[
  {
    "path": ".github/workflows/build.yaml",
    "content": "name: Build nn_tilde\non:\n  push:\n    tags:\n      - \"v*\"\n\njobs:\n  osx-arm64-build:\n    runs-on: macos-latest\n    steps:\n      - name: Check out code\n        uses: actions/checkout@v2\n      - run: | \n          git submodule update --init --recursive\n          git fetch --tags\n          curl -L  ${{ secrets.WHEEL_PATH }} -o src/models/wheel.ts\n          du -sh src/models/*\n      - name: Setup LLVM\n        run: |\n          brew install llvm \n      - name: Setup miniconda\n        run: |\n          curl -L https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh > miniconda.sh\n          chmod +x ./miniconda.sh\n          bash ./miniconda.sh -b -u -p ./env\n          source ./env/bin/activate\n          conda install python=3.11\n      - name: Installing torch and requirements\n        run: |\n          source ./env/bin/activate\n          conda install -c conda-forge curl\n          pip install -r requirements.txt\n      - name: Setup puredata\n        run: |\n          mkdir puredata_include\n          curl -L https://raw.githubusercontent.com/pure-data/pure-data/master/src/m_pd.h -o ${{ github.workspace }}/puredata_include/m_pd.h\n          cat ${{ github.workspace }}/puredata_include/m_pd.h\n      - name: Build\n        run: |\n          mkdir build\n          export CC=$(brew --prefix llvm)/bin/clang\n          export CXX=$(brew --prefix llvm)/bin/clang++\n          export SIGN_ID=${{ secrets.SIGN_ID }}\n          cd build\n          cmake ../src -DCMAKE_C_COMPILER=$CC -DCMAKE_CXX_COMPILER=$CXX -DTORCH_MAC_UB_URL=${{ secrets.TORCH_UB_PATH }} -DCMAKE_BUILD_TYPE=Release -DCMAKE_OSX_ARCHITECTURES=arm64\n          make\n      - name: Max/MSP Package creation\n        run: |\n          mkdir nn_tilde\n          mkdir nn_tilde/help\n          mv src/externals src/help src/support src/patchers src/extras src/package-info.json src/icon.png src/source src/models src/misc nn_tilde\n          tar -czvf nn_max_msp_macOS_arm64.tar.gz nn_tilde\n      - name: PureData Package creation\n        run: |\n          rm -fr nn_tilde\n          mv build/frontend/puredata/nn_tilde .\n          mv src/frontend/puredata/nn_tilde/nn~-help.pd nn_tilde\n          rm -fr nn_tilde/CMakeFiles/ nn_tilde/*.cmake nn_tilde/Makefile\n          tar -czvf nn_puredata_macOS_arm64.tar.gz nn_tilde\n      - name: Upload binaries\n        uses: actions/upload-artifact@v4\n        with:\n          name: nn_tilde_mac_arm64\n          path: |\n            nn_max_msp_macOS_arm64.tar.gz\n            nn_puredata_macOS_arm64.tar.gz\n\n  osx-x64-build:\n    runs-on: macos-latest\n    steps:\n      - name: Check out code\n        uses: actions/checkout@v2\n      - uses: actions/setup-python@v5\n        with:\n          python-version: \"3.11\"\n      - run: | \n          git submodule update --init --recursive\n          git fetch --tags\n          git describe --tags --always\n          curl -L  ${{ secrets.WHEEL_PATH }} -o src/models/wheel.ts\n\n      - name: Install x86_64 Homebrew\n        run: |\n          sudo rm -rf /opt/homebrew\n          sudo rm -rf /usr/local/homebrew\n          sudo mkdir -p /usr/local/homebrew\n          sudo chown -R $(whoami) /usr/local/homebrew\n          arch -x86_64 /bin/bash -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\" --prefix=/usr/local/homebrew\n          arch -x86_64 /usr/local/bin/brew shellenv >> $GITHUB_ENV\n\n      - name: Downloading wheels & libtorch for x64\n        run: |\n          curl -L ${{ secrets.TORCH_WHEEL }} > torch-2.5.1-cp313-cp313-macosx_14_0_x86_64.whl\n          curl -L ${{ secrets.TORCH_UB_PATH }} > torch_ub.zip\n          mkdir torch \n          unzip torch_ub.zip -d torch\n          mv torch/torch torch/libtorch\n\n      - name: Installing miniconda and dependencies...\n        run: |\n          ls \n          curl -L https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh > miniconda.sh\n          chmod 777 ./miniconda.sh\n          bash ./miniconda.sh -b -u -p ./env\n          source ./env/bin/activate\n\n      - name: Installing torch\n        run : |\n          source ./env/bin/activate\n          export DYLD_LIBRARY_PATH=${{ github.workspace }}/torch/lib:$DYLD_LIBRARY_PATH\n          arch -x86_64 conda install -c conda-forge cmake ninja numpy setuptools mkl mkl-include typing_extensions protobuf\n          arch -x86_64 pip install ./torch-2.5.1-cp313-cp313-macosx_14_0_x86_64.whl\n          arch -x86_64 pip install cached_conv>=2.5.0\n          arch -x86_64 pip install cmake==3.28\n          arch -x86_64 conda install -c conda-forge curl\n          TARGET_DIR=$(find env/lib -type d -name \"python3*\")\n          TARGET_DIR=\"${TARGET_DIR}/site-packages/torch/share/cmake/Caffe2/public\"\n          cp install/mkl.cmake \"${TARGET_DIR}\"  \n\n      - name: Setup LLVM\n        run: |\n          rm '/usr/local/bin/pydoc3'\n          rm /usr/local/bin/pydoc3.13\n          rm /usr/local/bin/python3\n          rm /usr/local/bin/python3-config\n          rm /usr/local/bin/python3.13\n          rm /usr/local/bin/python3.13-config\n          rm '/usr/local/bin/pip3.13'\n          rm '/usr/local/bin/idle3'\n          rm '/usr/local/bin/idle3.13'\n          arch -x86_64 /usr/local/bin/brew install llvm\n          export LDFLAGS=\"-L/usr/local/opt/llvm/lib\"\n          export CPPFLAGS=\"-I/usr/local/opt/llvm/include\"\n\n      - name: Setup puredata\n        run: |\n          mkdir puredata_include\n          curl -L https://raw.githubusercontent.com/pure-data/pure-data/master/src/m_pd.h -o puredata_include/m_pd.h\n\n      - name: Build\n        run: |\n          mkdir build\n          export CC=$(/usr/local/bin/brew --prefix llvm)/bin/clang\n          export CXX=$(/usr/local/bin/brew --prefix llvm)/bin/clang++\n          export SIGN_ID=${{ secrets.SIGN_ID }}\n          cd build\n          arch -x86_64 ../env/bin/cmake ../src -DCMAKE_C_COMPILER=$CC -DCMAKE_CXX_COMPILER=$CXX -DCMAKE_BUILD_TYPE=Release -DPUREDATA_INCLUDE_DIR=${{ github.workspace }}/puredata_include -DCMAKE_OSX_ARCHITECTURES=x86_64\n          arch -x86_64 make\n      - name: Max/MSP Package creation\n        run: |\n          mkdir nn_tilde\n          mkdir nn_tilde/help\n          mv src/externals src/help src/support src/patchers src/extras src/package-info.json src/icon.png src/source src/models src/misc nn_tilde\n          tar -czvf nn_max_msp_macOS_x64.tar.gz nn_tilde\n      - name: PureData Package creation\n        run: |\n          rm -fr nn_tilde\n          mv build/frontend/puredata/nn_tilde .\n          mv src/frontend/puredata/nn_tilde/nn~-help.pd nn_tilde\n          rm -fr nn_tilde/CMakeFiles/ nn_tilde/*.cmake nn_tilde/Makefile\n            tar -czvf nn_puredata_macOS_x64.tar.gz nn_tilde\n      - name: Upload binaries\n        uses: actions/upload-artifact@v4\n        with:\n          name: nn_tilde_mac_x86_64\n          path: |\n            nn_max_msp_macOS_x64.tar.gz\n            nn_puredata_macOS_x64.tar.gz\n\n  linux:\n    runs-on: ${{ matrix.runs-on }}\n    strategy:\n      matrix:\n        arch: [amd64]\n        include:\n          - arch: amd64\n            runs-on: ubuntu-latest\n            artifact-name: nn_linux_puredata_x86_64\n            conda-url: https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n    steps:\n      - name: Check out code\n        uses: actions/checkout@v2\n      - run: | \n          git submodule update --init --recursive\n          git fetch --tags\n          GIT_TRACE=1 git describe --tags --always\n      - name: Setup LLVM\n        run: |\n          sudo apt-get install -y ca-certificates\n          sudo apt install gcc-10 g++-10\n      - name: Setup miniconda\n        run: |\n          echo \"architecture : $(uname -m)\"\n          curl --cacert /etc/ssl/certs/ca-certificates.crt -L ${{ matrix.conda-url }} > miniconda.sh\n          chmod +x ./miniconda.sh\n          bash ./miniconda.sh -b -u -p ./env\n          source ./env/bin/activate\n          conda install python=3.11\n          pip3 install torch --index-url https://download.pytorch.org/whl/cpu\n          conda install -c conda-forge curl\n          # conda install -c pytorch torch==2.5.1 torchaudio==2.5.1\n          # pip install -r requirements.txt\n      - name: Setup puredata\n        run: |\n          mkdir puredata_include\n          curl --cacert /etc/ssl/certs/ca-certificates.crt -L https://raw.githubusercontent.com/pure-data/pure-data/master/src/m_pd.h -o puredata_include/m_pd.h\n      - name: Build\n        run: |\n          mkdir build\n          export CC=$(which gcc-10)\n          export CXX=$(which g++-10)\n          cd build\n          cmake ../src -DCMAKE_C_COMPILER=$CC -DCMAKE_CXX_COMPILER=$CXX -DCMAKE_PREFIX_PATH=${{ github.workspace }}/env/lib/python3.12/site-packages/torch -DCMAKE_BUILD_TYPE=Release -DPUREDATA_INCLUDE_DIR=${{ github.workspace }}/puredata_include\n          make\n      - name: PureData Package creation\n        run: |\n          mkdir nn_tilde\n          mv build/frontend/puredata/nn_tilde .\n          mv src/frontend/puredata/nn_tilde/nn~-help.pd nn_tilde\n          rm -fr nn_tilde/CMakeFiles/ nn_tilde/*.cmake nn_tilde/Makefile\n          tar -czvf ${{ matrix.artifact-name }}.tar.gz nn_tilde\n      - name: Upload binaries\n        uses: actions/upload-artifact@v4\n        with:\n          name: ${{ matrix.artifact-name }}\n          path: \n            ${{ matrix.artifact-name }}.tar.gz\n\n            \n  windows-x64-build:\n    runs-on: windows-latest\n    steps:\n      - name: Check out code\n        uses: actions/checkout@v2\n      - run: | \n          git submodule update --init --recursive\n          git fetch --tags\n          curl -L  ${{ secrets.WHEEL_PATH }} -o src/models/wheel.ts\n      - name: Setup puredata\n        run: |\n          mkdir pd\n          cd pd\n          curl -L https://msp.ucsd.edu/Software/pd-0.55-2.msw.zip -o pd.zip\n          unzip pd.zip\n          mv pd*/src .\n          mv pd*/bin .\n          cd ..\n      - name: Installing dependencies with vcpkg\n        run: |\n          git clone https://github.com/microsoft/vcpkg.git\n          cd vcpkg \n          ./bootstrap-vcpkg.bat\n          ./vcpkg.exe integrate install\n          ./vcpkg.exe install curl\n          cd ..\n\n      - name: Build\n        run: |\n          mkdir build\n          cd build\n          cmake ../src -G \"Visual Studio 17 2022\" -DPUREDATA_INCLUDE_DIR=${{ github.workspace }}/pd/src -DPUREDATA_BIN_DIR=${{ github.workspace }}/pd/bin -A x64\n          cmake --build . --config Release\n          \n      - name: Max/MSP Package creation\n        run: |\n          mkdir nn_tilde\n          mkdir nn_tilde/support\n          ls\n          cp torch/libtorch/lib/* nn_tilde/support\n          cp vcpkg/installed/x64-windows/lib/*.lib nn_tilde/support\n          cp vcpkg/installed/x64-windows/bin/*.dll nn_tilde/support\n          curl -L https://curl.se/ca/cacert.pem -o nn_tilde/support/cacert.pem\n          $files = @(\"src\\externals\", \"src\\help\", \"src\\patchers\", \"src\\extras\", \"src\\package-info.json\", \"src\\icon.png\", \"src\\source\", \"src\\models\", \"src\\misc\")\n          foreach ($file in $files) {\n            mv $file nn_tilde\n          }\n          curl -L https://nubo.ircam.fr/index.php/s/7xBPxJZA6nJFHkG -o ${{ secrets.WHEEL_PATH }}\n          tar -czvf nn_max_msp_windows_x64.tar.gz nn_tilde\n      - name: PureData Package creation\n        run: |\n          Remove-Item -Recurse -Force nn_tilde\n          mkdir nn_tilde\n          mv build/frontend/puredata/nn_tilde/Release/* nn_tilde/\n          cp torch/libtorch/lib/* nn_tilde/\n          cp vcpkg/installed/x64-windows/lib/*.lib nn_tilde/\n          cp vcpkg/installed/x64-windows/bin/*.dll nn_tilde/\n          curl -L https://curl.se/ca/cacert.pem -o nn_tilde/cacert.pem\n          tar -czvf nn_puredata_windows_x64.tar.gz nn_tilde\n      - name: Upload binaries\n        uses: actions/upload-artifact@v4\n        with:\n          name: nn_tilde_win_x64\n          path: |\n            nn_max_msp_windows_x64.tar.gz\n            nn_puredata_windows_x64.tar.gz\n\n  MakeMaxUniversalForMac:\n    runs-on: macos-latest\n    needs: [osx-arm64-build, osx-x64-build]\n    steps:\n      - name: Download build binaries (arm64)\n        uses: actions/download-artifact@v4\n        with:\n          name: nn_tilde_mac_arm64\n      - name: Download build binaries (x86)\n        uses: actions/download-artifact@v4\n        with:\n          name: nn_tilde_mac_x86_64\n      - name: Build ub max with lipo\n        run: |\n          tar -xvf nn_max_msp_macOS_arm64.tar.gz \n          mv nn_tilde nn_tilde_arm64\n          tar -xvf nn_max_msp_macOS_x64.tar.gz \n          mv nn_tilde nn_tilde_x64\n          curl -L https://raw.githubusercontent.com/domkirke/nn_tilde/refs/heads/dev/install/macos_max_makeub.sh > macos_max_makeub.sh\n          chmod 777 ./macos_max_makeub.sh\n          ./macos_max_makeub.sh\n          \n      - name: Install signing certificate\n        run: |\n          CERT_PATH=\"$RUNNER_TEMP/signing-identity.p12\"\n          echo \"${{ secrets.CERT }}\" | base64 -D > $CERT_PATH\n          security create-keychain -p \"\" build.keychain\n          security default-keychain -s build.keychain\n          security unlock-keychain -p \"\" build.keychain\n          security import $CERT_PATH -k build.keychain -P \"${{ secrets.CERT_PASSWORD }}\" -T /usr/bin/codesign\n          security set-key-partition-list -S apple-tool:,apple: -s -k \"\" build.keychain\n          codesign --deep --force --sign ${{ secrets.SIGN_ID }} nn_tilde/support/*.dylib\n          for i in $(find nn_tilde/externals/*/Contents/MacOS -type f -perm -111); do codesign --deep --force --sign ${{ secrets.SIGN_ID }} nn_tilde/externals/$(basename $i).mxo/Contents/MacOS/$(basename $i); done\n\n      - name: Compressing...\n        run: tar -czvf nn_max_msp_macos_ub.tar.gz nn_tilde\n\n      - name: Upload UB \n        uses: actions/upload-artifact@v4\n        with:\n          name: nn_tilde_macos_max_ub\n          path: |\n            nn_max_msp_macos_ub.tar.gz\n\n  MakePdUniversalForMac:\n    runs-on: macos-latest\n    needs: [osx-arm64-build, osx-x64-build]\n    steps:\n      - name: Download build binaries (arm64)\n        uses: actions/download-artifact@v4\n        with:\n          name: nn_tilde_mac_arm64\n      - name: Download build binaries (x86)\n        uses: actions/download-artifact@v4\n        with:\n          name: nn_tilde_mac_x86_64\n      - name: build ub max with lipo\n        run: |\n          tar -xvf nn_puredata_macOS_arm64.tar.gz \n          mv nn_tilde nn_tilde_arm64\n          tar -xvf nn_puredata_macOS_x64.tar.gz \n          mv nn_tilde nn_tilde_x64\n          curl -L https://raw.githubusercontent.com/domkirke/nn_tilde/refs/heads/dev/install/macos_pd_makeub.sh > macos_pd_makeub.sh\n          chmod 777 ./macos_pd_makeub.sh\n          ./macos_pd_makeub.sh\n\n      - name: Install signing certificate\n        run: |\n          CERT_PATH=\"$RUNNER_TEMP/signing-identity.p12\"\n          echo \"${{ secrets.CERT }}\" | base64 -D > $CERT_PATH\n          security create-keychain -p \"\" build.keychain\n          security default-keychain -s build.keychain\n          security unlock-keychain -p \"\" build.keychain\n          security import $CERT_PATH -k build.keychain -P \"${{ secrets.CERT_PASSWORD }}\" -T /usr/bin/codesign\n          security set-key-partition-list -S apple-tool:,apple: -s -k \"\" build.keychain\n          codesign --deep --force --sign ${{ secrets.SIGN_ID }} nn_tilde/*.dylib\n          codesign --deep --force --sign ${{ secrets.SIGN_ID }} nn_tilde/nn~.pd_darwin\n      \n      - name: Compressing archive...\n        run: tar -czvf nn_puredata_macos_ub.tar.gz nn_tilde\n\n      - name: Upload UB \n        uses: actions/upload-artifact@v4\n        with:\n          name: nn_tilde_macos_pd_ub\n          path: |\n            nn_puredata_macos_ub.tar.gz\n          \n\n  AutomaticRelease:\n    runs-on: ubuntu-latest\n    needs: [MakeMaxUniversalForMac, MakePdUniversalForMac, linux, windows-x64-build]\n    steps:\n      - name: Download Max build binaries (mac)\n        uses: actions/download-artifact@v4\n        with:\n          name: nn_tilde_macos_max_ub\n      - name: Download Pd binaries (mac)\n        uses: actions/download-artifact@v4\n        with:\n          name: nn_tilde_macos_pd_ub\n      - name: Download build binaries (linux_x86_64)\n        uses: actions/download-artifact@v4\n        with:\n          name: nn_linux_puredata_x86_64\n      - name: Download build binaries (windows mac_x86_64)\n        uses: actions/download-artifact@v4\n        with:\n          name: nn_tilde_win_x64 \n      - name: Automated Release\n        uses: \"marvinpinto/action-automatic-releases@latest\"\n        with:\n          repo_token: \"${{ secrets.GITHUB_TOKEN }}\"\n          prerelease: false\n          files: |\n            nn_max_msp_macos_ub.tar.gz\n            nn_puredata_macos_ub.tar.gz\n            nn_linux_puredata_x86_64.tar.gz\n            nn_max_msp_windows_x64.tar.gz\n            nn_puredata_windows_x64.tar.gz\n\n"
  },
  {
    "path": ".github/workflows/python-publish.yaml",
    "content": "name: Upload Python Package\n\non:\n  push:\n    tags:\n      - \"v*\"\n\npermissions:\n  contents: read\n\njobs:\n  deploy:\n\n    runs-on: ubuntu-latest\n\n    steps:\n    - uses: actions/checkout@v3\n    - name: Set up Python\n      uses: actions/setup-python@v3\n      with:\n        python-version: '3.10'\n    - name: Install dependencies\n      run: |\n        python -m pip install --upgrade pip setuptools wheel build\n        python -m pip install -r requirements.txt\n    - name: Build package\n      run: NN_TILDE_VERSION=${{  github.ref_name }} python -m build\n    - name: Publish package\n      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29\n      with:\n        verbose: true\n        user: __token__\n        password: ${{ secrets.PYPI_TOKEN }}"
  },
  {
    "path": ".gitignore",
    "content": "*build\nsrc/externals\nsrc/frontend/tests\nsrc/tests\n.vscode\n*libtorch\n*DS_Store*\nsrc/docs\ndist/\n*.egg-info*"
  },
  {
    "path": ".gitmodules",
    "content": "[submodule \"src/frontend/maxmsp/min-api\"]\n\tpath = src/frontend/maxmsp/min-api\n\turl = https://github.com/Cycling74/min-api.git\n[submodule \"src/json\"]\n\tpath = src/json\n\turl = https://github.com/nlohmann/json.git\n"
  },
  {
    "path": "LICENSE",
    "content": "Creative Commons Attribution-NonCommercial 4.0 International\n\nCreative Commons Corporation (\"Creative Commons\") is not a law firm and\ndoes not provide legal services or legal advice. Distribution of\nCreative Commons public licenses does not create a lawyer-client or\nother relationship. Creative Commons makes its licenses and related\ninformation available on an \"as-is\" basis. Creative Commons gives no\nwarranties regarding its licenses, any material licensed under their\nterms and conditions, or any related information. Creative Commons\ndisclaims all liability for damages resulting from their use to the\nfullest extent possible.\n\nUsing Creative Commons Public Licenses\n\nCreative Commons public licenses provide a standard set of terms and\nconditions that creators and other rights holders may use to share\noriginal works of authorship and other material subject to copyright and\ncertain other rights specified in the public license below. The\nfollowing considerations are for informational purposes only, are not\nexhaustive, and do not form part of our licenses.\n\n-   Considerations for licensors: Our public licenses are intended for\n    use by those authorized to give the public permission to use\n    material in ways otherwise restricted by copyright and certain other\n    rights. Our licenses are irrevocable. Licensors should read and\n    understand the terms and conditions of the license they choose\n    before applying it. Licensors should also secure all rights\n    necessary before applying our licenses so that the public can reuse\n    the material as expected. Licensors should clearly mark any material\n    not subject to the license. This includes other CC-licensed\n    material, or material used under an exception or limitation to\n    copyright. 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  },
  {
    "path": "MANIFEST.in",
    "content": "include requirements.txt"
  },
  {
    "path": "README.md",
    "content": "![banner](https://github.com/acids-ircam/nn_tilde/raw/master/assets/banner.png)\r\n\r\n# Demonstration video\r\n\r\n[![RAVE x nn~](http://img.youtube.com/vi/dMZs04TzxUI/mqdefault.jpg)](https://www.youtube.com/watch?v=dMZs04TzxUI)\r\n\r\n# Installation\r\n\r\nGrab the [latest release of nn~](https://github.com/acids-ircam/nn_tilde/releases/latest) ! Be sure to download the correct version for your installation.\r\n\r\n## MaxMSP\r\n\r\nUncompress the `.tar.gz` file in the Package folder of your Max installation, i.e. in `Documents/Max [your version]/Packages/`. You can then instantiate  an `nn~` object!  Alt-click the `nn~` object to open the help patch, or access the nn~ Overview patch in the Extras menu.\r\n\r\n##### Mac alert : codesigned with IRCAM identity and not trigger MacOS quarantine ; if it does so,  please launch in the terminal : \r\n\r\n```bash\r\ncd \"~/Max X/Packages/nn_tilde\r\nsudo codesign --deep --force --sign - support/*.dylib\r\nsudo codesign --deep --force --sign - externals/*/Contents/MacOS/*\r\nxattr -r -d com.apple.quarantine externals/*/Contents/MacOS/*  \r\n```\r\n\r\nAlt+click on the `nn~` object to open the help patch, and follow the tabs to learn more about this project.\r\n\r\n## PureData\r\n\r\nUncompress the `.tar.gz` file in the Package folder of your Pd installation, i.e. in `Documents/Pd/externals/`. You can then add a new path in the `Pd/File/Preferences/Path` menu pointing to the `nn_tilde` folder.\r\n\r\nSimilarly, the external should not be blocked on recent MacOS systems. It it still is, `cd` to the `nn_tilde` folder and fix with\r\n\r\n```bash\r\n\r\nxattr -r -d com.apple.quarantine Documents/Pd/externals/nn_tilde\r\nsudo codesign --deep --force --sign - Documents/Pd/externals/nn_tilde/*.dylib\r\nsudo codesign --deep --force --sign - Documents/Pd/externals/nn_tilde/nn\\~.pd_darwin\r\n```\r\n\r\n# Usage\r\n\r\n## Pretrained models\r\n\r\nAt its core, `nn~` is a translation layer between Max/MSP or PureData and the [libtorch c++ interface for deep learning](https://pytorch.org/). Alone, `nn~` is like an empty shell, and **requires pretrained models** to operate. Since v1.6.0, you can download them directly through Forum IRCAM API. Alternatively, you can find a few [RAVE](https://github.com/acids-ircam/RAVE) models [here](https://acids-ircam.github.io/rave_models_download) or [here](https://huggingface.co/Intelligent-Instruments-Lab/rave-models). Few [vschaos2](https://github.com/acids-ircam/vschaos2) models are also available[here](https://www.dropbox.com/sh/avdeiza7c6bn2of/AAAGZsnRo9ZVMa0iFhouCBL-a?dl=0).\r\n\r\nPretrained model for `nn~` are **torchscript files**, with a `.ts` extension. You can add these files to `nn_tilde/models` folders, or any place accessible through Max / Pd filesystem (Max: `Options/File Preferences`, PureData: `File/Preferences/Path`).\r\n\r\n**New** : since v1.6.0, some models are directly downloadable through IRCAM Forum API.\r\n\r\nOnce this is done, you can load a model with `nn~` by providing its name as first argument (for example, here `isis.ts` located inside `nn_tilde/models` for Max, or among the PureData patch):  \r\n\r\n<table>\r\n  <tr>\r\n    <th width=\"50%\">Max / MSP</th>\r\n    <th width=\"50%\">PureData</th>\r\n  </tr>\r\n  <tr>\r\n    <td><img width=\"100%\" src=\"assets/max_instance.png\" /></td>\r\n    <td><img width=\"100%\" src=\"assets/pd_instance.png\" /></td>\r\n  </tr>\r\n</table>\r\n\r\n## Model information fetching\r\n\r\n## Model information fetching\r\n\r\nComing with v1.6.0, the `nn.info` object allows model inspection and fetching avilable models for download on the IRCAM-API. With this object, you can get available methods and attributes for a given model. For example, you can see below that a RAVE model has three different methods : `encode`, `decode`, and `forward`.\r\n\r\n<center>\r\n<img src=\"assets/max_nninfo.png\"/>\r\n</center>\r\n\r\n### Methods\r\n\r\nModels can have several _methods_, that correspond to several processing pipelines the model can achieve. Hence, each method can have a different number in inlets / outlets. The method is given as the third argument (for exemple, `decode` above), and equals `forward` by default.\r\n\r\n### Attributes\r\n\r\nIt is possible the internal state of the module through _attributes_, that are **model-dependent** and defined at exportation. Model attributes can be set using _messages_, with the following syntax:\r\n\r\n```bash\r\nset ATTRIBUTE_NAME ATTRIBUTE_VAL_1 ATTRIBUTE_VAL_2\r\n```\r\n\r\nUsing Max/MSP and PureData graphical objects, this can lead to an intuitive way to modify the behavior of the model, as shown below where we have two model attributes (i.e. generation temperature and generation mode), and the special `enable` attribute.\r\n\r\n<table>\r\n  <tr>\r\n    <th width=\"50%\">Max / MSP</th>\r\n    <th width=\"50%\">PureData</th>\r\n  </tr>\r\n  <tr>\r\n    <td><img width=\"100%\" src=\"assets/max_attr.png\" /></td>\r\n    <td><img width=\"100%\" src=\"assets/pd_attr.png\" /></td>\r\n  </tr>\r\n</table>\r\n\r\n**New in 1.6.0**\r\n\r\n- Buffers (Max) / Array (Pd) attribute setting to allow the `.ts` model to access internal buffers / arrays.\r\n- `torch.Tensor` attributes can be set through Max/MSP `[array]`, allowing to set attributes of unlimited size.\r\n\r\n## Buffer configuration\r\n\r\nInternally, `nn~` has a circular buffer mechanism that helps maintain a reasonable computational load, if the given buffer size is greater tha 0. You can modify its size through the use of an additional integer after the method declaration, as shown below. \r\n\r\n**Important**For Windows users, the circular buffer is automatically disabled because of a memory leak [that occurs when a TorchScript model is used in a separate thread](https://github.com/pytorch/pytorch/issues/24237). Unfortunately, this implies a much lower efficiency in terms of CPU.  \r\n\r\n<table>\r\n  <tr>\r\n    <th width=\"50%\">Max / MSP</th>\r\n    <th width=\"50%\">PureData</th>\r\n  </tr>\r\n  <tr>\r\n    <td><img width=\"100%\" src=\"assets/max_buffer.png\" /></td>\r\n    <td><img width=\"100%\" src=\"assets/pd_buffer.png\" /></td>\r\n  </tr>\r\n</table>\r\n\r\n## Multichannel (Max/MSP)\r\n\r\nThe Max/MSP release of `nn~` includes additional externals, namely `mc.nn~` and `mcs.nn~`, allowing the use of the multicanal abilities of Max 8+ to simplify the patching process with `nn~` and optionally decrease the computational load.\r\n\r\nIn the following examples, two audio files are being encoded then decoded by the same model in parallel\r\n\r\n![regular](assets/max_regular.png)\r\n\r\nThis patch can be improved both visually _and_ computationally speaking by using `mc.nn~` and using _batch operations_\r\n\r\n![mc](assets/max_mc.png)\r\n\r\nUsing `mc.nn~` we build the multicanal signals **over the different batches**. In the example above, each multicanal signal will have 2 different canals. We also propose the `mcs.nn~` external that builds multicanal signals **over the different dimensions**, as shown in the example below\r\n\r\n![mcs](assets/max_mcs.png)\r\n\r\nIn the example above, the two multicanals signals yielded by the `nn~ rave encode 2` object have 16 canals each, corresponding to the 16 latent dimensions. This can help patching, while keeping the batching abilities of `mc.nn~` by creating an explicit number of inlets / oulets corresponding to the number of examples we want to process in parallel.\r\n\r\nTo recap, the regular `nn~` operates on a single example, and has as many inlets / outlets as the model has inputs / outputs. The `mc.nn~` external is like `nn~`, but can process multiple examples _at the same time_. The `mcs.nn~` variant is a bit different, and can process mulitple examples at the same time, but will **have one inlet / outlet per examples**.\r\n\r\n## Lazy mode (Max/MSP)\r\n\r\nSince v1.6.0, nn~ has a `void` mode, that allows to initialise it with a fixed number of inlets / outlets, and may be attached to a model afterwards. This can be done with the `void` special model, that enables this lazy initialisation.\r\n\r\n<center>\r\n<img src=\"assets/max_void.png\" width=\"30%\"/>\r\n</center>\r\n\r\n## Special messages\r\n\r\n### enable [0 / 1]\r\n\r\nEnable / Disable computation to save up computation without deleting the model. Similar to how a _bypass_ function would work.\r\n\r\n### reload\r\n\r\nDynamically reloads the model. Can be useful if you want to periodically update the state of a model during a training.\r\n\r\n### dump\r\n\r\nPrints methods / attributes of the loaded model. \r\n\r\n### print_available_models\r\n\r\nPrints models downloadable through API.\r\n\r\n### download\r\n\r\nDownload a model from the API. \r\n\r\n### delete\r\n\r\nDeletes a downloaded model.\r\n\r\n### load\r\n\r\nChange dynamically the incoming model.\r\n\r\n### method\r\n\r\nChange dynamically the used method.\r\n\r\n# Scripting any PyTorch model in nn~\r\n\r\nIn the [`scripting`](https://github.com/acids-ircam/nn_tilde/tree/master/scripting) subfolder, you can find a series of examples that demonstrate how to write simple scripts to incorporate any type of deep models from PyTorch into MaxMSP (and potentially running on GPU). The examples show different use cases that also help to understand the input/output shapes relationships.\r\n- `effects.py` : apply simple effects to the input (identical input and output shapes)\r\n- `features.py` : compute spectral descriptors from the PyTorch audio library (each input audio buffer produces a single float as output)\r\n- `unmix.py` : apply the unmix deep source separation model (input is split into 4 different audio streams containing « drums », « vocals », « bass » and « others »)\r\n\r\n\r\n# Build Instructions\r\n\r\n## macOS\r\n\r\n- Download the latest libtorch (CPU) [here](https://pytorch.org/get-started/locally/) and unzip it to a known directory\r\n- Run the following commands:\r\n\r\n```bash\r\ngit clone https://github.com/acids-ircam/nn_tilde --recurse-submodules\r\ncd nn_tilde\r\ncurl -L https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh > miniconda.sh\r\nchmod +x ./miniconda.sh\r\nbash ./miniconda.sh -b -u -p ./env\r\nsource ./env/bin/activate\r\npip install -r requirements.txt\r\nconda install -c conda-forge curl\r\nmkdir build\r\ncd build\r\nmkdir puredata_include\r\ncurl -L https://raw.githubusercontent.com/pure-data/pure-data/master/src/m_pd.h -o puredata_include/m_pd.h\r\nexport CC=$(brew --prefix llvm)/bin/clang\r\nexport CXX=$(brew --prefix llvm)/bin/clang++\r\ncd build\r\ncmake ../src -DCMAKE_C_COMPILER=$CC -DCMAKE_CXX_COMPILER=$CXX -DCMAKE_PREFIX_PATH=../env/lib/python3.12/site-packages/torch -DCMAKE_BUILD_TYPE=Release -DPUREDATA_INCLUDE_DIR=../puredata_include -DCMAKE_OSX_ARCHITECTURES=arm64\r\ncmake --build . --config Release\r\n```\r\nplease replace `arm64` in the last line by `x86_64` if you want compile for 64 bits. You can remove `-DPUREDATA_INCLUDE_DIR=../puredata_include` to compile only for Max. The Max package is produced in `src/`,  and Pd external in `build/frontend/puredata/Release`.\r\n\r\n\r\n## Windows\r\n\r\n- Download Libtorch (CPU) and dependencies [here](https://pytorch.org/get-started/locally/) and unzip to a known directory.\r\n- Install [Visual Studio Redistribuable](https://learn.microsoft.com/fr-fr/cpp/windows/latest-supported-vc-redist?view=msvc-170) \r\n- Run the following commands (here for Git Bash):\r\n```bash\r\ngit clone https://github.com/acids-ircam/nn_tilde --recurse-submodules\r\ncd nn_tilde\r\ncurl -L https://download.pytorch.org/libtorch/cpu/libtorch-win-shared-with-deps-2.6.0%2Bcpu.zip > \"libtorch.zip\"\r\nunzip libtorch.zip\r\nmkdir pd\r\ncd pd\r\ncurl -L https://msp.ucsd.edu/Software/pd-0.55-2.msw.zip -o pd.zip\r\nunzip pd.zip\r\nmv pd*/src .\r\nmv pd*/bin .\r\ncd ..\r\ngit clone https://github.com/microsoft/vcpkg.git\r\ncd vcpkg \r\n./bootstrap-vcpkg.bat\r\n./vcpkg.exe integrate install\r\n./vcpkg.exe install curl\r\ncd ..\r\nmkdir build\r\ncd build\r\nmkdir puredata_include\r\ncurl -L https://raw.githubusercontent.com/pure-data/pure-data/master/src/m_pd.h -o puredata_include/m_pd.h\r\nexport CC=$(brew --prefix llvm)/bin/clang\r\nexport CXX=$(brew --prefix llvm)/bin/clang++\r\ncd build\r\ncmake ../src -G \"Visual Studio 17 2022\" -DTorch_DIR=../libtorch/share/cmake/Torch -DPUREDATA_INCLUDE_DIR=../pd/src -DPUREDATA_BIN_DIR=../pd/bin -A x64\r\ncmake --build . --config Release\r\n```\r\nYou can remove `-DPUREDATA_INCLUDE_DIR=../puredata_include` to compile only for Max. The Max package is produced in `src/`,  and Pd external in `build/frontend/puredata/Release`.\r\n\r\n## Raspberry Pi\r\n\r\n**not availble in v1.6.0, planned in next version ; please take previous versions if needed**\r\n\r\nWhile nn~ can be compiled and used on Raspberry Pi, you may have to consider using lighter deep learning models. We currently only support 64bit OS.\r\n\r\nInstall nn~ for PureData using\r\n\r\n```bash\r\ncurl -s https://raw.githubusercontent.com/acids-ircam/nn_tilde/master/install/raspberrypi.sh | bash\r\n```\r\n\r\n# Funding\r\n\r\nThis work is led at IRCAM, and has been funded by the following projects\r\n\r\n* [ANR MakiMono](https://acids.ircam.fr/course/makimono/)\r\n* [ACTOR](https://www.actorproject.org/)\r\n* [DAFNE+](https://dafneplus.eu/) N° 101061548\r\n\r\n<img src=\"https://ec.europa.eu/regional_policy/images/information-sources/logo-download-center/eu_co_funded_en.jpg\" width=200px/>\r\n"
  },
  {
    "path": "docs/index.md",
    "content": "![banner](https://github.com/acids-ircam/nn_tilde/raw/master/assets/banner.png)\r\n\r\n# Demonstration video\r\n\r\n[![RAVE x nn~](http://img.youtube.com/vi/dMZs04TzxUI/mqdefault.jpg)](https://www.youtube.com/watch?v=dMZs04TzxUI)\r\n\r\n# Installation\r\n\r\nGrab the [latest release of nn~](https://github.com/acids-ircam/nn_tilde/releases/latest) ! Be sure to download the correct version for your installation.\r\n\r\n## MaxMSP\r\n\r\nUncompress the `.tar.gz` file in the Package folder of your Max installation, i.e. in `Documents/Max [your version]/Packages/`. You can then instantiate  an `nn~` object!  Alt-click the `nn~` object to open the help patch, or access the nn~ Overview patch in the Extras menu.\r\n\r\n##### Mac alert : codesigned with IRCAM identity and not trigger MacOS quarantine ; if it does so,  please launch in the terminal : \r\n\r\n```bash\r\ncd \"~/Max X/Packages/nn_tilde\r\nsudo codesign --deep --force --sign - support/*.dylib\r\nsudo codesign --deep --force --sign - externals/*/Contents/MacOS/*\r\nxattr -r -d com.apple.quarantine externals/*/Contents/MacOS/*  \r\n```\r\n\r\nAlt+click on the `nn~` object to open the help patch, and follow the tabs to learn more about this project.\r\n\r\n## PureData\r\n\r\nUncompress the `.tar.gz` file in the Package folder of your Pd installation, i.e. in `Documents/Pd/externals/`. You can then add a new path in the `Pd/File/Preferences/Path` menu pointing to the `nn_tilde` folder.\r\n\r\nSimilarly, the external should not be blocked on recent MacOS systems. It it still is, `cd` to the `nn_tilde` folder and fix with\r\n\r\n```bash\r\n\r\nxattr -r -d com.apple.quarantine Documents/Pd/externals/nn_tilde\r\nsudo codesign --deep --force --sign - Documents/Pd/externals/nn_tilde/*.dylib\r\nsudo codesign --deep --force --sign - Documents/Pd/externals/nn_tilde/nn\\~.pd_darwin\r\n```\r\n\r\n# Usage\r\n\r\n## Pretrained models\r\n\r\nAt its core, `nn~` is a translation layer between Max/MSP or PureData and the [libtorch c++ interface for deep learning](https://pytorch.org/). Alone, `nn~` is like an empty shell, and **requires pretrained models** to operate. Since v1.6.0, you can download them directly through Forum IRCAM API. Alternatively, you can find a few [RAVE](https://github.com/acids-ircam/RAVE) models [here](https://acids-ircam.github.io/rave_models_download) or [here](https://huggingface.co/Intelligent-Instruments-Lab/rave-models). Few [vschaos2](https://github.com/acids-ircam/vschaos2) models are also available[here](https://www.dropbox.com/sh/avdeiza7c6bn2of/AAAGZsnRo9ZVMa0iFhouCBL-a?dl=0).\r\n\r\nPretrained model for `nn~` are **torchscript files**, with a `.ts` extension. You can add these files to `nn_tilde/models` folders, or any place accessible through Max / Pd filesystem (Max: `Options/File Preferences`, PureData: `File/Preferences/Path`).\r\n\r\n**New** : since v1.6.0, some models are directly downloadable through IRCAM Forum API.\r\n\r\nOnce this is done, you can load a model with `nn~` by providing its name as first argument (for example, here `isis.ts` located inside `nn_tilde/models` for Max, or among the PureData patch):  \r\n\r\n<table>\r\n  <tr>\r\n    <th width=\"50%\">Max / MSP</th>\r\n    <th width=\"50%\">PureData</th>\r\n  </tr>\r\n  <tr>\r\n    <td><img width=\"100%\" src=\"assets/max_instance.png\" /></td>\r\n    <td><img width=\"100%\" src=\"assets/pd_instance.png\" /></td>\r\n  </tr>\r\n</table>\r\n\r\n## Model information fetching\r\n\r\n## Model information fetching\r\n\r\nComing with v1.6.0, the `nn.info` object allows model inspection and fetching avilable models for download on the IRCAM-API. With this object, you can get available methods and attributes for a given model. For example, you can see below that a RAVE model has three different methods : `encode`, `decode`, and `forward`.\r\n\r\n<center>\r\n<img src=\"assets/max_nninfo.png\"/>\r\n</center>\r\n\r\n### Methods\r\n\r\nModels can have several _methods_, that correspond to several processing pipelines the model can achieve. Hence, each method can have a different number in inlets / outlets. The method is given as the third argument (for exemple, `decode` above), and equals `forward` by default.\r\n\r\n### Attributes\r\n\r\nIt is possible the internal state of the module through _attributes_, that are **model-dependent** and defined at exportation. Model attributes can be set using _messages_, with the following syntax:\r\n\r\n```bash\r\nset ATTRIBUTE_NAME ATTRIBUTE_VAL_1 ATTRIBUTE_VAL_2\r\n```\r\n\r\nUsing Max/MSP and PureData graphical objects, this can lead to an intuitive way to modify the behavior of the model, as shown below where we have two model attributes (i.e. generation temperature and generation mode), and the special `enable` attribute.\r\n\r\n<table>\r\n  <tr>\r\n    <th width=\"50%\">Max / MSP</th>\r\n    <th width=\"50%\">PureData</th>\r\n  </tr>\r\n  <tr>\r\n    <td><img width=\"100%\" src=\"assets/max_attr.png\" /></td>\r\n    <td><img width=\"100%\" src=\"assets/pd_attr.png\" /></td>\r\n  </tr>\r\n</table>\r\n\r\n**New in 1.6.0**\r\n\r\n- Buffers (Max) / Array (Pd) attribute setting to allow the `.ts` model to access internal buffers / arrays.\r\n- `torch.Tensor` attributes can be set through Max/MSP `[array]`, allowing to set attributes of unlimited size.\r\n\r\n## Buffer configuration\r\n\r\nInternally, `nn~` has a circular buffer mechanism that helps maintain a reasonable computational load, if the given buffer size is greater tha 0. You can modify its size through the use of an additional integer after the method declaration, as shown below. \r\n\r\n**Important**For Windows users, the circular buffer is automatically disabled because of a memory leak [that occurs when a TorchScript model is used in a separate thread](https://github.com/pytorch/pytorch/issues/24237). Unfortunately, this implies a much lower efficiency in terms of CPU.  \r\n\r\n<table>\r\n  <tr>\r\n    <th width=\"50%\">Max / MSP</th>\r\n    <th width=\"50%\">PureData</th>\r\n  </tr>\r\n  <tr>\r\n    <td><img width=\"100%\" src=\"assets/max_buffer.png\" /></td>\r\n    <td><img width=\"100%\" src=\"assets/pd_buffer.png\" /></td>\r\n  </tr>\r\n</table>\r\n\r\n## Multichannel (Max/MSP)\r\n\r\nThe Max/MSP release of `nn~` includes additional externals, namely `mc.nn~` and `mcs.nn~`, allowing the use of the multicanal abilities of Max 8+ to simplify the patching process with `nn~` and optionally decrease the computational load.\r\n\r\nIn the following examples, two audio files are being encoded then decoded by the same model in parallel\r\n\r\n![regular](assets/max_regular.png)\r\n\r\nThis patch can be improved both visually _and_ computationally speaking by using `mc.nn~` and using _batch operations_\r\n\r\n![mc](assets/max_mc.png)\r\n\r\nUsing `mc.nn~` we build the multicanal signals **over the different batches**. In the example above, each multicanal signal will have 2 different canals. We also propose the `mcs.nn~` external that builds multicanal signals **over the different dimensions**, as shown in the example below\r\n\r\n![mcs](assets/max_mcs.png)\r\n\r\nIn the example above, the two multicanals signals yielded by the `nn~ rave encode 2` object have 16 canals each, corresponding to the 16 latent dimensions. This can help patching, while keeping the batching abilities of `mc.nn~` by creating an explicit number of inlets / oulets corresponding to the number of examples we want to process in parallel.\r\n\r\nTo recap, the regular `nn~` operates on a single example, and has as many inlets / outlets as the model has inputs / outputs. The `mc.nn~` external is like `nn~`, but can process multiple examples _at the same time_. The `mcs.nn~` variant is a bit different, and can process mulitple examples at the same time, but will **have one inlet / outlet per examples**.\r\n\r\n## Lazy mode (Max/MSP)\r\n\r\nSince v1.6.0, nn~ has a `void` mode, that allows to initialise it with a fixed number of inlets / outlets, and may be attached to a model afterwards. This can be done with the `void` special model, that enables this lazy initialisation.\r\n\r\n<center>\r\n<img src=\"assets/max_void.png\" width=\"30%\"/>\r\n</center>\r\n\r\n## Special messages\r\n\r\n### enable [0 / 1]\r\n\r\nEnable / Disable computation to save up computation without deleting the model. Similar to how a _bypass_ function would work.\r\n\r\n### reload\r\n\r\nDynamically reloads the model. Can be useful if you want to periodically update the state of a model during a training.\r\n\r\n### dump\r\n\r\nPrints methods / attributes of the loaded model. \r\n\r\n### print_available_models\r\n\r\nPrints models downloadable through API.\r\n\r\n### download\r\n\r\nDownload a model from the API. \r\n\r\n### delete\r\n\r\nDeletes a downloaded model.\r\n\r\n### load\r\n\r\nChange dynamically the incoming model.\r\n\r\n### method\r\n\r\nChange dynamically the used method.\r\n\r\n# Build Instructions\r\n\r\n## macOS\r\n\r\n- Download the latest libtorch (CPU) [here](https://pytorch.org/get-started/locally/) and unzip it to a known directory\r\n- Run the following commands:\r\n\r\n```bash\r\ngit clone https://github.com/acids-ircam/nn_tilde --recurse-submodules\r\ncd nn_tilde\r\ncurl -L https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh > miniconda.sh\r\nchmod +x ./miniconda.sh\r\nbash ./miniconda.sh -b -u -p ./env\r\nsource ./env/bin/activate\r\npip install -r requirements.txt\r\nconda install -c conda-forge curl\r\nmkdir build\r\ncd build\r\nmkdir puredata_include\r\ncurl -L https://raw.githubusercontent.com/pure-data/pure-data/master/src/m_pd.h -o puredata_include/m_pd.h\r\nexport CC=$(brew --prefix llvm)/bin/clang\r\nexport CXX=$(brew --prefix llvm)/bin/clang++\r\ncd build\r\ncmake ../src -DCMAKE_C_COMPILER=$CC -DCMAKE_CXX_COMPILER=$CXX -DCMAKE_PREFIX_PATH=../env/lib/python3.12/site-packages/torch -DCMAKE_BUILD_TYPE=Release -DPUREDATA_INCLUDE_DIR=../puredata_include -DCMAKE_OSX_ARCHITECTURES=arm64\r\ncmake --build . --config Release\r\n```\r\nplease replace `arm64` in the last line by `x86_64` if you want compile for 64 bits. You can remove `-DPUREDATA_INCLUDE_DIR=../puredata_include` to compile only for Max. The Max package is produced in `src/`,  and Pd external in `build/frontend/puredata/Release`.\r\n\r\n\r\n## Windows\r\n\r\n- Download Libtorch (CPU) and dependencies [here](https://pytorch.org/get-started/locally/) and unzip to a known directory.\r\n- Install [Visual Studio Redistribuable](https://learn.microsoft.com/fr-fr/cpp/windows/latest-supported-vc-redist?view=msvc-170) \r\n- Run the following commands (here for Git Bash):\r\n```bash\r\ngit clone https://github.com/acids-ircam/nn_tilde --recurse-submodules\r\ncd nn_tilde\r\ncurl -L https://download.pytorch.org/libtorch/cpu/libtorch-win-shared-with-deps-2.6.0%2Bcpu.zip > \"libtorch.zip\"\r\nunzip libtorch.zip\r\nmkdir pd\r\ncd pd\r\ncurl -L https://msp.ucsd.edu/Software/pd-0.55-2.msw.zip -o pd.zip\r\nunzip pd.zip\r\nmv pd*/src .\r\nmv pd*/bin .\r\ncd ..\r\ngit clone https://github.com/microsoft/vcpkg.git\r\ncd vcpkg \r\n./bootstrap-vcpkg.bat\r\n./vcpkg.exe integrate install\r\n./vcpkg.exe install curl\r\ncd ..\r\nmkdir build\r\ncd build\r\nmkdir puredata_include\r\ncurl -L https://raw.githubusercontent.com/pure-data/pure-data/master/src/m_pd.h -o puredata_include/m_pd.h\r\nexport CC=$(brew --prefix llvm)/bin/clang\r\nexport CXX=$(brew --prefix llvm)/bin/clang++\r\ncd build\r\ncmake ../src -G \"Visual Studio 17 2022\" -DTorch_DIR=../libtorch/share/cmake/Torch -DPUREDATA_INCLUDE_DIR=../pd/src -DPUREDATA_BIN_DIR=../pd/bin -A x64\r\ncmake --build . --config Release\r\n```\r\nYou can remove `-DPUREDATA_INCLUDE_DIR=../puredata_include` to compile only for Max. The Max package is produced in `src/`,  and Pd external in `build/frontend/puredata/Release`.\r\n\r\n## Raspberry Pi\r\n\r\n**not availble in v1.6.0, planned in next version ; please take previous versions if needed**\r\n\r\nWhile nn~ can be compiled and used on Raspberry Pi, you may have to consider using lighter deep learning models. We currently only support 64bit OS.\r\n\r\nInstall nn~ for PureData using\r\n\r\n```bash\r\ncurl -s https://raw.githubusercontent.com/acids-ircam/nn_tilde/master/install/raspberrypi.sh | bash\r\n```\r\n\r\n# Funding\r\n\r\nThis work is led at IRCAM, and has been funded by the following projects\r\n\r\n* [ANR MakiMono](https://acids.ircam.fr/course/makimono/)\r\n* [ACTOR](https://www.actorproject.org/)\r\n* [DAFNE+](https://dafneplus.eu/) N° 101061548\r\n\r\n<img src=\"https://ec.europa.eu/regional_policy/images/information-sources/logo-download-center/eu_co_funded_en.jpg\" width=200px/>\r\n"
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\"ticks\" ],\n\t\t\t\t\t\"originaltempo\" : 120.0,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\", \"signal\", \"\", \"dictionary\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 717.0, 535.0, 150.0, 30.0 ],\n\t\t\t\t\t\"pitchcorrection\" : 0,\n\t\t\t\t\t\"quality\" : \"basic\",\n\t\t\t\t\t\"timestretch\" : [ 0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-8\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 717.0, 579.0, 140.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"nn~ multieffect rms 2048\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\"id\" : \"obj-78\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 757.0, 127.0, 73.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"loadmess 1.\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-75\",\n\t\t\t\t\t\"linecount\" : 5,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 297.0, 127.0, 199.0, 74.0 ],\n\t\t\t\t\t\"text\" : \"an given attribute can be printed with the get method, followed by the attribute name.\\nsimilarly, attributes can be changed using the set method.\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-74\",\n\t\t\t\t\t\"linecount\" : 4,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 481.0, 520.0, 198.0, 60.0 ],\n\t\t\t\t\t\"text\" : \"arguments can also comprise several values, with different types. You can refer to the given python code to see how it is implemented.\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-73\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 297.0, 241.5, 197.0, 33.0 ],\n\t\t\t\t\t\"text\" : \"for a boolean, either true or false must be sent\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-72\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 297.0, 205.0, 197.0, 33.0 ],\n\t\t\t\t\t\"text\" : \"there can be three types ofd attribute : int, str, and string\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-71\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 706.0, 241.5, 197.0, 33.0 ],\n\t\t\t\t\t\"text\" : \"for strings, int and floats, arguments can be sent directly.\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-70\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 356.0, 63.0, 439.0, 33.0 ],\n\t\t\t\t\t\"text\" : \"also, attributes accesible from Max can also be listed by sending the get_settable_attributes message\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-69\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 356.0, 29.0, 439.0, 33.0 ],\n\t\t\t\t\t\"text\" : \"methods available for a given model can be printed by sending the get_available_methods message to nn~\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-66\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 356.0, 554.0, 94.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"set fractal $1 $2\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-64\",\n\t\t\t\t\t\"maxclass\" : \"number\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 356.0, 479.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-62\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 356.0, 520.0, 41.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"pak i f\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-57\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 106.0, 132.0, 96.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"get invert_signal\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-55\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 40.0, 74.0, 111.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"nn~ multieffect thru\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-54\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 186.0, 29.0, 130.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"get_settable_attributes\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-53\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 40.0, 29.0, 132.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"get_available_methods\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-51\",\n\t\t\t\t\t\"maxclass\" : \"scope~\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 356.0, 660.0, 130.0, 130.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"format\" : 6,\n\t\t\t\t\t\"id\" : \"obj-50\",\n\t\t\t\t\t\"maxclass\" : \"flonum\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 436.0, 479.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-46\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 356.0, 605.0, 138.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"nn~ multieffect fractalize\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-45\",\n\t\t\t\t\t\"maxclass\" : \"scope~\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 1334.0, 356.0, 130.0, 130.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-44\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 1334.0, 301.0, 110.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"nn~ multieffect rms\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-43\",\n\t\t\t\t\t\"maxclass\" : \"scope~\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 1175.0, 356.0, 130.0, 130.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-42\",\n\t\t\t\t\t\"maxclass\" : \"scope~\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 1030.0, 356.0, 130.0, 130.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-41\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 1030.0, 301.0, 164.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"nn~ multieffect midside\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"basictuning\" : 440,\n\t\t\t\t\t\"data\" : \t\t\t\t\t{\n\t\t\t\t\t\t\"clips\" : [ \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"absolutepath\" : \"drumLoop.aif\",\n\t\t\t\t\t\t\t\t\"filename\" : \"drumLoop.aif\",\n\t\t\t\t\t\t\t\t\"filekind\" : \"audiofile\",\n\t\t\t\t\t\t\t\t\"id\" : \"u599005403\",\n\t\t\t\t\t\t\t\t\"loop\" : 1,\n\t\t\t\t\t\t\t\t\"content_state\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"loop\" : 1\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n ]\n\t\t\t\t\t}\n,\n\t\t\t\t\t\"followglobaltempo\" : 0,\n\t\t\t\t\t\"formantcorrection\" : 0,\n\t\t\t\t\t\"id\" : \"obj-40\",\n\t\t\t\t\t\"maxclass\" : \"playlist~\",\n\t\t\t\t\t\"mode\" : \"basic\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 5,\n\t\t\t\t\t\"originallength\" : [ 0.0, \"ticks\" ],\n\t\t\t\t\t\"originaltempo\" : 120.0,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\", \"signal\", \"\", \"dictionary\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 1030.0, 238.0, 150.0, 30.0 ],\n\t\t\t\t\t\"pitchcorrection\" : 0,\n\t\t\t\t\t\"quality\" : \"basic\",\n\t\t\t\t\t\"timestretch\" : [ 0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-38\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 159.0, 191.0, 124.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"set invert_signal false\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-37\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 136.0, 162.0, 119.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"set invert_signal true\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-35\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"signal\" 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  {
    "path": "extras/generate_test_model.py",
    "content": "from typing import List, Tuple\n\nimport torch\nimport torch.nn as nn\n\nimport nn_tilde\n\n\nclass AudioUtils(nn_tilde.Module):\n\n    def __init__(self):\n        super().__init__()\n        # REGISTER ATTRIBUTES\n        self.register_attribute('gain_factor', 1.)\n        self.register_attribute('polynomial_factors', (1., 0., 0., 0.))\n        self.register_attribute('saturate_mode', 'tanh')\n        self.register_attribute('invert_signal', False)\n        self.register_attribute('fractal', (2, 0.))\n\n        # REGISTER METHODS\n        self.register_method(\n            'thru',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) input signal'],\n            output_labels=['(signal) output signal'],\n        )\n        self.register_method(\n            'invert',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) input signal'],\n            output_labels=['(signal) output signal'],\n        )\n        self.register_method(\n            'add',\n            in_channels=2,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) first signal', '(signal) second signal'],\n            output_labels=['(signal) output signal'],\n        )\n        self.register_method(\n            'saturate',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) signal to saturate'],\n            output_labels=['(signal) saturated signal'],\n        )\n        self.register_method(\n            'midside',\n            in_channels=2,\n            in_ratio=1,\n            out_channels=2,\n            out_ratio=1,\n            input_labels=['(signal) L channel', '(signal) R channel'],\n            output_labels=['(signal) Mid channel', '(signal) Side channel'],\n        )\n        self.register_method(\n            'rms',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1024,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['(signal) rms value'],\n        )\n        self.register_method(\n            'polynomial',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) signal to distort'],\n            output_labels=['(signal) distorted signal'],\n        )\n\n        self.register_method(\n            'fractalize',\n            in_channels=1,\n            in_ratio=512,\n            out_channels=1,\n            out_ratio=512,\n            input_labels=['(signal) signal to replicate'],\n            output_labels=['(signal) fractalized signal'],\n        )\n\n    @torch.jit.export\n    def thru(self, x: torch.Tensor):\n        return x\n\n    # defining main methods\n    @torch.jit.export\n    def invert(self, x: torch.Tensor):\n        if self.invert_signal[0]:\n            return x\n        else:\n            return -x\n\n    @torch.jit.export\n    def add(self, x: torch.Tensor):\n        return x.sum(-2, keepdim=True) / 2\n\n    @torch.jit.export\n    def fractalize(self, x: torch.Tensor):\n        fractal_order = int(self.fractal[0])\n        fractal_amount = float(self.fractal[1])\n        downsampled_signal = x[..., ::fractal_order]\n        return x\n\n    @torch.jit.export\n    def polynomial(self, x: torch.Tensor):\n        out = torch.zeros_like(x)\n        for i in range(4):\n            out += self.polynomial_factors[i] * x.pow(i + 1)\n        return out\n\n    @torch.jit.export\n    def saturate(self, x: torch.Tensor):\n        saturate_mode = self.saturate_mode[0]\n        if saturate_mode == 'tanh':\n            return torch.tanh(x * self.gain_factor[0])\n        elif saturate_mode == 'clip':\n            return torch.clamp(x * self.gain_factor[0], -1, 1)\n\n    @torch.jit.export\n    def midside(self, x: torch.Tensor):\n        l, r = x[..., 0, :], x[..., 1, :]\n        return torch.stack([(l + r) / 2, (l - r) / 2], dim=-2)\n\n    @torch.jit.export\n    def rms(self, x: torch.Tensor):\n        x = x.reshape(x.shape[0], x.shape[1], 1024, -1)\n        rms = x.pow(2).sum(-2).sqrt() / x.size(-1)\n        return rms\n\n    # defining attribute getters\n    # WARNING : typing the function's ouptut is mandatory\n    @torch.jit.export\n    def get_gain_factor(self) -> float:\n        return float(self.gain_factor[0])\n\n    @torch.jit.export\n    def get_polynomial_factors(self) -> List[float]:\n        polynomial_factors: List[float] = []\n        for p in self.polynomial_factors:\n            polynomial_factors.append(float(p))\n        return polynomial_factors\n\n    @torch.jit.export\n    def get_saturate_mode(self) -> str:\n        return self.saturate_mode[0]\n\n    @torch.jit.export\n    def get_invert_signal(self) -> bool:\n        return self.invert_signal[0]\n\n    @torch.jit.export\n    def get_fractal(self) -> Tuple[int, float]:\n        return (int(self.fractal[0]), float(self.fractal[1]))\n\n    # defining attribute setter\n    # setters must return an error code :\n    # return 0 if the attribute has been adequately set,\n    # return -1 if the attribute was wrong.\n    @torch.jit.export\n    def set_gain_factor(self, x: float) -> int:\n        self.gain_factor = (x, )\n        return 0\n\n    @torch.jit.export\n    def set_polynomial_factors(self, factor1: float, factor2: float,\n                               factor3: float, factor4: float) -> int:\n        factors = (factor1, factor2, factor3, factor4)\n        self.polynomial_factors = factors\n        return 0\n\n    @torch.jit.export\n    def set_saturate_mode(self, x: str):\n        if (x == 'tanh') or (x == 'clip'):\n            self.saturate_mode = (x, )\n            return 0\n        else:\n            return -1\n\n    @torch.jit.export\n    def set_invert_signal(self, x: bool):\n        self.invert_signal = (x, )\n        return 0\n\n    @torch.jit.export\n    def set_fractal(self, factor: int, amount: float):\n        if factor <= 0:\n            return -1\n        elif factor % 2 != 0:\n            return -1\n        self.fractal = (factor, float(amount))\n        return 0\n\n\nif __name__ == '__main__':\n    model = AudioUtils()\n    model.export_to_ts('multieffect.ts')\n"
  },
  {
    "path": "install/dylib_fix.py",
    "content": "import argparse\nimport fnmatch\nimport tqdm\nimport re\nimport subprocess\nimport stat\nimport sys, os\nfrom pathlib import Path\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-p', '--path', type=Path, required=True)\nparser.add_argument('-l', '--lib_paths', nargs=\"*\")\nparser.add_argument('-o', '--out_dir', type=Path, default=None, help=\"optional directory for copying dylibs\")\nparser.add_argument('--safe', action=\"store_true\", help=\"safe mode\")\nparser.add_argument('--sign_id', type=str, default=\"-\", help=\"codesign sign\")\nparser.add_argument('--noclean_rpath', action=\"store_true\", help=\"does not clean rpath\")\nparser.add_argument('--verbose', action=\"store_true\", help=\"verbose output\")\nargs = parser.parse_args()\n\n\nDEFAULT_EXCLUDE_PATHS_SOLVE = ['/System/Library/Frameworks', '@executable_path', '/usr/lib']\nDEFAULT_EXCLUDE_LIBS = [r'libc\\+\\+.*', r'libSystem\\.B\\.*', r'libobjc\\.A\\.*']\n\n\ndef is_executable(file_path):\n    # Check if the file exists and is a regular file\n    if not os.path.isfile(file_path):\n        return False\n    \n    try:\n        result = subprocess.run(\n            ['file', '--brief', file_path],\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n            text=True,\n            check=True\n        )\n        output = result.stdout.strip()\n        # Check if the file is considered a Mach-O dynamically linked shared library or executable\n        if 'Mach-O' in output and ('executable' in output or 'dynamically linked shared library' in output or 'bundle' in output):\n            return True\n    except subprocess.CalledProcessError:\n        pass\n\n    return False\n\n\ndef extract_rpaths(executable_path, replace_dynamic_paths = True):\n    # Run otool to get load commands\n    try:\n        result = subprocess.run(['otool', '-l', executable_path], \n                                check=True, \n                                text=True, \n                                capture_output=True)\n        output = result.stdout\n\n        # Regex pattern to capture RPATH entries\n        rpath_pattern = re.compile(r'LC_RPATH.*?\\n.*?path (.*?) \\(offset', re.S)\n        \n        # Find all RPATH entries\n        rpaths = rpath_pattern.findall(output)\n\n        if replace_dynamic_paths:\n            for i, r in enumerate(rpaths):\n                rpaths[i] = re.sub(\"@loader_path\", str(executable_path.parent), r)\n        \n\n        for i, r in enumerate(rpaths):\n            if r.startswith(\"@loader_path\") or r.startswith(\"@loader_path\") or r.startswith(\"@executable_path\"):\n                continue \n            rpaths[i] = Path(r)\n        return rpaths\n\n    except subprocess.CalledProcessError as e:\n        print(f\"An error occurred: {e}\")\n        return []\n\n\ndef get_library_name(libname):\n    return os.path.splitext(os.path.basename(libname))[0].split('.')[0]\n\n\ndef get_dependencies(filepath, dir_filter=[], lib_filter=[], rpath=None):\n    if not os.path.exists(filepath): return None\n    rpath = rpath or extract_rpaths(filepath)\n    try:\n        result = subprocess.run(['otool', '-L', filepath], \n                                check=True, \n                                text=True, \n                                capture_output=True)\n        # Return the output\n    except subprocess.CalledProcessError as e:\n        return f\"An error occurred: {e}\"\n    result_lines = result.stdout.split('\\n')\n    deps = {}\n    for r in result_lines:\n        result_parsed = re.match(r\"\\s*(.+)\\s+\\((.+)\\)$\", r)\n        if result_parsed is None: \n            continue\n        outs = result_parsed.groups()\n        path = outs[0]\n        dep_name = path.split('/')[-1]\n        original_path = str(path)\n        for rp in rpath:\n            path_tmp = Path(str(path).replace('@rpath', str(rp)))\n            if os.path.exists(path_tmp):\n                path = path_tmp\n                break\n        if True in [Path(path).is_relative_to(r) for r in dir_filter]: continue\n        if True in [re.search(f, str(path)) is not None for f in lib_filter]: continue\n        deps[get_library_name(dep_name)] = {'path': path, 'version': outs[1], 'origin': filepath, 'linked': original_path}\n    return deps\n\n\ndef get_architectures(file_path):\n    try:\n        result = subprocess.run(\n            ['file', file_path],\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n            text=True,\n            check=True\n        )\n        output = result.stdout.strip()\n        return output.split(\" \")[-1]\n    except subprocess.CalledProcessError as e:\n        return f\"Error: {e.stderr.strip()}\"\n\n\n\ndef get_find_results(directory, pattern):\n    try:\n        # Run the find command\n        command = ['find', directory, '-name', pattern]\n        print(\" \".join(command))\n        result = subprocess.run(command, \n                                check=True, \n                                text=True, \n                                capture_output=True)\n        file_list = result.stdout.strip().split('\\n')\n        file_list = [f for f in file_list if f]\n        return file_list\n\n    except subprocess.CalledProcessError as e:\n        return []\n\n\ndef find_candidates_for(lib_name, lib_dir, lib_arch, allow_different_arch: bool = True):\n    lib_name, lib_ext = os.path.splitext(lib_name)\n    lib_name_parts = lib_name.split('.')\n    candidates = []\n    for l in lib_dir: \n        print('parsing %s'%lib_name_parts)\n        for i in reversed(range(1, len(lib_name_parts)+1)):\n            results = get_find_results(l, \".\".join(lib_name_parts[:i]) + \"*\" + lib_ext)\n            if len(results) > 0:\n                break\n        candidates.extend(results)\n    print(\"candidates before filtering : \", candidates)\n    if len(candidates) == 0: \n        return [] \n    candidates_filt_arch = list(filter(lambda x: get_architectures(x) == lib_arch, candidates))\n    if len(candidates_filt_arch) == 0:\n        print('[Warning] Candidates found for %s, but with wrong architecture'%lib_name)\n        if not allow_different_arch:\n            return [] \n    else:\n        candidates = candidates_filt_arch\n    print(candidates)\n\n    for i, c in enumerate(candidates):\n        while os.path.islink(c):\n            c = os.readlink(os.path.abspath(c))\n            if not os.path.isabs(c):\n                c = os.path.join(os.path.dirname(candidates[i]), c)\n            candidates[i] = c\n    return list(set(candidates))\n\n\ndef find_most_relevant_dylib_candidate(name, candidates, accept_weak_matching=False):\n    name_ext = os.path.splitext(name)[1][1:]\n    re_result = re.match(rf\"^([\\d\\w_]+)\\.?([\\d\\.]*)\\.{name_ext}$\", name)\n    if re_result is None: \n        print(f'[Warning] Could not parse name {name} for dynamic lib retrieving ; taking first : {candidates[0]}')\n        return candidates[0]\n    lib_name, lib_version = re_result.groups()\n    matching_name_and_version = []\n    matching_name = []\n    matching = []\n    for c in candidates:\n        c_name = os.path.basename(c)\n        re_result = re.match(rf\"^([\\d\\w_]+)\\.?([\\d\\.]*)\\.{name_ext}$\", c_name)\n        if re_result is not None:\n            c_name, c_version = re_result.groups()\n            if lib_name == c_name and c_version.startswith(lib_version):\n                matching_name_and_version.append(c)\n            elif lib_name == c_name: \n                matching_name.append(c)\n            else:\n                matching.append(c)\n    if len(matching_name_and_version) > 0:\n        return matching_name_and_version[0]\n    elif len(matching_name) > 0: \n        print(f'[Warning] Could only find candidate with different version : {matching_name[0]}')\n        return matching_name[0]\n    else: \n        if accept_weak_matching:\n            print(f'[Warning] Could only find weakly matching library : {matching[0]}')\n            return matching[0]\n        else:\n            return None\n\n\ndef clean_rpath_for_target_executable(lib_path): \n    actions = []\n    rpaths = extract_rpaths(lib_path, replace_dynamic_paths=False)\n    for p in rpaths:\n        if not str(p).startswith('@loader_path'): actions.append(['-delete_rpath', p, lib_path])\n    return actions\n\n\ndef most_relevant_lib(lib_name, path_dicts, dep_paths=[], arch=\"arm64\"):\n    path_list = [Path(p['path']) for p in path_dicts]\n    path_filtered_list = []\n    for p in path_list:\n        print('looking in %s...'%p)\n        while os.path.islink(p):\n            p = Path(p.parent / os.readlink(p)).resolve().absolute()\n        if not p.exists(): continue\n        if not is_executable(p): continue\n        path_filtered_list.append(p)\n    path_list = list(set(path_filtered_list))\n\n    path_list = filter(lambda x: x.exists(), path_list)\n    path_list = filter(lambda x: is_executable(x), path_list)\n    path_list = list(path_list)\n    if len(path_list) == 0:\n        # not found; look for candidate\n        candidates = find_candidates_for(f\"{lib_name}.dylib\", dep_paths, arch)\n        candidate = find_most_relevant_dylib_candidate(f\"{lib_name}.dylib\", candidates)\n        if candidate is None: \n            raise RuntimeError('no valid library found for %s in %s (candidates : %s)'%(lib_name, dep_paths, candidates))\n        return candidate\n    # find in priority the librairies given in arguments\n    for libdir in map(Path, dep_paths):\n        for path in path_list:\n            if path.is_relative_to(libdir): return path\n    return path_list[0]\n\ndef lib_from_exc_path(exc_path, lib_path):\n    exc_parts = exc_path.parts\n    lib_parts = lib_path.parts\n    assert exc_parts[0] == lib_parts[0], \"files do not have a single common root\"\n    i = 0\n    while exc_parts[i] == lib_parts[i]:\n        i+=1\n    return os.path.join(*([\"..\"] * (len(exc_parts[i:])-1) + list(lib_parts[i:])))\n\ndef parse_actions_from_executable(exec_path, dep_paths=[], main_dir = None, verbose=False):\n    if not os.path.exists(exec_path):\n        raise FileNotFoundError(exec_path)\n    if verbose: print(f'parsing {str(exec_path)}...')\n    libs_deps = {}\n    libs_paths = {}\n    libs_analysed = []\n    libs_to_analyse = [exec_path]\n    libs_hash = {}\n    libs_hash_linked = {}\n\n    arch = get_architectures(exec_path)\n    # analyse dependencies\n    while len(libs_to_analyse) != 0:\n        if verbose: print('anlysing librairies %s'%libs_to_analyse)\n        parsed_deps = []\n\n        # fetch dependencies\n        for current_lib in libs_to_analyse:\n            if verbose: print('fetching dependencies from %s'%(current_lib))\n            lib_deps = get_dependencies(current_lib, dir_filter=DEFAULT_EXCLUDE_PATHS_SOLVE, lib_filter=DEFAULT_EXCLUDE_LIBS)\n            for dep_name, dep_params in lib_deps.items():\n                parsed_deps.append(dep_name)\n                libs_deps[dep_name] = libs_deps.get(dep_name, []) + [dep_params]\n                libs_hash[get_library_name(dep_params['path'])] = libs_hash.get(get_library_name(dep_params['path']), []) + [dep_params['origin']]\n                libs_hash_linked[get_library_name(dep_params['path'])] = libs_hash_linked.get(get_library_name(dep_params['path']), []) + [dep_params['linked']]\n            del libs_to_analyse[libs_to_analyse.index(current_lib)]\n            libs_analysed.append(get_library_name(current_lib))\n         \n        # parse dependencies\n        for p in parsed_deps:\n            if verbose: print('fetching paths for %s'%p)\n            if p not in libs_paths:\n                lib_path = most_relevant_lib(p, libs_deps[p], dep_paths, arch)\n                print('found lib : %s'%lib_path)\n                libs_paths[p] = Path(lib_path)\n            if p not in libs_analysed:\n                print('adding %s for parsing'%p)\n                libs_to_analyse.append(libs_paths[p])\n\n    actions = []\n    exec_dir = exec_path.parent\n    os.makedirs(str(main_dir.resolve()), exist_ok=True)\n    for k, v in libs_paths.items():\n        if not (main_dir / v.name).resolve().exists():\n            actions.append(['copy', str(v), str(main_dir)])\n    for k, v in libs_hash.items():\n        for i, v_tmp in enumerate(v):\n            # if str(libs_hash_linked[k][i]).startswith('@rpath'):\n            #     continue\n            if get_library_name(libs_hash_linked[k][i]) == get_library_name(v_tmp.name):\n                if v_tmp.stem == exec_path.stem:\n                    actions.append(['-id', f\"@loader_path/{v_tmp.name}\", str(exec_dir / v_tmp.name)])\n                else:\n                    actions.append(['-id', f\"@loader_path/{v_tmp.name}\", str(main_dir / v_tmp.name)])\n            else:\n                current_dep = str(libs_hash_linked[k][i]) \n                # new_dep = f\"@loader_path/{libs_paths[get_library_name(current_dep)].name}\"\n                if v_tmp.stem == exec_path.stem:\n                    new_dep = f\"@loader_path/{lib_from_exc_path(exec_path, main_dir / libs_paths[get_library_name(current_dep)].name)}\"\n                    actions.append(['-change', current_dep, new_dep, str(exec_dir / v_tmp.name)])\n                else:\n                    new_dep = f\"@loader_path/{libs_paths[get_library_name(current_dep)].name}\"\n                    actions.append(['-change', current_dep, new_dep, str(main_dir / v_tmp.name)])\n    return actions\n\n\ndef perform_action(action, main_dir):\n    try:\n        if action[0] == \"-change\": \n            # Run the find command\n            result = subprocess.run(['install_name_tool', \n                                     '-change', \n                                     action[1], \n                                     action[2], \n                                     action[3]],\n                                    check=True, \n                                    text=True, \n                                    capture_output=False)\n            return \n        elif action[0] == \"-id\":\n            target_path = str(main_dir / os.path.split(action[1])[-1]) \n            result = subprocess.run(['install_name_tool', \n                                     '-id', \n                                     action[1], \n                                     target_path],\n                                    check=True, \n                                    text=True, \n                                    capture_output=False)\n        elif action[0] == \"copy\":\n            target_path = main_dir / os.path.split(action[1])[-1]\n            if not target_path.exists(): \n                result = subprocess.run(['cp',\n                                    action[1],\n                                    str(target_path)],  \n                                    check=True, \n                                    text=True, \n                                    capture_output=False)\n        elif action[0] == \"-delete_rpath\":\n            target_path = str(main_dir / os.path.split(action[2])[-1]) \n            result = subprocess.run(['install_name_tool', \n                                     '-delete_rpath', \n                                     action[1], \n                                     target_path], \n                                    check=True, \n                                    text=True, \n                                    capture_output=False)\n        elif action[0] == \"-add_rpath\":\n            target_path = str(main_dir / os.path.split(action[2])[-1]) \n            result = subprocess.run(['install_name_tool', \n                                     '-add_rpath', \n                                     action[1], \n                                     target_path], \n                                    check=True, \n                                    text=True, \n                                    capture_outpu=False)\n        elif action[0] == \"clean_rpath\":\n            rpaths = extract_rpaths(str(main_dir / action[1]), replace_dynamic_paths=False)\n            for r in rpaths: \n                try:\n                    subprocess.run(['install_name_tool', '-delete_rpath', str(r), str(action[1])])\n                except subprocess.SubprocessError as e: \n                    print(\"problem with clean_rpath : %s\"%e)\n                    pass\n\n        else:\n            print('[Warning] not known action : %s'%action) \n\n    except subprocess.CalledProcessError as e: \n        raise e\n\n\ndef print_action(idx, action):\n    if action[0] == \"copy\":\n        print(f'{idx:02d} cp {\"|\".join(action[1:])} -> @rpath')\n    elif action[0] == \"-change\": \n        print(f\"{idx:02d} -change: {os.path.basename(action[3])}: {action[1]}->{action[2]}\")\n    elif action[0] == \"-id\":\n        print(f\"{idx:02d} -id: {os.path.basename(action[1])}\")\n    elif action[0] == \"-delete_rpath\":\n        print(f\"{idx:02d} -delete_rpath: {action[1]} -> {action[2]}\")\n    elif action[0] == \"clean_rpath\": \n        print(f\"{idx:02d} clean_rpath: {action[1]}\")\n    else:\n        print(idx, action)\n\n\nif __name__ == \"__main__\":\n    exec_path = Path(args.path)\n    main_dir = args.out_dir or args.path.parent \n    actions = parse_actions_from_executable(exec_path, args.lib_paths, main_dir, args.verbose)\n\n\n    if args.safe:\n        for i, a in enumerate(actions):\n            print_action(i, a)\n        print('continue?')\n        answ = \"\"\n        while answ.lower() not in ['y', 'n']:\n            answ = input('[y/n] : ')\n        if answ.lower() == \"n\": raise SystemExit(\"raised by user\") \n\n    os.makedirs(str(main_dir.resolve()), exist_ok=True)\n    for a in tqdm.tqdm(actions): \n        perform_action(a, main_dir = main_dir)\n\n    if not args.noclean_rpath:\n        for m in [os.path.join(main_dir, m) for m in os.listdir(main_dir)] + [args.path]:\n            if is_executable(m):\n                perform_action(['clean_rpath', m], main_dir)\n\n\n    if args.sign_id == \"\": args.sign_id = \"-\"\n    for m in [os.path.join(main_dir, m) for m in os.listdir(main_dir)] + [args.path]:\n        try:\n            subprocess.run(['chmod', '+x', m])\n            subprocess.run(['codesign', '--deep', '--force', '--options=runtime', '--sign', args.sign_id, m])\n            subprocess.run([\"xattr\", \"-r\", \"-d\", \"com.apple.quarantine\", m])\n        except subprocess.CalledProcessError as e: \n            print(f'Could not chmod / codesign file {m} ; codesign needed')\n                "
  },
  {
    "path": "install/macos_max_makeub.sh",
    "content": "TARGET_DIR=$1\nif [ -z $TARGET_DIR ]; then\n    TARGET_DIR=\"nn_tilde\"\nfi\n\necho \"${TARGET_DIR}\"\n\nif [[ ! -d \"${TARGET_DIR}_arm64\" ]]; then\n    echo \"[Error] folder ${TARGET_DIR}_arm64 not found\" \n    exit\nfi\n\nif [[ ! -d \"${TARGET_DIR}_x64\" ]]; then\n    echo \"[Error] folder ${TARGET_DIR}_arm64 not found\" \n    exit\nfi\n\nif [[ ! -d \"${TARGET_DIR}\" ]]; then\n    cp -r ${TARGET_DIR}_arm64 ${TARGET_DIR}\nfi \n\nfor i in $(find ${TARGET_DIR}_x64/externals/*/Contents/MacOS -type f -perm -111) \ndo \n    echo \"UBing file $i...\"\n    lipo -create \"${TARGET_DIR}_arm64/externals/$(basename $i).mxo/Contents/MacOS/$(basename $i)\" \"${TARGET_DIR}_x64/externals/$(basename $i).mxo/Contents/MacOS/$(basename $i)\" -output \"${TARGET_DIR}/externals/$(basename $i).mxo/Contents/MacOS/$(basename $i)\"\ndone\n\nfor i in $(find ${TARGET_DIR}_x64/support/*.dylib)\ndo \n    arch1=\"\"\n    arch2=\"\"\n    if [[ ! -f \"${TARGET_DIR}_x64/support/$(basename $i)\" || ! -f \"${TARGET_DIR}_arm64/support/$(basename $i)\" ]]\n    then \n        echo \"skipping $i\"\n        continue\n    fi\n    is_ub=$(file \"${TARGET_DIR}_x64/support/$(basename $i)\" | grep -Eo '2 architectures')\n    if [[ -n \"$is_ub\" ]]; then\n        echo \"$i / universal binary; skipping\"\n        continue\n    fi\n    arch1=$(file -b \"${TARGET_DIR}_x64/support/$(basename $i)\" | grep -Eo 'x86_64|arm64')\n    arch2=$(file -b \"${TARGET_DIR}_arm64/support/$(basename $i)\" | grep -Eo 'x86_64|arm64')\n    if [[ -z \"$arch1\" || -z \"$arch2\" ]]; then\n        echo \"skipping $i\"\n        continue\n    fi\n    if [[ ! \"$arch1\" == \"$arch2\" ]]; then\n        echo \"$i / arch1 : $arch1; arch2 : $arch2\"\n        lipo -create \"${TARGET_DIR}_arm64/support/$(basename $i)\" \"${TARGET_DIR}_x64/support/$(basename $i)\" -output \"${TARGET_DIR}/support/$(basename $i)\"\n    fi\ndone\n"
  },
  {
    "path": "install/macos_pd_makeub.sh",
    "content": "TARGET_DIR=$1\nif [ -z $TARGET_DIR ]; then\n    TARGET_DIR=\"nn_tilde\"\nfi\n\necho \"${TARGET_DIR}\"\n\nif [[ ! -d \"${TARGET_DIR}_arm64\" ]]; then\n    echo \"[Error] folder ${TARGET_DIR}_arm64 not found\" \n    exit\nfi\n\nif [[ ! -d \"${TARGET_DIR}_x64\" ]]; then\n    echo \"[Error] folder ${TARGET_DIR}_arm64 not found\" \n    exit\nfi\n\nif [[ ! -d \"${TARGET_DIR}\" ]]; then\n    cp -r ${TARGET_DIR}_arm64 ${TARGET_DIR}\nfi \n\nlipo -create \"${TARGET_DIR}_arm64/nn~.pd_darwin\" \"${TARGET_DIR}_x64/nn~.pd_darwin\" -output \"${TARGET_DIR}/nn~.pd_darwin\"\n\nfor i in $(find ${TARGET_DIR}_x64/*.dylib)\ndo \n    arch1=\"\"\n    arch2=\"\"\n    if [[ ! -f \"${TARGET_DIR}_x64$(basename $i)\" || ! -f \"${TARGET_DIR}_arm64/$(basename $i)\" ]]\n    then \n        echo \"skipping $i\"\n        continue\n    fi\n    is_ub=$(file \"${TARGET_DIR}_x64/$(basename $i)\" | grep -Eo '2 architectures')\n    if [[ -n \"$is_ub\" ]]; then\n        echo \"$i / universal binary; skipping\"\n        continue\n    fi\n    arch1=$(file -b \"${TARGET_DIR}_x64/$(basename $i)\" | grep -Eo 'x86_64|arm64')\n    arch2=$(file -b \"${TARGET_DIR}_arm64/$(basename $i)\" | grep -Eo 'x86_64|arm64')\n    if [[ -z \"$arch1\" || -z \"$arch2\" ]]; then\n        echo \"skipping $i\"\n        continue\n    fi\n    if [[ ! \"$arch1\" == \"$arch2\" ]]; then\n        echo \"$i / arch1 : $arch1; arch2 : $arch2\"\n        lipo -create \"${TARGET_DIR}_arm64/$(basename $i)\" \"${TARGET_DIR}_x64/$(basename $i)\" -output \"${TARGET_DIR}/$(basename $i)\"\n    fi\ndone\n"
  },
  {
    "path": "install/max-linker-flags.txt",
    "content": "'-Wl,-U,_addbang' '-Wl,-U,_addfloat' '-Wl,-U,_addftx' '-Wl,-U,_addint' '-Wl,-U,_addinx' '-Wl,-U,_addmess' '-Wl,-U,_advise' '-Wl,-U,_advise_explain' '-Wl,-U,_alias' '-Wl,-U,_AnyKeyDown' '-Wl,-U,_appbuilder_keyword' '-Wl,-U,_appbuilder_register' '-Wl,-U,_argpad' '-Wl,-U,_assist_string' '-Wl,-U,_asyncfile_callback_free' '-Wl,-U,_asyncfile_callback_new' '-Wl,-U,_asyncfile_close' '-Wl,-U,_asyncfile_create' '-Wl,-U,_asyncfile_geteof' '-Wl,-U,_asyncfile_makerequest' '-Wl,-U,_asyncfile_object_method' '-Wl,-U,_asyncfile_params_default' '-Wl,-U,_asyncfile_params_free' '-Wl,-U,_asyncfile_params_new' '-Wl,-U,_asyncfile_read' '-Wl,-U,_asyncfile_seteof' '-Wl,-U,_asyncfile_write' '-Wl,-U,_atom_alloc' '-Wl,-U,_atom_alloc_array' '-Wl,-U,_atom_arg_getdouble' '-Wl,-U,_atom_arg_getfloat' '-Wl,-U,_atom_arg_getlong' '-Wl,-U,_atom_arg_getsym' '-Wl,-U,_atom_dynamic_end' '-Wl,-U,_atom_dynamic_start' '-Wl,-U,_atom_equal' '-Wl,-U,_atom_getatom_array' '-Wl,-U,_atom_getchar_array' '-Wl,-U,_atom_getcharfix' '-Wl,-U,_atom_getdouble_array' '-Wl,-U,_atom_getfloat' '-Wl,-U,_atom_getfloat_array' '-Wl,-U,_atom_getformat' '-Wl,-U,_atom_getlong' '-Wl,-U,_atom_getlong_array' '-Wl,-U,_atom_getobj' '-Wl,-U,_atom_getobj_array' '-Wl,-U,_atom_getsym' '-Wl,-U,_atom_getsym_array' '-Wl,-U,_atom_gettext' '-Wl,-U,_atom_gettext_precision' '-Wl,-U,_atom_gettype' '-Wl,-U,_atom_setatom_array' '-Wl,-U,_atom_setattrval' '-Wl,-U,_atom_setbinbuf' '-Wl,-U,_atom_setchar_array' '-Wl,-U,_atom_setdouble_array' '-Wl,-U,_atom_setfloat' '-Wl,-U,_atom_setfloat_array' '-Wl,-U,_atom_setformat' '-Wl,-U,_atom_setlong' '-Wl,-U,_atom_setlong_array' '-Wl,-U,_atom_setobj' '-Wl,-U,_atom_setobj_array' '-Wl,-U,_atom_setobjval' '-Wl,-U,_atom_setparse' '-Wl,-U,_atom_setsym' '-Wl,-U,_atom_setsym_array' '-Wl,-U,_atom_string' '-Wl,-U,_atombuf_append' '-Wl,-U,_atombuf_count' '-Wl,-U,_atombuf_eval' '-Wl,-U,_atombuf_firstatom' '-Wl,-U,_atombuf_free' '-Wl,-U,_atombuf_misc' '-Wl,-U,_atombuf_new' '-Wl,-U,_atombuf_next' '-Wl,-U,_atombuf_prepend' '-Wl,-U,_atombuf_replace' '-Wl,-U,_atombuf_replacepoundargs' '-Wl,-U,_atombuf_save' '-Wl,-U,_atombuf_set' '-Wl,-U,_atombuf_subst' '-Wl,-U,_atombuf_text' '-Wl,-U,_atombuf_totext' '-Wl,-U,_atomisatomarray' '-Wl,-U,_atomisdictionary' '-Wl,-U,_atomisstring' '-Wl,-U,_atoms_totext' '-Wl,-U,_attr_addfilter_clip' '-Wl,-U,_attr_addfilter_clip_scale' '-Wl,-U,_attr_addfilterget_clip' '-Wl,-U,_attr_addfilterget_clip_scale' '-Wl,-U,_attr_addfilterget_proc' '-Wl,-U,_attr_addfilterset_clip' '-Wl,-U,_attr_addfilterset_clip_scale' '-Wl,-U,_attr_addfilterset_proc' '-Wl,-U,_attr_args_dictionary' '-Wl,-U,_attr_args_offset' '-Wl,-U,_attr_args_process' '-Wl,-U,_attr_args_process_flags' '-Wl,-U,_attr_dictionary_process' '-Wl,-U,_attr_dictionary_check' '-Wl,-U,_attr_filter_clip_new' '-Wl,-U,_attr_filter_proc_new' '-Wl,-U,_attr_offset_array_new' '-Wl,-U,_attr_offset_new' '-Wl,-U,_attr_typedfun_set' '-Wl,-U,_attribute_new' '-Wl,-U,_attribute_new_sym' '-Wl,-U,_attribute_new_long' '-Wl,-U,_attribute_new_float' '-Wl,-U,_attribute_new_obj' '-Wl,-U,_attribute_new_atoms' '-Wl,-U,_attribute_new_attrval' '-Wl,-U,_attribute_new_binbuf' '-Wl,-U,_attribute_new_format' '-Wl,-U,_attribute_new_objval' '-Wl,-U,_attribute_new_parse' '-Wl,-U,_auxtable_checksym' '-Wl,-U,_bangout' '-Wl,-U,_bf_singlefast' '-Wl,-U,_binbuf_addtext' '-Wl,-U,_binbuf_append' '-Wl,-U,_binbuf_delete' '-Wl,-U,_binbuf_eval' '-Wl,-U,_binbuf_getatom' '-Wl,-U,_binbuf_gethandle' '-Wl,-U,_binbuf_insert' '-Wl,-U,_binbuf_inshandle' '-Wl,-U,_binbuf_make' '-Wl,-U,_binbuf_new' '-Wl,-U,_binbuf_read' '-Wl,-U,_binbuf_set' '-Wl,-U,_binbuf_text' '-Wl,-U,_binbuf_totext' '-Wl,-U,_binbuf_vinsert' '-Wl,-U,_binbuf_write' '-Wl,-U,_bitwrap_bell_filter' '-Wl,-U,_bitwrap_box_filter' '-Wl,-U,_bitwrap_bspline_filter' '-Wl,-U,_bitwrap_filter_default' '-Wl,-U,_bitwrap_filter_filter' '-Wl,-U,_bitwrap_interp' '-Wl,-U,_bitwrap_lanczos3_filter' '-Wl,-U,_bitwrap_mitchell_filter' '-Wl,-U,_bitwrap_triangle_filter' '-Wl,-U,_bitwrap_wrap_gworld' '-Wl,-U,_box_getcolor' '-Wl,-U,_boxcolor_rgb2index' '-Wl,-U,_call_method_attrval' '-Wl,-U,_call_method_binbuf' '-Wl,-U,_call_method_char' '-Wl,-U,_call_method_char_array' '-Wl,-U,_call_method_double' '-Wl,-U,_call_method_double_array' '-Wl,-U,_call_method_float' '-Wl,-U,_call_method_float_array' '-Wl,-U,_call_method_format' '-Wl,-U,_call_method_long' '-Wl,-U,_call_method_long_array' '-Wl,-U,_call_method_obj' '-Wl,-U,_call_method_obj_array' '-Wl,-U,_call_method_objval' '-Wl,-U,_call_method_parse' '-Wl,-U,_call_method_sym' '-Wl,-U,_call_method_sym_array' '-Wl,-U,_call_method_typed' '-Wl,-U,_charset_convert' '-Wl,-U,_charset_utf8tounicode' '-Wl,-U,_charset_unicodetoutf8' '-Wl,-U,_charset_isvalidutf8' '-Wl,-U,_charset_utf8_count' '-Wl,-U,_charset_utf8_offset' '-Wl,-U,_charset_isspace' '-Wl,-U,_class_addadornment' '-Wl,-U,_class_addattr' '-Wl,-U,_class_addattr_atoms' '-Wl,-U,_class_addattr_atoms' '-Wl,-U,_class_addattr_format' '-Wl,-U,_class_addattr_format' '-Wl,-U,_class_addattr_parse' '-Wl,-U,_class_addattr_parse' '-Wl,-U,_class_addcommand' '-Wl,-U,_class_addmethod' '-Wl,-U,_class_addtypedwrapper' '-Wl,-U,_class_addtransform' '-Wl,-U,_class_adornment_get' '-Wl,-U,_class_attr_addattr' '-Wl,-U,_class_attr_addattr_atoms' '-Wl,-U,_class_attr_addattr_atoms' '-Wl,-U,_class_attr_addattr_format' '-Wl,-U,_class_attr_addattr_format' '-Wl,-U,_class_attr_addattr_parse' '-Wl,-U,_class_attr_addattr_parse' '-Wl,-U,_class_attr_attr_get' '-Wl,-U,_class_attr_attr_getvalueof' '-Wl,-U,_class_attr_attr_setvalueof' '-Wl,-U,_class_attr_get' '-Wl,-U,_class_attr_method' '-Wl,-U,_class_buildprototype' '-Wl,-U,_class_clonable' '-Wl,-U,_class_cloneprototype' '-Wl,-U,_class_extra_lookup' '-Wl,-U,_class_extra_store' '-Wl,-U,_class_extra_storeflags' '-Wl,-U,_class_findbyname' '-Wl,-U,_class_findbyname_casefree' '-Wl,-U,_class_getifloaded' '-Wl,-U,_class_getifloaded_casefree' '-Wl,-U,_class_free' '-Wl,-U,_class_getmethod_object' '-Wl,-U,_class_getpath' '-Wl,-U,_class_is_ui' '-Wl,-U,_class_is_box' '-Wl,-U,_class_mess' '-Wl,-U,_class_method' '-Wl,-U,_class_nameget' '-Wl,-U,_class_new' '-Wl,-U,_class_noinlet' '-Wl,-U,_class_obexoffset_get' '-Wl,-U,_class_obexoffset_set' '-Wl,-U,_class_register' '-Wl,-U,_class_alias' '-Wl,-U,_class_copy' '-Wl,-U,_class_dumpout_wrap' '-Wl,-U,_class_setname' '-Wl,-U,_class_namespace_fromsym' '-Wl,-U,_class_namespace_getclassnames' '-Wl,-U,_class_setpath' '-Wl,-U,_class_sticky' '-Wl,-U,_class_sticky_clear' '-Wl,-U,_class_typedwrapper_get' '-Wl,-U,_classname_openhelp' '-Wl,-U,_classname_openrefpage' '-Wl,-U,_classname_openquery' '-Wl,-U,_clock_delay' '-Wl,-U,_clock_fdelay' '-Wl,-U,_clock_fdelay2' '-Wl,-U,_clock_fset' '-Wl,-U,_clock_fset2' '-Wl,-U,_clock_getextfmt' '-Wl,-U,_clock_getftime' '-Wl,-U,_clock_getftime_nocache' '-Wl,-U,_clock_new' '-Wl,-U,_clock_new_withscheduler' '-Wl,-U,_clock_set' '-Wl,-U,_clock_unset' '-Wl,-U,_clock_xdelay' '-Wl,-U,_clock_xset' '-Wl,-U,_clock_xunset' '-Wl,-U,_clock_setowner' '-Wl,-U,_CmdKeyDown' '-Wl,-U,_compression_compressjson_headless' '-Wl,-U,_compression_decompressjson_headless' '-Wl,-U,_connection_client' '-Wl,-U,_connection_delete' '-Wl,-U,_connection_send' '-Wl,-U,_connection_server' '-Wl,-U,_CopyFromGWorld' '-Wl,-U,_cpost' '-Wl,-U,_critical_enter' '-Wl,-U,_critical_exit' '-Wl,-U,_critical_free' '-Wl,-U,_critical_new' '-Wl,-U,_critical_tryenter' '-Wl,-U,_crosshatch' '-Wl,-U,_CtrlKeyDown' '-Wl,-U,_CurrentOptionKeysDown' '-Wl,-U,_debug_printf' '-Wl,-U,_defer' '-Wl,-U,_defer_front' '-Wl,-U,_defer_low' '-Wl,-U,_defer_medium' '-Wl,-U,_defer_sys_low' '-Wl,-U,_defvolume' '-Wl,-U,_dialog_setkey' '-Wl,-U,_dictionary_appendatom' '-Wl,-U,_dictionary_appendatom_flags' '-Wl,-U,_dictionary_appendatomarray' '-Wl,-U,_dictionary_appendatoms' '-Wl,-U,_dictionary_appendatoms_flags' '-Wl,-U,_dictionary_appendattribute' '-Wl,-U,_dictionary_appendbinbuf' '-Wl,-U,_dictionary_appenddictionary' '-Wl,-U,_dictionary_appendfloat' '-Wl,-U,_dictionary_appendlong' '-Wl,-U,_dictionary_appendobject' '-Wl,-U,_dictionary_appendobject_flags' '-Wl,-U,_dictionary_appendstring' '-Wl,-U,_dictionary_appendsym' '-Wl,-U,_dictionary_chuckentry' '-Wl,-U,_dictionary_clear' '-Wl,-U,_dictionary_copyatoms' '-Wl,-U,_dictionary_copyatoms_ext' '-Wl,-U,_dictionary_copydefatoms' '-Wl,-U,_dictionary_copyentries' '-Wl,-U,_dictionary_copyunique' '-Wl,-U,_dictionary_deleteentry' '-Wl,-U,_dictionary_dump' '-Wl,-U,_dictionary_entry_getkey' '-Wl,-U,_dictionary_entry_getvalue' '-Wl,-U,_dictionary_entry_getvalues' '-Wl,-U,_dictionary_entryisatomarray' '-Wl,-U,_dictionary_entryisdictionary' '-Wl,-U,_dictionary_entryisstring' '-Wl,-U,_dictionary_freekeys' '-Wl,-U,_dictionary_funall' '-Wl,-U,_dictionary_getatom' '-Wl,-U,_dictionary_getatom_ext' '-Wl,-U,_dictionary_getatomarray' '-Wl,-U,_dictionary_getatoms' '-Wl,-U,_dictionary_getatoms_ext' '-Wl,-U,_dictionary_getattribute' '-Wl,-U,_dictionary_getdefatom' '-Wl,-U,_dictionary_getdefatoms' '-Wl,-U,_dictionary_getdeffloat' '-Wl,-U,_dictionary_getdeflong' '-Wl,-U,_dictionary_getdefstring' '-Wl,-U,_dictionary_getdefsym' '-Wl,-U,_dictionary_getdictionary' '-Wl,-U,_dictionary_getentrycount' '-Wl,-U,_dictionary_getfloat' '-Wl,-U,_dictionary_getkeys' '-Wl,-U,_dictionary_getkeys_ordered' '-Wl,-U,_dictionary_getlong' '-Wl,-U,_dictionary_getobject' '-Wl,-U,_dictionary_getobject_ext' '-Wl,-U,_dictionary_getstring' '-Wl,-U,_dictionary_getsym' '-Wl,-U,_dictionary_get_ex' '-Wl,-U,_dictionary_hasentry' '-Wl,-U,_dictionary_new' '-Wl,-U,_dictionary_prototypefromclass' '-Wl,-U,_dictionary_read' '-Wl,-U,_dictionary_sprintf' '-Wl,-U,_dictionary_write' '-Wl,-U,_dictobj_register' '-Wl,-U,_dictobj_unregister' '-Wl,-U,_dictobj_findregistered_clone' '-Wl,-U,_dictobj_findregistered_retain' '-Wl,-U,_dictobj_release' '-Wl,-U,_dictobj_namefromptr' '-Wl,-U,_dictobj_outlet_atoms' '-Wl,-U,_dictobj_atom_safety' '-Wl,-U,_dictobj_atom_safety_flags' '-Wl,-U,_dictobj_atom_release' '-Wl,-U,_dictobj_validate' '-Wl,-U,_dictobj_jsonfromstring' '-Wl,-U,_dictobj_dictionaryfromstring' '-Wl,-U,_dictobj_dictionaryfromatoms' '-Wl,-U,_dictobj_dictionaryfromatoms_extended' '-Wl,-U,_dictobj_dictionarytoatoms' '-Wl,-U,_dictobj_key_parse' '-Wl,-U,_dictobj_modify' '-Wl,-U,_dictobj_convertatoms' '-Wl,-U,_dictobj_convertatoms2' '-Wl,-U,_dictobj_atomtotype' '-Wl,-U,_dictobj_outlet_atoms_prefix' '-Wl,-U,_dictobj_outlet_atoms_prefix_ext' '-Wl,-U,_arrayobj_register' '-Wl,-U,_arrayobj_unregister' '-Wl,-U,_arrayobj_findregistered_retain' '-Wl,-U,_arrayobj_findregistered_clone' '-Wl,-U,_arrayobj_release' '-Wl,-U,_arrayobj_namefromptr' '-Wl,-U,_stringobj_register' '-Wl,-U,_stringobj_unregister' '-Wl,-U,_stringobj_findregistered_retain' '-Wl,-U,_stringobj_findregistered_clone' '-Wl,-U,_stringobj_release' '-Wl,-U,_stringobj_namefromptr' '-Wl,-U,_disposhandle' '-Wl,-U,_drawstr' '-Wl,-U,_ed_new' '-Wl,-U,_ed_settext' '-Wl,-U,_ed_vis' '-Wl,-U,_egetfn' '-Wl,-U,_error' '-Wl,-U,_error_subscribe' '-Wl,-U,_error_sym' '-Wl,-U,_error_unsubscribe' '-Wl,-U,_errorcount_get' '-Wl,-U,_errorcount_set' '-Wl,-U,_evnum_get' '-Wl,-U,_evnum_incr' '-Wl,-U,_expr_eval' '-Wl,-U,_expr_new' '-Wl,-U,_fileformat_filetype' '-Wl,-U,_fileformat_installsniffer' '-Wl,-U,_fileformat_sniff' '-Wl,-U,_fileformat_sniffdata' '-Wl,-U,_fileformat_stripsuffix' '-Wl,-U,_fileformat_typesuffix' '-Wl,-U,_fileformat_suffixtotype' '-Wl,-U,_fileformat_suffix' '-Wl,-U,_fileformat_divide' '-Wl,-U,_fileformat_cstrtofiletype' '-Wl,-U,_fileformat_symtofiletype' '-Wl,-U,_fileformat_filetypetosym' '-Wl,-U,_fileformat_typetosuffix' '-Wl,-U,_filekind_getfiletypes' '-Wl,-U,_filekind_getname' '-Wl,-U,_filekind_nametoicon' '-Wl,-U,_fileload' '-Wl,-U,_fileload_extended' '-Wl,-U,_fileload_type' '-Wl,-U,_fileusage_addfile' '-Wl,-U,_fileusage_addfilename' '-Wl,-U,_fileusage_addpackage' '-Wl,-U,_fileusage_addpathname' '-Wl,-U,_fileusage_copyfolder' '-Wl,-U,_fileusage_makefolder' '-Wl,-U,_fileusage_addfolder' '-Wl,-U,_filewatcher_new' '-Wl,-U,_filewatcher_start' '-Wl,-U,_filewatcher_stop' '-Wl,-U,_finder_addclass' '-Wl,-U,_floatin' '-Wl,-U,_floatout' '-Wl,-U,_fontinfo_getname' '-Wl,-U,_fontinfo_getnumber' '-Wl,-U,_fontinfo_getsize' '-Wl,-U,_fontmap_getmapping' '-Wl,-U,_force_install' '-Wl,-U,_freebytes' '-Wl,-U,_freeobject' '-Wl,-U,_gensym' '-Wl,-U,_gensym_tr' '-Wl,-U,_getbytes' '-Wl,-U,_getexttime' '-Wl,-U,_getfn' '-Wl,-U,_getfolder' '-Wl,-U,_getschedtime' '-Wl,-U,_gettime' '-Wl,-U,_gettime_forobject' '-Wl,-U,_globalmouse_addlistener' '-Wl,-U,_globalmouse_removelistener' '-Wl,-U,_globalsymbol_reference' '-Wl,-U,_globalsymbol_dereference' '-Wl,-U,_globalsymbol_bind' '-Wl,-U,_globalsymbol_unbind' '-Wl,-U,_globalsymbol_notify' '-Wl,-U,_growhandle' '-Wl,-U,_GWorldFromPict' '-Wl,-U,_handle2tempfile' '-Wl,-U,_hashtab_chuck' '-Wl,-U,_hashtab_chuckkey' '-Wl,-U,_hashtab_clear' '-Wl,-U,_hashtab_delete' '-Wl,-U,_hashtab_findfirst' '-Wl,-U,_hashtab_flags' '-Wl,-U,_hashtab_funall' '-Wl,-U,_hashtab_getflags' '-Wl,-U,_hashtab_getkeyflags' '-Wl,-U,_hashtab_getkeys' '-Wl,-U,_hashtab_getsize' '-Wl,-U,_hashtab_keyflags' '-Wl,-U,_hashtab_lookup' '-Wl,-U,_hashtab_lookupentry' '-Wl,-U,_hashtab_lookupflags' '-Wl,-U,_hashtab_lookuplong' '-Wl,-U,_hashtab_lookupsym' '-Wl,-U,_hashtab_methodall' '-Wl,-U,_hashtab_methodall_imp' '-Wl,-U,_hashtab_new' '-Wl,-U,_hashtab_objfunall' '-Wl,-U,_hashtab_print' '-Wl,-U,_hashtab_readonly' '-Wl,-U,_hashtab_store' '-Wl,-U,_hashtab_store_safe' '-Wl,-U,_hashtab_storeflags' '-Wl,-U,_hashtab_storelong' '-Wl,-U,_hashtab_storesym' '-Wl,-U,_helpstring' '-Wl,-U,_inisr_set' '-Wl,-U,_inlet_append' '-Wl,-U,_inlet_insert_after' '-Wl,-U,_inlet_count' '-Wl,-U,_inlet_new' '-Wl,-U,_inlet_nth' '-Wl,-U,_inlet_to' '-Wl,-U,_inlet4' '-Wl,-U,_inspector_open' '-Wl,-U,_intin' '-Wl,-U,_intload' '-Wl,-U,_intout' '-Wl,-U,_IsKeyDown' '-Wl,-U,_isbpatcher' '-Wl,-U,_isnewex' '-Wl,-U,_ispatcher' '-Wl,-U,_isr' '-Wl,-U,_isr_set' '-Wl,-U,_linklist_append' '-Wl,-U,_linklist_chuck' '-Wl,-U,_linklist_chuckindex' '-Wl,-U,_linklist_chuckptr' '-Wl,-U,_linklist_clear' '-Wl,-U,_linklist_deleteindex' '-Wl,-U,_linklist_findall' '-Wl,-U,_linklist_findfirst' '-Wl,-U,_linklist_flags' '-Wl,-U,_linklist_funall' '-Wl,-U,_linklist_funall_break' '-Wl,-U,_linklist_funindex' '-Wl,-U,_linklist_getflags' '-Wl,-U,_linklist_getindex' '-Wl,-U,_linklist_getsize' '-Wl,-U,_linklist_insert_sorted' '-Wl,-U,_linklist_insertafterobjptr' '-Wl,-U,_linklist_insertbeforeobjptr' '-Wl,-U,_linklist_insertindex' '-Wl,-U,_linklist_last' '-Wl,-U,_linklist_makearray' '-Wl,-U,_linklist_methodall' '-Wl,-U,_linklist_methodall_imp' '-Wl,-U,_linklist_methodindex' '-Wl,-U,_linklist_methodindex_imp' '-Wl,-U,_linklist_moveafterobjptr' '-Wl,-U,_linklist_movebeforeobjptr' '-Wl,-U,_linklist_new' '-Wl,-U,_linklist_next' '-Wl,-U,_linklist_objptr2index' '-Wl,-U,_linklist_prev' '-Wl,-U,_linklist_readonly' '-Wl,-U,_linklist_reverse' '-Wl,-U,_linklist_rotate' '-Wl,-U,_linklist_shuffle' '-Wl,-U,_linklist_sort' '-Wl,-U,_linklist_substitute' '-Wl,-U,_linklist_swap' '-Wl,-U,_linklist_match' '-Wl,-U,_linklist_chuckobject' '-Wl,-U,_linklist_deleteobject' '-Wl,-U,_linklist_prune' '-Wl,-U,_listout' '-Wl,-U,_loadbang_disabled' '-Wl,-U,_loadbang_dequeue' '-Wl,-U,_loadbang_queueobject' '-Wl,-U,_loadbang_resume' '-Wl,-U,_loadbang_suspend' '-Wl,-U,_loader_setpath' '-Wl,-U,_locatefile' '-Wl,-U,_locatefile_extended' '-Wl,-U,_locatefilelist' '-Wl,-U,_locatefiletype' '-Wl,-U,_lockout_set' '-Wl,-U,_lowload_jpatcher_frombuffer' '-Wl,-U,_lowload_jpatcher_frombuffer_withobexprototype' '-Wl,-U,_lowload_jpatcher_fromamxd_data' '-Wl,-U,_loader_loadamxd_tohandle' '-Wl,-U,_maxlogger_log' '-Wl,-U,_maxversion' '-Wl,-U,_max_unicodekeydown' '-Wl,-U,_max_unicodekeyup' '-Wl,-U,_maxcache_getpath' '-Wl,-U,_maxcache_checkfile' '-Wl,-U,_maxcache_usefile' '-Wl,-U,_maxdb_search' '-Wl,-U,_maxdb_search_sprintf' '-Wl,-U,_maxdb_tag' '-Wl,-U,_maxdb_filter' '-Wl,-U,_maxdb_getstate' '-Wl,-U,_maxdb_query' '-Wl,-U,_maxdb_query_direct' '-Wl,-U,_mayquote' '-Wl,-U,_method_false' '-Wl,-U,_method_object_free' '-Wl,-U,_method_object_getmesslist' '-Wl,-U,_method_object_getmethod' '-Wl,-U,_method_object_getname' '-Wl,-U,_method_object_new' '-Wl,-U,_method_object_new_messlist' '-Wl,-U,_method_object_setmesslist' '-Wl,-U,_method_object_setmethod' '-Wl,-U,_method_object_setname' '-Wl,-U,_method_true' '-Wl,-U,_mfl_guiidle' '-Wl,-U,_mfl_idle' '-Wl,-U,_mfl_init' '-Wl,-U,_mfl_exit' '-Wl,-U,_path_mfl_getapppath' '-Wl,-U,_movecursor' '-Wl,-U,_namedpipeconnection_isconnected' '-Wl,-U,_nameinpath' '-Wl,-U,_nameload' '-Wl,-U,_nameload_unique' '-Wl,-U,_nameload_unique_internal' '-Wl,-U,_nametab_filename' '-Wl,-U,_nametab_getmatches' '-Wl,-U,_newex_knows' '-Wl,-U,_newhandle' '-Wl,-U,_newinstance' '-Wl,-U,_newobject' '-Wl,-U,_newobject_fromdictionary' '-Wl,-U,_newobject_fromboxtext' '-Wl,-U,_newobject_sprintf' '-Wl,-U,_noloadbangdisable_get' '-Wl,-U,_noloadbangdisable_set' '-Wl,-U,_notify_free' '-Wl,-U,_nullfn' '-Wl,-U,_object_addattr' '-Wl,-U,_object_addattr_atoms' '-Wl,-U,_object_addattr_atoms' '-Wl,-U,_object_addattr_format' '-Wl,-U,_object_addattr_format' '-Wl,-U,_object_addattr_parse' '-Wl,-U,_object_addattr_parse' '-Wl,-U,_object_addmethod' '-Wl,-U,_object_alloc' '-Wl,-U,_object_attach' '-Wl,-U,_object_attach_byptr' '-Wl,-U,_object_attach_byptr_register' '-Wl,-U,_object_attr_addattr' '-Wl,-U,_object_attr_addattr_atoms' '-Wl,-U,_object_attr_addattr_atoms' '-Wl,-U,_object_attr_addattr_format' '-Wl,-U,_object_attr_addattr_format' '-Wl,-U,_object_attr_addattr_parse' '-Wl,-U,_object_attr_addattr_parse' '-Wl,-U,_object_attr_attr_get' '-Wl,-U,_object_attr_attr_getvalueof' '-Wl,-U,_object_attr_attr_setvalueof' '-Wl,-U,_object_attr_enforcelocal' '-Wl,-U,_object_attr_freeze' '-Wl,-U,_object_attr_get' '-Wl,-U,_object_attr_getchar' '-Wl,-U,_object_attr_getchar_array' '-Wl,-U,_object_attr_getdisabled' '-Wl,-U,_object_attr_getdouble_array' '-Wl,-U,_object_attr_getdump' '-Wl,-U,_object_attr_getfloat' '-Wl,-U,_object_attr_getfloat_array' '-Wl,-U,_object_attr_getinvisible' '-Wl,-U,_object_attr_getlong' '-Wl,-U,_object_attr_getlong_array' '-Wl,-U,_object_attr_getnames' '-Wl,-U,_object_attr_getobj' '-Wl,-U,_object_attr_getsym' '-Wl,-U,_object_attr_getsym_array' '-Wl,-U,_object_attr_getvalueof' '-Wl,-U,_object_attr_method' '-Wl,-U,_object_attr_setattrval' '-Wl,-U,_object_attr_setbinbuf' '-Wl,-U,_object_attr_setchar' '-Wl,-U,_object_attr_setchar_array' '-Wl,-U,_object_attr_setdisabled' '-Wl,-U,_object_attr_setdouble_array' '-Wl,-U,_object_attr_setfloat' '-Wl,-U,_object_attr_setfloat_array' '-Wl,-U,_object_attr_setformat' '-Wl,-U,_object_attr_setinvisible' '-Wl,-U,_object_attr_setlong' '-Wl,-U,_object_attr_setlong_array' '-Wl,-U,_object_attr_setobj' '-Wl,-U,_object_attr_setobjval' '-Wl,-U,_object_attr_setparse' '-Wl,-U,_object_attr_setsym' '-Wl,-U,_object_attr_setsym_array' '-Wl,-U,_object_attr_setvalueof' '-Wl,-U,_object_attr_usercanget' '-Wl,-U,_object_attr_usercanset' '-Wl,-U,_object_attr_getdirty' '-Wl,-U,_object_attrhash_apply' '-Wl,-U,_object_bug' '-Wl,-U,_object_chuckattr' '-Wl,-U,_object_chuckmethod' '-Wl,-U,_object_class' '-Wl,-U,_object_classname' '-Wl,-U,_object_namespace' '-Wl,-U,_class_namespace' '-Wl,-U,_object_classname_compare' '-Wl,-U,_object_clonable' '-Wl,-U,_object_clone' '-Wl,-U,_object_clone_generic' '-Wl,-U,_object_commandenabled' '-Wl,-U,_object_deleteattr' '-Wl,-U,_object_deletemethod' '-Wl,-U,_object_detach' '-Wl,-U,_object_detach_byptr' '-Wl,-U,_object_dictionary_fromnewargs' '-Wl,-U,_object_dictionaryarg' '-Wl,-U,_object_error' '-Wl,-U,_object_error_obtrusive' '-Wl,-U,_jpatcher_error_obtrusive' '-Wl,-U,_object_findregistered' '-Wl,-U,_object_findregisteredbyptr' '-Wl,-U,_object_free' '-Wl,-U,_object_getattrlist' '-Wl,-U,_object_getcommand' '-Wl,-U,_object_getenabler' '-Wl,-U,_object_getmethod' '-Wl,-U,_object_getmethod_object' '-Wl,-U,_object_getftime' '-Wl,-U,_object_getvalueof' '-Wl,-U,_object_getvalueof_ext' '-Wl,-U,_object_handlecommand' '-Wl,-U,_object_inspect' '-Wl,-U,_object_mess' '-Wl,-U,_object_method' '-Wl,-U,_object_method_imp' '-Wl,-U,_object_method_attrval' '-Wl,-U,_object_method_binbuf' '-Wl,-U,_object_method_char' '-Wl,-U,_object_method_char_array' '-Wl,-U,_object_method_double' '-Wl,-U,_object_method_double_array' '-Wl,-U,_object_method_float' '-Wl,-U,_object_method_float_array' '-Wl,-U,_object_method_format' '-Wl,-U,_object_method_long' '-Wl,-U,_object_method_long_array' '-Wl,-U,_object_method_obj' '-Wl,-U,_object_method_obj_array' '-Wl,-U,_object_method_objval' '-Wl,-U,_object_method_parse' '-Wl,-U,_object_method_sym' '-Wl,-U,_object_method_sym_array' '-Wl,-U,_object_method_typed' '-Wl,-U,_object_method_typedfun' '-Wl,-U,_object_new' '-Wl,-U,_object_new_imp' '-Wl,-U,_object_new_attrval' '-Wl,-U,_object_new_binbuf' '-Wl,-U,_object_new_format' '-Wl,-U,_object_new_objval' '-Wl,-U,_object_new_parse' '-Wl,-U,_object_new_typed' '-Wl,-U,_object_notify' '-Wl,-U,_object_obex_dumpout' '-Wl,-U,_object_obex_free' '-Wl,-U,_object_obex_get' '-Wl,-U,_object_obex_lookup' '-Wl,-U,_object_obex_lookuplong' '-Wl,-U,_object_obex_lookupsym' '-Wl,-U,_object_obex_quickref' '-Wl,-U,_object_obex_set' '-Wl,-U,_object_obex_store' '-Wl,-U,_object_obex_storelong' '-Wl,-U,_object_obex_storesym' '-Wl,-U,_object_post' '-Wl,-U,_object_poststring' '-Wl,-U,_object_refpage_get_class_info' '-Wl,-U,_object_refpage_get_class_info_fromclassname' '-Wl,-U,_object_refpage_method_is_undocumented' '-Wl,-U,_object_refpage_method_is_groupreference' '-Wl,-U,_object_register' '-Wl,-U,_object_register_unique' '-Wl,-U,_object_replaceargs' '-Wl,-U,_object_reveal' '-Wl,-U,_object_setvalueof' '-Wl,-U,_object_setvalueof_ext' '-Wl,-U,_object_show' '-Wl,-U,_object_sticky' '-Wl,-U,_object_sticky_clear' '-Wl,-U,_object_subpatcher' '-Wl,-U,_object_unregister' '-Wl,-U,_object_warn' '-Wl,-U,_object_zero' '-Wl,-U,_object_isnogood' '-Wl,-U,_onecopy_fileload' '-Wl,-U,_open_dialog' '-Wl,-U,_open_dialog_filetypelist' '-Wl,-U,_opendialog_pathset' '-Wl,-U,_open_messageset' '-Wl,-U,_open_promptset' '-Wl,-U,_OptionKeyDown' '-Wl,-U,_ouchstring' '-Wl,-U,_outlet_add' '-Wl,-U,_outlet_addmonitor' '-Wl,-U,_outlet_anything' '-Wl,-U,_outlet_append' '-Wl,-U,_outlet_insert_after' '-Wl,-U,_outlet_atoms' '-Wl,-U,_outlet_atoms_ext' '-Wl,-U,_outlet_bang' '-Wl,-U,_outlet_canadd' '-Wl,-U,_outlet_count' '-Wl,-U,_outlet_enable' '-Wl,-U,_outlet_float' '-Wl,-U,_outlet_int' '-Wl,-U,_outlet_list' '-Wl,-U,_outlet_msg' '-Wl,-U,_outlet_new' '-Wl,-U,_outlet_notify' '-Wl,-U,_outlet_nth' '-Wl,-U,_outlet_removemonitor' '-Wl,-U,_outlet_rm' '-Wl,-U,_outlet_array' '-Wl,-U,_outlet_string' '-Wl,-U,_packages_getpackagepath' '-Wl,-U,_packages_createsubpathlist' '-Wl,-U,_packages_getsubpathcontents' '-Wl,-U,_palette_getcolor' '-Wl,-U,_patcher_eachdo' '-Wl,-U,_path_addnamed' '-Wl,-U,_path_addpath' '-Wl,-U,_path_build' '-Wl,-U,_path_closefolder' '-Wl,-U,_path_copyfile' '-Wl,-U,_path_copyfolder' '-Wl,-U,_path_copytotempfile' '-Wl,-U,_path_createfolder' '-Wl,-U,_path_createressysfile' '-Wl,-U,_path_createsysfile' '-Wl,-U,_path_deletefile' '-Wl,-U,_path_tempfolder' '-Wl,-U,_path_desktopfolder' '-Wl,-U,_path_userdocfolder' '-Wl,-U,_path_usermaxfolder' '-Wl,-U,_path_extendedfileinfo' '-Wl,-U,_path_fileinfo' '-Wl,-U,_path_fileisresource' '-Wl,-U,_path_foldernextfile' '-Wl,-U,_path_frompathname' '-Wl,-U,_path_fromunicodepathname' '-Wl,-U,_path_toabsolutesystempath' '-Wl,-U,_path_absolutepath' '-Wl,-U,_path_absolutepath_filetypelist' '-Wl,-U,_path_getapppath' '-Wl,-U,_path_getdefault' '-Wl,-U,_path_getfilecreationdate' '-Wl,-U,_path_getfilemoddate' '-Wl,-U,_path_getmoddate' '-Wl,-U,_path_getname' '-Wl,-U,_path_getnext' '-Wl,-U,_path_getpath' '-Wl,-U,_path_getprefstring' '-Wl,-U,_path_getseparator' '-Wl,-U,_path_getstyle' '-Wl,-U,_path_getsupportpath' '-Wl,-U,_path_infoforopensysfile' '-Wl,-U,_path_movefile' '-Wl,-U,_path_nameconform' '-Wl,-U,_path_nameinpath' '-Wl,-U,_path_nameisrelative' '-Wl,-U,_path_openfolder' '-Wl,-U,_path_openresfile' '-Wl,-U,_path_openressysfile' '-Wl,-U,_path_opensysfile' '-Wl,-U,_path_removefromlist' '-Wl,-U,_path_removepath' '-Wl,-U,_path_renamefile' '-Wl,-U,_path_resolvefile' '-Wl,-U,_path_setdefault' '-Wl,-U,_path_setfileinfo' '-Wl,-U,_path_setpermanent' '-Wl,-U,_path_setprefstring' '-Wl,-U,_path_sysnameinpath' '-Wl,-U,_path_collpathnamefrompath' '-Wl,-U,_path_tempfilename' '-Wl,-U,_path_topathname' '-Wl,-U,_path_topotentialname' '-Wl,-U,_path_topotentialunicodename' '-Wl,-U,_path_frompotentialpathname' '-Wl,-U,_path_splitnames' '-Wl,-U,_path_usermaxfolder' '-Wl,-U,_path_addfiles' '-Wl,-U,_path_addfolders' '-Wl,-U,_path_removefiles' '-Wl,-U,_path_exists' '-Wl,-U,_path_inpath' '-Wl,-U,_plug_free' '-Wl,-U,_plug_init' '-Wl,-U,_plug_setloopfun' '-Wl,-U,_popup_free' '-Wl,-U,_popup_new' '-Wl,-U,_popup_show' '-Wl,-U,_post' '-Wl,-U,_post_displayrecent' '-Wl,-U,_post_getpos' '-Wl,-U,_post_sym' '-Wl,-U,_postatom' '-Wl,-U,_postdictionary' '-Wl,-U,_poststring' '-Wl,-U,_preferences_class_define' '-Wl,-U,_preferences_class_defineoption' '-Wl,-U,_preferences_define' '-Wl,-U,_preferences_defineoption' '-Wl,-U,_preferences_getatomforkey' '-Wl,-U,_preferences_getatoms' '-Wl,-U,_preferences_getchar' '-Wl,-U,_preferences_getlong' '-Wl,-U,_preferences_getsym' '-Wl,-U,_preferences_path' '-Wl,-U,_preferences_readdictionary' '-Wl,-U,_preferences_setatoms' '-Wl,-U,_preferences_setchar' '-Wl,-U,_preferences_setlong' '-Wl,-U,_preferences_setsym' '-Wl,-U,_preferences_subpath' '-Wl,-U,_preferences_writedictionary' '-Wl,-U,_preset_int' '-Wl,-U,_preset_set' '-Wl,-U,_preset_store' '-Wl,-U,_proxy_getinlet' '-Wl,-U,_proxy_new' '-Wl,-U,_qelem_free' '-Wl,-U,_qelem_front' '-Wl,-U,_qelem_idlefree' '-Wl,-U,_qelem_idlefront' '-Wl,-U,_qelem_idleset' '-Wl,-U,_qelem_idleunset' '-Wl,-U,_qelem_new' '-Wl,-U,_qelem_set' '-Wl,-U,_qelem_unset' '-Wl,-U,_qelem_setowner' '-Wl,-U,_quotestring' '-Wl,-U,_versioncmp' '-Wl,-U,_versioncanparse' '-Wl,-U,_qti_extra_flags_get' '-Wl,-U,_qti_extra_flags_set' '-Wl,-U,_qti_extra_free' '-Wl,-U,_qti_extra_matrix_get' '-Wl,-U,_qti_extra_matrix_set' '-Wl,-U,_qti_extra_new' '-Wl,-U,_qti_extra_pixelformat_get' '-Wl,-U,_qti_extra_pixelformat_set' '-Wl,-U,_qti_extra_rect_get' '-Wl,-U,_qti_extra_rect_set' '-Wl,-U,_qti_extra_scalemode_get' '-Wl,-U,_qti_extra_scalemode_set' '-Wl,-U,_qti_extra_time_get' '-Wl,-U,_qti_extra_time_set' '-Wl,-U,_qtimage_getrect' '-Wl,-U,_qtimage_open' '-Wl,-U,_quickmap_add' '-Wl,-U,_quickmap_drop' '-Wl,-U,_quickmap_lookup_key1' '-Wl,-U,_quickmap_lookup_key2' '-Wl,-U,_quickmap_readonly' '-Wl,-U,_quickmap_new' '-Wl,-U,_quittask_install' '-Wl,-U,_quittask_remove' '-Wl,-U,_quittask_remove2' '-Wl,-U,_readatom' '-Wl,-U,_readatom_flags' '-Wl,-U,_readtohandle' '-Wl,-U,_recent_add' '-Wl,-U,_recent_getlist' '-Wl,-U,_recent_project_getlist' '-Wl,-U,_reg_object_namespace_lookup' '-Wl,-U,_reg_object_singlesym' '-Wl,-U,_rerand' '-Wl,-U,_saveas_autoextension' '-Wl,-U,_saveas_dialog' '-Wl,-U,_saveas_messageset' '-Wl,-U,_saveas_promptset' '-Wl,-U,_saveas_setselectedtype' '-Wl,-U,_saveasdialog_extended' '-Wl,-U,_saveasdialog_extended_filetypelist' '-Wl,-U,_saveasdialog_pathset' '-Wl,-U,_sched_idledequeue' '-Wl,-U,_sched_isinpoll' '-Wl,-U,_sched_isinqueue' '-Wl,-U,_sched_resume' '-Wl,-U,_sched_set_takeover' '-Wl,-U,_sched_setpollthrottle' '-Wl,-U,_sched_setqueuethrottle' '-Wl,-U,_sched_suspend' '-Wl,-U,_schedule' '-Wl,-U,_schedule_defer' '-Wl,-U,_schedule_delay' '-Wl,-U,_schedule_fdefer' '-Wl,-U,_schedule_fdelay' '-Wl,-U,_schedulef' '-Wl,-U,_scheduler_gettime' '-Wl,-U,_scheduler_getaudiooffset' '-Wl,-U,_scheduler_new' '-Wl,-U,_scheduler_run' '-Wl,-U,_scheduler_get' '-Wl,-U,_scheduler_set' '-Wl,-U,_scheduler_settime' '-Wl,-U,_scheduler_setaudioschedulertime' '-Wl,-U,_scheduler_fromobject' '-Wl,-U,_scheduler_shift' '-Wl,-U,_serialno' '-Wl,-U,_setclock_delay' '-Wl,-U,_setclock_fdelay' '-Wl,-U,_setclock_fgettime' '-Wl,-U,_setclock_getftime' '-Wl,-U,_setclock_gettime' '-Wl,-U,_setclock_unset' '-Wl,-U,_setup' '-Wl,-U,_ShiftKeyDown' '-Wl,-U,_simpleprefs_dictionary' '-Wl,-U,_sndfile_info' '-Wl,-U,_sndfile_writeheader' '-Wl,-U,_sprintf' '-Wl,-U,_sscanf' '-Wl,-U,_sprintf_tr' '-Wl,-U,_stdinletinfo' '-Wl,-U,_stdlist' '-Wl,-U,_str_tr' '-Wl,-U,_string_getptr' '-Wl,-U,_string_new' '-Wl,-U,_string_reserve' '-Wl,-U,_string_append' '-Wl,-U,_string_chop' '-Wl,-U,_string_clone_to_existing' '-Wl,-U,_stringload' '-Wl,-U,_strncpy_zero' '-Wl,-U,_snprintf_zero' '-Wl,-U,_strncat_zero' '-Wl,-U,_symbol_tr' '-Wl,-U,_symbol_unique' '-Wl,-U,_symbolarray_sort' '-Wl,-U,_symobject_new' '-Wl,-U,_symobject_linklist_match' '-Wl,-U,_sysdateformat_strftimetodatetime' '-Wl,-U,_sysdateformat_formatdatetime' '-Wl,-U,_sysfile_close' '-Wl,-U,_sysfile_geteof' '-Wl,-U,_sysfile_geteof_64' '-Wl,-U,_sysfile_getpos' '-Wl,-U,_sysfile_getpos_64' '-Wl,-U,_sysfile_openhandle' '-Wl,-U,_sysfile_openptrsize' '-Wl,-U,_sysfile_read' '-Wl,-U,_sysfile_readtextfile' '-Wl,-U,_sysfile_readtohandle' '-Wl,-U,_sysfile_readtoptr' '-Wl,-U,_sysfile_seteof' '-Wl,-U,_sysfile_setobject' '-Wl,-U,_sysfile_setpos' '-Wl,-U,_sysfile_setpos_64' '-Wl,-U,_sysfile_spoolcopy' '-Wl,-U,_sysfile_write' '-Wl,-U,_sysfile_writetextfile' '-Wl,-U,_sysmem_copyptr' '-Wl,-U,_sysmem_freehandle' '-Wl,-U,_sysmem_freeptr' '-Wl,-U,_sysmem_handlesize' '-Wl,-U,_sysmem_lockhandle' '-Wl,-U,_sysmem_newhandle' '-Wl,-U,_sysmem_newhandleclear' '-Wl,-U,_sysmem_newptr' '-Wl,-U,_sysmem_newptrclear' '-Wl,-U,_sysmem_nullterminatehandle' '-Wl,-U,_sysmem_ptrandhand' '-Wl,-U,_sysmem_ptrbeforehand' '-Wl,-U,_sysmem_ptrsize' '-Wl,-U,_sysmem_resizehandle' '-Wl,-U,_sysmem_resizeptr' '-Wl,-U,_sysmem_resizeptrclear' '-Wl,-U,_sysmenu_appenditem' '-Wl,-U,_sysmenu_appendrawitem' '-Wl,-U,_sysmenu_appendseparator' '-Wl,-U,_sysmenu_assignsubmenu' '-Wl,-U,_sysmenu_checkitem' '-Wl,-U,_sysmenu_cmdid_popup' '-Wl,-U,_sysmenu_cmdid_set' '-Wl,-U,_sysmenu_copyitems' '-Wl,-U,_sysmenu_deleteallitems' '-Wl,-U,_sysmenu_deleteitem' '-Wl,-U,_sysmenu_dispose' '-Wl,-U,_sysmenu_getcheck' '-Wl,-U,_sysmenu_gethelp' '-Wl,-U,_sysmenu_getid' '-Wl,-U,_sysmenu_gettext' '-Wl,-U,_sysmenu_gettitle' '-Wl,-U,_sysmenu_insert' '-Wl,-U,_sysmenu_insertsubmenu' '-Wl,-U,_sysmenu_itemcount' '-Wl,-U,_sysmenu_new' '-Wl,-U,_sysmenu_setitem' '-Wl,-U,_sysmenu_setreference' '-Wl,-U,_sysmenu_setshortcut' '-Wl,-U,_sysmenu_settext' '-Wl,-U,_sysmidi_createport' '-Wl,-U,_sysmidi_deletemarked' '-Wl,-U,_sysmidi_enqbigpacket' '-Wl,-U,_sysmidi_getinstance' '-Wl,-U,_sysmidi_idtoport' '-Wl,-U,_sysmidi_indextoname' '-Wl,-U,_sysmidi_iterate' '-Wl,-U,_sysmidi_numinports' '-Wl,-U,_sysmidi_numoutports' '-Wl,-U,_sysmidi_uniqueid' '-Wl,-U,_sysmidi_data1toport' '-Wl,-U,_sysmidi_nametoport' '-Wl,-U,_sysparallel_processorcount' '-Wl,-U,_sysparallel_physical_processorcount' '-Wl,-U,_sysparallel_task_benchprint' '-Wl,-U,_sysparallel_task_cancel' '-Wl,-U,_sysparallel_task_data' '-Wl,-U,_sysparallel_task_execute' '-Wl,-U,_sysparallel_task_free' '-Wl,-U,_sysparallel_task_new' '-Wl,-U,_sysparallel_task_workerproc' '-Wl,-U,_sysparallel_worker_execute' '-Wl,-U,_sysparallel_worker_free' '-Wl,-U,_sysparallel_worker_new' '-Wl,-U,_systhread_cond_broadcast' '-Wl,-U,_systhread_cond_free' '-Wl,-U,_systhread_cond_new' '-Wl,-U,_systhread_cond_signal' '-Wl,-U,_systhread_cond_wait' '-Wl,-U,_systhread_create' '-Wl,-U,_systhread_exit' '-Wl,-U,_systhread_getspecific' '-Wl,-U,_systhread_ismainthread' '-Wl,-U,_systhread_istimerthread' '-Wl,-U,_systhread_isaudiothread' '-Wl,-U,_systhread_set_name' '-Wl,-U,_systhread_markasaudiothread_begin' '-Wl,-U,_systhread_markasaudiothread_end' '-Wl,-U,_systhread_join' '-Wl,-U,_systhread_timedjoin' '-Wl,-U,_systhread_detach' '-Wl,-U,_systhread_mutex_free' '-Wl,-U,_systhread_mutex_lock' '-Wl,-U,_systhread_mutex_new' '-Wl,-U,_systhread_mutex_newlock' '-Wl,-U,_systhread_mutex_trylock' '-Wl,-U,_systhread_mutex_unlock' '-Wl,-U,_systhread_rwlock_new' '-Wl,-U,_systhread_rwlock_free' '-Wl,-U,_systhread_rwlock_rdlock' '-Wl,-U,_systhread_rwlock_tryrdlock' '-Wl,-U,_systhread_rwlock_rdunlock' '-Wl,-U,_systhread_rwlock_wrlock' '-Wl,-U,_systhread_rwlock_trywrlock' '-Wl,-U,_systhread_rwlock_wrunlock' '-Wl,-U,_systhread_rwlock_setspintime' '-Wl,-U,_systhread_rwlock_getspintime' '-Wl,-U,_systhread_key_create' '-Wl,-U,_systhread_key_delete' '-Wl,-U,_systhread_self' '-Wl,-U,_systhread_equal' '-Wl,-U,_systhread_setpriority' '-Wl,-U,_systhread_getpriority' '-Wl,-U,_systhread_setspecific' '-Wl,-U,_systhread_eliminatedenormals' '-Wl,-U,_systhread_sleep' '-Wl,-U,_systhread_terminate' '-Wl,-U,_systime_datetime' '-Wl,-U,_systime_datetoseconds' '-Wl,-U,_systime_datetime_milliseconds' '-Wl,-U,_systime_ms' '-Wl,-U,_systime_seconds' '-Wl,-U,_systime_secondstodate' '-Wl,-U,_systime_ticks' '-Wl,-U,_systimer_gettime' '-Wl,-U,_tabfromhandle' '-Wl,-U,_table_dirty' '-Wl,-U,_table_get' '-Wl,-U,_textpreferences_add' '-Wl,-U,_textpreferences_addoption' '-Wl,-U,_textpreferences_addraw' '-Wl,-U,_textpreferences_addrect' '-Wl,-U,_textpreferences_close' '-Wl,-U,_textpreferences_default' '-Wl,-U,_textpreferences_open' '-Wl,-U,_textpreferences_read' '-Wl,-U,_time_stop' '-Wl,-U,_time_tick' '-Wl,-U,_time_getms' '-Wl,-U,_time_getticks' '-Wl,-U,_time_listen' '-Wl,-U,_time_getphase' '-Wl,-U,_time_setvalue' '-Wl,-U,_class_time_addattr' '-Wl,-U,_time_new' '-Wl,-U,_time_new_custom' '-Wl,-U,_time_getnamed' '-Wl,-U,_time_enable_attributes' '-Wl,-U,_time_isfixedunit' '-Wl,-U,_time_schedule' '-Wl,-U,_time_schedule_limit' '-Wl,-U,_time_setticks' '-Wl,-U,_time_now' '-Wl,-U,_time_getitm' '-Wl,-U,_time_calcquantize' '-Wl,-U,_time_setclock' '-Wl,-U,_toolfile_fread' '-Wl,-U,_toolfile_fwrite' '-Wl,-U,_toolfile_getc' '-Wl,-U,_toolfile_new' '-Wl,-U,_translation_getinterfacepath' '-Wl,-U,_typedmess' '-Wl,-U,_typelist_make' '-Wl,-U,_utils_setcontext' '-Wl,-U,_utils_usecontext' '-Wl,-U,_utils_getcontextnumber' '-Wl,-U,_utils_contextinuse' '-Wl,-U,_wind_advise' '-Wl,-U,_wind_advise_explain' '-Wl,-U,_wind_nocancel' '-Wl,-U,_wind_setcursor' '-Wl,-U,_xmltree_attr_symcompare' '-Wl,-U,_xmltree_attribute_free' '-Wl,-U,_xmltree_attribute_new' '-Wl,-U,_xmltree_cdata_free' '-Wl,-U,_xmltree_cdata_new' '-Wl,-U,_xmltree_cdata_splittext' '-Wl,-U,_xmltree_charnode_addinterface' '-Wl,-U,_xmltree_charnode_appenddata' '-Wl,-U,_xmltree_charnode_deletedata' '-Wl,-U,_xmltree_charnode_free' '-Wl,-U,_xmltree_charnode_insertdata' '-Wl,-U,_xmltree_charnode_new' '-Wl,-U,_xmltree_charnode_replacedata' '-Wl,-U,_xmltree_charnode_substringdata' '-Wl,-U,_xmltree_comment_free' '-Wl,-U,_xmltree_comment_new' '-Wl,-U,_xmltree_document_createattribute' '-Wl,-U,_xmltree_document_createcdatasection' '-Wl,-U,_xmltree_document_createcomment' '-Wl,-U,_xmltree_document_createelement' '-Wl,-U,_xmltree_document_createheader' '-Wl,-U,_xmltree_document_createtextnode' '-Wl,-U,_xmltree_document_filename' '-Wl,-U,_xmltree_document_free' '-Wl,-U,_xmltree_document_getelementsbytagname' '-Wl,-U,_xmltree_document_new' '-Wl,-U,_xmltree_document_print' '-Wl,-U,_xmltree_document_read' '-Wl,-U,_xmltree_document_write' '-Wl,-U,_xmltree_document_xmlparse_cdata_end' '-Wl,-U,_xmltree_document_xmlparse_cdata_start' '-Wl,-U,_xmltree_document_xmlparse_characterdata' '-Wl,-U,_xmltree_document_xmlparse_comment' '-Wl,-U,_xmltree_document_xmlparse_default' '-Wl,-U,_xmltree_document_xmlparse_doctype_end' '-Wl,-U,_xmltree_document_xmlparse_doctype_start' '-Wl,-U,_xmltree_document_xmlparse_element_end' '-Wl,-U,_xmltree_document_xmlparse_element_start' '-Wl,-U,_xmltree_element_free' '-Wl,-U,_xmltree_element_getattribute' '-Wl,-U,_xmltree_element_getattribute_float' '-Wl,-U,_xmltree_element_getattribute_float_array' '-Wl,-U,_xmltree_element_getattribute_long' '-Wl,-U,_xmltree_element_getattribute_long_array' '-Wl,-U,_xmltree_element_getattribute_sym' '-Wl,-U,_xmltree_element_getattribute_sym_array' '-Wl,-U,_xmltree_element_getattributenode' '-Wl,-U,_xmltree_element_getelementsbytagname' '-Wl,-U,_xmltree_element_new' '-Wl,-U,_xmltree_element_removeattribute' '-Wl,-U,_xmltree_element_removeattributenode' '-Wl,-U,_xmltree_element_setattribute' '-Wl,-U,_xmltree_element_setattribute_float' '-Wl,-U,_xmltree_element_setattribute_float_array' '-Wl,-U,_xmltree_element_setattribute_long' '-Wl,-U,_xmltree_element_setattribute_long_array' '-Wl,-U,_xmltree_element_setattribute_sym' '-Wl,-U,_xmltree_element_setattribute_sym_array' '-Wl,-U,_xmltree_element_setattributenode' '-Wl,-U,_xmltree_element_symcompare' '-Wl,-U,_xmltree_init' '-Wl,-U,_xmltree_node_addinterface' '-Wl,-U,_xmltree_node_appendchild' '-Wl,-U,_xmltree_node_clonenode' '-Wl,-U,_xmltree_node_free' '-Wl,-U,_xmltree_node_getnodevalasstring' '-Wl,-U,_xmltree_node_getnodevalue' '-Wl,-U,_xmltree_node_getnodevalue_float' '-Wl,-U,_xmltree_node_getnodevalue_float_array' '-Wl,-U,_xmltree_node_getnodevalue_long' '-Wl,-U,_xmltree_node_getnodevalue_long_array' '-Wl,-U,_xmltree_node_getnodevalue_sym' '-Wl,-U,_xmltree_node_getnodevalue_sym_array' '-Wl,-U,_xmltree_node_haschildnodes' '-Wl,-U,_xmltree_node_insertbefore' '-Wl,-U,_xmltree_node_new' '-Wl,-U,_xmltree_node_nodevalue' '-Wl,-U,_xmltree_node_nodevalue_float' '-Wl,-U,_xmltree_node_nodevalue_float_array' '-Wl,-U,_xmltree_node_nodevalue_long' '-Wl,-U,_xmltree_node_nodevalue_long_array' '-Wl,-U,_xmltree_node_nodevalue_sym' '-Wl,-U,_xmltree_node_nodevalue_sym_array' '-Wl,-U,_xmltree_node_removeallchildren' '-Wl,-U,_xmltree_node_removechild' '-Wl,-U,_xmltree_node_replacechild' '-Wl,-U,_xmltree_node_setnodevalasstring' '-Wl,-U,_xmltree_node_write' '-Wl,-U,_xmltree_text_free' '-Wl,-U,_xmltree_text_new' '-Wl,-U,_xmltree_text_splittext' '-Wl,-U,_xpcoll_dereference' '-Wl,-U,_xpcoll_load' '-Wl,-U,_xpcoll_open' '-Wl,-U,_xpcoll_opensysfile' '-Wl,-U,_xpcoll_reference' '-Wl,-U,_xpcoll_setclientcallback' '-Wl,-U,_xpcoll_getvol' '-Wl,-U,_xpcoll_openfile' '-Wl,-U,_xpcoll_fromnameddata' '-Wl,-U,_xpcoll_unfreezedevice' '-Wl,-U,_xsetpost' '-Wl,-U,_zgetfn' '-Wl,-U,_jbox_initclass' '-Wl,-U,_jbox_new' '-Wl,-U,_jbox_free' '-Wl,-U,_jbox_ready' '-Wl,-U,_jbox_redraw' '-Wl,-U,_jbox_redrawcontents' '-Wl,-U,_jbox_getoutlet' '-Wl,-U,_jbox_getinlet' '-Wl,-U,_jbox_updatetextfield' '-Wl,-U,_jbox_updatetextfield_safe' '-Wl,-U,_jbox_updatetextfield_lockmutex' '-Wl,-U,_jbox_grabfocus' '-Wl,-U,_jbox_show_caption' '-Wl,-U,_jbox_hide_caption' '-Wl,-U,_jbox_invalidate_layer' '-Wl,-U,_jbox_remove_layer' '-Wl,-U,_jbox_start_layer' '-Wl,-U,_jbox_end_layer' '-Wl,-U,_jbox_paint_layer' '-Wl,-U,_jbox_get_boxpath' '-Wl,-U,_jbox_validaterects' '-Wl,-U,_jbox_processlegacydefaults' '-Wl,-U,_jbox_isdefaultattribute' '-Wl,-U,_jpatcher_deleteobj' '-Wl,-U,_jpatcher_is_patcher' '-Wl,-U,_jpatcher_get_box' '-Wl,-U,_jpatcher_get_count' '-Wl,-U,_jpatcher_get_firstobject' '-Wl,-U,_jpatcher_get_lastobject' '-Wl,-U,_jpatcher_get_firstline' '-Wl,-U,_jpatcher_get_firstview' '-Wl,-U,_jpatcher_set_locked' '-Wl,-U,_jpatcher_get_title' '-Wl,-U,_jpatcher_set_title' '-Wl,-U,_jpatcher_get_name' '-Wl,-U,_jpatcher_get_filename' '-Wl,-U,_jpatcher_get_filepath' '-Wl,-U,_jpatcher_get_dirty' '-Wl,-U,_jpatcher_set_dirty' '-Wl,-U,_jpatcher_get_bgcolor' '-Wl,-U,_jpatcher_set_bgcolor' '-Wl,-U,_jpatcher_get_gridsize' '-Wl,-U,_jpatcher_set_gridsize' '-Wl,-U,_jpatcher_get_parentpatcher' '-Wl,-U,_jpatcher_get_toppatcher' '-Wl,-U,_jpatcher_get_hubholder' '-Wl,-U,_jpatcher_get_maxclass' '-Wl,-U,_jpatcher_get_parentclass' '-Wl,-U,_jpatcher_get_rect' '-Wl,-U,_jpatcher_set_rect' '-Wl,-U,_jpatcher_get_noedit' '-Wl,-U,_jpatcher_uniqueboxname' '-Wl,-U,_jpatcher_getboxfont' '-Wl,-U,_jpatcher_get_controller' '-Wl,-U,_jpatcher_addboxlistener' '-Wl,-U,_jpatcher_removeboxlistener' '-Wl,-U,_jpatcher_get_fileversion' '-Wl,-U,_jpatcher_get_currentfileversion' '-Wl,-U,_jpatcher_get_bglocked' '-Wl,-U,_jpatcher_get_presentation' '-Wl,-U,_jpatcher_inc_maxsendcontext' '-Wl,-U,_jpatcher_dictionary_modernui' '-Wl,-U,_jpatcher_dictionary_version' '-Wl,-U,_jpatcher_sortdictionary' '-Wl,-U,_jpatcher_getboxfromid' '-Wl,-U,_jpatcher_endlognewobjects' '-Wl,-U,_jpatcher_swapboxlist' '-Wl,-U,_jpatcher_swaplinelist' '-Wl,-U,_systemfontname' '-Wl,-U,_systemfontsym' '-Wl,-U,_jpatchercontroller_createobject' '-Wl,-U,_jpatchercontroller_setpatcherview' '-Wl,-U,_jpatchercontroller_pastefileintoobject' '-Wl,-U,_jpatchercontroller_pastefileat' '-Wl,-U,_jpatchercontroller_connectobjects' '-Wl,-U,_jpatchercontroller_begintransaction' '-Wl,-U,_jpatchercontroller_endtransaction' '-Wl,-U,_jpatchercontroller_setattr' '-Wl,-U,_jpatchercontroller_dictionary_setattr' '-Wl,-U,_object_attr_get_rect' '-Wl,-U,_object_attr_set_rect' '-Wl,-U,_object_attr_getcolor' '-Wl,-U,_object_attr_setcolor' '-Wl,-U,_object_attr_getpt' '-Wl,-U,_object_attr_setpt' '-Wl,-U,_object_attr_getsize' '-Wl,-U,_object_attr_setsize' '-Wl,-U,_jbox_get_maxclass' '-Wl,-U,_jbox_get_patcher' '-Wl,-U,_jbox_get_object' '-Wl,-U,_jbox_get_rect_for_view' '-Wl,-U,_jbox_set_rect_for_view' '-Wl,-U,_jbox_get_rect_for_sym' '-Wl,-U,_jbox_set_rect_for_sym' '-Wl,-U,_jbox_set_rect' '-Wl,-U,_jbox_get_patching_rect' '-Wl,-U,_jbox_set_patching_rect' '-Wl,-U,_jbox_get_presentation_rect' '-Wl,-U,_jbox_set_presentation_rect' '-Wl,-U,_jbox_set_position' '-Wl,-U,_jbox_get_patching_position' '-Wl,-U,_jbox_set_patching_position' '-Wl,-U,_jbox_get_presentation_position' '-Wl,-U,_jbox_set_presentation_position' '-Wl,-U,_jbox_set_size' '-Wl,-U,_jbox_get_patching_size' '-Wl,-U,_jbox_set_patching_size' '-Wl,-U,_jbox_get_presentation_size' '-Wl,-U,_jbox_set_presentation_size' '-Wl,-U,_jbox_get_hidden' '-Wl,-U,_jbox_set_hidden' '-Wl,-U,_jbox_get_fontname' '-Wl,-U,_jbox_set_fontname' '-Wl,-U,_jbox_get_fontsize' '-Wl,-U,_jbox_createfont' '-Wl,-U,_jbox_set_fontsize' '-Wl,-U,_jbox_fontface_to_weight_slant' '-Wl,-U,_jbox_get_font_slant' '-Wl,-U,_jbox_get_font_weight' '-Wl,-U,_jbox_get_color' '-Wl,-U,_jbox_set_color' '-Wl,-U,_jbox_get_nextobject' '-Wl,-U,_jbox_get_prevobject' '-Wl,-U,_jbox_get_varname' '-Wl,-U,_jbox_set_varname' '-Wl,-U,_jbox_get_id' '-Wl,-U,_jbox_get_canhilite' '-Wl,-U,_jbox_get_background' '-Wl,-U,_jbox_set_background' '-Wl,-U,_jbox_get_ignoreclick' '-Wl,-U,_jbox_set_ignoreclick' '-Wl,-U,_jbox_get_drawfirstin' '-Wl,-U,_jbox_get_outline' '-Wl,-U,_jbox_set_outline' '-Wl,-U,_jbox_get_growy' '-Wl,-U,_jbox_get_growboth' '-Wl,-U,_jbox_get_nogrow' '-Wl,-U,_jbox_get_drawinlast' '-Wl,-U,_jbox_get_mousedragdelta' '-Wl,-U,_jbox_set_mousedragdelta' '-Wl,-U,_jbox_get_textfield' '-Wl,-U,_jbox_set_hinttrack' '-Wl,-U,_jbox_get_hinttrack' '-Wl,-U,_jbox_set_hintstring' '-Wl,-U,_jpatchline_get_startpoint' '-Wl,-U,_jpatchline_get_endpoint' '-Wl,-U,_jpatchline_get_nummidpoints' '-Wl,-U,_jpatchline_get_pending' '-Wl,-U,_jpatchline_get_box1' '-Wl,-U,_jpatchline_get_outletnum' '-Wl,-U,_jpatchline_get_box2' '-Wl,-U,_jpatchline_get_inletnum' '-Wl,-U,_jpatchline_get_straightthresh' '-Wl,-U,_jpatchline_set_straightthresh' '-Wl,-U,_jpatchline_get_straightstart' '-Wl,-U,_jpatchline_get_straightend' '-Wl,-U,_jpatchline_set_straightstart' '-Wl,-U,_jpatchline_set_straightend' '-Wl,-U,_jpatchline_get_nextline' '-Wl,-U,_jpatchline_get_hidden' '-Wl,-U,_jpatchline_set_hidden' '-Wl,-U,_jpatchline_get_color' '-Wl,-U,_jpatchline_set_color' '-Wl,-U,_jpatchline_addpaintmethod' '-Wl,-U,_patcherview_get_visible' '-Wl,-U,_patcherview_set_visible' '-Wl,-U,_patcherview_get_locked' '-Wl,-U,_patcherview_set_locked' '-Wl,-U,_patcherview_get_zoomfactor' '-Wl,-U,_patcherview_set_zoomfactor' '-Wl,-U,_patcherview_get_nextview' '-Wl,-U,_patcherview_get_topview' '-Wl,-U,_patcherview_get_jgraphics' '-Wl,-U,_patcherview_set_jgraphics' '-Wl,-U,_patcherview_get_patcher' '-Wl,-U,_patcherview_get_rect' '-Wl,-U,_patcherview_set_rect' '-Wl,-U,_patcherview_canvas_to_screen' '-Wl,-U,_patcherview_screen_to_canvas' '-Wl,-U,_patcherview_get_presentation' '-Wl,-U,_textfield_get_owner' '-Wl,-U,_textfield_get_textcolor' '-Wl,-U,_textfield_set_textcolor' '-Wl,-U,_textfield_get_bgcolor' '-Wl,-U,_textfield_set_bgcolor' '-Wl,-U,_textfield_get_textmargins' '-Wl,-U,_textfield_set_textmargins' '-Wl,-U,_textfield_get_editonclick' '-Wl,-U,_textfield_set_editonclick' '-Wl,-U,_textfield_get_selectallonedit' '-Wl,-U,_textfield_set_selectallonedit' '-Wl,-U,_textfield_get_noactivate' '-Wl,-U,_textfield_set_noactivate' '-Wl,-U,_textfield_get_readonly' '-Wl,-U,_textfield_set_readonly' '-Wl,-U,_textfield_get_wordwrap' '-Wl,-U,_textfield_set_wordwrap' '-Wl,-U,_textfield_get_useellipsis' '-Wl,-U,_textfield_set_useellipsis' '-Wl,-U,_textfield_get_autoscroll' '-Wl,-U,_textfield_set_autoscroll' '-Wl,-U,_textfield_get_wantsreturn' '-Wl,-U,_textfield_set_wantsreturn' '-Wl,-U,_textfield_get_wantstab' '-Wl,-U,_textfield_set_wantstab' '-Wl,-U,_textfield_get_autofixwidth' '-Wl,-U,_textfield_set_autofixwidth' '-Wl,-U,_textfield_set_emptytext' '-Wl,-U,_textfield_get_emptytext' '-Wl,-U,_textfield_set_underline' '-Wl,-U,_textfield_get_underline' '-Wl,-U,_textfield_set_justification' '-Wl,-U,_textfield_get_justification' '-Wl,-U,_jdrag_getitemstring' '-Wl,-U,_jdrag_getobject' '-Wl,-U,_jdrag_getlocation' '-Wl,-U,_jdrag_createobject' '-Wl,-U,_jdrag_createnewobj' '-Wl,-U,_jdrag_createmessage' '-Wl,-U,_jdrag_add' '-Wl,-U,_jdrag_process_drop' '-Wl,-U,_jdrag_matchdragrole' '-Wl,-U,_jdrag_setboxlocation' '-Wl,-U,_jdrag_box_add' '-Wl,-U,_jdrag_object_add' '-Wl,-U,_jdrag_itemcount' '-Wl,-U,_jgraphics_getfiletypes' '-Wl,-U,_jgraphics_round' '-Wl,-U,_jgraphics_image_surface_clear' '-Wl,-U,_jgraphics_image_surface_create' '-Wl,-U,_jgraphics_image_surface_create_referenced' '-Wl,-U,_jgraphics_image_surface_create_from_file' '-Wl,-U,_jgraphics_image_surface_create_for_data' '-Wl,-U,_jgraphics_image_surface_create_from_filedata' '-Wl,-U,_jgraphics_image_surface_create_from_resource' '-Wl,-U,_jgraphics_image_surface_writepng' '-Wl,-U,_jgraphics_image_surface_writejpeg' '-Wl,-U,_jgraphics_surface_reference' '-Wl,-U,_jgraphics_surface_destroy' '-Wl,-U,_jgraphics_surface_set_device_offset' '-Wl,-U,_jgraphics_surface_get_device_offset' '-Wl,-U,_jgraphics_image_surface_get_width' '-Wl,-U,_jgraphics_image_surface_get_height' '-Wl,-U,_jgraphics_image_surface_set_pixel' '-Wl,-U,_jgraphics_image_surface_get_pixel' '-Wl,-U,_jgraphics_image_surface_scroll' '-Wl,-U,_jgraphics_image_surface_lockpixels_readonly' '-Wl,-U,_jgraphics_image_surface_unlockpixels_readonly' '-Wl,-U,_jgraphics_image_surface_lockpixels' '-Wl,-U,_jgraphics_image_surface_unlockpixels' '-Wl,-U,_jgraphics_image_surface_draw' '-Wl,-U,_jgraphics_image_surface_draw_fast' '-Wl,-U,_jgraphics_getfontscale' '-Wl,-U,_jgraphics_get_resource_data' '-Wl,-U,_jsvg_create_from_file' '-Wl,-U,_jsvg_create_from_resource' '-Wl,-U,_jsvg_create_from_xmlstring' '-Wl,-U,_jsvg_get_size' '-Wl,-U,_jsvg_destroy' '-Wl,-U,_jsvg_render' '-Wl,-U,_jgraphics_create' '-Wl,-U,_jgraphics_reference' '-Wl,-U,_jgraphics_destroy' '-Wl,-U,_jgraphics_new_path' '-Wl,-U,_jgraphics_copy_path' '-Wl,-U,_jgraphics_path_destroy' '-Wl,-U,_jgraphics_append_path' '-Wl,-U,_jgraphics_close_path' '-Wl,-U,_jgraphics_path_roundcorners' '-Wl,-U,_jgraphics_get_current_point' '-Wl,-U,_jgraphics_diagonal_line_fill' '-Wl,-U,_jgraphics_arc' '-Wl,-U,_jgraphics_arc_negative' '-Wl,-U,_jgraphics_piesegment' '-Wl,-U,_jgraphics_curve_to' '-Wl,-U,_jgraphics_rel_curve_to' '-Wl,-U,_jgraphics_line_to' '-Wl,-U,_jgraphics_rel_line_to' '-Wl,-U,_jgraphics_move_to' '-Wl,-U,_jgraphics_rel_move_to' '-Wl,-U,_jgraphics_rectangle' '-Wl,-U,_jgraphics_rectangle_rounded' '-Wl,-U,_jgraphics_ellipse' '-Wl,-U,_jgraphics_oval' '-Wl,-U,_jgraphics_ovalarc' '-Wl,-U,_jgraphics_in_fill' '-Wl,-U,_jgraphics_line_intersects_rect' '-Wl,-U,_jgraphics_path_intersects_line' '-Wl,-U,_jgraphics_path_intersectsline' '-Wl,-U,_jgraphics_path_getpathelems' '-Wl,-U,_jgraphics_rectintersectsrect' '-Wl,-U,_jgraphics_rectcontainsrect' '-Wl,-U,_jgraphics_ptinrect' '-Wl,-U,_jgraphics_ptinroundedrect' '-Wl,-U,_jgraphics_fill_extents' '-Wl,-U,_jgraphics_select_font_face' '-Wl,-U,_jgraphics_select_jfont' '-Wl,-U,_jgraphics_set_font_size' '-Wl,-U,_jgraphics_set_underline' '-Wl,-U,_jgraphics_show_text' '-Wl,-U,_jgraphics_font_extents' '-Wl,-U,_jgraphics_text_measure' '-Wl,-U,_jgraphics_text_measuretext_wrapped' '-Wl,-U,_jgraphics_text_path' '-Wl,-U,_jgraphics_jrgba_contrasting' '-Wl,-U,_jgraphics_jrgba_contrastwith' '-Wl,-U,_jgraphics_jrgba_darker' '-Wl,-U,_jgraphics_jrgba_brighter' '-Wl,-U,_jgraphics_jrgba_overlay' '-Wl,-U,_jgraphics_jrgba_interpolate' '-Wl,-U,_jgraphics_jrgba_gethsb' '-Wl,-U,_jgraphics_jrgba_fromhsb' '-Wl,-U,_jgraphics_clip' '-Wl,-U,_jfont_create_from_maxfont' '-Wl,-U,_jfont_create' '-Wl,-U,_jfont_reference' '-Wl,-U,_jfont_destroy' '-Wl,-U,_jfont_ellipsifytext' '-Wl,-U,_jfont_isequalto' '-Wl,-U,_jfont_set_family' '-Wl,-U,_jfont_get_family' '-Wl,-U,_jfont_set_slant' '-Wl,-U,_jfont_get_slant' '-Wl,-U,_jfont_set_weight' '-Wl,-U,_jfont_get_weight' '-Wl,-U,_jfont_set_font_size' '-Wl,-U,_jfont_get_font_size' '-Wl,-U,_jfont_set_underline' '-Wl,-U,_jfont_get_underline' '-Wl,-U,_jfont_get_heighttocharheightratio' '-Wl,-U,_jfont_extents' '-Wl,-U,_jfont_text_measure' '-Wl,-U,_jfont_text_measuretext_wrapped' '-Wl,-U,_jfont_getfontlist' '-Wl,-U,_jfont_get_em_dimensions' '-Wl,-U,_jgraphics_system_canantialiastexttotransparentbg' '-Wl,-U,_jtextlayout_create' '-Wl,-U,_jtextlayout_withbgcolor' '-Wl,-U,_jtextlayout_destroy' '-Wl,-U,_jtextlayout_set' '-Wl,-U,_jtextlayout_settext' '-Wl,-U,_jtextlayout_settextcolor' '-Wl,-U,_jtextlayout_measuretext' '-Wl,-U,_jtextlayout_draw' '-Wl,-U,_jtextlayout_getnumchars' '-Wl,-U,_jtextlayout_getcharbox' '-Wl,-U,_jtextlayout_getchar' '-Wl,-U,_jtextlayout_createpath' '-Wl,-U,_jgraphics_matrix_init' '-Wl,-U,_jgraphics_matrix_init_identity' '-Wl,-U,_jgraphics_matrix_init_translate' '-Wl,-U,_jgraphics_matrix_init_scale' '-Wl,-U,_jgraphics_matrix_init_rotate' '-Wl,-U,_jgraphics_matrix_translate' '-Wl,-U,_jgraphics_matrix_scale' '-Wl,-U,_jgraphics_matrix_rotate' '-Wl,-U,_jgraphics_matrix_invert' '-Wl,-U,_jgraphics_matrix_multiply' '-Wl,-U,_jgraphics_matrix_transform_point' '-Wl,-U,_jgraphics_pattern_create_rgba' '-Wl,-U,_jgraphics_pattern_create_for_surface' '-Wl,-U,_jgraphics_pattern_create_linear' '-Wl,-U,_jgraphics_pattern_create_radial' '-Wl,-U,_jgraphics_pattern_add_color_stop_rgba' '-Wl,-U,_jgraphics_pattern_reference' '-Wl,-U,_jgraphics_pattern_destroy' '-Wl,-U,_jgraphics_pattern_get_type' '-Wl,-U,_jgraphics_pattern_set_extend' '-Wl,-U,_jgraphics_pattern_get_extend' '-Wl,-U,_jgraphics_pattern_set_matrix' '-Wl,-U,_jgraphics_pattern_get_matrix' '-Wl,-U,_jgraphics_pattern_get_surface' '-Wl,-U,_jgraphics_pattern_translate' '-Wl,-U,_jgraphics_pattern_scale' '-Wl,-U,_jgraphics_pattern_rotate' '-Wl,-U,_jgraphics_translate' '-Wl,-U,_jgraphics_scale' '-Wl,-U,_jgraphics_rotate' '-Wl,-U,_jgraphics_transform' '-Wl,-U,_jgraphics_set_matrix' '-Wl,-U,_jgraphics_get_matrix' '-Wl,-U,_jgraphics_identity_matrix' '-Wl,-U,_jgraphics_user_to_device' '-Wl,-U,_jgraphics_device_to_user' '-Wl,-U,_jgraphics_save' '-Wl,-U,_jgraphics_restore' '-Wl,-U,_jgraphics_set_source_rgba' '-Wl,-U,_jgraphics_set_source_jrgba' '-Wl,-U,_jgraphics_set_source_rgb' '-Wl,-U,_jgraphics_set_source' '-Wl,-U,_jgraphics_set_source_surface' '-Wl,-U,_jgraphics_set_source_shared' '-Wl,-U,_jgraphics_scale_source_rgba' '-Wl,-U,_jgraphics_translate_source_rgba' '-Wl,-U,_jgraphics_set_dash' '-Wl,-U,_jgraphics_set_fill_rule' '-Wl,-U,_jgraphics_get_fill_rule' '-Wl,-U,_jgraphics_set_line_cap' '-Wl,-U,_jgraphics_get_line_cap' '-Wl,-U,_jgraphics_set_line_join' '-Wl,-U,_jgraphics_get_line_join' '-Wl,-U,_jgraphics_set_line_width' '-Wl,-U,_jgraphics_get_line_width' '-Wl,-U,_jgraphics_paint' '-Wl,-U,_jgraphics_paint_with_alpha' '-Wl,-U,_jgraphics_fill' '-Wl,-U,_jgraphics_fill_preserve' '-Wl,-U,_jgraphics_fill_preserve_with_alpha' '-Wl,-U,_jgraphics_fill_with_alpha' '-Wl,-U,_jgraphics_stroke' '-Wl,-U,_jgraphics_stroke_preserve' '-Wl,-U,_jgraphics_stroke_preserve_with_alpha' '-Wl,-U,_jgraphics_stroke_with_alpha' '-Wl,-U,_get_boxcolor_index_from_jrgba' '-Wl,-U,_set_jrgba_from_palette_index' '-Wl,-U,_set_jrgba_from_boxcolor_index' '-Wl,-U,_jgraphics_clip_rgba' '-Wl,-U,_jpopupmenu_create' '-Wl,-U,_jpopupmenu_destroy' '-Wl,-U,_jpopupmenu_clear' '-Wl,-U,_jpopupmenu_default_options' '-Wl,-U,_jpopupmenu_setcolors' '-Wl,-U,_jpopupmenu_setfont' '-Wl,-U,_jpopupmenu_additem' '-Wl,-U,_jpopupmenu_additemwithshortcut' '-Wl,-U,_jpopupmenu_addsubmenu' '-Wl,-U,_jpopupmenu_addsubmenu_owned' '-Wl,-U,_jpopupmenu_addseparator' '-Wl,-U,_jpopupmenu_addseperator' '-Wl,-U,_jpopupmenu_addownerdrawitem' '-Wl,-U,_jpopupmenu_popup' '-Wl,-U,_jpopupmenu_popup_nearbox' '-Wl,-U,_jpopupmenu_popup_nearbox_with_options' '-Wl,-U,_jpopupmenu_popup_abovebox' '-Wl,-U,_jpopupmenu_popup_belowrect' '-Wl,-U,_jpopupmenu_popup_leftofpt' '-Wl,-U,_jpopupmenu_closeall' '-Wl,-U,_jpopupmenu_setstandardstyle' '-Wl,-U,_jpopupmenu_setfixedwidth' '-Wl,-U,_jmouse_getposition_global' '-Wl,-U,_jmouse_setposition_global' '-Wl,-U,_jmouse_setposition_view' '-Wl,-U,_jmouse_setposition_box' '-Wl,-U,_jmouse_setcursor' '-Wl,-U,_jmouse_setcursor_surface' '-Wl,-U,_dictionary_appendjrgba' '-Wl,-U,_dictionary_getdefjrgba' '-Wl,-U,_dictionary_gettrect' '-Wl,-U,_dictionary_appendtrect' '-Wl,-U,_dictionary_gettpt' '-Wl,-U,_dictionary_appendtpt' '-Wl,-U,_atomstojrgba' '-Wl,-U,_jrgbatoatoms' '-Wl,-U,_qd_new' '-Wl,-U,_qd_initialize' '-Wl,-U,_qd_copystate' '-Wl,-U,_qd_PenNormal' '-Wl,-U,_qd_RGBForeColor' '-Wl,-U,_qd_RGBBackColor' '-Wl,-U,_qd_BoxcolorIndexForeColor' '-Wl,-U,_qd_InsetTRect' '-Wl,-U,_qd_OffsetTRect' '-Wl,-U,_qd_EqualTRect' '-Wl,-U,_qd_JRGBAToRGBColor' '-Wl,-U,_qd_GetForeColor' '-Wl,-U,_qd_GetBackColor' '-Wl,-U,_qd_GetForeJColor' '-Wl,-U,_qd_GetBackJColor' '-Wl,-U,_qd_Black' '-Wl,-U,_qd_White' '-Wl,-U,_qd_MoveTo' '-Wl,-U,_qd_LineTo' '-Wl,-U,_qd_Line' '-Wl,-U,_qd_EraseRect' '-Wl,-U,_qd_TRectToRect' '-Wl,-U,_qd_RectToTRect' '-Wl,-U,_qd_RGBColorToJRGBA' '-Wl,-U,_qd_TRectToRectZero' '-Wl,-U,_qd_InsetRect' '-Wl,-U,_qd_OffsetRect' '-Wl,-U,_qd_PaintTRect' '-Wl,-U,_qd_PaintRect' '-Wl,-U,_qd_FrameRect' '-Wl,-U,_qd_PenSize' '-Wl,-U,_qd_Move' '-Wl,-U,_qd_SetBackJColor' '-Wl,-U,_qd_SetRect' '-Wl,-U,_qd_PaintOval' '-Wl,-U,_qd_FrameOval' '-Wl,-U,_qd_PaintRoundRect' '-Wl,-U,_qd_FrameRoundRect' '-Wl,-U,_qd_GetPenLoc' '-Wl,-U,_qd_GetCPixel' '-Wl,-U,_qd_SetCPixel' '-Wl,-U,_qd_PaintArc' '-Wl,-U,_qd_FrameArc' '-Wl,-U,_qd_SetForeJColor' '-Wl,-U,_qd_KillPoly' '-Wl,-U,_qd_PaintPoly' '-Wl,-U,_qd_ClosePoly' '-Wl,-U,_qd_OpenPoly' '-Wl,-U,_qd_LineSegment' '-Wl,-U,_qd_TPtInTRect' '-Wl,-U,_qd_FramePoly' '-Wl,-U,_qd_CloseRgn' '-Wl,-U,_qd_DisposeRgn' '-Wl,-U,_qd_FrameRgn' '-Wl,-U,_qd_OpenRgn' '-Wl,-U,_qd_PaintRgn' '-Wl,-U,_jmenu_init' '-Wl,-U,_jmenu_command_setstate' '-Wl,-U,_jmenu_process' '-Wl,-U,_jmenu_command_enable' '-Wl,-U,_jmenu_command_getstate' '-Wl,-U,_jmenu_command_invert' '-Wl,-U,_jmenu_command_invalidate' '-Wl,-U,_jmenu_command_settext' '-Wl,-U,_jmenu_new' '-Wl,-U,_jmenu_addsubmenu' '-Wl,-U,_jmenu_addseparator' '-Wl,-U,_jmenu_appenditem' '-Wl,-U,_jmenu_interface_fromfile' '-Wl,-U,_jmenu_clearenums' '-Wl,-U,_jmenu_command_setid' '-Wl,-U,_jmenu_enumerate_getfile' '-Wl,-U,_jmenu_enumerate_path' '-Wl,-U,_jmenu_enumerate_data' '-Wl,-U,_jmenu_lookup' '-Wl,-U,_jcolor_getcolor' '-Wl,-U,_jcolor_linkcolor' '-Wl,-U,_jcommand_lookup' '-Wl,-U,_jmenu_update' '-Wl,-U,_jmenu_command_enableall_fortarget' '-Wl,-U,_jmenu_proxy_popup' '-Wl,-U,_jmonitor_getnumdisplays' '-Wl,-U,_jmonitor_getdisplayrect_foralldisplays' '-Wl,-U,_jmonitor_getdisplayrect' '-Wl,-U,_jmonitor_getdisplayrect_forpoint' '-Wl,-U,_jmonitor_getdisplayscalefactor' '-Wl,-U,_jmonitor_getdisplayscalefactor_forpoint' '-Wl,-U,_jmonitor_scale_pt' '-Wl,-U,_jmonitor_unscale_pt' '-Wl,-U,_jkeyboard_getcurrentmodifiers' '-Wl,-U,_jbox_notify' '-Wl,-U,_jgraphics_attr_setrgba' '-Wl,-U,_jgraphics_attr_getrgba' '-Wl,-U,_jgraphics_attr_setrgb_alias' '-Wl,-U,_jcolumn_setcheckbox' '-Wl,-U,_jcolumn_setvaluemsg' '-Wl,-U,_jcolumn_setrowcomponentmsg' '-Wl,-U,_jcolumn_setmaxwidth' '-Wl,-U,_jcolumn_setminwidth' '-Wl,-U,_jcolumn_setwidth' '-Wl,-U,_jcolumn_setlabel' '-Wl,-U,_jcolumn_sethideable' '-Wl,-U,_jcolumn_setvisible' '-Wl,-U,_jcolumn_getvisible' '-Wl,-U,_jcolumn_setinitiallysorted' '-Wl,-U,_jcolumn_setnumeric' '-Wl,-U,_jcolumn_setoverridesort' '-Wl,-U,_jcolumn_setcustomsort' '-Wl,-U,_jcolumn_getid' '-Wl,-U,_jcolumn_update' '-Wl,-U,_jcolumn_getname' '-Wl,-U,_jcolumn_getreference' '-Wl,-U,_jcolumn_setdraggable' '-Wl,-U,_jcolumn_setindentspacing' '-Wl,-U,_jcolumn_setreference' '-Wl,-U,_jcolumn_setsortable' '-Wl,-U,_jcolumn_setcustompaint' '-Wl,-U,_jcolumn_setcellcluemsg' '-Wl,-U,_jcolumn_setcelltextcolormsg' '-Wl,-U,_jcolumn_setcelltextstylemsg' '-Wl,-U,_jdataview_addcolumn' '-Wl,-U,_jdataview_addcolumn_hidden' '-Wl,-U,_jdataview_addrows' '-Wl,-U,_jdataview_addrow' '-Wl,-U,_jdataview_clear' '-Wl,-U,_jdataview_containersizechange' '-Wl,-U,_jdataview_deletecolumn' '-Wl,-U,_jdataview_deleterows' '-Wl,-U,_jdataview_deleterow' '-Wl,-U,_jdataview_editcell' '-Wl,-U,_jdataview_forcecellvisible' '-Wl,-U,_jdataview_getfontname' '-Wl,-U,_jdataview_getfontsize' '-Wl,-U,_jdataview_gethorizscrollvalues' '-Wl,-U,_jdataview_getnamedcolumn' '-Wl,-U,_jdataview_getnthcolumn' '-Wl,-U,_jdataview_getnumcolumns' '-Wl,-U,_jdataview_getnumrows' '-Wl,-U,_jdataview_getvertscrollvalues' '-Wl,-U,_jdataview_resort' '-Wl,-U,_jdataview_restorecolumnwidths' '-Wl,-U,_jdataview_savecolumnwidths' '-Wl,-U,_jdataview_setautosizeright' '-Wl,-U,_jdataview_setautosizebottom' '-Wl,-U,_jdataview_setautosizerightcolumn' '-Wl,-U,_jdataview_setbordercolor' '-Wl,-U,_jdataview_setborderthickness' '-Wl,-U,_jdataview_setcolumnheadercluemsg' '-Wl,-U,_jdataview_setcolumnheaderheight' '-Wl,-U,_jdataview_setcustomselectcolor' '-Wl,-U,_jdataview_setdragenabled' '-Wl,-U,_jdataview_setdrawgrid' '-Wl,-U,_jdataview_setfontname' '-Wl,-U,_jdataview_setfontsize' '-Wl,-U,_jdataview_setheight' '-Wl,-U,_jdataview_sethorizscrollvalues' '-Wl,-U,_jdataview_setkeyfocusable' '-Wl,-U,_jdataview_setscrollvisible' '-Wl,-U,_jdataview_setselectcolor' '-Wl,-U,_jdataview_setrowcolor2' '-Wl,-U,_jdataview_setrowcolor1' '-Wl,-U,_jdataview_setusegradient' '-Wl,-U,_jdataview_setusesystemfont' '-Wl,-U,_jdataview_setvertscrollvalues' '-Wl,-U,_jdataview_setclient' '-Wl,-U,_jdataview_new' '-Wl,-U,_jdataview_newsection' '-Wl,-U,_jdataview_numsections' '-Wl,-U,_jdataview_getnthsection' '-Wl,-U,_jdataview_section_getnumrows' '-Wl,-U,_jdataview_section_getallrows' '-Wl,-U,_jdataview_section_isopen' '-Wl,-U,_jdataview_section_setopen' '-Wl,-U,_jdataview_getsectionopenness' '-Wl,-U,_jdataview_setsectionopenness' '-Wl,-U,_jdataview_section_headervisible' '-Wl,-U,_jdataview_section_setheadervisible' '-Wl,-U,_jdataview_section_getname' '-Wl,-U,_jdataview_section_geticon' '-Wl,-U,_jdataview_scrolltosection' '-Wl,-U,_jdataview_scrolltotop' '-Wl,-U,_jdataview_addrowtosection' '-Wl,-U,_jdataview_addrowstosection' '-Wl,-U,_jdataview_deleterowfromsection' '-Wl,-U,_jdataview_deleterowsfromsection' '-Wl,-U,_jdataview_deleteselectedrows' '-Wl,-U,_jdataview_deleteselectedrowsforview' '-Wl,-U,_jdataview_gettextinrows' '-Wl,-U,_jdataview_iscelltextselected' '-Wl,-U,_jdataview_selectedrowcountforview' '-Wl,-U,_jdataview_selectedrowcount' '-Wl,-U,_jdataview_getselectedrowsforview' '-Wl,-U,_jdataview_applytoselectedrows' '-Wl,-U,_jdataview_applytorows' '-Wl,-U,_jdataview_cellcut' '-Wl,-U,_jdataview_cellcopy' '-Wl,-U,_jdataview_cellpaste' '-Wl,-U,_jdataview_setcancopy' '-Wl,-U,_jdataview_getcancopy' '-Wl,-U,_jdataview_setcanpaste' '-Wl,-U,_jdataview_getcanpaste' '-Wl,-U,_jdataview_getsortcolumn' '-Wl,-U,_jdataview_sortcolumn' '-Wl,-U,_jdataview_redrawcolumn' '-Wl,-U,_jdataview_repaintforview' '-Wl,-U,_jdataview_obscuring' '-Wl,-U,_jdataview_id2colname' '-Wl,-U,_jdataview_colname2id' '-Wl,-U,_jdataview_enablecell' '-Wl,-U,_jdataview_enablerow' '-Wl,-U,_jdataview_colname_setvisible' '-Wl,-U,_jdataview_colname_getvisible' '-Wl,-U,_jdataview_colname_delete' '-Wl,-U,_jdataview_patchervis' '-Wl,-U,_jdataview_patcherinvis' '-Wl,-U,_jdataview_showrow' '-Wl,-U,_jdataview_redrawcell' '-Wl,-U,_jdataview_redrawrow' '-Wl,-U,_jdataview_row2id' '-Wl,-U,_jdataview_selectcell' '-Wl,-U,_jdataview_selectcellinview' '-Wl,-U,_jdataview_sort' '-Wl,-U,_jdataview_setusecharheightfont' '-Wl,-U,_jdialog_showtext' '-Wl,-U,_jdialog_show2button_async' '-Wl,-U,_jdialog_cancel_async' '-Wl,-U,_newobject_sprintf' '-Wl,-U,_itm_initclass' '-Wl,-U,_itm_new' '-Wl,-U,_itm_getglobal' '-Wl,-U,_itm_getnamed' '-Wl,-U,_itm_clocksource_getnamed' '-Wl,-U,_itm_getclocksources' '-Wl,-U,_itmclock_new' '-Wl,-U,_itm_poke' '-Wl,-U,_itm_gettime' '-Wl,-U,_itm_getticks' '-Wl,-U,_itmclock_delay' '-Wl,-U,_itmclock_set' '-Wl,-U,_itmclock_unset' '-Wl,-U,_itm_sync' '-Wl,-U,_itm_settimesignature' '-Wl,-U,_itm_seek' '-Wl,-U,_itm_pause' '-Wl,-U,_itm_resume' '-Wl,-U,_itm_setresolution' '-Wl,-U,_itm_reference' '-Wl,-U,_itm_dereference' '-Wl,-U,_itm_tickstobarbeatunits_timesig' '-Wl,-U,_itm_barbeatunitstoticks_timesig' '-Wl,-U,_itm_barbeatunitstoticks' '-Wl,-U,_itm_tickstobarbeatunits' '-Wl,-U,_itm_nextbeat' '-Wl,-U,_itm_nextunit' '-Wl,-U,_itm_parse' '-Wl,-U,_itm_deleteeventlist' '-Wl,-U,_itm_geteventlistnames' '-Wl,-U,_itm_switcheventlist' '-Wl,-U,_itm_gettimesignature' '-Wl,-U,_itm_getstate' '-Wl,-U,_itm_getresolution' '-Wl,-U,_itm_dump' '-Wl,-U,_itm_getfromarg' '-Wl,-U,_itm_getglobal' '-Wl,-U,_itm_getnamed' '-Wl,-U,_itm_getfromarg' '-Wl,-U,_itm_reference' '-Wl,-U,_itm_dereference' '-Wl,-U,_itm_deleteeventlist' '-Wl,-U,_itm_eventlistseek' '-Wl,-U,_itm_geteventlistnames' '-Wl,-U,_itm_switcheventlist' '-Wl,-U,_itm_gettime' '-Wl,-U,_itm_getticks' '-Wl,-U,_itm_dump' '-Wl,-U,_itm_sync' '-Wl,-U,_itm_settimesignature' '-Wl,-U,_itm_gettimesignature' '-Wl,-U,_itm_seek' '-Wl,-U,_itm_pause' '-Wl,-U,_itm_resume' '-Wl,-U,_itm_getstate' '-Wl,-U,_itm_setresolution' '-Wl,-U,_itm_getresolution' '-Wl,-U,_itm_getname' '-Wl,-U,_itm_parse' '-Wl,-U,_itm_tickstoms' '-Wl,-U,_itm_mstoticks' '-Wl,-U,_itm_mstosamps' '-Wl,-U,_itm_sampstoms' '-Wl,-U,_itm_barbeatunitstoticks' '-Wl,-U,_itm_tickstobarbeatunits' '-Wl,-U,_itm_format' '-Wl,-U,_itm_isunitfixed' '-Wl,-U,_itm_gettempo' '-Wl,-U,_itm_getsr' '-Wl,-U,_itmclock_new' '-Wl,-U,_itmclock_delay' '-Wl,-U,_itmclock_set' '-Wl,-U,_itmclock_unset' '-Wl,-U,_patcher_removedefault' '-Wl,-U,_patcher_setdefault' '-Wl,-U,_patcher_getdefault' '-Wl,-U,_patcher_removedefault' '-Wl,-U,_patcher_setdefault' '-Wl,-U,_patcher_boxname' '-Wl,-U,_atom_alloc_array' '-Wl,-U,_atomarray_decodebinarydata' '-Wl,-U,_jgraphics_write_image_surface_to_filedata' '-Wl,-U,_binarydata_appendtodictionary' '-Wl,-U,_patcherview_findpatcherview' '-Wl,-U,_jpatcher_resolvepatcher' '-Wl,-U,_jpatcher_resolvebox' '-Wl,-U,_jpatcher_resolvebox_ex' '-Wl,-U,_jpatcher_resolveobj' '-Wl,-U,_jpatcher_resolvebox_boxpath' '-Wl,-U,_jpatcher_resolveobj_boxpath' '-Wl,-U,_jwind_canfullscreen' '-Wl,-U,_jwind_getactive' '-Wl,-U,_jwind_getcount' '-Wl,-U,_jwind_getat' '-Wl,-U,_jwind_nextuntitled' '-Wl,-U,_jgraphics_rectangle_fill_fast' '-Wl,-U,_jgraphics_rectangle_draw_fast' '-Wl,-U,_jgraphics_line_draw_fast' '-Wl,-U,_atomarray_new' '-Wl,-U,_atomarray_flags' '-Wl,-U,_atomarray_getflags' '-Wl,-U,_atomarray_setatoms' '-Wl,-U,_atomarray_getatoms' '-Wl,-U,_atomarray_copyatoms' '-Wl,-U,_atomarray_getsize' '-Wl,-U,_atomarray_getindex' '-Wl,-U,_atomarray_duplicate' '-Wl,-U,_atomarray_clone' '-Wl,-U,_atomarray_clone_to_existing' '-Wl,-U,_atomarray_setatoms_clone' '-Wl,-U,_atomarray_appendatom' '-Wl,-U,_atomarray_appendatoms' '-Wl,-U,_atomarray_chuckindex' '-Wl,-U,_atomarray_clear' '-Wl,-U,_atomarray_funall' '-Wl,-U,_atomarray_dispose' '-Wl,-U,_atomarray_insertatom_atindex' '-Wl,-U,_atomarray_prependatom' '-Wl,-U,_atomarray_prependatoms' '-Wl,-U,_common_symbols_gettable' '-Wl,-U,_object_obex_storeflags' '-Wl,-U,_object_obex_enforce' '-Wl,-U,_object_attr_getjrgba' '-Wl,-U,_object_attr_setjrgba' '-Wl,-U,_jrgba_to_atoms' '-Wl,-U,_atoms_to_jrgba' '-Wl,-U,_jrgba_set' '-Wl,-U,_jrgba_copy' '-Wl,-U,_jrgba_compare' '-Wl,-U,_jrgba_attr_set' '-Wl,-U,_class_parameter_init' '-Wl,-U,_class_parameter_mappable' '-Wl,-U,_class_parameter_setinfo' '-Wl,-U,_class_parameter_getinfo' '-Wl,-U,_class_parameter_register_default_color' '-Wl,-U,_object_parameter_init' '-Wl,-U,_object_parameter_init_flags' '-Wl,-U,_object_parameter_dictionary_process' '-Wl,-U,_object_parameter_hasminmax_true' '-Wl,-U,_object_parameter_hasminmax_false' '-Wl,-U,_class_parameter_addmethod' '-Wl,-U,_parameter_default_int' '-Wl,-U,_parameter_default_float' '-Wl,-U,_parameter_default_anything' '-Wl,-U,_object_parameter_free' '-Wl,-U,_object_parameter_notify' '-Wl,-U,_object_parameter_getinfo' '-Wl,-U,_object_parameter_setinfo' '-Wl,-U,_object_parameter_string_get' '-Wl,-U,_object_parameter_stringtovalue' '-Wl,-U,_object_parameter_value_set' '-Wl,-U,_object_parameter_value_get' '-Wl,-U,_object_parameter_color_get' '-Wl,-U,_object_parameter_value_getvalueof' '-Wl,-U,_object_parameter_value_setvalueof' '-Wl,-U,_object_parameter_value_setvalueof_nonotify' '-Wl,-U,_object_parameter_value_changed' '-Wl,-U,_object_parameter_value_changed_nonotify' '-Wl,-U,_object_parameter_current_to_initial' '-Wl,-U,_object_parameter_is_initialized' '-Wl,-U,_object_parameter_is_in_Live' '-Wl,-U,_object_parameter_is_automated' '-Wl,-U,_object_parameter_wants_focus' '-Wl,-U,_object_parameter_is_parameter' '-Wl,-U,_object_parameter_get_order' '-Wl,-U,_object_parameter_is_in_maxtilde' '-Wl,-U,_object_parameter_getenable_savestate' '-Wl,-U,_live_default_color_count' '-Wl,-U,_live_default_color_string' '-Wl,-U,_live_default_dynamic_color_string' '-Wl,-U,_live_default_color_symbol' '-Wl,-U,_live_default_color_rgbastring' '-Wl,-U,_live_default_color_rgba' '-Wl,-U,_live_default_color_rgba_from_symbol' '-Wl,-U,_param_global_initializecolors' '-Wl,-U,_project_newfromdevicepatcher' '-Wl,-U,_remote_object_new_typed' '-Wl,-U,_remote_object_new_typed_flags' '-Wl,-U,_remote_object_get' '-Wl,-U,_remote_object_get_flags' '-Wl,-U,_remote_object_method_typed' '-Wl,-U,_remote_object_method_typed_flags' '-Wl,-U,_remote_object_attr_setvalueof' '-Wl,-U,_remote_object_attr_setvalueof_flags' '-Wl,-U,_remote_object_attr_getvalueof' '-Wl,-U,_remote_object_attr_getvalueof_flags' '-Wl,-U,_maxserver_getremoteurl' '-Wl,-U,_maxserver_getcontent' '-Wl,-U,_sysshmem_alloc' '-Wl,-U,_sysshmem_open' '-Wl,-U,_sysshmem_close' '-Wl,-U,_sysshmem_getsize' '-Wl,-U,_sysshmem_getptr' '-Wl,-U,_syssem_create' '-Wl,-U,_syssem_open' '-Wl,-U,_syssem_close' '-Wl,-U,_syssem_wait' '-Wl,-U,_syssem_trywait' '-Wl,-U,_syssem_post' '-Wl,-U,_sysprocess_isrunning' '-Wl,-U,_sysprocess_isrunning_with_returnvalue' '-Wl,-U,_sysprocess_kill' '-Wl,-U,_sysprocess_launch' '-Wl,-U,_sysprocess_launch_withflags' '-Wl,-U,_sysprocess_activate' '-Wl,-U,_sysprocess_getid' '-Wl,-U,_sysprocess_getcurrentid' '-Wl,-U,_sysprocess_getpath' '-Wl,-U,_sysprocesswatcher_new' '-Wl,-U,_sysprocess_fitsarch' '-Wl,-U,_object_retain' '-Wl,-U,_object_release' '-Wl,-U,_multigraph_add' '-Wl,-U,_multigraph_remove' '-Wl,-U,_multinode_resizeio' '-Wl,-U,_multinode_hasdescendant' '-Wl,-U,_multigraph_connect' '-Wl,-U,_multigraph_connect_relaxed' '-Wl,-U,_multigraph_disconnect' '-Wl,-U,_multigraph_disconnectnode' '-Wl,-U,_multigraph_dependency_chain' '-Wl,-U,_multigraph_new' '-Wl,-U,_multinode_new' '-Wl,-U,_multinode_connect' '-Wl,-U,_multinode_disconnect' '-Wl,-U,_multinode_iterfun' '-Wl,-U,_multinode_dependency_chain' '-Wl,-U,_multiedge_disconnect' '-Wl,-U,_multiedge_new' '-Wl,-U,_multigraph_scheduler_new' '-Wl,-U,_multigraph_scheduler_acquire' '-Wl,-U,_multigraph_scheduler_release' '-Wl,-U,_multigraph_scheduler_complete' '-Wl,-U,_multigraph_parallel_iterator_new' '-Wl,-U,_multigraph_parallel_iterator_free' '-Wl,-U,_multigraph_parallel_iterator_scheduler' '-Wl,-U,_multigraph_parallel_iterator_data' '-Wl,-U,_multigraph_parallel_iterator_execute' '-Wl,-U,_multigraph_parallel_iterator_workerproc' '-Wl,-U,_jpatcher_bulk_load_begin' '-Wl,-U,_jpatcher_bulk_load_end' '-Wl,-U,_jpatcher_load' '-Wl,-U,_jpatcher_load_frombuffer' '-Wl,-U,_jpatcher_load_fromdictionary' '-Wl,-U,_jpatcher_load_namespace' '-Wl,-U,_jpatcher_load_frombuffer_namespace' '-Wl,-U,_jpatcher_load_fromdictionary_namespace' '-Wl,-U,_jdesktopui_new' '-Wl,-U,_jdesktopui_destroy' '-Wl,-U,_jdesktopui_setvisible' '-Wl,-U,_jdesktopui_setalwaysontop' '-Wl,-U,_jdesktopui_setrect' '-Wl,-U,_jdesktopui_getrect' '-Wl,-U,_jdesktopui_setposition' '-Wl,-U,_jdesktopui_setfadetimes' '-Wl,-U,_jdesktopui_get_jgraphics' '-Wl,-U,_jdesktopui_redraw' '-Wl,-U,_jdesktopui_redrawrect' '-Wl,-U,_object_subscribe' '-Wl,-U,_object_unsubscribe' '-Wl,-U,_sysparallel_task_workercount' '-Wl,-U,_backgroundtask_execute' '-Wl,-U,_backgroundtask_execute_method' '-Wl,-U,_backgroundtask_purge_object' '-Wl,-U,_backgroundtask_join_object' '-Wl,-U,_backgroundtask_cancel' '-Wl,-U,_backgroundtask_join' '-Wl,-U,_jgraphics_image_surface_create_for_data_premult' '-Wl,-U,_jgraphics_create_zoomed' '-Wl,-U,_jgraphics_get_target' '-Wl,-U,_jgraphics_pop_group' '-Wl,-U,_jgraphics_get_group_target' '-Wl,-U,_jgraphics_pop_group_surface' '-Wl,-U,_syntax_addtoken' '-Wl,-U,_object_register_getnames' '-Wl,-U,_dictionary_clone_to_existing' '-Wl,-U,_dictionary_clone' '-Wl,-U,_dictionary_merge_to_existing' '-Wl,-U,_dictionary_copy_nonunique_to_existing' '-Wl,-U,_patcherdomain_namespace_init' '-Wl,-U,_patcherdomain_node_new' '-Wl,-U,_patcherdomain_node_free' '-Wl,-U,_patcherdomain_makeinlets' '-Wl,-U,_patcherdomain_makeoutlets' '-Wl,-U,_patcherdomain_inlets_resize' '-Wl,-U,_patcherdomain_outlets_resize' '-Wl,-U,_patcherdomain_freeinlets' '-Wl,-U,_patcherdomain_freeoutlets' '-Wl,-U,_patcherdomain_class_register' '-Wl,-U,_patcherdomain_simple_connectionaccept' '-Wl,-U,_patcherdomain_simple_patchlineupdate' '-Wl,-U,_inlet_delete' '-Wl,-U,_outlet_delete' '-Wl,-U,_proxy_append' '-Wl,-U,_proxy_insert' '-Wl,-U,_proxy_new_forinlet' '-Wl,-U,_proxy_delete' '-Wl,-U,_proxy_setinletptr' '-Wl,-U,_proxy_getinletptr' '-Wl,-U,_jgraphics_bubble' '-Wl,-U,_class_subclass' '-Wl,-U,_class_super_construct' '-Wl,-U,_class_super_construct_imp' '-Wl,-U,_object_super_method' '-Wl,-U,_object_super_method_imp' '-Wl,-U,_object_this_method' '-Wl,-U,_object_this_method_imp' '-Wl,-U,_object_obex_chuck' '-Wl,-U,_object_attr_touch' '-Wl,-U,_object_attr_touch_parse' '-Wl,-U,_object_method_direct_getmethod' '-Wl,-U,_object_method_direct_getobject' '-Wl,-U,_object_super_getmethod' '-Wl,-U,_object_typedwrapper_get' '-Wl,-U,_usergesture_begin' '-Wl,-U,_usergesture_end' '-Wl,-U,_usergesture_add_live_undo_subscriber' '-Wl,-U,_db_open' '-Wl,-U,_db_close' '-Wl,-U,_db_query' '-Wl,-U,_db_query_direct' '-Wl,-U,_db_query_silent' '-Wl,-U,_db_query_getlastinsertid' '-Wl,-U,_db_query_table_new' '-Wl,-U,_db_query_table_addcolumn' '-Wl,-U,_db_transaction_start' '-Wl,-U,_db_transaction_end' '-Wl,-U,_db_transaction_flush' '-Wl,-U,_db_view_create' '-Wl,-U,_db_view_remove' '-Wl,-U,_db_view_getresult' '-Wl,-U,_db_view_setquery' '-Wl,-U,_db_result_nextrecord' '-Wl,-U,_db_result_reset' '-Wl,-U,_db_result_clear' '-Wl,-U,_db_result_numrecords' '-Wl,-U,_db_result_numfields' '-Wl,-U,_db_result_fieldname' '-Wl,-U,_db_result_string' '-Wl,-U,_db_result_long' '-Wl,-U,_db_result_float' '-Wl,-U,_db_result_datetimeinseconds' '-Wl,-U,_db_util_stringtodate' '-Wl,-U,_db_util_datetostring' '-Wl,-U,_eventcontext_begin' '-Wl,-U,_eventcontext_end' '-Wl,-U,_eventcontext_get' '-Wl,-U,_eventcontext_set' '-Wl,-U,_collectionlist_read' '-Wl,-U,_collection_updatefromdictionary' '-Wl,-U,_collection_deletenamed' '-Wl,-U,_collection_renamenamed' '-Wl,-U,_collection_getallnames' '-Wl,-U,_schedule_queue_new' '-Wl,-U,_schedule_queue' '-Wl,-U,_lockfreequeue_free' '-Wl,-U,_lockfreequeue_new' '-Wl,-U,_lockfreequeue_pop' '-Wl,-U,_lockfreequeue_push' '-Wl,-U,_lockfreequeue_isempty' '-Wl,-U,_class_attr_setstyle' '-Wl,-U,_class_attr_style_alias' '-Wl,-U,_class_attr_setfill' '-Wl,-U,_class_addstyleattr' '-Wl,-U,_style_getmenu' '-Wl,-U,_style_handlemenu' '-Wl,-U,_dynamiccolor_getmenu' '-Wl,-U,_dynamiccolor_handlemenu' '-Wl,-U,_object_attr_getinherited' '-Wl,-U,_object_attr_setinherited' '-Wl,-U,_object_style_setfillattribute' '-Wl,-U,_class_attr_stylemap' '-Wl,-U,_object_attr_attrname_forstylemap' '-Wl,-U,_object_attr_stylemapname' '-Wl,-U,_jgraphics_triangle' '-Wl,-U,_jgraphics_jrgba_set_brightness' '-Wl,-U,_jpopupmenu_setstandardstyle' '-Wl,-U,_jgraphics_attr_setfill' '-Wl,-U,_jgraphics_attr_setfill_transformed' '-Wl,-U,_jgraphics_attr_fillrect' '-Wl,-U,_object_attr_getfillcolor_atposition' '-Wl,-U,_object_attr_getfill' '-Wl,-U,_jsvg_remap_create' '-Wl,-U,_jsvg_remap_destroy' '-Wl,-U,_jsvg_remap_addcolor' '-Wl,-U,_jsvg_remap_addsinglecolor' '-Wl,-U,_jsvg_remap_perform' '-Wl,-U,_jsvg_load_cached' '-Wl,-U,_jgraphics_draw_jsvg' '-Wl,-U,_objectcollection_addobject' '-Wl,-U,_objectcollection_addtext' '-Wl,-U,_ctopcpy' '-Wl,-U,_ptoccpy' '-Wl,-U,_pstrcpy' '-Wl,-U,_plug_getoptions' '-Wl,-U,_path_fromfsref' '-Wl,-U,_path_tofsref' '-Wl,-U,_fontinfo_prefcheckencoding' '-Wl,-U,_fontinfo_getencoding' '-Wl,-U,_fontinfo_reconverthandle' '-Wl,-U,_fontinfo_convert' '-Wl,-U,_rescopy' '-Wl,-U,_max_debug_is_debugger_attached' '-Wl,-U,_unibrowser_search_dosearch' '-Wl,-U,_unibrowser_search_autocomplete_dosearch' '-Wl,-U,_unibrowser_search_collection_getall' '-Wl,-U,_unibrowser_search_snippets_dosearch' '-Wl,-U,_unibrowser_search_getsnippetdictionary' '-Wl,-U,_snapshotwriter_addpayload' '-Wl,-U,_snapshotwriter_addlist' '-Wl,-U,_snapshotreader_from_dictionary' '-Wl,-U,_snapshotreader_from_userpath' '-Wl,-U,_snapshotreader_fitstype' '-Wl,-U,_snapshotreader_getpayload' '-Wl,-U,_snapshotreader_getlist' '-Wl,-U,_snapshotreader_haspayload' '-Wl,-U,_snapshotreader_haslist' '-Wl,-U,_snapshotreader_getname' '-Wl,-U,_snapshotreader_getorigin' '-Wl,-U,_snapshotlist_setup' '-Wl,-U,_snapshotlist_will_restore' '-Wl,-U,_snapshotlist_initial_restore' '-Wl,-U,_snapshotlist_momentary_snapshot' '-Wl,-U,_snapshotlist_devalidate_snapshot' '-Wl,-U,_snapshotlist_forceupdatefilesnapshots' '-Wl,-U,_snapshotlist_appendtodictionary' '-Wl,-U,_snapshotlist_getvalueof' '-Wl,-U,_snapshotlist_setvalueof' '-Wl,-U,_snapshotlist_fileusage' '-Wl,-U,_object_attr_obsolete_getter' '-Wl,-U,_object_attr_obsolete_setter' '-Wl,-U,_object_method_obsolete' '-Wl,-U,_updatepath_pathhaschanged' '-Wl,-U,_sysinfo_getosversion' '-Wl,-U,_object_attr_lock' '-Wl,-U,_object_attr_unlock' '-Wl,-U,_dictionary_transaction_lock' '-Wl,-U,_dictionary_transaction_unlock' '-Wl,-U,_charset_urlencode' '-Wl,-U,_jpatcher_setnextobexprototype' '-Wl,-U,_jpatcher_getnextobexprototype' '-Wl,-U,_sysinfo_gestalt_get_sysv' '-Wl,-U,_sysinfo_gestalt_get_sysvers' '-Wl,-U,_sysinfo_gestalt_get_systemversion' '-Wl,-U,_sysinfo_gestalt_get_pid' '-Wl,-U,_sysinfo_gestalt_get_processname' '-Wl,-U,_sysinfo_gestalt_get_processorcount' '-Wl,-U,_sysinfo_gestalt_get_physicalmemory' '-Wl,-U,_sysinfo_gestalt_get_hostname' '-Wl,-U,_sysinfo_gestalt_get_arguments' '-Wl,-U,_sysinfo_gestalt_get_environment' '-Wl,-U,_sysinfo_getappname' '-Wl,-U,_sysinfo_getarchitecture' '-Wl,-U,_jgraphics_path_getpointalongpath' '-Wl,-U,_jgraphics_path_getnearestpoint' '-Wl,-U,_jgraphics_path_getlength' '-Wl,-U,_class_attr_dynamiccolor_init' '-Wl,-U,_object_attr_dynamiccolor_supported' '-Wl,-U,_object_attr_dynamiccolor_setsym_setup' '-Wl,-U,_object_attr_dynamiccolor_getname' '-Wl,-U,_object_attr_dynamiccolor_setname' '-Wl,-U,_object_attr_dynamiccolor_geton' '-Wl,-U,_object_attr_dynamiccolor_seton' '-Wl,-U,_object_attr_dynamiccolor_getregular' '-Wl,-U,_object_attr_dynamiccolor_setregular' '-Wl,-U,_object_attr_dynamiccolor_getregularrgba' '-Wl,-U,_object_attr_dynamiccolor_setregularrgba' '-Wl,-U,_object_attr_dynamiccolor_gethumanname' '-Wl,-U,_object_attr_dynamiccolor_apply' '-Wl,-U,_filetypelist_file_matches' '-Wl,-U,_filetypelist_gettypes' '-Wl,-U,_filetypelist_addtypes' '-Wl,-U,_filetypelist_gettypesym' '-Wl,-U,_filetypelist_to_types_and_suffixes' '-Wl,-U,_filetypelist_from_types_and_suffixes' '-Wl,-U,_filetypelist_from_types' '-Wl,-U,_types_and_suffixes_setup' '-Wl,-U,_types_and_suffixes_cleanup' '-Wl,-U,_types_and_suffixes_free' '-Wl,-U,_fuzzy_floatcmp' '-Wl,-U,_copyatoms' '-Wl,-U,_cloneatoms'"
  },
  {
    "path": "install/mkl.cmake",
    "content": "find_package(MKL QUIET)\n\nif(NOT TARGET caffe2::mkl)\n  add_library(caffe2::mkl INTERFACE IMPORTED)\nendif()\n\ntarget_include_directories(caffe2::mkl INTERFACE ${MKL_INCLUDE_DIR})\n#target_link_libraries(caffe2::mkl INTERFACE ${MKL_LIBRARIES})\nforeach(MKL_LIB IN LISTS MKL_LIBRARIES)\n  if(EXISTS \"${MKL_LIB}\")\n    get_filename_component(MKL_LINK_DIR \"${MKL_LIB}\" DIRECTORY)\n    if(IS_DIRECTORY \"${MKL_LINK_DIR}\")\n      target_link_directories(caffe2::mkl INTERFACE \"${MKL_LINK_DIR}\")\n    endif()\n  endif()\nendforeach()\n\n# TODO: This is a hack, it will not pick up architecture dependent\n# MKL libraries correctly; see https://github.com/pytorch/pytorch/issues/73008\nset_property(\n  TARGET caffe2::mkl PROPERTY INTERFACE_LINK_DIRECTORIES\n  ${MKL_ROOT}/lib ${MKL_ROOT}/lib/intel64 ${MKL_ROOT}/lib/intel64_win ${MKL_ROOT}/lib/win-x64)\n"
  },
  {
    "path": "install/patch_with_vst.sh",
    "content": "#!/bin/bash\n\n# Default values\npd=0\nmax=0\n\nfunction print_help() {\n    echo \"Usage: $0 [--pd_path[=val]] (default: ~/Documents/Pd) [--max[=val]] (by default, look in all ~/Documents/Max X/ ; if specified, look for externals sub-folder)\"\n}\n\n# Parse arguments\nwhile [[ $# -gt 0 ]]; do\n    case \"$1\" in\n        --pd=*)\n            pd=1\n            pd_path=\"${1#*=}\"\n            shift\n            ;;\n        --pd)\n            pd=1\n            shift\n            ;;\n        --max=*)\n            max=1\n            max_path=\"${1#*=}\"\n            shift\n            ;;\n        --max)\n            max=1\n            shift\n            ;;\n        --help|-h)\n            print_help\n            exit 0\n            ;;\n        *)\n            echo \"Unknown option: $1\"\n            shift\n            ;;\n    esac\ndone\n\n\nfunction patch_max_external() {\n    find \"$1\" -name \"nn_tilde\" -type d -mindepth 2 -print0 | while IFS= read -r -d '' ext_dir; do\n    if [[ -d \"$ext_dir/externals\" ]]; then\n        echo \"found nn_tilde at $ext_dir\"; \n        find \"$ext_dir/externals\" -name \"*.mxo\" -print0 | while IFS= read -r -d '' ext_path; do\n            # echo \"found external at $ext_path\"; \n            ext_name=$(basename \"$ext_path\")\n            find \"$ext_dir/support\" -name \"*.dylib\" -print0 | while IFS= read -r -d '' dylib_path; do\n                dylib_name=$(basename \"$dylib_path\")\n                echo \"fixing library : $dylib_name\"\n                new_path=\"/Library/Application Support/ACIDS/RAVE/$dylib_name\"\n                if [[ ! -e \"$new_path\" ]]; then \n                    echo \"[WARNING] library not found : $new_path. Patch may not work\"\n                fi\n                install_name_tool -change \"@loader_path/../../../../support/$dylib_name\" \"/Library/Application Support/ACIDS/RAVE/$dylib_name\" \"${ext_path}/Contents/MacOS/${ext_name%.*}\" 2> /dev/null\n            done\n            codesign --deep --force --sign - \"${ext_path}/Contents/MacOS/${ext_name%.*}\"\n        done\n    fi\n    done\n}\n\nfunction patch_pd_external() {\n    ext_dir=$1\n    if [[ ! \"$(basename $ext_dir)\" == \"nn_tilde\" ]]; then\n        ext_dir=\"${ext_dir}/externals/nn_tilde\"\n    fi\n    if [[ -e $(realpath $ext_dir) ]]; then\n        find \"$ext_dir\" -name \"nn~.pd_*\" -print0 | while IFS= read -r -d '' ext_path; do\n            ext_name=$(basename \"$ext_path\")\n            find \"$ext_dir\" -name \"*.dylib\" -print0 | while IFS= read -r -d '' dylib_path; do\n                dylib_name=$(basename \"$dylib_path\")\n                echo \"fixing library : $dylib_name\"\n                new_path=\"/Library/Application Support/ACIDS/RAVE/$dylib_name\"\n                if [[ ! -e \"$new_path\" ]]; then \n                    echo \"[WARNING] library not found : $new_path. Patch may not work\"\n                fi\n                echo install_name_tool -change \"@rpath/$dylib_name\" \"/Library/Application Support/ACIDS/RAVE/$dylib_name\" \"${ext_path}\" 2> /dev/null\n            done\n            codesign --deep --force --sign - \"${ext_path}\"\n        done\n    else\n        echo \"folder $ext_dir not found\".  \n    fi\n}\n\n\nif [[ \"$max\" -eq 1 ]]; then\n    if [[ -n \"$max_path\" ]]; then \n        patch_max_external \"$max_path\" \n    else\n        find ~/Documents -maxdepth 1 -name \"Max *\" -type d -print0 | while IFS= read -r -d '' max_dir; do\n            if [[ -d $max_dir ]]; then \n                patch_max_external \"$max_dir\" \n            else \n                echo \"$max_dir does not exists\" \n            fi\n        done \n    fi\nfi\n\nif [[ \"$pd\" -eq 1 ]]; then\n    if [[ -n \"$max_path\" ]]; then \n        patch_pd_external \"$pd_path\" \n    else\n        patch_pd_external \"$HOME/Documents/Pd\"\n    fi\nfi"
  },
  {
    "path": "package-info.json.in",
    "content": "{\n\t\"name\" : \"nn_tilde\",\n\t\"displayname\" : \"nn~\",\n\t\"version\" : \"${VERSION}\",\n\t\"author\" : \"ACIDS\",\n\t\"authors\" : [ \"Antoine Caillon\", \"Axel Chemla--Romeu-Santos\", \"Philippe Esling\", \"Nils Demerlé\"],\n\t\"description\" : \"Max interfaces for deep neural generation\",\n\t\"tags\" : [ \"audio\", \"ai\", \"neural synthesis\"],\n\t\"website\" : \"http://www.github.com/acids-ircam/nn_tilde\",\n\t\"extends\" : \"\",\n\t\"extensible\" : 1,\n\t\"max_version_min\" : \"8.0.2\",\n\t\"max_version_max\" : \"none\",\n\t\"os\" : \t{\n\t\t\"macintosh\" : \t\t{\n\t\t\t\"min_version\" : \"10.12.x\",\n\t\t\t\"platform\" : [ \"x64\", \"aarch64\" ]\n\t\t}\n,\n\t\t\"windows\" : \t\t{\n\t\t\t\"min_version\" : \"7\",\n\t\t\t\"platform\" : [ \"x64\" ]\n\t\t}\n\n\t}\n,\n\t\"homepatcher\" : \"help_hub.maxpat\",\n\t\"package_extra\" : \t{\n\t}\n,\n\t\"c74install\" : 1,\n\t\"installdate\" : 3745215604\n}\n"
  },
  {
    "path": "python_tools/__init__.py",
    "content": "\nimport pathlib\nimport torch\nTMP_FILE_OUTPUT = pathlib.Path(__file__).parent / \".tmpfile\"\nfrom .buffer import Buffer\nTYPE_HASH = {bool: 0, int: 1, float: 2, str: 3, torch.Tensor: 4, Buffer: 5}\n\nfrom . import templates\nfrom .module import Module"
  },
  {
    "path": "python_tools/buffer.py",
    "content": "import torch\nfrom typing import  List, Dict, Optional\n\nclass Buffer():\n    value: torch.Tensor\n    min_samples: int\n    max_samples: int\n    def __init__(self, tensor: Optional[torch.Tensor] = None, \n                 min_samples: int = -1, \n                 max_samples: int = -1, \n                 sr: int | float | None = -1):\n        self.value = torch.tensor(0)\n        self.init_value()\n        if tensor is not None:\n            self.value = tensor\n        self.min_samples = min_samples\n        self.max_samples = max_samples\n        self.sr = -1 if sr is None else int(sr)\n\n    def check_bounds(self, x: torch.Tensor) -> bool:\n        is_ok = True\n        if self.min_samples != -1:\n            is_ok = is_ok and x.shape[-1] >= self.min_samples\n        if self.max_samples != -1:\n            is_ok = is_ok and x.shape[-1] <= self.max_samples\n        return is_ok\n\n    def from_buffer(self, buffer: \"Buffer\"):\n        return self.set_value(buffer.value, sr = buffer.sr)\n\n    @staticmethod\n    def copy(buffer: \"Buffer\"):\n        buffer_n = Buffer()\n        buffer_n.from_buffer(buffer)\n        return buffer_n\n\n    def set_value(self, x: torch.Tensor, sr: int | float | None = None) -> int:        \n        _has_valid_bounds = self.check_bounds(x)\n        if not _has_valid_bounds:\n            return -1\n        if sr is None:\n            self.sr = -1\n        else:\n            self.sr = int(sr)\n        self.value = x.clone()\n        return 0\n\n    @property\n    def shape(self) -> List[int]:\n        if self.has_value: \n            return self.value.shape\n        else:\n            return torch.Size([])\n\n    @property\n    def has_value(self) -> bool:\n        return self.value.numel() != 0\n\n    def get_value(self) -> torch.Tensor:\n        return self.value\n\n    def init_value(self) -> None:\n        self.value = torch.zeros(0, 0, 0)\n        \n    def to_str(self) -> str:\n        if self.has_value:\n            out = f\"Buffer(min={self.value.min()}, max={self.value.max()}, sr={self.sr}, shape={self.shape})\"\n        else:\n            out = \"Buffer(empty)\"\n        return out\n\n\nBUFFER_ATTRIBUTES_TYPE = Dict[str, Buffer]"
  },
  {
    "path": "python_tools/codegen.py",
    "content": "import os\nimport shutil\nimport uuid\nfrom . import TMP_FILE_OUTPUT \n\n\nclass TmpFileSession(object):\n    def __init__(self, obj):\n        self._path = (TMP_FILE_OUTPUT / f\"{id(obj)}\").resolve()\n    def get(self): \n        if not self._path.exists():\n            os.makedirs(self._path)\n        unique_id = str(uuid.uuid4())\n        return self._path / f\"{unique_id}.py\"\n    def close(self):\n        \n        if len(os.listdir(TMP_FILE_OUTPUT)) == 0:\n            shutil.rmtree(TMP_FILE_OUTPUT, True)\n        else:\n            shutil.rmtree(self._path, True)\n            \n\ndef tmp_file_session(obj):\n    return TmpFileSession(obj)\n\n\ndef method_from_template(file_session: TmpFileSession, template: str, gl = {}, lo = {}):\n    target_path = file_session.get()\n    with open(target_path, 'w+') as f:\n        f.write(template)\n    code_compiled = compile(template, target_path, 'exec')\n    exec(code_compiled, gl, lo)\n    return lo\n\n"
  },
  {
    "path": "python_tools/module.py",
    "content": "import inspect\nimport logging\nfrom typing import Any,  List, Optional, Sequence, Tuple, Union\nfrom types import MethodType\n\nimport cached_conv as cc\nimport torch\n\nfrom . import TYPE_HASH\n\nfrom .buffer import Buffer, BUFFER_ATTRIBUTES_TYPE\nfrom . import templates\nfrom .codegen import *\n\n\nclass Module(torch.nn.Module):\n    __reserved_attribute_names__ = ['sr']\n    def __init__(self, sr: int | None = None) -> None:\n        super().__init__()\n        self._methods = []\n        self._attributes = torch.jit.Attribute([], List[str])\n        self._buffer_attributes = torch.jit.Attribute([], List[str])\n        self.tmp_file_session = tmp_file_session(self)\n        self._ready = False\n        self.sr = torch.jit.Attribute(sr, int | None) \n\n    def register_method(\n        self,\n        method_name: str,\n        in_channels: int,\n        in_ratio: int,\n        out_channels: int,\n        out_ratio: int,\n        input_labels: Optional[Sequence[str]] = None,\n        output_labels: Optional[Sequence[str]] = None,\n        test_method: bool = True,\n        test_buffer_size: int = 8192,\n    ):\n        \"\"\"Register a class method as usable by nn~.\n\n        The method must take as input and return a single 3D tensor.\n\n        Args:\n            method_name: name of the method to register\n            in_channels: number of channels of the input tensor\n            in_ratio: temporal compression ratio of the input tensor\n            in_channels: number of channels of the output tensor\n            in_ratio: temporal compression ratio of the output tensor\n            input_labels: labels used by max for the inlets\n            output_labels: labels used by max for the outlets\n            test_method: weither the method is tested during registration or not\n            test_buffer_size: duration of the test buffer\n        \"\"\"\n        logging.info(f'Registering method \"{method_name}\"')\n        self.register_buffer(\n            f'{method_name}_params',\n            torch.tensor([\n                in_channels,\n                in_ratio,\n                out_channels,\n                out_ratio,\n            ]))\n\n        if input_labels is None:\n            input_labels = [\n                f\"(signal) model input {i}\" for i in range(in_channels)\n            ]\n        if len(input_labels) != in_channels:\n            raise ValueError(\n                (f\"Method {method_name}, expected \"\n                 f\"{in_channels} input labels, got {len(input_labels)}\"))\n        setattr(self, f\"{method_name}_input_labels\", input_labels)\n\n        if output_labels is None:\n            output_labels = [\n                f\"(signal) model output {i}\" for i in range(out_channels)\n            ]\n        if len(output_labels) != out_channels:\n            raise ValueError(\n                (f\"Method {method_name}, expected \"\n                 f\"{out_channels} output labels, got {len(output_labels)}\"))\n        setattr(self, f\"{method_name}_output_labels\", output_labels)\n\n        if test_method:\n            logging.info(f\"Testing method {method_name} with nn~ API\")\n            x = torch.zeros(1, in_channels, test_buffer_size // in_ratio)\n            y = getattr(self, method_name)(x)\n\n            if len(y.shape) != 3:\n                raise ValueError(\n                    (\"Output tensor must have exactly 3 dimensions, \"\n                     f\"got {len(y.shape)}\"))\n            if y.shape[0] != 1:\n                raise ValueError(\n                    f\"Expecting single batch output, got {y.shape[0]}\")\n            if y.shape[1] != out_channels:\n                raise ValueError((\n                    f\"Wrong number of output channels for method \\\"{method_name}\\\", \"\n                    f\"expected {out_channels} got {y.shape[1]}\"))\n            if y.shape[2] != test_buffer_size // out_ratio:\n                raise ValueError(\n                    (f\"Wrong output length for method \\\"{method_name}\\\", \"\n                     f\"expected {test_buffer_size//out_ratio} \"\n                     f\"got {y.shape[2]}\"))\n            if y.dtype != torch.float:\n                raise ValueError(f\"Output tensor must be of type float, got {y.dtype}\")\n\n            if cc.MAX_BATCH_SIZE > 1:\n                logging.info(f\"Testing method {method_name} with mc.nn~ API\")\n                x = torch.zeros(4, in_channels, test_buffer_size // in_ratio)\n                y = getattr(self, method_name)(x)\n\n                if len(y.shape) != 3:\n                    raise ValueError(\n                        (\"Output tensor must have exactly 3 dimensions, \"\n                         f\"got {len(y.shape)}\"))\n                if y.shape[0] != 4:\n                    raise ValueError(\n                        f\"Expecting 4 batch output, got {y.shape[0]}\")\n                if y.shape[1] != out_channels:\n                    raise ValueError((\n                        f\"Wrong number of output channels for method \\\"{method_name}\\\", \"\n                        f\"expected {out_channels} got {y.shape[1]}\"))\n                if y.shape[2] != test_buffer_size // out_ratio:\n                    raise ValueError(\n                        (f\"Wrong output length for method \\\"{method_name}\\\", \"\n                         f\"expected {test_buffer_size//out_ratio} \"\n                         f\"got {y.shape[2]}\"))\n                logging.info((f\"Added method \\\"{method_name}\\\" \"\n                              f\"tested with buffer size {test_buffer_size}\"))\n            else:\n                logging.info(\n                    f\"Skipping method {method_name} with mc.nn~ API as cc.MAX_BATCH_SIZE={cc.MAX_BATCH_SIZE}\"\n                )\n        else:\n            logging.warn(f\"Added method \\\"{method_name}\\\" without testing it.\")\n\n        self._methods.append(method_name)\n\n    def register_attribute(self, attribute_name: str,\n                           values: Union[Any, Tuple[Any]]):\n        \"\"\"Register an attribute visible by nn~.\n\n        Args:\n            attribute_name: name of the attribute to register\n            values: a default value or tuple of default values\n        \"\"\"\n        assert attribute_name not in self.__reserved_attribute_names__, f\"attribute_name {attribute_name} is reserved.\"\n\n        if not isinstance(values, (List, Tuple)):\n            values = list([values])\n        else:\n            values = list(values)\n\n        type_hash = []\n        for v in values:\n            type_hash.append(TYPE_HASH[type(v)])\n\n        # if not hasattr(self, f\"get_{attribute_name}\"):\n        #     raise AttributeError(f\"Getter for {attribute_name} not defined\")\n\n        # if not hasattr(self, f\"set_{attribute_name}\"):\n        #     raise AttributeError(f\"Setter for {attribute_name} not defined\")\n\n        # signature = inspect.signature(getattr(self, f\"get_{attribute_name}\"))\n        # if signature.return_annotation == inspect._empty:\n        #     raise TypeError(\n        #         f\"Output type not defined for getter get_{attribute_name}\")\n\n        self.__setattr__(attribute_name, tuple(values))\n        self._attributes.value.append(attribute_name)\n        for i, v in enumerate(values):\n            if isinstance(v, Buffer):\n                self._buffer_attributes.value.append(f\"{attribute_name}#{i}\")\n        self.register_buffer(f\"{attribute_name}_params\", torch.LongTensor(type_hash))\n\n    @torch.jit.export\n    def get_methods(self):\n        return self._methods\n\n    @torch.jit.export\n    def get_attributes(self) -> List[str]:\n        return self._attributes\n\n    def export_to_ts(self, path):\n        self.eval()\n        scripted = torch.jit.script(self)\n        scripted.save(path)\n\n    # buffer attributes handling\n    @torch.jit.export\n    def get_buffer_attributes(self) -> List[str]:\n        if torch.jit.is_scripting():\n            return self._buffer_attributes\n        else:\n            return self._buffer_attributes.value\n\n    @torch.jit.export\n    def is_buffer_empty(self, buffer_name: str) -> bool:\n        if buffer_name not in self.get_buffer_attributes():\n            return True\n        if torch.jit.is_scripting():\n            for k, v in self._buffer_attributes.items():\n                if k == buffer_name:\n                    return v.has_value\n        else:\n            for k, v in self._buffer_attributes.value.items():\n                if k == buffer_name:\n                    return v.has_value\n        return True\n\n    @torch.jit.export\n    def get_sample_rate(self) -> int: \n        sr = self.sr\n        if torch.jit.isinstance(sr, Optional[int]):\n            if sr is None:\n                return -1\n            else:\n                return int(sr)\n        else:\n            if sr.value is None:\n                return -1\n            else:\n                return int(sr.value)\n\n    @torch.jit.export\n    def set_sample_rate(self, sample_rate: int | None) -> None:\n        \"\"\"set the operative sampling rate of the module on inference.\"\"\"\n        if sample_rate is not None:\n            self.sr = sample_rate\n\n    def __prepare_scriptable__(self):\n        if not self._ready:\n            self.finish()\n        return self\n\n    def finish(self):\n        self._build_buffer_attribute_methods()\n        self._build_missing_attribute_callbacks()\n        self._ready = True\n    \n    def _build_buffer_attribute_methods(self):\n        self.set_buffer_attribute = MethodType(\n            method_from_template(\n                self.tmp_file_session, \n                templates.buffers.set_buffer_attribute_template(self._buffer_attributes),\n                globals(), locals()\n            )[\"set_buffer_attribute\"], \n            self)\n\n        self.clear_buffer_attribute = MethodType(\n            method_from_template(\n                self.tmp_file_session, \n                templates.buffers.clear_buffer_attribute_template(self._buffer_attributes),\n                globals(), locals()\n                )[\"clear_buffer_attribute\"], \n            self)\n\n        self.is_buffer_empty = MethodType(\n            method_from_template(\n                self.tmp_file_session, \n                templates.buffers.is_buffer_empty_template(self._buffer_attributes),\n                globals(), locals()\n            )[\"is_buffer_empty\"], \n            self)\n\n    def _build_missing_attribute_callbacks(self):\n        for attr_name in self._attributes.value:\n            getter_name = f\"get_{attr_name}\"\n            if not hasattr(self, getter_name):\n                getter_cb = MethodType(\n                    method_from_template(\n                    self.tmp_file_session, \n                    templates.attributes.get_attribute_getter(attr_name, getattr(self, f\"{attr_name}_params\")),\n                    globals(), locals()\n                )[getter_name], \n                self)\n                setattr(self, getter_name, getter_cb)\n            setter_name = f\"set_{attr_name}\"\n            if not hasattr(self, setter_name):\n                setter_cb = MethodType(\n                    method_from_template(\n                    self.tmp_file_session, \n                    templates.attributes.get_attribute_setter(attr_name, getattr(self, f\"{attr_name}_params\")),\n                    globals(), locals()\n                )[setter_name], \n                self)\n                setattr(self, setter_name, setter_cb)\n\n\n    def __del__(self):\n        if getattr(self, \"tmp_file_session\", None):\n            self.tmp_file_session.close()\n\n        \n"
  },
  {
    "path": "python_tools/templates/__init__.py",
    "content": "from .buffers import *\nfrom .attributes import *"
  },
  {
    "path": "python_tools/templates/attributes.py",
    "content": "\nimport torch\nfrom .. import TYPE_HASH, Buffer\n\nTYPE_HASH_R = {v: k for k, v in TYPE_HASH.items()}\n\ndef _get_sig_type(param):\n    param = TYPE_HASH_R.get(int(param), None)\n    if param in [int, float, bool, str]:\n        return param.__name__\n    elif param in [torch.Tensor]:\n        return \"torch.Tensor\"\n    elif param in [Buffer]:\n        # return \"Tuple[torch.Tensor, int]\"\n        return \"str\"\n    else:\n        raise TypeError('type %s not known'%type(param))\n\ndef get_attribute_setter(attribute_name, attribute_params):\n    signature_atoms = [f'{attribute_name}{i}: {_get_sig_type(attribute_params[i])}' for i in range(len(attribute_params))]\n    signature = \", \".join(signature_atoms)\n    setter_atoms = []\n    for i in range(len(attribute_params)):\n        if attribute_params[i] == TYPE_HASH[Buffer]:\n            setter_atoms.append(f'Buffer.copy(self.{attribute_name}[{i}])')\n        else:\n            setter_atoms.append(f\"{attribute_name}{i}\")\n    setter = f\"self.{attribute_name} = (\" + \", \".join(setter_atoms) + \",)\"\n    template = f\"@torch.jit.export\\ndef set_{attribute_name}(self, {signature}) -> int:\\n\\tres=0\\n\"\n    template += f\"\\t{setter}\\n\"\n    template += \"\\treturn res\"\n    return template\n\ndef get_attribute_getter(attribute_name, attribute_params):\n    def _export_arg(attribute_name, attribute_params, i):\n        if attribute_params[i] == TYPE_HASH[Buffer]:\n            return 'self.' + attribute_name+'['+str(i)+'].to_str()' \n        else:\n            return 'self.' + attribute_name+'['+str(i)+']' \n    template = f\"@torch.jit.export\\ndef get_{attribute_name}(self):\\n\"\n    template+= f\"\\treturn {', '.join([_export_arg(attribute_name, attribute_params, i) for i in range(len(attribute_params))])},\"\n    return template\n\n"
  },
  {
    "path": "python_tools/templates/buffers.py",
    "content": "\n\ndef set_buffer_attribute_template(buffer_names):\n    template = \"@torch.jit.export\\ndef set_buffer_attribute(self, buffer_name: str, buffer: torch.Tensor, sr: int) -> int:\\n\"\n    if len(buffer_names.value) == 0:\n        template += \"\\treturn -1\"\n        return template\n    for b in buffer_names.value:\n        buffer_names_parts = b.split('#')\n        if len(buffer_names_parts) != 2:\n            raise ValueError('Invalid buffer name : '%b)\n        attribute_name, buffer_idx = buffer_names_parts\n        template += f\"\\tif (buffer_name == \\\"{b}\\\"): return self.{attribute_name}[{buffer_idx}].set_value(buffer, sr)\\n\"\n    template += \"\\treturn -1\"\n    return template\n\ndef clear_buffer_attribute_template(buffer_names):\n    template = \"@torch.jit.export\\ndef clear_buffer_attribute(self, buffer_name: str) -> None:\\n\"\n    if len(buffer_names.value) == 0: \n        template += \"\\treturn\"\n        return template\n    for b in buffer_names.value:\n        buffer_names_parts = b.split('#')\n        if len(buffer_names_parts) != 2:\n            raise ValueError('Invalid buffer name : '%b)\n        attribute_name, buffer_idx = buffer_names_parts\n        template += f\"\\tif (buffer_name == \\\"{b}\\\"): return self.{attribute_name}[{buffer_idx}].init_value()\\n\"\n    return template\n\ndef is_buffer_empty_template(buffer_names):\n    template = \"@torch.jit.export\\ndef is_buffer_empty(self, buffer_name: str) -> bool:\\n\"\n    if len(buffer_names.value) == 0:\n        template += \"\\treturn True\"\n        return template\n    for b in buffer_names.value:\n        buffer_names_parts = b.split('#')\n        if len(buffer_names_parts) != 2:\n            raise ValueError('Invalid buffer name : '%b)\n        attribute_name, buffer_idx = buffer_names_parts\n        template += f\"\\tif (buffer_name == \\\"{b}\\\"): return not self.{attribute_name}[{buffer_idx}].has_value\\n\"\n    template += \"\\treturn True\"\n    return template\n"
  },
  {
    "path": "python_tools/test/test_attributes.maxpat",
    "content": "{\n\t\"patcher\" : \t{\n\t\t\"fileversion\" : 1,\n\t\t\"appversion\" : \t\t{\n\t\t\t\"major\" : 8,\n\t\t\t\"minor\" : 6,\n\t\t\t\"revision\" : 5,\n\t\t\t\"architecture\" : \"x64\",\n\t\t\t\"modernui\" : 1\n\t\t}\n,\n\t\t\"classnamespace\" : \"box\",\n\t\t\"rect\" : [ 607.0, 354.0, 767.0, 480.0 ],\n\t\t\"bglocked\" : 0,\n\t\t\"openinpresentation\" : 0,\n\t\t\"default_fontsize\" : 12.0,\n\t\t\"default_fontface\" : 0,\n\t\t\"default_fontname\" : \"Arial\",\n\t\t\"gridonopen\" : 1,\n\t\t\"gridsize\" : [ 15.0, 15.0 ],\n\t\t\"gridsnaponopen\" : 1,\n\t\t\"objectsnaponopen\" : 1,\n\t\t\"statusbarvisible\" : 2,\n\t\t\"toolbarvisible\" : 1,\n\t\t\"lefttoolbarpinned\" : 0,\n\t\t\"toptoolbarpinned\" : 0,\n\t\t\"righttoolbarpinned\" : 0,\n\t\t\"bottomtoolbarpinned\" : 0,\n\t\t\"toolbars_unpinned_last_save\" : 0,\n\t\t\"tallnewobj\" : 0,\n\t\t\"boxanimatetime\" : 200,\n\t\t\"enablehscroll\" : 1,\n\t\t\"enablevscroll\" : 1,\n\t\t\"devicewidth\" : 0.0,\n\t\t\"description\" : \"\",\n\t\t\"digest\" : \"\",\n\t\t\"tags\" : \"\",\n\t\t\"style\" : \"\",\n\t\t\"subpatcher_template\" : \"\",\n\t\t\"assistshowspatchername\" : 0,\n\t\t\"boxes\" : [ \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 153.666666666666686, 278.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-2\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 69.0, 278.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-19\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 602.0, 154.0, 75.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"get attr_bool\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-18\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 525.0, 154.0, 66.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"get attr_str\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-17\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 454.0, 154.0, 65.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"get attr_int\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-16\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 378.0, 154.0, 65.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"get attr_int\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-14\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 419.0, 92.0, 91.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"set attr_bool $1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-13\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 296.0, 92.0, 82.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"set attr_str $1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-12\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 174.0, 92.0, 91.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"set attr_float $1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-11\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 69.0, 92.0, 81.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"set attr_int $1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-9\",\n\t\t\t\t\t\"maxclass\" : \"toggle\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"int\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 419.0, 37.0, 24.0, 24.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-7\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 296.0, 49.0, 42.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"cherry\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"format\" : 6,\n\t\t\t\t\t\"id\" : \"obj-5\",\n\t\t\t\t\t\"maxclass\" : \"flonum\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 174.0, 49.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-3\",\n\t\t\t\t\t\"maxclass\" : \"number\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 69.0, 44.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-1\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 4,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\", \"signal\", \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 69.0, 214.0, 273.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"nn~ test_attributes[AttributeFoo]\"\n\t\t\t\t}\n\n\t\t\t}\n ],\n\t\t\"lines\" : [ \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-2\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-1\", 0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-4\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-1\", 1 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-1\", 0 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  },
  {
    "path": "python_tools/test/test_attributes.py",
    "content": "import sys, os\nimport torch\nimport pytest\nfrom pathlib import Path\n\nsys.path.append(str(Path(__file__).parent / \"..\" / \"..\"))\nfrom python_tools import Module, TYPE_HASH, Buffer\nfrom types import MethodType\nfrom typing import NoReturn\nfrom utils import out_dir, test_name, import_code \n\n\nclass AttributeFoo(Module):\n    def __init__(self):\n        super().__init__()\n        self.register_attribute(\"attr_int\", 0)\n        self.register_attribute(\"attr_float\", 0.)\n        self.register_attribute(\"attr_str\", \"apple\")\n        self.register_attribute(\"attr_bool\", False)\n        self.register_method(\"forward\", 1, 1, 2, 1, test_method=False)\n        self.finish()\n\n    @torch.jit.export\n    def forward(self, x: torch.Tensor):\n        x = torch.zeros(x.shape[:-2] + (2, x.shape[-1]))\n        x[..., 0, :] = self.attr_int[0]\n        x[..., 1, :] = self.attr_float[0]\n        return x\n\n\nclass ListAttributeFoo(Module):\n    def __init__(self, n: int, attribute_type: type):\n        super().__init__()\n        self.n = n\n        if attribute_type == torch.Tensor:\n            attr = tuple([torch.tensor(i) for i in range(n)])\n        else:\n            attr = tuple([attribute_type(i) for i in range(n)])\n        self.register_attribute(\"attr\", attr)\n        self.register_method(\"forward\", 1, 1, n, 1, output_labels=[\"attribute %d\"%i for i in range(len(attr))])\n        self.finish()\n\n    def forward(self, x):\n        out = torch.zeros(x.shape[:1] + (len(self.attr),) + x.shape[2:])\n        for i, val in enumerate(self.attr):\n            out[..., i, :] = float(val)\n        return out\n\ndef _default(attr_hash: int):\n    type_hash_r = {v: k for k, v in TYPE_HASH.items()}\n    attr_hash = type_hash_r[int(attr_hash)]\n    if attr_hash in [bool, int, float, str]: \n        return attr_hash(1)\n    elif attr_hash in [torch.Tensor]:\n        return torch.tensor(0)\n    elif attr_hash in [Buffer]:\n        return (torch.tensor(0), 44100)\n    else:\n        raise TypeError('type not known')\n\n\nclass TensorAttributeFoo(Module): \n    def __init__(self):\n        super().__init__()\n        self.register_attribute(\"a\", torch.zeros(4))\n        self.register_method(\"forward\", 1, 1, 4, 1, test_method=False)\n        self.finish()\n\n    @torch.jit.export\n    def forward(self, x: torch.Tensor):\n        out = torch.zeros(x.shape[0], 4, x.shape[2])\n        for i in range(4):\n            out[:, i] = self.a[0][None, i]\n        return out\n        \n\n@pytest.mark.parametrize('module_class', [AttributeFoo])\ndef test_attributes(module_class, out_dir, test_name):\n    module = module_class()\n    for attr_name in module.get_attributes().value:\n        # test getter\n        attr_params = getattr(module, f\"{attr_name}_params\")\n        getattr(module, f\"get_{attr_name}\")()\n        # test setter\n        getattr(module, f\"set_{attr_name}\")(_default(attr_params[0]))\n    module(torch.zeros(1, 1, 16))\n    scripted = torch.jit.script(module)\n    torch.jit.save(scripted, out_dir/f\"{test_name}.ts\")\n\n\n@pytest.mark.parametrize('n', [1, 4])\n@pytest.mark.parametrize('attr_type', [str, bool, int, float])\n@pytest.mark.parametrize('module_class', [ListAttributeFoo])\ndef test_list_attributes(n, attr_type, module_class, out_dir, test_name):\n    module = module_class(n, attr_type)\n    module.get_attr()\n    module.set_attr(*([_default(module.attr_params[0])] * n))\n    module(torch.zeros(1, 1, 16))\n    scripted = torch.jit.script(module)\n    torch.jit.save(scripted, out_dir/f\"{test_name}.ts\")\n\n\n\n@pytest.mark.parametrize('n', [1, 4])\n@pytest.mark.parametrize('attr_type', [str, bool, int, float])\n@pytest.mark.parametrize('module_class', [ListAttributeFoo])\ndef test_list_attributes(n, attr_type, module_class, out_dir, test_name):\n    module = module_class(n, attr_type)\n    module.get_attr()\n    module.set_attr(*([_default(module.attr_params[0])] * n))\n    module(torch.zeros(1, 1, 16))\n    scripted = torch.jit.script(module)\n    torch.jit.save(scripted, out_dir/f\"{test_name}.ts\")\n    \n\n@pytest.mark.parametrize('module_class', [TensorAttributeFoo])\ndef test_tensor_attributes(module_class, out_dir, test_name):\n    module = module_class()\n    target_attr = torch.Tensor([1,2,3,4])\n    module.set_a(torch.Tensor(target_attr))\n    out = module.get_a()[0]\n    assert out.eq(target_attr).all()\n    module(torch.zeros(1, 1, 16))\n    scripted = torch.jit.script(module)\n    torch.jit.save(scripted, out_dir/f\"{test_name}.ts\")\n"
  },
  {
    "path": "python_tools/test/test_buffers.py",
    "content": "import sys, os\nimport torch\nimport pytest\nfrom pathlib import Path\n\nsys.path.append(str(Path(__file__).parent / \"..\" / \"..\"))\nfrom python_tools import Module, Buffer\nfrom utils import out_dir, test_name, import_code \nfrom typing import Tuple\n\n\nclass BufferFoo(Module):\n    buffer: Tuple[Buffer]\n    def __init__(self, test_method: bool = False):\n        super().__init__()\n        self.register_attribute(\"buf\", Buffer(None, 64, 2048))\n        self.register_method('loudness', 1, 1, 1, 1, test_method=test_method)\n        self.register_method('shape', 1, 1, 2, 1, test_method=test_method)\n        self.register_method('get_sr', 1, 1, 1, 1, test_method=test_method)\n        self.finish()\n\n    def get_loudness(self, x: torch.Tensor) -> float:\n        return x.pow(2).mean().sqrt().item()\n\n    @torch.jit.export\n    def loudness(self, x: torch.Tensor):\n        buffer = self.buf[0]\n        if buffer.has_value:\n            loudness = self.get_loudness(buffer.value)\n            return torch.full_like(x, fill_value=loudness)\n        else:\n            return torch.zeros_like(x)\n\n    @torch.jit.export\n    def shape(self, x: torch.Tensor):\n        is_batched = x.ndim > 2\n        if not is_batched:\n            x = x[None]\n        buffer = self.buf[0]\n        if buffer.has_value:\n            out = torch.zeros(x.shape[0], 2, x.shape[-1])\n            out[:, 0, :] = buffer.value.shape[0]\n            out[:, 1, :] = buffer.value.shape[1]\n            if not is_batched:\n                out = out[0]\n        else:\n            out = torch.zeros_like(x)\n        return out\n\n    @torch.jit.export\n    def get_sr(self, x: torch.Tensor):\n        buffer = self.buf[0]\n        if buffer.has_value:\n            if self.buf[0].sr is None:\n                sr = -1\n            else:\n                sr = buffer.sr\n            return torch.full_like(x, fill_value=sr)\n        else:\n            return torch.zeros_like(x)\n\n\n@pytest.mark.parametrize('module_class', [BufferFoo])\ndef test_buffer_attributes(module_class, out_dir, test_name):\n    module = module_class()\n    module.loudness(torch.randn(1, 1, 16))\n    module.shape(torch.randn(1, 1, 16))\n    module.get_sr(torch.randn(1, 1, 16))\n    module.get_buf()\n    module.set_buf((torch.zeros(1, 64), 44100))\n    module.set_buffer_attribute(\"buffer#0\", torch.zeros(1, 64), 44100)\n\n\n@pytest.mark.parametrize('module_class', [BufferFoo])\ndef test_scripted_buffer_attributes(module_class, out_dir, test_name):\n    module = module_class()\n    scripted = torch.jit.script(module)\n    module.loudness(torch.randn(1, 1, 16))\n    module.shape(torch.randn(1, 1, 16))\n    module.get_sr(torch.randn(1, 1, 16))\n    module.get_buf()\n    module.set_buf((torch.zeros(1, 64), 44100))\n    module.set_buffer_attribute(\"buffer#0\", torch.zeros(1, 64), 44100)\n    module.get_buffer_attributes()\n    torch.jit.save(scripted, out_dir/f\"{test_name}.ts\")\n    \n"
  },
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    "path": "python_tools/test/test_list_attributes.maxpat",
    "content": "{\n\t\"patcher\" : \t{\n\t\t\"fileversion\" : 1,\n\t\t\"appversion\" : \t\t{\n\t\t\t\"major\" : 8,\n\t\t\t\"minor\" : 6,\n\t\t\t\"revision\" : 5,\n\t\t\t\"architecture\" : \"x64\",\n\t\t\t\"modernui\" : 1\n\t\t}\n,\n\t\t\"classnamespace\" : \"box\",\n\t\t\"rect\" : [ 572.0, 172.0, 681.0, 480.0 ],\n\t\t\"bglocked\" : 0,\n\t\t\"openinpresentation\" : 0,\n\t\t\"default_fontsize\" : 12.0,\n\t\t\"default_fontface\" : 0,\n\t\t\"default_fontname\" : \"Arial\",\n\t\t\"gridonopen\" : 1,\n\t\t\"gridsize\" : [ 15.0, 15.0 ],\n\t\t\"gridsnaponopen\" : 1,\n\t\t\"objectsnaponopen\" : 1,\n\t\t\"statusbarvisible\" : 2,\n\t\t\"toolbarvisible\" : 1,\n\t\t\"lefttoolbarpinned\" : 0,\n\t\t\"toptoolbarpinned\" : 0,\n\t\t\"righttoolbarpinned\" : 0,\n\t\t\"bottomtoolbarpinned\" : 0,\n\t\t\"toolbars_unpinned_last_save\" : 0,\n\t\t\"tallnewobj\" : 0,\n\t\t\"boxanimatetime\" : 200,\n\t\t\"enablehscroll\" : 1,\n\t\t\"enablevscroll\" : 1,\n\t\t\"devicewidth\" : 0.0,\n\t\t\"description\" : 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\t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-23\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 351.0, 218.0, 167.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"set attr Pipi caca boudin prout\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-24\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 510.0, 347.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-25\",\n\t\t\t\t\t\"maxclass\" : 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\"obj-14\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 39.0, 218.0, 85.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"set attr 1 0 1 0\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-15\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 193.666666666666657, 347.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-16\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 114.333333333333329, 347.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-17\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 35.0, 347.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-18\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 4,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\", \"signal\", \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 35.0, 279.0, 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0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-9\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 503.0, 146.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-10\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 427.0, 146.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-11\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 351.0, 146.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-12\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 4,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\", \"signal\", \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 351.0, 78.0, 257.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"nn~ test_list_attributes[ListAttributeFoo-float-4]\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-2\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 263.0, 146.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-7\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 35.0, 43.0, 105.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"set attr 0. 1. 3. 23.\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-5\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 187.0, 146.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 111.0, 146.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-3\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 35.0, 146.0, 56.0, 22.0 ],\n\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-1\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 4,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\", \"signal\", \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 35.0, 78.0, 247.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"nn~ test_list_attributes[ListAttributeFoo-int-4]\"\n\t\t\t\t}\n\n\t\t\t}\n ],\n\t\t\"lines\" : [ \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-2\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-1\", 3 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-3\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-1\", 0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-4\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-1\", 1 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-5\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-1\", 2 ]\n\t\t\t\t}\n\n\t\t\t}\n, 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0 ],\n\t\t\t\t\t\"source\" : [ \"obj-27\", 3 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-24\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-27\", 2 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-25\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-27\", 1 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-26\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-27\", 0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-1\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-7\", 0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-12\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-8\", 0 ]\n\t\t\t\t}\n\n\t\t\t}\n ],\n\t\t\"dependency_cache\" : [ \t\t\t{\n\t\t\t\t\"name\" : \"nn~.mxo\",\n\t\t\t\t\"type\" : \"iLaX\"\n\t\t\t}\n ],\n\t\t\"autosave\" : 0\n\t}\n\n}\n"
  },
  {
    "path": "python_tools/test/utils.py",
    "content": "import os\nimport importlib\nimport time\nimport sys\nimport pytest\nfrom pathlib import Path\n\n@pytest.fixture\ndef out_dir() -> Path:\n    out_dir = (Path(__file__).parent / \"model_out\").resolve()\n    if not out_dir.exists():\n        os.makedirs(out_dir)\n    return out_dir\n\n@pytest.fixture\ndef test_name(request):\n    return request.node.name\n\ndef import_code(code, glob, loc):\n    outdir = Path('/tmp') / \"nn_tilde\" / \"code\" \n    os.makedirs(outdir, exist_ok=True)\n    module_name = f\"nntilde_tmp_{int(time.time())}\"\n    outpath = outdir / f\"{module_name}.py\"\n    with open(outpath, \"w+\") as f:\n        f.write(code)\n       # Load the module\n    spec = importlib.util.spec_from_file_location(module_name, outpath)\n    module = importlib.util.module_from_spec(spec)\n    exec(spec.loader.get_code(module_name), glob)\n    loc.update(module.__dict__)\n    \n\n\n"
  },
  {
    "path": "requirements.txt",
    "content": "torch==2.5\ncached_conv>=2.5.0"
  },
  {
    "path": "requirements_darwin_x64.txt",
    "content": "torch1\ncached_conv>=2.5.0"
  },
  {
    "path": "scripting/README.md",
    "content": "# Scripting examples in nn~\n\nThese examples demonstrate how to write simple scripts to incorporate any type of deep models from PyTorch into MaxMSP (and potentially running on GPU). The examples show a variety of different use cases that also help to understand the input/output shapes relationships.\n- `effects.py` : apply simple effects to the input (identical input and output shapes)\n- `features.py` : compute spectral descriptors from the PyTorch audio library (each input audio buffer produces a single float as output)\n- `unmix.py` : apply the unmix deep source separation model (input is split into 4 different audio streams containing « drums », « vocals », « bass » and « others »)\n"
  },
  {
    "path": "scripting/effects.py",
    "content": "#\n# NN~ - Scripting library\n# effects.py : Intermediate scripting example for waveform-to-waveform case.\n#\n# We provide here a simple example of how to use nn~ in order to transform incoming audio.\n# In this example, we do not rely on any ML model, but simply apply effects on input buffers.\n#\n# ACIDS - IRCAM : Philippe Esling, Axel Chemla--Romeu-Santos, Antoine Caillon\n#\n\nfrom typing import List, Tuple\nimport torch\nimport torch.nn as nn\nimport nn_tilde\n\nclass AudioUtils(nn_tilde.Module):\n\n    def __init__(self):\n        super().__init__()\n        # REGISTER ATTRIBUTES\n        self.register_attribute('gain_factor', 1.)\n        self.register_attribute('polynomial_factors', (1., 0., 0., 0.))\n        self.register_attribute('saturate_mode', 'tanh')\n        self.register_attribute('invert_signal', False)\n        self.register_attribute('fractal', (2, 0.))\n\n        # REGISTER METHODS\n        self.register_method(\n            'thru',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) input signal'],\n            output_labels=['(signal) output signal'],\n        )\n        self.register_method(\n            'invert',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) input signal'],\n            output_labels=['(signal) output signal'],\n        )\n        self.register_method(\n            'add',\n            in_channels=2,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) first signal', '(signal) second signal'],\n            output_labels=['(signal) output signal'],\n        )\n        self.register_method(\n            'saturate',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) signal to saturate'],\n            output_labels=['(signal) saturated signal'],\n        )\n        self.register_method(\n            'midside',\n            in_channels=2,\n            in_ratio=1,\n            out_channels=2,\n            out_ratio=1,\n            input_labels=['(signal) L channel', '(signal) R channel'],\n            output_labels=['(signal) Mid channel', '(signal) Side channel'],\n        )\n        self.register_method(\n            'rms',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1024,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['(signal) rms value'],\n        )\n        self.register_method(\n            'polynomial',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) signal to distort'],\n            output_labels=['(signal) distorted signal'],\n        )\n\n        self.register_method(\n            'fractalize',\n            in_channels=1,\n            in_ratio=512,\n            out_channels=1,\n            out_ratio=512,\n            input_labels=['(signal) signal to replicate'],\n            output_labels=['(signal) fractalized signal'],\n        )\n\n    @torch.jit.export\n    def thru(self, x: torch.Tensor):\n        return x\n\n    # defining main methods\n    @torch.jit.export\n    def invert(self, x: torch.Tensor):\n        if self.invert_signal[0]:\n            return x\n        else:\n            return -x\n\n    @torch.jit.export\n    def add(self, x: torch.Tensor):\n        return x.sum(-2, keepdim=True) / 2\n\n    @torch.jit.export\n    def fractalize(self, x: torch.Tensor):\n        fractal_order = int(self.fractal[0])\n        fractal_amount = float(self.fractal[1])\n        downsampled_signal = x[..., ::fractal_order]\n        return x\n\n    @torch.jit.export\n    def polynomial(self, x: torch.Tensor):\n        out = torch.zeros_like(x)\n        for i in range(4):\n            out += self.polynomial_factors[i] * x.pow(i + 1)\n        return out\n\n    @torch.jit.export\n    def saturate(self, x: torch.Tensor):\n        saturate_mode = self.saturate_mode[0]\n        if saturate_mode == 'tanh':\n            return torch.tanh(x * self.gain_factor[0])\n        elif saturate_mode == 'clip':\n            return torch.clamp(x * self.gain_factor[0], -1, 1)\n\n    @torch.jit.export\n    def midside(self, x: torch.Tensor):\n        l, r = x[..., 0, :], x[..., 1, :]\n        return torch.stack([(l + r) / 2, (l - r) / 2], dim=-2)\n\n    @torch.jit.export\n    def rms(self, x: torch.Tensor):\n        x = x.reshape(x.shape[0], x.shape[1], 1024, -1)\n        rms = x.pow(2).sum(-2).sqrt() / x.size(-1)\n        return rms\n\n    # defining attribute getters\n    # WARNING : typing the function's ouptut is mandatory\n    @torch.jit.export\n    def get_gain_factor(self) -> float:\n        return float(self.gain_factor[0])\n\n    @torch.jit.export\n    def get_polynomial_factors(self) -> List[float]:\n        polynomial_factors: List[float] = []\n        for p in self.polynomial_factors:\n            polynomial_factors.append(float(p))\n        return polynomial_factors\n\n    @torch.jit.export\n    def get_saturate_mode(self) -> str:\n        return self.saturate_mode[0]\n\n    @torch.jit.export\n    def get_invert_signal(self) -> bool:\n        return self.invert_signal[0]\n\n    @torch.jit.export\n    def get_fractal(self) -> Tuple[int, float]:\n        return (int(self.fractal[0]), float(self.fractal[1]))\n\n    # defining attribute setter\n    # setters must return an error code :\n    # return 0 if the attribute has been adequately set,\n    # return -1 if the attribute was wrong.\n    @torch.jit.export\n    def set_gain_factor(self, x: float) -> int:\n        self.gain_factor = (x, )\n        return 0\n\n    @torch.jit.export\n    def set_polynomial_factors(self, factor1: float, factor2: float,\n                               factor3: float, factor4: float) -> int:\n        factors = (factor1, factor2, factor3, factor4)\n        self.polynomial_factors = factors\n        return 0\n\n    @torch.jit.export\n    def set_saturate_mode(self, x: str):\n        if (x == 'tanh') or (x == 'clip'):\n            self.saturate_mode = (x, )\n            return 0\n        else:\n            return -1\n\n    @torch.jit.export\n    def set_invert_signal(self, x: bool):\n        self.invert_signal = (x, )\n        return 0\n\n    @torch.jit.export\n    def set_fractal(self, factor: int, amount: float):\n        if factor <= 0:\n            return -1\n        elif factor % 2 != 0:\n            return -1\n        self.fractal = (factor, float(amount))\n        return 0\n\nif __name__ == '__main__':\n    model = AudioUtils()\n    model.export_to_ts('effects.ts')"
  },
  {
    "path": "scripting/features.py",
    "content": "#\n# NN~ - Scripting library\n# features.py : Simple scripting example for waveform-to-float case.\n#\n# We demonstrate the basic mecanisms for using the nn~ environment.\n# In this case, any function from Python can be used to wrap it inside a nn~ model.\n#\n# ACIDS - IRCAM : Philippe Esling, Axel Chemla--Romeu-Santos, Antoine Caillon\n#\n\nfrom typing import List, Tuple\nimport librosa\n# Pytorch audio operations\nimport torch\nimport torchaudio.functional as F\nfrom torchaudio.transforms import Spectrogram\n# Import the nn~ library\nimport nn_tilde\nimport numpy as np\n\nclass AudioFeatures(nn_tilde.Module):\n\n    def __init__(self,\n                 nfft=1024,\n                 hop_size=256,\n                 skip_features=None):\n        super().__init__()\n        self.nfft = nfft\n        self.hop_size = hop_size\n        transform = Spectrogram(n_fft=nfft,\n                                win_length=nfft,\n                                hop_length=hop_size,\n                                center=False,\n                                normalized=True)\n        self.transform = transform\n        self.skip_features = skip_features\n        # -----------------\n        # Register attributes\n        # -----------------\n        self.register_attribute('sr', 44100)\n        self.register_buffer('audio_buffer', torch.zeros((1, 1, nfft - hop_size)))\n        # Pre-compute frequency bins\n        self.freq = torch.fft.rfftfreq(n = nfft, d = 1.0 / self.sr[0])\n\n        # -----------------\n        # Register methods\n        # -----------------\n        self.register_method(\n            'rms',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1024,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['(signal) rms value'],\n        )\n\n        # REGISTER METHODS\n        self.register_method(\n            'centroid',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=self.hop_size,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['(signal) spectral centroid value'],\n        )\n\n        # REGISTER METHODS\n        self.register_method(\n            'flatness',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=self.hop_size,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['(signal) flatness value'],\n        )\n\n        # REGISTER METHODS\n        self.register_method(\n            'bandwidth',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=self.hop_size,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['(signal) bandwidth value'],\n        )\n\n    def _compute_spectrogram(self, x: torch.Tensor):\n        # X : B x hop_size\n        if self.audio_buffer.shape[0] != x.shape[0]:\n            print(\"Resizing and resetting buffer - the batch size has changed\")\n            self.audio_buffer = torch.zeros((x.shape[0], 1, self.nfft - self.hop_size)).to(x)\n            self.freq = torch.fft.rfftfreq(n = self.nfft, d = 1.0 / self.sr[0])\n        # Using the previous buffer information\n        x = torch.cat([self.audio_buffer, x], dim=-1)\n        # Compute the transform\n        spec = self.transform(x)[:, 0]\n        self.audio_buffer = x[..., -(self.nfft - self.hop_size):]\n        if self.skip_features is not None:\n            spec = spec[:, :self.skip_features]\n        return spec\n\n    @torch.jit.export\n    def rms(self, x: torch.Tensor):\n        x = x.reshape(x.shape[0], x.shape[1], 1024, -1)\n        rms = x.pow(2).sum(-2).sqrt() / x.size(-1)\n        return rms\n    \n    @torch.jit.export\n    def centroid(self, x: torch.Tensor):\n        # Compute the current spectrogram\n        spectro = self._compute_spectrogram(x)\n        # Compute the center frequencies of each bin\n        if self.freq is None:\n            self.freq = torch.fft.rfftfreq(n = self.nfft, d = 1.0 / self.sr[0])\n        if len(self.freq.shape) == 1:\n            self.freq = self.freq[None, :, None].expand_as(spectro)\n        # Column-normalize S\n        centroid = torch.sum(self.freq * torch.nn.functional.normalize(spectro, p=1.0, dim=-2), dim=-2)\n        return centroid[:, None, :]\n\n    @torch.jit.export\n    def bandwidth(self, x: torch.Tensor, amin: float = 1e-10, power: float = 2.0, p: float = 2.0):\n        # Compute the current spectrogram\n        spectro = self._compute_spectrogram(x)\n        # Compute the center frequencies of each bin\n        if self.freq is None:\n            self.freq = torch.fft.rfftfreq(n = self.nfft, d = 1.0 / self.sr[0])\n        if len(self.freq.shape) == 1:\n            self.freq = self.freq[None, :, None].expand_as(spectro)\n        # Normalize spectro\n        spectro_normed = torch.nn.functional.normalize(spectro, p=1.0, dim=-2)\n        # Compute centroid\n        centroid = torch.sum(self.freq * spectro_normed, dim=-2)[:, None, :]\n        # Compute the deviation\n        deviation = torch.abs(self.freq - centroid)\n        # Compute bandwidth\n        bandwidth = torch.sum(spectro_normed * deviation**p, dim=-2, keepdim=True) ** (1.0 / p)\n        return bandwidth\n\n    @torch.jit.export\n    def flatness(self, x: torch.Tensor, amin: float = 1e-10, power: float = 2.0):\n        # Compute the current spectrogram\n        spectro = self._compute_spectrogram(x)\n        S_thresh = torch.maximum(spectro**power, torch.zeros(1) + amin)\n        gmean = torch.exp(torch.mean(torch.log(S_thresh), dim=-2, keepdim=True))\n        amean = torch.mean(S_thresh, dim=-2, keepdim=True)\n        flatness = gmean / amean\n        print(flatness.shape)\n        return flatness\n\n    # defining attribute getters\n    # WARNING : typing the function's ouptut is mandatory\n    @torch.jit.export\n    def get_sr(self) -> int:\n        return int(self.sr[0])\n\n    # defining attribute setter\n    # setters must return an error code :\n    # return 0 if the attribute has been adequately set,\n    # return -1 if the attribute was wrong.\n    @torch.jit.export\n    def set_sr(self, x: int) -> int:\n        self.sr = (x, )\n        return 0\n\nif __name__ == '__main__':\n    # Create your target class\n    model = AudioFeatures()\n    # Export it to a torchscript model\n    model.export_to_ts('features.ts')"
  },
  {
    "path": "scripting/scripting.maxpat",
    "content": "{\n\t\"patcher\" : \t{\n\t\t\"fileversion\" : 1,\n\t\t\"appversion\" : \t\t{\n\t\t\t\"major\" : 9,\n\t\t\t\"minor\" : 0,\n\t\t\t\"revision\" : 2,\n\t\t\t\"architecture\" : \"x64\",\n\t\t\t\"modernui\" : 1\n\t\t}\n,\n\t\t\"classnamespace\" : \"box\",\n\t\t\"rect\" : [ 134.0, 124.0, 1000.0, 959.0 ],\n\t\t\"gridsize\" : [ 15.0, 15.0 ],\n\t\t\"boxes\" : [ \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-7\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 193.0, 342.0, 121.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"Spectral centroid\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-9\",\n\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ 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  {
    "path": "scripting/unmix.py",
    "content": "#\n# NN~ - Scripting library\n# unmix.py : Advanced scripting example for integrating a deep waveform-to-waveform model.\n#\n# We provide here a simple example of how to use nn~ in order to transform incoming audio.\n# In this example, we do not rely on any ML model, but simply apply effects on input buffers.\n#\n# ACIDS - IRCAM : Philippe Esling, Axel Chemla--Romeu-Santos, Antoine Caillon\n#\n\n# System imports\nfrom typing import List, Tuple\nimport os\nimport math\n# Pytorch imports\nimport torch\nimport torch.nn as nn\nimport torch\nimport torchaudio\n# NN~ imports\nimport nn_tilde\n\nclass Unmix(nn_tilde.Module):\n\n    def __init__(self, \n                 pretrained):\n        super().__init__()\n        # REGISTER ATTRIBUTES\n        self.register_attribute('sr', 44100)\n        self.pretrained = pretrained\n\n        # REGISTER METHODS\n        self.register_method(\n            'forward',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=4,\n            out_ratio=1,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['drums', 'bass', 'vocals', 'others'],\n        )\n\n    @torch.jit.export\n    def forward(self, input: torch.Tensor):\n        # Preprocess the input buffer (representation)\n        in_r = preprocess(input, int(self.sr[0]), int(self.pretrained.sample_rate))\n        # Pass through the deep audio separation\n        out = self.pretrained(in_r)\n        # Return the separated channels\n        return out.mean(dim=2)\n\n    # defining attribute getters\n    # WARNING : typing the function's ouptut is mandatory\n    @torch.jit.export\n    def get_sr(self) -> int:\n        return int(self.sr[0])\n\n    # defining attribute setter\n    # setters must return an error code :\n    # return 0 if the attribute has been adequately set,\n    # return -1 if the attribute was wrong.\n    @torch.jit.export\n    def set_sr(self, x: int) -> int:\n        self.sr = (x, )\n        return 0\n\ndef preprocess(\n    audio: torch.Tensor,\n    rate: int,\n    model_rate: int,\n) -> torch.Tensor:\n    \"\"\"                                                                                                                                                                                                                                              \n    From an input tensor, convert it to a tensor of shape                                                                                                                                                                                            \n    shape=(nb_samples, nb_channels, nb_timesteps). This includes:                                                                                                                                                                                    \n    -  if input is 1D, adding the samples and channels dimensions.                                                                                                                                                                                   \n    -  if input is 2D                                                                                                                                                                                                                                \n        o and the smallest dimension is 1 or 2, adding the samples one.                                                                                                                                                                              \n        o and all dimensions are > 2, assuming the smallest is the samples                                                                                                                                                                           \n          one, and adding the channel one                                                                                                                                                                                                            \n    - at the end, if the number of channels is greater than the number                                                                                                                                                                               \n      of time steps, swap those two.                                                                                                                                                                                                                 \n    - resampling to target rate if necessary                                                                                                                                                                                                         \n                                                                                                                                                                                                                                                     \n    Args:                                                                                                                                                                                                                                            \n        audio (Tensor): input waveform                                                                                                                                                                                                               \n        rate (float): sample rate for the audio                                                                                                                                                                                                      \n        model_rate (float): sample rate for the model                                                                                                                                                                                                \n                                                                                                                                                                                                                                                     \n    Returns:                                                                                                                                                                                                                                         \n        Tensor: [shape=(nb_samples, nb_channels=2, nb_timesteps)]                                                                                                                                                                                    \n    \"\"\"\n    shape = torch.as_tensor(audio.shape, device=audio.device)\n\n    if len(shape) == 1:\n        # assuming only time dimension is provided.                                                                                                                                                                                                  \n        audio = audio[None, None, ...]\n    elif len(shape) == 2:\n        if shape.min() <= 2:\n            # assuming sample dimension is missing                                                                                                                                                                                                   \n            audio = audio[None, ...]\n        else:\n            # assuming channel dimension is missing                                                                                                                                                                                                  \n            audio = audio[:, None, ...]\n    if audio.shape[1] > audio.shape[2]:\n        # swapping channel and time                                                                                                                                                                                                                  \n        audio = audio.transpose(1, 2)\n    if audio.shape[1] > 2:\n        audio = audio[..., :2]\n\n    if audio.shape[1] == 1:\n        # if we have mono, we duplicate it to get stereo                                                                                                                                                                                             \n        audio = torch.repeat_interleave(audio, 2, dim=1)\n\n    if rate != model_rate:\n        # we have to resample to model samplerate if needed                                                                                                                                                                                          \n        # this makes sure we resample input only once                                                                                                                                                                                                \n        audio = torchaudio.functional.resample(audio,\n            orig_freq=rate, new_freq=model_rate, resampling_method=\"sinc_interpolation\"\n        ).to(audio.device)\n    return audio\n\nif __name__ == '__main__':\n    pretrained = torch.jit.load(\"unmix.pt\")  # Pretrained weights\n    model = Unmix(pretrained)\n    model.export_to_ts('unmix.ts')"
  },
  {
    "path": "setup.py",
    "content": "import os\n\nimport setuptools\n\nVERSION = os.environ[\"NN_TILDE_VERSION\"]\n\nwith open(\"README.md\", \"r\") as readme:\n    readme = readme.read()\n\nwith open(\"requirements.txt\", \"r\") as requirements:\n    requirements = requirements.read()\n\nsetuptools.setup(\n    name=\"nn_tilde\",\n    version=VERSION,\n    author=\"Antoine CAILLON & Axel CHEMLA--ROMEU-SANTOS\",\n    author_email=\"chemla@ircam.fr\",\n    description=\"Set of tools to create nn_tilde compatible models.\",\n    long_description=readme,\n    long_description_content_type=\"text/markdown\",\n    packages=['nn_tilde', 'nn_tilde.templates'],\n    package_dir={'nn_tilde': 'python_tools'},\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n    ],\n    install_requires=requirements.split(\"\\n\"),\n    python_requires='>=3.11',\n)\n"
  },
  {
    "path": "src/.nojekyll",
    "content": ""
  },
  {
    "path": "src/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 3.10 FATAL_ERROR)\nproject(nn_tilde)\n\nset(CMAKE_POSITION_INDEPENDENT_CODE ON)\n\nconfigure_file(\n  \"${CMAKE_SOURCE_DIR}/../install/max-linker-flags.txt\" \"${CMAKE_SOURCE_DIR}/frontend/maxmsp/min-api/max-sdk-base/script/max-linker-flags.txt\"\n  COPYONLY\n)\n\nif (NOT DEFINED SIGN_ID)\n  if (DEFINED $ENV{SIGN_ID})\n    set(SIGN_ID $ENV{SIGN_ID})\n  else()\n    set(SIGN_ID \"-\")\n  endif()\nendif()\n\nmessage(\"Copying ${CMAKE_SOURCE_DIR}/../install/MaxAPI.lib\" \"${CMAKE_SOURCE_DIR}/frontend/maxmsp/min-api/max-sdk-base/c74support/max-includes/x64\" )\nconfigure_file(\n    \"${CMAKE_SOURCE_DIR}/../install/MaxAPI.lib\" \"${CMAKE_SOURCE_DIR}/frontend/maxmsp/min-api/max-sdk-base/c74support/max-includes/x64/MaxAPI.lib\" \n    COPYONLY\n)\n\nconfigure_file(\n  \"${CMAKE_SOURCE_DIR}/../install/patch_with_vst.sh\" \"${CMAKE_SOURCE_DIR}/extras\"\n  COPYONLY\n)\n\n\ninclude(${CMAKE_SOURCE_DIR}/cmake/add_torch.cmake)\nlist(PREPEND CMAKE_PREFIX_PATH \"${torch_dir}/libtorch\")\nfind_package(Torch REQUIRED PATHS ${torch_dir}/libtorch/lib)\n\nset(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}\")\n\nset(CONDA_ENV_PATH \"${CMAKE_SOURCE_DIR}/../env\")\nif (MSVC)\n  set(CMAKE_PREFIX_PATH \"${CMAKE_PREFIX_PATH}:${CONDA_ENV_PATH}\")\nelse()\n  set(CMAKE_PREFIX_PATH \"${CMAKE_PREFIX_PATH};${CONDA_ENV_PATH}\")\nendif()\n\n\nif (APPLE)\n  set(CMAKE_OSX_DEPLOYMENT_TARGET \"10.12\")\nendif()\n\nset(CMAKE_CXX_STANDARD 20)\nset(CMAKE_CXX_STANDARD_REQUIRED ON)\nset(SET CMP00076)\n\n\n# import json\nadd_subdirectory(json)\n\nif (UNIX)\n  if (APPLE)\n    add_compile_options(-std=c++20)\n    set(CMAKE_CXX_FLAGS \"-faligned-allocation\")\n    if (CMAKE_OSX_ARCHITECTURES STREQUAL \"\")\n        set(CMAKE_OSX_ARCHITECTURES ${CMAKE_HOST_SYSTEM_PROCESSOR})\n    endif()\n    message(\"Building for architecture : ${CMAKE_OSX_ARCHITECTURES} \")\n  endif()\nendif()\n\n\n\nadd_subdirectory(backend) # DEEP LEARNING BACKEND\n\nexecute_process(\n\t\tCOMMAND git describe --tags\n\t\tWORKING_DIRECTORY ${CMAKE_CURRENT_LIST_DIR}\n\t\tOUTPUT_VARIABLE VERSION\n\t\tOUTPUT_STRIP_TRAILING_WHITESPACE\n)\n\nif (NOT DEFINED NO_PUREDATA)\n  set(NO_PUREDATA 0)\nendif()\n\nif (NO_PUREDATA EQUAL 0)\n  if (\"${PUREDATA_INCLUDE_DIR}\" STREQUAL \"\")\n    set(PUREDATA_INCLUDE_DIR \"${CMAKE_CURRENT_SOURCE_DIR}/pd_include\")\n    execute_process(\n      COMMAND cmake -E make_directory \"${PUREDATA_INCLUDE_DIR}\"\n    )\n    file(DOWNLOAD \"https://raw.githubusercontent.com/pure-data/pure-data/master/src/m_pd.h\" \"${PUREDATA_INCLUDE_DIR}/m_pd.h\")\n  endif()\n  add_subdirectory(frontend/puredata/nn_tilde) # PURE DATA EXTERNAL\nelse()\n  if (UNIX AND NOT APPLE) \n    message(FATAL_ERROR \"NO_PUREDATA needs to be off on Linux, otherwise no task are available\")\n  endif()\nendif()\n\nconfigure_file(${CMAKE_CURRENT_SOURCE_DIR}/../package-info.json.in \"${CMAKE_CURRENT_SOURCE_DIR}/package-info.json\")\n\nif(APPLE OR MSVC)\n    # MAX MSP EXTERNAL\n    add_subdirectory(frontend/maxmsp/nn.info) \n    add_subdirectory(frontend/maxmsp/nn_tilde)\n    add_subdirectory(frontend/maxmsp/mc.nn_tilde)\n    add_subdirectory(frontend/maxmsp/mcs.nn_tilde)\nendif()\n"
  },
  {
    "path": "src/backend/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 3.10 FATAL_ERROR)\nproject(backend)\n\nset(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} ${TORCH_CXX_FLAGS}\")\n\nadd_library(backend STATIC parsing_utils.cpp backend.cpp)\ntarget_link_libraries(backend \"${TORCH_LIBRARIES}\")\nset_property(TARGET backend PROPERTY CXX_STANDARD 20)\n\nif(MSVC)\n  set(CMAKE_CXX_FLAGS_RELEASE \"${CMAKE_CXX_FLAGS_RELEASE} /MT\")\n  set(CMAKE_CXX_FLAGS_MINSIZEREL  \"${CMAKE_CXX_FLAGS_MINSIZEREL} /MT\")\n  set(CMAKE_CXX_FLAGS_RELWITHDEBINFO \"${CMAKE_CXX_FLAGS_RELWITHDEBINFO} /MT\")\n  set(CMAKE_CXX_FLAGS_DEBUG \"${CMAKE_CXX_FLAGS_DEBUG} /MT\")\nendif()\n\n\n"
  },
  {
    "path": "src/backend/backend.cpp",
    "content": "#include \"backend.h\"\n#include <exception>\n#include \"parsing_utils.h\"\n#include <algorithm>\n#include <iostream>\n#include <exception>\n#include <stdio.h>\n#include <stdlib.h>\n\n#define CPU torch::kCPU\n#define CUDA torch::kCUDA\n#define MPS torch::kMPS\n\n#define REFRESH_THREAD_INTERVAL 50\n\n\nstd::string tensor_to_str(torch::Tensor &tsr) {\n  std::stringstream ss; \n  ss << \"Tensor(dim: \";\n  for (int i = 0; i < tsr.dim(); i++) {\n    ss << tsr.size(i); \n    if (i != tsr.dim() - 1)\n      ss << \", \";\n  }\n  ss << \")\"; \n  return ss.str(); \n}\n\n\nBackend::Backend() : m_loaded(0), m_device(CPU), m_use_gpu(false) {\n  at::init_num_threads();\n}\n\nvoid Backend::perform(std::vector<float *> &in_buffer,\n                      std::vector<float *> &out_buffer, \n                      std::string method, \n                      int n_batches, int n_out_channels, int n_vec) {\n  c10::InferenceMode guard;\n\n  auto params = get_method_params(method);\n  if (!params.size())\n    return;\n\n  auto in_dim = params[0];\n  auto in_ratio = params[1];\n  auto out_dim = params[2];\n  auto out_ratio = params[3];\n\n  if (!m_loaded)\n    return;\n\n  // COPY BUFFER INTO A TENSOR\n  std::vector<at::Tensor> tensor_in;\n  // for (auto buf : in_buffer)\n  for (int i(0); i < in_dim * n_batches; i++) {\n    if (i < in_buffer.size()) {\n      tensor_in.push_back(torch::from_blob(in_buffer[i], {1, 1, n_vec}).clone());\n    } else {\n      tensor_in.push_back(torch::zeros({1, 1, n_vec}));\n    }\n  }\n\n  auto cat_tensor_in = torch::cat(tensor_in, 1);\n  cat_tensor_in = cat_tensor_in.reshape({in_dim, n_batches, -1, in_ratio});\n  cat_tensor_in = cat_tensor_in.select(-1, -1);\n  cat_tensor_in = cat_tensor_in.permute({1, 0, 2});\n\n  // SEND TENSOR TO DEVICE\n  std::unique_lock<std::mutex> model_lock(m_model_mutex);\n  cat_tensor_in = cat_tensor_in.to(m_device);\n  std::vector<torch::jit::IValue> inputs = {cat_tensor_in};\n  auto kwargs = empty_kwargs();\n\n  // PROCESS TENSOR\n  at::Tensor tensor_out;\n  try {\n    tensor_out = m_model.get_method(method)(inputs, kwargs).toTensor();\n    tensor_out = tensor_out.repeat_interleave(out_ratio).reshape(\n        {n_batches, out_dim, -1});\n  } catch (const std::exception &e) {\n    std::cerr << e.what() << '\\n';\n    return;\n  }\n  model_lock.unlock();\n\n  int out_batches(tensor_out.size(0)), out_channels(tensor_out.size(1)),\n      out_n_vec(tensor_out.size(2));\n\n  if (out_n_vec != n_vec) {\n    std::cout << \"model output size is not consistent, expected \" << n_vec\n              << \" samples, got \" << out_n_vec << \"!\\n\";\n    return;\n  }\n\n  tensor_out = tensor_out.to(CPU);\n\n  for (int i(0); i < n_out_channels; i++) {\n    for (int j(0); j < n_batches; j++) {\n      if (i < tensor_out.size(1)) {\n        auto out_ptr = tensor_out.index({j, i}).contiguous().data_ptr<float>();\n        memcpy(out_buffer[j * n_out_channels + i], out_ptr, n_vec * sizeof(float));\n      } else {\n        // put zeros\n        memset(out_buffer[j * n_out_channels + i], 0, n_vec *sizeof(float));\n      }\n    }\n  }\n}\n\nint Backend::load(std::string path, double sampleRate, const std::string* target_method) {\n  try {\n    auto model = torch::jit::load(path);\n    if (target_method != nullptr) {\n      if (!(*target_method).empty()) {\n        // if target_method is not null, check if loaded model has it\n        auto locked_model = LockedModel(); \n        locked_model.model = &model;  \n        auto methods = get_available_methods(&locked_model);\n        if (std::find(methods.begin(), methods.end(), *target_method) == methods.end()) {\n          throw \"path \" + path + \" does not contain target method \" + (*target_method); \n        }\n      }\n    }\n    model.eval();\n    model.to(m_device);\n\n    std::unique_lock<std::mutex> model_lock(m_model_mutex);\n    m_model = model;\n    model_lock.unlock();\n\n    m_available_methods = get_available_methods();\n    m_buffer_attributes = retrieve_buffer_attributes();\n    m_path = path;\n    set_sample_rate(sampleRate);\n    m_loaded = 1;\n    return 0;\n  } catch (const std::exception &e) {\n    throw \"problem loading model \" + path + \". Exception : \" + e.what();\n  }\n}\n\nvoid Backend::set_sample_rate(double sampleRate) {\n if (has_method(\"set_sample_rate\")) {\n      m_sr = sampleRate;\n      std::vector<c10::IValue> sr_input = {sampleRate};\n      std::unique_lock<std::mutex> model_lock(m_model_mutex);\n      auto args = empty_args(); args.push_back((int)sampleRate);\n      auto kwargs = empty_kwargs();\n      std::string method_name = \"set_sample_rate\";\n      m_model.get_method(method_name)(args, kwargs); \n      model_lock.unlock();\n    } \n}\n\nint Backend::reload() {\n  auto return_code = load(m_path, m_sr);\n  return return_code;\n}\n\nbool Backend::has_method(std::string method_name) {\n  if (!is_loaded()){\n    return false; \n  }\n  std::unique_lock<std::mutex> model_lock(m_model_mutex);\n  for (const auto &m : m_model.get_methods()) {\n    if (m.name() == method_name)\n      return true;\n  }\n  return false;\n}\n\nbool Backend::has_settable_attribute(std::string attribute) {\n  for (const auto &a : get_settable_attributes()) {\n    if (a == attribute)\n      return true;\n  }\n  return false;\n}\n\nstd::vector<std::string> Backend::get_available_methods(LockedModel *target_model) {\n  std::vector<std::string> methods;\n  torch::jit::script::Module *model;\n  std::mutex *mutex; \n  if (target_model == nullptr) {\n    model = &m_model;\n    mutex = &m_model_mutex;    \n  } else {\n    model = target_model->model;\n    mutex = &target_model->mutex;\n  }\n  try {\n    std::vector<c10::IValue> dumb_input = {};\n    std::unique_lock<std::mutex> model_lock(*mutex);\n    auto methods_from_model =\n        model->get_method(\"get_methods\")(dumb_input).toList();\n    model_lock.unlock();\n\n    for (int i = 0; i < methods_from_model.size(); i++) {\n      methods.push_back(methods_from_model.get(i).toStringRef());\n    }\n  } catch (...) {\n    std::unique_lock<std::mutex> model_lock(*mutex);\n    for (const auto &m : model->get_methods()) {\n      try {\n        auto method_params = model->attr(m.name() + \"_params\");\n        methods.push_back(m.name());\n      } catch (...) {\n      }\n    }\n    model_lock.unlock();\n  }\n  return methods;\n}\n\nstd::vector<std::string> Backend::get_available_attributes() {\n  std::vector<std::string> attributes;\n  std::unique_lock<std::mutex> model_lock(m_model_mutex);\n  for (const auto &attribute : m_model.named_attributes())\n    attributes.push_back(attribute.name);\n  return attributes;\n}\n\nstd::vector<std::string> Backend::get_settable_attributes() {\n  std::vector<std::string> attributes;\n  try {\n    std::vector<c10::IValue> dumb_input = {};\n    std::unique_lock<std::mutex> model_lock(m_model_mutex);\n    auto methods_from_model =\n        m_model.get_method(\"get_attributes\")(dumb_input).toList();\n    model_lock.unlock();\n    for (int i = 0; i < methods_from_model.size(); i++) {\n      auto attr_name = methods_from_model.get(i).toStringRef();\n      if (attr_name != \"none\") {\n        attributes.push_back(attr_name);\n      }\n    }\n  } catch (...) {\n    std::unique_lock<std::mutex> model_lock(m_model_mutex);\n    for (const auto &a : m_model.named_attributes()) {\n      try {\n        auto method_params = m_model.attr(a.name + \"_params\");\n        if (a.name != \"none\") {\n          attributes.push_back(a.name);\n        }\n      } catch (...) {\n      }\n    }\n    model_lock.unlock();\n  }\n  return attributes;\n}\n\nstd::vector<c10::IValue> Backend::get_attribute(std::string attribute_name) {\n  std::string attribute_getter_name = \"get_\" + attribute_name;\n  std::unique_lock<std::mutex> model_lock(m_model_mutex);\n  try {\n    auto attribute_getter = m_model.get_method(attribute_getter_name);\n  } catch (...) {\n    model_lock.unlock();\n    throw \"getter for attribute \" + attribute_name + \" not found in model\";\n  }\n  std::vector<c10::IValue> getter_inputs = {}, attributes;\n  auto kwargs = empty_kwargs();\n  try {\n    try {\n      auto output_list = m_model.get_method(attribute_getter_name)(getter_inputs, kwargs).toList();\n      attributes = output_list.vec();\n    } catch (...) {\n      auto output_tuple_ref =\n          m_model.get_method(attribute_getter_name)(getter_inputs, kwargs).toTuple();\n      attributes = (*output_tuple_ref.get()).elements();\n    }\n  } catch (...) {\n    model_lock.unlock();\n    throw (\"could not access method \" + attribute_getter_name + \" from original script.\");\n  }\n  model_lock.unlock();\n  return attributes;\n}\n\nstd::string Backend::get_attribute_as_string(std::string attribute_name) {\n  std::vector<c10::IValue> getter_outputs = get_attribute(attribute_name);\n  // finstringd arguments\n  torch::Tensor setter_params;\n  std::unique_lock<std::mutex> model_lock(m_model_mutex);\n  try {\n    setter_params = m_model.attr(attribute_name + \"_params\").toTensor();\n    model_lock.unlock();\n  } catch (...) {\n    model_lock.unlock();\n    throw \"parameters to get attribute \" + attribute_name +\n        \" not found in model\";\n  }\n  std::string current_attr = \"\";\n  for (int i = 0; i < setter_params.size(0); i++) {\n    int current_id = setter_params[i].item().toInt();\n    switch (current_id) {\n    // bool case\n    case 0: {\n      current_attr += (getter_outputs[i].toBool()) ? \"true\" : \"false\";\n      break;\n    }\n    // int case\n    case 1: {\n      current_attr += std::to_string(getter_outputs[i].toInt());\n      break;\n    }\n    // float case\n    case 2: {\n      float result = getter_outputs[i].to<float>();\n      current_attr += std::to_string(result);\n      break;\n    }\n    // str case\n    case 3: {\n      current_attr += getter_outputs[i].toStringRef();\n      break;\n    }\n    // tensor case\n    case 4: {\n      auto tensor = getter_outputs[i].toTensor();\n      current_attr += tensor_to_str(tensor);\n      break; \n    }\n    case 5: {\n      current_attr += getter_outputs[i].toStringRef();\n      break;\n    }\n    default: {\n      throw \"bad type id : \" + std::to_string(current_id) + \" at index \" +\n          std::to_string(i);\n      break;\n    }\n    }\n    if (i < setter_params.size(0) - 1)\n      current_attr += \" \";\n  }\n  return current_attr;\n}\n\n\nvoid Backend::set_attribute(std::string attribute_name,\n                            std::vector<std::string> attribute_args, \n                            const Backend::BufferMap &buffer_array) {\n  // find setter\n  std::string attribute_setter_name = \"set_\" + attribute_name;\n  std::unique_lock<std::mutex> model_lock(m_model_mutex);\n  try {\n    auto attribute_setter = m_model.get_method(attribute_setter_name);\n  } catch (...) {\n    model_lock.unlock();\n    throw \"setter for attribute \" + attribute_name + \" not found in model\";\n  }\n  // find arguments\n  torch::Tensor setter_params;\n  try {\n    setter_params = m_model.attr(attribute_name + \"_params\").toTensor();\n  } catch (...) {\n    model_lock.unlock();\n    throw \"parameters to set attribute \" + attribute_name +\n        \" not found in model\";\n  }\n  model_lock.unlock();\n\n  // check attribute params\n  int setter_elements = setter_params.numel();\n  if (attribute_args.size() != setter_elements) {\n    std::stringstream exception;\n    exception << \"wrong number of elements : setter seems to have \" << std::to_string(setter_params.numel());\n    exception << \", but \" << attribute_args.size() << \" was given\";\n    throw exception.str();\n  }\n\n  // process inputs\n  std::vector<c10::IValue> setter_inputs = {};\n  auto n_args = setter_params.size(0);\n  for (int i = 0; i < n_args; i++) {\n    int current_id = setter_params[i].item().toInt();\n    switch (current_id) {\n      // bool case\n      case 0:\n        setter_inputs.push_back(c10::IValue(to_bool(attribute_args[i])));\n        break;\n      // int case\n      case 1:\n        setter_inputs.push_back(c10::IValue(to_int(attribute_args[i])));\n        break;\n      // float case\n      case 2:\n        setter_inputs.push_back(c10::IValue(to_float(attribute_args[i])));\n        break;\n      // str case\n      case 3:\n        setter_inputs.push_back(c10::IValue(attribute_args[i]));\n        break;\n      // tensor case\n      case 4: {\n        auto buffer_name = get_buffer_name(attribute_name, i);\n        if (!buffer_array.contains(buffer_name)) {\n          throw std::string(\"buffer for argument \") + buffer_name + std::string(\" not found. Did you initialise it?\");\n        } else {\n          auto buffer = buffer_array.at(buffer_name);\n          auto buf_tensor = buffer.to_tensor().index({0});\n          setter_inputs.push_back(buf_tensor.clone()); \n        }\n        break;\n      }\n      case 5: {\n        auto buffer_name = get_buffer_name(attribute_name, i);\n        if (!buffer_array.contains(buffer_name)) {\n          throw std::string(\"buffer for argument \") + buffer_name + std::string(\" not found. Did you initialise it?\");\n        } else {\n          auto buffer = buffer_array.at(buffer_name);\n          update_buffer(buffer_name, buffer); \n          setter_inputs.push_back(c10::IValue(buffer_name));\n        }\n        break;\n      }\n      default:\n        throw \"bad type id : \" + std::to_string(current_id) + \"at index \" +\n            std::to_string(i);\n        break;\n    }\n  }\n  try {\n    std::unique_lock<std::mutex> model_lock(m_model_mutex);\n    auto kwargs = empty_kwargs();\n    auto setter_out = m_model.get_method(attribute_setter_name)(setter_inputs, kwargs);\n    model_lock.unlock();\n    int setter_result = setter_out.toInt();\n    if (setter_result != 0) {\n      throw \"setter returned -1\";\n    }\n  } catch (std::string &e) {\n    throw \"setter for \" + attribute_name + \" failed : \" + e;\n  } catch (std::exception &e) {\n    throw \"setter for \" + attribute_name + \" failed : \" + e.what();\n  } catch (...) {\n    throw \"setter for \" + attribute_name + \" failed\";\n  }\n}\n\nstd::vector<int> Backend::get_method_params(std::string method) {\n  std::vector<int> params;\n\n  if (std::find(m_available_methods.begin(), m_available_methods.end(),\n                method) != m_available_methods.end()) {\n    try {\n      std::unique_lock<std::mutex> model_lock(m_model_mutex);\n      auto p = m_model.attr(method + \"_params\").toTensor();\n      model_lock.unlock();\n      for (int i(0); i < 4; i++)\n        params.push_back(p[i].item().to<int>());\n    } catch (...) {\n    }\n  }\n  return params;\n}\n\nint Backend::get_higher_ratio() {\n  int higher_ratio = 1;\n  for (const auto &method : m_available_methods) {\n    auto params = get_method_params(method);\n    if (!params.size())\n      continue; // METHOD NOT USABLE, SKIPPING\n    int max_ratio = std::max(params[1], params[3]);\n    higher_ratio = std::max(higher_ratio, max_ratio);\n  }\n  return higher_ratio;\n}\n\nbool Backend::is_loaded() { return m_loaded; }\n\nvoid Backend::use_gpu(bool value) {\n  std::unique_lock<std::mutex> model_lock(m_model_mutex);\n  if (value) {\n    if (torch::hasCUDA()) {\n      std::cout << \"sending model to cuda\" << std::endl;\n      m_device = CUDA;\n    } else if (torch::hasMPS()) {\n      std::cout << \"sending model to mps\" << std::endl;\n      m_device = MPS;\n    } else {\n      std::cout << \"sending model to cpu\" << std::endl;\n      m_device = CPU;\n    }\n  } else {\n    m_device = CPU;\n  }\n  m_model.to(m_device);\n}\n\nstd::string Backend::get_buffer_name(std::string attribute_name, int attribute_elt_idx) {\n  return attribute_name + \"#\" + std::to_string(attribute_elt_idx);\n}\n\nbool Backend::is_buffer_element_of_attribute(std::string attribute_name, int attribute_elt_idx) {\n  std::string target_buffer_name = get_buffer_name(attribute_name, attribute_elt_idx);\n  // std::cout << \"current registered buffers : \" << m_buffer_attributes.size() << std::endl; \n  for (auto buffer_name: m_buffer_attributes) {\n    // std::cout << \"target buffer name : \" << target_buffer_name << \"; compared buffer name : \" << buffer_name << std::endl;\n    if (buffer_name == target_buffer_name) {\n      return true;\n    }\n  }\n  return false;\n}\n\nbool Backend::is_tensor_element_of_attribute(std::string attribute_name, int attribute_elt_idx) {\n  auto attribute_params = m_model.attr(attribute_name + \"_params\").toTensor(); \n  if (id_to_string_hash.at(attribute_params.item().toInt()) == \"tensor\") {\n    return true;\n  } else {\n    return false;\n  }\n}\n\nstd::vector<std::string> Backend::retrieve_buffer_attributes() {\n  std::vector<std::string> bufferNames = {};\n  std::string getter_name = \"get_buffer_attributes\";\n  if (has_method(getter_name)) {\n    std::unique_lock<std::mutex> model_lock(m_model_mutex);\n    try {\n      auto model_method = m_model.get_method(getter_name);\n      auto model_out = model_method(empty_args(), empty_kwargs()).toList().vec();\n      for (int i = 0; i < model_out.size(); i++) {\n        // std::cout << \"adding \" << model_out[i].toStringRef() << \" to buffer list\" << std::endl; \n        bufferNames.push_back(model_out[i].toStringRef());\n      }\n      model_lock.unlock();\n    } catch (std::exception& e) {\n      model_lock.unlock();\n      throw e;\n    }\n  } \n  return bufferNames;\n}\n\nstd::vector<std::string> Backend::get_buffer_attributes() {\n  return m_buffer_attributes;\n}\n\nint Backend::reset_buffer(std::string buffer_name) {\n  std::string clear_method = \"clear_buffer_attribute\";\n  auto args = empty_args(); auto kwargs = empty_kwargs();\n  args.push_back(buffer_name);\n  std::unique_lock<std::mutex> model_lock(m_model_mutex);\n  auto model_out = m_model.get_method(clear_method)(args, kwargs);\n  int result = model_out.toInt();\n  return result;  \n}\n\nint Backend::update_buffer(std::string buffer_name, StaticBuffer<float> &buffer) {\n  std::string setter_method = \"set_buffer_attribute\";\n  auto args = empty_args(); auto kwargs = empty_kwargs();\n  auto tensor = buffer.to_tensor().to(m_device); auto sr = static_cast<int>(buffer.sr());\n  // auto tensor_min = tensor.min(); auto tensor_max = tensor.max(); \n  auto tensor_size = {tensor.size(0), tensor.size(1)};\n  args.push_back(buffer_name); args.push_back(tensor); args.push_back(sr);\n  std::unique_lock<std::mutex> model_lock(m_model_mutex);\n  auto model_out = m_model.get_method(setter_method)(args, kwargs);\n  model_lock.unlock();\n  int result = model_out.toInt();\n  return result; \n}\n\nModelInfo Backend::get_model_info() {\n  if (!m_loaded) {\n    throw \"no model loaded; cannot retrieve model information\";\n  }\n\n  auto methods = get_available_methods();\n  auto attributes = get_settable_attributes();\n  auto method_dict = ModelInfo::MethodDict();\n  auto attribute_dict = ModelInfo::AttributeDict();\n  std::unique_lock<std::mutex> model_lock(m_model_mutex);\n  model_lock.unlock();\n  try {\n    for (auto method : methods) {\n      auto method_properties = MethodProperties();\n      model_lock.lock();\n      auto setter_params = m_model.attr(method + \"_params\").toTensor();\n      model_lock.unlock();\n      method_properties.name = method; \n      method_properties.channels_in = setter_params[0].item().toInt(); \n      method_properties.ratio_in = setter_params[1].item().toInt(); \n      method_properties.channels_out = setter_params[2].item().toInt(); \n      method_properties.ratio_out = setter_params[3].item().toInt(); \n      method_dict.insert({method, method_properties});\n    }\n    for (auto attribute: attributes) {\n      model_lock.lock();\n      auto attribute_params = m_model.attr(attribute + \"_params\").toTensor();\n      model_lock.unlock();\n      auto attribute_properties = AttributeProperties();\n      attribute_properties.name = attribute;\n      auto attribute_types = std::vector<std::string>();\n      for (int i = 0; i < attribute_params.size(0); i++) {\n        auto param_idx = attribute_params[i].item().toInt();\n        auto param_str = this->id_to_string_hash.at(param_idx);\n        attribute_types.push_back(param_str);\n      }\n      attribute_properties.attribute_types = attribute_types;\n      attribute_dict.insert({attribute, attribute_properties});\n    }\n    auto model_info = ModelInfo();\n    model_info.method_properties = method_dict;\n    model_info.attribute_properties = attribute_dict;\n    return model_info;\n  } catch (std::exception& e) {\n    model_lock.unlock();\n    throw \"error fetching model information for model \" + m_path + \". Caught error : \" + e.what();\n  }\n}"
  },
  {
    "path": "src/backend/backend.h",
    "content": "#pragma once\n#include <mutex>\n#include <string>\n#include <torch/script.h>\n#include \"../shared/static_buffer.h\"\n#include <torch/torch.h>\n#include <vector>\n\n\n\nstruct MethodProperties {\n  std::string name = \"\"; \n  int channels_in = -1;\n  int channels_out = -1;\n  int ratio_in = -1;\n  int ratio_out = -1;\n};\n\nstruct AttributeProperties {\n  std::string name = \"\"; \n  std::vector<std::string> attribute_types = {};\n};\n\nstruct ModelInfo {\n  using MethodDict = std::unordered_map<std::string, MethodProperties>;\n  using AttributeDict = std::unordered_map<std::string, AttributeProperties>;\n  MethodDict method_properties = {};\n  AttributeDict attribute_properties = {}; \n};\n\nstruct LockedModel {\n  torch::jit::script::Module* model; \n  std::mutex mutex;\n};\n\n\nclass Backend {\n\nprotected:\n  torch::jit::script::Module m_model;\n  int m_loaded, m_in_dim, m_in_ratio, m_out_dim, m_out_ratio;\n  std::string m_path;\n  std::mutex m_model_mutex;\n  std::vector<std::string> m_available_methods;\n  std::vector<std::string> m_buffer_attributes;\n  c10::DeviceType m_device;\n  bool m_use_gpu;\n  std::vector<std::string> retrieve_buffer_attributes();\n  std::unique_ptr<std::thread> set_attribute_thread; \n  double m_sr; \n\npublic:\n  using DataType = float; \n  using ArgsType = std::vector<c10::IValue>;\n  using KwargsType = std::unordered_map<std::string, c10::IValue>;\n  using BufferMap = std::map<std::string, StaticBuffer<float>>;\n\n  Backend();\n  void perform(std::vector<float *> &in_buffer,\n                      std::vector<float *> &out_buffer, \n                      std::string method, \n                      int n_batches, int n_out_channels, int n_vec);\n  bool has_method(std::string method_name);\n  bool has_settable_attribute(std::string attribute);\n  std::vector<std::string> get_available_methods(LockedModel *model = nullptr);\n  std::vector<std::string> get_available_attributes();\n  std::vector<std::string> get_settable_attributes();\n  std::vector<c10::IValue> get_attribute(std::string attribute_name);\n  std::string get_attribute_as_string(std::string attribute_name);\n  void set_attribute(std::string attribute_name,\n                     std::vector<std::string> attribute_args, \n                     const Backend::BufferMap &buffer_array);\n\n  // buffer attributes\n  bool is_buffer_element_of_attribute(std::string attribute_name, int attribute_elt_idx);\n  bool is_tensor_element_of_attribute(std::string attribute_name, int attribute_elt_idx);\n  // auto get_buffer_attribtues() { return m_buffer_attributes; }\n  std::string get_buffer_name(std::string attribute_name, int attribute_elt_idx);\n  int update_buffer(std::string buffer_id, StaticBuffer<float> &buffer);\n  int reset_buffer(std::string);\n\n  std::vector<int> get_method_params(std::string method);\n  int get_higher_ratio();\n  int load(std::string path, double sampleRate, const std::string* target_method = nullptr);\n  int reload();\n  void set_sample_rate(double sampleRate);\n  bool is_loaded();\n  torch::jit::script::Module get_model() { return m_model; }\n  void use_gpu(bool value);\n  std::vector<std::string> get_buffer_attributes();\n  \n  ArgsType empty_args() { return ArgsType(); }\n  KwargsType empty_kwargs() { return KwargsType(); }\n  std::pair<ArgsType, KwargsType> empty_inputs() {\n    return std::make_pair(empty_args(), empty_kwargs());\n  } \n\n  ModelInfo get_model_info();\n  const std::unordered_map<int, std::string> id_to_string_hash = {\n    {0, \"bool\"}, \n    {1, \"int\"}, \n    {2, \"float\"},\n    {3, \"string\"}, \n    {4, \"tensor\"}, \n    {5, \"buffer\"} \n  };\n};\n"
  },
  {
    "path": "src/backend/parsing_utils.cpp",
    "content": "#include \"parsing_utils.h\"\n\nbool to_bool(std::string str) {\n  if ((str == \"0\") || (str == \"false\")) {\n    return false;\n  } else {\n    return true;\n  }\n}\n\nint to_int(std::string str) { return stoi(str); }\n\nfloat to_float(std::string str) { return stof(str); }"
  },
  {
    "path": "src/backend/parsing_utils.h",
    "content": "#pragma once\n#include <algorithm>\n#include <cctype>\n#include <iomanip>\n#include <sstream>\n#include <string>\n\nbool to_bool(std::string str);\nint to_int(std::string str);\nfloat to_float(std::string str);"
  },
  {
    "path": "src/cmake/add_torch.cmake",
    "content": "set(torch_dir ${CMAKE_CURRENT_BINARY_DIR}/../torch)\nset(torch_lib_name torch)\n\nmessage(\"first looking for lib in : ${torch_dir}\")\nfind_library(torch_lib\n  NAMES ${torch_lib_name}\n  PATHS ${torch_dir}/libtorch/lib\n)\n\nfunction (download_library url out)\n  message(\"download ${url} to ${out}...\")\n  file(DOWNLOAD\n      ${url}\n      ${out}/torch_cc.zip\n      SHOW_PROGRESS\n    )\n    execute_process(COMMAND ${CMAKE_COMMAND} -E tar -xf torch_cc.zip\n                    COMMAND remove -f ${out}/torch_cc.zip\n                    WORKING_DIRECTORY ${out})\nendfunction()\n\nif (DEFINED torch_version)\n  message(\"setting torch version : ${torch_version}\")\nelse()\n  set(torch_version \"2.5.1\")\n  message(\"torch version : ${torch_version}\")\nendif()\n\nif (NOT torch_lib)\n  set(NEEDS_DL TRUE)\nelse()\n  set(NEEDS_DL FALSE)\n  if (UNIX AND NOT APPLE)\n    if (torch_lib STREQUAL \"/usr/lib/libtorch.so\")\n      set(NEEDS_DL TRUE)\n    endif()\n  endif()\nendif()\n\n\nif (NEEDS_DL)\n  message(STATUS \"Downloading torch C API pre-built\")\n  # Download\n  if (UNIX AND NOT APPLE)  # Linux\n    set(torch_url \"https://download.pytorch.org/libtorch/cpu/libtorch-shared-with-deps-${torch_version}%2Bcpu.zip\")\n    download_library(${torch_url} ${torch_dir})\n  elseif (UNIX AND APPLE)  # OSX\n    if (NOT IS_DIRECTORY ${torch_dir})\n      if (EXISTS ${CMAKE_SOURCE_DIR}/../install/torch_ub)\n        execute_process(COMMAND cp -r ${CMAKE_SOURCE_DIR}/../install/torch_ub ${torch_dir})\n      elseif(DEFINED TORCH_MAC_UB_URL)\n        download_library(${TORCH_MAC_UB_URL} /tmp)\n        execute_process(\n          COMMAND mkdir -p ${torch_dir}\n          COMMAND echo $(ls /tmp)\n          COMMAND mv /tmp/torch ${torch_dir}/libtorch\n        )\n      else()\n        execute_process(COMMAND ${CMAKE_COMMAND} -E make_directory ${torch_dir})\n        set(torch_url \"https://download.pytorch.org/libtorch/cpu/libtorch-macos-arm64-${torch_version}.zip\")\n        download_library(${torch_url} ${torch_dir})\n      endif()\n    endif()\n  else()\n\n    execute_process(COMMAND ${CMAKE_COMMAND} -E make_directory ${torch_dir})\n    download_library(\"https://download.pytorch.org/libtorch/cpu/libtorch-win-shared-with-deps-${torch_version}%2Bcpu.zip\" ${torch_dir})\n  endif()\n    # Check if architecutre == ARM64\n    # if (NOT DEFINED APPLE_ARM64)\n    #   set (APPLE_ARM64 (CMAKE_SYSTEM_PROCESSOR STREQUAL \"arm64\"))\n    # endif()\n    ## If ARM, download both libraries and pre-compile UB libraries\n    # if (APPLE_ARM64)\n      # download arm64 library\n      # if (NOT IS_DIRECTORY ${torch_dir})\n      #   download_library(\"https://anaconda.org/pytorch/pytorch/${torch_version}/download/osx-arm64/pytorch-${torch_version}-py3.10_0.tar.bz2\" ${torch_dir}-arm64)\n      #   execute_process(COMMAND mkdir ${torch_dir})\n      #   execute_process(COMMAND cp -r ${torch_dir}-arm64/lib/python3.10/site-packages/torch ${torch_dir}/libtorch)\n      # endif()\n      # # download x86 library\n      # if (EXISTS ${CMAKE_SOURCE_DIR}/../install/torch_x86)\n      #   execute_process(COMMAND cp -r ${CMAKE_SOURCE_DIR}/../install/torch_x86 ${torch_dir}-x86)\n      # else()\n      #   if (NOT DEFINED TORCH_MAC_UB_URL)\n      #     message(FATAL_ERROR \"If not provided, please give a valid URL for Apple universal library\")\n      #   endif()\n      #   download_library(${TORCH_MAC_X86_URL} ${torch_dir})\n      # endif()\n      # message(\"found libtorch for x86 at : \" ${torch_dir}-x86)\n      # # export UB libs to main path\n      # execute_process(COMMAND mkdir ${torch_dir}-x86)\n      # execute_process(COMMAND cp /opt/homebrew/opt/llvm/lib/libomp.dylib ${torch_dir}/libtorch/lib/)\n      # execute_process(COMMAND find ${torch_dir}/libtorch/lib -maxdepth 1 -type f -execdir lipo -create ${torch_dir}/libtorch/lib/{} ${torch_dir}-x86/libtorch/lib/{} -output ${torch_dir}/libtorch/lib/{} \\;)\n    # else()\n    #   if (EXISTS ${CMAKE_SOURCE_DIR}/../install/torch_x86)\n    #     execute_process(COMMAND cp ${CMAKE_SOURCE_DIR}/../install/torch_x86 ${torch_dir})\n    #   else()\n    #     if (NOT DEFINED TORCH_MAC_X86_URL)\n    #       message(FATAL_ERROR \"If not provided, please give a valid URL for Apple x86 library\")\n    #     endif()\n    #     download_library(${TORCH_MAC_X86_URL} ${torch_dir})\n    #   endif()\n    #   message(\"found libtorch for x86 at : \" ${torch_dir})\n\nendif()\n\n# Find the libraries again\nmessage(\"${torch_dir}\")\nfind_library(torch_lib\n  NAMES ${torch_lib_name}\n  PATHS ${torch_dir}/libtorch/lib\n)\n\nif (NOT torch_lib)\n  message(FATAL_ERROR \"torch could not be included\")\nendif()\n"
  },
  {
    "path": "src/extras/nn~ Overview.maxpat",
    "content": "{\n\t\"patcher\" : \t{\n\t\t\"fileversion\" : 1,\n\t\t\"appversion\" : \t\t{\n\t\t\t\"major\" : 9,\n\t\t\t\"minor\" : 0,\n\t\t\t\"revision\" : 0,\n\t\t\t\"architecture\" : \"x64\",\n\t\t\t\"modernui\" : 1\n\t\t}\n,\n\t\t\"classnamespace\" : \"box\",\n\t\t\"rect\" : [ 59.0, 106.0, 1000.0, 780.0 ],\n\t\t\"gridsize\" : [ 15.0, 15.0 ],\n\t\t\"boxes\" : [  ],\n\t\t\"lines\" : [  ],\n\t\t\"originid\" : \"pat-152\",\n\t\t\"dependency_cache\" : [  ],\n\t\t\"autosave\" : 0\n\t}\n\n}\n"
  },
  {
    "path": "src/extras/patch_with_vst.sh",
    "content": "#!/bin/bash\n\n# Default values\npd=0\nmax=0\n\nfunction print_help() {\n    echo \"Usage: $0 [--pd_path[=val]] (default: ~/Documents/Pd) [--max[=val]] (by default, look in all ~/Documents/Max X/ ; if specified, look for externals sub-folder)\"\n}\n\n# Parse arguments\nwhile [[ $# -gt 0 ]]; do\n    case \"$1\" in\n        --pd=*)\n            pd=1\n            pd_path=\"${1#*=}\"\n            shift\n            ;;\n        --pd)\n            pd=1\n            shift\n            ;;\n        --max=*)\n            max=1\n            max_path=\"${1#*=}\"\n            shift\n            ;;\n        --max)\n            max=1\n            shift\n            ;;\n        --help|-h)\n            print_help\n            exit 0\n            ;;\n        *)\n            echo \"Unknown option: $1\"\n            shift\n            ;;\n    esac\ndone\n\n\nfunction patch_max_external() {\n    find \"$1\" -name \"nn_tilde\" -type d -mindepth 2 -print0 | while IFS= read -r -d '' ext_dir; do\n    if [[ -d \"$ext_dir/externals\" ]]; then\n        echo \"found nn_tilde at $ext_dir\"; \n        find \"$ext_dir/externals\" -name \"*.mxo\" -print0 | while IFS= read -r -d '' ext_path; do\n            # echo \"found external at $ext_path\"; \n            ext_name=$(basename \"$ext_path\")\n            find \"$ext_dir/support\" -name \"*.dylib\" -print0 | while IFS= read -r -d '' dylib_path; do\n                dylib_name=$(basename \"$dylib_path\")\n                echo \"fixing library : $dylib_name\"\n                new_path=\"/Library/Application Support/ACIDS/RAVE/$dylib_name\"\n                if [[ ! -e \"$new_path\" ]]; then \n                    echo \"[WARNING] library not found : $new_path. Patch may not work\"\n                fi\n                install_name_tool -change \"@loader_path/../../../../support/$dylib_name\" \"/Library/Application Support/ACIDS/RAVE/$dylib_name\" \"${ext_path}/Contents/MacOS/${ext_name%.*}\" 2> /dev/null\n            done\n            codesign --deep --force --sign - \"${ext_path}/Contents/MacOS/${ext_name%.*}\"\n        done\n    fi\n    done\n}\n\nfunction patch_pd_external() {\n    ext_dir=$1\n    if [[ ! \"$(basename $ext_dir)\" == \"nn_tilde\" ]]; then\n        ext_dir=\"${ext_dir}/externals/nn_tilde\"\n    fi\n    if [[ -e $(realpath $ext_dir) ]]; then\n        find \"$ext_dir\" -name \"nn~.pd_*\" -print0 | while IFS= read -r -d '' ext_path; do\n            ext_name=$(basename \"$ext_path\")\n            find \"$ext_dir\" -name \"*.dylib\" -print0 | while IFS= read -r -d '' dylib_path; do\n                dylib_name=$(basename \"$dylib_path\")\n                echo \"fixing library : $dylib_name\"\n                new_path=\"/Library/Application Support/ACIDS/RAVE/$dylib_name\"\n                if [[ ! -e \"$new_path\" ]]; then \n                    echo \"[WARNING] library not found : $new_path. Patch may not work\"\n                fi\n                echo install_name_tool -change \"@rpath/$dylib_name\" \"/Library/Application Support/ACIDS/RAVE/$dylib_name\" \"${ext_path}\" 2> /dev/null\n            done\n            codesign --deep --force --sign - \"${ext_path}\"\n        done\n    else\n        echo \"folder $ext_dir not found\".  \n    fi\n}\n\n\nif [[ \"$max\" -eq 1 ]]; then\n    if [[ -n \"$max_path\" ]]; then \n        patch_max_external \"$max_path\" \n    else\n        find ~/Documents -maxdepth 1 -name \"Max *\" -type d -print0 | while IFS= read -r -d '' max_dir; do\n            if [[ -d $max_dir ]]; then \n                patch_max_external \"$max_dir\" \n            else \n                echo \"$max_dir does not exists\" \n            fi\n        done \n    fi\nfi\n\nif [[ \"$pd\" -eq 1 ]]; then\n    if [[ -n \"$max_path\" ]]; then \n        patch_pd_external \"$pd_path\" \n    else\n        patch_pd_external \"$HOME/Documents/Pd\"\n    fi\nfi"
  },
  {
    "path": "src/frontend/maxmsp/mc.nn_tilde/CMakeLists.txt",
    "content": "# Copyright 2018 The Min-DevKit Authors. All rights reserved.\n# Use of this source code is governed by the MIT License found in the License.md file.\n\ncmake_minimum_required(VERSION 3.10 FATAL_ERROR)\n\nset(CMAKE_CXX_STANDARD 20)\nset(CMAKE_CXX_STANDARD_REQUIRED ON)\nset(CMAKE_CXX_EXTENSIONS OFF)\n\nset(C74_MIN_API_DIR ${CMAKE_CURRENT_SOURCE_DIR}/../min-api)\ninclude(${C74_MIN_API_DIR}/script/min-pretarget.cmake)\n\nif (APPLE)\n\tset(CMAKE_OSX_DEPLOYMENT_TARGET \"10.12\")\nendif()\n\n\n#############################################################\n# MAX EXTERNAL\n#############################################################\n\nexecute_process(\n\t\tCOMMAND git describe --tags\n\t\tWORKING_DIRECTORY ${CMAKE_CURRENT_LIST_DIR}\n\t\tOUTPUT_VARIABLE VERSION\n\t\tOUTPUT_STRIP_TRAILING_WHITESPACE\n)\nmessage(${VERSION})\nadd_definitions(-DVERSION=\"${VERSION}\")\n\n\n\nset(\nSOURCE_FILES\n\tmc.nn_tilde.cpp\n)\n\nadd_library( \n\t${PROJECT_NAME} \n\tMODULE\n\t${SOURCE_FILES}\n)\n\n\ninclude(${C74_MIN_API_DIR}/script/min-posttarget.cmake)\n\nif (MSVC)\n\tset(CMAKE_CXX_FLAGS_RELEASE \"${CMAKE_CXX_FLAGS_RELEASE} /MT\")\n\tset_property(TARGET ${PROJECT_NAME} PROPERTY CXX_STANDARD 20)\n\tset_property(TARGET ${PROJECT_NAME} PROPERTY CXX_STANDARD_REQUIRED ON)\n\ttarget_compile_features(${PROJECT_NAME} PUBLIC cxx_std_20)\nendif()\n\ninclude_directories( \n\t\"${C74_INCLUDES}\"\n\t\"${CMAKE_CURRENT_SOURCE_DIR}/../shared\"\n\t\"${CMAKE_CURRENT_SOURCE_DIR}/../../shared\"\n)\n\nif (MSVC)\n\tinclude_directories(${VCPKG_INCLUDE_DIR})\n\tlink_directories(${VCPKG_LIB_DIR})\nendif()\n\n\ntarget_link_libraries(${PROJECT_NAME} PRIVATE backend)\n\nif (UNIX)\n\tset(CONDA_ENV_PATH \"${CMAKE_SOURCE_DIR}/../env\")\n\tset(CURL_INCLUDE_DIR \"${CONDA_ENV_PATH}/include\")\n\tset(CURL_LIBRARY \"${CONDA_ENV_PATH}/lib/libcurl.dylib\")\n\tinclude_directories(${CURL_INCLUDE_DIR})\nelseif(MSVC)\n\tset(VCPKG_PATH \"${CMAKE_SOURCE_DIR}/../vcpkg\")\n\tset(CURL_INCLUDE_DIR \"${VCPKG_PATH}/packages/curl_x64-windows/include\")\n\tset(CURL_LIBRARY \"${VCPKG_PATH}/packages/curl_x64-windows/lib/libcurl.lib\")\nendif()\n\ntarget_link_libraries(${PROJECT_NAME} PRIVATE nlohmann_json::nlohmann_json)\ntarget_link_libraries(${PROJECT_NAME} PRIVATE ${CURL_LIBRARY})\n\n\nif (APPLE) # SEARCH FOR TORCH DYLIB IN THE LOADER FOLDER\nset_target_properties(${PROJECT_NAME} PROPERTIES\n\tBUILD_WITH_INSTALL_RPATH FALSE\n\tLINK_FLAGS \"-Wl,-rpath,@loader_path/\"\n)\nendif()\n\n\nif (APPLE) # COPY DYLIBS IN THE LOADER FOLDER\n\tadd_custom_command(\n\t\tTARGET ${PROJECT_NAME}\n\t\tPOST_BUILD \n\t\tCOMMAND ${CMAKE_COMMAND} -E copy  \"${CMAKE_SOURCE_DIR}/../env/ssl/cert.pem\" \"$<TARGET_FILE_DIR:${PROJECT_NAME}>\"\n\t\tCOMMAND ${CMAKE_SOURCE_DIR}/../env/bin/python ${CMAKE_SOURCE_DIR}/../install/dylib_fix.py -p \"$<TARGET_FILE:${PROJECT_NAME}>\" -o \"${CMAKE_SOURCE_DIR}/support\" -l \"${torch_dir}/libtorch\" \"${CMAKE_BINARY_DIR}/_deps\" \"${CMAKE_SOURCE_DIR}/../env\" \"${HOMEBREW_PREFIX}\" --sign_id \"${SIGN_ID}\"\n\t\tCOMMENT \"Fixing libraries, certificates, permissions, codesigning, quarantine\"\n\t)\n\t\nendif()\n\n"
  },
  {
    "path": "src/frontend/maxmsp/mc.nn_tilde/mc.nn_tilde.cpp",
    "content": "#include \"../shared/nn_base.h\"\n#include \"c74_min.h\"\n\n\ntemplate <typename nn_class>\nvoid model_perform(nn_class* nn_instance) {\n  std::vector<float *> in_model, out_model;\n  for (int c(0); c < nn_instance->n_inlets; c++)\n    in_model.push_back(nn_instance->m_in_model[c].get());\n  for (int c(0); c < nn_instance->n_outlets; c++)\n    out_model.push_back(nn_instance->m_out_model[c].get());\n    \n  if (nn_instance->wait_for_buffer_reset) {\n    nn_instance->init_buffers(); \n  }\n\n  nn_instance->m_model->perform(in_model, out_model, nn_instance->m_method, \n                                nn_instance->get_batches(), nn_instance->n_outlets, nn_instance->m_buffer_size);\n}\n\ntemplate <typename nn_class>\nvoid model_perform_async(nn_class* nn_instance) {\n  while (!nn_instance->can_perform()) {\n    std::this_thread::sleep_for(std::chrono::milliseconds(REFRESH_THREAD_INTERVAL));\n    if (nn_instance->m_should_stop_perform_thread) {\n      return;\n    }\n  }\n  std::vector<float *> in_model, out_model;\n  if (nn_instance->wait_for_buffer_reset) {\n    nn_instance->init_buffers(); \n  }\n  \n  for (int c(0); c < nn_instance->n_inlets; c++)\n    in_model.push_back(nn_instance->m_in_model[c].get());\n  for (int c(0); c < nn_instance->n_outlets; c++)\n    out_model.push_back(nn_instance->m_out_model[c].get());\n\n  while (!nn_instance->m_should_stop_perform_thread) {\n    if (nn_instance->m_data_available_lock.try_acquire_for(\n            std::chrono::milliseconds(REFRESH_THREAD_INTERVAL))) {\n        if (nn_instance->wait_for_buffer_reset) {\n          nn_instance->init_buffers(); \n        }\n        if (nn_instance->had_buffer_reset) {\n          in_model.clear(); \n          for (int c(0); c < nn_instance->m_model_in * nn_instance->get_batches(); c++) {\n            in_model.push_back(nn_instance->m_in_model[c].get());\n          }\n          out_model.clear(); \n          for (int c(0); c < nn_instance->m_model_out * nn_instance->get_batches(); c++) {\n            out_model.push_back(nn_instance->m_out_model[c].get());\n          }\n        }\n        nn_instance->m_model->perform(in_model, \n                                      out_model,\n                                      nn_instance->m_method, \n                                      nn_instance->get_batches(),\n                                      nn_instance->m_out_model.size() / nn_instance->get_batches(),\n                                      nn_instance->m_buffer_size);\n        nn_instance->m_result_available_lock.release();\n    }\n  }\n}\n\nlong simplemc_multichanneloutputs(c74::max::t_object *x, long index,\n                                  long count);\nlong simplemc_inputchanged(c74::max::t_object *x, long index, long count);\n\n\nclass mc_nn: public nn_base<mc_nn, mc_operator<>> {\n\npublic:\n    MIN_DESCRIPTION{\"Multi-channel interface for deep learning models\"};\n    MIN_TAGS{\"audio, deep learning, ai\"};\n    MIN_AUTHOR{\"Antoine Caillon & Axel Chemla--Romeu-Santos\"};\n    MIN_RELATED{\"nn.info, nn~, mcs.nn~\"};\n\n    static std::string get_external_name() { return \"mc.nn~\";} \n    mc_nn(const atoms &args = {}) {\n        init_external(args); \n    }\n\n    int get_sample_rate() override {\n        return samplerate(); \n    }\n\n    void init_external(const atoms &args) override {\n        init_model(); \n        init_downloader(); \n        if (!args.size()) { return; } \n        init_inputs_and_outputs(args);\n        init_inlets_and_outlets();\n        // init_buffers(); \n        init_process();\n    } \n    void perform(audio_bundle input, audio_bundle output) override;\n\n    // channel handling\n    std::vector<int> channel_map;\n    int n_batches_arg = 0;\n    int get_batches(); \n    int get_batches_out(); \n\n    void init_inputs_and_outputs(const atoms& atoms) override; \n    void init_inlets_and_outlets() override; \n    void init_buffers() override; \n    void init_process() override; \n    bool init_buffers_at_init() override { return true; }\n    bool update_channel_map(const long& index, const long& count); \n\n    message<> maxclass_setup{\n      this, \"maxclass_setup\",\n      [this](const c74::min::atoms &args, const int inlet) -> c74::min::atoms {\n        cout << \"mc.nn~ \" << VERSION << \" - torch \" << TORCH_VERSION\n             << \" - 2024-2025 - Antoine Caillon & Axel Chemla--Romeu-Santos\" << endl;\n        cout << \"visit https://www.github.com/acids-ircam\" << endl;\n        c74::max::t_class *c = args[0];\n        c74::max::class_addmethod(\n            c, (c74::max::method)simplemc_multichanneloutputs,\n            \"multichanneloutputs\", c74::max::A_CANT, 0);\n        c74::max::class_addmethod(c, (c74::max::method)simplemc_inputchanged,\n                                  \"inputchanged\", c74::max::A_CANT, 0);\n        return {};\n      }};\n\n    attribute<int> chans_attr {\n      this, \n      \"chans\", \n      0,\n      description{\"set a fixed number of output channels\"}, \n      setter{\n        MIN_FUNCTION {\n          if (args.size() == 0)\n            return args; \n          int in_chans = args[0];\n          if (in_chans > 0) {\n            n_batches_arg = in_chans;\n            DEBUG_PRINT(\"setting out channels to %d\", in_chans);\n          }\n          return args;\n        }\n      }};\n};\n\nint mc_nn::get_batches() {\n  if (channel_map.size() > 0) {\n    return *std::max_element(channel_map.begin(), channel_map.end());\n  } else {\n    return 1; \n  }\n}\n\nint mc_nn::get_batches_out() {\n  if (n_batches_arg > 0) {\n    return n_batches_arg; \n  } else {\n    return get_batches(); \n  }\n}\n\nvoid mc_nn::init_inputs_and_outputs(const atoms& args)  {\n    nn_base::init_inputs_and_outputs(args);\n    for (int i(0); i < m_model_in; i++)\n        channel_map.push_back(1);\n}\n\nvoid mc_nn::init_inlets_and_outlets() {\n  for (int i(0); i < n_inlets; i++) {\n    std::string input_label = \"\";\n    try {\n      input_label = m_model->get_model()\n                        .attr(m_method + \"_input_labels\")\n                        .toList()\n                        .get(i)\n                        .toStringRef();\n    } catch (...) {\n      input_label = \"(multichannel) model input \" + std::to_string(i);\n    }\n    m_inlets.push_back(std::make_unique<inlet<>>(this, input_label, \"multichannelsignal\"));\n  }\n\n  for (int i(0); i < n_outlets; i++) {\n    std::string output_label = \"\";\n    try {\n      output_label = m_model->get_model()\n                         .attr(m_method + \"_output_labels\")\n                         .toList()\n                         .get(i)\n                         .toStringRef();\n    } catch (...) {\n      output_label = \"(multichannel) model output \" + std::to_string(i);\n    }\n    m_outlets.push_back(\n        std::make_unique<outlet<>>(this, output_label, \"multichannelsignal\"));\n  }\n}\n\nvoid mc_nn::init_buffers() {\n  if (channel_map.size() == 0) {\n    for (int i(0); i < n_inlets; i++)\n        channel_map.push_back(1);\n  }\n\n  update_method(); \n\n  // if (m_buffer_size == -1) {\n  //   // NO THREAD MODE\n  //   m_buffer_size = DEFAULT_BUFFER_SIZE;\n  // }\n  // if (m_buffer_size < m_higher_ratio) {\n  //   cerr << \"buffer size too small, switching to \" << m_buffer_size << endl;\n  //   m_buffer_size = m_higher_ratio;\n  // } else {\n  //   m_buffer_size = power_ceil(m_buffer_size);\n  // }\n\n  m_buffer_in = n_inlets * get_batches();\n  if (m_in_buffer.get() != nullptr) { m_in_buffer.release(); }\n  m_in_buffer = std::make_unique<circular_buffer<double, float>[]>(m_buffer_in); \n  for (int i = 0; i < m_buffer_in; i++) {\n    m_in_buffer[i].initialize(m_buffer_size);\n  }\n\n  m_buffer_out = n_outlets * get_batches_out();\n  if (m_out_buffer.get() == nullptr) { m_out_buffer.release(); }\n  m_out_buffer = std::make_unique<circular_buffer<float, double>[]>(m_buffer_out); \n  for (int i = 0; i < m_buffer_out; i++) {\n    m_out_buffer[i].initialize(m_buffer_size);\n  }\n\n  m_in_model.clear(); \n  for (int i = 0; i < m_model_in * get_batches(); i++) {\n    m_in_model.push_back(std::make_unique<float[]>(m_buffer_size));\n    std::fill(m_in_model[i].get(), m_in_model[i].get() + m_buffer_size, 0.); \n  }\n\n  m_out_model.clear(); \n  for (int i = 0; i < m_model_out * get_batches(); i++) {\n    m_out_model.push_back(std::make_unique<float[]>(m_buffer_size));\n    std::fill(m_out_model[i].get(), m_out_model[i].get() + m_buffer_size, 0.); \n  }\n\n  wait_for_buffer_reset = false; \n  had_buffer_reset = true; \n  buffer_initialised = true; \n}\n\nvoid mc_nn::init_process() {\n    nn_base<mc_nn, mc_operator<>>::init_process(); \n    if (m_use_thread) {\n        m_compute_thread = std::make_unique<std::thread>(model_perform_async<mc_nn>, this);\n    }\n}\n\nvoid mc_nn::perform(audio_bundle input, audio_bundle output) {\n  auto vec_size = input.frame_count();\n  auto chan_size = input.channel_count(); \n\n  // COPY INPUT TO CIRCULAR BUFFER\n  int current_batch = 0; \n  int current_chan = 0; \n  int n_batches = get_batches(); \n\n  for (int c_in(0); c_in < chan_size; c_in++) {\n    auto in = input.samples(c_in); \n    auto buf_idx = current_batch * n_batches + current_chan; \n    if (buf_idx < m_buffer_in) {\n      m_in_buffer[buf_idx].put(in, vec_size);\n    }\n    current_chan++; \n    if (current_chan >= channel_map[current_batch]) {\n      current_batch++;\n      current_chan=0; \n    }\n  }\n\n  if (m_in_buffer[0].full()) { // BUFFER IS FULL\n    if (!m_use_thread) {\n      // TRANSFER MEMORY BETWEEN INPUT CIRCULAR BUFFER AND MODEL BUFFER\n      auto n_ins = std::min(n_inlets, m_model_in);\n      for (int b(0); b < n_batches; b++) {\n        for (int c(0); c < n_ins; c++) {\n          auto buf_idx = c * get_batches() + b; \n          if ((buf_idx < m_buffer_in) && (c * n_batches + b < m_in_model.size()))\n            m_in_buffer[buf_idx].get(m_in_model[c * n_batches + b].get(), m_buffer_size);\n        }\n      }\n\n      // CALL MODEL PERFORM IN CURRENT THREAD\n      model_perform(this);\n\n      // TRANSFER MEMORY BETWEEN OUTPUT CIRCULAR BUFFER AND MODEL BUFFER\n      auto n_outs = std::min(n_outlets, m_model_out);\n      auto n_batches_out = std::min(get_batches_out(), n_batches);\n      for (int b(0); b < n_batches_out; b++) {\n        for (int c(0); c < n_outs; c++) {\n          auto b_idx = c * get_batches_out() + b;\n          if ((b_idx < m_buffer_out) && (b * m_model_out + c < m_out_model.size()))\n            m_out_buffer[b_idx].put(m_out_model[b * m_model_out + c].get(), m_buffer_size);\n        }\n      }\n\n    } else if (m_result_available_lock.try_acquire()) {\n      \n      // TRANSFER MEMORY BETWEEN INPUT CIRCULAR BUFFER AND MODEL BUFFER\n      auto n_ins = std::min(n_inlets, m_model_in);\n      for (int b(0); b < n_batches; b++) {\n        for (int c(0); c < n_ins; c++) {\n          auto buf_idx = c * get_batches() + b; \n          if ((buf_idx < m_buffer_in) && (c * n_batches + b < m_in_model.size()))\n            m_in_buffer[buf_idx].get(m_in_model[c * n_batches + b].get(), m_buffer_size);\n        }\n      }\n\n      // TRANSFER MEMORY BETWEEN OUTPUT CIRCULAR BUFFER AND MODEL BUFFER\n      auto n_outs = std::min(n_outlets, m_model_out);\n      auto n_batches_out = std::min(get_batches_out(), n_batches);\n      for (int b(0); b < n_batches_out; b++) {\n        for (int c(0); c < n_outs; c++) {\n          auto b_idx = c * get_batches_out() + b;\n          if ((b_idx < m_buffer_out) && (b * m_model_out + c < m_out_model.size()))\n            m_out_buffer[b_idx].put(m_out_model[b * m_model_out + c].get(), m_buffer_size);\n        }\n      }\n\n      // SIGNAL PERFORM THREAD THAT DATA IS AVAILABLE\n      m_data_available_lock.release();\n    }\n  }\n\n  // COPY CIRCULAR BUFFER TO OUTPUT\n  for (int b(0); b < output.channel_count() ; b++) {\n    if (b < m_buffer_out) {\n      auto out = output.samples(b); \n      m_out_buffer[b].get(out, vec_size);\n    }\n  }\n  \n}\n\nbool mc_nn::update_channel_map(const long& index, const long& count) {\n  bool needs_refresh = false; \n  if (channel_map.size() == 0) {\n    for (int i(0); i < n_inlets; i++)\n        channel_map.push_back(1);\n  }\n  if (index > channel_map.size()) {\n    return false; \n  }\n  if (channel_map[index] != count) {\n    channel_map[index] = count; \n    wait_for_buffer_reset = true; \n  }\n  return true; \n}\n\n\nlong simplemc_multichanneloutputs(c74::max::t_object *x, long index,\n                                  long count) {\n  minwrap<mc_nn> *ob = (minwrap<mc_nn> *)(x);\n  return ob->m_min_object.get_batches_out();\n}\n\nlong simplemc_inputchanged(c74::max::t_object *x, long index, long count) {\n  minwrap<mc_nn> *ob = (minwrap<mc_nn> *)(x);\n  ob->m_min_object.update_channel_map(index, count);\n  return true; \n}\n\n\n\nMIN_EXTERNAL(mc_nn);"
  },
  {
    "path": "src/frontend/maxmsp/mcs.nn_tilde/CMakeLists.txt",
    "content": "# Copyright 2018 The Min-DevKit Authors. All rights reserved.\n# Use of this source code is governed by the MIT License found in the License.md file.\n\ncmake_minimum_required(VERSION 3.10 FATAL_ERROR)\n\nset(CMAKE_CXX_STANDARD 20)\nset(CMAKE_CXX_STANDARD_REQUIRED ON)\nset(CMAKE_CXX_EXTENSIONS OFF)\n\nset(C74_MIN_API_DIR ${CMAKE_CURRENT_SOURCE_DIR}/../min-api)\ninclude(${C74_MIN_API_DIR}/script/min-pretarget.cmake)\n\nif (APPLE)\n\tset(CMAKE_OSX_DEPLOYMENT_TARGET \"10.12\")\nendif()\n\n\n#############################################################\n# MAX EXTERNAL\n#############################################################\n\nexecute_process(\n\t\tCOMMAND git describe --tags\n\t\tWORKING_DIRECTORY ${CMAKE_CURRENT_LIST_DIR}\n\t\tOUTPUT_VARIABLE VERSION\n\t\tOUTPUT_STRIP_TRAILING_WHITESPACE\n)\nmessage(${VERSION})\nadd_definitions(-DVERSION=\"${VERSION}\")\n\n\nset(\nSOURCE_FILES\n\tmcs.nn_tilde.cpp\n)\n\nadd_library( \n\t${PROJECT_NAME} \n\tMODULE\n\t${SOURCE_FILES}\n)\n\n\ninclude(${C74_MIN_API_DIR}/script/min-posttarget.cmake)\n\nif (MSVC)\n\tset(CMAKE_CXX_FLAGS_RELEASE \"${CMAKE_CXX_FLAGS_RELEASE} /MT\")\n\tset_property(TARGET ${PROJECT_NAME} PROPERTY CXX_STANDARD 20)\n\tset_property(TARGET ${PROJECT_NAME} PROPERTY CXX_STANDARD_REQUIRED ON)\n\ttarget_compile_features(${PROJECT_NAME} PUBLIC cxx_std_20)\nendif()\n\ninclude_directories( \n\t\"${C74_INCLUDES}\" \n\t\"${CMAKE_CURRENT_SOURCE_DIR}/../shared\"\n\t\"${CMAKE_CURRENT_SOURCE_DIR}/../../shared\"\n)\n\nif (MSVC)\n\tinclude_directories(${VCPKG_INCLUDE_DIR})\n\tlink_directories(${VCPKG_LIB_DIR})\nendif()\n\n\ntarget_link_libraries(${PROJECT_NAME} PRIVATE backend)\n\nif (UNIX)\n\tset(CONDA_ENV_PATH \"${CMAKE_SOURCE_DIR}/../env\")\n\tset(CURL_INCLUDE_DIR \"${CONDA_ENV_PATH}/include\")\n\tset(CURL_LIBRARY \"${CONDA_ENV_PATH}/lib/libcurl.dylib\")\n\tinclude_directories(${CURL_INCLUDE_DIR})\nelseif(MSVC)\n\tset(VCPKG_PATH \"${CMAKE_SOURCE_DIR}/../vcpkg\")\n\tset(CURL_INCLUDE_DIR \"${VCPKG_PATH}/packages/curl_x64-windows/include\")\n\tset(CURL_LIBRARY \"${VCPKG_PATH}/packages/curl_x64-windows/lib/libcurl.lib\")\nendif()\n\ntarget_link_libraries(${PROJECT_NAME} PRIVATE nlohmann_json::nlohmann_json)\ntarget_link_libraries(${PROJECT_NAME} PRIVATE ${CURL_LIBRARY})\n\n\nif (APPLE) # SEARCH FOR TORCH DYLIB IN THE LOADER FOLDER\nset_target_properties(${PROJECT_NAME} PROPERTIES\n\tBUILD_WITH_INSTALL_RPATH FALSE\n\tLINK_FLAGS \"-Wl,-rpath,@loader_path/\"\n)\nendif()\n\nset(CMAKE_CXX_FLAGS_DEBUG \"${CMAKE_CXX_FLAGS_DEBUG} -g\")\n\nif (APPLE) # COPY DYLIBS IN THE LOADER FOLDER\n\n\tadd_custom_command(\n\t\tTARGET ${PROJECT_NAME}\n\t\tPOST_BUILD \n\t\tCOMMAND ${CMAKE_COMMAND} -E copy  \"${CMAKE_SOURCE_DIR}/../env/ssl/cert.pem\" \"$<TARGET_FILE_DIR:${PROJECT_NAME}>\"\n\t\tCOMMAND ${CMAKE_SOURCE_DIR}/../env/bin/python ${CMAKE_SOURCE_DIR}/../install/dylib_fix.py -p \"$<TARGET_FILE:${PROJECT_NAME}>\" -o \"${CMAKE_SOURCE_DIR}/support\" -l \"${torch_dir}/libtorch\" \"${CMAKE_BINARY_DIR}/_deps\" \"${CMAKE_SOURCE_DIR}/../env\" \"${HOMEBREW_PREFIX}\" --sign_id \"${SIGN_ID}\"\n\t\tCOMMENT \"Fixing libraries, certificates, permissions, codesigning, quarantine\"\n\t)\n\t\nendif()\n\n"
  },
  {
    "path": "src/frontend/maxmsp/mcs.nn_tilde/mcs.nn_tilde.cpp",
    "content": "#include \"../shared/nn_base.h\"\n#include \"c74_min.h\"\n\n\ntemplate <typename nn_class>\nvoid model_perform(nn_class* nn_instance) {\n  std::vector<float *> in_model, out_model;\n  for (int c(0); c < nn_instance->m_model_in * nn_instance->n_batches; c++)\n    in_model.push_back(nn_instance->m_in_model[c].get());\n  for (int c(0); c < nn_instance->m_model_out * nn_instance->n_batches; c++)\n    out_model.push_back(nn_instance->m_out_model[c].get());\n\n  if (nn_instance->had_buffer_reset) {\n    nn_instance->had_buffer_reset = false; \n  }\n\n  nn_instance->m_model->perform(in_model, out_model, nn_instance->m_method, \n                                nn_instance->n_inlets, nn_instance->m_model_out, nn_instance->m_buffer_size);\n}\n\ntemplate <typename nn_class>\nvoid model_perform_async(nn_class* nn_instance) {\n  \n  while (!nn_instance->can_perform()){\n    std::this_thread::sleep_for(std::chrono::milliseconds(REFRESH_THREAD_INTERVAL));\n    if (nn_instance->m_should_stop_perform_thread) {\n      return;\n    }\n  }\n\n  if (nn_instance->wait_for_buffer_reset) {\n    nn_instance->init_buffers();\n  }\n  std::vector<float *> in_model, out_model;\n  for (int c(0); c < nn_instance->m_model_in * nn_instance->n_batches; c++)\n    in_model.push_back(nn_instance->m_in_model[c].get());\n  for (int c(0); c < nn_instance->m_model_out * nn_instance->n_batches; c++)\n    out_model.push_back(nn_instance->m_out_model[c].get());\n\n  while (!nn_instance->m_should_stop_perform_thread) {\n    if (nn_instance->wait_for_buffer_reset) {\n      nn_instance->init_buffers();\n    }\n    if (nn_instance->had_buffer_reset) {\n      in_model.clear(); \n      for (int c(0); c < nn_instance->m_model_in * nn_instance->n_batches; c++) {\n        in_model.push_back(nn_instance->m_in_model[c].get());\n      }\n      out_model.clear(); \n      for (int c(0); c < nn_instance->m_model_out * nn_instance->n_batches; c++) {\n        out_model.push_back(nn_instance->m_out_model[c].get());\n      }\n      nn_instance->had_buffer_reset = false; \n    }\n\n    if (nn_instance->m_data_available_lock.try_acquire_for(\n            std::chrono::milliseconds(REFRESH_THREAD_INTERVAL))) {\n      nn_instance->m_model->perform(in_model, out_model, nn_instance->m_method, \n                                    nn_instance->n_inlets, nn_instance->m_model_out, nn_instance->m_buffer_size);\n      nn_instance->m_result_available_lock.release();\n    }\n  }\n}\n\n\nlong simplemc_multichanneloutputs(c74::max::t_object *x, long index,\n                                  long count);\nlong simplemc_inputchanged(c74::max::t_object *x, long index, long count);\n\n\nclass mcs_nn: public nn_base<mcs_nn, mc_operator<>> {\n\npublic:\n    MIN_DESCRIPTION{\"Multi-channel interface for deep learning models\"};\n    MIN_TAGS{\"audio, deep learning, ai\"};\n    MIN_AUTHOR{\"Antoine Caillon & Axel Chemla--Romeu-Santos\"};\n    MIN_RELATED{\"nn.info, nn~, mc.nn~\"};\n\n    static std::string get_external_name() { return \"mcs.nn~\";} \n    mcs_nn(const atoms &args = {}) {\n        init_external(args); \n    }\n\n    int get_sample_rate() override {\n        return samplerate(); \n    }\n\n    void init_external(const atoms &args) override {\n        init_model(); \n        init_downloader(); \n        if (!args.size()) { return; } \n        init_inputs_and_outputs(args);\n        init_inlets_and_outlets();\n        // init_buffers(); \n        wait_for_buffer_reset = true; \n        init_process();\n    } \n    void perform(audio_bundle input, audio_bundle output) override;\n\n    void dump_object() override;\n\n    // channel handling\n    int get_batches(); \n    int n_mc_inputs() {\n      return std::accumulate(channel_map.begin(), channel_map.end(), 0);\n    } \n    bool check_inputs(); \n    std::vector<long> channel_map;\n\n    void init_inputs_and_outputs(const atoms& atoms) override; \n    void init_inlets_and_outlets() override; \n    void init_buffers() override; \n    void init_process() override; \n    void update_method(std::string method = \"\") override;\n    bool update_channel_map(const long& index, const long& count); \n\n    int m_out_channels = 0; \n    int m_out_channels_arg = 0; \n    void update_out_channels() {\n      if ((m_out_channels_arg == 0) && (m_model_out != m_out_channels)) {\n        DEBUG_PRINT(\"updating out channels to %d\", m_model_out);\n        m_out_channels = m_model_out; \n        wait_for_buffer_reset = true; \n      }\n      // if method is changed, multi channels outputs will stay the same. \n    }\n\n    message<> maxclass_setup{\n      this, \"maxclass_setup\",\n      [this](const c74::min::atoms &args, const int inlet) -> c74::min::atoms {\n        cout << \"mcs.nn~ \" << VERSION << \" - torch \" << TORCH_VERSION\n             << \" - 2024-2025 - Antoine Caillon & Axel Chemla--Romeu-Santos\" << endl;\n        cout << \"visit https://www.github.com/acids-ircam\" << endl;\n        c74::max::t_class *c = args[0];\n        c74::max::class_addmethod(\n            c, (c74::max::method)simplemc_multichanneloutputs,\n            \"multichanneloutputs\", c74::max::A_CANT, 0);\n        c74::max::class_addmethod(c, (c74::max::method)simplemc_inputchanged,\n                                  \"inputchanged\", c74::max::A_CANT, 0);\n        return {};\n      }};\n\n      attribute<int> chans_attr {\n        this, \n        \"chans\", \n        0,\n        description{\"set a fixed number of output channels\"}, \n        setter{\n          MIN_FUNCTION {\n            if (args.size() == 0)\n              return args; \n            int in_chans = args[0];\n            if (in_chans > 0) {\n              m_out_channels_arg = in_chans;\n              m_out_channels = in_chans;\n              DEBUG_PRINT(\"setting out channels to %d\", in_chans);\n            }\n            return args;\n          }\n        }};\n};\n\nbool mcs_nn::check_inputs() {\n    return true;\n}\n\nint mcs_nn::get_batches() {\n  return *std::max_element(channel_map.begin(), channel_map.end());\n}\n\nvoid mcs_nn::init_inputs_and_outputs(const atoms &args) {\n  bool empty_mode = false; \n  DEBUG_PRINT(\"parsing inputs & outputs...\");\n  if (args.size() > 0) { // ONE ARGUMENT IS GIVEN\n    auto model_path = std::string(args[0]);\n    if (model_path == \"void\") {\n      empty_mode = true;    \n      m_model_in = 1; \n      m_model_out = 1; \n    } else {\n      try {\n        m_path = to_model_path(model_path);\n      } catch (std::string &e) {\n        error(e);\n      }\n    }\n  }\n  \n  if (empty_mode) {\n    DEBUG_PRINT(\"empty mode\");\n    if (args.size() > 1) { // FOUR ARGUMENTS ARE GIVEN\n      n_batches = int(args[1]);\n    }\n    if (args.size() > 2) { // THREE ARGUMENTS ARE GIVEN\n      m_buffer_size = int(args[2]);\n    }\n    channel_map = std::vector<long>(n_batches, 1); \n  } else {\n    if (args.size() > 1) { // TWO ARGUMENTS ARE GIVEN\n      m_method = std::string(args[1]);\n    }\n    if (args.size() > 2) { // TWO ARGUMENTS ARE GIVEN\n      n_batches = int(args[2]);\n    }\n    if (args.size() > 3) { // THREE ARGUMENTS ARE GIVEN\n      m_buffer_size = int(args[3]);\n    }\n    channel_map = std::vector<long>(n_batches, 1); \n    DEBUG_PRINT(\"loading model...\"); \n    load_model(m_path);\n    if (m_ready) {\n      m_out_channels = m_model_out; \n    } \n  }\n  if (m_buffer_size == -1) {\n    // NO THREAD MODE\n    m_buffer_size = DEFAULT_BUFFER_SIZE;\n  }\n}\n\nbool mcs_nn::update_channel_map(const long& index, const long& count)\n{\n  if (channel_map[index] != count) {\n    channel_map[index] = count;\n    wait_for_buffer_reset = true; \n    if (count != m_model_in) {\n      return false;\n    } else {\n      return true;\n    }\n  }\n  return true; \n}\n\n\nvoid mcs_nn::init_inlets_and_outlets() {\n   \n  DEBUG_PRINT(\"loading model...\"); \n  DEBUG_PRINT(\"n_batches : %d\", n_batches); \n  std::string input_label; \n  for (int i(0); i < n_batches; i++) {\n    if (m_model_in > 0) {\n      input_label = \"(multichannel) batch \" + std::to_string(i) + \"(\" + std::to_string(m_model_in) + \" dimensions)\";\n    } else {\n      input_label = \"(multichannel) batch \" + std::to_string(i); \n    }\n    m_inlets.push_back(\n      std::make_unique<inlet<>>(this, input_label, \"multichannelsignal\"));\n  }\n\n  std::string output_label;\n  for (int i(0); i < n_batches; i++) {\n    output_label = \"(multichannel) batch \" + std::to_string(i) + \"(\" + std::to_string(m_out_channels) + \" dimensions)\";\n    m_outlets.push_back(\n        std::make_unique<outlet<>>(this, output_label, \"multichannelsignal\"));\n  }\n  n_inlets = n_batches; \n  n_outlets = n_batches; \n}\n\nvoid mcs_nn::init_buffers() {\n\n  update_method(); \n\n  if (m_out_channels == 0) {\n    error(\"could not retrieve number of output channels\");\n  }\n  DEBUG_PRINT(\"initializing buffers...\");\n  if (!m_ready) { return; }\n  if (m_buffer_size == -1) {\n    // NO THREAD MODE\n    m_buffer_size = DEFAULT_BUFFER_SIZE;\n  }\n  DEBUG_PRINT(\"buffer size : %d\", m_buffer_size);\n  if (m_buffer_size == 0) {\n    m_use_thread = false; \n    m_buffer_size = m_higher_ratio; \n  } else {\n    if (m_buffer_size < m_higher_ratio) {\n      cerr << \"buffer size too small, switching to \" << m_buffer_size << endl;\n      m_buffer_size = m_higher_ratio;\n    } else {\n      m_buffer_size = power_ceil(m_buffer_size);\n    }\n  }\n\n  m_buffer_in = n_mc_inputs(); \n  DEBUG_PRINT(\"initializing with n_mc_inputs : %d\", m_buffer_in); \n  if (m_in_buffer.get() != nullptr) { m_in_buffer.release(); }\n  m_in_buffer = std::make_unique<circular_buffer<double, float>[]>(m_buffer_in); \n  for (int i = 0; i < m_buffer_in; i++) {\n    m_in_buffer[i].initialize(m_buffer_size);\n  }\n\n  DEBUG_PRINT(\"initializing with outputs : %d x %d\", n_outlets, m_out_channels);\n  if (m_out_buffer.get() != nullptr) { m_out_buffer.release(); }\n  m_buffer_out = n_outlets * m_out_channels;\n  m_out_buffer = std::make_unique<circular_buffer<float, double>[]>(m_buffer_out); \n  for (int i = 0; i < m_buffer_out; i++) {\n    m_out_buffer[i].initialize(m_buffer_size);\n  }\n\n  DEBUG_PRINT(\"initializing with model buffer inputs : %d x %d\", m_model_in, n_inlets);\n  m_in_model.clear(); \n  for (int i = 0; i < m_model_in * n_inlets; i++) {\n    m_in_model.push_back(std::make_unique<float[]>(m_buffer_size));\n    std::fill(m_in_model[i].get(), m_in_model[i].get() + m_buffer_size, 0.); \n  }\n\n  DEBUG_PRINT(\"initializing with model buffer outputs : %d x %d\", m_model_out, n_outlets);\n  m_out_model.clear(); \n  for (int i = 0; i < m_model_out * n_outlets; i++) {\n    m_out_model.push_back(std::make_unique<float[]>(m_buffer_size));\n    std::fill(m_out_model[i].get(), m_out_model[i].get() + m_buffer_size, 0.); \n  }\n  DEBUG_PRINT(\"buffers initialized\");\n  wait_for_buffer_reset = false; \n  had_buffer_reset = true; \n  buffer_initialised = true; \n}\n\n\n\nvoid mcs_nn::update_method(std::string method) {\n  if (!method.empty()) {\n      set_method(method);\n  }\n  if (!m_model->is_loaded()) {\n    cerr << \"no model is set yet\" << endl;\n    return; \n  }\n  if (m_model->has_method(m_method)) {\n    auto params = m_model->get_method_params(m_method);\n    // input parameters\n    m_model_in = params[0];\n    m_in_ratio = params[1];\n    // output parameters\n    m_model_out = params[2];\n    m_out_ratio = params[3]; \n\n    if (m_out_channels == 0) { \n      m_out_channels = m_model_out; \n    } \n    wait_for_buffer_reset = true; \n  } else {\n    cerr << \"method \" << method << \" not present in model\" << endl;\n    m_ready = false; \n  }\n}\n\nvoid mcs_nn::init_process() {\n    nn_base<mcs_nn, mc_operator<>>::init_process(); \n    if (m_use_thread) {\n        m_compute_thread = std::make_unique<std::thread>(model_perform_async<mcs_nn>, this);\n    }\n}\n\nvoid mcs_nn::perform(audio_bundle input, audio_bundle output) {\n  auto chan_size = input.channel_count(); \n  auto vec_size = input.frame_count();\n\n  // if (buffer_initialised) {\n    int current_batch = 0; \n    int current_channel = 0; \n    int n_channels_in = std::min<int>(chan_size, m_buffer_in);\n    for (int in_c(0); in_c < n_channels_in; in_c++) {\n      auto in = input.samples(in_c);\n      m_in_buffer[in_c].put(in, vec_size);\n    }\n\n    if (m_in_buffer[0].full()) { // BUFFER IS FULL\n      if (!m_use_thread) {\n        // TRANSFER MEMORY BETWEEN INPUT CIRCULAR BUFFER AND MODEL BUFFER\n        int current_chan = 0; \n        int current_batch = 0; \n        for (int i(0); i < m_buffer_in; i++) {\n          if (current_chan < m_model_in) {\n            auto c_idx = current_chan * n_batches + current_batch; \n            m_in_buffer[i].get(m_in_model[c_idx].get(), m_buffer_size);\n          }\n          current_chan++; \n          if (current_chan >= channel_map[current_batch]) {\n            current_batch += 1;\n            current_chan = 0;\n          }\n        }\n\n        // CALL MODEL PERFORM IN CURRENT THREAD\n        model_perform(this);\n\n        // TRANSFER MEMORY BETWEEN OUTPUT CIRCULAR BUFFER AND MODEL BUFFER\n        auto current_idx = 0; \n        for (int c(0); c < n_outlets * m_model_out; c++){ \n            m_out_buffer[c].put(m_out_model[c].get(), m_buffer_size);\n        }\n\n      } else if (m_result_available_lock.try_acquire()) {\n\n        // TRANSFER MEMORY BETWEEN INPUT CIRCULAR BUFFER AND MODEL BUFFER\n        // for (int c(0); c < m_model_in * get_batches(); c++)\n        int current_chan = 0; \n        int current_batch = 0; \n        int i = 0;\n        while (i < n_channels_in) {\n          if (current_chan >= m_model_in) {\n            i += (channel_map[current_batch] - current_chan); \n            current_batch += 1; \n            current_chan = 0; \n          } else { \n            if (current_chan < m_model_in) {\n              auto c_idx = current_chan * n_batches + current_batch; \n              m_in_buffer[i].get(m_in_model[c_idx].get(), m_buffer_size);\n            } \n            current_chan++; \n            if (current_chan >= channel_map[current_batch]) {\n              current_batch += 1;\n              current_chan = 0;\n            }\n            i++; \n          }\n        }\n\n        // TRANSFER MEMORY BETWEEN OUTPUT CIRCULAR BUFFER AND MODEL BUFFER\n        auto current_idx = 0; \n        auto n_channels = std::min(m_model_out, m_out_channels);\n        for (int b(0); b < n_outlets; b++) {\n          for (int c(0); c < n_channels; c++){ \n              m_out_buffer[b * m_out_channels + c].put(m_out_model[b * m_model_out + c].get(), m_buffer_size);\n          }\n        }\n\n        // SIGNAL PERFORM THREAD THAT DATA IS AVAILABLE\n        m_data_available_lock.release();\n      }\n    }\n    \n    // for (int b(0); b < n_outlets; b++) {\n    //   for (int c(0); c < n_channels; c++) {\n    for (int i(0); i < m_buffer_out; i++) {\n        auto out = output.samples(i); \n        m_out_buffer[i].get(out, vec_size); \n    }\n  // }\n}\n\nlong simplemc_multichanneloutputs(c74::max::t_object *x, long index,\n                                  long count) {\n  minwrap<mcs_nn> *ob = (minwrap<mcs_nn> *)(x);\n  return ob->m_min_object.m_out_channels;\n}\n\nlong simplemc_inputchanged(c74::max::t_object *x, long index, long count) {\n  minwrap<mcs_nn> *ob = (minwrap<mcs_nn> *)(x);\n  auto is_full = ob->m_min_object.update_channel_map(index, count);\n  ob->m_min_object.update_out_channels();\n\n  if (!is_full) {\n    auto n_channels = ob->m_min_object.m_model_in; \n    c74::max::object_warn(\n        x, (std::string(\"got \" + std::to_string(count) + \n            \" for \" + std::to_string(n_channels) + \n            \" model inputs\").c_str())\n    );\n  }\n  return true;\n}\n\nvoid mcs_nn::dump_object() {\n  cout << \"model_path: \" << std::string(m_path) << endl;\n  if (m_model) {\n    if (m_model->is_loaded()) {\n      cout << \"input dimension: \" << m_model_in << endl;\n      cout << \"output dimension: \" << m_model_out << endl;\n    } else {\n      cout << \"input dimension: no model yet\" << endl;\n      cout << \"output dimension: no model yet\" << endl;\n    }\n  } else {\n    cout << \"input dimension: no model yet\" << endl;\n    cout << \"output dimension: no model yet\" << endl;\n  }\n  cout << \"input ratio: \" << std::to_string(m_in_ratio) << endl; \n  cout << \"output ratio: \" << std::to_string(m_out_ratio) << endl; \n  cout << \"methods: \";\n  for (auto method: m_model->get_available_methods())\n    cout << method << \"; \";\n  cout << endl; \n  cout << \"attributes: \";\n  for (auto attribute: m_model->get_settable_attributes())\n    cout << attribute << \"; \";\n  cout << endl;\n}\n\nMIN_EXTERNAL(mcs_nn);"
  },
  {
    "path": "src/frontend/maxmsp/nn.info/CMakeLists.txt",
    "content": "# Copyright 2018 The Min-DevKit Authors. All rights reserved.\n# Use of this source code is governed by the MIT License found in the License.md file.\n\ncmake_minimum_required(VERSION 3.10 FATAL_ERROR)\n\nset(CMAKE_CXX_STANDARD 20)\nset(CMAKE_CXX_STANDARD_REQUIRED ON)\nset(CMAKE_CXX_EXTENSIONS OFF)\n\nset(C74_MIN_API_DIR ${CMAKE_CURRENT_SOURCE_DIR}/../min-api)\ninclude(${C74_MIN_API_DIR}/script/min-pretarget.cmake)\n\nif (APPLE)\n\tset(CMAKE_OSX_DEPLOYMENT_TARGET \"10.12\")\nendif()\n\n\n#############################################################\n# MAX EXTERNAL\n#############################################################\n\nexecute_process(\n\t\tCOMMAND git describe --tags\n\t\tWORKING_DIRECTORY ${CMAKE_CURRENT_LIST_DIR}\n\t\tOUTPUT_VARIABLE VERSION\n\t\tOUTPUT_STRIP_TRAILING_WHITESPACE\n)\nmessage(\"version : ${VERSION}\")\nadd_definitions(-DVERSION=\"${VERSION}\")\n\n\n\nset(\nSOURCE_FILES\n\tnn.info.cpp\n)\n\nadd_library( \n\t${PROJECT_NAME} \n\tMODULE\n\t${SOURCE_FILES}\n)\n\ninclude_directories( \n\t\"${C74_INCLUDES}\", \n\t\"${CMAKE_CURRENT_SOURCE_DIR}/../shared\"\n\t\"${CMAKE_CURRENT_SOURCE_DIR}/../../shared\"\n)\n\nif (MSVC)\n\tinclude_directories(${VCPKG_INCLUDE_DIR})\n\tlink_directories(${VCPKG_LIB_DIR})\nendif()\n\n\ntarget_link_libraries(${PROJECT_NAME} PRIVATE backend)\n\nif (UNIX)\n\tset(CONDA_ENV_PATH \"${CMAKE_SOURCE_DIR}/../env\")\n\tset(CURL_INCLUDE_DIR \"${CONDA_ENV_PATH}/include\")\n\tset(CURL_LIBRARY \"${CONDA_ENV_PATH}/lib/libcurl.dylib\")\n\tinclude_directories(${CURL_INCLUDE_DIR})\nelseif(MSVC)\n\tset(VCPKG_PATH \"${CMAKE_SOURCE_DIR}/../vcpkg\")\n\tset(CURL_INCLUDE_DIR \"${VCPKG_PATH}/packages/curl_x64-windows/include\")\n\tset(CURL_LIBRARY \"${VCPKG_PATH}/packages/curl_x64-windows/lib/libcurl.lib\")\nendif()\n\ntarget_link_libraries(${PROJECT_NAME} PRIVATE nlohmann_json::nlohmann_json)\ntarget_link_libraries(${PROJECT_NAME} PRIVATE ${CURL_LIBRARY})\n\n\n\ninclude(${C74_MIN_API_DIR}/script/min-posttarget.cmake)\n\nif (MSVC)\n\tset(CMAKE_CXX_FLAGS_RELEASE \"${CMAKE_CXX_FLAGS_RELEASE} /MT\")\n\tset_property(TARGET ${PROJECT_NAME} PROPERTY CXX_STANDARD 20)\n\tset_property(TARGET ${PROJECT_NAME} PROPERTY CXX_STANDARD_REQUIRED ON)\n\ttarget_compile_features(${PROJECT_NAME} PUBLIC cxx_std_20)\nendif()\n\n\nif (APPLE) # SEARCH FOR TORCH DYLIB IN THE LOADER FOLDER\nset_target_properties(${PROJECT_NAME} PROPERTIES\n\tBUILD_WITH_INSTALL_RPATH FALSE\n\tLINK_FLAGS \"-Wl,-rpath,@loader_path/\"\n)\nendif()\n\n\n\n\nif (APPLE) # COPY DYLIBS IN THE LOADER FOLDER\n\n\tadd_custom_command(\n\t\tTARGET ${PROJECT_NAME}\n\t\tPOST_BUILD \n\t\tCOMMAND echo \"signing with ${SIGN_ID}\"\n\t\tCOMMAND ${CMAKE_COMMAND} -E copy  \"${CMAKE_SOURCE_DIR}/../env/ssl/cert.pem\" \"$<TARGET_FILE_DIR:${PROJECT_NAME}>\"\n\t\tCOMMAND ${CMAKE_SOURCE_DIR}/../env/bin/python ${CMAKE_SOURCE_DIR}/../install/dylib_fix.py -p \"$<TARGET_FILE:${PROJECT_NAME}>\" -o \"${CMAKE_SOURCE_DIR}/support\" -l \"${torch_dir}/libtorch\" \"${CMAKE_BINARY_DIR}/_deps\" \"${CMAKE_SOURCE_DIR}/../env\" \"${HOMEBREW_PREFIX}\" --sign_id \"${SIGN_ID}\"\n\t\tCOMMENT \"Fixing libraries, certificates, permissions, codesigning, quarantine\"\n\t)\n\t\nendif()\n"
  },
  {
    "path": "src/frontend/maxmsp/nn.info/nn.info.cpp",
    "content": "#include \"../../../backend/backend.h\"\n#include \"../shared/max_model_download.h\"\n#include \"../shared/buffer_tools.h\"\n#include \"../shared/dict_utils.h\"\n#include \"c74_min.h\"\n#include <chrono>\n#include <semaphore>\n#include <string>\n#include <thread>\n#include <vector>\n\n#ifndef VERSION\n#define VERSION \"UNDEFINED\"\n#endif\n\nusing namespace c74::min;\n\nclass nn_info : public object<nn_info>, public vector_operator<> {\npublic:\n  MIN_DESCRIPTION{\"Fetching information from deep learning models\"};\n  MIN_TAGS{\"audio, deep learning, ai\"};\n  MIN_AUTHOR{\"Axel Chemla--Romeu-Santos\"};\n  MIN_RELATED{ \"nn~, mc.nn~, mcs.nn~\"};\n\n  nn_info(const atoms &args = {});\n  ~nn_info();\n\n  // INLETS OUTLETS\n  std::vector<std::unique_ptr<inlet<>>> m_inlets;\n  std::vector<std::unique_ptr<outlet<>>> m_outlets;\n\n  // BACKEND RELATED MEMBERS\n  ModelInfo m_model_info; \n  std::string m_path;\n  bool has_model = false;\n  std::unique_ptr<dict> m_model_dict;\n  std::unique_ptr<dict> m_available_models_dict;\n  std::unique_ptr<MaxModelDownloader> m_downloader;\n  bool is_valid_print_key(std::string string);\n\n  // ONLY FOR DOCUMENTATION\n  argument<symbol> path_arg{this, \"model path\",\n                            \"Absolute path to the pretrained model.\"};\n\n  // FUNCTION\n  void set_model_path(const std::string& model_path);\n  void dump_path();\n  void dump_methods();\n  void dump_attributes();\n  void dump_method_parameters(const std::string& method);\n  void dump_attribute_parameters(const std::string& method);\n  void dump_dictionary();\n  void dump_object();\n  void dump_downloadable_models(); \n  void scan_model(const path path);\n  void update_dictionary(); \n  void bind_dictionary(const std::string &dict_name);\n  void operator()(audio_bundle input, audio_bundle output) {}\n  void perform(audio_bundle input, audio_bundle output) {}\n\n  // BOOT STAMP\n  message<> maxclass_setup{\n      this, \"maxclass_setup\",\n      [this](const c74::min::atoms &args, const int inlet) -> c74::min::atoms {\n        cout << \"nn.info \" << VERSION << \" - torch \" << TORCH_VERSION\n             << \" - 2025 - Antoine Caillon & Axel Chemla--Romeu-Santos\" << endl;\n        cout << \"visit https://forum.ircam.fr\" << endl;\n        return {};\n      }};\n\n  // ATTRIBUTES\n  attribute<symbol> dict_attribute{\n    this, \"dict\", symbol(\"\"), \n    description{\"bind model information to target dictionary\"}, \n    setter{ MIN_FUNCTION {\n      std::string dictionary_name = args[0];\n      this->bind_dictionary(dictionary_name);\n      return {};\n    }}\n  };\n\n\n  message<> print {\n    this, \"print\", \n    MIN_FUNCTION {\n      bool is_valid = is_valid_print_key(args[0]);\n      if (!is_valid) {\n        return {};\n      }\n      if (args[1] == \"cout\") {\n        cout << args[2] << endl;\n      } else if (args[1] == \"cerr\") {\n        cerr << args[2] << endl;\n      } else if (args[1] == \"cwarn\") {\n        cwarn << args[2] << endl;\n      }\n      return {};\n    }\n  };\n\n  // MESSAGES\n  message<> bang_callback{\n      this, \"bang\", \n        description{\"dumps every model information available\"},\n        MIN_FUNCTION {\n          this->dump_object();  \n          return {};\n        }};\n\n  // MESSAGES\n  message<> dump_callback{\n      this, \"dump\", \n        description{\"dumps every model information available\"},\n        MIN_FUNCTION {\n          this->dump_object();  \n          return {};\n        }};\n\n  message<> set_model_callback{\n      this, \"set\", \n        description{\"set model path\"},\n        MIN_FUNCTION {\n          if (args.size() == 0) {\n            cerr << \"set message needs a path to a valid model.\" << endl;\n          }\n          std::string model_path = args[0];\n          set_model_path(model_path);  \n          return {};\n        }};\n\n  message<> get_path_callback {\n    this, \"path\", \n      description{\"dumps current model path\"},\n      MIN_FUNCTION {\n        this->dump_path();  \n        return {};\n      }};\n\n  message<> get_methods_callback {\n    this, \"methods\", \n    description{\"get available methods for provided path\"}, \n    MIN_FUNCTION {\n      this->dump_methods(); \n      return {};\n    }};\n  \n  message<> get_attributes_callback {\n    this, \"attributes\", \n    description{\"provides settable attributes for provided path\"}, \n    MIN_FUNCTION{\n      this->dump_attributes(); \n      return {};\n    }\n  };\n\n  message<> get_params_callback {\n    this, \"parameters\", \n    description{\"provides processing parameters for the given method\"}, \n    MIN_FUNCTION{\n      if (args.size() == 0) {\n        cerr << \"parameters takes a valid method as first argument.\" << endl;\n        return {}; \n      }\n      std::string method_or_attribute = args[0];\n      if (m_model_info.attribute_properties.contains(method_or_attribute)) {\n        this->dump_attribute_parameters(method_or_attribute);\n      } else if (m_model_info.method_properties.contains(method_or_attribute)) {\n        this->dump_method_parameters(method_or_attribute);\n      } else {\n        cerr << method_or_attribute << \" not found in model\" << endl;\n      }\n      return {};\n    }\n  };\n\n  message<> get_dict_callback {\n    this, \"dump_dict\", \n    description{\"dump information as dictionary\"}, \n    MIN_FUNCTION{\n      this->dump_dictionary();\n      return {};\n    }\n  };\n\n  message<> get_models_callback {\n    this, \"get_available_models\", \n    description{\"dump available models as a dictionary, with additional informations\"}, \n    MIN_FUNCTION{\n      this->dump_downloadable_models();\n      return {};\n    }\n  };\n\n  message <> download_models {\n    this, \"download\", \n    description{\"download a model from IRCAM Forum API\"}, \n    MIN_FUNCTION {\n      std::string model_card, optional_name;\n      if (args.size() == 0) {\n        cerr << \"please provide a model card (print downloadable models with get_available_models messages)\" << endl;\n      } else if (args.size() == 1) {\n        min::symbol model_card_s = args[0];\n        model_card = std::string(model_card_s.c_str());\n        optional_name = \"\";\n      } else {\n        min::symbol model_card_s = args[0];\n        min::symbol optional_name_s = args[1]; \n        model_card = std::string(model_card_s.c_str());\n        optional_name = std::string(optional_name_s.c_str()); \n      }\n      try {\n        if (this->m_downloader.get()->is_ready())\n          this->m_downloader.get()->download(model_card, optional_name);\n      } catch (std::string &e) {\n        cerr << e << endl;\n      }\n      return {}; \n    }\n  };\n\n  message <> delete_models {\n    this, \"delete\", \n    description{\"delete a model downloaded from IRCAM Forum API\"}, \n    MIN_FUNCTION {\n      if (args.size() == 0) {\n        cerr << \"please provide a model to delete\" << endl;\n      }\n      std::string model_card = args[0];\n      try {\n        if (this->m_downloader.get()->is_ready())\n          this->m_downloader.get()->remove(model_card);\n      } catch (std::string &e) {\n        cerr << e << endl;\n      }\n      return {}; \n    }};\n};\n\n\nnn_info::nn_info(const atoms &args)\n{\n  // make inlets\n  m_inlets.push_back(std::make_unique<inlet<>>(this, \"input for nn.info\"));\n  // make outlets\n  m_outlets.push_back(\n    std::make_unique<outlet<>>(this, \"model path\", \"symbol\")\n  );\n  m_outlets.push_back(\n    std::make_unique<outlet<>>(this, \"available methods\")\n  );\n  m_outlets.push_back(\n    std::make_unique<outlet<>>(this, \"available attributes\")\n  );\n  m_outlets.push_back(\n    std::make_unique<outlet<>>(this, \"processing parameters\")\n  );\n  m_outlets.push_back(\n    std::make_unique<outlet<>>(this, \"dict output\", \"dictionary\")\n  );\n  m_outlets.push_back(\n    std::make_unique<outlet<>>(this, \"available models for download\", \"dictionary\")\n  );\n\n\n  try {\n    m_downloader = std::make_unique<MaxModelDownloader>(this, std::string(\"nn.info\")); \n  } catch (...) {\n    cwarn << \"could not initialise model downloader\" << endl;\n  }\n\n  // import informations from model\n  if (args.size() > 0) { // ONE ARGUMENT IS GIVEN\n    auto model_path = std::string(args[0]);\n    set_model_path(model_path);\n    if (m_path == \"\") {\n      error(std::string(\"could not find model : \") + model_path);\n    }\n  }\n}\n\nbool nn_info::is_valid_print_key(std::string id_string) {\n  if (id_string == m_downloader.get()->string_id()) {\n    return true;\n  }\n  return false;\n}\n\n\nvoid nn_info::update_dictionary() {\n  if ((m_model_dict.get() == nullptr) || (!has_model)) {\n    return;\n  }\n  auto dict_ref = m_model_dict.get();\n  if (!dict_ref->valid()) {\n    cwarn << \"problem with dictionary : \" << dict_ref->name() << \" seems to be unvalid\" << endl;\n    return;\n  }\n  dict_ref->clear();\n  auto model_dict = nn_tools::dict_from_model_info(m_model_info);\n  dict_ref->copyunique(model_dict);\n  return;\n}\n\nvoid nn_info::bind_dictionary(const std::string& dict_name) {\n    if (m_model_dict.get() != nullptr) {\n      m_model_dict.release();\n    }\n  if (dict_name == \"\") {\n    m_model_dict = std::make_unique<dict>(symbol(true));\n  } else {\n    m_model_dict = std::make_unique<dict>(symbol(dict_name));\n  }\n  update_dictionary();\n}\n\nvoid nn_info::scan_model(path path) {\n  std::string model_path = path;\n  cout << \"parsing model : \" << model_path << endl; \n  auto m_model = Backend();\n  if (m_model.load(model_path, samplerate())) {\n    // cerr << \"error loading path \" << model_path << endl;\n    throw \"error loading path \" + model_path;\n    return;\n  }\n  // parse things\n  try {\n    m_model_info = m_model.get_model_info();\n    has_model = true; \n    m_path = model_path;\n    update_dictionary();\n  } catch (std::string &error) {\n    throw error; \n  }\n}\n\nvoid nn_info::set_model_path(const std::string& model_path) {\n  auto model_path_checked = model_path;\n  try {\n    if (model_path.substr(model_path.length() - 3) != \".ts\")\n      model_path_checked = model_path_checked + \".ts\";\n    min::path current_path; \n    if (m_downloader) {\n      auto download_path = m_downloader->get_download_path() / model_path_checked; \n      if (std::filesystem::exists(download_path)) {\n        current_path = path(download_path.string());\n        scan_model(current_path);\n        return;\n      }\n    }\n    current_path = path(model_path_checked);\n    scan_model(current_path);\n  } catch (std::string& stringerr) {\n    cerr << stringerr << endl;\n  } catch (std::exception& e) {\n    cerr << e.what() << endl; \n  }\n}\n\nvoid nn_info::dump_path() {\n  // Iterating over the keys\n  auto outlet = m_outlets[0].get();\n  if (!has_model) {\n    outlet->send(symbol(\"none\"));\n  } else {\n    std::string path = m_path;\n    outlet->send(symbol(path));\n  }\n  \n}\n\nvoid nn_info::dump_methods() {\n  if (!has_model) {\n    cerr << \"please set a model before\" << endl; \n    return;\n  }\n  // Iterating over the keys\n  auto outlet = m_outlets[1].get();\n  for (const auto& pair : m_model_info.method_properties) {\n    auto key = pair.first;\n    outlet->send({symbol(\"method\"), symbol(key)});\n  }\n}\n\nvoid nn_info::dump_attributes() {\n  if (!has_model) {\n    cerr << \"please set a model before\" << endl; \n    return;\n  }\n  // Iterating over the keys\n  auto outlet = m_outlets[2].get();\n  for (const auto& pair : m_model_info.attribute_properties) {\n    auto key = pair.first;\n    outlet->send({symbol(\"attribute\"), symbol(key)});\n  }\n}\n\n\nvoid nn_info::dump_method_parameters(const std::string& method) {\n  if (!has_model) {\n    cerr << \"please set a model before\" << endl;\n    return;\n  }\n  auto method_props = m_model_info.method_properties;\n  if (method_props.find(method) == method_props.end()) {\n    cerr << \"method \" << method << \" does not seem to be valid.\" << endl;\n    return;\n  }\n  // Iterating over the keys\n  auto outlet = m_outlets[3].get();\n  auto params = m_model_info.method_properties[method];  \n  outlet->send({symbol(params.name), symbol(\"channels_in\"), params.channels_in});\n  outlet->send({symbol(params.name), symbol(\"channels_out\"), params.channels_in});\n  outlet->send({symbol(params.name), symbol(\"ratio_in\"), params.ratio_out});\n  outlet->send({symbol(params.name), symbol(\"ratio_out\"), params.ratio_out});\n}\n\nvoid nn_info::dump_attribute_parameters(const std::string& attribute) {\n  if (!has_model) {\n    cerr << \"please set a model before\" << endl;\n    return;\n  }\n  auto attribute_props = m_model_info.attribute_properties;\n  if (attribute_props.find(attribute) == attribute_props.end()) {\n    cerr << \"attribute \" << attribute << \" does not seem to be valid.\" << endl;\n    return;\n  }\n  // Iterating over the keys\n  auto outlet = m_outlets[3].get();\n  auto params = m_model_info.attribute_properties[attribute];  \n  atoms attr_types = {symbol(params.name), symbol(\"attribute_type\")};\n  for (auto attr_type: params.attribute_types) \n    attr_types.push_back(symbol(attr_type));\n  outlet->send(attr_types); \n}\n\nvoid nn_info::dump_dictionary() {\n  // if (!has_model) {\n  //   cerr << \"please set a model before\" << endl; \n  // }\n  // auto dict = nn_tools::dict_from_model_info(m_model_info);\n  auto outlet = m_outlets[4].get();\n  auto dict_ref = m_model_dict.get();\n  if (dict_ref->valid()) {\n    std::string name = dict_ref->name();\n    outlet->send({\"dictionary\", symbol(name)});\n  } else {\n    cerr << \"internal dictionary (id: )\" << dict_ref->name() << \" is invalid.\" << endl;\n  }\n}\n\nvoid nn_info::dump_downloadable_models() {\n  auto outlet = m_outlets[5].get(); \n  if (m_available_models_dict.get() == nullptr) {\n    m_available_models_dict = std::make_unique<dict>(symbol(true));\n  }   \n  try {\n    if (m_downloader.get()->is_ready()) {\n      m_downloader.get()->fill_dict(m_available_models_dict.get());\n      outlet->send({\"dictionary\", symbol(m_available_models_dict.get()->name())});\n    }\n  } catch (std::string& e) {\n    cerr << \"could not get models from api\" << endl;\n    cerr << \"reason : \" << e << endl;\n  }\n}\n\nvoid nn_info::dump_object() {\n  if (!has_model) {\n    cerr << \"please set a model before\" << endl; \n  }\n  this->dump_methods(); \n  this->dump_attributes(); \n  for (const auto& pair : m_model_info.attribute_properties)\n    this->dump_attribute_parameters({pair.first});\n  for (const auto& pair : m_model_info.method_properties)\n    this->dump_method_parameters({pair.first});\n  this->dump_dictionary(); \n  this->dump_downloadable_models(); \n}\n\nnn_info::~nn_info() {\n\n}\n\nMIN_EXTERNAL(nn_info);\n"
  },
  {
    "path": "src/frontend/maxmsp/nn_tilde/CMakeLists.txt",
    "content": "# Copyright 2018 The Min-DevKit Authors. All rights reserved.\n# Use of this source code is governed by the MIT License found in the License.md file.\n\ncmake_minimum_required(VERSION 3.10 FATAL_ERROR)\n\nset(CMAKE_CXX_STANDARD 20)\nset(CMAKE_CXX_STANDARD_REQUIRED ON)\nset(CMAKE_CXX_EXTENSIONS OFF)\n\nset(C74_MIN_API_DIR ${CMAKE_CURRENT_SOURCE_DIR}/../min-api)\ninclude(${C74_MIN_API_DIR}/script/min-pretarget.cmake)\n\nif (APPLE)\n\tset(CMAKE_OSX_DEPLOYMENT_TARGET \"10.12\")\nendif()\n\n\n#############################################################\n# MAX EXTERNAL\n#############################################################\n\nexecute_process(\n\t\tCOMMAND git describe --tags\n\t\tWORKING_DIRECTORY ${CMAKE_CURRENT_LIST_DIR}\n\t\tOUTPUT_VARIABLE VERSION\n\t\tOUTPUT_STRIP_TRAILING_WHITESPACE\n)\nmessage(${VERSION})\nadd_definitions(-DVERSION=\"${VERSION}\")\n\n\n\nset(\nSOURCE_FILES\n\tnn_tilde.cpp\n)\n\nadd_library( \n\t${PROJECT_NAME} \n\tMODULE\n\t${SOURCE_FILES}\n)\n\n\ninclude(${C74_MIN_API_DIR}/script/min-posttarget.cmake)\n\nif (MSVC)\n\tset(CMAKE_CXX_FLAGS_RELEASE \"${CMAKE_CXX_FLAGS_RELEASE} /MT\")\n\tset_property(TARGET ${PROJECT_NAME} PROPERTY CXX_STANDARD 20)\n\tset_property(TARGET ${PROJECT_NAME} PROPERTY CXX_STANDARD_REQUIRED ON)\n\ttarget_compile_features(${PROJECT_NAME} PUBLIC cxx_std_20)\nendif()\n\ninclude_directories( \n\t\"${C74_INCLUDES}\" \n\t\"${CMAKE_CURRENT_SOURCE_DIR}/../shared\"\n\t\"${CMAKE_CURRENT_SOURCE_DIR}/../../shared\"\n)\n\ninclude_directories( \n\t\"${MAX_SDK_INCLUDES}\"\n\t\"${MAX_SDK_MSP_INCLUDES}\"\n\t\"${MAX_SDK_JIT_INCLUDES}\"\n)\n\nif (MSVC)\n\tinclude_directories(${VCPKG_INCLUDE_DIR})\n\tlink_directories(${VCPKG_LIB_DIR})\nendif()\n\ntarget_include_directories(${PROJECT_NAME} PRIVATE \"${CMAKE_SOURCE_DIR}/frontend/maxmsp/min-api/max-sdk-base/c74support/max-includes\")\ntarget_link_libraries(${PROJECT_NAME} PRIVATE backend)\n\nif (UNIX)\n\tset(CONDA_ENV_PATH \"${CMAKE_SOURCE_DIR}/../env\")\n\tset(CURL_INCLUDE_DIR \"${CONDA_ENV_PATH}/include\")\n\tset(CURL_LIBRARY \"${CONDA_ENV_PATH}/lib/libcurl.dylib\")\n\tinclude_directories(${CURL_INCLUDE_DIR})\nelseif(MSVC)\n\tset(VCPKG_PATH \"${CMAKE_SOURCE_DIR}/../vcpkg\")\n\tset(CURL_INCLUDE_DIR \"${VCPKG_PATH}/packages/curl_x64-windows/include\")\n\tset(CURL_LIBRARY \"${VCPKG_PATH}/packages/curl_x64-windows/lib/libcurl.lib\")\nendif()\n\ntarget_link_libraries(${PROJECT_NAME} PRIVATE nlohmann_json::nlohmann_json)\ntarget_link_libraries(${PROJECT_NAME} PRIVATE ${CURL_LIBRARY})\n\n\nif (APPLE) # SEARCH FOR TORCH DYLIB IN THE LOADER FOLDER\n\tset_target_properties(${PROJECT_NAME} PROPERTIES\n\t\tBUILD_WITH_INSTALL_RPATH FALSE\n\t\t# LINK_FLAGS \"-Wl,-rpath,@loader_path/\"\n\t\tLINK_FLAGS \"-Wl\"\n\t)\n\n\tadd_custom_command(\n\t\tTARGET ${PROJECT_NAME}\n\t\tPOST_BUILD \n\t\tCOMMAND ${CMAKE_COMMAND} -E copy  \"${CMAKE_SOURCE_DIR}/../env/ssl/cert.pem\" \"$<TARGET_FILE_DIR:${PROJECT_NAME}>\"\n\t\tCOMMAND ${CMAKE_SOURCE_DIR}/../env/bin/python ${CMAKE_SOURCE_DIR}/../install/dylib_fix.py -p \"$<TARGET_FILE:${PROJECT_NAME}>\" -o \"${CMAKE_SOURCE_DIR}/support\" -l \"${torch_dir}/libtorch\" \"${CMAKE_BINARY_DIR}/_deps\" \"${CMAKE_SOURCE_DIR}/../env\" \"${HOMEBREW_PREFIX}\" --sign_id \"${SIGN_ID}\"\n\t\tCOMMENT \"Fixing libraries, certificates, permissions, codesigning, quarantine\"\n\t)\nendif()\n\n\nif (MSVC)\n\tset_property(TARGET ${PROJECT_NAME} PROPERTY CXX_STANDARD 20)\nendif()\n"
  },
  {
    "path": "src/frontend/maxmsp/nn_tilde/nn_tilde.cpp",
    "content": "#include \"../shared/nn_base.h\"\n#include \"c74_min.h\"\n\ntemplate <typename nn_class>\nvoid model_perform(nn_class* nn_instance) {\n  std::vector<float *> in_model, out_model;\n  for (int c(0); c < nn_instance->m_model_in; c++)\n    in_model.push_back(nn_instance->m_in_model[c].get());\n  for (int c(0); c < nn_instance->m_model_out; c++)\n    out_model.push_back(nn_instance->m_out_model[c].get());\n\n  if (nn_instance->had_buffer_reset) {\n    nn_instance->had_buffer_reset = false; \n  }\n\n  nn_instance->m_model->perform(in_model, out_model, \n                                nn_instance->m_method, \n                                1, nn_instance->m_out_model.size(), nn_instance->m_buffer_size);\n}\n\ntemplate <typename nn_class>\nvoid model_perform_async(nn_class *nn_instance) {\n  while (!nn_instance->can_perform()){\n    std::this_thread::sleep_for(std::chrono::milliseconds(REFRESH_THREAD_INTERVAL));\n    if (nn_instance->m_should_stop_perform_thread) {\n      return;\n    }\n  }\n  std::vector<float *> in_model, out_model;\n\n  if (nn_instance->wait_for_buffer_reset) {\n    nn_instance->init_buffers(); \n  }\n\n  for (int c(0); c < nn_instance->m_model_in; c++)\n    in_model.push_back(nn_instance->m_in_model[c].get());\n  for (int c(0); c < nn_instance->m_model_out; c++)\n    out_model.push_back(nn_instance->m_out_model[c].get());\n\n  while (!nn_instance->m_should_stop_perform_thread) {\n    if (nn_instance->m_data_available_lock.try_acquire_for(\n            std::chrono::milliseconds(REFRESH_THREAD_INTERVAL))) {\n          if (nn_instance->wait_for_buffer_reset) {\n            nn_instance->init_buffers(); \n          }\n          if (nn_instance->had_buffer_reset) {\n            in_model.clear(); \n            for (int c(0); c < nn_instance->m_model_in; c++) {\n              in_model.push_back(nn_instance->m_in_model[c].get());\n            }\n            out_model.clear(); \n            for (int c(0); c < nn_instance->m_model_out; c++) {\n              out_model.push_back(nn_instance->m_out_model[c].get());\n            }\n            nn_instance->had_buffer_reset = false; \n          }\n          nn_instance->m_model->perform(in_model, out_model,\n                                      nn_instance->m_method, \n                                      1, nn_instance->m_out_model.size(), nn_instance->m_buffer_size);\n          nn_instance->m_result_available_lock.release();\n        }\n    }\n  }\n\n\n\nclass nn: public nn_base<nn, vector_operator<>> {\n\npublic:\n    MIN_DESCRIPTION{\"Interface for deep learning models\"};\n    MIN_TAGS{\"audio, deep learning, ai\"};\n    MIN_AUTHOR{\"Antoine Caillon & Axel Chemla--Romeu-Santos\"};\n    MIN_RELATED{\"nn.info, mc.nn~, mcs.nn~\"};\n\n    static std::string get_external_name() {\n       return std::string(\"nn~\");\n    } \n    nn(const atoms &args = {}) {\n        init_external(args); \n     }\n\n    int get_sample_rate() override {\n        return samplerate(); \n    }\n\n    void init_process() override { \n        nn_base<nn>::init_process(); \n        if (m_use_thread) {\n            m_compute_thread = std::make_unique<std::thread>(model_perform_async<nn>, this);\n        }\n    }\n\n    void init_external(const atoms &args) override {\n      DEBUG_PRINT(\"initializing model\");\n      init_model(); \n      DEBUG_PRINT(\"initializing downloader\");\n      init_downloader(); \n      if (!args.size()) { return; } \n      DEBUG_PRINT(\"initializing inputs & outputs\");\n      init_inputs_and_outputs(args); \n      DEBUG_PRINT(\"initializing inlets & outlets\");\n      init_inlets_and_outlets(); \n      // DEBUG_PRINT(\"initializing buffers\");\n      // init_buffers(); \n      DEBUG_PRINT(\"initializing process\"); \n      init_process(); \n    } \n    void perform(audio_bundle input, audio_bundle output) override;\n\n    message<> maxclass_setup{\n      this, \"maxclass_setup\",\n      [this](const c74::min::atoms &args, const int inlet) -> c74::min::atoms {\n        cout << \"nn~ \" << VERSION << \" - torch \" << TORCH_VERSION\n             << \" - 2023-2025 - Antoine Caillon & Axel Chemla--Romeu-Santos\" << endl;\n        cout << \"visit https://www.github.com/acids-ircam\" << endl;\n        return {};\n      }};\n      \n};\n\n\nvoid nn::perform(audio_bundle input, audio_bundle output) {\n  auto vec_size = input.frame_count();\n\n  if (m_ready) {\n\n    // COPY INPUT TO CIRCULAR BUFFER\n    for (int c(0); c < input.channel_count(); c++) {\n      auto in = input.samples(c);\n      m_in_buffer[c].put(in, vec_size);\n    }\n\n\n    if (m_in_buffer[0].full()) { // BUFFER IS FULL\n      if (!m_use_thread) {\n        if (wait_for_buffer_reset) {\n              init_buffers();\n          }\n          // TRANSFER MEMORY BETWEEN INPUT CIRCULAR BUFFER AND MODEL BUFFER\n          auto n_ins = std::min(n_inlets, m_model_in);\n          for (int c(0); c < n_ins; c++)\n            m_in_buffer[c].get(m_in_model[c].get(), m_buffer_size);\n\n          // CALL MODEL PERFORM IN CURRENT THREAD\n          model_perform<nn>(this);\n          // TRANSFER MEMORY BETWEEN OUTPUT CIRCULAR BUFFER AND MODEL BUFFER\n          auto n_outs = std::min(n_outlets, m_model_out);\n          for (int c(0); c < n_outs; c++)\n            m_out_buffer[c].put(m_out_model[c].get(), m_buffer_size);\n      } else { \n        if (m_result_available_lock.try_acquire()) {\n          // TRANSFER MEMORY BETWEEN INPUT CIRCULAR BUFFER AND MODEL BUFFER\n          if (wait_for_buffer_reset) {\n              init_buffers();\n          }\n          auto n_ins = std::min(n_inlets, m_model_in);\n          for (int c(0); c < n_ins; c++)\n            m_in_buffer[c].get(m_in_model[c].get(), m_buffer_size);\n\n          // TRANSFER MEMORY BETWEEN OUTPUT CIRCULAR BUFFER AND MODEL BUFFER\n          auto n_outs = std::min(n_outlets, m_model_out);\n          for (int c(0); c < n_outs; c++)\n            m_out_buffer[c].put(m_out_model[c].get(), m_buffer_size);\n\n\n          // SIGNAL PERFORM THREAD THAT DATA IS AVAILABLE\n          m_data_available_lock.release();\n        } \n      }\n    } \n\n    // COPY CIRCULAR BUFFER TO OUTPUT\n    for (int c(0); c < output.channel_count(); c++) {\n      auto out = output.samples(c);\n      m_out_buffer[c].get(out, vec_size);\n    }\n  }\n}\n\n\nMIN_EXTERNAL(nn);"
  },
  {
    "path": "src/frontend/maxmsp/nn_tilde/nn_tilde_test.cpp",
    "content": "#include \"c74_min.h\"\n#include \"c74_min_unittest.h\"\n#include \"nn_tilde.cpp\"\n#include <iostream>\n\nSCENARIO(\"object produces correct output\") {\n  ext_main(nullptr);\n\n  GIVEN(\"An instance of nn~ without parameters\") {\n    nn my_object;\n    WHEN(\"a buffer is given\") {\n      sample_vector input(4096);\n      sample_vector output;\n\n      for (int i(0); i < 10; i++) {\n        for (auto x : input) {\n          auto y = my_object(x);\n          output.push_back(y);\n        }\n      }\n    }\n  }\n\n  GIVEN(\"An instance of nn~ with parameters\") {\n    atom path(\"/Users/acaillon/Desktop/nn.ts\"), method(\"forward\");\n    atoms args = {path, method};\n    nn my_object = nn(args);\n\n    WHEN(\"a buffer is given\") {\n      sample_vector input(4096);\n      sample_vector output;\n\n      for (int i(0); i < 10; i++) {\n        for (auto x : input) {\n          auto y = my_object(x);\n          output.push_back(y);\n        }\n      }\n    }\n  }\n}"
  },
  {
    "path": "src/frontend/maxmsp/nn_tilde/nn~.maxhelp",
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You can also use nn~ to interface your own generators by using the provided Python interface (see documentation for advanced use).\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-13\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 173.0, 56.0, 355.0, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Antoine Caillon & Axel Chemla--Romeu-Santos - ACIDS - ircam\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 18.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-9\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 173.0, 14.0, 233.0, 47.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"a max external for real-time ai audio processing\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 48.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-1\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 14.0, 14.0, 149.0, 62.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-60\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"toggle\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"int\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 23.0, 346.0, 43.0, 43.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-55\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 9,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 101.0, 270.0, 160.0, 141.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"First time here ? Download a pretrained RAVE model by clicking this button ! (~160MB)\\n\\nOnce downloaded (toggle enabled), re-open this help patch.\\n\\n\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-49\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"int\" ],\n\t\t\t\t\t\t\t\t\t\"patcher\" : \t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"fileversion\" : 1,\n\t\t\t\t\t\t\t\t\t\t\"appversion\" : \t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t\"major\" : 8,\n\t\t\t\t\t\t\t\t\t\t\t\"minor\" : 6,\n\t\t\t\t\t\t\t\t\t\t\t\"revision\" : 5,\n\t\t\t\t\t\t\t\t\t\t\t\"architecture\" : \"x64\",\n\t\t\t\t\t\t\t\t\t\t\t\"modernui\" : 1\n\t\t\t\t\t\t\t\t\t\t}\n,\n\t\t\t\t\t\t\t\t\t\t\"classnamespace\" : \"box\",\n\t\t\t\t\t\t\t\t\t\t\"rect\" : [ 615.0, 160.0, 676.0, 522.0 ],\n\t\t\t\t\t\t\t\t\t\t\"bglocked\" : 0,\n\t\t\t\t\t\t\t\t\t\t\"openinpresentation\" : 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]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"patchline\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"destination\" : [ \"obj-60\", 0 ],\n\t\t\t\t\t\t\t\t\t\"source\" : [ \"obj-49\", 0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n ]\n\t\t\t\t\t}\n,\n\t\t\t\t\t\"patching_rect\" : [ 26.0, 32.0, 47.0, 22.0 ],\n\t\t\t\t\t\"saved_object_attributes\" : \t\t\t\t\t{\n\t\t\t\t\t\t\"description\" : \"\",\n\t\t\t\t\t\t\"digest\" : \"\",\n\t\t\t\t\t\t\"globalpatchername\" : \"\",\n\t\t\t\t\t\t\"tags\" : \"\"\n\t\t\t\t\t}\n,\n\t\t\t\t\t\"text\" : \"p basic\"\n\t\t\t\t}\n\n\t\t\t}\n ],\n\t\t\"lines\" : [  ],\n\t\t\"dependency_cache\" : [ \t\t\t{\n\t\t\t\t\"name\" : \"drumLoop.aif\",\n\t\t\t\t\"bootpath\" : \"C74:/media/msp\",\n\t\t\t\t\"type\" : \"AIFF\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"eroica.aiff\",\n\t\t\t\t\"bootpath\" : \"C74:/docs/tutorial-patchers/msp-tut\",\n\t\t\t\t\"type\" : \"AIFF\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"huge.aiff\",\n\t\t\t\t\"bootpath\" : \"C74:/docs/tutorial-patchers/msp-tut\",\n\t\t\t\t\"type\" : \"AIFF\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"morph.256.rom.aif\",\n\t\t\t\t\"bootpath\" : \"C74:/packages/BEAP/misc\",\n\t\t\t\t\"type\" : \"AIFF\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"nn~.mxo\",\n\t\t\t\t\"type\" : \"iLaX\"\n\t\t\t}\n ],\n\t\t\"autosave\" : 0\n\t}\n\n}\n"
  },
  {
    "path": "src/frontend/maxmsp/shared/array_tools.h",
    "content": "#include \"ext.h\"\t\t\t\t\t\t\t// standard Max include, always required\n#include \"ext_obex.h\"\t\t\t\t\t\t// required for new style Max object\n#include \"ext_atomarray.h\"                  // atomarrays\n\n#include \"../../../shared/static_buffer.h\"\n\n#include <type_traits>\n\n\nnamespace ArrayTools {\n\n    namespace min = c74::min; \n    namespace max = c74::max;\n\n    extern \"C\" max::t_atomarray* arrayobj_findregistered_retain(max::t_symbol* name);\n    extern \"C\" max::t_max_err arrayobj_release(max::t_atomarray* aa);\n    // extern void atomarray_dispose(max::t_atomarray* x);\n    // extern max::t_atomarray* arrayobj_register(max::t_atomarray* aa, max::t_symbol** name);\n    // extern max::t_max_err arrayobj_unregister(max::t_atomarray* aa);\n    // extern max::t_atomarray* arrayobj_findregistered_clone(max::t_symbol* name);\n    // extern max::t_symbol* arrayobj_namefromptr(max::t_atomarray* aa);\n    // extern void* max::outlet_array(max::t_outlet* x, max::t_symbol* s);\n\n\n    bool is_array(const min::atom &atom) {\n        bool result = false;\n        auto name_max = max::atom_getsym(&atom); \n        max::t_atomarray* aa = arrayobj_findregistered_retain(name_max);\n        if (aa) {\n            arrayobj_release(aa);\n            return true;\n        }\n        return false;\n    }\n\n    long get_length(const min::atom &atom) {\n        bool result = false;\n        auto name_max = max::atom_getsym(&atom); \n        max::t_atomarray* aa = arrayobj_findregistered_retain(name_max);\n        if (aa) {\n            long size = atomarray_getsize(aa); \n            arrayobj_release(aa);\n            return size;\n        }\n        return -1; \n    }\n\n    void fill_long_vector(std::vector<long> &array, const min::atom &atom) {\n        auto name_max = max::atom_getsym(&atom); \n        max::t_atomarray* aa = arrayobj_findregistered_retain(name_max);\n        if (aa) {\n            if (array.size() != 0) {\n                throw \"array not empty\";\n            }\n            max::t_atomarray* clone = (max::t_atomarray*)object_clone((max::t_object*)aa); // CLONE, do not potentially modify upstream data\n            max::t_atom atom_elt; \n            for (long i = 0; i < max::atomarray_getsize(clone); i++) {\n                max::atomarray_getindex(clone, i, &atom_elt);                \n                array.emplace_back(max::atom_getlong(&atom_elt)); \n            }\n            arrayobj_release(aa);\n            max::atomarray_clear(clone);\n            max::object_free(clone);\n        } else {\n            throw \"could not create array\"; \n        }\n    }\n\n    void fill_float_vector(std::vector<float> &array, const min::atom &atom) {\n        auto name_max = max::atom_getsym(&atom); \n        max::t_atomarray* aa = arrayobj_findregistered_retain(name_max);\n        if (aa) {\n            if (array.size() != 0) {\n                throw \"array not empty\";\n            }\n            max::t_atomarray* clone = (max::t_atomarray*)object_clone((max::t_object*)aa); // CLONE, do not potentially modify upstream data\n            max::t_atom atom_elt; \n            for (long i = 0; i < max::atomarray_getsize(clone); i++) {\n                max::atomarray_getindex(clone, i, &atom_elt);                \n                array.emplace_back(max::atom_getfloat(&atom_elt)); \n            }\n            arrayobj_release(aa);\n            max::atomarray_clear(clone);\n            max::object_free(clone);\n        } else {\n            throw \"could not create array\"; \n        }\n    }\n\n    StaticBuffer<float> static_buffer_from_array(const min::atom &atom) {\n        auto name_max = max::atom_getsym(&atom); \n        max::t_atomarray* aa = arrayobj_findregistered_retain(name_max);\n        if (aa) {\n            max::t_atomarray* clone = (max::t_atomarray*)object_clone((max::t_object*)aa); // CLONE, do not potentially modify upstream data\n            long array_size = max::atomarray_getsize(clone);\n            StaticBuffer<float> out_buffer(1, array_size);\n            max::t_atom atom_elt; \n            for (long i = 0; i < max::atomarray_getsize(clone); i++) {\n                max::atomarray_getindex(clone, i, &atom_elt);                \n                out_buffer.put(max::atom_getfloat(&atom_elt), 0, i); \n            }\n            arrayobj_release(aa);\n            max::atomarray_clear(clone);\n            max::object_free(clone);\n            return out_buffer;\n        } else {\n            throw std::string(\"could not create array\"); \n        }\n    }\n\n}"
  },
  {
    "path": "src/frontend/maxmsp/shared/buffer_tools.h",
    "content": "#pragma once\n#include \"array_tools.h\"\n#include \"../../../backend/backend.h\"\n#include \"../../../shared/static_buffer.h\"\n#include \"c74_min.h\"\n\nclass BufferManager {\n\n    std::vector<std::unique_ptr<c74::min::buffer_reference>> m_max_buffers; \n    std::vector<std::string> buffer_attributes;\n    bool buffer_track = false;\n\npublic: \n\n    BufferManager();\n\n    void init_buffer_list(Backend *backend, c74::min::object_base* object);\n    void link_attribute_to_buffer(std::string buffer_name, c74::min::symbol target_max_buffer);\n    void append_if_buffer_element(Backend *model, Backend::BufferMap &buffers, c74::min::symbol target_max_buffer, std::string attribute_name, int index);\n    static int get_buffer_index(std::vector<std::string> buffer_attributes, std::string buffer_name);\n\n    void set_buffer_tracking(bool buffer_tracking) { buffer_track = buffer_tracking; }\n\n    int bind_buffer_attribute(Backend* backend, std::string element, c74::min::object_base* object);\n    int unbind_buffer_attribute(Backend* backend, std::string element, c74::min::object_base* object);\n    int modify_buffer_attribute(Backend* backend, std::string element, c74::min::object_base* object);\n\n    template <typename data_type>\n    StaticBuffer<data_type> static_buffer_from_name(std::string buffer_name);\n    template <typename data_type>\n    static StaticBuffer<data_type> static_buffer_from_max_buffer(c74::min::buffer_reference* max_buffer);\n\n    c74::min::function get_notification_callback(std::string element, c74::min::object_base* object, Backend* backend);\n\n    using iterator = std::vector<std::unique_ptr<c74::min::buffer_reference>>::iterator;\n    auto begin() { return m_max_buffers.begin(); }\n    auto end() { return m_max_buffers.end(); }\n\n    std::string string_id() {\n      std::stringstream str_id; \n      str_id << this; \n      return str_id.str();\n    }\n    \n};\n\n\nBufferManager::BufferManager() {\n}\n\n\nint BufferManager::get_buffer_index(std::vector<std::string> buffer_attributes, std::string buffer_name) {\n  int buffer_idx = -1;\n  for (int i = 0; i < buffer_attributes.size(); i++) {\n    if (buffer_name == buffer_attributes[i]) {\n      buffer_idx = i;\n      break;\n    }\n  }\n  return buffer_idx;\n}\n\nvoid BufferManager::link_attribute_to_buffer(std::string buffer_name, c74::min::symbol target_max_buffer) {\n  int buffer_index = get_buffer_index(buffer_attributes, buffer_name);\n  if (buffer_index > -1) {\n    m_max_buffers[buffer_index].get()->set(target_max_buffer);\n  } else {\n    throw \"could not link\" + buffer_name + \"to max buffer\" + target_max_buffer.c_str();\n  }\n}\n\nvoid BufferManager::append_if_buffer_element(Backend *model, Backend::BufferMap &buffers, c74::min::symbol target_max_buffer, std::string attribute_name, int index)\n{\n    if (model->is_buffer_element_of_attribute(attribute_name, index)) {\n      auto buffer_name = model->get_buffer_name(attribute_name, index);\n      link_attribute_to_buffer(buffer_name, target_max_buffer);\n      buffers[buffer_name] = static_buffer_from_name<float>(buffer_name);\n    } else if (model->is_tensor_element_of_attribute(attribute_name, index)) {\n      try {\n        auto buffer_name = model->get_buffer_name(attribute_name, index);\n        buffers[buffer_name] = ArrayTools::static_buffer_from_array(target_max_buffer);\n      } catch (std::string &e) {\n        throw \"could not populate element \" + std::to_string(index) + \" of \" + attribute_name + \". Got : \"  + e; \n      }\n    }\n}\ntemplate <typename data_type>\nStaticBuffer<data_type> BufferManager::static_buffer_from_name(std::string buffer_name) {\n\n  int buffer_idx = get_buffer_index(buffer_attributes, buffer_name);\n  if ((buffer_idx  == -1) || (buffer_idx > m_max_buffers.size())) {\n    throw std::string(\"could not retrieve buffer from name\") + buffer_name;\n  }\n  c74::min::buffer_reference *buffer_ref = m_max_buffers[buffer_idx].get();\n  try {\n    return static_buffer_from_max_buffer<data_type>(buffer_ref);\n  } catch (std::string &e) {\n    // cerr << \"could not link buffer \" << buffer_name;\n    // if (buffer_ref->name() != symbol()) {\n    //   cerr << \"; buffer name \" << buffer_ref->name() << \" seems to be invalid.\";\n    // } \n    // cerr << endl;\n    throw e;\n  }\n}\n\ntemplate <typename data_type>\nStaticBuffer<data_type> BufferManager::static_buffer_from_max_buffer(c74::min::buffer_reference* max_buffer) {\n    c74::min::buffer_lock<false> b(*max_buffer);\n    const size_t n_channels = b.channel_count();\n    const size_t n_samples = b.frame_count();\n    const double sample_rate = b.samplerate();\n    if (b.valid()) {\n        auto data = StaticBuffer<data_type>(n_channels, n_samples, sample_rate);\n        for (auto c = 0; c < n_channels; c++) {\n            for (auto t = 0; t < n_samples; t++) {\n                data.put(b.lookup(t, c), c, t);\n            }\n        }\n        return data;\n    } else {\n        throw \"given max buffer is invalid.\";\n    }\n};\n\n\nint BufferManager::bind_buffer_attribute(Backend *backend, std::string element, c74::min::object_base* object) {\nif (this->buffer_track) {\n    std::string method = \"print\";\n    // c74::min::atoms args = {string_id(), \"cout\", element + \" modified\"};\n    // object->try_call(method, args);\n    int buffer_idx = get_buffer_index(buffer_attributes, element);\n    if (buffer_idx == -1) { return -1; }\n    auto buffer_name = m_max_buffers[buffer_idx].get()->name();\n    m_max_buffers[buffer_idx].release();\n    m_max_buffers[buffer_idx] = std::make_unique<c74::min::buffer_reference>(object, get_notification_callback(element, object, backend), false);\n    m_max_buffers[buffer_idx].get()->set(buffer_name);\n\n    // if (current_buffer_ref != bufferRef) {\n    //   m_max_buffers[buffer_idx].release();\n    //   m_max_buffers[buffer_idx] = std::unique_ptr<c74::min::buffer_reference>(bufferRef);\n    // }\n    try {\n      auto buffer = static_buffer_from_max_buffer<float>(m_max_buffers[buffer_idx].get());\n      int res = backend->update_buffer(element, buffer);\n      return res;\n    } catch (...) {\n      c74::min::atoms args = {string_id(), \"cerr\", \"failed to bind buffer \" + element};\n      object->try_call(method, args);\n    }\n  }\n  return -1;\n}\n\n\nint BufferManager::unbind_buffer_attribute(Backend *backend, std::string element, c74::min::object_base* object) {\n  if (this->buffer_track) {\n    std::string method = \"print\";\n    // c74::min::atoms args = {string_id(), \"cout\", element + \" unbounded\"};\n    // object->try_call(method, args);\n    // get buffer\n    int buffer_idx = get_buffer_index(buffer_attributes, element);\n    if (buffer_idx == -1) { return -1; }\n    auto current_buffer_ref = m_max_buffers[buffer_idx].get();\n    // if (current_buffer_ref != bufferRef) {\n    //   m_max_buffers[buffer_idx].release();\n    //   m_max_buffers[buffer_idx] = std::unique_ptr<c74::min::buffer_reference>(bufferRef);\n    // }\n    try {\n      int res = backend->reset_buffer(element);\n      return res;\n    } catch (...) {\n      c74::min::atoms args = {string_id(), \"cerr\", \"failed to unbind buffer \" + element};\n      object->try_call(method, args);\n    }\n  }\n  return -1;\n}\n\nint BufferManager::modify_buffer_attribute(Backend *backend, std::string element, c74::min::object_base* object) {\n  if (this->buffer_track) {\n    std::string method = \"print\";\n    // c74::min::atoms args = {string_id(), \"cout\", element + \" modified\"};\n    // object->try_call(method, args);\n    int buffer_idx = get_buffer_index(buffer_attributes, element);\n    if (buffer_idx == -1) { return -1; }\n    auto buffer_name = m_max_buffers[buffer_idx].get()->name();\n    m_max_buffers[buffer_idx].release();\n    m_max_buffers[buffer_idx] = std::make_unique<c74::min::buffer_reference>(object, get_notification_callback(element, object, backend), false);\n    m_max_buffers[buffer_idx].get()->set(buffer_name);\n\n    // if (current_buffer_ref != bufferRef) {\n    //   m_max_buffers[buffer_idx].release();\n    //   m_max_buffers[buffer_idx] = std::unique_ptr<c74::min::buffer_reference>(bufferRef);\n    // }\n    try {\n      auto buffer = static_buffer_from_max_buffer<float>(m_max_buffers[buffer_idx].get());\n      int res = backend->update_buffer(element, buffer);\n      return res;\n    } catch (...) {\n      c74::min::atoms args = {string_id(), \"cerr\", \"failed to bind buffer \" + element};\n      object->try_call(method, args);\n    }\n  }\n  return -1;\n}\n\nc74::min::function BufferManager::get_notification_callback(std::string element, c74::min::object_base* object, Backend* backend) {\n  c74::min::function bufferCallback = \n        [this, object, element, backend](const c74::min::atoms& args, const int inlet) -> c74::min::atoms {\n            int out;\n            c74::min::symbol buffer_message = args[0];\n            // cout << \"callback\" << buffer_message << \"for element \" << element << \"called\" << endl;\n            if (buffer_message == \"binding\") {\n              out = this->bind_buffer_attribute(backend, element, object);\n            } else if (buffer_message == \"unbinding\") {\n              out = this->unbind_buffer_attribute(backend, element, object);\n            } else if (buffer_message == \"modified\") {\n              out = this->modify_buffer_attribute(backend, element, object);\n            } else {\n            //   cerr << \"got unsupported buffer notification \" << args << \"for element\" << element << endl;\n            }\n            if (out != 0) {\n            //   cerr << \"problem setting buffer \" << element << endl;\n            }\n            return std::vector<c74::min::atom>();\n        };\n    return bufferCallback;\n}\n\nvoid BufferManager::init_buffer_list(Backend *backend, c74::min::object_base* object) {\n    // clear previous buffers\n    m_max_buffers.clear();\n    buffer_attributes.clear();\n    // init model buffers\n    std::vector<std::string> model_buffers;\n    try {\n      model_buffers = backend->get_buffer_attributes();\n    } catch (std::exception &e) {\n        throw std::string(\"could not retrieve buffers from model. Caught error : \") + e.what();\n    }\n    // create buffer references for each of model buffers\n    for (auto & element : model_buffers) {\n      std::unique_ptr<c74::min::buffer_reference> bufferRef;\n      \n      // m_max_buffer_callbacks.push_back(bufferCallback);\n      // add buffer id to buffer_attributes\n      buffer_attributes.push_back(element);\n      // add pointer to buffer_reference\n      m_max_buffers.push_back(std::make_unique<c74::min::buffer_reference>(object, get_notification_callback(element, object, backend), false));\n    }\n}\n\n\nvoid fill_with_zero(c74::min::audio_bundle output) {\n  for (int c(0); c < output.channel_count(); c++) {\n    auto out = output.samples(c);\n    for (int i(0); i < output.frame_count(); i++) {\n      out[i] = 0.;\n    }\n  }\n}\n"
  },
  {
    "path": "src/frontend/maxmsp/shared/dict_utils.h",
    "content": "#pragma once\n#include \"c74_min.h\"\n#include <nlohmann/json.hpp>\n#include \"../../../backend/backend.h\"\n\nnamespace nn_tools {\n\n    namespace min = c74::min; \n    namespace max = c74::max;\n\n    void append_to_dictionary(max::t_dictionary* d, max::t_symbol* key, max::t_dictionary* value) {\n        auto parsed_value  = reinterpret_cast<max::t_object*>(value);\n        max::dictionary_appenddictionary(d, key, parsed_value);\n    }\n\n    min::dict dict_from_model_info(const ModelInfo & info) {\n        auto str = std::stringstream();\n        auto new_dict = max::dictionary_new();\n\n        // // append methods\n        std::vector<std::string> method_names {};\n        auto method_dict = max::dictionary_new();\n        for (auto method_pair: info.method_properties) {\n            method_names.push_back(method_pair.first);\n            auto current_method_dict = max::dictionary_new();\n            auto method_props = method_pair.second;\n            max::dictionary_appendlong(current_method_dict, min::symbol(\"channels_in\"), (long)method_props.channels_in);\n            max::dictionary_appendlong(current_method_dict, min::symbol(\"channels_out\"), (long)method_props.channels_out);\n            max::dictionary_appendlong(current_method_dict, min::symbol(\"ratio_in\"), (long)method_props.ratio_in);\n            max::dictionary_appendlong(current_method_dict, min::symbol(\"ratio_out\"), (long)method_props.ratio_out);\n            append_to_dictionary(method_dict, min::symbol(method_pair.first), current_method_dict);\n        }\n        append_to_dictionary(new_dict, min::symbol(\"methods\"), method_dict);\n\n        // // append methods\n        std::vector<std::string> attribute_names {};\n        auto attribute_dict = max::dictionary_new();\n        for (auto attribute_pair: info.attribute_properties) {\n            attribute_names.push_back(attribute_pair.first);\n            auto current_attribute_dict = max::dictionary_new();\n            auto attr_types = attribute_pair.second.attribute_types;\n            min::atoms attr_types_atoms(attr_types.size());\n            std::transform(attr_types.begin(), attr_types.end(), attr_types_atoms.begin(),\n                [](const std::string& str) {\n                return min::atom(str); \n            });\n            max::dictionary_appendatoms(current_attribute_dict, min::symbol(\"attribute_type\"), attr_types_atoms.size(), attr_types_atoms.data());\n            append_to_dictionary(attribute_dict, min::symbol(attribute_pair.first), current_attribute_dict);\n        }\n        append_to_dictionary(new_dict, min::symbol(\"attributes\"), attribute_dict);\n        auto out_dict = min::dict(new_dict);\n        return out_dict;\n    }\n    \n    void json_walk(max::t_dictionary* dict, nlohmann::json json) {\n        for (auto& el : json.items()) {\n            // std::cout << \"key: \" << el.key() << \", value:\" << el.value() << '\\n';\n            min::symbol key = el.key(); \n            auto val = el.value();\n            if (val.is_null()) {\n            } else if ((json.is_boolean())||(json.is_number_integer())||(json.is_number_unsigned())) {\n                max::dictionary_appendlong(dict, key, val.get<long>());\n            } else if (val.is_number_float()) {\n                max::dictionary_appendfloat(dict, key, val.get<float>());\n            } else if (val.is_string()) {\n                max::dictionary_appendsym(dict, key, min::symbol(val.get<std::string>())); \n            } else if (val.is_array()) {\n                std::vector<min::atom> atoms; \n                for (const auto& v: val){\n                    if ((v.is_boolean())||(v.is_number_integer())||(v.is_number_unsigned())) {\n                        atoms.emplace_back(v.get<long>());\n                    } else if (v.is_number_float()) {\n                        atoms.emplace_back(v.get<float>());\n                    } else if (v.is_string()) {\n                        atoms.emplace_back(v.get<std::string>());\n                    }\n                }\n                max::dictionary_appendatoms(dict, key, atoms.size(), atoms.data());\n            } else if (val.is_object()) {\n                auto sub_dict = max::dictionary_new();\n                json_walk(sub_dict, val);\n                append_to_dictionary(dict, key, sub_dict);\n            } else {\n                std::cerr << \"Unknown type\" << std::endl;\n            }\n        }\n                \n    }\n\n    void fill_dict_with_json(min::dict* dict_to_fill, nlohmann::json json) {\n        auto global_dict = max::dictionary_new();\n        json_walk(global_dict, json);\n        auto min_dict = min::dict(global_dict);\n        dict_to_fill->copyunique(min_dict);\n    }\n}\n\n"
  },
  {
    "path": "src/frontend/maxmsp/shared/max_model_download.h",
    "content": "#pragma once\n\n#include <cstdio>\n#include <filesystem>\n#include <iostream>\n#include <string>\n#include <curl/curl.h>\n#include <nlohmann/json.hpp>\n#include \"c74_min.h\"\n#include <thread>\n#include \"dict_utils.h\"\n#include \"../../../shared/model_download.h\"\n\n\n\n#ifndef MAX_DOWNLOADS\n#define MAX_DOWNLOADS 2\n#endif\n\n\n\nnamespace max = c74::max;\nnamespace min = c74::min;\n\n\nclass MaxModelDownloader: public ModelDownloader {\n    c74::min::object_base* d_parent;\n\npublic:\n    MaxModelDownloader(c74::min::object_base* obj);\n    MaxModelDownloader(c74::min::object_base* obj, std::string external_name); \n    MaxModelDownloader(c74::min::object_base* obj, fs::path download_location); \n\n    void fill_dict(void* dict_to_fill);\n    void print_to_parent(const std::string &message, const std::string &canal);\n    fs::path cert_path_from_path(fs::path path) {\n        #if defined(_WIN32) || defined(_WIN64)\n            std::string perm_path = (path / \"..\" / \"..\" / \"support\" / \"cacert.pem\").string();\n            find_and_replace_char(perm_path, '/', '\\\\');\n        #elif defined(__APPLE__) || defined(__MACH__)\n            std::string perm_path = path / \"Contents\" / \"MacOS\" / \"cert.pem\";\n        #else\n            std::string perm_path = (path / \"..\").string();\n        #endif\n        return perm_path;\n    }\n\n    void set_model_directory(const std::string &external_path) {\n        d_path = fs::absolute(fs::path(external_path) / \"..\" / \"..\" / \"models\");\n        if (!fs::exists(d_path)) {\n            fs::create_directories(d_path);\n        }\n    }\n};\n\n\nMaxModelDownloader::MaxModelDownloader(c74::min::object_base* obj): d_parent(obj) {\n    // d_path = d_path / \"..\" / \"nn_tilde\" / \"models\";\n    min::path path = min::path(\"nn~\", min::path::filetype::external); \n    std::string path_str = path;\n    if (path) {\n        set_model_directory(path_str);\n        d_cert_path = cert_path_from_path(fs::path(path_str));\n    }\n}\n\nMaxModelDownloader::MaxModelDownloader(c74::min::object_base* obj, std::string external_name): d_parent(obj) {\n    min::path path = min::path(external_name, min::path::filetype::external);\n    std::string path_str = path;\n    fs::path fs_path(path_str);\n    if (path) {\n        d_cert_path = cert_path_from_path(fs::path(path_str));\n        set_model_directory(path_str);\n    }\n}\n\nMaxModelDownloader::MaxModelDownloader(c74::min::object_base* obj, fs::path download_location): ModelDownloader(download_location), d_parent(obj) {\n    d_path = download_location;\n}\n\n\nvoid MaxModelDownloader::print_to_parent(const std::string &message, const std::string &canal) {\n    std::string method = \"print\";\n    min::atoms args = {string_id(), canal, message};\n    d_parent->try_call(method, args);\n}\n\nvoid MaxModelDownloader::fill_dict(void* dict_to_fill) {\n    if (dict_to_fill == nullptr) {\n        throw \"dict is empty\";\n    }\n    min::dict* max_dict = static_cast<min::dict*>(dict_to_fill);\n    if (!max_dict->valid()) {\n        throw \"dict is invalid\";\n    }\n    auto json_models = d_available_models; \n    nn_tools::fill_dict_with_json(max_dict, json_models);     \n}"
  },
  {
    "path": "src/frontend/maxmsp/shared/nn_base.h",
    "content": "\n#include <chrono>\n#include <semaphore>\n#include <string>\n#include <thread>\n#include <vector>\n#include <algorithm>\n#include \"c74_min.h\"\n#include \"max_model_download.h\"\n\n#ifndef CLASS_FLAG_OBJECTAWARE\n#define CLASS_FLAG_OBJECTAWARE (0x10000000L) ///< this object can work with 'array' and 'string' objects\n#endif\n\n#include \"buffer_tools.h\"\n#include \"dict_utils.h\"\n#include \"../../../backend/backend.h\"\n#include \"../../../shared/circular_buffer.h\"\n#include \"../../../shared/static_buffer.h\"\n\n#ifndef VERSION\n#define VERSION \"UNDEFINED\"\n#endif\n\n#ifndef REFRESH_THREAD_INTERVAL\n#define REFRESH_THREAD_INTERVAL 100\n#endif\n\n#ifndef DEFAULT_BUFFER_SIZE\n#define DEFAULT_BUFFER_SIZE 4096\n#endif\n\nusing namespace c74::min;\n\n#define DEBUG 0\n#ifdef DEBUG\n    #if DEBUG == 1\n        #define DEBUG_PRINT(fmt, ...) printf(\"DEBUG: \" fmt \"\\n\", ##__VA_ARGS__)\n    #else\n        #define DEBUG_PRINT(fmt, ...)\n    #endif\n#else\n    #define DEBUG_PRINT(fmt, ...)\n#endif\n\n\nunsigned power_ceil(unsigned x) {\n  if (x <= 1)\n    return 1;\n  int power = 2;\n  x--;\n  while (x >>= 1)\n    power <<= 1;\n  return power;\n}\n\ntemplate <typename nn_name, typename op_type> \nvoid dumb(nn_name* obj) {\n\n}\n\ntemplate <typename nn_name, typename op_type = vector_operator<>> \nclass nn_base : public object<nn_name>, public op_type {\npublic:\n  \n  using BufferList = std::map<std::string, StaticBuffer<float>>;\n\n  nn_base(const atoms &args = {});\n  ~nn_base<nn_name, op_type>();\n  static std::string get_external_name() { return \"\"; } \n\n  // INLETS OUTLETS\n  std::vector<std::unique_ptr<inlet<>>> m_inlets;\n  std::vector<std::unique_ptr<outlet<>>> m_outlets;\n  \n  virtual void init_model(); \n  virtual void init_downloader(); \n  virtual void init_inputs_and_outputs(const atoms& args); \n  virtual void init_inlets_and_outlets();\n  virtual void init_buffers();\n  virtual void init_process();\n  virtual int get_sample_rate() = 0;\n  virtual void init_external(const atoms &args) {\n    init_model(); \n    init_downloader(); \n    if (!args.size()) { return; } \n    init_inputs_and_outputs(args);\n    init_inlets_and_outlets();\n    if (init_buffers_at_init()) {\n      init_buffers(); \n    } else {\n      wait_for_buffer_reset = true; \n    }\n    init_process();\n  } \n  virtual bool init_buffers_at_init() { return true; }\n\n  // BACKEND RELATED MEMBERS\n  std::unique_ptr<Backend> m_model;\n  bool m_is_backend_init = false;\n  bool m_ready = false;\n  std::string m_method;\n  std::vector<std::string> settable_attributes;\n  c74::min::path m_path;\n  int n_inlets, m_model_in, m_in_ratio, n_outlets, m_model_out, m_out_ratio, m_higher_ratio, n_batches;\n  bool has_settable_attribute(std::string attribute);\n  bool is_valid_print_key(std::string string);\n  bool buffer_initialised = false; \n  bool wait_for_buffer_reset = false; \n  bool had_buffer_reset = true; \n  bool can_perform() {\n    if (m_model->is_loaded()) {\n      if (m_model->has_method(m_method)) {\n        return true;\n      }\n    }\n    return false;\n  }\n  void update_model(const std::string &model);\n  virtual void load_model(const std::string &model);\n  virtual void set_method(std::string method);  \n  virtual void update_method(std::string method = \"\"); \n  path to_model_path(std::string model_path);\n  \n  // BUFFER ATTRIBUTES MANAGER\n  BufferManager m_buffer_manager;\n\n  // BUFFER RELATED MEMBERS\n  int m_buffer_size;\n  int m_buffer_in, m_buffer_out; \n  std::unique_ptr<circular_buffer<double, float>[]> m_in_buffer;\n  std::unique_ptr<circular_buffer<float, double>[]> m_out_buffer;\n  std::vector<std::unique_ptr<float[]>> m_in_model, m_out_model;\n\n  // AUDIO PERFORM\n  bool m_force_refresh, m_use_thread, m_should_stop_perform_thread;\n  std::binary_semaphore m_data_available_lock, m_result_available_lock;\n  std::unique_ptr<std::thread> m_compute_thread;\n\n  void operator()(audio_bundle input, audio_bundle output);\n  virtual void perform(audio_bundle input, audio_bundle output) { }\n\n  // FLUX\n  logger cout;\n  logger cerr;\n  logger cwarn;\n\n  // HELPERS\n  virtual void dump_object();\n  void print_to_cout(std::string &message);\n  void print_to_cerr(std::string &message);\n\n  // DOWNLOAD RELATED ATTRIBUTES\n  std::unique_ptr<MaxModelDownloader> m_downloader;\n  void dump_available_models(); \n\n  // ONLY FOR DOCUMENTATION\n  argument<symbol> path_arg{this, \"model path\",\n                            \"Absolute path to the pretrained model.\"};\n  argument<symbol> method_arg{this, \"method\",\n                              \"Name of the method to call during synthesis.\"};\n  argument<int> buffer_arg{\n      this, \"buffer size\",\n      \"Size of the internal buffer (can't be lower than the method's ratio).\"};\n\n  // ENABLE / DISABLE ATTRIBUTE\n  attribute<bool> enable{this, \"enable\", true,\n                         description{\"Enable / disable tensor computation\"}};\n\n  // ENABLE / DISABLE ATTRIBUTE\n  attribute<bool> gpu{this, \"gpu\", false,\n                      description{\"Enable / disable gpu usage when available\"},\n                      setter{[this](const c74::min::atoms &args,\n                                    const int inlet) -> c74::min::atoms {\n                        if (m_is_backend_init)\n                          m_model->use_gpu(bool(args[0]));\n                        return args;\n                      }}};\n\n  // TRACK BUFFER ATTRIBUTE\n  attribute<bool> track_buffers{this, \n                                \"track_buffers\", \n                                false, \n                                description{\"tracks buffer change for buffer attributes\"}, \n                                setter{\n                                  MIN_FUNCTION{\n                                    bool enable_buffer_tracking = args[0];\n                                    if (m_is_backend_init) {\n                                      this->m_buffer_manager.set_buffer_tracking(enable_buffer_tracking);\n                                    }\n                                    return args;\n                                  }}};\n\n\n  // BOOT STAMP\n  // message<> maxclass_setup;  \n  message<> print {\n    this, \"print\", \n    MIN_FUNCTION {\n      bool is_valid = is_valid_print_key(args[0]);\n      if (!is_valid) {\n        return {};\n      }\n      DEBUG_PRINT(\"asked to print %s on %s\", std::string(args[2]).c_str(), std::string(args[1]).c_str());\n      if (args[1] == \"cout\") {\n        cout << args[2] << endl;\n      } else if (args[1] == \"cerr\") {\n        cerr << args[2] << endl;\n      } else if (args[1] == \"cwarn\") {\n        cwarn << args[2] << endl;\n      }\n      return {};\n    }\n  };\n\n  message<> buffer_notify{\n      this, \"notify\",\n        MIN_FUNCTION {\n          return buffer_reference::handle_notification<BufferManager::iterator>(\n            this, \n            args, \n            m_buffer_manager.begin(), \n            m_buffer_manager.end()\n          );\n      }};\n\n  message<> dump_callback{\n      this, \"dump\", \n        description{\"dumps model information to console\"},\n        MIN_FUNCTION {\n          this->dump_object();  \n          return {};\n        }};\n\n  message <> get_available_models_callback{\n    this, \"print_available_models\", \n    description{\"print available models to console\"}, \n    MIN_FUNCTION {\n        this->dump_available_models(); \n        return {};\n    }};\n\n  message <> download_models {\n    this, \"download\", \n    description{\"download a model from IRCAM Forum API\"}, \n    MIN_FUNCTION {\n      std::string model_card, optional_name;\n      if (args.size() == 0) {\n        cerr << \"please provide a model card (print downloadable models with get_available_models messages)\" << endl;\n      } else if (args.size() == 1) {\n        min::symbol model_card_s = args[0];\n        model_card = std::string(model_card_s.c_str());\n        optional_name = \"\";\n      } else {\n        min::symbol model_card_s = args[0];\n        min::symbol optional_name_s = args[1];\n        model_card = std::string(model_card_s.c_str());\n        optional_name = std::string(optional_name_s.c_str()); \n      }\n      try {\n        if (this->m_downloader.get()->is_ready())\n          this->m_downloader.get()->download(model_card, optional_name);\n      } catch (std::string &e) {\n        cerr << e << endl;\n      }\n      return {}; \n    }\n  };\n\n  message <> update_method_fn {\n    this, \"method\", \n    description{\"set the current method of model\"}, \n    MIN_FUNCTION {\n      if (args.size() == 0) {\n        cerr << \"please provide a method\" << endl; \n      }\n      std::string method = args[0]; \n      try {\n        if (m_model->is_loaded()) {\n          if (!m_model->has_method(method)) {\n            cerr << \"current model does not have method \" << method << endl; \n            return args; \n          }\n        }\n        this->set_method(method);\n        this->wait_for_buffer_reset = true; \n      } catch (std::string &e) {\n        cerr << e << endl;   \n      }\n      return {};\n    }\n  };\n  \n  message <> delete_models {\n    this, \"delete\", \n    description{\"delete a model downloaded from IRCAM Forum API\"}, \n    MIN_FUNCTION {\n      if (args.size() == 0) {\n        cerr << \"please provide a model to delete\" << endl;\n      }\n      std::string model_card = args[0];\n      try {\n        if (this->m_downloader.get()->is_ready())\n          this->m_downloader.get()->remove(model_card);\n      } catch (std::string &e) {\n        cerr << e << endl;\n      }\n      return {}; \n  }};\n\n  message<> anything{\n      this, \"anything\", \"callback for attributes\",\n      [this](const c74::min::atoms &args, const int inlet) -> c74::min::atoms {\n        symbol attribute_name = args[0];\n        if (attribute_name == \"reload\") {\n          m_model->reload();\n        } else if (attribute_name == \"load\") {\n          if (args.size() < 2) {\n            cerr << \"a model path must be given along the model message\" << endl; \n          } else {\n            std::string model_path = args[1];\n            update_model(model_path); \n          }\n          return {}; \n        } else if (attribute_name == \"get_attributes\") {\n          for (std::string attr : settable_attributes) {\n            cout << attr << endl;\n          }\n          return {};\n        } else if (attribute_name == \"get_methods\") {\n          for (std::string method : m_model->get_available_methods())\n            cout << method << endl;\n          return {};\n        } else if (attribute_name == \"get\") {\n          if (args.size() < 2) {\n            cerr << \"get must be given an attribute name\" << endl;\n            return {};\n          }\n          attribute_name = args[1];\n          if (m_model->has_settable_attribute(attribute_name)) {\n            try {\n              cout << attribute_name << \": \"\n                 << m_model->get_attribute_as_string(attribute_name) << endl;\n            } catch (std::string& e) {\n              cout << e << endl; \n            }\n          } else {\n            cerr << \"no attribute \" << attribute_name << \" found in model\"\n                 << endl;\n          }\n          return {};\n        } else if (attribute_name == \"set\") {\n          if (args.size() < 3) {\n            cerr << \"set must be given an attribute name and corresponding \"\n                    \"arguments\"\n                 << endl;\n            return {};\n          }\n          attribute_name = args[1];\n          std::vector<std::string> attribute_args;\n\n          BufferList buffers;\n          if (has_settable_attribute(attribute_name)) {\n\n            for (int i = 2; i < args.size(); i++) {\n              // get if argument is buffer\n              attribute_args.push_back(args[i]);\n              try {\n                m_buffer_manager.append_if_buffer_element(m_model.get(), buffers, args[i], attribute_name, i - 2);\n              } catch (std::string &message) {\n                cerr << message << endl; \n                return args; \n              }\n            }\n            try {\n              m_model->set_attribute(attribute_name, attribute_args, buffers);\n            } catch (std::string message) {\n              cerr << message << endl;\n            }\n          } else {\n            cerr << \"model does not have attribute \" << attribute_name << endl;\n          }\n        } else {\n          cerr << \"no corresponding method for \" << attribute_name << endl;\n        }\n        return {};\n      }};\n};\n\n\ntemplate <typename nn_name, typename op_type>\nbool nn_base<nn_name, op_type>::is_valid_print_key(std::string id_string) {\n  if (id_string == m_buffer_manager.string_id()) {\n    return true;\n  } else if (id_string == m_downloader.get()->string_id()) {\n    return true;\n  }\n  return false;\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::init_model() {\n  m_model = std::make_unique<Backend>();\n  m_is_backend_init = true;\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::init_downloader() {\n  try {\n    m_downloader = std::make_unique<MaxModelDownloader>(this, nn_name::get_external_name()); \n  } catch (std::string &e) {\n    cerr << \"failed to init downloader. caught exception : \" << e << endl;\n  } catch (std::exception &e) {\n    cerr << \"failed to init downloader. caught exception : \" << e.what() << endl;\n  }\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::init_inputs_and_outputs(const atoms &args) {\n  DEBUG_PRINT(\"parsing inputs & outputs...\");\n  bool empty_mode; \n  if (args.size() > 0) { // ONE ARGUMENT IS GIVEN\n    auto model_path = std::string(args[0]);\n    if (model_path == \"void\") {\n      empty_mode = true;    \n    } else {\n      try {\n        m_path = to_model_path(model_path);\n      } catch (std::string &e) {\n        error(e);\n      }\n      empty_mode = false;\n    }\n  }\n  DEBUG_PRINT(\"empty mode set to %d\", empty_mode);\n  \n  if (empty_mode) {\n    if (args.size() > 1) { // FOUR ARGUMENTS ARE GIVEN\n      auto inlets_arg = int(args[1]);\n      if (inlets_arg >= 1) { n_inlets = inlets_arg; }\n    }\n    if (args.size() > 2) { // FIVE ARGUMENTS ARE GIVEN\n      auto outlets_arg = int(args[2]);\n      if (outlets_arg >= 1) { n_outlets = outlets_arg; }\n    } \n    if (args.size() > 3) { // THREE ARGUMENTS ARE GIVEN\n      m_buffer_size = int(args[3]);\n    }\n    if (n_outlets == -1) { n_outlets = 1;}\n    DEBUG_PRINT(\"empty mode\");\n    DEBUG_PRINT(\"%d inlets\", n_inlets);\n    DEBUG_PRINT(\"%d outlets\", n_outlets);\n  } else {\n    if (args.size() > 1) { // TWO ARGUMENTS ARE GIVEN\n      m_method = std::string(args[1]);\n    }\n    if (args.size() > 2) { // THREE ARGUMENTS ARE GIVEN\n      m_buffer_size = int(args[2]);\n    }\n    if (args.size() > 3) { // FOUR ARGUMENTS ARE GIVEN\n      auto inlets_arg = int(args[3]);\n      if (inlets_arg >= 1) { n_inlets = inlets_arg; }\n    }\n    if (args.size() > 4) { // FIVE ARGUMENTS ARE GIVEN\n      auto outlets_arg = int(args[4]);\n      if (outlets_arg >= 1) { n_outlets = outlets_arg; }\n    }\n    DEBUG_PRINT(\"loading model : %s\", std::string(m_path).c_str()); \n    DEBUG_PRINT(\"%d inlets\", n_inlets);\n    DEBUG_PRINT(\"%d outlets\", n_outlets);\n    load_model(m_path);\n    if ((!m_method.empty()) && (m_model->is_loaded()))\n      update_method(); \n    if ((n_inlets == -1) || (n_outlets == -1)) {\n      error(\"could not initialise object\");\n    }\n  }\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::init_inlets_and_outlets() {\n  for (int i(0); i < n_inlets; i++) {\n    std::string input_label = \"\";\n    try {\n      input_label = m_model->get_model()\n                        .attr(m_method + \"_input_labels\")\n                        .toList()\n                        .get(i)\n                        .toStringRef();\n    } catch (...) {\n      input_label = \"(signal) model input \" + std::to_string(i);\n    }\n    m_inlets.push_back(std::make_unique<inlet<>>(this, input_label, \"signal\"));\n  }\n\n  for (int i(0); i < n_outlets; i++) {\n    std::string output_label = \"\";\n    try {\n      output_label = m_model->get_model()\n                         .attr(m_method + \"_output_labels\")\n                         .toList()\n                         .get(i)\n                         .toStringRef();\n    } catch (...) {\n      output_label = \"(signal) model output \" + std::to_string(i);\n    }\n    m_outlets.push_back(\n        std::make_unique<outlet<>>(this, output_label, \"signal\"));\n  }\n\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::init_process() {\n  if (m_buffer_size == 0) {\n    m_use_thread = false;\n  }\n  // Calling forward in a thread causes memory leak in windows.\n  // See https://github.com/pytorch/pytorch/issues/24237\n  #ifdef _WIN32\n  m_use_thread = false;\n  #endif\n\n}\n\ntemplate <typename nn_name, typename op_type>\nnn_base<nn_name, op_type>::nn_base(const atoms &args)\n    : m_compute_thread(nullptr), n_inlets(-1), m_in_ratio(1), n_outlets(-1),\n      m_out_ratio(1), m_buffer_size(-1), n_batches(1), \n      m_method(\"forward\"), m_force_refresh(false),\n      m_use_thread(true), m_data_available_lock(0), m_result_available_lock(1),\n      m_should_stop_perform_thread(false), m_in_model(), m_out_model(),\n      cout(this, logger::type::message), cwarn(this, logger::type::warning), cerr(this, logger::type::error) \n{\n  init_external(args);\n}\n\n\n\ntemplate <typename nn_name, typename op_type>\nnn_base<nn_name, op_type>::~nn_base() {\n  m_should_stop_perform_thread = true;\n  if (m_compute_thread)\n    m_compute_thread->join();\n}\n\ntemplate <typename nn_name, typename op_type>\npath nn_base<nn_name, op_type>::to_model_path(std::string model_path) {\n  try {\n    if (model_path.substr(model_path.length() - 3) != \".ts\")\n      model_path = model_path + \".ts\";\n    // first look in \"models\" subfolder\n    if (m_downloader) {\n      auto download_path = m_downloader->get_download_path() / model_path; \n      if (std::filesystem::exists(download_path)) {\n        return path(download_path.string());\n      }\n    }\n    auto parsed_path = path(model_path);\n    return parsed_path;\n  } catch (std::string &e) {\n    throw e; \n  } catch (std::exception& e) {\n    std::string exception_str = e.what();\n    throw exception_str;\n  }\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::update_model(const std::string &model) {\n  load_model(model);\n  if ((m_model->is_loaded()) && m_model->has_method(m_method)) {\n    update_method(); \n  }\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::set_method(std::string method) {\n  m_method = method; \n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::update_method(std::string method) {\n  try {\n    // if (!m_model->is_loaded()) {\n    //   cerr << \"no model is set yet\" << endl;\n    //   return; \n    // }\n    if (method.empty()) {\n      if (m_method.empty()) {\n        return;\n      } \n      method = m_method; \n    }\n    if (m_model->has_method(method)) {\n      auto params = m_model->get_method_params(method);\n      if (params.size() == 0) {\n        throw std::format(\"method {} present in model, but not initialised\", method);\n      }\n      // input parameters\n      m_model_in = params[0];\n      if (n_inlets == -1) {\n        n_inlets = m_model_in;\n      } \n      m_in_ratio = params[1];\n\n      // output parameters\n      m_model_out = params[2];\n      if (n_outlets == -1) {\n        n_outlets = params[2];\n      }\n      m_out_ratio = params[3]; \n\n      if (m_model_in != n_inlets) {\n        cwarn << \"nn_base~ has been initialised with \" << n_inlets << \" inputs, but current model has \" << m_model_in << endl;\n      }\n      if (m_model_out != n_outlets) {\n        cwarn << \"nn_base~ has been initialised with \" << n_outlets << \" outputs, but current model has \" << m_model_out << endl;\n      }\n      \n      set_method(method); \n\n      if (m_buffer_size == -1) {\n        // NO THREAD MODE\n        m_buffer_size = m_higher_ratio;\n      }\n      DEBUG_PRINT(\"buffer size : %d\", m_buffer_size);\n      if (m_buffer_size == 0) {\n        m_use_thread = false; \n        m_buffer_size = m_higher_ratio; \n      } else {\n        if (m_buffer_size < m_higher_ratio) {\n          cerr << \"buffer size too small, switching to \" << m_buffer_size << endl;\n          m_buffer_size = m_higher_ratio;\n        } else {\n          m_buffer_size = power_ceil(m_buffer_size);\n        }\n      }\n    } else {\n      cerr << \"model \" << m_path << \" does not have method \" << method << endl; \n      m_ready = false; \n    }\n    wait_for_buffer_reset = true; \n  } catch (std::string &e) {\n    cerr << \"failed to set method to \" << method << \". Caught exception : \" << e << endl; \n  } catch (std::exception &e) {\n    cerr << \"failed to set method to \" << method << \". Caught exception : \" << e.what() << endl; \n  }\n\n  DEBUG_PRINT(\"updating buffer size by default\");\n  if (m_buffer_size == -1) {\n    // NO THREAD MODE\n    m_buffer_size = DEFAULT_BUFFER_SIZE;\n  }\n  if (m_buffer_size == 0) {\n    m_use_thread = false; \n    m_buffer_size = DEFAULT_BUFFER_SIZE; \n  } \n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::load_model(const std::string& model_path) {\n  DEBUG_PRINT(\"model path: %s\", model_path.c_str());\n  if (m_method.empty()) \n    m_method = \"forward\";\n\n  try {\n    auto path = to_model_path(model_path);\n    m_model.get()->load(path, get_sample_rate()); \n    DEBUG_PRINT(\"model loaded\"); \n    m_model->use_gpu(gpu);\n    m_higher_ratio = m_model->get_higher_ratio();\n    settable_attributes = m_model->get_settable_attributes(); \n    DEBUG_PRINT(\"attributes setted\"); \n    m_buffer_manager.init_buffer_list(m_model.get(), this);\n    m_ready = true; \n\n    if (!m_model->has_method(m_method)) {\n      cwarn << model_path << \" loaded, but does not have method \" << m_method << endl; \n\n    }\n  } catch (std::string &e) {\n    cerr << e << endl;\n    return; \n  }\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::init_buffers() { \n  if (!m_ready) { return; }\n  update_method(); \n\n  // if (m_buffer_size == -1) {\n  //   m_buffer_size = DEFAULT_BUFFER_SIZE;\n  // } else if (m_buffer_size == 0) {\n  //   m_use_thread = false; \n  //   m_buffer_size = m_higher_ratio;\n  // } else {\n  //   if (m_buffer_size < m_higher_ratio) {\n  //     cerr << \"buffer size too small, switching to \" << m_buffer_size << endl;\n  //     m_buffer_size = m_higher_ratio;\n  //   } else {\n  //     m_buffer_size = power_ceil(m_buffer_size);\n  //   }\n  // }\n\n  // Calling forward in a thread causes memory leak in windows.\n  // See https://github.com/pytorch/pytorch/issues/24237\n  #ifdef _WIN32\n  m_use_thread = false;\n  #endif\n  \n  if (m_in_buffer.get() != nullptr) { m_in_buffer.release(); }\n  m_in_buffer = std::make_unique<circular_buffer<double, float>[]>(n_inlets);\n  if (m_in_buffer.get()->max_size() < m_buffer_size) {\n    for (int i = 0; i < n_inlets; i++) {\n      m_in_buffer[i].initialize(m_buffer_size);\n    }\n  }\n\n  if (m_out_buffer.get() != nullptr) { m_out_buffer.release(); }\n  m_out_buffer = std::make_unique<circular_buffer<float, double>[]>(n_outlets);\nif (m_out_buffer.get()->max_size() < m_buffer_size) {\n    for (int i = 0; i < n_outlets; i++) {\n      m_out_buffer[i].initialize(m_buffer_size);\n    }\n  }\n\n  m_in_model.clear(); \n  for (int i = 0; i < m_model_in; i++) {\n    m_in_model.push_back(std::make_unique<float[]>(m_buffer_size));\n    std::fill(m_in_model[i].get(), m_in_model[i].get() + m_buffer_size, 0.); \n  }\n\n  m_out_model.clear(); \n  for (int i = 0; i < m_model_out; i++) {\n    m_out_model.push_back(std::make_unique<float[]>(m_buffer_size));\n    std::fill(m_out_model[i].get(), m_out_model[i].get() + m_buffer_size, 0.); \n  }\n\n  wait_for_buffer_reset = false; \n  had_buffer_reset = true; \n  buffer_initialised = true; \n}\n\ntemplate <typename nn_name, typename op_type>\nbool nn_base<nn_name, op_type>::has_settable_attribute(std::string attribute) {\n  for (std::string candidate: settable_attributes) {\n    if (candidate == attribute)\n      return true;\n  }\n  return false;\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::operator()(audio_bundle input, audio_bundle output) {\n\n  // CHECK IF MODEL IS LOADED AND ENABLED\n  if (!m_ready || !enable) {\n    fill_with_zero(output);\n    return;\n  }\n\n  if ((!m_use_thread) && (!buffer_initialised)) {\n    init_buffers(); \n  }\n\n  if (can_perform()) {\n    if (buffer_initialised) {\n      auto dsp_vec_size = output.frame_count();\n      // CHECK IF DSP_VEC_SIZE IS LARGER THAN BUFFER SIZE\n      if (dsp_vec_size > m_buffer_size) {\n        cerr << \"vector size (\" << dsp_vec_size << \") \";\n        cerr << \"larger than buffer size (\" << m_buffer_size << \"). \";\n        cerr << \"disabling model.\";\n        cerr << endl;\n        enable = false;\n        fill_with_zero(output);\n        return;\n      } \n      perform(input, output);\n    } \n    // else {\n    //   fill_with_zero();\n    // }\n  } else {\n    fill_with_zero(output); \n  }\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::dump_object() {\n  cout << \"model_path: \" << std::string(m_path) << endl;\n  cout << \"input dimension: \" << std::to_string(m_inlets.size()) << endl;\n  cout << \"output dimension: \" << std::to_string(m_outlets.size()) << endl;\n  cout << \"input ratio: \" << std::to_string(m_in_ratio) << endl; \n  cout << \"output ratio: \" << std::to_string(m_out_ratio) << endl; \n  cout << \"methods: \";\n  for (auto method: m_model->get_available_methods())\n    cout << method << \"; \";\n  cout << endl; \n  cout << \"attributes: \";\n  for (auto attribute: m_model->get_settable_attributes())\n    cout << attribute << \"; \";\n  cout << endl;\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::print_to_cout(std::string &message) {\n  cout << message << endl;\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::print_to_cerr(std::string &message) {\n  cerr << message << endl;\n}\n\ntemplate <typename nn_name, typename op_type>\nvoid nn_base<nn_name, op_type>::dump_available_models() {\n  try {\n    if (m_downloader.get()->is_ready()) {\n      m_downloader.get()->print_available_models();\n    }\n  } catch (std::string &e) {\n    cerr << \"error from model downloader : \" << e << endl;\n  }\n}\n"
  },
  {
    "path": "src/frontend/puredata/nn_tilde/CMakeLists.txt",
    "content": "cmake_minimum_required(VERSION 3.10)\nproject(nn_tilde_pd)\n\nset(CMAKE_CXX_STANDARD 20)\nset(CMAKE_CXX_STANDARD_REQUIRED ON)\nset(CMAKE_CXX_EXTENSIONS OFF)\n\nfind_package(Torch REQUIRED)\n\nfile(GLOB SRC *.cpp)\n\nadd_library(nn SHARED ${SRC})\n\nif (MSVC)\n\tset_property(TARGET nn PROPERTY CXX_STANDARD 20)\n\ttarget_compile_features(nn PUBLIC \"cxx_std_20\")\nendif()\n\n# Get version from git for all platforms\nexecute_process(\n    COMMAND git describe --tags\n    WORKING_DIRECTORY ${CMAKE_CURRENT_LIST_DIR}\n    OUTPUT_VARIABLE VERSION\n    OUTPUT_STRIP_TRAILING_WHITESPACE\n)\nif(VERSION)\n    message(STATUS \"Building version: ${VERSION}\")\n    add_definitions(-DVERSION=\"${VERSION}\")\nendif()\n\n# COPY HELP FILES\nadd_custom_command(TARGET nn POST_BUILD\n    COMMAND ${CMAKE_COMMAND} -E copy_if_different\n        \"${CMAKE_SOURCE_DIR}/frontend/puredata/nn_tilde/nn~-help.pd\"\n        \"${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_CFG_INTDIR}/nn~-help.pd\"\n    COMMENT \"Copy Help File\"\n)\n\nif (APPLE)\n    set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -stdlib=libc++\")\n    set(CMAKE_SHARED_LINKER_FLAGS \"${CMAKE_SHARED_LINKER_FLAGS} -undefined dynamic_lookup\")\n\n    add_custom_command(\n        TARGET nn\n        POST_BUILD\n        COMMAND cp \"${TORCH_INSTALL_PREFIX}/lib/*.dylib\" \"${CMAKE_CURRENT_BINARY_DIR}/\"\n        COMMENT \"Copy Torch Libraries\"\n    )\n\n    set_target_properties(nn PROPERTIES\n        PREFIX \"\"\n        SUFFIX \"~.pd_darwin\"\n        BUILD_WITH_INSTALL_RPATH FALSE\n        LINK_FLAGS \"-Wl,-rpath,@loader_path/\"\n    )\n\n    add_custom_command(\n        TARGET nn\n        POST_BUILD \n        COMMAND ${CMAKE_SOURCE_DIR}/../env/bin/python ${CMAKE_SOURCE_DIR}/../install/dylib_fix.py -p \"${CMAKE_CURRENT_BINARY_DIR}/*.pd_darwin\" -l \"${CMAKE_CURRENT_BINARY_DIR}/\" \"${CMAKE_BINARY_DIR}/_deps\"  \"${CMAKE_SOURCE_DIR}/../env\" \n        COMMENT \"Fixing libraries and codesigning\"\n    )\n\nendif()\n\n\nfunction(resolve_symlink symlink_path resolved_path)\n    execute_process(\n        COMMAND readlink -f ${symlink_path}\n        OUTPUT_VARIABLE resolved\n        OUTPUT_STRIP_TRAILING_WHITESPACE\n    )\n    set(${resolved_path} ${resolved} PARENT_SCOPE)\nendfunction()\n\n\nif (UNIX AND NOT APPLE)\n    # set(TORCH_ESSENTIAL_LIBS\n    #     \"libtorch.so*\"\n    #     \"libtorch_cpu.so*\"\n    #     \"libc10.so*\"\n    #     \"libgomp*so*\"\n    #     \"libtorch_global_deps.so*\"\n    # )\n    file(GLOB TORCH_ESSENTIAL_LIBS \"${torch_dir}/libtorch/lib/*.so*\")\n    set(CURL_ESSENTIAL_LIBS\n        \"libnghttp2.so*\"\n        \"libssh2.so*\"\n        \"libssl.so*\" \n        \"libkrb5.so*\"\n        \"libk5crypto.so*\"\n        \"libkrb5support.so*\"\n        \"libcrypto.so*\" \n        \"libgssapi_krb5.so*\" \n        \"libzstd.so*\"\n        \"libcom_err.so*\"\n        \"libz.so*\"\n        \"libcurl.so*\"\n    )\n\n    # Copy essential Torch libraries\n    add_custom_target(copy_torch_libs)\n\n    foreach(LIB_PATTR ${TORCH_ESSENTIAL_LIBS})\n        message(\"${LIB_PATTR} -> ${CMAKE_CURRENT_BINARY_DIR}\")\n        get_filename_component(LIB_NAME ${LIB_PATTR} NAME)\n        add_custom_command(\n            TARGET copy_torch_libs\n            PRE_BUILD\n            COMMAND ${CMAKE_COMMAND} -E copy_if_different\n                \"${LIB_PATTR}\"\n                \"${CMAKE_CURRENT_BINARY_DIR}/$(basename ${LIB_PATTR})\"\n            COMMENT \"Copying ${LIB_NAME}\"\n        )\n    endforeach()\n\n    # Copy essential curl libs\n    foreach(LIB_PATTR ${CURL_ESSENTIAL_LIBS})\n        file(GLOB CURRENT_PATHS \"${CMAKE_SOURCE_DIR}/../env/lib/${LIB_PATTR}\")\n        list(LENGTH CURRENT_PATHS N_PATHS)\n        if (NOT ${N_PATHS} EQUAL 0)\n            list(GET CURRENT_PATHS 0 LIB)\n            resolve_symlink(\"${LIB}\" original_path)\n            get_filename_component(LIB_NAME ${LIB} NAME)\n            message(\"${original_path} -> ${CMAKE_CURRENT_BINARY_DIR}/${LIB_NAME}\")\n            add_custom_command(\n                TARGET copy_torch_libs\n                PRE_BUILD\n                COMMAND ${CMAKE_COMMAND} -E copy_if_different\n                    \"${original_path}\"\n                    \"${CMAKE_CURRENT_BINARY_DIR}/${LIB_NAME}\"\n                COMMENT \"Copying ${LIB_NAME}\"\n            )\n        else() \n            message(\"${LIB_PATTR} not found\")\n        endif()\n    endforeach()\n\n    add_dependencies(nn copy_torch_libs)\n\n    set_target_properties(nn PROPERTIES \n        PREFIX \"\"\n        SUFFIX \"~.pd_linux\"\n        BUILD_WITH_INSTALL_RPATH TRUE\n        INSTALL_RPATH_USE_LINK_PATH TRUE\n        INSTALL_RPATH \"$ORIGIN\"\n    )\n\n    # Add libgomp as a link dependency\n    target_link_libraries(nn PRIVATE gomp)\nendif()\n\nif(MSVC)\n    set(CMAKE_CXX_FLAGS_RELEASE \"${CMAKE_CXX_FLAGS_RELEASE} /MT\")\n\n    # Base DLLs (these work for CPU version)\n    set(REQUIRED_DLLS\n        \"torch_cpu.dll\"\n        \"c10.dll\"\n        \"fbgemm.dll\"\n        \"libiomp5md.dll\"\n        \"libiompstubs5md.dll\"\n        \"uv.dll\"\n        \"asmjit.dll\"\n        \"torch.dll\"\n        \"torch_global_deps.dll\"\n    )\n\n    # CUDA DLL setup\n    if(CUDA_FOUND OR EXISTS \"${TORCH_INSTALL_PREFIX}/lib/torch_cuda.dll\")\n        list(APPEND REQUIRED_DLLS\n            # PyTorch CUDA DLLs\n            \"torch_cuda.dll\"\n            \"c10_cuda.dll\"\n            # Core CUDA Runtime DLLs\n            \"cudart64_12.dll\"\n            # Additional CUDA DLLs\n            \"cudnn64_9.dll\"\n            \"cudnn_graph64_9.dll\"\n            \"cudnn_engines_precompiled64_9.dll\"\n            \"cudnn_engines_runtime_compiled64_9.dll\"\n            \"cudnn_heuristic64_9.dll\"\n            \"nvrtc-builtins64_120.dll\"\n            \"cudadevrt.dll\"\n        )\n\n        # VC Runtime handling\n        if(DEFINED ENV{VCREDIST_PATH} AND EXISTS \"$ENV{VCREDIST_PATH}/vcruntime140_1.dll\")\n            message(STATUS \"Found VC Runtime at: $ENV{VCREDIST_PATH}\")\n            list(APPEND REQUIRED_DLLS \"$ENV{VCREDIST_PATH}/vcruntime140_1.dll\")\n        else()\n            message(WARNING \"VC Runtime not found in VCREDIST_PATH\")\n        endif()\n\n        # CUDA runtime DLL handling\n        if(DEFINED ENV{CUDA_PATH})\n            file(GLOB CUDA_RUNTIME_DLLS \"$ENV{CUDA_PATH}/bin/*.dll\")\n            foreach(CUDA_DLL ${CUDA_RUNTIME_DLLS})\n                get_filename_component(DLL_NAME ${CUDA_DLL} NAME)\n                foreach(REQUIRED_DLL ${REQUIRED_DLLS})\n                    if(DLL_NAME STREQUAL REQUIRED_DLL)\n                        add_custom_command(TARGET nn POST_BUILD\n                            COMMAND ${CMAKE_COMMAND} -E copy_if_different\n                                \"${CUDA_DLL}\"\n                                \"${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_CFG_INTDIR}/${DLL_NAME}\"\n                            COMMENT \"Copying CUDA runtime DLL: ${DLL_NAME}\"\n                        )\n                    endif()\n                endforeach()\n            endforeach()\n        endif()\n    endif()\n\n    # Copy libtorch DLLs\n    foreach(DLL ${REQUIRED_DLLS})\n        if(EXISTS \"${TORCH_INSTALL_PREFIX}/lib/${DLL}\")\n            add_custom_command(TARGET nn POST_BUILD\n                COMMAND ${CMAKE_COMMAND} -E copy_if_different\n                    \"${TORCH_INSTALL_PREFIX}/lib/${DLL}\"\n                    \"${CMAKE_CURRENT_BINARY_DIR}/${CMAKE_CFG_INTDIR}/${DLL}\"\n                COMMENT \"Copying ${DLL}\"\n            )\n        endif()\n    endforeach()\n\n    set_target_properties(nn PROPERTIES PREFIX \"\" SUFFIX \"~.dll\")\nendif()\n\nif(NOT $ENV{PD_EXTERNAL_PATH} STREQUAL \"\")\n    add_custom_command(\n        TARGET nn\n        POST_BUILD\n        COMMAND ${CMAKE_COMMAND} -E copy_directory  \"${CMAKE_CURRENT_BINARY_DIR}\" \"$ENV{PD_EXTERNAL_PATH}/nn_tilde\"\n        COMMENT \"Copying ${CMAKE_CURRENT_BINARY_DIR} to $ENV{PD_EXTERNAL_PATH}/nn_tilde\"\n    )\nendif()\n\ntarget_link_libraries(nn PRIVATE backend)\ntarget_include_directories(nn PRIVATE \"${PUREDATA_INCLUDE_DIR}\")\n\nif (APPLE)\n    add_custom_command(\n            TARGET nn\n            POST_BUILD\n            COMMAND ${CMAKE_COMMAND} -E copy \"${CMAKE_SOURCE_DIR}/../install/patch_with_vst.sh\" \"${CMAKE_BINARY_DIR}/frontend/puredata/nn_tilde/\"\n        )\nendif()\n\nset(CONDA_ENV_PATH \"${CMAKE_SOURCE_DIR}/../env\")\nset(CURL_INCLUDE_DIR \"${CONDA_ENV_PATH}/include\")\n\nif (UNIX)\n    if (APPLE)\n        set(CURL_LIBRARY \"${CONDA_ENV_PATH}/lib/libcurl.dylib\")\n    else()\n        set(CURL_LIBRARY \"${CONDA_ENV_PATH}/lib/libcurl.so\")\n        add_custom_command(\n            TARGET nn\n            POST_BUILD\n            COMMAND ${CMAKE_COMMAND} -E copy ${CURL_LIBRARY} \"${CMAKE_BINARY_DIR}/frontend/puredata/nn_tilde/\"\n        )\n    endif()\nendif()\n\ninclude_directories(${CURL_INCLUDE_DIR})\ntarget_link_libraries(nn PRIVATE ${CURL_LIBRARY})\n\ntarget_link_libraries(nn PRIVATE nlohmann_json::nlohmann_json)\nif (MSVC)\n    target_link_libraries(nn PRIVATE \"${PUREDATA_BIN_DIR}/pd.lib\" shlwapi.lib)\nendif()"
  },
  {
    "path": "src/frontend/puredata/nn_tilde/nn_tilde.cpp",
    "content": "\n#include <stdio.h>\n#include <stdlib.h>\n\n#include <limits.h>\n#include <memory>\n#include <mutex>\n#include <exception>\n#include <string>\n#include <vector>\n\n#ifndef VERSION\n#define VERSION \"UNDEFINED\"\n#endif\n\n#include \"m_pd.h\"\n#ifndef CLASS_MULTICHANNEL\n#if PD_MINOR_VERSION >= 54\n#define PD_HAVE_MULTICHANNEL\n#else\n#pragma message(\"building without multi-channel support; requires Pd 0.54+\")\n#define CLASS_MULTICHANNEL 0\n#endif\n#else\n#define PD_HAVE_MULTICHANNEL CLASS_MULTICHANNEL\n#endif\n\n#if defined(__APPLE__) && defined(__MACH__)\n#include <mach-o/dyld.h>\n#endif\n\n#ifdef __unix__\n#include <unistd.h>\n#endif\n\n\n#include \"../../../backend/backend.h\"\n#include \"../../../shared/circular_buffer.h\"\n#include \"../shared/pd_model_download.h\"\n#include \"../shared/pd_buffer_manager.h\"\n\n#ifdef _WIN32\n  #define NOMINMAX\n  #include <windows.h>\n  #include <shlwapi.h>\n\n  std::string get_executable_path()\n  {\n    HMODULE hModule = GetModuleHandle(\"nn~.dll\");\n    if (hModule)\n    {\n      char path[MAX_PATH];\n      GetModuleFileName(hModule, path, sizeof(path));\n      PathRemoveFileSpec(path); // Remove filename, leaving the path\n      SetDllDirectory(path);    // Add to DLL search path\n      \n      std::string path_std = path;\n      return path_std;\n    }\n  }\n#else\n  #include <dlfcn.h>\n#endif\n\n\n#if defined(__APPLE__) && defined(__MACH__)\n#include <cstdint>\nstd::string get_executable_path()\n{\n  char buf[PATH_MAX];\n  uint32_t bufsize = PATH_MAX;\n  if (!_NSGetExecutablePath(buf, &bufsize))\n    puts(buf);\n  std::string path_std = buf;\n  return path_std;\n}\n#endif\n#if defined(__unix__)\nstd::string get_executable_path()\n{\n  char path[PATH_MAX];\n  ssize_t count = readlink(\"/proc/self/exe\", path, PATH_MAX - 1);\n  if (count != -1)\n  {\n    path[count] = '\\0'; // Null-terminate the string\n    std::string path_std = path;\n  }\n  else\n  {\n    throw \"could not extract file position\";\n  }\n  return path;\n}\n#endif\n\n\nusing t_signal_setmultiout = void (*)(t_signal **, int);\nstatic t_signal_setmultiout g_signal_setmultiout;\nstatic t_class *nn_tilde_class;\n\nunsigned power_ceil(unsigned x)\n{\n  if (x <= 1)\n    return 1;\n  int power = 2;\n  x--;\n  while (x >>= 1)\n    power <<= 1;\n  return power;\n}\n\n// CLASS LIKE INITIALISATION\ntypedef struct _nn_tilde\n{\n  t_object x_obj;\n  t_sample f;\n  bool m_multichannel;\n  bool m_gpu;\n  bool m_dims_changed;\n  t_canvas *m_canvas;\n  t_outlet *m_info_outlet;\n\n  bool m_enabled;\n  // BACKEND RELATED MEMBERS\n  std::unique_ptr<Backend> m_model;\n  std::unique_ptr<PdBufferManager<_nn_tilde>> m_buf_manager;\n  std::vector<float *> m_in_model_ptrs;\n  std::vector<float *> m_out_model_ptrs;\n  std::vector<std::string> settable_attributes;\n  t_symbol *m_method, *m_path;\n  std::unique_ptr<std::thread> m_compute_thread;\n\n  // BUFFER RELATED MEMBERS\n  int m_head, m_in_dim, m_in_ratio, m_out_dim, m_out_ratio, m_buffer_size;\n\n  std::unique_ptr<circular_buffer<float, float>[]> m_in_buffer;\n  std::unique_ptr<circular_buffer<float, float>[]> m_out_buffer;\n  std::vector<std::unique_ptr<float[]>> m_in_model, m_out_model;\n\n  bool m_use_thread;\n\n  // DSP RELATED MEMBERS\n  int m_dsp_vec_size;\n  std::vector<float *> m_dsp_in_vec;\n  std::vector<float *> m_dsp_out_vec;\n\n  // DOWNLOAD RELATED MEMBERS\n  std::unique_ptr<PdModelDownloader<_nn_tilde>> m_downloader;\n\n} t_nn_tilde;\n\nclass PdErrorCatcher\n{\n  std::streambuf *old_cerr;\n  std::stringstream error_stream;\n  t_nn_tilde *x; // Store Pd object pointer\n\npublic:\n  PdErrorCatcher(t_nn_tilde *obj) : x(obj)\n  {\n    old_cerr = std::cerr.rdbuf(error_stream.rdbuf());\n  }\n\n  ~PdErrorCatcher()\n  {\n    // Output any caught errors to Pd console\n    std::string errors = error_stream.str();\n    if (!errors.empty())\n    {\n      pd_error(x, \"nn~: %s\", errors.c_str());\n    }\n    // Restore original stderr\n    std::cerr.rdbuf(old_cerr);\n  }\n};\n\nvoid model_perform(t_nn_tilde *x)\n{\n  PdErrorCatcher error_catcher(x);\n  try\n  {\n    x->m_model->perform(x->m_in_model_ptrs, x->m_out_model_ptrs,\n                        x->m_method->s_name, 1, x->m_out_dim, x->m_buffer_size);\n  }\n  catch (const std::exception &e)\n  {\n    pd_error(x, \"nn~: model perform error: %s\", e.what());\n  }\n}\n\n// DSP CALL\nt_int *nn_tilde_perform(t_int *w)\n{\n  t_nn_tilde *x = (t_nn_tilde *)(w[1]);\n\n  if (!x->m_model->is_loaded() || !x->m_enabled)\n  {\n    for (int c(0); c < x->m_out_dim; c++)\n    {\n      float *out = x->m_dsp_out_vec[c];\n      memset(out, 0, x->m_dsp_vec_size * sizeof(float));\n    }\n    return w + 2;\n  }\n\n  // COPY INPUT TO CIRCULAR BUFFER\n  for (int c(0); c < x->m_in_dim; c++)\n    x->m_in_buffer[c].put(x->m_dsp_in_vec[c], x->m_dsp_vec_size);\n\n  if (x->m_in_buffer[0].full())\n  { // BUFFER IS FULL\n    // IF USE THREAD, CHECK THAT COMPUTATION IS OVER\n    if (x->m_compute_thread && x->m_use_thread)\n      x->m_compute_thread->join();\n\n    // TRANSFER MEMORY BETWEEN INPUT CIRCULAR BUFFER AND MODEL BUFFER\n    for (int c(0); c < x->m_in_dim; c++)\n      x->m_in_buffer[c].get(x->m_in_model[c].get(), x->m_buffer_size);\n\n    if (!x->m_use_thread) // PROCESS DATA RIGHT NOW\n      model_perform(x);\n\n    // TRANSFER MEMORY BETWEEN OUTPUT CIRCULAR BUFFER AND MODEL BUFFER\n    for (int c(0); c < x->m_out_dim; c++)\n      x->m_out_buffer[c].put(x->m_out_model[c].get(), x->m_buffer_size);\n\n    if (x->m_use_thread) // PROCESS DATA LATER\n      x->m_compute_thread = std::make_unique<std::thread>(model_perform, x);\n  }\n\n  // COPY CIRCULAR BUFFER TO OUTPUT\n  for (int c(0); c < x->m_out_dim; c++)\n    x->m_out_buffer[c].get(x->m_dsp_out_vec[c], x->m_dsp_vec_size);\n\n  return w + 2;\n}\n\nvoid nn_tilde_dsp(t_nn_tilde *x, t_signal **sp)\n{\n  x->m_dsp_vec_size = sp[0]->s_n;\n  x->m_dsp_in_vec.clear();\n  x->m_dsp_out_vec.clear();\n\n  if (x->m_multichannel)\n  {\n// Get number of available input channels\n#if PD_HAVE_MULTICHANNEL\n    int nchans = sp[0]->s_nchans;\n#else\n    int nchans = 1;\n#endif\n\n    // Map input channels, wrapping if needed\n    for (int i(0); i < x->m_in_dim; i++)\n      x->m_dsp_in_vec.push_back(sp[0]->s_vec + x->m_dsp_vec_size * (i % nchans));\n\n    // Configure multichannel output\n    g_signal_setmultiout(&sp[1], x->m_out_dim);\n    for (int i(0); i < x->m_out_dim; i++)\n      x->m_dsp_out_vec.push_back(sp[1]->s_vec + x->m_dsp_vec_size * i);\n  }\n  else\n  {\n    // Standard mode - separate signals\n    for (int i(0); i < x->m_in_dim; i++)\n      x->m_dsp_in_vec.push_back(sp[i]->s_vec);\n    for (int i(x->m_in_dim); i < x->m_in_dim + x->m_out_dim; i++)\n    {\n      if (g_signal_setmultiout)\n        g_signal_setmultiout(&sp[i], 1); // Ensure single channel\n      x->m_dsp_out_vec.push_back(sp[i]->s_vec);\n    }\n  }\n  dsp_add(nn_tilde_perform, 1, x);\n}\n\nvoid nn_tilde_free(t_nn_tilde *x)\n{\n  if (x->m_compute_thread)\n    x->m_compute_thread->join();\n}\n\nstd::string resolve_file_path(t_nn_tilde *x, const char *filename)\n{\n  char dirresult[MAXPDSTRING];\n  char *nameresult;\n  int fd;\n\n  // Add .ts extension if not present\n  std::string fname(filename);\n  if (fname.substr(fname.length() - 3) != \".ts\")\n    fname += \".ts\";\n\n  const char *canvas_dir = canvas_getdir(x->m_canvas)->s_name;\n\n  // Try to open from canvas dir first, then search other paths\n  fd = open_via_path(canvas_dir, fname.c_str(), \"\", dirresult, &nameresult, MAXPDSTRING, 1);\n\n  if (fd >= 0)\n  {\n    sys_close(fd);\n    std::filesystem::path fullpath = dirresult;\n    fullpath /= nameresult;\n    // char fullpath[MAXPDSTRING];\n    // std::ostringstream str;\n    // str <<fullpath << std::to_string(MAXPDSTRING).c_str(), \"%s/%s\", dirresult, nameresult);\n    char normalized[MAXPDSTRING];\n    sys_unbashfilename(fullpath.string().c_str(), normalized);\n    return std::string(normalized);\n  }\n\n  pd_error(x, \"nn~: could not find file '%s'\", fname.c_str());\n  return \"\";\n}\n\nvoid create_buffers(t_nn_tilde *x, int in_dim, int out_dim)\n{\n  x->m_in_buffer = std::make_unique<circular_buffer<float, float>[]>(in_dim);\n  x->m_out_buffer = std::make_unique<circular_buffer<float, float>[]>(out_dim);\n\n  x->m_in_model.clear();\n  x->m_out_model.clear();\n\n  for (int i(0); i < in_dim; i++)\n  {\n    x->m_in_buffer[i].initialize(x->m_buffer_size);\n    x->m_in_model.push_back(std::make_unique<float[]>(x->m_buffer_size));\n  }\n\n  for (int i(0); i < out_dim; i++)\n  {\n    x->m_out_buffer[i].initialize(x->m_buffer_size);\n    x->m_out_model.push_back(std::make_unique<float[]>(x->m_buffer_size));\n  }\n\n  // Pre-allocate pointer vectors\n  x->m_in_model_ptrs.clear();\n  x->m_out_model_ptrs.clear();\n  x->m_in_model_ptrs.reserve(in_dim);\n  x->m_out_model_ptrs.reserve(out_dim);\n\n  for (int i(0); i < in_dim; i++)\n    x->m_in_model_ptrs.push_back(x->m_in_model[i].get());\n  for (int i(0); i < out_dim; i++)\n    x->m_out_model_ptrs.push_back(x->m_out_model[i].get());\n}\n\nbool nn_tilde_update_model_params(t_nn_tilde *x, t_symbol *method)\n{\n  x->m_method = method;\n\n  // Get the method parameters\n  auto params = x->m_model->get_method_params(x->m_method->s_name);\n\n  // Try to fallback to forward method if method not found\n  if (!params.size())\n  {\n    post(\"nn~: method %s not found in model, using forward instead\", x->m_method->s_name);\n    x->m_method = gensym(\"forward\");\n    params = x->m_model->get_method_params(x->m_method->s_name);\n    if (!params.size())\n    {\n      pd_error(x, \"nn~: forward method not found in model\");\n      return false;\n    }\n  }\n\n  // Store old dimensions to check if they changed\n  int old_in_dim = x->m_in_dim;\n  int old_out_dim = x->m_out_dim;\n  x->m_dims_changed = false;\n\n  // Update dimensions and ratios\n  x->m_in_dim = params[0];\n  x->m_in_ratio = params[1];\n  x->m_out_dim = params[2];\n  x->m_out_ratio = params[3];\n\n  // Check/adjust buffer size\n  auto higher_ratio = x->m_model->get_higher_ratio();\n  if (!x->m_buffer_size)\n  {\n    // NO THREAD MODE\n    x->m_use_thread = false;\n    x->m_buffer_size = higher_ratio;\n  }\n  else if (x->m_buffer_size < higher_ratio)\n  {\n    x->m_buffer_size = higher_ratio;\n    post(\"nn~: buffer size too small, switching to %d\", x->m_buffer_size);\n  }\n  else\n  {\n    x->m_buffer_size = power_ceil(x->m_buffer_size);\n  }\n\n  // Create new buffers if dimensions changed\n  x->m_dims_changed = (x->m_in_dim != old_in_dim) || (x->m_out_dim != old_out_dim);\n  if (x->m_dims_changed)\n  {\n    create_buffers(x, x->m_in_dim, x->m_out_dim);\n    post(\"nn~: dimensions changed: %d in, %d out\", x->m_in_dim, x->m_out_dim);\n  }\n\n  return true;\n}\n\n// MODEL LOADER\nbool nn_tilde_load_model(t_nn_tilde *x, const char *path)\n{\n  // Resolve the file path\n  std::string fullpath = resolve_file_path(x, path);\n  if (fullpath.empty())\n    return false;\n\n  // Create and load new backend instance\n  auto new_model = std::make_unique<Backend>();\n  if (new_model->load(fullpath.c_str(), (double)sys_getsr()))\n  {\n    pd_error(x, \"error loading model %s\", path);\n    return false;\n  }\n\n  // Store the new model and path\n  x->m_model = std::move(new_model);\n  x->m_path = gensym(fullpath.c_str());\n  x->settable_attributes = x->m_model->get_settable_attributes();\n\n  if (x->m_gpu && !(torch::hasCUDA() || torch::hasMPS()))\n  {\n    post(\"nn~: GPU mode not available\");\n    x->m_gpu = 0;\n  }\n  else if (x->m_gpu)\n  {\n    const char *gputype = torch::hasCUDA() ? \"CUDA\" : \"MPS\";\n    post(\"nn~: %s found\", gputype);\n  }\n\n  if (x->m_gpu)\n    x->m_model->use_gpu(true);\n  try\n  {\n    x->m_buf_manager->init_buffer_list(x->m_model.get());\n  }\n  catch (std::string &e)\n  {\n    pd_error(x, \"nn~: %s\", e.c_str());\n  }\n\n  // Update parameters using current method (or fallback to forward)\n  return nn_tilde_update_model_params(x, x->m_method);\n}\n\nvoid nn_tilde_bang(t_nn_tilde *x)\n{\n  // Output \"is_loaded\" status\n  t_atom is_loaded;\n  SETFLOAT(&is_loaded, x->m_model->is_loaded());\n  outlet_anything(x->m_info_outlet, gensym(\"loaded\"), 1, &is_loaded);\n\n  // Return if no model is loaded\n  if (!x->m_model->is_loaded())\n    return;\n\n  // Output \"enabled\" status\n  t_atom enabled;\n  SETFLOAT(&enabled, x->m_enabled);\n  outlet_anything(x->m_info_outlet, gensym(\"enabled\"), 1, &enabled);\n\n  // Output \"gpu\" status\n  t_atom gpu;\n  SETFLOAT(&gpu, x->m_gpu);\n  outlet_anything(x->m_info_outlet, gensym(\"gpu\"), 1, &gpu);\n\n  // Output model path\n  t_atom model;\n  SETSYMBOL(&model, x->m_path);\n  outlet_anything(x->m_info_outlet, gensym(\"model\"), 1, &model);\n\n  // Output modes\n  std::vector<std::string> methods = x->m_model->get_available_methods();\n  std::vector<t_atom> method_atoms(methods.size());\n  for (size_t i = 0; i < methods.size(); i++)\n    SETSYMBOL(&method_atoms[i], gensym(methods[i].c_str()));\n  outlet_anything(x->m_info_outlet, gensym(\"methods\"),\n                  methods.size(), method_atoms.data());\n\n  // Output selected method\n  t_atom method;\n  SETSYMBOL(&method, x->m_method);\n  outlet_anything(x->m_info_outlet, gensym(\"method\"), 1, &method);\n\n  if (x->settable_attributes.empty())\n  {\n    // Output empty symbol when no attributes\n    t_atom empty;\n    SETSYMBOL(&empty, gensym(\"\")); // or you could use gensym(\"none\")\n    outlet_anything(x->m_info_outlet, gensym(\"attributes\"), 1, &empty);\n  }\n  else\n  {\n    std::vector<t_atom> attr_atoms(x->settable_attributes.size());\n    for (size_t i = 0; i < x->settable_attributes.size(); i++)\n    {\n      SETSYMBOL(&attr_atoms[i], gensym(x->settable_attributes[i].c_str()));\n    }\n    outlet_anything(x->m_info_outlet, gensym(\"attributes\"),\n                    attr_atoms.size(), attr_atoms.data());\n  }\n  // Output buffer size\n  t_atom bufsize;\n  SETFLOAT(&bufsize, x->m_buffer_size);\n  outlet_anything(x->m_info_outlet, gensym(\"bufsize\"), 1, &bufsize);\n\n  // Output dimensions\n  t_atom dims[2];\n  SETFLOAT(dims, x->m_in_dim);\n  SETFLOAT(dims + 1, x->m_out_dim);\n  outlet_anything(x->m_info_outlet, gensym(\"dim\"), 2, dims);\n\n  // Output ratios\n  t_atom ratios[2];\n  SETFLOAT(ratios, x->m_in_ratio);\n  SETFLOAT(ratios + 1, x->m_out_ratio);\n  outlet_anything(x->m_info_outlet, gensym(\"ratio\"), 2, ratios);\n}\n\nvoid *nn_tilde_new(t_symbol *s, int argc, t_atom *argv)\n{\n  t_nn_tilde *x = (t_nn_tilde *)pd_new(nn_tilde_class);\n\n  x->m_model = std::make_unique<Backend>();\n  x->m_buf_manager = std::make_unique<PdBufferManager<_nn_tilde>>(x->m_model.get(), x);\n  x->m_head = 0;\n  x->m_compute_thread = nullptr;\n  x->m_in_dim = 1;\n  x->m_in_ratio = 1;\n  x->m_out_dim = 1;\n  x->m_out_ratio = 1;\n  x->m_buffer_size = 4096;\n  x->m_method = gensym(\"forward\");\n  x->m_enabled = true;\n  x->m_use_thread = true;\n  x->m_multichannel = false;\n  x->m_gpu = false;\n  x->m_dims_changed = true;\n  x->m_canvas = canvas_getcurrent();\n\n  // init downloader\n  auto canvas_dir = canvas_getdir(x->m_canvas)->s_name;\n  x->m_downloader = std::make_unique<PdModelDownloader<_nn_tilde>>(x, canvas_dir);\n  // #endif\n\n  // Create minimum outlet (we already have main inlet from CLASS_MAINSIGNALIN)\n  outlet_new(&x->x_obj, &s_signal);\n  create_buffers(x, 1, 1); // minimum buffers for one in/out\n\n  // CHECK ARGUMENTS\n  // First check for optional flags\n  while (argc > 0 && argv->a_type == A_SYMBOL)\n  {\n    t_symbol *flag = atom_getsymbol(argv);\n\n    if (flag == gensym(\"-m\"))\n    {\n      if (g_signal_setmultiout)\n      {\n        x->m_multichannel = true;\n      }\n      else\n      {\n        int maj = 0, min = 0, bug = 0;\n        sys_getversion(&maj, &min, &bug);\n        pd_error(x, \"nn~: no multichannel support in Pd %i.%i-%i, ignoring '-m' flag\",\n                 maj, min, bug);\n      }\n      argc--;\n      argv++;\n    }\n    else if (flag == gensym(\"-gpu\") || flag == gensym(\"-g\"))\n    { // activate gpu mode\n      x->m_gpu = true;\n      argc--;\n      argv++;\n    }\n    else if (flag == gensym(\"-d\"))\n    { // start in disabled mode\n      x->m_enabled = false;\n      argc--;\n      argv++;\n    }\n    else\n      break; // Not a flag, should be the model path\n  }\n\n  if (!argc)\n    return (void *)x;\n\n  // Process remaining regular arguments\n  x->m_path = atom_getsymbol(argv);\n  if (argc > 1)\n    x->m_method = atom_gensym(argv + 1);\n  if (argc > 2)\n    x->m_buffer_size = atom_getint(argv + 2);\n\n  if (nn_tilde_load_model(x, x->m_path->s_name))\n  {\n    // Add any additional inlets/outlets needed for model\n    if (!x->m_multichannel)\n    {\n      for (int i(1); i < x->m_in_dim; i++)\n        inlet_new(&x->x_obj, &x->x_obj.ob_pd, &s_signal, &s_signal);\n      for (int i(1); i < x->m_out_dim; i++)\n        outlet_new(&x->x_obj, &s_signal);\n    }\n  }\n  x->m_info_outlet = outlet_new(&x->x_obj, &s_anything);\n\n  return (void *)x;\n}\n\nvoid nn_tilde_method(t_nn_tilde *x, t_symbol *s)\n{\n  if (!x->m_multichannel)\n  {\n    pd_error(x, \"nn~: method change is only supported in multichannel mode\");\n    return;\n  }\n\n  if (x->m_compute_thread)\n  {\n    x->m_compute_thread->join();\n    x->m_compute_thread = nullptr;\n  }\n\n  if (nn_tilde_update_model_params(x, s) && x->m_dims_changed)\n    canvas_update_dsp();\n}\n\nvoid nn_tilde_bufsize(t_nn_tilde *x, t_floatarg size)\n{\n  if (x->m_compute_thread)\n  {\n    x->m_compute_thread->join();\n    x->m_compute_thread = nullptr;\n  }\n\n  // Store and validate new size\n  x->m_buffer_size = (int)size;\n  auto higher_ratio = x->m_model->get_higher_ratio();\n  if (!x->m_buffer_size)\n  {\n    // NO THREAD MODE\n    x->m_use_thread = false;\n    x->m_buffer_size = higher_ratio;\n  }\n  else if (x->m_buffer_size < higher_ratio)\n  {\n    x->m_buffer_size = higher_ratio;\n    post(\"nn~: buffer size too small, switching to %d\", x->m_buffer_size);\n  }\n  else\n  {\n    x->m_buffer_size = power_ceil(x->m_buffer_size);\n  }\n\n  // Recreate buffers with new size\n  create_buffers(x, x->m_in_dim, x->m_out_dim);\n}\n\nvoid nn_tilde_load(t_nn_tilde *x, t_symbol *s)\n{\n  if (!x->m_multichannel)\n  {\n    pd_error(x, \"nn~: dynamically loading models is only supported in multichannel mode\");\n    return;\n  }\n\n  if (x->m_compute_thread)\n  {\n    x->m_compute_thread->join();\n    x->m_compute_thread = nullptr;\n  }\n\n  if (nn_tilde_load_model(x, s->s_name))\n  {\n    if (x->m_dims_changed)\n    {\n      canvas_update_dsp();\n    }\n  }\n}\n\nvoid nn_tilde_gpu(t_nn_tilde *x, t_floatarg arg)\n{\n  bool want_gpu = (bool)arg;\n  if (want_gpu == x->m_gpu)\n    return;\n\n  if (!x->m_model->is_loaded())\n  {\n    pd_error(x, \"nn~: no model loaded\");\n    return;\n  }\n\n  if (want_gpu && !(torch::hasCUDA() || torch::hasMPS()))\n  {\n    post(\"nn~: GPU mode not available\");\n    x->m_gpu = false;\n    return;\n  }\n\n  x->m_gpu = want_gpu;\n  x->m_model->use_gpu(want_gpu);\n}\n\nvoid nn_tilde_enable(t_nn_tilde *x, t_floatarg arg) { x->m_enabled = (bool)arg; }\nvoid nn_tilde_reload(t_nn_tilde *x) { x->m_model->reload(); }\n\nvoid nn_tilde_set(t_nn_tilde *x, t_symbol *s, int argc, t_atom *argv)\n{\n  if (argc < 2)\n  {\n    pd_error(x, \"nn~: set needs at least 2 arguments [set argname argval1 ...(\");\n    return;\n  }\n  std::vector<std::string> attribute_args;\n\n  auto argname = argv[0].a_w.w_symbol->s_name;\n  std::string argname_str = argname;\n\n  if (!std::count(x->settable_attributes.begin(), x->settable_attributes.end(),\n                  argname_str))\n  {\n    pd_error(x, \"nn~: argument name not settable in current model\");\n    return;\n  }\n  auto buffers = Backend::BufferMap();\n\n  for (int i(1); i < argc; i++)\n  {\n    if (argv[i].a_type == A_SYMBOL)\n    {\n      attribute_args.push_back(argv[i].a_w.w_symbol->s_name);\n      try\n      {\n        x->m_buf_manager->append_if_buffer_element<Backend>(buffers, argv[i].a_w.w_symbol->s_name, argname, i - 1);\n      }\n      catch (std::string &e)\n      {\n        pd_error(x, \"%s\", e.c_str());\n      }\n      catch (const std::exception &e)\n      {\n        pd_error(x, \"nn~: %s\", e.what());\n      }\n    }\n    else if (argv[i].a_type == A_FLOAT)\n    {\n      attribute_args.push_back(std::to_string(argv[i].a_w.w_float));\n    }\n  }\n  try\n  {\n    x->m_model->set_attribute(argname, attribute_args, buffers);\n  }\n  catch (const std::string &e)\n  {\n    pd_error(x, \"nn~: %s\", e.c_str());\n  }\n  catch (const std::exception &e)\n  {\n    pd_error(x, \"nn~: %s\", e.what());\n  }\n}\n\nvoid nn_tilde_available_models(t_nn_tilde *x)\n{\n  try\n  {\n    if (x->m_downloader->is_ready())\n    {\n      auto available_models = x->m_downloader->get_available_models();\n      for (auto model : available_models)\n      {\n        post(model.c_str());\n      }\n      return;\n    }\n    else\n    {\n      throw \"download could be initailized\";\n    }\n  }\n  catch (std::string &e)\n  {\n    pd_error(x, \"nn~: %s\", e.c_str());\n  }\n  catch (std::exception &e)\n  {\n    pd_error(x, \"nn~: %s\", e.what());\n  }\n  return;\n}\n\nvoid nn_tilde_download(t_nn_tilde *x, t_symbol *s, int argc, t_atom *argv)\n{\n  if (argc == 0)\n  {\n    pd_error(x, \"nn~: %s\", \"download needs at least a model card.\");\n    return;\n  }\n  if (argv[0].a_type != A_SYMBOL)\n  {\n    pd_error(x, \"nn~: %s\", \"download takes a symbol as a model card.\");\n    return;\n  }\n  std::string model_name = argv[0].a_w.w_symbol->s_name;\n  std::string rename_model = \"\";\n  if (argc == 2)\n  {\n    if (argv[1].a_type != A_SYMBOL)\n    {\n      pd_error(x, \"nn~: %s\", \"download takes a symbol as a model card, and an optional symbol as a custom name.\");\n      return;\n    }\n    rename_model = argv[1].a_w.w_symbol->s_name;\n  }\n\n  try\n  {\n    if (x->m_downloader->is_ready())\n    {\n      x->m_downloader->download(model_name, rename_model);\n    }\n  }\n  catch (std::string &e)\n  {\n    pd_error(x, \"nn~: %s\", e.c_str());\n  }\n  catch (std::exception &e)\n  {\n    pd_error(x, \"nn~: %s\", e.what());\n  }\n}\n\nvoid nn_tilde_remove(t_nn_tilde *x, t_symbol *s)\n{\n  try\n  {\n    std::string model_name = s->s_name;\n    x->m_downloader->remove(model_name);\n  }\n  catch (std::string &e)\n  {\n    pd_error(x, \"nn~: %s\", e.c_str());\n  }\n  catch (std::exception &e)\n  {\n    pd_error(x, \"nn~: %s\", e.what());\n  }\n}\n\nvoid startup_message()\n{\n  std::string startmessage = \"nn~ - \";\n  startmessage += VERSION;\n  startmessage += \" - \";\n  startmessage += \"torch \";\n  startmessage += TORCH_VERSION;\n  startmessage += \" - 2025 - Antoine Caillon, Axel Chemla--Romeu-Santos, Benjamin Wesch\";\n  post(startmessage.c_str());\n}\n\n#ifdef _WIN32\n#define EXPORT extern \"C\" __declspec(dllexport)\n#elif __GNUC__ >= 4\n#define EXPORT extern \"C\" __attribute__((visibility(\"default\")))\n#else\n#define EXPORT extern \"C\"\n#endif\n\nEXPORT void nn_tilde_setup(void)\n{\n\n  // multichannel handling copied from\n  // https://github.com/Spacechild1/vstplugin/blob/v0.6.0/pd/src/vstplugin~.cpp#L4120\n#ifdef PD_HAVE_MULTICHANNEL\n// runtime check for multichannel support:\n#ifdef _WIN32\n  // get a handle to the module containing the Pd API functions.\n  // NB: GetModuleHandle(\"pd.dll\") does not cover all cases.\n  HMODULE module;\n  if (GetModuleHandleEx(\n          GET_MODULE_HANDLE_EX_FLAG_FROM_ADDRESS | GET_MODULE_HANDLE_EX_FLAG_UNCHANGED_REFCOUNT,\n          (LPCSTR)&pd_typedmess, &module))\n  {\n    g_signal_setmultiout = (t_signal_setmultiout)(void *)GetProcAddress(\n        module, \"signal_setmultiout\");\n  }\n#else\n  // search recursively, starting from the main program\n  g_signal_setmultiout = (t_signal_setmultiout)dlsym(\n      dlopen(nullptr, RTLD_NOW), \"signal_setmultiout\");\n#endif\n#endif // PD_HAVE_MULTICHANNEL\n\n// Handle path for dynamically loaded cudnn_graph64_9.dll\n// FIXME: is this really the way to do this??\n#ifdef _WIN32\n  // Get the directory of the current DLL\n  HMODULE hModule = GetModuleHandle(\"nn~.dll\");\n  if (hModule)\n  {\n    char path[MAX_PATH];\n    GetModuleFileName(hModule, path, sizeof(path));\n    PathRemoveFileSpec(path); // Remove filename, leaving the path\n    SetDllDirectory(path);    // Add to DLL search path\n  }\n#endif\n\n  startup_message();\n  nn_tilde_class = class_new(gensym(\"nn~\"), (t_newmethod)nn_tilde_new, 0,\n                             sizeof(t_nn_tilde), CLASS_MULTICHANNEL, A_GIMME, 0);\n  class_addmethod(nn_tilde_class, (t_method)nn_tilde_dsp, gensym(\"dsp\"), A_CANT, 0);\n  class_addmethod(nn_tilde_class, (t_method)nn_tilde_enable, gensym(\"enable\"), A_FLOAT, 0);\n  class_addmethod(nn_tilde_class, (t_method)nn_tilde_load, gensym(\"load\"), A_SYMBOL, 0);\n  class_addmethod(nn_tilde_class, (t_method)nn_tilde_reload, gensym(\"reload\"), A_NULL);\n  class_addmethod(nn_tilde_class, (t_method)nn_tilde_set, gensym(\"set\"), A_GIMME, 0);\n  class_addmethod(nn_tilde_class, (t_method)nn_tilde_bufsize, gensym(\"bufsize\"), A_FLOAT, 0);\n  class_addmethod(nn_tilde_class, (t_method)nn_tilde_gpu, gensym(\"gpu\"), A_FLOAT, 0);\n  class_addmethod(nn_tilde_class, (t_method)nn_tilde_method, gensym(\"method\"), A_SYMBOL, 0);\n  class_addmethod(nn_tilde_class, (t_method)nn_tilde_available_models, gensym(\"print_available_models\"), A_NULL);\n  class_addmethod(nn_tilde_class, (t_method)nn_tilde_download, gensym(\"download\"), A_GIMME, 0);\n  class_addmethod(nn_tilde_class, (t_method)nn_tilde_remove, gensym(\"remove\"), A_SYMBOL, 0);\n  class_addbang(nn_tilde_class, (t_method)nn_tilde_bang);\n  CLASS_MAINSIGNALIN(nn_tilde_class, t_nn_tilde, f);\n}\n"
  },
  {
    "path": "src/frontend/puredata/nn_tilde/nn~-help.pd",
    "content": "#N canvas 9 53 1467 851 12;\n#X obj 10 51 cnv 1 960 1 empty empty empty 8 12 0 13 #000000 #000000 0;\n#X obj 22 18 nn~;\n#X text 59 18 - real-time ai audio processing;\n#X obj 574 67 bng 19 250 50 0 \\$0-browse-vschaos2 empty IRCAM\\ vschaos2\\ models 24 9 0 12 #fcfcfc #000000 #000000;\n#X obj 768 92 bng 19 250 50 0 \\$0-browse-rave-iil empty iiL\\ RAVE\\ models 24 9 0 12 #fcfcfc #000000 #000000;\n#X obj 574 92 bng 19 250 50 0 \\$0-browse-rave-ircam empty IRCAM\\ RAVE\\ models 24 9 0 12 #fcfcfc #000000 #000000;\n#N canvas 38 607 670 504 guts 0;\n#X obj 57 272 pdcontrol;\n#X msg 60 162 browse https://acids-ircam.github.io/rave_models_download;\n#X msg 60 222 browse https://www.dropbox.com/sh/avdeiza7c6bn2of/AAAGZsnRo9ZVMa0iFhouCBL-a?dl=0, f 80;\n#X obj 60 198 r \\$0-browse-vschaos2;\n#X obj 60 138 r \\$0-browse-rave-ircam;\n#X obj 61 73 r \\$0-browse-rave-iil;\n#X msg 61 98 browse https://huggingface.co/Intelligent-Instruments-Lab/rave-models, f 69;\n#X obj 84 335 loadbang;\n#X msg 84 359 0;\n#X obj 84 383 s \\$0-enable;\n#X connect 1 0 0 0;\n#X connect 2 0 0 0;\n#X connect 3 0 2 0;\n#X connect 4 0 1 0;\n#X connect 5 0 6 0;\n#X connect 6 0 0 0;\n#X connect 7 0 8 0;\n#X connect 8 0 9 0;\n#X restore 901 800 pd guts;\n#X msg 24 659 gpu \\$1;\n#X obj 24 635 tgl 19 0 empty empty empty 0 -10 0 12 #fcfcfc #000000 #000000 0 1;\n#X text 545 621 <-- latent space;\n#X obj 419 549 vsl 19 162 -10 10 0 0 empty empty empty 0 -9 0 12 #fcfcfc #000000 #000000 0 1;\n#X obj 506 252 tgl 19 0 \\$0-enable empty empty 0 -10 0 12 #dfdfdf #000000 #000000 0 1;\n#X text 394 540 10;\n#X text 401 621 0;\n#X text 387 703 -10;\n#X obj 450 549 vsl 19 162 -10 10 0 0 empty empty empty 0 -9 0 12 #fcfcfc #000000 #000000 0 1;\n#X obj 482 549 vsl 19 162 -10 10 0 0 empty empty empty 0 -9 0 12 #fcfcfc #000000 #000000 0 1;\n#X obj 514 549 vsl 19 162 -10 10 0 0 empty empty empty 0 -9 0 12 #fcfcfc #000000 #000000 0 1;\n#X obj 419 276 noise~;\n#X obj 506 300 f;\n#X obj 506 348 mtof;\n#X obj 419 800 dac~;\n#X obj 506 372 lop~ 12;\n#X obj 584 378 +~ 3;\n#X obj 419 397 bob~;\n#X obj 584 354 *~ 3;\n#X obj 437 324 line~;\n#X obj 419 373 *~;\n#X obj 437 348 pow~ 2;\n#X obj 536 300 + 4;\n#X obj 506 324 + 50;\n#X obj 566 300 mod 41;\n#X obj 584 330 osc~ 7;\n#X text 21 65 At its core \\, nn~ is a translation layer between Pure Data and the libtorch C++ interface for deep learning. Alone \\, nn~ is like an empty shell \\, and requires pretrained models to operate. You can find a few models here:, f 74;\n#X obj 697 378 dac~;\n#X text 21 297 optional leading flags:;\n#X obj 24 800 s \\$0-nn;\n#X msg 24 752 bufsize 0;\n#X msg 24 728 bufsize 4096;\n#X text 120 727 <-- default;\n#X obj 506 276 metro 150;\n#X text 29 238 3rd :;\n#X text 29 203 2nd :;\n#X text 72 203 <method> - optional \\, defaults to \"forward\", f 21;\n#X text 29 183 1st :;\n#X text 73 183 <model_path>;\n#X obj 267 635 tgl 19 0 empty \\$0-enable empty 0 -10 0 12 #fcfcfc #000000 #000000 0 1;\n#X msg 267 659 enable \\$1;\n#X obj 697 252 tgl 19 0 empty empty empty 0 -10 0 12 #dfdfdf #000000 #000000 0 1;\n#X text 29 326 -m :;\n#X text 69 326 activate multichannel mode (in- and output signal for all methods combined in 1 inlet and 1 outlet), f 33;\n#X text 29 374 -g :;\n#X text 68 374 activate GPU mode (if available);\n#X text 29 395 -d :;\n#X text 68 395 initialize in disabled state;\n#X msg 24 473 load nasa;\n#X obj 613 444 r \\$0-nn;\n#X obj 614 737 r \\$0-nn;\n#X obj 419 732 snake~ in 4;\n#X obj 24 683 s \\$0-nn;\n#X obj 267 683 s \\$0-nn;\n#X obj 24 497 s \\$0-nn;\n#X obj 605 800 print;\n#X text 413 223 timbre transfer;\n#X text 695 223 decoder-only;\n#X obj 267 800 s \\$0-nn;\n#X obj 267 775 bng 19 250 50 0 empty empty empty 0 -10 0 12 #fcfcfc #000000 #000000;\n#X text 264 725 bang to output state on info outlet, f 14;\n#X text 22 151 CREATION ARGS:;\n#X text 23 442 MESSAGES:;\n#X text 72 238 <buffer size> - optional \\, defaults to 4096 0 sets no-thread-mode \\, resulting in lower latency, f 34;\n#X text 650 800 <-- check info log on bang;\n#X msg 24 542 reload;\n#X msg 24 566 method forward;\n#X obj 697 587 dac~;\n#X obj 697 444 tgl 19 0 empty empty empty 0 -10 0 12 #dfdfdf #000000 #000000 0 1;\n#N canvas 237 262 668 332 snapshotall 0;\n#X obj 49 158 snapshot~;\n#X obj 139 158 snapshot~;\n#X obj 230 158 snapshot~;\n#X obj 323 158 snapshot~;\n#X obj 49 182 outlet;\n#X obj 139 182 outlet;\n#X obj 230 182 outlet;\n#X obj 323 182 outlet;\n#X obj 49 44 inlet~;\n#X obj 404 110 route enable;\n#X obj 404 139 metro 25;\n#X obj 404 86 r \\$0-nn;\n#X obj 49 101 snake~ out 4;\n#X connect 0 0 4 0;\n#X connect 1 0 5 0;\n#X connect 2 0 6 0;\n#X connect 3 0 7 0;\n#X connect 8 0 12 0;\n#X connect 9 0 10 0;\n#X connect 10 0 2 0;\n#X connect 10 0 3 0;\n#X connect 10 0 1 0;\n#X connect 10 0 0 0;\n#X connect 11 0 9 0;\n#X connect 12 0 0 0;\n#X connect 12 1 1 0;\n#X connect 12 2 2 0;\n#X connect 12 3 3 0;\n#X restore 419 509 pd snapshotall;\n#X msg 24 590 set <attribute> <value>;\n#X text 695 416 prior;\n#X msg 697 276 enable \\$1;\n#X obj 697 525 nn~ -m -g;\n#X obj 697 563 nn~ -m -g;\n#X msg 697 492 enable \\$1;\n#X obj 771 468 sel 1;\n#X obj 697 468 t f f;\n#X text 73 542 reload model;\n#X msg 24 776 bufsize 16384;\n#X text 97 752 <-- no-thread-mode;\n#X text 197 584 see info log for attributes, f 14;\n#X text 133 565 switch method (multichannel only);\n#X text 80 651 activate if available (check Pd's CPU meter), f 22;\n#X msg 771 530 load vintage \\, method decode \\, bufsize 8192, f 20;\n#X text 411 151 EXAMPLES (these require the percussion.ts and vintage.ts models from the IRCAM RAVE collection above):, f 38;\n#X msg 437 300 1 \\, 0 200;\n#X obj 770 314 osc~ 5.1;\n#X obj 835 314 osc~ 7.1;\n#X msg 771 492 load vintage \\, method prior \\, bufsize 8192, f 20;\n#X obj 900 314 osc~ 9.1;\n#X obj 705 314 osc~ 4.1;\n#X text 574 645 combining \"encode\" and \"decode\" objects like this without manipulation of the latent trajectory is similar to using the \"forward\" method. number of in- and output channels depends on the model. see \"dim\" output on info outlet., f 52;\n#X text 100 466 dynamically load another model (multichannel only), f 30;\n#X msg 1062 233 print_available_models;\n#X obj 1062 276 nn~;\n#X obj 1060 392 nn~;\n#X text 1055 151 DIRECT DOWNLOAD you can directly download nn~ compatible models within nn~., f 37;\n#X msg 1081 357 remove nasa;\n#X obj 419 463 nn~ -m -g nasa encode;\n#X obj 420 756 nn~ -m -g nasa decode;\n#X obj 697 338 nn~ -d -g nasa decode;\n#X msg 1060 325 download ircam/rave/nasa;\n#X connect 7 0 59 0;\n#X connect 8 0 7 0;\n#X connect 10 0 58 0;\n#X connect 11 0 40 0;\n#X connect 15 0 58 1;\n#X connect 16 0 58 2;\n#X connect 17 0 58 3;\n#X connect 18 0 27 0;\n#X connect 19 0 29 0;\n#X connect 19 0 30 0;\n#X connect 20 0 22 0;\n#X connect 22 0 24 1;\n#X connect 23 0 24 2;\n#X connect 24 0 106 0;\n#X connect 25 0 23 0;\n#X connect 26 0 28 0;\n#X connect 27 0 24 0;\n#X connect 28 0 27 1;\n#X connect 29 0 31 0;\n#X connect 30 0 20 0;\n#X connect 31 0 19 1;\n#X connect 32 0 25 0;\n#X connect 37 0 36 0;\n#X connect 38 0 36 0;\n#X connect 40 0 19 0;\n#X connect 40 0 93 0;\n#X connect 46 0 47 0;\n#X connect 47 0 60 0;\n#X connect 48 0 79 0;\n#X connect 55 0 61 0;\n#X connect 56 0 106 0;\n#X connect 57 0 107 0;\n#X connect 58 0 107 0;\n#X connect 66 0 65 0;\n#X connect 75 0 84 0;\n#X connect 76 0 10 0;\n#X connect 76 1 15 0;\n#X connect 76 2 16 0;\n#X connect 76 3 17 0;\n#X connect 79 0 108 0;\n#X connect 80 0 81 0;\n#X connect 81 0 74 0;\n#X connect 82 0 80 0;\n#X connect 82 0 81 0;\n#X connect 83 0 96 0;\n#X connect 83 0 91 0;\n#X connect 84 0 82 0;\n#X connect 84 1 83 0;\n#X connect 86 0 36 0;\n#X connect 91 0 81 0;\n#X connect 93 0 26 0;\n#X connect 94 0 108 1;\n#X connect 95 0 108 2;\n#X connect 96 0 80 0;\n#X connect 97 0 108 4;\n#X connect 98 0 108 0;\n#X connect 101 0 102 0;\n#X connect 105 0 103 0;\n#X connect 106 0 76 0;\n#X connect 107 0 21 0;\n#X connect 107 1 62 0;\n#X connect 108 0 34 0;\n#X connect 108 1 34 1;\n#X connect 109 0 103 0;\n"
  },
  {
    "path": "src/frontend/puredata/shared/pd_buffer_manager.h",
    "content": "#pragma once\n#include \"m_pd.h\"\n#include \"../../../backend/backend.h\"\n#include \"../../../shared/static_buffer.h\"\n\n#ifndef CHECK_BUFFERS_INTERVAL\n#define CHECK_BUFFERS_INTERVAL 100\n#endif\n\n\ntemplate <typename pd_class>\nclass PdBufferManager {\n\n    Backend *m_backend; \n    pd_class *m_obj;\n    t_clock *m_clock; \n    std::vector<std::string> m_buffer_attributes;\n    std::unordered_map<std::string, std::string> m_array_to_buffer; \n    double m_sample_rate;\n    \n    public:\n    bool m_monitor_arrays; \n\n    PdBufferManager(Backend *backend, pd_class* nn_obj): \n        m_backend(backend), m_obj(nn_obj)\n    { }\n\n    void init_buffer_list(Backend *backend = nullptr) {\n        if (backend != nullptr) {\n            m_backend = backend; \n        }\n        // clear previous buffers\n        m_buffer_attributes.clear();\n        // init model buffers\n        std::vector<std::string> model_buffers;\n        try {\n            model_buffers = m_backend->get_buffer_attributes();\n        } catch (std::exception &e) {\n            throw std::string(\"could not retrieve buffers from model. Caught error : \") + e.what();\n        }\n        // if (!model_buffers.size()) {\n        //     std::cout << \"no buffers found\" << std::endl;\n        // }\n        // create buffer references for each of model buffers\n        for (auto & element : model_buffers) {\n            // std::cout << \"adding buffer \" << element << \" to buffer manager.\";\n            m_buffer_attributes.push_back(element);\n            post(\"nn~: %s\", element.c_str());\n        }\n    } \n\n    void add_array_monitor(const std::string target_pd_buffer, const std::string buffer_name) {\n        t_garray *garray;\n        if (!(garray = (t_garray *)pd_findbyclass(gensym(target_pd_buffer.c_str()), garray_class))) {\n            throw std::string(\"table \" + buffer_name + \" not found\");\n        }\n        m_array_to_buffer[target_pd_buffer] = buffer_name; \n    }\n\n    template <typename backend_type>\n    StaticBuffer<typename backend_type::DataType> static_buffer_from_name(const std::string buffer_name) {\n        t_garray *garray;\n        if (!(garray = (t_garray *)pd_findbyclass(gensym(buffer_name.c_str()), garray_class))) {\n            throw std::string(\"table \" + buffer_name + \" not found\");\n        }\n        t_word *table_data;\n        int table_size;\n        if (!(garray_getfloatwords(garray, &table_size, &table_data))) {\n            throw std::string(\"could not access table \") + std::string(buffer_name); \n        }\n        auto buffer = StaticBuffer<typename backend_type::DataType>(1, table_size, (double)sys_getsr()); \n        for (int i(0); i < table_size; ++i) \n            buffer.put(static_cast<typename backend_type::DataType>((table_data + i)->w_float), 0, i); \n        return buffer; \n    }\n\n    template <typename backend_type>\n    void append_if_buffer_element(Backend::BufferMap &buffers, std::string target_pd_buffer, std::string attribute_name, int index) {\n        if (m_backend->is_buffer_element_of_attribute(attribute_name, index)) {\n            auto buffer_name = m_backend->get_buffer_name(attribute_name, index);\n            buffers[buffer_name] = static_buffer_from_name<backend_type>(target_pd_buffer);\n        } else {\n            std::ostringstream error; \n            // error << std::to_string(index) << \"th element of attribute \" << attribute_name << \"does not seem to be a buffer\"; \n            throw error.str(); \n        }\n    }\n\n    auto get_buffer_attributes() { return m_buffer_attributes; }\n    \n};"
  },
  {
    "path": "src/frontend/puredata/shared/pd_model_download.h",
    "content": "#pragma once\n#include \"m_pd.h\"\n#include \"../../../shared/model_download.h\"\n\nnamespace fs = std::filesystem;\n\ntemplate <typename pd_struct>\nclass PdModelDownloader : public ModelDownloader\n{\n    const pd_struct *d_parent;\n\npublic:\n    PdModelDownloader(const pd_struct *parent) : d_parent(parent)\n    {\n        d_cert_path = cert_path_from_path(\"\");\n    }\n    PdModelDownloader(const pd_struct *parent, const std::string path) : d_parent(parent)\n    {\n        d_path = path;\n        d_cert_path = cert_path_from_path(path);\n    }\n\n    void print_to_parent(const std::string &message, const std::string &canal) override\n    {\n        if (d_parent != nullptr)\n        {\n            if (canal == \"cout\")\n            {\n                post(message.c_str());\n            }\n            else if (canal == \"cwarn\")\n            {\n                post(message.c_str());\n            }\n            else if (canal == \"cerr\")\n            {\n                pd_error(d_parent, \"nn~: %s\", message.c_str());\n            }\n        }\n    };\n\n    fs::path cert_path_from_path(fs::path path)\n    {\n#if defined(_WIN32) || defined(_WIN64)\n        const char *homeDir = std::getenv(\"USERPROFILE\");\n        std::string perm_path_str = std::string(homeDir) + \"/Documents/Pd/externals/nn_tilde/cacert.pem\";\n        find_and_replace_char(perm_path_str, '/', '\\\\');\n        fs::path perm_path = perm_path_str;\n        if (fs::exists(perm_path))\n        {\n            return perm_path;\n        }\n        perm_path = \"C:\\\\Program Files\\\\Pd\\\\extra\\\\cacert.pem\";\n        if (fs::exists(perm_path))\n        {\n            return perm_path;\n        }\n        std::string path_str = path.string();\n        find_and_replace_char(path_str, '/', '\\\\');\n        perm_path = path_str + \"\\\\cacert.pem\";\n        return perm_path;\n#elif defined(__APPLE__) || defined(__MACH__)\n        std::string perm_path = \"/etc/ssl/cert.pem\";\n#elif defined(__linux__)\n        std::string perm_path = \"/etc/ssl/certs/ca-certificates.crt\";\n#else\n        std::string perm_path = \"\";\n#endif\n        return perm_path;\n    }\n\n    void fill_dict(void *dict_to_fill) override\n    {\n        return;\n    }\n};\n"
  },
  {
    "path": "src/help/mc.nn~.maxhelp",
    "content": "{\n\t\"patcher\" : \t{\n\t\t\"fileversion\" : 1,\n\t\t\"appversion\" : \t\t{\n\t\t\t\"major\" : 8,\n\t\t\t\"minor\" : 6,\n\t\t\t\"revision\" : 5,\n\t\t\t\"architecture\" : \"x64\",\n\t\t\t\"modernui\" : 1\n\t\t}\n,\n\t\t\"classnamespace\" : \"box\",\n\t\t\"rect\" : [ 134.0, 125.0, 683.0, 680.0 ],\n\t\t\"bglocked\" : 0,\n\t\t\"openinpresentation\" : 0,\n\t\t\"default_fontsize\" : 12.0,\n\t\t\"default_fontface\" : 0,\n\t\t\"default_fontname\" : \"Arial\",\n\t\t\"gridonopen\" : 1,\n\t\t\"gridsize\" : [ 15.0, 15.0 ],\n\t\t\"gridsnaponopen\" : 1,\n\t\t\"objectsnaponopen\" : 1,\n\t\t\"statusbarvisible\" : 2,\n\t\t\"toolbarvisible\" : 1,\n\t\t\"lefttoolbarpinned\" : 0,\n\t\t\"toptoolbarpinned\" : 0,\n\t\t\"righttoolbarpinned\" : 0,\n\t\t\"bottomtoolbarpinned\" : 0,\n\t\t\"toolbars_unpinned_last_save\" : 2,\n\t\t\"tallnewobj\" : 0,\n\t\t\"boxanimatetime\" : 200,\n\t\t\"enablehscroll\" : 1,\n\t\t\"enablevscroll\" : 1,\n\t\t\"devicewidth\" : 0.0,\n\t\t\"description\" : \"\",\n\t\t\"digest\" : \"\",\n\t\t\"tags\" : \"\",\n\t\t\"style\" : \"\",\n\t\t\"subpatcher_template\" : \"\",\n\t\t\"showontab\" : 1,\n\t\t\"assistshowspatchername\" : 0,\n\t\t\"boxes\" : [ \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 0,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patcher\" : \t\t\t\t\t{\n\t\t\t\t\t\t\"fileversion\" : 1,\n\t\t\t\t\t\t\"appversion\" : \t\t\t\t\t\t{\n\t\t\t\t\t\t\t\"major\" : 8,\n\t\t\t\t\t\t\t\"minor\" : 6,\n\t\t\t\t\t\t\t\"revision\" : 5,\n\t\t\t\t\t\t\t\"architecture\" : \"x64\",\n\t\t\t\t\t\t\t\"modernui\" : 1\n\t\t\t\t\t\t}\n,\n\t\t\t\t\t\t\"classnamespace\" : \"box\",\n\t\t\t\t\t\t\"rect\" : [ 0.0, 26.0, 683.0, 654.0 ],\n\t\t\t\t\t\t\"bglocked\" : 0,\n\t\t\t\t\t\t\"openinpresentation\" : 0,\n\t\t\t\t\t\t\"default_fontsize\" : 12.0,\n\t\t\t\t\t\t\"default_fontface\" : 0,\n\t\t\t\t\t\t\"default_fontname\" : \"Arial\",\n\t\t\t\t\t\t\"gridonopen\" : 1,\n\t\t\t\t\t\t\"gridsize\" : [ 15.0, 15.0 ],\n\t\t\t\t\t\t\"gridsnaponopen\" : 1,\n\t\t\t\t\t\t\"objectsnaponopen\" : 1,\n\t\t\t\t\t\t\"statusbarvisible\" : 2,\n\t\t\t\t\t\t\"toolbarvisible\" : 1,\n\t\t\t\t\t\t\"lefttoolbarpinned\" : 0,\n\t\t\t\t\t\t\"toptoolbarpinned\" : 0,\n\t\t\t\t\t\t\"righttoolbarpinned\" : 0,\n\t\t\t\t\t\t\"bottomtoolbarpinned\" : 0,\n\t\t\t\t\t\t\"toolbars_unpinned_last_save\" : 0,\n\t\t\t\t\t\t\"tallnewobj\" : 0,\n\t\t\t\t\t\t\"boxanimatetime\" : 200,\n\t\t\t\t\t\t\"enablehscroll\" : 1,\n\t\t\t\t\t\t\"enablevscroll\" : 1,\n\t\t\t\t\t\t\"devicewidth\" : 0.0,\n\t\t\t\t\t\t\"description\" : \"\",\n\t\t\t\t\t\t\"digest\" : \"\",\n\t\t\t\t\t\t\"tags\" : \"\",\n\t\t\t\t\t\t\"style\" : \"\",\n\t\t\t\t\t\t\"subpatcher_template\" : \"\",\n\t\t\t\t\t\t\"showontab\" : 1,\n\t\t\t\t\t\t\"assistshowspatchername\" : 0,\n\t\t\t\t\t\t\"boxes\" : [ \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-13\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 14.0, 81.0, 528.0, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"ACIDS - IRCAM : Antoine Caillon, Axel Chemla--Romeu Santos, Nils Demerlé, Philippe Esling\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-7\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 269.0, 230.0, 70.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"loadmess 0\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-10\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 4,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 239.0, 396.0, 209.0, 64.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Each patch cord contains the same latent for all of the inputs. The number of channels is adapted to the number of inputs.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-5\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 36.0, 139.0, 401.0, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Batches inputs among mc channels, allows to process latents in parallel.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 18.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-37\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 192.0, 14.0, 455.0, 27.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Multichannel audio generation\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 48.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-40\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 14.0, 14.0, 167.0, 62.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.nn~\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-3\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 193.0, 40.0, 453.0, 33.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"We support two variants of multichannel generation (mc.nn~ and mcs.nn~). Please check the respective help patches for each object\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontface\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-16\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 36.0, 114.0, 282.0, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Batch multichannels with mc.nn~\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-14\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"mc.ezdac~\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 52.0, 524.0, 45.0, 45.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-9\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 52.0, 321.0, 70.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.pack~ 3\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"basictuning\" : 440,\n\t\t\t\t\t\t\t\t\t\"data\" : \t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"clips\" : [ \t\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t\t\"absolutepath\" : \"morph.256.rom.aif\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"filename\" : \"morph.256.rom.aif\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"filekind\" : \"audiofile\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"id\" : \"u741009561\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"loop\" : 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\"content_state\" : \t\t\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t\t\t\"loop\" : 1\n\t\t\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\t\t}\n ]\n\t\t\t\t\t\t\t\t\t}\n,\n\t\t\t\t\t\t\t\t\t\"followglobaltempo\" : 0,\n\t\t\t\t\t\t\t\t\t\"formantcorrection\" : 0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-8\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"playlist~\",\n\t\t\t\t\t\t\t\t\t\"mode\" : \"basic\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 5,\n\t\t\t\t\t\t\t\t\t\"originallength\" : [ 0.0, \"ticks\" ],\n\t\t\t\t\t\t\t\t\t\"originaltempo\" : 120.0,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\", \"signal\", \"\", \"dictionary\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 78.0, 217.0, 150.0, 30.0 ],\n\t\t\t\t\t\t\t\t\t\"pitchcorrection\" : 0,\n\t\t\t\t\t\t\t\t\t\"quality\" : \"basic\",\n\t\t\t\t\t\t\t\t\t\"timestretch\" : [ 0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"basictuning\" : 440,\n\t\t\t\t\t\t\t\t\t\"data\" : \t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"clips\" : [ \t\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t\t\"absolutepath\" : \"eroica.aiff\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"filename\" : \"eroica.aiff\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"filekind\" : \"audiofile\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"id\" : \"u702009436\",\n\t\t\t\t\t\t\t\t\t\t\t\t\"loop\" : 1,\n\t\t\t\t\t\t\t\t\t\t\t\t\"content_state\" : \t\t\t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t\t\t\"loop\" : 1\n\t\t\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t\t\t}\n ]\n\t\t\t\t\t\t\t\t\t}\n,\n\t\t\t\t\t\t\t\t\t\"followglobaltempo\" : 0,\n\t\t\t\t\t\t\t\t\t\"formantcorrection\" : 0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-6\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"playlist~\",\n\t\t\t\t\t\t\t\t\t\"mode\" : \"basic\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 5,\n\t\t\t\t\t\t\t\t\t\"originallength\" : [ 0.0, \"ticks\" ],\n\t\t\t\t\t\t\t\t\t\"originaltempo\" : 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],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 445.5, 503.0, 56.0, 34.0 ],\n\t\t\t\t\t\t\t\t\t\"sig\" : [ 0.0, 0.0, 0.0, 0.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-19\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"mc.number~\",\n\t\t\t\t\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\", \"float\", \"int\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 375.0, 503.0, 56.0, 34.0 ],\n\t\t\t\t\t\t\t\t\t\"sig\" : [ 0.0, 0.0, 0.0, 0.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-17\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"mc.number~\",\n\t\t\t\t\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\", \"float\", \"int\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 163.0, 503.0, 56.0, 34.0 ],\n\t\t\t\t\t\t\t\t\t\"sig\" : [ 0.0, 0.0, 0.0, 0.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-39\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"mc.number~\",\n\t\t\t\t\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\", \"float\", \"int\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 99.0, 503.0, 56.0, 34.0 ],\n\t\t\t\t\t\t\t\t\t\"sig\" : [ 0.0, 0.0, 0.0, 0.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-15\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"mc.number~\",\n\t\t\t\t\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\", \"float\", \"int\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 35.0, 503.0, 56.0, 34.0 ],\n\t\t\t\t\t\t\t\t\t\"sig\" : [ 0.0, 0.0, 0.0, 0.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-12\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\", \"multichannelsignal\", \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 375.0, 459.0, 150.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.nn~ demo_mc channel\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-1\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\", \"multichannelsignal\", \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 35.0, 459.0, 137.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.nn~ demo_mc batch\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-8\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 160.0, 168.0, 60.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"loadmess\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-7\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 36.0, 168.0, 60.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"loadmess\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-9\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 160.0, 197.0, 35.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"4 5 6\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-41\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 160.0, 225.0, 114.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mcs.sig~ @chans 3\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 36.0, 195.0, 45.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"1 2 3 4\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-2\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 4,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 36.0, 224.0, 114.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mcs.sig~ @chans 4\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-6\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\", \"multichannelsignal\", \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 35.0, 278.0, 232.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.nn~ demo_mc identity\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-13\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 14.0, 81.0, 528.0, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"ACIDS - IRCAM : Antoine Caillon, Axel Chemla--Romeu Santos, Nils Demerlé, Philippe Esling\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-5\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 34.5, 133.0, 401.0, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Batches inputs among mc channels, allows to process latents in parallel.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 18.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-37\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 192.0, 14.0, 455.0, 27.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Multichannel audio generation\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 48.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-40\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 14.0, 14.0, 167.0, 62.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.nn~\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-3\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 193.0, 40.0, 453.0, 33.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"We support two variants of multichannel generation (mc.nn~ and mcs.nn~). 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2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\", \"float\", \"int\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 53.0, 481.0, 56.0, 34.0 ],\n\t\t\t\t\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-12\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\", \"multichannelsignal\", \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 439.0, 437.0, 166.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mcs.nn~ demo_mc channel 3\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-1\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\", \"multichannelsignal\", \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 53.0, 437.0, 153.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mcs.nn~ demo_mc batch 3\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-8\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 196.0, 169.0, 60.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"loadmess\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-7\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" 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3\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 54.0, 197.0, 35.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"1 2 3\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-2\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 54.0, 226.0, 114.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mcs.sig~ @chans 3\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" 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1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 214.0, 40.0, 453.0, 33.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"This variant allows to group the inputs/outputs of the objects inside a single connection per input (eg. here all encoded latents of one input)\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontface\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-26\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 53.0, 111.0, 141.0, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Wraps inputs/output\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-25\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"mc.ezdac~\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 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  {
    "path": "src/help/nn.info.maxhelp",
    "content": "{\n\t\"patcher\" : \t{\n\t\t\"fileversion\" : 1,\n\t\t\"appversion\" : \t\t{\n\t\t\t\"major\" : 9,\n\t\t\t\"minor\" : 0,\n\t\t\t\"revision\" : 0,\n\t\t\t\"architecture\" : \"x64\",\n\t\t\t\"modernui\" : 1\n\t\t}\n,\n\t\t\"classnamespace\" : \"box\",\n\t\t\"rect\" : [ 100.0, 100.0, 755.0, 713.0 ],\n\t\t\"gridsize\" : [ 15.0, 15.0 ],\n\t\t\"toolbars_unpinned_last_save\" : 2,\n\t\t\"showrootpatcherontab\" : 0,\n\t\t\"showontab\" : 0,\n\t\t\"boxes\" : [ \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-25\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 0,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patcher\" : \t\t\t\t\t{\n\t\t\t\t\t\t\"fileversion\" : 1,\n\t\t\t\t\t\t\"appversion\" : \t\t\t\t\t\t{\n\t\t\t\t\t\t\t\"major\" : 9,\n\t\t\t\t\t\t\t\"minor\" : 0,\n\t\t\t\t\t\t\t\"revision\" : 0,\n\t\t\t\t\t\t\t\"architecture\" : \"x64\",\n\t\t\t\t\t\t\t\"modernui\" : 1\n\t\t\t\t\t\t}\n,\n\t\t\t\t\t\t\"classnamespace\" : 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0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 426.0, 219.5, 150.0, 37.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"dump outputs all available information\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-7\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"button\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"bang\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 77.0, 435.0, 24.0, 24.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-3\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 238.0, 199.0, 113.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"parameters forward\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-24\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"dict.view\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 366.0, 326.0, 285.0, 119.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-23\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 366.0, 227.0, 39.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"dump\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-21\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 121.0, 169.0, 32.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"path\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-20\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"bang\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 366.0, 192.0, 58.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"loadbang\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-19\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 150.0, 289.0, 96.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"print parameters\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-18\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 126.0, 314.0, 85.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"print attributes\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-17\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 101.0, 340.0, 81.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"print methods\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-16\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 77.0, 366.0, 59.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"print path\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-13\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"dict.view\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 77.0, 509.0, 354.0, 150.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-11\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 5,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"dictionary\", \"\", \"\", \"\", \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 77.0, 466.0, 89.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"saved_object_attributes\" : \t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"embed\" : 0,\n\t\t\t\t\t\t\t\t\t\t\"legacy\" : 0,\n\t\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\t\"parameter_mappable\" : 0\n\t\t\t\t\t\t\t\t\t}\n,\n\t\t\t\t\t\t\t\t\t\"text\" : \"dict model_info\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-10\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 121.0, 227.0, 64.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"dump_dict\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-9\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" 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isis\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 77.0, 130.0, 140.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"download ircam/rave/isis\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-2\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 6,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"symbol\", \"\", \"\", \"\", \"dictionary\", \"dictionary\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 366.0, 262.0, 285.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn.info isis\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-1\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 6,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"symbol\", \"\", \"\", \"\", \"dictionary\", \"dictionary\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 77.0, 262.0, 141.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn.info @dict model_info\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-12\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 3,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 185.0, 43.0, 528.0, 47.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn.info allows to retrieve all the information from a given model ; nn.info does not retain the model, such that information parsing can be very memory light. It can also be used as a proxy to handle download models from the API. \"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 18.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-37\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 185.0, 14.0, 455.0, 27.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Model inspection with nn.info\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 48.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-40\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 6,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"symbol\", \"\", \"\", \"\", \"dictionary\", \"dictionary\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 14.0, 14.0, 153.0, 62.0 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\"\",\n\t\t\t\t\t\t\"showontab\" : 1,\n\t\t\t\t\t\t\"assistshowspatchername\" : 0,\n\t\t\t\t\t\t\"boxes\" : [ \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-44\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 101.0, 455.5, 265.0, 37.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"all the attributes of a given model can be retrieved with nn.info.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-41\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 3,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 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Export code can be found at nn_tilde/source/attributes.py\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-36\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 226.0, 213.0, 82.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"set attr_str $1\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-29\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 270.0, 174.0, 46.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"orange\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : 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2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 508.0, 272.0, 67.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"get attr_list\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-19\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 421.0, 272.0, 75.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"get attr_bool\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-17\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 324.0, 272.0, 83.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"get attr_enum\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-15\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 148.0, 272.0, 75.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"get attr_float\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-14\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 69.0, 272.0, 65.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"get attr_int\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-51\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 595.0, 214.0, 114.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"prepend set attr_list\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-48\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 672.0, 149.0, 33.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"yves\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-47\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 660.0, 122.0, 29.5, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"luc\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-45\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"toggle\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"int\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 790.0, 133.0, 24.0, 24.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"format\" : 6,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-42\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"flonum\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" 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0.0\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-13\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 390.0, 564.0, 141.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~ demo_buffers shape\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-1\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 87.0, 506.0, 45.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"get buf\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : 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\"obj-20\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 703.0, 564.0, 142.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~ demo_buffers get_sr\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-6\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"number~\",\n\t\t\t\t\t\t\t\t\t\"mode\" : 2,\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\", \"float\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 390.0, 603.0, 56.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : 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\"obj-3\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 10.0, 85.0, 627.0, 33.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Some models are able to directly access Max buffers to perform some internal computations and set their state. 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1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 214.0, 200.0, 140.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"download ircam/rave/isis\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-3\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 14.0, 101.0, 699.0, 33.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"set the model \\\"void\\\" to activate the empty mode. Then, the arguments are the number of inlets, and the number of outets, and the buffer size. If the given number of inlets or outlets does not fit the loaded model, a warning will appear in the Max console. \"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-2\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 4,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 4,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\", \"signal\", \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 214.0, 397.0, 104.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~ void 4 4 2048\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 18.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-37\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 173.0, 14.0, 532.0, 27.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Empty mode\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 48.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-40\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 14.0, 14.0, 149.0, 62.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 174.0, 40.0, 453.0, 33.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"v1.6.0 created an \\\"empty mode\\\" initialization for nn~ : create the object before, and set the model after!\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n 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mode, meaning that audio thread waits for the model's output. This is higly CPU-intensive, and unfortunately default on Windows due to a libtorch memory leak.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"attr\" : \"enable\",\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-25\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"attrui\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 601.0, 307.0, 150.0, 22.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-26\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 601.0, 339.0, 150.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~ wheel forward 0\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"bubbleside\" : 0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-24\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 5,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 330.0, 377.0, 157.0, 93.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Some models cannot go beyond a given buffer size; by default, nn~ takes the smallest (for this RAVE model, 2048)\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"attr\" : \"enable\",\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-21\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"attrui\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" 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\"Some models cannot go beyond a given buffer size; by default, nn~ takes the smallest (for this RAVE model, 2048)\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-12\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"int\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 403.0, 182.0, 40.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"active\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"format\" : 6,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-11\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"flonum\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 330.0, 237.0, 50.0, 22.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-9\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"bang\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 330.0, 182.0, 63.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"metro 100\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"attr\" : \"enable\",\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-8\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"attrui\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 86.0, 307.0, 150.0, 22.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-6\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 86.0, 339.0, 150.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~ wheel forward\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-5\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\", \"int\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 330.0, 209.0, 77.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"adstatus cpu\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : 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: \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"bang\", \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 35.0, 587.0, 34.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"sel 1\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-20\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"int\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 35.0, 554.0, 40.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"active\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"comment\" : \"\",\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-16\",\n\t\t\t\t\t\t\t\t\t\"index\" : 1,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"inlet\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 46.0, 514.0, 30.0, 30.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-1\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 3,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 14.0, 89.5, 678.0, 47.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Settings of the buffer size highly depends on the model, but have to be initialised at creation, so be careful!  An increased buffer size adds latency, but decreases your CPU load ; inversely, a reduced buffer size reduces the latency but may be quite intensive for your CPU load. \"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 18.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-37\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 170.0, 14.0, 427.0, 27.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Buffer size handling\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 48.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-40\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 14.0, 14.0, 149.0, 62.0 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\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"bubbleside\" : 3,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-69\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 14,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 451.546366453170776, 418.845327496528625, 169.0, 198.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"as batches are among inlets, the number of inlets of mc.nn~ is defined by an arbitrary fourth argument (here 2). 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The number of out multi-channels is the same as the maximum of the number of input multi-channels.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-67\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 3,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 226.804111003875732, 219.587616562843323, 247.422666549682617, 51.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc_example is a model that only copies the input signal, and has three inputs & outputs.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\t\t\t\t\"fontname\" : \"Arial\",\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-65\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : 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[ 23.711338877677917, 285.566994309425354, 56.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"sig\" : 0.0\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-7\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\", \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 24.0, 238.144316554069519, 141.391744375228882, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~ demo_mc identity\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-30\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 891.752527356147766, 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855.670055150985718, 6.185566663742065, 30.0, 30.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 48.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-11\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 15.0, 14.0, 149.0, 62.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-5\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 237.113388776779175, 354.639155387878418, 217.0, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Batches inputs among mc channels\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 18.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-37\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 192.0, 14.0, 455.0, 27.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Multichannel audio generation\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-3\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 3,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 193.0, 44.329894423484802, 453.0, 47.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"We support two variants of multichannel generation (mc.nn~ and mcs.nn~). For more detailed information, please check the respective help patches for each object.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontface\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-26\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 625.773160815238953, 328.123683094978333, 111.5, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mcs.nn~ version\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-20\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\", \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 627.0, 587.917482972145081, 176.536038517951965, 22.0 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\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"angle\" : 270.0,\n\t\t\t\t\t\t\t\t\t\"bgcolor\" : [ 0.0, 0.0, 0.0, 0.61 ],\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-54\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"panel\",\n\t\t\t\t\t\t\t\t\t\"mode\" : 0,\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 14.0, 597.0, 596.0, 72.0 ],\n\t\t\t\t\t\t\t\t\t\"proportion\" : 0.5\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"bubblepoint\" : 0.68,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-31\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 7,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 594.0, 212.0, 219.0, 104.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"wheel is a RAVE model, such that forward is composed by two methods : encode and decode.\\nThe latent variables are slower than audio rate, such that nn~ downsamples the corresponding outputs.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-33\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"scope~\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 578.0, 402.0, 130.0, 130.0 ],\n\t\t\t\t\t\t\t\t\t\"range\" : [ -6.0, 6.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-32\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 8,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 571.0, 348.0, 106.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.pack~ 8\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, 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\"obj-23\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"bang\", \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 703.0, 87.0, 34.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"sel 1\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-20\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"int\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 703.0, 54.0, 40.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"active\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"comment\" : \"\",\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-16\",\n\t\t\t\t\t\t\t\t\t\"index\" : 1,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"inlet\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 714.0, 14.0, 30.0, 30.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-12\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 173.0, 43.0, 528.0, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Antoine Caillon, Axel Chemla--Romeu Santos, Nils Demerlé, Philippe Esling\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"bubbleside\" : 3,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-7\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 22.0, 203.0, 197.0, 37.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"If available on your computer, you can use GPU processing.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 18.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-37\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 173.0, 14.0, 455.0, 27.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Real-time AI audio generation\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 48.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-40\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 14.0, 14.0, 149.0, 62.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-22\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 8,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 455.0, 348.0, 106.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~ wheel decode\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-19\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 8,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\", \"signal\", \"signal\", \"signal\", 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455.0, 144.0, 65.0, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Input\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-11\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"ezdac~\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 214.0, 521.0, 45.0, 45.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"bubbleside\" : 3,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-9\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 4,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 18.0, 274.0, 206.0, 64.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Arguments:\\n- model (str)\\n- method (str) - default: forward\\n- buffer (int) - default 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\"\",\n\t\t\t\t\t\t\"tags\" : \"\",\n\t\t\t\t\t\t\"style\" : \"\",\n\t\t\t\t\t\t\"subpatcher_template\" : \"\",\n\t\t\t\t\t\t\"showontab\" : 1,\n\t\t\t\t\t\t\"assistshowspatchername\" : 0,\n\t\t\t\t\t\t\"boxes\" : [ \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-30\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 583.783783435821533, 515.46396392583847, 60.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"download\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-28\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 583.783783435821533, 556.905405342578888, 75.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"s #0-nn-help\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-23\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"bang\", \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 583.783783435821533, 483.932432413101196, 34.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"sel 1\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-42\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"int\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 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1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 216.0, 272.0, 128.0, 93.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn.info can provide more detailed information about downloadable models. \"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"bubbleside\" : 2,\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-21\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 4,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 277.0, 423.0, 162.0, 79.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"You can also delete a model, only using its name (name of the model file in the Max File Path)\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-20\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 327.0, 523.0, 62.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"delete isis\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-19\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 114.0, 523.0, 140.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"download ircam/rave/isis\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"bubbleside\" : 2,\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-17\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 5,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 114.0, 423.0, 152.0, 93.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"You can download a model using the corresponding model card, namely :\\n\\\"source\\\"/\\\"model\\\"/\\\"name\\\"\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-15\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"dict.view\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 379.279279053211212, 198.198198080062866, 368.468468248844147, 176.576576471328735 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-14\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 219.0, 198.0, 125.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"get_available_models\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-12\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 6,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"symbol\", \"\", \"\", \"\", \"dictionary\", \"dictionary\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 219.0, 238.0, 122.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn.info\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-11\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 114.0, 141.0, 463.0, 24.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Click here to output available models on the IRCAM Forum API.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-8\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"button\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"bang\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 80.0, 141.0, 24.0, 24.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-3\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 10.0, 85.0, 680.0, 33.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Since v1.6.0, models can be directly downloaded inside IRCAM Forum. 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0VizPhKG3y3SnijbbSo9DYleYfCGXVqtEoAYWIvQDQbRUGhjjTaTl4WilSxoO4NpWy3.1xHh+V0gHo533lR8AxLOLfcq5NjFj8G.FUDwEUcHRRRcCxL+z.ecfUt5VjFD8j.aeDwut5PjTMbbSodXYlyKvwBr4U2hzfjD3LANpHhKs5Xjjj5FkY9wA1If0o3TjFrLQfuUDwgVcHRZ3miaJ0iJybI.9E.u2hSQZvvDANeZt4y+iE2hjjTOgLyOAvN.7AptEoAIGNvdGQLgpCQRCebbSodPYlqBvoC7VptEoYPSD37.N3Hhqp5Xjjj5EkYt9.6C9aJt5MLZfsKh3eWcHRZ3giaJ0iIybiANQf4u5VjlA7R.mDvOHh3ZpNFIIo9AYlqCvdAr9EmhzLpq.XKhHFW0gHogdNtoTOjLycD3v.lypaQZ5zyAbp.+jHhqn5Xjjj5G042r7sGXSptEoY.2NM2j59z+H0iywMk5QjYte.e6p6PZ5zD.t.fCIh3JqNFIIIAYlaJvWCu3gT2q+EvVGQb9UGhjF533lRc4xLmMfiD3KTcKRSmNKfCzeW0kjjZm57N47aC7tptEooCSDXmhHN1pCQRCMbbSotXYluNfeIvFWcKRSGNWfCKh3OWcHRRRZpKyby.1Cf0r5VjlNreQD6e0QHoAeNtoTWpLyQRy6lP+lKU2l+DvOLh3rpNDIIIMsKy7KArS.qP0sHMM5vA1qHhIVcHRZviiaJ0EJybE.NSfkp5VjlFb8.emHhQWcHRRRZFWl4NCry.KY0sHMM3TA1lHhwWcHRZvwLUc.RZZSl45.76wgMU2iaklWj6qjCaJII06Hh3GN5QO5c.XOAdnp6QZ.ZK.9MYlKX0gHoAGdxMk5hjYtE.GMv7UcKRC.2MvOHh3vqNDIIIMzJybw.1dfcD+dUU2gqB3SEQbaUGhjlw33lRcIxL2FfeFdhqU62CCbbzLromhCIIo9HYlKMv9B7wAlqhyQZpYrzLv40Tc3OAmg0...H.jDQAQERZ5miaJ0EHybO.Nzp6PZp34A9kz7d07tqNFIIIUmLyUE36BrQU2hzTwi.7QiH9qUGhjl933lRsbYlGNvtTcGRSEiF3PhH9GUGhjjjZOxL2.f8CXMJNEoWKOIv1EQb5UGhjl143lRsTYlyJvQArcU2hzqg+Fv2Kh3LqNDIII0dkYti.6Jvas5VjdULAfuRDwwVcHRZZiiaJ0BkYNO.GCM2jeRsQ2Iv2Oh3HqNDIII08Hy7fA9B.u9paQ5UwtFQ7CpNBIMv4EShTKSl47AbV3vlpc5oANvQO5QuCNrojjjlVEQr2.qFvwWcKRuJN7Ly8u5Hjz.mmbSoVjLyEE3L.VypaQZJ3WRyif9MVcHRRRp6Wl4GBXu.d+U2hzTv2OhX2pNBIM043lRsDYluEZtTVVopaQ5U3uC7ciHN+pCQRRR8dxL+xz7937sTcKRuBGKvNFQ7BUGhjd043lRs.Ylq.vYBrTU2hzj4g.NnHhin5PjjjTuuLyQA7kAFQ0sHMYNcfOSDwDqNDIMk43lREKyb0n4DatXU2hTGSD3GA7ChHt2piQRRR8OxLe6.eSfMu5VjlLW.vVEQ7DUGhj9+xwMkJTl4ZQyvlKb0sH0wEAbfQD+kpCQRRR8uxL+T.6KvxWcKRcbw.aYDwiUcHR5+jiaJUjLy0G3z.lmpaQBXb.GPDwOq5PjjjjdYct0p+J.ye0sHAbk.ezHh6u5Pjzj33lREHyby.NQf4p5VTeuW.3vG+3G+QNhQLh6t5XjjjjdkxLWZfCAXyptEIfqC3iDQLtpCQRMbbSogYYlaEvOGX1qtE026R.9lQDWd0gHIIIM0jY9I.9N.KW0sn9d2.vmLhXLUGhjbbSogUYlaMvwALSU2h5qcO.GdDwgWcHRRRRSqxLOLfuHvbWcKpu1X.9XQD2R0gH0uywMkFljYt8.GE9+tS0IANdf8Ih3gpNFIIIooWYlqLv+CvGn5VTeswArwQD2T0gH0OySOlzvfLyuBvOFG1T04ZAVuHhs0gMkjjT2tHhqMhXcA1FfGt5dTeqEG3ByLeGUGhT+LG2TZHVl4NAbD3+6MUimBX+iHVkHhKs5XjjjjFLEQbB.qNMOcJRUXj.iNy7cVcHR8q7TjIMDJybW.78ZnpxkB70hHttpCQRRRZnVl4GF3PAV1paQ8ktefMMh3pqNDo9MdRxjFhjYtG3vlpFOJv1GQrtNrojjj5WDQbtQDKGvn.d9p6Q8cdi.WTl4pUcHR8abbSog.Yl6EvgTcGpuzIBrZQD+zpCQRRRpBQD6Iv6G3xptE02YAANmLy2a0gH0OwGKcoAYcNwlGZ0cn9N2AvADQ36aJIIIoNxL2efcB30UcKpuxCArYQDWd0gH0OvStozfnLy8EG1TC+NtQMpQ88bXSIIIo+SQDeaf2Kver3TT+kEF3BxLWipCQpefmbSoAIYl6CvAVcGpuxsRyEFzETcHRRRRscYl6DMe+5yS0sn9FOBMmfSeEIHMDxStozffLy8DG1TCeRfevXG6X2bG1TRRRZfIh3HAd2.mc0sn9FKHvYjY9tqNDodYdxMklAkYta.GV0cn9FiEXOhHN2pCQRRRpaUl4tBrO.KP0sn9BOHvlDQbUUGhTuHG2TZFPl4tC78ptC023HhH14piPRRRpWPl4aE3nAV2paQ8EdXfMHh3ZpNDodM9XoKMcJybW.FU0cn9B2FvF5vlRRRRCdhHt8Hh0C3qB7jU2i54sP.mSl46r5Pj503I2TZ5Pl41AbrU2g5K7y.9FQDOT0gHIII0qJybkANbf0o3TTuu6.3CGQbSUGhTuBG2TZZTl41.7yw+2OZn0C.7UiHNipCQRRRpeQl42fl2EmyY0sndZ2Iv5GQbaUGhTu.GmQZZPl4mC3X.l8paQ8zNIf8Nh3dqNDIIIo9MYlqAvOF3cTcKpm1X.1zHhau5Pj5143lRCPYleTfSGXVptE0y5QA1mHBekGHIIIUrLyCAX2vu+eMz45.9nQD2U0gH0MywMkF.xL2HfyDOwlZnyk.rSQDio5PjjjjTiLyMkl2EmukpaQ8rtFfMJh3AqNDotUNtozTQl46G37.lqpaQ8jlHv9GQbPUGhjjjjlxxLOYfOc0cndVWIMCb9nUGhT2nYp5.jZy57914zwgM0PiqGXccXSIIIo1sHhsBXao40HjzfsUCXzYlyS0gH0MxStozqhLyUA3B.VjpaQ8jNpHhub0QHIIIoAtLykE3HAV2paQ8jtPfOdDwyUcHRcS7jaJMEjYtb.mANroF78P.akCaJIII08IhXLQDqGvAB3.TZv1FA7KxLm4pCQpahmbSoWgLyEG32.rbU2h54bw.eAuMDkjjj5904cy+wArDEmh58bL.aeDQVcHRcC7jaJMYxLWHfQiCapAWOOv9EQ7AcXSIIIodCQD+Af2Evut5VTOmuHvnpNBotEdxMk5nyKu4yC38VcKpmxc.rCQD+tpCQRRRRCMxL+5.6Cv7UcKpmx2Jh36VcDRscNtoDPl4rQy6XyMs5VTOkyb7ie765HFwHt6pCQRRRRCsxLWMfeA9TfoAW6bDwQTcDRsY9XoK033vgM0fmI.rqQDebG1TRRRp+PDwUN1wN1sD3mUcKpmxOHy7yUcDRsYdxMUesLy.3G.7UqtE0yXLz7x+9OUcHRRRRpFYl6.vAgOl5ZvwD.1xHhyt5PjZi7jap9ceKbXSM34TF6XG6V3vlRRRR82hH9I.qGv0UcKpmvrC7KxLWqpCQpMxSto5akYta.GV0cndBuHvtEQ7CqNDIIII0tjY9y.11p6P8DdXf0Kh3FpNDo1DG2T8kxL2VfiFXVptE006VA9RQD+wpCQRRRRsSYlaOv2GXNqtE0061.13Hhaq5PjZKbbS02Iy7C.bQ.yZ0sntdmKvNFQbeUGhjjjjZ2xLWafiEXYptE006ePy.mOb0gH0F36bS0WIy7cBb13vlZFSB7chH9HNrojjjjFHhH9yicri8SBbVU2h558NANiLyYq5PjZC7jap9FYluMfyGXIqtE0U6gA1tHhyq5PjjjjT2oLy8E3.ptC006TA9zQDuT0gHUIO4lpuPl4BCbx3vlZFykA7AbXSIIIIMiHh3.A1BfGr5VTWss.3PpNBop4I2T87xLmCfKFXsptE0U63iH97UGgjjjj5cjYtr.m.vZTbJp619DQbvUGgTU7japdZYlyDvOCG1TS+l.vN6vlRRRRZvVDwXhHVSfiq5VTWsCHyzedE02xSto5okY98.18p6PcstKfsMh3RqNDIIII0aKybW.NXf4n5VTWoIBrg9ytn9QNto5YkYtG.GZ0cntV+IfsIh3NqNDIIII0eHybS.NZfEs5VTWoGAXChHt5pCQZ3jiapdRYlaAvoTcGpq0OMhX6qNBIIII0+Iy7MC7qAV8paQckFKv5GQLtpCQZ3hiapdNYluWfKBXNqtE004kn48q4QVcHRRRRp+Vl4OGv2ihZ5wUB79iHdlpCQZ3fWnPpmRl4JBbR3vlZZ28CrINrojjjjZChH1Vf8FHqtE00Y0.NwLyYt5PjFN33lpmQl4BAbh.KV0sntNWIvGLh32TcHRRRRRurHhCAXK.drpaQcc1LfCp5HjFN3ikt5Iz42Qp+.vZWcKpqyoDQ7opNBIIIIoWMcdB0NEfkq5VTWmcOh3+o5HjFJ4I2Tc8xLCfeLNrol1cfNrojjjjZ6hHtdf0E3bqtE004fxL+DUGgzPIO4lpqWl49.bfU2g5p7j.6YDwQWcHRRRRRSKxLOFfuP0cntJONv5FQbMUGhzPAG2Tc0xL+r.+B7+trF3d.fsHh3OWcHRRRRRSOxL2CZNfGyR0sntF2AvGHhXbUGhzfMGDRcsxLWCfKAXtptE003pA1xHhaq5PjjjjjlQjY9Qo4fdLOU2h5Z7WAVuHhwWcHRCl7cto5JkY9V.NUbXSMvcFicricabXSIIII0KHh3rA9f.982pAp2MfuZtTOGO4lpqSl47Abg.uqpaQcM9wQD6T0QHIIIIMXKybI.NYZFtRZfX+hH1+piPZvhiaptNYlmFvlWcGpqPBrGQDGV0gHIIIIMTJy7WA7optC003yDQbxUGgzfAerzUWkLyCFG1TCLOIMueMcXSIIII0yKh3SCLpp6PcMN5Ly2a0QHMXvSto5ZjYt0.mP0cntB2MvmNh3xpNDIIIIogSYleMfCAXVqtE05c6.enHh6r5PjlQ33lpqPl4ZB7GAl8hSQseWOvlGQbqUGhjjjjTExL2BfiAuI00T2eG38EQ7bUGhzzKerzUqWl4hCbR3vlZp6RF+3G+l5vlRRRRpeVDwoBrA.OP0snVuUG3HqNBoYDdxMUqVl4r.7mva9OM08KiH15piPRRRRpsHybEo4lTeEptE05sGQDeupiPZ5gmbS0ZkYF.+PbXSM0cDNrojjjjz+oHhqGXiA7cQulZN3LyMt5Hjld3I2TsVYleEfin5NTq29FQbPUGgjjjjTaVl44ArIU2gZ0dPf0IhXrUGhzzBG2TsRYlaDvYALaU2hZsdQfuXDwwUcHRRRRRcCxLOVfsq5NTq10SyELz+t5PjFn7wRWsNYlKEvQgCapWcOAvmvgMkjjjjF3hH9B.Gb0cnVsUD3HxLm4pCQZfxwMUqRl4bB7q.V7paQsV2OvlDQb1UGhjjjjT2lHh8A3qWcGpU6yBrWUGgz.kOV5pUwGSBMUb6.erHhan5Pjjjjj5lkYtM.+TfYs5VTqzKBrkQDmQ0gHM033lp0Hyb2A9dU2gZstZfMOh3NpNDIIIIodAYlaFvu.XtqtE0J8Hz7927VpNDoWKNtoZExLWOfKD+cMTSY+YfOYDwCVcHRRRRR8RxLe+.mFvBTcKpU5pAVuHhGu5Pjd033lpbYliD3u.rXU2hZktfHhMo5Hjjjjj5UkYtp.mM9yjoorSHhXapNBoWMdgBoRkYN6.mB9ODUSYmjCaJIIIIMzJh3pA1PfwTcKpU5+NyzKXH0Z43lpZeWf2U0QnVoiOh3yVcDRRRRR8ChHtIfOBv0VcKpUZ+xLeeUGgzThOV5pLYledfed0cnVoiHhXmqNBIIIIo9MYlKJv4.7NptE05bO.umHh6t5PjlbdxMUIxLWYfCq5NTqzA5vlRRRRR0Hh39F0nF0OC3xptE05rX.Gel4rTcHRStYt5.T+mLy4C37.V7paQsN6aDw9WcDRRRRR8yt3K9h+GiXDiX1Vq0ZsdIfko5dTqxRBLq6+9u+WR0gH8x7wRWCqxLmYfiC3yUcKpUIA1qHhQUcHRRRRRZRxLOGfOb0cnVkWB3yDQ7qqNDIvwM0vrLyuJvOr5NTqyWNh3npNBIIIII8+Ul4Y.7wqtC0p7H.u6Hhau5PjbbSMrIy78B7aAlipaQsFIvWHhvKVJIIIIoVrLyiCXaptC0pbU.qcDw3qND0eyKTHMrHybgANVbXSMISD3y6vlRRRRRseQDedfio5NTqx6.3fpNBIG2TC457d17mBrzU2hZMdNfuXDwITcHRRRRRZfIh3KA3gSPStcIy7yWcDp+lOV5ZHWl4d.bnU2gZMdIfOYDwnqNDIIIIIMsKy7n.1gp6PsF+KZd7zu4pCQ8mbbSMjJyb8ANOfYs5VTqvK.rEQDmY0gHIIIIooeYlGNvtTcGp035AVqHhmt5PT+Gerz0PlLy2.M2L5Nro.3Yn4croCaJIIII0kKhXWANpp6PsFqHv2MyzCQmF14+kNMjIy7b.9vU2gZEddfOSDwoUcHRRRRRZviOh55U3yEQbhUGg5u3I2TCIxL2cbXS03Eo4croCaJIIII0iIhXGo4I1SBfuWl4xWcDp+hmbSMnKybMA9y.yR0snx8b.aSDwoTcHRRRRRZnSl4OBXmptC0JbEzbAC87UGh5O3I2TCpxLWPfiAG1TvD.9hNrojjjjTuuHhuBvOu5NTqvZ.b.UGg5e3I2TCpxLOQfOS0cnVgsJh3WUcDRRRRRZ3Sl4wArMU2gZE1jHhKn5HTuOO4lZPSl41CrUU2gJWRyshtCaJIIII8+i8tuiVxJqRXi+rEPnIojFBRPDQZjfjSRzHneFAQLqiIFyQPGmYDLLJMNJlyiXDU.yfhQTIifJHgljjjfX.EoQR896ONECHdu29Fpp104TO+VqZ08nNvSCzEUsOugwLQD+q.elp6PiD9nYlab0QntOW4lpuHy7gBbp.qb0snRsXfWcDwGp5PjjjjjTcxLOJfCn5NT491.O4HhEWcHp6xUtolyxLmGMOYNGrod8NXSIIIIIEQ7L.9dU2gJ2S.3fpNB0s4vMU+vaEXGpNBUtCJh3HpNBIIIIIMZHhXe.99U2gJ2gjYtsUGg5tb3lZNIy7+Gvqu5NT4daQDGd0QHIIIIoQKG6wdruWfSr5NToVVfiLybMpND0M4Ytol0xLWGfSAXCptEUp2WDwqq5HjjjjjznoLy0B33A15paQk5iFQ7xpNB083J2TyJYl2Gf2ONXywce.GrojjjjjlJQDWGviC3bqtEUp+sLymU0QntGW4lZVIy7k.7wqtCUpOcDwKp5HjjjjjT6Pl4FC7MA1zpaQk4Z.1kHhqn5PT2gC2TyXYlOLfeLvpVcKpLeiHhmR0QHIIIIo1kLys.33.VupaQk46A7DhHtipCQcCtsz0LRl4RA7QvAaNN6G6fMkjjjjzrQDw4B7T.9iU2hJydC3Yuo5ab3lZl5P.1kpiPk4LV3BW3qs5HjjjjjT6UDwYAru.Kp5VTYd2YlaS0QntA2V5ZZKybmA94.KU0snRbd.6SDwUUcHRRRRRp8Ky7oB7U.V5paQk3W.7viHtspCQsatxM0zRl4pC7IvAaNt5xA1OGrojjjjj5WhH9Z31Sdb11AbnUGgZ+b3lZ55sBr4UGgJwM.r+QDWX0gHIIIIotkHhOIvAWcGpLuwLSO56zbhaKcsD0aqB7UwUs43naC3wDQ7SqNDIIIII0ckYt.f2X0cnRbg.6VDwen5PT6jqbSMkxLWCfEfC1bbzhAdANXSIIIIIMnEQbP.e5p6PkX9.ukpiPsWNbSMoxLCfCGXiptEUh2PDwWp5Hjjjjjz3gHhWDvwWcGpDupLymX0Qn1I2V5ZR0a6nerU2gJwgFQbHUGgjjjjjF+jYdlzbYynwKWBvNDQ7mqND0t3J2TSnLyGDv6q5NTI9PNXSIIIIIUnmJv4UcDZn6AC795sKRkl17efQSnLyuBv9WcGZn66FQ73pNBIIIIIMdKybq.NNf0o5VzP2SMh3qWcDp8vgap+IYlOWfOa0cngtyHhXGqNBIIIIII.xL2YfeBvxVcKZn5Jn41S+ppND0N31RW+CxLWeZtcz03kK.3.pNBIIIIIo6RDwoB7bAVb0sngpM.3cTcDp8vgap+OYl2GZFr4ZVcKZn5O.7LiH9sUGhjjjjjz8TDwWE3MWcGZn6YmY5BvQSKtsz0+G2N5iktCf8Ih3GVcHRRRRRRSlLy2Cvqu5NzP0UBriQDWW0gnQatxME.jY5x9d7zqzAaJIIIIoQcQDuAfis5NzP05C7tqNBM5ygap6x6AX8pNBMT81hH9XUGgjjjjjzzQDw9A7KptCMT87xLeJUGgFs41RWjYtu.eUbX2iSNxHhWP0QHIIIIIMSjYtQ.+.fMr5VzPyEBrmQDWe0gnQSNbywbYlqMvYB7.ptEMzbZQD6b0QHIIIIIMajYtS.eefUp5VzPymJh3EWcDZzjqTOcH3fMGmbg.O0piPRRRRRZ1Jh3z.ddU2gFpd9YlO5piPilb3liw5ctU7hptCMzbC.O6Hhqs5PjjjjjjlKhH95.Gb0cnglkF3ClYtxUGhF83vMGSkYd+n41Q2+YfwC2IvKNh3rpNDIIIIIo9gHhE.7IqtCMzrI.uwpiPid7L2bLUl4G.3UVcGZn40EQ79pNBIIIIIo9sLye.vip5NzPwcBr6QDmR0gnQGNbywPYl6.vIArLU2hFJ9vQDuhpiPRRRRRZPHybco4FTe9U2hFJNMfcKh3NpNDMZvsj7XlLyU.3igC1bbwOxAaJIIIIotrHhqF3YBbiU2hFJ1If2T0QnQGNbywOuZfst5HzPwBA9WqNBIIIIIoAsHheIvARy1VVceGTl4lTcDZzfC2bLRl4liOciwEKB3YDQbkUGhjjjjjzvPDwWglKNW08sR.efLykp5PT8b3liW9en4M.T21hAdI8dxkRRRRRRiMhHNDfOW0cnghGCMGGAZLmC2bLQl4ygleiu59N7HhuX0QHIIIIIUgErfEbp.tXOFObn8tPozXLuszGCjYtN.mEvZUcKZf6XhHdZUGgjjjjjTkxL2XfSB3eo5Vz.2mIhv6ahwXtxMGO7VvAaNN3b.7lQWRRRRRi8hHtXfmOvcTbJZv6YmYtGUGgpiqbyNtLyGMv2EvCY2ts+HvdFQ7apNDIIIIIoQEYlGDvgUcGZf6b.1oHhao5PzvmqbyNrLykF3+FGrYW2cB7JbvlRRRRRR+ihHV.vQVcGZfaKAdYUGgpgC2ra6f.1tpiPCbuqHhub0QHIIIIIMJJh3E.btU2gF3NjLy4WcDZ3yskdGUl4FRykHzpTcKZf5XiH1upiPRRRRRZTVl4lB7i.V6paQCTekHhCn5HzvkqbyNnLykB3cgC1rq6hAdUUGgjjjjjzntHhK.30Qyw5k5t1+LymV0QngKW4lcPYlOAfuU0cnApEAr6QDmU0gHIIIII0VjY9VANjp6PCTKDXq8xEZ7gqbyNlLyU.3cVcGZf6M5fMkjjjjjlYhHNTfiq5Nz.0l.7lpNBM73vM6ddC.aQ0QnApOdDwGo5HjjjjjjZodo.WV0QnAp2fWtPiObao2gjY9fANMfUq5Vz.yYEQrcUGgjjjjjTaVl4tCbB.KW0snAluJvyHhXwUGhFrb3lcDYlAvwB7TptEMv7W.1oHhKr5PzbSl4CBXk.tez7vHVQf4AbeAVpd+O6NAtMf+NvsPye++OSy4s5MufErf89fO3C9iMjSWRRRpyJybcn4ynsBz74yV0d+765yoszz7cnWLvsSymSaQ.+IZ9bZ2DveIh35G5wqYkLy2HvBptCMPs+QDGc0QnAKGtYGQl4iA36g+8ztpEC7riHNppCQSe81FDaKvNArt.qIv+Bvp.rxz7AjmotEfaF3u160MvcO3yKG3RnYK1bcQDW8b6WARRRRcGYlaJvC.XiA1Hf0llgYtF.2eZ97Y20vMmoGgaIMC27FA9C.WKv0C7K.NyHheQe3WBZ.Hy7yA7bptCMv7a.1Aubg51bPXc.YlqHvofm0lcYGQDwqs5HzzWl4NC7Cn4CGWgqmlOT8kB7qnYnmWCvUDQbEE0jjjjz.Wl4V.rAzLHysDXy58yWap4ylk.u5HhOXA+4VKAYlqIvOC3gTcKZf4PiHNjpiPCNNbyNfLy2Dv6p5Nz.yIGQrqUGglYxL+z.+qU2wD3FnYEd9qn4gh7KO1i8XWq8a+1uSnzpjjjjlExLWeZVIl6HM6VlGBvFRy1HeTxuNhXqpNBMwxL2VfeJ0svDzf0eCXqiHtjpCQCFNbyVtLyMjlATrVU2hFHtAfGdDwEWcHZlIy7xoYECLp61AtZfqC3jn4C0ctQDWYoUIIIIMAxLeX.aOviB3gxcer+Lp6N.1sHhSq5PzDKy7UCbDU2gFX9RQDOqpiPCFNbyVtLyu.f+FztoEC7zhH9ZUGhlYxLet.e1p6XN32Cbt.mCvoQy4D0us1jjjjz3ndqntGNv1.r0zLPyYy4V9nfOSDwn3N6Q8jY9Y.d9U2gFHRfmPDwwUcHp+yga1hkYtSzb1frLU2hFHd+QDulpiPybYl+Df8r5N5i9izrM1OdfSYgKbgKZ9ye9mSwMIIIoNnLy65x94wCr6.aNy7K2mQU+IfsHh3ZpNDMw5c9adh.yu3TzfwoAr6QD2d0gn9KGtYKUl4x.7yo4rkQcOmZDwtTcDZlKybyANa51OzgqfliCiuNvoDQ76JtGIII0h06B.5QC7jo4B.59UaQCTOiHhub0QnIWl41Qyw0zxVcKZf30DQ79qNB0e4vMaoxLed.GY0cnAh+DMOMoyq5PzLWl4a.3vqtignqE3hA9N.e2HheSw8HIIoVfLyGAv9Sy4m4lw3yfjNgHh8t5HzTKy7MBrfp6PCDWMv1DQbCUGh5eb3lsPYlqNvISyMAn5dd9QDs4yqwwZYlmMMmGTiitUfSG3XoYEc9KJtGIIIMh3vNrC6.OnC5f9s.OVf8FXSKNopbK.6bDwut5PzTKy7qSypIVcOu2HhWe0Qn9GGtYKTl46F3fqtCMP7YiHd9UGglcxL2SfeL9dq2keAvw.7shHtfpiQRRRCeYl6JMW.pOVfMr3bFUbnQDGR0QnoVl4FPy4u4Cr1Rz.vsArstqy5N7Kf2xjY9foYkQspU2h56NuHhMu5HzrWl46E30VcGiftYfymlAc90iHt3h6QRRRCPYl6LvyF3QfWLKSjSOhXmpNBsjkY9X.Ngp6PCDGcDw9WcDp+vga1hjYF.eIfCn5VTe2sB7HhHNkpCQydYlmKM2nmZx8Wn4If+cANtHhqt1bjjjT+Pl4Cilsv6dC3f6lZ2IvCOh3zqNDsj4NmryJAdpQDeipCQycNbyVjLychlasskp5VTe2AGQ3AVcKVl4illmpquu5z20A7s.NpHhSr3VjjjzLTl45Ary.u.f8hwmKEn9gORDwKu5HzzSl4YPykek5VNMfcKh3NpNDM23WBukHyboA9Q.6d0sn9teXDwit5HzbSl4mE34VcGsXKD3KPyfNuzpiQRRRStLycD3EA7D.Vyhyos5RiHdvUGgldxL2BfeJvpTcKpu6.iH93UGglab3lsDYlOKZ9h+pa45n41R7xqNDM2jYd4.aP0czA7mANCfOSDwWo5XjjjTidqRyC.3oArc32kbt5N.1yHhSt5PzzSl4AB7QqtC02cE.aUDwMVcHZ1y+ERs.Ylq.MKWZOK+5VVLvyOh3yWcHZtIybu.9A3QFQ+1E.7ko4RH5bqNFYjMexD...B.IQTPTIIowQYlOBf8C3ohqRy9M2Z5sLYleYfmd0cn9tCMh3PpNBM64vMaAxLeS.uqp6P8cerHh+spiPycYlebfWR0czg82A91.ezHheR0wHII00kYtl.OZfWFMmolZv3phHV+piPSe898FmNtis5Z9a.aSDwEWcHZ149Tc.ZpkYtF.upp6P8cW3BW3BcKMzAbXG1gcf.6S0czwsbzrE39wYl+lLyWyEdgW3VVcTRRRcMYlaRl46A3LA973fMGzVuLymT0QnouHhqG3URytvScGqHvqu5HzrmqbyQbYl+O.utp6P8UKFXOhHNopCQycYlOFZtkz0v0kC7M.9rQD+phaQRRpUKybuAd1.OQfUp3bF27diHbnJsLYlePfWQ0cn9pailaN8yn5PzLmqbyQXYlaFMaED0s7dbvlcJ6d0ALl5AB7Z.NsLyuTl4dVaNRRRsOYlOsLyeFv2E3YgC1rBO1pCPybKXAK37.Nmp6P8U2WfCs5HzriqbyQXYleFfme0cn9pyJhX6pNB0+jY9qAbKROZ3zANhHhub0gHIIMpp2YF3yA3kB7fKNGAIvizyU71mLycB3jwEMVWxcBr2QD+vpCQyL9aBGQkYtGzr0PT2wh.NvpiP8O89.MaV0cn+O6HvQkY9qyLeidtbJIIc2xLePYlGAvY.b33fMGUDzbSzqVlHhSC3sWcGpuZo.NrLykq5PzLiqbyQPYlAvwCr2U2h5qN3HhETcDp+Iy7+F3MWcGZRcE.eZfuPDwus5XjjjpPl41B7uBb..qZw4nI1uJhXqqNBM6jYdl.t675VdFtavZWb3lifxLeb.eabk01kbxQD6Z0Qn9qLyeLvdUcGZI5ORyPN+eiHVX0wHIIMLjYtC.uJZFp4RUbNZpcy.6bDw4VcHZlKyba.9Y.qP0sn9lyGXGiH9aUGhldb3YiXxLWVf2A92a5R9qz7zxUGRl4F.3SXucX0.NHfyLy7SkYN+pCRRRZPIyb2yL+t.mBMWRPNXyQeq.tq8ZshHNaf2V0cn9pGJvKp5HzzmCPazySAGXRWygFQbQUGg561Kf6e0QnYjUB3ERyMr9mKyzsOjjj5LxLeRYleOfeJMCJygZ1t7jpN.M6063G6mWcGpu5UjY5Q4QKgC2bDRl4JA7eUcGpu5DhHduUGgFHdhUGfl0tezbKwdxYlexdmEYRRRsRYl6SugZ9M.drU2il0dXYlOnpiPyIGHMWhrpaXi.dsUGgldb3liVd1.aZ0Qn9laD30WcDp+q2sv8NTcGZN69Ry1M4zxL+hYld6pKIoViLyGYuy+6iGGpYWvJBrEUGgl8hHNeb6o20bfYlqU0QnkLGt4HhLy6G9TA5ZdyQDmW0Qn9uMYS1j4Ar1U2g5aVZfmINjSII0BjY9nxLOQfeHdwF1034tYKWDwgQyQCg5FVcf2P0QnkLGt4niWNvFWcDpu4mEQ7wpNBMv7Hv2+rKZdzLjySIy7CmYtYUGjjjzcIybuxL+p.+.f8n5dz.win5.TewqCvaY6tiWZloypYDme47Q.YlqBv+V0cn9l+LvKo5Hz.09Tc.ZfZE.dY.mdl4BxL2npCRRRiuxL2lLyiB3GC7zptGMP8fyL24piPyM8t8zOjp6P8MqHvqn5HzTygaNZ30.rtUGg5adWQDKr5HzfQl4FB7vptCMTrB.uQfyLy7vVzhVz5WcPRRZ7Ql4FmY94ANEfCn5dzPw8AOpA5DhH9e.Nyp6P8Mu3LyMu5HzjygaVrLy0C3UUcGpu4GEQb3UGgFn1dfUt5HzP0p.bPyady6mmYdnG1gcXGX0AIIotqLy0Oy7CBbFzbgitrEmjFt1lpCP8MuHfat5HTew7.N3piPStn5.F2kY9N.dKU2g5KtMfsOh3bpNDM3jY9t.dSU2gJ0kRyJz9SWcHRRp6HybMAdg.uZf+khyQ04JhHdfUGg5OxL+uAdyU2g5KtYfcNh3bqND8OyUtYgxLev3Y2PWxayAaNVX2pN.UtMB3SkYdNYlO0piQRRseYlGHMag02INXywcqel4tWcDp+Hh3eG3rqtC0WrB.+WYltHAGA4vMq0qD39UcDpu3bhHdmUGgFrxLef.dVqn6xV.brYl+D+RHRRZ1Hyb+yL+U.eTf0q5dzHg.3wTcDpu50Cr3piP8EOIfsp5Hz+LGtYQ5c669rptC0Wba3sc+3hsGefD5e1dB7CyLOROnwkjzzQl4iHy73.9J3EUn9m4kJTGRDwIB7dqtC0WrL3Yu4HIGtYcdS.qV0Qn9hOVDwoTcDZnXOqN.MxZY.dd.mZl4aMybcpNHIIM5Iy7gjY9wA99.Otp6QirV+K7Buvsr5HT+yBVvBtTfKu5NTewSKybOpNB8OxyJfBjYtw.+RZNyFT61EuvEtv8a9ye9dVaNFHy7rvavRM8bMzboC8gpNDIIMZHy7sB75.V4paQi7tSfcIh3LpND0+z6rZ+nwEYVWvWKhXeqNBc272TUiWGNXythWuC1b7Pl4ZA3pwSSWqCvGLy7LxLehUGijjpSl4KIy7hANDbvlZ5YovKwxNmHhuFvwTcGpu3IjY5uGcDhC2bHKyby.dNU2g5K9ZQDe6piPCMOX71KUybaOv2r24w4lUcLRRZ3Iy7gmY98.93z74HjlI10pCPCD+m.We0Qn4rkA3M6Mm9nCGt4v2+FtpM6B9C.u1piPCU6J9dlZ164AbFYlGR0gHIoAqLy0My7S.7y.drU2iZs15pCP8eQDWDvgVcGpu3QArcUGgZ3WTeHJybS.dtU2g5KNrHhqr5HzP0Cu5.Tq2xC7VyLujLy+0piQRR8eYluIfyD3EieWKM2rdYl6P0Qn9uHhOJMuOgZ2VFf+iLSeu9Q.tDZGhxL+j.unp6PyYmbDgaSjwLYlWNvFTcGpS46B71hHNspCQi2xLWSf6KMeH86auWKcuWKGMCled89ueY58e9c8+9k5d7Zo68iPyPcBt6OqYdudsXZtvLtSf6n2Od62qW2AvsBrnduti6w+421c8ZgKbgqgm+0pZYlOYf+Cfss5VTmxyIh3KTcDp+Kyb6.NYZ92mp1qECraQDmR0gLtygaNjjYtA.mMvpVcKZN4NAdjQD+zpCQCOYlaOvoxc+k1k5WtUfi.3CFQ76pNF0dbXG1gcfGzAcPGGMG0MKK28fGWQfUA39060JeOdsR2ie9xdOdceuWutqAXNJtRDtqAbdWC27t942ZuW2FvMC7W.todu9q2iW2Xu+6tQf+N28fRWDvhhHttg3uVTGPl4FA7V.dAU2h5jVPDwAWcDZvHy7iPywVmZ29hQDO6piXbmC2bHIy7CA7xqtCMm8AhHd0UGgFtxLO.fip5NTm10B7uGQbjUGhpSuUP4J060pArVzbQlsF.qduWqIMCvbEtGutqgapYtESy.RWDMCE8td8m.98zbFa+G.tgd+eeszaPoQDWUEAqQGYluYf2HMOPAoAgSJhvaj4Npi4XNlG69tu66GFXiptEMmbm.6p6FqZ4vMGBxL2PfygluLhZu9c.aaDg2tciYxLOTf+qp6PiE9w.u4Hhyn5PT+Sl45RyvOVcf0E3Az60ZSyvJmGMCn7dtBKckhO55VnYEg9WnYPn2Vue90BbM.Wcue72A7mW3BW37bay2sjY93.d6.aS0snNuqGXqbUk2ckY97.Nxp6PyYGcDw9WcDiyb3lCAYluCZ1tJpc6EEQ7oqNBM7kYdb.Otp6PiMVDvGXgKbgGkCDYzWl4CflUY4ZCr9zrUvWMf0ilyo2Uilijl6OMCvTiW9q.+YZVInWCvU16G+qzrZPuBfaXAKXAOxC9fO3OVYUpokLyGHMel9WH98nzvQBr6QDmT0gnAmLyuCviu5NzbxMCryQDma0gLtx+kxCX89PP+BZ9xMp85DhH16piP0Hy7x.1vp6PictTfCNh3XqNjwYYlqEM67hUF3ARy6ErQ.OHf0glUi4pgWH.Z16uPyvOuAfKmleu+k06GudZNKPu7phSMxLOPZVslqd0snwNu9Hh2a0QnAmLyGJMyLXdU2hlS9R.O6Hhr5PFG4vMGvbUa1Ib6.6RDwun5PzvWl4CC3LvAWn57MAdsQD+1pCoKq2kBxZArw.yG3gPyvLWCt6gaNJdA6nts65lh+FAtNfKFXg.WHMCB85c6pNXkYtC.uOfco5VzXqiLhvKrpNtLyCC3fptCMmrHfsIhXgUGx3HGt4.Tl4pQySf4AVbJZtwaovwXYlOKfuP0cnwd+df2aDwgUcHsY81B4qCMCtbSnYEXsgzr8wWK7RAQsK2AMC77tF540Ry474E.b4QDWPgs0IzaXCuBfku5VzXseXDwit5HzfWl4EQyCYUsWenHhWY0QLNxgaN.kY9uC7NqtCMm76.1NWUDiuxLeO.u9p6PpmSB3MEQbxUGxnrdWje20sM9lCrEzLPy65R7QpqaQzbwFcU.mGMWrkKD3FW3BW38wyy2oVl4SD3cCroU2hDvkEQ3so8XfLyW.v+a0cn4jaBXGhHtvpCYbiC2b.Iybk.NWZVMHp85EDQbjUGgpSl4w.ruU2gz8vhA9ehHbqKAjY9Pn4Lv7gAr0zLDy0COqqklH+cZVomWAvuA3r68iWQDw0WYXiBxLWSf2Cvyt5VjtGtUfs2KpjwCYlGOv9TcGZN4cGQ7lqNhwMNbyAjLyWDvmr5NzbxoFQ34qzXtLySGXGptCoIvEC7VhHN5pCYXn2PG1PZVEl6.MqnpUAXco47vTRydWMMG+EWOMGoRmEvEMNs01yLeY.uIZd3HRiZd5QDe0piPCdYlaGvoArTU2hl0tVZ18mWS0gLNwgaN.jYtx.mNMWHApc51AdjQD+7pCQ0Iybso4K341XUix9j.GZDwuq5P5WxL2.t6yEyGDv1QyfMW2J6RZLyMSykVzEB7q68yOuHhytvl56xL2bf2AvSp5VjlBuyHh+ipiPCGYlGAvqt5NzbxaOh3+p5HFm3vMG.xLO.fip5NzbxmHh3kVcDpVYlaOvohO4TM56Z.dyQDetpCYlp2k7yZPyYi4CGXanYPlqQkcIoIzhAtLfKhlGj+oBbks0aF1LyWKvgBrRU2hzRv2Lh3IWcDZ3XQKZQq+7l27NMbAVzlcE.aQDwMUcHiKb3l8YYlKEvOGXmqtEMq8mn4P.9RqNDUqLymKvms5NjlA9N.uxHhKu5PlLYlyGXy.1cZ1d4a.MCxboqrKIMqcSzrk1u.ZtzyNMZ1R6WaoUMExL2FfOHfG+Pps37iH1rpiPCOYluJf2e0cn4jWVDwGs5HFW3vM6yxL2WfiF+qssYutHh2W0Qn5kY9tn472RpM4ZAN7Qg2Gq24j4lBrq.aEMqHyGBvJVYWRZf62AbI.WJvo.bVQD+pZSpQl4aG30fuOjZW9yzbF9cYUGhFdxLOMfcr5Nzr1ESyhl5FqNjwAN.t9rLyeBvdVcGZVagQDdVoJ.Hy7afmAWp85aQyVU+7GV+Ir2pxbio4R+Y2oYEZ54jojtSt6ytyeJMeguENLurExL2EZtIzc2Uo1nESyMldm5LuUSsLymDvWGmaSa1yJh3KUcDiC72jzGkYtCzrcbVlpaQyJKllahvio5PzngLyyklyAPo1pahlAb9gGD+AOybCo42i7nnY6cNeb0PIoomqA37A9I.mHvkFQb8Ch+DkY91nYmX3mQWsY6cDwITcDZ3Jy7n.Nfp6PyZmJvtEQbmUGRWmC2rOJy7nA1up6PyZeuHh8o5HzngLy0A3WfGj2pa3GQyYw4ELW9CRl4CllK7m8AXqoYUYtZy87jj3pnY6r+yn48rNmHhqat7GvLycC3Hn48sjZ6d0QDefpiPCWYlaFMemjkq5VzrRBreQDespCoqyga1mjY9PANK7McZqtcf8Jh3jqNDMZHy7gPyGjvaPU0U7m.9OiH9HS2+eHybinYn.aEvtQy1M+9OXxSR5evUCb1zrpWNGfydlLrydqVyWOvxOXxSZn6CFQ7ppNBM7kY99n4rBVsSemHhmP0Qz04vM6SxLeOz7AnT6zmHh3kVcDZzQl4NRyWnx2mTcMeKf2PDwEeu+un2E.zF.7HA1aZFl4pLbySRZBc0.+RfiC3zmrKnnd+6u+..6vPrMoggSHhXuqNBM7snEsn0edyadmJv5TcKZV4uCrKQD+xpCoKyuzdePl45SyG1ZUqtEMq72.15HhKo5PzniLymIvWr5NjFP9yzbVb9w6cI.sG.OQfsDu.fjznu6.3JnYGV7c.NoHhKOy7+B3fwUqo5lNuHBOK3GSkY9FAVP0cnYsuTDwyp5H5xb3l8AYl+G.u8p6PyZ+WQD92+z+fLy2Bv6n5NjFv9k.ODfUn5PjjlC9Czbdc9vpNDoAneOv1FQb0UGhpQl4ujliJH09bS.aUDwkUcHcU2mpCnsKybE.dlU2gl0tLGrolDaX0AHMDr03fMkT62piC1TceqJvZTcDpT98VauVIfmW0Qzk4vMm6d5.aZ0QnYsCu5.zHKGtojjjjFUrz3QGyXsd231mX0cnYsWRl4ZUcDcUNby4fLykA3kUcGZV6ziH9XUGgFYslUGfjjjjz8vCr5.T4NXfau5HzrxZAruUGQWkC2btYWvy7h1rCq5.znoLy0AugnkjjjznEW0Wi4hHNCfuQ0cnYsWPl48s5H5hb3lyRYl2GfWMvRUcKZV4DhH95UGgFYc+wgaJIIIoQKqT0AnQBGBveq5Hzrx1B7DqNhtHGt4r2VB7DpNBMqbm.+mUGgFos5.yq5HjjjjjtGt+UGfpWDw4C74ptCMqcfUGPWjC2b16YPyg5rZeN1Hhyr5HzHsGP0AHIIIIcu3EJjtKuWf+X0QnYkcIybKqNhtFGt4rPl4ZB7bptCMqbSzrL9klJtkzkjjjznlUu5.zngHhKE38WcGZVYd.u7pinqwgaN67b.V6piPyJGcDwETcDZj2JTc.RRRRR2Kq7EdgWnq3KcW9D.+9piPyJGPl45WcDcINbyYnLykhlsjtZetQf2c0QnVAOr1kjjjznlUZS1jMwKRFA.QDWO98aaqVYfmT0Qzk3vMm4dr.aU0QnYkOZDwEWcDpUXMpN.IIIIo6kUBXEqNBM5Hh38AbIU2glUNvLS2wf8INbyYfdqZyWA9W2Zi9izrr8klNVspCPRRRR5dYY.teUGgF47ApN.Mq7PAdhUGQWgCoalYS.1ypiPyJu2HhKu5HTqgqbSIIIIMJxOmp9GDQ7AA9UU2glUNfpCnqvgaNy7bo4lsRsKWSDw+c0QnVk6e0AHIIIIMAV0pCPijdeUGflUdLYldIg0G3vMmlxLWSZFtoZe9vUGfZc7gXHIIIoQQqR0AnQOQDeNfeY0cnYrkC3kWcDcANbyou8CXsqNBMicwtpM0LQuGjgC2TRRRRihV4pCPirdaUGflU12deGTMG3vMmFxLC7rPns5iUc.p0YYo4InIIIIIMpwiOIMghH9F3p2rMZ0.9+UcDscNbyomcEXmqNBMicIKXAKXQUGgZclGvxWcDRRRRRSfUp5.zHs2IPVcDZF6eKy79VcDsYQ0AzFjY9EAdlU2glwdwQDeppiPsK8NPmOS.+WtHIIIoQMGcDw9WcDZzUl4IBrGU2glwdrQDe+pinsxUt4RPl4ZC7XqtCMicYNXSMKcewAaJIIIoQStxM0RxGp5.zrhOzh4.Gt4R19QyYffZWVP0AnVKuLgjjjjznJuPgzTJh3X.Nwp6PyXOkLy0q5HZqb3lSgLyU.3kVcGZF6BiH93UGgZsVgpCPRRRRZRrrUGfZEN7pCPyXqJvys5HZqb3lSscCXypNBMi4fM0bgWlPRRRRZT0RWc.ZzWDwwSy8HfZW1+Lyko5HZib3lSsmd0AnYrKJh3HpNB0p4SCWRRRRipVppCPsFuMfEWcDZFYy.10pinMxgaNIxLeP.O4p6PyXuupCPsd9zvkjjjznJ+N7ZZIh36.bVU2glQVJfCr5HZi7MFmbOCf6e0QnYjq5Vtka43qNB054vMkjjjznpn5.Tqx6u5.zL19jYtgUGQaiC2bBjYtT.Okp6PyX+OK+xu7WY0QnVOGtojjjjFU4vM0zVDwWD3LptCMirR.6S0Qz13vMmX6FvVUcDZF4ZiH7oRo9Ae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p5B6NBMZ3EJzTfjro.mJ9sLMM6+E3dWU8M5NDIoEhj77ANnt6PRiLmPU0Cr6HjjVUkjCDXe5tCMRcl.aQU0ep6PzngO4lSApp9R.uot6PiT2PfOT2QHIsPjjcF3U2cGRZj5AjjCq6HjjVUjjcDeaRl1cY.6oCaNcywMmdbP.WT2QnQp6PRdmcGgjz7QR1Bf2A9VFHMK3wM2SBkjzXqjrd.uV7MZcZ2GXtGHLMEywMmRTUcw.urt6PibOkj7L5NBIoUGIYi.9X.WmtaQRKZ1mjrWcGgjzUiODvZ2cDZj5hAdUcGgF8bbyoKuGfSq6HzH2xRx8r6HjjVUL2sm7gAbS5tEIsn6.Sx10cDRR+8RxaA3tzcGZj6kVU886NBM54ie8TljbuANQfqc2snQpuKvVVUcIcGhjzUmj7I.1lt6PRs4mAbeqpN6tCQRBfjrKL3ACRS29p.2yppKs6PznmO4lSYpp9B.uqt6Pib2d7ePVRi4Rxq.G1TZV2MA3CeNmy4rwcGhjTRtq.ugt6PibWNCtDgbXyYD9jaNEJIqKvYBbS6tEMxs+UUu7tiPR5uWRdD.e7t6PRiMNxppcr6Hjzrsj7s.1ft6PibGZU0N0cDZwiO4lSgpp9g.ujt6PKJ1ujr8cGgjzesjr9.ust6PRiUdrI4E0cDRZ1UR9n3vlyBtPf8q6HzhKexMmRkxfR0u...f.PRDEDUjqMvoBbO5tEMx8S.d.UUe6tCQRBfjbh.2ut6PRictLFb9a5EfojVTkj8A3.6tCsn3YVU8V6NBs3xmbyoTUUWNvdxfODolts1.GQ2QHIAPRN.bXSIck65.bnd9aJoESIYa.d0c2gVT7kAd2cGgV74St4Ttj7lAdlc2gVT7AqpdhcGgjlckjs.3K.rFc2hjFq4mYQRKJRxF.b7.qS2snQt+DCtczOitCQK97I2b52qF376NBsnXmRxKr6Hjzro4dRrd23vlR5Z1NkDG2TRKFNTbXyYEuKG1b1kO4ly.RxiF3C2cGZQwJA1tppOQ2gHoYKI40CrWc2gjlX7iAt2UUeutCQRSmRxG.vaL6YCmOvcqp5W1cHpG9jaNa3iBbrcGgVTrF.uqjrztCQRyNRxChAmyyRRqpVGf2X2QHooSIYuwgMmk7xcXyYaNt4LfppUxfeoy+2taQKJto.G0xW9x25tCQRyLdM3qitjV88fSxSn6Hjzzkjrc.GP2cnEMKmAG+.ZFluV5yPRx9C7R6tCsn4HppdbcGgjltkjWAvKt6NjzDqKhAuJg+3tCQRS9RxFBbh.qU2snEE+dF7ug7c5ND0KexMms7p.NqtiPKZ1wj7J6NBIM8JI2Af8t6NjzDs+C7KHQRCAKaYKa2ANJbXyYIubG1TfO4lybRx8A3jv++8yJtBfcqp581cHRZ5SRNFfGV2cHoIdWAvCpp5D5NDIM4JIGMvip6NzhluNv8np5O1cHpe9jaNiop5yCbvc2gVzbs.diI4t0cHRZ5RRdL3vlRZ33ZAr+cGgjlbkjC.G1bVxkArGNro9ybbyYSubfKn6HzhlqOvQkjaU2gHooJ6a2AHooJaQR18tiPRSdRxthetjYMukppSo6Hz3Ce0jmQkjc.3Ci+uAlkb5UUaQ2QHoIeI44wfaHcIogoeTU051cDRZxQR1TfOGv+R2snEMmGvlWU8y5NDM9vmbyYTUUeDfCq6NzhpMOIevtiPRS1Nmy4b1Xf8p6NjzToadRdocGgjlLjjaGCt.gbXyYK6oCap+dNt4rsWFvOo6Hzhpmf+RCRZgXoKcoaKvZ2cGRZp0dM2fERRWkV9xW9VC7AAt4c2hVTc3UUeltiPie7URdFWR1IfOP2cnEc6bU06u6Hjzjk4N6d+Z.2ftaQRS0dKUU6Q2QHowWdynOS5GBroUUWT2gnwO9japCCvu4iYOusj7.5NBIMwY2vgMkzn2SJIqe2QHowSI40iCaNKZ+cXScUwwMmwUUsRf8D3WzcKZQ0R.duIYi5NDIMYHI2Rfcs6NjzLgqOv9zcDRZ7SRdl3Y+8rnOLCNFBjtR43lhppyC3.6tCsn6lAbDIYM6NDIMQ3oCbS5NBIMy3QjjaS2QHowGI4gAbvc2gVz8K.dIUUWQ2gnwWNto9yd8.GW2QnEcaDCtgAkjtJsrksrcGXG6tCIMS4FB775NBIMdHI2Yf2OvZzbJZw29TU8+zcDZ7lWnP5+ybuhxeQF7p.oYKu2ppco6Hjz3ojr2.Kq6NjzLmeGvcnp562cHRpOIYcANQfaW2snEcGOvC1mZScMwmbS8+op5rAdYc2gZwSNIuhtiPRis1stCPRyjt93SMtjFbdK5vlyd9o.OKG1TqJbbS826fYvSuol87hSxSq6Hjz3kjrs.21t6PRyrdZKe4Keq6NBI0ijbj.aZ2cnV7JqpNmtiPSF70RW+CRxF.bZL3rNRyVtBfGVU0mo6Pjz3gj74.tOc2gjlo8zqpd6cGgjVbkjCF3Y0cGpEeJfGYU0k2cHZxfO4l5ePU02Fu8zmUcs.N7jr4cGhj5WRtW.2qt6PRy7dbcGfjVbkj8EG1bV0uF343vlZ0giapqJuVfOa2QnVbCANzjr9cGhjZ21i2JoRpea9beYKRZFPRd7.GP2cn17hpp9tcGglr33l5JUU0JAdF.qn6VTKt0.ezjrVcGhj5w4bNmyFyfwMkj510B3o2cDRZzKIOHf2W2cn1bLUUu0tiPSdbbScUpp5bA16t6PsY8AN5tiPR8XoKcoaFvZ2cGRRy49mj0r6HjznSR1TfCE3Z2cKpE+DfmW2QnISNtotZM22Zxw1cGpMaVR9ncGgjZwSr6.jj9qbiAd3cGgjFMRxsG3vYv+stlMsu95nq4KG2TqJ1SF7snnYSaaRdOcGgjV7jj0C3NzcGRR+c10tCPRCeIYsYvaL1sp6VTa9vL3o1UZdwwM00npp+GfWb2cnVsKI40zcDRZQy1Cb86NBIo+N20jbW6NBIMz8Q.1vtiPs4GCrWUUWQ2gnIWNtoVU8AYvqIflc87RhmAqRyF1gtCPR5JwZ.7.6NBIM7jjOFvl2cGpMWAvyqp5h5NDMYywM0pj4t8z2afKr6VTqVVRdJcGgjFcRxlguR5RZ7010c.RZ3HIuKfGY2cnVcHUUGY2QnIeNtoVkUU8iA1CF7sqnYWusj7X5NBIMxbe.ptiPR5pvcLI9TdIMgKIKCX25tC0pyA3E0cDZ5fiapUKUUeb.ubYlssF.uuj3qElzzoGU2AHIc03ZgO8lRSzRx9xf2JPM6J.O0ppeV2gnoCNtolO1Wfud2QnVsDfObR1ztCQRCOI4t.bG6tCIoqA2ytCPRyOIYOANft6Ps6.qpN4tiPSObbSsZqp5W.7L.VY2snVcCAN5jbm6NDIMz7v.9m5NBIoqA2gjrwcGgjV8jjcB3MzcGpcmIv90cDZ5hiap4kppSCX+6tC0t+CfOZRVZ2gHoghMq6.jjVErDfGT2QHoUcIYa.de3FDy59M.6VU0k2cHZ5h+fEsPbf.mR2Qn1cKA9DI41zcHRZ9KIqMfOI1RZRwin6.jzplj7P.9P39CBdQUUdD2ogN+gKZdqpZk.OEfeR2sn1sTfkmj0s6Pjz71lCbS5NBIoUQarewpRi+RxV.7A.9W6tE0tkC715NBMcxwM0BRU04.7B5tCMV3NxfANWytCQRyKOztCPRZ0v0GXC6NBIcUKIaBvGC3F2cKpc+.fmYU0UzcHZ5jiapggODv6n6HzXg6FvGu6Hjz7xVzc.RRqltucGfjtxM2k90mBeqPzfKh3cup5R5NDM8xwM0BVUU.1Wfys6VzXgMMImX2QHoUcycofsNc2gjzpo6c2AHo+QIY8A9v3msPC75ppN1tiPS2bbSMTTU8q.dh.+wtaQiEteI4S1cDRZU1l.bc6NBIoUSq+bOcXRZLQRtEL3rUb85tEMV3KCr+cGgl943lZnop5KC7J6tCM13gmjOQ2QHoUI9pcJoIQ+y3QpgzXijrNL3UQeC5tEMV32C7jqp9CcGhl943lZX6.YvgFsD.aSR7L3TZ72l1c.RRyS2gtCPRPRt4.eB7h9R+E6YU02t6HzrAG2TCUUUWNvyE3B6tEM13QjjOX2QHoqbIY8.tsc2gjz7zlzc.Ry5RxZwfyXy6R2snwFGZU06s6HzrCG2TCcUUW.vtBbEc2hFa7DRxg2cDR5J0cD350cDRRySq+bmweRpAKe4KeqANFfMq6VzXiyF3Y0cDZ1x0t6.zzoppiKIGDvKn6VzXicLIW6ppGc2gHo+F9TaJoIY2HfMB3GzcHRyZVwJVw5tjkrj8G3t1cKZrweBXWl6BGVZQiO4lZT5k.745NBMVYG7I3TZrycr6.jjVf7bCVZQVRVykrjkbn3Sro9a87ppNitiPydbbSMxL24u4y.3h5tEMVYGSx6q6Hjz+GuLNjzjNepwjVDsrksrcG3i.rkc2hFq7wppdScGglMUcGfl9kjsgA2bdR+09vUUO1tiPZVVRVefyB351cKRRK.e2ppk1cDRyJRxWB3dzcGZrx2EXypp9kcGhlM4StoF4pp9j.GT2cnwNOlj7w6NBoYbqGNrojl78ukj0s6Hjl1kj0LImBNro9acY.6rCapN43lZwx9BbJcGgF67HRxg0cDRyvtScGfjzPv+Nf2X5RiPqXEqXcANBf6Y2snwNufppSu6HzrMG2TKJppVIvtB7i6tEM14wkjOV2QHMixyaSIMMn.1jtiPZZ04bNmyFujkrjiBXq5tEM14n.N3tiPxwM0hlppuKvSq6NzXoGYRNttiPZFzMq6.jjFR7KqQZDHI+GKcoK8PvWEc8O56.r6UUo6PjbbSsnpp5X.dYc2gFK8.SxIs7ku7st6PjlgbC6N.IogjMr6.jl1jjaECtXXuqc2hF67aXv4r4up6Pj.G2T83kA745NBMVZq1tsa612jrVcGhzztjb6A7+VSRSKVG+BRkFdRxsD3iAb2ZNEMdZepp9xcGgzeV0c.Z1TRVGfu.vso6VzXouBv1TU8S5NDooUI49CbBc2gjzPxkBbWqp91cGhzjtjbaA9T.29taQikdGUUdbyowJ9japVTU8iA1EfKu6VzXo6BvwlDG+VZzYocGfjzPzRvyQXoErjrI.GKNrotx8eC776NBo+dNtoZSU0W.Xe5tCM15N.7oRhmgVRiF25tCPRZH6eu6.jljkj6JvQiucc5J2k.73qp9ccGhzeOG2Tc6M.bXcGgFasTfiII24tCQZJjiaJooM+GcGfzjpjro.GC94CzUtq.3Y6Q+gFW43lpUUUAX2A9lc2hFacq.9zIYy5NDooL2htCPRZH6V0c.RShRxVBbb3EMntp8JppNxtiP5phiap1M2i09NA7S6tEM1ZMYv.mOntCQZZvb2nv2nt6PRZH6V1c.RSZRx1B7IAtAc2hFa8Y.NftiP5pi2V5ZrQR1QF7Jp6+6RcU4RAdRUUGU2gHMIKI2dfyD+EYjzzkuVU0cp6HjlTL2u+0gvfKjKoqLmGvVVUcQcGhzUGexM0Xippi.3U1cGZr1R.N7j7T6NDoIbWOfqe2QHIMjcCm6ISWRWCRxtyfGrDG1TWU98L3BDxgM0XOeB4zXkjbs.9D.OrtaQi0BvKpp5U2cHRShl6Lr8z6tCIogreMvcsp575NDowYIYe.Nvt6Pi810ppCo6HjVU3StoFqTUcE.6BvY2cKZrVA7pRhenLo4m+8tCPRZD35iG2FRWsl6yO6mgVWSdcNrolj33lZrSU0OEXmYv29tzUm8IIu6tiPZBzZ1c.RRi.qAvMs6HjFWkj2Av9zcGZr2o.7B5NBoUGNtoFKUUcV.Okt6PSD10j7o6NBoILNtojlVsVcGfz3nj7Q.7bqWWSNOfGSU0k2cHRqNbbSM1ZtaD6Wd2cnIBO3jbxI4l2cHRSH70RWRSqtwcGfz3jjrlI4j.19taQi89s.OAu.gzjHG2Ti0pp1efOd2cnIB2KfiIIaX2gHMAvwMkzzJG2TZNI41B7oA1ptaQSD1yppuT2QHMe33lZRvt.705NBMQXS.NtjbO5NDowb+acGfjzHh+7MIfjbm.NAf6b2snIBGXU06u6HjlubbSM1qp5WB7D.9Yc2hlHrN.GeRdjcGhzXL+k+kzzpqa2AH0sjr0.GGvsr4TzjgOMvKp6HjVHbbSMQnp5rYvMn9UzcKZhvM.3Cmjcu6PjFSc86N.IoQj+4tCPpSIYGAVNvMo6VzDguIvNWU4umsln43lZhQU0mB3E1cGZhw0A3smjWV2gHMF55zc.RRiH9y2zLqjrG.GN9kXpUM+Lfcpp5m1cHRKTNtolnTUcP.u6t6PST1uj7d6NBowEKe4KeqA9W5tCIoQD+4aZlTRds.uot6PST1opJuaKzTgp6.jVckjqKCdUKdvc2hlnb7.OwppKt6Pj5TRVafyDXs6tEIoQfSqp5d1cDRKlRxGF3Q2cGZhxyop5MzcDRCK9japINUUq.X2.N2taQSTdf.GaR1ftCQpY+Sy8GIooQ9japYFIYsSxIfCapUOuEG1TSabbSMQpp5GCr8.+htaQST1DfOWR1xtCQpQNtojllcs6N.oECIYiXvalz8u6VzDkiEXu5NBogMG2TSr9qtA0WY2snIJ2TfOUR1stCQpINtojll43lZpWRdf.mHvF1cKZhx2B3IUUc4cGhzvliapIZUUGCvyu6NzDmqOv6zaRcMiZMl6ORRSibbSMUKI6Bvm.XM6tEMQ4hAdLUUWR2gHMJ33lZh2bmWHGb2cnINECtI0e2cGhzhLG2TRSy7muooVIY+AdO3YKqV8rBfmbU02p6PjFUbbSMs34B7o6NBMQZWSxolj0s6PjVjTy8GIooQWqksrks6cGgzvVRNTfWZ2cnIRO2ppOS2QHMJ43lZpPU0U.73.NqtaQSj1BfOeR17tCQZQfiaJooY0s41batftiPZXII2xjbx.O9taQSjdYUUuitiPZTywM0TippeMvNAb9c2hlHcq.N1j7X6NDoQLG2TRSypMZi1nKp6HjFFRxcG3y.bu5tEMQ5H.d4cGgzhAG2TSUpp9NL3I37R6tEMQ5eE3Hl67LRRRRSd7KuQSERx1yfaD80q6VzDouDCtYzuhtCQZwfiapoNUUeYFLvY5tEMw5kljip6Hjjjjzrmjru.GEC9h2kVc8s.1gppKq6PjVr33lZpTU0GG342cGZh1NjjuTRV+tCQRRRRyFRxg.b.3SgrletDf+yppKr6PjVL43lZpUU0qCXYc2glncO.Nwjr0cGhjjjjldM2EGzo.7j6tEMw5OxfmXyud2gHsXywM0zt8E3CzcDZh1ZC7ekj8p6PjjjjzzmjbOANEf6Y2snIVWAvSop5T5NDoN33lZp1bGfxOUfSt6VzDsqMvqOIuutCQRRRRSORxyD3SAby5tEMQaeqp9fcGgTWbbSM0qp5OB7nA7wyWKTOojbpIwasRIIIIsfjj2HvaF3FzcKZh1qspxiiMMSywM0LgppKAXGAtftaQS71BfOumCmRRRRZ9HI2rjbb.6Y2snIdGIv9zcDRcywM0LippuCCdBN+sc2hl3sl.epjrucGhjjjjlb7Wc9Z9.6tEMw6T.dhycTrIMSywM0LkppyBXG.t7taQS7VCfCHIenku7k6SwojjjjtZkjmFvmA3V1bJZx2WE3QWUcYcGhz3.G2TybppNNfmAvJ6tEMU3+b61tsaYI4N2cHRRRRZ7TRd8.uMfqe2snIdW.viqp5h6NDowENtolIUU8t.dwc2glZrI.etjrycGhjjjjFejjaYRNUf8p6VzTgeEvipp5b6NDowINtolYUUcf.uxt6PSMtA.u2j7V6NDIIII0uj7v.NMFbgTJsP86A11ppud2gHMtwwM0rt8C3c2cDZpxSOIetjrdcGhjjjj5QRdg.ebf0t6VzTg+DvtUU846NDowQNtoloUUEfmJvQ2cKZpx8A3jSxNzcHRRRRZwSRVyjrbfWECt.JkFFdlUUGQ2QHMtxwM0Lu4F37I.bhc2hlpbS.Npj7Z6NDIIIIM5kjMC3TA1ttaQSUdAycmQHoqBNtoDPU0J.1Qfyn6VzTmmaRNojrztCQRRRRiFI44Bbr.21taQSUNvppk0cDRi6bbSo4TU8yA1dfuU2snoNaECtM08awWRRRZJx4bNmyFmj2GvqkAWvjRCKusppWX2QHMIvwMk9qTU8i.1Vfyu6VzTm+Cfk6qotjjjzzgjb2V5RW5Q.7j5tEM04v.1itiPZRgiaJ82op56xfmfyKp6VzTomaRNsjrAcGhjjjjleRxyF3D.7yzogsiC3IWUcEcGhzjBG2T5JQU0+MCF37+s6VzToMG3ymjmX2gHIIIoUcKe4KeqSx6E3M.7+q6dzTmSC3QWUcYcGhzjDG2T5pPU0oCrC.+gtaQSktI.u+j71V1xV1t2cLRRRR5pWR17sa61tWGvN2cKZpzYArcUU+ltCQZRiiaJc0np5DA9OAt7taQSsdZ68du2OyjrYcGhjjjjtxkj8F3DA1vtaQSk9NLXXyKo6PjlD43lRWCppNZfcEvy7DMprg.mPRdQcGhjjjj9KRxMOIebfkArjt6QSk9t.aSU0Or6PjlT43lRqBpp9..OcfzcKZp00C3UljkmjaU2wHIIIMqKIOLfOOvin4TzzqyGXGppNutCQZRliaJsJpp5cB7b5tCM0a6.9xI4wzcHRRRRypRxABbL.25taQSstHF7pn+M5NDoIcNtozpgppCF3EzcGZp2MA3HSx6t6Pjjjjlkjj6PRNcf8o6VzTseNvCup5q0cHRSCbbSoUSUUKCvyFQsXXWSxYmjGb2gHIIIMsKI6IvIA3E8nFk9E.OhppuR2gHMsvwMklGppdU.upt6PyD1PfiII6e2gHIIIMMJIqcR9P.uQfab28noZ+JfGWU0o2cHRSSbbSo4oppWDvqo6NzLg0.3kljuTRticGijjjzzhj7n.NCf+ytaQS89C.aeU0w2cHRSabbSoEfpp8F3f5tCMy3d.74RxKt6Pjjjjlzkj2NvxAVmtaQS8tLfsop5j5NDooQNtozB2K.3szcDZlwMD3UjjOQRVZ2wHIIIMoII2yjbV.6N96DqQueOvSrp5D5NDooU9CxkVfppRU0d.715tEMSYa.9hI4YzcHRRRRSJRx9A7YAtKc2hlIbY.O4ppir6Pjll43lRCIUUOCf2d2cnYJ2Hf2RR9LI4V2cLRRRRiqRxljjSE3kAbc5tGMSHL3xC5n5NDoocNtozv0yD3s1cDZlyCB3zRxdzcHRRRRiaRx9.bR.aQ2snYFWNCF17i1cHRyBbbSognppq.XOAdmc2hl4rV.uoj7wRxss6Xjjjj5VR1vjb7.GHv+V28nYFWJvt4qhtzhGG2TZHqp5Jpp1cf2Q2snYRORfyHIO8tCQRRRpKI4YAb5.OftaQyTVIvSpp582cHRyRbbSoQmmAvar6HzLoaDvaMIGaR1vtiQRRRZwRR1fjbb.GLvMn6dzLkUBr8dFaJs3ywMkFQl6UT+4.7l5tEMyZqAN0jrWcGhjjjzn1bOslmBvCr6VzLmeOviup5i2cHRyhbbSoQn4dE0eV3Svo5yMD30mjOeRtycGijjjzv1bmsl+4mVSOaM0hseOCdUz8L1TpINtozhfppmMvqs6NzLs6MvojjWQ2gHIIIMrL2Mg9WDeZMUOtLfsspZ4cGhzrLG2TZQRU0yG3U2cGZl10E3EmjyJIOjtiQRRRZ9JIaZRNQFbSn+u1cOZlzuE3QVUc7cGhzrNG2TZQTU09Bb.c2gl4cW.9uRxaHI+GcGijjjzpij7h.9b.2utaQyr9k.OlppOS2gHIG2TZQWU0KF3kzcGZlWA7rANyj736NFIIIoqIIYKSxWG3UB7uzcOZl0uB3Q3vlRiObbSoFTU8JAd9c2gDv5.bnI4XRx52cLRRRR+8V9xW9Vmj2NvwArwc2ilocw.O3ppSs6Pjzew0t6.jlUUU8ZSxuG3sxfmhNoN8v.1hjrrppk0cLRRRR.jjcD3kCba6tEMy6B.1gppuR2gHo+V9jaJ0npp2NvtwfaYOot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0VizPhKG3y3SnijbbSo9DYleYfCGXVqtEoAYWIvQDQbRUGhjjTaTl4WilSxoO4NpWy3.1xHh+V0gHo533lR8AxLOLfcq5NjFj8G.FUDwEUcHRRRcCxL+z.ecfUt5VjFD8j.aeDwut5PjTMbbSodXYlyKvwBr4U2hzfjD3LANpHhKs5Xjjj5FkY9wA1If0o3TjFrLQfuUDwgVcHRZ3miaJ0iJybI.9E.u2hSQZvvDANeZt4y+iE2hjjTOgLyOAvN.7AptEoAIGNvdGQLgpCQRCebbSodPYlqBvoC7VptEoYPSD37.N3Hhqp5Xjjj5EkYt9.6C9aJt5MLZfsKh3eWcHRZ3giaJ0iIybiANQf4u5VjlA7R.mDvOHh3ZpNFIIo9AYlqCvdAr9EmhzLpq.XKhHFW0gHogdNtoTOjLycD3v.lypaQZ5zyAbp.+jHhqn5Xjjj5G042r7sGXSptEoY.2NM2j59z+H0iywMk5QjYte.e6p6PZ5zD.t.fCIh3JqNFIIIAYlaJvWCu3gT2q+EvVGQb9UGhjF533lRc4xLmMfiD3KTcKRSmNKfCzeW0kjjZm57N47aC7tptEooCSDXmhHN1pCQRCMbbSotXYluNfeIvFWcKRSGNWfCKh3OWcHRRRZpKyby.1Cf0r5VjlNreQD6e0QHoAeNtoTWpLyQRy6lP+lKU2l+DvOLh3rpNDIIIMsKy7KArS.qP0sHMM5vA1qHhIVcHRZviiaJ0EJybE.NSfkp5VjlFb8.emHhQWcHRRRZFWl4NCry.KY0sHMM3TA1lHhwWcHRZvwLUc.RZZSl45.76wgMU2iaklWj6qjCaJII06Hh3GN5QO5c.XOAdnp6QZ.ZK.9MYlKX0gHoAGdxMk5hjYtE.GMv7UcKRC.2MvOHh3vqNDIIIMzJybw.1dfcD+dUU2gqB3SEQbaUGhjlw33lRcIxL2FfeFdhqU62CCbbzLromhCIIo9HYlKMv9B7wAlqhyQZpYrzLv40Tc3OAmg0...H.jDQAQERZ5miaJ0EHybO.Nzp6PZp34A9kz7d07tqNFIIIUmLyUE36BrQU2hzTwi.7QiH9qUGhjl933lRsbYlGNvtTcGRSEiF3PhH9GUGhjjjZOxL2.f8CXMJNEoWKOIv1EQb5UGhjl143lRsTYlyJvQArcU2hzqg+Fv2Kh3LqNDIII0dkYti.6Jvas5VjdULAfuRDwwVcHRZZiiaJ0BkYNO.GCM2jeRsQ2Iv2Oh3HqNDIII08Hy7fA9B.u9paQ5UwtFQ7CpNBIMv4EShTKSl47AbV3vlpc5oANvQO5QuCNrojjjlVEQr2.qFvwWcKRuJN7Ly8u5Hjz.mmbSoVjLyEE3L.VypaQZJ3WRyif9MVcHRRRp6Wl4GBXu.d+U2hzTv2OhX2pNBIM043lRsDYluEZtTVVopaQ5U3uC7ciHN+pCQRRR8dxL+xz7937sTcKRuBGKvNFQ7BUGhjd043lRs.Ylq.vYBrTU2hzj4g.NnHhin5PjjjTuuLyQA7kAFQ0sHMYNcfOSDwDqNDIMk43lREKyb0n4DatXU2hTGSD3GA7ChHt2piQRRR8OxLe6.eSfMu5VjlLW.vVEQ7DUGhj9+xwMkJTl4ZQyvlKb0sH0wEAbfQD+kpCQRRR8uxL+T.6KvxWcKRcbw.aYDwiUcHR5+jiaJUjLy0G3z.lmpaQBXb.GPDwOq5PjjjjdYct0p+J.ye0sHAbk.ezHh6u5Pjzj33lREHyby.NQf4p5VTeuW.3vG+3G+QNhQLh6t5XjjjjdkxLWZfCAXyptEIfqC3iDQLtpCQRMbbSogYYlaEvOGX1qtE026R.9lQDWd0gHIIIM0jY9I.9N.KW0sn9d2.vmLhXLUGhjbbSogUYlaMvwALSU2h5qcO.GdDwgWcHRRRRSqxLOLfuHvbWcKpu1X.9XQD2R0gH0uywMkFljYt8.GE9+tS0IANdf8Ih3gpNFIIIooWYlqLv+CvGn5VTeswArwQD2T0gH0OySOlzvfLyuBvOFG1T04ZAVuHhs0gMkjjT2tHhqMhXcA1FfGt5dTeqEG3ByLeGUGhT+LG2TZHVl4NAbD3+6MUimBX+iHVkHhKs5XjjjjFLEQbB.qNMOcJRUXj.iNy7cVcHR8q7TjIMDJybW.78ZnpxkB70hHttpCQRRRZnVl4GF3PAV1paQ8ktefMMh3pqNDo9MdRxjFhjYtG3vlpFOJv1GQrtNrojjj5WDQbtQDKGvn.d9p6Q8cdi.WTl4pUcHR8abbSog.Yl6EvgTcGpuzIBrZQD+zpCQRRRpBQD6Iv6G3xptE02YAANmLy2a0gH0OwGKcoAYcNwlGZ0cn9N2AvADQ36aJIIIoNxL2efcB30UcKpuxCArYQDWd0gH0OvStozfnLy8EG1TC+NtQMpQ88bXSIIIo+SQDeaf2Kver3TT+kEF3BxLWipCQpefmbSoAIYl6CvAVcGpuxsRyEFzETcHRRRRscYl6DMe+5yS0sn9FOBMmfSeEIHMDxStozffLy8DG1TCeRfevXG6X2bG1TRRRZfIh3HAd2.mc0sn9FKHvYjY9tqNDodYdxMklAkYta.GV0cn9FiEXOhHN2pCQRRRpaUl4tBrO.KP0sn9BOHvlDQbUUGhTuHG2TZFPl4tC78ptC023HhH14piPRRRpWPl4aE3nAV2paQ8EdXfMHh3ZpNDodM9XoKMcJybW.FU0cn9B2FvF5vlRRRRCdhHt8Hh0C3qB7jU2i54sP.mSl46r5Pj503I2TZ5Pl41AbrU2g5K7y.9FQDOT0gHIII0qJybkANbf0o3TTuu6.3CGQbSUGhTuBG2TZZTl41.7yw+2OZn0C.7UiHNipCQRRRpeQl42fl2EmyY0sndZ2Iv5GQbaUGhTu.GmQZZPl4mC3X.l8paQ8zNIf8Nh3dqNDIIIo9MYlqAvOF3cTcKpm1X.1zHhau5Pj5143lRCPYleTfSGXVptE0y5QA1mHBekGHIIIUrLyCAX2vu+eMz45.9nQD2U0gH0MywMkF.xL2HfyDOwlZnyk.rSQDio5PjjjjTiLyMkl2EmukpaQ8rtFfMJh3AqNDotUNtozTQl46G37.lqpaQ8jlHv9GQbPUGhjjjjlxxLOYfOc0cndVWIMCb9nUGhT2nYp5.jZy57914zwgM0PiqGXccXSIIIo1sHhsBXao40HjzfsUCXzYlyS0gH0MxStozqhLyUA3B.VjpaQ8jNpHhub0QHIIIoAtLykE3HAV2paQ8jtPfOdDwyUcHRcS7jaJMEjYtb.mANroF78P.akCaJIII08IhXLQDqGvAB3.TZv1FA7KxLm4pCQpahmbSoWgLyEG32.rbU2h54bw.eAuMDkjjj5904cy+wArDEmh58bL.aeDQVcHRcC7jaJMYxLWHfQiCapAWOOv9EQ7AcXSIIIodCQD+Af2Evut5VTOmuHvnpNBotEdxMk5nyKu4yC38VcKpmxc.rCQD+tpCQRRRRCMxL+5.6Cv7UcKpmx2Jh36VcDRscNtoDPl4rQy6XyMs5VTOkyb7ie765HFwHt6pCQRRRRCsxLWMfeA9TfoAW6bDwQTcDRsY9XoK033vgM0fmI.rqQDebG1TRRRp+PDwUN1wN1sD3mUcKpmxOHy7yUcDRsYdxMUesLy.3G.7UqtE0yXLz7x+9OUcHRRRRpFYl6.vAgOl5ZvwD.1xHhyt5PjZi7jap9ceKbXSM34TF6XG6V3vlRRRR82hH9I.qGv0UcKpmvrC7KxLWqpCQpMxSto5akYta.GV0cndBuHvtEQ7CqNDIIII0tjY9y.11p6P8DdXf0Kh3FpNDo1DG2T8kxL2VfiFXVptE006VA9RQD+wpCQRRRRsSYlaOv2GXNqtE0061.13Hhaq5PjZKbbS02Iy7C.bQ.yZ0sntdmKvNFQbeUGhjjjjZ2xLWafiEXYptE006ePy.mOb0gH0F36bS0WIy7cBb13vlZFSB7chH9HNrojjjjFHhH9yicri8SBbVU2h558NANiLyYq5PjZC7jap9FYluMfyGXIqtE0U6gA1tHhyq5PjjjjT2oLy8E3.ptC006TA9zQDuT0gHUIO4lpuPl4BCbx3vlZFykA7AbXSIIIIMiHh3.A1BfGr5VTWss.3PpNBop4I2T87xLmCfKFXsptE0U63iH97UGgjjjj5cjYtr.m.vZTbJp619DQbvUGgTU7japdZYlyDvOCG1TS+l.vN6vlRRRRZvVDwXhHVSfiq5VTWsCHyzedE02xSto5okY98.18p6PcstKfsMh3RqNDIIII0aKybW.NXf4n5VTWoIBrg9ytn9QNto5YkYtG.GZ0cntV+IfsIh3NqNDIIII0eHybS.NZfEs5VTWoGAXChHt5pCQZ3jiapdRYlaAvoTcGpq0OMhX6qNBIIII0+Iy7MC7qAV8paQckFKv5GQLtpCQZ3hiapdNYluWfKBXNqtE004kn48q4QVcHRRRRp+Vl4OGv2ihZ5wUB79iHdlpCQZ3fWnPpmRl4JBbR3vlZZ28CrINrojjjjZChH1Vf8FHqtE00Y0.NwLyYt5PjFN33lpmQl4BAbh.KV0sntNWIvGLh32TcHRRRRRurHhCAXK.drpaQcc1LfCp5HjFN3ikt5Iz42Qp+.vZWcKpqyoDQ7opNBIIIIoWMcdB0NEfkq5VTWmcOh3+o5HjFJ4I2Tc8xLCfeLNrol1cfNrojjjjZ6hHtdf0E3bqtE004fxL+DUGgzPIO4lpqWl49.bfU2g5p7j.6YDwQWcHRRRRRSKxLOFfuP0cntJONv5FQbMUGhzPAG2Tc0xL+r.+B7+trF3d.fsHh3OWcHRRRRRSOxL2CZNfGyR0sntF2AvGHhXbUGhzfMGDRcsxLWCfKAXtptE003pA1xHhaq5PjjjjjlQjY9Qo4fdLOU2h5Z7WAVuHhwWcHRCl7cto5JkY9V.NUbXSMvcFicricabXSIIII0KHh3rA9f.982pAp2MfuZtTOGO4lpqSl47Abg.uqpaQcM9wQD6T0QHIIIIMXKybI.NYZFtRZfX+hH1+piPZvhiaptNYlmFvlWcGpqPBrGQDGV0gHIIIIMTJy7WA7optC003yDQbxUGgzfAerzUWkLyCFG1TCLOIMueMcXSIIII0yKh3SCLpp6PcMN5Ly2a0QHMXvSto5ZjYt0.mP0cntB2MvmNh3xpNDIIIIogSYleMfCAXVqtE05c6.enHh6r5PjlQ33lpqPl4ZB7GAl8hSQseWOvlGQbqUGhjjjjTExL2BfiAuI00T2eG38EQ7bUGhzzKerzUqWl4hCbR3vlZp6RF+3G+l5vlRRRRpeVDwoBrA.OP0snVuUG3HqNBoYDdxMUqVl4r.7mva9OM08KiH15piPRRRRpsHybEo4lTeEptE05sGQDeupiPZ5gmbS0ZkYF.+PbXSM0cDNrojjjjz+oHhqGXiA7cQulZN3LyMt5Hjld3I2TsVYleEfin5NTq29FQbPUGgjjjjTaVl44ArIU2gZ0dPf0IhXrUGhzzBG2TsRYlaDvYALaU2hZsdQfuXDwwUcHRRRRRcCxLOVfsq5NTq10SyELz+t5PjFn7wRWsNYlKEvQgCapWcOAvmvgMkjjjjF3hH9B.Gb0cnVsUD3HxLm4pCQZfxwMUqRl4bB7q.V7paQsV2OvlDQb1UGhjjjjT2lHh8A3qWcGpU6yBrWUGgz.kOV5pUwGSBMUb6.erHhan5Pjjjjj5lkYtM.+TfYs5VTqzKBrkQDmQ0gHM033lp0Hyb2A9dU2gZstZfMOh3NpNDIIIIodAYlaFvu.XtqtE0J8Hz7927VpNDoWKNtoZExLWOfKD+cMTSY+YfOYDwCVcHRRRRR8RxLe+.mFvBTcKpU5pAVuHhGu5Pjd033lpbYliD3u.rXU2hZktfHhMo5Hjjjjj5UkYtp.mM9yjoorSHhXapNBoWMdgBoRkYN6.mB9ODUSYmjCaJIIIIMzJh3pA1PfwTcKpU5+NyzKXH0Z43lpZeWf2U0QnVoiOh3yVcDRRRRR8ChHtIfOBv0VcKpUZ+xLeeUGgzThOV5pLYledfed0cnVoiHhXmqNBIIIIo9MYlKJv4.7NptE05bO.umHh6t5PjlbdxMUIxLWYfCq5NTqzA5vlRRRRR0Hh39F0nF0OC3xptE05rX.Gel4rTcHRStYt5.T+mLy4C37.V7paQsN6aDw9WcDRRRRR8yt3K9h+GiXDiX1Vq0ZsdIfko5dTqxRBLq6+9u+WR0gH8x7wRWCqxLmYfiC3yUcKpUIA1qHhQUcHRRRRRZRxLOGfOb0cnVkWB3yDQ7qqNDIvwM0vrLyuJvOr5NTqyWNh3npNBIIIII8+Ul4Y.7wqtC0p7H.u6Hhau5PjbbSMrIy78B7aAlipaQsFIvWHhvKVJIIIIoVrLyiCXaptC0pbU.qcDw3qND0eyKTHMrHybgANVbXSMISD3y6vlRRRRRseQDedfio5NTqx6.3fpNBIG2TC457d17mBrzU2hZMdNfuXDwITcHRRRRRZfIh3KA3gSPStcIy7yWcDp+lOV5ZHWl4d.bnU2gZMdIfOYDwnqNDIIIIIMsKy7n.1gp6PsF+KZd7zu4pCQ8mbbSMjJyb8ANOfYs5VTqvK.rEQDmY0gHIIIIooeYlGNvtTcGp035AVqHhmt5PT+Gerz0PlLy2.M2L5Nro.3Yn4croCaJIIII0kKhXWANpp6PsFqHv2MyzCQmF14+kNMjIy7b.9vU2gZEddfOSDwoUcHRRRRRZviOh55U3yEQbhUGg5u3I2TCIxL2cbXS03Eo4croCaJIIII0iIhXGo4I1SBfuWl4xWcDp+hmbSMnKybMA9y.yR0snx8b.aSDwoTcHRRRRRZnSl4OBXmptC0JbEzbAC87UGh5O3I2TCpxLWPfiAG1TvD.9hNrojjjjTuuHhuBvOu5NTqvZ.b.UGg5e3I2TCpxLOQfOS0cnVgsJh3WUcDRRRRRZ3Sl4wArMU2gZE1jHhKn5HTuOO4lZPSl41CrUU2gJWRyshtCaJIIII8+i8tuiVxJqRXi+rEPnIojFBRPDQZjfjSRzHneFAQLqiIFyQPGmYDLLJMNJlyiXDU.yfhQTIifJHgljjjfX.EoQR896ONECHdu29Fpp104TO+VqZ08nNvSCzEUsOugwLQD+q.elp6PiD9nYlab0QntOW4lpuHy7gBbp.qb0snRsXfWcDwGp5PjjjjjTcxLOJfCn5NT491.O4HhEWcHp6xUtolyxLmGMOYNGrod8NXSIIIIIEQ7L.9dU2gJ2S.3fpNB0s4vMU+vaEXGpNBUtCJh3HpNBIIIIIMZHhXe.99U2gJ2gjYtsUGg5tb3lZNIy7+Gvqu5NT4daQDGd0QHIIIIoQKG6wdruWfSr5NToVVfiLybMpND0M4Ytol0xLWGfSAXCptEUp2WDwqq5HjjjjjznoLy0B33A15paQk5iFQ7xpNB083J2TyJYl2Gf2ONXywce.GrojjjjjlJQDWGviC3bqtEUp+sLymU0QntGW4lZVIy7k.7wqtCUpOcDwKp5HjjjjjT6Pl4FC7MA1zpaQk4Z.1kHhqn5PT2gC2TyXYlOLfeLvpVcKpLeiHhmR0QHIIIIo1kLys.33.VupaQk46A7DhHtipCQcCtsz0LRl4RA7QvAaNN6G6fMkjjjjzrQDw4B7T.9iU2hJydC3Yuo5ab3lZl5P.1kpiPk4LV3BW3qs5HjjjjjT6UDwYAru.Kp5VTYd2YlaS0QntA2V5ZZKybmA94.KU0snRbd.6SDwUUcHRRRRRp8Ky7oB7U.V5paQk3W.7viHtspCQsatxM0zRl4pC7IvAaNt5xA1OGrojjjjj5WhH9Z31Sdb11AbnUGgZ+b3lZ55sBr4UGgJwM.r+QDWX0gHIIIIotkHhOIvAWcGpLuwLSO56zbhaKcsD0aqB7UwUs43naC3wDQ7SqNDIIIII0ckYt.f2X0cnRbg.6VDwen5PT6jqbSMkxLWCfEfC1bbzhAdANXSIIIIIMnEQbP.e5p6PkX9.ukpiPsWNbSMoxLCfCGXiptEUh2PDwWp5Hjjjjjz3gHhWDvwWcGpDupLymX0Qn1I2V5ZR0a6nerU2gJwgFQbHUGgjjjjjF+jYdlzbYynwKWBvNDQ7mqND0t3J2TSnLyGDv6q5NTI9PNXSIIIIIUnmJv4UcDZn6AC795sKRkl17efQSnLyuBv9WcGZn66FQ73pNBIIIIIMdKybq.NNf0o5VzP2SMh3qWcDp8vgap+IYlOWfOa0cngtyHhXGqNBIIIIII.xL2YfeBvxVcKZn5Jn41S+ppND0N31RW+CxLWeZtcz03kK.3.pNBIIIIIo6RDwoB7bAVb0sngpM.3cTcDp8vgap+OYl2GZFr4ZVcKZn5O.7LiH9sUGhjjjjjz8TDwWE3MWcGZn6YmY5BvQSKtsz0+G2N5iktCf8Ih3GVcHRRRRRRSlLy2Cvqu5NzP0UBriQDWW0gnQatxME.jY5x9d7zqzAaJIIIIoQcQDuAfis5NzP05C7tqNBM5ygap6x6AX8pNBMT81hH9XUGgjjjjjzzQDw9A7KptCMT87xLeJUGgFs41RWjYtu.eUbX2iSNxHhWP0QHIIIIIMSjYtQ.+.fMr5VzPyEBrmQDWe0gnQSNbywbYlqMvYB7.ptEMzbZQD6b0QHIIIIIMajYtS.eefUp5VzPymJh3EWcDZzjqTOcH3fMGmbg.O0piPRRRRRZ1Jh3z.ddU2gFpd9YlO5piPilb3liw5ctU7hptCMzbC.O6Hhqs5PjjjjjjlKhH95.Gb0cnglkF3ClYtxUGhF83vMGSkYd+n41Q2+YfwC2IvKNh3rpNDIIIIIo9gHhE.7IqtCMzrI.uwpiPid7L2bLUl4G.3UVcGZn40EQ79pNBIIIIIo9sLye.vip5NzPwcBr6QDmR0gnQGNbywPYl6.vIArLU2hFJ9vQDuhpiPRRRRRZPHybco4FTe9U2hFJNMfcKh3NpNDMZvsj7XlLyU.3igC1bbwOxAaJIIIIotrHhqF3YBbiU2hFJ1If2T0QnQGNbywOuZfst5HzPwBA9WqNBIIIIIoAsHheIvARy1VVceGTl4lTcDZzfC2bLRl4liOciwEKB3YDQbkUGhjjjjjzvPDwWglKNW08sR.efLykp5PT8b3liW9en4M.T21hAdI8dxkRRRRRRiMhHNDfOW0cnghGCMGGAZLmC2bLQl4ygleiu59N7HhuX0QHIIIIIUgErfEbp.tXOFObn8tPozXLuszGCjYtN.mEvZUcKZf6XhHdZUGgjjjjjTkxL2XfSB3eo5Vz.2mIhv6ahwXtxMGO7VvAaNN3b.7lQWRRRRRi8hHtXfmOvcTbJZv6YmYtGUGgpiqbyNtLyGMv2EvCY2ts+HvdFQ7apNDIIIIIoQEYlGDvgUcGZf6b.1oHhao5PzvmqbyNrLykF3+FGrYW2cB7JbvlRRRRRR+ihHV.vQVcGZfaKAdYUGgpgC2ra6f.1tpiPCbuqHhub0QHIIIIIMJJh3E.btU2gF3NjLy4WcDZ3yskdGUl4FRykHzpTcKZf5XiH1upiPRRRRRZTVl4lB7i.V6paQCTekHhCn5HzvkqbyNnLykB3cgC1rq6hAdUUGgjjjjjzntHhK.30Qyw5k5t1+LymV0QngKW4lcPYlOAfuU0cnApEAr6QDmU0gHIIIII0VjY9VANjp6PCTKDXq8xEZ7gqbyNlLyU.3cVcGZf6M5fMkjjjjjlYhHNTfiq5Nz.0l.7lpNBM73vM6ddC.aQ0QnApOdDwGo5HjjjjjjZodo.WV0QnAp2fWtPiObao2gjY9fANMfUq5Vz.yYEQrcUGgjjjjjTaVl4tCbB.KW0snAluJvyHhXwUGhFrb3lcDYlAvwB7TptEMv7W.1oHhKr5PzbSl4CBXk.tez7vHVQf4AbeAVpd+O6NAtMf+NvsPye++OSy4s5MufErf89fO3C9iMjSWRRRpyJybcn4ynsBz74yV0d+765yoszz7cnWLvsSymSaQ.+IZ9bZ2DveIh35G5wqYkLy2HvBptCMPs+QDGc0QnAKGtYGQl4iA36g+8ztpEC7riHNppCQSe81FDaKvNArt.qIv+Bvp.rxz7AjmotEfaF3u160MvcO3yKG3RnYK1bcQDW8b6WARRRRcGYlaJvC.XiA1Hf0llgYtF.2eZ97Y20vMmoGgaIMC27FA9C.WKv0C7K.NyHheQe3WBZ.Hy7yA7bptCMv7a.1Aubg51bPXc.YlqHvofm0lcYGQDwqs5HzzWl4NC7Cn4CGWgqmlOT8kB7qnYnmWCvUDQbEE0jjjjz.Wl4V.rAzLHysDXy58yWap4ylk.u5HhOXA+4VKAYlqIvOC3gTcKZf4PiHNjpiPCNNbyNfLy2Dv6p5Nz.yIGQrqUGglYxL+z.+qU2wD3FnYEd9qn4gh7KO1i8XWq8a+1uSnzpjjjjlExLWeZVIl6HM6VlGBvFRy1HeTxuNhXqpNBMwxL2VfeJ0svDzf0eCXqiHtjpCQCFNbyVtLyMjlATrVU2hFHtAfGdDwEWcHZlIy7xoYECLp61AtZfqC3jn4C0ctQDWYoUIIIIMAxLeX.aOviB3gxcer+Lp6N.1sHhSq5PzDKy7UCbDU2gFX9RQDOqpiPCFNbyVtLyu.f+FztoEC7zhH9ZUGhlYxLet.e1p6XN32Cbt.mCvoQy4D0us1jjjjz3ndqntGNv1.r0zLPyYy4V9nfOSDwn3N6Q8jY9Y.d9U2gFHRfmPDwwUcHp+yga1hkYtSzb1frLU2hFHd+QDulpiPybYl+Df8r5N5i9izrM1OdfSYgKbgKZ9ye9mSwMIIIoNnLy65x94wCr6.aNy7K2mQU+IfsHh3ZpNDMw5c9adh.yu3TzfwoAr6QD2d0gn9KGtYKUl4x.7yo4rkQcOmZDwtTcDZlKybyANa51OzgqfliCiuNvoDQ76JtGIII0h06B.5QC7jo4B.59UaQCTOiHhub0QnIWl41Qyw0zxVcKZf30DQ79qNB0e4vMaoxLed.GY0cnAh+DMOMoyq5PzLWl4a.3vqtignqE3hA9N.e2HheSw8HIIoVfLyGAv9Sy4m4lw3yfjNgHh8t5HzTKy7MBrfp6PCDWMv1DQbCUGh5eb3lsPYlqNvISyMAn5dd9QDs4yqwwZYlmMMmGTiitUfSG3XoYEc9KJtGIIIMh3vNrC6.OnC5f9s.OVf8FXSKNopbK.6bDwut5PzTKy7qSypIVcOu2HhWe0Qn9GGtYKTl46F3fqtCMP7YiHd9UGglcxL2SfeL9dq2keAvw.7shHtfpiQRRRCeYl6JMW.pOVfMr3bFUbnQDGR0QnoVl4FPy4u4Cr1Rz.vsArstqy5N7Kf2xjY9foYkQspU2h56NuHhMu5HzrWl46E30VcGiftYfymlAc90iHt3h6QRRRCPYl6LvyF3QfWLKSjSOhXmpNBsjkY9X.Ngp6PCDGcDw9WcDp+vga1hjYF.eIfCn5VTe2sB7HhHNkpCQydYlmKM2nmZx8Wn4If+cANtHhqt1bjjjT+Pl4Cilsv6dC3f6lZ2IvCOh3zqNDsj4NmryJAdpQDeipCQycNbyVjLychlasskp5VTe2AGQ3AVcKVl4illmpquu5z20A7s.NpHhSr3VjjjzLTl45Ary.u.f8hwmKEn9gORDwKu5HzzSl4YPykek5VNMfcKh3NpNDM23WBukHyboA9Q.6d0sn9teXDwit5HzbSl4mE34VcGsXKD3KPyfNuzpiQRRRStLycD3EA7D.Vyhyos5RiHdvUGgldxL2BfeJvpTcKpu6.iH93UGglab3lsDYlOKZ9h+pa45n41R7xqNDM2jYd4.aP0czA7mANCfOSDwWo5XjjjTidqRyC.3oArc32kbt5N.1yHhSt5PzzSl4AB7QqtC02cE.aUDwMVcHZ1y+ERs.Ylq.MKWZOK+5VVLvyOh3yWcHZtIybu.9A3QFQ+1E.7ko4RH5bqNFYjMexD...B.IQTPTIIowQYlOBf8C3ohqRy9M2Z5sLYleYfmd0cn9tCMh3PpNBM64vMaAxLeS.uqp6P8cerHh+spiPycYlebfWR0czg82A91.ezHheR0wHII00kYtl.OZfWFMmolZv3phHV+piPSe898FmNtis5Z9a.aSDwEWcHZ149Tc.ZpkYtF.upp6P8cW3BW3BcKMzAbXG1gcf.6S0czwsbzrE39wYl+lLyWyEdgW3VVcTRRRcMYlaRl46A3LA973fMGzVuLymT0QnouHhqG3URytvScGqHvqu5HzrmqbyQbYl+O.utp6P8UKFXOhHNopCQycYlOFZtkz0v0kC7M.9rQD+phaQRRpUKybuAd1.OQfUp3bF27diHbnJsLYlePfWQ0cn9pailaN8yn5PzLmqbyQXYlaFMaED0s7dbvlcJ6d0ALl5AB7Z.NsLyuTl4dVaNRRRsOYlOsLyeFv2E3YgC1rBO1pCPybKXAK37.Nmp6P8U2WfCs5HzriqbyQXYleFfme0cn9pyJhX6pNB0+jY9qAbKROZ3zANhHhub0gHIIMpp2YF3yA3kB7fKNGAIvizyU71mLycB3jwEMVWxcBr2QD+vpCQyL9aBGQkYtGzr0PT2wh.NvpiP8O89.MaV0cn+O6HvQkY9qyLeidtbJIIc2xLePYlGAvY.b33fMGUDzbSzqVlHhSC3sWcGpuZo.NrLykq5PzLiqbyQPYlAvwCr2U2h5qN3HhETcDp+Iy7+F3MWcGZRcE.eZfuPDwus5XjjjpPl41B7uBb..qZw4nI1uJhXqqNBM6jYdl.t675VdFtavZWb3lifxLeb.eabk01kbxQD6Z0Qn9qLyeLvdUcGZI5ORyPN+eiHVX0wHIIMLjYtC.uJZFp4RUbNZpcy.6bDw4VcHZlKyba.9Y.qP0sn9lyGXGiH9aUGhldb3YiXxLWVf2A92a5R9qz7zxUGRl4F.3SXucX0.NHfyLy7SkYN+pCRRRZPIyb2yL+t.mBMWRPNXyQeq.tq8ZshHNaf2V0cn9pGJvKp5HzzmCPazySAGXRWygFQbQUGg561Kf6e0QnYjUB3ERyMr9mKyzsOjjj5LxLeRYleOfeJMCJygZ1t7jpN.M6063G6mWcGpu5UjY5Q4QKgC2bDRl4JA7eUcGpu5DhHduUGgFHdhUGfl0tezbKwdxYlexdmEYRRRsRYl6SugZ9M.drU2il0dXYlOnpiPyIGHMWhrpaXi.dsUGgldb3liVd1.aZ0Qn9laD30WcDp+q2sv8NTcGZN69Ry1M4zxL+hYld6pKIoViLyGYuy+6iGGpYWvJBrEUGgl8hHNeb6o20bfYlqU0QnkLGt4HhLy6G9TA5ZdyQDmW0Qn9uMYS1j4Ar1U2g5aVZfmINjSII0BjY9nxLOQfeHdwF1034tYKWDwgQyQCg5FVcf2P0QnkLGt4niWNvFWcDpu4mEQ7wpNBMv7Hv2+rKZdzLjySIy7CmYtYUGjjjzcIybuxL+p.+.f8n5dz.win5.TewqCvaY6tiWZloypYDme47Q.YlqBv+V0cn9l+LvKo5Hz.09Tc.ZfZE.dY.mdl4BxL2npCRRRiuxL2lLyiB3GC7zptGMP8fyL24piPyM8t8zOjp6P8MqHvqn5HzTygaNZ30.rtUGg5adWQDKr5HzfQl4FB7vptCMTrB.uQfyLy7vVzhVz5WcPRRZ7Ql4FmY94ANEfCn5dzPw8AOpA5DhH9e.Nyp6P8Mu3LyMu5HzjygaVrLy0C3UUcGpu4GEQb3UGgFn1dfUt5HzP0p.bPyady6mmYdnG1gcXGX0AIIotqLy0Oy7CBbFzbgitrEmjFt1lpCP8MuHfat5HTew7.N3piPStn5.F2kY9N.dKU2g5KtMfsOh3bpNDM3jY9t.dSU2gJ0kRyJz9SWcHRRp6HybMAdg.uZf+khyQ04JhHdfUGg5OxL+uAdyU2g5KtYfcNh3bqND8OyUtYgxLev3Y2PWxayAaNVX2pN.UtMB3SkYdNYlO0piQRRseYlGHMag02INXywcqel4tWcDp+Hh3eG3rqtC0WrB.+WYltHAGA4vMq0qD39UcDpu3bhHdmUGgFrxLef.dVqn6xV.brYl+D+RHRRZ1Hyb+yL+U.eTf0q5dzHg.3wTcDpu50Cr3piP8EOIfsp5Hz+LGtYQ5c669rptC0Wba3sc+3hsGefD5e1dB7CyLOROnwkjzzQl4iHy73.9J3EUn9m4kJTGRDwIB7dqtC0WrL3Yu4HIGtYcdS.qV0Qn9hOVDwoTcDZnXOqN.MxZY.dd.mZl4aMybcpNHIIM5Iy7gjY9wA99.Otp6QirV+K7Buvsr5HT+yBVvBtTfKu5NTewSKybOpNB8OxyJfBjYtw.+RZNyFT61EuvEtv8a9ye9dVaNFHy7rvavRM8bMzboC8gpNDIIMZHy7sB75.V4paQi7tSfcIh3LpND0+z6rZ+nwEYVWvWKhXeqNBc272TUiWGNXythWuC1b7Pl4ZA3pwSSWqCvGLy7LxLehUGijjpSl4KIy7hANDbvlZ5YovKwxNmHhuFvwTcGpu3IjY5uGcDhC2bHKyby.dNU2g5K9ZQDe6piPCMOX71KUybaOv2r24w4lUcLRRZ3Iy7gmY98.93z74HjlI10pCPCD+m.We0Qn4rkA3M6Mm9nCGt4v2+FtpM6B9C.u1piPCU6J9dlZ164AbFYlGR0gHIoAqLy0My7S.7y.drU2iZs15pCP8eQDWDvgVcGpu3QArcUGgZ3WTeHJybS.dtU2g5KNrHhqr5HzP0Cu5.Tq2xC7VyLujLy+0piQRR8eYluIfyD3EieWKM2rdYl6P0Qn9uHhOJMuOgZ2VFf+iLSeu9Q.tDZGhxL+j.unp6PyYmbDgaSjwLYlWNvFTcGpS46B71hHNspCQi2xLWSf6KMeH86auWKcuWKGMCled89ueY58e9c8+9k5d7Zo68iPyPcBt6OqYdudsXZtvLtSf6n2Od62qW2AvsBrnduti6w+421c8ZgKbgqgm+0pZYlOYf+Cfss5VTmxyIh3KTcDp+Kyb6.NYZ92mp1qECraQDmR0gLtygaNjjYtA.mMvpVcKZN4NAdjQD+zpCQCOYlaOvoxc+k1k5WtUfi.3CFQ76pNF0dbXG1gcfGzAcPGGMG0MKK28fGWQfUA39060JeOdsR2ie9xdOdceuWutqAXNJtRDtqAbdWC27t942ZuW2FvMC7W.todu9q2iW2Xu+6tQf+N28fRWDvhhHttg3uVTGPl4FA7V.dAU2h5jVPDwAWcDZvHy7iPywVmZ29hQDO6piXbmC2bHIy7CA7xqtCMm8AhHd0UGgFtxLO.fip5NTm10B7uGQbjUGhpSuUP4J060pArVzbQlsF.qduWqIMCvbEtGutqgapYtESy.RWDMCE8td8m.98zbFa+G.tgd+eeszaPoQDWUEAqQGYluYf2HMOPAoAgSJhvaj4Npi4XNlG69tu66GFXiptEMmbm.6p6FqZ4vMGBxL2PfygluLhZu9c.aaDg2tciYxLOTf+qp6PiE9w.u4Hhyn5PT+Sl45RyvOVcf0E3Az60ZSyvJmGMCn7dtBKckhO55VnYEg9WnYPn2Vue90BbM.Wcue72A7mW3BW37bay2sjY93.d6.aS0snNuqGXqbUk2ckY97.Nxp6PyYGcDw9WcDiyb3lCAYluCZ1tJpc6EEQ7oqNBM7kYdb.Otp6PiMVDvGXgKbgGkCDYzWl4CflUY4ZCr9zrUvWMf0ilyo2Uilijl6OMCvTiW9q.+YZVInWCvU16G+qzrZPuBfaXAKXAOxC9fO3OVYUpokLyGHMel9WH98nzvQBr6QDmT0gnAmLyuCviu5NzbxMCryQDma0gLtx+kxCX89PP+BZ9xMp85DhH16piP0Hy7x.1vp6PictTfCNh3XqNjwYYlqEM67hUF3ARy6ErQ.OHf0glUi4pgWH.Z16uPyvOuAfKmleu+k06GudZNKPu7phSMxLOPZVslqd0snwNu9Hh2a0QnAmLyGJMyLXdU2hlS9R.O6Hhr5PFG4vMGvbUa1Ib6.6RDwun5PzvWl4CC3LvAWn57MAdsQD+1pCoKq2kBxZArw.yG3gPyvLWCt6gaNJdA6nts65lh+FAtNfKFXg.WHMCB85c6pNXkYtC.uOfco5VzXqiLhvKrpNtLyCC3fptCMmrHfsIhXgUGx3HGt4.Tl4pQySf4AVbJZtwaovwXYlOKfuP0cnwd+df2aDwgUcHsY81B4qCMCtbSnYEXsgzr8wWK7RAQsK2AMC77tF540Ry474E.b4QDWPgs0IzaXCuBfku5VzXseXDwit5HzfWl4EQyCYUsWenHhWY0QLNxgaN.kY9uC7NqtCMm76.1NWUDiuxLeO.u9p6PpmSB3MEQbxUGxnrdWje20sM9lCrEzLPy65R7QpqaQzbwFcU.mGMWrkKD3FW3BW38wyy2oVl4SD3cCroU2hDvkEQ3so8XfLyW.v+a0cn4jaBXGhHtvpCYbiC2b.Iybk.NWZVMHp85EDQbjUGgpSl4w.ruU2gz8vhA9ehHbqKAjY9Pn4Lv7gAr0zLDy0COqqklH+cZVomWAvuA3r68iWQDw0WYXiBxLWSf2Cvyt5VjtGtUfs2KpjwCYlGOv9TcGZN4cGQ7lqNhwMNbyAjLyWDvmr5NzbxoFQ34qzXtLySGXGptCoIvEC7VhHN5pCYXn2PG1PZVEl6.MqnpUAXco47vTRydWMMG+EWOMGoRmEvEMNs01yLeY.uIZd3HRiZd5QDe0piPCdYlaGvoArTU2hl0tVZ18mWS0gLNwgaN.jYtx.mNMWHApc51AdjQD+7pCQ0Iybso4K341XUix9j.GZDwuq5P5WxL2.t6yEyGDv1QyfMW2J6RZLyMSykVzEB7q68yOuHhytvl56xL2bf2AvSp5VjlBuyHh+ipiPCGYlGAvqt5NzbxaOh3+p5HFm3vMG.xLO.fip5NzbxmHh3kVcDpVYlaOvohO4TM56Z.dyQDetpCYlp2k7yZPyYi4CGXanYPlqQkcIoIzhAtLfKhlGj+oBbks0aF1LyWKvgBrRU2hzRv2Lh3IWcDZ3XQKZQq+7l27NMbAVzlcE.aQDwMUcHiKb3l8YYlKEvOGXmqtEMq8mn4P.9RqNDUqLymKvms5NjlA9N.uxHhKu5PlLYlyGXy.1cZ1d4a.MCxboqrKIMqcSzrk1u.ZtzyNMZ1R6WaoUMExL2FfOHfG+Pps37iH1rpiPCOYluJf2e0cn4jWVDwGs5HFW3vM6yxL2WfiF+qssYutHh2W0Qn5kY9tn472RpM4ZAN7Qg2Gq24j4lBrq.aEMqHyGBvJVYWRZf62AbI.WJvo.bVQD+pZSpQl4aG30fuOjZW9yzbF9cYUGhFdxLOMfcr5Nzr1ESyhl5FqNjwAN.t9rLyeBvdVcGZVagQDdVoJ.Hy7afmAWp85aQyVU+7GV+Ir2pxbio4R+Y2oYEZ54jojtSt6ytyeJMeguENLurExL2EZtIzc2Uo1nESyMldm5LuUSsLymDvWGmaSa1yJh3KUcDiC72jzGkYtCzrcbVlpaQyJKllahvio5PzngLyyklyAPo1pahlAb9gGD+AOybCo42i7nnY6cNeb0PIoomqA37A9I.mHvkFQb8Ch+DkY91nYmX3mQWsY6cDwITcDZ3Jy7n.Nfp6PyZmJvtEQbmUGRWmC2rOJy7nA1up6PyZeuHh8o5HzngLy0A3WfGj2pa3GQyYw4ELW9CRl4CllK7m8AXqoYUYtZy87jj3pnY6r+yn48rNmHhqat7GvLycC3Hn48sjZ6d0QDefpiPCWYlaFMemjkq5VzrRBreQDespCoqyga1mjY9PANK7McZqtcf8Jh3jqNDMZHy7gPyGjvaPU0U7m.9OiH9HS2+eHybinYn.aEvtQy1M+9OXxSR5evUCb1zrpWNGfydlLrydqVyWOvxOXxSZn6CFQ7ppNBM7kY99n4rBVsSemHhmP0Qz04vM6SxLeOz7AnT6zmHh3kVcDZzQl4NRyWnx2mTcMeKf2PDwEeu+un2E.zF.7HA1aZFl4pLbySRZBc0.+RfiC3zmrKnnd+6u+..6vPrMoggSHhXuqNBM7snEsn0edyadmJv5TcKZV4uCrKQD+xpCoKyuzdePl45SyG1ZUqtEMq72.15HhKo5PzniLymIvWr5NjFP9yzbVb9w6cI.sG.OQfsDu.fjznu6.3JnYGV7c.NoHhKOy7+B3fwUqo5lNuHBOK3GSkY9FAVP0cnYsuTDwyp5H5xb3l8AYl+G.u8p6PyZ+WQD92+z+fLy2Bv6n5NjFv9k.ODfUn5PjjlC9Czbdc9vpNDoAneOv1FQb0UGhpQl4ujliJH09bS.aUDwkUcHcU2mpCnsKybE.dlU2gl0tLGrolDaX0AHMDr03fMkT62piC1TceqJvZTcDpT98VauVIfmW0Qzk4vMm6d5.aZ0QnYsCu5.zHKGtojjjjFUrz3QGyXsd231mX0cnYsWRl4ZUcDcUNby4fLykA3kUcGZV6ziH9XUGgFYslUGfjjjjz8vCr5.T4NXfau5HzrxZAruUGQWkC2btYWvy7h1rCq5.znoLy0AugnkjjjznEW0Wi4hHNCfuQ0cnYsWPl48s5H5hb3lyRYl2GfWMvRUcKZV4DhH95UGgFYc+wgaJIIIoQKqT0AnQBGBveq5Hzrx1B7DqNhtHGt4r2VB7DpNBMqbm.+mUGgFos5.yq5HjjjjjtGt+UGfpWDw4C74ptCMqcfUGPWjC2b16YPyg5rZeN1Hhyr5HzHsGP0AHIIIIcu3EJjtKuWf+X0QnYkcIybKqNhtFGt4rPl4ZB7bptCMqbSzrL9klJtkzkjjjznlUu5.zngHhKE38WcGZVYd.u7pinqwgaN67b.V6piPyJGcDwETcDZj2JTc.RRRRR2Kq7EdgWnq3KcW9D.+9piPyJGPl45WcDcINbyYnLykhlsjtZetQf2c0QnVAOr1kjjjznlUZS1jMwKRFA.QDWO98aaqVYfmT0Qzk3vMm4dr.aU0QnYkOZDwEWcDpUXMpN.IIIIo6kUBXEqNBM5Hh38AbIU2glUNvLS2wf8INbyYfdqZyWA9W2Zi9izrr8klNVspCPRRRR5dYY.teUGgF47ApN.Mq7PAdhUGQWgCoalYS.1ypiPyJu2HhKu5HTqgqbSIIIIMJxOmp9GDQ7AA9UU2glUNfpCnqvgaNy7bo4lsRsKWSDw+c0QnVk6e0AHIIIIMAV0pCPijdeUGflUdLYldIg0G3vMmlxLWSZFtoZe9vUGfZc7gXHIIIoQQqR0AnQOQDeNfeY0cnYrkC3kWcDcANbyou8CXsqNBMicwtpM0LQuGjgC2TRRRRihV4pCPirdaUGflU12deGTMG3vMmFxLC7rPns5iUc.p0YYo4InIIIIIMpwiOIMghH9F3p2rMZ0.9+UcDscNbyomcEXmqNBMicIKXAKXQUGgZclGvxWcDRRRRRSfUp5.zHs2IPVcDZF6eKy79VcDsYQ0AzFjY9EAdlU2glwdwQDeppiPsK8NPmOS.+WtHIIIoQMGcDw9WcDZzUl4IBrGU2glwdrQDe+pinsxUt4RPl4ZC7XqtCMicYNXSMKcewAaJIIIoQStxM0RxGp5.zrhOzh4.Gt4R19QyYffZWVP0AnVKuLgjjjjznJuPgzTJh3X.Nwp6PyXOkLy0q5HZqb3lSgLyU.3kVcGZF6BiH93UGgZsVgpCPRRRRZRrrUGfZEN7pCPyXqJvys5HZqb3lSscCXypNBMi4fM0bgWlPRRRRZT0RWc.ZzWDwwSy8HfZW1+Lyko5HZib3lSsmd0AnYrKJh3HpNB0p4SCWRRRRipVppCPsFuMfEWcDZFYy.10pinMxgaNIxLeP.O4p6PyXuupCPsd9zvkjjjznJ+N7ZZIh36.bVU2glQVJfCr5HZi7MFmbOCf6e0QnYjq5Vtka43qNB054vMkjjjznpn5.Tqx6u5.zL19jYtgUGQaiC2bBjYtT.Okp6PyX+OK+xu7WY0QnVOGtojjjjFU4vM0zVDwWD3LptCMirR.6S0Qz13vMmX6FvVUcDZF4ZiH7oRo9Ae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p5B6NBMZ3EJzTfjro.mJ9sLMM6+E3dWU8M5NDIoEhj77ANnt6PRiLmPU0Cr6HjjVUkjCDXe5tCMRcl.aQU0ep6PzngO4lSApp9R.uot6PiT2PfOT2QHIsPjjcF3U2cGRZj5AjjCq6HjjVUjjcDeaRl1cY.6oCaNcywMmdbP.WT2QnQp6PRdmcGgjz7QR1Bf2A9VFHMK3wM2SBkjzXqjrd.uV7MZcZ2GXtGHLMEywMmRTUcw.urt6PibOkj7L5NBIoUGIYi.9X.WmtaQRKZ1mjrWcGgjzUiODvZ2cDZj5hAdUcGgF8bbyoKuGfSq6HzH2xRx8r6HjjVUL2sm7gAbS5tEIsn6.Sx10cDRR+8RxaA3tzcGZj6kVU886NBM54ie8TljbuANQfqc2snQpuKvVVUcIcGhjzUmj7I.1lt6PRs4mAbeqpN6tCQRBfjrKL3ACRS29p.2yppKs6PznmO4lSYpp9B.uqt6Pib2d7ePVRi4Rxq.G1TZV2MA3CeNmy4rwcGhjTRtq.ugt6PibWNCtDgbXyYD9jaNEJIqKvYBbS6tEMxs+UUu7tiPR5uWRdD.e7t6PRiMNxppcr6Hjzrsj7s.1ft6PibGZU0N0cDZwiO4lSgpp9g.ujt6PKJ1ujr8cGgjzesjr9.ust6PRiUdrI4E0cDRZ1UR9n3vlyBtPf8q6HzhKexMmRkxfR0u...f.PRDEDUjqMvoBbO5tEMx8S.d.UUe6tCQRBfjbh.2ut6PRictLFb9a5EfojVTkj8A3.6tCsn3YVU8V6NBs3xmbyoTUUWNvdxfODolts1.GQ2QHIAPRN.bXSIck65.bnd9aJoESIYa.d0c2gVT7kAd2cGgV74St4Ttj7lAdlc2gVT7AqpdhcGgjlckjs.3K.rFc2hjFq4mYQRKJRxF.b7.qS2snQt+DCtczOitCQK97I2b52qF376NBsnXmRxKr6Hjzro4dRrd23vlR5Z1NkDG2TRKFNTbXyYEuKG1b1kO4ly.RxiF3C2cGZQwJA1tppOQ2gHoYKI40CrWc2gjlX7iAt2UUeutCQRSmRxG.vaL6YCmOvcqp5W1cHpG9jaNa3iBbrcGgVTrF.uqjrztCQRyNRxChAmyyRRqpVGf2X2QHooSIYuwgMmk7xcXyYaNt4LfppUxfeoy+2taQKJto.G0xW9x25tCQRyLdM3qitjV88fSxSn6Hjzzkjrc.GP2cnEMKmAG+.ZFluV5yPRx9C7R6tCsn4HppdbcGgjltkjWAvKt6NjzDqKhAuJg+3tCQRS9RxFBbh.qU2snEE+dF7ug7c5ND0KexMms7p.NqtiPKZ1wj7J6NBIM8JI2Af8t6NjzDs+C7KHQRCAKaYKa2ANJbXyYIubG1TfO4lybRx8A3jv++8yJtBfcqp581cHRZ5SRNFfGV2cHoIdWAvCpp5D5NDIM4JIGMvip6NzhluNv8np5O1cHpe9jaNiop5yCbvc2gVzbs.diI4t0cHRZ5RRdL3vlRZ33ZAr+cGgjlbkjC.G1bVxkArGNro9ybbyYSubfKn6HzhlqOvQkjaU2gHooJ6a2AHooJaQR18tiPRSdRxthetjYMukppSo6Hz3Ce0jmQkjc.3Ci+uAlkb5UUaQ2QHoIeI44wfaHcIogoeTU051cDRZxQR1TfOGv+R2snEMmGvlWU8y5NDM9vmbyYTUUeDfCq6NzhpMOIevtiPRS1Nmy4b1Xf8p6NjzToadRdocGgjlLjjaGCt.gbXyYK6oCap+dNt4rsWFvOo6Hzhpmf+RCRZgXoKcoaKvZ2cGRZp0dM2fERRWkV9xW9VC7AAt4c2hVTc3UUeltiPie7URdFWR1IfOP2cnEc6bU06u6Hjzjk4N6d+Z.2ftaQRS0dKUU6Q2QHowWdynOS5GBroUUWT2gnwO9japCCvu4iYOusj7.5NBIMwY2vgMkzn2SJIqe2QHowSI40iCaNKZ+cXScUwwMmwUUsRf8D3WzcKZQ0R.duIYi5NDIMYHI2Rfcs6NjzLgqOv9zcDRZ7SRdl3Y+8rnOLCNFBjtR43lhppyC3.6tCsn6lAbDIYM6NDIMQ3oCbS5NBIMy3QjjaS2QHowGI4gAbvc2gVz8K.dIUUWQ2gnwWNto9yd8.GW2QnEcaDCtgAkjtJsrksrcGXG6tCIMS4FB775NBIMdHI2Yf2OvZzbJZw29TU8+zcDZ7lWnP5+ybuhxeQF7p.oYKu2ppco6Hjz3ojr2.Kq6NjzLmeGvcnp562cHRpOIYcANQfaW2snEcGOvC1mZScMwmbS8+op5rAdYc2gZwSNIuhtiPRis1stCPRyjt93SMtjFbdK5vlyd9o.OKG1TqJbbS826fYvSuol87hSxSq6Hjz3kjrs.21t6PRyrdZKe4Keq6NBI0ijbj.aZ2cnV7JqpNmtiPSF70RW+CRxF.bZL3rNRyVtBfGVU0mo6Pjz3gj74.tOc2gjlo8zqpd6cGgjVbkjCF3Y0cGpEeJfGYU0k2cHZxfO4l5ePU02Fu8zmUcs.N7jr4cGhj5WRtW.2qt6PRy7dbcGfjVbkj8EG1bV0uF343vlZ0giapqJuVfOa2QnVbCANzjr9cGhjZ21i2JoRpea9beYKRZFPRd7.GP2cn17hpp9tcGglr33l5JUU0JAdF.qn6VTKt0.ezjrVcGhj5w4bNmyFyfwMkj510B3o2cDRZzKIOHf2W2cn1bLUUu0tiPSdbbScUpp5bA16t6PsY8AN5tiPR8XoKcoaFvZ2cGRRy49mj0r6HjznSR1TfCE3Z2cKpE+DfmW2QnISNtotZM22Zxw1cGpMaVR9ncGgjZwSr6.jj9qbiAd3cGgjFMRxsG3vYv+stlMsu95nq4KG2TqJ1SF7snnYSaaRdOcGgjV7jj0C3NzcGRR+c10tCPRCeIYsYvaL1sp6VTa9vL3o1UZdwwM00npp+GfWb2cnVsKI40zcDRZQy1Cb86NBIo+N20jbW6NBIMz8Q.1vtiPs4GCrWUUWQ2gnIWNtoVU8AYvqIflc87RhmAqRyF1gtCPR5JwZ.7.6NBIM7jjOFvl2cGpMWAvyqp5h5NDMYywM0pj4t8z2afKr6VTqVVRdJcGgjFcRxlguR5RZ7010c.RZ3HIuKfGY2cnVcHUUGY2QnIeNtoVkUU8iA1CF7sqnYWusj7X5NBIMxbe.ptiPR5pvcLI9TdIMgKIKCX25tC0pyA3E0cDZ5fiapUKUUeb.ubYlssF.uuj3qElzzoGU2AHIc03ZgO8lRSzRx9xf2JPM6J.O0ppeV2gnoCNtolO1Wfud2QnVsDfObR1ztCQRCOI4t.bG6tCIoqA2ytCPRyOIYOANft6Ps6.qpN4tiPSObbSsZqp5W.7L.VY2snVcCAN5jbm6NDIMz7v.9m5NBIoqA2gjrwcGgjV8jjcB3MzcGpcmIv90cDZ5hiap4kppSCX+6tC0t+CfOZRVZ2gHoghMq6.jjVErDfGT2QHoUcIYa.de3FDy59M.6VU0k2cHZ5h+fEsPbf.mR2Qn1cKA9DI41zcHRZ9KIqMfOI1RZRwin6.jzplj7P.9P39CBdQUUdD2ogN+gKZdqpZk.OEfeR2sn1sTfkmj0s6Pjz71lCbS5NBIoUQarewpRi+RxV.7A.9W6tE0tkC715NBMcxwM0BRU04.7B5tCMV3NxfANWytCQRyKOztCPRZ0v0GXC6NBIcUKIaBvGC3F2cKpc+.fmYU0UzcHZ5jiapggODv6n6HzXg6FvGu6Hjz7xVzc.RRqltucGfjtxM2k90mBeqPzfKh3cup5R5NDM8xwM0BVUU.1Wfys6VzXgMMImX2QHoUcycofsNc2gjzpo6c2AHo+QIY8A9v3msPC75ppN1tiPS2bbSMTTU8q.dh.+wtaQiEteI4S1cDRZU1l.bc6NBIoUSq+bOcXRZLQRtEL3rUb85tEMV3KCr+cGgl943lZnop5KC7J6tCM13gmjOQ2QHoUI9pcJoIQ+y3QpgzXijrNL3UQeC5tEMV32C7jqp9CcGhl943lZX6.YvgFsD.aSR7L3TZ72l1c.RRyS2gtCPRPRt4.eB7h9R+E6YU02t6HzrAG2TCUUUWNvyE3B6tEM13QjjOX2QHoqbIY8.tsc2gjz7zlzc.Ry5RxZwfyXy6R2snwFGZU06s6HzrCG2TCcUUW.vtBbEc2hFa7DRxg2cDR5J0cD350cDRRySq+bmweRpAKe4KeqANFfMq6VzXiyF3Y0cDZ1x0t6.zzoppiKIGDvKn6VzXicLIW6ppGc2gHo+F9TaJoIY2HfMB3GzcHRyZVwJVw5tjkrj8G3t1cKZrweBXWl6BGVZQiO4lZT5k.745NBMVYG7I3TZrycr6.jjVf7bCVZQVRVykrjkbn3Sro9a87ppNitiPydbbSMxL24u4y.3h5tEMVYGSx6q6Hjz+GuLNjzjNepwjVDsrksrcG3i.rkc2hFq7wppdScGglMUcGfl9kjsgA2bdR+09vUUO1tiPZVVRVefyB351cKRRK.e2ppk1cDRyJRxWB3dzcGZrx2EXypp9kcGhlM4StoF4pp9j.GT2cnwNOlj7w6NBoYbqGNrojl78ukj0s6Hjl1kj0LImBNro9acY.6rCapN43lZwx9BbJcGgF67HRxg0cDRyvtScGfjzPv+Nf2X5RiPqXEqXcANBf6Y2snwNufppSu6HzrMG2TKJppVIvtB7i6tEM14wkjOV2QHMixyaSIMMn.1jtiPZZ04bNmyFujkrjiBXq5tEM14n.N3tiPxwM0hlppuKvSq6NzXoGYRNttiPZFzMq6.jjFR7KqQZDHI+GKcoK8PvWEc8O56.r6UUo6PjbbSsnpp5X.dYc2gFK8.SxIs7ku7st6PjlgbC6N.IogjMr6.jl1jjaECtXXuqc2hF67aXv4r4up6Pj.G2T83kA745NBMVZq1tsa612jrVcGhzztjb6A7+VSRSKVG+BRkFdRxsD3iAb2ZNEMdZepp9xcGgzeV0c.Z1TRVGfu.vso6VzXouBv1TU8S5NDooUI49CbBc2gjzPxkBbWqp91cGhzjtjbaA9T.29taQikdGUUdbyowJ9japVTU8iA1EfKu6VzXo6BvwlDG+VZzYocGfjzPzRvyQXoErjrI.GKNrotx8eC776NBo+dNtoZSU0W.Xe5tCM15N.7oRhmgVRiF25tCPRZH6eu6.jljkj6JvQiucc5J2k.73qp9ccGhzeOG2Tc6M.bXcGgFasTfiII24tCQZJjiaJooM+GcGfzjpjro.GC94CzUtq.3Y6Q+gFW43lpUUUAX2A9lc2hFacq.9zIYy5NDooL2htCPRZH6V0c.RShRxVBbb3EMntp8JppNxtiP5phiap1M2i09NA7S6tEM1ZMYv.mOntCQZZvb2nv2nt6PRZH6V1c.RSZRx1B7IAtAc2hFa8Y.NftiP5pi2V5ZrQR1QF7Jp6+6RcU4RAdRUUGU2gHMIKI2dfyD+EYjzzkuVU0cp6HjlTL2u+0gvfKjKoqLmGvVVUcQcGhzUGexM0Xippi.3U1cGZr1R.N7j7T6NDoIbWOfqe2QHIMjcCm6ISWRWCRxtyfGrDG1TWU98L3BDxgM0XOeB4zXkjbs.9D.OrtaQi0BvKpp5U2cHRShl6Lr8z6tCIogreMvcsp575NDowYIYe.Nvt6Pi810ppCo6HjVU3StoFqTUcE.6BvY2cKZrVA7pRhenLo4m+8tCPRZD35iG2FRWsl6yO6mgVWSdcNrolj33lZrSU0OEXmYv29tzUm8IIu6tiPZBzZ1c.RRi.qAvMs6HjFWkj2Av9zcGZr2o.7B5NBoUGNtoFKUUcV.Okt6PSD10j7o6NBoILNtojlVsVcGfz3nj7Q.7bqWWSNOfGSU0k2cHRqNbbSM1ZtaD6Wd2cnIBO3jbxI4l2cHRSH70RWRSqtwcGfz3jjrlI4j.19taQi89s.OAu.gzjHG2Ti0pp1efOd2cnIB2KfiIIaX2gHMAvwMkzzJG2TZNI41B7oA1ptaQSD1yppuT2QHMe33lZRvt.705NBMQXS.NtjbO5NDowb+acGfjzHh+7MIfjbm.NAf6b2snIBGXU06u6HjlubbSM1qp5WB7D.9Yc2hlHrN.GeRdjcGhzXL+k+kzzpqa2AH0sjr0.GGvsr4TzjgOMvKp6HjVHbbSMQnp5rYvMn9UzcKZhvM.3Cmjcu6PjFSc86N.IoQj+4tCPpSIYGAVNvMo6VzDguIvNWU4umsln43lZhQU0mB3E1cGZhw0A3smjWV2gHMF55zc.RRiH9y2zLqjrG.GN9kXpUM+Lfcpp5m1cHRKTNtolnTUcP.u6t6PST1uj7d6NBowEKe4KeqA9W5tCIoQD+4aZlTRds.uot6PST1opJuaKzTgp6.jVckjqKCdUKdvc2hlnb7.OwppKt6Pj5TRVafyDXs6tEIoQfSqp5d1cDRKlRxGF3Q2cGZhxyop5MzcDRCK9japINUUq.X2.N2taQSTdf.GaR1ftCQpY+Sy8GIooQ9japYFIYsSxIfCapUOuEG1TSabbSMQpp5GCr8.+htaQST1DfOWR1xtCQpQNtojllcs6N.oECIYiXvalz8u6VzDkiEXu5NBogMG2TSr9qtA0WY2snIJ2TfOUR1stCQpINtojll43lZpWRdf.mHvF1cKZhx2B3IUUc4cGhzvliapIZUUGCvyu6NzDmqOv6zaRcMiZMl6ORRSibbSMUKI6Bvm.XM6tEMQ4hAdLUUWR2gHMJ33lZh2bmWHGb2cnINECtI0e2cGhzhLG2TRSy7muooVIY+AdO3YKqV8rBfmbU02p6PjFUbbSMs34B7o6NBMQZWSxolj0s6PjVjTy8GIooQWqksrks6cGgzvVRNTfWZ2cnIRO2ppOS2QHMJ43lZpPU0U.73.NqtaQSj1BfOeR17tCQZQfiaJooY0s41batftiPZXII2xjbx.O9taQSjdYUUuitiPZTywM0TippeMvNAb9c2hlHcq.N1j7X6NDoQLG2TRSypMZi1nKp6HjFFRxcG3y.bu5tEMQ5H.d4cGgzhAG2TSUpp9NL3I37R6tEMQ5eE3Hl67LRRRRSd7KuQSERx1yfaD80q6VzDouDCtYzuhtCQZwfiapoNUUeYFLvY5tEMw5kljip6Hjjjjzrmjru.GEC9h2kVc8s.1gppKq6PjVr33lZpTU0GG342cGZh1NjjuTRV+tCQRRRRyFRxg.b.3SgrletDf+yppKr6PjVL43lZpUU0qCXYc2glncO.Nwjr0cGhjjjjldM2EGzo.7j6tEMw5OxfmXyud2gHsXywM0zt8E3CzcDZh1ZC7ekj8p6PjjjjzzmjbOANEf6Y2snIVWAvSop5T5NDoN33lZp1bGfxOUfSt6VzDsqMvqOIuutCQRRRRSORxyD3SAby5tEMQaeqp9fcGgTWbbSM0qp5OB7nA7wyWKTOojbpIwasRIIIIsfjj2HvaF3FzcKZh1qspxiiMMSywM0LgppKAXGAtftaQS71BfOumCmRRRRZ9HI2rjbb.6Y2snIdGIv9zcDRcywM0LippuCCdBN+sc2hl3sl.epjrucGhjjjjlb7Wc9Z9.6tEMw6T.dhycTrIMSywM0LkppyBXG.t7taQS7VCfCHIenku7k6SwojjjjtZkjmFvmA3V1bJZx2WE3QWUcYcGhz3.G2TybppNNfmAvJ6tEMU3+b61tsaYI4N2cHRRRRZ7TRd8.uMfqe2snIdW.viqp5h6NDowENtolIUU8t.dwc2glZrI.etjrycGhjjjjFejjaYRNUf8p6VzTgeEvipp5b6NDowINtolYUUcf.uxt6PSMtA.u2j7V6NDIIII0uj7v.NMFbgTJsP86A11ppud2gHMtwwM0rt8C3c2cDZpxSOIetjrdcGhjjjj5QRdg.ebf0t6VzTg+DvtUU846NDowQNtoloUUEfmJvQ2cKZpx8A3jSxNzcHRRRRZwSRVyjrbfWECt.JkFFdlUUGQ2QHMtxwM0Lu4F37I.bhc2hlpbS.Npj7Z6NDIIIIM5kjMC3TA1ttaQSUdAycmQHoqBNtoDPU0J.1Qfyn6VzTmmaRNojrztCQRRRRiFI44Bbr.21taQSUNvppk0cDRi6bbSo4TU8yA1dfuU2snoNaECtM08awWRRRZJx4bNmyFmj2GvqkAWvjRCKusppWX2QHMIvwMk9qTU8i.1Vfyu6VzTm+Cfk6qotjjjzzgjb2V5RW5Q.7j5tEM04v.1itiPZRgiaJ82op56xfmfyKp6VzTomaRNsjrAcGhjjjjleRxyF3D.7yzogsiC3IWUcEcGhzjBG2T5JQU0+MCF37+s6VzToMG3ymjmX2gHIIIoUcKe4KeqSx6E3M.7+q6dzTmSC3QWUcYcGhzjDG2T5pPU0oCrC.+gtaQSktI.u+j71V1xV1t2cLRRRR5pWR17sa61tWGvN2cKZpzYArcUU+ltCQZRiiaJc0np5DA9OAt7taQSsdZ68du2OyjrYcGhjjjjtxkj8F3DA1vtaQSk9NLXXyKo6PjlD43lRWCppNZfcEvy7DMprg.mPRdQcGhjjjj9KRxMOIebfkArjt6QSk9t.aSU0Or6PjlT43lRqBpp9..OcfzcKZp00C3UljkmjaU2wHIIIMqKIOLfOOvin4TzzqyGXGppNutCQZRliaJsJpp5cB7b5tCM0a6.9xI4wzcHRRRRypRxABbL.25taQSstHF7pn+M5NDoIcNtozpgppCF3EzcGZp2MA3HSx6t6Pjjjjlkjj6PRNcf8o6VzTseNvCup5q0cHRSCbbSoUSUUKCvyFQsXXWSxYmjGb2gHIIIMsKI6IvIA3E8nFk9E.OhppuR2gHMsvwMklGppdU.upt6PyD1PfiII6e2gHIIIMMJIqcR9P.uQfab28noZ+JfGWU0o2cHRSSbbSo4oppWDvqo6NzLg0.3kljuTRticGijjjzzhj7n.NCf+ytaQS89C.aeU0w2cHRSabbSoEfpp8F3f5tCMy3d.74RxKt6Pjjjjlzkj2NvxAVmtaQS8tLfsop5j5NDooQNtozB2K.3szcDZlwMD3UjjOQRVZ2wHIIIMoII2yjbV.6N96DqQueOvSrp5D5NDooU9CxkVfppRU0d.715tEMSYa.9hI4YzcHRRRRSJRx9A7YAtKc2hlIbY.O4ppir6Pjll43lRCIUUOCf2d2cnYJ2Hf2RR9LI4V2cLRRRRiqRxljjSE3kAbc5tGMSHL3xC5n5NDoocNtozv0yD3s1cDZlyCB3zRxdzcHRRRRiaRx9.bR.aQ2snYFWNCF17i1cHRyBbbSognppq.XOAdmc2hl4rV.uoj7wRxss6Xjjjj5VR1vjb7.GHv+V28nYFWJvt4qhtzhGG2TZHqp5Jpp1cf2Q2snYRORfyHIO8tCQRRRpKI4YAb5.OftaQyTVIvSpp582cHRyRbbSoQmmAvar6HzLoaDvaMIGaR1vtiQRRRZwRR1fjbb.GLvMn6dzLkUBr8dFaJs3ywMkFQl6UT+4.7l5tEMyZqAN0jrWcGhjjjzn1bOslmBvCr6VzLmeOviup5i2cHRyhbbSoQn4dE0eV3Svo5yMD30mjOeRtycGijjjzv1bmsl+4mVSOaM0hseOCdUz8L1TpINtozhfppmMvqs6NzLs6MvojjWQ2gHIIIMrL2Mg9WDeZMUOtLfsspZ4cGhzrLG2TZQRU0yG3U2cGZl10E3EmjyJIOjtiQRRRZ9JIaZRNQFbSn+u1cOZlzuE3QVUc7cGhzrNG2TZQTU09Bb.c2gl4cW.9uRxaHI+GcGijjjzpij7h.9b.2utaQyr9k.OlppOS2gHIG2TZQWU0KF3kzcGZlWA7rANyj736NFIIIoqIIYKSxWG3UB7uzcOZl0uB3Q3vlRiObbSoFTU8JAd9c2gDv5.bnI4XRx52cLRRRR+8V9xW9Vmj2NvwArwc2ilocw.O3ppSs6Pjzew0t6.jlUUU8ZSxuG3sxfmhNoN8v.1hjrrppk0cLRRRR.jjcD3kCba6tEMy6B.1gppuR2gHo+V9jaJ0npp2NvtwfaYOot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0VizPhKG3y3SnijbbSo9DYleYfCGXVqtEoAYWIvQDQbRUGhjjTaTl4WilSxoO4NpWy3.1xHh+V0gHo533lR8AxLOLfcq5NjFj8G.FUDwEUcHRRRcCxL+z.ecfUt5VjFD8j.aeDwut5PjTMbbSodXYlyKvwBr4U2hzfjD3LANpHhKs5Xjjj5FkY9wA1If0o3TjFrLQfuUDwgVcHRZ3miaJ0iJybI.9E.u2hSQZvvDANeZt4y+iE2hjjTOgLyOAvN.7AptEoAIGNvdGQLgpCQRCebbSodPYlqBvoC7VptEoYPSD37.N3Hhqp5Xjjj5EkYt9.6C9aJt5MLZfsKh3eWcHRZ3giaJ0iIybiANQf4u5VjlA7R.mDvOHh3ZpNFIIo9AYlqCvdAr9EmhzLpq.XKhHFW0gHogdNtoTOjLycD3v.lypaQZ5zyAbp.+jHhqn5Xjjj5G042r7sGXSptEoY.2NM2j59z+H0iywMk5QjYte.e6p6PZ5zD.t.fCIh3JqNFIIIAYlaJvWCu3gT2q+EvVGQb9UGhjF533lRc4xLmMfiD3KTcKRSmNKfCzeW0kjjZm57N47aC7tptEooCSDXmhHN1pCQRCMbbSotXYluNfeIvFWcKRSGNWfCKh3OWcHRRRZpKyby.1Cf0r5VjlNreQD6e0QHoAeNtoTWpLyQRy6lP+lKU2l+DvOLh3rpNDIIIMsKy7KArS.qP0sHMM5vA1qHhIVcHRZviiaJ0EJybE.NSfkp5VjlFb8.emHhQWcHRRRZFWl4NCry.KY0sHMM3TA1lHhwWcHRZvwLUc.RZZSl45.76wgMU2iaklWj6qjCaJII06Hh3GN5QO5c.XOAdnp6QZ.ZK.9MYlKX0gHoAGdxMk5hjYtE.GMv7UcKRC.2MvOHh3vqNDIIIMzJybw.1dfcD+dUU2gqB3SEQbaUGhjlw33lRcIxL2FfeFdhqU62CCbbzLromhCIIo9HYlKMv9B7wAlqhyQZpYrzLv40Tc3OAmg0...H.jDQAQERZ5miaJ0EHybO.Nzp6PZp34A9kz7d07tqNFIIIUmLyUE36BrQU2hzTwi.7QiH9qUGhjl933lRsbYlGNvtTcGRSEiF3PhH9GUGhjjjZOxL2.f8CXMJNEoWKOIv1EQb5UGhjl143lRsTYlyJvQArcU2hzqg+Fv2Kh3LqNDIII0dkYti.6Jvas5VjdULAfuRDwwVcHRZZiiaJ0BkYNO.GCM2jeRsQ2Iv2Oh3HqNDIII08Hy7fA9B.u9paQ5UwtFQ7CpNBIMv4EShTKSl47AbV3vlpc5oANvQO5QuCNrojjjlVEQr2.qFvwWcKRuJN7Ly8u5Hjz.mmbSoVjLyEE3L.VypaQZJ3WRyif9MVcHRRRp6Wl4GBXu.d+U2hzTv2OhX2pNBIM043lRsDYluEZtTVVopaQ5U3uC7ciHN+pCQRRR8dxL+xz7937sTcKRuBGKvNFQ7BUGhjd043lRs.Ylq.vYBrTU2hzj4g.NnHhin5PjjjTuuLyQA7kAFQ0sHMYNcfOSDwDqNDIMk43lREKyb0n4DatXU2hTGSD3GA7ChHt2piQRRR8OxLe6.eSfMu5VjlLW.vVEQ7DUGhj9+xwMkJTl4ZQyvlKb0sH0wEAbfQD+kpCQRRR8uxL+T.6KvxWcKRcbw.aYDwiUcHR5+jiaJUjLy0G3z.lmpaQBXb.GPDwOq5PjjjjdYct0p+J.ye0sHAbk.ezHh6u5Pjzj33lREHyby.NQf4p5VTeuW.3vG+3G+QNhQLh6t5XjjjjdkxLWZfCAXyptEIfqC3iDQLtpCQRMbbSogYYlaEvOGX1qtE026R.9lQDWd0gHIIIM0jY9I.9N.KW0sn9d2.vmLhXLUGhjbbSogUYlaMvwALSU2h5qcO.GdDwgWcHRRRRSqxLOLfuHvbWcKpu1X.9XQD2R0gH0uywMkFljYt8.GE9+tS0IANdf8Ih3gpNFIIIooWYlqLv+CvGn5VTeswArwQD2T0gH0OySOlzvfLyuBvOFG1T04ZAVuHhs0gMkjjT2tHhqMhXcA1FfGt5dTeqEG3ByLeGUGhT+LG2TZHVl4NAbD3+6MUimBX+iHVkHhKs5XjjjjFLEQbB.qNMOcJRUXj.iNy7cVcHR8q7TjIMDJybW.78ZnpxkB70hHttpCQRRRZnVl4GF3PAV1paQ8ktefMMh3pqNDo9MdRxjFhjYtG3vlpFOJv1GQrtNrojjj5WDQbtQDKGvn.d9p6Q8cdi.WTl4pUcHR8abbSog.Yl6EvgTcGpuzIBrZQD+zpCQRRRpBQD6Iv6G3xptE02YAANmLy2a0gH0OwGKcoAYcNwlGZ0cn9N2AvADQ36aJIIIoNxL2efcB30UcKpuxCArYQDWd0gH0OvStozfnLy8EG1TC+NtQMpQ88bXSIIIo+SQDeaf2Kver3TT+kEF3BxLWipCQpefmbSoAIYl6CvAVcGpuxsRyEFzETcHRRRRscYl6DMe+5yS0sn9FOBMmfSeEIHMDxStozffLy8DG1TCeRfevXG6X2bG1TRRRZfIh3HAd2.mc0sn9FKHvYjY9tqNDodYdxMklAkYta.GV0cn9FiEXOhHN2pCQRRRpaUl4tBrO.KP0sn9BOHvlDQbUUGhTuHG2TZFPl4tC78ptC023HhH14piPRRRpWPl4aE3nAV2paQ8EdXfMHh3ZpNDodM9XoKMcJybW.FU0cn9B2FvF5vlRRRRCdhHt8Hh0C3qB7jU2i54sP.mSl46r5Pj503I2TZ5Pl41AbrU2g5K7y.9FQDOT0gHIII0qJybkANbf0o3TTuu6.3CGQbSUGhTuBG2TZZTl41.7yw+2OZn0C.7UiHNipCQRRRpeQl42fl2EmyY0sndZ2Iv5GQbaUGhTu.GmQZZPl4mC3X.l8paQ8zNIf8Nh3dqNDIIIo9MYlqAvOF3cTcKpm1X.1zHhau5Pj5143lRCPYleTfSGXVptE0y5QA1mHBekGHIIIUrLyCAX2vu+eMz45.9nQD2U0gH0MywMkF.xL2HfyDOwlZnyk.rSQDio5PjjjjTiLyMkl2EmukpaQ8rtFfMJh3AqNDotUNtozTQl46G37.lqpaQ8jlHv9GQbPUGhjjjjlxxLOYfOc0cndVWIMCb9nUGhT2nYp5.jZy57914zwgM0PiqGXccXSIIIo1sHhsBXao40HjzfsUCXzYlyS0gH0MxStozqhLyUA3B.VjpaQ8jNpHhub0QHIIIoAtLykE3HAV2paQ8jtPfOdDwyUcHRcS7jaJMEjYtb.mANroF78P.akCaJIII08IhXLQDqGvAB3.TZv1FA7KxLm4pCQpahmbSoWgLyEG32.rbU2h54bw.eAuMDkjjj5904cy+wArDEmh58bL.aeDQVcHRcC7jaJMYxLWHfQiCapAWOOv9EQ7AcXSIIIodCQD+Af2Evut5VTOmuHvnpNBotEdxMk5nyKu4yC38VcKpmxc.rCQD+tpCQRRRRCMxL+5.6Cv7UcKpmx2Jh36VcDRscNtoDPl4rQy6XyMs5VTOkyb7ie765HFwHt6pCQRRRRCsxLWMfeA9TfoAW6bDwQTcDRsY9XoK033vgM0fmI.rqQDebG1TRRRp+PDwUN1wN1sD3mUcKpmxOHy7yUcDRsYdxMUesLy.3G.7UqtE0yXLz7x+9OUcHRRRRpFYl6.vAgOl5ZvwD.1xHhyt5PjZi7jap9ceKbXSM34TF6XG6V3vlRRRR82hH9I.qGv0UcKpmvrC7KxLWqpCQpMxSto5akYta.GV0cndBuHvtEQ7CqNDIIII0tjY9y.11p6P8DdXf0Kh3FpNDo1DG2T8kxL2VfiFXVptE006VA9RQD+wpCQRRRRsSYlaOv2GXNqtE0061.13Hhaq5PjZKbbS02Iy7C.bQ.yZ0sntdmKvNFQbeUGhjjjjZ2xLWafiEXYptE006ePy.mOb0gH0F36bS0WIy7cBb13vlZFSB7chH9HNrojjjjFHhH9yicri8SBbVU2h558NANiLyYq5PjZC7jap9FYluMfyGXIqtE0U6gA1tHhyq5PjjjjT2oLy8E3.ptC006TA9zQDuT0gHUIO4lpuPl4BCbx3vlZFykA7AbXSIIIIMiHh3.A1BfGr5VTWss.3PpNBop4I2T87xLmCfKFXsptE0U63iH97UGgjjjj5cjYtr.m.vZTbJp619DQbvUGgTU7japdZYlyDvOCG1TS+l.vN6vlRRRRZvVDwXhHVSfiq5VTWsCHyzedE02xSto5okY98.18p6PcstKfsMh3RqNDIIII0aKybW.NXf4n5VTWoIBrg9ytn9QNto5YkYtG.GZ0cntV+IfsIh3NqNDIIII0eHybS.NZfEs5VTWoGAXChHt5pCQZ3jiapdRYlaAvoTcGpq0OMhX6qNBIIII0+Iy7MC7qAV8paQckFKv5GQLtpCQZ3hiapdNYluWfKBXNqtE004kn48q4QVcHRRRRp+Vl4OGv2ihZ5wUB79iHdlpCQZ3fWnPpmRl4JBbR3vlZZ28CrINrojjjjZChH1Vf8FHqtE00Y0.NwLyYt5PjFN33lpmQl4BAbh.KV0sntNWIvGLh32TcHRRRRRurHhCAXK.drpaQcc1LfCp5HjFN3ikt5Iz42Qp+.vZWcKpqyoDQ7opNBIIIIoWMcdB0NEfkq5VTWmcOh3+o5HjFJ4I2Tc8xLCfeLNrol1cfNrojjjjZ6hHtdf0E3bqtE004fxL+DUGgzPIO4lpqWl49.bfU2g5p7j.6YDwQWcHRRRRRSKxLOFfuP0cntJONv5FQbMUGhzPAG2Tc0xL+r.+B7+trF3d.fsHh3OWcHRRRRRSOxL2CZNfGyR0sntF2AvGHhXbUGhzfMGDRcsxLWCfKAXtptE003pA1xHhaq5PjjjjjlQjY9Qo4fdLOU2h5Z7WAVuHhwWcHRCl7cto5JkY9V.NUbXSMvcFicricabXSIIII0KHh3rA9f.982pAp2MfuZtTOGO4lpqSl47Abg.uqpaQcM9wQD6T0QHIIIIMXKybI.NYZFtRZfX+hH1+piPZvhiaptNYlmFvlWcGpqPBrGQDGV0gHIIIIMTJy7WA7optC003yDQbxUGgzfAerzUWkLyCFG1TCLOIMueMcXSIIII0yKh3SCLpp6PcMN5Ly2a0QHMXvSto5ZjYt0.mP0cntB2MvmNh3xpNDIIIIogSYleMfCAXVqtE05c6.enHh6r5PjlQ33lpqPl4ZB7GAl8hSQseWOvlGQbqUGhjjjjTExL2BfiAuI00T2eG38EQ7bUGhzzKerzUqWl4hCbR3vlZp6RF+3G+l5vlRRRRpeVDwoBrA.OP0snVuUG3HqNBoYDdxMUqVl4r.7mva9OM08KiH15piPRRRRpsHybEo4lTeEptE05sGQDeupiPZ5gmbS0ZkYF.+PbXSM0cDNrojjjjz+oHhqGXiA7cQulZN3LyMt5Hjld3I2TsVYleEfin5NTq29FQbPUGgjjjjTaVl44ArIU2gZ0dPf0IhXrUGhzzBG2TsRYlaDvYALaU2hZsdQfuXDwwUcHRRRRRcCxLOVfsq5NTq10SyELz+t5PjFn7wRWsNYlKEvQgCapWcOAvmvgMkjjjjF3hH9B.Gb0cnVsUD3HxLm4pCQZfxwMUqRl4bB7q.V7paQsV2OvlDQb1UGhjjjjT2lHh8A3qWcGpU6yBrWUGgz.kOV5pUwGSBMUb6.erHhan5Pjjjjj5lkYtM.+TfYs5VTqzKBrkQDmQ0gHM033lp0Hyb2A9dU2gZstZfMOh3NpNDIIIIodAYlaFvu.XtqtE0J8Hz7927VpNDoWKNtoZExLWOfKD+cMTSY+YfOYDwCVcHRRRRR8RxLe+.mFvBTcKpU5pAVuHhGu5Pjd033lpbYliD3u.rXU2hZktfHhMo5Hjjjjj5UkYtp.mM9yjoorSHhXapNBoWMdgBoRkYN6.mB9ODUSYmjCaJIIIIMzJh3pA1PfwTcKpU5+NyzKXH0Z43lpZeWf2U0QnVoiOh3yVcDRRRRR8ChHtIfOBv0VcKpUZ+xLeeUGgzThOV5pLYledfed0cnVoiHhXmqNBIIIIo9MYlKJv4.7NptE05bO.umHh6t5PjlbdxMUIxLWYfCq5NTqzA5vlRRRRR0Hh39F0nF0OC3xptE05rX.Gel4rTcHRStYt5.T+mLy4C37.V7paQsN6aDw9WcDRRRRR8yt3K9h+GiXDiX1Vq0ZsdIfko5dTqxRBLq6+9u+WR0gH8x7wRWCqxLmYfiC3yUcKpUIA1qHhQUcHRRRRRZRxLOGfOb0cnVkWB3yDQ7qqNDIvwM0vrLyuJvOr5NTqyWNh3npNBIIIII8+Ul4Y.7wqtC0p7H.u6Hhau5PjbbSMrIy78B7aAlipaQsFIvWHhvKVJIIIIoVrLyiCXaptC0pbU.qcDw3qND0eyKTHMrHybgANVbXSMISD3y6vlRRRRRseQDedfio5NTqx6.3fpNBIG2TC457d17mBrzU2hZMdNfuXDwITcHRRRRRZfIh3KA3gSPStcIy7yWcDp+lOV5ZHWl4d.bnU2gZMdIfOYDwnqNDIIIIIMsKy7n.1gp6PsF+KZd7zu4pCQ8mbbSMjJyb8ANOfYs5VTqvK.rEQDmY0gHIIIIooeYlGNvtTcGp035AVqHhmt5PT+Gerz0PlLy2.M2L5Nro.3Yn4croCaJIIII0kKhXWANpp6PsFqHv2MyzCQmF14+kNMjIy7b.9vU2gZEddfOSDwoUcHRRRRRZviOh55U3yEQbhUGg5u3I2TCIxL2cbXS03Eo4croCaJIIII0iIhXGo4I1SBfuWl4xWcDp+hmbSMnKybMA9y.yR0snx8b.aSDwoTcHRRRRRZnSl4OBXmptC0JbEzbAC87UGh5O3I2TCpxLWPfiAG1TvD.9hNrojjjjTuuHhuBvOu5NTqvZ.b.UGg5e3I2TCpxLOQfOS0cnVgsJh3WUcDRRRRRZ3Sl4wArMU2gZE1jHhKn5HTuOO4lZPSl41CrUU2gJWRyshtCaJIIII8+i8tuiVxJqRXi+rEPnIojFBRPDQZjfjSRzHneFAQLqiIFyQPGmYDLLJMNJlyiXDU.yfhQTIifJHgljjjfX.EoQR896ONECHdu29Fpp104TO+VqZ08nNvSCzEUsOugwLQD+q.elp6PiD9nYlab0QntOW4lpuHy7gBbp.qb0snRsXfWcDwGp5PjjjjjTcxLOJfCn5NT491.O4HhEWcHp6xUtolyxLmGMOYNGrod8NXSIIIIIEQ7L.9dU2gJ2S.3fpNB0s4vMU+vaEXGpNBUtCJh3HpNBIIIIIMZHhXe.99U2gJ2gjYtsUGg5tb3lZNIy7+Gvqu5NT4daQDGd0QHIIIIoQKG6wdruWfSr5NToVVfiLybMpND0M4Ytol0xLWGfSAXCptEUp2WDwqq5HjjjjjznoLy0B33A15paQk5iFQ7xpNB083J2TyJYl2Gf2ONXywce.GrojjjjjlJQDWGviC3bqtEUp+sLymU0QntGW4lZVIy7k.7wqtCUpOcDwKp5HjjjjjT6Pl4FC7MA1zpaQk4Z.1kHhqn5PT2gC2TyXYlOLfeLvpVcKpLeiHhmR0QHIIIIo1kLys.33.VupaQk46A7DhHtipCQcCtsz0LRl4RA7QvAaNN6G6fMkjjjjzrQDw4B7T.9iU2hJydC3Yuo5ab3lZl5P.1kpiPk4LV3BW3qs5HjjjjjT6UDwYAru.Kp5VTYd2YlaS0QntA2V5ZZKybmA94.KU0snRbd.6SDwUUcHRRRRRp8Ky7oB7U.V5paQk3W.7viHtspCQsatxM0zRl4pC7IvAaNt5xA1OGrojjjjj5WhH9Z31Sdb11AbnUGgZ+b3lZ55sBr4UGgJwM.r+QDWX0gHIIIIotkHhOIvAWcGpLuwLSO56zbhaKcsD0aqB7UwUs43naC3wDQ7SqNDIIIII0ckYt.f2X0cnRbg.6VDwen5PT6jqbSMkxLWCfEfC1bbzhAdANXSIIIIIMnEQbP.e5p6PkX9.ukpiPsWNbSMoxLCfCGXiptEUh2PDwWp5Hjjjjjz3gHhWDvwWcGpDupLymX0Qn1I2V5ZR0a6nerU2gJwgFQbHUGgjjjjjF+jYdlzbYynwKWBvNDQ7mqND0t3J2TSnLyGDv6q5NTI9PNXSIIIIIUnmJv4UcDZn6AC795sKRkl17efQSnLyuBv9WcGZn66FQ73pNBIIIIIMdKybq.NNf0o5VzP2SMh3qWcDp8vgap+IYlOWfOa0cngtyHhXGqNBIIIIII.xL2YfeBvxVcKZn5Jn41S+ppND0N31RW+CxLWeZtcz03kK.3.pNBIIIIIo6RDwoB7bAVb0sngpM.3cTcDp8vgap+OYl2GZFr4ZVcKZn5O.7LiH9sUGhjjjjjz8TDwWE3MWcGZn6YmY5BvQSKtsz0+G2N5iktCf8Ih3GVcHRRRRRRSlLy2Cvqu5NzP0UBriQDWW0gnQatxME.jY5x9d7zqzAaJIIIIoQcQDuAfis5NzP05C7tqNBM5ygap6x6AX8pNBMT81hH9XUGgjjjjjzzQDw9A7KptCMT87xLeJUGgFs41RWjYtu.eUbX2iSNxHhWP0QHIIIIIMSjYtQ.+.fMr5VzPyEBrmQDWe0gnQSNbywbYlqMvYB7.ptEMzbZQD6b0QHIIIIIMajYtS.eefUp5VzPymJh3EWcDZzjqTOcH3fMGmbg.O0piPRRRRRZ1Jh3z.ddU2gFpd9YlO5piPilb3liw5ctU7hptCMzbC.O6Hhqs5PjjjjjjlKhH95.Gb0cnglkF3ClYtxUGhF83vMGSkYd+n41Q2+YfwC2IvKNh3rpNDIIIIIo9gHhE.7IqtCMzrI.uwpiPid7L2bLUl4G.3UVcGZn40EQ79pNBIIIIIo9sLye.vip5NzPwcBr6QDmR0gnQGNbywPYl6.vIArLU2hFJ9vQDuhpiPRRRRRZPHybco4FTe9U2hFJNMfcKh3NpNDMZvsj7XlLyU.3igC1bbwOxAaJIIIIotrHhqF3YBbiU2hFJ1If2T0QnQGNbywOuZfst5HzPwBA9WqNBIIIIIoAsHheIvARy1VVceGTl4lTcDZzfC2bLRl4liOciwEKB3YDQbkUGhjjjjjzvPDwWglKNW08sR.efLykp5PT8b3liW9en4M.T21hAdI8dxkRRRRRRiMhHNDfOW0cnghGCMGGAZLmC2bLQl4ygleiu59N7HhuX0QHIIIIIUgErfEbp.tXOFObn8tPozXLuszGCjYtN.mEvZUcKZf6XhHdZUGgjjjjjTkxL2XfSB3eo5Vz.2mIhv6ahwXtxMGO7VvAaNN3b.7lQWRRRRRi8hHtXfmOvcTbJZv6YmYtGUGgpiqbyNtLyGMv2EvCY2ts+HvdFQ7apNDIIIIIoQEYlGDvgUcGZf6b.1oHhao5PzvmqbyNrLykF3+FGrYW2cB7JbvlRRRRRR+ihHV.vQVcGZfaKAdYUGgpgC2ra6f.1tpiPCbuqHhub0QHIIIIIMJJh3E.btU2gF3NjLy4WcDZ3yskdGUl4FRykHzpTcKZf5XiH1upiPRRRRRZTVl4lB7i.V6paQCTekHhCn5HzvkqbyNnLykB3cgC1rq6hAdUUGgjjjjjzntHhK.30Qyw5k5t1+LymV0QngKW4lcPYlOAfuU0cnApEAr6QDmU0gHIIIII0VjY9VANjp6PCTKDXq8xEZ7gqbyNlLyU.3cVcGZf6M5fMkjjjjjlYhHNTfiq5Nz.0l.7lpNBM73vM6ddC.aQ0QnApOdDwGo5HjjjjjjZodo.WV0QnAp2fWtPiObao2gjY9fANMfUq5Vz.yYEQrcUGgjjjjjTaVl4tCbB.KW0snAluJvyHhXwUGhFrb3lcDYlAvwB7TptEMv7W.1oHhKr5PzbSl4CBXk.tez7vHVQf4AbeAVpd+O6NAtMf+NvsPye++OSy4s5MufErf89fO3C9iMjSWRRRpyJybcn4ynsBz74yV0d+765yoszz7cnWLvsSymSaQ.+IZ9bZ2DveIh35G5wqYkLy2HvBptCMPs+QDGc0QnAKGtYGQl4iA36g+8ztpEC7riHNppCQSe81FDaKvNArt.qIv+Bvp.rxz7AjmotEfaF3u160MvcO3yKG3RnYK1bcQDW8b6WARRRRcGYlaJvC.XiA1Hf0llgYtF.2eZ97Y20vMmoGgaIMC27FA9C.WKv0C7K.NyHheQe3WBZ.Hy7yA7bptCMv7a.1Aubg51bPXc.YlqHvofm0lcYGQDwqs5HzzWl4NC7Cn4CGWgqmlOT8kB7qnYnmWCvUDQbEE0jjjjz.Wl4V.rAzLHysDXy58yWap4ylk.u5HhOXA+4VKAYlqIvOC3gTcKZf4PiHNjpiPCNNbyNfLy2Dv6p5Nz.yIGQrqUGglYxL+z.+qU2wD3FnYEd9qn4gh7KO1i8XWq8a+1uSnzpjjjjlExLWeZVIl6HM6VlGBvFRy1HeTxuNhXqpNBMwxL2VfeJ0svDzf0eCXqiHtjpCQCFNbyVtLyMjlATrVU2hFHtAfGdDwEWcHZlIy7xoYECLp61AtZfqC3jn4C0ctQDWYoUIIIIMAxLeX.aOviB3gxcer+Lp6N.1sHhSq5PzDKy7UCbDU2gFX9RQDOqpiPCFNbyVtLyu.f+FztoEC7zhH9ZUGhlYxLet.e1p6XN32Cbt.mCvoQy4D0us1jjjjz3ndqntGNv1.r0zLPyYy4V9nfOSDwn3N6Q8jY9Y.d9U2gFHRfmPDwwUcHp+yga1hkYtSzb1frLU2hFHd+QDulpiPybYl+Df8r5N5i9izrM1OdfSYgKbgKZ9ye9mSwMIIIoNnLy65x94wCr6.aNy7K2mQU+IfsHh3ZpNDMw5c9adh.yu3TzfwoAr6QD2d0gn9KGtYKUl4x.7yo4rkQcOmZDwtTcDZlKybyANa51OzgqfliCiuNvoDQ76JtGIII0h06B.5QC7jo4B.59UaQCTOiHhub0QnIWl41Qyw0zxVcKZf30DQ79qNB0e4vMaoxLed.GY0cnAh+DMOMoyq5PzLWl4a.3vqtignqE3hA9N.e2HheSw8HIIoVfLyGAv9Sy4m4lw3yfjNgHh8t5HzTKy7MBrfp6PCDWMv1DQbCUGh5eb3lsPYlqNvISyMAn5dd9QDs4yqwwZYlmMMmGTiitUfSG3XoYEc9KJtGIIIMh3vNrC6.OnC5f9s.OVf8FXSKNopbK.6bDwut5PzTKy7qSypIVcOu2HhWe0Qn9GGtYKTl46F3fqtCMP7YiHd9UGglcxL2SfeL9dq2keAvw.7shHtfpiQRRRCeYl6JMW.pOVfMr3bFUbnQDGR0QnoVl4FPy4u4Cr1Rz.vsArstqy5N7Kf2xjY9foYkQspU2h56NuHhMu5HzrWl46E30VcGiftYfymlAc90iHt3h6QRRRCPYl6LvyF3QfWLKSjSOhXmpNBsjkY9X.Ngp6PCDGcDw9WcDp+vga1hjYF.eIfCn5VTe2sB7HhHNkpCQydYlmKM2nmZx8Wn4If+cANtHhqt1bjjjT+Pl4Cilsv6dC3f6lZ2IvCOh3zqNDsj4NmryJAdpQDeipCQycNbyVjLychlasskp5VTe2AGQ3AVcKVl4illmpquu5z20A7s.NpHhSr3VjjjzLTl45Ary.u.f8hwmKEn9gORDwKu5HzzSl4YPykek5VNMfcKh3NpNDM23WBukHyboA9Q.6d0sn9teXDwit5HzbSl4mE34VcGsXKD3KPyfNuzpiQRRRStLycD3EA7D.Vyhyos5RiHdvUGgldxL2BfeJvpTcKpu6.iH93UGglab3lsDYlOKZ9h+pa45n41R7xqNDM2jYd4.aP0czA7mANCfOSDwWo5XjjjTidqRyC.3oArc32kbt5N.1yHhSt5PzzSl4AB7QqtC02cE.aUDwMVcHZ1y+ERs.Ylq.MKWZOK+5VVLvyOh3yWcHZtIybu.9A3QFQ+1E.7ko4RH5bqNFYjMexD...B.IQTPTIIowQYlOBf8C3ohqRy9M2Z5sLYleYfmd0cn9tCMh3PpNBM64vMaAxLeS.uqp6P8cerHh+spiPycYlebfWR0czg82A91.ezHheR0wHII00kYtl.OZfWFMmolZv3phHV+piPSe898FmNtis5Z9a.aSDwEWcHZ149Tc.ZpkYtF.upp6P8cW3BW3BcKMzAbXG1gcf.6S0czwsbzrE39wYl+lLyWyEdgW3VVcTRRRcMYlaRl46A3LA973fMGzVuLymT0QnouHhqG3URytvScGqHvqu5HzrmqbyQbYl+O.utp6P8UKFXOhHNopCQycYlOFZtkz0v0kC7M.9rQD+phaQRRpUKybuAd1.OQfUp3bF27diHbnJsLYlePfWQ0cn9pailaN8yn5PzLmqbyQXYlaFMaED0s7dbvlcJ6d0ALl5AB7Z.NsLyuTl4dVaNRRRsOYlOsLyeFv2E3YgC1rBO1pCPybKXAK37.Nmp6P8U2WfCs5HzriqbyQXYleFfme0cn9pyJhX6pNB0+jY9qAbKROZ3zANhHhub0gHIIMpp2YF3yA3kB7fKNGAIvizyU71mLycB3jwEMVWxcBr2QD+vpCQyL9aBGQkYtGzr0PT2wh.NvpiP8O89.MaV0cn+O6HvQkY9qyLeidtbJIIc2xLePYlGAvY.b33fMGUDzbSzqVlHhSC3sWcGpuZo.NrLykq5PzLiqbyQPYlAvwCr2U2h5qN3HhETcDp+Iy7+F3MWcGZRcE.eZfuPDwus5XjjjpPl41B7uBb..qZw4nI1uJhXqqNBM6jYdl.t675VdFtavZWb3lifxLeb.eabk01kbxQD6Z0Qn9qLyeLvdUcGZI5ORyPN+eiHVX0wHIIMLjYtC.uJZFp4RUbNZpcy.6bDw4VcHZlKyba.9Y.qP0sn9lyGXGiH9aUGhldb3YiXxLWVf2A92a5R9qz7zxUGRl4F.3SXucX0.NHfyLy7SkYN+pCRRRZPIyb2yL+t.mBMWRPNXyQeq.tq8ZshHNaf2V0cn9pGJvKp5HzzmCPazySAGXRWygFQbQUGg561Kf6e0QnYjUB3ERyMr9mKyzsOjjj5LxLeRYleOfeJMCJygZ1t7jpN.M6063G6mWcGpu5UjY5Q4QKgC2bDRl4JA7eUcGpu5DhHduUGgFHdhUGfl0tezbKwdxYlexdmEYRRRsRYl6SugZ9M.drU2il0dXYlOnpiPyIGHMWhrpaXi.dsUGgldb3liVd1.aZ0Qn9laD30WcDp+q2sv8NTcGZN69Ry1M4zxL+hYld6pKIoViLyGYuy+6iGGpYWvJBrEUGgl8hHNeb6o20bfYlqU0QnkLGt4HhLy6G9TA5ZdyQDmW0Qn9uMYS1j4Ar1U2g5aVZfmINjSII0BjY9nxLOQfeHdwF1034tYKWDwgQyQCg5FVcf2P0QnkLGt4niWNvFWcDpu4mEQ7wpNBMv7Hv2+rKZdzLjySIy7CmYtYUGjjjzcIybuxL+p.+.f8n5dz.win5.TewqCvaY6tiWZloypYDme47Q.YlqBv+V0cn9l+LvKo5Hz.09Tc.ZfZE.dY.mdl4BxL2npCRRRiuxL2lLyiB3GC7zptGMP8fyL24piPyM8t8zOjp6P8MqHvqn5HzTygaNZ30.rtUGg5adWQDKr5HzfQl4FB7vptCMTrB.uQfyLy7vVzhVz5WcPRRZ7Ql4FmY94ANEfCn5dzPw8AOpA5DhH9e.Nyp6P8Mu3LyMu5HzjygaVrLy0C3UUcGpu4GEQb3UGgFn1dfUt5HzP0p.bPyady6mmYdnG1gcXGX0AIIotqLy0Oy7CBbFzbgitrEmjFt1lpCP8MuHfat5HTew7.N3piPStn5.F2kY9N.dKU2g5KtMfsOh3bpNDM3jY9t.dSU2gJ0kRyJz9SWcHRRp6HybMAdg.uZf+khyQ04JhHdfUGg5OxL+uAdyU2g5KtYfcNh3bqND8OyUtYgxLev3Y2PWxayAaNVX2pN.UtMB3SkYdNYlO0piQRRseYlGHMag02INXywcqel4tWcDp+Hh3eG3rqtC0WrB.+WYltHAGA4vMq0qD39UcDpu3bhHdmUGgFrxLef.dVqn6xV.brYl+D+RHRRZ1Hyb+yL+U.eTf0q5dzHg.3wTcDpu50Cr3piP8EOIfsp5Hz+LGtYQ5c669rptC0Wba3sc+3hsGefD5e1dB7CyLOROnwkjzzQl4iHy73.9J3EUn9m4kJTGRDwIB7dqtC0WrL3Yu4HIGtYcdS.qV0Qn9hOVDwoTcDZnXOqN.MxZY.dd.mZl4aMybcpNHIIM5Iy7gjY9wA99.Otp6QirV+K7Buvsr5HT+yBVvBtTfKu5NTewSKybOpNB8OxyJfBjYtw.+RZNyFT61EuvEtv8a9ye9dVaNFHy7rvavRM8bMzboC8gpNDIIMZHy7sB75.V4paQi7tSfcIh3LpND0+z6rZ+nwEYVWvWKhXeqNBc272TUiWGNXythWuC1b7Pl4ZA3pwSSWqCvGLy7LxLehUGijjpSl4KIy7hANDbvlZ5YovKwxNmHhuFvwTcGpu3IjY5uGcDhC2bHKyby.dNU2g5K9ZQDe6piPCMOX71KUybaOv2r24w4lUcLRRZ3Iy7gmY98.93z74HjlI10pCPCD+m.We0Qn4rkA3M6Mm9nCGt4v2+FtpM6B9C.u1piPCU6J9dlZ164AbFYlGR0gHIoAqLy0My7S.7y.drU2iZs15pCP8eQDWDvgVcGpu3QArcUGgZ3WTeHJybS.dtU2g5KNrHhqr5HzP0Cu5.Tq2xC7VyLujLy+0piQRR8eYluIfyD3EieWKM2rdYl6P0Qn9uHhOJMuOgZ2VFf+iLSeu9Q.tDZGhxL+j.unp6PyYmbDgaSjwLYlWNvFTcGpS46B71hHNspCQi2xLWSf6KMeH86auWKcuWKGMCled89ueY58e9c8+9k5d7Zo68iPyPcBt6OqYdudsXZtvLtSf6n2Od62qW2AvsBrnduti6w+421c8ZgKbgqgm+0pZYlOYf+Cfss5VTmxyIh3KTcDp+Kyb6.NYZ92mp1qECraQDmR0gLtygaNjjYtA.mMvpVcKZN4NAdjQD+zpCQCOYlaOvoxc+k1k5WtUfi.3CFQ76pNF0dbXG1gcfGzAcPGGMG0MKK28fGWQfUA39060JeOdsR2ie9xdOdceuWutqAXNJtRDtqAbdWC27t942ZuW2FvMC7W.todu9q2iW2Xu+6tQf+N28fRWDvhhHttg3uVTGPl4FA7V.dAU2h5jVPDwAWcDZvHy7iPywVmZ29hQDO6piXbmC2bHIy7CA7xqtCMm8AhHd0UGgFtxLO.fip5NTm10B7uGQbjUGhpSuUP4J060pArVzbQlsF.qduWqIMCvbEtGutqgapYtESy.RWDMCE8td8m.98zbFa+G.tgd+eeszaPoQDWUEAqQGYluYf2HMOPAoAgSJhvaj4Npi4XNlG69tu66GFXiptEMmbm.6p6FqZ4vMGBxL2PfygluLhZu9c.aaDg2tciYxLOTf+qp6PiE9w.u4Hhyn5PT+Sl45RyvOVcf0E3Az60ZSyvJmGMCn7dtBKckhO55VnYEg9WnYPn2Vue90BbM.Wcue72A7mW3BW37bay2sjY93.d6.aS0snNuqGXqbUk2ckY97.Nxp6PyYGcDw9WcDiyb3lCAYluCZ1tJpc6EEQ7oqNBM7kYdb.Otp6PiMVDvGXgKbgGkCDYzWl4CflUY4ZCr9zrUvWMf0ilyo2Uilijl6OMCvTiW9q.+YZVInWCvU16G+qzrZPuBfaXAKXAOxC9fO3OVYUpokLyGHMel9WH98nzvQBr6QDmT0gnAmLyuCviu5NzbxMCryQDma0gLtx+kxCX89PP+BZ9xMp85DhH16piP0Hy7x.1vp6PictTfCNh3XqNjwYYlqEM67hUF3ARy6ErQ.OHf0glUi4pgWH.Z16uPyvOuAfKmleu+k06GudZNKPu7phSMxLOPZVslqd0snwNu9Hh2a0QnAmLyGJMyLXdU2hlS9R.O6Hhr5PFG4vMGvbUa1Ib6.6RDwun5PzvWl4CC3LvAWn57MAdsQD+1pCoKq2kBxZArw.yG3gPyvLWCt6gaNJdA6nts65lh+FAtNfKFXg.WHMCB85c6pNXkYtC.uOfco5VzXqiLhvKrpNtLyCC3fptCMmrHfsIhXgUGx3HGt4.Tl4pQySf4AVbJZtwaovwXYlOKfuP0cnwd+df2aDwgUcHsY81B4qCMCtbSnYEXsgzr8wWK7RAQsK2AMC77tF540Ry474E.b4QDWPgs0IzaXCuBfku5VzXseXDwit5HzfWl4EQyCYUsWenHhWY0QLNxgaN.kY9uC7NqtCMm76.1NWUDiuxLeO.u9p6PpmSB3MEQbxUGxnrdWje20sM9lCrEzLPy65R7QpqaQzbwFcU.mGMWrkKD3FW3BW38wyy2oVl4SD3cCroU2hDvkEQ3so8XfLyW.v+a0cn4jaBXGhHtvpCYbiC2b.Iybk.NWZVMHp85EDQbjUGgpSl4w.ruU2gz8vhA9ehHbqKAjY9Pn4Lv7gAr0zLDy0COqqklH+cZVomWAvuA3r68iWQDw0WYXiBxLWSf2Cvyt5VjtGtUfs2KpjwCYlGOv9TcGZN4cGQ7lqNhwMNbyAjLyWDvmr5NzbxoFQ34qzXtLySGXGptCoIvEC7VhHN5pCYXn2PG1PZVEl6.MqnpUAXco47vTRydWMMG+EWOMGoRmEvEMNs01yLeY.uIZd3HRiZd5QDe0piPCdYlaGvoArTU2hl0tVZ18mWS0gLNwgaN.jYtx.mNMWHApc51AdjQD+7pCQ0Iybso4K341XUix9j.GZDwuq5P5WxL2.t6yEyGDv1QyfMW2J6RZLyMSykVzEB7q68yOuHhytvl56xL2bf2AvSp5VjlBuyHh+ipiPCGYlGAvqt5NzbxaOh3+p5HFm3vMG.xLO.fip5NzbxmHh3kVcDpVYlaOvohO4TM56Z.dyQDetpCYlp2k7yZPyYi4CGXanYPlqQkcIoIzhAtLfKhlGj+oBbks0aF1LyWKvgBrRU2hzRv2Lh3IWcDZ3XQKZQq+7l27NMbAVzlcE.aQDwMUcHiKb3l8YYlKEvOGXmqtEMq8mn4P.9RqNDUqLymKvms5NjlA9N.uxHhKu5PlLYlyGXy.1cZ1d4a.MCxboqrKIMqcSzrk1u.ZtzyNMZ1R6WaoUMExL2FfOHfG+Pps37iH1rpiPCOYluJf2e0cn4jWVDwGs5HFW3vM6yxL2WfiF+qssYutHh2W0Qn5kY9tn472RpM4ZAN7Qg2Gq24j4lBrq.aEMqHyGBvJVYWRZf62AbI.WJvo.bVQD+pZSpQl4aG30fuOjZW9yzbF9cYUGhFdxLOMfcr5Nzr1ESyhl5FqNjwAN.t9rLyeBvdVcGZVagQDdVoJ.Hy7afmAWp85aQyVU+7GV+Ir2pxbio4R+Y2oYEZ54jojtSt6ytyeJMeguENLurExL2EZtIzc2Uo1nESyMldm5LuUSsLymDvWGmaSa1yJh3KUcDiC72jzGkYtCzrcbVlpaQyJKllahvio5PzngLyyklyAPo1pahlAb9gGD+AOybCo42i7nnY6cNeb0PIoomqA37A9I.mHvkFQb8Ch+DkY91nYmX3mQWsY6cDwITcDZ3Jy7n.Nfp6PyZmJvtEQbmUGRWmC2rOJy7nA1up6PyZeuHh8o5HzngLy0A3WfGj2pa3GQyYw4ELW9CRl4CllK7m8AXqoYUYtZy87jj3pnY6r+yn48rNmHhqat7GvLycC3Hn48sjZ6d0QDefpiPCWYlaFMemjkq5VzrRBreQDespCoqyga1mjY9PANK7McZqtcf8Jh3jqNDMZHy7gPyGjvaPU0U7m.9OiH9HS2+eHybinYn.aEvtQy1M+9OXxSR5evUCb1zrpWNGfydlLrydqVyWOvxOXxSZn6CFQ7ppNBM7kY99n4rBVsSemHhmP0Qz04vM6SxLeOz7AnT6zmHh3kVcDZzQl4NRyWnx2mTcMeKf2PDwEeu+un2E.zF.7HA1aZFl4pLbySRZBc0.+RfiC3zmrKnnd+6u+..6vPrMoggSHhXuqNBM7snEsn0edyadmJv5TcKZV4uCrKQD+xpCoKyuzdePl45SyG1ZUqtEMq72.15HhKo5PzniLymIvWr5NjFP9yzbVb9w6cI.sG.OQfsDu.fjznu6.3JnYGV7c.NoHhKOy7+B3fwUqo5lNuHBOK3GSkY9FAVP0cnYsuTDwyp5H5xb3l8AYl+G.u8p6PyZ+WQD92+z+fLy2Bv6n5NjFv9k.ODfUn5PjjlC9Czbdc9vpNDoAneOv1FQb0UGhpQl4ujliJH09bS.aUDwkUcHcU2mpCnsKybE.dlU2gl0tLGrolDaX0AHMDr03fMkT62piC1TceqJvZTcDpT98VauVIfmW0Qzk4vMm6d5.aZ0QnYsCu5.zHKGtojjjjFUrz3QGyXsd231mX0cnYsWRl4ZUcDcUNby4fLykA3kUcGZV6ziH9XUGgFYslUGfjjjjz8vCr5.T4NXfau5HzrxZAruUGQWkC2btYWvy7h1rCq5.znoLy0AugnkjjjznEW0Wi4hHNCfuQ0cnYsWPl48s5H5hb3lyRYl2GfWMvRUcKZV4DhH95UGgFYc+wgaJIIIoQKqT0AnQBGBveq5Hzrx1B7DqNhtHGt4r2VB7DpNBMqbm.+mUGgFos5.yq5HjjjjjtGt+UGfpWDw4C74ptCMqcfUGPWjC2b16YPyg5rZeN1Hhyr5HzHsGP0AHIIIIcu3EJjtKuWf+X0QnYkcIybKqNhtFGt4rPl4ZB7bptCMqbSzrL9klJtkzkjjjznlUu5.zngHhKE38WcGZVYd.u7pinqwgaN67b.V6piPyJGcDwETcDZj2JTc.RRRRR2Kq7EdgWnq3KcW9D.+9piPyJGPl45WcDcINbyYnLykhlsjtZetQf2c0QnVAOr1kjjjznlUZS1jMwKRFA.QDWO98aaqVYfmT0Qzk3vMm4dr.aU0QnYkOZDwEWcDpUXMpN.IIIIo6kUBXEqNBM5Hh38AbIU2glUNvLS2wf8INbyYfdqZyWA9W2Zi9izrr8klNVspCPRRRR5dYY.teUGgF47ApN.Mq7PAdhUGQWgCoalYS.1ypiPyJu2HhKu5HTqgqbSIIIIMJxOmp9GDQ7AA9UU2glUNfpCnqvgaNy7bo4lsRsKWSDw+c0QnVk6e0AHIIIIMAV0pCPijdeUGflUdLYldIg0G3vMmlxLWSZFtoZe9vUGfZc7gXHIIIoQQqR0AnQOQDeNfeY0cnYrkC3kWcDcANbyou8CXsqNBMicwtpM0LQuGjgC2TRRRRihV4pCPirdaUGflU12deGTMG3vMmFxLC7rPns5iUc.p0YYo4InIIIIIMpwiOIMghH9F3p2rMZ0.9+UcDscNbyomcEXmqNBMicIKXAKXQUGgZclGvxWcDRRRRRSfUp5.zHs2IPVcDZF6eKy79VcDsYQ0AzFjY9EAdlU2glwdwQDeppiPsK8NPmOS.+WtHIIIoQMGcDw9WcDZzUl4IBrGU2glwdrQDe+pinsxUt4RPl4ZC7XqtCMicYNXSMKcewAaJIIIoQStxM0RxGp5.zrhOzh4.Gt4R19QyYffZWVP0AnVKuLgjjjjznJuPgzTJh3X.Nwp6PyXOkLy0q5HZqb3lSgLyU.3kVcGZF6BiH93UGgZsVgpCPRRRRZRrrUGfZEN7pCPyXqJvys5HZqb3lSscCXypNBMi4fM0bgWlPRRRRZT0RWc.ZzWDwwSy8HfZW1+Lyko5HZib3lSsmd0AnYrKJh3HpNB0p4SCWRRRRipVppCPsFuMfEWcDZFYy.10pinMxgaNIxLeP.O4p6PyXuupCPsd9zvkjjjznJ+N7ZZIh36.bVU2glQVJfCr5HZi7MFmbOCf6e0QnYjq5Vtka43qNB054vMkjjjznpn5.Tqx6u5.zL19jYtgUGQaiC2bBjYtT.Okp6PyX+OK+xu7WY0QnVOGtojjjjFU4vM0zVDwWD3LptCMirR.6S0Qz13vMmX6FvVUcDZF4ZiH7oRo9Ae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p5B6NBMZ3EJzTfjro.mJ9sLMM6+E3dWU8M5NDIoEhj77ANnt6PRiLmPU0Cr6HjjVUkjCDXe5tCMRcl.aQU0ep6PzngO4lSApp9R.uot6PiT2PfOT2QHIsPjjcF3U2cGRZj5AjjCq6HjjVUjjcDeaRl1cY.6oCaNcywMmdbP.WT2QnQp6PRdmcGgjz7QR1Bf2A9VFHMK3wM2SBkjzXqjrd.uV7MZcZ2GXtGHLMEywMmRTUcw.urt6PibOkj7L5NBIoUGIYi.9X.WmtaQRKZ1mjrWcGgjzUiODvZ2cDZj5hAdUcGgF8bbyoKuGfSq6HzH2xRx8r6HjjVUL2sm7gAbS5tEIsn6.Sx10cDRR+8RxaA3tzcGZj6kVU886NBM54ie8TljbuANQfqc2snQpuKvVVUcIcGhjzUmj7I.1lt6PRs4mAbeqpN6tCQRBfjrKL3ACRS29p.2yppKs6PznmO4lSYpp9B.uqt6Pib2d7ePVRi4Rxq.G1TZV2MA3CeNmy4rwcGhjTRtq.ugt6PibWNCtDgbXyYD9jaNEJIqKvYBbS6tEMxs+UUu7tiPR5uWRdD.e7t6PRiMNxppcr6Hjzrsj7s.1ft6PibGZU0N0cDZwiO4lSgpp9g.ujt6PKJ1ujr8cGgjzesjr9.ust6PRiUdrI4E0cDRZ1UR9n3vlyBtPf8q6HzhKexMmRkxfR0u...f.PRDEDUjqMvoBbO5tEMx8S.d.UUe6tCQRBfjbh.2ut6PRictLFb9a5EfojVTkj8A3.6tCsn3YVU8V6NBs3xmbyoTUUWNvdxfODolts1.GQ2QHIAPRN.bXSIck65.bnd9aJoESIYa.d0c2gVT7kAd2cGgV74St4Ttj7lAdlc2gVT7AqpdhcGgjlckjs.3K.rFc2hjFq4mYQRKJRxF.b7.qS2snQt+DCtczOitCQK97I2b52qF376NBsnXmRxKr6Hjzro4dRrd23vlR5Z1NkDG2TRKFNTbXyYEuKG1b1kO4ly.RxiF3C2cGZQwJA1tppOQ2gHoYKI40CrWc2gjlX7iAt2UUeutCQRSmRxG.vaL6YCmOvcqp5W1cHpG9jaNa3iBbrcGgVTrF.uqjrztCQRyNRxChAmyyRRqpVGf2X2QHooSIYuwgMmk7xcXyYaNt4LfppUxfeoy+2taQKJto.G0xW9x25tCQRyLdM3qitjV88fSxSn6Hjzzkjrc.GP2cnEMKmAG+.ZFluV5yPRx9C7R6tCsn4HppdbcGgjltkjWAvKt6NjzDqKhAuJg+3tCQRS9RxFBbh.qU2snEE+dF7ug7c5ND0KexMms7p.NqtiPKZ1wj7J6NBIM8JI2Af8t6NjzDs+C7KHQRCAKaYKa2ANJbXyYIubG1TfO4lybRx8A3jv++8yJtBfcqp581cHRZ5SRNFfGV2cHoIdWAvCpp5D5NDIM4JIGMvip6NzhluNv8np5O1cHpe9jaNiop5yCbvc2gVzbs.diI4t0cHRZ5RRdL3vlRZ33ZAr+cGgjlbkjC.G1bVxkArGNro9ybbyYSubfKn6HzhlqOvQkjaU2gHooJ6a2AHooJaQR18tiPRSdRxthetjYMukppSo6Hz3Ce0jmQkjc.3Ci+uAlkb5UUaQ2QHoIeI44wfaHcIogoeTU051cDRZxQR1TfOGv+R2snEMmGvlWU8y5NDM9vmbyYTUUeDfCq6NzhpMOIevtiPRS1Nmy4b1Xf8p6NjzToadRdocGgjlLjjaGCt.gbXyYK6oCap+dNt4rsWFvOo6Hzhpmf+RCRZgXoKcoaKvZ2cGRZp0dM2fERRWkV9xW9VC7AAt4c2hVTc3UUeltiPie7URdFWR1IfOP2cnEc6bU06u6Hjzjk4N6d+Z.2ftaQRS0dKUU6Q2QHowWdynOS5GBroUUWT2gnwO9japCCvu4iYOusj7.5NBIMwY2vgMkzn2SJIqe2QHowSI40iCaNKZ+cXScUwwMmwUUsRf8D3WzcKZQ0R.duIYi5NDIMYHI2Rfcs6NjzLgqOv9zcDRZ7SRdl3Y+8rnOLCNFBjtR43lhppyC3.6tCsn6lAbDIYM6NDIMQ3oCbS5NBIMy3QjjaS2QHowGI4gAbvc2gVz8K.dIUUWQ2gnwWNto9yd8.GW2QnEcaDCtgAkjtJsrksrcGXG6tCIMS4FB775NBIMdHI2Yf2OvZzbJZw29TU8+zcDZ7lWnP5+ybuhxeQF7p.oYKu2ppco6Hjz3ojr2.Kq6NjzLmeGvcnp562cHRpOIYcANQfaW2snEcGOvC1mZScMwmbS8+op5rAdYc2gZwSNIuhtiPRis1stCPRyjt93SMtjFbdK5vlyd9o.OKG1TqJbbS826fYvSuol87hSxSq6Hjz3kjrs.21t6PRyrdZKe4Keq6NBI0ijbj.aZ2cnV7JqpNmtiPSF70RW+CRxF.bZL3rNRyVtBfGVU0mo6Pjz3gj74.tOc2gjlo8zqpd6cGgjVbkjCF3Y0cGpEeJfGYU0k2cHZxfO4l5ePU02Fu8zmUcs.N7jr4cGhj5WRtW.2qt6PRy7dbcGfjVbkj8EG1bV0uF343vlZ0giapqJuVfOa2QnVbCANzjr9cGhjZ21i2JoRpea9beYKRZFPRd7.GP2cn17hpp9tcGglr33l5JUU0JAdF.qn6VTKt0.ezjrVcGhj5w4bNmyFyfwMkj510B3o2cDRZzKIOHf2W2cn1bLUUu0tiPSdbbScUpp5bA16t6PsY8AN5tiPR8XoKcoaFvZ2cGRRy49mj0r6HjznSR1TfCE3Z2cKpE+DfmW2QnISNtotZM22Zxw1cGpMaVR9ncGgjZwSr6.jj9qbiAd3cGgjFMRxsG3vYv+stlMsu95nq4KG2TqJ1SF7snnYSaaRdOcGgjV7jj0C3NzcGRR+c10tCPRCeIYsYvaL1sp6VTa9vL3o1UZdwwM00npp+GfWb2cnVsKI40zcDRZQy1Cb86NBIo+N20jbW6NBIMz8Q.1vtiPs4GCrWUUWQ2gnIWNtoVU8AYvqIflc87RhmAqRyF1gtCPR5JwZ.7.6NBIM7jjOFvl2cGpMWAvyqp5h5NDMYywM0pj4t8z2afKr6VTqVVRdJcGgjFcRxlguR5RZ7010c.RZ3HIuKfGY2cnVcHUUGY2QnIeNtoVkUU8iA1CF7sqnYWusj7X5NBIMxbe.ptiPR5pvcLI9TdIMgKIKCX25tC0pyA3E0cDZ5fiapUKUUeb.ubYlssF.uuj3qElzzoGU2AHIc03ZgO8lRSzRx9xf2JPM6J.O0ppeV2gnoCNtolO1Wfud2QnVsDfObR1ztCQRCOI4t.bG6tCIoqA2ytCPRyOIYOANft6Ps6.qpN4tiPSObbSsZqp5W.7L.VY2snVcCAN5jbm6NDIMz7v.9m5NBIoqA2gjrwcGgjV8jjcB3MzcGpcmIv90cDZ5hiap4kppSCX+6tC0t+CfOZRVZ2gHoghMq6.jjVErDfGT2QHoUcIYa.de3FDy59M.6VU0k2cHZ5h+fEsPbf.mR2Qn1cKA9DI41zcHRZ9KIqMfOI1RZRwin6.jzplj7P.9P39CBdQUUdD2ogN+gKZdqpZk.OEfeR2sn1sTfkmj0s6Pjz71lCbS5NBIoUQarewpRi+RxV.7A.9W6tE0tkC715NBMcxwM0BRU04.7B5tCMV3NxfANWytCQRyKOztCPRZ0v0GXC6NBIcUKIaBvGC3F2cKpc+.fmYU0UzcHZ5jiapggODv6n6HzXg6FvGu6Hjz7xVzc.RRqltucGfjtxM2k90mBeqPzfKh3cup5R5NDM8xwM0BVUU.1Wfys6VzXgMMImX2QHoUcycofsNc2gjzpo6c2AHo+QIY8A9v3msPC75ppN1tiPS2bbSMTTU8q.dh.+wtaQiEteI4S1cDRZU1l.bc6NBIoUSq+bOcXRZLQRtEL3rUb85tEMV3KCr+cGgl943lZnop5KC7J6tCM13gmjOQ2QHoUI9pcJoIQ+y3QpgzXijrNL3UQeC5tEMV32C7jqp9CcGhl943lZX6.YvgFsD.aSR7L3TZ72l1c.RRyS2gtCPRPRt4.eB7h9R+E6YU02t6HzrAG2TCUUUWNvyE3B6tEM13QjjOX2QHoqbIY8.tsc2gjz7zlzc.Ry5RxZwfyXy6R2snwFGZU06s6HzrCG2TCcUUW.vtBbEc2hFa7DRxg2cDR5J0cD350cDRRySq+bmweRpAKe4KeqANFfMq6VzXiyF3Y0cDZ1x0t6.zzoppiKIGDvKn6VzXicLIW6ppGc2gHo+F9TaJoIY2HfMB3GzcHRyZVwJVw5tjkrj8G3t1cKZrweBXWl6BGVZQiO4lZT5k.745NBMVYG7I3TZrycr6.jjVf7bCVZQVRVykrjkbn3Sro9a87ppNitiPydbbSMxL24u4y.3h5tEMVYGSx6q6Hjz+GuLNjzjNepwjVDsrksrcG3i.rkc2hFq7wppdScGglMUcGfl9kjsgA2bdR+09vUUO1tiPZVVRVefyB351cKRRK.e2ppk1cDRyJRxWB3dzcGZrx2EXypp9kcGhlM4StoF4pp9j.GT2cnwNOlj7w6NBoYbqGNrojl78ukj0s6Hjl1kj0LImBNro9acY.6rCapN43lZwx9BbJcGgF67HRxg0cDRyvtScGfjzPv+Nf2X5RiPqXEqXcANBf6Y2snwNufppSu6HzrMG2TKJppVIvtB7i6tEM14wkjOV2QHMixyaSIMMn.1jtiPZZ04bNmyFujkrjiBXq5tEM14n.N3tiPxwM0hlppuKvSq6NzXoGYRNttiPZFzMq6.jjFR7KqQZDHI+GKcoK8PvWEc8O56.r6UUo6PjbbSsnpp5X.dYc2gFK8.SxIs7ku7st6PjlgbC6N.IogjMr6.jl1jjaECtXXuqc2hF67aXv4r4up6Pj.G2T83kA745NBMVZq1tsa612jrVcGhzztjb6A7+VSRSKVG+BRkFdRxsD3iAb2ZNEMdZepp9xcGgzeV0c.Z1TRVGfu.vso6VzXouBv1TU8S5NDooUI49CbBc2gjzPxkBbWqp91cGhzjtjbaA9T.29taQikdGUUdbyowJ9japVTU8iA1EfKu6VzXo6BvwlDG+VZzYocGfjzPzRvyQXoErjrI.GKNrotx8eC776NBo+dNtoZSU0W.Xe5tCM15N.7oRhmgVRiF25tCPRZH6eu6.jljkj6JvQiucc5J2k.73qp9ccGhzeOG2Tc6M.bXcGgFasTfiII24tCQZJjiaJooM+GcGfzjpjro.GC94CzUtq.3Y6Q+gFW43lpUUUAX2A9lc2hFacq.9zIYy5NDooL2htCPRZH6V0c.RShRxVBbb3EMntp8JppNxtiP5phiap1M2i09NA7S6tEM1ZMYv.mOntCQZZvb2nv2nt6PRZH6V1c.RSZRx1B7IAtAc2hFa8Y.NftiP5pi2V5ZrQR1QF7Jp6+6RcU4RAdRUUGU2gHMIKI2dfyD+EYjzzkuVU0cp6HjlTL2u+0gvfKjKoqLmGvVVUcQcGhzUGexM0Xippi.3U1cGZr1R.N7j7T6NDoIbWOfqe2QHIMjcCm6ISWRWCRxtyfGrDG1TWU98L3BDxgM0XOeB4zXkjbs.9D.OrtaQi0BvKpp5U2cHRShl6Lr8z6tCIogreMvcsp575NDowYIYe.Nvt6Pi810ppCo6HjVU3StoFqTUcE.6BvY2cKZrVA7pRhenLo4m+8tCPRZD35iG2FRWsl6yO6mgVWSdcNrolj33lZrSU0OEXmYv29tzUm8IIu6tiPZBzZ1c.RRi.qAvMs6HjFWkj2Av9zcGZr2o.7B5NBoUGNtoFKUUcV.Okt6PSD10j7o6NBoILNtojlVsVcGfz3nj7Q.7bqWWSNOfGSU0k2cHRqNbbSM1ZtaD6Wd2cnIBO3jbxI4l2cHRSH70RWRSqtwcGfz3jjrlI4j.19taQi89s.OAu.gzjHG2Ti0pp1efOd2cnIB2KfiIIaX2gHMAvwMkzzJG2TZNI41B7oA1ptaQSD1yppuT2QHMe33lZRvt.705NBMQXS.NtjbO5NDowb+acGfjzHh+7MIfjbm.NAf6b2snIBGXU06u6HjlubbSM1qp5WB7D.9Yc2hlHrN.GeRdjcGhzXL+k+kzzpqa2AH0sjr0.GGvsr4TzjgOMvKp6HjVHbbSMQnp5rYvMn9UzcKZhvM.3Cmjcu6PjFSc86N.IoQj+4tCPpSIYGAVNvMo6VzDguIvNWU4umsln43lZhQU0mB3E1cGZhw0A3smjWV2gHMF55zc.RRiH9y2zLqjrG.GN9kXpUM+Lfcpp5m1cHRKTNtolnTUcP.u6t6PST1uj7d6NBowEKe4KeqA9W5tCIoQD+4aZlTRds.uot6PST1opJuaKzTgp6.jVckjqKCdUKdvc2hlnb7.OwppKt6Pj5TRVafyDXs6tEIoQfSqp5d1cDRKlRxGF3Q2cGZhxyop5MzcDRCK9japINUUq.X2.N2taQSTdf.GaR1ftCQpY+Sy8GIooQ9japYFIYsSxIfCapUOuEG1TSabbSMQpp5GCr8.+htaQST1DfOWR1xtCQpQNtojllcs6N.oECIYiXvalz8u6VzDkiEXu5NBogMG2TSr9qtA0WY2snIJ2TfOUR1stCQpINtojll43lZpWRdf.mHvF1cKZhx2B3IUUc4cGhzvliapIZUUGCvyu6NzDmqOv6zaRcMiZMl6ORRSibbSMUKI6Bvm.XM6tEMQ4hAdLUUWR2gHMJ33lZh2bmWHGb2cnINECtI0e2cGhzhLG2TRSy7muooVIY+AdO3YKqV8rBfmbU02p6PjFUbbSMs34B7o6NBMQZWSxolj0s6PjVjTy8GIooQWqksrks6cGgzvVRNTfWZ2cnIRO2ppOS2QHMJ43lZpPU0U.73.NqtaQSj1BfOeR17tCQZQfiaJooY0s41batftiPZXII2xjbx.O9taQSjdYUUuitiPZTywM0TippeMvNAb9c2hlHcq.N1j7X6NDoQLG2TRSypMZi1nKp6HjFFRxcG3y.bu5tEMQ5H.d4cGgzhAG2TSUpp9NL3I37R6tEMQ5eE3Hl67LRRRRSd7KuQSERx1yfaD80q6VzDouDCtYzuhtCQZwfiapoNUUeYFLvY5tEMw5kljip6Hjjjjzrmjru.GEC9h2kVc8s.1gppKq6PjVr33lZpTU0GG342cGZh1NjjuTRV+tCQRRRRyFRxg.b.3SgrletDf+yppKr6PjVL43lZpUU0qCXYc2glncO.Nwjr0cGhjjjjldM2EGzo.7j6tEMw5OxfmXyud2gHsXywM0zt8E3CzcDZh1ZC7ekj8p6PjjjjzzmjbOANEf6Y2snIVWAvSop5T5NDoN33lZp1bGfxOUfSt6VzDsqMvqOIuutCQRRRRSORxyD3SAby5tEMQaeqp9fcGgTWbbSM0qp5OB7nA7wyWKTOojbpIwasRIIIIsfjj2HvaF3FzcKZh1qspxiiMMSywM0LgppKAXGAtftaQS71BfOumCmRRRRZ9HI2rjbb.6Y2snIdGIv9zcDRcywM0LippuCCdBN+sc2hl3sl.epjrucGhjjjjlb7Wc9Z9.6tEMw6T.dhycTrIMSywM0LkppyBXG.t7taQS7VCfCHIenku7k6SwojjjjtZkjmFvmA3V1bJZx2WE3QWUcYcGhz3.G2TybppNNfmAvJ6tEMU3+b61tsaYI4N2cHRRRRZ7TRd8.uMfqe2snIdW.viqp5h6NDowENtolIUU8t.dwc2glZrI.etjrycGhjjjjFejjaYRNUf8p6VzTgeEvipp5b6NDowINtolYUUcf.uxt6PSMtA.u2j7V6NDIIII0uj7v.NMFbgTJsP86A11ppud2gHMtwwM0rt8C3c2cDZpxSOIetjrdcGhjjjj5QRdg.ebf0t6VzTg+DvtUU846NDowQNtoloUUEfmJvQ2cKZpx8A3jSxNzcHRRRRZwSRVyjrbfWECt.JkFFdlUUGQ2QHMtxwM0Lu4F37I.bhc2hlpbS.Npj7Z6NDIIIIM5kjMC3TA1ttaQSUdAycmQHoqBNtoDPU0J.1Qfyn6VzTmmaRNojrztCQRRRRiFI44Bbr.21taQSUNvppk0cDRi6bbSo4TU8yA1dfuU2snoNaECtM08awWRRRZJx4bNmyFmj2GvqkAWvjRCKusppWX2QHMIvwMk9qTU8i.1Vfyu6VzTm+Cfk6qotjjjzzgjb2V5RW5Q.7j5tEM04v.1itiPZRgiaJ82op56xfmfyKp6VzTomaRNsjrAcGhjjjjleRxyF3D.7yzogsiC3IWUcEcGhzjBG2T5JQU0+MCF37+s6VzToMG3ymjmX2gHIIIoUcKe4KeqSx6E3M.7+q6dzTmSC3QWUcYcGhzjDG2T5pPU0oCrC.+gtaQSktI.u+j71V1xV1t2cLRRRR5pWR17sa61tWGvN2cKZpzYArcUU+ltCQZRiiaJc0np5DA9OAt7taQSsdZ68du2OyjrYcGhjjjjtxkj8F3DA1vtaQSk9NLXXyKo6PjlD43lRWCppNZfcEvy7DMprg.mPRdQcGhjjjj9KRxMOIebfkArjt6QSk9t.aSU0Or6PjlT43lRqBpp9..OcfzcKZp00C3UljkmjaU2wHIIIMqKIOLfOOvin4TzzqyGXGppNutCQZRliaJsJpp5cB7b5tCM0a6.9xI4wzcHRRRRypRxABbL.25taQSstHF7pn+M5NDoIcNtozpgppCF3EzcGZp2MA3HSx6t6Pjjjjlkjj6PRNcf8o6VzTseNvCup5q0cHRSCbbSoUSUUKCvyFQsXXWSxYmjGb2gHIIIMsKI6IvIA3E8nFk9E.OhppuR2gHMsvwMklGppdU.upt6PyD1PfiII6e2gHIIIMMJIqcR9P.uQfab28noZ+JfGWU0o2cHRSSbbSo4oppWDvqo6NzLg0.3kljuTRticGijjjzzhj7n.NCf+ytaQS89C.aeU0w2cHRSabbSoEfpp8F3f5tCMy3d.74RxKt6Pjjjjlzkj2NvxAVmtaQS8tLfsop5j5NDooQNtozB2K.3szcDZlwMD3UjjOQRVZ2wHIIIMoII2yjbV.6N96DqQueOvSrp5D5NDooU9CxkVfppRU0d.715tEMSYa.9hI4YzcHRRRRSJRx9A7YAtKc2hlIbY.O4ppir6Pjll43lRCIUUOCf2d2cnYJ2Hf2RR9LI4V2cLRRRRiqRxljjSE3kAbc5tGMSHL3xC5n5NDoocNtozv0yD3s1cDZlyCB3zRxdzcHRRRRiaRx9.bR.aQ2snYFWNCF17i1cHRyBbbSognppq.XOAdmc2hl4rV.uoj7wRxss6Xjjjj5VR1vjb7.GHv+V28nYFWJvt4qhtzhGG2TZHqp5Jpp1cf2Q2snYRORfyHIO8tCQRRRpKI4YAb5.OftaQyTVIvSpp582cHRyRbbSoQmmAvar6HzLoaDvaMIGaR1vtiQRRRZwRR1fjbb.GLvMn6dzLkUBr8dFaJs3ywMkFQl6UT+4.7l5tEMyZqAN0jrWcGhjjjzn1bOslmBvCr6VzLmeOviup5i2cHRyhbbSoQn4dE0eV3Svo5yMD30mjOeRtycGijjjzv1bmsl+4mVSOaM0hseOCdUz8L1TpINtozhfppmMvqs6NzLs6MvojjWQ2gHIIIMrL2Mg9WDeZMUOtLfsspZ4cGhzrLG2TZQRU0yG3U2cGZl10E3EmjyJIOjtiQRRRZ9JIaZRNQFbSn+u1cOZlzuE3QVUc7cGhzrNG2TZQTU09Bb.c2gl4cW.9uRxaHI+GcGijjjzpij7h.9b.2utaQyr9k.OlppOS2gHIG2TZQWU0KF3kzcGZlWA7rANyj736NFIIIoqIIYKSxWG3UB7uzcOZl0uB3Q3vlRiObbSoFTU8JAd9c2gDv5.bnI4XRx52cLRRRR+8V9xW9Vmj2NvwArwc2ilocw.O3ppSs6Pjzew0t6.jlUUU8ZSxuG3sxfmhNoN8v.1hjrrppk0cLRRRR.jjcD3kCba6tEMy6B.1gppuR2gHo+V9jaJ0npp2NvtwfaYOot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swpXzptCZDVBF1cMBYNhhu94IdML9Ebj9yFstCvjeF3k57vc6bn7ZmounAi0w3JqCbkiOGtAdb4IZPb5xAiM0OFctOt7QIBX.9rMvwkQBG6wmdLha3cIB35m1CtFKAQWeEhHfH5Nwwe5xX2qdPs7T5mUy.LCvBNmaIyrUcN2CAjwLaefsoJcoUksf12D3Hl784w6bU1ji0x.OLvFNm6c0pUqML3zyO+7OjCxr2M2CbbHwKpquXsZ01tYylaArYsZ01pDr4WtYysqUq1KBrCEY2K9u3h69DOwSrWE3lsWgCYdFvUmXJ4LcaASDTuRIkq6BNVinj5uNwovScv+PvueZfVpBo0+UAFzdcB4JiJuN8y6zuGCmbaTxqOdo52lA3sF3e0zN8uLFauLv11hfsSc7HDOZOpctoWOcRMc8Rubp+K80BrAFWdCfKOd6mSW1Skqdii6V4qo66ycreXjV+y8d6eokAtsAyXw36vccHj5DxBDxkus1Kmt+ywkW2.RJ+Mc8sS+2k7FCuT0uc2dLd2s9+W.B6WIoNp1SbLFuNtwa26UZ++kWGb+NCSlJ5aqE.0ii7a83H.WOtCnYIIhvab7BBlhz1qLmzn9L8nXe2FgwSJ5omz88FcS1H1smMSS+XNoQbXrQacraqSv5iMRDWgBY5BYfxYWAxSKlAZLCvLcoZdtJ43xjCVIGTHGPVJQVfrKA4.xmbKWAHa7mStiiDxK0MkwVe82nF71.fqL5erjQX2mKS.rbV3z4.xQ83y20O9b+L.4WAxB0iKasVRYutjgsGqdxEI6xrbtj+eVnRxWWJWEXFtNyBWadf4uNqLGawrzkYf54gkh+8V+kbjydot8fJip3Ujh9lYA.9lU1Gv6u5EdAuxfEuA4bhqgIG+915wuOVZx2Kc.t4medmCb6t2dt81aOvEeeVRGoZ0pk6Qq8nFPPiFMl4rm+hmxb1BPoEdlm4bmhdL6i+3OddfbsY4brEY3pjEJjEpDOcc1Xh1DVEkLSZ...H.jDQAQEUWVb4uEy1Ex2sZRYyQ0eQbYmBjqPb8X4.x0fkx1GxAEyu7j0+kuM0yyURaee86T8ZSmgAJKndigaazSYxySI2e8fqFWuUthvrrMyAaOGvL6PwbzfLzl.XkjaiZ+Kt9piukT+WRYz35+x7RW+W8jeVkLEfbbYxBWNstwjLMXzNP0zqsTxqetS8c4N0lzwih+wY+VPR8JYfUxVExmz92bLY6eyBUmAVLOws+lAHX8iq6KKPtxPdX0Y1Al45ESp+qQZYpjxeqSPxh5430m4Aa3sA.WFCtrM0eGpeYu96UZeoGu++9rwnE00Wx9+2mJ4oM4nMYfEyDmwm3yJD.jYw6s9+e25il75n6mm.FuP2XUtgsTbjagIKL.wcHLj34G9Ish9qnp8R63nsuNdbkQcP.tyK9emzG9No2quWFklou.fWubRMTAGOpTSO5pS+dvwuONYVfvR.iJ+dZLFfOsI3V25VYlc10xB8CpBzp.gzmADWdN84LpBD0N96G8Yie0e0eU668686Mj3Qj9Hf3sEzIyffTSedXrW+aXISEjwyXA84mWajdNYrF1VyCtpUAr1UHcTElnQ2+n+n+H9V9V9VROF9.dNmijo1w.N9beZ4uzN76A3pAtloeNtTR4itoO1xYfNYcNW..lYiOZtg.CqBG0BNjIKWN9eOoRGYioGo1nw9+OnHsNfbkf45FmUHqPQVEOVv014u2d6cyScp02D51tLrUm5rGMlHCSFudl.frm6bmalyd1ytDwYXx6HICSdmI++EINyUbUpT4f1sauWkJU1orYu3WtYyctzktzMNy4Nyd.2n0y0ZGmy8h0p8nWG5c8BvK1uH6QOtIvALV8IKCgaeb6.SegJA.Aewu3Wz+e8+m+qseo+O9ktsy4qB1lokUVAvgw134bt.yJjA1zqJ3ZUfgzmC43xzCABWCBu5wYHvz0SC24rf5AoxaudZ7ARX5AZwUGbMRKOTGO1m.1hb27l2L6by8MlC5FrJXatJN1bTaXomiIYM+I87pGw0+4RpqZ719f6k5+7HJI9jFTxftTBnaYbzYTYnz55ltek2sLdRdsyzscN9sS57xn5nJAV2ze+B.g3w1okSplCZky4bYfIZ+aT4fRvftGeMFoG6.fLNmKiYl+96uO.QyLyLie8HQ.gKAQWO80WAbDLp7Gok+p.t10SlBm2delOoax8G2sqgc5e130ycR+roClO.tU.1J8QsDdLG9zfLMZzHa85O5cp++iZGsLD04319B.B9C+C+C4a8a8aMj359lt++SmgI2sLl6d453j6SteEvjoCVR5nC32saWuRuq2kid8bT.uZ8InY7iAhKbLdAlouXS0wo6tiazYcB3JSDAbHsQi50CoQCGqia8q.W41GMu6TJDNdJic29f53WjE70+yWi2363c3K90xZqEwUupiMvs9kwtxcN5+iegtd.1xfKNyfWlZrs0bxK.JCPNmyEr4laZAAAQKszRSzAMmyMzJWdHc6FRY7JzgL8i+8rjG2gDeANoWnQZi5FfUBnKDAaDljxwi5z4Xulg5DQiQcRX7zgWt+X7N7c6yC+EwiclnLX.Plqe8q6uz2z2Dzsqih3sZOB1LttQONt9uzfYPxuaVmyk0LKn.X8ACJDUj9Q8N97a5qk.mykwJa9zE33.POz4bCAFXqt5Ar0VGPANpRehZe7eO2s5+GurT52CO3Tlxi3LvH2VvhPgUKP+h8btULylGv8q+q+qu62y2y2Sef1TlMoC6A2V.SFcrn.YJ0mY6FGC1SywALYCRBXhYVfKxEVsV0CZ0p0dF1MbDsSsZeK61p4WZWLaOmysasZ01sUqV6TsZ0qCrCk3EoK6ASDvjnKe4KGtwe2+tgzsaDUwUtEdclbz2Odjzt8LN0JBVOvCV0Jvlt92dpsG3btzxxCMyRaGOss7Ar3hGxN6Lf5DUuAz33m2wmxFCYxKTAT6+uV5jtfgw6+V.fcZvcMHBVIZE1hshebYHYDRAlw4bY9K+K+K8eaus2FiEXDHNvHQVoRgzqmiRPgtDzO4XS746CStcTxuW5ExNd8e.EbI0+M8T6ws+96yLmdFG8mX.3RuMf50GRiF2oTe+t89hJ28JyzuGdmBHm+XOlwGrmQOt5fe71wdYqLcnyws8llIQ4bNWNqhEPmQ8oJDHz4bQ.gVwhCne+ArLgKuMr8w04MpbdxqiwKeDADVBB6BgvJgEXqn92oo15wAKY5qcIh50iRJ+cuLMEk6MSe8GmzO+15iyFjrXsN4O6j5+uO.EgndfCJQQ5Z8lbPsxRb4uL25V2xN5niFu++Nfn6P++yxw8++HlrNvwGXAlp++mT+5Oo5qTYqWib+LfIoE1xBjmZ0xQygAP2zBFN.uqcsqk4ge3GNaIy76E+6lVf4fjaCVe80Gdkqbk60sEwGjMcTQyDOpele7.3TEncZPORqL2.7pCdMlrCxG+d7owwtDQ.gzeTGONorzX7uldLd83b0zAKIX6s21e4k+FLXG31mmz9UgfV2dZgGWQXYBvgmqiyLyFMpnIYE.PQfd9TlfBcHa+I67erRkftdgP6i33KfwRFIjrkLyq2wUXtexs3KbtNPDw6TFsusrvBntAM7YY7Vba71I9Nm9wM8mcjWcFqL1ZwKvWKi+o2F+qc6cBLfUWMCalwGZab74B+O5G8il8m6m6malJlksyjAMaPxiy24b4rxVdhHK8iO1IiHaxHmVJD5FQInT2Qi9lQb.5BMyF3btglUc3Xk+1miaPlJfW66PmJnbxm8uECY6IBh83qe.uUtbUbcpwAgdVhCvQImyUFX4KbgmY1yblmH7282+2+E+G+9e+85AsHdSvY5.ljdrFuSVyBrRAXs9wqgIuaf2QqVspUsZ0EZ0pkOFCqVo5AsZ05l0pUauFMZrW8502qYyl6VqVs8fh2nYyma2ZOVsaz54Z8hUqV8FIAOYWmycKyrChO+WLB5OcVmE.js.iBb63AKI9qEwCGdtdNOyrQ2eRlBL.JND5ERInXW7RZGexLapb4P5DND5e.vsHt72fjWGAIO+oCbxPNNS6Rq+5jxro2JWl6qWNo1sgiO+mkpjkAjw0aT6eSk0HKmA1NOkHe4tLSm3.mL4zUnbYO5.PmzQQMj3xPY.xU1Lu3KDfCItrwAi8XxdGp+Ko8sxCfNCoHQk5QT2ie8MLtbucjy4FX1pCfsFO3c2qsMN9.Iwc4wIS5tMHZSzO0Egfct8fUD+49SiGGQ.CIfMGc92NttnxAPmLTlrE6Pldw0i4Sb+qBMyhhqqpnKo9u3.mcbY3fRwqAEYX7AVKs9lBI2Quz56hK6bvAGLLe97gPgHneDkvUpax59zzYIWwhPOyAcGOKRGutM02rW4lNPG290wtwFdb4a5EuPjO5wl1Wduk.uqO4fNdbvhKPFb3456lH.vIkAMnpGzJfUIawMIeuIKGEax9+m11WZ++ykb8utj6O8ZeiCZREhvk72TmQYKWZcnokel98BPYwzq4teDvji6PXIxRWxSbmBy6btfpl4ZOpC2ELneFJPdWOWV.yLany4NLYAzKofyxCfsGtNDdkIqLRMfMowqvHfRIq+AiMBhKC11i9.ToPnKT.OWOmuUy7n0DoFsCvUq16JrQi+zAOrYCZ.GsNL3J2dVKj77tg2cXJg706.l3wFoqcDGmRukA5L50ScGzvnHAtttrVIKC8F8XGMpCISuAqhYVGfxf0434BsiR3nK3bNOyJ5C88SFkUeyLefLasUOukWtPXYyNn6nQ8sXDzKfRjw0w4C3R5b2AlYiBXHT1AcnLPamKrjYC5MdzmWlzTh2yrJ9PGbNWnshM9E3prL49mICLY7my7cNmuYU8g1AIkYxXlMZt26bNuj5+R5jdAenedJxbtttYAxZlYIcxeT.SfxYgNy3btb.YJaVvKbzQteyeyeyvu6u6u6g6s2dClat4FBDYV4Hnajy4H9+aoMtNDXPA3n9itfjhGB8FREX6+rsskeGK6c7T6Y7NtVJz45bTEyNpCb3ZvQW81G4+2JWWbZPky1.lqJrRKnhy4pbgKbgkOy4OyLzgA+D+D+DW+m5m5S2A52DnOUXOZygbxS6I+30dgFyRUVkV72fx7HCdgAuq82e+uoc2c2Z.mBvCrg0pU8flMadKfaZvMcvsLysa0p0287W776dlm3L6dzQGcirYe3a.cugy41AXWyraQbGuBArBLZ6L1fJdNWqLVEKuqsK2W9K+ky8deuu2I1YH94+4+489A+A+AC.7KYVPOveyM2LyW4q7U7dGui2Q3pqt5gTlC68m16nBEJLzrJNnSXR1LElbA1d+k+k+k7M9M9MNrpY2rMbCfcgB6C8in.d+p+u9ql4wdrGKyZqsFIWjS5.mL.pODZL8.l7V8xbe8vzYT43Rufgbvp4ct9YRCpQMH5C9w9Xtm4Ydl3GSUxRKxEmEHUmAZmy4bYAxYlkCH2Mtw0ybpS8PT1rgcGkAck7ft4nH4ccGMEBOx4b6mz+uihecUNKzI+30+8+6e0ek6EewWbvi9nO5Q+O9S7TG9u7+4OwQ.CLqxPnywYS2wkiNhimNFIAiaoAv0GP7hR7Imo.uwX.fdynouH1TiWOXb4mkHqaaWVaUKCagex.QMVeGq4CMCJ.A8bN+xlEzcrr1jJjw0xkAHvrJAPmjckIRCv2nmyjLjK4hNKG4bswJaAtNtfW7EewfkVZozKjdz.JlzWNqhYtNvQkgi5LdPOJQHcILtdqhQP+wq+KM6Ow4bQ0LKr0nLHnx.n8Km9loxbmrw5O1FdIqeLr95qyUtxUfwFDzEAucFUdbQGriQU7bMc9VAyOMfbL1LiHInFdULiNPX7LsdrrRoHdzC+3oyUoLPufo6++st0s7lYlYFlz++8.N.J4ftAThbtNtLDWdMz4bGYlkF33CgBCg9QkfnNN2vxlcT2imEFQrNQwKYd.rNIqed7deuu2nuzW5Kood3qgteEvDeVi.tJ4XUlkMYNmykOYQxKJo.Q575Jn.LyWYyMyu5pqlMoxxzzR+.f8WANbqIlWWqMDt5K0HC7fZgiIiP5JDvViVvpFcgawYFQ7nKGmlqq5AalbQeVPud87JVrnADkzIizQkdenvQP+SLKSVC7tJ.qAb0WyRe+6TZxd6o54JjgslHnQVRjgGEc4hPldTNGzIWRmxxYlkOIMfylDfD.b+J+J+J78888Oy24Z6e8qecuqe8qya6s89iftNmyQYyhGg+hXziLTjb27u5l4lc1Yyr2d6EdpScpa4btzKjY..kffNwuumNhHiW9+nUgvMSSmOyF.kNB5lN5qQPERxbgzND.UHj1LjJbDsusQRSd4a5QKarQgX4.Xaeh2cQ75FGrxrzkbTfYnOokk7H973fjNR4CjuHbptN2B.ySbYuzNsEQbP9xzINfy4iC.So.mqiiQS4ghG4bcGBLrjYg8fPJQDcIjhLjdSLMGOx4b6WoRk86zoygDGDkn9DeozIe1v24b1W6q803s81daQEgA8hKKljY.ENL4y+i2P7akaXNs9jr.yCkWE5T8S9IuP4m5odxUbNWdfit10t11eaqsVm1PSJQujoDyzALI83klF4yUAJzlxeCm+Sdl24O9G6G+cEDD7M0pUqpwOW3avvJUqdPqVstEwmCtEwAccOyrce1m8R6dly7j6ArWqVstQ0pUuAkYG5vtTl8oSb8LI+MDjVGOIYWRQXlW3vCymKWtYRtP2QSAQyLuxPP63LULIiEKmw453QbcJGXVk8gNG3btiJa1ftvgTlinCQTFiNjw4b1+1O+mev+vOvGXOmyssY10S9a3n3NfVxG5FjT9ahol6hvvclXpfsdTxNPkx3zW4FOnuFrtGbkwyBfzxmyTFx2YhoM3p.alNUbxBkxBcyt+96m8aXlYx1AxQIxSWlihLuqqadhmxNdIY61AlYCGNbnWPPPtRP9Nwk67HtNswy.NWxn+mar5+7Sp+6n3.qTdemq8A.GVwrAcfAIk+NhhrO8lXJtdTR+OSyfoiVBFd83Kdc51HM.aCRFBnIGnNUV6tapxWSj4HL1OKSbfnKjE5mKMinG6wakIdZClzuUenXFnWFmykyLKOTN2uyuyuTvuvuvyF7u8+8OseGvmh3SO7iS92IxlEemy4BCCCCBBBAnD32YrLwlwFTVyLqL32N941EOXtkNz45dHwk+FBEG.8F3btQCpvn9cE2Nb5eug.Ct4Mu49yM2bGPR8aa.gW9dqbkpm61kVNwCvO45O7nDFcwXEL1ZhrMOM6jF0etkAusohOz1OsO+GczQ4xlMadhWXpCdz+K+u197+led2i7HORzy+78v455+U+peUu29a+s6CE8fd9NmyqrY9ciyJyz9+m8m6+oet7ezO5GMnSmNCqToxMcN2MLytIwkG7KBY6Fu14j19WZV3sOv9EgC6ACR523gPoCgtistjsbDrsCvMZ.QpPzXYi9ak6a1qqBdoeH2UGWI4UShR2lUxboKcwLlUHSRG0hGEzUIhMIjUIp+lDt5pq5JEGQtjQ0nTdn6LPkY1h1oYaRxHEb0oWeStaWH3ChEPN9hX1pTDz0AK6AdYct9yXVkrTBO5RHUv0uM.aZki6PSFfrEK9NyR7na65RmCINpnIoZVeORxvg5PTCvw533JqYWkqlNUURCHAb6oi4qTSeQqmzOe7FpCXqSG.WyGV0Gx46bM7MqrGEvm93QYB50gbPm7kf7lYyBLOTZNyrYfhY5PuzQ8L566666C.+abiajY4kW1Odj765nLNyJE0pUKWkJUbIUHmgdkmY1YmMexEYDFO5YU1ixrGc3v+h+h+hv29a+salYdkhe+2Q77sMYzwJc3lz8nO8m9SmF04iftiFwrxvvNzNjUIjMKF0K90paT6AsKYISCtw6zxChel3UiSJkO8.BVFB1lsCnL9zoj0ktwML2MYzY6WX1Kcoe14MqbtjN7EYVkgIMn4QQx2qGKXl8PkgGpCbpCO7vbYyl0C.yJ6010If3rOIcwb0OIMiSFwzdGYksgzsT5b0NL4hNCu3+hKN3IdhmHMnJC.NzrRGP4dwWTRQNreOF.8GV73LBv2rpdwSeuhg8n2Aou1ABSRqYOhaf10GBYiQa6huUbZ5L0Hw2IIXBQVw3uAmyYCFDYsSKqz8d73VFu1cvC53ctm5LdO4Yexj5JMmgy4.mCrVsZ4UsZUOyLqYyltZ0p4ZzngCvcly7jzrYSCv6Ye1mMYjtr3oBamxFzIBJ66bsyAj2rJ4nH4nGYoHY60iYxkKW9RvLlY4+pe0uZl+V+s9O2h+inj0ItLc.PlVsZE.cSGMrHmycHzYeJwAlU9f3+O6mTWUHcJ44bcxXVIbttGE2YwJoWPSVJw9s6x.nqqZ7wL483B.8Mnl+NzLMfuIs2eko2VZUPSd4435vh2ty8gq3uBXaM58uULXqLPsLcnYR1ijdgriBVRZPbyQYxNyL+Mxvwq2HyBrvK9UewE.Vzrxy4bc7iqapzQThgAAAly4x74+7e9blYYKAd++0qmy4boCXvQlUNpSbv4Fu9OOyrnm+4e9CJA62kN2zJY6SuxGxwWXQbpu2q3sfd6AkOHInJGYV0CoHGQONhRbz06xQP6CWFNZ6QkqVC3pFTwtb7J54zYS28q9z7VQS2doe76mP76oa3X8KabEBXI7abc7g99Effj5sxlr3k6AE85POuO+m+y6kT2P.zKKvr6u+9yUDlqGcx+s+8+s6SuxoKfvAI8iMyMu599yMyL9ICxPFffgCGRlGNSb8Ikv8c7c7Q7R5+kuYl8A+99f749U9chhm9LksNw026A39re1O6gPuCR6K1nfuUj8MqzM+s+s+s28a+a+a+lICNx.5V14bsMypfy0dnYFyM223PhWPriXY3xaiGP3FPzkeoG.3GTK6c2FL+je1FbUtrkzWk3yYaU2CZ3AE8gPemay3c0t3rBwn.118wCZ6ULNifyCLW1rO7b.yBEy0ld9s9B+6HoNGGw0+jl84wYQRQ7Mqjs4laxJqrx38+O+G8i9QmwrxANWmgNm6llUddJwMoKC9Y+Y+Y4G6G6GKvLKnbb4e+c1YG6niNJbokV5fffp2pG8t0uvuvuv91nrNu69DW+6fZvvlrcXbVNUIpOsSl9Yo8UKdjq4sl8K60cuZxvjwtPh5YfF4Hdqha1ja4HoRGfAkgCSRqMXzH2UZ9s29qLyxK+ME3bcgjEArjzyb+jT07PX4Cqv1G1NcTSiSIoSpiSOnFQsiCXPxnDALGvB.y0ue+7uyBEB5EOZ2Qoos3+7+4+ys+U+q9WkAJk245LCPtFMZX0qW+PyraTnPgazue+a.k1aU5dvlooVaIBq2MIvI0wnQcfFNt+t.QdRo34zMbXwc.bMO3po+sOZcbgiS0trEfr8IMMhs7DuX0MiY17PwEbttKBL+1u3Klc4G5gblYCJWtbXmNcv4bAoomNwYrBDOcZfhDQuRQIAoHHcTPRdMDQxH0+G7G7Gbq22668MZwXL9keQqWum2Ur3ify0Mc8L4fjx+2xEudDbHT9vhz4ndwMXenUzNnReNpMLjBDQ+RIM1eaK3cuTAWTtciOBFIetZMuJbUu1Ie9pSmNAuyxk82B7oLdzY0rvl4IdJUr.vBNma1jxfNfnqe8qyRKsjOwi757.ODTZ4O6m8hOzG9C+gmiiGQ2wmOsiusXFALLLLbP7n1FF56mIBvs0VaEsxJqDADEEEMDX3m6y84N5C8g9Poiv5AkL6fdI0o9o+ze58+Q+Q+Q2OYjeCgR3bc3y7Y9ey8C+C+OcPR4tcKVr3t8506lvJGLwBd2hLrzNSryC7VooKYZ8MAqrxJ41ZqslGJtZQ5U5oN+4K8jO4StrU1xWrKC+sd9m+5Ox24izl1kZBc6A20LLIMiUNEwQG3uATZCmqy6rUq1+Mme94pr2d6sfCBLHBGGUsV0a0pUq8.6F3b2.iaLyL426fC1+lOV05G7kZ17fpUqtuYkN.5cKRBpcR.riCfBLW61sm8QqTYl1IYSGwWLZt81au7YxjI2y+7Oel2y648X99oqAiSr32MJS8XxEo3CANbXzvCx3W+fjQ.KLcTgSZm4.hmNNW+e3G3Cr8+O+692sCvtDmwLGkj5wNnn6vCegnb4xklMVoKZwIo3d8gKQivqe6qQS2uyjw2JZ7.9NwB860t103zm9z.m1AWKMCSl04byYlMSkJUx0tc6.nPlRzOW2j92kjgUYMyxPIxP2x4fNyAbJmysnY1BO+y+7y8NemuyfiN5Hmuu+Pee+zyUSTtJJJBOOOWTTzPOOuA.tnnHyyya5cqmPfCihht0gGd3s9Flc186FGrjglUdny04nO4m7Sdqm9oe5wGQ2z0NmCKAG0Ms8yQYzxx6CaeHr3PXmQ8cLY.HBWe80CuxUthVOvdoMd.lSWuiFeP8o.32+3Ep0zsE5bP67Pw7NW2bI2ePxE+ldgvYghyTh++Yu2znjkypyz8YG4bVYMWYFYFYIcPnCBiP5nIDPC5BHLM3llkuH6Fuv1fa4FDvRX2XijLr.YnoYRBvfW9B80VB41zxs3ZlQFvfvskwfwcaPBgDVFPGA5nSkQjC0TV4PkSQru+3KhrhpTI.g8Oz.eqUbp5TUVUEYD6X+s2u6286t47Mf4UUmsYylYNlssUC.UU41u8aO4S9I+jyjHQhrISlLM6+uSRL22lB.an8Ub1v.6+9q0N6riXYYoEJTXDvvxhLrAL5q9U+JiunK5YM.S6OtM1rEMJuMTOx2uZNszfof3A65.Cc2uN53CE8gVAgsJdbaq3r37wh1dGLl+n0CbO0UIAqsbRXio9TlBpQLMyoDjt4drsORKbRY7aYOmp0m6rO6iN6G8i9YxbVm0YkPDQKUpTvm6y843BuvKzprHoarWK6DAhKTB8AI9+Dg6isKPWCPuwi8GKvNwvg2ehLYNEKUanXre5KhzAnaXbX6B16VhFCZFAXWUYTIWF2DlvxLgMJ4CMiG2+CVwENrqi+r0Cg0OsLLIdREIWh0RsYnhASYRScR.kEU8Hxo42Y80YkUVIJnqviFr3hKZUlloDQRXaC0qqYssISiFS6G1gTdyAd0iILlGeeitoCFr9iEQiMZILOIoMYfx4f54AlYkUVISSHYqVshDNM+0Wec+v6GIKSiLhHynple0UWMY4xxjoURBRwJMRt95zEylXCEQz0ht1uF5YxZZHEV+W6J+EupXGjsDlu2ci0RbhDaFI3vNjx3rrXBnUBLIHjukA.oYBqDVtu6286l2vnDlyllyiQXGmcoEVH00cc+wZTaTzoSG9M9O8qa0HzI4EdgWjkuuujHQhHfShrmmlTjF1CjpQk1m1tM999C+4+4+4Geq25sN4FtgOr9JekWFEKVjRzDQj.aaFWuttqsM8ZzfNgA8MjR0G1rI6F5.saiFM5Xaaa1ToE9KSifMhNGNUf6GENhBmHNqe9Qsdr5yKQqCxjo3IXXwRmHo2l60mqkKWdu.xpSJaVOaiH.JKy7pmNmHkyGV4eK.YwEWzJbi1zD9Z2c2cWLWtbKPXq4v9SPH9jPBBC5KQhDgaHZE355pNNNr7xKOErXKKKefIuzW5Kc7K8k9RGGDDLBXz2cyMGs3hKN3S7I9D8eMulWSeftG6Buvt.CKQC+wiG6+leyupIqu95CpTQRpSE3XDb1HUKWylvWy0bM9uw23abTiG30r3OG7Hc6IAP1XiML2CJ0jlMM96joBJnnNNNA3gZSC8GAASNHKkD1qMAAPcbpDryN6D333D355p.hXIVtttIpVspUM2ZTsZ0nf98UUCpCTsZ0j.4rgLW4688N6UcUWkRYrtnm6y0BJkFZlEXlYlc1Ytk67NmYmc1IStb4RmJUpTg9nRAj57O+yOIf35VScbpFcNmH1Qz4sR7oOBLpY8liTsdDcyC.SRL20ccWgrqS5.rvM+49bKIhrCFU3dGftgBB63JzLHSlLSJWlIhHiJWlQ0qSDvI9r7ZI2Zio+citVN4oSyL...B.IQTPTEMUohWIse1Z+q31cSSd.iOnjm5odpBftB2OqCV11jpQCxUtrjSUsfoXBjC6V4pWWyIhjSUMOFlYlEHyG5s7gR8xe4u7LyN6r4w3WaVU0B.4mLYRxzoSKAAAGrsWlBLrkkkElOYZrbgeo3OuD88FALLWtb69C50aX974m.3qZ8f1saOZbv3coL8.ZC1cfFcw.ZxvPcTYbXwH5AzA1rCvtUo8vZvjxkwudcBVXAwGPCG7.GzO2A2S8Q5959W5Z+wWD1OShbJw+dQfkjgv1D27QuLarwFYeRKubdQj7kKSla6erVpRkHQylSqpeNraNaiFrjp5h2wcbmydtm6wx356ikkUvINwI3EdAWPx5FP7x+FdSugLW665Zi9abvIwkOfVudcbbbhN+D.000EGGmn6sxbyMGtttZgBEFCLptpiCBBFOXvfw.CBBB1AXSZPdapmsgwm1tThwZCcrH1Ceiuw2n+0bMWSBLCX.M77IZexIgs4p+7rVPaPWZIzM2D01lfFMdLKCmN39kVgxzQ7BhuWNFqgVjMzVgE4fxg.4ZjbfHf+y1z.1atvbZBAWodJfB1zXAfEtq653yAjw1dJf+5S8o9Tk68duWq5pl5K7W8ER+B+28ByLZznToSm1RMI.D22Vzf2HJ9+CNELm.3eW20cEb1m8YqhHR5zosroYBQDsbYF64o8KUhNMaR2v3+Gfcica1vj+qHR+v8UMrOYCFUllQZriEyiParBGLDOX5NGw9Z+r0Cg0OsLLIdBEoIzXDSU6yHhSRvSt4O4mT+E+k+kCt669tm7g9PenQenOzGZB11AmmiSxm94e94elW7EO6u9u9ud9ve1Tus2w6N4+ke+2TT6PDlno8PUqOPDoeQnWqUn2pqyv0Bql1K4k7R7+3e7OdbC2GqhFaBfz1vLMXk4Us07hHyCjuDjrgp7W9W8WM4e2+1+sSlLYxj2y648L4S8+3Swa889VS8Beguv7e7O9Ge9W1K6kMOPtK5htH4y9Y+rCVYkU57I9De5s+k+kewaJhrcIncyRzawlLXq8OBrNbmY+K68DXD0oDvcaAXYCRCynITZ8.qB+TAaMRHvHTy.rg4qq57.yKkKWPqWOuHR9PFlLqp57850agYlYlYFNbXpLYxne+u+22+LNiyH1jAXZUVimPaz6ynMbS345lnhiydBDaXer545MthSkwtddSbpTIpRGXYYE4PKHHHX7jISFjNc5dhHgiHTi8OP2xhztQI1nbS1p9dUyNhII9Xp31HQNxH39iOa2+wcu3QKrC3g55fUw3ffkjDN0jv8mDHUEHoWQRUtEo8L.JF42qP3wblJrVdVnQ9a6a7MReF+b+bo9A+fePxicriEoIDohk3wbXXbPdfLttdIbbpbvpqHQ.iPXvViFMJHc5z66dU3qQCBBBZ0Z8.e+ISbbbl.365VariS0IAAAirrr1c73w8NwO7G18nmwYzU1SrEi.1qGlf+BSr0taHkPMTeuhL1tNCZDaplwidp5e76+gA3uzLvlK9G7G7Gr768Jthh0KwRZCMGf+O3DmXqS+w83pC3wO1ojyYlDt6Lr7xyxFaTDC2Y+4TUeRdddGUUsR0pUm6889deouhq3Jr5zoyjNc5rqiiSWWW2sbbb11VjsaVhdkaxt2tq6DGGGBA1JN.HI60qWxISljJUpTYxmOeNee+7MazLeEmJwmlIGDPDXuDSm9LPPPPTRsQ1gJfuqqquiiiOvXWWWiMfpAHBkKWVAlXACvxZ2M1X8dKt3R8rrr5hwtZ6PvS5vdASFI5c8BCHLTylJ5Cs7+9e+u+3K5LNiwMWkwEWiQs1OK5dr1d9+jrlZKelf0cSkjfWJrsyn0qGlTw97yDEKW9icriU3Nuy6bN0rm4b6t6tylOedy9kkkbZcMGPdee+rIRjHJI33LLNG6M4bhXY7gcOZJvIiFNh02Xc0wwgv8RiNuh9+A9999IRjX7nQiFs95qOY14lye1BEBvr+6XGGmgDDzGKqcZ1rYWaa6tpp8qHxPOSqiMb73w8RkJUaQjsUU2Jbe1H1YEo6IwNNyIvceXL07fIv9XM6uCauyvONUHNs.rlGR0lhYgV4TUyLZznzYxjIZzTGAz1Lg++HcMIIPlO8m9SOyYbFmwhqt5pKe764dV5LexO4YylMapv+VSBYKcB0vdtbg+NhCVR7BNL8dnaMWbp5bv7ehhiSBeMJhgYJNNNSB84MAiOqt.a1e29qOS9Y1DiesPVAX20zVXrCP+hvvlF8nSCKhVj3refVOz.pSqVs7KVrXb1z8Xo7ZNP7WEsLspI5pf+Z6u6Bj8dcDwZ77hXmAZNMu.aH+m7q80J7LelOy49TepO0L+R+R+RYDQxDxFjHVwM+m7S8Yl44dwO6zVIRXMS97Z+98kYmcVASqakRM5tTbM+BL1LAg1LwYwGLEXLF644MoRkJSp44MoZkJQf2AfDx3IBes61qWudEJTnOvt20ccWCOqy5rF.zyVjtMsoS4FzIL9+d2zMcS8+09090lNkcLw+WcB3FE6evQ.8DbTENdblK8u1E29wDqeZ.LIdB5QFp4wPUybhHIGNbHYVMS3HoshenxPOFXxJPv5gpwdoRjuYyPQFqHYdlG44l4S84+noJUpTBQDsQiFAkJUZ7ccW20ficri0Gnitm.ZFSDb12H8BdrkAPr.7mOKzdNfkvlEoAE9i+i+iSs5pq5+hdQunw.Cq.i7ButUtLTuNoJBEZYd8KAL2nQix.DjNc5c+u9e8s19s7V9+cCn45.afYiACyG1yQO7i9guGp2ON.EOqjTU285SwCToLl5jxNYnPBlRJIonEYgRyAMWVUcYQjEKAEt2Ncxee228kKQBI2YbF+bE912wcL64eAWfoZZ6QgyozyL1gbHGGLHpCN0gBBcnNsO76zoSPmNcBLRg.p.JhDjLQxIS7mLpRkJ6Bzud856VrXwQIRjnOvNhHqiQnAVGSxFQfVE97UoAPycA5yoxtb+SC.7fmiwuu7ul.c8HoU7.8N3813.vkRDmzfW5XZEQFL97lUUcVLfdLKvr25sdqydAWvEL6e9M8mm+U9e5UlMSlijQUuz.ou268dSc5m9oGkXR7DLhBxKAfr1ZqwpqtZ7yQVe800UVYESvddtpSEmo2mhRvv00CGmJAgUFKna2tAfD.pOPfiiyXWW2Q.CTn27yMWeKKqcEQFjKWtQG+dN9tG8IbztwXBPaC81s6BMiWYCiMF6SbEOrwO9izrkd..lbsW60l+M7F9CmC7VDXYU0Ee+u+2e9q3Jthf67NuysO1wNVSf5.qCGoWndacH.lX1yaYX12zevePwq3JthiXayOW855SxqVsmvLyMakd6zaNUzL.VEJTvua2tCJVrX2VsVuMXlBNsZ0Z2y8bO2QgU.y5889deIuxq7Jyc8W+0m823RuzrIsrxD1NCQIMDOg13i90CZ2GAtaPrjFlBnhqqqDlDqFDDn0qW2vvl81+0ue+9Z61soRkJpqq6DSqiwHPGtvBKLbmc1Y2xkK2AnsHR6v8x6ppFkbQaQjn11Y.Sa4hhSf0GCL70+5e8id+u+2eD.e+roA1guhYGezDvwiX1V5PAcMKP5hEwpYSkRhHsJSxeq+u+sx7A+S9fQIvNGvBppKdxSdx4toa5lx+ZdMulb228ce4N2y8bmIhEIDZeUtb4T0qWOxNKpB+GVKOD.DTy0EADQjDnpkJX4Tw.VBPn+LWI7y0PlWoNNN9tt07mat4CJTnPvfACzs1ZKsRkJ9ttdicbpLLRrjSlL4tKu7x6lHQhg20+zcM7Xm0wFTF5dS+M+Ma9betO2MJUhVkJcVaeW20csi3H6ptlQxM6MkcBAM4n9g5nyCVrMOR0e2C15GGCZNXAFNXdDBmIB2MVvxofMx.Ly0e8We92xkcY471aXDjKjYyQ.ljWDIyZqsVpW6q60l4y7I9L4.l6Nuy6b4icrisBvBgutTDyeU3e+H.XllPanNPYXPhmKg6YZ1C00EQ.UCs0LeeB+dhiiCCFLP60qWvxKubTLbA.9MZ1Xr+D+9VVVcBBBZ633ztc61sme942IbuysoDaRS1BpzE7hFY1A1feXqYDo8NiKCS7h0JhrWdMwKDwC18hGMsN39ug6gUITe01WKmrOVc.jCJV.ZUPUM+E8btnz+8ek+9jgscS9PPQVzFl+6s81yrvBKj627272H20e8+oyjHQhYGLXvLYylMK6GLjH+WIhc9DsuZ78MOH641GHcg.sMsk42Zqslzev.+ozL03+CBvGwzV9NNNwisZHPOQjcTU2NDr2HastgG8.69gsF6HJxXZwX3TCf6eJ.IU.eu8.m6wp4L+S85gJfISMnOJj33KSZ1vnWF2vMbC4dEuhWQlJfU3XDdTjZ+x9ErUEvBJlBZkdEHy56EPWdfY9ve3ObtW4q7UNUk1+Vequ0nm+4cd8aQocfl6.zwA56VjgFihGSBXRLVXrW0gVAlecXEU0ElLYRNee+frYytKlGphF4jiCmk7RYHccSxdKCTLDXgY888SbIWxkL4lu4atip5FUDoUcpzD71DnSXO4MkF17i9Z9C0.JN36sjKCI1vTs+TX5IwLMKSV0SSeu268l7nG8nQnImpQn8jMj+BdguvEt4a9lWNQhDqDDDr327a9Mm8o9TepYGOdb1zoSGUo+PAexniDRHRFb.5CyCLPg3UCC.7bcEEjxkKSi50UES.eiGONnUqVZDs6iBBbJnIpnpnANNNiAFExFfgiFMZzNc5r6f98a+Q9ebist5q9M2.XiRPml1LRqq9XnZ7.L8J4NMLI614Tg92+dZpv9NuCmzNAgBO1iE6U1Cit5GFfbY.xZ7SUJSn.zksLTntMyo004DQl81tsaalmzS5IMSlLYlYznQExlMajVNEAHRTOZm.HQnnfEOAi3Ix9ftZ1roVpTI.3jqsFmxpqtOavXr.X5gwNaZ0sl333LBXnqq6.LZF0nJUpLDXvm6y+458w9K9Xctwa7FaqlQU6VhHaaCa2fxcf58TU64HRWuJzWc0AgrA3vRf8QZIRbP.yhzAjBUf48nzhPyETUyYKh9J+8eS67td6uqVXXWxlDaRevCBfILUCSJ83fl+bppmIvS.vASRpYbccsvbcbjiiS+1c5z8IN2bc+Vtt6533LZs0Vy2xxRcNWmjZCM0fACxlMa1npqltW2dIlovLwYPxA0CmCxhonyWeLU+TUyziHNfI333HgI5Fw3I000M.y+Wc8bQTTB8k0Ymc7mct4lXRx0cR30lcWZok5lMa1t20ccWcN6y9r6fgwIa9Y9Lel0ewu3W7F.QSTmvJlICKBCaQ0APsgXlzNi8NSlvc+XtJv9iaMcuyyDRb26Ms7xxdLANen9QjvFnQXBJ59YM2LutW2qa1+y+m+OO+i+w+3mmPeYgI3VfP8Qi8.hK9wTveiCzFwhSSDaU0FQ1XVt0bsPPhuWZ7UHPJpBZBKq.MHPUAbpX1GMh4SdttSpD5eiPVi355NxwwYXPPv.fNVVVaJR4VPiF1vF0MIgzAiM2TcOgXrN4Hv3SvYFDM+bNSf61z9IOZZuS4.e9CFCZN3dmS+4NSHp8rsrgDgwhEJr9LCFaqzPkzfW9RvLMLs+0Lm8S3Ij6N+9e+re8u9WO8y3Y7LxJhLCFcKYYLwmNW3uqHFlbv1gNNyRlddUudcrrrXxjIZb6qHv4.CnIdttDFuV78TU.0y0UUDEz..+v8PG35512wwoGPugiF1QCzs8882rPgBqWB1noAH3oSHmvONHr3VCJCi7zo6gNJ1qKtPW+nAaqeRVw2+MEP5a8Vu0jOkmxSIwoO6rzDmIfqOfeQPaYlHNVrWtiyY1mlYUUyTVjT0ME4JOv7.Ke8W+0uzkdoW5bISlblwiGmKUpT4wHnvYykKWj1jHppR61ssle94iw1ERVy0MY08XP99h8Zh+DRlH4TedFaFh7ME366GDDDLo8N63WbkmbPsZ2dXtvHhJRX7+QSLmoEgpYyliVd4k6u0Va04S9o+za+ZdUup1kfsm8obr1+k+O9+ameieiei1eyu42bGL4k0qw90so3ZYXDiWl.UlDaDW+Xo7l+Wz5mF.SBMfpjD7hpRZzXDNEfdC23MN7U7xe4gUhb0gKwZS1Da+PgoDpffGVv7Ig1QavlgoU1n77MZ7sKXaamM7u4Xf9+s+s+scdNOme01f2NPwtPqn.TOLGKOZ9l+AqFdpSERe+lqeK.rjmm2bkKWNoHUFVj5sagcanggYHkYB0QgxBTOJH9UTUKCTRD6EUsQ1OvG38y4dtWP+m6y84r0sdq2ZqK9hu3FP40g5aCK0C1Lda4bXmivCdK67i68GreAvLLAlkyn55YMZDQ87g8RsocI1CA3LrG87V3+8W+yrzwN2ycou62+6tv4cNm2L.Y1d6sSuvBKLkcTD1VXQWWCor4C.rjNc5H6rSGpFiNmtt0Hr2+ibfJgUDikWdExjIMtttJBZHQBUTnbYG0vDOTWOWEkPcLvaBpNAgIfLAzANNNcFNb3VYxjYiWvK5Es0s74+l8Us9nPMoYnpZuvpytQ3wl.6.U5uHdi2hkTXSy47h.agFBZ1Am.EQ2ydz75f.xEOoxjPwjPqnJVEWffyhAzjLf8LiFcxYAl8U+peEE9B+2uwbMBs4.JDoIPXBTLBzj3.i7.DVyXIWH.QsaAiFMhzoM1PyM2bzt8NHhIvtff.pW2CPhGnWTEYCWpBRjcWrMMiBNSG63Tcrqq6Xaa6cSjHQ+67NtytG6bOVafMDQ1PUccQjsXJn0U1A7hDvygyCiau+.8djH6kh6SMNnYQrIZAQpLmpd4DwV+E+Euvt27M+42DXiEgs2ZOVOdH.lD1RNvbTjhzhSCJ8jTswYB7Db8bcJLSg451saFPjxksCrrrF455NnZ0p6VqVsggLDJvwwQa2tsL+7yGY2NULW4AlvvgwljCaMsJpCFLT2byMDGGGKWW2DCFLPxlMiDYiUqlKkJUDQDRlLo55VCPhZmh8wj.QDsRkJAtttAH3S.iwhgnrKH8rsK0QDYqDIRrAFfmVGr2DZrClDXiezGJMDZNFVdLrQbAt6mAZxgTnAX0rKwZ41vHB0y.TfxTP8zreouzechegeg+sQ+boIzO2IOYsLtqsVlm9y3oG0tgypFgfcFn7rp5MG6k7Zj1KE4W6frXBfH6hnpzGUzfowSFxlDIxGFJ3T8Av3Dv3VDQP26qopfnUL98BTvWBGA6lp3qiU0zh2EJTn6byM210qWe8xkK2TDIBrycBOhWo1cicD1hXqpvZRXAGh.Y7QC1eOfhAEtNr2SGr8CrBGuowesg9OWN6JrwLqyJETskQ23JSZ0SSIhcdn4LSlLoP2tcm43G+34dJOkmRznNOmHRAUUCioMGyw9iSK94kEfU+98sxmOufoUGnQiFRkJU.P60qGs2tMHfhhLcOSS7a6wTyPEGYuA+nF6KD5aS7gfn8MiXEPeWW2NJrUUGmMA1Pjx6XSi9W9a6cr6a4s7lGHhzmxzU8zthTpGzJBjtnhYNDVcLr1Aa2vGoZW8PYcXErXJ3qFVUVQg5Z3yeQ1eYKAEZZ.EYAfBpQSaRJRkjkndtu2VaM+BKrvx+9+9+9q71e6u8E.JDlCQ7135f.FdvVW8P8sEDDPmNcX94mG2PP2DL9rBicScc8TDMPBDeUz.EMnpImAK.KOOO.HbuRe.eQz.Uk.fwNNNCccc6uzRK0Ia1r6LXvfcxlMaDKSZGx7jH8AqCPuq65tt9upW0aYzJTeRq8ljhiwlQzfQPkQfWbBG7XAar+EsdnH5qGfB6dgFPkSA02SUhKxjWwK+kG.NiA2QvZi1Dl.M1KwZisg0Qo83iO0XzYTEbC7fDP8LarwF4wzahIEobBUqGHhLtXQF1pkYrXBDDl82nvysn9BSic99nMif3n6mHjoNYt+vJgSERqtpkHU7eUupewIP8dsfcBC.sOvXpatNsD0Sro45SRn7tkEYPcUGAM8AjW+q+JRpplCrGewuzKdWU0tNNROOO1MDrjfPwEZuqwGEkiiD90i9pw6YO3g78jUgkVSXSRXauQ5vpfEJfqkyB0S6pZJee+TIRjHEP1FMZj63G+3y749b+oy8zeFOi4v3DMr5FjHa1rG353TGhV.hSUGHVhGQ.gL6ryRmN6DlnZj3gsmeVGGG1Ymc.c5uCbccoToRzrYSy68v28VVl.IylMKNUbh5Eae.KmpNILizSMfoi2SRIB49RetO2BDlb1e0e0e03ie7iO.n667c9t25ptpqHe5zoy.kRAMSBdIMIxsoOf98+9eeMTaVBT0H1rwroNXPHOZbcXzI1RU0xQjjdPZUalVbjr3Q1SuTob2aSxKkkb6ryNomc1YS666m9Zu12Wl69tu6Tmy4bNI+ve3+rjI9ueio.RI1RJZRxW+q6pRJlwh4AlL.6Kg13LaQh5c5omngBlcHamP.oPgBTnPA777HZS1Hv577pOktww8.53T0J7qG2uH.rxJqXs95qmz00Ms.S1XiMx366muvbElIHHH+jISxBkxH1lVQ5i9Qe+o+U+UeYVp5FX.qyvTs1QOCsHSXKl.GwGNwCV0We3rMl45ypHgtuTXYeVYiwhHirsYTXqApEJL+HnzXno+V+nCpUh0S+InkAjr2+662KwjISRzpUqDnjna2tg1AUrb8bAcpHxotdtIeeuu2Svq+0eEBfDV4q8U8K1usUz6k316nGX5U.SsKl9yjMaFS1D6YSJdddBphmmKVVv5qudr2aSyrHFnIBYylQFLX.tttIDQrTUSfPBTCKDVbwER0nQiTkKWNgpZh64dtmjmwYbFYUs9LhszllrMvVpphTTBXc7cbZpFRtrQ30S6IgSnfn3KdrBnuwWGH1Lr1qfVqkaSydkQ.eLqHxr.4dAufmWzT7hnjTe0u5KK20cce3zTZ5DeHioUWIYnurzG3HxlKNPvGjESQUgcJSkhDXyPPhmZmAP4xkUKKq3rJQL9zTPPpVsp555NsnDNNUkM1XSSK7HHhFVwVyaMoRkpQfxjnWudVc61UVXgEhtVEMtjiXPSGUq2cvfA8xkKW3T0XUEmZJtvxKultwFHp5pl8NOhF5m6QC1a6ArpokZfG7j1CesKm3+1+s2dxK+xuby85SEk6eJCxyBajacn.r9LhY5gjVDIoHRx+nOvGH0y949bS+rdVOqze8u98lwlFQraJqXKQS5wY.xIkkLZcMIPh+la8+Uhm6E+yGsm19Dv0s2daIDvDZzXeRwsryN6fXYNMcp3Lc+SQrvyauATgHBHPkvVzoW+9R6s2Vip6f48snfjJjMfIEQRswFalwwwIqqqaVWW2bppETsdTQE1UDo+G6i8w57G8G8G0VDocYHYcy41DvdjMMFYNiW6QC1R+zrhdeGDNITCE46JfsmkHh0Jqfr95j.7B2uqbJndtll36W.XdQj7hTN063c7aaopWBLhTcFU0Lu829aOp3BYUsdTAwhZEGIF6JE.qm+EewV2xsdq6SuubcqINNUk3f4N+7yO8ymF+EDUnK.TmJNVdF6k.IzPx00URkJUDndXZUeIQHvIQfyk1yyKCPls1ZyrUp3Lylat071EK0UUs6IO4I2Y3vQsAZSI1RanaIRk1upW0qZGf9qaXQbn.YqQwuHKhmFF2Rz.TI98fe15PVOTXXR7DLStHj4qbm2Y1icriEVg9JYAuDg8kW+kXotaxl8fiL.NQzMjniClzRzFW4AVnHT7e33G29nG8nKpplwVjfSNbR+SISxNMB664xPm560G8C.6wPiCaTp9nMCAg8S21PDRWIW3nMMR7cs9O7e3EO9S9I+5cro01M1quviBrTvlDzXZETWBvtc61U9d+yeuxO0m9ScALARD7U9JektOmmyyY8xP8aqVsFUqVcikg1aX98EducYfMlVU0EYQqsXKfkCpvF9dG9HH8A6829n7YQHUKHiCj2kxEf5yZCEZXzMmL.IEwNIzL0IO4IScJmxoj8U7a9Jx8g+S+vyJh871zboPQesPnFTHX.iKZhkDwBfC1ZDSChy0ySbBcpY1nUmtgZzJFJw344QjSvISlvnQiHe97zpYKc7Dyz0N7AAMLRvoh9UHE1iSO9.Pl33TYXMW2ABLzwwYjqq6jEVXgw4yme2O+m+Kz8E8h922VUcigCG1LSlLgUOydyxzna880S1gLLXIlvl+DMFxdj55Ay+1AAKKNfF4B6y+b.4twa7Fy9xdYurrhHosgTunW0qM4S+BO2Tu4K6xx1bud8ORKSl49tu6alS6zNsYTUmIHHHehDN4h1X9i9Q+noOsG2ok5o+u4oGO4hC1pWSWwsgb87voREb87PXOarnUkJUhWceMYhj5D+IQ9ZkHqsvVBKd+QOoZ0pA0pUap8G6w9jcme946LyLyz9Nty6byegy4b1ptgoIqWFZVeuJytKTbDzJx9ZzRrz3MYSe3H9mImH3t2OSzd3H3ISqNenu0npIkJrsQyuBL25Td12y640m889686Qkicr924cdmaCrErTGXyX9CI10938YMK.XCbZXySbRsIOoFMZbTGGmx0bcm8idS2T5eseseMIZTqBLQfIKt7xAYyjA1ucygcbXrH4PeNPUMpW9UGmp7c9NeG8rNqyR+zepOMOsm9SK5myT0eOOIUhDTrToo9+.YpcnZP0Sw.ZhumqmeEmJJFPfid8FFNEJjhHQLMgdpp8Vd4k2Ia1rc9c9s+c14O7+m+v1.aJhzpDrdSXyJPGOJO.pOpBL5n+e8+03u5W8qF5WaJEiezTKR7iacXwQYAjbdlOUaZmCVovJrdgV6ATxreyu42b1K3BtfrhsXQSystv8Eyopl6u9u9uN8y6487REB3aTLE4sEYlFF+bQhUcTaFFG7tD999VIRj3vXp.XrGznufI0TUrrRfePvzJyFyWFoRljQSlPUGGh0RXwYQmFGq+lMaFToREeee+3Zqj+hKt3jb4xMdxjI861sa2d862fvLzBA..f.PRDEDUY4kVZmjIStSpTohXXRaancCn83wiamJUpsJB6zB5az+MGEb0SEz6eOek+jFayCWWwYlj0QfDmXuKnG1ySRnOxn8LSAqj.VeZr7kob15TOWkJOgYTu6Yl5lXISJhXcC2vMP850sdy+Qu4rzfBppyGDDLWhDIlUUsPYQxU2vHpBwZ+qYYuVaMtNS7.700ueexmOO.zoSGlc1YOz2zQ6kFEKmqmGEWYk3fAC.yLyLZmtcQ.M+LyDzuWOeQT+vp+GdcYpVgMxwwYWOWutUbpzEnumq6tKr3hCxkKWeQjtCGNrclLY1Jj0lafQS51hv1l21LUmdv7k8HQ6qGJq8kmIPxSERc+PpkgjaX.HIRvVi9XVnTAn4bg1KYtka4VjWvK3EHfchxzHWcif7ufHxRpIWfHcmKN6xsdPNOrbc8rbbpLskV87bkeRi+2yyi4meNc6saGQso39+h+YA.9kKW1ud85GHGVIvwoxDWWuwfNJjwI6BrqkkUuxkK24C8g9P67Zesu1sA1nhTYia9e7Su4S8odI6XS890MS2zI.C++7+4+yfm1S6oMfxrK0m1tXwKp8i1sw9odY8i+kb3qs.N1wNFfYDIBdQZXi.vlKuYny1SDiFabvOe+AoOuIX6Vv3idzi52tca.rZnpUlLISzLVRMeKOunISQgkVhYfF4VhklNK24ARcpGJfC8v00zGhu68nrVtxkofpsJThRyD90TwQF8I+je19PqdJrKr7H.+iBJGILH9Fj.brXok.XBkY77yO+jAiF3iop6Y.x8re1O6YTUmYlS+zmoZ0p4AxuwdNZxVoBYdqu0eqTmJmZz09ja4rUx2065ckD1H4HHAbjCsxSOHu+BSxX0jrLoaE1hMm8K3EjGpOKvr0MAtkWDIissjb3vSJkor9E+heQEH3F9B2PHbwMRT2TYrLDNxeCqTVBQjDhT1ZxjIGLfyCl3wTvR.iCwHmkau81.fmmKUpTQqToh5VyDHmaHK.RlLI4ymGWW2PvRTHTrWcbbTDqoS4FB6KVP5AReEc2e2e2e2Qf5655JBjRDIqqqadf7au814bccyedm24THTXqVISlLkkxRUfUgFUUvFJufpZdaHMECeteS.p.m4Y9nMGjO.P23vu2JvpBKsj40U0T46vf6xAkx8xupWd5uzW5KICFLHXswiGeCW2GZ3kcYW1vl6sICm3DmHEPNawd1G2i6wMWHMhm0xxZlPvRRIR4D+p+p+pVO8+MO8G.yRbccwyyS.1GHHQKOSRBlpfwgBVh55VKtuU+IA9gAwK9BD.FaLOW2IfNFXbxjIGALoVsZwEatX5MkX0tc6rt0pM2oeZm1J0U0909ZesU.pTGJCTBX4hEYIUatP366B.yrYwMCaCoSjdKHIbz39je3peYAhTo.3Zu1qEVEVObeqVpF.0CrrrzVfdy27Mq.pw84l5YxYdveWBfbzvJwBjrxdicvTzfjIRjvBQjNc5PUGG8JuxqL.Hnb4xANNNrvBKHUbbrxlISTqqFo6MwEWyHZKeXU3+A85qHBUqVE+.U.3rNqyRZ2ts0k7KcIV.QBlHdttxwum6gI99SAGQUyGBCLTiEikF09ghHQ.+NYgEVz.9iXZALfwnLILYljqt5pY2XqMlqVsZK91dGusU.JdMWy6onpZwlFz3W1U0kf5KBrfqpK7U+pe04.lckUXFvKaQJFWDv+wpGPOBecPPeSVgJQ6yks8JsC0pj0y+C1YmYDoxLX.CN8S4E8TR.f1P0M2byf2zUe0JDg4EVmwYbzTFVbZW.SREEDQx+C60aehpISYDhDc9..IRXFRDsa2NteJoVsZfA.NKGGGoZ3GymeFBBAKAHxWlosGD7GOYhuXlLSARH.hdddQ5AQXaLDDMokFUpXow9AAiTXDHCOupUGBLbqs1ZnsHiZ0pwjd85QUGmTat4lEt+SdxkTUK8O9O9OZqpVpgQK2VJUppyQEloU3T+wCx.towz9ySiy7HG4vYUyivVS8GeBLLQCVU3HGNvr2cLPxVZIR8O+O+UyTjh4wXuLe8h0WTUcYOu6YYOUWTUc1u7W9KmqDj7M8tdExa9M+lMxWO1hToRRKKqbQfiDVbqEhkXaTqeEWHgevNlBVB.oRkZpMXDqL87BYFPkJgI0ZDRNAXyM2j34Kpppc6zAIRXq60aBvXU2SyQDgw0pcxH.NDuZdIUgrhHyHhLqUhDymLYxEcccWQUszcbm2QELZVkyYe1WnM11qnpt.Un.PtFSAFpRJN5Cve1ijswdHtNpRQ39CyGX0y4bRC0ygwezb.KJhrP4xr.zbVU0bW20ccot+6+9sdAu7WP30nFhmphoFEDnpNc.jD1N6v9Yp4AE20j.IbbpL85umm2g.VhaXdAUvy0Xu454gqqqVoREMe9YLuPw.PLJhPDCNCAKwTHgw0qWe.60lV6hpCAc78ce2Wfo3pHtttIXuAGvLtttyeIWxkr73wiKBTpd45Eu7e2qbYn9B0UcVQrmAHmMj8o8hdZlhrWmLfSZrsOHyT+YqGj0CkKPwXHxYFGnCeQjfs2daMTrLi3O9OtpIpG3Hf1LAVdbwPGQyM2biIT88iNeMTCubj1SLKvratIyATXS1LZb1klU2mP28iq+sejvJV.RG05I9DehSm1Fu+2+MkEHWSZlwyyKIf9ott+xQD1+sMggPtI.AGGDNQwjmJjlxgA.r4lFjSqiB15uxy5YY4qZ5+8u3+84FLXPAQJWPDovW4q7UlgxLKXGuJSE77H+a6s81xd+b+6ELkh7e7+3+QAvZCPLsU7OxDkhmfgww0otVV1f7P4B.y9k9Reo4oDyMd73YwTMrz.xm3S724ewW7EOtN0GcYW1kM5+x+k2xjPmOv9YQvTwuSUMmpZVUqmJYxjSC.rVXuTygr4zjISldxFs46t6tKttt5t6NPcccYqs1xTHUUUz.M50dfpgEfPTubG3Tohuss8HfcUQ5IvNftMhtEJs+.efOPGGGmcAFU87qNdvfgSG6gUqV0jXrJ344kbmc1I+N6ryhZc0FnJvp0UsBvJhHy0.xRKmTkgDLOVfmvce2O..h9Iwf7goq8UsLld++nwZagil7HPRpPRXsjr4lludMRA1osinadklInNboW9k6mMa1gmxobJ6hMCfxi.7emuy2I.IO0S8TSCjqg1XFLOSL8YCBC1V05oN4IO4gkTKNNNSojYzGUU2CXDQl90AyFwQKUUSE7UYJXIhnSP0IW1k8aNYqs1Lte5IgIQL.XvD+ICAFIpLF0HFcUqdd8EnmJRePMBFrkUxNc5j200c9q9pu5kUUKFp2QU.pzpE1hHEEQVBSORNGvL850KGPVyTQncrfPN5gED3CGVJPDrG5a387Ff0PgULYDJBTD8Jux2Sfpp+W7K9WG.nato4m6t4tOLfGsNNXAAV.I7.q2266OTTUMvMAfpZmNcBbcc8qYFWvS.lLYxD+74yql+z6aBgcviC5a8Ar9a+a+JQsD3zUThDe1O0mg66GdeBfzsWOfPQPzyS9S9S9SDDgmvS3IDBThtOQE9Pr+Ba2O0uVsZ9DpkDqu9FipVs5.EF.xtXnn9f4latQNNN9tq4hXp2Rh4me9T+S+S+SY+898txBqs1ZyC1q.TTDoDknnp5JhHKigUjKt95LOvrsnUThUwSn+gK1V+q45.EVvT7DO71SXWkoBYd9Ymc1bPP5+7OxGIQcHP8zgspWu2W+q+06rzRK08c+NemQix4g.iuvibZbO2y8jR05QwXMGPg74ymCHiHkiCPmTutdnfzM+7yO0uUsZ0vxxRHDfX.Y80WG2ZtrvBKPxDIU.cgEVPssKariDM.EeDw2wwwWDwLcQL9u5Cz+s9V+uFlXgDoED8PnGvtatw58qV0o+2pVsdfzUDo62pVscTU5XYY0SJKiAHalLYpUq1rWvEbAK.rTYXYQjkvt473wbXJDUNU0rXSFnbDXkoARchS7nh3LmFexQ.DQTprlvIPBK1k483QIAbj3ZLQlM2jrOomzSJeKZMKvbeuu22aNRrGyKEQl4C7A9ix77e9O+jMApeb0uro.c9pVGpWOw2467cRCjWD6YwnEEQ6gFMtgS828282EsuwOQwo34YhGKdAFLBltD98MwugB08pippNw2WKUrjVoREcJq4Lssbj82P0Hv98qVsZef9iGOY2pUWcX0pUGALVEcx0bsWaPsZ0nVsZVA99Iu3K94moZ0p4877l6rexm8Rpp1.Utq65aTgFMJKhTDOVBrWzN74MJ6MCGmrf8AAo7mH.weD3J98UKpb7v1WsXZfre6u82NOUHOTIupZdU0bu6286Ne85lQQ8G7C9A4U+pe09G4HGYr1PGioET3nkKKpVG.UDIPjx9kEw2vb6o186q8Va2t8TvR3.wrUYeLMO7il3uCO62e7+tttGPS4DIZjV6VyUcbbBVbwEmfxP03CqKlIF21.sQjtNNN8SmN8tUO+pCAIRPpGUsZ0IAAAJfkHR50We87c5zdV0SW3a92+2uHvBhHySnNT0.lgVkxWLbp1BtYnQiCiY8+r0grdndgIdUMhplQXK4TJOzLop5jhm2ou652wOn6RPuM2+3m8AqR1BPhJPZEloNrBP4986WJe97yBXYCSZXT77ggJTcT0QGZYYMrRkysOzn2Jvf0Ca+fJvDu8O9jdjJ01l5bLFcwmJRtlMkJmGZjpDDzvP+pdgTIcGJR+voID.IfSMCb+YTUSKkkDzXkjv54rgkqqZUfSUDYUee+kZ0pURee+9me0pqeKe6uc8m+4bNsZ.as1Zq0Y0UWcOgpp3oOjV26nhFFB4CnNf5BJKhuQWC9Qp72B6uUix.UyA0xBjuDjOrOEiD50DevO3GjBEJ3eoW5kNFJoPyDkfLeiSbhBG4HGYtRvRMTcYorTTqqKgYi3H52EWQ+i2O1wmjD.60NDwWat4FLbvHp3TQcccIPU0BQQlR9bAPTIRN.PCoiW.hDDJLE5m9y7o0K4EeI9pHCsf9mmiS2FPOic94E.MRTqVsThHIAPUMjFv5XPlnpNQDYb0pm+DngeYXxe+O3GL4wejSaT.A8srr14K8k+xa7K77e9s.ZBrITdGnt4d2pLh0lJfhGVKs8HkU7MaE1iZwVTDKZE+0sLvFlMAKSRpubJXizUBGQ5tFAdMSsZ0R7Y+reV8xu7KOvFBZ.REHsmYyl4CSZyVUs33wiWIUpTQ1XyxzoAv9Rn0pYqVVkJVbeB7ZD8Mu2iebxOyLSOQiHp4dT2L9RYg4WT2Z6MAj.PBDTeM138U.Q2ekClnvHKQFFlTdPsZ0DPBTzIurekekA2zG6iMzoZ0Q0pUy+27xtb8V9BeVCPh0pEDFvX+zoS2Ie976jJUp1+C+C+CseVOqmUWnb2SdxuQuK7TNk90CEoyxP+5vtrBCY83i.9kCfMd33XSL99aIA6DPiz.YKB4aYD277gTAVEQ1kPZ7GJB4CY+sdjIHLaRQiosjybPkhf2i6Zeuu2y326JuxmHviGnnqqaNQDq74yy7yO+ACJ9ASTM+oJ3FOOOTfc2sOm9i+ziQOcypRr1+xve38Z4FyqvLPzWXw4ks2tMJnVg.1EDxB.TLBWsH9X7aMcDnWsZ0Iq455u544n0t8ZAe7O9Ge7K4k7RFd9UqtaCCK610w4bGryNGuW+AC5erRk51D5qp1WDIZRlr6d+e6dPidkf9M2abWePgS7QCqCZSDVLfJoAuz1P55plw11NciFMxIhjmxji5j8a7+9+chzEJv4bVOOEpadtqDPSyn27O389dm+ZupqZgFpt7wN1wJdm24ctLF+Y4wvrwH1Qcvo9UjsnEfUMWWo5Azio3qFMaRfuOAFZL.lxlpxz6QloQxW7V9xAuheyKMXsZ0hdc9QB4Jni.YewRTsZUtkuzWhy7rOaUBz.QXRfniErFKlw35nX9+FiwulkpRBHfKX0UG65622xxp663c7N15pu5qtkHRSfVkg10gcCioL3q809Z9WzEcQQi7yggBn3C27m8PYE22W38yiHvILIIhQ5AsAIbpJEU7on3+yAMyTBR+BdEWZpa7F9yRaCouz2zaH6U76bEo9m+m+mkm8y9YOAXHTdnpdHhjSUcQyuVpPHiEYOAENxNae1ZQ5dyAeC345QEmJOf+ez9qQ97h9Amt+ZHxHxde4nOIzlT7CYEWLFMwHCXdFIHVMhzjngShDQkQH5n.zQhocqC.TUj.QU+e3O7GN7bO2ys6a92+Mu0e36+OrgHRiRldZpcCy9m6BqL.VeWfAKCC2XOQU+f4w7HwbZN3JdgtB8ocpog6OKFlQjuto8.C0UIicwvgCSccW20k32929smHL+yjQ5oopZpe8K8WO2+y+r+mE1c2cmMLWx4z8ZqvniBrmvuNsvCAAAXYlJCxO3duWd7m9ouOatH6qFMZXZmvPin.0XHL+Byqa2daUhYYoBRL6u.QHPLwwGN8jnW0pU6WqVscqV878Mw+6ld3fcSlIaVQ.eDYhp53P1xLQDYR3qchML51bc60qSmNmwYbFs888amrZxNzvvVEU0AhTY.TuGPWVgNr9Jcg06uJLZs8mCP3o4OaEsdnRAm3AM4isYFQ6.Ca29dFfQP77W+N9AJf0l6ILc+jDXm5AA0i5e9hL71tsaajssc.PhFkHkpZFGGmLgBfSVrIqiiS9ysRk7Pi7tttYCGQwIeiuw2XBukW9fZEvCWoB9OIKANyD2Mj.VxjreYS.SkDIAzHAkwpIHuq206J.XbqJUL82cK7OyHC+kIQ3n4MiHRla6KbaYg0yhMYaXDGorhH4eku5WYgd85MqsscAQjBeK20l8bNmm+bMf4Was0Vb0UWc40VasU.VtBrTs63uaNpP9V6oZ9h6pqZrU1BJFYCblO.lLD+XuVMpJ4FL3dKfIfs4eO+Ye34TShroTUCti63NF9a81+s5doW5k1QUsGzbWU0wMANxQNRBU0TMssSIhXs82a63U+L9l8QLRJdfe.P8F0mdgOt1kDcL+7KPEmJ3VyHnSUJWN.KSBCUcb7QHvopSfrmxpqH3ivDzoa9N5RdwWxHQjgB5tADz+1qUqasZ053bdN6Tq1s2tVsZsA1Q0fNNNmWWfcpV872VU1p54WcaQj1mW0pcbcust0pUq+sUq1nbYypWwUcEosrrlsa2tK+29272Tx1DLRQJyxQzZGXNViBvF4Y9CM.3GIrNrJtH.Vmv11TwfVjZ0HP3pPFXiHg9JO0IOrQdJStO+se6Y7LB3FkOZY+M2bywW9ke4iA7aXCPoDlIwjcFfLFFJooARmNc5zRYItXHdvVlHIPhREKl.3A.VB.4lIhJwlGUKaaSkJUBs+1+9VphVyqFnRf.AfNQMATMjPccphiy.fAgUNdWf9Wy64c2UUsSMicV3zGQ6Inc+y+X+EcPj1qs1ZaU87qt4+8q++1l999at1ZqssJ5NXzZhgqrxSV2d6sSaYYM6y7Y9LW54+bdNq.0KM+7yWLDr6kUUWrdoRFviVmYpDEXcQxAa7vYaMyypmIAPio.ezZErLjbP.pXD71JlffKAJwPjKbsmOtFnWy0bMQ6YNB7F.r6a3ptpnD6G.rqiiSukVZoNyO+7cvTkoHMQIJo++EWUw38Ys.73OsGOvdzS+K7E9hTtbY7hCRrpZHUfiZUhIf3iE9aucaeIbpjnv3nDRAFDVo+NnZj9PL8v00siEz6lu9atGP2WxK4+vN+u9e8k251qUaSW201z3+6aMna2tT20Mqmu+7pYj2urp5RXnr+b.y9c+te2YfF4UUy17AB78ilV6GrjUMIV3.otka4ijlRjsgY5hjsYylYkRkRAjP8T0FFOybys6477NqtlobGaBroQXcq2QUs+UbUW0vlFcyx5tZcWoGMZTzT9Z1fffYUUiXsRTxEwYw6zjYqFazstupvF9w.e+ouYHzlpZ3zyIL4z.UU8e2K34o0pUKpFDQfkLpZ0yeWP5W87q1UToy4WsZafMq4Vaiy5rO6VVPq16zdCE1RTos.6b9NN6.zd0yu51NNNa6551Fn8uxuxuxNhncrrn2sUasA0q2v+Vtkaw5pu5qNRSWlAnv2x6jy.ky4Y76m5htnKJopZRfTkgTfWpv6GORlZ66ikXwEyVWUsfkSz.RZFI0jCmoSPoYCaIhYaBybi2veVZ.9cd2u6wej+zOxtkJUZvy5Y8rLCIfRHPcqHgmTDwRDIgTVRFpiNSODQR+C+g+vGP6FF01+Qqo9zhCVh2dfmDWSIh+CFAVh4yMTBv79UMS3qoigUcHn6FxhoNO6m8ufwOlEsqVc01BzVTssBaeu268tEvFXw5pHqKHQSsvsv755JhL3zNsSK3u3u3uH068Zeuy.r3YcgGckFpZ2.JqpZSIrUs0JDxXyMJxLG4HmSXKwcDS7+m4Cvd6Q5961CrjUICb+Q9elqQIhZA+zm8YegBf+se629v69tu69uie6e6dPydTNBnICX4R4x5M8QtIKQjj4xkKElVxeZ6rJlo55CZKdEwHN.4we5mtbvV6phio8aBBBL6eFZuIl+Qa2tcPHXY9FfNvWj8jfhvhJLRUcu8JQ1w0cssqd9U2tVsaa60Vas1f1NStrcqV875pPWGmyqip5NqdAqtiHxNme0pcccusd0pUqecU2UTc7S7I9DAH8sca2VgW2K80sPYXoy5rNqUDaoHTuXud8L1Vqybv5EXExu1gmKzizso9W00OMWLh9YRXdv8DFgG01NOMZjAyEZeLA60uHraq852+eTUt1JbzKlESxbk.pnpVBnfsHIZZy3S9MO4fS4TNkvw2X4IP8wtttibN+yuO0quCgBmT3e+IEA+VPvpft1daF7fI.gObbsGx+Ggjbh3iN2h4gVETUKH1140FMR.LVDoKPafsWF5sg45e.lGDx.ja2c2Metb4xhYC+z0g7P4kg5mhp5iC3TAVdznQIRmNceaQ17F97+kMeQuvWzlhHcMT30dDzbHPeJUZGZ1LZjVM85+oB92O3+Vequ0f21a6s8fwvjvpksZRXsHgrcNLnAWPDIeIHcyxXM9ji0mwy3YL9a7M9FCgxCg5l9jw1NMMZjGisyxppK8FdCugEu1q8ZmCXtxhLecUW3BunKZ9uwW6qEGQ4CS.N2WRIQHM644wngC4HOtGWbgN7+et6cObIor5bweWc026888tqp5upmYfgAPGDAlQwnjDEMwyCAyiQMnPPwaGhWHXjXv3QMlbdBdINhPf3i5OdxObRL3ENZLdvfADU7FZDF.Qmi.CWlY5p5tpt28du66cWWVm+36q5t2aFLlKFGN0yy1MNyd5ceYUqu05c899t.fj0HhRkBUa2BP.I33GGFfRfHDQAQDGj.HPXYEUwtRDAJhHZj58rt0p401vnXG.LzxxJ9yrj111IUeFNhHZfjkU5g.dxUoHyQVVV7C+vOLUnPAMgPjF.Z6e+6O5RtjKoG.ViHpAy7p.XchntP1.buhD0uAPOfx8.pDS2uiEKfNd65IC3swrDHdhCLywEoowLGuUuRxLmzfnTd.IedOumu1266cmZ21scazK9E+hmdJtIKAj1Qt5zyCfBmzIcRy8HORmk.bW4m9S+oqbpm5otBQzhwMwAYLbbCEietFEEQIRjXSaqjexO9mfS6YbZ.3IZbX.3XRwjwzEdpo1CfgPZjlirrrBUdFfFXPTBDt+8+2N70bIWR+xVVibbbhhPDRfDfYN7Zu1qc3e3e3UzC.8KWVLv11NNFHAwjFR.HNKACWDToRkH.vkKuG9HG4tC2111V.jNwdOl4A.nCQTa.ztDPqGe3vtaOSlgt.idsuo2j+y5Y7LFc4W9kOYRcGeYXhwwNwx3KK.x+Q9HejBui2w6X7ZLG.QPZ.osAv5nXwNn9SjgIJFyoUDPq9DiecQHyudpgggOstc5dhs6zdIgPjD.wV0eR0DXi8MhoYEWR7yA3ISCFGvlm352+6+8wK6k8xF+2444gvvPDDDhjI0.Cf+ouxWA+V+V+VwLiiAgPhQHOY5Swwywwg9.v+1u8ae3K9272b.CzaOVV8OPEaeHKTLhIJprPDB.11V56NwLWhYtikkUuJUpLBIRvkEhwTjds0ViVXgEhiy5.fN.FsAbayL2hz0ag50a+Y9G9LsunW1EEW37zwV.GeDe8u2qsxrjjxAmzTxPRCirv0MKKMAwzw48L.zpwbhu025aA0D9i8nnXIqlx.nfKvB.l5.0LTxsyB.l+0+0+0K8G7G7GD6MZSyxoD.Pqa2tTgBE15PnH.ro7WwMWvzzS0mAAJhjLbKTxrD.4f8Ag350XDRDTxITBtHyb+xk26Pa6CLhHZHy7n21a6s4ecW20EB.XYYQ.fL.vArsC2ikkuKvHaa6AVRFzE..D239kdoWZBMsr3S7ItdVHDgUpTYjkkUGEs3W2.XCWCzAtiaHaDvJ9.Mj+2kKODUprEOf539yO250VOOcrL.KBjn9hPCqMgcypZyjFlutd1G567cRdxm7ICHm58ncsqcE14PGRqFPVCfLJujimx6HxAfEYlM888KkJUJCLggIEvjF3FWm1nQinzoSON9ZS421hQaN9ZJpZxDPCOOTTWeqFkdDFObqopqS50RwMzF+0.l4Qemuy2M3BuvWEaaayV6cuv9dtGlIxurPDOfhA5.9dPZ70QQQZkKWNlUyIsssIlY+CdvC16E8hdQszzDc.biYMWe.zwvvXCWW2Vq.zpwxnKVc5EsPoPfpw42dpP8ZOYWpXscqAbvoUtPAVtNyyqCjwSGD7juVufK3BBu4a9aFOnhDPWOM77RqFpZV02KnjD1BrbC5D+UrTuJfXILNkG4DuIuhexwRSwdS0lIMKc4OhuuORkJEJUpDrcbhYCWLnagrLlmQjTXtR83N9yrXqSHN1pG.5wDMTcNo1i9nOZproSmV9ukBAvnq7JuxQ2zM80BLLnvCbfCDAfn8u+8GdQWzEEjNcZeKKqP.DZ.DtuO0eavq409ZBHhBul+p+J+29a6skW8swi...H.jDQAQEeF55c5zo4IMyLq6BrAfUW.6oxu8+yb14+oc8uWzil9f63FbyCY.Xp2xa6sfG8Adzg+yei+YolRKgAn53l1i9Y7XpY.jwUlvTG.hq9puZwq5U8pVJQhDoEBQHybexf5a5YN7u61+67+M9M9MBmpgy1.nELQaTC8g7vqHrDXzbbyEacif.b7cvP760ikOBybZAIxTEUiW8ZypnJdB.LDlXCTCqAf0Q7pDVdECXhxvVMx0oyijdlYlIm5vOccfs6x7IzqWuskHQhEZ1rYBgPzmHZCaa6Usrr1.Fnmoq4n614tCPDFUtb4t111qSD0THDqCSzB0PLso8UaikoSrOcSEiatExh2x5BT3LNiyX96+9u+X.SxxLm3q9U+pQ+I+I+Ii9Q09Q808z64IAqIT8XjiYd1QiFsb5zoKB4jtWB.yPlzLvU5R1P1rRbCso.PhW0q5Uk3y849bD.nG6QezDm3N2ozDNUE5McBS0dUOtHP0FfX7qqHKKqHaGaY6Ewe1QfYPgzXlkP9P5n5w+6FRD0SHDcejG9g6jMe9AVVV9.H5c+te2zkcYWV78o9DQ8EmoXvg+gGdTxjIiDBArss4DHQjnrxaALQRm61IS+g8yjKWNsCbeGH779ucd8zzzZAfVrbSS0CPuKfW2WzK5E08Nti6nM.ZUFnSE0JKFG+eH7VKrS9msK.bnozjp.ZvYSMapZ9zPCvUiYV6u3u3uHw66889nK7U7Jvm8K7EhKwZ56WlQc3cAo1OQAhn4YlW9FtgaXkK8RuzkwjXqXcWOcisS+bd70zzH1opCno+qO1ZwQoOV.l3HKgUfiiSrDEG.f9Vmk0.6601mjZuFrTqt9S8yDXYYwe0u5WEaaaaiMMMGs7xKO..8u0a8VGrzRKM347bdNwRZfqToBsvBKfYlYF1wwIRblBX5YpUkqpUsZUsnnnDVVVQQQQgZZZAv.C4Zb2w.2pi1va7zeFD+7DXo9.M+4Qxl+W40z.lDapZwleozrzzQJ1kiN7gOb+S3DNgVPN8v3sP1zqnO4i0tQBbvw43hGHPY.8SILr5ojHQhc.fEa2ts1tlaN1SBpWV0u23FGdBMOfmD.SlhJw+LupVsJRpkDE0KhpUqhLYxfgCGN4GfAXvbPPPz1291ibbbFK4f8XsGTiqQiy+wXDHLzxxZfsicWvnKjrIoi0drFZe.6wMxJAxioG5gdX5bO2yMxwwYfPH5Bft5RO2JFn3z.HiRRhIXliTxwsG.53551xzzLl0JsG+kA5pZr8IqnuiGhy9250j7bkPpEqtXp0vZw9HwzLlLMJhTn933BV+jzi7dDuX.sBgLEBQjHIj0PLGjMppyJuU.SjHwRPFCtUcterh+F+eOcdswRhvo5DZkvQx4IvHJgVhvnnnHKKK1w1F7jGaljdMPLPOx7a6wpuyAb5xfiMFwt.nukkk7bKCDU4dpjXvfAZ4xkKwW7K8E4W1K8kIYnBw9IPhPgPDBXv.tQLyQDIAqwDf9ak.ly.xo+pZdURgcftnH5tR8U52.MF75e8u9g23MdiiXoLnGKUbb7+YmOYWSCZxzdlT7YmogAxY3ZTnFWaFHm5eVkTsHhnHe1eXJJUbdcMXhBnlTN0pGKlHJwG9C+gy8Nemuykvj3twLp.GalLQXKrxb5qo2dWa8rSFXxxOW8mvRnfgvRD433Dc3ibjvSX6aWJOGhXNhCOZkiNXaaaacEBQahnVNNNcDmknutq9vCXe.eRIKLhona8e9VCeiuw23nu3W5K1+reVmcO.ziHZj0drX6CXSVVVofLeVtO0m5Fy7ddOWeJ.Wx111+Fuwab36487d7MHJ3tOxQF0sa2gO8m9SuCy7FPNfq0fLOWWXB+x0JGVAUdx1RSOUIlK9Si352RscfLGYxYty.InIYAfVPPPPpToF8o96+TCtxK9JGVG0iuGKIj0+mmHZFniYXWdFhn410t10BO7C+vKpFl0hJvSTwslYYtVld85kIe97oWs4pZKuzxIvTwXNNNrhMviMle.LAD3sLTqoyqoFRPn0YYEU4dskLVRJAGUNMRwdIYtEKKq9F.CuWGmQJICFB.XaaG+9SDQTfXOhQN2syHl3frYyFMb3vP0.Sk43MPn8Ar4d85QLyI98ure+De5a7SmvvvHwW5K+k3e2W9ua.QTel4MHhZBLtWwVF.smHIrmvfSepRb0uvt92K0A2p94jgKFPiYNyG+5934+m+F+yy.gPlzq53h79WiNwraLpt.CgAFlNc5fJUpfRkJkhYNqIQ4XWNWMTK2YcVmUNhnwf07g+ne37l.4PMEsbKURlruIzdrG6wl1XtdplIWRRqHrHI.RPqPZUQ0TF.o95e8udJH0WbRnCsO3eweAPMUi3ae6aMI53oRyLGhRtgyLyLLFKSE8btLm6RtvKNW974yjMa1TBgHoSUmTNNNYDBwr111K.fkqgZ5IhfAQjosssgkkUQwYJV1DXATCy433jCRVNjPAV0TT87IBVxh.oALx5pP789u+6OGKmneFXfjWvEbgz4cdu1HuezOJDIPfG7BzmxuF.PDoSXaYxDO0HMxzTdPuKRd0W8UO9y9pUqtIVkDCVB.nSbmRJpWpTIXVxD..0pUSYrSx8pdLERbrcPbxTgPPx+LaRTRAbgxrv.i.R40H.XzC+vOjb55DFadXLycrss6dRm7IOvxxx2wwIz11N7xtrKaDHzyxZOaPL07LEhUQBzHYxjM.vpDQMIhZJJKVClXUGGmFNGvwSTV3lMa15QQQMOqy3rZ655NjYNQPPPt3F8gtWQl4h2wcbGqTBXQ.iYqLAHoetMWseIcsU1jj.kgVYfD3Pp4IUpj78emwwbwduTZl4znna71RBW2668EwLG7Y+BegISfUGj5mM+G5CsuYIxH149miHZ13oeboW5kpzw8lmTwVdNNNeyC7.Ov3WDxCccTufHk5G.bqUClkLAiIFA6zpqVXIXhI1woJC.VwFo.gP36beNirrr5yL2lYdM.TmHx0xxpF.7rrrVE.MO8S+zW8zNsSqd+98qYBT011t54cdmWMKKKO.3AcTC.Nk2a4JyLyIczJUpbzyRHp.B1Gv4.UMk56uQ4xkWG.8+p21WMx.HEWiKr1ZqsHLMWgYdE3IKBVUvbrobpg4a9jxpqeIeIiolWwxjRHsTFVF4LkFpYV.jNJJZyfWr6mviiLu2A2DyFiabcHz85lHQhV.XcWW2lyM2I2zk40TEI2ES4CGDY9ycQKOYfkLMkhcTEDJAKQF+sjbc+L92.qhFSlLIbbpFQLw+fu+2OBDhtmJ2C633DKih.PvGD5aaa2AL1.xUko6a6s8VpBWXaYYYSD4XaaWyxxpNSIZbtW341v11ttPHpaaaWGPeUu3lBT9Ulss8fUVYk9BwY08V9xekNkj9Wxvy4bNGeCCi3b+xOyJhDEka+M4mGkKOsQ49TYpqO99ic.ngQPaMrVRNdaKE6CWlpMvTcj..7nQi7+jexquu2i30ARvjhioBIhHXTMlAUEXlmae6aeyAfYIhhYRUr7ll1.DOVFb536cmVdDSaN0RyqFRS1DPMOA4+pnnHh.fiiCEyJSRlNTsAFA85e8WJ.Hl.X660NJhiBAigVVVcIh1vxxZUXf51110rOfcsxkKWMa97UYlq8xdouLW.T2xxp49uw8ugPH5.fd11GnC.ZQDslIjmcVCX0S6zNMYyo5X3e9e9eNXozKmCvXIOOuEgFluAZTnHPla7Fuw3yPRfsiDkN99LyedtlN+h7y0hHAv10.zSYBjAIPFW3lQPTRRtnG7Ih5SFTmu5W8q1NEkRxxaSSY9qZfg78nLjbywLG.V7c9QemKwLuDy7h6xzbVHkSVLvbOAodE+j5XAVhb61.TpjP92S.ZISBcESRn3WMiysMI9ywQJ6qSXG6.BKKFLhXliPBDtssss.q8XMzwwomSEmNmkPz9w+gO9F2q88t9drJ2DxyScYhcdi+Odi1ezOxGoxy4Y+bpTtb4Jk2ydpXYYUAt3nVVVGE.UpTohskkU0W6q804Bc25UpTYUKKqMdCug2POhLBbYNgttd1m1S6oMCzw7DQK.f4kKZgh40Ax18Q5lpBpj7HG4HGqsN2SEtdB0tUFHIvhoNhZKecjibjLJ4yjhJQZDI3FMZDX.L308G855VG06XNIm1..LhLoHc..OnQDk9HG4H4e3G9gKnXA0r.Xlvvv3gPji4ZY.Pp74yqAfDKuzxwC..kJUhO7QN7XvRh+yXUAZwLla7emXxFWpPgBSxhA.660lIIXG9fQelY4f8gTppVVVsjLx0nyArs6yL66XaGo5AXHHzwxZOqCfUOKgn9Me82bclXO.Te4kW1Cxba0gA7rss8rOfsmkkUi74yuZgBE13l1+M0gHZPpToBeNO6miVPPPNl4YoRzhLyq.fh5pMClqzBDjC7y3Iz29Skhw9Ex0+QdCHdBroPYjCUvrrzHDmExlLB20t1U2G4QdjM.LZA31EOwI9r0GuXlTDSa4R.XaO1i8XV4mYlELJVLM.BTn9OB5vGdHzwohOhRzmItkkk0Z.XMcf1JyeKDvLBnlTOhFH.taZhlGuyzjoQ5OMjq7zrDQEPIT.Uwrv.yAWjQMI41DQqVBnQUorbhm5o7yqkQZr53ImF+0bPhtuEy7IPDcB6e+627RtjKYFGGGXYYMphsc+DDOPHJOz1112xxJ7Vu0aM7Y9Leli.g1VBqUAP8G7AevUO0S8TaZBzLXkUVuQiFs.VtGvpGKYdLtA13mKwToCpI0ypMgCQTXkJUBJWtbrwa0GqfQnAhPIngpX5IkYXBnWCXEVZvSynZvcNLgRdwL.XbQz1Sze8lYAvXSCyA.zlMDQVoued7luQRyb.1qdctXwhQJJsG.Fir1q0H3hg1119PV35nG7AevAm5odpcDBQeaa6QLyA6cu6M5.G3.A.Xfkk0fCcnC0Ke9r8DhxwL2YDL.CWow0YYYAaa6XTlgNPRuwdogQxG5g91zobJmBMXv.JHHflYlY.jFgWeHO3o48e+2e8y7LOyUgLtIlYRGKSF63gqIrbaWHANDzTLAhA.uK.9Piu2YwT.qI8rDoAHFKcPfmbVOkd0UWM2xKu77PRa3EU.MMCjd8SAXfY4Z7BjAsL6xKgIq.wmvp2DPAvlk5.XFnZsMuBgYH8Rhoarc7VyA.T7+qJnilnwZehSLjItukkUOGGmtHJpuPZJy8.L553bu8DBwHG4VXAO7C+vb974CTzYuqkkUL6O7cbbFEEEMpb4xCgJFPEaw..NNNIDBQRniTvCYfAxAW45sqYyl4SkJU5YlYF5RuzKMnWudctoa5qs9a5M8xZ9I+jex0fAZCW0jZUqMucu6cO5fG7fGOHKmoKhKNmTAl4ETe1W.xO60.vnO9M7wa+Vtz2xZPflvAsf79xXFlL87LiKpMIlGYvFXFHmd+1FLXvNZznQIKKq4fz+bzfLO3BPlKaALwT5NVd+xO2mgu1ZqgEWbwiIU1mc1YQK45yD.3XPWcDRDGJDkCsssYFfZznNJtRQFD7sDVCrss6XYY0xwwYsyRHV0UBZxZJCYUxxQSngZR5nqdtGtwFaLb26d2CAfussM52uexcsqck4Zu1qMY1rYS7leyuYFxXvfXfAqVspeoynz.3gAvDC3p7.kLCaCf1BfdNiyeUhAptU439ToIlMIW2zlTtERCaI3upAGkkkxMDu6286dzG3C7ATxdSeDfmR5pPSUqPFHOWLtnYchHcVZf0KmJUpXFZN+e292e9Wyq80dr7bnmTFkXXX.WWW4eo5NgstNzYl4QiFwoSmdqSFeZD+hAXTsEur5+XO1i0MUlLsHvqYIrVywoRSgnbqJUpzob4x8AfuNPj23mql.nFCozHRHDhD27Meyz4bNmSnpY+XSvlAfliiShnnnjkKWNE.R+Nd6u8zu+OzGJwQNxiGcJmxSa.jwXafRXcTEswJnKZLl8bGKYF9Tk3r3qwCxBXZIfi3XrbDQYgIRgZfPIDhpikEZ.jRAfcAR9betO2720c8XKvb0kfLWVAEnH4fjYzKhoxyMECNiy2kD.Z852mxmK2SHW2zrWZ53uovFYRl3sx5DdSRuIB.gequ02he9O+mu5HV3C4YTavL2rb4xMAv5111sAPWq8Z0E0fby44hX4XMBRo3LP0+wHkzYj81XhrnFx433jikxmSSHDwmOj111NapToRoqqm.RFN0oUqVqO+7yG6EJaf35+LQ.pAeX.eUOMOUHlaqfrNkwUiIdKmZ.Tpbazm+y+4CdkuxWYLSujKrfhHB0gFJhbnNlS0+4hLyyWhnkpx7xPsR5gLWlz29Tqo5G+we7TmvIbBwfAer7dnwa2FUkWD.QYylECFNjl9rxMydNl+61+94Wyq80JO2joPlFapq8rrr5633LjkFgtOjriqOjm+EA.njT8HEyy6CfAJ4DFKk4gPGAvCQ+UW+Gk+Cu7+nnJUpDUtb4..DoCvdieeUO8+m+O2Y1m9S+oWv11Netb4xL+7ym3M+ley92vMbCcGMZTmToRsdPPvZoRkR5sUx3rX.12Ze.GuFa8K7q+iBXRbCu4fZ+qqJzq.QDUsZ09kJURZvXyiVXiwET9jUXb7MPY.JNKP8h.Xal.aW5aYXNUAkApC5hYXvHaa6dVVVs.vp.EWEndbSeibccCLNSi.UR8ooK4VY8vwiABiARpHP55pIBoCLmm7l+Ef7fmjarwFiVXgEVCnnGP8FPNstoksxXZNiXvRJh7ntwB.tFLyaiLocFZGriDZZ5NNN4a2tM+7eZOs.W.eGa6fHlCIhB9y9e7d7+e9Ae+9DQCXoo9spkkUC.zvwwooPHZfwELiNXWXHNzlZlPBBTQjF0MxC3V..y.CjGtn.fQNe+Jo+te2uK8JeAufPOf.Sfvu9AOX3t28t8efG3AF8LdFOiQpIbjTUv3bLyKSjoNy0L.PQHWqVynZzMFo4XZtmLJJJQrSXO062iADYqE4EeMSgBnS2tX94mmWe80QwhE450qGEFFx.faznQzC8POTzEdgWXjsssDLEl8gZkfYYY4+7ddOufa9+0MOBL02xRHABw.ibNfc.K8pj9LysHhZIDh15.8dWezOZ+K5htnAmQoRi7ja4jDVVVwI7Uwz5QxEhCzpToRxxkKmTsoNRCfrCFLHaxjISqooA.LhHyt.tMgjUA0gGVCX6c.NxwZSSL88H+x59ki0AtSA.xth.NzzxpIMVA4PC4VNARC+hhOvhHg+Xs.uBRv04jDULKPiBPZtjKAfkZ1r4BKu7xyAo7blU0D87PdO37W9ke4Et9q+5OVLLYSWiFMBqt5p..nTIS33TCFF5nQiFX1YmEarwFvvv.arwFHSlLXiM1P8yVhA.ZznA7G4yLXNe97g862OfYVYdXzfG+wdrt+p+p+p8rssayL2tb4xsfT+8wzEmcbbBhhh5WdOk6.O8V.d8.vvJUp3WtbYe.8Q1126nnnH+m811VPMoePwJqMMgIf1O7nGM41111RCf7W3Edgyb0W8UOukk0b+3e7OdlS6zNsrJYTLD.snRzpnlrfOVJILolc2N5iiTZHP0iGj.1TwTFoAbyCfYVAXgFx3fBjbC4.ShFrym6ys8ccW20ZPVfwVkjyzkmGGGFq++7PNP.yO7U+gK8NeGeTi8su2wr+w+w+wRiC1Eyvxh8LHhVQAz6lYmywXphGKppq.QbS+Lw.y0qWOr1ZMAkPCyO2bHa1rnd85nXwhv0yCIHhylMKxlIaTM2ZgYRmdr+kPLQgbHRjHQDIM70A.nSXX3Fae6O60.bkrTBnw8du26Fm0+syZPkCTI..T4xkieelcbpDJD6I.vS9YuNzbtOmTrzPMIl4HWWW+8t285aBDt6W3KD25sdqXGYxvG02OLUpTLjKlf.HWUwsADsAb5hIE5EhkPHZ9DnuNvwmm4O80XP2T9gSJHMdzzzDSxLCjMxpFtfY.fa7p2UVvqAhfqbyQnXORd.LGzwxvS2fYWC.nSFzxvSZZyXxvEldiesIl5zrYyILSBS1nbqs95XP+9HUpTfYFAgA.pcQR974Q1rYw5quNGFF.FDaIDb850QPP.z00Q2t8n1sawLyQc5zInVsZ9mxobJC61sa+BEJz80eAu5V2126azTIS33B7UlkrQe.2g.HzwwITHDQPGL7FOPtjxsiCGV9YUdDpYLBvMNuHIafUOoiy8kF.41iPT3u5y84x8JdEuhTZZZQ850qe974aojIw5S98J2bS.FiT.D9To3r3qsBNWlU.x1.HmNPAOkTYzAR4ov7hHZ5Xs..SFnVREnbyRDsj5LzkqWu9bElc1YxmM6r.X921UbYKccWyGa5gMjA.Y9oO3Cl4ocpm5OSiAe55xLMMQsZRprjKaNL2byhUWcUjHQBEXvsPfuOhG6vxKsD788Q1rYYWOOl.h50qWT5zoYgP.Ou5QPtYb5Sf1PXIV011dU.zzxxZsC8POzZ4JTXC.zNtA3nnHeYCq59.dS6mMAPtlgn27a9Mm7S7I9DiM11pUqnUpzdRB3IkqoIJL3wGj+H11o+Z+S+S7a8xu7AF.spw7ZkHpYMYbdGAvHmIO9S98b7MnIalQvx5+0P8smB3Hw4vxJyOYjFvME.nQiFEkNc5gl.8qo7GQ01phfLdYFHOedAhnk.Lmm4pKQDs7W6q80V9E8hdQw.lL+vgCmMSlLwC05XN7AGGGv.rkPDEEEwIRjfULbJ94NQfHP.EKVD999na2tv22W9FdLZaiFEQDEkJUpPHu2nGjRuI9dkHaG6HlgeY0JoF5Hx4.UBYIaz6PDsAy7FW0UcUs+G93e7dug286dvkcYW1f8ZYMrFfekJU3xkKCro5+MCUDD.Ht9WcjGdS5+4xu7+vLefOvUQyLyLADYLfY2NDQqASrJWkaPTw0AZzF.CL.F4d78vS+urq+S.vjckbwEOT10VSFzVBXwpRfSxPR2xeCEaGZZ.z0cRC7OY.lDircd.rLfoEPsSLHHX6ttd5VVhB111INqy5WO79tuucvcdmeqQ+5+5+Z8srr5.f0uyu9WuwgqTY0K4RtDYi5.8s.FYCLZ6.9GQ0.XIH2JOXyqcX7j775WlWGqh3mGxBtWhYdQSSpPsZL9g+veXme6y9rq6BTCRvJ5fXebQQ6MkSHmU4UH4YoGgDylmSfYdm.X6NNNqHDhrNNUYc8hgdddgpV.TS0VxxCKq810w4.aDEE0jHZ0dc5rZ9YlYUDSSLfFKAzZKqW5weNuHPl0jMfN2AO3Am6zNsSKFPiz+M+M+Mza7M9dB.poPp2H75u92K+xe4u7PKKK0ADFIJUxMsiCWfHZASfkpxbQHWUc5XJekvjnB0jMLu0B.AvDMJFCRxRKuD52a.xjIMVe80wJqTDMZTG4ymGiFMBAAxg1EONBBfEBA633DsxJqDkNc5nFMZvoSkJpSmt7i+3OVP1b47Ih7CCC8+betOm+UbEWwHKKIkOihh5SDMpWudCymOuB.PiF.tpIKX1U8dg58CDBTJggQ0TW609YR97ddOOnooEUtb4.GGmHEcBSToREsxkKmRGHmGvLNNNyztc6Bu9W+qO8i789dbMIisVGRGzJFjKUCfiW4vSCr3urYk0T2SrqDkJcHs3gJMk4NCnhwJAjoJPtR.ETqL3jl.bM.eS.+ZSJ3.Xy9xzLLyyIODFKwLujp3uXyCaQ.Lmuu+rqs1ZEz002pQBON+ZbiECGNDoSmF0pI2BSKu7xXvf9na2da5EnjESROMY7jvlPyDHDBD6eD555gISlLnc619yN6rCWas05mJUpdtttcVa80Z8RdVO61t.cL.F5JYPhO.5JDh00AZ5ArliiSWhnAkJUJtPO0WBe.G08ZkX4xkbZssalrToZ4pVEy.f4cbblWHDyedm24M69129xeZm1oAee+doSmtoIP8pLuNQTaVZrcwdVQrdY+ksYINcyBoAP9q4ZtlBWwUbEK.f4129tlBL6m9c9Nemrhkis.L2XkUpsdiFnM17Ya7TOdD.zJAjrJPpq8Zu1rW0a+sOWCfkL.J5JY22bPs4kT.woC.AjiGeQLEHu3eirKgYFiFMBN11X3nQXW6ZWXznQXiM1PwjDaN9ghHhlBzkXOZRcOOEA4Fj.ZZZDyLRjHAOZzvfnHdTud85M2ry19q+M9Fqctm64V+du+6u1u84e90rssaXYY0RUvafssczU+W9WF9QutqKpDPXUIvu..Ity67NS968686E+ZLBvzGnlrXRYiXSCPpFybpRknT0pATqVM+mooYWuIT0dD.7enG5gBNkS4T72Nvnin9yvwWFM7OqqohIKmDnR7z9U.kTJcwhUyTutb6zY.D5pljo5qgKBDrF.I.zbT41TM+tvu8u0uUw+gu7W1HYxjlr7byomF6BXhwHlB.ICBBzRlL433uXP5BBBPiFM.GEgRBwXv6bp5f4madTnPALXv.r1ZqsosVhbBtLvXCeUdIDBX6XCsDZQQQQQDQALiQoRkbnuueO.p8O3G78W+k+xeKqYZ5tVsZXCaa6MHl2PTtbqJUpzVw1jgUO5QGUZaaKRsRb0d6u82t1e+0dsnNPXb9+pUqFBLVtGSkCvLOPsY.vrWzq5Rl4l9r6OCjzquOy75UqVcsy3LDa34IAAdYf9qNgoIOUDzjoYzbF.yrLWMOMw.Mm4FtgaHykdoWpFKWEpc90909UZ+c9N+f1XxZWlvjyPmUGXIWlWwjnhtw0rRzR0l56W1kc4y7w9XW+zx.KkSUGMQIwwLWWsZ0.yLjC8gwhKsLZswFX4kWdS.GGal0InDvvzX7+9XS6OQhDHJJjwTayPUcSQQQQA0p4N3PG5gauicri0OgS34rps8AZDWWK.VEPecccu1ddxZxhYAmpQ1H.DyL5.CfHEmqHkIfqYBnUCHkiiSJ.j6U7JdEybW20cU35u1qOW+f9ZW463JCUwZcHxnEfWrjEUfyaEnqa664IY2xt.7OzjyQAN9IlaZ.GzpGUxrI...B.IQTPTIBjptzyBS4tEFLA4+eB.XznQAoSmVwNCyA.0F6sZP5Cc4KVzc950k8Coh0VP0SyhPxN3k9J2xWYgy+kb9yBI3Jw0+uUeYZ5qHojSoPc8hPIKUszoSkHUpTIzzzvfACHeeIwIiOre75qlYVHDrsiMWbkhgMp2XT1rY5MX3vtVVV8N5QNxnK7htH9l96+6o1cZGt3hKEuEvBd7G+wGchm3I1SHDqqp+uA.VGPuiZvVwrYaDfNWrnWh50kxViYlsrrBO5QOZ31111h..YaamxxxJmNvLd.y9HOxiLy4bRmT9u1O9Gmdm6bmZ4ymmUOdsXlaRDo98g1.E6ATW86Z2A.G7XMTqiWhw9E90+dALYSA+P5+E4A7lWNwBikYtVA.DQjnEP05Pf5RZKu69.Gb5anOVO1wli27PVz3IEDDrSOOucTpTokcbbxVsZUHLM8EkKOrRkJsa0p056d26tIzQi8uuOciW6q8Uq9.2rKPs9XEL.MvPfkB.ZFce228wm4YdlakN9GOiJqF.RqB5WBlnHWkKpnfVVHYjvZnDphpEqATuIjELIaDbWPCGBY.LxB3FKEmX.SVARWw+DU.lrMHS1jQIImHGmJQPtQMXH8eit6QX04y9M9FsO4S8TWur3rVGvsIzQS3Y1zw4.dBgvCvrAPsMvtP+oXXBvlog2rP0.ppA0YTrFIDxjCwSuIPGfuOGmPwdEgEqhn5Jv0TSgMVdDqPDYvRVIsDjwQwIJmlZ6a5vXWWWXXXfNc5frYyhjIS9DjiyzWwS7eJSVLl1uioxtB.E.BrnjHRMwq3oAD333L51u8aez49BO29+Yuu+rAepO0mZH.5aaa2RwXGGHA.q4kbIWRmO3G7C1OQhDCKc5k70afPO4SpTG8n0RtssYRxIIVJ3nG8eIXaaaaLfNC3QnDRYe214rr16rUpb2yVtb4YUTeMd66zCSLWr0m587XJFG.fvcADN+d2az8bO2ySFqS9E80Vm3pRikFDfKicfHb3c.fCqxMUNEPkrRlTgb.kRCTE7TdJClTTabgdYwDWTON1bYl4kMHR2UN8+kf7dD4VB3Iwf5heR2qWOLb3.rvBKNdh+GKFAro3LVZrv.fcbrQbQcJvRHfw.mDIjqky.njTStb4F1ue+dDScWt3xcqXWo626t9tCO2m+KbDQTWKKq0Afq5qX1nEucqhapb5IWEA.7RdIuD5VtkaIAJhDqTGTiXi.D54+Nemu3L+p+t+pyiZFK.3t3a8M+Vm+ZutqKS5zI8+J25WYiy+7N+UAJsFP03XqdS88AS86KFTN06B+W10VALI1erlERV7kUsQkXTBCuyO6c184+7e9JSG0nKf6zxNL9RNMMorwRBrXRf0RqdbmGJJCyRoCF6IWK.4Fhq7Ue0Ws063c7NVFx3r3U19Spl0mNlZslqgEWZQDDDnxkMkjBcpB0H+ACf1sawyM6bPHDi0wcbUfxbX1QP4gDr7cJRTRD+SEAf.ee+AG3dOPu4ma9VdddM10t1UM.XaYYU8Yt2mY8a8KeqsrrdV8ApNcNkfI+lFyBmDpFNhmdZrgwSG8nGMollVJwYJRa3gLti2jBFoA7XXhgnlYWfZpXpUFAzXDJhgnN5Cr8dJlyc7.il9W6Z55rRBfTJveyfhHqYcjo13yxJlDndDy7PhJ1Eng5drh9.0iPQj.0kzyFvK1f9WDRVXZvLaRDshss8xBgXI.DKAwBCFLHShDIRmNc53Oeh2XInUqVzxKu7SfES..fk9oSTTzljjyzaxj3yPw3yNswz4+DVBF.QUqUMji3PBvujPLvwwoGAzY4UVokqqa6UWcsV99CW+reIm85vynYkJ2ypkKWdCfhsAp2ElXzm7+40G9ldSe.xw4dvT4KmlwwSeEyd5rerO1GqvkcYW17.XwQiFMWpToxPkH1rF5pl3eK.y1.05.A5BGQO.G0Tj2kOvgll0Y3X7653kqsFuE2DabNv43XIoZPvzC8qIqUXcfU1.nQerLBwpKQ.MU43LlEvcQniUfGL.LMXtZwK6xt7U9XerqeZv4hyuM8YnRC3zoJEK2lVarAxlKGBBBl.36zq.cr43OfwmgBGGaHDV..v11I1hIXFfWes03uzW5Kwum2y6YbtQCCiHWW2QBgnW6NsamfRr1G78+9a79+fev5vDtl0P8ZikISwN.06KkIgrlqJUp.MMsvRkJMBlXzQ9WNxnsu8mcfZc0GmyaZYoGy7qBNNN4DBQ5fffDISljAf+O3G7CF7a+bdN88.5gRXndU36MlgIhg.N8QQL.028HUisGOAHr505t0.NXLq3Fal4m9dO8LOv87.YYlSefCb.s2+6+8G9E+hewwF8r56iD.ANxyeSrTUjoobnVKPj4JNN26xhyTrD6xwl757.X9y4rOm4+dG46MKWaSqE83e2aZ.D99iXe+.122m888CIhhVYkUX0hbHteWB.T7lzgmHEb..L84mlkL4Z0pEBBAhRhANNU6KDkhA8A999TiFMvsc62V3y64977+Jekaw+Jth+n9NNNsEBQSr4ZzlHGrmkUe8pXjxfzS7XOVEsS7DKSXE.zPOzw49Bj43DLfiFJgr12sc950aVnXwkxaYYkG.Y+G+e+Ol6y+Y+7o+ze5OcDjLzbcLwDX6vL2UwfrA.veW.gG5Xy37imOG8+zt9OC.Sh04XVn7PBl4ULHZ9eTsZzW6q8059pe0u55nH7D0wZNRzQiY8vOKVljF5XN3oanCuc98ezG8TNwS7DOYHareNGGGMkl76A4GtqBfFLy0KWtbSCfVt.scbb5JDhtnD5YVE8qoZPpTIDUs5S0XXRL8yLVQI4DcUhgj.nKQTcHav1cIfMZJCxkMBZfzBWj0QBtPdgfJTsp7fPSfhUYda.3DAvNAfvw1YAgkHkiSUPf4RxF+4G9ge3vSdWm7.PRyJx11dc.rtkk05111qqZDqoAPCWIkrqCf0wNPOb3IEGuicfjGVthjyUDX15vbQfZwSuelez88izNiy6LF8u7k+W57RN6ytk2lLAQi..2XYFsU.WVRQiccHozdQE8hGSs3vvvTZxQSjv00k788gttA.XjNc5wF75z2cXZZNtnPGaGlI.qXvP1BZqBg.UcbT12oj4IkDBDEEwtUciXhiDhwfm3W0wwmAFjKWtgO5i9nCJLagd+ieg+wMdMutWi20dMWWk8uu+xJdx2KU5Jr3Pf5wMmEGaDyTlP.DpCD3Mcgf5HooGxTSABftNl0yCyvLmyfnjtxMEfDbJczdeW495bk66J6Auh8En9PmM0Dc4.Hcn8sZhua88heQb8DjiCKWQuzT+tok.zZNYxEYgAxUxEYpJeeJvzDCqUSFOcAWvED9deuuW5LNiyPVjXITfc3YnRzLnlwL5vcAOYdshJf3VA.K466uPpTohoQ7zFj3XiXaZIQvQQnVM2s17.633.lAnoyFOI9aL3apuhAIg1TysxodFpjYzXoJBkdWEBw..LpWudCVe80aem24Wuwu2u2qoJ.rMAbqAil.tRPSLPe3hgvD9n179KiMBWcSM0VNAPkoMPy3s0QdnnGqAvxt.KNXvfBarwFjwyznmtKV2SECqqi9G3.UFTtb49R+lnTbizAX2HDG7IsY1eQFesY.SDHKbrxIfcAmMwJMcFvK389deu8upq5ph2dF8vjF64od7j2aVBZKWEZqp.EvxBYssGed4RDYLqpQ1bpbXkXYN4xPx.koAL4ma1kTsZUTrXQPDAMMswZsNNtS8bkii+FybIFzVh+hAqiUMTPSwxIlHJrToR9wa7lVsZsdmNcbu3K9hs+leyuoMzQUSOznlxTWWYEzuQCYQvxIupS.dwuWQnJBk9MlHXE3DzP9xQ0bwJoAZLVVI2xsbKye9m+4W.RyOOhYdPIhF7Y+ley9ufWvKPp26Rn2JUQmFpMcxt1EFbnCcb01Y5XcQ.Hwt.RdnMIkVQd.mwLkjk9VR3AO3AGt6cu6XOboiyDIhAXfjy6hTanjDltNl86+8ezE24N2YQ0Yk5.XYhnkgAV9K8I9RK9R+cdowLLICQTrLoR1ndCMsTIoEWXAZJVYFGOAgPPa0qRDBAbrUfgPahgbwweLQp3LB73rMLwp5OTS7EgBgHvQtIIF533DC3Z2hEK157O+yesa+1u8UM.7TdnibZokPaipXfarGaXf.WouSDJi+L.fqLhuHHTGIjaWshoMP8rpM23RPd+5Bp2ShTLkSslqQ2q4Ztl1WwUbEsMAZWaBS.dx7.fi2h6lN+mxukJlkYu7jzaBiANNC.htpq5p57m9m9mtF17VBKlMyYpDK6KfEgNJZ5AypLaPDoaZhhUqJk5JlLPqwCcvw1IgvRL9LlMKi0IfjbrjK8XpXxi8nDlAfkP.Gamo.AVk6BHhkrmSFWVcb9O4JsFXTlrY6szRK0V8ZcUGGmXllrpggwptttqCYtsA.HrHTK71XC9dEzuTCLrp5rYU+GL.Hc.MOXkBvVVqRIjEUQVXfLv0LCyUSSDEuw8B.LGxb0gDQi9I+jeh+t28tGJDhAUqVc7ZOFG+AH7z.hmckUPt50GuBfSQjYFlqklHJk7VQLRduUwtVnQO6oj6ppduXutbN04kEgzyBWA.KYPzhe+G8QW3DOwSLNtMOj9OVF0.ly.fzNNNoDBglssCYYInnnHtVsZ.LhPBDAlBIvnjj0QRCH11gXBj0lygM9ZK41.ADURHBcrsCfbSZEHDhvopgiAnPHk.1nO399fCzB0Z+xufWdyEleAuW7YbF0bm.ZRSfwRnV4Qmapd.F.Ql.A0l74eBniTp5+yBfLFFHqq63s+3LPdeWHQTWXhMN7O3vs1wd2QWzPzqDb5WEi25rA.kh.pFWS5z0ld7Rr1uvt926VxYSWWvEbAr.hXvG7Ih78LMCLLLvEewWrjIA0QJm+UlNl5ZRCBdk8A7F3Azam6bmwz1FPM8uG6wd77W7EewwnDRVVVb4xkgsscBWc8j111YDBgDIwpHUsIEaxoqtii0GxGu8gMsouVV8cC2omnSH.FcO+K2yzSrcTSCi3aVj.P4BMGER1DQYbb33oGTnlTmoyB4gaEBBBxAJFAVNCCjw1wIC.ReJ65TRRInDOzC8PTrIi9Y9beFeGGmPKKqnO8m9SG+9+zSiRCGdxVoA.I6b3kiQUOScfL.0RCYAuoHxH4C7StOsJ2cEsy9rOaxC.UpTgQQ0dKunqhxqE8gbi+.cnK2LNROtgfjc4Dy7VMurjwfkr95qiEVXAnoogzoSMFrDcc8sxPXPDAmpUQylMYgkfs1RAbDn.ZBqQBfjcLQBgHhkrNIpVsZbIqRrPHfqqKwLzbbbRwpF662uel8t28l6ocJOsBurWwuyL851at8su+x47hS3WTwJlhpihwJLTMFWDEmdWt2wSWu6T++6BOzVMMrl.nA7z8fb6orlqDE4PHO.YF1kW3JuxqbQ3YtHP84skITKfkTLzYwJJvA10wZqS8ycyb+G3ZSnZSDgRnzzSrIYyRkRi3sgCPFtFm1gYMlYdu6cugKOyoMFLga9luY+y3LNi3IMB1gI.jfqxZLWKoKyoTEIlA.YdfexCjF.oRkJ0z4x1Twvc5zAc5zgI05ntZ0pnlq6X2T2slKhh3wrQJtYAgPHoDLMd3+w4UCVdkkC.OVRMi77p6SD4+E9G9B9BgHrToRQBgH5N+leKPDow.ouo+9aJGjxvZdGGmEVe80WD.KcQWzqd4e7O9mT7zO8SWuFPQIFqXN.jCtBIv.0LAvFQqFyBOC0AikqndIKX0ysQ5Pe.jwZsAvFtEK1rc61MWc0UWWWWuCWi8cka7hT.HC4YDuFTyu7oe54ApFazaYwAKFC9zuLhuHfcK+c3Xl.vlb.38su8EfhRYHxrae.L3ptpOwXlWUFk+YA9uFl.Vh70hs03en8su8gq9p+SFmajHSM.n8Q9HezXP.+YxjqpUqhff.YWmLipNUGuIb.CTudc344MdSkDeIDhwwW4xkMN2UH.EBBg.T78DgKt3hALfeXXnuPTxWHD9sa2NPHDgBgHFntwRgqSmNoEBQga4VtkEN2y4bVDdXwZSLbuTnwJ3htnKJ.RYh0GvKF3o1n5xRV63JZA3zRAxQGLuJWlUCkbSJN..CeIujWhussM+fO3ClD.4Was0l6u61u84eAufWvB29se6K.f4YGdt621dbSYsOjw+lk1z+EeMdnTGZB.dx0sogy7xg.XrD.VhHZl0We8L6d26FPUClyRKsYYG4BdC.vx6A0z7Jkbm6bmi2vdj7BCFLf4ZL+R+cdoSyf3DLyIZ0tMU0oJVo3JXwEVf..LLL3s.9lbfBLyQLyJFjHyyQDKrDQYxjIxwwIh4IdHGQxywDBwPQIwP.dHXLDfGxxMlzn4leN+O4G+SFpxYpAfLBgXl25k+VWPHDqjJUJya61tMqFMZX8ttlqoLjrSVG.yipHqDpZLB.cS3VJFjiNx0noarDlFf5aeH.F.mR8Ap2wM9bScoYHRFTe.f23a7MkkHZd.rhILMXl0uhq3JJBfkqoqOMqV2peIb731ZZpgftK4m6aTNgrDAB.HB5xOinRROK4O8O8uN1.NGrDVZ5FzSTQtgBG6uNP1rV52065cmx11VqZ0Mc91VqEmiq+pe+9DyLZr5pRlIwSL00ouJURdt57yOO.OwXzgJFiT9YCTq50UWc0Xl95y.i.3QPsYt.iQDgQfUx2WHvRKsTRaGm3gysrPHLBCCsN5QOZ4e2W4uqkALLwDoUlqtoYxidziFUoREo70kfkDu1o8GTcwwFDqTRp18fLdbCT0nI.ZryBmdcfZqpjvpOKqeIOyUmSF2YN6ocZm1LNNNE7q5Gug9jmct6ce7bdsDoaHhqIOMQTF.2zJPYS.WC7s+1e6PhLGAzXncLqWMjf.QKSpXE4BZPUmOUudcM.n0tc6TtLm8m9fOXNH8SyXVkjVwb8w2+kKWNzrYSXoLieWWInoBKAAFD.qwp7f1NNjmmGIrDvRH31saGW+eHAJjTmU533DppmVd1nTdOLHBBgPyzzbrrJcbbja3TvIAPJgPj95ulqO667c8NKr28r24uu669V5h+i9iV9QezGcIXp.VzvSArnYbuNgFvH1TY6Cfd0LMi6ET1Wfr9+0gDzEuDtkpAIy0cIhVExsE1.l4HTyPaG6XGo45bZ.GIiFAxBc02EUUJTXwokP2+lFjySUu9ONCSjR8XZyrScXNlG.YLMM82647b13e5K9kaTDnYcYRgo0g1S1ierrbVD.ay.3Tpw7SG.mLj9sQ9pNNAh8X019.1ddMZT8rNiyvC.qpCz59bb5KDhdkJUp6cbG2Q2Ymc11m011VmFSlD3wZB4GOAXxlljdY.sJxDL4gjMEEa0p0JG7mdvY9UN6ekne+2xao0+ee7Odc.3UFX8JxWmg6d26lN3AOXR.jcIfb+vG4QJbRmzIMijFyzbAAAKpzu7NFLXvIjMa1s633nqjESrVBY0zqCHhFwL2Axa9Zpoo07a8s9VMekuxW4FNNNsAP2yTHZeOG8nq8.OvCT+M7FdCd0pUaM.zsLPfpUqIS9WNAqXuhXte3O7GN2Ed9WX1u888sI0DjZKDhV5.scYtuhdXCAPTQfT0KhYQcrzq++9+8U9++FtgXOlXIEyRhM90kg7frr.H0ZquVh985S.DIDallvAAAXznQHe97nd8Fv2eDuE5lLtIC0TUiANY5XG4zW2xzLTzYOBSwEhACFDsycty.EKS7gZ0vB4meqUqVsZ+ze5OsxEdtmaUEichkKy.Cfg2yXS5DAdddA555gBfPGUr8RKsD2rYSF.Xd.sMTrA6y7Y9L4tnK5hlA.yqqqO+8ce22bBgHGTLv.5nG6xchmdlNPus79+nR.9U2EBwgdBM08KZ1ZMI+ytQBbvoZLU5UDoppznpPHx3HKzIsAPhm+q7U5+4+7e9dXxtlO..Pou+3bXyACLObw7PIaBc.c2IShcQ.LmAQy3JARI9v9j.Py22OYpToHGaGhRH8DB4jvjwQwZ2OdppZZZbTXHywutjyD.JIbEQDGxLEBPgVVB111VBxhRiDS8tLSDwmkPf60wIdpuQW5a6sF9+9y+ECqUqZfPX4GDDzud85shhhZZYY03ltoax6Jt3K1ySNTrUAvZUpTY8xkK2Fp7k6.H5vp2iO7gObxcricj3vG9vQ63YtCFaf3FXRAfrl.EpJk52r555y344kiYNoAQQtRy5NtvwgRIDPCT9aQrCvO.KgQn4lXh3zwV+hHe8wX5pi2hXYkSAbx6yPl+YX8kv.zbSRJJ94mjc.6ZWZG5PGJ0G5C8gztt206Joyl7G.LKjSEaIShVvUlGrfh0fEg7bNCHyeEuYulVJgnW+dHet7nYylX3vgiYtjllFz00GaxqD1r9pgZp9VVhvojzEWsZUoKUk.HJhU.4wQDnPFTjBXOvLSD.YHMaQlAGRfBMLLBTzXefiiSGSSy0SjHQCGGGu8XY44BTW4oIqholLc7YCG4HGAae6ammxjVCA.uK.5PaVp.YE.EtaGm4DBwBPZ5eycW20ckaG6XGjZagMRswmFVtb4tk.1npDv33bna0TqAN9oFf33wjPl6J+McG2wruvW3KLdUiNGybta6NtMsW7K5EOX+6e+sdcutWWS.zrDPqpS1xYprIH01ARejILxbQl4kI4lwYrDCUZ+eko9ylIJJJM.RVqlqF.mHQhDIhhhjMVHmlJyfYBDRlLEBB7wTm+E6qWSRUEyfD.3U2iEkDQAAASLnWFQKs7Rb1rYQPPPTmNc3985wLHXYInVsZQskazIlkOVLKiQGoqq2utqaqRVVq444U+tu66147O+y2F.1F.tta1fliqCD6F.GbJFgUudcpXwhwG5qsLPtUkmEXToREyUWcUihEKNWIIv7..ipVsZWgPrgsscy33aSSyV0pUqOjLox2chDvOda5+iG1..RhxHMpHA54jNohY57H0y5JMkyL..DQiLA5VyBsf8X1+FA.MUbVAn5EfHZYU7zJPlKaQZ75xEKnjNwXvk52uepLYxjD.iMj+oYRRbdrUVYEzngj6YrhRHEJTf6zoC..WHegnd86ExR1yxLGwS2xyT4+BDBQbytie+XJVexjzXq8I4VkqO.5SL2sjkUq986u12869cW827272rA.V0Dn08Xa21xxpittdGOOuN.nuxnVCaznQzJqrxzarKx.HgKPxibjins8suckYn9+k6dyiRRtptS3e2H2yZeIyHy2K6pTK0ffVHg5pwMFPfQHjvijLFc.iMLZPryXOF8ABDlEO1vLiMxZAKrFi4.1CFIKLivrHwmLHZr3XKP.B5tAYo1nVkPzcmuWjQtVUlUtmQb+9iWDYkUKgwiQ.Z9dmSepp6tpHhLha7d2289aYrgZLWFfEtjW6aXgOwm3uLcvF9G95dMuldeh65l6BWzlYt8m9S+oa9pdUupVyAr0laiHCuvOt+zGl7S0H7cqvOSSCCcJSSDkvFHgqI1C.X.xisfCZBy6pcJ.Lr31zlNB.huHP554vbnDVBl77yh.Kx00r94xHHWMr8ZmQ.PzACFDsZ0pwDBQz.myhPPb1byNGuwlafsQ8137+2QywH.e6b47JUpjev7eleHiNfwZklOk4+7YCRfY..ee.O+g7J6ZEOGs1iAFEKVL+gCGN4d.ZBfZ+CG7fUdwWzEUFaizjF4.ZdXCCJ5CfAA4+Op.fWv8Jdx7+WDfpeJH8AAnyOGvbe4u22K04dtmqE.FhbnGJMtfKcO4IOYucsqcsCMlaI.uZ4vPTZbg.mjdi+hNd6mIie5QXx5i+NFXYOXzRiNDQsHxtUoRk57e37uvg.43J6rBT+jJViOPdOjwzQ.Wfl+Q+Q+2aQD0wwwYnYQZXoNrB.f22y7Y5CX6A.+inTvHbS4nCcnCQ6cu6k20tViq9X6V2SVQWxjEjJJLh0ZH2NIr7xd.n+ryNa6QCF0D.a9w9K9K1DvtE.5VbbhR6kN5QOZvKGYiUGH1YbFmQTDTUVjEQhDIRbX3Ne5jISlzwwItPHhBli.PiQER974I.F9FqK1mHxSJkd4xka348bet8e4+5u7ABw9FHDhQGQo3csqcYcwW7qMZoRkBttWIZwcZKho93exOdnpMOCQTZl4D+R+RWZjGo7i.SGKyNRbthg.vqbvlAA.AikqEoBPzfEUR7I9K+KGy+See+o.vTDkaG1SW2tci..pSmtjPHfPjersgY5HqFQiFEoSmFZslMEKAf4wZGQXgRFAiku0Uq0aQAVYI.05s+1e6sAPGsV2EjoZuZstM.sECzhA2hYzhHZKPncpTo5.fdLg9fvPobsvEEhcIWxudZl4YeguvW3xZOOaX1.U9.9Csz25QezYKTnPRXtu5mM64X5R73IslipWudv6Y19aFzQC.z9U8pdUsPFrI.1rb4xsymO+3NX.fYu8O5maAhxs3q7xekyCiiLMcf9Jj33G+3wfQ.KiFTrzfmwq9iyd19Ywvr44iNYQOy..DwAHloXeKGWq0AU.21xEfe5O8m915hvBaKto5wzMx1C.d4bgOPV.ayFVJCjv22O4gNzghS11QAPj.DSDR8FZznQPq0T4JkMZLBMgkyAffhkvpsQUhO.7777FwD7.AOBF6olX3o0NAwYT+HQhzGvefRo5GTzptfns.GD6YhoZCfNGVo5wLOPqMh05e4G9uXToRk7.He.fXwhEct4lOsTJWPq0Ye0u5Ws7ddnGZ0uzW5KsJP1B.vdsBEV.X4oB5pPjiiwk4iWMP6mVc0U8OybmYXx+i.VYv6487d5Uxnt6a.joZ4xkKCfpDY27D85MHnckwYlSvFA3MM.lN.8AypLn.HsccjDXkjXowhyVXxNgcx3IxwDEm1zc07ahn.4ChiPzxaS2pHHSlnvHZcwQ8+06tx5quN..+te2+IPCfq65ttsKtWlwz2JRIy4IN.RdcW20EVTjPaGbrcchIRXgYF8602HP0ANUhiiCN1CeL344scwZJo4C...f.PRDEDUQ7YlYlCo.V3BcDY57OQjOQjmRq7XlGM2ByMDF2uvXe6f5v.clYlo63CzQru80gHpMCzpToRMAvlDnl.TqnQi1gA2mHxG.QbccSq054ylMalGtYybLy4dYurWVV.rDxFfnIy5aD.7WYk8MD.CJTGCMhLmYykquMMyFhEWb..5++9dtmtBgnELE.oAQ42747bdNcxjIyHyyhbwDBQhBEJjhYNky1qEXhmxLwyzGKJldxwXu..fbAnK3Bt.KhnnHWt3AwkQNy8bl70cMWyvW6q801y3NLnmy1EBX7iZfLzI14FiibO2y8DGFXparI1.D4EbriAiKxE0xxJpkkUTgHuU5zoIe+wxOW35m9DLEzczngga7zGAtXnixYH.Fv.8Yv8XdbgQGjMS1AiFMx72IzUJWqCHzoQ85aQTtlkKWdyNc5zHuPrIfeSkR0dqs1pM.5v.8.anAmEaQDQIJWt7z9DVrd851dddxK9hu3c8M+FeycAjMuqYy5gTayBELeF.f2QGWDi83A.uyISFOf7iPACkJpE3vE.XyBOqm0lmy4bNsBDH6nDQSq05EDhyc40We8kjR4BEKVbt63NtioKUpTftIjIo61Zzw+trE7eFOB1HUln.HwREQR.6TXYj5QdjJgalkIh5SFzq1rDPSnljJh4s.PzSLtwCYmoToRyAf4Hxdlf7GhA.pd8MFiHNLARQCGkJUhKUpzX8fHDAIgU5EvHj5iu3CPUxVasEXivT60tSmQLyCAPelQOXbnPCJR.0mrB+dzmHpKQT2HQhzNSlLsAvVDgPTH0hMhjdOl4gx0VC.HZjXwR1sa2YRkJ0BunWzKJyIO4Iseaus2lcIfEkR4L.1wKWtLYt2X6EjWl2xl728vdFidTufBoMbkU1+..zawJnCvJagkLm+J.cTpGcvM8Q9HVv1NE.l8Sby27rFGkDoHhh+pdU++DC.Q2bm5xwS1Pyz1ywZiHATqIT3WivLiNc5LJqCF.H5gULhmbwIW+yHlt90A7QI3uM5Fy5yL6Wd6XowEbFFDbmD.IqWud73wiGSHDQBc+FsVCkVw4ymmSOUZyEJEV3LhPPSPYyw1GlBn0OnXnsILlVdcXl69nO5iFl+eWirQPsYf1VVVs8YtM.5XYgNQiDsimmWGlPGPnW1rm8.gP3444wDkKR+98S.foufK7BWrb4x1e069tkv1V..6CoTKJDhoC97YkM6yvG.9EGOu+37+Yf7d02lh1lFFtBBW6biR.atmm1d5ZbXGDIaIDmYNIxYzUmcsqcYbwTfocbblA.ybOG8nSiRSpeeq9jw40dBc7DxFaBpDOYJ7k8HfbAB0Y4VjM04s9V+iG.ThWBfV7e6EMgAb7PEL.BzE4yu0u+u+ePKl41Ly8EBguPHHhHq0VaMpXwhV.tV+5+5uhHjOEjXcIRreACjwGnhuMf+hOlywN95SlFlEvVGwvJHAxkKLQuHnZUFAPK87NuyayezO5GEHROtaATnuQiI.CbTKrDhZD5xxID.Ixt8joHqokoQe+u+2+XQXJLQLFvhAaYJjJHsiC.HtfTx.vmYeekR4oTpQ6a+6ezm81+rCTpC2GHaexmFbGe96vCnLPFXYfN1IhtBPrbaKjXS86eEuoY9u9A9.yBylkl4G7C9Aod4u7yKJ.XoTN7j5uWu22a5c2CYPOjO+PGGGSlZkKGJTVishrZ0pE.we6zVVVSyLOMykl9tN3ckZzHu3kKWNZiFMrzZM0uaen0ZzndivVsFffSC8Izpwduto6plM15GzIVurYyN.f6FrA0lroSoM.3Muwa7FaJDhsfw9vZ0eP+lfwl.bi0jxFVfZP.MXla.Fa5ybKsV2VJjckh05yboAZsdjVqwcdm2dzDIhkF.yGQHrYl20G8i7QOMTt7oA.4t28ty.fYxkKWbyyzRLvb9.KDTR7MsvJvBXQBvkWEve0PN0BzIaErEvxsfYCt8A.+i9Q+nDLyy7RurKad.2E+S+S9SWD1XAjIyXa+a0UWMr3cQAP74latfM6c7nAa37m0a7Xx2amn6vUHfbQXlilgxDGnZbjGwAri9+3+wakxCL5i+A9.FDL.LHeCLDXOSHpoB+.swwygYOlcY3BhYN5ev6+8G2xxJ192+9ixttQu9q+5B27dD.DsUqVQJW1nACRgjfo6TAu2DbwxrAoQl9q5S.dbvlJdSuw2znF02viAaRlhnQFXBy8.POOe+d.TehoA9Fq5sSvlE2jHpAHrAy7lFzeQgvKuiPH5.fNRorK.5+M+leygJkhO8Se2Q.PRFXNhnkmd5oy8q9q9qJ+hew+pB.HeYfL.UmGkwTHORfLYBKJfOZzXrUF9POzCMpPgBAEL4Di9fevORef7AcFoRcrLpBjoJP4MZ2tcmgCG5A.Khn34IJoQrvwzJkZ1idziNqTJmE.S6BLEvIRiZHEDHAVXgvXteVFeYArtE.rLZcii0MbC2fgVe4yaEffnDnRkz.Xp69dtmz.H0xKub3lfl75YBDwTvGngeA.95u5qN37fn4pfX.4CngHEKja0W8Ue0wIhrBRLaHkiBKXxNPxViFMvBKrvXN8+Q+neT..7T2ySErugTDAztY7zbmR7mO.4yL6oTJOBViHhFTsZ0ABorO.2EfMIWQT6Ymc11x74aW7vGdKl4lfPCgPT6QdjGIvwH3FJkpoTH6vLOPJk99v2BfRFMZzYeJyN6RNJk8G9CeS4APtrkQF.6PcxHNvJDPMiyR.LZBgKbx+3g50GBfAO+m+yuK.ZkISlFlyeoZDkeiACFzE.dJ0gh.ajPqKkzwwIoMPxhEKlDlD7RJpfD.KFVLtIKbBvSNR3ivQAPFvv1NbtNe1wIrn88NsS6zZcC23MFH7vtaATnGPgIoGAArJATgD.zRaersdkufWPzfBjjzT7xboOmy4bRmKWtzUqVM4HOu3kJ4DE.QpVspkVqsle94oHQiB.lme94Yaaa+f3Te.1CfLPTmog.9CAssi87e6O7OrCApCYP2WHJ+ZCfN0pUqsTrVak5vFZYQTSk5vMHhqAfZTtb0.nM.iM8CZPgTJaJDhsDBQGl3AAabhHXEqWudgBA+x6Z0UsGMRm2FvN61n0J0hEQrf0q.F+955l4+AFA3LBEwHrWLB4yO.gc7sToFDQaPD0N.4sQEBwT.km8LNiyXVfryTnPgoeouzW5zvFSCXOEPkz2xsbKIJrcASdxP7EvNJhV9H.UR.fTUYdJ.2o3JbZjGIQtbwBJTwPToR3yNC5CWc0.cTvIBPt3.1Iu3K7hSCTd57ma9YZ2t8rL6NcVfj1Ay0s3hyGPGrbw.PribjiDgMzmlZznAA.Ja1rHb5NGGGzsaWPLfSoRfYe1yyiyjICmOed12HSIAyqYED+w8Yh5APcjRQGBns2nQcDBQWoTziXzkA5dMWy0zQoTcTJUaOOu1uvW3KbKoT1B.MkR4lAqy1hHpsPH5vkJ0SHD8SkJ0vToR4WrXwHNttIiDIxrW8Ue0K9MtmuwR.Xd.2ogMRBrbL.Wq4.f8jyks9DyoA3YJPbkg.XfYysmnOpgNPf1HSlsN3Au6t+Nuk2xHtTonFp4vy.fYsssCbxpxIM56zxm57Y+htXviO21SfvgLtiaJQb.DkY1RoT9oSmdXYf9.5A3DXH16NZTfOJV7TQvPvwubD.D48+G7eaxOugnlJdrXwh6n0w60qWLsVGE9vZGPNBDzZMOQ9+.fIozHtqZslkBg+7yOenKg1Al3hMXiXotI.ZIDhsRDOQaoTFPQYpI.u49jxMXe+MHfMCxUaS.ZSWW2lRgbKoXs1LWpqVq6655Nj4RiRjHACfXT97yjISlLWv4e9hG4du2cAfBRozF.KjKWtf0OKGAHOATH3y8lQBx+2BvA6Eic3zQ.XPgSfdFQP23pbSm7L5xryn1saStF6mOA6vofMlB4yGpELyl278yt28t2Y.vz1lB3k.33wBno+SVla6I7w+d+fsyfwEQbTOeb.mD1.wbWFwPTDEkLvGN.Rb8QfvXgGK7ke7FgPWNEBnkC.NSl4yB.OU.XGz831LwkKH1uqRc3J6WJ2nTVS.PwCWrYgBEBU72PWfHDFtmJ7gdxRQSlDZ3gEEHAaBfi..ZYfnQ.hWxLQCQ4nAv0tMf6VY.5VYOXHhCFGcLrqBg.Wx.N7EBq4E.ffY9zxQzYTxHxf4zZ07fnjvGQB5qLCBCO9wO9fUWc0dHnCxBgnF.pp05JBw4VEn7F4.ZUxFcK98J1odk5sNmy4bBu22Elm2gzJZF.L+i9nO5r6d26d5r.Is.hVBfKVr3v+l+1+lNu6q9c2BA9NO.FDXOafYlxQTLWymoEPNrLJkcYfxK2oS6LO7irdly4YbNY9W9W9WV9LOyybtM1Xio50qWhvJJ6yLrHvAjZHbusg26gg4LDSf7C5VAC.9y+4+77kcYWlWPrbmfBizwHzg6yGvMhRohQDE0flOxOfFSC8YeOvvmHxWJk.Aw2ZcQKlIqfSL0sWOqjISDI37OjA2SJk8.P+FMZzuSmNcylMaqq5scU0to+7axE.kfMJA2L0.pzZNf9at.XLCrNwW+DD.vJqrRXmpOUGoHdPbwR4.Duneieiccq21sYS4oYACKXDEuV5h5FhBhZLyUIh1HCPSOft0CQxTdvm3aeB+UVYkQE1Y2.9Y86US7tRgn.ESrLPppYPZXgjvcLuUs9JekuxnWxK4kzElEGBcokAg2OrAhEAHoFXJXiYgElGNX4ie7imakUVQ..AYD70EggNNS4xbZee+DtttwYFQgo.iVXBnYFdgN27ywazXif6GjYyELFAC288MQhgI2v.DMB9X.YQ8C3ZsuRoB5VEF8e7U9er2sda2ZeoTNPoTduoW6uM82ev6HhMPzCqTQIhrBndgO.7u1a3Zw65c7t..rX.KxDi6AvCXB8lYpY5N6rytkRoZXYgxh8UPCWnfABn0w1TXvaOATtZ8c77cuDvQsJ.PEsgEFAKTaa64EF3WOGy7bG37dQo9B21eSzf2CXlY+uy25aM5W647bF3BzEYwVfQWTYLr4G709Zes9m+4e9SB8ymnDYrvmQgttTLr.hh3HQP7SB.j7C8g9PItpq5phCfDY.hUlYpQiF8VbwEahbnNJM2l.aFRwK+IN1guqMI55R9m9mdMy71e6u6P5OrbdhVjAlKfVNogg60yvF5sFj.9XgQLpVqMnDDLxmWLVLWQ.+DNk6MLCFKL+BzFargoKYiSTmGAiVkLwZhlM9RfFwfGJkROkR4IkRVq07m81tMuW9q7UNHDJvl+u8AfxQUJU7u22+6Ecem69rBnDlW.7hGbK2xsz+BtfKniTJa9e+O5CT4+566OzAFep1MCPMKiPs2E6ACv5+Xs8Wq8BDooghpFzSZi3v0NMf67YAV1k4kHaxTrOSxxdvj2QHGtCoBT3b.CWBXXJCsfNUKT+WDicfDDrSXrOadfYu5q8Ck5pdWWECfdHCZhJ1aB3tUdfNNamaEv1vuNZwhEiUnPgv47WlYV.inBmOz8upUq1hKrvBKZYYYJboqaRuQdQAgSovBi+d.B9eku7W06085uBVoJxloxfOYrIyIchrwqIHkRbWe46Bm84bVrOCeKPiPvOOQ7PFz.3iAL3AAEj0RYlWKNXNx4JkzQTJHkxHJsJ923dt2DOuy64FaMoLxgUJeD3JEv7buNLwYJhnSBiv3WFl3fsviKhbdLCiQD.LUdfE8Ax4BrK1jyjM.lxwwwe5omdyVsZUQtlrBbQcGGml4ymOr.1gEYHDUF+h1F0ABhy1CPzZ.Ia.LEDXFL.oQUC5BYiHa5OXvf9wiGuaNf1DPamPZerJ.NtcD.2DRfzEYdZxlV.kMysQFQ3e1G3AdfoN6y9rCoe87e6u02d9C7rOvbXBA4Wq0QEBQDsiNB7AQVAj.bblY7Ni8B+OM4nxDfGCLDFMHYLRlBnLHC.hMhdAAF9fvHl3gVLM.DMhY1OL+OD7NjTJgxDqQeguvWv++0G6i48W7w+3ijR4H.LToUCkh0FA3NToTcYha85dSut5G7NOnI+rcNmyjBx5igJ2mx2SX67kW.FKlWxFplOC.fiipqXeE1PcXUcoT1HCPiJ1XSXgsfyXat+TkefedGuE9YIbeGgNtzrDQg6KIxq+JthQeha9lairXCDAa.mwyQO4ZpVqBDsIPhFlBsOOxgLnjcd.WAanM8xTNZY3hk1ZqsVNUpTK3TxYJoPFy00MxHOOh.SgjZlA34maddyM2Dl6KLEDSQiwUKEp0RzHv7.N.o1RoriRo5Jkq4A3FQqTw61qWrjISBrcAJB0ZQuIx+G..SLeIvXMLirXvf7oQLwijRou4mU4CfdBgn4O5G9ipbgujKTu95q6frnDJmuFfyFKCzsZdLD.P8cUvyyyOH++Sc9Ffs2CvTKCLmEvR+K0pM+hKt3TTNJBbMnuKXs+QAW+d+fG7A8+UNqyx+88m8mM3W9W9WtyAt3Cz9o3+La+v0+9cxCzeh0edxrfp+uqwO0HLo..g5fXVaA.qRLSnJXTBd.4FZPbBXjyvi1b+aGRhguTOBYP+7ltVt0sbK2xDNR.w.HhErRB3NsTJm6l9Le14PYLKJizLywti63NLeFyA..ZksSd8TEf1mLUUrcVzjbHQFJSxu6286lD.IphbQcAXhxMPPTG3NVzx5WAqNBqCeitNjwJni6wfMBqj6jUeNhcPQYTiFkv22O.xnTDvSTQZKvDHr5pqN95iYNhRohaGTLmhEObD.3cHkZ.bQ2B4Kz6bNmW7f.gQBXIyjAAHXIdP2TS8r28tmhYN8i1tcx69nGMhVqYKKqAG6e4XgPfsEVFssgcmBEJzG.iHRPF3slcJafYXGdFfxyvLOS5z6dlm4Y+LmA.yblm4YltTI2jyN6rwDBgkVqIvLmOWNeDHrgBgzifk2DBfnuYCsgalXrsVN3xtrKa..5wL5BezonR0VoTMEBQSk5vapTpMAnVLysjBYKl4Mkx0ZvLWuvZEpSDUe+RYckRUWoT0TJUU.T6Ftg+zFYssaN27y29zO8SuqTVnKQTegTLzwwgKpKFSWTOc2tcWTHD1kKWN+e1+y+L4eym5SIyBjCtXYfJyBfjah7QQC.bBvqrx4wqrxYGHTmS1w6wP8ePAi0S1tDvVep+wOSG.LTcHUjO+G8yOkRolmYdIwZhEUJ0bgKtUA1ILNPyRQy.DY86YcqUVYEK.PECv47OGFS1wDKfhQ.PrpvNApfjvEoAxmFvNA.rdIWwKwG.CVZm1jbvD56Et.jNuwUafKLoWCPqt5pQHaJdNJWPWXoj.HgimWhQiFEqToRwXliXam0RHDVBg7wTrjfN6G1U+QLYt2SVg10F0KuwEa5YJFG0EL5bsWyGbKl4VJsZKsROYGY25V+eeqsXfMzEK1PJk0+3+0+EU877pdXktJ.pvLWF.tR49bAP42063cUUJkMHl2j.ZFFmt7xY1JW1bc2ZqsFzqWO1xxJNyzLbIdgW5kbIFcLHKlKOxOEBfx95XOX8siiBdm4ndHbCmtX.pg9.q1CHi48XSQr2xlntetO8M6IDhH.HUVfoIxdlC7bdNy4lMfVNGQMGpfY.vzOzC8PIgMh8ad9+l+rTTwH.fitGS5Qe32+Glg6DtLzxn+G7p9fifY8hDkM5LxzVVwSkEHAJgn.adphY9jIl5GB+5BA7v9s+1e2Q.P7a3FtgDDkOoCyobsQpq+5u9j2vMbCgBY9jelI.iaQ.XzNh67u+Kib4Bzfo.a2j8m77R9AECwiHxaiM1XDALhlXNMhrBQbUaPnELwFMAPSl4l.noVqaRDs0cbG2wVLyMe4uxWYiC9UNX0hZsqVoJIkxRJ0Qpn055DSadtOyyssPH5533zUJWqiVq6Q4nQu3W7K1RHDIKp0y85esuwLEKVL2q+0+exFvd4J.y4ZGHHrq+XDF9ISv2+nFDnLDXuCVEnGbQW.21.nUYX2hHp2H8H+ie7iGSoToKVr3TLySci23MlxdaaRcJl4oxBLEfLcMfjEWXr.18jgtwFVrjnXNDMPTyA.7zL2+pdWWUa.6lYA1.UvlFzkftNFZLMgN5rGywaAPEJT.YL+6iEm+fe1HkMEha5DIRLS.MRRoU5XdddQ.M99.i.gL2bLHeBvm8Y9kbQW.TJECNbsSLfYzWJWqC.ZKWS1hHp09BbQuhEKV8rOmytBfUkla1pFaPc4lDQMEhBMAiMkEjaTnPgMJpK1HKxV+23232nN.pxfpdDkpB.UwwwohTHqbdOumSU.T6HE00EBwFfMMHinbCCtWNEQz7rwEMx.i9rLaNfT.4Gi5Vr8ZjSjOv3+LD.8bvhsLBAa1lG7fe41vPCcNc5zTylMME0wEIAxNU97OyYAvrEKVbZ.jN+iU.X+E4Xbr15.QZj2nAUrhSipXr6hP4n3YPF567c9N9.XTIr7.CsuJ3sG.FGGDfaT.DWgLIHhRfxHAfAUzA47EMHewjv7t2LG3YefYfYSughGYHMIrD4EzNtJw3hkXdtvA5AAn.DYZhoCn6UWCEInV+J+J+pMgQPK2TJkaR.aPLuI.13Q9gORC.TyBVU3sWyrL.E5tiUAPMkR03qbWekMeyu42byCbfCz71uy6bi27q6MW2yyqlVqqKExl5hGpyvgCGQLEE9XpCdmGbgd85s7u4u4qNK.VF4w7.XJwXmVy7QBm55D6LtyfbykP+Ewhg4C21wwo2m9S8o4C9O7OD+bEER+Uu0u5LRobFl4o+BeiuwTvEogCRCXmBnfon+1H9d1yd9EoPWS.vZkIPlL.hmCHNfcDhH9o9ze5CAPWTFcgyNzXncrw6iiBTC.K.oUVfHlFy6lHnQCyPDMKbMMdHUpTIJUpTLoPFQq0Q7FMxRFlmFAJnwj7latoePyq71dMyfhMYgQDFSC+dLnwHGQq0FGBs3g1nXwhaxLZlLYxVRobK.pkTtVSl4MkqI2fHZi0BbSzhEK1fYtN.USJWqgPH1LRjHaM8zS2QHD8.idhBh9RobjRqXkREgHJoPHlA.KbZm9os7+3+3+n8Mey2rswttcVF.KTEYlBNHAbfkT9rvJqbdl3oUeLwZL.72CvHfE5WEna4rn8RKsTW.L7rV9rH.j7o7TdJyJWStvK8RdoKEnuUy9zNqyepin0Itxq7OJ1K8.GHp990Qd35e+n.HpCV7wihq++aFOAfvjBw.JF2FHgqwNrhwrgPoHXC+4M5c.CylUlrCuS1I5ebmiXBfo0FA8Y2HG1K6v6cvfAqVqVs4CD.p1qIDMOrVu4a6s8117FuwarA.pJDhx.nTFfZULHUnmRoFFHHbmRW0dRCJSlDFYIAPptc6lNUpTFspHCr.AFkMI97b2+ys+8dn6s6J.cOQAzCEGihfvpGFXauFKjM.cIghN3h.XEl4SG.mA.1kiiyR4ymeJsVaPGjok4lp1usFXzAFt50rRkJ0yjISYhHG49kNnDphsQySG.z0Fniq42GXr0Hi4BN+yyLO0AO3AidQWzEMJ33GZeaa.fMVBncMyuOgEQLXgznJlmMB6j8cdm2o8a3RuzrkLhXWlQiFsL.V7Dm3Dys6cu6TNZcbe.KXb3UeXR5yyITp1MPA0ZhpIyf2N9XeRo2QLBsoGCLj.2AfZ6551x11tM.5ueozujMh.WDSoTQfYR9ADy8VqPgAAV7EBEHVgPficr0wTSkJpTJC4LdLkREG.Qt0a8Voq9puZlHhUJUjkVZoX0pUKpPHXGGUelolBgnV+98KkLYxSlE3jksgCbQif66iioCDApSUn4Be+LALThJK.V49u+6+zN6y9rKzpUqklYlYhq05A.ntTJKA.8gNzgJu+Kc+a.mPDGr3Pf5ixC34rsPcdph04SziI1Pydi.bzXBfj57XJ3Lt6EoHhhA.1zA.oQA52N1L7ZbRz1DpCNyvLOOQjM.JvLuJ.VEFMjYAOOuoGLXPpFMZj.fhID4in05waxaGEKw2mAQLGTrDrMRe5SFATNrC9gu2Sff2M+Iu4AulWyqoqTJGXbtDyw1mYu+rO7Mz+JuxqpC.5VnvZ8TpiDlXgEQTDeeeqBEJvvfJElHhBJPQrfN3EUoTb5zo8VXgE1gFYn0Zu74y2ywwo4vgCqs5pqVMGPkRghkYAzFdnGb1QWDl74xo98Qx.DuRfH.BfkN+y+7ybvCdvEhFM5LvzUQR655s+y8b6SFqstsVq2RHDsN4IOYy8uqcsU4PQ5cULHvdxO0y8+dGiQXB1YgzGS2p7.obBPDGy772vMbsS+Nem+dQ9g+ve3Vm9o+rqATor.ntdUzFG+wfbxSEwfgBc8B.HCarYSa1HJhyiIT0eNPXtCPYxbsa2N8TSMUbW2RQ78YqQiFgnQM5j2e+cdm3+vEewSlLTf.Zx9Awef88QfHJFDGRC+pe0CN3BuvKrGybWoT1UoTi69IQjuPH7rA3CGHzvLQCsfeGlosjRYmhE0C.7w9KTHlqMRAWLkRoRFHld9.vaMozKvgRrBmarRkJcylMai74y6d228cqufK3BzXmnYJTzB+w0E1IKpQjk.hWaYLMphkgA0jEbbbxIDhYUJEIkx1Ly0HhpwLuQ.cJCSJu2C9fOX2y5rNqwtLGdbRT+mSiSEYIwsMb7OzoBRAKDGk.8O8O8O0+E7Bd4sApr0R.cpsWzeB63FgGmLF81YxFEEhZWA.1M.Ni50qeZKrvBRsVurPHlUq0o.PLFHhocqAwUgcZMzFuLNIAQFK1cxNpN.l6k8.yckEJLPoTCBlyava8s9VGdS2zM4CCZSFO2YP2LGBCxk5EhfN.y7p9.V+W9ses.HE9y+y+yoBE1eDs9HFWLiPbvicLQykGy9NNNCymOeWhnVZst9W7K9Eq7VdKukRvTV7JvjmQnsYdpyoL46wVXUDCCwTPiEAPA.6cyboUzZcFgPDiL5K0l.n4e2e2e2VuhWwqnqiiS674yGhV1IQ67jBy4uHx+bxbMSfsmWZg.5LEiHxOKP2CcxS1dW6ZWag+3Ryw...H.jDQAQkbnErvVPitlq68.f0iI.RnEHMzlhgDr4pkXlWvlnEJaVSdN.rz8ce22RG3.GXY.Luuu+TkJUJTaWlj9HfHhJWtLxlMan.lyL.SjQXfAFqqclhoPXH3w4mZPyCy8HfA+Se8uwneqeqeSVoTb.hQXhngAnjyDmF77OP2TrDBgELMUL5gUpnvfGAOhoADYP9YwhZeoLuUNhh4ZiXvEwzFAWuO.Z544UqPznUJYlaKTT0ahchxdfsyI6TGVqBD63lmMKhbHO7QA1ksCVaHBQTOafltYQcTF0fIFqEL62Z3x.CqNgC8fe9iroIW+KDsoyGfvjzA6WbDLy6uwDW+StFv1Gm7HFbPxG3AdfoeFOiyeQfJ4JUpzJ111mFLHlytXwhKVnPg4.vLZkZJPVIXliLZzPq3wiSS3rWg4Qbpq0PgSpQa++OfA5RfaiIn8LQT+8IDdt1HxO7a9CiGOd7XAG.O.L3ce0W8fa4S8oFAX6oTG1G.7m7S9I8+s9sdUdISlfCDb1n.HgVqSvfiBFzBKr.mNcZRoTQEBQBGGmX9L6G3fSMEBwFOymxyr1W5e7KUcMorraNTNX+WgHmqO.FkGXjyN0.MfctGfXX67yVD.KcnCcn42+92+b.HEybj.WPsmVq6H1mnGbQmCbfCz49tuSzBnT6r.8JuD5YZT13Xscn4Z+zEB8jiw+dqv8DIlUzG.dtB3AmwIZALwjwNYxj73G+3SCaLctc525+jPXB.fuQPFs6lCXKTBMKUpTypUq1Me97iHhh3Cj96pKNO.V51tsaaYl4E888CsvvjUrsMBGXdD6bjxvJ7ep776IaUCi..VFFquBHG1ZqsrPEDgcGKJaV26gtW..+SrH7PwG2D7nJYyx.fobDa5CIhvF0NOM.RSDMkmmWJDX6VvrXQ3CQiytXTu9wS1JkRPVTjrYyFiHJpbexHOqBOqvDq7Af2a7JtBF.vMSFyBg4gUlf6yLynWud9Lydewa+18t669qNg3QN97LHuQv05CrX+EA5g5nOpNdglH.H4kdoW5Lt4P3BzKRDMmqq6TVDkPq0QY.KoPPDMdAVesV4OgcXNBfGF74aH7GCkytDQsOhpnQDv.ZEzc9lRor0LyMSG.zWJkCKYiQumW+6ouRo5RAE7gHplnPgZe6ie7pJkpNybc362v22uA.p8Tep6opTJKqTpxOxirdEsVWSJk0.i5W9ke4Mbbb1vyyaKoT1819Lel9zXWzo.dfG3Ah633LShDIVjYNS4rXY3h4QnscNQmD+wPQlvmSgEopM.ZcNmy4rE.587ddOO9889dewykKWpRkJMkVqSaCjd+6e+oyi7oO4IOYPWgpGA.ji.Lp+ykIFmrCrQANZLDlrloXIgNayr.HMybDoQjR6ikPuEe7gesE1YxZDYTh9H16rPJSAfopToR5FMZjDDhAvQTAEKIbDdgxLybP7FYb+nIK3Xal4sjRYKkR0DlE4pCfpjOW907ZdMkAPEsRWSHDM992+82XiFMpmJYxJW609gJWnPgJ2+Cb+U91e6uXUoTVQteoqTJc7guZ94m+j.3jZs9jRo7jhy8bKZ.4hVwLqUJUQe+QEWXgEN4e8M+WeBsVeBstXQlYGgPTE.aM8zS6s5pqFmYdp+tu9WeNf7yaCLKJhTvYmN0xDiSsaY9.vqxd1yPXb9l1YAZ+09ZesdQhDgYi9ILsPHl6Ysu8Me61sm6RtjKYNl4YDBQZ.DescsKKiTKkyeIS2LmLF6Ih4qO02G1w7PyALz.wTwnftya8Nem+dQAPzs1ZqH.Ur..oE.33+DNS4wj3uhA.ttOz0Al4vhrQH.EdLyIIhRwLmB.wqUqVzNs6DA.Vdd9ThDIFWrDF.W7kbI..Xj2HNzwG.vHPzP4ZqMTJDCshXEtI1N.nkTJ17BuvKrNLIx6nTJkTJKBfh.PKDBWkRU8vph0O1wN1FFMXBUEhBUjRYU.TqPAwFEJTnkqMZCWzVoUsHh1zwwoNybcoTV6Cea2VckR0fYdy.MmnelLY7C1f4TOsm1SaNX1n17XmtAziGphdbiwpU.iP0waRuKQT+jISxJkJVf6eMEkilIqoCjyAfETJ0BAm24Nqy5rlZhy6iWr8OOFmxbaHVFfDkXNEvxoxBjBkCQzDvK3E7B7.pL..CpU.iBD.6GybvUrsYlYeLG7E6rPii2r7byMW37aow1hSZHUQ8AYPShEY4AFgVIcnqIMNWMhnQz1MVomTJ6Bh5nTpVjgm+UYlK+49beNGoTpjRoRq0p0We8RJkp7m6y84ppKVLLdooVqaKkx1R4ZasOgnoLe9Z29s+Upb629sWtPgBU9JekaorPHJK2mzQJjJhHkPHT.PWrXwR.nh3bO2F.nsRoFwLG6xtrKape6e62xbX63sPwHcR92ep2GM+8iCOnGGm0BvcS.rkPH55aTC2Xvji67uhWwqX4QiFsrmm2xvrYWybnl6yaqIc+hI+ySEISS5bgog4dRBl4HkA3csqCLLCPOTB8fdLkRXf0I.DQmAwXEGZSrwQ.xRHhh5xbD.Dkro34IJ0ANvAlFl0QSscwR3w2GHr85ng5XhVqAXvRgvCLFd7ie79fPOl49Ly8Agdm7DmrKLhy5lRorA.pKKTntXs0p87e9mWMkRUl.b0ZsyW9K+kTBgn3m6K74NQwhEOdwhEeTkR8CAvOjY9QEBwwAfRWT6919f+QUkx0pslTVC9nlTJqJDh5.Xiq+5u1sHh54lC8gKFoTJu.WvKpVqSEMZzYcXdwa4VtkkfMVvd642lDsGgiGOT.vGGvCHS+k.ZiRXSTFM95e8u9lLy8Hh.ybhRLOMJiYqUq1rHnfDAec1OwW7KNM.Ri7HwR+7WCc1tv11vxNnnsSzTcF.depO0mZRawsO9WSxFLtJs0y3Y7Lr.pDA.wrssiSFQEd5QiFMaznQmUq0Sq05TvxJlPjOBQvJd73D.fVaLKjfB.OFM4Rob.ssC90kBPzKQTKBvfPIxpgTJ2HPma5JDhAt1X.bQuDIRrEQzlDQMHhpKkx5+IW+0WWoT0qU6nMHhpSDU+89deuUO8Se2UDBQYsVWY80OV0icriU8U+pe00.iFDQatwFazTqzsu1q8C1867c9NcO3W9t5HEhtRgr+e6e6equuueru+C+8m408FeiK+ParQ9q+cd8gVa8LvL2BA.Om8rmSs3+SdOMLmmcT3v0VasH.XpG7AevEAPlFMZrjMvbBgXJ8QzwUJUz669tOxv3LfxY.U76WbL8Owie78+W+3mVHAtcRlZSBlrgypitoa5lXXfQZbToR5omd5o4R7LkBff2J+ay6lmnaYt8KYBdaN6ry1b5omdK.LrQiFVDyodO+dumYYlWPoTKBfEJr+8OOPtYAvLv00HJRNHQsfhkj8wufIO4YrGvXQvUC39FPI+omdZFSjLEybrkCuGV+TdYXO6AFpuCFkK6CfQbIdn1zEnH4IJAy7TLyyLZznoJWtbpACFDBIYKS0W4I2DwHPlIT.vvO1G6iMhCfCoTJ8UGQ4ekW4UFx0Nf74s9K+jeYyBwUpXJXkChUI33mmHjLYROhnguzW1Kq+0bMWa++4+4+YC5XxB333vHKXmwm+5ipO1wHVbH.FQ4H+bDEkYNE6vyBfEJUpz7kJUZF.jJV7DiWbPo0PHDLyruYCEzHkREZeWC.n9fPOhHi5VaTs5sN1wd3V.zlLQav.MjRYc.TWq0a7TdNOkVqEzUV0gUi9c+c+cG.f1BobShnFBw4VC.UiFMpQ35HpNSTiBEJTOmApmUTJkqTJcSkJsKyrawhEcAAWw9Dt.9UhDIRM.z3kbQWzFLvFZstIf8VWzEcQ8E6S..jzwwY166+26KbCGFW.Xt4h.TXxMU73tXL1ldNgh+zl0qWeq6+9u+9+w+u9iQoRkhs1ZqkTHDocARWrXwobb3o10t10THKRlIzG10+LWXwl7XaRxyFQKDJhvZLELHCY1O3G7CNCYamlYNZ61s8QkJFpUTCCpuyNIDVg8IcKCi3yYiHHKhVh4XW20ccIAPRee+jdddICDY2PaD1xsTIJe97DXL1dpCgOLYPyzH1Xwo8fQ.VaSlD6Zq05sjRYSXJVRYlYGlHsbMoC.bERQE.T6bN6ytxdOqyxsa2tN.4zJkx4heMWr6tjRCLhKAW.nKHJTb5om9jEKV7jBwZmD.J0QNhlHR466qjRYQgPTb+6Z0h.n3E8huHkPHzBQAGhHW.TkxQat1yastAcgKwt28tm947BOyYcMKDOEPVyFK26Nl27GW7kOVecCGZqidkC1PqMQC+9e+uukiiSRsVOC.lc5omd167NuyoaznQZkREGYPzJiWipDpsy3fmnG6XC3XOvCEf2li+2z9U..YzFlH.HxW8q9UijK75QO9Z6G+w2AbwftdGH9eidWui2kwAQL1q7P.3c8enqmfI9JI.RNZznDKszRwxjMiUfp9iEWbQNbfweEbzHQ4Pjc7s9Ve6QDQiJdnCYh8XiSevABEbwhEqxLWlY1gHRekW0Up.PQ49jJhHsxP2lJDYU87O+yu5gUpJBgnhVqqBf5vFMsCzEL8Qz8UJUmkWZ4lBgn1W7NuyxEJre2OzG5CU9EbdmWYl4xDQUzZcEhn5kJUpEy7.lYRJkoTJUXh9yhLXZjGIQ9+OHoqhmRweykqyBKrPnUmFkYNEbwLtLOO.VxFXYoTlA.KoTpIKTSTj+WnhjHg8.KrDhfbHVEfDFZ.VMU4.KtlYN16889dIjC9KEVfuhOtED27GWWiUVtIXcPQ4r21B0mgYdl.mOHM.Rpczw.Aqd86BDF+yrGHxy222CFds6CfPTkD5TEiXlGbEu1WqwAu.0RoTAz7BaHDhZLykKTnPorlBzoKVrnVHD5zoSqYhKcYW1k45agJx8Iq+IukOwlg5DlRc3sbMTqnAPtZZstJxgpm8d2aU.TFkQo2+ev62QHD5fXWcgBq47Y+reVG8266UlHplPHZIkxQYxjI1G4i7QCQ45jEv3mzFIClinfINKO1JiQzGaUud8dkzk3fmOyq05kuu669xFMZzrEJTHC.V9G8s9QK5BLmRolNfZXiy2Zhy6OO2H6oVrDCc0xhTjMkfronDQ7fACF.3zqh4cq9AhJrO1KP9vFMTAQCPzow.AxkKNQ1wYlMTzwTruobXdJ.jtV0poxjISBgPDC.QhFMlkssMIDhsI.VPnLyf4fM2533LBfFr5pq1StO41BHLiN6ZW6psTJap05MKp0Mjx0pe7ie75pibj5RorJybYgT5vLqeOug2Xwq+5u9hO6eomcwBEJTrPgBECJV7Ik6WVD1Po0EcDEDtWwU75p.3V4vJUkBEJTVoTUPv7ee5a7F6jMa1A5CqGMb3vgBgnOQTOhnAR4Zb.RsSe4W9kOKbwbAqgltv1EK6mzy6fXtJipYV6rIfc8m+y+42.FZSNzwwIpVqSCf4e5O8m9h111KBa6EYlW3du26cteseseMy51NHYse95RSSFSaAWDIvskhlynGiV.FjXboW5kFRMz9XAL.64eMzIXa9R1wEfIZPQ5RzoSmTVVVoMBgpwUGE4yGUErtI.P974YgvXdESkdpf7eoPTsMfMN1UG.NDUX0Ylqw.0jRYMeeTC.MJp0sHh5o05gpCqFoTpdvX1CM.PMgXeU9LelOSHUuprzRKUF.UDBQYafxJkxUoTtBgvMYxzkld5oKcq25sVhItjXehJc61otPJ13C+g+etgnPgFWwq+00nXwhM.r23c7NdGsijOx.GGGq65u+uOc61sW324242IzJkmG4xkFHqgdoqu9olSV3XxFOrMEjsg2ccW2UDjEo51s6b.Xgy5rNq4cMqUjVHN2jRoLd9y3LhlMLu4JHRgBEBe+OFVXgn.ENUJE++0O9o8CwNmzcEDGmXrMPl.FNaNqCvrqu95INiy3LFRDsIxiJvA0C7Z6Ssium5wmVEHd.jzBokySmc3mF.1sV6rL.mJ35XHLPQplgW0J09kR8gTJGoTVAHaSfxcdGui2wvq7Juxgq9Ku5f.gQ5TEkreQNF2gok.hWCHg.HkNPY+APBjAwQEParwF8me9m9V.NFgQcaH4FBwq3YAR4FzsRfLIApLELdWdVjEEPYral4UcbbjLyKEXSUwBRLmw1uH0Cf6RjUWw9D8TG1L4.LcFu7ke4WdoO4eymzYE4J0AvVZsdfPH7xBLp7Xn.Zy.tQfMR.WjB4PJTBoN+y+7Sdq25sFy22ORgBE7O1wNV6idzitwa5k8xpVAn1B.MaXp.JCiH+MEbQF.rBP1mJytOUXvFpvQqWLuPLsiVG2mQToTPgeVHPd9j+HhogjwVvFOobkJU3LYx3SD466ydVVT+8IDccA5FXkqCXlG7vOzC0+oblm41TkvGz91+9nibjivrgdE8+gqud2joS1QHJzIGPuRiEWTaRoNrkbeRT7vE8J7rJ3gRXxhfEG.Q+A+fiE4o8zdpQTJUDoTl3XG6XImZpoRvLGSJkQnbjEWhogCGRUpTcnPju0C9fOXkmwE7LzvEEgA9mafsEWtebP9MLVKDRdY.vt.xsmgCO4YTwshv2BIsLUQu1Ye1mg6+7+7iTA1XC0gUaX1nucKGmuWm74y2Cqfd3Dig84SjPZemK7tWXgiFJflKmDn5TMZzXlEdZKr.bw7LyoIhHl4N4HptqgKx0AVrEP8PQelBNNicHJLtCa4lBnzhLyBhncaCrGsm2tsrrDNZ8bLgTvmiJjRKsRCxhn.KaE.gEKgAyjuEAO1vw8dLysYvcHh5QL5KVSNPeDcOee+tEJTHryJ8JVT2uPAwPcQ8HQAAd3G9goolZJel4A.nqbextnrA1i5SpG5S9CJTnPeXigv0rYFowMq3.5HDZs3QAJGE1vBt.HG7MZM0NolTPRtwxADojYyP8cbb15HG4H0ujK4RpibnAJ8XDvtebnKZLxI.VJAPs4PdXCGrBan5TNX1zRDGGmd28ce2st7K+c1PqObk0DhJk.ptLvFU2llFCvdvHr9iKcMdhHFKLQxH4Crn5L.opXtFWB.YYyltid1m8Y25AdfJkAbMB6WAzFECsXycfBlf2yJDCnXBrLRgpXFjEKd8uqqe42467ctbvwdI1PMmkAvx850agM1Xi4ykK2XTYp0ZqYlcFp0lMYXz7UHjhP62jo.GVJXtngJkZHQzvJkKOb4LY5yD2qv9JzUeDcaw9DsUGVEVvtPw0tOPVOfxTNiHbGQoTg2S7Zs4l8eZun81GkvPkRgtc6FYO6YOItwa7FikLcRq+yu4+yL.FoTpQRozKKfWYy8CKfbQ05CmPrOQR8gJFWr+BQO428jd6ZW6pOyby67Nuy5W5kdoU.xTAnR8f48+IQMmsQGVFDCUP5f6k6B.mtmm2JkJUZQh4nfr5Ijh1vFcUG1XO2Bgnya3M7F13u5u5upJFqaAK0BnVnfINIEM9YYtAOl3jL.IqXPIWJXrNzzrQOH7Ih5.fM.xtAP41.iE3wv2COUDDXArXLf5oPdr.bf.Ywddn64gNyy3LNimpqq6pvL++z.TbgHO4ncXFreHcaFLX.GOdbeXnr5jmCfs6RYfyQvsDhBa8nO5i1Nd73aQD0TH1WSk5vsjqI6nNrpubM4v.gEbRJvAfrLfwZ1EBQjO6m4yPOmm2yyiYdHQz.w9Dg+dT.UFIoTRHK.JCqfeuPjrNFsf.1w1yYOS70qrdjK4Y7hG9AtlqYym0y5YEpXUUv1Tk3es4yrvdQTbTjD.K.jU.Td2Ly6F.Ebz5E.QwOxg9t7Eeo+Z8APWGGmlv2uAfUUQAgKxCW3fZXQzD02gQD7yKZfE9baLssAvzO2m6yc968du2ECleKIL5bSqb.UCnjoQ792KFFflIK.6X.tg5RxzDQyBfY62u+hIRjHb9rkHivu9+G68lGjjcVdlu+9N4dUUWUWK4xYo6VRcqVPK.0coF7XYgD1XMBD3.KCRvEY.4wN3Z6qXUBOXrIb3QDgwHIjXwbGSXFVjAT.CvXaPHYtiwHKCCKc0RHodvVqc0m0LyJyrxrx8LOe2+36bxJ6RKtMHLybi6ITFk5tq7r9dd+dWddedVAXk1cZu7ryL67.4bccSJhDyf3SNoDhwoYzHQLIoNAzSFUz864dtmAWxK9RBi9lgQwn0TnDjfF.a4Za2QJDQDmegdP4ATfQTlQThA3Sbh5SwYFkDfexRPFeHmiiSVgPjJLLTXYYERAFSYDNNNY.xYZZN6C9fOXlM1XiDW5kdohuvcbGbUu1WKBgfhP3Qu7KevW8ttqlCFLXiLYx3SQJS.0XY1JZDFhGi9mtm6S+rZNzYI7nDfQmNcLxjIyhZZZYDEEijAxVBgnoss8VVVVsKBcCh3Bv0We8F6cu6sEKSmni6NGQims1lNdsohwzJKXOGEXAYfbdQQwLx.olPHFv1iiylPw1Pvzizwz6qDJR9lY.lGJTPJCLA1unn3bkAxyw000THjKEFpMyL4xjtaudSP.bD5eTMyRvXPL7S9I+j8u1q8ZmD2pTRnPv.Ah9FlFccbb5FYa0Gnq4plcIfgtttHknEkiwXIx9lFlJoq11tugkU+BP+xS3LuBg21s8dBe6+Iuco6wcGabXiwT9zHm6T.Y9e9+7eN8y84dvzNNNoLMMy555lRWWOkmmSBccSoiiCQibsv01US2TWqd85i61s6VUpToxgO7giQIZvBvlad5Dm6SUN.B1lXwWDzMAuyoRPkyYkBqX544Miqqa+idzi1.XCaW65Zn0zvvXSPugs82ukkkUOxynO5ezGc30ccWmBQwbZ4V+uEnO+m5aOaP5TJmYGfQrNCXum1rxM1UJGKkxvO6m8yBQu7TziDvJIv6Lh3W4ja2EbELH8K1.XSGGusLLzGZXX.SwCABgX1nNVsnOrTiFMVDXdnbNfT+k2xsnsu8cQZQG+3Ep+e4pDVTGUwEio+qETIORoTd7iebLvSYHlGImH5eep.kJq30fn6yUD.IJ4SNnvBx.4Rnfj77wP+N56ILLLhFEHkgtholEikR4Pm0b6IkxHIMUVGn927DeyM0jZsojJ.WCCCdnG5gzJCot+6+9SKkxzPPZoTlVOPQzSROo7i+I93C2v1tuggwvH1vGOOuD+p+p+pZUhdVTmEmbd.HInT7BnTfxwrWeNfYjBx455lVBIQflqqiXokWVBDJQNVHiHuID8A5KQ1Gn+gO7g6aZZ1SJkcEBZKCkaslsSSGGm5llWX0Wyq40DHDB+C7bNnmo4pAn3HmFRgr4ZqsVKohHw1vwwo74bfCDXXXU9Vtsaqxw8rqhJg8JNNqU0zzrVoxzvx5nMwW04MTAiTw000Gv6ReNGzEvKh2P7O2y8bcymOuskk0oDBwo9v+AeTGgPDTsZ0pqZZrIJnOKk9xTTfbrc2qNS5X1NQYxVEvuUxjI6napORHkIz00mAX9G3Adzk788W42++vevxupW0qZQf4KRvr555YQmTrNIYomT2Kd158osC9+D5wHBIkTpPuzhOmESS.IUwWQ3Farw.gPzMXatzoOTKNnT01I1tPUR0HpkAHSd7yfxORNfb9a+ukT2vHIRgFBgv00ECSCgttNqt5pnqqOEK9KBEBFiPFi7t9.8rLs5tpgYWIz08Xtc+Q+y+nNVVVa433roiiSMfMV0xnJvFFVF0.1HWtYqHkReSSSWSSSWJWzCv211t7p6wnrkkUEGGmpm5GbppThJlllAnJZVYyHDnnrsJ6A3+5uzqtrss8F3OYVgiCZe.vvhBw.aa6AGywYD.BQoDFFFouhq3JTcfzuzDHaa8jk52ctMkM1Fi.5WxiNfdGgPzqZ0pgn5PTFcc8bWy0bM4JfeVCCirGywIKP1pJDsj8E9BuTE7seD8eZ1grIKpGo6LhJrh.fOvG3VAfa9luU.DejOxGSqHApyCcTgn7LtojCwBUIDJLhxz+FtganiTJaeS2zM0FEo+NrjPHEBQhrYylNLLLtKzIhQWRqlsBQHjkJURhXaBEFHTJh51ufwNNNi.Y+Por8JJo2shPJBbVywWJkAq+8WOdzZpgpfIade2280nDka3dJ2M9A11UApXZtZfooouPH7eNG5PUwuXcTcRqyLyjoqiiS6q5ptpMekWwqr5wN1w7AbMMMcunWxKwc8d8hUBGWGmi4ZXX3VL.eCqKrJAz3AOwC1AX3cdm2IuhWwqH46487dxTjJYQoBB+KAe7s8wrODTAIVLhIiKAM0zz5XXXL9wdhmHogowtbssWtX.EPweLKKkxc+e5+z+o4rssi8YlZA1XZ6qc5K6m1wGHrhLjJq7kgPTJNo+3IUXBpO0o7N4lpc1AwoHm4Z.jXEOxIkxEnLKevCdv7IRjXIlZTnLLz0bcbiTeZjG9vWhDPdVm0YIAvzX0niiHTnHJw9HhF0KAaAroggUMGGmpoSmt7q8W+WOPMdWqEXZZVtP.aXZdg0HfFnZ3xF.Ubbb7A7JP4.TchsFP8+cWzE0HLLrgooYcCCi5DTpAPCGGm5G0zrloo4FNNNUcNtSUJo5f6O5g+QA.9BgvCvUJkdPPka9Fu4Ft+.6Ne7O8md7EdgWXha38bCwib4SEZOdp1jQEKXDPuBphU098+9e+8DBQ3s8Q+Po00020wt+6a2+6ekuxEEBwBRobdoPLmgkwL9NNYK5Q5ScpSkhZSFM7yjt9+SC6uIwJJkRs+w+w+woK90fpUq1GnqOEigpuJwusG8KJRfZ+nGYbV.gTJ0xjIilToHiYPwedSFSjrYxNqmmmRAdPpMYIy3SJAJYQOb6hkDMlgCkQEkyzzr0kbIWZ8UsL2PHDa.rQXX3FJj8ZT200sgiiSMCKqM5zoSYaaaea60bAbsWy10000CeTHEQUHHEGYAczwuMPye0eqe65.UMMORfggQfllV.PUJWpNJYsdqkVZo1111sVbwEabIWxkrgsmcvU+5dc9BgnriiSsi6Z25qc22cWfQoSmlG3AdfT5AjAJlgMlXyclPn4aifNukZQTwE92+u+mGWAGtH...B.IQTPToShDIjs61NKkYwvvvBtttkrrrJBTHPU73EA10d26dmAH8BaPBXeOU90d1v15zat0zjWM1oAxTpbz6akIkPEGUHwna.FtOBdlKbXfBgIkKWVSmxIEBQFQIQNYfbpQISjRHjI61qqlLpXI.wEKA.oTRXnLL723Zu1PSSywQba4PgfdBonMRE5RLMWspggQLI.WmfBwxGbKgHbKaa6sjRYSjT2wwoxuyuyuSfgkk++w2y6w+3JeZ9.d11q481e6u8xEBnpgwgqS4Sy+W4neO2Wxy8fd.AQwtEHkRe.GCCqSIDBGTqmVtZ0pUW0xntPH1ZwEWbnPJRdAWvEDyEIK.L6l+KibtchxjgEwa.vnwiGKARnqqmQWWONuhbVFGM2fACxYa6kKOd4rrrRSQRREDW2Mdch0We8+sb8x+Mc6YqWPlfJBX4TvFogkxBYmCb2ETbWTJHmzSJul2z0z9y8Y9b0ApsDzp11cP5YpCkwECY9U.8pv9eaW+a647G+d+iOucu6cuWGamkmaWykaqs1BIzS.sVasiW6U9JeEUbbb7srV00089bLLLBnH0HP048HzODMRFGZPjRO7yBhda5q03q2Xh3JFYI4jRYlRBQl.HIEHjxqzCpFC401rsLopA4SCIxA94fUxRgpYoL4PmciGF2xsdq66c91e6mCvdcccKYXXrjmm2rKu7xoqVsZbgzhYF934aKpSQFSRtKJf6p6e+6eiG8Qez5aWc37xO5G8OR6JuxqDSyiLhRkGiOBxSJYYYVgPjtHnELwgZojNNGKUylMEKrvB8i5PPU1ljr5CHiH.u4nH4Inv4.kOzW6q80N+W1K6kcPMMMSfEbccyr7xqjXiMpBQA1gPNFICEBQeoT1yzzbfqiyHYzyZEfDjiAwPSSi9NNNwinRShVbzwwoypll8OlsaegPFJDhDc5zIYtb4RDIwbcht96.E5CZC.+9Pogj2eLUPihj5yeae9jWzEcQjHQhwVVVQx10QA7h6p1jwtJRJHiCjSq.jXMa2zll5YDhRYjR+jBEOYzGn4C+vO7Fm64dtUfRUgzMg06vSe0kis4z.8Lf2tfBEgx6+k+xe4Om+h+h+hC.TTHDoz0060oSmFmyryVO.pSQpRPwpPPLIS07q+s95se4W5+m8f0mdFT+IsCFmVA.KBICTvlOEdlYAmbDwsHpNbULGTNgTJGHD5MA+MfkqwxazZptp.flNjxSsHvrQnRIGjOGTYNoh.g2yuvuvuvYcu268d1ttt60vvHumq6bgRRYZZjvyySL4JSnPVhDjKt6cKazXynDYjSyaIsLW0bKm0bhI7u3NxtkTJZYYYDIQclw2+FYaaORSSangREc5V.5cettCjR4XSyiLFJGSNwQ79S9QPkQfdz8aOMnfF4KqQERPDwzEYakzxxJ4C8POTpy+7eoocbNVZMMsz555wiqXJGGGMCCCoHRV3K.sJCMQmlp.2p8uFYhOIKRNpyR.lkf86IkmsPHLbccmUWWOTHJ0ABpST29uhq3Jpdm24cVGnkmm2VWftdmxmN4uF+77mTe1Su.e76eIuka4VRc8W+0mEUBkKW.xWFVjBjr7CtwVEJrbETxTZU1VhReZPXBIYQxPcE72KB6JHhHbgBEfxkjRotPHLkRodDAbtfmmWtHjEH.vvvP554JQoHhwGCIBBkRFIlBUf850qc1r4ZBxFqs1Za9q7a8qzx8XtcLrL5633z+u6u+uavu3k9K169u+6u6q3U7Jl19aDfz11Vdy27MKusa61j.iK.gG2wA.su025ak70+5e8w9lhVqnPen7PHezykJgPAIEKKIfDTjrDPNaa6YS.yzrc6zG7fGTHDkFAAckRYqW+q+0W+y+4+7JRF9zI.1oiQX56qZurW1KSbW20cEihhYgB4Aw9tnKZ+G3Nti6Xu6YO6YYgPjywwQHPLR2PumtPrUTmyCdausq26C8gtEO0i2Jax11z6DNyOUEl3Yismp09iGalb.4dkuxWYtu7W9Km3M8a9aN3ye229VT1nA3FiJhmYT3b.RxiPVJwtwGSfCdW20ccnK+xu7mKvY455VvvvXNWW2zS88lVRRkn3hoP4N45GIiLsLG535zQhnoFrggwgqAhF11Gaya3F98ZcGeyOWSBXKJPWJyvH9DIzxxJz00MzvXUI3GZaaGZYYMoqzu829aO4sca2V74xvBvv62yarTJkFFqB3KO8q0hBUshKBHjpbPJJHePJpPNJvt5ep9ylISlLtttiz002THDUfUJCUqCVsA6oQM2S8yoCPJdDqYA6U.1KvAkR4AANqWyq40r7G9C+QRF46eKSyUq43rVEf.yKzzEe7A1XEnU0Ib1vR8gZOURMrXGG6cVPreb11IBSlAJNKDLOnOuT5NqPIN.8PE+SbAUiQr5N6Jd1ScpSMyd1ydlEJsKozaAfkEhRkFNz1LYxjVsa21LWtbE8882MRlEwjD4zl9pzvv3zRpkss0F9XO1i08bNmyYKoTzPHja9DOwSr0YcVmUefwemuy2Q9y+y+yOjnho.EZ53b7llllsghsnXvVDPGxy.pnd1FgJTwoN0oXO64BAJO111djkk0NIZzInzpHj3X11IzzzRe0W8+G4t268ak8Ceqe3Lu02waUnNOKLDJKgRofvLiF4lx22Wy5nVivmtThsvWUbZSXSGUb66b8ym13z1mh.Xmg7rDUJYB9myvgCO6jISpexSdx4RlNcnkgQGnXKGm0ZIDhlggg0UiVonL4opRUsn6AfQOxScwV+Iw21T9kOjFbhDvxIQeiL3wrTfEnrZzwK.oCTnFqETrFDTaJz88z4uWYyVjYIPeIvyD3b.NOoTdPWWu8AxhBgXWRoLclLYRLXvfoPujThRNo2V1ykzGgXPz6q8iH2z102r9lKtvhwbTTOGGmAMp0X3tWZ2CEJRpU9XO1iocVm0YgkkUTCGKzVg3uR8AYeUtPkFPd+gTAI4QiJJTvIkRooo43ScpSMdO64EFB9Baa6DVVVw48k4QezGM092+9ieuWTDzVywIgh+uJkTJ8yD0XuY.Raa6NzzTutPn6BRaE5WWrAGpdWNwSad1w13YfB6BJqCr+O3s8AOu2wa6cbN.K444IVZokZlISlZBgnAEnIkKto5Y1jhN1AJNPcMa0CF1eJjB8+OBSh1l9ksPXi3tYDBtQAtEzufO8EBwvO2m4yAQNZqg0zUW8YZK7.wU8FZSA17NtkOT8EVXgFRobKIh9as0ViLMMkBIBSSyDG4HGNoPHRoookFJmNLLTMOWAp8Gf7G56i5Xur.NwOKma4o2lDrPQEY0lALxDwUDoBTJkCd2u2Xn5HJwvnYpcGN3qj.7SpP1Q0LEKGQlWdJEpYtYxtDvRgggKBh4bccyHkxjoSmVAm0HSakLVJPDUARoT1e80Wu8Cb+2eCSSyMJbfBaXa6V+QezGMR4iBhPXTkgW20ccCDBQOn7f79QKHTYa4ENfBQIATHD7GZZZ1a94muqggQWGGmdkJUJJvkkkGH5ZK.zXIRppRe4rTjbWwUbE4zzzlHEmFFFhM1nJEKFMqiHkuq2w6LDEevDMqh18LLWs+67c9NGfhCAFHDx9frqiiSafsVc0UacG2wcroo4p0KpJNTk.HvZUifUMM8MLL7OvuvAhq.bTW8C7ABdSuoWdEOuu+FtttM.+MKTgl.MIflW7EewM2291WKKqKrEPqvvvlfWCfZFFFa.rgss8FkJUJBl348yCtW7EewNkAWqUMbKJD9W9u3gCD5hpPwFW8U+ZZ6551+.G3.iu8a++hD7Ev5mI1xQu65Egxjx8A590+5e8dQIpKBkxrttt6pQiFKcbW6UbcsW1831K53r1tt0OxsNqZTuHyK+Re4of0mVMF9I8coIcq3PQAsD.IwfjK6QZvIMEi5Vihr4Dn53Z++x+x+xNn5TTGXi9Va7jYAcOhHiXkr1oTZApLgsvKTnvR28ce2KVud84QRNWW2TRPSnPtzjQvITJkRYnBlmRB2rQiwpNuJG.Lne+9CVUkDZ+u2ey2qqPH1RHjMkRQCSSkbyYYYnjxUEbZa533r4W3K7EpATwv3v95ptJ3UF7MV0nxKzzrJTdCJPMnXDTVYSnRSfs.u1uxW4EFgtlxcVohh7xHPoRY+C+C+CssrNbWfAm+4+KEJkdB.Qz0TnTJCKVr3XExxHLpKfoKCy7ley+G1UQOlGpMG4IqpH4mQ7QUH0i5HMz0mRsA59C+g+vggggBWW2LE2+byMd73c6Z6tTgBEV9Nuy6bo+ium2yh4g4000mqLjCcRm+jS5F7yFqg8T0ULwRKsj35u9qGXIgdz0VYhJLVfTt1ZeeIEQpfv8YzwPPc0Ouoa5lHfRQIcVL7CbK2ffnjOjR4LsZ0JmHlzpmL7Mp6gddtiERFKjgQ7FgHlzpmPXc.8DBY2rYytkoowlqZZtwuxuxuR.A3aXYDXaaWwz7H0eCWyankk0E18vG9v8cccGXZZ1a+ufWPGuS40TWWutkkUsa6197UKo5.V4xPUyiZtwEZZV60+5e80HOUsssiGqAWnrOPYnxFtt2eMWW2MgxapGDYeFP8G3Adf5VVVanacgabdm24UywwoQjr31SHDi+7e9OuFPRcc8n3ClzMzmtM4ccW2Uz8mZJ9xgx8ff1e6u82dq8rm8zEkMbRfbRjy8e9+7Gati4nTBFGGmDenOzsDUnhJwiTb5kWd4HBh2J49dpk44eptUrXwnXpzCWAB+pe0u53zoSO3y++ys2ekxzCbGP9mww3XRBPG3Q..M7iSPl4u7K+xW.XdWW2XN7HoDzjP7HsD8tfPCo5ZNZGNV.CQJ6JDhsPHZ53XuIR1THk0UqgUthiyZk0zzpbG2wmqZIk5s0jxz9a8s9VsMMM6XYsZL5+ZC9s.1zxxZSfF+4+4+4MxmOes2065cUEVoRAk8WkxvF5Oe85u.CiFfecxSCHebmZa7Q9H+gQ9BC176889qahhXVamuBaArEkoYlLYZJkxlFFq19a9M+lCO+y+BApF2E7yD+XviPHXOhRzGx2EnSXX3.fvOxG9Cm.jY.xZZZl1wYsjQHDJliDE.IpRoT23MdiofURB0dpP2xN8KI1wuyyF1fRvZLDD4W1S8LEZ9I9Dehs.5.K0awSezK296ZgDVHbO6YOQEs2efPH5qFyhfQoRkRHDhzyN6r4788yAjgHxRe94WPDcUD8tqT535FWPtQBEmNM.D8.ZeNmy4zRHjMzzj0u7KY0MNqy5rpXdDSeSyU89y9y9fNBgvQJktt11AP4pBQXcf5PPi7J6u5Tg3D7ZnqejV.c2yddgi77teoqqa7nNLZ80WuejBH1DzaTR8cpE.0rNpUiCaXT+du2uUMzo5q409Z7.b.rgx1.tfumTFDzrYyJVVV0K4yV.ivmjREOtrKGEGKNKrPbrqOSqkIgHBfcYFZVgdfeWJPujISN100MQpTolwY8Ss6a8CdSK63r1Jlllq.rjoo4tKpFUpYnxDxFN4ifdjM+gd1hGG2g85IhhCbin30HiLPlE0nXksLEiPAcIPgpjwvFOcMYa67LKBEBP.dInDYfBy8w9X+YyOd73c0qa2YEJDCm.jZ862e6cfLhSFESxkQYeIDCQQ5qiQMNoCBCC69R+Eeoc52ueaCiizpHroooYiy+4e9M.peDCiZFFFUu3esKNF4QkApBkiPro+F11GqlqqacvuQoJQiaTEZ93O9i2RwmRWvV.akHQhVS4+qAPcOOuZOvC7.UylMa.n6UHx9J.bMW0zWWHBrsOVfPWD.kpHkx5c5zosFD9FulWa5a7F++ZFHh6NodJqSblxOSkiiOq2G38+A5CLx00KwQLLxJDhcIDhcSQ1sywc1sq6wWv00cd0HrqlNgHU.ZLXOFB9YI3C9ox1yVK5uc0+rHI1jVp3MiIJKAptjjTJkiO3AOX6G9ge33f7epl+1mp8erL6t.J4v6.2y8bOOmW7K9EetnlW4EiHXoQRXKMgPAAYgHXrT5PXnskkkKJDKzlsqj+3EgA0mLmY+LUh2D.IJVjzAApBbHkxYDED4hbzESJki+g+veXuK3Btf1.MWBZWa66gQAdajCbmEEbHi6R0bBgHuqq6dz00Ovcc22847K8K9RL1n5FKJgY.YJPjnToR366KAFKDhgRj8DH1JZrSpp3CF7PUjfMJBaF.s+A+fePmi9xOZOpPeUGFqLcmnDnd9sqnOYkRo3IdhmXz+ty9rGcbW2QRoLTQJazes0N0V6YO6QEviNcviQLoJnr6H9k3f862+7SlL4gBBB1uTJyifYkRRIT+tSfpNaiTlNn5BQOaW2gBYnpSsBB60qyv8u+ysmqqcaCCqVNNNaZZZ1fhro6wc27i8m+wZ999ieewnCXbz4SxRf3XNNiusa615cS29M0QMWrkFA9gSc7kw+9LYVsU2ahP5zXCCBccAccDddS5diF5.dS5CSLWmLCvLEJTXlG7AevTJ1jm9TjlJnNWpdjS3XDl7LwOOZ.IVZIxUqFKWrH6KHfCIkxmKpp2unqqqlooYeGGmMCCCq82eO2SvK3487bufK3BBJ.UKGgxDVhNTitP9AJjNLIf9ebbdtcGRiJx5q5U8pR8G8G8GkZ0UWMt6qyJUxupRoCJhf.FbEWwqp8cdm+UwRTWbWpiuGbZ2Gijc64u268dW3hu3KdoBEXkxkQGkjauGWWGcPrhDlSnr+Tv77zOWiWLdrpK+hgfL1lqGPGgTr4nvQMzzzZXYY0Dnya3MbMc9au8Oa6.ncwhz4u7S+M5dnm+g5G0keETnySWpbZy59ow345P3DxQdYBYCjph.OYzBlNPrmTGr0gY7fbEKRlf.R355pYXXj3Vu0aM463c7NRRID3S3W7K+EGeU+ZW03G5gdn9e4+5+51u2e+eeUQ9fMc2Vplel5La73yEyGU6sRkJ6eokV5r0zzJ544Lmttovyys2G5C8ga9m9m9mV6ge3Gtx49hO2p3OIPWkc1Ydm4NS1lt6UZ55jH58O06qKQVpo3vDoTthPH1MPBaa6VVVVUKAd9OyHLYmjq3zpQwBEfBARoUoRhyx2WtO.KWW2BkJUZAMMsrCFNHQ5ToEJTknryjfzxzT5DI2uekuxWI7JuxqbX+A8G7pt5W6f65+1+sXTM0.XiibjiTob4xUi7oEOSyxeyeyqU6q9I9Tx.XPoRz02msz0YKOuIxr5HLXLtmFGeMEuXfXEXb0XTNsBioJRXYsH6uchbmsIFxnQgHBoWZkJgz2mQRor2wO9was5pqpJxrNcijw5cxQGvNSfbejfSRFnztAeCfy4s+Nemmyu2MbC60vvHuiiyrFFFBWW2dlllMbbbBd3G9gceIujWhWgBDTtL0xmmVUpLQsFFyhDR8oH+7e5v0DSrAsrHos8D6jbThbO924wyd1m8YmVJkiEJo2tEaK8l6zlam623666BEzK1Ovyyww44IDxCJkBcf4UI4KhWaZ6lfojw0wDkDaLWJEFF1QSSq6q5xu7d+0+s+s8jR4VRgr9McK2ZkW4K6UT4xtreoIxa4W4q7U5ekW4UFGi0DYLGXbIXjOLpXQFFDvXCCjttS7aIoDR7eRxh4jm44AhGeWVFAafFnqoPW2ooZCoO5QOZ1evO3GDeuUCkra1wC1zvfFttz5.GftOxyLGIEu9QFf40AcO3.862+7CCCeN0pUaOFFFyMd73wIRjnoPHJiBEZwimVrTF2FnisscWKKqd4yS2JUdRy7+zEIQvzOW9w2m2oglo8BYWGxkOOyToBynqSJOODqu95826d2aaJQK7eR7g1DTysDjtVDJnoDyhOyCEWAB1iTJ2OvAZ1r49le94KnRvhnwwIpVIp3ujBAQwhICkxIiI7.P1EDsQPSAhMehm3IZbQWzE0.n0q9U+p67O7k9RCJGKkoJDL0y11qmkkdL2jzAkcXGcc8dJhiMNNYxlGxTARoqizyiAqrB8pVcxZsiPmvoh+J9coIiRkJ7rnQZPmQpQ8ekzP03wXXFhPLvW7K9ESdUW0UEhZs8MAZrDznFz5.Gf9mA1cwqgNOQuK2qWumSlLY1OfNPtVsaOd94Nmttt2WSoT13c86+6W4y8Y9LQEzVgtI1dspwVvX6SmCG+I0tRnJByIRFMVVwxI7tjR4hBkjSOaz3S2ofPrQEnhtN077lfT9mN91XxzFfpRKG31u8a+49q+q+q+77771utpiOy655kAjIEhs0c5oQVBR4.ohWF6KPLPBi9a+a+5i78pz+M9ldisMMMiFGey5QqYt0Mey2bma3Ftg9exO4mr+uw69cOfffAO+m+yOL3Ad.Vy1NrZ0pCO7ke3dDPOXk9P0HQp3z7aMc7XRfvH+eiWZIj0pgnXQREDL03nt85uv1wsFq1qYJByErsTMOSzwqQ73HhpXNwnz+oK9LsCcHRdhSvL4yyxUpvYA7bkR44ArOWW2cgfwF5FaIDhl.M+m+m+m27fG7fUK.9kisqJwlJeEK2C1XmHl6+se6YityM8lbe1HgklnO5.HkRsHF6MiPWO2Zqs1Lrs70clxD9gVvHJQ+RQDDzkbIWRi9862x00sqqqaz71KDBUGRjFFFgiGOd3EZXLxx5npEbMLllD2DtttT+m8nJIdS.nEDLI35zBQwLxxxz.oJAIoDhhBQ3EbAWPLrqGVapEwNfhb2RlO+vIAlVPHxHDEUrsegBYijYwrurK+xyjNclzEKUJEPBCCSM.7THuQhpnrgHYrTJGspo4.gPLvwwoussc7XCLN.v11V6nG8nZ5QvMKpXIC.5UD5C4Gx1cpVCH4latYhy9rOas.0Lp12zzbKfMqTgFQEKoCrz.7Tuvc.PxBJXoE4DNyi+3OdVee+rQPVWSHmjHqL9+h6NqTHBEBQnooYnqiSnPJGaZZM.gZbIxlclMUxYnHhMpo9ZqsVCBnw8e+2ei+h+322ltttMJFsPGQx.qOT8a7M9FUuoa5lpshOM.8Ff+lnBnssIzCLTKnWr3zKf2FXqxpe110U0ssnEM1R88WpEdJTAXBaQQ1x11NB4Dzub4xCxmO+jpiqGv3HD6LcBaS+ympMIfrVM0h9AAwApvvn4KMgooYFGGmbHXFiRF4dCWy0j4BtfKHKP10Ty++r.ytRMlALmgHdHf+k43hy3McP9WcO+UxqX0U29DWsnqlTplEZoubrTJGdm24e0DhbS+I6zd5D3zpDkL6EewWbNfEJWl7862uz27a9MKBrLHlOLLLqFjLVts62qmhYWO8hxMVNNbj.E7NEJtkoOPu+qew+qcC0nydeguvshJVRCf5u+2+GnwZNN0cbbpGDPsK6kcYwi4VE0m7UnhZ1YWD1jko0xwJCPDbt8lndTLRgjFqSqSMJbvXnNOUPGMdLg5CzyC51rYyN+f6yaqu3W7K1xvvnEPqkWd4s9ve3Oba7oiTJ6ec+ZW03+o+o+Iseoy+7y9tugaHtX34bOc9x4YpyrRNzgNM9nJe97M0zz1BXfggk.HqTxtdqu025ht1tKetm6uvJtq4tBvREgc644sqUNcEl3mT6qc1E23hkD8ojF0T66RPha91tYsa4ltEA.wDqq+Yzg4PpigttlZ8vIEcHcfTlSHDyEDvBc61cAGkpAMiumeZfDUqTEWW2PAhI7QgPoTIiQnRh8JuxqrOP+9c608Pmy4F6eoIJdlZy669tuVu923ars4QNRah727E97eg1ehOwmZqiYa2xwwo0wNlSSaa6lddSRFeKnTabo80bMWS6hPaJQ6hD0s9nQVrJQnmCZsbU1BVtMrQL5A5sBLHRYyFRwhwRx5V.stnK5hZdi+E+EMAZ64I6IUPzd7pp2wUwM38LpjHSWL1vCcRBUEq2e.P2uw23ar0c7A+fcbcc6MXvfwlqtpv00MYwhESezm2Qy.j4k7RdIYAxVtLyBL2Zqcp3F7jExmw6DdY.RuDjj88SeEzw1VseUqUVTfOZm8Ye1ZEA9nezOZnw1Ec3Lkn5EPQULEkJkCXtgCGNOvtjRQNCCizFFFIjnPAgL96Hi9pphzMV.ijHGb6e5auKP68rm8zxzzrwO3AevZnJXXvEZX4+68Nem9W1k8KED82UuHz5JuxemdNNNCoPgXeUwERt6G3y7Y5BzMHPwgDQqCFMNrVwb8Uq7p0+ZmeadopGPuJp8Uefd6cC5BFcAuIieYI0HJmDP7CN0oFGE6RbAnCcUpdUFWWkbJ+HOxynuro+6k.i8h7mlISldISlb.HBGLXfV850S455l568c+tIcbbRBjz00Msqqqp3BQx2rkkRMIpT4IYWMMh2RrOPi88jN29w0FbRQSVOZ7KqTQkjlmR1VGu28t2gf4foJV0SocVsoNWK5iFjWCBRhB0mY61oSt1sam0wyKMwD7pTAWICCSUA4fwRoXLBFEJmr9TaProPnsAPf.gmwpFdm0dOqxpN2SyuzW5KsUYkHCzDXiRkoJTphkk9Fqu95aBzd80WuOTbDvXuAChe2IN9fdUfdddd8N1wb6BzsZ0IEgb.r2gQEqcPdnGKQu8NAUTJerdvlG3.GnNPiE7nIr2MgpQH9jVEfNP9Qm5TmJwU8VdKYkR4bRob2e2+wu6h.KTKR4PejGgj7L6WQhJN3SqAfYyls8nQi5EkCjHWlLo6z4IxBgyXZZNye2m4yLiuu+L111yftdtK4Rtj3XyRUqVsD1O0qe9iqcUjMxI.PJDBohAFM.PHJT.oTJ2XiMBAF0rYygUhhewy6IQx1OkaEO8BWkqc61yEFFNmLLblF0qmw00KIH0hi+mHz+x1igy.hGQZSqFxnFwbYW9Ur4a7M8FiV2i1+nS7i53551wzzryMdi23V27MbCsbcca7t+M9MZPPPMfMdfG3ApVFpXYYswCc+OzlkBXqG8QWuMTMdc1t5v.PWU7MEp4m36CnieTrb0po9yAJT.GkiQ9Z3RcfMKBMYYZpPjYLA4Ryfn04kR4l5BQbQzkDUPTCHIrz+RnHRdhSn7mUoxDaqHDl3BPhE8Cy...f.PRDEDUFjrKfEcbbV1wwYka8i7Qx655l+3NNKipfMyj2mTpwhbiorE9+6r8rUASlTUxSFONN5DCk6HBKqbpu7W9Km4s9pe0410t10L.4VYEEIioeFRNk1vX7Yf+1RfZiLYxzzzzriPHFFIarZQDIj.Hzx5BGe8efOvPveDPng5gebG00LLLz1KHhxp3mUaSm.WbGfS7nO5il.EolpAns9fAB7QZqb5eZjqKfDNj3QfDPsTURTIpCdExTARKkAoARWrb4ze5O8mNiPICWobccS566oYXXHbclnMlwOOi5nlX38du26f0bb5KkxAQ70gz11VfREERXYYkvwwIgGnYXXHhf+lDJNN.FCUj.hRBQh27a9MmPJkZG7f6VywwQpCiIOSbhw1J1P2n45cLf7QP8uDrcklSYYYkNRZ5z.Q7P8GE.sHDgXxLW2tUqwRobjiiy3PXjPHGFIEXsLuPyXxWppoo4FVVV0LMMaTpTolPwVu7W9KuoOzzvvnUDqi2111N9bs90dsWacvngJ4AusXAhbXZzyAF.tiVe80GsTPv.fdAAA8JURkv6dgAr3jjd2gC0Zc+xe4ubOfdNPOBnmkk0zUtch8iTJ07fDP4jjOV1ryOQ913L3crn60C.5+09ZesAO5i9viADNNNo.RKQjIQpDo+N+iem3NDOqkk07111K.E2cUXAvYWTj4.xsC3l9u1jLjS8SEJJpyX+o5xqPQPln9niPHBO4IO4PJF+9whC8dxA6I.D6cupBsTRghmTREwGO+Mdi23xoSmtvO2O2OWdfcChY0zzRKkjPIOcRxlIqjHI0j3hk.iDZZC.4.yUM6KgdQbgS6WyU+ZZIjxlNqs1l.Mrss2rXQZYZZ1ZUSysLW0bhsDLMZJpzPotOzoNziMn+FaSPqi.FE0wrwv9h5TjcHVp2Ad7G+wC8fvSdxuSHP3h11wcIenooJvToT1Y98Oeq8nqu4UcUW0D3s+FeiuwFuk2xaYSPukPHZG.8Nuy67F5KkxToRk3O3c+GrsLxAIgC7uzyXIm3DwjKV274UDDYQUGK5DcsPXXXpjISlsc+1yNdr2BFFGY2Robw0bbVPWWeWUI11ZRA49IkDXm96o89e+u+X+vI.+DPoDnTLHsa3seChq+cc8.vccW20N1MVOCGhSnJ5hmWLAzkrTIEBeJJDySIV7O6i7w1ctb4VP.yYXXjEAIiP6i.TbWRTUfi8wMF4DnEOzzb0AyuvBC9fevap6a928M2Vo9MhMMLrZIkxs9belOSW2669ljb4081t5t.s0zzZYtpYSSSyMiJlWKhJJmm2w6B589re1Oau.nO9zOHx2zIO4I6RjhUn9bfdJayM5iUb.WKLPg9jZiO4IO4XyffIxnrgA891e6ucu26u0u0.Jv.gPLTHDit5q9pmBMdkSCFwbpyzwILcWPm3y+DPHTYL5pjhtrK6ZZ6Ca8betO2tUpTYf6wNVnPHz7CBR+Wc2+UYAlo.LKEYNGGmc455tq8rm8DIe1jCpjUW+noARUCRxIOiFg3eb2lNg4H6j.MnPBoTlH.zdKuk2Bta6y4LoycQ6ufj.YJ56OKv7ISlbdT1Y4.R454lPHUGTsHBRLtrMQGmQFll8+jepOc62v09FZgTQ5pNNN0N4IOYEoRNzcB.aSSSGJP.P067N+FaFnF4ltBgnud4xCt669t6aaa2uPAkMx63M9F6SgIwxri0+r6B6qKwEFIfAUN8XeFUoRknjfyObc0ZsCvJpYNAARePqb4GRSJkhRAAg+1+1+1CAFVr3DThltYylwbE2zj+5j6BOM2WI59eT7Xk5mLYxAFF5iqVsJAAAZ.IO34cdojBYZfrG1vXFT9ulqXQlw11NcdHAEdRGm33AItXqu0a4VDbxS6X+ShuNMckrcmxDxfN4hQhrTJSs+m+9E.gfyXXooQT0Na5vTwstRJEAoWYFJvb.y+c+te24q2nwbRoLqotdJgp3TBCSCRkNo7q+0+5gB053iQJGIkxgZJUSIhKRj0jRYfoooqgggC93c3eQixRor9k8Rtr1epO0mpqqqaWCCi1NNNs7g5feMzo9d26d2z11t8d26d665d7w.xU1XiIw0ZYoV+y00sm9En2wvvHtIDwMSXXkJGaTTwVFUAFQMhrwn+i+3Odruu1Oxi7Hc.qtaB8f06wRw9Dy2qLLBpfkkUBomWJgPjEH2K5hdQyALmNQxn9oSx0Osqg9HSS.rQEtIYxjsMLL5JDhwISlTqd85ojRQZnXl0bcyTRUnzYz87l4dtm6IGPlUVgTKszRIWRMY.SxyXGOa+wNdM0mhgJ+Utg.gEpTITHDiWd4kGUrnX3BKrvf7v.LeRHH7o0uVvoWvjLunWzKJmuueVDhLc51MIH0LLLDBglPoH0BoggQX+98GQTwRBEhlBAaXaaWV.AlqZ5KjxIE4867c+Na9RurWZKHrsiiS626688tkuhnWaFDM9ym5TmJtwo0AZbMuoqeSeXq8u+81AiX6n7C7fQfWnssszz2eLvPee+9kJo7+s.LjEYZtnaB21AU1RwKhzI.5xFzMBgnS9Hkx1.aIDEa5qjd8NnViEnXBWUNgmIix2De8DYi+.Ov8OTUZSRgjYcccWvzzbIfU9+9i7Qx+C+g+vBlllE.VAzWnBLCDjlEeVYDu9e41dVGgIDmzkGiMhRnAUxbItxq7JS9g+y9yRUud8zCFHSWsJoWVAG2yjYCN9g4PTcsZKJo5rkTJaKkx9tttiEfrYylZgggBGGGgq6w426262KdeHbi5J40ccWWxZ0HwJf15f.uel8v8opXIo.Rs+8u+DnhgQpCgoRkJDEaaOr.LfRriDmOg1hQvLDe037.kyEMdTYAxE.y7ldSuoY9deuuW1Nc5jpToRIDBMMWGWQzcfsCFO5EXIxAu0W6qseud8GHiJPiFf0EZk.HUDZORXZZJJEeQIDQy8evzWWICfje7O9GOA.kKi7HllgufK+xG68C8llbYi+riwj5.w2yzJnBxH4ryNabvzZFF5RgXRWXGKfwyN6LiA4na9lu4gyM2bSBJSfXjTJFVqVsdlllagOMLMMq9k9JeoJNNNSTNBCCiVmx69ZoCaAElT8Yaa61V+bVS2I2lf6VKG24qMoudbvappzNZu6cuCqEAyzhEuvA99LbAX75fLlaCht9lvX0.C909090FrrpKsSN+KAi9u+e++93FMZDJTLVaBgPjtTDIlRElU8+WIt6+mIuqOsyxdttt8ewu3Kc327u+uWJhTxDT7YRte0ege9YbbblCEzHWvxxZ2NNqsPQ0edWOze2CMaQHGr4Yp5C7zsMMJNhsIGUBFQwI1FQ+zebQX3YcVm0PBTDDHT+oJXO.cw5qivRHD9PB7HoPHxUqVsc8G9G9GtafEqWu9BtttyYXnmUnTcIAQEhSHXrPaaaMfQBQDxbjz6O325OnqPELSKSSyFHUEA4y849b0.Znoos42467XMu4a9l25X11sIfIc9ms6feDy8WaaEJXJnryAT+TWWOJ3jSNoS6XirHDd1m8YGBDtu8suvy67Nuw0mb9t2QutW20OXIEuQrEJN1ItngwepIDhZddqUOuRt+Z0nQi1BkDIO588m79fs8WkDdjyH9nRcMr29UpP6hJRjsEPGWW29RobbhDIBKTnfXlryjLQhDYu6uwe4bNNN6xz7Hwiy2Lkfbrzj4w9LQkINS2ju6286F.toa5lh7K6mHOj.JngBMShh.G5PGZGe0mQYxIFMJREpnJkv2mrEgcUFVT5IW52859cVzUgtjbQiWZBCCCMWWWg.vwwASCC.gTnFKmQnj9yg.CccO9vUMMGJjh9e7O1GusiiSSSkJZsooo4V111cLLL59DOwSzwwwo8wN1o1pjxGWcBHljUmpX0zSWWuO3MHOLDzmTT2hvv88h1WbhqQ1lOR76BiwlQ6CFAaNBXz8bO2y38su8MxYx47xCbcUn+xCFQ4sGykuvW3K.pmmQEk0M6dgzf0S0y3mpBmLFOFVD5Ak6.zYlYloqoo4PzzjRoLgk4poMhRdsLLu6wbWvzzbdCCi4We80misQ.aJvI429a+sS.F+zBYI6Dw.63SYs64dtmjfdBJUJAfVdPP9y38aLhUyEnd+YAT9om000MiqqaBBQf3IccoZ5PzZBNNtct1q8Zagj5BMQMyUM2PJkUEBQYSSSOKCCaOaaa.OJSYcn9UbEWVSf1999cMLL54A8t7K+x6YYcz9kKS+7vfMfX40bm95ir2N4oMBOrC+e4ymOZMfJi1211eiK.iKVrXnTJCKTnPnqqanOL5q9U+pCgRC88kgQbSTp4me9IDsHO8pyzjmQGPc+e50qGB98a1rYLxakm+4e9Z.IWXgERaYXkCX10rsm2vvX2PoEBBXtUsrxVARR4SiiR1QhqmPrOPd8W+sN0ykS6mmoaSEq49R34Qp7PFGHGdLqmm2b.yJDhr+O969ejX6mE01IIzp9bnSGALRYkTm7jmLKvbTlc+89deuEeAufWvtylM6bFFFYcccSFArDQXXHCGNRd3CeARDBkJ3HXLBEO4Az0wwIlvYK6333BEccbb79V+0+OqbDSy5epO6mp4u7u7ubaoT19G8i9QszzzZpGykWdrYAnokkUafdFFFi777BqN45X4PaaFsWnuggQGJOYs23QwaHvHksUP3ANchNdrNL5r+2c1SPhq5icrc5HpwHERvpDBHcbbHp4NgRoLTHJJ.R9XO1ik0Sc+emEL4YZSpCiHO8iNma827272zzyyqsiiSeg59oPHDIcbVK4FarQxG7AevjVVVo9911QM4POU0pjpHjnFng8oYysSer+q0e2TuGGLcwcCCjxn2UKNLHP1+BuvWP+Jv.b9Wbrwi2TMd.RPIUrGG4HGIcDJ4TwYJUnbWWOl36kRWW2wm8Ye1ijpmssERYSojZWnkUYCSSO6iY6XZZ5XZZ588+9e+f8sm8sAPCCCqlRgnUgn3+e7G+waBzx00cq8r5dhQYYDZ37acpScJEu+3ROCEWNNBHb80WWZYYE5DEqXoRGcnuOCWDFu4oG+ebA8hssFrMuPdfIq2VBFvRp+egRxi6Ak6VHx9UHDQivyj7uNS3TvcVvjd1198PMZ4fPjtToRy333rKfEdfG7AV7E7BdAK633j+K7E9B4AukxqVeI2AyevyDfP7+1s8rYASdRvS2EFA5iJ.iEB8wQNKj6d26VjNsHIjO4FO4ND9LsEBGXLvvpn2Ee1RHDsFMZTLOnLBgPtqcsKgkkUxSbhSjxvX0TEgTPoTO1i8XpY+pDo+nezOZZHe5pmdv1+rppXSGTSZkhkLo6ofJnxA.Cde+Quu9.CJqfJ4NUlBs5aOm7yHDhYkR4r5BwrPo4PIevKPQl+E9BegyMyLyjUSSKkTJ0RlNIp8iLDDiPF+BqnulPzaMGm94xkcfk4ENx11MzvZUbVyQCPXXXfiiy326688NzOJ3YOOuwd.PdsUfjPgznHiojUqVMxwPowqYaGd228cGFkzWjyzEiSLbGiRwi.XoAjnLkRQYR666mFUx7BTIiLVp3dkQs6zYT61sGIPL3c7NdGCPnpnq4QLGXZZLPHDCVbwkhQAvl.0d0W4qdCSyirAJT.zDXq8nq21Kh7Lgk5.z0x5E1EmHoTD5TLpHOS08+gpJKaMZYXngw4ucxEKw.vd.jezlO4YaTDMDEwctZHvvMfgJYwcog.C7I+fW5K8kNXgEVHdgFAPFuHBESJk6xGlEVN6RPJEik+z5vbGApVZ.Pu27a9M2CXv4cvCJ0MLzz00SKfrFFFybbW2cYZZtvse629BNNNwDG3LAQD504e9me1iYamFVJ0BaSbi+jrEc9cnPfPeLBIXax6Jl.u7UcPX.v.UmcrdxnK4PH.OAXgy1nyIAPpkVZoIbiBQnXPEnmTC1dbujHBQxX0BSpf.d++Iu+Alll8GMdTme2e2e2lRnAHp9s+1+iULMORYSSyxW8Ue0UoH0MLLZdNmyEs0MbC2PaKKq3fzF.L3085dc8m5OOMz62tfHPHOxj+twG5z65bHpQkaLnOpXTxt+S+S+SJaEcFAqO7Vtk+K8qE0gLhf0o5mVS24j5555Mp.Mgfl6d2mabQC6GUL7D.oW4LqnESYms9Hvn+Tx9bWoT1WJkCMLNx3nQmS333j54cnCk1xxJiiywy8o9DeprTnPVexmw+D9SHvth+3WTto6nUz8tkCAjuq206hn8YxJPJnbRgnTBgPjvWJ0FOdvjq0ROc68mz0MiEVBI3mDxmyWJm+lto+zEu4a8lWDUhrJzkfhXDiPJoPpFCwXjSJkQiHgoo4.oT1uamN8kR4f0bb5GRXWfs1byMa.EpWDZXa60zxxZKf1m0O2Y01zzr0d1yKZyu+oN0jmyLguexOsJNM.XfB1zdSJXW.GXDAOoQC4zrAOILFrFuLL5RtjeksQOfEQJBxRCbUu2N59u+6ex3kDMlcojRY1VsZkCEGKjFrSF4G6L443n.nOrR2RPm6+9u+t1t1Cz00CMLLD1NGKkqqW1tc6tq21a6srfgkwtA1sqq6B6cuunoPXBoCBBRbQWzEgZbvK9SSXFG8dyxOoD0uzK8R0jR2j3GlDHYEVVKh.02YmC24Gs7JHYmI5ZZ9gCGtfqq67FFFyDgNyDaWrDYrRRDcuTNVUPNYWP15D+vGrgoo4F.UIPgHy8t28VtfZF180srh6RaCOnIX1tHzoTom+1D75xzA76BF8prs8yTm+SV+amHo4zeO8Q1tX0GJxF7jaG2vPEuVrb+BBQenT+IclUmdfeegn3.WUBbBNMt0I+yjuLA.OBG.pvDhjrXzwrc61wIMG555JLMORROOmrttty.rKKKq4cccm2w4X6x11dlxaipkDaeLs1Y7nQH21dbz5e6rHgmIaaaib.R.mLETJcEH6K6k8xxALqggwLsa2NqTJSkOe9oSdamHYRi+e4t28njjq5677yuHeVO5p55UFYFQ1cIgZdnRFiTIAFM3EYOyvNVlY.6iWXVarGav3mXyrHj.6cXGNLX7.5AL1fAOCCiN9r90ZViQ.dEXFaCdGrEVp6V.lFAzHT2cFQjYVO5tdkui329G2ajUVU2p6RHI.u2yoNY20i3wMtwu6uGe+88KK4vo10uYWHiHR9q5ptpwwRZ5O+m+yethEKNSmNclLLLpvBKrPFrDXcT8H0rsKnZRJWLEipCvZWCCJS1v22+72xsr7ZPi078ug0u1q8ZOeSJeAKh31v22+B2xy44rQkJUrq4LsvUSisrs7gVfWmJUpX7WdFRLhSQ0Tzhj5GmEU.KND8R16+3Su67PLPbDKMp8uKEWsYQVQ4jEfXe+k6qp1SUsiTQ5.M6JhaxUe0WcJmjLburqfuZ.nQrXBqLr0h1909xdYaWoRkVSO8zc+FeiuQbXXHIIINuhWwqJ6ryNa1YlYlr.YqVsZNCZgixCKjqwvhcLb8WV3XirV7ao8SS+TgE0YAEVX22eKAPCEPO9w+hCayT1663WtgTGWGpuawkiBCGFGoZuhssRhJHIhHwgQg8A54eC9c888aAxVML660rZ0pQAAAA+J+J+x0dAufWPnmmWivvv0.1npmmss4atyUe0ees.1wya4cr7JWKfcNpsXWG4HGYXqRGB8giFWFhO5Quwg1p3nzGpM.lK976lDxQRTpg38Y+IHlSGCGa.vf5bzA+qdQ+qRs00yyfJqdMM9CmfwegrP47kg71Vx7x1RNLz1pWJJy67C+C+C0w1BSIflod8FEUkw88Wdx4lclC8vO7COsmm2gas81y.b3ufgihF+q809ZEh1csz++Fzl7TMBSfgS5UrFSh59W+k+xc52+bcv3jceKJ.nBqLRuhW8JMolFzrMa9QV3nUZqrYylBg3tUpTouHRRPPfrzRKkIL7D4Z.4CBNdgmwy3YXpfPcCgpEF9EFAN4SmYwmdpdzkaLRF+qj4nCIRrnhThB5PojbgXVfdhHcde+meK1duLv1pJGauAbWxj3EU0boDuacXRn9T.GFXFstdXfIiBCGKzXnwYP+AHPBHwfNvy2qWlLY5BZGUM85opZOW25CDQiglpuueRsZgIuy246ruuue229a+s2gEL+twwwVNKoYlUgBPywwjDmhyO+74DQjxTWqVsZBFmKz4G9R64S3XWRilRkJ0rP0tdgW5K8kVrb4xEBCCyoJNggQ3uruJPrlnCFe7wsHjgdG8nGsKpEl3MoaPPPuq2yyJ8Wz9u7u7udG2Tz.PyKDDDrwcdm241.sN24p2gxl.H77V2N+GY+rZWfdM1KZXFFzJTafIYGmOMiwcYcyuaUVY3FEm8rmkc0u7HXoQOFCcBbP0pqaONqzAn8u7u7quEV0k.Hya623sMlp5jhTw5z+ZEWGxYYr7K6Z6kF5fT891pWzMLLrmmmWbXTnDEDjUDJFFVabfIpUq1j+f+f+ylv22u3q5U8yjSDICkHiIS+tYqVsZNX8ra.NvYdx7t0v0BKszor+qP62urJhDKhz+wdrGq2q9m+UaSXhu0QlZ6u5XvoLuyU1nHBN.Nh35jlzDLPTOGPNSu8aZ2Kzg6kq.puuugievb9+0+0+06DDDrc1rYOOPSU0HPCtpq5pqEDbx.f5Kt3hqZql+Fkn9loUjHHHnSPPPOf9+w+w+wCrpew9cB6RWMcH9TWrSG1JED0uwdC1cfk.kGTkym1q3sgJsqNzwwZswcz9zd9svksTU2pToU1BXGQjN.Iu1W6uXNfBql53e0CDZlR.hKYPeUGfVwwws7775HhzsVsi2GWRBBBbTUynhjixj222O2K3luwbq8U9J4fUxUtb4r110vndXWLbhehLFY9bsAL6tH2687ddOYrs3VdUqW.Cp5xt81syBkx.H0OvIijDBPAxTxvyOG5Nti27gu82vsOMvjflVoQGa94DEjImbRwjHggq+R7886GDD1Cn6XiOQGPZYe1rUqVstvRKsz4CBN44+rOxibgpUqroshsahUwZVfnKbjibDKpRpt0bCCZXkQaOhcqZ5dcda+eu8GHmccap8uMRUumdTKsJYq2SLN50+68686MkujPDICkM7evgNzgFCbKhmInxEW7TGDmuTnpc8+p8pCcu9q+566WwO11sIYDjbddUJN93iOwO7O7+xod9O++IGtLb3jjjof5GhR6lvDW2qygYMG2JUZbPbn+akQpRf4.qIm8rm0dePLTZfZBrWUsg0eg0F0uo8Ck9Q4Hmbq.EMRsLSAb3rYydXfCEFFN1ZqsVtz+VQFgGqkcQ4gpzFjsQ0Mutm60sNvZdddqBrxlat4JAAAq1zjjj0XgTRYtpUgUBZ0.1oQi+gTjysCqYVmMm48+9.IAAApAbJfgWT2i8t8+U5O+RY+y98VxVkz0Zuhokf11p9c6PjIPmEnYawHW5wPYms1ZKafqqjmouRphzoU.DwvMTMrIL4rm8r12WDEvIH3j4erG6rE877lTUcxfffISRRl.XbGGmQZAH2rkGF3ZMmWwq3UL557QRbzotTIv3fNLqMLbzRNepWPUsvm7S9IKBkJnplehIlHyFargR4gUZNFVbzyWJpWrqwlwArpWHjkRLFvTf6Lsa2dHh477pjakUVQDCy3jHHwBoszpjhjoXPF.zKLLrKP6vvvsculm6l+M+MmbSfMKUpoMg802rToRW3du26ccf0ZXPD4EnTZ6DtfMX1UZG.sfvNAAAFjibdyZlkn1HUxeotKMrh9mYTaa6mjmsuSdpQ+YWh.9Olc8Y8AMUs227a9.cDQ1QDYGpaPxRYZ1CPOwINQFnRd2gEc3xZiyd7Oi84igS7r7fWqM1bitWy0bMC777PDw4u8u8yj022ufuueZQXK366mGHePvCmRbsYgplVyAx.mNikqlNHswwUXbFVGDXEqeVRFZtKsCr6wsrV8JmrDYognZpg8XTNWRRRVECY0mxgk1ihhAIlwppC7p30GnW3CG1MHHnyM360xx8MWHHHX0az2uw66889i777piKM777VKHHXiff.ivS.sWXgl10U0s6UtPGfNmMkSbpjx+MUs1AN6f5vfO8m9ObWDibVSgvpxZCKH7H9+CDs+jCOx+9z12SN6fO9G+iO3wdrGqGPOaA0GlrDnrU0GqWrNT.ZbPZ2K6bVXJEIzAnyLyLSuolZpXTU77pjUDJDFdxhUp3O1y6487FOLLb7Wx+henwqUKbBuk8l.XbXAKOyc5KiMz+w23oqVxIYQxaSrwr89mccWWmb4x0Fn0ZqsVaftppwOl1UY3l60NHN6ld7GLOovQq4VsZ0ZKP1VDosHR+JUpjVUYIIgrkfBppo8nZQLUMNu2yyK+i7HOh0nwFYNy2lkMv8NhbNqUtccg7ZCMsUFjRrRBqPOOnya7+8e6TduvVYlSaCDbQigjllq80WecQDw4O4O4OIq8dexOxG4iLElr+eHfIp34UvjERy8pZCxRP5GFF1qb4x87775truWGUktK6626DmHXfkGPFDFF1CR57U+pe01XjHzswHeosuop2TWfAh3SEiSAiggYrGCSBcj5ksYhOLLwyySW0NSLKHb5KtpNUAIJx0AHmKj+c7NdGi.IeiLhEbh..zBEKLrsVDQ5GDDXj6NU5Z23r6IMvWrsuue6myy4Y01Vs6cJCa666u8u8u8u810pUq0xGobapS2RPuvPCBON1vMZqsmJQvEGz5vqC1avGCpszt+MG8nGM4nG8nI.IUAkScQUXKAHtVMCoYVw1+pefOv605Xpam669tu325a4s5.juDIEe2u62UQnbgRGrpuqmxdM6BCvvV+87775GEEEiJfHYRTxppTP0jhUqVsnHZtff.me+e+6U877hsPqWqetuf8cHWw6ov2kN0oLsAgGXIEw5VGWpz8ptpqp808rtt1.cbYPJ22bIpNlIXh5yOZ6jzLq8cgr.47p3kcs0LpLifUlMEQLpqIhmWEIsBF.wpRu+1+1+1cTUOuHRSQjvpUqdVe+kO6xddmSDMUcDlmA+SZ...H.jDQAQUOE4RMKwNG4HGYGf1999oDDauYLy+6mnZGcr+fF1+Fqit1YzpTrmjvUaOqKi5UazfjaLTdZaOMq1lFzxWj1MaNeafNKXNFY9fevOfsx0kMaPV6.KmzIM81UhgylMaZh+F3fCXI9ZQj72nmWgZmHLmKjcok9mmYt4lywcHGWDloQiFYfi9joRF6Yd6XPBqOb9Rdmug2PFVgbu665tJHhaAU07t1joY4XJwHXAGnyQ5yImlkovce22cJmFLwpqtZQPxGEFsm6EAjs2dGyxOSx4hEXPPPv.LRWcGe+aXGP2VDYCU0y+8+L+9WGXce+a372xy44bAfKjjjjRDga.rwJlVZ0FfQsTzwMZvB629ydQ3zdue3R74kx92vupOynRfrz0yyqOPxK4G7k3P8gI8O21a+MxQHY8fLm4LUOHqsziQQ65buAU18bFGTKf33XGee+rggg4hiiK9C7C7CLwG8i9gOzwCBlRD4PAAASFbxfI.F6m7m7mLeEVMCqCGEznnpOUmvjg2Kmx9dyLfbzidzz4u9dzrmMv9zmOp6vpAWy5+xwFUMXxVwfR0hPkwcMqulFXVfYWas0l1yyaBOOuBc61MCFzyYkG8877sOPmjj3c.YqkqV8BpQ8AOeTsZq+67676b9kVZoKXJxvBl0QqXJrvBz0F74B8lE5355lxMDC4Gh0VbH5RF366GWsZ0XfjEuxID3wy92H1.OkMX3JcmwhhNee+zVtvvIJya9TDoupQL4jSlCnv7PA1XHWl73YOydNirm2ER.heguvW3.fXW2RpmmmCP9EWbwwCBBlHLLbRfwqVs5X9994UUGp1Em8rOX151moyBY9ve3O79sksea7iNObPFoIRaHRlCVfBhH1.oalCS6FpO6Ce3ATeWzMtjI378brL9hEgo.8fQzyHi1PKXH0z5Se9yu9TG5PGZBOOuTEIBEh877FHhLHYDegr1wRsGzMIIIkTL2guwWZaOOuc9U+k+ka+fO3Y5fgHMac7ie7sd0u5Wsk2u7t.VYqFn6Rrf83sXWW6wzhZkgH.5T6YuwSM3T6hn4KBsHr28HtT66N5yCcIxaSrha+xhz9pu5qdavTzAfs+w+w+waU20z5D2vMbCBDkqgcsf6UFcGijrvFVDxTxDHuRGqu4JF9ELu0m6Itsa61rAyZRDr+M3mCH6YO6Yy.0bV2tNOJJR3LWr+2eqMpJGcus5is8szQQQEP8KeCsZGV++DO66JW3BORVGGmrhkLgWXASeJ546oX3uDSLMh1KJJpKJcUU6kjjz6jl3.5fw1vl0ssj7Bvp0Nds0.Nuuu+lWuu+N+edm+ws7f1e+e+upN.cN1robz0JoEJ0rGSzv1yxttt5fEf9ujWxKYzhPL.Ht1w1ce0Q7+WW7Rmb38T3kp1y4UcUWk8cl4Gl7YQDwEMKPgM1XiBt61lgGDPInyBIrxtsD43iOd+ImbxDDQBLEXOGplWDIepOy992PtpU8xGdBCgV6his8FmI2wdxWLquqY7zABS.POCmwNgutk.aJsipZqklat13Z13ufT.XdGVvRLkKcvbzFHY0cgR2VWy0LwlSM0g1tRkJsBBB5EFFF6eC9Z4xkEQzLBj624C7eo.PwxPQJSAOOubrBYeNOmaIKkJYbzXVxr3hK9s+9tZokL22tHhHYRqVpHtNTFml166Pn+c75e88.5d1cMtKKtHNvYxLMSm97LdtklKFH9U9Jekfw.UgezezezwKaj54wRRRJ.jUUbrMZPhEkBCTR5CR+ffvdgQg8NQsZ8DQ61v1ubhH888864440qZ0pc9jex6sETu07LeZUo2otacaaFDFiA6k4UUK7+yG+iW.WKafWmjG3AdfXum2yKtVsZIrfohtqOz35Ri9BtTCDaO44z.x9re1OaSvsddYLAuXM.qHc60SspXQhpZhci53kq5M.SO40AnkpxN35Zp7TIZctyctV0s8EJL+VUWt51+cm9zs.5Xf365Cv.QyKELLuTUB6wOv0SQBrTLUqZL35ZLlV6RCQQqwRiifQiPLTUpv1pVu0O2K+mqiHx..ZVtYla61dy4f54F.YMx74AI6xnMFd8VdPnQtmiwP03piH1M4jrAAAYVd4kcVdYe022O1E2gIpr7xkcdfG3AbfF1yW0Kyo8JNF4Z1PdlglJhl97sGD0tBUZcG2wcrCP6FS2HcirK1oRWyl128u1cmEJmiRCaWr7.4ihBxGFFkagEVHSXTnybysKD4E6bXXXnsh+n.862ua6a9lu4MEQVyyyqg2M3E.TCZTKAh77tol.qWA1788e98s8O32+OnsuocaMT0iVv.kyyuGhb9wspKWJGmuTqAuTId6waMY7999C3XLXCqSsFHlt5.f3ULuSmSDY7s2d6If5i6h6nD769dtcIt1CIFJkpbTcN8id5dppIdU8xTqVXAe+kGa1Ymcr6+DmnPUOu7mLLLSPvIRe+enyWW605lANqiKtOY1Pd3b0oG9tWUvCRqd5a31u8b1.KxUW0LyM2bN.35BPCp73yb3CsEr.nLmctoNYu8a+1KnpVLNNtXu98xO0TSkQQ260uA35IVTFXZAsQRF7xK6283G+Ou0x9Kuop54+O8e5+zpMJ0vTweZttCb9fffKbjibjMJCahqsR+0oErXaVZogJdFWdaYWp+OboWidor+M5Zq9bd5Ay1gxkSaUht.C9K9q9K.vQjJ4jxR9ImbRCAkaRPvUpnFJ.mdoSaW2GNHA5qpNHIIIFAsYy5hZTpnrG4H2Tt74yW.Xb+k8m3VtkaYBee+I788GOHHn3u+e4ue9H6Z5yBxUfmZ9VcXtWLTzkb9g2GUGfK8BsANZqPcGLsamfs8aO5Qo.b5hyxropuxDQtQ1jjDMi.yCtk.JopNW2tcmNLvftTQDmzoQYDaEl8Qjd.cxHRKeeusOQPvVU871NHHXqJUqt0q60851DXyvvvcfUZufIH0A.IqTYE6y5U5eHSEW6Az8XiVzfyfAh4UpXPChc+uybwqAuTiCh8uXHp+TPWvHwrK.cXFakdWk9v7Cv0Mwhtlb.EWcHgRWMOtCaYgQBtauWCttnvJIknTb56nYxjIIHLzADKpgkh999lfHLGyb999YgRYYgExdzidzr3aR105CSTiqCGa3Z7Gu2IOHizigiKjAOagBVgbpp4w0MMwPHhLnwvhA41GWhO0Em.KsFnVNhHoRETnIKvBN.EDiOlS544OwVascwvvvrggQhmQgJGDFF1SUsqX7mbWdqSFlTqcbbbFpDbVRlt6688+96s3hK1Gn+Ihh5Za+gsmcV1FB2w0f1h9.CNkwOoX3LClC5aaylzBccws25i+dmOdqs3R7yFNNEojaditpofVaAromHappt8Q8NZKZPWnbLoDXZISa4z3fuGZLP+RkLb0z+v+v+PaqZSN.SQdxAL1i9nO5j26u2u2T21scaS8Q+nezCgqQliCNYPNvKyQu4a1bNlwbbsOWUKYCO584Szg.0jDP9Zesul.HU.mnnnQVSuPByOu47U8xd+ZG65+Gfb3CeXIJJH0WLYkUVATzvvPUMn7xntWpSWOOuNhizFnsiiSm2467c1yy6F5gqaWfNm4LmoEvNq.aUsZ0s.2snLaexZ014m5m5+0VgPm+r+r+ft.8N856sks3h8mx9UsASaV+0Gn2Ri92bZKR31m++WA6eJPRsz8PgdddzEVsyBrPWQjtpp8qq0U.Y7wGOaBjIU7GN1UFwP55CeunbLkM2aQQQJf3644HPVExFDDjwtOpTq1wkRTJimmWtvyEVrQ4FVanmO2VW5Vy4eTlzjmtZImQx.J8paz651hHsqqZGZrPuxhz2Df7pBqXaMmScfx.Fr6hk1.aWuNaN4jStYTTTawpVN3fSlLYxppluAT7+363+x3Q0pMdcXLpuPgfffrQQQNPCGZ1zj480IyYNSu8Cu0mtGBmx1lAMPfRZILxwEtMUpm1lr14SW5a43i3z+9yXXt+LavFCSXBwjxqCwhHHRYGLjtZdfbNNNYCCCyLwDiaa0.RaufAK6WMVQiQT8Te4Sk3W8lR788iCBBL8g4tU6x.GLaVUWkUGVg.5uakjrZTO.YeourWVVZTxrgPIzW3K7ElvJqjTsZUkUPf4bpllQxEO0nOKLiEfTgR8C8gtWBCC0nnHIJHxwyyyQDwAwTwrffZCWGJPRXPPbcHFb5Khz0eY+VUq5scsie7s888aQSZS1rcJmtAt2psnI6bri8BRkfv9rzdHmpqjCLWtMg2MAH0pYLX1XOFguRIfIkjb6DEQaQj1MzF6VwrHM409Z+Y.PLIfRGc87kYc8BpW50Vo5wppCvjDlTNHJQA788AfSdxSJm3DABfzvjLqr.4B+BgYeguvWnC.gWrCFeqLLIQqJNvLiZ7MMQeciHZX0CYbd7PXBzvkJfb620smQ0nrzrYdfhhHiswFaMlpRAOuJ4RRRxfhr1ZqsmqCc2pkf832MWt7aiohDq.zL7jgM.ZBrZSXcHdiG7Aevshfsemu86cmG4+we8N+I+I+IsfF6XIltVlpyVIcS0zmyWowSl0e6OoJW72+znVdavb7cAK4mlQDIupZwIlXhh.4aXThiCjsyk.kYPglwXTNpdG6YbrA99KKpp4md5CMVsviO15qudw+1G3AJDFFlWUMu+M5mCJkx2.4CBBxs95FGLsq+dpylc0Z1N+pD.b228cqpppHFGJ+TepOUBf1ngYtIx.k1GugBFZOf0lixfPIx7tuy6N6u3O2uXtLYxjyqhW1ImbxLdddCUFG.luToTxrdf+x98zck7utppcazftttka+59O751x22+B28ce2mu1WH57QQm6B.aDAa36e8a.rYcXGZLW6EGZ+9LC3Tm5fj72KkiaWo.2tbq0rATrdOpW2TouH5Bk5iAB0Bt0yPCxAUFVIdSqRbrq7y4cQn2.Sh9K20fHRFbnCMsZQGFAAmPdf+tGvAkL0NdsrelOyeSNaapZHy7HxYkjViMG2mF6G6SCfqceXhYlZwl8DbSUCicXdZCyGem24clx8Aic1yZjC40Y8z1tcVL6TVFvuNbDPqBTILLbNOOuI878xKh3XsvnnoOSLnWJIIoOn8DQ53U8FaakWy13R6O4m9SmZmsUsZ0Z6440An+WL5KlZuRHY21I4Ll.G5CLXeEZvzZWQQonZa+6+cPFWp0g6wGzyXaMQf9q.C37KDWATisrUgFMDfLc61MGrPACYkyXLWshzfBvbiRb4i5ShI4sMrepMTQjj986GGEEk364oXPuSFPyFDDjAHS3ICyDDDj48967dyFU6KjkUVwXOKfBW2M8+TgERWqOaiLb5KYRZ9VYX2ilLDtPNU0bTh7hH4nQibpQcIUU0X2zVGnZiAz3w0WjgIAMJxXyXEVQrUbtPbbrUVt0T9YgvvvXf9992fAt+BsHIUMPXST1nVX3Fhpaln519K62xr1pTZ0y68U9JektP4NhHCEG.qbr1qAMtT9NYQRhoMaN8SL6bGz8VuTe+gw+zDZCxN.aGA6HRoV2687t5X7kutJhj4QdjGIOMMxXt2ieB5tTWCwMaZZehm6y841MIIoupp5uruiHR9d85M9y3Y7Ll7m4ey+loqVs5guoa5lllFkltDLo+06OFDlCy5Rgya1qewgyMm5RgdlmviZf7rdV2hCfDAZ4xkGT1PT48fU5ypqZJr0t4h9w2+gkPgiNbt+c9NuyjJU7UQkDOOOc1YmUsMjiJPLnwJzGztgAAsTU2w9rn868C+d6BM5G9PmzfriEWLAVXPEK+cPoFsoN6Ts50OL4cLKc2scathIZSwT.lz0e6u0ouR9+u+wnG2zVBqWXnINfUXkg7Am3ZRrdtb4zUFo.Cm9.3a1bomqx0MLSEPkJUvyyivnPG0zZUN.xEtvFFz+WspzjlYBCCy6cDuhAGOXX2bX3ImcdxvKNeWy3oMDlvtOT6aj3sU5ggCS5AN8aXf8Cat4lNP4rXHtuCpZ4jdr6h0XaiUVYSRXGKwNI0dnZ4BqENV0kqNAtbHUilpR0p19DWJ566m85ewuXw3K7B7A9.e.oJHtDc.Bp7o5wwD.bA0klIVh6omVeXBORRSZRkFjrOk+vACTJy.jY9zq6UQKKRxwdtGyfzVstHhj4cc22c1tc6lKLLLqmmmyNs1wVz7zWf03SDDFKPhpjbcW20QsZGWcMNw1aYe+NU8757k9Reoz.RUS1GKk.DOc5KxqS+YSIsRW5IhLPDI1bsz.QDnIJtjXCHQlGb7YMmZfyq407ZDLvudzg5tBIPTLP+1s2omp5fJUpDmHpFFDJUpTwQQj25+9+8BpnpnwXQNS+ACh+6enGJ12+F5qp1kFzhRriiiy1XZEm1KWoR25oPCMrrEBwq2c1TNJ4TWRijWowUxn59yL8kayXX2MiSgBXOX9NhHcbGAxyW609bMqSlE0P17WwgLGqLL68zrDNNNpZcvv5HuV0vcGlEjIIhuueFaPrEKCEdnG9gxIwhyBoqollDn1SJG8vhlf4pQV37CgJ8tISzMFne0Tm8htnMzFY3nQfRCTojj9tSAWXrolZxw777FKLLLuldNrvTMsG+GtnTIACZkZqhron54CBBVG37ppaTNscGJSqZ0NYqm+y+4a66UsUDz9U9JeksnDsgpsAZWE5tDQCFYSUt3q8mxFOAVC2fpf7te2uaYgF3j1FJppx1aus82w.E1CJNhNE.mGEORrx1Xef3vvSJLTlOcFCn3OxK+kW3F77J366W3+kuuWdQn4v1qz1a14f4yB7TEOTYlSpcTavqMiKCCti63N5CzS0nNhHc9d9d9dFgTdqbPRt0vQcPnIhlSx769A+cy.jIJLLSTXnDFFRXXX5CEc0UVMQMjgXufSDzQfcDQGk6a5Jhz86+E8hZ6ZQGW0JU15FpbDCRRJwNvJ65zGyz6L6s5XOQrk8TwX2JLtDIV9NafOzuLMGX4jnDsthpZVHpPY6y6xaPtCfZLo.5BoIkoLcgFs+He3+zV.sspk1.ERPPu4+I+HpJVu.E0w1JEY51sqsEWVI666889xB3rPimVJjhBnKAZUZjXSNVBmetTX2Onj0Nu6pL.VUeSuo2TVrx59u5u5u5Tf6L.yUNMQIMnZYXQfqlRbUqrxW9Hppk877lILLb7nnnrI.pXgstXpDKn80Qp7+YNyYZGDbx1nZmZ0p0M73058y9y7yLjXs2Uh6KmToRElKssOZfyh6NOsejs8s59eOgmS22weX.fQfCMLJsgYMF4JTnPg+9+9+7hMLILYbVaAa6KrVgYsIwn5tjjY5m1j4UNqHhiKn4xkKYgEVHNsUisI+zQUMquuedOukyCT3W808qVnxxUJ.UJBTzEFqwC8+awUfBMZzHGqezCZ6Md4Fi5OsM4M4yJhjiljix6dsqppt6hvjATalQeVL575k.wXtJf3ZPyb9l0alOJLbDB4WAEUEINH7gME6QoiJ5NhHa466u4x99aHptwNs6tcUe+V0NdnsEzZFGDDj.D+CdsWaOndmxFToMzWmQH2cYeWqilf1Q+J8m+zwXuA0Vk9P8cKlHYLDReChMEioT1m8y9YWnBTzkUJFd.kW3Qt2F.t8.5433DGDDnAmHHSTXXt74ymOLLrXTTzX.S9284+7S69Lm9vMgYXElxf1j4yCjYFPpPC8L6dbepXH3AVd4v1dLk61vx4JF+epL.HoZ5u+kovdFJr6rCma+090dSw.Cp3WIIJLLYs0Va3ZRQM98ig2L644620z5yK2QDoK0oOkHw6HKK.YJAN0pcRMBFbu2681klKz9+3a8s1BVcGaQT6Tc8gsayAAYROdq8dxX+a+IjyFGvLcAZUxxgYzjcpHR62xa8sziRj.qbPWqKqkN2Wur.HIIIRTPfwGXEbccM0MUQO7gmFSxSJkxclEv3WVw4SIx3xjChFMIf+iVTl7zUBSfQevViAyaMBab7qQ5KiYNzgNTNndNlmbi.EsqzDoYwhG8.ZUF1rboRa3VwcqVsZ0od85HhjGGlPanSSClIJHX1G6wdrC+4dfO2TPywBBBx13q+0UZRb85eojeo2xujVCng43O54+o6GpBbZgYQZ.RcSDfwRYwp.GlMAjED.OhvjEX.MEBuSap1aVlmrqtaeLmoQoRYN8W5zYEQx4JRdfBuo23arPlLYR68rLnHddURuKU.022KAgXTM4c8tdWwfN3DgA8EQ51.5DDDzc1YmMlRHe8u9WOyw+7edSu76grwHFIV20MFnGMn6BjRFR6IPVo1wqYB.qBNO327alIv97++1G8+19g+rBKpFk+nbeft2wccGc8886FEF1WPS778HLLT787bdaus2lff9y+Z+4i+De7Odef9YylsuekJCfFC50qW+vvvtzjVddd6TKp1NGOHXmURkUNW5cly74GRTSqW8RBY8mriKkg0qjyh69yqZ1.pBzW0UFvBzuAz6C8g9PcEQ599u260LeuNIyc4udG5T0ZoNUUlLPyLUpTQp54kddAfff.sWudl7QIhDVqV1+p+p+3hTlwOQX33ye34KT4HUxrxBKX1LXCRV5RgziC9PXIbrppUd6F74YAxIhjYAPTstxBCgp3kBYI5teFs6lMqPxi9nOpCkH+PGkMAhjyQFUYbzTnds6yKgXP5IPmpdd63ciU2122eaLr6e25kJM3U7JdECnN8uISfEcwkNPyzdmsCMoMTqCGid0fuckrjmnCoVEbtsa6dxXpVwBf4Zr2i9nOpMohqzCWFT6I16G5K6ldYwPi9Tl9ekuxWd.fFEEjwyyKuekJ4788yIPtSDFjKLLL++2228UrVsZECBBJ9Y+re1z.MJ.qlmJj8LOUwCUUA3roH6necW5bO2y8z1zVDk2An8m9S+o6.zedCz+UV7.bO6BvZCCf41usaW.brUtQrhHA.Xg9WhXkaSLsHQafc77ptsJ5NufWvKnyx9989C+C+C68bt1qsaCi7LuCvNmHLzjfjlzd5zfKVh9VBa86TIJYuHP5T12Gmgj.HtdZed6NTA3b.J7QenGXLnzX0g7vrGHjLshQ40FX4epV+aus+21YgEVHkqb5IF95JNL7jIUW1GEDOeemOym4yjkRj8pu5qN28e+2eNfb+Juiekrv710+Kwk679DbLztzoXD6WUIFVa36RMKSBLWRCShqAHCKPAfI9+589dmFZLKkXg5P4+n+n+H+69tu6i7U2XiEUUOp1POR+tcKGEEMqTVlDnfmmWFw.i8DPGHNRe0TPqgJ+FPqibjizx22q0u6G782pZ0ap0cd22canjAsNKXR5lgOipqTZj8OfLmoREm8cedPBr3o50jiX2G0Tg5URSfQFpaPRzK8k8RxIhj+4+7e94ulq4ZJRIFS0lEs7+Vg0KSA3nEpAEfiZRX6BFjnLOLlpQEvkbM.m986qMa1LAQhCCCSr7IgTsZ0LAAAYg54EQxGFFV3O8C7mVDhJhKi+PQmahUspyj606lGN6SkUi07thGNPvvBrQcxzoyi4XAQ2fGqUKSRfmiAv42exEz88uS.TSmbzPvcHBlKnhVPgbyO+7YTUcDPPLYY22zZNwVNEoaRRR62x+G+56XChdmm4y7Z1ILLrc0pd8N24NWbPPf5eC9IPEq8g4MIdnjofbtXKHywFMoPWTbDWp0bOcORi+IYtz02UHApmd9k4latLPyrgMBy+e9i8wxOT0Zl8JFGjvhledIHAZDSIiu0hHRiFMxlnZNaa4jWUsPXX33una9lm5je1O6r.yEFFNGM3vvpShKEOOjKJMn1ktn8P+VecXHpuMw.ppcUsdJ4juCMoKDMfiB0tXxq9hlOM7r2ronse.P+ye9yOHLLbPEOuQRf.8q3WoOPewF2YsZ0FPY5GDb7tIIIcCBB5Gbx.00UkvvP4D0pgUDJF7peyu5dvJc90eaus1.cpOmQ8jpsWDxAW90QWIadeqZ+a++cCfy2mJz0R9uaoptUjpst1m001ilDuvtHzdzmq6ON2zuxvBjEpmRJvYTGwoho26zFMZl.x.DhEUR.jffSjCWJnpV3Vu0asPTTT9UccMHyqNY9leyuo4Y6wdJX8z2AGOclvDXjLgk012pejOxGYX6Y.jWDofKTjUo.TYz9E8w8kF6mwXHfy10gMw08BNNNaL93i2RUcP4xkyVoRkIhBCm9gdvGZlanZ0Y+leyu4LunW3KZJfw888sJPCIWe4xwrFIVndM53oyGpCWfVAbXcx.ymB0IrRlZ50i7Z9W7pD7RIYRKS8e5iAfrAX3AlUGJMd4.xoMZjxKCE.FSUcr0We8wxlMaAU0bgggFXxEFAJpsX5wAAAwpx.Qjdu427a1TsIcWIz0RPkCnoqFEE47u7G4Gwg4mWLvWG60HIznQxQsnLJytD5U+2+6+8OrchpVsZlxP1nSDk8pu5WX5yck0uHCHvRmw98paRjQyxcCCC6pPeDm3vvvT33kNR9u9A+f8+E9E9ELDHlRa0jvmdm6bmqmmmW2vvvNkgVUqTcae+aZGf1QQQcnA8VbwckWtkp83zdGO4GOdFPux+c0VTAzHPkxkSXEhu+O882+m8m8msKPm+t+p+pNKXl2iyNRRpr+862vYFnRZEyJR8gRpaNKThc777bt2+q+Wc.xTnPAmpdKmw22OiW0p4e1O6m8XT2HMgW0UcUE.xvJqvr16mS8jadSV7T3vzqjEHupqX5E7UJmGiJPjQJW1gUdbmOSue2+FMw.I270bMBMI2G5CcuEZzHs2KImpFXGhBpdQ+s8MnkitJzNHHnEMnUsnZsA5FDDDSylI+Me3ObBP7CdtyYVy1fgUmEavgKACFQhL+ttjk.HtQ3fWfzoSGAi5N0SjxsddOum2NhHaiqaa681AEN8JPxG6i8whcg9TmdKsz+LKgc6PXXnSXXX1fffras0V4V1yOup5XAAAiUsZ0I788m3VtkW43.icxvvhL+74I5IjD0e4G01qCIKzftuw67M1RUc665tt8c.Zce22mnKP+UgDRxU8VO...B.IQTPToVU4LGfi6tHTHEkNoqwjcaCGyRNrJGglnCPoGh1Ai82V.a6fy1AAA6bxvvcdUupW0PHp6440x9YWK756WLspwm56Xqw1ektG9LZQvxcjKnVNfnGMLnQ0htxLee232WdJ2zRx2qevdFGEgq4dbfis8E60qW6z97VEM1xqBDdxPw2e4LgggY+A9A9AxQSCxAt0a8VyCj2MhrL6p1q6S8zQAUF0o4XpsakvUU0x0AleMEHMId76c2+dNpp4aZHS8o0F57.t2y8bOku8a+1qL0TSU9O+i+wcequk25bpHSSBiq007tttYBBBDC5RHcMVOfNIlVjnkZq.7sdq25Ng0B25W3m6WdiyctGbi2za5MsQsZmXKOOu1gegvtQQQCVARvyKglnesu1Wi50qOb9ej6sQ+b+22OcGL6tm+SM76khXPQU04Sbe+ENP4L.Y+FeiuQNZRAQjhhHEAFqRcFiYOqUt4O6D.SxJyeHfoVElRbcmjFFxz7QezG0wyyCuJUzk88S.R1byMIHHvAH2wO9wyu1ZqUvyyq3O1O1O1XkfInAG5HUtwIqUq13.EH5.o1XOQGBgl0qofkTUUuphEi0FZeQb6M93iaZUu0tHdzZz4x878NO3v7ymyz9R11YBJ533ja0UW07dpf344gJhDFXHl+29u4uo566mHhD+a71+ON.JmpLNsSLnmqclLY5JhzqbSFTq1CZQh3pFaYM8F.nM.nZUw19ROdAc+s6jCum4LG64VCGQp8LI9DUUwuruyK9E+h2kG9V+w0txt1QMHv1oIHTtrZIb+3CcnCgqqqCP1xVR4TDx444Uzyy6PppyDFFNumm27QQQyTFNDMXbqziaBx8T6YuzqDep73NpXuue3ULbZjXQ6A65+S7rfvYwAl8R01F6+bpv513KJO.n+LyLyPxTUMsOdewnzR8Dne6VsGHBIN3nAG2r1qZ0pI999Iat4lIzvTl7pUuQaxGnKMSUOPSgFN1ZCUOoQ8u4ftV5xs16Ii8uc+8O1whsDNaJIp2QDo2s8pdUI.N4RU.oZWRBXM8+mpdQ4YER4Cqw52ueQTxEFE5XKdnBj36ubBNXQApjmFTrZ0pEu+6+9KjjjjmFMxVwV.qa4puZye5o2y09+na7sCDlnrHw0sY86G6G6GafosLTGnbdfwOWudVnOFUXlCnhdX9Z8AX0gbZzXiG3AdfKHhr0TSOU250qy63c7Nxqv3ysvbSdhffIelOym4jAAASTqVnUxiVHC.0Gd7NK1LwI2xsbKe6HCXBfDMTwGVM276B0ygHE425252Jy+s+f+.GaBITphtDKwHq9D0xCKppYSk.SrIjRUcrFpNVRRx3EJVvJkcZFvDPnt66+psplwUMDGUOU0t999se8u9WeKU0THd2tVsZcN0o9q6ericLChQVc0cStww18F7r.TAM84OP+W2q60Ea1rnrCfyIGRBTMT+8BMs8lzDiyNITh3RTpOTu2TSMUuxkK22yPbbInosHj0QPXPPXPOee+N9U86r4FazQDo6K9E+h6bm24c1lX1oto0I1Bh1xC1oRkJongYXOEtOhO66RdY+L6d8zvjDsm2087Rnro2FKUpT+UfXpPRi8FnxnesqQRhFypnBSY+ZxCe3COF6BQTmW8q80lAgrA0BxVK3DYAx8XO1ik2VItwCCqMVsZ0RI+SYcXXSQNx3fV8+gF0OC3vF6xv+knTQndQLUOIKMZrKL8O1U93Y9uyC.Msjt1q407yTLNNNUpG20gUYOajECzWP5lPxt8LpJFYSOQ5QYh888Sdqu02pg.cKyfibjiLzIgyblTYKr5kpuV+1oScGjgfgjUEBQJVrnRYhmm46dty8PswzRjsnQigJ3wtbE2kbLx8noZNMFxGOM5EEFNnRkJ5m4y7YRWel8VtkaI+IBBFCShtmDWlrDklDZLQXX3XdddE9u7a9atqpVr3SRzkbwWuwqX5w3NhHcti65N5VhR8eCugW+tO2FJIpW1wdRXv8bO2SlxR4LgggY.bBCCEwR3qxnI0SrImSwz69tzJHHXGU0s8882RUcKU0sdjG4Q1NHHXaJaB18085dccZ.cYF523hQc02oViIuhWwqv9OWJ8ca67xJT1PjyopmTWLIlTeOum2SFpaJD.kIaEp734ivdd12.TpPR59OIIICknWGb3l9dtoLppYUUyAMxqpV3rm8rEBCCKTqVsB0pUqPPPfoxuVtxwviRO4BjXeiQelrenamHhn0AkUwjPIWhmm4G7S+S+SmHhP+98yJhLlHxjppS8fO3CNMF62G5VeouzI9O7g9OLluueQupd4BCCy1z3qf3Xt7SCRnuuuemUZVuiuue6p99cPoy8e+2eaupd6366u4QNxQtfmm2EpdSU2rLk21yyqakJUFbW20ckfQRnSdVOqmUb4an72IPuzAcLRxxmOVUMVDYPPPPLTeHxmgg9PUDXrHXRVmo1byMmBWltDkl9BW3zoyySswW+qOkp5D.ExkK2PkG7DAAJf9bdNOGQTICP1a7Fuw7yN6rE.2wBBBFuoUkrflimISlBAobJwEOG9jctTARN6YOqsJ7y2CnajpcDQZ0qWsVkRQX6dQB7kaXHrzUWs.FzYNwNs2YbfhyLyL4DQxffnJ7U+peUwAPsxk9q4m9mVBqkxkPAI0BenAK6620yyqkX70bKOOus877ZottcpVsZua8Vu0A3QrKtwm4L+c69dhw9qDDDHlLv9cMUxVAzUreZ4Ilzh0LTwvTUGb3Ce3DKsgu+q8K5dXIVxwR.1l0J0qm.LX0UWs+Vas0.TTOOO4DAAYts631xnJYCBBxCXsSDO0W7K9EmpRkJG5DF0aZLX8caihciC4ISagYZ84oQWXgEF0ll8dZgr.4rjbb1eoeo+0YV3hCl+hGKf5ieLTeXKoaUXs99UpLPDI1yD+x.EhKNdwDOOeGupCEFBw0PE.CJVr3fFPeOOudAAmnm6tJ4UZ6dYH40KMmk7cKCkSe5c26vaHJaRpqpPExFZkTZt3msoyIYwzxgEwfxsowvGVSu5pqNAPATxpfCBhfHPCw19pYEUxGFFlOHHHOtjyQcxBjIBDlC4re2471S3wS2HLAfDNiohrXCXv1ixYTMpHvj4ymeJW3Pv7iedyC0CRRSRNlkTkvDzxl27O5MuQ2tc2ZxIlrMP+W8q9Um3444jOW9rhHErx413Uq5MVsZ0Jh6JCcxdNSE1yrh0QnO6m8ydkew8I+XHDBm2Hov4Wc2E1EnjYwcgIJjAJKfuNMnjA8Tbp8DWnHhvB3TQjLrhw.jToRVU07epO0mpfHx3NNNiswE1n31aucdPxvHpwfHhwXNn999ZXPPhaY2Xee+9m7KbxN+o+o+OZWs5M1tjEkIUqVs0gO7g2wyyqUPPPaf9LOwyApMONBUpXN9QCCzrmHR2VsZYpdQYSknpTohtbkJwv7CBtzpOS5PmETDRZRy9kftSN4jcbbb5DFF1Gw72Z62+AZJa7qC2Xp2RKsTG60bqehepehc7Nh21.a6AaCysS3tYUtmExeeWavr6Ku.ppZxyyya.0s22ksNtFsmrGua+Wu3tNE5BS.tS0vPXfyCLeiUZL64uvENjUV.20.qpYQH+e4m5SW31u82bwq5ptphUqVsXXXXAUk7NNNlDrTwFTyZWDZV1OJWt7ikPfEbfxNtPForjqg1Heo8tIPFlEmY.YjrXmN1GRZryEdqlNWjRzeiCTLLLLuU4kD6Ca6lQRrHx.D5pnsjDYaP1DX6DmjV3R2k88GPchqUqVxq8W7WzrFt9dkU0EWbQy5piU6oKXn+T6XF.Jwm8S9Y0RPBNDuJq1+EbjirWxOqDxrfik7tuxDVm49OFXPIHFJEO6byk.vOwOwOgCPVee+7G+3Gu.vXOve2e+DtvgPY5FZioJAGxyyabfB+7+7+74vESEMNyPm79VEoIl0JSa97ttq6BnRLTdfKz+t90tqAMoYx0ccW2deds3Srywa7c8Fy74N8mKyLyLyPhd0T5Q67iLLX1ddd2PWT5hpoNwklrpsTU1v22ei+o+3+S2bYe+snN67Reouz1+NejOhQ0HN+2UYCS+ve3Or4eYZHcAv4nf.yKM.8K+k+xFUSorQBHoLwug2vaPfJYKCYIlLQDckIW3JlvOFsptG5PGB.GEx5c8d4uuO08YH7Yy68EAJlISlhdddEqtb0BUqVsf+x94A27vBlye28TktCDIGePlW12WIF9cgDliDSxEKGWRjX5S+UY0tUrITJWtbw0pUCJY7kHLLzQUUjxk050qqAGOPCLAtO5SdsRZKKY2WNLLraIW2NA0B5DFDzCGip3EFF1Ab2AJsYYXChYy5TemELqA6Y41mALG8qrqzZNfkV56VSbhweD2UGjVs6O3u6uaWvs+W5K8kRvtOgZT1uhThIfRSAKLy0L0TyhCyzjly7rN7gO7FarwgAldpol5PRYYBWn3JFRb0AfLYxfmmmAQGNHn3DDDjUEIGkZjVnghQ0pU79tu6KWkJUbrDpdZK29TYxRTfjidziZBVu7pcEQZKhriKrS9796zbzVmdoqXBSR2OMGvXTlIusW+q+PiUXrIDQJr15qmqRkJNnl2ONzgNDppBn344IhHhJl+u.IhJCNYXno.nvl92f+E.t.kXyG9ge3c.2tG+9u+Azk3FzH9FVbw8ZWqJ52mueJR+9NchgGcLRR5XfXI7TwyvSPhHc9v+Q+Q8.23nc+aFpZVWhimbJNkLm0tyEtvE.SKpz64tvBc8775gPbnAgWx64teONhHY788yFTKHmpZdUxje94mu.PdOOu7lhbUtPZ7Gggg4MDrMNl1g9ITrP60N1F6AcyYvRV00q+kFGqLG6A4+.efOZlUtxmCkrjDPPbYK4r1oSm1gggcBCC6EYU0wfffD+kWNAzDTHHHPBBBxXSZIMZPRsnSz+pu5qt6a4s9V5VB536eSCUjIfdbr8PBqOU1J9OUOLyYyhLOvPj965J.Y928Z92kl32wG4qwrszYdfByZkf9ZlD.ahAnLk9hewu3rhHSCxXkcKmKUMRq3UIym3i8wyFDDjCjbpn48tAu7hp4oAYiINCttFesW663Is7orw2NZImQMVz2x6HwXhwOup53.S9q71e6SBqZfi3UVVsT.8zoUGwynk1Tmsbbb1JHHXG1Mv2NhJ8UUiCqUSpEVKWXXXwpUqNtEBkiAT7eHJJODMZ121uy1OU9Pe+AvkcUCq7m2tIcAU0Bzj7AAAYeQeeuHwz2iAIaXR.k49OMhYO6mqfDYf3sSIHC0qmSDI+OzOzOcJ7pJBTXyM2LKlVrX38jpZpbhQPPfnfzndCAHoz7khgF8BBNQ2lP6fSFrSXX3ldWu2F.ajjjrMPaVk9qYgr1RvPH4Z49gXqyucGarw5IhDScR6u2A+O9FeidXTYm8y.0vHFfWGRngoBjMTs0m+y+42te+96.zxqh2vrBK1u7775gQJ65GDDzKHHnSXX3N2y8bOaUsRUChYf1gPWXsTT.XLR136JMNNbXe9qFGpMIjZECpf5opNf50saRM+vDCvQYHgYxYLRPIvTMfYfFKfgkEpryN6TdPuAyU02eJQjwTHSXXfEQJRVUjB+y+g9edr69teWSDDFX1zyyKkabb95ekuhPTjMv4oGElr6+S3x+tkvoPL8cdcibx1fLetO2mKWSWS0+DWo3s9O+Vyy53b9K+wxgJiT8vvgPHdhFl4gT9KIaTTjSXXXZk9sN5jLPUs+EN+E5phri+M5uouu2Fhna9e+S9euU3IC6ZQKQ+apZ03iTohEoTk1kfdcGQVMO820uQL.JmmDnY7K9ewKNtIDajCXRhl29p8B1pL1j7qagz6wNHsFSZhUAoIHPSINNVvf1BGQjbAAA4EQJN0TSU7EdyO+wOdsvICNYvzAQQy7R9odUSALQX34LHFrA4rR.a1pW4V67warKJP1vj3m63NtCAh.pqM.81eC2tBve1e1el42eA6w+LGfi9tnsRnIxW4q7UbN+4OuSXXnSPXn4baNZpfj344MPDoesfSzCgdpscUX2Dlbgp2n24erG6wtP3ICuPCCZ4Z82+m+m2wp9HoDx32oG60IZPsJYC.xYAGXUAPutq65R4dj9hHCnNJfCDkotQNTScj+x+b0t+iH9oemg62JP9fSFTzyyaLq8sw777FCKg0g4mmOHHHKMHasZGOCrRl5fCqPZ+Xeof27ScySmxlvj0HApEC0im4YciCXcq5z4ZqDZE53662QancEQ55440ILLrUvINQZ6a0AneXsZw92ne7G6i8wF.z2pZPCUaI0jLtdux+0ux9JzesUWafp5.OOuAPi9Pyd0gtF07hNqXjA5dehOwmvjL30nWjsnXKA84Tm5JEv82NFWpD0aVCZ7inGP221uwuQWnQum6y84FCfpZFQj7hHiSSlBZNyFab54VAlWC04.loYYN7zSO8TppSJhLgVWKVW0bKu7xNVhaVcccS.zff.0yyCwQb788ERRbBNYfyC+vOblvvvrIhj4k+xe4o97t+1x5op4PkYGdLGPcqZf.sZ.6nZyTzkX3OnScYOuVdEirV4vcBpygtmeqeqCUud8ITUKhpYCCCSSXhIuHBhuu+tsdNPsZAId99I9998SRR57u8e6+eT2ad7V1YYc998YsGOy0YXOrVqcUGzTDgBRnxo.QtLXPBw.RqBJQafnfcS3Jhh1p3.nHHbQHBBpMpneDPHsZ3JefVMgtwlzhfbUSUIg.kzPEMmSsF1Cm4g87d8b+i20Ze10ImToR.BIu4y9yopJm8Z3csdedeF98762qeOWW2soNaBrE0YWaa6VPst0gArlY+m0KDeub73e5wfQ3vruQhLmGtiCZya.16+NGgLrMJu9W1KqqpUGvnE049qPRWvwdMydIIswXOpQmpPmc2c2tNNN8i64GIIgU9d9DmD9AhZZGQOCxbRUoRkLP0bDGifiiSV1JtHY6W.jGNn1QgBIH2R9HejORFU0w.lrb4xS+Y9Lelo.lvf.hvGLx71LLpxT+pEoCPykWd4ccbb1ESqDNrsf8OyY.DbLbvmfhbed2WRq3oUrqL.n2a6s71Z+l9c+caAgiPhvGp+Yeq1V1EaHrNVqZ3lIqRfnUMB8wa6s81xhw21onDCQeHMXJhQ215vTP3QvTrzx.N9m12dgEVnfiiyLfNlUJqLgggoPIcPPPlq5otTFfLhDkUUMC0HqSkJYe+u+2e1mZkJYoVs3BzVdejJc7uoCBguoNdj.gIfoZIlfFB1Of3W2q60k5u6u6uK64N24F6M8ldSil0qLPkKkpTZLBELDkI6t6t6tsiiy1NNN6333raPPvtNUbZphzQEFTwohkpZt+z+z+zwAljRLITdBaaaSxZVHtG9LP98f8C42HePabJeNRQgEMAyB4jhRNyF0kyhoBqVO+q3JTfAEOHrpSpj99bGhX1HpP5ZplAHmw3TsIpVudbaOQVER433XYjVwjD+BAAAx0ccWmo0dfTUCCs788shhhv22OZo31zw00s0obb1IdCsMO5QO51wy+CMVc1QLxHhDgio+EoJ8jRk5AL3c9NempySwIBJ0+xtrKyjrhYePynaDPuhlLyuy202020VM2auMDQ1hj1CvX3roqqaKee+NNNN8btJmjp72NB1687ddOFV5tDchCn0bNKjbdKk7LRNwCUDQ7H03bwaDuF8UU6HhXRPnTpSLgAigb2VMKtwYX1HAkS.LEvzr.GAJr.wIJgh37w9K+Ks2byMKBLmp5jA9A4DShRRNyVmxwIMJ4788yKQ5X9994.REA33bR8w+DehifpksRQoK.9ei1arOPymCWusX7ZkEFoEh9geVOqrwI7b7d98xea+c21HLK+g1SNliWnwIOMd8AkXLn3D+z+z+zSNXvfIDQx633joXwhV.TnPgn3.WGnpzWDo2QNxQ5XEQSpw1ddda53TYqmzU7j10wwoEPWnX26vyq64M7VRen9.CYuQTrLMdPVV+QqCEHxNNgQ1lJi0CnuCLfUMFOV9NVNMLZ+qam+bizFWb31PMueTlzwslYVfrat4lYCCBRqFBCzHuqHY2d6sysjakwEQmTDY5mpiyLyL0Ly.LkiyQSrsY31FJm06BO+OLV2VPv37gTL9dnbryHhHVEAqK6xtLy8ViGBUd6BTnZjWzK5EIpHVkJUxx0wQhctCPTaa6nfffApp8q35l.c6j9Ttkqq6d.6DbmAa83dbOtsTUMpxDkZ0fQ4SlZb.GUdj1FVx63QG3SLIbZHw7a+1u8nXm+6CEFtOPAHpe+9I78RJHFIYm8Pcf+fAJoPfFSpkR+98S0qWuLwnlKWPXPNaa6DIpNqHRt.OubPwbtttoEQR466aUoREqx6iNsTuzW5KMEm6Bro8Mmjl.QUh8yY0UWs+W8qd5d.8b1mOjZSHMMjQrrCvNhXusqq6VtttarxJqroiiy1.6phzx+L9cd9W601Fy9j6gvdpHsbccaeS2zM01cI212xsbKslbxIasvBKzRDoUPPPGOOudkMEdvXK.FPH8CCC68hdQungnG174D8Oj1L7aEiCtu88OwIkPghC9hewuXbaRZ3MFQJMTEhJCSqpN2kOyLyqptfHRghvBTk4TUmQDYRfw+ReouTdQjLYydzTwqiiBBBFDOmEEDXTJNOeeArX6s2lWz+oWj33rj355ZAHwAvFAy8Mm4u8EKOAPYAF.NFDOHRWlOg6RpbwBTzLGd138iM78S91saONv3wbkQlX35KI+mXoIHrwz5LlGACpT4nFE.y2uSkSUoy66889ZAka5440DnYAy6pctq65tFVw+4LD1twus84+qCqUtezw3Dw9pEdApaXOCw0VrGPzW3K7EjX+jx.ktXEbv7Lwj3n9wb3QBQx2YxImrSPPPefAkKWdfhFEoZjaE29wDs6P94nxRU5EWzRy4sHY877xNRPtoJw8yN2k5Hd9uAwEUH0MbC2PFQj7IEJ2Jq0DfcLEIPJl4hmjHRPIEzOlmQ19I7DdBafgEr1Nl+t5kvMU.DDDf6U4pHD83N1iKRUM5q809ZC.56440EJ14m4m4mwfbyRIEL8D2eNT7QuiQsyYIhM0XHWWYJ1PYxigqZRjc94.l03+uyrjHE8kLIK4u7u7i49Jt9qurp5bgAgS.jcHxEMI6LEZLh0UIUbwsx366m809Z+MxVExDFl..gpoe5eOOcyu649FFhL+Vx3QpDl.mEszHvRq.L326262K5ZtlqQN9wOdFQDiC2MHdwiWRefdwlbU33CpXTvjV.67Dmc1sA1LHHXSfsbcc2sDrWEGmNfz222GQjL+J+D+DiCLcwZLMTcxf.Cul3cWdwFJBuXvt8qmG16WASHs85jkFKOTypoAYLjnSsT.V+o+oeDsZ77VcrGkvghuFJfiCr2d6E+2vBZjIlrxlPDYZU0oWXt4lJLHXbQjLtNNoBRplY79lhHhiii7o9TeJAPDvprokZTKKqHWW2n6zKHABusCiaCJvY6xkY234+DmzG0PSTAHh.FrPBJipWuup5fidziFUrAQPs82DYiKZuyNDIEwrA8FPwFybjiTKBZ.rtiiSRhS1y22uUkJU5FDDz2+L9II6oSEGmVkhudCtyfHndRv8iD7SMILLDvVN6iNWfGO+ZOXlYnabxRZZZYp5IYIGwnhLYNYtSN1Bv3as0VS7c7c7cLYYSBSNRwUYAnQwenenWZ4Se5SWx+N8K9Le1O6EbccOhiiyTAAgiY6XmrVLoRIbFSeVmAjbpHYUQRGDDHK43nP83JBa3FkBPFpQJ6QB3Xw6euw9.l3jki+8ZXT0AAHUXLYF+1e6u87oSmNNIqjpBHv4Nzi0I1+7H27MeyFRQqFYuwa76er2467cNdpToFSUMaPPPp50pK.znwppsicrTeJ8GFvpE6466usHxlEKxVNNN6DDDzrDzNH3t5ToRkNG0zxJCrAkPnvgUk88eV9n0QTX751pL6PBq8ratYRxSF7ze5KBPl+r+r+rwrgIfvDj5jzxi2OdyIVdNyYWkwfpIIvaRGGmwUHmER55MpmBHshlFHaMSPr4UUGKT0wdiuw23XddAiUtrIQM+w+w+wiYCikvsMbgxy3CkQLhlPDohU8XmIqRgrw6SkstAERofhxBK7PZeAEPsGwNtEQjJUJ.iyc.p.pef+PR.MNnqAu3W7KtePPPhZlrGTd2kVxYWfc9PenOzN1vdvH7ICHyCVOJvQk6GZAYHJSpEAL349bet8ECo80GZDmvjhQ0UUyjICU8pBw6c9ffjrKX3XPyiUQvJc5zoVc0gshWVzj2SjLtttYUUyuyd6MVPvckCHiigXqs.rpRoguC+w9Xer3pmUHckJWPu9+MxgBnIUMegEdRCUAhf3VLc0UWsksM6Ar84O+42rHrgu+o23Zu1qccf0O1wN15kEYcQkMWx0c6O7G9Cuc9b41Qfsccc2BksEMZGe+fc+E+E+E2K3NC1SUcmYlYlsTU2TDYaU0c6zoS6pTruiiyfye9yqgwAkZauzfEVfjjML3Dv.3rOXshyijItKwWqTGej8eh2KJcwZjBpackW4UBlfLDfzpVyfZBJOUnpy.bjppNmHx7ppKbts2t.v7hT5Has0VSAL9S9I+jyEyENVwAgNPMxNerOMVXaaaUw005FeU+3VOwm3STJUCApZAjxyyypRkJwyIIY133e8LOcAILpxHIpNV9eSYuJVwUbavcbG2wPza.dWJsiiETLcwhFa44ymOIw2YBBBR4GFZoDumcDhssaJhUzQIN1iqxsx.Hpmuue2ffftA2YPbf+Ui+Y41MhQJ9IO42S2vXTys9ElXjClbmGss2pxYQiS7YjpZja7e9+168+1.ndD.OimwyH0U+Du5XkIoVbacchGnjTD6K778JUZehjOlju6GihrAUqVMxwwIRL6gz222uCPykbcaFDDz16LdChQZdZfb+Q+l+Q4pToRVnZrMM6z0FlrjS7vIFHs..MPc.51sqZ6xsGC..f.PRDEDU.Hat4lo777R+c+L+ty.g6yoFacIwekQqXr+0DJuUoRrZ8UWsliiSiZUqtIvdAAAcUnuhFIv.+6zuqHRBmL1Y7wGuKFj.2Cp24tu661zFN0RJX6YitHWCOZaLrfDyCCTMr+HEVoKv.MTkxPtVsZM4sca21LuieyeyYAls3pLODr.Pg0We8hWYoqrjpZoNsaU3idK2xQbbblv1wNS7YIZpolZfgCdjg9ubUK4lx000HhKplCpmyyyKmsscFOOuL1Pl+oOy+T5JWXh2fGcFW0Ec7HUBSDNAhgKhsUhIQu39GMwwtTu9W+qOQQWFkyDtHIKAENWjmG8.m1.6VsHaAroiiyVhH656Gz7zddc7886455N.D0wwI8o87F64+7e9S+OdtycDfoS5E9JUpbP1gNE7vNCqOvyGwATFNhB1.jWUMmHxPG8+g+g+AfgFGCirSfDxwQpXRNhDDfNwDFE9wqSGKn3PXuopNMvzoRkZBaGm7pANURLAcm.WMTLRqEfLrGZgAelOymouqqaeu.uANUbF7Bt1qcfuueeJQGGnEDzrZUZAKdPICNdiqRCZDKAXMTsWbkC6Khz+Ztlqw.we12gKt+a9cfm2w7VSA1qHrATu1O1OwOQfqsc.PsvvvMbbb1MQIe78ChbccUGGG077uTWOOujVmHJNao6yqGiDHusssXfh+i5xx7Hyug82ZK5By1FnU8EnEX29O+O+OO9Ygiz.ReWMuqLMTM6Ly73y8U9JekwpFStq0TcVee+4+q9q9XyWtb44cbbNhiiyT999ieyezaNKnoiQhTLYmZ3fn3yukqqikJhkEfiiSzc56O.JpAAARPPPZnXlFPFNFoLjabIKeeeYYy8wk5luwAuJR430khsjZyM2L6u5u5uZVy5kBoADuJvC.YRDi5oRQ.CdEuhWQD.UgTefOveR1wFarrwbWRZfDG8.TB7CTee+HPSfSaqHMZuqx0cGWW2sqWmsbcWZGfl0JRaGmqZHYgAKz+e97mOBmDhe6DG7c6GM8d0niQuNiIntM5NeLD9Oxkcj1wn7pc0pL.JYcC2vMjOrLSBklhX4wDl2DP5wS3OGCZ57LPDcxPCCoLOv7sa2d1vP+IcbbxonojXUJx7S0x2+Ns788SIhHAAAoVx0MkHZ5SeZ+rPwbu5W8qNWXAxe9ye9XjlPFnzCmVyY35KU8H96mEZLVYXbU0wnH491+1+1SC0Ss5pHf8k3wt.PrUk3QDVRPXfDDDHSO8TwW.pHpQJKbccSPdRzm7S7I5GEE0w4jNsC87ZAU26zm1eWOOuseiuw23Ng1rW7dgCL7VQEiTuV5PQ5yiziCA8Gizh.6yyOCKLfp0PLDAaT4JkiVHY+gYtXb67vg.krBLUXKUbhurrsss50qWJQjTKuxxVAAAobccR666mY94mO6ke4WdVU0LwR.K.wq+qoNI6c6PJSae0HsmGogE+lEJSFlzLnV+ES1qzl9yAcWXgE5DFRSvdmidzit8G91tssbcO0VenOzGZKfMgRqWCV0YImF+Zu+e25W1k83WEXUEVCXcU0MAYKQLDetiiy1hHa566ulqq6pNNNq455t8kcYWVSnd2hPzQO5QiXgjBfENX0UY.LSDPzYu310Nr.t9l06hW3weQrNGjJln+SE25do921cWq68duWKXnhUY3iCSBbmEpNOvBhHKHhrP4X98ZpolZAU04g5yM8zSOip5j2y8bOiIkkbppYEwvUBBRjqqaj6RtnnVAAAo788S+G+g9PoWYkUREGLZp68du2TwemC3244d39d0Ed+eBDOPfiYR9mpotu669RENR.LO0m5Scz22df1WZ30ho3b0spWmTP4TMa1LEf01auiEf3ZaKDYTGGDizoa3tP0RATUG78+Z9O2SUSgrdZOsmVGGGmt+F+F+l8vHw0chSbR7m067zssMIfZwCQT.Nb6KOZYndfByGIhD4Ges+xdYurQK.Zpa+1u8gh8.f0HJx09nh5B1Wdsd0pQuXTpzuVYy7x+g+C+Gh84cI9deteuVDipbW2qRd4uxWYTMHxwwQqToxPNEAH+q407ZRJ9TRgisVd4kG8Z4g750FwH4J.hxl8XC.FL6ryNv3O99EN3252525R0ePkEouiIwZaUqFqVpvUTEnZjpqVpToscccaIPOAq9Ntt8MsleTKUk87BBRZSwN2gmWWftOkmxSYnp8vicPVxHCChXVqD8EwNo.psfhMEQZKkjdgppiM1iK80ccWW9ekesesI.lolpyda21sMOv7yM2by8o9T25QVYkUl94cMOuwEUxDFFRPPPeD5hP6c2Ym30hZWId95LmwW88CLH2VjLP4rhHYAxToxSy32uCV+Um8eRf4+Vo+GecO9lcBS1eRoKhQAZBUf9EKNDBYsJYfZr7deuuWCL4chq5i8kTBJhMbDXZKm5riHxNgFdLoiHDUoREQEwpLjx00IMP1+9+9aera61tsIGarwlpHLITHAVXoY9XiUEvBJkpx8GV4W381Cs4i3fwGxkB4sgwEo7DppSHhjOtmPSqpJKu7xQk1OgBQFGsWT3bIa.lLVC.IWtbog54KIxDppSBL4W8q9UmfXtY.iSApkkwfPBOMjOWdcvfAJFmCSXv6teOeOeOcJBcq3Tomuuezs8+7+o55dRkZDEbAI6X4CiEoihqdXe5kP5uM5rxJqzVUsSgBE5BzqZ0pC.mK0dELBNdeZPq5vV.M9HevOXfHhOl3PVUDYGee+NwA4J+e9+7+IUPXfUICxQTKKqnhEMHkne+9VgggogxIZNdZlINoIE.nhNhne7s5pkcvQRvr8gML8E6pzAB68y8e7+Xefn+1+1+3gWyehOwmPf5VhHYrsMvMtZ0pS7Sdi23jddAS355NtHRdJQF.qm6y64BFhNsGHl9jGZq5977huueTb+vNH98lHnNK43jJJJJCT2rlpGoLjTVMbcc0EWb30+n+7fCANgTZj43pfg3LphdjibjjJ1nPCy6OdvEgLIzDzLEWUzA.TJ1AUGCxqrFBkXhQfkLRh5flhJ6JHaeWAAaQLZ19m+m+q2VUcGpSSnVqhwIL4M+l+o5czidz9jl3jcd1GKjrjjwnAsM.n+ZPOnPGViV0iUHHftPMUDIye9uye9XPsIJMT4GVyztLMXrYfwfkm.XpBE3H.EArKAtO+m+Of8Zqs1B11tyDDDLFJoEKQbcWBr.QrF5vnpZjqq6fy36G455ptttRPvcYrm1frG8nGMWo3V7IAod7vHYIbLhDYdMtULxppN1+k206Z728652YLpS1+5+5+5DxcTtvTf7.NFhbEa682Gw01FGaGbbbX6s2YzeaAvx222JVRREDYPkJmpG0oSTbK34551rRkJ6ArGgzDB5Twn5IC.O8SeK2BvkTBFdjZbv.bF.Ge+jlWBCwaVBKQJIEGwN2pv.vc.aQz4dvsGaA0LsXZnZUBREEEYEDDX0nQcQUUV7XKJ.Rud8D.q0Vasz9g9YhaGGKnL+9uu2WbU0PCLIcUH.KOH0W4q7UhSH2xeipXJG1bUx5uAKmLGER+0GptTzBBaBr2K3E7B1Ept6SwwY2vvvsWYk+kM.V0+N8q9R99eIA+H+Huz.U0pW0U4V222e0JUprdqlM2LJR2x00caLPaeUU0ZAAAUgh0KBq644sCPq5PWn7.VknZPT850i22dqQC76A5Sx++KEdr5aD6qt+wd.hgvQaXAXM+7Fe5lXhIjm0kcYD2pcoAF6i+I+3SKhTfRTBJaiom9KATHT0EDobALINo.ljoLmHxLW4UdkSqU0I.xaaamSDIcbkWS6cF+zDoYYeNxIWrh3jIHHHc974kX0KJtMEIiYe34S3CpGt9aZ7cc6DgMXEK.oPAw5a6a6aKEP5XzljDKfFu96AMfwDB5zLuVUFe7wE.1cWiMrlMahaEWBBBLkiNRMLnAnf1WDo8a8M+VaZYYsqmWvdddds.5bi23+oN.ccWxsKrPmEFQsRVA5BK1ikOTU+5Rw+wGoGCulNADAqM.neoRjnVWQkA8xtrSQL5lR3YMKCoienVsOjjoV2LeX36I000Mduiyj9CcyevLNNN4TUyG3cW49e+o+z4JW1TnVee+wMw+Tb72467cNVXXXlDUaixXAErdlKt3WOqEMWqVDUB5qZXWfNkKSaQjNhH8KaPRn9K+K+Ka9F1OnGuHVlAFdGjcAVEpFJh3WnPgvUWc0FA99aAzDztA9987CC6cRmJcpTwoSEGm1m5TtcCBB5UoRk3yeg8eeZex18RIVuGMj..MFQL8MsoY0NwHneuq+5u5cwvqYwzUPst.QEKRZf7hHS9i+BdASAklx22eBGmJ4W76bwz.nh1WU5HPST1EXWE1AkcUXOM12uImbx9hPjqqK6t6txa5M8pjqJFUlPLOFpH+fm3oKwwo9no0mOjFORfvDyKRmyzyDwNdzOccmN.MEQZU0v+BC9JekuhIC+AEMILIb9KEmcUfAUnROLIfYuNc5rWIa6NXBlJkpZVQ072gQh.y8LelOyrO2m6yKW0p0yCL1crxJiAMFCH6oO8oSyZwOraLmETS909i9iFlk9K3d5gy7P7lX8CKLjOFBTMipUyEC267TuTNfz862mq3JthDY2b.yF+hVkkE.4Fuwab3bv7F1DbX1hqWr3D.SIhLwke4WddLvjzxw0QcbbTTYHbFKUtbT61sGjJUpgL3MP6efefWZKf10M8W5.2kbi.TOu6L1f8BWrMnF0ndeV2vD+.sO1wNVaawtS74Jpb4xLJQrbQFInJJN3UmcwrBLrHEOu+4OuusscMSUynMlrnm5xu7KOifj8K0vnJRQQQVTun.vQO5QsrssSGCAwXXAVw7NWiB.db1C+96QxpkcXiKHwTmfSLZO71u5by0WUs+22222m4eqJQu3W7KNIi9RTXICjbssS+e+u8uM08bO2s0sca2l344ITqD.CPoKncTUMjSVoj.j0VXz58tXZyo9AdA8bcc64Z34lAUKWdeG..KBQVajM4mX4SbPmxd.LhdVoVxwvAAPEaYfgfBKOTVesw9vP3zAmqFF3QpUKkrAI0NPPVwsEQxWUKWtbjB8PLs8mJ5lhHa9C+C+x1DSeytyK567EsaLAK1Bna8xk6Az6s7VdKl0tqvfvGs0W0WZiCj.T5ywaDGn1LCYTdari9.u+2u7i9i9il4tu66NesxkMbDk4yTrESukQp5lEXAZTnLfKvw9Jarww9ze5OoKPgfffo.IqiqiE.99mQUU0Hy9FQttt8UQS5C61wPLt2IcNYT34OODm7q637m+fxl2kx5xK78jUlMBVOpp46lEH+a3M7FF6m+M7ykCHyq407ZFdbsu3d4c+BdTCKe+rUDDDP1rYYj+886W33flbbbTJWKVFXupN.s7882qHE2EF1Zjc7LIitOP+m+0e8CnF54dzW.Eib8bt8WaTKduwZEsf5TS0App8jxwbnyB9w1wtH5W8IFBebChzDwpFHVVIIdyPFfIHrrd85IeSQTKq+q+W+8S43bRApxq60+5ipTox.bhC2qXMAv5S9I+6rdBOgmfIAXOHd4+vbLpswQa+fX67mXXgMJPggqI.Z1nPg8JWt7NG6XGaKf0eNOmmSCmJUBA7EQ7+re1uRULtjs1we7O90Eg0877VOHHXUW2kpWoRkZNNmrNTes6JHXyJUpr64CCaCz87m+eIA8hZwhEE.4XFjKj5.eFsM7jQ+6mfSbX7X0gkbE3huu5EK4L6O7HY+HAvRWaNydIhDExvVwIKvjWyy8ZmSUsjVUcUMrhHRks1ZKGU0x.EUs5BhHyCLup5BppyqpNGF6aSs81aOQPPvX1114887y666mWfwQjweyu42x3ppS7Q9vejIbccy655lv0GI9FCUKZJbXM67vZYO2CcBr9fyaV3g0Zi7cytpSxyiT0LsISxwWol8kn8gBJ.hHTFi8r0WecKL1ojM2bSB7C1+BRDUEi++hX0IJhlNNN6nptckJNw7uzPwZnC0VnKrZ2USPdVkD9Ob4QQWB7nG6YWrgd1QJ7PlZUR70teUn+4N2crOulEtfwV3Vy.Ghj+kb7F4mpM6+LKIovAAAY.xZ63jOHHXhfffoTQmw22+HZ0Ry566OKvr0gY78uyI+k9k9kxYaamxwYIyApZYfFLrvrG+gz77n6yDQHCpwPRFdOpVdOU0l.sVoa2tP888cKrxkBJgF.GOtsbXyhTrNf+N6riWoRkBscbVSUcm1sa211woun5PzrGDDzOpZw9w7bhZzpyF6m.2yYePt16vV2cP6TeqJwIGnvCl43ZkJ0TUcua4VtkcvPbxaJhrIv1hH6YUubGU0Auo2zah6JHP78Oi08bO2S75+x8EQ635tTKWWm8t6u3WbWE1wcI2sA1BK1LetbaIhriqq6t6s2dMIhV999smZpoZ+Z++9mt6c5626Fdkux8aqoPHb+8wdzZxMePGORjvjgu7aaZABSOYaPDR66y+9ZJhzrHz8I7DdBQ28ce2of5YLYYesQ44fC6EwgN35gWehq1xQykq0N6rS21saig.UkIbccmRDYJfI+7e9O+DNNN4qTwImqqadKKq7kLHvXrScpSkC7yXHeo0s.307ZdMrHLJq9+P8gbx09PGFZXpzQxlLhsHV+E+E+EoR3ujOwm3SHYx3l3PTuxPe1vDrKdlu2e8G3CLT0EVi0LPhR0wJAS98+c8cMMvTkJUZBf7gggYbbbrDQjvvPTysf533LnZ0p8RRlAlrItGB69I+jer8bccaED304pbc6QMiboVohwf1BrpgpfdfMRjrHtGPRVOaBzpJU6BwURDrJcoSPiCCbqDAsoHaCrZcpGT4XGyamc1IrQ85qArmqqa+xkKK9AAYTUy8jKTHqpZFWW2z0M7VxvJjXHG0DG87rty67NkXinG1ywGpUK6alCELxLc7eNpBLf0WuuHROaCDVSHJvgjhVMpo.5K7G7EN.n2O9K3Ez8Lm4LcMahWqKPGUokqqayX3M1x+LAsvPltlDCDE0VEsiuuem+1+G2VGf1+5+5+FcBBB5R0p8LsDk+PmarGwY+yZ5y8GnDHbAA6.jp.jl.LD8PU5Khzxlp6HNxVevO3Gb6PB2iKjCctnyY0n1vykoBYj3nGnjDLkBnUqVsukHcbrcZBrqqq61ppa8u8O9+d2q7TW4t.6Um564662Dn8JqrROpVsOvfhEKlzpA8OwCb6l8Xgw9IS3bI7P0V8w1buERX+a709ZGHhnW6S4ojRMD90XEJTXRLrx9r1lpxVBvoAMN5evevezwTUW7O7O7O7nppkEQlEXbTM8W6beMhhz305Zj.Qsa2oOPOQkt.c1byMa4dJ21dddsqS8tZpT8AhJB51auM.x9bB.vk95x3y6FCgLbQv5c+te2F4ZWDKUUN1wNlRYy7RnA4jOPuKCfr3HuOW0vaAV862+Br40saWPQmbxIUUSPOkQKN62uejuu+ffyDzGnWn2Y55440100sUcpOLPiJPWZzHQBq2WUldzWu8mLF8ZwfBfBHpVCHljuEoK0hCXZ0jfkN6C78vYOK.L+7yGKVDlW7hhhTDvwwQ88ChBCCGdLbccku6u2uaKPs9AewuXKOuyLrp6kM7vkY9qt440q9G3Z.6Gzm+e8NNXRkG4yYGpPHMLb9ReftgggsoQi8DQ1sb4x6.r48du26ZEgZ.Attt9SN4jA+N+Nuupuge02PcLINYUKKqFe3OxGtNTqNkoQPvcsFl1admRPyiZa2Bn6QO5Q6CLvk.k4P.RuBqLj3lKXPpYFfLG+3G+fItzBH0Y4rIAjbXIIw5P9bXIE49kHFtv8guf4vRwEK.PVm0S9ch.FTFPM7h0T++dK2x7RIIIgtUTUqL8zS6ho12kDQJnptPIQVf3VzASxSlUDY5omd5IAFOvOXbDlPGh1Nch2xa4MOQPXv32vO9MLlmWP934INkq6fkVZo38spmxP7lgIx9Ypi+PucvE.NQbhCCCCicUyXaKffHUUR16aDDlvHsg7gMF99XAZj3+oDaOK8jSN4EljZY+CT7eL5t9h2UOU01fZjOXW2sKA6XYYsGPaGGmte4u7WtGrZRKRLnDninZPOZ0N1Cz39UvFO7RrK2ajOwbQxp8MImcqGr6sgOKB2GgiV+9+t+tVyLyLoJTnPZfL9ggwRJqNMlBVTnF0J555VPUcdee+ou268dyi48jAP09r.8gpQUMJsXTAPiEYfGp14F5udYCwB2BnYUptGPytc61Ia1r8rufmodWJGSSgSqXPYRcpuFP3byM24um64d71ZqsBpToxp4ymemXgenuuuWjppN+7yyc5emfYMRJU0LkXeI1EByPgg7u1gQp2Gl++GVhdejbLLYb1PWpUqiHRKaa68vfvjsrwHRG.aEpgaCr2a6s81Z533zVDo80ccWWGiDxWscDzx2+LMAZdEWwUz7Ttt6EbmA6Jhriqi61Ku7xapptoWf2Vppaoht4cbG2wlNNNa81eq+Fa6551b9ibjt111Ch2iUq7X3DkjLdj3AaxKRIFRSSROys.iypCqB4XXd4smHx1K.arJk2D6p6Q3nL9+gd7iQVQgogFk.91t9q+5u726688dYppN+a26+1LOqm8yJcPPPuXkSYWP1Vkncp3TY6fffMbbbV2Kza0J1KsFTei4gsWii0hBqzaHabe+6YxGNyAINJjkBjmFLMvQvXHap3MsUf8DwYCJEtN0XSLIav3zagBPiFIm+39qmIgxEfptppK1rYyEmXhu8ic629egyUe0W8BlJ2RdEMChkknXgnfReQj1JZqa81t08dgW2KbWWW2cBBB1JFoFanprNDs94O+xq+LdFOyMBCC2v11dKhkvR1WJdOr9eM99tRZvKGFtKXZLLO+XXptSKLsWyVPg8fFcF448EKf5D04XRf4gh1P8ippdzff.6YlYlElXhIl1vkFzwwwYCQjPLvYYUU0siO2swTc1cAm8ffNGC5sByM.606S3CHxEF0X4naJ9HkAgQMTmBrSGSVwwumUNETMVYObSCAo888y455NFv3TjwusO5sM1K3ZeAiCLoWf2zhJGQU8HVhLoFK0tpJYEQu.xZJFxmC788aArgqqaCee+ZtttqArouuebkiprEv1vL6Ba0lQThfQliN3b092SkHK0H+69c+ty+y+y+ya1TqToTZ0pIRSXRV0aAU1C7ZwA3Ag62wL4clhTf57sA7j51q6UTuQ8KWTwEXJAxpHpiicOflAAA67Zesu50+jexasAEKVk508Ap466ulkk01111Mghsf5sA5TqVstkJsTGvOFNwK18PZasGKLNLm.Fc8WdfITUmpa2timKWt7Xb.efHkihkJwThXmqWuym+pu1qdxO+s+4mAXVU0BXB.onuueADlUTYBEMS74IBg9tNtc7882cvfAakJUpMdNOmmyp+8+8+8qVoRkF.qhwIfcfhIDdbbkrlqEr9PYUkG38PN3HNvqR4nTsInFE.JqpVDXZob4zTqVqO0m5Ss90ccWWcf5.qCK1DV1HImWPbBHvIRCmMCF6eyBrHE4D876cE0qW+DHbTU0iXgUVEkolbx96r6dcPTSBrgsdqu027p+5+5ukP.eWW2f3y6ZlyMaBGaWXkj0XG797fNp7nk2+FccYF3X4Tc47EEY75plWbjbDRZ.UUssHxdTpztTq1nIHcz6qQNdymGVaRfBPoiA0dhCFL3DQpd40qUyFi8MwwwomHRu63z2QGmxNsTzMAp45tzJdAm9euhSkUvjrAy9vKrv.Vc0QQ6QRvNWLBK+azyYv99RjAH2m6yc57Oqm0oR3+MKvAHP9Rm4KY8jW56wTHJiOGFetJyjAmIXbGGmL25m5V4jW4I633bxcf56TB1qFE5PoF8oF8+PenOTmW4q7UZVaM+78Ys0zkWdYYwEWLIIEJL+f3VNH9YxwhfUh+6KN.VdXBKTUQdbhxx2OeFD.43f04XQgiurNTM3tv2cOXRSD33JbtCtGb7uywsfyk36YVLsXWBpdmBSBObDo7wZ2d4i1oSG2omd54v7NR134YEHZmc1IZpol5PViOzWrgDeJfQVWkgW2wUEWVCz.QDeUUeWW2p.qCk212+z60pUql+C+C+S68p94e46v5rKFaXWpuecvBzkxn5igVruM6IUUGO99GF5CT4cgpWfhGdHmuQ2GcFLun8cr7xKeEoSm9IqhdYhJEAYBPyDe+mvSQsTXqJtt0CBB7e1euO6v68dt2Z999a555tKTZOn1t1vlgTZSbpsEAjrVeTdt6wRnKAt+qYSywHGqPBRLmvv8M18gp6MOr0ZL+Nbh05vYGZO+vdNj77bNn7iCp9jO8oO8UtzRK8DAp.Lk48PIBztKrvBMsrr1oQiF6nptEl8Np4555C3CEBgFaVBZVi46P405Q0Kv91AKH0Ea9ej0EKlFVNa7053.iyBjiUIU7wLlDycZBAGzGtKVL.InpeRn77wvhoxK4k7RN563c7Nc+a9a9uW3G8G4GcRDTGGmlhHIsb3p.q8ze1O8M+m+b+y6vv1Vo7dP0NNPu.lsOk1neLQvdXw1jr9Zz4huUUTrCDK.Vwq4i+2lyBVe+XugwnLiSURDbj7.S566OEvrWkq6b2UPvrJ5LtNtS344kavfARpTozJm5TQTqVhc9dAAAsIhccprzV99mdCWW2X+Qr2DBi8+2YGHHAAYi5iziUV+B7HCBSfQxt5h6mU0NZCssYABMMPr2NgDNytJjCplgPREq26WLjGDe7aDS9q1stka4VZaaa2Gv5Y8reV4BLJDyDAAAS.LlpQ4OkSkb9994bbbFCXhJ1mZBn93.4WCxBqjhFPbUbd3tHXnQiiCV16mzHqBMf3dmqmpZWnXOQjdhHc62ueGHrM0nMknGyN6.LNMBMZL5wMUwhEiWHTMGTZruzW7KM43iO9zpVcpq9pu5ICBBGiXRPzBIknpEnhDMT4O5hRqW8+o2TyO9G+iuGvdwDm5tttt6ZYwdUN0oZ8LdFOFIr2s...H.jDQAQEyt.8ssGB+OKfTyO+7CgL9g7LJ9YiWz7vfB6uPQ777RAjy11NdAqSVnwkhhCj7bX.P24latXEyodMnfmHkO+VarkelLoqGmLrlKrvB8N2W8qp+z+j+jCSdmsHoghYu0a8ViyrbwrPPFfzq.Vv5FYncQr3Dm3fU5ZnyHwDm02LjWxGhiP0.q24ichuZWNFcEwsKDzcNn6U451c4kWtKPm20q+s27Ebsuxci23byJNmZqmyy44rckJU1ww7reGQzsEQ2EQ16U7JdEMcccaqhzIHvqsuuemeqeq2U6DzI45511jzfxMccc2qRkJ6QIZAk6.acXHr3Ae8TM.G3m+c+tSpBlRsZChOm6Ara7y+lfW2XXp+.gvD43i7bag5jvcMoGzefkqsqE.hJwT.tRPfuQV5VZI9C9C9SDeOeg50sBB7RAjx0005jm7jwOqqqPwHXgAkJUZ.3GYpTTgHX4GKitjCq5JP7ZvxkK2UDoctb4ZZR3ocykWd4NPsdhsfHESCUGa80We5O2m4yMafWvr999yFDDLyJm+7SGy0RioJYUTKTTWWWicCE022GDgzoSKtttxm8y9Yku7W9dRV+2OV4D5+re1eGCLy0kipVsZDrdDyRDr3CmphATSoFJ1LfRkF7ddOum9.8zpFEb3u5S7I5.zqToRwNTs7CTh+rfyZMKjd93faAxVtNYRmNc5YmcVKTDSrUFT0ryt61GzNnz1wwok6RK05m6m6WnsqqaWWW29efOvePx648KWtbL5BVY.b7QqB6AqH6i1RVxnCEpnvJQhH6SJ7gzkEnG11ChI+UgZ0fKI6qqEQATCQOWSKCpkkkVyT0TMYxNHHvx22OkcY6zJZpOxG4ij5q809ZV99mwphSEKOOOKJUR.ja+1ucX0UU.ckULICv119RMQbeiZL5yQMd+3nm0y5TC.2dwsoVKHnI1z7Iuzyq4m6y8wat.raoFrMlfjZXWkZNNmLLHHH7EdcuvPGmSVCpuJvF0fsgF6EdWg6gMMek+R+RCa+QVasNXPuz..JWtrnplV0UyB14.mX4EekwnD4gikEVNcEvJVoVPDgEWFhaqpQ2SMEPFirjubFNGYvlzb7iOZq9jz9rY.xN6ryF6+v4xAkxXBNaTDmbBANm0bPpxweOU0zEDQL3NBTUEQrSqZ07QQQSNsg8kmA3H6ryNyPbQd.ldpolZJhU0KFRt0C+jGHWfePFGGmzHjFC2njVRtlUxHB4.x63bU4cccycq25slAJYAUEWWW43G+3xq5U8xEVGXFDN9CIFH5Bd+vPHjgCp.CleDdmqrHCLzyBwIRYgzwJ1yCIjrTpToT.Y777x533jUTqzl1LRkQWTD+PVEkHee+App8u26Ym9AAACLsxaoAPsnvyGpgf.0DBPvFKlatGHzC8XwgBvLqfByODAFh3zCLHScMCemfA3vW7wbyMW7ZmpVPIqmzS5IkJHHHUPf+v1iBCI1mZsUWMa850GuToRSBLgq6ox+4+7e9TF99qTm3hU1tFz8Lm4S2ipzm4Y.Kt3nIo5fno3hEWV7mkGXC8mwj3kN.cJsJcgEF.n2xsbKVFBZOHYs8kBULn.CNwINQbRHqtATtJT57e7O9G+9JVbg66m8m8m02w0ogiiyNXTQH0yyyRDIUIQx7O+4VNumm2De4u7WdJCY0WcJfI7UcLXi7TibLOY4BECjK.UaI9+e7ie7CCMbORMF02x303gwI55Dcg06TAZMuwm4c.1pXUVGJutpZbQWr2v00cKQksqC6pptmisSSee+1hHc9C+C+C6VoRkd9m4L8BB75FySjMupq5p11ohyFP00+d+d+A2.JEqTogseG+1uidfy.HPOFnKd+rM8XqwinsjCPzx6avtmgIeCZAzpjHcNyY9ai.RWsZ0bw5FsgwlO6EcwyHaJP+RDzkxgsn3vdgrugzozzppYUUy9y7e4WLaTTTlZPFWW2L9994B77xGDbm4Axey2xMmkBinXJl1H7qWX.JmCrBikyMfTFRypgBDYKxfS8jK1SUsqpZ6LGMig2HfNTidrwFCvzRSLOHyNhiC0SWOKTJKPtWy+4uu7Ww0dEiIkkI.lra2ti63XmWQyBjVgTfX5YVTEzAJZ2kbc6D3c5VujWxKo063c8+SSee+8B7BRXN+jJ50FC7UU6QLZbcurqShUIhK5yn0HQoPPuwW6MxQO5QSIF4nMaYHaABNHY+9fYvLBn+5ysdGX1cAVuHMpRwZm+DWyIV4U+puwy633D.znwZM1drIln0O1OwOwfXHZm5+4W7Klc4k+WF6E9BegSXCSVf5SBLNkHGEFxT3oYYxvYO6npnyEXT77m+7BT4Pp30iHFLu.CPGUjHrWa.UhSL4JzELR025Pm5P6EWbwV.MeCuw23dPscDQ1pHrYYps4m8y9Y2z22eKeCoYs0uyuy6aKWW2MQ0s+nezO5t999sNkiSGTqthHcdouzen1NmzoYfWPSCWJTeWe+SuCELpt.0nYLS22+rO7VCoDfRPPjHRjpZzBvfh62lA8VOy58fYuXxA2vmGmajJ7rJjQUij1YYYkNLHvrgmUDHfiiiHhk7+5S++JUvcdmoUUyfPt.e+bpJIP3LaMoVVihZfUQpqTX0HrI5K7E9BCLv3uwi41XHdbvDkL56zCW+UsS0tlDkQyBzXWVHb2G2+WONS0DpR2XB4U50qWZQjrRJIqqqaVGGmLKdrikxOzOEPB4RDgE88C76cye3adXfnN11IjwaJfTO4m7UNrcIruJaEJy+v+v+fr.MDJWkxOkxlquMPOjDYboL12AjPhnVs9CFLnqHR6RhzVUsy+5W5K0EnWsT0F.ycwpXh.XsAj9W9ltIib1B4pFSx3quw5o.rldxIM+xhFgPeQktlVhLXO+ybl8ld5oa9pdUupNkfd+Z23O4.fn+0+0+UspU0nX9DH5DlJr+f84QkiiajxTisqER3q.FvpnDFlnhIVE.q4efsudg2qMX.TaPud8FTMw9ikklvcIUbcsTwHa0RLx.tgeraH0U+ib0oDURAjtRkJooVsz.odtO2mqUA.aH5XG6XQ.CByFdXUe7a1ig1QMm+4G.zuH98bOoaWRHU+PZC0Z+S8K7S0dUSfPMA1ILLbCvdUndMU0p1PshTuNvFTjcnLMIli4HjlTq1vV9Zdn6rr+ymvvvHQJiHhUAByPgfbesu1WKOPdsplCS65jwCRMBQ0qKCjn7FGmDUGhLLCYwgbas0VFzZDRFN24FhbiBP14hUWPfw2H0FiCyONvXNTKOysbVrYeRb2frqLqCYqFmrRQjLqBoRHE5RhjpDUyHhj8ldOuqji8X.4mZpoR7EM4ynUkcneBau81Y1YmsM9HXQpff.C5RHI86wnqQvJga8fZY888ydxSdxLEolIPwxHl7XD+E2Bky8.xkEOXuiDEKyyC7LAhmTvxt0fdpFNHt0brrYUSBolmQ4quK1P.jZQ0RAES2rYyLCFLHCFk0yBvHyWRb1IwjfRcez1.TWUUiBBBhJRMC+RXE0+2988aueRHCga8i9QOry8iUFGz2PK.YKvJgDLKAQEHbfp5.JPDEujrUK.rttt.EMEOkZodau82lEBhiiaxuWTbAHTMN4XUqVMinRFe+SackW4UFcJW29kolI.aa5QY5tzRKYViuF8Y4kiNv4cz6G3h+7PAzPXvVijvtppN.VUO0y9TVutq+5S6PMyZbGxLj3auDRHyYO6YG.mnCvtEn55TtVUrYkYmc166o+ze52GfWPPvphH6ELXvfXYhOacXLn1jUpTY5SbhSbjxTaVfYn.SINhIV.XLVi734kmX6OGGRehQRXhw+ea4bm6bViTPtuUlzjCv0UmsOPeOn2ZljU0DX25vNP0sDQ1pDrcQB2FXKmkb1lRrsnxVggIHDgcecutWWKOOudK451WUY.PeU0N0nVy.ufc7771t9W5eYGOuSGWnT59q7K7qzekU9+a.PzJl3+StNeL43QRDlL5CyDXz20nlDE5TG5sz0sjppl5HG4H4hgJjIgIktTPbvYi.FTi45RUZQc18y+4+76ARyxkK2Yr7iM.LAA869dtI4nG8nR61sGxeAqswFVQQQo.R8xu9Wt0O00+Ss+byI95NqXIa.IyDqC8D6bvBPZvNUUPN8peIDQFHhz8xm9x2m7qJPuJTYHgctFyjZCHMTLmKjm.xoZ0r.Y+i9S9axoU0bZUM++1+1+d9UWcsbAAAYDQR433HNNNTpbIy8hQ8OhDjnZv.0HWr89wd4+3IDzpgyQJRSOOu19998.hpRY4Kbe2mETVJCby+d2Lbw6gaIge9JF+O7Ad+e.EPKCxcbG2gUnpoaXbxIEgOD4yjyQeXi1.6Tm4Vi5DPUV9O6O6+w+9OzOzO58Ab9JWUkZttta8hdZOsNF9ciLW4Udki8ct3hSppdjPXtFkXNfiPMlTqqCMPhCYOFGy7mMRiZ702hhMF4tE7hMTdnYY9aVFMO36kQdbhADRe7RfJ9IRHLst.sO197HydqrxJ6ZaZkgcpCaWs.6jOe9DjEs2m7S9I280+5e8aSI1FXaU0ccccacFe+1JZGGmS193G+3MoN6ohtqqq6tm97g6355tCMRZQhYen1NDG79Jh4neIJ0Cn2xKub+Uo3.CEMVRrgTTCKXiKIaYII56lu4aNEPFaQxBEys5pqlQGxOEhfhDFDfsss0y6ZedVnl0qhHYTSEJy3EDjsHjkpjUhhx.jtNErnAPH5y3Y7CpvLOlHX0KkwMdi23AQWhAl0aPu3VQp4ey+z+zdrJ6YfQcoVfSGftWwS6J5YYY0qHzy11tmuue2ffftkgtwbRRWD5533zAkNnz4k+i+xMv.VHpe+9366K999B.ggg5a8s+NnHPvYB.pJ.o9hAAVTEwPQMkTChS95ZtWo.QknTu2va3Mz4ltoapccJ2QDo2m6y80FTHIgdr9E+nTxXG3c8K9KlTY7L2zMcSoARYYTka1Y2cELE6VEjHUTC4tdUtc1am8ZGEE09C9A+fcNsmWuS64EAkzolZJ0vW1M.fydgDg5iod26blhnGgGQw7ThhI.xzWy280joWudYAR2f4RYHvxKBouBJrXTr5qEkwIyvjZ3ZaSPP.HXED3mRTqzXJlfYeYkTTiTNUbR644kgRjwlgDAHMnnFZTVg9UfAwJ1w2JlmMmukIBVKBXPcpziFzi5zElqCLaGfN28x2cGftKu7xsAZZaauSHgat.rtq6Sc0PX051rgmm210tmZ6QUZAzpb4xsnLslYeBdt2ZTo+FPeX191CkY9ZQ.RCbRSCx93e7O9blVdIl.+MJ+R5BGXuQiBDVJy4fbdULI.gsXbBX7YlYFCxXKQNarSRVwXMn7XqCS.NSTDljUYRUWcbU0wBXt7rN4HjbvL4fiYRrQQywdgDYAOFIHkgrPor0gb0JSVU0r0BpmrW+nEI4fR9ax+dZQjThHold5oSM0TSmBvJV0qTQjnQ+333D4Z3FKDQj+w+wOepXeyReWlV3I04+WNeJpNRfWyfrHK9P0Gh6muA.CNwHDCOP+hhLPDCJSBKEeOtFVb1GTeVDXdwFrnAof5oulq4ZRmJUpgDHqAYCwHJJ9vIir9v00Uh8GL5k+xe48qWx3ihqqa6q3IbEIsVYepPzM7BugCt15Q81yhGi5Cn0wuv1xWLEuqXzxsaO3+et6cOJI6r77d+8tqq88q0k8koaMRsFjFIYqYjjkQiyIQANFC1APN9jSrkcriiMgDrOb.YAIfCwFYHKLHv1wdYvKaehuErMKaAlv0.XxxKIvflQhXkAjzDFlYp8dW6p5aUes5pqZ+d9iu8t5paMynYD1Hj+Vqdpdptp8ku82k2KOuOOMKm7roIwzmCpu72mGEDVAvvIeDop7.u8GPPEILLTBCC0ff.LHHAULAMQbccshIV.za3F9GEWG5UO87GROp2OgTc4n6y1MKLD17EizluXs8sGTkC3LeYH9j+UAbpZ0x7g9ROrIYBAjivDeAd18QcvfBtSSlYSpyxDRcn7EdzG87eCQjy633TWUc0nvvcDQX6s2NOvHUgIfJS+a9a9aNS8xLMvTzjIz.ME8XiPEFYZl1D7DnvYnTtSa7aypRe6+MpAyYL6Sb0f37+11GgKURRtXkPZaizLOS6HX6FkXKU0soAaRDqohthiiyRt2l6hXLvYcOOusNkueGSEQvNVhzlH1FK1xyyaqFTcaOOucds2yOytP0tPYShEl0L9YgW3Lu8h19VU.Sf8+vqe8OYjotlFClavtu829aOtPgBVj.2RfbSGcE5.sMwvxcIodL+d9d9dZ43XuVTTzlSO8zcJTnPrHBtttVhHVEKVjvvP000s6sbK2xtqr1J8k1veieiei9FZezS2+5+4dybLRhpb+5NN2hPNHLmpZVMTs.henG5g18odpmJE5Z650jd0LYeClBKp1JGvPPig8Y1gAJVQjBTl7pFkSLZfc9q8ZObdGGayBOpH.ZPPPbmc14fNQH999hqqKZhjc533zywyoWPPP2JMnqmmWOWWWkpHggOlbMWy0HPcpaXm63Etz0jl4YlYCXoQxjW0HoZc+ZqtZ263NtiXyZNoPC8hhjiKUaOG2Vf1vxqShx4.Qm6O+O+O4qWUju9a7du+ye+2+aN5wCCWCyBFV.EiLvpcxOxG8iNiVWmgDtjQDIcAxh+im66t34zyYLXaBxSoykbMdtLgTQ.nDHTAK3LCBa3uUUlNCDHxSOnZ4j9+6RMCwhc98Xp7smat41ND1lTBirIaOyLyrcsZ0ZCryq5U8p1wyya6JQroqq6lVVVatmR4vV0pcpMA17oe5mdii65tNUX8CcH6AjvrFsgU1cg8CkStH+9AaChhAsxxzKBixfbMm3Z1kJMTLYGLefpEvX3aVS++kuONgZzj68duWSlVf7+d+du67pHEbbbxYaaagPBBrPBBB.EKMIuXwI80VVVpmiCOdPf0S8TOkUrHYnJVu427O4.WCQ.s96F8y34g1u0u0u0Ama2etmMrKSSm67Nuy9R.og7fC1sLr6m7C+I2wwwY6Fvlppa333rtqq6F0SJqJUjMA1HHHXSLD17VXp281nrSiFM18q9U+p8Pn6YO6Wu2se62du21a8eeuFTomiiiFDDnWn9EhS+cy0VTJ5ddtEj6j6wxMINhntpp6d+um6eWJWuKf9W9W9GSSi1iKSCWN+2Mk1CFD18fO3CBfb+2+8O3ZB6ab6HiLhI.n998Bd7fcGYrQ1QDoCUnq2w8RlOEY44cLKLHtvhowJMi8OGtee9tIbl86bAP1c1Ym710Yn+6e9+6CkOe9BTlbXubRBTN8yx844THPKk5DBDGGGqgggXPqjjw11ICnYJWt793noDobNqmmWtfGKHWHg4RdenZij0Za1qloD.e9LfTJf5AwThtKt3iO.QRt7tvJcJA6RnwH4DR.NMYHarHrNlLKtFgrtmm2lUpTIMYMsAZOcc1oEsLbZwBDy70RteWINj45Az0AhYVrRfTedRBtLzLWEHG0Mnwq49CDQtZlr7NBvXTauxdgD9yBX3pQLTHgCu7xKOBUYDJWOojXBl3IWYiI.FurHiZPU6xCm78FAZMJb9Qq.iQCywcQXBQjILeeFqNLBDMpp5HTmQDQF5G+m3GOkvGubNDJj3XjobjzmgSjIiyTU033DkdJHHX2fffcA5JhDeW20IzjikkiiSFfrG5PGJiuue+9ImVj4bFYq9psjTdFNNc5C3.USHlDBfeOdP+YskLGcIqPpjAHSkAr2IIIjI3HQSNeRrpRLhDKIIOw22OtgwVvt+k+k+k6PDalT5Dq8xdYur9J90L0X2kXoA4OiACFz2N2Fb8rL.YOydi+MnpTDAZnEKVrGM5GjhKGg3uuVpBNVFTJChH5YO6YUGGarssw11VbbbPAbccU0DzjXee+XSfNo2e1e1ugAARmp1f7Byd9oc59+egEvBuSOXPAtj6gcflBnG9696tO4jJhzsAzS0fXOOOqS7cchz0N1KYkWYpb2.1+tTpTCmTh9M7q.WPDI3zm9zMrMD1emkWdYAnvq707ZFU05S9u4ey+loutxW2LXjwhz0GFAXjG+S83i9R9+5kX38oYXXbZZRjOgYhnj.Hy.BSQVL6SjhFtA8AXv9mClL0+tH4pWp.mru4+AzOX6wzj369tu6z.prUhLyuT05zv00Dzje4G78zREccL1nsYrJa566u0wcb1lJrcPvoZCry0e8GNo7xZXHr4jDf7bBmbeaT6akALAt3AMoODAA14M9Fei6.z8QezGELZDe1kMY0+YeRSngQ8AZ+9deuu0AVsa2tq5brisNv16ryN6566G666q990zZ0pE633zgDoYblImYSf12y2+8zm.2pr2lLey2p.NlnJaQxFsLP.BDQDUU8dtm6ouQOyXfRYLr.ULQSNahwGEAF5jm7SUDnXipTfFjWDofpZgNc5jeqs1JmeJwigpAgApiiS7JqrROP6kJqVI8qRPPPRzRM8m999bqNNDAJTUKCp+I8STtgzmgg8.hOyEOfIFCeSq+3ovpr485Ihrqp5NSN4jsMLlNce0u5WsIihCvyKbUizj9D35x.0AtPTkJm689de2ma5RS5WpTolppanp180+5e8V.E+gd0u5Qek+.+.oDQ4jXLXaTfgsgQ9bewu3vhTdDaXHZwPzjTNgIODkCHayoHCQ6q1pOXfeFbQyuYWn7h8cRWDbvnIO3umlgoAXi88QjW8mCJhztVsZ6TqVsNUfNOVPscfJsuUGmsCdrfs9u9e8+5F.a344sgiiyFW+0e8aDUgMHpudumV9V6BDeFS8WakvEQWMF8IK.I1wMQbUnG9nFDkPdRIOLnX8YHGQWRjn0eb4.L0cpSY49Q9Q9QJHplOgbfyLPgWqBhoOLQcu7bb5555lFg8tNNNwG4HGQ7NtmPcrdWuq2U5ws+31P6WvFxjmsMc6u4a3BzikeFF7A.MpTI100sya39eCaWAVWDYMQj0.VKHHnEvphpqfZj9NWW209C+C+CW200ccw.Gz0+4eausMN5MbzMQYqumum+AaEDDrMP6ffGqCPGma0oygpdnN.61qWuTUG3alRkH0QTZ.w+xuu2WOaQ5RjI6eppxm4y+YrHQsyVdJjKR8lueCTli3pPu669tudpp8IRtjfizO30hJxlato.FHsqZbrq6wiOlqaOhH98+1e+344kLFqYFfrKYSVV9pV9Qe9tcPCHGf2ILj42gJTnXHLjHUGVUsHMH+.HPbviwkpoMKYJWIpr25.AA9BnVAAAVNNNRiFMFzljX0nbZY+re1OadGGmBfctRPVWWWg56crSTpmmucbSqcT5QS5M6rytWlgStWZ54YlONQhBpse04HUc7RKA39RENvtkfcWF5BdpmInVY3bCt214s.rBpPFVjre9O+mOOP9G4QdjTGFxEYaW.n3xKubwY1qTWLAIwrW6zXzakY.ltUqVSb9ye9wUUGsLLRcXLnx32vzSOA0YRZXHIeU0ImZpQm.Xrl1LJQLBl8sGGXhDtGYxnRklX0UWcBrYpRvTsZ0ZJU0ovP19SRhCRppi.LzMeS27fALIcroBnR+hLYe1oLXPfxBXEDDHppRMeebbbTWGmdIxHaeNbPUcmZA01UUsasZ056jruue+yQIHS.jAJYczqLGSubsA4vqA+4fsK234AmqlIw9mbQkMiG51sqUBxRHHHHIOcnm+7mSEQ6kjnrdhno2uo8KF4l0rd+ZX3YgMKA6rzdk+0yGk91eazL8USSt41OpkDRS1Wx7wj9hqz.lzOvQMpfRCy2yoZ03fffXv7LHHLTDfjwUFGkC7S6668O8e5+zX.cu8T1aLw.4.vL14LXQMrtu669N39LWIiI0E+hew8c+hQ4bRe1Z867676z22FfBkt5CZRWLyw1BiPRzLx1tNf+QO5QCu8a4VZr5pqtpiiSaUU8C7A9.YEQFBXry72blISWWPDYbfwpBieq25sN1G5C8WMdIXbVhQInuHkzmyEWZNxxJ8EzjTzvYVG4nG8fINMs+Zv96ql9x+1H3JGbMrr.Y97e9Ou3e9y2y22emfff0q.K+n0psHTYwi45t7O5O7OxpdNdsDQZArtmmyFtttaUuJaSjgu0.149tu6quuEOlYrnxLnG8Edyc2W6a0ALA1uS0GbP91iM1XskJxt292+sCUHaT+HMdtmMh.ZviU62v65csATs0K9E+hao0quguue6QFYjt.wNNNZinlXYYkZ3ZGWW2cRBdxtOzW9ghoJJ1PzeaFAvHj..QDojI5xoCVEQDTU68o9TeptTgcoDcvltK0uTFNS50hETMa61sy80+5e8bG+3G2DQy58qo2gDQFpYylEVc0UyIl.wX433ffnUqVMVgdfzKHHn+Byqu95oY3HiuuuUPPfk6wbsd7ffj645ZCnmqqaWGmuSyhckn6zCXjIOSx1zb8dlyXlXtBYZjrIgsYgxsTU2TDYKfc9ve3Obue7e7ebKlkb3QNlgKlgKWp1fO+2Aa6Mg4VEnIQQA.0dKu42he1rYiDQVFXiG7AevNppw+YejORpC3CIhLBUYLJy3ThwCMp4y3PywCgwO7gO7XPYCgu4xHylRzaqXVj7i+w+3lEK87J.SMXjlO3BiGbwR3JegxK2mcv4VOCGaS3QjdkFvf5SdxSl9raWfcbuM21.aKhz9wBB53330Ah14w78a633rw8du265tttqVKHXkG7c+tWsVsZstvidg0XvfkXa2AVvL18Lmwb+d5qnMF1q+YAxbl9YxnUl58WbupQskr6mExhrD43JXbRhf0IyZ9LYAxctyct7pp4cbbxBHI4GKFgdJpouQ09vYz22eWWW2coL6VA5VqVsd0NUZVWSN+UpX8G7G7GXtdBC+lesimeaGLXb5A9aJm4.ALsREKv0b+GEECz488teeaEkP3X0pUa0SbhSrJl.atnqqaSWW2F.K566u7ce228x999KgQEbZ9.OvCrnimSB4j0QGnJH...B.IQTPTUY0d85sluu+FG2wwf3oF8c3aqCcnC0lJU1El9fpRzU2l0yC0RVW6M8FdCwug206JFnuTb9+7zOUF6zwbq.7rs2z4oa8DiDqXLLdWfdiO93w.5niMpgaMr.aaaATKWW2jCaDmpVM.jwFarL999YnJYeu+x+x6wyC6YL9KDCZBPReqMFhdrLYq0oSdU0hPz.YcrjwYi4e1t+l2zGzro40H3u3u3uf33XQDKS.oDwfhLnGhwPdUztdG2K163dV23Mdi49BeguPdHLWy9N5T1z+ZSZMqe053vea01KXbmdvZVu+Z6l+dsjU8ZA0Rdu4LuyA+NlwjSQWlhdLMpoPuPfZYpYFeUDpZB5wrLzzob4QjgaO9G9O7eXQU0gtqevevg.6gAFVCBFgRkFc5omdrkRHR0plfjLKFVG0VU0SU0EvdhiLQ449tlaFohLSi9ARIZ1HipZUBrKALqTQlVU0Dri.cLy9zLIvLToR4wFagx.knYyYmbxImYsmbsoaBSO93iOisHy.LcRBRRIv0gUUK9o9zep7.YVa80rVas01WP8z8jfdKfLgAAoy65mQ7jfvY433XIIAOAPUU64551WFYccc65430000smkkU+jZ3db2TtrPO44Oex4qo0o+laNs.XclDUpzdOmRKhgSWx1+XOMCVF5G7Xz+XQYLnGBxSCyZOMZzXf4ChJIiOmat45OFyMgCDDQ58U+pe0dtttcSHpXSf6lgswi1ThNMuzAK4EJNdsm8LKSl+lVsr.xjjBEMEo0rW.D5wrzi4HFuq38rTh1KoXewuzWZPjVJnpjFoujiW7Ovc7c0UUsaiFM1EiMLw.5EtvEDbPvCKrQN8d2CYNS+w8yX8fO3CJXibz896O6WiFDFDCzap874qSsZ0RUjI4m7m7mLO1LLUYTpxHM2imftR8AHMv26B1aCrFggKBTWDw+gezGs1TSMUXEQZ9Y+re1VrmpsHjlDtJLFUYbpx30gw1XiMFW0vwZZ7EXBnrIgpNLb4z.ib99jIcdU07LyLE.aydym9zCFfrCVVe6Ko4b42G4hsOyykjsZUYff8Vud8zfTU.Hq6cNGhHcbNtyVQlxvoEDs5opUaEGGmUBBBV100cEee+Udv286tUsZ0VK3TAa566mh971ToxNIOW6533X7ObIzSu2yoWP1d9HfIos8g1jjL1ZL5sgcGhPefelGHKPAGdVcbdPXFYPYP85a.0W6BO5itFv5Vhr8latYGQjtRUItbkxwPRjJ16XZJUlF14oNElIzrIn8U1f4qfVIyKSCM2ay2zVOQjNEKVrMQkZSS5Lw9ky1zyq7Veq2KEKVjq8ZuVDwNsLVFRUcTU0w.F011dHGGmbXPxhDZx3gdxScxXQ5GUeCLugtiM1XwIQf1x083lrF83AxS+zOMUg3Oym4yzsVsZ6.z9q809qLv0sIcVF6CdMN30JKjLA0buZx7.tzKD1gYYKQjMt669tSkwN989898xxhUJRMFtxRlnL6sWTluxWvLLrCb9smg9pBPCvIDHDahDQVLqa1U.V6oe5mdyj9BEHmFpiPClrRSlFX5JhLs4oFSe1yd1ooZioUUmFelZQp1OCUTggeEuhWgwHjZ0JBqTzYvHMuWseZ9YgmQ8Qe4t+NXjoG70Cd+ewVPZeANoYRFG+m+O+ed7sca2V+4Nuu226qM0s2zyyaii65tQLrkuu+1u6286dKU0M788Wy4XNqBrhmiyx+e+C+CurHxJG5PGZMbF.YIgg8fyDmxs+m+7mGX9Cdeb4Zh8YLyGc1yfTSPAqVeXU0QHLIXIoiO7thPuhPUjEgLI0Uetq65ttbIjObhLPJfhJHwNNNoNezCvXDhjXbRCz28+e+g344odddJUSydcfEQQxO1O1Olk8ysLH7sSsCFbjzWO3XLiito2uQQY9U9UteqR.ppcUU2wHYjrN1rlkkUqG9oe3kcbbVDnQPPP8fff5ppQfzDn4iepGuoqqaCQjner68daDDTqAvRm8rewkOzgNTKSI8TMcS5sSHZLyF1QQcfk68MSFMV3bl6S2Di0eyu42L.hXKVhHYO2S9jYBo5dOau3BYw.6MYaLjqJ61v759bDXi02fff.MNNo+DgZ99HhH999VhHYCpUK289Ft2bttt4oNEdiuoGrHvPenOzGpHtWw088211LklYUgPjRMPxmOee0Pgz0QmsoYL14tjFUl754Nv7uJxK8k9RM+uAJafO8m9Sq.8bcb2Enimyw6D73AcCdr.MFrd0u3Wb+xLAHGS2vbMER1ApY8mOCPU552CFrjX.cvLDetycNy7SGrNOXgGVOzC8PC5HE.BqLmEqPFOChkx6sGAqNJvXTp93sa2dBVjIVlJonzXJpvjhHSJhLA0qOIDNUUXFQjYoYyRPkJ.UoBN0AOU04ANLv0IhbchHWGv0PT0CQcbq1.G.2xF4Q0SDwCvkJg1ppk0HcVQpNsp5ThHSKUkz.v315odJOHxSU0I48pL93KTRUsjXKkBUcVU0YDyd6SHhLJlDlj+U8JeU4.xrw5aXsggWg5mM1PydD8S1gcRYzPRfRvTZMVAA9V99AYDShwrBSBZx.OqzDdknWPPPWGGmtIbDWGhr6344sa4lDO2byIybkiz1KWyJg60xwzTLzvMCoxYawDzNaN1KO8kScVjDjhlgFjMhR4TU2WfZMAHR0jIYw4xkKEAqcUzt+9+A+989U+U9UhcNlidi23MpAAAwttt6M1cox8nF87ZtOjV7bOv2eaSKILk1ngfhC8KKlzeTU6whkTNOpWsqnC5d8I1l8Rzd8hIIwvBFdazRUKW2iKJJhHZcU6Jhzob4xcN6YOaGOOut.bnCcHKBpjgZXUxTh7Vyuem7sXlkLm4v9bl0ULRX.TlGcEHN45s2wN1wRSzZVQjgz.cLpy3kqaJMOmAsw6pJnIg6ZarGMoL8sCKVrXMpxEZ.0do26KM3C8g9PQpQkXVaokVpsHBq70VIO0YXMTGCXrEFczwSJiuIDQlD6FSppNIALdCJsG4vVghhH4EQJvRKU.BKVcu8MxBKXlibzjfUTZeAZ8JoD.Se8hY++UZxVElGqHvJ092pUq1GQLW3BWvhvx8DQZWstgqCKmnrNVVVqEDDrZh8+q555t5O7O781xyyacGGmMbuc28Jkynn1dddoAMIInmdWNEr7EDsmu1fevHsMXcqMDvXkfwZnZwtc6p4xkaCnzJPyVPe8X+hQfjlACGkrbZFByl3t.Knp9hBCCuFaa6oC88y7K9K7Kz4C7a+WrQPsGeMGOmV.qTqVsU877VGXiJPqn8AMvI1FZsCGLyMWY2mv.PLFnvzPwkozPPSCSxqZAQjbppwhHa6.qEXfS1FI2uwIGiDtKwYHHnHPwpPw5v3Tlxzf4TUu1d85c3LYx3hI6LilTpAHhzSUsq.6pPGT5fEcbcb6DDDrCF9En0u06+8uxu3C7.KAzz22ugq6wVDZrhoufsgpaA02FX6EfNmgKZfcF79dekphppkHdY.+AMFoeTVumu+ue4g9XerThAtM3sMTKElvWI8+G77mxYL4AJNKLzhL6nvhS.L4W9K+kG+Iexmb368du2r.wUDoSjp6lBUvtc6FmISl3kVZotFHOWsah7u00F5FB6hMFIvzltjDnqs1ZqtCO7v8ynDl9IsBHlZerYpQToA5avETt3iuuHvDl8aLwkqMvwX9rv4RcBYeJ.PIX3lvH+GdK+GF4AdmOvPFEXxvf+O1i8XxwN1KSqU6T6Jhz93ttqGAqREVgHVkoXCVgz4JoOCFzAtChFoK1849lunplSDIUcBFApNFTezj5FusHRKJwhzjUXd1hysOcdev66L.4KCi1fJkfnqQU8n.2LvM.3FDDLt.4rcbz.+fttd8koys.ZIhzPUMz00sNvhUfU+3m7jabauhaaiy+kO+FycmysAgIpa0dPaeOBr6pa8iucus23oEHCmgbLG4376qNsyj7YSQeS1xvPmpVsQ777RkqyTn4OBvP+0+0+04uy67N6O2HHHnWRVJRjR5JsfnU.VMHHXMGGmM+5e8u91W60ds6Lv7uAcd7JtlvOv8UVfhvziAKOop5zhHS8ddOumgtu6693U8p9A23K9W7PK0.ZvbrBmmsvLO+fOeSGSmCiAfyFP0CC0uo1cZeKK0boavwwwCX7DB5NVUsMJqIVxxNNGaEHpkuueKScEWoUPvisZbbbZIL0hRrLMoENrFA8uN9Vsb29bocPaAxZCEBMFgNEvrI86ETU2RDYIvNBBWj8rG3hMWueY6k78mC3npp2BvMFDDNGpNgZoYEU5Rh5w.rpHxJppsbccaUqVskN+4OezccW2UHP8KbgKr7gNzssQBIGuyzokrxQoKm9xt98eW1jKyuOXYijEHSYPZPYLxfd+.sDydYeLacUSIuSKnRZoWjrGQ4bPi9bWQDjmxTjFTjJTjHRSTiljDh3Daa.vpLjogYOkhXbXOUZdsVas05cjIlXm5p1QDoqpZOQrUUC2mspoG2xPbCnGUoG0QnBYz5ZNQjLppLfCohpZVQjBUfbel+l+F4VtkaI8dOSx0wzXBtRIRrcJ4d1JAERjptRo8ylR6J4RSAGWyeOHHPUPsfXcurouijveLJrFJK554F5666qpF5440.XkJvFQ6sewNfy1PfQ0wt5mWuu89.FtLL1E1YmIVas0lb1YmcXS2orElDKszTvZqXNWcY+qkkZ6PN3bEAFuJTNT04kJxMnQ5MBbjff.WGGGyZYfZ63r623a7M1Ie97skDtyww8XqTq1oh777t.v2.3BXJg5UJAa1z33UpyVuPsTbFruuu8sS.zBy+x9s+MipJUDoWi8JUtAsY3hc7y.TDGlh.lixbSum2z64679tu66lBBBNjS0pS3WudNIA0E+2+ze51u4+k+K25T9AqpZbjmmWMfy8vO7CW6Dm3DMAVtDzpow2ijwad.0rhhhjJUpXYxc3xw.wG0n9gWIjdc+0kmGxdNlIGrTBh0XXR3THfQJC49odSukcem+xuyM.VEbVEBV+Jn+3hd9X+Abu3rPwEo7PXTEy9m2yctyMzbyMW1d85QlLY5kX+euZ0B545Z2KYsjcUU2QDYafssgsCMI+nG0IFaRIJOUM9PjZ6WpuKwk.qlLUFXEChlMqSltFwfIQAt36q8bw9+Cz+Oc9ffmnfiiyv.i7jO4SN5XiMVQGGGqpPb88VmQ.6bP3v.CWqVshddGOqu+iEaYYs6sZa2NB19Tm5Tac7ie7swlsIru8+Grb+uZJ2rusr87Qln5GgvitunEVMYPRkXib6Rtb4xkjYilWIQXzLf4zoNeVZGnzl.q8a+a+61ZlYlYsfffMTKY6Ovu8u8t99OlhnYqUqVQU0wTUlDXFee+YNYsZy566OElIRCCsRiB3UaD8169cgj+uCbgM2DnII74gkwvjpISZJ0IHgrWY+CtRgRaOHvH8XTQSJSghZjNJFjNLwYO6YGKHHXXfBIYMOCForyBvRAbcciEgcccb6DTKncb7dYm8W7Adf1AAAsCBBZ6551NgTP2FncEXan9NLAc.utm4hO3+.Yhd9Xy0KwTkXQjXvua48wiFkvjkipC+PerO13c5zYJHQ0Zn1XrGQPc0Dk48pWxilTiryPqEgUfEWrpgdLBui63NB+Q+QeiQhHK+jO4Stdjp69k9ReIIAdcCmMa1wDQlX1YmcZU0YTMbVJyrOxi7HyFjjoJMPmBXbBsGQUcHnZwgGdtBppEfYxS0j.SLGEhfBPyhUY.kfBuKWcZt+wRPFlueFitZPtv.apctX6Ajl2KbgKzAncsZ01rYhiCOv67AVFXoZ0N4RAAAKCrx22wN1pPiU8Nt2ptt25RQFB1cYhnEvVrh2fK5umA4Gsug4Ctv9UzhlevO3GLM.J4dculWWgnnuRQpRAQrSiJOzL4Xct4u7GrRHM.4ge3+b1igyIEV9.vTSOcejjjT1Z8W7WMjhrFDDnu+2+uk9W8TOkZW1tGQzat4lqGg18OQW3BWH42mHE8EOelE5+tsYHrSN2e04RdiYi+ze5OchA+k1Ngrf2jYMxYmmm2ZkgVepO6mZUfVlffXd8Nuy6b0fffU.VAHEFnq366uZEn0EtvisFkYCnxlu5W8qdaf1W60dslMjCmHYrWIMQpySaOW660DEvwRDISUH2O2O2OWAwVJd+2+arPiTzGd9qvmuNXD0FpKUASgeADDDpA6Ic1l4HBpppku+oxfgDMK366WnVsGKmywbx54c7TTQY8Y9feFiysAUuTqg7Bj1zRJwLSxZEc61Mta2tJPhZqFhgIk8tTGjj0U7T.DojlHOaZbbbBGQnFL7nhTtbYAL7Eiq6wALJ3AIOyCLkcQdfB29gNTQnQApPdnTtG6bmyrt8oedc98AWS8.FYWxh4RBB8Lj8jW3B4fF4cGD0LXm75LEiLHsXTRKuEhlEnBPUU0ppFYSUrgJN0U0UUcNMRmWUcdhpNup57ppGVD6qSUcAn5QDa4EAbippGMR0a5O5O58dzpvQa0p0M.7h.NBvQFe7wu95pdDQjinpdDfWjpguHQjiHhb8.KHhbsTlqQUc9Ff47VmCCbsZc85AtdU0iHR0qWUcg0VasEnLWqHx0r1ZqcnH3P27MeydppdXP5hCTorHxThTcDfhgggYCCCSKmFIsDa.De+fAdeWbbbL+XBVR+wAtNNRxnr9RhtNfZLIVhkuuOttt877N9tlrwVc2n81mT788sffKFWGb0LNSX9Dm1mgbMf7EJTH+2QoR4Ei.AjCJkf10pVqr2Xm8eL5+y4LkrAUk5fAUXIp5RPPflVNHkKWNVgdAAgcymO+tXRTWWTQ88eLwRDKeeeqZ0pIXHOeAJI+uZzP341842t1hSTZltTgcaAcgVcmde1NWF.QjYowUeBUTBRrupQkt228cecCBB6BRuvnnTXra8k+xe4L+e9x9dybJeeKPkAc96Dm3dD.qa5lt8L+we1O6ADrfZ.nUpXjQRXYXFDvlmKkI14.EVR6uOG.TMQlsqNTCXj2w65cjVhciAAiPBJSVXeWWWw9.rKGksA1joo0hvxPiEKaBPW.PMnbs4medeQp13M8ldSq.r0hKtXWUUwyyImHRZvcMI2oJSBUlLP0oUUm5tt16xfx7PaCWaQ47h3j2X+uaAJmXquGEaBEfUJLaRPbLkC2bIyuO5AG2ev4BeSa+uo+e43uKSRn5Bz8E8hNwtu829auSsZ05TeONspMP6ffS1122eSf0ucOu0fFsbOt6Z11emqFAqBrxwO9waArY3oBSBzY4NfSRxBqzgEdFIU+EjsmOgtq0oMD4pkYvR8jG7QV.Y9Q+g+QyppV3odpmpHXWnJOC3FeoFbj3jbyNPys.V+m9m9eUq74y2BXyDX2hqqadEYHOOuwDo5jVVlZp000cVKKqobccmnZ0piBT7M7FdK4l6Jeh5fWaoQDzZ9TE.H.Mgj+3B6rifIpxYf5RqVsTnYe34M+7yOXTaUf3yCJyhpMTAhxREJBUGVDYze0e0e0w.FcgEVXXGGmBAA96UJQ59g6kuuuZ63zy22uCVrskEapZ7FPBwcFyF25sdqa.rwe5e5e5lkSl.EA6fGcJ0htXTtmKWzMSVv5bovqrK0o6C9fO3t.61fzxcXZEZJFT1DNDv34ymep+G+O9ejTJLkmnpIaOWMPyK87afm2oSBbxRoYol0pS4kAZBkiLLpMMuga3FVVDYs67Nuys6zoyt.5latY1qiqaHfwfdSJUjYpzfYuq65tL0RsXmBs2ofvIDQFCpOBr3PlEZWZnp0SHnzyuG6+GpZwW2q60UvXrZsCFvjKlAKlWmGN+e04E3bjHW0WrO+yV+RbngiQ1ElnygNzg1AXGOOusS5eVuToRq9DOwSrhm2wV93NNKArRDrhuu+p+G+oeqq.MWAaVAlNE8W6.0NXvRL+rW1WuRIyL011NFH9G4G4+WCSaCxuwu0ugU4xkspVGQ0PQbDMIXqoAB5xerMAVQOwINgFQk9ysbLR8npfVrXw8tPLg+OF0veO.cCBB5opF+Zesul3i7+wQ55bnasKP2m9oe5XHL4ZoodnCcH.Dnkj.u0+9lgfC1T.RVypGr3teueueuIaX1r8LQIAjcQRGesYCJu4K6k7x1JLLbaHZaee+sqU6jaBrtp55999qUqVs0fpq+Q+vez0bMnYZ8LYXiRMXSHZquz49RaCylhlmcgVcAhCC+epIRc9y41QO5QAP.WojorByUGx+fO3ubAMTKL4jSlmJji8jb1m88EB5mUPqet2y60pPgBItsalaDqZrBoDxMXJQx7A0BJXLDiBd2lS9JQjMH3wk63exc.TlW5K8klLlpdx07K3FmkrW4x8g9c4xkAHNWN2dtYy1EnWcHlYI1IDEpco1yI40ZIqAsXbkFl0c9q+q+xOiuP1rYELIRvpVvikRF64pToRtuxW4qj6G5G5eatZ0pkACZJx95dcutrkhHKzzZ9SLuzuTae9G4XWBz50Li84SbXdIxcnCcnB.C4yrIDvpcQHLAguKM787O6e1X.SVsZ0YApnp5TwfVW2eoeoeIOopbnx0YNHZ9S9Xm7ZEw95DQVPDYgNsO+B.WuHxBu025+pq+Idhm3Has0W+HpeuaX80W+FAtQfa7k7RdI2vI8CNxniN5B.G9TO1olKNNdNL0s40npds.WWxOWqp5gUUObxe6ZzH8ZR98Cm72utSdxStPbb70Abcm5Tm5v85Ebs.W6XiM10t4Y27ZUUulwFar42XiMNDvg.lSUcN.OUqaqpN6idxO93.CYaamy11Niiii3GDHA98Cnt355fiiiD1me21qEFDRXn4yZPeBBzm2Sx3551ujbiiQTUigx6FD73c7775jfbUsVsZRoRkRRlVEqpO2lG2eM.uykLVXIy+eyM2jPJoFT3XKp1HYco5ChHzK94zClqFppgfMJTt+97NNNDFDP4xkoQiF.nNN1wG20M1M4GrLiS0zqMuaSfHiTeSSob4xV.VyLyLuPtTVgAsc+zI1+FsGmrrLyjf5xpVIH1xBVp+9HyO+yRxe5eNrGH4fQ695++4mYGP2Ah6nIjK95qutbG2wcHnoqInVZeNTrRVnQNfrexO4Ck4k7RdIV.VyM2bI82yqymj3qkgXlA8W+W7WWgPtDkg5yd+ADKxzIiaJoI6aYoZXh+PCV4AkJVIIgomAxlTRKWcbZxoSP40xzlDzD1.mV.KCkZBMRBdRTv6889diDQV7lu4adss2d6sSJoXI4ZpnTQFsRcFGhlVpJyJhcoG4QdjDToENkgvXaLJDNhHxvf+vkajHQw01i28ZpZtG3AdfLPIANex8xoGL3nWLaJDlGzugd0Z++fATOFnWMlIgzusaCKt0uvuvaYSOOuMS5eZO6ry19q7U9JscbN112pq6l.qWGZEDDz58b+umUgFsnLqBylxegaYaamjvzFcgfDj0GsKm4Yj7+WP19V8BPomuAKAirUg70g7kJQwlMYhxkYpFFYfK6G4i7Q19U8S+pVhFzDSFGS46hKkiWICrmoHrzD.N.GVU85BBBNjqq6r999CioVS6JhryG6i9w19U7C7J1LLzeaUk0AVd80Wewa3FtgHflXyxDxFrmxeLHjvtXFoj95AG7mVZHIkVfolfKAEanp53HaGFxpttrpuOCBAMcf9rzu6XXPTxrppkANjHx0npN21aus6pqt5rIPcMmBVIWToQTrs.aE0r45G6Vu0M788W200csJUXsnHV8S9I+3qdK2x24R.McccahMKQnobbBBBRIG2qjRLX+QE8hyWGYlAJrDLhsMiEDnSTohLVTjV.fW+q+0u8u1e5u1ZTmUXuxTpuBrvUNDnO3ykTXJWrJLbcXXaaF5G5U8yl+C8g+OmutgDcKBLRkEXn5OsNLPwO1G6Sj6G3G3UjMAtuw.cKC6Do51O9i+3acrW9wZSc1FJ2NE11IWq8F.VxosXfNy.sWB1x1lNgg6qbRF7dJs+JcbTZ6YqDWtT8E8OV1PlPHqmGYqUiLyLC4VZoAfwcExlp.PgggY.v11tyK+k+x25S7I9DaNGr042OpntTiEF78uTWmG79LGdjmZ8gs4DIYdXXQD8s9Veqa969NdGqDZHGzVr.ayYtjkEQJrjGECgCdM.2jp5MGDDbCNNNtAAAi633jKgvz5Bz183ta849fet0OxMbCqZAMcbbhvjSsl999KebW2VQlf.rEkXmZOVs1dd2dandGfcO6YOa2Ce3CmZnzeuXiiAZC9rJCkHy22s88Y8I+jeIfk2yfk8Fe1uTIH44YIXplv3e3O7e1v2wc7cm6XGyUZzneo3DSYhoA8BBB5.rchzStQTTz5UpTYaOncsTHu5QRBw7Hwg4uYleXVu0gIHfYUUqJhTVUcLQDoa2tqlMa15UA+5PS1eITdfr72+9dLLdXeXfaZ2c28nYylcg0VaM6wGe7QSPy.IqwtkHxVAA01Br15i+o9Dq+S8u7e0pAAAMUUa3551DXoJvJmrVsU777ViprN0YS1+bwuccb1f8MYmdZxu7xjWUMeRF8FsSmNSj+P4GWqq4vT9cq.6yVfKVlqROtIkvJSig.AtoM2byuigGd3aJLHXtXXxJkKmqYTyXUz1fttqq2ZAA0VKwNf0.Vz8Xt0qcpZ9dddQ.qTud80qVsZZf+11F5DdUJGn+sT6RY+1f64lCnPiFMJTtb4AUugbI6g0sJzstY+oLhHCUsJiT2ve.i+HOxiL7K9E+hKHhTTUsXPPPQWW2hIOixCLTmc1Y3b4yO7N6ryPEJTHAwjWRhNePa.1GgGFFFH11N8m2mnzGZpZ94GDD6ZBrc+.x2saWsQiFpiiijP1pY8CBx3lnTKIe19pFBXPKRPPfjbbiCMkOijP52EwfL2hIb.mEfDFFJ5.c3wIdP433PxdEo+tjV1NIueriiSbXXX2DH5uqppgLJgPWW2yVA95QvERhwvFI1ADCnAAAcbNlyVDYbHgqt40C1utJHVX...f.PRDEDUmCS4eOrIgNFkDBXjpfUnpaWohrRiFzzyiUpUq+4JsudeyUS5mFCXlxvbMRPODvKJHHvKojbxlbstG+DlPL2hHshglVDe9a0w6bMfK7O9e7sU+y84N4ZyLCaszROCk76Ex6Ydv.X221FU0rkDovhPtJUHyW6qshtvBS0Yok5mg+mMapr7f70LOKroJGQC0aF3l.NbPPPIwxZXMNNSxwIMggsTXQT02BtfJRsieb2F+4+4O7R+f+fmnUTDoNAuSR4FN38QZ.05Szrbks+5yz1NnfMLRnQ0qlBXbaaoP85nXTRpM+.+Nef0ds+Tu1zqmsY+9dbw3OwK04tee1.m+bUfbQPtpUIa85juREJDE02OqQnJCWkpCEFFVnWudYO0oNUl63NtCIor.0Oym5S06y94+7cemuy24tREYWZPGnxNPTpzr2gD9oYfRDLsea2YfNKA6L8zr6xKSrsMwIy+OXBoOXfTTt56+gC3OZRIvVnRExEEgU4xPCihKYZUvJQAJyFFFl0111BPe6u82dm21a6ssyrvNKlVJxNv49BmKd94OVOX4TjY+rkX8Wvzddsjb.DU0T0uHy+6+2qkvz1jUUMipZ1W4q7UlkFlMUq.YF.Z6WtVLFcaeGfMqBq+cb8eGaN7vC2w.wQJ333LRPPvn999i78+O46enfff7eoG8TYRfhK2vMbC366KvrBgly2.hC5kJp2WrfkHdf7ZdMuF.TlatAG7mCnXBSPWHznzAY78eFSLD.7.AOrpZ1vpvYuvEFFXzjM.GEX3Se5SWbkUVIupZFArPQjjwnJHjTGw1NNRkYKShdru6q+m40uSTjAJVeeeeuhsbcc2z00cKnzVI0jVmRPWmuamtktx2vdvHJaBVy7ymxH38WvawDolKLjdhHwQQJXT2mB+Z+Z+Iibuuj6cLfIp.igCi3s+xy4JE0O6K51LfbVm.AssBCYi+yu++yqUuNop2wJP0Uq+z5F.sqHR2a61Nlnpl6Iexmr3G8i8wF9tu6SLxWa4kGSDYhicriMS05LqpZo0W++8rjvv+UfogpFxuy1dFL0l+DjvYCKkTa3gOSkt3hcuoGkK5hovU9hQ6qeHLoenVMCuarzRzNQ1V2Bb2LwXs0mEVy931qca11s.V6S7I9Da.r84u37Jzf82bfWubWmCdsECd8nlA1fkR3JlphzQLjU7tdddcCGjjCOyyRefm2fm+ClEdEHkc4A0747Okud228cGKpQVDSP6h9u3m3m.WW2DY21fJrv+mgc87NVeNtAn6se3C2C796AjV2kr0+9e9lz8S9Iehcgk6KafLHWhbzilZvvtjTJXMgcd3G9gae624cssqq6lm5T0VCnkiiSqZ0p0hFrVPPvZ2piyZN2ly5ylDbpJUprCPmZCrdxQqQO3n8fZ8IpWt5leb.CamJEUHVUEICU1yAukVxjMv5W50eFX+fiZLPY1YMkEoweq9iG1XiMhEQzO3G7CBXHNXSvSpjWUiypqs3xEnJYIFKWWWRHMxtQP2LYxzEnmWc5kTJJuPZrl.Xs7xFi3DSlOy.H4yOuRDc61samphzAa109Y2Ap8sViY9pAx+EKVr+yIWGGZznApQWrr9Fm6BYBBBxEqRdfBhJ4DQxV6T0DOOOirlWgdwww6iebBe9giEtXAgXv8CSe+rUfrkK+cXxVqgyJFGXh1saOAvj0SJkWQLkkb85TkDRY8ttq6Y9D9ewqpHNNNN1sVokSud8bTix13kKedO.2BEJ3fwLIaLUOUULkxS5OkAJEDDjt23TAAAFnrCiXa6LBvnNNNiALtiiyDNNNSfY+xojDhWO46NSPPvrMZ1njHRIfRNNNyFFDLsqiyjAAA6QFsvzBhQscDY5fffoDXxfffo.lx1wYRQkICBBGmjr+533jMXfRuw11t+jYaaa00wA0rWQJxJF70j8PLkbPXPPrAIIn1116asnfffz8OhO0oLJURXHZsZ0RB1xs2iHy9ad6eN8U4XMO.TY1YOncPwgp4P0ngYbSsZWz0x5+8VXP64fdQI1ukb7TGGGsc61HfHpXI6MVzxHZ5RGaa6s8bb17e2+t2xVOdhDw+49bmbWftKsDwOwS7DIyml+p617aOaC1euumchHYVLwOf500hSM0TEVZomAJpuT11p.Zs8bZtSk5rsHU2NHHX6vffNtttc+e8272DKHFTzJhAkOJXohHhjUEIqqqa1nHrNwINgUTzfm2JRRvR5uG+Q2qDkO35dWIiIGzI+tXB17NXBh15hHqFFpoIGcSfcds+TuVEC2mUjYXH6DIGlmcw.4h8L3fm+c+pKu7N.aVutoTzOxQ9GjpNeKBUVRC0UCCCWuhHal0M6Nuxuquq3d85koSmNCopN5K8k8xl7+z+o+SyHhTtbCppp5nZ8z0AsKCNPEaQjpXfM4zXVyaHf7KkDz3kW1v0JggCJKwyOnu.vQgE1m8wWU66j94NX++1.aFYryeyFMXq50qmDrmJaSjAw4y.qYea1spjvumus21aacfMWj9b1WmYBny7ye66BKeP0S8EJ1hbYaOefvj9alWpD4Z1j7yBEVrBEpFsvv04LiiIZiiYpsKZCUVFhZhwI1TDFb4bZ2Jg7WGFJOMzXNfinpdjfffCKhTJV0hVB8.Y6MVe80FYzQWw00c0ZlL0sDFtYH4bVYEHZc1KK5WtAARx4OQFU8DnV+H6MGj67lMk6Shf+PuxWYg26u9ud26+M7ues+j+r+nkvj8rAHco8EfkQvXfyzXxTYk2w63cX+VequUWnrmpQNAAAkR9LEcbbxjj0CSMxCcUiSLaJhrliiyp999KKhrRbb7JhHK2sa2kylM6htttKZ5GlsEr3Vrexq7pIiZGLB6lHi6gUsZj0AxG3vvDvfY9XTf7INVryi7HOxFm3DmXM1CkICFA9q1LPH.VG8nj4zml7NPAeSFyFjmZRI4tQA6wgv8TCGXDee+b4ym2Z1YmEfdezO5Gs2q7U9ZTUCMJPTEZuxWaksmZpabGUC6jDY431sa2qPgB8RxzTaQjsp.aEUgMIZeDc1AIqR8.W+v92D34xBRWpLebfeJaAMFDptCFvoA4ZmmKafd4ttRtdlIOrT+ZHMI.gC+w+3ebqWwq3UzFyXhkMkFzxChJqC1mMX1XJWtLWaiFbSpp2TPPvQvrA2nXd1i49R2AjMAZ455truueSLxf6R999K455lDXMZ8e7W5WZ8ewe9edipsTk1TmNPktPzfYA3u2r4w.sK17a3YNtbeY6zwgQBBXzJv3QUX3DoIUXf0WdnG5gz64d92RR40jF.l8jut8VS9RUirWMAL4fYfIYMfpiC0mQUM0Avw.3IdhmXka4Vtk5.9.MAuMfZoHLgCbrRU7swBLnaZtJU3FpWWuwPe+ErccsC8CG210NePPfL5niFuwFajTRSRaPSH.OVTEMzywKDHLHHngiob4ZASuNr7dJU029Wyv6gDBaxQH4UCAnmhtugAFAJOjpQRBQ6kLWm0f9jKW7E4XmzmOwvPqYv340MqFRe8nAAAygY877BDq6MtZcWW2M9BeguvFyM2bqJhF43b60f5eCL069xzm7yeFYA9aE82CNGaPEaQLPyOoj.mGgyQVvKOTaHRxVpp5nhHinpVnREQhhzd1hracaTLpB33ppyhI3FSg4YPl1saKEKVrOwgt45aVbjwFYHRUHhAjkRdlIxP.HNNVrrrNXxljf.eYxImhgGdXf97fQRIZXxyCnoumVoREhhhzDjbn.rwFavZqsNNN1xdG2.F76hI3EXmbrEQhsssiCB7icbbACRRxXaamIHHHSJmkzmeqTCotFD3qNNt6KnCCbtrlbxIsVc0UwXyUXOGG6dAgA6hxthgGO1AXcQnQbrdti6480aTkKPchvL1ZvfL2FlcyD6uR2a6pYb1fnBIeYX3Fl83lnSmNikKWtQqHR1HisHqgwQwk4hij6AG2kZK5rXlacTU0aF3FBBBNDl4V4.QAsKv1kJUZyEa1bUGW2kpEDz31NlS3oNkesay0sVcapSHKQJpF1aM8mK128sqsA6+xNwDTnUKFdVXjEKwHzj7XtW2h8ryMcMtKsuNlmsCCklEZdXRPMKFB30AXLDxhRWRP3iqq65999KCT+3tt0hJyEpcpZ0877VN4budx0QaX9Nv4tTJbxykDRj9pEP1ofBqLCivR8WKI0N7ji4rwvh6KImreR0+hw2iWtyu.HI1+mwFjvJHDgx9GemrtY0wf5iY9cRI+zgsvZnM2dyBKrvBYAx769e4225m7m3egkHBpp69pe0u5NejOxecGUCSEPhN6ryNamOe9MDQ1rTI1rYS1xA1NvXuXO.8q9U+p5Mdi2X58aGva2jD.cv9v9Hs6RbOeo5G125uGExbZOxPsmQP2S5KmNNgG2jC72RsqMoueAfyXN1KflTFNoetK20yKXZeqFgICX.8Q0lMIdBHdQnmVW6UmyzCn2i7HOhgfnRp+t6+e+OoTFx.NOaRuzdmmSSbISsTkpvEa0sa21.6Fqp553XARNaa6rG4E8hx355J99987775jnJFcBCCSFjFkna0S0WB4N59GXk11yQuSShjFVaeFNb99NAVJqHRdU0b+9+w+wYO0W9T7m7m8GoXxNQ5wq+857fELceFuWUMsl1G9s7VdKCqpNhpQCuyN6jBI1LR+yqll07XsuSahpppURFz633raqUVoiHRm4me9NtttCfBjEiO6YOaOXtAk.sq1Hadvnh1qVM54AcCfcInOrM2RUcCL7XvVm+7meWQDq64Dmn3scamHkXamrDLNy1mWSFDsIWIbI..5oOMpMDG.8DQ5Vud8TlIuMvV+29u8eaSfMfv0qZtd13KexStAvF+A+W9urYqVs1VDosHxt29se6r6tWHGvH850aJhX1ImbxJP8p+q+W+S6.3r81aaWrXwJ.yBkm5m8m8mcRU0IifIIpe1yRQeRBgCat+7fbIQaNClE4tXNodkfzlK2ykAI3zAT2mFIDRmSZ8etUIXar6irD3.np5p3Z3YqIvRRYvBpjVFT8p.ce4u7W9A1jbYN5k+XwdJ0WEhhTzT4EUAQjzLDNv3bQKUpjJPLTomZPYRruQtWs.xTqVMK.9E+4+46UwzmsC0YGOnCDcwJEm+9V6Yj0RtHDVM6yvpIhCBX2xvNQvF9mxuUXX3x.K466aHRXXk64dtmUfvVkg0BCCSqu1clE1EmK443fYzCtx62OXfCyB0srAQDaMIvmcsEo6m3y9oO.JVdF5.49xzOPtf9YDqrUTDDGGqiN93Rf+++r26dXR1U44896qt0cOc2SO8kp10dsqo6AoAj8nqyLBDXYcrwlPbBIX7iisCXmfAbbvOFSHHguQN1gaFLRB6ClX7AGH1wFysjiv9jiQjfSxSvx151LFgzXPL.pmo1qcco6o6d5a0s8967Gq8t5dFMilQBICBm0ySMcOcU095ZuVeq2u2u2WaNDMm0ZyIhj+C8g9PCoLrhlMVdRPPvfO9ezGefsd89Tg96p7H6Amoe4m7Kn5aMZQti2cWxhohBXBzZPUG.yCAns7SbfwBjYo8qsa5Gm6tu66Nk0.Bm5TmxQ+xTpUKfh3Vf7K5E8hT2XM4TnQRkzm+Ic7w4fXl+u0WD2v3KV.JxrLB6TdtSBQSZRYsHKxHyCEg5Y195H3JiwIbfN6u2u1Wai85IxTQpNMQLyO8q60MGfmHh+Jqrhe+988+090908hiimazQGcZQptWbLZc7wmb7c6vDS.Lt0ZS0DkgfmLLSoqs1ZkxkK2tsPyBsZ0p.PdiIH2pqr54T5xo.Vj2XLELF+gtLhBEa1rYAfhgVagzx9H+YO6YyYL94.xkxNqz64ZdPGV5O9tDHkCDw22Wx5KP58QU0r3uTq0lXs1DM88LAYkCjjDYsw1Haeq01yZs8LFSu8M8T8MFS7pqtZrwXRhr1XPisV6.T2B8TnmwX5IhLHIQ0b4jbsfhzfhomK4.znnn3vvvz4hWJdVHANzS09YCWbSqcDu79kJUZPEQbVb9kW4MLbb0EfDpcQchrgOO4n2riBKsa2NQg3vvvt0LGdqicrvMNbPvVMfNDcQA38oRbMeqdS.ubqsFE7fQVBFm1tDElUtw7TxfCZOHM9i9c61MCjE.UL9tRTakUVQCBBRBCck6lHjzDRnEZMG6akSe5Su6xkKOrXtKv96o57qm+2OdEm1BlUhiYIE8ruhWwqHUW7VJdNGqLG49tu6abfImClDexrz2r0.b9f09Dt+OwIFxv5cqsLo55wPsNYSnwFUg060qmSeGgMOZsZaaNpo6y849b6KhD2nQC8e7K6ePAfw1ZislBn7m9S+oMPi4ANvwO9weNppyOxHiXdauy2lG3MaqV5zppSYgonAYIjcpuyuyuyLmCL08vpORYnD3mUl74qA43fmiVCcgVCvEquy4bO3DPFKt2crpYuF.mYvBPOXgrqKaWC5vAOmD7N.N4PVUyIunt8yypaeynjb.PgSj.KjrV5EXQjgHG9c8c8C2cW1wT768W6WSaA4.a9T2D4xZAwsgXvquSvRqrwq+m60uQkJU1JvMgUxDSLgXs1BVa8h.ECBBxAnAAGIAH4ge3Glz8U9lPQXkhvzE.Jbhy0UStPGOhUUAJKkAwIrOoAtUgBP6BP47OzC8PxXiMV7q3G9ULrCqwbiIbtK.M+hPN3L4ASdfBh3ULEvkQDQxBTYrkWd4QMFSIbJx9tb+Cw446JIHjjNIl9f0swf1OLLr6UesW61FioC30yZsmSl6dNOmmiBmJIszBdpTdAWvEUUemEo2Enip5lh3sNv5hHquvBKrkpZ+Vf7.Ove9Hp5bCn13uOVZHvBiUdmrac9kyzS3wTztr8ppUqltviZcA59O5ez+ngCPzvwHm0e9G8e3Z0qWekew25+Wm4fG7fK8k9ReokCCCW8HFylsa2teTTjDEEMhp5jqu95SCdU9S9P+6qppZFarw7ApJUkJPqxefOvmbtT5D6AT8k+xe4oTYtpmGdygC.ko.lnNLFrnywcfRmvM34tlT6BlUuKqmSt.2aNe5zkTqVsDvN.J2GnWaNX+T6aUAn1EtDh9FIHmyYwlN2HoYAGaWPZhG.IG+3GWAzfzuzItb1xUQglpHUyr8xLGIQihBSRe9OVDM1XLwsa2NFQSr1iGWq1QiqUq1.UkXiwn0qWWt268u.+zqcM2E6Hpet0p5S1Lw7rs142GZ2+sy8ys.JrVb85062J0lICBBNquu+p.qFDb3TmywYq62xsbKq2JUnXgpc.58C7S7SDicXFgxbbsy+X4hcLbgZ6hUSGLKfwgkHWDHPiXvqGPmF304gdf6cnt8Xtv6GANj3.aepgL1RUMmSBb7T.Ve80AAQgbhn4TE4U8JeUCeF1sPdc.Pu67Ntyt21scacUQ54pUZ5EEE0KHMqzse1HXIOt4EBR.RDQh60q2fd85MnwPadetXfj1oW6dBZxIAfZLGH862W.jW7K9Em5pI9bi23MpphpZhhfagw5NYsKEbzA+E+E+EwGOLLwwTLuDq0lrDkUNka+.Ot.Welns6wCKsH01CKyT3JOEuxtxeoh08+mBX7SAiAUG0Hxnf2X3rX48HUkwUMZ7Il3JmrkqTblFX1G9u4uYtG4Q9hyopN6Zqs1LEKVbe+x+x+BSkOe985.ZowtYSxn.ilxFnQ.JkF2wv4jrVadG3Eg4lZpobZAhMJW2tcyYir4xbmnnHqL4dmThhhjkWdIwXLhfHQQVIc9zbt0eiHfnJhwXHXWZEh.IQQgWnj4HYeFajKoQHjXL9wYNfVpV1MH62A5aLlzEAHCjz+t0ZGXsQCLFSOE5gROiwz0XLc8Do6dFaO8rVaeiwDas1DeiQMFSxQBBRmOQhCBBFHdxfDMINWtbpwDj2ZqWxZsijE2F36J6mffgmOK6hY9oR+lcONnatnYnuHROU0Nmm88ldsq1k5YKcQNnR8cdtMqLUsVKpHpwXRTGvLCDj3Ymc1XDzff.0E+YyAehOwmpWKnaXXX19FfbvbmeIl83XqzyhahygSZV.lsTS2ySSnpNkp5995e8u9dA+ww8L0k8Zclcm3OR.RJVrnZsVJTnPNiwIy.Figo22zj5RXJN75GNukyshP1+9eA4wkHngIHdWGGWfje7Td9lr9joLofNN88Y1sA57o+ze5t.8eiuw2XxRf70+5e8hufWvKaLfw+u7W8WMIQjArvD96.bxtMGjmn9KmexctPuFdb0.1pToRadpScpMrV65Mo55zj0sV6Y60q25G12eiJUprs0ZGrxZqj+U+pe06AWkRT4Q+ReIyMbC2P.Ngy17u8e2+1pPSOQpVQDY2kqXZoJVdFU0ygQ6sgwbBycsQ.FoNSWhSdN.DcgdcohCO6914jHan74uFujEgjTVF0GnecneJnHCNuu+SmLM+a4ZeSDvDzCwhYWXG.l9yj14Dh5fGcR0ofAhTkSbBm5A66W+hosCOtsuaa2LMn7Vq+g+c9vmsPgBqCrUXX3fm6d2q.TTUYzvvvQsggkpTgBggGKGUP96826um.T3y7Y9yJ4rQPJZLqTXokVZ2LG4hzpgHh.sk1PdG.IoAf6zjkb+Kecub85u9qu+JqrRWnRGugzuy5DZmgTCqbAl0MAR0p1z5euEpphHUy8nO5iVTDoTRRRIjLjVkcc7IZZxSzfZAImZwSM75iH5fffZc94949WuUXX3V.aAM6jjjz+O4O4OIsSuWBUcODbvCV+7eX3Ia6Bs.8AfW+ZPp+l2ZC7XcRcrGQLaqpNvc8zUlLqu9ilVixdSALY6LpA6cYAbx4CNv.nVOugT8qdelZHCK5BU6npto0ZO6+w+iumUqUq1RPqVqrxJs9N9N9daFDDzpArrwXVy22e6Z0pEGFFUXu6cu64Dm3+w9Nc+9yATVUshpZEZREU0xUnkGtx.IPUc9Owm3Srvq608SNOznVyxMM3BBtLTcFpv9dIujWxTyN6xoHOGkh97T6xZheBAO4Iy8kDNztyfvzJyj4bSsSQf9jof.bP.j+hS4V8vSQW6Y2sceLm9LfWA7nnpZQe+Voz0roHhv7oZBTX129RQwDPog69dUZFCD2ue+g8m88CHL7XIhHwpJ8ihh5qPeUkA999Cr1ikpcJl920ccWw0pciI+v+v+nDkssmYn1q7Dw7gucsoWje+b+LKhBDec0p0mgYxox5kSqMVn0lTkL1jr8m+y+k1l4XKee+NkK2nKP++v+v+vz9ey.PtSbweF+IaSfSlionvu4u4uYJfIUKPUj26688lTsZyd296616.M29c+tuyNo.nLv93oqbZe3SHMAd2u6ewgAbdm24cl.j3QS0snIS5bEH99AordX2iMI8SfNAAAa+du8acq50qu8NITnROee+9gYK1o1yp5esqmINThW54qp06mxdsdkJUpWoR6umTUF.DWt7RomeMuL2E0YIfhEcUY2HiLBwwt0l0pUKDQzJU7RDjjiDDnm5TKpKt3hIg1v3fff9hHCVXgEhCNbfBfmWSLFi.sSGaZ5KmrZ9MZa3XgkKmwpj56CnB3U6Aev+SKz1IhvKfG0.7fJo.mzXhHXbn4dTUGSDYLsgNpHxndzbO+x+x+xSDEEs2UW8rSudy0m4PG5Z1mHxdOvANv4.NxK9k9h2iywGXrTlsNBtRlc2y6r64ayIoMiIPhhhb8uM9LxHifjdoJJJBeeCarwF366Sud8HIII8Jo6yn.FSfXCCw8rh5..wZSLFSrwXFjNFc+TggtOH6BveIwXLwBouuRWq01QDIiMocTU6DYsampcWaairoum1QS+LtjIocrVaGbhO81VaTmScpSs8wCscr1ntFioKv.QkjnnH0ZsRiLVLl1BOdnJHI.3XRSsQLlaXnSf.Q4Ri2ZPqVsF.yGi+2PrYZ2y8DyYbIHxyEe8PgUctg.6WWuzRGxIS+4bBPtlY26cHZoc5zc.jBLCza4kWd.JZXXXliMJ+5u42jBN2mqd85wyA58bO2i.KkiJCYkWAvOOG5Yzmu9ai1NwzrnqTT7X4R.i8U9JekIDwaRfI2Zq0GGhF049Zmiqf9D1VN6W7c8O1d6tXLFoRkJh0ZydAhC3jfffgeWmVMVI2QpUKWTzoyk1+KWsZYIEdFgC9z17q696m8yDXg3YGl37kGvr6rP72+6+Sn3CWwUbEx202U07.ibS2zMkpES96CX5nLvELrGfQfCtafdN+XfOePD0y62ckTxv0GLSeU0N.acS2z7aXLlMfFqGFFd1jjjUKUpzxMb2BVwyyaifffdu6286VDQFQDYxImZp8s7xKOMvr860aVR04POZ5gKFeemtmnF.OncYbfmLCv9fp6kJNfgLl5iqptGXkTcbY1yg0drP1XwG5xcNoyG3KEZm7A9.efc8+O3EZMaWJ18Bei2G4a4ZeyBvD.3DtE2jdCvFelTzqJC89O7d9Oz8QdjGoGvfpzTOzgNTNCTLJZth3JIlcmUmKTyM4.k6G3lvacfURRRV4ttq6ZciwzsgiofYtVyXJL1wOd3nGMHnDsbBvCPoW6+fu+QnIiTEJYskKN2byku7EmpS.n0F9mlJuIs90YmfLJ.v+2+W9vCTU6NyLyrMzpSZ1oUOHOMGt+KNEsKvxj2Cx0ngmHhndtLvknZCddOumWtzsYQiuovYO6Yy43Ypd9CRnggg57yOeh0ZiQX3BV9s9s9M1LHHXCq0tYUnyO4O4OYuW9K+kOnQiFwUoYBMHwGhO4IeJwtjK18mcgxayAoNcQmYgNzjTvaXKehxJCjth3mnpVXzQGcO862eJn4z.SCloA1KMYBfw7v67qm5KDHa65g+5CZlxvmZv.VamRSoBM5Jhr0QMl0+m+O+WbEfy3CKO8zS2FZjINTKArr0ZWEXCQzdehOwmPNzgdwkJTnvdrV6dO8oO8951s6zppy.LWqJNKaTUslHx7iM1Xy+g+v+dKnpNOsY+ouW0pznBsn7e8m6yM6xKWYVfYliLJ7sVJ.JlyQHxpKds...H.jDQAQErN3NSXbo.W7BcOQ4D6fT7G3C7Nh4LjTChWbwCcdnOeREPme9aw0W3DOshpr.j6S9I+sxQSJTQjRVql4vC4.X1YmM83Xlr8+k9bK87pguaP+0VaM03xFnXsVQPjzxAH1XL8yA8UwAdhwbj9.8ipWu+OzOzOz.nQBf9XO1i41+mwcs1m3ucHaXOUZOQ222IncH9LKLrVj2BZsY6TWGYZnCM1ghr0ncOVhd0ftsaOUGfdyjlQz1s+xYSp+z1IfOHrFxa5M8lxCj2iF490+4ee566m+mueiFU2NmlaqJvlelOymYanYpdCL84uflLf.TeP+k9k9kFlAma61tsXf3ltEEoVqEeeevAlh566OTP5.5IncEU55Acdy216qasZ05eXSlkr2Jc6V1se2QvFe1TeOENg1LEvj8KRehF12n2bzrOMIta2tZ61tySuK6McMlaWO6mjjPylMYvfA5icpEAfBEJnpp5CZsIyO+BIuvW3KLFk9ggg8LFSuCGDLfVjTEnYSO.xYFlLiURCVcgKGQZ7oRaHXIyBiztc4I.lIkQI6GZ9bRRzC9Zdcutm6q407ZNn1PeN.6uLpOPYve5WzK3ELEvjo.dLhHRQU0h+I2+8V5085dc6w22ex8su8N0CcxGZe.6spiJ3YYqsHvn+2+r+22SJ6NmrZ0piuwFqeNTgua2tCO+SAGgomd5r90C+YjMZ3+2F498g1vaTDfPylMw22e3qA86Sn0pHBgVaxHiLZrj97QVIwHhzKHHnWJSkG.5fomdZGCQDou0F1SUsCpi5+hHaj3J82MsV6l.anN516.sU4roZ5wYyAqasQab5Se5M.YcQjMlZpo1TDYKP2tPgBcBBN71fts0Z6DEE0SEc.jPpi7j2XLE888KlFmVN.wbXC.RXXXtvviW7Ar1R3bsi7NWVkjJUpDOCmJlnmxIo5hFebKuyMivKkNu97.r3S71bgg8KWJE3vptXMbr+QUMI1XL8UkdGIHnGNcLQEUxYLlhgggEePqMqOiVqVsjkfja9luYM.jG7y7fCYYxrDk2oGfeaCKSxAjugCrwRO2m6ysDzZDfRW8UecEq5h++Iy4o3HVFjYPEat45YLqBIc6jBThXs1TVlfVqVMbLquU9VPAe+8muJHz.IJ84yY3L6fOlq8z0hf2ULYKlrb5hv8gjL6uFHmGMgHhqBCtm64ghUUEOmCcMdiFGee.yBUb15q0wH6on8X.kfCcoVCvtON1osy4ahGmouHRWOXqnHyl.aT1o0UmUDY0q3Jthy.UVF3LEJTHM9eo6W9K+2LHLLT888ktc6l2ZsE9De7O4nMZzXbfo9Jm8ryzsa2Jpy88LhHFbhDqmHxrppSqptuJzXezx4lfVak8IhL0bCKGxkS0UkzDmtnaMi9rxS13+GdunFnug2vaXXb9+.+.G7w8Yt.utPelusq8MK.Sb27NA.9BkK69qUcAS2FF7ZdMul3q9p+9hWZokzFoBvmEJAKUBn3It7PeUg1CRfNP00AV0Oe9U99+9+9WyZsa4J6DIyN4JIhLZPPvXMf83pG2JiQEF89qWeDfQZ.kRYJRt1Ot8y4960qUGf7vZEEXLvaO.i4CiHdRA.zF5.Qp1COudG6XGKN0FdJ1L0+wCCCGgxTbszy0l.p1HAHt4NKZU77bAu0nQiBVqsvlquYNEUbVttS0xcrLgTsXvsPPTGXIVqcyfffMviMEQ1tAr8m6y845BU6U8vU62HsDChdpUJNWh6O6BzjEbfTr7vrdTtqp5VQtAf1vsfpFcDQRJTnPghEKNNvzUpvbfsrO9kIEU1lkaloAHkV3wSSuyulLOGZ4sSoTLyf27a9M2uUlK5.qCMOKvZ22oOcVICbVOO2e2CuUEQNCvppHm8lukaY6vvi2Oy1Bme94yu7xKWxZs64M8FeS6c4+lkmFGRxdppUUU8SEVReUUeQ7LhmX9OeO2iOf+RfupMqBTYoTDnUU2mwvTfcx4XtTVmvXmblSNTKa3IGnI655xgTpQR5fmwN0X+D6FvjcQiuEeFnzSpASA+n+n+n4.x2FJHtIJKJNmzfG4QdjjpUIANSxrL6kb+cHPY9zisH.ppwwwXsVwFYEiwjSwAFiHRBdL.UG7neouTeU0AggGKFHVykK4ZtlqI42+2+2ONJJJ4.G3.IUoJjsngxQOaOiXeiztPYY37ypy.VLETfCcHW4XtfCv7UF1+ZJk4gTW.XfyMbVqOP+yjNlT4xkiqUaHvcv23iKoQCOdmSu8a+10lPxuva9MOnAzU0nstsa611ropa8S+S+S2En+LyPLrhdnGuFCnvIzwckF5.f9y.8wKELnTQdSEQihbE8yq7U9JSxJUfffiLvXL8+i9Xer9.8ZB8+EdKu43vvvjl.29se6JP5zmsYJlBd7iu8rk9fJGZXYZlxtuY6ybLXIW4wnNGqscZI4cNmWOAmiY5JSE.HWtbf.EJTPNv7K..VqUDQjZG1Um+gggwfzOH3HcCCC6d7vvdVqMtAnPSAHm0QY8B.Ele9g04+S2WqyFyNOvHKCS.smAvusyljeNum2y68JDgqXp8rmq3E77eAWgHxATUmusW6f68du2pPzbe0669lRUcOppkTUycza9Zzewe9ewjvSGJW4U9hJ9Y+u8eany4HNMMYbfQEwqjTUbtqyNZkxDMZzX7IlXxyAf9QFYjrEjLDbjQGcT.ne+9.orIwrCvIR5m022L7jM66GEEM7Ug740L6DVfjdc6Fqo8QRYgbOU0d1PaeiwLPTYvfACFblUVoOncQ0NJhKALBaDbjfMzDcCA1HH3HqiHaDEEsQvQBNKvZhnqljvp999qXLlUUXEPWIe97q.5p862es0VasyFFFtAvlhvVggGaSwIN3aqp1wXLCRTQs1nbBTHzZKZs1RQVaQDxCP3wBSLlCmMmYt8eXSAfhGKLLETESBFhOyENatOYZCKs64gBLaZ4F1Lk8xUq5FuvEFtdpKi8QJdJBtRLLOzXnyVAnqrxJIVqsePfo2wCC6B45MyLyDinh0ZKDDDTRiiKAdEvUZVJUbmigfdzidTfxR850kke1yXXWdM20YwwpvxBUpjkflju3W7KF2XXbTd5kAiASu1rr3g2v3aqTohDNjUWFAPN0oNUpl8jVGhpFas13vvvj50qS8HG68SWuk36eCfG5YdleAvtXBVvc+OZ3bXyU3V+kt07oi0mzHkAJhHCZ5XsUopUqNop5Luo2zOV4JToBtqtyrV00RcgxSLlOTZWLN4Bw7hKx4mm9U+pe0jzDo1oIrE3.W8O6gdnMHEzj0W+qsFzZ0pTcEq0tZ850WUU8riO9ja.xFO5i9napparguuWx2Wu3jXUDova+s+1GqToRS.rukVZooUUmQUcllNfRlVjp6S7koallf01vbp1rLP4k1gAJSWoB6EVax4Xt8.L5C+vObonYhNeF1bot9O79P8gqEZgDNHI28ce2Ocutum019lJCSbzmOR+.+p+p.nz.EBR7Sq8Q7a1et4lKNsPiK.Th4nDT4xYgfCylYDzC2jYq1BVYu6cuqBrgi9jZrMzp.4MFSovvv834r1tInRq8T+X0GsVsiNhME8emsRV8RGbT+cp23PXTPF6u99tuQifQnkeAOHmXDEZl7R9NuV8HG4HPDE.+QpjRCViwTh1C2e.nRfnY4W6fG7ZyEGGmuYSJt4lalkMn79A94LFijUiuQVap3aQBhj.xvZyKHHXqjAIa97ulm+Vzjs88ugs8fNmNJZ6nniusyRgqloZ6YKP9oy1NflrHIf+fofAqu958vztqHx1oh.qKyOdrAToiicMZQf8zrotu65ttq4hHpBY0CXtg0x8jKB6AlMKaXY8ct.pA84ABf2Yheeuu2WOn1NB.kOaBdat+8u+LUquWyltqkMo4FFiYMq0tbsCaNSMi4LftpIHXMANaXX3lhHcBBNxfegeg2hzoSmRhH64+we1+iIrV6D.SJheVfqSqZy4nEd27M+C4CX.LKVewp5Nt0QYf4rV2.mKwR6CWfuieSO2aZ2Azt6ZP8IASSNQBNaZ8hJramy0qGesK9TosyfxKTWYM2BWSeu7pp49y+y+yyAfpZRbb7fFMb8MWlkuj66S.volRKCJkQ+n+A24v8WpvnI.hSbSTI7XgnhjbUW0UoAAADbj.A7xYLF4+5+0+q7pe0uZ022O9Nuy6LtAMhARNUV4I4Ze6PFwdp1t..HL7m6.T4INgqu0htIo8fj1samvzqobJRdzG8Q2o7TNHCR6OtiVwT+xRvBubNVU.8fYGW9K0+s7VdK8UUyX6v1RUoC32IMS1w.5YNiaCbhGO8lTfjSBIvnNV0.wzrZRYHgJo6yjjgG8JtNKAAA5Zm8QShhhhekuxW4.f363NtCEOj+vO1mHOP9W0q5UU.He61twxdnEenyue1yl52otKet6+.wL2xJKAPYQDImpZNlCIUOvtraKsCaKkjjDPEhR01qS80WTp3VvhDdrPHU6TBBL8wq4ffff3Se5SqtxvgbTlbvbRTTjTqVMoUqVxoN0SI.ouTscAVhWQbf+OMdTEpt+2869magOym4CM+2y2yB0.7+G9O9Fplq359.ACFLnl1PCtoa5lpBLWSU2qH9i7a+a+ayq+0+56e5ux1c98t8e8t2vMbC8gVxK8k7RF0IDrRlnSNAvnp1rj1Py.LI0whF5TDiBTx5XIvPvRxXWRV1oAXokVJszaRAK47.NIxFM7IVM88G9frpJhjYeuwHLHIErjfffdppcEQ5txJqzWcZw1.Uz9EJTnmfNbdaA13K7E9BqGDDrd3wBOqHb1fiDbVa8iuNI55FiYiviEtQPvg2PUYi8u+f0kpxFhHmMH3HqIhrhHxJAGIXkEV3Erlp55CFDudPPvlII5lwwwaZBLaFDDLzkpDzDi67LmfjIZs4DDUfAHzyV+3cCBB5GDDDGdrPApjYq14vXA6iq7NepzGJGTq.PoSAivxkGqBLFUXLUaMJMZ3hIpM.95T678eh1eCmqLU7yyAjywRyP4Q+JeEEHILLbfl5nIqblyzMEnKrggExmOeQnYQbh+aNZAPPldbnTocpPjdoSDxypZs8zpPBkIQ0Vwzp0fG4QdjdoFHPmpYtDjoYbpNwboZBPtlzLaMG4RRRxQRhHhHQNWxLWwBExqplOH3n4xAT6n0R788iCBBhyQNsleMApluLT3qd5SmGuloNGi22HkC1kpsSrjKhBypUAmX7O2Rxc9tuygr8EnGUnCTsq3DAbvEe6Duy246desnUl6dUAYHfB6MBl.5sKFwcAYe8iG.kZMStxq7JGpae.cwmNPPmq65ttsIcrk1sc5qVCZrtwXVEX4fiDrTsZ0V5K+k+Rsupq5pZVq1gaBzJII4LAlirdXX31u427adfHBhH4Z1rYQq0Nzc3DQl3ZuwJ6kFLsHUKiiQgFwSxJcGefpppdsZ4.IZIVZJfItlq4ZFiRmSY5e4xNqyK4wKNH0oaN+3p+1qmGeRz9loFl3V8RM3M7FdGtEr.5LDpQYYfLpRlfvlfqzHK3sDkfVmu+a+DseRnF8gnN3nb4pW2Mdiqgi0BcLFy.bhQUNaXXIf8zDl7c9Nem6M73giWqVsQflEudio3W9K+kKVtIEfFWJ5NIzDoZpfU1oSmBe0u58T35e9O+zNvIEZBEHh73g7G7w9CDf7kghMN88U5u1ANSAQBR2GMjzR7QpZyDLJJcxS9EGsPgB6QUcLMCfEmE.lyFZIEHnD0AtPBPhwXhwQOxdJzEO5Ta9ar68+v2euO5G8i12ml8aB81uueWe+qKsNdazegm4s1MEHYAhRVCFL4jS1Ga0N37m8MCb261nZS1.ZsopZmACFLvoiKxHuhWwqXBU0o61sqSDUahmWlPJUg8AKmEH3tAO4hU+4t9MMOX5hxp2a5TQo8W8m9WsCzLq1m6Vud89QQQ8g45ArcUXMiwrLMoYXXXjwXr0covLBnowXVNL7Xqs15quUPPPuvvv3q567pRLFCQgg5xK+H.Hc5zo3W5K8n6QUcpG9g+eNmGTQU0agZK38q7q7qLTnnDQpnpVFpLGvL9vzO1i8X68du26chYS84cGHemCirjmfWrqelNQ0ItPho24yX.8h79OUaJKtP51YIWYRjFXv28282s.nhHIW20ccofzT8BURDWzVaPoM5O9s9iqddNAjLxZI0BtyIplGUxGDDj+q80+54OZPPtvvvbgGKLm0dbWfIICAaK9Vu06X.vfxP77yOeBsQOz+aT4uPM8B7Z38tCwhIMgjxkKqrxrI.IOum2yyQSWHgSRxt5O9T00tdBO9N4v4el1URLhzsbpvH52jAoVbr7XO1iML.UX5KTPWY+LgczqKEDsM3z8Ufff.RxMrCtnfXs1bar9loaOI4vAA5sca2F1iay8O6U8OMOPgfffcIJeyHKrvBR4K7wvyFZxAAoFKlwdDgklU.xoZqbdoiQO2R.z9hAD24zFRj34f98c.jjwvjzNdx9eNyKEJTHGPNA3zm5TJNm7Ht9CZiARdguvWnFkJHhkai.Ko999I3JYhz6qW9EIzShVp3C2br4b.gOWklX9Pen+k6+E+hegA+E2ywp7ddO+6m5y+me5wiZ1cO23M7bl7C+691l93egiOmHhmmS2r1m3K64G4G4Vje1e1e19+N+N+Na0p05a1D19JthqXfpZNQ7GEXRU0oHEvcxl63beMrrhIMXbiwbNyq366Sq1sGBNB3XLhmmGQ1ncUFNNPQ7MtQ1y.PY2S.466qEJTPUGyRbBNnyZd6AzqSmNcA5ZLltSO8Lc+3ezOVOft8GLnKJaiKqtqqNKgesq+5u90pasmEQVCQVq9wBWSE8rHrQXX3l+9ejOxVdzZ6fffssV610Otcyvvv0CCO1ZCFL3LFi4L1iEtRX3wVoVsZqVnP9UCCCWUDY074yuFv5m5TmZSbBYcOiIHNUDTkT6NFbX.EqPu.SPmjbIc.utVqcvQBBnJsxkx347XyRTVc3oFXIt9PGjbP8rR7dTH2XMUcLsoNl3KiBUGQDovbPNehXsmBia3zUORm+Lfm2y84JBHNQd0A.oBc2byMcNDjp5RKsTAmlyUwIZvPQeBysLn2y8bOwzxOcr8kiOzS+ra9usa65XuXRCHg1DK9RLTt+Ue0WcmpvVWy0bMa1v0+oO1oNeF6d9sc83x7BP9JoLIJWtbRNGHyhhy4nzzDEFZOd9DfSdOmLV7jAfW+9w8i8.IJ534aCEtx8u+7zrR51+bzKpmot1qGBTOVNoQJn0ae5siAFbe22Wne57ucmqEcfFaArsHR2TsMjwGe7RppiqptOq0NGQT12kPwxTk4fSkpGHLIzIqjCGgCdNfJr60CmP8CkVZrsGrPZ4wNaDcgPmQPznwVOxi7Ho1Ku+l.q6Cm4ccGumlzDaylMO8K9E+hO0G42626TggGawfff50pUKxZOd6fffUMFy5VqcSq0t8gNzg5d3ff9UEIIIIQTUK9P2+Csmuqidz8AMK6kpyIQegnZ+V+V+V0.1Ov9EQp81dauMCT1CXVe243doAiWIqrJW3IzTHtvfEsqR0iyKFsmFum+rt12L.L4bCjrNIPyDlkDKot8LnhmDCsxDdyAhQRvUVJERYXRA7uLE+05DOk6AtsviM1NLb874yuYXXXWq0NvXLfG42tSmR.iEFFN9q407Z1SPPvPADssO4tpq56MeanPp8FmOUXVun.1zvySdqu02JiN5n7htxqzIredjGZVRc1B7HzjR999EO8oOcw1Pwp6e+EiiiKvbjCrCudU2f.ky4JKnJiopNAUbznEXxyt1Z6AXDiwTXzQGUPPImyBVQ0rZ9cPnMLCszdBzilL.ZDSEzumuuumLwqzUJBzNSPv5u3NKJ4Yh1vISVLKSx9zEZzgTqFNrFaAk21MYRksDQ1r39KlQA19hHhTUFoc61SppNa850qzrREe.eZgueFiLJyLvxoCbNrVsKkNv44oF3mzsvIn+J9zGJ26s81dacybQGlkd0pUq205622ZentejOxGYqFNsx4L.MCN5QshHmplwr3uy+t+cKFDDbZ7vFDDz59u+6+L.qopttwX1TpJaYpUaqNc5rkHR2QWXzAOum2AkjjjQt5q96bxW6a8WXZQpNqHR42w63CUAG5xF.eOQ7UsompZ4HXlCbfCrOfIN9oNkS.rZRwZW5ALSyD047YtPKtky6mOQ+suQZRJwecYrzfl5pHIe1O6elae3D3LsBnPCE+mfsVV6P.r1NSJzhjjjjjTU8Giwf0FldsHoPXXXgq347bJ9Gee+UEwkkvhFygKBjOIWRt50qqNwpLZ.9zu8trlsS7j..m+NX6BBbxIxxvwBL.VdHSRhN3EjgSm+j5v2XWm2Y6dHhgUxzQj31dj.dRja9mQ.F8t+b28X.iTwokE4YgKZvG67xC8NtiaM68E.Y80WWlbhwkDzbtr0p4ckEVlFToZqJn29semppZxG8i9wz50qSl0oVAxCmIG0RAB7YOYCZm.9OD4NIjqd1XPFxAKmGHe+98y2rL4EwHKAjUdMWhldxCdP.TVBMnXwD.8y9Y+rpweWKNWcKxCmKEI6e9EzLAQTDU.xEFFl2+H0xeO2y8lVJtkUfAT18b9zNM35oyfIyFKNON.JFeoprOnZk2367M3ejibsd2+82Xlq7f2zX+T+T+qxM1HioiVbF8QOYm7W20eMic7G7yMEvzMTceOvC7.S7y7C8yT3y+o9TI.cjpx1PqTwkGQDo3i8X+UigKYBCsxxzRwY2rwLu0FsKg37Bmg1nnHhGLfnnHZzzsPqnnng.Us6GNDXHyShhrn65QXUU0ZC0ACFjL1niNPQ6BRJiQjsA1d4yr71nrsMLbaP29e5OwqbafsKVnvVl.yFKu5JmMHHXsie7iuhMLbEfUDzUL99qZLlUQ0UCBBNKv5AGIXqW8q80143Va2vvvdJZ2jA85LXvfsBBBNqCPDuUTXEfUBCCOCvxw8iaCrjwXNSXX3ZyO+7aLXvfsA5YihhEQRPG5DIoIrxwNFfthJ8fl8MG1LnIjbLqUpWudN7IOz3xwfCtT8iDNI4qk55WNV41rnHhiQxMnnpQETUyuDHQYemKGIgpp6Gd.hys4.RedBx4aL4bh7ppUfAAAA8Fehw6ZLld0qWOd1YmUrG2VDZMp0ZGyBiDkFu8M+CeyJDMDP7S7zGf3eytoP8cl6pACf18kpR2FNP.5.zk.5Cqc4bNKtaUmRXNHsTUX6s2VTmEs6XAzNimjGUyIhHiM1XJsHFZFuvyegjlPNee+hKt3holbQqB72hrT7DPRyLfQqRuwFarN.aeS2zMrc6.5.yzospaCdaALzXHTU2TDoqTUzuxW4qTx22eRU0YhpV0YpBMv36XncUpPEn9r3.VXBNYJa4pcNIPM8b9DCY15hLTt.5CyzCnS0qtZmq9pu5N9PmvvGXKJy5QvY9f+lefl.gdW60d5+5+5+5Ees+j+jKdzffSCTGOZXLlkvMNxpppq466ul3ImsErwws1sxmOeOwSTfR+29e8+ZbU0oanZ4+z+z61y22u5a7M9tBTU2up5B.K7a+q9qteUaEnp5G4lbbVf80pbY234Kxn9ma44e4poKWr39+6rsuYVRNCmD4fNQ9QSsyrrEx3JKmT5WVIhXwSzW6q80lK8A4BDM64m07K19IoTl001jsNYiFa444ssJZO.MJJJW3wByO5niVJEjjQMFyHgtZIUBCC0xQjTu98q.7U+pedWmrlkehn4jRylI+GdWuqDfXICrAmXtNlHx3as0VSbi23MNdEXO6e+6eTveDfh6e+6OGKgTud8gaLOK4wucZFBZsWQjYzl5b.yljjLEv3okMT9zIr.GqRbNO.z+K9E+hcQoyG6O7ObafNChi6Vud8AgggJsfZ9GUxDCJZdNVq04uPkmoZYK9Y.QrCM3ftd0oKS0tCv1p1z4W6M7VIkprqRkJa7X26i0ua2t4DQFqVsZ6Sa1r7Farg+M+7u4fXnFPMZS.N5rkMvxTdvjbRFCJegJgEEH4PQL.mCwzCp6.LY4Zc.5tDzwbC2PmW6q80tMorXJJJZYZznQYHD3Ten206ZQfECOV3oBCCCeIujWRT850aWqVskihhV4T2+oVAmFnrVXX3YoIajOe9tMa1TsVawete1+UipZiwi62euPyYUU8vYSY0ZA0jpUMhHUwkUwY.lZ94mehxN5SOxVmqK5j6B+5PWH67K6ZvST1cd5LyOmOSWD.lyhpZiXQjj+9+8+9UfbD4U.nXKn.dd4RE7rm3I3SqZha4VtkrfVhWasr.SD0YC2hpfXLA4.JHpTnVv9KDbjTfRoY950qWnl4n4qUqVtlfPkJZpv7cgVD++61kWa38jT8MYmWm7bJIvK1j4Occ8VSEu3g5D0bMoH9MGUUcbn0DppS9cbvuiwAFqETDe+br3kjsiJMQusa61fcsPyMVeCAEQPDee+bhjKGoNIgppJpjXOV8j2xseqwhpCdKukaMtVsZIFyQDf7sf734keWB9Z196YCM2w7I.nVVlpEr6n2TkJUJu1RygWT53RstTAw6N2O4I0ob+d17uw27MeyZj0p.Z1BzqToR5uJfS+hxsK16TJHHnT0VT3leE2T57Bsc8Qa6O.HdEHgZWRqX8Iy0iL.SJhioG6sZClipMpbi230V9ttqOy9Ve8sGoZUedOum2cuW+OyOSmer2zap+q7U9Oi++t6+mEuga3pG+23Nuy8NXvfoZ0p03evO3Gbjzsm5nXOR14Vbb7nKrvBiAL1VasUlcAWRari3jlcfkxRhKFaD20EdGqRzjDhhhXt4lyUhNJmCySxt9GkJ1qBR56Kouin.wc1tSeP5hntxhE04fVprsjS5nPmfCe3Nn398ffs9S9i+i2bl8suMrggm8vG9vqpvJAAAqhxphHqFFFtZsZ0xXHx5gGKbCRckK21lMKTnz5EJTXMfUDQNSX3wVNHHXYQjyDDDrbPvQZO+Q3wlkI...B.IQTPTAluUPPPqnnnkEQVCXyyd1y5JoBmsTmXLlrRJRIM64gggCHE3jvvvd1iaG.UiARpUqlPzvE1T.H+AO26EWtKhUR+d4iO2EHo.ZJHGBPJPs6pTyO4EYKt6a0M7U.sIjfm2vwlcZyA4RKWqB.4OdXHgggwp5RnPwhEiCBBDiwTzZsiYLl8.dYL+MWln2xEdL+ms21Mf+wpp8zFZ2G4QdjLlK2mvKaVSpmDf4mWYoc.tZrwFSQyTwPv2XjgkUX57JYGGgggPKxAUK.TpToRkRWmxEh80OS.fx4b8.nOMXX4zArkIjsw+Lcc.J0zEmcSyphHmQDYY77Vs9CVei+O+29+YOw0FSihlZs0VqLf++fW6OQ.v9oE6G2ZAFtFf.XJpyjv7oNryiq5ENuiuy3VWxxyuMNsMb6fidzsoMa.rFvxGrZ0FzpU3K8vGNDHLwk86V0ev5KEUu9JVqc0ey2+6esfffU8EYUZwZf2Y8882rd85cnE8EQnd85khBiFyZsie3Ce86UUcep1XVb3TZTUq0D1u34USDoFNG2znp5Q61yUMEXHYGGOa2tpyEx5tuP2W91km69Ft8M2RxIsS3IG1YrdRylMyFDIVjgVCm1BfVj6C+g+vEnJELXJllApKGwVSam8fXp.d99de2w16au6qmntx8AGEmKFFFVTUsjHRofffh3Bdh1NgmKAH4JukqT8wGn8EKvgrNXwVHlxjzz8+yCLhKnalZ7wuh8VdlxS0JUL0BCefwptSG5b0pUaXvSMg7DQQU08PU1a61smAXtNc5LaiFM1GoBbp0ZyYbZWxvGxCNRPeftW60dscPXy+o+3+y1LHHXyB4y2QDYPPPPxu6u6uaBzLoSmNIQQ6frO+s2h+x196VSLFtfol63bM8k4jsqPkMflqgCo1yPqVqbfCbfydEWwUzY6s2VUUKAL4DSLwrexO8mz6XVavwN1w1OvARes.3UCvuYpPQAs2q+NzQ9bF37D.NMVgXNHCpQs9P8Tavltzp0P1vvLrguu+Z.moMzlpznADAXCBBrAAAViwDUq1QZ7td6uqVppsme94W9K+k+esxgMlUCBBVCGSU1xXL8BBBRRRRxKhLxC8vO73oTmdt65SeWdoBEqu1nwPsMQpJCskr1vTTlIOSZ+C1sEjwA2M.IEfSTn749Ydln17ex1z68duWcogVNGCnBZUplW0FE+HejOh6XsYycCx0S31CP+7e9OulxXE5zoiFFFpFiuJPxd1ydTAzG5K7PhwXDUzb.R3wBIHHfvvPMG43O5S7aHevO3GzEXaqV6Lwi22zul8rw14C7wtpk1GGXsWrLf7zcSH0MTVBFkHFWDYRp5X02byM83jMNQzSnH+dg.0QfJBPNEM2FargnnRj0Njd6SLwDtwBygZpUKN73gCL0p0mJz0CudV6whO8oOsVudcglM2oO2k1Zs+Vo1tt2UmJ6JqnjN1Tud8JHhTfloiaMC4mkYubdFSWywVxrLWlLwDSj3aLpHpJHpnRx8e+2e58EGiR9K+K+KchhITJLLr3e4e4eYgGnd871uf0EuTURpR0XHJdokVx0+rd8mIXXRl1kr2FvLzf416d269toWzO3dN5Qd94dGui2Qu64dtmN28ce2cznnA850i+e9O8IKpZmwlatxS7ReoeuS9xdYurIoJ6oQptnAL5OxOxO9Xoh.6dxmO+n.kZznQwel2vaXWyMPAQj7IIIOQfvMzIMxb+FiuOkFozv+O3zvjru3NZahhjUaTC+K5v2+d+q9qxPTQQby6GXbrxH3HAcEjdAAl9ppCBBBRBO9w0fffjZAAIgggI23y+4GWqVsAlffdAGInaPPv10qWeqfffspW2tUPPvVgggaEDDrswX5zue+tAAA89Y9o9o5DDDrAvJjPaQjlgggsLFSqfffVgggsTUaFFF1rd8GrIt5UnswXVwXLqGFVe6q8kbsYZ91vRpHKlLGVBtSWq0lDFFFKhz2XL8gF8SRRF.Dacf5MjU.m7wmDiKU+GAPNIHLyPliPZL08wITtYrgLws94FZ0Ky9vKvBjVZhIppw3haOY5omV888kG4gejTWjhhHTLHHn3C8Ednb4DQrVqdzidTMkgbEMFSp1MzbzFN8TIK1C3aeytsByLLNWQjdW8Ue08SSR7ka716bsIsTBwMd2v9c4KTPQbtkiMzhaaKI4xQRJfjfS.qKBMJ9xeYurhIIIE1k1Dcg.I8Yh1v0LACSVZVL0crP2TmSqGSQGClMA6Ywwl6knYy10pUa4O9G8iuFdrU50w7u829aeOm5TmZeui2wudk50q6OXvf8Cr.vA.uE.pE5XeRE3TyLqqjD2MvIm+ZLUf3CxA6AmpyviwFMxzzj0oFqcxFMVFncan0wN1wZ0xY6vqVqVsy5Wq1lFiYq2za7csIvFOXX35.msd8GbcOQ1nVsZaBz4V+Weq8tpq5phM0LfSefJDFFVRpJ6YiM1Xu.S2oSm4TU8zlM8UUqALu3KyKhre.SCnBUXFqqRDRkjfr4Blcnncyku.w92oaeq.CSTfjEfDX1XOOuL+aePpWzqpp4.+BP0hUDoDCXDK1h9W95XRV.3NVl.aeq25aYqs1dqN9A9CpTohL891Wdvq.PoOxG52sTXX3HTIsdcqRNnB6e+G0IVeBZDQLy4VFCm+9LgolxkIqVZVFcFCXBQjo9Me+u+8cG+FukY9v+de3oAl4i9G7GLcPvQ26wr1rfvSEXVJP.Efpk.uQqJx3nr2xkKOCvriLxHSiCvkwPnf.h0ZoToRJNzlSp+f0yNu2TRj0EQOaXX35AGIXqffftVa89+a9W7uX.To2UdkWY2ACF3xPh+4vtjryqmIam+hfNuL1WKdZX.EoeKZ00Oa.JmfvtFdbVOQ17.G3.cEQToZ0RVa8wMFyz+n+S9mT9vG9vlTprcfSe5SeEuzW10+bvAdx9cT1aNunT5rALIUSGfYVFw0+agbGDDJgVm5I01ATG2.7UoWUnGmw0GipU2DXcZvYwAryR3Tt.WfVUZ07s9q7VaAz968696d4ImbxUNt0tFv5VqcCq0tYXX31uyes2Y+b4xk.d7Cb3CmOIIoDvXeueOeuSBru50qOyq+M75mSpJkUU8noSLnvyyogKswY4x6tDjfhT9jCCPtLT.ltX6crRxBrvkEXjOy0Vv0O3l9AuoXW12pNvCFb2+Gu63H0oiDG3.GXmiuxOI29dNJiO4jSRPP.VqU8MFcqM2REPu9W50mXsV0ogRRBHw150GDbzfXSMSx+5erWk9C9C9ClsvtBTlhUfBz7oryD820aWHfP9aKvQ1cyceqb5BVliR29se6inpNVEXbMRGWDY7O9ezmJSmGbyCctxvv4u8N2sMHTsUlPIlS.wUGktO6G+i8wzM1bCMHHP+i9X+QwgggwAAGcf0Z6bG+72w1MoYGIV5t+8u+A0pUyM9bUbZXxIt3L.3awZmGPRKnokTjad0xtEKVZ9R4UUKnpVrLTfQH+xrrarXW6Byvyre17wWG199AfPhJptvBymH6jY7Bn5HVqczrjlL+7ymuVsilyXNBkS2tMnASA5byM2yT8KSKKCJc+2+mbO2+8+edhG399OOdohEG6q+0NY9u1W+qEex64d5Czct41bvG+S9w0ZGpV9ek+Mu+RIZxHeGGZrQeu2wadrG3A9j649++8SkUtM6EXpO0m5ilomWiopNBPgpUqJ+9ejORV7L4AJnplOWtb4jptU4uxYV4h1WpznkFB1wryLK9F+T1hjxUjr+I8pjlcKKKE333XR5axK7E9Bytql9QEE7ziDDP3wCQQEOHePPPwTl0NRnMbDuT8VQDozO3O1OVAqsdd6ws4ulq4ZxWqVs7.4EQK.TH3HAY.iU7EbfCT.pH+w+o+oCr0saFDDbFyQMMMFSjS+w7ZBdMORPPyfiDzRUs0QqUaIbKX6r3JQfsEjd1iaGXs1XW7WpZcZiE3XqbNU0bJjybCFIHHHwXLCrVaWnZ2Z0p0EnupZBlzmCLjGJmCN3tiy7x+45y3BhlchWwUR1Ujd3h0NE3j4RZbYNN6hrnVigLlYnnumyIPubK+ebKo8izRnt3nutq+5JopVv3JUmb.R850yYs0yGEEU.7JTsZ0czSho9V5wt9FsoL6YTfXQjAkgAP43cUZSBydYG+farMORpBwoi2kXLFcvfAYuuhPBJIfljnhFbTStW1K6kUHHHnzQpUaDq0VHNcgyFiQSkkIgoIGrvkqvg9MZa2qUyIICGjAPsXvefGDSIFXw9+O68lGkjcUclu+12XHmyrxJyLh3dOQlYAUooRRkTURXrjX507vVXL7ZYnke.1FZ7.Xr6Vu1cwfAZFbyfsjM1X2O2sw7vV7nM1psaimZKvd85EvBICVpJPhtPfJ4VUUw8bigbNhLyX7te+w4FQFUIIPX.QYZNqUrxbkUTQbu2yz97s+1eesmcu3+2.Xc7YceQ1jJT+RtjKYGoPgN+a+29uQWbwEyXLlILFyroRkJupZQU0kih9BOMfmNvSq.rDTvrpi0I6GXZ7SRd5bLJ3mEVNygSNq2o4zwG97A2oMGlVGBZQIm0kyRKUGXqicri4VeHgsL3blzcHWkc.10XL6d8G4H634403+9+i+G0ApGFFV+G5k7C0HLLbGx4JUq2za3M275JVrc3IB6Ihvm3+1ec5Nc5NFvjerO1Ga+RAI2W4K+kCzHsnpZQxm27deuu2BTcuxzgDIb.XbJrpSGWfrtyReng0xkmJ5q+mbsKFdXL7lz8QkdfZA+K8K8KM9a+s+16iJVZU0dkJUpwhKt3Fr.ajPCpV7XszzgaIhmFiQNlipb.fCqpdkgggGzXLyCjNLJp8O1q3UryK+k+xqu81auxOxOxOh8.G3.kvQmpUvQ2p9pfdalgNrYxD5Sed.K3sLj9LCriO+ofnYvYCryAL2K8k9Rm41u8aejCcnC4AzMIaGM.1nToRqWrXwMu669t25lu4adGbKdjF2fcefC9G9G9GdoO6myy9RB7CJZsgyEDXFGHS+xwQfXDompZaAYGD15G7Ze1a9fxirIUXyvvv5FiYyRkJsQwhEWsToRqTrXwUwcv99Sv66DLeif582paCmsQmSIkbf9el+u9YjO3uwGTvmzDwn3BHbVfYsV69hiimtXwhS7Y9TelrO6m6yN6OvM87R8e7i7+i2XiMF999cDQ18gdnGpw4N24Z7BdAuf53V7c6jW6R+9Y23q1Lj9T.n3ii5ryP5y9fm0aokVpeHgw6GhWK4Z+y7Y9Lxy9Y+rcKDMGYXUGvYgggSYLloYOFsLb.QowAvwLkJUZ5hEKNwe9e9GO60cceeoEgQigwDUGIHHvqWud8.Z95dcutc+PenOztppaCrkHx5ppq.rpjX2wDvNXcyWTU6GryfLM3.mDUDo+FVmWlx9VVu5W6V+0D5GP6n3dFMwPuFIgEZ6P.qikMvMlsMOw5sS+0YFG2FHG.3pTUORXX3kiHFOQlbzQGM0t6ta+Ms2EmvAtVQioVXXXMiwrNvVVqcKU0MMWmY82xq4sr56889d6eMradmMv1mgTOUA332q8MWa3xgHMI5GAIqqnpteQj4UU2GPpO0m5S03487ddkYANK0nLt0OFdun96u1OHjrjiIoJyC7zti63Ntxie7ieUVq8xABTXZwMd2o2DhTOHHXivvvUTUqHhT1XL82KZiyEctsVzewso.0orabG8oz8icM6KVG6M7y79qw2e99j3d1OoS6EnsHxVjjsNND6NToZ8XX+Pxm2nf+9gnkAtpd85cDOOuqDXIq0NSx6QMFS2vvv9Y0rtwXVMLLrOqBhTUsEKVLBGX2qCrSx9+coH8btI12RDe3ArZJ49eAfEeM+Tulm1DiOwS+k7hu1Bv7isxZM69xusezlTld28c+WOxO3O3MOy8cemXe2y87ec7K6xVx6tu6uT6q3JtpsVekUVQi8p8leau4UeM+Duls+89D+dwcscylNc5owcvf4UUmSDYN04PNiyd069EdM83Id3DEYGXMv6oII6wvj9yDdL+8gZVqk4medxlMK850itc6pYylMNNNtq0Za869g+v69+9+aOuVO6m8ys8JqTK925292N947rtItwa3FE7PVo5JdekuxWgm0y5YIqt5pZTXXuic8We6yd1yt8q8G40tcyoZ17M7Kb71W40dUceV23yp6McS2TOiwnkJUJ0Ue0WcJ.uu+a3FhujCcnFevO3Gb0OxG4irBtwY8mSGasmKNHXw922dgggdhHoCt1fwJchRS2sa2ENvANP.PQaXXfBKDDDLUjMJCBJp1VE1Fk0TUKWrXwPbJ5Z4vvv0MFS+3O5WNBN8aKLb2DMOYX2J7q09wWvbpEx.0bwMMGoX09h+JSppNIPJQjV3huse7eCG22E1bqONCiylLG4YYp31GE3JvIHkSmvRg96g1X+6e+MF6.i0H7DgMTU2nXwhqCrRXXXMU0ZEGB.p65ttqsu0a8VaReGi46NDcx98K82aYbU0IEQlD25cdppsEQ1FWeQC1qe3I5du+m2n3h88..WYud8tlJUpbk.Kow59DOIsFqcQXGiwrg0ZqEDDzOdlUA1pj0tUwffshhh1v22ecb.QrEtwh86G9Vo.q+D0FFH7gSJ84Wp79.Q3wRjgyNX+hIAlr.LQY2uOAjeJnxj0qWe7oNzTidm29cl5m3m3mPEQ5pp1pWud6VoRkFIL6dijW0IQ7lYuX+GNd3dbHTN8iAfQEPNL3cpAqYFjEri.L9W9K+kG6JthqXBfQCCCy7Y+re1T25sdqCeN79LKrerGSFFFNowXFKLLbDUkQg3ww4jNiDDDjRJH7n26ipAECnZ0pwFioIvVWxkbIq9vO7CWoc61qLxHirAPCU095jSeGFqOfO8u2Fd8k+oP7COk1tXAvDn+DhhjhRjEVXLn1D.SUvQI0IUUyjPovF4f0p5Vbu+.6uVGpenfmXe3VP+x50q2UUakZWV2dw9Fe+wrVar4XllZYc6O0m5Su9kbIGprwXByCkNQXXz0YLql6HGYqG3Adfcu268daeC2vMzePV+ER5Mz22f5OFXlG8bO5bGXwCr.NMlXAQj8ALVIaIOUkNdtEGWybTSsvSFVaiMZr9UdkW1V.MmGhWwc.wYSpSsCopdo.GDHHIvugNvsnAA9p0ZcVwErsBa5I5ZAAEWOLLbCiwT2ZsaFKwaPOVsXwh0VXgEVsVpZq6XDQwcgRMG5d66TaVM.vDeHSTBfIyCocB.H8ot5H3FiLCvrhHyBrOq0Nsuu+XRAYTpP1RkJkwXLoAHJJpmuueSQxuaoRmXmwFarFycEy0fpz.xsCTcWflEfcJC6r.zrVB3IEgtI9U9vKpK.wvr8f0O+whK.T671rzcv+EXRpkaryct6ejEWbwzjCuvSFJFywx.UFGXpvvvovAPRFUjTFiYjbv3eAqcrs2d6rWxkbIDFF1apolpW1rY689e++5seNOmm812zMcSaIhrJvpppqHhze9xNzO3q.55Ti8MAW1rDmUFNWWX0K7fWOUCXRJfrEfwJCiW.FubBcIUUSm.pyNz2xo269p+30Kr0+fv8AL4oQNtJshdDq0dYAAA9VqcxfffTgVaOouPQ61.cUfpFiY0RkJs4wJVbqSl.5HvFVqc8fffs.19rm8r6tzRK0eSnmpz+muW6at1E.tgeFHZDfITmkqtu7vbUbk519TUS+fO3CV+HG4HQ.mEdL.lv484sLo3LjEW1clG3oQeP6s1K0DDDXs1oEQxfpwJRKPqCrlHRk+f+f+fHQ0vezW9KOrXwh0V.1r1dyk2AXmRkJ0rXwhsgk6.m4IpTltXr0esyzLGYc.JmeBUKOgHxj4cNWW5jCSrEvZKAadV288SB.SXV7YYh3pTUuFfqDXotc6Nc0pURkKWdsZ0p8oFeKfsiiYUQzxFiI5du26szMbC2fcAnRMGCA2ZAXmZ6c.1gWm7aE.l3AKmANy3.6u.D7C9pdUK+LelOiCjISiBG8nW5jO7Wcad4uhe796+L9+lie7YVs54l9e0+pWV1O4m3yFu3RGa60VasM2YmcVUUcsCbfCT+U7JdEsEmyYLpHxTppyJ4k8eauxiO6uw6+N5aI88Au+wyU0NOlyMrUACPmNcn1JqLfHIBfmmGKrvBr4laxXiMFc61EOOOMNVoa2NL4TSR0p0jfg9bhhhH1UWZw3XvPGU01AAAsA5ZsVU.0eOMYvKIQQR+RewZswhHs888aBran01F23mdIBuZef.REZsdILLp4LyLyZ+W9u7e4bu5W8a5bPEKv5O5i9nsNvANfFEEEmb+JK.opsGX9S.LSXX3Bqs1ZAfr3r6e1hlff7IyoyFCHN6IeWE1PfphHgAGKnDkyYgpq.rUTTz19996BKrKTaGfc8gcifVK6De+mr6Ge9fPteRyZjNYeyrzOADKv3ZUMcx39sXd1fUFbP8udIdXLbftsLvU0oSmqoRkJGVDukTzYR.+smHRyXU2VPa749be9FOym4yrAvFFiYMxSMpjuBToLPsyctys5hKt3VNg8rR+DV8chXP91Qa34Qi.LF4YRpjaJUqLtHR5DCLXavrADVm8..+qEfI8WmKounvUpZz0DEEcU.KopNiBoEzdfrCvlfrJnqhPMTVyXLqas10d8u9W+Ze7O9GecnvFP45.aW.ZV1Eya6RO0Bd04AXRQH0mKLTLFitLnmAzm6y84JepO0m57.fBXBQjI.lnToRSVrXwoHGyPUlJLLbBU0wDQxTnPAOOOODoPm+5+5e+ct5q9pqO6ryt9DSLw5Pt5Iw+2JOzrBzLvUZPMAZk7rXOvSdr5ry4mn2.FAKic1yd1QWZoAZkRlvvvTFy0SX384DabejnSDk12+niBkm.XRaI63AECFMOjoRx8YNXhSTpzXEKVLKPpvvvzhHY1rdcuK+Ruzt3.dcyj3+WiBroFoMDQFDu.NSPYWsr1RDGayXA5QM5AGJFN82K10Knk96zW.r2fKGkAc5bZWnVGbTSrq3rEWEPdkuxWo2G8i9QSMPkuwOsueTpnnutcrZQnWxD9cJ.M9a9a9ap+7ddOusGYjQ5PhFODdhPQDAanMQY1yu6Ismb6fffcfB6V9AdfV.ctga3F5BjBlIdgE1LUsZm2jk9HhlFHSdXzCr3AFOOL4s8deuyHhrue42y6Y1e7W8qdhiEXRchvv1AAAdhH6hSKUfAaTUvaEJmBXLQ7mDX5jLAME6oKE8QdUkDp2Ys191wWeQmsqpR2vvvNOzC8PsymXStWmewlUbKBzrVsZ8qIu1PoK5NrWTBjxppCoJ64zEVnZbsZN5wUPj1UFRrXCBBZYKUxyd+VOUTBBBPDwKLLLCP1mwQNxnpVYhtc61Z6s2dZshtCvNu825au4K4k9RZ9Ltte3lkohyu0I+1PksA1oTesKg46kK2JwUcVDpdpScJ8vG9vCXZBfaw8ZtnJNSR+57f7kJWVulBEh+adf+1VKt3hiXs1LhHduk2xaIETIcRFOcAYo5H3.KwCXjS5V3eBQjQeSuo2PJiw30qWOoR4xd+b+budld5oSKhLpp5TEDomHRbd56xTjYdXmUflKXQp4.Kw0Oa6qcDq93sIvS0MAlSJypd.oJStztDsKCw.l7cymuRbkJOouFEbA7J0.OsRhHShLf9ghHZXXX+9uNppsKVrXSaIaKRnv7O8a8s20XLcfbsKTnZq9AyOvVgepuLR9ds+w2tPlfjB56HHAIUpAwUnPWnb2jZ+Wuq65t5.E5lKW43pUeb6mEvI7hm9LjN.xXSJAtBfWYPZ0pkJnp0Fo.5DSLAMZzPbKki.BppwG+3GuKTn8+12vanMP6ZCxJTgN4yWtakJzqXwhwK.ZMNyvAZNL6ljGmqwKVZtq2U6mUtJIrYHmTVqDmLeePIhd1uAtOB.rQNS8HN1YZIppTsZU.QpVsphyC67TUSAjVDRqhlBxwMbC2fZsVMH3Z064d93d23MdiItkGwvRZ97msWkJeKI4SCQA5ynt6WSyxDV+Nuy6bs67N+DSbu26eZpSdx+1NG8nWwnu628qO8ezezmYjQllrG7.Sm4G8kcK7ENwC19ydOe0c+We827F+3+3+3qIhrVNndUn0q3U7JDfL+o+o+oinpNtHx3ppiuxJqLFvHIGXaHMsxc8ToREIe97BfjX65.vjSNIfCfiwFeb5ztMhpL0zSyniNJUqUiEVXApToRefUT.Zzngt0VaQxmiD36qQQQCd90GDlPanXBLRBSExj78mJHHPG5ZIUhts4t9BCIoTjUee+QBsgiXBLiYBB5mg3dCI.qdytu8kx39+1gDV097e9OeOnRGfsA+sNvANvttOui1+dPpk.leXXXmq+5M5e+Ih7L99YCCCmLHHnAvtVqscPPfqrNU2NLISB8R.SXL68UZLUjwLFyX.s888SFiWqSXXnmw7LHBmHEeFNbLbpuQ1SQwYpB8N8ZvrtA4BPx5Dn4pgJNlH6bzmUdR8YOD.r9wEJDokKSb5zoiMFiZs1AEbkJpmnjVfLBRlzoSm0XLYCCCGMLLbDbhpbef45s3hKlLOuRxygugum+mDs7fVAHWEjpT0SDmC78k9ReojyODl7L4P7jQAdSZpOnQ6oXtJZr5gnIBO7fCxKndAFSpj3f6OGxcFLRbNSfidzav6tu6Odp74y6UBeAh9Vw5beizFz2WhANXllDGs9o9T0jhEoWoRN1QjSj9NUXafLEKVrYXXXFQkr9F+z3XSkWdH0IBCyBjQ0xis95qO5ryN6n3RF2jhH69U+pe0VW5k9raWgJs.11xBaC0Z.znjKI8sfEZGDTqi0599WF5clyGz73hPuRV53CRRrgw9P6SDEk9H99o.vXL54N24XwEWT788SCzpToRc.ZKojsCCCyJpj9K9fewLuvW3KbhSFFNgmm23gggiJhlcsM1LyryLyHSOwDY9y+y+y0+9+9+9Nuq+8+6oUqVYWbjQlpZYRkWjQA14gdnGpwy8xu7zU.s9GNWA..f.PRDEDUoPkAh+L.B0nKfGb5tC87mGme++krcw.CS52FNn0AHFhCXf8opN01au8nGbxIkJPyibcGoQs6+A1LxkY4sgC0LoS9IJCOITcZowgyt.NpqcEppWIvAsVatfffwRVToII1CqpZYQjR2xs9BO2m+y9.g9PsHXS1O6leM574OyY5r7xK2C76BQc4v.mZYO3LYAlDJrOUiV.HuHhuppOPAq0NGN8LI0JqrxNyO+7qDGGGVrXwyfyYUVwceQuvvvLIktQdU0kEQNTOs2S2CuEsV6bAAAi6r7OUQH1Dbz3vvS5V7SoMB0EQVOHHX0vP6ZfttwX5W5Oq8o+ze5UOzgNzZAAA8oi4dkczSMTv6qU67PpcVH65PZq05crf.Rp4Vu+h+h+hLu3W7KdLlmoXk7ynZ48ce228M8y3Y7hm788dtswdY+e9xxdIG7Rx.4yFFdhQMFy3ppi7kenublYldFXnLYYLlNgggcBBB5BzoSmNM+gukadmO4e0+eMxC0q.Mn.08Ky1QCdNk2sI+7zkU76.QIYcrXLTJlCi5bBhCIvoyPRsVSRMDxdZgfSSQVXgz0pUa32WJ.uBP5xvHTfIKTlIJ6rA6LFyQy.USQdRaOo06889de8t8a+1a2qWulSN4j6ppVGngH4qCU2RUstHR+xtpuMS16w402I.L6BKKhwUUmBX7BhLREHEKfjuFcS.6qATnNTdXFlbgYFK4y7PogSOAtr7e.bkjyUYirWtijm5DfWJP6hxter+vOV8ie7i2W+YVMOrQEXi69tu6M+It4adipvZm6bmasEW76aCHpAArK1EZA0F9442UEr22E1FdMlgK8uQnel.SXuluHyF4.r1qbXXc+hEqBDA90fnswM9aPoY5bdpS0OCui.4m7W8W8Mr+333kdiuw23k2oSmqHc5zW5V02JnQ8Fy39dE.sMtCvshwXrVaoyEDT7b4AaEmPRuwu5w+Uq2qWucdSuo2ztPgcgxsIOcoRw3DqqTgCqI1i3EM.eeAsge1ObF66K9180bI.ZmCZTE1fYYqCsNMO8Sb1WS9LWXTn1.FlzqWuq4BJImzJnIhEZOP6.x13JM.qwXJgiAQg.U7g0hfsIOMq9fUakKWtN.sfC09qS7GOYdNz+m8yfeeVpNCNNJ5W.xWFl6+zG78N00cMW4DwzZZUimYi0qO4IN4Cj9G5E8S1rb4Ja72cu26Juy246bEH2lP0co.PYxB4lZkUN0ryM2byiikc6myWP.GreiMz5EXBD.r1PBBLB.0qWWlZponWudToRUxkaARmdubuMnrbRD9UUGXWGZPfQCsV03NbFVq0KAKgj6ekf.2ghHgoH3zigdVqsq.872CvDOUIkHjJANBoeZiBB7Ufd1PaOjDF.IzEktAAA5N6riL93imJALFvkbkM.N2C+vO7W4RuzK8KC7H3JAqcR5Wo+gZHOoluBYWAF8bQQisn+0LETcVxggp4NnpUNnMztLt8YFCmI.0wDbrlggmngprQwhlUBCCiLFSDPMbLUrQPPfyE.cz9re4QObIQ7MTxAvwvszbFRCEyBk5WdqCJED1Kl2gKEjmnuKAH0Bvn0NeVMbsQQQWoprLnyDDDj0ADriYM.6XLGcm+j+q+m196+Y9Lq+fO3Ct1Mey2bkRkJcVfykThRUStFtv8xuXbsq+wzFvbVby2lAGq2mFGK5Z1mEc3dN7jjgICuNWgqR0nqA3Jsg1kPXZfTIZXxtjb1FiwrVXX3ppHqVLHXMbO6qAr1Mey27l28ce26FEE0x22uECRP34wf2mJ5ONOFbeXPN0ikIGof4xBqNJ4YBpjeh1sO23Yylcbnv3P4IIOSQEl.xOp0dxwBBBlPUcRq0NtBo+ku82m9K9F+E6.zxkHLmwi.Das1VoSmdm74yWOGrYUXKxSibUXmpIIaFx2Dpzh.Zic1tv5IiaKpPIHOdNssqXZnzvBa7E59UCWRN8Oav.6cOgkIiSdlL7DgSXLlj8GymEpjk7jN7DghpZuhEK1sd85ce6us2du2+uw6uu1E0TDogp5lhH8K4psYu0W5bO2y8z4Fuwab3RP56ll+8MU6hAFlzuc9LMA5vRzhyt+T9rVpjr700QovBwew66K5kPsvQ.tvfUd75XSxX7Y6BratbrU0pr1ce2ex0t4a9GXgfffYrV6nAAAohhhRqwZF0Sy5Idi1sa2Q+7e1GXzRkJkMUpTo88utTrlUp.r7xKmLgNxsP3oPgy3UDRWBRCkyHhjkBLZRVclDXpvvRSKh2jIYtAU0QKVrXl65ttqL25+5aMCkIC4IKUJnWuypimBXZQjo2b8MmxCuIrV6H.oiii8RxHohBggmfAOGDhEnavQC5DFF1Az1Fios0VpUPPwl.Mu0myyoUYx0eQYIYxsGGB4IO31eauo.wqCcgYIHHXX89Pewu3WrGPOVgtPkN9hzrrCXk3fffl+hefewTOzC8PomZpoRaLlwihhlLJJZxK6Rurw8SkJy09C7+g2O6O6qNysbK2RZUUGpq4PJcBarHZme+e2OZafchiiab7a6MV+O7O4OXqH2FOIfNTwsv4JzBhZs.zxkE3RtLlrMwGBzS6df1agEncBqX5lCZVk8mFVyATRlZoqYqklEHM07SUfHuxIKlVFRC4FixUm3y9HOxDiLxHiXLlr3bHozppYBBBRkGze1e1eV8e3e3eHVUM926idmi7V9we0BtMkSmOuLndP8Art6Y2h7Ghdb5GSf+O0mc5CixolSWfUGvnHWeSNgZ07JqZ5a61tsL+l+l+lYvwBqKbymGmq2S6AHAAHIIBy8dTQSrMSOaj0KvOP1d6skW9K+kK+Ce0up2a8c7NxfKaXi.4yd0W8UmtZRc9u3hKtmqNYoKT6aU5Yv2q8s+1viWbAeMKoXcRw9wSWUEeQnbhcfBzRjBaCUzO9G+i2WH2ZAQWHHcIetmR.3C7N9.79tsaixTI93G+3w4x4.SKc5z8hr1dSL4T6ARondSNwjRiFM7.RYCCSGD3lieRm.WlIHHH0wO9wGhIakciAqPLThhfTBjgxNqbA+7hwl.HILHLgIg4o.U0xzetunfQY8P8zOotOpM32JfqDQhhh3rm8QYwEWtuUaJkJEBNGj.RdlphpggV8rm8LbC2vMPXXHFiQfBBUJK6t6tCkvoukrQoKf4CiGmBOWYbVrmOkZGkPw5xPljmOc9ve3Ob7ZqUK6y648zG+t+DOPqicrmQyy7nmcqG7A+B09semuyDsVoZCbA8lwUkKnhHoxkirUpniFGGOlmmWew+qufe6DiXGXIB.AAFpWutL4jSxDSLAsZ2lF0qSPvimtjjrjJpBPfIvA9QXnqTYf3nHqln6IdBjJoKW.gjjVE6x3sDGD32mUHw9AA8DIupZEIHHfDVlHINtBn3tlrVPDIvDPj05ECdF+.ObYXFUUwZsYBBBRGYsphDiydcYkUVwMWZA5Ps7sJPkcKmTlcKt3hBTviJkSsRx70E886e3hXpxnP08CzP7jlZhqmnfmmhFFdBMHHfnnPuRkJkFGKUFAXLiwzRUsaXXXWQjLAWafGUCXFrN2d5ebGbw89mfXGqFJAfTn.ohhbrIxWDIR6q4w3fQa0u9ISs1ve9NVMn.3HDtqeTDwwMbfiZLdgkBSIdRZU0zG4HGISXXnS.dMGse7bw3SOhdbsT3uqokOOTohC7DU0z.YdjG4QRIhjl4QXEbJRx5GVS7b8uNsZ.HDg.kINNFOOOBLAZj0FqBhnzS6y3bQSDRbSOGPw4hs1u..RvQCj69tuaOJf36esLOnq7c19fj83Rbqxy+um7yUcumJzCpzwjMa6yctykJc5zdIL1HksTodysvBMGYjQ1MedZEEE0EPOlwLZUxm9+vG3+PJbImSrgVuybly3kNcVDgNc61s4t6taiUWc0s9q9K9qqe6+1+lMdjJO3.QbkDVnikcg0asen8ZPGnjaO8NDuef0bmGn2RKg2YOqa+t..6dr2IM9jgH5RAZS47oxQEwQfcRUAx.EFmJkaHhLVXX3nt3+qjoToRoEQRaLFIm686s4lal5m9m9mLCP57hLVU29.oJ3Dx69rcQinf.k8.ja7VtQ2yR27vKlHUw2waemzkbd7Z8mT5Dbwy52BVa2H2gR2h4otHxN3WoqHRp2xa4czW7pFYVl8IiMnFSh0BKUyWGX8W3K7Gb0Nc5rNv1EJTni0Zw222yTzjpXvwR8G+G+GmJUpToARW75Kl122OEErIHCtvfZDcFlY.6..RUx22CmHN4VTLRSKhLxC+vO73m5TmZBQ7lHNlwaznwnpyBbyDFFNxsdq25XTtvD.SQkbSAkm708tdWSgqTb1mp59l4JlYJfwCBBxBjpb4xRPP.xPA2Lzy.UAsz8WpmwX5ZLlNO7C+vsUUZCzxZsMKCsgp8.3C7A9.onxbN5Pe5hWLnVxCOlvcnzhqmTpJ4GnSDNadzo.7pp6d+gga+g+HejMSXMyJq8PqU8xu7KurwXp.T4Z8u1Z999qmJUp5mLLb2O4m7OqysbK2BppoDovHppiqUzILF+oAlM1INdELFyh+Z+l2wATUe5+o+o+YGD3fufWzK3oWfBKAXvEW9B0xmeO21AlfyvXmdOVjHoq42WTQaVE1AVqAP8Z0pUG69aDFFVmZ42Bh1nbeqSaAGKGJU5Dq7leyu4pG7f2X4hEKFUpTonBTHJgMTQ.kqjOekCe3CuxO7O7O7l+yeQunce4urez362QAwotsa611mW0ByRRlEifQS.ezAhxoOT+m8mG597T6X.0A93pw05KFU4SXAStZJfHhe5DwfL89Y+esrU3K7PwdXOeq2FThhhbAdq3Ys1Tat4VoLFS52563cLnluKUpz3u1et+4iYLlQRnyZFfTADbgVO2i2O+dsKNam2Zb+5u8ec2uu19423232PK2eLhOcTUadGu+iWGXqa+1+MGHHd6m8egYca3ep21scaZ4gbUKupEFjAG+fftMZTef1JHHZiF06y5hr9AAiBLdIm95LYv0FLJt.mFX8lKw4WFXkJVjesesesgu+F9mWr1T.UDC.HxB.UkxfWmNc75zoi652Dl71eR6exBQHIfNK999xy7YdChptR7vZso77j9.tpfz0XLcEU5JBcWbwE6Anli5ttvurBnKu70O3Y9g3POAe0OIu916mdbJmatPsYSAk7RfiPofa+BIuTGXiWyq4Wb8+x+x+5M.YyCeEW8Vqu95q8qe6u+Zus21auR0b4phS6p1RUsIkoqHBhHd.oqTQy.jMUpTOtYxjglSj.LjL93iiHBUpTgQxlk1saSb7iUhK78CvUBSAf5XIh0Z6EXLcc1mKsUk1VqcfXCJxfCG2KHHnqHZGmywnMsVaKcPlbokpUZYs1VVqsIIwvHHCmA719AAsUUaas1NJzwDDzwZscO6YOW2vvvtQ1n3ff.mKLhCjkG7AePUUMd1Ym0MOsV9tPkNk6Whu4ck4KyVte7GN2vC1ddluuaczmgF6np1IYNsJNPS5yZF8nAWGhCMgTIGxIMtCyjwXttzAAAdTMmfCrju4Or5oPpLz9eZ47C1Su74u9mvpy8j7CcOaoq.E5+qpFKpHRrBwZrN.H4SFFh34J80O6m4yJwwPRIVHP0899iJbw95TeS2hqrvf9BQjzhHoO3AOnKt6U7c2+qOKO4.KAIwl0gKXuLan04VlJwAFSuAIECuXiwLHwNunWzyvAtYPfPk7h0Zgx9JTo2J8S9yxeGoDsGdezg+9OujSrfarnhiYHcp462ZwEWrYhFFsSo6uTC77pmH9oqoUxsVtb41vXL0OYXXSnRObmWKqHEFWQmZ4kWdZiweZQjY.lcs0VaAiwD7Q9i98W7zOvCb.q0dvOym4ybnWxK8kbI4I+gv43NG.Xw0xmu+YAlGXerFStVhwk.H0O6r8OKZaqKgK8W+pEQ4ZctyctlTtvNPksp12EfBXUfUs1STCnRPv0T1XLQVqMJO4iDQhRDE9np4yWAXkhEKtwgupqpQXXXyJp1687ddeoDQFsbYcbbrlchHXbUiFoerzTYA2ZCQ6oqTeulqcw3BSCeHsAzzM.Fy5nvVeGyIylatY2YNzL6PsATWbGNeZKdgsgEqpYAJBbYppGF3RAJZs1Imc1Y0u3W7Kt8RKszFFioxYNyYBSmNcoD54VA2.3cBf118SLqgxPkzvL.a5B.YBee+YihhxCrnp5ADQNfppwZs6+9tu6erWxK4EGGEEs0QCBpTAhTUiDQpYs1Mc5lBJvH4g8WA72ZqsVZ7wGeos2da+sazXV+ffwsVaZEUlceyFuwFa1Cz9AezEn4a6W5cr464c7KsxQCBpcRm5XuJvpus21aa02869cuJvV4gcqrGs61kBrKkGnRzesTo6mpZOlCueHPNcQ7nz.JNNBvnIhD5H.YWXgERUqVMO.RzMf9tjxLkJUZ1hEKNaoRkl5dtm6Yha8Vu0wTUyZs1L.dFiwKLzEb9wLFNQXXLPuwGe7tau81stthEachvvcCBB14dtm6Y6a5ltoF.MJ.aWNInoBEJrSYmGs2Lvs33vpQc+wowN0YYuROhBjhxjpb4xZgBEzBflDXS+xzYjbP1pNzn6+2bHICoSBNJUgBExTtb4QAFUUcremememLu1W6qsaekXWUcMQjMV.1pFCbZiNrLwblG2MqdpxwW5SWwL.iZfIBc8a8ooelOwe4eo2bKVn80eMWeC1SQ2e7TU9gG6jEmPdteQjkwUVdWEvgihhJppNMJowitIzWcafFSO8zM1Zqs1TDYifff0dqu025Jen2y6Y0SZsUBBBpwPtnkOzJZOwQ66wxjKta82CbXWZIMAjA6fLtORNXrpv32wcbGi7FdCugT.c7gchlm5rx4Qq0g26o+XNOfT6Gxr1dtYvhjTRnas0VW11MZTLvXlILLbDTRgKI3MEQqC5pfTUDwFGGWRDojwXhxCq7F9U+U233G+38yzUKfdIU8t.vxKuLm4LCD.1KFcqogma1ujbFm4YhVgslXjQFYbfQyAT0c+02h125vvtm5IduIAH8byM2nqt5pyBbfBvUFtWI4rbXncehPVb0yQrBsO6YOaikVZo0vIvyQkJUpTwhEstDBSsDASuAPy7PyJKQGNKOdzW9erOC5aouoEQFPc+4gYt6669l8O5O5OZx63Ntir3zFswqjm8ee+U20bexO4oGWUsya8s9VWOGT4Nty6b0W0q5UsiuSePxlnkUyVV04wEHuONKzbdbkCvv5glGNl9Lf0AOQtaCv4I7qm8rmkzoS2mhIwiL5HwsZ0J4fYZOP5kH7pZoRVouE+N6rylZ80WWDQbGx1iNZLcEgthncUUFDSShdLf.hl.viSqLDADOAkNc6R5zoUQj3omd53ImbxXaXXOEhMFCN68krhLfQMMEQV022+Q+ROvW5TW8OvUeJpvWEHBn9gfNmdnwU.oBfzgp5k.BUFbIGoXd3JNo0d0wwwWgwXJZs1obeGZGPZgv1BRCU0MLFSMfp4fUNYX35hHaFDDT2ZsqE3JSh0S99ac5uwGeM73pz6Gxt1i0w4FCPRD1x9f9TmCSKN0Wy4VtRxIfYw5D80DAU9vVqcwfffosVaJQj3XU6H6oqDMA1IwQfVMLLrBv4NpwbtpNGCpJvFLGaypCJU3uSG642pZ6whwDSJ.m6qMeAQlorioI6HhrFEnFkYC1yPKdh5G5Odr+YZNPd3J+erxJGocy1G123WLLJZRiuum0F1AGvhaArtwXV0ZsqFDDrVx5ZqkH5tqw7rNqPiyblyr8xKu7PLl96nLm8BOu5EdNwL.YR1mMCP1b4xMZ0pUGGXzRkJk9487ddxoO8oSi6Y+9AVHLLb150qO4TSM0XAAAYsg1LpmlpXPQuRkJEKB8tNSwd2ensGncwIN3cOZPPuSXC6F3GzFX2JUprcgBE1NGrS0DWtJI9+swoal6VduyXMri6zWSgf9wfjmTk+hkkBEJDi6LkwatWecFfQBfr1bLBUOuR5OUNvqZx6qPgBiVtb4w.F8O4O4tF8Y9LuwrFigFMZzdxImrQR4esYRottmXvVjlT5oTmQ5eRztXigIWP6PPdGpg1jLyop1g74iUUY5omNE0FbXwzP9udYBeO1q.MIG0AV+HG4HqYs1MCiB2An65quNe+e+e+oLli4EFF5kNcZwQEWWPcOvC7.YAF0BiwZLJtIii3ruMRsYxyUUUMJJJYPVgT.Y+O967ebDq0lEjLW+0ecodj+gGwSUM0m9q7U5Gr3j+7+7+7ydrff4UUWfBr.vBOz5qOOvbSM0T6KUpTSM8zSOl5rQ3T.hnBarwlpHn5..SbAY7t+28thUUiek+B+B8TU64JMG57t+fu61VqsCPbEfRkJkBxmNJJJUtx3AyIIIw6hAf0tPllz6zLvNGiA59K7K7KzAnU4jC4BE1pVsZaU9rk2DVnd48Jgls.1PbtFy5dddaciOqab6vvvVVqsqHRuiZL8BCscQniwX5Twk4KBBBRO6ke4iYLlYNQX37AAA9.KcS2zM8zrV6gTUujHUujO5G8idoP9CUtb4CB7zf7Kac.zsGpyyyjjPE5nAyEy2CnyrkoMrTqBWVgVI2S8qizcnPgF.NjmiXcbLPYMXgUsV6pUfUa2t85VqcixkKm.lXtsEQZduet6s6wO9wSopNN4YZw4VSSWaP8LmeDfQl8LLBL6nr+A0RY+rO9T0ZFt96kc84g6oGHcoPgd.89hm5T8t9q45Gh5ty80ZA8yKi+Ikb04YaZwtySJiOw3B5dG10XLo1ZqsRAZpDQFTd8u9Wu2IBC8hiiSCj9m5m5mJCjOCP5nGmrz9sxGLeu12Ram23B3P8.5MqktPPGVvEXSUn6uxuxuRm2va3Mzh742AXm64+4+ycYkGiKNbge1IiMObu01KvC2q7t+OMZz.+f.JE5T77jQKwhzmL6tCN+e9+7GcrhEKNl45ttQvUKydIkkSRyIrlmdv26giS.K4wi4KWj1lQAhWXEhGYjhwsa2V6zoiVMoTc.TLFEP+5j6UAfUWc0j4wEjxI.ADFFJVaDIXA3JCH2uqKuzx8RbLfcg7aetyctsCCC2IIYBchii6wBKDSxdlb1KH67eSOe2UhuKKhGj2AbGjcEH60e8We5WvK3Eve2e2eWGfFUfUoBU.uHfHOuzkApTEV4U8pdUa.9MhhhZ5bdiBwU.Ymc1IUh.SlZnWdWvKoamNRe1iXSzhDee+yCbj9+snnHhrQDEEQ5zo0DwiLFQbfknzAjNh30VQaqp1NLz1o30EzUDoa2tc6t95q6zBFklHrinriHzvbLSi3XoAPi91taTTTCiwrk5bAhMHNdCiuYcmFjnqGitQ5zo2.XiNc5r4lat4Vgg1FZxgABCC2AjsEg9kT2NhHM888aEEF09pulqtagJzCVHlEbyc1aNEP97J.V29H6w7UVnCP2JI5efmmmBn8KsHPhMFSOUINQWWbeNVqV0AjS2ffi1AnUbbbGnPOly8d9lrfuDnnrlKC58Gep36ttUU6UV0j0ubrK4vOIH1PsjGBC05KrnwMa1LgIQZKOUZM2byMPiDv4zQcBsgcMFSmO+m+y2spSqHDHeJfTytJdN8d66J26zce4HniJhDeOOxi3pIMwwL87kQfYDNzWWVyNjPkuPZbksQp4laNuXbBirjzmGDX79W9S+uTRmNsi8IVWmWoRkFvzmEW7YH.rvJnP9tK+8sbWft0t3PK19ZwtE8QdjGIFnyZCrkayNUqVcmG39efcfBaWrXwFm9zmtNtyArUXX3Vggg0A1YxomrkJZ6u5W8q1V7jc8vaaq0tkHxVFSw5tyTnMCBBhMG6Xo788G8jV6TA9AyA3KhrbudwGTU8xpn5UbxSdxCelyT5JJWt7kCbYP9CV1w9j8NCvBLKNvpmnVRR.gk7.zfJzqPgquCy3.Zby8LxhcX1Y2FXKKrIUou8OuNjaiRkJsQUXqRkJUOJJpw8e+2ehtDUX8W5K8VW607ZeMqehSbhF0pUqkTPTRXwZ0jjsBAo9k+k+k87KgGrP+weeu3XSZWrBXRRmyocBkiOoX+62CbRYOUpnIzKM0688d6oymOeRovTIEb3uVzOt+jst.sVnJ6.427AevGbiBEJrkw2rchaWPbbr2m6y+WjFzzliZRS978EhmQOxQNReltLNv3EJTHoNxpk1QSZmPoIh.9HP9TWxQlJSbb7H+P27OzHet68ym0XBRKPpQGcLOfTSN4jYf7iJhLwu0u0u0z2eX3rhHy+S7C7JxQNxuu8suEvcP6Ywc31QCBB5mcjj.9TUUMtn45R1DOt6m8d9rcv401c9nu+2e2ilP0Xq01oPUZGDbsCXjSwhWKPEw22Ool4VsOi.uXIH6gWvbvAONzgbGx48+9e+sghMg7ILCn71jmsKrzUuKTqYdn8B6svy1GyX1LLLb8fff0KFTbCUksLFSchowWvZanZbCSfY6Pa3tgggMEQZKhzK7DmPEQRIhjMLLbBfYJEFtfppIJJ5.hj+P+X+X+XWZX3It750qeEWR9q9JfJWdA3RK3rB5C.TbgUHOL+bLHCeKk0MFFVuud6rYRon3TOemyvTtbep.uCvN6e+6eaf5PssBBNxlP95Yylc6fffcwmlIVjbiG7Aev528u2c1viLs.PKqiB4mVUce.y.KLMTYRfIVGl.VeBViIHOiwRKkrf94Ysje6ucFT1OJ4xE+w9XeLmPbUtbOfduw23ar2q7U9J6tmMQtp9DPS+AGVb4DZaBzKeU5A4iAhcLIxiff.7DOvk8RATOxgqBfQTaIaufffdFiI1XLZwhEEee+zenOzGJKTICr+rPw9h504YCme68A02q8MYKYMkSGCz0oURVmE64SLyOeu2za5M4NTWkJs.Z8I+jex1.cFxUj3I3mJbJMOPggx5a9JCFa3YsVOAOuImbROzA++bAoJf4nG0SDuTgggoBu+6e.XbgNaVLATfHIY7exZimZX.ZtXYM7uFsh.ap.wt.jthPhb...H.jDQAQ0q0Ka1r8xrXldc5zYOcAx4fUOYteDlAOHmGTNUdvyyyy6bm8bBnI5WhaiSTTPhUzXMV6YLl1PklKt3h6ZtNSKJTnCjTdN0bkDHI.NrzRKcgy0+lncF.jyBBTIEVRSdR+nO5ilBfWvK3EzczQGcfnzCT8E8h94hhEIT0dVHWUnvZppaAQaSdZJR9NpVtWdfwGebAF3VJOlXkZ2tM.jNSFRkJEQQVb4DfA.ir2vS2eSUUicwd3J4jPaR7GZWE5fHcLlfVnZKOwq0FquQaPaaOQXGUoc5zo2a+LGHFM9BewuXciwTO7Dk1R7XKywN1VVqsNJ0CBBpGFF1vXLMTUabrq+5qKhT2c3lisom5slnx5FiY8k+9VdSUk5c60owwLlsEQ1tWmdMLlf5GyX1REYqtc6V222ug0FsSPwfV2wcbGcJ6bpwXpQL9WvgzpT47N.w..hnlBEhgBwAAApuuuFZsZTXjlT4OwppwhlXWxwRbIqMVUMNLLrKjui0dx1.sKVrXGnbOVEXIRgyMM9lXujRxRfrfiMLBf3GAvBtACN.57fUE3wnUDeMaCChVPPfFDDDmJUpt.sMG0z5y84+7sVc0UaeTioiHtxcSUsKJcBCC6bK2xszEnWPvwz+r+rOH.dq6bpiuabOyAicluFRePtO3AOXraFjKAqNFBu4SDRYCybHu74ymvLxZYfBYfbo2c2cSIh54z5OwyZsohrVue+e2eOub4x4YLlznZ5ffqM0OyOyOSh3GmOUoR2m2K7E9BoFDCU5Qkj3OO7guPVFbwPeiBnG9vGVAhO3AOXBnkykDieXSLz7HW2+rcgx6jC1Yg8had6iYL0UQ1vXLaTLn3Feku7CuwkcYW15ILvulpZUiwTKLLbUq0tgwXpKhznzIO4tuzW5+71ppwVqMUTTz3ggg6CzBevO3u6RhT3oGdtvKYokLWtp5UDFFcEe3O766JxAWVNWkLbPfmV9ZrHjO.HOvbPgogyNFPZKHPIkMoGEoGty33h+e80Ou3+me9429K8k9RaCUaTr30tMjemhEK1z22uk45LMgbaCk2r.r5W7u5SV85ttqq1q60851TKq6BEhUU8b5nS9zfM8a9M+lSG4FOklSSJNziAT8+W13YuX6lduEBNDo3zCXOxvNVvH4gwDXzxPpUWc01yc4y0fZ9qCQacHX2uNTWrefNihSiIL.Wpp5UVPjK6DggEKTH+jkKWIVP1xFYq366GZLFaXXXYf0NpwzHEz7yety0IUpTcCtlftTa9cfUbzzMfdzkzTkIwAxQQfC8k+xe4CM8zSe.btJ1TfjNHHnanMbaSfYsnvv0TQVGgsMG0zQKqJtCdMtHx99DehOw7O+m+yedQj8644MoSzW0Tty0ghPWPc.jnCn.4N3PfbEbNtPsvvvULFyJI+s0A1sToRcRmNcutc61t3ynnaxXzfRx4hAzkuv1vA6kBNjGb5TK.d0lkTLAodG+juizuq206ZfyWjHZuY7886W9JN5eCSVpTooMFyzQQQSnpNhHRlfffTgggopWudlK+xu7rIZVQZmPwE4syt63s3hK5s5pqlx2EPS5lMaJiN5npMx1Ek1hHsxjISy4me9cBCC2od85Mthq3J5qF8ak7pd9jR3oBzhBzgrzkyNfMMmW4krPR5pVMIyB+29z+2R8C8bd0dPUY+Pp0xSFRwHXcyW9I+I+Iy9Ne2uyLK5uXeG+Xheme2emw9W7R+Wjc+6e+w20ccW67yeq25VhyAX1hBzDgXh.U09V54trWcj28w455aG8uCrl6jq6w.FaAXrTPlxf7bdN2PmO8ot2FrRtsfpWXYQnWvm2ERe0kAtRU0iDEEck.KopNCnoAoGJsPXGf5hHanptlwXV0VpTMUj0LFyF+b+butM++9O4+zlTdgMgZaEEE0v+Z72kNzj0OeZW9swmUeu127s8NDzgIEqS5jRcq+5D8qwWOeQztP2U5WywyRSVe4VvY5WFVOQi6xhiF76GXIfKWctz1UXs1hBrOEx.JHRaQkcCLAaaKY2REG88CCCKAb1iZLkp1u7PKv1TN+tPklKCsOy4Sg6guVtXb72f87WFReFHqp5nRfLJQ6UBtIkGRafsVHQmHps2b8GOGwRv02MNt8fWF3J60q2U644c3tc6tX5zomww1SWRTDQZ5.ZfZ.kLFyYANGNEsbs68du2Mtga3FZjC1MMzwBw2288ohu9e3maWJu+VvZ8Kgfmry0GdOr8nW9bjgQHqFpiJNqfbbbhQ3n0qWmCM8zsUX2DscJK9L9q94+Sk0Kar9W9g+vMqlisIEsSDruQvoUUyiyk8J7I+a+a8u1ib0ExkKeNbTJXZfwqu0VYmZ5oS0qWOoZ0pN9OHI2Ih6fw80ljjrSq8+GCBBzpUqp851MVctwQWfdINOWm4lat1qt5pcLFSuRVq9w9C++kWwO5OVpNc53kISFAPEnqBsMGyzN7jgsIl1HzAktHme4jcme36j2x+t2x.vFIFTwwr1d850comwRDdhP.jjRqs+y5XPTiIfyblyjNUpToMFSlHqsGhTWUMZ80W+Qtp+YW0CSU9ehaNV8j9U.RMGj4+9C7.oOxQNhSyaJBTpPJn7TEfhkKvUFd+gGIHH3v3J8tICsgHHsMFSyvvvcLFy1gkBWGm7dV45bZqVsxvp3ylzgFrx.JxOr9rL7duOYFe0Od29qiMpp5nhHiMzumRUsSdQF3.ULGM4ze8bfpjxKzmkIJ2UpZkqA3xa1rYvZqs1n.pwX5FFF1dOwE0Ux2FWxpVEHxXLkx4J8op+1+w+wq+xtsWVCZPC17wDqw+TtM7b7zrWrmy.LaAXlH29KMEeYcTpQkAkjyE1Or2ZE7+O68tGkjjUWuue1Q9pdzUUc8HyHi8N5tFFZFX5dXX5tmYt73f58dTDPWHKvAOqAOGW3R.e.h3vfJiG3NJxUEPEudv6QTj6UYPXT.uGDgiO.zkWv4PWMy.SOBqVGptyXGYjupJqm4yXe+icDYkUMc2zyviyLn60JWY0UmUDYDw9wu8ueeePVli7zdgogVKZLlkEkEGOXkfS.7Tw5p5yjfL19RoriVGtAXZqTplI6EnkKr9JAAqAzPcRUDPMpQKr88+ZMu1+y5YyX2SOtCbNwxfX0kAFhfJibYwIpVsZ9lMal4Dm3Do26J.bne5e5ex49s+se2yGEEMaoRkl7889deYedOumGpSofHHofDY.xFDDjUHDY877xVVHxEYG+LkVqmVJkSBLQXXPNOOkSXXXbhfO2MSlL6355tMv1wwwad+2+WbyScpaZarnaaafsKYi+eyZv1TjcIGcQShVIMZOXoiCRcKH6yeexQE2rPTFEjIvkbHXBpZcXtKdwKJHKli3cjzYzyAT3K+k+xEttq65xJDh3hPurvtgvNTlcvvtDMxoT6O14+RIDyOQer4Uc6wqHLANucvv+z+z+jCKPlO4m7SNpZb+Zuu2GUAVas0bNwhKlk5jEByAj873e0Tkmzp20qDrKTZqelelelMOqVusPH5TsZzPoTJLFSla9lu4bFgovK4E8RJ7I+jexBJkZhUpToPHT3HG4Vx6LbXVpi.ZH.LQ3BZDTCGvKGTdBiwLMtbnq+5u9CIkxIKUpTdW2xYxjIiXmc1FkTQXXHxS6CfP5IcHh7BgXZf4DkEKbWuo6Zgm2y64c3LYxLShp1my.NPZkhR5yZDFgc+zCA5AhNJkpC1Eo5nqTomwh3jAW7hWreQKTR64662q7MTtquueOBoGgOBKr6alaL9wBjlGev5P37CA5Wmi0m0nOUn2ce2+1cAe6lZVjd9998d5dd80Zc226688tK1DUzBJW222OT3IpHkxK9te2u6KJkx.fHkR079tu6acWb2xXL6HkxcEhx6HUxslXpo13HEJzVJkaPBWSmXhIrZjigYAVzXnb+9880g5qQoTW6ryN6wBBBdJZs959G+G+GeJkgmBT5IGAWSjG9.tTkE4BLGobL9njedleDpEpyhhl.vBliBlW32wKbHTa.PuVH6PDcPSmJUpzoZ0pc9C9C9C14Hm5HaBrlWRhyd0uxW85+3+3+36ZLFtsa61lnFLSTIlqd85yQUliPl0XLyHDhYLFyzEo3TLhZNKevpc8MSH6YvEiD4HDEUmhl26G+iy+O2y8HN2e2mEZfHQz3txGm8dejdrTNousmmGoUH0BtDPjT6sjqqzf+5XDhcsiob6uzRKEGd1PGK5xHqm2MlkZjk0HKrXlwfU7+pMq7OAos27bmi3TWZ3s81daFVjXWbGJDhABgXPUVZfUH7jFWvvZPBx.tbsTns5rn0UD1GUHBBBERozZuphDGV0x9DaeVGPffJUrVErRcJpY2BqSIHKUImEcSjcU7tbhO7i6CpY0kARo4P3dBjKkommvKMvwA0o7v5esudDvw.7Swd49FCVqVsDW7XOwDOI.2gqt5pbgKbgzMjjIHHvoLkcdVOqmk.PTihBM3.dYFLnPFphCz5Qak2FOYI1J2crj2ahCZxlPclbTDmxTN95ttqa2Ymc1MqQ410g1fZcOnAgD899i+8q9deuu2p0XoFTi1DxNPotV6tmgThglDZW779t+tw0sr.P3lf3f986yLyNKZslLYxbI+lljrDqU+l33FBabFC0Z8fACFzuvDS1CSZ0OEaYLlsA1pYylaCrUPkJahwrws+C8CuAP674y2VJkq2rYyVxSoZb1yd15AqDTGC0AZJkxlpSqZIDVDiXLlMTpSt0OxO5Ox1AAA6p05NFioyfA81cvfAaqTmbiLYxzlHVCawfVKHHnEPigCGVW.0kRuFAAAsVd4kaEGGulPTdMCr9byM2lJkZm+g+g+gtTaLt66a6Wl9pItw23MdiwvQsTqoBFvJDvUoLTcDUaPq0FsVmDSFwZaRCFp059Hn+IUpdJ0o5clffdmIHnGP+J+OpLfFiRPTVVhrGkidoVy8pu4Z+7KXmiYjgDHsHKICPlZESlSZMbRi+9JePKhDro5fZitG0pUqQqUGDnGBDGnChEBwPgwLvXL8CrWqcNoR0oRkvN0ft07n2O3O3O3.BXHs+ldbm+Oyl.rZDjEwgEoZx8KgPLjpDSzk85drjD3mYdHGsIOzp.PAgvq.QjGrE7CDiudfvR6IiCfi0UULYA2bqjjL.kRkM3rAYn1nTkFywurEA3QfPsGq2KtDutZaIwyctg.CWEFvpLfJL.VtOrPOfNkOQ4Nm3DmniGzwRIO1BXsOzu86tF3pcccq7E9hegK9JdEuhKpTp.hrIvSoTsnLqWhRsEBw5dddqIDkWKBVqhVuVY67Rsiii2FnqmmxfcM9B.SAhYGNb3BgggkFNbnrdTzxttEuViwbLiw7T+c+c+ub8ulWyO80WCdp0Jww.tFpiBMkvVTuYvkomm4SEl6LvwSP.1xlhfwdsF0Cna.xcIxp+jutW2qamKbgp6bjibjcNxMcjs.1XI6bhMAZ8TepO01tBwNFiYPcvIrD18pVkYHh4vlLuYYQlwCuTsCMOr7kTbver83+IVsGucQdvrvlCH+hPgl1J7UPTRT.GJPjkyWIU.eGIrttDqSM1h8KNQWpVpHcMM1JubM.G2XLWuVqexHXIomLWB+eacJkpZTRFvwkVHXiDAQsCTrCTe2JUprs+s5uCZ5lbNRE2OkKbsUMlmRsZMdxCG12KgBDSjTol9JkZqJAAMEVDfrN1A0hjiwLB3vHDy644kPcBlLoxXocXsSZHX.FKkSTJU2fffswX17T99sNSEcSe+QhHV5fl5Xg06VrWEL5yiTXh9l0BWWpIcubPa+JcLF+Xk1+YTfnK.YZwnrMml3gQ++gggY877FgfojWSToRkI.lNIwUynTpIqnqj2LbXlibjkMUpTYH.9994bgIOqVOqwXl4AevGbxumumumbgggY9mdnGR7zt9qmXiIV.CxlMa2gCG1wyyaafsa1r4V850ayjjtLtJ6mXWwrqKzMJoO8a6s81F7l90dSCocZe64bf1LGPaq.DC6csV.HmGjIbu6iohd7r.y+4+7e9Yttq65xMyLyL7k+e5kuy87GcOau6t61cxImLcQxAImeKse7XaFPGpSOtxYZ9q29Kid978+8+8m6i8w9XohA4THXJpSA.we1e1eVuW5K8ktcQnc8u1BE29qLF6IVcAggmPXLGE63qrX4Td+fffcdq+R+Ra7K9ley0UJUUbILXkfHkR0FXGvsCD0407ZdMa867m96rIUYSfseKuk2xt28ce2i1j2k3d0+V6wesCt9SVf7KAEZjpSVKkL2RiTsvZwdPyc8gNU1SneuTUCLsphyfEgIGAKBSNNvSaiM13HyN6rykH3zwX62rKV8UXi2+6+8u1cdm2YMJSELbQhPCz.717c7NticdGug2vtUcwFrz9QdwSD5us+JllfnzzJgact.gvXL8Edhswgsr1239P+3AOdiPXxRvRM1CgI2niiy0OXv.+rYyNqVqyBLz.c8UpNZsdaiwz9BW3B0N5QOZfRop3BgQPifff0T2jZCLrq095IFjCAc+4fNs8oKUthhN+3e+fGo1gjFD59PTmmUWj.nOKwtTfcIHs56yAzNaIHSMqx3l9L2AaEPmQJDK7vc5rzDSLQpXupLFiDvc6s2dwIlXhCEEEMIPtkVZImFMZHDBgvyyCcnElJoIS1.FgfgXHVJkwIZVQZ7GCSQUowXFGQD8AQOr5WReee+gAAAFgP3HkRwG8i9QM2xsbKCEBQWfcMX5pjp9AAACTJ0PsVmdtbhINivXcUjwR7n8qnvz2W52Sq0cMFSrRoPWohyI88IZrJzVoREgPHxoTpbewu3WL2MbC2Pdg0156XLlFe9O+m+B2xsbKOLVzE0bYXmUsissOyVlLrZZLDKHfVHgbZWlgHNJ1XIuQsVeboT5q05oAQrwX5JDiDR7MAZlJ5qUz5Z9Vp.zBaL.6VoRkdYxjoeud85t7xKONJStZQ243HLI0QISEN8zM.MAVQFtmPH1BFoIBoHa3RMGxXqiJmGzKWBNQj8Z9ogUiFlH4uK84+.qV5vtJkZqfffVJkplVq0RoTiEIOMgQBnc506iUgT9waswWaIkd+SCLW0pUmy008PIH8oiPHVm8tWbPQec74JyCxBFSvjRg3PgdrDgrLvSKHH33.OYoT5p05oLV66tuTJ2Uq0I53mXcvJ5+RorM1m8MvpLMZrYAaiiCcGSbsuT6Y7wJRC9ZE++iFT5co9YGRPDVS65JoyyB6slb9xkKOYpvnxdq6msRkJ4MNNEbLlBe3O7GN+q809ZyPIbvgrTkrFioPPP3j99xC4By9Y9J+SyMyzyLCvjRoLWPPfC.m12O9LAAwhDG.Sq08jR4.RSbJzsToRac1yd10u0a8VSG+0N40ltvlIweuKyQWxy.pmF+eQGntXQvZtU1JoNZMTfbEAQ88bnugI2aRQ65zehOwmXpu2u2u2IRRPOLZLqEQ4IBBsc7nKcovHgNebzS+3QAk+a3sGugvjwypbLvv4gAMgAfbPIgX.0Y.Q6UMHrqk6ncIK01WVufK8faF63OfTgzIQPPkR4NDSea7YlLRoL+JAASFn0S8hdduvoHhooJSo05IAJjTYYG+awWfdeSlkyXLS.L8WrQiYa2t8rkJszLdddSIkxBI1HmiPHDAAA7.2+8O90uPHDYTJUAoTNoTolxyyap333ozZcdsVmc5om1wxA6w5gZ16dWB7GGfPzekffABgYHkvDDDfVqEUpTYTkyStOlRgmCpLxeypMdBNxbfW6Kq3eMNNGrOyHQgkjA9sVdY6FWOJcJsm3olHLTrkmm2FjZcu1EtqBn888CuYe+PkREoTmr1a6teqMEFQqibjkW6K+k+xq466uluueKflQtrlTJaqTpMuga3F1VHD6ZLlt+u8u+e+.qtxnbjRYtACFLgmm2zBgX9vvvRKt3hpCe3CeMAAAOYsVerfffigkiiWKv0TBNZD3AEKtDL2a5M8lll1TfQI8oswXLwsWjgzZeW2oOK6FVt7tFiYGVfsTIBdK1EGacy27Muwe5G7CtCvf64O5db.JL4jSlBE9IMFyDQP9G7AevbKA4IjbVDcsOM53f7a7p441USy.vG6i8wDfTHAgotwg56UosWxK4k..0KBz7w7j0Fmjt5RojhEsV92ccW2kPfP7K9leyFDhAAAAcCVIXGkRsK1jZ1GhLAAAN+NevemrTcu6I28ce2OdKYz+asqt13ymXNJXZ.Fvy7q+q+qCMvgF1DR9o9TepIfl4AxUw8pP+JlGVvtd09BvbyM2js1ZqQhv2G7C7AfwRXnwXbt8W9KGcEs4seGucSvJ14vIQ2Rt8a+1EmMLTPz23uY7sv1n07IwEwDBwfh12GBkLdBgwNy7nO+U5Xk9toQoR10xbI99tu6KFvTqVMFLXfAHtToRCDPufffdFqidXN5QOZFfBAAASFASDFFV3jJUdpSNZrWk0t3E+GM.wsWfXKRCtpaGL4bib4M1aCsS+xeYu7I0FS9G3Ad.QYnOMnCAiSOx1c.1tlKakTrnwSXFBgHSnqatIlXhBThBk2aSxSzqWuI5zoS9nnnQZgViFMDhD4vMbTxRhsiGLVZyfktuc0g5cA19y949bagfsDH1DAaXLl1JkZsDTgzxHDqoTx0.ZcJe+VZstoRoZHkxZAAAQ2xsbKQJkJRJkQRorpRpBqTohNwxLiR9bMtIorguzu4vgCaIkxVupehWUSkRUCHxHLgBiHrhtRUoTVSoT0BBBpYDhnUBBhpToRDkHpLT222ukRoZq05Me5O8m9VBKUb15AdfGX6UWc0suka4VRoAS+4sUsd+1Y5pihWpOzpOvP8QGgJfQ8AFgFGgHFLw9mVMTLpusoOvvJ5Jw.3mDKG6MNXfuu+fS64MX4kWNFNpw6q2MiHGgzp8Em0dSG8XoYGLVq7iXypBkRkpuRBvf+oTFf3JUpLxwb9TepO0tV6ideIm4RUDrmnud5kJN0Xf3xkKmJdvidO8O5JXb5IycnyJDhbgEo.ginbUA.KkxsqSfHA8Ju3uuuuA.CrhObrA.oTl4E9BedYpToRtjDHOpuwh.m6Rzu61tsa6q2mGWo3+ez7L+fErae6eDXPyicLq6L4RmDMLIU+O1Dnc0pUSKdbsUVYk5XS6v5999adKR4NJkZ2W6q8stSIX6fyFrEUYyJUprgPHZ66KWGn0+w23an4S65dZMUJUKf0DBQaGGms7882YEstmuRMTJkTVHxHkxBBg3PZs9v.kjRopVsZKKkxqMHH3Xc618o7W7W7W7T.ttRvSNBtFvyuD3RaVf5LCiDI15N0qWmlEwPy8E6e59a1t9RKsc0pU2AO18niogKIW+a+7e9O+NRoHNoXMyXLlE.JZLlRZstn+oeFEA2EKAySDGlKvLXWe5flAw2NLN8J1d7VBSRaoPAKQ78nOn6W2p518.2tBgnCEsYa200EhFE3wA231kqMdBS1FXi+xO4e4F.aIUxtAVgkKCvDJkZZLlY+c+CeOGF3v+Q+Q2ybRobFsVOYwzNMg6kYRf735NgPTdJfCUZokl4e4e4eYVfo0Z8jAZcdiwjMLLL6gO7gyBj8E7BdAYUJkky7kHuwXJ77999tlPHDS827272LIvjNNNoZuQls2daQZ.1IWnFgQbvjFDu1ZqY1XiMPoTBpQFgc.qyO2q+myAPbwKdQQ5m+i+w+3VgE0ae7k6alUEOcxxTzDYecrGQhStZFHd4Sbh0oHFvEnesDNAVdOtAlJfRoSfzFOViRzjxTupMK60fZ0eSukew5.Me1O6+WaMyLyrlVqWuDztRkJsCVInstht8a8s9V2vXLaR4xaoTmZqgCGtkTJ24+9e8ecGgPzKIQewAAANRobBfYmbxIW.njmmmBqNZ7jZ1r4w9IdMu5qqF7T.tVn9QaXqbyBPQ6DldjaNax0Lzb+KByQOZbpi6P0p8DBQWZQmfjq0R6k3jV+nuxW45CFLXariGxfqs5tFiovpqtZNfrm3DmHacqs2kHrXVwHzm80u+aFBcZxwP6nsIXzA7DFiwj3xH1mw0ezUkC096qXLFgQJk.Hr6K.wO4O0OovlCRhsUGSzUoTcsPcm9pSoFFFFZGaUGmfff8bQnxkIw0dN3q+s1iua19aGGCtvEF86Bcdiuw2XBxs7lDX5+pO8mdpx1M2linqBGjZMnkMnXS4w1D1lat49N+u963Nra33jJG.mOvG3C3fwHj9Rty67NA.oTJvEyRPrTJG5448D4J8r+4s8YHyy.Jxv5iFeFYpN5iaGmdUdbioVM67DQDe5Se5zMlXpUaDMBhEBwPkRYojyEtfQHDYTmRkWoTSn05I777JrmSB3l4BW3BBbwbSG4H1uystpGiePjPlZivoZLwzTlYnLyfGG58+gd+SAj+FuwaTTMgFwIhZXZE9rabOZz5Y1MdJkjP0h7DEM4Fas0zCzCloJL6fAClse+9yjOe9oVbwEKHkxrV36mbOczc18.IkALHRCH25hPBDaKfMdVOymYaLrtwXVGShy035ttTdxVAAAqILl0vZiosNaPPyXKZcpQRwILFS0W5K8kVshtR0fffna3Vtgp92reDknFI5rlRoZWKopqG8n25Z.M+K9G+Kpo0ZsRotfuzeUkRUQXDVzJ35FoTmJRoT0LFSMee+FTiVUSPvoKrSbbbmfffN.c0Zc2m9S+o24ZtlqoKkoKdzkEn+Z6GsPOhMiQZUVuv9JtjArNFiTJMjPEpfUBLRkxnTJyIkV5W6K8yBjWq0ERzHMGVhXJReTzSOJNkKLH7qWjVnGQGmL850KMtKmDz0rWrCyggkuJNdKhvKsubUD+cel+NgTJGQiUiw3DDDjHtxHRn4g39tu6SbRqSWE+xe4u7XoTlduyIJJJCKRF7IKdeKTb4+VWy.D6tWQ8Fh0UkrH3xXfhXmhad3bWoq+EfhI2mSJhUdfB0pUq.VGzzQYc1w3jywf28648L.nuVq6+pdUup9FiYHP76487GJRP39OTfOF...B.IQTPTQ1fffLTFGjHZdYhi6du268aDquboh+OKG+wDUOtTwZY6We9yammLhdIVibuimp+X6O1+MN0oN0FTjsnD6fK6TE1Uq06BQ6VyJqA6di23MusiiyVewyctMJCqq050dgO+WXyfffTcgrwO2uvuPyj4+ZO2ryt0G+i+w24G4G4GoyJAAcAFXFNznTpLtttEvhh84vgkjRoW974O5y3jOimrwXttybwK9z.dZP30UyVDUenXIfCyhLsGjuXwhYrVV0XE+d4k6lX9E6PiF6Vtb4NDR2Kjb8O+9u12NLjNqu45lG5gdn76ryNGxXLGV3JVLWtbKclyb+KdQ8YW79CCW.3vf6r.SSQlbtwry3GCOydBW6wqWX6q5KIPpJkCW4nH4RrS3I.27FSUiPH5fKaPDahMCZWInwZO9GibbdlF6TTWCVHRe8AAgWqPXJlb95M0DSr416taKee+0.ZWQWYMeo+5.spTIbMeeu1ehOwmXim+O7yemDn5lEKVYK6BW6e3G6iccOum+y+XQQQGQJkKp05IRt1heKuk2Ru69tu6sDBQq4latlqu95anTmpePvJYSRJybe0G9gO7y947bR0zhTXi4n05z7wmt.deAzUZEVrcwpDzaEAs0AAsMPaivrFBZwPZ3eZ+ZDQcrnMXKRppBbr9IZBx2LnQv3UOXbpWkNfanci9QCONDetGoKOb09cY791IIQyKGDlc0UWMyxKurCGkXtvn.fD.h+9+9+dwy849C4.Zmj9cSVDNzWHHXFkRMCvjX897L1uKkDPMK8tJwbAmM3vJkZ5+a+29yycpScyoIQHGIY7WHDoAR4XLFqf0Aj3zK8sTz5TCz5y1Oe9786zq2NyMyLaLyLyrNVTvzjhzh5oOy71EBsPedYhSjRgzyY1DOYOAZrKL.ZYXNxPaJDFFNkmm2gv55RSuwFaL8ryN6D0qWOWwhEEtP+HX2DekeOZBsH6lP8kgvBwPKaeDWLDsOwg5qmDELZCEEg702S3CmVHEERRPYbBkXRoKzNDNhpPWJzQYWf1kBDw7TjiRcNtwXdFZs9FvBa+4MFQdgvDKPzKFylNBQi333J999W.H.WZjfzj9PYSPvYF99e+u+Nuwey23VDNRLe2Fnywgdm6IVzi3es1FmlDB1acm7JXxe1246bh63NtiIIA0UBgHypqtZ+kW9l2BpagP6xrKqdIqTZBLrmu.r1T3xBDwQnDOMSj4FzZ80KkxijTwobfv.l9.ckR4NgZc62+8bO0uy67NqpqToh7z9ADQXPPPjRcx0gZ107VfNz5IzT.KYL+wxBmOuKLYDbHWXpHHa2tc6WnPgcviMsZzwkkRNoGqrKBSzzR+tqAJeBiI7YDFFdBOOuiNXvfYqUqVljiyNJkZmfffcUpS0IHXksDHZJUxnfff.K8AtXs3XwZ9mxeKpkRYgk5AMrtmzwoKm6JNVe79CN3SVprOgseBr8ulRHDS4ASbg98Anatb41.65zswmcSn9yAcshwfpOSCb3xfaUPZLFegv0e3vPYrI1KalrkzZ8gkR4TiSuWK0m.ozZWvwVHIb.D4Z5nT96BraPftCXFXEy0JCEFQOkRsaPPv1.6JLl9FgXPx61DNWldTkQTtAaADhSnqyt1DRWq+89mbuba+GtsrkgrUsZtfiuueFqilgYvfA8Wd4asCDYQnPYrHPBDZ8EQJOBtfHBbBBBbRJVTgfffoFLXvgV9VWdpTpcCfwX1MLLrtTdxKBQWnDTqlciTi2O6fHnLcSeSfGGNgRDG2XL2.vSSq0JgPLMFi.DcMX1EAaKLrEBwVRobCsVutTJqCT0RQkSWEBWKYc2dKZQY83Pp+Qy5poEPLG3OIT4P3xrlploAlPHDYvVPfNITxoML+FvZoBt5kiRNITLr7BP0qAK0Ve5IWydEKVbxZ0ZXr5isMwdBD8kJ4NejOxGYia8Vu0ZpSpzTiJjHpxPZr6t6BQ6LOzYs8SKmuYh34uU0R6ujCISglCCLehdwky9bnTan9ZyCsWaTL46g9b169edvcJH5PFiYdQYgzT07jDBwS0XLOkf.suRImWq04EBQLF5Zvrs5TpMCVIXCgvrgwHZmLu2NXst6lP4.n5E8fpgv5GG5LFkbRuFRaOZiKO8u+RE+O.CA+APkwel+0yZXWNDOev8gLFcPWr.zbpG5gdnIu9q+5m1ElXkffb+C+C+CYdYurWlfxPx7LYfRYgZ1DcWhY0eA8zRobpfff7FiofiiiUJILlbdJUVgP37U9JeEw0ccWWdiwTH4YSlDmlZXR7+wFS0AgAg8MBytYylcqBEJr9q9U+i17C9A+v0oD0oFMvZWvaAUsnJzmdV8LYz8KmkAmUGcstrAV0vxHXUbtsa61xcu268NA13+SQUxR.yHDhBk.SjwzqrPzIJI4JFiYagqX629c9127Nuy6bKnztPM67uGm9btKonv9sMsGOivjQYHrIEGOC3CntWJx.5rDQ8DBwPiwHHhL3dUAqK6w97DicxncvtIm0.V2wIQSOrkWN6Nc5Lguu+LUpTY9fff4EFwbI1IaAgHNyC9fOn34+e74KnAYXIK+iMFyg.lKBN7q8W3WXtNc5LiTJmJTqyasCXQNgQj6tu66NuvxW6ot1q8ZmQHDyVlnYykK2L5.8TRobhm8y44juUqVYa0pUFcn1Qq0iPWBhDgpzpL8CM1AcCQXWb8r5JwUBBD+M+s+sBkRI7k9wmxyefu+o5u58s53UpZrWm+aFc5G+YwAgZovXLNUpTwAHyBVq0M64djzzAtzOOuTsKAT8BiAhuIKDWGOYIoACN349bet8KhtKP2lISRTmhaoTpMHoBWW7LWbzlh05ufUI6sPhdSgPrdPPPqSd5S2DntRopqTplFiYsye9yuNvFFiYyfffMEBw1+W+C9utCvtBqkEGKkxrtDMcbb7Bc61srvXN5gNzgtViwrGccp6dTrnMYdOBmhDgAjUQjTYHCfIHHv7Ez5QWaPqA.C7ZSOX9TcTI85ZqSdxS1cvfAbCEKlyXL4iRRPYjEYI6UEflkyk.i6r9zJKP1G9ge3rDQVJNJayeCKiy0o3nqIgPLznMiDowxDYSPiDCgWcKd6FAvhwT2lbmd85M.XvgO7gGBDKDlgRobnwXFHf9FKu7GoV3UNSEiRoLZs1ThpCUJ0fCcnCMfPFhKwUpTYz3my8soKb7sYs8sQ1ikz+Mg2y4BnT963NtiT9+OsPTdFfYNyYNyTtT2RONINWFcecr9+qAfv1+qLTChiiIAYSi97BLBL1x5q0Zy6+d9fla+1uczZMRe+T2+vnTpXOpYg29bziVOBGY5Ij86NNm2.oVqINUSl+oPgBVDjFV9pEp9ll6aMfpDGa2y0fACHa1rBCFgHIX4fffbJkJaPvJY.bLLZMojjZeD788gZkFcBVhF1evhc8ql44RBPe4LTYequMRy19E+4+4KXLl7gPlrYyZxkSZSHSplfT43WtmwB7FoAJE.ltJLqwXVPHDEgZkbbbJlMS1E50q2LXXBcndDcbDBgnb4xHDfNPa777LBv74+7mYDhJDBQegvoqVq6DDn6HDlcUJ0NThc7k96.rSPPv1IV96FRe+MTV66schtOsdvYBVSq0sRbpu5XEWwZ9990CBBZUlZqAz919ObaaBr4YBB1Rq0a+q7q7qtEvFNNNsUJUqb4xUuLQQ.grDgTkpqt5p0.ZHDYaBzLJQfCUJUaoTtMIBMZtb457k9u+k57E+hewt.89pe0uZWfNwwwcJkj.lZWZsB4RWI6E.BQPhC6FDFXBBCiASrUYXQXvjU.4EFJDaDSlrwpoINdZsVOUkJUxKkxLdDJ.HoPGzjEGuuyi10RGCULUFBD6lHnnVffX+LITAIFHdAV6pFsTtT0N9vEm6+9u+LgggYjRYl50qmUHLYEPtDjSm2fofVqm3Vu0acBfI+vu6O7D.EpToRNfbetO2mKKPNIQ4Axu1nMRe7Gsw9838lAV1f9.nBNoULAt.qckOFw.w9DYo3UQQLQXDtBwK4k7RbzgZgRISEp4gFiI1fIVHDwAqDD+Jek+DwlXwPkRECLToTCEBQesV2uDUGBXRzMIw4rBW+A99+Hhu9wRaew+aLFGfL2na+CZesWtWWMsKOxS1O88SnXGckzrKPmomd5cA1IhxaqTpcdNOmmiU3pOSvljfF8vvyX0ZjxzlZrVrH15zPJUCee+5RorQBsDWWHDsA1X5omdyf.8VIInbKiwrkPH1QHDckRIkHJmNPeHfExlMqWoRktl4latmxexexe1SsQqFOUpU9ofs.+ROptHvLThInB4X4QIdxPpH3N5ZaUa7AqxP3XCu268dS0ojT5IsoPHRAafoFjse+94qZLoTD8PZsdZp4N4zSOcAfBRpYAwvbjiyQVN9WGBS8S.ZOdMgIGnUOEtZwEggPXeTzCJ1qAz2XrdNHPVhVLYCaG6QIsbb2DXcgn7ZdddskR4NHneRvr4CBBNjuu+bB3vRobV0oTSBkypTJNwINQbxbbNznTBDqbmEbWvXLK749a+TyO8zSOiVqmvXgJWVozKCBSVgPjyXE.1oWYkUlQJuo49BUqN6fAClVpjSHDhb.YlbxIc51sqivPp2IjLnWXGzaXHNio1+Va8quT5OP.Cd523SePkJU5o05N0fcfZamMa1T9N2ElebKq6azUC+fvP1ArVi0nOfHIXTjNs1e.jozr5fIb4fG2K2y5wmjbH9LzBy1KL7q7U9JisX0xIUv4X8qumdtjjXt56TsZ0s.13AdfGX8ibjirtVqaewKFtwccW20F.aDrRv5AAAMMFSjwXBEFQfRcx..sVqCcbbhDBQ82va7MzToTqUtb40CBBV+YcKOq0TJ0ZZsdciwroVq6T0JjwYSrpr4BCCKFDDnjR40XLlq8y72+m9jnLGAJ4FZqd5zIhfZVV0KYL87Fk5YMzJtTyODuQOaGDBCf05snMgPIHGoz1+y+y+ycyjIiopwj8FuwarPBRnlTXs1xIMFSBEzpVv8lbyCKkqRRRTdROomjMYJ0OZBzJ8y.Geb8D5wZSXioFiwXFlnoACnHwBQoTX5KXerS6J1LQfAZF6krXR9746Czuc61ifXsVqGfvtPiPv.kREGDDfKjVkSGo7TjDTcuG3Ad.6FZhVpmuueeX9gf++FpR1q80afOeytk7c4XNm+fTDzhfrBXQ3zgfnYnDGpToRSFkpb85uFWK91++2467cJh.GnpSoj40rI.OTj90vSJ22cla+1+gPoT.HbSSdbYDPQBSWGn8i.YWOgsct8ViHKP9jJulyXLYVxduSf6nO9Uw0pzf2XqCDCQ0qiVqEBDYLFxBh7BnPPfNUenb.v22eeaNnDXfZFJhA7LmyRqmTw16J0NvZVq5L+XEDHYyBNP4L+J+Z+ZowkM3i7Q9Hcg5o7tuqcNqycoSVB3P3BinM1W9K+kmlDw81XLKZLlE0Z8B.yznQiIkJYdLjAydwAVsZUKu.DX0eCX3MeymdOMwxX5ZLltFLcA5XL13GBNaP2.cPmjumao05199mdcfV+r+rugVBgXsW7K9EuVhXe1JVHZVoRkFXo7ZzEuXXTXXXck5laV0RUzMpToxF.abwKdw1Rob865t94WqDzxyyqwpqtZco7T0qZWbnAMnIPykW9VVCXcOuS0F7RDNQ213wFUpTYSJwlqToxlRobi4WZ9Mtga3F1vXLar7xKuIvl9927101SvSuRim1+89VvMdq2nCgkc.bTdJgxyynT9wBaAsb.xIOopfAlRHLS6ZQAzTFDELFSVKcYJIB2OJBxBMG4PdGe+TN+peSiGOYdhkXXj0tmMk2qeyXND2hlVWEIJw917hzjZRDYld5oyXL3jP2mL.4jJUtf.c9O8m4SW.qkgWPJk4MFS9986lOHHHuuueNnryy7Y9LyDDDjQCY+y+y+yGqvKm6wSqS7Mn1pl41+F2SJHjG0uZJ1KXfiMrR5FhaXK7oIxD+S8S8SEK8jF.dkuxWoIs3pJkxR2XCh2y642kS4qHHHHVWQO.b6Qbbm333NeAsNEQUX+Nb9C9c4fIe3q6lPrrwtEtiJdfnnCpKdWMIO4qYxyuBuFqnoKOHgFbcN5QOZha5Tcyvvv1JkZihvFJkZiKdwKtATbCOuirAP6Je9JqWoRkVPlZAAAQZsV+W9W9WpAKRPylMaMsV2HHHn45qudKvzTq0MOyYNScf5I+eqGFFtUjwzCAwdJurCFLXptc6NuVqcAN5hyu30ZLgO4OvG7C7jgRKGBJfhTiCCLEqtvizLPtjuN+viumdmrKnRDB3har1ZqsswX5YLl374ymILLLGvDCFLXxSoTS.QE9I+e+mLG3lUC49nezOZNZmDqz47+1Z2y4w6ILIoCse77I7LtlwLjxLnT.8g58wiA+m+O+KaXDTTalPcmyO9FsuBGaFBy2EhRPYRz5.sa2t8V.cEBAlD01Wq0yznYyCEFFLk9LUxCUcnDfEAL.jUq+BIpecsCaLUW7du26cwolZxCCbHoTV.Hmqq0F27jxLFa0yxq05IkR4rUzeg4FNb3rRob5G5gdnBZsN6YO6WHSgBEbLFiXwkVJov.FK+XELzXD1M6aXfvH5qTpdBgnqPX5lvQ2cWZwk11wwYSiwrwu+u+ueafMUpmQhZjWtOr12rT73wG3rOwcZuLWWLs5GlDQDKCP1uzW5Kkrno2ACT3fG2C96tTM6jGU1i2nW208bS94iMDVM4mO+n++icrikLgxQ67zKWdGfMuwa7F2.WZ+Ljx1G4Hds+C+3+gaBW6FJkZckR0PoTU888qnTpJPs.fPoTFcSRYsicriU+0+5d8M.ZkISllJ0oZ7LdFOiFu1W6qqtTJqKDhVJkZCsVusTJ6Ttb4AXikIWi5MRgMW4icsWqudE8QfZRJhKrzhXCLdxD60V.qMDtPRlkWq+wB22FpFBzuIGKIgPk2EpsK196C2Ymcb9j+kexItoa5lNDVQfZtxvbBgX15vLTjoVJjBPi7nHGrvXat7BIuWIKbtGqYa9.+MEw2N9eP8DM1oXcLPcmtc6lAJ8XQuahCo3.fdkK+T5JkxdFiYO+l2vvScpSMDHVJOIAAAYTJU9ypqT.Wl3TJkc7uKFvsewhGNYyBMRDvt0FrWe5+US6pIfluVAA8s513yc3.mejPFWrXQmeyeyeyLu829aOSIa+6Idmuy24gnDGxDYl5Ftgan.tjEJ+0L47KmHHn2wcbGomKQMJ63333HkRgT5kfzDCinZoEEIFPDqCBLwwwhUBBbTmTkwsJYd6u82nkq4rzk5d2SD62MdejbkJUp.vjP4IKKDEDBQtF1pYmfRrq1qQMdgi8Oc.wd1LrCBQVvT3qt5pELXxCjY0UW0XEaVFpTmdXplyb+gWzPYhKU2hXw3333wRL5UyFMGcdakTQUPlIwgBx.UEXq3e+xkK24c8tdWaCxsWxtI9dg6GZ7G7XlAZkPU4vodpO0m5gvttvbwwwyOb3v4kR4rZsdZoTle2c20FXaROGiI1DaRhqvXQymPH5afdXrUhz.aIDrMF1Qojc.5d1yd+8DBSWr1I7VBgosTJWChZ9G+G+G232323cz7teyu4lezO5GsoEx+zv2yqou+ISbEFuVG4HdM877ZBgqAzVq0ab5Se5M.Z+Lele+qsDzz22uQMnNtT6Ys7x0gpMYIVCjoH9bSn1F1eNbyKdw6KoX.QasTHa466uo9KnamPs5FJ0IqKDhFBgWSfVBgXciIL0YR5svBKbUN+8wAv4AtuGvAplAKcoSSDg8Yjw97I3rAIh6qovJAA4EBQFDvdZnSszyoimmWBEAJl2KAsmmi8UE2qll86+4Rh+owdwADZLlpUqB3N17Gesy72ds0fhHfRhs2dat+6+9QJ8DMZTOUqTxZ0kES9uquyuqb.Yuu66+giVqc788c928c7cjbcTx.US0YiX.yOvOvOfXokVJYchk+1tMdAHrdaH.EGg1GiIbz05byM2k4O09QgyGiaJE1GIT1CKUrnIg1Z7ddOuGG.GoTlIHHHCFx9pd0upr.YhrO2Q5exgUpbldRe+N999cjRYOrhQaxbw9eiZ84KShWJFCWHYNzKjtOgrI8+ylT3szhnNNhyublMvi1umik3jUG4PLyM2bIILYwMNsmWaf0qCswkMt4ibjMg5agGaCE2122eCee+V9ddMREv5WvK3ETEnpRohh62utTJqqTp5m3DmngRopKkmr5Mey2bnwXzRoLToT0MFyZZsdi4O7763551AXXylsbFNX3jZsd10We8kBCC89NetO2ir81O7x3xQgx9.t.K.sND1h6LdwJujIH5bin4mWOHHo3o02XgEVHU7vG.HjxSl6m809yNQ1rYm5AazXRJQduZjChxgjbu3W7qNUaMyAUNnHv9sUsGumvD.LbrJwoh+pPH5SUFTKklAgD++0a8MyEu3EsAJTlD2qYtQPMkuFah10lrfNddrIv5wwwsmat4RsZWiPXCV100cxEWbwILFQAoue9OzG5CkiZjyk5o7PdB4ojSALK3dXgPL+N6ryBSM0TyALkNvxU3nnnwl.xjQrmMBN887+8e7r.yFDDbnie7iOoTJKbxSdSYcbbxf.QiFM.vHkJzZcr.wPgvLPHD8A5ZbL8BBB5XLz4E8hdw6BrcBcRVWJkqoTp09w9w9wZCrIzXGftyQ0KU0T9FMBSF2xHSpXx4y.HRgfnTZ+bEsz5H6MbC2fsRuKDl8b6exxK0jlWoMMueTlLhhJQIIIZDEjFOgQCO+4OuEpddWna8DG0AXChX85vZEKVrMQz1k+k1XqJVcJm5iCtAXsh5p.Q+iO7CGoqniTJUDPsvJUpGDrRMoTF8g9+7cU8AevGLJLrRsu7W9KW+TmR0LHHX8UVYk1JkZKfcK4VZPPXXlO7G9iNkPHNrmmWwd854Qc7JSCOr5vybTjIs2acSuV6Cz+76+ZKMKyC.5OOU6Az0yyxW3O9G+iIdM+zulB0pU6P.y+UuvWcgp1j0LuVqmk5bnFopjc.405uzdB95RjYkUVICL+2HD9U6eew5TYbg1Kj35.FiIagBExIsH.vlfziuuENOX6.KZTe.P2G7A+bcA5JDl9kJUx12PP7JqrRL.A5UxbO2y8juhVOgT5OEQL4YshzWVh.OhF7K+K+qZS9ToTnyu7AC19aGy39USRQFerY5b.i+5xk7juU1D.briYe+c8tdWB.pWut40+5e8w2467Nol0sZx8FdCug7lHSAf7yM2bYHBgGUubaVdT+sUOvFpeGui2gHkHzgZMarwFIeQD1DmHre9fffgBQbbrwX7884dtmOnSvYCxEAEty67NKPUx6QiwCR4IppUe52WmEWzVc5DwKbBiIbhpFSp3xIHBwiBCAR.jV0dbbbvyyiKbgUwfU7bEXx.j8Ztlky5VpTF.wy9Y+rikR4fO6m8y16+uO2GdvYrtN2v+k+kvXpRbZ7GkKWdv7ITcfGkUbMUbLKi1pUSEsA4VrHwFioWXX3NelOymYaT5sarmVFb430uc7UQKcbLFyTX4l9LBgXVGGmYxjIygRJLSdfrsVaMGvPFmLi5mJfXqytv.iwz2XL8DI5tgwVc01FCq+S9S+S2NHHXKfseAm7YryC+vqtsPH1.XMozuYkJ5F.M9g+g+ga.T+s7K8KUqRkJ0vhHj5.MNyY9Ds.VuDgqCrtTZsSSkhMjR4lQQQVQX2swZMR0uKnAQzRmpmKMXKOz64ZPtot.wQ24HG4Hi98MRnWqTJScDu5PsHfHiIrlPHZVtLsEBw1ttV84pUqVWMzNPr73EFnDoTKvQq0YDVqS1QJkYvPFoTlEHq.QNiP3.wlSpT8iii635RGfdEKxvKbgKPXXnnRkJNW3BmIS33w8btG0vd+xUQcS4xkwkHGKcrrGuEV3ppfC1VcLtTKd5omN9E8hdQw5PswHbDEKVLCPVK8yswFHLBwsdq2Bic9GpTpgUprx.fdKsDc8886VrH89q9q9qFznQCa+7kWce2uuJtdeBRqsCfXIpKLFiimUjac77rOGZ2t8UJVF6y9HDtV6Qy9LoL7zuwaDkR4n05QIZvhpcaeueueueuL.NUzmUXLFitxYFHDhtttryK9E9h2IINvg1jogCtU95cyuWt3CD.3ZYP.RocNLWWq30FFFZK9cwyk5fXoZ8zdBDKj83ORG14xcO6J0Ferw.fdsa215fpKzbKcBkUvF++lQo54WH6Tj5ohF65TJ0oMKFYeOQXqcbBCBBphcQ+pZsNrh9rZoTV4z99U.Bz5JgBgH5u+u+uq1TSOUiyd1yt1K6k8xZKDwamIalt.wau81YAlpToxGtc61kzmUqd3G9y5iEoIt3x7.GBVXLmz7QbMl9yw.CONgC.5tzR183TpDa8JdEuhNBgX.fCkhJ7ybm+LSALc2tcOD0X5vTaIWSg1s+J6smtilFi2xeaIJSd7bBS1qC74saTxG5iG8b2SM36CLrAvQNxQx7a8a8aUvqJSBEm.ZOtPhdEOOQ1Mh0MzJjbalISlMhii2JopylDHqlc6s2NGPNAhrAAA4eYurW1j.SGAyf80gt6eh6dFvcVio5bFiYtu6u6u6YzZ8TUqVMOBx555tmpgis1BdVUpOePX3ju7+Su7ojR4gjR4gLFyzAAASPBruEFRsRXgVqMXQePr0hvLCvX5SBDYUJY2+e+y+y67J9geEa4BsEBgMXCW2VX2b+lIBCa21ORQB7aTIKYrIH8sbq12OcxuTKoJW8DHvq01IDqWmBFa.x4gh4nkcRxhPN7I6w1axwC5NKWoMMbv.FRQTykxQfF+yMjPK2F8fcoHaOucxxMqWu9F.adVs1NQJrdopzBba.QMAZVoRkl+m+4+4q+jdROyHourZkvvHrvPttwXpCTOBpehSbh5dd90e8u9WesUVInluueciwzPq0MTJUKoT11A14Vel2bbhiJLS974W5bm6KIeNuzWpBqtpWxqNG11+OJC6O3nCFj8nFOCjXB..f.PRDEDUDHkjLxdgVASse1r4M+5+5+54pToxzAAAGtPtBK9vO7CujKr3t6tqMoI1fwmBKcxR2LiypmYUwoN0KjT8Z3QaGF1+BdBvWjp.3RHlEIdI.iwjIAp9406sHZlictqpInGk3ra9lu4dKszR8.5YLh90pYqvWRUVs8cMj81u8aOuCLItLYYK8jxmHXd7hd0u5QIyQViAf2PX0wE9yCtvwSzW.4JkjjLVQ6bOJsrLjE7sIUdwEGe7eZ.PGL4IomiuU0L.b9yaGS75dcutwbAik5SUKbmKKDwFiQ7a7N9MDBgfO8m9SCfI7pJIyG2NdaQqyI8FdCug8cxmc1YAf64CbOlj41SFyJhMFQriiiwE3Ne6+rNInQXjMzFt28xrG6I1U1Q.HZ1bbmiobVfrCFLHimkJRNEu5t9FMdqLHvJtDi9cKezkQXGeK7RVSM1fSsZ0D.we1O6mcPPPPum0y5E2+n9GsuRoFTBF9b9At0AE2iW38.5s1d5.1URTJubvAWTMYbSsZlLFiQTuNCSzzpcgx6PfcizWgywnwfx5jyXLEDBwTXiIYtM1XiYC0gGJvlrjBZsNqNP6HrfevLLdXR+cmw0xrQV+pR4uCvVNvFZsd8SoTq8e4c8tVCXMivr9YBBV647bdNsN6YNaiSoT0fRQ21s8RqAzH7hgM8fFR4Ia3662.orA1DVr9oO8o2DXqZVAxdGs15XCAAX0ELXGn71DYWucNXSVfMKZ2bxVIeltgiGOXD8fk6AWH0EL54C8.WqqPXqdZpd00hDjk.rY0pVgDNJ5phZaitmu5dICNSwZ3HDtiRRRPPPVsVmMTGlAAYBCCc.xX.6rIFwfZPWee+NqrRPWf90qS7QO5QM.Fe++WLI+LW3BW.VjqJCr4xzLXcLkXiwLLQ6+FFAC2d6sMUS32ZqVW0GuXfgQI8WxkKWeLLnbI2XqCTYbzA5LfktQtdtB0oNE.nqnMIz.J122e.vfFMrz9pdc5887878jzWedX0usacyQ8W.x1.q6Up0FG.Rbya.LvwtbGiQq4FMVrutUw4K8k9RYhiiGKIBVMfRq04RSfkvHbHNV366aj99CUJUuUVIn6G8i+Q6DFROsVOv5TndPzWWa5c7mciecOJ98nj3DRh+ehnHlvX0MiI.2IntULrcgonLSIIsn3108N2Xw+wd6A3qSjlLpXicokUuLWF1Ae10iws0c5txEuXGRbZUYMVGTqA0aBzTq0M7fZR4yHRoTUCBBh.pYLlHeorJP0njhpJk9UkRY0+ceGemiRrxG7C9AqUsZs520ccWMaznQ6+p+5+psLFSuLYx3Hkxo+LelO8BRorbkJUj.dtQTBJeXn0zjRW3G48g8sFTJRSZz.qE0Wiseeuu22tFiYnwXxPMlvwwYZsVOqqq6rkSPsH12mdt4laxjyUV8mSmElKCr5AQ9y2Vzd7bBSfCrA2J1pK2OZb6bKIHBiwvq6085xrRXXVndNV5Rp8EWti+PVf9rmlNrAVd3lpT3rxJq3LyLy3XDhLRkLmRoJXLloAlKgWvK.kW3s7VdKK.QGVTVb3yctyMagBSLsTJmnb4x4jRoSlLYF88ILLTHkRgVG3.jQ44kCXh333I0Z8z.SIkxQ9csmT5Dn0BoTRxl4.fDKPdnToFQGmffftFnyuz+G+R6DAa9c889c0NHHX8xQQibviSdRUWrIh5aAbeuh.HylOzCk8bm6b4AxWpj023WzNQ3zX2D9gvZyVSCLETeJPNIvT0gIoBSb98uYqwd4NpZFbkQZR7XuevDEcoSZhU6O5Qc5t1dSVtKvtRobGfcpVs510Rf.LtjvI5S29W9W8WcsjDnz3TddMf++4t283srqp5786Xsecdepyi8q0ZWmxj5vqBBwTkoCH1FDtn2lV4J4izeBzsbArwGMh5MRLpejqWaaEaBgtQA61VaZ.oAe8I1pbSatwOnHn.ITU.kTHzURnN0d8ZuOuesO6Wqw8Oly09rOmppjpxCZpdVeVe1m5r2m0dMmq4ZLGyeiw32OZVsVskO4IqsLvJEgUohw4s669tuU777Z566Gelu3Yh9Tep+xHnbruuey29O1ae8i5czc9bO3msmkaalrToJy8C7C7CXMXVoRHLGzbJJyHrOyi+D0+ziYHM0AjC0q809Z0ie7imUDYbOOui35dSy8Q+nezhwPwie7iOuqq6rXLXNAvHV8a2Ab0icrik.QIL6SKv2F5dV8A0Wc.nrBrrI3roaJee0yB2Lm6JyHsBjTrH89BeguP2RPmfvfAR9rqqqJhHhiHepO0ekim2Iy355ly00Mu+Y7yeZe+rv.vK0eqeqeKa+zMwTLEg1rzFGKg0cXf7tVtc4b.JETjBv4FgEXTfQgEF87vnP8QAF866a+aerm+y+4ONvXULYC3HyBEnLC+r62HINrKJhKbfMMtbWnRGfNw1MFeGui6nOPxO9+xe7CY+3xtUFENK.57qrx.6JUpLHp9J.A9A75e8udrx7oBjHRRhm2Mknplbmum2iV+KFfqqqSP8fbAAAEBBBFB74ExdtuwN18Lc6.fvYR.iHEHIa1rIgPBQnMu3O+k57XecFmHPfvAO6466KUccw0PJhDD3KHXF0sk92BKrfEvf3N0pcxdXHAzt+M+I+Mcadv.1jBVxUp5kL3d6zSa9bejOxGAJa5uhHpZj5yN.6AQ6AydEp7QSKVoWOOkXbU0ofxGYxImbZEcBAFwVO5YTAw0y0LGCLjDuojD6.rmpZKU0cA11lIIapJa355tdLrV+d8WyyyasZt0Vyyya050qu7McpapQLD+g9Pu63O6i8YWFX0pG8EuZHrFDuJvZECBrY2J6TqlAviEfNLyfwzTB82xgXQcr8+NaX27Ry8e+gApZnrD8783DoOCWrecnOD2WUsa0UW0D0XX2YmkcA1oHrKknspZO6ZMWp4VG9mG19mEv2JYZBNPCILLLiMB+YAxphlAHyM45t+Z.BpmmWuf506REFnXPb.6P9cgx8.5uvBKjvJnGHeKtJatF9rPEQRJWl9hH8qB82c2cuZWq9.QiGXuy9O7OrGPmFMh6hPRgBif3HBHhh5DEEkobrgP++GN2WK6oNUsr.NU.ghGXSpCcecMEp8znG+MMsCutoy7yamanZVJRFYeV3cn6EmCt36KomiAAl3c8tdW4fx4hgbufWvKHaRRRZFkjw00KCBYbccSmS53Vy04TmplDDDfuueBkouQ0+Li+ttt14igpMakGtO7Tou6.jo1Pja8zSa.7eNXLJaxFNLk.9DV++GGhGCpLNvDwvDZnNQfk6ev.bxH1iBvbWNIt8p459v98m5OP2yC8nNcCOns+NG8nGscTTTanRq.XGveaVv.xqq6MrdHrJzbYfkOkojDWwyya0JFe9WqDrNUHUELaVy0M5O5d+iBVc0UCdyu4+E9m7j+u6+q7q7qDN+7yG8leSu4U7771TDYu50qq29s+5yu7xKOgmm2LO3C9fEighPzb3lVh9UuJnlBRIW78.5zue+Dy3Y4w777lz00cp333ibZe+YvvchS666Oo89PdnbNybtMLRC9019hbIaeyNfIvEOANAn+hFPNFNZH8DQRpToB.BKiyrWbssc4O+qZxxDr.l333rEvNAAgc.5exSdR8QezGSDUc9pe0uZ1xlGRm3G+G+GeFWW24TUKBQEKAyu1ZqMGwLyjSLwjyO+bix9nedQSbCCCkb4xi88x355l8y7Y9L4s7cxH6ryNoaHLiUBZkff.Ay+br0JpEY5vjRFRvsSXXXaf1G8nGsU85024+wW9+wNddd6DYIONvqSiFzqnAHpmMxtjz1APCexImL6INwIxCkGsQCFuHL4JvTDYPr7QdjGIE4xzr1YJHXZU0IgxoY0vnULx3W9G3Adfg3Oi3by8DLVaaGFv.3x2uOLvICugptbrAFXZCrWkJUZOS5+Ol8V.1EhSAfacf0ZXipETbkG9g8WsLrtemNaZYd6zO2pdddMeMeuul3a+1eCQPbzo77Z799.+FM888W6u9u5Sui04tLyM2bie629sOqmmWokV5AqTBJALCwL9BWdDlOP+67fRv.Clc.59q9q9qhHRdfI78OyL+B+B+By566O2OyO+O+r.yTBl9U7O4UMouu+n1nVHP.kSGmVkjiseFVb01Nv8EQDcNH0wIGfrEYPjTSWvLODjs7SNnD16mypMaR+xXJuuREK02wwIw00UCBBjO3G7CJppxsdqeWTO3gIz3XA.5e2e2eW+S540ixlLb6c9Nem1q2.Y.ccdVijtAm8voK5+qPzxDfLr3AJyt7P8BUM2KFikXhpvjvRSx9.gNwe1e1e1D+C+C+CSppNYTDS5BiuJjJumC6vySjs6mMVD9vOiOzQT+RPBTJQDQum64dnBH2+m49Oz0wS3VYTfjksQ2EpjDEYr6pfIqRrLcP5l1777PUQ88eX8jddIu9W+qOAPeOum2Cmrlq3dStoo3u0Q3kbpdsoyIGNpoBVm35zoSGQjVUEokp5.IFs7k+bM3bZd9asCWBmhk.cIHH.QfRkJa4rCFD8cOOu9ddd8CBBR78e39kf9PwdurW1KqGPuogdT6.Yo3Uy5mJbLciMPKAIuw23abfbrai7+vY3QWq5lcEvmFaPYyXWVMVGspHSoZzQ.l100cBLkpiI6FL8eUDRp551CkNhLPwD1VDYKQjMDQ1Tsp6fWsaZi5AAqegKbg0ylK6ZTg0.Vqd85qVqVskcccaVFZ7ldSuoUHl0bgMt+6+ilpBaaArUSSvZZAk2qdc5LGz4zMa1i0Nfr4N338+9e+8gU6erCpjeGlfiu3fdbVTC7kMGrVhHRR3f0umoypqRmJP2Fp1mFjJb.WpRD7R0DKWhLj8un7ks1uTUcbc2+4STS.7Niuu0maCY86662GGm9XsEXC90vfQzEh6NMzCl6opBXMvNZvf9TIhiQKB8Cg9Yyl8pUFWUpMrnIPqUVoYKAZoIZGT50tc69FaYJXyR6y36mGH+y+49byely3m+O+O+OO2u48duYo4AJe.ieImOMKMq+rXf79FRKseMTVXtP1kWlrkfbhHY0FpCC.I9xZKYfMxZPFld+4d+b+b+bEf3B21q40THIIoPiFMxEFDL.7.2pthkWcD.w2OPh2utF0xwkSvTNdCopJo.WE+TYN2v86A.lTevyKUFYiMXrxvDq.SQLGALGhHo9+a4fonoLf+N+zou27vDTlwrYhhMnAqjatKcY9N7X2Ux5iWpfldQkrOGa+LwqRkJsgn1kS8+eI1qLrKrxvJQ4ZQVkXEJs9W3KTeiJvVmod8cBNSvNVfoWGX4efa6GH5ttq6x++x+kO1EfFW3jddW39u+622VVOq366uINNsDQ5aABaT2ZtSeWui2wb.yS.yXHU5vgKMmmn8AbvrpA5bm24cpurW12U1ktvWXjfffwUUmvyyaJU0oKCSqpN0uyuyuy3Lv+63Ldoq0tBYK+M9fe8rd6ZoNvgQ32RxqLNvTEgIZZdvoO6KSvaCGaO37o096k5A9zajomqRPkqChdAc5z4E7I9Dehq+1tsaqjue8w.Qwrf+JddmbEHdUU0MCBB16Tddc9teiuQ4i7Q9HinpdDQjJc61sZ1rYKiwPPJvIWxINgggTsZUMHvWcc8z.+.00ykjjDVe80k81qkX8yAUG7SIfzCz1friH5FpJq644sNlzmcCfl2jqaXLUBgnkA1HNNdqxkKuKLytvZCqNNOSBXxfEINAj4rTLOzzD044YDVlbPwrPygR+Z6hGCD8nAHetOK8Sk1lnNUoGF4OyHUdfZUWl8Az3JJpbOk5So+7.dZnFH022Q+z2WfYyr.qlYIplABMKfUgrDQFX9LX3efznqayThxY+6+h+E4tgu0aHOPNee+Q877Fud85SUq1olx2+Li64cpB99mNqmmmfwI+VhHqeu268Fda21sUGvGnITdSLxj3kxg6z4hYvXzaF.WU0quc61KlKWtEbbblOIIoPlLY5YLnKq3441LHHngqq6JXV.XyJvVQlmM1W9KgdvI5AmMcgFtBuOb.P1X+noT.pLADMCTdFHdBUUc80Wemm2LyrRCSMtuo8Z3IJ80yfA88YfvuEfWnp5MB7BBBB7.l.EAg1frKna544stuu+JhHwtttwlr94LqZ4GncBCC21JSy6Lz2OPYw2+Lpm2snVRf8pcyUeyTan6KKlwRr1YAxTAxDYuG84+7e972xsbK4UUcDojThlIMnDPCGU0LRUIig9Np1GB6.zVUcOQD6bmpcfvgGqtb2GGd76o6X4kvo1z4bLNVBWFpTFhlFJIpFulHRHfeEnYjAr8Nbv0ZFdtrYMFWlk.V.Jch98CeQQQQm.gihxzXTElDU01ttt6FDDLLHpKCkWAhWIHHX0jjjUqUq1x.qVw3LVJYsM70v0BywFNZ84A2BPPZTDGAi8wDybjpaCQaCUaYm6bo3JH1+bUYbWhlMfRK.MNgp5K5y849bm3k7RdIKDDDbjRkJUnQiFXxvBZA5FfzvyyKDCCjGADUud83Z0p0DleEX4MgYZAqkZW8pYsyg5qEyCMGYHfeKfYy1p8Yggsmb47gQ.bVDxdNiOFyfod1eNsa29EjMa1mOv2RTTTQfIQo.BNl5MTra3U634Uqikf36.zRTYu+k+vuo89s+s+Ps77baUud8scbb1x08l1zDDfxs78OSm81autG+3GeO66uw2+2+2+5O3C9foDmZWnTWWZzKfY5MfX4WD0D37iI1T2FNfMwEwpLGou2vaX4Iy14vOukEJlcZZlcCby.1RcaN5a31T2714YoQ1NmArpxaYI++MwDs0Th1c3..jtlTpBxMgHxrXP0+4pp9B.dd16ESEDDly0spF3GzCgV.a.xxfF344sTPPP8jjD+Z0pECkVEZr09igCmAMUSvv4.WMqiLzyVLNEYxse7smXhINdZo61yZ+0xECk2AhG9YqK04O0d1D.kKAKdra9lugG7AevaXiM1Xwomd5xAAAiYFiTAjD6yX68fOzCt4Mey2biZdmpND+3.OJvEvTpxaBy2FVdXe3ROtVxlVZ6vfAmkYIGqxHP4wg3oUUmVDYby5kxtv7qAKuBlMYmN+KYnySVnXNqsiwEQlrBLaDbTfmip5KHLLbwpUq5FDDLo.4TDEztnrSkpU1LJJZEfkUUiEQh777hBBBBM91TYEHZSfceUupWUmG3AdfgEDhqlw+KxeAU0bREIOwFESRDYTfw9TepO0Hememem4EQxnGzO9DFxWv8ATtZaHrM1.DWSjtOTPPaKg0dXNY7vWyOc7a3h.ABi++T+ff2Z+LKJU4bYBY1rVB4N+W9Q+x4dQG+EkElOq0++gyXdGCILGOvm2G5gdnwu4a9lGy22eLOuSNZ85mdzZ0N4Hat44JL0TSkEfxlRiqsp5Fqu95MmYlYB.Bf4ZTjU1zlUdC6G9k59TNfwfhyAMOJv0qpdc.UEQlbokVx4nG8ns788W2yyaUQjk888Wd0UWck+2tgaX8XCn3sdaus2VmOvG3CXyPvZcg5WpmcuV5Y3AsqExvjz1kJi.RfZ8.5XmPzUUM43G+3BT15L+SZsTkdN6OKokkSzVThMxkK2F2xsbKaWOHXODomqo9Ow000od8GNquuetffvQ777FOT0i7g+ve34TUKJhT7O4O9Oc1rYyNE66vWZVfjRbqX++.P0pUIHH.WWOoQiFxLyNiSRRhCfSq81yw00yjaiBhiSZeQDPb.ICPFUcbvPVf8++4+6eoNtttVcDmcMo1KcBCC6Vtb49.8mi0NL+V7LEnBGXCuFYhr49QidYiCFUo4j.SnpN1Mdi2RgRlLZHmHRAU0QO9wO9XppiqpNAkLN0Ttbzjsa2dBHxVpRtVF+FGQjLTMMZhUuTHL+zscoPdtOokKFzkSjZT5X1wzU6uDzCB6Bz4u99tuNDYhj2br7NX.2qETpSUn8OzOzabuvvu312v2yMXHQVH1y6lhpWudTsZ0he8u9WVCOOukUMbcfsVe80aUud8dgggYTUG609ZesSCLMTzlpbwCykOGFvmg5WkSl2xN3hHsF4XizwwwQAx433Llp5juq+M+al52727e+z.SYx3Glv22ebfQiRSQxxooGYJYuc1mpLl8PH6unRQ.voXwnb.4+a9at27ofrMyLyjsQJnSybEkUY.ntDN7BboKjHCQ5lIf1Gj999987775opZSU93Vddds7886.zuZ0p.3XKEvrvr1HcDKFPspe3910xMwRXy4TUK.LVjATgIoDSdK2xsLMT8HlMPzb1XUmCZLKvLhHyPDynpNaIBmCXNyuuxzVY1bDHrfk7JubjBqygdEd5+78AWHu1.f0LNmVwjEapFl+tum6tfpwEhiiya4XiLQOwqubP6EAF.pKSi9NNN5DSLghZ9ysxXOHHoxAIF11ne85068deu2YWfdttt8qUqV+vvvDfjnzyesKZMxuYuM395wFbuNvwP4HXKMkx6RE1UDo0bykRP4gOQ7ExPyUhDeUcfFN.RRRhrvBKLXrsQiFlwIkjkVZo9fzGP8C7cBBBx566mEpXk7Uv0cY64cM67uS7TYMFEPqRyDRkIcp10Z+suHReaIJXu+s3SD3..HmKs+VJcdao7+g+g+gi.LZTTz9YukQSbD4fKEn9998.Z644sCvlpit18ce++sJnq366uh3Hqljjr1oO8mvBjP7VdddaM5nitQPPvZ0pUaMWW20dvkdvM.1FJZqy+FcBFnZX10FOmwt9IL77TpR0kbhA1hOmxhCu95ItTkN6UXqo7o+696Dq56YF6WwbNpY.PwBBRQmphXIr0XG7dROw166SKujWxKI02lb1RKLmue3gJwP0IHHvg8G30S54ljJW8tteqbpZ0ra.qQVJNbVQsfvLluupF4d5oWzZahL93i6nZz.6mhToe4A2KhuR2XSBLcOfNMf8dnG5gZgQcQ5F3GzCHwjMWBKszRNnF6o+it4+QYPISf+YxDDD3366mAplYdHy8du2aFXYGJOb1lbYm+esRan0RHKqZVaSUihJYhNnQoh1Zq0DXYwZ+6vmigBhTyr.4DoXdfQrq+NNkYbrbrFFtQQro3i0VC8ihhR.3TddRsZ0bdqu02piuuuyIMkXLPjRQybgG3AdfmpY0TZy92bN.LpmTrAnwxFdVZLfwdzG8QG8K+k+xi.TPDo.Unfp5HfMKRpvX.iWohLoIaSBsYgRwQEQx6axNeGlNcLJ8YoKoh57zwugCuddOft0u3reyB3y45aT1rUGTtguni+h1CnkmQzMr9+Oe6xvdggWnUPvCuEUYMr9+ey272WfpZfmmWT4xwMpUq1J99O7Zat4lq666uwpqt5NmtdPGa+Z7ibjibDnjsr4WY7lGLSyur82SLnO0rOUMfUkjjHAAA4d7G+wGcgEVXzf.+wpUq1XVftFwy6jEtga3FJDa4SMpvnefOvGXDKoyl2nVlWxRR+Zoj0XP6ZI.SRaCaHOwF01d159UEQx7nO5iVnJN1MtMStEOnC2WtyotpYB+dyCaSCVONNd8RkJsYkRk1MiS116t6t8sorOmplK.hqaUGfbhTYLfoDQl8m+m+me11c1aZFrAf84SkpUqR2tcurWDIIITpTIYzQGUbbbHJJxj9rggBhP0pt.xPd7XnaAiCcpw0On2O7O7ascPP8cUU2ojkjhBBB5TsZ0AoZ2JUeFm6RNL5qNvhNEGptEIMk8gQCKwH1n5H+H+H+e1243GuyoO8o6np14ttq6p6YNyY5+XO1io.x64m4eWFU0BwwkGqPgBST0XHs.DXS2LizZpAF4hrLICuAqCe7LQ6RmtdF46iZz298TlhCYj867U+pGjwLq3ZRkUf8pW+LsOSXX6+y+m+H68sVs5tDyVXxdiUJQil0pUqYQH9i+w+aipWudbPPPCOOuUNxQNxF0pUampUq10VxVinpNdwAy8pjq1ESllWBiVw8W1VKxUgcI1Dcqff.BCBxGDDL5ez+g+Ci8i8u5mbLee+w.Fqd85i544YIWvxlxmJlI.F8y7Y9CroBXwLm3peb+.a56DbNklHEgLMaVMKPNe+ymUDIiMhDY.iTcyZW4jFZf2AtGpIIIokjyfOimmGsZsKJHkA77Nk5662qBz8U7JdEcr08aOKQc5vxlEoWfIrKRLmT8hAZa3930ZMAPJZiVlHhU5WKapA4FoQqMXxa+1esSAkltrHSqpllxsSaec1FkYNU04t0a86XVH9HVxyd74gQn4AJOmKBD1S7rGuvHm.DpahdjpZVWZlkHaslCYeG2w6HKPlLYxjAR2zQomr43CCvZef9wULy61YmsTW2pnXJShAeZa6jdtnIhdpZ0z63Nd2Z850S788S.TKPcJ3ZlaU+ZFfRtn14suppBgnVRmqy7D2gHZ6BcWYkx8.RV7J1A9RRk8KaUGGGmA97nG7dR2EVXgthH8DD0y0KipZdOOuBe1O6ebdOOurUgrAAUx.jsxfrB7pFP3A1bBWbX9xIr+x10PpB5u7Owu7f+fEMa13x0ry6pZ5iML1eJij8k+xe44ihhxWpTobXH9QG1m6yD8h.Mm9999sEjc8b81NHHXSOOuMNom2ZdtdqcpZ0V6Tm56c8G+we7M9c9O8eZSfsbcc2z00cCn7F.aPjIKmpQAaVFNa+h66iwP9E.m8.o494RNq85ZQP3bCxfrD3rWMkJxgFqKmbqu3WbZoVc.fWpuf40G4QdDEZJglH7aHUX+K6ZVGx98F749beNDopYCZwjSUMmm29bGvPkBQp+YI.8Oiueu50SUXoFDCR850cpBNW3LWXvF9bYIGVCoYylDFdw6j9Jab3fGhH7W+W+WODnDwZrcro1U1ZT1y0F1MMV1FQY53662CA000MsLVkEV3nlw.a+WDoWhH81d6ssRIbntLH21scaNtfyPDMp7jL++alaCulkQzCF5PbkAyQTMBQDchINhAqjvKxm0A9SC33N..lkyaJKECOenQ5D.iJH4s9ChqtHWD3...H.jDQAQkqaeP6VoRkNpZJyud85kbZeew22249tu6KimmWluP85N.NUAn4AxVhmlfkbf+1AkwViJCD2Am2xa4sjbu+I+Ic+ReouT2O3G7C18c9Vem81d6s6ea21sk79e+ueUCU9jexOYl+n+nOcAQjwKYJa+ofliCT.JY3quMLi6koqcLulywtXk07oZP7Nbe6h8++hGyRNzwfrk220vAh.s9I+I+mu6CszRspV8nsNoq6tDx11dyJUHpoHRiJPbbbkF0qWuop5x0pUaEOOu0lc1Y2zyq5d0qWOwVF8SpZr0Wq4mXt82C5ST+UNqctVYPIzb8e9yedw00MatbEJXJ+duQqWu9nAA0s7mVr0+upFvuhLbWWQxXAouVthWZEQ7Zx10h.lLbSAz4m2fZPESz9FUUc7vpgiCLJrVgsFBvhmfyUBFGX5rrkwiqT4FWuc61aDGEsS4xkZu95q2WDIAPOiueeOOu9hHI999hu+YxGDDLV850m3s8u5sM4q6e1qaB1uLbN.fMVmb4v+rmqKVo7h25a8GERi1sp3Z+bgggbjibjzS0f9ipIpXJYnd999cRbnUBN6544YQvrZaWW2NkgtTxhDZ3SHq9+TsYLtehTCSmK2nPNKhiFTiqX2LTCjs2du9Tpzdus21aa23cezsN0oN0FhHa7te2u6Mmd5m2VG+kc7sEQ18c7N9+piHht0VmKKPgPXDqwgrF4HaYJNjd1GWNN8ZY3HQ+LM5lWjiHoG0qUmeweweQfXLHYMmBjLMjvzVGkCnKTqKEK1oVsZsq9hq1FXuXqDHBytMywlMLFNWsIrbPvYZVqVsXOOuF.M788a566ulHx1mz6jcrWW4aVtoEg2nbsuz7mwgGST60U6PCp26DEEsmqqa+t85I.4Nsu+H077Fsa61i344MRsZ0FAXLee+I88OyQVZoklAKYv9c7Z+Nr0VZyrad4Ig2mrwV.KcYBRSHCDlQU04e1+r2vveVGHNSQJd0XPVwGEJ0mxk62oSm9NNNIAAApMJ+BHNTlLKt3h47bcyeFe+BAAmIummW1HP9jexOI.ZwhF4Icdl24S+o+zN.YVxcI60wJjAzghR50psCL2o49QLy5vV7XppiFFFlFM6b+d+d+w4TMN+G9+9+8BhTwJQr5nKu7xiJhLtFoSALym5q70lCXlu5W8qNMvDKOHBYKjixWTcv5.j4r663yy3OWe1AeOUcDOwIv5fkkTNGjUY+w+w+wF9znLIPCsF0tbNUNvww4fDpXcjJJ0dgC.pmqq9w9XeLU0jDPrGjDCIe7O9+0jGNHv78TqFVd3P.nTI.BXZl9Yht++yqcL.yl4rappRhTVRr.IXH8YiDZllQEWt1PN32.ATnh.HIIIXIqYJWpjtzRWPwjUG8vjgpcSzjDeeeAHquue9W5K8klGHWHjEhxszRKUHZHxl9XG6ojC3pISKp0momNk+RT.d781i2467cBLOLCWtsKNXN+hKhCDloDkRAXLWbo3bddd4xlMWtlMZjUQLWehIKUY.ZIx9afPL76hqmahuueOW2u0NAAA6cFe+c9O9e7+3lwv5u226cs90ccW2FeWuxW4lXJYksJWlsf3sbw0xOIzo9r060rYy9vpIYMkRB.RMP3XGZcmT6hFhxV1m3hWb36kWo.MenMqD2OKzGlqmHR+xlrvxrolkPgY0W3q3UL37ppJMGXSYZgEeRCzVxbygBQX.tmrVBYOWPPPJuygAeJTQrDrqEnLQj9dm5TJf366K0pUSBAN5QOp.F+ASguuXwhpgDNeJuIVsFzmxF6W25sdqcsfRZ1rmmYbo9SxI4P8esREy3bEpzuQiFIdddJ.19ti.NVb55u7xM6fvdtttsp45t6y849czp1IOooDVsYU0CYd1yAbcXtgmObM4FsL.vSYGLkdddpZK85PxBkcnbYrkgYhkmcr2amNMSGGJaINWFfLAGrrQyCQinFoDeTfBJZ1LYy5nppAAA8muXoNNNNsEGYOuS50IWlbIX1eQVJWNMytyAjMbv3cQ3DGXN1Si.OtHX1LtiM6Qy+tui2cNQp5PkJ8AZ8K999E15Fuwabi2xa4sr4u7u7u7VSN4haeuel6c629a+suiHRqWwq3UrWkJU52qWuLu2O5Gcj64dtmI.lrTIFCZjaNlafc334hcTUEntSmgx3jZPFN1ULOE8j0NPf8tDGGt+qESKIPnKAzAVnMkJ05889desV3TKXDOBRkF8h6PI1JZe9OY0G5g9yVtVsZMLktGwAAAM+DehOwJhHadpZmpspJg9g4azngUV4WdrrPAX5K2df2eLnlYbIdfeUkkq+kb8NMa1LqmW0B.iIhLtiHi65VabWW2wCBBlnd85iCgS366OtMXpi9kB9RV5End1N1.mBj4DWimkIWKCXxfIq1JbISDQ4UUGUDYhM+palRPnEhgrVjYgKeTCrNvLWWWyD1MoR75iO93q6U6TaikWB7C70u3W5ue3GJDfLddd4bccGoVsZi93e8GOkHhRSEJG.Is7aRec3VXf42klR1+5+5+61+ME4.+Mqs95G7hWUEURTLrbummWqZmzc2ZttaCrSCn0ce22QaRU5gFt8lCRFRTGdlXB7A2L9l6in9RPAHaALfbjinxNe9O+mO4M7FeCsmbxQ2gFMLZXdHqAkVsngKJZR0nFDwxPoz54b2u9W+q2SUUdrG6wRyZkLAAlu6GaqGaezcSN7BMm3YBTkubM8P+rRFzeoeoeIEPiAExl.jrtp8Yi4Lo+ZY5C06Qyllz0qIsgp6opZTfG2U2iUXOnxtX3mfMbuI2UfxMAhWc0Ua3440zyv71qGoQa+9deuuNhHIev20GLEI+rlM2t3vkwf43DG.0cXCR.2dVhqp0XiMVqtc61Natb8wt35Ox+pejBc50KknUGEXbOOuIUUmdgEVXZrYI.YHkroxT+osyNKtuQcSp5p1nE1GP+ZesulcS7MuRWDTqBJySBznOwwcu628c2Yqs1pqZ13DpIisxt041Juuu+HVUqxdTZXVZuPSa1PrLKm4e7+3+wlwyDbd7G+wELNfpvYO7hqWK1FNZ1obtyHhHir4laNhHRgpUqlAfO1u+ueu81auNhHcTU69a7a7Kz+28282MIEvYUU4F91tg7O94d7wnQioTUm4487ddGApNYIiJYMJkVJuI8cqs+ytKh.0DiyPOq.B59B4X0PTeEnR5l55op1VjJs.Zce228aroZkgz57DtUCEPWAfnpZU.KvI53iON.r4laxq+M7FzeuO1GOQERvjF0FhgLMB4kIodPPhMKS56662+m8m88l.jrAab34VWKrIi8WK87j.kSVHMKbbi6SCRL7XsYyrTDGX5mn64C2+SKWojtcuPBf94+7edBBBUAnQiFbritf555lnIZhmmWOOuS1yR3qootYFnTt50qaHVvxTXgEt4CHI1m+7OkcBTg5JargB1zUmxYN1HiXluO2xhQc1W7x82K.x4LYigDqwNjFAawb80qW2bJjwQjgtFkAnk.pfhHpjAkL99AYd0upWUNUjrTtQFWWW77NUuezezez8.19N92dGaBk133G+3afgmz15ge3fsA1Iff8lcVqh9TfdEKVrOfcCfYT.9799BmulbhK0XwYAnlrzRKY9Mm3bGFjjqF6lCxjilPWXhtfUgEquXxwfDlkDX0DhiGvUGh3pym9byBajloKWzX9v++UVw3CnrubemGHmqq6vDtdBPWE5fJsA1y6jdlwpnnjf5ovhTJsZLT7BzJUpnvB6GA6EuHhY8JcLwDHGHg3Y65BcdrG6w5HhzVDompZB9.TSpdv4xOYym0HKvuQDkTpTojvPeEi+BN.Yp55JFPKo27EK1RP1JLHXCee+0gFa4+vO7t.soIcgRI2rEvEpD3vJ3XAaX38p7M61zNPy..e2TvzxRXwbUfbTjLpFIZTTJnscUU6VJk6XldCak7drz9uU8Ql1lUJl0.skKXNL7e0.I1sboRztc6DWW29K2rY2fffNtttc8Oie+aplqJhXJ6vybl7.E777xe9ye9rkR8Ib1lNb1Ai0WJeqdxtOLTPN1w.jKjmlUxWFx+S8S8SkU0Pknn8.1hlrF3tx7vx.KSw3UnAq.UsBk.a7bdYOmsyl0syq6085xbG2wcT.Xr3XcLfQVgU1WvGR1WPLBAASFeKOTXnCmupyh66K7S21UB.lJjWAz+tvPkUJmTa.oguTGZzvP11MYGX9chhhLA4d5l6QC1CVXf++27q9lWEJsLFoINx00M568686sIvJO34evMRRR1spW0dkJUx4O3O3OHOl8+lGFaX.StT1zxPcxBkxU0N+Q0HQiUFczQcrJgog7WgIeiuw23TXH90iTqVsY.Nhmm2TdddiCTne+9CtWrAH+a+29ND.4rG767Zt10p.lb3EKRQvMGlrXXzfffQKmJ2QtjEN2UxlkSpwJ8BLaXbGhJs0+G+S+mtkpQ6FDDzclYlQEEmW8+jumLdddCWlIE9t9te0iHhLxe4m7Sl+k9ReoCW+8C9NqVspZe07kkre.mq5VkvvvAe1QGcT7CBHHHfzzlH8uyriwD0ldiFGCDoqnRmS54s6i+3O9V+5+7+5a366uouu+1TgV24cdmFhia946CNFG2OOBK9LJ586aHsNNOvC7.l6ItjCZjWUMmMs14k7RdI89XejOVKpvNPks.1zE1nFMVuooTTVkvxqBrBzvH8Vv1uxa3F1awEWr+0ccWmvAR0q4jImbxTmMDZhSwTvRpRF3rOakkIosCZ377jbhTG+WzHqgjVe5jueLjPLIlnpXbHZdn27D1UDwPbUAyZI8rntUMyI2sZLaBwqArxLyLyxlwGVUUccQjs9I+I+Iac++E2eu2xa4sPozwm8m+aGCrRc6YIC0HS5Bxy.hGAZrk6.ld5myd4xkqsa0p8Dffffbu+226uvXiM1nX.OXBee+wusa61F+1+W7lGKndvn999ibgvvBDQtvvPiCike5NVeNlw9SUhMoyqMhv8UUSdtO2mKPU465a6aWpbENWNDTVl9TjNP48tyel6r0jSN4dhp850qm5.Y.I+DSLwHUpTYTL.jLNpNEzHsrRlNH3BSBL58du2aALp2iYLNBN40ccl4BK9zJsV+e1sgcRxldqgoaLvT2pvHSM0h4AbJA5K8U8c04e9se66drQFYKfsd0uoW81u829aemevevevc9e7XO1tyO+KpkHR6u7o+x8ttEuNm67NuqQ.FuDLUEBmpQJnaMJaAdtd1Z1EeO14Pf5olDOLPkOizNW54Kz0lsCQZYH4dtm6oeYQ5Bw6Ihr2XEJjRFm8GTRLO4MEDMDTCw2B6ryN5latoN0TSoBn29a3Mn0L7kUeS5TK89m+FdCFdOIFslqq53n8Mav2q2cbG2QWvr4zSbf0Dup13y+yto.ZMhSVJ8YkfzL8pQZIf4LeSDXiqj9ldrz0Fmmjb4xk.jbi23MpttUsR2ApJlr5PETeeeBBdXUDoe850GTlu+Me1+DmSVqVVf7UiIODmsd85YuvEtvS+0Nqg.Kjll8YKRb1Gc6sMqosxr1y6ksjDFZ91LhHRFqytiXUcpQvtwcUUA8R7.i.hiHpnFmlQy967g+vYEUyU+gCxDDDP8fyzOHHnMvtkiYanwl.aFFFtoqq61ttt6Bk1CnyXqtpYMqPqRFZV+KwpzIpI6Cxnm8xDU1iQccgEVv7+O6INTe7JtcnrLwJ0vC3Xfy0+7fxpnThDX9A.RnZfZYXNgkpMb.FNbz0cF5HKTMGPgxPguze+ee9P+vrg9gYLtnQeP6JpzQg8PLxZr+Y7a4cRu19998bq4lbSddZIZngoW29yYutVpe0zwxy8zhb9s+cY5G.ct9q+5aSEZCU5Jhj3ATk5NgGrDduBOuUGbMUpTEDUF7bQPXHesu1WKAnSXTTKW2aZaWOusTU2lxrim2o1CniKzqBM5+4VZICfPQU0UWcUf3THCuVvN1E2VDL62GLbjiZHH8ljUJIYDoBX7ko2O1a8s1qQ57zMlwFP1yKkAGUUmYWYf8uz0.ErfmHC3fGbDUjvf.xWHO.IttC3KGEH4L99pqqaZ.ecpWutyRe8udla4XGKSizRgc0oG.TyINXorb0D3Q6dLF3yvHfLZLjOWtbN29O3s2GJtGv1yAaTlf0V13++Zzz07Jgq4YF.WoZCVGZrcgBE5X624MDlc4Q.JLaJWMsFYlN85rHBDltGJARNbVK8r47J6yomE.sZ0pIPtj5PB0oOmftvwZCzZVX2oY4VUpTwP91aT1ReBK0YVC4+taolrEzXMfkEQZhgmSZBkW9XG6XqDEEsFv1qrxJs+N91+NTfrFdUJb3xaFtX6XYcg7UoQgPiec4DoriHhyDSLQZ.xFoZ0piWtb4I90909UmBCHIy366OKT4H.S566OBP1idziJkR6+SidW20ccXaVWK5K70r.lLbSmGTHlVsZwW7K9EcDQx77e9O+7wUqZP6OfbkexePeeD3gdLOsgF69mde2Wqs2d68Pn2OwOwOEpQBGyuyN6Lhuue5FoF6Sd+++Nhuuetb4ymomoDFRWrd3izuG.HsbpGl3WsflH.ha0plXbwEUFOpMJQFjogdPR2DmjVmtd8cttq6515m3m3mXKuSdxc777ZQD6UE5Tud8tjY49P8zqEGN2yZjwi7pdyuYSJCFP9+QuvWXAQjBREImkY1UftFBPMpEyRq.nUca4fPM1wJIuaCrUixrMTd6FPqG8Q2sGfXIhw7yC4gUxxbjMbHsou4zSmEHiFnNU+FWpfMvIsA0n84FhDnVj9Co.HIlZEcInB5xPZ5m2i4nmUNI6Az6QV8Q6BKrWHrCTbKfMDWYcf0KAF4eT0sEQ1869U9c2EPZTspYQp.1Wt0NANvYE.4O8O8OUnNBbdGJRl0fr9oFUqfBM5Az4rm8r8TA0y6lxznQiBfyX0qWe75A0G2yyareieieiB+9+W+P4cOoaNOOubGsZ0b.Yqdi2nYLOlm1s0lEEluenp8LpnBVm8RcBNT+KC9a4If7MG99y9NR2jNP7tEVnvtIIIsbcca2nQi9FV3TyFGGmMJJJmHZNPxu9FaLZPPvT999yPElw08nGoLL4sca21n9994ZNTF6r1LyX99N2UTTH9lw1vOu3.Kl47PFKg9ZKEGF4QdjGIOzHipZxOzO2OW6O6C7WtCvlwyO+F.qQCV2B34FOmW50uokA92Fyy5ce2u6eM1c2cy2.FOzPlvSZH443TURoP84HKTMy4MkJi45xjgbOSMddv4MEggHJRhKg7S+Se2z.z2y648zGn6G7C+A6AjXV+IfgxXuKe6Pm2z1TSMkr4laZs8qhs1+ILLLwyyMw8jd8+XereujXn+ce22SeW2i1kRF4LuLzAZ1kpzOs703fND8MB6dOSz1ub.lGGHvxOQjoYQDQphYyrSO7yQWpmoT.87KtnBzmkMxGNP+wFarAJujkD20ff.UPcTTGUww22WqcpZ87775np14aYgE52nDxe6e6ealPHCLuSsZ03nujiBXyVsq9l4dScx.KkkRTH.FoILx3iOdAiStq9D4e1f9uYZ2ZBUwIzFzn+h+h+hQCBBRKOtLhojavy0EUUMSlLHfhhpppgQ9pZzeOERLYagpIpp8p45108jtsCBBZEC6.U1FXqpeqU2FXaCGo0nMUoa8g3nmycvM4aNNA8gyeXEdYvw42mK.5a4tjmp1MG1N+gI298uGz.GXYGJYhHsMpzYnHYsDVnyEernwGiYG1+ivBppEhgQdwunWTgpdUypNH1xjtu.cUQ2yQjVdVx32yya2fGNXOQj10Cp24L0q2uQZVQEEovJC5CgG6.W+WjujWwsYQglIppFtTHhtPTeJB9PlvzMKerq7ME6AJDpTxbsEGGqpn5N6ri8pT0m2y841200sa0JUZGD7v6AzpVsZsIlt99mtGUII.zHH4Tm5T8ohgXmmc1YU.8bm3Zx0OA65+Vd7IQpIIpZj5ZUUgl.DqTlDQj9ex+5Oq44j4QM3F..NwygiHy5rpY82z4nV9JhbhH4oB452ue1Nc53nhJFslTjff.wVdTCmk0I9998RypEGG5uv2x2hFkBLRYxBaL.fjyViL1L87JsLX2e8myQlpF6PippNNDNFFxc042+i96m.M6vrr2JPKi.UvtTicg.ydAfs8mgsfRaFBaXT9QRy5+Dyd6hyQIJr5PY92FF+eyXCdp45rH.wGd8wmsZCuFkwl1In+PpkXeNqEL2SP2Ug1ajpxjGitP7.Rj8U95dccgx60.1ElYKfMnjQ87p.q.wqbgKbgk877VQDY84me9VteamLACuJk2K845SbIAJJCyP1.H+K+0+5K.j+e+6+eeNnQF.mVsZ4HhlASYFNRbb73yLybS7E+Rewo7q6Osn5T0q+ElHHHXja8Vu0TtRQaT1HU0rAI0tXNdY3wmqYZWKCXxfA8kgDpVs2niNZ2a5ltot1HCwe0G+imsjcyyc1OiOfm3EBLSrWdfdTu2DSLQaAo2uz+5eQroc4nW+0e8SJhLMvzYylchfvvQcccyM93iS974M0n1AkEs9.I9AAWjQ+JUpL3mM.inzOIY+LPQMfpDDDnppZbbrljjjfgQ+6uW61cEwosnRKbb1Aiwjcpe5SuaUS4E0tOzoVsZcIde4Y69u+6GHszgeRGWdxZG1IVndcwRNU4V5QdjbppoxIVtie7imw9Y6u.zkUOf1u2k5jRhXsAZOaL6w7w6Y9+g8AbHpTAfwWFFmpUGiUX..V.iwFaLhqYwDmjm8QRd+98k1QsCCdx.mAmFT+S6qAAAojBUeV4fZ99KX1i2ihKYmS1zvwIgdokb0NpQVgaAzwjEK37kefGHeQ6FNGOMaSN69FLeMulWi.3LmYQEqT0YPWlHbrWe8edOmmWeAQ88eXGAxCIi444MdM2SN9u8u8u8npp48N0oxPCxTud88IXqFMb1el8SqwTs3pjPkk6YJwi.SIKEUoEFU8oKkJ0mfqJCvJlw51kfVDyNc61cGe+v8bcc6YydKRRRDQE8lbqoHpzoSmLekuxWo.vX9m1eRfIOc85iSkJi544YF6RcLYs0jp6+ccM2hC1l.jYQHCkOmELxfB.iJREiLgC4oREm0We8j2065cYUZL1gkWdq4MRM2VwpZ.+L1XaByb1cEQZWVjjwFarrP4QDQlPUcBMVG+89deuC.Lo5JTfoCsDAanYN74O1guNG90mp8UyQyCB5PwFjQ0PGLQ5S.jO4m7SA.KCBtt6yZoOQslW5u2vvP1d6swfOtH5AJgBzxwvcd2+z366ycdm+zIggg8nQkN.sigNLyL8rbRUZTCOrBA7MyfkbvMwBYXYiTyaqqdmhMAUCsajdig2H8kuctyoybHawl0MM+r0daeSjVEtvRWHc7Rqe55cBBB10yyaWQj8pel5891+9+1S.zO6m8Oyr9teot.8Bu5bB7fNpB47fBzfQfRiopNlHxHDefx48Ircd.VXAgPRyx1Qt0a8VG000c.fIX4sjT+O52u+f0VDjtUq30wQb1yyyskpxNppaArop5l0qWeqJQr8oO8osxbbj403J6.raCnEUq1gv88y4xbX2fvkD7B8.etK8m4pscIAkw9doQMMWIHOMHOTLU40xZVOb+mgNw9kvZN3blrqa0xi.LxxlrZtfXT2u7hHYCBBx3Vsp.jfRO03WSGqJq0QDoS850635519lbc63fSuZ091RARhJUpbfM0br8ssb0tYiCt4HahNX4IH.nhY8+bopNGPVN+EkYMW1wXePoTo9z3.JDRxFarQhqqqhRBhzOHHnGVE9JkbqCBBjuMOOGB2WYCa1rIDc.BxNkL8uVciVIERCDlOCxN1TdKoBnZjpPU8q80dDSeaegzzAvYgUHKUWyd+IvL+qYoQAlHFljJUlPC0wylM6H4ymOGP1wFcTC.fpgWcN+4OuoT6r.l3440d4kWt0IqUaWvoEToKF6hBwGX8CoZcjkV5uM8Z5JcsVwJ3FY6XALQLxm7X.E9c+3ebGf9EM9+2YXhCdH++6.z4XqQalsQKfsg3s.15BW3BaKhzJR0dpphFqYY+rdsfFDjur0erj8WWe39vy1YcdZaeaam8h7+O82mtOQy9eN+AUbmG4O7OrOKFaIL10LhEQi41EX6HXKJWdyidziZyHGC8Engg82d6sc.x0Kc+um8.8aGL.tkg0HC3k425252JKPtepe7epzr2OyniNpCpjQDIqXyr3elelelQ+Vuwu6wDGYBUjw+pe0u5XtttE9q9z+UY.HJJRINtuEnj9iruc8qk8E9ZZ.Sf8mv0ivv1ppoxzzdppIu7W9KOSCHOEI+Z6S9pWIQsIciqc.16C+g+v6op1AkDU0rtttidlyblIcccmNJJZpd85Mga0pEDQbtga3FRjJx.vVrGVFim9dlzfa3uqADUJ.sa2FPnQbLMhaX+.fkP3Tfj9I86mwISOUR5AzoPgBsUUaAraMW2cojQ4UpUqV6G7BWnKUnaiCBfSefjumumenDfzzi8.WSOMZC6XhD.N+l+l+lYiL0W4.iY+29u8eKURNkk1+u6hcvxdrJziksf8TDopTMupwiqpNEvzDFNMlZpaZJRJWZLZ.jmEHS7231vvkJ5mWtHco.5FlEu56551alAxQ1Ab9neLzyTiuzkZzYNlqM32FqbZKhz4we7GuCFdV.rJXRSXDlmBKcH9zwb4UT.jUL+tbUu9Iyqpl2n9PjyFcWIa9rTsZUmjjjrJTvwwYjJRkwfFi9pe0u5B0pUKGQQY.a8zVY+nwEABkeZOllzD5SjAfCorrWYJ2BhZIhzRUs8O6O3ao6fwzxOoK.ZueTrOFIQrEvNEJTXGGQaw9Oulnp1WEs2Y786hRmhEK1+U9FdkbRuSlQEMmuuedGGm7DEkVFdYMQmzr.UHHbhuodypWt1ARYyyAYI1VBhkIeQJV.hGUUcjW3K7ElinHYlYlIAnKU2Grya6G9GtMkosHRa6uGXwYvL...B.IQTPToKFx0riHk6.zqAnc61MqpQiT1B1oHxH1ZTdDfQBgQXCqjUWi7yxrYOjjweoNdZ0+eeuu22.mJZBYtm64dxppl4c7NdGYJSYmBikc+uGixJcU9cdP3DUTACXLNhpNn3nplw22OyY78y3+v9l9ZY3lpdS.Q6aeYs0R+9SKyx8q68uw3T3Sm1fqsx6OmKErjbX3gIYPYXMG5hWdd83.s0.nbJORg533nAAAZoRkRJUpTOfdggg8.RN5BG0T2JfVqVstttt6Arqqq6d0pUqS3WJrGPuW5q8k1cVlsCzXv55b0s4dKXIylCHuOLxW4q7UFEZXHrQixQ7DUy4G9bIrzRC3w.JyHe8G6qWHHHHOP1ff.GyRBndttoAZoGH8PoqhtGvNerO9GeKee+01byMW1Rr3QNNNMpUq1xQv5eeeeeeaArKko0zL8dPzdupW0qxTVZlwvKalivAW26v.Lc3.Mbo9LOcZGFPNmSbhSjAJmEHerY8tBp1HkaZxBj4C8g9PCdN5rlRX0DPgRLlKtS.wSfozTGUDIeJXIppYbcccrpsVhqmaeOOOiuKhI6PRRRjZ0NI.ZrIam566e5jx6C34vO+5b9qr4BOYsCYernCPtngJuxhV9WgCl99Wt192aaXxF0nnn1tttCJUwfffDDRRTUM7gimDDDjw.NSkBtttFa6V4ju399oCW77lq0ZCl+Wee+5F.BfpZmW9K6k2KBRL.XEh02MXe.UyBTXIXDBskE77LQIJMEzXVfhppEIJpnHxQTUmX6s2dDWW2rG4HGwjgdNFvmylIadwnVVhmmWufff1u3W7Kd2FvNttt6Vu9WncQnuU3INP1IFB5BKrvv8qge8vsA9NbNHCtjaEy7qQAF8tu6+ciBj+VN0ob.n4.e8iubfs1+7POCnJzlprSIJs0QNxQ1FnksDsEK+sLJkMiShHiFaddM+JCGHKPt669tApc3q4mMZOQ11NrMvKUeWARNKjXC15P.qrRZ.kaQb71O5i9naCrkcev8.jwGe7rTjrwGPhySsqbrrvvumelm6TSkxMNYTUynplIIIISoxkyTsZ0LJjIJNJ2O6c8yV.ZLhp5HhHi7Jekux7.4N4MVKCkwIEv25V+UN2ECX80hOO++R.XRZ1brmTT1oBrsHRKKQV4.jmljF42TzUeBQLm8KKm8TU28M8y9l1w0jFkcAjvP+BtttS3W2ep98SlxyyarG4Qdjb9A9RlLY5SrAnEXeYiBFT9.Gv3+vj4ZXXHEJTX+qDAJWtLUqVUmbxI0++4t27vkjzp57+yIhH2y7tmYjQDYUEBEnT8dUMLJBB3Fz.5f7CXTjGPAQEF0gF5FGWFWQTnra7wAlQw9mHLzsiHfr9SPPVjl9Qn6tptoaZFkpcnpJiH2t2aU28kLy376OdiHuYsWcyVUdpm74du0MuQD4a7Fumy62y2y2CJwBLHSlL8UzsrTYSP1.j0UUW+V9Stk0qAa7fe5Gz3vpVsg6ZW6J9zPqemMiuuliGn227mHadnw407ZdMoZ8R1zLwToRkrTq8HV+r2yLaSmVvVMTvS8AAab9322GufmHS.LMvb.yQUlyUj49S+u8mNaylMmhZ0p.jmicJfkc5av5aU14JyVmdPjCA5anUe09mXjlHbpLLgwuuMACWfEF5eZKt9XdLOlXWS1ijACF3ru8sOSfPyOJHnSkhwyzKMKaYCfbs92VNuHRAIoGqK0EyyM0vYkUVy1xxxoTgBYTUyc3nCmOpYTdUk7IGa6lQMs.jl2SSqlMaZgWRqtryi5LbO1X0digp88f9jgs6PmsplrAbQjg+Q2xeDLGRMy46h4dqhWOyF7gMbSn9oBqmTu9CRXpyPDFDDr+9fLJXm6s48NX+9MhCBBv22mlMaBdPylME5g0wO9wER5FGos4mKSsD0SeZGUUmYfLLjr8nWta6O+1xt95qmpKQB0QmChSX5PLyLS763c7NFRmQykQSADPD4Jex0r.ru5q+pytqrYK.TpSpfuB4deev2mgds0I2W4q7UJ.tE7g7rI4VjEyLCmRVfO8WeC+L9+k2z+ESPiUwFbctoa5O1otHNhm3zgNNO8m5S+TxB2oF+0EgU2HhIkJUBOOODDJUtLnIW3VxH.aBB1uCfy9CBrCOTn08z7dD2cxRtYbnJ16wPAZAPtka4VNcPRtTDrjSw5L18MUU6s1ZqDpm6YC0sbAAE4HW3VsKr2Dwhtyottuuuu533D633DGDDLT03gBL73G63wJnhJwQggCw0vJ.bYqVsZssm2U2uNrMCY6EYw9ytyZxOZx1uM9Klxnu7+.OwmXNn9oBTR0jOG64BdrLA.WGm5PV5PtRUJky22OS0pUsccckj+h3nnnghPeCiGzsQL0F+W3NuyUtoW+a4jhHKLwDSzAnEtD4662FWl+49be9mDptRMXc1lsVhk1dOP+O4m7SlF.+oW9LmO+ebZ+em9OetdOeyxjG5gdHalsiCIIxw.1gWpORa.qele8eFy7lYwxzkG1c14fBnTNhnIRRViQukfhhmj20b7x.X666KIso9cZowJRXXniHRlvvCmMLLzAvtFHAAGPtuwJI6lMaZ99cikAbi8N987G09SWd4kMioy0yJYs6rlM93kuWht2.3bQVVN6DCdc13O4Vu0MVd4k2.XSee+zDzEu8VaEGEEQXXnsuueVf7QQGJOP9uz89kJXXBqatO8C7.6zN4mN4y49Fcdtb0RmGONnIaIUks+b20mqOljbE+vO7Cqus21aK8uwxTVLjCOJByUApM4bvTXyLco6b0gZpp0A7TUqopNyC+vObkM1XiBIhzosuuuEZBvKBNDicBxywpp8CCCMs31vvsaz3562Ch2+92u47OWx3uG.6Qg8FuuK79E1wWy91mYsonQ.+3npl4lu4azFv5cc6uKCCrHo6QxdOcvDNiXkqB8YHa0kta9DlXhz8TIX.JtXcnBcXhnnnJTspowezMIQKi0wRu4a9lgFOB5GTeiYmu01tPuNUlnbpLvKc9Tefsdb+GdbaVyDObLfHFA3OyXrkabPXcXlilnoLoZ+RM61IwlAXKthSREU3zoSGSKN222ota8LUqUMq6N6oKaXXXFpQl669hxjjXsLTspYcjNeSoMNeIg8uG.LIlFF.SXdVusYiOa7beVOKS2tP0Lvb6j4f8bFYb6rcLMN.lmMEQVk1FZkC5lfDqporbd22w6tBnUBCaV5JuxqLWc25VG5PGBFCDGfMEwcbVlbJ.lb1Zwvm7jmbz2mlQrkWYkXDFpP+9a2eKE1PEV+du26YMQhWsQiFqt7IVd8CGFt4UdkW410fAst+6e3m+y+4GBysCaE16XLW3LoG12zLW.Z2V.j+9+9+dIA7JawUrTUsusa61rLc7QiMRnEOmVSfVRDXQaxr6cu6hscYJfp3Rc.e5gWWn9O6O6OasCznwrzs6j.kf5ocsnL3MZwiuYlM5KV6rsf3PfAcf9PuzLvmNO4LPZlcnmZhlHh7k9ReIy2HBsMYnvtgiyNajhp1ppVOA7rXlwVzbQFQgwPnHLeZmfonpZA5fIyaczrNNVYAxr1FalAHWrp4+7egOewFM7Kb6+0+ux+Y+ze1LM7aXEDDPiFMzqqQCAinuZCS+MZ1wT3HwPugsf9DReU0A8pwPLNzMN.lmLIBVl0dO+kemYru0HGNa1AVSDYsJUprVRlk2NoNuG9VO3ebLzIVj33fffggGJbPiFM199hh1JLLbylsZtUiFM1lVzuQiFC5zoyvqYW6RSXcP546xQybOqIVvIrEQxrnA.tb.Y+49s94x1oSmLRcwLOqM1yaJYEyFaWbwj4ZtoNoyH0kzrYV3A+ROXAfR2+ce+kamncICGNbTVaegO+WnIPm1T3Jthqn.zovW73GOOyabRuneRf8t6naQrCvfmd6H9BYm4ylcLe8sdyuEfNbK2xqS5.h1xHrd+C+i+Co58i43ewD+U0wFaauy0VTTzNbjWfxUpniwpPZFdHQD0pCXEDDXIh3zoZRlgmKIfvd3jlI5F.u9W+qe7Oai+0KksQWiau81Rtb4romQiHf1Y6.YXwKp6qVbjij.nzbiVKLNN1BPhLBptFFFFCxP+ffg6d261j8ehE+f.I7PglqmNLLNNteqVGZ61ltYVefAK38nlhwlmGhF0Vty1Cx.sS2Li471K4XezKvQy.rh8JesUbZmL+2Ko98yjIiXaaK.pjjHHU09RZlISzLrW7+o+SqDFdnSBrPPPPOftzgd.y27dadhO1G6CtLza8tvVbBCn9G8LA1+BMVb5fm7n887n0Fm4FBKvNhjoHVIkb2NyohR9aV.anWF3XYmGJbiuzarLvD+b+h+bSALI0XJfIzVZkNP4jlNP1j1JLBRrQmbFwZfL.EBBBRKe37GpYyrPaaOSRFD.Zzvf.6Q+7GESs7cdAI77Y677+rnSLwDlZ8d9ctdLr+sc5FKMc3tidd2jyNanqgg4mzl0eyG7fqTtb4U7882IwCHCykKmhgkX1G6XGKEjpb.4Z30HaOHaX3gxbUW0UsiP9ehjy+C8s0Xz9Vgsy09L.oa1c9c.bUDQebOtGG+x+x+xJoySLkkWAZQIU6UQ0NS0S0YnCyATqsKtXB0tpHxr.St5pqVpe+94AxLb3PSasVMq2HFcLJc8OACSSDL9T.Zm5KxbsNex0cKT3nwvQhenKNF+Xte8POTx8sZRJyYt+G79UQD8s7VdK5G9C7gGslwRlX7NWGuQqIzCRK8q31tI.kTix.SSGlsMLmp5bGv2eF50aBfxP8hsZ0xD+uO1iNpeaCujy7ywE464rAtx3uOy8BuDVmLOoZfjnpZqsZkvpF2c.KYlQITJKKNh0OEWDxCcSYgiMfk1QseFOimgSBSyGsuAQjLcme9bGJLLePPPdDxoplmtTv22uDPo5PY50KoS05NJopb48ywWVCXxNShZNBfhjxfYts9XehOwfjfOx.ymi4R1v7QOu0E7Xn5McZs3uhKbx67Nuyk.YUeeus888kVQQYeY+zurB999E78Cxqplcqs1x9.G3.RRftCvf12Vp1IcivmMFcbF1TSMEQILOIYwqQA6rxJqrMvlhxFA9AqEDDrhpxRgggK+q9q9qtZPPvFfa+tIKD+zdZOMElWwM4y0QNEzJGGAS37+f7iHaLc9Ttga3FPpKJfpcTEH9M9Feilw.WTX2miixoZIDX2BHyCd+OXgnCGMQqVspRG7A1kp5t.BpT4w49K7a8aMGF2SSBsKiGEvPqww2P03Nh+1sS4SGzjwecAy1lG.l1or7jexO4QNjLy4ckVIABZd0SDOQ9WokUxlMrwP4tr3RdndY.SmeoF6zZfSZctQQQ4NwINQBKUzL.Y2ePPgm1S6oUDn7y7o+CU7Y7C9LxFFFljwf5wGtYycb7yIrR5w6eiL9F+r26dGwTJOQhS.by9icaerrppocEhbfelibwgnsxzL.psElMNrZ974SDiTYaee+AhH5Au0a0DnsJ1ggg1AAA3Zz9f9AAAaxvQrHaSfsbcutsW7TA95xYSlMgIC0Gi510fbZaM6i8w9XyPGb909090bN5QOZRqSrtSmw5fXPm7T0PGWssVRUsLl4XlRnClre+9S.LgsmcEU0JIyIKATxKA.OOnvt10txAjyWjLDkbN5LJH+j6+yb1nU9ij4dJTczymuo2vaPATINcocSWI3C729ALqYz9Qvwu2YcMGw22GAHUnDWYkUwDDKr+f.wRDArD.qnnHqCDDXSOSIBbve0Cl94OICZ6IU7xOeqmbooYJeuXvMNWtb51auscRYBTLo12yCjw8LW+N0NSlWv7Yn93cTMDXr5fEQCCC4XG6XTqVMKCCAbyFDDjMJJxoUyVRPPP79810nxuoA.sNsMfuy4+7YmBKSTUsEwaDqnTUUCy1pOxG8dO+2yFo2NUpTIkYNiNGIB9nZ3kiFCLHHHXaPRX.qtNHqFFFtbPPvR9Wm+RQQQKA0WFi9Cs501nwZ.a5sSLL59N6f+eopM97gSoT0j5BPc50qGhHHFx3jvnCCPrIYRMWRWWrzS3I7DJWGl719ytso.litTUUslHRULwbLAlxzww222xy2SB1ef3m.zIPVPRaZ.EfZ4MLNIzgZHlNYR0QO+ds6YOoyCfG4i0mZ7CKbp5LhTUHMYVOvC7.N+S+S+SIfNO2YKFoy1wNllljNfQ67V1xxZ4nVsVMUS0778FBnAAAhppisssiuuelff.6jXFLwFpplzAwApxQO5QAPt3J9tKIscdVOsLIW7TjE.0rdmqpppOtG2iaHykFuPUGLq0UpNTVDYh5hLwu0u0u0T.y3Byt3WcwYvzs9l3e3e3enDPgq4Ztlbf3.X0oSGYs0VSH4eZxbeUUKeeemfffrMiZlqYynTemYnNNVCGZC0GsIY2cVe6rso8yo8re1O6juqqJ0jXfAW6Ucs8ey+w+w8eCug2vfG3AdfXi315cwNedz6y2.nT9nnnIzN5b.dCFLH.HPDWu6MJpFvr.SpZqxdWmWQfbIfTuSxT16k7fwcgYfRqSY+bBTKk4bIsY5NNTKsyGsCaetwa7FyRUxu4laNB3VpaXidp9g8Y9LeF60VaMKeeeqVsZY80+5ecaU0LhpYemuy2YtnnnbRrlO4uubXX3jf6z2aX3zXX++jPmRTm7bwWloWxZWNCXRpktoeifRA8+6969KF.LbgEVPDQx3B4YdJ3mhb9YFjyY43chDm.UWoCbh+r+r+rEmbxIOYTTzZ.88Lzs2INNNaqVQY.rJTn.ITub3pqt530a1oKFZm2EHZ0xDI13rOAP888GVoRk9AAAaKVxlQQQqUud8UDQVNHHXohEKtRXX3FPm9.CMsvJzpIYK06bGjy2pB3QAFppNjNLnNz+U9Jek8AFTOYyjtcPgicQctauSM+a8Xe7O1r999k877lILLz6P2280.XWQMa5Ccc+4+4+4mEXJU0IbgxzhRFZ8abLz3TEEwSOazemfwImg1lv449yNs9upVddFbRRh2PFLHbGE+uFRU.ZiTamLbYWKQY085P923a7WpBlwpYzN5L.SIhLgpZILAIlCvw22O84FmCGFkEnPXXTQyWCyEDDXapA61w23Mdi5wLLLxxCroYhv48niZdJf9wOxQFEDY6jwCUUmmyy44jWDoHPw5PdH5TpW0yywLt5IXXhVDrNvpYxjYUyy35lgQgCFLXnZz..MCpjUDwILLz5daFk.Jp6V6e+MR0pnMMs+4nwZ2rWxuYhKfsWVHYdSKin.lCHeWHeRc6modcruga3Fr1yd1SRq7tyn2GPAOnD8nTMSfekeNOmmyD0MA5MJfurYyVJNNtXyC0rTTTTYUaY5VNTu78FEURDoTK7JRx7wjVeWVX1ryY.CO+m5S8oJLCTDVrvt28tOUA38h2Rte0K4qdZRMVquta9VLapx7Rd6u82NfKioY2mO6T1He8wVuY0UWUhhZMZytRhxuFFFZ466a2wPEV663N9ea2rYSw222pMXYDX45NujWxKYD.Qy.VIx.5YigZWJZmJ3QFj1UnSL.YyFX2yzcEJKheELAlkqyEFLrQ.lnpZSarSFysZ0pk366MBnJPkff.Y26d2Vc610AHaTzgKBTz2+ZKDKwN.Rqc.Qyp4HlBT0oQiSIlhKJalju5IhEzNkoCfYiiwP6DvVmVOx4cMj8wrylNlglTRlitu6mpYZJpmWfBLLLLrOnaYRthrFnqopr5G4i7QVIQTlWGZuIXJEo4SzcnVr6gom0G5r6m7Rw4Xidlau6nIDo.tYWqCBzVqVsJpp7NeyuyDfR5kCnHLmQTMgJcnVEfJu5W8qdh1FejyB0pUuNd.9.9pp0vrlVQOOuLgQFcGJ7PgVggg1ppNAAAiR7fnZtvvCmMHHHaPPfCcw1j0+dIIRYxAmHIFxi7MVxsFOFi3jt1hN27jpgdVW4Udk1EJTHAXw4SV2bumOlZBf55xPLIqbMWXoUWc0kP0ke0+R+Ranh1OJJZnsssl.Nh.XEFFRXXX70EDLHozc1pQiF8oapeydw6YO6IF7hOK5evkSlrOSYR5LYBCHu0a8Vc7R1zZsNXkz0Vza+1uck4S2zaOC6Rndw1PwO0m5SkuC0y+68686U.nRaUmX5omdBQpWFH+O5O5O5X95L.lpfTrTQ.CboryZhNtPlu3W7Kl2RkRhnkAJ8Q9Hejhzlb020txBsSlG3loyni69FWaYtf1G+i+wAP8fX5wfZIBe7u5McS8UUGd7ieb0adjjV96EaLgljFXFKK644MSqVsbUUC5zoSinlMCftd.o.lLkqHSPaJWOIAf.4ZjxDuibFBi9kcajeO6Yz2JUAKU6jlPTaUU65fCcwo9XLvsJ37VequUm58HStb4x9ley2pgsIsMI1RDovcbG2QdQpmsb4xNQQQVdddVYyl0ZlYlwod85Ye9O+meNU07W2AZT.nXXX3DhpSAclMHHXVfYNdqiOUMnBsovt2QentrE2gKauvSrwVH0zBAilkg+D+Du5g.wyN6rhpZ11plmZTLxr.Tp3Wd9dvHE.lsfdqBbxa+1u8EJUpzIxkIyJl+eF566Ke9O+cZ444KCFLHdqs1Zf3IocVmjRvwcHfFGGe1Vv+LV3Y3vgi.Joe+9366iuuOHl1eXx01PU0An51AAAaDq5pf6p.qGbffsgZixp88ce2G8.EOzVm447ak.kjbsVe3G+i+wGPM52F5+W9W9Q6Khz+m709ZGBD24h67mb7ZG6k.7ze069uRTUyDF1pRlLYlc+W6yxc3vgtpkTMLLbZQjIBCCqHhW4NPYpS4q3JthhfaAfbM2QbyNEZnw4uTN9VgctPN97YByZVbD5QqVnppCEQFz0kA111wplvn+t0zdfPUr6ZxbTVfbcSxZVKn3u4u4u4DppyHhTc3v9UUUmES.gU.J366mEA6VsZsSaWDM6DSLQ9Z0pVLFJEDr+h.E5Tmbfal+1+1+1L6td8LvbYaAYXZbdnGc.SMd.hw6MMnuYXXMylCDL0FatUVYk7smibf2o+L94LXud6T9ba.r5sbKGbEfU8882HvOn+t28t3q9U9p1QQgYTQy555lMHHvQD0pNnQQGdX2tLD7F.Lr0rL.7GiIY6I87eYmiXicDHIHEQDm69tu6r.4UUyWOAHsVsT6efefWr.PWUA7NkZiOR0BppE+8+y+eTDpU4O6u3uXxVpN8FarwThQGhJpplavfAYCBBxGDDTJo0MOIzdRee+IAl.ZUNJJxjIDWxMGjCVH+7ySdnZge3e3e3BKVkBfagicrik.XRiGs5YRLPrpQikQ11CukCdvgjTVCpp8gNCZ2lgf+4CThwlGNoEfU6SUuUrP1gU.phkZDmuLQQQ4BaFlKLrY1a5ldcNhH1Ma1TvEY+MZvAO3qWBBBrf4D.qEmFIYNWpuhKGXWxom8eqc9+5kse+9EEQpnZzD0LrNJGMvYOWX.QAlSEQne+9iZ43iS4qTpoGFENBfkDwIuPXXXYnawfffDJE2yoVZaqeFxVCxA8x0rIYf8bwNOSAzESt+z1nk.ocsDUDIEvij6am3BbO6gzEVXDinF1oN8gZ8+LelOyHAo022O1nkFgpuuerJx.Uksihh1.XMUjUazvesq65ttM.1122ejtrbpIs4XxLo2WZf09t7YMMg8kHb0r6r.Y6jD7dWvZ6s2NsrbrOzgt6z55uDvDv7o.6NKzclDfdmRUcJiext0Z0R8.BTU8.pFGGOouue9nvHaQkcZoqhYtShtkjIHHHiJhSPPfSXXXJXNiqASZRGg5aztFzoGmrl1rPlemm8DQD6ekekWyottDG4BMeNtSGFB9aCrdGWVoRk8tzryN6Ju8+z+z0C7B1FHd3vgRPPf0W9K+.VJvG8i9+mBLr6NLCeKfsZ2t83kk7Pn0kKqgc51H.JdHidr3rDjAlI6q6085x0Bx8i8i8iksqIa9VhHx26262a5mMaL++4TsUdU0r+v+v+vYf1oyMKJ0kR0EonpsygI9mzXyD+DsSSTLtULPvHO7+1Ca.PVDmCEFlawEWrnBkCBBlHLLbherererRMa1LsyZkDabmwJy0G5h0O5Xy2b0VI6+vTtHUiAiH2VtbYo0YBVw46Xm10Aswir0MfeLYLTUj50CBB7TQpGFFVCXtlMaNcXX3zcSXNcaXBLfdVpo4uMeqScS7Wtrd13lbzih.9BfzqJhHd5ccW2kBve9e9sIsAabIaaHG3kGHWuj0ZZa7247FdC2XZBtJkjnzRujWxKo.zIukkU1Z0p4DEEYIf0hKtnU61ssmd5oy.j8i9wtm7MZbfhAAAkTQp7W7W7WT48+9e+S75esu9JxPmxcgR3SgiAYMZA0kufSc4NfIolZz3.hYAhg1CqB5m7S9ISnWqQ0jmCJkTmlN6aemyaZoOrODW1FlacOSaZZQQjEmsZ0khhZsNP+nnn3m9S+GPa0p0PGGm94ymeCskl1VeWGXKUaODPs8sunlbXaaO5pX94mmjZs9TbS7o9jeZSvvhzOJJZyW7K5EsNzYsfffMMcRjtZylMkVsZYcsW60Z9LpiVrY7Omey1FcLaLFHG2vMbCCwhAtPenaeU0AOkmxSYT8aZZ8imShWldLiAhaY.gZ6eye8eyA.pHpyryNaga+1u0x111UPonuue9qyjwlLQQGJGPgnCEUrNTF5TBnX0pjx1jbyfYS9vi7LE9c.Sf8AK.8RxFop5.QDihY2YjJrO3s+1e6Cgt.XgCYpB4f4K.TzyiR.kAlnNLsH0mSU00wIqqTWbwTKnSk7dxM2ry4366aZopIhH1RKsT1LYxj2BJFFdnRgggknMEgNIh3F4g4MeMGYm8LUp6jOOWPaz7JS1llMlEQ6lLGKEzjJUpjwnuFsRYPzEUMX2.5CdaBrxMcSugk2XqMVFXsHiFkL7G5G4GR.CPQVVVVggg1A6e+1sAqDV2HQQ2q47r..QL4n.QO5oSW+KGbRL103dYlcF+st9q+5c.2rhH4R5jZoFtA..f.PRDEDUtBNlw+j5RQDAZ4TOoiXkJhvhqT7+1uvqoLzch8znwT0qKyTnPgYRliURDubYylMqHRtvnvBAAAkBttfJetO2maxZvTu6286dJfIiiiq.0KRGx+.sZkMLLzHLrlLxkM59ixd7ieOY7GADZyG0LZhoIVjZCmKICuppCdc2zMssp5VhHaDFFNl1TEct1LS541xrA+kr.rqV8znFrJ6jAPQMrWPHGH4Qn.XDh4fffLhHVzA5Bw27Meyw.wG+3G1bMeBT3nOZ.g86D1XOWr2QkJnQjWwg5jA7x0HSlhIAwUrKjG7xPSrN5Ewy1v7w.CxrqLCqk3uwhQk5ZBXSlw+j0QDE09n+eOZdfR20c8OWJoyMk6ttq6JyG7ttKGvKCKRttPtNc5j323nWr0ms.P0pi.zZnuHC62u+HgAcj.gFfByjn4kmWSgdC+.efOv.ZyVP2M.1LJJZSf9shhhCihTTznvHUTc3ANfeeU0s.13.99azrYzlMZzXaLLPwrEKOrgZ1UGCHqEAq21a6sIzD4xJsrdajY.a3XNppYqUij0MHairYyB0yppl6M7F90xiqArjDPQl4jm7jyhQX4q1R04.2Yw.hxbCGNrlHRpVRLW+98m1xxpTqnnb9A91AMBLkAAXgZXUfuuuCFVVH.oUgh.XGFFlEujMrNKNvLoBh+2j.M3HJ.UMZoj709ZesjNyRUAv5ttqujynxWyCanw4ymU50wPHxjzgNrBzYob4xsjkk0Z.aKf566KQggVW8UcUVVhH+1+1upQGWymeOEHtd85w3lvJ6FeqSi89VrcJrIDvl7jQUMKrnojVqQgOxG4ijGHquHNc5rX562l4LkZ4W3K7EJHha95hjqd854vTlEEf5E01Zo1pV.HWpHCqpZm.NmHfjbEHHfpH6865wZIp3Hplc+6OH+MbC2PQfRQQQkCB1eYfhVVV4ntoyZYt+6M5XxivX0LVGy3gZDjeU6J.VttXuXhFmYVeowEkO5Vf.yJzBq1P1gCGlOvyqrpsmPUcpomd5oDQm7578mrQiFSEDDLMvzMu+lSGEEMEzcBfxG+3G2ThNPh3wumuSvv7uQL4TeEYAf10vNwWv2+2+1.a+K7K7yM..rHSUn.zpHPgpUIEXLGCayqmCCHwUDWYBU0JppkSJ08Lc6101222RAADQ2AT0LW+ANP1vvCkqYyl4+W9W9Wx+pdUupbeueueu4tk+jaIaiFdYihhxvvzw4dWNCN0kzaL7QiMJqZ8pUS+Q9Q9QXvfANhH4u6O1cWZ9z5zBxr7C03BEfSLc16.X9sZYDR1knFmjXV122a8nnns888GzqWuASM0TCNwINwV.qKhrhHxxX5VOahIXaz1JWfy2HqUqV346ct905S7J9tURXYhp5Vuu226ayZTaiVMatUylMGzrYSswANf3k1kR.g1Hs9V+hAiN1MabJsLBk1l1mWJSH9M9U9MFI.tmv.30ExgnxLIrAnZms.17C8g9Pa666G+676+6X8LeFOyQ0ndTTj1QUsYTj366mACKIJe2G+3UvTewUnW0xjPOuEgrIHoe50X2kJOTepWK66g.PAughHCEQ56gWZKFaSQjMEOuMihh5ShHPQKb5pZ9jMdTItk6jjDzWanJzwEntpp21GeaWLxS4zX.LI+7yOuyHAxamt2gSylMS0OjhAAAkZ1rYYfR+D+7+7E+G+betczUj1iZsaOZAMYmwhYVH486NJPROwSSpceK.qliIddW.KtIL.ZsAFfNOomq2RXpe+M888GdG2wcn0pUyjnFQrFNbnUzgOrcRsh5DFFZec9W23elrVZ26Vle94OMmZWVXmF3NGgEM+rR8D.Qb6XopNpLBTU05lxkyDvG3zwyajdm.jmtT3Jdl+nkL5SBS2tsN6Jqrxrqu95SIhTd3vvBhH41XiMxgJEhhZVht0q73dbOtI6BS+xdYurYZFEMciFMlj5sKAjyy6pxZn2NYqQMm6+9ueaeeeYW6ZW7kZ9nRQ2FeiIwbB2APuAympGU9FPIk5x5.q8o9Tep0vvLo9yvLmuxuR.jiNFqRr6U25fu4CZgAzMI8Oy22mD5Saa1fkNZicMihxX.qK.f3Cdv25nx97.65.mM8JAtzeiFoyyDL0WuM8lwPo3VpCzJSaSa1M+vgCMrFxsUxyZdmsmoFeSk6TVrsYPWXX+98i87L0LupiJ6IKPIQfZzi90Ol7888+84.RtGyiY2Et2jrt9TdAOEmmxS4o3faKG.m1sa63555.XUMoEsy4947S4+2pm6H.SZACyjIyvwKkF.kvYTXwKTS1RAh2M6dvK3E7BR6Neq+ze5O8zN621JLPLIXIFwrIzCe3n9AAA8A52A52nge+nnnA2+8e+wAAWmzrYSKZU2pYyCY0K8yURHI+R+t+RIm28M90vkxlvQPVDjmzS5oaC3X0s9nRFrKTDZW7e6e6eqzt10tJSmZU.l7U7JdUSIhLyTSM0b+LupWUMREYS2NUEQlUj5SaYYM8fAClljRKLSlLEAx446aCXE0LBeeeIHobVSZ2vB.pZt2kp2bQQQVVVVYnUs7.EXA2bvho5wUp8MxX8nmMjd0z67Nuy3G+i+wiHhnZ2TweMKsMYglVykAZdwniIpmggkagwG5Rqu95K444sRqvVa546O3C7A9.nfEhYi8G5PQNAAAYwTdmELh5bRWrriq431buWtwtjS2e+nRXlVoBqqQGbztUKiIa94aAYbcmIQaaHOyWsHPom5S8EUD5VrCTnc61ERzvoRQsOTp9dktel70Ma3MCPlnvHaDwzZWMiXh.hJhkJFled3CGkqYyvbXXKZtvvCk8q9U+plxttcc7884+6cEBzhDFqHz3QTLLmheHskR2w.PR6Tyd1Ym0AvtGXAMu3iQZ1ERFqqI111N+W+0+ulqUqVE.JTnPgBDaU3PggEaFEUAXhlMaNcCuFS+09ZesT84q30uqqOGP1Nc5XunIwVWtDeFbV.jyEi3KYz+J1pspqKhrdBishoE1ule6e6bow+mr+mzjPTB5TdkUVwz0Q65NiHxzjLVgYdocXBaLQTKKQr.wwjT5nb.4dvG7Ay7C9C9C5.XEr+8SBv6wppJcfEOSFjd4Tbw.+6C.SNiLoUEh0NlZfNIq3Yu5q9pKTamZXKayyelGSNVGYTaGsJrJcYYrXYL.nrYbb71UqVcaaa6s9B20csgp5Z.qbeG9vKKhrxfACRaEwor.9bNIQUkEWbQSaFVgkWd4wnCqYmffDCLTPFDDDLPDoOPeee+s6R2sesu9WeeKKqAWeiFwzoiBiJiG.T2GkCvOJLgllwWOOSshqphmgJdCTU6eS+t2z3Z6hdAxhl49whI.lzy.Lvy64871RpKC98+c98UEUZ0pkDDDnWWPfJhPCee6Dj4KAL4SZW6ZZfoBCCmrG8JAjuYylYAbdWuq20khs9pwmqry2+PoAI2ZHyX1PPKZ0mD5sppt4y8.GXq2za5MssqqaLlOWY8EIuHRQU0I5PmoAppp5pp58y+Jek9uzW5K06K8k9R0yjIScU04XL.S.xdxSdRGRxjQPPfs.NVVVNXnOZ1vvv7VVV4qC4eGui2Qtm3i+wmsYylN.N+F+F+FNt6.VxYCzjK9wjEMiE0oi999Puugppa+O+0+m2Bn+7yexAUgX1occe9teltlwfpiEr2INwINYTTzxAAAaDEE0+k7RdI5W6q80nQP.KrvBr6cuaq26688ZWmtNgggVAAAV2ywumzLj6LG3vwNl8byMmU0pUuTZ9z4yNaA5Mx41rPJIRPaqHhfaRoCrvBKnsMJruspZFWHmFEkUUMqIqZF0X+S+o+DixbKvLkKWdlhEKNsZD90x.EVXgEx666U3AevGpDzdhFMZLUXX3LgggyLr+vo.pDcnnB.Yqy7orQvpKcstlq4Z.H1EFznQiA.CaznwiFQ2MYyrcF019pAaqQ5FhHqos0UAV8u487dVi4XCfsWjEOasW1we10Z1wFSaSa4l+Uu4zykAnDRJQD0jWPUO06C+uui+Z4Nti6fvCYBD4sby23PbYvAO3AGzktiqSVWpxnjyosGyWDCsxWzl5XKhLpz.bEQRXeYpNmfGsN6GLio.5jSN4HPSbgg111wCFND.Kvb7ihhb.i1QDDDvd1ytoYTS.UN5QOp0wO9QsZ1rYp39pzwz9vup50ELZ0qzidWrrKwBvpiQ2BfD.R7RRlfpZrHRhXnu3E7yWxqgGiiktg00qCqYaauVTTz5sZ0Js0aNTRAiQL5JVXX3.QjggggCCCCi8880q4ZtFML7vZiCzfuxW4eTazngBnc61M1zsLHldof57PeKQv3+VfofwY1ce2eNQDwpsQiFxqpVTUsjKT9w9XerU.lP0NSfKS7Nem21DXRvxjuy2w6XFssNmHRUssNKvzqt5COAPYaa6QwUxXkUSTTjEVXKhXYxRuJQQQRhXNqhvvjM3LX+AAC888wyyKSylGp.dTB5L5Xtuu4UpvIfzzcvS8o9TSiYXvfACTRJs0MV4eKOdjGl+z0bfy04NtEsFoiIdvIKUpzI.NoWf2pcme9sSJ2DqDwdMuuueAU0RgggUhhhJ6BocxDGnivrPR4fd4xZYmdbZV.Vtti5PbN0gbpgUHk6QuJppUDQJATntQmlJBLAzaZpyLPqowHL5S3BURzUtI8q6OgHxD24cdmkIQPzaqsK.j222OiX.kRZ2ps3G3OZvSRY6RR7ZMZDjESIYXAjtA2gP6ALGC9t9tBFUJXyZD22Gw.wm.NcrHhVESBmTUc5R2LKrvBiDVXuKd1qnrf4q0nqt95qKu4+v2rkiiSlvvvbhH4ttF94788KPbboZ0pM4m8e5yNIvj6cuO1IHojNu2l2qCf35Z1UTiy4o7RNaTbZ6yTpMN.Y5XR3qMl7W0WDYKfstlq4Z1Fy3l8e9u6uadQjRppSr6Cr6owjvzYTUmwElsb4xUAppZ64TUm9o9TelUDQxCjw222Jv22x22W787SDVb0No7BcTT64latQZSTy68dGDDDz200MEX9g2+8e+5o843xN6eO.XxXVCEHt2nLK4Mna2tJfU1rYyzcjPAlPW68cA2fbLlVG318LkXyJOvC7.q.rhuu+Zsa2dcf0ylM6pOum6ycULYkdkq85ttUAV6k8x9Y2fj9hc2tcgyChZhHr4VaB.yN2rr1ZqQTTjZDsMQQjXQhGpvfW8q9UuUTTzVITpcT8d92727YG366OnMz2HradCFNbXRfzdZmu8ROaA7jVsZIef+5OfHhu1BFt3hKNPDo+e0e0s2G+cZyxOzE9ZJMibCnVss.1v11dyvCEtcTylw.RbLVgggVcSP0OJJJ6gNzgJfoU+Mwa9c8tlDXRQDC6RpS1FMZX+rdVOK4k+xe4Imi87srAjGg1orYKNSv1hOFLjEM26UUGB0GBLTDo+G8i9Q2Fp0uSmNwFl.3loURMJl33cVbo1e8eyesmHh2e9sca0eOum2Ssm7S9Im1cgRKGmhIHH6L0TS4nh3TsZU6vvP6uvccW1IrLvtQiF1hH1ppNsGqTCRaMh+A+A++RmcXff42u2K9ZW8Tds6caAHsAdg+GegCDQ15AevGbSQ72Xt4lZqdi133EkavXfg8LkVwZPskZznwI788WJLLbMD1JJJZvjSNYrppNyLyPTTj7hdQuHq6oYSqff.a.mcsqc4.Uyd0W8Umc9QLoogUud8tb.E8Setl8d.GvywPKbrSD9UTUQpWOMynwhH5sdvCZAjApWTDobmDwcUDuxINnKqpNgHxThHSKhLCINq+fenO3T.SXYYUILJrbh9kT5JuhqnRXXzT3xrAAAyALqssLEPkTZh1FroGRylMkie7iC3FiIi4IkkV0AMMqM7HADgwVibewLqAj1tv1hHadvCdvMj50WGXiO8m+yu83sFRN6adLYrcur.vevexeP5uOlZ6HBiQshT0fThh.9d9pXxO3v2ycbGCBBBF7S8S8S1+m5m9mdPvANPLF+bPG3lu4aNYswce55bvkCazPAFqy4ZXpjaaDnJXH10vnACR8yMf50iAL0F+44XB6SWZokRtu3pc.0xxBGSa10RrzQcLfZ0pYGFEZGEZn17A7a.H522y+6KdW656ZXiFM1QGIpWOtQiFw8fXhNk4KmqOim0MSkvXCEHNxv7R0TcGHFwfz2hIuf06c587AI5HwlsMIzYMfM7771DXvzSOSb0Z0hAz.+fzNvj.lxRJAbJKnNAAAZzgiFbEWwyrO0MB9ZsZ01Q.626dOWrY5a21omQ+yy5r6MgIqnXXxTFbIuYcJYh1pNwq3W7UT4tu66tBPE2NTVUsTBfJkqWu9DjHL5X1jwTuhWwqXBwH13mq1lonpZEFFZkfEn366qFlLIwpnC888GDEE0uyHeV0rrrrxFdOg4AuB3ZzIpG5a7j4rCy4R0sqZiZsza433LH426jub9rUaQdXtr3cA02gwNty1GX8VlRXegvvVKDEEsTs4laCPFlvtlLMCCKDEEUNJJZxfffohiimrCTw6o5U.byAjYxEvFZbofVG7HIS3i7eZd4Y2oSmjmqbsaaVqIGFPNJiI9pR.EaoZYQjIWYkUlAXtZsopp5bpQa4ltspSppNwW4q7UlPUcBU0JOsm1SqRhNyUFnzvgCKDEEkUMhDtEhozCS5W8FIMA0RS.hNLJDfgu+2+6e6fffM+d122ylf6V.a6NOaCt8opw+1BmYSI37YiVSrUqVI+cAwIhmtHhXMXv.6+6+O+uOJNwyKz2m9w1rN1ftvfhEKNPUMte+9hHhipZ1NiJEXqxG9vGtxK8k7RmLLLbhffcUBHWylMcZzngLl+2gMOyFtvkh139ObdHHSRqmNYesF1NZprTi9Q9I9DehDhSVy1ngITRDYpu3W7KVkZ3B3Ih30VUeQDOLRXeUfoty67yTVMs9aCa4hBIJJxjT+wYOE3HH1W+0+bkfff3id7il3ur1vNc5jDezbCulq4ZFO1jKx0suzx92C.lL1fcS0DzneB6KZ0+ce6u6AhTOFiyK6ZioSE68gtHx9bKFhO8wyaCfUu5q9pWBiCgk888WI46OIvhe5O8m9DppKC0VUUcsa+1e2afIi+CpUq145AQEf0Wec.gpUqxByuvneQTTHHoZUgLPfs+PenOzVppahIH9wN1chMkgQ8sBBB1JJ5d2bWOocsMDzGZMXOe6sVPUvnFoufep+yJzJ1Eh+tmYlA.Ctq65yMXtHhgZOR1DiIvvtc2FXymzS8otEPeuf.UA6f.ur27M+qUHg1Yk888q7deeu2IfZS9A+vevIdtO2m6DgggU788KCTnZax.0r9DehOAlisaroey+c7ENO8MvZw9123hA2orHSsZ0v79ZaA0k986qhHwarwQAWrEwKGzonGTtVRFy.lwsCy8R9IuwpCGNr5C+vO7r+A+gu4ow76KCTb3vg4a0JJSRMW6DEEYKpZ2c94c.bdJOkmhMf892+9sRo1dPv9svPOP022OFWR.xocrKnM.tm64dD.YOG4Q7BklO2G6XosQ4XLAyu4y6487VCZsF3uILSBqtZdw3XezwA72.5tLvIVe80Oguu+xnLhkXQQQHhvC7.Of.X0nQCafL0qWOwoUube4ub6720ccuIfx1zlYPtDBDtS2NCpcl7x4nPFnUFVfLTiLTudpyXQa2VSL.r9KeWuqb.kg1SAtynplPQ81SPMlTD2oEQloFLSMylMRZ2bT44+e74WFnRbbbYeO+xhHUBBBlvOHXJUimgNLmp5r999SaYYMEPkm8y9YWBFUCt1u7W9KW10t1kZ5rJtC.2jLm1abvLdjZJ7PJKPLtDybyMDXvMey27.LrWbzwrd85WH+H.Ggpf9m9Z+MRWGK1s6XLBQQAQULsc8nnnXf9hna+R+oeIadva4VVGX8F99aFdu26fjis0sbK2h0Nm+iwXfDdoZPeiaiCrShf.1NVUMtCDC8hoK8k5xVN99IsAW1h1sGAz9433lLm9gDlzPcani3lrlYXXnQuRTrzDQrta2tNphyW+neca.q+t65thUgAgGJb6FGvuOF.CLykZ2djOhidzitSmn47W7LBrCjJpp32BEBF04uL9e5hppUWvFhrYIrXOm2MqBi1Dbz.yFdpuAvFIkjyV.8OwIVbXud8TAzjr.Jsa21x2+Zchhhb788cLYKrsfo65L.5tMsYSpwVL8z6HFmG4HiKBoiec7sK6zC19zAd+rDL9QL0ep4mcDQxUqCkA2IwHv4S9L99elUdROomT4s2d6RsUsnHh4UcoTGSF9mDCQUlFX526688lxLtQqEIRcilVEFI999hN55SETURzjNUDhshshg56nYM3pQQ2mDGGmTtJsxQGxRcb3huDSuPVBKBn+t6x1PvlXJi2sRZPAVhHY6Yz6rbzhLor36Bb9igEF.02DbWAXwFM7mud85KFF1ZEeeusSzMnrVhTPUs7a4O7OrBTq7ryNaovvvB7v0yCcxBjYoT+memi0uiGi0oC34Y68l708YkHz31PKaCfmthpsoJX4Y.qHKPg5hT526M9GVgD1i.0l509ZesyppVsioaKU8s7V9iliZLiTu9zhHScEWwUjH.5TIQmIRALonsscNPxfhcTXj.pooQD3qus+GuMc4kVREDrLO0pRrL.Xye4e4e4UBCCW9m4U9ed4nnCuJvFcfsfNa+t9ieW8YFFhgolORiIVYunvzJDp0.bS.Qzwww5sdv2pvNc9RtHSvkR2toB0+l21scaa1pUqs888iAr+E+EeM4BCiJ9d9e8dJED3WAnxwN1wKKhTJLLrnKjuQiFN.VtcQAuK2XjYZRGG0IBcccyqplW01YAuLfmippMz195tgqyVDWGnaVWCi5JALkss8btcwSUM.y.ehnU2tNF8ZZps1ZqRhH4hiichZ0xRDKAPhSZa8ILSRpUqVhe0CEC0Ftmcsm9ggg8a17PCLfR4ECyqyA5t24yv3qaCem4Y7Gw1kEWjW.6TB3eFH2hyRYxvTzlTw4pbRz8qHhzEiRXdRLrF47E7U5lHJjbb1Mv2ip5SrUqVOFOOuIA5Ktxhz0sCzY9ZvZcqik1RKKhTCvyLQjZX1jPALS1OiIKsZ0J4mU.golZJ8Dm3Di0wdXYfSJhbx866uz8zr4xhHK0ue+EylM67999KTCV5s89euq7heMu30nCoBPaZs1mpb9eqZAhz6E13QFZYpQXLkBUQU0bhHNIu2MSttVFXs8Aa9Pm+qsz6EEcM2K1SGy8h8A73aE1plJZFP2LX+MVJ7PgmHHHXonnn07882BX6nnns788WCCWm6ALOvI.VYZXiRv1M4rlw3ucZmty4SOnAcO.aB1IJ8eAlixL+bkf4KUCJz0kr.Bc1o2rqplMgdcEUS1LlJorabAb2byMmKe97U.xK0kLZacDHI9991I0csBnBLTG0YYzUEwZQMV690O5WO5E78+8GcuQMaiv7M7Zr.lmyVgDQPlD1e8i7ib0C+jexubJc4uPNsF+Y7L6FxuJTbQymk7RUwg4qpPu9UgM6MCaRQ1flrIW316qA7CHaenRGnFt7XP4Inczuaf8DEFMKBY.1TUc482nwh.c6BKzrYy4ar+Fmjt0V43G+dW8520tVqSMVGk0oGqatUwkZsY3S2Ik09.qkA6lfMShCkv9e8y9uZ8DdBOgTsAJsyQLYMSqdYx1P4CbfqJ+8bOe4rhT2R01.DK0kXssFm3PMahS5IWYkUlsRkJoqIOMoc7Df+k+O+eF7c+878rUTTzV2wey6Yqa5FeCaiQXsGHprMVrtp5IBBB57w9Xeri+y9betQcMOCuJywVsdfVa+E9Begs+u7BegaD5yFLjMoynw7Go.EO9ydiJoua8Vu0IdcutWW4pP1tpN3G+G+Ge8OxG4ibRpxIoW0UgdoyuG+d73GqjVEISiwWx9TUupvvv8YIxt878mJJJJKBZ4xk6u5xqtNBKALePPP2vvv1.c2ePv7JbhtFv5MZqU25KAsWwC1rkYc9w2P6kx1nwGOHSKHKyggUf8zIFLXvDNNNESnx85hHmrNL+FvRKwHvLO8moFOnxRj3u.XeCGN7JiUceN116JJLZpjmqAXPhlistIoGrXPPP2nnnl999GuYylMaznQmlMatzSpQiMG.86YV6ZaZvVzzcKnSJKXNaWOvoAJIi8bkGLwQ2d6xYylMKtL3q9Y+pq7DehOwSh4d75Xtmdt.+azb0YfoVzkF.Ow9M6eUNNNWQTTziAXxYmcV6b4xssQz50UEUVyug+ZgggKQhOQU0N11z06ZaziNr.F+iqgYMrz0p+NIKlFerzL2YeHIko5o+5T7af4Y4JXd9qJP8u7W9K6dUW0UM2wN1wlnUXXl+Ceeeewhqrk1Q2LojmIgQaUv.rxTIrCHEjjczooDVlbO2y8HW+0e8LVWMz32Tjgpp8Mk5YySRrUO+F9QggggWWPvBBrbGXabY6V2Wq0hiiWJHHH8dvpri+yuQ7gLN33YwDa1jtvLsUcZQjxppHdxpzl4wDizI4b+rVpkF+cgEqyzLjFzimfp5UFEE88nn6QPlQDIiojAzkzXo6fgCZZmw9que+FGSfvNPu2za529D+52xu6prvH+mWLwH7MS6bAPx3m+S+4aALst5i3gsFo1hIU+mx3LvzppyIhLopZgDlco0.qNownUi7HjSaqYv.fkElxnofHxjtvjsUMMwVoMu.i3ZhZCh366KI.ToX..MNJJZHl0Q1.SLY8BBBBCCCO90EDbLKH7dCC6Dr+fEwlkIZz87wKe9K16CiXBQx02DXVGtJlxLJCvFhHKBzwCVzFVq4N9tNWywblDJlElqmYM8u6gCGtu1sa+ckbryiPLJaEDDrYXX35.qFDDbRfNMa1LpQiFFwYwHQ+KCrwdg9G4R2RZc707rbgrYLc5Sy9qbIOclKKLedWnPGWJos0B.NunWzKJ9889deCRJ0yLXzMmIRVGaD6jpKRwDQDtvvgCyZaamQDI27yOe9YmMmEVFg...H.jDQAQU1hQQQoIoxwbAIwJZZGtZkJUprvJqrRDPXvABh.5QaVLLLbwjw9kwrFVZL4oymtTcL+rZWtyvD4z9dYQlAVfgltESsTkfOEE8sqel5Ix4yL2DcY3bL2VXBb3j.K544sXTTzh.KRWVD5bBU0k5Rs0oMac0W8SpOTafoTIX3RKszvOzG5CkN43LlnDEEkJ5bitrNwhmH8GUAI8uYfu+01+PMi52nwAFr+f.8w7XdL.PTTj1k58ew++7h2jNrNTacnpoSNrmus3zYmfUZQLL8PXNy3cUDQDGWS.FoheUdyO23hgxooG6gcXls6.a.0WKNNdcfM8B7LKHnRovCENEvrQQQy56ecSEFFVFWJt7xKm2ExEEEkOIHnbldS9L1m.jlMFcd9NgctyPVCjO7G9COxo7QopSGHCTKqKji4IOL+++T26dXVxUY89+4sp8k99tus20tpZO8DfNgv.HYlDhBQdDPOh5Q4lhdTDOOpXHJGMBIA+QPO.GQ7mZBGPEMFQdziXPOnbS+8fBHWTHQfvLQEF4xDgYlcU0t16998deod+8Gqp18t6YxLSBgDx54YmtSOcWWV0pVq2022uue+Nhp5XMo5DDy3+Vu5aabvYbvYBfo9y+K9ymQUshCTQDoLodTOl.AGcngFpucxkBVReMFIy5MyD+KESwBnhQL1MMkq65tNNdPft0FakTysVOvoWZIQ.9l6wrbH7Q9H+aOXFGtu.UNCS2aIiaI00SjNZKsMzpCPRKl05Y7DeF4nN4goxr6yK57b0gj34HSo+WilrLvRgggqN53it4w7OVaOOOUDw5xutmStnd8JBUF9pqUajv6Kbz50O9HG5PGZjXbFglLDsnva+s+1ycop96OB0NXVYSCbdd6SB10mI0xTWk7DRgq3JtBy6pkonCNY5QR9lTsPjpEo5SXzOym4DSHhL8W8q9oJC3HR0pZCspHhWbbKevolHRsm924S2ezQGMK6EY.GmUi7EFehIFJLJbDfwt4W0qYhvvvRgggSJhTRszRnL9oO8om.nzt61oTSiEANpKjmE.WW2d+H+H+HcCX1dDRhQmKNxAu2en0eMC1N3j+U+pe0CALRKpNlXrOuQmEFlVjGZcwDQbSfqm6l5TKKKUSYUhHROT5Nw3SzVP5u1UEprquueaee+Nw3n22YCsBBBriN6YyQSxYXWFVQF1c8Xor1zm8fQymtt3BzkEniHxt4ymeaoprUmNc1TDYKfcZvzcWE5AG4AB7fzONVPECvIU2SaIxYaKgggBBoBZnfQTm098W999ZXPfoTGqPuqo10zCHoVsZZDylzBz50qmTFTSs8GeglOSOv2mkk+dtFG5pcDzofegdppp1PkmzS5IsWoK5btrJ7A3bjrDNcHlcHlM61MYSLfsryYO6Y6s3hKpgggV99d1.48p4UHHHHOFFWX4eLeoVsZnpklB1t4yTjTyTtyCB.vAuudjrkN+071tPdNYlEntuxGIc9siXd9OsYdlY2qjHFGnzS8o9TmBnzzSO8neGunmYQQj7ZrlmTA3TUcDopj4nbYVt4HPeWlaz669tur4xxCXeMWy0XktY0DRiayyyqqpZOw3BQIpZJImzMw1qIz8DggcAZGb7f1IIIc888Mf9LM1SwTObx1h9y83ZJCndwPWohjEabNMRyhQqHUIuKtWr0QSG+UqCMXaZwZPkkDo5B4ymeYQj0A1USRRY6sXaYaYkOusTy6XRSP96N9myFH2sdquw7rXe.EyJc9Goa6MF5H6Su0tP8+xonlEQY5Bxb1fQreYVy3upvvokw0nMZzXr2467cLlp53MgwqWu9XppisyY1YLsgNZp9lLtZbqooDQlVUcxFFM+pOSfAFpa2t4877rybasAAKAnWXXXmfffcQ0c08rw4cBBB1x2+Xq2DVqQYVy22echYKBIyA35v7OnAKYf17l9xpl9OUU1byM41tsaKQDIoLj.yRDH0cuzliaUJ2sErC3rdEXEKKqkA1PUssmmmfRAOOugCBBFAXjUWc0QBCCGILLbzqtVsQek+R+R8AYZFHOTJ2o9VO8K7fs9IVHloxUGJ7RdI+jEcfgda25aanT2ubHo5kODwFiV3c9NemE+q+q+my.ycTfRFGwT565WYeZnZVhrl7q809Zim96O7LyLSlLVjCiFDgmmGpwasxLsZci0VSATe+i0qRRkdzfdP081qs4Yqky.BeOoIpiGCEuxi0ALA1CYWKbwBVJqSuGzrc2tc2Y6s2dSQjMA1twdnjm82dwdHkb3X5s.KXz3.WVUpJK0tc6E777VXs0VaQU0kq.qZbOglsA58u8u84TUiSxD0qRkJ08E7BdAcwb9OGTa2iVu66NKcPojnjZ4fhzCml8TQ6EDb7jOe8vzEf0Dui50AZrCvVwwwaYrWvV6TCZyo6Og2iDszIUWVSohkPKxpuvgUUG6du26cLfQiggf5Y0+6EGzjZzipK0FXKnwZppqDFFtNv1ppJxdYQ5u8u8ucpv5e9RGy2ez5e9vBiM1X4ZnZNU07hHEN0oNUAi0ntj47W+RZ7v2LZGLiF6SvMcqi77e9OeyOeFroTqTEUu4vw3LJvXUgRhiLMzXlO2m6yU9W4W4lKCwkg3xUfJurW5KqpHR0W6a8s5ppVQUcFQjRoKJmov582TAfcRRhsldML6ryRXXno7eDUQPEUS.oml57QohHXmq3Jth1.seuu2+vt0pUKYV.BLGy5GfsLG3yEqkvQHAmkRYlRbmW0u9ucGQjtUgDphnZqb2y8bOoY8a4BvoyDLuKdcXeFxDttMvvjqE877VZ7wFesSDdhcBBB5AX8+887tJ7Y+re1gBCuugNQX3Pug2van30TqVwvvvBPbg50qmGH2q7U9Jsf58KEfGka6iIdrurbeJCPIlfTyml8mrMMTvoEC0PaLjHxP3vPTswHu427adTZb+i+YtmOSIU0o9eea2VZs82XFRydakJy5qZiZpp09bepOmqkkUYLfzMZ5wdvEOyiYg8rrYLlmm2XppidLO+QTzQO7gO7nAAAi2s61k.lnd8iO7wihxAvrPOilHsPxd2mm7giMYHdKhcLw4usa61JRUF9s7VdUCqpNxe2e2e2PKfSAfbyYzjmK17GR3Ad+ta2tB.JnXDiytHzAnsJ5N99969WdW2UmlzrWXXXBUPwIVw179h6gtF.znnHLZhQ72.2pOp1TNUlVbXt+IMv9JwrS9742EnMdzkxYBr6IuPabWfXKnosppMM1SnoyboDfTcByUia1LAMUQ+ERBBBTOee8qelyzyoI897Aedy50Nz8S8od+YfmjzxnaN5Cnm1cv6w8+IIB5AQl64VljZHhHunWzKp+XjxwmShgN+G24IAh6RJ6MGd3hqCrt.aU6PGp8+m+OuqDOOOBBBrVYkUru669ewdmc1w9Tm5ThpZFHI8777RRE.R.rlaYrpabwhAO+OZw9xAlG6T1QLcNfbSC4vYeymjNmxIKVCFhkXXfgWv.VxnhHiqpVRDYhM1XiwVe80GRan4UUyIhj+ttq6ZHfQeGui2wXZCc7TcOaTopjIvqFmjAJdUW0Uk4jU4HSrWMWipuuW1FVMfAZDJ6zjeIc877xFq2NQR1AXGee+s888yXSh3tD1Kyxeyn7TR0AnlFfwZ0m8JCxHmhyzf7QDcwzSjzwz0GXMzlq.wKLyLyzRUCSd8782AnqmmWhpp54UKIH3D8.zm9wd5.Xe1yd1LvRxAXe3S9PRb3en1NGF9ld9OX4PevOoZxV8r0UyCmIODVvAFhELVUcCC6llVUcZWW2o9Y9Yd4YN2xn0pUaDfgKVr3vhorkFIIIYrphLAFlojwLyRQQQ8c4QfbMa1zL9PPR2TaevR.5.xtdd96hHaaIx1999aigoIaCwlu1hsYOFwNPI38Mh0NeJYNPnAF8OSDFczQStka4VR.zV.vBlmuQWRisUNbqz43hWuIUWVDYwolZpkDQVOJJpsHhXhEig788G9G64+7GIIgQ+G+G+3iGCS71+c+cmHsOejEggfUOn6M9n09.NX6bStkC1TaYafbui2waOeLyV3FuwarniAXygih9JCSJnbMa1bDHdr+KO6mcIw35MyDYzEmYUUmsc61SiA.kLwsdLfQle94GFCXwCkjjLTZeoMfDDFJgFsuAATO+iYFSXYk1iESylM0ie7i2KL738788My8EQxYO6Y0rnSd+u+2Ov9rl9uUn+9h1drLfICNwl0opgEQ8Grq.89LelOyt4ymeygmat0A1nUqVaoplQ4qCdLNeMEfSC8fo63BaQDqRLKTnPgl.MKUpzh.qzrBanMzc.5jVSpHh.FWJIql6ZCzIIIYe.lDFFlDFZBPQUiYy0Wf.PTUSxNFIhpZvwCDeeeKeeeoVMOyByGyuWJEz2EXWGGm9kBP8ibjGoo8z9nE6BttVpp1REICg8QeZOsm13evO3GLCg7rfMtPOOLGu5ziFzgT19jKWtE877V.X0zZ1lzi2HW+0e8iqhL1wCBFtVMuB0t5Z4hRc0DfbyO+74MzXre1gdzzivGbyr82f+ggTufnrLCHrH1rJ4dBOgmPQU0gUswn.SDo5TZrNS850KeEWwU3npVUU0UU0qoiimHhqpZ0a7FuQGLrLYZU0I9pe0uZeaCK679F+Ue81.Vqt5poTHQYokMRlWylMUzAYIk1y22OAQ546620T66FPAewu3WbO.cAWWqFMZXiG4bNW.Sdv0NIIYA1Cz807q8Z5.zowrzK0EKr08blkB.4OYsKpEhmcszCn8G5C8g1DCMnW3e3C8OzxxxZYWW2M8886566a445kat4lqfmmWdWW2b24cdmVMbvxyyy9K9E+h10167YVBI0FSeTpsuf5Nxd.kjEjeF3H1W+0e81Xn.b+emJPwFpNjHdiTEFkXlPizIdcutWWIU0IdlemOyIDQF+seG2wXjUK1IISMP4d4l90Aso5hozC0BP50qmXaaagZ1jijdsktPcwimo9899CCLxy7Y9rFMHHXzZ0pMrqqad.YgxnD1+9874DSOjBBZp9fb3ZYkjXqQZtjD67.4e4u7WddHNGf8YpgvEVSdDbPb16c78MWi.InofEjXJePfcCCB67e6m3GOILLDRvJ79BsHFR2vQuzMH2y8ntYtXx2xSq0KPaPVLzCnWEi39k.U5oppDhRqGb2ihiiIeXNHVVGHjGA9Wtm6QEHw222HZ1l42zff.dbG5vDaxbVhCjPL89N+N+NGfgnF8rIp1kz0zAY3U13k92uhHJf79deuOaLk3PpsadjK97W6A3zNXnb+J.KO4Lyr13iM1V+T+2+I6FYJSDYpoJIW1kMGO9G+iOY94mO6duap14noqQZ6.4NigEl4N7CfFZ8nPK6bao5hBf0u4+2+Hg3AYMW+M7ORcOOCaPpv3kSEjZ03PIimVdpC655VTDIa9vgdouzW5XppS7q8y8yMYZ4SLNvnep+lOUVxENH3L8O2dotIAfDDDH9G8nfAPzjqxyqGjBLJZafcSAGYGIYOFkQZoIDEEIQfMT01Y+yi8MZ+eevF2ZqsR.5pp1wTtLRO.1d6ssbgbKB4fR4l+hOOZ16tokqKq.rvMdS2T7TSNYCOOu9k.QXXXGSe.cSLrNQeOum2C.xgNzg5O+cMidZ8v488kRqebnGF3qqp.Glib9O+le17Xyo5mjggXOVHMd7ryNIvLTAmzxyu5W3K7uU4u+u+ueZ03XbSfYb4noex.0aTKKqwRK+lRoel.XTWW2LFAmKYOffALf.mVNXFcjSjtf1t1U6uiHx1.aEDDrIvV6t6t67k9Reo8.GY+reevOOTYUldlRkTfDwQ5U0r+n1X9Z2ud8u9ASd8E54rY8ASxeSEo+FKCzZ3gGtkmm2xdddapp14c8tdWZyFMrBBBxcO+q+qErrXnW1M+RGIHHXzvvvLw1cXfhm9zmdPAa9RY70ijiASmW4vFWlLlbT277tToRVp1xFnPCUGVizQDiH.O9ce228Du1W6qcRfY9H+G+GU.boxdwhIhLcgBElTDY7d850uu.nfHRwSbhSTDnnkkUQQz7HhMJV9oLxL6ZLH33hBxoO8os787sCBBrArcbbvyLWWGuq5p5.z8PG8P87RGa8BeguvCFqxiIha4wx.l.CNvsNYBAI.JSOcmu8u8ePiNYzp05.aTtb4cDQ5hIiPGbx+KBx4K0MxrP1pPkEtwa7Fi.Ba2tcrHxhzj0SoL7t.sEW2NppcN68du8yTFFQZsikkUBoNLAlI0xN8lrF54lZyjZpH.JpZBhII6RMLLTdWuq2kUPPf366qDSu50q22ZYoZ01opVdWN4IenJ7geizTnbZ44DY9ZKjHUs1c2cymKWthO+m+yu+KoboxvDi3n0AXKJyJUflhHM9JekuRyvvvU7771NcQBaQpVPDYuMmECqt95IAAFAnBCEyRyFvdzNiuA1f0En8.sPvACh1hZXiK1TCqS2+5ok0h8ybg+P2+8e+CKtxnNoYePDYZQjYum64dJWpToJ.NOmmyyySDwWazvm8uw0YRo3Yo4me9Qwnp38Y4yq+M8Fs.r1d6sjhEKBHzqWurqbMabq+w7S.R50qWOOOuj84HIUqhQ1dvhnHqpUqZSHV+I+c+cVjdwj1dvVVNJLuYCFtzclLfGWHEPQQrDiZomGS1+yS85WJYNnevde++Le+aiKqgCK988C78s.l56e0nnnc.5cG2weD.DDDfqIigIDSxq7FdkIO4m7S1jk5JUTnp4daOKN9Q514jghSlNFp1d.lzmcQ24cdmlf+cHGtTPUcnlvHhHic4OkYlnAL8S4o7TlVD2Y9LelOyThiLAvXufWvKXvrtNhXaOFo0q8.exrzugvnqI8elDGGK850q+lHc87RAsQs+reVCMsUUyeBS4CjO8c5BTgBUydms0dYDLNN11HRsk5aagOj58NhA0LSKReU2xsnhHby27uChHxm3S7IxrCQg5WBGuXjXv9+2a+1yrX6bVVV1tttL93imjJbc8vht9GyuKB8778R.QTUyohl2yyKuoTSPqcM05VIiwhMdDuN+e3pctfHLEBU.p.mc2cE.Q03L6uzBl8fqS7.DbcYy6zMalHhjPLIG+3GWg8rwYT363Y7LTL.jn.IpwchT+i5yoO6oSOmwVw8+KLtDEUH4Lm4Ll2+qeA62O38n8zmKnYJl7jnppxW9K+ksoL4WpOvem7RAH+8z6rxkWGptzlat4hKsvBKMwDSrNJ6nF8mJ4nd0RTU6FEE09q7U9J69E+hew1XXWhFFFJ3fMUoPb+RncoBm9Q2DJLXyzO6fNS5ZQuhezWwfavvl4lq.L6v.iQXXIU0IoIkZYX31jNhLIPoa61tswxmO+Ho56UwM2bygTi63LNvjQ8EvZy7WW20cc8ALQpJCBTxfwMXEFE0GHkv669r.vy0K4DFVJ1IUeTFjkw8788M.lVlN3POph1sa2ziaCq3G962UfjQFYjtoWSsEQ1kJzNNNt6vyMLQ8Gmtp8kXYKXlCqTocfJqCz52+s9VC2YmcpuxJqDBrfZJOmcRRR59ZesuV8PG5P.HOym4yzbuVEaph8ezezezAYj5iTs9fCbZ.S7NmlCjM7AWWMWJXIEw.Tx3ToRIvcJfYYgEp.3owZMfC0qWOeKqbNOum2yaFw3ZbkTCnHiqFGkabfwwgIj8bAmA2jeevR50qm01ato344kUxzTsZUxbWyvvvDmTWkgXZqptShoL81x22eqgFZnctxq7JaCz6e4e4ewTBXkwXqyy2uu3gLXI.JqtpIgwMo88u4l6HhryW5K8k1UDo6g8O7AkHgKkDaY.kykspZz3oEDQZljjzJHHXYfM+IeYur1ULtolr5JqX655lO33AEAJ9t+q9qJFFFVHJJJOf8gO7gOe6E7fWKG7m8MS.712ZE.4gSWHtuVIMsYuJNX6XzDxho.sUBXxa8Vu0oelu3W7r268duFMzrggkuZbeszbZ0TVWiqpNpkk0HhH8K8FU0bG8nGMOP9jjjbtt9FQjULk5UFXvhQEXsDvZt4lyJndfURRhMfk+QOpjJhzcnYSy9eWf1go.yc3yUSbfGCD2xi0ALYeHTIU5C5PBKsTWn0tNFQbK0l8l0PyQWjHvhouj1jNvAxbS4lK769696F849betfBEpEhQrzVlJrNvFhHapQQaBrYsZ01VLhI2tXPwu+.jd85IIII8O2dddR0pUIHHTMnEKYTqSEnOMY8OpmnpZ8betOWoa2DMHHP+nezOpV6pqsWl+aznGr3AYVxijCHUnUhaJEq+i+i+iaCNsEQ5V7PESvTOiClo6K0M2jvQhMYvnEq1jxs.Buhq3JB777hCCCWJLLbSzjNp1.GGGaU0bgAAVfihEIpklzDTbPBBBxn.5.0A87ObBZx4CTtK3Dx5YUKmDGwotYA4o1eFyFFufQAlfFTpIUmTMA0MyK+Fd4S+LelOyoRyZwze7O9+PYU0pc5zopppCvroTyaRLBuUVsJNTZYXrOm3Y3gFQlYlYF3B2X95pln.IAGOHAEMNN1rYCKvXghXe++S+S4fl4ql1m9g9PeHKvQ9A+A+AUlpukf9Pa73QNk4c7HRVvnQPlMs3sGqlDQxAw4md+kZz46Y5fuaXxbQT0sIh0JGyR.MqZDJ5kxmO+5pp6bC2vqninZOee+dQp1Gnna8MbqYYIt2Yt26UgF3t+m0WrLn7vY6faRKWpsykGOx+y+a7ajmYnv.keSV+Tdh8JPDCWwTtVkpBS+U+Begx.U9m+m+mc50Kn70dsW6TZrNlHNi79e+u+h.EEWYHCqmzQHUnmS+LXPd6qzu51sqL4jSlcMq.DFlAvjv0dsO88sYDiEnphmmmMMIWCpjGHWPPfMNleGGGGI5DmPfUyJGpGR82GwDgr5mtIfrxqjpw8.Rld5o0n8f9StHlgj.SaAX8+yu4MYzfHm8X015qutHR55WpPvwCPLRpgknpsuueVIKULL7ya..tA5uxse66kQvReqe.GO.sACNLGKWyllXUtYZIo.16ABJ4X5EtDKOfVFVgzOqoU58TepO0DDznzrwB8EacA.eeezDiQbVsIVyM2b1+K28cueFK4zvbdahN2bemOXWaUXFr9OWYECn3m67RVh3l+m+m+munSKifhV4AWBELkDQqVa.MV5wO1XM888aEFFtT0pU2.XWQn6IBB53eL+cuu6691947bdNaWpTocCBB5BPRhjiXJTtAEN6YOawO9G+iuu5X+hbM7My1f8yIDSxRTdOP5mt+3n7blyTDVXja9lu4wAJ8Kb8W+TPkY.lgpLarpyr81aO4y5Y8rlnPgBilNu0v+T+T+TihgobSxdkOQFfui.TTpJ4AxoMzCFmP+OdFWsIqDGsArRsiydhH8DnmHpx..El9umPqJIgmHLoZCTMeds7d26Ob1GlVBW8KAtLAOdSZ5tU0pUauxWcEsx7ya6s+0GtPu6s25nqtZan4FUMBqY3LyLyY+QeQ+nmAHDRVDXSKKqt+l+l+lR5wtfuuuYbViJ4zH09+4q3UXAHkejY8xGv6kibtrrPnerRGNK1wh3wHXLXhIz33oTMblJlMn5hQE2pA3sxJq3bjibjYYuwVSgojax99IAlTaXrR3FMZLlrm8Uuu2Csssk0VaMIJJJytWoQiFDFFvsbK+JJXhOS.MSzW+m9jehc7882LHHXSOOus.1MJJpy2w2w2gY7XqYrXQrRcwvGp86Gjsfc.mcFczQ2VUcqq7JuxsopwkOOP+5E6blcb6RD61.+0wjPq3+8u3+dCOOOCSSbc2zyyqsHRhuuur4lalIdtV2zu4qxxyyy18o4dv4dEN2w2G76OH3nvCuiMO34xdp8JWYiAZTdIilIESglTMSSlJ8b9u7blFnrETVihpb0W8UuOWv4cbmuCGQjowjLqQG3yvpQSG6Ge1MbC2fMfskkkc61ss7MIvGv3bqdddhppQOCytlEv11l50qq0u26MAhSpt27KcfYLLY5Hz4z6mESOlItkGKCXxfnRkv7jPKRXl8IppchMr5HkhiKzEP0PUfYsYoyIXkKzh.YTMbSZ4sDP70dsWanCMit+6+9aJtxRzjUTUWAXYQjUDQVa4EWbcLpAclJ2S54z1xxx1xxxZfZdkFMZXFBplyoghrR+AU999RvIBjm2y64odddIc61Nw22O464m36oGwjf691.3iFCD22FPiRWL95u9qeaHdKfszXcWQb5ktAgAY3wEiI.li6IIACk71vkVKAzPbjyBbVOOuT0uV1HHJpSbiF7q95e8Vd994fX6mzS7IYIIh.UsHFaee+7G5PGJCzlB.EJyoFjhd7.bsbozN3jeWnIj6OgcEQj33Xq3zwIKSM6zMKLDvXDVYJLh1ZYGZTAnLUY1+ve2+vo2byMm7G9G9Gtz.A7MS974mkTAcRUc7jjjwN0o9OGsQiFYYs3bVHtamtxN6rCg6swBCSmDxFOZ9XzcASM86US7OpucXXXwmvUbECCLbiT2C36+6+62JUXDSlYYRRYIxCkwo5.NhPReFd3ROBoGNjr6t6p.npZuz.0BMW3f86WlOyXzAnMaAKqp1JFmF2zMcSwyN6rKBrdXXXaOiM6gqHV0pUKGNX6W02hxli+byMm.XsWF5NGpam8r+g61AGOkA1V9TKarvrgT3085d6EXQC8KY+t8vvtDNJv3MUcJU0YaPUG.uff.+ff.OKKqxo.uMtpwYfgLjFoCmVi+ifgYJCJFhYAbuu9hEWZQYkUVAFXbffRkxUv22ee2K+u9e8ane1O6msmpRxa8s9V0ff.qpzLGPN+i4aSLRp3BiqqK.x2HJ5QZFE0.lIg9znmtZj1SUsmqqaRYhLiiqgxouXOSVR.rqtfgh8+Nu5eGaKKK6Hy7+VAAAV20ccW1fZO1XikSw3TUt994BBBJ7te2+UEAF1y6XCkpSNV+12zMYNCyhIWaWRNy32xzN33TafbUM5.fcKPJVrHsHsjNMYSylkbujoOc7dqce2puPG...B.IQTPTk.MUaaaUTTEAzTQeU6uwWqff.wRT4zm9zRCCvq10l6wsGXhP93+s39qMThyXN9G4RZcWAPldQjIm7oHDgnptu6AQDKGZj6i8w9XEu021aaDfgahaFqFtz1rZe5pyxMSYfommWqFMZrh.agxtJzt9mu9tOsq5osSud81oVsZc788ILLL2wp4V.XnVvvG5PGZnmyy4+1A03pGo.88fsCd9TGZY1j2bXwRFlwwdhJ+nWwU73GGXp+f67NmEZVAnpFoU2Zqsp7R+wdoy7s+s+smw7sQDQF4u4u4uYLmT8MgGH1wk5fbLv5JGPWbjvvPKOOOaAxoFQ00VUU7884nddFQ.SEq5g0yEDDTnd85EbcMiqcnYhpZuFP24pVsaKpNn6D8vUSS0lBiSOkBXhHxlu9W+0uMPmImbRs4oNUtPpLXRsdvjfwsaXlUpIP8Oxm3ibFfyN1Hi0PUYEU01pp1gggFl.AiYr90lCIhjONcrVKJmcbuPIe3ajXzdfNFI.Imb+88Vv780ojo3zlwZtLJgLFUYbQjwEQJ8q8FdCS0zLybkkVZIGfJqs5pyL8zSOoHUm.nzu7s7KWZi0WeZfYDQlIcCsYZUxj.kpVs5XpwAS1GKn2bysjFMhjz05xVyq+s0a8s8VHaI0a9VtEQUU888S9Xe3ORavYafMCCC2nd85a355tCPmyd1ylLCKBf0oe3APfr4j5.w6.rUEQ1Fb1VizcA58Rd9OeX+fPbwNe82OlGAlR+xgVW021UE0sa2HLtm2JhHamJ1x5G8i9QACK80fSDXXIXy8MOce.Jl+7WJuC.TxQtTEA3GLsCBZS+DHrLd49E+E+EK.L7t6t6HzpRV4dMhCMFSUcRopL6G6C+wbnBU+e9FdCUEQphwyly9T9ke8u7oUUmnpIQXYIxJ6Se.SpJRt63Nti95wzBKrP50kjtFinFqQWzTPe6kB7UGui500xxpWs4lKAPOdZogYddsnIF6S1OV6GSwtD3w1.lj0LOPNU5CgEc6Aj7G7G7GzCbSWPnbO7Smva1rMPrP9p6eiTWrVBPmZvNP35TlkfpKDCK7DdBOgknAq.rpHtFqdjpq.Nq9ttq6ZSU0cTUyr3RROe1I85YBRTwZ.a.ybOI6sANQ1i1lAFk6u6+v+v+P6vvvsGd3g2rd85aEdeg6bO2y8zgnxohdnmUJ0sezHvFy8PePlpsC9rIv5Tl0A1rSmfsUU6kFvXdvIMfrZWJOORWPt7NQYKH2j.QjS2sa29flrwZqutBseSuw2HggglE4bxBzoQtT1OTHNNtHLaJc2loXq8pQ4Chh7CkEoMS.djzMDbj9aL3.fnbjziWYo0AlvDpmOs9pG4M+leykflynFga0MFbEQbW7KtXkyb1yLyke4W9ju2266chT2wYv5dcbR2Hqss8vyO+iejpUq1W353.LwHW9b354lc+0ei.B6Sz55.rqnFFTcG2wczo98EhoLmpT.pT7Nuy6L+u1u1ulUJPd8XN5tHzEN02HHKqbXRfY50HairQlqIsg1qXwhIKszRHRUS1FY5hTKci6G47VqpCNocuE2qFrWWDYAHN5s7VdKgas0VM1c2cWDX85g0aCUzHS1CyGdhPywuU0zmYylClMEHvpEX5z924ufflboLt5RInwAW3MGLUd7RADbFxu.jCByWduMAVDpNLvnfy3QokSiHxr+.+.+.UgF09S+S+SOTmNcp8jexOY20WeyJ3XzijTPQ567U6dl1ibS2zMkk0hAq2+8QYc.YkUVQlY5YTLhdcOw3pDIJn14rIHHXeOid8u9e0jenq8Z656624k7RdIcU0J43l4LELBioRvLoA25l.kRCxcetkykZaf9xEAylmIsT9RjpR2Ymc1tsxV3u9EYr77oGqpHMLTr291+UtE6c2cWKUwVDwVfb27Mey4.JrwFajGnPPPPgfffBppE9w+w+wJFFFVLH3DEsrrJ.UxkQS+2xs9VLmm5O1Bwjzlj5lTVppVM16ciz4chztc6JppxrfEDITBouCLbdOdvdE9mqjtmKs++AELTLVzT.aR0tGaOeeqCe3CaEDDXkjjH0p4JYLmyEx433jipXCUjU2OH9W74ypYRGJTGW1q7FyJeUUUodmN4TUG5FuwaLE7wn9VV6Cv86fsz0FKsM3rJvBfSCfFdddK554sFpwZGu5Z054642y11NwTslX64cz7mHHH684hAAAEf39uCOyCeLu770tPy+s+41lGKvwNNSOlNiwJpIxYbfIfxS.tSb8W+uvTfyrhi3.3SEpsxJq38ptkaoxeyG3uYxjjj9VyZ5lRGI1Xav8sbSRchD1O3u6a8RKKKAf.Ck0UOOORGaYYIhsqgwIRPXfbh5gBnVGy2OWMuZC466OLXmtokJ1w.oZJS6FMZrKznMmq059MxFLNHqJ6789898lBZR0cdiuw2Xes36W9W9WVZ17KzGbH2K9FaG33Nep376tBtzDHPpJmsToRg+6+6+qK566usHhkqq6nW208rmJHHXZe+iMU7dwpjBPPqyWhMen.dxCzu64qrpN38C.bXvBNUdy540JtbldkDkls9FUG4i8w9Xi.NC+q+FdCiggIuSO0TSM0+4+4+YoIJUZLfQTswH.i7+3U7+X7QGarR.S1qWuL1A2OlsO4m7S9.lTqBExKUq5RTJiLihhFXPgncZ2QytE+ctseG.HHLH4M9ldScqGdhcA11yyaqZ0psETdaf1G5PGKYw85qd3.P.y9.bSYZfKsaA6BwcRS1k064C9AykF+edn7AKK9GnioBzKLi0+wr.PT979mEHHLLbgfff0R0KE8HG4Hlxvzw.ZBzfe5W1Kafm+No6Kb5bm5biYoe73G1Tdj1bXrgibwR19Eq8.MN7.LdOL2u2u2uWNf7EJTXn27u8MOJNLN3LYrAnsJ+kus+xpuxeoWoai+sFUeiuw2XEvoeRRY.suAXj38zqjA+zWS6ZXX9et1saa644YAjYQ5pjE6uIQoIXbArtpps888aSbk1dddcM+9USKemJR5RwCR9fGsSr+Co1i0ALYO5Y1+STBPxuvuvuP12qPKbCRop4BTTDYnYmkgZfeZ.8WzMo2+AccnCGlcJ2hMfFqQUVCbx7X5MgFok+Sisf3s+k9k9k1IkcIY.lj.PRRhfHxVaskUZfajRqDU2CgdiaInrqzWCTzc+0+0eC6.r0Q8N55dG0asZ0N1Z.a9LdFOi1PqT.SBsLtDvEkAMeynM3KFcg5scCXGLkQylhHalOe9sEwss4W2IWV1ug5WL1.LvBxsZyzrI3sLPCfylOe9uNvYt5q9pabkW4SbIfM8775lh9dNsgVvyyq.PwO5W3KTDXHGmm5vvBlrjqKzG7fCe3CevIMevrP89lHLUo0ywIOGghy1TBPmL89t0dBtYkJoSl4VzyLI2X25sdqSppVtpHtc610WU0+M+l+splKWtxO9G+ieJRK0FUabvf85OwX5FYGTzO2WedRRBQgQR5jjnlIJSyxdRhHz6X99cQS1UDYGuZGcaee+ctga3F5TyyqWXXcMH3DTu9w0q+5u9jumumumdkiRGOeluQrntAlf8zzCVrKyPGQNbl18zIEo6dSO8zRZf9CCKMhecCcp4jmSVRO3wtuN.L2bysAvxvrwUg5iN5nm8O4O4ONrZ0pK565uwevu+anqIyhN42byMKppNjpQoA9tvv6taPJ58MJVcoTPoN04E.gKDnGGbrzAWb87I9fY+No+rkyoAokyvhXu81aaCHMMf8jSUs.zHs18aLtpZo63Ntiocbbpbm24cVcs0W26oe0OcuK6xtLWfJiO9nSqMTikT6zutpGQDYjBExOxse62dV1KJ.jOErxy4dcxImjlMapdddIgggIl48LzTOyJqAjzwe8TU6bhv56hC6bTe+cDIYWuqxqKTNkIdjjVFhc+Zes6tKrZ53rS9fYbV+yaZsMaAd1UR6uiUUEWoW0X592+2+22FW5.Uunimm+ToeSC31u8aGUUsAUY2c2EOeSIVN5XiYGDFlWgbYyQ466W.QJbM0pk+ttq6JupZA+i5mVJUMyQYrAWqW8q9UmNV4RQLU9VtllccKhjI34ZESs2qPYsV97J.K.BNHysJBbpKvgDALkZipglB2DjOvG3uEX.MLw7isv.ViMf8ce22ctTQOMs4noWSlR4sJVQmHRflvznvgGDv0Kz3Lc.sNIIhrRnsRuz0sUQDxmOu0q3k+JJjsAdn7v9l4tuXrjaf0FWsMDmJd0wMcDoQXXXLvRd9da1oSm1MLkGrDDDX2qmQ7y61Mn3ce228P0qWenJUpTv22u.3Ze7iebCiv.gC+vdLEWpaB1Le2gIW4SQdHtHyxHfy.ZlTroran0LPzr.kUsgiFq9.GpWTuZSN4jt+Qu829rhTsjkkkYcgzOo..OX4DNTXXTALZmzfLKYeLLoZ0p.n9ddIggAohtoI69ppVYIrw2yOuquaNQzbGOHLuAPppCA8RW2nY1yYc1Ymsa0pU6.tcJue.Sd3pktd2789ve3ObpoDzHSzY2Enya8s9VSpT4oIyll7lHv1.V0E74d53vSkx1onsJGwJ.MIl.Qjy988888EEFFtxW6q805BT7S8o93S366OSpKqMCoLq.XXnZgxC1meDxcjC3neb9W+6RbL0Q5yXDN+kua+e2SC4nD4zE0hP8hTkg.2gUUGw7IZ3m6y84VDhyD90wEQJEEDLwryNaVBFFZ0UWcHfgu7K+xyrm5RVVV6ysR.F965656JCnjCpwdrPqVC.RhxTSMkJobqETUjLyhP6klnKUST022Wq44k36ezTShn7NPqc.5.s590a1TAvww4hN.5hz5Gu1giRS.rINvdl0+brvTxZCAwowLz5fLY5BMWWONBcfo2BibiECMCDQp6440PDc4nnns.5d4W9kqgggnMTQUUBCqK2467clFiT49ruU0Eyh+OesZ0RGyc3b0RGWbZv9ltoeRK2SiEbxyWLaWr1EKVtL11WbuDZ5mub56ehHEds2xsLZkXlDhm0Tp8NUebOtGW0eu25umiiiS42za5MMipM5q6RrGCRFh8J+lg1c2c6G+ep4Wj+e8e8esOPMKt3hVof9120kzrRS1vp7T1kKc8886FDDzIH3DY6yUgFo2SMsqFcdeV9XFfRxZORsA5uY21av2Qv1rozYxAKlMIaeKqTUcnzZYqCFPNVeFXyES0XDtDnTK6eiJ4JAVql8yllBrDiAkmEZ4opdHf4vPV5LQ2YRfQBBCKHnVnhXdRnJHIhPOUMVMGvNHrsum+1AgAafxphwtSWHHHnIPqq12e4FlILVmprAMXqnnnsbccGzdvdjVP.GruJGl.RxVbXBLOKxKhjztc6cKTnv5NNrdbLa33v1wwjUBSOPWyYG67kfgV0Dvzr3f2Je4U7JUpjGF5PNdPTjsuqam21+621V+vuje3MNzgNz5XbQf0A179u+6eyBEJr40bMGZi3XVyyiMCCIS.e6xQHYPE+JscvfjOeTKavwJ4TUsRKCoAqcOwArhA64lC6ybFxM6rjagEFXwaGJPLihgllN.dppthHU50q2zeOOqm0DerO8mNydfyVvevRfPDQxn+c1FosF3q6iIMppznQCbccMA9IzCkDE5JohJrBaIFUvewi56uvIBBVz22ek5g0WolWsUgpq7O9O9Wr52828285XTF+0vPS7s4arwiCtPiMfcYC88MiwpxPzfQLLFZgB.xK5E8hZ+9deuusw77dCLADN30v463mED0P3x3DwzXd2sZylMmoQiFi8s8s8skOstf6544sKvlou+sATdSnUJ3orAFaud2LpKx9Ui9yW6f8MOfA8UCj5Y+MyixoPg4E3T809F0XWlYAhLX.h4cfBwvPUqxvMZvnNNLYbLy.37I9DehJyO+7y566OElL+MT5eaV1ISpJBoYknuUDy9cRhr2C1282VaskNxHiza0UVIYxolRCCC.UrvTyDYG+1HrMJqpPSTMTDoNPnuueSfEcfkiMLMaSbYGSPYyjogSC1OewFuk8NvfAuLLUXBZxjXpy7QwD3RWLioW1CVJzb92gycMjrmS4vD3RILiidb.OoNc57j1dqstbEb2Xi0GWvJGFLC10yyaqvvvMQXSAYCU0U888WJHHH122OBiAZ0nBrXSy6haPM1g5OpLe+Ck1f824bbHWbL477HWXX56dvnsa2d7BEJLjpZWQjTPLMtsAm6FIGbcmr0blF3v.Ooc1YmmRwhEORTX3gRfIEnHJBBcEgcPkMUzUvPqnXee+5TgyPSpigp2qGDDr0f1x47P6Scg2P6AiaH26+8+9sdguvWn.HdPtPXzJUXx3XcFWWYpq8Ze9C+A9.efjeieiei09U+89UWfXZh4Y7Vmm64yy45H4fSNDlXMbAdbewu3W7IL0TScY.ydC2vKu3e3e3ebOfMOpu+F2WX3luq20e11ulWyqcqJUXilMY8nnnUbccWEGVgXVEy7X6st3CbMnO3yhKk14C7ky2beYymLnajL7rvHsL5lzPqs1ZEKUpTQnxnpFOgHxLppkAlsc61yjKWtIsrrx1PZ9ziehHRZFSwNIIofkk0AY0SewoLsbarHsTb777389deu7hewu388NW1+VJXtYNXng4hvpJrjuu+hg0CWDKV3ptJuVMaxBNNrTbL6kDt4XKNy4rl0CGuWuu05JUhhqtJCqFGZIK9LTiy4rElm8aydwTdgra1AGumoyBSHhLCotm1W9K+kqdEWwULSTX3ntFgjbaQjUTUWDHVDIFnEUYYZvl7.vxFG5W5cJGoOk+yddob9GekcMZ6.1wf8TSgr7xj35RunnANFySlCnM.iL2aistvv+0e5O8nW20cciRUFApNBMXLnwj.Ud2+k+kNe6W60V4w+3e7SmpabYqgN3ZiCtmBqA92N3lw6+0nnn9kgSTTTeflI0xpw3BSYe1lTmk7du26coq4ZtlFpHA077pWAp2DVnbY1rUK1sREZ2r49rV3KTb3Wr1frlHCDow95e8u9XW1kcYi.TvARhgccbXs3XVat4Xiybl9ysegLrBAvZdH2oLqsNAtTlHpAUtLUiurvvPOOOuR.Rud81HWtbs.BR+z.XIGXiXX2JUnWylzc1YoyBKze+GILMYzBLquef94GTL+Zvmg8e1dDPNIHttXGEQ9pUoPiF8igxhYwFy9ALZWnYN8owr+FmO+m+y69zdZOsJ111Sk96LXLXVXh+uuyqRVENjjjyxx5fjEXeycmNGlFFFljtOft999ZPPP16G6Hhr5W5K8kZ9DehOwP+i4GRLwfyBP7h0qWuUsZ0Vjr3SXf90GC1drNCSxZ6MX8jY5ivhI.IpQ0arfYyopVPbcGQSob47yN+X.Cu390OiKkySxfeVcfEM7WBAbESrUn.ccEYWLKFzW7WWes0TeiRVimumIM9lZDKIegh641Mvt9d96FDDr869O+css+w72PUcUfkDQVv22ewFlWmWGX664u7SzAbS1e8L9nRq+yDinYUtGPmT6xbGLun0FPKTnPNfQhiMHqGG2ei+WLV+j.zcUS+z5.K5FS3jSdkmUD4LXnlW7U64shHU25FeU2X2C8ze5Z850sZDFZ+I+3ex7ppEdBOgmPwCcnCULN1D.VX3fzTa5BbxLsMgBTN8qWDsYXvOtfMLsgx2yM2AnaG1wPtyblyj6LmwrH7BKvPoYrXbvYJhMSL5.t3feEi.h4E0ngqkkUkO1m9SOClMhM1ApOw9zHtufIB4gj9WuQQQ6KSJc61knFMPUUCCLzKFEcOgpS5JHcEyDe6BraSniuueufffdGyqVmvvvcfFa8c+c+cuAvFNvlPUiNBYr35KkEWtPO222BVs1ysA550fDnJo1sVQU0Qduu2263PUSPxFV6bgxfwfGaS4jEwZ.KTAhvgy7sUoxY.BTUa444sQ9746fISs40HcHnxHp1b3u9W+qOTTTTgpPAvIuHRdbcGLSVGbbyA0ak8QGzi.1Ti7t6EzVQfh0GfAQlLu5lCNU+iaYvRDey4nb4AAKo.vvwofX1nASALabLUd1O6mc03n3pOqm0yxoVsZUvr.cF0Nynr93.i0vLeZ+LxxdYCaeYDav96EWbQYkkWgvvPYys1hff.TUDkLAl.PSY6iA73csTscsqtV6i462N0JN2En8u+648XTd8xGtaoH5Bk6BKdPKR7Rc71A.DubNZRta+1u8b21scaVhiX4JxfA7Hg682cAZGAfDLNcV+qmb4xwFatISLwDhhHJpUJvl1CjUZa.4n99DDDn+Uu6+JRuFy8VdK+1EZN..UoBE8iURDxffL2KN1vBsvv9Ap2AlsS9742KveG55u+Mpe91r99.yNsB+APJVrX+9F4.aqWUAEMAn6YNyYZCrSXX31286+t2AX2xPmFMZz4pLtYR1XK8TW5yiobjin.IuvW3KrOXdgoyi0rIct+6+9a2nAc9fevOXOQDdcutWmMwl2kbMu+eIFixI6qkIyBKANMdxO4mbva5M+lqOyLyD+A9.++slmm2tdddRSnfqqawWyq40VLHHnvINQPd.6d85YAtRz8EIjsNVUxM04ojT3BmY+GHVZdNyww4vfi46OW0TF.wGwOMi7jpQWK.y9S+S+SOqHRkqnTI2W9K+5qAMmCShplqpH01byM8KTnfqkkUYLykkU1GCgYchhoNkyPVVV8sj9AtGyAjKLJz1yyyFvJvrYBAPdwu3WL+4+Y+4.n+W+u9B.LrXJLHTDPDj8MGuBV9ouOiEPBVG+3Q4qWudg3XJl4fGvzvY9lJE1yhip2pqZh4LEbjMLfSVcqz3znJjmJTXt8KR+WLFOkTC5BSsiHx5Tt7B.g.m8I9Deh0AhEKqUwLV0NErlR2+8e+SqpNM3LIMLkmxzvnLKiVdOlyNBlRLH66KV9jTHUHVOPRgl2LdpVsAKop7.4hSA9X4kMYgOJhAd96jwJzBdPQlggqjtoeLiglHBl35ttqqDPImFLIMzoTMZZfY5zoyzO+enenIebOtG2Do66XPc8ZvXAOXLkGLVx88N0xKuLJv1aucewd0PNSTPRf91V8NGa.vcEQ18E7BdAcADRRxAjuY54qUK3Lm4LIMa9vlfbNv3hZlue1YU.trK6x.H2pqt5PMLfHUJN13FUm4L8KGqKowWmZ.C4vIhEMkfXy5wwwmcpolJHJJXgvvv0hhh1MHHHod85YfeU.XnzwOi0roIdlEVfAhgt7HrjQmObLy+Tv6bie6frcZvmWmOV.uuXvNY5y9nHy3vFMX3W+q+0OpY7R4IXAl.nTEnDUY5pPk0VaMOfC8o+z+y0Fe7wc+HejOxr.kpJx9rLY1K9+9mutc6lGXPvRjvfv8MWbXXnDFDlAZhwdp0z0qBBMrKQjrRyo2S7I9DUeeeI7Dg1PkbPbtvvPaQDaiwGT4RX3x252drRfUWps8PLuF4nN4.2ho0.7X.kJCkZYVfr25qu95SLwDKVFVpUY1fVXbQmKL5WmS1hREjNaSc3EW.X7+i+i+iotxq7Jc.pJh3B3pp5A3tyN6TtWudkVasUKpJ4.U.QQIAgTpQZ.Uv22emffvc.cy0Vcs0dRG4IsPPPPz.YXbQLfErE31FhL+8UYGZvtvgaCm9aFhF1kR6bxV+LF.pFUUcDGGY3lMYnJPt22m9SmbcW20kZayrZ58TVFauTxbWdn1vP8wAllxTlVTQUc1d85MQtb4FxPEuvt999sA1QUcaQjsq.aEabRosRyfYFy.RyjV0DCXzmSFvxzyizu2sGDY99ifxIQ.WaHp+DqU.oYZPO0pQu50AOPBcvl39KZlgD+nl.HpNADOcZlxb+4tgeF221a42ub2tcmbhIlXPlkb9B.8fuimMYtEFT2w11V.XkUVgwFaLZ0pE111Z2tcSKWhndf1w22uSPPvtftCHqixxhkrjp5x.q7k+pe0UFajQV3Zu1qsIUoEMXYnxFPyMYOZ99v0XwCxzj7PshP8QAF+9tu6a7q5ptpQgpE+y9y9sRxkK2luzW5KcUeX4fKNKSF73aYB.6zYYqdPsgozpqt5XkJUZDfBoVtlQ+SbXiEO4hqOyLOk0gn0Ic7zLjogKzapon6xKShCjDmllLNWPYkT1hLHifN3ByVl+lp8fFlwjygvYR2vuglqYrIwNNcgyT11MRJqIFGnTpXtN6ZqsV4Owm7ST9G5G7GZZFvgHXurV.6O6OGL3fAYUx9FC1saWha1TEP877RhBCSTAp5TkFMZv.G6cA1z2+XaDDb7U.Vv22uA6kgnXRs7Yf0AmMSEVt1orB6fA88fkcIE.FFbm.hlTUcRwDPRNU0te7O9Ge8m6y84tDvB.KCyuMbpNbtapIK.shy.SrnIonOtJU3IGGqO4nnfKWUwCkwQHG6Iv3aC5Vf05fthnxhIVrv69c+Wz5k9i8RWDXwDRVJMCNKCyrJr3Fr+2y9VYFl.6ueeeqqRJCSLkbQbQLiI1h8Xq1CDqPGXNA2QSKMiCCbDU0mRXX3SB3PXxTWwJUpPqVs5PBaohlp6GzDHTDstm2UWGhivLVKaticG3qWrrsmcMMHHAx7yCm5THNfcrKCSDiigESSJFgG0Z2c2cmgFZtUflKfIKcaB6i8kOPsz9QmBP7XXxFoqCT6KuxJ9ppUlbxIGOLLL2w780FptKvNQQQa344sNFAudYQjUfJqBM2.yltxtey9zG3nC7I845Qxntt4+CzSl9ueji.m7jv7fbpZXQ8yIoUp4+ulcZo5lAT7vat4liN5niNtp5XouSNBTYDn43oYweBQjIa2t8j4ymeJLyWON6sg0rwaCxBsr9srykMz2JM22yxvfPwy27yR+20fvvj16tqVrXQCjuZ+6EEw.7qmm2NgggYN6QKee+VAAAK3eT+kqe75qTqVsUAVIHHXEee+U.VElZcX4r98Gtemdv0QyhS4.Z0RYank0O6O6Oq9m7m7mLnHwlwBgGHFHbv3+xUEFpgYczobcoRTDUUUcttmy0M0mONG8...f.PRDEDUG9C9gGdsMViT1Zt5Ow+8ehEtq+r6pE3t.DsAvtpp8DQNe.l1ClsCrfAr0Zzg5jXz4n9qcZOGXcFyuexTSAKuLVNFcaHOfUplBkwbfd.bkW4SW9ReoOW1XhgIM90zxqIU6uXLnx3pFOlHxXuo27aZh+z24e5jm5TqOspMxzRhrX0Fj8HCx.iygsuG7gUFIAZD0.WO29fkjVyfYZLQ268yeu6bMW80rku+w1LH33aohtoEVahoD02JHHXsd85rzbycYMp7+O281GjjbWdmmedx58p526pqrxLqo6QRCFwHMBzHgj4sEVCXIaeK1guf07xtAdiXEgu8Nr8YPvZv64f337a7RXSrarQX10ducCAdwKWvZ6EFv9PmDfFiPZDVBM.qdcloyLqrp98Wptq2xm6O9kY0U2ZjzHC3U3eQTQ+V0UkUl+xmW+978KrbqZzjVrNG19VpzW+218bi+YLKl7tJJll5MY85L8S+z6OYgBEJ84+7ed8m6m6mqiHx5Xt23xYq64x1ZFnQNX4x.yPMpQKbgZMTMpNlXXxVWj8hLHTbUU0UEQ1xF1qogiIiEibem1Ll9ImZSjV5C0n7TaFiEOqcLDY9dS7+LVraBfjDlmEPFWWxDDfTGjl1HDQFL26MRcAUUyeK2xqK+27a90JhQFfmWDwVU09a+s+10t9q+5qhYu0Db33yNZCme1Pq7ynwgsZ0hACFj7+nJXMTDFnw5.CRL0XMVLHNwrWYKQjUiiIxxhVX7ethqqaavoED1lC7cdk365Esq+9BBSN5JlkYn8AbZxnf5ao5H4rcpolJ+BP91PNZeHm0OeERJE9eJlaxRdOhh2c2cUfgurW1KaPcQ5KhzWM2LNheEJVrXbkJUTGmTmvhotvhgD3xjIyvDR+ZX5rvJf7xN4a.eee0yyafMziZiBZKYSXXpLJFSSzEfX3B+vnqDWoqil72fDB0rmHN8hhz90AMR0rW5RWpxuyG4ijPXabzh.7bUg4zW+9vx6snIX5UnMg1vxhHW5O4O4OI7u4u4uosHxlttt6Az+IexmTEQrpAYaYfXeNQj7ppE+3e7OdpSvJ.kUMrhswg3DXLZMgMLA0ML6NipFcX4ES61+4ovbPZw5FwF0sLFAKC0Ku7xTddnX.THoXIERla8zNnMuHhipMOlMbUelOyexU++4G4ib7+s+q+C8JWtbsolZpzjYGeNEOJxWNZUsOjS4zhk.vzSOMsaaF1+ImbR.RTIGUEQhSjcxAfzSDom2M40y00cfgDsfeh2vaHNoXT6QS5TE1CZkVXhiBcwePrNTmjgkwK46eEuhWgZCwarw2M9c8tdW563c7ND.K+QmSrGOw9K29qw1acg9m.1GlOMP2V0fPbHb5omtkp5Fpp6UsZ0g1fkpZNhnvKa94K.giTeFfxqBk5zoSIvqz5qSYaCBOJlb8OaBOJLdPT4fGuvzPAvs.l8H4mGxiG4M7OhoCBpFlagzjJtHEcfxP6zwfaRfoapZBTNsWPDolsoftthHMDQVrYylKc1yd1klbxIa7O5+o+Q1X5FaZASJhovPo6uNJogktm6nnJ4PqVsam9KSIcSKTjnlMEjQO+3D6kc88O2dXPjV+kCBh8884BW3BhuuO.wgggIIqFEuP5dry+L1qckhtjzuJe7O9GOYuQXhz1VOczBK6HRkpUqlVDoj8TO9y1doz2ec0Dagpp5cdmezjiOKSBYBoXqgOym4yj72jXPGJPeUnaCW28ty26c18Fa3Nv8lbkj4rN4Zvpoc+5GUQYxQ8WjDTZzfZFRnL0NRhu8myqs5RflT.acu81SAHNNNI4WAWWWDAsUqVwZrFewK9zCEQ56kffIOOutttMFjruRMDIr4XqJnIJRzQQSwy2m0QetdbiJkLLxHQloIgtiHx1InHc6hEK14i769d6VCFBNiWbxmu2ujD+h5ioHSqCzLBtzLyLyS+O+W7e9k1e+8aMwDSr0CEDruHxPQD0wwIMoLIY7QEUix3XPIWIbcqnpVwCJi8gHOvb.4VZrttZ9eNeZC.JBT37i5j9B4O+4Mn07wgbrr4+OwdV5yOoS9KOYUXZvNcr3l6ZpToJ0nFlVWVSUslpQ1pp0qK0850qWi1sa6kKWNGfEvfFkw6vedfr862e7lTL5yxX7tjUfAElVwwwBoigimKc1qi.HIESQ7bcMHXRwhDh7O8ZjqyAEWI42Y444I0fXOOugzh9MZzn+xKu7QKBs.qKK8buu56m0y.wlbPw.SRbt8v82eeq+8+6+KRa73jtPEZL559ymezzB.2q4nBd5tRXHA1vkDQtv89WcuKOHdPKGGms2au8Fr1Zqk8t9ObWU.lABmuFLO3LuHxbppy3.SyBlXwHgWFTsc9po9gVFiORd7wUMoxmakURRFcgRquNEW.JDY3mlLhHo6+R8YWDn3246b+krgxP8IIgDWsEYgs2d65XP66wTUWT0nEqKxRsa2doepa6m5XO1i8Xdp1L0GZJhlFcNKgZ.FOdrmyhk.PXylllJHv5ar9g9aBIbMmxfa9lt4Q9NEQ2QLpRzFtttaArqHR+LYxguuel+lvvbz5PIX+Cx3zNz9qjBRzCb59DOwtcOVwh8EQzy+HmOsXTGkjauRP9exw7x8cLE0cSZQKa3RPqmRD4o1d6suDPqlptyxAACMnffR1PkHnhHRkfffxppURZfTZbSS.LQMXRvdRLxFsAkS1GRZdKCQkNR7+EgGOkSjpPx6k468lHHfIUUmpILAQlhjXxMfzFXMqHRsyd1++bsgEg5WkHxUqpdU862eoq+5u9FXr+cYsswyN+9L55xEu3ENTgTTUkgCGJtttXd3oIkiKFQFpv.Uk9hPW8.aD66551qQC2g2nqaLwntttIisS3y2nm+iTq+9VASNTWNhfXX534AEbSKtwPf3uxW4qDqpR6mYGauRf7p4qGzAygym3roRkJoHDoWDzEp2CCKBOJ.uACFjVAuQnnqXohlCZMVGNLNcFwF43TAqkW9AyHhXs7xARjQVrhSbvNz22e3evevevvHX3S8TO0Pfgs+ACr59AwJ0oog+FpROnYOQpMno4ZRl21a6sU3C7g9PIFJbRMX974LN80djS4KZLXtEvpQPDrP368ex+jfa3FtgnACFrpHx1TidW8Ue0JTKSjpYRUjBf7hHkduu2euwG4foDQlJBlAZMKTeVQjYiXgooISd1yd1IAlrVhg9KNlQ10LFYmxDrW8zwYXJwVlBZNMXO8pGLhCSfQ0QlApmNmudX5D5wifi2oytK9g9feP2rYyt.vLAA9ommFEvJWdnROxobBz6jfv.ILLbz4zd85gjnm0NNNr9ZqyXaazj8uCA5qp4QvCMJ3NQDQ9U9U9UzT18Gn2JP+vvvAydPv9+fZe3QCpPRF.M8eym+ymtOqWSU2elYt18bNXtqUfLTkDkenwURQShYDrOWsK3rCvFsX9UIj1.sEQVc3vgar+9624gBB5mLun4ZAkfZS5Xt9mxV4SV9pJOA3OITcxDEAXz9cCR0lKs3ViPazlPEHnD3VTDovpXW.exKhjCHiqHVhHVi3xEnbHL4FarwLNvbvB0vnnRMvlEgniC0OdSUWBS22WpWudKkOe9kt5q9DdX1+MOl8moIFkEHk4zsvL1HiCY8KGY4MZs0VaYH4TGGPLIZnJhkkkjTyXPSJRflJiuReOOut2wc7OqaPPv.KP777xtzR2RVOOynp333fShcf1W9tbdktR9eOoBnu2266czqUavR0l4.6BppkBUsX850GE7qyUdByJ1FhS6NuyO1Pf3JUpnAAA5DSLAXF+M8ceGu6XOOuXPF.zSg8+ze56pCTqCPmGJHnq+476CnKu7xVehOwmHKPtfm40fWrutLc6ZoidrqQplzXB6X.M5.6IOq1Tt.nPCsJDWpzRJfZYYQPngf0CBBzDtxIFKFbrkVZfp5.MQlzwTPuL.YM2WElAbw22WWgpvxGFwHi844xsFu3NWtNUlpLW6BtaIhroHxV.69a7A9.caYZHBvBWoWaGkjxIfdvb6fAMpgTmK849u94tPoRG+RSM0TMcbbVy22e6kWd48EQFHRcMA15VXZlPgPnBDMEACmQDYFermlnQJISI2D+PW3fhmVHzn5KUlCl.7RsyUFGJAsKA0K09vE5OYrXpml3zT.S+.OvCL+JPUHZAn9BhHKzD6ZzB6tc65np5VWDOw7v8rO4YcymOuS0pUswfrloYLarjTT2fffL4xkKEMYow.jKHHHahMNAfa9ltIAflMaZJ9QRgPJWpLgggDFFHQFjwItttVEKULkiSDWW2CcgJQpzGY67gLpuTruueefdMZznKTqehezQw+cgq7hx8210XwQsz36OU.oXwh4ttqqZoDz6Lc.1SwxOCDSb4dMYrWKC5TVhNPvVv7qFkLdNYyl8hyLyL9.sJU53a80+pe89XtNUQUMQddCqB1yIhLSH1SQaiJsopl2QjrhHYWYLtEYUnHXWZdnLXWAXhpUqNItLAzdBnVk1GNNSKCOeUcbT9VVDYhHXJn4rP8phH0ZQs5+p+p+xdlBknKAb7emememkt02xaYwYmc1Fm9zm1kC7glt+aj+hfffLpQRksBBBNTdGas0VOiykggAnpxTSNIpZHj082aOyIYMN4ZmAkIHiEqMr+M51XWUksRPrzV9996zrUy89y9y9K5444MzwwQSZ9Px0rEuB2x77tNpMuTkxYeHrSkJW8tsS3DoOz+GeH8K7E9B4LMLzIsHDGsnIOu4.DZ9L2AXiHnET8R0fmZpodIO8c9Atyk61sa674yt6t69TCEwNSDj+q7U9KyCj2yyKuHRQQrKyAw+OoHxTsfognYfZyHhLSapNEQL4JqrxjXJnREtrw+We5EfYfpow+MMtLM3OCTc1DD8NJ1PQjTEtbVU0p.NYxjYwH3pUM7pUUup333EykKmaPPPJ5RF+b0ylB+XAX0oSGAPCBBhCBBiWbwkF4CMNNV2e+8wwwg.eyfFmTfWET0yyMVLE3suhzUP26a8s9Vc.53662IHX4NOTPvdtMb2+S9I+jc8775C1CLE62cbe0+nRrIOi0ORdP+7rdFv.61tsaq7YNyYlPDIEVlEAvF1Oxjb8Ff81KPTGijWcEAAL3vUF1T3koIGaRYSBxMWPU0SDogp5h.Kt2d64UnPgZVVVSGFDTTSHkSHANch1WUIknW2OHHnug.X0877719y849rst0a8UuLvEaznwxPsVIvk0fzj4oOqROXtdvZWN4n6+QrF+5QJA7VpNLQyDRLJoSAYvLVLqgKqRPJr7NYujYx9JExyYf4J.qUhjhdPB+K7POzCM4q3U7JFMijhgr25JhS2ZzreqjNYJNROMLkjNqC1MynM0L11hzpEwpg.BGB18gnAO7C+v82au8Fdq+r2pRSTnt.MSgXW9jw1xRUUS6rvYNyY31u8aOInYGoFgEaASopNmTWpRDKnpN+C+ve6pUqN27IjW03iGwHD3DDDP97EnZ04G+7wy14oCsTUYiM1fYmcVBCC.jCH7UHFj9H59nzQD1y00aWe+fcEgsccuwc88O2tpJa1ngaarYYh3RPsHn0l.ct8a+16dlyblQcGlu+1KdHT.vgG+nbXSAhnD3VTU+7l.fFA85XNf6U1eVXu0uxIE4iZWIYbMVXBn8Lj3zSUsRPPPAOuazBZoIel6kL9Wc5zoydkKWtGInLPDYvi9nOZ2q65dC6OOs2aUywSLNXQXZwOpVP01VhHTsJwqrh43w.UzEhgUR6LnRxncopVRDohuu+DdddSgYeUZ..ocxnjHR4s2d6JVVVSVtb4QrzePXPYWG2B.YWc00xL+7yMJf8f.ebc8jmkyUOq9TFNbHsZ0B.iS4DopybtWhEzXM4mccc6FDDrKv5JrdiazaqkO2x6XYw1.q451HDvGbhfv0dmuy+4acW20+tNXSWhNjMuqzQxY7qwoizYB7XqOK1MqoM0ZNNxbggZQL6YVSj5Mgn.LvPcWt7vlOAxvjCSvTK.bba35Zp5oBBBdofz.zovr2Z3jSNY2s2d6cA1xy6zq46et1dddQ11zNJxPv2Ku7x6znwo2CZsSPPvVtttaRM1hViFWkueuW6uKVitGdIHyERuedZxxliN+WV0vbhHCrsY2nnQjO5yE7dSuOsLljVVB35t++5+5a3Udq25IANVPPvzW7hWL2Mey2bbqVs6JncTzsTk0abSMV0+b9q344Ece228Ed7ie7.WW2l0qyZMMDPYeee+9ddWeeX8wssckzfhidOhfona4BS6F9BTh1jy72VnuyUmsW3SFt+ryR20WmT+5WIWeOpMqxX7ENmMrvuxu0u0b+5+5+5S2sa2hKVrX1Gx2GWW29RcYOsotsHxtfcWrih0lJNNRbylzWUceQj8.68gntZJgVWiXZgZJrS6Te84Ll5FQ30w.xd6smTpToDjM3p1DjIJgusjZRdZmZisdYUCKIhjx0HkUUmFXFQpOspMmRDoxS9jOU4q5pN93RB73cz+PHe6QdjGgScpSkdN5YTj0jwrQ.HHLDWGGBCBHegBzqWW.g50qiHB6u+9r1ZqovH0jvjvgL50N100sePPv3Dv41.qHhD355tLTKv2+gZ644sguu+1pp6znQijQCdwNz3h+vjLmOpuzrLGYYMRPaQ0xTckIXElTMJrgZ1WvZvBa.sSGCvmOR5mwdexLOje0QnJn5z1rxLQlDEmZyM2r7LyLSNiOt5CUsY54tthH6qlQHqqH0RU3EyXTTigzxzR7DHRkt2unZHwVK.50q2Pu74G1dzXk4p0IvpogGUrjZhnsReMpmi5Mqng5zNNxrMax7ppII6ZOCDMkp5Deuu22q7K8k9RK0ue+B4xkaDpp3vMwhidNZLRDV1auNRoRkOzItCStqA333NZTbF2Opqq6vvvv9ppcSFEmMAV0yya0fffUSPWRWee+dIiW8tppaXYY0x08zsqRyMVIcjbroWhezwG4w+1rma7bkFebWKZCSDYPGwbP8oroY9HXvq5U8p19rO0YWyLR2rErPGNY6db9WHhxQibvxE3fBuNpnDXPjTFRP7hw1k8fZzhVX5NnTWFpM0ghHZPPf3ditYRr+oMax.0PDx8Si++Lm4LClat4FdKukaI9xD+e1jsi5i7HOR7oN0oxRhZUJhj47m+7CO4IeCCSd+KnpNo3HyQSlyLBN0mamcdhYqToxQkH3RbPA3rpWutXYYM94hwWGpP8hHpZ3JMKfLwwX0ueWJTnfbgKbAVZokFO9+gflBDftf1Ej8d8u9WemG+w2Nce1Zdddq7W9+6e4pu423adsG8QezMttq65VGSQ5GwwlbkMhUunc822PXR5ZrKDyxC9k9RIcO+fpbppNHxNoiyPdHJe6Cyb0WIu9i5hCovYbSyqeUFL.XfHNCTS2wT.JUpjzrYSibMk.+bKKw.tD.vBAQPAeee000cnHL3qb22Seee+9YyUbfkk0vatQijigVIetrMUueUS28cnvKD1a9uKVieC6flMa1qosQpgUU6HFBwsOfXC4IXzXLTzlyOdUSe9px7PfAmf4LpVBNa3bvLo25Fuwarc2tcWa80Weq01XiNpp8LWaZl8Se22cAfIbDYZZxrhHyALWMZNazCGMKvLQQ5L.yZOpxvQy.L0MbC21T25sdqSQSlVUc15lNaUCpaqpVCSRRyKFVhu5G8i9Qsu8ewauAl.4ONDd7VvwAtp65+zcc0ZS8ZdfG3AtZfieC2v02PMcqXNLN.NTWQvzIKoZ04OD7vCO.EII+b3k8bmpJyN6rDDFN5zaRkkAPEAEUTAhccOcruuugdDUMiu+4xBjSDMmuue1TBJrJwIGa1x8elyL90mePrFqvXoNhc.HlHixpXyvtl.5c6T+.k4AU07+7+7+7EAJsdsC2wwwN2cYOMwXHY5jlBTtKzdSfUMEtjVhHqN0TSs0G8idm8MiIiNgpZRPALa4xkS4+jYDGYNf4utq65lCZO0pIcf0CJS3nwxZtZrxb.yZCSuxJNSBTYASwNJjnDPYMRiG4MEKhIbDmoAl2y6lrqAd862eIfqQD4DO1i8X+X.uj333Snpd0SLwDKUtb4CgpDWG2zhJlc94maTGJBBCkjhkjdc3xt+JMftzuFGGikkE10LRUXPPfpG5bpNLN4dWAoePPvHGzVhzE4Z5dSMZzSUYfpxPeee8RW5RjNxE20c8+iYuUjYuwI3DGEtyuPViK+qZcZpDY99vPUEQFZKR+68du2dPTRxqK9BIQlXfgQ0HUgkh4fhEA.au81i5Jmu+45CLvFzycNeAvpFHMZzPgVC.5Ob3PSf+sdQAhBuRWGBcIWHof5Mf7r4AcJtJMKHhjeAHaTzBYv3i3Joi6JLWr8Xj+6q3zF6WAAAn.KsTZG1T00yS.IqkkUI+y4OIvT999SuzRKMoqqaYaHeyl1V.r.nddd.qmXK5Dierbkh9iwej1cztKB6Qa1UUsCP2Ens9O3U9SZMGja80cxAjcoqbjDMd2cS7Ix5.shbv+C9A+fWRD4REKVLHR0U77715a+ve6dDgHhjGnbMhlJQFwmMLTmGXd6DeiPzr.SKhwtDsnzi9nOZoEFAq75SqpNy29a+sGOX+o.ltzwJM8A+tfISHe5I+te2u6Lzl4ApC3VilM.N1m5S8oNlHNKhgHWOlHxwTsYiu025a4pp5bUW0wqatz7LFivQIUjd95Tm5TGx1Ef3GDHAAFeltttRpsKWGGBBCww0kt85AHrvBKfHBgggTrXQMYrbzyctyguuexUDkTDlDF5CIHPASiZxphlRF6Y78eH0yPlv6eyddcZznw9.8Z1r4fY4hw+cfZgOxm5bLpXI4AxWmUxwJjqFjWD6h.kO249lIn.ncx42kd9had785CAFr5IGsebCXk1QFHT4WSjfa9lu4Vppab1yd18sIRIo4mutehaqHlQhrjAMRsSJHwBlBB1xvoH1h8j.SCNyTKw2axiY.lNe97S2tNSmT3soffIZlNtQhLEsY5jFrNyBzbNZxB.0CCMxS8xKu7wTUanZSuu62865.X+Reouzp.yjKWtwSn8xUvtiLpD5HeTGsXI.GRIbNnXI5neW5JHHHUIThCBdnj64k9A99cccc2+ttq6JUkl5.rk2o81nQiFqaJjRycWIgSnV6Idh3D9T6GDqwQYTZic5CzMZACh5TU2U0v8+p+2+uOPUUdhyd1Bqc90RGkkRtzt.mmWH4.DCKmfXu41pVJx5LZW+xXP0T669du6M52u+NhHcgVwsLu1ErEoBQLoHxj.SbZW2IzlFjUEFZJTqsHyfQFymEX5a+1+mM0sbK2xTzjYTUmar3+qopZa9dpdpScppjntM.t.MN4IOYCnkWKChxMnJuIGCXw6+9u+Eu264ytXkJURUY0E3YxYIYbccsrrrNj8rc1YmCESVPPvHeEIEKARZ.lkErxpqx5quNKszRLddCBpnhXIfkBVfP7fACtm64d556etcMiPprMv1u423adKfctu6695.z8S9I+2NHQbpGeu+OJg.1Cs96iEL4HcSbcTP.SEbUUgZnhHwlfqWH2YNyYLNGpen4+945h43+siB6rXf3UrWIo5rMSHGM6gIEMQlbxIMSqt4+TiiSjCLwz0Y0Huv.Rref+fO2m6ys+a7m3Mz4i+w+3aey2zMs85qtZmliUot50IFhF3fSZRF8CMj+5K1lcL8DvPVfA0qWuGQrOrvtRsZoR7aGQjAQi5Ln8DTmJQGLadWwFLebd7j.DC6zA1BptFP6pPqhWUwn4latVyO6rq7e6+1Y1PUc2m9oe5duo21+P49u+6OenYNFmVUcVrYtVv7111yIhLa61smAX5HUmQUcVU0YpUiYfvoqaF6lYOyYNS0l0aZewKdw5PSG.GQbrwLWD0nN1uu226ySC0kTUuFU0eLfWJ17iA7R1cusultc6d02zMcSKgw.5BYrrRMRVDH2e1e1e13iRxk6ANNt333bHmpfgY02byMG8yQQQDFFN1I0wx2RLaPAM49nHIwvYFOOOKOOOKQPDQk66qeeVKu7xFk9o1JIPyNJi4+qwOHLNd4PWRFGGDHTVfEFELVz7QIcjJn2.Cw8BPVGQJN4jEMA60hRP8BrvBGsPoOW6uhAhO+AjRZmEWjMgVq4haaf1+T+T+Tqcme76b6u3W7K1se+9vXnpxFJScpLXvfozPcFU04oVs4AlCaS.c9ijLt51TG6HUsEQrifEfv4AlokpiPLR85LEzd55TeVLE7Xg+zu5eZc.WnYiHUWLWtFG+q+0+5Wkp5UYW293m6AO2RVVVGCy9q56t6tGEdmGEZmBXRd3nqThZcbk4ZLtPv7bhhPDgnVQlSi5nSxFwgAhabZOyHeQ7nYn+d+p2aWWW2dD8D8SF2o9c5zYfmmW7wN1w.PpWGf0nggTID.qGuwiO9w8UZhrGYMGTCsYp7Fyn.71NR0seOum2yN.cbcoGbwgmzLJOGccn2+5PRs8PSKtQPXn555hWxbCa1lkTfeiOggdddZDHhHY788y1xLRAw.8sso6wN1w55g2kCQg+nPwSL2KOGVppYWdDr5qkmZTnspE.JzR07InVHSzUbSMVSsfXnthYTbUOOOULE.VAHa1rJ.9A9BPVUiKPB+Y3cZuI.J666Wz3WJR.z1igjjEV.R3ulzOKuPWiG6vv8VfDNOq5.pAsgL+m+O++c9q8U+pKlvIREtvUdgdG+023SzIII0PZCNATu9kd4u7WtuTWBu669tWswhM1dvfA8UUs94+E94K1xLpnSKhLy4N24lAX1HUmSSdTqFyBMmwAmo.l55ttqap1UamzU2lyBL2oN0aZNLnZYVpyLppSqs0YUUmi50mGXdroJvBurW1KKgaHrWTUcoHUOtHxwui63NtJn4Us4lad7K1s6hCGNrAf6q3U7JpSRBI7Lmm+T0lRpToh.jfhxCVMihD.YpImTbcckKmBCJXryIXrskMaVv78ZPP.AA9JfdZsArRV...H.jDQAQ0SeZ0yySSl+eBBBvxxBUMIyTWDAwvU.hZ5hmuuuppNjZFdrILQQSfpcqey06u9Obii6fXFNABP10FgD354TUy1LYOVjpzr4CaAj8zm9UlxyGEpUi7vExdRN4URxPGjT64S1OtzRFdMw1vOXsgvG+we7PQbZ8M+leyUap5lequ02piHRuu1c+kU.qPUymf1kR0pQEn8D0nVJRhm8q+Xe84wLFOKDoZ0jQoYVU0YIoPI69D6ZTZIy9upTmp.UCLjq+B862uF1TuswhcCQjEwzfqEmbxIO127a9M8.p+ReouzTNxYJL7gwQKTxkaTUGqf9hLF21LZYF2qPhiieFwvc4.PfXz8KE03yPEYnHL3F87FrbPvv2467cNvF5+E9RegN.as7Ct7ZfypXauNv1T2f.f4tlqIY+1H+Ye+5C4PEFlzhlzltfydhs8thH69RdIuj8AFzBjYmcVCmIUmxAGVwWdA03TXs8EXGXwM.VYAHhZzTDo4Owq+mnctbKtN0qusp5dIiioDoZ1KbgKTXvfAknNUZZr+kLtLLCvLQlX+mSUct50YVHbVarmEXtMVYiEZZ2r9SeoK4.McApKhsMPs5vBXyBpp0A7TUOVxHccUTmq5M7FdCWsHxU8s9VO7wUUW5jm7jMdc+C9G3DDDjpTgSBT5u3u3uHkmRNZbNit2ahIlHM9+C82Vas0FId.sZ0ZTCvrDQLptzgrOJppVhpVJjQRdurxlU8775644sOvtVVwaWiZohaPm63CcG6Az6W9W9+kXSndUy.j0FxvI+QJNV6Pqej7f9JXkZPZLl12cRHXRU0hhTOmpMEotf1T6iIQ8sA1DVXWn8yE7BubcWc7+VFfh3xjpuYFzDQV7q809ZWyq407ZNQbb7RVVV0AlLHLn.wXgwZ2PQjgwpNvRjTH7uqqq6N9AAahpa344sF1zlnZsCB9Vq355tRXX3Zm6bmaielelelTXJeTER3EKAMmdtJCrTFatPlHy3KkFbpAFs1WcQs4SXALTrk8oE6.1aCQFV5+jz+J.Zdi+9krOX9bvpIN4qVdAVoT6ZThVFB95TWyoJ9.m+AJmISlJm+Qdjh2vMdiYoFx1Ow1xjS9RrfllQfvQTZVWgliL9m.G4C3HCHO0nHsFwJ3Y+NemuidsW60FaP5jcdahpDUiIzHsDXjvwLYxjCnRPPvTIieyj.kBBBFMahoP27.DmdXHa1ueexkK2y3jQ5yY7uVnPAlZponcKCQu535LB9w9AAFBD6fNBjlv39LZV6YaQjsccc2Y4kWdCQj0777ZAD8nO5i1NARdaZdtN6wAEw6us6IG+dtL.Y9Reouj0sca2132ClpbUYSFGmDxIcghl4lm7ppZcQ5FUmcnYpBSMR0bFO3zW.6uFw2HFkzoNyPSl827272bp+U+q9WU.fLYxz2rOolVmV4ZViBZjlACgP2AXGXgtNzVBgBXSYsoVVDmB6t6SPkJUFPc5QyZ8gVovyN8Q5mcyXnYyzDYf6pp5b6tam42e+8ld94mehgCGVZiM1H+7yO+kQ9EeFb4zUjOhvfvQOyomdZJW1zorNc5vf98Y2NcNzyWUUccc0.+fXLJHQeD56551KHHXeE5HvNppad22y8r8Owa3MrimmWGrYmkenfMZ35tBPKee+1dddqgYzJ6jbMLNgbrGG8eWI1BO70zYo.qSwjwtxDLtMS768d+DYd+u+est+b+bu0M+7e9+zU4.F8eb+FWtWy7XlI+4rEYwV03jZjd8AAAmjDUa4t9L+I4dmu821PUj8DU2xyya8fffUUUW6S+o+zaJYjMd6+Bu8MEQVK09uiiyX2m8LTipWLX6+YaM98xI2C4jS0fDI31fzp986OQ1rYyJhzkZrIs7VC72hCTQsmM+zFewvbfyRP30u4ladC6t6tWuiiyhm8u9ryb7ic7rKXuPbqVs5644069N6Y6d7EWb+XQ1QPW2y0q8YO6Yuzq5U8pdZfmhZ3SK20gf8.FtxJqLrZ0piqpF+scjIFctXzn4LGkYMJa3MmlhHxvu5W8qt+q608ysugOkFMNgWoic6n8hMfbKOG4XsCHjZfIgZSViVS9GelyT91dSuoBYxjImH0yCQ4TUys6d6JST9DCgliFswCFOmQDPuRMxQKJoFUroHXKAAOTeiJhUeXcZRSy3CZMXv.Ma1rIWupUpFslHR0oj5xTZScJQrqnZTolMaVXgEVnPlLYJM1wbo0We8ByN6riK0mo1wF4u3x4+ywwgtc6RgBEFY+Zt4lmBExOxOX55Pi.QXHyO2br1ZqQ0pU0b4xkZGK0Nbx6qJffqqqFDDN3.XsK6A51hHq355F.bgfkCdZ2FtKC0alLZD6hKcIf9Pi9vxiqtb+f7d5i3Gat799ORdOOu7pp4EQxl7UCguZyjZSMuHx.U0sEQRUzjzwB+niJ1Ux9wwrArXg44hkV0HgvSvJTApUBZUPUM2vgCylISlLIA+LdB3C9G+O9er9Y+reVymCaxRznh9mau81K6ce22s0O8O8OcLTanMsFNlxzMp4KarwFY+wlYlrs.KpiPSxA1kfnYVe80melYlwzbiiv8XA9AYb8bG2m4Q8kdzbEzUVYEpVsJgAgRph2jt2Lsj44xki4ladhhZNZ7aT.OWWBCCk4laNsPgBLxOZRSbPjcP0TkXZUvd0m7IO6lEKVbaU0M+E+m9Ocs+xy+UVkliTYtjwIswfSvxwO9gKh6OnQGr45sM4SH6zDz.wjfSIUCr.FHNRWZxtfytPXGfcwl8I5j8uBFQ+weurRT5vDeLUKXyJEhpRNVY7wqlI1e+8mnPgBk+re1Oatege4egLFkqodNnYdU0bRcwhHaECpmTXz34zK43wBi7aWfVjdOTlc1YmgUpTYfHRLTKWcZMYSalVapkvzHDc3vgxN6rSgolXhIwxJEMdSDDDTFibmmqd85OCR0e73+Miv1yLjsDoAN8qZpxeMXv.Ia1rRfe.10sknVsH8FK2zwxWjgSNwDC2d6sMpKJrqX1yrhpZ663Nti1ewu3Wr8G3C7AV828282ccfM8882wyyaWfNggg663b88Nvm0R8GS4VewR9oWQqr+O5CfeXtNAviCJUQO+89WoWy0bMIDGW8gxA6phEwL2o0.TZS6m+W5TChiaDTSdO0GGhIfA0EwP7q0Xvq809ZUUUoYylY.rlXhIrRrJpfDqBCQ0AVIiKDlaDMIqp59XPfw50iXsGX4ysoqq6t.8bbbRbPUaHzZ3ILDT4K1JVR5JIAlKXHj2MIl4Hl0rUSgeAM5IACQaUne+9YMjxVjUhuM047ngGTA4zWyms2KXDCsuZZPs8fb621.eyhTmBzzozi7DORkkJTnuB5E61kM1XiRSO8zE.xu+9OctBEJjQDAaH9O9K7Go+z+z+zxW7K9Eka+1u8D07vtODgZ3pjh69T6Vrb4xEEQJRMxcsW60ZP0Dv8e++449JekuxDu4exexTlSO0H3HB6LHHnhqqaQf7tttGJI1vvPwwwgvfPbbcvwwggCGpYxjgb4xQPXnLdvdoD4T5yOLLjYlYFJTrHVhLpPIgIAIFXJVRpL0oZBlQUvZuNcrJYRFNgOPh6fQVw1TTYqq+DmX+uz8bOCshsrpBFRXylLDE9CppJOp86.ba21sk7cyKvpLKv5XHNy4.VyD3SFMrUpJHkWDCHA6dgtCJ3UnOqL+PX03z4Q47GruI02wy1wQ5yqObxX37Ca.CVF5SS5UC59g+ve3Ne3+se3Rzhr+QepOkl.Y1L+G+O9eL665c8tx.T7s+1emkTUKYKREg1CCMEDqneXXoO0m5SUR0v70EQ1c2ciKWtbxryNJQQTUsDotEDkyFJ0zvx6if99v33oqTo7TUpTdBfRYxjIWud8NprycHaZAAAjpSFGsiqGclpAydnC8bBBoRkxTpbYJWtL61oynjMR1iYdhVlw8REUsDINzfhj8Q0Nu9a+mc264L+W2+e567c1Ov2e3ccW20v+2emuy9Mbc6466OvyySOfDEcEHf4wf+VOOOcIPuvy.wgWQKic80mV98+8+v7q9q9qNrNzsIriSDCd+u+eMKf9OwC+cGSBeWZHbgmsWuwOFTQVvXWtUp8YgIlnB6ryN7O4s+1vw0UBBBjIlXRAP9O8o+L7weeuW4N+neT9X+ZuOQGpbm24cBf533jXWyYHDFu.DeE3+5EaKEPmGhKQX53qZQncVHpPtb4JXa31B0Ujbg3+7odZGd4hRfYzslZpoXpolhACFvq7lek.Hsa21Bvx222RDgKbgKnKt3hwfjRFhVIxwclEZgUaN.ZyUqVM43OIZiu+NG.Lx+lvZNYfvbPTpL3N7s95dcvynC0XwAbCxyURDirYsLzm0HlEHl1MF.K2qFzEZsWKXuela+1m.pOw8bO+mKs0VOlN4jSZAj4ZJOQ1Deb49O7G8ePdW+ydW4RJHxDPTum5we79W0INgRKx8c9NemxhHSPMJoQMsDCg62GhG1DfHxfMYyl0LlJF4cMpnHxDqs1ZS9zeyKNUmNcpnZTYfB0qWOOFU5Juqqa5WyM6rydTRN+Pn5Zb6UiaKKXLTUdT6WxX+8wc3jhDy0VaUzXz1saofnHFNXBi+xzQuYr8nFT0454FGDDjxqU8L7fyMF61vkZfHzzJJ8+KvEHHFV9uSRr3DftIqkP1zLTj4SkZ035f1DxXGQVwPp4RBefjzPhp8gUTaPRP+0URiGR+LMp3GKwEiuPZbZq3sG3uMzpTcnnHRgZPg23a+sWnYyl4sssyce228k8U+pe0YWe80k4laNKaHayDkiijQJUrkrkJUJaBhGi6z4oGVtbYkHrnNYoocdUalEHmsHYaAY5zoiUoRkxbK2vMj89e3Gt31au8TSN4jy.LcPPvjtttk.JDD3my00Kiqm6QsEIA9AVpnVdtdR+98kizHKomA8o335vfACNzdyT+tarwFzLpInpl5ybTBGwpt5pq.HnlV+q.wJLzy0sefu+fe++0+AwXi5etyodddCWd4k61nwMs6xK+fa2nQisvlcHxsCDz8DvfG+fhk7Cq7GNHtoH5y7rOqZKIw+ODB6kLJfVfskpMKKhjwFxFAhSDRnIBswO1d9r2MLQsPS7UJ8hf8XExRUxwJ1Efnd0ggKUr3vHyyq3W7K+kKd6u42bFfr228ceE9deuuWA6HxzzT7Z4Kcluj0O4s8ShItd6gPTrIVLI2884uuBupW0qJuHRdrISkJURU8uXPx8T6s2DYxjYp68qcukesu5Walj8lVSM0T4+Fei6u7wNViIbccKCTz008PnIIHHvLxfGN9exjIyAcQcrUXXHtttR+980wUtqN6sG4ykyDKlkHsZ0xvUSI137CBPRPrz1au8H4WWfghnCI1ZnWCu9m3Dmn6xKGreiFtcqAcO2xK2yyyqqsQ3SF3bSNCAzuw23avsdq25ORCRiej9f+4XY1TbxSlkye97.kSkOJbHuFnYGqB0FR+rtrKFxjKkXZtbctJ87kwY3RjgKfLVf4oIYYjHqpUmkUVwA3D1vKqopWaPPvU655t.lJGlVvpgf12yqQeee+dhn8TjtnzQLLk+Jt2naHQ0B78enHOuaZsyctuvVKrvB6bricrsbgsCfcwg8Ijue6h+OLWWNz4XpvqAsGGTo45TVC0Leuu22q+0dsW6NXyFD4sA3uCrz9u.qP4AUZ9jXw4G0QyToQsD0nxgd+sYBhLDGmpZoe+O4mH6u7+a+pDGGqIP3NsaVRbbrZYYE+HOxiXcpScpzwun3fACJkMa1ThYR52ueblLYXqs1J6d6sWoOym4yLwuzuzuT4xkKmEv5FtgWh0C+vOVZEueFPPLspvomGSqZ7XKM84APghEkdc6xBKrvHHDGFFhkkE4ymm82e+C8upi9hBHIAMICTU6+5u8SO3dNyC0Ez8AYaOO20888aCrxo87V8b99q5cZuMIhzwqZWVXgch91e6ssss2YdnypGz46ue1adv0xSPFdbrrASPllrkSCRMCUqlWa2tnHR45P4PUKI1RdMRAnWRWQMctXA12.QzQGiWoD333GSiqtMkXgEpP61U.Jkz0XqTRYEXhUVYkome94mne+94srrjm3IdB8ZtlqgLYxjUpKEIhRIDCVArwRapvX7wfXKhFo4Z1rY150qm8g9adnb23K+FK.TNNNtRrpUxlIShLJZH.wfvf7njYDgFlzwgzOHAAgVPrkqqmDD3KhXIGTbjPN52m1Y1iN5WVVVXaaO52UpTI1aDq9qGDzDLLNNdfkkUeEceAoimm21IjX5V.6366uu2o816i7d9+Z2ewew20lIHYZ8Owm7Srwu1u7u1Fggga533rIMXWVdLTdbxSpb9y+BoKYGbczgbDNRlQKRUxyJ14fn7+t+teB4C7A90hwk8HfcWD18hF+Fi2c0i95ZRtXQJwEYNLRQv0opdJee+qyyyaQ+.+Y7b8xY5pC6gpaCxF.q3cSdqQS60Wd4Gb8FMtoMfnU+XerOV622668sRcXslv13RGBHkbD+9AsC+c05.+omfr73iYS1lRDYHqu986OUNub4zHcOQj0gZsgVavRzgKbYIY2zW6TDlLqCrTHb8ppu7vvvq2wwYwfffYcccy8W+W+WyhKdr9pPWQkcAcGuS2XK+ys7FfrppZfHxkTUuPiFMB.60fncYQ5yEYfCLL7EtMimsyGFDJZPWyT.S9.OvCT5l+otYKZaOvHSvFhFjZziVzsAr2xP2DzW9BryqI9wNfjcSrcQEZa3zAU0Ij5Rku1m6qU7G+G+GO23RQ+Kw9kHOVziYAX0tcasVsZCIMFHa6JZylS.TttHYaZTZsAX7qfZHdwb.YGLXPtgCGl+Lm4KW7m8m8eTpzZVlw3AhD0qI0ualfvfLtNiZlvnXJBBBPD4xNZMiiTjze1X65.xzzn5HBqt5JTs5BR5eGjwscYVJwhPbkIlX3t6tabR2akjOaonbQcccGDDDjxgDIBMfga0DQZ5ditADQSLbs1FTisSHu4wiG8GV2OONhHL9vVjrbww3c.GxSnSYHbJU0JhH4vl9ZSsScQ1MB1i4XeVajOzWHHeJ8X.Ris1P51GndQ0IOMqV.VoXBhOJQcJ8G+Q9iK7Ze8u1Bm3DmHGPt+7+7+7BW60dsEum64dJcG+F+FEIJJeJOwHRBSWZZbkUxuuHPwuxc+Wk+TW+KOW+98S2ikAHye5e5eZ125a8sV.nzfACpjMa1x.ECBBx455lMHHHSgBEj4medBBCTTv00k0WecY1YmM875nlbEGGSTzADDxgKfW.BF9wISlLr5pqP+9CR1yoP5H2L94rQUySiEwpmp5dXP6y1hHanpt9oO8oWmnnUNmueauS60lHh.Zio2BFjYdR5kX63xUDheX3+3vny0Xuu7i7HORoScpSUDC5aKHFodVkZx9zt1VPqsfFaCKONhfuRZJx34bb385Pd7n.9iPsV4kWd4JMZzXBfoFLXvTY8xNw+yu1egh+W9u7mjcmc1QlXhIRsCMhFG1ZqsXpolxBHaPPPwEpUKeFKqBVVVY3.NbQa0pU1rYyVd+82ehjBukES6oR2SlJC1oE9KUUujwi2er7ANzuijWrms31TU0YlYlTT.KobhiYqjIsXGGW0LlgxXMZlt.644c5N99maKfU51saqe7q9pa9fW5RQG6lN1pg+MgqGGGuomm21TsZGVYkt.CpACaA8SDkjWn1FdQy5ueWvjTnmBEpBkVAJ9jO4Sl+pu5qNClt3phHwK.8Mpiiy9PX5MhWNHMeDHDtjkpOso1Km3Dw2zzSG+fO3CBG.M9Yc.uP3k9u4ey+5q6s+1dGmb14l83gAAKnPYEMifnBx.EMYCozEzdJz8ceG2w9epO0mZaLRWpu6qv8hzh.LrN7NPscfV6BrCyOeGVc0i5X8EqqwKbRlFP1kghf6Dp5OkHxTu6286dx+v+v+vBppLXvf8NVtba1rJavJifd+QSJ.d9MXl90z.AxBjeAnXanLTqBzZBLZs9DhHSztc6IxmOekc2c2Be9+rOu7u3W5eQRG8kB999EbccMLutssUvC8PYcbbJjLCqk788KIPYTo.VHDi534nI7PPgzJHyg4OCqjBoMxPYR2pFYfL0nXJz5vDPFvnpBKX5xECGNTVYkUHNNFTHS1LTsZURYS6iF.4XJWx3xcYxL0KcQ08TCb7VEjlddtFmu0quBMatAvtu+2+6eueueueu8CBB1+ztt61D5r.rW6m8BQ9BYMdfUG37y11RaZp7exeOCPNWnX.T9S+o+zkeGui2QZwqzwfQY2yblyr+se62dOnVWnUJrxGGBzbEb7dzjPxATzCJ3CEbgrAG3ftDFzeLKvL0fxsLb1PVUix7a8a8ak6e4+x+kEsrrJNXvfhYylMcdnsF63Hs6dEWe80ymBIc+ff7BjWfhNttoEcK8Qtj442JkjB4fhVnQQQ333XEDDLtzAO947CAaXfmw2Cvt6tKC52mNIEHIMnNmCF0K.TEMVPFeO19frqHr0a4s9VW++5m8ytAGvt560oSmcpToxltttIifRss+ve3+W2527272ba771Be+wUqgKGJ6tRtFlFLUZQKKBj+i8w9X4deer2WVZNZlzUijw2zLJUNNcHL7YS0VFu3vkRttuDlBlbCgggWGvhppyBRdLcptKl.e2DkUPXUuS6sFQr1G8i9QW6Nuy6b0kWd4VMZzXUf0wwYG+G3A534482ljU9eTqwuWNCXmyH22iFSyo62u+LYylcJGQxEZFS00vDr+5vRcfK7bVvjDa6iJXxW3Lm4k+Jtga35cbbVxOveFAIufPh+2Tjbtkmm2FAAAq655tluueyaxyKnoMWJIo10A5.N8RFyv9bxSNfyeEAS7mqU5dubjLZepQ83JUSDq1FEnIFfc2cWsRkSLLIdkcwwYWBCSsc8BRQ4bfLgfELaFX8wPMfgGW.lnNToYcJRSxmLtiVfAAPqu95ExjISoImbxbIc5LSx+eph0TAy8SvgsoZs+96mqXwh4hiiyaYYkGHebbbAKKqhqrxJE60qWAWW2Qj1ZpcojjGrZ2tsrvBKLpqoAggib.LdxnCGNj1saSrpHXJf6LyLyn+dZhD.Ttb4C82Fek3Cdje2jGCEiZ6MLwTmk.YTyHzJobMDGP5ta344sNl33VMX4fUvhVttmtcPv4ZOb3vMN1wN1VvnwEMkzx+gUivNZhjGEoNVNPtPWJQ.SdwKdwJG6XGKO.RpxGUqVeZ0J8dnzGuPTyoK2wxn3zlFxtYcxQyQi9ZQndYnoonZ0pU7u5S+oK9ldSuox.S7G8u6e2D23McSUt9q+5KATHqoiQVc5zQJVrnkkkUZwRJCTZkUVoPud8xlTDjwKZRpu7QxNMlDhyTpTYqYmcFqwZbkBGPtltttRbbrr+96ylatojppRG0e4QWau81zqWOle9QJdnt81aqau81whAASokI4.jTIxPTsulLJqdddaArYvxKutJx56u+9sdzG8QidKuk2RSfvW+q+029dtm6YcfcNAr+ief+hwW+vzuwnqyKAYu.T.rK.QopKznhlpplEXfXK6PK1.SQd1Mw96KjQ.6PuuIOxNKjacHGTMWBI5m99OIvLAKGLawxEmZ1Yms3q4U9Jy80+leSqm7IexbWy07ZJpZXw2y648T7W+C9AyhpYbccy7k+xe4721scaECBBJlvyNYEQUhshc7bzu18duYt5SbhBIHHOUsKG0vsfff7pIFtb.YqWutkkkkbYh+mff.011lLYxnvyH9+Qnqqa2tr1pqgJvzSME4ymmb4xcYiiCLR3njVjGkdhPW2S6sev472SgNlwwyZUUia2nQiHrsaRTzJXJBmgSbf8t1q8Z68c+te2gtvPma5lF7fO3C9BUQ4dQ05uuVvDXbG.NjMoagiqS0hgnhifCp9W+ogdadvrAeTC8GtJ3i0AAFOPfkHKWfRXl0wkvlW1vfgmpYXyWFBKBLmqqawjjWhEizVk5rYOOOud.87W1u6e3m5Ob62869c2F3hdddOMfONrZ0vp6rBqrKFGpGkCFdwbPxiuF2IcAfxK.S19fwInTRwI52qWucxmO+V.agCclOb9tqxpiy4DWoIFM10vSlAiB7jJWgk1d6sKM4jSVx22ujmmWpdvW4a7M9FEN1wNl0oO8osZpMyYGYW4b9mqhmmWAU0r999Y877J7a7a7gJ81e6uiJW+0e8UTizsVoe+9EyjISllMaBBpmqWlgCGlqYylovsyJsPEBfiYDbF0Iszi4wKNRfef554Nd.bi9LO4jSZM4jSZEDDLp.B0qWmnlQLwjSv1auMi74l9EUUkQDGloSfFdknGFzXzWMR4WGfMNs2oWIhnlXXe7V999s7771.nC0nKsFEf3drH6yEGs+7GDIwczhlbXh.yAHbTBpEvlxIpGSpyojBBYO.hF9Feiuwg+1+1+1Ctka4V5kL5EOaIc9BwYbZhO41ZqsxN0TSMNGgjTvjZy9POvWZl9w8m3Vtkaoz297muva7jmLeSybuVZqs1p3q407ZJ8vO7CWFHWRg1rbccy7RbdI4ervGqT5naE3GjEw75mjfQtfffrYxjI67UmOS1LYMA5Y5JaZgPhC9+m7dyiRRtJuSzeeQtU6UWK4RbuQ2ExzHK0VRntEVxR.OIFVF8NbNOaC1FPxCGr8w1Xv3wFPH7XvbX.6YFvRLdW9v3mOfAg7fELiGCOa4Ay1XwwBT2MHTYVZTqp53dibOqbeIxH9d+wMhLypTIg.jrkv2C4IEcWckQFw2869s762uOsNzLItnPWkKjBokuuehToRkXr+XqACGPsa29P6Vq1SCgsHFNn..X3vgnW+dXPuA66zkM1XCtZkpw8DCl39rBIf.FrezXNb.AzItfbRorpqqaCGGmNZstaud85b7ie7Vn.ZksX11UPEC5fh5nFlpgSG1gwOdCVe1BlLOlDb9j8iIQdjJeo7zewm4uXzMbC2P2nO6YnmygZqD+6cNLofIEtLl8tBqPIjfN...B.IQTPTMOueHl4iwfWyBTZF.24cdmitoa5l5JDhldddUCCCq8Q9HejZ.n5s76bK0iF4sUgIoq3BKYNGXKLB67zlBlru8KGCH0tSSVOVj8VNJnYCBSxgJNkc1yEtSzrF7XgvDAVCZrE.trG5gdnm8EcQWzko05sh9cmN5e6HXR1nEaB3qQj13TWq0kDBQQDMcEvlno6Yc6633DqsSGlVh7c68iX+FK.Sg6WjLiS2DwnxfDTJVyIO6YOKN4IOYbR3w1f8moawOZWKGnwOHQDEKnnHhl56LJ4gOym4yL2MbC2fIFp7Ho5zly6.vBO3C9fK8K+y+Kuxe++ve+h+E+Eejzuxe0aJCJh4hJ1yJHZhNnUZKP.RojXli65dRLIIANkEnT1Q9uvTTVZPThIQTLCbzmhpDkl.APDHaaSyC777v7yOOVYkUXlYjHQhIc5OdECqc.vJsBVf34WXA9HG4HwiF3CFirY+j4bSFl32B3PdLlpgIwmem.lDWBTZ8HhQePnCQTSl4FR4o1S6dlFgHrNQTMoTVOGP0x.64551zwwoUzyzGK+ZOQrlbe7D.I1d+ZjPbx4wwnMODXgb5bYJixSzIluxW4qfK+xu7.hnQHO5iRShI861lOL600rwam.NH0ltaloJpNq96D+9RHOVs3Wt3pEJTXk+w+w+wktlezqYd2SqSmayMSkNc5jQ9Rx.fE7TdKXKsm+U7JdEou822smTJjw94SGDDjLHHHY0pUiafvrES5f2iLB3sVCPvBgvJhlNVu9ekWO9C+89CoYoD8r5nCfofdkJWBKtvhHSlLnQiF.LfsvNtHLSDEZP.jYNENKsDiEl7dfPaDhlfvdHpnbLykrrrJF4GqH.pBTnUTA++WpQ85iHVo7.oKMEgEKE4CYtxkKib4x0mHpMxh1aVYydUQ0Yat8iVSRdz9bm8yNllgID.V5ohz+RHZpJoTpUXly333jjY1p.UHcITZ9c1YmkRlL4h.XdgPjlHJcbtCQw+O2YO6YSckW4URDQbj1pYoTpj.H0rECN9ZIJmfYyUcRCSw293+oLykg1X8M1W7+LLB1e2tcwhKt3DDyMEgRFmZzTTzwfQHnYhi2retG.5eR4IaeZ0oa7u+W4ee869tu6xJkpnTJKAfxHKZfJSZzk+m+q74Cd9WwyehVCcHOqdZy56q0vDD6f1j7TrnbNFwN6xVhPEiS3B.IKBgUSnm0IHi86jO17Z1tvSy7YY962A..Vv1NE77lCkvhVVVKBx.yJgPjPOcbOwLGFRj0r7Z0jfJ3wG8nGMPdJYf68qCzZ83SJD9kBwnpn5POOuQ111OZNLd5xhA.af0rieE3FW3nTHuYpPf7HLUpTbV7rR.7MSWwBg0PMZM.qF6+69iG34MyyvsmTnLIQDvlX4kWN..ijRYeXidvCcPNL20bM+noA3D4PojHGlWMVMRlLYfqqddsVmxwwIExgvy+OddqzoSmQoTAJsNzh3PlovI5PBCKkRknXwhoDBQhXX1IDB1yyK.XxgiHJ.YfIHBfXBbn1y3rzSab5o0dFMf.V.Las7xKCsVSL.ANjEBI444gM1XCTqZsI701yyPmhjISBee+YKVhAFdVXDXS2hLEziFRbXWlntmVc5t.n2G6i8w5+xdYurdRorGJfd4KhdkJGk.wFaLB0pMB69HJj22q1mwOCiEYqHJX.bhsA11TnbB11L77hG0rLyruRoRXFGqfy9LC4JeKfO0m5SQuzq9ps.xmp3Tw7BvA.tG5AwOV1V.w9LN9wAN243UVYkwX+InD8b9rjPHBYlGxLOOQ1YTJUl++9Deh49o9I9IFtxyZkETmVE7.OvCDd4W9kmY2cuPR.XQ4oTbINQzAnIzZcR1hSPfrxtY13hqwfnvfffwISjbB+mm72YREfEBQnVq3nQXd7OGQ.V1BAuTpkn1saarUlb3JCaaAHlvnQiP5zog1yLMH1X8Mvf9ClbVbrl3jNcZSBFlSkY.hAwL.EBFgJkZB8uHlFvD2UoTs.PCjC6IDWYG.zMGP2xgnSETomqqae.zOZDb5CaLFdOBcb36dasB.+m+U+OS25sdqVDUHAPPx7nZ5RVHcQtn0u4u9uoEL9e5CCsRnGCYr3fEzkQghFeeQhqlEYEFO0Mtoa5lLOGHhdu29605Vdi+Nv08LrkEBeU27qJPJjbN.Td1DIxAZ8x.024wks5SoVFE.IK1gKOQz5l.e97Hjn7ALWJn.PPQBgtvMLRuv91+8aFQ+uZ0p3XG6XG1OESDEHDBesqdDP3PkVM.L5uyN6zWq0C1Ymc7eYW60F5kBVNNNIk.rZloaClsiue2eeO1GrOxgAQGKMlH6D.4RZSTFXizQ5wTx2xa4+Pl2y6421vu8bfJTFnnQDlls.NG10y9hk4nDAfsBKY35O.vXjEiQE3irX3MbC2TJfbo1DkSVkPRoTlLOvbk.Vz11dvm9d+zATAJPcZ0hrGSDQo18BWHHYhDigYzYmDDHhojJkJ9bOqHXnmPHDI777rXlIsRaleLDYPDBARXaN+KS5Tr1aBTzszZOBfsXhAAhrsELfAl4gLy850EG4HGYplIwwI0FoOSBCcaXF3HqdDte+9gAiCXOslmHG+SdlRrQ8N.KDB1yH.mgLyiAMINDTnPgjVVVP64QfYRGKf5DBjRYrOVKjuDIbLIUqTJKfbVmUe1DBgHgiiShKbgKXczidTKrNHTeKKfch0ssmzVQ52k46qCLiW8iiPbtsBA5wNnRn6HLtLJmNKPpJFsEJcD0IRoTJKy31F.YyRnRk36eiQ7Sf8+9gsNXgtii0N3D.VaCLtJp56.LzMR3gAv7n.FjuH7KUBgEJT.+92wcPWy0bMw9Q3O1G6iQvPMGKkRkPdJokuqOo0J9+5668wVIRDp0ZFFcyzxVHRlHQhIzgH5rq..lEBI6o0rsgZqgMZzHre+9gISjfykOukVqSVoZUK+QCoei25ugYeFAJlBqwZ4UbQSRjHAV6HqgjIShpUqN4oPDhAfPHfm1iAXHtRYn5LJh.BYPzd60fW8HqN1xPImgfw.xh5GBzwBgsDBmFJkZum2M97ZBfNW3BWn+QO5QGgiUbL18Iz3x9tYw.HbKffcvFVkLMA0XyXCKhHB1HDdERvrGW.HUQKLeUTkhPsabys7wVXL1YR7ZOd0OGF.gBhXf7AZyTPy36Tf.nQHxCeo7T8Anz4AR7C+79gsJgRoK.rvVas0RiFMZ4JUprHUfVHOvhBgftvEtPZsm2XNLLHWtbV.v588699HhoXjhvLyiiPmjkPHrhZfNqUZhHXAiuw3hDiucw+aFVkf1X8MRDINv6K9+RkJgM2Xynu7zD6u3iBlTa3nSFDRQnRqCn3g9fwtZ.PX+ynNyPBXzce22cfRohhmAr59UikRo44QgBiQwhAO+q34Oa9YOcLG0IqueFgI.GnKJvL4CRfF6mRFHhirQNVMUo0z0lCihKGr52wq3+dK.jNGvxkMyxzK9m8l9Yu728uy69xrsseVZsNOLUtLVAnFCiC+tQulTw2nDH5BfpRo7BW208Ce968d+hW..UO6YOaqq7kdk8fB8MJG8j.jd5lwXbkdmT0+ntpsXylMmekUVIYjH.5C.++z+z+zQefOvGv+y849b9Xc3i5vOhK4we+O3yqGs6EGryEyhni8owIdddosssiq3aFXPcxpZsdEl44kWkLkQM0w7e9O+meokWd4i7re1O6kIhV7du26cws1ZKCz6XjBVHovVLAdmZsNwce22M+xe4u7..LVJkglfmLznHJPDK..Sxs5IhoVgBEnhEKR.LDBISDQZsNwL7m1B.z7yOOsvBKf50pOoCG.ORZ3v.LM0tKhuhxtJkpmTJGnTtC.n9Qv8rNxiJt2uaImmiSYTDUfoS28.vnc1YmQas0VQcecqQOIoJ1OZ6Ei+yhd1tVZfFlNUtNrPcfnqAJtyBQ2uXl4wjQTUmENwy95wqs0L1WGmhF6nDxCqHMLYdX7CrHlhjgoZlQT2xTJ0hQHcJCybx74eVIJW9bYfYZqrpmm2p.gKXaKi0kIKkRkPHDIeaus2VheqeqeKKsq1hINIwTZaocJ.jzyyiXlonhjDKVg7uvO+Ou0m3S7IhGEmwz2gLBf3z1QLSiIPpTIQ5zYP2tcm.qcgPfRkJQ4ymOtCYlNVXgfn9yFeuKtyXw9.i41umTJ8foaXMhTc89.n+8ce22fq9pu5AXSL.UwvH+eGjq+OVOedzVwO2lU47i6bYl2w63cj4c9NemYhRPv5y9Y+rCu9q+5aiBnNJhlXpNl7ni3grXNTYJkb788uhxkKGOkbVC.Yh3ZevRKsTuNc5zjYtBrrJ6HDwH4pF.p644U+C9A+fstoa5lZczidzNXZ2ne5D0LeDcYDS2CrDLZXxRFw+FCfA8GlISzww.bN3iC2mxTDl.wZ.5s.vk466+rSlL4k444crG9ge3ir0VaklHxTzRhZSLu2N6rS8q65tt5ZstpHZZLALg2+MPjtRn05wBgXDbvvHsy460N0N68iYoAP7YSY.lL4YVB4wbnDRn05QQh.eqnWc1.nesGaZqN6m0r9Om13m8y0+YOSwb9nYRPr.xhUhroW6AdfGX4WzK5EsbwhEWhHZQ.rr1UujvQLmxUMm7TxLbIdBcSi7YkPoTI.PBoTlPoTIjRYFkREitqTLQjzTLwPXD1RRZfbdBFbBgsvJpKsDLn5XRGYiRzEfAh55+9z4DsVyEJT.VVVg.HrYylAc61c16SSPjWjsRXzYkQE2h8AHeoTNVq0fYNUrnzRDkfM7.ZDAzWbJYe0oUcHhZwL2.Fec0fAQc0gw9ZOXPY2rzxINwvmLR33vPbTbgKms.zyFqTpnIgzbvFyCOLQq1.v37.CKY1mzC3XC.18fzy46TezydVu4bUCxwSg7HCWjioB8RdddqDFFdDoTtJ.V9C8A+PK+BdgufEkR4RZsdAl4452u+bG+3Gedl4LZsNC.RIkxjvLE8xn053yjSEkLaz0JA.yHV0hHKaaaRqUgfo.oiLN4QqXp8.fjfgEHPl+29ohimmGVXgEvpqt5DQFdV5QDedJCDR.A.THQHLDlfWhtEFPr0HXgdLycjRYSkRUG.kMc8OWIyzZF0Pdz.klPqkXT.8uTmU7ngzi8qufXhskE.329a+sG7tdWuqwnfQb8gMFAuI9em0N6wJVsChdo3+r32mf5cSyrnXZQOU2SL18Ki7X4H8NbY.rhRoVUHDGgHZo74ym4zm9zoti63Nr9k9E+krXK1ZFcWJ1+Ghh4OgTJSOieu4hh+OAPbyslF+e1rYoJUpDG+OdtO2mK8W9W9WZEgfpDesu1Wy5RtjKADHZ0irJHhv7yOO777lfJ38YqE8EOxlKV6R5CftLQcbDhdZstK.5IDhtJkpsTJaoTp5iFMp7EcQWTb7Z0WGnS8IHtM6XfJyFC8SqZlyrKqu8+Heewhwwid.0.6+fv7Hog6bQblKW7nBSDK3lGFjLieO7.uGsxZUNV3WAV7VeG25RglwEaLk.r.Pj5Jyi4nDFjRYWxLdi6HkmpWTRqS5NeoysS7lb9EekWY.TQEInz9Tj7mNtl1EAAhzVBwvUWc0ADQ8sifipMf0O2O2OWlO2m6ysTVfUQcyLQG6h3oMSjSsSLKUVl80AWy1cPCzyriOje8XX9274XauG.1ydlfXxCzNLLr4e6e6e6dnnAB2.n9y6487pekW4+1ZZstlVqq+LdFOioABQnkTH6oz5A.Xnmm2.Pn+a3M7F59.OvCzQHDsUJUGgPzkiBRJhpV9BgXjmQz3L7ClwfhEKNHWtbCIP9ZsNPoTggbTgki9BZYYg0VaMTqdc.hmLx5.lneISN0klFPX.Gc+PoT8Il5.fVDnl.XOsV2ToTsUmVYDkthGLQgB7VasU780.fc9NgVKemrNr8hw+2SQJCZLBaDMNjquOJrMjxSCADiAPvm7S9IAkmhnigXdDQGKais0b1y.Ob7XaWM60T.v4hO7wOpvlCu7K+40G.sQdrGfcc.T8hu3KtJrQ0986WCHWc.T+ttq6pdNfZc5zoZdhpd5S+Ypp05pZWcUsVaB1loFZst4G+i+waqTp1.niVq691dausdLy8DNhdBgnKSgc0ZcekmWeaa6g.XTsZUmPqs4maN79e+u+nK+oeshUM8Y+yH1PyqzoSiLoyfUVYE111lm01JHHfGOdb7nPLDDB.iw7L5hSDjOiKXRGhnlDQMjxSU+qs810.P8B.0kRYi7.Mccc6b0W8U24YgbcQ0nmkkdDzQbVaiuaVLf8jDEhBu05O9c9NS.fjDQIxBPQ5bPHJ9HPOxi9Z5HrgA.rrrX.vKu7xy1AFv.T2tcS.fDVjE4HDgELETcjVqGT.Xfss8na8VuU+jISdffCO92Ce0+m0E+HdsZ79F6Y1+hw0pUKH+L6wWC.3bOdeFqMzzC.epO0mBJOOryN6fs1ZKPDGFZDizvnY0NecW20ACccMF7wet4L+JH.PYMIkS6t6tVv8aqufuSueLqlWza8HZ2lMpgJDQ8YlGfRv222msssS827272Dy49kAxsTsHeVXpVXcX9rNnuxYsihgPc70wfsl1HoNqCzFkQS.rGpfI5wwZqsV8kVJWChnFZstQ850qIbDUc0t0Yv6gxnsxyqG.Fn05gZs6Pl49+8+8+88DBQOkR0SHDCJVrzPgP3mHBgJRa6wdlBCMXt4lafTH5WqVsg111iD1B+nDIB3n6gJslylManPHLi6WBAfLTPTMiXo666iHs9hA.7773EWbQVJO4j6IDQiizpiwLy9RobjkglpF5pBZjTJG7q8q8q0Kj4t.zDwZkYN.DBEBAX.K0oUIXfTLG8bg.IkmLTJkAvPQxn7VxOIgNmnjqNwrzN8I90r9uXhdFG7rzYiSZjMv.TN5LUuIEygAPBfrYJx7bau81QEad24gXRCAl8LzCVrtucWeSiSDHHZ.GLBkP7XstK.ZYaeEMjRY02za5MU100s7O8q9mtLQTM.rGybauRk59LelOyN.nkVq2iYpA.1SoTs9m9m9mZCfdFDEXhQPq0i.vn+uN4IGCvikR4XKfw111AkKWNDfB60u2XkRMRHDCEBwvnhXNNx9iY.L+ByYZdPTBpdddXkUVACGNDX+2D3P1r..V6HqAZRx8rwMU3jjZGBP89e8I9e0gYtMLiz0F.n1O0O0OU0c1Ymx.kKiBn11aucyHpRMsvaa8u3MMe1mqgTgXQFcBcGGHh1KUv78Mw65c8txvLuX0uZUCkM8hxCvP+uEAVK1u2iPPnOjO6Y88Md5qSLQygxYxIqUtow+2JKPK2unqQ7lKMI9+FJkZOlol228ceM0Zcyyd1y1TJk68Z+kds6wD2TJjchJ5PeOOu9ZsaWl4tRoriPH5p05tBgnGGY6YYY4e1ydVegP3OS7+8AP+JUpLfHZHAZjVq8+nezOZPT7+LCfK4RtDF.X4UVF60rI1auFSh+OlB0dd58kP7D53XPad..7IPCbDh9ZstKybmvvvN4MMmoyC+vObW.z6K9E+hyPAurA022y0Jg336qvqOsrXI.+qGDlXAyXqKYwnNJanfCRmEX9+Oe8u9hW7EewK.SE+FgnDkAPGfSLDX6GMja7H5p8I.RtsoPIa.fmA.Nguu+UjLYxS344cTaa60zZcFxXQFP.8Yiw2dDQ6IDhN.Xj1UClXBlCca533TZmGZmceo++7Rc+pe0uZQXBPoMP99m3Dk72d68UzjmNYLdvtpE2cw4PTW0MBLGr.rSB3kDFz.QDQ7K+k+J8+7e96pe4xlf4NFvfcml7ziGdMdvhpbfNYrQRfZQcbtvbJ08OuTJiQFvB.4Rq0mMoPHRo05zmRHVrngy8Kq77VxQHVHOv76Nb3bUqVcgwiGuvwN1wl2SqmmmhxnfHMBYL.fTJSAf4c0tKPLEIZTTnsscfVq8iF6eTbUoAfESjEwbxuv+v+Ppq849bSlNc5joRm1Z0UVg777PlzYv5ar99D1IfXGj7DGjuw23ab7se629HXbJ2A.MkRYSsV2VHNYWs9LceMulWSm64dtGCJSPt5EJTtQQSG16bgKbgdG8nGcVge6etDfxGMeYS5hvV.V6D8LNZ7GZcLfj6Fglm8aqE+xliD1wA.hA.5C1AiGstXPOF+2IrAR5YPhVlx6uaxw19Q1W4WPqOybBgH0LceIMLid5kEhSdDs9LqzqWuEO+4Oe5e5W7KNYIfjkKWNsuuerXhMq1ljA.o7z5jyuvBI60qmUlLosFNzm.XqHnwmDLRZKrS..KuX5CxXFDJQv1t.5zoCZ0pEGK.YvzZx3ebBXht3LgxMRoLHl1YFadyjWBFcinJ.Jp0ZOgP3AjuLPoHzajaHvDgELZZLjczVaUY7NOwoYGwcWJhGyqu.P8Xwma9YzSB9s+1e68eWuq2eyM1n3d0pg13wVCSlAwCSDgzeHl4KuUqVWZmNcOpTJNhRoliHJwRKtD2tS6g.nkPHJo0ZWo7TtJ0o024cdmktka4VLnr.Y6HDU5q0lfn1.XvgfrfmJedvrc46f6CV.nvx.EWJpqYiHhZByYeMwzti9Xd+V.rlFXqB.W18ZzvjeH.bLsVuJLIuEk3A5Af1RoroVoavfqxLUY2ce3hW20cckArq54c5F111wc7eDvF9BQsgZ89708DgM3A69ZB.mL.twHuYIaa6E87FO+MeyuvTe3O7cAl4QWVtb8dvJU5fonUc3F.ipAL9D.Aa+H8YM6m4i107AOWL95JIvwRKk6tnRgUtvEtvJW0QO5xkAVQq0KFBrniPrXAfENe+9Kznd8kDR4RLyKbgKbg4RjHQj+1PDFZYQDm..wZixhtJ2EIPKD0sdDMoYhO+CQnRIIQHUHiTDPRxH1pTjXqZR92fHkXZ7jPq0VykIiU5LYPy81iHKhEBIfofEw9oBAPvBKrH50qKL2ynvrY2bbkJUF+m7m79Cesu1eAlYNjHLNjwPKhFxgggRGmTJkZwnmSyEgPgYE25dDQsDBQEsR4ILHoqrI4ctkTJa6440x11tMfnCftO.FBb7QQEe+ISTlLaBlyVzD9P94RAbrLQO+Wpa2tK9LVbw4JazctXjCNbSfgUMuOpZjd+XCL16v637i2uSS7a33fjttFTujEHckYDyU.rXdfkKAbDl40.vpdddKbRgXt620MEQTJ.jNxlaNl440Z8hvPsh4.PFsVmLUpTI78GaID1wHYJZOIYIL5LhO.FoUp.oiiEybJsVOG.xP.IsinecLpl777PgBE.yLrrr1mnCGsXFLMiD5DqoD6uAefF9.OvWt+ke4WQWhnNmTHZUxn6RUYlKJkxhE.prX1m4dsp7s5UIp3maBLnJP+HZM8jExkd7rNHROlE8+otm64+SlWxK44EU3sByATbeMcFHO9u7e4MFbq25sNB.82Dna0YZHFL98B29wWd.G7+ehYy4.HaJW2yjxwwIViQx.ja9ekekW4Buk2xaYQhnktJobkh.qxLupRoVhHJ8ojRqy3pIl3jQZd3bZsdtHjMCi6IqXzNmQHDKBfEUJ0BDQyEgvjPgP3e.+eV.TBDg3IBHo+vgoRkISJ.jjAr1X80oX8v4QOaeFAAgSZdC.BiFDICAnN.7dsa2t4kbIWeKk5zcjRY2O1G6i24k8x9waAjusVel5BgnB.plGndISdCC1d6s8OwINguCfu690wnmJGOxi45eMfvDiQvI.WBHjnbg.fKBvc61kq.fevevePCYwIJIalg6yTI7serpp+iXC31GGDfHF9rKBje4RkJsjmm27Lyo877LSnmnJkyQirIhnQQHJYH.FxDOPJk8enG5g5emen6bnRoFmZtT3YeYWl4Cp.BOFP.Povs29o0Fg6qByX+U50OhdDiAP.y5PlYKl4jTNZdl4Eu669tV99tuGdYfrKAfk1ccSRlXBMGxaPcxINztZbXOWOv0QsXX9MDn3vSZDj2A228ce8xBzBn7dBgnIPglLyMKlGM.xUmHplUHp8i9Re40OiqtV0pUqJkxJISlr54O+4aHLBjZSoT1RHDsDBQKoT1F.sipjaOhon.kvPaa6QDQCgo.Z9JkZbTPIARoLLdi709betHSlLjkkE51tiQT6rswvQCiD5IfgiFgQiFMyWXZxyga+1us3hmDJkxfd85EnbU9gHrOPoNetO2mq4a8s9VahbnsRo5ATdv8e+JefbA.f+ze5OMCr9LcK+DGrxxOYsdjcq9.1U6Lsio9abheRe.3u6TXbNJpKhDL68WjYdUl0qAi3MuFfd0MPDZl1.Ybdz6d6rAadXWagQAK5W1fhm3hKEOli64440A.sTpS2PHD0YlqQDUKuQ.2phbn7UJDEAJ4IDhROzC8P0tzK8R2qTdzJHHnsuu+D3bKkx9xSI6s2dM5RTgNZstCCzcs0VqK.5Ob3nAvLkP70ZcfAx4LqUZVq0LGFIL+zzwR8pGYE..zpU6Ip0tPHBgEEJDByXOlQHwj44OgPlCGCfQJkZ.y7.1LBDGvFZd0Kh9WcN9wu7dBgHhhMkhKN0Xs9r94hed4fg4AFBTw+IvhkLqsTjca8.l4f26688NlHxOa7meVL7O9c8tF.TbTsZXLP9Gm5lRV.XTJYDYqrxJqPfXRq0zRKsDwLrZ2ocLjcszJskIuuR7Ikxw2zMcSiPNLrf46+PsF9.qG..t1F..acXA+8T409tuMEE5H.nXbm9F566OJebfWqCbhGW+pyZjvDaCVgO5QO5relgvnuFSnJvN6rikRoRABoIlx33HhKf+7.dycJC0LSfrfcccCApEn0HzTX0mv7uMqeqY5.p63BF+W8wlniVqaATo0G9CeWcpZFei3qVtbZfrKBS2VWEH+J0.VFBrv1.yAXePTxM6m4re1GlOz80M1S.3Cr6HkB8Ar67te2u6VkKf8.x2PHDMPXXCf70KBT+241+cpKjx5ZsdOkmWyicri0RJksEBQGaaYWGGQWoT1Ef6oTpgLyiHP9v3SxWHDihoJIy74G2dTC..f.PRDEDUPkVEGqzHgP5G8yNFDMdiM1HXJz0oPFfkmRxZOMdauseSlAvQVaMtUqVvJQBHDRn0Zn0ZbjirZ78iP.DzqWWifGRnGQnaESwnZ8K9K9y2DLuG.ZwL5P.8BCC6KuJmA.4mYzdSrVYzrDoTZn6hg5AVJkhER4XWW2QZsdnPHFJkR+KbAuvoz1PS5notRDsNexbcvmyGVCAl7yrEPHvtgJEB.1LXwEWLnLyy13tjLyYpZPt8JUAVEH2p.XYODij6IBw9AmNOOdVg.Hz0EiA1xG.CqrlwVDFZMUCnPkR.kQNT9Feo+aJST9J.nZIfJNNNUjRY4a7FuwJZsthVqq64o1SJk6QDUWqz0zZcM.rmu+n1.b+hEKN..CEBgINclGpz5QZs2HsV6KuJmQJkdnVq8IlFC1f5ISw6L2Fi0sDhHTpTIn2WwR3IEnJ5rSCkx.yBovzABivu5S.CXFctwa7+6lvfdlFmVop65paHDhFRorIPtVEA57s3uU+J.i9DehOQ..P0nhT39jKpkd7tNL+KA.H3k7RddSNWmYOhYSOYPd.St.EScq25stvC8POzJ.3HUyi0.xeDXhSaI.GieO68gro8Oj.ldMbvWAQSgmn3+qL7pbbFBfgEKVL572x8+89898553bUcXlaULO1CHWCxLM2pAfpmVoJKtJQIoTVx0UW8u5u9uduHee8DBw.aaY7dei8jROHNWv32EBwvXDswLORoU9.vOtATTjMSpLY.h5X0JKuLZTe1hkXLwrsswrklKjAOsXIbH.EvfCts2ysMFf8AvnK4RtjAJ0o6AfNeouzWp8K609i25u6u6uqEPoFgDUGH+d.nUobn2N6ryH.DbhSbh.frgtemWLzmxt998BlL0Q+1H73.gwboZ0Uw3EWbww.v2DDR9XA0JAJDMFNyiT.4OrMWOZKKbNj.PmxNp51EK9kW1hnkBYdNxfLhH53.fHmDDPnPbxPozbvMxAlXZLxiAOym4yr+s8VeK8.PegPL78ba21XfrAnHB1c+haDl4Z7IB3A+O2qoEp3XHnToRiQAD.jMXyoPiMBdr1g9ZeFQUhdqs1J8W9K++1DTacrDPokKLAdxkLcpuHlK+DJ67HNf1B3DydXMM60ywmApdkAF8NdG+1Ct5q9p6VAncmNch3MdwVgggsQIzBnbq7.sENhN+O+D2cGgink7TxFWd970kRYsLYxTSqca.flZstkVqaCTnK.5XYY0kYtiVo6PD0Ef5Af9ZsdfRo7APfV6wL.rssogCGRJkx507ZdMDQj0Fato0nQCoACFP4xmahU.CXv4YHyOis1hSkJYLjO4UWd43hjXdmLPTWoTgyO+hiOoizmXZ..57Jekux1WxkbIs0mU2QJk8.vPCMANa..Bd0u5Wc.P8f0lbn21+KMUwNX.+g.Hb6s+nSNbdCfw.1wA5QHqYJUP4okAvp4Lv8bM.rVMfi.jaUTCq3NMnuIiaPL0FZVeFGr3I6KwiKSJ8ADi.vHWW2AHK5Yaa2w00skTdUMxATKBRwMJAzjnB6gxn9YTpx.njRoJ+RdIujZKu7xMUmV0tXQuV+z+z+Ts1c2cimbFccOiaud852EnTGee+1.bakYL71k.5KkmbH.7SlL4X.DDAk3YtjM+errrPlLyg81qYrfgsuQrovVDnMBEVPLL3IfPlQPTRMCAv.hlTbntDPWkR08ttq6pK.5++3u5iLPoTiPNDn0ZNa7uagHrb7zMxEiKc3nH6It0FHDHenMQiuka4V7AvnJ.iHpvPtLOHtao.XLPoGOWCDPEB.VEJLUOHzZcBNDIXlSzsaWKXPVnE.rXFV20c8Qrty67iXo0ZpL.KkmJDkQPw8I100C2c2cCQM.fIT27fHC3oxqIAUQ1DfYeH.xGXaJl4vzoSOpDvXfMCQcva+35W6DQmjJ.POvW9A..3wiGaDTVf.sVOVJOU..3s1ZKqnFmjhINEP9z+3W20kw00MsmmWZOSmFITAvwwYFeJdOQa+cvjTA.BKBLFH+PTEcIhLPBGn4latYGhnQDQz+o+Suw49I+I+wVF.GAnzZ.XM3iiHAVAvaA.LmiygBYcfC2N4PKfx1SatwH.u9+I+I+IcPQzFnTq7.scbbZCTpE.1qT0pMxAzPHD0QXXcs1sgqqaSkREc9W9t.nW1r45QD0WoT8Ag9G37ug.vWqz9LanoJLEbdTiF08ymOuOyreDMCiJhpoc.pyn.w.+5+52JkvxBVVVPHDHSlLQEoahvJFOhNiQHQ+SIkc.iVro.I0IlpIkxJrgfcUgAN9cHhFfRS7E...gvl.AKFvRqTVHB+cjA07AJkZzU43LPHlRAjid01FTLJPHffuJg3eNR13fmOM66G7uG.f2YlFK8m8m8d8goAWiHhFkOp.ju5W8q1LwmRfUcccWCn75.X8M2DqAfUAvRGCXAfrypQDGLVrucWubDseCQCLFva3pQnECnnYRwj.0N6m7SWEnbYgPT100szN6rSIkRUYNlqHDWYcgPTOLD0c0tUsssKCBkXlJcJorBQV0+C9C9CZFFF1VHDsTZcqu427a1RJEcIfd.7PhngpSqFQTng9VjgNVu6+i+GmTB3ToRwKu7xvySOsvIXB5mX.JLhBWAvBA.T.HDNZjenVqiZvJ4CFCYfdNNhNQzvskTJaIkxVDwMyAzx7mWtqVq6gTneVfguzW5K0WoTA+p+p+BOUI40CZ2MsvIqaN+cFpXFZxAHmOJYlbijYbdSWzEcQotm64dVXm6amU.Js5lQwqA3ZnpS.V1Yl30N9weLKdR70Sref3FsMpDvPfBCKTnvfhlwqazT5qTKGGmVnDZCTtUNflNNN6IkxFRorFXTo.PUGGQsmyoNUCs1skRo5nTpdZsd.P9XMXYH.OPq08APe.pOlw+GQjuVoGC1DiFQHdx6fnuCzBKLG..Vd4kgsvFLGBdluQdddfHFQr9hISOJh1KSwEIY3a5VeSCAng2we7e7PXjLh9tttcEEDc90928lZ+hewu31.nkiscqH+7cQYLXqs1ZzpShKZh1k78EEM462KXR7hA.etoAX6OWy7SFIfEA7AJMlM77vp3YKZffUo0SAT5vRB5vdMKrhmyy1NtKOKy.KR.ywylPESSpxmPJIs9LVJkJIybR0YTViC8Y8Yz9Bgn28651QJksQAzY73w8PgJwhoyrb1+vRT6o5AIevkYC0tqGjOe9.Tb8w.UFWgMafwlltv+CbYKz6XG6X8Ipv.lYehnvWzy9EYgHgB7qb+ekiTz1dC.rILTiZMTGqVxTDkEAv7YMGPGmnapnwKbj35c7YEBJbtXm3qZbb9Ne++GhFyVY6tzRK0A.syhrcN5QOZmG5gdnt.nao746pTptJkp8q609ZacaukaauGnTo5JkplTJqgPqZRorAybK.zUot+dLyCrss6IkmpKHz9M7F9UaCvsAPW1TkY++r+a+Yg.L4HkIzZcxLQvu6C9A9fIYlspUspkssf..JUtL3PlmgpDLHDIbdTHyl8CsZ2dL.Ok+lb7ADjOQr+m7K8kFBf9memc5BfNhqTzwHxf45eO2y8zG.CuRwUF6v2G.9Mxm+6zQx6SlqGsNlx.Hr1zDdLvvrRgjLyI3Rbxd85ktDyyyLub+98WC.ah7kirq1bMX1iuvZFdydXiCtCtmb1qm..32.vGP6CfgNNNCPkM6Af1NNWUSfh6UNRH.0ZcS.z1y6rc.PKo7phG+t0UJUi69i9QaIkx1RoS6O81mq0wN1wZRL0RoTcrfUW.zgYt0oO8oa9M9FeyV.nqTJ6y.iTpy3SDEjOe9.yDaRwCFLz7LiLZhybyOOBCBQ+98l7kYZfd.dZChTjRIChlD3isPDP.i4n.OjR4nSJNULhZhE65tO+m+yuuTJGbYm3eyHh3wnb9.gPDdFWWF.ge568Sa7eWHdRFs026VFO5KF0xGBTZbwXUhWDMQFxVZnoa2SQlfCbh+2cXA4eP5UjLrXtT.H0C9fOXJgPjzxhhEawDfoYNWlwq5ltI5VtkaIg3JEIbccSfbkh7OUvRoTD.nMwl6e5ubhSbvyq.dp44AOx8lEMn0fKyg.kF6EC+77QcsOa0n8pG+Q6r3GILu8fUQ.quxW8ArzZMJWtLGFxyDfdIF.wCOERJkD.n68d+37G+duW1wwAiYl8tfGibUh92rwD+ZQO+ex79Ki7fYlCvwJEQQvBcfgS8MKfBwh33325a8sZ8Q+L+CYXlWpc61GA.afJXSU7Yg.KCWm3wr9rE58vhy4vuVl9d..72.arODxUJe9du7Wwqnq9B5t.n8ev662sYYCG+a333rGyzdNNNMiDP7dZ8Y5wL2Oc5z8EhS1gHzBL1y2eTrHn1VHDFpPYAeGi.qNQKjVes08O6YO6H.L5c7a7t7gA8tg..gggVar9FIxWnfkkkEEFFRQE4Equ95..LYn5.uvBKDNdbP..6+POz46+G8G8G04zJ0dLy0AgJ.nLSbI.TZu81qjTJq.f5eiu12nkTJGpTpv27a7VIgPjjARFFFZIDBKxzQBRHE..ALOs3wk.5EMdW63551w8K5Zz3f62aHf12KlBnm3I8ySOXg8O3e1g8yGBfwu4el2bLUI6CfAkxlcH.B9y+y+yIl4z965ufiiypLyqCfropZmC.4.vl6BrFPkXcnaN.mCpyIea05jSLEuYL.BalO+riC0tvCsdQ27qYuVsZUG.0Nkyops0VaUSJk09he0uZiG9guuF.ntiiSMKXUgHpjTdxhNNhhW5MbCkBYtxq+W9WtJ.po055NRYim0y5Y0zfxKCEPh0rDgvgkm7jSRR8s8a9aZJJhThQiFg1saC.S7+GfJNl60LYPHESglN9ifzoSGq0gCAwwISOPq0CjRY+SJOYunFfzF.sOiQjz6BSiVG.OLLhNNCkRo+c8e8u5fi54+kbc3wlUOaHPofRwnvOOMjYtO1rRjVJUHVSmFXS1Am9zml1ZqsRyLub074WCF3blC.4QYroqAovGA.KM3bNwhs+g0D0Ys0ldMc7n3hWuXjcU99llZh1BHZCfNdddcAPux4x0C.cN8oO8dtttMJXUndQfZJkptTJ2iYpoTJiEo8AZ8YF0nQigkJUpuTdxt.n0fACaxbXS.zlMOGGcG+g2Q.HynYWq0VwTHWq0IxmOuEQDcjirFIDBpZ0pvS6Axxx7kgH.S0QhApyjl2QTbgumrmoOXpK.2+09K8KMPoT8c05tWy0bMcEmRz48ca2VW.SNN.nSgnmEHpnuMssiot9Skry9dd8Tw.ndxXcvtsMawMlG.KkEX0JFsmHEQjO1DcPUrGLcns+wA7iJ3Bv9O.I92YLu2WJOvlk.dF.3RYluLsV+CB.I.eDgPlIBhkgLiQDgd.nCAzpaudsSjHQmLYxzSZTScCep0tUuRgSoxlNZDqT+srA56cBLFaOSKfejID9zkJ5MaBEovpHEZhzQ75iVGfpOkaioAPlB.oKlGIZ9MZRqt5pw+6SlCHcICOZ474y6WpTog.XTNfgkmv87II2G..9X.3yd9yyWzEcQL.FisP.1YxlbxFHg2T90m..VqCP0kffZ5D0A.ykGXo620cIX3v77DQIjRoUjt4LmVqWnYylKs7xKufkk0be7O9GO0O1O1OlEhdtIkRKWsaZhoXdPuHALmvnsIoTJUpFMZjY80WOECNIXjfAaQfRfHNMB.qXqBFFE8mLjkzLVWMVESRp0fH.JfYdLXLDD5Jkx1Jkpd4xkpdim7TkJM01qiqqaW.zy4TNcPYzE.8uvEtvfidziNbcfg0ejSpimJYGdvDXSYCLmWzjogYdQxllGEmfdjDOqBER7MKVD.f+Feiuw3K9hu3gYA5TIK5hJ6Ss48OFPvtQA4d7ie7fycty8nEn6jjS1xnuJSmZSGCVX2o5WPAfzEiz2jc2c2zutW2qK48+W+WmzKGlCkwZtttaFDDrVlLYV7JKTH48alzDIiTo8z20ccWIeUupWgkP3XEMEJlSoTFgtNx9xVHlSq0ogQc1SyLmB.IVas0n81aOZyM2bxHOLFx3gggbwhEACNj.Y5HlIHONWtbb4xkAlHdXX.al7RC0Z8nH3jNPoTcOkT15zJ0d+L+7+L6869d9cqcounKs5m5C+YZboW5EumPH5H.5pmHpXlWaA3uyS71XlmIm.Iv1SzTiTqCjtl4rgzv.0bjkH+pBL.5IO+OLgmcx4C4ARWxreNK.9AfYJ4bYISl7GTq0BXJ9VZvffEB3PLjHzBl8c567Nuqcuka4McAXFTtUxAznbdzNRH+lXC5.DDCC1SbhSvau81+yEs39tcEueLRvkQFrIxvULiqTDQgA.vQzxnO.5mGXXoS.e7Hoj5jyBDBw7ZsdC.rUNfeHuffKyxx5h0ZsHWtbKUtbYKX7+EKFwwSIu8jRYEWW2RVVnzUJbJ8+7K7EJesW60VElD36tAfes7XbjPNO64IOQQQ1IwqbBfDaeftgtA.UaUj.MMZZDhfgddf4KZljSI788sRkJEcy27MG728g+vCKaJNYLxyh2OEjEHnxiTzWOrDnOrqwoSNBf4yBr3CTr3BEJTXgBEJj4K9E+hIO5QOZ5b.yU1H98K8S7S7Sr3+8+6+kySDOmTJmSq0oih8JgPHRpTpTvngYYHBoAnLesu12HyK7E9BRpTpDDQVmTHnR4QR0oUoYvoIPoDBQx7Dk3LJUJvH0pGY0jYxjwJYxjjVqnHsJgzZM77JxEJjGDQ7RKsD2oSmPgPDr6Cu6vjYR1kC4NNNNwBocOoT1SoT8u9q+5GdtycN+b.b47HAJgzOzC8PyM2bysHy7Jeiu9W+H2vK3ErlVqWAFe1Vvfj3Qrw1pI.pvLtfiibG.rimqqmsiSiIOWxignTrNMgQyD+4SU1Ga1icbjDmaRrOoYlSminjkMwsktPgByWrXwEgY5NkNRCtrpVsZvlatY7dYy3h2XaZzdh0fOZ7H1OcP6wYiqOA.rTJE8bjR3EkflssskmmWF.rvl.KVc+hgbJsVmIxGwB111Kn054jRY5ye9ym7htnKJE.xn0tK9M+5eyEt9WvKXgH6xjRoLcdf4JkGy+0+be8LKszRQ5EFYQDRaaaOuVoWHjCmOa1roSkJUBKKKK.yHuF.Fkii.hGq7XZAHCkmTF5dZMrr.LCkIZ1DZGTqVs9atwFcEmR1QcZUKXro1SJk0K.T6K45V2wwodgBEZTrXwl.nqMvHu8ihi3DZehzm02qqYedlBQ1TRhxnM9Wh8+wB.KcdjReF8bBgHdpFkVoTIi7iPeguvWfu1q8ZGCfgE.5WLtX.Sz.MSAWWEXby8OUDOrX01Wg3WGHQ8igDXWjDSmpaq9ddOumUt4a9lW71u8aO0a9M+l4vvPeoTFFE++hJk5HLyqBfkrrrlWHDYzJUJFH4C7Ue.5F+2diIh8+AfEHhmiYZN4z3+S.fTmRJSdZsJIXLSydoT.ro.3LHxxLhQMiPXMXdx2ovHQxIHBkN9Q5n3PvXzM+ydyC9S+i++s+y7Y9CzY6s2t4pqt5rSxq1tttcRjHQmfffFNNN0QTQsAv.GfwtG991mJXe88z5eMfvjC1Y232Cg8TnwWY+aTrxWEo.JLGjlQ844lN9gSi8I5OSDnt4izQiUJYpj4l.E1Tq0GA.KtvBKjwjLqoSC.TLsbH.XwDrlew4oefefqkAPXgRXLQjuq1suP3z4LQS+B.zGarwP.D3AvX6o+N.1JNAqmp2UwGs0jNVjuI7AVaHcTZHLIfOQcnYl6AftEA5fRn2pqt5Py+tBIYlWrrApma.jKawhEy+A+y9f1.4sKalUB1vLtmyAAVGle1k2EXwK5htnE.xNO.V3X6f4AVeNj0fX.uoUfFEKVjAPPc.+nIUzHCbgy1G.8KAzob4xscbdNMcbbZHkxFdddM9U9s+sa.fFggg0uzK8Rq4bUN0EBQyW+q+02kHZnTJGG6754HbFJkW0P4ojiXlGKjRVoTIbibjt1Zqk111NsvVjLc5zINxpGIA.QBgMwfmZYAvDiP.ZLHxmAOh4nNkSSFkqc.QsYlaQfZCxzkhyd1ubqd850La1bMugWwqnsqqqQLAymuCylfIQYDONS6dzidM8idVcXA57Tx0Mdi2H..4EsmwHheje9hX.fcOjG8FLXvnuYwhfYd9ye9yu5E+7e9YYly8sZ0JOpfbvj.bVfbYAP1cQrcU9kN24NWT.Z1yRamCBCzYf27ViAvH6cwPf7CvFFa9YNnevwdNOmA2wcbG88.5EUrp1NNNsSlLYmBEJz69Up9RorqVq6.f1DSsekuxWUKgvokRoZIkxtLyCjmRNNBJmVrQXgSRDkrZ0pVfYqb4xQ..yO+7..HUpTSdP544AOOOTpTISyKXhLg+wTzYxAkKWwGlNu0GfFvQA+qTpwLCe.LPoTcO4IOY6m4K35ZwD25d9je4VenOxGpUghn8K7E9JZKtJQG.zQOMHG+ye9yGDcO6IZ6pI9MO91f.xCrlwmTcf3wMsO1bywDQgUXlfdRQbSZDHt8A02YFQ44SWZlQkK.VCEvQRlL4xQhCXRPQ1Dl1QyDQQSsJhkRIcy27qHgqVmz00MA.R7leOuGqhe4hyTn70Mb5ex2EGqs2dhFb8zgyCX.mP.DjuJBHxI.4mPGyvntokB4xkA.yUBHM1dBhtRG0Y5YNaVDKfiKAjaoxS6nXRgPfxkKGinDK1jDTpKr6ElQaOxE5bUNiEBmgkAFbsW601C.8Q1rifAYZ3K7+3KLCRVN9Sz2amDuxLh0Zv0e8WuYxAA3ilSKfHfXDyreon6W.4oToRk5AevGb9OvG3CrbIlW6Lm4LqCCRS1.EhgsNNRkHzvB3Dg.ynN8e7Ink6vFuvGf1gaDBfwU.FMd73g.45UrXwno2Q1VeYOul.Xue+e+e+F268du64bUhlQ51U2PD1WJk8Xl6xL27M75dcUkxS44bUNJ.xSJkUtjK4h2SoT8HhFYYYEbFsNTcZESDwTTXUDQ3dO24nrYyRyM+7zhKtHkLYRK.XIDRRoUjxjbZ3UcUmJPJkiAv3Nc6LtPgBiAv3TYRE.FANNWkOxa53Zz0YKoT15y9Y+rcbcc6dZW2tnD5occ6kISlgBwICjRIcMW6ORJ.jJc5zIEBgkPHH.vfn.gP3KDhQRobHYMQDXGa63DkPrX..5mqD5CbrAHqowCOEqXIwKFmaBkEFBbrADQ8qDMIm.x1uXwh8Yl6yLOx3KiSxLuvC9f+Sq455l8C8g9P1.PLM1rMyAfMrafUAxsLN1jBbLCZfmTftCXGZSRSwRht+bhPOOuP.3CXOrJPuc2c2N.n0EtvEZhBnkPHZAC5kZSD0RJOUK.z7Y7LdFs.x0F4QGgvo8cdW2USXDk7FBgnoVqae+tt8TmVMb4kVxGLBd+u++ajTJRBBo0Zc5ir1ZoJTnPhLYxXYYYMAUSwMaHJ9+3weMLS+ZB4xki0mUCh3vy8sNW.LTYc..2KZRZ14JthqnKCz8r+smsK.5xL2oVsZMUJ0dEA1ywwoIPtNm4LmI5bSwPunhsf3hEbh8Mlmep1hANd.xhwDQihZVROLkFL8z.cQIzMZPYzEvd.LZ5WRivolasejejejrLyEN+4OurHvQAvwhd4fbP.y3PKayIwrUXI.rvF.yAjcNjGyhDciMmsMA.tNP.1cJccLhROFby27M2G.ctsa6C2111NRaBy2oXDRZYla633z14TNQH0FC3HpseEW9ULjYt+IkxNR4o5HO0++j2adTRxU4ch96KhHibu1yk3dypajnYqQMnt5FjzHKFPxHaDFKFaL1xRXCO+XS1FX.jsAKaVL7NOuAC1i0vlMLHwhDFCBLOPBqQXawhPpqVr03GzRPWcbuQFYsuj6YFeyebiHqrKI3w6LhA0x2yoNUUcUcVQl4Mtea+Vj6vL0QJkCTJEoTpD8wL8w05zSTbhTYxjw11xN9dfDbMgQmLSiPxjoeIbr1NEe9SOl41fPSlYiqmQX0m5S3PqjMalk+C9C+CV4O6O6crBLBstowHUqtokk01ddd6TqVsQ4kATaH.X+yT6idj1YV+uz5QxIO8vwZ7jD2Kj9n8CXeJLcZf0yyLWD.EHhxbW20cYcwW7EOfHxL4NA5f9k5ikW9gZRVzryNq8pqtpK.J.TcFfHIPiysSmNOtUVasGKEwRgTLcryn3Dex3PvnGSnMArMQzlBw4uoVeeaJDhcTJUyXNytRsiVKDjWCDDrJF4dOnELIJEcfCb.bxSdRqkVZIru8cILP9HfSb1Huw16j+MACKABnDvxKOVwI6R+gxkK61nQiDzAL4W5K8Um3m4m4hxxLaqpq3ZdGcnRcrASN4jCBCC6+XerO1tvLgxlgnbmXm2XnQGaBS579tNsyrfAOCg0Va7DDGcnvLyLi0ZlelCvbo.rRCzHM.RiJvEgkRArrSY.WBHcXLJSNrPTXQkpfTJmPq04DhEbApm72vAlD7Kd6egau3DEmn3QNxQx1nQi3jG3TDYY644QAAAnb4xTXXHIDhXQhDDHFLH1L4etOAzGDMfi3gfvPhoALgdvH3mC.ngRoXnuueWKKqlBgXCkRsxy3m+Yr58Gt8p+pOqm0Z27Meyac5Se5sme94SbhgcpToR6vvv3GiZCA72KL7dj19uw1mc.afSlB.YdSuo2Tl2xa4sjFFsFxNd51oQEjCb4IQiFS7abU+F4+u+Q9u6F2XqVDQsdGui2Q6W6q80F+bt7..qt.0MM3nD5fkEcAz8D.CZCLX83Wa7.3.TiA7iuG8..3jieMBX1+agr6KEVZoDAnLAxxN.vsRkJSDFFNC.l02WWjH1kXhD0D1nBR4eLsasZBmJwEFde95LhZhIz99SGYYMCw7DZsNWq1sSevmzSJU1rYSkOedGkVaQfiaDRLpNAQLAPiAdhXwvgID2XNl6CxLML1XKc8IxXImuvm+yevs7o9T8APm+oa+1a9yd4+baAvaHkxMTJUhSgsJLBl1F.XKLKZgzhtXzlyfUt...H.jDQAQkPN9fDGtGt1ms2IV5vLaSSQ1nXMK366f3IdEu2fN2y87G1pU8d0qWOF4ZyL.vNBXYy0RIPX4osAV2sBPABXt3D2db862+I333b.sVKEBwLZcPV.ih3G6TGsA3sHxZM133AJsu1OBQpZ0psLhcKlJUprSXXX7zYqMvnqjmh9K+K+Kwq6u50w3TG7rg3AiPEB1EIelBjpfTvV3BCJDriEm4DgRtmPHFtqcrV0BHxBngYpekPQrbox.Kuup.OtE05GGhv9Xhm8vRYtiqzNBiyOEwFWJqCLwXWgIpNhh7IhTxibj.Tudh6z0Dw1nZ850iN7gO7vfffjo+NNhmd350kwiKl7uk73aM6ry5t5pqlD+qHQkyCrrQPMKiznQEWs93olc1YIaa69NNNc.PmJ.cBQo9.KGGuqROfvQTUFInDbFLD1khvxKuWzlv.f2+92ON0oNU76chT.cSCrp48NArgtpEPcmXaYOa.P9u3W7Kl+W6Y9LyeuZcdDEUnQiFYWXgmiKPHAfn68du2gG8nGkAfCpfr5iqmTHDS466OEQT9Nc5jd00Vy4o+zdZopToh6m5S8obOuy67R0saOmtc65.lSIjhTJs1dVS7YpToRbpTohojJFRLEEQ7PKhXl4Dm9JBL5+k+Je4lKrvBakIS5MXBaPLkPgwN.X3ce22czEbAW..fkuuepZ0pkG.SexSdx4.n4xlMyz.bAPTJy7K3Q5gxh9p0AgkOrTFz.U7UpEWRJkA.UaDq4FiYQm+Hm58OsW6MeMafYsAFXCroAg.UPVDVIOPXgx.Es.lnNPwSbhSj6YdvClJzPGk9.nCQUaCD1BwCfI9i1PhtnWoAX4keHcmtRkJgkWNwu1KA.mDJ1Fg8CBmphMPXpJ.tN.tp3XmUARUGyk4Vu0+1bW4Udk4UJUVoTl4ltoax8Ztlqw022OikEx.XkkYNM.Rm3lNvfTkItwa7CW7RuzmYNoTlftA2xkKmYqs1J6LyLSZcfNEhfsPJF41bdddnSmNX80VGQfimtEXNVOCietML96iaFUL0hXzlIz1h3NOietmSya78992d+6e9MhhhV847bdNq949betUpU6vqCr7VAAA63c9dMQXkl.gc1OPu73LbLFfGYFSX7Xv1vCVn2r1X0UenLsAmJUpjNLLLGLLDXBh7l7iey+0S7Bdguf7p.U5pkqZEFFBgPD0sa2ANNN8bbb5TFncCTtMPi3lwTsUbdaiiVzHTBLnxLZzHoA.i2D.J1ErhcUuJ4Anr.0MngoDXrbI.rr807q8qk91+Xer7MLCMo3y8Y+rK9d+fevBRorfRoxHkG1Enw3rfvEwBl7+5+vcMw4dNmSgxkKmays1xsamNNffkvSjPqKB.VBgvVqCb.yNfXhAQVwJVR7iaLEbLNVHCz0hnN.nUDPye0ekekl2xMeKMAYt+KdvaaIN7g2Dgga.isU2RJksUJUSoTtSoRk1Y4kWtI.5ZhgTbvOAcyqept92CMLYDr2wYRaEfwnkSUyAf4pCjEUPJvfdaux21fq+sb8IPsMJ4wnZ7ic8jGaOjBAkxBr7DUAJUuLpwg79AviQq0R.ThIt.wTJyeSJhnn9LScAPal3srXZcFXchnM.h1gYpIwzlhZhUwthK1l.XaoQnDa4aNHcHN.Xbx8S.mB6G.m5LCpL9y2yFV6MAwwO.w.yZfT8.bWIAxtkoLX4R4AVdxx.S0nJl7ldG2T1q4W+Zb..oTJDOIcVq0C8775Ob3vt2y8bOstnK5h5.fdULvxNpNPDJigvB8Q8Gjk3B.vk.FZALHL4mMGHrhf.zVU.rCKAGXCGTeTR+oG6iDaiMKLGbNA.lTq0SvLmgY1o1B0rug2xM37h+MewYVe80Srv3Bw++bEBgCLhEIYzahfHDAvlw7aymIxw33mC8Xl6VqVsd9Jko4I.8.3NRYstULPRe3h99CIh5xDucMQs0u7K+xW8C7A9.qFWP6lRobmxFJjsSXRRMyfNXsZ8A72KjtS96+Hs036orAPp8A31AvsAPJTE1fgEBgCL6wJRDMM.lgYdBkRk0xxBKszRctvK7B6gJnOWmA.r+betOKth+O9ELPMt9Hq8b7DfGmamiJ5eZSxQ7Z.LlM9pa0y35bDUz788ScjZ0RElr2pLJhFUmRoN1zGVJK9o+Ze0zW3uvEZiFl+OJkJsTJSESEGiH7ALkRolQoTSIkxIDBQl0WaM2omYFacf1V3Ir52uOs7xKi74ySMa1jfI4fQSDyrhqSMtIvDw8YXNWCrg9MG6du29G4nGcnRoF.fARoreL2W2BwSsawEWb8JUproTJ2DkwZvBa77dJOuM9L29mIg5.ir9vC.vtm4T2e3Jn7tudanjyXhB8tTFwCHSefzqXd8mfGFhfx8.ZzeVfgqVBQf.iFidLSn94Tvfts8666e.oT9Xihhlud85k.PwXqIm.gHvnGXzFD15pegW85+B+m9EBupq5pzRozuBfOADVGXs2467+6s9O+m+GrCzhV.5N6GX3oN.XzGDN0nmWmMDOX2jjqAGzBo3U4TwP3OMhEQc.3FDDvGxyq2JI6KJiHznL.ZXI.r0UPJXAWDX1mCSUTyu5pqt+Nc5rO.TcpolbxMWeqrLwNwzYjIyYfF53rfrgZQkZokVx+htnKxGFiMJD.q6AzpmAgGIHUcHBlsarip8vcCSF+0lGJwIjPEjBgdYABJVFnPixHOHj4D24IRcvCdPW.3d7ie7TG9vGNE.rh0pMtUqVC+9e+u+vm7S9IykAFPlXZ8P0XzbVeDptNCapdNyERTij8T0.CeS7u4.bVQ.GvfPvYnIJIMBKax6k5SqyKlWT..EhsPyzLy1xiHY8h5AWh3Rv8i62AlyqlFwZvhRoJdXoL6G5y+4ScnCcHm4latTMBajxx1x0.GcxFDrAuKR9hsn033Rz.fnA.z.h3ANob4O++OeNbc+l+lzw0ZvLOPHDsWckU19q+M9FacYW1kcFCopBPzG+K9uDcWe46Buw23ajPEXivJ49pe0acxCcnCMatb4lSq0SKDh7Zs1I90sdvTz6NLQaZgnUEhZM7880DQm9HRodHP3x.qAA1ALZifG7q8OLuu5gi0Yju1A.rNYMXg96F+D6lqSrPDio9re1Oawq3JthrDQIFg.CSih6d62ws25xu5Keai.BOhlNizqlJlNxLPmb+2bfwJki.ZDI.hz6CQXaDg0Oi77GCsei95j8i4KGS84Fl7+SCFoQiQ6USPllqRoxrfTlcQkp..lTJkEe4W6KO6e7e3aJybyMqqqqqKLVQb53yyc.fECFVjEA.DwLSHdDCTxvFHi3aBLjHNhYZPr0t16qc2es1O8K3o2D.6DSKLCR2.1gHdSw4WaUzn5J.0M15ZUXDfy5ix6X7lu8PsO5Qh6oR973wfGQEdFfVO4bEIRCkWAffIJAL0xUvTfwj+UW+eUgW0q5UMlimBGeeeKXYEI875Wud8Nau81cN4IOY2WxUbEsAPyPflnBZgHzEKeF5uwngzLGv.KiAPXdsrBHDVxFXY2R.tKWEtHBNnwYXQxI47aLgBf7OvC7.4t4a9iV3M7F9CK9s+1e6bSO4joYhrkKHsTKprt1W705dyelaN+O3A9AEOmG64TbkUVIO.xJDh35HA.PjNPODLEughsQLMVAuacuL3XT3ggLQCHl6A1nGNLP6iHqsCCr88o0aINrXS+i6uUMQss0ZcKl4ch0akc.vNU.Zy.sajjaqDsgxqyZq8s6+jlYlAgG.CvIw.7Hylw8+RqGM2vjyrP68CabJjzPgjB5nZ.19XlT.qkFBjAZSAs6ryNoJTnf0K5E8hvM9EtQpb8x1MPiX3.VwxLIjxVUQiTwZKPd.L427a9MKedm244YZTB4Af4.3hfPFvvtZkJvx1dnVq6AfNKHkst5W60s4q608ZVStfbUDZ3GsVqaYrqVrNphUKWu75MPisui63NZdYW1udSfvl.y1Y+X09m5LETmjMnORM.6ONqGJjAkf5BG.gKfNMP4LUQiL02Ep6SvLOEQTwq3J9Ey7o+z+CNgggVRoz1W66TSTyVoTjPH33oT1y22uOQzfq4ZtF9NOwcRuu216CuzW5KMFFmweTFCqznRTnA8ICAJ22fJkx8qfFCCOiq6xV.MhObuhC.4BTOiYRKHaUfL02swI4KCLw64SdqS99deumhu62y6Nmskcpb4x4L0TSYqU5zfPFgPjMPGjgAGCKPC0G.Hl.GAhXgPv23Mdiz0bMWikVqsF6V6H.d.HzcP+Acd56e+cNlR0whntBgniVqZwL5FyewA.XvZqsV6Ymc1sYlWWJkqipXix0KuUCzHNowRMAVdGfYaMIVs0lO3Iqt2IQ9Hw0XEgreGfSkB0PJ3OJIafj.cygIJuR4YWzewYOwINwDW9keM4.ZXcm24cN3pdVOqg0APrdejvM5ngCG1667c9+s4gNzk1FHrMJi1nA5UAna3tEgzuJP+5Xt9.qL.UQDpO50vcuGnJRN+ItoIykBXEW.O2xHHywiQoD.lT6qKJpIhuNpjRoVLiTJyd5SeZ24medmC.394O4IyUoRkI1ZqslrXwhSr816jGfcEBgytVXooMHr4KHXLaBq3FlPvDAN4hjqVsJWud8glfvlogIkKriRsXahn9hCKhTKpFPL2OBnCQzVDQqsfPrVcTY863N9HadYW0ksMZfsCBB1xyyKwVjaBOz6fAGbvIvI16dqeRrWa7hSMIbreP0NUMxG9wMXuTFfkywLm4lu4aN025a8svw9pe0nO2+z+zPS+PpGe8TE.rEPXZ3g7H.yTEPbi2wcr+C9DehOlrYyN+0e8ugJW+0+lljYNCnXzGZRLqKAzhA1FlIbuhPJB.fOPEeS+Rpt5bn9lqraS45BbfA.mLAR5.69Z1iziGr2oUmffpTPfrU0UKTG0KxLmcmc1wpXwhC.pzqJBGVe26Wb.7RADDWjV4B.VSsxJeqRSO8zdAAAdVDUoT4xyznQiB3LszT.B8AiV.XMl450pUyG.mVoT9Lygwn5Yi3IQ1GnTTErbThnDB7P1f8GtdsYrlOb.J98X9f.zIvnBSyyLWfHJ++7+7+b5CcnCk5Buv+iNe2u62zwzTEjoLPlPlyDqEOVLyze6e6Gv52525kf+o67ehe1W5ydH1E96lF95gNUBpzON9WjwBkCh.pLPfv95c01MK.OKff3BfqhX.gG2TA3VFHcL0nxAfbU.JDhxEAZTHNFYpEWbQZgEVHY.UoPETPsnZx2065cMyq9U+pmD.4KVrXld8Fjpa21NDQoVYkUblc1YcLSXU+PMIZFlflCyWnvfc1YmdF2cSMfMEafw9+LD.cHfsSmIy1qr5JMIl5r3wWrWXX3f2+6+lhdau0qO59WZon+rW8uK9R2+8a84+re1L+e9xe4S355NsVqSDWxb.jCLZ5TBBSZsnRk3tQqdDor9wTJkTJ0nJBQcjfh3wEQwQMK9g48UObs9gju19sA1IEvpYPUjK33AEdJddS1HNGMkRUXAoLWHpj02+XYpcjZo9ju6OIddOumWWaa6lgggaeIWxkz76s82qW45kGz.MhGxS0d.0G.TYXUDZFx0n68hikBLDPD+ZklPEXgPPk.rVdrAkTAHa79uI0ZcAgPjPAnzJkJKyb1ZKTKakFURunZwzRoLKP4be6u0clmAJVtbo7yN6rYrrrFWv2So0AN.rMQVVLGsacVLCgTNRyRHBLuq3tNt1hzm.5ywnK4vxCuSCzX6idzi17S+o+zMQDZJpc3sKivMZXFlZLhC81.HvPceTsCP2d.qmjK6izaZ93q8tmZLTmWxx39YSaIv5NekScpz6e+6u..lpe+9SkJUJivViJY2byua5ImbRWCBNj4TJUVgPXSFo8nuRo5yLMPJ85u7xK2trQrViikZPvOpfnpgUGVG0GBf9vfJu9.dC7P.aTjljquxN.MhuNqXaZfaiLnLxiFHuRoRPiTF.jsLPtFnZw+q+Wu9r+N+I+NY+ZelulSsZ0hE+c3lj+uVqGiZZjU73QiHiMdOPJkQvz9M6fffT7nlEy6t2yLTtg7th+a63lu0B.6vDuIwzZw5Tx5vLPq1nLZWsQ010Q8NAAAs77NeSNsXt1BrRG8tmQkfv7GtQ96iXVOZugID.nXASyxLXESSdOxQNRzwN1wRRzJIAsjhdx.fLc610Mc5ztd.o0LmcvfAYcbbRDdHKyFSNCQTgEDhIVTqmRHDywLWJHHXNl4YHlKxDkEFw3IIvcrBdSc.3l.XCln0pIDqpTpMRT2Zee+sqUq1FUAV+d8823zm9z6bQO+KpYv8EzzyyKtKy6uGvo1KbMOaon0eTqw2atamZmAoPZ3h.SPMfJYXtdZAQ4+5q1nXV2rE2byMyeq25s59a+a+a6vLaWknzGyWmQJ8RmH5Xi03D..xWqsIvohmJEyLOHc5z8qWud+G+i+wOLc5z76889didJm+4O3O3262t6W7KdOw71q5Pf5bU.pdUPn9C5.d2p.Y9u7wtkr21sc6Yeiuwe+zarwFtG7fGzciM1HK.xmJUphkJUJ+fACxtxJqjJlmuiumLEL7t2F.jNPCXrALlHJxyyKJdp+DQDESSmDXFGEn0Ci.5BFsIBsIhZGEE0Nl1Ms.Pm226+826x+4d181+76uC.ZdG2wcr4S3I7D1pVsZaUFXq2x+s+asdkuxWYqS789dsO3y3w0RcupVFaEtTGfkGeJX.mcrmaunLIo.swmhqMLh36jgloaNCphIAPVTG1lI05Af5NwB.WQ0hiBHOToTcHl61e3vdUpTo+kbIWZ+64d9xibGArqviMNxShF65y.gW.qfjyoJCGXAGT2fXIsVmQHD4q.TLrBJhPDCuSYZ.j9U+676j4Z9M9MxdtO1C3BPo51scJl4LwIFlWq0IS1M44dxJRHDQZslI.xy.6TKFfL56OA.D+YNQPgGPD0gYdjZ8iQTGjFxbTel4t1116vLugTJWEI7h0Dbt4EbAWP669tu6wQlS6CBz+DOXtV+SpIj8Pk7ex2agRHEbPlXzKjKt4I1UAn5ljxsioMRxqk1UARGXJjcFee+pRorF.lWqCp.vSiXjS.yq+7G8i8QGdU+ZWUm3oItSr8wtJ.ZHVPn8uW+SWqVs5.XY.wV.5l0.Z4aDJxjDiG+0kyVhGrKJSFSHQk.4T.SzqWuIbm2MGXXiFHpUqVCxk6bYlCvm8VuU6m6UdkIn4KK.Jn05IDBwze2u62ctBEJTx11YtpUqL8W4q7UJtu8suLisu2lA.YnIaSPXUvPSDsjPHNcE.cHPC.rlVq2PbXQaDZteUoTCkR4vJ.8hEf1GtEPwy7bp8CKLDD7SZB1AAvIrlAvcsRHGVF4vXZ0ByrUYhrW1DCIG.Jd7ie7IJWtbdgPjNtwIt999In4gHh5cq25s19hu3Kt4S4o7TZUwnKWIEOX1KElX6td8.BFTAfCKCq+0Ow+JcIWxk.LVS5p.fvwDwwO3G7Cl9E+hewIStOGLnrrPbwDoAf8q407Zn65ttK6m8O+Ou60+FdC41byMmnRkJEsrrxqCzY.iTF6eF1Bg2d0zBB.TLBLgVqAav.GG6FDwEdScIBCDBQjuRQDYDJw3edKgPrCQTyS8C9Ac2292+PsVyF5eohDBi1mDDXbWMl4zxEjEQHlVoTibOM.XSDwLid.bhKBsCYzLrM.vxDQABgH..gAAAq344kbd3N.n0AA5dhGYILm+vVzd9rE.blEH0p69dcQ.T71u8au3ke4WdBBamvWqm.HpfErbYliZ2tcmCbfCzA.chQELehSbhgO4K6I2GQnOZjbNW4HfFCqBzq9tBk63ZnFJAPKu6003WaIhk7DvzfqXpQKhysDo8UpLGQJy7WdS2T5K3Btfz0le9LDP1zoSmE.4z5fLDAWvrCGuGLtocl8gLSfHXYYigCGlX3bIHcJBfSJtLAwoChGdUO.zqcmNcxkNcaXY0jMNp3NxCK2Qee5c.vNBwg2ToVbsiJkqFTAqiPLx0dPr1RbPfAmkr+Yuq8hzDa3AaDABgI2muOafkbKATXYOLk+83OU974mbpolJ2EbAWP569tu6T2+8e+tG3.GHOybwW0q5UU7M7FdCoYlsEBA9Begu.t7K+xsUJEGSSkdvLPmtddd81byMi99e+ueT4xkit1q8ZGtuG6io+e8G9c0ONN6PfxLPCpLfUipvB0OCT0YCfTUAxdo+5+F4unK5om2yqZl4medWgP37JdE+Nt+0+0uyLoSmNe4xky1X4FYDdhTc5zwds0Vyghs2dBvk2EY4V5.chsMDAfABgXXfVGwlAaYA.aKKK6nnHRHDHPqAChM5yGh0CmQNZV6wxyXCgPrhVqaHL0htoTJ6TAn+hJUeoT16z0Oc64Oz7swxnsuue6Z0NZGf58p.LHbWix3gaj+9Hl0i1aXBv3v4Z+.6+T.mZObfDOXXil.eprUAxWGn.PkhLWOOLNHQRBcoYlK.fICBBl1yyapFMZL8ryN6jMZzXBl4BUqVMaPPPZhH63I0FmnQ7TbHzj.sEybRm81P6qaIpI5n05chhh1jHZcobgMqfvDZPLpXBjPKmG5hUezvl0QMfX+.NmZWMDvc80W2c5omN8oO8oSO+QmOKBMPc6tu66NasZ0bdFG4HN2e85tnRk7pEWLubAYVtNmRq0NBgH00e8Wepq5ptpzm24cdtIbTse+9t111V0qWOB.CWPJGpGNLJLLDdddQ.XfVq6RD0gA2W3IFlb80rYSZ3vgVSLwDPq0DQjsmmmiJHHszyKSbwsozZsCQrCyT5hEKloXwhYiE9QW.3TsZUq1saas4laZE2jDSC5zZjNSFLyLyDEDDLLtQIQwb3c7jARlRczXecePn8BBYqEMzgn0oO8oadgW3E1D.s788aYYYrdVgPjTrayJ.sNlue6Z0NbGOrb6fcg3YBWOGOAky1NfbuEnYGFFRUpTYzYGwN6PQfJybe26mc5dQQEd5O8md1u025a4bdm24YwF2XxcyM2L+EewW7Deyu42rH.bIhHee+9RojqdfCv0O4IQPP.444MLHHnKQT2VsZ044+Be9cOz49D68Q93e7waXxHDNTFvpA.ATkph5V0GKPL.Re0W8Um4C+g+vYOzQOT9O8G+Sm+6dxua9sVeyLWwy84lNSlLt.HikkUlfffTdddNCGLv11wIEfQX5hebREyC23.rALGamgGVJ4iqTDHhHlsheyki0vEFFTnvjAN08YvsAise6u829V2vMbCa4q0Mqc3C2Qs3h8IxPUmXg1aMXZBv5ZsdyXwOqM.5d5Se5tyO+78.Pm8Cz6T+vmZ+Oo1qQODe83IAkTTdNl4Luvq9pc+3+O9HNb.mH.3tDQoTJkU4xkSkRJSW+a7MxW8oVcZ0hp4.PEh3J.VywfKBFYA3TXWshYHQTOXD3tcjR41ZsdSDgUXhC9XerOl5085dcZXlp3F+E+E+EMe8uiWeKnGapXmcFOXuwrclFvccf7.dSTAASTm4rXTQnlIfvLaQkIarrA4dLy4IhlnSmNSsxZqMSsCe3YTKt3ryM2bSs5JqTfMvZ1Uq0oVe80SkMaVmLYxj75dSv7ZLfRJkKUAXoiqOsVHdZqTF0W6S+U+pacgW3ElneX6VrSELVRz+jQCS1yfeFG4PwzxwTj2cdm2Y5m0y5YMxM2pWutU0pUcQUj+6bmemIekuxW4L24cdmS9w9XerbW0UcU4fwASRZfgK.XkR0iHpyBRYm5L2QEDzu5byE8Ut66lusa61X.D8bedO2dWzEbQFzEZ.V03nya2brpVEndcKTANUCga8cEL+z2xsbKY+vevOXg+n25aM+29a+sydgW3Eld9Z0brrssWas0b777FQim.sNqmP3BCcTsDBQRSQr0A5yfBNwWC7XP0iggpawnAh6AP8fY5rrRo..fPHh.PWs1XGxGVJaeeZ+AQvhrLm+wewu3Wb3Ue0WcOkRkneNVUqVMc850KJDhoAvzZsdR.jiArIyqCcMBgZBxchZwLssTJWG.g9990srrBEBwJ999qax4St0z.MWeWDb9P4RiORbclmYt+86fScJWTFYqz.4BiclMXZdxj0qWelpUqNyce228jWvEbAY0991kpTIJUpTCYlSNGyVq0PHDQ+p+p+pCdmuy24.WWWdvfA7exa+syu5e2e29OgmvSHIu5jXolWmpTAHLjPYPka.tg4eOIe+b.Xx2065+xjYxjchm4y7Yl6bNmyI8C7.OPpie7i6doW5k5RDbGLXnqmmmK.bGLXfaiFMbIhb877REEE4TOrtEXXkMaVq1saOJWLdOi3OdvBCMMJgFtayQ39w6G6SF2Txf.JktCCtCHp487Ut6MeZW3Se6Ymc1syjIyNUOP0V2ycdO6XYYsk7HxMPcSS1VZokZsu8su8la1YqEvNJ++CBXcB.63btR9YFTeZPv4jvzzqhLyYjRoQCx77Ri50yepScpISYaOArrx4444RD4pTJWoTlIFgvV228ce74e9meehnAJkp+BRYzhJUDSTjzj+e+fffArAYGbrfNOB8Kc610Zmc1g50qGA.KOOOGcPPFgmWNUfJqzS5FDDXGSCMmzYR6N6LylQqUoMCUGNfgsPJRNiaTtoAZM4ID..I5vTDL6mXZrZYymKGZ1pEPx64FILgAF4TWc.PqFMZzrb4xsiQZx1vfpjU.PnTJWA.aUFn2wN8oile9iFUBg8WdWyGHo9SCxJMzvYuB8JvYe629QtdzbCS.NS3bMdAkie3AumeOGLMbgSLuKW1zA73OxGWXsCQjCJibUZfINlu+LVVVyDSGjI777xGDDjiYNC.ROyLy3t1ZqEa6XHAxqiBdxLuEQzFuo+j2zZu4q+MuIrPqs1XqNm3e6DMeA+RufsfYibhEG2BPzBP2FkPmCtL5chyrgI.O5ZS5dQrQJf8kBXoTXFjp5Zvo9XV5KRfsVoRovxVo.ByNXvfhNNNSnTpbLyt0pUKUEfz28O3GjKUpXBhxFE...B.IQTPTUIAuSRXLytTSXT5VjPHnff.344M..8zZcRvnwC9Zicosf4he2ll3RbBkZfEYDfyTBgvMHHXjEtJDBSGjSddymw91QcUF.C.QQjgRriqyBIMHYfTtvPkZwHhnnvFM5UtToQthygkxVK56uSbSRZJDhsdEW6qXy28M7t2HHHXKuC60DQna7jbRDzvtujWxKoyG3CbqcAVqCJgtGXYz+j+jAF5+ui03nHHIvDAu3e5.XgkgK.JrT85StupUmfYNO.RSDkxOHv4qu3hN+c+c+cY9DehOQAsudhUVakINzgNTZeeeaKKaHkBal4TJsNMYJTfXlGPD0qToRcSkJU+fff9LyCXhGJ8jrJPQ111N7vgNyLybVqt5pINVCYYYQqu95Vc5zw100MEyb5986mw.UXjUq0YSt9h2S4JDFwOrzbyY455ZES6lXmiXzzvR9.toc4d85YNSwfjIDWYJgw3xabSS1sobL5gXaRWtvBaoVbwshsl5VwhkXaXNCay+k+k+k0dFOimwFeiuw2XymxS4ojnO.l6opg9nEFfbnO7OCk7++ct+ZuwFG6LnRo.VNMjHaIERu7ttkVVXhQjipVMiZwEcjRYJl4zuzW9KO26+89dm.FMXXF.LiTJmVY3Buwkb10gyXPX.XLBkIRobKee+U+nezOZ8q659K0.00uk2xaY4a3M8l1HbW8UXuEKb1XRK61zDiNxXD9tD69lnz.ksAZPnDHukgcvtnQIM.x366Wr1QNxDHjl12+dmSJkyRDMiRoJBSQrtV.NBit9jblbxd6VKszRqA.0EckWzR56SuzfACT6ae6aWQHFU6.TuGpfghPLTuqHgONr2+IABSh2+se.bJfChnXKUFX2lnZd9L2bNXkHKf0FuIe4We80mb5omd1x.SceZcQOOuBDQ4O0oNUdGGmBvzLImXJCjH9vCDBwvjlTA.DifwdwE7EQDYkfVmIlXBZmc1Ixwwc3TSMA2ueejNcZq1sa6355lZiM1vYP+9tdBQBJ4LvMmPZgmHkVqSzfjQSYkIJkzyyIltM..QfHlL9joMQjcLpLiE7bJ1PHXVHDITfHBFZNzyxT3P+pUqN767c9NQSO8ziGasEHrs7vxcPH5.fgwEoYG+zefVq6JWP1sZHFVGvNVvPmToTyTsZ0oCCCmLtITVwOti6xGsVnVstGWo5.fsXlWIFwX0888Wt1gqsJrvFvFaAM1Am8NThwisN59S.jsBPtv3Fl.fYtga3Fl4Zu1qcJl47TUJy+v69ev9W5U7K4fvQhpapd85YuxJq.lY1xxhihh.QjUTTjkTJGBChe5EEE0yxxZPbrIpUqVzNsZYUoTI5zm9zQOgmvSHJJJBau81Nat4lYd7O9Gews2d6I52uewXzW5FOTKalIGDit23Askneb1vruyZO4eYP0TP.ahcx7ryNKVc0UAFK+MZWaLu6wN1w57K9K9K1QoTcIh5xf6IOrz39hKp5rvBKzNLLb6XzmuEP4s9S+Secs98eG+9cKGhNM.Z8I9Dehc9k+k+kShCzF6aecvR86BDb139lwW6EIvV.0H.efZ.d9fB10fDJD+QNTspKpy1G8nyacu268lDCYhd85MYiFMJrPsZ4ZXF3QAkRUfHJWhd5HDB9c8W8th9UdAufH.BRojYlizZcjPHFBfn3ySrHls43bsRPu9dx+2xyyKY3ToSdNP.Dax22I9mkrOKI+LB6h3s8V653h.Mvt0J.PiUe.n9.734BDgXqM+HFpA1jhE7Ul4sIh13J+UtxUtm6+dVFLVCFWHc..3q5E8hh9n23MNHlRNs.BaiIQGuMQufGLc7OaLuierV+6gFlj74wet9C6MTBFKfLEPX1J.ECAljYdB.TjHxjXaY37s9e70Scdm2SMu1WOknlXta9lu4Yt3K4hmzhoBLPVh3zBQszZkJMLP+MAx0CAPGoT1FUPSDhVJkZaoTtA.Vybn3Bs.B6DqBwa4ArYfIYsl.nUXXXqJUpLtOhe17Ah+3rNyhZKAGrbRAtUsApaBHWFtpiqbkR4HwVsDPgkAlToTS.frRoLU7z3xdW20cUnPgBS7TepO0hDQ4+ReouTty4bNmLwuWMNMXRBRhO4m3SL3+zu7ubeh4thZ0FpTJDmTmKHjhXXylNOyJUPjkE.yrU0pUcBCCchOPlHhrhsHrTvz.NKdjfomTyDEihXyarwTjXHHzuaq18O2CbfgJklgwYMrheLhOvj6APCHCmE6SD0CQncjUzNu+226u4a9M8laEKnSIhI15vL0+0AprUkJnsUX3f6w2OZ6s2dvS5I8jRnRRW3gdH3AQijyV2+MdC4hmN5AAvInZ.V9.oJCjswtSEKK7PZDjTbR4z.MxBfhZstnPHJByTtcHpJUFgtMLSvMefNnPDhbkBISDMDUv.0hpHgPD8G8G8Gwus21aKJdRmVLytwBzpcfVayiqeHFn9Z8xd4ubm+w+w+wTZs1MldAtISNONYuQSmfHh.CJhhXhI9s8Veq70+G+Gy.iT48wR5i..GQDMLxP.6QABMI5wC.ndRobvW6t+ZCi3nn4me9gfPWhQagTtiR4ucqVs25hebOtsZ.rsRoZ9Q9HejlW20cc6DinjMQErEBenJ1+LbqpeZy65wSXybFTE3fvDmbQ5BnL7W2LkqX6YE4fgi7oBioNE.JHDhIXlmfHZJsVOYTTTAKKqQB4lRorXlnf.EKEx9BonsRoZt1Zqs4gNzgVE.g9ZecMQBkbJuARzVnYPar1iptmzF.N0.x5atGJCQTJrKzmsM2mEOYtxHMGxYIhJBfIeaus+ul8u95eikNtVOmPHlUq0Eh3nLDQ1RgzdryeSA.JFoAsAn0AX8BR4Rg.Ko0ZUryMYFZg.cf1fvjScpSMb+6e+wba+mXM26LG7yAAgS7fRNkvAfMN4YX0pV6CfVJlZn.kxAr7DnLlEMvzeyu42bhK6xtrIBCCKPDMR7UkxCmVoNdpEjRm5lhAM+ALS0kUZEAFrTJGpTpHoTRJkJEQTB0DHgPDoT5AjIAdRHDVA5.mHDkRJjNlynPJlwnyrhe+1lYXQDrDBgEhQSBLm+YYbDhHFLFJqUKJtPFSwTDbHFVwi4fiXJhnQMzkIPQQHpGQTWoP1Sq08YliDBA+C9A+fny4bNmAJkpyy+4e06boW5Er8G7O8Ocm5L2A.C0ZskTJs..TJ0v33+cigjeJ.jWoUSIExYTJ0zDvDQLbsrHKl4gDQcAi1BonEpfNHDCzZUunHdG.rZbCSB.PCfRqAr7Fd.6D.zZVftq9f0GmyVtudrFMWIEPnKybZhnrnBx6eL+Irssm1yyaJ.L4G5C8gJdYW1kkSJk4z95LQVHc2lMcerOtGmKyrSPPfSDhrpIpQvb+ep38NVvj20vwP.PBZMsiKb0REnhHlFJkxHkRQ.HsPHxo0ZSg1ioqQwza9Ln3k4qYKvfN0RKgGy9dLDS7nBbiUCGlHdXTDMLd++X.cxTPqTJ68G9G9G18Zu1qsM.ho2LZo05tLy8hsQVi3waxOaSee+0pUq1lnL1FMPG.L3XG6XCNxQNhQj0mCsO8wOc64me9wo56Yi6Y165g57u30AHfSZNeaNjkWlyZZpt48vp.N0GQ8pxEAZTDFpKWDkwDpiqlTHDSRDkuRkJoO9wOt8eyey6gt1q8kQDHxS3Mp1QhnH.Do09LyjkTJcTJsKQFym3GU9+kJUJ0JKuhCCCUftzeleFqa5VtkjAs57u8u8uY8DehOQhHhhhX1xJ9vVd27+KWtL2nQiHPXvoO0RCuvK5hXkRSw4+mbM1mYtKEKLqDQ8DBQjuuOaQVQQHp2e+e+ee6Wyq90zLwsazZc6XTkuI.V6a+s+1q9jexO4sLtHDFVEfWzzrn9X27LhY2vA6Cbh8NjfyV2m8+mqGs2vDfGL7p+QAQYB.10.R4GaoqvzvjBDQYgwtQsHppMy0cIhJvLOsVqmStfbZ0wTSHOhLmZQUZPHCCJMwQYXPYocUp4H.zKQGIfEZQLsEQz5wIksdEiCjz122uUsZ01An519926V0pUKlJNdsABdzRAq+3r1yT1NfEvII.XMRDu1GrwRHElENX0QVN7Hmnw22u3c7E9Bo+MeIujTkAR0.URyb8hDQSD2Lk7.HuTJypTpz.HiTJSqTpzwIzYC.HDhge9O+s06m+m+mquVqGFO4gws6UagPv5.8PlMNuCCPVDYWpTI6FMZbFp9MC1g.YGCs7wgy1n2KqToBGFFBLp6xTeSCQLPzUJk1ZsNogKQ.nuTJ6+29A9.8+sdIuj9999c.PBkaZdXgnUHJ2V6eeshrhZEqF1qKDh0.v5k.1dYydq3qg4FFKjZw64lrOvlIST8rMsKYuqeXMUM46cdpO0mZ5u9W+quK5kPE2G6isf68e+2ua61syjMa1bnBJ9m+5+yKbc+EWW9ct+cRWnPAaes1lLMeKOQjwx6LS3N08bO2KdZG8nfIlrHKZpolxJa1rwLegsCTAoXvoAAm3oPXkISFqtc6RLCJSlzVc5zwJ9ms6jkSzgk3oSDOICpZ0pnd85IPBd..MXhIJNbqs2BwEY3DOAUiT9yTDiX6m1Lk4joZDCcXt6a9M+VG7ley+QC.ngLwCHl5JOhrs5XplfvNLnsrhh1.VVaIDhs8882oUqVMujG+ie6PTcGf5iKrgiZ96AwYX8g+zdu0danVrXtEtqsCSTJTAoQHxgRXJtAOQ850KDEEkJtoWoeYurWV526688lGFs0n30cc+AS7g+venoTZUAJhyxwSmRJkTx8xDSQLwcYlZRDGa0sb8ZhEpCznA.VsDvlKCrCJglXYzA0PW3+HdQh7Gm0tH9LAQOl831.RBPYCS7XSg5UgKpaDpQoTVD65pJUTJ0bweedhH2CKD1+i2ywHgWUaXgTfQRAWQDQcN0oN0lHB0unK9hNM.VBnrFnwpXjtRH6Bn5Cfg6CX3R.CQsehhFpeTC9g2yOauhc5tvVONdXEfoCQooB7+FSd9Ww4OQ32HrfRoxmISl7yN6r40ZcVl4rwwBSDCZK..gPfa5l9vz0bMWcBhKoFMZ3Vtb4L0qWOSTTTp3F6NTHDCVas0FNyLyPqt5pVyN6rwBhoNV+QXGFriEH64JUxJUpTThVbMZxpzHzcN54cbbxg.zvLYRGM6ryRJkxFFiHwNgpf.XXbSc33vqQDPuCKkct5W6qu8q609etKHD0sa2nzoSGAf9268dusN5QO51LyaWqVsVejOxGoyUcUW0fXJVRdddv11KZ3vf9111CXls9BeguP5CddGrPMQsoN4Cbxo8p3M4latY9XD2LJlLabotNJkpK.M.f6BB6.FqxLGRDEHkxkqBrdcCkXiKJYecAVJId6Yan47LQuMPJS90ykFXkDWCo3q+0+5m3085dcE877JpTpIjR4DJkpPPPP1ibji3RD4p05TLXGvlGmM2bS2mzS5IkgHJqVqSwLaGmgOuvyXA9X+qGmqIDVJkxVHD1MZzvZvfAQsa0pet74G344w.HUfVmwynoWYiik5jKWNpcy1FYL1yKAgRHFsRirx3olZJrwFaL54Xx9LPX.ynOs64AI+NLAD6jbbaPnIwXGPzNvfz2NJkZfTJGp808Wd0kacsW6qYquzW5NW89tu6asy+7O+MAJ2zX5.H5zm9zQyO+7CPIzEKitvniKiWavYiZWxdW+vN+K4eyBdvAAv0Xi5mQtPo.f6oO8oyN+7ymG.EJCTnApVToN1jLwSqWxeR47yWv111sZ0pNZslXlskRoiVqc7LHaCvzHjHhpBlqaGSy4DT8l011N8wV7X1Wwy4JXcfdvdy+GlF7YicueHI++wQG9CJt8XHVIgJzwZdCw.r8pqtp8byMmEGwLrPWvnkTJaFqAlsia7VDQTDyb+EjxNgnRSkZwVLwcpIp0ToTMiOyayx.a0Xj1ygHee+nZ0pkj+eafpsAp+noFx8i85eOzvjj0CEBSdn9cRRtHCLvqtHQTdee+z0pUyxCfBLnbvEKWsHP8oAvr999SWqVs7995r0pIRalFV0b.gYApjQqOtg1ElaKh.P2Ejxtg.stsO2ss448TNzJG+X2aiegewW1xZ8hIAKaKDh1.nYPPPSOOu1yAzdEf1lo7evA.m3QCGH9iyZuIAR.GjfoUyiEPdeN.K4B3k12+dxVqVsB.UKfp0yh5vMQ+RNsVm9oID4qa7t8hAAAEDBQdfJY51coLqrxJYGLXPt8su8kKd5vFAwCneDi9jEF.lXoT3BfLJkJGHjgYXSlCVGn059F0qtBTpEMP50LcBaFv5q9k+xNWz+g+C61cXCZPFhXA2D.HIPM18.ogQQQ8m+ny2WsnZH.vfACrbbbRNvMQ.w5yL0mHt6a8s912487otgsPnYR+.nMST2EDh1M.Zq05sEhCu4K4Ec4q+AtwaayG2iaxleuu22aP7eeJ9ZYH.FrOf9KAL.0vfeJRWhGtW6sQIi+0iDeR.3NCPp0.Rg4fKVYD5RxgXzmn05rBgHkYx2d1.0cApjSqOdAgPLoRolnc61YefevOH007y9yZ2.vtQXCm9C5OJPZ7zvRnYlqVqcxlMqS61ssRmNsU2tcs.AKgmvNHHvgYdLnBGXQDrXFPH7hCzRLLXWOB.CqToxf5ggcs.FHLSU2TzuY5vizf.hnAEJTn+N6rSel49DQ8AycYCe76wLOHAJz.n+2+6+8635515Juxqr4sdq25NDQaIDhM.ptIJWeaz.MQIzBKaZRREfNgiZVRodFkm+L1O8S6IV7ffCbr.7ZgYAgUm1BX8jBARCf7.UlDHbBC8Q7bAp6.T0UqWL9mi7BgXBesdxiJDSUGnXbCZcP7yy986StttV..Ly8a2tcyO4m7StA.V8u35ttv2+m4yD979sddqnNtZMaa6MqVsZy50q2rZ0pc.p0Cv+QKMLYroSalX3j.VaBPnJbP8YSArpK.RWudc2pUqlAnZVf5EAJOcmNKM6ZqsV41saOSlrYlFLxQD4FEEYQF3R3XlRnJ4rctcmNcxjIy1LyMN1wNl5JuxqLwRgWKHHXKhnVUqVsKJiAng2PiawLhRN+j77veTC94g52arOePK3cBSAEyf7XMLwoCBl3HddEa.LQnNL+PLLuI9W4bsaepbqt5pEpUqVAl4rKszRocbbb1yisEyjcsZBW.jUoT4PrkWtq3nFzWJECAp.kdQavHEHSCpXhbrXNA4bIMJAIOuh0Ffw+2S96RrA1kwZ.AhPD.SLkOedqlMaQvHJ0QkJM2fkWd4Aum2y6K5k+xeow74O1ttAZR.sERY+lMaFswFaDAfdR4BsTpE2Flo52lYtKYbTuHee+Q7qo1QqwndUBndJ.j022uXsZ0lToTSPDUvyyKcLpTHfJjRsXzK7E9B6eK2xsziXpKSb2iHks+ytoOzl+rOye1UE0N+F.MBUJ0pRob63gkk37RIedbsL4ro6s2sgyF2pzAFja51vjmcN.ub.A4ApV.n9TLySAfI0ZcNoTlItgHNDQtBgvEkQFNjyp05DqVMM.RozZa2Tor52efkP3QwM5yAFJdQBgfQL84zZ8PoTZyLmNonW.JkP3YmruKtAII4+DEW.LG25CFfoXDVYg3JXiaRROCJLEC.pDoTKl7Z.f48v1GVJa0.XKkRsE.sEwQ6Hpcjt.gCzZcDybel4cpUq1FW0UcUq7Q+nez08.1NvfljgXk4fQxIF4RWcAlN1UbFUavdikd155GVNZIeebLhYb.VKEPYmSe5ikZ94m2EvyEkBxfkQ1W0q5Uk626262KOQTgCKkEa.LEy7jAAA4Mn5cAKs93DyriTJSyLmIdfninBHyfrLMlMsTJyAXb+F.jgMzsgEBQ+8l+OLCxxFlATY2uWOaWW2379LNdYb9+Ci2mh8b9GHfnM2d6gOo+iOwgpEUwzBhriQ4TDLMhqoTtv1J0haYrC3xsuu6616e9m+4OLFUcIN1U6Pf1FlLb3V.MZBTtoRc7VRorG.F7.OvCDctm64lffxdyAzYEjzXtC1+eGU+I.92WML4Gm03cBOc4xky1nQib.UyVB0cWNIItpvtTc3dgWwyq3m4d+Lyvg7LAApoDhZ4fQHXSSDkQoT4Ihxztc6zYyjwkAREmfFQDOjYqt.bKXznjFRoLFl0F6bpZ0psqWudGfxsKgFsWNQvMKgNwZGwYyh4z++c8PzvD.blSXauB2aN.j6q809ZYetO8mt6xi45LFwdZgr.gE.JWPquuhuhWwKM+M7teeYHCcHxpTp7c61sv4dtWTNsZQWgTZzeBOZPk5XvhF53j1zkYUd.JKLz3gSDMJgPDEDD.X7tvXX7ohKBfr.FA4YF.C6OXvvGy92+PkREsvBKvKt3hQLQ7QDBdQkhAvPhvPl4A.zP4gkPcbEfo6yCXlG7Buxetge7O8sOjYtePPP6pUqt4QpUaiPCj6ZAin10SHVnCP81.UapTGaaC+Xw1vrOKQLYAPY1CMFF.L769c+tCe7Wxie3ABwfGEt+6gJPLAb.mJ3jNgl8MNxmpLk2JHU.PZTFYz2mNiPHxpTpzRozMNwNGXPfPJhnLwb1dR.L0sca21DG5PO4rBwQcGNT4Tud8Tw7m0VJk1.vZ80W24I9DehtKt3hoXfTGQJcVTorgwpBsAy1LyNvhhm3F6X1OMher.6Nwhw+HNYNtqTdj9+M+M+w7y+4+7Go+AbbgiXDZRPWobgdZ+i2W7+j8d2iRttpty+Om68VO6Vpk5GUU2Gc29QC1HhAoVFXBwXyCClL+RBIqLvLCXlEIP94fIrRvjj4gcviAmjYHfAlfIX.y.+BPxXxjgPHS.VNI717PpaYOLBrsjk6tu2a8naIYY8n6tpacO+9iy4V8sK0RVFrczi620pVsTWUWcW2y9tO6yd+c+c64zNHveU2o8VsRSZuGe+nXHVHL5nd+bWF335VL7X.Gw22+HdddOtld4IBErtZotcdmuy+Mse+u+OQa3HIGJ3etaAmznmuFsfaZJkRgnlPTsIzDfQvfCNgELehPvtYcR1Ku+8u+7WxkbIVBgHudbhmzi0CEDDLDvPZVJVP0NcwRWWOI.A9Aho24zLyLyzFDmXZWmGqopk4VJLLboc33bnlvgTs0TsiAMROJDOeopO8yRBUk37PLhOVGTk3xBrTs7PiB.EpWudIaa6M8fO3CNzfCN3vttti9i9Q+nsr3hKNzUe0W8.56QsBC8Mbbl1b+6+9xUpTIi33XgmmWreX3xBo7ntttKEDDzPHD0cb1YqZT+PMRzJlQnSkCR2VfDG5R3ViRM5Ne59vrOQE9YC8gose6UHn27a9MOvce2+cCBMFDpNnu+tK644M.P428se6kdW2xsrovvvMALfiyNJUil4ajH3r0DFUah4LpDMUxwwYfvvfAAQYT6+gd+uDQQEgVytbccyultwHLFYjgEG7fGRp+nHAj5CfxccW2k3Ftga.PHm10QLipEJTMWnjXoT1EEM4WWhUEJ8MoqTJSR1PRkdk5jerrTFeLW2cdLn4pAAAI2uzF33ZeWGOHHXUWW2NPk1UoUTSkdggnlvrZSLl02OmTHJ533LPPPvl877FJzObPoPVTyrLQPPf3vG9vlacqaUn+r0Q01WbbgPbz333C44syCBMWBkHXmz50qZaaG889deutyN6rQu8Wyqo87qMM0NWLgIv5zgGLgsZAGNGLZNXoDV5NHvVdku7W4Vu+e38OzryN6.ttSWrBMy0BxcEWwkl+q7U9lEg3xDy.N6za.+cGVRHjJMhPRdgfbRHudRgX533zqsVQcMKBUxw5pObbATwJlvRXCHgrmxXIDKjz00yMVJkw0qWOd6NNxY88kHDnS3WJVBHibc8VUwjnd1eni+SDDDzEXYPbTPdXgP7X+R+q9kd7+lO+eywzGTsquue7zSOc6YlYlG2ya5CWiVGpQR7aUoSslHa.BpBzzMBBRrMR6++7g8.5GazYV2njqmv517MZzH+1qUqPi0zPmx999k8714fPyMC01ru+tFPGylv00UhhIbk28t28f+h+h+FCTkF4aBB88+hpMwZlff7HnrisyfgqM1eMTrPQzII9+vvvdhStqqqYPPf19ecw+GKDxtwRQWOU6hkD+uDDLsqCZ+eZ1xE2EDR2c3J7mI.kFfKz5Hm7Df3XRo7HdddGIHH3n5jizIg8RAAAscc2wJPqkWXgEN93iO9I.V929c9NW9C89e+sA59U9Jek3q65ttXvoaEB6zBZGDDrh6U3116Pzw+byj29SExRXx5gJ3hsg0d2apQLrllWAAABcerlTw4MAr0Jvv6ILby.kc1gSNZR9G9ge3hCLv.kkRYAoZptXoCVHuqqatvf.jp.bOZivFG567c+NMeOum2dyVs3fiMFO9hK1SMhWgwXkG5a+Pq7re1O69UA7yGcHdpP+ANmf0OsiT8wcNvtHTu3sdq2Zwa61ts7.VG6vGybvsNXNfb0WXgB1iOdIfAzZUylpTgAZ0h7Uqh0l1zTE2291WYkfPIGTJEEjBgoPJiccci.51rYShhh5I1i.kDBgULfXsVXHFTzp6.G3.FEJTv7M8ldSVe5O0mRz6OZoHFCPJiieSugeM4m9y8o5Jk8D1otnT2ePfTFKkdJsSoKJwnSoGFIAArC2N96IrqmyNhCBloCvIjBwQ7bbNbXX3iqYrTTXXXGGGm1.qXCmnNbrwFiis3hbBaaZWudJapIHl42vCdetd637Dg9oSbhvbZAjSKFc4qAE1kuedOOOKpPNZ0SGjRZCu75Cps4fffs555N7QNxQF7XG6XEArt9e4WWt+vOzeRtK5hlLmiiaNcKYX455l6.G3.l4ymOYy+bBgvxwwwLHHvzxxxJJJJokcziGUoo5LEpjk333DEDF1UnpZgRCbzZZS2tc6XXX.RrPPt+C+GdGV+4+42iPe.hDeMIsKiRj5jx15Je1NHHHJNNtqggQGgPrrTJOVXX3QsccOpmy1OJzJYZKcLkXgYmz1MITItKPmgg1GZ8hl4H6NnQC..f.PRDEDUYK9z1n.8OoD0NBXcPH+7yOewI9WLwfDRYXrRvh8zRIoTVRHDCFDDLnqq6VBBBFRJDad+O7CM33td418ryJ93e7On7q9U+1x.UhQku025aM9C7A9.qVrTwiKP73NNNGILL7wbbbNLpfmOLvQFFN1gRnu+SOi11+4D8SG6jGIAEWXXH+gzL.SuW8.vXaBVbnfffs355tYTL4YPfx+F+F+FE93e7OtkqVzWERgkimC0pQmFM33gggOFvRRorkqq6hgggGxww4HnRXxJjZBbrqcsq3W4UdkcN7Z1umsE.YZa3j3VRZuvA.FnFLXCXfZ0nTiFT5c7N9sK8A9.enAA1juu+.XPYhofPkbVCWWWK.q1samawEWrrNoeChZevb5escQI3fISSBCsOpbG9vGN2ke4WtktMSSFI4pYDkT.BogTl59L0oME5p2Z7m8m8mE+Vequ001eLU664Nsar+LAcE51VUHDq533zNHHHF.MszWUHkGywy6n.Is.Qb0pztYSNd0pbrlMUTR+K7E9BseguvWXac+6Go+aJGPt21a6sU3Nuy6r7G7NtiM86bS2zlCBBFxcZ2ABlIH+sbK2p4se62lv000X94mOmoooYylMoZ0pQ5DK2apTTAVpEbnJU3wZ0hGuZUVtYydsDqjpzUOAlRRpb+h574Bn+325o0NCAlGALGcTxuzRTdzQYyKsjZL+BLfuuegtc6lyxxJ+0cc+xE9g+vePY82e.T5RWgvvv7Rorf2N8J5OaX467O8NK9a8aci4bbbLBBCM22C+vlWy0bMI5gSRw.5hxlpfDJKDT58ba2d9+fa8OHgceoaKzXcbXwr93tj5BTHzeJkRIQBUaqtBvJRoriggQ7wN1wLFXfALtq65th+MugaXYIbDgPdvWhyzGZ+z5wqVki2roh4KRorqqq6p.GkpbDZxQt5q8pO123d+Fqp+6AeeegmmmrQiFcpUqlx9XJ5v9NuqPVOQHsONMaSpZAMS72oJFj5bX8RZB5QbcEXSsfx0pgUiFDWoBwsZgEJMcZyBgbyRonHBR7+Y.XoS7aIgpMqU5eipUnSRJWuynoaqFC.ScKzK5oYgRhQn8e45FGFFDCh3T5vCnrwLLDBCGGGoePPrPaWJjxXoxVrq6ztcBlIX0a7Fe6G+i7Q9SOhPHNrTJeLs36uphwKjzxjcXs3+OQsZrbiFrhsMQ0qmJt9JzM0veHwGT+9gtPvNKKgI8gDm5IUbMOTMu9fO4FCLVbTLpsDV+fEVH+G6S9IG78bq25V.1x91291DPwqZpor1s5FoBZgqJ218bLaQUKe+cm2vvnnTJKt5JqHZTu4pu3q5E+3.GLHHnkqqauJLrvBKr73iO9xvnKCKsLvJCAcNhRvMe5PM9OaFq6fKSBlys1ykdyA8FxdVfeAzszPUvpIiY.KZPsZVznQdfBOxi7HktjK4RFDUvzaBn76487drdVOqmkw67c9Nyo5G9oGT2NEkzNHEBDw1N1c0h.kYPPPg29a+sW5O8O8Osnho.X53Xyeze36g2zu1aVNsqKy5GJkBoAZE8mXPhL10ys6BKrPWECw0D90vHVnlLAQOzC8PQW1kcYwAAAxc55J97eyuo7RujKM1w0oKP2fvftHoidy0UTIBYGQ996tqPH5.bhocce7q+24c93u+O36+3TsZGZ1rKJJFG444sJ8F0gjLZWi.jGZ+GhguzojvghAh8.o+TDus8gbum+VAizXc1cSAF6yFyQpi4AWKAJoeXZCV0oRtvv8j2wwoX8Epm64OtsUKpkGZTlJUFNX1YG4q72+2uke82xaoTxOWPPPNoPjWHk4kfIJ1lX7W+W++xbqacK4trK6xxOsqa9YBBxIPjShpZERozHLLzZ4kWNWoRkRD4UC8e7cQJhde+I+W67696+uuarPs4JpMl6zpUqt5ouiUfePNogvPn5++UO5QO5xaZSa53tt6X4ffYa655FE56GKEhHgTzVJjcTUektgggq333bBfGuJ73Mo1QCCm43NpQE7IpBK2jwVAVrMiLRWVahAjPO5zUNMs5ue1.RGnufsgXa6E165Y1Vuw7rCTLjQKt5pAEJTnPdT9eJ9s229JO0TSMXPPvlbcc2x92+92rTJ2zTSMUITLWxPWfRoVPBSlzUq.MOtuu+wtROui2nFGKX2AG000M4.WIGjOomiOeMQ5qqcPnmPl1aBEoDoavJTIDyk+w+3e7.W9ke4CVAFbOggC533jjzjAbccKD5GVzwyo.fkuuu3G+POzJa6xu7ir8su8C1pUqV.KAUOLz7wANlMrb8JrpcK5VGj2zsdqh631tsmoZImeZvZ1vpQ7ZR69kzJgC9Zesu1xenOzGpjiZr8VDnrxVc5ABBlY.WW2hUgbyDDjWHDV1114zhsawfffR.CzoSmhpI2kZ+u5ggRjh3c34HmMLDc+6agR2HLrcbj25sdqcusa615BflcaIqs333HqWut7nG8nhAGbvjD9nodtRmtBBChDHh2+92e2ux+6uR2a7seiQR8THS29fqLsqam+o8t2taaaaSDDDHevG7A67xe4u7Sna6kDFuE6GFtpAbrD+V999s87dAc9Q+n+w1OmmyyocUnSypHraR922m8yV3M7FdCIG7ZPfg12912lKWrXYoPjSHDlNJ8yvZZWWquy92uYwhEERgnqPJWckUV4wuzK8RODpdpXofffGKNN9HiO93IsisN4L1.0k.Q1Pm5mclTtyDrQILIMSgS7iVDXv23a7Mt467NuyA27lurxP87.4q.4aUkRO526QKeQWzEUFnbXXXAsfYmCnvq7k9RK+o9re1x.kEPdIXLsqq4rAg4hQOISjRbcUrEQnzVhBA9AkPzKge5FtQHu4+f2U7se621ZLXRPrPJ5pD9bhDqMo3zcBlHVmrvN+i+C26pu7Ww0t7NbcWoEUi782snSmNl4ymm+hO6mcke2e+e+GC05+gBCCOBvIRRLWPPPjRvNm9DPiiAb7pvJMcIZ3.jGZsqY8ZWBN61GzS2n+8G5w1jI.q4olEzHG0pkiFMJBLP8EpuY6wsSDo8ByLyLhZ0pEs8su8tsZ0xRUbicrkffYGx00cfPe+7ZsFyrb4xl6+QdDqqbm6LePPPgUVYkBEKVrfhIcBbbrk2165OP9VtgeS4N87XV+PjBo.gv.ozLo.ntttcme946ZYXIiMzm.Po0Rc25V2ZzhKtX2ImbRBCCM1tiStYSZ8KorqmmWTPPPjTJ6n0XjNemuy2oye0e0ec6a5l9cV1vfGONlCOsm2i0hpONzbUpTQRqVlnX+VWMilRFQvIeMIFBATS36uqXOOuNdPa+wn8jKRzbqOVsKXryxRXx5Q+UTN2W9KeO4d0u5eqbPKKeeeCOOuTISQkkxjCaKkFE77rUGdRUoqh5ppHzJ9d9jd8UJDFexOwGak2065+7iGFFdHmocNDM3HnNz5J6d26d4ctycpMhqbBnUZ5Ve9dk86GoqrnIfw7yOuXhIlHI6+8OhsrXLxoFIrUxAs5IDdZUyNGPAaaJVuNCVu9BC1sqHY7dZ5440ay6fffxG8nGcfMsoMUFnve8W3KX8q9q7ZDRoHVJkQdddxff.CgPjWpGizBgHmy1cxQKL0JwN5L6lHni8DPVgPzIz2uqyN8jzDiff.zUunil9tcbcmVFDLSxmMIB5JhEckBY2jfCcccWAprRPvrsEBQjyNb5nD+qZGWKtlGGncMH91tq6ha3FtA4XiQ2EWjNMZL+x0thINAKpr8HUETUecxXXt9GaXx0+ZNuEazFwqS3dW6e2SLPyiCEIjhyO+74uwa7Fs9ReoujIPoZvVlILbzNc5r074yOfsssUPPfg6ztVzj7ggg4+K+K+KM+W+u9ecudi10cG4BC2SQYrrHBJHDRKaaWi.UKgIPkDNKojbfTMwkPJPRWPD455zSbdCCBjwRYrNobwB0F34jfkPJDRgrKvxZZoqGIvS2IHXlXPYGGF52INldLMILL7DezO5G8Xu628G8wgVONvInBqPKVEptJzr2D8pBHaoulVoBxVsnyXP6E2FsOKkYD8yvgM5401AiXo0Ti7ZQfM+0e8+54+LelOYxHKev599CZ64MDpopz.5DwZoW+MBCCkR0T0nitx3KqmZZKqSL0J908W1y163AAAGIUazcBXp1v9NWSTHexfMhsDEZ0pUtJUpXA0LpWeVgsscNpRdZRQs1HzSbIqBaZFMSSPM4cJYXPAv.hYYoPdXWW2Vn53JsPuZehvvcuriiSxdv82FHIL76r4J6ld+SKf7UpPwVsXf50qW1d61koIEVXgEz88uhAJgggC9u5e0qef+p+pOWIoTVTUMe+7RoHGppklnALEjPdAjWJkEdnG5grd4u7WtUnJQIRMM2Eg99lRgvRBFtpVWsaXneWmcza+uD+oxXhiMvP55NsQPvLILJRHDxHaa2U0STjNhjpq5NcWMqJ63Ns6pgyFt5Ue0W2p6ae+eR7+QMP9cdjGIJe97qZZxx11dqF56GggQ2333UMMMOtsschXT2VU.hmeGXwjD5ZrvBKje7wGuHpjNUPecZSwwrou5W8KW9W+W+WufRicv7tu6+6VW208JShoPnEf1HoTdLgP9XNNdG7Fdyu4CcW+c28iQiDQEt2dv50ssJgCmZbgdN4AU5OFNSMisYRHdtIAlCCVKt5joRWAvNOTOmN4HkBCCKqEayhttt4ejG4QrtjK4RrfpECBlozC+vOb4W5+lWZofcGXo0Ymj30K9fO3ClavAGzLo5Z51fMQCSJfhEmha9ccyw29sc6wFFBYbrdJ3nFbgwnF46sERwpp1TMn+BXJihhhlbxIaGDDdBPtr9foxCbfCXkKWNgPHZKDxG2ww6PUfC0RMw3Vds1.qWRXaCUW8ttq28J2vMbC8RV1byMGSN4jx0+5VmlFBmaYe7TE5qMNmxPOEcLoWAsTIkqZUF5c7N9ur423a7MNvc7gtCq24uy6TFGGmj7AKfABC82hTJFBURQSFDDVBov79ef6me9+k+7F5VrOwFyTHDlNa2wjVXni+OoMeHHHnW7+111cMMMiBC8iz9+D999hDFiqi+uqq6zBe+caIDh7RgvzfXjRQuDkomDNsuy+r6p8u7uzuPm+g+g+g1+69c+2sLs3XPsGCZbDTwHztFH9tO5iZc228cadW206gsu8WUzm7S9IWwaGdK++8q8+83O2m6ycY86qDPbO2y8Hdcut2dLzLwNK8fd3BN6rrDlrdze.YIAWXUCLavXlvhV.4bf7giQIVjjfgKfhlVFZp9VPJkE777LCBBv0003U8pdU49nezOZoK4Rtjx5W2pAqMRgOhuu+wO7gO7JkKWt8kdoW5pZVlbBTUoscS5IxNWPQCJ1.pwOIfdS19CRM40XMLja14lybxImzH06i4byMm4jS9ByCMKLFT5qN6rCricriRn6eUeeewm9S+oMu4a9lyGFFVzwwobEnbKpTBZUHLLzZ6NNLaPPjiiSj1AWRBwJpEorjwSrAHoc6Nw4ymWBXnb5oRXRXXPjiiaTnenDidN56IZTNNNcCBBjS65JZRUCphLXlYh1gqaTKEUciT8in6J.qVEVsoth8UfkmQUEsD8MH9c+te27betOWwu5u5uk.pGqY2xpdddIYXdEN4DxI4jcNdgPxRRvF2V.pwaW5oRgwVAyCqCNaTnzRPtEVXAqwGe7jJhODvHAAAC655NXXXXNGGG788Mdo6bmVeiYm0RGDmz22GOOOSoTVnpPTpEUK6GNaQgTlamtthcGDHccc04WSXhViUDRgoimi324s+1i+.+29uEGDDjbnhd2mzocG4EcwWDZAe0LNN1vvvPp66+kANpqq6w78CW1yyIpJD2jpBnIe0u5Wsya7U8pZu6vvUINdkCbfCb7WxK4kbTfi1XgENZswGeEfNOvC7.cedOumWR6ekrYqDv3du26UbsW60J0hHbR0wNaLgIv52m7jZIGRkzjw.qE0T.1ExGrVKPT5Vu0aszsca2VRqNLnmmWYs+kbg9gVPrYrA34nTj9fffUbccWILLbkocbVoAUZCs536629E34sR8w3XrXuCYomnFm0dM7oJHPw1Kq8MB44fTPqYPl.hgA4gz6aaC4CUzvt3UcUWUou025aMf959lfJCBsJGDDTRqgAHDhkARRXxhnXvywAVUMZWq0AZzFnaUP1zFI064a7r4jkjfTI2qZNnYQWnz8M+7EmXhIJBXM2byYM4jSlqd85ErssK+e7+3sT9O9O91GrFTtA0JCMJGFFVXZGGqcGDHbbb307ZdMxu3W7KZ.XFFDj2w0sjuueIMaZM09Uh0NpLzSOFScBgSFOwI6cK.DW+0e8w+4+4+4QdddxtRoYy50y63r879AyZHfN6z0c0FUYYZxp5hFzEHJLHnii5voqVEZOaXXjRuunqJYugckRYGW2czAZsZPPPmTUns8B0W3D+ZuwesSbu268llwVQtPT.CKgCoz2NnXKaxsv2egbiO93I5Wz.ZQ+t.pXAxsxJqXUpTAgiiGggg7pe0uZwC7.O.5CFezJvi0RYm8XnaeQVaOX.DMa1jmW0pxlLUDruyEry1HzWLbSZBygJDtdedRKf1klat4JL4jSVX9FMrdQ0pkuNiU.VrDP4pPol0n.MHQLXygdc4W9s7+agO1m3ikuFXVWJMCBBxaXXTDUBRKHEBq69i8wMt0+y2pQ850shiiyIjhBRgLu1ORRR9hCBBD2569ca72dW2k4LAghO7e1GQ9a8Veqcl10ckcqz6nU.VMoMsCTiYa0nmVyvWH9D25sdaq9w+3eb4+z+z+j4K6k8xL88Ci77bRR58ioDdemj3zRyTjt.QUgnl3FAAw.xw.4hUINUqZc1dBaelD8GulQpupsurG.puYee+MIkxh53yR2pVV.kqWu9lsssGRMYcpVBZVJLLzZZGGycE5Kcsc6JTBHRRa5jHj34zLbxDPDGGKMLTD+sQyFl10rQ0hzqK9ePydj0JVpabPPf4Nccy2fp4+q9e9QL+W7hdQxc54E0rBcnEcBCCWU2d8qVCZ2P4+X4pvwmML7wkJM45D5OaB0fuX64fVhG7Aev3u7W9K2929292Nc7+II.r24QFA5dP7h.+9Yv5ET1YYIL4jw5B.N0W0IOYrbpdSer75QcXATJ9sQq094xAj+i7Q9HEtwa7caUgFxVTSnlXBTPqmAF999c777NNpMIS1nrMTqsNvrUR8n+CTbAkgJmjyusIf8l9v7oO.SJweZXysxgDGlshJtDLvACB6UQ3DJcmn2DFPMCnQR0NxWud8hIiOLGmsWNHX1htt6vDZk3fMox48nnrVzXK9HOxiXN3.CH2wz+7wPyXPkk4jfEkRYWCC5533IALBC8ENNSKgVQnDoottttDF5KN7gOBaYKaI100MJLLLxwwoquueWOOuN088aGKDqd8W+0u5+eetO2pG3ge3Ut5q9pWFFc4ZrzJMXrNpy.ffQQvRH.GfvjOCI1YIa9lNgHaTxQtPy9CV+FwazWSRVWBk2KBUxOJsrVhZ5IlCCRUFllrUe+vAEBYN.bccA05ugiyUJfFR.odC3Bgg9kAix.EWHHHmmiigNoJc877DgAABoRjYSN7nA.+F+F2HehO9Goqi6zwAAynpD6Nckz.9XerOg3xu7msnRkwXSaZyRftO5idfNW7EeIKSLmPJjGWmT2tG+3GWNv.CnKJmLxya5NZVusBJ5BerlJeXGm0UE9ZRWZzMfwhfEUa1ZiA0AeeeYBUR4bCplmd+RYeeuz9cRwBwgyWkCkuI0JBMTT32lAoNk09IJpSZVNTUcEW2c1QO19VYgEVX0wGe7dLzQq2Bc788W0yya4a5ltoieG2wcbB8DG5bQAg7IKzWm2lIr2jp5kaTvbI0ymT7X8Zvn4pxR4ZhcAndo8u+8W9R+YuzAoECBUJCsJBX8i+w+XYqVsV9pu5q9HPsCuvB+fCO93imrmbxgXZCizANXDLULru92+4r8q6oYIWNXq4fCmLtlWajjqnudh1bU9q809ZC7Reouzx999kEBw.0a1rvUN8OedsrG28G7C9AQ++7BdAc0w+jOHHX.WW2ACBBJKkhbsW4DrzgNb7K7E9BhADAgAVtNt478CsDpo6.IUfUkj3cFefC7cit3K9h6DFFF633XED3mGD4ALaszRcpL5nq355tbPPvJutW2qoym+y+ESZEmNwwwqpYcYWnpbWeuuX7U9h9E6Bshle94WchWvDcTGzrZGnY+SglUfQW4RcFZk8Gt+DeRozgkN4fEKBNEfPqjqm0f7ZQkrjdhJV7G8i9QEdNOmWlUMZPCpI09zMdvG7AEW1kcYcCCCW1ww4XZgwNoJvGWyJudILQ+09YS6Y61ZaDNcwvk591jXr6ESVO8J5K9E+hk9k9k9kJqigtvm4y7Yxc8W+0mSqgI40L8MIgbFPUqJzrPKpT5y7YuiRurW5KKu6zt4nIV228ceFiO9DlKu7wMKWd.KgPX0pUKywFqBJsdEDBCAHMEBoUbL5llPFs28t2UdUupW4I.Vw22e4c540ooN9b89ZwZ1AubEZtbKprBzJFvHLLzPqmXq333brfffi455lzNzqBi1FVJBHVOwoheaus2V267NuyXnZLzTpG0q82NfWnd1fSE1nBcYpYedITI.NoPoIs2TWvV9ZesWUtO+m+ymHBwa5Zu1W1.e5O8morq6zk88mMmm2NLglIL7IpJHap2+OQa6BBBJdu26+Xtq8Ze4Ftt6Dngx+2oN9ecBjmNFZ1FUqYI0BEadcRAMbbbj5yEDoSpRmfffU050zp999m.XYUQPqbb0.CwdEndG.XTLYIrfpF5yjDgx2m19qZj962uumzOR99WPgrDlbxn+.fEvTFXuOSpikiC4tm64aU3ptpqpv8e+2e9m+y+UkagE1kw3iOtxQUEDZQeLOzLe850srssS+9Ygpmo4J87hlsd8UrssWFprrCsVMLYyac1CAZCS04b3JK7TENUTieitdzmZruNQhs2Ac1JXb3dLIZTSWVxLHYcpJFzLYi5QKPkkJRKJUsJCr6c6W1vvHG.NN6HJLb1N5w.lEppdTBpVDZVP6bDf3uzW5KE8K7K7KHA035c6NNh8n1PDnhrBsL1SnuvwwiDGhnOzYX3LRf3sucmtsZslJnqqnwZ8wZEVkVrhdVo2eRPRe8Q3.DpdehfIZay7qVe8T9M80q9+2WHiSUqYj5PyCmGNTg4latbSN4jIGHIGJ5Fu4fffg1gq6.+seuuWtWzK5EATiZzfcoDxMB6YWfETqPX3LEkRYwom1sPylj+G9C2q4V1xlkFFFwNNNha3FtQtq65iXQULoIFPEgu+LBOOOCee+X.YROupq.FnnJJ.cgpQ0nYmFI5IRUVklpCJ9714OW7e+W7d5dM6XG73zJdlYlKxyaxn50q21194uZPvrKqC36D3vxDRGnhDZIt8a+1E2xsbKIssv5zmjsBwGds1Y3b4.91fjlT0BZlSmLjjf+SpDcYee+R6zyqXScB2gJV0nk4LgKHcbFuyrOvrqrim2NzZ6RsUOvAtu1W7EewsqTgN56+Snq8J5VdJ8TF5bwqgmonuVKYq4USZCslHLF57BOp.VxDvZ26d2V6bm6LI4Jk.F3ge3GdfRkJUxyySKRoU5VgVqzJIoeiwwYwDemtcpWeWssssaqED6SUU1Na+Z95RpmVrh06OZaTi5hFIwnTkbpVZhRvXk78msjmmWopUo7ry5WRJE4EBgvwYGcCCmsy1290zoUqGFpQNZv.P0MAMKGFFlW6GSp2qRhpRrEDPADhbZAgUhJYrwqMEGb01y0LCCmIuTJyM8ztFMaRzm7S9oV85tN0AVS0BCQTg1zh1PkN5CnJ9Zesul7a+s+1w27Mey8DM+pUqFE2rYmE6ceTsUqRijjN1lgnCGIYc1Shsuf5XZaSg50oXiFMx2oSGywGeZIrnjjDzYSApSwj8+0GdR0JsqYeX366GekddczUC9Xf8wbo9wBzsjMaitr2jjHrMPIbRmqqaXmtX3Rfx9rJVzLI9MaSFsdNVhBZgIVIvlPdnhEzJw+ZdpfIsP.UHLbOF5wbc9tc6VZ7wGu389U+p4u1W0qJuuuukPHRlJcBnpnBMMZgpMJRJp.fgeXno.oERLmdZWlYlftnStlq6NVw2elUzSeottS6FSSjPktPqNIrDj0mLawANvAh+WbwWb6ljLbGFckprT6lI1iCSGNTx5cUI1MioNwCMDwG4HoY8qW2TU8+7YlE9SC52tyBHmGj2W2FMvvLJGJVmzcCcK0oEG6JkezG86W9u3y7YJ+A+SukR+i+i+eyOzPCaAcwya5nffY6555F+29292J9Eey+hBZgo97eEBTC4CK.dnG5ghe1O6mc+w+i5rCUnBsXVMi6zEDUGydEC+5yXIhEV6XGthVsTq8999wO5iNe2q5pdw8lng5Vg93f8xP8jj82+fBQ.XVCDMT1KQPsNCSiUOz5KbUZzegoufzNKKgIaL1nLSZn5+sgxCGIuCTHDx666a444ALpDVJop.lUoZtlzLGTyJLbFgiii.f5KTWXOts.UkVinNI5.vpIzVeBHZ90N354yiHrmrnepwuQGlO8AWM.Oyp3azbsW+Z23OIBl6jnsmXqf3voXohCjKDJTEJ1TSk6ZPgFfoVso6pOfn4C+vOb91saW79m49y+5eiudKnpgu+tk5pAzUmHj0wzDTUcPDCFdNNF999bvCdP4V1xVjSN4jIYStaMHpAU536OSWS.aOujOyweyu48E8RdI+rsQmoXee+UTB55HsqxAiZtVVgSfgN4doqRwoRA9uP1l6LEqikI1XmqN0yA14Fk54VR2lF59vtLqQe6jpaPROcO+7yyDSLgnVsZhFMZjbnaqJfUKpkKHX24DBokiiWxXRjvvPhiiM777D27Meybi23Mp6c1cXFFtGdGui2t7+w+i+mcQMxBwyyyfpH72sebhVjfdbSuCW5R9zg..PG1lDQAQEmnMM0TQe8u9WOx00sqpcbHpBD2hpcglcsg35qY2rJL5JIhSMPzPLDGgiH.GCaBE0WeUJT+aaho95Zmgy0C3qmujIYRq4XNKXHqpbj7M0AzefCbf7W7EewEXMlFlG89HBgvv00UlZBV0FH5Nti6X0a5lto1iBqtjh8gQUfnVqq5hqq+0OW9Z3YBRRXRRatkbvodUDqQiFTqVMCv0rFAlMRILujnOBUof+t8MzrbZUfUt268dW4MbsW6pMgNMZzHpVsZQi.QGDZqllSdczGPo+fJOa+ZdZ+SpqcigIKdxsXldpkzS2Gz60kuBTLgV5UfBs.y8rm8Du8su8NPsHMCJx466W5C+e6COvU77uhRug2vMYEDLSRhYi0smrRnMCBJIDhBNNN4zIvMVSA8Hs1jzQe+fgiii0a4M8lL9Dep+dBCmMRJkIsg5J.cBUsdSGsVQrZXXXz8ce2G+p+p2nYUZJz6+0AXkW3K7E146+8+9QLJcq++odGaa6UXsVZqsMDUe8skJ.BU7eST.lufNl.sJsSL3JffdSgnpTsXSZVDkX+a3o1qNVyXF.j+3e7Ot6ke4WdDJljchwGe7iCbhIgUl6j+8mrFct9AVNcwvk78RyDJiw.yEAKkMmx9SqiL8XeR850yEGGaoimBck4Mfp4qQyBMVajA2aRURUrBlIvPmDOo9mioccEynrGMbccMpAldu3Wp4BemulwtCCEdNNw+G+2+604O9+5eRp31U1+ZM6I93O9w69reNO6NPsU7820pdddc.hVXgE5N93iGCDsP85cFesIGWz7yOe2IlXhn4me9NSLwD8XMxVA4ggX09v1RnNdfzOqp+OYw58A5gI9qax2AfXBvbdvZ94mO+DSLQdanPcU6dURa+UnJjqoJwmcUw1qXP123a7M3pu5q1nBUrZQqbPk7gg6wTe9ujBblvzjjotpQXXHcAw3pooS7INwI5dcW200c+6e+B8zPRTADsnBAAyJgdrxKFUxnizLLYYfSnkygNvXcpxhQMWu9GotFLFBVLIgIqK9+MJFhy0867TBxRXxoGaDcqS1TL4gY0pHZ1TkwudBF5nXon8DlUAF3J1o3a+U+RxsaaKaZSWpmX.6FwXsiXwEiXHh3HmTRRNWgtuOShSU0IR+7IAUaXClyTutv11VNIDO2FmA0MHIYdBFy2jEw7ZtlqI2W+q+0STE+xKrvBE0aXyXiQ7e4e4+.W9ke4ltttp.MUUnyDfJfbFeeoooY2ossipWgHTYIFeeeCuq7JsnQCSc0QRXDi.pA0ZHoAIi40dikUTNbw00UXai76+8WHxvvnsqdZ4fKqRfxFyCh+1yMmbxImTNDvQl.Ayqqjh6KPxVWNhCe3NrMhNKU3MOWAqsg7TXw9TAyoBzdzb996wZZOubsrIO0I4Py4cmdZKZ1TPELTUGKw1spIUZZQqdLjRRJAuLLLzzwwgpUQL6rKDClBGGGY3Bg37BbfFp2mDgGVJkwWomWbipXDLSfoPHLzUHd026688txG7u3CthRnfSrApEGDr6XWW2nOwm3Sz4s7VdK85S550qisssDnakJz4lu4OT6+3e6+3UanZkjH0vcnmttXbXFRbnCc.4yZ3giOnx+W+92NeIfuzA6aAXNAjadpj+FtgeEquvccW4ZNFVrXpDn87e94YwEyQUDzrWBLifZcYrFQrXuQ7buVyg0NPU5jMc9x0vmHjd+3bSLA4+te2PKgPHzr4ThReQL.LFAL1yBKXL93iaMJjeown7m4C7YF3U7JdE4ss2NAAyz1008DTiUngZDZCDCCKa05GGWoRkzATl9Zu5204FXs801FlrWL2yd1iw1290Ad4hw2Olsgj8llIrd49bet2atW+q+0mtsHJATRmrub.xZ0n6t1keD.ZF6ThJThVT.vrBvrpCfJ+T28cyM+geWFzR09pgggE+O+G9Gl+icm2ofp.qEGUWnZWp1T24OPPPPrTJiSZiuDAxrRE5LyL9Q5DrnRznZTXl1+mzvvnyz11cpWgn8ee6WVtb4Xa6qnMN4WgvvkYDVkCdJ0RozSlo7MZzvpVsZ.DaairdcDsZ0xpRkJpVwbLJxhJcsyFD+.e+XSSyt6v1tai0RTrbe6aecm5k7Rhnd8UXLVgE6k3ljX.Sv4RIm6LAmpX35ONrzrENYjvlnQLFtttFUpfYqVXo2OTkDF08+lnRlR93337RoL+U54kqQEJ3OieACCi7NNaWDDLab2tc6ZjyH1y1St1u6pFAAyX355ZDFFlTzSAfTWnrHfNUpPzLy3GCnJDAHRMJnWsBz9d9Zesnm0y5Y0YZW21MGkNrjhQfWwUbkQ+eZsPLMal72bRh8ZCiDg8AS1q7Twlsym167YBbxcPv11lf8tWXaf1+m9bdSXRs4snQOeeILTrnNoaZwak3csq.oTJkIh6L0vjFp83qBF61utvvPFuSGmt0U9lRhg2zcm6zhFMLzw+k.ITUp8+krtutjnpi+WVoBx6+9Wn6W6q8sZ+u8e6+VUxeULMOBPN+7yGGWrX7EUoRWOHx2CvOIgMu.ICc7tbjizgsQGc7+mpyZdAu8UVBSNyvZNumBS1WUqjQMLqMdzRLxL.LqWutosscROtB3HfPc.tNcFkvnkroCqQuW8MCSEC6q+pslkcumbX88rnhoIBvu+dwai1nt+2CCXRCXNkSyQnDGbrRZ8qQSm60s1mC0gYsDhtl11iqe9pn6K4HnVmQoQ2kFCotBeVKrvBlFFFVtt6zrJMLalRE4+t6ZWQG+HGYkWwq3UrhpUspJgloGMeIYItGSAnFcnQ5.+5ogKRXqhI3vFyqzxkzYX9bUE3+rIjNPuTGznlkVWbL.WypDX0bTxwRj+09Zds49P+YeHS.CGmcXLFMMWT01d49te2uq0O6O6OqLNNNZ7wGOBajO723.VOqm0EW.v589duCye+e+aB5Owp0.ZzKXSATA5sabMqpzvpoZC5tnGobWxkbIq7s9VeqU2oiS7Ov2W5sCOopBDUiz8TaGbnKgtRHHc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MyLKCRZUUE9fwNFl4BuVTMd2ibtdLjYlYYSScCrcMa35GYYwNKVqMW30hlRadvdB.eVu5tlYV1k.VYnl9LdLkYwjK7ZQS0PgKSPWVrygYlYMNjLHhORsyXDO1IsnwEdsnnzlGrGIsYOFxLyrrOBziD9ndUdsXwEdsnXhP6aDDWTrygYlYyMDvGnV2i5222hBW30lyM4gWP2rWcWyLq0AA5IA5lW+Mo4G6rXsdbgWaNWPIWAjVUrygYlYy0zkzwHU9vwNEVqGW30lSsgqejkAwqwWxDlYVqGB1A.J4U40lq4RG1blxkUHTK4FkzZhcVLyLKRHtvNGYjMF6XXsVbgWaNytewgWk.9vd0cMyrVWDrCPby9xnvlK4hG1bhxkU.gbWOI8cptYl0hSRqITK4ZhcNrVGtvqMmXxU2UWQrygYlYw2jWFE7x5s+g7MsoMmvEdsFtRkTADB2rWcWyLyNFJct4Q9qsbY4tHVCmeQl0vM40II8p6ZlY1uBYdjnab2u3vdLUZMbtvq0PstxJujtFP3GakYlY+ZH4RQtf2KuVCmK7ZMTc+RibdPr2XmCyLyZRIr9R8Wwa4MqgxEdsFlRaQcE.9Ld0cMyL63Q.qbBoq26kWqQxu3xZXpM1QuHIcYwNGlYl07hjAH7w7d40ZjbgWqgnTIUHg7pIYWwNKlYl0by6kWqQyEdsFhw6djykfWZrygYlYoDdu7ZMPtvqU248tqYlYyTdu7ZMR9EUVcm26tlYlMScr8x6ScfwVYryhk83BuVcUoRpfBgM68tqYlYyTjbogZIW+5Jq7wNKV1hK7Z0Ui0yXKW.mWrygYlYoSjZ8K7kGYwwNGV1hK7Z0MkJoBgZIeRBrjXmEyLyRmjvx8d40p27Klr5lImLC3CF6bXlYV5EIC.3C+ur2QWVryhkc3BuVcw5Jq7AoM6IyfYlYyZBKKoM8A8p7Z0K9ERVcwaauitT.bIwNGlYlk9Qx.E9P69WNrWDEqtvEdsYsxkUP4zGV.dfgalYVcgfVkpFtxXmCKavEdsYsm5.isRAc8SsuqLyLyl0HXAP1Wu8OjWkWaVyETrYkxkUHWsZ8QRu5tlYlUmo01lx4CCsMq4Bu1rxt+kCu.P5aUMyLyp6HXAI7g5seUL1YwR2bgWaVIoZ9KWPqJ14vLyrrIBtpBXjyM14vR2bgW6TVu8OzBBH45HXgXmEyLyxnHVfD9z80m5H1QwRubgW6TVtj15UD9ScalYVikvE8psOhu15sSYtvqcJYC8cjtCHYyd0cMyLqgyqxqMK4Bu1oD1d6qFjqN14vLyrVCB37d01GaYwNGV5jK7ZyXkJoBTZy.n6XmEyLyZQHs3DjrwXGCKcxEdsYrj4O7Jk35icNLyLq0AICLQWco9q349tMi4Bu1Lx5Jq7IgbeRRrnXmEyLyZsHfUVUvWDE1LlK7ZyH8r+JKRPWZrygYlYsdHYP.W8ltgWcgwNKV5hK7ZyHIHbo.XIwNGlYl0ZhRKGIbswNGV5hK7ZSaanuizMQx03QQlYlYQCYQIdUkJI+yhroMW30l1XaseYfvC9ayLyhJJd4i2sutgsoOW30lV5seUjDWMA8P+1Lyr3hXAAfqtbY4dL1zheghMszV0gOKIu5tlYl0rPW5t2ekEG6TXoCtvqMsjjK2kCnED6bXlYlA.Hfkx.unXmCKcvEdsSpR8WYoAo9Hoe8hYlYMEHXgjDcMq+lz7icVrlet.icRMQhtbArxXmCyLyr2fyuiwF87icHrletvqcBM4nHCeTu5tlYl0rgjcQkr495S9.UamPtDich0VaKCfd0cMyLq4jv4MXGi5q6d6DxEdsiqxkUH.dUfvGVMyLyZJIhEiZ3hicNrlatvqcbs68WYwB3JhcNLyLyNdHXg.S5yGdM6DwEdsiOhqf.KO1wvLyL6DQBmawJG8BicNrlWtvq8VZC8cjtgvGEj4icVLyLyNQHYWIjWcoRpPryh0bxEds2ZEZeU.X0wNFlYlYSGj7hpcFicVwNGVyIW30dKEDtXP58CkYlYoCRKLoVx4F6XXMmbgW6MozVFdQfnTrygYlY1zFYdFvGp29UwXGEq4iK7ZuI0pFtDH4syfYlYoMWXNMxYG6PXMebgW6WSosntR7gUyLyrzotovGcckk+YX1uFW3090jTsxJIvED6bXlYlcpfPWd2GXzkD6bXMWbgW6WSBzYCothcNLyLyNUHhEETsUF6bXMWbgW60TZKpKIdUd6LXlYVZEA6.Jrgxkk63XuF+hA60LQ0QNaBt1XmCyLyrYCAcY+K6czkE6bXMObgWC..kKq.E9n.n6XmEyLyrYEgkobIWQrig07vEdM..rqCL5REzkE6bXlYlMaQxf.1z5uI4KPIC.tvqMEJrB.r3XmCyLyr5ARtlNpTwGdMC.tvqgI2NCP58RvNhcVLyLypGjz7Y.9pF1.fK7ZXxsy.f7dcxLyrLio1VCu+RawiZSyEdM.jS5BnvRicNLyLypmH3ZmnpupgMW3skWu8qhRvydWyLyxh5FI3p7L407K.Zw0NprJ4qRXyLyxnHzE+TuXkEE6bXwkK71hqVBVKA5I14vLyLqQPDKOPb9wNGVb4BusvVWYkG.qK14vLyLqQgfE.4phcNr3xEdag81d4wVNHunXmCyLyrFIB8d2PeGw2jnsvbg2VXR0tDH4KaByLyxzDvZBs0wZicNr3wEdaQUZKpKA79IoeMfYlYYZjrKA898zZn0k+BeKpjpUVI.8rIzLyrVBdZMzZyEdaQ4oyfYlYsR7zZn0lK71BxSmAyLyZ03o0PqMW3sET2GXzkPeYSXlYVKFR8GVZKpqXmCatmK71BJmzE.fkD6bXlYlMWRfqYxyvh0pwEdawzWepCkfRfLeryhYlY1bIBzSMgME6bXy8bg2VLuZ6isLPbdwNGlYlYw.AuTeITz5wEdawjjjrFHsvXmCyLyrHYEnP69vq0hwEdawPneGucFLyLqEV2AHOG5aw3BusPJs4A6QfWRrygYlYVLIgeuoFQmVKBW3sEx3n80.He5TMyLqkFAtftOvndZE0BwEdagvftHR54OnYlYszDwhY0j0F6bXycbg2VDq+lz7oz6I14vLyLK1HXAR7GUtrbOnVD9KzsHZezQVsuREMyLylj.tfcu+JKN14vla3BusHnzpAfm6flYlY..DmEH71ZnEgK71BnbYEH44D6bXlYl0rffE.v5hcNr4FdjbzBX2+xgWfPtKjwNHVpgjRHvnfbPAMJEN..RdS+BIVn.KBotAPQR5ODsU+HUE.UDvfDXX.b32zuFxBPZxWGRMeB1wbdNsTKAtz0UV4ejAX0XmEqwxEdaAjLQ9UQlrLPW40NtFDRCBhWPf6hfOEC3ERRBGfnVk7EJN3HUeyEdCUqzcdwNpkGKhI3r.z+UM4UW8x.X2PpaWB1lNjTB.ODoFDfOOD9mE3dfzKn7gCRVsxDIcM7a7etpsi7m1Xiz8XLzUtZZQBIKmj+N.3rkzh.POd5zXGeZs8r+JKB.6K1IwZrbg2V.Lj7tI7a3a+5jzvDX2Ij2GSzC2VNd.juyCu8uLeSkJNApL0+4u..ON.Pu8qhskToGDRVH.OeA89A3YSfdp6+OBK0SBGf.OAX3GvZUexZHb31e0NOz12NGeF7ayPS8e9r.3m..rg9NR2niN5FUwxHRduB7RA0p7J.auAKA.mO.9NwNHVikWxuLtRaQcUchJ+..dwwNKV7InwovKAxGBhe6jIFcm221NiAaj+YVZKpqIFqxpH4UCpKS.K0kNZwIUQj6AP2Oqk7WN+ZcsmssMNZi7Oxd6enEjC4tPJ9QIvEHoE4m9fA..g+u2w8T7lhcLrFKW3Mi682+QO2Zh+Hu5Zs1jTBHeV.9kZi5Gev2dmuzb8dVqbYEdpWrxhBjWBotQ.bdfzOkoVISVz8ggR9JLudxc7meZGbtNB80m53UZarkRlbEP5ZDvJcw2VaBX2LWs2cLd8nM2wEdy3130cz+6D7NicNr3Xxht3En38lOfuw12Zw8F6LAL4pskOI+GGD8AnUN0ok1xnl5fO9yXfekb4676OC21LMLk5uxRqlnaT.eD.rDW7sEkzPIju266tK93wNJViiK7lwsoqqxcAf9icNrnXPA7kBU4232doc7BCL.eySYgHqzVFdQUqxOHRvMCxkE67XMB54kvWRSL9eUid6ybpXckU921KO1xkpcc.7S.OuxaMI8+9Ntm4UN1wvZbbg2LLu+caMM0HE6wozmO2Py6GOCO7Oy4JWVgcs+itFxvcPoKyaygrgIWUW9fLD9re2s1wyE67bxTpjJL9YT4xChkkzZ7p81pQescb2y6ZhcJrFGW3MCaS8O7ZkB+cd+61RYPHs07EzWZ6e4tNPrCyLwltgWcgpZXif713jmbZKsR5EXfetb0F+A29818ad141DaCW+HKKjnaERebPVL14wly7jIiO16oY7oPX0G9SvlgIk6rm5BAvZM7R.3Fy+JEuszVYW.fc7meZG7cdlEuGxjqS.OSryiMyIoDIryDEtlu6VK9sRakcA.tu6pyWXB14mAD2BDNTryiMmYELegUD6PXMNtvaF05Jq7DI+A9wxk8IoDH8Xj3pN7h676zruEFNQFX.lris10CVKgaR.emIuLBrzfo9Z0eUnFKce2amObryyrw8uUV4vKt3VI4UIgcF67XyI5VAu8+xx7VZHipzVFdQUGm+8fbUwNKVC2SVMgezev814dhcPpm1z0dzkHh6..eXOEGZtInwg38v70FHqMZm1T+CuVkDtaRr1XmEqg69yOXmu+z7hFXGed0+xnFerbqT.KM14vZbl5wG+fnVsMm0J6B.ri6Yd6azNJdST3OQP9G.0jZpxt+4i1Yme1rVYW.fcr0t1ISp0mDdP+DGx3jV838L5hicLrFCW3Mih4z466O9rMB73sEv0uiu5okY2uqO3cxgFoyhedH9m6RuMedckcusG7N4Pm7+IRm1wW8zdl1B354TWe1V1jHVLqUa0wNGVigK7lQQoeqXmAqw30VY2jjarY4hjnQ5AuSNznc14sAvurWgslHRUoveRVur6wr8sVbuHI4FkvCF6rXMFDr.H+cicNrFCW3MCZ82jlOD7oMMyh6JquxtuQO3cxgRpouH.ueW5M9l7qA79mHT6OtUnr6wriu5o8LgP3lkTS+bE1N0P.ukFxnbg2LnNG6nKWjqL14vZ.jdAD3msUXkcei99e048RD5SRfebryhwGBP2z8u042xM1tl7RzP2h.1Wryh0HvUsg9NhGmmYPtvaFjm+tYTRCIva8c916nksv2Ntm4sOE3siIm4vVDHf8Aox63dlWKagu24YNuGfBeNIMbryhUeIfkGZusyJ14vp+bg2LlxkUfJ42yye2rEIk.xuU0Pm2+.CvV5Go+67s24imH94DPp6RMHsS.GVh216bIE+YwNKwz.CvjIBc9c.3ek2hMYLRcqDddwNFV8mKEkw7r66U5VfmcrygUeQfGOeaICb+akUhcVhsAFfICdlc7MHzWK1YoUCg9ZCdlc7MZ0+PW.Sd4TPnAHvSD6rX0OjL.he2xkk6Gkw3uflwTMW9kAhkE6bX0UuDPnbZ75BtQ4QFfUqlD9J9JHdN0tplD9JOx.rZrCRyhI2VGgaCdK1jsHt5mceuh2VfYLtvaFi2+tYPRe80blc7vwNFMa9A2am6AIpLjZYlR.QizPLI4ykEufSlsVyY1wCCoudrygUGQrrp4xurXGCq9xEdyXnz438ua1g.dlpJbu9QH+V6zqV7AD3Wy6ixFGIkHvu1oUcd+jXmklQCL.Sppv85m1PFh2GuYRtXTFx5Jq7fZgwNGV8gjFFIprWUsiussMNJSpc2jza2iFDRd.lT6t2113nwNKMqN1SavSsgrARFH46L14vpubg2LjS6ke0tAnuvIxHHvi2V6EenXmilcq4cz0yJHuJuM.St5t5qsl2QWOaryRyt1Zu3C4qd3rD0y5Jq7wNEV8iK7lgjuVtyBRd9AlAHnQYfaa6eY5UL5jXfAXRaj2EA7seEoRHJC...H.jDQAQUcFAdt1HuKukZN4l76U42TPiG6rX0ABqtme4v8D6XX0OtvaVRHrZAzUrigM6Qfmn1Xi8.wNGoEaeqE2aB417p7V+HojDxs0Jdq9cpZhP0G.xiorr.QtHjvkD6bX0OtvaFguvIxNjzvR5O9911YLXryRZRNF99fziGp5ExWJGCe+XGizjIupk0Wz6k2L.otkx4YZeFhKGkQ7SOL5BjqN14vpK14ncNuGM1gHs4+3s29dHfWU75DB7.+Gu818AlbFZpu2cmwNG1rCICTI+d9BnH6vegLinv3isDIrzXmCa1Sj+vG7NomsryPOx.rZf5tgvghcVR8DNTf5t8kLwL2CdmbHQ9CicNr5.xU+SOr2lfYEtvaFQtjZKGTdC1mxIgCDpUyqR4ontFq3y.fGK14HC3wl5eWZmBB0p8.RviJuzukzVkQ8n9LivEdyHDv6hfEhcNrYIpGJ+q10yG6XjVM4rhU9jxOKL4+tSeSO2cO0M42CqGN14vlcDzBxwZqL14vpObg2LfRkTAHu+cS6DznPZ6ae6zk0lEBJ44gnOvempDGLnD+gtlE19143Jfusf7GZHEifEHBqI14vpObg2LfQNCjmvamgTOwmsZHwiznYotp10yShmL14HshDOYWU8SYX1RUwSRgeQrygM6Hf2UoRxO8zL.W3MCHTsR2fXQwNG1rCg10ji0Ha1XaaiiJneTryQZjjRjzOzamgYuy4cT7.fzSakTNB0yHmA7MtVFfK7lAvbbYPvar9TNQ7TwNCYE4RRdTIbvXmizFRdfjb4bIs5fAFfIRxeOcZGwBCUqzcrigM64BuY.Dbkfb9wNG1rfzPBzyty5jiVrqWfPdFxNCInmKQs+BwNGYEDg8.oJwNG1oNAtfb4keBpY.tvaF.kNmXmAa1Qf6oPx3OWryQVwTywXOdxloD+Gt+sRWPqNoVRxyI.e0LmlI0MRB9PgmA3Buob81uJBJ+MiobDZWqZImtmr.0QB7mCIewILMM43HKYWwNGYIux6n3AAf+2ooXjLHBunRY.tvaJWaIU5Qv2vZoYRJQL7OMv.LI1YIKIIW3YD3gicNRKH3g.x6oyPcziL.qRfeZrygM6PoU0WepiXmCa1wEdS4RHWpOvZodUDj2NC0YsqjggmCpSeRi2VHw+6q5LEBOqmGuodKYnNG1mSlTNW3MkKHrb.TL14vN0QfCg.eoXmirlpgZiRfgicNRMHO3q1dmda0TmkPtOHNTrygcpSjKJoJWbrygM63BuocTuSR5uNlt8REx0gm+t0YyejtFRDGH14H8PO+6pG+ADp2xwINDg72emhQo4SDVQrygM63hRoXkJoBRb4wNG1rDw9N3uA7i7rNaaaiiReB4m9D++06i75uIR5ZXA3BuoYj4A06L1wvlcbg2TLekBmQHdvGY.5oIPCf.7VEY5Jj3KpiFfem2NFkzevqzNBr70UV9FWKEyEdSwZKLbW.vEdS4D0+ZryPlkv+driPZffFUHmKk0.L4pl6YabpmvhOsWFEhcLrSctvaJVM01BD3BhcNrYIgwicDxpDB9e2NMPgD.eif0v3O3U5GYgpsCuBuoXtvaJVtjZKGT9N91rii.vd8Hg5jSjipjDef0ZPjOzZYAKosJi5Q.ZJlK7lhIwkSP+HVL63PAM9jqdocBIMbgPvWRGlc7H0ca4jGMYoXtvaJFg9uF6LXlYlk0IfNTBVVrygcpyEdSo5seUDDmUrygYlYVVGICdzjkt4BuoTgpU5F.9wqXlYlM2XIdzjkd4BuoTLnE5IzfYlY1bEtrNOLJF6TXmZbg2TJFxsP.4uwyrSHVTzuO2ICIyOQRniXmiLL+ZvL.Ar31GeTO66So72DlRQkrbB5e.kYm.D0Vp+9joAotQHwiboFDR7eI1YvpCj5FIdRMjV4BuoTRzSnAyNITB89saZJ3edPCD6J1Ivl8HPGTXIwNG1oF+FboP80m5fPKO14vpOn2K1MLdk0ldDPGI0vRicNxhJUREjvhhcNr5.x7Dv+r2TJW3ME5+ncD.7FmOyfxkxZb79saZfjAD7s1XCwuAJPH+gZyJ76WmZ4BuoPclGA3u1kYHfk1Wex6yz5rd6WEA7fheZS7+oXGgrnpiWoaQuBuYE98qSubooTH+FnYKTXQC04vyO14Hqwyp5YFBsb+Cxq+RfV..8pmmQPgBS8TVsTF+EsTH+FnYKhbQIUoKlUm0ddlWBt.2zEwxNRtJdKfTmQDVAk7GnMqfrP01gOLroPtvaJT.45wyf2rD0UfzGXn5rpHYAf9zwOcIgNXagBwNGYMDX4fdZgjUHoEcZiMhWvoTHW3MEJAvyVzLDBV.B+twNGYNIgUC4Ch0zEA7LFsNqTIU.zeucVBA5dhZxaovTHW3MEhLwmRzLFQbdScHqr5m+mIoeOtoKx4SoUG6XjkL1oUYA.ZkwNGV8iHJfbA+AoSg7OLHEh9f3j4PvUV.i4s0PcxF56HcChyK14HsgD+9qqr7ieuNgLrBOCdyVHXGD076UmB4BuoL80m5PvCH9LGoEJkb1wNFYEg1a6rjGP7yXR3bO8WrhuhgqSHSVA89HOyQh+micFrYNW3MkoR9WoHA8IoNqgLu.dWwNFYFI7h7928T.wxBAs1XGirfxkUfjmSrygU+4mxZ5jK7lxLd91mujbg2LIsFuOdm85qO0gHe2d+6NyQvNHCuuxkk+2cyRO0KVYQP5hhcNr5Oe4SjN42TKkgUSVHA7JWkAQw0zFNpOfKyRCme3U.gyM14HsRPW5tNvndaSMKkKGuHQusZxhHXOUx+JdwIRYbg2TFxPWhvyJyrHhEHw2eriQZW0bgyBzamgSUTXQnpuRlmMVWYkWRuOB52qNCRR8fbE7dyNkwEdSY7L3MaiBabSW6QWRryQZUe8oNBBWkKZLKPVLvj2q2VCm55d+Gc0.7RhcNrFDxtFOIwasvTF+FZoLDd+6lwshDDtzXGhzpgZ+nqBxEMl0H13y9xi3O30oJFtLRONxxpnTGLD7J7lx3BuoLjvW5DYYj4I0eToRxqP4LT4xJnDdUfXAwNKocRX4UE9fwNGoQk1h5hRu6XmCqgp.R7jZHswEdSQl7QLJ+oJy5Dtvpm1vqH1wHs4o2+XKGvkzpGHY.RWi2dMybSTczK..meryg0.QlG9osl53BuoH+7WFcH4KchrNRrHExcidUdmYBgjyE9wHW2HhkK4aqtYhd6WEQRRe9xlH6yOs0zGW3MEoZ6HOoOvZsBHvGb7tGwiVqood6enEnD8I8gUq9gfEPfatzV7SUZ5pPsidw.3JhcNrFOIN+XmAalwEdSQNswFoaH4q8yVADKH.b09jxexUtrBskj6iI5YuacmzEUc7QtxXGizf95Scn.8p61hfP9xmHkw+vzTF4ul0xPBa7o2WEWh6j3o2+XKW.2rWc25uIKuoa06k2StWM+QuDHr9XmCaNSw+i18OONMwewxrlTjXQHvaYC8cDeIJbbTtrBjIWgua6anVAB3J8Sa33aS2vqtvj.+Lf9wb2Bom1BC6UyOEwuAVJRsIXdP5ul0RQWAaqvGN1onY0SuuJmqD9LSdposFBx7HA27tewgWUriRSqpgOBDt3XGCati.5JeRNukFRQb4oTjZ40hf7UlZqjIeL8byk5uhmNGuA81+PK.jeNOf+mCPtLjKbqq+ljWAy2fMdsitBQtY5EivrlZ9aPSQHQf9qYsfzZqJMPu8qhwNIMKJWVg7J2GlPWdryRqBA7A6XjJejXmilIq+lz7IS97DX0wNKlYmXt7TJBk5QD9Q21hYxKA.7AyqJeBuOJmzTGlua1akg4NDr.It4M0+vqM1YoYP4xJz4HU9DxenqVRjL+DIAukFRQ7O7LEQBKxmD8VTjEA3s9LubkV9xFa35GYYLv6jfmUryRqGtBjv+TO0F.d5WdjKPf2JgmM5sjj5FgDOlPSQbg2zDBW1sEFAVRMguvFt9QVVryRrTZKpqPM8YjjGWaQh.t..bSsxWHEWY+itRlnuf2+3lkd3BuoHT72J1YvhKBdIgD8G2JNpxJURElXhJeVQcs9.BEOjLHhOU0wG4S0Jd8WWZyC1iRp8mBxKH1YwhGQjmR8D6bXSe9GZjl3U30.ff5MzVgasU5PrstxJ+3m9neDJ7e2OB43axuFnas5oOxGtUZeku9aRyeBVnrnGAYs5HXAIuB+oIsLuQUZ2j+PkVmBN1w2T6i6OUaIUJ2Jb0VVtrBc+xU5kPedOX+ahPNeQbG69kpzaqPo295SczwHUtMPcC9rTXV5Sl+MoxJ94uL5PBdVrZShrn.1xPsU4Fxxqza4xJ7zu7nWJEuSueIa9PfkHv+rrdo2d6WEGpsJ2..bYW60PvED6LXSeY12fJKxyfW60ijcAf6HuF4NxhWH.kJoBO89qbsHI4tIPK+TAnY0qU5c+i7wxh6o2RadvdxmLxWD.2wTeOmYSh32L1Qvl9bAJyRyHKBnszwHib6k17fYlCPQoRpP0tqbsD3KRR+jMZxM4GHQ2Y0tqbsYoRuk17f8TkEFfP8O42qYlkV4BuoDgpU5FDdl+YuIDr.ntgIXgu4l9DuZp+FeZS2vqtvI5dju..uCumcSQHmO.uiI5djuvltgWM0+dUa5S7pqdBV3aJp98Ebh8VSEyxakmrF+EpTh1yy7RHye.krSMScKXsdjK2+iM1+HW15JqT2OftbYEdeadjkqp4taH8o.PK2nWKCnaH8oT0b28UdMijJuXPVWYkeiW6v8pb49KIw58d10NdjvR+4ur+4xoEtvqYYKmMSz2rm8W41KskgSMGxq95Scr6WpxMjOj7CATudN6ldM4W6TuJWxOXS8O70llljHk1xvKpm8W41IB+EDH0+zRrFKetZRWXrCfM8rgqejkwZIOh2Oi1zgjRHviy.+b4NRmOw12NGO1Y5sR4xJ7u7xisBkT6VDvGzGJnrEIMLA9qYH2W729s29yOv.LI1Y5sRoRpPsyXjyWI5NDvE3OvkMsH8XSDJ9dt+sxJwNJ1ImK7lRrwMezyiD+cdOMZyDR3f.5GKnu3Yelya2MSEN1z0dzk.hqW.eDBlJe721zif9ED3aAg6ZG2y71WryywTtrB6Z+GcMD7V.3kReNIrYlckOY7+vseuce3XGD6jyEdSI1X+UtPlnejOov1oBArOJ8sRxE9J22c04KDyrza+CsfbIs0af5SKo03USq0fjRH4tSD+SqEl39u+sN+CEy7rgqejkEpkbih7i3wdmcpPR6ss.+829VKt2XmE6jyEdSIbgWa1Zps4vyAxGkf+MU5nim7AuSNzbwe1kJoBUOiitZjvMHxKCPq0GFnVSBZb.tSJ8PHn6K+Ql2yLWskaV+Mo4WbzQOOA89gzEIfU5OvkcpxEdSWbg2TBW30pqjFR.6hDeaVM7f4Fti8UuKcrtxJ+o+hUVXtb7hjzFH3kBBeyDY+JBGRP+XRde0poG8bdGEOP8da2TpjJLVOis7Psj0CnMPfy1aMLqdvEdSWbg2ThM1+HWFTx8QvTyId1RAjpJxCPfmDBujD9oI4C6LGm3P4+O5ZvoaI30UV4W39dk4OA6XA.UWAH+cIvxkvZ.wY4Uy0NQDz3TXOfXmBXuP5etlx8bSTriC9t5ACOcKA2WepiJ4ekhiy7KkJrZR7t.whAv4KoE4Uy0pmbg2zEW3MkXS8O70BEt6XmCKiSpp.OLgNj.2CA1qn9Wgv3PrRHG1aBPBRvhAT2fn.E+s.0BA3J.vBEzBbAWa1PPiSvCAf8AnmG.GTB+a.HgDGPjGlIrPBvRIRJ.vtHwuEjVFHWn.VJkluuvHrFIIbvjbg+fu+c0wyF6rXmb9MCRITBCze7DqQiLOAVH.WHAVE..AAHffRTBFcpWFV30JSvW6+Gdse8lMKL0GXZwS9+wyC.3Xu+mfFmIppnBAvN90V2lo9EwW++.l0fPntBJIybktm04BulYSKS83f8dH2hpIuJsQAWm0Lalv6mIyLyLyrLMW30LyLyLKSyEdMyLyLyxzbgWyLyLyrLMW30LyLyLKSyEdMyLyLaFRDATSKL14vldbgWyLyLylgHXGf7OL14vldbgWyLyLyNEP56yfzBW30LyLyLKSyEdMyLyLyxzbgWyLyLyrLMW30LyLyLKSyEdMyLyLyxzbgWyLyLyrLMW30LyLyLKSyEdMyLyLyxzbgWyLyLylgDz3B7mF6bXSOtvqYlYlYyPTnJnd9XmCa5wEdMyLyLyxzbgWyLyLyrLMW30LyLyLKSyEdMyLyLyxzbgWyLyLyrLMW30LyLyLKSyEdMyLyLyxzbg2TBRb.AMdrygYlYlA.xAQR3fwNF1ziK7lRHxCSgpwNGlYlYFfjp1VHYzXmCa5wEdMyLyLyxzbgWyLyLyrLMW30LyLyrYJhpUSTRrigM83BuoEIgCBxAicLLyLyL.Jbf7EJ5etbJgK7lRzVHYTI4CslYlYVygjQpBuBuoDtvqYlYlYVllK7lRTMQIfdrjYlYl0LPDi+1FyqvaZgK7lRjuPwAovAhcNLyLyL.Br2ssM54vaJgK7lRL09DxeRRyLyrlBz29ooHtvqYlYlYVllK7lRz4QPUAd3XmCyLyLC.B+6wNB1zmK7lRr8sywI8d30LyLqYffNTryfM84BulYlYlYYZtvaZhe7IlYlYQmf7ScMkwEdSQ7iOwLyLK9nPUQetZRSbg2TDFjGKYlYlYQl.Rj7nBMMwEdSQDxsWA4gbsYlYVLQNXtpzaogTDW3MUQUn+DklYlYwkTRt1T0XGCa5yEdSSpowAf+FrLHAMtf7s1iYYHBZbHWJxrlAtvaJhxGNn.FL14vpyjd1.40DHuFH8XPpRrijY1oNIMr.d.J7QA4mUBGL1YxrVctvaJRA4CsVVi.dFF3M9c2Zwu02cqE+VISL96C.8AoG2EeMKkQZHA7.AoOTas04GZG2y79NqYwc9m.faR.6K1wypiHpVMw+L4zDF6.XSek5uxRmHQ+ijbowNK1rijRHw2pZRXfev814ddi+86s+gVPA01YKkbUfb8.XwQHllYSCRZu.3uVj6nPact6s+k4vu9+9kKqvSuuJmKItcPdYQJlV8jzimuPw2ya7q0VyKW3MEwEdyHjp.vu5DgpCb+ac9mvYq75Jq787xGcMPgqSRWt+ZuYMGl5CstGH78pkK21NmE09yMv.7Dthea35GYYgZ5KAndAY94prZM.RO1Dghum6eqzOItTBW3MEozlGrmpgB+8.3ricVrSYujjt8pghe8YxaTVtrBO0AFakgjZWBEde.3BAYwFXNMydKHogA4iBo6KTK7i+sWZGuvIqn6qWu8OzBZKIe+f3V.P2MvnZMT5u9vKt3G8QFf9PIlR3BuoH81uJ1VRkeDHuvXmEalSB6jf2xZNyNd3YxOf7MZ82jlemiV4hE3USoKCjyudlSyr2RCJoG.gv1ZKeGO9r4QYWpjJLwoOxFA0mmfmU8Lj1blsti6t30G6PXSe9Qpjh7pucLdOuDdoXmCaFRph.+V0D+B+f6sy8riY4ucO3cxg.v2u290Ot.G4bqIb9TnDnVMA6ndDYyL.AMJDeFQrc.7XcOQwctsswY8k+y12NGubY8c109O5yCx6.Rqmj9PjmlH7uG6HXyLdEdSY1z0U4t.P+wNG1zijFl.21DghasQtWu5s+gVPNj6BChaF.WH7iJ0rYiAAvikPcu0PsG6jsW6mM1zM7pKTUyUlP8680a5gjt9u28LusF6bXSe9atRYjzOG.CSxthcVroCVAgjgq1di860l5GH+85se8P4zHmcP3hAz6E.q060WylFlbL.tS.92lP7v0Xm6ZN4.IUMTfPGRDUo+YxoBR3.RgmO14vlY7J7lxzWepigZajOnHtCBrjXmG6jSPiBveRNpxq9sWbmyl8u6LwF56HcmKeaWfBrj.uT+5EydyjvA.zOgDe6jwG+QuuscFyIWtOStOdO5kQFtcIsFukFRIjdVxvm92dwc7Slqdubq9vEdSgJWVgm9kG8RoR9rP3B8iAKcXxAOu95Igbeyu+c0wyNW8m65Jq7m9AFaELIoWBrNBb1RZQ9GvZspjvA.wNEvi.k7PCdly6YlqNs8S992ibAT3pEvGf.8LW7mqM6L0BWb+jIe9cr0t1YryiMy4BuoXa5Fd0EhZ4tcH8w7isNMQ6Qhekpgpe8F4dC7sRe8oNNRGicVLIoWJThPqxu1wZEbrCfFndHVK4ub905ZO0iCf1Lwlt1itD.bSB7+FIV3b4e11rfvgRH9hEZqyurunIRubg2Ttd6WEaCG8ifDdyfbUwNO1zjTUA7vj7dymL9Cs86s6COWGgRadvdplK+4Aw2MDVsHOWuZSVVxTGZzcKvcQn+g7ZheRT9dssL7hlXbtQRdMR5b8SWIcXxKWD9yjRt8ibly6A8L2McyEdyH1zm3UWMxk6yCo06s3P5gfFmBOLC7NxcjNehsucNdLxQu8qhsiJqplvlf3k4U90Rql56o1CHdvjjvO.UGcWyU6K22nd6WEyWqx5Yf2pK5lxHUAjecHcG63dl29hcbrYOW3MCo29GZAsobeZIdsjXQwNO1Lfvg.w2ARaK+qTbmwp3KvawJ+Bd1.ZA9GVaMwFDROq.2EI9GqUSO547NJdfXcnhJsE00DUG8BPRRe.3J7T0IsQOuD9iqFJ9M7UGb1gK7lwbrCDARveJIVaryiMCIbHA7WmjK7mcNKp8mK1mB3d6WEaq5vmkB4tLP8GAwU4OLk0jXP.77P39jzOVUG+4i0J4dLkJoBi28Hma.3yHoKyEcSWlZKL7.jga46t0NdtXmGq9xEdyn130N5JHpcqf7JguDBRcjzdA48BE9qdmmY66I1EeAdckeyEtXJ9GBhkIok6entMWXxCcFdABrWQ7yD3ODiO1yF6Rt.SUz8zO5pHC8QgOFHVPryjMyHgCBp6g4R9S2we9ocvXmGq9yEdyvJUREp18HWIfF.fqH14wlgl7fs8Rf3azF4cs8sVbuwNRGSoRpvHmwqN+b0Za0Lj7tIvEHv0.ot8VevpWjzv.74HvSHj7i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0VizPhKG3y3SnijbbSo9DYleYfCGXVqtEoAYWIvQDQbRUGhjjTaTl4WilSxoO4NpWy3.1xHh+V0gHo533lR8AxLOLfcq5NjFj8G.FUDwEUcHRRRcCxL+z.ecfUt5VjFD8j.aeDwut5PjTMbbSodXYlyKvwBr4U2hzfjD3LANpHhKs5Xjjj5FkY9wA1If0o3TjFrLQfuUDwgVcHRZ3miaJ0iJybI.9E.u2hSQZvvDANeZt4y+iE2hjjTOgLyOAvN.7AptEoAIGNvdGQLgpCQRCebbSodPYlqBvoC7VptEoYPSD37.N3Hhqp5Xjjj5EkYt9.6C9aJt5MLZfsKh3eWcHRZ3giaJ0iIybiANQf4u5VjlA7R.mDvOHh3ZpNFIIo9AYlqCvdAr9EmhzLpq.XKhHFW0gHogdNtoTOjLycD3v.lypaQZ5zyAbp.+jHhqn5Xjjj5G042r7sGXSptEoY.2NM2j59z+H0iywMk5QjYte.e6p6PZ5zD.t.fCIh3JqNFIIIAYlaJvWCu3gT2q+EvVGQb9UGhjF533lRc4xLmMfiD3KTcKRSmNKfCzeW0kjjZm57N47aC7tptEooCSDXmhHN1pCQRCMbbSotXYluNfeIvFWcKRSGNWfCKh3OWcHRRRZpKyby.1Cf0r5VjlNreQD6e0QHoAeNtoTWpLyQRy6lP+lKU2l+DvOLh3rpNDIIIMsKy7KArS.qP0sHMM5vA1qHhIVcHRZviiaJ0EJybE.NSfkp5VjlFb8.emHhQWcHRRRZFWl4NCry.KY0sHMM3TA1lHhwWcHRZvwLUc.RZZSl45.76wgMU2iaklWj6qjCaJII06Hh3GN5QO5c.XOAdnp6QZ.ZK.9MYlKX0gHoAGdxMk5hjYtE.GMv7UcKRC.2MvOHh3vqNDIIIMzJybw.1dfcD+dUU2gqB3SEQbaUGhjlw33lRcIxL2FfeFdhqU62CCbbzLromhCIIo9HYlKMv9B7wAlqhyQZpYrzLv40Tc3OAmg0...H.jDQAQERZ5miaJ0EHybO.Nzp6PZp34A9kz7d07tqNFIIIUmLyUE36BrQU2hzTwi.7QiH9qUGhjl933lRsbYlGNvtTcGRSEiF3PhH9GUGhjjjZOxL2.f8CXMJNEoWKOIv1EQb5UGhjl143lRsTYlyJvQArcU2hzqg+Fv2Kh3LqNDIII0dkYti.6Jvas5VjdULAfuRDwwVcHRZZiiaJ0BkYNO.GCM2jeRsQ2Iv2Oh3HqNDIII08Hy7fA9B.u9paQ5UwtFQ7CpNBIMv4EShTKSl47AbV3vlpc5oANvQO5QuCNrojjjlVEQr2.qFvwWcKRuJN7Ly8u5Hjz.mmbSoVjLyEE3L.VypaQZJ3WRyif9MVcHRRRp6Wl4GBXu.d+U2hzTv2OhX2pNBIM043lRsDYluEZtTVVopaQ5U3uC7ciHN+pCQRRR8dxL+xz7937sTcKRuBGKvNFQ7BUGhjd043lRs.Ylq.vYBrTU2hzj4g.NnHhin5PjjjTuuLyQA7kAFQ0sHMYNcfOSDwDqNDIMk43lREKyb0n4DatXU2hTGSD3GA7ChHt2piQRRR8OxLe6.eSfMu5VjlLW.vVEQ7DUGhj9+xwMkJTl4ZQyvlKb0sH0wEAbfQD+kpCQRRR8uxL+T.6KvxWcKRcbw.aYDwiUcHR5+jiaJUjLy0G3z.lmpaQBXb.GPDwOq5PjjjjdYct0p+J.ye0sHAbk.ezHh6u5Pjzj33lREHyby.NQf4p5VTeuW.3vG+3G+QNhQLh6t5XjjjjdkxLWZfCAXyptEIfqC3iDQLtpCQRMbbSogYYlaEvOGX1qtE026R.9lQDWd0gHIIIM0jY9I.9N.KW0sn9d2.vmLhXLUGhjbbSogUYlaMvwALSU2h5qcO.GdDwgWcHRRRRSqxLOLfuHvbWcKpu1X.9XQD2R0gH0uywMkFljYt8.GE9+tS0IANdf8Ih3gpNFIIIooWYlqLv+CvGn5VTeswArwQD2T0gH0OySOlzvfLyuBvOFG1T04ZAVuHhs0gMkjjT2tHhqMhXcA1FfGt5dTeqEG3ByLeGUGhT+LG2TZHVl4NAbD3+6MUimBX+iHVkHhKs5XjjjjFLEQbB.qNMOcJRUXj.iNy7cVcHR8q7TjIMDJybW.78ZnpxkB70hHttpCQRRRZnVl4GF3PAV1paQ8ktefMMh3pqNDo9MdRxjFhjYtG3vlpFOJv1GQrtNrojjj5WDQbtQDKGvn.d9p6Q8cdi.WTl4pUcHR8abbSog.Yl6EvgTcGpuzIBrZQD+zpCQRRRpBQD6Iv6G3xptE02YAANmLy2a0gH0OwGKcoAYcNwlGZ0cn9N2AvADQ36aJIIIoNxL2efcB30UcKpuxCArYQDWd0gH0OvStozfnLy8EG1TC+NtQMpQ88bXSIIIo+SQDeaf2Kver3TT+kEF3BxLWipCQpefmbSoAIYl6CvAVcGpuxsRyEFzETcHRRRRscYl6DMe+5yS0sn9FOBMmfSeEIHMDxStozffLy8DG1TCeRfevXG6X2bG1TRRRZfIh3HAd2.mc0sn9FKHvYjY9tqNDodYdxMklAkYta.GV0cn9FiEXOhHN2pCQRRRpaUl4tBrO.KP0sn9BOHvlDQbUUGhTuHG2TZFPl4tC78ptC023HhH14piPRRRpWPl4aE3nAV2paQ8EdXfMHh3ZpNDodM9XoKMcJybW.FU0cn9B2FvF5vlRRRRCdhHt8Hh0C3qB7jU2i54sP.mSl46r5Pj503I2TZ5Pl41AbrU2g5K7y.9FQDOT0gHIII0qJybkANbf0o3TTuu6.3CGQbSUGhTuBG2TZZTl41.7yw+2OZn0C.7UiHNipCQRRRpeQl42fl2EmyY0sndZ2Iv5GQbaUGhTu.GmQZZPl4mC3X.l8paQ8zNIf8Nh3dqNDIIIo9MYlqAvOF3cTcKpm1X.1zHhau5Pj5143lRCPYleTfSGXVptE0y5QA1mHBekGHIIIUrLyCAX2vu+eMz45.9nQD2U0gH0MywMkF.xL2HfyDOwlZnyk.rSQDio5PjjjjTiLyMkl2EmukpaQ8rtFfMJh3AqNDotUNtozTQl46G37.lqpaQ8jlHv9GQbPUGhjjjjlxxLOYfOc0cndVWIMCb9nUGhT2nYp5.jZy57914zwgM0PiqGXccXSIIIo1sHhsBXao40HjzfsUCXzYlyS0gH0MxStozqhLyUA3B.VjpaQ8jNpHhub0QHIIIoAtLykE3HAV2paQ8jtPfOdDwyUcHRcS7jaJMEjYtb.mANroF78P.akCaJIII08IhXLQDqGvAB3.TZv1FA7KxLm4pCQpahmbSoWgLyEG32.rbU2h54bw.eAuMDkjjj5904cy+wArDEmh58bL.aeDQVcHRcC7jaJMYxLWHfQiCapAWOOv9EQ7AcXSIIIodCQD+Af2Evut5VTOmuHvnpNBotEdxMk5nyKu4yC38VcKpmxc.rCQD+tpCQRRRRCMxL+5.6Cv7UcKpmx2Jh36VcDRscNtoDPl4rQy6XyMs5VTOkyb7ie765HFwHt6pCQRRRRCsxLWMfeA9TfoAW6bDwQTcDRsY9XoK033vgM0fmI.rqQDebG1TRRRp+PDwUN1wN1sD3mUcKpmxOHy7yUcDRsYdxMUesLy.3G.7UqtE0yXLz7x+9OUcHRRRRpFYl6.vAgOl5ZvwD.1xHhyt5PjZi7jap9ceKbXSM34TF6XG6V3vlRRRR82hH9I.qGv0UcKpmvrC7KxLWqpCQpMxSto5akYta.GV0cndBuHvtEQ7CqNDIIII0tjY9y.11p6P8DdXf0Kh3FpNDo1DG2T8kxL2VfiFXVptE006VA9RQD+wpCQRRRRsSYlaOv2GXNqtE0061.13Hhaq5PjZKbbS02Iy7C.bQ.yZ0sntdmKvNFQbeUGhjjjjZ2xLWafiEXYptE006ePy.mOb0gH0F36bS0WIy7cBb13vlZFSB7chH9HNrojjjjFHhH9yicri8SBbVU2h558NANiLyYq5PjZC7jap9FYluMfyGXIqtE0U6gA1tHhyq5PjjjjT2oLy8E3.ptC006TA9zQDuT0gHUIO4lpuPl4BCbx3vlZFykA7AbXSIIIIMiHh3.A1BfGr5VTWss.3PpNBop4I2T87xLmCfKFXsptE0U63iH97UGgjjjj5cjYtr.m.vZTbJp619DQbvUGgTU7japdZYlyDvOCG1TS+l.vN6vlRRRRZvVDwXhHVSfiq5VTWsCHyzedE02xSto5okY98.18p6PcstKfsMh3RqNDIIII0aKybW.NXf4n5VTWoIBrg9ytn9QNto5YkYtG.GZ0cntV+IfsIh3NqNDIIII0eHybS.NZfEs5VTWoGAXChHt5pCQZ3jiapdRYlaAvoTcGpq0OMhX6qNBIIII0+Iy7MC7qAV8paQckFKv5GQLtpCQZ3hiapdNYluWfKBXNqtE004kn48q4QVcHRRRRp+Vl4OGv2ihZ5wUB79iHdlpCQZ3fWnPpmRl4JBbR3vlZZ28CrINrojjjjZChH1Vf8FHqtE00Y0.NwLyYt5PjFN33lpmQl4BAbh.KV0sntNWIvGLh32TcHRRRRRurHhCAXK.drpaQcc1LfCp5HjFN3ikt5Iz42Qp+.vZWcKpqyoDQ7opNBIIIIoWMcdB0NEfkq5VTWmcOh3+o5HjFJ4I2Tc8xLCfeLNrol1cfNrojjjjZ6hHtdf0E3bqtE004fxL+DUGgzPIO4lpqWl49.bfU2g5p7j.6YDwQWcHRRRRRSKxLOFfuP0cntJONv5FQbMUGhzPAG2Tc0xL+r.+B7+trF3d.fsHh3OWcHRRRRRSOxL2CZNfGyR0sntF2AvGHhXbUGhzfMGDRcsxLWCfKAXtptE003pA1xHhaq5PjjjjjlQjY9Qo4fdLOU2h5Z7WAVuHhwWcHRCl7cto5JkY9V.NUbXSMvcFicricabXSIIII0KHh3rA9f.982pAp2MfuZtTOGO4lpqSl47Abg.uqpaQcM9wQD6T0QHIIIIMXKybI.NYZFtRZfX+hH1+piPZvhiaptNYlmFvlWcGpqPBrGQDGV0gHIIIIMTJy7WA7optC003yDQbxUGgzfAerzUWkLyCFG1TCLOIMueMcXSIIII0yKh3SCLpp6PcMN5Ly2a0QHMXvSto5ZjYt0.mP0cntB2MvmNh3xpNDIIIIogSYleMfCAXVqtE05c6.enHh6r5PjlQ33lpqPl4ZB7GAl8hSQseWOvlGQbqUGhjjjjTExL2BfiAuI00T2eG38EQ7bUGhzzKerzUqWl4hCbR3vlZp6RF+3G+l5vlRRRRpeVDwoBrA.OP0snVuUG3HqNBoYDdxMUqVl4r.7mva9OM08KiH15piPRRRRpsHybEo4lTeEptE05sGQDeupiPZ5gmbS0ZkYF.+PbXSM0cDNrojjjjz+oHhqGXiA7cQulZN3LyMt5Hjld3I2TsVYleEfin5NTq29FQbPUGgjjjjTaVl44ArIU2gZ0dPf0IhXrUGhzzBG2TsRYlaDvYALaU2hZsdQfuXDwwUcHRRRRRcCxLOVfsq5NTq10SyELz+t5PjFn7wRWsNYlKEvQgCapWcOAvmvgMkjjjjF3hH9B.Gb0cnVsUD3HxLm4pCQZfxwMUqRl4bB7q.V7paQsV2OvlDQb1UGhjjjjT2lHh8A3qWcGpU6yBrWUGgz.kOV5pUwGSBMUb6.erHhan5Pjjjjj5lkYtM.+TfYs5VTqzKBrkQDmQ0gHM033lp0Hyb2A9dU2gZstZfMOh3NpNDIIIIodAYlaFvu.XtqtE0J8Hz7927VpNDoWKNtoZExLWOfKD+cMTSY+YfOYDwCVcHRRRRR8RxLe+.mFvBTcKpU5pAVuHhGu5Pjd033lpbYliD3u.rXU2hZktfHhMo5Hjjjjj5UkYtp.mM9yjoorSHhXapNBoWMdgBoRkYN6.mB9ODUSYmjCaJIIIIMzJh3pA1PfwTcKpU5+NyzKXH0Z43lpZeWf2U0QnVoiOh3yVcDRRRRR8ChHtIfOBv0VcKpUZ+xLeeUGgzThOV5pLYledfed0cnVoiHhXmqNBIIIIo9MYlKJv4.7NptE05bO.umHh6t5PjlbdxMUIxLWYfCq5NTqzA5vlRRRRR0Hh39F0nF0OC3xptE05rX.Gel4rTcHRStYt5.T+mLy4C37.V7paQsN6aDw9WcDRRRRR8yt3K9h+GiXDiX1Vq0ZsdIfko5dTqxRBLq6+9u+WR0gH8x7wRWCqxLmYfiC3yUcKpUIA1qHhQUcHRRRRRZRxLOGfOb0cnVkWB3yDQ7qqNDIvwM0vrLyuJvOr5NTqyWNh3npNBIIIII8+Ul4Y.7wqtC0p7H.u6Hhau5PjbbSMrIy78B7aAlipaQsFIvWHhvKVJIIIIoVrLyiCXaptC0pbU.qcDw3qND0eyKTHMrHybgANVbXSMISD3y6vlRRRRRseQDedfio5NTqx6.3fpNBIG2TC457d17mBrzU2hZMdNfuXDwITcHRRRRRZfIh3KA3gSPStcIy7yWcDp+lOV5ZHWl4d.bnU2gZMdIfOYDwnqNDIIIIIMsKy7n.1gp6PsF+KZd7zu4pCQ8mbbSMjJyb8ANOfYs5VTqvK.rEQDmY0gHIIIIooeYlGNvtTcGp035AVqHhmt5PT+Gerz0PlLy2.M2L5Nro.3Yn4croCaJIIII0kKhXWANpp6PsFqHv2MyzCQmF14+kNMjIy7b.9vU2gZEddfOSDwoUcHRRRRRZviOh55U3yEQbhUGg5u3I2TCIxL2cbXS03Eo4croCaJIIII0iIhXGo4I1SBfuWl4xWcDp+hmbSMnKybMA9y.yR0snx8b.aSDwoTcHRRRRRZnSl4OBXmptC0JbEzbAC87UGh5O3I2TCpxLWPfiAG1TvD.9hNrojjjjTuuHhuBvOu5NTqvZ.b.UGg5e3I2TCpxLOQfOS0cnVgsJh3WUcDRRRRRZ3Sl4wArMU2gZE1jHhKn5HTuOO4lZPSl41CrUU2gJWRyshtCaJIIII8+i8tuiVxJqRXi+rEPnIojFBRPDQZjfjSRzHneFAQLqiIFyQPGmYDLLJMNJlyiXDU.yfhQTIifJHgljjjfX.EoQR896ONECHdu29Fpp104TO+VqZ08nNvSCzEUsOugwLQD+q.elp6PiD9nYlab0QntOW4lpuHy7gBbp.qb0snRsXfWcDwGp5PjjjjjTcxLOJfCn5NT491.O4HhEWcHp6xUtolyxLmGMOYNGrod8NXSIIIIIEQ7L.9dU2gJ2S.3fpNB0s4vMU+vaEXGpNBUtCJh3HpNBIIIIIMZHhXe.99U2gJ2gjYtsUGg5tb3lZNIy7+Gvqu5NT4daQDGd0QHIIIIoQKG6wdruWfSr5NToVVfiLybMpND0M4Ytol0xLWGfSAXCptEUp2WDwqq5HjjjjjznoLy0B33A15paQk5iFQ7xpNB083J2TyJYl2Gf2ONXywce.GrojjjjjlJQDWGviC3bqtEUp+sLymU0QntGW4lZVIy7k.7wqtCUpOcDwKp5HjjjjjT6Pl4FC7MA1zpaQk4Z.1kHhqn5PT2gC2TyXYlOLfeLvpVcKpLeiHhmR0QHIIIIo1kLys.33.VupaQk46A7DhHtipCQcCtsz0LRl4RA7QvAaNN6G6fMkjjjjzrQDw4B7T.9iU2hJydC3Yuo5ab3lZl5P.1kpiPk4LV3BW3qs5HjjjjjT6UDwYAru.Kp5VTYd2YlaS0QntA2V5ZZKybmA94.KU0snRbd.6SDwUUcHRRRRRp8Ky7oB7U.V5paQk3W.7viHtspCQsatxM0zRl4pC7IvAaNt5xA1OGrojjjjj5WhH9Z31Sdb11AbnUGgZ+b3lZ55sBr4UGgJwM.r+QDWX0gHIIIIotkHhOIvAWcGpLuwLSO56zbhaKcsD0aqB7UwUs43naC3wDQ7SqNDIIIII0ckYt.f2X0cnRbg.6VDwen5PT6jqbSMkxLWCfEfC1bbzhAdANXSIIIIIMnEQbP.e5p6PkX9.ukpiPsWNbSMoxLCfCGXiptEUh2PDwWp5Hjjjjjz3gHhWDvwWcGpDupLymX0Qn1I2V5ZR0a6nerU2gJwgFQbHUGgjjjjjF+jYdlzbYynwKWBvNDQ7mqND0t3J2TSnLyGDv6q5NTI9PNXSIIIIIUnmJv4UcDZn6AC795sKRkl17efQSnLyuBv9WcGZn66FQ73pNBIIIIIMdKybq.NNf0o5VzP2SMh3qWcDp8vgap+IYlOWfOa0cngtyHhXGqNBIIIIII.xL2YfeBvxVcKZn5Jn41S+ppND0N31RW+CxLWeZtcz03kK.3.pNBIIIIIo6RDwoB7bAVb0sngpM.3cTcDp8vgap+OYl2GZFr4ZVcKZn5O.7LiH9sUGhjjjjjz8TDwWE3MWcGZn6YmY5BvQSKtsz0+G2N5iktCf8Ih3GVcHRRRRRRSlLy2Cvqu5NzP0UBriQDWW0gnQatxME.jY5x9d7zqzAaJIIIIoQcQDuAfis5NzP05C7tqNBM5ygap6x6AX8pNBMT81hH9XUGgjjjjjzzQDw9A7KptCMT87xLeJUGgFs41RWjYtu.eUbX2iSNxHhWP0QHIIIIIMSjYtQ.+.fMr5VzPyEBrmQDWe0gnQSNbywbYlqMvYB7.ptEMzbZQD6b0QHIIIIIMajYtS.eefUp5VzPymJh3EWcDZzjqTOcH3fMGmbg.O0piPRRRRRZ1Jh3z.ddU2gFpd9YlO5piPilb3liw5ctU7hptCMzbC.O6Hhqs5PjjjjjjlKhH95.Gb0cnglkF3ClYtxUGhF83vMGSkYd+n41Q2+YfwC2IvKNh3rpNDIIIIIo9gHhE.7IqtCMzrI.uwpiPid7L2bLUl4G.3UVcGZn40EQ79pNBIIIIIo9sLye.vip5NzPwcBr6QDmR0gnQGNbywPYl6.vIArLU2hFJ9vQDuhpiPRRRRRZPHybco4FTe9U2hFJNMfcKh3NpNDMZvsj7XlLyU.3igC1bbwOxAaJIIIIotrHhqF3YBbiU2hFJ1If2T0QnQGNbywOuZfst5HzPwBA9WqNBIIIIIoAsHheIvARy1VVceGTl4lTcDZzfC2bLRl4liOciwEKB3YDQbkUGhjjjjjzvPDwWglKNW08sR.efLykp5PT8b3liW9en4M.T21hAdI8dxkRRRRRRiMhHNDfOW0cnghGCMGGAZLmC2bLQl4ygleiu59N7HhuX0QHIIIIIUgErfEbp.tXOFObn8tPozXLuszGCjYtN.mEvZUcKZf6XhHdZUGgjjjjjTkxL2XfSB3eo5Vz.2mIhv6ahwXtxMGO7VvAaNN3b.7lQWRRRRRi8hHtXfmOvcTbJZv6YmYtGUGgpiqbyNtLyGMv2EvCY2ts+HvdFQ7apNDIIIIIoQEYlGDvgUcGZf6b.1oHhao5PzvmqbyNrLykF3+FGrYW2cB7JbvlRRRRRR+ihHV.vQVcGZfaKAdYUGgpgC2ra6f.1tpiPCbuqHhub0QHIIIIIMJJh3E.btU2gF3NjLy4WcDZ3yskdGUl4FRykHzpTcKZf5XiH1upiPRRRRRZTVl4lB7i.V6paQCTekHhCn5HzvkqbyNnLykB3cgC1rq6hAdUUGgjjjjjzntHhK.30Qyw5k5t1+LymV0QngKW4lcPYlOAfuU0cnApEAr6QDmU0gHIIIII0VjY9VANjp6PCTKDXq8xEZ7gqbyNlLyU.3cVcGZf6M5fMkjjjjjlYhHNTfiq5Nz.0l.7lpNBM73vM6ddC.aQ0QnApOdDwGo5HjjjjjjZodo.WV0QnAp2fWtPiObao2gjY9fANMfUq5Vz.yYEQrcUGgjjjjjTaVl4tCbB.KW0snAluJvyHhXwUGhFrb3lcDYlAvwB7TptEMv7W.1oHhKr5PzbSl4CBXk.tez7vHVQf4AbeAVpd+O6NAtMf+NvsPye++OSy4s5MufErf89fO3C9iMjSWRRRpyJybcn4ynsBz74yV0d+765yoszz7cnWLvsSymSaQ.+IZ9bZ2DveIh35G5wqYkLy2HvBptCMPs+QDGc0QnAKGtYGQl4iA36g+8ztpEC7riHNppCQSe81FDaKvNArt.qIv+Bvp.rxz7AjmotEfaF3u160MvcO3yKG3RnYK1bcQDW8b6WARRRRcGYlaJvC.XiA1Hf0llgYtF.2eZ97Y20vMmoGgaIMC27FA9C.WKv0C7K.NyHheQe3WBZ.Hy7yA7bptCMv7a.1Aubg51bPXc.YlqHvofm0lcYGQDwqs5HzzWl4NC7Cn4CGWgqmlOT8kB7qnYnmWCvUDQbEE0jjjjz.Wl4V.rAzLHysDXy58yWap4ylk.u5HhOXA+4VKAYlqIvOC3gTcKZf4PiHNjpiPCNNbyNfLy2Dv6p5Nz.yIGQrqUGglYxL+z.+qU2wD3FnYEd9qn4gh7KO1i8XWq8a+1uSnzpjjjjlExLWeZVIl6HM6VlGBvFRy1HeTxuNhXqpNBMwxL2VfeJ0svDzf0eCXqiHtjpCQCFNbyVtLyMjlATrVU2hFHtAfGdDwEWcHZlIy7xoYECLp61AtZfqC3jn4C0ctQDWYoUIIIIMAxLeX.aOviB3gxcer+Lp6N.1sHhSq5PzDKy7UCbDU2gFX9RQDOqpiPCFNbyVtLyu.f+FztoEC7zhH9ZUGhlYxLet.e1p6XN32Cbt.mCvoQy4D0us1jjjjz3ndqntGNv1.r0zLPyYy4V9nfOSDwn3N6Q8jY9Y.d9U2gFHRfmPDwwUcHp+yga1hkYtSzb1frLU2hFHd+QDulpiPybYl+Df8r5N5i9izrM1OdfSYgKbgKZ9ye9mSwMIIIoNnLy65x94wCr6.aNy7K2mQU+IfsHh3ZpNDMw5c9adh.yu3TzfwoAr6QD2d0gn9KGtYKUl4x.7yo4rkQcOmZDwtTcDZlKybyANa51OzgqfliCiuNvoDQ76JtGIII0h06B.5QC7jo4B.59UaQCTOiHhub0QnIWl41Qyw0zxVcKZf30DQ79qNB0e4vMaoxLed.GY0cnAh+DMOMoyq5PzLWl4a.3vqtignqE3hA9N.e2HheSw8HIIoVfLyGAv9Sy4m4lw3yfjNgHh8t5HzTKy7MBrfp6PCDWMv1DQbCUGh5eb3lsPYlqNvISyMAn5dd9QDs4yqwwZYlmMMmGTiitUfSG3XoYEc9KJtGIIIMh3vNrC6.OnC5f9s.OVf8FXSKNopbK.6bDwut5PzTKy7qSypIVcOu2HhWe0Qn9GGtYKTl46F3fqtCMP7YiHd9UGglcxL2SfeL9dq2keAvw.7shHtfpiQRRRCeYl6JMW.pOVfMr3bFUbnQDGR0QnoVl4FPy4u4Cr1Rz.vsArstqy5N7Kf2xjY9foYkQspU2h56NuHhMu5HzrWl46E30VcGiftYfymlAc90iHt3h6QRRRCPYl6LvyF3QfWLKSjSOhXmpNBsjkY9X.Ngp6PCDGcDw9WcDp+vga1hjYF.eIfCn5VTe2sB7HhHNkpCQydYlmKM2nmZx8Wn4If+cANtHhqt1bjjjT+Pl4Cilsv6dC3f6lZ2IvCOh3zqNDsj4NmryJAdpQDeipCQycNbyVjLychlasskp5VTe2AGQ3AVcKVl4illmpquu5z20A7s.NpHhSr3VjjjzLTl45Ary.u.f8hwmKEn9gORDwKu5HzzSl4YPykek5VNMfcKh3NpNDM23WBukHyboA9Q.6d0sn9teXDwit5HzbSl4mE34VcGsXKD3KPyfNuzpiQRRRStLycD3EA7D.Vyhyos5RiHdvUGgldxL2BfeJvpTcKpu6.iH93UGglab3lsDYlOKZ9h+pa45n41R7xqNDM2jYd4.aP0czA7mANCfOSDwWo5XjjjTidqRyC.3oArc32kbt5N.1yHhSt5PzzSl4AB7QqtC02cE.aUDwMVcHZ1y+ERs.Ylq.MKWZOK+5VVLvyOh3yWcHZtIybu.9A3QFQ+1E.7ko4RH5bqNFYjMexD...B.IQTPTIIowQYlOBf8C3ohqRy9M2Z5sLYleYfmd0cn9tCMh3PpNBM64vMaAxLeS.uqp6P8cerHh+spiPycYlebfWR0czg82A91.ezHheR0wHII00kYtl.OZfWFMmolZv3phHV+piPSe898FmNtis5Z9a.aSDwEWcHZ149Tc.ZpkYtF.upp6P8cW3BW3BcKMzAbXG1gcf.6S0czwsbzrE39wYl+lLyWyEdgW3VVcTRRRcMYlaRl46A3LA973fMGzVuLymT0QnouHhqG3URytvScGqHvqu5HzrmqbyQbYl+O.utp6P8UKFXOhHNopCQycYlOFZtkz0v0kC7M.9rQD+phaQRRpUKybuAd1.OQfUp3bF27diHbnJsLYlePfWQ0cn9pailaN8yn5PzLmqbyQXYlaFMaED0s7dbvlcJ6d0ALl5AB7Z.NsLyuTl4dVaNRRRsOYlOsLyeFv2E3YgC1rBO1pCPybKXAK37.Nmp6P8U2WfCs5HzriqbyQXYleFfme0cn9pyJhX6pNB0+jY9qAbKROZ3zANhHhub0gHIIMpp2YF3yA3kB7fKNGAIvizyU71mLycB3jwEMVWxcBr2QD+vpCQyL9aBGQkYtGzr0PT2wh.NvpiP8O89.MaV0cn+O6HvQkY9qyLeidtbJIIc2xLePYlGAvY.b33fMGUDzbSzqVlHhSC3sWcGpuZo.NrLykq5PzLiqbyQPYlAvwCr2U2h5qN3HhETcDp+Iy7+F3MWcGZRcE.eZfuPDwus5XjjjpPl41B7uBb..qZw4nI1uJhXqqNBM6jYdl.t675VdFtavZWb3lifxLeb.eabk01kbxQD6Z0Qn9qLyeLvdUcGZI5ORyPN+eiHVX0wHIIMLjYtC.uJZFp4RUbNZpcy.6bDw4VcHZlKyba.9Y.qP0sn9lyGXGiH9aUGhldb3YiXxLWVf2A92a5R9qz7zxUGRl4F.3SXucX0.NHfyLy7SkYN+pCRRRZPIyb2yL+t.mBMWRPNXyQeq.tq8ZshHNaf2V0cn9pGJvKp5HzzmCPazySAGXRWygFQbQUGg561Kf6e0QnYjUB3ERyMr9mKyzsOjjj5LxLeRYleOfeJMCJygZ1t7jpN.M6063G6mWcGpu5UjY5Q4QKgC2bDRl4JA7eUcGpu5DhHduUGgFHdhUGfl0tezbKwdxYlexdmEYRRRsRYl6SugZ9M.drU2il0dXYlOnpiPyIGHMWhrpaXi.dsUGgldb3liVd1.aZ0Qn9laD30WcDp+q2sv8NTcGZN69Ry1M4zxL+hYld6pKIoViLyGYuy+6iGGpYWvJBrEUGgl8hHNeb6o20bfYlqU0QnkLGt4HhLy6G9TA5ZdyQDmW0Qn9uMYS1j4Ar1U2g5aVZfmINjSII0BjY9nxLOQfeHdwF1034tYKWDwgQyQCg5FVcf2P0QnkLGt4niWNvFWcDpu4mEQ7wpNBMv7Hv2+rKZdzLjySIy7CmYtYUGjjjzcIybuxL+p.+.f8n5dz.win5.TewqCvaY6tiWZloypYDme47Q.YlqBv+V0cn9l+LvKo5Hz.09Tc.ZfZE.dY.mdl4BxL2npCRRRiuxL2lLyiB3GC7zptGMP8fyL24piPyM8t8zOjp6P8MqHvqn5HzTygaNZ30.rtUGg5adWQDKr5HzfQl4FB7vptCMTrB.uQfyLy7vVzhVz5WcPRRZ7Ql4FmY94ANEfCn5dzPw8AOpA5DhH9e.Nyp6P8Mu3LyMu5HzjygaVrLy0C3UUcGpu4GEQb3UGgFn1dfUt5HzP0p.bPyady6mmYdnG1gcXGX0AIIotqLy0Oy7CBbFzbgitrEmjFt1lpCP8MuHfat5HTew7.N3piPStn5.F2kY9N.dKU2g5KtMfsOh3bpNDM3jY9t.dSU2gJ0kRyJz9SWcHRRp6HybMAdg.uZf+khyQ04JhHdfUGg5OxL+uAdyU2g5KtYfcNh3bqND8OyUtYgxLev3Y2PWxayAaNVX2pN.UtMB3SkYdNYlO0piQRRseYlGHMag02INXywcqel4tWcDp+Hh3eG3rqtC0WrB.+WYltHAGA4vMq0qD39UcDpu3bhHdmUGgFrxLef.dVqn6xV.brYl+D+RHRRZ1Hyb+yL+U.eTf0q5dzHg.3wTcDpu50Cr3piP8EOIfsp5Hz+LGtYQ5c669rptC0Wba3sc+3hsGefD5e1dB7CyLOROnwkjzzQl4iHy73.9J3EUn9m4kJTGRDwIB7dqtC0WrL3Yu4HIGtYcdS.qV0Qn9hOVDwoTcDZnXOqN.MxZY.dd.mZl4aMybcpNHIIM5Iy7gjY9wA99.Otp6QirV+K7Buvsr5HT+yBVvBtTfKu5NTewSKybOpNB8OxyJfBjYtw.+RZNyFT61EuvEtv8a9ye9dVaNFHy7rvavRM8bMzboC8gpNDIIMZHy7sB75.V4paQi7tSfcIh3LpND0+z6rZ+nwEYVWvWKhXeqNBc272TUiWGNXythWuC1b7Pl4ZA3pwSSWqCvGLy7LxLehUGijjpSl4KIy7hANDbvlZ5YovKwxNmHhuFvwTcGpu3IjY5uGcDhC2bHKyby.dNU2g5K9ZQDe6piPCMOX71KUybaOv2r24w4lUcLRRZ3Iy7gmY98.93z74HjlI10pCPCD+m.We0Qn4rkA3M6Mm9nCGt4v2+FtpM6B9C.u1piPCU6J9dlZ164AbFYlGR0gHIoAqLy0My7S.7y.drU2iZs15pCP8eQDWDvgVcGpu3QArcUGgZ3WTeHJybS.dtU2g5KNrHhqr5HzP0Cu5.Tq2xC7VyLujLy+0piQRR8eYluIfyD3EieWKM2rdYl6P0Qn9uHhOJMuOgZ2VFf+iLSeu9Q.tDZGhxL+j.unp6PyYmbDgaSjwLYlWNvFTcGpS46B71hHNspCQi2xLWSf6KMeH86auWKcuWKGMCled89ueY58e9c8+9k5d7Zo68iPyPcBt6OqYdudsXZtvLtSf6n2Od62qW2AvsBrnduti6w+421c8ZgKbgqgm+0pZYlOYf+Cfss5VTmxyIh3KTcDp+Kyb6.NYZ92mp1qECraQDmR0gLtygaNjjYtA.mMvpVcKZN4NAdjQD+zpCQCOYlaOvoxc+k1k5WtUfi.3CFQ76pNF0dbXG1gcfGzAcPGGMG0MKK28fGWQfUA39060JeOdsR2ie9xdOdceuWutqAXNJtRDtqAbdWC27t942ZuW2FvMC7W.todu9q2iW2Xu+6tQf+N28fRWDvhhHttg3uVTGPl4FA7V.dAU2h5jVPDwAWcDZvHy7iPywVmZ29hQDO6piXbmC2bHIy7CA7xqtCMm8AhHd0UGgFtxLO.fip5NTm10B7uGQbjUGhpSuUP4J060pArVzbQlsF.qduWqIMCvbEtGutqgapYtESy.RWDMCE8td8m.98zbFa+G.tgd+eeszaPoQDWUEAqQGYluYf2HMOPAoAgSJhvaj4Npi4XNlG69tu66GFXiptEMmbm.6p6FqZ4vMGBxL2PfygluLhZu9c.aaDg2tciYxLOTf+qp6PiE9w.u4Hhyn5PT+Sl45RyvOVcf0E3Az60ZSyvJmGMCn7dtBKckhO55VnYEg9WnYPn2Vue90BbM.Wcue72A7mW3BW37bay2sjY93.d6.aS0snNuqGXqbUk2ckY97.Nxp6PyYGcDw9WcDiyb3lCAYluCZ1tJpc6EEQ7oqNBM7kYdb.Otp6PiMVDvGXgKbgGkCDYzWl4CflUY4ZCr9zrUvWMf0ilyo2Uilijl6OMCvTiW9q.+YZVInWCvU16G+qzrZPuBfaXAKXAOxC9fO3OVYUpokLyGHMel9WH98nzvQBr6QDmT0gnAmLyuCviu5NzbxMCryQDma0gLtx+kxCX89PP+BZ9xMp85DhH16piP0Hy7x.1vp6PictTfCNh3XqNjwYYlqEM67hUF3ARy6ErQ.OHf0glUi4pgWH.Z16uPyvOuAfKmleu+k06GudZNKPu7phSMxLOPZVslqd0snwNu9Hh2a0QnAmLyGJMyLXdU2hlS9R.O6Hhr5PFG4vMGvbUa1Ib6.6RDwun5PzvWl4CC3LvAWn57MAdsQD+1pCoKq2kBxZArw.yG3gPyvLWCt6gaNJdA6nts65lh+FAtNfKFXg.WHMCB85c6pNXkYtC.uOfco5VzXqiLhvKrpNtLyCC3fptCMmrHfsIhXgUGx3HGt4.Tl4pQySf4AVbJZtwaovwXYlOKfuP0cnwd+df2aDwgUcHsY81B4qCMCtbSnYEXsgzr8wWK7RAQsK2AMC77tF540Ry474E.b4QDWPgs0IzaXCuBfku5VzXseXDwit5HzfWl4EQyCYUsWenHhWY0QLNxgaN.kY9uC7NqtCMm76.1NWUDiuxLeO.u9p6PpmSB3MEQbxUGxnrdWje20sM9lCrEzLPy65R7QpqaQzbwFcU.mGMWrkKD3FW3BW38wyy2oVl4SD3cCroU2hDvkEQ3so8XfLyW.v+a0cn4jaBXGhHtvpCYbiC2b.Iybk.NWZVMHp85EDQbjUGgpSl4w.ruU2gz8vhA9ehHbqKAjY9Pn4Lv7gAr0zLDy0COqqklH+cZVomWAvuA3r68iWQDw0WYXiBxLWSf2Cvyt5VjtGtUfs2KpjwCYlGOv9TcGZN4cGQ7lqNhwMNbyAjLyWDvmr5NzbxoFQ34qzXtLySGXGptCoIvEC7VhHN5pCYXn2PG1PZVEl6.MqnpUAXco47vTRydWMMG+EWOMGoRmEvEMNs01yLeY.uIZd3HRiZd5QDe0piPCdYlaGvoArTU2hl0tVZ18mWS0gLNwgaN.jYtx.mNMWHApc51AdjQD+7pCQ0Iybso4K341XUix9j.GZDwuq5P5WxL2.t6yEyGDv1QyfMW2J6RZLyMSykVzEB7q68yOuHhytvl56xL2bf2AvSp5VjlBuyHh+ipiPCGYlGAvqt5NzbxaOh3+p5HFm3vMG.xLO.fip5NzbxmHh3kVcDpVYlaOvohO4TM56Z.dyQDetpCYlp2k7yZPyYi4CGXanYPlqQkcIoIzhAtLfKhlGj+oBbks0aF1LyWKvgBrRU2hzRv2Lh3IWcDZ3XQKZQq+7l27NMbAVzlcE.aQDwMUcHiKb3l8YYlKEvOGXmqtEMq8mn4P.9RqNDUqLymKvms5NjlA9N.uxHhKu5PlLYlyGXy.1cZ1d4a.MCxboqrKIMqcSzrk1u.ZtzyNMZ1R6WaoUMExL2FfOHfG+Pps37iH1rpiPCOYluJf2e0cn4jWVDwGs5HFW3vM6yxL2WfiF+qssYutHh2W0Qn5kY9tn472RpM4ZAN7Qg2Gq24j4lBrq.aEMqHyGBvJVYWRZf62AbI.WJvo.bVQD+pZSpQl4aG30fuOjZW9yzbF9cYUGhFdxLOMfcr5Nzr1ESyhl5FqNjwAN.t9rLyeBvdVcGZVagQDdVoJ.Hy7afmAWp85aQyVU+7GV+Ir2pxbio4R+Y2oYEZ54jojtSt6ytyeJMeguENLurExL2EZtIzc2Uo1nESyMldm5LuUSsLymDvWGmaSa1yJh3KUcDiC72jzGkYtCzrcbVlpaQyJKllahvio5PzngLyyklyAPo1pahlAb9gGD+AOybCo42i7nnY6cNeb0PIoomqA37A9I.mHvkFQb8Ch+DkY91nYmX3mQWsY6cDwITcDZ3Jy7n.Nfp6PyZmJvtEQbmUGRWmC2rOJy7nA1up6PyZeuHh8o5HzngLy0A3WfGj2pa3GQyYw4ELW9CRl4CllK7m8AXqoYUYtZy87jj3pnY6r+yn48rNmHhqat7GvLycC3Hn48sjZ6d0QDefpiPCWYlaFMemjkq5VzrRBreQDespCoqyga1mjY9PANK7McZqtcf8Jh3jqNDMZHy7gPyGjvaPU0U7m.9OiH9HS2+eHybinYn.aEvtQy1M+9OXxSR5evUCb1zrpWNGfydlLrydqVyWOvxOXxSZn6CFQ7ppNBM7kY99n4rBVsSemHhmP0Qz04vM6SxLeOz7AnT6zmHh3kVcDZzQl4NRyWnx2mTcMeKf2PDwEeu+un2E.zF.7HA1aZFl4pLbySRZBc0.+RfiC3zmrKnnd+6u+..6vPrMoggSHhXuqNBM7snEsn0edyadmJv5TcKZV4uCrKQD+xpCoKyuzdePl45SyG1ZUqtEMq72.15HhKo5PzniLymIvWr5NjFP9yzbVb9w6cI.sG.OQfsDu.fjznu6.3JnYGV7c.NoHhKOy7+B3fwUqo5lNuHBOK3GSkY9FAVP0cnYsuTDwyp5H5xb3l8AYl+G.u8p6PyZ+WQD92+z+fLy2Bv6n5NjFv9k.ODfUn5PjjlC9Czbdc9vpNDoAneOv1FQb0UGhpQl4ujliJH09bS.aUDwkUcHcU2mpCnsKybE.dlU2gl0tLGrolDaX0AHMDr03fMkT62piC1TceqJvZTcDpT98VauVIfmW0Qzk4vMm6d5.aZ0QnYsCu5.zHKGtojjjjFUrz3QGyXsd231mX0cnYsWRl4ZUcDcUNby4fLykA3kUcGZV6ziH9XUGgFYslUGfjjjjz8vCr5.T4NXfau5HzrxZAruUGQWkC2btYWvy7h1rCq5.znoLy0AugnkjjjznEW0Wi4hHNCfuQ0cnYsWPl48s5H5hb3lyRYl2GfWMvRUcKZV4DhH95UGgFYc+wgaJIIIoQKqT0AnQBGBveq5Hzrx1B7DqNhtHGt4r2VB7DpNBMqbm.+mUGgFos5.yq5HjjjjjtGt+UGfpWDw4C74ptCMqcfUGPWjC2b16YPyg5rZeN1Hhyr5HzHsGP0AHIIIIcu3EJjtKuWf+X0QnYkcIybKqNhtFGt4rPl4ZB7bptCMqbSzrL9klJtkzkjjjznlUu5.zngHhKE38WcGZVYd.u7pinqwgaN67b.V6piPyJGcDwETcDZj2JTc.RRRRR2Kq7EdgWnq3KcW9D.+9piPyJGPl45WcDcINbyYnLykhlsjtZetQf2c0QnVAOr1kjjjznlUZS1jMwKRFA.QDWO98aaqVYfmT0Qzk3vMm4dr.aU0QnYkOZDwEWcDpUXMpN.IIIIo6kUBXEqNBM5Hh38AbIU2glUNvLS2wf8INbyYfdqZyWA9W2Zi9izrr8klNVspCPRRRR5dYY.teUGgF47ApN.Mq7PAdhUGQWgCoalYS.1ypiPyJu2HhKu5HTqgqbSIIIIMJxOmp9GDQ7AA9UU2glUNfpCnqvgaNy7bo4lsRsKWSDw+c0QnVk6e0AHIIIIMAV0pCPijdeUGflUdLYldIg0G3vMmlxLWSZFtoZe9vUGfZc7gXHIIIoQQqR0AnQOQDeNfeY0cnYrkC3kWcDcANbyou8CXsqNBMicwtpM0LQuGjgC2TRRRRihV4pCPirdaUGflU12deGTMG3vMmFxLC7rPns5iUc.p0YYo4InIIIIIMpwiOIMghH9F3p2rMZ0.9+UcDscNbyomcEXmqNBMicIKXAKXQUGgZclGvxWcDRRRRRSfUp5.zHs2IPVcDZF6eKy79VcDsYQ0AzFjY9EAdlU2glwdwQDeppiPsK8NPmOS.+WtHIIIoQMGcDw9WcDZzUl4IBrGU2glwdrQDe+pinsxUt4RPl4ZC7XqtCMicYNXSMKcewAaJIIIoQStxM0RxGp5.zrhOzh4.Gt4R19QyYffZWVP0AnVKuLgjjjjznJuPgzTJh3X.Nwp6PyXOkLy0q5HZqb3lSgLyU.3kVcGZF6BiH93UGgZsVgpCPRRRRZRrrUGfZEN7pCPyXqJvys5HZqb3lSscCXypNBMi4fM0bgWlPRRRRZT0RWc.ZzWDwwSy8HfZW1+Lyko5HZib3lSsmd0AnYrKJh3HpNB0p4SCWRRRRipVppCPsFuMfEWcDZFYy.10pinMxgaNIxLeP.O4p6PyXuupCPsd9zvkjjjznJ+N7ZZIh36.bVU2glQVJfCr5HZi7MFmbOCf6e0QnYjq5Vtka43qNB054vMkjjjznpn5.Tqx6u5.zL19jYtgUGQaiC2bBjYtT.Okp6PyX+OK+xu7WY0QnVOGtojjjjFU4vM0zVDwWD3LptCMirR.6S0Qz13vMmX6FvVUcDZF4ZiH7oRo9Ae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p5B6NBMZ3EJzTfjro.mJ9sLMM6+E3dWU8M5NDIoEhj77ANnt6PRiLmPU0Cr6HjjVUkjCDXe5tCMRcl.aQU0ep6PzngO4lSApp9R.uot6PiT2PfOT2QHIsPjjcF3U2cGRZj5AjjCq6HjjVUjjcDeaRl1cY.6oCaNcywMmdbP.WT2QnQp6PRdmcGgjz7QR1Bf2A9VFHMK3wM2SBkjzXqjrd.uV7MZcZ2GXtGHLMEywMmRTUcw.urt6PibOkj7L5NBIoUGIYi.9X.WmtaQRKZ1mjrWcGgjzUiODvZ2cDZj5hAdUcGgF8bbyoKuGfSq6HzH2xRx8r6HjjVUL2sm7gAbS5tEIsn6.Sx10cDRR+8RxaA3tzcGZj6kVU886NBM54ie8TljbuANQfqc2snQpuKvVVUcIcGhjzUmj7I.1lt6PRs4mAbeqpN6tCQRBfjrKL3ACRS29p.2yppKs6PznmO4lSYpp9B.uqt6Pib2d7ePVRi4Rxq.G1TZV2MA3CeNmy4rwcGhjTRtq.ugt6PibWNCtDgbXyYD9jaNEJIqKvYBbS6tEMxs+UUu7tiPR5uWRdD.e7t6PRiMNxppcr6Hjzrsj7s.1ft6PibGZU0N0cDZwiO4lSgpp9g.ujt6PKJ1ujr8cGgjzesjr9.ust6PRiUdrI4E0cDRZ1UR9n3vlyBtPf8q6HzhKexMmRkxfR0u...f.PRDEDUjqMvoBbO5tEMx8S.d.UUe6tCQRBfjbh.2ut6PRictLFb9a5EfojVTkj8A3.6tCsn3YVU8V6NBs3xmbyoTUUWNvdxfODolts1.GQ2QHIAPRN.bXSIck65.bnd9aJoESIYa.d0c2gVT7kAd2cGgV74St4Ttj7lAdlc2gVT7AqpdhcGgjlckjs.3K.rFc2hjFq4mYQRKJRxF.b7.qS2snQt+DCtczOitCQK97I2b52qF376NBsnXmRxKr6Hjzro4dRrd23vlR5Z1NkDG2TRKFNTbXyYEuKG1b1kO4ly.RxiF3C2cGZQwJA1tppOQ2gHoYKI40CrWc2gjlX7iAt2UUeutCQRSmRxG.vaL6YCmOvcqp5W1cHpG9jaNa3iBbrcGgVTrF.uqjrztCQRyNRxChAmyyRRqpVGf2X2QHooSIYuwgMmk7xcXyYaNt4LfppUxfeoy+2taQKJto.G0xW9x25tCQRyLdM3qitjV88fSxSn6Hjzzkjrc.GP2cnEMKmAG+.ZFluV5yPRx9C7R6tCsn4HppdbcGgjltkjWAvKt6NjzDqKhAuJg+3tCQRS9RxFBbh.qU2snEE+dF7ug7c5ND0KexMms7p.NqtiPKZ1wj7J6NBIM8JI2Af8t6NjzDs+C7KHQRCAKaYKa2ANJbXyYIubG1TfO4lybRx8A3jv++8yJtBfcqp581cHRZ5SRNFfGV2cHoIdWAvCpp5D5NDIM4JIGMvip6NzhluNv8np5O1cHpe9jaNiop5yCbvc2gVzbs.diI4t0cHRZ5RRdL3vlRZ33ZAr+cGgjlbkjC.G1bVxkArGNro9ybbyYSubfKn6HzhlqOvQkjaU2gHooJ6a2AHooJaQR18tiPRSdRxthetjYMukppSo6Hz3Ce0jmQkjc.3Ci+uAlkb5UUaQ2QHoIeI44wfaHcIogoeTU051cDRZxQR1TfOGv+R2snEMmGvlWU8y5NDM9vmbyYTUUeDfCq6NzhpMOIevtiPRS1Nmy4b1Xf8p6NjzToadRdocGgjlLjjaGCt.gbXyYK6oCap+dNt4rsWFvOo6Hzhpmf+RCRZgXoKcoaKvZ2cGRZp0dM2fERRWkV9xW9VC7AAt4c2hVTc3UUeltiPie7URdFWR1IfOP2cnEc6bU06u6Hjzjk4N6d+Z.2ftaQRS0dKUU6Q2QHowWdynOS5GBroUUWT2gnwO9japCCvu4iYOusj7.5NBIMwY2vgMkzn2SJIqe2QHowSI40iCaNKZ+cXScUwwMmwUUsRf8D3WzcKZQ0R.duIYi5NDIMYHI2Rfcs6NjzLgqOv9zcDRZ7SRdl3Y+8rnOLCNFBjtR43lhppyC3.6tCsn6lAbDIYM6NDIMQ3oCbS5NBIMy3QjjaS2QHowGI4gAbvc2gVz8K.dIUUWQ2gnwWNto9yd8.GW2QnEcaDCtgAkjtJsrksrcGXG6tCIMS4FB775NBIMdHI2Yf2OvZzbJZw29TU8+zcDZ7lWnP5+ybuhxeQF7p.oYKu2ppco6Hjz3ojr2.Kq6NjzLmeGvcnp562cHRpOIYcANQfaW2snEcGOvC1mZScMwmbS8+op5rAdYc2gZwSNIuhtiPRis1stCPRyjt93SMtjFbdK5vlyd9o.OKG1TqJbbS826fYvSuol87hSxSq6Hjz3kjrs.21t6PRyrdZKe4Keq6NBI0ijbj.aZ2cnV7JqpNmtiPSF70RW+CRxF.bZL3rNRyVtBfGVU0mo6Pjz3gj74.tOc2gjlo8zqpd6cGgjVbkjCF3Y0cGpEeJfGYU0k2cHZxfO4l5ePU02Fu8zmUcs.N7jr4cGhj5WRtW.2qt6PRy7dbcGfjVbkj8EG1bV0uF343vlZ0giapqJuVfOa2QnVbCANzjr9cGhjZ21i2JoRpea9beYKRZFPRd7.GP2cn17hpp9tcGglr33l5JUU0JAdF.qn6VTKt0.ezjrVcGhj5w4bNmyFyfwMkj510B3o2cDRZzKIOHf2W2cn1bLUUu0tiPSdbbScUpp5bA16t6PsY8AN5tiPR8XoKcoaFvZ2cGRRy49mj0r6HjznSR1TfCE3Z2cKpE+DfmW2QnISNtotZM22Zxw1cGpMaVR9ncGgjZwSr6.jj9qbiAd3cGgjFMRxsG3vYv+stlMsu95nq4KG2TqJ1SF7snnYSaaRdOcGgjV7jj0C3NzcGRR+c10tCPRCeIYsYvaL1sp6VTa9vL3o1UZdwwM00npp+GfWb2cnVsKI40zcDRZQy1Cb86NBIo+N20jbW6NBIMz8Q.1vtiPs4GCrWUUWQ2gnIWNtoVU8AYvqIflc87RhmAqRyF1gtCPR5JwZ.7.6NBIM7jjOFvl2cGpMWAvyqp5h5NDMYywM0pj4t8z2afKr6VTqVVRdJcGgjFcRxlguR5RZ7010c.RZ3HIuKfGY2cnVcHUUGY2QnIeNtoVkUU8iA1CF7sqnYWusj7X5NBIMxbe.ptiPR5pvcLI9TdIMgKIKCX25tC0pyA3E0cDZ5fiapUKUUeb.ubYlssF.uuj3qElzzoGU2AHIc03ZgO8lRSzRx9xf2JPM6J.O0ppeV2gnoCNtolO1Wfud2QnVsDfObR1ztCQRCOI4t.bG6tCIoqA2ytCPRyOIYOANft6Ps6.qpN4tiPSObbSsZqp5W.7L.VY2snVcCAN5jbm6NDIMz7v.9m5NBIoqA2gjrwcGgjV8jjcB3MzcGpcmIv90cDZ5hiap4kppSCX+6tC0t+CfOZRVZ2gHoghMq6.jjVErDfGT2QHoUcIYa.de3FDy59M.6VU0k2cHZ5h+fEsPbf.mR2Qn1cKA9DI41zcHRZ9KIqMfOI1RZRwin6.jzplj7P.9P39CBdQUUdD2ogN+gKZdqpZk.OEfeR2sn1sTfkmj0s6Pjz71lCbS5NBIoUQarewpRi+RxV.7A.9W6tE0tkC715NBMcxwM0BRU04.7B5tCMV3NxfANWytCQRyKOztCPRZ0v0GXC6NBIcUKIaBvGC3F2cKpc+.fmYU0UzcHZ5jiapggODv6n6HzXg6FvGu6Hjz7xVzc.RRqltucGfjtxM2k90mBeqPzfKh3cup5R5NDM8xwM0BVUU.1Wfys6VzXgMMImX2QHoUcycofsNc2gjzpo6c2AHo+QIY8A9v3msPC75ppN1tiPS2bbSMTTU8q.dh.+wtaQiEteI4S1cDRZU1l.bc6NBIoUSq+bOcXRZLQRtEL3rUb85tEMV3KCr+cGgl943lZnop5KC7J6tCM13gmjOQ2QHoUI9pcJoIQ+y3QpgzXijrNL3UQeC5tEMV32C7jqp9CcGhl943lZX6.YvgFsD.aSR7L3TZ72l1c.RRyS2gtCPRPRt4.eB7h9R+E6YU02t6HzrAG2TCUUUWNvyE3B6tEM13QjjOX2QHoqbIY8.tsc2gjz7zlzc.Ry5RxZwfyXy6R2snwFGZU06s6HzrCG2TCcUUW.vtBbEc2hFa7DRxg2cDR5J0cD350cDRRySq+bmweRpAKe4KeqANFfMq6VzXiyF3Y0cDZ1x0t6.zzoppiKIGDvKn6VzXicLIW6ppGc2gHo+F9TaJoIY2HfMB3GzcHRyZVwJVw5tjkrj8G3t1cKZrweBXWl6BGVZQiO4lZT5k.745NBMVYG7I3TZrycr6.jjVf7bCVZQVRVykrjkbn3Sro9a87ppNitiPydbbSMxL24u4y.3h5tEMVYGSx6q6Hjz+GuLNjzjNepwjVDsrksrcG3i.rkc2hFq7wppdScGglMUcGfl9kjsgA2bdR+09vUUO1tiPZVVRVefyB351cKRRK.e2ppk1cDRyJRxWB3dzcGZrx2EXypp9kcGhlM4StoF4pp9j.GT2cnwNOlj7w6NBoYbqGNrojl78ukj0s6Hjl1kj0LImBNro9acY.6rCapN43lZwx9BbJcGgF67HRxg0cDRyvtScGfjzPv+Nf2X5RiPqXEqXcANBf6Y2snwNufppSu6HzrMG2TKJppVIvtB7i6tEM14wkjOV2QHMixyaSIMMn.1jtiPZZ04bNmyFujkrjiBXq5tEM14n.N3tiPxwM0hlppuKvSq6NzXoGYRNttiPZFzMq6.jjFR7KqQZDHI+GKcoK8PvWEc8O56.r6UUo6PjbbSsnpp5X.dYc2gFK8.SxIs7ku7st6PjlgbC6N.IogjMr6.jl1jjaECtXXuqc2hF67aXv4r4up6Pj.G2T83kA745NBMVZq1tsa612jrVcGhzztjb6A7+VSRSKVG+BRkFdRxsD3iAb2ZNEMdZepp9xcGgzeV0c.Z1TRVGfu.vso6VzXouBv1TU8S5NDooUI49CbBc2gjzPxkBbWqp91cGhzjtjbaA9T.29taQikdGUUdbyowJ9japVTU8iA1EfKu6VzXo6BvwlDG+VZzYocGfjzPzRvyQXoErjrI.GKNrotx8eC776NBo+dNtoZSU0W.Xe5tCM15N.7oRhmgVRiF25tCPRZH6eu6.jljkj6JvQiucc5J2k.73qp9ccGhzeOG2Tc6M.bXcGgFasTfiII24tCQZJjiaJooM+GcGfzjpjro.GC94CzUtq.3Y6Q+gFW43lpUUUAX2A9lc2hFacq.9zIYy5NDooL2htCPRZH6V0c.RShRxVBbb3EMntp8JppNxtiP5phiap1M2i09NA7S6tEM1ZMYv.mOntCQZZvb2nv2nt6PRZH6V1c.RSZRx1B7IAtAc2hFa8Y.NftiP5pi2V5ZrQR1QF7Jp6+6RcU4RAdRUUGU2gHMIKI2dfyD+EYjzzkuVU0cp6HjlTL2u+0gvfKjKoqLmGvVVUcQcGhzUGexM0Xippi.3U1cGZr1R.N7j7T6NDoIbWOfqe2QHIMjcCm6ISWRWCRxtyfGrDG1TWU98L3BDxgM0XOeB4zXkjbs.9D.OrtaQi0BvKpp5U2cHRShl6Lr8z6tCIogreMvcsp575NDowYIYe.Nvt6Pi810ppCo6HjVU3StoFqTUcE.6BvY2cKZrVA7pRhenLo4m+8tCPRZD35iG2FRWsl6yO6mgVWSdcNrolj33lZrSU0OEXmYv29tzUm8IIu6tiPZBzZ1c.RRi.qAvMs6HjFWkj2Av9zcGZr2o.7B5NBoUGNtoFKUUcV.Okt6PSD10j7o6NBoILNtojlVsVcGfz3nj7Q.7bqWWSNOfGSU0k2cHRqNbbSM1ZtaD6Wd2cnIBO3jbxI4l2cHRSH70RWRSqtwcGfz3jjrlI4j.19taQi89s.OAu.gzjHG2Ti0pp1efOd2cnIB2KfiIIaX2gHMAvwMkzzJG2TZNI41B7oA1ptaQSD1yppuT2QHMe33lZRvt.705NBMQXS.NtjbO5NDowb+acGfjzHh+7MIfjbm.NAf6b2snIBGXU06u6HjlubbSM1qp5WB7D.9Yc2hlHrN.GeRdjcGhzXL+k+kzzpqa2AH0sjr0.GGvsr4TzjgOMvKp6HjVHbbSMQnp5rYvMn9UzcKZhvM.3Cmjcu6PjFSc86N.IoQj+4tCPpSIYGAVNvMo6VzDguIvNWU4umsln43lZhQU0mB3E1cGZhw0A3smjWV2gHMF55zc.RRiH9y2zLqjrG.GN9kXpUM+Lfcpp5m1cHRKTNtolnTUcP.u6t6PST1uj7d6NBowEKe4KeqA9W5tCIoQD+4aZlTRds.uot6PST1opJuaKzTgp6.jVckjqKCdUKdvc2hlnb7.OwppKt6Pj5TRVafyDXs6tEIoQfSqp5d1cDRKlRxGF3Q2cGZhxyop5MzcDRCK9japINUUq.X2.N2taQSTdf.GaR1ftCQpY+Sy8GIooQ9japYFIYsSxIfCapUOuEG1TSabbSMQpp5GCr8.+htaQST1DfOWR1xtCQpQNtojllcs6N.oECIYiXvalz8u6VzDkiEXu5NBogMG2TSr9qtA0WY2snIJ2TfOUR1stCQpINtojll43lZpWRdf.mHvF1cKZhx2B3IUUc4cGhzvliapIZUUGCvyu6NzDmqOv6zaRcMiZMl6ORRSibbSMUKI6Bvm.XM6tEMQ4hAdLUUWR2gHMJ33lZh2bmWHGb2cnINECtI0e2cGhzhLG2TRSy7muooVIY+AdO3YKqV8rBfmbU02p6PjFUbbSMs34B7o6NBMQZWSxolj0s6PjVjTy8GIooQWqksrks6cGgzvVRNTfWZ2cnIRO2ppOS2QHMJ43lZpPU0U.73.NqtaQSj1BfOeR17tCQZQfiaJooY0s41batftiPZXII2xjbx.O9taQSjdYUUuitiPZTywM0TippeMvNAb9c2hlHcq.N1j7X6NDoQLG2TRSypMZi1nKp6HjFFRxcG3y.bu5tEMQ5H.d4cGgzhAG2TSUpp9NL3I37R6tEMQ5eE3Hl67LRRRRSd7KuQSERx1yfaD80q6VzDouDCtYzuhtCQZwfiapoNUUeYFLvY5tEMw5kljip6Hjjjjzrmjru.GEC9h2kVc8s.1gppKq6PjVr33lZpTU0GG342cGZh1NjjuTRV+tCQRRRRyFRxg.b.3SgrletDf+yppKr6PjVL43lZpUU0qCXYc2glncO.Nwjr0cGhjjjjldM2EGzo.7j6tEMw5OxfmXyud2gHsXywM0zt8E3CzcDZh1ZC7ekj8p6PjjjjzzmjbOANEf6Y2snIVWAvSop5T5NDoN33lZp1bGfxOUfSt6VzDsqMvqOIuutCQRRRRSORxyD3SAby5tEMQaeqp9fcGgTWbbSM0qp5OB7nA7wyWKTOojbpIwasRIIIIsfjj2HvaF3FzcKZh1qspxiiMMSywM0LgppKAXGAtftaQS71BfOumCmRRRRZ9HI2rjbb.6Y2snIdGIv9zcDRcywM0LippuCCdBN+sc2hl3sl.epjrucGhjjjjlb7Wc9Z9.6tEMw6T.dhycTrIMSywM0LkppyBXG.t7taQS7VCfCHIenku7k6SwojjjjtZkjmFvmA3V1bJZx2WE3QWUcYcGhz3.G2TybppNNfmAvJ6tEMU3+b61tsaYI4N2cHRRRRZ7TRd8.uMfqe2snIdW.viqp5h6NDowENtolIUU8t.dwc2glZrI.etjrycGhjjjjFejjaYRNUf8p6VzTgeEvipp5b6NDowINtolYUUcf.uxt6PSMtA.u2j7V6NDIIII0uj7v.NMFbgTJsP86A11ppud2gHMtwwM0rt8C3c2cDZpxSOIetjrdcGhjjjj5QRdg.ebf0t6VzTg+DvtUU846NDowQNtoloUUEfmJvQ2cKZpx8A3jSxNzcHRRRRZwSRVyjrbfWECt.JkFFdlUUGQ2QHMtxwM0Lu4F37I.bhc2hlpbS.Npj7Z6NDIIIIM5kjMC3TA1ttaQSUdAycmQHoqBNtoDPU0J.1Qfyn6VzTmmaRNojrztCQRRRRiFI44Bbr.21taQSUNvppk0cDRi6bbSo4TU8yA1dfuU2snoNaECtM08awWRRRZJx4bNmyFmj2GvqkAWvjRCKusppWX2QHMIvwMk9qTU8i.1Vfyu6VzTm+Cfk6qotjjjzzgjb2V5RW5Q.7j5tEM04v.1itiPZRgiaJ82op56xfmfyKp6VzTomaRNsjrAcGhjjjjleRxyF3D.7yzogsiC3IWUcEcGhzjBG2T5JQU0+MCF37+s6VzToMG3ymjmX2gHIIIoUcKe4KeqSx6E3M.7+q6dzTmSC3QWUcYcGhzjDG2T5pPU0oCrC.+gtaQSktI.u+j71V1xV1t2cLRRRR5pWR17sa61tWGvN2cKZpzYArcUU+ltCQZRiiaJc0np5DA9OAt7taQSsdZ68du2OyjrYcGhjjjjtxkj8F3DA1vtaQSk9NLXXyKo6PjlD43lRWCppNZfcEvy7DMprg.mPRdQcGhjjjj9KRxMOIebfkArjt6QSk9t.aSU0Or6PjlT43lRqBpp9..OcfzcKZp00C3UljkmjaU2wHIIIMqKIOLfOOvin4TzzqyGXGppNutCQZRliaJsJpp5cB7b5tCM0a6.9xI4wzcHRRRRypRxABbL.25taQSstHF7pn+M5NDoIcNtozpgppCF3EzcGZp2MA3HSx6t6Pjjjjlkjj6PRNcf8o6VzTseNvCup5q0cHRSCbbSoUSUUKCvyFQsXXWSxYmjGb2gHIIIMsKI6IvIA3E8nFk9E.OhppuR2gHMsvwMklGppdU.upt6PyD1PfiII6e2gHIIIMMJIqcR9P.uQfab28noZ+JfGWU0o2cHRSSbbSo4oppWDvqo6NzLg0.3kljuTRticGijjjzzhj7n.NCf+ytaQS89C.aeU0w2cHRSabbSoEfpp8F3f5tCMy3d.74RxKt6Pjjjjlzkj2NvxAVmtaQS8tLfsop5j5NDooQNtozB2K.3szcDZlwMD3UjjOQRVZ2wHIIIMoII2yjbV.6N96DqQueOvSrp5D5NDooU9CxkVfppRU0d.715tEMSYa.9hI4YzcHRRRRSJRx9A7YAtKc2hlIbY.O4ppir6Pjll43lRCIUUOCf2d2cnYJ2Hf2RR9LI4V2cLRRRRiqRxljjSE3kAbc5tGMSHL3xC5n5NDoocNtozv0yD3s1cDZlyCB3zRxdzcHRRRRiaRx9.bR.aQ2snYFWNCF17i1cHRyBbbSognppq.XOAdmc2hl4rV.uoj7wRxss6Xjjjj5VR1vjb7.GHv+V28nYFWJvt4qhtzhGG2TZHqp5Jpp1cf2Q2snYRORfyHIO8tCQRRRpKI4YAb5.OftaQyTVIvSpp582cHRyRbbSoQmmAvar6HzLoaDvaMIGaR1vtiQRRRZwRR1fjbb.GLvMn6dzLkUBr8dFaJs3ywMkFQl6UT+4.7l5tEMyZqAN0jrWcGhjjjzn1bOslmBvCr6VzLmeOviup5i2cHRyhbbSoQn4dE0eV3Svo5yMD30mjOeRtycGijjjzv1bmsl+4mVSOaM0hseOCdUz8L1TpINtozhfppmMvqs6NzLs6MvojjWQ2gHIIIMrL2Mg9WDeZMUOtLfsspZ4cGhzrLG2TZQRU0yG3U2cGZl10E3EmjyJIOjtiQRRRZ9JIaZRNQFbSn+u1cOZlzuE3QVUc7cGhzrNG2TZQTU09Bb.c2gl4cW.9uRxaHI+GcGijjjzpij7h.9b.2utaQyr9k.OlppOS2gHIG2TZQWU0KF3kzcGZlWA7rANyj736NFIIIoqIIYKSxWG3UB7uzcOZl0uB3Q3vlRiObbSoFTU8JAd9c2gDv5.bnI4XRx52cLRRRR+8V9xW9Vmj2NvwArwc2ilocw.O3ppSs6Pjzew0t6.jlUUU8ZSxuG3sxfmhNoN8v.1hjrrppk0cLRRRR.jjcD3kCba6tEMy6B.1gppuR2gHo+V9jaJ0npp2NvtwfaYOot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  },
  {
    "path": "src/patchers/latent_remote/M4L.latent_remote.js",
    "content": "inlets = 1;\noutlets = 1;\n\nvar MAX_SLIDERS = 64;\nvar CURRENT_SLIDERS = 1;\nvar MAX_COLUMNS = 8;\nvar SLIDER_X_MARGIN = 4; \nvar SLIDER_Y_MARGIN = 3; \n\n// Global UI objects\nvar InputRoute = null;\nvar LatentSlider = new Array(MAX_SLIDERS);\nvar PathInlet = this.patcher.getnamed(\"input1\");\nvar PathOutlet = this.patcher.getnamed(\"output1\");\nvar PathSymbolInlet = this.patcher.getnamed(\"symbol_in\")\n\n\n// Global UI objects position\nvar InputRoutPos = [10, 80];\nvar OutputRoutPos = [10, 400];\nvar LatentSliderPos = [10, 120];\nvar LatentSliderSize = [51, 168];\n\n\nvar IN_PACK_NAME = \"input_unpack\"\nvar OUT_UNPACK_NAME = \"output_pack\"\n\n// make presentation layout\nfunction make_presentation_layout() {\n\tvar x_grid = 0;\n\tvar y_grid = 0;\n\tfor (var i = 0; i < this.CURRENT_SLIDERS; i++) {\n\t\tpost(i, \"\\n\"); \n\t\tvar posX = x_grid * (LatentSliderSize[0] + SLIDER_X_MARGIN);\n\t\tvar posY = y_grid * (LatentSliderSize[1] + SLIDER_Y_MARGIN);\n\t\tthis.LatentSlider[i].setboxattr(\"presentation_rect\", posX, posY, LatentSliderSize[0], LatentSliderSize[1]);\n\t\tx_grid += 1;\n\t\tif (x_grid >= MAX_COLUMNS) {\n\t\t\tx_grid = 0;\n\t\t\ty_grid += 1;\n\t\t}\n\t}\n\t//\n}\n\n\n// generate sliders\nfunction sliders(n_sliders) {\n\tif (n_sliders > MAX_SLIDERS) error('cannot generate more than ' + String(MAX_SLIDERS) + ' sliders.')\n\tif (InputRoute != null) this.patcher.remove(InputRoute);\n\t// create input routing\n\tthis.patcher.remove(this.patcher.getnamed(IN_PACK_NAME));\n\tInputRoute = this.patcher.newdefault(InputRoutPos[0], InputRoutPos[1], \"mc.unpack~\", n_sliders); \n\tInputRoute.setattr(\"varname\", IN_PACK_NAME);\n\tthis.patcher.connect(PathInlet, 0, InputRoute, 0);\n\t// create output routing\n\tthis.patcher.remove(this.patcher.getnamed(OUT_UNPACK_NAME));\n\tOutputRoute = this.patcher.newdefault(OutputRoutPos[0], OutputRoutPos[1], \"mc.pack~\", n_sliders); \n\tOutputRoute.setattr(\"varname\", OUT_UNPACK_NAME);\n\tthis.patcher.connect(OutputRoute, 0, PathOutlet, 0);\n\t// delete existing sliders\n\tfor (var i = 0; i < this.CURRENT_SLIDERS; i++) {\n\t\tthis.patcher.remove(LatentSlider[i]);\n\t}\n\t// create sliders\n\tif (n_sliders < 1) {\n\t\treturn\n\t}\n\t// create pak for symout\n\tvar symoutOutlet = this.patcher.getnamed(\"symout\");\n\tvar symoutPos = symoutOutlet.getattr(\"patching_rect\");\n\tvar pakName = \"pak\"\n\tfor (var i = 0; i < n_sliders; i++) {\n\t\tpakName = pakName + \" f\";\n\t}\n\tvar PakObject = this.patcher.newdefault(symoutPos[0], symoutPos[1] - 20, pakName);\n\tthis.patcher.connect(PakObject, 0, symoutOutlet, 0)\n\tvar x_grid = 0;\n\tvar y_grid = 0;\n\tfor (var i = 0; i < n_sliders; i++) {\n\t\tvar posX = LatentSliderPos[0] + i * LatentSliderSize[0];\n\t\tvar posY = LatentSliderPos[1];\n\t\tvar currentSlider = this.patcher.newdefault(posX, posY, \"bpatcher\", \"M4L.latent_slider\");\n\t\t//post(i, posX, posY, LatentSliderSize[0], LatentSliderSize[1], \"\\n\");\n\t\t//currentSlider.setboxattr(\"patching_rect\", posX, posY, LatentSliderSize[0], LatentSliderSize[1]);\n\t\t\n\t\tcurrentSlider.setboxattr(\"varname\", \"slider\"+String(i+1));\n\t\tthis.patcher.connect(InputRoute, i, currentSlider, 0);\n\t\t\n\t\t// make layout\n\t\tvar posX = x_grid * (LatentSliderSize[0] + SLIDER_X_MARGIN);\n\t\tvar posY = y_grid * (LatentSliderSize[1] + SLIDER_Y_MARGIN);\n\t\tcurrentSlider.setboxattr(\"patching_rect\", posX, posY, LatentSliderSize[0], LatentSliderSize[1]);\n\t\tcurrentSlider.setboxattr(\"presentation\", 1); \n\t\tx_grid += 1;\n\t\tif (x_grid >= this.MAX_COLUMNS) {\n\t\t\tx_grid = 0;\n\t\t\ty_grid += 1;\n\t\t}\n\t\t// connect to output\n\t\tthis.patcher.connect(currentSlider, 0, OutputRoute, i);\n\t\tthis.LatentSlider[i] = currentSlider;\n\t\t// connect to symout\n\t\tthis.patcher.connect(currentSlider, 1, PakObject, i);\n\t\t// make receive obj\n\t\tvar receiveObj = this.patcher.newdefault(posX, posY - 20, \"receive\", jsarguments[1]+\"_\"+String(i))\n\t\tthis.patcher.connect(receiveObj, 0, currentSlider, 0)\n\t}\n\tCURRENT_SLIDERS = n_sliders;\n\t\n\tupdate_patching_rect(); \n}\n\nfunction max_columns(n_columns) {\n\tif (n_columns == 0) {\n\t\treturn;\n\t}\n\tif (n_columns > this.CURRENT_SLIDERS) error(\"Cannot set max columns \" + String(n_columns) + \" with \" + String(this.CURRENT_SLIDERS) + \" sliders\\n\");\n\tthis.MAX_COLUMNS = n_columns;\n\t//make_presentation_layout();\n\tupdate_patching_rect(); \n}\n\nfunction update_patching_rect() {\n\tif (this.patcher.box != null) {\n\t\tvar slider_rect = this.patcher.getnamed(\"slider1\").getboxattr(\"patching_rect\");\n\t\tvar patching_rect = this.patcher.box.getboxattr(\"patching_rect\")\n\t\tvar presentation_rect = this.patcher.box.getboxattr(\"presentation_rect\") \n\t\tvar target_width = Math.min(this.CURRENT_SLIDERS, this.MAX_COLUMNS) * (slider_rect[2] + SLIDER_X_MARGIN);\n\t\tvar target_height = Math.ceil(this.CURRENT_SLIDERS / this.MAX_COLUMNS) * (slider_rect[3] + SLIDER_Y_MARGIN);\n\t\tthis.patcher.box.setboxattr(\"patching_rect\", patching_rect[0], patching_rect[1], target_width, target_height);\n\t\tthis.patcher.box.setboxattr(\"presentation_rect\", presentation_rect[0], presentation_rect[1], target_width, target_height);\n\t}\n}\n\nfunction slider_check_size() {\n\tif (this.patcher.box != null) {\n\t\tvar slider_rect = LatentSliderSize;\n\t\tvar patching_rect = this.patcher.box.getboxattr(\"patching_rect\");\n\t\tthis.patcher.box.setboxattr(\"patching_rect\", patching_rect[0], patching_rect[1], LatentSliderSize[0], LatentSliderSize[1])\n\t\tvar presentation_rect = this.patcher.box.getboxattr(\"presentation_rect\");\n\t\tthis.patcher.box.setboxattr(\"presentation_rect\", patching_rect[0], patching_rect[1], LatentSliderSize[0], LatentSliderSize[1])\n\t}\n}\n\nfunction clear() {\n\tfor (var i = 0; i < this.CURRENT_SLIDERS; i++) {\n\t\tthis.patcher.remove(LatentSlider[i]); \n\t}\n\tthis.patcher.remove(this.patcher.getnamed(IN_PACK_NAME));\n\tthis.patcher.remove(this.patcher.getnamed(OUT_UNPACK_NAME));\n}\n\n\nfunction faders() {\n\tvar args = arrayfromargs(arguments);\n\tvar mess = args.shift();\n\t//post(\"n sliders\", CURRENT_SLIDERS);\n\tfor (var i = 0; i < CURRENT_SLIDERS; i++) {\n\t\t//post('setting slider', i, 'to', args[i], '\\n'); \n\t\tmessnamed(jsarguments[1]+\"_\"+String(i), mess, args[i])\n\t}\n}\n\nfunction faders_all() {\n\tvar args = arrayfromargs(arguments);\n\tvar mess = args.shift();\n\t//post(\"n sliders\", CURRENT_SLIDERS);\n\tfor (var i = 0; i < CURRENT_SLIDERS; i++) {\n\t\t//post('setting slider', i, 'to', args[i], '\\n'); \n\t\tmessnamed(jsarguments[1]+\"_\"+String(i), mess, args)\n\t}\n}\n\n\nfunction fader() {\n\tvar args = arrayfromargs(arguments);\n\tvar fader_idx = args.shift() - 1;\n\tvar mess = args.shift();\n\tif (fader_idx > CURRENT_SLIDERS) {\n\t\terror('fader_idx '+String(fader_idx)+' too big')\n\t}\n\t//post(\"n sliders\", CURRENT_SLIDERS);\n\tmessnamed(jsarguments[1]+\"_\"+String(fader_idx), mess, args)\n}\n\nfunction dump_all() {\n\tpost('dump all!!!')\n}"
  },
  {
    "path": "src/patchers/latent_remote/M4L.latent_remote.maxhelp",
    "content": "{\n\t\"patcher\" : \t{\n\t\t\"fileversion\" : 1,\n\t\t\"appversion\" : \t\t{\n\t\t\t\"major\" : 8,\n\t\t\t\"minor\" : 6,\n\t\t\t\"revision\" : 5,\n\t\t\t\"architecture\" : \"x64\",\n\t\t\t\"modernui\" : 1\n\t\t}\n,\n\t\t\"classnamespace\" : \"box\",\n\t\t\"rect\" : [ 100.0, 100.0, 814.0, 598.0 ],\n\t\t\"bglocked\" : 0,\n\t\t\"openinpresentation\" : 0,\n\t\t\"default_fontsize\" : 12.0,\n\t\t\"default_fontface\" : 0,\n\t\t\"default_fontname\" : \"Arial\",\n\t\t\"gridonopen\" : 1,\n\t\t\"gridsize\" : [ 15.0, 15.0 ],\n\t\t\"gridsnaponopen\" : 1,\n\t\t\"objectsnaponopen\" : 1,\n\t\t\"statusbarvisible\" : 2,\n\t\t\"toolbarvisible\" : 1,\n\t\t\"lefttoolbarpinned\" : 0,\n\t\t\"toptoolbarpinned\" : 0,\n\t\t\"righttoolbarpinned\" : 0,\n\t\t\"bottomtoolbarpinned\" : 0,\n\t\t\"toolbars_unpinned_last_save\" : 0,\n\t\t\"tallnewobj\" : 0,\n\t\t\"boxanimatetime\" : 200,\n\t\t\"enablehscroll\" : 1,\n\t\t\"enablevscroll\" : 1,\n\t\t\"devicewidth\" : 0.0,\n\t\t\"description\" : \"\",\n\t\t\"digest\" : \"\",\n\t\t\"tags\" : \"\",\n\t\t\"style\" : \"\",\n\t\t\"subpatcher_template\" : \"\",\n\t\t\"assistshowspatchername\" : 0,\n\t\t\"boxes\" : [ \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\"id\" : \"obj-17\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 699.0, 103.0, 70.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"loadmess 1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-16\",\n\t\t\t\t\t\"maxclass\" : \"number\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 372.0, 99.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-13\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 372.0, 128.0, 105.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"join i f @triggers 1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-11\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 148.0, 453.0, 190.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"0.4414 0.618 0.752156 0.469841\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-8\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 489.0, 386.0, 119.0, 22.0 ],\n\t\t\t\t\t\"presentation_linecount\" : 2,\n\t\t\t\t\t\"text\" : \"faders_all range -5 5\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-7\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 489.0, 345.0, 60.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"loadmess\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\"bubbleside\" : 0,\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-6\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 360.0, 423.0, 268.0, 52.0 ],\n\t\t\t\t\t\"presentation_linecount\" : 2,\n\t\t\t\t\t\"text\" : \"user faders_all \\\"param\\\" \\\"values\\\" to set all slider's parameters to a single value\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-3\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 512.0, 289.0, 268.0, 37.0 ],\n\t\t\t\t\t\"presentation_linecount\" : 2,\n\t\t\t\t\t\"text\" : \"user faders \\\"fader_idx\\\" \\\"param\\\" \\\"values\\\" to set individual slider's parameters with a list\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-2\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 466.0, 156.0, 268.0, 37.0 ],\n\t\t\t\t\t\"presentation_linecount\" : 2,\n\t\t\t\t\t\"text\" : \"user fader \\\"fader_idx\\\" \\\"param\\\" \\\"values\\\" to set individually each slider's parameters\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"format\" : 6,\n\t\t\t\t\t\"id\" : \"obj-46\",\n\t\t\t\t\t\"maxclass\" : \"flonum\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 376.0, 348.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-45\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 376.0, 386.0, 97.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"faders_all set $1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-44\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 376.0, 296.0, 128.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"faders set $1 $2 $3 $4\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"contdata\" : 1,\n\t\t\t\t\t\"id\" : \"obj-42\",\n\t\t\t\t\t\"maxclass\" : \"multislider\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 376.0, 217.0, 77.0, 63.0 ],\n\t\t\t\t\t\"size\" : 4\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"format\" : 6,\n\t\t\t\t\t\"id\" : \"obj-39\",\n\t\t\t\t\t\"maxclass\" : \"flonum\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 485.0, 99.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-37\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 372.0, 161.0, 89.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"fader $1 set $2\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-12\",\n\t\t\t\t\t\"maxclass\" : \"scope~\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 38.0, 453.0, 96.0, 94.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-9\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 38.0, 161.0, 72.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"mc.cycle~ 4\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"args\" : [ 4 ],\n\t\t\t\t\t\"bgmode\" : 0,\n\t\t\t\t\t\"border\" : 0,\n\t\t\t\t\t\"clickthrough\" : 0,\n\t\t\t\t\t\"enablehscroll\" : 0,\n\t\t\t\t\t\"enablevscroll\" : 0,\n\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\"lockeddragscroll\" : 0,\n\t\t\t\t\t\"lockedsize\" : 0,\n\t\t\t\t\t\"maxclass\" : 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2,\n\t\t\t\t\t\"bubbletextmargin\" : 3,\n\t\t\t\t\t\"fontsize\" : 10.0,\n\t\t\t\t\t\"id\" : \"obj-22\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 503.0, 88.0, 113.0, 44.0 ],\n\t\t\t\t\t\"text\" : \"raw value of slider, between 0 and 1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"format\" : 6,\n\t\t\t\t\t\"id\" : \"obj-18\",\n\t\t\t\t\t\"maxclass\" : \"flonum\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 465.0, 138.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-8\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 465.0, 199.0, 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\"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 224.0, 370.0, 150.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"← general parameters\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-15\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 5.0, 370.0, 150.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"modulation parameters →\",\n\t\t\t\t\t\"textjustification\" : 2\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-14\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 5.0, 227.0, 150.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"enables audio input →\",\n\t\t\t\t\t\"textjustification\" : 2\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-13\",\n\t\t\t\t\t\"maxclass\" : 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    "path": "src/patchers/latent_remote/frand.maxpat",
    "content": "{\n\t\"patcher\" : \t{\n\t\t\"fileversion\" : 1,\n\t\t\"appversion\" : \t\t{\n\t\t\t\"major\" : 8,\n\t\t\t\"minor\" : 6,\n\t\t\t\"revision\" : 5,\n\t\t\t\"architecture\" : \"x64\",\n\t\t\t\"modernui\" : 1\n\t\t}\n,\n\t\t\"classnamespace\" : \"box\",\n\t\t\"rect\" : [ 59.0, 119.0, 640.0, 480.0 ],\n\t\t\"bglocked\" : 0,\n\t\t\"openinpresentation\" : 0,\n\t\t\"default_fontsize\" : 12.0,\n\t\t\"default_fontface\" : 0,\n\t\t\"default_fontname\" : \"Arial\",\n\t\t\"gridonopen\" : 1,\n\t\t\"gridsize\" : [ 15.0, 15.0 ],\n\t\t\"gridsnaponopen\" : 1,\n\t\t\"objectsnaponopen\" : 1,\n\t\t\"statusbarvisible\" : 2,\n\t\t\"toolbarvisible\" : 1,\n\t\t\"lefttoolbarpinned\" : 0,\n\t\t\"toptoolbarpinned\" : 0,\n\t\t\"righttoolbarpinned\" : 0,\n\t\t\"bottomtoolbarpinned\" : 0,\n\t\t\"toolbars_unpinned_last_save\" : 0,\n\t\t\"tallnewobj\" : 0,\n\t\t\"boxanimatetime\" : 200,\n\t\t\"enablehscroll\" : 1,\n\t\t\"enablevscroll\" : 1,\n\t\t\"devicewidth\" : 0.0,\n\t\t\"description\" : \"\",\n\t\t\"digest\" : \"\",\n\t\t\"tags\" : \"\",\n\t\t\"style\" : \"\",\n\t\t\"subpatcher_template\" : \"\",\n\t\t\"assistshowspatchername\" : 0,\n\t\t\"boxes\" : [ \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"comment\" : \"\",\n\t\t\t\t\t\"id\" : \"obj-1\",\n\t\t\t\t\t\"index\" : 1,\n\t\t\t\t\t\"maxclass\" : \"inlet\",\n\t\t\t\t\t\"numinlets\" : 0,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 231.0, 45.0, 30.0, 30.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"comment\" : \"\",\n\t\t\t\t\t\"id\" : \"obj-7\",\n\t\t\t\t\t\"index\" : 1,\n\t\t\t\t\t\"maxclass\" : \"outlet\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 231.0, 190.0, 30.0, 30.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-6\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 6,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 231.0, 159.0, 97.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"scale 0. 1. #1 #2\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-5\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"float\" ],\n\t\t\t\t\t\"patching_rect\" : [ 231.0, 126.0, 52.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"/ 10000.\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 231.0, 97.0, 86.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"random 10000\"\n\t\t\t\t}\n\n\t\t\t}\n ],\n\t\t\"lines\" : [ \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-4\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-1\", 0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-5\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-4\", 0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-6\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-5\", 0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-7\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-6\", 0 ]\n\t\t\t\t}\n\n\t\t\t}\n ]\n\t}\n\n}\n"
  },
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    "path": "src/patchers/latent_remote/ierf.gendsp",
    "content": "{\n\t\"patcher\" : \t{\n\t\t\"fileversion\" : 1,\n\t\t\"appversion\" : \t\t{\n\t\t\t\"major\" : 8,\n\t\t\t\"minor\" : 6,\n\t\t\t\"revision\" : 0,\n\t\t\t\"architecture\" : \"x64\",\n\t\t\t\"modernui\" : 1\n\t\t}\n,\n\t\t\"classnamespace\" : \"dsp.gen\",\n\t\t\"rect\" : [ 100.0, 668.0, 600.0, 450.0 ],\n\t\t\"bglocked\" : 0,\n\t\t\"openinpresentation\" : 0,\n\t\t\"default_fontsize\" : 12.0,\n\t\t\"default_fontface\" : 0,\n\t\t\"default_fontname\" : \"Arial\",\n\t\t\"gridonopen\" : 1,\n\t\t\"gridsize\" : [ 15.0, 15.0 ],\n\t\t\"gridsnaponopen\" : 1,\n\t\t\"objectsnaponopen\" : 1,\n\t\t\"statusbarvisible\" : 2,\n\t\t\"toolbarvisible\" : 1,\n\t\t\"lefttoolbarpinned\" : 0,\n\t\t\"toptoolbarpinned\" : 0,\n\t\t\"righttoolbarpinned\" : 0,\n\t\t\"bottomtoolbarpinned\" : 0,\n\t\t\"toolbars_unpinned_last_save\" : 2,\n\t\t\"tallnewobj\" : 0,\n\t\t\"boxanimatetime\" : 200,\n\t\t\"enablehscroll\" : 1,\n\t\t\"enablevscroll\" : 1,\n\t\t\"devicewidth\" : 0.0,\n\t\t\"description\" : \"\",\n\t\t\"digest\" : \"\",\n\t\t\"tags\" : \"\",\n\t\t\"style\" : \"\",\n\t\t\"subpatcher_template\" : \"\",\n\t\t\"assistshowspatchername\" : 0,\n\t\t\"boxes\" : [ \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-6\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 0,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 302.0, 23.0, 38.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"in 2 2\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"code\" : \"x = pow(in1, in2);\\nout1 = 0.8862269254527579 * x + 0.23201366653465444 * pow(x,3) + 0.12755617530559793 * pow(x,5) + 0.08655212924154752 * pow(x,7) + 0.0649596177453854 * pow(x,9) + 0.051731281984616365 * pow(x,11) + 0.04283672065179733 * pow(x,13) + 0.03646592930853161 * pow(x,15) + 0.03168900502160544 * pow(x,17) + 0.027980632964995214 * pow(x,19);\\n\",\n\t\t\t\t\t\"fontface\" : 0,\n\t\t\t\t\t\"fontname\" : \"<Monospaced>\",\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-5\",\n\t\t\t\t\t\"maxclass\" : \"codebox\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 85.0, 93.0, 200.0, 200.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-1\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 0,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 50.0, 14.0, 28.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"in 1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 176.0, 418.0, 35.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"out 1\"\n\t\t\t\t}\n\n\t\t\t}\n ],\n\t\t\"lines\" : [ \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-5\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-1\", 0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-4\", 0 ],\n\t\t\t\t\t\"source\" : [ \"obj-5\", 0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"patchline\" : \t\t\t\t{\n\t\t\t\t\t\"destination\" : [ \"obj-5\", 1 ],\n\t\t\t\t\t\"source\" : [ \"obj-6\", 0 ]\n\t\t\t\t}\n\n\t\t\t}\n ]\n\t}\n\n}\n"
  },
  {
    "path": "src/patchers/latent_remote/latent_remote.js",
    "content": "inlets = 1;\noutlets = 1;\n\nvar MAX_SLIDERS = 64;\nvar CURRENT_SLIDERS = 1;\nvar MAX_COLUMNS = 8;\nvar SLIDER_X_MARGIN = 10; \nvar SLIDER_Y_MARGIN = 3; \n\n// Global UI objects\nvar InputRoute = null;\nvar LatentSlider = new Array(MAX_SLIDERS);\nvar PathInlet = this.patcher.getnamed(\"input1\");\nvar PathOutlet = this.patcher.getnamed(\"output1\");\nvar PathSymbolInlet = this.patcher.getnamed(\"symbol_in\")\n\n\n// Global UI objects position\nvar InputRoutPos = [10, 80];\nvar OutputRoutPos = [10, 400];\nvar LatentSliderPos = [10, 120];\nvar LatentSliderSize = [65, 220];\n\n\nvar IN_PACK_NAME = \"input_unpack\"\nvar OUT_UNPACK_NAME = \"output_pack\"\n\n// make presentation layout\nfunction make_presentation_layout() {\n\tvar x_grid = 0;\n\tvar y_grid = 0;\n\tfor (var i = 0; i < this.CURRENT_SLIDERS; i++) {\n\t\tpost(i, \"\\n\"); \n\t\tvar posX = x_grid * (LatentSliderSize[0] + SLIDER_X_MARGIN);\n\t\tvar posY = y_grid * (LatentSliderSize[1] + SLIDER_Y_MARGIN);\n\t\tthis.LatentSlider[i].setboxattr(\"presentation_rect\", posX, posY, LatentSliderSize[0], LatentSliderSize[1]);\n\t\tx_grid += 1;\n\t\tif (x_grid >= MAX_COLUMNS) {\n\t\t\tx_grid = 0;\n\t\t\ty_grid += 1;\n\t\t}\n\t}\n\t//\n}\n\n\n// generate sliders\nfunction sliders(n_sliders) {\n\tif (n_sliders > MAX_SLIDERS) error('cannot generate more than ' + String(MAX_SLIDERS) + ' sliders.')\n\tif (InputRoute != null) this.patcher.remove(InputRoute);\n\t// create input routing\n\tthis.patcher.remove(this.patcher.getnamed(IN_PACK_NAME));\n\tInputRoute = this.patcher.newdefault(InputRoutPos[0], InputRoutPos[1], \"mc.unpack~\", n_sliders); \n\tInputRoute.setattr(\"varname\", IN_PACK_NAME);\n\tthis.patcher.connect(PathInlet, 0, InputRoute, 0);\n\t// create output routing\n\tthis.patcher.remove(this.patcher.getnamed(OUT_UNPACK_NAME));\n\tOutputRoute = this.patcher.newdefault(OutputRoutPos[0], OutputRoutPos[1], \"mc.pack~\", n_sliders); \n\tOutputRoute.setattr(\"varname\", OUT_UNPACK_NAME);\n\tthis.patcher.connect(OutputRoute, 0, PathOutlet, 0);\n\t// delete existing sliders\n\tfor (var i = 0; i < this.CURRENT_SLIDERS; i++) {\n\t\tthis.patcher.remove(LatentSlider[i]);\n\t}\n\t// create sliders\n\tif (n_sliders < 1) {\n\t\treturn\n\t}\n\t// create pak for symout\n\tvar symoutOutlet = this.patcher.getnamed(\"symout\");\n\tvar symoutPos = symoutOutlet.getattr(\"patching_rect\");\n\tvar pakName = \"pak\"\n\tfor (var i = 0; i < n_sliders; i++) {\n\t\tpakName = pakName + \" f\";\n\t}\n\tvar PakObject = this.patcher.newdefault(symoutPos[0], symoutPos[1] - 20, pakName);\n\tthis.patcher.connect(PakObject, 0, symoutOutlet, 0); \n\tvar x_grid = 0;\n\tvar y_grid = 0;\n\tfor (var i = 0; i < n_sliders; i++) {\n\t\tvar posX = LatentSliderPos[0] + i * LatentSliderSize[0];\n\t\tvar posY = LatentSliderPos[1];\n\t\tvar currentSlider = this.patcher.newdefault(posX, posY, \"bpatcher\", \"latent_slider\");\n\t\t//post(i, posX, posY, LatentSliderSize[0], LatentSliderSize[1], \"\\n\");\n\t\t//currentSlider.setboxattr(\"patching_rect\", posX, posY, LatentSliderSize[0], LatentSliderSize[1]);\n\t\t\n\t\tcurrentSlider.setboxattr(\"varname\", \"slider\"+String(i+1));\n\t\tthis.patcher.connect(InputRoute, i, currentSlider, 0);\n\t\t\n\t\t// make layout\n\t\tvar posX = x_grid * (LatentSliderSize[0] + SLIDER_X_MARGIN);\n\t\tvar posY = y_grid * (LatentSliderSize[1] + SLIDER_Y_MARGIN);\n\t\tcurrentSlider.setboxattr(\"patching_rect\", posX, posY, LatentSliderSize[0], LatentSliderSize[1]);\n\t\tcurrentSlider.setboxattr(\"presentation\", 1); \n\t\tx_grid += 1;\n\t\tif (x_grid >= this.MAX_COLUMNS) {\n\t\t\tx_grid = 0;\n\t\t\ty_grid += 1;\n\t\t}\n\t\t// connect to output\n\t\tthis.patcher.connect(currentSlider, 0, OutputRoute, i);\n\t\tthis.LatentSlider[i] = currentSlider;\n\t\t// connect to symout\n\t\tthis.patcher.connect(currentSlider, 1, PakObject, i);\n\t\t// make receive obj\n\t\tvar receiveObj = this.patcher.newdefault(posX, posY - 20, \"receive\", jsarguments[1]+\"_\"+String(i))\n\t\tthis.patcher.connect(receiveObj, 0, currentSlider, 0)\n\t}\n\tCURRENT_SLIDERS = n_sliders;\n\t\n\tupdate_patching_rect(); \n}\n\nfunction max_columns(n_columns) {\n\tif (n_columns == 0) {\n\t\treturn;\n\t}\n\tif (n_columns > this.CURRENT_SLIDERS) error(\"Cannot set max columns \" + String(n_columns) + \" with \" + String(this.CURRENT_SLIDERS) + \" sliders\\n\");\n\tthis.MAX_COLUMNS = n_columns;\n\t//make_presentation_layout();\n\tupdate_patching_rect(); \n}\n\nfunction update_patching_rect() {\n\tif (this.patcher.box != null) {\n\t\tvar slider_rect = this.patcher.getnamed(\"slider1\").getboxattr(\"patching_rect\");\n\t\tvar patching_rect = this.patcher.box.getboxattr(\"patching_rect\")\n\t\tvar presentation_rect = this.patcher.box.getboxattr(\"presentation_rect\") \n\t\tvar target_width = Math.min(this.CURRENT_SLIDERS, this.MAX_COLUMNS) * (slider_rect[2] + SLIDER_X_MARGIN);\n\t\tvar target_height = Math.ceil(this.CURRENT_SLIDERS / this.MAX_COLUMNS) * (slider_rect[3] + SLIDER_Y_MARGIN);\n\t\tthis.patcher.box.setboxattr(\"patching_rect\", patching_rect[0], patching_rect[1], target_width, target_height);\n\t\tthis.patcher.box.setboxattr(\"presentation_rect\", presentation_rect[0], presentation_rect[1], target_width, target_height);\n\t}\n}\n\nfunction slider_check_size() {\n\tif (this.patcher.box != null) {\n\t\tvar slider_rect = LatentSliderSize;\n\t\tvar patching_rect = this.patcher.box.getboxattr(\"patching_rect\");\n\t\tthis.patcher.box.setboxattr(\"patching_rect\", patching_rect[0], patching_rect[1], LatentSliderSize[0], LatentSliderSize[1])\n\t\tvar presentation_rect = this.patcher.box.getboxattr(\"presentation_rect\");\n\t\tthis.patcher.box.setboxattr(\"presentation_rect\", patching_rect[0], patching_rect[1], LatentSliderSize[0], LatentSliderSize[1])\n\t}\n}\n\nfunction clear() {\n\tfor (var i = 0; i < this.CURRENT_SLIDERS; i++) {\n\t\tthis.patcher.remove(LatentSlider[i]); \n\t}\n\tthis.patcher.remove(this.patcher.getnamed(IN_PACK_NAME));\n\tthis.patcher.remove(this.patcher.getnamed(OUT_UNPACK_NAME));\n}\n\n\nfunction faders() {\n\tvar args = arrayfromargs(arguments);\n\tvar mess = args.shift();\n\t//post(\"n sliders\", CURRENT_SLIDERS);\n\tfor (var i = 0; i < CURRENT_SLIDERS; i++) {\n\t\t//post('setting slider', i, 'to', args[i], '\\n'); \n\t\tmessnamed(jsarguments[1]+\"_\"+String(i), mess, args[i])\n\t}\n}\n\nfunction faders_all() {\n\tvar args = arrayfromargs(arguments);\n\tvar mess = args.shift();\n\t//post(\"n sliders\", CURRENT_SLIDERS);\n\tfor (var i = 0; i < CURRENT_SLIDERS; i++) {\n\t\t//post('setting slider', i, 'to', args[i], '\\n'); \n\t\tmessnamed(jsarguments[1]+\"_\"+String(i), mess, args)\n\t}\n}\n\n\nfunction fader() {\n\tvar args = arrayfromargs(arguments);\n\tvar fader_idx = args.shift() - 1;\n\tvar mess = args.shift();\n\tif (fader_idx > CURRENT_SLIDERS) {\n\t\terror('fader_idx '+String(fader_idx)+' too big')\n\t}\n\t//post(\"n sliders\", CURRENT_SLIDERS);\n\tmessnamed(jsarguments[1]+\"_\"+String(fader_idx), mess, args)\n}\n\nfunction dump_all() {\n\tpost(\"dump all!!\")\n}"
  },
  {
    "path": "src/patchers/latent_remote/latent_remote.maxhelp",
    "content": "{\n\t\"patcher\" : \t{\n\t\t\"fileversion\" : 1,\n\t\t\"appversion\" : \t\t{\n\t\t\t\"major\" : 8,\n\t\t\t\"minor\" : 6,\n\t\t\t\"revision\" : 5,\n\t\t\t\"architecture\" : \"x64\",\n\t\t\t\"modernui\" : 1\n\t\t}\n,\n\t\t\"classnamespace\" : \"box\",\n\t\t\"rect\" : [ 100.0, 100.0, 814.0, 598.0 ],\n\t\t\"bglocked\" : 0,\n\t\t\"openinpresentation\" : 0,\n\t\t\"default_fontsize\" : 12.0,\n\t\t\"default_fontface\" : 0,\n\t\t\"default_fontname\" : \"Arial\",\n\t\t\"gridonopen\" : 1,\n\t\t\"gridsize\" : [ 15.0, 15.0 ],\n\t\t\"gridsnaponopen\" : 1,\n\t\t\"objectsnaponopen\" : 1,\n\t\t\"statusbarvisible\" : 2,\n\t\t\"toolbarvisible\" : 1,\n\t\t\"lefttoolbarpinned\" : 0,\n\t\t\"toptoolbarpinned\" : 0,\n\t\t\"righttoolbarpinned\" : 0,\n\t\t\"bottomtoolbarpinned\" : 0,\n\t\t\"toolbars_unpinned_last_save\" : 0,\n\t\t\"tallnewobj\" : 0,\n\t\t\"boxanimatetime\" : 200,\n\t\t\"enablehscroll\" : 1,\n\t\t\"enablevscroll\" : 1,\n\t\t\"devicewidth\" : 0.0,\n\t\t\"description\" : \"\",\n\t\t\"digest\" : \"\",\n\t\t\"tags\" : \"\",\n\t\t\"style\" : \"\",\n\t\t\"subpatcher_template\" : \"\",\n\t\t\"assistshowspatchername\" : 0,\n\t\t\"boxes\" : [ \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\"id\" : \"obj-17\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 699.0, 103.0, 70.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"loadmess 1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-16\",\n\t\t\t\t\t\"maxclass\" : \"number\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 372.0, 99.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-13\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 372.0, 128.0, 105.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"join i f @triggers 1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-11\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 148.0, 453.0, 190.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"0.4414 0.618 0.752156 0.469841\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-8\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 489.0, 386.0, 119.0, 22.0 ],\n\t\t\t\t\t\"presentation_linecount\" : 2,\n\t\t\t\t\t\"text\" : \"faders_all range -5 5\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-7\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 489.0, 345.0, 60.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"loadmess\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\"bubbleside\" : 0,\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-6\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 360.0, 423.0, 268.0, 52.0 ],\n\t\t\t\t\t\"presentation_linecount\" : 2,\n\t\t\t\t\t\"text\" : \"user faders_all \\\"param\\\" \\\"values\\\" to set all slider's parameters to a single value\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-3\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 512.0, 289.0, 268.0, 37.0 ],\n\t\t\t\t\t\"presentation_linecount\" : 2,\n\t\t\t\t\t\"text\" : \"user faders \\\"fader_idx\\\" \\\"param\\\" \\\"values\\\" to set individual slider's parameters with a list\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\"fontsize\" : 12.0,\n\t\t\t\t\t\"id\" : \"obj-2\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 466.0, 156.0, 268.0, 37.0 ],\n\t\t\t\t\t\"presentation_linecount\" : 2,\n\t\t\t\t\t\"text\" : \"user fader \\\"fader_idx\\\" \\\"param\\\" \\\"values\\\" to set individually each slider's parameters\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"format\" : 6,\n\t\t\t\t\t\"id\" : \"obj-46\",\n\t\t\t\t\t\"maxclass\" : \"flonum\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 376.0, 348.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-45\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 376.0, 386.0, 97.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"faders_all set $1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-44\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 376.0, 296.0, 128.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"faders set $1 $2 $3 $4\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"contdata\" : 1,\n\t\t\t\t\t\"id\" : \"obj-42\",\n\t\t\t\t\t\"maxclass\" : \"multislider\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 376.0, 217.0, 77.0, 63.0 ],\n\t\t\t\t\t\"size\" : 4\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"format\" : 6,\n\t\t\t\t\t\"id\" : \"obj-39\",\n\t\t\t\t\t\"maxclass\" : \"flonum\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 485.0, 99.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-37\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 372.0, 161.0, 89.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"fader $1 set $2\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-12\",\n\t\t\t\t\t\"maxclass\" : \"scope~\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 38.0, 453.0, 96.0, 94.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-9\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 38.0, 161.0, 72.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"mc.cycle~ 4\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"args\" : [ 4 ],\n\t\t\t\t\t\"bgmode\" : 0,\n\t\t\t\t\t\"border\" : 0,\n\t\t\t\t\t\"clickthrough\" : 0,\n\t\t\t\t\t\"enablehscroll\" : 0,\n\t\t\t\t\t\"enablevscroll\" : 0,\n\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\"lockeddragscroll\" : 0,\n\t\t\t\t\t\"lockedsize\" : 0,\n\t\t\t\t\t\"maxclass\" : 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between 0 and 1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-40\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 164.5, 452.0, 50.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"0.5\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\"bubblepoint\" : 0.1,\n\t\t\t\t\t\"bubbleside\" : 2,\n\t\t\t\t\t\"bubbletextmargin\" : 3,\n\t\t\t\t\t\"fontsize\" : 10.0,\n\t\t\t\t\t\"id\" : \"obj-32\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 398.0, 88.0, 93.0, 44.0 ],\n\t\t\t\t\t\"text\" : \"︎value of slider in provided range\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\"bubblepoint\" : 0.1,\n\t\t\t\t\t\"bubbleside\" : 2,\n\t\t\t\t\t\"bubbletextmargin\" : 3,\n\t\t\t\t\t\"fontsize\" : 10.0,\n\t\t\t\t\t\"id\" : \"obj-22\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 503.0, 88.0, 113.0, 44.0 ],\n\t\t\t\t\t\"text\" : \"raw value of slider, between 0 and 1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"format\" : 6,\n\t\t\t\t\t\"id\" : \"obj-18\",\n\t\t\t\t\t\"maxclass\" : \"flonum\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 465.0, 138.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-8\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 465.0, 199.0, 67.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"set_raw $1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"format\" : 6,\n\t\t\t\t\t\"id\" : \"obj-3\",\n\t\t\t\t\t\"maxclass\" : \"flonum\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", \"bang\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 363.0, 303.0, 50.0, 22.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-35\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 391.0, 199.0, 41.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"set $1\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"format\" : 6,\n\t\t\t\t\t\"id\" : \"obj-33\",\n\t\t\t\t\t\"maxclass\" : \"flonum\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"\", 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\"obj-21\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 277.0, 169.0, 76.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"pak f f\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-19\",\n\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"patching_rect\" : [ 277.0, 199.0, 73.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"range $1 $2\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-17\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 193.0, 401.0, 150.0, 21.0 ],\n\t\t\t\t\t\"text\" : \"↖︎ listening parameters\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-16\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 224.0, 370.0, 150.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"← general parameters\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-15\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 5.0, 370.0, 150.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"modulation parameters →\",\n\t\t\t\t\t\"textjustification\" : 2\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-14\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 5.0, 227.0, 150.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"enables audio input →\",\n\t\t\t\t\t\"textjustification\" : 2\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-13\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 5.0, 211.0, 150.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"enables modulation →\",\n\t\t\t\t\t\"textjustification\" : 2\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-11\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 157.0, 142.0, 60.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"cycle~ 10\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\"bubblepoint\" : 0.7,\n\t\t\t\t\t\"bubbleside\" : 2,\n\t\t\t\t\t\"fontsize\" : 14.0,\n\t\t\t\t\t\"id\" : \"obj-10\",\n\t\t\t\t\t\"linecount\" : 3,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 20.0, 71.0, 206.0, 72.0 ],\n\t\t\t\t\t\"text\" : \"create a 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Dahj+fn8IuEUTQ4Bbht6oSNKdY07TWc0c9vl1SFHYR9746yRR6zWp2AI+9jbT5d+iPy74y2U0qSLND50rR1HLpxKu7r84y2mgjah8p+C5P0QEUTwjz8N0XAEUTQIPiaZpcG6XGqs29se66S2wQX3t08wOQrGRNin8ItM0TSeqdJuFZnguQzt7hgT+N1wNxVmmeDpnwSc4yP8kosBPxGxiGOyjxX7RPRUYkU1Y0mSRVrtiK6BRF2QO5Qmne+9ud5bmLYB7lu4adN5deoSme+9+F59.YOV+5W+J+m+y+4UOzKoswOS2G+DwVHYBznK6E0DHPfsiOoQpNomp7fo3hKN89KYJLRxt10tB6YnZa.EImIIeLKXWTUj7+fjSmRkwE8fjeU1OYsDm7r6Yz1q+5udZMzPCKijOuEbgqo5sdq25JCmsURlHiv9odrlfeIaHOdDhlppppNL.ha26d22jtikvv8p6ighXKj72DsOo8sdq2Z78Tdae6auuMf0.4u..TbwEOpnZvYi0Ymc9t56LCyQvwSzoSx6lFyaJCGtI4VH42kFS3hRtzT7IH4Dqqt5lzANvAl3fbRjsr+fY2rgMrgDpnhJNq.ABzyLnosWUUU08Eh+vq33mjR.+Oh56Ls4762eeGXzZy5W+5W..vgNzgVmtikvfTwbgogjmWz9D1JqrxueuJRUf.AdmP7iN4fw3OKBJ1NnwD91lH4cPxuFIWIIWPmc14o41s6ITd4kmcQEUTpEWbwIFb.oFGMZwUUu9WbEUTQo51s6uQDDCCaG5PG5KY8mUD8PRUs0VaFjbAj7lI4aw9+67eLRtflZpobJpnhR.1z9VuvBwgnB0jb4gxEUd734pnLnACKETPAt18t28r84y2CM.WvZmreRl+fs8TUUUMld+AZs0VGmEsqz14UdkWIIZSNlVQEUrcD7l8tc6Np2hgln6SyGFQAETfK2tcmNIyljigFYngj4PL6HKrWHYJLJOVO750a0.3DmWricL3QNpL...H.jDQAQki4FhezeWvXLyA38K1iGO+pJqrxqaO6YOKqvBK7zdgW3EFcvYRznQ2YP0c2caR6UBcu4a9lyJJrsXqPirWVBjLMRlNIiW2wjvFhjW5f7dwyvazjewVYrGKofBJvUIkTxbBDHvSX52wybsxAZafjSgjrvBKL4foYyQroOQ2tcey58vzmXCaXCmdOwUKszxKo63ILb+Z33V5jbwj7g3P2WjKlj2IMdLyIZ0wpHzPiJCskn5Ypj7sdq2Z98pXiyue+gx3KJ.ClkyH4kQxMSitK5LnQWWPKsdJ0PVj4W+q+0iVGaqBgsCI24f7dqNbtvp4ladlVYrGqZCaXCITZoktn.ABD0+xjHT+lq529129XIIKnfBFQWIk0st0krtO.0iJpnh+QuiMud8VrtiovfkL4BEregddj7sGlw6lI4mBxih1VgFU3Mpps1Z6cQuNtWYkUFpikiKSi6ZFPjb6Qy8W8mm5odJoOTKDz3QpPD7wgQxb548BNv0BqG8W4kW9H1VHMZ4EewWL0FZnguHIKw7uUXDqC1+sFtKRxidziNQKeGkMBMF071BO7C+v42mXyVj5FCQQ0JlSimH30PyeexgI4E.oB5ZGIyhVPWJ64dtm6DMJUgEVX9g3G6sfM8bDZL3EsTaXCaHAcucKDZGIGOI41111tfMrgMj.I2EBNPPBDHvFCyqq1qt2dhkQR0t10tFuGOdtCZOxzG8aklH40SxwZ06erKnQeY0VnxJq7u0mvSo6XJL8fQwiSKk8S1kxjUBMZAcgdnH46FkOFSe97QDrB1268duYwPbV9st5pKSMu+Y.QxyM5tW6TAa5ORQHrTj7766EGc0UWaIPf.uUDbc00q6smQPbUc0UedjLTGw+QKinaY79CIucMeL4D9M+leSeO93R2wTXJZzGyUjzpmjk90TFjWVNRZI4r+t6taVXgEl9d1ydlICwTjmOe9r0YfDZL.msRMp6sYgvVfjlV5bqs1ZKWcu8LRzd26dGse+9ucpmL.RIAS8VmBR5Hl81LSjLUMbLneczidzB5a7Ym566gHSOqrPxerl1VJixzkskoolZxNmOvOQVRxNiFoeQqxeQ2auB6mQpyfRmmIsdZM8zSuFSZcIBCyYNyo93iO9ew+3e7ORps1Z6yAfRsvhelKaYKaas2d6mGIWBO4TH28Xgwgcwsp6.nGuvK7Bqsuu1nF0nbZCJ2tMyUVmc1Y9.3mXlqyvvz.PkjLOMU9ijnxJqrdEcGDCj5qu9UA.p63XnzbyM+Ssvh6uagkkPXeQya5h++T2aKhSPc7ie7YQRcjV71LIiuiN5XJjirlMXoMp0xqrxJ+88WLtksrkwq6XKL8yL4iQupt2fnw3CImgNZEQJ+98aIcgkHzin68OgpRJojjrv8Kmkt2dE1OiDawbE.LqAlzyZRqGwvGG23FWIJk5xO1wN1XAviZgk8UA.eojRJGA.X0qd0S1BKacy1LSm9TO0S886uWO8zSOcqNVFl7YVqnppppTAfcXdVHA.rCJ847nhlZpobb4x0eT2ww.olZp4+R2wPnZhSbhmgEUTcAfhsnxR3fLhqh4ETPAlUpIpK.TgIstDlnINwIVmRoV692+9yD.2kFBgOmFJSKW0UWcZPecQhSxQNxQ1vcbG2Q8826kQFYXayBDC.+l0JJu7xatl05xDLUXSNeIFiJqrx5U0cPLHtmbyM210cPDJH4mJ0TSsHqnrZrwF+iJkJfUTVBmkQbULO0TS0rRl+OtRoL09BpvbM6YO6VUJ08TSM0jNhR886VZoE7we7Gi.ANo6udyCzxSxKIZDG5vDlvD9g5NF5wy7LOysOPuWRIkjSavGZZsXN.VlIttLC2NImjtChXI986+Zf4MtohFdbcG.gAKq6oVTQE8AVUYIbVFwUw7oMsoMFyX83ymum2LVOhnubyM21UJ0c0XiMlM.9eLy0cpolJdlm4YvMdi2HN1wNVOu7Y89u+6mx.7Q1fcNO9Fpb61c5.Xc5NN..Ju7xeza+1u8ALsikZpoZJWyagLyVQ6yahqKyxF0c.DqnkVZYLtb45o0cbLH1G.bq6fHLXY4e+rxJqxspxR3rLhqh44kWdlR++srxJ6.87+I4YPiIsieqYrtEQGiZTipYkRccd73YF.3.C4GHDDe7wiezO5GgpppJbEWwUf8u+8C.f4Mu4cd..jbQjr2oHr0O5QO5ejYT15z3F23rM8s7srks7iGr2OszRyok24MyVL+SahqKyxmu81aWxRKCSjLtLxHi2P2wwPXMJkx1mIV5k1rpBZlybl0ZUkkPXqQxa1LFJ08dZzkjtH4BBNPqDN.jLNR9cMiyEHIqt5p4C7.O.OsS6zXM0TCYvYS15qu9ISxEzqxMdRdEZbSeXq5pqNMyZ+1vUYkU1PN802TSM8P5NNCSeay33DMtujckiIKcXWQxustOHNX762us3IpENH4iXg6hxW2auBgs.I+SlwUTXD3SaHVjGOdNcR5wLNmfzX1vqGG4HG4y41s6IPxCCGvDqQnhj2oYs+Z35du26MqgJd6pqt9K5NNCSlxrILIsxz9VXqnhJxrFH9i3Px7z8wu95e8u9W89Oao2MdkSAMwIevPv3081qPXKPxFMiqnbh2zQz+ZngFlpYbNQe06JoSx4q6sSyPUUUksIukWZokFRyPljrXcGqgoulYbrhjYn6MjASSM0zLLisyQZnwSBoDce7q2ZngFH.ne+9IIYmc14mU26mhDj7RsvcaRd8Wzuh4a0WRNUFrO9FrxzlxTl9UcUW0rLi0iP+F0nF0Q5t6t+Cl45ryN6D20ccWn81OQVByJyq5QM4kWd2htigd7xu7KGpyPeNsqU8ZRqmjLo0STQVYk0Yp6Xvg51.vL0cPzaomd53q7U9J87m6KkTR4s0Y7Do74yW+lxUiVEmEVVBGjX9JlqTpiBf02Vask6pV0pLsQb8nF0n9ajbzl05SnWMzPC+2l45KkTRAYmc13QdjGAjD.Xwzg+nKO5QOZJ.3mq63..nrxJ6WdK2xszRHrnwAiI2FmDy5KrSzjVOQKKV2AfSCIOM.XpyLrlgDRHArwMtQ3xkK.iIbMmz.97DJszRsrYs4JpnBIGlK5Ww7ULG.7HG4HOaZok1wlvDlvtMiUne+9+hJkJekRYk+5ZQTTKszhoer7e+e+eGqYMqo2437q0rKCqzjm7jWqtigd7DOwSDpUNwx9hVSTWlz5wtWwb6XFiw1hjI.f2S2ww.I3DragJkZu5NVhTc1Yml07bxPxiGOx7fhneMRnh43ge3G9RMy02G+wercdVVSDAF6XGqozEm5sDRHALyYNSDe7mXVHecjzQdMW4kWdx.3Wp63..3fG7f+h6+9u+VCkk8odpmxtW4z9iY0h418mTvB0c.3v7P.Xb5NHFBeCcG.CGiabiadVUY0RKsHULWzuhenWDmMRdAczQG2gYtNW3BWnoMkYKrGxHiLrhJIjR80Wed.nRKnrLU4me9eKcGC83Iexm7WDpKa5omdxQyXIJwr5i4186um.IcISK4CMRtHLHynv1DOmRopP2AwvQt4lqkkJaas0Vcjc2GQzmir06BUjbl.3cRMUSM8h6CNz9Omn+EbvAaISXNidzidoVQ4XlHYRvn05ztCdvC9yC0VKG.HqrxJ8nY7DkXVsXtcuh4.1+V0W6HYx.3+U2wQH36p6.X3n7xKO43hKtKvpJuUrhUH0iPzuhYqXNIiG.6HJrpcbs1oXv4wimoB.qZlHzT6VUVjqC1j6U7Nuy6bugyxmXhIZ5cQIKvHkVLG.vI9DMrLAazfmB.on6XYH7jJk535NHBUj7TF6I4me9KyJigBJn.qr3DNH1hurMJ4b.PzXfbX2mBjEgGUxIm7u0BKuKyBKqgMRlH.dLcGG.FsV9MbC2PH2Z4..YjQFNwLmjY0h4Ng6uKyVxCtKD.qV2AQH3Gn6.HTErR4KqOulB.+J8DQBwIyIbi6HUlQo06SDkVuBMvue++e.vEYgE4n5Iu56PbMvlbehvs0xA.RIkTr6CVt9iYMFVbBmmYYYACmFRlB.dMcGGgf+KkREJotT6hoF7e81XAfokNkCEqYMqwJKNgChs3KbiF73wSog5xRRzc2C8.jtrxJaBJkZmCq.SXWn5pqt9Ztb45Opgx1QbcWvzy1Fzcb..bfCbfvt0xA.xHiLlXzHdhxLqVLWpXty1uG17IIJXbt5Cp6fHLc8.XR8409xVcPTXgEZ6u9jFyxrYSxYSxUQx0Qx+FIcSRujrVZLyJWPv2aUd73YVUUUUxSBaXvIzGDiHacqa06EcQgVCgVRIkfbyMWjYlCdirOiYLCGSenSLvH4bBDHvK6xkqooix+i9nOJN.3DxDEqF1jAm269tuaX2Z4..ojRJ4axgRTmGOdFI0h4Yn6.vNhjmObFcgkqQoTlUd2OpqvBKLY.bG.3I640B9DLuKsET1.A2GjB.xE.mAL5pOWI.FpuibLA+2bQvyWSJojPd4kGHYc.39AvyCfxUJkjdHCQNhVtKRrjkrjqMTW1st0shLxXn+9g8t28NpgSLIzORd1.XO5pR4..KbgKz1OZ7CN3o2ntiCfHu0xA.RIkTNMyNdh1H4HozwpSLq4DUEbbc3DFKS0.iJc4Xje94+eE7+d187ZM0TSYAfIX0wRs0VqVp+EIiqgFZHKRdlj75I4VfwjZV6.3P.3uBfuOF5JkOTFCLl6KND.ZgjeaRJWuGBhIqXNI+JokVZgzLCXWc0Ept5pgRMzMtzrm8rqijNwASl3S7mzc.TXgEp6PHTbYvljIHhzVKG.HojRxR62nlARZVOMEmPKlKeQ8o59fy3IIrJmTqf9QezGsr7yO+6L3et.D75ijRJo4pi3YricrVQ8uT0UWcYRxyhjeKR9F.HPN4jSS.3igQiurJD8exnoAfeC.Zkj+bRJcgsAQLWEyCl6x+yg5xWRIkfYMqYExq+N6rSouS4PsgMrgD.vr0cbrhUrBcGBCpfYsfMo63..Xe6aeQbqkGj1dxHQpt6taoEyGghjmF.tEcGGgf2C.Eo6fHTQxIO+4O+dmK3iaSaZSYA.jRJo7uoovxz6JwkWd4I6wimYRxqljalj9G8nGcy.XmvHwUXkI5fAxsCf1BFiwb0A0LDSsSIX+j5kBmOyG7Aev+S94ment3s+xu7KervMtD1CW1kcYeFcGCAY2aIyOK.rE4+6MsoMEwsVNbn2eyDawbm.okyBJXkTbBcgE.fUqTJaeWxqWpF.ePuegEtvE9MqpppFC.zxrZb6s29vpUpKrvBiu1ZqMOR94I4iPx5yO+76LojRpD.7GAvUA688.+i.XajLZkA8D1AAG4vgrt5pq8BfrN5QO5Psntat4lOOoar3b85u9qmFI8DNmeDs7W+q+Ua6ioljwQiQbu1s28t2Pp6nMHRT2aCQhlZpobLoikmmt2VBA2tYrsFKfF82Wm.mVVXA.mHCi7Z5dmWOdoW5kB4LFEIi6nG8nixqWuKlj2NI2ulCeyjWRp8mjscRrVVY4ABmEd6ae6eU.3a7ie7C0hNurxJKIir3PUTQEkvBVvB9.XSR8X4kWdYCfgS2yHZZI.P649aRNbasbrwMtQaww6vU2gRtaMD3ymOjPB1hjpyfwVLNFzMRlErIC15gfO.7eMjKkMjRoBPxqB1j68lRJoLZ.TU+7Vp5pqtLF8nG8zAvRAvWA.Wvjm7jsz3yBk..1KIWgRo9G5NXrCryOlivBMlLFBmo678r3Eu3c+C+g+PWIlXhC1x8ycRS0vhS19129xXAKXAeL.lmtikdblm4YNecGC8G9IS+2Z292+9+o2+8e+Cqu.cricrNxwCRf.ALqJla26xT..Iq6.vl3+Q2APH5pTJkGcGDQBRp9W+q+UN..G+3GGEWbwZMdl6bm674ImgTtAR9pjr6f8K7c.fGE.WfVCTqSgj7Sq6fvNHloh4.XQgyBWc0U+M..ufK3BFzVUq4la1Q9X6Foq3hKNQe97cEm9oe50Cav.9r2RJojteZOm8OOaXSFrjaZSa59FtqijRJIaaWFZv30qWSoOl6wiG634X80H9JlSx4CfqP2wQH3iUJUXMFtrCHYJj7sAP2KZQK5n..MzPC3gdnGBj5qaxOgILgMgSNCo7j.3h0V.YOrURlutCBcKVph4eyvXYOdd4k21A.l+7m+f9nTSJojxjjNxVdajnfs7P0yctysq3iO9+BrISPN8wLAfsJ+ZG7GJXKxDK6YO6YX2Z4..omd51hAvZ3JPf.lRsE762uTwbaNZjAjdUcGGgnKC.NoA7YO9Y.3jREVYjQFXSaZSX+6e+ZJjDChcPRGY2PzrDSTw7BJn.W.3ZB0kuzRK8lPvavL1wN1AcDAmbxI+FJkpygWDJrPEBMLYQDAra8s1YAfyR2AAIwi+3O9uzLVWomd5iwLVOVMOd7XJckEOd73Dt+tc65.q1MBavX5HD7yUJUk5NHBWzHS27856qmat4hm7IexPZhETX4xF.+NcGD5TLwf+bQKZQgThHuwFaDG3.G.O8S+zmHkTkXhINTylmeA3LakfQb7506E..Gwry5G8QezQzcLzGaP2A..PwEW7O8we7GuMyXckRJo3D9AZmBud8ZJ2uoqt5xkYrdhxbBwXTQvA74uQ2wQHnc.7+S2AQjn4laN6rxJqS40iKt3vMbC2fFhHQH5ZH4uPoT6Q2AhNDSTw7ryN6PZBB3Mdi2.e0u5W8iAPu+h+ALkrzc2c+xtb45fC23SD0o762+Zb4xUHOwRoY6agKbgMq6fnGjbx.3BsAwAdhm3ILkVKG.HszRaRl05xJUas0ZJsXdGczgSnRuNgV0OZwIjEV..tLkR4U2AQjH0TS0VLlYDQjMCf4hQfMLZrvMEiK6ry9GDJK3jlzj.Ll8q5soNPKeM0TyH5GmhSPIkTxLH464fpTNf8al8yVL.mMyVKG.H0TSMeyZcYkps1ZMkuHpiN5XPS2T1DNge7foKXda9pzcbDB9..7+NjKkMUBIjvJ0cLHhXyljZu6UpCN9Jl2byMOCDhCvu4Mu4gK9hu380mWdlCzxmWd48WH4xFNwmv7QR0QO5QOMR91ybly7P.Xw5NlBCsBf2V2AQOH4X.vpsAwgo1Z4..ojRJ1pAXanZ0qd0l0f+zILvJiIdpsgif86YmR1M4K6vlgOOgfi8rertiCwvxSA6+LksoywWw7LyLyqOLVV7m+y+4b68q0VasclCwG62FQAlvTPREImVu96wBf+9jm7jOH5yHs2gX0JkxNMkqem5N..L+VKG.HwDSzVklLsZABDHccGCh90p.vLzcPDBdHkRUstChHj5Juxq7W.YvE6zMut5pq4p6fvp4nqXdvTM0OLTV1ibjifibjizZZokl6d+5s0Vam8P7Qkgssdc1d73g..j7R.vwgyrB4..6C.uwPtTVDRlNrAcqlnQqkC.UbwEmSHaWbJLqVnzmOeNxrRSrrfoAtBzcbDB5thJpvV7i1CWG9vGNWR9lwGe7gTcCD1aIlXhOGFg0p4N5JlCfOEFhsgN6rS7.OvCrh7yO+3yO+7yLgDRnvd81w0c2c2u+h5t6t6eoGOdlAbtUBLlP2c28C9ge3GFn1ZqMC.7J5NdFltDa1iE9aq6...XW6ZWldqkCm881LqtxR9lw5IJyutC.K1+ErmysBmj.ABb8SYJSwwjlfKpnhR0mOeeFRtsoN0odL.7Y0cLILMeJe97cQ5NHrRN8922sNXuoe+9wO4m7S97268du+iAXQRxkq9crGcr3hKtaOkTRwNUIpQbJnfBRLt3haEKaYK6n5NVLA+TkRYaRQhEWbwIB.ytUpCajD+1e6u0zii0st0Y6q7SzlOe9VttiAwmH334vIzmmaeqacq+AcGDCFRFWGczQtolZpKG.+G.3bzbHIhhhO93+qEWbwYeFmwY3HyNPgKGaqJQxDgwjyv.ZG6XG0bu2689lCxhjd7wep+1jibjirdaVKaNRjZAKXA+TcGDljZfMKO.O24N2uDrAW+GkZsbje946DF3i8Geu9q+5ogge1JIAud85DxnAijtOqsXl0cnbricr+sUrhUXqdRFjLtVas0wSxKijaF.ARM0TqD.+AHUJejfTl6bm6Cn6fvp3jaw7yenVfryN66.C9M9GcRIcpy7qG7fGrpgQbIF9b8tu669ql9zm92Q2AhIY4Jkx17EcAGaF1hbn769tuaToU6SJojRMZrds.IrxUtx1HI.P2vXbI7Q.XO.nX.TN.pB.sNXMdPiM13Wu+lXUrgrMWWDMEL8HdY5NNBAsNwINwWT2AAIiqs1ZaLomd5KD.eM.7URO8z0dCIHzp01UWc8emTRIsScGHQaN4SzuqA6M2111Fdxm7Iec.D292+9+4s2d6Wd+rXSJ0TO0u+9y849bCXJTTDc8XO1iM45pqtCrzktzXkJk+iUJ0AzcPzGW..RS2AA.v2869cakggPc8NpQMpLilwsEINXLAa70gQ2N5uAf8BflAPWj72RxyIX52C.mHKFciYmc1aTobDiWptzc.XAT.3un6fHTzVascS.PKYMp5pqtLI4xI4FAfuzSOc2v3b9+OvYWWEgIIwDS78c61sjsoriHYJC0Wfum8rGBf3eoW5ktmfuzozJ3qbkq7lFfOdsjzQ7sZwPR7fG7f+jvoRZN.6k8pRS1DJRdHcuiIREhaiwcvCdv6V2wpEp38rm8bdd858BHYw5NXBSw7oiVRdQ5dmbnp7xK2R6BXszRKikj2HIOpt21ENFai1uuW0T4T6JKCUJNDG4HGAKe4K+KuxUtxdR4S4AiVt3De49YbFmwBFfO9X.vn.P8C2.UL3JnfBbcVm0YspYMqY8L.3T6WQNaKSoTlxzqtYgjmNbF4P4HxS+zO8jV0pV0qkYlYNRJ22N24Lm4rMcGDQnX5VLmjwCfmW2wQH5+dZSaZdrhBhFyMEOC.NOqn7DwTNO.bc.HlclY2oVw7u9Ps.ae6aGemuy24OmXhexrR8JW4Jmwa7Fuwg54um1zl1fMqdNG.r0gUTJFLwsqcsqO6bm6b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6OWO3LP9c.faqgFZXJadyaNdkR80TJ0GHUJ2ZYWaw7ICizOnPLbUJ.VXt4la6g6GL3fkQqCXGwHF+u.3pUJUzn6cbLXbOU6lPJEkZCDK1Z4u4DlvDbq6fvrzUWcUptigAS6s2tseR0IXCA33mMaiEXKawb.by5N.DwDdH.b5JkJRyJE4ahwhPzu76++e6cuGeTVcs2.+2JISlbkbgPBPf.ALnHAQJ3EzhZphWnspUKwVsd9T6QqZa8zSa0Z0dQssGeaea01i5odrpG8T0d7jz5EN7pBpPDEE0DEABfbK2SfDBSBISlIyjYVu+wL3IDRHSl444YuelY88yG+HjLyduRlGRVy9YsW6gVIQTElTR4..5Z4Jn8mdlwqqV9.CLvco5XvHwLq0MIBWtbMVajRg33ncqXdUUUUx.3Go53PXq0I.t.hncFiiyYZDAiPLVZqs1V7LlwLL6N9iVdf3zbyMGyG1JVfGT0AfYHiLx3STcLXjV1xVlV2p9prxJ04MgsPyncqX9ke4W9bgFFWBai+YDpFci0jxA.NeCXLDhwzN1wNFDleaqUKqu0csqco02del4hPni.83MOIQT7Vhh5bKebPn2wmPyncI.6zoyqV0wfvV5k.P9DQ+qDQF0FZ50Av2bqacqyb+6e+yxfFSg3yrhUrhcvLGjY1Ey7qwLufva9cijVVxHd73Q2u89+AUG.lA+98+npNFLAi6o2rBsCUG.B6EcqTVH.b6pNHD1J6D.WsAsB4GChnpG1eMYl04e1uvF6kZrwF+AkVZoMAyIACsr9aSM0T8o5XXrDd0x+5pNNLCM1XiaW0wfQiHh03e97tUc.HrWzpDy22912j.P9pNND1BGF.WEQzFgErZI+g+veHaydNDIbFzmOemgSmN2lIOOZYh4qbkqTmqK33xUKG.qYdyadwqs9Ncse8q0cLFg9QqtHdNyYNye7eThDbtAvWA.ERD8VvhtElKdwK9aYEyiHgR4VPR4..8YAyQzPKq6Vl4og3zUKG.2upC.Sjt9FNZU0AfvdQqVwb.rRUG.BskG.7s.veyp23RO+y+7kedm24EO1KiEpyyRDsWKZtzwDy04SBwn4DB1tnVUG.lHccCspqsqTglR2RL+qo5.PncbCf+A.rZqNg7G6wdLGW5kdo2XIkTxexJmWQ7uibji7KsvoaBe3ZYA1kpCfQCy7L.vUo53vj7vDQ5b4CEqz0u1zx1UpPeoMIlyLmL.JS0wgPazCBkP9qXfcYkHFy7B.Pc.voUO2h3eqcsqc+V3zoic+DcstaeLUG.ln+npC.SlVVZTPS6JRB8kNUi4Yo5.PnEZ..U.fBHh9eTQR4gkE.1.zzd.svV6uUYkUZkWWqiIl2npCfQZfAFXVH9sbJ6Dg9Yqwyz0Dy0xMesPeoMIl6wimbUcLHTpM.fERDMWhnZTXB4..fH58IhtLhnoPgj7u427axeKaYKUDHPf0nxXSXuM3fC9ea0SoEOeQh1Tc.LRomd5OkpiASzOfHRa6mfFDcMw7ATc.HrWzlDyegW3ElgpiAgRb+82e+EQD8EHh1Nz2CJhf20ccWtV7hWbMojRJe4CbfCjE.tAnmI8HzXG3.GnNKdJ0tqQGZng5T0wvv40q2xPn6TW7pUq5.vBnqIl6U0AfvdQaRLuhJp3yq5XPDcd9m+4uZ.jL.RdvAG7JhfmR8.3Rpqt5RkH5mlc1YqU+R5Hwzl1zbSD8zHTIubcPe23QBMSCMzfU2kFztCxGe97cXUGCCC4zoymS0AgIpZhn9UcPXAzt2.ZXxuaPLgXzG8yQMl4FAfbrmqHtc6FYlYlviGOXKaYKHqrxByadyCNcN968Qhnjw+6pUPLyCgi+M80N.t21au8Wr3hKNtqtsYlSC.+N.78TcrHzZCBfzsxxJfYNS.nUIl0UWcMuBKrv8n53..nu95agYkUVaU0wgI5TMiSFYcCy7t.vIq53XTjhpKMSg8hVzUV5omdxGRR4J2C8PODFbvAwO9G+iA.v0ccWGdzG8QQ1YeBOzKaBG6sPjAv2D.eU.7Q.XiM2byaojRJom34ZbjHxK.tsAGbvGK0TS8cAfbRgJFMqVA+6.sq+N61saco2pSYkUVVcM+akNDQjV1ZJMA5ZIinqkXiPSoEkxRN4jyMp5XHQWlYlIV0pVExKu7vUe0WMJpnhvy8bOG1wN1w38TOt1KFQzyPDcEDQ2GQzFl0rlkq34jxGNmNct8VZokhPhQMcJl3dcELmZ2p0ELXPsnSwzau8dl.Hd9Dm96C8ce6XzzwtehmDke2mv3n7RYY6ae6otfEr.cs1vDiuBHhjCPgQAy7jAvkfP2AgKDG6aDNX2c2cI986umoN0otW.LUEDhBKle+9OiTSMUK8zWjYNInYImWSM0jdEUTgRWgyveeoQ.LSUFGlrLIhRH5JHLyuH.txd6s2+obxImGR0wSX0SDUtpCBg8hxWw7ErfErTUGChn1uPRJerQD0MQzekH5hIhRo5pq1YM0TS1g6nKNJnfBZaZSaZtesW609hpNV0TuTe8027qu95cBfjB21Jo8u+8aaaspc2c2MqfoUGWwNkWdMc2c2WDhuSJ++HQIo7vZA.ORN4jyin5.YXZQ0Afv9Q4qXNy7CBfefpiCwD1l.v4QDI0OWrKElYYm6++x+QNxQt5bxIm+mQ6SVas0lwRVxRzwiY9wUc0UWpKcoK0xeslYN.zfEh4nHhRBp8MLjLy7g.fs8M4EAJkHpQUGDVEl4qrwFa70JszR8xL2Lzi2z0SRDIkpqXBQG17mWtpC.wD1fszRKqnjRJQRJ2.DNooDMAAviejibj+Re802m1SO8zWlYlYR4jSNNyKu75OmbxYL+dRZoklxWs0nTepHo7vZC5QhJ.gZebJcU7OvANv0f36jx2GQTSpNHrRDQuzQ+yACFryjRJIc35cqt0nJhCnCqfxbUc.HlXpu95u4RJoDsXyaEO3gdnGJUUGCVnOr0VacY0TSMNIhtkbxIm2cFyXFcWd4k6qzRK0a94meuiWqEaG6XG102HiJOwXOfBm6QZ+pbxqpppRsnhJ5oUYLXA9NPOKgIqhtzdPi6ZMvBymRWw7MrgMnCqXuXBpnhJpdUGCwSV9xW9on5XvrELXvWYm6bm2Z4kWdLWi0UVYkAY1VlywFT3bePEN2iz9T4jWQEU7OC.GpLFLYAqqt5T40ZJWRIkjtrvQRh4hILklX7hW7hkdWtMTAETvivLeYDQ8fD6UkwPTVYkcKpNFLQ02RKsbkkTRI60.GS650behBmacJwbksg3dhm3IxtfBJ32pp42h7yUXISoKzkdZtNcB2JrITVorvLS4jSNGWOvVXKbV.3vLyAYlalY9JTc.YW8XO1i4HqrxJtLwbe97c8.3zL3jxOJcpzLhTprle6Tgy8Hor5tcUqZU+AUM2Vkd6s2+cUGCZ.c4Mlni8VcglSk0X9BPnd6rvdal.3k74y2RXlkRSZB5htnK5rTcLLN1Vas0147xu7KOom7IexIsu8suudD7b5bfAFXFNc57YMwt1Salz3ZlbopINPf.ZSaM0qWuJ4MUs4Mu4YLoIMo+QUL2Vn0lat4JqRKfOUG.goK05tvFQkIR8iT3bKLXNb3nV.fv09aO.Xa.3sAPsd85ca0We8sujkrD4TPaDlyblyuW0wvXIPf.2PJojx+IN1RG44c4xkubyM2+9X7zVS80W+UWd4ka1+hw1.vRL44vH4lHRYIK31s6COoIMIUM8GC2tc2kBlV5LNiynJELuVsaW0AflPWVwbaYacUnVJYEyYlSFgNMDEwmxE.KG.2M.dgzRKs8DtuSeXl4GfY97b4xUtPC5i9pzt10tJ.gJKHsie+9WZJojxSiQodte8W+0eY.LxSqW+.3ZHhtbKHob.8ploiDprirf96u+dT47Obd73wxuyAG7fG7bSJojVlUOuVrNIhjMleH5RmaRW1DpBaDUUJKknn4UnV4BfeH.dqbyMWWLy8xL+uvLuHlYmpN3rZm7IexeWUGCiFe97UdpolZci0muxJqL..90.nj8u+8OK.bpHzQ+cUv51Xl1sDyeSUN4tb4pWUN+CmWudszXY6ae6oVXgE9JV4bpH2LruaLZiltbVGnKaBUgMhpJkkKRQyqPujMBsp52M..y76CfeC.1H.bEOW1K6d261I.tWUGGije+9OKmNcFIq51ukHRk2tXsoloiPaQkSNybepb9GNe97Yowx7m+7e.D5m0DWq95qOQ3MeDozke2gtTq6BaDUsh4Z4JEJTtyB.uHBkzU6Ly2Jy7zQbXIuTVYkckpNFFogFZnKI0TS8ChjGqhSJGv9kXtpOEF0lMglOe9Fvplq95quElTRI88rp4Sg9UVTIjIlXzkUtWXiX4IlWas05..KxpmWgsyTAveB.sEtjW9gLySmY11mjNybR.P2ZoYeSGNbrtH4AxLqCmVuZSMSGgTVGYA.HojRxxRFd73ymOK416WUUUkbVYk0aXEykp8oe5m9PpNFzLZwumn95qWWp0cgMhkmX9RVxRJ1pmSgsW1.3APnNwwAXluQl4BTbLEKNaDpd60E+bhn+yH4Atu8suh.vEaxwSjPapY5HvgT8cXHPf.ZSstNv.CLxMNroXUqZU2J.JzJlKEasmxobJxIL4vzSO8jtpiA.fcricHIlKlvTQornkcgBgsQg.3wAPWLy6kYtRlYaS8iFdE+eZUGGCyyBf+kH8AOm4LmBIhzgU62Ncvc7hpN.BDHfkjLbDxzu89Ly4.fG1rmGcPe802OT0wfto0VaU4Il61saTYkUZVmiCh3XpHw7JUvbJhOMW.7eCfivLudl4kYCNjilO.JS0AQXeH.9lSjMYKQz1fdrwpzlMy33IPf.qWChAcp9ish5t8Qsf4PGz9jlzj1opCBcSJojhxWrFud8BnG+rRgMiUmXNA.kto2dm24ctiMtwMdt6e+6eEd734OoxXQXnp..uK.FjY9ewsa2Eqo0i9eV0APX8.fKfHxVdqVc61ss4f6n1ZqcqpNFRKszzlMgVEUTgotJhLyk.fH4DpMdvMAI4uiSVYkUNpNFRN4jsM+LJgdwRSL2kKW4X0y4Hl+WX4Ke4+9y+7O+2ctyctuQFYjw288du26VTU7HLEIAf6NiLxnU.7oLyWLyrCUGT..LyyD.mqpiivVLQj1rg.mn74ymtcvcTeGczwrIhRlHJo.ABbMG8S79u+62pJCL.sqVWMsDIC+lw+ql03qYBBfWW0AgNJqrxR46AorxJqCn5XPXOYoqnHy7R.PsV4bNbc1Ym2cQEUz+mg+wB2gLtdDptk0hD3DFtf.3WAf+MhHUbbfC..l4mG.Wy39.MeeChnmS0AQrH7ARktrgFOP80W+rFd6paDwWxDQptVSIlYUGC..dHhxvrFbl4EB.keGJrH2AQzuW0AgNZngF5HImbxptbVdOhnyQwwfvFxpW85OuEOeGiLyLyTG4GiHJX3NRQF.3KB02ugEFuj.v8.fNYleCl4E.q+MklOzfjxaokVdahn3gUTT08Q8Oy92+9+GFkdH8Q+6ukFjTNPnUoVGtKCsahiMAfpMwwWq3xkqGW0wftRCRJGv9cVKHzDVch4JMwjLyLyw71aQDMDQzq.fRAvbPndnsH9yEBfsyL2.ybELyIaQy6OwhlmSnlat4uFhCpI0vI6pEcZjppppi6t.NrMT6e2hCmSjFUc..fNMqAlY9yAfS1rFeMy+U94mucpkgZYBeWv0AGV0AfvdxJu.NI.rLKb9FMyX7d.DQLQTCDQeW.3D.WD.9XSOxDVsYAf0C.WLyekpppJSKA81ZqsL.vcXVi+DPOm64dtcn5fv.EQmRols1au8ST8a+NVVfL9zg6FnYsJhD.dFSZr0Q2spC.cUM0TitjXtROTwD1WV1EvM2by5vApxzlHOXhHeDQuIQzmC.4iP6.d4fbH9R1.3EV0pVUutb45pfI7uIl9zm92xnGynzOXhzZDsAdWUG...21scaSYL9TCBf8Zkwx3PGRL2TVEwCe3CWNB0JRSDTOziWK0Ryd1yVWZYt1oyZAgFwxRLepScp5vw38wUi4QJhHWDQOABc.2LKDZyDpM04pHlkYt4l6emYts1au8y.FTMnWas05..OnQLVwnWC.+EUGDFr2S0A..PYkU1LGiOUi.neKLTFO5vcKwLJ+BJu7xKQZ0x+1wYuAaCUVYkkVzDGBDHfjXtHpXYIl6vgiy1plqSfVh0AHbotzLQzu..oCfEBfmLliLgtXpSaZS6CXlWKybLeWdVzhVzWBpua+r5pqt5ujlrIDMRaW0A.Bsp3exX74tSMKAJcn8sY3snSWtbsH.rHidb0T8AfMq5fPmUPAE3T0w..Pu81qN8lxE1HVYsX8Urv4Zr7rF4fQDEfHZ6DQ2HBsZ7KC.qwHmCgxrBDp9yu9X3fJJoTRIkmvHCpnvGVc0UeUUVYk5Ter1nzrBm6N862+YTc0UmIQzXUKoq1Rinw2AUc...C8PWgYNobyM2+lQNlZtaIN7MXazRW0A..PvfAkCXHQTwpRLmPnSlQU5FfI1gDHh7SDsYhnubiM1X5HzlFcSl07IrL+E.71LySZh9DGXfANaDZuInJGB.mWbZR4fHxO.9CpXt6ryNWTpolZsmnu2pYqVNfdzkHLzjU7506J.fNTljVEcpK+nqzgVkHRJojzkyYAgMikjX9N24NyxJlmwxZW6ZOKhnm1pVogRKsTug2zne9N6ryrGZngtbL12tag96bQnUOe4SfmCkd5o+eXVATjvsa2KjHJt9WNTe80+SfBZahEVXglVa+yDo7tDQf.ALrDyerG6wbjVZokHkn5ulHRKZQnZt7Tc...je94qCma.BaHKIw7hKt3wsMEZlV8pW8NT0bWTQE0uCGN9eHhN88u+8maf.ApD.6QUwiHpkD.1Hy7uDQv+toqt5ZdPg8T4gFZnJRDNRnKu7x8cvCdvepUOutc6tPqdNM.JuuWGHP.CKFtga3Ftc.joQMd5tibji7GUcLXSLVcIIql7lnDQEKIw7ryN6SyJlmwRYkUlVz9jl6bmauojRJUSDMu5qu9I6wimqGRauxt4myLu9cu6ceB2fQETPAOpUEPih6ygCG0nv42Rsl0rFKuFiyLyLeAK7voxnn7ZdkY1P5TE6ZW6p.GNbb+FwXYS7R4jSNxIIYjoXUG...tb4RRL2hvLSLyIwL6fYNMl4LYlygYNel4BYlmJy7zYlKN7etPWtbkayM2b5ZzAR0mwpBnKzhlmQ0sdq25zU47OZJu7xObFYjwyRDUZM0TyT5qu9tQnGcMAw367Kqrx9Tl4QsVFas0VmIT2dp38.vuTQysR31saU7uaVF.doXXiAqBJurlBFLnQrh4zIexmbhzF9Dd8581UcLXiLKUG...6XG6PZmxQIl4jYlSObh0kvLuvvmT2eMl4amY9gYleYl4sxLO..BBf..vG.7fPso1dPnCzrChPsJ11.Pqg+yGL2by00Lm4LG..ABON2Iy77YlU9B4ZI+REl4tgZ2DbeQhnWQgyejhdu268Jb9ye9ekbxImeE.JP0Aj3D5v.XNDQGSxFLyuJ.tTEDOd5ryNKrnhJJQqMcQLyppSULahHawc8hY1AB8KtTl96u+SK6rydawxX3wimKLszR6MLpXxFXmDQK..51lIVKwL+x.3xUcbzRKsblkTRIenpiCcSUUUUxqZUqJc.jC.JB.yD.kBfSE.md3+S0sX3+D.9c.nIUrI9M8Dyqs1ZcrjkrDk8KCb4x0rxKu7NBQTOpJFhRza8Vu0TW3BW3UmWd4cePsuwFwXqcDJ47AA.1+92+IWZoktKUDH9746zc5zYB4lLlY1K.TQ+K96PDoxxVJhEd08UZq1yiGOyJiLxHpaykM2byoOyYNSWPMuVqD986+bSM0T0hS4V6.l4FgdrDd+vPB..f.PRDEDUp4KgH5iTcPnBgKOjLQnDumC.NMDpIJbA.PGNE3iT6C.eC.7AVYaJ0zSL+.G3.EUTQEorRzXaaaaS8zNsSSG5euwB5ce22c5yadyaUSdxS9df85B6DA+6DQ2J.RhYd2PMsuseBQzuUAyqVfYd6.XAJXpWMQzUnf4MpvL6BJ7mezau8let4laT2cXXleV.bcFXHo65C.4J8t7HiN7lOGlSmHJdegRHl4il.97Av4.fqL7eNdRKHzcg4SrhUP2zSLmY9rfZOoxlGQT7TWPgdi23Ml9RVxRtlbyM26EZROaUfj8506uJszR6tUvb+g.3rSj+k2LyOJ.tEEL0dHhxPAyaTgYtF.b9JLDRKZa4e9746y6vgi21nCHM2WmH54UcPXWDt9f0hZ61mOeKxoSmaU0wgAgps1ZSeAKXAElVZoMO.bV.3KF9+mHYs.nRhHCYSrOVrhDyuUDpdcTg0.fqHNNgE5C9fOXFkWd4Uld5oeOPRRWkN..lpBl2fc2c24UPAEXp+fBcGy7UBfWTQSeRZ3gIznhY9g.vsovPHp9dEybVHzAlUBSIr..rm8rmzl27lmzcOhPc0UWYqK+rPWtbsn7yOeaUh4LyIUe80mQ1YmcgEVXgkkVZocl.3xPnM6tHjf.3RIhdcyZBrhce5EaAywX4ZiiSJG.fOyy7LaA.O..dv8rm8LiRKszqI4jS9W.KHI8.ABfZpoFzbyMioLkofhKtXL6YOajWdZw46fUSEIkC.bg5xuHRwjSY2HyNU3b+BQYR4DBsHKITIkCfetjT9DiGOdlvmPylEhHsqM7cTUUUUIWRIkj4LlwLlZd4k2ImQFYb1HTB3KdAKPEUDnsRR.XcLy+Vhn6Blvlx1JVwbUsor..bPDMjhlaUhFXfAlQ5om9WG.+LXxIo6xkKricrCrm8rG7ge3Gh96uebdm24gktzkhEtvEhjRRa+4S1cOFQjJJeCsS3j21Ir9C0I+DQoZwyYTyiGOWbZok1ZUwb60q2xRO8z26D84o365pxTWc0k6RW5RU9gBkcRO8zyRxImbpU0wA.vgNzgNioLkonzXgYNIWtbkclYl4LRM0TW..97HTsRqCaN13AOL.99F8cL0TSLmYNUn1S+pjiyWw7HwQSR+Z.fkrR5ACFD8zSOfYFSdxS1rmtDUGpt5pa5KcoKUKpmRcPe80W4YkUVwTq3KJ7dDQmiEOmQsFZngSY1yd1JYUyewW7EK3ptpqZBcH4vLOK.zn4DQZsmiH5an5fvtwsa2e0LxHipUcb..zWe8s7IMoI8NV07s8su8TWvBVvT.vofPIfe0.XgV07m.aEDQFZ6a0rKkEUVSCusjTN..3LxHiV.vuG.O.y7L.foljdRIkDxOeo6NZlZu81WtjT9wJ6rytdl45g01cV9KV3bEyZs0VO3rm8rUxb+du26MgZatg2HeIZa1S..r28t26R0wfcTFYjwhTcLbTolZplV9O6d261YYkU1TQn1P3ECfuFjy8DUY0LySxHqNCytFCJwjG+wzfCN3uSUysFiIhZgH52SDkCB85ysiPsjKKSiM1380Ymc9qCFL3Gakya7hfAC9aKt3hURuRWywd734lsv4qS.7LV37EyV9xWtx1OBACFbhtPI+TD5vGIQyGVVYk0hpCBapUn5.3nb5zogz1bqpppRt+96epLyqfY9AXl6prxJyKBcmjVM.9dPRJWkRGgZQjFFyNw7SyjG+wTu81qbhachczjzefvIoOS.7iPniwVSSWc0Et1q8Ze4hJpneQxIm7miHJkMtwMVRmc142B.0alycbhd93O9i+4pNHzUqYMqYyvZJetRHhJhHxsELWFo..PIsO1rxJqHtNLYlmM.tWSKXzX8zSOeaUGC1TDzq122kDMOocu6c6zqWuyiY9lXl27pV0pFJyLyrC.rN.7CgjDtN5wCenJYHL6DyUVsW1UWccXUM21PLQTqDQOHQT9.XF.3G.SHI8oLkofMtwM9QG7fGro1au8atolZZVACFrihJpnmhHp7ZpolrapoltvfACpjMnltyiGOeQoDVFaUVYkA.fUzK46vBlCyx+iJlz4N24FQ6oIl4jAv5M4vQW0dd4kW79gRioXm6bmYo5XXDtzppppS3FCmYlN3AOXVLymNy7syLu2xJqLuNc57SAveF50azPL1xE.eNUGDQDl4NX0wz63LI.Hl4hYluMl4tM4WudzAFXfiozmtm64dRq4la97YlWuIO21EC.KnSJY2sicriIa1uP31s6kn5uNiVLyWqY+8mQyi7HORDk3Dy7Mqh3SG32ueU1dgs074y2RU8qeiTc0U2y9TO0SkK.Rt1Zq0QWc0U1LymDy7UxL+mYl6WsQnv.sQi5ZYS6WxygV0CU0pB2CQz7TzbGuhXlmJBsSuuGXR2NsN6ryWpnhJ5ZAfmg+wW6ZWalKZQK5BKpnh9+Bquk3oKtZhnWP0Agc.ybs.vLSdty5qu9YVd4kOg1Pi5.e97cFNb33Cr548Ue0WMmUtxUdBqwcl4bfIWNcZrAqt5pyL7c8QLAvgVHt2B.KW0whHwUu81a94latth0wwLKkEUdLUK2JPiGSD0AQziPDUH.lF.9NHzFfyvTXgEdkCMzP8tksrkuFFVWC5RtjKw8Tm5TWMQzort0sth5omd9AXDIuGGqk1au8YIIkG45u+9ucSdJJbAKXAqqpppJYSddLb80WeGPEya1Ymcj78pG2zCD802URJOpMSHIkKTrbxImulQLNlYh4prAV2lBm6DALQzAHhdTD5Dubp.3VPnik9XVxImriEsnE8ewLejVas0u48bO2SZC+yeIWxkzYd4k2ejHJqMu4MuHud89RFw7poNPc0U2oTbwE2rpCD6jlZpo20BllyeUqZU++XCbS+XERJojrztvzQMzPCcBSLmCsgOWk0DMZomS0AfMVkpN.DB.7agATIJl4uPQYsJQXvqhqXrQDwDQGjH5w.vzAPQ.3FAP6FvvmdwEW7Scu2685YvAG7eq1ZqsDbrWzGbYKaYaM8zS+q7xu7KOo1ZqsuEhutM3A+zO8SW3RW5RGP0AhcS3RL4wrfo5RPnMJssQt4lqRtSSYmc1NFmGxSXIAhd5WSD4U0AgM1OR0AfP.fr83wSLeppZlIlqxZ7VY8p2DYgSRuShnmDg5rKSA.2..h4dxapol52YIKYIMwL+os2d6m+HKgfq7Jux9lwLlwSQDM42+8e+SevAG7Mi04T05qu9N+S4TNkCo53vtp6t69e0hlpeOybZi+CSanj89yjm7jcNVet1ZqsR.vEZggit4AUc.XW0byMmNBcWaEBkKszR6eLVGCyLw7Eahi83QUa5TQXgSR+PDQOM.lEBkj92Bw9JoW1zl1zpYUqZUtb4x0WE.i71iG7rO6y9SRKsztnG4QdjBZs0VuuXb9TkGyJONmiGUPAE7ovZ5o4..mgEMOwLUchHO4IO4zGqO2zm9z+yVYrnY9uHhh4MLVhpIMoIMMUGCBwvbmwZ4MZlIlqx9uorAZzHCKI8mB.yn+96uH.7sQrUxQYmat4VMyrqN5niu.Fk5551tsaq6YNyYduOzC8PoUWc0cMCMzP1kxbom5qu9+IUGDwABBfeoEMWWpEMOFAFV2aX4yjc1YOpMDf1au8khn7vXINwcp5.vNKmbxIQsKcIzSN.vbhkAHtbEy6u+9OgM0eg5PDwYmc1cRD83DQS0sa2Gs6tDskrQ1ScpS8M84yWcu4a9lmLFkDz+9e+u+fKcoKsJGNbL40u90e9d73YewxWClMe97cA1w1vmNps1Z6YrnoxtkX4Gof4LmQ9A5omdxaZSaZFV++0F58Hhh4R8KA2xTc.HDivWOVdxlUh4Iahi8354dtmKOUM2hIDNqrx5.DQOJQTgc2c2yD.+DDEsAQGNbr3uvW3KrKud8to0st0MeL56L5fW3EdgaLiLxnr0st0s3AGbv5i0u.LAOrSmNk18oAYFyXFsB.q3T.VkktWz3uof4L+g+WXlWbN4jygAvXVhKI.tYUG.wAtRUG.BwHbmHF5NKlRxyu669tJcEqO0S8TKUkyuHpvETPAsRD8aAPV81aukgPaHpIT8v5zoykshUrhc3ymuOYyadyKAi9+3fujK4R1RZok1B27l27hBFL3mZ.wuQ3..3Gp5fHNCCfepELOIYy5o4ulBlyhFwe+mofXPmzD.1tpCBatj.vBUcPHDiPlgOPFiJlRh4olZpJMw7ku7kuBUN+hXCQTvbyM28RD8ipolZb1d6suDLAWgOGNbrvy5rNqZYlac+6e+WLN9MIJ..urksrslbxIO+1au8OOh9xownrLhHYiKavZu81sjUGtiN5Hkw+QoM1Er91J6LGwe+Br34W27MIhXUGD1Y8zSOGW4QIDZhntKSYJIlO0oNUU+KnlY3i1YgMWEUTwPEWbweDQzpt268dSukVZ4RCFL3DoTOldokV5ZYlc2c2ceSqd0qdz1.ZbwEW7lBWy62nQE6SP2BQTiJZtiqUbwEeH.rSKXpFy1AntIbmY4ygI3cjJFM+gGBXDk1RBl9.vaq5fvtKiLxnLUGCBwX3eNZehlRh4EWbwV9N9eX9jibjibJ.nXEFCBSv8ce2m2RJoj0lbxIe5UWc0St0Va8VPjefB4L+7y+O+k+xeY2CN3fO3V25VG4sUG.HPVYk0S9pu5qly.CLvZMvPe77ZDQIxsKNqvsa1Svoe5mtsZw.HhZafAF3brvo7LO5engFZv17lXLI2HQjz8vhQNb3XkpNFDhwvRXl0qFQhWudeWVAZt4lk5MKwB8BuvKL2N6ryGNJtb4cZqs19bXzeCpzZVyZpr2d60vuFcDNBauNbZrkZngFRynegyue+6hYNMl41Xl48rm8Xa5NDLy+Nl4uIy7YZzee4DAg+2Z9746rsx4UyDfYd7NETEiCl4j3P+7SgPWM6n4ZaSqyoLv.CnjM0R0UW8dUw7JTF9ptpqZeEVXg2V0UWsy8su8cgACF7iivm64N8oO85XlOR+82+M85u9qO7U7j+ReouTU4jSNetFarQSHrCwue+UHGE2luRKsTu.3wMxwLkTRoyvu1c4..Se5S+LGmmhNYV.3o.v6akS5gNzglFyrSGNb77V47pY9QDQ9UcPXmwLSABD3qCfrUcrHDm.mtpCfiAGZEYrT986mQLzhZDwO1vF1PA97461Yl8NQtFZngF5c15V25Jti63NxFguVZfAFXylzkr0B45UKCy7bL3W+ZN73lDyrKl4sp5uFiTLyWuA+8BQDpiN5HSU+5ucTs0VqCl4alYdWp90PgHB8qT8+t4XvLe+V82AZu816R0ecKzKUUUUI2SO8rTl4ZlnWO0byM6acqaccXBWpxLyra2tU4oiaBGlYhYd+F4qgH7arhY9GxLyu669t1hdxMybYF42GDQrGT0u1aCQ9746bYl6R0u3IDSD986+eKZtfWYGBPlggFZHs9DcTX8prxJCjat4VKQzEzRKsLYDZS.FQ2F4YNyY5XEqXEQcuHc7jQFYrEyZrEGuvsltawHGyitIFOxQNxZ..Niy3LNeib7MKG3.GncUGCIh5qu9teUGC1HI4ymuymYtAGNb7N.n.UGPBwDQJojRuQyyyLSL2x2bKojRJcX0yov9njRJ4vDQO..x.g5gxVQKzar3mHRkcunDUqG.F122m8rmc9..0We8s..jRJo7exLq8GzPSaZSa.UGCIfdoIMoIo5yJAs2t28tcxLeMLy83vgiZPn8CgPXG0Rz7jLyDyGs9EsoJiLxHRacdhDXDQCQD8V.XAd85cN.nZEDFefBlyDdgO.m9tF3PNG.fksrkczj8KD.WqAN9lEF.5xIdaBAud8Jmpum.s1ZqSlY9dJqrx7BfmGxF6TX+MQNyU9LlYh4SxDG6QUlYlorJPhHFQDmd5o2.QTk81auSF.+NKZpa..WuEMWhi2yZfi04.7YGXOGsDo9Kr83.NaipN.Rf7IokVZMp5fP2vLmTe802BYlesvGDX2qpiIgv.EocHtigYlXdtl3XOpRIkTjRCPDUxM2bOLQzOdW6ZWSB.OhYLGtc6t5ZpolzIhlCQTClwbHFegKgn6vfFtgu53CeOC7eXPiuoIPf.aS0wPhB+98eSg2iCB.zbyMmNy7WC.ckUVYsU.bIpNlDBC1kQD4IZdhlYh4p31PImjZhXx7m+76iH515niNNUCdn22q7JuxWuhJpP5Y45gG0fFmEwLmR3+7OYXe7qhYdNFzbXJb61srY4sFG1gCG0o5fP0Xlot6t6YxL+nyblyb..7eAf7UcbIDFrWA.yhH50h1AHtpTVPn5lTHhYuy67N61fGxUUYkUJuwQMAQjabrIRG0ZpollR3wb8aZSaZ3+bu+ciX7MKtc69fpNFRPbSgK0oDRbn97+YCfON+7yuYXvcFIgPgFDgRD+63ymuSC.oSD8EIhZNVFTS6vMgCcXarPyZ7GC+dhHi5VTKRfEd0NMpUTrIhnRg7FG0JLyNAPeHF6fTtc69KlUVY8JCabWO.pH7es.hntikw2rzTSMMmRJoDSYUy6ryNwTlxT.Qx4mU80WuyxKubepNNrZLyIgPknxyBYkwE1SGB.0hPahycgP6Or1C+wcSDYJ+65TF+GRTSEqPPbUeYWnTWkQMPCMzP2Hjjx0NDQCxL+OfP2R8nVlYl4+.BspIG0MAf8F9O+UAviEKiuYYvAGrOidL84yGdhm3IPu81Ktq65tL5g2N59RPSJ+T.vaBfoq5XQHFEAAP8.ntCe3Cu8t5pq84ymuVSN4j6xmOe85vgiAJu7x8CE86sMyDy62DG6wh12+fE1BD.9oFzX0hCGNVuAMVBiW0.3gQrc3kbMHzl.MH..Qz9XleR.7OBfeC.dbnlEp3DxmOeQ0FS5DYm6bm3u9W+qXMqYMF8PaK0d6saJajbcFy7kgi8MpJDFBWtbcA4kWdmEB08dFqSYY2.Xqg+usiPKRRaHzpb2G.7Bf.57lw1LKkkWF.WtYM9ig+DQjQ1ihEIfXlmJ.LhCqJ+.XtDQQ0gLfvZvLuT.7gwxXTWc0M8ktzk9YWyDtLYN..x8PG5PW3TlxTzt2bVs0VqikrjkXnqlamc1I5s2dQYkUlQNr1UafH5Kn5fvJE9v0ZHUGGB6k8t289idsW609nJqrR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Dahj+fn8IuEUTQ4Bbht6oSNKdY07TWc0c9vl1SFHYR9746yRR6zWp2AI+9jbT5d+iPy74y2U0qSLND50rR1HLpxKu7r84y2mgjah8p+C5P0QEUTwjz8N0XAEUTQIPiaZpcG6XGqs29se66S2wQX3t08wOQrGRNin8ItM0TSeqdJuFZnguQzt7hgT+N1wNxVmmeDpnwSc4yP8kosBPxGxiGOyjxX7RPRUYkU1Y0mSRVrtiK6BRF2QO5Qmne+9ud5bmLYB7lu4adN5deoSme+9+F59.YOV+5W+J+m+y+4UOzKoswOS2G+DwVHYBznK6E0DHPfsiOoQpNomp7fo3hKN89KYJLRxt10tB6YnZa.EImIIeLKXWTUj7+fjSmRkwE8fjeU1OYsDm7r6Yz1q+5udZMzPCKijOuEbgqo5sdq25JCmsURlHiv9odrlfeIaHOdDhlppppNL.ha26d22jtikvv8p6ighXKj72DsOo8sdq2Z78Tdae6auuMf0.4u..TbwEOpnZvYi0Ymc9t56LCyQvwSzoSx6lFyaJCGtI4VH42kFS3hRtzT7IH4Dqqt5lzANvAl3fbRjsr+fY2rgMrgDpnhJNq.ABzyLnosWUUU08Eh+vq33mjR.+Oh56Ls4762eeGXzZy5W+5W..vgNzgVmtikvfTwbgogjmWz9D1JqrxueuJRUf.AdmP7iN4fw3OKBJ1NnwD91lH4cPxuFIWIIWPmc14o41s6ITd4kmcQEUTpEWbwIFb.oFGMZwUUu9WbEUTQo51s6uQDDCCaG5PG5KY8mUD8PRUs0VaFjbAj7lI4aw9+67eLRtflZpobJpnhR.1z9VuvBwgnB0jb4gxEUd734pnLnACKETPAt18t28r84y2CM.WvZmreRl+fs8TUUUMld+AZs0VGmEsqz14UdkWIIZSNlVQEUrcD7l8tc6Np2hgln6SyGFQAETfK2tcmNIyljigFYngj4PL6HKrWHYJLJOVO750a0.3DmWricL3QNpL...H.jDQAQki4FhezeWvXLyA38K1iGO+pJqrxqaO6YOKqvBK7zdgW3EFcvYRznQ2YP0c2caR6UBcu4a9lyJJrsXqPirWVBjLMRlNIiW2wjvFhjW5f7dwyvazjewVYrGKofBJvUIkTxbBDHvSX52wybsxAZafjSgjrvBKL4foYyQroOQ2tcey58vzmXCaXCmdOwUKszxKo63ILb+Z33V5jbwj7g3P2WjKlj2IMdLyIZ0wpHzPiJCskn5Ypj7sdq2Z98pXiyue+gx3KJ.ClkyH4kQxMSitK5LnQWWPKsdJ0PVj4W+q+0iVGaqBgsCI24f7dqNbtvp4ladlVYrGqZCaXCITZoktn.ABD0+xjHT+lq529129XIIKnfBFQWIk0st0krtO.0iJpnh+QuiMud8VrtiovfkL4BEregddj7sGlw6lI4mBxih1VgFU3Mpps1Z6cQuNtWYkUFpikiKSi6ZFPjb6Qy8W8mm5odJoOTKDz3QpPD7wgQxb548BNv0BqG8W4kW9H1VHMZ4EewWL0FZnguHIKw7uUXDqC1+sFtKRxidziNQKeGkMBMF071BO7C+v42mXyVj5FCQQ0JlSimH30PyeexgI4E.oB5ZGIyhVPWJ64dtm6DMJUgEVX9g3G6sfM8bDZL3EsTaXCaHAcucKDZGIGOI41111tfMrgMj.I2EBNPPBDHvFCyqq1qt2dhkQR0t10tFuGOdtCZOxzG8aklH40SxwZ06erKnQeY0VnxJq7u0mvSo6XJL8fQwiSKk8S1kxjUBMZAcgdnH46FkOFSe97QDrB1268duYwPbV9st5pKSMu+Y.QxyM5tW6TAa5ORQHrTj7766EGc0UWaIPf.uUDbc00q6smQPbUc0UedjLTGw+QKinaY79CIucMeL4D9M+leSeO93R2wTXJZzGyUjzpmjk90TFjWVNRZI4r+t6taVXgEl9d1ydlICwTjmOe9r0YfDZL.msRMp6sYgvVfjlV5bqs1ZKWcu8LRzd26dGse+9ucpmL.RIAS8VmBR5Hl81LSjLUMbLneczidzB5a7Ym566gHSOqrPxerl1VJixzkskoolZxNmOvOQVRxNiFoeQqxeQ2auB6mQpyfRmmIsdZM8zSuFSZcIBCyYNyo93iO9ew+3e7ORps1Z6yAfRsvhelKaYKaas2d6mGIWBO4TH28Xgwgcwsp6.nGuvK7Bqsuu1nF0nbZCJ2tMyUVmc1Y9.3mXlqyvvz.PkjLOMU9ijnxJqrdEcGDCj5qu9UA.p63XnzbyM+Ssvh6uagkkPXeQya5h++T2aKhSPc7ie7YQRcjV71LIiuiN5XJjirlMXoMp0xqrxJ+88WLtksrkwq6XKL8yL4iQupt2fnw3CImgNZEQJ+98aIcgkHzin68OgpRJojjrv8Kmkt2dE1OiDawbE.LqAlzyZRqGwvGG23FWIJk5xO1wN1XAviZgk8UA.eojRJGA.X0qd0S1BKacy1LSm9TO0S886uWO8zSOcqNVFl7YVqnppppTAfcXdVHA.rCJ847nhlZpobb4x0eT2ww.olZp4+R2wPnZhSbhmgEUTcAfhsnxR3fLhqh4ETPAlUpIpK.TgIstDlnINwIVmRoV692+9yD.2kFBgOmFJSKW0UWcZPecQhSxQNxQ1vcbG2Q8826kQFYXayBDC.+l0JJu7xatl05xDLUXSNeIFiJqrx5U0cPLHtmbyM210cPDJH4mJ0TSsHqnrZrwF+iJkJfUTVBmkQbULO0TS0rRl+OtRoL09BpvbM6YO6VUJ08TSM0jNhR886VZoE7we7Gi.ANo6udyCzxSxKIZDG5vDlvD9g5NF5wy7LOysOPuWRIkjSavGZZsXN.VlIttLC2NImjtChXI986+Zf4MtohFdbcG.gAKq6oVTQE8AVUYIbVFwUw7oMsoMFyX83ymum2LVOhnubyM21UJ0c0XiMlM.9eLy0cpolJdlm4YvMdi2HN1wNVOu7Y89u+6mx.7Q1fcNO9Fpb61c5.Xc5NN..Ju7xeza+1u8ALsikZpoZJWyagLyVQ6yahqKyxF0c.DqnkVZYLtb45o0cbLH1G.bq6fHLXY4e+rxJqxspxR3rLhqh44kWdlR++srxJ6.87+I4YPiIsieqYrtEQGiZTipYkRccd73YF.3.C4GHDDe7wiezO5GgpppJbEWwUf8u+8C.f4Mu4cd..jbQjr2oHr0O5QO5ejYT15z3F23rM8s7srks7iGr2OszRyok24MyVL+SahqKyxmu81aWxRKCSjLtLxHi2P2wwPXMJkx1mIV5k1rpBZlybl0ZUkkPXqQxa1LFJ08dZzkjtH4BBNPqDN.jLNR9cMiyEHIqt5p4C7.O.OsS6zXM0TCYvYS15qu9ISxEzqxMdRdEZbSeXq5pqNMyZ+1vUYkU1PN802TSM8P5NNCSeay33DMtujckiIKcXWQxustOHNX762us3IpENH4iXg6hxW2auBgs.I+SlwUTXD3SaHVjGOdNcR5wLNmfzX1vqGG4HG4y41s6IPxCCGvDqQnhj2oYs+Z35du26MqgJd6pqt9K5NNCSlxrILIsxz9VXqnhJxrFH9i3Px7z8wu95e8u9W89Oao2MdkSAMwIevPv3081qPXKPxFMiqnbh2zQz+ZngFlpYbNQe06JoSx4q6sSyPUUUksIukWZokFRyPljrXcGqgoulYbrhjYn6MjASSM0zLLisyQZnwSBoDce7q2ZngFH.ne+9IIYmc14mU26mhDj7RsvcaRd8Wzuh4a0WRNUFrO9FrxzlxTl9UcUW0rLi0iP+F0nF0Q5t6t+Cl45ryN6D20ccWn81OQVByJyq5QM4kWd2htigd7xu7KGpyPeNsqU8ZRqmjLo0STQVYk0Yp6Xvg51.vL0cPzaomd53q7U9J87m6KkTR4s0Y7Do74yW+lxUiVEmEVVBGjX9JlqTpiBf02Vask6pV0pLsQb8nF0n9ajbzl05SnWMzPC+2l45KkTRAYmc13QdjGAjD.Xwzg+nKO5QOZJ.3mq63..nrxJ6WdK2xszRHrnwAiI2FmDy5KrSzjVOQKKV2AfSCIOM.XpyLrlgDRHArwMtQ3xkK.iIbMmz.97DJszRsrYs4JpnBIGlK5Ww7ULG.7HG4HOaZok1wlvDlvtMiUne+9+hJkJekRYk+5ZQTTKszhoer7e+e+eGqYMqo2437q0rKCqzjm7jWqtigd7DOwSDpUNwx9hVSTWlz5wtWwb6XFiw1hjI.f2S2ww.I3DragJkZu5NVhTc1Yml07bxPxiGOx7fhneMRnh43ge3G9RMy02G+wercdVVSDAF6XGqozEm5sDRHALyYNSDe7mXVHecjzQdMW4kWdx.3Wp63..3fG7f+h6+9u+VCkk8odpmxtW4z9iY0h418mTvB0c.3v7P.Xb5NHFBeCcG.CGiabiadVUY0RKsHULWzuhenWDmMRdAczQG2gYtNW3BWnoMkYKrGxHiLrhJIjR80Wed.nRKnrLU4me9eKcGC83Iexm7WDpKa5omdxQyXIJwr5i4186um.IcISK4CMRtHLHynv1DOmRopP2AwvQt4lqkkJaas0Vcjc2GQzmir06BUjbl.3cRMUSM8h6CNz9Omn+EbvAaISXNidzidoVQ4XlHYRvn05ztCdvC9yC0VKG.HqrxJ8nY7DkXVsXtcuh4.1+V0W6HYx.3+U2wQH36p6.X3n7xKO43hKtKvpJuUrhUH0iPzuhYqXNIiG.6HJrpcbs1oXv4wimoB.qZlHzT6VUVjqC1j6U7Nuy6bugyxmXhIZ5cQIKvHkVLG.vI9DMrLAazfmB.on6XYH7jJk535NHBUj7TF6I4me9KyJigBJn.qr3DNH1hurMJ4b.PzXfbX2mBjEgGUxIm7u0BKuKyBKqgMRlH.dLcGG.FsV9MbC2PH2Z4..YjQFNwLmjY0h4Ng6uKyVxCtKD.qV2AQH3Gn6.HTErR4KqOulB.+J8DQBwIyIbi6HUlQo06SDkVuBMvue++e.vEYgE4n5Iu56PbMvlbehvs0xA.RIkTr6CVt9iYMFVbBmmYYYACmFRlB.dMcGGgf+KkREJotT6hoF7e81XAfokNkCEqYMqwJKNgChs3KbiF73wSog5xRRzc2C8.jtrxJaBJkZmCq.SXWn5pqt9Ztb45Opgx1QbcWvzy1Fzcb..bfCbfvt0xA.xHiLlXzHdhxLqVLWpXty1uG17IIJXbt5Cp6fHLc8.XR8409xVcPTXgEZ6u9jFyxrYSxYSxUQx0Qx+FIcSRujrVZLyJWPv2aUd73YVUUUUxSBaXvIzGDiHacqa06EcQgVCgVRIkfbyMWjYlCdirOiYLCGSenSLvH4bBDHvK6xkqooix+i9nOJN.3DxDEqF1jAm269tuaX2Z4..ojRJ4axgRTmGOdFI0h4Yn6.vNhjmObFcgkqQoTlUd2OpqvBKLY.bG.3I640B9DLuKsET1.A2GjB.xE.mAL5pOWI.FpuibLA+2bQvyWSJojPd4kGHYc.39AvyCfxUJkjdHCQNhVtKRrjkrjqMTW1st0shLxXn+9g8t28NpgSLIzORd1.XO5pR4..KbgKz1OZ7CN3o2ntiCfHu0xA.RIkTNMyNdh1H4HozwpSLq4DUEbbc3DFKS0.iJc4Xje94+eE7+d187ZM0TSYAfIX0wRs0VqVp+EIiqgFZHKRdlj75I4VfwjZV6.3P.3uBfuOF5JkOTFCLl6KND.ZgjeaRJWuGBhIqXNI+JokVZgzLCXWc0Ept5pgRMzMtzrm8rqijNwASl3S7mzc.TXgEp6PHTbYvljIHhzVKG.HojRxR62nlARZVOMEmPKlKeQ8o59fy3IIrJmTqf9QezGsr7yO+6L3et.D75ijRJo4pi3YricrVQ8uT0UWcYRxyhjeKR9F.HPN4jSS.3igQiurJD8exnoAfeC.Zkj+bRJcgsAQLWEyCl6x+yg5xWRIkfYMqYExq+N6rSouS4PsgMrgD.vr0cbrhUrBcGBCpfYsfMo63..Xe6aeQbqkGj1dxHQpt6taoEyGghjmF.tEcGGgf2C.Eo6fHTQxIO+4O+dmK3iaSaZSYA.jRJo7uoovxz6JwkWd4I6wimYRxqljalj9G8nGcy.XmvHwUXkI5fAxsCf1BFiwb0A0LDSsSIX+j5kBmOyG7Aev+S94ment3s+xu7KervMtD1CW1kcYeFcGCAY2aIyOK.rE4+6MsoMEwsVNbn2eyDawbm.okyBJXkTbBcgE.fUqTJaeWxqWpF.ePuegEtvE9MqpppFC.zxrZb6s29vpUpKrvBiu1ZqMOR94I4iPx5yO+76LojRpD.7GAvUA688.+i.XajLZkA8D1AAG4vgrt5pq8BfrN5QO5Psntat4lOOoar3b85u9qmFI8DNmeDs7W+q+Ua6ioljwQiQbu1s28t2Pp6nMHRT2aCQhlZpobLoikmmt2VBA2tYrsFKfF82Wm.mVVXA.mHCi7Z5dmWOdoW5kB4LFEIi6nG8nixqWuKlj2NI2ulCeyjWRp8mjscRrVVY4ABmEd6ae6eU.3a7ie7C0hNurxJKIir3PUTQEkvBVvB9.XSR8X4kWdYCfgS2yHZZI.P649aRNbasbrwMtQaww6vU2gRtaMD3ymOjPB1hjpyfwVLNFzMRlErIC15gfO.7eMjKkMjRoBPxqB1j68lRJoLZ.TU+7Vp5pqtLF8nG8zAvRAvWA.Wvjm7jsz3yBk..1KIWgRo9G5NXrCryOlivBMlLFBmo678r3Eu3c+C+g+PWIlXhC1x8ycRS0vhS19129xXAKXAeL.lmtikdblm4YNecGC8G9IS+2Z292+9+o2+8e+Cqu.cricrNxwCRf.ALqJla26xT..Iq6.vl3+Q2APH5pTJkGcGDQBRp9W+q+UN..G+3GGEWbwZMdl6bm674ImgTtAR9pjr6f8K7c.fGE.WfVCTqSgj7Sq6fvNHloh4.XQgyBWc0U+M..ufK3BFzVUq4la1Q9X6Foq3hKNQe97cEm9oe50Cav.9r2RJojteZOm8OOaXSFrjaZSa59FtqijRJIaaWFZv30qWSoOl6wiG634X80H9JlSx4CfqP2wQH3iUJUXMFtrCHYJj7sAP2KZQK5n..MzPC3gdnGBj5qaxOgILgMgSNCo7j.3h0V.YOrURlutCBcKVph4eyvXYOdd4k21A.l+7m+f9nTSJojxjjNxVdajnfs7P0yctysq3iO9+BrISPN8wLAfsJ+ZG7GJXKxDK6YO6YX2Z4..omd51hAvZ3JPf.lRsE762uTwbaNZjAjdUcGGgnKC.NoA7YO9Y.3jREVYjQFXSaZSX+6e+ZJjDChcPRGY2PzrDSTw7BJn.W.3ZB0kuzRK8lPvavL1wN1AcDAmbxI+FJkpygWDJrPEBMLYQDAra8s1YAfyR2AAIwi+3O9uzLVWomd5iwLVOVMOd7XJckEOd73Dt+tc65.q1MBavX5HD7yUJUk5NHBWzHS27856qmat4hm7IexPZhETX4xF.+NcGD5TLwf+bQKZQgThHuwFaDG3.G.O8S+zmHkTkXhINTylmeA3LakfQb7506E..Gwry5G8QezQzcLzGaP2A..PwEW7O8we7GuMyXckRJo3D9AZmBud8ZJ2uoqt5xkYrdhxbBwXTQvA74uQ2wQHnc.7+S2AQjn4laN6rxJqS40iKt3vMbC2fFhHQH5ZH4uPoT6Q2AhNDSTw7ryN6PZBB3Mdi2.e0u5W8iAPu+h+ALkrzc2c+xtb45fC23SD0o762+Zb4xUHOwRoY6agKbgMq6fnGjbx.3BsAwAdhm3ILkVKG.HszRaRl05xJUas0ZJsXdGczgSnRuNgV0OZwIjEV..tLkR4U2AQjH0TS0VLlYDQjMCf4hQfMLZrvMEiK6ry9GDJK3jlzj.Ll8q5soNPKeM0TyH5GmhSPIkTxLH464fpTNf8al8yVL.mMyVKG.H0TSMeyZcYkps1ZMkuHpiN5XPS2T1DNge7foKXda9pzcbDB9..7+NjKkMUBIjvJ0cLHhXyljZu6UpCN9Jl2byMOCDhCvu4Mu4gK9hu380mWdlCzxmWd48WH4xFNwmv7QR0QO5QOMR91ybly7P.Xw5NlBCsBf2V2AQOH4X.vpsAwgo1Z4..ojRJ1pAXanZ0qd0l0f+zILvJiIdpsgif86YmR1M4K6vlgOOgfi8rertiCwvxSA6+LksoywWw7LyLyqOLVV7m+y+4b68q0VasclCwG62FQAlvTPREImVu96wBf+9jm7jOH5yHs2gX0JkxNMkqem5N..L+VKG.HwDSzVklLsZABDHccGCh90p.vLzcPDBdHkRUstChHj5Juxq7W.YvE6zMut5pq4p6fvp4nqXdvTM0OLTV1ibjifibjizZZokl6d+5s0Vam8P7Qkgssdc1d73g..j7R.vwgyrB4..6C.uwPtTVDRlNrAcqlnQqkC.UbwEmSHaWbJLqVnzmOeNxrRSrrfoAtBzcbDB5thJpvV7i1CWG9vGNWR9lwGe7gTcCD1aIlXhOGFg0p4N5JlCfOEFhsgN6rS7.OvCrh7yO+3yO+7yLgDRnvd81w0c2c2u+h5t6t6eoGOdlAbtUBLlP2c28C9ge3GFn1ZqMC.7J5NdFltDa1iE9aq6...XW6ZWldqkCm881LqtxR9lw5IJyutC.K1+ErmysBmj.ABb8SYJSwwjlfKpnhR0mOeeFRtsoN0odL.7Y0cLILMeJe97cQ5NHrRN8922sNXuoe+9wO4m7S97268du+iAXQRxkq9crGcr3hKtaOkTRwNUIpQbJnfBRLt3haEKaYK6n5NVLA+TkRYaRQhEWbwIB.ytUpCajD+1e6u0zii0st0Y6q7SzlOe9VttiAwmH334vIzmmaeqacq+AcGDCFRFWGczQtolZpKG.+G.3bzbHIhhhO93+qEWbwYeFmwY3HyNPgKGaqJQxDgwjyv.ZG6XG0bu2689lCxhjd7wep+1jibjirdaVKaNRjZAKXA+TcGDljZfMKO.O24N2uDrAW+GkZsbje946DF3i8Geu9q+5ogge1JIAud85DxnAijtOqsXl0cnbricr+sUrhUXqdRFjLtVas0wSxKijaF.ARM0TqD.+AHUJejfTl6bm6Cn6fvp3jaw7yenVfryN66.C9M9GcRIcpy7qG7fGrpgQbIF9b8tu669ql9zm92Q2AhIY4Jkx17EcAGaF1hbn769tuaToU6SJojRMZrds.IrxUtx1HI.P2vXbI7Q.XO.nX.TN.pB.sNXMdPiM13Wu+lXUrgrMWWDMEL8HdY5NNBAsNwINwWT2AAIiqs1ZaLomd5KD.eM.7URO8z0dCIHzp01UWc8emTRIsScGHQaN4SzuqA6M2111Fdxm7Iec.D292+9+4s2d6Wd+rXSJ0TO0u+9y849bCXJTTDc8XO1iM45pqtCrzktzXkJk+iUJ0AzcPzGW..RS2AA.v2869cakggPc8NpQMpLilwsEINXLAa70gQ2N5uAf8BflAPWj72RxyIX52C.mHKFciYmc1aTobDiWptzc.XAT.3un6fHTzVascS.PKYMp5pqtLI4xI4FAfuzSOc2v3b9+OvYWWEgIIwDS78c61sjsoriHYJC0Wfum8rGBf3eoW5ktmfuzozJ3qbkq7lFfOdsjzQ7sZwPR7fG7f+jvoRZN.6k8pRS1DJRdHcuiIREhaiwcvCdv6V2wpEp38rm8bdd858BHYw5NXBSw7oiVRdQ5dmbnp7xK2R6BXszRKikj2HIOpt21ENFai1uuW0T4T6JKCUJNDG4HGAKe4K+KuxUtxdR4S4AiVt3De49YbFmwBFfO9X.vn.P8C2.UL3JnfBbcVm0YspYMqY8L.3T6WQNaKSoTlxzqtYgjmNbF4P4HxS+zO8jV0pV0qkYlYNRJ22N24Lm4rMcGDQnX5VLmjwCfmW2wQH5+dZSaZdrhBhFyMEOC.NOqn7DwTNO.bc.HlclY2oVw7u9Ps.ae6aGemuy24OmXhexrR8JW4Jmwa7Fuwg54um1zl1fMqdNG.r0gUTJFLwsqcsqO6bm6b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6OWO3LP9c.faqgFZXJadyaNdkR80TJ0GHUJ2ZYWaw7ICizOnPLbUJ.VXt4la6g6GL3fkQqCXGwHF+u.3pUJUzn6cbLXbOU6lPJEkZCDK1Z4u4DlvDbq6fvrzUWcUptigAS6s2tseR0IXCA33mMaiEXKawb.by5N.DwDdH.b5JkJRyJE4ahwhPzu76++e6cuGeTVcs2.+2JISlbkbgPBPf.ALnHAQJ3EzhZphWnspUKwVsd9T6QqZa8zSa0Z0dQssGeaea01i5odrpG8T0d7jz5EN7pBpPDEE0DEABfbK2SfDBSBISlIyjYVu+wL3IDRHSl444YuelY88yG+HjLyduRlGRVy9YsW6gVIQTElTR4..5Z4Jn8mdlwqqV9.CLvco5XvHwLq0MIBWtbMVajRg33ncqXdUUUUx.3Go53PXq0I.t.hncFiiyYZDAiPLVZqs1V7LlwLL6N9iVdf3zbyMGyG1JVfGT0AfYHiLx3STcLXjV1xVlV2p9prxJ04MgsPyncqX9ke4W9bgFFWBai+YDpFci0jxA.NeCXLDhwzN1wNFDleaqUKqu0csqco02del4hPni.83MOIQT7Vhh5bKebPn2wmPyncI.6zoyqV0wfvV5k.P9DQ+qDQF0FZ50Av2bqacqyb+6e+yxfFSg3yrhUrhcvLGjY1Ey7qwLufva9cijVVxHd73Q2u89+AUG.lA+98+npNFLAi6o2rBsCUG.B6EcqTVH.b6pNHD1J6D.WsAsB4GChnpG1eMYl04e1uvF6kZrwF+AkVZoMAyIACsr9aSM0T8o5XXrDd0x+5pNNLCM1XiaW0wfQiHh03e97tUc.HrWzpDy22912j.P9pNND1BGF.WEQzFgErZI+g+veHaydNDIbFzmOemgSmN2lIOOZYh4qbkqTmqK33xUKG.qYdyadwqs9Ncse8q0cLFg9QqtHdNyYNye7eThDbtAvWA.ERD8VvhtElKdwK9aYEyiHgR4VPR4..8YAyQzPKq6Vl4og3zUKG.2upC.Sjt9FNZU0AfvdQqVwb.rRUG.BskG.7s.veyp23RO+y+7kedm24EO1KiEpyyRDsWKZtzwDy04SBwn4DB1tnVUG.lHccCspqsqTglR2RL+qo5.PncbCf+A.rZqNg7G6wdLGW5kdo2XIkTxexJmWQ7uibji7KsvoaBe3ZYA1kpCfQCy7L.vUo53vj7vDQ5b4CEqz0u1zx1UpPeoMIlyLmL.JS0wgPazCBkP9qXfcYkHFy7B.Pc.voUO2h3eqcsqc+V3zoic+DcstaeLUG.ln+npC.SlVVZTPS6JRB8kNUi4Yo5.PnEZ..U.fBHh9eTQR4gkE.1.zzd.svV6uUYkUZkWWqiIl2npCfQZfAFXVH9sbJ6Dg9Yqwyz0Dy0xMesPeoMIl6wimbUcLHTpM.fERDMWhnZTXB4..fH58IhtLhnoPgj7u427axeKaYKUDHPf0nxXSXuM3fC9ea0SoEOeQh1Tc.LRomd5OkpiASzOfHRa6mfFDcMw7ATc.HrWzlDyegW3ElgpiAgRb+82e+EQD8EHh1Nz2CJhf20ccWtV7hWbMojRJe4CbfCjE.tAnmI8HzXG3.GnNKdJ0tqQGZng5T0wvv40q2xPn6TW7pUq5.vBnqIl6U0AfvdQaRLuhJp3yq5XPDcd9m+4uZ.jL.RdvAG7JhfmR8.3Rpqt5RkH5mlc1YqU+R5Hwzl1zbSD8zHTIubcPe23QBMSCMzfU2kFztCxGe97cXUGCCC4zoymS0AgIpZhn9UcPXAzt2.ZXxuaPLgXzG8yQMl4FAfbrmqHtc6FYlYlviGOXKaYKHqrxByadyCNcN968Qhnjw+6pUPLyCgi+M80N.t21au8Wr3hKNtqtsYlSC.+N.78TcrHzZCBfzsxxJfYNS.nUIl0UWcMuBKrv8n53..nu95agYkUVaU0wgI5TMiSFYcCy7t.vIq53XTjhpKMSg8hVzUV5omdxGRR4J2C8PODFbvAwO9G+iA.v0ccWGdzG8QQ1YeBOzKaBG6sPjAv2D.eU.7Q.XiM2byaojRJom34ZbjHxK.tsAGbvGK0TS8cAfbRgJFMqVA+6.sq+N61saco2pSYkUVVcM+akNDQjV1ZJMA5ZIinqkXiPSoEkxRN4jyMp5XHQWlYlIV0pVExKu7vUe0WMJpnhvy8bOG1wN1w38TOt1KFQzyPDcEDQ2GQzFl0rlkq34jxGNmNct8VZokhPhQMcJl3dcELmZ2p0ELXPsnSwzau8dl.Hd9Dm96C8ce6XzzwtehmDke2mv3n7RYY6ae6otfEr.cs1vDiuBHhjCPgQAy7jAvkfP2AgKDG6aDNX2c2cI986umoN0otW.LUEDhBKle+9OiTSMUK8zWjYNInYImWSM0jdEUTgRWgyveeoQ.LSUFGlrLIhRH5JHLyuH.txd6s2+obxImGR0wSX0SDUtpCBg8hxWw7ErfErTUGChn1uPRJerQD0MQzekH5hIhRo5pq1YM0TS1g6nKNJnfBZaZSaZtesW609hpNV0TuTe8027qu95cBfjB21Jo8u+8aaaspc2c2MqfoUGWwNkWdMc2c2WDhuSJ++HQIo7vZA.ORN4jyin5.YXZQ0Afv9Q4qXNy7CBfefpiCwD1l.v4QDI0OWrKElYYm6++x+QNxQt5bxIm+mQ6SVas0lwRVxRzwiY9wUc0UWpKcoK0xeslYN.zfEh4nHhRBp8MLjLy7g.fs8M4EAJkHpQUGDVEl4qrwFa70JszR8xL2Lzi2z0SRDIkpqXBQG17mWtpC.wD1fszRKqnjRJQRJ2.DNooDMAAviejibj+Re802m1SO8zWlYlYR4jSNNyKu75OmbxYL+dRZoklxWs0nTepHo7vZC5QhJ.gZebJcU7OvANv0f36jx2GQTSpNHrRDQuzQ+yACFryjRJIc35cqt0nJhCnCqfxbUc.HlXpu95u4RJoDsXyaEO3gdnGJUUGCVnOr0VacY0TSMNIhtkbxIm2cFyXFcWd4k6qzRK0a94meuiWqEaG6XG102HiJOwXOfBm6QZ+pbxqpppRsnhJ5oUYLXA9NPOKgIqhtzdPi6ZMvBymRWw7MrgMnCqXuXBpnhJpdUGCwSV9xW9on5XvrELXvWYm6bm2Z4kWdLWi0UVYkAY1VlywFT3bePEN2iz9T4jWQEU7OC.GpLFLYAqqt5T40ZJWRIkjtrvQRh4hILklX7hW7hkdWtMTAETvivLeYDQ8fD6UkwPTVYkcKpNFLQ02RKsbkkTRI60.GS650behBmacJwbksg3dhm3IxtfBJ32pp42h7yUXISoKzkdZtNcB2JrITVorvLS4jSNGWOvVXKbV.3vLyAYlalY9JTc.YW8XO1i4HqrxJtLwbe97c8.3zL3jxOJcpzLhTprle6Tgy8Hor5tcUqZU+AUM2Vkd6s2+cUGCZ.c4Mlni8VcglSk0X9BPnd6rvdal.3k74y2RXlkRSZB5htnK5rTcLLN1Vas0147xu7KOom7IexIsu8suudD7b5bfAFXFNc57YMwt1Salz3ZlbopINPf.ZSaM0qWuJ4MUs4Mu4YLoIMo+QUL2Vn0lat4JqRKfOUG.goK05tvFQkIR8iT3bKLXNb3nV.fv09aO.Xa.3sAPsd85ca0We8sujkrD4TPaDlyblyuW0wvXIPf.2PJojx+IN1RG44c4xkubyM2+9X7zVS80W+UWd4ka1+hw1.vRL44vH4lHRYIK31s6COoIMIUM8GC2tc2kBlV5LNiynJELuVsaW0AflPWVwbaYacUnVJYEyYlSFgNMDEwmxE.KG.2M.dgzRKs8DtuSeXl4GfY97b4xUtPC5i9pzt10tJ.gJKHsie+9WZJojxSiQodte8W+0eY.LxSqW+.3ZHhtbKHob.8ploiDprirf96u+dT47Obd73wxuyAG7fG7bSJojVlUOuVrNIhjMleH5RmaRW1DpBaDUUJKknn4UnV4BfeH.dqbyMWWLy8xL+uvLuHlYmpN3rZm7IexeWUGCiFe97UdpolZci0muxJqL..90.nj8u+8OK.bpHzQ+cUv51Xl1sDyeSUN4tb4pWUN+CmWudszXY6ae6oVXgE9JV4bpH2LruaLZiltbVGnKaBUgMhpJkkKRQyqPujMBsp52M..y76CfeC.1H.bEOW1K6d261I.tWUGGije+9OKmNcFIq51ukHRk2tXsoloiPaQkSNybepb9GNe97Yowx7m+7e.D5m0DWq95qOQ3MeDozke2gtTq6BaDUsh4Z4JEJTtyB.uHBkzU6Ly2Jy7zQbXIuTVYkckpNFFogFZnKI0TS8ChjGqhSJGv9kXtpOEF0lMglOe9Fvplq95quElTRI88rp4Sg9UVTIjIlXzkUtWXiX4IlWas05..KxpmWgsyTAveB.sEtjW9gLySmY11mjNybR.P2ZoYeSGNbrtH4AxLqCmVuZSMSGgTVGYA.HojRxxRFd73ymOK416WUUUkbVYk0aXEykp8oe5m9PpNFzLZwumn95qWWp0cgMhkmX9RVxRJ1pmSgsW1.3APnNwwAXluQl4BTbLEKNaDpd60E+bhn+yH4Atu8suh.vEaxwSjPapY5HvgT8cXHPf.ZSstNv.CLxMNroXUqZU2J.JzJlKEasmxobJxIL4vzSO8jtpiA.fcricHIlKlvTQornkcgBgsQg.3wAPWLy6kYtRlYaS8iFdE+eZUGGCyyBf+kH8AOm4LmBIhzgU62Ncvc7hpN.BDHfkjLbDxzu89Ly4.fG1rmGcPe802OT0wfto0VaU4Il61saTYkUZVmiCh3XpHw7JUvbJhOMW.7eCfivLudl4kYCNjilO.JS0AQXeH.9lSjMYKQz1fdrwpzlMy33IPf.qWChAcp9ish5t8Qsf4PGz9jlzj1opCBcSJojhxWrFud8BnG+rRgMiUmXNA.kto2dm24ctiMtwMdt6e+6eEd734OoxXQXnp..uK.FjY9ewsa2Eqo0i9eV0APX8.fKfHxVdqVc61ss4f6n1ZqcqpNFRKszzlMgVEUTgotJhLyk.fH4DpMdvMAI4uiSVYkUNpNFRN4jsM+LJgdwRSL2kKW4X0y4Hl+WX4Ke4+9y+7O+2ctyctuQFYjw288du26VTU7HLEIAf6NiLxnU.7oLyWLyrCUGT..LyyD.mqpiivVLQj1rg.mn74ymtcvcTeGczwrIhRlHJo.ABbMG8S79u+62pJCL.sqVWMsDIC+lw+ql03qYBBfWW0AgNJqrxR46AorxJqCn5XPXOYoqnHy7R.PsV4bNbc1Ym2cQEUz+mg+wB2gLtdDptk0hD3DFtf.3WAf+MhHUbbfC..l4mG.Wy39.MeeChnmS0AQrH7ARktrgFOP80W+rFd6paDwWxDQptVSIlYUGC..dHhxvrFbl4EB.keGJrH2AQzuW0AgNZngF5HImbxptbVdOhnyQwwfvFxpW85OuEOeGiLyLyTG4GiHJX3NRQF.3KB02ugEFuj.v8.fNYleCl4E.q+MklOzfjxaokVdahn3gUTT08Q8Oy92+9+GFkdH8Q+6ukFjTNPnUoVGtKCsahiMAfpMwwWq3xkqGW0wftRCRJGv9cVKHzDVch4JMwjLyLyw71aQDMDQzq.fRAvbPndnsH9yEBfsyL2.ybELyIaQy6OwhlmSnlat4uFhCpI0vI6pEcZjppppi6t.NrMT6e2hCmSjFUc..fNMqAlY9yAfS1rFeMy+U94mucpkgZYBeWv0AGV0AfvdxJu.NI.rLKb9FMyX7d.DQLQTCDQeW.3D.WD.9XSOxDVsYAf0C.WLyekpppJSKA81ZqsL.vcXVi+DPOm64dtcn5fv.EQmRols1au8ST8a+NVVfL9zg6FnYsJhD.dFSZr0Q2spC.cUM0TitjXtROTwD1WV1EvM2by5vApxzlHOXhHeDQuIQzmC.4iP6.d4fbH9R1.3EV0pVUutb45pfI7uIl9zm92xnGynzOXhzZDsAdWUG...21scaSYL9TCBf8Zkwx3PGRL2TVEwCe3CWNB0JRSDTOziWK0Ryd1yVWZYt1oyZAgFwxRLepScp5vw38wUi4QJhHWDQOABc.2LKDZyDpM04pHlkYt4l6emYts1au8y.FTMnWas05..OnQLVwnWC.+EUGDFr2S0A..PYkU1LGiOUi.neKLTFO5vcKwLJ+BJu7xKQZ0x+1wYuAaCUVYkkVzDGBDHfjXtHpXYIl6vgiy1plqSfVh0AHbotzLQzu..oCfEBfmLliLgtXpSaZS6CXlWKybLeWdVzhVzWBpua+r5pqt5ujlrIDMRaW0A.Bsp3exX74tSMKAJcn8sY3snSWtbsH.rHidb0T8AfMq5fPmUPAE3T0w..Pu81qN8lxE1HVYsX8Urv4Zr7rF4fQDEfHZ6DQ2HBsZ7KC.qwHmCgxrBDp9yu9X3fJJoTRIkmvHCpnvGVc0UeUUVYk5Ter1nzrBm6N862+YTc0UmIQzXUKoq1Rinw2AUc...C8PWgYNobyM2+lQNlZtaIN7MXazRW0A..PvfAkCXHQTwpRLmPnSlQU5FfI1gDHh7SDsYhnubiM1X5HzlFcSl07IrL+E.71LySZh9DGXfANaDZuInJGB.mWbZR4fHxO.9CpXt6ryNWTpolZsmnu2pYqVNfdzkHLzjU7506J.fNTljVEcpK+nqzgVkHRJojzkyYAgMikjX9N24NyxJlmwxZW6ZOKhnm1pVogRKsTug2zne9N6ryrGZngtbL12tag96bQnUOe4SfmCkd5o+eXVATjvsa2KjHJt9WNTe80+SfBZahEVXglVa+yDo7tDQf.ALrDyerG6wbjVZokHkn5ulHRKZQnZt7Tc...je94qCma.BaHKIw7hKt3wsMEZlV8pW8NT0bWTQE0uCGN9eHhN88u+8maf.ApD.6QUwiHpkD.1Hy7uDQv+toqt5ZdPg8T4gFZnJRDNRnKu7x8cvCdvepUOutc6tPqdNM.JuuWGHP.CKFtga3Ftc.joQMd5tibji7GUcLXSLVcIIql7lnDQEKIw7ryN6SyJlmwRYkUlVz9jl6bmauojRJUSDMu5qu9I6wimqGRauxt4myLu9cu6ceB2fQETPAOpUEPih6ygCG0nv42Rsl0rFKuFiyLyLeAK7voxnn7ZdkY1P5TE6ZW6p.GNbb+FwXYS7R4jSNxIIYjoXUG...tb4RRL2hvLSLyIwL6fYNMl4LYlygYNel4BYlmJy7zYlKN7etPWtbkayM2b5ZzAR0mwpBnKzhlmQ0sdq25zU47OZJu7xObFYjwyRDUZM0TyT5qu9tQnGcMAw367Kqrx9Tl4QsVFas0VmIT2dp38.vuTQysR31saU7uaVF.doXXiAqBJurlBFLnQrh4zIexmbhzF9Dd8581UcLXiLKUG...6XG6PZmxQIl4jYlSObh0kvLuvvmT2eMl4amY9gYleYl4sxLO..BBf..vG.7fPso1dPnCzrChPsJ11.Pqg+yGL2by00Lm4LG..ABON2Iy77YlU9B4ZI+REl4tgZ2DbeQhnWQgyejhdu268Jb9ye9ekbxImeE.JP0Aj3D5v.XNDQGSxFLyuJ.tTEDOd5ryNKrnhJJQqMcQLyppSULahHawc8hY1AB8KtTl96u+SK6rydawxX3wimKLszR6MLpXxFXmDQK..51lIVKwL+x.3xUcbzRKsblkTRIenpiCcSUUUUxqZUqJc.jC.JB.yD.kBfSE.md3+S0sX3+D.9c.nIUrI9M8Dyqs1ZcrjkrDk8KCb4x0rxKu7NBQTOpJFhRza8Vu0TW3BW3UmWd4cePsuwFwXqcDJ47AA.1+92+IWZoktKUDH9746zc5zYB4lLlY1K.TQ+K96PDoxxVJhEd08UZq1yiGOyJiLxHpaykM2byoOyYNSWPMuVqD986+bSM0T0hS4V6.l4FgdrDd+vPB..f.PRDEDUp4KgH5iTcPnBgKOjLQnDumC.NMDpIJbA.PGNE3iT6C.eC.7AVYaJ0zSL+.G3.EUTQEorRzXaaaaS8zNsSSG5euwB5ce22c5yadyaUSdxS9df85B6DA+6DQ2J.RhYd2PMsuseBQzuUAyqVfYd6.XAJXpWMQzUnf4MpvL6BJ7mezau8let4laT2cXXleV.bcFXHo65C.4J8t7HiN7lOGlSmHJdegRHl4il.97Av4.fqL7eNdRKHzcg4SrhUP2zSLmY9rfZOoxlGQT7TWPgdi23Ml9RVxRtlbyM26EZROaUfj8506uJszR6tUvb+g.3rSj+k2LyOJ.tEEL0dHhxPAyaTgYtF.b9JLDRKZa4e9746y6vgi21nCHM2WmH54UcPXWDt9f0hZ61mOeKxoSmaU0wgAgps1ZSeAKXAElVZoMO.bV.3KF9+mHYs.nRhHCYSrOVrhDyuUDpdcTg0.fqHNNgE5C9fOXFkWd4Uld5oeOPRRWkN..lpBl2fc2c24UPAEXp+fBcGy7UBfWTQSeRZ3gIznhY9g.vsovPHp9dEybVHzAlUBSIr..rm8rmzl27lmzcOhPc0UWYqK+rPWtbsn7yOeaUh4LyIUe80mQ1YmcgEVXgkkVZocl.3xPnM6tHjf.3RIhdcyZBrhce5EaAywX4ZiiSJG.fOyy7LaA.O..dv8rm8LiRKszqI4jS9W.KHI8.ABfZpoFzbyMioLkofhKtXL6YOajWdZw46fUSEIkC.bg5xuHRwjSY2HyNU3b+BQYR4DBsHKITIkCfetjT9DiGOdlvmPylEhHsqM7cTUUUUIWRIkj4LlwLlZd4k2ImQFYb1HTB3KdAKPEUDnsRR.XcLy+Vhn6Blvlx1JVwbUsor..bPDMjhlaUhFXfAlQ5om9WG.+LXxIo6xkKricrCrm8rG7ge3Gh96uebdm24gktzkhEtvEhjRRa+4S1cOFQjJJeCsS3j21Ir9C0I+DQoZwyYTyiGOWbZok1ZUwb60q2xRO8z26D84o365pxTWc0k6RW5RU9gBkcRO8zyRxImbpU0wA.vgNzgNioLkonzXgYNIWtbkclYl4LRM0TW..97HTsRqCaN13AOL.99F8cL0TSLmYNUn1S+pjiyWw7HwQSR+Z.fkrR5ACFD8zSOfYFSdxS1rmtDUGpt5pa5KcoKUKpmRcPe80W4YkUVwTq3KJ7dDQmiEOmQsFZngSY1yd1JYUyewW7EK3ptpqZBcH4vLOK.zn4DQZsmiH5an5fvtwsa2e0LxHipUcb..zWe8s7IMoI8NV07s8su8TWvBVvT.vofPIfe0.XgV07m.aEDQFZ6a0rKkEUVSCusjTN..3LxHiV.vuG.O.y7L.foljdRIkDxOeo6NZlZu81WtjT9wJ6rytdl45g01cV9KV3bEyZs0VO3rm8rUxb+du26MgZatg2HeIZa1S..r28t26R0wfcTFYjwhTcLbTolZplV9O6d261YYkU1TQn1P3ECfuFjy8DUY0LySxHqNCytFCJwjG+wzfCN3uSUysFiIhZgH52SDkCB85ysiPsjKKSiM1380Ymc9qCFL3Gakya7hfAC9aKt3hURuRWywd734lsv4qS.7LV37EyV9xWtx1OBACFbhtPI+TD5vGIQyGVVYk0hpCBapUn5.3nb5zogz1bqpppRt+96epLyqfY9AXl6prxJyKBcmjVM.9dPRJWkRGgZQjFFyNw7SyjG+wTu81qbhachczjzefvIoOS.7iPniwVSSWc0Et1q8Ze4hJpneQxIm7miHJkMtwMVRmc142B.0alycbhd93O9i+4pNHzUqYMqYyvZJetRHhJhHxsELWFo..PIsO1rxJqHtNLYlmM.tWSKXzX8zSOeaUGC1TDzq122kDMOocu6c6zqWuyiY9lXl27pV0pFJyLyrC.rN.7CgjDtN5wCenJYHL6DyUVsW1UWccXUM21PLQTqDQOHQT9.XF.3G.SHI8oLkofMtwM9QG7fGro1au8atolZZVACFrihJpnmhHp7ZpolrapoltvfACpjMnltyiGOeQoDVFaUVYkA.fUzK46vBlCyx+iJlz4N24FQ6oIl4jAv5M4vQW0dd4kW79gRioXm6bmYo5XXDtzppppS3FCmYlN3AOXVLymNy7syLu2xJqLuNc57SAveF50azPL1xE.eNUGDQDl4NX0wz63LI.Hl4hYluMl4tM4WudzAFXfiozmtm64dRq4la97YlWuIO21EC.KnSJY2sicriIa1uP31s6kn5uNiVLyWqY+8mQyi7HORDk3Dy7Mqh3SG32ueU1dgs074y2RU8qeiTc0U2y9TO0SkK.Rt1Zq0QWc0U1LymDy7UxL+mYl6WsQnv.sQi5ZYS6WxygV0CU0pB2CQz7TzbGuhXlmJBsSuuGXR2NsN6ryWpnhJ5ZAfmg+wW6ZWalKZQK5BKpnh9+Bquk3oKtZhnWP0Agc.ybs.vLSdty5qu9YVd4kOg1Pi5.e97cFNb33Cr548Ue0WMmUtxUdBqwcl4bfIWNcZrAqt5pyL7c8QLAvgVHt2B.KW0whHwUu81a94latth0wwLKkEUdLUK2JPiGSD0AQziPDUH.lF.9NHzFfyvTXgEdkCMzP8tksrkuFFVWC5RtjKw8Tm5TWMQzort0sth5omd9AXDIuGGqk1au8YIIkG45u+9ucSdJJbAKXAqqpppJYSddLb80WeGPEya1Ymcj78pG2zCD802URJOpMSHIkKTrbxImulQLNlYh4prAV2lBm6DALQzAHhdTD5Dubp.3VPnik9XVxImriEsnE8ewLejVas0u48bO2SZC+yeIWxkzYd4k2ejHJqMu4MuHud89RFw7poNPc0U2oTbwE2rpCD6jlZpo20BllyeUqZU++XCbS+XERJojrztvzQMzPCcBSLmCsgOWk0DMZomS0AfMVkpN.DB.7agATIJl4uPQYsJQXvqhqXrQDwDQGjH5w.vzAPQ.3FAP6FvvmdwEW7Scu2685YvAG7eq1ZqsDbrWzGbYKaYaM8zS+q7xu7KOo1ZqsuEhutM3A+zO8SW3RW5RGP0AhcS3RL4wrfo5RPnMJssQt4lqRtSSYmc1NFmGxSXIAhd5WSD4U0AgM1OR0AfP.fr83wSLeppZlIlqxZ7VY8p2DYgSRuShnmDg5rKSA.2..h4dxapol52YIKYIMwL+os2d6m+HKgfq7Jux9lwLlwSQDM42+8e+SevAG7Mi04T05qu9N+S4TNkCo53vtp6t69e0hlpeOybZi+CSanj89yjm7jcNVet1ZqsR.vEZggit4AUc.XW0byMmNBcWaEBkKszR6eLVGCyLw7Eahi83QUa5TQXgSR+PDQOM.lEBkj92Bw9JoW1zl1zpYUqZUtb4x0WE.i71iG7rO6y9SRKsztnG4QdjBZs0VuuXb9TkGyJONmiGUPAE7ovZ5o4..mgEMOwLUchHO4IO4zGqO2zm9z+yVYrnY9uHhh4MLVhpIMoIMMUGCBwvbmwZ4MZlIlqx9uorAZzHCKI8mB.yn+96uH.7sQrUxQYmat4VMyrqN5niu.Fk5551tsaq6YNyYduOzC8PoUWc0cMCMzP1kxbom5qu9+IUGDwABBfeoEMWWpEMOFAFV2aX4yjc1YOpMDf1au8khn7vXINwcp5.vNKmbxIQsKcIzSN.vbhkAHtbEy6u+9OgM0eg5PDwYmc1cRD83DQS0sa2Gs6tDskrQ1ScpS8M84yWcu4a9lmLFkDz+9e+u+fKcoKsJGNbL40u90e9d73YewxWClMe97cA1w1vmNps1Z6YrnoxtkX4Gof4LmQ9A5omdxaZSaZFV++0F58Hhh4R8KA2xTc.HDivWOVdxlUh4Iahi8354dtmKOUM2hIDNqrx5.DQOJQTgc2c2yD.+DDEsAQGNbr3uvW3KrKud8to0st0MeL56L5fW3EdgaLiLxnr0st0s3AGbv5i0u.LAOrSmNk18oAYFyXFsB.q3T.VkktWz3uof4L+g+WXlWbN4jygAvXVhKI.tYUG.wAtRUG.BwHbmHF5NKlRxyu669tJcEqO0S8TKUkyuHpvETPAsRD8aAPV81aukgPaHpIT8v5zoykshUrhc3ymuOYyadyKAi9+3fujK4R1RZok1B27l27hBFL3mZ.wuQ3..3Gp5fHNCCfepELOIYy5o4ulBlyhFwe+mofXPmzD.1tpCBatj.vBUcPHDiPlgOPFiJlRh4olZpJMw7ku7kuBUN+hXCQTvbyM28RD8ipolZb1d6suDLAWgOGNbrvy5rNqZYlac+6e+WLN9MIJ..urksrslbxIO+1au8OOh9xownrLhHYiKavZu81sjUGtiN5Hkw+QoM1Er91J6LGwe+Br34W27MIhXUGD1Y8zSOGW4QIDZhntKSYJIlO0oNUU+KnlY3i1YgMWEUTwPEWbweDQzpt268dSukVZ4RCFL3DoTOldokV5ZYlc2c2ceSqd0qdz1.ZbwEW7lBWy62nQE6SP2BQTiJZtiqUbwEeH.rSKXpFy1AntIbmY4ygI3cjJFM+gGBXDk1RBl9.vaq5fvtKiLxnLUGCBwX3eNZehlRh4EWbwV9N9eX9jibjibJ.nXEFCBSv8ce2m2RJoj0lbxIe5UWc0St0Va8VPjefB4L+7y+O+k+xeY2CN3fO3V25VG4sUG.HPVYk0S9pu5qly.CLvZMvPe77ZDQIxsKNqvsa1Svoe5mtsZw.HhZafAF3brvo7LO5engFZv17lXLI2HQjz8vhQNb3XkpNFDhwvRXl0qFQhWudeWVAZt4lk5MKwB8BuvKL2N6ryGNJtb4cZqs19bXzeCpzZVyZpr2d60vuFcDNBauNbZrkZngFRynegyue+6hYNMl41Xl48rm8Xa5NDLy+Nl4uIy7YZzee4DAg+2Z9746rsx4UyDfYd7NETEiCl4j3P+7SgPWM6n4ZaSqyoLv.CnjM0R0UW8dUw7JTF9ptpqZeEVXg2V0UWsy8su8cgACF7iivm64N8oO85XlOR+82+M85u9qO7U7j+ReouTU4jSNetFarQSHrCwue+UHGE2luRKsTu.3wMxwLkTRoyvu1c4..Se5S+LGmmhNYV.3o.v6akS5gNzglFyrSGNb77V47pY9QDQ9UcPXmwLSABD3qCfrUcrHDm.mtpCfiAGZEYrT986mQLzhZDwO1vF1PA97461Yl8NQtFZngF5c15V25Jti63NxFguVZfAFXylzkr0B45UKCy7bL3W+ZN73lDyrKl4sp5uFiTLyWuA+8BQDpiN5HSU+5ucTs0VqCl4alYdWp90PgHB8qT8+t4XvLe+V82AZu816R0ecKzKUUUUI2SO8rTl4ZlnWO0byM6acqaccXBWpxLyra2tU4oiaBGlYhYd+F4qgH7arhY9GxLyu669t1hdxMybYF42GDQrGT0u1aCQ9746bYl6R0u3IDSD986+eKZtfWYGBPlggFZHs9DcTX8prxJCjat4VKQzEzRKsLYDZS.FQ2F4YNyY5XEqXEQcuHc7jQFYrEyZrEGuvsltawHGyitIFOxQNxZ..Niy3LNeib7MKG3.GncUGCIh5qu9teUGC1HI4ymuymYtAGNb7N.n.UGPBwDQJojRuQyyyLSL2x2bKojRJcX0yov9njRJ4vDQO..x.g5gxVQKzar3mHRkcunDUqG.F122m8rmc9..0We8s..jRJo7exLq8GzPSaZSa.UGCIfdoIMoIo5yJAs2t28tcxLeMLy83vgiZPn8CgPXG0Rz7jLyDyGs9EsoJiLxHRacdhDXDQCQD8V.XAd85cN.nZEDFefBlyDdgO.m9tF3PNG.fksrkczj8KD.WqAN9lEF.5xIdaBAud8Jmpum.s1ZqSlY9dJqrx7BfmGxF6TX+MQNyU9LlYh4SxDG6QUlYlorJPhHFQDmd5o2.QTk81auSF.+NKZpa..WuEMWhi2yZfi04.7YGXOGsDo9Kr83.NaipN.Rf7IokVZMp5fP2vLmTe802BYlesvGDX2qpiIgv.EocHtigYlXdtl3XOpRIkTjRCPDUxM2bOLQzOdW6ZWSB.OhYLGtc6t5ZpolzIhlCQTClwbHFegKgn6vfFtgu53CeOC7eXPiuoIPf.aS0wPhB+98eSg2iCB.zbyMmNy7WC.ckUVYsU.bIpNlDBC1kQD4IZdhlYh4p31PImjZhXx7m+76iH515niNNUCdn22q7JuxWuhJpP5Y45gG0fFmEwLmR3+7OYXe7qhYdNFzbXJb61srY4sFG1gCG0o5fP0Xlot6t6YxL+nyblyb..7eAf7UcbIDFrWA.yhH50h1AHtpTVPn5lTHhYuy67N61fGxUUYkUJuwQMAQjabrIRG0ZpollR3wb8aZSaZ3+bu+ciX7MKtc69fpNFRPbSgK0oDRbn97+YCfON+7yuYXvcFIgPgFDgRD+63ymuSC.oSD8EIhZNVFTS6vMgCcXarPyZ7GC+dhHi5VTKRfEd0NMpUTrIhnRg7FG0JLyNAPeHF6fTtc69KlUVY8JCabWO.pH7es.hntikw2rzTSMMmRJoDSYUy6ryNwTlxT.Qx4mU80WuyxKubepNNrZLyIgPknxyBYkwE1SGB.0hPahycgP6Or1C+wcSDYJ+65TF+GRTSEqPPbUeYWnTWkQMPCMzP2Hjjx0NDQCxL+OfP2R8nVlYl4+.BspIG0MAf8F9O+UAviEKiuYYvAGrOidL84yGdhm3IPu81Ktq65tL5g2N59RPSJ+T.vaBfoq5XQHFEAAP8.ntCe3Cu8t5pq84ymuVSN4j6xmOe85vgiAJu7x8CE86sMyDy62DG6wh12+fE1BD.9oFzX0hCGNVuAMVBiW0.3gQrc3kbMHzl.MH..Qz9XleR.7OBfeC.dbnlEp3DxmOeQ0FS5DYm6bm3u9W+qXMqYMF8PaK0d6saJajbcFy7kgi8MpJDFBWtbcA4kWdmEB08dFqSYY2.Xqg+usiPKRRaHzpb2G.7Bf.57lw1LKkkWF.WtYM9ig+DQjQ1ihEIfXlmJ.LhCqJ+.XtDQQ0gLfvZvLuT.7gwxXTWc0M8ktzk9YWyDtLYN..x8PG5PW3TlxTzt2bVs0VqikrjkXnqlamc1I5s2dQYkUlQNr1UafH5Kn5fvJE9v0ZHUGGB6k8t289idsW609nJqrR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\"Guidance[3]\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"bubbleside\" : 2,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-64\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 3,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 784.0, 220.0, 141.0, 66.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Or even entirely generate our own latent positions from scratch.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-63\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 609.0, 412.0, 29.5, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"+~\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-61\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"live.dial\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\", \"float\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 1,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 636.0, 309.0, 45.0, 48.0 ],\n\t\t\t\t\t\t\t\t\t\"saved_attribute_attributes\" : \t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"valueof\" : \t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_longname\" : \"Bias z1[2]\",\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_mmax\" : 3.0,\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_mmin\" : -3.0,\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_modmode\" : 3,\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_osc_name\" : \"<default>\",\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_shortname\" : \"Bias\",\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_type\" : 0,\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_unitstyle\" : 1\n\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t}\n,\n\t\t\t\t\t\t\t\t\t\"varname\" : \"Guidance[2]\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-54\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 460.0, 549.0, 91.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"s~ audio.output\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-55\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 461.0, 255.0, 82.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"r~ audio.input\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-56\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 442.0, 119.0, 70.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"loadmess 0\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-57\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 442.0, 224.0, 61.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"enable $1\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-58\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"toggle\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"int\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 442.0, 148.0, 26.0, 26.0 ],\n\t\t\t\t\t\t\t\t\t\"svg\" : \"\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-59\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 8,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 461.0, 481.0, 106.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~ wheel decode\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-60\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 8,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\", \"signal\", \"signal\", \"signal\", \"signal\", \"signal\", \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 461.0, 292.0, 106.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~ wheel encode\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-53\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 474.0, 176.0, 306.40000456571579, 33.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"The advantage of this modularity is that we can directly work in the latent space rather than the audio space.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-52\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 271.0, 177.0, 159.200002372264862, 33.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Expose the latent variables (here identical to forward)\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-51\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 62.0, 177.0, 163.0, 33.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Go through the whole model (encode then decode)\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontname\" : \"Ableton Sans Bold\",\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 16.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-50\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 474.0, 148.0, 144.0, 26.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Advanced usage\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-46\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 256.0, 404.0, 91.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"s~ audio.output\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-47\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 256.0, 256.0, 82.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"r~ audio.input\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-44\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 55.0, 396.0, 91.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"s~ audio.output\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-43\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 55.0, 256.0, 82.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"r~ audio.input\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-42\",\n\t\t\t\t\t\t\t\t\t\"lastchannelcount\" : 0,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"live.gain~\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 5,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\", \"signal\", \"\", \"float\", \"list\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 1,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 55.0, 488.0, 43.0, 83.0 ],\n\t\t\t\t\t\t\t\t\t\"saved_attribute_attributes\" : \t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"valueof\" : \t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_longname\" : \"live.gain~[4]\",\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_mmax\" : 6.0,\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_mmin\" : -70.0,\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_modmode\" : 3,\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_osc_name\" : \"<default>\",\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_shortname\" : \"live.gain~[4]\",\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_type\" : 0,\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_unitstyle\" : 4\n\t\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t}\n,\n\t\t\t\t\t\t\t\t\t\"varname\" : \"live.gain~\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-41\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 55.0, 436.0, 89.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"r~ audio.output\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-38\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"ezdac~\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 55.0, 582.0, 45.0, 45.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-35\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 701.0, 110.5, 84.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"s~ audio.input\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-26\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 238.0, 119.0, 70.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"loadmess 0\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-29\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 237.0, 225.0, 61.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"enable $1\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-30\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"toggle\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"int\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 237.0, 149.0, 26.0, 26.0 ],\n\t\t\t\t\t\t\t\t\t\"svg\" : \"\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontname\" : \"Ableton Sans Bold\",\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 16.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-31\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 271.0, 149.0, 144.0, 26.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Encode / decode\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"hidden\" : 1,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-12\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 29.0, 119.0, 70.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"loadmess 0\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-36\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 28.0, 226.0, 61.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"enable $1\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-39\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"toggle\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"int\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 28.0, 149.0, 26.0, 26.0 ],\n\t\t\t\t\t\t\t\t\t\"svg\" : \"\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontname\" : \"Ableton Sans Bold\",\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 16.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-17\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 62.0, 149.0, 99.0, 26.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"Forward\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"fontname\" : \"Ableton Sans Bold\",\n\t\t\t\t\t\t\t\t\t\"fontsize\" : 24.0,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-28\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 184.0, 310.5, 41.0, 35.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"==\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-27\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 55.0, 317.0, 107.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"nn~ wheel forward\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-20\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 609.0, 382.0, 29.5, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"*~\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"bubbleside\" : 2,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-19\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 3,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 613.0, 220.0, 141.0, 66.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"We can mess up the scale and bias of each dimension separately.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-98\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"live.dial\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\", \"float\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 1,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 602.0, 309.0, 45.0, 48.0 ],\n\t\t\t\t\t\t\t\t\t\"saved_attribute_attributes\" : \t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\"valueof\" : \t\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_longname\" : \"Bias z1[1]\",\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_mmax\" : 3.0,\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_mmin\" : -3.0,\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_modmode\" : 3,\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_osc_name\" : \"<default>\",\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_shortname\" : \"Scale\",\n\t\t\t\t\t\t\t\t\t\t\t\"parameter_type\" : 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0,\n\t\t\t\t\t\"patching_rect\" : [ 401.0, 406.0, 73.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"Polynomial\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-71\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 10.0, 84.0, 505.0, 33.0 ],\n\t\t\t\t\t\"text\" : \"Check the \\\"source/loudness.py\\\" and \\\"source/effects.py\\\" files in package to see how to export a model in Torchscript. We promise, more documentation will come soon!\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-68\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 401.0, 389.0, 73.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"Saturate\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-67\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 401.0, 373.0, 73.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"DC Invert\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-66\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 401.0, 357.0, 73.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"Mute\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-63\",\n\t\t\t\t\t\"maxclass\" : \"ezdac~\",\n\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 307.0, 492.0, 45.0, 45.0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"disabled\" : [ 0, 0, 0, 0 ],\n\t\t\t\t\t\"id\" : \"obj-62\",\n\t\t\t\t\t\"itemtype\" : 0,\n\t\t\t\t\t\"maxclass\" : \"radiogroup\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 381.0, 358.0, 18.0, 66.0 ],\n\t\t\t\t\t\"size\" : 4,\n\t\t\t\t\t\"value\" : 2\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-60\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 4,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 307.0, 452.0, 68.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"selector~ 3\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-54\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 517.0, 317.0, 114.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"nn~ effects saturate\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-53\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\"patching_rect\" : [ 324.0, 317.0, 100.0, 22.0 ],\n\t\t\t\t\t\"text\" : \"nn~ effects invert\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-51\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 307.0, 175.0, 212.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"Waveform-to-waveform examples\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-50\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 14.0, 175.0, 212.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"Waveform-to-label examples\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontsize\" : 18.0,\n\t\t\t\t\t\"id\" : \"obj-37\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 173.0, 14.0, 532.0, 27.0 ],\n\t\t\t\t\t\"text\" : \"Adding your own Python-scripted real-time deep models\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontsize\" : 48.0,\n\t\t\t\t\t\"id\" : \"obj-40\",\n\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 14.0, 14.0, 149.0, 62.0 ],\n\t\t\t\t\t\"text\" : \"nn~\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\"linecount\" : 2,\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 174.0, 40.0, 453.0, 33.0 ],\n\t\t\t\t\t\"text\" : \"Here we demonstrate how nn~ can actually host any type of models scripted from Python by using the Torchscript formalism\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"basictuning\" : 440,\n\t\t\t\t\t\"data\" : \t\t\t\t\t{\n\t\t\t\t\t\t\"clips\" : [ \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"absolutepath\" : \"social.aif\",\n\t\t\t\t\t\t\t\t\"filename\" : \"social.aif\",\n\t\t\t\t\t\t\t\t\"filekind\" : \"audiofile\",\n\t\t\t\t\t\t\t\t\"id\" : \"u068005979\",\n\t\t\t\t\t\t\t\t\"loop\" : 1,\n\t\t\t\t\t\t\t\t\"content_state\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"loop\" : 1\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n 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\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\"expression\" : \"\"\n\t\t\t\t\t\t}\n,\n\t\t\t\t\t\t\"candicane6\" : \t\t\t\t\t\t{\n\t\t\t\t\t\t\t\"expression\" : \"\"\n\t\t\t\t\t\t}\n,\n\t\t\t\t\t\t\"candicane7\" : \t\t\t\t\t\t{\n\t\t\t\t\t\t\t\"expression\" : \"\"\n\t\t\t\t\t\t}\n,\n\t\t\t\t\t\t\"candicane8\" : \t\t\t\t\t\t{\n\t\t\t\t\t\t\t\"expression\" : \"\"\n\t\t\t\t\t\t}\n\n\t\t\t\t\t}\n,\n\t\t\t\t\t\"timestretch\" : [ 0 ]\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 1,\n\t\t\t\t\t\"fontsize\" : 16.0,\n\t\t\t\t\t\"id\" : \"obj-48\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 307.0, 149.0, 212.0, 24.0 ],\n\t\t\t\t\t\"text\" : \"Audio effects\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"fontface\" : 1,\n\t\t\t\t\t\"fontsize\" : 16.0,\n\t\t\t\t\t\"id\" : \"obj-42\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 14.0, 149.0, 212.0, 24.0 ],\n\t\t\t\t\t\"text\" : \"Audio features\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-39\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 142.0, 385.0, 103.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"Spectral centroid\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-38\",\n\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\"patching_rect\" : [ 14.0, 385.0, 78.0, 20.0 ],\n\t\t\t\t\t\"text\" : \"RMS (dB)\"\n\t\t\t\t}\n\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"box\" : \t\t\t\t{\n\t\t\t\t\t\"id\" : \"obj-35\",\n\t\t\t\t\t\"maxclass\" : \"gain~\",\n\t\t\t\t\t\"multichannelvariant\" : 0,\n\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\"numoutlets\" : 2,\n\t\t\t\t\t\"outlettype\" : [ \"signal\", 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\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"bubbleside\" : 3,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-20\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 9,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 38.5, 417.0, 191.0, 131.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"in vschaos we can set the temperature (how the encoding distribution is sampled), the dimensionality reduction method, and the inversion mode (for direct use of the decoder, as without any encoding process the phase is missing)\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"autofit\" : 1,\n\t\t\t\t\t\t\t\t\t\"data\" : [ 286615, \"png\", 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kFrAu03xdWEneb.RB2hRoaYuXl4CkyAcBxka1nxzOnC3SY7nwIdbSCFRDrUHvvx8X3YNy4GBDFDDDUsZUZ0p0negZ0R9+IqzINvpEmEJY.x7Ae7ORNfbsa2N66oRkr8f.piGMlHvOZp4HhHhHx8AJCSDQD4AU1X27f59WMtcwzL1HcaAF.6BW3BANW6rNmK2y9rOa1NoY2QmWxmm3Em0pD0ABgNgES1FfiyvDKI.GlqUqVQyO+7gXDVsZ0Pfvycty4Z1roqH3UudEeJSlJUdrr8hChR.MverWyhHhHhH2mn.lHhHxC5RBXRCuOwm3S3C3UnPgj.oTxa0jczlm7Iexr.YKYV1+Y+y9uOCEwCJMdPWt6ZMZq+MpGkhFLXf6Tm5TtpUq3.mCbtKcoKEs2t6EBLnUqVCclEc9ydVGf8bMa5cwKdN+hcHS6W3OLCkHCTKY6Ndh.8n.mHhHhHx8ApSUhHh7fpzfK3WuNAMZPvRKg20u9netGqvLrEODPQfB.Ojy47+k+k+k28C9A+f8JAs5B8A1C3PNda9c7mizoTyr.KCrFvFGczAuqM6s4FNyNM3VzvKCFNmycHv9.2zL2MhhX650q2GnWylM6TqVst.cA5WD1tGrKv9rAGwkI7DdMHhHhHh7JfxvDQDQdfWi30fD65WeT.N7fJ9rEA.YbNWfy47u34unYlQwJUb.ttGGXhSJ.Eiuth3QkJSjEHYxjiJ0pQ0pUoZ0ZToZEhbNGfqVsZtZ0dLmyEOccZ0ng0pUKuGsVsfVsZkEHGPtdPVhCFi+ZWdhLLQCHhHhHhHuJo.lHhHhDy8LOyy.wsMF.syBExBjwLKvLy2YNCv8Muw6Jc50jrK3T+NcLGE3hJsaSb7PfRkh+Ysa1jVsaQqls3YN+4SevVylMslM+xlYlQxST0pU89xsZDTsZ0Lm6bmKa61sy.D7BuvK3Am16pJHIhHhHhbek5bkHhHOnZrE7UBRtkYruOKvrEgE65bKYlsjy4lsjY7u7m+memej+I+S5CzAXSfaR7VP73SGlz1X8SNtyArDTbMn26v4buqabiarAvoWXgEdnVsakAGtjiyA.64Lam2a0pae1yewMOyYdhs.1tYylaU6Qq0mdwSKGfqCrWc3fFGuEH6PSKGQDQDQdUQYXhHhHOnxM1sjfLrniiCfRlKbgKDzCLyLmy4BO2EtvvdTZ3202w+EoqUHT4te7G+4IjBDA8BgRN.2t6tK6cy8R2JgMvLb3ANCv7.9RMZ3NyYdBGTllMaFeD6c7yQsjieiI+6QAKQDQDQjWkT.SDQD4AciEzjcbTDdlm4Y7A7exm7mwmBwsUVtr4dpybFGz0c3gg.kAf12aG+3ovS+3Ek0xwaWwNOOOW0JUoZ0pIuFbNLR2pgihbtnK84tTXylMGdwK9jCLycjY1Qsa2d.TdHPXyjsnXVWAIQDQDQj6mT.SDQD4AYimgIQKCN5g6i8w9X3bNaU5azONzHc5jr9i.196uuAcRlxM2kbLYxrLIcpxD0ob58GAf6F23FS75f3ffD9X0pE9g+u8CG9K9rO6fm3IN2gUqVeeH5VUpT4fFM9iNJ9wUH928JJyRDQDQD49IEvDQDQjj.mr8n.VTIxpYtMi+YdO44NmuYluy4763bdUpTw.rJU.nsU+NunuN5XuB.URBnQmzfaD2L7d6sGwOutHvBAKrZ0pg8RBdxSd1yFB8NDX+pUqeSfaUud8iJTfgP+nBwAMQDQDQD49HEvDQDQjXQ0SxrCVtcnqoKBpX.Am8rmMKkIcGywOHHvCf1wyGGWCZ7Rdv2BbztpqB3RlMOPzwYfR72XtZ0p5LCWqVsnYqV.PylMiZ0p0PnzgDufvd.vgO0Sctg.t9z+9zaAhHhHhHoT.SDQD4AciVnTajNcX1tPnYVDz1Jlri43Z6xYlkEv222ercYt65Tx41zFh2ac.vCZFG0Ea7sT3zLXoV0pd.9lY9OZ0p9PWqc6WXzT24rm8rNnhVnWEQDQD40.JfIhHhHwhCDQIBg9CAB+DehOgqWE7bNWFfrPwL.9yO+7F.UAGz1wZ2CAqn..sl3tlat4Lu30wDle9SYfyCCuVsZ4A30rYSOv44btLcgrP4rUp7vYoBA.9kACZCktO9tfHhHhHBfBXhHhHBL91waWBAFBD9zO8SGVnMty+Lm2.7N24Nquy479c+c+cAv0BbTsJb06gmgSXVybyadSpToFUqV01au87.ya94l2i31msZ0pgYdd.9W77mO64O+YxCLCsKlqQiFAc.eJW1nKFL5FS88hHhHhHuBnNSIhHhDyHNPE9.4.lGXIh2jbJ4btU.xW1Je3bu645+09S+ZsIdF1rEvMAFvw6DNSeb8Axmb7VCJ+HNW62S61sej4lat0VXgEV7hW3BYe7OzGxAbDvMMytwy9rO6NNm65m8rm85MZzXy5OV8tk5UpyWpwWpa8502DXafcSd9OjzsX3Xi+5PSWGQDQDQdYRYXhHhHxwhyxjRDALjUY.T3vy8y7LGVxritvybgAcoa3W4+zWYx.Pr1KymkxoKhIQrvBKvt6tKO9G5Cg4LmAQXDE4bgm4bmI7C+g+vQPQyLyu4y0L6WpwWJ22R85YARuEPcBpDGTFuotMdFmHhHhHh7xf5.kHhHRrwyvjr.y.r.vJ.EAVEXVmyE0oemsqTrRWVk1r48XFlTf7zmkIN7JOhy4dO23F23ct2d6slCVzfLI+9G.rGvNlY6TsZ0cZznwtlYuHvV0pUqKkoKcXyW3Edgse3G9guQxi+.fgqCQWY7oXzwulTVlHhHhHxKCJCSDQDQlX.DVez5+Qwj.NTFF5btgNmKrjYg86zON.Da9x332eh0YDCvVXgErpUqF+eh2mbbNyb0pUKBHx4bQMa1z8dqW2A3pUqVTYH5Bm8BQ.t+uuzkFOHOdPIuqD+8o2ldcMQDQDQD4djBXhHu5L8Bsn7fEc9+sVRNWdkjHW3nW78G014hLyFBLrGD9K9K9KFAUbqt58TVaXabb1q3UZroLyt6tq0pU6QOe.XNmqYylNvnVsZ.35RoPyrAMa17v+nFMN3Iexm7Pnxve5y+TQoutOM3AcM.6m7m7mz.XiWaCVhJ+KmjoCLnHh7VM2s53T8euESvq2u.D4Moro9d2X2mR6825605y+ie7U4ou9Y7owxnL9.vYlEVNdAUMz4btJl4.batI.UY5sK3wX.bYvfS6AWyuabauA.96t6tdXX3N9btYl4hbFlyZ1nI0pUiyctyERTzgX1sp+dquWI3lco8985wf1saOrRkJQWaQbrSAf9tO9G+i6f03xb0zxO2OKGo5+jSxKUv4tWKan5+j2n6jJiqxpOX3t092zON013aQnLLQjW9FuSgdS80w+4xaM8Z44+SZ5SnQo3qOFuyLQTgPndXkjfjbtyctvNvPypDZl49t+Q9QR6HjcWBVRJu5fGbMeffxkICIALAvCmMwHx6bNCCuZ0dLCCqQiFtOxG4iLnZ85GTq1itGcYutwqYJ6ewKdwCqTox.fP1gPne3ZwqYIQvUmdsK49Yv7T8ex3RK+dRK5v2qkMT8exaFLd80dS8+k2Z6t092IUtPsM9VDJfIh7Jy3qa.iuyTnJDevv3WXv8qy+5BQeig3.LzdsHnwv1vPfgm4LmYHPHE6D4bN2OvOvOvnNGUgJujokaiwpuv0oj+4+jm2CvqZ0p1XwwvLaTvSnYyub7HTYVnC2QMa1beJ16V.2DJd.vgOwS7DCJRw3WaIAJ4pIA4Yr6698Hao5+jT29EJr959bbVTEv8V8Xp9O4MCl9hhmNvfpb5a8MQ6eavFosANc4hSpsQU93MoT.SD4kmQUHtA3wZ21EMbuznoZX8MulbTOWaz48wWbMe4b6jN9drw87Eh9x4XKuzFaWk4poAbX3pv.yrAW3BWXH8hCBw+guvW.JG+9baZ+RdfKjbd4bm6bVW5xYdpy.fqUqVSbxJM6R.7pUqlcoKcI68Vql6RO6uTXsZ0F87WjdNJF+5sG8Rxnja61KU1k7xsby8i5+j2b6NUeiGfecHfqbkL.YVYkUxvsW9X7iwIcre4T+m7fsWMs+8J42cxxuabhae6fJu9VUmX6eWlKOcV04A3u1ZqMdPSFurkZi7MgdsNfIpC7xakLQkcWF73p3+E9BegjJHW6jZz7U5ENKuw0wkCtJbgKbgj++FSed7UR.TL.u0tLdPc+wBHyzkUtsKTga+BLzEv9pii5DADtYRVl7jO4SNzEu3u5969c7c3ny3Agn9c8f0O4Xd1yd13fYTlvgCGFcpScpnJUq5b3nZ0p.jrdlXdMa1zdxm7IoKvi+3ePCvmhEC.x0CxSOxAoSum5SeQow+MbmcRkkta0Kch0+0tc66T8exa8LdY.O.uu+u+u+jueCO.uFipK5zAat4lIee86zzz4UZ8ei+5Qsk9fmWMs+cu76duTVx3xXimog2C+Nxadc2Z+yer1+RtU29q+q+qStu0Oo5oTYl2j40p.lbGaHj6bClxqd5Chwd092+cqRsaqg1G+we7j0hfq5AqemZ3c7emSJU8dP8b0a58LOyyj7cWFt8557uK2NoxId.dWE7ctWviqNZTLlNcOGUdZ80WOf3KZNCrVFhSOzoKmoxX26NdgesANNMQTt7nr1vLKBv8e3K9Ec.r5pqlDThF20CZIvUZT1qvvhchmxL6t6tIYBhASTVvYfyZ2tsctKdQuKcoKE.ju0e7e77.KBrvEtvENUoRklCHOzHKrzzAN4NEjsoqSJ.1330TkS9wMca3woi7FaLV8ean5zdyuWpN1O94+fm64dtjocyk8gR9vR9NmyGtluUy7gE8gF9qkNpr241+ln9u1s+Ocmp+a5xj5BQdqq6141WIs+MU8dqO1zFasLbmKWNc8ni7e7+3+woCJsJ28laur5++Xs+kDzjzfizvrpFPECthMUcep++uIzqEmjd4zf0IktvZUD9kuoqHO8qi+9Ki882oyKmzJ87alNeXS88S+Z+NsRVeRGiS5wY.15f2tPP2zEswpXzptCZDVBF1cMBYNhhu94IdML9Ebj9yFstCvjeF3k57vc6bn7ZmounAi0w3JqCbkiOGtAdb4IZPb5xAiM0OFctOt7QIBX.9rMvwkQBG6wmdLha3cIB35m1CtFKAQWeEhHfH5Nwwe5xX2qdPs7T5mUy.LCvBNmaIyrUcN2CAjwLaefsoJcoUksf12D3Hl784w6bU1ji0x.OLvFNm6c0pUqML3zyO+7OjCxr2M2CbbHwKpquXsZ01tYylaArYsZ01pDr4WtYysqUq1KBrCEY2K9u3h69DOwSrWE3lsWgCYdFvUmXJ4LcaASDTuRIkq6BNVinj5uNwovScv+PvueZfVpBo0+UAFzdcB4JiJuN8y6zuGCmbaTxqOdo52lA3sF3e0zN8uLFauLv11hfsSc7HDOZOpctoWOcRMc8Rubp+K80BrAFWdCfKOd6mSW1Skqdii6V4qo66ycreXjV+y8d6eokAtsAyXw36vccHj5DxBDxkus1Kmt+ywkW2.RJ+Mc8sS+2k7FCuT0uc2dLd2s9+W.B6WIoNp1SbLFuNtwa26UZ++kWGb+NCSlJ5aqE.0ii7a83H.WOtCnYIIhvab7BBlhz1qLmzn9L8nXe2FgwSJ5omz88FcS1H1smMSS+XNoQbXrQacraqSv5iMRDWgBY5BYfxYWAxSKlAZLCvLcoZdtJ43xjCVIGTHGPVJQVfrKA4.xmbKWAHa7mStiiDxK0MkwVe82nF71.fqL5erjQX2mKS.rbV3z4.xQ83y20O9b+L.4WAxB0iKasVRYutjgsGqdxEI6xrbtj+eVnRxWWJWEXFtNyBWadf4uNqLGawrzkYf54gkh+8V+kbjydot8fJip3Ujh9lYA.9lU1Gv6u5EdAuxfEuA4bhqgIG+915wuOVZx2Kc.t4medmCb6t2dt81aOvEeeVRGoZ0pk6Qq8nFPPiFMl4rm+hmxb1BPoEdlm4bmhdL6i+3OddfbsY4brEY3pjEJjEpDOcc1Xh1DVEkLSZ...H.jDQAQEUWVb4uEy1Ex2sZRYyQ0eQbYmBjqPb8X4.x0fkx1GxAEyu7j0+kuM0yyURaee86T8ZSmgAJKndigaazSYxySI2e8fqFWuUthvrrMyAaOGvL6PwbzfLzl.XkjaiZ+Kt9piukT+WRYz35+x7RW+W8jeVkLEfbbYxBWNstwjLMXzNP0zqsTxqetS8c4N0lzwih+wY+VPR8JYfUxVExmz92bLY6eyBUmAVLOws+lAHX8iq6KKPtxPdX0Y1Al45ESp+qQZYpjxeqSPxh5430m4Aa3sA.WFCtrM0eGpeYu96UZeoGu++9rwnE00Wx9+2mJ4oM4nMYfEyDmwm3yJD.jYw6s9+e25il75n6mm.FuP2XUtgsTbjagIKL.wcHLj34G9Ish9qnp8R63nsuNdbkQcP.tyK9emzG9No2quWFklou.fWubRMTAGOpTSO5pS+dvwuONYVfvR.iJ+dZLFfOsI3V25VYlc10xB8CpBzp.gzmADWdN84LpBD0N96G8Yie0e0eU668686Mj3Qj9Hf3sEzIyffTSedXrW+aXISEjwyXA84mWajdNYrF1VyCtpUAr1UHcTElnQ2+n+n+H9V9V9VROF9.dNmijo1w.N9beZ4uzN76A3pAtloeNtTR4itoO1xYfNYcNW..lYiOZtg.CqBG0BNjIKWN9eOoRGYioGo1nw9+OnHsNfbkf45FmUHqPQVEOVv014u2d6cyScp02D51tLrUm5rGMlHCSFudl.frm6bmalyd1ytDwYXx6HICSdmI++EINyUbUpT4f1sauWkJU1orYu3WtYyctzktzMNy4Nyd.2n0y0ZGmy8h0p8nWG5c8BvK1uH6QOtIvALV8IKCgaeb6.SegJA.Aewu3Wz+e8+m+qseo+O9ktsy4qB1lokUVAvgw134bt.yJjA1zqJ3ZUfgzmC43xzCABWCBu5wYHvz0SC24rf5AoxaudZ7ARX5AZwUGbMRKOTGO1m.1hb27l2L6by8MlC5FrJXatJN1bTaXomiIYM+I87pGw0+4RpqZ719f6k5+7HJI9jFTxftTBnaYbzYTYnz55ltek2sLdRdsyzscN9sS57xn5nJAV2ze+B.g3w1okSplCZky4bYfIZ+aT4fRvftGeMFoG6.fLNmKiYl+96uO.QyLyLie8HQ.gKAQWO80WAbDLp7Gok+p.t10SlBm2delOoax8G2sqgc5e130ycR+roClO.tU.1J8QsDdLG9zfLMZzHa85O5cp++iZGsLD04319B.B9C+C+C4a8a8aMj359lt++SmgI2sLl6d453j6SteEvjoCVR5nC32saWuRuq2kid8bT.uZ8InY7iAhKbLdAlouXS0wo6tiazYcB3JSDAbHsQi50CoQCGqia8q.W41GMu6TJDNdJic29f53WjE70+yWi2363c3K90xZqEwUupiMvs9kwtxcN5+iegtd.1xfKNyfWlZrs0bxK.JCPNmyEr4laZAAAQKszRSzAMmyMzJWdHc6FRY7JzgL8i+8rjG2gDeANoWnQZi5FfUBnKDAaDljxwi5z4Xulg5DQiQcRX7zgWt+X7N7c6yC+EwiclnLX.Plqe8q6uz2z2Dzsqih3sZOB1LttQONt9uzfYPxuaVmyk0LKn.X8ACJDUj9Q8N97a5qk.mykwJa9zE33.POz4bCAFXqt5Ar0VGPANpRehZe7eO2s5+GurT52CO3Tlxi3LvH2VvhPgUKP+h8btULylGv8q+q+qu62y2y2Sef1TlMoC6A2V.SFcrn.YJ0mY6FGC1SywALYCRBXhYVfKxEVsV0CZ0p0dF1MbDsSsZeK61p4WZWLaOmysasZ01sUqV6TsZ0qCrCk3EoK6ASDvjnKe4KGtwe2+tgzsaDUwUtEdclbz2Odjzt8LN0JBVOvCV0Jvlt92dpsG3btzxxCMyRaGOss7Ar3hGxN6Lf5DUuAz33m2wmxFCYxKTAT6+uV5jtfgw6+V.fcZvcMHBVIZE1hshebYHYDRAlw4bY9K+K+K8eaus2FiEXDHNvHQVoRgzqmiRPgtDzO4XS746CStcTxuW5ExNd8e.EbI0+M8T6ws+96yLmdFG8mX.3RuMf50GRiF2oTe+t89hJ28JyzuGdmBHm+XOlwGrmQOt5fe71wdYqLcnyws8llIQ4bNWNqhEPmQ8oJDHz4bQ.gVwhCne+ArLgKuMr8w04MpbdxqiwKeDADVBB6BgvJgEXqn92oo15wAKY5qcIh50iRJ+cuLMEk6MSe8GmzO+15iyFjrXsN4O6j5+uO.EgndfCJQQ5Z8lbPsxRb4uL25V2xN5niFu++Nfn6P++yxw8++HlrNvwGXAlp++mT+5Oo5qTYqWib+LfIoE1xBjmZ0xQygAP2zBFN.uqcsqk4ge3GNaIy76E+6lVf4fjaCVe80Gdkqbk60sEwGjMcTQyDOpele7.3TEncZPORqL2.7pCdMlrCxG+d7owwtDQ.gzeTGONorzX7uldLd83b0zAKIX6s21e4k+FLXG31mmz9UgfV2dZgGWQXYBvgmqiyLyFMpnIYE.PQfd9TlfBcHa+I67erRkftdgP6i33KfwRFIjrkLyq2wUXtexs3KbtNPDw6TFsusrvBntAM7YY7Vba71I9Nm9wM8mcjWcFqL1ZwKvWKi+o2F+qc6cBLfUWMCalwGZab74B+O5G8il8m6m6malJlksyjAMaPxiy24b4rxVdhHK8iO1IiHaxHmVJD5FQInT2Qi9lQb.5BMyF3btglUc3Xk+1miaPlJfW66PmJnbxm8uECY6IBh83qe.uUtbUbcpwAgdVhCvQImyUFX4KbgmY1yblmH7282+2+E+G+9e+85AsHdSvY5.ljdrFuSVyBrRAXs9wqgIuaf2QqVspUsZ0EZ0pkOFCqVo5AsZ05l0pUauFMZrW8502qYyl6VqVs8fh2nYyma2ZOVsaz54Z8hUqV8FIAOYWmycKyrChO+WLB5OcVmE.js.iBb63AKI9qEwCGdtdNOyrQ2eRlBL.JND5ERInXW7RZGexLapb4P5DND5e.vsHt72fjWGAIO+oCbxPNNS6Rq+5jxro2JWl6qWNo1sgiO+mkpjkAjw0aT6eSk0HKmA1NOkHe4tLSm3.mL4zUnbYO5.PmzQQMj3xPY.xU1Lu3KDfCItrwAi8XxdGp+Ko8sxCfNCoHQk5QT2ie8MLtbucjy4FX1pCfsFO3c2qsMN9.Iwc4wIS5tMHZSzO0Egfct8fUD+49SiGGQ.CIfMGc92NttnxAPmLTlrE6Pldw0i4Sb+qBMyhhqqpnKo9u3.mcbY3fRwqAEYX7AVKs9lBI2Quz56hK6bvAGLLe97gPgHneDkvUpax59zzYIWwhPOyAcGOKRGutM02rW4lNPG290wtwFdb4a5EuPjO5wl1Wduk.uqO4fNdbvhKPFb3456lH.vIkAMnpGzJfUIawMIeuIKGEax9+m11WZ++ykb8utj6O8ZeiCZREhvk72TmQYKWZcnokel98BPYwzq4teDvji6PXIxRWxSbmBy6btfpl4ZOpC2ELneFJPdWOWV.yLany4NLYAzKofyxCfsGtNDdkIqLRMfMowqvHfRIq+AiMBhKC11i9.ToPnKT.OWOmuUy7n0DoFsCvUq16JrQi+zAOrYCZ.GsNL3J2dVKj77tg2cXJg706.l3wFoqcDGmRukA5L50ScGzvnHAtttrVIKC8F8XGMpCISuAqhYVGfxf0434BsiR3nK3bNOyJ5C88SFkUeyLefLasUOukWtPXYyNn6nQ8sXDzKfRjw0w4C3R5b2AlYiBXHT1AcnLPamKrjYC5MdzmWlzTh2yrJ9PGbNWnshM9E3prL49mICLY7my7cNmuYU8g1AIkYxXlMZt26bNuj5+R5jdAenedJxbtttYAxZlYIcxeT.SfxYgNy3btb.YJaVvKbzQteyeyeyvu6u6u6g6s2dClat4FBDYV4Hnajy4H9+aoMtNDXPA3n9itfjhGB8FREX6+rsskeGK6c7T6Y7NtVJz45bTEyNpCb3ZvQW81G4+2JWWbZPky1.lqJrRKnhy4pbgKbgkOy4OyLzgA+D+D+DW+m5m5S2A52DnOUXOZygbxS6I+30dgFyRUVkV72fx7HCdgAuq82e+uoc2c2Z.mBvCrg0pU8flMadKfaZvMcvsLysa0p0287W776dlm3L6dzQGcirYe3a.cugy41AXWyraQbGuBArBLZ6L1fJdNWqLVEKuqsK2W9K+ky8deuu2I1YH94+4+489A+A+AC.7KYVPOveyM2LyW4q7U7dGui2Q3pqt5gTlC68m16nBEJLzrJNnSXR1LElbA1d+k+k+k7M9M9MNrpY2rMbCfcgB6C8in.d+p+u9ql4wdrGKyZqsFIWjS5.mL.pODZL8.l7V8xbe8vzYT43Rufgbvp4ct9YRCpQMH5C9w9Xtm4Ydl3GSUxRKxEmEHUmAZmy4bYAxYlkCH2Mtw0ybpS8PT1rgcGkAck7ft4nH4ccGMEBOx4b6mz+uihecUNKzI+30+8+6e0ek6EewWbvi9nO5Q+O9S7TG9u7+4OwQ.CLqxPnywYS2wkiNhimNFIAiaoAv0GP7hR7Imo.uwX.fdynouH1TiWOXb4mkHqaaWVaUKCagex.QMVeGq4CMCJ.A8bN+xlEzcrr1jJjw0xkAHvrJAPmjckIRCv2nmyjLjK4hNKG4bswJaAtNtfW7EewfkVZozKjdz.JlzWNqhYtNvQkgi5LdPOJQHcILtdqhQP+wq+KM6Ow4bQ0LKr0nLHnx.n8Km9loxbmrw5O1FdIqeLr95qyUtxUfwFDzEAucFUdbQGriQU7bMc9VAyOMfbL1LiHInFdULiNPX7LsdrrRoHdzC+3oyUoLPufo6++st0s7lYlYFlz++8.N.J4ftAThbtNtLDWdMz4bGYlkF33CgBCg9QkfnNN2vxlcT2imEFQrNQwKYd.rNIqed7deuu2nuzW5Kood3qgteEvDeVi.tJ4XUlkMYNmykOYQxKJo.Q575Jn.LyWYyMyu5pqlMoxxzzR+.f8WANbqIlWWqMDt5K0HC7fZgiIiP5JDvViVvpFcgawYFQ7nKGmlqq5AalbQeVPud87JVrnADkzIizQkdenvQP+SLKSVC7tJ.qAb0WyRe+6TZxd6o54JjgslHnQVRjgGEc4hPldTNGzIWRmxxYlkOIMfylDfD.b+J+J+J78888Oy24Z6e8qecuqe8qya6s89iftNmyQYyhGg+hXziLTjb27u5l4lc1Yyr2d6EdpScpa4btzKjY..kffNwuumNhHiW9+nUgvMSSmOyF.kNB5lN5qQPERxbgzND.UHj1LjJbDsusQRSd4a5QKarQgX4.Xaeh2cQ75FGrxrzkbTfYnOokk7H973fjNR4CjuHbptN2B.ySbYuzNsEQbP9xzINfy4iC.So.mqiiQS4ghG4bcGBLrjYg8fPJQDcIjhLjdSLMGOx4b6WoRk86zoygDGDkn9DeozIe1v24b1W6q803s81daQEgA8hKKljY.ENL4y+i2P7akaXNs9jr.yCkWE5T8S9IuP4m5odxUbNWdfit10t11eaqsVm1PSJQujoDyzALI83klF4yUAJzlxeCm+Sdl24O9G6G+cEDD7M0pUqpwOW3avvJUqdPqVstEwmCtEwAccOyrce1m8R6dly7j6ArWqVstQ0pUuAkYG5vtTl8oSb8LI+MDjVGOIYWRQXlW3vCymKWtYRtP2QSAQyLuxPP63LULIiEKmw453QbcJGXVk8gNG3btiJa1ftvgTlinCQTFiNjw4b1+1O+mev+vOvGXOmyssY10S9a3n3NfVxG5FjT9ahol6hvvclXpfsdTxNPkx3zW4FOnuFrtGbkwyBfzxmyTFx2YhoM3p.alNUbxBkxBcyt+96m8aXlYx1AxQIxSWlihLuqqadhmxNdIY61AlYCGNbnWPPPtRP9Nwk67HtNswy.NWxn+mar5+7Sp+6n3.qTdemq8A.GVwrAcfAIk+NhhrO8lXJtdTR+OSyfoiVBFd83Kdc51HM.aCRFBnIGnNUV6tapxWSj4HL1OKSbfnKjE5mKMinG6wakIdZClzuUenXFnWFmykyLKOTN2uyuyuTvuvuvyF7u8+8OseGvmh3SO7iS92IxlEemy4BCCCCBBBAnD32YrLwlwFTVyLqL32N941EOXtkNz45dHwk+FBEG.8F3btQCpvn9cE2Nb5eug.Ct4Mu49yM2bGPR8aa.gW9dqbkpm61kVNwCvO45O7nDFcwXEL1ZhrMOM6jF0etkAusohOz1OsO+GczQ4xlMadhWXpCdz+K+u197+led2i7HORzy+78v455+U+peUu29a+s6CE8fd9NmyqrY9ciyJyz9+m8m6+oet7ezO5GMnSmNCqToxMcN2MLytIwkG7KBY6Fu14j19WZV3sOv9EgC6ACR523gPoCgtistjsbDrsCvMZ.QpPzXYi9ak6a1qqBdoeH2UGWI4UShR2lUxboKcwLlUHSRG0hGEzUIhMIjUIp+lDt5pq5JEGQtjQ0nTdn6LPkY1h1oYaRxHEb0oWeStaWH3ChEPN9hX1pTDz0AK6AdYct9yXVkrTBO5RHUv0uM.aZki6PSFfrEK9NyR7na65RmCINpnIoZVeORxvg5PTCvw533JqYWkqlNUURCHAb6oi4qTSeQqmzOe7FpCXqSG.WyGV0Gx46bM7MqrGEvm93QYB50gbPm7kf7lYyBLOTZNyrYfhY5PuzQ8L566666C.+abiajY4kW1Odj765nLNyJE0pUKWkJUbIUHmgdkmY1YmMexEYDFO5YU1ixrGc3v+h+h+hv29a+salYdkhe+2Q77sMYzwJc3lz8nO8m9SmF04iftiFwrxvvNzNjUIjMKF0K90paT6AsKYISCtw6zxChel3UiSJkO8.BVFB1lsCnL9zoj0ktwML2MYzY6WX1Kcoe14MqbtjN7EYVkgIMn4QQx2qGKXl8PkgGpCbpCO7vbYyl0C.yJ6010If3rOIcwb0OIMiSFwzdGYksgzsT5b0NL4hNCu3+hKN3IdhmHMnJC.NzrRGP4dwWTRQNreOF.8GV73LBv2rpdwSeuhg8n2Aou1ABSRqYOhaf10GBYiQa6huUbZ5L0Hw2IIXBQVw3uAmyYCFDYsSKqz8d73VFu1cvC53ctm5LdO4Yexj5JMmgy4.mCrVsZ4UsZUOyLqYyltZ0p4ZzngCvcly7jzrYSCv6Ye1mMYjtr3oBamxFzIBJ66bsyAj2rJ4nH4nGYoHY60iYxkKW9RvLlY4+pe0uZl+V+s9O2h+inj0ItLc.PlVsZE.cSGMrHmycHzYeJwAlU9f3+O6mTWUHcJ44bcxXVIbttGE2YwJoWPSVJw9s6x.nqqZ7wL483B.8Mnl+NzLMfuIs2eko2VZUPSd4435vh2ty8gq3uBXaM58uULXqLPsLcnYR1ijdgriBVRZPbyQYxNyL+Mxvwq2HyBrvK9UewE.Vzrxy4bc7iqapzQThgAAAly4x74+7e9blYYKAd++0qmy4boCXvQlUNpSbv4Fu9OOyrnm+4e9CJA62kN2zJY6SuxGxwWXQbpu2q3sfd6AkOHInJGYV0CoHGQONhRbz06xQP6CWFNZ6QkqVC3pFTwtb7J54zYS28q9z7VQS2doe76mP76oa3X8KabEBXI7abc7g99Effj5sxlr3k6AE85POuO+m+y6kT2P.zKKvr6u+9yUDlqGcx+s+8+s6SuxoKfvAI8iMyMu599yMyL9ICxPFffgCGRlGNSb8Ikv8c7c7Q7R5+kuYl8A+99f749U9chhm9LksNw026A39re1O6gPuCR6K1nfuUj8MqzM+s+s+s28a+a+a+lICNx.5V14bsMypfy0dnYFyM223PhWPriXY3xaiGP3FPzkeoG.3GTK6c2FL+je1FbUtrkzWk3yYaU2CZ3AE8gPemay3c0t3rBwn.118wCZ6ULNifyCLW1rO7b.yBEy0ld9s9B+6HoNGGw0+jl84wYQRQ7Mqjs4laxJqrx38+O+G8i9QmwrxANWmgNm6llUddJwMoKC9Y+Y+Y4G6G6GKvLKnbb4e+c1YG6niNJbokV5fffp2pG8t0uvuvuv91nrNu69DW+6fZvvlrcXbVNUIpOsSl9Yo8UKdjq4sl8K60cuZxvjwtPh5YfF4Hdqha1ja4HoRGfAkgCSRqMXzH2UZ9s29qLyxK+ME3bcgjEArjzyb+jT07PX4Cqv1G1NcTSiSIoSpiSOnFQsiCXPxnDALGvB.y0ue+7uyBEB5EOZ2Qoos3+7+4+ys+U+q9WkAJk245LCPtFMZX0qW+PyraTnPgazue+a.k1aU5dvlooVaIBq2MIvI0wnQcfFNt+t.QdRo34zMbXwc.bMO3po+sOZcbgiS0trEfr8IMMhs7DuX0MiY17PwEbttKBL+1u3Klc4G5gblYCJWtbXmNcv4bAoomNwYrBDOcZfhDQuRQIAoHHcTPRdMDQxH0+G7G7Gbq22668MZwXL9keQqWum2Ur3ify0Mc8L4fjx+2xEudDbHT9vhz4ndwMXenUzNnReNpMLjBDQ+RIM1eaK3cuTAWTtciOBFIetZMuJbUu1Ie9pSmNAuyxk82B7oLdzY0rvl4IdJUr.vBNma1jxfNfnqe8qyRKsjOwi757.ODTZ4O6m8hOzG9C+gmiiGQ2wmOsiusXFALLLLbP7n1FF56mIBvs0VaEsxJqDADEEEMDX3m6y84N5C8g9Poiv5AkL6fdI0o9o+ze58+Q+Q+Q2OYjeCgR3bc3y7Y9ey8C+C+OcPR4tcKVr3t8506lvJGLwBd2hLrzNSryC7VooKYZ8MAqrxJ41ZqslGJtZQ5U5oN+4K8jO4StrU1xWrKC+sd9m+5Ox24izl1kZBc6A20LLIMiUNEwQG3uATZCmqy6rUq1+Mme94pr2d6sfCBLHBGGUsV0a0pUq8.6F3b2.iaLyL426fC1+lOV05G7kZ17fpUqtuYkN.5cKRBpcR.riCfBLW61sm8QqTYl1IYSGwWLZt81au7YxjI2y+7Oel2y648X99oqAiSr32MJS8XxEo3CANbXzvCx3W+fjQ.KLcTgSZm4.hmNNW+e3G3Cr8+O+692sCvtDmwLGkj5wNnn6vCegnb4xklMVoKZwIo3d8gKQivqe6qQS2uyjw2JZ7.9NwB860t103zm9z.m1AWKMCSl04byYlMSkJUx0tc6.nPlRzOW2j92kjgUYMyxPIxP2x4fNyAbJmysnY1BO+y+7y8NemuyfiN5Hmuu+Pee+zyUSTtJJJBOOOWTTzPOOuA.tnnHyyya5cqmPfCihht0gGd3s9Flc186FGrjglUdny04nO4m7Sdqm9oe5wGQ2z0NmCKAG0Ms8yQYzxx6CaeHr3PXmQ8cLY.HBWe80CuxUthVOvdoMd.lSWuiFeP8o.32+3Ep0zsE5bP67Pw7NW2bI2ePxE+ldgvYghyTh++Yu2znjkypyz8YG4bVYMWYFYFYIcPnCBiP5nIDPC5BHLM3llkuH6Fuv1fa4FDvRX2XijLr.YnoYRBvfW9B80VB41zxs3ZlQFvfvskwfwcaPBgDVFPGA5nSkQjC0TV4PkSQru+3KhrhpTI.g8Oz.eqUbp5TUVUEYD6X+s2u6286t47Mf4UUmsYylYNlssUC.UU41u8aO4S9I+jyjHQhrISlLM6+uSRL22lB.an8Ub1v.6+9q0N6riXYYoEJTXDvvxhLrAL5q9U+JiunK5YM.S6OtM1rEMJuMTOx2uZNszfof3A65.Cc2uN53CE8gVAgsJdbaq3r37wh1dGLl+n0CbO0UIAqsbRXio9TlBpQLMyoDjt4drsORKbRY7aYOmp0m6rO6iN6G8i9YxbVm0YkPDQKUpTvm6y843BuvKzprHoarWK6DAhKTB8AI9+Dg6isKPWCPuwi8GKvNwvg2ehLYNEKUanXre5KhzAnaXbX6B16VhFCZFAXWUYTIWF2DlvxLgMJ4CMiG2+CVwENrqi+r0Cg0OsLLIdREIWh0RsYnhASYRScR.kEU8Hxo42Y80YkUVIJnqviFr3hKZUlloDQRXaC0qqYssISiFS6G1gTdyAd0iILlGeeitoCFr9iEQiMZILOIoMYfx4f54AlYkUVISSHYqVshDNM+0Wec+v6GIKSiLhHynple0UWMY4xxjoURBRwJMRt95zEylXCEQz0ht1uF5YxZZHEV+W6J+EupXGjsDlu2ci0RbhDaFI3vNjx3rrXBnUBLIHjukA.oYBqDVtu6286l2vnDlyllyiQXGmcoEVH00cc+wZTaTzoSG9M9O8qa0HzI4EdgWjkuuujHQhHfShrmmlTjF1CjpQk1m1tM999C+4+4+4Geq25sN4FtgOr9JekWFEKVjRzDQj.aaFWuttqsM8ZzfNgA8MjR0G1rI6F5.saiFM5Xaaa1ToE9KSifMhNGNUf6GENhBmHNqe9Qsdr5yKQqCxjo3IXXwRmHo2l60mqkKWdu.xpSJaVOaiH.JKy7pmNmHkyGV4eK.YwEWzJbi1zD9Z2c2cWLWtbKPXq4v9SPH9jPBBC5KQhDgaHZE355pNNNr7xKOErXKKKefIuzW5Kc7K8k9RGGDDLBXz2cyMGs3hKN3S7I9D8eMulWSeftG6Buvt.CKQC+wiG6+leyupIqu95CpTQRpSE3XDb1HUKWylvWy0bM9uw23abTiG30r3OG7Hc6IAP1XiML2CJ0jlMM96joBJnnNNNA3gZSC8GAASNHKkD1qMAAPcbpDryN6D333D355p.hXIVtttIpVspUM2ZTsZ0nf98UUCpCTsZ0j.4rgLW4688N6UcUWkRYrtnm6y0BJkFZlEXlYlc1Ytk67NmYmc1IStb4RmJUpTg9nRAj57O+yOIf35VScbpFcNmH1Qz4sR7oOBLpY8liTsdDcyC.SRL20ccWgrqS5.rvM+49bKIhrCFU3dGftgBB63JzLHSlLSJWlIhHiJWlQ0qSDvI9r7ZI2Zio+citVN4oSyL...B.IQTPTEMUohWIse1Z+q31cSSd.iOnjm5odpBftB2OqCV11jpQCxUtrjSUsfoXBjC6V4pWWyIhjSUMOFlYlEHyG5s7gR8xe4u7LyN6r4w3WaVU0B.4mLYRxzoSKAAAGrsWlBLrkkkElOYZrbgeo3OuD88FALLWtb69C50aX974m.3qZ8f1saOZbv3coL8.ZC1cfFcw.ZxvPcTYbXwH5AzA1rCvtUo8vZvjxkwudcBVXAwGPCG7.GzO2A2S8Q5959W5Z+wWD1OShbJw+dQfkjgv1D27QuLarwFYeRKubdQj7kKSla6erVpRkHQylSqpeNraNaiFrjp5h2wcbmydtm6wx356ikkUvINwI3EdAWPx5FP7x+FdSugLW665Zi9abvIwkOfVudcbbbhN+D.000EGGmn6sxbyMGtttZgBEFCLptpiCBBFOXvfw.CBBB1AXSZPdapmsgwm1tThwZCcrH1Ceiuw2n+0bMWSBLCX.M77IZexIgs4p+7rVPaPWZIzM2D01lfFMdLKCmN39kVgxzQ7BhuWNFqgVjMzVgE4fxg.4ZjbfHf+y1z.1atvbZBAWodJfB1zXAfEtq653yAjw1dJf+5S8o9Tk68duWq5pl5K7W8ER+B+28ByLZznToSm1RMI.D22Vzf2HJ9+CNELm.3eW20cEb1m8YqhHR5zosroYBQDsbYF64o8KUhNMaR2v3+Gfcica1vj+qHR+v8UMrOYCFUllQZriEyiParBGLDOX5NGw9Z+r0Cg0OsLLIdBEoIzXDSU6yHhSRvSt4O4mT+E+k+kCt669tm7g9PenQenOzGZB11AmmiSxm94e94elW7EO6u9u9ud9ve1Tus2w6N4+ke+2TT6PDlno8PUqOPDoeQnWqUn2pqyv0Bql1K4k7R7+3e7OdbC2GqhFaBfz1vLMXk4Us07hHyCjuDjrgp7W9W8WM4e2+1+sSlLYxj2y648L4S8+3Swa889VS8Beguv7e7O9Ge9W1K6kMOPtK5htH4y9Y+rCVYkU57I9De5s+k+kewaJhrcIncyRzawlLXq8OBrNbmY+K68DXD0oDvcaAXYCRCynITZ8.qB+TAaMRHvHTy.rg4qq57.yKkKWPqWOuHR9PFlLqp57850agYlYlYFNbXpLYxne+u+22+LNiyH1jAXZUVimPaz6ynMbS345lnhiydBDaXer545MthSkwtddSbpTIpRGXYYE4PKHHHX7jISFjNc5dhHgiHTi8OP2xhztQI1nbS1p9dUyNhII9Xp31HQNxH39iOa2+wcu3QKrC3g55fUw3ffkjDN0jv8mDHUEHoWQRUtEo8L.JF42qP3wblJrVdVnQ9a6a7MReF+b+bo9A+fePxicriEoIDohk3wbXXbPdfLttdIbbpbvpqHQ.iPXvViFMJHc5z66dU3qQCBBBZ0Z8.e+ISbbbl.365VariS0IAAAirrr1c73w8NwO7G18nmwYzU1SrEi.1qGlf+BSr0taHkPMTeuhL1tNCZDaplwidp5e76+gA3uzLvlK9G7G7Gr768Jthh0KwRZCMGf+O3DmXqS+w83pC3wO1ojyYlDt6Lr7xyxFaTDC2Y+4TUeRdddGUUsR0pUm6889deouhq3Jr5zoyjNc5rqiiSWWW2sbbb11VjsaVhdkaxt2tq6DGGGBA1JN.HI60qWxISljJUpTYxmOeNee+7MazLeEmJwmlIGDPDXuDSm9LPPPPTRsQ1gJfuqqquiiiOvXWWWiMfpAHBkKWVAlXACvxZ2M1X8dKt3R8rrr5hwtZ6PvS5vdASFI5c8BCHLTylJ5Cs7+9e+u+3K5LNiwMWkwEWiQs1OK5dr1d9+jrlZKelf0cSkjfWJrsyn0qGlTw97yDEKW9icriU3Nuy6bN0rm4b6t6tylOedy9kkkbZcMGPdee+rIRjHJI33LLNG6M4bhXY7gcOZJvIiFNh02Xc0wwgv8RiNuh9+A9999IRjX7nQiFs95qOY14lye1BEBvr+6XGGmgDDzGKqcZ1rYWaa6tpp8qHxPOSqiMb73w8RkJUaQjsUU2Jbe1H1YEo6IwNNyIvceXL07fIv9XM6uCauyvONUHNs.rlGR0lhYgV4TUyLZznzYxjIZzTGAz1Lg++HcMIIPlO8m9SOyYbFmwhqt5pKe764dV5LexO4YylMapv+VSBYKcB0vdtbg+NhCVR7BNL8dnaMWbp5bv7ehhiSBeMJhgYJNNNSB84MAiOqt.a1e29qOS9Y1DiesPVAX20zVXrCP+hvvlF8nSCKhVj3refVOz.pSqVs7KVrXb1z8Xo7ZNP7WEsLspI5pf+Z6u6Bj8dcDwZ77hXmAZNMu.aH+m7q80J7LelOy49TepO0L+R+R+RYDQxDxFjHVwM+m7S8Yl44dwO6zVIRXMS97Z+98kYmcVASqakRM5tTbM+BL1LAg1LwYwGLEXLF644MoRkJSp44MoZkJQf2AfDx3IBes61qWudEJTnOvt20ccWCOqy5rF.zyVjtMsoS4FzIL9+d2zMcS8+09090lNkcLw+WcB3FE6evQ.8DbTENdblK8u1E29wDqeZ.LIdB5QFp4wPUybhHIGNbHYVMS3HoshenxPOFXxJPv5gpwdoRjuYyPQFqHYdlG44l4S84+noJUpTBQDsQiFAkJUZ7ccW20ficri0Gnitm.ZFSDb12H8BdrkAPr.7mOKzdNfkvlEoAE9i+i+iSs5pq5+hdQunw.Cq.i7ButUtLTuNoJBEZYd8KAL2nQix.DjNc5c+u9e8s19s7V9+cCn45.afYiACyG1yQO7i9guGp2ON.EOqjTU285SwCToLl5jxNYnPBlRJIonEYgRyAMWVUcYQjEKAEt2Ncxee228kKQBI2YbF+bE912wcL64eAWfoZZ6QgyozyL1gbHGGLHpCN0gBBcnNsO76zoSPmNcBLRg.p.JhDjLQxIS7mLpRkJ6Bzud856VrXwQIRjnOvNhHqiQnAVGSxFQfVE97UoAPycA5yoxtb+SC.7fmiwuu7ul.c8HoU7.8N3813.vkRDmzfW5XZEQFL97lUUcVLfdLKvr25sdqydAWvEL6e9M8mm+U9e5UlMSlijQUuz.ou268dSc5m9oGkXR7DLhBxKAfr1ZqwpqtZ7yQVe800UVYESvddtpSEmo2mhRvv00CGmJAgUFKna2tAfD.pOPfiiyXWW2Q.CTn27yMWeKKqcEQFjKWtQG+dN9tG8IbztwXBPaC81s6BMiWYCiMF6SbEOrwO9izrkd..lbsW60l+M7F9CmC7VDXYU0Ee+u+2e9q3Jthf67NuysO1wNVSf5.qCGoWndacH.lX1yaYX12zevePwq3JthiXayOW855SxqVsmvLyMakd6zaNUzL.VEJTvua2tCJVrX2VsVuMXlBNsZ0Z2y8bO2QgU.y5889deIuxq7Jyc8W+0m823RuzrIsrxD1NCQIMDOg13i90CZ2GAtaPrjFlBnhqqqDlDqFDDn0qW2vvl81+0ue+9Z61soRkJpqq6DSqiwHPGtvBKLbmc1Y2xkK2AnsHR6v8x6ppFkbQaQjn11Y.Sa4hhSf0GCL70+5e8id+u+2eD.e+roA1guhYGezDvwiX1V5PAcMKP5hEwpYSkRhHsJSxeq+u+sx7A+S9fQIvNGvBppKdxSdx4toa5lx+ZdMulb228ce4N2y8bmIhEIDZeUtb4T0qWOxNKpB+GVKOD.DTy0EADQjDnpkJX4Tw.VBPn+LWI7y0PlWoNNN9tt07mat4CJTnPvfACzs1ZKsRkJ9ttdicbpLLRrjSlL4tKu7x6lHQhg20+zcM7Xm0wFTF5dS+M+Ma9betO2MJUhVkJcVaeW20csi3H6ptlQxM6MkcBAM4n9g5nyCVrMOR0e2C15GGCZNXAFNXdDBmIB2MVvxofMx.Ly0e8We92xkcY471aXDjKjYyQ.ljWDIyZqsVpW6q60l4y7I9L4.l6Nuy6b4icrisBvBgutTDyeU3e+H.XllPanNPYXPhmKg6YZ1C00EQ.UCs0LeeB+dhiiCCFLP60qWvxKubTLbA.9MZ1Xr+D+9VVVcBBBZ633ztc61sme942IbuysoDaRS1BpzE7hFY1A1feXqYDo8NiKCS7h0JhrWdMwKDwC18hGMsN39ug6gUITe01WKmrOVc.jCJV.ZUPUM+E8btnz+8ek+9jgscS9PPQVzFl+6s81yrvBKj627272H20e8+oyjHQhYGLXvLYylMK6GLjH+WIhc9DsuZ78MOH641GHcg.sMsk42Zqslzev.+ozL03+CBvGwzV9NNNwisZHPOQjcTU2NDr2HastgG8.69gsF6HJxXZwX3TCf6eJ.IU.eu8.m6wp4L+S85gJfISMnOJj33KSZ1vnWF2vMbC4dEuhWQlJfU3XDdTjZ+x9ErUEvBJlBZkdEHy56EPWdfY9ve3ObtW4q7UNUk1+Vequ0nm+4cd8aQocfl6.zwA56VjgFihGSBXRLVXrW0gVAlecXEU0ElLYRNee+frYytKlGphF4jiCmk7RYHccSxdKCTLDXgY888SbIWxkL4lu4atip5FUDoUcpzD71DnSXO4MkF17i9Z9C0.JN36sjKCI1vTs+TX5IwLMKSV0SSeu268l7nG8nQnImpQn8jMj+BdguvEt4a9lWNQhDqDDDr327a9Mm8o9TepYGOdb1zoSGUo+PAexniDRHRFb.5CyCLPg3UCC.7bcEEjxkKSi50UES.eiGONnUqVZDs6iBBbJnIpnpnANNNiAFExFfgiFMZzNc5r6f98a+Q9ebist5q9M2.XiRPml1LRqq9XnZ7.L8J4NMLI614Tg92+dZpv9NuCmzNAgBO1iE6U1Cit5GFfbY.xZ7SUJSn.zksLTntMyo004DQl81tsaalmzS5IMSlLYlYznQExlMajVNEAHRTOZm.HQnnfEOAi3Ix9ftZ1roVpTI.3jqsFmxpqtOavXr.X5gwNaZ0sl333LBXnqq6.LZF0nJUpLDXvm6y+458w9K9Xctwa7FaqlQU6VhHaaCa2fxcf58TU64HRWuJzWc0AgrA3vRf8QZIRbP.yhzAjBUf48nzhPyETUyYKh9J+8eS67td6uqVXXWxlDaRevCBfILUCSJ83fl+bppmIvS.vASRpYbccsvbcbjiiS+1c5z8IN2bc+Vtt6533LZs0Vy2xxRcNWmjZCM0fACxlMa1npqltW2dIlovLwYPxA0CmCxhonyWeLU+TUyziHNfI333HgI5Fw3I000M.y+Wc8bQTTB8k0Ymc7mct4lXRx0cR30lcWZok5lMa1t20ccWcN6y9r6fgwIa9Y9Lel0ewu3W7F.QSTmvJlICKBCaQ0APsgXlzNi8NSlvc+XtJv9iaMcuyyDRb26Ms7xxdLANen9QjvFnQXBJ59YM2LutW2qa1+y+m+OO+i+w+3mmPeYgI3VfP8Qi8.hK9wTveiCzFwhSSDaU0FQ1XVt0bsPPhuWZ7UHPJpBZBKq.MHPUAbpX1GMh4SdttSpD5eiPVi355NxwwYXPPv.fNVVVaJR4VPiF1vF0MIgzAiM2TcOgXrN4Hv3SvYFDM+bNSf61z9IOZZuS4.e9CFCZN3dmS+4NSHp8rsrgDgwhEJr9LCFaqzPkzfW9RvLMLs+0Lm8S3Ij6N+9e+re8u9WO8y3Y7LxJhLCFcKYYLwmNW3uqHFlbv1gNNyRlddUudcrrrXxjIZb6qHv4.CnIdttDFuV78TU.0y0UUDEz..+v8PG35512wwoGPugiF1QCzs8882rPgBqWB1noAH3oSHmvONHr3VCJCi7zo6gNJ1qKtPW+nAaqeRVw2+MEP5a8Vu0jOkmxSIwoO6rzDmIfqOfeQPaYlHNVrWtiyY1mlYUUyTVjT0ME4JOv7.Ke8W+0uzkdoW5bISlblwiGmKUpT4wHnvYykKWj1jHppR61ssle94iw1ERVy0MY08XP99h8Zh+DRlH4TedFaFh7ME366GDDDLo8N63WbkmbPsZ2dXtvHhJRX7+QSLmoEgpYyliVd4k6u0Va04S9o+za+ZdUup1kfsm8obr1+k+O9+ameieiei1eyu42bGL4k0qw90so3ZYXDiWl.UlDaDW+Xo7l+Wz5mF.SBMfpjD7hpRZzXDNEfdC23MN7U7xe4gUhb0gKwZS1Da+PgoDpffGVv7Ig1QavlgoU1n77MZ7sKXaamM7u4Xf9+s+s+scdNOme01f2NPwtPqn.TOLGKOZ9l+AqFdpSERe+lqeK.rjmm2bkKWNoHUFVj5sagcanggYHkYB0QgxBTOJH9UTUKCTRD6EUsQ1OvG38y4dtWP+m6y84r0sdq2ZqK9hu3FP40g5aCK0C1Lda4bXmivCdK67i68GreAvLLAlkyn55YMZDQ87g8RsocI1CA3LrG87V3+8W+yrzwN2ycou62+6tv4cNm2L.Y1d6sSuvBKLkcTD1VXQWWCor4C.rjNc5H6rSGpFiNmtt0Hr2+ibfJgUDikWdExjIMtttJBZHQBUTnbYG0vDOTWOWEkPcLvaBpNAgIfLAzANNNcFNb3VYxjYiWvK5Es0s74+l8Us9nPMoYnpZuvpytQ3wl.6.U5uHdi2hkTXSy47h.agFBZ1Am.EQ2ydz75f.xEOoxjPwjPqnJVEWffyhAzjLf8LiFcxYAl8U+peEE9B+2uwbMBs4.JDoIPXBTLBzj3.i7.DVyXIWH.QsaAiFMhzoM1PyM2bzt8NHhIvtff.pW2CPhGnWTEYCWpBRjcWrMMiBNSG63Tcrqq6Xaa6cSjHQ+67NtytG6bOVafMDQ1PUccQjsXJn0U1A7hDvygyCiau+.8djH6kh6SMNnYQrIZAQpLmpd4DwV+E+Euvt27M+42DXiEgs2ZOVOdH.lD1RNvbTjhzhSCJ8jTswYB7Db8bcJLSg451saFPjxksCrrrF455NnZ0p6VqVsggLDJvwwQa2tsL+7yGY2NULW4AlvvgwljCaMsJpCFLT2byMDGGGKWW2DCFLPxlMiDYiUqlKkJUDQDRlLo55VCPhZmh8wj.QDsRkJAtttAH3S.iwhgnrKH8rsK0QDYqDIRrAFfmVGr2DZrClDXiezGJMDZNFVdLrQbAt6mAZxgTnAX0rKwZ41vHB0y.TfxTP8zreouzechegeg+sQ+boIzO2IOYsLtqsVlm9y3oG0tgypFgfcFn7rp5MG6k7Zj1KE4W6frXBfH6hnpzGUzfowSFxlDIxGFJ3T8Av3Dv3VDQP26qopfnUL98BTvWBGA6lp3qiU0zh2EJTn6byM210qWe8xkK2TDIBrycBOhWo1cicD1hXqpvZRXAGh.Y7QC1eOfhAEtNr2SGr8CrBGuowesg9OWN6JrwLqyJETskQ23JSZ0SSIhcdn4LSlLoP2tcm43G+34dJOkmRznNOmHRAUUCioMGyw9iSK94kEfU+98sxmOufoUGnQiFRkJU.P60qGs2tMHfhhLcOSS7a6wTyPEGYuA+nF6KD5aS7gfn8MiXEPeWW2NJrUUGmMA1Pjx6XSi9W9a6cr6a4s7lGHhzmxzU8zthTpGzJBjtnhYNDVcLr1Aa2vGoZW8PYcXErXJ3qFVUVQg5Z3yeQ1eYKAEZZ.EYAfBpQSaRJRkjkndtu2VaM+BKrvx+9+9+9q71e6u8E.JDlCQ7135f.FdvVW8P8sEDDPmNcX94mG2PP2DL9rBicScc8TDMPBDeUz.EMnpImAK.KOOO.HbuRe.eQz.Uk.fwNNNCccc6uzRK0Ia1r6LXvfcxlMaDKSZGx7jH8AqCPuq65tt9upW0aYzJTeRq8ljhiwlQzfQPkQfWbBG7XAar+EsdnH5qGfB6dgFPkSA02SUhKxjWwK+kG.NiA2QvZi1Dl.M1KwZisg0Qo83iO0XzYTEbC7fDP8LarwF4wzahIEobBUqGHhLtXQF1pkYrXBDDl82nvysn9BSic99nMif3n6mHjoNYt+vJgSERqtpkHU7eUupewIP8dsfcBC.sOvXpatNsD0Sro45SRn7tkEYPcUGAM8AjW+q+JRpplCrGewuzKdWU0tNNROOO1MDrjfPwEZuqwGEkiiD90i9pw6YO3g78jUgkVSXSRXauQ5vpfEJfqkyB0S6pZJee+TIRjHEP1FMZj63G+3y749b+oy8zeFOi4v3DMr5FjHa1rG353TGhV.hSUGHVhGQ.gL6ryRmN6DlnZj3gsmeVGGG1Ymc.c5uCbccoToRzrYSy68v28VVl.IylMKNUbh5Eae.KmpNILizSMfoi2SRIB49RetO2BDlb1e0e0e03ie7iO.n667c9t25ptpqHe5zoy.kRAMSBdIMIxsoOf98+9eeMTaVBT0H1rwroNXPHOZbcXzI1RU0xQjjdPZUalVbjr3Q1SuTob2aSxKkkb6ryNomc1YS666m9Zu12Wl69tu6Tmy4bNI+ve3+rjI9ueio.RI1RJZRxW+q6pRJlwh4AlL.6Kg13LaQh5c5omngBlcHamP.oPgBTnPA777HZS1Hv577pOktww8.53T0J7qG2uH.rxJqXs95qmz00Ms.S1XiMx366muvbElIHHH+jISxBkxH1lVQ5i9Qe+o+U+UeYVp5FX.qyvTs1QOCsHSXKl.GwGNwCV0We3rMl45ypHgtuTXYeVYiwhHirsYTXqApEJL+HnzXno+V+nCpUh0S+InkAjr2+662KwjISRzpUqDnjna2tg1AUrb8bAcpHxotdtIeeuu2Svq+0eEBfDV4q8U8K1usUz6k316nGX5U.SsKl9yjMaFS1D6YSJdddBphmmKVVv5qudr2aSyrHFnIBYylQFLX.tttIDQrTUSfPBTCKDVbwER0nQiTkKWNgpZh64dtmjmwYbFYUs9LhszllrMvVpphTTBXc7cbZpFRtrQ30S6IgSnfn3KdrBnuwWGH1Lr1qfVqkaSydkQ.eLqHxr.4dAufmWzT7hnjTe0u5KK20cce3zTZ5DeHioUWIYnurzG3HxlKNPvGjESQUgcJSkhDXyPPhmZmAP4xkUKKq3rJQL9zTPPpVsp555NsnDNNUkM1XSSK7HHhFVwVyaMoRkpQfxjnWudVc61UVXgEhtVEMtjiXPSGUq2cvfA8xkKW3T0XUEmZJtvxKultwFHp5pl8NOhF5m6QC1a6ArpokZfG7j1CesKm3+1+s2dxK+xuby85SEk6eJCxyBajacn.r9LhY5gjVDIoHRx+nOvGH0y949bS+rdVOqze8u98lwlFQraJqXKQS5wY.xIkkLZcMIPh+la8+Uhm6E+yGsm19Dv0s2daIDvDZzXeRwsryN6fXYNMcp3Lc+SQrvyauATgHBHPkvVzoW+9R6s2Vip6f48snfjJjMfIEQRswFalwwwIqqqaVWW2bppETsdTQE1UDo+G6i8w57G8G8G0VDocYHYcy41DvdjMMFYNiW6QC1R+zrhdeGDNITCE46JfsmkHh0Jqfr95j.7B2uqbJndtll36W.XdQj7hTN063c7aaopWBLhTcFU0Lu829aOp3BYUsdTAwhZEGIF6JE.qm+EewV2xsdq6SuubcqINNUk3f4N+7yO8ymF+EDUnK.TmJNVdF6k.IzPx00URkJUDndXZUeIQHvIQfyk1yyKCPls1ZyrUp3Lylat071EK0UUs6IO4I2Y3vQsAZSI1RanaIRk1upW0qZGf9qaXQbn.YqQwuHKhmFF2Rz.TI98fe15PVOTXXR7DLStHj4qbm2Y1icriEVg9JYAuDg8kW+kXotaxl8fiL.NQzMjniClzRzFW4AVnHT7e33G29nG8nKpplwVjfSNbR+SISxNMB664xPm560G8C.6wPiCaTp9nMCAg8S21PDRWIW3nMMR7cs9O7e3EO9S9I+5cro01M1quviBrTvlDzXZETWBvtc61U9d+yeuxO0m9ScALARD7U9JektOmmyyY8xP8aqVsFUqVcikg1aX98EducYfMlVU0EYQqsXKfkCpvF9dG9HH8A6829n7YQHUKHiCj2kxEf5yZCEZXzMmL.IEwNIzL0IO4IScJmxoj8U7a9Jx8g+S+vyJh871zboPQesPnFTHX.iKZhkDwBfC1ZDSChy0ySbBcpY1nUmtgZzJFJw344QjSvISlvnQiHe97zpYKc7Dyz0N7AAMLRvoh9UHE1iSO9.Pl33TYXMW2ABLzwwYjqq6jEVXgw4yme2O+m+Kz8E8h922VUcigCG1LSlLgUOydyxzna880S1gLLXIlvl+DMFxdj55Ay+1AAKKNfF4B6y+b.4twa7Fy9xdYurrhHosgTunW0qM4S+BO2Tu4K6xx1bud8ORKSl49tu6alS6zNsYTUmIHHHehDN4h1X9i9Q+noOsG2ok5o+u4oGO4hC1pWSWwsgb87voREb87PXOarnUkJUhWceMYhj5D+IQ9ZkHqsvVBKd+QOoZ0pA0pUap8G6w9jcme946LyLyz9Nty6byegy4b1ptgoIqWFZVeuJytKTbDzJx9ZzRrz3MYSe3H9mImH3t2OSzd3H3ISqNenu0npIkJrsQyuBL25Td12y640m889686Qkicr924cdmaCrErTGXyX9CI10938YMK.XCbZXySbRsIOoFMZbTGGmx0bcm8idS2T5eseseMIZTqBLQfIKt7xAYyjA1ucygcbXrH4PeNPUMpW9UGmp7c9NeG8rNqyR+zepOMOsm9SK5myT0eOOIUhDTrToo9+.YpcnZP0Sw.ZhumqmeEmJJFPfid8FFNEJjhHQLMgdpp8Vd4k2Ia1rc9c9s+c14O7+m+v1.aJhzpDrdSXyJPGOJO.pOpBL5n+e8+03u5W8qF5WaJEiezTKR7iacXwQYAjbdlOUaZmCVovJrdgV6ATxreyu42b1K3BtfrhsXQSystv8Eyopl6u9u9uN8y6487REB3aTLE4sEYlFF+bQhUcTaFFG7tD999VIRj3vXp.XrGznufI0TUrrRfePvzJyFyWFoRljQSlPUGGh0RXwYQmFGq+lMaFToREeee+3Zqj+hKt3jb4xMdxjI861sa2d862fvLzBA..f.PRDEDUY4kVZmjIStSpTohXXRaancCn83wiamJUpsJB6zB5az+MGEb0SEz6eOek+jFayCWWwYlj0QfDmXuKnG1ySRnOxn8LSAqj.VeZr7kob15TOWkJOgYTu6Yl5lXISJhXcC2vMP850sdy+Qu4rzfBppyGDDLWhDIlUUsPYQxU2vHpBwZ+qYYuVaMtNS7.700ueexmOO.zoSGlc1YOz2zQ6kFEKmqmGEWYk3fAC.yLyLZmtcQ.M+LyDzuWOeQT+vp+GdcYpVgMxwwYWOWutUbpzEnumq6tKr3hCxkKWeQjtCGNrclLY1Jj0lafQS51hv1l21LUmdv7k8HQ6qGJq8kmIPxSERc+PpkgjaX.HIRvVi9XVnTAn4bg1KYtka4VjWvK3EHfchxzHWcif7ufHxRpIWfHcmKN6xsdPNOrbc8rbbpLskV87bkeRi+2yyi4meNc6saGQso39+h+YA.9kKW1ud85GHGVIvwoxDWWuwfNJjwI6BrqkkUuxkK24C8g9P67Zesu1sA1nhTYia9e7Su4S8odI6XS890MS2zI.C++7+4+yfm1S6oMfxrK0m1tXwKp8i1sw9odY8i+kb3qs.N1wNFfYDIBdQZXi.vlKuYny1SDiFabvOe+AoOuIX6Vv3idzi52tca.rZnpUlLISzLVRMeKOunISQgkVhYfF4VhklNK24ARcpGJfC8v00zGhu68nrVtxkofpsJThRyD90TwQF8I+je19PqdJrKr7H.+iBJGILH9Fj.brXok.XBkY77yO+jAiF3iop6Y.x8re1O6YTUmYlS+zmoZ0p4AxuwdNZxVoBYdqu0eqTmJmZz09ja4rUx2065ckD1H4HHAbjCsxSOHu+BSxX0jrLoaE1hMm8K3EjGpOKvr0MAtkWDIissjb3vSJkor9E+heQEH3F9B2PHbwMRT2TYrLDNxeCqTVBQjDhT1ZxjIGLfyCl3wTvR.iCwHmkau81.fmmKUpTQqToh5VyDHmaHK.RlLI4ymGWW2PvRTHTrWcbbTDqoS4FB6KVP5AReEc2e2e2e2Qf5655JBjRDIqqqadf7au814bccyedm24THTXqVISlLkkxRUfUgFUUvFJufpZdaHMECeteS.p.m4Y9nMGjO.P23vu2JvpBKsj40U0T46vf6xAkx8xupWd5uzW5KICFLHXswiGeCW2GZ3kcYW1vl6sICm3DmHEPNawd1G2i6wMWHMhm0xxZlPvRRIR4D+p+p+pVO8+MO8G.yRbccwyyS.1GHHQKOSRBlpfwgBVh55VKtuU+IA9gAwK9BD.FaLOW2IfNFXbxjIGALoVsZwEatX5MkX0tc6rt0pM2oeZm1J0U0909ZesU.pTGJCTBX4hEYIUatP366B.yrYwMCaCoSjdKHIbz39je3peYAhTo.3Zu1qEVEVObeqVpF.0CrrrzVfdy27Mq.pw84l5YxYdveWBfbzvJwBjrxdicvTzfjIRjvBQjNc5PUGG8JuxqL.Hnb4xANNNrvBKHUbbrxlISTqqFo6MwEWyHZKeXU3+A85qHBUqVE+.U.3rNqyRZ2ts0k7KcIV.QBlHdttxwum6gI99SAGQUyGBCLTiEikF09ghHQ.+NYgEVz.9iXZALfwnLILYljqt5pY2XqMlqVsZK91dGusU.JdMWy6onpZwlFz3W1U0kf5KBrfqpK7U+pe04.lckUXFvKaQJFWDv+wpGPOBecPPeSVgJQ6yks8JsC0pj0y+C1YmYDoxLX.CN8S4E8TR.f1P0M2byf2zUe0JDg4EVmwYbzTFVbZW.SREEDQx+C60aehpISYDhDc9..IRXFRDsa2NteJoVsZfA.NKGGGoZ3GymeFBBAKAHxWlosGD7GOYhuXlLSARH.hdddQ5AQXaLDDMokFUpXow9AAiTXDHCOupUGBLbqs1ZnsHiZ0pwjd85QUGmTat4lEt+SdxkTUK8O9O9OZqpVpgQK2VJUppyQEloU3T+wCx.towz9ySiy7HG4vYUyivVS8GeBLLQCVU3HGNvr2cLPxVZIR8O+O+UyTjh4wXuLe8h0WTUcYOu6YYOUWTUc1u7W9KmqDj7M8tdExa9M+lMxWO1hToRRKKqbQfiDVbqEhkXaTqeEWHgevNlBVB.oRkZpMXDqL87BYFPkJgI0ZDRNAXyM2j34Kpppc6zAIRXq60aBvXU2SyQDgw0pcxH.NDuZdIUgrhHyHhLqUhDymLYxEcccWQUszcbm2QELZVkyYe1WnM11qnpt.Un.PtFSAFpRJN5Cve1ijswdHtNpRQ39CyGX0y4bRC0ygwezb.KJhrP4xr.zbVU0bW20ccot+6+9sdAu7WP30nFhmphoFEDnpNc.jD1N6v9Yp4AE20j.IbbpL85umm2g.VhaXdAUvy0Xu454gqqqVoREMe9YLuPw.PLJhPDCNCAKwTHgw0qWe.60lV6hpCAc78ce2Wfo3pHtttIXuAGvLtttyeIWxkr73wiKBTpd45Eu7e2qbYn9B0UcVQrmAHmMj8o8hdZlhrWmLfSZrsOHyT+YqGj0CkKPwXHxYFGnCeQjfs2daMTrLi3O9OtpIpG3Hf1LAVdbwPGQyM2biIT88iNeMTCubj1SLKvratIyATXS1LZb1klU2mP28iq+sejvJV.RG05I9DehSm1Fu+2+MkEHWSZlwyyKIf9ott+xQD1+sMggPtI.AGGDNQwjmJjlxgA.r4lFjSqiB15uxy5YY4qZ5+8u3+84FLXPAQJWPDovW4q7UlgxLKXGuJSE77H+a6s81xd+b+6ELkh7e7+3+QAvZCPLsU7OxDkhmfgww0otVV1f7P4B.y9k9Reo4oDyMd73YwTMrz.xm3S724ewW7EOtN0GcYW1kM5+x+k2xjPmOv9YQvTwuSUMmpZVUqmJYxjSC.rVXuTygr4zjISldxFs46t6tKttt5t6NPcccYqs1xTHUUUz.M50dfpgEfPTubG3Tohuss8HfcUQ5IvNftMhtEJs+.efOPGGGmcAFU87qNdvfgSG6gUqV0jXrJ344kbmc1I+N6ryhZc0FnJvp0UsBvJhHy0.xRKmTkgDLOVfmvce2O..h9Iwf7goq8UsLld++nwZagil7HPRpPRXsjr4lludMRA1osinadklInNboW9k6mMa1gmxobJ6hMCfxi.7emuy2I.IO0S8TSCjqg1XFLOSL8YCBC1V05oN4IO4gkTKNNNSojYzGUU2CXDQl90AyFwQKUUSE7UYJXIhnSP0IW1k8aNYqs1Lte5IgIQL.XvD+ICAFIpLF0HFcUqdd8EnmJRePMBFrkUxNc5j200c9q9pu5kUUKFp2QU.pzpE1hHEEQVBSORNGvL850KGPVyTQncrfPN5gED3CGVJPDrG5a387Ff0PgULYDJBTD8Jux2Sfpp+W7K9WG.nato4m6t4tOLfGsNNXAAV.I7.q2266OTTUMvMAfpZmNcBbcc8qYFWvS.lLYxD+74yql+z6aBgcviC5a8Ar9a+a+JQsD3zUThDe1O0mg66GdeBfzsWOfPQPzyS9S9S9SDDgmvS3IDBThtOQE9Pr+Ba2O0uVsZ9DpkDqu9FipVs5.EF.xtXnn9f4latQNNN9tq4hXp2Rh4me9T+S+S+SY+898txBqs1ZyC1q.TTDoDknnp5JhHKigUjKt95LOvrsnUThUwSn+gK1V+q45.EVvT7DO71SXWkoBYd9Ymc1bPP5+7OxGIQcHP8zgspWu2W+q+06rzRK08c+NemQix4g.iuvibZbO2y8jR05QwXMGPg74ymCHiHkiCPmTutdnfzM+7yO0uUsZ0vxxRHDfX.Y80WG2ZtrvBKPxDIU.cgEVPssKariDM.EeDw2wwwWDwLcQL9u5Cz+s9V+uFlXgDoED8PnGvtatw58qV0o+2pVsdfzUDo62pVscTU5XYY0SJKiAHalLYpUq1rWvEbAK.rTYXYQjkvt473wbXJDUNU0rXSFnbDXkoARchS7nh3LmFexQ.DQTprlvIPBK1k483QIAbj3ZLQlM2jrOomzSJeKZMKvbeuu22aNRrGyKEQl4C7A9ix77e9O+jMApeb0uro.c9pVGpWOw2467cRCjWD6YwnEEQ6gFMtgS828282EsuwOQwo34YhGKdAFLBltD98MwugB08pippNw2WKUrjVoREcJq4Lssbj82P0Hv98qVsZef9iGOY2pUWcX0pUGALVEcx0bsWaPsZ0nVsZVA99Iu3K94moZ0p4877l6rexm8Rpp1.Utq65aTgFMJKhTDOVBrWzN74MJ6MCGmrf8AAo7mH.weD3J98UKpb7v1WsXZfre6u82NOUHOTIupZdU0bu6286Ne85lQQ8G7C9A4U+pe09G4HGYr1PGioET3nkKKpVG.UDIPjx9kEw2vb6o186q8Va2t8TvR3.wrUYeLMO7il3uCO62e7+tttGPS4DIZjV6VyUcbbBVbwEmfxP03CqKlIF21.sQjtNNN8SmN8tUO+pCAIRPpGUsZ0IAAAJfkHR50We87c5zdV0SW3a92+2uHvBhHySnNT0.lgVkxWLbp1BtYnQiCiY8+r0grdndgIdUMhplQXK4TJOzLop5jhm2ou652wOn6RPuM2+3m8AqR1BPhJPZEloNrBP4986WJe97yBXYCSZXT77ggJTcT0QGZYYMrRkysOzn2Jvf0Ca+fJvDu8O9jdjJ01l5bLFcwmJRtlMkJmGZjpDDzvP+pdgTIcGJR+voID.IfSMCb+YTUSKkkDzXkjv54rgkqqZUfSUDYUee+kZ0pURee+9me0pqeKe6uc8m+4bNsZ.as1Zq0Y0UWcOgpp3oOjV26nhFFB4CnNf5BJKhuQWC9Qp72B6uUix.UyA0xBjuDjOrOEiD50DevO3GjBEJ3eoW5kNFJoPyDkfLeiSbhBG4HGYtRvRMTcYorTTqqKgYi3H52EWQ+i2O1wmjD.60NDwWat4FLbvHp3TQcccIPU0BQQlR9bAPTIRN.PCoiW.hDDJLE5m9y7o0K4EeI9pHCsf9mmiS2FPOic94E.MRTqVsThHIAPUMjFv5XPlnpNQDYb0pm+DngeYXxe+O3GL4wejSaT.A8srr14K8k+xa7K77e9s.ZBrITdGnt4d2pLh0lJfhGVKs8HkU7MaE1iZwVTDKZE+0sLvFlMAKSRpubJXizUBGQ5tFAdMSsZ0R7Y+reV8xu7KOvFBZ.REHsmYyl4CSZyVUs33wiWIUpTQ1XyxzoAv9Rn0pYqVVkJVbeB7ZD8Mu2iebxOyLSOQiHp4dT2L9RYg4WT2Z6MAj.PBDTeM138U.Q2ekClnvHKQFFlTdPsZ0DPBTzIurekekA2zG6iMzoZ0Q0pUy+27xtb8V9BeVCPh0pEDFvX+zoS2Ie976jJUp1+C+C+CseVOqmUWnb2SdxuQuK7TNk90CEoyxP+5vtrBCY83i.9kCfMd33XSL99aIA6DPiz.YKB4aYD277gTAVEQ1kPZ7GJB4CY+sdjIHLaRQiosjybPkhf2i6Zeuu2y326JuxmHviGnnqqaNQDq74yy7yO+ACJ9ASTM+oJ3FOOOTfc2sOm9i+ziQOcypRr1+xve38Z4FyqvLPzWXw4ks2tMJnVg.1EDxB.TLBWsH9X7aMcDnWsZ0Iq455u544n0t8ZAe7O9Ge7K4k7RFd9UqtaCCK610w4bGryNGuW+AC5erRk51D5qp1WDIZRlr6d+e6dPidkf9M2abWePgS7QCqCZSDVLfJoAuz1P55plw11NciFMxIhjmxji5j8a7+9+chzEJv4bVOOEpadtqDPSyn27O389dm+ZupqZgFpt7wN1wJdm24ctLF+Y4wvrwH1Qcvo9UjsnEfUMWWo5Azio3qFMaRfuOAFZL.lxlpxz6QloQxW7V9xAuheyKMXsZ0hdc9QB4Jni.YewRTsZUtkuzWhy7rOaUBz.QXRfniErFKlw35nX9+FiwulkpRBHfKX0UG65622xxp663c7N15pu5qtkHRSfVkg10gcCioL3q809Z9WzEcQQi7yggBn3C27m8PYE22W38yiHvILIIhQ5AsAIbpJEU7on3+yAMyTBR+BdEWZpa7F9yRaCouz2zaH6U76bEo9m+m+mkm8y9YOAXHTdnpdHhjSUcQyuVpPHiEYOAENxNae1ZQ5dyAeC345QEmJOf+ez9qQ97h9Amt+ZHxHxde4nOIzlT7CYEWLFMwHCXdFIHVMhzjngShDQkQH5n.zQhocqC.TUj.QU+e3O7GN7bO2ys6a92+Mu0e36+OrgHRiRldZpcCy9m6BqL.VeWfAKCC2XOQU+f4w7HwbZN3JdgtB8ocpog6OKFlQjuto8.C0UIicwvgCSccW20k32929smHL+yjQ5oopZpe8K8WO2+y+r+mE1c2cmMLWx4z8ZqvniBrmvuNsvCAAAXYlJCxO3duWd7m9ouOatH6qFMZXZmvPin.0XHL+Byqa2daUhYYoBRL6u.QHPLwwGN8jnW0pU6WqVscqV878Mw+6ld3fcSlIaVQ.eDYhp53P1xLQDYR3qchML51bc60qSmNmwYbFs888amrZxNzvvVEU0AhTY.TuGPWVgNr9Jcg06uJLZs8mCP3o4OaEsdnRAm3AM4isYFQ6.Ca29dFfQP77W+N9AJf0l6ILc+jDXm5AA0i5e9hL71tsaajssc.PhFkHkpZFGGmLgBfSVrIqiiS9ysRk7Pi7tttYCGQwIeiuw2XBukW9fZEvCWoB9OIKANyD2Mj.VxjreYS.SkDIAzHAkwpIHuq206J.XbqJUL82cK7OyHC+kIQ3n4MiHRla6KbaYg0yhMYaXDGorhH4eku5WYgd85MqsscAQjBeK20l8bNmm+bMf4Was0Vb0UWc40VasU.VtBrTs63uaNpP9V6oZ9h6pqZrU1BJFYCblO.lLD+XuVMpJ4FL3dKfIfs4eO+Ye34TShroTUCti63NF9a81+s5doW5k1QUsGzbWU0wMANxQNRBU0TMssSIhXs82a63U+L9l8QLRJdfe.P8F0mdgOt1kDcL+7KPEmJ3VyHnSUJWN.KSBCUcb7QHvopSfrmxpqH3ivDzoa9N5RdwWxHQjgB5tADz+1qUqasZ053bdN6Tq1s2tVsZsA1Q0fNNNmWWfcpV872VU1p54WcaQj1mW0pcbcust0pUq+sUq1nbYypWwUcEosrrlsa2tK+29272Tx1DLRQJyxQzZGXNViBvF4Y9CM.3GIrNrJtH.Vmv11TwfVjZ0HP3pPFXiHg9JO0IOrQdJStO+se6Y7LB3FkOZY+M2bywW9ke4iA7aXCPoDlIwjcFfLFFJooARmNc5zRYItXHdvVlHIPhREKl.3A.VB.4lIhJwlGUKaaSkJUBs+1+9VphVyqFnRf.AfNQMATMjPccphiy.fAgUNdWf9Wy64c2UUsSMicV3zGQ6Inc+y+X+EcPj1qs1ZaU87qt4+8q++1l999at1ZqssJ5NXzZhgqrxSV2d6sSaYYM6y7Y9LW54+bdNq.0KM+7yWLDr6kUUWrdoRFviVmYpDEXcQxAa7vYaMyypmIAPio.ezZErLjbP.pXD71JlffKAJwPjKbsmOtFnWy0bMQ6YNB7F.r6a3ptpnD6G.rqiiSukVZoNyO+7cvTkoHMQIJo++EWUw38Ys.73OsGOvdzS+K7E9hTtbY7hCRrpZHUfiZUhIf3iE9aucaeIbpjnv3nDRAFDVo+NnZj9PL8v00siEz6lu9atGP2WxK4+vN+u9e8k251qUaSW201z3+6aMna2tT20Mqmu+7pYj2urp5RXnr+b.y9c+te2YfF4UUy17AB78ilV6GrjUMIV3.otka4ijlRjsgY5hjsYylYkRkRAjP8T0FFOybys6477NqtlobGaBroQXcq2QUs+UbUW0vlFcyx5tZcWoGMZTzT9Z1fffYUUiXsRTxEwYw6zjYqFazstupvF9w.e+ouYHzlpZ3zyIL4z.UU8e2K34o0pUKpFDQfkLpZ0yeWP5W87q1UToy4WsZafMq4Vaiy5rO6VVPq16zdCE1RTos.6b9NN6.zd0yu51NNNa6551Fn8uxuxuxNhncrrn2sUasA0q2v+Vtkaw5pu5qNRSWlAnv2x6jy.ky4Y76m5htnKJopZRfTkgTfWpv6GORlZ66ikXwEyVWUsfkSz.RZFI0jCmoSPoYCaIhYaBybi2veVZ.9cd2u6wej+zOxtkJUZvy5Y8rLCIfRHPcqHgmTDwRDIgTVRFpiNSODQR+C+g+vGP6FF01+Qqo9zhCVh2dfmDWSIh+CFAVh4yMTBv79UMS3qoigUcHn6FxhoNO6m8ufwOlEsqVc01BzVTssBaeu268tEvFXw5pHqKHQSsvsv755JhL3zNsSK3u3u3uH068Zeuy.r3YcgGckFpZ2.JqpZSIrUs0JDxXyMJxLG4HmSXKwcDS7+m4Cvd6Q5961CrjUICb+Q9elqQIhZA+zm8YegBf+se629v69tu69uie6e6dPydTNBnICX4R4x5M8QtIKQjj4xkKElVxeZ6rJlo55CZKdEwHN.4we5mtbvV6phio8aBBBL6eFZuIl+Qa2tcPHXY9FfNvWj8jfhvhJLRUcu8JQ1w0cssqd9U2tVsaa60Vas1f1NStrcqV875pPWGmyqip5NqdAqtiHxNme0pcccusd0pUqecU2UTc7S7I9DAH8sca2VgW2K80sPYXoy5rNqUDaoHTuXud8L1Vqybv5EXExu1gmKzizso9W00OMWLh9YRXdv8DFgG01NOMZjAyEZeLA60uHraq852+eTUt1JbzKlESxbk.pnpVBnfsHIZZy3S9MO4fS4TNkvw2X4IP8wtttibN+yuO0quCgBmT3e+IEA+VPvpft1daF7fI.gObbsGx+Ggjbh3iN2h4gVETUKH1140FMR.LVDoKPafsWF5sg45e.lGDx.ja2c2Metb4xhYC+z0g7P4kg5mhp5iC3TAVdznQIRmNceaQ17F97+kMeQuvWzlhHcMT30dDzbHPeJUZGZ1LZjVM85+oB92O3+Vequ0f21a6s8fwvjvpksZRXsHgrcNLnAWPDIeIHcyxXM9ji0mwy3YL9a7M9FCgxCg5l9jw1NMMZjGisyxppK8FdCugEu1q8ZmCXtxhLecUW3BunKZ9uwW6qEGQ4CS.N2WRIQHM644wngC4HOtGWbgN7+et6cObIor5bweWc026888tqp5upmYfgAPGDAlQwnjDEMwyCAyiQMnPPwaGhWHXjXv3QMlbdBdINhPf3i5OdxObRL3ENZLdvfADU7FZDF.Qmi.CWlY5p5tpt28du66cWWVm+36q5t2aFLlKFGN0yy1MNyd5ceYUqu05c899t.fj0HhRkBUa2BP.I33GGFfRfHDQAQDGj.HPXYEUwtRDAJhHZj58rt0p401vnXG.LzxxJ9yrj111IUeFNhHZfjkU5g.dxUoHyQVVV7C+vOLUnPAMgPjF.Z6e+6O5RtjKoG.ViHpAy7p.XchntP1.buhD0uAPOfx8.pDS2uiEKfNd65IC3swrDHdhCLywEoowLGuUuRxLmzfnTd.IedOumu1266cmZ21scazK9E+hmdJtIKAj1Qt5zyCfBmzIcRy8HORmk.bW4m9S+oqbpm5otBQzhwMwAYLbbCEietFEEQIRjXSaqjexO9mfS6YbZ.3IZbX.3XRwjwzEdpo1CfgPZjlirrrBUdFfFXPTBDt+8+2N70bIWR+xVVibbbhhPDRfDfYN7Zu1qc3e3e3UzC.8KWVLv11NNFHAwjFR.HNKACWDToRkH.vkKuG9HG4tC2111V.jNwdOl4A.nCQTa.ztDPqGe3vtaOSlgt.idsuo2j+y5Y7LFc4W9kOYRcGeYXhwwNwx3KK.x+Q9HejBui2w6X7ZLG.QPZ.osAv5nXwNn9SjgIJFyoUDPq9DiecQHyudpgggOstc5dhs6zdIgPjD.wV0eR0DXi8MhoYEWR7yA3ISCFGvlm352+6+8wK6k8xF+2444gvvPDDDhjI0.Cf+ouxWA+V+V+VwLiiAgPhQHOY5Swwywwg9.v+1u8ae3K9272b.CzaOVV8OPEaeHKTLhIJprPDB.11V56NwLWhYtikkUuJUpLBIRvkEhwTjds0ViVXgEhiy5.fN.FsAbayL2hz0ag50a+Y9G9LsunW1EEW37zwV.GeDe8u2qsxrjjxAmzTxPRCirv0MKKMAwzw48L.zpwbhu025aA0D9i8nnXIqlx.nfKvB.l5.0LTxsyB.l+0+0+0K8G7G7GD6MZSyxoD.Pqa2tTgBE15PnH.ro7WwMWvzzS0mAAJhjLbKTxrD.4f8Ag350XDRDTxITBtHyb+xk26Pa6CLhHZHy7n21a6s4ecW20EB.XYYQ.fL.vArsC2ikkuKvHaa6AVRFzE..D239kdoWZBMsr3S7ItdVHDgUpTYjkkUGEs3W2.XCWCzAtiaHaDvJ9.Mj+2kKODUprEOf539yO250VOOcrL.KBjn9hPCqMgcypZyjFlutd1G567cRdxm7ICHm58ncsqcE14PGRqFPVCfLJujimx6HxAfEYlM888KkJUJCLggIEvjF3FWm1nQinzoSON9ZS421hQaN9ZJpZxDPCOOTTWeqFkdDFObqopqS50RwMzF+0.l4Qemuy2M3BuvWEaaayV6cuv9dtGlIxurPDOfhA5.9dPZ70QQQZkKWNlUyIsssIlY+CdvC16E8hdQszzDc.biYMWe.zwvvXCWW2Vq.zpwxnKVc5EsPoPfpw42dpP8ZOYWpXscqAbvoUtPAVtNyyqCjwSGD7juVufK3BBu4a9aFOnhDPWOM77RqFpZV02KnjD1BrbC5D+UrTuJfXILNkG4DuIuhexwRSwdS0lIMKc4OhuuORkJEJUpDrcbhYCWLnagrLlmQjTXtR83N9yrXqSHN1pG.5wDMTcNo1i9nOZproSmV9ukBAvnq7JuxQ2zM80BLLnvCbfCDAfn8u+8GdQWzEEjNcZeKKqP.DZ.DtuO0eavq409ZBHhBul+p+J+29a6skW8swi...H.jDQAQEeF55c5zo4IMyLq6BrAfUW.6oxu8+yb14+oc8uWzil9f63FbyCY.Xp2xa6sfG8Adzg+yei+YolRKgAn53l1i9Y7XpY.jwUlvTG.hq9puZwq5U8pVJQhDoEBQHybexf5a5YN7u61+67+M9M9MBmpgy1.nELQaTC8g7vqHrDXzbbyEacif.b7cvP760ikOBybZAIxTEUiW8ZypnJdB.LDlXCTCqAf0Q7pDVdECXhxvVMx0oyijdlYlIm5vOccfs6x7IzqWuskHQhEZ1rYBgPzmHZCaa6Usrr1.Fnmoq4n614tCPDFUtb4t111qSD0THDqCSzB0PLso8UaikoSrOcSEiatExh2x5BT3LNiyX96+9u+X.SxxLm3q9U+pQ+I+I+Ii9Q09Q808z64IAqIT8XjiYd1QiFsb5zoKB4jtWB.yPlzLvU5R1P1rRbCso.PhW0q5Uk3y849bD.nG6QezDm3N2ozDNUE5McBS0dUOtHP0FfX7qqHKKqHaGaY6Ewe1QfYPgzXlkP9P5n5w+6FRD0SHDcejG9g6jMe9AVVV9.H5c+te2zkcYWV78o9DQ8EmoXvg+gGdTxjIiDBArss4DHQjnrxaALQRm61IS+g8yjKWNsCbeGH779ucd8zzzZAfVrbSS0CPuKfW2WzK5E08Nti6nM.ZUFnSE0JKFG+eH7VKrS9msK.bnozjp.ZvYSMapZ9zPCvUiYV6u3u3uHw66889nK7U7Jvm8K7EhKwZ56WlQc3cAo1OQAhn4YlW9FtgaXkK8RuzkwjXqXcWOcisS+bd70zzH1opCno+qO1ZwQoOV.l3HKgUfiiSrDEG.f9Vmk0.6601mjZuFrTqt9S8yDXYYwe0u5WEaaaaiMMMGs7xKO..8u0a8VGrzRKM347bdNwRZfqToBsvBKfYlYF1wwIRblBX5YpUkqpUsZUsnnnDVVVQQQQgZZZAv.C4Zb2w.2pi1va7zeFD+7DXo9.M+4Qxl+W40z.lDapZwleozrzzQJ1kiN7gOb+S3DNgVPN8v3sP1zqnO4i0tQBbvw43hGHPY.8SILr5ojHQhc.fEa2ts1tlaN1SBpWV0u23FGdBMOfmD.SlhJw+LupVsJRpkDE0KhpUqhLYxfgCGN4GfAXvbPPPz1291ibbbFK4f8XsGTiqQiy+wXDHLzxxZfsicWvnKjrIoi0drFZe.6wMxJAxioG5gdX5bO2yMxwwYfPH5Bft5RO2JFn3z.HiRRhIXliTxwsG.53551xzzLl0JsG+kA5pZr8IqnuiGhy9250j7bkPpEqtXp0vZw9HwzLlLMJhTn933BV+jzi7dDuX.sBgLEBQjHIj0PLGjMppyJuU.SjHwRPFCtUcterh+F+eOcdswRhvo5DZkvQx4IvHJgVhvnnnHKKK1w1F7jGaljdMPLPOx7a6wpuyAb5xfiMFwt.nukkk7bKCDU4dpjXvfAZ4xkKwW7K8E4W1K8kIYnBw9IPhPgPDBXv.tQLyQDIAqwDf9ak.ly.xo+pZdURgcftnH5tR8U52.MF75e8u9g23MdiiXoLnGKUbb7+YmOYWSCZxzdlT7YmogAxY3ZTnFWaFHm5eVkTsHhnHe1eXJJUbdcMXhBnlTN0pGKlHJwG9C+gy8Nemuykvj3twLp.GalLQXKrxb5qo2dWa8rSFXxxOW8mvRnfgvRD433Dc3ibjvSX6aWJOGhXNhCOZkiNXaaaacEBQahnVNNNcDmknutq9vCXe.eRIKLhona8e9VCeiuw23nu3W5K1+reVmcO.ziHZj0drX6CXSVVVofLeVtO0m5Fy7ddOWeJ.Wx111+Fuwab36487d7MHJ3tOxQF0sa2gO8m9SuCy7FPNfq0fLOWWXB+x0JGVAUdx1RSOUIlK9Si352RscfLGYxYty.InIYAfVPPPPpToF8o96+TCtxK9JGVG0iuGKIj0+mmHZFniYXWdFhn410t10BO7C+vKpFl0hJvSTwslYYtVld85kIe97oWs4pZKuzxIvTwXNNNrhMviMle.LAD3sLTqoyqoFRPn0YYEU4dskLVRJAGUNMRwdIYtEKKq9F.CuWGmQJICFB.XaaG+9SDQTfXOhQN2syHl3frYyFMb3vP0.Sk43MPn8Ar4d85QLyI98ure+De5a7SmvvvHwW5K+k3e2W9ua.QTel4MHhZBLtWwVF.smHIrmvfSepRb0uvt92K0A2p94jgKFPiYNyG+5934+m+F+yy.gPlzq53h79WiNwraLpt.CgAFlNc5fJUpfRkJkhYNqIQ4XWNWMTK2YcVmUNhnwf07g+ne37l.4PMEsbKURlruIzdrG6wl1XtdplIWRRqHrHI.RPqPZUQ0TF.o95e8udJH0WbRnCsO3eweAPMUi3ae6aMI53oRyLGhRtgyLyLLFKSE8btLm6RtvKNW974yjMa1TBgHoSUmTNNNYDBwr111K.fkqgZ5IhfAQjosssgkkUQwYJV1DXATCy433jCRVNjPAV0TT87IBVxh.oALx5pP789u+6OGKmneFXfjWvEbgz4cdu1HuezOJDIPfG7BzmxuF.PDoSXaYxDO0HMxzTdPuKRd0W8UO9y9pUqtIVkDCVB.nSbmRJpWpTIXVxD..0pUSYrSx8pdLERbrcPbxTgPPx+LaRTRAbgxrv.i.R40H.XzC+vOjb55DFadXLycrss6dRm7IOvxxx2wwIz11N7xtrKaDHzyxZOaPL07LEhUQBzHYxjM.vpDQMIhZJJKVClXUGGmFNGvwSTV3lMa15QQQMOqy3rZ655NjYNQPPPt3F8gtWQl4h2wcbGqTBXQ.iYqLAHoetMWseIcsU1jj.kgVYfD3Pp4IUpj78emwwbwduTZl4znna71RBW2668EwLG7Y+BegISfUGj5mM+G5CsuYIxH149miHZ13oeboW5kpzw8lmTwVdNNNeyC7.Ov3WDxCccTufHk5G.bqUClkLAiIFA6zpqVXIXhI1woJC.VwFo.gP36beNirrr5yL2lYdM.TmHx0xxpF.7rrrVE.MO8S+zW8zNsSqd+98qYBT011t54cdmWMKKKO.3AcTC.Nk2a4JyLyIczJUpbzyRHp.B1Gv4.UMk56uQ4xkWG.8+p21WMx.HEWiKr1ZqsHLMWgYdE3IKBVUvbrobpg4a9jxpqeIeIiolWwxjRHsTFVF4LkFpYV.jNJJZyfWr6mviiLu2A2DyFiabcHz85lHQhV.XcWW2lyM2I2zk40TEI2ES4CGDY9ycQKOYfkLMkhcTEDJAKQF+sjbc+L92.qhFSlLIbbpFQLw+fu+2OBDhtmJ2C633DKih.PvGD5aaa2AL1.xUko6a6s8VpBWXaYYYSD4XaaWyxxpNSIZbtW341v11ttPHpaaaWGPeUu3lBT9Ulss8fUVYk9BwY08V9xekNkj9Wxvy4bNGeCCi3b+xOyJhDEka+M4mGkKOsQ49TYpqO99ic.ngQPaMrVRNdaKE6CWlpMvTcj..7nQi7+jexquu2i30ARvjhioBIhHXTMlAUEXlmae6aeyAfYIhhYRUr7ll1.DOVFb536cmVdDSaN0RyqFRS1DPMOA4+pnnHh.fiiCEyJSRlNTsAFA85e8WJ.Hl.X660NJhiBAigVVVcIh1vxxZUXf51110rOfcsxkKWMa97UYlq8xdouLW.T2xxp49uw8ugPH5.fd11GnC.ZQDslIjmcVCX0S6zNMYyo5X3e9e9eNXozKmCvXIOOuEgFluAZTnHPla7Fuw3yPRfsiDkN99LyedtlN+h7y0hHAv10.zSYBjAIPFW3lQPTRRtnG7Ih5SFTmu5W8q1NEkRxxaSSY9qZfg78nLjbywLG.V7c9QemKwLuDy7h6xzbVHkSVLvbOAodE+j5XAVhb61.TpjP92S.ZISBcESRn3WMiysMI9ywQJ6qSXG6.BKKFLhXliPBDtssss.q8XMzwwomSEmNmkPz9w+gO9F2q88t9drJ2DxyScYhcdi+Odi1ezOxGoxy4Y+bpTtb4Jk2ydpXYYUAt3nVVVGE.UpTohskkU0W6q804Bc25UpTYUKKqMdCug2POhLBbYNgttd1m1S6oMCzw7DQK.f4kKZgh40Ax18Q5lpBpj7HG4HGqsN2SEtdB0tUFHIvhoNhZKecjibjLJ4yjhJQZDI3FMZDX.L308G855VG06XNIm1..LhLoHc..OnQDk9HG4H4e3G9gKnXA0r.Xlvvv3gPji4ZY.Pp74yqAfDKuzxwC..kJUhO7QN7XvRh+yXUAZwLla7emXxFWpPgBSxhA.660lIIXG9fQelY4f8gTppVVVsjLx0nyArs6yL66XaGo5AXHHzwxZOqCfUOKgn9Me82bclXO.Te4kW1Cxba0gA7rss8rOfsmkkUi74yuZgBE13l1+M0gHZPpToBeNO6miVPPPNl4YoRzhLyq.fh5pMClqzBDjC7y3Iz29Skhw9Ex0+QdCHdBroPYjCUvrrzHDmExlLB20t1U2G4QdjM.LZA31EOwI9r0GuXlTDSa4R.XaO1i8XV4mYlELJVLM.BTn9OB5vGdHzwohOhRzmItkkk0Z.XMcf1JyeKDvLBnlTOhFH.taZhlGuyzjoQ5OMjq7zrDQEPIT.Uwrv.yAWjQMI41DQqVBnQUorbhm5o7yqkQZr53ImF+0bPhtuEy7IPDcB6e+627RtjKYFGGGXYYMphsc+DDOPHJOz1112xxJ7Vu0aM7Y9Leli.g1VBqUAP8G7AevUO0S8TaZBzLXkUVuQiFs.VtGvpGKYdLtA13mKwToCpI0ypMgCQTXkJUBJWtbrwa0GqfQnAhPIngpX5IkYXBnWCXEVZvSynZvcNLgRdwL.XbQz1Sze8lYAvXSCyA.zlMDQVoued7luQRyb.1qdctXwhQJJsG.Fir1q0H3hg1119PV35nG7AevAm5odpcDBQeaa6QLyA6cu6M5.G3.A.Xfkk0fCcnC0Ke9r8DhxwL2YDL.CWow0YYYAaa6XTlgNPRuwdogQxG5g91zobJmBMXv.JHHflYlY.jFgWeHO3o48e+2e8y7LOyUgLtIlYRGKSF63gqIrbaWHANDzTLAhA.uK.9Piu2YwT.qI8rDoAHFKcPfmbVOkd0UWM2xKu77PRa3EU.MMCjd8SAXfY4Z7BjAsL6xKgIq.wmvp2DPAvlk5.XFnZsMuBgYH8Rhoarc7VyA.T7+qJnilnwZehSLjItukkUOGGmtHJpuPZJy8.L553bu8DBwHG4VXAO7C+vb974CTzYuqkkUL6O7cbbFEEEMpb4xCgJFPEaw..NNNIDBQRniTvCYfAxAW45sqYyl4SkJU5YlYF5RuzKMnWudctoa5qs9a5M8xZ9I+jex0fAZCW0jZUqMucu6cO5fG7fGOHKmoKhKNmTAl4ETe1W.xO60.vnO9M7wa+Vtz2xZPflvAsf79xXFlL87LiKpMIlGYvFXFHmd+1FLXvNZznQIKKq4fz+bzfLO3BPlKaALwT5NVd+xO2mgu1ZqgEWbwiIU1mc1YQK45yD.3XPWcDRDGJDkCsssYFfZznNJtRQFD7sDVCrss6XYY0xwwYsyRHV0UBZxZJCYUxxQSngZR5nqdtGtwFaLb26d2CAfussM52uexcsqck4Zu1qMY1rYS7leyuYFxXvfXfAqVspeoynz.3gAvDC3p7.kLCaCf1BfdNiyeUhAptU439ToIlMIW2zlTtERCaI3upAGkkkxMDu6286dzG3C7ATxdSeDfmR5pPSUqPFHOWLtnYchHcVZf0KmJUpXFZN+e292e9Wyq80dr7bnmTFkXXX.WWW4eo5NgstNzYl4QiFwoSmdqSFeZD+hAXTsEur5+XO1i0MUlLsHvqYIrVywoRSgnbqJUpzob4x8AfuNPj23mql.nFCozHRHDhD27Meyz4bNmSnpY+XSvlAfliiShnnnjkKWNE.R+Nd6u8zu+OzGJwQNxiGcJmxSa.jwXafRXcTEswJnKZLl8bGKYF9Tk3r3qwCxBXZIfi3XrbDQYgIRgZfPIDhpikEZ.jRAfcAR9betO2720c8XKvb0kfLWVAEnH4fjYzKhoxyMECNiy2kD.Z852mxmK2SHW2zrWZ53uovFYRl3sx5DdSRuIB.gequ02he9O+mu5HV3C4YTavL2rb4xMAv5111sAPWq8Z0E0fby44hX4XMBRo3LP0+wHkzYj81XhrnFx433jikxmSSHDwmOj111NapToRoqqm.RFN0oUqVqO+7yG6EJaf35+LQ.pAeX.eUOMOUHlaqfrNkwUiIdKmZ.Tpbazm+y+4CdkuxWYLSujKrfhHB0gFJhbnNlS0+4hLyyWhnkpx7xPsR5gLWlz29Tqo5G+we7TmvIbBwfAer7dnwa2FUkWD.QYylECFNjl9rxMydNl+61+94Wyq80JO2joPlFapq8rrr5633LjkFgtOjriqOjm+EA.njT8HEyy6CfAJ4DFKk4gPGAvCQ+UW+Gk+Cu7+nnJUpDUtb4..DoCvdieeUO8+m+O2Y1m9S+oWv11Netb4xL+7ym3M+ley92vMbCcGMZTmToRsdPPvZoRkR5sUx3rX.12Ze.GuFa8K7q+iBXRbCu4fZ+qqJzq.QDUsZ09kJURZvXyiVXiwET9jUXb7MPY.JNKP8h.Xal.aW5aYXNUAkApC5hYXvHaa6dVVVs.vp.EWEndbSeibccCLNSi.UR8ooK4VY8vwiABiARpHP55pIBoCLmm7l+Ef7fmjarwFiVXgEVCnnGP8FPNstoksxXZNiXvRJh7ntwB.tFLyaiLocFZGriDZZ5NNN4a2tM+7eZOs.W.eGa6fHlCIhB9y9e7d7+e9Ae+9DQCXoo9spkkUC.zvwwooPHZfwELiNXWXHNzlZlPBBTQjF0MxC3V..y.CjGtn.fQNe+Jo+te2uK8JeAufPOf.Sfvu9AOX3t28t8efG3AF8LdFOiQpIbjTUv3bLyKSjoNy0L.PQHWqVynZzMFo4XZtmLJJJQrSXO062iADYqE4EeMSgBnS2tX94mmWe80QwhE450qGEFFx.faznQzC8POTzEdgWXjsssDLEl8gZkfYYY4+7ddOufa9+0MOBL02xRHABw.ibNfc.K8pj9LysHhZIDh15.8dWezOZ+K5htnAmQoRi7ja4jDVVVwI7Uwz5QxEhCzpToRxxkKmTsoNRCfrCFLHaxjISqooA.LhHyt.tMgjUA0gGVCX6c.NxwZSSL88H+x59ki0AtSA.xth.NzzxpIMVA4PC4VNARC+hhOvhHg+Xs.uBRv04jDULKPiBPZtjKAfkZ1r4BKu7xyAo7blU0D87PdO37W9ke4Et9q+5OVLLYSWiFMBqt5p..nTIS33TCFF5nQiFX1YmEarwFvvv.arwFHSlLXiM1P8yVhA.ZznA7G4yLXNe97g862OfYVYdXzfG+wdrt+p+p+p8rssayL2tb4xsfT+8wzEmcbbBhhh5WdOk6.O8V.d8.vvJUp3WtbYe.8Q1126nnnH+m811VPMoePwJqMMgIf1O7nGM41111RCf7W3Edgyb0W8UOukk0b+3e7OdlS6zNsrJYTLD.snRzpnlrfOVJILolc2N5iiTZHP0iGj.1TwTFoAbyCfYVAXgFx3fBjbC4.ShFrym6ys8ccW20ZPVfwVkjyzkmGGGFq++7PNP.yO7U+gK8NeGeTi8su2wr+w+w+wRiC1Eyvxh8LHhVQAz6lYmywXphGKppq.QbS+Lw.y0qWOr1ZMAkPCyO2bHa1rnd85nXwhv0yCIHhylMKxlIaTM2ZgYRmdr+kPLQgbHRjHQDIM70A.nSXX3Fae6O60.bkrTBnw8du26Fm0+syZPkCTI..T4xkieelcbpDJD6I.vS9YuNzbtOmTrzPMIl4HWWW+8t285aBDt6W3KD25sdqXGYxvG02OLUpTLjKlf.HWUwsADsAb5hIE5EhkPHZ9DnuNvwmm4O80XP2T9gSJHMdzzzDSxLCjMxpFtfY.fa7p2UVvqAhfqbyQnXORd.LGzwxvS2fYWC.nSFzxvSZZyXxvEldiesIl5zrYyILSBS1nbqs95XP+9HUpTfYFAgA.pcQR974Q1rYw5quNGFF.FDaIDb850QPP.z00Q2t8n1sawLyQc5zInVsZ9mxobJC61sa+BEJz80eAu5V2126azTIS33B7UlkrQe.2g.HzwwITHDQPGL7FOPtjxsiCGV9YUdDpYLBvMNuHIafUOoiy8kF.41iPT3u5y84x8JdEuhTZZZQ850qe974aojIw5S98J2bS.FiT.D9To3r3qsBNWlU.x1.HmNPAOkTYzAR4ov7hHZ5Xs..SFnVREnbyRDsj5LzkqWu9bElc1YxmM6r.X921UbYKccWyGa5gMjA.Y9oO3Cl4ocpm5OSiAe55xLMMQsZRprjKaNL2byhUWcUjHQBEXvsPfuOhG6vxKsD788Q1rYYWOOl.h50qWT5zoYgP.Ou5QPtYb5Sf1PXIV011dU.zzxxZsC8POzZ4JTXC.zNtA3nnHeYCq59.dS6mMAPtlgn27a9Mm7S7I9DiM11pUqnUpzdRB3IkqoIJL3wGj+H11o+Z+S+S7a8xu7AF.spw7ZkHpYMYbdGAvHmIO9S98b7MnIalQvx5+0P8smB3Hw4vxJyOYjFvME.nQiFEkNc5gl.8qo7GQ01phfLdYFHOedAhnk.Lmm4pKQDs7W6q80V9E8hdQw.lL+vgCmMSlLwC05XN7AGGGv.rkPDEEEwIRjfULbJ94NQfHP.EKVD999na2tv22W9FdLZaiFEQDEkJUpPHu2nGjRuI9dkHaG6HlgeY0JoF5Hx4.UBYIaz6PDsAy7FW0UcUs+G93e7dug286dvkcYW1f8ZYMrFfekJU3xkKCro5+MCUDD.Ht9WcjGdS5+4xu7+vLefOvUQyLyLADYLfY2NDQqASrJWkaPTw0AZzF.CL.F4d78vS+urq+S.vjckbwEOT10VSFzVBXwpRfSxPR2xeCEaGZZ.z0cRC7OY.lDircd.rLfoEPsSLHHX6ttd5VVhB111INqy5WO79tuucvcdmeqQ+5+5+Z8srr5.f0uyu9WuwgqTY0K4RtDYi5.8s.FYCLZ6.9GQ0.XIH2JOXyqcX7j775WlWGqh3mGxBtWhYdQSSpPsZL9g+veXme6y9rq6BTCRvJ5fXebQQ6MkSHmU4UH4YoGgDylmSfYdm.X6NNNqHDhrNNUYc8hgdddgpV.TS0VxxCKq810w4.aDEE0jHZ0dc5rZ9YlYUDSSLfFKAzZKqW5weNuHPl0jMfN2AO3Am6zNsSKFPiz+M+M+Mza7M9dB.poPp2H75u92K+xe4u7PKKK0ADFIJUxMsiCWfHZASfkpxbQHWUc5XJekvjnB0jMLu0B.AvDMJFCRxRKuD52a.xjIMVe80wJqTDMZTG4ymGiFMBAAxg1EONBBfEBA633DsxJqDkNc5nFMZvoSkJpSmt7i+3OVP1b47Ih7CCC8+betOm+UbEWwHKKIkOihh5SDMpWudCymOuB.PiF.tpIKX1U8dg58CDBTJggQ0TW609YR97ddOOnooEUtb4.GGmHEcBSToREsxkKmRGHmGvLNNNyztc6Bu9W+qO8i789dbMIisVGRGzJFjKUCfiW4vSCr3urYk0T2SrqDkJcHs3gJMk4NCnhwJAjoJPtR.ETqL3jl.bM.eS.+ZSJ3.Xy9xzLLyyIODFKwLujp3uXyCaQ.Lmuu+rqs1ZEz002pQBON+ZbiECGNDoSmF0pI2BSKu7xXvf9na2da5EnjESROMY7jvlPyDHDBD6eD555gISlLnc619yN6rCWas05mJUpdtttcVa80Z8RdVO61t.cL.F5JYPhO.5JDh00AZ5ArliiSWhnAkJUJtPO0WBe.G08ZkX4xkbZssalrToZ4pVEy.f4cbblWHDyedm24M69129xeZm1oAee+doSmtoIP8pLuNQTaVZrcwdVQrdY+ksYINcyBoAP9q4ZtlBWwUbEK.f4129tlBL6m9c9Nemrhkis.L2XkUpsdiFnM17Ya7TOdD.zJAjrJPpq8Zu1rW0a+sOWCfkL.J5JY22bPs4kT.woC.AjiGeQLEHu3eirKgYFiFMBN11X3nQXW6ZWXznQXiM1PwjDaN9ghHhlBzkXOZRcOOEA4Fj.ZZZDyLRjHAOZzvfnHdTud85M2ry19q+M9Fqctm64V+du+6u1u84e90rssaXYY0RUvafssczU+W9WF9QutqKpDPXUIvu..Ity67NS968686E+ZLBvzGnlrXRYiXSCPpFybpRknT0pATqVM+mooYWuIT0dD.7enG5gBNkS4T72Nvnin9yvwWFM7OqqohIKmDnR7z9U.kTJcwhUyTutb6zY.D5pljo5qgKBDrF.I.zbT41TM+tvu8u0uUw+gu7W1HYxjlr7byomF6BXhwHlB.ICBBzRlL433uXP5BBBPiFM.GEgRBwXv6bp5f4madTnPALXv.r1ZqsosVhbBtLvXCeUdIDBX6XCsDZQQQQQDQALiQoRkbnuueO.p8O3G78W+k+xeKqYZ5tVsZXCaa6MHl2PTtbqJUpzVw1jgUO5QGUZaaKRsRb0d6u82t1e+0dsnNPXb9+pUqFBLVtGSkCvLOPsY.vrWzq5Rl4l9r6OCjzquOy75UqVcsy3LDa34IAAdYf9qNgoIOUDzjoYzbF.yrLWMOMw.Mm4FtgaHykdoWpFKWEpc90909UZ+c9N+f1XxZWlvjyPmUGXIWlWwjnhtw0rRzR0l56W1kc4y7w9XW+zx.KkSUGMQIwwLWWsZ0.yLjC8gwhKsLZswFX4kWdS.GGal0InDvvzX7+9XS6OQhDHJJjwTayPUcSQQQQA0p4N3PG5gauicri0OgS34rps8AZDWWK.VEPecccu1ddxZxhYAmpQ1H.DyL5.CfHEmqHkIfqYBnUCHkiiSJ.j6U7JdEybW20cU35u1qOW+f9ZW463JCUwZcHxnEfWrjEUfyaEnqa664IY2xt.7OzjyQAN9IlaZ.GzpGUxrI...B.IQTPTIBjptzyBS4tEFLA4+eB.XznQAoSmVwNCyA.0F6sZP5Cc4KVzc950k8Coh0VP0SyhPxN3k9J2xWYgy+kb9yBI3Jw0+uUeYZ5qHojSoPc8hPIKUszoSkHUpTIzzzvfACHeeIwIiOre75qlYVHDrsiMWbkhgMp2XT1rY5MX3vtVVV8N5QNxnK7htH9l96+6o1cZGt3hKEuEvBd7G+wGchm3I1SHDqqp+uA.VGPuiZvVwrYaDfNWrnWh50kxViYlsrrBO5QOZ31111h..YaamxxxJmNvLd.y9HOxiLy4bRmT9u1O9Gmdm6bmZ4ymmUOdsXlaRDo98g1.E6ATW86Z2A.G7XMTqiWhw9E90+dALYSA+P5+E4A7lWNwBikYtVA.DQjnEP05Pf5RZKu69.Gb5anOVO1wli27PVz3IEDDrSOOucTpTokcbbxVsZUHLM8EkKOrRkJsa0p056d26tIzQi8uuOciW6q8Uq9.2rKPs9XEL.MvPfkB.ZFce228wm4YdlakN9GOiJqF.RqB5WBlnHWkKpnfVVHYjvZnDphpEqATuIjELIaDbWPCGBY.LxB3FKEmX.SVARWw+DU.lrMHS1jQIImHGmJQPtQMXH8eit6QX04y9M9FsO4S8TWur3rVGvsIzQS3Y1zw4.dBgvCvrAPsMvtP+oXXBvlog2rP0.ppA0YTrFIDxjCwSuIPGfuOGmPwdEgEqhn5Jv0TSgMVdDqPDYvRVIsDjwQwIJmlZ6a5vXWWWXXXfNc5frYyhjIS9DjiyzWwS7eJSVLl1uioxtB.E.BrnjHRMwq3oAD333L51u8aez49BO29+Yuu+rAepO0mZH.5aaa2RwXGGHA.q4kbIWRmO3G7C1OQhDCKc5k70afPO4SpTG8n0RtssYRxIIVJ3nG8eIXaaaaLfNC3QnDRYe214rr16rUpb2yVtb4YUTeMd66zCSLWr0m587XJFG.fvcADN+d2az8bO2ySFqS9E80Vm3pRikFDfKicfHb3c.fCqxMUNEPkrRlTgb.kRCTE7TdJClTTabgdYwDWTON1bYl4kMHR2UN8+kf7dD4VB3Iwf5heR2qWOLb3.rvBKNdh+GKFAro3LVZrv.fcbrQbQcJvRHfw.mDIjqky.njTStb4F1ue+dDScWt3xcqXWo626t9tCO2m+KbDQTWKKq0Afq5qX1nEucqhapb5IWEA.7RdIuD5VtkaIAJhDqTGTiXi.D54+Nemu3L+p+t+pyiZFK.3t3a8M+Vm+ZutqKS5zI8+J25WYiy+7N+UAJsFP03XqdS88AS86KFTN06B+W10VALI1erlERV7kUsQkXTBCuyO6c184+7e9JSG0nKf6zxNL9RNMMorwRBrXRf0RqdbmGJJCyRoCF6IWK.4Fhq7Ue0Ws063c7NVFx3r3U19Spl0mNlZslqgEWZQDDDnxkMkjBcpB0H+ACf1sawyM6bPHDi0wcbUfxbX1QP4gDr7cJRTRD+SEAf.ee+AG3dOPu4ma9VdddM10t1UM.XaYYU8Yt2mY8a8KeqsrrdV8ApNcNkfI+lFyBmDpFNhmdZrgwSG8nGMollVJwYJRa3gLti2jBFoA7XXhgnlYWfZpXpUFAzXDJhgnN5Cr8dJlyc7.il9W6Z55rRBfTJveyfhHqYcjo13yxJlDndDy7PhJ1Eng5drh9.0iPQj.0kzyFvK1f9WDRVXZvLaRDshss8xBgXI.DKAwBCFLHShDIRmNc53Oeh2XInUqVzxKu7SfES..fk9oSTTzljjyzaxj3yPw3yNswz4+DVBF.QUqUMji3PBvujPLvwwoGAzY4UVokqqa6UWcsV99CW+reIm85vynYkJ2ypkKWdCfhsAp2ElXzm7+40G9ldSe.xw4dvT4KmlwwSeEyd5rerO1GqvkcYW17.XwQiFMWpToxPkH1rF5pl3eK.y1.05.A5BGQO.G0Tj2kOvgll0Y3X7653kqsFuE2DabNv43XIoZPvzC8qIqUXcfU1.nQerLBwpKQ.MU43LlEvcQniUfGL.LMXtZwK6xt7U9XerqeZv4hyuM8YnRC3zoJEK2lVarAxlKGBBBl.36zq.cr43OfwmgBGGaHDV..v11I1hIXFfWes03uzW5Kwum2y6YbtQCCiHWW2QBgnW6NsamfRr1G78+9a79+fev5vDtl0P8ZikISwN.06KkIgrlqJUp.MMsvRkJMBlXzQ9WNxnsu8mcfZc0GmyaZYoGy7qBNNN4DBQ5fffDISljAf+O3G7CF7a+bdN88.5gRXndU36MlgIhg.N8QQL.028HUisGOAHr505t0.NXLq3Fal4m9dO8LOv87.YYlSefCb.s2+6+8G9E+hewwF8r56iD.ANxyeSrTUjoobnVKPj4JNN26xhyTrD6xwl757.X9y4rOm4+dG46MKWaSqE83e2aZ.D99iXe+.122m888CIhhVYkUX0hbHteWB.T7lzgmHEb..L84mlkL4Z0pEBBAhRhANNU6KDkhA8A999TiFMvsc62V3y64977+Jekaw+Jth+n9NNNsEBQSr4ZzlHGrmkUe8pXjxfzS7XOVEsS7DKSXE.zPOzw49Bj43DLfiFJgr12sc950aVnXwkxaYYkG.Y+G+e+Ol6y+Y+7o+ze5OcDjLzbcLwDX6vL2UwfrA.veW.gG5Xy37imOG8+zt9OC.Sh04XVn7PBl4ULHZ9eTsZzW6q8059pe0u55nH7D0wZNRzQiY8vOKVljF5XN3oanCuc98ezG8TNwS7DOYHareNGGGMkl76A4GtqBfFLy0KWtbSCfVt.scbb5JDhtnD5YVE8qoZPpTIDUs5S0XXRL8yLVQI4DcUhgj.nKQTcHav1cIfMZJCxkMBZfzBWj0QBtPdgfJTsp7fPSfhUYda.3DAvNAfvw1YAgkHkiSUPf4RxF+4G9ge3vSdWm7.PRyJx11dc.rtkk05111qqZDqoAPCWIkrqCf0wNPOb3IEGuicfjGVthjyUDX15vbQfZwSuelez88izNiy6LF8u7k+W57RN6ytk2lLAQi..2XYFsU.WVRQiccHozdQE8hGSs3vvvTZxQSjv00k788gttA.XjNc5wF75z2cXZZNtnPGaGlI.qXvP1BZqBg.UcbT12oj4IkDBDEEwtUciXhiDhwfm3W0wwmAFjKWtgO5i9nCJLagd+ieg+wMdMutWi20dMWWk8uu+xJdx2KU5Jr3Pf5wMmEGaDyTlP.DpCD3Mcgf5HooGxTSABftNl0yCyvLmyfnjtxMEfDbJczdeW495bk66J6Auh8En9PmM0Dc4.Hcn8sZhua88heQb8DjiCKWQuzT+tok.zZNYxEYgAxUxEYpJeeJvzDCqUSFOcAWvED9deuuW5LNiyPVjXITfc3YnRzLnlwL5vcAOYdshJf3VA.K466uPpTohoQ7zFj3XiXaZIQvQQnVM2s17.633.lAnoyFOI9aL3apuhAIg1TysxodFpjYzXoJBkdWEBw..LpWudCVe80aem24Wuwu2u2qoJ.rMAbqAil.tRPSLPe3hgvD9n179KiMBWcSM0VNAPkoMPy3s0QdnnGqAvxt.KNXvfBarwFjwyznmtKV2SECqqi9G3.UFTtb49R+lnTbizAX2HDG7IsY1eQFesY.SDHKbrxIfcAmMwJMcFvK389deu8upq5ph2dF8vjF64od7j2aVBZKWEZqp.EvxBYssGed4RDYLqpQ1bpbXkXYN4xPx.koAL4ma1kTsZUTrXQPDAMMswZsNNtS8bkii+FybIFzVh+hAqiUMTPSwxIlHJrToR9wa7lVsZsdmNcbu3K9hs+leyuoMzQUSOznlxTWWYEzuQCYQvxIupS.dwuWQnJBk9MlHXE3DzP9xQ0bwJoAZLVVI2xsbKye9m+4W.RyOOhYdPIhF7Y+ley9ufWvKPp26Rn2JUQmFpMcxt1EFbnCcb01Y5XcQ.Hwt.RdnMIkVQd.mwLkjk9VR3AO3AGt6cu6XOboiyDIhAXfjy6hTanjDltNl86+8ezE24N2YQ0Yk5.XYhnkgAV9K8I9RK9R+cdowLLICQTrLoR1ndCMsTIoEWXAZJVYFGOAgPPa0qRDBAbrUfgPahgbwweLQp3LB73rMLwp5OTS7EgBgHvQtIIF533DC3Z2hEK157O+yesa+1u8UM.7TdnibZokPaipXfarGaXf.WouSDJi+L.fqLhuHHTGIjaWshoMP8rpM23RPd+5Bp2ShTLkSslqQ2q4Ztl1WwUbEsMAZWaBS.dx7.fi2h6lN+mxukJlkYu7jzaBiANNC.htpq5p57m9m9mtF17VBKlMyYpDK6KfEgNJZ5AypLaPDoaZhhUqJk5JlLPqwCcvw1IgvRL9LlMKi0IfjbrjK8XpXxi8nDlAfkP.Gamo.AVk6BHhkrmSFWVcb9O4JsFXTlrY6szRK0V8ZcUGGmXllrpggwptttqCYtsA.HrHTK71XC9dEzuTCLrp5rYU+GL.Hc.MOXkBvVVqRIjEUQVXfLv0LCyUSSDEuw8B.LGxb0gDQi9I+jeh+t28tGJDhAUqVc7ZOFG+AH7z.hmckUPt50GuBfSQjYFlqklHJk7VQLRduUwtVnQO6oj6ppduXutbN04kEgzyBWA.KYPzhe+G8QW3DOwSLNtMOj9OVF0.ly.fzNNNoDBglssCYYInnnHtVsZ.LhPBDAlBIvnjj0QRCH11gXBj0lygM9ZK41.ADURHBcrsCfbSZEHDhvopgiAnPHk.1nO399fCzB0Z+xufWdyEleAuW7YbF0bm.ZRSfwRnV4Qmapd.F.Ql.A0l74eBniTp5+yBfLFFHqq63s+3LPdeWHQTWXhMN7O3vs1wd2QWzPzqDb5WEi25rA.kh.pFWS5z0ld7Rr1uvt926VxYSWWvEbAr.hXvG7Ih78LMCLLLvEewWrjIA0QJm+UlNl5ZRCBdk8A7F3Azam6bmwz1FPM8uG6wd77W7EewwnDRVVVb4xkgsscBWc8j111YDBgDIwpHUsIEaxoqtii0GxGu8gMsouVV8cC2omnSH.FcO+K2yzSrcTSCi3aVj.P4BMGER1DQYbb33oGTnlTmoyB4gaEBBBxAJFAVNCCjw1wIC.ReJ65TRRInDOzC8PTrIi9Y9beFeGGmPKKqnO8m9SG+9+zSiRCGdxVoA.I6b3kiQUOScfL.0RCYAuoHxH4C7StOsJ2cEsy9rOaxC.UpTgQQ0dKunqhxqE8gbi+.cnK2LNROtgfjc4Dy7VMurjwfkr95qiEVXAnoogzoSMFrDcc8sxPXPDAmpUQylMYgkfs1RAbDn.ZBqQBfjcLQBgHhkrNIpVsZbIqRrPHfqqKwLzbbbRwpF662uel8t28l6ocJOsBurWwuyL851at8su+x47hS3WTwJlhpihwJLTMFWDEmdWt2wSWu6T++6BOzVMMrl.nA7z8fb6orlqDE4PHO.YF1kW3JuxqbQ3YtHP84skITKfkTLzYwJJvA10wZqS8ycyb+G3ZSnZSDgRnzzSrIYyRkRi3sgCPFtFm1gYMlYdu6cugKOyoMFLga9luY+y3LNi3IMB1gI.jfqxZLWKoKyoTEIlA.YdfexCjF.oRkJ0z4x1Twvc5zAc5zgI05ntZ0pnlq6X2T2slKhh3wrQJtYAgPHoDLMd3+w4UCVdkkC.OVRMi77p6SD4+E9G9B9BgHrToRQBgH5N+leKPDow.ouo+9aJGjxvZdGGmEVe80WD.KcQWzqd4e7O9mT7zO8SWuFPQIFqXN.jCtBIv.0LAvFQqFyBOC0AikqndIKX0ysQ5Pe.jwZsAvFtEK1rc61MWc0UWWWWuCWi8cka7hT.HC4YDuFTyu7oe54ApFazaYwAKFC9zuLhuHfcK+c3Xl.vlb.38su8EfhRYHxrae.L3ptpOwXlWUFk+YA9uFl.Vh70hs03en8su8gq9p+SFmajHSM.n8Q9HezXP.+YxjqpUqhff.YWmLipNUGuIb.CTudc344MdSkDeIDhwwW4xkMN2UH.EBBg.T78DgKt3hALfeXXnuPTxWHD9sa2NPHDgBgHFntwRgqSmNoEBQga4VtkEN2y4bVDdXwZSLbuTnwJ3htnKJ.RYh0GvKF3o1n5xRV63JZA3zRAxQGLuJWlUCkbSJN..CeIujWhussM+fO3ClD.4Was0l6u61u84eAufWvB29se6K.f4YGdt621dbSYsOjw+lk1z+EeMdnTGZB.dx0sogy7xg.XrD.VhHZl0We8L6d26FPUClyRKsYYG4BdC.vx6A0z7Jkbm6bmi2vdj7BCFLf4ZL+R+cdoSyf3DLyIZ0tMU0oJVo3JXwEVf..LLL3s.9lbfBLyQLyJFjHyyQDKrDQYxjIxwwIh4IdHGQxywDBwPQIwP.dHXLDfGxxMlzn4leN+O4G+SFpxYpAfLBgXl25k+VWPHDqjJUJya61tMqFMZX8ttlqoLjrSVG.yipHqDpZLB.cS3VJFjiNx0noarDlFf5aeH.F.mR8Ap2wM9bScoYHRFTe.f23a7MkkHZd.rhILMXl0uhq3JJBfkqoqOMqV2peIb731ZZpgftK4m6aTNgrDAB.HB5xOinRROK4O8O8uN1.NGrDVZ5FzSTQtgBG6uNP1rV52065cmx11VqZ0Mc91VqEmiq+pe+9DyLZr5pRlIwSL00ouJURdt57yOO.OwXzgJFiT9YCTq50UWc0Xl95y.i.3QPsYt.iQDgQfUx2WHvRKsTRaGm3gysrPHLBCCsN5QOZ4e2W4uqkALLwDoUlqtoYxidziFUoREo70kfkDu1o8GTcwwFDqTRp18fLdbCT0nI.ZryBmdcfZqpjvpOKqeIOyUmSF2YN6ocZm1LNNNE7q5Gug9jmct6ce7bdsDoaHhqIOMQTF.2zJPYS.WC7s+1e6PhLGAzXncLqWMjf.QKSpXE4BZPUmOUudcM.n0tc6TtLm8m9fOXNH8SyXVkjVwb8w2+kKWNzrYSXoLieWWInoBKAAFD.qwp7f1NNjmmGIrDvRH31saGW+eHAJjTmU533DppmVd1nTdOLHBBgPyzzbrrJcbbja3TvIAPJgPj95ulqO667c8NKr28r24uu669V5h+i9iV9QezGcIXp.VzvSArnYbuNgFvH1TY6Cfd0LMi6ET1Wfr9+0gDzEuDtkpAIy0cIhVExsE1.l4HTyPaG6XGo45bZ.GIiFAxBc02EUUJTXwokP2+lFjySUu9ONCSjR8XZyrScXNlG.YLMM82647b13e5K9kaTDnYcYRgo0g1S1ierrbVD.ay.3Tpw7SG.mLj9sQ9pNNAh8X019.1ddMZT8rNiyvC.qpCz59bb5KDhdkJUp6cbG2Q2Ymc11m011VmFSlD3wZB4GOAXxlljdY.sJxDL4gjMEEa0p0JG7mdvY9UN6ekne+2xao0+ee7Odc.3UFX8JxWmg6d26lN3AOXR.jcIfb+vG4QJbRmzIMijFyzbAAAKpzu7NFLXvIjMa1s633nqjESrVBY0zqCHhFwL2Axa9Zpoo07a8s9VMekuxW4FNNNsAP2yTHZeOG8nq8.OvCT+M7FdCd0pUaM.zsLPfpUqIS9WNAqXuhXte3O7GN2Ed9WX1u888sI0DjZKDhV5.scYtuhdXCAPTQfT0KhYQcrzq++9+8U9++FtgXOlXIEyRhM90kg7frr.H0ZquVh985S.DIDallvAAAXznQHe97nd8Fv2eDuE5lLtIC0TUiANY5XG4zW2xzLTzYOBSwEhACFDsycty.EKS7gZ0vB4meqUqVsZ+ze5OsxEdtmaUEichkKy.Cfg2yXS5DAdddA555gBfPGUr8RKsD2rYSF.Xd.sMTrA6y7Y9L4tnK5hlA.yqqqO+8ce22bBgHGTLv.5nG6xchmdlNPus79+nR.9U2EBwgdBM08KZ1ZMI+ytQBbvoZLU5UDoppznpPHx3HKzIsAPhm+q7U5+4+7e9dXxtlO..Pou+3bXyACLObw7PIaBc.c2IShcQ.LmAQy3JARI9v9j.Py22OYpToHGaGhRH8DB4jvjwQwZ2OdppZZZbTXHywutjyD.JIbEQDGxLEBPgVVB111VBxhRiDS8tLSDwmkPf60wIdpuQW5a6sF9+9y+ECqUqZfPX4GDDzud85shhhZZYY03ltoax6Jt3K1ySNTrUAvZUpTY8xkK2Fp7k6.H5vp2iO7gObxcricj3vG9vQ63YtCFaf3FXRAfrl.EpJk52r555y344kiYNoAQQtRy5NtvwgRIDPCT9aQrCvO.KgQn4lXh3zwV+hHe8wX5pi2hXYkSAbx6yPl+YX8kv.zbSRJJ94mjc.6ZWZG5PGJ0G5C8gztt206Joyl7G.LKjSEaIShVvUlGrfh0fEg7bNCHyeEuYulVJgnW+dHet7nYylX3vgiYtjllFz00GaxqD1r9pgZp9VVhvojzEWsZUoKUk.HJhU.4wQDnPFTjBXOvLSD.YHMaQlAGRfBMLLBTzXefiiSGSSy0SjHQCGGGu8XY44BTW4oIqholLc7YCG4HGAae6ammxjVCA.uK.5PaVp.YE.EtaGm4DBwBPZ5eycW20ckaG6XGjZagMRswmFVtb4tk.1npDv33bna0TqAN9oFf33wjPl6J+McG2wruvW3KLdUiNGybta6NtMsW7K5EOX+6e+sdcutWWS.zrDPqpS1xYprIH01ARejILxbQl4kI4lwYrDCUZ+eko9ylIJJJM.RVqlqF.mHQhDIhhhjMVHmlJyfYBDRlLEBB7wTm+E6qWSRUEyfD.3U2iEkDQAAASLnWFQKs7Rb1rYQPPPTmNc3985wLHXYInVsZQskazIlkOVLKiQGoqq2utqaqRVVq444U+tu66147O+y2F.1F.tta1fliqCD6F.GbJFgUudcpXwhwG5qsLPtUkmEXToREyUWcUihEKNWIIv7..ipVsZWgPrgsscy33aSSyV0pUqOjLox2chDvOda5+iG1..RhxHMpHA54jNohY57H0y5JMkyL..DQiLA5VyBsf8X1+FA.MUbVAn5EfHZYU7zJPlKaQZ75xEKnjNwXvk52uepLYxjD.iMj+oYRRbdrUVYEzngj6YrhRHEJTf6zoC..WHegnd86ExR1yxLGwS2xyT4+BDBQbytie+XJVexjzXq8I4VkqO.5SL2sjkUq986u12869cW827272rA.V0Dn08Xa21xxpittdGOOuN.nuxnVCaznQzJqrxzarKx.HgKPxibjins8suckYn9+k6dyiRRtptS3e2H2yZeIyHy2K6pTK0ffVHg5pwMFPfQHjvijLFc.iMLZPryXOF8ABDlEO1vLiMxZAKrFi4.1CFIKLivrHwmLHZr3XKP.B5tAYo1nVkPzcmuWjQtVUlUtmQb+9iWDYkUKgwiQ.Z9dmSepp6tpHhLha7d2289aYrgZLWFfEtjW6aXgOwm3uLcvF9G95dMuldeh65l6BWzlYt8m9S+oa9pdUupVyAr0laiHCuvOt+zGl7S0H7cqvOSSCCcJSSDkvFHgqI1C.X.xisfCZBy6pcJ.Lr31zlNB.huHP554vbnDVBl77yh.Kx00r94xHHWMr8ZmQ.PzACFDsZ0pwDBQz.myhPPb1byNGuwlafsQ8137+2QywH.e6b47JUpjev7eleHiNfwZklOk4+7YCRfY..ee.O+g7J6ZEOGs1iAFEKVL+gCGN4d.ZBfZ+CG7fUdwWzEUFaizjF4.ZdXCCJ5CfAA4+Op.fWv8Jdx7+WDfpeJH8AAnyOGvbe4u22K04dtmqE.FhbnGJMtfKcO4IOYucsqcsCMlaI.uZ4vPTZbg.mjdi+hNd6mIie5QXx5i+NFXYOXzRiNDQsHxtUoRk57e37uvg.43J6rBT+jJViOPdOjwzQ.Wfl+Q+Q+2aQD0wwwYnYQZXoNrB.f22y7Y5CX6A.+inTvHbS4nCcnCQ6cu6k20tViq9X6V2SVQWxjEjJJLh0ZH2NIr7xd.n+ryNa6QCF0D.a9w9K9K1DvtE.5VbbhR6kN5QOZvKGYiUGH1YbFmQTDTUVjEQhDIRbX3Ne5jISlzwwItPHhBli.PiQER974I.F9FqK1mHxSJkd4xka348bet8e4+5u7ABw9FHDhQGQo3csqcYcwW7qMZoRkBttWIZwcZKho93exOdnpMOCQTZl4D+R+RWZjGo7i.SGKyNRbthg.vqbvlAA.AikqEoBPzfEUR7I9K+KGy+See+o.vTDkaG1SW2tci..pSmtjPHfPjersgY5HqFQiFEoSmFZslMEKAf4wZGQXgRFAiku0Uq0aQAVYI.05s+1e6sAPGsV2EjoZuZstM.sECzhA2hYzhHZKPncpTo5.fdLg9fvPobsvEEhcIWxudZl4YeguvW3xZOOaX1.U9.9Csz25QezYKTnPRXtu5mM64X5R73IslipWudv6Y19aFzQC.z9U8pdUsPFrI.1rb4xsymO+3NX.fYu8O5maAhxs3q7xekyCiiLMcf9Jj33G+3wfQ.KiFTrzfmwq9iyd19Ywvr44iNYQOy..DwAHloXeKGWq0AU.21xEfe5O8m915hvBaKto5wzMx1C.d4bgOPV.ayFVJCjv22O4gNzghS11QAPj.DSDR8FZznQPq0T4JkMZLBMgkyAffhkvpsQUhO.7777FwD7.AOBF6olX3o0NAwYT+HQhzGvefRo5GTzptfns.GD6YhoZCfNGVo5wLOPqMh05e4G9uXToRk7.He.fXwhEct4lOsTJWPq0Ye0u5Ws7ddnGZ0uzW5KsJP1B.vdsBEV.X4oB5pPjiiwk4iWMP6mVc0U8OybmYXx+i.VYv6487d5Uxnt6a.joZ4xkKCfpDY27D85MHnckwYlSvFA3MM.lN.8AypLn.HsccjDXkjXowhyVXxNgcx3IxwDEm1zc07ahn.4ChiPzxaS2pHHSlnvHZcwQ8+06tx5quN..+te2+IPCfq65ttsKtWlwz2JRIy4IN.RdcW20EVTjPaGbrcchIRXgYF8602HP0ANUhiiCN1CeL344scwZJo4C...f.PRDEDUQ7YlYlCo.V3BcDY57OQjOQjmRq7XlGM2ByMDF2uvXe6f5v.clYlo63CzQru80gHpMCzpToRMAvlDnl.TqnQi1gA2mHxG.QbccSq054ylMalGtYybLy4dYurWVV.rDxFfnIy5aD.7WYk8MD.CJTGCMhLmYykquMMyFhEWb..5++9dtmtBgnELE.oAQ42747bdNcxjIyHyyhbwDBQhBEJjhYNky1qEXhmxLwyzGKJldxwXu..fbAnK3Bt.KhnnHWt3AwkQNy8bl70cMWyvW6q801y3NLnmy1EBX7iZfLzI14FiibO2y8DGFXparI1.D4EbriAiKxE0xxJpkkUTgHuU5zoIe+wxOW35m9DLEzczngga7zGAtXnixYH.Fv.8Yv8XdbgQGjMS1AiFMx72IzUJWqCHzoQ85aQTtlkKWdyNc5zHuPrIfeSkR0dqs1pM.5v.8.anAmEaQDQIJWt7z9DVrd851dddxK9hu3c8M+FeycAjMuqYy5gTayBELeF.f2QGWDi83A.uyISFOf7iPACkJpE3vE.XyBOqm0lmy4bNsBDH6nDQSq05EDhyc40We8kjR4BEKVbt63NtioKUpTftIjIo61Zzw+trE7eFOB1HUln.HwREQR.6TXYj5QdjJgalkIh5SFzq1rDPSnljJh4s.PzSLtwCYmoToRyAf4Hxdlf7GhA.pd8MFiHNLARQCGkJUhKUpzX8fHDAIgU5EvHj5iu3CPUxVasEXivT60tSmQLyCAPelQOXbnPCJR.0mrB+dzmHpKQT2HQhzNSlLsAvVDgPTH0hMhjdOl4gx0VC.HZjXwR1sa2YRkJ0BunWzKJyIO4Iseaus2lcIfEkR4L.1wKWtLYt2X6EjWl2xl728vdFidTufBoMbkU1+..zawJnCvJagkLm+J.cTpGcvM8Q9HVv1NE.l8Sby27rFGkDoHhh+pdU++DC.Q2bm5xwS1Pyz1ywZiHATqIT3WivLiNc5LJqCF.H5gULhmbwIW+yHlt90A7QI3uM5Fy5yL6Wd6XowEbFFDbmD.IqWud73wiGSHDQBc+FsVCkVw4ymmSOUZyEJEV3LhPPSPYyw1GlBn0OnXnsILlVdcXl69nO5iFl+eWirQPsYf1VVVs8YtM.5XYgNQiDsimmWGlPGPnW1rm8.gP3444wDkKR+98S.foufK7BWrb4x1e069tkv1V..6CoTKJDhoC97YkM6yvG.9EGOu+37+Yf7d02lh1lFFtBBW6biR.atmm1d5ZbXGDIaIDmYNIxYzUmcsqcYbwTfocbblA.ybOG8nSiRSpeeq9jw40dBc7DxFaBpDOYJ7k8HfbAB0Y4VjM04s9V+iG.ThWBfV7e6EMgAb7PEL.BzE4yu0u+u+ePKl41Ly8EBguPHHhHq0VaMpXwhV.tV+5+5uhHjOEjXcIRreACjwGnhuMf+hOlywN95SlFlEvVGwvJHAxkKLQuHnZUFAPK87NuyayezO5GEHROtaATnuQiI.CbTKrDhZD5xxID.Ixt8joHqokoQe+u+2+XQXJLQLFvhAaYJjJHsiC.HtfTx.vmYeekR4oTpQ6a+6ezm81+rCTpC2GHaexmFbGe96vCnLPFXYfN1IhtBPrbaKjXS86eEuoY9u9A9.yBylkl4G7C9Aod4u7yKJ.XoTN7j5uWu22a5c2CYPOjO+PGGGSlZkKGJTVishrZ0pE.we6zVVVSyLOMykl9tN3ckZzHu3kKWNZiFMrzZM0uaen0ZzndivVsFffSC8Izpwduto6plM15GzIVurYyN.f6FrA0lroSoM.3Muwa7FaJDhsfw9vZ0eP+lfwl.bi0jxFVfZP.MXla.Fa5ybKsV2VJjckh05yboAZsdjVqwcdm2dzDIhkF.yGQHrYl20G8i7QOMTt7oA.4t28ty.fYxkKWbyyzRLvb9.KDTR7MsvJvBXQBvkWEve0PN0BzIaErEvxsfYCt8A.+i9Q+nDLyy7RurKad.2E+S+S9SWD1XAjIyXa+a0UWMr3cQAP74latfM6c7nAa37m0a7Xx2amn6vUHfbQXlilgxDGnZbjGwAri9+3+wakxCL5i+A9.FDL.LHeCLDXOSHpoB+.swwygYOlcY3BhYN5ev6+8G2xxJ192+9ixttQu9q+5B27dD.DsUqVQJW1nACRgjfo6TAu2DbwxrAoQl9q5S.dbvlJdSuw2znF02viAaRlhnQFXBy8.POOe+d.TehoA9Fq5sSvlE2jHpAHrAy7lFzeQgvKuiPH5.fNRorK.5+M+leygJkhO8Se2Q.PRFXNhnkmd5oy8q9q9qJ+hew+pB.HeYfL.UmGkwTHORfLYBKJfOZzXrUF9POzCMpPgBAEL4Di9fevORef7AcFoRcrLpBjoJP4MZ2tcmgCG5A.Khn34IJoQrvwzJkZ1idziNqTJmE.S6BLEvIRiZHEDHAVXgvXteVFeYArtE.rLZcii0MbC2fgVe4yaEffnDnRkz.Xp69dtmz.H0xKub3lfl75YBDwTvGngeA.95u5qN37fn4pfX.4CngHEKja0W8Ue0wIhrBRLaHkiBKXxNPxViFMvBKrvXN8+Q+neT..7T2ySErugTDAztY7zbmR7mO.4yL6oTJOBViHhFTsZ0ABorO.2EfMIWQT6Ymc11x74aW7vGdKl4lfPCgPT6QdjGIvwH3FJkpoTH6vLOPJk99v2BfRFMZzYeJyN6RNJk8G9CeS4APtrkQF.6PcxHNvJDPMiyR.LZBgKbx+3g50GBfAO+m+yuK.ZkISlFlyeoZDkeiACFzE.dJ0gh.ajPqKkzwwIoMPxhEKlDlD7RJpfD.KFVLtIKbBvSNR3ivQAPFvv1NbtNe1wIrn88NsS6zZcC23MFH7vtaATnGPgIoGAArJATgD.zRaersdkufWPzfBjjzT7xboOmy4bRmKWtzUqVM4HOu3kJ4DE.QpVspkVqsle94oHQiB.lme94Yaaa+f3Te.1CfLPTmog.9CAssi87e6O7OrCApCYP2WHJ+ZCfN0pUqsTrVak5vFZYQTSk5vMHhqAfZTtb0.nM.iM8CZPgTJaJDhsDBQGl3AAabhHXEqWudgBA+x6Z0UsGMRm2FvN61n0J0hEQrf0q.F+955l4+AFA3LBEwHrWLB4yO.gc7sToFDQaPD0N.4sQEBwT.km8LNiyXVfryTnPgoeouzW5zvFSCXOEPkz2xsbKIJrcASdxP7EvNJhV9H.UR.fTUYdJ.2o3JbZjGIQtbwBJTwPToR3yNC5CWc0.cTvIBPt3.1Iu3K7hSCTd57ma9YZ2t8rL6NcVfj1Ay0s3hyGPGrbw.PribjiDgMzmlZznAA.Ja1rHb5NGGGzsaWPLfSoRfYe1yyiyjICmOed12HSIAyqYED+w8Yh5APcjRQGBns2nQcDBQWoTziXzkA5dMWy0zQoTcTJUaOOu1uvW3KbKoT1B.MkR4lAqy1hHpsPH5vkJ0SHD8SkJ0vToR4WrXwHNttIiDIxrW8Ue0K9MtmuwR.Xd.2ogMRBrbL.Wq4.f8jyks9DyoA3YJPbkg.XfYysmnOpgNPf1HSlsN3Au6t+Nuk2xHtTonFp4vy.fYsssCbxpxIM56zxm57Y+htXviO21SfvgLtiaJQb.DkY1RoT9oSmdXYf9.5A3DXH16NZTfOJV7TQvPvwubD.D48+G7eaxOugnlJdrXwh6n0w60qWLsVGE9vZGPNBDzZMOQ9+.fIozHtqZslkBg+7yOenKg1Al3hMXiXotI.ZIDhsRDOQaoTFPQYpI.u49jxMXe+MHfMCxUaS.ZSWW2lRgbKoXs1LWpqVq6655Nj4RiRjHACfXT97yjISlLWv4e9hG4du2cAfBRozF.KjKWtf0OKGAHOATH3y8lQBx+2BvA6Eic3zQ.XPgSfdFQP23pbSm7L5xryn1saStF6mOA6vofMlB4yGpELyl278yt28t2Y.vz1lB3k.33wBno+SVla6I7w+d+fsyfwEQbTOeb.mD1.wbWFwPTDEkLvGN.Rb8QfvXgGK7ke7FgPWNEBnkC.NSl4yB.OU.XGz831LwkKH1uqRc3J6WJ2nTVS.PwCWrYgBEBU72PWfHDFtmJ7gdxRQSlDZ3gEEHAaBfi..ZYfnQ.hWxLQCQ4nAv0tMf6VY.5VYOXHhCFGcLrqBg.Wx.N7EBq4E.ffY9zxQzYTxHxf4zZ07fnjvGQB5qLCBCO9wO9fUWc0dHnCxBgnF.pp05JBw4VEn7F4.ZUxFcK98J1odk5sNmy4bBu22Elm2gzJZF.L+i9nO5r6d26d5r.Is.hVBfKVr3v+l+1+lNu6q9c2BA9NO.FDXOafYlxQTLWymoEPNrLJkcYfxK2oS6LO7irdly4YbNY9W9W9WV9LOyybtM1Xio50qWhvJJ6yLrHvAjZHbusg26gg4LDSf7C5VAC.9y+4+77kcYWlWPrbmfBizwHzg6yGvMhRohQDE0flOxOfFSC8YeOvvmHxWJk.Aw2ZcQKlIqfSL0sWOqjISDI37OjA2SJk8.P+FMZzuSmNcylMaqq5scU0to+7axE.kfMJA2L0.pzZNf9at.XLCrNwW+DD.vJqrRXmpOUGoHdPbwR4.Duneieiccq21sYS4oYACKXDEuV5h5FhBhZLyUIh1HCPSOft0CQxTdvm3aeB+UVYkQE1Y2.9Y86US7tRgn.ESrLPppYPZXgjvcLuUs9JekuxnWxK4kzElEGBcokAg2OrAhEAHoFXJXiYgElGNX4ie7imakUVQ..AYD70EggNNS4xbZee+DtttwYFQgo.iVXBnYFdgN27ywazXif6GjYyELFAC288MQhgI2v.DMB9X.YQ8C3ZsuRoB5VEF8e7U9er2sda2ZeoTNPoTduoW6uM82ev6HhMPzCqTQIhrBndgO.7u1a3Zw65c7t..rX.KxDi6AvCXB8lYpY5N6rytkRoZXYgxh8UPCWnfABn0w1TXvaOATtZ8c77cuDvQsJ.PEsgEFAKTaa64EF3WOGy7bG37dQo9B21eSzf2CXlY+uy25aM5W647bF3BzEYwVfQWTYLr4G709Zes9m+4e9SB8ymnDYrvmQgttTLr.hh3HQP7SB.j7C8g9PItpq5phCfDY.hUlYpQiF8VbwEahbnNJM2l.aFRwK+IN1guqMI55R9m9mdMy71e6u6P5OrbdhVjAlKfVNogg60yvF5sFj.9XgQLpVqMnDDLxmWLVLWQ.+DNk6MLCFKL+BzFargoKYiSTmGAiVkLwZhlM9RfFwfGJkROkR4IkRVq07m81tMuW9q7UNHDJvl+u8AfxQUJU7u22+6Ecem69rBnDlW.7hGbK2xsz+BtfKniTJa9e+O5CT4+566OzAFep1MCPMKiPs2E6ACv5+Xs8Wq8BDooghpFzSZi3v0NMf67YAV1k4kHaxTrOSxxdvj2QHGtCoBT3b.CWBXXJCsfNUKT+WDicfDDrSXrOadfYu5q8Ck5pdWWECfdHCZhJ1aB3tUdfNNamaEv1vuNZwhEiUnPgv47WlYV.inBmOz8upUq1hKrvBKZYYYJboqaRuQdQAgSovBi+d.B9eku7W06085uBVoJxloxfOYrIyIchrwqIHkRbWe46Bm84bVrOCeKPiPvOOQ7PFz.3iAL3AAEj0RYlWKNXNx4JkzQTJHkxHJsJ923dt2DOuy64FaMoLxgUJeD3JEv7buNLwYJhnSBiv3WFl3fsviKhbdLCiQD.LUdfE8Ax4BrK1jyjM.lxwwwe5omdyVsZUQtlrBbQcGGml4ymOr.1gEYHDUF+h1F0ABhy1CPzZ.Ia.LEDXFL.oQUC5BYiHa5OXvf9wiGuaNf1DPamPZerJ.NtcD.2DRfzEYdZxlV.kMysQFQ3e1G3AdfoN6y9rCoe87e6u02d9C7rOvbXBA4Wq0QEBQDsiNB7AQVAj.bblY7Ni8B+OM4nxDfGCLDFMHYLRlBnLHC.hMhdAAF9fvHl3gVLM.DMhY1OL+OD7NjTJgxDqQeguvWv++0G6i48W7w+3ijR4H.LToUCkh0FA3NToTcYha85dSut5G7NOnI+rcNmyjBx5igJ2mx2SX67kW.FKlWxFplOC.fiipqXeE1PcXUcoT1HCPiJ1XSXgsfyXat+TkefedGuE9YIbeGgNtzrDQg6KIxq+JthQeha9lairXCDAa.mwyQO4ZpVqBDsIPhFlBsOOxgLnjcd.WAanM8xTNZY3hk1ZqsVNUpTK3TxYJoPFy00MxHOOh.SgjZlA34maddyM2Dl6KLEDSQiwUKEp0RzHv7.N.o1RoriRo5Jkq4A3FQqTw61qWrjISBrcAJB0ZQuIx+G..SLeIvXMLirXvf7oQLwijRou4mU4CfdBgn4O5G9ipbgujKTu95q6frnDJmuFfyFKCzsZdLD.P8cUvyyyOH++Sc9Ffs2CvTKCLmEvR+K0pM+hKt3TTNJBbMnuKXs+QAW+d+fG7A8+UNqyx+88m8mM3W9W9WtyAt3Cz9o3+La+v0+9cxCzeh0edxrfp+uqwO0HLo..g5fXVaA.qRLSnJXTBd.4FZPbBXjyvi1b+aGRhguTOBYP+7ltVt0sbK2xDNR.w.HhErRB3NsTJm6l9Le14PYLKJizLywti63NLeFyA..ZksSd8TEf1mLUUrcVzjbHQFJSxu6286lD.IphbQcAXhxMPPTG3NVzx5WAqNBqCeitNjwJni6wfMBqj6jUeNhcPQYTiFkv22O.xnTDvSTQZKvDHr5pqN95iYNhRohaGTLmhEObD.3cHkZ.bQ2B4Kz6bNmW7f.gQBXIyjAAHXIdP2TS8r28tmhYN8i1tcx69nGMhVqYKKqAG6e4XgPfsEVFssgcmBEJzG.iHRPF3slcJafYXGdFfxyvLOS5z6dlm4Y+LmA.yblm4YltTI2jyN6rwDBgkVqIvLmOWNeDHrgBgzifk2DBfnuYCsgalXrsVN3xtrKa..5wL5BezonR0VoTMEBQSk5vapTpMAnVLysjBYKl4Mkx0ZvLWuvZEpSDUe+RYckRUWoT0TJUU.T6Ftg+zFYssaN27y29zO8SuqTVnKQTegTLzwwgKpKFSWTOc2tcWTHD1kKWN+e1+y+L4eym5SIyBjCtXYfJyBfjah7QQC.bBvqrx4wqrxYGHTmS1w6wP8ePAi0S1tDvVep+wOSG.LTcHUjO+G8yOkRolmYdIwZhEUJ0bgKtUA1ILNPyRQy.DY86YcqUVYEK.PECv47OGFS1wDKfhQ.PrpvNApfjvEoAxmFvNA.rdIWwKwG.CVZm1jbvD56Et.jNuwUafKLoWCPqt5pQHaJdNJWPWXoj.HgimWhQiFEqToRwXliXam0RHDVBg7wTrjfN6G1U+QLYt2SVg10F0KuwEa5YJFG0EL5bsWyGbKl4VJsZKsROYGY25V+eeqsXfMzEK1PJk0+3+0+EU877pdXktJ.pvLWF.tR49bAP42063cUUJkMHl2j.ZFFmt7xY1JW1bc2ZqsFzqWO1xxJNyzLbIdgW5kbIFcLHKlKOxOEBfx95XOX8siiBdm4ndHbCmtX.pg9.q1CHi48XSQr2xlntetO8M6IDhH.HUVfoIxdlC7bdNy4lMfVNGQMGpfY.vzOzC8PIgMh8ad9+l+rTTwH.fitGS5Qe32+Glg6DtLzxn+G7p9fifY8hDkM5LxzVVwSkEHAJgn.adphY9jIl5GB+5BA7v9s+1e2Q.P7a3FtgDDkOoCyobsQpq+5u9j2vMbCgBY9jelI.iaQ.XzNh67u+Kib4Bzfo.a2j8m77R9AECwiHxaiM1XDALhlXNMhrBQbUaPnELwFMAPSl4l.noVqaRDs0cbG2wVLyMe4uxWYiC9UNX0hZsqVoJIkxRJ0Qpn055DSadtOyyssPH5533zUJWqiVq6Q4nQu3W7K1RHDIKp0y85esuwLEKVL2q+0+exFvd4J.y4ZGHHrq+XDF9ISv2+nFDnLDXuCVEnGbQW.21.nUYX2hHp2H8H+ie7iGSoToKVr3TLySci23MlxdaaRcJl4oxBLEfLcMfjEWXr.18jgtwFVrjnXNDMPTyA.7zL2+pdWWUa.6lYA1.UvlFzkftNFZLMgN5rGywaAPEJT.YL+6iEm+fe1HkMEha5DIRLS.MRRoU5XdddQ.M99.i.gL2bLHeBvm8Y9kbQW.TJECNbsSLfYzWJWqC.ZKWS1hHp09BbQuhEKV8rOmytBfUkla1pFaPc4lDQMEhBMAiMkEjaTnPgMJpK1HKxV+23232nN.pxfpdDkpB.UwwwohTHqbdOumSU.T6HE00EBwFfMMHinbCCtWNEQz7rwEMx.i9rLaNfT.4Gi5Vr8ZjSjOv3+LD.8bvhsLBAa1lG7fe41vPCcNc5zTylMME0wEIAxNU97OyYAvrEKVbZ.jN+iU.X+E4Xbr15.QZj2nAUrhSipXr6hP4n3YPF567c9N9.XTIr7.CsuJ3sG.FGGDfaT.DWgLIHhRfxHAfAUzA47EMHewjv7t2LG3YefYfYSughGYHMIrD4EzNtJw3hkXdtvA5AAn.DYZhoCn6UWCEInV+J+J+pMgQPK2TJkaR.aPLuI.13Q9gORC.TyBVU3sWyrL.E5tiUAPMkR03qbWekMeyu42byCbfCz71uy6bi27q6MW2yyqlVqqKExl5hGpyvgCGQLEE9XpCdmGbgd85s7u4u4qNK.VF4w7.XJwXmVy7QBm55D6LtyfbykP+Ewhg4C21wwo2m9S8o4C9O7OD+bEER+Uu0u5LRobFl4o+BeiuwTvEogCRCXmBnfon+1H9d1yd9EoPWS.vZkIPlL.hmCHNfcDhH9o9ze5CAPWTFcgyNzXncrw6iiBTC.K.oUVfHlFy6lHnQCyPDMKbMMdHUpTIJUpTLoPFQq0Q7FMxRFlmFAJnwj7latoePyq71dMyfhMYgQDFSC+dLnwHGQq0FGBs3g1nXwhaxLZlLYxVRobK.pkTtVSl4MkqI2fHZi0BbSzhEK1fYtN.USJWqgPH1LRjHaM8zS2QHD8.idhBh9RobjRqXkREgHJoPHlA.KbZm9os7+3+3+n8Mey2rswttcVF.KTEYlBNHAbfkT9rvJqbdl3oUeLwZL.72CvHfE5WEna4rn8RKsTW.L7rV9rH.j7o7TdJyJWStvK8RdoKEnuUy9zNqyepin0Itxq7OJ1K8.GHp990Qd35e+n.HpCV7wihq++aFOAfvjBw.JF2FHgqwNrhwrgPoHXC+4M5c.CylUlrCuS1I5ebmiXBfo0FA8Y2HG1K6v6cvfAqVqVs4CD.p1qIDMOrVu4a6s8117FuwarA.pJDhx.nTFfZULHUnmRoFFHHbmRW0dRCJSlDFYIAPptc6lNUpTFspHCr.AFkMI97b2+ys+8dn6s6J.cOQAzCEGihfvpGFXauFKjM.cIghN3h.XEl4SG.mA.1kiiyR4ymeJsVaPGjok4lp1usFXzAFt50rRkJ0yjISYhHG49kNnDphsQySG.z0Fniq42GXr0Hi4BN+yyLO0AO3AidQWzEMJ33GZeaa.fMVBncMyuOgEQLXgznJlmMB6j8cdm2o8a3RuzrkLhXWlQiFsL.V7Dm3Dys6cu6TNZcbe.KXb3UeXR5yyITp1MPA0ZhpIyf2N9XeRo2QLBsoGCLj.2AfZ6551x11tM.5ueozujMh.WDSoTQfYR9ADy8VqPgAAV7EBEHVgPficr0wTSkJpTJC4LdLkREG.Qt0a8Voq9puZlHhUJUjkVZoX0pUKpPHXGGUelolBgnV+98KkLYxSlE3jksgCbQif66iioCDApSUn4Be+LALThJK.V49u+6+zN6y9rKzpUqklYlYhq05A.ntTJKA.8gNzgJu+Kc+a.mPDGr3Pf5ixC34rsPcdph04SziI1Pydi.bzXBfj57XJ3Lt6EoHhhA.1zA.oQA52N1L7ZbRz1DpCNyvLOOQjM.JvLuJ.VEFMjYAOOuoGLXPpFMZj.fhID4in05waxaGEKw2mAQLGTrDrMRe5SFATNrC9gu2Sff2M+Iu4AulWyqoqTJGXbtDyw1mYu+rO7Mz+JuxqpC.5VnvZ8TpiDlXgEQTDeeeqBEJvvfJElHhBJPQrfN3EUoTb5zo8VXgE1gFYn0Zu74y2ywwo4vgCqs5pqVMGPkRghkYAzFdnGb1QWDl74xo98Qx.DuRfH.BfkN+y+7ybvCdvEhFM5LvzUQR655s+y8b6SFqstsVq2RHDsN4IOYy8uqcsU4PQ5cULHvdxO0y8+dGiQXB1YgzGS2p7.obBPDGy772vMbsS+Nem+dQ9g+ve3Vm9o+rqATor.ntdUzFG+wfbxSEwfgBc8B.HCarYSa1HJhyiIT0eNPXtCPYxbsa2N8TSMUbW2RQ78YqQiFgnQM5j2e+cdm3+vEewSlLTf.Zx9Awef88QfHJFDGRC+pe0CN3BuvKrGybWoT1UoTi69IQjuPH7rA3CGHzvLQCsfeGlosjRYmhE0C.7w9KTHlqMRAWLkRoRFHld9.vaMozKvgRrBmarRkJcylMai74y6d228cqufK3BzXmnYJTzB+w0E1IKpQjk.hWaYLMphkgA0jEbbbxIDhYUJEIkx1Ly0HhpwLuQ.cJCSJu2C9fOX2y5rNqwtLGdbRT+mSiSEYIwsMb7OzoBRAKDGk.8O8O8O0+E7Bd4sApr0R.cpsWzeB63FgGmLF81YxFEEhZWA.1M.Ni50qeZKrvBRsVurPHlUq0o.PLFHhocqAwUgcZMzFuLNIAQFK1cxNpN.l6k8.yckEJLPoTCBlyava8s9VGdS2zM4CCZSFO2YP2LGBCxk5EhfN.y7p9.V+W9ses.HE9y+y+yoBE1eDs9HFWLiPbvicLQykGy9NNNCymOeWhnVZst9W7K9Eq7VdKukRvTV7JvjmQnsYdpyoL46wVXUDCCwTPiEAPA.6cyboUzZcFgPDiL5K0l.n4e2e2e2VuhWwqnqiiS674yGhV1IQ67jBy4uHx+bxbMSfsmWZg.5LEiHxOKP2CcxS1dW6ZWag+3Ryw...H.jDQAQkbnErvVPitlq68.f0iI.RnEHMzlhgDr4pkXlWvlnEJaVSdN.rz8ce22RG3.GXY.Luuu+TkJUJTaWlj9HfHhJWtLxlMan.lyL.SjQXfAFqqclhoPXH3w4mZPyCy8HfA+Se8uwneqeqeSVoTb.hQXhngAnjyDmF77OP2TrDBgELMUL5gUpnvfGAOhoADYP9YwhZeoLuUNhh4ZiXvEwzFAWuO.Z544UqPznUJYlaKTT0ahchxdfsyI6TGVqBD63lmMKhbHO7QA1ksCVaHBQTOafltYQcTF0fIFqEL62Z3x.CqNgC8fe9iroIW+KDsoyGfvjzA6WbDLy6uwDW+StFv1Gm7HFbPxG3AdfoeFOiyeQfJ4JUpzJ111mFLHlytXwhKVnPg4.vLZkZJPVIXliLZzPq3wiSS3rWg4Qbpq0PgSpQa++OfA5RfaiIn8LQT+8IDdt1HxO7a9CiGOd7XAG.O.L3ce0W8fa4S8oFAX6oTG1G.7m7S9I8+s9sdUdISlfCDb1n.HgVqSvfiBFzBKr.mNcZRoTQEBQBGGmX9L6G3fSMEBwFOymxyr1W5e7KUcMorraNTNX+WgHmqO.FkGXjyN0.MfctGfXX67yVD.KcnCcn42+92+b.HEybj.WPsmVq6H1mnGbQmCbfCz49tuSzBnT6r.8JuD5YZT13Xscn4Z+zEB8jiw+dqv8DIlUzG.dtB3AmwIZALwjwNYxj73G+3SCaLctc525+jPXB.fuQPFs6lCXKTBMKUpTypUq1Me97iHhh3Cj96pKNO.V51tsaaYl4E888CsvvjUrsMBGXdD6bjxvJ7ep776IaUCi..VFFquBHG1ZqsrPEDgcGKJaV26gtW..+SrH7PwG2D7nJYyx.fobDa5CIhvF0NOM.RSDMkmmWJDX6VvrXQ3CQiytXTu9wS1JkRPVTjrYyFiHJpbexHOqBOqvDq7Af2a7JtBF.vMSFyBg4gUlf6yLynWud9Lydewa+18t669qNg3QN97LHuQv05CrX+EA5g5nOpNdglH.H4kdoW5Lt4P3BzKRDMmqq6TVDkPq0QY.KoPPDMdAVesV4OgcXNBfGF74aH7GCkytDQsOhpnQDv.ZEzc9lRor0LyMSG.zWJkCKYiQumW+6ouRo5RAE7gHplnPgZe6ie7pJkpNybc362v22uA.p8Tep6opTJKqTpxOxirdEsVWSJk0.i5W9ke4Mbbb1vyyaKoT1819Lel9zXWzo.dfG3Ah633LShDIVjYNS4rXY3h4QnscNQmD+wPQlvmSgEopM.ZcNmy4rE.587ddOO9889dewykKWpRkJMkVqSaCjd+6e+oyi7oO4IOYPWgpGA.ji.Lp+ykIFmrCrQANZLDlrloXIgNayr.HMybDoQjR6ikPuEe7gesE1YxZDYTh9H16rPJSAfopToR5FMZjDDhAvQTAEKIbDdgxLybP7FYb+nIK3Xal4sjRYKkR0DlE4pCfpjOW907ZdMkAPEsRWSHDM992+82XiFMpmJYxJW609gJWnPgJ2+Cb+U91e6uXUoTVQteoqTJc7guZ94m+j.3jZs9jRo7jhy8bKZ.4hVwLqUJUQe+QEWXgEN4e8M+WeBsVeBstXQlYGgPTE.aM8zS6s5pqFmYdp+tu9WeNf7yaCLKJhTvYmN0xDiSsaY9.vqxd1yPXb9l1YAZ+09ZesdQhDgYi9ILsPHl6Ysu8Me61sm6RtjKYNl4YDBQZ.DescsKKiTKkyeIS2LmLF6Ih4qO02G1w7PyALz.wTwnftya8Nem+dQAPzs1ZqH.Ur..oE.33+DNS4wj3uhA.ttOz0Al4vhrQH.EdLyIIhRwLmB.wqUqVzNs6DA.Vdd9ThDIFWrDF.W7kbI..Xj2HNzwG.vHPzP4ZqMTJDCshXEtI1N.nkTJ17BuvKrNLIx6nTJkTJKBfh.PKDBWkRU8vph0O1wN1FFMXBUEhBUjRYU.TqPAwFEJTnkqMZCWzVoUsHh1zwwoNybcoTV6Cea2VckR0fYdy.MmnelLY7C1f4TOsm1SaNX1n17XmtAziGphdbiwpU.iP0waRuKQT+jISxJkJVf6eMEkilIqoCjyAfETJ0BAm24Nqy5rlZhy6iWr8OOFmxbaHVFfDkXNEvxoxBjBkCQzDvK3E7B7.pL..CpU.iBD.6GybvUrsYlYeLG7E6rPii2r7byMW37aow1hSZHUQ8AYPShEY4AFgVIcnqIMNWMhnQz1MVomTJ6Bh5nTpVjgm+UYlK+49beNGoTpjRoRq0p0We8RJkp7m6y84ppKVLLdooVqaKkx1R4ZasOgnoLe9Z29s+Upb629sWtPgBU9JekaorPHJK2mzQJjJhHkPHT.PWrXwR.nh3bO2F.nsRoFwLG6xtrKape6e62xbX63sPwHcR92ep2GM+8iCOnGGm0BvcS.rkPH55aTC2Xvji67uhWwqX4QiFsrmm2xvrYWybnl6yaqIc+hI+ySEISS5bgog4dRBl4HkA3csqCLLCPOTB8fdLkRXf0I.DQmAwXEGZSrwQ.xRHhh5xbD.Dkro34IJ0ANvAlFl0QSscwR3w2GHr85ng5XhVqAXvRgvCLFd7ie79fPOl49Ly8Agdm7DmrKLhy5lRorA.pKKTntXs0p87e9mWMkRUl.b0ZsyW9K+kTBgn3m6K74NQwhEOdwhEeTkR8CAvOjY9QEBwwAfRWT6919f+QUkx0pslTVC9nlTJqJDh5.Xiq+5u1sHh54lC8gKFoTJu.WvKpVqSEMZzYcXdwa4VtkkfMVvd642lDsGgiGOT.vGGvCHS+k.ZiRXSTFM95e8u9lLy8Hh.ybhRLOMJiYqUq1rHnfDAec1OwW7KNM.Ri7HwR+7WCc1tv11vxNnnsSzTcF.depO0mZRawsO9WSxFLtJs0y3Y7Lr.pDA.wrssiSFQEd5QiFMaznQmUq0Sq05TvxJlPjOBQvJd73D.fVaLKjfB.OFM4Rob.ssC90kBPzKQTKBvfPIxpgTJ2HPma5JDhAt1X.bQuDIRrEQzlDQMHhpKkx5+IW+0WWoT0qU6nMHhpSDU+89deuUO8Se2UDBQYsVWY80OV0icriU8U+pe00.iFDQatwFazTqzsu1q8C1867c9NcO3W9t5HEhtRgr+e6e6equuueru+C+8m408FeiK+ParQ9q+cd8gVa8LvL2BA.Om8rmSs3+SdOMLmmcT3v0VasH.XpG7AevEAPlFMZrjMvbBgXJ8QzwUJUz669tOxv3LfxY.U76WbL8Owie78+W+3mVHAtcRlZSBlrgypitoa5lXXfQZbToR5omd5o4R7LkBff2J+ay6lmnaYt8KYBdaN6ry1b5omdK.LrQiFVDyodO+dumYYlWPoTKBfEJr+8OOPtYAvLv00HJRNHQsfhkj8wufIO4YrGvXQvUC39FPI+omdZFSjLEybrkCuGV+TdYXO6AFpuCFkK6CfQbIdn1zEnH4IJAy7TLyyLZznoJWtbpACFDBIYKS0W4I2DwHPlIT.vvO1G6iMhCfCoTJ8UGQ4ekW4UFx0Nf74s9K+jeYyBwUpXJXkChUI33mmHjLYROhnguzW1Kq+0bMWa++4+4+YC5XxB333vHKXmwm+5ipO1wHVbH.FQ4H+bDEkYNE6vyBfEJUpz7kJUZF.jJV7DiWbPo0PHDLyruYCEzHkREZeWC.n9fPOhHi5VaTs5sN1wd3V.zlLQav.MjRYc.TWq0a7TdNOkVqEzUV0gUi9c+c+cG.f1BobShnFBw4VC.UiFMpQ35HpNSTiBEJTOmApmUTJkqTJcSkJsKyrawhEcAAWw9Dt.9UhDIRM.z3kbQWzFLvFZstIf8VWzEcQ8E6S..jzwwY166+26KbCGFW.Xt4h.TXxMU73tXL1ldNgh+zl0qWeq6+9u+9+w+u9iQoRkhs1ZqkTHDocARWrXwobb3o10t10THKRlIzG10+LWXwl7XaRxyFQKDJhvZLELHCY1O3G7CNCYamlYNZ61s8QkJFpUTCCpuyNIDVg8IcKCi3yYiHHKhVh4XW20ccIAPRee+jdddICDY2PaD1xsTIJe97DXL1dpCgOLYPyzH1Xwo8fQ.VaSlD6Zq05sjRYSXJVRYlYGlHsbMoC.bERQE.T6bN6ytxdOqyxsa2tN.4zJkx4heMWr6tjRCLhKAW.nKHJTb5om9jEKV7jBwZmD.J0QNhlHR466qjRYQgPTb+6Z0h.n3E8huHkPHzBQAGhHW.TkxQat1yastAcgKwt28tm947BOyYcMKDOEPVyFK26Nl27GW7kOVecCGZqidkC1PqMQC+9e+uukiiSRsVOC.lc5omd167NuyoaznQZkREGYPzJiWipDpsy3fmnG6XC3XOvCEf2li+2z9U..YzFlH.HxW8q9UijK75QO9Z6G+w2AbwftdGH9eidWui2kwAQL1q7P.3c8enqmfI9JI.RNZznDKszRwxjMiUfp9iEWbQNbfweEbzHQ4Pjc7s9Ve6QDQiJdnCYh8XiSevABEbwhEqxLWlY1gHRekW0Up.PQ49jJhHsxP2lJDYU87O+yu5gUpJBgnhVqqBf5vFMsCzEL8Qz8UJUmkWZ4lBgn1W7NuyxEJre2OzG5CU9EbdmWYl4xDQUzZcEhn5kJUpEy7.lYRJkoTJUXh9yhLXZjGIQ9+OHoqhmRweykqyBKrPnUmFkYNEbwLtLOO.VxFXYoTlA.KoTpIKTSTj+WnhjHg8.KrDhfbHVEfDFZ.VMU4.KtlYN16889dIjC9KEVfuhOtED27GWWiUVtIXcPQ4r21B0mgYdl.mOHM.Rpczw.Aqd86BDF+yrGHxy222CFds6CfPTkD5TEiXlGbEu1WqwAu.0RoTAz7BaHDhZLykKTnPorlBzoKVrnVHD5zoSqYhKcYW1k45agJx8Iq+IukOwlg5DlRc3sbMTqnAPtZZstJxgpm8d2aU.TFkQo2+ev62QHD5fXWcgBq47Y+reVG8266UlHplPHZIkxQYxjI1G4i7QCQ45jEv3mzFIClinfINKO1JiQzGaUud8dkzk3fmOyq05kuu669xFMZzrEJTHC.V9G8s9QK5BLmRolNfZXiy2Zhy6OO2H6oVrDCc0xhTjMkfronDQ7fACF.3zqh4cq9AhJrO1KP9vFMTAQCPzow.AxkKNQ1wYlMTzwTruobXdJ.jtV0poxjISBgPDC.QhFMlkssMIDhsI.VPnLyf4fM2533LBfFr5pq1StO41BHLiN6ZW6psTJap05MKp0Mjx0pe7ie75pibj5RorJybYgT5vLqeOug2Xwq+5u9hO6eomcwBEJTrPgBECJV7Ik6WVD1Po0EcDEDtWwU75p.3V4vJUkBEJTVoTUPv7ee5a7F6jMa1A5CqGMb3vgBgnOQTOhnAR4Zb.RsSe4W9kOKbwbAqgltv1EK6mzy6fXtJipYV6rIfc8m+y+42.FZSNzwwIpVqSCf4e5O8m9h111KBa6EYlW3du26cteseseMy51NHYse95RSSFSaAWDIvskhlynGiV.FjXboW5kFRMz9XAL.64eMzIXa9R1wEfIZPQ5RzoSmTVVVoMBgpwUGE4yGUErtI.P974YgvXdESkdpf7eoPTsMfMN1UG.NDUX0Ylqw.0jRYMeeTC.MJp0sHh5o05gpCqFoTpdvX1CM.PMgXeU9LelOSHUuprzRKUF.UDBQYafxJkxUoTtBgvMYxzkld5oKcq25sVhItjXehJc61otPJ13C+g+etgnPgFWwq+00nXwhM.r23c7NdGsijOx.GGGq65u+uOc61sW324242IzJkmG4xkFHqgdoqu9olSV3XxFOrMEjsg2ccW2UDjEo51s6b.Xgy5rNq4cMqUjVHN2jRoLd9y3LhlMLu4JHRgBEBe+OFVXgn.ENUJE++0O9o8CwNmzcEDGmXrMPl.FNaNqCvrqu95INiy3LFRDsIxiJvA0C7Z6Ssium5wmVEHd.jzBokySmc3mF.1sV6rL.mJ35XHLPQplgW0J09kR8gTJGoTVAHaSfxcdGui2wvq7Juxgq9Ku5f.gQ5TEkreQNF2gok.hWCHg.HkNPY+APBjAwQEParwF8me9m9V.NFgQcaH4FBwq3YAR4FzsRfLIApLELdWdVjEEPYral4UcbbjLyKEXSUwBRLmw1uH0Cf6RjUWw9D8TG1L4.LcFu7ke4WdoO4eymzYE4J0AvVZsdfPH7xBLp7Xn.Zy.tQfMR.WjB4PJTBoN+y+7Sdq25sFy22ORgBE7O1wNV6idzitwa5k8xpVAn1B.MaXp.JCiH+MEbQF.rBP1mJytOUXvFpvQqWLuPLsiVG2mQToTPgeVHPd9j+HhogjwVvFOobkJU3LYx3SD466ydVVT+8IDccA5FXkqCXlG7vOzC0+oblm41TkvGz91+9nibjivrgdE8+gqud2joS1QHJzIGPuRiEWTaRoNrkbeRT7vE8J7rJ3gRXxhfEG.Q+A+fiE4o8zdpQTJUDoTl3XG6XImZpoRvLGSJkQnbjEWhogCGRUpTcnPju0C9fOXkmwE7LzvEEgA9mafsEWtebP9MLVKDRdY.vt.xsmgCO4YTwshv2BIsLUQu1Ye1mg6+7+7iTA1XC0gUaX1nucKGmuWm74y2Cqfd3Dig84SjPZemK7tWXgiFJflKmDn5TMZzXlEdZKr.bw7LyoIhHl4N4HptqgKx0AVrEP8PQelBNNicHJLtCa4lBnzhLyBhncaCrGsm2tsrrDNZ8bLgTvmiJjRKsRCxhn.KaE.gEKgAyjuEAO1vw8dLysYvcHh5QL5KVSNPeDcOee+tEJTHryJ8JVT2uPAwPcQ8HQAAd3G9goolZJel4A.nqbextnrA1i5SpG5S9CJTnPeXigv0rYFowMq3.5HDZs3QAJGE1vBt.HG7MZM0NolTPRtwxADojYyP8cbb15HG4H0ujK4RpibnAJ8XDvtebnKZLxI.VJAPs4PdXCGrBan5TNX1zRDGGmd28ce2st7K+c1PqObk0DhJk.ptLvFU2llFCvdvHr9iKcMdhHFKLQxH4Crn5L.opXtFWB.YYyltid1m8Y25AdfJkAbMB6WAzFECsXycfBlf2yJDCnXBrLRgpXFjEKd8uqqe42467ctbvwdI1PMmkAvx850agM1Xi4ykK2XTYp0ZqYlcFp0lMYXz7UHjhP62jo.GVJXtngJkZHQzvJkKOb4LY5yD2qv9JzUeDcaw9DsUGVEVvtPw0tOPVOfxTNiHbGQoTg2S7Zs4l8eZun81GkvPkRgtc6FYO6YOItwa7FikLcRq+yu4+yL.FoTpQRozKKfWYy8CKfbQ05CmPrOQR8gJFWr+BQO428jd6ZW6pOyby67Nuy5W5kdoU.xTAnR8f48+IQMmsQGVFDCUP5f6k6B.mtmm2JkJUZQh4nfr5Ijh1vFcUG1XO2Bgnya3M7F13u5u5upJFqaAK0BnVnfINIEM9YYtAOl3jL.IqXPIWJXrNzzrQOH7Ih5.fM.xtAP41.iE3wv2COUDDXArXLf5oPdr.bf.Ywddn64gNyy3LNimpqq6pvL++z.TbgHO4ncXFreHcaFLX.GOdbeXnr5jmCfs6RYfyQvsDhBa8nO5i1Nd73aQD0TH1WSk5vsjqI6nNrpubM4v.gEbRJvAfrLfwZ1EBQjO6m4yPOmm2yyiYdHQz.w9Dg+dT.UFIoTRHK.JCqfeuPjrNFsf.1w1yYOS70qrdjK4Y7hG9AtlqYym0y5YEpXUUv1Tk3es4yrvdQTbTjD.K.jU.Td2Ly6F.Ebz5E.QwOxg9t7Eeo+Z8APWGGmlv2uAfUUQAgKxCW3fZXQzD02gQD7yKZfE9baLssAvzO2m6yc968du2ECleKIL5bSqb.UCnjoQ792KFFflIK.6X.tg5RxzDQyBfY62u+hIRjHb9rkHivu9+G68lGjjcVdlu+9N4dUUWUWK4xYo6VRcqVPK.0coF7XYgD1XMBD3.KCRvEY.4wN3Z6qXUBOXrIb3QDgwHIjXwbGSXFVjAT.CvXaPHYtiwHKCCKc0RHodvVqc0m0LyJyrxrx8LOe2+36bxJ6RKtMHLybi6ITFk5tq7r9dd+dWddedVAXk1cZu7ryL67.4bccSJhDyf3SNoDhwoYzHQLIoNAzSFUz864dtmAWxK9RBi9lgQwn0TnDjfF.a4Za2QJDQDmegdP4ATfQTlQThA3Sbh5SwYFkDfexRPFeHmiiSVgPjJLLTXYYERAFSYDNNNY.xYZZN6C9fOXlM1XiDW5kdohuvcbGbUu1WKBgfhP3Qu7KevW8ttqlCFLXiLYx3SQJS.0XY1JZDFhGi9mtm6S+rZNzYI7nDfQmNcLxjIyhZZZYDEEijAxVBgnoss8VVVVsKBcCh3Bv0We8F6cu6sEKSmni6NGQims1lNdsohwzJKXOGEXAYfbdQQwLx.olPHFv1iiylPw1Pvzizwz6qDJR9lY.lGJTPJCLA1unn3bkAxyw000THjKEFpMyL4xjtaudSP.bD5eTMyRvXPL7S9I+j8u1q8ZmD2pTRnPv.Ah9FlFccbb5FYa0Gnq4plcIfgtttHknEkiwXIx9lFlJoq11tugkU+BP+xS3LuBg21s8dBe6+Iuco6wcGabXiwT9zHm6T.Y9e9+7eN8y84dvzNNNoLMMy555lRWWOkmmSBccSoiiCQibsv01US2TWqd85i61s6VUpToxgO7giQIZvBvlad5Dm6SUN.B1lXwWDzMAuyoRPkyYkBqX544Miqqa+idzi1.XCaW65Zn0zvvXSPugs82ukkkUOxynO5ezGc30ccWmBQwbZ4V+uEnO+m5aOaP5TJmYGfQrNCXum1rxM1UJGKkxvO6m8yBQu7TziDvJIv6Lh3W4ja2EbELH8K1.XSGGusLLzGZXX.SwCABgX1nNVsnOrTiFMVDXdnbNfT+k2xsnsu8cQZQG+3Ep+e4pDVTGUwEio+qETIORoTd7iebLvSYHlGImH5eep.kJq30fn6yUD.IJ4SNnvBx.4Rnfj77wP+N56ILLLhFEHkgtholEikR4Pm0b6IkxHIMUVGn927DeyM0jZsojJ.WCCCdnG5gzJCot+6+9SKkxzPPZoTlVOPQzSROo7i+I93C2v1tuggwvH1vGOOuD+p+p+pZUhdVTmEmbd.HInT7BnTfxwrWeNfYjBx455lVBIQflqqiXokWVBDJQNVHiHuID8A5KQ1Gn+gO7g6aZZ1SJkcEBZKCkaslsSSGGm5llWX0Wyq40DHDB+C7bNnmo4pAn3HmFRgr4ZqsVKohHw1vwwo74bfCDXXXU9Vtsaqxw8rqhJg8JNNqU0zzrVoxzvx5nMwW04MTAiTw000Gv6ReNGzEvKh2P7O2y8bcymOuskk0oDBwo9v+AeTGgPDTsZ0pqZZrIJnOKk9xTTfbrc2qNS5X1NQYxVEvuUxjI6napORHkIz00mAX9G3Adzk788W42++vevxupW0qZQf4KRvr555YQmTrNIYomT2Kd158osC9+D5wHBIkTpPuzhOmESS.IUwWQ3Farw.gPzMXatzoOTKNnT01I1tPUR0HpkAHSd7yfxORNfb9a+ukT2vHIRgFBgv00ECSCgttNqt5pnqqOEK9KBEBFiPFi7t9.8rLs5tpgYWIz08Xtc+Q+y+nNVVVa433roiiSMfMV0xnJvFFVF0.1HWtYqHkReSSSWSSSWJWzCv211t7p6wnrkkUEGGmpm5GbppThJlllAnJZVYyHDnnrsJ6A3+5uzqtrss8F3OYVgiCZe.vvhBw.aa6AGywYD.BQoDFFFouhq3JTcfzuzDHaa8jk52ctMkM1Fi.5WxiNfdGgPzqZ0pgn5PTFcc8bWy0bM4JfeVCCirGywIKP1pJDsj8E9BuTE7seD8eZ1grIKpGo6LhJrh.fOvG3VAfa9luU.DejOxGSqHApyCcTgn7LtojCwBUIDJLhxz+FtganiTJaeS2zM0FEo+NrjPHEBQhrYylNLLLtKzIhQWRqlsBQHjkJURhXaBEFHTJh51ufwNNNi.Y+Por8JJo2shPJBbVywWJkAq+8WOdzZpgpfIade2280nDka3dJ2M9A11UApXZtZfooouPH7eNG5PUwuXcTcRqyLyjoqiiS6q5ptpMekWwqr5wN1w7AbMMMcunWxKwc8d8hUBGWGmi4ZXX3VL.eCqKrJAz3AOwC1AX3cdm2IuhWwqH46487dxTjJYQoBB+KAe7s8wrODTAIVLhIiKAM0zz5XXXL9wdhmHogowtbssWtX.EPweLKKkxc+e5+z+o4rssi8YlZA1XZ6qc5K6m1wGHrhLjJq7kgPTJNo+3IUXBpO0o7N4lpc1AwoHm4Z.jXEOxIkxEnLKevCdv7IRjXIlZTnLLz0bcbiTeZjG9vWhDPdVm0YIAvzX0niiHTnHJw9HhF0KAaAroggUMGGmpoSmt7q8W+WOPMdWqEXZZVtP.aXZdg0HfFnZ3xF.Ubbb7A7JP4.TchsFP8+cWzE0HLLrgooYcCCi5DTpAPCGGm5G0zrloo4FNNNUcNtSUJo5f6O5g+QA.9BgvCvUJkdPPka9Fu4Ft+.6Ne7O8md7EdgWXha38bCwib4SEZOdp1jQEKXDPuBphU098+9e+8DBQ3s8Q+Po00020wt+6a2+6ekuxEEBwBRobdoPLmgkwL9NNYK5Q5ScpSkhZSFM7yjt9+SC6uIwJJkRs+w+w+woK90fpUq1GnqOEigpuJwusG8KJRfZ+nGYbV.gTJ0xjIilToHiYPwedSFSjrYxNqmmmRAdPpMYIy3SJAJYQOb6hkDMlgCkQEkyzzr0kbIWZ8UsL2PHDa.rQXX3FJj8ZT200sgiiSMCKqM5zoSYaaaea60bAbsWy10000CeTHEQUHHEGYAczwuMPye0eqe65.UMMORfggQfllV.PUJWpNJYsdqkVZo1111sVbwEabIWxkrgsmcvU+5dc9BgnriiSsi6Z25qc22cWfQoSmlG3AdfT5AjAJlgMlXyclPn4aifNukZQTwE92+u+mGWAGtH...B.IQTPToShDIjs61NKkYwvvvBtttkrrrJBTHPU73EA10d26dmAH8BaPBXeOU90d1v15zat0zjWM1oAxTpbz6akIkPEGUHwna.FtOBdlKbXfBgIkKWVSmxIEBQFQIQNYfbpQISjRHjI61qqlLpXI.wEKA.oTRXnLL723Zu1PSSywQba4PgfdBonMRE5RLMWspggQLI.WmfBwxGbKgHbKaa6sjRYSjT2wwoxuyuyuSfgkk++w2y6w+3JeZ9.d11q481e6u8xEBnpgwgqS4Sy+W4neO2Wxy8fd.AQwtEHkRe.GCCqSIDBGTqmVtZ0pUW0xntPH1ZwEWbnPJRdAWvEDyEIK.L6l+KibtchxjgEwa.vnwiGKARnqqmQWWONuhbVFGM2fACxYa6kKOd4rrrRSQRREDW2Mdch0We8+sb8x+Mc6YqWPlfJBX4TvFogkxBYmCb2ETbWTJHmzSJul2z0z9y8Y9b0ApsDzp11cP5YpCkwECY9U.8pv9eaW+a647G+d+iOucu6cuWGamkmaWykaqs1BIzS.sVasiW6U9JeEUbbb7srV00089bLLLBnH0HP048HzODMRFGZPjRO7yBhda5q03q2Xh3JFYI4jRYlRBQl.HIEHjxqzCpFC401rsLopA4SCIxA94fUxRgpYoL4PmciGF2xsdq66c91e6mCvdcccKYXXrjmm2rKu7xoqVsZbgzhYF934aKpSQFSRtKJf6p6e+6eiG8Qez5aWc37xO5G8OR6JuxqDSyiLhRkGiOBxSJYYYVgPjtHnELwgZojNNGKUylMEKrvB8i5PPU1ljr5CHiH.u4nH4Inv4.kOzW6q80N+W1K6kcPMMMSfEbccyr7xqjXiMpBQA1gPNFICEBQeoT1yzzbfqiyHYzyZEfDjiAwPSSi9NNNwinRShVbzwwoypll8OlsaegPFJDhDc5zIYtb4RDIwbcht96.E5CZC.+9Pogj2eLUPihj5yeae9jWzEcQjHQhwVVVQx10QA7h6p1jwtJRJHiCjSq.jXMa2zll5YDhRYjR+jBEOYzGn4C+vO7Fm64dtUfRUgzMg06vSe0kis4z.8Lf2tfBEgx6+k+xe4Om+h+h+hC.TTHDoz0060oSmFmyryVO.pSQpRPwpPPLIS07q+s95se4W5+m8f0mdFT+IsCFmVA.KBICTvlOEdlYAmbDwsHpNbULGTNgTJGHD5MA+MfkqwxazZptp.flNjxSsHvrQnRIGjOGTYNoh.g2yuvuvuvYcu268d1ttt60vvHumq6bgRRYZZjvyySL4JSnPVhDjKt6cKazXynDYjSyaIsLW0bKm0bhI7u3NxtkTJZYYYDIQclw2+FYaaORSSangREc5V.5cettCjR4XSyiLFJGSNwQ79S9QPkQfdz8aOMnfF4KqQERPDwzEYakzxxJ4C8POTpy+7eoocbNVZMMsz555wiqXJGGGMCCCoHRV3K.sJCMQmlp.2p8uFYhOIKRNpyR.lkf86IkmsPHLbccmUWWOTHJ0ABpST29uhq3Jpdm24cVGnkmm2VWftdmxmN4uF+77mTe1Su.e76eIuka4VRc8W+0mEUBkKW.xWFVjBjr7CtwVEJrbETxTZU1VhReZPXBIYQxPcE72KB6JHhHbgBEfxkjRotPHLkRodDAbtfmmWtHjEH.vvvP554JQoHhwGCIBBkRFIlBUf850qc1r4ZBxFqs1Za9q7a8qzx8XtcLrL5633z+u6u+uavu3k9K169u+6u6q3U7Jl19aDfz11Vdy27MKusa61j.iK.gG2wA.su025ak70+5e8w9lhVqnPen7PHezykJgPAIEKKIfDTjrDPNaa6YS.yzrc6zG7fGTHDkFAAckRYqW+q+0W+y+4+7JRF9zI.1oiQX56qZurW1KSbW20cEihhYgB4Aw9tnKZ+G3Nti6Xu6YO6YYgPjywwQHPLR2PumtPrUTmyCdausq26C8gtEO0i2Jax11z6DNyOUEl3Yismp09iGalb.4dkuxWYtu7W9Km3M8a9aN3ye229VT1nA3FiJhmYT3b.RxiPVJwtwGSfCdW20ccnK+xu7mKvY455VvvvXNWW2zS88lVRRkn3hoP4N45GIiLsLG535zQhnoFrggwgqAhF11Gaya3F98ZcGeyOWSBXKJPWJyvH9DIzxxJz00MzvXUI3GZaaGZYYMoqzu829aO4sca2V74xvBvv62yarTJkFFqB3KO8q0hBUshKBHjpbPJJHePJpPNJvt5ep9ylISlLtttiz002THDUfUJCUqCVsA6oQM2S8yoCPJdDqYA6U.1KvAkR4AANqWyq40r7G9C+QRF46eKSyUq43rVEf.yKzzEe7A1XEnU0Ib1vR8gZOURMrXGG6cVPreb11IBSlAJNKDLOnOuT5NqPIN.8PE+SbAUiQr5N6Jd1ScpSMyd1ydlEJsKozaAfkEhRkFNz1LYxjVsa21LWtbE8882MRlEwjD4zl9pzvv3zRpkss0F9XO1i08bNmyYKoTzPHja9DOwSr0YcVmUefwemuy2Q9y+y+yOjnho.EZ53b7llllsghsnXvVDPGxy.pnd1FgJTwoN0oXO64BAJO111djkk0NIZzInzpHj3X11IzzzRe0W8+G4t268ak8Ceqe3Lu02waUnNOKLDJKgRofvLiF4lx22Wy5nVivmtThsvWUbZSXSGUb66b8ym13z1mh.Xmg7rDUJYB9myvgCO6jISpexSdx4RlNcnkgQGnXKGm0ZIDhlggg0UiVonL4opRUsn6AfQOxScwV+Iw21T9kOjFbhDvxIQeiL3wrTfEnrZzwK.oCTnFqETrFDTaJz88z4uWYyVjYIPeIvyD3b.NOoTdPWWu8AxhBgXWRoLclLYRLXvfoPujThRNo2V1ykzGgXPz6q8iH2z102r9lKtvhwbTTOGGmAMp0X3tWZ2CEJRpU9XO1iocVm0YgkkUTCGKzVg3uR8AYeUtPkFPd+gTAI4QiJJTvIkRooo43ScpSMdO64EFB9Baa6DVVVw48k4QezGM092+9ieuWTDzVywIgh+uJkTJ8yD0XuY.Raa6NzzTutPn6BRaE5WWrAGpdWNwSad1w13YfB6BJqCr+O3s8AOu2wa6cbN.K444IVZokZlISlZBgnAEnIkKto5Y1jhN1AJNPcMa0CF1eJjB8+OBSh1l9ksPXi3tYDBtQAtEzufO8EBwvO2m4yAQNZqg0zUW8YZK7.wU8FZSA17NtkOT8EVXgFRobKIh9as0ViLMMkBIBSSyDG4HGNoPHRoookFJmNLLTMOWAp8Gf7G56i5Xur.NwOKma4o2lDrPQEY0lALxDwUDoBTJkCd2u2Xn5HJwvnYpcGN3qj.7SpP1Q0LEKGQlWdJEpYtYxtDvRgggKBh4bccyHkxjoSmVAm0HSakLVJPDUARoT1e80Wu8Cb+2eCSSyMJbfBaXa6V+QezGMR4iBhPXTkgW20ccCDBQOn7f79QKHTYa4ENfBQIATHD7GZZZ1a94muqggQWGGmdkJUJJvkkkGH5ZK.zXIRppRe4rTjbWwUbE4zzzlHEmFFFhM1nJEKFMqiHkuq2w6LDEevDMqh18LLWs+67c9NGfhCAFHDx9frqiiSafsVc0UacG2wcroo4p0KpJNTk.HvZUifUMM8MLL7OvuvAhq.bTW8C7ABdSuoWdEOuu+FtttM.+MKTgl.MIflW7EewM2291WKKqKrEPqvvvlfWCfZFFFa.rgss8FkJUJBl348yCtW7EewNkAWqUMbKJD9W9u3gCD5hpPwFW8U+ZZ6551+.G3.iu8a++hD7Ev5mI1xQu65Egxjx8A590+5e8dQIpKBkxrttt6pQiFKcbW6UbcsW1831K53r1tt0OxsNqZTuHyK+Re4of0mVMF9I8coIcq3PQAsD.IwfjK6QZvIMEi5Vihr4Dn53Z++x+x+xNn5TTGXi9Va7jYAcOhHiXkr1oTZApLgsvKTnvR28ce2KVud84QRNWW2TRPSnPtzjQvITJkRYnBlmRB2rQiwpNuJG.Lne+9CVUkDZ+u2ey2qqPH1RHjMkRQCSSkbyYYYnjxUEbZa533r4W3K7EpATwv3v95ptJ3UF7MV0nxKzzrJTdCJPMnXDTVYSnRSfs.u1uxW4EFgtlxcVohh7xHPoRY+C+C+CssrNbWfAm+4+KEJkdB.Qz0TnTJCKVr3XExxHLpKfoKCy7ley+G1UQOlGpMG4IqpH4mQ7QUH0i5HMz0mRsA59C+g+vggggBWW2LE2+byMd73c6Z6tTgBEV9Nuy6bo+ium2yh4g4000mqLjCcRm+jS5F7yFqg8T0ULwRKsj35u9qGXIgdz0VYhJLVfTt1ZeeIEQpfv8YzwPPc0Ouoa5lHfRQIcVL7CbK2ffnjOjR4LsZ0JmHlzpmL7Mp6gddtiERFKjgQ7FgHlzpmPXc.8DBY2rYytkoowlqZZtwuxuxuR.A3aXYDXaaWwz7H0eCWyankk0E18vG9v8cccGXZZ1a+ufWPGuS40TWWutkkUsa6197UKo5.V4xPUyiZtwEZZV60+5e80HOUsssiGqAWnrOPYnxFtt2eMWW2MgxapGDYeFP8G3Adf5VVVanacgabdm24UywwoQjr31SHDi+7e9OuFPRcc8n3ClzMzmtM4ccW2Uz8mZJ9xgx8ff1e6u82dq8rm8zEkMbRfbRjy8e9+7Gati4nTBFGGmDenOzsDUnhJwiTb5kWd4HBh2J49dpk44eptUrXwnXpzCWAB+pe0u53zoSO3y++ys2ekxzCbGP9mww3XRBPG3Q..M7iSPl4u7K+xW.XdWW2XN7HoDzjP7HsD8tfPCo5ZNZGNV.CQJ6JDhsPHZ53XuIR1THk0UqgUthiyZk0zzpbG2wmqZIk5s0jxz9a8s9VsMMM6XYsZL5+ZC9s.1zxxZSfF+4+4+4MxmOes2065cUEVoRAk8WkxvF5Oe85u.CiFfecxSCHebmZa7Q9H+gQ9BC176889qahhXVamuBaArEkoYlLYZJkxlFFq19a9M+lCO+y+BApF2E7yD+XviPHXOhRzGx2EnSXX3.fvOxG9Cm.jY.xZZZl1wYsjQHDJliDE.IpRoT23MdiofURB0dpP2xN8KI1wuyyF1fRvZLDD4W1S8LEZ9I9Dehs.5.K0awSezK296ZgDVHbO6YOQEs2efPH5qFyhfQoRkRHDhzyN6r4788yAjgHxRe94WPDcUD8tqT535FWPtQBEmNM.D8.ZeNmy4zRHjMzzj0u7KY0MNqy5rpXdDSeSyU89y9y9fNBgvQJktt11AP4pBQXcf5PPi7J6u5Tg3D7ZnqejV.c2yddgi77teoqqa7nNLZ80WuejBH1DzaTR8cpE.0rNpUiCaXT+du2uUMzo5q409Z7.b.rgx1.tfumTFDzrYyJVVV0K4yV.ivmjREOtrKGEGKNKrPbrqOSqkIgHBfcYFZVgdfeWJPujISN100MQpTolwY8Ss6a8CdSK63r1Jlllq.rjoo4tKpFUpYnxDxFN4ifdjM+gd1hGG2g85IhhCbin30HiLPlE0nXksLEiPAcIPgpjwvFOcMYa67LKBEBP.dInDYfBy8w9X+YyOd73c0qa2YEJDCm.jZ862e6cfLhSFESxkQYeIDCQQ5qiQMNoCBCC69R+Eeoc52ueaCiizpHroooYiy+4e9M.peDCiZFFFUu3esKNF4QkApBkiPro+F11GqlqqacvuQoJQiaTEZ93O9i2RwmRWvV.akHQhVS4+qAPcOOuZOvC7.UylMa.n6UHx9J.bMW0zWWHBrsOVfPWD.kpHkx5c5zosFD9FulWa5a7F++ZFHh6NodJqSblxOSkiiOq2G38+A5CLx00KwQLLxJDhcIDhcSQ1sywc1sq6wWv00cd0HrqlNgHU.ZLXOFB9YI3C9ox1yVK5uc0+rHI1jVp3MiIJKAptjjTJkiO3AOX6G9ge33f7epl+1mp8erL6t.J4v6.2y8bOOmW7K9EetnlW4EiHXoQRXKMgPAAYgHXrT5PXnskkkKJDKzlsqj+3EgA0mLmY+LUh2D.IJVjzAApBbHkxYDED4hbzESJki+g+veXuK3Btf1.MWBZWa66gQAdajCbmEEbHi6R0bBgHuqq6dz00Ovcc22847K8K9RL1n5FKJgY.YJPjnToR366KAFKDhgRj8DH1JZrSpp3CF7PUjfMJBaF.s+A+fePmi9xOZOpPeUGFqLcmnDnd9sqnOYkRo3IdhmXz+ty9rGcbW2QRoLTQJazes0N0V6YO6QEviNcviQLoJnr6H9k3f862+7SlL4gBBB1uTJyifYkRRIT+tSfpNaiTlNn5BQOaW2gBYnpSsBB60qyv8u+ysmqqcaCCqVNNNaZZZ1fhro6wc27i8m+wZ999ieewnCXbz4SxRf3XNNiusa615cS29M0QMWrkFA9gSc7kw+9LYVsU2ahP5zXCCBccAccDddS5diF5.dS5CSLWmLCvLEJTXlG7AevTJ1jm9TjlJnNWpdjS3XDl7LwOOZ.IVZIxUqFKWrH6KHfCIkxmKpp2unqqqlooYeGGmMCCCq82eO2SvK3487bufK3BBJ.UKGgxDVhNTitP9AJjNLIf9ebbdtcGRiJx5q5U8pR8G8G8GkZ0UWMt6qyJUxupRoCJhf.FbEWwqp8cdm+UwRTWbWpiuGbZ2Gijc64u268dW3hu3KdoBEXkxkQGkjauGWWGcPrhDlSnr+Tv77zOWiWLdrpK+hgfL1lqGPGgTr4nvQMzzzZXYY0Dnya3MbMc9au8Oa6.ncwhz4u7S+M5dnm+g5G0keETnySWpbZy59ow345P3DxQdYBYCjph.OYzBlNPrmTGr0gY7fbEKRlf.R355pYXXj3Vu0aM463c7NRRID3S3W7K+EGeU+ZW03G5gdn9e4+5+51u2e+eeUQ9fMc2Vplel5La73yEyGU6sRkJ6eokV5r0zzJ544Lmttovyys2G5C8ga9m9m9mV6ge3Gtx49hO2p3OIPWkc1Ydm4NS1lt6UZ55jH58O06qKQVpo3vDoTthPH1MPBaa6VVVVUKAd9OyHLYmjq3zpQwBEfBARoUoRhyx2WtO.KWW2BkJUZAMMsrCFNHQ5ToEJTknryjfzxzT5DI2uekuxWI7JuxqbX+A8G7pt5W6f65+1+sXTM0.XiibjiTob4xUi7oEOSyxeyeyqU6q9I9Tx.XPoRz02msz0YKOuIxr5HLXLtmFGeMEuXfXEXb0XTNsBioJRXYsH6uchbmsIFxnQgHBoWZkJgz2mQRor2wO9was5pqpJxrNcijw5cxQGvNSfbejfSRFnztAeCfy4s+Nemmyu2MbC60vvHuiiyrFFFBWW2dlllMbbbBd3G9gceIujWhWgBDTtL0xmmVUpLQsFFyhDR8oH+7e5v0DSrAsrHos8D6jbThbO924wyd1m8YmVJkiEJo2tEaK8l6zlam623666BEzK1Ovyyww44IDxCJkBcf4UI4KhWaZ6lfojw0wDkDaLWJEFF1QSSq6q5xu7d+0+s+s8jR4VRgr9McK2ZkW4K6UT4xtreoIxa4W4q7U5ekW4UFGi0DYLGXbIXjOLpXQFFDvXCCjttS7aIoDR7eRxh4jm44AhGeWVFAafFnqoPW2ooZCoO5QOZ1evO3GDeuUCkra1wC1zvfFttz5.GftOxyLGIEu9QFf40AcO3.862+7CCCeN0pUaOFFFyMd73wIRjnoPHJiBEZwimVrTF2FnisscWKKqd4yS2JUdRy7+zEIQvzOW9w2m2oglo8BYWGxkOOyToBynqSJOODqu95826d2aaJQK7eR7g1DTysDjtVDJnoDyhOyCEWAB1iTJ2OvAZ1r49le94KnRvhnwwIpVIp3ujBAQwhICkxIiI7.P1EDsQPSAhMehm3IZbQWzE0.n0q9U+p67O7k9RCJGKkoJDL0y11qmkkdL2jzAkcXGcc8dJhiMNNYxlGxTARoqizyiAqrB8pVcxZsiPmvoh+J9coIiRkJ7rnQZPmQpQ8ekzP03wXXFhPLvW7K9ESdUW0UEhZs8MAZrDznFz5.Gf9mA1cwqgNOQuK2qWumSlLY1OfNPtVsaOd94Nmttt2WSoT13c86+6W4y8Y9LQEzVgtI1dspwVvX6SmCG+I0tRnJByIRFMVVwxI7tjR4hBkjSOaz3S2ofPrQEnhtN077lfT9mN91XxzFfpRKG31u8a+49q+q+q+77771utpiOy655kAjIEhs0c5oQVBR4.ohWF6KPLPBi9a+a+5i78pz+M9ldisMMMiFGey5QqYt0Mey2bma3Ftg9exO4mr+uw69cOfffAO+m+yOL3Ad.Vy1NrZ0pCO7ke3dDPOXk9P0HQp3z7aMc7XRfvH+eiWZIj0pgnXQREDL03nt85uv1wsFq1qYJByErsTMOSzwqQ73HhpXNwnz+oK9LsCcHRdhSvL4yyxUpvYA7bkR44ArOWW2cgfwF5FaIDhl.M+m+m+m27fG7fUK.9kisqJwlJeEK2C1XmHl6+se6YityM8lbe1HgklnO5.HkRsHF6MiPWO2Zqs1Lrs70clxD9gVvHJQ+RQDDzkbIWRi9862x00sqqqaz71KDBUGRjFFFgiGOd3EZXLxx5npEbMLllD2DtttT+m8nJIdS.nEDLI35zBQwLxxxz.oJAIoDhhBQ3EbAWPLrqGVapEwNfhb2RlO+vIAlVPHxHDEUrsegBYijYwrurK+xyjNclzEKUJEPBCCSM.7THuQhpnrgHYrTJGspo4.gPLvwwoussc7XCLN.v11V6nG8nZ5QvMKpXIC.5UD5C4Gx1cpVCH4latYhy9rOas.0Lp12zzbKfMqTgFQEKoCrz.7Tuvc.PxBJXoE4DNyi+3OdVee+rQPVWSHmjHqL9+h6NqTHBEBQnooYnqiSnPJGaZZM.gZbIxlclMUxYnHhMpo9ZqsVCBnw8e+2ei+h+322ltttMJFsPGQx.qOT8a7M9FUuoa5lpshOM.8Ff+lnBnssIzCLTKnWr3zKf2FXqxpe110U0ssnEM1R88WpEdJTAXBaQQ1x11NB4Dzub4xCxmO+jpiqGv3HD6LcBaS+ympMIfrVM0h9AAwApvvn4KMgooYFGGmbHXFiRF4dCWy0j4BtfKHKP10Ty++r.ytRMlALmgHdHf+k43hy3McP9WcO+UxqX0U29DWsnqlTplEZoubrTJGdm24e0DhbS+I6zd5D3zpDkL6EewWbNfEJWl7862uz27a9MKBrLHlOLLLqFjLVts62qmhYWO8hxMVNNbj.E7NEJtkoOPu+qew+qcC0nydeguvshJVRCf5u+2+GnwZNN0cbbpGDPsK6kcYwi4VE0m7UnhZ1YWD1jko0xwJCPDbt8lndTLRgjFqSqSMJbvXnNOUPGMdLg5CzyC51rYyN+f6yaqu3W7K1xvvnEPqkWd4s9ve3Oba7oiTJ6ec+ZW03+o+o+Iseoy+7y9tugaHtX34bOc9x4YpyrRNzgNM9nJe97M0zz1BXfggk.HqTxtdqu025ht1tKetm6uvJtq4tBvREgc644sqUNcEl3mT6qc1E23hkD8ojF0T66RPha91tYsa4ltEA.wDqq+Yzg4PpigttlZ8vIEcHcfTlSHDyEDvBc61cAGkpAMiumeZfDUqTEWW2PAhI7QgPoTIiQnRh8JuxqrOP+9c608Pmy4F6eoIJdlZy669tuVu923ars4QNRah727E97eg1ehOwmZqiYa2xwwo0wNlSSaa6lddSRFeKnTabo80bMWS6hPaJQ6hD0s9nQVrJQnmCZsbU1BVtMrQL5A5sBLHRYyFRwhwRx5V.stnK5hZdi+E+EMAZ64I6IUPzd7pp2wUwM38LpjHSWL1vCcRBUEq2e.P2uw23ar0c7A+fcbcc6MXvfwlqtpv00MYwhESezm2Qy.j4k7RdIYAxVtLyBL2Zqcp3F7jExmw6DdY.RuDjj88SeEzw1VseUqUVTfOZm8Ye1ZEA9nezOZnw1Ec3Lkn5EPQULEkJkCXtgCGNOvtjRQNCCizFFFIjnPAgL96Hi9pphzMV.ijHGb6e5auKP68rm8zxzzrwO3AevZnJXXvEZX4+68Nem9W1k8KED82UuHz5JuxemdNNNCoPgXeUwERt6G3y7Y5BzMHPwgDQqCFMNrVwb8Uq7p0+ZmeadopGPuJp8Uefd6cC5BFcAuIieYI0HJmDP7CN0oFGE6RbAnCcUpdUFWWkbJ+HOxynuro+6k.i8h7mlISldISlb.HBGLXfV850S455l568c+tIcbbRBjz00Msqqqp3BQx2rkkRMIpT4IYWMMh2RrOPi88jN29w0FbRQSVOZ7KqTQkjlmR1VGu28t2gf4foJV0SocVsoNWK5iFjWCBRhB0mY61oSt1sam0wyKMwD7pTAWICCSUA4fwRoXLBFEJmr9TaProPnsAPf.gmwpFdm0dOqxpN2SyuzW5KsUYkHCzDXiRkoJTphkk9Fqu95aBzd80WuOTbDvXuAChe2IN9fdUfdddd8N1wb6BzsZ0IEgb.r2gQEqcPdnGKQu8NAUTJerdvlG3.GnNPiE7nIr2MgpQH9jVEfNP9Qm5TmJwU8VdKYkR4bRob2e2+wu6h.KTKR4PejGgj7L6WQhJN3SqAfYyls8nQi5EkCjHWlLo6z4IxBgyXZZNye2m4yLiuu+L111yftdtK4Rtj3XyRUqVsD1O0qe9iqcUjMxI.PJDBohAFM.PHJT.oTJ2XiMBAF0rYygUhhewy6IQx1OkaEO8BWkqc61yEFFNmLLblF0qmw00KIH0hi+mHz+x1igy.hGQZSqFxnFwbYW9Ur4a7M8FiV2i1+nS7i53551wzzryMdi23V27MbCsbcca7t+M9MZPPPMfMdfG3ApVFpXYYswCc+OzlkBXqG8QWuMTMdc1t5v.PWU7MEp4m36CnieTrb0po9yAJT.GkiQ9Z3RcfMKBMYYZpPjYLA4Ryfn04kR4l5BQbQzkDUPTCHIrz+RnHRdhSn7mUoxDaqHDl3BPhE8Cy...f.PRDEDUFjrKfEcbbV1wwYka8i7Qx655l+3NNKipfMyj2mTpwhbiorE9+6r8rUASlTUxSFONN5DCk6HBKqbpu7W9Km4s9pe0410t10L.4VYEEIioeFRNk1vX7Yf+1RfZiLYxzzzzriPHFFIarZQDIj.Hzx5BGe8efOvPveDPng5gebG00LLLz1KHhxp3mUaSm.WbGfS7nO5il.EolpAns9fAB7QZqb5eZjqKfDNj3QfDPsTURTIpCdExTARKkAoARWrb4ze5O8mNiPICWobccS566oYXXHbclnMlwOOi5nlX38du26f0bb5KkxAQ70gz11VfREERXYYkvwwIgGnYXXHhf+lDJNN.FCUj.hRBQh27a9MmPJkZG7f6VywwQpCiIOSbhw1J1P2n45cLf7QP8uDrcklSYYYkNRZ5z.Q7P8GE.sHDgXxLW2tUqwRobjiiy3PXjPHGFIEXsLuPyXxWppoo4FVVV0LMMaTpTolPwVu7W9KuoOzzvvnUDqi2111N9bs90dsWacvngJ4AusXAhbXZzyAF.tiVe80GsTPv.fdAAA8JURkv6dgAr3jjd2gC0Zc+xe4ubOfdNPOBnmkk0zUtch8iTJ07fDP4jjOV1ryOQ913L3crn60C.5+09ZesAO5i9viADNNNo.RKQjIQpDo+N+iem3NDOqkk07111K.E2cUXAvYWTj4.xsC3l9u1jLjS8SEJJpyX+o5xqPQPln9niPHBO4IO4PJF+9whC8dxA6I.D6cupBsTRghmTREwGO+Mdi23xoSmtvO2O2OWdfcChY0zzRKkjPIOcRxlIqjHI0j3hk.iDZZC.4.yUM6KgdQbgS6WyU+ZZIjxlNqs1l.Mrss2rXQZYZZ1ZUSysLW0bhsDLMZJpzPotOzoNziMn+FaSPqi.FE0wrwv9h5TjcHVp2Ad7G+wC8fvSdxuSHP3h11wcIenooJvToT1Y98Oeq8nqu4UcUW0D3s+FeiuwFuk2xaYSPukPHZG.8Nuy67F5KkxToRk3O3c+GrsLxAIgC7uzyXIm3DwjKV274UDDYQUGK5DcsPXXXpjISlsc+1yNdr2BFFGY2Robw0bbVPWWeWUI11ZRA49IkDXm96o89e+u+X+vI.+DPoDnTLHsa3seChq+cc8.vccW20N1MVOCGhSnJ5hmWLAzkrTIEBeJJDySIV7O6i7w1ctb4VP.yYXXjEAIiP6i.TbWRTUfi8wMF4DnEOzzb0AyuvBC9fevap6a928M2Vo9MhMMLrZIkxs9belOSW2669ljb4081t5t.s0zzZYtpYSSSyMiJlWKhJJmm2w6B589re1Oau.nO9zOHx2zIO4I6RjhUn9bfdJayM5iUb.WKLPg9jZiO4IO4XyffIxnrgA891e6ucu26u0u0.Jv.gPLTHDit5q9pmBMdkSCFwbpyzwILcWPm3y+DPHTYL5pjhtrK6ZZ6Ca8betO2tUpTYf6wNVnPHz7CBR+Wc2+UYAlo.LKEYNGGmc455tq8rm8DIe1jCpjUW+noARUCRxIOiFg3eb2lNg4H6j.MnPBoTlH.zdKuk2Bta6y4LoycQ6ufj.YJ56OKv7ISlbdT1Y4.R454lPHUGTsHBRLtrMQGmQFll8+jepOc62v09FZgTQ5pNNN0N4IOYEoRNzcB.aSSSGJP.P067N+FaFnF4ltBgnud4xCt669t6aaa2uPAkMx63M9F6SgIwxri0+r6B6qKwEFIfAUN8XeFUoRknjfyObc0ZsCvJpYNAARePqb4GRSJkhRAAg+1+1+1CAFVr3DThltYylwbE2zj+5j6BOM2WI59eT7Xk5mLYxAFF5iqVsJAAAZ.IO34cdojBYZfrG1vXFT9ulqXQlw11NcdHAEdRGm33AItXqu0a4VDbxS6X+ShuNMckrcmxDxfN4hQhrTJSs+m+9E.gfyXXooQT0Na5vTwstRJEAoWYFJvb.y+c+te24q2nwbRoLqotdJgp3TBCSCRkNo7q+0+5gB053iQJGIkxgZJUSIhKRj0jRYfoooqgggC93c3eQixRor9k8Rtr1epO0mpqqqaWCCi1NNNs7g5feMzo9d26d2z11t8d26d665d7w.xU1XiIw0ZYoV+y00sm9En2wvvHtIDwMSXXkJGaTTwVFUAFQMhrwn+i+3Odruu1Oxi7Hc.qtaB8f06wRw9Dy2qLLBpfkkUBomWJgPjEH2K5hdQyALmNQxn9oSx0Osqg9HSS.rQEtIYxjsMLL5JDhwISlTqd85ojRQZnXl0bcyTRUnzYz87l4dtm6IGPlUVgTKszRIWRMY.SxyXGOa+wNdM0mhgJ+Utg.gEpTITHDiWd4kGUrnX3BKrvf7v.LeRHH7o0uVvoWvjLunWzKJmuueVDhLc51MIH0LLLDBglPoH0BoggQX+98GQTwRBEhlBAaXaaWV.AlqZ5KjxIE4867c+Na9RurWZKHrsiiS626688tkuhnWaFDM9ym5TmJtwo0AZbMuoqeSeXq8u+81AiX6n7C7fQfWnssszz2eLvPee+9kJo7+s.LjEYZtnaB21AU1RwKhzI.5xFzMBgnS9Hkx1.aIDEa5qjd8NnViEnXBWUNgmIix2De8DYi+.Ov8OTUZSRgjYcccWvzzbIfU9+9i7Qx+C+g+vBlllE.VAzWnBLCDjlEeVYDu9e41dVGgIDmzkGiMhRnAUxbItxq7JS9g+y9yRUud8zCFHSWsJoWVAG2yjYCN9g4PTcsZKJo5rkTJaKkx9tttiEfrYylZgggBGGGgq6w426262KdeHbi5J40ccWWxZ0HwJf15f.uel8v8opXIo.Rs+8u+DnhgQpCgoRkJDEaaOr.LfRriDmOg1hQvLDe037.kyEMdTYAxE.y7ldSuoY9deuuW1Nc5jpToRIDBMMWGWQzcfsCFO5EXIxAu0W6qseud8GHiJPiFf0EZk.HUDZORXZZJJEeQIDQy8evzWWICfje7O9GOA.kKi7HllgufK+xG68C8llbYi+riwj5.w2yzJnBxH4ryNabvzZFF5RgXRWXGKfwyN6LiA4na9lu4gyM2bSBJSfXjTJFVqVsdlllagOMLMMq9k9JeoJNNNSTNBCCiVmx69ZoCaAElT8Yaa61V+bVS2I2lf6VKG24qMoudbvappzNZu6cuCqEAyzhEuvA99LbAX75fLlaCht9lvX0.C909090FrrpKsSN+KAi9u+e++93FMZDJTLVaBgPjtTDIlRElU8+WIt6+mIuqOsyxdttt8ewu3Kc327u+uWJhTxDT7YRte0ege9YbbblCEzHWvxxZ2NNqsPQ0edWOze2CMaQHGr4Yp5C7zsMMJNhsIGUBFQwI1FQ+zebQX3YcVm0PBTDDHT+oJXO.cw5qivRHD9PB7HoPHxUqVsc8G9G9GtafEqWu9BtttyYXnmUnTcIAQEhSHXrPaaaMfQBQDxbjz6O325OnqPELSKSSyFHUEA4y849b0.Znoos42467XMu4a9l25X11sIfIc9ms6feDy8WaaEJXJnryAT+TWWOJ3jSNoS6XirHDd1m8YGBDtu8suvy67Nuw0mb9t2QutW20OXIEuQrEJN1ItngwepIDhZddqUOuRt+Z0nQi1BkDIO588m79fs8WkDdjyH9nRcMr29UpP6hJRjsEPGWW29RobbhDIBKTnfXlryjLQhDYu6uwe4bNNN6xz7Hwiy2Lkfbrzj4w9LQkINS2ju6286F.toa5lh7K6mHOj.JngBMShh.G5PGZGe0mQYxIFMJREpnJkv2mrEgcUFVT5IW52859cVzUgtjbQiWZBCCCMWWWg.vwwASCC.gTnFKmQnj9yg.CccO9vUMMGJjh9e7O1GusiiSSSkJZsooo4V111cLLL59DOwSzwwwo8wN1o1pjxGWcBHljUmpX0zSWWuO3MHOLDzmTT2hvv88h1WbhqQ1lOR76BiwlQ6CFAaNBXz8bO2y38su8MxYx47xCbcUn+xCFQ4sGykuvW3K.pmmQEk0M6dgzf0S0y3mpBmLFOFVD5Ak6.zYlYloqoo4PzzjRoLgk4poMhRdsLLu6wbWvzzbdCCi4We80misQ.aJvI429a+sS.F+zBYI6Dw.63SYs64dtmjfdBJUJAfVdPP9y38aLhUyEnd+YAT9om000MiqqaBBQf3IccoZ5PzZBNNtct1q8Zagj5BMQMyUM2PJkUEBQYSSSOKCCaOaaa.OJSYcn9UbEWVSf1999cMLL54A8t7K+x6YYcz9kKS+7vfMfX40bm95ir2N4oMBOrC+e4ymOZMfJi1211eiK.iKVrXnTJCKTnPnqqanOL5q9U+pCgRC88kgQbSTp4me9IDsHO8pyzjmQGPc+e50qGB98a1rYLxakm+4e9Z.IWXgERaYXkCX10rsm2vvX2PoEBBXtUsrxVARR4SiiR1QhqmPrOPd8W+sN0ykS6mmoaSEq49R34Qp7PFGHGdLqmm2b.yJDhr+O969ejX6mE01IIzp9bnSGALRYkTm7jmLKvbTlc+89deuEeAufWvtylM6bFFFYcccSFArDQXXHCGNRd3CeARDBkJ3HXLBEO4Az0wwIlvYK6333BEccbb79V+0+OqbDSy5epO6mp4u7u7ubaoT19G8i9QszzzZpGykWdrYAnokkUafdFFFi777BqN45X4PaaFsWnuggQGJOYs23QwaHvHksUP3ANchNdrNL5r+2c1SPhq5icrc5HpwHERvpDBHcbbHp4NgRoLTHJJ.R9XO1ik0Sc+emEL4YZSpCiHO8iNma827272zzyyqsiiSeg59oPHDIcbVK4FarQxG7AevjVVVo9911QM4POU0pjpHjnFng8oYysSer+q0e2TuGGLcwcCCjxn2UKNLHP1+BuvWP+Jv.b9Wbrwi2TMd.RPIUrGG4HGIcDJ4TwYJUnbWWOl36kRWW2wm8Ye1ijpmssERYSojZWnkUYCSSO6iY6XZZ5XZZ588+9e+f8sm8sAPCCCqlRgnUgn3+e7G+waBzx00cq8r5dhQYYDZ37acpScJEu+3ROCEWNNBHb80WWZYYE5DEqXoRGcnuOCWDFu4oG+ebA8hssFrMuPdfIq2VBFvRp+egRxi6Ak6VHx9UHDQivyj7uNS3TvcVvjd1198PMZ4fPjtToRy333rKfEdfG7AV7E7BdAK633j+K7E9B4AukxqVeI2AyevyDfP7+1s8rYASdRvS2EFA5iJ.iEB8wQNKj6d26VjNsHIjO4FO4ND9LsEBGXLvvpn2Ee1RHDsFMZTLOnLBgPtqcsKgkkUxSbhSjxvX0TEgTPoTO1i8XpY+pDo+nezOZZHe5pmdv1+rppXSGTSZkhkLo6ofJnxA.Cde+Quu9.CJqfJ4NUlBs5aOm7yHDhYkR4r5BwrPo4PIevKPQl+E9BegyMyLyjUSSKkTJ0RlNIp8iLDDiPF+BqnulPzaMGm94xkcfk4ENx11MzvZUbVyQCPXXXfiiy326688NzOJ3YOOuwd.PdsUfjPgznHiojUqVMxwPowqYaGd228cGFkzWjyzEiSLbGiRwi.XoAjnLkRQYR666mFUx7BTIiLVp3dkQs6zYT61sGIPL3c7NdGCPnpnq4QLGXZZLPHDCVbwkhQAvl.0d0W4qdCSyirAJT.zDXq8nq21Kh7Lgk5.z0x5E1EmHoTD5TLpHOS08+gpJKaMZYXngw4ucxEKw.vd.jezlO4YaTDMDEwctZHvvMfgJYwcog.C7I+fW5K8kNXgEVHdgFAPFuHBESJk6xGlEVN6RPJEik+z5vbGApVZ.Pu27a9M2CXv4cvCJ0MLzz00SKfrFFFybbW2cYZZtvse629BNNNwDG3LAQD504e9me1iYamFVJ0BaSbi+jrEc9cnPfPeLBIXax6Jl.u7UcPX.v.UmcrdxnK4PH.OAXgy1nyIAPpkVZoIbiBQnXPEnmTC1dbujHBQxX0BSpf.d++Iu+Alll8GMdTme2e2e2lRnAHp9s+1+iULMORYSSyxW8Ue0UoH0MLLZdNmyEs0MbC2PaKKq3fzF.L3085dc8m5OOMz62tfHPHOxj+twG5z65bHpQkaLnOpXTxt+S+S+SJaEcFAqO7Vtk+K8qE0gLhf0o5mVS24j5555Mp.Mgfl6d2mabQC6GUL7D.oW4LqnESYms9Hvn+Tx9bWoT1WJkCMLNx3nQmS333j54cnCk1xxJiiywy8o9DeprTnPVexmw+D9SHvth+3WTto6nUz8tkCAjuq206hn8YxJPJnbRgnTBgPjvWJ0FOdvjq0ROc68mz0MiEVBI3mDxmyWJm+lto+zEu4a8lWDUhrJzkfhXDiPJoPpFCwXjSJkQiHgoo4.oT1uamN8kR4f0bb5GRXWfs1byMa.EpWDZXa60zxxZKf1m0O2Y01zzr0d1yKZyu+oN0jmyLguexOsJNM.XfB1zdSJXW.GXDAOoQC4zrAOILFrFuLL5RtjeksQOfEQJBxRCbUu2N59u+6ex3kDMlcojRY1VsZkCEGKjFrSF4G6L443n.nOrR2RPm6+9u+t1t1Cz00CMLLD1NGKkqqW1tc6tq21a6srfgkwtA1sqq6B6cuunoPXBoCBBRbQWzEgZbvK9SSXFG8dyxOoD0uzK8R0jR2j3GlDHYEVVKh.02YmC24Gs7JHYmI5ZZ9gCGtfqq67FFFyDgNyDaWrDYrRRDcuTNVUPNYWP15D+vGrgoo4F.UIPgHy8t28VtfZF180srh6RaCOnIX1tHzoTom+1D75xzA76BF8prs8yTm+SV+amHo4zeO8Q1tX0GJxF7jaG2vPEuVrb+BBQenT+IclUmdfeegn3.WUBbBNMt0I+yjuLA.OBG.pvDhjrXzwrc61wIMG555JLMORROOmrttty.rKKKq4cccm2w4X6x11dlxaipkDaeLs1Y7nQH21dbz5e6rHgmIaaaib.R.mLETJcEH6K6k8xxALqggwLsa2NqTJSkOe9oSdamHYRi+e4t28njjq5677yuHeVO5p55UFYFQ1cIgZdnRFiTIAFM3EYOyvNVlY.6iWXVarGav3mXyrHj.6cXGNLX7.5AL1fAOCCiN9r90ZViQ.dEXFaCdGrEVp6V.lFAzHT2cFQjYVO5tdkui329G2ajUVU2p6RHI.u2yoNY20i3wMtwu6uGe+88KK4vo10uYWHiHR9q5ptpwwRZ5O+m+yethEKNSmNclLLLpvBKrPFrDXcT8H0rsKnZRJWLEipCvZWCCJS1v22+72xsr7ZPi078ug0u1q8ZOeSJeAKh31v22+B2xy44rQkJUrq4LsvUSisrs7gVfWmJUpX7WdFRLhSQ0Tzhj5GmEU.KND8R16+3Su67PLPbDKMp8uKEWsYQVQ4jEfXe+k6qp1SUsiTQ5.M6JhaxUe0WcJmjLburqfuZ.nQrXBqLr0h1909xdYaWoRkVSO8zc+FeiuQbXXHIIINuhWwqJ6ryNa1YlYlr.YqVsZNCZgixCKjqwvhcLb8WV3XirV7ao8SS+TgE0YAEVX22eKAPCEPO9w+hCayT1663WtgTGWGpuawkiBCGFGoZuhssRhJHIhHwgQg8A54eC9c888aAxVML660rZ0pQAAAA+J+J+x0dAufWPnmmWivvv0.1npmmss4atyUe0ees.1wya4cr7JWKfcNpsXWG4HGYXqRGB8giFWFhO5Quwg1p3nzGpM.lK976lDxQRTpg38Y+IHlSGCGa.vf5bzA+qdQ+qRs00yyfJqdMM9CmfwegrP47kg71Vx7x1RNLz1pWJJy67C+C+C0w1BSIflod8FEUkw88Wdx4lclC8vO7COsmm2gas81y.b3ufgihF+q809ZEh1csz++Fzl7TMBSfgS5UrFSh59W+k+xc52+bcv3jceKJ.nBqLRuhW8JMolFzrMa9QV3nUZqrYylBg3tUpTouHRRPPfrzRKkIL7D4Z.4CBNdgmwy3YXpfPcCgpEF9EFAN4SmYwmdpdzkaLRF+qj4nCIRrnhThB5PojbgXVfdhHcde+meK1duLv1pJGauAbWxj3EU0boDuacXRn9T.GFXFstdXfIiBCGKzXnwYP+AHPBHwfNvy2qWlLY5BZGUM85opZOW25CDQiglpuueRsZgIuy246ruuue229a+s2gEL+twwwVNKoYlUgBPywwjDmhyO+74DQjxTWqVsZBFmKz4G9R64S3XWRilRkJ0rP0tdgW5K8kVrb4xEBCCyoJNggQ3uruJPrlnCFe7wsHjgdG8nGsKpEl3MoaPPPuq2yyJ8Wz9u7u7udG2Tz.PyKDDDrwcdm241.sN24p2gxl.H77V2N+GY+rZWfdM1KZXFFzJTafIYGmOMiwcYcyuaUVY3FEm8rmkc0u7HXoQOFCcBbP0pqaONqzAn8u7u7quEV0k.Hya623sMlp5jhTw5z+ZEWGxYYr7K6Z6kF5fT891pWzMLLrmmmWbXTnDEDjUDJFFVabfIpUq1j+f+f+ylv22u3q5U8yjSDICkHiIS+tYqVsZNX8ra.NvYdx7t0v0BKszor+qP62urJhDKhz+wdrGq2q9m+UaSXhu0QlZ6u5XvoLuyU1nHBN.Nh35jlzDLPTOGPNSu8aZ2Kzg6kq.puuugievb9+0+0+06DDDrc1rYOOPSU0HPCtpq5pqEDbx.f5Kt3hqZql+Fkn9loUjHHHnSPPPOf9+w+w+wCrpew9cB6RWMcH9TWrSG1JED0uwdC1cfk.kGTkym1q3sgJsqNzwwZswcz9zd9svksTU2pToU1BXGQjN.Iu1W6uXNfBql53e0CDZlR.hKYPeUGfVwwws7775HhzsVsi2GWRBBBbTUynhjixj222O2K3luwbq8U9J4fUxUtb4r110vndXWLbhehLFY9bsAL6tH2687ddOYrs3VdUqW.Cp5xt81syBkx.H0OvIijDBPAxTxvyOG5Nti27gu82vsOMvjflVoQGa94DEjImbRwjHggq+R7886GDD1Cn6XiOQGPZYe1rUqVstvRKsz4CBN44+rOxibgpUqroshsahUwZVfnKbjibDKpRpt0bCCZXkQaOhcqZ5dcda+eu8GHmccap8uMRUumdTKsJYq2SLN50+68686MkujPDICkM7evgNzgFCbKhmInxEW7TGDmuTnpc8+p8pCcu9q+566WwO11sIYDjbddUJN93iOwO7O7+xod9O++IGtLb3jjjof5GhR6lvDW2qygYMG2JUZbPbn+akQpRf4.qIm8rm0dePLTZfZBrWUsg0eg0F0uo8Ck9Q4Hmbq.EMRsLSAb3rYydXfCEFFN1ZqsVtz+VQFgGqkcQ4gpzFjsQ0Mutm60sNvZdddqBrxlat4JAAAq1zjjj0XgTRYtpUgUBZ0.1oQi+gTjysCqYVmMm48+9.IAAApAbJfgWT2i8t8+U5O+RY+y98VxVkz0Zuhokf11p9c6PjIPmEnYawHW5wPYms1ZKafqqjmouRphzoU.DwvMTMrIL4rm8r12WDEvIH3j4erG6rE877lTUcxfffISRRl.XbGGmQZAH2rkGF3ZMmWwq3UL557QRbzotTIv3fNLqMLbzRNepWPUsvm7S9IKBkJnplehIlHyFargR4gUZNFVbzyWJpWrqwlwArpWHjkRLFvTf6Lsa2dHh477pjakUVQDCy3jHHwBoszpjhjoXPF.zKLLrKP6vvvsculm6l+M+MmbSfMKUpoMg802rToRW3du26ccf0ZXPD4EnTZ6DtfMX1UZG.sfvNAAAFjibdyZlkn1HUxeotKMrh9mYTaa6mjmsuSdpQ+YWh.9Olc8Y8AMUs227a9.cDQ1QDYGpaPxRYZ1CPOwINQFnRd2gEc3xZiyd7Oi84igS7r7fWqM1bitWy0bMC777PDw4u8u8yj022ufuueZQXK366mGHePvCmRbsYgplVyAx.mNikqlNHswwUXbFVGDXEqeVRFZtKsCr6wsrV8JmrDYognZpg8XTNWRRRVECY0mxgk1ihhAIlwppC7p30GnW3CG1MHHnyM360xx8MWHHHX0az2uw66889i777piKM777VKHHXiff.ivS.sWXgl10U0s6UtPGfNmMkSbpjx+MUs1AN6f5vfO8m9ObWDibVSgvpxZCKH7H9+CDs+jCOx+9z12SN6fO9G+iO3wdrGqGPOaA0GlrDnrU0GqWrNT.ZbPZ2K6bVXJEIzAnyLyLSuolZpXTU77pjUDJDFdxhUp3O1y6487FOLLb7Wx+henwqUKbBuk8l.XbXAKOyc5KiMz+w23oqVxIYQxaSrwr89mccWWmb4x0Fn0ZqsVaftppwOl1UY3l60NHN6ld7GLOovQq4VsZ0ZKP1VDosHR+JUpjVUYIIgrkfBppo8nZQLUMNu2yyK+i7HOh0nwFYNy2lkMv8NhbNqUtccg7ZCMsUFjRrRBqPOOnya7+8e6TduvVYlSaCDbQigjllq80WecQDw4O4O4OIq8dexOxG4iLElr+eHfIp34UvjERy8pZCxRP5GFF1qb4x87775truWGUktK6626DmHXfkGPFDFF1CR57U+pe01XjHzswHeosuop2TWfAh3SEiSAiggYrGCSBcj5ksYhOLLwyySW0NSLKHb5KtpNUAIJx0AHmKj+c7NdGi.IeiLhEbh..zBEKLrsVDQ5GDDXj6NU5Z23r6IMvWrsuue6myy4Y01Vs6cJCa666u8u8u8u810pUq0xGobapS2RPuvPCBON1vMZqsmJQvEGz5vqC1avGCpszt+MG8nGM4nG8nI.IUAkScQUXKAHtVMCoYVw1+pefOv605Xpam669tu325a4s5.juDIEe2u62UQnbgRGrpuqmxdM6BCvvV+87775GEEEiJfHYRTxppTP0jhUqVsnHZtff.me+e+6U877hsPqWqetuf8cHWw6ov2kN0oLsAgGXIEw5VGWpz8ptpqp808rtt1.cbYPJ22bIpNlIXh5yOZ6jzLq8cgr.47p3kcs0LpLifUlMEQLpqIhmWEIsBF.wpRu+1+1+1cTUOuHRSQjvpUqdVe+kO6xddmSDMUcDlmA+SZ...H.jDQAQUOE4RMKwNG4HGYGf1999oDDauYLy+6mnZGcr+fF1+Fqit1YzpTrmjvUaOqKi5UazfjaLTdZaOMq1lFzxWj1MaNeafNKXNFY9fevOfsx0kMaPV6.KmzIM81UhgylMaZh+F3fCXI9ZQj72nmWgZmHLmKjcok9mmYt4lywcHGWDloQiFYfi9joRF6Yd6XPBqOb9Rdmug2PFVgbu665tJHhaAU07t1joY4XJwHXAGnyQ5yImlkovce22cJmFLwpqtZQPxGEFsm6EAjs2dGyxOSx4hEXPPPv.LRWcGe+aXGP2VDYCU0y+8+L+9WGXce+a372xy44bAfKjjjjRDga.rwJlVZ0FfQsTzwMZvB629ydQ3zdue3R74kx92vupOynRfrz0yyqOPxK4G7k3P8gI8O21a+MxQHY8fLm4LUOHqsziQQ65buAU18bFGTKf33XGee+rggg4hiiK9C7C7CLwG8i9gOzwCBlRD4PAAASFbxfI.F6m7m7mLeEVMCqCGEznnpOUmvjg2Kmx9dyLfbzidzz4u9dzrmMv9zmOp6vpAWy5+xwFUMXxVwfR0hPkwcMqulFXVfYWas0l1yyaBOOuBc61MCFzyYkG8877sOPmjj3c.YqkqV8BpQ8AOeTsZq+67676b9kVZoKXJxvBl0QqXJrvBz0F74B8lE5355lxMDC4Gh0VbH5RF366GWsZ0XfjEuxID3wy92H1.OkMX3JcmwhhNee+zVtvvIJya9TDoupQL4jSlCnv7PA1XHWl73YOydNirm2ER.heguvW3.fXW2RpmmmCP9EWbwwCBBlHLLbRfwqVs5X9994UUGp1Em8rOX151moyBY9ve3O79sksea7iNObPFoIRaHRlCVfBhH1.oalCS6FpO6Ce3ATeWzMtjI378brL9hEgo.8fQzyHi1PKXH0z5Se9yu9TG5PGZBOOuTEIBEh877FHhLHYDegr1wRsGzMIIIkTL2guwWZaOOuc9U+k+ka+fO3Y5fgHMac7ie7sd0u5Wsk2u7t.VYqFn6Rrf83sXWW6wzhZkgH.5T6YuwSM3T6hn4KBsHr28HtT66N5yCcIxaSrha+xhz9pu5qdavTzAfs+w+w+waU20z5D2vMbCBDkqgcsf6UFcGijrvFVDxTxDHuRGqu4JF9ELu0m6Itsa61rAyZRDr+M3mCH6YO6Yy.0bV2tNOJJR3LWr+2eqMpJGcus5is8szQQQEP8KeCsZGV++DO66JW3BORVGGmrhkLgWXASeJ546oX3uDSLMh1KJJpKJcUU6kjjz6jl3.5fw1vl0ssj7Bvp0Nds0.Nuuu+lWuu+N+edm+ws7f1e+e+upN.cN1robz0JoEJ0rGSzv1yxttt5fEf9ujWxKYzhPL.Ht1w1ce0Q7+WW7Rmb38T3kp1y4UcUWk8cl4Gl7YQDwEMKPgM1XiBt61lgGDPInyBIrxtsD43iOd+ImbxDDQBLEXOGplWDIepOy992PtpU8xGdBCgV6his8FmI2wdxWLquqY7zABS.POCmwNgutk.aJsipZqklat13Z13ufT.XdGVvRLkKcvbzFHY0cgR2VWy0LwlSM0g1tRkJsBBB5EFFF6eC9Z4xkEQzLBj624C7eo.PwxPQJSAOOubrBYeNOmaIKkJYbzXVxr3hK9s+9tZokL22tHhHYRqVpHtNTFml166Pn+c75e88.5d1cMtKKtHNvYxLMSm97LdtklKFH9U9Jekfw.UgezezezwKaj54wRRRJ.jUUbrMZPhEkBCTR5CR+ffvdgQg8NQsZ8DQ61v1ubhH888864440qZ0pc9jex6sETu07LeZUo2otacaaFDFiA6k4UUK7+yG+iW.WKafWmjG3AdfXum2yKtVsZIrfohtqOz35Ri9BtTCDaO44z.x9re1OaSvsddYLAuXM.qHc60SspXQhpZhci53kq5M.SO40AnkpxN35Zp7TIZctyctV0s8EJL+VUWt51+cm9zs.5Xf365Cv.QyKELLuTUB6wOv0SQBrTLUqZL35ZLlV6RCQQqwRiifQiPLTUpv1pVu0O2K+mqiHx..ZVtYla61dy4f54F.YMx74AI6xnMFd8VdPnQtmiwP03piH1M4jrAAAYVd4kcVdYe022O1E2gIpr7xkcdfG3AbfF1yW0Kyo8JNF4Z1PdlglJhl97sGD0tBUZcG2wcrCP6FS2HcirK1oRWyl128u1cmEJmiRCaWr7.4ihBxGFFkagEVHSXTnybysKD4E6bXXXnsh+n.862ua6a9lu4MEQVyyyqg2M3E.TCZTKAh77tol.qWA1788e98s8O32+OnsuocaMT0iVv.kyyuGhb9wspKWJGmuTqAuTId6waMY7999C3XLXCqSsFHlt5.f3ULuSmSDY7s2d6If5i6h6nD769dtcIt1CIFJkpbTcN8id5dppIdU8xTqVXAe+kGa1Ymcr6+DmnPUOu7mLLLSPvIRe+enyWW605lANqiKtOY1Pd3b0oG9tWUvCRqd5a31u8b1.KxUW0LyM2bN.35BPCp73yb3CsEr.nLmctoNYu8a+1KnpVLNNtXu98xO0TSkQQ260uA35IVTFXZAsQRF7xK6283G+Ou0x9Kuop54+O8e5+zpMJ0vTweZttCb9fffKbjibjMJCahqsR+0oErXaVZogJdFWdaYWp+OboWidor+M5Zq9bd5Ay1gxkSaUht.C9K9q9K.vQjJ4jxR9ImbRCAkaRPvUpnFJ.mdoSaW2GNHA5qpNHIIIFAsYy5hZTpnrG4H2Tt74yW.Xb+k8m3VtkaYBee+I788GOHHn3u+e4ue9H6Z5yBxUfmZ9VcXtWLTzkb9g2GUGfK8BsANZqPcGLsamfs8aO5Qo.b5hyxropuxDQtQ1jjDMi.yCtk.JopNW2tcmNLvftTQDmzoQYDaEl8Qjd.cxHRKeeusOQPvVU871NHHXqJUqt0q60851DXyvvvcfUZufIH0A.IqTYE6y5U5eHSEW6Az8XiVzfyfAh4UpXPChc+uybwqAuTiCh8uXHp+TPWvHwrK.cXFakdWk9v7Cv0Mwhtlb.EWcHgRWMOtCaYgQBtauWCttnvJIknTb56nYxjIIHLzADKpgkh999lfHLGyb999YgRYYgExdzidzr3aR105CSTiqCGa3Z7Gu2IOHizigiKjAOagBVgbpp4w0MMwPHhLnwvhA41GWhO0Em.KsFnVNhHoRETnIKvBN.EDiOlS544OwVascwvvvrggQhmQgJGDFF1SUsqX7mbWdqSFlTqcbbbFpDbVRlt6688+96s3hK1Gn+Ihh5Za+gsmcV1FB2w0f1h9.CNkwOoX3LClC5aaylzBccws25i+dmOdqs3R7yFNNEojaditpofVaAromHappt8Q8NZKZPWnbLoDXZISa4z3fuGZLP+RkLb0z+v+v+PaqZSN.SQdxAL1i9nO5j26u2u2T21scaS8Q+nezCgqQliCNYPNvKyQu4a1bNlwbbsOWUKYCO584Szg.0jDP9Zesul.HU.mnnnQVSuPByOu47U8xd+ZG65+Gfb3CeXIJJH0WLYkUVATzvvPUMn7xntWpSWOOuNhizFnsiiSm2467c1yy6F5gqaWfNm4LmoEvNq.aUsZ0s.2snLaexZ014m5m5+0VgPm+r+r+ft.8N856sks3h8mx9UsASaV+0Gn2Ri92bZKR31m++WA6eJPRsz8PgdddzEVsyBrPWQjtpp8qq0U.Y7wGOaBjIU7GN1UFwP55CeunbLkM2aQQQJf3644HPVExFDDjwtOpTq1wkRTJimmWtvyEVrQ4FVanmO2VW5Vy4eTlzjmtZImQx.J8paz651hHsqqZGZrPuxhz2Df7pBqXaMmScfx.Fr6hk1.aWuNaN4jStYTTTawpVN3fSlLYxppluAT7+363+x3Q0pMdcXLpuPgfffrQQQNPCGZ1zj480IyYNSu8Cu0mtGBmx1lAMPfRZILxwEtMUpm1lr14SW5a43i3z+9yXXt+LavFCSXBwjxqCwhHHRYGLjtZdfbNNNYCCCyLwDiaa0.RaufAK6WMVQiQT8Te4Sk3W8lR788iCBBL8g4tU6x.GLaVUWkUGVg.5uakjrZTO.YeourWVVZTxrgPIzW3K7ElvJqjTsZUkUPf4bpllQxEO0nOKLiEfTgR8C8gtWBCC0nnHIJHxwyyyQDwAwTwrffZCWGJPRXPPbcHFb5Khz0eY+VUq5scsie7s888aQSZS1rcJmtAt2psnI6bri8BRkfv9rzdHmpqjCLWtMg2MAH0pYLX1XOFguRIfIkjb6DEQaQj1MzF6VwrHM409Z+Y.PLIfRGc87kYc8BpW50Vo5wppCvjDlTNHJQA788AfSdxSJm3DABfzvjLqr.4B+BgYeguvWnC.gWrCFeqLLIQqJNvLiZ7MMQeciHZX0CYbd7PXBzvkJfb620smQ0nrzrYdfhhHiswFaMlpRAOuJ4RRRxfhr1ZqsmqCc2pkf832MWt7aiohDq.zL7jgM.ZBrZSXcHdiG7Aevshfsemu86cmG4+we8N+I+I+IsfF6XIltVlpyVIcS0zmyWowSl0e6OoJW72+znVdavb7cAK4mlQDIupZwIlXhh.4aXThiCjsyk.kYPglwXTNpdG6YbrA99KKpp4md5CMVsviO15qudw+1G3AJDFFlWUMu+M5mCJkx2.4CBBxs95FGLsq+dpylc0Z1N+pD.b228cqpppHFGJ+TepOUBf1ngYtIx.k1GugBFZOf0lixfPIx7tuy6N6u3O2uXtLYxjyqhW1ImbxLdddCUFG.luToTxrdf+x98zck7utppcazftttka+59O751x22+B28ce2mu1WH57QQm6B.aDAa36e8a.rYcXGZLW6EGZ+9LC3Tm5fj72KkiaWo.2tbq0rATrdOpW2TouH5Bk5iAB0Bt0yPCxAUFVIdSqRbrq7y4cQn2.Sh9K20fHRFbnCMsZQGFAAmPdf+tGvAkL0NdsrelOyeSNaapZHy7HxYkjViMG2mF6G6SCfqceXhYlZwl8DbSUCicXdZCyGem24clx8Aic1yZjC40Y8z1tcVL6TVFvuNbDPqBTILLbNOOuI878xKh3XsvnnoOSLnWJIIoOn8DQ53U8FaakWy13R6O4m9SmZmsUsZ0Z6440An+WL5KlZuRHY21I4Ll.G5CLXeEZvzZWQQonZa+6+cPFWp0g6wGzyXaMQf9q.C37KDWATisrUgFMDfLc61MGrPACYkyXLWshzfBvbiRb4i5ShI4sMrepMTQjj986GGEEk364oXPuSFPyFDDjAHS3ICyDDDj48967dyFU6KjkUVwXOKfBW2M8+TgERWqOaiLb5KYRZ9VYX2ilLDtPNU0bTh7hH4nQibpQcIUU0X2zVGnZiAz3w0WjgIAMJxXyXEVQrUbtPbbrUVt0T9YgvvvXf9992fAt+BsHIUMPXST1nVX3Fhpaln519K62xr1pTZ0y68U9JektP4NhHCEG.qbr1qAMtT9NYQRhoMaN8SL6bGz8VuTe+gw+zDZCxN.aGA6HRoV2687t5X7kutJhj4QdjGIOMMxXt2ieB5tTWCwMaZZehm6y841MIIoupp5uruiHR9d85M9y3Y7Ll7m4ey+loqVs5guoa5lllFkltDLo+06OFDlCy5Rgya1qewgyMm5RgdlmviZf7rdV2hCfDAZ4xkGT1PT48fU5ypqZJr0t4h9w2+gkPgiNbt+c9NuyjJU7UQkDOOOc1YmUsMjiJPLnwJzGztgAAsTU2w9rn868C+d6BM5G9PmzfriEWLAVXPEK+cPoFsoN6Ts50OL4cLKc2scathIZSwT.lz0e6u0ouR9+u+wnG2zVBqWXnINfUXkg7Am3ZRrdtb4zUFo.Cm9.3a1bomqx0MLSEPkJUvyyivnPG0zZUN.xEtvFFz+WspzjlYBCCy6cDuhAGOXX2bX3ImcdxvKNeWy3oMDlvtOT6aj3sU5ggCS5AN8aXf8Cat4lNP4rXHtuCpZ4jdr6h0XaiUVYSRXGKwNI0dnZ4BqENV0kqNAtbHUilpR0p19DWJ566m85ewuXw3K7B7A9.e.oJHtDc.Bp7o5wwD.bA0klIVh6omVeXBORRSZRkFjrOk+vACTJy.jY9zq6UQKKRxwdtGyfzVstHhj4cc22c1tc6lKLLLqmmmyNs1wVz7zWf03SDDFKPhpjbcW20QsZGWcMNw1aYe+NU8757k9Reoz.RUS1GKk.DOc5KxqS+YSIsRW5IhLPDI1bsz.QDnIJtjXCHQlGb7YMmZfyq407ZDLvudzg5tBIPTLP+1s2omp5fJUpDmHpFFDJUpTwQQj25+9+8BpnpnwXQNS+ACh+6enGJ12+F5qp1kFzhRriiiy1XZEm1KWoR25oPCMrrEBwq2c1TNJ4TWRijWowUxn59yL8kayXX2MiSgBXOX9NhHcbGAxyW609bMqSlE0P17WwgLGqLL68zrDNNNpZcvv5HuV0vcGlEjIIhuueFaPrEKCEdnG9gxIwhyBoqollDn1SJG8vhlf4pQV37CgJ8tISzMFne0Tm8htnMzFY3nQfRCTojj9tSAWXrolZxw777FKLLLuldNrvTMsG+GtnTIACZkZqhron54CBBVG37ppaTNscGJSqZ0NYqm+y+4a66UsUDz9U9JeksnDsgpsAZWE5tDQCFYSUt3q8mxFOAVC2fpf7te2uaYgF3j1FJppx1aus82w.E1CJNhNE.mGEORrx1Xef3vvSJLTlOcFCn3OxK+kW3F77J366W3+kuuWdQn4v1qz1a14f4yB7TEOTYlSpcTavqMiKCCti63N5CzS0nNhHc9d9d9dFgTdqbPRt0vQcPnIhlSx769A+cy.jIJLLSTXnDFFRXXX5CEc0UVMQMjgXufSDzQfcDQGk6a5Jhz86+E8hZ6ZQGW0JU15FpbDCRRJwNvJ65zGyz6L6s5XOQrk8TwX2JLtDIV9NafOzuLMGX4jnDsthpZVHpPY6y6xaPtCfZLo.5BoIkoLcgFs+He3+zV.sspk1.ERPPu4+I+HpJVu.E0w1JEY51sqsEWVI666889xB3rPimVJjhBnKAZUZjXSNVBmetTX2Onj0Nu6pL.VUeSuo2TVrx59u5u5u5Tf6L.yUNMQIMnZYXQfqlRbUqrxW9Hppk877lILLb7nnnrI.pXgstXpDKn80Qp7+YNyYZGDbx1nZmZ0p0M73058y9y7yLjXs2Uh6KmToRElKssOZfyh6NOsejs8s59eOgmS22weX.fQfCMLJsgYMF4JTnPg+9+9+7hMLILYbVaAa6KrVgYsIwn5tjjY5m1j4UNqHhiKn4xkKYgEVHNsUisI+zQUMquuedOukyCT3W808qVnxxUJ.UJBTzEFqwC8+awUfBMZzHGqezCZ6Md4Fi5OsM4M4yJhjiljix6dsqppt6hvjATalQeVL575k.wXtJf3ZPyb9l0alOJLbDB4WAEUEINH7gME6QoiJ5NhHa466u4x99aHptwNs6tcUe+V0NdnsEzZFGDDj.D+CdsWaOndmxFToMzWmQH2cYeWqilf1Q+J8m+zwXuA0Vk9P8cKlHYLDReChMEioT1m8y9YWnBTzkUJFd.kW3Qt2F.t8.5433DGDDnAmHHSTXXt74ymOLLrXTTzX.S9284+7S69Lm9vMgYXElxf1j4yCjYFPpPC8L6dbepXH3AVd4v1dLk61vx4JF+epL.HoZ5u+kovdFJr6rCma+090dSw.Cp3WIIJLLYs0Va3ZRQM98ig2L644620z5yK2QDoK0oOkHw6HKK.YJAN0pcRMBFbu2681klKz9+3a8s1BVcGaQT6Tc8gsayAAYROdq8dxX+a+IjyFGvLcAZUxxgYzjcpHR62xa8sziRj.qbPWqKqkN2Wur.HIIIRTPfwGXEbccM0MUQO7gmFSxSJkxclEv3WVw4SIx3xjChFMIf+iVTl7zUBSfQevViAyaMBab7qQ5KiYNzgNTNndNlmbi.EsqzDoYwhG8.ZUF1rboRa3VwcqVsZ0od85HhjGGlPanSSClIJHX1G6wdrC+4dfO2TPywBBBx13q+0UZRb85eojeo2xujVCng43O54+o6GpBbZgYQZ.RcSDfwRYwp.GlMAjED.OhvjEX.MEBuSap1aVlmrqtaeLmoQoRYN8W5zYEQx4JRdfBuo23arPlLYR68rLnHddURuKU.022KAgXTM4c8tdWwfN3DgA8EQ51.5DDDzc1YmMlRHe8u9WOyw+7edSu76grwHFIV20MFnGMn6BjRFR6IPVo1wqYB.qBNO327alIv97++1G8+19g+rBKpFk+nbeft2wccGc8886FEF1WPS778HLLT787bdaus2lff9y+Z+4i+De7Odef9YylsuekJCfFC50qW+vvvtzjVddd6TKp1NGOHXmURkUNW5cly74GRTSqW8RBY8mriKkg0qjyh69yqZ1.pBzW0UFvBzuAz6C8g9PcEQ599u260LeuNIyc4udG5T0ZoNUUlLPyLUpTQp54kddAfff.sWudl7QIhDVqV1+p+p+3hTlwOQX33ye34KT4HUxrxBKX1LXCRV5RgziC9PXIbrppUd6F74YAxIhjYAPTstxBCgp3kBYI5teFs6lMqPxi9nOpCkH+PGkMAhjyQFUYbzTnds6yKgXP5IPmpdd63ciU2122eaLr6e25kJM3U7JdECnN8uISfEcwkNPyzdmsCMoMTqCGid0fuckrjmnCoVEbtsa6dxXpVwBf4Zr2i9nOpMohqzCWFT6I16G5K6ldYwPi9Tl9ekuxWd.fFEEjwyyKuekJ4788yIPtSDFjKLLL++2228UrVsZECBBJ9Y+re1z.MJ.qlmJj8LOUwCUUA3roH6necW5bO2y8z1zVDk2An8m9S+o6.zedCz+UV7.bO6BvZCCf41usaW.brUtQrhHA.Xg9WhXkaSLsHQafc77ptsJ5NufWvKnyx9989C+C+C68bt1qsaCi7LuCvNmHLzjfjlzd5zfKVh9VBa86TIJYuHP5T12Gmgj.HtdZed6NTA3b.J7QenGXLnzX0g7vrGHjLshQ40FX4epV+aus+21YgEVHkqb5IF95JNL7jIUW1GEDOeemOym4yjkRj8pu5qN28e+2eNfb+Juiekrv710+Kwk679DbLztzoXD6WUIFVa36RMKSBLWRCShqAHCKPAfI9+589dmFZLKkXg5P4+n+n+H+69tu6i7U2XiEUUOp1POR+tcKGEEMqTVlDnfmmWFw.i8DPGHNRe0TPqgJ+FPqibjizx22q0u6G782pZ0ap0cd22canjAsNKXR5lgOipqTZj8OfLmoREm8cedPBr3o50jiX2G0Tg5URSfQFpaPRzK8k8RxIhj+4+7e94ulq4ZJRIFS0lEs7+Vg0KSA3nEpAEfiZRX6BFjnLOLlpQEvkbM.m986qMa1LAQhCCCSr7IgTsZ0LAAAYg54EQxGFFV3O8C7mVDhJhKi+PQmahUspyj606lGN6SkUi07thGNPvvBrQcxzoyi4XAQ2fGqUKSRfmiAv42exEz88uS.TSmbzPvcHBlKnhVPgbyO+7YTUcDPPLYY22zZNwVNEoaRRR62x+G+56XChdmm4y7Z1ILLrc0pd8N24NWbPPf5eC9IPEq8g4MIdnjofbtXKHywFMoPWTbDWp0bOcORi+IYtz02UHApmd9k4latLPyrgMBy+e9i8wxOT0Zl8JFGjvhledIHAZDSIiu0hHRiFMxlnZNaa4jWUsPXX33una9lm5je1O6r.yEFFNGM3vvpShKEOOjKJMn1ktn8P+VecXHpuMw.ppcUsdJ4juCMoKDMfiB0tXxq9hlOM7r2ronse.P+ye9yOHLLbPEOuQRf.8q3WoOPewF2YsZ0FPY5GDb7tIIIcCBB5Gbx.00UkvvP4D0pgUDJF7peyu5dvJc90eaus1.cpOmQ8jpsWDxAW90QWIadeqZ+a++cCfy2mJz0R9uaoptUjpst1m001ilDuvtHzdzmq6ON2zuxvBjEpmRJvYTGwoho26zFMZl.x.DhEUR.jffSjCWJnpV3Vu0asPTTT9UccMHyqNY9leyuo4Y6wdJX8z2AGOclvDXjLgk012pejOxGYX6Y.jWDofKTjUo.TYz9E8w8kF6mwXHfy10gMw08BNNNaL93i2RUcP4xkyVoRkIhBCm9gdvGZlanZ0Y+leyu4LunW3KZJfw888sJPCIWe4xwrFIVndM53oyGpCWfVAbXcx.ymB0IrRlZ50i7Z9W7pD7RIYRKS8e5iAfrAX3AlUGJMd4.xoMZjxKCE.FSUcr0We8wxlMaAU0bgggFXxEFAJpsX5wAAAwpx.Qjdu427a1TsIcWIz0RPkCnoqFEE47u7G4Gwg4mWLvWG60HIznQxQsnLJytD5U+2+6+8OrchpVsZlxP1nSDk8pu5WX5yck0uHCHvRmw98paRjQyxcCCC6pPeDm3vvvT33kNR9u9A+f8+E9E9ELDHlRa0jvmdm6bmqmmmW2vvvNkgVUqTcae+aZGf1QQQcnA8VbwckWtkp83zdGO4GOdFPux+c0VTAzHPkxkSXEhu+O882+m8m8msKPm+t+p+pNKXl2iyNRRpr+862vYFnRZEyJR8gRpaNKThc777bt2+q+Wc.xTnPAmpdKmw22OiW0p4e1O6m8XT2HMgW0UcUE.xvJqvr16mS8jadSV7T3vzqjEHupqX5E7UJmGiJPjQJW1gUdbmOSue2+FMw.I270bMBMI2G5CcuEZzHs2KImpFXGhBpdQ+s8MnkitJzNHHnEMnUsnZsA5FDDDSylI+Me3ObBP7CdtyYVy1fgUmEavgKACFQhL+ttjk.HtQ3fWfzoSGAi5N0SjxsddOum2NhHaiqaa681AEN8JPxG6i8whcg9TmdKsz+LKgc6PXXnSXXX1fffras0V4V1yOup5XAAAiUsZ0I788m3VtkW43.icxvvhL+74I5IjD0e4G01qCIKzftuw67M1RUc665tt8c.Zce22mnKP+UgDRxU8VO...B.IQTPToVU4LGfi6tHTHEkNoqwjcaCGyRNrJGglnCPoGh1Ai82V.a6fy1AAA6bxvvcdUupW0PHp6440x9YWK756WLspwm56Xqw1ektG9LZQvxcjKnVNfnGMLnQ0htxLee232WdJ2zRx2qevdFGEgq4dbfis8E60qW6z97VEM1xqBDdxPw2e4LgggY+A9A9AxQSCxAt0a8VyCj2MhrL6p1q6S8zQAUF0o4XpsakvUU0x0AleMEHMId76c2+dNpp4aZHS8o0F57.t2y8bOku8a+1qL0TSU9O+i+wcequk25bpHSSBiq007tttYBBBDC5RHcMVOfNIlVjnkZq.7sdq25Ng0B25W3m6WdiyctGbi2za5MsQsZmXKOOu1gegvtQQQCVARvyKglnesu1Wi50qOb9ej6sQ+b+22OcGL6tm+SM76khXPQU04Sbe+ENP4L.Y+FeiuQNZRAQjhhHEAFqRcFiYOqUt4O6D.SxJyeHfoVElRbcmjFFxz7QezG0wyyCuJUzk88S.R1byMIHHvAH2wO9wyu1ZqUvyyq3O1O1O1XkfInAG5HUtwIqUq13.EH5.o1XOQGBgl0qofkTUUuphEi0FZeQb6M93iaZUu0tHdzZz4x878NO3v7ymyz9R11YBJ533ja0UW07dpf344gJhDFXHl+29u4uo566mHhD+a71+ON.JmpLNsSLnmqclLY5JhzqbSFTq1CZQh3pFaYM8F.nM.nZUw19ROdAc+s6jCum4LG64VCGQp8LI9DUUwuruyK9E+h2kG9V+w0txt1QMHv1oIHTtrZIb+3CcnCgqqqCP1xVR4TDx444Uzyy6PppyDFFNumm27QQQyTFNDMXbqziaBx8T6YuzqDep73NpXuue3ULbZjXQ6A65+S7rfvYwAl8R01F6+bpv513KJO.n+LyLyPxTUMsOdewnzR8Dne6VsGHBIN3nAG2r1qZ0pI999Iat4lIzvTl7pUuQaxGnKMSUOPSgFN1ZCUOoQ8u4ftV5xs16Ii8uc+8O1whsDNaJIp2QDo2s8pdUI.N4RU.oZWRBXM8+mpdQ4YER4Cqw52ueQTxEFE5XKdnBj36ubBNXQApjmFTrZ0pEu+6+9KjjjjmFMxVwV.qa4puZye5o2y09+na7sCDlnrHw0sY86G6G6GafosLTGnbdfwOWudVnOFUXlCnhdX9Z8AX0gbZzXiG3AdfKHhr0TSOU250qy63c7Nxqv3ysvbSdhffIelOym4jAAASTqVnUxiVHC.0Gd7NK1LwI2xsbKe6HCXBfDMTwGVM276B0ygHE425252Jy+s+f+.GaBITphtDKwHq9D0xCKppYSk.SrIjRUcrFpNVRRx3EJVvJkcZFvDPnt66+psplwUMDGUOU0t999se8u9WeKU0THd2tVsZcN0o9q6ericLChQVc0cStww18F7r.TAM84OP+W2q60Ea1rnrCfyIGRBTMT+8BMs8lzDiyNITh3RTpOTu2TSMUuxkK22yPbbInosHj0QPXPPXPOee+N9U86r4FazQDo6K9E+h6bm24c1lX1oto0I1Bh1xC1oRkJongYXOEtOhO66RdY+L6d8zvjDsm2087Rnro2FKUpT+UfXpPRi8FnxnesqQRhFypnBSY+ZxCe3COF6BQTmW8q80lAgrA0BxVK3DYAx8XO1ik2VItwCCqMVsZ0RI+SYcXXSQNx3fV8+gF0OC3vF6xv+knTQndQLUOIKMZrKL8O1U93Y9uyC.Msjt1q407yTLNNNUpG20gUYOajECzWP5lPxt8LpJFYSOQ5QYh888Sdqu02pg.cKyfibjiLzIgyblTYKr5kpuV+1oScGjgfgjUEBQJVrnRYhmm46dty8PswzRjsnQigJ3wtbE2kbLx8noZNMFxGOM5EEFNnRkJ5m4y7YRWel8VtkaI+IBBFCShtmDWlrDklDZLQXX3XdddE9u7a9atqpVr3SRzkbwWuwqX5w3NhHcti65N5VhR8eCugW+tO2FJIpW1wdRXv8bO2SlxR4LgggY.bBCCEwR3qxnI0SrImSwz69tzJHHXGU0s8882RUcKU0sdjG4Q1NHHXaJaB18085dccZ.cYF523hQc02oViIuhWwqv9OWJ8ca67xJT1PjyopmTWLIlTeOum2SFpaJD.kIaEp734ivdd12.TpPR59OIIICknWGb3l9dtoLppYUUyAMxqpV3rm8rEBCCKTqVsB0pUqPPPfoxuVtxwviRO4BjXeiQelrenamHhn0AkUwjPIWhmm4G7S+S+SmHhP+98yJhLlHxjppS8fO3CNMF62G5VeouzI9O7g9OLluueQupd4BCCy1z3qf3Xt7SCRnuuuemUZVuiuue6p99cPoy8e+2eaupd6366u4QNxQtfmm2EpdSU2rLk21yyqakJUFbW20ckfQRnSdVOqmUb4an72IPuzAcLRxxmOVUMVDYPPPPLTeHxmgg9PUDXrHXRVmo1byMmBWltDkl9BW3zoyySswW+qOkp5D.ExkK2PkG7DAAJf9bdNOGQTICP1a7Fuw7yN6rE.2wBBBFuoUkrflimISlBAobJwEOG9jctTARN6YOqsJ7y2CnajpcDQZ0qWsVkRQX6dQB7kaXHrzUWs.FzYNwNs2YbfhyLyL4DQxffnJ7U+peUwAPsxk9q4m9mVBqkxkPAI0BenAK6620yyqkX70bKOOus877ZottcpVsZua8Vu0A3QrKtwm4L+c69dhw9qDDDHlLv9cMUxVAzUreZ4Ilzh0LTwvTUGb3Ce3DKsgu+q8K5dXIVxwR.1l0J0qm.LX0UWs+Vas0.TTOOO4DAAYts631xnJYCBBxCXsSDO0W7K9EmpRkJG5DF0aZLX8caihciC4ISagYZ84oQWXgEF0ll8dZgr.4rjbb1eoeo+0YV3hCl+hGKf5ieLTeXKoaUXs99UpLPDI1yD+x.EhKNdwDOOeGupCEFBw0PE.CJVr3fFPeOOudAAmnm6tJ4UZ6dYH40KMmk7cKCkSe5c26vaHJaRpqpPExFZkTZt3msoyIYwzxgEwfxsowvGVSu5pqNAPATxpfCBhfHPCw19pYEUxGFFlOHHHOtjyQcxBjIBDlC4re2471S3wS2HLAfDNiohrXCXv1ixYTMpHvj4ymeJW3Pv7iedyC0CRRSRNlkTkvDzxl27O5MuQ2tc2ZxIlrMP+W8q9Um3444jOW9rhHErx413Uq5MVsZ0Jh6JCcxdNSE1yrh0QnO6m8ydkew8I+XHDBm2Hov4Wc2E1EnjYwcgIJjAJKfuNMnjA8Tbp8DWnHhvB3TQjLrhw.jToRVU07epO0mpfHx3NNNiswE1n31aucdPxvHpwfHhwXNn999ZXPPhaY2Xee+9m7KbxN+o+o+OZWs5M1tjEkIUqVs0gO7g2wyyqUPPPaf9LOwyApMONBUpXN9QCCzrmHR2VsZYpdQYSknpTohtbkJwv7CBtzpOS5PmETDRZRy9kftSN4jcbbb5DFF1Gw72Z62+AZJa7qC2Xp2RKsTG60bqehepehc7Nh21.a6AaCysS3tYUtmExeeWavr6Ku.ppZxyyya.0s22ksNtFsmrGua+Wu3tNE5BS.tS0vPXfyCLeiUZL64uvENjUV.20.qpYQH+e4m5SW31u82bwq5ptphUqVsXXXXAUk7NNNlDrTwFTyZWDZV1OJWt7ikPfEbfxNtPForjqg1Heo8tIPFlEmY.YjrXmN1GRZryEdqlNWjRzeiCTLLLLuU4kD6Ca6lQRrHx.D5pnsjDYaP1DX6DmjV3R2k88GPchqUqVxq8W7WzrFt9dkU0EWbQy5piU6oKXn+T6XF.Jwm8S9Y0RPBNDuJq1+EbjirWxOqDxrfik7tuxDVm49OFXPIHFJEO6byk.vOwOwOgCPVee+7G+3Gu.vXOve2e+DtvgPY5FZioJAGxyyabfB+7+7+74vESEMNyPm79VEoIl0JSa97ttq6BnRLTdfKz+t90tqAMoYx0ccW2deds3Srywa7c8Fy74N8mKyLyLyPhd0T5Q67iLLX1ddd2PWT5hpoNwklrpsTU1v22ei+o+3+S2bYe+snN67Reouz1+NejOhQ0HN+2UYCS+ve3Or4eYZHcAv4nf.yKM.8K+k+xFUSorQBHoLwug2vaPfJYKCYIlLQDckIW3JlvOFsptG5PGB.GEx5c8d4uuO08YH7Yy68EAJlISlhdddEqtb0BUqVsf+x94A27vBlye28TktCDIGePlW12WIF9cgDliDSxEKGWRjX5S+UY0tUrITJWtbw0pUCJY7kHLLzQUUjxk050qqAGOPCLAtO5SdsRZKKY2WNLLraIW2NA0B5DFDzCGip3EFF1Ab2AJsYYXChYy5TemELqA6Y41mALG8qrqzZNfkV56VSbhweD2UGjVs6O3u6uaWvs+W5K8kRvtOgZT1uhThIfRSAKLy0L0TyhCyzjly7rN7gO7FarwgAldpol5PRYYBWn3JFRb0AfLYxfmmmAQGNHn3DDDjUEIGkZjVnghQ0pU79tu6KWkJUbrDpdZK29TYxRTfjidziZBVu7pcEQZKhriKrS9796zbzVmdoqXBSR2OMGvXTlIusW+q+PiUXrIDQJr15qmqRkJNnl2ONzgNDppBn344IhHhJl+u.IhJCNYXno.nvl92f+E.t.kXyG9ge3c.2tG+9u+Azk3FzH9FVbw8ZWqJ52mueJR+9NchgGcLRR5XfXI7TwyvSPhHc9v+Q+Q8.23nc+aFpZVWhimbJNkLm0tyEtvE.SKpz64tvBc8775gPbnAgWx64teONhHY788yFTKHmpZdUxje94mu.PdOOu7lhbUtPZ7Gggg4MDrMNl1g9ITrP60N1F6AcyYvRV00q+kFGqLG6A4+.efOZlUtxmCkrjDPPbYK4r1oSm1gggcBCC6EYU0wfffD+kWNAzDTHHHPBBBxXSZIMZPRsnSz+pu5qt6a4s9V5VB536eSCUjIfdbr8PBqOU1J9OUOLyYyhLOvPj965J.Y928Z92kl32wG4qwrszYdfByZkf9ZlD.ahAnLk9hewu3rhHSCxXkcKmKUMRq3UIym3i8wyFDDjCjbpn48tAu7hp4oAYiINCttFesW663Is7orw2NZImQMVz2x6HwXhwOup53.S9q71e6SBqZfi3UVVsT.8zoUGwynk1Tmsbbb1JHHXG1Mv2NhJ8UUiCqUSpEVKWXXXwpUqNtEBkiAT7eHJJODMZ121uy1OU9Pe+AvkcUCq7m2tIcAU0Bzj7AAAYeQeeuHwz2iAIaXR.k49OMhYO6mqfDYf3sSIHC0qmSDI+OzOzOcJ7pJBTXyM2LKlVrX38jpZpbhQPPfnfzndCAHoz7khgF8BBNQ2lP6fSFrSXX3ldWu2F.ajjjrMPaVk9qYgr1RvPH4Z49gXqyucGarw5IhDScR6u2A+O9FeidXTYm8y.0vHFfWGRngoBjMTs0m+y+42te+96.zxqh2vrBK1u7775gQJ65GDDzKHHnSXX3N2y8bOaUsRUChYf1gPWXsTT.XLR136JMNNbXe9qFGpMIjZECpf5opNf50saRM+vDCvQYHgYxYLRPIvTMfYfFKfgkEpryN6TdPuAyU02eJQjwTHSXXfEQJRVUjB+y+g9edr69teWSDDFX1zyyKkabb95ekuhPTjMv4oGElr6+S3x+tkvoPL8cdcibx1fLetO2mKWSWS0+DWo3s9O+Vyy53b9K+wxgJiT8vvgPHdhFl4gT9KIaTTjSXXXZk9sN5jLPUs+EN+E5phri+M5uouu2Fhna9e+S9euU3IC6ZQKQ+apZ03iTohEoTk1kfdcGQVMO820uQL.JmmDnY7K9ewKNtIDajCXRhl29p8B1pL1j7qagz6wNHsFSZhUAoIHPSINNVvf1BGQjbAAA4EQJN0TSU7EdyO+wOdsvICNYvzAQQy7R9odUSALQX34LHFrA4rR.a1pW4V67warKJP1vj3m63NtCAh.pqM.81eC2tBve1e1el42eA6w+LGfi9tnsRnIxW4q7UbN+4OuSXXnSPXn4baNZpfj344MPDoesfSzCgdpscUX2Dlbgp2n24erG6wtP3ICuPCCZ4Z82+m+m2wp9HoDx32oG60IZPsJYC.xYAGXUAPutq65R4dj9hHCnNJfCDkotQNTScj+x+b0t+iH9oemg62JP9fSFTzyyaLq8sw777FCKg0g4mmOHHHKMHasZGOCrRl5fCqPZ+Xeof27ScySmxlvj0HApEC0im4YciCXcq5z4ZqDZE53662QancEQ55440ILLrUvINQZ6a0AneXsZw92ne7G6i8wF.z2pZPCUaI0jLtdux+0ux9JzesUWafp5.OOuAPi9Pyd0gtF07hNqXjA5dehOwmvjL30nWjsnXKA84Tm5JEv82NFWpD0aVCZ7inGP221uwuQWnQum6y84FCfpZFQj7hHiSSlBZNyFab54VAlWC04.loYYN7zSO8TppSJhLgVWKVW0bKu7xNVhaVcccS.zff.0yyCwQb788ERRbBNYfyC+vOblvvvrIhj4k+xe4o97t+1x5op4PkYGdLGPcqZf.sZ.6nZyTzkX3OnScYOuVdEirV4vcBpygtmeqeqCUud8ITUKhpYCCCSSXhIuHBhuu+tsdNPsZAId99I9998SRR57u8e6+eT2ad7V1YYc998YsGOy0YXOrVqcUGzTDgBRnxo.QtLXPBw.RqBJQafnfcS3Jhh1p3.nHHbQHBBpMpneDPHsZ3JefVMgtwlzhfbUSUIg.kzPEMmSsF1Cm4g87d8b+i20Ze10ImToR.BIu4y9yopJm8Z3csdedeF98762qeOWW2soNaBrE0YWaa6VPst0gArlY+m0KDeub73e5wfQ3vruQhLmGtiCZya.16+NGgLrMJu9W1KqqpUGvnE049qPRWvwdMydIIswXOpQmpPmc2c2tNNN8i64GIIgU9d9DmD9AhZZGQOCxbRUoRkLP0bDGifiiSV1JtHY6W.jGNn1QgBIH2R9HejORFU0w.lrb4xS+Y9Lelo.lvf.hvGLx71LLpxT+pEoCPykWd4ccbb1ESqDNrsf8OyY.DbLbvmfhbed2WRq3oUrqL.n2a6s71Z+l9c+caAgiPhvGp+Yeq1V1EaHrNVqZ3lIqRfnUMB8wa6s81xhw21onDCQeHMXJhQ215vTP3QvTrzx.N9m12dgEVnfiiyLfNlUJqLgggoPIcPPPlq5otTFfLhDkUUMC0HqSkJYe+u+2e1mZkJYoVs3BzVdejJc7uoCBguoNdj.gIfoZIlfFB1Of3W2q60k5u6u6uK64N24F6M8ldSil0qLPkKkpTZLBELDkI6t6t6tsiiy1NNN6333raPPvtNUbZphzQEFTwohkpZt+z+z+zwAljRLITdBaaaSxZVHtG9LP98f8C42HePabJeNRQgEMAyB4jhRNyF0kyhoBqVO+q3JTfAEOHrpSpj99bGhX1HpP5ZplAHmw3TsIpVudbaOQVER433XYjVwjD+BAAAx0ccWmo0dfTUCCs788shhhv22OZo31zw00s0obb1IdCsMO5QO51wy+CMVc1QLxHhDgio+EoJ8jRk5AL3c9NempySwIBJ0+xtrKyjrhYePynaDPuhlLyuy202020VM2auMDQ1hj1CvX3roqqaKee+NNNN8btJmjp72NB1687ddOFV5tDchCn0bNKjbdKk7LRNwCUDQ7H03bwaDuF8UU6HhXRPnTpSLgAigb2VMKtwYX1HAkS.LEvzr.GAJr.wIJgh37w9K+Ks2byMKBLmp5jA9A4DShRRNyVmxwIMJ4788yKQ5X9994.REA33bR8w+DehifpksRQoK.9ei1arOPymCWusX7ZkEFoEh9geVOqrwI7b7d98xea+c21HLK+g1SNliWnwIOMd8AkXLn3D+z+z+zSNXvfIDQx633joXwhV.TnPgn3.WGnpzWDo2QNxQ5XEQSpw1ddda53TYqmzU7j10wwoEPWnX26vyq64M7VRen9.CYuQTrLMdPVV+QqCEHxNNgQ1lJi0CnuCLfUMFOV9NVNMLZ+qam+bizFWb31PMueTlzwslYVfrat4lYCCBRqFBCzHuqHY2d6sysjakwEQmTDY5mpiyLyL0Ly.LkiyQSrsY31FJm06BO+OLV2VPv37gTL9dnbryHhHVEAqK6xtLy8ViGBUd6BTnZjWzK5EIpHVkJUxx0wQhctCPTaa6nfffApp8q35l.c6j9Ttkqq6d.6DbmAa83dbOtsTUMpxDkZ0fQ4SlZb.GUdj1FVx63QG3SLIbZHw7a+1u8nXm+6CEFtOPAHpe+9I78RJHFIYm8Pcf+fAJoPfFSpkR+98S0qWuLwnlKWPXPNaa6DIpNqHRt.OubPwbtttoEQR466aUoREqx6iNsTuzW5KMEm6Bro8Mmjl.QUh8yY0UWs+W8qd5d.8b1mOjZSHMMjQrrCvNhXusqq6VtttarxJqroiiy1.6phzx+L9cd9W601Fy9j6gvdpHsbccaeS2zM01cI212xsbKslbxIasvBKzRDoUPPPGOOudkMEdvXK.FPH8CCC68hdQungnG174D8Oj1L7aEiCtu88OwIkPghC9hewuXbaRZ3MFQJMTEhJCSqpN2kOyLyqptfHRghvBTk4TUmQDYRfw+ReouTdQjLYydzTwqiiBBBFDOmEEDXTJNOeeArX6s2lWz+oWj33rj355ZAHwAvFAy8Mm4u8EKOAPYAF.NFDOHRWlOg6RpbwBTzLGd138iM78S91saONv3wbkQlX35KI+mXoIHrwz5LlGACpT4nFE.y2uSkSUoy66889ZAka5440DnYAy6pctq65tFVw+4LD1twus84+qCqUtezw3Dw9pEdApaXOCw0VrGPzW3K7EjX+jx.ktXEbv7Lwj3n9wb3QBQx2YxImrSPPPefAkKWdfhFEoZjaE29wDs6P94nxRU5EWzRy4sHY877xNRPtoJw8yN2k5Hd9uAwEUH0MbC2PFQj7IEJ2Jq0DfcLEIPJl4hmjHRPIEzOlmQ19I7DdBafgEr1Nl+t5kvMU.DDDf6U4pHD83N1iKRUM5q809ZC.56440EJ14m4m4mwfbyRIEL8D2eNT7QuiQsyYIhM0XHWWYJ1PYxigqZRjc94.l03+uyrjHE8kLIK4u7u7i49Jt9qurp5bgAgS.jcHxEMI6LEZLh0UIUbwsx366m809Z+MxVExDFl..gpoe5eOOcyu649FFhL+Vx3QpDl.mEszHvRq.L326262K5ZtlqQN9wOdFQDiC2MHdwiWRefdwlbU33CpXTvjV.67Dmc1sA1LHHXSfsbcc2sDrWEGmNfz222GQjL+J+D+DiCLcwZLMTcxf.Cul3cWdwFJBuXvt8qmG16WASHs85jkFKOTypoAYLjnSsT.V+o+oeDsZ77VcrGkvghuFJfiCr2d6E+2vBZjIlrxlPDYZU0oWXt4lJLHXbQjLtNNoBRplY79lhHhiii7o9TeJAPDvprokZTKKqHWW2n6zKHABusCiaCJvY6xkY234+DmzG0PSTAHh.FrPBJipWuup5fidziFUrAQPs82DYiKZuyNDIEwrA8FPwFybjiTKBZ.rtiiSRhS1y22uUkJU5FDDz2+L9II6oSEGmVkhudCtyfHndRv8iD7SMILLDvVN6iNWfGO+ZOXlYnabxRZZZYp5IYIGwnhLYNYtSN1Bv3as0VS7c7c7cLYYSBSNRwUYAnQwenenWZ4Se5SWx+N8K9Le1O6EbccOhiiyTAAgiY6XmrVLoRIbFSeVmAjbpHYUQRGDDHK43nP83JBa3FkBPFpQJ6QB3Xw6euw9.l3jki+8ZXT0AAHUXLYF+1e6u87oSmNNIqjpBHv4Nzi0I1+7H27MeyFRQqFYuwa76er2467cNdpToFSUMaPPPp50pK.znwppsicrTeJ8GFvpE6466usHxlEKxVNNN6DDDzrDzNH3t5ToRkNG0zxJCrAkPnvgUk88eV9n0QTX751pL6PBq8ratYRxSF7ze5KBPl+r+r+rwrgIfvDj5jzxi2OdyIVdNyYWkwfpIIvaRGGmwUHmER55MpmBHshlFHaMSPr4UUGKT0wdiuw23XddAiUtrIQM+w+w+wiYCikvsMbgxy3CkQLhlPDohU8XmIqRgrw6SkstAERofhxBK7PZeAEPsGwNtEQjJUJ.iyc.p.pef+PR.MNnqAu3W7KtePPPhZlrGTd2kVxYWfc9PenOzN1vdvH7ICHyCVOJvQk6GZAYHJSpEAL349bet8ECo80GZDmvjhQ0UUyjICU8pBw6c9ffjrKX3XPyiUQvJc5zoVc0gshWVzj2SjLtttYUUyuyd6MVPvckCHiigXqs.rpRoguC+w9Xer3pmUHckJWPu9+MxgBnIUMegEdRCUAhf3VLc0UWsksM6Ar84O+42rHrgu+o23Zu1qccf0O1wN15kEYcQkMWx0c6O7G9Cuc9b41Qfsccc2BksEMZGe+fc+E+E+E2K3NC1SUcmYlYlsTU2TDYaU0c6zoS6pTruiiyfye9yqgwAkZauzfEVfjjML3Dv.3rOXshyijItKwWqTGej8eh2KJcwZjBpackW4UBlfLDfzpVyfZBJOUnpy.bjppNmHx7ppKbts2t.v7hT5Has0VSAL9S9I+jyEyENVwAgNPMxNerOMVXaaaUw005FeU+3VOwm3STJUCApZAjxyyypRkJwyIIY133e8LOcAILpxHIpNV9eSYuJVwUbavcbG2wPza.dWJsiiETLcwhFa44ymOIw2YBBBR4GFZoDumcDhssaJhUzQIN1iqxsx.Hpmuue2ffftA2YPbf+Ui+Y41MhQJ9IO42S2vXTys9ElXjClbmGss2pxYQiS7YjpZja7e9+168+1.ndD.OimwyH0U+Du5XkIoVbacchGnjTD6K778JUZehjOlju6GihrAUqVMxwwIRL6gz222uCPykbcaFDDz16LdChQZdZfb+Q+l+Q4pToRVnZrMM6z0FlrjS7vIFHs..MPc.51sqZ6xsGC..f.PRDEDU.Hat4lo777R+c+L+ty.g6yoFacIwekQqXr+0DJuUoRrZ8UWsliiSiZUqtIvdAAAcUnuhFIv.+6zuqHRBmL1Y7wGuKFj.2Cp24tu661zFN0RJX6YitHWCOZaLrfDyCCTMr+HEVoKv.MTkxPtVsZM4sca21LuieyeyYAls3pLODr.Pg0We8hWYoqrjpZoNsaU3idK2xQbbblv1wNS7YIZpolZfgCdjg9ubUK4lx000HhKplCpmyyyKmsscFOOuL1Pl+oOy+T5JWXh2fGcFW0Ec7HUBSDNAhgKhsUhIQu39GMwwtTu9W+qOQQWFkyDtHIKAENWjmG8.m1.6VsHaAroiiyVhH656Gz7zddc7886455N.D0wwI8o87F64+7e9S+OdtycDfoS5E9JUpbP1gNE7vNCqOvyGwATFNhB1.jWUMmHxPG8+g+g+AfgFGCirSfDxwQpXRNhDDfNwDFE9wqSGKn3PXuopNMvzoRkZBaGm7pANURLAcm.WMTLRqEfLrGZgAelOymouqqaeu.uANUbF7Bt1qcfuueeJQGGnEDzrZUZAKdPICNdiqRCZDKAXMTsWbkC6Khz+Ztlqw.we12gKt+a9cfm2w7VSA1qHrATu1O1OwOQfqsc.PsvvvMbbb1MQIe78ChbccUGGG077uTWOOujVmHJNao6yqGiDHusssXfh+i5xx7Hyug82ZK5By1FnU8EnEX29O+O+OO9Ygiz.ReWMuqLMTM6Ly73y8U9JekwpFStq0TcVee+4+q9q9XyWtb44cbbNhiiyT999ieyezaNKnoiQhTLYmZ3fn3yukqqikJhkEfiiSzc56O.JpAAARPPPZnXlFPFNFoLjabIKeeeYYy8wk5luwAuJR430khsjZyM2L6u5u5uZVy5kBoADuJvC.YRDi5oRQ.CdEuhWQD.UgTefOveR1wFarrwbWRZfDG8.TB7CTee+HPSfSaqHMZuqx0cGWW2sqWmsbcWZGfl0JRaGmqZHYgAKz+e97mOBmDhe6DG7c6GM8d0niQuNiIntM5NeLD9Oxkcj1wn7pc0pL.JYcC2vMjOrLSBklhX4wDl2DP5wS3OGCZ57LPDcxPCCoLOv7sa2d1vP+IcbbxonojXUJx7S0x2+Ns788SIhHAAAoVx0MkHZ5SeZ+rPwbu5W8qNWXAxe9ye9XjlPFnzCmVyY35KU8H96mEZLVYXbU0wnH491+1+1SC0Ss5pHf8k3wt.PrUk3QDVRPXfDDDHSO8TwW.pHpQJKbccSPdRzm7S7I5GEE0w4jNsC87ZAU26zm1eWOOuseiuw23Ng1rW7dgCL7VQEiTuV5PQ5yiziCA8Gizh.6yyOCKLfp0PLDAaT4JkiVHY+gYtXb67vg.krBLUXKUbhurrsss50qWJQjTKuxxVAAAobccR666mY94mO6ke4WdVU0LwR.K.wq+qoNI6c6PJSae0HsmGogE+lEJSFlzLnV+ES1qzl9yAcWXgE5DFRSvdmidzit8G91tssbcO0VenOzGZKfMgRqWCV0YImF+Zu+e25W1k83WEXUEVCXcU0MAYKQLDetiiy1hHa566ulqq6pNNNq455t8kcYWVSnd2hPzQO5QiXgjBfENX0UY.LSDPzYu310Nr.t9l06hW3weQrNGjJln+SE25do921cWq68duWKXnhUY3iCSBbmEpNOvBhHKHhrP4X98ZpolZAU04g5yM8zSOip5j2y8bOiIkkbppYEwvUBBRjqqaj6RtnnVAAAo788S+G+g9PoWYkUREGLZp68du2TwemC3244d39d0Ed+eBDOPfiYR9mpotu669RENR.LO0m5Scz22df1WZ30ho3b0spWmTP4TMa1LEf01auiEf3ZaKDYTGGDizoa3tP0RATUG78+Z9O2SUSgrdZOsmVGGGmt+F+F+l8vHw0chSbR7m067zssMIfZwCQT.Nb6KOZYndfByGIhD4Ges+xdYurQK.Zpa+1u8gh8.f0HJx09nh5B1Wdsd0pQuXTpzuVYy7x+g+C+Gh84cI9deteuVDipbW2qRd4uxWYTMHxwwQqToxPNEAH+q407ZRJ9TRgisVd4kG8Z4g750FwH4J.hxl8XC.FL6ryNv3O99EN3252525R0ePkEouiIwZaUqFqVpvUTEnZjpqVpToscccaIPOAq9Ntt8MsleTKUk87BBRZSwN2gmWWftOkmxSYnp8vicPVxHCChXVqD8EwNo.psfhMEQZKkjdgppiM1iK80ccWW9ekesesI.lolpyda21sMOv7yM2by8o9T25QVYkUl94cMOuwEUxDFFRPPPeD5hP6c2Ym30hZWId95LmwW88CLH2VjLP4rhHYAxToxSy32uCV+Um8eRf4+Vo+GecO9lcBS1eRoKhQAZBUf9EKNDBYsJYfZr7deuuWCL4chq5i8kTBJhMbDXZKm5riHxNgFdLoiHDUoREQEwpLjx00IMP1+9+9aera61tsIGarwlpHLITHAVXoY9XiUEvBJkpx8GV4W381Cs4i3fwGxkB4sgwEo7DppSHhjOtmPSqpJKu7xQk1OgBQFGsWT3bIa.lLVC.IWtbog54KIxDppSBL4W8q9UmfXtY.iSApkkwfPBOMjOWdcvfAJFmCSXv6teOeOeOcJBcq3Tomuuezs8+7+o55dRkZDEbAI6X4CiEoihqdXe5kP5uM5rxJqzVUsSgBE5BzqZ0pC.mK0dELBNdeZPq5vV.M9HevOXfHhOl3PVUDYGee+NwA4J+e9+7+IUPXfUICxQTKKqnhEMHkne+9VgggogxIZNdZlINoIE.nhNhne7s5pkcvQRvr8gML8E6pzAB68y8e7+Xefn+1+1+3gWyehOwmPf5VhHYrsMvMtZ0pS7Sdi23jddAS355NtHRdJQF.qm6y64BFhNsGHl9jGZq5977huueTb+vNH98lHnNK43jJJJJCT2rlpGoLjTVMbcc0EWb30+n+7fCANgTZj43pfg3LphdjibjjJ1nPCy6OdvEgLIzDzLEWUzA.TJ1AUGCxqrFBkXhQfkLRh5flhJ6JHaeWAAaQLZ19m+m+q2VUcGpSSnVqhwIL4M+l+o5czidz9jl3jcd1GKjrjjwnAsM.n+ZPOnPGViV0iUHHftPMUDIye9uye9XPsIJMT4GVyztLMXrYfwfkm.XpBE3H.EArKAtO+m+Of8Zqs1B11tyDDDLFJoEKQbcWBr.QrF5vnpZjqq6fy36G455ptttRPvcYrm1frG8nGMWo3V7IAod7vHYIbLhDYdMtULxppN1+k206Z728652YLpS1+5+5+5DxcTtvTf7.NFhbEa682Gw01FGaGbbbX6s2YzeaAvx222JVRREDYPkJmpG0oSTbK34551rRkJ6ArGgzDB5Twn5IC.O8SeK2BvkTBFdjZbv.bF.Ge+jlWBCwaVBKQJIEGwN2pv.vc.aQz4dvsGaA0LsXZnZUBREEEYEDDX0nQcQUUV7XKJ.Rud8D.q0Vasz9g9YhaGGKnL+9uu2WbU0PCLIcUH.KOH0W4q7UhSH2xeipXJG1bUx5uAKmLGER+0GptTzBBaBr2K3E7B1Ept6SwwY2vvvsWYk+kM.V0+N8q9R99eIA+H+Huz.U0pW0U4V222e0JUprdqlM2LJR2x00caLPaeUU0ZAAAUgh0KBq644sCPq5PWn7.VknZPT850i22dqQC76A5Sx++KEdr5aD6qt+wd.hgvQaXAXM+7Fe5lXhIjm0kcYD2pcoAF6i+I+3SKhTfRTBJaiom9KATHT0EDobALINo.ljoLmHxLW4UdkSqU0I.xaaamSDIcbkWS6cF+zDoYYeNxIWrh3jIHHHc974kX0KJtMEIiYe34S3CpGt9aZ7cc6DgMXEK.oPAw5a6a6aKEP5XzljDKfFu96AMfwDB5zLuVUFe7wE.1cWiMrlMahaEWBBBLkiNRMLnAnf1WDo8a8M+VaZYYsqmWvdddds.5bi23+oN.ccWxsKrPmEFQsRVA5BK1ikOTU+5Rw+wGoGCulNADAqM.neoRjnVWQkA8xtrSQL5lR3YMKCoienVsOjjoV2LeX36I000Mduiyj9CcyevLNNN4TUyG3cW49e+o+z4JW1TnVee+wMw+Tb72467cNVXXXlDUaixXAErdlKt3WOqEMWqVDUB5qZXWfNkKSaQjNhH8KaPRn9K+K+Ka9F1OnGuHVlAFdGjcAVEpFJh3WnPgvUWc0FA99aAzDztA9987CC6cRmJcpTwoSEGm1m5TtcCBB5UoRk3yeg8eeZex18RIVuGMj..MFQL8MsoY0NwHneuq+5u5cwvqYwzUPst.QEKRZf7hHS9i+BdASAklx22eBGmJ4W76bwz.nh1WU5HPST1EXWE1AkcUXOM12uImbx9hPjqqK6t6txa5M8pjqJFUlPLOFpH+fm3oKwwo9no0mOjFORfvDyKRmyzyDwNdzOccmN.MEQZU0v+BC9JekuhIC+AEMILIb9KEmcUfAUnROLIfYuNc5rWIa6NXBlJkpZVQ072gQh.y8LelOyrO2m6yKW0p0yCL1crxJiAMFCH6oO8oSyZwOraLmETS909i9iFlk9K3d5gy7P7lX8CKLjOFBTMipUyEC267TuTNfz862mq3JthDY2b.yF+hVkkE.4Fuwab3bv7F1DbX1hqWr3D.SIhLwke4WddLvjzxw0QcbbTTYHbFKUtbT61sGjJUpgL3MP6efefWZKf10M8W5.2kbi.TOu6L1f8BWrMnF0ndeV2vD+.sO1wNVaawtS74Jpb4xLJQrbQFInJJN3UmcwrBLrHEOu+4OuusscMSUynMlrnm5xu7KOifj8K0vnJRQQQVTun.vQO5QsrssSGCAwXXAVw7NWiB.db1C+96QxpkcXiKHwTmfSLZO71u5by0WUs+22222m4eqJQu3W7KNIi9RTXICjbssS+e+u8uM08bO2s0sca2l344ITqD.CPoKncTUMjSVoj.j0VXz58tXZyo9AdA8bcc64Z34lAUKWdeG..KBQVajM4mX4SbPmxd.LhdVoVxwvAAPEaYfgfBKOTVesw9vP3zAmqFF3QpUKkrAI0NPPVwsEQxWUKWtbjB8PLs8mJ5lhHa9C+C+x1DSeytyK567EsaLAK1Bna8xk6Az6s7VdKl0tqvfvGs0W0WZiCj.T5ywaDGn1LCYTdari9.u+2u7i9i9il4tu66NesxkMbDk4yTrESukQp5lEXAZTnLfKvw9Jarww9ze5OoKPgfffo.IqiqiE.99mQUU0Hy9FQttt8UQS5C61wPLt2IcNYT34OODm7q637m+fxl2kx5xK78jUlMBVOpp46lEH+a3M7FF6m+M7ykCHyq407ZFdbsu3d4c+BdTCKe+rUDDDP1rYYj+886W33flbbbTJWKVFXupN.s7882qHE2EF1Zjc7LIitOP+m+0e8CnF54dzW.Eib8bt8WaTKduwZEsf5TS0App8jxwbnyB9w1wtH5W8IFBebChzDwpFHVVIIdyPFfIHrrd85IeSQTKq+q+W+8S43bRApxq60+5ipTox.bhC2qXMAv5S9I+6rdBOgmfIAXOHd4+vbLpswQa+fX67mXXgMJPggqI.Z1nPg8JWt7NG6XGaKf0eNOmmSCmJUBA7EQ7+re1uRULtjs1we7O90Eg0877VOHHXUW2kpWoRkZNNmrNTes6JHXyJUpr64CCaCz87m+eIA8hZwhEE.4XFjKj5.eFsM7jQ+6mfSbX7X0gkbE3huu5EK4L6O7HY+HAvRWaNydIhDExvVwIKvjWyy8ZmSUsjVUcUMrhHRks1ZKGU0x.EUs5BhHyCLup5BppyqpNGF6aSs81aOQPPvX1114887y666mWfwQjweyu42x3ppS7Q9vejIbccy655lv0GI9FCUKZJbXM67vZYO2CcBr9fyaV3g0Zi7cytpSxyiT0LsISxwWol8kn8gBJ.hHTFi8r0WecKL1ojM2bSB7C1+BRDUEi++hX0IJhlNNN6nptckJNw7uzPwZnC0VnKrZ2USPdVkD9Ob4QQWB7nG6YWrgd1QJ7PlZUR70teUn+4N2crOulEtfwV3Vy.Ghj+kb7F4mpM6+LKIovAAAY.xZ63jOHHXhfffoTQmw22+HZ0Ry566OKvr0gY78uyI+k9k9kxYaamxwYIyApZYfFLrvrG+gz77n6yDQHCpwPRFdOpVdOU0l.sVoa2tP888cKrxkBJgF.GOtsbXyhTrNf+N6riWoRkBscbVSUcm1sa211woun5PzrGDDzOpZw9w7bhZzpyF6m.2yYePt16vV2cP6TeqJwIGnvCl43ZkJ0TUcua4VtkcvPbxaJhrIv1hH6YUubGU0Auo2zah6JHP78Oi08bO2S75+x8EQ635tTKWWm8t6u3WbWE1wcI2sA1BK1LetbaIhriqq6t6s2dMIhV999smZpoZ+Z++9mt6c5626Fdkux8aqoPHb+8wdzZxMePGORjvjgu7aaZABSOYaPDR66y+9ZJhzrHz8I7DdBQ28ce2of5YLYYesQ44fC6EwgN35gWehq1xQykq0N6rS21saig.UkIbccmRDYJfI+7e9O+DNNN4qTwImqqadKKq7kLHvXrScpSkC7yXHeo0s.307ZdMrHLJq9+P8gbx09PGFZXpzQxlLhsHV+E+E+EoR3ujOwm3SHYx3l3PTuxPe1vDrKdlu2e8G3CLT0EVi0LPhR0wJAS98+c8cMMvTkJUZBf7gggYbbbrDQjvvPTysf533LnZ0p8RRlAlrItGB69I+jer8bccaED304pbc6QMiboVohwf1BrpgpfdfMRjrHtGPRVOaBzpJU6BwURDrJcoSPiCCbqDAsoHaCrZcpGT4XGyamc1IrQ85qArmqqa+xkKK9AAYTUy8jKTHqpZFWW2z0M7VxvJjXHG0DG87rty67NkXinG1ywGpUK6alCELxLc7eNpBLf0WuuHROaCDVSHJvgjhVMpo.5K7G7EN.n2O9K3Ez8Lm4LcMahWqKPGUokqqayX3M1x+LAsvPltlDCDE0VEsiuuem+1+G2VGf1+5+5+FcBBB5R0p8LsDk+PmarGwY+yZ5y8GnDHbAA6.jp.jl.LD8PU5Khzxlp6HNxVevO3Gb6PB2iKjCctnyY0n1vykoBYj3nGnjDLkBnUqVsukHcbrcZBrqqq61ppa8u8O9+d2q7TW4t.6Um564662Dn8JqrROpVsOvfhEKlzpA8OwCb6l8Xgw9IS3bI7P0V8w1buERX+a709ZGHhnW6S4ojRMD90XEJTXRLrx9r1lpxVBvoAMN5evevezwTUW7O7O7O7nppkEQlEXbTM8W6beMhhz305Zj.Qsa2oOPOQkt.c1byMa4dJ21dddsqS8tZpT8AhJB51auM.x9bB.vk95x3y6FCgLbQv5c+te2F4ZWDKUUN1wNlRYy7RnA4jOPuKCfr3HuOW0vaAV862+Br40saWPQmbxIUUSPOkQKN62uejuu+ffyDzGnWn2Y55440100sUcpOLPiJPWZzHQBq2WUldzWu8mLF8ZwfBfBHpVCHljuEoK0hCXZ0jfkN6C78vYOK.L+7yGKVDlW7hhhTDvwwQ88ChBCCGdLbccku6u2uaKPs9AewuXKOuyLrp6kM7vkY9qt440q9G3Z.6Gzm+e8NNXRkG4yYGpPHMLb9ReftgggsoQi8DQ1sb4x6.r48du26ZEgZ.Attt9SN4jA+N+Nuupuge02PcLINYUKKqFe3OxGtNTqNkoQPvcsFl1admRPyiZa2Bn6QO5Q6CLvk.k4P.RuBqLj3lKXPpYFfLG+3G+fItzBH0Y4rIAjbXIIw5P9bXIE49kHFtv8guf4vRwEK.PVm0S9ch.FTFPM7h0T++dK2x7RIIIgtUTUqL8zS6ho12kDQJnptPIQVf3VzASxSlUDY5omd5IAFOvOXbDlPGh1Nch2xa4MOQPXv32vO9MLlmWP934INkq6fkVZo38spmxP7lgIx9Ypi+PucvE.NQbhCCCCicUyXaKffHUUR16aDDlvHsg7gMF99XAZj3+oDaOK8jSN4EljZY+CT7eL5t9h2UOU01fZjOXW2sKA6XYYsGPaGGmte4u7WtGrZRKRLnDninZPOZ0N1Cz39UvFO7RrK2ajOwbQxp8MImcqGr6sgOKB2GgiV+9+t+tVyLyLoJTnPZfL9ggwRJqNMlBVTnF0J555VPUcdee+ou268dyi48jAP09r.8gpQUMJsXTAPiEYfGp14F5udYCwB2BnYUptGPytc61Ia1r8rufmodWJGSSgSqXPYRcpuFP3byM24um64d71ZqsBpToxp4ymemXgenuuuWjppN+7yyc5emfYMRJU0LkXeI1EByPgg7u1gQp2Gl++GVhdejbLLYb1PWpUqiHRKaa68vfvjsrwHRG.aEpgaCr2a6s81Z533zVDo80ccWWGiDxWscDzx2+LMAZdEWwUz7Ttt6EbmA6Jhriqi61Ku7xapptoWf2Vppaoht4cbG2wlNNNa81eq+Fa6551b9ibjt111Ch2iUq7X3DkjLdj3AaxKRIFRSSROys.iypCqB4XXd4smHx1K.arJk2D6p6Q3nL9+gd7iQVQgogFk.91t9q+5u726688dYppN+a26+1LOqm8yJcPPPuXkSYWP1Vkncp3TY6fffMbbbV2Kza0J1KsFTei4gsWii0hBqzaHabe+6YxGNyAINJjkBjmFLMvQvXHap3MsUf8DwYCJEtN0XSLIav3zagBPiFIm+39qmIgxEfptppK1rYyEmXhu8ic629egyUe0W8BlJ2RdEMChkknXgnfReQj1JZqa81t08dgW2KbWWW2cBBB1JFoFanprNDs94O+xq+LdFOyMBCC2v11dKhkvR1WJdOr9eM99tRZvKGFtKXZLLO+XXptSKLsWyVPg8fFcF448EKf5D04XRf4gh1P8ippdzff.6YlYlElXhIl1vkFzwwwYCQjPLvYYUU0siO2swTc1cAm8ffNGC5sByM.606S3CHxEF0X4naJ9HkAgQMTmBrSGSVwwumUNETMVYObSCAo888y455NFv3TjwusO5sM1K3ZeAiCLoWf2zhJGQU8HVhLoFK0tpJYEQu.xZJFxmC788aArgqqaCee+ZtttqArouuebkiprEv1vL6Ba0lQThfQliN3b092SkHK0H+69c+ty+y+y+ya1TqToTZ0pIRSXRV0aAU1C7ZwA3Ag62wL4clhTf57sA7j51q6UTuQ8KWTwEXJAxpHpiicOflAAA67Zesu50+jexasAEKVk508Ap466ulkk01111Mghsf5sA5TqVstkJsTGvOFNwK18PZasGKLNLm.Fc8WdfITUmpa2timKWt7Xb.efHkihkJwThXmqWuym+pu1qdxO+s+4mAXVU0BXB.onuueADlUTYBEMS74IBg9tNtc7882cvfAakJUpMdNOmmyp+8+8+8qVoRkF.qhwIfcfhIDdbbkrlqEr9PYUkG38PN3HNvqR4nTsInFE.JqpVDXZob4zTqVqO0m5Ss90ccWWcf5.qCK1DV1HImWPbBHvIRCmMCF6eyBrHE4D876cE0qW+DHbTU0iXgUVEkolbx96r6dcPTSBrgsdqu027p+5+5ukP.eWW2f3y6ZlyMaBGaWXkj0XG797fNp7nk2+FccYF3X4Tc47EEY75plWbjbDRZ.UUssHxdTpztTq1nIHcz6qQNdymGVaRfBPoiA0dhCFL3DQpd40qUyFi8MwwwomHRu63z2QGmxNsTzMAp45tzJdAm9euhSkUvjrAy9vKrv.Vc0QQ6QRvNWLBK+azyYv99RjAH2m6yc57Oqm0oR3+MKvAHP9Rm4KY8jW56wTHJiOGFetJyjAmIXbGGmL25m5V4jW4I633bxcf56TB1qFE5PoF8oF8+PenOTmW4q7UZVaM+78Ys0zkWdYYwEWLIIEJL+f3VNH9YxwhfUh+6KN.VdXBKTUQdbhxx2OeFD.43f04XQgiurNTM3tv2cOXRSD33JbtCtGb7uywsfyk36YVLsXWBpdmBSBObDo7wZ2d4i1oSG2omd54v7NR134YEHZmc1IZpol5PViOzWrgDeJfQVWkgW2wUEWVCz.QDeUUeWW2p.qCk212+z60pUql+C+C+S68p94e46v5rKFaXWpuecvBzkxn5igVruM6IUUGO99GF5CT4cgpWfhGdHmuQ2GcFLun8cr7xKeEoSm9IqhdYhJEAYBPyDe+mvSQsTXqJtt0CBB7e1euO6v68dt2Z999a555tKTZOn1t1vlgTZSbpsEAjrVeTdt6wRnKAt+qYSywHGqPBRLmvv8M18gp6MOr0ZL+Nbh05vYGZO+vdNj77bNn7iCp9jO8oO8UtzRK8DAp.Lk48PIBztKrvBMsrr1oQiF6nptEl8Np4555C3CEBgFaVBZVi46P405Q0Kv91AKH0Ea9ej0EKlFVNa7053.iyBjiUIU7wLlDycZBAGzGtKVL.InpeRn77wvhoxK4k7RN563c7Nc+a9a9uW3G8G4GcRDTGGmlhHIsb3p.q8ze1O8M+m+b+y6vv1Vo7dP0NNPu.lsOk1neLQvdXw1jr9Zz4huUUTrCDK.Vwq4i+2lyBVe+XugwnLiSURDbj7.S566OEvrWkq6b2UPvrJ5LtNtS344kavfARpTozJm5TQTqVhc9dAAAsIhccprzV99mdCWW2X+Qr2DBi8+2YGHHAAYi5iziUV+B7HCBSfQxt5h6mU0NZCssYABMMPr2NgDNytJjCplgPREq26WLjGDe7aDS9q1stka4VZaaa2Gv5Y8reV4BLJDyDAAAS.LlpQ4OkSkb9994bbbFCXhJ1mZBn93.4WCxBqjhFPbUbd3tHXnQiiCV16mzHqBMf3dmqmpZWnXOQjdhHc62ueGHrM0nMknGyN6.LNMBMZL5wMUwhEiWHTMGTZruzW7KM43iO9zpVcpq9pu5ICBBGiXRPzBIknpEnhDMT4O5hRqW8+o2TyO9G+iuGvdwDm5tttt6ZYwdUN0oZ8LdFOFIr2s...H.jDQAQEyt.8ssGB+OKfTyO+7CgL9g7LJ9YiWz7vfB6uPQ777RAjy11NdAqSVnwkhhCj7bX.P24latXEyodMnfmHkO+VarkelLoqGmLrlKrvB8N2W8qp+z+j+jCSdmsHoghYu0a8ViyrbwrPPFfzq.Vv5FYncQr3Dm3fU5ZnyHwDm02LjWxGhiP0.q24ichuZWNFcEwsKDzcNn6U451c4kWtKPm20q+s27Ebsuxci23byJNmZqmyy44rckJU1ww7reGQzsEQ2EQ16U7JdEMcccaqhzIHvqsuuemeqeq2U6DzI45511jzfxMccc2qRkJ6QIZAk6.acXHr3Ae8TM.G3m+c+tSpBlRsZChOm6Ara7y+lfW2XXp+.gvD43i7bag5jvcMoGzefkqsqE.hJwT.tRPfuQV5VZI9C9C9SDeOeg50sBB7RAjx0005jm7jwOqqqPwHXgAkJUZ.3GYpTTgHX4GKitjCq5JP7ZvxkK2UDoctb4ZZR3ocykWd4NPsdhsfHESCUGa80We5O2m4yMafWvr999yFDDLyJm+7SGy0RioJYUTKTTWWWicCE022GDgzoSKtttxm8y9Yku7W9dRV+2OV4D5+re1eGCLy0kipVsZDrdDyRDr3CmphATSoFJ1LfRkF7ddOum9.8zpFEb3u5S7I5.zqToRwNTs7CTh+rfyZMKjd93faAxVtNYRmNc5YmcVKTDSrUFT0ryt61GzNnz1wwok6RK05m6m6WnsqqaWWW29efOvePx648KWtbL5BVY.b7QqB6AqH6i1RVxnCEpnvJQhH6SJ7gzkEnG11ChI+UgZ0fKI6qqEQATCQOWSKCpkkkVyT0TMYxNHHvx22OkcY6zJZpOxG4ij5q809ZV99mwphSEKOOOKJUR.ja+1ucX0UU.ckULICv119RMQbeiZL5yQMd+3nm0y5TC.2dwsoVKHnI1z7Iuzyq4m6y8wat.raoFrMlfjZXWkZNNmLLHHH7EdcuvPGmSVCpuJvF0fsgF6EdWg6gMMek+R+RCa+QVasNXPuz..JWtrnplV0UyB14.mX4EekwnD4gikEVNcEvJVoVPDgEWFhaqpQ2SMEPFirjubFNGYvlzb7iOZq9jz9rY.xN6ryF6+v4xAkxXBNaTDmbBANm0bPpxweOU0zEDQL3NBTUEQrSqZ07QQQSNsg8kmA3H6ryNyPbQd.ldpolZJhU0KFRt0C+jGHWfePFGGmzHjFC2njVRtlUxHB4.x63bU4cccycq25slAJYAUEWWW43G+3xq5U8xEVGXFDN9CIFH5Bd+vPHjgCp.CleDdmqrHCLzyBwIRYgzwJ1yCIjrTpToT.Y777x533jUTqzl1LRkQWTD+PVEkHee+App8u26Ym9AAACLsxaoAPsnvyGpgf.0DBPvFKlatGHzC8XwgBvLqfByODAFh3zCLHScMCemfA3vW7wbyMW7ZmpVPIqmzS5IkJHHHUPf+v1iBCI1mZsUWMa850GuToRSBLgq6ox+4+7e9TF99qTm3hU1tFz8Lm4S2ipzm4Y.Kt3nIo5fno3hEWV7mkGXC8mwj3kN.cJsJcgEF.n2xsbKVFBZOHYs8kBULn.CNwINQbRHqtATtJT57e7O9G+9JVbg66m8m8m02w0ogiiyNXTQH0yyyRDIUIQx7O+4VNumm2De4u7WdJCY0WcJfI7UcLXi7TibLOY4BECjK.UaI9+e7ie7CCMbORMF02x303gwI55Dcg06TAZMuwm4c.1pXUVGJutpZbQWr2v00cKQksqC6pptmisSSee+1hHc9C+C+C6VoRkd9m4L8BB75FySjMupq5p11ohyFP00+d+d+A2.JEqTogseG+1uidfy.HPOFnKd+rM8XqwinsjCPzx6avtmgIeCZAzpjHcNyY9ai.RWsZ0bw5FsgwlO6EcwyHaJP+RDzkxgsn3vdgrugzozzppYUUy9y7e4WLaTTTlZPFWW2L9994B77xGDbm4Axey2xMmkBinXJl1H7qWX.JmCrBikyMfTFRypgBDYKxfS8jK1SUsqpZ6LGMig2HfNTidrwFCvzRSLOHyNhiC0SWOKTJKPtWy+4uu7Ww0dEiIkkI.lra2ti63XmWQyBjVgTfX5YVTEzAJZ2kbc6D3c5VujWxKo063c8+SSee+8B7BRXN+jJ50FC7UU6QLZbcurqShUIhK5yn0HQoPPuwW6MxQO5QSIF4nMaYHaABNHY+9fYvLBn+5ysdGX1cAVuHMpRwZm+DWyIV4U+puwy633D.znwZM1drIln0O1OwOwfXHZm5+4W7Klc4k+WF6E9BegSXCSVf5SBLNkHGEFxT3oYYxvYO6npnyEXT77m+7BT4Pp30iHFLu.CPGUjHrWa.UhSL4JzELR025Pm5P6EWbwV.MeCuw23dPscDQ1pHrYYps4m8y9Y2z22eKeCoYs0uyuy6aKWW2MQ0s+nezO5t999sNkiSGTqthHcdouzen1NmzoYfWPSCWJTeWe+SuCELpt.0nYLS22+rO7VCoDfRPPjHRjpZzBvfh62lA8VOy58fYuXxA2vmGmajJ7rJjQUij1YYYkNLHvrgmUDHfiiiHhk7+5S++JUvcdmoUUyfPt.e+bpJIP3LaMoVVihZfUQpqTX0HrI5K7E9BCLv3uwi41XHdbvDkL56zCW+UsS0tlDkQyBzXWVHb2G2+WONS0DpR2XB4U50qWZQjrRJIqqqaVGGmLKdrikxOzOEPB4RDgE88C76cye3adXfnN11IjwaJfTO4m7UNrcIruJaEJy+v+v+fr.MDJWkxOkxlquMPOjDYboL12AjPhnVs9CFLnqHR6RhzVUsy+5W5K0EnWsT0F.ycwpXh.XsAj9W9ltIib1B4pFSx3quw5o.rldxIM+xhFgPeQktlVhLXO+ybl8ld5oa9pdUupNkfd+Z23O4.fn+0+0+UspU0nX9DH5DlJr+f84QkiiajxTisqER3q.FvpnDFlnhIVE.q4efsudg2qMX.TaPud8FTMw9ikklvcIUbcsTwHa0RLx.tgeraH0U+ib0oDURAjtRkJooVsz.odtO2mqUA.aH5XG6XQ.CByFdXUe7a1ig1QMm+4G.zuH98bOoaWRHU+PZC0Z+S8K7S0dUSfPMA1ILLbCvdUndMU0p1PshTuNvFTjcnLMIli4HjlTq1vV9Zdn6rr+ymvvvHQJiHhUAByPgfbesu1WKOPdsplCS65jwCRMBQ0qKCjn7FGmDUGhLLCYwgbas0VFzZDRFN24FhbiBP14hUWPfw2H0FiCyONvXNTKOysbVrYeRb2frqLqCYqFmrRQjLqBoRHE5RhjpDUyHhj8ldOuqji8X.4mZpoR7EM4ynUkcneBau81Y1YmsM9HXQpff.C5RHI86wnqQvJga8fZY888ydxSdxLEolIPwxHl7XD+E2Bky8.xkEOXuiDEKyyC7LAhmTvxt0fdpFNHt0brrYUSBolmQ4quK1P.jZQ0RAES2rYyLCFLHCFk0yBvHyWRb1IwjfRcez1.TWUUiBBBhJRMC+RXE0+2988aueRHCga8i9QOry8iUFGz2PK.YKvJgDLKAQEHbfp5.JPDEujrUK.rttt.EMEOkZodau82lEBhiiaxuWTbAHTMN4XUqVMinRFe+SackW4UFcJW29kolI.aa5QY5tzRKYViuF8Y4kiNv4cz6G3h+7PAzPXvVijvtppN.VUO0y9TVutq+5S6PMyZbGxLj3auDRHyYO6YG.mnCvtEn55TtVUrYkYmc166o+ze52GfWPPvphH6ELXvfXYhOacXLn1jUpTY5SbhSbjxTaVfYn.SINhIV.XLVi734kmX6OGGRehQRXhw+ea4bm6bViTPtuUlzjCv0UmsOPeOn2ZljU0DX25vNP0sDQ1pDrcQB2FXKmkb1lRrsnxVggIHDgcecutWWKOOudK451WUY.PeU0N0nVy.ufc7771t9W5eYGOuSGWnT59q7K7qzekU9+a.PzJl3+StNeL43QRDlL5CyDXz20nlDE5TG5sz0sjppl5HG4H4hgJjIgIktTPbvYi.FTi45RUZQc18y+4+76ARyxkK2Yr7iM.LAA869dtI4nG8nR61sGxeAqswFVQQQo.R8xu9Wt0O00+Ss+byI95NqXIa.IyDqC8D6bvBPZvNUUPN8peIDQFHhz8xm9x2m7qJPuJTYHgctFyjZCHMTLmKjm.xoZ0r.Y+i9S9axoU0bZUM++1+1+d9UWcsbAAAYDQR433HNNNTpbIy8hQ8OhDjnZv.0HWr89wd4+3IDzpgyQJRSOOu19998.hpRY4Kbe2mETVJCby+d2Lbw6gaIge9JF+O7Ad+e.EPKCxcbG2gUnpoaXbxIEgOD4yjyQeXi1.6Tm4Vi5DPUV9O6O6+w+9OzOzO58Ab9JWUkZttta8hdZOsNF9ciLW4Udki8ct3hSppdjPXtFkXNfiPMlTqqCMPhCYOFGy7mMRiZ702hhMF4tE7hMTdnYY9aVFMO36kQdbhADRe7RfJ9IRHLst.sO197HydqrxJ6ZaZkgcpCaWs.6jOe9DjEs2m7S9I280+5e8aSI1FXaU0ccccacFe+1JZGGmS193G+3MoN6ohtqqq6tm97g6355tCMRZQhYen1NDG79Jh4neIJ0Cn2xKub+Uo3.CEMVRrgTTCKXiKIaYII56lu4aNEPFaQxBEys5pqlQGxOEhfhDFDfsss0y6ZedVnl0qhHYTSEJy3EDjsHjkpjUhhx.jtNErnAPH5y3Y7CpvLOlHX0KkwMdi23AQWhAl0aPu3VQp4ey+z+zdrJ6YfQcoVfSGftWwS6J5YYY0qHzy11tmuue2ffftkgtwbRRWD5533zAkNnz4k+i+xMv.VHpe+9366K999B.ggg5a8s+NnHPvYB.pJ.o9hAAVTEwPQMkTChS95ZtWo.QknTu2va3Mz4ltoapccJ2QDo2m6y80FTHIgdr9E+nTxXG3c8K9KlTY7L2zMcSoARYYTka1Y2cELE6VEjHUTC4tdUtc1am8ZGEE09C9A+fcNsmWuS64EAkzolZJ0vW1M.fydgDg5iod26blhnGgGQw7ThhI.xzWy280joWudYAR2f4RYHvxKBouBJrXTr5qEkwIyvjZ3ZaSPP.HXED3mRTqzXJlfYeYkTTiTNUbR644kgRjwlgDAHMnnFZTVg9UfAwJ1w2JlmMmukIBVKBXPcpziFzi5zElqCLaGfN28x2cGftKu7xsAZZaauSHgat.rtq6Sc0PX051rgmm210tmZ6QUZAzpb4xsnLslYeBdt2ZTo+FPeX191CkY9ZQ.RCbRSCx93e7O9blVdIl.+MJ+R5BGXuQiBDVJy4fbdULI.gsXbBX7YlYFCxXKQNarSRVwXMn7XqCS.NSTDljUYRUWcbU0wBXt7rN4HjbvL4fiYRrQQywdgDYAOFIHkgrPor0gb0JSVU0r0BpmrW+nEI4fR9ax+dZQjThHold5oSM0TSmBvJV0qTQjnQ+333D4Z3FKDQj+w+wOepXeyReWlV3I04+WNeJpNRfWyfrHK9P0Gh6muA.CNwHDCOP+hhLPDCJSBKEeOtFVb1GTeVDXdwFrnAof5oulq4ZRmJUpgDHqAYCwHJJ9vIir9v00Uh8GL5k+xe48qWx3ihqqa6q3IbEIsVYepPzM7BugCt15Q81yhGi5Cn0wuv1xWLEuqXzxsaO3+et6cOJI6r77d+8tqq88q0k8koaMRsFjFIYqYjjkQiyIQANFC1APN9jSrkcriiMgDrOb.YAIfCwFYHKLHv1wdYvKaehuErMKaAlv0.XxxKIvflQhXkAjzDFlYp8dW6p5aUes5pqZ+d9iu8t5paMynYD1Hj+Vqdpdptp8ku82k2KOuOOMKm7roIwzmCpu72mGEDVAvvIeDop7.u8GPPEILLTBCC0ff.LHHAULAMQbccshIV.za3F9GEWG5UO87GROp2OgTc4n6y1MKLD17EizluXs8sGTkC3LeYH9j+UAbpZ0x7g9ROrIYBAjivDeAd18QcvfBtSSlYSpyxDRcn7EdzG87eCQjy633TWUc0nvvcDQX6s2NOvHUgIfJS+a9a9aNS8xLMvTzjIz.ME8XiPEFYZl1D7DnvYnTtSa7aypRe6+MpAyYL6Sb0f37+11GgKURRtXkPZaizLOS6HX6FkXKU0soAaRDqohthiiyRt2l6hXLvYcOOusNkueGSEQvNVhzlH1FK1xyyaqFTcaOOucds2yOytP0tPYShEl0L9YgW3Lu8h19VU.Sf8+vqe8OYjotlFClavtu829aOtPgBVj.2RfbSGcE5.sMwvxcIodL+d9d9dZ43XuVTTzlSO8zcJTnPrHBtttVhHVEKVjvvP000s6sbK2xtqr1J8k1veieiei9FZezS2+5+4dybLRhpb+5NN2hPNHLmpZVMTs.henG5g18odpmJE5Z650jd0LYeClBKp1JGvPPig8Y1gAJVQjBTl7pFkSLZfc9q8ZObdGGayBOpH.ZPPPbmc14fNQH999hqqKZhjc533zywyoWPPP2JMnqmmWOWWWkpHggOlbMWy0HPcpaXm63Etz0jl4YlYCXoQxjW0HoZc+ZqtZ263NtiXyZNoPC8hhjiKUaOG2Vf1vxqShx4.Qm6O+O+O4qWUju9a7du+ye+2+aN5wCCWCyBFV.EiLvpcxOxG8iNiVWmgDtjQDIcAxh+im66t34zyYLXaBxSoykbMdtLgTQ.nDHTAK3LCBa3uUUlNCDHxSOnZ4j9+6RMCwhc98Xp7smat41ND1lTBirIaOyLyrcsZ0ZCryq5U8p1wyya6JQroqq6lVVVatmR4vV0pcpMA17oe5mdii65tNUX8CcH6AjvrFsgU1cg8CkStH+9AaChhAsxxzKBixfbMm3Z1kJMTLYGLefpEvX3aVS++kuONgZzj68duWSlVf7+d+du67pHEbbbxYaaagPBBrPBBB.EKMIuXwI80VVVpmiCOdPf0S8TOkUrHYnJVu427O4.WCQ.s96F8y34g1u0u0u0Ama2etmMrKSSm67Nuy9R.og7fC1sLr6m7C+I2wwwY6Fvlppa333rtqq6F0SJqJUjMA1HHHXSLD17VXp281nrSiFM18q9U+p8Pn6YO6Wu2se62du21a8eeuFTomiiiFDDnWn9EhS+cy0VTJ5ddtEj6j6wxMINhntpp6d+um6eWJWuKf9W9W9GSSi1iKSCWN+2Mk1CFD18fO3CBfb+2+8O3ZB6ab6HiLhI.n998Bd7fcGYrQ1QDoCUnq2w8RlOEY44cLKLHtvhowJMi8OGtee9tIbl86bAP1c1Ym710Yn+6e9+6CkOe9BTlbXubRBTN8yx844THPKk5DBDGGGqgggXPqjjw11ICnYJWt793noDobNqmmWtfGKHWHg4RdenZij0Za1qloD.e9LfTJf5AwThtKt3iO.QRt7tvJcJA6RnwH4DR.NMYHarHrNlLKtFgrtmm2lUpTIMYMsAZOcc1oEsLbZwBDy70RteWINj45Az0AhYVrRfTedRBtLzLWEHG0Mnwq49CDQtZlr7NBvXTauxdgD9yBX3pQLTHgCu7xKOBUYDJWOojXBl3IWYiI.FurHiZPU6xCm78FAZMJb9Qq.iQCywcQXBQjILeeFqNLBDMpp5HTmQDQF5G+m3GOkvGubNDJj3XjobjzmgSjIiyTU033DkdJHHX2fffcA5JhDeW20IzjikkiiSFfrG5PGJiuue+9ImVj4bFYq9psjTdFNNc5C3.USHlDBfeOdP+YskLGcIqPpjAHSkAr2IIIjI3HQSNeRrpRLhDKIIOw22OtgwVvt+k+k+k6PDalT5Dq8xdYur9J90L0X2kXoA4OiACFz2N2Fb8rL.YOydi+MnpTDAZnEKVrGM5GjhKGg3uuVpBNVFTJChH5YO6YUGGarssw11VbbbPAbccU0DzjXee+XSfNo2e1e1ugAARmp1f7Byd9oc59+egEvBuSOXPAtj6gcflBnG9696tO4jJhzsAzS0fXOOOqS7cchz0N1KYkWYpb2.1+tTpTCmTh9M7q.WPDI3zm9zMrMD1emkWdYAnvq707ZFU05S9u4ey+loutxW2LXjwhz0GFAXjG+S83i9R9+5kX38oYXXbZZRjOgYhnj.Hy.BSQVL6SjhFtA8AXv9mClL0+tH4pWp.mru4+AzOX6wzj369tu6z.prUhLyuT05zv00Dzje4G78zREccL1nsYrJa566u0wcb1lJrcPvoZCry0e8GNo7xZXHr4jDf7bBmbeaT6akALAt3AMoODAA14M9Fei6.z8QezGELZDe1kMY0+YeRSngQ8AZ+9deuu0AVsa2tq5brisNv16ryN6566G666q990zZ0pE633zgDoYblImYSf12y2+8zm.2pr2lLey2p.NlnJaQxFsLP.BDQDUU8dtm6ouQOyXfRYLr.ULQSNahwGEAF5jm7SUDnXipTfFjWDofpZgNc5jeqs1JmeJwigpAgApiiS7JqrROP6kJqVI8qRPPPRzRM8m999bqNNDAJTUKCp+I8STtgzmgg8.hOyEOfIFCeSq+3ovpr485Ihrqp5NSN4jsMLlNce0u5WsIihCvyKbUizj9D35x.0AtPTkJm689de2ma5RS5WpTolppanp180+5e8V.E+gd0u5Qek+.+.oDQ4jXLXaTfgsgQ9bewu3vhTdDaXHZwPzjTNgIODkCHayoHCQ6q1pOXfeFbQyuYWn7h8cRWDbvnIO3umlgoAXi88QjW8mCJhztVsZ6TqVsNUfNOVPscfJsuUGmsCdrfs9u9e8+5F.a344sgiiyFW+0e8aDUgMHpudumV9V6BDeFS8WakvEQWMF8IK.I1wMQbUnG9nFDkPdRIOLnX8YHGQWRjn0eb4.L0cpSY49Q9Q9QJHplOgbfyLPgWqBhoOLQcu7bb5555lFg8tNNNwG4HGQ7NtmPcrdWuq2U5ws+31P6WvFxjmsMc6u4a3BzikeFF7A.MpTI100sya39eCaWAVWDYMQj0.VKHHnEvphpqfZj9NWW209C+C+CW200ccw.Gz0+4eausMN5MbzMQYqumum+AaEDDrMP6ffGqCPGma0oygpdnN.61qWuTUG3alRkH0QTZ.w+xuu2WOaQ5RjI6eppxm4y+YrHQsyVdJjKR8lueCTli3pPu669tudpp8IRtjfizO30hJxlato.FHsqZbrq6wiOlqaOhH98+1e+344kLFqYFfrKYSVV9pV9Qe9tcPCHGf2ILj42gJTnXHLjHUGVUsHMH+.HPbviwkpoMKYJWIpr25.AA9BnVAAAVNNNRiFMFzljX0nbZY+re1OadGGmBfctRPVWWWg56crSTpmmucbSqcT5QS5M6rytWlgStWZ54YlONQhBpse04HUc7RKA39RENvtkfcWF5BdpmInVY3bCt214s.rBpPFVjre9O+mOOP9G4QdjTGFxEYaW.n3xKubwY1qTWLAIwrW6zXzakY.ltUqVSb9ye9wUUGsLLRcXLnx32vzSOA0YRZXHIeU0ImZpQm.Xrl1LJQLBl8sGGXhDtGYxnRklX0UWcBrYpRvTsZ0ZJU0ovP19SRhCRppi.LzMeS27fALIcroBnR+hLYe1oLXPfxBXEDDHppRMeebbbTWGmdIxHaeNbPUcmZA01UUsasZ056jruue+yQIHS.jAJYczqLGSubsA4vqA+4fsK234AmqlIw9mbQkMiG51sqUBxRHHHHIOcnm+7mSEQ6kjnrdhno2uo8KF4l0rd+ZX3YgMKA6rzdk+0yGk91eazL8USSt41OpkDRS1Wx7wj9hqz.lzOvQMpfRCy2yoZ03fffXv7LHHLTDfjwUFGkC7S6668O8e5+zX.cu8T1aLw.4.vL14LXQMrtu669N39LWIiI0E+hew8c+hQ4bRe1Z867676z22FfBkt5CZRWLyw1BiPRzLx1tNf+QO5QCu8a4VZr5pqtpiiSaUU8C7A9.YEQFBXry72blISWWPDYbfwpBieq25sN1G5C8WMdIXbVhQInuHkzmyEWZNxxJ8EzjTzvYVG4nG8fINMs+Zv96ql9x+1H3JGbMrr.Y97e9Ou3e9y2y22emfff0q.K+n0psHTYwi45t7O5O7OxpdNdsDQZArtmmyFtttaUuJaSjgu0.149tu6quuEOlYrnxLnG8Edyc2W6a0ALA1uS0GbP91iM1XskJxt292+sCUHaT+HMdtmMh.ZviU62v65csATs0K9E+hao0quguue6QFYjt.wNNNZinlXYYkZ3ZGWW2cRBdxtOzW9ghoJJ1PzeaFAvHj..QDojI5xoCVEQDTU68o9TeptTgcoDcvltK0uTFNS50hETMa61sy80+5e8bG+3G2DQy58qo2gDQFpYylEVc0UyIl.wX433ffnUqVMVgdfzKHHn+Byqu95oY3HiuuuUPPfk6wbsd7ffj645ZCnmqqaWGmuSyhckn6zCXjIOSx1zb8dlyXlXtBYZjrIgsYgxsTU2TDYKfc9ve3Obue7e7ebKlkb3QNlgKlgKWp1fO+2Aa6Mg4VEnIQQA.0dKu42he1rYiDQVFXiG7AevNppw+YejORpC3CIhLBUYLJy3ThwCMp4y3PywCgwO7gO7XPYCgu4xHylRzaqXVj7i+w+3lEK87J.SMXjlO3BiGbwR3JegxK2mcv4VOCGaS3QjdkFvf5SdxSl9raWfcbuM21.aKhz9wBB53330Ah14w78a633rw8du265tttqVKHXkG7c+tWsVsZstvidg0XvfkXa2AVvL18Lmwb+d5qnMF1q+YAxbl9YxnUl58WbupQskr6mExhrD43JXbRhf0IyZ9LYAxctyct7pp4cbbxBHI4GKFgdJpouQ09vYz22eWWW2coL6VA5VqVsd0NUZVWSN+UpX8G7G7GXtdBC+lesimeaGLXb5A9aJm4.ALsREKv0b+GEECz488teeaEkP3X0pUa0SbhSrJl.atnqqaSWW2F.K566u7ce228x999KgQEbZ9.OvCrnimSB4j0QGnJH...B.IQTPTUY0d85sluu+FG2wwf3oF8c3aqCcnC0lJU1El9fpRzU2l0yC0RVW6M8FdCwug206JFnuTb9+7zOUF6zwbq.7rs2z4oa8DiDqXLLdWfdiO93w.5niMpgaMr.aaaATKWW2jCaDmpVM.jwFarL999YnJYeu+x+x6wyC6YL9KDCZBPReqMFhdrLYq0oSdU0hPz.YcrjwYi4e1t+l2zGzro40H3u3u3uf33XQDKS.oDwfhLnGhwPdUztdG2K163dV23Mdi49BeguPdHLWy9N5T1z+ZSZMqe053vea01KXbmdvZVu+Z6l+dsjU8ZA0Rdu4LuyA+NlwjSQWlhdLMpoPuPfZYpYFeUDpZB5wrLzzob4QjgaO9G9O7eXQU0gtqevevg.6gAFVCBFgRkFc5omdrkRHR0plfjLKFVG0VU0SU0EvdhiLQ449tlaFohLSi9ARIZ1HipZUBrKALqTQlVU0Dri.cLy9zLIvLToR4wFagx.knYyYmbxImYsmbsoaBSO93iOisHy.LcRBRRIv0gUUK9o9zep7.YVa80rVas01WP8z8jfdKfLgAAoy65mQ7jfvY433XIIAOAPUU64551WFYccc65430000smkkU+jZ3db2TtrPO44Oex4qo0o+laNs.XclDUpzdOmRKhgSWx1+XOMCVF5G7Xz+XQYLnGBxSCyZOMZzXf4ChJIiOmat45OFyMgCDDQ58U+pe0dtttcSHpXSf6lgswi1ThNMuzAK4EJNdsm8LKSl+lVsr.xjjBEMEo0rW.D5wrzi4HFuq38rTh1KoXewuzWZPjVJnpjFoujiW7Ovc7c0UUsaiFM1EiMLw.5EtvEDbPvCKrQN8d2CYNS+w8yX8fO3CJXibz896O6WiFDFDCzap874qSsZ0RUjI4m7m7mLO1LLUYTpxHM2imftR8AHMv26B1aCrFggKBTWDw+gezGs1TSMUXEQZ9Y+re1VrmpsHjlDtJLFUYbpx30gw1XiMFW0vwZZ7EXBnrIgpNLb4z.ib99jIcdU07LyLE.aydym9zCFfrCVVe6Ko4b42G4hsOyykjsZUYff8Vud8zfTU.Hq6cNGhHcbNtyVQlxvoEDs5opUaEGGmUBBBV100cEee+Udv286tUsZ0VK3TAa566mh971ToxNIOW6533X7ObIzSu2yoWP1d9HfIos8g1jjL1ZL5sgcGhPefelGHKPAGdVcbdPXFYPYP85a.0W6BO5itFv5Vhr8latYGQjtRUItbkxwPRjJ16XZJUlF14oNElIzrIn8U1f4qfVIyKSCM2ay2zVOQjNEKVrMQkZSS5Lw9ky1zyq7Veq2KEKVjq8ZuVDwNsLVFRUcTU0w.F011dHGGmbXPxhDZx3gdxScxXQ5GUeCLugtiM1XwIQf1x083lrF83AxS+zOMUg3Oym4yzsVsZ6.z9q809qLv0sIcVF6CdMN30JKjLA0buZx7.tzKD1gYYKQjMt669tSkwN989898xxhUJRMFtxRlnL6sWTluxWvLLrCb9smg9pBPCvIDHDahDQVLqa1U.V6oe5mdyj9BEHmFpiPClrRSlFX5JhLs4oFSe1yd1ooZioUUmFelZQp1OCUTggeEuhWgwHjZ0JBqTzYvHMuWseZ9YgmQ8Qe4t+NXjoG70Cd+ewVPZeANoYRFG+m+O+ed7sca2V+4Nuu226qM0s2zyyaii65tQLrkuu+1u6286dKU0M788Wy4XNqBrhmiyx+e+C+CurHxJG5PGZMbF.YIgg8fyDmxs+m+7mGX9Cdeb4Zh8YLyGc1yfTSPAqVeXU0QHLIXIoiO7thPuhPUjEgLI0Uetq65ttbIjObhLPJfhJHwNNNoNezCvXDhjXbRCz28+e+g344odddJUSydcfEQQxO1O1Olk8ysLH7sSsCFbjzWO3XLiito2uQQY9U9UteqR.ppcUU2wHYjrN1rlkkUqG9oe3kcbbVDnQPPP8fff5ppQfzDn4iepGuoqqaCQjner68daDDTqAvRm8rewkOzgNTKSI8TMcS5sSHZLyF1QQcfk68MSFMV3bl6S2Di0eyu42L.hXKVhHYO2S9jYBo5dOau3BYw.6MYaLjqJ61v759bDXi02fff.MNNo+DgZ99HhH999VhHYCpUK289Ft2bttt4oNEdiuoGrHvPenOzGpHtWw088211LklYUgPjRMPxmOee0Pgz0QmsoYL14tjFUl754Nv7uJxK8k9RM+uAJafO8m9Sq.8bcb2Enimyw6D73AcCdr.MFrd0u3Wb+xLAHGS2vbMER1ApY8mOCPU552CFrjX.cvLDetycNy7SGrNOXgGVOzC8PC5HE.BqLmEqPFOChkx6sGAqNJvXTp93sa2dBVjIVlJonzXJpvjhHSJhLA0qOIDNUUXFQjYoYyRPkJ.UoBN0AOU04ANLv0IhbchHWGv0PT0CQcbq1.G.2xF4Q0SDwCvkJg1ppk0HcVQpNsp5ThHSKUkz.v315odJOHxSU0I48pL93KTRUsjXKkBUcVU0YDyd6SHhLJlDlj+U8JeU4.xrw5aXsggWg5mM1PydD8S1gcRYzPRfRvTZMVAA9V99AYDShwrBSBZx.OqzDdknWPPPWGGmtIbDWGhr6344sa4lDO2byIybkiz1KWyJg60xwzTLzvMCoxYawDzNaN1KO8kScVjDjhlgFjMhR4TU2WfZMAHR0jIYw4xkKEAqcUzt+9+A+989U+U9UhcNlidi23MpAAAwttt6M1cox8nF87ZtOjV7bOv2eaSKILk1ngfhC8KKlzeTU6whkTNOpWsqnC5d8I1l8Rzd8hIIwvBFdazRUKW2iKJJhHZcU6Jhzob4xcN6YOaGOOut.bnCcHKBpjgZXUxTh7Vyuem7sXlkLm4v9bl0ULRX.TlGcEHN45s2wN1wRSzZVQjgz.cLpy3kqaJMOmAsw6pJnIg6ZarGMoL8sCKVrXMpxEZ.0do26KM3C8g9PQpQkXVaokVpsHBq70VIO0YXMTGCXrEFczwSJiuIDQlD6FSppNIALdCJsG4vVghhH4EQJvRKU.BKVcu8MxBKXlibzjfUTZeAZ8JoD.Se8hY++UZxVElGqHvJ092pUq1GQLW3BWvhvx8DQZWstgqCKmnrNVVVqEDDrZh8+q555t5O7O781xyyacGGmMbuc28Jkynn1dddoAMIInmdWNEr7EDsmu1fevHsMXcqMDvXkfwZnZwtc6p4xkaCnzJPyVPe8X+hQfjlACGkrbZFByl3t.Knp9hBCCuFaa6oC88y7K9K7Kz4C7a+WrQPsGeMGOmV.qTqVsU877VGXiJPqn8AMvI1FZsCGLyMWY2mv.PLFnvzPwkozPPSCSxqZAQjbppwhHa6.qEXfS1FI2uwIGiDtKwYHHnHPwpPw5v3Tlxzf4TUu1d85c3LYx3hI6LilTpAHhzSUsq.6pPGT5fEcbcb6DDDrCF9En0u06+8uxu3C7.KAzz22ugq6wVDZrhoufsgpaA02FX6EfNmgKZfcF79dekphppkHdY.+AMFoeTVumu+ue4g9XerThAtM3sMTKElvWI8+G77mxYL4AJNKLzhL6nvhS.L4W9K+kG+Iexmb368du2r.wUDoSjp6lBUvtc6FmISl3kVZotFHOWsah7u00F5FB6hMFIvzltjDnqs1ZqtCO7v8ynDl9IsBHlZerYpQToA5avETt3iuuHvDl8aLwkqMvwX9rv4RcBYeJ.PIX3lvH+GdK+GF4AdmOvPFEXxvf+O1i8XxwN1KSqU6T6Jhz93ttqGAqREVgHVkoXCVgz4JoOCFzAtChFoK1849lunplSDIUcBFApNFTezj5FusHRKJwhzjUXd1hysOcdev66L.4KCi1fJkfnqQU8n.2LvM.3FDDLt.4rcbz.+fttd8koys.ZIhzPUMz00sNvhUfU+3m7jabauhaaiy+kO+FycmysAgIpa0dPaeOBr6pa8iucus23oEHCmgbLG4376qNsyj7YSQeS1xvPmpVsQ777RkqyTn4OBvP+0+0+04uy67N6O2HHHnWRVJRjR5JsfnU.VMHHXMGGmM+5e8u91W60ds6Lv7uAcd7JtlvOv8UVfhvziAKOop5zhHS8ddOumgtu6693U8p9A23K9W7PK0.ZvbrBmmsvLO+fOeSGSmCiAfyFP0CC0uo1cZeKK0boavwwwCX7DB5NVUsMJqIVxxNNGaEHpkuueKScEWoUPvisZbbbZIL0hRrLMoENrFA8uN9Vsb29bocPaAxZCEBMFgNEvrI86ETU2RDYIvNBBWj8rG3hMWueY6k78mC3npp2BvMFDDNGpNgZoYEU5Rh5w.rpHxJppsbccaUqVskN+4OezccW2UHP8KbgKr7gNzssQBIGuyzokrxQoKm9xt98eW1jKyuOXYijEHSYPZPYLxfd+.sDydYeLacUSIuSKnRZoWjrGQ4bPi9bWQDjmxTjFTjJTjHRSTiljDh3Daa.vpLjogYOkhXbXOUZdsVas05cjIlXm5p1QDoqpZOQrUUC2mspoG2xPbCnGUoG0QnBYz5ZNQjLppLfCohpZVQjBUfbel+l+F4VtkaI8dOSx0wzXBtRIRrcJ4d1JAERjptRo8ylR6J4RSAGWyeOHHPUPsfXcurouijveLJrFJK554F5666qpF5440.XkJvFQ6sewNfy1PfQ0wt5mWuu89.FtLL1E1YmIVas0lb1YmcXS2orElDKszTvZqXNWcY+qkkZ6PN3bEAFuJTNT04kJxMnQ5MBbjff.WGGGyZYfZ63r623a7M1Ie97skDtyww8XqTq1oh777t.v2.3BXJg5UJAa1z33UpyVuPsTbFruuu8sS.zBy+x9s+MipJUDoWi8JUtAsY3hc7y.TDGlh.lixbSum2z64679tu66lBBBNjS0pS3WudNIA0E+2+ze51u4+k+K25T9AqpZbjmmWMfy8vO7CW6Dm3DMAVtDzpow2ijwad.0rhhhjJUpXYxc3xw.wG0n9gWIjdc+0kmGxdNlIGrTBh0XXR3THfQJC49odSukcem+xuyM.VEbVEBV+Jn+3hd9X+Abu3rPwEo7PXTEy9m2yctyMzbyMW1d85QlLY5kX+euZ0B545Z2KYsjcUU2QDYafssgsCMI+nG0IFaRIJOUM9PjZ6WpuKwk.qlLUFXEChlMqSltFwfIQAt36q8bw9+Cz+Oc9ffmnfiiyv.i7jO4SN5XiMVQGGGqpPb88VmQ.6bP3v.CWqVshddGOqu+iEaYYs6sZa2NB19Tm5Tac7ie7swlsIru8+Grb+uZJ2rusr87Qln5GgvitunEVMYPRkXib6Rtb4xkjYilWIQXzLf4zoNeVZGnzl.q8a+a+61ZlYlYsfffMTKY6Ovu8u8t99OlhnYqUqVQU0wTUlDXFee+YNYsZy566OElIRCCsRiB3UaD8169cgj+uCbgM2DnII74gkwvjpISZJ0IHgrWY+CtRgRaOHvH8XTQSJSghZjNJFjNLwYO6YGKHHXXfBIYMOCForyBvRAbcciEgcccb6DTKncb7dYm8W7Adf1AAAsCBBZ6551NgTP2FncEXan9NLAc.utm4hO3+.Yhd9Xy0KwTkXQjXvua48wiFkvjkipC+PerO13c5zYJHQ0Zn1XrGQPc0Dk48pWxilTiryPqEgUfEWrpgdLBui63NB+Q+QeiQhHK+jO4Stdjp69k9ReIIAdcCmMa1wDQlX1YmcZU0YTMbVJyrOxi7HyFjjoJMPmBXbBsGQUcHnZwgGdtBppEfYxS0j.SLGEhfBPyhUY.kfBuKWcZt+wRPFlueFitZPtv.apctX6Ajl2KbgKzAncsZ01rYhiCOv67AVFXoZ0N4RAAAKCrx22wN1pPiU8Nt2ptt25RQFB1cYhnEvVrh2fK5umA4Gsug4Ctv9UzhlevO3GLM.J4dculWWgnnuRQpRAQrSiJOzL4Xct4u7GrRHM.4ge3+b1igyIEV9.vTSOcejjjT1Z8W7WMjhrFDDnu+2+uk9W8TOkZW1tGQzat4lqGg18OQW3BWH42mHE8EOelE5+tsYHrSN2e04RdiYi+ze5OchA+k1Ngrf2jYMxYmmm2ZkgVepO6mZUfVlffXd8Nuy6b0fffU.VAHEFnq366uZEn0EtvisFkYCnxlu5W8qdaf1W60dslMjCmHYrWIMQpySaOW660DEvwRDISUH2O2O2OWAwVJd+2+arPiTzGd9qvmuNXD0FpKUASgeADDDpA6Ic1l4HBpppku+oxfgDMK366WnVsGKmywbx54c7TTQY8Y9feFiysAUuTqg7Bj1zRJwLSxZEc61Mta2tJPhZqFhgIk8tTGjj0U7T.DojlHOaZbbbBGQnFL7nhTtbYAL7Eiq6wALJ3AIOyCLkcQdfB29gNTQnQApPdnTtG6bmyrt8oedc98AWS8.FYWxh4RBB8Lj8jW3B4fF4cGD0LXm75LEiLHsXTRKuEhlEnBPUU0ppFYSUrgJN0U0UUcNMRmWUcdhpNup57ppGVD6qSUcAn5QDa4EAbippGMR0a5O5O58dzpvQa0p0M.7h.NBvQFe7wu95pdDQjinpdDfWjpguHQjiHhb8.KHhbsTlqQUc9Ff47VmCCbsZc85AtdU0iHR0qWUcg0VasEnLWqHx0r1ZqcnH3P27MeydppdXP5hCTorHxThTcDfhgggYCCCSKmFIsDa.De+fAdeWbbbL+XBVR+wAtNNRxnr9RhtNfZLIVhkuuOttt877N9tlrwVc2n81mT788sffKFWGb0LNSX9Dm1mgbMf7EJTH+2QoR4Ei.AjCJkf10pVqr2Xm8eL5+y4LkrAUk5fAUXIp5RPPflVNHkKWNVgdAAgcymO+tXRTWWTQ88eLwRDKeeeqZ0pIXHOeAJI+uZzP341842t1hSTZltTgcaAcgVcmde1NWF.QjYowUeBUTBRrupQkt228cecCBB6BRuvnnTXra8k+xe4L+e9x9dybJeeKPkAc96Dm3dD.qa5lt8L+we1O6ADrfZ.nUpXjQRXYXFDvlmKkI14.EVR6uOG.TMQlsqNTCXj2w65cjVhciAAiPBJSVXeWWWw9.rKGksA1joo0hvxPiEKaBPW.PMnbs4medeQp13M8ldSq.r0hKtXWUUwyyImHRZvcMI2oJSBUlLP0oUUm5tt16xfx7PaCWaQ47h3j2X+uaAJmXquGEaBEfUJLaRPbLkC2bIyuO5AG2ev4BeSa+uo+e43uKSRn5Bz8E8hNwtu829auSsZ05TeONspMP6ffS1122eSf0ucOu0fFsbOt6Z11emqFAqBrxwO9waArY3oBSBzY4NfSRxBqzgEdFIU+EjsmOgtq0oMD4pkYvR8jG7QV.Y9Q+g+QyppV3odpmpHXWnJOC3FeoFbj3jbyNPys.V+m9m9eUq74y2BXyDX2hqqadEYHOOuwDo5jVVlZp000cVKKqobccmnZ0piBT7M7FdK4l6Jeh5fWaoQDzZ9TE.H.Mgj+3B6rifIpxYf5RqVsTnYe34M+7yOXTaUf3yCJyhpMTAhxREJBUGVDYze0e0e0w.FcgEVXXGGmBAA96UJQ59g6kuuuZ63zy22uCVrskEapZ7FPBwcFyF25sdqa.rwe5e5e5lkSl.EA6fGcJ0htXTtmKWzMSVv5bovqrK0o6C9fO3t.61fzxcXZEZJFT1DNDv34ymep+G+O9ejTJLkmnpIaOWMPyK87afm2oSBbxRoYol0pS4kAZBkiLLpMMuga3FVVDYs67Nuys6zoyt.5latY1qiqaHfwfdSJUjYpzfYuq65tL0RsXmBs2ofvIDQFCpOBr3PlEZWZnp0SHnzyuG6+GpZwW2q60UvXrZsCFvjKlAKlWmGN+e04E3bjHW0WrO+yV+RbngiQ1ElnygNzg1AXGOOusS5eVuToRq9DOwSrhm2wV93NNKArRDrhuu+p+G+oeqq.MWAaVAlNE8W6.0NXvRL+rW1WuRIyL011NFH9G4G4+WCSaCxuwu0ugU4xkspVGQ0PQbDMIXqoAB5xerMAVQOwINgFQk9ysbLR8npfVrXw8tPLg+OF0veO.cCBB5opF+Zesul3i7+wQ55bnasKP2m9oe5XHL4ZoodnCcH.Dnkj.u0+9lgfC1T.RVypGr3teueueuIaX1r8LQIAjcQRGesYCJu4K6k7x1JLLbaHZaee+sqU6jaBrtp55999qUqVs0fpq+Q+vez0bMnYZ8LYXiRMXSHZquz49RaCylhlmcgVcAhCC+epIRc9y41QO5QAP.WojorByUGx+fO3ubAMTKL4jSlmJji8jb1m88EB5mUPqet2y60pPgBItsalaDqZrBoDxMXJQx7A0BJXLDiBd2lS9JQjMH3wk63exc.TlW5K8klLlpdx07K3FmkrW4x8g9c4xkAHNWN2dtYy1EnWcHlYI1IDEpco1yI40ZIqAsXbkFl0c9q+q+xOiuP1rYELIRvpVvikRF64pToRtuxW4qj6G5G5eatZ0pkACZJx95dcutrkhHKzzZ9SLuzuTae9G4XWBz50Li84SbXdIxcnCcnB.C4yrIDvpcQHLAguKM787O6e1X.SVsZ0YApnp5TwfVW2eoeoeIOopbnx0YNHZ9S9Xm7ZEw95DQVPDYgNsO+B.WuHxBu025+pq+Idhm3Has0W+HpeuaX80W+FAtQfa7k7RdI2vI8CNxniN5B.G9TO1olKNNdNL0s40npds.WWxOWqp5gUUObxe6ZzH8ZR98Cm72utSdxStPbb70Abcm5Tm5v85Ebs.W6XiM10t4Y27ZUUulwFar42XiMNDvg.lSUcN.OUqaqpN6idxO93.CYaamy11Niiii3GDHA98Cnt355fiiiD1me21qEFDRXn4yZPeBBzm2Sx3551ujbiiQTUigx6FD73c7775jfbUsVsZRoRkRRlVEqpO2lG2eM.uykLVXIy+eyM2jPJoFT3XKp1HYco5ChHzK94zClqFppgfMJTt+97NNNDFDP4xkoQiF.nNN1wG20M1M4GrLiS0zqMuaSfHiTeSSob4xV.VyLyLuPtTVgAsc+zI1+FsGmrrLyjf5xpVIH1xBVp+9HyO+yRxe5eNrGH4fQ695++4mYGP2Ah6nIjK95qutbG2wcHnoqInVZeNTrRVnQNfrexO4Ck4k7RdIV.VyM2bI82yqymj3qkgXlA8W+W7WWgPtDkg5yd+ADKxzIiaJoI6aYoZXh+PCV4AkJVIIgomAxlTRKWcbZxoSP40xzlDzD1.mV.KCkZBMRBdRTv6889diDQV7lu4adss2d6sSJoXI4ZpnTQFsRcFGhlVpJyJhcoG4QdjDToENkgvXaLJDNhHxvf+vkajHQw01i28ZpZtG3AdfLPIANex8xoGL3nWLaJDlGzugd0Z++fATOFnWMlIgzusaCKt0uvuvaYSOOuMS5eZO6ry19q7U9JscbN112pq6l.qWGZEDDz58b+umUgFsnLqBylxegaYaamjvzFcgfDj0GsKm4Yj7+WP19V8BPomuAKAirUg70g7kJQwlMYhxkYpFFYfK6G4i7Q19U8S+pVhFzDSFGS46hKkiWICrmoHrzD.N.GVU85BBBNjqq6r999CioVS6JhryG6i9w19U7C7J1LLzeaUk0AVd80Wewa3FtgHflXyxDxFrmxeLHjvtXFoj95AG7mVZHIkVfolfKAEanp53HaGFxpttrpuOCBAMcf9rzu6XXPTxrppkANjHx0npN21aus6pqt5rIPcMmBVIWToQTrs.aE0r45G6Vu0M788W200csJUXsnHV8S9I+3qdK2x24R.McccahMKQnobbBBBRIG2qjRLX+QE8hyWGYlAJrDLhsMiEDnSTohLVTjV.fW+q+0u8u1e5u1ZTmUXuxTpuBrvUNDnO3ykTXJWrJLbcXXaaF5G5U8yl+C8g+OmutgDcKBLRkEXn5OsNLPwO1G6Sj6G3G3UjMAtuw.cKC6Do51O9i+3acrW9wZSc1FJ2NE11IWq8F.VxosXfNy.sWB1x1lNgg6qbRF7dJs+JcbTZ6YqDWtT8E8OV1PlPHqmGYqUiLyLC4VZoAfwcExlp.PgggY.v11tyK+k+x25S7I9DaNGr042OpntTiEF78uTWmG79LGdjmZ8gs4DIYdXXQD8s9Veqa969NdGqDZHGzVr.ayYtjkEQJrjGECgCdM.2jp5MGDDbCNNNtAAAi633jKgvz5Bz183ta849fet0OxMbCqZAMcbbhvjSsl999KebW2VQlf.rEkXmZOVs1dd2dandGfcO6YOa2Ce3CmZnzeuXiiAZC9rJCkHy22s88Y8I+jeIfk2yfk8Fe1uTIH44YIXplv3e3O7e1v2wc7cm6XGyUZzneo3DSYhoA8BBB5.rchzStQTTz5UpTYaOncsTHu5QRBw7Hwg4uYleXVu0gIHfYUUqJhTVUcLQDoa2tqlMa15UA+5PS1eITdfr72+9dLLdXeXfaZ2c28nYylcg0VaM6wGe7QSPy.IqwtkHxVAA01Br15i+o9Dq+S8u7e0pAAAMUUa3551DXoJvJmrVsU777ViprN0YS1+bwuccb1f8MYmdZxu7xjWUMeRF8FsSmNSj+P4GWqq4vT9cq.6yVfKVlqROtIkvJSig.AtoM2byuigGd3aJLHXtXXxJkKmqYTyXUz1fttqq2ZAA0VKwNf0.Vz8Xt0qcpZ9dddQ.qTud80qVsZZf+11F5DdUJGn+sT6RY+1f64lCnPiFMJTtb4AUugbI6g0sJzstY+oLhHCUsJiT2ve.i+HOxiL7K9E+hKHhTTUsXPPPQWW2hIOixCLTmc1Y3b4yO7N6ryPEJTHAwjWRhNePa.1GgGFFFH11N8m2mnzGZpZ94GDD6ZBrc+.x2saWsQiFpiiijP1pY8CBx3lnTKIe19pFBXPKRPPfjbbiCMkOijP52EwfL2hIb.mEfDFFJ5.c3wIdP433PxdEo+tjV1NIueriiSbXXX2DH5uqppgLJgPWW2yVA95QvERhwvFI1ADCnAAAcbNlyVDYbHgqt40C1utJHVX...f.PRDEDUmCS4eOrIgNFkDBXjpfUnpaWohrRiFzzyiUpUq+4JsudeyUS5mFCXlxvbMRPODvKJHHvKojbxlbstG+DlPL2hHshglVDe9a0w6bMfK7O9e7sU+y84N4ZyLCaszROCk76Ex6Ydv.X221FU0rkDovhPtJUHyW6qshtvBS0Yok5mg+mMapr7f70LOKroJGQC0aF3l.NbPPPIwxZXMNNSxwIMggsTXQT02BtfJRsieb2F+4+4O7R+f+fmnUTDoNAuSR4FN38QZ.05Szrbks+5yz1NnfMLRnQ0qlBXbaaoP85nXTRpM+.+Nef0ds+Tu1zqmsY+9dbw3OwK04tee1.m+bUfbQPtpUIa85juREJDE02OqQnJCWkpCEFFVnWudYO0oNUl63NtCIor.0Oym5S06y94+7cemuy24tREYWZPGnxNPTpzr2gD9oYfRDLsea2YfNKA6L8zr6xKSrsMwIy+OXBoOXfTTt56+gC3OZRIvVnRExEEgU4xPCihKYZUvJQAJyFFFl0111BPe6u82dm21a6ssyrvNKlVJxNv49BmKd94OVOX4TjY+rkX8Wvzddsjb.DU0T0uHy+6+2qkvz1jUUMipZ1W4q7UlkFlMUq.YF.Z6WtVLFcaeGfMqBq+cb8eGaN7vC2w.wQJ333LRPPvn999i78+O46enfff7eoG8TYRfhK2vMbC366KvrBgly2.hC5kJp2WrfkHdf7ZdMuF.TlatAG7mCnXBSPWHznzAY78eFSLD.7.AOrpZ1vpvYuvEFFXzjM.GEX3Se5SWbkUVIupZFArPQjjwnJHjTGw1NNRkYKShdru6q+m40uSTjAJVeeeeuhsbcc2z00cKnzVI0jVmRPWmuamtktx2vdvHJaBVy7ymxH38WvawDolKLjdhHwQQJXT2mB+Z+Z+Iibuuj6cLfIp.igCi3s+xy4JE0O6K51LfbVm.AssBCYi+yu++yqUuNop2wJP0Uq+z5F.sqHR2a61Nlnpl6Iexmr3G8i8wF9tu6SLxWa4kGSDYhicriMS05LqpZo0W++8rjvv+UfogpFxuy1dFL0l+DjvYCKkTa3gOSkt3hcuoGkK5hovU9hQ6qeHLoenVMCuarzRzNQ1V2Bb2LwXs0mEVy931qca11s.V6S7I9Da.r84u37Jzf82bfWubWmCdsECd8nlA1fkR3JlphzQLjU7tdddcCGjjCOyyRefm2fm+ClEdEHkc4A0747Okud228cGKpQVDSP6h9u3m3m.WW2DY21fJrv+mgc87NVeNtAn6se3C2C796AjV2kr0+9e9lz8S9Iehcgk6KafLHWhbzilZvvtjTJXMgcd3G9gae624cssqq6lm5T0VCnkiiSqZ0p0hFrVPPvZ2piyZN2ly5ylDbpJUprCPmZCrdxQqQO3n8fZ8IpWt5leb.CamJEUHVUEICU1yAukVxjMv5W50eFX+fiZLPY1YMkEoweq9iG1XiMhEQzO3G7CBXHNXSvSpjWUiypqs3xEnJYIFKWWWRHMxtQP2LYxzEnmWc5kTJJuPZrl.Xs7xFi3DSlOy.H4yOuRDc61samphzAa109Y2Ap8sViY9pAx+EKVr+yIWGGZznApQWrr9Fm6BYBBBxEqRdfBhJ4DQxV6T0DOOOirlWgdwww6iebBe9giEtXAgXv8CSe+rUfrkK+cXxVqgyJFGXh1saOAvj0SJkWQLkkb85TkDRY8ttq6Y9D9ewqpHNNNN1sVokSud8bTix13kKedO.2BEJ3fwLIaLUOUULkxS5OkAJEDDjt23TAAAFnrCiXa6LBvnNNNiALtiiyDNNNSfY+xojDhWO46NSPPvrMZ1njHRIfRNNNyFFDLsqiyjAAA6QFsvzBhQscDY5fffoDXxfffo.lx1wYRQkICBBGmjr+533jMXfRuw11t+jYaaa00wA0rWQJxJF70j8PLkbPXPPrAIIn1116asnfffz8OhO0oLJURXHZsZ0RB1xs2iHy9ad6eN8U4XMO.TY1YOncPwgp4P0ngYbSsZWz0x5+8VXP64fdQI1ukb7TGGGsc61HfHpXI6MVzxHZ5RGaa6s8bb17e2+t2xVOdhDw+49bmbWftKsDwOwS7DIyml+p617aOaC1euumchHYVLwOf500hSM0TEVZomAJpuT11p.Zs8bZtSk5rsHU2NHHX6vffNtttc+e8272DKHFTzJhAkOJXohHhjUEIqqqa1nHrNwINgUTzfm2JRRvR5uG+Q2qDkO35dWIiIGzI+tXB17NXBh15hHqFFpoIGcSfcds+TuVEC2mUjYXH6DIGlmcw.4h8L3fm+c+pKu7N.aVutoTzOxQ9GjpNeKBUVRC0UCCCWuhHal0M6Nuxuquq3d85koSmNCopN5K8k8xl7+z+o+SyHhTtbCppp5nZ8z0AsKCNPEaQjpXfM4zXVyaHf7KkDz3kW1v0JggCJKwyOnu.vQgE1m8wWU66j94NX++1.aFYryeyFMXq50qmDrmJaSjAw4y.qYea1spjvumus21aacfMWj9b1WmYBny7ye66BKeP0S8EJ1hbYaOefvj9alWpD4Z1j7yBEVrBEpFsvv04LiiIZiiYpsKZCUVFhZhwI1TDFb4bZ2Jg7WGFJOMzXNfinpdjfffCKhTJV0hVB8.Y6MVe80FYzQWw00c0ZlL0sDFtYH4bVYEHZc1KK5WtAARx4OQFU8DnV+H6MGj67lMk6Shf+PuxWYg26u9ud26+M7ues+j+r+nkvj8rAHco8EfkQvXfyzXxTYk2w63cX+VequUWnrmpQNAAAkR9LEcbbxjj0CSMxCcUiSLaJhrliiyp999KKhrRbb7JhHK2sa2kylM6htttKZ5GlsEr3Vrexq7pIiZGLB6lHi6gUsZj0AxG3vvDvfY9XTf7INVryi7HOxFm3DmXM1CkICFA9q1LPH.VG8nj4zml7NPAeSFyFjmZRI4tQA6wgv8TCGXDee+b4ym2Z1YmEfdezO5Gs2q7U9ZTUCMJPTEZuxWaksmZpabGUC6jDY431sa2qPgB8RxzTaQjsp.aEUgMIZeDc1AIqR8.W+v92D34xBRWpLebfeJaAMFDptCFvoA4ZmmKafd4ttRtdlIOrT+ZHMI.gC+w+3ebqWwq3UzFyXhkMkFzxChJqC1mMX1XJWtLWaiFbSpp2TPPvQvrA2nXd1i49R2AjMAZ455truueSLxf6R999K455lDXMZ8e7W5WZ8ewe9edipsTk1TmNPktPzfYA3u2r4w.sK17a3YNtbeY6zwgQBBXzJv3QUX3DoIUXf0WdnG5gz64d92RR40jF.l8jut8VS9RUirWMAL4fYfIYMfpiC0mQUM0Avw.3IdhmXka4Vtk5.9.MAuMfZoHLgCbrRU7swBLnaZtJU3FpWWuwPe+ErccsC8CG210NePPfL5niFuwFajTRSRaPSH.OVTEMzywKDHLHHngiob4ZASuNr7dJU029Wyv6gDBaxQH4UCAnmhtugAFAJOjpQRBQ6kLWm0f9jKW7E4XmzmOwvPqYv340MqFRe8nAAAygY877BDq6MtZcWW2M9BeguvFyM2bqJhF43b60f5eCL069xzm7yeFYA9aE82CNGaPEaQLPyOoj.mGgyQVvKOTaHRxVpp5nhHinpVnREQhhzd1hracaTLpB33ppyhI3FSg4YPl1saKEKVrOwgt45aVbjwFYHRUHhAjkRdlIxP.HNNVrrrNXxljf.eYxImhgGdXf97fQRIZXxyCnoumVoREhhhzDjbn.rwFavZqsNNN1xdG2.F76hI3EXmbrEQhsssiCB7icbbACRRxXaamIHHHSJmkzmeqTCotFD3qNNt6KnCCbtrlbxIsVc0UwXyUXOGG6dAgA6hxthgGO1AXcQnQbrdti6480aTkKPchvL1ZvfL2FlcyD6uR2a6pYb1fnBIeYX3Fl83lnSmNikKWtQqHR1HisHqgwQwk4hij6AG2kZK5rXlacTU0aF3FBBBNDl4V4.QAsKv1kJUZyEa1bUGW2kpEDz31NlS3oNkesay0sVcapSHKQJpF1aM8mK128sqsA6+xNwDTnUKFdVXjEKwHzj7XtW2h8ryMcMtKsuNlmsCCklEZdXRPMKFB30AXLDxhRWRP3iqq65999KCT+3tt0hJyEpcpZ0877VN4budx0QaX9Nv4tTJbxykDRj9pEP1ofBqLCivR8WKI0N7ji4rwvh6KImreR0+hw2iWtyu.HI1+mwFjvJHDgx9GemrtY0wf5iY9cRI+zgsvZnM2dyBKrvBYAx769e4225m7m3egkHBpp69pe0u5NejOxecGUCSEPhN6ryNamOe9MDQ1rTI1rYS1xA1NvXuXO.8q9U+p5Mdi2X58aGva2jD.cv9v9Hs6RbOeo5G125uGExbZOxPsmQP2S5KmNNgG2jC72RsqMoueAfyXN1KflTFNoetK20yKXZeqFgICX.8Q0lMIdBHdQnmVW6UmyzCn2i7HOhgfnRp+t6+e+OoTFx.NOaRuzdmmSSbISsTkpvEa0sa21.6Fqp553XARNaa6rG4E8hx355J99987775jnJFcBCCSFjFkna0S0WB4N59GXk11yQuSShjFVaeFNb99NAVJqHRdU0b+9+w+wYO0W9T7m7m8GoXxNQ5wq+857fELceFuWUMsl1G9s7VdKCqpNhpQCuyN6jBI1LR+yqll07XsuSahpppURFz633raqUVoiHRm4me9NtttCfBjEiO6YOaOXtAk.sq1Hadvnh1qVM54AcCfcInOrM2RUcCL7XvVm+7meWQDq64Dmn3scamHkXamrDLNy1mWSFDsIWIbI..5oOMpMDG.8DQ5Vud8TlIuMvV+29u8eaSfMfv0qZtd13KexStAvF+A+W9urYqVs1VDosHxt29se6r6tWHGvH850aJhX1ImbxJP8p+q+W+S6.3r81aaWrXwJ.yBkm5m8m8mcRU0IifIIpe1yRQeRBgCat+7fbIQaNClE4tXNodkfzlK2ykAI3zAT2mFIDRmSZ8etUIXar6irD3.np5p3Z3YqIvRRYvBpjVFT8p.ce4u7W9A1jbYN5k+XwdJ0WEhhTzT4EUAQjzLDNv3bQKUpjJPLTomZPYRruQtWs.xTqVMK.9E+4+46UwzmsC0YGOnCDcwJEm+9V6Yj0RtHDVM6yvpIhCBX2xvNQvF9mxuUXX3x.K466aHRXXk64dtmUfvVkg0BCCSqu1clE1EmK443fYzCtx62OXfCyB0srAQDaMIvmcsEo6m3y9oO.JVdF5.49xzOPtf9YDqrUTDDGGqiN93Rf+++r26dXR1U44896qt0cOc2SO8kp10dsqo6AoAj8nqyLBDXYcrwlPbBIX7iisCXmfAbbvOFSHHguQN1gaFLRB6ClX7AGH1wFysjiv9jiQjfSxSvx151LFgzXPL.pmo1qcco6o6d5a0s8967Gq8t5dFMilQBICBm0ySMcOcU095ZuVeq2u2u2WaNDMm0ZyIhj+C8g9PCoLrhlMVdRPPvfO9ezGefsd89Tg96p7H6Amoe4m7Kn5aMZQti2cWxhohBXBzZPUG.yCAns7SbfwBjYo8qsa5Gm6tu66Nk0.Bm5TmxQ+xTpUKfh3Vf7K5E8hT2XM4TnQRkzm+Ic7w4fXl+u0WD2v3KV.JxrLB6TdtSBQSZRYsHKxHyCEg5Y195H3JiwIbfN6u2u1Wai85IxTQpNMQLyO8q60MGfmHh+Jqrhe+988+090908hiimazQGcZQptWbLZc7wmb7c6vDS.Lt0ZS0DkgfmLLSoqs1ZkxkK2tsPyBsZ0p.PdiIH2pqr54T5xo.Vj2XLELF+gtLhBEa1rYAfhgVagzx9H+YO6YyYL94.xkxNqz64ZdPGV5O9tDHkCDw22Wx5KP58QU0r3uTq0lXs1DM88LAYkCjjDYsw1Haeq01yZs8LFSu8M8T8MFS7pqtZrwXRhr1XPisV6.T2B8TnmwX5IhLHIQ0b4jbsfhzfhomK4.znnn3vvvz4hWJdVHANzS09YCWbSqcDu79kJUZPEQbVb9kW4MLbb0EfDpcQchrgOO4n2riBKsa2NQg3vvvt0LGdqicrvMNbPvVMfNDcQA38oRbMeqdS.ubqsFE7fQVBFm1tDElUtw7TxfCZOHM9i9c61MCjE.UL9tRTakUVQCBBRBCck6lHjzDRnEZMG6akSe5Su6xkKOrXtKv96o57qm+2OdEm1BlUhiYIE8ruhWwqHUW7VJdNGqLG49tu6abfImClDexrz2r0.b9f09Dt+OwIFxv5cqsLo55wPsNYSnwFUg060qmSeGgMOZsZaaNpo6y849b6KhD2nQC8e7K6ePAfw1ZislBn7m9S+oMPi4ANvwO9weNppyOxHiXdauy2lG3MaqV5zppSYgonAYIjcpuyuyuyLmCL08vpORYnD3mUl74qA43fmiVCcgVCvEquy4bO3DPFKt2crpYuF.mYvBPOXgrqKaWC5vAOmD7N.N4PVUyIunt8yypaeynjb.PgSj.KjrV5EXQjgHG9c8c8C2cW1wT768W6WSaA4.a9T2D4xZAwsgXvquSvRqrwq+m60uQkJU1JvMgUxDSLgXs1BVa8h.ECBBxAnAAGIAH4ge3Glz8U9lPQXkhvzE.Jbhy0UStPGOhUUAJKkAwIrOoAtUgBP6BP47OzC8PxXiMV7q3G9ULrCqwbiIbtK.M+hPN3L4ASdfBh3ULEvkQDQxBTYrkWd4QMFSIbJx9tb+Cw446JIHjjNIl9f0swf1OLLr6UesW61FioC30yZsmSl6dNOmmiBmJIszBdpTdAWvEUUemEo2Enip5lh3sNv5hHquvBKrkpZ+Vf7.Ove9Hp5bCn13uOVZHvBiUdmrac9kyzS3wTztr8ppUqltviZcA59O5ez+ngCPzvwHm0e9G8e3Z0qWekew25+Wm4fG7fK8k9ReokCCCW8HFylsa2teTTjDEEMhp5jqu95SCdU9S9P+6qppZFarw7ApJUkJPqxefOvmbtT5D6AT8k+xe4oTYtpmGdygC.ko.lnNLFrnywcfRmvM34tlT6BlUuKqmSt.2aNe5zkTqVsDvN.J2GnWaNX+T6aUAn1EtDh9FIHmyYwlN2HoYAGaWPZhG.IG+3GWAzfzuzItb1xUQglpHUyr8xLGIQihBSRe9OVDM1XLwsa2NFQSr1iGWq1QiqUq1.UkXiwn0qWWt268u.+zqcM2E6Hpet0p5S1Lw7rs142GZ2+sy8ys.JrVb85062J0lICBBNquu+p.qFDb3TmywYq62xsbKq2JUnXgpc.58C7S7SDicXFgxbbsy+X4hcLbgZ6hUSGLKfwgkHWDHPiXvqGPmF304gdf6cnt8Xtv6GANj3.aepgL1RUMmSBb7T.Ve80AAQgbhn4TE4U8JeUCeF1sPdc.Pu67Ntyt21scacUQ54pUZ5EEE0KHMqzse1HXIOt4EBR.RDQh60q2fd85MnwPadetXfj1oW6dBZxIAfZLGH862W.jW7K9Em5pI9bi23MpphpZhhfagw5NYsKEbzA+E+E+EwGOLLwwTLuDq0lrDkUNka+.Ot.Welns6wCKsH01CKyT3JOEuxtxeoh08+mBX7SAiAUG0Hxnf2X3rX48HUkwUMZ7Il3JmrkqTblFX1G9u4uYtG4Q9hyopN6Zqs1LEKVbe+x+x+BSkOe985.ZowtYSxn.ilxFnQ.JkF2wv4jrVadG3Eg4lZpobZAhMJW2tcyYir4xbmnnHqL4dmThhhjkWdIwXLhfHQQVIc9zbt0eiHfnJhwXHXWZEh.IQQgWnj4HYeFajKoQHjXL9wYNfVpV1MH62A5aLlzEAHCjz+t0ZGXsQCLFSOE5gROiwz0XLc8Do6dFaO8rVaeiwDas1DeiQMFSxQBBRmOQhCBBFHdxfDMINWtbpwDj2ZqWxZsijE2F36J6mffgmOK6hY9oR+lcONnatnYnuHROU0Nmm88ldsq1k5YKcQNnR8cdtMqLUsVKpHpwXRTGvLCDj3Ymc1XDzff.0E+YyAehOwmpWKnaXXX19FfbvbmeIl83XqzyhahygSZV.lsTS2ySSnpNkp5995e8u9dA+ww8L0k8Zclcm3OR.RJVrnZsVJTnPNiwIy.Figo22zj5RXJN75GNukyshP1+9eA4wkHngIHdWGGWfje7Td9lr9joLofNN88Y1sA57o+ze5t.8eiuw2XxRf70+5e8hufWvKaLfw+u7W8WMIQjArvD96.bxtMGjmn9KmexctPuFdb0.1pToRadpScpMrV65Mo55zj0sV6Y60q25G12eiJUprs0ZGrxZqj+U+pe06AWkRT4Q+ReIyMbC2P.Ngy17u8e2+1pPSOQpVQDY2kqXZoJVdFU0ygQ6sgwbBycsQ.FoNSWhSdN.DcgdcohCO6914jHan74uFujEgjTVF0GnecneJnHCNuu+SmLM+a4ZeSDvDzCwhYWXG.l9yj14Dh5fGcR0ofAhTkSbBm5A66W+hosCOtsuaa2LMn7Vq+g+c9vmsPgBqCrUXX3fm6d2q.TTUYzvvvQsggkpTgBggGKGUP96826um.T3y7Y9yJ4rQPJZLqTXokVZ2LG4hzpgHh.sk1PdG.IoAf6zjkb+Kecub85u9qu+JqrRWnRGugzuy5DZmgTCqbAl0MAR0p1z5euEpphHUy8nO5iVTDoTRRRIjLjVkcc7IZZxSzfZAImZwSM75iH5fffZc94949WuUXX3V.aAM6jjjz+O4O4OIsSuWBUcODbvCV+7eX3Ia6Bs.8AfW+ZPp+l2ZC7XcRcrGQLaqpNvc8zUlLqu9ilVixdSALY6LpA6cYAbx4CNv.nVOugT8qdelZHCK5BU6npto0ZO6+w+iumUqUq1RPqVqrxJs9N9N9daFDDzpArrwXVy22e6Z0pEGFFUXu6cu64Dm3+w9Nc+9yATVUshpZEZREU0xUnkGtx.IPUc9Owm3Srvq608SNOznVyxMM3BBtLTcFpv9dIujWxTyN6xoHOGkh97T6xZheBAO4Iy8kDNztyfvzJyj4bSsSQf9jof.bP.j+hS4V8vSQW6Y2sceLm9LfWA7nnpZQe+Voz0roHhv7oZBTX129RQwDPog69dUZFCD2ue+g8m88CHL7XIhHwpJ8ihh5qPeUkA999Cr1ikpcJl920ccWw0pciI+v+v+nDkssmYn1q7Dw7gucsoWje+b+LKhBDec0p0mgYxox5kSqMVn0lTkL1jr8m+y+k1l4XKee+NkK2nKP++v+v+vz9ey.PtSbweF+IaSfSlionvu4u4uYJfIUKPUj26688lTsZyd296616.M29c+tuyNo.nLv93oqbZe3SHMAd2u6ewgAbdm24cl.j3QS0snIS5bEH99AordX2iMI8SfNAAAa+du8acq50qu8NITnROee+9gYK1o1yp5esqmINThW54qp06mxdsdkJUpWoR6umTUF.DWt7RomeMuL2E0YIfhEcUY2HiLBwwt0l0pUKDQzJU7RDjjiDDnm5TKpKt3hIg1v3fff9hHCVXgEhCNbfBfmWSLFi.sSGaZ5KmrZ9MZa3XgkKmwpj56CnB3U6Aev+SKz1IhvKfG0.7fJo.mzXhHXbn4dTUGSDYLsgNpHxndzbO+x+x+xSDEEs2UW8rSudy0m4PG5Z1mHxdOvANv4.NxK9k9h2iywGXrTlsNBtRlc2y6r64ayIoMiIPhhhb8uM9LxHifjdoJJJBeeCarwF366Sud8HIII8Jo6yn.FSfXCCw8rh5..wZSLFSrwXFjNFc+TggtOH6BveIwXLwBouuRWq01QDIiMocTU6DYsampcWaairoum1QS+LtjIocrVaGbhO81VaTmScpSs8wCscr1ntFioKv.QkjnnH0ZsRiLVLl1BOdnJHI.3XRSsQLlaXnSf.Q4Ri2ZPqVsF.yGi+2PrYZ2y8DyYbIHxyEe8PgUctg.6WWuzRGxIS+4bBPtlY26cHZoc5zc.jBLCza4kWd.JZXXXliMJ+5u42jBN2mqd85wyA58bO2i.KkiJCYkWAvOOG5Yzmu9ai1NwzrnqTT7X4R.i8U9JekIDwaRfI2Zq0GGhF049Zmiqf9D1VN6W7c8O1d6tXLFoRkJh0ZydAhC3jfffgeWmVMVI2QpUKWTzoyk1+KWsZYIEdFgC9z17q696m8yDXg3YGl37kGvr6rP72+6+Sn3CWwUbEx202U07.ibS2zMkpES96CX5nLvELrGfQfCtafdN+XfOePD0y62ckTxv0GLSeU0N.acS2z7aXLlMfFqGFFd1jjjUKUpzxMb2BVwyyaifffdu6286VDQFQDYxImZp8s7xKOMvr860aVR04POZ5gKFeemtmnF.OncYbfmLCv9fp6kJNfgLl5iqptGXkTcbY1yg0drP1XwG5xcNoyG3KEZm7A9.efc8+O3EZMaWJ18Bei2G4a4ZeyBvD.3DtE2jdCvFelTzqJC89O7d9Oz8QdjGoGvfpzTOzgNTNCTLJZth3JIlcmUmKTyM4.k6G3lvacfURRRV4ttq6ZciwzsgiofYtVyXJL1wOd3nGMHnDsbBvCPoW6+fu+QnIiTEJYskKN2byku7EmpS.n0F9mlJuIs90YmfLJ.v+2+W9vCTU6NyLyrMzpSZ1oUOHOMGt+KNEsKvxj2Cx0ngmHhndtLvknZCddOumWtzsYQiuovYO6Yy43Ypd9CRnggg57yOeh0ZiQX3BV9s9s9M1LHHXCq0tYUnyO4O4OYuW9K+kOnQiFwUoYBMHwGhO4IeJwtjK18mcgxayAoNcQmYgNzjTvaXKehxJCjth3mnpVXzQGcO862eJn4z.SCloA1KMYBfw7v67qm5KDHa65g+5CZlxvmZv.VamRSoBM5Jhr0QMl0+m+O+WbEfy3CKO8zS2FZjINTKArr0ZWEXCQzdehOwmPNzgdwkJTnvdrV6dO8oO8951s6zppy.LWqJNKaTUslHx7iM1Xy+g+v+dKnpNOsY+ouW0pznBsn7e8m6yM6xKWYVfYliLJ7sVJ.JlyQHxpKds...H.jDQAQErN3NSXbo.W7BcOQ4D6fT7G3C7Nh4LjTChWbwCcdnOeREPme9aw0W3DOshpr.j6S9I+sxQSJTQjRVql4vC4.X1YmM83Xlr8+k9bK87pguaP+0VaM03xFnXsVQPjzxAH1XL8yA8UwAdhwbj9.8ipWu+OzOzOz.nQBf9XO1i41+mwcs1m3ucHaXOUZOQ222IncH9LKLrVj2BZsY6TWGYZnCM1ghr0ncOVhd0ftsaOUGfdyjlQz1s+xYSp+z1IfOHrFxa5M8lxCj2iF490+4ee566m+mueiFU2NmlaqJvlelOymYanYpdCL84uflLf.TeP+k9k9kFlAma61tsXf3ltEEoVqEeeevAlh566OTP5.5IncEU55Acdy216qasZ05eXSlkr2Jc6V1se2QvFe1TeOENg1LEvj8KRehF12n2bzrOMIta2tZ61tySuK6McMlaWO6mjjPylMYvfA5icpEAfBEJnpp5CZsIyO+BIuvW3KLFk9ggg8LFSuCGDLfVjTEnYSO.xYFlLiURCVcgKGQZ7oRaHXIyBiztc4I.lIkQI6GZ9bRRzC9Zdcutm6q407ZNn1PeN.6uLpOPYve5WzK3ELEvjo.dLhHRQU0h+I2+8V5085dc6w22ex8su8N0CcxGZe.6spiJ3YYqsHvn+2+r+22SJ6NmrZ0piuwFqeNTgua2tCO+SAGgomd5r90C+YjMZ3+2F498g1vaTDfPylMw22e3qA86Sn0pHBgVaxHiLZrj97QVIwHhzKHHnWJSkG.5fomdZGCQDou0F1SUsCpi5+hHaj3J82MsV6l.anN516.sU4roZ5wYyAqasQab5Se5M.YcQjMlZpo1TDYKP2tPgBcBBN71fts0Z6DEE0SEc.jPpi7j2XLE888KlFmVN.wbXC.RXXXtvviW7Ar1R3bsi7NWVkjJUpDOCmJlnmxIo5hFebKuyMivKkNu97.r3S71bgg8KWJE3vptXMbr+QUMI1XL8UkdGIHnGNcLQEUxYLlhgggEePqMqOiVqVsjkfja9luYM.jG7y7fCYYxrDk2oGfeaCKSxAjugCrwRO2m6ysDzZDfRW8UecEq5h++Iy4o3HVFjYPEat45YLqBIc6jBThXs1TVlfVqVMbLquU9VPAe+8muJHz.IJ84yY3L6fOlq8z0hf2ULYKlrb5hv8gjL6uFHmGMgHhqBCtm64ghUUEOmCcMdiFGee.yBUb15q0wH6on8X.kfCcoVCvtON1osy4ahGmouHRWOXqnHyl.aT1o0UmUDY0q3Jthy.UVF3LEJTHM9eo6W9K+2LHLLT888ktc6l2ZsE9De7O4nMZzXbfo9Jm8ryzsa2Jpy88LhHFbhDqmHxrppSqptuJzXezx4lfVak8IhL0bCKGxkS0UkzDmtnaMi9rxS13+GdunFnug2vaXXb9+.+.G7w8Yt.utPelusq8MK.Sb27NA.9BkK69qUcAS2FF7ZdMul3q9p+9hWZokzFoBvmEJAKUBn3It7PeUg1CRfNP00AV0Oe9U99+9+9WyZsa4J6DIyN4JIhLZPPvXMf83pG2JiQEF89qWeDfQZ.kRYJRt1Ot8y4960qUGf7vZEEXLvaO.i4CiHdRA.zF5.Qp1COudG6XGKN0FdJ1L0+wCCCGgxTbszy0l.p1HAHt4NKZU77bAu0nQiBVqsvlquYNEUbVttS0xcrLgTsXvsPPTGXIVqcyfffMviMEQ1tAr8m6y845BU6U8vU62HsDChdpUJNWh6O6BzjEbfTr7vrdTtqp5VQtAf1vsfpFcDQRJTnPghEKNNvzUpvbfsrO9kIEU1lkaloAHkV3wSSuyulLOGZ4sSoTLyf27a9M2uUlK5.qCMOKvZ22oOcVICbVOO2e2CuUEQNCvppHm8lukaY6vvi2Oy1Bme94yu7xKWxZs64M8FeS6c4+lkmFGRxdppUUU8SEVReUUeQ7LhmX9OeO2iOf+RfupMqBTYoTDnUU2mwvTfcx4XtTVmvXmblSNTKa3IGnI655xgTpQR5fmwN0X+D6FvjcQiuEeFnzSpASA+n+n+n4.x2FJHtIJKJNmzfG4QdjjpUIANSxrL6kb+cHPY9zisH.ppwwwXsVwFYEiwjSwAFiHRBdL.UG7neouTeU0AggGKFHVykK4ZtlqI42+2+2ONJJJ4.G3.IUoJjsngxQOaOiXeiztPYY37ypy.VLETfCcHW4XtfCv7UF1+ZJk4gTW.XfyMbVqOP+yjNlT4xkiqUaHvcv23iKoQCOdmSu8a+10lPxuva9MOnAzU0nstsa611ropa8S+S+S2En+LyPLrhdnGuFCnvIzwckF5.f9y.8wKELnTQdSEQihbE8yq7U9JSxJUfffiLvXL8+i9Xer9.8ZB8+EdKu43vvvjl.29se6JP5zmsYJlBd7iu8rk9fJGZXYZlxtuY6ybLXIW4wnNGqscZI4cNmWOAmiY5JSE.HWtbf.EJTPNv7K..VqUDQjZG1Um+gggwfzOH3HcCCC6d7vvdVqMtAnPSAHm0QY8B.Ele9g04+S2WqyFyNOvHKCS.smAvusyljeNum2y68JDgqXp8rmq3E77eAWgHxATUmusW6f68du2pPzbe0669lRUcOppkTUycza9Zzewe9ewjvSGJW4U9hJ9Y+u8eany4HNMMYbfQEwqjTUbtqyNZkxDMZzX7IlXxyAf9QFYjrEjLDbjQGcT.ne+9.orIwrCvIR5m022L7jM66GEEM7Ug740L6DVfjdc6Fqo8QRYgbOU0d1PaeiwLPTYvfACFblUVoOncQ0NJhKALBaDbjfMzDcCA1HH3HqiHaDEEsQvQBNKvZhnqljvp999qXLlUUXEPWIe97q.5p862es0VasyFFFtAvlhvVggGaSwIN3aqp1wXLCRTQs1nbBTHzZKZs1RQVaQDxCP3wBSLlCmMmYt8eXSAfhGKLLETESBFhOyENatOYZCKs64gBLaZ4F1Lk8xUq5FuvEFtdpKi8QJdJBtRLLOzXnyVAnqrxJIVqsePfo2wCC6B45MyLyDinh0ZKDDDTRiiKAdEvUZVJUbmigfdzidTfxR850kke1yXXWdM20YwwpvxBUpjkflju3W7KF2XXbTd5kAiASu1rr3g2v3aqTohDNjUWFAPN0oNUpl8jVGhpFas13vvvj50qS8HG68SWuk36eCfG5YdleAvtXBVvc+OZ3bXyU3V+kt07oi0mzHkAJhHCZ5XsUopUqNop5Luo2zOV4JToBtqtyrV00RcgxSLlOTZWLN4Bw7hKx4mm9U+pe0jzDo1oIrE3.W8O6gdnMHEzj0W+qsFzZ0pTcEq0tZ850WUU8riO9ja.xFO5i9napparguuWx2Wu3jXUDova+s+1GqToRS.rukVZooUUmQUcllNfRlVjp6S7koallf01vbp1rLP4k1gAJSWoB6EVax4Xt8.L5C+vObonYhNeF1bot9O79P8gqEZgDNHI28ce2Ocutum019lJCSbzmOR+.+p+p.nz.EBR7Sq8Q7a1et4lKNsPiK.Th4nDT4xYgfCylYDzC2jYq1BVYu6cuqBrgi9jZrMzp.4MFSovvv834r1tInRq8T+X0GsVsiNhME8emsRV8RGbT+cp23PXTPF6u99tuQifQnkeAOHmXDEZl7R9NuV8HG4HPDE.+QpjRCViwTh1C2e.nRfnY4W6fG7ZyEGGmuYSJt4lalkMn79A94LFijUiuQVap3aQBhj.xvZyKHHXqjAIa97ulm+Vzjs88ugs8fNmNJZ6nniusyRgqloZ6YKP9oy1NflrHIf+fofAqu958vztqHx1oh.qKyOdrAToiicMZQf8zrotu65ttq4hHpBY0CXtg0x8jKB6AlMKaXY8ct.pA84ABf2Yheeuu2WOn1NB.kOaBdat+8u+LUquWyltqkMo4FFiYMq0tbsCaNSMi4LftpIHXMANaXX3lhHcBBNxfegeg2hzoSmRhH64+we1+iIrV6D.SJheVfqSqZy4nEd27M+C4CX.LKVewp5Nt0QYf4rV2.mKwR6CWfuieSO2aZ2Azt6ZP8IASSNQBNaZ8hJramy0qGesK9TosyfxKTWYM2BWSeu7pp49y+y+yyAfpZRbb7fFMb8MWlkuj66S.volRKCJkQ+n+A24v8WpvnI.hSbSTI7XgnhjbUW0UoAAADbj.A7xYLF4+5+0+q7pe0uZ022O9Nuy6LtAMhARNUV4I4Ze6PFwdp1t..HL7m6.T4INgqu0htIo8fj1samvzqobJRdzG8Q2o7TNHCR6OtiVwT+xRvBubNVU.8fYGW9K0+s7VdK8UUyX6v1RUoC32IMS1w.5YNiaCbhGO8lTfjSBIvnNV0.wzrZRYHgJo6yjjgG8JtNKAAA5Zm8QShhhhekuxW4.f363NtCEOj+vO1mHOP9W0q5UU.He61twxdnEenyue1yl52otKet6+.wL2xJKAPYQDImpZNlCIUOvtraKsCaKkjjDPEhR01qS80WTp3VvhDdrPHU6TBBL8wq4ffff3Se5SqtxvgbTlbvbRTTjTqVMoUqVxoN0SI.ouTscAVhWQbf+OMdTEpt+2869magOym4CM+2y2yB0.7+G9O9Fplq359.ACFLnl1PCtoa5lpBLWSU2qH9i7a+a+ayq+0+56e5ux1c98t8e8t2vMbC8gVxK8k7RF0IDrRlnSNAvnp1rj1Py.LI0whF5TDiBTx5XIvPvRxXWRV1oAXokVJszaRAK47.NIxFM7IVM88G9frpJhjYeuwHLHIErjfffdppcEQ5txJqzWcZw1.Uz9EJTnmfNbdaA13K7E9BqGDDrd3wBOqHb1fiDbVa8iuNI55FiYiviEtQPvg2PUYi8u+f0kpxFhHmMH3HqIhrhHxJAGIXkEV3Erlp55CFDudPPvlII5lwwwaZBLaFDDLzkpDzDi67LmfjIZs4DDUfAHzyV+3cCBB5GDDDGdrPApjYq14vXA6iq7NepzGJGTq.PoSAivxkGqBLFUXLUaMJMZ3hIpM.95T678eh1eCmqLU7yyAjywRyP4Q+JeEEHILLbfl5nIqblyzMEnKrggExmOeQnYQbh+aNZAPPldbnTocpPjdoSDxypZs8zpPBkIQ0Vwzp0fG4QdjdoFHPmpYtDjoYbpNwboZBPtlzLaMG4RRRxQRhHhHQNWxLWwBExqplOH3n4xAT6n0R788iCBBhyQNsleMApluLT3qd5SmGuloNGi22HkC1kpsSrjKhBypUAmX7O2Rxc9tuygr8EnGUnCTsq3DAbvEe6Duy246desnUl6dUAYHfB6MBl.5sKFwcAYe8iG.kZMStxq7JGpae.cwmNPPmq65ttsIcrk1sc5qVCZrtwXVEX4fiDrTsZ0V5K+k+Rsupq5pZVq1gaBzJII4LAlirdXX31u427adfHBhH4Z1rYQq0Nzc3DQl3ZuwJ6kFLsHUKiiQgFwSxJcGefpppdsZ4.IZIVZJfItlq4ZFiRmSY5e4xNqyK4wKNH0oaN+3p+1qmGeRz9loFl3V8RM3M7FdGtEr.5LDpQYYfLpRlfvlfqzHK3sDkfVmu+a+DseRnF8gnN3nb4pW2Mdiqgi0BcLFy.bhQUNaXXIf8zDl7c9Nem6M73giWqVsQflEudio3W9K+kKVtIEfFWJ5NIzDoZpfU1oSmBe0u58T35e9O+zNvIEZBEHh73g7G7w9CDf7kghMN88U5u1ANSAQBR2GMjzR7QpZyDLJJcxS9EGsPgB6QUcLMCfEmE.lyFZIEHnD0AtPBPhwXhwQOxdJzEO5Ta9ar68+v2euO5G8i12ml8aB81uueWe+qKsNdazegm4s1MEHYAhRVCFL4jS1Ga0N37m8MCb261nZS1.ZsopZmACFLvoiKxHuhWwqXBU0o61sqSDUahmWlPJUg8AKmEH3tAO4hU+4t9MMOX5hxp2a5TQo8W8m9WsCzLq1m6Vud89QQQ8g45ArcUXMiwrLMoYXXXjwXr0covLBnowXVNL7Xqs15quUPPPuvvv3q567pRLFCQgg5xK+H.Hc5zo3W5K8n6QUcpG9g+eNmGTQU0agZK38q7q7qLTnnDQpnpVFpLGvL9vzO1i8X68du26chYS84cGHemCirjmfWrqelNQ0ItPho24yX.8h79OUaJKtP51YIWYRjFXv28282s.nhHIW20ccofzT8BURDWzVaPoM5O9s9iqddNAjLxZI0BtyIplGUxGDDj+q80+54OZPPtvvvbgGKLm0dbWfIICAaK9Vu06X.vfxP77yOeBsQOz+aT4uPM8B7Z38tCwhIMgjxkKqrxrI.IOum2yyQSWHgSRxt5O9T00tdBO9N4v4el1URLhzsbpvH52jAoVbr7XO1iML.UX5KTPWY+LgczqKEDsM3z8Ufff.RxMrCtnfXs1bar9loaOI4vAA5sca2F1iay8O6U8OMOPgfffcIJeyHKrvBR4K7wvyFZxAAoFKlwdDgklU.xoZqbdoiQO2R.z9hAD24zFRj34f98c.jjwvjzNdx9eNyKEJTHGPNA3zm5TJNm7Ht9CZiARdguvWnFkJHhkai.Ko999I3JYhz6qW9EIzShVp3C2br4b.gOWklX9Pen+k6+E+hegA+E2ywp7ddO+6m5y+me5wiZ1cO23M7bl7C+691l93egiOmHhmmS2r1m3K64G4G4Vje1e1e19+N+N+Na0p05a1D19JthqXfpZNQ7GEXRU0oHEvcxl63beMrrhIMXbiwbNyq366Sq1sGBNB3XLhmmGQ1ncUFNNPQ7MtQ1y.PY2S.466qEJTPUGyRbBNnyZd6AzqSmNcA5ZLltSO8Lc+3ezOVOft8GLnKJaiKqtqqNKgesq+5u90pasmEQVCQVq9wBWSE8rHrQXX3l+9ejOxVdzZ6fffssV610Otcyvvv0CCO1ZCFL3LFi4L1iEtRX3wVoVsZqVnP9UCCCWUDY074yuFv5m5TmZSbBYcOiIHNUDTkT6NFbX.EqPu.SPmjbIc.utVqcvQBBnJsxkx347XyRTVc3oFXIt9PGjbP8rR7dTH2XMUcLsoNl3KiBUGQDovbPNehXsmBia3zUORm+Lfm2y84JBHNQd0A.oBc2byMcNDjp5RKsTAmlyUwIZvPQeBysLn2y8bOwzxOcr8kiOzS+ra9usa65XuXRCHg1DK9RLTt+Ue0WcmpvVWy0bMa1v0+oO1oNeF6d9sc83x7BP9JoLIJWtbRNGHyhhy4nzzDEFZOd9DfSdOmLV7jAfW+9w8i8.IJ534aCEtx8u+7zrR51+bzKpmot1qGBTOVNoQJn0ae5siAFbe22Wne57ucmqEcfFaArsHR2TsMjwGe7RppiqptOq0NGQT12kPwxTk4fSkpGHLIzIqjCGgCdNfJr60CmP8CkVZrsGrPZ4wNaDcgPmQPznwVOxi7Ho1Ku+l.q6Cm4ccGumlzDaylMO8K9E+hO0G42626TggGawfff50pUKxZOd6fffUMFy5VqcSq0t8gNzg5d3ff9UEIIIIQTUK9P2+Csmuqidz8AMK6kpyIQegnZ+V+V+V0.1Ov9EQp81dauMCT1CXVe243doAiWIqrJW3IzTHtvfEsqR0iyKFsmFum+rt12L.L4bCjrNIPyDlkDKot8LnhmDCsxDdyAhQRvUVJERYXRA7uLE+05DOk6AtsviM1NLb874yuYXXXWq0NvXLfG42tSmR.iEFFN9q407Z1SPPvPADssO4tpq56MeanPp8FmOUXVun.1zvySdqu02JiN5n7htxqzIredjGZVRc1B7HzjR999EO8oOcw1Pwp6e+EiiiKvbjCrCudU2f.ky4JKnJiopNAUbznEXxyt1Z6AXDiwTXzQGUPPImyBVQ0rZ9cPnMLCszdBzilL.ZDSEzumuuumLwqzUJBzNSPv5u3NKJ4Yh1vISVLKSx9zEZzgTqFNrFaAk21MYRksDQ1r39KlQA19hHhTUFoc61SppNa850qzrREe.eZgueFiLJyLvxoCbNrVsKkNv44oF3mzsvIn+J9zGJ26s81dacybQGlkd0pUq205622ZentejOxGYqFNsx4L.MCN5QshHmplwr3uy+t+cKFDDbZ7vFDDz59u+6+L.qopttwX1TpJaYpUaqNc5rkHR2QWXzAOum2AkjjjQt5q96bxW6a8WXZQpNqHR42w63CUAG5xF.eOQ7UsompZ4HXlCbfCrOfIN9oNkS.rZRwZW5ALSyD047YtPKtky6mOQ+suQZRJwecYrzfl5pHIe1O6elae3D3LsBnPCE+mfsVV6P.r1NSJzhjjjjjTU8Giwf0FldsHoPXXXgq347bJ9Gee+UEwkkvhFygKBjOIWRt50qqNwpLZ.9zu8trlsS7j..m+NX6BBbxIxxvwBL.VdHSRhN3EjgSm+j5v2XWm2Y6dHhgUxzQj31dj.dRja9mQ.F8t+b28X.iTwokE4YgKZvG67xC8NtiaM68E.Y80WWlbhwkDzbtr0p4ckEVlFToZqJn29semppZxG8i9wz50qSl0oVAxCmIG0RAB7YOYCZm.9OD4NIjqd1XPFxAKmGHe+98y2rL4EwHKAjUdMWhldxCdP.TVBMnXwD.8y9Y+rpweWKNWcKxCmKEI6e9EzLAQTDU.xEFFl2+H0xeO2y8lVJtkUfAT18b9zNM35oyfIyFKNON.JFeoprOnZk2367M3ejibsd2+82Xlq7f2zX+T+T+qxM1HioiVbF8QOYm7W20eMic7G7yMEvzMTceOvC7.S7y7C8yT3y+o9TI.cjpx1PqTwkGQDo3i8X+UigKYBCsxxzRwY2rwLu0FsKg37Bmg1nnHhGLfnnHZzzsPqnnng.Us6GNDXHyShhrn65QXUU0ZC0ACFjL1niNPQ6BRJiQjsA1d4yr71nrsMLbaP29e5OwqbafsKVnvVl.yFKu5JmMHHXsie7iuhMLbEfUDzUL99qZLlUQ0UCBBNKv5AGIXqW8q80143Va2vvvdJZ2jA85LXvfsBBBNqCPDuUTXEfUBCCOCvxw8iaCrjwXNSXX3ZyO+7aLXvfsA5YihhEQRPG5DIoIrxwNFfthJ8fl8MG1LnIjbLqUpWudN7IOz3xwfCtT8iDNI4qk55WNV41rnHhiQxMnnpQETUyuDHQYemKGIgpp6Gd.hys4.RedBx4aL4bh7ppUfAAAA8Fehw6ZLld0qWOd1YmUrG2VDZMp0ZGyBiDkFu8M+CeyJDMDP7S7zGf3eytoP8cl6pACf18kpR2FNP.5.zk.5Cqc4bNKtaUmRXNHsTUX6s2VTmEs6XAzNimjGUyIhHiM1XJsHFZFuvyegjlPNee+hKt3holbQqB72hrT7DPRyLfQqRuwFarN.aeS2zMrc6.5.yzospaCdaALzXHTU2TDoqTUzuxW4qTx22eRU0YhpV0YpBMv36XncUpPEn9r3.VXBNYJa4pcNIPM8b9DCY15hLTt.5CyzCnS0qtZmq9pu5N9PmvvGXKJy5QvY9f+lefl.gdW60d5+5+5+5Ees+j+jKdzffSCTGOZXLlkvMNxpppq466ul3ImsErwws1sxmOeOwSTfR+29e8+ZbU0oanZ4+z+z61y22u5a7M9tBTU2up5B.K7a+q9qteUaEnp5G4lbbVf80pbY234Kxn9ma44e4poKWr39+6rsuYVRNCmD4fNQ9QSsyrrEx3JKmT5WVIhXwSzW6q80lK8A4BDM64m07K19IoTl001jsNYiFa444ssJZO.MJJJW3wByO5niVJEjjQMFyHgtZIUBCC0xQjTu98q.7U+pedWmrlkehn4jRylI+GdWuqDfXICrAmXtNlHx3as0VSbi23MNdEXO6e+6eTveDfh6e+6OGKgTud8gaLOK4wucZFBZsWQjYzl5b.yljjLEv3okMT9zIr.GqRbNO.z+K9E+hcQoyG6O7ObafNChi6Vud8AgggJsfZ9GUxDCJZdNVq04uPkmoZYK9Y.QrCM3ftd0oKS0tCv1p1z4W6M7VIkprqRkJa7X26i0ua2t4DQFqVsZ6Sa1r7Farg+M+7u4fXnFPMZS.N5rkMvxTdvjbRFCJegJgEEH4PQL.mCwzCp6.LY4Zc.5tDzwbC2PmW6q80tMorXJJJZYZznQYHD3Ten206ZQfECOV3oBCCCeIujWRT850aWqVskihhV4T2+oVAmFnrVXX3YoIajOe9tMa1TsVawete1+UipZiwi62euPyYUU8vYSY0ZA0jpUMhHUwkUwY.lZ94mehxN5SOxVmqK5j6B+5PWH67K6ZvST1cd5LyOmOSWD.lyhpZiXQjj+9+8+9UfbD4U.nXKn.dd4RE7rm3I3SqZha4VtkrfVhWasr.SD0YC2hpfXLA4.JHpTnVv9KDbjTfRoY950qWnl4n4qUqVtlfPkJZpv7cgVD++61kWa38jT8MYmWm7bJIvK1j4Occ8VSEu3g5D0bMoH9MGUUcbn0DppS9cbvuiwAFqETDe+br3kjsiJMQusa61fcsPyMVeCAEQPDee+bhjKGoNIgppJpjXOV8j2xseqwhpCdKukaMtVsZIFyQDf7sf734keWB9Z196YCM2w7I.nVVlpEr6n2TkJUJu1RygWT53RstTAw6N2O4I0ob+d17uw27MeyZj0p.Z1BzqToR5uJfS+hxsK16TJHHnT0VT3leE2T57Bsc8Qa6O.HdEHgZWRqX8Iy0iL.SJhioG6sZClipMpbi230V9ttqOy9Ve8sGoZUedOum2cuW+OyOSmer2zap+q7U9Oi++t6+mEuga3pG+23Nuy8NXvfoZ0p03evO3Gbjzsm5nXOR14Vbb7nKrvBiAL1VasUlcAWRari3jlcfkxRhKFaD20EdGqRzjDhhhXt4lyUhNJmCySxt9GkJ1qBR56Kouin.wc1tSeP5hntxhE04fVprsjS5nPmfCe3Nn398ffs9S9i+i2bl8suMrggm8vG9vqpvJAAAqhxphHqFFFtZsZ0xXHx5gGKbCRckK21lMKTnz5EJTXMfUDQNSX3wVNHHXYQjyDDDrbPvQZO+Q3wlkI...B.IQTPTAluUPPPqnnnkEQVCXyyd1y5JoBmsTmXLlrRJRIM64gggCHE3jvvvd1iaG.UiARpUqlPzvE1T.H+AO26EWtKhUR+d4iO2EHo.ZJHGBPJPs6pTyO4EYKt6a0M7U.sIjfm2vwlcZyA4RKWqB.4OdXHgggwp5RnPwhEiCBBDiwTzZsiYLl8.dYL+MWln2xEdL+ms21Mf+wpp8zFZ2G4QdjLlK2mvKaVSpmDf4mWYoc.tZrwFSQyTwPv2XjgkUX57JYGGgggPKxAUK.TpToRkRWmxEh80OS.fx4b8.nOMXX4zArkIjsw+Lcc.J0zEmcSyphHmQDYY77Vs9CVei+O+29+YOw0FSihlZs0VqLf++fW6OQ.v9oE6G2ZAFtFf.XJpyjv7oNryiq5ENuiuy3VWxxyuMNsMb6fidzsoMa.rFvxGrZ0FzpU3K8vGNDHLwk86V0ev5KEUu9JVqc0ey2+6esfffU8EYUZwZf2Y8882rd85cnE8EQnd85khBiFyZsie3Ce86UUcep1XVb3TZTUq0D1u34USDoFNG2znp5Q61yUMEXHYGGOa2tpyEx5tuP2W91km69Ft8M2RxIsS3IG1YrdRylMyFDIVjgVCm1BfVj6C+g+vEnJELXJllApKGwVSam8fXp.d99de2w16au6qmntx8AGEmKFFFVTUsjHRofffh3Bdh1NgmKAH4JukqT8wGn8EKvgrNXwVHlxjzz8+yCLhKnalZ7wuh8VdlxS0JUL0BCefwptSG5b0pUaXvSMg7DQQU08PU1a61smAXtNc5LaiFM1GoBbp0ZyYbZWxvGxCNRPeftW60dscPXy+o+3+y1LHHXyB4y2QDYPPPPxu6u6uaBzLoSmNIQQ6frO+s2h+x196VSLFtfol63bM8k4jsqPkMflqgCo1yPqVqbfCbfydEWwUzY6s2VUUKAL4DSLwrexO8mz6XVavwN1w1OvARes.3UCvuYpPQAs2q+NzQ9bF37D.NMVgXNHCpQs9P8Tavltzp0P1vvLrguu+Z.moMzlpznADAXCBBrAAAViwDUq1QZ7td6uqVppsme94W9K+k+esxgMlUCBBVCGSU1xXL8BBBRRRRxKhLxC8vO73oTmdt65SeWdoBEqu1nwPsMQpJCskr1vTTlIOSZ+C1sEjwA2M.IEfSTn749Ydln17ex1z68duWcogVNGCnBZUplW0FE+HejOh6XsYycCx0S31CP+7e9OulxXE5zoiFFFpFiuJPxd1ydTAzG5K7PhwXDUzb.R3wBIHHfvvPMG43O5S7aHevO3GzEXaqV6Lwi22zul8rw14C7wtpk1GGXsWrLf7zcSH0MTVBFkHFWDYRp5X02byM83jMNQzSnH+dg.0QfJBPNEM2FargnnRj0Njd6SLwDtwBygZpUKN73gCL0p0mJz0CudV6whO8oOsVudcglM2oO2k1Zs+Vo1tt2UmJ6JqnjN1Tud8JHhTfloiaMC4mkYubdFSWywVxrLWlLwDSj3aLpHpJHpnRx8e+2e58EGiR9K+K+KchhITJLLr3e4e4eYgGnd871uf0EuTURpR0XHJdokVx0+rd8mIXXRl1kr2FvLzf416d269toWzO3dN5Qd94dGui2Qu64dtmN28ce2cznnA850i+e9O8IKpZmwlatxS7ReoeuS9xdYurIoJ6oQptnAL5OxOxO9Xoh.6dxmO+n.kZznQwel2vaXWyMPAQj7IIIOQfvMzIMxb+FiuOkFozv+O3zvjru3NZahhjUaTC+K5v2+d+q9qxPTQQby6GXbrxH3HAcEjdAAl9ppCBBBRBO9w0fffjZAAIgggI23y+4GWqVsAlffdAGInaPPv10qWeqfffspW2tUPPvVgggaEDDrswX5zue+tAAA89Y9o9o5DDDrAvJjPaQjlgggsLFSqfffVgggsTUaFFF1rd8GrIt5UnswXVwXLqGFVe6q8kbsYZ91vRpHKlLGVBtSWq0lDFFFKhz2XL8gF8SRRF.Dacf5MjU.m7wmDiKU+GAPNIHLyPliPZL08wITtYrgLws94FZ0Ky9vKvBjVZhIppw3haOY5omV888kG4gejTWjhhHTLHHn3C8Ednb4DQrVqdzidTMkgbEMFSp1MzbzFN8TIK1C3aeytsByLLNWQjdW8Ue08SSR7ka716bsIsTBwMd2v9c4KTPQbtkiMzhaaKI4xQRJfjfS.qKBMJ9xeYurhIIIE1k1Dcg.I8Yh1v0LACSVZVL0crP2TmSqGSQGClMA6Ywwl6knYy10pUa4O9G8iuFdrU50w7u829aeOm5TmZeui2wudk50q6OXvf8Cr.vA.uE.pE5XeRE3TyLqqjD2MvIm+ZLUf3CxA6AmpyviwFMxzzj0oFqcxFMVFncan0wN1wZ0xY6vqVqVsy5Wq1lFiYq2za7csIvFOXX35.msd8GbcOQ1nVsZaBz4V+Weq8tpq5phM0LfSefJDFFVRpJ6YiM1Xu.S2oSm4TU8zlM8UUqALu3KyKhre.SCnBUXFqqRDRkjfr4Blcnncyku.w92oaeq.CSTfjEfDX1XOOuL+aePpWzqpp4.+BP0hUDoDCXDK1h9W95XRV.3NVl.aeq25aYqs1dqN9A9CpTohL891Wdvq.PoOxG52sTXX3HTIsdcqRNnB6e+G0IVeBZDQLy4VFCm+9LgolxkIqVZVFcFCXBQjo9Me+u+8cG+FukY9v+de3oAl4i9G7GLcPvQ26wr1rfvSEXVJP.Efpk.uQqJx3nr2xkKOCvriLxHSiCvkwPnf.h0ZoToRJNzlSp+f0yNu2TRj0EQOaXX35AGIXqffftVa89+a9W7uX.To2UdkWY2ACF3xPh+4vtjryqmIam+hfNuL1WKdZX.EoeKZ00Oa.JmfvtFdbVOQ17.G3.cEQToZ0RVa8wMFyz+n+S9mT9vG9vlTprcfSe5SeEuzW10+bvAdx9cT1aNunT5rALIUSGfYVFw0+agbGDDJgVm5I01ATG2.7UoWUnGmw0GipU2DXcZvYwAryR3Tt.WfVUZ07s9q7VaAz968696d4ImbxUNt0tFv5VqcCq0tYXX31uyes2Y+b4xk.d7Cb3CmOIIoDvXeueOeuSBru50qOyq+M75mSpJkUU8noSLnvyyogKswY4x6tDjfhT9jCCPtLT.ltX6crRxBrvkEXjOy0Vv0O3l9AuoXW12pNvCFb2+Gu63H0oiDG3.GXmiuxOI29dNJiO4jSRPP.VqU8MFcqM2REPu9W50mXsV0ogRRBHw150GDbzfXSMSx+5erWk9C9C9ClsvtBTlhUfBz7oryD820aWHfP9aKvQ1cyceqb5BVliR29se6inpNVEXbMRGWDY7O9ezmJSmGbyCctxvv4u8N2sMHTsUlPIlS.wUGktO6G+i8wzM1bCMHHP+i9X+QwgggwAAGcf0Z6bG+72w1MoYGIV5t+8u+A0pUyM9bUbZXxIt3L.3awZmGPRKnokTjad0xtEKVZ9R4UUKnpVrLTfQH+xrrarXW6Byvyre17wWG199AfPhJptvBymH6jY7Bn5HVqczrjlL+7ymuVsilyXNBkS2tMnASA5byM2yT8KSKKCJc+2+mbO2+8+edhG399OOdohEG6q+0NY9u1W+qEex64d5Czct41bvG+S9w0ZGpV9ek+Mu+RIZxHeGGZrQeu2wadrG3A9j649++8SkUtM6EXpO0m5ilomWiopNBPgpUqJ+9ejORV7L4AJnplOWtb4jptU4uxYV4h1WpznkFB1wryLK9F+T1hjxUjr+I8pjlcKKKE333XR5axK7E9Bytql9QEE7ziDDP3wCQQEOHePPPwTl0NRnMbDuT8VQDozO3O1OVAqsdd6ws4ulq4ZxWqVs7.4EQK.TH3HAY.iU7EbfCT.pH+w+o+oCr0saFDDbFyQMMMFSjS+w7ZBdMORPPyfiDzRUs0QqUaIbKX6r3JQfsEjd1iaGXs1XW7WpZcZiE3XqbNU0bJjybCFIHHHwXLCrVaWnZ2Z0p0EnupZBlzmCLjGJmCN3tiy7x+45y3BhlchWwUR1Ujd3h0NE3j4RZbYNN6hrnVigLlYnnumyIPubK+ebKo8izRnt3nutq+5JopVv3JUmb.R850yYs0yGEEU.7JTsZ0czSho9V5wt9FsoL6YTfXQjAkgAP43cUZSBydYG+farMORpBwoi2kXLFcvfAYuuhPBJIfljnhFbTStW1K6kUHHHnzQpUaDq0VHNcgyFiQSkkIgoIGrvkqvg9MZa2qUyIICGjAPsXvefGDSIFXw9+O68lGkjcUclu+12XHmyrxJyLh3dOQlYAUooRRkTURXrjX507vVXL7ZYnke.1FZ7.Xr6Vu1cwfAZFbyfsjM1X2O2sw7vV7nM1psaimZKvd85EvBICVpJPhtPfJ4VUUw8bigbNhLyX7te+w4FQFUIIPX.QYZNqUrxbkUTQbu2yz97s+1eesmcu3+2.Xc7YceQ1jJT+RtjKYGoPgN+a+29uQWbwEyXLlILFyroRkJupZQU0kih9BOMfmNvSq.rDTvrpi0I6GXZ7SRd5bLJ3mEVNygSNq2o4zwG97A2oMGlVGBZQIm0kyRKUGXqicri4VeHgsL3blzcHWkc.10XL6d8G4H634403+9+i+G0ApGFFV+G5k7C0HLLbGx4JUq2za3M275JVrc3IB6Ihvm3+1ec5Nc5NFvjerO1Ga+RAI2W4K+kCzHsnpZQxm27deuu2BTcuxzgDIb.XbJrpSGWfrtyReng0xkmJ5q+mbsKFdXL7lz8QkdfZA+K8K8KM9a+s+16iJVZU0dkJUpwhKt3Fr.ajPCpV7XszzgaIhmFiQNlipb.fCqpdkgggGzXLyCjNLJp8O1q3UryK+k+xqu81auxOxOxOh8.G3.kvQmpUvQ2p9pfdalgNrYxD5Sed.K3sLj9LCriO+ofnYvYCryAL2K8k9Rm41u8aejCcnC4AzMIaGM.1nToRqWrXwMu669t25lu4adGbKdjF2fcefC9G9G9GdoO6myy9RB7CJZsgyEDXFGHS+xwQfXDompZaAYGD15G7Ze1a9fxirIUXyvvv5FiYyRkJsQwhEWsToRqTrXwUwcv99Sv66DLeif582paCmsQmSIkbf9el+u9YjO3uwGTvmzDwn3BHbVfYsV69hiimtXwhS7Y9TelrO6m6yN6OvM87R8e7i7+i2XiMF999cDQ18gdnGpw4N24Z7BdAuf53V7c6jW6R+9Y23q1Lj9T.n3ii5ryP5y9fm0aokVpeHgw6GhWK4Z+y7Y9Lxy9Y+rcKDMGYXUGvYgggSYLloYOFsLb.QowAvwLkJUZ5hEKNwe9e9GO60cceeoEgQigwDUGIHHvqWud8.Z95dcutc+PenOztppaCrkHx5ppq.rpjX2wDvNXcyWTU6GryfLM3.mDUDo+FVmWlx9VVu5W6V+0D5GP6n3dFMwPuFIgEZ6P.qikMvMlsMOw5sS+0YFG2FHG.3pTUORXX3kiHFOQlbzQGM0t6ta+Ms2EmvAtVQioVXXXMiwrNvVVqcKU0MMWmY82xq4sr56889d6eMradmMv1mgTOUA332q8MWa3xgHMI5GAIqqnpteQj4UU2GPpO0m5S03487ddkYANK0nLt0OFdun96u1OHjrjiIoJyC7zti63Ntxie7ieUVq8xABTXZwMd2o2DhTOHHXivvvUTUqHhT1XL82KZiyEctsVzewso.0orabG8oz8icM6KVG6M7y79qw2e99j3d1OoS6EnsHxVjjsNND6NToZ8XX+Pxm2nf+9gnkAtpd85cDOOuqDXIq0NSx6QMFS2vvv9Y0rtwXVMLLrOqBhTUsEKVLBGX2qCrSx9+coH8btI12RDe3ArZJ49eAfEeM+Tulm1DiOwS+k7hu1Bv7isxZM69xusezlTld28c+WOxO3O3MOy8cemXe2y87ec7K6xVx6tu6uT6q3JtpsVekUVQi8p8leau4UeM+Duls+89D+dwcscylNc5owcvf4UUmSDYN04PNiyd069EdM83Id3DEYGXMv6oII6wvj9yDdL+8gZVqk4medxlMK850itc6pYylMNNNtq0Za869g+v69+9+aOuVO6m8ys8JqTK925292N947rtItwa3FE7PVo5JdekuxWgm0y5YIqt5pZTXXuic8We6yd1yt8q8G40tcyoZ17M7Kb71W40dUceV23yp6McS2TOiwnkJUJ0Ue0WcJ.uu+a3FhujCcnFevO3Gb0OxG4irBtwY8mSGasmKNHXw922dgggdhHoCt1fwJchRS2sa2ENvANP.PQaXXfBKDDDLUjMJCBJp1VE1Fk0TUKWrXwPbJ5Z4vvv0MFS+3O5WNBN8aKLb2DMOYX2J7q09wWvbpEx.0bwMMGoX09h+JSppNIPJQjV3huse7eCG22E1bqONCiylLG4YYp31GE3JvIHkSmvRg96g1X+6e+MF6.i0H7DgMTU2nXwhqCrRXXXMU0ZEGB.p65ttqsu0a8VaReGi46NDcx98K82aYbU0IEQlD25cdppsEQ1FWeQC1qe3I5du+m2n3h88..WYud8tlJUpbk.Kow59DOIsFqcQXGiwrg0ZqEDDzOdlUA1pj0tUwffshhh1v22ecb.QrEtwh86G9Vo.q+D0FFH7gSJ84Wp79.Q3wRjgyNX+hIAlr.LQY2uOAjeJnxj0qWe7oNzTidm29cl5m3m3mPEQ5pp1pWud6VoRkFIL6dijW0IQ7lYuX+GNd3dbHTN8iAfQEPNL3cpAqYFjEri.L9W9K+kG6JthqXBfQCCCy7Y+re1T25sdqCeN79LKrerGSFFFNowXFKLLbDUkQg3ww4jNiDDDjRJH7n26ipAECnZ0pwFioIvVWxkbIq9vO7CWoc61qLxHirAPCU095jSeGFqOfO8u2Fd8k+oP7COk1tXAvDn+DhhjhRjEVXLn1D.SUvQI0IUUyjPovF4f0p5Vbu+.6uVGpenfmXe3VP+x50q2UUakZWV2dw9Fe+wrVar4XllZYc6O0m5Su9kbIGprwXByCkNQXXz0YLql6HGYqG3Adfcu268daeC2vMzePV+ER5Mz22f5OFXlG8bO5bGXwCr.NMlXAQj8ALVIaIOUkNdtEGWybTSsvSFVaiMZr9UdkW1V.MmGhWwc.wYSpSsCopdo.GDHHIvugNvsnAA9p0ZcVwErsBa5I5ZAAEWOLLbCiwT2ZsaFKwaPOVsXwh0VXgEVsVpZq6XDQwcgRMG5d66TaVM.vDeHSTBfIyCocB.H8ot5H3FiLCvrhHyBrOq0Nsuu+XRAYTpP1RkJkwXLoAHJJpmuueSQxuaoRmXmwFarFycEy0fpz.xsCTcWflEfcJC6r.zrVB3IEgtI9U9vKpK.wvr8f0O+whK.T671rzcv+EXRpkaryct6ejEWbwzjCuvSFJFywx.UFGXpvvvovAPRFUjTFiYjbv3eAqcrs2d6rWxkbIDFF1apolpW1rY689e++5seNOmm812zMcSaIhrJvpppqHhze9xNzO3q.55Ti8MAW1rDmUFNWWX0K7fWOUCXRJfrEfwJCiW.FubBcIUUSm.pyNz2xo269p+30Kr0+fv8AL4oQNtJshdDq0dYAAA9VqcxfffTgVaOouPQ61.cUfpFiY0RkJs4wJVbqSl.5HvFVqc8fffs.19rm8r6tzRK0eSnmpz+muW6at1E.tgeFHZDfITmkqtu7vbUbk519TUS+fO3CV+HG4HQ.mEdL.lv484sLo3LjEW1clG3oQeP6s1K0DDDXs1oEQxfpwJRKPqCrlHRk+f+f+fHQ0vezW9KOrXwh0V.1r1dyk2AXmRkJ0rXwhsgk6.m4IpTltXr0esyzLGYc.JmeBUKOgHxj4cNWW5jCSrEvZKAadV288SB.SXV7YYh3pTUuFfqDXotc6Nc0pURkKWdsZ0p8oFeKfsiiYUQzxFiI5du26szMbC2fcAnRMGCA2ZAXmZ6c.1gWm7aE.l3AKmANy3.6u.D7C9pdUK+LelOiCjISiBG8nW5jO7Wcad4uhe796+L9+lie7YVs54l9e0+pWV1O4m3yFu3RGa60VasM2YmcVUUcsCbfCT+U7JdEsEmyYLpHxTppyJ4k8eauxiO6uw6+N5aI88Au+wyU0NOlyMrUACPmNcn1JqLfHIBfmmGKrvBr4laxXiMFc61EOOOMNVoa2NL4TSR0p0jfg9bhhhH1UWZw3XvPGU01AAAsA5ZsVU.0eOMYvKIQQR+RewZswhHs888aBran01F23mdIBuZef.REZsdILLp4LyLyZ+W9u7e4bu5W8a5bPEKv5O5i9nsNvANfFEEEmb+JK.opsGX9S.LSXX3Bqs1ZAfr3r6e1hlff7IyoyFCHN6IeWE1PfphHgAGKnDkyYgpq.rUTTz19996BKrKTaGfc8gcifVK6De+mr6Ge9fPteRyZjNYeyrzOADKv3ZUMcx39sXd1fUFbP8udIdXLbftsLvU0oSmqoRkJGVDukTzYR.+smHRyXU2VPa749be9FOym4yrAvFFiYMxSMpjuBToLPsyctys5hKt3VNg8rR+DV8chXP91Qa34Qi.LF4YRpjaJUqLtHR5DCLXavrADVm8..+qEfI8WmKounvUpZz0DEEcU.KopNiBoEzdfrCvlfrJnqhPMTVyXLqas10d8u9W+Ze7O9GecnvFP45.aW.ZV1Eya6RO0Bd04AXRQH0mKLTLFitLnmAzm6y84JepO0m57.fBXBQjI.lnToRSVrXwoHGyPUlJLLbBU0wDQxTnPAOOOODoPm+5+5e+ct5q9pqO6ryt9DSLw5Pt5Iw+2JOzrBzLvUZPMAZk7rXOvSdr5ry4mn2.FAKic1yd1QWZoAZkRlvvvTFy0SX384DabejnSDk12+niBkm.XRaI63AECFMOjoRx8YNXhSTpzXEKVLKPpvvvzhHY1rdcuK+Ruzt3.dcyj3+WiBroFoMDQFDu.NSPYWsr1RDGayXA5QM5AGJFN82K10Knk96zW.r2fKGkAc5bZWnVGbTSrq3rEWEPdkuxWo2G8i9QSMPkuwOsueTpnnutcrZQnWxD9cJ.M9a9a9ap+7ddOusGYjQ5PhFODdhPQDAanMQY1yu6Ismb6fffcfB6V9AdfV.ctga3F5BjBlIdgE1LUsZm2jk9HhlFHSdXzCr3AFOOL4s8deuyHhrue42y6Y1e7W8qdhiEXRchvv1AAAdhH6hSKUfAaTUvaEJmBXLQ7mDX5jLAME6oKE8QdUkDp2Ys191wWeQmsqpR2vvvNOzC8PsymXStWmewlUbKBzrVsZ8qIu1PoK5NrWTBjxppCoJ64zEVnZbsZN5wUPj1UFRrXCBBZYKUxyd+VOUTBBBPDwKLLLCP1mwQNxnpVYhtc61Z6s2dZshtCvNu825au4K4k9RZ9Ltte3lkohyu0I+1PksA1oTesKg46kK2JwUcVDpdpScJ8vG9vCXZBfaw8ZtnJNSR+57f7kJWVulBEh+adf+1VKt3hiXs1LhHduk2xaIETIcRFOcAYo5H3.KwCXjS5V3eBQjQeSuo2PJiw30qWOoR4xd+b+budld5oSKhLpp5TEDomHRbd56xTjYdXmUflKXQp4.Kw0Oa6qcDq93sIvS0MAlSJypd.oJStztDsKCw.l7cymuRbkJOouFEbA7J0.OsRhHShLf9ghHZXXX+9uNppsKVrXSaIaKRnv7O8a8s20XLcfbsKTnZq9AyOvVgepuLR9ds+w2tPlfjB56HHAIUpAwUnPWnb2jZ+Wuq65t5.E5lKW43pUeb6mEvI7hm9LjN.xXSJAtBfWYPZ0pkJnp0Fo.5DSLAMZzPbKki.BppwG+3GuKTn8+12vanMP6ZCxJTgN4yWtakJzqXwhwK.ZMNyvAZNL6ljGmqwKVZtq2U6mUtJIrYHmTVqDmLeePIhd1uAtOB.rQNS8HN1YZIppTsZU.QpVsphyC67TUSAjVDRqhlBxwMbC2fZsVMH3Z064d93d23MdiItkGwvRZ97msWkJeKI4SCQA5ynt6WSyxDV+Nuy6bs67N+DSbu26eZpSdx+1NG8nWwnu628qO8ezezmYjQllrG7.Sm4G8kcK7ENwC19ydOe0c+We827F+3+3+3qIhrVNndUn0q3U7JDfL+o+o+oinpNtHx3ppiuxJqLFvHIGXaHMsxc8ToREIe97BfjX65.vjSNIfCfiwFeb5ztMhpL0zSyniNJUqUiEVXApToRefUT.Zzngt0VaQxmiD36qQQQCd90GDlPanXBLRBSExj78mJHHPG5ZIUhts4t9BCIoTjUee+QBsgiXBLiYBB5mg3dCI.qdytu8kx39+1gDV097e9OeOnRGfsA+sNvANvttOui1+dPpk.leXXXmq+5M5e+Ih7L99YCCCmLHHnAvtVqscPPfqrNU2NLISB8R.SXL68UZLUjwLFyX.s888SFiWqSXXnmw7LHBmHEeFNbLbpuQ1SQwYpB8N8ZvrtA4BPx5Dn4pgJNlH6bzmUdR8YOD.r9wEJDokKSb5zoiMFiZs1AEbkJpmnjVfLBRlzoSm0XLYCCCGMLLbDbhpbef45s3hKlLOuRxygugum+mDs7fVAHWEjpT0SDmC78k9ReojyODl7L4P7jQAdSZpOnQ6oXtJZr5gnIBO7fCxKndAFSpj3f6OGxcFLRbNSfidzav6tu6Odp74y6UBeAh9Vw5beizFz2WhANXllDGs9o9T0jhEoWoRN1QjSj9NUXafLEKVrYXXXFQkr9F+z3XSkWdH0IBCyBjQ0xis95qO5ryN6n3RF2jhH69U+pe0VW5k9raWgJs.11xBaC0Z.znjKI8sfEZGDTqi0599WF5clyGz73hPuRV53CRRrgw9P6SDEk9H99o.vXL54N24XwEWT788SCzpToRc.ZKojsCCCyJpj9K9fewLuvW3KbhSFFNgmm23gggiJhlcsM1LyryLyHSOwDY9y+y+y0+9+9+9Nuq+8+6oUqVYWbjQlpZYRkWjQA14gdnGpwy8xu7zU.s9GNWA..f.PRDEDUoPkAh+L.B0nKfGb5tC87mGme++krcw.CS52FNn0AHFhCXf8opN01au8nGbxIkJPyibcGoQs6+A1LxkY4sgC0LoS9IJCOITcZowgyt.NpqcEppWIvAsVatfffwRVToII1CqpZYQjR2xs9BO2m+y9.g9PsHXS1O6leM574OyY5r7xK2C76BQc4v.mZYO3LYAlDJrOUiV.HuHhuppOPAq0NGN8LI0JqrxNyO+7qDGGGVrXwyfyYUVwceQuvvvLIktQdU0kEQNTOs2S2CuEsV6bAAAi6r7OUQH1Dbz3vvS5V7SoMB0EQVOHHX0vP6ZfttwX5W5Oq8o+ze5UOzgNzZAAA8oi4dkczSMTv6qU67PpcVH65PZq05crf.Rp4Vu+h+h+hLu3W7KdLlmoXk7ynZ48ce228M8y3Y7hm788dtswdY+e9xxdIG7Rx.4yFFdhQMFy3ppi7kenublYldFXnLYYLlNgggcBBB5BzoSmNM+gukadmO4e0+eMxC0q.Mn.08Ky1QCdNk2sI+7zkU76.QIYcrXLTJlCi5bBhCIvoyPRsVSRMDxdZgfSSQVXgz0pUa32WJ.uBP5xvHTfIKTlIJ6rA6LFyQy.USQdRaOo06889de8t8a+1a2qWulSN4j6ppVGngH4qCU2RUstHR+xtpuMS16w402I.L6BKKhwUUmBX7BhLREHEKfjuFcS.6qATnNTdXFlbgYFK4y7PogSOAtr7e.bkjyUYirWtijm5DfWJP6hxter+vOV8ie7i2W+YVMOrQEXi69tu6M+It4adipvZm6bmasEW76aCHpAArK1EZA0F9442UEr22E1FdMlgK8uQnel.SXuluHyF4.r1qbXXc+hEqBDA90fnswM9aPoY5bdpS0OCui.4m7W8W8Mr+333kdiuw23k2oSmqHc5zW5V02JnQ8Fy39dE.sMtCvshwXrVaoyEDT7b4AaEmPRuwu5w+Uq2qWucdSuo2ztPgcgxsIOcoRw3DqqTgCqI1i3EM.eeAsge1ObF66K9180bI.ZmCZTE1fYYqCsNMO8Sb1WS9LWXTn1.FlzqWuq4BJImzJnIhEZOP6.x13JM.qwXJgiAQg.U7g0hfsIOMq9fUakKWtN.sfC09qS7GOYdNz+m8yfeeVpNCNNJ5W.xWFl6+zG78N00cMW4DwzZZUimYi0qO4IN4Cj9G5E8S1rb4Ja72cu26Juy246bEH2lP0co.PYxB4lZkUN0ryM2byiikc6myWP.GreiMz5EXBD.r1PBBLB.0qWWlZponWudToRUxkaARmdubuMnrbRD9UUGXWGZPfQCsV03NbFVq0KAKgj6ekf.2ghHgoH3zigdVqsq.872CvDOUIkHjJANBoeZiBB7Ufd1PaOjDF.IzEktAAA5N6riL93imJALFvkbkM.N2C+vO7W4RuzK8KC7H3JAqcR5Wo+gZHOoluBYWAF8bQQisn+0LETcVxggp4NnpUNnMztLt8YFCmI.0wDbrlggmngprQwhlUBCCiLFSDPMbLUrQPPfyE.cz9re4QObIQ7MTxAvwvszbFRCEyBk5WdqCJED1Kl2gKEjmnuKAH0Bvn0NeVMbsQQQWoprLnyDDDj0ADriYM.6XLGcm+j+q+m196+Y9Lq+fO3Ct1Mey2bkRkJcVfykThRUStFtv8xuXbsq+wzFvbVby2lAGq2mFGK5Z1mEc3dN7jjgICuNWgqR0nqA3Jsg1kPXZfTIZXxtjb1FiwrVXX3ppHqVLHXMbO6qAr1Mey27l28ce26FEE0x22uECRP34wf2mJ5ONOFbeXPN0ikIGof4xBqNJ4YBpjeh1sO23Yylcbnv3P4IIOSQEl.xOp0dxwBBBlPUcRq0NtBo+ku82m9K9F+E6.zxkHLmwi.Das1VoSmdm74yWOGrYUXKxSibUXmpIIaFx2Dpzh.Zic1tv5IiaKpPIHOdNssqXZnzvBa7E59UCWRN8Oav.6cOgkIiSdlL7DgSXLlj8GymEpjk7jN7DghpZuhEK1sd85ce6us2du2+uw6uu1E0TDogp5lhH8K4psYu0W5bO2y8z4Fuwab3RP56ll+8MU6hAFlzuc9LMA5vRzhyt+T9rVpjr700QovBwew66K5kPsvQ.tvfUd75XSxX7Y6BratbrU0pr1ce2ex0t4a9GXgfffYrV6nAAAohhhRqwZF0Sy5Idi1sa2Q+7e1GXzRkJkMUpTo88utTrlUp.r7xKmLgNxsP3oPgy3UDRWBRCkyHhjkBLZRVclDXpvvRSKh2jIYtAU0QKVrXl65ttqL25+5aMCkIC4IKUJnWuypimBXZQjo2b8MmxCuIrV6H.oiii8RxHohBggmfAOGDhEnavQC5DFF1Az1Fios0VpUPPwl.Mu0myyoUYx0eQYIYxsGGB4IO31eauo.wqCcgYIHHXX89Pewu3WrGPOVgtPkN9hzrrCXk3fffl+hefewTOzC8PomZpoRaLlwihhlLJJZxK6Rurw8SkJy09C7+g2O6O6qNysbK2RZUUGpq4PJcBarHZme+e2OZafchiiab7a6MV+O7O4OXqH2FOIfNTwsv4JzBhZs.zxkE3RtLlrMwGBzS6df1agEncBqX5lCZVk8mFVyATRlZoqYqklEHM07SUfHuxIKlVFRC4FixUm3y9HOxDiLxHiXLlr3bHozppYBBBRkGze1e1eV8e3e3eHVUM926idmi7V9we0BtMkSmOuLndP8Art6Y2h7Ghdb5GSf+O0mc5CixolSWfUGvnHWeSNgZ07JqZ5a61tsL+l+l+lYvwBqKbymGmq2S6AHAAHIIBy8dTQSrMSOaj0KvOP1d6skW9K+kK+Ce0up2a8c7NxfKaXi.4yd0W8UmtZRc9u3hKtmqNYoKT6aU5Yv2q8s+1viWbAeMKoXcRw9wSWUEeQnbhcfBzRjBaCUzO9G+i2WH2ZAQWHHcIetmR.3C7N9.79tsaixTI93G+3w4x4.SKc5z8hr1dSL4T6ARondSNwjRiFM7.RYCCSGD3lieRm.WlIHHH0wO9wGhIakciAqPLThhfTBjgxNqbA+7hwl.HILHLgIg4o.U0xzetunfQY8P8zOotOpM32JfqDQhhh3rm8QYwEWtuUaJkJEBNGj.RdlphpggV8rm8LbC2vMPXXHFiQfBBUJK6t6tCkvoukrQoKf4CiGmBOWYbVrmOkZGkPw5xPljmOc9ve3Ob7ZqUK6y648zG+t+DOPqicrmQyy7nmcqG7A+B09semuyDsVoZCbA8lwUkKnhHoxkirUpniFGGOlmmWew+qufe6DiXGXIB.AAFpWutL4jSxDSLAsZ2lF0qSPvimtjjrjJpBPfIvA9QXnqTYf3nHqln6IdBjJoKW.gjjVE6x3sDGD32mUHw9AA8DIupZEIHHfDVlHINtBn3tlrVPDIvDPj05ECdF+.ObYXFUUwZsYBBBRGYsphDiydcYkUVwMWZA5Ps7sJPkcKmTlcKt3hBTviJkSsRx70E886e3hXpxnP08CzP7jlZhqmnfmmhFFdBMHHfnnPuRkJkFGKUFAXLiwzRUsaXXXWQjLAWafGUCXFrN2d5ebGbw89mfXGqFJAfTn.ohhbrIxWDIR6q4w3fQa0u9ISs1ve9NVMn.3HDtqeTDwwMbfiZLdgkBSIdRZU0zG4HGISXXnS.dMGse7bw3SOhdbsT3uqokOOTohC7DU0z.YdjG4QRIhjl4QXEbJRx5GVS7b8uNsZ.HDg.kINNFOOOBLAZj0FqBhnzS6y3bQSDRbSOGPw4hs1u..RvQCj69tuaOJf36esLOnq7c19fj83Rbqxy+um7yUcumJzCpzwjMa6yctykJc5zdIL1HksTodysvBMGYjQ1MedZEEE0EPOlwLZUxm9+vG3+PJbImSrgVuybly3kNcVDgNc61s4t6taiUWc0s9q9K9qqe6+1+lMdjJO3.QbkDVnikcg0asen8ZPGnjaO8NDuef0bmGn2RKg2YOqa+t..6dr2IM9jgH5RAZS47oxQEwQfcRUAx.EFmJkaHhLVXX3nt3+qjoToRoEQRaLFIm686s4lal5m9m9mLCP57hLVU29.oJ3Dx69rcQinf.k8.ja7VtQ2yR27vKlHUw2waemzkbd7Z8mT5Dbwy52BVa2H2gR2h4otHxN3WoqHRp2xa4czW7pFYVl8IiMnFSh0BKUyWGX8W3K7Gb0Nc5rNv1EJTni0Zw222yTzjpXvwR8G+G+GmJUpToARW75Kl122OEErIHCtvfZDcFlY.6..RUx22CmHN4VTLRSKhLxC+vO73m5TmZBQ7lHNlwaznwnpyBbyDFFNxsdq25XTtvD.SQkbSAkm708tdWSgqTb1mp59l4JlYJfwCBBxBjpb4xRPP.xPA2Lzy.UAsz8WpmwX5ZLlNO7C+vsUUZCzxZsMKCsgp8.3C7A9.onxbN5Pe5hWLnVxCOlvcnzhqmTpJ4GnSDNadzo.7pp6d+gga+g+HejMSXMyJq8PqU8xu7KurwXp.T4Z8u1Z999qmJUp5mLLb2O4m7OqysbK2BppoDovHppiqUzILF+oAlM1INdELFyh+Z+l2wATUe5+o+o+YGD3fufWzK3oWfBKAXvEW9B0xmeO21AlfyvXmdOVjHoq42WTQaVE1AVqAP8Z0pUG69aDFFVmZ42Bh1nbeqSaAGKGJU5Dq7leyu4pG7f2X4hEKFUpTonBTHJgMTQ.kqjOekCe3CuxO7O7O7l+yeQunce4urez362QAwotsa611mW0ByRRlEifQS.ezAhxoOT+m8mG597T6X.0A93pw05KFU4SXAStZJfHhe5DwfL89Y+esrU3K7PwdXOeq2FThhhbAdq3Ys1Tat4VoLFS52563cLnluKUpz3u1et+4iYLlQRnyZFfTADbgVO2i2O+dsKNam2Zb+5u8ec2uu19423232PK2eLhOcTUadGu+iWGXqa+1+MGHHd6m8egYca3ep21scaZ4gbUKupEFjAG+fftMZTef1JHHZiF06y5hr9AAiBLdIm95LYv0FLJt.mFX8lKw4WFXkJVjesesesgu+F9mWr1T.UDC.HxB.UkxfWmNc75zoi652Dl71eR6exBQHIfNK999xy7YdChptR7vZso77j9.tpfz0XLcEU5JBcWbwE6Anli5ttvurBnKu70O3Y9g3POAe0OIu916mdbJmatPsYSAk7RfiPofa+BIuTGXiWyq4Wb8+x+x+5M.YyCeEW8Vqu95q8qe6u+Zus21auR0b4phS6p1RUsIkoqHBhHd.oqTQy.jMUpTOtYxjglSj.LjL93iiHBUpTgQxlk1saSb7iUhK78CvUBSAf5XIh0Z6EXLcc1mKsUk1VqcfXCJxfCG2KHHnqHZGmywnMsVaKcPlbokpUZYs1VVqsIIwvHHCmA719AAsUUaas1NJzwDDzwZscO6YOW2vvvtQ1n3ff.mKLhCjkG7AePUUMd1Ym0MOsV9tPkNk6Whu4ck4KyVte7GN2vC1ddluuaczmgF6np1IYNsJNPS5yZF8nAWGhCMgTIGxIMtCyjwXttzAAAdTMmfCrju4Or5oPpLz9eZ47C1Su74u9mvpy8j7CcOaoq.E5+qpFKpHRrBwZrN.H4SFFh34J80O6m4yJwwPRIVHP0899iJbw95TeS2hqrvf9BQjzhHoO3AOnKt6U7c2+qOKO4.KAIwl0gKXuLan04VlJwAFSuAIECuXiwLHwNunWzyvAtYPfPk7h0Zgx9JTo2J8S9yxeGoDsGdezg+9OujSrfarnhiYHcp462ZwEWrYhFFsSo6uTC77pmH9oqoUxsVtb41vXL0OYXXSnRObmWKqHEFWQmZ4kWdZiweZQjY.lcs0VaAiwD7Q9i98W7zOvCb.q0dvOym4ybnWxK8kbI4I+gv43NG.Xw0xmu+YAlGXerFStVhwk.H0O6r8OKZaqKgK8W+pEQ4ZctyctlTtvNPksp12EfBXUfUs1STCnRPv0T1XLQVqMJO4iDQhRDE9np4yWAXkhEKtwgupqpQXXXyJp1687ddeoDQFsbYcbbrlchHXbUiFoerzTYA2ZCQ6oqTeulqcw3BSCeHsAzzM.Fy5nvVeGyIylatY2YNzL6PsATWbGNeZKdgsgEqpYAJBbYppGF3RAJZs1Imc1Y0u3W7Kt8RKszFFioxYNyYBSmNcoD54VA2.3cBf118SLqgxPkzvL.a5B.YBee+YihhxCrnp5ADQNfppwZs6+9tu6erWxK4EGGEEs0QCBpTAhTUiDQpYs1Mc5lBJvH4g8WA72ZqsVZ7wGeos2da+sazXV+ffwsVaZEUlceyFuwFa1Cz9AezEn4a6W5cr464c7KsxQCBpcRm5XuJvpus21aa02869cuJvV4gcqrGs61kBrKkGnRzesTo6mpZOlCueHPNcQ7nz.JNNBvnIhD5H.YWXgERUqVMO.RzMf9tjxLkJUZ1hEKNaoRkl5dtm6Yha8Vu0wTUyZs1L.dFiwKLzEb9wLFNQXXLPuwGe7tau81stthEachvvcCBB14dtm6Y6a5ltoF.MJ.aWNInoBEJrSYmGs2Lvs33vpQc+wowN0YYuROhBjhxjpb4xZgBEzBflDXS+xzYjbP1pNzn6+2bHICoSBNJUgBExTtb4QAFUUcremememLu1W6qsaekXWUcMQjMV.1pFCbZiNrLwblG2MqdpxwW5SWwL.iZfIBc8a8ooelOwe4eo2bKVn80eMWeC1SQ2e7TU9gG6jEmPdteQjkwUVdWEvgihhJppNMJowitIzWcafFSO8zM1Zqs1TDYifff0dqu025Jen2y6Y0SZsUBBBpwPtnkOzJZOwQ66wxjKta82CbXWZIMAjA6fLtORNXrpv32wcbGi7FdCugT.c7gchlm5rx4Qq0g26o+XNOfT6Gxr1dtYvhjTRnas0VW11MZTLvXlILLbDTRgKI3MEQqC5pfTUDwFGGWRDojwXhxCq7F9U+U233G+38yzUKfdIU8t.vxKuLm4LCD.1KFcqogma1ujbFm4YhVgslXjQFYbfQyAT0c+02h125vvtm5IduIAH8byM2nqt5pyBbfBvUFtWI4rbXncehPVb0yQrBsO6YOaikVZo0vIvyQkJUpTwhEstDBSsDASuAPy7PyJKQGNKOdzW9erOC5aouoEQFPc+4gYt6669l8O5O5OZx63Ntir3zFswqjm8ee+U20bexO4oGWUsya8s9VWOGT4Nty6b0W0q5UsiuSePxlnkUyVV04wEHuONKzbdbkCvv5glGNl9Lf0AOQtaCv4I7qm8rmkzoS2mhIwiL5HwsZ0J4fYZOP5kH7pZoRVouE+N6rylZ80WWDQbGx1iNZLcEgthncUUFDSShdLf.hl.viSqLDADOAkNc6R5zoUQj3omd53ImbxXaXXOEhMFCN68krhLfQMMEQV022+Q+ROvW5TW8OvUeJpvWEHBn9gfNmdnwU.oBfzgp5k.BUFbIGoXd3JNo0d0wwwWgwXJZs1obeGZGPZgv1BRCU0MLFSMfp4fUNYX35hHaFDDT2ZsqE3JSh0S99ac5uwGeM73pz6Gxt1i0w4FCPRD1x9f9TmCSKN0Wy4VtRxIfYw5D80DAU9vVqcwfffosVaJQj3XU6H6oqDMA1IwQfVMLLrBv4NpwbtpNGCpJvFLGaypCJU3uSG642pZ6whwDSJ.m6qMeAQlorioI6HhrFEnFkYC1yPKdh5G5Odr+YZNPd3J+erxJGocy1G123WLLJZRiuum0F1AGvhaArtwXV0ZsqFDDrVx5ZqkH5tqw7rNqPiyblyr8xKu7PLl96nLm8BOu5EdNwL.YR1mMCP1b4xMZ0pUGGXzRkJk9487ddxoO8oSi6Y+9AVHLLb150qO4TSM0XAAAYsg1LpmlpXPQuRkJEKB8tNSwd2ensGncwIN3cOZPPuSXC6F3GzFX2JUprcgBE1NGrS0DWtJI9+swoal6VduyXMri6zWSgf9wfjmTk+hkkBEJDi6LkwatWecFfQBfr1bLBUOuR5OUNvqZx6qPgBiVtb4w.F8O4O4tF8Y9LuwrFigFMZzdxImrQR4esYRottmXvVjlT5oTmQ5eRztXigIWP6PPdGpg1jLyop1g74iUUY5omNE0FbXwzP9udYBeO1q.MIG0AV+HG4HqYs1MCiB2An65quNe+e+e+oLli4EFF5kNcZwQEWWPcOvC7.YAF0BiwZLJtIii3ruMRsYxyUUUMJJJYPVgT.Y+O967ebDq0lEjLW+0ecodj+gGwSUM0m9q7U5Gr3j+7+7+7ydrff4UUWfBr.vBOz5qOOvbSM0T6KUpTSM8zSOl5rQ3T.hnBarwlpHn5..SbAY7t+28thUUiek+B+B8TU64JMG57t+fu61VqsCPbEfRkJkBxmNJJJUtx3AyIIIw6hAf0tPllz6zLvNGiA59K7K7KzAnU4jC4BE1pVsZaU9rk2DVnd48Jgls.1PbtFy5dddaciOqab6vvvVVqsqHRuiZL8BCscQniwX5Twk4KBBBRO6ke4iYLlYNQX37AAA9.KcS2zM8zrV6gTUujHUujO5G8idoP9CUtb4CB7zf7Kac.zsGpyyyjjPE5nAyEy2CnyrkoMrTqBWVgVI2S8qizcnPgF.NjmiXcbLPYMXgUsV6pUfUa2t85VqcixkKm.lXtsEQZduet6s6wO9wSopNN4YZw4VSSWaP8LmeDfQl8LLBL6nr+A0RY+rO9T0ZFt96kc84g6oGHcoPgd.89hm5T8t9q45Gh5ty80ZA8yKi+Ikb04YaZwtySJiOw3B5dG10XLo1ZqsRAZpDQFTd8u9Wu2IBC8hiiSCj9m5m5mJCjOCP5nGmrz9sxGLeu12Ram23B3P8.5MqktPPGVvEXSUn6uxuxuRm2va3Mzh742AXm64+4+ycYkGiKNbge1IiMObu01KvC2q7t+OMZz.+f.JE5T77jQKwhzmL6tCN+e9+7GcrhEKNl45ttQvUKydIkkSRyIrlmdv26giS.K4wi4KWj1lQAhWXEhGYjhwsa2V6zoiVMoTc.TLFEP+5j6UAfUWc0j4wEjxI.ADFFJVaDIXA3JCH2uqKuzx8RbLfcg7aetyctsCCC2IIYBchii6wBKDSxdlb1KH67eSOe2UhuKKhGj2AbGjcEH60e8We5WvK3Eve2e2eWGfFUfUoBU.uHfHOuzkApTEV4U8pdUa.9MhhhZ5bdiBwU.Ymc1IUh.SlZnWdWvKoamNRe1iXSzhDee+yCbj9+snnHhrQDEEQ5zo0DwiLFQbfknzAjNh30VQaqp1NLz1o30EzUDoa2tc6t95q6zBFklHrinriHzvbLSi3XoAPi91taTTTCiwrk5bAhMHNdCiuYcmFjnqGitQ5zo2.XiNc5r4lat4Vgg1FZxgABCC2AjsEg9kT2NhHM888aEEF09pulqtagJzCVHlEbyc1aNEP97J.V29H6w7UVnCP2JI5efmmmBn8KsHPhMFSOUINQWWbeNVqV0AjS2ffi1AnUbbbGnPOly8d9lrfuDnnrlKC58Gep36ttUU6UV0j0ubrK4vOIH1PsjGBC05KrnwMa1LgIQZKOUZM2byMPiDv4zQcBsgcMFSmO+m+y2spSqHDHeJfTytJdN8d66J26zce4HniJhDeOOxi3pIMwwL87kQfYDNzWWVyNjPkuPZbksQp4laNuXbBirjzmGDX79W9S+uTRmNsi8IVWmWoRkFvzmEW7YH.rvJnP9tK+8sbWft0t3PK19ZwtE8QdjGIFnyZCrkayNUqVcmG39efcfBaWrXwFm9zmtNtyArUXX3Vggg0A1YxomrkJZ6u5W8q1V7jc8vaaq0tkHxVFSw5tyTnMCBBhMG6Xo788G8jV6TA9AyA3KhrbudwGTU8xpn5UbxSdxCelyT5JJWt7kCbYP9CV1w9j8NCvBLKNvpmnVRR.gk7.zfJzqPgquCy3.Zby8LxhcX1Y2FXKKrIUou8OuNjaiRkJsQUXqRkJUOJJpw8e+2ehtDUX8W5K8VW607ZeMqehSbhF0pUqkTPTRXwZ0jjsBAo9k+k+k87KgGrP+weeu3XSZWrBXRRmyocBkiOoX+62CbRYOUpnIzKM0688d6oymOeRovTIEb3uVzOt+jst.sVnJ6.427AevGbiBEJrkw2rchaWPbbr2m6y+WjFzzliZRS978EhmQOxQNReltLNv3EJTHoNxpk1QSZmPoIh.9HP9TWxQlJSbb7H+P27OzHet68ym0XBRKPpQGcLOfTSN4jYf7iJhLwu0u0u0z2eX3rhHy+S7C7JxQNxuu8suEvcP6Ywc31QCBB5mcjj.9TUUMtn45R1DOt6m8d9rcv401c9nu+2e2ilP0Xq01oPUZGDbsCXjSwhWKPEw22Ool4VsOi.uXIH6gWvbvAONzgbGx48+9e+sghMg7ILCn71jmsKrzUuKTqYdn8B6svy1GyX1LLLb8fff0KFTbCUksLFSchowWvZanZbCSfY6Pa3tgggMEQZKhzK7DmPEQRIhjMLLbBfYJEFtfppIJJ5.hj+P+X+X+XWZX3It750qeEWR9q9JfJWdA3RK3rB5C.TbgUHOL+bLHCeKk0MFFVuud6rYRon3TOemyvTtbep.uCvN6e+6eaf5PssBBNxlP95Yylc6fffcwmlIVjbiG7Aev528u2c1viLs.PKqiB4mVUce.y.KLMTYRfIVGl.VeBViIHOiwRKkrf94Ysje6ucFT1OJ4xE+w9XeLmPbUtbOfduw23ar2q7U9J6tmMQtp9DPS+AGVb4DZaBzKeU5A4iAhcLIxiff.7DOvk8RATOxgqBfQTaIaufffdFiI1XLZwhEEee+zenOzGJKTICr+rPw9h504YCme68A02q8MYKYMkSGCz0oURVmE64SLyOeu2za5M4NTWkJs.Z8I+jex1.cFxUj3I3mJbJMOPggx5a9JCFa3YsVOAOuImbROzA++bAoJf4nG0SDuTgggoBu+6e.XbgNaVLATfHIY7exZimZX.ZtXYM7uFsh.ap.wt.jthPhb...H.jDQAQ0q0Ka1r8xrXldc5zYOcAx4fUOYteDlAOHmGTNUdvyyyy6bm8bBnI5WhaiSTTPhUzXMV6YLl1PklKt3h6ZtNSKJTnCjTdN0bkDHI.NrzRKcgy0+lncF.jyBBTIEVRSdR+nO5ilBfWvK3EzczQGcfnzCT8E8h94hhEIT0dVHWUnvZppaAQaSdZJR9NpVtWdfwGebAF3VJOlXkZ2tM.jNSFRkJEQQVb4DfA.ir2vS2eSUUicwd3J4jPaR7GZWE5fHcLlfVnZKOwq0FquQaPaaOQXGUoc5zo2a+LGHFM9BewuXciwTO7Dk1R7XKywN1VVqsNJ0CBBpGFF1vXLMTUabrq+5qKhT2c3lisom5slnx5FiY8k+9VdSUk5c60owwLlsEQ1tWmdMLlf5GyX1REYqtc6V222ug0FsSPwfV2wcbGcJ6bpwXpQL9WvgzpT47N.w..hnlBEhgBwAAApuuuFZsZTXjlT4OwppwhlXWxwRbIqMVUMNLLrKjui0dx1.sKVrXGnbOVEXIRgyMM9lXujRxRfrfiMLBf3GAvBtACN.57fUE3wnUDeMaCChVPPfFDDDmJUpt.sMG0z5y84+7sVc0UaeTioiHtxcSUsKJcBCC6bK2xszEnWPvwz+r+rOH.dq6bpiuabOyAicluFRePtO3AOXraFjKAqNFBu4SDRYCybHu74ymvLxZYfBYfbo2c2cSIh54z5OwyZsohrVue+e2eOub4x4YLlznZ5ffqM0OyOyOSh3GmOUoR2m2K7E9BoFDCU5Qkj3OO7guPVFbwPeiBnG9vGVAhO3AOXBnkykDieXSLz7HW2+rcgx6jC1Yg8had6iYL0UQ1vXLaTLn3Feku7CuwkcYW15ILvulpZUiwTKLLbUq0tgwXpKhznzIO4tuzW5+71ppwVqMUTTz3ggg6CzBevO3u6RhT3oGdtvKYokLWtp5UDFFcEe3O766JxAWVNWkLbPfmV9ZrHjO.HOvbPgogyNFPZKHPIkMoGEoGty33h+e80Ou3+me9429K8k9RaCUaTr30tMjemhEK1z22uk45LMgbaCk2r.r5W7u5SV85ttqq1q60851TKq6BEhUU8b5nS9zfM8a9M+lSG4FOklSSJNziAT8+W13YuX6lduEBNDo3zCXOxvNVvH4gwDXzxPpUWc01yc4y0fZ9qCQacHX2uNTWrefNihSiIL.Wpp5UVPjK6DggEKTH+jkKWIVP1xFYq366GZLFaXXXYf0NpwzHEz7yety0IUpTcCtlftTa9cfUbzzMfdzkzTkIwAxQQfC8k+xe4CM8zSe.btJ1TfjNHHnanMbaSfYsnvv0TQVGgsMG0zQKqJtCdMtHx99DehOw7O+m+yedQj8644MoSzW0Tty0ghPWPc.jnCn.4N3PfbEbNtPsvvvULFyJI+s0A1sToRcRmNcutc61t3ynnaxXzfRx4hAzkuv1vA6kBNjGb5TK.d0lkTLAodG+juizuq206ZfyWjHZuY7886W9JN5eCSVpTooMFyzQQQSnpNhHRlfffTgggopWudlK+xu7rIZVQZmPwE4syt63s3hK5s5pqlx2EPS5lMaJiN5npMx1Ek1hHsxjISy4me9cBCC2od85Mthq3J5qF8ak7pd9jR3oBzhBzgrzkyNfMMmW4krPR5pVMIyB+29z+2R8C8bd0dPUY+Pp0xSFRwHXcyW9I+I+Iy9Ne2uyLK5uXeG+Xheme2emw9W7R+Wjc+6e+w20ccW67yeq25VhyAX1hBzDgXh.U09V54trWcj28w455aG8uCrl6jq6w.FaAXrTPlxf7bdN2PmO8ot2FrRtsfpWXYQnWvm2ERe0kAtRU0iDEEck.KopNCnoAoGJsPXGf5hHanptlwXV0VpTMUj0LFyF+b+butM++9O4+zlTdgMgZaEEE0v+Z72kNzj0OeZW9swmUeu127s8NDzgIEqS5jRcq+5D8qwWOeQztP2U5WywyRSVe4VvY5WFVOQi6xhiF76GXIfKWctz1UXs1hBrOEx.JHRaQkcCLAaaKY2REG88CCCKAb1iZLkp1u7PKv1TN+tPklKCsOy4Sg6guVtXb72f87WFReFHqp5nRfLJQ6UBtIkGRafsVHQmHps2b8GOGwRv02MNt8fWF3J60q2U644c3tc6tX5zomww1SWRTDQZ5.ZfZ.kLFyYANGNEsbs68du2Mtga3FZjC1MMzwBw2288ohu9e3maWJu+VvZ8Kgfmry0GdOr8nW9bjgQHqFpiJNqfbbbhQ3n0qWmCM8zsUX2DscJK9L9q94+Sk0Kar9W9g+vMqlisIEsSDruQvoUUyiyk8J7I+a+a8u1ib0ExkKeNbTJXZfwqu0VYmZ5oS0qWOoZ0pN9OHI2Ih6fw80ljjrSq8+GCBBzpUqp851MVctwQWfdINOWm4lat1qt5pcLFSuRVq9w9C++kWwO5OVpNc53kISFAPEnqBsMGyzN7jgsIl1HzAktHme4jcme36j2x+t2x.vFIFTwwr1d850comwRDdhP.jjRqs+y5XPTiIfyblyjNUpToMFSlHqsGhTWUMZ80W+Qtp+YW0CSU9ehaNV8j9U.RMGj4+9C7.oOxQNhSyaJBTpPJn7TEfhkKvUFd+gGIHH3v3J8tICsgHHsMFSyvvvcLFy1gkBWGm7dV45bZqVsxvp3ylzgFrx.JxOr9rL7duOYFe0Od29qiMpp5nhHiMzumRUsSdQF3.ULGM4ze8bfpjxKzmkIJ2UpZkqA3xa1rYvZqs1n.pwX5FFF1dOwE0Ux2FWxpVEHxXLkx4J8op+1+w+wq+xtsWVCZPC17wDqw+TtM7b7zrWrmy.LaAXlH29KMEeYcTpQkAkjyE1Or2ZE7+O68tGkjjUWuue1Q9pdzUUc8HyHi8N5tFFZFX5dXX5tmYt73f58dTDPWHKvAOqAOGW3R.e.h3vfJiG3NJxUEPEudv6QTj6UYPXT.uGDgiO.zkWv4PWMy.SOBqVGptyXGYjupJqm4yXe+icDYkUMc2zyviyLn60JWY0UmUDYDw9wu8ueeePVli7zdgogVKZLlkEkEGOXkfS.7Tw5p5yjfL19RoriVGtAXZqTplI6EnkKr9JAAqAzPcRUDPMpQKr88+ZMu1+y5YyX2SOtCbNwxfX0kAFhfJibYwIpVsZ9lMal4Dm3Do26J.bne5e5ex49s+se2yGEEMaoRkl7889deYedOumGpSofHHofDY.xFDDjUHDY877xVVHxEYG+LkVqmVJkSBLQXXPNOOkSXXXbhfO2MSlL6355tMv1wwwad+2+WbyScpaZarnaaafsKYi+eyZv1TjcIGcQShVIMZOXoiCRcKH6yeexQE2rPTFEjIvkbHXBpZcXtKdwKJHKli3cjzYzyAT3K+k+xEttq65xJDh3hPurvtgvNTlcvvtDMxoT6O14+RIDyOQer4Uc6wqHLANucvv+z+z+jCKPlO4m7SNpZb+Zuu2GUAVas0bNwhKlk5jEByAj873e0Tkmzp20qDrKTZqelelelMOqVusPH5TsZzPoTJLFSla9lu4bFgovK4E8RJ7I+jexBJkZhUpToPHT3HG4Vx6LbXVpi.ZH.LQ3BZDTCGvKGTdBiwLMtbnq+5u9CIkxIKUpTdW2xYxjIiXmc1FkTQXXHxS6CfP5IcHh7BgXZf4DkEKbWuo6Zgm2y64c3LYxLShp1my.NPZkhR5yZDFgc+zCA5AhNJkpC1Eo5nqTomwh3jAW7hWreQKTR64662q7MTtquueOBoGgOBKr6alaL9wBjlGev5P37CA5Wmi0m0nOUn2ce2+1cAe6lZVjd9998d5dd80Zc226688tK1DUzBJW222OT3IpHkxK9te2u6KJkx.fHkR079tu6acWb2xXL6HkxcEhx6HUxslXpo13HEJzVJkaPBWSmXhIrZjigYAVzXnb+9880g5qQoTW6ryN6wBBBdJZs959G+G+GeJkgmBT5IGAWSjG9.tTkE4BLGobL9njedleDpEpyhhl.vBliBlW32wKbHTa.PuVH6PDcPSmJUpzoZ0pc9C9C9C14Hm5HaBrlWRhyd0uxW85+3+3+36ZLFtsa61lnFLSTIlqd85yQUliPl0XLyHDhYLFyzEo3TLhZNKevpc8MSH6YvEiD4HDEUmhl26G+iy+O2y8HN2e2mEZfHQz3txGm8dejdrTNousmmGoUH0BtDPjT6sjqqzf+5XDhcsiob6uzRKEGd1PGK5xHqm2MlkZjk0HKrXlwfU7+pMq7OAos27bmi3TWZ3s81daFVjXWbGJDhABgXPUVZfUH7jFWvvZPBx.tbsTns5rn0UD1GUHBBBERozZuphDGV0x9DaeVGPffJUrVErRcJpY2BqSIHKUImEcSjcU7tbhO7i6CpY0kARo4P3dBjKkommvKMvwA0o7v5esudDvw.7Swd49FCVqVsDW7XOwDOI.2gqt5pbgKbgzMjjIHHvoLkcdVOqmk.PTihBM3.dYFLnPFphCz5Qak2FOYI1J2crj2ahCZxlPclbTDmxTN95ttqa2Ymc1MqQ410g1fZcOnAgD899i+8q9deuu2p0XoFTi1DxNPotV6tmgThglDZW779t+tw0sr.P3lf3f986yLyNKZslLYxbI+lljrDqU+l33FBabFC0Z8fACFzuvDS1CSZ0OEaYLlsA1pYylaCrUPkJahwrws+C8CuAP674y2VJkq2rYyVxSoZb1yd15AqDTGC0AZJkxlpSqZIDVDiXLlMTpSt0OxO5Ox1AAA6p05NFioyfA81cvfAaqTmbiLYxzlHVCawfVKHHnEPigCGVW.0kRuFAAAsVd4kaEGGulPTdMCr9byM2lJkZm+g+g+gtTaLt66a6Wl9pItw23MdiwvQsTqoBFvJDvUoLTcDUaPq0FsVmDSFwZaRCFp059Hn+IUpdJ0o5clffdmIHnGP+J+OpLfFiRPTVVhrGkidoVy8pu4Z+7KXmiYjgDHsHKICPlZESlSZMbRi+9JePKhDro5fZitG0pUqQqUGDnGBDGnChEBwPgwLvXL8CrWqcNoR0oRkvN0ft07n2O3O3O3.BXHs+ldbm+Oyl.rZDjEwgEoZx8KgPLjpDSzk85drjD3mYdHGsIOzp.PAgvq.QjGrE7CDiudfvR6IiCfi0UULYA2bqjjL.kRkM3rAYn1nTkFywurEA3QfPsGq2KtDutZaIwyctg.CWEFvpLfJL.VtOrPOfNkOQ4Nm3DmniGzwRIO1BXsOzu86tF3pcccq7E9hegK9JdEuhKpTp.hrIvSoTsnLqWhRsEBw5dddqIDkWKBVqhVuVY67Rsiii2FnqmmxfcM9B.SAhYGNb3BgggkFNbnrdTzxttEuViwbLiw7T+c+c+ub8ulWyO80WCdp0Jww.tFpiBMkvVTuYvkomm4SEl6LvwSP.1xlhfwdsF0Cna.xcIxp+jutW2qamKbgp6bjibjcNxMcjs.1XI6bhMAZ8TepO01tBwNFiYPcvIrD18pVkYHh4vlLuYYQlwCuTsCMOr7kTbver83+IVsGucQdvrvlCH+hPgl1J7UPTRT.GJPjkyWIU.eGIrttDqSM1h8KNQWpVpHcMM1JubM.G2XLWuVqexHXIomLWB+eacJkpZTRFvwkVHXiDAQsCTrCTe2JUprs+s5uCZ5lbNRE2OkKbsUMlmRsZMdxCG12KgBDSjTol9JkZqJAAMEVDfrN1A0hjiwLB3vHDy644kPcBlLoxXocXsSZHX.FKkSTJU2fffswX17T99sNSEcSe+QhHV5fl5Xg06VrWEL5yiTXh9l0BWWpIcubPa+JcLF+Xk1+YTfnK.YZwnrMml3gQ++gggY877FgfojWSToRkI.lNIwUynTpIqnqj2LbXlibjkMUpTYH.9994bgIOqVOqwXl4AevGbxumumumbgggY9mdnGR7zt9qmXiIV.CxlMa2gCG1wyyaafsa1r4V850ayjjtLtJ6mXWwrqKzMJoO8a6s81F7l90dSCocZe64bf1LGPaq.DC6csV.HmGjIbu6iohd7r.y+4+7e9Yttq65xMyLyL7k+e5kuy87GcOau6t61cxImLcQxAImeKse7XaFPGpSOtxYZ9q29Kid978+8+8m6i8w9XohA4THXJpSA.we1e1eVuW5K8ktcQnc8u1BE29qLF6IVcAggmPXLGE63qrX4Td+fffcdq+R+Ra7K9ley0UJUUbILXkfHkR0FXGvsCD0407ZdMa867m96rIUYSfseKuk2xt28ce2i1j2k3d0+V6wesCt9SVf7KAEZjpSVKkL2RiTsvZwdPyc8gNU1SneuTUCLsphyfEgIGAKBSNNvSaiM13HyN6rykH3zwX62rKV8UXi2+6+8u1cdm2YMJSELbQhPCz.717c7NticdGug2vtUcwFrz9QdwSD5us+JllfnzzJgact.gvXL8Edhswgsr1239P+3AOdiPXxRvRM1CgI2niiy0OXv.+rYyNqVqyBLz.c8UpNZsdaiwz9BW3B0N5QOZfRop3BgQPifff0T2jZCLrq095IFjCAc+4fNs8oKUthhN+3e+fGo1gjFD59PTmmUWj.nOKwtTfcIHs56yAzNaIHSMqx3l9L2AaEPmQJDK7vc5rzDSLQpXupLFiDvc6s2dwIlXhCEEEMIPtkVZImFMZHDBgvyyCcnElJoIS1.FgfgXHVJkwIZVQZ7GCSQUowXFGQD8AQOr5WReee+gAAAFgP3HkRwG8i9QM2xsbKCEBQWfcMX5pjp9AAACTJ0PsVmdtbhINivXcUjwR7n8qnvz2W52Sq0cMFSrRoPWohyI88IZrJzVoREgPHxoTpbewu3WL2MbC2Pdg0156XLlFe9O+m+B2xsbKOLVzE0bYXmUsissOyVlLrZZLDKHfVHgbZWlgHNJ1XIuQsVeboT5q05oAQrwX5JDiDR7MAZlJ5qUz5Z9Vp.zBaL.6VoRkdYxjoeud85t7xKONJStZQ243HLI0QISEN8zM.MAVQFtmPH1BFoIBoHa3RMGxXqiJmGzKWBNQj8Z9ogUiFlH4uK84+.qV5vtJkZqfffVJkplVq0RoTiEIOMgQBnc506iUgT9waswWaIkd+SCLW0pUmy008PIH8oiPHVm8tWbPQec74JyCxBFSvjRg3PgdrDgrLvSKHH33.OYoT5p05oLV66tuTJ2Uq0I53mXcvJ5+RorM1m8MvpLMZrYAaiiCcGSbsuT6Y7wJRC9ZE++iFT5co9YGRPDVS65JoyyB6slb9xkKOYpvnxdq6msRkJ4MNNEbLlBe3O7GN+q809ZyPIbvgrTkrFioPPP3j99xC4By9Y9J+SyMyzyLCvjRoLWPPfC.m12O9LAAwhDG.Sq08jR4.RSbJzsToRac1yd10u0a8VSG+0N40ltvlIweuKyQWxy.pmF+eQGntXQvZtU1JoNZMTfbEAQ88bnugI2aRQ65zehOwmXpu2u2u2IRRPOLZLqEQ4IBBsc7nKcovHgNebzS+3QAk+a3sGugvjwypbLvv4gAMgAfbPIgX.0Y.Q6UMHrqk6ncIK01WVufK8faF63OfTgzIQPPkR4NDSea7YlLRoL+JAASFn0S8hdduvoHhooJSo05IAJjTYYG+awWfdeSlkyXLS.L8WrQiYa2t8rkJszLdddSIkxBI1HmiPHDAAA7.2+8O90uPHDYTJUAoTNoTolxyyap333ozZcdsVmc5om1wxA6w5gZ16dWB7GGfPzekffABgYHkvDDDfVqEUpTYTkyStOlRgmCpLxeypMdBNxbfW6Kq3eMNNGrOyHQgkjA9sVdY6FWOJcJsm3olHLTrkmm2FjZcu1EtqBn888CuYe+PkREoTmr1a6teqMEFQqibjkW6K+k+xq466uluueKflQtrlTJaqTpMuga3F1VHD6ZLlt+u8u+e+.qtxnbjRYtACFLgmm2zBgX9vvvRKt3hpCe3CeMAAAOYsVerfffigkiiWKv0TBNZD3AEKtDL2a5M8lll1TfQI8oswXLwsWjgzZeW2oOK6FVt7tFiYGVfsTIBdK1EGacy27Muwe5G7CtCvf64O5db.JL4jSlBE9IMFyDQP9G7AevbKA4IjbVDcsOM53f7a7p441USy.vG6i8wDfTHAgotwg56UosWxK4k..0KBz7w7j0Fmjt5RojhEsV92ccW2kPfP7K9leyFDhAAAAcCVIXGkRsK1jZ1GhLAAAN+NevemrTcu6I28ce2OdKYz+asqt13ymXNJXZ.Fvy7q+q+qCMvgF1DR9o9TepIfl4AxUw8pP+JlGVvtd09BvbyM2js1ZqQhv2G7C7AfwRXnwXbt8W9KGcEs4seGucSvJ14vIQ2Rt8a+1EmMLTPz23uY7sv1n07IwEwDBwfh12GBkLdBgwNy7nO+U5Xk9toQoR10xbI99tu6KFvTqVMFLXfAHtToRCDPufffdFqidXN5QOZFfBAAASFASDFFV3jJUdpSNZrWk0t3E+GM.wsWfXKRCtpaGL4bib4M1aCsS+xeYu7I0FS9G3Ad.QYnOMnCAiSOx1c.1tlKakTrnwSXFBgHSnqatIlXhBThBk2aSxSzqWuI5zoS9nnnQZgViFMDhD4vMbTxRhsiGLVZyfktuc0g5cA19y949bagfsDH1DAaXLl1JkZsDTgzxHDqoTx0.ZcJe+VZstoRoZHkxZAAAQ2xsbKQJkJRJkQRorpRpBqTohNwxLiR9bMtIorguzu4vgCaIkxVupehWUSkRUCHxHLgBiHrhtRUoTVSoT0BBBpYDhnUBBhpToRDkHpLT222ukRoZq05Me5O8m9VBKUb15AdfGX6UWc0suka4VRoAS+4sUsd+1Y5pihWpOzpOvP8QGgJfQ8AFgFGgHFLw9mVMTLpusoOvvJ5Jw.3mDKG6MNXfuu+fS64MX4kWNFNpw6q2MiHGgzp8Em0dSG8XoYGLVq7iXypBkRkpuRBvf+oTFf3JUpLxwb9TepO0tV6ideIm4RUDrmnud5kJN0Xf3xkKmJdvidO8O5JXb5IycnyJDhbgEo.ginbUA.KkxsqSfHA8Ju3uuuuA.CrhObrA.oTl4E9BedYpToRtjDHOpuwh.m6Rzu61tsa6q2mGWo3+ez7L+fErae6eDXPyicLq6L4RmDMLIU+O1Dnc0pUSKdbsUVYk5XS6v5999adKR4NJkZ2W6q8stSIX6fyFrEUYyJUprgPHZ66KWGn0+w23an4S65dZMUJUKf0DBQaGGms7882YEstmuRMTJkTVHxHkxBBg3PZs9v.kjRopVsZKKkxqMHH3Xc618o7W7W7W7T.ttRvSNBtFvyuD3RaVf5LCiDI15N0qWmlEwPy8E6e59a1t9RKsc0pU2AO18niogKIW+a+7e9O+NRoHNoXMyXLlE.JZLlRZstn+oeFEA2EKAySDGlKvLXWe5flAw2NLN8J1d7VBSRaoPAKQ78nOn6W2p518.2tBgnCEsYa200EhFE3wA231kqMdBS1FXi+xO4e4F.aIUxtAVgkKCvDJkZZLlY+c+CeOGF3v+Q+Q2ybRobFsVOYwzNMg6kYRf735NgPTdJfCUZokl4e4e4eYVfo0Z8jAZcdiwjMLLL6gO7gyBj8E7BdAYUJkky7kHuwXJ77999tlPHDS827272LIvjNNNoZuQls2daQZ.1IWnFgQbvjFDu1ZqY1XiMPoTBpQFgc.qyO2q+myAPbwKdQQ5m+i+w+3VgE0ae7k6alUEOcxxTzDYecrGQhStZFHd4Sbh0oHFvEnesDNAVdOtAlJfRoSfzFOViRzjxTupMK60fZ0eSukew5.Me1O6+WaMyLyrlVqWuDztRkJsCVInstht8a8s9V2vXLaR4xaoTmZqgCGtkTJ24+9e8ecGgPzKIQewAAANRobBfYmbxIW.njmmmBqNZ7jZ1r4w9IdMu5qqF7T.tVn9QaXqbyBPQ6DldjaNax0Lzb+KByQOZbpi6P0p8DBQWZQmfjq0R6k3jV+nuxW45CFLXariGxfqs5tFiovpqtZNfrm3DmHacqs2kHrXVwHzm80u+aFBcZxwP6nsIXzA7DFiwj3xH1mw0ezUkC096qXLFgQJk.Hr6K.wO4O0OovlCRhsUGSzUoTcsPcm9pSoFFFFZGaUGmfff8bQnxkIw0dN3q+s1iua19aGGCtvEF86Bcdiuw2XBxs7lDX5+pO8mdpx1M2linqBGjZMnkMnXS4w1D1lat49N+u963Nra33jJG.mOvG3C3fwHj9Rty67NA.oTJvEyRPrTJG5448D4J8r+4s8YHyy.Jxv5iFeFYpN5iaGmdUdbioVM67DQDe5Se5zMlXpUaDMBhEBwPkRYojyEtfQHDYTmRkWoTSn05I777JrmSB3l4BW3BBbwbSG4H1uystpGiePjPlZivoZLwzTlYnLyfGG58+gd+SAj+FuwaTTMgFwIhZXZE9rabOZz5Y1MdJkjP0h7DEM4Fas0zCzCloJL6fAClse+9yjOe9oVbwEKHkxrV36mbOczc18.IkALHRCH25hPBDaKfMdVOymYaLrtwXVGShy035ttTdxVAAAqILl0vZiosNaPPyXKZcpQRwILFS0W5K8kVshtR0fffna3Vtgp92reDknFI5rlRoZWKopqG8n25Z.M+K9G+Kpo0ZsRotfuzeUkRUQXDVzJ35FoTmJRoT0LFSMee+FTiVUSPvoKrSbbbmfffN.c0Zc2m9S+o24ZtlqoKkoKdzkEn+Z6GsPOhMiQZUVuv9JtjArNFiTJMjPEpfUBLRkxnTJyIkV5W6K8yBjWq0ERzHMGVhXJReTzSOJNkKLH7qWjVnGQGmL850KMtKmDz0rWrCyggkuJNdKhvKsubUD+cel+NgTJGQiUiw3DDDjHtxHRn4g39tu6SbRqSWE+xe4u7XoTlduyIJJJCKRF7IKdeKTb4+VWy.D6tWQ8Fh0UkrH3xXfhXmhad3bWoq+EfhI2mSJhUdfB0pUq.VGzzQYc1w3jywf28648L.nuVq6+pdUup9FiYHP76487GJRP39OTfOF...B.IQTPTQ1fffLTFGjHZdYhi6du268aDquboh+OKG+wDUOtTwZY6We9yammLhdIVibuimp+X6O1+MN0oN0FTjsnD6fK6TE1Uq06BQ6VyJqA6di23MusiiyVewyctMJCqq050dgO+WXyfffTcgrwO2uvuPyj4+ZO2ryt0G+i+w24G4G4GoyJAAcAFXFNznTpLtttEvhh84vgkjRoW974O5y3jOimrwXttybwK9z.dZP30UyVDUenXIfCyhLsGjuXwhYrVV0XE+d4k6lX9E6PiF6Vtb4NDR2Kjb8O+9u12NLjNqu45lG5gdn76ryNGxXLGV3JVLWtbKclyb+KdQ8YW79CCW.3vf6r.SSQlbtwry3GCOydBW6wqWX6q5KIPpJkCW4nH4RrS3I.27FSUiPH5fKaPDahMCZWInwZO9GibbdlF6TTWCVHRe8AAgWqPXJlb95M0DSr416taKee+0.ZWQWYMeo+5.spTIbMeeu1ehOwmXim+O7yemDn5lEKVYK6BW6e3G6iccOum+y+XQQQGQJkKp05IRt1heKuk2Ru69tu6sDBQq4latlqu95anTmpePvJYSRJybe0G9gO7y947bR0zhTXi4n05z7wmt.deAzUZEVrcwpDzaEAs0AAsMPaivrFBZwPZ3eZ+ZDQcrnMXKRppBbr9IZBx2LnQv3UOXbpWkNfanci9QCONDetGoKOb09cY791IIQyKGDlc0UWMyxKurCGkXtvn.fD.h+9+9+dwy849C4.Zmj9cSVDNzWHHXFkRMCvjX897L1uKkDPMK8tJwbAmM3vJkZ5+a+29yycpScyoIQHGIY7WHDoAR4XLFqf0Aj3zK8sTz5TCz5y1Oe9786zq2NyMyLaLyLyrNVTvzjhzh5oOy71EBsPedYhSjRgzyY1DOYOAZrKL.ZYXNxPaJDFFNkmm2gv55RSuwFaL8ryN6D0qWOWwhEEtP+HX2DekeOZBsH6lP8kgvBwPKaeDWLDsOwg5qmDELZCEEg702S3CmVHEERRPYbBkXRoKzNDNhpPWJzQYWf1kBDw7TjiRcNtwXdFZs9FvBa+4MFQdgvDKPzKFylNBQi333J999W.H.WZjfzj9PYSPvYF99e+u+Nuwey23VDNRLe2Fnywgdm6IVzi3es1FmlDB1acm7JXxe1246bh63NtiIIA0UBgHypqtZ+kW9l2BpagP6xrKqdIqTZBLrmu.r1T3xBDwQnDOMSj4FzZ80KkxijTwobfv.l9.ckR4NgZc62+8bO0uy67NqpqToh7z9ADQXPPPjRcx0gZ107VfNz5IzT.KYL+wxBmOuKLYDbHWXpHHa2tc6WnPgcviMsZzwkkRNoGqrKBSzzR+tqAJeBiI7YDFFdBOOuiNXvfYqUqVljiyNJkZmfffcUpS0IHXksDHZJUxnfff.K8AtXs3XwZ9mxeKpkRYgk5AMrtmzwoKm6JNVe79CN3SVprOgseBr8ulRHDS4ASbg98Anatb41.65zswmcSn9yAcshwfpOSCb3xfaUPZLFegv0e3vPYrI1KalrkzZ8gkR4TiSuWK0m.ozZWvwVHIb.D4Z5nT96BraPftCXFXEy0JCEFQOkRsaPPv1.6JLl9FgXPx61DNWldTkQTtAaADhSnqyt1DRWq+89mbuba+GtsrkgrUsZtfiuueFqilgYvfA8Wd4asCDYQnPYrHPBDZ8EQJOBtfHBbBBBbRJVTgfffoFLXvgV9VWdpTpcCfwX1MLLrtTdxKBQWnDTqlciTi2O6fHnLcSeSfGGNgRDG2XL2.vSSq0JgPLMFi.DcMX1EAaKLrEBwVRobCsVutTJqCT0RQkSWEBWKYc2dKZQY83Pp+Qy5poEPLG3OIT4P3xrlploAlPHDYvVPfNITxoML+FvZoBt5kiRNITLr7BP0qAK0Ve5IWydEKVbxZ0ZXr5isMwdBD8kJ4NejOxGYia8Vu0ZpSpzTiJjHpxPZr6t6BQ6LOzYs8SKmuYh34uU0R6ujCISglCCLehdwky9bnTan9ZyCsWaTL46g9b169edvcJH5PFiYdQYgzT07jDBwS0XLOkf.suRImWq04EBQLF5Zvrs5TpMCVIXCgvrgwHZmLu2NXst6lP4.n5E8fpgv5GG5LFkbRuFRaOZiKO8u+RE+O.CA+APkwel+0yZXWNDOev8gLFcPWr.zbpG5gdnIu9q+5m1ElXkffb+C+C+CYdYurWlfxPx7LYfRYgZ1DcWhY0eA8zRobpfff7FiofiiiUJILlbdJUVgP37U9JeEw0ccWWdiwTH4YSlDmlZXR7+wFS0AgAg8MBytYylcqBEJr9q9U+i17C9A+v0oD0oFMvZWvaAUsnJzmdV8LYz8KmkAmUGcstrAV0vxHXUbtsa61xcu268NA13+SQUxR.yHDhBk.SjwzqrPzIJI4JFiYagqX629c9127Nuy6bKnztPM67uGm9btKonv9sMsGOivjQYHrIEGOC3CntWJx.5rDQ8DBwPiwHHhL3dUAqK6w97DicxncvtIm0.V2wIQSOrkWN6Nc5Lguu+LUpTY9fff4EFwbI1IaAgHNyC9fOn34+e74KnAYXIK+iMFyg.lKBN7q8W3WXtNc5LiTJmJTqyasCXQNgQj6tu66NuvxW6ot1q8ZmQHDyVlnYykK2L5.8TRobhm8y44juUqVYa0pUFcn1Qq0iPWBhDgpzpL8CM1AcCQXWb8r5JwUBBD+M+s+sBkRI7k9wmxyefu+o5u58s53UpZrWm+aFc5G+YwAgZovXLNUpTwAHyBVq0M64djzzAtzOOuTsKAT8BiAhuIKDWGOYIoACN349bet8KhtKP2lISRTmhaoTpMHoBWW7LWbzlh05ufUI6sPhdSgPrdPPPqSd5S2DntRopqTplFiYsye9yuNvFFiYyfffMEBw1+W+C9utCvtBqkEGKkxrtDMcbb7Bc61srvXN5gNzgtViwrGccp6dTrnMYdOBmhDgAjUQjTYHCfIHHv7Ez5QWaPqA.C7ZSOX9TcTI85ZqSdxS1cvfAbCEKlyXL4iRRPYjEYI6UEflkyk.i6r9zJKP1G9ge3rDQVJNJayeCKiy0o3nqIgPLznMiDowxDYSPiDCgWcKd6FAvhwT2lbmd85M.XvgO7gGBDKDlgRobnwXFHf9FKu7GoV3UNSEiRoLZs1ThpCUJ0fCcnCMfPFhKwUpTYz3my8soKb7sYs8sQ1ikz+Mg2y4BnT963NtiT9+OsPTdFfYNyYNyTtT2RONINWFcecr9+qAfv1+qLTChiiIAYSi97BLBL1x5q0Zy6+d9fla+1uczZMRe+T2+vnTpXOpYg29bziVOBGY5Ij86NNm2.oVqINUSl+oPgBVDjFV9pEp9ll6aMfpDGa2y0fACHa1rBCFgHIX4fffbJkJaPvJY.bLLZMojjZeD788gZkFcBVhF1evhc8ql44RBPe4LTYequMRy19E+4+4KXLl7gPlrYyZxkSZSHSplfT43WtmwB7FoAJE.ltJLqwXVPHDEgZkbbbJlMS1E50q2LXXBcndDcbDBgnb4xHDfNPa777LBv74+7mYDhJDBQegvoqVq6DDn6HDlcUJ0NThc7k96.rSPPv1IV96FRe+MTV66schtOsdvYBVSq0sRbpu5XEWwZ9990CBBZUlZqAz919ObaaBr4YBB1Rq0a+q7q7qtEvFNNNsUJUqb4xUuLQQ.grDgTkpqt5p0.ZHDYaBzLJQfCUJUaoTtMIBMZtb457k9u+k57E+hewt.89pe0uZWfNwwwcJkj.lZWZsB4RWI6E.BQPhC6FDFXBBCiASrUYXQXvjU.4EFJDaDSlrwpoINdZsVOUkJUxKkxLdDJ.HoPGzjEGuuyi10RGCULUFBD6lHnnVffX+LITAIFHdAV6pFsTtT0N9vEm6+9u+LgggYjRYl50qmUHLYEPtDjSm2fofVqm3Vu0acBfI+vu6O7D.EpToRNfbetO2mKKPNIQ4Axu1nMRe7Gsw9838lAV1f9.nBNoULAt.qckOFw.w9DYo3UQQLQXDtBwK4k7RbzgZgRISEp4gFiI1fIVHDwAqDD+Jek+DwlXwPkRECLToTCEBQesV2uDUGBXRzMIw4rBW+A99+Hhu9wRaew+aLFGfL2na+CZesWtWWMsKOxS1O88SnXGckzrKPmomd5cA1IhxaqTpcdNOmmiU3pOSvljfF8vvyX0ZjxzlZrVrH15zPJUCee+5RorQBsDWWHDsA1X5omdyf.8VIInbKiwrkPH1QHDckRIkHJmNPeHfExlMqWoRktl4latmxexexe1SsQqFOUpU9ofs.+ROptHvLThInB4X4QIdxPpH3N5ZaUa7AqxP3XCu268dS0ojT5IsoPHRAafoFjse+94qZLoTD8PZsdZp4N4zSOcAfBRpYAwvbjiyQVN9WGBS8S.ZOdMgIGnUOEtZwEggPXeTzCJ1qAz2XrdNHPVhVLYCaG6QIsbb2DXcgn7ZdddskR4NHneRvr4CBBNjuu+bB3vRobV0oTSBkypTJNwINQbxbbNznTBDqbmEbWvXLK749a+TyO8zSOiVqmvXgJWVozKCBSVgPjyXE.1oWYkUlQJuo49BUqN6fAClVpjSHDhb.YlbxIc51sqivPp2IjLnWXGzaXHNio1+Va8quT5OP.Cd523SePkJU5o05N0fcfZamMa1T9N2ElebKq6azUC+fvP1ArVi0nOfHIXTjNs1e.jozr5fIb4fG2K2y5wmjbH9LzBy1KL7q7U9JisX0xIUv4X8qumdtjjXt56TsZ0s.13AdfGX8ibjirtVqaewKFtwccW20F.aDrRv5AAAMMFSjwXBEFQfRcx..sVqCcbbhDBQ82va7MzToTqUtb40CBBV+YcKOq0TJ0ZZsdciwroVq6T0JjwYSrpr4BCCKFDDnjR40XLlq8y72+m9jnLGAJ4FZqd5zIhfZVV0KYL87Fk5YMzJtTyODuQOaGDBCf05snMgPIHGoz1+y+y+ycyjIiopwj8FuwarPBRnlTXs1xIMFSBEzpVv8lbyCKkqRRRTdROomjMYJ0OZBzJ8y.Geb8D5wZSXioFiwXFlnoACnHwBQoTX5KXerS6J1LQfAZF6krXR9746Czuc61ifXsVqGfvtPiPv.kREGDDfKjVkSGo7TjDTcuG3Ad.6FZhVpmuueeX9gf++FpR1q80afOeytk7c4XNm+fTDzhfrBXQ3zgfnYnDGpToRSFkpb85uFWK91++2467cJh.GnpSoj40rI.OTj90vSJ22cla+1+gPoT.HbSSdbYDPQBSWGn8i.YWOgsct8ViHKP9jJulyXLYVxduSf6nO9Uw0pzf2XqCDCQ0qiVqEBDYLFxBh7BnPPfNUenb.v22eeaNnDXfZFJhA7LmyRqmTw16J0NvZVq5L+XEDHYyBNP4L+J+Z+ZowkM3i7Q9Hcg5o7tuqcNqycoSVB3P3BinM1W9K+kmlDw81XLKZLlE0Z8B.yznQiIkJYdLjAydwAVsZUKu.DX0eCX3MeymdOMwxX5ZLltFLcA5XL13GBNaP2.cPmjumao05199mdcfV+r+rugVBgXsW7K9EuVhXe1JVHZVoRkFXo7ZzEuXXTXXXck5laV0RUzMpToxF.abwKdw1Rob865t94WqDzxyyqwpqtZco7T0qZWbnAMnIPykW9VVCXcOuS0F7RDNQ213wFUpTYSJwlqToxlRobi4WZ9Mtga3F1vXLar7xKuIvl9927101SvSuRim1+89VvMdq2nCgkc.bTdJgxyynT9wBaAsb.xIOopfAlRHLS6ZQAzTFDELFSVKcYJIB2OJBxBMG4PdGe+TN+peSiGOYdhkXXj0tmMk2qeyXND2hlVWEIJw917hzjZRDYld5oyXL3jP2mL.4jJUtf.c9O8m4SW.qkgWPJk4MFS9986lOHHHuuueNnryy7Y9LyDDDjQCY+y+y+yGqvKm6wSqS7Mn1pl41+F2SJHjG0uZJ1KXfiMrR5FhaXK7oIxD+S8S8SEK8jF.dkuxWoIs3pJkxR2XCh2y642kS4qHHHHVWQO.b6Qbbm333NeAsNEQUX+Nb9C9c4fIe3q6lPrrwtEtiJdfnnCpKdWMIO4qYxyuBuFqnoKOHgFbcN5QOZha5Tcyvvv1JkZihvFJkZiKdwKtATbCOuirAP6Je9JqWoRkVPlZAAAQZsV+W9W9WpAKRPylMaMsV2HHHn45qudKvzTq0MOyYNScf5I+eqGFFtUjwzCAwdJurCFLXptc6NuVqcAN5hyu30ZLgO4OvG7C7jgRKGBJfhTiCCLEqtvizLPtjuN+viumdmrKnRDB3har1ZqsswX5YLl374ymILLLGvDCFLXxSoTS.QE9I+e+mLG3lUC49nezOZNZmDqz47+1Z2y4w6ILIoCse77I7LtlwLjxLnT.8g58wiA+m+O+KaXDTTalPcmyO9FsuBGaFBy2EhRPYRz5.sa2t8V.cEBAlD01Wq0yznYyCEFFLk9LUxCUcnDfEAL.jUq+BIpecsCaLUW7du26cwolZxCCbHoTV.Hmqq0F27jxLFa0yxq05IkR4rUzeg4FNb3rRob5G5gdnBZsN6YO6WHSgBEbLFiXwkVJov.FK+XELzXD1M6aXfvH5qTpdBgnqPX5lvQ2cWZwk11wwYSiwrwu+u+ueafMUpmQhZjWtOr12rT73wG3rOwcZuLWWLs5GlDQDKCP1uzW5Kkrno2ACT3fG2C96tTM6jGU1i2nW208bS94iMDVM4mO+n++icrikLgxQ67zKWdGfMuwa7F2.WZ+Ljx1G4Hds+C+3+gaBW6FJkZckR0PoTU888qnTpJPs.fPoTFcSRYsicriU+0+5d8M.ZkISllJ0oZ7LdFOiFu1W6qqtTJqKDhVJkZCsVusTJ6Ttb4AXikIWi5MRgMW4icsWqudE8QfZRJhKrzhXCLdxD60V.qMDtPRlkWq+wB22FpFBzuIGKIgPk2EpsK196C2Ymcb9j+kexItoa5lNDVQfZtxvbBgX15vLTjoVJjBPi7nHGrvXat7BIuWIKbtGqYa9.+MEw2N9eP8DM1oXcLPcmtc6lAJ8XQuahCo3.fdkK+T5JkxdFiYO+l2vvScpSMDHVJOIAAAYTJU9ypqT.Wl3TJkc7uKFvsewhGNYyBMRDvt0FrWe5+US6pIfluVAA8s513yc3.mejPFWrXQmeyeyeyLu829aOSIa+6Idmuy24gnDGxDYl5Ftgan.tjEJ+0L47KmHHn2wcbGomKQMJ63333HkRgT5kfzDCinZoEEIFPDqCBLwwwhUBBbTmTkwsJYd6u82nkq4rzk5d2SD62MdejbkJUp.vjP4IKKDEDBQtF1pYmfRrq1qQMdgi8Oc.wd1LrCBQVvT3qt5pELXxCjY0UW0XEaVFpTmdXplyb+gWzPYhKU2hXw3333wRL5UyFMGcdakTQUPlIwgBx.UEXq3e+xkK24c8tdWaCxsWxtI9dg6GZ7G7XlAZkPU4vodpO0m5gvttvbwwwyOb3v4kR4rZsdZoTle2c20FXaROGiI1DaRhqvXQymPH5afdXrUhz.aIDrMF1Qojc.5d1yd+8DBSWr1I7VBgosTJWChZ9G+G+G232323cz7teyu4lezO5GsoEx+zv2yqou+ISbEFuVG4HdM877ZBgqAzVq0ab5Se5M.Z+Lele+qsDzz22uQMnNtT6Ys7x0gpMYIVCjoH9bSn1F1eNbyKdw6KoX.QasTHa466uo9KnamPs5FJ0IqKDhFBgWSfVBgXciIL0YR5svBKbUN+8wAv4AtuGvAplAKcoSSDg8Yjw97I3rAIh6qovJAA4EBQFDvdZnSszyoimmWBEAJl2KAsmmi8UE2qll86+4Rh+owdwADZLlpUqB3N17Gesy72ds0fhHfRhs2dat+6+9QJ8DMZTOUqTxZ0kES9uquyuqb.Yuu66+giVqc788c928c7cjbcTx.US0YiX.yOvOvOfXokVJYchk+1tMdAHrdaH.EGg1GiIbz05byM2k4O09QgyGiaJE1GIT1CKUrnIg1Z7ddOuGG.GoTlIHHHCFx9pd0upr.YhrO2Q5exgUpbldRe+N999cjRYOrhQaxbw9eiZ84KShWJFCWHYNzKjtOgrI8+ylT3szhnNNhyublMvi1umik3jUG4PLyM2bIILYwMNsmWaf0qCswkMt4ibjMg5agGaCE2122eCee+V9ddMREv5WvK3ETEnpRohh62utTJqqTp5m3DmngRopKkmr5Mey2bnwXzRoLToT0MFyZZsdi4O7763551AXXylsbFNX3jZsd10We8kBCC89NetO2ir81O7x3xQgx9.t.K.sND1h6LdwJujIH5bin4mWOHHo3o02XgEVHU7vG.HjxSl6m809yNQ1rYm5AazXRJQduZjChxgjbu3W7qNUaMyAUNnHv9sUsGumvD.LbrJwoh+pPH5SUFTKklAgD++0a8MyEu3EsAJTlD2qYtQPMkuFah10lrfNddrIv5wwwsmat4RsZWiPXCV100cxEWbwILFQAoue9OzG5CkiZjyk5o7PdB4ojSALK3dXgPL+N6ryBSM0TyALkNvxU3nnnwl.xjQrmMBN887+8e7r.yFDDbnie7iOoTJKbxSdSYcbbxf.QiFM.vHkJzZcr.wPgvLPHD8A5ZbL8BBB5XLz4E8hdw6BrcBcRVWJkqoTp09w9w9wZCrIzXGftyQ0KU0T9FMBSF2xHSpXx4y.HRgfnTZ+bEsz5H6MbC2fsRuKDl8b6exxK0jlWoMMueTlLhhJQIIIZDEjFOgQCO+4OuEpddWna8DG0AXChX85vZEKVrMQz1k+k1XqJVcJm5iCtAXsh5p.Q+iO7CGoqniTJUDPsvJUpGDrRMoTF8g9+7cU8AevGLJLrRsu7W9KW+TmR0LHHX8UVYk1JkZKfcK4VZPPXXlO7G9iNkPHNrmmWwd854Qc7JSCOr5vybTjIs2acSuV6Cz+76+ZKMKyC.5OOU6Az0yyxW3O9G+iIdM+zulB0pU6P.y+UuvWcgp1j0LuVqmk5bnFopjc.405uzdB95RjYkUVICL+2HD9U6eew5TYbg1Kj35.FiIagBExIsH.vlfziuuENOX6.KZTe.P2G7A+bcA5JDl9kJUx12PP7JqrRL.A5UxbO2y8juhVOgT5OEQL4YshzWVh.OhF7K+K+qZS9ToTnyu7AC19aGy39USRQFerY5b.i+5xk7juU1D.briYe+c8tdWB.pWut40+5e8w2467Nol0sZx8FdCug7lHSAf7yM2bYHBgGUubaVdT+sUOvFpeGui2gHkHzgZMarwFIeQD1DmHre9fffgBQbbrwX7884dtmOnSvYCxEAEty67NKPUx6QiwCR4IppUe52WmEWzVc5DwKbBiIbhpFSp3xIHBwiBCAR.jV0dbbbvyyiKbgUwfU7bEXx.j8Ztlky5VpTF.wy9Y+rikR4fO6m8y16+uO2GdvYrtN2v+k+kvXpRbZ7GkKWdv7ITcfGkUbMUbLKi1pUSEsA4VrHwFioWXX3NelOymYaT5sarmVFb430uc7UQKcbLFyTX4l9LBgXVGGmYxjIygRJLSdfrsVaMGvPFmLi5mJfXqytv.iwz2XL8DI5tgwVc01FCq+S9S+S2NHHXKfseAm7YryC+vqtsPH1.XMozuYkJ5F.M9g+g+ga.T+s7K8KUqRkJ0vhHj5.MNyY9Ds.VuDgqCrtTZsSSkhMjR4lQQQVQX2swZMR0uKnAQzRmpmKMXKOz64ZPtot.wQ24HG4Hi98MRnWqTJScDu5PsHfHiIrlPHZVtLsEBw1ttV84pUqVWMzNPr73EFnDoTKvQq0YDVqS1QJkYvPFoTlEHq.QNiP3.wlSpT8iii635RGfdEKxvKbgKPXXnnRkJNW3BmIS33w8btG0vd+xUQcS4xkwkHGKcrrGuEV3ppfC1VcLtTKd5omN9E8hdQw5PswHbDEKVLCPVK8yswFHLBwsdq2Bic9GpTpgUprx.fdKsDc8886VrH89q9q9qFznQCa+7kWce2uuJtdeBRqsCfXIpKLFiimUjac77rOGZ2t8UJVF6y9HDtV6Qy9LoL7zuwaDkR4n05QIZvhpcaeueueueuL.NUzmUXLFitxYFHDhtttryK9E9h2IINvg1jogCtU95cyuWt3CD.3ZYP.RocNLWWq30FFFZK9cwyk5fXoZ8zdBDKj83ORG14xcO6J0Ferw.fdsa215fpKzbKcBkUvF++lQo54WH6Tj5ohF65TJ0oMKFYeOQXqcbBCBBphcQ+pZsNrh9rZoTV4z99U.Bz5JgBgH5u+u+uq1TSOUiyd1yt1K6k8xZKDwamIalt.wau81YAlpToxGtc61kzmUqd3G9y5iEoIt3x7.GBVXLmz7QbMl9yw.CONgC.5tzR183TpDa8JdEuhNBgX.fCkhJ7ybm+LSALc2tcOD0X5vTaIWSg1s+J6smtilFi2xeaIJSd7bBS1qC74saTxG5iG8b2SM36CLrAvQNxQx7a8a8aUvqJSBEm.ZOtPhdEOOQ1Mh0MzJjbalISlMhii2JopylDHqlc6s2NGPNAhrAAA4eYurW1j.SGAyf80gt6eh6dFvcVio5bFiYtu6u6u6YzZ8TUqVMOBx555tmpgis1BdVUpOePX3ju7+Su7ojR4gjR4gLFyzAAASPBruEFRsRXgVqMXQePr0hvLCvX5SBDYUJY2+e+y+y67J9geEa4BsEBgMXCW2VX2b+lIBCa21ORQB7aTIKYrIH8sbq12OcxuTKoJW8DHvq01IDqWmBFa.x4gh4nkcRxhPN7I6w1axwC5NKWoMMbv.FRQTykxQfF+yMjPK2F8fcoHaOucxxMqWu9F.adVs1NQJrdopzBba.QMAZVoRkl+m+4+4q+jdROyHourZkvvHrvPttwXpCTOBpehSbh5dd90e8u9WesUVInluueciwzPq0MTJUKoT11A14Vel2bbhiJLS974W5bm6KIeNuzWpBqtpWxqNG11+OJC6O3nCFj8nFOCjXB..f.PRDEDUDHkjLxdgVASse1r4M+5+5+54pToxzAAAGtPtBK9vO7CujKr3t6tqMoI1fwmBKcxR2LiypmYUwoN0KjT8Z3QaGF1+BdBvWjp.3RHlEIdI.iwjIAp9406sHZlictqpInGk3ra9lu4dKszR8.5YLh90pYqvWRUVs8cMj81u8aOuCLItLYYK8jxmHXd7hd0u5QIyQViAf2PX0wE9yCtvwSzW.4JkjjLVQ6bOJsrLjE7sIUdwEGe7eZ.PGL4IomiuU0L.b9yaGS75dcutwbAik5SUKbmKKDwFiQ7a7N9MDBgfO8m9SCfI7pJIyG2NdaQqyI8FdCug8cxmc1YAf64CbOlj41SFyJhMFQriiiwE3Ne6+rNInQXjMzFt28xrG6I1U1Q.HZ1bbmiobVfrCFLHimkJRNEu5t9FMdqLHvJtDi9cKezkQXGeK7RVSM1fSsZ0D.we1O6mcPPPPum0y5E2+n9GsuRoFTBF9b9At0AE2iW38.5s1d5.1URTJubvAWTMYbSsZlLFiQTuNCSzzpcgx6PfcizWgywnwfx5jyXLEDBwTXiIYtM1XiYC0gGJvlrjBZsNqNP6HrfevLLdXR+cmw0xrQV+pR4uCvVNvFZsd8SoTq8e4c8tVCXMivr9YBBV647bdNsN6YNaiSoT0fRQ21s8RqAzH7hgM8fFR4Ia3662.orA1DVr9oO8o2DXqZVAxdGs15XCAAX0ELXGn71DYWucNXSVfMKZ2bxVIeltgiGOXD8fk6AWH0EL54C8.WqqPXqdZpd00hDjk.rY0pVgDNJ5phZaitmu5dICNSwZ3HDtiRRRPPPVsVmMTGlAAYBCCc.xX.6rIFwfZPWee+NqrRPWf90qS7QO5QM.Fe++WLI+LW3BW.VjqJCr4xzLXcLkXiwLLQ6+FFAC2d6sMUS32ZqVW0GuXfgQI8WxkKWeLLnbI2XqCTYbzA5LfktQtdtB0oNE.nqnMIz.J122e.vfFMrz9pdc5887878jzWedX0usacyQ8W.x1.q6Up0FG.Rbya.LvwtbGiQq4FMVrutUw4K8k9RYhiiGKIBVMfRq04RSfkvHbHNV366aj99CUJUuUVIn6G8i+Q6DFROsVOv5TndPzWWa5c7mciecOJ98nj3DRh+ehnHlvX0MiI.2IntULrcgonLSIIsn3108N2Xw+wd6A3qSjlLpXicokUuLWF1Ae10iws0c5txEuXGRbZUYMVGTqA0aBzTq0M7fZR4yHRoTUCBBh.pYLlHeorJP0njhpJk9UkRY0+ceGemiRrxG7C9AqUsZs520ccWMaznQ6+p+5+psLFSuLYx3Hkxo+LelO8BRorbkJUj.dtQTBJeXn0zjRW3G48g8sFTJRSZz.qE0Wiseeuu22tFiYnwXxPMlvwwYZsVOqqq6rkSPsH12mdt4laxjyUV8mSmElKCr5AQ9y2Vzd7bBSfCrA2J1pK2OZb6bKIHBiwvq6085xrRXXVndNV5Rp8EWti+PVf9rmlNrAVd3lpT3rxJq3LyLy3XDhLRkLmRoJXLloAlKgWvK.kW3s7VdKK.QGVTVb3yctyMagBSLsTJmnb4x4jRoSlLYF88ILLTHkRgVG3.jQ44kCXh333I0Z8z.SIkxQ9csmT5Dn0BoTRxl4.fDKPdnToFQGmffftFnyuz+G+R6DAa9c889c0NHHX8xQQibviSdRUWrIh5aAbeuh.HylOzCk8bm6b4AxWpj023WzNQ3zX2D9gvZyVSCLETeJPNIvT0gIoBSb98uYqwd4NpZFbkQZR7XuevDEcoSZhU6O5Qc5t1dSVtKvtRobGfcpVs510Rf.LtjvI5S29W9W8WcsjDnz3TddMf++4t283srqp5786Xsecdepyi8q0ZWmxj5vqBBwTkoCH1FDtn2lV4J4izeBzsbArwGMh5MRLpejqWaaEaBgtQA61VaZ.oAe8I1pbSatwOnHn.ITU.kTHzURnN0d8ZuOuesO6Wqw8Oly09rOmppjpxCZpdVeVe1m5r2m0dMmq4ZLGyeiw32OZVsVskO4IqsLvJEgUohw4s669tuU777Z566Gelu3Yh9Tep+xHnbruuey29O1ae8i5czc9bO3msmkaalrToJy8C7C7CXMXVoRHLGzbJJyHrOyi+D0+ziYHM0AjC0q809Z0ie7imUDYbOOui35dSy8Q+nezhwPwie7iOuqq6rXLXNAvHV8a2Ab0icrik.QIL6SKv2F5dV8A0Wc.nrBrrI3roaJee0yB2Lm6JyHsBjTrH89BeguP2RPmfvfAR9rqqqJhHhiHepO0ekim2Iy355ly00Mu+Y7yeZe+rv.vK0eqeqeKa+zMwTLEg1rzFGKg0cXf7tVtc4b.JETjBv4FgEXTfQgEF87vnP8QAF866a+aerm+y+4ONvXULYC3HyBEnLC+r62HINrKJhKbfMMtbWnRGfNw1MFeGui6nOPxO9+xe7CY+3xtUFENK.57qrx.6JUpLHp9J.A9A75e8udrx7oBjHRRhm2Mknplbmum2iV+KFfqqqSP8fbAAAEBBBFB74ExdtuwN18Lc6.fvYR.iHEHIa1rIgPBQnMu3O+k57XecFmHPfvAO6466KUccw0PJhDD3KHXF0sk92BKrfEvf3N0pcxdXHAzt+M+I+Mcadv.1jBVxUp5kL3d6zSa9bejOxGAJa5uhHpZj5yN.6AQ6AydEp7QSKVoWOOkXbU0ofxGYxImbZEcBAFwVO5YTAw0y0LGCLjDuojD6.rmpZKU0cA11lIIapJa355tdLrV+d8WyyyasZt0Vyyya050qu7McpapQLD+g9Pu63O6i8YWFX0pG8EuZHrFDuJvZECBrY2J6TqlAviEfNLyfwzTB82xgXQcr8+NaX27Ry8e+gApZnrD8783DoOCWrecnOD2WUsa0UW0D0XX2YmkcA1oHrKknspZO6ZMWp4VG9mG19mEv2JYZBNPCILLLiMB+YAxphlAHyM45t+Z.BpmmWuf506REFnXPb.6P9cgx8.5uvBKjvJnGHeKtJatF9rPEQRJWl9hH8qB82c2cuZWq9.QiGXuy9O7OrGPmFMh6hPRgBif3HBHhh5DEEkobrgP++GN2WK6oNUsr.NU.ghGXSpCcecMEp8znG+MMsCutoy7yamanZVJRFYeV3cn6EmCt36KomiAAl3c8tdW4fx4hgbufWvKHaRRRZFkjw00KCBYbccSmS53Vy04TmplDDDfuueBkouQ0+Li+ttt14igpMakGtO7Tou6.jo1Pja8zSa.7eNXLJaxFNLk.9DV++GGhGCpLNvDwvDZnNQfk6ev.bxH1iBvbWNIt8p459v98m5OP2yC8nNcCOns+NG8nGscTTTanRq.XGveaVv.xqq6MrdHrJzbYfkOkojDWwyya0JFe9WqDrNUHUELaVy0M5O5d+iBVc0UCdyu4+E9m7j+u6+q7q7qDN+7yG8leSu4U7771TDYu50qq29s+5yu7xKOgmm2LO3C9fEighPzb3lVh9UuJnlBRIW78.5zue+Dy3Y4w777lz00cp333ibZe+YvvchS666Oo89PdnbNybtMLRC9019hbIaeyNfIvEOANAn+hFPNFNZH8DQRpToB.BKiyrWbssc4O+qZxxDr.l333rEvNAAgc.5exSdR8QezGSDUc9pe0uZ1xlGRm3G+G+GeFWW24TUKBQEKAyu1ZqMGwLyjSLwjyO+bix9nedQSbCCCkb4xi88x355l8y7Y9L4s7cxH6ryNoaHLiUBZkff.Ay+br0JpEY5vjRFRvsSXXXaf1G8nGsU85024+wW9+wNddd6DYIONvqSiFzqnAHpmMxtjz1APCexImL6INwIxCkGsQCFuHL4JvTDYPr7QdjGIE4xzr1YJHXZU0IgxoY0vnULx3W9G3Adfg3Oi3by8DLVaaGFv.3x2uOLvICugptbrAFXZCrWkJUZOS5+Ol8V.1EhSAfacf0ZXipETbkG9g8WsLrtemNaZYd6zO2pdddMeMeuul3a+1eCQPbzo77Z799.+FM888W6u9u5Sui04tLyM2bie629sOqmmWokV5AqTBJALCwL9BWdDlOP+67fRv.Clc.59q9q9qhHRdfI78OyL+B+B+By566O2OyO+O+r.yTBl9U7O4UMouu+n1nVHP.kSGmVkjiseFVb01Nv8EQDcNH0wIGfrEYPjTSWvLODjs7SNnD16mypMaR+xXJuuREK02wwIw00UCBBjO3G7CJppxsdqeWTO3gIz3XA.5e2e2eW+S540ixlLb6c9Nem1q2.Y.ccdVijtAm8voK5+qPzxDfLr3AJyt7P8BUM2KFikXhpvjvRSx9.gNwe1e1e1D+C+C+CSppNYTDS5BiuJjJumC6vySjs6mMVD9vOiOzQT+RPBTJQDQum64dnBH2+m49Oz0wS3VYTfjksQ2EpjDEYr6pfIqRrLcP5l1777PUQ88eX8jddIu9W+qOAPeOum2Cmrlq3dStoo3u0Q3kbpdsoyIGNpoBVm35zoSGQjVUEokp5.IFs7k+bM3bZd9asCWBmhk.cIHH.QfRkJa4rCFD8cOOu9ddd8CBBR78e39kf9PwdurW1KqGPuogdT6.Yo3Uy5mJbLciMPKAIuw23abfbrai7+vY3QWq5lcEvmFaPYyXWVMVGspHSoZzQ.l100cBLkpiI6FL8eUDRp551CkNhLPwD1VDYKQjMDQ1Tsp6fWsaZi5AAqegKbg0ylK6ZTg0.Vqd85qVqVskcccaVFZ7ldSuoUHl0bgMt+6+ilpBaaArUSSvZZAk2qdc5LGz4zMa1i0Nfr4N338+9e+8gU6erCpjeGlfiu3fdbVTC7kMGrVhHRR3f0umoypqRmJP2Fp1mFjJb.WpRD7R0DKWhLj8un7ks1uTUcbc2+4STS.7Niuu0maCY86662GGm9XsEXC90vfQzEh6NMzCl6opBXMvNZvf9TIhiQKB8Cg9Yyl8pUFWUpMrnIPqUVoYKAZoIZGT50tc69FaYJXyR6y36mGH+y+49byely3m+O+O+OO2u48duYo4AJe.ieImOMKMq+rXf79FRKseMTVXtP1kWlrkfbhHY0FpCC.I9xZKYfMxZPFld+4d+b+b+bEf3B21q40THIIoPiFMxEFDL.7.2pthkWcD.w2OPh2utF0xwkSvTNdCopJo.WE+TYN2v86A.lTevyKUFYiMXrxvDq.SQLGALGhHo9+a4fonoLf+N+zou27vDTlwrYhhMnAqjatKcY9N7X2Ux5iWpfldQkrOGa+LwqRkJsgn1kS8+eI1qLrKrxvJQ4ZQVkXEJs9W3KTeiJvVmod8cBNSvNVfoWGX4efa6GH5ttq6x++x+kO1EfFW3jddW39u+622VVOq366uINNsDQ5aABaT2ZtSeWui2wb.yS.yXHU5vgKMmmn8AbvrpA5bm24cpurW12U1ktvWXjfffwUUmvyyaJU0oKCSqpN0uyuyuy3Lv+63Ldoq0tBYK+M9fe8rd6ZoNvgQ32RxqLNvTEgIZZdvoO6KSvaCGaO37o096k5A9zajomqRPkqChdAc5z4E7I9Dehq+1tsaqjue8w.Qwrf+JddmbEHdUU0MCBB16Tddc9teiuQ4i7Q9HinpdDQjJc61sZ1rYKiwPPJvIWxINgggTsZUMHvWcc8z.+.00ykjjDVe80k81qkX8yAUG7SIfzCz1friH5FpJq644sNlzmcCfl2jqaXLUBgnkA1HNNdqxkKuKLytvZCqNNOSBXxfEINAj4rTLOzzD044YDVlbPwrPygR+Z6hGCD8nAHetOK8Sk1lnNUoGF4OyHUdfZUWl8Az3JJpbOk5So+7.dZnFH022Q+z2WfYyr.qlYIplABMKfUgrDQFX9LX3efznqayThxY+6+h+E4tgu0aHOPNee+Q877Fud85SUq1olx2+Li64cpB99mNqmmmfwI+VhHqeu268Fda21sUGvGnITdSLxj3kxg6z4hYvXzaF.WU0quc61KlKWtEbbblOIIoPlLY5YLnKq3441LHHngqq6JXV.XyJvVQlmM1W9KgdvI5AmMcgFtBuOb.P1X+noT.pLADMCTdFHdBUUc80Wemm2LyrRCSMtuo8Z3IJ80yfA88YfvuEfWnp5MB7BBBB7.l.EAg1frKna544stuu+JhHwtttwlr94LqZ4GncBCC21JSy6Lz2OPYw2+Lpm2snVRf8pcyUeyTan6KKlwRr1YAxTAxDYuG84+7e972xsbK4UUcDojThlIMnDPCGU0LRUIig9Np1GB6.zVUcOQD6bmpcfvgGqtb2GGd76o6X4kvo1z4bLNVBWFpTFhlFJIpFulHRHfeEnYjAr8Nbv0ZFdtrYMFWlk.V.Jch98CeQQQQm.gihxzXTElDU01ttt6FDDLLHpKCkWAhWIHHX0jjjUqUq1x.qVw3LVJYsM70v0BywFNZ84A2BPPZTDGAi8wDybjpaCQaCUaYm6bo3JH1+bUYbWhlMfRK.MNgp5K5y849bm3k7RdIKDDDbjRkJUnQiFXxvBZA5FfzvyyKDCCjGADUud83Z0p0DleEX4MgYZAqkZW8pYsyg5qEyCMGYHfeKfYy1p8Yggsmb47gQ.bVDxdNiOFyfod1eNsa29EjMa1mOv2RTTTQfIQo.BNl5MTra3U634Uqikf36.zRTYu+k+vuo89s+s+Ps77baUud8scbb1x08l1zDDfxs78OSm81autG+3GeO66uw2+2+2+5O3C9foDmZWnTWWZzKfY5MfX4WD0D37iI1T2FNfMwEwpLGou2vaX4Iy14vOukEJlcZZlcCby.1RcaN5a31T2714YoQ1NmArpxaYI++MwDs0Th1c3..jtlTpBxMgHxrXP0+4pp9B.dd16ESEDDly0spF3GzCgV.a.xxfF344sTPPP8jjD+Z0pECkVEZr09igCmAMUSvv4.WMqiLzyVLNEYxse7smXhINdZo61yZ+0xECk2AhG9YqK04O0d1D.kKAKdra9lugG7AevaXiM1Xwomd5xAAAiYFiTAjD6yX68fOzCt4Mey2biZdmpND+3.OJvEvTpxaBy2FVdXe3ROtVxlVZ6vfAmkYIGqxHP4wg3oUUmVDYby5kxtv7qAKuBlMYmN+KYnySVnXNqsiwEQlrBLaDbTfmip5KHLLbwpUq5FDDLo.4TDEztnrSkpU1LJJZEfkUUiEQh777hBBBBM91TYEHZSfceUupWUmG3AdfgEDhqlw+KxeAU0bREIOwFESRDYTfw9TepO0Hememem4EQxnGzO9DFxWv8ATtZaHrM1.DWSjtOTPPaKg0dXNY7vWyOc7a3h.ABi++T+ff2Z+LKJU4bYBY1rVB4N+W9Q+x4dQG+EkElOq0++gyXdGCILGOvm2G5gdnwu4a9lGy22eLOuSNZ85mdzZ0N4Hat44JL0TSkEfxlRiqsp5Fqu95MmYlYB.Bf4ZTjU1zlUdC6G9k59TNfwfhyAMOJv0qpdc.UEQlbokVx4nG8ns788W2yyaUQjk888Wd0UWck+2tgaX8XCn3sdaus2VmOvG3CXyPvZcg5WpmcuV5Y3AsqExvjz1kJi.RfZ8.5XmPzUUM43G+3BT15L+SZsTkdN6OKokkSzVThMxkK2F2xsbKaWOHXODomqo9Ow000od8GNquuetffvQ777FOT0i7g+ve34TUKJhT7O4O9Oc1rYyNE66vWZVfjRbqX++.P0pUIHH.WWOoQiFxLyNiSRRhCfSq81yw00yjaiBhiSZeQDPb.ICPFUcbvPVf8++4+6eoNtttVcDmcMo1KcBCC6Vtb49.8mi0NL+V7LEnBGXCuFYhr49QidYiCFUo4j.SnpN1Mdi2RgRlLZHmHRAU0QO9wO9XppiqpNAkLN0Ttbzjsa2dBHxVpRtVF+FGQjLTMMZhUuTHL+zscoPdtOokKFzkSjZT5X1wzU6uDzCB6Bz4u99tuNDYhj2br7NX.2qETpSUn8OzOzabuvvu312v2yMXHQVH1y6lhpWudTsZ0he8u9WVCOOukUMbcfsVe80aUud8dgggYTUG609ZesSCLMTzlpbwCykOGFvmg5WkSl2xN3hHsF4XizwwwQAx433Llp5juq+M+al52727e+z.SYx3Glv22ebfQiRSQxxooGYJYuc1mpLl8PH6unRQ.voXwnb.4+a9at27ofrMyLyjsQJnSybEkUY.ntDN7BboKjHCQ5lIf1Gj999987775opZSU93Vddds7886.zuZ0p.3XKEvrvr1HcDKFPspe3910xMwRXy4TUK.LVjATgIoDSdK2xsLMT8HlMPzb1XUmCZLKvLhHyPDynpNaIBmCXNyuuxzVY1bDHrfk7JubjBqygdEd5+78AWHu1.f0LNmVwjEapFl+tum6tfpwEhiiya4XiLQOwqubP6EAF.pKSi9NNN5DSLghZ9ysxXOHHoxAIF11ne85068deu2YWfdttt8qUqV+vvvDfjnzyesKZMxuYuM395wFbuNvwP4HXKMkx6RE1UDo0bykRP4gOQ7ExPyUhDeUcfFN.RRRhrvBKLXrsQiFlwIkjkVZo9fzGP8C7cBBBx566mEpXk7Uv0cY64cM67uS7TYMFEPqRyDRkIcp10Z+suHReaIJXu+s3SD3..HmKs+VJcdao7+g+g+gi.LZTTz9YukQSbD4fKEn9998.Z644sCvlpit18ce++sJnq366uh3Hqljjr1oO8mvBjP7VdddaM5nitQPPvZ0pUaMWW20dvkdvM.1FJZqy+FcBFnZX10FOmwt9IL77TpR0kbhA1hOmxhCu95ItTkN6UXqo7o+696Dq56YF6WwbNpY.PwBBRQmphXIr0XG7dROw166SKujWxKI02lb1RKLmue3gJwP0IHHvg8G30S54ljJW8tteqbpZ0ra.qQVJNbVQsfvLluupF4d5oWzZahL93i6nZz.6mhToe4A2KhuR2XSBLcOfNMf8dnG5gZgQcQ5F3GzCHwjMWBKszRNnF6o+it4+QYPISf+YxDDD3366mAplYdHy8du2aFXYGJOb1lbYm+esRan0RHKqZVaSUihJYhNnQoh1Zq0DXYwZ+6vmigBhTyr.4DoXdfQrq+NNkYbrbrFFtQQro3i0VC8ihhR.3TddRsZ0bdqu02piuuuyIMkXLPjRQybgG3AdfmpY0TZy92bN.LpmTrAnwxFdVZLfwdzG8QG8K+k+xi.TPDo.Unfp5HfMKRpvX.iWohLoIaSBsYgRwQEQx6axNeGlNcLJ8YoKoh57zwugCuddOft0u3reyB3y45aT1rUGTtguni+h1CnkmQzMr9+Oe6xvdggWnUPvCuEUYMr9+ey272WfpZfmmWT4xwMpUq1J99O7Zat4lq666uwpqt5NmtdPGa+Z7ibjibDnjsr4WY7lGLSyur82SLnO0rOUMfUkjjHAAA4d7G+wGcgEVXzf.+wpUq1XVftFwy6jEtga3FJDa4SMpvnefOvGXDKoyl2nVlWxRR+Zoj0XP6ZI.SRaCaHOwF01d159UEQx7nO5iVnJN1MtMStEOnC2WtyotpYB+dyCaSCVONNd8RkJsYkRk1MiS116t6t8sorOmplK.hqaUGfbhTYLfoDQl8m+m+me11c1aZFrAf84SkpUqR2tcurWDIIITpTIYzQGUbbbHJJxj9rggBhP0pt.xPd7XnaAiCcpw0On2O7O7ascPP8cUU2ojkjhBBB5TsZ0AoZ2JUeFm6RNL5qNvhNEGptEIMk8gQCKwH1n5H+H+H+e1243GuyoO8o6np14ttq6p6YNyY5+XO1io.x64m4eWFU0BwwkGqPgBST0XHs.DXS2LizZpAF4hrLICuAqCe7LQ6RmtdF46iZz298TlhCYj867U+pGjwLq3ZRkUf8pW+LsOSXX6+y+m+H68sVs5tDyVXxdiUJQil0pUqYQH9i+w+aipWudbPPPCOOuUNxQNxF0pUampUq10VxVinpNdwAy8pjq1ESllWBiVw8W1VKxUgcI1Dcqff.BCBxGDDL5ez+g+Ci8i8u5mbLee+w.Fqd85i544YIWvxlxmJlI.F8y7Y9CroBXwLm3peb+.a56DbNklHEgLMaVMKPNe+ymUDIiMhDY.iTcyZW4jFZf2AtGpIIIokjyfOimmGsZsKJHkA77Nk5662qBz8U7JdEcr08aOKQc5vxlEoWfIrKRLmT8hAZa3930ZMAPJZiVlHhU5WKapA4FoQqMXxa+1esSAkltrHSqpllxsSaec1FkYNU04t0a86XVH9HVxyd74gQn4AJOmKBD1S7rGuvHm.DpahdjpZVWZlkHaslCYeG2w6HKPlLYxjAR2zQomr43CCvZef9wULy61YmsTW2pnXJShAeZa6jdtnIhdpZ0z63Nd2Z850S788S.TKPcJ3ZlaU+ZFfRtn14suppBgnVRmqy7D2gHZ6BcWYkx8.RV7J1A9RRk8KaUGGGmA97nG7dR2EVXgthH8DD0y0KipZdOOuBe1O6ebdOOurUgrAAUx.jsxfrB7pFP3A1bBWbX9xIr+x10PpB5u7Owu7f+fEMa13x0ry6pZ5iML1eJij8k+xe44ihhxWpTobXH9QG1m6yD8h.Mm9999sEjc8b81NHHXSOOuMNom2ZdtdqcpZ0V6Tm56c8G+we7M9c9O8eZSfsbcc2z00cCn7F.aPjIKmpQAaVFNa+h66iwP9E.m8.o494RNq85ZQP3bCxfrD3rWMkJxgFqKmbqu3WbZoVc.fWpuf40G4QdDEZJglH7aHUX+K6ZVGx98F749beNDopYCZwjSUMmm29bGvPkBQp+YI.8Oiueu50SUXoFDCR850cpBNW3LWXvF9bYIGVCoYylDFdw6j9Jab3fGhH7W+W+WODnDwZrcro1U1ZT1y0F1MMV1FQY53662CA000MsLVkEV3nlw.a+WDoWhH81d6ssRIbntLH21scaNtfyPDMp7jL++alaCulkQzCF5PbkAyQTMBQDchINhAqjvKxm0A9SC33N..lkyaJKECOenQ5D.iJH4s9ChqtHWD3...H.jDQAQkqaeP6VoRkNpZJyud85kbZeew22249tu6KimmWluP85N.NUAn4AxVhmlfkbf+1AkwViJCD2Am2xa4sjbu+I+Ic+ReouT2O3G7C18c9Vem81d6s6ea21sk79e+ueUCU9jexOYl+n+nOcAQjwKYJa+ofliCT.JY3quMLi6koqcLulywtXk07oZP7Nbe6h8++hGyRNzwfrk220vAh.s9I+I+mu6CszRspV8nsNoq6tDx11dyJUHpoHRiJPbbbkF0qWuop5x0pUaEOOu0lc1Y2zyq5d0qWOwVF8SpZr0Wq4mXt82C5ST+UNqctVYPIzb8e9yedw00MatbEJXJ+duQqWu9nAA0s7mVr0+upFvuhLbWWQxXAouVthWZEQ7Zx10h.lLbSAz4m2fZPESz9FUUc7vpgiCLJrVgsFBvhmfyUBFGX5rrkwiqT4FWuc61aDGEsS4xkZu95q2WDIAPOiueeOOu9hHI999hu+YxGDDLV850m3s8u5sM4q6e1qaB1uLbN.fMVmb4v+rmqKVo7h25a8GERi1sp3Z+bgggbjibjzS0f9ipIpXJYnd999cRbnUBN6544YQvrZaWW2NkgtTxhDZ3SHq9+TsYLtehTCSmK2nPNKhiFTiqX2LTCjs2du9Tpzdus21aa23cezsN0oN0FhHa7te2u6Mmd5m2VG+kc7sEQ18c7N9+piHht0VmKKPgPXDqwgrF4HaYJNjd1GWNN8ZY3HQ+LM5lWjiHoG0qUmeweweQfXLHYMmBjLMjvzVGkCnKTqKEK1oVsZsq9hq1FXuXqDHBytMywlMLFNWsIrbPvYZVqVsXOOuF.M788a566ulHx1mz6jcrWW4aVtoEg2nbsuz7mwgGST60U6PCp26DEEsmqqa+t85I.4Nsu+H077Fsa61i344MRsZ0FAXLee+I88OyQVZoklAKYv9c7Z+Nr0VZyrad4Ig2mrwV.KcYBRSHCDlQU04e1+r2vveVGHNSQJd0XPVwGEJ0mxk62oSm9NNNIAAApMJ+BHNTlLKt3h47bcyeFe+BAAmIummW1HP9jexOI.ZwhF4Icdl24S+o+zN.YVxcI60wJjAzghR50psCL2o49QLy5vV7XppiFFFlFM6b+d+d+w4TMN+G9+9+8BhTwJQr5nKu7xiJhLtFoSALym5q70lCXlu5W8qNMvDKOHBYKjixWTcv5.j4r663yy3OWe1AeOUcDOwIv5fkkTNGjUY+w+w+wF9znLIPCsF0tbNUNvww4fDpXcjJJ0dgC.pmqq9w9XeLU0jDPrGjDCIe7O9+0jGNHv78TqFVd3P.nTI.BXZl9Yht++yqcL.yl4rappRhTVRr.IXH8YiDZllQEWt1PN32.ATnh.HIIIXIqYJWpjtzRWPwjUG8vjgpcSzjDeeeAHquue9W5K8klGHWHjEhxszRKUHZHxl9XG6ojC3pISKp0momNk+RT.d781i2467cBLOLCWtsKNXN+hKhCDloDkRAXLWbo3bddd4xlMWtlMZjUQLWehIKUY.ZIx9afPL76hqmahuueOW2u0NAAA6cFe+c9O9e7+3lwv5u226cs90ccW2FeWuxW4lXJYksJWlsf3sbw0xOIzo9r060rYy9vpIYMkRB.RMP3XGZcmT6hFhxV1m3hWb36kWo.MenMqD2OKzGlqmHR+xlrvxrolkPgY0W3q3UL37ppJMGXSYZgEeRCzVxbygBQX.tmrVBYOWPPPJuygAeJTQrDrqEnLQj9dm5TJf366K0pUSBAN5QOp.F+ASguuXwhpgDNeJuIVsFzmxF6W25sdqcsfRZ1rmmYbo9SxI4P8esREy3bEpzuQiFIdddJ.19ti.NVb55u7xM6fvdtttsp45t6y849czp1IOooDVsYU0CYd1yAbcXtgmObM4FsL.vSYGLkdddpZK85PxBkcnbYrkgYhkmcr2amNMSGGJaINWFfLAGrrQyCQinFoDeTfBJZ1LYy5nppAAA8muXoNNNNsEGYOuS50IWlbIX1eQVJWNMytyAjMbv3cQ3DGXN1Si.OtHX1LtiM6Qy+tui2cNQp5PkJ8AZ8K999E15Fuwabi2xa4sr4u7u7u7VSN4haeuel6c629a+suiHRqWwq3UrWkJU52qWuLu2O5Gcj64dtmI.lrTIFCZjaNlafc334hcTUEntSmgx3jZPFN1ULOE8j0NPf8tDGGt+qESKIPnKAzAVnMkJ05889desV3TKXDOBRkF8h6PI1JZe9OY0G5g9yVtVsZMLktGwAAAM+DehOwJhHadpZmpspJg9g4azngUV4WdrrPAX5K2df2eLnlYbIdfeUkkq+kb8NMa1LqmW0B.iIhLtiHi65VabWW2wCBBlnd85iCgS366OtMXpi9kB9RV5End1N1.mBj4DWimkIWKCXxfIq1JbISDQ4UUGUDYhM+palRPnEhgrVjYgKeTCrNvLWWWyD1MoR75iO93q6U6TaikWB7C70u3W5ue3GJDfLddd4bccGoVsZi93e8GOkHhRSEJG.Is7aRec3VXf42klR1+5+5+61+ME4.+Mqs95G7hWUEURTLrbummWqZmzc2ZttaCrSCn0ce22QaRU5gFt8lCRFRTGdlXB7A2L9l6in9RPAHaALfbjinxNe9O+mO4M7FeCsmbxQ2gFMLZXdHqAkVsngKJZR0nFDwxPoz54b2u9W+q2SUUdrG6wRyZkLAAlu6GaqGaezcSN7BMm3YBTkubM8P+rRFzeoeoeIEPiAExl.jrtp8Yi4Lo+ZY5C06Qyllz0qIsgp6opZTfG2U2iUXOnxtX3mfMbuI2UfxMAhWc0Ua3440zyv71qGoQa+9deuuNhHIev20GLEI+rlM2t3vkwf43DG.0cXCR.2dVhqp0XiMVqtc61Natb8wt35Ox+pejBc50KknUGEXbOOuIUUmdgEVXZrYI.YHkroxT+osyNKtuQcSp5p1nE1GP+ZesulcS7MuRWDTqBJySBznOwwcu628c2Yqs1pqZ13DpIisxt041Juuu+HVUqxdTZXVZuPSa1PrLKm4e7+3+wlwyDbd7G+wELNfpvYO7hqWK1FNZ1obtyHhHir4laNhHRgpUqlAfO1u+ueu81auNhHcTU69a7a7Kz+28282MIEvYUU4F91tg7O94d7wnQioTUm4487ddGApNYIiJYMJkVJuI8cqs+ytKh.0DiyPOq.B59B4X0PTeEnR5l55op1VjJs.Zce228aroZkgz57DtUCEPWAfnpZU.KvI53iON.r4laxq+M7FzeuO1GOQERvjF0FhgLMB4kIodPPhMKS56662+m8m88l.jrAab34VWKrIi8WK87j.kSVHMKbbi6SCRL7XsYyrTDGX5mn64C2+SKWojtcuPBf94+7edBBBUAnQiFbritf555lnIZhmmWOOuS1yR3qootYFnTt50qaHVvxTXgEt4CHI1m+7OkcBTg5JargB1zUmxYN1HiXluO2xhQc1W7x82K.x4LYigDqwNjFAawb80qW2bJjwQjgtFkAnk.pfhHpjAkL99AYd0upWUNUjrTtQFWWW77NUuezezez8.19N92dGaBk133G+3afgmz15ge3fsA1Iff8lcVqh9TfdEKVrOfcCfYT.9799BmulbhK0XwYAnlrzRKY9Mm3bGFjjqF6lCxjilPWXhtfUgEquXxwfDlkDX0DhiGvUGh3pym9byBajloKWzX9v++UVw3CnrubemGHmqq6vDtdBPWE5fJsA1y6jdlwpnnjf5ovhTJsZLT7BzJUpnvB6GA6EuHhY8JcLwDHGHg3Y65BcdrG6w5HhzVDompZB9.TSpdv4xOYym0HKvuQDkTpTojvPeEi+BN.Yp55JFPKo27EK1RP1JLHXCee+0gFa4+vO7t.soIcgRI2rEvEpD3vJ3XAaX38p7M61zNPy..e2TvzxRXwbUfbTjLpFIZTTJnscUU6VJk6XldCak7drz9uU8Ql1lUJl0.skKXNL7e0.I1sboRztc6DWW29K2rY2fffNtttc8Oie+aplqJhXJ6vybl7.E777xe9ye9rkR8Ib1lNb1Ai0WJeqdxtOLTPN1w.jKjmlUxWFx+S8S8SkU0Pknn8.1hlrF3tx7vx.KSw3UnAq.UsBk.a7bdYOmsyl0syq6085xbG2wcT.Xr3XcLfQVgU1WvGR1WPLBAASFeKOTXnCmupyh66K7S21UB.lJjWAz+tvPkUJmTa.oguTGZzvP11MYGX9chhhLA4d5l6QC1CVXf++27q9lWEJsLFoINx00M568686sIvJO34evMRRR1spW0dkJUx4O3O3OHOl8+lGFaX.StT1zxPcxBkxU0N+Q0HQiUFczQcrJgog7WgIeiuw23TXH90iTqVsY.Nhmm2TdddiCTne+9CtWrAH+a+29ND.4rG767Zt10p.lb3EKRQvMGlrXXzfffQKmJ2QtjEN2UxlkSpwJ8BLaXbGhJs0+G+S+mtkpQ6FDDzclYlQEEmW8+jumLdddCWlIE9t9te0iHhLxe4m7Sl+k9ReoCW+8C9NqVspZe07kkre.mq5VkvvvAe1QGcT7CBHHHfzzlH8uyriwD0ldiFGCDoqnRmS54s6i+3O9V+5+7+5a366uouu+1TgV24cdmFhia946CNFG2OOBK9LJ586aHsNNOvC7.l6ItjCZjWUMmMs14k7RdI89XejOVKpvNPks.1zE1nFMVuooTTVkvxqBrBzvH8Vv1uxa3F1awEWr+0ccWmvAR0q4jImbxTmMDZhSwTvRpRF3rOakkIosCZ377jbhTG+WzHqgjVe5jueLjPLIlnpXbHZdn27D1UDwPbUAyZI8rntUMyI2sZLaBwqArxLyLyxlwGVUUccQjs9I+I+Iac++E2eu2xa4sPozwm8m+aGCrRc6YIC0HS5Bxy.hGAZrk6.ld5myd4xkqsa0p8Dffffbu+226uvXiM1nX.OXBee+wusa61F+1+W7lGKndvn999ibgvvBDQtvvPiCike5NVeNlw9SUhMoyqMhv8UUSdtO2mKPU465a6aWpbENWNDTVl9TjNP48tyel6r0jSN4dhp850qm5.Y.I+DSLwHUpTYTL.jLNpNEzHsrRlNH3BSBL58du2aALp2iYLNBN40ccl4BK9zJsV+e1sgcRxldqgoaLvT2pvHSM0h4AbJA5K8U8c04e9se66drQFYKfsd0uoW81u829aemevevevc9e7XO1tyO+KpkHR6u7o+x8ttEuNm67NuqQ.FuDLUEBmpQJnaMJaAdtd1Z1EeO14Pf5olDOLPkOizNW54Kz0lsCQZYH4dtm6oeYQ5Bw6Ihr2XEJjRFm8GTRLO4MEDMDTCw2B6ryN5latoN0TSoBn29a3Mn0L7kUeS5TK89m+FdCFdOIFslqq53n8Mav2q2cbG2QWvr4zSbf0Dup13y+yto.ZMhSVJ8YkfzL8pQZIf4LeSDXiqj9ldrz0Fmmjb4xk.jbi23MpttUsR2ApJlr5PETeeeBBdXUDoe850GTlu+Me1+DmSVqVVf7UiIODmsd85YuvEtvS+0Nqg.Kjll8YKRb1Gc6sMqosxr1y6ksjDFZ91LhHRFqytiXUcpQvtwcUUA8R7.i.hiHpnFmlQy967g+vYEUyU+gCxDDDP8fyzOHHnMvtkiYanwl.aFFFtoqq61ttt6Bk1CnyXqtpYMqPqRFZV+KwpzIpI6Cxnm8xDU1iQccgEVv7+O6INTe7JtcnrLwJ0vC3Xfy0+7fxpnThDX9A.RnZfZYXNgkpMb.FNbz0cF5HKTMGPgxPguze+ee9P+vrg9gYLtnQeP6JpzQg8PLxZr+Y7a4cRu19998bq4lbSddZIZngoW29yYutVpe0zwxy8zhb9s+cY5G.ct9q+5aSEZCU5Jhj3ATk5NgGrDduBOuUGbMUpTEDUF7bQPXHesu1WKAnSXTTKW2aZaWOusTU2lxrim2o1CniKzqBM5+4VZICfPQU0UWcUf3THCuVvN1E2VDL62GLbjiZHH8ljUJIYDoBX7ko2O1a8s1qQ57zMlwFP1yKkAGUUmYWYf8uz0.ErfmHC3fGbDUjvf.xWHO.IttC3KGEH4L99pqqaZ.ecpWutyRe8udla4XGKSizRgc0oG.TyINXorb0D3Q6dLF3yvHfLZLjOWtbN29O3s2GJtGv1yAaTlf0V13++Zzz07Jgq4YF.WoZCVGZrcgBE5X624MDlc4Q.JLaJWMsFYlN85rHBDltGJARNbVK8r47J6yomE.sZ0pIPtj5PB0oOmftvwZCzZVX2oY4VUpTwP91aT1ReBK0YVC4+taolrEzXMfkEQZhgmSZBkW9XG6XqDEEsFv1qrxJs+N91+NTfrFdUJb3xaFtX6XYcg7UoQgPiec4DoriHhyDSLQZ.xFoZ0piWtb4I90909UmBCHIy366OKT4H.S566OBP1idziJkR6+SidW20ccXaVWK5K70r.lLbSmGTHlVsZwW7K9EcDQx77e9O+7wUqZP6OfbkexePeeD3gdLOsgF69mde2Wqs2d68Pn2OwOwOEpQBGyuyN6Lhuue5FoF6Sd+++Nhuuetb4ymomoDFRWrd3izuG.HsbpGl3WsflH.ha0plXbwEUFOpMJQFjogdPR2DmjVmtd8cttq6515m3m3mXKuSdxc777ZQD6UE5Tud8tjY49P8zqEGN2yZjwi7pdyuYSJCFP9+QuvWXAQjBREImkY1UftFBPMpEyRq.nUca4fPM1wJIuaCrUixrMTd6FPqG8Q2sGfXIhw7yC4gUxxbjMbHsou4zSmEHiFnNU+FWpfMvIsA0n84FhDnVj9Co.HIlZEcInB5xPZ5m2i4nmUNI6Az6QV8Q6BKrWHrCTbKfMDWYcf0KAF4eT0sEQ1869U9c2EPZTspYQp.1Wt0NANvYE.4O8O8OUnNBbdGJRl0fr9oFUqfBM5Az4rm8r8TA0y6lxznQiBfyX0qWe75A0G2yyareieieiB+9+W+P4cOoaNOOubGsZ0b.Yqdi2nYLOlm1s0lEEluenp8LpnBVm8RcBNT+KC9a4If7MG99y9NR2jNP7tEVnvtIIIsbcca2nQi9FV3TyFGGmMJJJmHZNPxu9FaLZPPvT999yPElw08nGoLL4sca21n9994ZNTF6r1LyX99N2UTTH9lw1vOu3.Kl47PFKg9ZKEGF4QdjGIOzHipZxOzO2OW6O6C7WtCvlwyO+F.qQCV2B34FOmW50uokA92Fyy5ce2u6eM1c2cy2.FOzPlvSZH443TURoP84HKTMy4MkJi45xjgbOSMddv4MEggHJRhKg7S+Se2z.z2y648zGn6G7C+A6AjXV+IfgxXuKe6Pm2z1TSMkr4laZs8qhs1+ILLLwyyMw8jd8+XereujXn+ce22SeW2i1kRF4LuLzAZ1kpzOs703fND8MB6dOSz1ub.lGGHvxOQjoYQDQphYyrSO7yQWpmoT.87KtnBzmkMxGNP+wFarAJujkD20ff.UPcTTGUww22WqcpZ87775np14aYgE52nDxe6e6ealPHCLuSsZ03nujiBXyVsq9l4dScx.KkkRTH.FoILx3iOdAiStq9D4e1f9uYZ2ZBUwIzFzn+h+h+hQCBBRKOtLhojavy0EUUMSlLHfhhpppgQ9pZzeOERLYagpIpp8p45108jtsCBBZEC6.U1FXqpeqU2FXaCGo0nMUoa8g3nmycvM4aNNA8gyeXEdYvw42mK.5a4tjmp1MG1N+gI298uGz.GXYGJYhHsMpzYnHYsDVnyEernwGiYG1+ivBppEhgQdwunWTgpdUypNH1xjtu.cUQ2yQjVdVx32yya2fGNXOQj10Cp24L0q2uQZVQEEovJC5CgG6.W+WjujWwsYQglIppFtTHhtPTeJB9PlvzMKerq7ME6AJDpTxbsEGGqpn5N6ri8pT0m2y841200sa0JUZGD7v6AzpVsZsIlt99mtGUII.zHH4Tm5T8ohgXmmc1YU.8bm3Zx0OA65+Vd7IQpIIpZj5ZUUgl.DqTlDQj9ex+5Oq44j4QM3F..NwygiHy5rpY82z4nV9JhbhH4oB452ue1Nc53nhJFslTjff.wVdTCmk0I9998RypEGG5uv2x2hFkBLRYxBaL.fjyViL1L87JsLX2e8myQlpF6PippNNDNFFxc042+i96m.M6vrr2JPKi.UvtTicg.ydAfs8mgsfRaFBaXT9QRy5+Dyd6hyQIJr5PY92FF+eyXCdp45rH.wGd8wmsZCuFkwl1In+PpkXeNqEL2SP2Ug1ajpxjGitP7.Rj8U95dccgx60.1ElYKfMnjQ87p.q.wqbgKbgk877VQDY84me9VteamLACuJk2K845SbIAJJCyP1.H+K+0+5K.j+e+6+eeNnQF.mVsZ4HhlASYFNRbb73yLybS7E+Rewo7q6Osn5T0q+ElHHHXja8Vu0TtRQaT1HU0rAI0tXNdY3wmqYZWKCXxfA8kgDpVs2niNZ2a5ltot1HCwe0G+imsjcyyc1OiOfm3EBLSrWdfdTu2DSLQaAo2uz+5eQroc4nW+0e8SJhLMvzYylchfvvQcccyM93iS974M0n1AkEs9.I9AAWjQ+JUpL3mM.inzOIY+LPQMfpDDDnppZbbrljjjfgQ+6uW61cEwosnRKbb1Aiwjcpe5SuaUS4E0tOzoVsZcIde4Y69u+6GHszgeRGWdxZG1IVndcwRNU4V5QdjbppoxIVtie7imw9Y6u.zkUOf1u2k5jRhXsAZOaL6w7w6Y9+g8AbHpTAfwWFFmpUGiUX..V.iwFaLhqYwDmjm8QRd+98k1QsCCdx.mAmFT+S6qAAAojBUeV4fZ99KX1i2ihKYmS1zvwIgdokb0NpQVgaAzwjEK37kefGHeQ6FNGOMaSN69FLeMulWi.3LmYQEqT0YPWlHbrWe8edOmmWeAQ88eXGAxCIi444MdM2SN9u8u8u8npp48N0oxPCxTud88IXqFMb1el8SqwTs3pjPkk6YJwi.SIKEUoEFU8oKkJ0mfqJCvJlw51kfVDyNc61cGe+v8bcc6YydKRRRDQE8lbqoHpzoSmLekuxWo.vX9m1eRfIOc85iSkJi544YF6RcLYs0jp6+ccM2hC1l.jYQHCkOmELxfB.iJREiLgC4oREm0We8j2065cYUZL1gkWdq4MRM2VwpZ.+L1XaByb1cEQZWVjjwFarrP4QDQlPUcBMVG+89deuC.Lo5JTfoCsDAanYN74O1guNG90mp8UyQyCB5PwFjQ0PGLQ5S.jO4m7SA.KCBtt6yZoOQslW5u2vvP1d6swfOtH5AJgBzxwvcd2+z366ycdm+zIggg8nQkN.sigNLyL8rbRUZTCOrBA7MyfkbvMwBYXYiTyaqqdmhMAUCsajdig2H8kuctyoybHawl0MM+r0daeSjVEtvRWHc7Rqe55cBBB10yyaWQj8pel5891+9+1S.zO6m8Oyr9teot.8Bu5bB7fNpB47fBzfQfRiopNlHxHDefx48Ircd.VXAgPRyx1Qt0a8VG000c.fIX4sjT+O52u+f0VDjtUq30wQb1yyyskpxNppaArop5l0qWeqJQr8oO8osxbbj403J6.raCnEUq1gv88y4xbX2fvkD7B8.etK8m4pscIAkw9doQMMWIHOMHOTLU40xZVOb+mgNw9kvZN3blrqa0xi.LxxlrZtfXT2u7hHYCBBx3Vsp.jfRO03WSGqJq0QDoS850635519lbc63fSuZ091RARhJUpbfM0br8ssb0tYiCt4HahNX4IH.nhY8+bopNGPVN+EkYMW1wXePoTo9z3.JDRxFarQhqqqhRBhzOHHnGVE9JkbqCBBjuMOOGB2WYCa1rIDc.BxNkL8uVciVIERCDlOCxN1TdKoBnZjpPU8q80dDSeaegzzAvYgUHKUWyd+IvL+qYoQAlHFljJUlPC0wylM6H4ymOGP1wFcTC.fpgWcN+4OuoT6r.l3440d4kWt0IqUaWvoEToKF6hBwGX8CoZcjkV5uM8Z5JcsVwJ3FY6XALQLxm7X.E9c+3ebGf9EM9+2YXhCdH++6.z4XqQalsQKfsg3s.15BW3BaKhzJR0dpphFqYY+rdsfFDjur0erj8WWe39vy1YcdZaeaam8h7+O82mtOQy9eN+AUbmG4O7OrOKFaIL10LhEQi41EX6HXKJWdyidziZyHGC8Engg82d6sc.x0Kc+um8.8aGL.tkg0HC3k425252JKPtepe7epzr2OyniNpCpjQDIqXyr3elelelQ+Vuwu6wDGYBUjw+pe0u5XtttE9q9z+UY.HJJRINtuEnj9iruc8qk8E9ZZ.Sf8mv0ivv1ppoxzzdppIu7W9KOSCHOEI+Z6S9pWIQsIciqc.16C+g+v6op1AkDU0rtttidlyblIcccmNJJZpd85Mga0pEDQbtga3FRjJx.vVrGVFim9dlzfa3uqADUJ.sa2FPnQbLMhaX+.fkP3Tfj9I86mwISOUR5AzoPgBsUUaAraMW2cojQ4UpUqV6G7BWnKUnaiCBfSefjumumenDfzzi8.WSOMZC6XhD.N+l+l+lYiL0W4.iY+29u8eKURNkk1+u6hcvxdrJziksf8TDopTMupwiqpNEvzDFNMlZpaZJRJWZLZ.jmEHS7231vvkJ5mWtHco.5FlEu56551alAxQ1Ab9neLzyTiuzkZzYNlqM32FqbZKhz4we7GuCFdV.rJXRSXDlmBKcH9zwb4UT.jUL+tbUu9Iyqpl2n9PjyFcWIa9rTsZUmjjjrJTvwwYjJRkwfFi9pe0u5B0pUKGQQY.a8zVY+nwEABkeZOllzD5SjAfCorrWYJ2BhZIhzRUs8O6O3ao6fwzxOoK.ZueTrOFIQrEvNEJTXGGQaw9Oulnp1WEs2Y786hRmhEK1+U9FdkbRuSlQEMmuuedGGm7DEkVFdYMQmzr.UHHbhuodypWt1ARYyyAYI1VBhkIeQJV.hGUUcjW3K7ElinHYlYlIAnKU2Grya6G9GtMkosHRa6uGXwYvL...B.IQTPToKFx0riHk6.zqAnc61MqpQiT1B1oHxH1ZTdDfQBgQXCqjUWi7yxrYOjjweoNdZ0+eeuu22.mJZBYtm64dxppl4c7NdGYJSYmBikc+uGixJcU9cdP3DUTACXLNhpNn3nplw22OyY78y3+v9l9ZY3lpdS.Q6aeYs0R+9SKyx8q68uw3T3Sm1fqsx6OmKErjbX3gIYPYXMG5hWdd83.s0.nbJORg533nAAAZoRkRJUpTOfdggg8.RN5BG0T2JfVqVstttt6Arqqq6d0pUqS3WJrGPuW5q8k1cVlsCzXv55b0s4dKXIylCHuOLxW4q7UFEZXHrQixQ7DUy4G9bIrzRC3w.JyHe8G6qWHHHHOP1ff.GyRBndttoAZoGH8PoqhtGvNerO9GeKee+01byMW1Rr3QNNNMpUq1xQv5eeeeeeaArKko0zL8dPzdupW0qxTVZlwvKalivAW26v.Lc3.Mbo9LOcZGFPNmSbhSjAJmEHerY8tBp1HkaZxBj4C8g9PCdN5rlRX0DPgRLlKtS.wSfozTGUDIeJXIppYbcccrpsVhqmaeOOOiuKhI6PRRRjZ0NI.ZrIam566e5jx6C34vO+5b9qr4BOYsCYernCPtngJuxhV9WgCl99Wt192aaXxF0nnn1tttCJUwfffDDRRTUM7gimDDDjw.NSkBtttFa6V4ju399oCW77lq0ZCl+Wee+5F.BfpZmW9K6k2KBRL.XEh02MXe.UyBTXIXDBskE77LQIJMEzXVfhppEIJpnHxQTUmX6s2dDWW2rG4HGwjgdNFvmylIadwnVVhmmWufff1u3W7Kd2FvNttt6Vu9WncQnuU3INP1IFB5BKrvv8qge8vsA9NbNHCtjaEy7qQAF8tu6+ciBj+VN0ob.n4.e8iubfs1+7POCnJzlprSIJs0QNxQ1FnksDsEK+sLJkMiShHiFaddM+JCGHKPt669tApc3q4mMZOQ11NrMvKUeWARNKjXC15P.qrRZ.kaQb71O5i9naCrkcev8.jwGe7rTjrwGPhySsqbrrvvumelm6TSkxMNYTUynplIIIISoxkyTsZ0LJjIJNJ2O6c8yV.ZLhp5HhHi7Jekux7.4N4MVKCkwIEv25V+UN2ECX80hOO++R.XRZ1brmTT1oBrsHRKKQV4.jmljF42TzUeBQLm8KKm8TU28M8y9l1w0jFkcAjvP+BtttS3W2ep98SlxyyarG4Qdjb9A9RlLY5SrAnEXeYiBFT9.Gv3+vj4ZXXHEJTX+qDAJWtLUqVUmbxI0++4t27vkjzp57+yIhH2y7tmYjQDYUEBEnT8dUMLJBB3Fz.5f7CXTjGPAQEF0gF5FGWFWQTnra7wAlQw9mHLzsiHfr9SPPVjl9Qn6tptoaZFkpcnpJiH2t2aU28kLy376OdiHuYsWcyVUdpm74du0MuQD4a7Fumy62y2y2CJwBLHSlL8UzsrTYSP1.j0UUW+V9Stk0qAa7fe5Gz3vpVsg6ZW6J9zPqemMiuuliGn227mHadnw407ZdMoZ8R1zLwToRkrTq8HV+r2yLaSmVvVMTvS8AAab9322GufmHS.LMvb.yQUlyUj49S+u8mNaylMmhZ0p.jmicJfkc5av5aU14JyVmdPjCA5anUe09mXjlHbpLLgwuuMACWfEF5eZKt9XdLOlXWS1ijACF3ru8sOSfPyOJHnSkhwyzKMKaYCfbs92VNuHRAIoGqK0EyyM0vYkUVy1xxxoTgBYTUyc3nCmOpYTdUk7IGa6lQMs.jl2SSqlMaZgWRqtryi5LbO1X0digp88f9jgs6PmsplrAbQjg+Q2xeDLGRMy46h4dqhWOyF7gMbSn9oBqmTu9CRXpyPDFDDr+9fLJXm6s48NX+9MhCBBv22mlMaBdPylME5g0wO9wER5FGos4mKSsD0SeZGUUmYfLLjr8nWta6O+1xt95qmpKQB0QmChSX5PLyLS763c7NFRmQykQSADPD4Jex0r.ru5q+pytqrYK.TpSpfuB4deev2mgds0I2W4q7UJ.tE7g7rI4VjEyLCmRVfO8WeC+L9+k2z+ESPiUwFbctoa5O1otHNhm3zgNNO8m5S+TxB2oF+0EgU2HhIkJUBOOODDJUtLnIW3VxH.aBB1uCfy9CBrCOTn08z7dD2cxRtYbnJ16wPAZAPtka4VNcPRtTDrjSw5L18MUU6s1ZqDpm6YC0sbAAE4HW3VsKr2Dwhtyottuuuu533D633DGDDLT03gBL73G63wJnhJwQggCw0vJ.bYqVsZssm2U2uNrMCY6EYw9ytyZxOZx1uM9Klxnu7+.OwmXNn9oBTR0jOG64BdrLA.WGm5PV5PtRUJky22OS0pUsccckj+h3nnnghPeCiGzsQL0F+W3NuyUtoW+a4jhHKLwDSzAnEtD4662FWl+49be9mDptRMXc1lsVhk1dOP+O4m7SlF.+oW9LmO+ebZ+em9OetdOeyxjG5gdHalsiCIIxw.1gWpORa.qele8eFy7lYwxzkG1c14fBnTNhnIRRViQukfhhmj20b7x.X666KIso9cZowJRXXniHRlvvCmMLLzAvtFHAAGPtuwJI6lMaZ99cikAbi8N987G09SWd4kMioy0yJYs6rlM93kuWht2.3bQVVN6DCdc13O4Vu0MVd4k2.XSee+zDzEu8VaEGEEQXXnsuueVf7QQGJOP9uz89kJXXBqatO8C7.6zN4mN4y49Fcdtb0RmGONnIaIUks+b20mqOljbE+vO7Cqus21aK8uwxTVLjCOJByUApM4bvTXyLco6b0gZpp0A7TUqopNyC+vObkM1XiBIhzosuuuEZBvKBNDicBxywpp8CCCMs31vvsaz3562Ch2+92u47OWx3uG.6Qg8FuuK79E1wWy91mYsonQ.+3npl4lu4azFv5cc6uKCCrHo6QxdOcvDNiXkqB8YHa0kta9DlXhz8TIX.JtXcnBcXhnnnJTspowezMIQKi0wRu4a9lgFOB5GTeiYmu01tPuNUlnbpLvKc9Tefsdb+GdbaVyDObLfHFA3OyXrkabPXcXlilnoLoZ+RM61IwlAXKthSREU3zoSGSKN222ota8LUqUMq6N6oKaXXXFpQl669hxjjXsLTspYcjNeSoMNeIg8uG.LIlFF.SXdVusYiOa7beVOKS2tP0Lvb6j4f8bFYb6rcLMN.lmMEQVk1FZkC5lfDqporbd22w6tBnUBCaV5JuxqLWc25VG5PGBFCDGfMEwcbVlbJ.lb1Zwvm7jmbz2mlQrkWYkXDFpP+9a2eKE1PEV+du26YMQhWsQiFqt7IVd8CGFt4UdkW410fAst+6e3m+y+4GBysCaE16XLW3LoG12zLW.Z2V.j+9+9+dIA7JawUrTUsusa61rLc7QiMRnEOmVSfVRDXQaxr6cu6hscYJfp3Rc.e5gWWn9O6O6OasCznwrzs6j.kf5ocsnL3MZwiuYlM5KV6rsf3PfAcf9PuzLvmNO4LPZlcnmZhlHh7k9ReIy2HBsMYnvtgiyNajhp1ppVOA7rXlwVzbQFQgwPnHLeZmfonpZA5fIyaczrNNVYAxr1FalAHWrp4+7egOewFM7Kb6+0+ux+Y+ze1LM7aXEDDPiFMzqqQCAinuZCS+MZ1wT3HwPugsf9DReU0A8pwPLNzMN.lmLIBVl0dO+kemYru0HGNa1AVSDYsJUprVRlk2NoNuG9VO3ebLzIVj33fffggGJbPiFM199hh1JLLbylsZtUiFM1lVzuQiFC5zoyvqYW6RSXcP546xQybOqIVvIrEQxrnA.tb.Y+49s94x1oSmLRcwLOqM1yaJYEyFaWbwj4ZtoNoyH0kzrYV3A+ROXAfR2+ce+kamncICGNbTVaegO+WnIPm1T3Jthqn.zovW73GOOyabRuneRf8t6naQrCvfmd6H9BYm4ylcLe8sdyuEfNbK2xqS5.h1xHrd+C+i+Co58i43ewD+U0wFaauy0VTTzNbjWfxUpniwpPZFdHQD0pCXEDDXIh3zoZRlgmKIfvd3jlI5F.u9W+qe7Oai+0KksQWiau81Rtb4romQiHf1Y6.YXwKp6qVbjij.nzbiVKLNN1BPhLBptFFFFCxP+ffg6d261j8ehE+f.I7PglqmNLLNNteqVGZ61ltYVefAK38nlhwlmGhF0Vty1Cx.sS2Li471K4XezKvQy.rh8JesUbZmL+2Ko98yjIiXaaK.pjjHHU09RZlISzLrW7+o+SqDFdnSBrPPPPOftzgd.y27dadhO1G6CtLza8tvVbBCn9G8LA1+BMVb5fm7n887n0Fm4FBKvNhjoHVIkb2NyohR9aV.anWF3XYmGJbiuzarLvD+b+h+bSALI0XJfIzVZkNP4jlNP1j1JLBRrQmbFwZfL.EBBBRKe37GpYyrPaaOSRFD.Zzvf.6Q+7GESs7cdAI77Y677+rnSLwDlZ8d9ctdLr+sc5FKMc3tidd2jyNanqgg4mzl0eyG7fqTtb4U7882IwCHCykKmhgkX1G6XGKEjpb.4Z30HaOHaX3gxbUW0UsiP9ehjy+C8s0Xz9Vgsy09L.oa1c9c.bUDQebOtGG+x+x+xJoySLkkWAZQIU6UQ0NS0S0YnCyATqsKtXB0tpHxr.St5pqVpe+94AxLb3PSasVMq2HFcLJc8OACSSDL9T.Zm5KxbsNex0cKT3nwvQhenKNF+Xte8POTx8sZRJyYt+G79UQD8s7VdK5G9C7gGslwRlX7NWGuQqIzCRK8q31tI.kTix.SSGlsMLmp5bGv2eF50aBfxP8hsZ0xD+uO1iNpeaCujy7ywE464rAtx3uOy8BuDVmLOoZfjnpZqsZkvpF2c.KYlQITJKKNh0OEWDxCcSYgiMfk1QseFOimgSBSyGsuAQjLcme9bGJLLePPPdDxoplmtTv22uDPo5PY50KoS05NJopb48ywWVCXxNShZNBfhjxfYts9XehOwfjfOx.ymi4R1v7QOu0E7Xn5McZs3uhKbx67Nuyk.YUeeus888kVQQYeY+zurB999E78Cxqplcqs1x9.G3.RRftCvf12Vp1IcivmMFcbF1TSMEQILOIYwqQA6rxJqrMvlhxFA9AqEDDrhpxRgggK+q9q9qtZPPvFfa+tIKD+zdZOMElWwM4y0QNEzJGGAS37+f7iHaLc9Ttga3FPpKJfpcTEH9M9Feilw.WTX2miixoZIDX2BHyCd+OXgnCGMQqVspRG7A1kp5t.BpT4w49K7a8aMGF2SSBsKiGEvPqww2P03Nh+1sS4SGzjwecAy1lG.l1or7jexO4QNjLy4ckVIABZd0SDOQ9WokUxlMrwP4tr3RdndY.SmeoF6zZfSZctQQQ4NwINQBKUzL.Y2ePPgm1S6oUDn7y7o+CU7Y7C9LxFFFljwf5wGtYycb7yIrR5w6eiL9F+r26dGwTJOQhS.by9icaerrppocEhbfelibwgnsxzL.psElMNrZ974SDiTYaee+AhH5Au0a0DnsJ1ggg1AAA3Zz9f9AAAaxvQrHaSfsbcutsW7TA95xYSlMgIC0Gi510fbZaM6i8w9XyPGb909090bN5QOZRqSrtSmw5fXPm7T0PGWssVRUsLl4XlRnClre+9S.LgsmcEU0JIyIKATxKA.OOnvt10txAjyWjLDkbN5LJH+j6+yb1nU9ij4dJTczymuo2vaPATINcocSWI3C729ALqYz9Qvwu2YcMGw22GAHUnDWYkUwDDKr+f.wRDArD.qnnHqCDDXSOSIBbve0Cl94OICZ6IU7xOeqmbooYJeuXvMNWtb51auscRYBTLo12yCjw8LW+N0NSlWv7Yn93cTMDXr5fEQCCC4XG6XTqVMKCCAbyFDDjMJJxoUyVRPPP79810nxuoA.sNsMfuy4+7YmBKSTUsEwaDqnTUUCy1pOxG8dO+2yFo2NUpTIkYNiNGIB9nZ3kiFCLHHHXaPRX.qtNHqFFFtbPPvR9Wm+RQQQKA0WFi9Cs501nwZ.a5sSLL59N6f+eopM97gSoT0j5BPc50qGhHHFx3jvnCCPrIYRMWRWWrzS3I7DJWGl719ytso.litTUUslHRULwbLAlxzww222xy2SB1ef3m.zIPVPRaZ.EfZ4MLNIzgZHlNYR0QO+ds6YOoyCfG4i0mZ7CKbp5LhTUHMYVOvC7.N+S+S+SIfNO2YKFoy1wNllljNfQ67V1xxZ4nVsVMUS0778FBnAAAhppisssiuuelff.6jXFLwFpplzAwApxQO5QAPt3J9tKIscdVOsLIW7TjE.0rdmqpppOtG2iaHykFuPUGLq0UpNTVDYh5hLwu0u0u0T.y3Byt3WcwYvzs9l3e3e3enDPgq4Ztlbf3.X0oSGYs0VSH4eZxbeUUKeeemfffrMiZlqYynTemYnNNVCGZC0GsIY2cVe6rso8yo8re1O6juqqJ0jXfAW6Ucs8ey+w+w8eCug2vfG3AdfXi315cwNedz6y2.nT9nnnIzN5b.dCFLH.HPDWu6MJpFvr.SpZqxdWmWQfbIfTuSxT16k7fwcgYfRqSY+bBTKk4bIsY5NNTKsyGsCaetwa7FyRUxu4laNB3VpaXidp9g8Y9LeF60VaMKeeeqVsZY80+5ecaU0LhpYemuy2YtnnnbRrlO4uubXX3jf6z2aX3zXX++jPmRTm7bwWloWxZWNCXRpktoeifRA8+6969KF.LbgEVPDQx3B4YdJ3mhb9YFjyY43chDm.UWoCbh+r+r+rEmbxIOYTTzZ.88Lzs2INNNaqVQY.rJTn.ITub3pqt530a1oKFZm2EHZ0xDI13rOAP888GVoRk9AAAaKVxlQQQqUud8UDQVNHHXohEKtRXX3FPm9.CMsvJzpIYK06bGjy2pB3QAFppNjNLnNz+U9Jek8AFTOYyjtcPgicQctauSM+a8Xe7O1r999k877lILLz6P2280.XWQMa5Ccc+4+4+4mEXJU0IbgxzhRFZ8abLz3TEEwSOazemfwImg1lv449yNs9upVddFbRRh2PFLHbGE+uFRU.ZiTamLbYWKQY085P923a7WpBlwpYzN5L.SIhLgpZILAIlCvw22O84FmCGFkEnPXXTQyWCyEDDXapA61w23Mdi5wLLLxxCroYhv48niZdJf9wOxQFEDY6jwCUUmmyy44jWDoHPw5PdH5TpW0yywLt5IXXhVDrNvpYxjYUyy35lgQgCFLXnZz..MCpjUDwILLz5daFk.Jp6V6e+MR0pnMMs+4nwZ2rWxuYhKfsWVHYdSKin.lCHeWHeRc6modcruga3Fr1yd1SRq7tyn2GPAOnD8nTMSfekeNOmmyD0MA5MJfurYyVJNNtXyC0rTTTTYUaY5VNTu78FEURDoTK7JRx7wjVeWVX1ryY.CO+m5S8oJLCTDVrvt28tOUA38h2Rte0K4qdZRMVquta9VLapx7Rd6u82NfKioY2mO6T1He8wVuY0UWUhhZMZytRhxuFFFZ466a2wPEV663N9ea2rYSw222pMXYDX45NujWxKYD.Qy.VIx.5YigZWJZmJ3QFj1UnSL.YyFX2yzcEJKheELAlkqyEFLrQ.lnpZSarSFysZ0pk366MBnJPkff.Y26d2Vc610AHaTzgKBTz2+ZKDKwN.Rqc.Qyp4HlBT0oQiSIlhKJalju5IhEzNkoCfYiiwP6DvVmVOx4cMj8wrylNlglTRlitu6mpYZJpmWfBLLLLrOnaYRthrFnqopr5G4i7QVIQTlWGZuIXJEo4SzcnVr6gom0G5r6m7Rw4Xidlau6nIDo.tYWqCBzVqVsJpp7NeyuyDfR5kCnHLmQTMgJcnVEfJu5W8qdh1FejyB0pUuNd.9.9pp0vrlVQOOuLgQFcGJ7PgVggg1ppNAAAiR7fnZtvvCmMHHHaPPfCcw1j0+dIIRYxAmHIFxi7MVxsFOFi3jt1hN27jpgdVW4Udk1EJTHAXw4SV2bumOlZBf55xPLIqbMWXoUWc0kP0ke0+R+Ranh1OJJZnsssl.Nh.XEFFRXXX70EDLHozc1pQiF8oapeydw6YO6IF7hOK5evkSlrOSYR5LYBCHu0a8Vc7R1zZsNXkz0Vza+1uck4S2zaOC6Rndw1PwO0m5SkuC0y+68686U.nRaUmX5omdBQpWFH+O5O5O5X95L.lpfTrTQ.CboryZhNtPlu3W7Kl2RkRhnkAJ8Q9Hejhzlb020txBsSlG3loyni69FWaYtf1G+i+wAP8fX5wfZIBe7u5McS8UUGd7ieb0adjjV96EaLgljFXFKK644MSqVsbUUC5zoSinlMCftd.o.lLkqHSPaJWOIAf.4ZjxDuibFBi9kcajeO6Yz2JUAKU6jlPTaUU65fCcwo9XLvsJ37VequUm58HStb4x9ley2pgsIsMI1RDovcbG2QdQpmsb4xNQQQVdddVYyl0ZlYlwod85Ye9O+meNU07W2AZT.nXXX3DhpSAclMHHXVfYNdqiOUMnBsovt2QentrE2gKauvSrwVH0zBAilkg+D+Du5g.wyN6rhpZ11plmZTLxr.Tp3Wd9dvHE.lsfdqBbxa+1u8EJUpzIxkIyJl+eF566Ke9O+cZ444KCFLHdqs1Zf3IocVmjRvwcHfFGGe1Vv+LV3Y3vgi.Joe+9366iuuOHl1eXx01PU0An51AAAaDq5pf6p.qGbffsgZixp88ce2G8.EOzVm447ak.kjbsVe3G+i+wGPM52F5+W9W9Q6Khz+m709ZGBD24h67mb7ZG6k.7ze069uRTUyDF1pRlLYlc+W6yxc3vgtpkTMLLbZQjIBCCqHhW4NPYpS4q3JthhfaAfbM2QbyNEZnw4uTN9VgctPN97YByZVbD5QqVnppCEQFz0kA111wplvn+t0zdfPUr6ZxbTVfbcSxZVKn3u4u4u4DppyHhTc3v9UUUmES.gU.J366mEA6VsZsSaWDM6DSLQ9Z0pVLFJEDr+h.E5Tmbfal+1+1+1L6td8LvbYaAYXZbdnGc.SMd.hw6MMnuYXXMylCDL0FatUVYk7smibf2o+L94LXud6T9ba.r5sbKGbEfU8882HvOn+t28t3q9U9p1QQgYTQy555lMHHvQD0pNnQQGdX2tLD7F.Lr0rL.7GiIY6I87eYmiXicDHIHEQDm69tu6r.4UUyWOAHsVsT6efefWr.PWUA7NkZiOR0BppE+8+y+eTDpU4O6u3uXxVpN8FarwThQGhJpplavfAYCBBxGDDTJo0MOIzdRee+IAl.ZUNJJxjIDWxMGjCVH+7ySdnZge3e3e3BKVkBfagicrik.XRiGs5YRLPrpQikQ11CukCdvgjTVCpp8gNCZ2lgf+4CThwlGNoEfU6SUuUrP1gU.phkZDmuLQQQ4BaFlKLrY1a5ldcNhH1Ma1TvEY+MZvAO3qWBBBrf4D.qEmFIYNWpuhKGXWxom8eqc9+5kse+9EEQpnZzD0LrNJGMvYOWX.QAlSEQne+9iZ43iS4qTpoGFENBfkDwIuPXXXYnawfffDJE2yoVZaqeFxVCxA8x0rIYf8bwNOSAzESt+z1nk.ocsDUDIEvij6am3BbO6gzEVXDinF1oN8gZ8+LelOyHAo022O1nkFgpuuerJx.Uksihh1.XMUjUazvesq65ttM.1122ejtrbpIs4XxLo2WZf09t7YMMg8kHb0r6r.Y6jD7dWvZ6s2NsrbrOzgt6z55uDvDv7o.6NKzclDfdmRUcJiext0Z0R8.BTU8.pFGGOouue9nvHaQkcZoqhYtShtkjIHHHiJhSPPfSXXXJXNiqASZRGg5aztFzoGmrl1rPlemm8DQD6ekekWyottDG4BMeNtSGFB9aCrdGWVoRk8tzryN6Ju8+z+z0C7B1FHd3vgRPPf0W9K+.VJvG8i9+mBLr6NLCeKfsZ2t83kk7Pn0kKqgc51H.JdHidr3rDjAlI6q6085x0Bx8i8i8iksqIa9VhHx26262a5mMaL++4TsUdU0r+v+v+vYf1oyMKJ0kR0EonpsygI9mzXyD+DsSSTLtULPvHO7+1Ca.PVDmCEFlawEWrnBkCBBlHLLbherererRMa1LsyZkDabmwJy0G5h0O5Xy2b0VI6+vTtHUiAiH2VtbYo0YBVw46Xm10Aswir0MfeLYLTUj50CBB7TQpGFFVCXtlMaNcXX3zcSXNcaXBLfdVpo4uMeqScS7Wtrd13lbzih.9BfzqJhHd5ccW2kBve9e9sIsAabIaaHG3kGHWuj0ZZa7247FdC2XZBtJkjnzRujWxKo.zIukkU1Z0p4DEEYIf0hKtnU61ssmd5oy.j8i9wtm7MZbfhAAAkTQp7W7W7WT48+9e+S75esu9JxPmxcgR3SgiAYMZA0kufSc4NfIolZz3.hYAhg1CqB5m7S9ISnWqQ0jmCJkTmlN6aemyaZoOrODW1FlacOSaZZQQjEmsZ0khhZsNP+nnn3m9S+GPa0p0PGGm94ymeCskl1VeWGXKUaODPs8sunlbXaaO5pX94mmjZs9TbS7o9jeZSvvhzOJJZyW7K5EsNzYsfffMMcRjtZylMkVsZYcsW60Z9LpiVrY7Omey1FcLaLFHG2vMbCCwhAtPenaeU0AOkmxSYT8aZZ8imShWldLiAhaY.gZ6eye8eyA.pHpyryNaga+1u0x111UPonuue9qyjwlLQQGJGPgnCEUrNTF5TBnX0pjx1jbyfYS9vi7LE9c.Sf8AK.8RxFop5.QDihY2YjJrO3s+1e6Cgt.XgCYpB4f4K.TzyiR.kAlnNLsH0mSU00wIqqTWbwTKnSk7dxM2ry4366aZopIhH1RKsT1LYxj2BJFFdnRgggknMEgNIh3F4g4MeMGYm8LUp6jOOWPaz7JS1llMlEQ6lLGKEzjJUpjwnuFsRYPzEUMX2.5CdaBrxMcSugk2XqMVFXsHiFkL7G5G4GR.CPQVVVVggg1A6e+1sAqDV2HQQ2q47r..QL4n.QO5oSW+KGbRL103dYlcF+st9q+5c.2rhH4R5jZoFtA..f.PRDEDUtBNlw+j5RQDAZ4TOoiXkJhvhqT7+1uvqoLzch8znwT0qKyTnPgYRliURDubYylMqHRtvnvBAAAkBttfJetO2maxZvTu6286dJfIiiiq.0KRGx+.sZkMLLzHLrlLxkM59ixd7ieOY7GADZyG0LZhoIVjZCmKICuppCdc2zMssp5VhHaDFFNl1TEct1LS541xrA+kr.rqV8znFrJ6jAPQMrWPHGH4Qn.XDh4fffLhHVzA5Bw27Meyw.wG+3G1bMeBT3nOZ.g86D1XOWr2QkJnQjWwg5jA7x0HSlhIAwUrKjG7xPSrN5Ewy1v7w.CxrqLCqk3uwhQk5ZBXSlw+j0QDE09n+eOZdfR20c8OWJoyMk6ttq6JyG7ttKGvKCKRttPtNc5j323nWr0ms.P0pi.zZnuHC62u+HgAcj.gFfByjn4kmWSgdC+.efOv.ZyVP2M.1LJJZSf9shhhCihTTznvHUTc3ANfeeU0s.13.99azrYzlMZzXaLLPwrEKOrgZ1UGCHqEAq21a6sIzD4xJsrdajY.a3XNppYqUij0MHairYyB0yppl6M7F90xiqArjDPQl4jm7jyhQX4q1R04.2Yw.hxbCGNrlHRpVRLW+98m1xxpTqnnb9A91AMBLkAAXgZXUfuuuCFVVH.oUgh.XGFFlEujMrNKNvLoBh+2j.M3HJ.UMZoj709ZesjNyRUAv5ttqujynxWyCanw4ymU50wPHxjzgNrBzYob4xsjkk0Z.aKf566KQggVW8UcUVVhH+1+1upQGWymeOEHtd85w3lvJ6FeqSi89VrcJrIDvl7jQUMKrnojVqQgOxG4ijGHquHNc5rX562l4LkZ4W3K7EJHha95hjqd854vTlEEf5E01Zo1pV.HWpHCqpZm.NmHfjbEHHfpH6865wZIp3Hplc+6OH+MbC2PQfRQQQkCB1eYfhVVV4ntoyZYt+6M5XxivX0LVGy3gZDjeU6J.VttXuXhFmYVeowEkO5Vf.yJzBq1P1gCGlOvyqrpsmPUcpomd5oDQm7578mrQiFSEDDLMvzMu+lSGEEMEzcBfxG+3G2ThNPh3wumuSvv7uQL4TeEYAf10vNwWv2+2+1.a+K7K7yM..rHSUn.zpHPgpUIEXLGCayqmCCHwUDWYBU0JppkSJ08Lc6101222RAADQ2AT0LW+ANP1vvCkqYyl4+W9W9Wx+pdUupbeueueu4tk+jaIaiFdYihhxvvzw4dWNCN0kzaL7QiMJqZ8pUS+Q9Q9QXvfANhH4u6O1cWZ9z5zBxr7C03BEfSLc16.X9sZYDR1knFmjXV122a8nnns888GzqWuASM0TCNwINwV.qKhrhHxxX5VOahIXaz1JWfy2HqUqV346ct905S7J9tURXYhp5Vuu226ayZTaiVMatUylMGzrYSswANf3k1kR.g1Hs9V+hAiN1MabJsLBk1l1mWJSH9M9U9MFI.tmv.30ExgnxLIrAnZms.17C8g9Pa666G+676+6X8LeFOyQ0ndTTj1QUsYTj366mACKIJe2G+3UvTewUnW0xjPOuEgrIHoe50X2kJOTepWK66g.PAughHCEQ56gWZKFaSQjMEOuMihh5ShHPQKb5pZ9jMdTItk6jjDzWanJzwEntpp21GeaWLxS4zX.LI+7yOuyHAxamt2gSylMS0OjhAAAkZ1rYYfR+D+7+7E+G+betczUj1iZsaOZAMYmwhYVH486NJPROwSSpceK.qliIddW.KtIL.ZsAFfNOomq2RXpe+M888GdG2wcn0pUyjnFQrFNbnUzgOrcRsh5DFFZec9W23elrVZ26Vle94OMmZWVXmF3NGgEM+rR8D.Qb6XopNpLBTU05lxkyDvG3zwyajdm.jmtT3Jdl+nkL5SBS2tsN6Jqrxrqu95SIhTd3vvBhH41XiMxgJEhhZVht0q73dbOtI6BS+xdYurYZFEMciFMlj5sKAjyy6pxZn2NYqQMm6+9ueaeeeYW6ZW7kZ9nRQ2FeiIwbB2APuAympGU9FPIk5x5.q8o9Tep0vvLo9yvLmuxuR.jiNFqRr6U25fu4CZgAzMI8Oy22mD5Saa1fkNZicMihxX.qK.f3Cdv25nx97.65.mM8JAtzeiFoyyDL0WuM8lwPo3VpCzJSaSa1M+vgCMrFxsUxyZdmsmoFeSk6TVrsYPWXX+98i87L0LupiJ6IKPIQfZzi90Ol7888+84.RtGyiY2Et2jrt9TdAOEmmxS4o3faKG.m1sa63555.XUMoEsy4947S4+2pm6H.SZACyjIyvwKkF.kvYTXwKTS1RAh2M6dvK3E7BR6Neq+ze5O8zN621JLPLIXIFwrIzCe3n9AAA8A52A52nge+nnnA2+8e+wAAWmzrYSKZU2pYyCY0K8yURHI+R+t+RIm28M90vkxlvQPVDjmzS5oaC3X0s9nRFrKTDZW7e6e6eqzt10tJSmZU.l7U7JdUSIhLyTSM0b+LupWUMREYS2NUEQlUj5SaYYM8fAClljRKLSlLEAx446aCXE0LBeeeIHobVSZ2vB.pZt2kp2bQQQVVVVYnUs7.EXA2bvho5wUp8MxX8nmMjd0z67Nuy3G+i+wiHhnZ2TweMKsMYglVykAZdwniIpmggkagwG5Rqu95K444sRqvVa546O3C7A9.nfEhYi8G5PQNAAAYwTdmELh5bRWrriq431buWtwtjS2e+nRXlVoBqqQGbztUKiIa94aAYbcmIQaaHOyWsHPom5S8EUD5VrCTnc61ERzvoRQsOTp9dktel70Ma3MCPlnvHaDwzZWMiXh.hJhkJFled3CGkqYyvbXXKZtvvCk8q9U+plxttcc7884+6cEBzhDFqHz3QTLLmheHskR2w.PR6Tyd1Ym0AvtGXAMu3iQZ1ERFqqI111N+W+0+ulqUqVE.JTnPgBDaU3PggEaFEUAXhlMaNcCuFS+09ZesT84q30uqqOGP1Nc5XunIwVWtDeFbV.jyEi3KYz+J1pspqKhrdBishoE1ule6e6bow+mr+mzjPTB5TdkUVwz0Q65NiHxzjLVgYdocXBaLQTKKQr.wwjT5nb.4dvG7Ay7C9C9C5.XEr+8SBv6wppJcfEOSFjd4Tbw.+6C.SNiLoUEh0NlZfNIq3Yu5q9pKTamZXKayyelGSNVGYTaGsJrJcYYrXYL.nrYbb71UqVcaaa6s9B20csgp5Z.qbeG9vKKhrxfACRaEwor.9bNIQUkEWbQSaFVgkWd4wnCqYmffDCLTPFDDDLPDoOPeee+s6R2sesu9WeeKKqAWeiFwzoiBiJiG.T2GkCvOJLgllwWOOSshqphmgJdCTU6eS+t2z3Z6hdAxhl49whI.lzy.Lvy64871RpKC98+c98UEUZ0pkDDDnWWPfJhPCee6Dj4KAL4SZW6ZZfoBCCmrG8JAjuYylYAbdWuq20khs9pwmqry2+PoAI2ZHyX1PPKZ0mD5sppt4y8.GXq2za5MssqqaLlOWY8EIuHRQU0I5PmoAppp5pp58y+Jek9uzW5K06K8k9R0yjIScU04XL.S.xdxSdRGRxjQPPfs.NVVVNXnOZ1vvv7VVV4qC4eGui2Qtm3i+wmsYylN.N+F+F+FNt6.VxYCzjK9wjEMiE0oi999Puugppa+O+0+m2Bn+7yexAUgX1occe9teltlwfpiEr2INwINYTTzxAAAaDEE0+k7RdI5W6q80nQP.KrvBr6cuaq26688ZWmtNgggVAAAV2ywumzLj6LG3vwNl8byMmU0pUuTZ9z4yNaA5Mx41rPJIRPaqHhfaRoCrvBKnsMJruspZFWHmFEkUUMqIqZF0X+S+o+DixbKvLkKWdlhEKNsZD90x.EVXgEx666U3AevGpDzdhFMZLUXX3LgggyLr+vo.pDcnnB.Yqy7orQvpKcstlq4Z.H1EFznQiA.CaznwiFQ2MYyrcF019pAaqQ5FhHqos0UAV8u487dVi4XCfsWjEOasW1we10Z1wFSaSa4l+Uu4zykAnDRJQD0jWPUO06C+uui+Z4Nti6fvCYBD4sby23PbYvAO3AGzktiqSVWpxnjyosGyWDCsxWzl5XKhLpz.bEQRXeYpNmfGsN6GLio.5jSN4HPSbgg111wCFND.Kvb7ihhb.i1QDDDvd1ytoYTS.UN5QOp0wO9QsZ1rYp39pzwz9vup50ELZ0qzidWrrKwBvpiQ2BfD.R7RRlfpZrHRhXnu3E7yWxqgGiiktg00qCqYaauVTTz5sZ0Js0aNTRAiQL5JVXX3.QjggggCCCCi8880q4ZtFML7vZiCzfuxW4eTazngBnc61M1zsLHldof57PeKQv3+VfofwY1ce2eNQDwpsQiFxqpVTUsjKT9w9XerU.lP0NSfKS7Nem21DXRvxjuy2w6XFssNmHRUssNKvzqt5COAPYaa6QwUxXkUSTTjEVXKhXYxRuJQQQRhXNqhvvjM3LX+AAC888wyyKSylGp.dTB5L5Xtuu4UpvIfzzcvS8o9TSiYXvfACTRJs0MV4eKOdjGl+z0bfy04NtEsFoiIdvIKUpzI.NoWf2pcme9sSJ2DqDwdMuuueAU0RgggUhhhJ6BocxDGnivrPR4fd4xZYmdbZV.Vtti5PbN0gbpgUHk6QuJppUDQJATntQmlJBLAzaZpyLPqowHL5S3BURzUtI8q6OgHxD24cdmkIQPzaqsK.j222OiX.kRZ2ps3G3OZvSRY6RR7ZMZDjESIYXAjtA2gP6ALGC9t9tBFUJXyZD22Gw.wm.NcrHhVESBmTUc5R2LKrvBiDVXuKd1qnrf4q0nqt95qKu4+v2rkiiSlvvvbhH4ttF94788KPbboZ0pM4m8e5yNIvj6cuO1IHojNu2l2qCf35Z1UTiy4o7RNaTbZ6yTpMN.Y5XR3qMl7W0WDYKfstlq4Z1Fy3l8e9u6uadQjRppSr6Cr6owjvzYTUmwElsb4xUAppZ64TUm9o9TelUDQxCjw222Jv22x22W787SDVb0No7BcTT64latQZSTy68dGDDDz200MEX9g2+8e+5o843xN6eO.XxXVCEHt2nLK4Mna2tJfU1rYyzcjPAlPW68cA2fbLlVG318LkXyJOvC7.q.rhuu+Zsa2dcf0ylM6pOum6ycULYkdkq85ttUAV6k8x9Y2fj9hc2tcgyChZhHr4VaB.yN2rr1ZqQTTjZDsMQQjXQhGpvfW8q9UuUTTzVITpcT8d92727YG366OnMz2HradCFNbXRfzdZmu8ROaA7jVsZIef+5OfHhu1BFt3hKNPDo+e0e0s2G+cZyxOzE9ZJMibCnVss.1v11dyvCEtcTylw.RbLVgggVcSP0OJJJ6gNzgJfoU+Mwa9c8tlDXRQDC6RpS1FMZX+rdVOK4k+xe4Imi87srAjGg1orYKNSv1hOFLjEM26UUGB0GBLTDo+G8i9Q2Fp0uSmNwFl.3loURMJl33cVbo1e8eyesmHh2e9sca0eOum2Ssm7S9Im1cgRKGmhIHH6L0TS4nh3TsZU6vvP6uvccW1IrLvtQiF1hH1ppNsGqTCRaMh+A+A++RmcXff42u2K9ZW8Tds6caAHsAdg+GegCDQ15AevGbSQ72Xt4lZqdi133EkavXfg8LkVwZPskZznwI788WJLLbMD1JJJZvjSNYrppNyLyPTTj7hdQuHq6oYSqff.a.mcsqc4.Uyd0W8Umc9QLoogUud8tb.E8Setl8d.GvywPKbrSD9UTUQpWOMynwhH5sdvCZAjApWTDobmDwcUDuxINnKqpNgHxThHSKhLCINq+fenO3T.SXYYUILJrbh9kT5JuhqnRXXzT3xrAAAyALqssLEPkTZh1FroGRylMkie7iC3FiIi4IkkV0AMMqM7HADgwVibewLqAj1tv1hHadvCdvMj50WGXiO8m+yu83sFRN6adLYrcur.vevexeP5uOlZ6HBiQshT0fThh.9d9pXxO3v2ycbGCBBBF7S8S8S1+m5m9mdPvANPLF+bPG3lu4aNYswce55bvkCazPAFqy4ZXpjaaDnJXH10vnACR8yMf50iAL0F+44XB6SWZokRtu3pc.0xxBGSa10RrzQcLfZ0pYGFEZGEZn17A7a.H522y+6KdW656ZXiFM1QGIpWOtQiFw8fXhNk4KmqOim0MSkvXCEHNxv7R0TcGHFwfz2hIuf06c587AI5HwlsMIzYMfM7771DXvzSOSb0Z0hAz.+fzNvj.lxRJAbJKnNAAAZzgiFbEWwyrO0MB9ZsZ01Q.626dOWrY5a21omQ+yy5r6MgIqnXXxTFbIuYcJYh1pNwq3W7UT4tu66tBPE2NTVUsTBfJkqWu9DjHL5X1jwTuhWwqXBwH13mq1lonpZEFFZkfEn366qFlLIwpnC888GDEE0uyHeV0rrrrxFdOg4AuB3ZzIpG5a7j4rCy4R0sqZiZsza433LH426jub9rUaQdXtr3cA02gwNty1GX8VlRXegvvVKDEEsTs4laCPFlvtlLMCCKDEEUNJJZxfffohiimrCTw6o5U.byAjYxEvFZbofVG7HIS3i7eZd4Y2oSmjmqbsaaVqIGFPNJiI9pR.EaoZYQjIWYkUlAXtZsopp5bpQa4ltspSppNwW4q7UlPUcBU0JOsm1SqRhNyUFnzvgCKDEEkUMhDtEhozCS5W8FIMA0RS.hNLJDfgu+2+6e6fffM+d122ylf6V.a6NOaCt8opw+1BmYSI37YiVSrUqVI+cAwIhmtHhXMXv.6+6+O+uOJNwyKz2m9w1rN1ftvfhEKNPUMte+9hHhipZ1NiJEXqxG9vGtxK8k7RmLLLbhffcUBHWylMcZzngLl+2gMOyFtvkh139ObdHHSRqmNYesF1NZprTi9Q9I9DehDhSVy1ngITRDYpu3W7KVkZ3B3Ih30VUeQDOLRXeUfoty67yTVMs9aCa4hBIJJxjT+wYOE3HH1W+0+bkfff3id7il3ur1vNc5jDezbCulq4ZFO1jKx0suzx92C.lL1fcS0DzneB6KZ0+ce6u6AhTOFiyK6ZioSE68gtHx9bKFhO8wyaCfUu5q9pWBiCgk888WI46OIvhe5O8m9DppKC0VUUcsa+1e2afIi+CpUq145AQEf0Wec.gpUqxByuvneQTTHHoZUgLPfs+PenOzVppahIH9wN1chMkgQ8sBBB1JJ5d2bWOocsMDzGZMXOe6sVPUvnFoufep+yJzJ1Eh+tmYlA.Ctq65yMXtHhgZOR1DiIvvtc2FXymzS8otEPeuf.UA6f.ur27M+qUHg1Yk888q7deeu2IfZS9A+vevIdtO2m6DgggU788KCTnZax.0r9DehOAlisaroey+c7ENO8MvZw9123hA2orHSsZ0v79ZaA0k986qhHwarwQAWrEwKGzonGTtVRFy.lwsCy8R9IuwpCGNr5C+vO7r+A+gu4ow76KCTb3vg4a0JJSRMW6DEEYKpZ2c94c.bdJOkmhMf892+9sRo1dPv9svPOP022OFWR.xocrKnM.tm64dD.YOG4Q7BklO2G6XosQ4XLAyu4y6487VCZsF3uILSBqtZdw3XezwA72.5tLvIVe80Oguu+xnLhkXQQQHhvC7.Of.X0nQCafL0qWOwoUube4ub6720ccuIfx1zlYPtDBDtS2NCpcl7x4nPFnUFVfLTiLTudpyXQa2VSL.r9KeWuqb.kg1SAtynplPQ81SPMlTD2oEQloFLSMylMRZ2bT44+e74WFnRbbbYeO+xhHUBBBlvOHXJUimgNLmp5r999SaYYMEPkm8y9YWBFUCt1u7W9KW10t1kZ5rJtC.2jLm1abvLdjZJ7PJKPLtDybyMDXvMey27.LrWbzwrd85WH+H.Ggpf9m9Z+MRWGK1s6XLBQQAQULsc8nnnXf9hna+R+oeIadva4VVGX8F99aFdu26fjis0sbK2h0Nm+iwXfDdoZPeiaiCrShf.1NVUMtCDC8hoK8k5xVN99IsAW1h1sGAz9433lLm9gDlzPcani3lrlYXXnQuRTrzDQrta2tNphyW+neca.q+t65thUgAgGJb6FGvuOF.CLykZ2djOhidzitSmn47W7LBrCjJpp32BEBF04uL9e5hppUWvFhrYIrXOm2MqBi1Dbz.yFdpuAvFIkjyV.8OwIVbXud8TAzjr.Jsa21x2+Zchhhb788cLYKrsfo65L.5tMsYSpwVL8z6HFmG4HiKBoiec7sK6zC19zAd+rDL9QL0ep4mcDQxUqCkA2IwHv4S9L99elUdROomT4s2d6RsUsnHh4UcoTGSF9mDCQUlFX526688lxLtQqEIRcilVEFI999hN55SETURzjNUDhshshg56nYM3pQQ2mDGGmTtJsxQGxRcb3huDSuPVBKBn+t6x1PvlXJi2sRZPAVhHY6Yz6rbzhLor36Bb9igEF.02DbWAXwFM7mud85KFF1ZEeeusSzMnrVhTPUs7a4O7OrBTq7ryNaovvvB7v0yCcxBjYoT+memi0uiGi0oC34Y68l708YkHz31PKaCfmthpsoJX4Y.qHKPg5hT526M9GVgD1i.0l509ZesyppVsioaKU8s7V9iliZLiTu9zhHScEWwUjH.5TIQmIRALonsscNPxfhcTXj.pooQD3qus+GuMc4kVREDrLO0pRrL.Xye4e4e4UBCCW9m4U9ed4nnCuJvFcfsfNa+t9ieW8YFFhgolORiIVYunvzJDp0.bS.Qzwww5sdv2pvNc9RtHSvkR2toB0+l21scaa1pUqs888iAr+E+EeM4BCiJ9d9e8dJED3WAnxwN1wKKhTJLLrnKjuQiFN.VtcQAuK2XjYZRGG0IBcccyqplW01YAuLfmippMz195tgqyVDWGnaVWCi5JALkss8btcwSUM.y.ehnU2tNF8ZZps1ZqRhH4hiichZ0xRDKAPhSZa8ILSRpUqVhe0CEC0Ftmcsm9ggg8a17PCLfR4ECyqyA5t24yv3qaCem4Y7Gw1kEWjW.6TB3eFH2hyRYxvTzlTw4pbRz8qHhzEiRXdRLrF47E7U5lHJjbb1Mv2ip5SrUqVOFOOuIA5Ktxhz0sCzY9ZvZcqik1RKKhTCvyLQjZX1jPALS1OiIKsZ0J4mU.golZJ8Dm3Di0wdXYfSJhbx866uz8zr4xhHK0ue+EylM67999KTCV5s89euq7heMu30nCoBPaZs1mpb9eqZAhz6E13QFZYpQXLkBUQU0bhHNIu2MSttVFXs8Aa9Pm+qsz6EEcM2K1SGy8h8A73aE1plJZFP2LX+MVJ7PgmHHHXonnn07882BX6nnns788WCCWm6ALOvI.VYZXiRv1M4rlw3ucZmty4SOnAcO.aB1IJ8eAlixL+bkf4KUCJz0kr.Bc1o2rqplMgdcEUS1LlJorabAb2byMmKe97U.xK0kLZacDHI9991I0csBnBLTG0YYzUEwZQMV690O5WO5E78+8GcuQMaiv7M7Zr.lmyVgDQPlD1e8i7ib0C+jexubJc4uPNsF+Y7L6FxuJTbQymk7RUwg4qpPu9UgM6MCaRQ1flrIW316qA7CHaenRGnFt7XP4Inczuaf8DEFMKBY.1TUc482nwh.c6BKzrYy4ar+Fmjt0V43G+dW8520tVqSMVGk0oGqatUwkZsY3S2Ik09.qkA6lfMShCkv9e8y9uZ8DdBOgTsAJsyQLYMSqdYx1P4CbfqJ+8bOe4rhT2R01.DK0kXssFm3PMahS5IWYkUlsRkJoqIOMoc7Df+k+O+eF7c+878rUTTzV2wey6Yqa5FeCaiQXsGHprMVrtp5IBBB57w9Xeri+y9betQcMOCuJywVsdfVa+E9Begs+u7BegaD5yFLjMoynw7Go.EO9ydiJoua8Vu0IdcutWW4pP1tpN3G+G+Ge8OxG4ibRpxIoW0UgdoyuG+d73GqjVEISiwWx9TUupvvv8YIxt878mJJJJKBZ4xk6u5xqtNBKALePPP2vvv1.c2ePv7JbhtFv5MZqU25KAsWwC1rkYc9w2P6kx1nwGOHSKHKyggUf8zIFLXvDNNNESnx85hHmrNL+FvRKwHvLO8moFOnxRj3u.XeCGN7JiUceN116JJLZpjmqAXPhlistIoGrXPPP2nnnl999GuYylMaznQmlMatzSpQiMG.86YV6ZaZvVzzcKnSJKXNaWOvoAJIi8bkGLwQ2d6xYylMKtL3q9Y+pq7DehOwSh4d75Xtmdt.+azb0YfoVzkF.Ow9M6eUNNNWQTTziAXxYmcV6b4xssQz50UEUVyug+ZgggKQhOQU0N11z06ZaziNr.F+iqgYMrz0p+NIKlFerzL2YeHIko5o+5T7af4Y4JXd9qJP8u7W9K6dUW0UM2wN1wlnUXXl+Ceeeewhqrk1Q2LojmIgQaUv.rxTIrCHEjjczooDVlbO2y8HW+0e8LVWMz32Tjgpp8Mk5YySRrUO+F9QggggWWPvBBrbGXabY6V2Wq0hiiWJHHH8dvpri+yuQ7gLN33YwDa1jtvLsUcZQjxppHdxpzl4wDizI4b+rVpkF+cgEqyzLjFzimfp5UFEE88nn6QPlQDIiojAzkzXo6fgCZZmw9que+FGSfvNPu2za529D+52xu6prvH+mWLwH7MS6bAPx3m+S+4aALst5i3gsFo1hIU+mx3LvzppyIhLopZgDlco0.qNownUi7HjSaqYv.fkElxnofHxjtvjsUMMwVoMu.i3ZhZCh366KI.ToX..MNJJZHl0Q1.SLY8BBBBCCCO90EDbLKH7dCC6Dr+fEwlkIZz87wKe9K16CiXBQx02DXVGtJlxLJCvFhHKBzwCVzFVq4N9tNWywblDJlElqmYM8u6gCGtu1sa+ckbryiPLJaEDDrYXX35.qFDDbRfNMa1LpQiFFwYwHQ+KCrwdg9G4R2RZc707rbgrYLc5Sy9qbIOclKKLedWnPGWJos0B.NunWzKJ9889deCRJ0yLXzMmIRVGaD6jpKRwDQDtvvgCyZaamQDI27yOe9YmMmEVFg...H.jDQAQU1hQQQoIoxwbAIwJZZGtZkJUprvJqrRDPXvABh.5QaVLLLbwjw9kwrFVZL4oymtTcL+rZWtyvD4z9dYQlAVfgltESsTkfOEE8sqel5Ix4yL2DcY3bL2VXBb3j.K544sXTTzh.KRWVD5bBU0k5Rs0oMac0W8SpOTafoTIX3RKszvOzG5CkN43LlnDEEkJ5bitrNwhmH8GUAI8uYfu+01+PMi52nwAFr+f.8w7XdL.PTTj1k58ew++7h2jNrNTacnpoSNrmus3zYmfUZQLL8PXNy3cUDQDGWS.FoheUdyO23hgxooG6gcXls6.a.0WKNNdcfM8B7LKHnRovCENEvrQQQy56ecSEFFVFWJt7xKm2ExEEEkOIHnbldS9L1m.jlMFcd9NgctyPVCjO7G9COxo7QopSGHCTKqKji4IOL+++T26dXVxUY89+4sp8k99tus20tpZO8DfNgv.HYlDhBQdDPOh5Q4lhdTDOOpXHJGMBIA+QPO.GQ7mZBGPEMFQdziXPOnbS+8fBHWTHQfvLQEF4xDgYlcU0t16998deod+8Gqp18t6YxLSBgDx54YmtSOcWWV0pVq2022uue+Nhp5XMo5DDy3+Vu5aabvYbvYBfo9y+K9ymQUshCTQDoLodTOl.AGcngFpucxkBVReMFIy5MyD+KESwBnhQL1MMkq65tNNdPft0FakTysVOvoWZIQ.9l6wrbH7Q9H+aOXFGtu.UNCS2aIiaI00SjNZKsMzpCPRKl05Y7DeF4nN4goxr6yK57b0gj34HSo+WilrLvRgggqN53it4w7OVaOOOUDw5xutmStnd8JBUF9pqUajv6Kbz50O9HG5PGZjXbFglLDsnva+s+1ycop96OB0NXVYSCbdd6SB10mI0xTWk7DRgq3JtBy6pkonCNY5QR9lTsPjpEo5SXzOym4DSHhL8W8q9oJC3HR0pZCspHhWbbKevolHRsm924S2ezQGMK6EY.GmUi7EFehIFJLJbDfwt4W0qYhvvvRgggSJhTRszRnL9oO8om.nzt61oTSiEANpKjmE.WW2d+H+H+HcCX1dDRhQmKNxAu2en0eMC1N3j+U+pe0CALRKpNlXrOuQmEFlVjGZcwDQbSfqm6l5TKKKUSYUhHROT5Nw3SzVP5u1UEprquueaee+Nw3n22YCsBBBriN6YyQSxYXWFVQF1c8Xor1zm8fQymtt3BzkEniHxt4ymeaoprUmNc1TDYKfcZvzcWE5AG4AB7fzONVPECvIU2SaIxYaKgggBBoBZnfQTm098W999ZXPfoTGqPuqo10zCHoVsZZDylzBz50qmTFTSs8GeglOSOv2mkk+dtFG5pcDzofegdppp1PkmzS5IsWoK5btrJ7A3bjrDNcHlcHlM61MYSLfsryYO6Y6s3hKpgggV99d1.48p4UHHHHOFFWX4eLeoVsZnpklB1t4yTjTyTtyCB.vAuudjrkN+071tPdNYlEntuxGIc9siXd9OsYdlY2qjHFGnzS8o9TmBnzzSO8neGunmYQQj7ZrlmTA3TUcDopj4nbYVt4HPeWlaz669tur4xxCXeMWy0XktY0DRiayyyqqpZOw3BQIpZJImzMw1qIz8DggcAZGb7f1IIIc888Mf9LM1SwTObx1h9y83ZJCndwPWohjEabNMRyhQqHUIuKtWr0QSG+UqCMXaZwZPkkDo5B4ymeYQj0A1USRRY6sXaYaYkOusTy6XRSP96N9myFH2sdquw7rXe.EyJc9Goa6MF5H6Su0tP8+xonlEQY5Bxb1fQreYVy3upvvokw0nMZzXr2467cLlp53MgwqWu9XppisyY1YLsgNZp9lLtZbqooDQlVUcxFFM+pOSfAFpa2t4877rybasAAKAnWXXXmfffcQ0c08rw4cBBB1x2+Xq2DVqQYVy22echYKBIyA35v7OnAKYf17l9xpl9OUU1byM41tsaKQDIoLj.yRDH0cuzliaUJ2sErC3rdEXEKKqkA1PUssmmmfRAOOugCBBFAXjUWc0QBCCGILLbzqtVsQek+R+R8AYZFHOTJ2o9VO8K7fs9IVHloxUGJ7RdI+jEcfgda25aanT2ubHo5kODwFiV3c9NemE+q+q+my.ycTfRFGwT565WYeZnZVhrl7q809Zim96O7LyLSlLVjCiFDgmmGpwasxLsZci0VSATe+i0qRRkdzfdP081qs4Yqky.BeOoIpiGCEuxi0ALA1CYWKbwBVJqSuGzrc2tc2Y6s2dSQjMA1twdnjm82dwdHkb3X5s.KXz3.WVUpJK0tc6E777VXs0VaQU0kq.qZbOglsA58u8u84TUiSxD0qRkJ08E7BdAcwb9OGTa2iVu66NKcPojnjZ4fhzCml8TQ6EDb7jOe8vzEf0Dui50AZrCvVwwwaYrWvV6TCZyo6Og2iDszIUWVSohkPKxpuvgUUG6du26cLfQiggf5Y0+6EGzjZzipK0FXKnwZppqDFFtNv1ppJxdYQ5u8u8ucpv5e9RGy2ez5e9vBiM1X4ZnZNU07hHEN0oNUAi0ntj47W+RZ7v2LZGLiF6SvMcqi77e9OeyOeFroTqTEUu4vw3LJvXUgRhiLMzXlO2m6yU9W4W4lKCwkg3xUfJurW5KqpHR0W6a8s5ppVQUcFQjRoKJmov582TAfcRRhsldML6ryRXXno7eDUQPEUS.oml57QohHXmq3Jth1.seuu2+vt0pUKYV.BLGy5GfsLG3yEqkvQHAmkRYlRbmW0u9ucGQjtUgDphnZqb2y8bOoY8a4BvoyDLuKdcXeFxDttMvvjqE877VZ7wFesSDdhcBBB5AX8+887tJ7Y+re1gBCuugNQX3Pug2van30TqVwvvvBPbg50qmGH2q7U9Jsf58KEfGka6iIdrurbeJCPIlfTyml8mrMMTvoEC0PaLjHxP3vPTswHu427adTZb+i+YtmOSIU0o9eea2VZs82XFRydakJy5qZiZpp09bepOmqkkUYLfzMZ5wdvEOyiYg8rrYLlmm2XppidLO+QTzQO7gO7nAAAi2s61k.lnd8iO7wihxAvrPOilHsPxd2mm7giMYHdKhcLw4usa61JRUF9s7VdUCqpNxe2e2e2PKfSAfbyYzjmK17GR3Ad+ta2tB.JnXDiytHzAnsJ5N99969WdW2UmlzrWXXXBUPwIVw179h6gtF.znnHLZhQ72.2pOp1TNUlVbXt+IMv9JwrS9742EnMdzkxYBr6IuPabWfXKnosppMM1SnoyboDfTcByUia1LAMUQ+ERBBBTOee8qelyzyoI897Aedy50Nz8S8od+YfmjzxnaN5Cnm1cv6w8+IIB5AQl64VljZHhHunWzKp+XjxwmShgN+G24IAh6RJ6MGd3hqCrt.aU6PGp8+m+OuqDOOOBBBrVYkUru669ewdmc1w9Tm5ThpZFHI8777RRE.R.rlaYrpabwhAO+OZw9xAlG6T1QLcNfbSC4vYeymjNmxIKVCFhkXXfgWv.VxnhHiqpVRDYhM1XiwVe80GRan4UUyIhj+ttq6ZHfQeGui2wXZCc7TcOaTopjIvqFmjAJdUW0Uk4jU4HSrWMWipuuW1FVMfAZDJ6zjeIc877xFq2NQR1AXGee+s888yXSh3tD1Kyxeyn7TR0AnlFfwZ0m8JCxHmhyzf7QDcwzSjzwz0GXMzlq.wKLyLyzRUCSd8782AnqmmWhpp54UKIH3D8.zm9wd5.Xe1yd1LvRxAXe3S9PRb3en1NGF9ld9OX4PevOoZxV8r0UyCmIODVvAFhELVUcCC6llVUcZWW2o9Y9Yd4YN2xn0pUaDfgKVr3vhorkFIIIYrphLAFlojwLyRQQQ8c4QfbMa1zL9PPR2TaevR.5.xtdd96hHaaIx1999aigoIaCwlu1hsYOFwNPI38Mh0NeJYNPnAF8OSDFczQStka4VR.zV.vBlmuQWRisUNbqz43hWuIUWVDYwolZpkDQVOJJpsHhXhEig788G9G64+7GIIgQ+G+G+3iGCS71+c+cmHsOejEggfUOn6M9n09.NX6bStkC1TaYafbui2waOeLyV3FuwarniAXygih9JCSJnbMa1bDHdr+KO6mcIw35MyDYzEmYUUmsc61SiA.kLwsdLfQle94GFCXwCkjjLTZeoMfDDFJgFsuAATO+iYFSXYk1iESylM0ie7i2KL738788My8EQxYO6Y0rnSd+u+2Ov9rl9uUn+9h1drLfICNwl0opgEQ8Grq.89LelOyt4ymeygmat0A1nUqVaoplQ4qCdLNeMEfSC8fo63BaQDqRLKTnPgl.MKUpzh.qzrBanMzc.5jVSpHh.FWJIql6ZCzIIIYe.lDFFlDFZBPQUiYy0Wf.PTUSxNFIhpZvwCDeeeKeeeoVMOyByGyuWJEz2EXWGGm9kBP8ibjGoo8z9nE6BttVpp1REICg8QeZOsm13evO3GLCg7rfMtPOOLGu5ziFzgT19jKWtE877V.X0zZ1lzi2HW+0e8iqhL1wCBFtVMuB0t5Z4hRc0DfbyO+74MzXre1gdzzivGbyr82f+ggTufnrLCHrH1rJ4dBOgmPQU0gUswn.SDo5TZrNS850KeEWwU3npVUU0UU0qoiimHhqpZ0a7FuQGLrLYZU0I9pe0uZeaCK679F+Ue81.Vqt5poTHQYokMRlWylMUzAYIk1y22OAQ546620T66FPAewu3WbO.cAWWqFMZXiG4bNW.Sdv0NIIYA1Cz807q8Z5.zowrzK0EKr08blkB.4OYsKpEhmcszCn8G5C8g1DCMnW3e3C8OzxxxZYWW2M8886566a445kat4lqfmmWdWW2b24cdmVMbvxyyy9K9E+h10167YVBI0FSeTpsuf5Nxd.kjEjeF3H1W+0e81Xn.b+emJPwFpNjHdiTEFkXlPizIdcutWWIU0IdlemOyIDQF+seG2wXjUK1IISMP4d4l90Aso5hozC0BP50qmXaaagZ1jijdsktPcwimo9899CCLxy7Y9rFMHHXzZ0pMrqqad.YgxnD1+9874DSOjBBZp9fb3ZYkjXqQZtjD67.4e4u7WddHNGf8YpgvEVSdDbPb16c78MWi.InofEjXJePfcCCB67e6m3GOILLDRvJ79BsHFR2vQuzMH2y8ntYtXx2xSq0KPaPVLzCnWEi39k.U5oppDhRqGb2ihiiIeXNHVVGHjGA9Wtm6QEHw222HZ1l42zff.dbG5vDaxbVhCjPL89N+N+NGfgnF8rIp1kz0zAY3U13k92uhHJf79deuOaLk3PpsadjK97W6A3zNXnb+J.KO4Lyr13iM1V+T+2+I6FYJSDYpoJIW1kMGO9G+iOY94mO6duap14noqQZ6.4NigEl4N7CfFZ8nPK6bao5hBf0u4+2+Hg3AYMW+M7ORcOOCaPpv3kSEjZ03PIimVdpC655VTDIa9vgdouzW5XppS7q8y8yMYZ4SLNvnep+lOUVxENH3L8O2dotIAfDDDH9G8nfAPzjqxyqGjBLJZafcSAGYGIYOFkQZoIDEEIQfMT01Y+yi8MZ+eevF2ZqsR.5pp1wTtLRO.1d6ssbgbKB4fR4l+hOOZ16tokqKq.rvMdS2T7TSNYCOOu9k.QXXXGSe.cSLrNQeOum2C.xgNzg5O+cMidZ8v488kRqebnGF3qqp.Glib9O+le17Xyo5mjggXOVHMd7ryNIvLTAmzxyu5W3K7uU4u+u+ueZ03XbSfYb4noex.0aTKKqwRK+lRoel.XTWW2LFAmKYOffALf.mVNXFcjSjtf1t1U6uiHx1.aEDDrIvV6t6t67k9Reo8.GY+reevOOTYUldlRkTfDwQ5U0r+n1X9Z2ud8u9ASd8E54rY8ASxeSEo+FKCzZ3gGtkmm2xdddapp14c8tdWZyFMrBBBxcO+q+qErrXnW1M+RGIHHXzvvvLw1cXfhm9zmdPAa9RY70ijiASmW4vFWlLlbT277tToRVp1xFnPCUGVizQDiH.O9ce228Du1W6qcRfY9H+G+GU.boxdwhIhLcgBElTDY7d850uu.nfHRwSbhSTDnnkkUQQz7HhMJV9oLxL6ZLH33hBxoO8os787sCBBrArcbbvyLWWGuq5p5.z8PG8P87RGa8BeguvCFqxiIha4wx.l.CNvsNYBAI.JSOcmu8u8ePiNYzp05.aTtb4cDQ5hIiPGbx+KBx4K0MxrP1pPkEtwa7Fi.Ba2tcrHxhzj0SoL7t.sEW2NppcN68du8yTFFQZsikkUBoNLAlI0xN8lrF54lZyjZpH.JpZBhII6RMLLTdWuq2kUPPf366qDSu50q22ZYoZ01opVdWN4IenJ7geizTnbZ44DY9ZKjHUs1c2cymKWthO+m+yu+KoboxvDi3n0AXKJyJUflhHM9JekuRyvvvU7771NcQBaQpVPDYuMmECqt95IAAFAnBCEyRyFvdzNiuA1f0En8.sPvACh1hZXiK1TCqS2+5ok0h8ybg+P2+8e+CKtxnNoYePDYZQjYum64dJWpToJ.NOmmyyySDwWazvm8uw0YRo3Yo4me9Qwnp38Y4yq+M8Fs.r1d6sjhEKBHzqWurqbMabq+w7S.R50qWOOOuj84HIUqhQ1dvhnHqpUqZSHV+I+c+cVjdwj1dvVVNJLuYCFtzclLfGWHEPQQrDiZomGS1+yS85WJYNnevde++Le+aiKqgCK988C78s.l56e0nnnc.5cG2weD.DDDfqIigIDSxq7FdkIO4m7S1jk5JUTnp4daOKN9Q514jghSlNFp1d.lzmcQ24cdmlf+cHGtTPUcnlvHhHic4OkYlnAL8S4o7TlVD2Y9LelOyThiLAvXufWvKXvrtNhXaOFo0q8.exrzugvnqI8elDGGK850q+lHc87RAsQs+reVCMsUUyeBS4CjO8c5BTgBUydms0dYDLNN11HRsk5aagOj58NhA0LSKReU2xsnhHby27uChHxm3S7IxrCQg5WBGuXjXv9+2a+1yrX6bVVV1tttL93imjJbc8vht9GyuKB8778R.QTUyohl2yyKuoTSPqcM05VIiwhMdDuN+e3pctfHLEBU.p.mc2cE.Q03L6uzBl8fqS7.DbcYy6zMalHhjPLIG+3GWg8rwYT363Y7LTL.jn.IpwchT+i5yoO6oSOmwVw8+KLtDEUH4Lm4Ll2+qeA62O38n8zmKnYJl7jnppxW9K+ksoL4WpOvem7RAH+8z6rxkWGptzlat4hKsvBKMwDSrNJ6nF8mJ4nd0RTU6FEE09q7U9J69E+hew1XXWhFFFJ3fMUoPb+RncoBm9Q2DJLXyzO6fNS5ZQuhezWwfavvl4lq.L6v.iQXXIU0IoIkZYX31jNhLIPoa61tswxmO+Ho56UwM2bygTi63LNvjQ8EvZy7WW20cc8ALQpJCBTxfwMXEFE0GHkv669r.vy0K4DFVJ1IUeTFjkw8788M.lVlN3POph1sa2ziaCq3G962UfjQFYjtoWSsEQ1kJzNNNt6vyMLQ8Gmtp8kXYKXlCqTocfJqCz52+s9VC2YmcpuxJqDBrfZJOmcRRR59ZesuV8PG5P.HOym4yzbuVEaph8ezezezAYj5iTs9fCbZ.S7NmlCjM7AWWMWJXIEw.Tx3ToRIvcJfYYgEp.3owZMfC0qWOeKqbNOum2yaFw3ZbkTCnHiqFGkabfwwgIj8bAmA2jeevR50qm01ato344kUxzTsZUxbWyvvvDmTWkgXZqptShoL81x22eqgFZnctxq7JaCz6e4e4ewTBXkwXqyy2uu3gLXI.JqtpIgwMo88u4l6HhryW5K8k1UDo6g8O7AkHgKkDaY.kykspZz3oEDQZljjzJHHXYfM+IeYur1ULtolr5JqX655lO33AEAJ9t+q9qJFFFVHJJJOf8gO7gOe6E7fWKG7m8MS.712ZE.4gSWHtuVIMsYuJNX6XzDxho.sUBXxa8Vu0oelu3W7r268duFMzrggkuZbeszbZ0TVWiqpNpkk0HhH8K8FU0bG8nGMOP9jjjbtt9FQjULk5UFXvhQEXsDvZt4lyJndfURRhMfk+QOpjJhzcnYSy9eWf1go.yc3yUSbfGCD2xi0ALYeHTIU5C5PBKsTWn0tNFQbK0l8l0PyQWjHvhouj1jNvAxbS4lK769696F849betfBEpEhQrzVlJrNvFhHapQQaBrYsZ01VLhI2tXPwu+.jd85IIII8O2dddR0pUIHHTMnEKYTqSEnOMY8OpmnpZ8betOWoa2DMHHP+nezOpV6pqsWl+aznGr3AYVxijCHUnUhaJEq+i+i+iaCNsEQ5V7PESvTOiClo6K0M2jvQhMYvnEq1jxs.Buhq3JB777hCCCWJLLbSzjNp1.GGGaU0bgAAVfihEIpklzDTbPBBBxn.5.0A87ObBZx4CTtK3Dx5YUKmDGwotYA4o1eFyFFufQAlfFTpIUmTMA0MyK+Fd4S+LelOyoRyZwze7O9+PYU0pc5zopppCvroTyaRLBuUVsJNTZYXrOm3Y3gFQlYlYF3B2X95pln.IAGOHAEMNN1rYCKvXghXe++S+S4fl4ql1m9g9PeHKvQ9A+A+AUlpukf9Pa73QNk4c7HRVvnQPlMs3sGqlDQxAw4md+kZz46Y5fuaXxbQT0sIh0JGyR.MqZDJ5kxmO+5pp6bC2vqninZOee+dQp1Gnna8MbqYYIt2Yt26UgF3t+m0WrLn7vY6faRKWpsykGOx+y+a7ajmYnv.keSV+Tdh8JPDCWwTtVkpBS+U+Begx.U9m+m+mc50Kn70dsW6TZrNlHNi79e+u+h.EEWYHCqmzQHUnmS+LXPd6qzu51sqL4jSlcMq.DFlAvjv0dsO88sYDiEnphmmmMMIWCpjGHWPPfMNleGGGGI5DmPfUyJGpGR82GwDgr5mtIfrxqjpw8.Rld5o0n8f9StHlgj.SaAX8+yu4MYzfHm8X015qutHR55WpPvwCPLRpgknpsuueVIKULL7ya..tA5uxse66kQvReqe.GO.sACNLGKWyllXUtYZIo.16ABJ4X5EtDKOfVFVgzOqoU58TepO0DDznzrwB8EacA.eeezDiQbVsIVyM2b1+K28cueFK4zvbdahN2bemOXWaUXFr9OWYECn3m67RVh3l+m+m+munSKifhV4AWBELkDQqVa.MV5wO1XM888aEFFtT0pU2.XWQn6IBB53eL+cuu6691947bdNaWpTocCBB5BPRhjiXJTtAEN6YOawO9G+iuu5X+hbM7My1f8yIDSxRTdOP5mt+3n7blyTDVXja9lu4wAJ8Kb8W+TPkY.lgpLarpyr81aO4y5Y8rlnPgBilNu0v+T+T+TihgobSxdkOQFfui.TTpJ4AxoMzCFmP+OdFWsIqDGsArRsiydhH8DnmHpx..El9umPqJIgmHLoZCTMeds7d26Ob1GlVBW8KAtLAOdSZ5tU0pUauxWcEsx7ya6s+0GtPu6s25nqtZan4FUMBqY3LyLyY+QeQ+nmAHDRVDXSKKqt+l+l+lR5wtfuuuYbViJ4zH09+4q3UXAHkejY8xGv6kibtrrPnerRGNK1wh3wHXLXhIz33oTMblJlMn5hQE2pA3sxJq3bjibjYYuwVSgojax99IAlTaXrR3FMZLlrm8Uuu2Csssk0VaMIJJJytWoQiFDFFvsbK+JJXhOS.MSzW+m9jehc7882LHHXSOOus.1MJJpy2w2w2gY7XqYrXQrRcwvGp86Gjsfc.mcFczQ2VUcqq7JuxsopwkOOP+5E6blcb6RD61.+0wjPq3+8u3+dCOOOCSSbc2zyyqsHRhuuur4lalIdtV2zu4qxxyyy18o4dv4dEN2w2G76OH3nvCuiMO34xdp8JWYiAZTdIilIESglTMSSlJ8b9u7blFnrETVihpb0W8UuOWv4cbmuCGQjowjLqQG3yvpQSG6Ge1MbC2fMfskkkc61ss7MIvGv3bqdddhppQOCytlEv11l50qq0u26MAhSpt27KcfYLLY5Hz4z6mESOlItkGKCXxfnRkv7jPKRXl8IppchMr5HkhiKzEP0PUfYsYoyIXkKzh.YTMbSZ4sDP70dsWanCMit+6+9aJtxRzjUTUWAXYQjUDQVa4EWbcLpAclJ2S54z1xxx1xxxZfZdkFMZXFBplyoghrR+AU999RvIBjm2y64odddIc61Nw22O464m36oGwjf691.3iFCD22FPiRWL95u9qeaHdKfszXcWQb5ktAgAY3wEiI.li6IIACk71vkVKAzPbjyBbVOOuT0uV1HHJpSbiF7q95e8Vd994fX6mzS7IYIIh.UsHFaee+7G5PGJCzlB.EJyoFjhd7.bsbozN3jeWnIj6OgcEQj33Xq3zwIKSM6zMKLDvXDVYJLh1ZYGZTAnLUY1+ve2+vo2byMm7G9G9Gtz.A7MS974mkTAcRUc7jjjwN0o9OGsQiFYYs3bVHtamtxN6rCg6swBCSmDxFOZ9XzcASM86US7OpucXXXwmvUbECCLbiT2C36+6+62JUXDSlYYRRYIxCkwo5.NhPReFd3ROBoGNjr6t6p.npZuz.0BMW3f86WlOyXzAnMaAKqp1JFmF2zMcSwyN6rKBrdXXXaOiM6gqHV0pUKGNX6W02hxli+byMm.XsWF5NGpam8r+g61AGOkA1V9TKarvrgT3085d6EXQC8KY+t8vvtDNJv3MUcJU0YaPUG.uff.+ff.OKKqxo.uMtpwYfgLjFoCmVi+ifgYJCJFhYAbuu9hEWZQYkUVAFXbffRkxUv22ee2K+u9e8ane1O6msmpRxa8s9V0ff.qpzLGPN+i4aSLRp3BiqqK.x2HJ5QZFE0.lIg9znmtZj1SUsmqqaRYhLiiqgxouXOSVR.rqtfgh8+Nu5eGaKKK6Hy7+VAAAV20ccW1fZO1XikSw3TUt994BBBJ7te2+UEAF1y6XCkpSNV+12zMYNCyhIWaWRNy32xzN33TafbUM5.fcKPJVrHsHsjNMYSylkbujoOc7dqce2puPG...B.IQTPTk.MUaaaUTTEAzTQeU6uwWqff.wRT4zm9zRCCvq10l6wsGXhP93+s39qMThyXN9G4RZcWAPldQjIm7oHDgnptu6AQDKGZj6i8w9XEu021aaDfgahaFqFtz1rZe5pyxMSYfommWqFMZrh.agxtJzt9mu9tOsq5osSud81oVsZc788ILLL2wp4V.XnVvvG5PGZnmyy4+1A03pGo.88fsCd9TGZY1j2bXwRFlwwdhJ+nWwU73GGXp+f67NmEZVAnpFoU2Zqsp7R+wdoy7s+s+smw7sQDQF4u4u4uYLmT8MgGH1wk5fbLv5JGPWbjvvPKOOOaAxoFQ00VUU7884nddFQ.SEq5g0yEDDTnd85EbcMiqcnYhpZuFP24pVsaKpNn6D8vUSS0lBiSOkBXhHxlu9W+0uMPmImbRs4oNUtPpLXRsdvjfwsaXlUpIP8Oxm3ibFfyN1Hi0PUYEU01pp1gggFl.AiYr90lCIhjONcrVKJmcbuPIe3ajXzdfNFI.Imb+88Vv780ojo3zlwZtLJgLFUYbQjwEQJ8q8FdCS0zLybkkVZIGfJqs5pyL8zSOoHUm.nzu7s7KWZi0WeZfYDQlIcCsYZUxj.kpVs5XpwAS1GKn2bysjFMhjz05xVyq+s0a8s8VHaI0a9VtEQUU888S9Xe3ORavYafMCCC2nd85a355tCPmyd1ylLCKBf0oe3APfr4j5.w6.rUEQ1Fb1VizcA58Rd9OeX+fPbwNe82OlGAlR+xgVW021UE0sa2HLtm2JhHamJ1x5G8i9QACK80fSDXXIXy8MOce.Jl+7WJuC.TxQtTEA3GLsCBZS+DHrLd49E+E+EK.L7t6t6HzpRV4dMhCMFSUcRopL6G6C+wbnBU+e9FdCUEQphwyly9T9ke8u7oUUmnpIQXYIxJ6Se.SpJRt63Nti95wzBKrP50kjtFinFqQWzTPe6kB7UGui500xxpWs4lKAPOdZogYddsnIF6S1OV6GSwtD3w1.lj0LOPNU5CgEc6Aj7G7G7GzCbSWPnbO7Smva1rMPrP9p6eiTWrVBPmZvNP35TlkfpKDCK7DdBOgknAq.rpHtFqdjpq.Nq9ttq6ZSU0cTUyr3RROe1I85YBRTwZ.a.ybOI6sANQ1i1lAFk6u6+v+v+P6vvvsGd3g2rd85aEdeg6bO2y8zgnxohdnmUJ0sezHvFy8PePlpsC9rIv5Tl0A1rSmfsUU6kFvXdvIMfrZWJOORWPt7NQYKH2j.QjS2sa29flrwZqutBseSuw2HggglE4bxBzoQtT1OTHNNtHLaJc2loXq8pQ4Chh7CkEoMS.djzMDbj9aL3.fnbjziWYo0AlvDpmOs9pG4M+leykflynFga0MFbEQbW7KtXkyb1yLyke4W9ju2266chT2wYv5dcbR2Hqss8vyO+iejpUq1W353.LwHW9b354lc+0ei.B6Sz55.rqnFFTcG2wczo98EhoLmpT.pT7Nuy6L+u1u1ulUJPd8XN5tHzEN02HHKqbXRfY50HairQlqIsg1qXwhIKszRHRUS1FY5hTKci6G47VqpCNocuE2qFrWWDYAHN5s7VdKgas0VM1c2cWDX85g0aCUzHS1CyGdhPywuU0zmYylClMEHvpEX5z924ufflboLt5RInwAW3MGLUd7RADbFxu.jCByWduMAVDpNLvnfy3QokSiHxr+.+.+.UgF09S+S+SOTmNcp8jexOY20WeyJ3XzijTPQ567U6dl1ibS2zMkk0hAq2+8QYc.YkUVQlY5YTLhdcOw3pDIJn14rIHHXeOid8u9e0jenq8Z656624k7RdIcU0J43l4LELBioRvLoA25l.kRCxcetkykZaf9xEAylmIsT9RjpR2Ymc1tsxV3u9EYr77oGqpHMLTr291+UtE6c2cWKUwVDwVfb27Mey4.JrwFajGnPPPPgfffBppE9w+w+wJFFFVLH3DEsrrJ.UxkQS+2xs9VLmm5O1Bwjzlj5lTVppVM16ciz4chztc6JppxrfEDITBouCLbdOdvdE9mqjtmKs++AELTLVzT.aR0tGaOeeqCe3CaEDDXkjjH0p4JYLmyEx433jipXCUjU2OH9W74ypYRGJTGW1q7FyJeUUUodmN4TUG5FuwaLE7wn9VV6Cv86fsz0FKsM3rJvBfSCfFdddK554sFpwZGu5Z054642y11NwTslX64cz7mHHH684hAAAEf39uCOyCeLu770tPy+s+41lGKvwNNSOlNiwJpIxYbfIfxS.tSb8W+uvTfyrhi3.3SEpsxJq38ptkaoxeyG3uYxjjj9VyZ5lRGI1Xav8sbSRchD1O3u6a8RKKKAf.Ck0UOOORGaYYIhsqgwIRPXfbh5gBnVGy2OWMuZC466OLXmtokJ1w.oZJS6FMZrKznMmq059MxFLNHqJ6789898lBZR0cdiuw2Xes36W9W9WVZ17KzGbH2K9FaG33Nep376tBtzDHPpJmsToRg+6+6+qK566usHhkqq6nW208rmJHHXZe+iMU7dwpjBPPqyWhMen.dxCzu64qrpN38C.bXvBNUdy540JtbldkDkls9FUG4i8w9Xi.NC+q+FdCiggIuSO0TSM0+4+4+YoIJUZLfQTswH.i7+3U7+X7QGarR.S1qWuL1A2OlsO4m7S9.lTqBExKUq5RTJiLihhFXPgncZ2QytE+ctseG.HHLH4M9ldScqGdhcA11yyaqZ0psETdaf1G5PGKYw85qd3.P.y9.bSYZfKsaA6BwcRS1k064C9AykF+edn7AKK9GnioBzKLi0+wr.PT979mEHHLLbgfff0R0KE8HG4Hlxvzw.ZBzfe5W1Kafm+No6Kb5bm5biYoe73G1Tdj1bXrgibwR19Eq8.MN7.LdOL2u2u2uWNf7EJTXn27u8MOJNLN3LYrAnsJ+kus+xpuxeoWoai+sFUeiuw2XEvoeRRY.suAXj38zqjA+zWS6ZXX9et1saa644YAjYQ5pjE6uIQoIXbArtpps888aSbk1dddcM+9USKemJR5RwCR9fGsSr+Co1i0ALYO5Y1+STBPxuvuvuP12qPKbCRop4BTTDYnYmkgZfeZ.8WzMo2+AccnCGlcJ2hMfFqQUVCbx7X5MgFok+Sisf3s+k9k9k1IkcIY.lj.PRRhfHxVaskUZfajRqDU2CgdiaInrqzWCTzc+0+0eC6.r0Q8N55dG0asZ0N1Z.a9LdFOi1PqT.SBsLtDvEkAMeynM3KFcg5scCXGLkQylhHalOe9sEwss4W2IWV1ug5WL1.LvBxsZyzrI3sLPCfylOe9uNvYt5q9pabkW4SbIfM8775lh9dNsgVvyyq.PwO5W3KTDXHGmm5vvBlrjqKzG7fCe3CevIMevrP89lHLUo0ywIOGghy1TBPmL89t0dBtYkJoSl4VzyLI2X25sdqSppVtpHtc610WU0+M+l+splKWtxO9G+ieJRK0FUabvf85OwX5FYGTzO2WedRRBQgQR5jjnlIJSyxdRhHz6X99cQS1UDYGuZGcaee+ctga3F5TyyqWXXcMH3DTu9w0q+5u9jumumumdkiRGOeluQrntAlf8zzCVrKyPGQNbl18zIEo6dSO8zRZf9CCKMhecCcp4jmSVRO3wtuN.L2bysAvxvrwUg5iN5nm8O4O4ONrZ0pK565uwevu+anqIyhN42byMKppNjpQoA9tvv6taPJ58MJVcoTPoN04E.gKDnGGbrzAWb87I9fY+No+rkyoAokyvhXu81aaCHMMf8jSUs.zHs18aLtpZo63Ntiocbbpbm24cVcs0W26oe0OcuK6xtLWfJiO9nSqMTikT6zutpGQDYjBExOxse62dV1KJ.jOErxy4dcxImjlMapdddIgggIl48LzTOyJqAjzwe8TU6bhv56hC6bTe+cDIYWuqxqKTNkIdjjVFhc+Zes6tKrZ53rS9fYbV+yaZsMaAd1UR6uiUUEWoW0X592+2+22FW5.Uunimm+ToeSC31u8aGUUsAUY2c2EOeSIVN5XiYGDFlWgbYyQ466W.QJbM0pk+ttq6JupZA+i5mVJUMyQYrAWqW8q9UmNV4RQLU9VtllccKhjI34ZESs2qPYsV97J.K.BNHysJBbpKvgDALkZipglB2DjOvG3uEX.MLw7isv.ViMf8ce22ctTQOMs4noWSlR4sJVQmHRflvznvgGDv0Kz3Lc.sNIIhrRnsRuz0sUQDxmOu0q3k+JJjsAdn7v9l4tuXrjaf0FWsMDmJd0wMcDoQXXXLvRd9da1oSm1MLkGrDDDX2qmQ7y61Mn3ce228P0qWenJUpTv22u.3Ze7iebCiv.gC+vdLEWpaB1Le2gIW4SQdHtHyxHfy.ZlTroran0LPzr.kUsgiFq9.GpWTuZSN4jt+Qu829rhTsjkkkYcgzOo..OX4DNTXXTALZmzfLKYeLLoZ0p.n9ddIggAohtoI69ppVYIrw2yOuquaNQzbGOHLuAPppCA8RW2nY1yYc1Ymsa0pU6.tcJue.Sd3pktd2789ve3ObpoDzHSzY2Enya8s9VSpT4oIyll7lHv1.V0E74d53vSkx1onsJGwJ.MIl.Qjy988888EEFFtxW6q805BT7S8o93S366OSpKqMCoLq.XXnZgxC1meDxcjC3neb9W+6RbL0Q5yXDN+kua+e2SC4nD4zE0hP8hTkg.2gUUGw7IZ3m6y84VDhyD90wEQJEEDLwryNaVBFFZ0UWcHfgu7K+xyrm5RVVV6ysR.F965656JCnjCpwdrPqVC.RhxTSMkJobqETUjLyhP6klnKUST022Wq44k36ezTShn7NPqc.5.s590a1TAvww4hN.5hz5Gu1giRS.rINvdl0+brvTxZCAwowLz5fLY5BMWWONBcfo2BibiECMCDQp6440PDc4nnns.5d4W9kqgggnMTQUUBCqK2467clFiT49ruU0Eyh+OesZ0RGyc3b0RGWbZv9ltoeRK2SiEbxyWLaWr1EKVtL11WbuDZ5mub56ehHEds2xsLZkXlDhm0Tp8NUebOtGW0eu25umiiiS42za5MMipM5q6RrGCRFh8J+lg1c2c6G+ep4Wj+e8e8esOPMKt3hVof9120kzrRS1vp7T1kKc8886FDDzIH3DY6yUgFo2SMsqFcdeV9XFfRxZORsA5uY21av2Qv1rozYxAKlMIaeKqTUcnzZYqCFPNVeFXyES0XDtDnTK6eiJ4JAVql8yllBrDiAkmEZ4opdHf4vPV5LQ2YRfQBBCKHnVnhXdRnJHIhPOUMVMGvNHrsum+1AgAafxphwtSWHHHnIPqq12e4FlILVmprAMXqnnnsbccGzdvdjVP.GruJGl.RxVbXBLOKxKhjztc6cKTnv5NNrdbLa33v1wwjUBSOPWyYG67kfgV0Dvzr3f2Je4U7JUpjGF5PNdPTjsuqam21+621V+vuje3MNzgNz5XbQf0A179u+6eyBEJr40bMGZi3XVyyiMCCIS.e6xQHYPE+JscvfjOeTKavwJ4TUsRKCoAqcOwArhA64lC6ybFxM6rjagEFXwaGJPLihgllN.dppthHU50q2zeOOqm0DerO8mNydfyVvevRfPDQxn+c1FosF3q6iIMppznQCbccMA9IzCkDE5JohJrBaIFUvewi56uvIBBVz22ek5g0WolWsUgpq7O9O9Wr52828285XTF+0vPS7s4arwiCtPiMfcYC88MiwpxPzfQLLFZgB.xK5E8hZ+9deuusw77dCLADN30v463mED0P3x3DwzXd2sZylMmoQiFi8s8s8skOstf6544sKvlou+sATdSnUJ3orAFaud2LpKx9Ui9yW6f8MOfA8UCj5Y+MyixoPg4E3T809F0XWlYAhLX.h4cfBwvPUqxvMZvnNNLYbLy.37I9DehJyO+7y566OElL+MT5eaV1ISpJBoYknuUDy9cRhr2C1282VaskNxHiza0UVIYxolRCCC.UrvTyDYG+1HrMJqpPSTMTDoNPnuueSfEcfkiMLMaSbYGSPYyjogSC1OewFuk8NvfAuLLUXBZxjXpy7QwD3RWLioW1CVJzb92gycMjrmS4vD3RILiidb.OoNc57j1dqstbEb2Xi0GWvJGFLC10yyaqvvvMQXSAYCU0U888WJHHH122OBiAZ0nBrXSy6haPM1g5OpLe+Ck1f824bbHWbL477HWXX56dvnsa2d7BEJLjpZWQjTPLMtsAm6FIGbcmr0blF3v.Ooc1YmmRwhEORTX3gRfIEnHJBBcEgcPkMUzUvPqnXee+5TgyPSpigp2qGDDr0f1x47P6Scg2P6AiaH26+8+9sdguvWn.HdPtPXzJUXx3XcFWWYpq8Ze9C+A9.efjeieiei09U+89UWfXZh4Y7Vmm64yy45H4fSNDlXMbAdbewu3W7IL0TScY.ydC2vKu3e3e3ebOfMOpu+F2WX3luq20e11ulWyqcqJUXilMY8nnnUbccWEGVgXVEy7X6st3CbMnO3yhKk14C7ky2beYymLnajL7rvHsL5lzPqs1ZEKUpTQnxnpFOgHxLppkAlsc61yjKWtIsrrx1PZ9ziehHRZFSwNIIofkk0AY0SewoLsbarHsTb777389deu7hewu388NW1+VJXtYNXng4hvpJrjuu+hg0CWDKV3ptJuVMaxBNNrTbL6kDt4XKNy4rl0CGuWuu05JUhhqtJCqFGZIK9LTiy4rElm8aydwTdgra1AGumoyBSHhLCotm1W9K+kqdEWwULSTX3ntFgjbaQjUTUWDHVDIFnEUYYZvl7.vxFG5W5cJGoOk+yddob9GekcMZ6.1wf8TSgr7xj35RunnANFySlCnM.iL2aistvv+0e5O8nW20cciRUFApNBMXLnwj.Ud2+k+kNe6W60V4w+3e7SmpabYqgN3ZiCtmBqA92N3lw6+0nnn9kgSTTTeflI0xpw3BSYe1lTmk7du26coq4ZtlFpHA077pWAp2DVnbY1rUK1sREZ2r49rV3KTb3Wr1frlHCDow95e8u9XW1kcYi.TvARhgccbXs3XVat4Xiybl9ysegLrBAvZdH2oLqsNAtTlHpAUtLUiurvvPOOOuR.Rud81HWtbs.BR+z.XIGXiXX2JUnWylzc1YoyBKze+GILMYzBLquef94GTL+Zvmg8e1dDPNIHttXGEQ9pUoPiF8igxhYwFy9ALZWnYN8owr+FmO+m+y69zdZOsJ111Sk96LXLXVXh+uuyqRVENjjjyxx5fjEXeycmNGlFFFljtOft999ZPPP16G6Hhr5W5K8kZ9DehOwP+i4GRLwfyBP7h0qWuUsZ0Vjr3SXf90GC1drNCSxZ6MX8jY5ivhI.IpQ0arfYyopVPbcGQSob47yN+X.Cu390OiKkySxfeVcfEM7WBAbESrUn.ccEYWLKFzW7WWes0TeiRVimumIM9lZDKIegh641Mvt9d96FDDr869O+css+w72PUcUfkDQVv22ewFlWmWGX664u7SzAbS1e8L9nRq+yDinYUtGPmT6xbGLun0FPKTnPNfQhiMHqGG2ei+WLV+j.zcUS+z5.K5FS3jSdkmUD4LXnlW7U64shHU25FeU2X2C8ze5Z850sZDFZ+I+3ex7ppEdBOgmPwCcnCULN1D.VX3fzTa5BbxLsMgBTN8qWDsYXvOtfMLsgx2yM2AnaG1wPtyblyj6LmwrH7BKvPoYrXbvYJhMSL5.t3feEi.h4E0ngqkkUkO1m9SOClMhM1ApOw9zHtufIB4gj9WuQQQ6KSJc61knFMPUUCCLzKFEcOgpS5JHcEyDe6BraSniuueufffdGyqVmvvvcfFa8c+c+cuAvFNvlPUiNBYr35KkEWtPO222BVs1ysA550fDnJo1sVQU0Qduu2263PUSPxFV6bgxfwfGaS4jEwZ.KTAhvgy7sUoxY.BTUa444sQ9746fISs40HcHnxHp1b3u9W+qOTTTTgpPAvIuHRdbcGLSVGbbyA0ak8QGzi.1Ti7t6EzVQfh0GfAQlLu5lCNU+iaYvRDey4nb4AAKo.vvwofX1nASALabLUd1O6mc03n3pOqm0yxoVsZUvr.cF0Nynr93.i0vLeZ+LxxdYCaeYDav96EWbQYkkWgvvPYys1hff.TUDkLAl.PSY6iA73csTscsqtV6i462N0JN2En8u+648XTd8xGtaoH5Bk6BKdPKR7Rc71A.DubNZRta+1u8b21scaVhiX4JxfA7Hg682cAZGAfDLNcV+qmb4xwFatISLwDhhHJpUJvl1CjUZa.4n99DDDn+Uu6+JRuFy8VdK+1EZN..UoBE8iURDxffL2KN1vBsvv9Ap2AlsS9742KveG55u+Mpe91r99.yNsB+APJVrX+9F4.aqWUAEMAn6YNyYZCrSXX31286+t2AX2xPmFMZz4pLtYR1XK8TW5yiobjin.IuvW3KrOXdgoyi0rIct+6+9a2nAc9fevOXOQDdcutWmMwl2kbMu+eIFixI6qkIyBKANMdxO4mbva5M+lqOyLyD+A9.++slmm2tdddRSnfqqawWyq40VLHHnvINQPd.6d85YAtRz8EIjsNVUxM04ojT3BmY+GHVZdNyww4vfi46OW0TF.wGwOMi7jpQWK.y9S+S+SOqHRkqnTI2W9K+5qAMmCShplqpH01byM8KTnfqkkUYLykkU1GCgYchhoNkyPVVV8sj9AtGyAjKLJz1yyyFvJvrYBAPdwu3WL+4+Y+4.n+W+u9B.LrXJLHTDPDj8MGuBV9ouOiEPBVG+3Q4qWudg3XJl4fGvzvY9lJE1yhip2pqZh4LEbjMLfSVcqz3znJjmJTXt8KR+WLFOkTC5BSsiHx5Tt7B.g.m8I9Deh0AhEKqUwLV0NErlR2+8e+SqpNM3LIMLkmxzvnLKiVdOlyNBlRLH66KV9jTHUHVOPRgl2LdpVsAKop7.4hSA9X4kMYgOJhAd96jwJzBdPQlggqjtoeLiglHBl35ttqqDPImFLIMzoTMZZfY5zoyzO+enenIebOtG2Do66XPc8ZvXAOXLkGLVx88N0xKuLJv1aucewd0PNSTPRf91V8NGa.vcEQ18E7BdAcADRRxAjuY54qUK3Lm4LIMa9vlfbNv3hZlue1YU.trK6x.H2pqt5PMLfHUJN13FUm4L8KGqKowWmZ.C4vIhEMkfXy5wwwmcpolJHJJXgvvv0hhh1MHHHod85YfeU.XnzwOi0roIdlEVfAhgt7HrjQmObLy+Tv6bie6frcZvmWmOV.uuXvNY5y9nHy3vFMX3W+q+0OpY7R4IXAl.nTEnDUY5pPk0VaMOfC8o+z+y0Fe7wc+HejOxr.kpJx9rLY1K9+9mutc6lGXPvRjvfv8MWbXXnDFDlAZhwdp0z0qBBMrKQjrRyo2S7I9DUeeeI7Dg1PkbPbtvvPaQDaiwGT4RX3x252drRfUWps8PLuF4nN4.2ho0.7X.kJCkZYVfr25qu95SLwDKVFVpUY1fVXbQmKL5WmS1hREjNaSc3EW.X7+i+i+iotxq7Jc.pJh3B3pp5A3tyN6TtWudkVasUKpJ4.U.QQIAgTpQZ.Uv22emffvc.cy0Vcs0dRG4IsPPPPz.YXbQLfErE31FhL+8UYGZvtvgaCm9aFhF1kR6bxV+LF.pFUUcDGGY3lMYnJPt22m9SmbcW20kZayrZ58TVFauTxbWdn1vP8wAllxTlVTQUc1d85MQtb4FxPEuvt999sA1QUcaQjsq.aEabRosRyfYFy.RyjV0DCXzmSFvxzyizu2sGDY99ifxIQ.WaHp+DqU.oYZPO0pQu50AOPBcvl39KZlgD+nl.HpNADOcZlxb+4tgeF221a42ub2tcmbhIlXPlkb9B.8fuimMYtEFT2w11V.XkUVgwFaLZ0pE111Z2tcSKWhndf1w22uSPPvtftCHqixxhkrjp5x.q7k+pe0UFajQV3Zu1qsIUoEMXYnxFPyMYOZ99v0XwCxzj7PshP8QAF+9tu6a7q5ptpQgpE+y9y9sRxkK2luzW5KcUeX4fKNKSF73aYB.6zYYqdPsgozpqt5XkJUZDfBoVtlQ+SbXiEO4hqOyLOk0gn0Ic7zLjogKzapon6xKShCjDmllLNWPYkT1hLHifN3ByVl+lp8fFlwjygvYR2vuglqYrIwNNcgyT11MRJqIFGnTpXtN6ZqsV4Owm7ST9G5G7GZZFvgHXurV.6O6OGL3fAYUx9FC1saWha1TEP877RhBCSTAp5TkFMZv.G6cA1z2+XaDDb7U.Vv22uA6kgnXRs7Yf0AmMSEVt1orB6fA88fkcIE.FFbm.hlTUcRwDPRNU0te7O9Ge8m6y84tDvB.KCyuMbpNbtapIK.shy.SrnIonOtJU3IGGqO4nnfKWUwCkwQHG6Iv3aC5Vf05fthnxhIVrv69c+Wz5k9i8RWDXwDRVJMCNKCyrJr3Fr+2y9VYFl.6ueeeqqRJCSLkbQbQLiI1h8Xq1CDqPGXNA2QSKMiCCbDU0mRXX3SB3PXxTWwJUpPqVs5PBaohlp6GzDHTDstm2UWGhivLVKaticG3qWrrsmcMMHHAx7yCm5THNfcrKCSDiigESSJFgG0Z2c2cmgFZtUflKfIKcaB6i8kOPsz9QmBP7XXxFoqCT6KuxJ9ppUlbxIGOLLL2w780FptKvNQQQa344sNFAudYQjUfJqBM2.yltxtey9zG3nC7I845Qxntt4+CzSl9ueji.m7jv7fbpZXQ8yIoUp4+ulcZo5lAT7vat4liN5niNtp5XouSNBTYDn43oYweBQjIa2t8j4ymeJLyWON6sg0rwaCxBsr9srykMz2JM22yxvfPwy27yR+20fvvj16tqVrXQCjuZ+6EEw.7qmm2NgggYN6QKee+VAAAK3eT+kqe75qTqVsUAVIHHXEee+U.VElZcX4r98Gtemdv0QyhS4.Z0RYank0O6O6Oq9m7m7mLnHwlwBgGHFHbv3+xUEFpgYczobcoRTDUUUcttmy0M0mONG8...f.PRDEDUG9C9gGdsMViT1Zt5Ow+8ehEtq+r6pE3t.DsAvtpp8DQNe.l1ClsCrfAr0Zzg5jXz4n9qcZOGXcFyuexTSAKuLVNFcaHOfUplBkwbfd.bkW4SW9ReoOW1XhgIM90zxqIU6uXLnx3pFOlHxXuo27aZh+z24e5jm5TqOspMxzRhrX0Fj8HCx.iygsuG7gUFIAZD0.WO29fkjVyfYZLQ268yeu6bMW80rku+w1LH33aohtoEVahoD02JHHXsd85rzbycYMp7+O281GjjbWdmmedx58p526pqrxLqo6QRCFwHMBzHgj4sEVCXIaeK1guf07xtAdiXEgu8Nr8YPvZv64f337a7RXSrarQX10ducCAdwKWvZ6EFv9PmDfFiPZDVBM.qdcloyLqrp98Wptq2xm6O9kY0U2ZjzHC3U3eQTQ+V0UkUl+xmW+978KrbqZzjVrNG19VpzW+218bi+YLKl7tJJll5MY85L8S+z6OYgBEJ84+7ed8m6m6mqiHx5Xt23xYq64x1ZFnQNX4x.yPMpQKbgZMTMpNlXXxVWj8hLHTbUU0UEQ1xF1qogiIiEibem1Ll9ImZSjV5C0n7TaFiEOqcLDY9dS7+LVraBfjDlmEPFWWxDDfTGjl1HDQFL26MRcAUUyeK2xqK+27a90JhQFfmWDwVU09a+s+10t9q+5qhYu0Db33yNZCme1Pq7ynwgsZ0hACFj7+nJXMTDFnw5.CRL0XMVLHNwrWYKQjUiiIxxhVX7ethqqaavoED1lC7cdk365Esq+9BBSN5JlkYn8AbZxnf5ao5H4rcpolJ+BP91PNZeHm0OeERJE9eJlaxRdOhh2c2cUfgurW1KaPcQ5KhzWM2LNheEJVrXbkJUTGmTmvhotvhgD3xjIyvDR+ZX5rvJf7xN4a.eee0yyafMziZiBZKYSXXpLJFSSzEfX3B+vnqDWoqil72fDB0rmHN8hhz90AMR0rW5RWpxuyG4ijPXabzh.7bUg4zW+9vx6snIX5UnMg1vxhHW5O4O4OI7u4u4uosHxlttt6Az+IexmTEQrpAYaYfXeNQj7ppE+3e7OdpSvJ.kUMrhswg3DXLZMgMLA0ML6NipFcX4ES61+4ovbPZw5FwF0sLFAKC0Ku7xTddnX.THoXIERla8zNnMuHhipMOlMbUelOyexU++4G4ib7+s+q+C8JWtbsolZpzjYGeNEOJxWNZUsOjS4zhk.vzSOMsaaF1+ImbR.RTIGUEQhSjcxAfzSDom2M40y00cfgDsfeh2vaHNoXT6QS5TE1CZkVXhiBcwePrNTmjgkwK46eEuhWgZCwarw2M9c8tdW563c7ND.K+QmSrGOw9K29qw1acg9m.1GlOMP2V0fPbHb5omtkp5Fpp6UsZ0g1fkpZNhnvKa94K.giTeFfxqBk5zoSIvqz5qSYaCBOJlb8OaBOJLdPT4fGuvzPAvs.l8H4mGxiG4M7OhoCBpFlagzjJtHEcfxP6zwfaRfoapZBTNsWPDolsoftthHMDQVrYylKc1yd1klbxIa7O5+o+Q1X5FaZASJhovPo6uNJogktm6nnJ4PqVsam9KSIcSKTjnlMEjQO+3D6kc88O2dXPjV+kCBh8884BW3BhuuO.wgggIIqFEuP5dry+L1qckhtjzuJe7O9GOYuQXhz1VOczBK6HRkpUqlVDoj8TO9y1doz2ec0Dagpp5cdmezjiOKSBYBoXqgOym4yj72jXPGJPeUnaCW28ty26c18Fa3Nv8lbkj4rN4Zvpoc+5GUQYxQ8WjDTZzfZFRnL0NRhu8myqs5RflT.acu81SAHNNNI4WAWWWDAsUqVwZrFewK9zCEQ56kffIOOutttMFjruRMDIr4XqJnIJRzQQSwy2m0QetdbiJkLLxHQloIgtiHx1InHc6hEK14i769d6VCFBNiWbxmu2ujD+h5ioHSqCzLBtzLyLyS+O+W7e9k1e+8aMwDSr0CEDruHxPQD0wwIMoLIY7QEUix3XPIWIbcqnpVwCJi8gHOvb.4VZrttZ9eNeZC.JBT37i5j9B4O+4Mn07wgbrr4+OwdV5yOoS9KOYUXZvNcr3l6ZpToJ0nFlVWVSUslpQ1pp0qK0850qWi1sa6kKWNGfEvfFkw6vedfr862e7lTL5yxX7tjUfAElVwwwBoigimKc1qi.HIESQ7bcMHXRwhDh7O8ZjqyAEWI42Y444I0fXOOugzh9MZzn+xKu7QKBs.qKK8buu56m0y.wlbPw.SRbt8v82eeq+8+6+KRa73jtPEZL559ymezzB.2q4nBd5tRXHA1vkDQtv89WcuKOHdPKGGms2au8Fr1Zqk8t9ObWU.lABmuFLO3LuHxbppy3.SyBlXwHgWFTsc9po9gVFiORd7wUMoxmakURRFcgRquNEW.JDY3mlLhHo6+R8YWDn3246b+krgxP8IIgDWsEYgs2d65XP66wTUWT0nEqKxRsa2doepa6m5XO1i8Xdp1L0GZJhlFcNKgZ.FOdrmyhk.PXylllJHv5ar9g9aBIbMmxfa9lt4Q9NEQ2QLpRzFtttaArqHR+LYxguuel+lvvbz5PIX+Cx3zNz9qjBRzCb59DOwtcOVwh8EQzy+HmOsXTGkjauRP9exw7x8cLE0cSZQKa3RPqmRD4o1d6suDPqlptyxAACMnffR1PkHnhHRkfffxppURZfTZbSS.LQMXRvdRLxFsAkS1GRZdKCQkNR7+EgGOkSjpPx6k468lHHfIUUmpILAQlhjXxMfzFXMqHRsyd1++bsgEg5WkHxUqpdU862eoq+5u9FXr+cYsswyN+9L55xEu3ENTgTTUkgCGJtttXd3oIkiKFQFpv.Uk9hPW8.aD66551qQC2g2nqaLwntttIisS3y2nm+iTq+9VASNTWNhfXX534AEbSKtwPf3uxW4qDqpR6mYGauRf7p4qGzAygym3roRkJoHDoWDzEp2CCKBOJ.uACFjVAuQnnqXohlCZMVGNLNcFwF43TAqkW9AyHhXs7xARjQVrhSbvNz22e3evevevvHX3S8TO0Pfgs+ACr59AwJ0oog+FpROnYOQpMno4ZRl21a6sU3C7g9PIFJbRMX974LN80djS4KZLXtEvpQPDrP368ex+jfa3FtgnACFrpHx1TidW8Ue0JTKSjpYRUjBf7hHkduu2euwG4foDQlJBlAZMKTeVQjYiXgooISd1yd1IAlrVhg9KNlQ10LFYmxDrW8zwYXJwVlBZNMXO8pGLhCSfQ0QlApmNmudX5D5wifi2oytK9g9feP2rYyt.vLAA9ommFEvJWdnROxobBz6jfv.ILLbz4zd85gjnm0NNNr9ZqyXaazj8uCA5qp4QvCMJ3NQDQ9U9U9UzT18Gn2JP+vvvAydPv9+fZe3QCpPRF.M8eym+ymtOqWSU2elYt18bNXtqUfLTkDkenwURQShYDrOWsK3rCvFsX9UIj1.sEQVc3vgar+9624gBB5mLun4ZAkfZS5Xt9mxV4SV9pJOA3OITcxDEAXz9cCR0lKs3ViPazlPEHnD3VTDovpXW.exKhjCHiqHVhHVi3xEnbHL4FarwLNvbvB0vnnRMvlEgniC0OdSUWBS22WpWudKkOe9kt5q9DdX1+MOl8moIFkEHk4zsvL1HiCY8KGY4MZs0VaYH4TGGPLIZnJhkkkjTyXPSJRflJiuReOOut2wc7OqaPPv.KP777xtzR2RVOOynp333fShcf1W9tbdktR9eOoBnu2266czqUavR0l4.6BppkBUsX850GE7qyUdByJ1FhS6NuyO1Pf3JUpnAAA5DSLAXF+M8ceGu6XOOuXPF.zSg8+ze56pCTqCPmGJHnq+476CnKu7xVehOwmHKPtfm40fWrutLc6ZoidrqQplzXB6X.M5.6IOq1Tt.nPCsJDWpzRJfZYYQPngf0CBBzDtxIFKFbrkVZfp5.MQlzwTPuL.YM2WElAbw22WWgpvxGFwHi844xsFu3NWtNUlpLW6BtaIhroHxV.69a7A9.caYZHBvBWoWaGkjxIfdvb6fAMpgTmK849u94tPoRG+RSM0TMcbbVy22e6kWd48EQFHRcMA15VXZlPgPnBDMEACmQDYFermlnQJISI2D+PW3fhmVHzn5KUlCl.7RsyUFGJAsKA0K09vE5OYrXpml3zT.S+.OvCL+JPUHZAn9BhHKzD6ZzB6tc65np5VWDOw7v8rO4YcymOuS0pUswfrloYLarjTT2fffL4xkKEMYow.jKHHHahMNAfa9ltIAflMaZJ9QRgPJWpLgggDFFHQFjwItttVEKULkiSDWW2CcgJQpzGY67gLpuTruueefdMZznKTqehezQw+cgq7hx8210XwQsz36OU.oXwh4ttqqZoDz6Lc.1SwxOCDSb4dMYrWKC5TVhNPvVv7qFkLdNYyl8hyLyL9.sJU53a80+pe89XtNUQUMQddCqB1yIhLSH1SQaiJsopl2QjrhHYWYLtEYUnHXWZdnLXWAXhpUqNItLAzdBnVk1GNNSKCOeUcbT9VVDYhHXJn4rP8phH0ZQs5+p+p+xdlBknKAb7emememkt02xaYwYmc1Fm9zm1kC7glt+aj+hfffLpQRksBBBNTdGas0VOiykggAnpxTSNIpZHj082aOyIYMN4ZmAkIHiEqMr+M51XWUksRPrzV9996zrUy89y9y9K5444MzwwQSZ9Px0rEuB2x77tNpMuTkxYeHrSkJW8tsS3DoOz+GeH8K7E9B4LMLzIsHDGsnIOu4.DZ9L2AXiHnET8R0fmZpodIO8c9Atyk61sa674yt6t69TCEwNSDj+q7U9KyCj2yyKuHRQQrKyAw+OoHxTsfognYfZyHhLSapNEQL4JqrxjXJnREtrw+We5EfYfpow+MMtLM3OCTc1DD8NJ1PQjTEtbVU0p.NYxjYwH3pUM7pUUup333EykKmaPPPJ5RF+b0ylB+XAX0oSGAPCBBhCBBiWbwkF4CMNNV2e+8wwwg.eyfFmTfWET0yyMVLE3suhzUP26a8s9Vc.53662IHX4NOTPvdtMb2+S9I+jc8775C1CLE62cbe0+nRrIOi0ORdP+7rdFv.61tsaq7YNyYlPDIEVlEAvF1Oxjb8Ff81KPTGijWcEAAL3vUF1T3koIGaRYSBxMWPU0SDogp5h.Kt2d64UnPgZVVVSGFDTTSHkSHANch1WUIknW2OHHnug.X0877719y849rst0a8UuLvEaznwxPsVIvk0fzj4oOqROXtdvZWN4n6+QrF+5QJA7VpNLQyDRLJoSAYvLVLqgKqRPJr7NYujYx9JExyYf4J.qUhjhdPB+K7POzCM4q3U7JFMijhgr25JhS2ZzreqjNYJNROMLkjNqC1MynM0L11hzpEwpg.BGB18gnAO7C+v82au8Fdq+r2pRSTnt.MSgXW9jw1xRUUS6rvYNyY31u8aOInYGoFgEaASopNmTWpRDKnpN+C+ve6pUqN27IjW03iGwHD3DDDP97EnZ04G+7wy14oCsTUYiM1fYmcVBCC.jCH7UHFj9H59nzQD1y00aWe+fcEgsccuwc88O2tpJa1ngaarYYh3RPsHn0l.ct8a+16dlyblQcGlu+1KdHT.vgG+nbXSAhnD3VTU+7l.fFA85XNf6U1eVXu0uxIE4iZWIYbMVXBn8Lj3zSUsRPPPAOuazBZoIel6kL9Wc5zoydkKWtGInLPDYvi9nOZ2q65dC6OOs2aUywSLNXQXZwOpVP01VhHTsJwqrh43w.UzEhgUR6LnRxncopVRDohuu+DdddSgYeUZ..ocxnjHR4s2d6JVVVSVtb4QrzePXPYWG2B.YWc00xL+7yMJf8f.ebc8jmkyUOq9TFNbHsZ0B.iS4DopybtWhEzXM4mccc6FDDrKv5JrdiazaqkO2x6XYw1.q451HDvGbhfv0dmuy+4acW20+tNXSWhNjMuqzQxY7qwoizYB7XqOK1MqoM0ZNNxbggZQL6YVSj5Mgn.LvPcWt7vlOAxvjCSvTK.bba35Zp5oBBBdofz.zovr2Z3jSNY2s2d6cA1xy6zq46et1dddQ11zNJxPv2Ku7x6znwo2CZsSPPvVtttaRM1hViFWkueuW6uKVitGdIHyERuedZxxliN+WV0vbhHCrsY2nnQjO5yE7dSuOsLljVVB35t++5+5a3Udq25IANVPPvzW7hWL2Mey2bbqVs6JncTzsTk0abSMV0+b9q344Ece228Ed7ie7.WW2l0qyZMMDPYeee+9ddWeeX8wssckzfhidOhfona4BS6F9BTh1jy72VnuyUmsW3SFt+ryR20WmT+5WIWeOpMqxX7ENmMrvuxu0u0b+5+5+5S2sa2hKVrX1Gx2GWW29RcYOsotsHxtfcWrih0lJNNRbylzWUceQj8.68gntZJgVWiXZgZJrS6Te84Ll5FQ30w.xd6smTpToDjM3p1DjIJgusjZRdZmZisdYUCKIhjx0HkUUmFXFQpOspMmRDoxS9jOU4q5pN93RB73cz+PHe6QdjGgScpSkdN5YTj0jwrQ.HHLDWGGBCBHegBzqWW.g50qiHB6u+9r1ZqovH0jvjvgL50N100sePPv3Dv41.qHhD355tLTKv2+gZ644sguu+1pp6znQijQCdwNz3h+vjLmOpuzrLGYYMRPaQ0xTckIXElTMJrgZ1WvZvBa.sSGCvmOR5mwdexLOje0QnJn5z1rxLQlDEmZyM2r7LyLSNiOt5CUsY54tthH6qlQHqqH0RU3EyXTTigzxzR7DHRkt2unZHwVK.50q2Pu74G1dzXk4p0IvpogGUrjZhnsReMpmi5Mqng5zNNxrMax7ppII6ZOCDMkp5Deuu22q7K8k9RK0ue+B4xkaDpp3vMwhidNZLRDV1auNRoRkOzItCStqA333NZTbF2Opqq6vvvv9ppcSFEmMAV0yya0fffUSPWRWee+dIiW8tppaXYY0x08zsqRyMVIcjbroWhezwG4w+1rma7bkFebWKZCSDYPGwbP8oroY9HXvq5U8p19rO0YWyLR2rErPGNY6db9WHhxQibvxE3fBuNpnDXPjTFRP7hw1k8fZzhVX5NnTWFpM0ghHZPPf3ditYRr+oMax.0PDx8Si++Lm4LClat4FdKukaI9xD+e1jsi5i7HOR7oN0oxRhZUJhj47m+7CO4IeCCSd+KnpNo3HyQSlyLBN0mamcdhYqToxQkH3RbPA3rpWutXYYM94hwWGpP8hHpZ3JMKfLwwX0ueWJTnfbgKbAVZokFO9+gflBDftf1Ej8d8u9WemG+w2Nce1Zdddq7W9+6e4pu423adsG8QezMttq65VGSQ5GwwlbkMhUunc822PXR5ZrKDyxC9k9RIcO+fpbppNHxNoiyPdHJe6Cyb0WIu9i5hCovYbSyqeUFL.XfHNCTS2wT.JUpjzrYSibMk.+bKKw.tD.vBAQPAeee000cnHL3qb22Seee+9YyUbfkk0vatQijigVIetrMUueUS28cnvKD1a9uKVieC6flMa1qosQpgUU6HFBwsOfXC4IXzXLTzlyOdUSe9px7PfAmf4LpVBNa3bvLo25Fuwarc2tcWa80Weq01XiNpp8LWaZl8Se22cAfIbDYZZxrhHyALWMZNazCGMKvLQQ5L.yZOpxvQy.L0MbC21T25sdqSQSlVUc15lNaUCpaqpVCSRRyKFVhu5G8i9Qsu8ewauAl.4ONDd7VvwAtp65+zcc0ZS8ZdfG3AtZfieC2v02PMcqXNLN.NTWQvzIKoZ04OD7vCO.EII+b3k8bmpJyN6rDDFN5zaRkkAPEAEUTAhccOcruuugdDUMiu+4xBjSDMmuue1TBJrJwIGa1x8elyL90mePrFqvXoNhc.HlHixpXyvtl.5c6T+.k4AU07+7+7+7EAJsdsC2wwwN2cYOMwXHY5jlBTtKzdSfUMEtjVhHqN0TSs0G8idm8MiIiNgpZRPALa4xkS4+jYDGYNf4utq65lCZO0pIcf0CJS3nwxZtZrxb.yZCSuxJNSBTYASwNJjnDPYMRiG4MEKhIbDmoAl2y6lrqAd862eIfqQD4DO1i8X+X.uj333Snpd0SLwDKUtb4CgpDWG2zhJlc94maTGJBBCkjhkjdc3xt+JMftzuFGGikkE10LRUXPPfpG5bpNLN4dWAoePPvHGzVhzE4Z5dSMZzSUYfpxPeee8RW5RjNxE20c8+iYuUjYuwI3DGEtyuPViK+qZcZpDY99vPUEQFZKR+68du2dPTRxqK9BIQlXfgQ0HUgkh4fhEA.au81i5Jmu+45CLvFzycNeAvpFHMZzPgVC.5Ob3PSf+sdQAhBuRWGBcIWHof5Mf7r4AcJtJMKHhjeAHaTzBYv3i3Joi6JLWr8Xj+6q3zF6WAAAn.KsTZG1T00yS.IqkkUI+y4OIvT999SuzRKMoqqaYaHeyl1V.r.nddd.qmXK5Dierbkh9iwej1cztKB6Qa1UUsCP2Ens9O3U9SZMGja80cxAjcoqbjDMd2cS7Ix5.shbv+C9A+fWRD4REKVLHR0U77715a+ve6dDgHhjGnbMhlJQFwmMLTmGXd6DeiPzr.SKhwtDsnzi9nOZoEFAq75SqpNy29a+sGOX+o.ltzwJM8A+tfISHe5I+te2u6Lzl4ApC3VilM.N1m5S8oNlHNKhgHWOlHxwTsYiu025a4pp5bUW0wqatz7LFivQIUjd95Tm5TGx1Ef3GDHAAFeltttRpsKWGGBBCww0kt85AHrvBKfHBgggTrXQMYrbzyctyguuexUDkTDlDF5CIHPASiZxphlRF6Y78eH0yPlv6eyddcZznw9.8Z1r4fY4hw+cfZgOxm5bLpXI4AxWmUxwJjqFjWD6h.kO249lIn.ncx42kd9had785CAFr5IGsebCXk1QFHT4WSjfa9lu4Vppab1yd18sIRIo4mutehaqHlQhrjAMRsSJHwBlBB1xvoH1h8j.SCNyTKw2axiY.lNe97S2tNSmT3soffIZlNtQhLEsY5jFrNyBzbNZxB.0CCMxS8xKu7wTUanZSuu62865.X+Reouzp.yjKWtwSn8xUvtiLpD5HeTGsXI.GRIbNnXI5neW5JHHHUIThCBdnj64k9A99cccc2+ttq6JUkl5.rk2o81nQiFqaJjRycWIgSnV6Idh3D9T6GDqwQYTZic5CzMZACh5TU2U0v8+p+2+uOPUUdhyd1Bqc90RGkkRtzt.mmWH4.DCKmfXu41pVJx5LZW+xXP0T669du6M52u+NhHcgVwsLu1ErEoBQLoHxj.SbZW2IzlFjUEFZJTqsHyfQFymEX5a+1+mM0sbK2xTzjYTUmar3+qopZa9dpdpScppjntM.t.MN4IOYCnkWKChxMnJuIGCXw6+9u+Eu264ytXkJURUY0E3YxYIYbccsrrrNj8rc1YmCESVPPvHeEIEKARZ.lkErxpqx5quNKszRLddCBpnhXIfkBVfP7fACtm64d556etcMiPprMv1u423adKfctu6695.z8S9I+2NHQbpGeu+OJg.1Cs96iEL4HcSbcTP.SEbUUgZnhHwlfqWH2YNyYLNGpen4+945h43+siB6rXf3UrWIo5rMSHGM6gIEMQlbxIMSqt4+TiiSjCLwz0Y0Huv.Rref+fO2m6ys+a7m3Mz4i+w+3aey2zMs85qtZmliUot50IFhF3fSZRF8CMj+5K1lcL8DvPVfA0qWuGQrOrvtRsZoR7aGQjAQi5Ln8DTmJQGLadWwFLebd7j.DC6zA1BptFP6pPqhWUwn4latVyO6rq7e6+1Y1PUc2m9oe5duo21+P49u+6OenYNFmVUcVrYtVv7111yIhLa61smAX5HUmQUcVU0YpUiYfvoqaF6lYOyYNS0l0aZewKdw5PSG.GQbrwLWD0nN1uu226ySC0kTUuFU0eLfWJ17iA7R1cusultc6d02zMcSKgw.5BYrrRMRVDH2e1e1e13iRxk6ANNt333bHmpfgY02byMG8yQQQDFFN1I0wx2RLaPAM49nHIwvYFOOOKOOOKQPDQk66qeeVKu7xFk9o1JIPyNJi4+qwOHLNd4PWRFGGDHTVfEFELVz7QIcjJn2.Cw8BPVGQJN4jEMA60hRP8BrvBGsPoOW6uhAhO+AjRZmEWjMgVq4haaf1+T+T+Tqcme76b6u3W7K1se+9vXnpxFJScpLXvfozPcFU04oVs4AlCaS.c9ijLt51TG6HUsEQrifEfv4AlokpiPLR85LEzd55TeVLE7Xg+zu5eZc.WnYiHUWLWtFG+q+0+5Wkp5UYW293m6AO2RVVVGCy9q56t6tGEdmGEZmBXRd3nqThZcbk4ZLtPv7bhhPDgnVQlSi5nSxFwgAhabZOyHeQ7nYn+d+p2aWWW2dD8D8SF2o9c5zYfmmW7wN1w.PpWGf0nggTID.qGuwiO9w8UZhrGYMGTCsYp7Fyn.71NR0seOum2yN.cbcoGbwgmzLJOGccn2+5PRs8PSKtQPXn555hWxbCa1lkTfeiOggdddZDHhHY788y1xLRAw.8sso6wN1w55g2kCQg+nPwSL2KOGVppYWdDr5qkmZTnspE.JzR07InVHSzUbSMVSsfXnthYTbUOOOULE.VAHa1rJ.9A9BPVUiKPB+Y3cZuI.J666Wz3WJR.z1igjjEV.R3ulzOKuPWiG6vv8VfDNOq5.pAsgL+m+O++c9q8U+pKlvIREtvUdgdG+023SzIII0PZCNATu9kd4u7WtuTWBu669tWswhM1dvfA8UUs94+E94K1xLpnSKhLy4N24lAX1HUmSSdTqFyBMmwAmo.l55ttqap1UamzU2lyBL2oN0aZNLnZYVpyLppSqs0YUUmi50mGXdroJvBurW1KKgaHrWTUcoHUOtHxwui63NtJn4Us4lad7K1s6hCGNrAf6q3U7JpSRBI7Lmm+T0lRpToh.jfhxCVMihD.YpImTbcckKmBCJXryIXrskMaVv78ZPP.AA9JfdZsArRV...H.jDQAQ0SeZ0yySSl+eBBBvxxBUMIyTWDAwvU.hZ5hmuuuppNjZFdrILQQSfpcqey06u9Obii6fXFNABP10FgD354TUy1LYOVjpzr4CaAj8zm9UlxyGEpUi7vExdRN4URxPGjT64S1OtzRFdMw1vOXsgvG+we7PQbZ8M+leyUap5lequ02piHRuu1c+kU.qPUymf1kR0pQEn8D0nVJRhm8q+Xe84wLFOKDoZ0jQoYVU0YIoPI69D6ZTZIy9upTmp.UCLjq+B862uF1TuswhcCQjEwzfqEmbxIO127a9M8.p+ReouzTNxYJL7gwQKTxkaTUGqf9hLF21LZYF2qPhiieFwvc4.PfXz8KE03yPEYnHL3F87FrbPvv2467cNvF5+E9RegN.as7Ct7ZfypXauNv1T2f.f4tlqIY+1H+Ye+5C4PEFlzhlzltfydhs8thH69RdIuj8AFzBjYmcVCmIUmxAGVwWdA03TXs8EXGXwM.VYAHhZzTDo4Owq+mnctbKtN0qusp5dIiioDoZ1KbgKTXvfAknNUZZr+kLtLLCvLQlX+mSUct50YVHbVarmEXtMVYiEZZ2r9SeoK4.McApKhsMPs5vBXyBpp0A7TUOVxHccUTmq5M7FdCWsHxU8s9VO7wUUW5jm7jMdc+C9G3DDDjpTgSBT5u3u3uHkmRNZbNit2ahIlHM9+C82Vas0FId.sZ0ZTCvrDQLptzgrOJppVhpVJjQRdurxlU8775644sOvtVVwaWiZohaPm63CcG6Az6W9W9+kXSndUy.j0FxvI+QJNV6Pqej7f9JXkZPZLl12cRHXRU0hhTOmpMEotf1T6iIQ8sA1DVXWn8yE7BubcWc7+VFfh3xjpuYFzDQV7q809ZWyq407ZNQbb7RVVV0AlLHLn.wXgwZ2PQjgwpNvRjTH7uqqq6N9AAahpa344sF1zlnZsCB9Vq355tRXX3Zm6bmaielelelTXJeTER3EKAMmdtJCrTFatPlHy3KkFbpAFs1WcQs4SXALTrk8oE6.1aCQFV5+jz+J.Zdi+9krOX9bvpIN4qVdAVoT6ZThVFB95TWyoJ9.m+AJmISlJm+Qdjh2vMdiYoFx1Ow1xjS9RrfllQfvQTZVWgliL9m.G4C3HCHO0nHsFwJ3Y+NemuidsW60FaP5jcdahpDUiIzHsDXjvwLYxjCnRPPvTIieyj.kBBBFMahoP27.DmdXHa1ueexkK2y3jQ5yY7uVnPAlZponcKCQu535LB9w9AAFBD6fNBjlv39LZV6YaQjsccc2Y4kWdCQj0777ZAD8nO5i1NARdaZdtN6wAEw6us6IG+dtL.Y9Reouj0sca2132ClpbUYSFGmDxIcghl4lm7ppZcQ5FUmcnYpBSMR0bFO3zW.6uFw2HFkzoNyPSl827272bp+U+q9WU.fLYxz2rOolVmV4ZViBZjlACgP2AXGXgtNzVBgBXSYsoVVDmB6t6SPkJUFPc5QyZ8gVovyN8Q5mcyXnYyzDYf6pp5b6tam42e+8ld94mehgCGVZiM1H+7yO+kQ9EeFb4zUjOhvfvQOyomdZJW1zorNc5vf98Y2NcNzyWUUccc0.+fXLJHQeD56551KHHXeE5HvNppad22y8r8Owa3MrimmWGrYmkenfMZ35tBPKee+1dddqgYzJ6jbMLNgbrGG8eWI1BO70zYo.qSwjwtxDLtMS768d+DYd+u+est+b+bu0M+7e9+zU4.F8eb+FWtWy7XlI+4rEYwV03jZjd8AAAmjDUa4t9L+I4dmu821PUj8DU2xyya8fffUUUW6S+o+zaJYjMd6+Bu8MEQVK09uiiyX2m8LTipWLX6+YaM98xI2C4jS0fDI31fzp986OQ1rYyJhzkZrIs7VC72hCTQsmM+zFewvbfyRP30u4ladC6t6tWuiiyhm8u9ryb7ic7rKXuPbqVs5644069N6Y6d7EWb+XQ1QPW2y0q8YO6Yuzq5U8pdZfmhZ3SK20gf8.FtxJqLrZ0piqpF+scjIFctXzn4LGkYMJa3MmlhHxvu5W8qt+q608ysugOkFMNgWoic6n8hMfbKOG4XsCHjZfIgZSViVS9GelyT91dSuoBYxjImH0yCQ4TUys6d6JST9DCgliFswCFOmQDPuRMxQKJoFUroHXKAAOTeiJhUeXcZRSy3CZMXv.Ma1rIWupUpFslHR0oj5xTZScJQrqnZTolMaVXgEVnPlLYJM1wbo0We8ByN6riK0mo1wF4u3x4+ywwgtc6RgBEFY+Zt4lmBExOxOX55Pi.QXHyO2br1ZqQ0pU0b4xkZGK0Nbx6qJffqqqFDDN3.XsK6A51hHq355F.bgfkCdZ2FtKC0alLZD6hKcIf9Pi9vxiqtb+f7d5i3Gat799ORdOOu7pp4EQxl7UCguZyjZSMuHx.U0sEQRUzjzwB+niJ1Ux9wwrArXg44hkV0HgvSvJTApUBZUPUM2vgCylISlLIA+LdB3C9G+O9er9Y+reVymCaxRznh9mau81K6ce22s0O8O8OcLTanMsFNlxzMp4KarwFY+wlYlrs.KpiPSxA1kfnYVe80melYlwzbiiv8XA9AYb8bG2m4Q8kdzbEzUVYEpVsJgAgRph2jt2Lsj44xki4ladhhZNZ7aT.OWWBCCk4laNsPgBLxOZRSbPjcP0TkXZUvd0m7IO6lEKVbaU0M+E+m9Ocs+xy+UVkliTYtjwIswfSvxwO9gKh6OnQGr45sM4SH6zDz.wjfSIUCr.FHNRWZxtfytPXGfcwl8I5j8uBFQ+weurRT5vDeLUKXyJEhpRNVY7wqlI1e+8mnPgBk+re1Oatege4egLFkqodNnYdU0bRcwhHaECpmTXz34zK43wBi7aWfVjdOTlc1YmgUpTYfHRLTKWcZMYSalVapkvzHDc3vgxN6rSgolXhIwxJEMdSDDDTFibmmqd85OCR0e73+Miv1yLjsDoAN8qZpxeMXv.Ia1rRfe.10sknVsH8FK2zwxWjgSNwDC2d6sMpKJrqX1yrhpZ663Nti1ewu3Wr8G3C7AV828282ccfM8882wyyaWfNggg663b88Nvm0R8GS4VewR9oWQqr+O5CfeXtNAviCJUQO+89WoWy0bMIDGW8gxA6phEwL2o0.TZS6m+W5TChiaDTSdO0GGhIfA0EwP7q0Xvq809ZUUUoYylY.rlXhIrRrJpfDqBCQ0AVIiKDlaDMIqp59XPfw50iXsGX4ysoqq6t.8bbbRbPUaHzZ3ILDT4K1JVR5JIAlKXHj2MIl4Hl0rUSgeAM5IACQaUne+9YMjxVjUhuM047ngGTA4zWyms2KXDCsuZZPs8fb621.eyhTmBzzozi7DORkkJTnuB5E61kM1XiRSO8zE.xu+9OctBEJjQDAaH9O9K7Go+z+z+zxW7K9Eka+1u8D07vtODgZ3pjh69T6Vrb4xEEQJRMxcsW60ZP0Dv8e++449JekuxDu4exexTlSO0H3HB6LHHnhqqaQf7tttGJI1vvPwwwgvfPbbcvwwggCGpYxjgb4xQPXnLdvdoD4T5yOLLjYlYFJTrHVhLpPIgIAIFXJVRpL0oZBlQUvZuNcrJYRFNgOPh6fQVw1TTYqq+DmX+uz8bOCshsrpBFRXylLDE9CppJOp86.ba21sk7cyKvpLKv5XHNy4.VyD3SFMrUpJHkWDCHA6dgtCJ3UnOqL+PX03z4Q47GruI02wy1wQ5yqObxX37Ca.CVF5SS5UC59g+ve3Ne3+se3Rzhr+QepOkl.Y1L+G+O9eL665c8tx.T7s+1emkTUKYKREg1CCMEDqneXXoO0m5SUR0v70EQ1c2ciKWtbxryNJQQTUsDotEDkyFJ0zvx6if99v33oqTo7TUpTdBfRYxjIWud8NprycHaZAAAjpSFGsiqGclpAydnC8bBBoRkxTpbYJWtL61oynjMR1iYdhVlw8REUsDINzfhj8Q0Nu9a+mc264L+W2+e567c1Ov2e3ccW20v+2emuy9Mbc6466OvyySOfDEcEHf4wf+VOOOcIPuvy.wgWQKic80mV98+8+v7q9q9qNrNzsIriSDCd+u+eMKf9OwC+cGSBeWZHbgmsWuwOFTQVvXWtUp8YgIlnB6ryN7O4s+1vw0UBBBjIlXRAP9O8o+L7weeuW4N+neT9X+ZuOQGpbm24cBf533jXWyYHDFu.DeE3+5EaKEPmGhKQX53qZQncVHpPtb4JXa31B0Ujbg3+7odZGd4hRfYzslZpoXpolhACFvq7lek.Hsa21Bvx222RDgKbgKnKt3hwfjRFhVIxwclEZgUaN.ZyUqVM43OIZiu+NG.Lx+lvZNYfvbPTpL3N7s95dcvynC0XwAbCxyURDirYsLzm0HlEHl1MF.K2qFzEZsWKXuela+1m.pOw8bO+mKs0VOlN4jSZAj4ZJOQ1Deb49O7G8ePdW+ydW4RJHxDPTum5we79W0INgRKx8c9NemxhHSPMJoQMsDCg62GhG1DfHxfMYyl0LlJF4cMpnHxDqs1ZS9zeyKNUmNcpnZTYfB0qWOOFU5Juqqa5WyM6rydTRN+Pn5Zb6UiaKKXLTUdT6WxX+8wc3jhDy0VaUzXz1saofnHFNXBi+xzQuYr8nFT0454FGDDjxqU8L7fyMF61vkZfHzzJJ8+KvEHHFV9uSRr3DftIqkP1zLTj4SkZ035f1DxXGQVwPp4RBefjzPhp8gUTaPRP+0URiGR+LMp3GKwEiuPZbZq3sG3uMzpTcnnHRgZPg23a+sWnYyl4sssyce228k8U+pe0YWe80k4laNKaHayDkiijQJUrkrkJUJaBhGi6z4oGVtbYkHrnNYoocdUalEHmsHYaAY5zoiUoRkxbK2vMj89e3Gt31au8TSN4jy.LcPPvjtttk.JDD3my00Kiqm6QsEIA9AVpnVdtdR+98kizHKomA8o335vfACNzdyT+tarwFzLpInpl5ybTBGwpt5pq.HnlV+q.wJLzy0sefu+fe++0+AwXi5etyodddCWd4k61nwMs6xK+fa2nQisvlcHxsCDz8DvfG+fhk7Cq7GNHtoH5y7rOqZKIw+ODB6kLJfVfskpMKKhjwFxFAhSDRnIBswO1d9r2MLQsPS7UJ8hf8XExRUxwJ1Efnd0ggKUr3vHyyq3W7K+kKd6u42bFfr228ceE9deuuWA6HxzzT7Z4Kcluj0O4s8ShItd6gPTrIVLI2884uuBupW0qJuHRdrISkJURU8uXPx8T6s2DYxjYp68qcukesu5Walj8lVSM0T4+Fei6u7wNViIbccKCTz008PnIIHHvLxfGN9exjIyAcQcrUXXHtttR+980wUtqN6sG4ykyDKlkHsZ0xvUSI137CBPRPrz1au8H4WWfghnCI1ZnWCu9m3Dmn6xKGreiFtcqAcO2xK2yyyqqsQ3SF3bSNCAzuw23avsdq25ORCRiej9f+4XY1TbxSlkye97.kSkOJbHuFnYGqB0FR+rtrKFxjKkXZtbctJ87kwY3RjgKfLVf4oIYYjHqpUmkUVwA3D1vKqopWaPPvU655t.lJGlVvpgf12yqQeee+dhn8TjtnzQLLk+Jt2naHQ0B78enHOuaZsyctuvVKrvB6bricrsbgsCfcwg8Ijue6h+OLWWNz4XpvqAsGGTo45TVC0Leuu22q+0dsW6NXyFD4sA3uCrz9u.qP4AUZ9jXw4G0QyToQsD0nxgd+sYBhLDGmpZoe+O4mH6u7+a+pDGGqIP3NsaVRbbrZYYE+HOxiXcpScpzwun3fACJkMa1ThYR52ueblLYXqs1J6d6sWoOym4yLwuzuzuT4xkKmEv5FtgWh0C+vOVZEueFPPLspvomGSqZ7XKM84APghEkdc6xBKrvHHDGFFhkkE4ymm82e+C8upi9hBHIAMICTU6+5u8SO3dNyC0Ez8AYaOO20888aCrxo87V8b99q5cZuMIhzwqZWVXgch91e6ssss2YdnypGz46ue1adv0xSPFdbrrASPllrkSCRMCUqlWa2tnHR45P4PUKI1RdMRAnWRWQMctXA12.QzQGiWoD333GSiqtMkXgEpP61U.Jkz0XqTRYEXhUVYkome94mne+94srrjm3IdB8ZtlqgLYxjUpKEIhRIDCVArwRapvX7wfXKhFo4Z1rY150qm8g9adnb23K+FK.TNNNtRrpUxlIShLJZH.wfvf7njYDgFlzwgzOHAAgVPrkqqmDD3KhXIGTbjPN52m1Y1iN5WVVVXaaO52UpTI1aDq9qGDzDLLNNdfkkUeEceAoimm21IjX5V.6366uu2o816i7d9+Z2ewew20lIHYZ8Owm7Srwu1u7u1Fggga533rIMXWVdLTdbxSpb9y+BoKYGbczgbDNRlQKRUxyJ14fn7+t+teB4C7A90hwk8HfcWD18hF+Fi2c0i95ZRtXQJwEYNLRQv0opdJee+qyyyaQ+.+Y7b8xY5pC6gpaCxF.q3cSdqQS60Wd4Gb8FMtoMfnU+XerOV622668sRcXslv13RGBHkbD+9AsC+c05.+omfr73iYS1lRDYHqu986OUNub4zHcOQj0gZsgVavRzgKbYIY2zW6TDlLqCrTHb8ppu7vvvq2wwYwfffYcccy8W+W+WyhKdr9pPWQkcAcGuS2XK+ys7FfrppZfHxkTUuPiFMB.60fncYQ5yEYfCLL7EtMimsyGFDJZPWyT.S9.OvCT5l+otYKZaOvHSvFhFjZziVzsAr2xP2DzW9BryqI9wNfjcSrcQEZa3zAU0Ij5Rku1m6qU7G+G+GO23RQ+Kw9kHOVziYAX0tcasVsZCIMFHa6JZylS.TttHYaZTZsAX7qfZHdwb.YGLXPtgCGl+Lm4KW7m8m8eTpzZVlw3AhD0qI0ualfvfLtNiZlvnXJBBBPD4xNZMiiTjze1X65.xzzn5HBqt5JTs5BR5eGjwscYVJwhPbkIlX3t6tabR2akjOaonbQcccGDDDjxgDIBMfga0DQZ5ditADQSLbs1FTisSHu4wiG8GV2OONhHL9vVjrbww3c.GxSnSYHbJU0JhH4vl9ZSsScQ1MB1i4XeVajOzWHHeJ8X.Ris1P51GndQ0IOMqV.VoXBhOJQcJ8G+Q9iK7Ze8u1Bm3DmHGPt+7+7+7BW60dsEum64dJcG+F+FEIJJeJOwHRBSWZZbkUxuuHPwuxc+Wk+TW+KOW+98S2ikAHye5e5eZ125a8sV.nzfACpjMa1x.ECBBx455lMHHHSgBEj4medBBCTTv00k0WecY1YmM875nlbEGGSTzADDxgKfW.BF9wISlLr5pqP+9CR1yoP5H2L94rQUySiEwpmp5dXP6y1hHanpt9oO8oWmnnUNmueauS60lHh.Zio2BFjYdR5kX63xUDheX3+3vny0Xuu7i7HORoScpSUDC5aKHFodVkZx9zt1VPqsfFaCKONhfuRZJx34bb385Pd7n.9iPsV4kWd4JMZzXBfoFLXvTY8xNw+yu1egh+W9u7mjcmc1QlXhIRsCMhFG1ZqsXpolxBHaPPPwEpUKeFKqBVVVY3.NbQa0pU1rYyVd+82ehjBukES6oR2SlJC1oE9KUUujwi2er7ANzuijWrms31TU0YlYlTT.KobhiYqjIsXGGW0LlgxXMZlt.644c5N99maKfU51saqe7q9pa9fW5RQG6lN1pg+MgqGGGuomm21TsZGVYkt.CpACaA8SDkjWn1FdQy5ueWvjTnmBEpBkVAJ9jO4Sl+pu5qNClt3phHwK.8Mpiiy9PX5MhWNHMeDHDtjkpOso1Km3Dw2zzSG+fO3CBG.M9Yc.uP3k9u4ey+5q6s+1dGmb14l83gAAKnPYEMifnBx.EMYCozEzdJz8ceG2w9epO0mZaLRWpu6qv8hzh.LrN7NPscfV6BrCyOeGVc0i5X8EqqwKbRlFP1kghf6Dp5OkHxTu6286dx+v+v+vBppLXvf8NVtba1rJavJifd+QSJ.d9MXl90z.AxBjeAnXanLTqBzZBLZs9DhHSztc6IxmOekc2c2Be9+rOu7u3W5eQRG8kB999EbccMLutssUvC8PYcbbJjLCqk788KIPYTo.VHDi534nI7PPgzJHyg4OCqjBoMxPYR2pFYfL0nXJz5vDPFvnpBKX5xECGNTVYkUHNNFTHS1LTsZURYS6iF.4XJWx3xcYxL0KcQ08TCb7VEjlddtFmu0quBMatAvtu+2+6eueueueu8CBB1+ztt61D5r.rW6m8BQ9BYMdfUG37y11RaZp7exeOCPNWnX.T9S+o+zkeGui2QZwqzwfQY2yblyr+se62dOnVWnUJrxGGBzbEb7dzjPxATzCJ3CEbgrAG3ftDFzeLKvL0fxsLb1PVUix7a8a8ak6e4+x+kEsrrJNXvfhYylMcdnsF63Hs6dEWe80ymBIc+ff7BjWfhNttoEcK8Qtj442JkjB4fhVnQQQ333XEDDLtzAO947CAaXfmw2Cvt6tKC52mNIEHIMnNmCF0K.TEMVPFeO19frqHr0a4s9VW++5m8ytAGvt560oSmcpToxltttIifRss+ve3+W2527272ba771Be+wUqgKGJ6tRtFlFLUZQKKBj+i8w9X4deer2WVZNZlzUijw2zLJUNNcHL7YS0VFu3vkRttuDlBlbCgggWGvhppyBRdLcptKl.e2DkUPXUuS6sFQr1G8i9QW6Nuy6b0kWd4VMZzXUf0wwYG+G3A534482ljU9eTqwuWNCXmyH22iFSyo62u+LYylcJGQxEZFS00vDr+5vRcfK7bVvjDa6iJXxW3Lm4k+Jtga35cbbVxOveFAIufPh+2Tjbtkmm2FAAAq655tluueyaxyKnoMWJIo10A5.N8RFyv9bxSNfyeEAS7mqU5dubjLZepQ83JUSDq1FEnIFfc2cWsRkSLLIdkcwwYWBCSsc8BRQ4bfLgfELaFX8wPMfgGW.lnNToYcJRSxmLtiVfAAPqu95ExjISoImbxbIc5LSx+eph0TAy8SvgsoZs+96mqXwh4hiiyaYYkGHebbbAKKqhqrxJE60qWAWW2Qj1ZpcojjGrZ2tsrvBKLpqoAggib.LdxnCGNj1saSrpHXJf6LyLyn+dZhD.Ttb4C82Fek3Cdje2jGCEiZ6MLwTmk.YTyHzJobMDGP5ta344sNl33VMX4fUvhVttmtcPv4ZOb3vMN1wN1VvnwEMkzx+gUivNZhjGEoNVNPtPWJQ.SdwKdwJG6XGKO.RpxGUqVeZ0J8dnzGuPTyoK2wxn3zlFxtYcxQyQi9ZQndYnoonZ0pU7u5S+oK9ldSuox.S7G8u6e2D23McSUt9q+5KATHqoiQVc5zQJVrnkkkUZwRJCTZkUVoPud8xlTDjwKZRpu7QxNMlDhyTpTYqYmcFqwZbkBGPtltttRbbrr+96ylatojppRG0e4QWau81zqWOle9QJdnt81aqau81whAASokI4.jTIxPTsulLJqdddaArYvxKutJx56u+9sdzG8QidKuk2RSfvW+q+029dtm6YcfcNAr+ief+hwW+vzuwnqyKAYu.T.rK.QopKznhlpplEXfXK6PK1.SQd1Mw96KjQ.6PuuIOxNKjacHGTMWBI5m99OIvLAKGLawxEmZ1Yms3q4U9Jy80+leSqm7IexbWy07ZJpZXw2y648T7W+C9AyhpYbccy7k+xe4721scaECBBJlvyNYEQUhshc7bzu18duYt5SbhBIHHOUsKG0vsfff7pIFtb.YqWutkkkkbYh+mff.011lLYxnvyH9+Qnqqa2tr1pqgJvzSME4ymmb4xcYiiCLR3njVjGkdhPW2S6sev472SgNlwwyZUUia2nQiHrsaRTzJXJBmgSbf8t1q8Z68c+te2gtvPma5lF7fO3C9BUQ4dQ05uuVvDXbG.NjMoagiqS0hgnhifCp9W+ogdadvrAeTC8GtJ3i0AAFOPfkHKWfRXl0wkvlW1vfgmpYXyWFBKBLmqqawjjWhEizVk5rYOOOud.87W1u6e3m5Ob62869c2F3hdddOMfONrZ0vp6rBqrKFGpGkCFdwbPxiuF2IcAfxK.S19fwInTRwI52qWucxmO+V.agCclOb9tqxpiy4DWoIFM10vSlAiB7jJWgk1d6sKM4jSVx22ujmmWpdvW4a7M9FEN1wNl0oO8osZpMyYGYW4b9mqhmmWAU0r999Y877J7a7a7gJ81e6uiJW+0e8UTizsVoe+9EyjISllMaBBpmqWlgCGlqYylovsyJsPEBfiYDbF0Iszi4wKNRfef554Nd.bi9LO4jSZM4jSZEDDLp.B0qWmnlQLwjSv1auMi74l9EUUkQDGloSfFdknGFzXzWMR4WGfMNs2oWIhnlXXe7V999s7771.nC0nKsFEf3drH6yEGs+7GDIwczhlbXh.yAHbTBpEvlxIpGSpyojBBYO.hF9Feiuwg+1+1+1Ctka4V5kL5EOaIc9BwYbZhO41ZqsxN0TSMNGgjTvjZy9POvWZl9w8m3Vtkaoz297muva7jmLeSybuVZqs1p3q407ZJ8vO7CWFHWRg1rbccy7RbdI4ervGqT5naE3GjEw75mjfQtfffrYxjI67UmOS1LYMA5Y5JaZgPhC9+m7dyiRRtJuSzeeQtU6UWK4RbuQ2ExzHK0VRntEVxR.OIFVF8NbNOaC1FPxCGr8w1Xv3wFPH7XvbX.6YFvRLdW9v3mOfAg7fELiGCOa4Ay1XwwBT2MHTYVZTqp53dibOqbeIxH9d+wMhLypTIg.jrkv2C4IEcWckQFw2869s762uOsNzLItnPWkKjBokuuehToRkXr+XqACGPsa29P6Vq1SCgsHFNn..X3vgnW+dXPuA66zkM1XCtZkpw8DCl39rBIf.FrezXNb.AzItfbRorpqqaCGGmNZstaud85b7ie7Vn.ZksX11UPEC5fh5nFlpgSG1gwOdCVe1BlLOlDb9j8iIQdjJeo7zewm4uXzMbC2P2nO6YnmygZqD+6cNLofIEtLl8tBqPIjfN...B.IQTPTMOueHl4iwfWyBTZF.24cdmitoa5l5JDhldddUCCCq8Q9HejZ.n5s76bK0iF4sUgIoq3BKYNGXKLB67zlBlru8KGCH0tSSVOVj8VNJnYCBSxgJNkc1yEtSzrF7XgvDAVCZrE.trG5gdnm8EcQWzko05sh9cmN5e6HXR1nEaB3qQj13TWq0kDBQQDMcEvlno6Yc6633DqsSGlVh7c68iX+FK.Sg6WjLiS2DwnxfDTJVyIO6YOKN4IOYbR3w1f8moawOZWKGnwOHQDEKnnHhl56LJ4gOym4yL2MbC2fIFp7Ho5zly6.vBO3C9fK8K+y+Kuxe++ve+h+E+Eejzuxe0aJCJh4hJ1yJHZhNnUZKP.RojXli65dRLIIANkEnT1Q9uvTTVZPThIQTLCbzmhpDkl.APDHaaSyC777v7yOOVYkUXlYjHQhIc5OdECqc.vJsBVf34WXA9HG4HwiF3CFirY+j4bSFl32B3PdLlpgIwmem.lDWBTZ8HhQePnCQTSl4FR4o1S6dlFgHrNQTMoTVOGP0x.64551zwwoUzyzGK+ZOQrlbe7D.I1d+ZjPbx4wwnMODXgb5bYJixSzIluxW4qfK+xu7.hnQHO5iRShI861lOL600rwam.NH0ltaloJpNq96D+9RHOVs3Wt3pEJTXk+w+w+wktlezqYd2SqSmayMSkNc5jQ9Rx.fE7TdKXKsm+U7JdEou822smTJjw94SGDDjLHHHY0pUiafvrES5f2iLB3sVCPvBgvJhlNVu9ekWO9C+89CoYoD8r5nCfofdkJWBKtvhHSlLnQiF.LfsvNtHLSDEZP.jYNENKsDiEl7dfPaDhlfvdHpnbLykrrrJF4GqH.pBTnUTA++WpQ85iHVo7.oKMEgEKE4CYtxkKib4x0mHpMxh1aVYydUQ0Yat8iVSRdz9bm8yNllgID.V5ohz+RHZpJoTpUXly333jjY1p.UHcITZ9c1YmkRlL4h.XdgPjlHJcbtCQw+O2YO6YSckW4URDQbj1pYoTpj.H0rECN9ZIJmfYyUcRCSw293+oLykg1X8M1W7+LLB1e2tcwhKt3DDyMEgRFmZzTTzwfQHnYhi2retG.5eR4IaeZ0oa7u+W4ee869tu6xJkpnTJKAfxHKZfJSZzk+m+q74Cd9WwyehVCcHOqdZy56q0vDD6f1j7TrnbNFwN6xVhPEiS3B.IKBgUSnm0IHi86jO17Z1tvSy7YY962A..Vv1NE77lCkvhVVVKBx.yJgPjPOcbOwLGFRj0r7Z0jfJ3wG8nGMPdJYf68qCzZ83SJD9kBwnpn5POOuQ111OZNLd5xhA.af0rieE3FW3nTHuYpPf7HLUpTbV7rR.7MSWwBg0PMZM.qF6+69iG34MyyvsmTnLIQDvlX4kWN..ijRYeXidvCcPNL20bM+noA3D4PojHGlWMVMRlLYfqqddsVmxwwIExgvy+OddqzoSmQoTAJsNzh3PlovI5PBCKkRknXwhoDBQhXX1IDB1yyK.XxgiHJ.YfIHBfXBbn1y3rzSab5o0dFMf.V.Las7xKCsVSL.ANjEBI444gM1XCTqZsI701yyPmhjISBee+YKVhAFdVXDXS2hLEziFRbXWlntmVc5t.n2G6i8w5+xdYurdRorGJfd4KhdkJGk.wFaLB0pMB69HJj22q1mwOCiEYqHJX.bhsA11TnbB11L77hG0rLyruRoRXFGqfy9LC4JeKfO0m5SQuzq9ps.xmp3Tw7BvA.tG5AwOV1V.w9LN9wAN243UVYkwX+InD8b9rjPHBYlGxLOOQ1YTJUl++9Deh49o9I9IFtxyZkETmVE7.OvCDd4W9kmY2cuPR.XQ4oTbINQzAnIzZcR1hSPfrxtY13hqwfnvfffwISjbB+mm72YREfEBQnVq3nQXd7OGQ.V1BAuTpkn1saarUlb3JCaaAHlvnQiP5zog1yLMH1X8Mvf9ClbVbrl3jNcZSBFlSkY.hAwL.EBFgJkZB8uHlFvD2UoTs.PCjC6IDWYG.zMGP2xgnSETomqqae.zOZDb5CaLFdOBcb36dasB.+m+U+OS25sdqVDUHAPPx7nZ5RVHcQtn0u4u9uoEL9e5CCsRnGCYr3fEzkQghFeeQhqlEYEFO0Mtoa5lLOGHhdu29605Vdi+Nv08LrkEBeU27qJPJjbN.Td1DIxAZ8x.024wks5SoVFE.IK1gKOQz5l.e97Hjn7ALWJn.PPQBgtvMLRuv91+8aFQ+uZ0p3XG6XG1OESDEHDBesqdDP3PkVM.L5uyN6zWq0C1Ymc7eYW60F5kBVNNNIk.rZloaClsiue2eeO1GrOxgAQGKMlH6D.4RZSTFXizQ5wTx2xa4+Pl2y6421vu8bfJTFnnQDlls.NG10y9hk4nDAfsBKY35O.vXjEiQE3irX3MbC2TJfbo1DkSVkPRoTlLOvbk.Vz11dvm9d+zATAJPcZ0hrGSDQo18BWHHYhDigYzYmDDHhojJkJ9bOqHXnmPHDI777rXlIsRaleLDYPDBARXaN+KS5Tr1aBTzszZOBfsXhAAhrsELfAl4gLy850EG4HGYplIwwI0FoOSBCcaXF3HqdDte+9gAiCXOslmHG+SdlRrQ8N.KDB1yH.mgLyiAMINDTnPgjVVVP64QfYRGKf5DBjRYrOVKjuDIbLIUqTJKfbVmUe1DBgHgiiShKbgKXczidTKrNHTeKKfch0ssmzVQ52k46qCLiW8iiPbtsBA5wNnRn6HLtLJmNKPpJFsEJcD0IRoTJKy31F.YyRnRk36eiQ7Sf8+9gsNXgtii0N3D.VaCLtJp56.LzMR3gAv7n.FjuH7KUBgEJT.+92wcPWy0bMw9Q3O1G6iQvPMGKkRkPdJokuqOo0J9+5668wVIRDp0ZFFcyzxVHRlHQhIzgH5rq..lEBI6o0rsgZqgMZzHre+9gISjfykOukVqSVoZUK+QCoei25ugYeFAJlBqwZ4UbQSRjHAV6HqgjIShpUqN4oPDhAfPHfm1iAXHtRYn5LJh.BYPzd60fW8HqN1xPImgfw.xh5GBzwBgsDBmFJkZum2M97ZBfNW3BWn+QO5QGgiUbL18Iz3x9tYw.HbKffcvFVkLMA0XyXCKhHB1HDdERvrGW.HUQKLeUTkhPsabys7wVXL1YR7ZOd0OGF.gBhXf7AZyTPy36Tf.nQHxCeo7T8Anz4AR7C+79gsJgRoK.rvVas0RiFMZ4JUprHUfVHOvhBgftvEtPZsm2XNLLHWtbV.v588699HhoXjhvLyiiPmjkPHrhZfNqUZhHXAiuw3hDiucw+aFVkf1X8MRDINv6K9+RkJgM2Xynu7zD6u3iBlTa3nSFDRQnRqCn3g9fwtZ.PX+ynNyPBXzce22cfRohhmAr59UikRo44QgBiQwhAO+q34Oa9YOcLG0IqueFgI.GnKJvL4CRfF6mRFHhirQNVMUo0z0lCihKGr52wq3+dK.jNGvxkMyxzK9m8l9Yu728uy69xrsseVZsNOLUtLVAnFCiC+tQulTw2nDH5BfpRo7BW208Ce968d+hW..UO6YOaqq7kdk8fB8MJG8j.jd5lwXbkdmT0+ntpsXylMmekUVIYjH.5C.++z+z+zQefOvGv+y849b9Xc3i5vOhK4we+O3yqGs6EGryEyhni8owIdddosssiq3aFXPcxpZsdEl44kWkLkQM0w7e9O+meokWd4i7re1O6kIhV7du26cws1ZKCz6XjBVHovVLAdmZsNwce22M+xe4u7..LVJkglfmLznHJPDK..Sxs5IhoVgBEnhEKR.LDBISDQZsNwL7m1B.z7yOOsvBKf50pOoCG.ORZ3v.LM0tKhuhxtJkpmTJGnTtC.n9Qv8rNxiJt2uaImmiSYTDUfoS28.vnc1YmQas0VQcecqQOIoJ1OZ6Ei+yhd1tVZfFlNUtNrPcfnqAJtyBQ2uXl4wjQTUmENwy95wqs0L1WGmhF6nDxCqHMLYdX7CrHlhjgoZlQT2xTJ0hQHcJCybx74eVIJW9bYfYZqrpmm2p.gKXaKi0kIKkRkPHDIeaus2VheqeqeKKsq1hINIwTZaocJ.jzyyiXlonhjDKVg7uvO+Ou0m3S7IhGEmwz2gLBf3z1QLSiIPpTIQ5zYP2tcm.qcgPfRkJQ4ymOtCYlNVXgfn9yFeuKtyXw9.i41umTJ8foaXMhTc89.n+8ce22fq9pu5AXSL.UwvH+eGjq+OVOedzVwO2lU47i6bYl2w63cj4c9NemYhRPv5y9Y+rCu9q+5aiBnNJhlXpNl7ni3grXNTYJkb788uhxkKGOkbVC.Yh3ZevRKsTuNc5zjYtBrrJ6HDwH4pF.p644U+C9A+fstoa5lZczidzNXZ2ne5D0LeDcYDS2CrDLZXxRFw+FCfA8GlISzww.bN3iC2mxTDl.wZ.5s.vk466+rSlL4k444crG9ge3ir0VaklHxTzRhZSLu2N6rS8q65tt5ZstpHZZLALg2+MPjtRn05wBgXDbvvHsy460N0N68iYoAP7YSY.lL4YVB4wbnDRn05QQh.eqnWc1.nesGaZqN6m0r9Om13m8y0+YOSwb9nYRPr.xhUhroW6AdfGX4WzK5EsbwhEWhHZQ.rr1UujvQLmxUMm7TxLbIdBcSi7YkPoTI.PBoTlPoTIjRYFkREitqTLQjzTLwPXD1RRZfbdBFbBgsvJpKsDLn5XRGYiRzEfAh55+9z4DsVyEJT.VVVg.HrYylAc61c16SSPjWjsRXzYkQE2h8AHeoTNVq0fYNUrnzRDkfM7.ZDAzWbJYe0oUcHhZwL2.Fec0fAQc0gw9ZOXPY2rzxINwvmLR33vPbTbgKms.zyFqTpnIgzbvFyCOLQq1.v37.CKY1mzC3XC.18fzy46TezydVu4bUCxwSg7HCWjioB8RdddqDFFdDoTtJ.V9C8A+PK+BdgufEkR4RZsdAl4452u+bG+3Gedl4LZsNC.RIkxjvLE8xn053yjSEkLaz0JA.yHV0hHKaaaRqUgfo.oiLN4QqXp8.fjfgEHPl+29ohimmGVXgEvpqt5DQFdV5QDedJCDR.A.THQHLDlfWhtEFPr0HXgdLycjRYSkRUG.kMc8OWIyzZF0Pdz.klPqkXT.8uTmU7ngzi8qufXhskE.329a+sG7tdWuqwnfQb8gMFAuI9em0N6wJVsChdo3+r32mf5cSyrnXZQOU2SL18Ki7X4H8NbY.rhRoVUHDGgHZo74ym4zm9zoti63Nr9k9E+krXK1ZFcWJ1+Ghh4OgTJSOieu4hh+OAPbyslF+e1rYoJUpDG+OdtO2mK8W9W9WZEgfpDesu1Wy5RtjKADHZ0irJHhv7yOO777lfJ38YqE8EOxlKV6R5CftLQcbDhdZstK.5IDhtJkpsTJaoTp5iFMp7EcQWTb7Z0WGnS8IHtM6XfJyFC8SqZlyrKqu8+Heewhwwid.0.6+fv7Hog6bQblKW7nBSDK3lGFjLieO7.uGsxZUNV3WAV7VeG25RglwEaLk.r.Pj5Jyi4nDFjRYWxLdi6HkmpWTRqS5NeoysS7lb9EekWY.TQEInz9Tj7mNtl1EAAhzVBwvUWc0ADQ8sifipMf0O2O2OWlO2m6ysTVfUQcyLQG6h3oMSjSsSLKUVl80AWy1cPCzyriOje8XX9274XauG.1ydlfXxCzNLLr4e6e6e6dnnAB2.n9y6487pekW4+1ZZstlVqq+LdFOioABQnkTH6oz5A.Xnmm2.Pn+a3M7F59.OvCzQHDsUJUGgPzkiBRJhpV9BgXjmQz3L7ClwfhEKNHWtbCIP9ZsNPoTggbTgki9BZYYg0VaMTqdc.hmLx5.lneISN0klFPX.Gc+PoT8Il5.fVDnl.XOsV2ToTsUmVYDkthGLQgB7VasU780.fc9NgVKemrNr8hw+2SQJCZLBaDMNjquOJrMjxSCADiAPvm7S9IAkmhnigXdDQGKais0b1y.Ob7XaWM60T.v4hO7wOpvlCu7K+40G.sQdrGfcc.T8hu3KtJrQ0986WCHWc.T+ttq6pdNfZc5zoZdhpd5S+Ypp05pZWcUsVaB1loFZst4G+i+waqTp1.niVq691dausdLy8DNhdBgnKSgc0ZcekmWeaa6g.XTsZUmPqs4maN79e+u+nK+oeshUM8Y+yH1PyqzoSiLoyfUVYE111lm01JHHfGOdb7nPLDDB.iw7L5hSDjOiKXRGhnlDQMjxSU+qs810.P8B.0kRYi7.Mccc6b0W8U24YgbcQ0nmkkdDzQbVaiuaVLf8jDEhBu05O9c9NS.fjDQIxBPQ5bPHJ9HPOxi9Z5HrgA.rrrX.vKu7xy1AFv.T2tcS.fDVjE4HDgELETcjVqGT.Xfss8na8VuU+jISdffCO92Ce0+m0E+HdsZ79F6Y1+hw0pUKH+L6wWC.3bOdeFqMzzC.epO0mBJOOryN6fs1ZKPDGFZDizvnY0NecW20ACccMF7wet4L+JH.PYMIkS6t6tVv8aqufuSueLqlWza8HZ2lMpgJDQ8YlGfRv222msssS827272Dy49kAxsTsHeVXpVXcX9rNnuxYsihgPc70wfsl1HoNqCzFkQS.rGpfI5wwZqsV8kVJWChnFZstQ850qIbDUc0t0Yv6gxnsxyqG.Fn05gZs6Pl49+8+8+88DBQOkR0SHDCJVrzPgP3mHBgJRa6wdlBCMXt4lafTH5WqVsg111iD1B+nDIB3n6gJslylManPHLi6WBAfLTPTMiXo666iHs9hA.7773EWbQVJO4j6IDQiizpiwLy9RobjkglpF5pBZjTJG7q8q8q0Kj4t.zDwZkYN.DBEBAX.K0oUIXfTLG8bg.IkmLTJkAvPQxn7VxOIgNmnjqNwrzN8I90r9uXhdFG7rzYiSZjMv.TN5LUuIEygAPBfrYJx7bau81QEad24gXRCAl8LzCVrtucWeSiSDHHZ.GLBkP7XstK.ZYaeEMjRY02za5MU100s7O8q9mtLQTM.rGybauRk59LelOyN.nkVq2iYpA.1SoTs9m9m9mZCfdFDEXhQPq0i.vn+uN4IGCvikR4XKfw111AkKWNDfB60u2XkRMRHDCEBwvnhXNNx9iY.L+ByYZdPTBpdddXkUVACGNDX+2D3P1r..V6HqAZRx8rwMU3jjZGBP89e8I9e0gYtMLiz0F.n1O0O0OU0c1Ymx.kKiBn11aucyHpRMsvaa8u3MMe1mqgTgXQFcBcGGHh1KUv78Mw65c8txvLuX0uZUCkM8hxCvP+uEAVK1u2iPPnOjO6Y88Md5qSLQygxYxIqUtow+2JKPK2unqQ7lKMI9+FJkZOlol228ceM0Zcyyd1y1TJk68Z+kds6wD2TJjchJ5PeOOu9ZsaWl4tRoriPH5p05tBgnGGY6YYY4e1ydVegP3OS7+8AP+JUpLfHZHAZjVq8+nezOZPT7+LCfK4RtDF.X4UVF60rI1auFSh+OlB0dd58kP7D53XPad..7IPCbDh9ZstKybmvvvN4MMmoyC+vObW.z6K9E+hyPAurA022y0Jg336qvqOsrXI.+qGDlXAyXqKYwnNJanfCRmEX9+Oe8u9hW7EewK.SE+FgnDkAPGfSLDX6GMja7H5p8I.RtsoPIa.fmA.Nguu+UjLYxS344cTaa60zZcFxXQFP.8Yiw2dDQ6IDhN.Xj1UClXBlCca533TZmGZmceo++7Rc+pe0uZQXBPoMP99m3Dk72d68UzjmNYLdvtpE2cw4PTW0MBLGr.rSB3kDFz.QDQ7K+k+J8+7e96pe4xlf4NFvfcml7ziGdMdvhpbfNYrQRfZQcbtvbJ08OuTJiQFvB.4Rq0mMoPHRo05zmRHVrngy8Kq77VxQHVHOv76Nb3bUqVcgwiGuvwN1wl2SqmmmhxnfHMBYL.fTJSAf4c0tKPLEIZTTnsscfVq8iF6eTbUoAfESjEwbxuv+v+Ppq849bSlNc5joRm1Z0UVg777PlzYv5ar99D1IfXGj7DGjuw23ab7se629HXbJ2A.MkRYSsV2VHNYWs9LceMulWSm64dtGCJSPt5EJTtQQSG16bgKbgdG8nGcVge6etDfxGMeYS5hvV.V6D8LNZ7GZcLfj6Fglm8aqE+xliD1wA.hA.5C1AiGstXPOF+2IrAR5YPhVlx6uaxw19Q1W4WPqOybBgH0LceIMLid5kEhSdDs9LqzqWuEO+4Oe5e5W7KNYIfjkKWNsuuerXhMq1ljA.o7z5jyuvBI60qmUlLosFNzm.XqHnwmDLRZKrS..KuX5CxXFDJQv1t.5zoCZ0pEGK.YvzZx3ebBXht3LgxMRoLHl1YFadyjWBFcinJ.Jp0ZOgP3AjuLPoHzajaHvDgELZZLjczVaUY7NOwoYGwcWJhGyqu.P8Xwma9YzSB9s+1e68eWuq2eyM1n3d0pg13wVCSlAwCSDgzeHl4KuUqVWZmNcOpTJNhRoliHJwRKtD2tS6g.nkPHJo0ZWo7TtJ0o024cdmktka4VLnr.Y6HDU5q0lfn1.XvgfrfmJedvrc46f6CV.nvx.EWJpqYiHhZByYeMwzti9Xd+V.rlFXqB.W18ZzvjeH.bLsVuJLIuEk3A5Af1RoroVoavfqxLUY2ce3hW20cckArq54c5F111wc7eDvF9BQsgZ89708DgM3A69ZB.mL.twHuYIaa6E87FO+MeyuvTe3O7cAl4QWVtb8dvJU5fonUc3F.ipAL9D.Aa+H8YM6m4i107AOWL95JIvwRKk6tnRgUtvEtvJW0QO5xkAVQq0KFBrniPrXAfENe+9Kznd8kDR4RLyKbgKbg4RjHQj+1PDFZYQDm..wZixhtJ2EIPKD0sdDMoYhO+CQnRIIQHUHiTDPRxH1pTjXqZR92fHkXZ7jPq0VykIiU5LYPy81iHKhEBIfofEw9oBAPvBKrH50qKL2ynvrY2bbkJUF+m7m79Cesu1eAlYNjHLNjwPKhFxgggRGmTJkZwnmSyEgPgYE25dDQsDBQEsR4ILHoqrI4ctkTJa6440x11tMfnCftO.FBb7QQEe+ISTlLaBlyVzD9P94RAbrLQO+Wpa2tK9LVbw4JazctXjCNbSfgUMuOpZjd+XCL16v637i2uSS7a33fjttFTujEHckYDyU.rXdfkKAbDl40.vpdddKbRgXt620MEQTJ.jNxlaNl440Z8hvPsh4.PFsVmLUpTI78GaID1wHYJZOIYIL5LhO.FoUp.oiiEybJsVOG.xP.IsinecLpl777PgBE.yLrrr1mnCGsXFLMiD5DqoD6uAefF9.OvWt+ke4WQWhnNmTHZUxn6RUYlKJkxhE.prX1m4dsp7s5UIp3maBLnJP+HZM8jExkd7rNHROlE8+otm64+SlWxK44EU3sByATbeMcFHO9u7e4MFbq25sNB.82Dna0YZHFL98B29wWd.G7+ehYy4.HaJW2yjxwwIViQx.ja9ekekW4Buk2xaYQhnktJobkh.qxLupRoVhHJ8ojRqy3pIl3jQZd3bZsdtHjMCi6IqXzNmQHDKBfEUJ0BDQyEgvjPgP3e.+eV.TBDg3IBHo+vgoRkISJ.jjAr1X80oX8v4QOaeFAAgSZdC.BiFDICAnN.7dsa2t4kbIWeKk5zcjRY2O1G6i24k8x9waAjusVel5BgnB.plGndISdCC1d6s8OwINguCfu690wnmJGOxi45eMfvDiQvI.WBHjnbg.fKBvc61kq.fevevePCYwIJIalg6yTI7serpp+iXC31GGDfHF9rKBje4RkJsjmm27Lyo877LSnmnJkyQirIhnQQHJYH.FxDOPJk8enG5g5emen6bnRoFmZtT3YeYWl4Cp.BOFP.Povs29o0Fg6qByX+U50OhdDiAP.y5PlYKl4jTNZdl4Eu669tV99tuGdYfrKAfk1ccSRlXBMGxaPcxINztZbXOWOv0QsXX9MDn3vSZDj2A228ce8xBzBn7dBgnIPglLyMKlGM.xUmHplUHp8i9Re40OiqtV0pUqJkxJISlr54O+4aHLBjZSoT1RHDsDBQKoT1F.sipjaOhon.kvPaa6QDQCgo.Z9JkZbTPIARoLLdi709betHSlLjkkE51tiQT6rswvQCiD5IfgiFgQiFMyWXZxyga+1us3hmDJkxfd85EnbU9gHrOPoNetO2mq4a8s9VahbnsRo5ATdv8e+JefbA.f+ze5OMCr9LcK+DGrxxOYsdjcq9.1U6Lsio9abheRe.3u6TXbNJpKhDL68WjYdUl0qAi3MuFfd0MPDZl1.Ybdz6d6rAadXWagQAK5W1fhm3hKEOli64440A.sTpS2PHD0YlqQDUKuQ.2phbn7UJDEAJ4IDhROzC8P0tzK8R2qTdzJHHnsuu+D3bKkx9xSI6s2dM5RTgNZstCCzcs0VqK.5Ob3nAvLkP70ZcfAx4LqUZVq0LGFIL+zzwR8pGYE..zpU6Ip0tPHBgEEJDByXOlQHwj44OgPlCGCfQJkZ.y7.1LBDGvFZd0Kh9WcN9wu7dBgHhhMkhKN0Xs9r94hed4fg4AFBTw+IvhkLqsTjca8.l4f26688NlHxOa7meVL7O9c8tF.TbTsZXLP9Gm5lRV.XTJYDYqrxJqPfXRq0zRKsDwLrZ2ocLjcszJskIuuR7Ikxw2zMcSiPNLrf46+PsF9.qG..t1F..acXA+8T409tuMEE5H.nXbm9F566OJebfWqCbhGW+pyZjvDaCVgO5QO5relgvnuFSnJvN6rikRoRABoIlx33HhKf+7.dycJC0LSfrfcccCApEn0HzTX0mv7uMqeqY5.p63BF+W8wlniVqaATo0G9CeWcpZFei3qVtbZfrKBS2VWEH+J0.VFBrv1.yAXePTxM6m4re1GlOz80M1S.3Cr6HkB8Ar67te2u6VkKf8.x2PHDMPXXCf70KBT+241+cpKjx5ZsdOkmWyicri0RJksEBQGaaYWGGQWoT1Ef6oTpgLyiHP9v3SxWHDihoJIy74G2dTC..f.PRDEDUPkVEGqzHgP5G8yNFDMdiM1HXJz0oPFfkmRxZOMdauseSlAvQVaMtUqVvJQBHDRn0Zn0ZbjirZ78iP.DzqWWifGRnGQnaESwnZ8K9K9y2DLuG.ZwL5P.8BCC6KuJmA.4mYzdSrVYzrDoTZn6hg5AVJkhER4XWW2QZsdnPHFJkR+KbAuvoz1PS5notRDsNexbcvmyGVCAl7yrEPHvtgJEB.1LXwEWLnLyy13tjLyYpZPt8JUAVEH2p.XYODij6IBw9AmNOOdVg.Hz0EiA1xG.CqrlwVDFZMUCnPkR.kQNT9Feo+aJST9J.nZIfJNNNUjRY4a7FuwJZsthVqq64o1SJk6QDUWqz0zZcM.rmu+n1.b+hEKN..CEBgINclGpz5QZs2HsV6KuJmQJkdnVq8IlFC1f5ISw6L2Fi0sDhHTpTIn2WwR3IEnJ5rSCkx.yBovzABivu5S.CXFctwa7+6lvfdlFmVop65paHDhFRorIPtVEA57s3uU+J.i9DehOQ..P0nhT39jKpkd7tNL+KA.H3k7RddSNWmYOhYSOYPd.St.EScq25stvC8POzJ.3HUyi0.xeDXhSaI.GieO68gro8Oj.ldMbvWAQSgmn3+qL7pbbFBfgEKVL572x8+89898553bUcXlaULO1CHWCxLM2pAfpmVoJKtJQIoTVx0UW8u5u9uduHee8DBw.aaY7dei8jROHNWv32EBwvXDswLORoU9.vOtATTjMSpLY.h5X0JKuLZTe1hkXLwrsswrklKjAOsXIbH.EvfCts2ysMFf8AvnK4RtjAJ0o6AfNeouzWp8K609i25u6u6uqEPoFgDUGH+d.nUobn2N6ryH.DbhSbh.frgtemWLzmxt998BlL0Q+1H73.gwboZ0Uw3EWbww.v2DDR9XA0JAJDMFNyiT.4OrMWOZKKbNj.PmxNp51EK9kW1hnkBYdNxfLhH53.fHmDDPnPbxPozbvMxAlXZLxiAOym4yr+s8VeK8.PegPL78ba21XfrAnHB1c+haDl4Z7IB3A+O2qoEp3XHnToRiQAD.jMXyoPiMBdr1g9ZeFQUhdqs1J8W9K++1DTacrDPokKLAdxkLcpuHlK+DJ67HNf1B3DydXMM60ywmApdkAF8NdG+1Ct5q9p6VAncmNch3MdwVgggsQIzBnbq7.sENhN+O+D2cGgink7TxFWd970kRYsLYxTSqca.flZstkVqaCTnK.5XYY0kYtiVo6PD0Ef5Af9ZsdfRo7APfV6wL.rssogCGRJkx507ZdMDQj0Fato0nQCoACFP4xmahU.CXv4YHyOis1hSkJYLjO4UWd43hjXdmLPTWoTgyO+hiOoizmXZ..57Jekux1WxkbIs0mU2QJk8.vPCMANa..Bd0u5Wc.P8f0lbn21+KMUwNX.+g.Hb6s+nSNbdCfw.1wA5QHqYJUP4okAvp4Lv8bM.rVMfi.jaUTCq3NMnuIiaPL0FZVeFGr3I6KwiKSJ8ADi.vHWW2AHK5Yaa2w00skTdUMxATKBRwMJAzjnB6gxn9YTpx.njRoJ+RdIujZKu7xMUmV0tXQuV+z+z+Ts1c2cimbFccOiaud852EnTGee+1.bakYL71k.5KkmbH.7SlL4X.DDAk3YtjM+errrPlLyg81qYrfgsuQrovVDnMBEVPLL3IfPlQPTRMCAv.hlTbntDPWkR08ttq6pK.5++3u5iLPoTiPNDn0ZNa7uagHrb7zMxEiKc3nH6It0FHDHenMQiuka4V7AvnJ.iHpvPtLOHtao.XLPoGOWCDPEB.VEJLUOHzZcBNDIXlSzsaWKXPVnE.rXFV20c8Qrty67iXo0ZpL.KkmJDkQPw8I100C2c2cCQM.fIT27fHC3oxqIAUQ1DfYeH.xGXaJl4vzoSOpDvXfMCQcva+35W6DQmjJ.POvW9A..3wiGaDTVf.sVOVJOU..3s1ZKqnFmjhINEP9z+3W20kw00MsmmWZOSmFITAvwwYFeJdOQa+cvjTA.BKBLFH+PTEcIhLPBGn4latYGhnQDQz+o+Suw49I+I+wVF.GAnzZ.XM3iiHAVAvaA.LmiygBYcfC2N4PKfx1SatwH.u9+I+I+IcPQzFnTq7.scbbZCTpE.1qT0pMxAzPHD0QXXcs1sgqqaSkREc9W9t.nW1r45QD0WoT8Ag9G37ug.vWqz9LanoJLEbdTiF08ymOuOyreDMCiJhpoc.pyn.w.+5+52JkvxBVVVPHDHSlLQEoahvJFOhNiQHQ+SIkc.iVro.I0IlpIkxJrgfcUgAN9cHhFfRS7E...gvl.AKFvRqTVHB+cjA07AJkZzU43LPHlRAjid01FTLJPHffuJg3eNR13fmOM66G7uG.f2YlFK8m8m8d8goAWiHhFkOp.ju5W8q1LwmRfUcccWCn75.X8M2DqAfUAvRGCXAfrypQDGLVrucWubDseCQCLFva3pQnECnnYRwj.0N6m7SWEnbYgPT100szN6rSIkRUYNlqHDWYcgPTOLD0c0tUsssKCBkXlJcJorBQV0+C9C9CZFFF1VHDsTZcqu427a1RJEcIfd.7PhngpSqFQTng9VjgNVu6+i+GmTB3ToRwKu7xvySOsvIXB5mX.JLhBWAvBA.T.HDNZjenVqiZvJ4CFCYfdNNhNQzvskTJaIkxVDwMyAzx7mWtqVq6gTneVfguzW5K0WoTA+p+p+BOUI40CZ2MsvIqaN+cFpXFZxAHmOJYlbijYbdSWzEcQotm64dVXm6amU.Js5lQwqA3ZnpS.V1Yl30N9weLKdR70Sref3FsMpDvPfBCKTnvfhlwqazT5qTKGGmVnDZCTtUNflNNN6IkxFRorFXTo.PUGGQsmyoNUCs1skRo5nTpdZsd.P9XMXYH.OPq08APe.pOlw+GQjuVoGC1DiFQHdx6fnuCzBKLG..Vd4kgsvFLGBdluQdddfHFQr9hISOJh1KSwEIY3a5VeSCAng2we7e7PXjLh9tttcEEDc90928lZ+hewu31.nkiscqH+7cQYLXqs1ZzpShKZh1k78EEM462KXR7hA.etoAX6OWy7SFIfEA7AJMlM77vp3YKZffUo0SAT5vRB5vdMKrhmyy1NtKOKy.KR.ywylPESSpxmPJIs9LVJkJIybR0YTViC8Y8Yz9Bgn28651QJksQAzY73w8PgJwhoyrb1+vRT6o5AIevkYC0tqGjOe9.Tb8w.UFWgMafwlltv+CbYKz6XG6X8Ipv.lYehnvWzy9EYgHgB7qb+ekiTz1dC.rILTiZMTGqVxTDkEAv7YMGPGmnapnwKbj35c7YEBJbtXm3qZbb9Ne++GhFyVY6tzRK0A.syhrcN5QOZmG5gdnt.nao746pTptJkp8q609ZacaukaauGnTo5JkplTJqgPqZRorAybK.zUot+dLyCrss6IkmpKHz9M7F9UaCvsAPW1TkY++r+a+Yg.L4HkIzZcxLQvu6C9A9fIYlspUspkssf..JUtL3PlmgpDLHDIbdTHyl8CsZ2dL.Ok+lb7ADjOQr+m7K8kFBf9memc5BfNhqTzwHxf45eO2y8zG.CuRwUF6v2G.9Mxm+6zQx6SlqGsNlx.Hr1zDdLvvrRgjLyI3Rbxd85ktDyyyLub+98WC.ah7kirq1bMX1iuvZFdydXiCtCtmb1qm..32.vGP6CfgNNNCPkM6Af1NNWUSfh6UNRH.0ZcS.z1y6rc.PKo7phG+t0UJUi69i9QaIkx1RoS6O81mq0wN1wZRL0RoTcrfUW.zgYt0oO8oa9M9FeyV.nqTJ6y.iTpy3SDEjOe9.yDaRwCFLz7LiLZhybyOOBCBQ+98l7kYZfd.dZChTjRIChlD3isPDP.i4n.OjR4nSJNULhZhE65tO+m+yuuTJGbYm3eyHh3wnb9.gPDdFWWF.ge568Sa7eWHdRFs026VFO5KF0xGBTZbwXUhWDMQFxVZnoa2SQlfCbh+2cXA4eP5UjLrXtT.H0C9fOXJgPjzxhhEawDfoYNWlwq5ltI5VtkaIg3JEIbccSfbkh7OUvRoTD.nMwl6e5ubhSbvyq.dp44AOx8lEMn0fKyg.kF6EC+77QcsOa0n8pG+Q6r3GILu8fUQ.quxW8ArzZMJWtLGFxyDfdIF.wCOERJkD.n68d+37G+duW1wwAiYl8tfGibUh92rwD+ZQO+ex79Ki7fYlCvwJEQQvBcfgS8MKfBwh33325a8sZ8Q+L+CYXlWpc61GA.afJXSU7Yg.KCWm3wr9rE58vhy4vuVl9d..72.arODxUJe9du7Wwqnq9B5t.n8ev662sYYCG+a333rGyzdNNNMiDP7dZ8Y5wL2Oc5z8EhS1gHzBL1y2eTrHn1VHDFpPYAeGi.qNQKjVes08O6YO6H.L5c7a7t7gA8tg..gggVar9FIxWnfkkkEEFFRQE4Equ95..LYn5.uvBKDNdbP..6+POz46+G8G8G04zJ0dLy0AgJ.nLSbI.TZu81qjTJq.f5eiu12nkTJGpTpv27a7VIgPjjARFFFZIDBKxzQBRHE..ALOs3wk.5EMdW63551w8K5Zz3f62aHf12KlBnm3I8ySOXg8O3e1g8yGBfwu4el2bLUI6CfAkxlcH.B9y+y+yIl4z965ufiiypLyqCfropZmC.4.vl6BrFPkXcnaN.mCpyIea05jSLEuYL.BalO+riC0tvCsdQ27qYuVsZUG.0Nkyops0VaUSJk09he0uZiG9guuF.ntiiSMKXUgHpjTdxhNNhhW5MbCkBYtxq+W9WtJ.po055NRYim0y5Y0zfxKCEPh0rDgvgkm7jSRR8s8a9aZJJhThQiFg1saC.S7+GfJNl60LYPHESglN9ifzoSGq0gCAwwISOPq0CjRY+SJOYunFfzF.sOiQjz6BSiVG.OLLhNNCkRo+c8e8u5fi54+kbc3wlUOaHPofRwnvOOMjYtO1rRjVJUHVSmFXS1Am9zml1ZqsRyLub074WCF3blC.4QYroqAovGA.KM3bNwhs+g0D0Ys0ldMc7n3hWuXjcU99llZh1BHZCfNdddcAPux4x0C.cN8oO8dtttMJXUndQfZJkptTJ2iYpoTJiEo8AZ8YF0nQigkJUpuTdxt.n0fACaxbXS.zlMOGGcG+g2Q.HynYWq0VwTHWq0IxmOuEQDcjirFIDBpZ0pvS6Axxx7kgH.S0QhApyjl2QTbgumrmoOXpK.2+09K8KMPoT8c05tWy0bMcEmRz48ca2VW.SNN.nSgnmEHpnuMssiot9Skry9dd8Tw.ndxXcvtsMawMlG.KkEX0JFsmHEQjO1DcPUrGLcns+wA7iJ3Bv9O.I92YLu2WJOvlk.dF.3RYluLsV+CB.I.eDgPlIBhkgLiQDgd.nCAzpaudsSjHQmLYxzSZTScCep0tUuRgSoxlNZDqT+srA56cBLFaOSKfejID9zkJ5MaBEovpHEZhzQ75iVGfpOkaioAPlB.oKlGIZ9MZRqt5pw+6SlCHcICOZ474y6WpTog.XTNfgkmv87II2G..9X.3yd9yyWzEcQL.FisP.1YxlbxFHg2T90m..VqCP0kffZ5D0A.ykGXo620cIX3v77DQIjRoUjt4LmVqWnYylKs7xKufkk0be7O9GO0O1O1OlEhdtIkRKWsaZhoXdPuHALmvnsIoTJUpFMZjY80WOECNIXjfAaQfRfHNMB.qXqBFFE8mLjkzLVWMVESRp0fH.JfYdLXLDD5Jkx1Jkpd4xkpdim7TkJM01qiqqaW.zy4TNcPYzE.8uvEtvfidziNbcfg0ejSpimJYGdvDXSYCLmWzjogYdQxllGEmfdjDOqBER7MKVD.f+Feiuw3K9hu3gYA5TIK5hJ6Ss48OFPvtQA4d7ie7fycty8nEn6jjS1xnuJSmZSGCVX2o5WPAfzEiz2jc2c2zutW2qK48+W+WmzKGlCkwZtttaFDDrVlLYV7JKTH48alzDIiTo8z20ccWIeUupWgkP3XEMEJlSoTFgtNx9xVHlSq0ogQc1SyLmB.IVas0n81aOZyM2bxHOLFx3gggbwhEACNj.Y5HlIHONWtbb4xkAlHdXX.al7RC0Z8nH3jNPoTcOkT15zJ0d+L+7+L6869d9cqcounKs5m5C+YZboW5EumPH5H.5pmHpXlWaA3uyS71XlmIm.Iv1SzTiTqCjtl4rgzv.0bjkH+pBL.5IO+OLgmcx4C4ARWxreNK.9AfYJ4bYISl7GTq0BXJ9VZvffEB3PLjHzBl8c567Nuqcuka4McAXFTtUxAznbdzNRH+lXC5.DDCC1SbhSvau81+yEs39tcEueLRvkQFrIxvULiqTDQgA.vQzxnO.5mGXXoS.e7Hoj5jyBDBw7ZsdC.rUNfeHuffKyxx5h0ZsHWtbKUtbYKX7+EKFwwSIu8jRYEWW2RVVnzUJbJ8+7K7EJesW60VElD36tAfes7XbjPNO64IOQQQ1IwqbBfDaeftgtA.UaUj.MMZZDhfgddf4KZljSI788sRkJEcy27MG728g+vCKaJNYLxyh2OEjEHnxiTzWOrDnOrqwoSNBf4yBr3CTr3BEJTXgBEJj4K9E+hIO5QOZ5b.yU1H98K8S7S7Sr3+8+6+kySDOmTJmSq0oih8JgPHRpTpTvngYYHBoAnLesu12HyK7E9BRpTpDDQVmTHnR4QR0oUoYvoIPoDBQx7Dk3LJUJvH0pGY0jYxjwJYxjjVqnHsJgzZM77JxEJjGDQ7RKsD2oSmPgPDr6Cu6vjYR1kC4NNNNwBocOoT1SoT8u9q+5GdtycN+b.b47HAJgzOzC8PyM2bysHy7Jeiu9W+H2vK3ErlVqWAFe1Vvfj3Qrw1pI.pvLtfiibG.rimqqmsiSiIOWxignTrNMgQyD+4SU1Ga1icbjDmaRrOoYlSminjkMwsktPgByWrXwEgY5NkNRCtrpVsZvlatY7dYy3h2XaZzdh0fOZ7H1OcP6wYiqOA.rTJE8bjR3EkflssskmmWF.rvl.KVc+hgbJsVmIxGwB111Kn054jRY5ye9ym7htnKJE.xn0tK9M+5eyEt9WvKXgH6xjRoLcdf4JkGy+0+be8LKszRQ5EFYQDRaaaOuVoWHjCmOa1roSkJUBKKKK.yHuF.Fkii.hGq7XZAHCkmTF5dZMrr.LCkIZ1DZGTqVs9atwFcEmR1QcZUKXro1SJk0K.T6K45V2wwodgBEZTrXwl.nqMvHu8ihi3DZehzm02qqYedlBQ1TRhxnM9Wh8+wB.KcdjReF8bBgHdpFkVoTIi7iPeguvWfu1q8ZGCfgE.5WLtX.Sz.MSAWWEXby8OUDOrX01Wg3WGHQ8igDXWjDSmpaq9ddOumUt4a9lW71u8aO0a9M+l4vvPeoTFFE++hJk5HLyqBfkrrrlWHDYzJUJFH4C7Ue.5F+2diIh8+AfEHhmiYZN4z3+S.fTmRJSdZsJIXLSydoT.ro.3LHxxLhQMiPXMXdx2ovHQxIHBkN9Q5n3PvXzM+ydyC9S+i++s+y7Y9CzY6s2t4pqt5rSxq1tttcRjHQmfffFNNN0QTQsAv.GfwtG991mJXe88z5eMfvjC1Y232Cg8TnwWY+aTrxWEo.JLGjlQ844lN9gSi8I5OSDnt4izQiUJYpj4l.E1Tq0GA.KtvBKjwjLqoSC.TLsbH.XwDrlew4oefefqkAPXgRXLQjuq1suP3z4LQS+B.zGarwP.D3AvX6o+N.1JNAqmp2UwGs0jNVjuI7AVaHcTZHLIfOQcnYl6AftEA5fRn2pqt5Py+tBIYlWrrApma.jKawhEy+A+y9f1.4sKalUB1vLtmyAAVGle1k2EXwK5htnE.xNO.V3X6f4AVeNj0fX.uoUfFEKVjAPPc.+nIUzHCbgy1G.8KAzob4xscbdNMcbbZHkxFdddM9U9s+sa.fFggg0uzK8Rq4bUN0EBQyW+q+02kHZnTJGG6754HbFJkW0P4ojiXlGKjRVoTIbibjt1Zqk111NsvVjLc5zINxpGIA.QBgMwfmZYAvDiP.ZLHxmAOh4nNkSSFkqc.QsYlaQfZCxzkhyd1ubqd850La1bMugWwqnsqqqQLAymuCylfIQYDONS6dzidM8idVcXA57Tx0Mdi2H..4EsmwHheje9hX.fcOjG8FLXvnuYwhfYd9ye9yu5E+7e9YYly8sZ0JOpfbvj.bVfbYAP1cQrcU9kN24NWT.Z1yRamCBCzYf27ViAvH6cwPf7CvFFa9YNnevwdNOmA2wcbG88.5EUrp1NNNsSlLYmBEJz69Up9RorqVq6.f1DSsekuxWUKgvokRoZIkxtLyCjmRNNBJmVrQXgSRDkrZ0pVfYqb4xQ..yO+7..HUpTSdP544AOOOTpTISyKXhLg+wTzYxAkKWwGlNu0GfFvQA+qTpwLCe.LPoTcO4IOY6m4K35ZwD25d9je4VenOxGpUghn8K7E9JZKtJQG.zQOMHG+ye9yGDcO6IZ6pI9MO91f.xCrlwmTcf3wMsO1bywDQgUXlfdRQbSZDHt8A02YFQ44SWZlQkK.VCEvQRlL4xQhCXRPQ1Dl1QyDQQSsJhkRIcy27qHgqVmz00MA.R7leOuGqhe4hyTn70Mb5ex2EGqs2dhFb8zgyCX.mP.DjuJBHxI.4mPGyvntokB4xkA.yUBHM1dBhtRG0Y5YNaVDKfiKAjaoxS6nXRgPfxkKGinDK1jDTpKr6ElQaOxE5bUNiEBmgkAFbsW601C.8Q1rifAYZ3K7+3KLCRVN9Sz2amDuxLh0Zv0e8WuYxAA3ilSKfHfXDyreon6W.4oToRk5AevGb9OvG3CrbIlW6Lm4LqCCRS1.EhgsNNRkHzvB3Dg.ynN8e7Ink6vFuvGf1gaDBfwU.FMd73g.45UrXwno2Q1VeYOul.Xue+e+e+F268du64bUhlQ51U2PD1WJk8Xl6xL27M75dcUkxS44bUNJ.xSJkUtjK4h2SoT8HhFYYYEbFsNTcZESDwTTXUDQ3dO24nrYyRyM+7zhKtHkLYRK.XIDRRoUjxjbZ3UcUmJPJkiAv3Nc6LtPgBiAv3TYRE.FANNWkOxa53Zz0YKoT15y9Y+rcbcc6dZW2tnD5occ6kISlgBwICjRIcMW6ORJ.jJc5zIEBgkPHH.vfn.gP3KDhQRobHYMQDXGa63DkPrX..5mqD5CbrAHqowCOEqXIwKFmaBkEFBbrADQ8qDMIm.x1uXwh8Yl6yLOx3KiSxLuvC9f+Sq455l8C8g9P1.PLM1rMyAfMrafUAxsLN1jBbLCZfmTftCXGZSRSwRht+bhPOOuP.3CXOrJPuc2c2N.n0EtvEZhBnkPHZAC5kZSD0RJOUK.z7Y7LdFs.x0F4QGgvo8cdW2USXDk7FBgnoVqae+tt8TmVMb4kVxGLBd+u++ajTJRBBo0Zc5ir1ZoJTnPhLYxXYYYMAUSwMaHJ9+3weMLS+ZB4xki0mUCh3vy8sNW.LTYc..2KZRZ14JthqnKCz8r+smsK.5xL2oVsZMUJ0dEA1ywwoIPtNm4LmI5bSwPunhsf3hEbh8Mlmep1hANd.xhwDQihZVROLkFL8z.cQIzMZPYzEvd.LZ5WRivolasejejejrLyEN+4OurHvQAvwhd4fbP.y3PKayIwrUXI.rvF.yAjcNjGyhDciMmsMA.tNP.1cJccLhROFby27M2G.ctsa6C2111NRaBy2oXDRZYla633z14TNQH0FC3HpseEW9ULjYt+IkxNR4o5HO0++j2adTRxU4ch96KhHibu1yk3dypajnYqQMnt5FjzHKFPxHaDFKFaL1xRXCO+XS1FX.jsAKaVL7NOuAC1i0vlMLHwhDFCBLOPBqQXawhPpqVr03GzRPWcbuQFYsuj6YFeyebiHqrKI3w6LhA0x2yoNUUcUcVQl4Mtea+Vj6vL0QJkCTJEoTpD8wL8w05zSTbhTYxjw11xN9dfDbMgQmLSiPxjoeIbr1NEe9SOl41fPSlYiqmQX0m5S3PqjMalk+C9C+CV4O6O6crBLBstowHUqtokk01ddd6TqVsQ4kATaH.X+yT6idj1YV+uz5QxIO8vwZ7jD2Kj9n8CXeJLcZf0yyLWD.EHhxbW20cYcwW7EOfHxL4NA5f9k5ikW9gZRVzryNq8pqtpK.J.TcFfHIPiysSmNOtUVasGKEwRgTLcryn3Dex3PvnGSnMArMQzlBw4uoVeeaJDhcTJUyXNytRsiVKDjWCDDrJF4dOnELIJEcfCb.bxSdRqkVZIru8cILP9HfSb1Huw16j+MACKABnDvxKOVwI6R+gxkK61nQiDzAL4W5K8Um3m4m4hxxLaqpq3ZdGcnRcrASN4jCBCC6+XerO1tvLgxlgnbmXm2XnQGaBS579tNsyrfAOCg0Va7DDGcnvLyLi0ZlelCvbo.rRCzHM.RiJvEgkRArrSY.WBHcXLJSNrPTXQkpfTJmPq04DhEbApm72vAlD7Kd6egau3DEmn3QNxQx1nQi3jG3TDYY644QAAAnb4xTXXHIDhXQhDDHFLH1L4etOAzGDMfi3gfvPhoALgdvH3mC.ngRoXnuueWKKqlBgXCkRsxy3m+Yr58Gt8p+pOqm0Z27Meyac5Se5sme94SbhgcpToR6vvv3GiZCA72KL7dj19uw1mc.afSlB.YdSuo2Tl2xa4sjFFsFxNd51oQEjCb4IQiFS7abU+F4+u+Q9u6F2XqVDQsdGui2Q6W6q80F+bt7..qt.0MM3nD5fkEcAz8D.CZCLX83Wa7.3.TiA7iuG8..3jieMBX1+agr6KEVZoDAnLAxxN.vsRkJSDFFNC.l02WWjH1kXhD0D1nBR4eLsasZBmJwEFde95LhZhIz99SGYYMCw7DZsNWq1sSevmzSJU1rYSkOedGkVaQfiaDRLpNAQLAPiAdhXwvgID2XNl6CxLML1XKc8IxXImuvm+yevs7o9T8APm+oa+1a9yd4+baAvaHkxMTJUhSgsJLBl1F.XKLKZgzhtXzlyfUt...H.jDQAQkPN9fDGtGt1ms2IV5vLaSSQ1nXMK366f3IdEu2fN2y87G1pU8d0qWOF4ZyL.vNBXYy0RIPX4osAV2sBPABXt3D2db862+I333b.sVKEBwLZcPV.ih3G6TGsA3sHxZM133AJsu1OBQpZ0psLhcKlJUprSXXX7zYqMvnqjmh9K+K+Kwq6u50w3TG7rg3AiPEB1EIelBjpfTvV3BCJDriEm4DgRtmPHFtqcrV0BHxBngYpekPQrbox.Kuup.OtE05GGhv9Xhm8vRYtiqzNBiyOEwFWJqCLwXWgIpNhh7IhTxibj.Tudh6z0Dw1nZ850iN7gO7vfffjo+NNhmd350kwiKl7uk73aM6ry5t5pqlD+qHQkyCrrQPMKiznQEWs93olc1YIaa69NNNc.PmJ.cBQo9.KGGuqROfvQTUFInDbFLD1khvxKuWzlv.f2+92ON0oNU76chT.cSCrp48NArgtpEPcmXaYOa.P9u3W7Kl+W6Y9LyeuZcdDEUnQiFYWXgmiKPHAfn68du2gG8nGkAfCpfr5iqmTHDS466OEQT9Nc5jd00Vy4o+zdZopToh6m5S8obOuy67R0saOmtc65.lSIjhTJs1dVS7YpToRbpTohojJFRLEEQ7PKhXl4Dm9JBL5+k+Je4lKrvBakIS5MXBaPLkPgwN.X3ce22czEbAW..fkuuepZ0pkG.SexSdx4.n4xlMyz.bAPTJy7K3Q5gxh9p0AgkOrTFz.U7UpEWRJkA.UaDq4FiYQm+Hm58OsW6MeMafYsAFXCroAg.UPVDVIOPXgx.Es.lnNPwSbhSj6YdvClJzPGk9.nCQUaCD1BwCfI9i1PhtnWoAX4keHcmtRkJgkWNwu1KA.mDJ1Fg8CBmphMPXpJ.tN.tp3XmUARUGyk4Vu0+1bW4Udk4UJUVoTl4ltoax8Ztlqw022OikEx.XkkYNM.Rm3lNvfTkItwa7CW7RuzmYNoTlftA2xkKmYqs1J6LyLSZcfNEhfsPJF41bdddnSmNX80VGQfimtEXNVOCietML96iaFUL0hXzlIz1h3NOietmSya78992d+6e9MhhhV847bdNq949betUpU6vqCr7VAAA63c9dMQXkl.gc1OPu73LbLFfGYFSX7Xv1vCVn2r1X0UenLsAmJUpjNLLLGLLDXBh7l7iey+0S7Bdguf7p.U5pkqZEFFBgPD0sa2ANNN8bbb5TFncCTtMPi3lwTsUbdaiiVzHTBLnxLZzHoA.i2D.J1ErhcUuJ4Anr.0MngoDXrbI.rr807q8qk91+Xer7MLCMo3y8Y+rK9d+fevBRorfRoxHkG1Enw3rfvEwBl7+5+vcMw4dNmSgxkKmays1xsamNNffkvSjPqKB.VBgvVqCb.yNfXhAQVwJVR7iaLEbLNVHCz0hnN.nUDPye0ekekl2xMeKMAYt+KdvaaIN7g2Dgga.isU2RJksUJUSoTtSoRk1Y4kWtI.5ZhgTbvOAcyqept92CMLYDr2wYRaEfwnkSUyAf4pCjEUPJvfdaux21fq+sb8IPsMJ4wnZ7ic8jGaOjBAkxBr7DUAJUuLpwg79AviQq0R.ThIt.wTJyeSJhnn9LScAPal3srXZcFXchnM.h1gYpIwzlhZhUwthK1l.XaoQnDa4aNHcHN.Xbx8S.mB6G.m5LCpL9y2yFV6MAwwO.w.yZfT8.bWIAxtkoLX4R4AVdxx.S0nJl7ldG2T1q4W+Zb..oTJDOIcVq0C8775Ob3vt2y8bOstnK5h5.fdULvxNpNPDJigvB8Q8Gjk3B.vk.FZALHL4mMGHrhf.zVU.rCKAGXCGTeTR+oG6iDaiMKLGbNA.lTq0SvLmgY1o1B0rug2xM37h+MewYVe80Srv3Bw++bEBgCLhEIYzahfHDAvlw7aymIxw33mC8Xl6VqVsd9Jko4I.8.3NRYstULPRe3h99CIh5xDucMQs0u7K+xW8C7A9.qFWP6lRobmxFJjsSXRRMyfNXsZ8A72KjtS96+Hs036orAPp8A31AvsAPJTE1fgEBgCL6wJRDMM.lgYdBkRk0xxBKszRctvK7B6gJnOWmA.r+betOKth+O9ELPMt9Hq8b7DfGmamiJ5eZSxQ7Z.LlM9pa0y35bDUz788ScjZ0RElr2pLJhFUmRoN1zGVJK9o+Ze0zW3uvEZiFl+OJkJsTJSESEGiH7ALkRolQoTSIkxIDBQl0WaM2omYFacf1V3Ir52uOs7xKi74ySMa1jfI4fQSDyrhqSMtIvDw8YXNWCrg9MG6du29G4nGcnRoF.fARoreL2W2BwSsawEWb8JUproTJ2DkwZvBa77dJOuM9L29mIg5.ir9vC.vtm4T2e3Jn7tudanjyXhB8tTFwCHSefzqXd8mfGFhfx8.ZzeVfgqVBQf.iFidLSn94Tvfts8666e.oT9Xihhlud85k.PwXqIm.gHvnGXzFD15pegW85+B+m9EBupq5pzRozuBfOADVGXs2467+6s9O+m+GrCzhV.5N6GX3oN.XzGDN0nmWmMDOX2jjqAGzBo3U4TwP3OMhEQc.3FDDvGxyq2JI6KJiHznL.ZXI.r0UPJXAWDX1mCSUTyu5pqt+Nc5rO.TcpolbxMWeqrLwNwzYjIyYfF53rfrgZQkZokVx+htnKxGFiMJD.q6AzpmAgGIHUcHBlsarip8vcCSF+0lGJwIjPEjBgdYABJVFnPixHOHj4D24IRcvCdPW.3d7ie7TG9vGNE.rh0pMtUqVC+9e+u+vm7S9IykAFPlXZ8P0XzbVeDptNCapdNyERTij8T0.CeS7u4.bVQ.GvfPvYnIJIMBKax6k5SqyKlWT..EhsPyzLy1xiHY8h5AWh3Rv8i62AlyqlFwZvhRoJdXoL6G5y+4ScnCcHm4latTMBajxx1x0.GcxFDrAuKR9hsn033Rz.fnA.z.h3ANob4O++OeNbc+l+lzw0ZvLOPHDsWckU19q+M9FacYW1kcFCopBPzG+K9uDcWe46Buw23ajPEXivJ49pe0acxCcnCMatb4lSq0SKDh7Zs1I90sdvTz6NLQaZgnUEhZM7880DQm9HRodHP3x.qAA1ALZifG7q8OLuu5gi0Yju1A.rNYMXg96F+D6lqSrPDio9re1Oawq3JthrDQIFg.CSih6d62ws25xu5Keai.BOhlNizqlJlNxLPmb+2bfwJki.ZDI.hz6CQXaDg0Oi77GCsei95j8i4KGS84Fl7+SCFoQiQ6USPllqRoxrfTlcQkp..lTJkEe4W6KO6e7e3aJybyMqqqqqKLVQb53yyc.fECFVjEA.DwLSHdDCTxvFHi3aBLjHNhYZPr0t16qc2es1O8K3o2D.6DSKLCR2.1gHdSw4WaUzn5J.0M15ZUXDfy5ix6X7lu8PsO5Qh6oR973wfGQEdFfVO4bEIRCkWAffIJAL0xUvTfwj+UW+eUgW0q5UMlimBGeeeKXYEI875Wud8Nau81cN4IOY2WxUbEsAPyPflnBZgHzEKeF5uwngzLGv.KiAPXdsrBHDVxFXY2R.tKWEtHBNnwYXQxI47aLgBf7OvC7.4t4a9iV3M7F9CK9s+1e6bSO4joYhrkKHsTKprt1W705dyelaN+O3A9AEOmG64TbkUVIO.xJDh35HA.PjNPODLEughsQLMVAuacuL3XT3ggLQCHl6A1nGNLP6iHqsCCr88o0aINrXS+i6uUMQss0ZcKl4ch0akc.vNU.Zy.sajjaqDsgxqyZq8s6+jlYlAgG.CvIw.7Hylw8+RqGM2vjyrP68CabJjzPgjB5nZ.19XlT.qkFBjAZSAs6ryNoJTnf0K5E8hvM9EtQpb8x1MPiX3.VwxLIjxVUQiTwZKPd.L427a9MKedm244YZTB4Af4.3hfPFvvtZkJvx1dnVq6AfNKHkst5W60s4q608ZVStfbUDZ3GsVqaYrqVrNphUKWu75MPisui63NZdYW1udSfvl.y1Y+X09m5LETmjMnORM.6ONqGJjAkf5BG.gKfNMP4LUQiL02Ep6SvLOEQTwq3J9Ey7o+z+CNgggVRoz1W66TSTyVoTjPH33oT1y22uOQzfq4ZtF9NOwcRuu216CuzW5KMFFmweTFCqznRTnA8ICAJ22fJkx8qfFCCOiq6xV.MhObuhC.4BTOiYRKHaUfL02swI4KCLw64SdqS99deumhu62y6Nmskcpb4x4L0TSYqU5zfPFgPjMPGjgAGCKPC0G.Hl.GAhXgPv23Mdiz0bMWikVqsF6V6H.d.HzcP+Acd56e+cNlR0whntBgniVqZwL5FyewA.XvZqsV6Ymc1sYlWWJkqipXix0KuUCzHNowRMAVdGfYaMIVs0lO3Iqt2IQ9Hw0XEgreGfSkB0PJ3OJIafj.cygIJuR4YWzewYOwINwDW9keM4.ZXcm24cN3pdVOqg0APrdejvM5ngCG1667c9+s4gNzk1FHrMJi1nA5UAna3tEgzuJP+5Xt9.qL.UQDpO50vcuGnJRN+ItoIykBXEW.O2xHHywiQoD.lT6qKJpIhuNpjRoVLiTJyd5SeZ24medmC.394O4IyUoRkI1ZqslrXwhSr816jGfcEBgytVXooMHr4KHXLaBq3FlPvDAN4hjqVsJWud8glfvlogIkKriRsXahn9hCKhTKpFPL2OBnCQzVDQqsfPrVcTY863N9HadYW0ksMZfsCBB1xyyKwVjaBOz6fAGbvIvI16dqeRrWa7hSMIbreP0NUMxG9wMXuTFfkywLm4lu4aN025a8svw9pe0nO2+z+zPS+PpGe8TE.rEPXZ3g7H.yTEPbi2wcr+C9DehOlrYyN+0e8ugJW+0+lljYNCnXzGZRLqKAzhA1FlIbuhPJB.fOPEeS+Rpt5bn9lqraS45BbfA.mLAR5.69Z1iziGr2oUmffpTPfrU0UKTG0KxLmcmc1wpXwhC.pzqJBGVe26Wb.7RADDWjV4B.VSsxJeqRSO8zdAAAdVDUoT4xyznQiB3LszT.B8AiV.XMl450pUyG.mVoT9Lygwn5Yi3IQ1GnTTErbThnDB7P1f8GtdsYrlOb.J98X9f.zIvnBSyyLWfHJ++7+7+b5CcnCk5Buv+iNe2u62zwzTEjoLPlPlyDqEOVLyze6e6Gv52525kf+o67ehe1W5ydH1E96lF95gNUBpzON9WjwBkCh.pLPfv95c01MK.OKff3BfqhX.gG2TA3VFHcL0nxAfbU.JDhxEAZTHNFYpEWbQZgEVHY.UoPETPsnZx2065cMyq9U+pmD.4KVrXld8Fjpa21NDQoVYkUblc1YcLSXU+PMIZFlflCyWnvfc1YmdF2cSMfMEafw9+LD.cHfsSmIy1qr5JMIl5r3wWrWXX3f2+6+lhdau0qO59WZon+rW8uK9R2+8a84+re1L+e9xe4S355NsVqSDWxb.jCLZ5TBBSZsnRk3tQqdDor9wTJkTJ0nJBQcjfh3wEQwQMK9g48UObs9gju19sA1IEvpYPUjK33AEdJddS1HNGMkRUXAoLWHpj02+XYpcjZo9ju6OIddOumWWaa6lgggaeIWxkz76s82qW45kGz.MhGxS0d.0G.TYXUDZFx0n68hikBLDPD+ZklPEXgPPk.rVdrAkTAHa79uI0ZcAgPjPAnzJkJKyb1ZKTKakFURunZwzRoLKP4be6u0clmAJVtbo7yN6rYrrrFWv2So0AN.rMQVVLGsacVLCgTNRyRHBLuq3tNt1hzm.5ywnK4vxCuSCzX6idzi17S+o+zMQDZJpc3sKivMZXFlZLhC81.HvPceTsCP2d.qmjK6izaZ93q8tmZLTmWxx39YSaIv5NekScpz6e+6u..lpe+9SkJUJivViJY2byua5ImbRWCBNj4TJUVgPXSFo8nuRo5yLMPJ85u7xK2trQrViikZPvOpfnpgUGVG0GBf9vfJu9.dC7P.aTjljquxN.MhuNqXaZfaiLnLxiFHuRoRPiTF.jsLPtFnZw+q+Wu9r+N+I+NY+ZelulSsZ0hE+c3lj+uVqGiZZjU73QiHiMdOPJkQvz9M6fffT7nlEy6t2yLTtg7th+a63lu0B.6vDuIwzZw5Tx5vLPq1nLZWsQ010Q8NAAAs77NeSNsXt1BrRG8tmQkfv7GtQ96iXVOZugID.nXASyxLXESSdOxQNRzwN1wRRzJIAsjhdx.fLc610Mc5ztd.o0LmcvfAYcbbRDdHKyFSNCQTgEDhIVTqmRHDywLWJHHXNl4YHlKxDkEFw3IIvcrBdSc.3l.XCln0pIDqpTpMRT2Zee+sqUq1FUAV+d8823zm9z6bQO+KpYv8EzzyyKtKy6uGvo1KbMOaon0eTqw2atamZmAoPZ3h.SPMfJYXtdZAQ4+5q1nXV2rE2byMyeq25s59a+a+a6vLaWknzGyWmQJ8RmH5Xi03D..xWqsIvohmJEyLOHc5z8qWud+G+i+wOLc5z76889didJm+4O3O3262t6W7KdOw71q5Pf5bU.pdUPn9C5.d2p.Y9u7wtkr21sc6Yeiuwe+zarwFtG7fGzciM1HK.xmJUphkJUJ+fACxtxJqjJlmuiumLEL7t2F.jNPCXrALlHJxyyKJdp+DQDESSmDXFGEn0Ci.5BFsIBsIhZGEE0Nl1Ms.Pm226+826x+4d181+76uC.ZdG2wcr4S3I7D1pVsZaUFXq2x+s+asdkuxWYqS789dsO3y3w0RcupVFaEtTGfkGeJX.mcrmaunLIo.swmhqMLh36jgloaNCphIAPVTG1lI05Af5NwB.WQ0hiBHOToTcHl61e3vdUpTo+kbIWZ+64d9xibGArqviMNxShF65y.gW.qfjyoJCGXAGT2fXIsVmQHD4q.TLrBJhPDCuSYZ.j9U+676j4Z9M9MxdtO1C3BPo51scJl4LwIFlWq0IS1M44dxJRHDQZslI.xy.6TKFfL56OA.D+YNQPgGPD0gYdjZ8iQTGjFxbTel4t1116vLugTJWEI7h0Dbt4EbAWP669tu6wQlS6CBz+DOXtV+SpIj8Pk7ex2agRHEbPlXzKjKt4I1UAn5ljxsioMRxqk1UARGXJjcFee+pRorF.lWqCp.vSiXjS.yq+7G8i8QGdU+ZWUm3oItSr8wtJ.ZHVPn8uW+SWqVs5.XY.wV.5l0.Z4aDJxjDiG+0kyVhGrKJSFSHQk.4T.SzqWuIbm2MGXXiFHpUqVCxk6bYlCvm8VuU6m6UdkIn4KK.Jn05IDBwze2u62ctBEJTx11YtpUqL8W4q7UJtu8suLisu2lA.YnIaSPXUvPSDsjPHNcE.cHPC.rlVq2PbXQaDZteUoTCkR4vJ.8hEf1GtEPwy7bp8CKLDD7SZB1AAvIrlAvcsRHGVF4vXZ0ByrUYhrW1DCIG.Jd7ie7IJWtbdgPjNtwIt999In4gHh5cq25s19hu3Kt4S4o7TZUwnKWIEOX1KElX6td8.BFTAfCKCq+0Ow+JcIWxk.LVS5p.fvwDwwO3G7Cl9E+hewIStOGLnrrPbwDoAf8q407Zn65ttK6m8O+Ou60+FdC41byMmnRkJEsrrxqCzY.iTF6eF1Bg2d0zBB.TLBLgVqAav.GG6FDwEdScIBCDBQjuRQDYDJw3edKgPrCQTyS8C9Ac2292+PsVyF5eohDBi1mDDXbWMl4zxEjEQHlVoTibOM.XSDwLid.bhKBsCYzLrM.vxDQABgH..gAAAq344kbd3N.n0AA5dhGYILm+vVzd9rE.blEH0p69dcQ.T71u8au3ke4WdBBamvWqm.HpfErbYliZ2tcmCbfCzA.chQELehSbhgO4K6I2GQnOZjbNW4HfFCqBzq9tBk63ZnFJAPKu6003WaIhk7DvzfqXpQKhysDo8UpLGQJy7WdS2T5K3Btfz0le9LDP1zoSmE.4z5fLDAWvrCGuGLtocl8gLSfHXYYigCGlX3bIHcJBfSJtLAwoChGdUO.zqcmNcxkNcaXY0jMNp3NxCK2Qee5c.vNBwg2ToVbsiJkqFTAqiPLx0dPr1RbPfAmkr+Yuq8hzDa3AaDABgI2muOafkbKATXYOLk+83OU974mbpolJ2EbAWP569tu6T2+8e+tG3.GHOybwW0q5UU7M7FdCoYlsEBA9Begu.t7K+xsUJEGSSkdvLPmtddd81byMi99e+ueT4xkit1q8ZGtuG6io+e8G9c0ONN6PfxLPCpLfUipvB0OCT0YCfTUAxdo+5+F4unK5om2yqZl4medWgP37JdE+Nt+0+0uyLoSmNe4xky1X4FYDdhTc5zwds0Vyghs2dBvk2EY4V5.chsMDAfABgXXfVGwlAaYA.aKKK6nnHRHDHPqAChM5yGh0CmQNZV6wxyXCgPrhVqaHL0htoTJ6TAn+hJUeoT16z0Oc64Oz7swxnsuue6Z0NZGf58p.LHbWix3gaj+9Hl0i1aXBv3v4Z+.6+T.mZObfDOXXil.eprUAxWGn.PkhLWOOLNHQRBcoYlK.fICBBl1yyapFMZL8ryN6jMZzXBl4BUqVMaPPPZhH63I0FmnQ7TbHzj.sEybRm81P6qaIpI5n05chhh1jHZcobgMqfvDZPLpXBjPKmG5hUezvl0QMfX+.NmZWMDvc80W2c5omN8oO8oSO+QmOKBMPc6tu66NasZ0bdFG4HN2e85tnRk7pEWLubAYVtNmRq0NBgH00e8Wepq5ptpzm24cdtIbTse+9t111V0qWOB.CWPJGpGNLJLLDdddQ.XfVq6RD0gA2W3IFlb80rYSZ3vgVSLwDPq0DQjsmmmiJHHszyKSbwsozZsCQrCyT5hEKloXwhYiE9QW.3TsZUq1saas4laZE2jDSC5zZjNSFLyLyDEDDLLtQIQwb3c7jARlRczXecePn8BBYqEMzgn0oO8oadgW3E1D.s788aYYYrdVgPjTrayJ.sNlue6Z0NbGOrb6fcg3YBWOGOAky1NfbuEnYGFFRUpTYzYGwN6PQfJybe26mc5dQQEd5O8md1u025a4bdm24YwF2XxcyM2L+EewW7Deyu42rH.bIhHee+9RojqdfCv0O4IQPP.444MLHHnKQT2VsZ044+Be9cOz49D68Q93e7waXxHDNTFvpA.ATkph5V0GKPL.Re0W8Um4C+g+vYOzQOT9O8G+Sm+6dxua9sVeyLWwy84lNSlLt.HikkUlfffTdddNCGLv11wIEfQX5hebREyC23.rALGamgGVJ4iqTDHhHlsheyki0vEFFTnvjAN08YvsAise6u829V2vMbCa4q0Mqc3C2Qs3h8IxPUmXg1aMXZBv5ZsdyXwOqM.5d5Se5tyO+78.Pm8Cz6T+vmZ+Oo1qQODe83IAkTTdNl4Luvq9pc+3+O9HNb.mH.3tDQoTJkU4xkSkRJSW+a7MxW8oVcZ0hp4.PEh3J.VywfKBFYA3TXWshYHQTOXD3tcjR41ZsdSDgUXhC9XerOl5085dcZXlp3F+E+E+EMe8uiWeKnGapXmcFOXuwrclFvccf7.dSTAASTm4rXTQnlIfvLaQkIarrA4dLy4IhlnSmNSsxZqMSsCe3YTKt3ryM2bSs5JqTfMvZ1Uq0oVe80SkMaVmLYxj75dSv7ZLfRJkKUAXoiqOsVHdZqTF0W6S+U+pacgW3ElneX6VrSELVRz+jQCS1yfeFG4PwzxwTj2cdm2Y5m0y5YMxM2pWutU0pUcQUj+6bmemIekuxW4L24cdmS9w9XerbW0UcU4fwASRZfgK.XkR0iHpyBRYm5L2QEDzu5byE8Ut66lusa61X.D8bedO2dWzEbQFzEZ.V03nya2brpVEndcKTANUCga8cEL+z2xsbKY+vevOXg+n25aM+29a+sydgW3Eld9Z0brrssWas0b777FQim.sNqmP3BCcTsDBQRSQr0A5yfBNwWC7XP0iggpawnAh6AP8fY5rrRo..fPHh.PWs1XGxGVJaeeZ+AQvhrLm+wewu3Wb3Ue0WcOkRkneNVUqVMc850KJDhoAvzZsdR.jiArIyqCcMBgZBxchZwLssTJWG.g9990srrBEBwJ999qax4St0z.MWeWDb9P4RiORbclmYt+86fScJWTFYqz.4BiclMXZdxj0qWelpUqNyce228jWvEbAY0991kpTIJUpTCYlSNGyVq0PHDQ+p+p+pCdmuy24.WWWdvfA7exa+syu5e2e29OgmvSHIu5jXolWmpTAHLjPYPka.tg4eOIe+b.Xx2065+xjYxjchm4y7Yl6bNmyI8C7.OPpie7i6doW5k5RDbGLXnqmmmK.bGLXfaiFMbIhb877REEE4TOrtEXXkMaVq1saOJWLdOi3OdvBCMMJgFtayQ39w6G6SF2Txf.JktCCtCHp487Ut6MeZW3Se6Ymc1syjIyNUOP0V2ycdO6XYYsk7HxMPcSS1VZokZsu8su8la1YqEvNJ++CBXcB.63btR9YFTeZPv4jvzzqhLyYjRoQCx77Ri50yepScpISYaOArrx4444RD4pTJWoTlIFgvV228ce74e9meehnAJkp+BRYzhJUDSTjzj+e+fffArAYGbrfNOB8Kc610Zmc1g50qGA.KOOOGcPPFgmWNUfJqzS5FDDXGSCMmzYR6N6LylQqUoMCUGNfgsPJRNiaTtoAZM4ID..I5vTDL6mXZrZYymKGZ1pEPx64FILgAF4TWc.PqFMZzrb4xsiQZx1vfpjU.PnTJWA.aUFn2wN8oile9iFUBg8WdWyGHo9SCxJMzvYuB8JvYe629QtdzbCS.NS3bMdAkie3AumeOGLMbgSLuKW1zA73OxGWXsCQjCJibUZfINlu+LVVVyDSGjI777xGDDjiYNC.ROyLy3t1ZqEa6XHAxqiBdxLuEQzFuo+j2zZu4q+MuIrPqs1XqNm3e6DMeA+RufsfYibhEG2BPzBP2FkPmCtL5chyrgI.O5ZS5dQrQJf8kBXoTXFjp5Zvo9XV5KRfsVoRovxVo.ByNXvfhNNNSnTpbLyt0pUKUEfz28O3GjKUpXBhxFE...B.IQTPTUIAuSRXLytTSXT5VjPHnff.344M..8zZcRvnwC9Zicosf4he2ll3RbBkZfEYDfyTBgvMHHXjEtJDBSGjSddymw91QcUF.C.QQjgRriqyBIMHYfTtvPkZwHhnnvFM5UtToQthygkxVK56uSbSRZJDhsdEW6qXy28M7t2HHHXKuC60DQna7jbRDzvtujWxKoyG3CbqcAVqCJgtGXYz+j+jAF5+ui03nHHIvDAu3e5.XgkgK.JrT85StupUmfYNO.RSDkxOHv4qu3hN+c+c+cY9DehOQAsudhUVakINzgNTZeeeaKKaHkBal4TJsNMYJTfXlGPD0qToRcSkJU+fff9LyCXhGJ8jrJPQ111N7vgNyLybVqt5pINVCYYYQqu95Vc5zw100MEyb5986mw.UXjUq0YSt9h2S4JDFwOrzbyY455ZES6lXmiXzzvR9.toc4d85YNSwfjIDWYJgw3xabSS1sobL5gXaRWtvBaoVbwshsl5VwhkXaXNCay+k+k+k0dFOimwFeiuw2XymxS4ojnO.l6opg9nEFfbnO7OCk7++ct+ZuwFG6LnRo.VNMjHaIERu7ttkVVXhQjipVMiZwEcjRYJl4zuzW9KO26+89dm.FMXXF.LiTJmVY3Buwkb10gyXPX.XLBkIRobKee+U+nezOZ8q659K0.00uk2xaY4a3M8l1HbW8UXuEKb1XRK61zDiNxXD9tD69lnz.ksAZPnDHukgcvtnQIM.x366Wr1QNxDHjl12+dmSJkyRDMiRoJBSQrtV.NBit9jblbxd6VKszRqA.0EckWzR56SuzfACT6ae6aWQHFU6.TuGpfghPLTuqHgONr2+IABSh2+se.bJfChnXKUFX2lnZd9L2bNXkHKf0FuIe4We80mb5omd1x.SceZcQOOuBDQ4O0oNUdGGmBvzLImXJCjH9vCDBwvjlTA.DifwdwE7EQDYkfVmIlXBZmc1Ixwwc3TSMA2ueejNcZq1sa6355lZiM1vYP+9tdBQBJ4LvMmPZgmHkVqSzfjQSYkIJkzyyIltM..QfHlL9joMQjcLpLiE7bJ1PHXVHDITfHBFZNzyxT3P+pUqN767c9NQSO8ziGasEHrs7vxcPH5.fgwEoYG+zefVq6JWP1sZHFVGvNVvPmToTyTsZ0oCCCmLtITVwOti6xGsVnVstGWo5.fsXlWIFwX0888Wt1gqsJrvFvFaAM1Am8NThwisN59S.jsBPtv3Fl.fYtga3Fl4Zu1qcJl47TUJy+v69ev9W5U7K4fvQhpapd85YuxJq.lY1xxhihh.QjUTTjkTJGBChe5EEE0yxxZPbrIpUqVzNsZYUoTI5zm9zQOgmvSHJJJBau81Nat4lYd7O9Gews2d6I52uewXzW5FOTKalIGDit23Askneb1vruyZO4eYP0TP.ahcx7ryNKVc0UAFK+MZWaLu6wN1w57K9K9K1QoTcIh5xf6IOrz39hKp5rvBKzNLLb6XzmuEP4s9S+Secs98eG+9cKGhNM.Z8I9Dehc9k+k+kShCzF6aecvR86BDb139lwW6EIvV.0H.efZ.d9fB10fDJD+QNTspKpy1G8nyacu268lDCYhd85MYiFMJrPsZ4ZXF3QAkRUfHJWhd5HDB9c8W8th9UdAufH.BRojYlizZcjPHFBfn3ySrHls43bsRPu9dx+2xyyKY3ToSdNP.Dax22I9mkrOKI+LB6h3s8V653h.Mvt0J.PiUe.n9.734BDgXqM+HFpA1jhE7Ul4sIh13J+UtxUtm6+dVFLVCFWHc..3q5E8hh9n23MNHlRNs.BaiIQGuMQufGLc7OaLuierV+6gFlj74wet9C6MTBFKfLEPX1J.ECAljYdB.TjHxjXaY37s9e70Scdm2SMu1WOknlXta9lu4Yt3K4hmzhoBLPVh3zBQszZkJMLP+MAx0CAPGoT1FUPSDhVJkZaoTtA.Vybn3Bs.B6DqBwa4ArYfIYsl.nUXXXqJUpLtOhe17Ah+3rNyhZKAGrbRAtUsApaBHWFtpiqbkR4HwVsDPgkAlToTS.frRoLU7z3xdW20cUnPgBS7TepO0hDQ4+ReouTty4bNmLwuWMNMXRBRhO4m3SL3+zu7ubeh4thZ0FpTJDmTmKHjhXXylNOyJUPjkE.yrU0pUcBCCchOPlHhrhsHrTvz.NKdjfomTyDEihXyarwTjXHHzuaq18O2CbfgJklgwYMrheLhOvj6APCHCmE6SD0CQncjUzNu+226u4a9M8laEKnSIhI15vL0+0AprUkJnsUX3f6w2OZ6s2dvS5I8jRnRRW3gdH3AQijyV2+MdC4hmN5AAvInZ.V9.oJCjswtSEKK7PZDjTbR4z.MxBfhZstnPHJByTtcHpJUFgtMLSvMefNnPDhbkBISDMDUv.0hpHgPD8G8G8Gwus21aKJdRmVLytwBzpcfVayiqeHFn9Z8xd4ubm+w+w+wTZs1MldAtISNONYuQSmfHh.CJhhXhI9s8Veq70+G+Gy.iT48wR5i..GQDMLxP.6QABMI5wC.ndRobvW6t+ZCi3nn4me9gfPWhQagTtiR4ucqVs25hebOtsZ.rsRoZ9Q9HejlW20cc6DinjMQErEBenJ1+LbqpeZy65wSXybFTE3fvDmbQ5BnL7W2LkqX6YE4fgi7oBioNE.JHDhIXlmfHZJsVOYTTTAKKqQB4lRorXlnf.EKEx9BonsRoZt1Zqs4gNzgVE.g9ZecMQBkbJuARzVnYPar1iptmzF.N0.x5atGJCQTJrKzmsM2mEOYtxHMGxYIhJBfIeaus+ul8u95eikNtVOmPHlUq0Eh3nLDQ1RgzdryeSA.JFoAsAn0AX8BR4Rg.Ko0ZUryMYFZg.cf1fvjScpSMb+6e+wba+mXM26LG7yAAgS7fRNkvAfMN4YX0pV6CfVJlZn.kxAr7DnLlEMvzeyu42bhK6xtrIBCCKPDMR7UkxCmVoNdpEjRm5lhAM+ALS0kUZEAFrTJGpTpHoTRJkJEQTB0DHgPDoT5AjIAdRHDVA5.mHDkRJjNlynPJlwnyrhe+1lYXQDrDBgEhQSBLm+YYbDhHFLFJqUKJtPFSwTDbHFVwi4fiXJhnQMzkIPQQHpGQTWoP1Sq08YliDBA+C9A+fny4bNmAJkpyy+4e06boW5Er8G7O8Ocm5L2A.C0ZskTJs..TJ0v33+cigjeJ.jWoUSIExYTJ0zDvDQLbsrHKl4gDQcAi1BonEpfNHDCzZUunHdG.rZbCSB.PCfRqAr7Fd.6D.zZVftq9f0GmyVtudrFMWIEPnKybZhnrnBx6eL+Irssm1yyaJ.L4G5C8gJdYW1kkSJk4z95LQVHc2lMcerOtGmKyrSPPfSDhrpIpQvb+ep38NVvj20vwP.PBZMsiKb0REnhHlFJkxHkRQ.HsPHxo0ZSg1ioqQwza9Ln3k4qYKvfN0RKgGy9dLDS7nBbiUCGlHdXTDMLd++X.cxTPqTJ68G9G9G18Zu1qsM.ho2LZo05tLy8hsQVi3waxOaSee+0pUq1lnL1FMPG.L3XG6XCNxQNhQj0mCsO8wOc64me9wo56Yi6Y165g57u30AHfSZNeaNjkWlyZZpt48vp.N0GQ8pxEAZTDFpKWDkwDpiqlTHDSRDkuRkJoO9wOt8eyey6gt1q8kQDHxS3Mp1QhnH.Do09LyjkTJcTJsKQFym3GU9+kJUJ0JKuhCCCUftzeleFqa5VtkjAs57u8u8uY8DehOQhHhhhX1xJ9vVd27+KWtL2nQiHPXvoO0RCuvK5hXkRSw4+mbM1mYtKEKLqDQ8DBQjuuOaQVQQHp2e+e+ee6Wyq90zLwsazZc6XTkuI.V6a+s+1q9jexO4sLtHDFVEfWzzrn9X27LhY2vA6Cbh8NjfyV2m8+mqGs2vDfGL7p+QAQYB.10.R4GaoqvzvjBDQYgwtQsHppMy0cIhJvLOsVqmStfbZ0wTSHOhLmZQUZPHCCJMwQYXPYocUp4H.zKQGIfEZQLsEQz5wIksdEiCjz122uUsZ01An519926V0pUKlJNdsABdzRAq+3r1yT1NfEvII.XMRDu1GrwRHElENX0QVN7Hmnw22u3c7E9Bo+MeIujTkAR0.URyb8hDQSD2Lk7.HuTJypTpz.HiTJSqTpzwIzYC.HDhge9O+s06m+m+mquVqGFO4gws6UagPv5.8PlMNuCCPVDYWpTI6FMZbFp9MC1g.YGCs7wgy1n2KqToBGFFBLp6xTeSCQLPzUJk1ZsNogKQ.nuTJ6+29A9.8+sdIuj9999c.PBkaZdXgnUHJ2V6eeshrhZEqF1qKDh0.v5k.1dYydq3qg4FFKjZw64lrOvlIST8rMsKYuqeXMUM46cdpO0mZ5u9W+quK5kPE2G6isf68e+2ua61syjMa1bnBJ9m+5+yKbc+EWW9ct+cRWnPAaes1lLMeKOQjwx6LS3N08bO2KdZG8nfIlrHKZpolxJa1rwLegsCTAoXvoAAm3oPXkISFqtc6RLCJSlzVc5zwJ9ms6jkSzgk3oSDOICpZ0pnd85IPBd..MXhIJNbqs2BwEY3DOAUiT9yTDiX6m1Lk4joZDCcXt6a9M+VG7ley+QC.ngLwCHl5JOhrs5XplfvNLnsrhh1.VVaIDhs8882oUqVMujG+ie6PTcGf5iKrgiZ96AwYX8g+zdu0danVrXtEtqsCSTJTAoQHxgRXJtAOQ850KDEEkJtoWoeYurWV526688lGFs0n30cc+AS7g+venoTZUAJhyxwSmRJkTx8xDSQLwcYlZRDGa0sb8ZhEpCznA.VsDvlKCrCJglXYzA0PW3+HdQh7Gm0tH9LAQOl831.RBPYCS7XSg5UgKpaDpQoTVD65pJUTJ0bweedhH2CKD1+i2ywHgWUaXgTfQRAWQDQcN0oN0lHB0unK9hNM.VBnrFnwpXjtRH6Bn5Cfg6CX3R.CQsehhFpeTC9g2yOauhc5tvVONdXEfoCQooB7+FSd9Ww4OQ32HrfRoxmISl7yN6r40ZcVl4rwwBSDCZK..gPfa5l9vz0bMWcBhKoFMZ3Vtb4L0qWOSTTTp3F6NTHDCVas0FNyLyPqt5pVyN6rwBhoNV+QXGFriEH64JUxJUpTThVbMZxpzHzcN54cbbxg.zvLYRGM6ryRJkxFFiHwNgpf.XXbSc33vqQDPuCKkct5W6qu8q609etKHD0sa2nzoSGAf9268dusN5QO51LyaWqVsVejOxGoyUcUW0fXJVRdddv11KZ3vf9111CXls9BeguP5CddGrPMQsoN4Cbxo8p3M4latY9XD2LJlLabotNJkpK.M.f6BB6.FqxLGRDEHkxkqBrdcCkXiKJYecAVJId6Yan47LQuMPJS90ykFXkDWCo3q+0+5m3085dcE877JpTpIjR4DJkpPPPP1ibji3RD4p05TLXGvlGmM2bS2mzS5IkgHJqVqSwLaGmgOuvyXA9X+qGmqIDVJkxVHD1MZzvZvfAQsa0pet74G344w.HUfVmwynoWYiik5jKWNpcy1FYL1yKAgRHFsRirx3olZJrwFaL54Xx9LPX.ynOs64AI+NLAD6jbbaPnIwXGPzNvfz2NJkZfTJGp808Wd0kacsW6qYquzW5NW89tu6asy+7O+MAJ2zX5.H5zm9zQyO+7CPIzEKitvniKiWavYiZWxdW+vN+K4eyBdvAAv0Xi5mQtPo.f6oO8oyN+7ymG.EJCTnApVToN1jLwSqWxeR47yWv111sZ0pNZslXlskRoiVqc7LHaCvzHjHhpBlqaGSy4DT8l011N8wV7X1Wwy4JXcfdvdy+GlF7YicueHI++wQG9CJt8XHVIgJzwZdCw.r8pqtp8byMmEGwLrPWvnkTJaFqAlsia7VDQTDyb+EjxNgnRSkZwVLwcpIp0ToTMiOyayx.a0Xj1ygHee+nZ0pkj+eafpsAp+noFx8i85eOzvjj0CEBSdn9cRRtHCLvqtHQTdee+z0pUyxCfBLnbvEKWsHP8oAvr999SWqVs7995r0pIRalFV0b.gYApjQqOtg1ElaKh.P2Ejxtg.stsO2ss448TNzJG+X2aiegewW1xZ8hIAKaKDh1.nYPPPSOOu1yAzdEf1lo7evA.m3QCGH9iyZuIAR.GjfoUyiEPdeN.K4B3k12+dxVqVsB.UKfp0yh5vMQ+RNsVm9oID4qa7t8hAAAEDBQdfJY51coLqrxJYGLXPt8su8kKd5vFAwCneDi9jEF.lXoT3BfLJkJGHjgYXSlCVGn059F0qtBTpEMP50LcBaFv5q9k+xNWz+g+C61cXCZPFhXA2D.HIPM18.ogQQQ8m+ny2WsnZH.vfACrbbbRNvMQ.w5yL0mHt6a8s912487otgsPnYR+.nMST2EDh1M.Zq05sEhCu4K4Ec4q+AtwaayG2iaxleuu22aP7eeJ9ZYH.FrOf9KAL.0vfeJRWhGtW6sQIi+0iDeR.3NCPp0.Rg4fKVYD5RxgXzmn05rBgHkYx2d1.0cApjSqOdAgPLoRolnc61YefevOH007y9yZ2.vtQXCm9C5OJPZ7zvRnYlqVqcxlMqS61ssRmNsU2tcs.AKgmvNHHvgYdLnBGXQDrXFPH7hCzRLLXWOB.CqToxf5ggcs.FHLSU2TzuY5vizf.hnAEJTn+N6rSel49DQ8AycYCe76wLOHAJz.n+2+6+8635515Juxqr4sdq25NDQaIDhM.ptIJWeaz.MQIzBKaZRREfNgiZVRodFkm+L1O8S6IV7ffCbr.7ZgYAgUm1BX8jBARCf7.UlDHbBC8Q7bAp6.T0UqWL9mi7BgXBesdxiJDSUGnXbCZcP7yy986StttV..Ly8a2tcyO4m7StA.V8u35ttv2+m4yD979sddqnNtZMaa6MqVsZy50q2rZ0pc.p0Cv+QKMLYroSalX3j.VaBPnJbP8YSArpK.RWudc2pUqlAnZVf5EAJOcmNKM6ZqsV41saOSlrYlFLxQD4FEEYQF3R3XlRnJ4rctcmNcxjIy1LyMN1wNl5JuxqLwRgWKHHXKhnVUqVsKJiAng2PiawLhRN+j77veTC94g52arOePK3cBSAEyf7XMLwoCBl3HddEa.LQnNL+PLLuI9W4bsaepbqt5pEpUqVAl4rKszRocbbb1yisEyjcsZBW.jUoT4PrkWtq3nFzWJECAp.kdQavHEHSCpXhbrXNA4bIMJAIOuh0Ffw+2S96RrA1kwZ.AhPD.SLkOedqlMaQvHJ0QkJM2fkWd4Aum2y6K5k+xeow74O1ttAZR.sERY+lMaFswFaDAfdR4BsTpE2Flo52lYtKYbTuHee+Q7qo1QqwndUBndJ.j022uXsZ0lToTSPDUvyyKcLpTHfJjRsXzK7E9B6eK2xsziXpKSb2iHks+ytoOzl+rOye1UE0N+F.MBUJ0pRob63gkk37RIedbsL4ro6s2sgyF2pzAFja51vjmcN.ub.A4ApV.n9TLySAfI0ZcNoTlItgHNDQtBgvEkQFNjyp05DqVMM.RozZa2Tor52efkP3QwM5yAFJdQBgfQL84zZ8PoTZyLmNonW.JkP3YmruKtAII4+DEW.LG25CFfoXDVYg3JXiaRROCJLEC.pDoTKl7Z.f48v1GVJa0.XKkRsE.sEwQ6Hpcjt.gCzZcDybel4cpUq1FW0UcUq7Q+nez08.1NvfljgXk4fQxIF4RWcAlN1UbFUavdikd155GVNZIeebLhYb.VKEPYmSe5ikZ94m2EvyEkBxfkQ1W0q5Uk626262KOQTgCKkEa.LEy7jAAA4Mn5cAKs93DyriTJSyLmIdfninBHyfrLMlMsTJyAXb+F.jgMzsgEBQ+8l+OLCxxFlATY2uWOaWW2379LNdYb9+Ci2mh8b9GHfnM2d6gOo+iOwgpEUwzBhriQ4TDLMhqoTtv1J0haYrC3xsuu6616e9m+4OLFUcIN1U6Pf1FlLb3V.MZBTtoRc7VRorG.F7.OvCDctm64lffxdyAzYEjzXtC1+eGU+I.92WML4Gm03cBOc4xky1nQib.UyVB0cWNIItpvtTc3dgWwyq3m4d+Lyvg7LAApoDhZ4fQHXSSDkQoT4Ihxztc6zYyjwkAREmfFQDOjYqt.bKXznjFRoLFl0F6bpZ0psqWudGfxsKgFsWNQvMKgNwZGwYyh4z++c8PzvD.blSXauB2aN.j6q809ZYetO8mt6xi45LFwdZgr.gE.JWPquuhuhWwKM+M7teeYHCcHxpTp7c61sv4dtWTNsZQWgTZzeBOZPk5XvhF53j1zkYUd.JKLz3gSDMJgPDEDD.X7tvXX7ohKBfr.FA4YF.C6OXvvGy92+PkREsvBKvKt3hQLQ7QDBdQkhAvPhvPl4A.zP4gkPcbEfo6yCXlG7Buxetge7O8sOjYtePPP6pUqt4QpUaiPCj6ZAin10SHVnCP81.UapTGaaC+Xw1vrOKQLYAPY1CMFF.L769c+tCe7Wxie3ABwfGEt+6gJPLAb.mJ3jNgl8MNxmpLk2JHU.PZTFYz2mNiPHxpTpzRozMNwNGXPfPJhnLwb1dR.L0sca21DG5PO4rBwQcGNT4Tud8Tw7m0VJk1.vZ80W24I9DehtKt3hoXfTGQJcVTorgwpBsAy1LyNvhhm3F6X1OMher.6Nwhw+HNYNtqTdj9+M+M+w7y+4+7Go+AbbgiXDZRPWobgdZ+i2W7+j8d2iRttpty+Om68VO6Vpk5GUU2Gc29QC1HhAoVFXBwXyCClL+RBIqLvLCXlEIP94fIrRvjj4gcviAmjYHfAlfIX.y.+BPxXxjgPHS.VNI717PpaYOLBrsjk6tu2a8naIYY8n6tpacO+9iy4V8sK0RVFrczi620pVsTWUWcW2y9tO6yd+c+c64zNHveU2o8VsRSZuGe+nXHVHL5nd+bWF335VL7X.Gw22+HdddOtld4IBErtZotcdmuy+Mse+u+OQa3HIGJ3etaAmznmuFsfaZJkRgnlPTsIzDfQvfCNgELehPvtYcR1Ku+8u+7WxkbIVBgHudbhmzi0CEDDLDvPZVJVP0NcwRWWOI.A9Aho24zLyLyzFDmXZWmGqopk4VJLLboc33bnlvgTs0TsiAMROJDOeopO8yRBUk37PLhOVGTk3xBrTs7PiB.EpWudIaa6M8fO3CNzfCN3vttti9i9Q+nsr3hKNzUe0W8.56QsBC8Mbbl1b+6+9xUpTIi33XgmmWreX3xBo7ntttKEDDzPHD0cb1YqZT+PMRzJlQnSkCR2VfDG5R3ViRM5Ne59vrOQE9YC8gose6UHn27a9MOvce2+cCBMFDpNnu+tK644M.P428se6kdW2xsrovvvMALfiyNJUil4ajH3r0DFUah4LpDMUxwwYfvvfAAQYT6+gd+uDQQEgVytbccyultwHLFYjgEG7fGRp+nHAj5CfxccW2k3Ftga.PHm10QLipEJTMWnjXoT1EEM4WWhUEJ8MoqTJSR1PRkdk5jerrTFeLW2cdLn4pAAAI2uzF33ZeWGOHHXUWW2NPk1UoUTSkdggnlvrZSLl02OmTHJ533LPPPvl877FJzObPoPVTyrLQPPf3vG9vlacqaUn+r0Q01WbbgPbz333C44syCBMWBkHXmz50qZaaG889deutyN6rQu8Wyqo87qMM0NWLgIv5zgGLgsZAGNGLZNXoDV5NHvVdku7W4Vu+e38OzryN6.ttSWrBMy0BxcEWwkl+q7U9lEg3xDy.N6za.+cGVRHjJMhPRdgfbRHudRgX533zqsVQcMKBUxw5pObbATwJlvRXCHgrmxXIDKjz00yMVJkw0qWOd6NNxY88kHDnS3WJVBHibc8VUwjnd1eni+SDDDzEXYPbTPdXgP7X+R+q9kd7+lO+eywzGTsquue7zSOc6YlYlG2ya5CWiVGpQR7aUoSslHa.BpBzzMBBRrMR6++7g8.5GazYV2njqmv517MZzH+1qUqPi0zPmx999k8714fPyMC01ru+tFPGylv00UhhIbk28t28f+h+h+FCTkF4aBB88+hpMwZlff7HnrisyfgqM1eMTrPQzII9+vvvdhStqqqYPPf19ecw+GKDxtwRQWOU6hkD+uDDLsqCZ+eZ1xE2EDR2c3J7mI.kFfKz5Hm7Df3XRo7HdddGIHH3n5jizIg8RAAAscc2wJPqkWXgEN93iO9I.V929c9NW9C89e+sA59U9Jek3q65ttXvoaEB6zBZGDDrh6U3116Pzw+byj29SExRXx5gJ3hsg0d2apQLrllWAAABcerlTw4MAr0Jvv6ILby.kc1gSNZR9G9ge3hCLv.kkRYAoZptXoCVHuqqatvf.jp.bOZivFG567c+NMeOum2dyVs3fiMFO9hK1SMhWgwXkG5a+Pq7re1O69UA7yGcHdpP+ANmf0OsiT8wcNvtHTu3sdq2Zwa61ts7.VG6vGybvsNXNfb0WXgB1iOdIfAzZUylpTgAZ0h7Uqh0l1zTE2291WYkfPIGTJEEjBgoPJiccci.51rYShhh5I1i.kDBgULfXsVXHFTzp6.G3.FEJTv7M8ldSVe5O0mRz6OZoHFCPJiieSugeM4m9y8o5Jk8D1otnT2ePfTFKkdJsSoKJwnSoGFIAArC2N96IrqmyNhCBloCvIjBwQ7bbNbXX3iqYrTTXXXGGGm1.qXCmnNbrwFiis3hbBaaZWudJapIHl42vCdetd637Dg9oSbhvbZAjSKFc4qAE1kuedOOOKpPNZ0SGjRZCu75Cps4fffs555N7QNxQF7XG6XEArt9e4WWt+vOzeRtK5hlLmiiaNcKYX455l6.G3.l4ymOYy+bBgvxwwwLHHvzxxxJJJJokcziGUoo5LEpjk333DEDF1UnpZgRCbzZZS2tc6XXX.RrPPt+C+GdGV+4+42iPe.hDeMIsKiRj5jx15Je1NHHHJNNtqggQGgPrrTJOVXX3QsccOpmy1OJzJYZKcLkXgYmz1MITItKPmgg1GZ8hl4H6NnQC..f.PRDEDUYK9z1n.8OoD0NBXcPH+7yOewI9WLwfDRYXrRvh8zRIoTVRHDCFDDLnqq6VBBBFRJDad+O7CM33td418ryJ93e7On7q9U+1x.UhQku025aM9C7A9.qVrTwiKP73NNNGILL7wbbbNLpfmOLvQFFN1gRnu+SOi11+4D8SG6jGIAEWXXH+gzL.SuW8.vXaBVbnfffs355tYTL4YPfx+F+F+FE93e7OtkqVzWERgkimC0pQmFM33gggOFvRRorkqq6hgggGxww4HnRXxJjZBbrqcsq3W4UdkcN7Z1umsE.YZa3j3VRZuvA.FnFLXCXfZ0nTiFT5c7N9sK8A9.enAA1juu+.XPYhofPkbVCWWWK.q1samawEWrrNoeChZevb5escQI3fISSBCsOpbG9vGN2ke4WtktMSSFI4pYDkT.BogTl59L0oME5p2Z7m8m8mE+Vequ001eLU664Nsar+LAcE51VUHDq533zNHHHF.MszWUHkGywy6n.Is.Qb0pztYSNd0pbrlMUTR+K7E9BseguvWXac+6Go+aJGPt21a6sU3Nuy6r7G7NtiM86bS2zlCBBFxcZ2ABlIH+sbK2p4se62lv000X94mOmoooYylMoZ0pQ5DK2apTTAVpEbnJU3wZ0hGuZUVtYydsDqjpzUOAlRRpb+h574Bn+325o0NCAlGALGcTxuzRTdzQYyKsjZL+BLfuuegtc6lyxxJ+0cc+xE9g+vePY82e.T5RWgvvv7Rorf2N8J5OaX467O8NK9a8aci4bbbLBBCM22C+vlWy0bMI5gSRw.5hxlpfDJKDT58ba2d9+fa8OHgceoaKzXcbXwr93tj5BTHzeJkRIQBUaqtBvJRoriggQ7wN1wLFXfALtq65th+MugaXYIbDgPdvWhyzGZ+z5wqVki2roh4KRorqqq6p.GkpbDZxQt5q8pO123d+Fqp+6AeeegmmmrQiFcpUqlx9XJ5v9NuqPVOQHsONMaSpZAMS72oJFj5bX8RZB5QbcEXSsfx0pgUiFDWoBwsZgEJMcZyBgbyRonHBR7+Y.XoS7aIgpMqU5eipUnSRJWuynoaqFC.ScKzK5oYgRhQn8e45FGFFDCh3T5vCnrwLLDBCGGGoePPrPaWJjxXoxVrq6ztcBlIX0a7Fe6G+i7Q9SOhPHNrTJeLs36uphwKjzxjcXs3+OQsZrbiFrhsMQ0qmJt9JzM0veHwGT+9gtPvNKKgI8gDm5IUbMOTMu9fO4FCLVbTLpsDV+fEVH+G6S9IG78bq25V.1x91291DPwqZpor1s5FoBZgqJ218bLaQUKe+cm2vvnnTJKt5JqHZTu4pu3q5E+3.GLHHnkqqauJLrvBKr73iO9xvnKCKsLvJCAcNhRvMe5PM9OaFq6fKSBlys1ykdyA8FxdVfeAzszPUvpIiY.KZPsZVznQdfBOxi7HktjK4RFDUvzaBn76487drdVOqmkw67c9Nyo5G9oGT2NEkzNHEBDw1N1c0h.kYPPPg29a+sW5O8O8Osnho.X53Xyeze36g2zu1aVNsqKy5GJkBoAZE8mXPhL10ys6BKrPWECw0D90vHVnlLAQOzC8PQW1kcYwAAAxc55J97eyuo7RujKM1w0oKP2fvftHoidy0UTIBYGQ996tqPH5.bhocce7q+24c93u+O36+3TsZGZ1rKJJFG444sJ8F0gjLZWi.jGZ+GhguzojvghAh8.o+TDus8gbum+VAizXc1cSAF6yFyQpi4AWKAJoeXZCV0oRtvv8j2wwoX8Epm64OtsUKpkGZTlJUFNX1YG4q72+2uke82xaoTxOWPPPNoPjWHk4kfIJ1lX7W+W++xbqacK4trK6xxOsqa9YBBxIPjShpZERozHLLzZ4kWNWoRkRD4UC8e7cQJhde+I+W67696+uuarPs4JpMl6zpUqt5ouiUfePNogvPn5++UO5QO5xaZSa53tt6X4ffYa655FE56GKEhHgTzVJjcTUektgggq333bBfGuJ73Mo1QCCm43NpQE7IpBK2jwVAVrMiLRWVahAjPO5zUNMs5ue1.RGnufsgXa6E165Y1Vuw7rCTLjQKt5pAEJTnPdT9eJ9s229JO0TSMXPPvlbcc2x92+92rTJ2zTSMUITLWxPWfRoVPBSlzUq.MOtuu+wtROui2nFGKX2AG000M4.WIGjOomiOeMQ5qqcPnmPl1aBEoDoavJTIDyk+w+3e7.W9ke4CVAFbOggC533jjzjAbccKD5GVzwyo.fkuuu3G+POzJa6xu7ir8su8C1pUqV.KAUOLz7wANlMrb8JrpcK5VGj2zsdqh631tsmoZImeZvZ1vpQ7ZR69kzJgC9Zesu1xenOzGpjiZr8VDnrxVc5ABBlY.WW2hUgbyDDjWHDV1114zhsawfffR.CzoSmhpI2kZ+u5ggRjh3c34HmMLDc+6agR2HLrcbj25sdqcusa615BflcaIqs333HqWut7nG8nhAGbvjD9nodtRmtBBChDHh2+92e2ux+6uR2a7seiQR8THS29fqLsqam+o8t2taaaaSDDDHevG7A67xe4u7Sna6kDFuE6GFtpAbrD+V999s87dAc9Q+n+w1OmmyyocUnSypHraR922m8yV3M7FdCIG7ZPfg12912lKWrXYoPjSHDlNJ8yvZZWWquy92uYwhEERgnqPJWckUV4wuzK8RODpdpXofffGKNN9HiO93IsisN4L1.0k.Q1Pm5mclTtyDrQILIMSgS7iVDXv23a7Mt467NuyA27lurxP87.4q.4aUkRO526QKeQWzEUFnbXXXAsfYmCnvq7k9RK+o9re1x.kEPdIXLsqq4rAg4hQOISjRbcUrEQnzVhBA9AkPzKge5FtQHu4+f2U7se621ZLXRPrPJ5pD9bhDqMo3zcBlHVmrvN+i+C26pu7Ww0t7NbcWoEUi782snSmNl4ymm+hO6mcke2e+e+GC05+gBCCOBvIRRLWPPPjRvNm9DPiiAb7pvJMcIZ3.jGZsqY8ZWBN61GzS2n+8G5w1jI.q4olEzHG0pkiFMJBLP8EpuY6wsSDo8ByLyLhZ0pEs8su8tsZ0xRUbicrkffYGx00cfPe+7ZsFyrb4xl6+QdDqqbm6LePPPgUVYkBEKVrfhIcBbbrk2165OP9VtgeS4N87XV+PjBo.gv.ozLo.ntttcme946ZYXIiMzm.Po0Rc25V2ZzhKtX2ImbRBCCM1tiStYSZ8KorqmmWTPPPjTJ6n0XjNemuy2oye0e0ec6a5l9cV1vfGONlCOsm2i0hpONzbUpTQRqVlnX+VWMilRFQvIeMIFBATS36uqXOOuNdPa+wn8jKRzbqOVsKXryxRXx5Q+UTN2W9KeO4d0u5eqbPKKeeeCOOuTISQkkxjCaKkFE77rUGdRUoqh5ppHzJ9d9jd8UJDFexOwGak2065+7iGFFdHmocNDM3HnNz5J6d26d4ctycpMhqbBnUZ5Ve9dk86GoqrnIfw7yOuXhIlHI6+8OhsrXLxoFIrUxAs5IDdZUyNGPAaaJVuNCVu9BC1sqHY7dZ5440ay6fffxG8nGcfMsoMUFnve8W3KX8q9q7ZDRoHVJkQdddxff.CgPjWpGizBgHmy1cxQKL0JwN5L6lHni8DPVgPzIz2uqyN8jzDiff.zUunil9tcbcmVFDLSxmMIB5JhEckBY2jfCcccWAprRPvrsEBQjyNb5nD+qZGWKtlGGncMH91tq6ha3FtA4XiQ2EWjNMZL+x0thINAKpr8HUETUecxXXt9GaXx0+ZNuEazFwqS3dW6e2SLPyiCEIjhyO+74uwa7Fs9ReoujIPoZvVlILbzNc5r074yOfsssUPPfg6ztVzj7ggg4+K+K+KM+W+u9ecudi10cG4BC2SQYrrHBJHDRKaaWi.UKgIPkDNKojbfTMwkPJPRWPD455zSbdCCBjwRYrNobwB0F34jfkPJDRgrKvxZZoqGIvS2IHXlXPYGGF52INldLMILL7DezO5G8Xu628G8wgVONvInBqPKVEptJzr2D8pBHaoulVoBxVsnyXP6E2FsOKkYD8yvgM5401AiXo0Ti7ZQfM+0e8+54+LelOYxHKev599CZ64MDpopz.5DwZoW+MBCCkR0T0nitx3KqmZZKqSL0J908W1y163AAAGIUazcBXp1v9NWSTHexfMhsDEZ0pUtJUpXA0LpWeVgsscNpRdZRQs1HzSbIqBaZFMSSPM4cJYXPAv.hYYoPdXWW2Vn53JsPuZehvvcuriiSxdv82FHIL76r4J6ld+SKf7UpPwVsXf50qW1d61koIEVXgEz88uhAJgggC9u5e0qef+p+pOWIoTVTUMe+7RoHGppklnALEjPdAjWJkEdnG5grd4u7WtUnJQIRMM2Eg99lRgvRBFtpVWsaXneWmcza+uD+oxXhiMvP55NsQPvLILJRHDxHaa2U0STjNhjpq5NcWMqJ63Ns6pgyFt5Ue0W2p6ae+eR7+QMP9cdjGIJe97qZZxx11dqF56GggQ2333UMMMOtsschXT2VU.hmeGXwjD5ZrvBKje7wGuHpjNUPecZSwwrou5W8KW9W+W+WufRicv7tu6+6VW208JShoPnEf1HoTdLgP9XNNdG7Fdyu4CcW+c28iQiDQEt2dv50ssJgCmZbgdN4AU5OFNSMisYRHdtIAlCCVKt5joRWAvNOTOmN4HkBCCKqEayhttt4ejG4QrtjK4RrfpECBlozC+vOb4W5+lWZofcGXo0Ymj30K9fO3ClavAGzLo5Z51fMQCSJfhEmha9ccyw29sc6wFFBYbrdJ3nFbgwnF46sERwpp1TMn+BXJihhhlbxIaGDDdBPtr9foxCbfCXkKWNgPHZKDxG2ww6PUfC0RMw3Vds1.qWRXaCUW8ttq28J2vMbC8RV1byMGSN4jx0+5VmlFBmaYe7TE5qMNmxPOEcLoWAsTIkqZUF5c7N9ur423a7MNvc7gtCq24uy6TFGGmj7AKfABC82hTJFBURQSFDDVBov79ef6me9+k+7F5VrOwFyTHDlNa2wjVXni+OoMeHHHnW7+111cMMMiBC8iz9+D999hDFiqi+uqq6zBe+caIDh7RgvzfXjRQuDkomDNsuy+r6p8u7uzuPm+g+g+g1+69c+2sLs3XPsGCZbDTwHztFH9tO5iZc228cadW206gsu8WUzm7S9IWwaGdK++8q8+83O2m6ycY86qDPbO2y8Hdcut2dLzLwNK8fd3BN6rrDlrdze.YIAWXUCLavXlvhV.4bf7giQIVjjfgKfhlVFZp9VPJkE777LCBBv0003U8pdU49nezOZoK4Rtjx5W2pAqMRgOhuu+wO7gO7JkKWt8kdoW5pZVlbBTUoscS5IxNWPQCJ1.pwOIfdS19CRM40XMLja14lybxImzH06i4byMm4jS9ByCMKLFT5qN6rCricriRn6eUeeewm9S+oMu4a9lyGFFVzwwobEnbKpTBZUHLLzZ6NNLaPPjiiSj1AWRBwJpEorjwSrAHoc6Nw4ymWBXnb5oRXRXXPjiiaTnenDidN56IZTNNNcCBBjS65JZRUCphLXlYh1gqaTKEUciT8in6J.qVEVsoth8UfkmQUEsD8MH9c+te27betOWwu5u5uk.pGqY2xpdddIYXdEN4DxI4jcNdgPxRRvF2V.pwaW5oRgwVAyCqCNaTnzRPtEVXAqwGe7jJhODvHAAAC655NXXXXNGGG788Mdo6bmVeiYm0RGDmz22GOOOSoTVnpPTpEUK6GNaQgTlamtthcGDHccc04WSXhViUDRgoimi324s+1i+.+29uEGDDjbnhd2mzocG4EcwWDZAe0LNN1vvvPp66+kANpqq6w78CW1yyIpJD2jpBnIe0u5Wsya7U8pZu6vvUINdkCbfCb7WxK4kbTfi1XgENZswGeEfNOvC7.cedOumWR6ekrYqDv3du26UbsW60J0hHbR0wNaLgIv52m7jZIGRkzjw.qE0T.1ExGrVKPT5Vu0aszsca2VRqNLnmmWYs+kbg9gVPrYrA34nTj9fffUbccWILLbkocbVoAUZCs536629E34sR8w3XrXuCYomnFm0dM7oJHPw1Kq8MB44fTPqYPl.hgA4gz6aaC4CUzvt3UcUWUou025aMf959lfJCBsJGDDTRqgAHDhkARRXxhnXvywAVUMZWq0AZzFnaUP1zFI064a7r4jkjfTI2qZNnYQWnz8M+7EmXhIJBXM2byYM4jSlqd85ErssK+e7+3sT9O9O91GrFTtA0JCMJGFFVXZGGqcGDHbbb307ZdMxu3W7KZ.XFFDj2w0sjuueIMaZM09Uh0NpLzSOFScBgSFOwI6cK.DW+0e8w+4+4+4QdddxtRoYy50y63r879AyZHfN6z0c0FUYYZxp5hFzEHJLHnii5voqVEZOaXXjRuunqJYugckRYGW2czAZsZPPPmTUns8B0W3D+ZuwesSbu268llwVQtPT.CKgCoz2NnXKaxsv2egbiO93I5Wz.ZQ+t.pXAxsxJqXUpTAgiiGggg7pe0uZwC7.O.5CFezJvi0RYm8XnaeQVaOX.DMa1jmW0pxlLUDruyEry1HzWLbSZBygJDtdedRKf1klat4JL4jSVX9FMrdQ0pkuNiU.VrDP4pPol0n.MHQLXygdc4W9s7+agO1m3ikuFXVWJMCBBxaXXTDUBRKHEBq69i8wMt0+y2pQ850shiiyIjhBRgLu1ORRR9hCBBD2569ca72dW2k4LAghO7e1GQ9a8Veqcl10ckcqz6nU.VMoMsCTiYa0nmVyvWH9D25sdaq9w+3eb4+z+z+j4K6k8xL88Ci77bRR58ioDdemj3zRyTjt.QUgnl3FAAw.xw.4hUINUqZc1dBaelD8GulQpupsurG.puYee+MIkxh53yR2pVV.kqWu9lsssGRMYcpVBZVJLLzZZGGycE5Kcsc6JTBHRRa5jHj34zLbxDPDGGKMLTD+sQyFl10rQ0hzqK9ePydj0JVpabPPf4Nccy2fp4+q9e9QL+W7hdQxc54E0rBcnEcBCCWU2d8qVCZ2P4+X4pvwmML7wkJM45D5OaB0fuX64fVhG7Aev3u7W9K2929292Nc7+II.r24QFA5dP7h.+9Yv5ET1YYIL4jw5B.N0W0IOYrbpdSer75QcXATJ9sQq094xAj+i7Q9HEtwa7caUgFxVTSnlXBTPqmAF999c777NNpMIS1nrMTqsNvrUR8n+CTbAkgJmjyusIf8l9v7oO.SJweZXysxgDGlshJtDLvACB6UQ3DJcmn2DFPMCnQR0NxWud8hIiOLGmsWNHX1htt6vDZk3fMox48nnrVzXK9HOxiXN3.CH2wz+7wPyXPkk4jfEkRYWCC5533IALBC8ENNSKgVQnDoottttDF5KN7gOBaYKaI100MJLLLxwwoquueWOOuN088aGKDqd8W+0u5+eetO2pG3ge3Ut5q9pWFFc4ZrzJMXrNpy.ffQQvRH.GfvjOCI1YIa9lNgHaTxQtPy9CV+FwazWSRVWBk2KBUxOJsrVhZ5IlCCRUFllrUe+vAEBYN.bccA05ugiyUJfFR.odC3Bgg9kAix.EWHHHmmiigNoJc877DgAABoRjYSN7nA.+F+F2HehO9Goqi6zwAAynpD6Nckz.9XerOg3xu7msnRkwXSaZyRftO5idfNW7EeIKSLmPJjGWmT2tG+3GWNv.CnKJmLxya5NZVusBJ5BerlJeXGm0UE9ZRWZzMfwhfEUa1ZiA0AeeeYBUR4bCplmd+RYeeuz9cRwBwgyWkCkuI0JBMTT32lAoNk09IJpSZVNTUcEW2c1QO19VYgEVX0wGe7dLzQq2Bc788W0yya4a5ltoieG2wcbB8DG5bQAg7IKzWm2lIr2jp5kaTvbI0ymT7X8Zvn4pxR4ZhcAndo8u+8W9R+YuzAoECBUJCsJBX8i+w+XYqVsV9pu5q9HPsCuvB+fCO93imrmbxgXZCizANXDLULru92+4r8q6oYIWNXq4fCmLtlWajjqnudh1bU9q809ZC7Reouzx999kEBw.0a1rvUN8OedsrG28G7C9AQ++7BdAc0w+jOHHX.WW2ACBBJKkhbsW4DrzgNb7K7E9BhADAgAVtNt478CsDpo6.IUfUkj3cFefC7cit3K9h6DFFF633XED3mGD4ALaszRcpL5nq355tbPPvJutW2qoym+y+ESZEmNwwwqpYcYWnpbWeuuX7U9h9E6Bshle94WchWvDcTGzrZGnY+SglUfQW4RcFZk8Gt+DeRozgkN4fEKBNEfPqjqm0f7ZQkrjdhJV7G8i9QEdNOmWlUMZPCpI09zMdvG7AEW1kcYcCCCW1ww4XZgwNoJvGWyJudILQ+09YS6Y61ZaDNcwvk591jXr6ESVO8J5K9E+hk9k9k9kJqigtvm4y7Yxc8W+0mSqgI40L8MIgbFPUqJzrPKpT5y7YuiRurW5KKu6zt4nIV228ceFiO9DlKu7wMKWd.KgPX0pUKywFqBJsdEDBCAHMEBoUbL5llPFs28t2UdUupW4I.Vw22e4c540ooN9b89ZwZ1AubEZtbKprBzJFvHLLzPqmXq333brfffi455lzNzqBi1FVJBHVOwoheaus2V267NuyXnZLzTpG0q82NfWnd1fSE1nBcYpYedITI.NoPoIs2TWvV9ZesWUtO+m+ymHBwa5Zu1W1.e5O8morq6zk88mMmm2NLglIL7IpJHap2+OQa6BBBJdu26+Xtq8Ze4Ftt6Dngx+2oN9ecBjmNFZ1FUqYI0BEadcRAMbbbj5yEDoSpRmfffU050zp999m.XYUQPqbb0.CwdEndG.XTLYIrfpF5yjDgx2m19qZj962uumzOR99WPgrDlbxn+.fEvTFXuOSpikiC4tm64aU3ptpqpv8e+2e9m+y+UkagE1kw3iOtxQUEDZQeLOzLe850srssS+9Ygpmo4J87hlsd8UrssWFprrCsVMLYyac1CAZCS04b3JK7TENUTieitdzmZruNQhs2Ac1JXb3dLIZTSWVxLHYcpJFzLYi5QKPkkJRKJUsJCr6c6W1vvHG.NN6HJLb1N5w.lEppdTBpVDZVP6bDf3uzW5KE8K7K7KHA035c6NNh8n1PDnhrBsL1SnuvwwiDGhnOzYX3LRf3sucmtsZslJnqqnwZ8wZEVkVrhdVo2eRPRe8Q3.DpdehfIZay7qVe8T9M80q9+2WHiSUqYj5PyCmGNTg4latbSN4jIGHIGJ5Fu4fffg1gq6.+seuuWtWzK5EATiZzfcoDxMB6YWfETqPX3LEkRYwom1sPylj+G9C2q4V1xlkFFFwNNNha3FtQtq65iXQULoIFPEgu+LBOOOCee+X.YROupq.FnnJJ.cgpQ0nYmFI5IRUVklpCJ9714OW7e+W7d5dM6XG73zJdlYlKxyaxn50q21194uZPvrKqC36D3vxDRGnhDZIt8a+1E2xsbKIssv5zmjsBwGds1Y3b4.91fjlT0BZlSmLjjf+SpDcYee+R6zyqXScB2gJV0nk4LgKHcbFuyrOvrqrim2NzZ6RsUOvAtu1W7EewsqTgN56+Snq8J5VdJ8TF5bwqgmonuVKYq4USZCslHLF57BOp.VxDvZ26d2V6bm6LI4Jk.F3ge3GdfRkJUxyySKRoU5VgVqzJIoeiwwYwDemtcpWeWssssaqED6SUU1Na+Z95RpmVrh06OZaTi5hFIwnTkbpVZhRvXk78msjmmWopUo7ry5WRJE4EBgvwYGcCCmsy1290zoUqGFpQNZv.P0MAMKGFFlW6GSp2qRhpRrEDPADhbZAgUhJYrwqMEGb01y0LCCmIuTJyM8ztFMaRzm7S9oV85tN0AVS0BCQTg1zh1PkN5CnJ9Zesul7a+s+1w27Mey8DM+pUqFE2rYmE6ceTsUqRijjN1lgnCGIYc1Shsuf5XZaSg50oXiFMx2oSGywGeZIrnjjDzYSApSwj8+0GdR0JsqYeX366GekddczUC9Xf8wbo9wBzsjMaitr2jjHrMPIbRmqqaXmtX3Rfx9rJVzLI9MaSFsdNVhBZgIVIvlPdnhEzJw+ZdpfIsP.UHLbOF5wbc9tc6VZ7wGu389U+p4u1W0qJuuuukPHRlJcBnpnBMMZgpMJRJp.fgeXno.oERLmdZWlYlftnStlq6NVw2elUzSeottS6FSSjPktPqNIrDj0mLawANvAh+WbwWb6ljLbGFckprT6lI1iCSGNTx5cUI1MioNwCMDwG4HoY8qW2TU8+7YlE9SC52tyBHmGj2W2FMvvLJGJVmzcCcK0oEG6JkezG86W9u3y7YJ+A+SukR+i+i+eyOzPCaAcwya5nffY6555F+29292J9Eey+hBZgo97eEBTC4CK.dnG5ghe1O6mc+w+i5rCUnBsXVMi6zEDUGydEC+5yXIhEV6XGthVsTq8999wO5iNe2q5pdw8lng5Vg93f8xP8jj82+fBQ.XVCDMT1KQPsNCSiUOz5KbUZzegoufzNKKgIaL1nLSZn5+sgxCGIuCTHDx666a444ALpDVJop.lUoZtlzLGTyJLbFgiii.f5KTWXOts.UkVinNI5.vpIzVeBHZ90N354yiHrmrnepwuQGlO8AWM.Oyp3azbsW+Z23OIBl6jnsmXqf3voXohCjKDJTEJ1TSk6ZPgFfoVso6pOfn4C+vOb91saW79m49y+5eiudKnpgu+tk5pAzUmHj0wzDTUcPDCFdNNF999bvCdP4V1xVjSN4jIYStaMHpAU536OSWS.aOujOyweyu48E8RdI+rsQmoXee+UTB55HsqxAiZtVVgSfgN4doqRwoRA9uP1l6LEqikI1XmqN0yA14Fk54VR2lF59vtLqQe6jpaPROcO+7yyDSLgnVsZhFMZjbnaqJfUKpkKHX24DBokiiWxXRjvvPhiiM777D27Meybi23Mp6c1cXFFtGdGui2t7+w+i+mcQMxBwyyyfpH72sebhVjfdbSuCW5R9zg..PG1lDQAQEmnMM0TQe8u9WOx00sqpcbHpBD2hpcglcsg35qY2rJL5JIhSMPzPLDGgiH.GCaBE0WeUJT+aaho95Zmgy0C3qmujIYRq4XNKXHqpbj7M0AzefCbf7W7EewEXMlFlG89HBgvv00UlZBV0FH5Nti6X0a5lto1iBqtjh8gQUfnVqq5hqq+0OW9Z3YBRRXRRatkbvodUDqQiFTqVMCv0rFAlMRILujnOBUof+t8MzrbZUfUt268dW4MbsW6pMgNMZzHpVsZQi.QGDZqllSdczGPo+fJOa+ZdZ+SpqcigIKdxsXldpkzS2Gz60kuBTLgV5UfBs.y8rm8Du8su8NPsHMCJx466W5C+e6COvU77uhRug2vMYEDLSRhYi0smrRnMCBJIDhBNNN4zIvMVSA8Hs1jzQe+fgiii0a4M8lL9Dep+dBCmMRJkIsg5J.cBUsdSGsVQrZXXXz8ce2G+p+p2nYUZJz6+0AXkW3K7E146+8+9QLJcq++odGaa6UXsVZqsMDUe8skJ.BU7eST.lufNl.sJsSL3JffdSgnpTsXSZVDkX+a3o1qNVyXF.j+3e7Ot6ke4WdDJljchwGe7iCbhIgUl6j+8mrFct9AVNcwvk78RyDJiw.yEAKkMmx9SqiL8XeR850yEGGaoimBck4Mfp4qQyBMVajA2aRURUrBlIvPmDOo9mioccEynrGMbccMpAldu3Wp4BemulwtCCEdNNw+G+2+604O9+5eRp31U1+ZM6I93O9w69reNO6NPsU7820pdddc.hVXgE5N93iGCDsP85cFesIGWz7yOe2IlXhn4me9NSLwD8XMxVA4ggX09v1RnNdfzOqp+OYw58A5gI9qax2AfXBvbdvZ94mO+DSLQdanPcU6dURa+UnJjqoJwmcUw1qXP123a7M3pu5q1nBUrZQqbPk7gg6wTe9ujBblvzjjotpQXXHcAw3pooS7INwI5dcW200c+6e+B8zPRTADsnBAAyJgdrxKFUxnizLLYYfSnkygNvXcpxhQMWu9GotFLFBVLIgIqK9+MJFhy0867TBxRXxoGaDcqS1TL4gY0pHZ1TkwudBF5nXon8DlUAF3J1o3a+U+RxsaaKaZSWpmX.6FwXsiXwEiXHh3HmTRRNWgtuOShSU0IR+7IAUaXClyTutv11VNIDO2FmA0MHIYdBFy2jEw7ZtlqI2W+q+0STE+xKrvBE0aXyXiQ7e4e4+.W9ke4ltttp.MUUnyDfJfbFeeoooY2ossipWgHTYIFeeeCuq7JsnQCSc0QRXDi.pA0ZHoAIi40dikUTNbw00UXai76+8WHxvvnsqdZ4fKqRfxFyCh+1yMmbxImTNDvQl.Ayqqjh6KPxVWNhCe3NrMhNKU3MOWAqsg7TXw9TAyoBzdzb996wZZOubsrIO0I4Py4cmdZKZ1TPELTUGKw1spIUZZQqdLjRRJAuLLLzzwwgpUQL6rKDClBGGGY3Bg37BbfFp2mDgGVJkwWomWbipXDLSfoPHLzUHd026688txG7u3CthRnfSrApEGDr6XWW2nOwm3Sz4s7VdK85S550qisssDnakJz4lu4OT6+3e6+3UanZkjH0vcnmttXbXFRbnCc.4yZ3giOnx+W+92NeIfuzA6aAXNAjadpj+FtgeEquvccW4ZNFVrXpDn87e94YwEyQUDzrWBLifZcYrFQrXuQ7buVyg0NPU5jMc9x0vmHjd+3bSLA4+te2PKgPHzr4ThReQL.LFAL1yBKXL93iaMJjeown7m4C7YF3U7JdE4ss2NAAyz1008DTiUngZDZCDCCKa05GGWoRkzATl9Zu5204FXs801FlrWL2yd1iw1290Ad4hw2Olsgj8llIrd49bet2atW+q+0mtsHJATRmrub.xZ0n6t1keD.ZF6ThJThVT.vrBvrpCfJ+T28cyM+geWFzR09pgggE+O+G9Gl+icm2ofp.qEGUWnZWp1T24OPPPPrTJiSZiuDAxrRE5LyL9Q5DrnRznZTXl1+mzvvnyz11cpWgn8ee6WVtb4Xa6qnMN4WgvvkYDVkCdJ0RozSlo7MZzvpVsZ.DaairdcDsZ0xpRkJpVwbLJxhJcsyFD+.e+XSSyt6v1tai0RTrbe6aecm5k7Rhnd8UXLVgE6k3ljX.Sv4RIm6LAmpX35ONrzrENYjvlnQLFtttFUpfYqVXo2OTkDF08+lnRlR93337RoL+U54kqQEJ3OieACCi7NNaWDDLab2tc6ZjyH1y1St1u6pFAAyX355ZDFFlTzSAfTWnrHfNUpPzLy3GCnJDAHRMJnWsBz9d9Zesnm0y5Y0YZW21MGkNrjhQfWwUbkQ+eZsPLMal72bRh8ZCiDg8AS1q7Twlsym167YBbxcPv11lf8tWXaf1+m9bdSXRs4snQOeeILTrnNoaZwak3csq.oTJkIh6L0vjFp83qBF61utvvPFuSGmt0U9lRhg2zcm6zhFMLzw+k.ITUp8+krtutjnpi+WVoBx6+9Wn6W6q8sZ+u8e6+VUxeULMOBPN+7yGGWrX7EUoRWOHx2CvOIgMu.ICc7tbjizgsQGc7+mpyZdAu8UVBSNyvZNumBS1WUqjQMLqMdzRLxL.LqWutosscROtB3HfPc.tNcFkvnkroCqQuW8MCSEC6q+pslkcumbX88rnhoIBvu+dwai1nt+2CCXRCXNkSyQnDGbrRZ8qQSm60s1mC0gYsDhtl11iqe9pn6K4HnVmQoQ2kFCotBeVKrvBlFFFVtt6zrJMLalRE4+t6ZWQG+HGYkWwq3UrhpUspJgloGMeIYItGSAnFcnQ5.+5ogKRXqhI3vFyqzxkzYX9bUE3+rIjNPuTGznlkVWbL.WypDX0bTxwRj+09Zds49P+YeHS.CGmcXLFMMWT01d49te2uq0O6O6OqLNNNZ7wGOBajO723.VOqm0EW.v589duCye+e+aB5Owp0.ZzKXSATA5sabMqpzvpoZC5tnGobWxkbIq7s9VeqU2oiS7Ov2W5sCOopBDUiz8TaGbnKgtRHHc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swpXzptCZDVBF1cMBYNhhu94IdML9Ebj9yFstCvjeF3k57vc6bn7ZmounAi0w3JqCbkiOGtAdb4IZPb5xAiM0OFctOt7QIBX.9rMvwkQBG6wmdLha3cIB35m1CtFKAQWeEhHfH5Nwwe5xX2qdPs7T5mUy.LCvBNmaIyrUcN2CAjwLaefsoJcoUksf12D3Hl784w6bU1ji0x.OLvFNm6c0pUqML3zyO+7OjCxr2M2CbbHwKpquXsZ01tYylaArYsZ01pDr4WtYysqUq1KBrCEY2K9u3h69DOwSrWE3lsWgCYdFvUmXJ4LcaASDTuRIkq6BNVinj5uNwovScv+PvueZfVpBo0+UAFzdcB4JiJuN8y6zuGCmbaTxqOdo52lA3sF3e0zN8uLFauLv11hfsSc7HDOZOpctoWOcRMc8Rubp+K80BrAFWdCfKOd6mSW1Skqdii6V4qo66ycreXjV+y8d6eokAtsAyXw36vccHj5DxBDxkus1Kmt+ywkW2.RJ+Mc8sS+2k7FCuT0uc2dLd2s9+W.B6WIoNp1SbLFuNtwa26UZ++kWGb+NCSlJ5aqE.0ii7a83H.WOtCnYIIhvab7BBlhz1qLmzn9L8nXe2FgwSJ5omz88FcS1H1smMSS+XNoQbXrQacraqSv5iMRDWgBY5BYfxYWAxSKlAZLCvLcoZdtJ43xjCVIGTHGPVJQVfrKA4.xmbKWAHa7mStiiDxK0MkwVe82nF71.fqL5erjQX2mKS.rbV3z4.xQ83y20O9b+L.4WAxB0iKasVRYutjgsGqdxEI6xrbtj+eVnRxWWJWEXFtNyBWadf4uNqLGawrzkYf54gkh+8V+kbjydot8fJip3Ujh9lYA.9lU1Gv6u5EdAuxfEuA4bhqgIG+915wuOVZx2Kc.t4medmCb6t2dt81aOvEeeVRGoZ0pk6Qq8nFPPiFMl4rm+hmxb1BPoEdlm4bmhdL6i+3OddfbsY4brEY3pjEJjEpDOcc1Xh1DVEkLSZ...H.jDQAQEUWVb4uEy1Ex2sZRYyQ0eQbYmBjqPb8X4.x0fkx1GxAEyu7j0+kuM0yyURaee86T8ZSmgAJKndigaazSYxySI2e8fqFWuUthvrrMyAaOGvL6PwbzfLzl.XkjaiZ+Kt9piukT+WRYz35+x7RW+W8jeVkLEfbbYxBWNstwjLMXzNP0zqsTxqetS8c4N0lzwih+wY+VPR8JYfUxVExmz92bLY6eyBUmAVLOws+lAHX8iq6KKPtxPdX0Y1Al45ESp+qQZYpjxeqSPxh5430m4Aa3sA.WFCtrM0eGpeYu96UZeoGu++9rwnE00Wx9+2mJ4oM4nMYfEyDmwm3yJD.jYw6s9+e25il75n6mm.FuP2XUtgsTbjagIKL.wcHLj34G9Ish9qnp8R63nsuNdbkQcP.tyK9emzG9No2quWFklou.fWubRMTAGOpTSO5pS+dvwuONYVfvR.iJ+dZLFfOsI3V25VYlc10xB8CpBzp.gzmADWdN84LpBD0N96G8Yie0e0eU668686Mj3Qj9Hf3sEzIyffTSedXrW+aXISEjwyXA84mWajdNYrF1VyCtpUAr1UHcTElnQ2+n+n+H9V9V9VROF9.dNmijo1w.N9beZ4uzN76A3pAtloeNtTR4itoO1xYfNYcNW..lYiOZtg.CqBG0BNjIKWN9eOoRGYioGo1nw9+OnHsNfbkf45FmUHqPQVEOVv014u2d6cyScp02D51tLrUm5rGMlHCSFudl.frm6bmalyd1ytDwYXx6HICSdmI++EINyUbUpT4f1sauWkJU1orYu3WtYyctzktzMNy4Nyd.2n0y0ZGmy8h0p8nWG5c8BvK1uH6QOtIvALV8IKCgaeb6.SegJA.Aewu3Wz+e8+m+qseo+O9ktsy4qB1lokUVAvgw134bt.yJjA1zqJ3ZUfgzmC43xzCABWCBu5wYHvz0SC24rf5AoxaudZ7ARX5AZwUGbMRKOTGO1m.1hb27l2L6by8MlC5FrJXatJN1bTaXomiIYM+I87pGw0+4RpqZ719f6k5+7HJI9jFTxftTBnaYbzYTYnz55ltek2sLdRdsyzscN9sS57xn5nJAV2ze+B.g3w1okSplCZky4bYfIZ+aT4fRvftGeMFoG6.fLNmKiYl+96uO.QyLyLie8HQ.gKAQWO80WAbDLp7Gok+p.t10SlBm2delOoax8G2sqgc5e130ycR+roClO.tU.1J8QsDdLG9zfLMZzHa85O5cp++iZGsLD04319B.B9C+C+C4a8a8aMj359lt++SmgI2sLl6d453j6SteEvjoCVR5nC32saWuRuq2kid8bT.uZ8InY7iAhKbLdAlouXS0wo6tiazYcB3JSDAbHsQi50CoQCGqia8q.W41GMu6TJDNdJic29f53WjE70+yWi2363c3K90xZqEwUupiMvs9kwtxcN5+iegtd.1xfKNyfWlZrs0bxK.JCPNmyEr4laZAAAQKszRSzAMmyMzJWdHc6FRY7JzgL8i+8rjG2gDeANoWnQZi5FfUBnKDAaDljxwi5z4Xulg5DQiQcRX7zgWt+X7N7c6yC+EwiclnLX.Plqe8q6uz2z2Dzsqih3sZOB1LttQONt9uzfYPxuaVmyk0LKn.X8ACJDUj9Q8N97a5qk.mykwJa9zE33.POz4bCAFXqt5Ar0VGPANpRehZe7eO2s5+GurT52CO3Tlxi3LvH2VvhPgUKP+h8btULylGv8q+q+qu62y2y2Sef1TlMoC6A2V.SFcrn.YJ0mY6FGC1SywALYCRBXhYVfKxEVsV0CZ0p0dF1MbDsSsZeK61p4WZWLaOmysasZ01sUqV6TsZ0qCrCk3EoK6ASDvjnKe4KGtwe2+tgzsaDUwUtEdclbz2Odjzt8LN0JBVOvCV0Jvlt92dpsG3btzxxCMyRaGOss7Ar3hGxN6Lf5DUuAz33m2wmxFCYxKTAT6+uV5jtfgw6+V.fcZvcMHBVIZE1hshebYHYDRAlw4bY9K+K+K8eaus2FiEXDHNvHQVoRgzqmiRPgtDzO4XS746CStcTxuW5ExNd8e.EbI0+M8T6ws+96yLmdFG8mX.3RuMf50GRiF2oTe+t89hJ28JyzuGdmBHm+XOlwGrmQOt5fe71wdYqLcnyws8llIQ4bNWNqhEPmQ8oJDHz4bQ.gVwhCne+ArLgKuMr8w04MpbdxqiwKeDADVBB6BgvJgEXqn92oo15wAKY5qcIh50iRJ+cuLMEk6MSe8GmzO+15iyFjrXsN4O6j5+uO.EgndfCJQQ5Z8lbPsxRb4uL25V2xN5niFu++Nfn6P++yxw8++HlrNvwGXAlp++mT+5Oo5qTYqWib+LfIoE1xBjmZ0xQygAP2zBFN.uqcsqk4ge3GNaIy76E+6lVf4fjaCVe80Gdkqbk60sEwGjMcTQyDOpele7.3TEncZPORqL2.7pCdMlrCxG+d7owwtDQ.gzeTGONorzX7uldLd83b0zAKIX6s21e4k+FLXG31mmz9UgfV2dZgGWQXYBvgmqiyLyFMpnIYE.PQfd9TlfBcHa+I67erRkftdgP6i33KfwRFIjrkLyq2wUXtexs3KbtNPDw6TFsusrvBntAM7YY7Vba71I9Nm9wM8mcjWcFqL1ZwKvWKi+o2F+qc6cBLfUWMCalwGZab74B+O5G8il8m6m6malJlksyjAMaPxiy24b4rxVdhHK8iO1IiHaxHmVJD5FQInT2Qi9lQb.5BMyF3btglUc3Xk+1miaPlJfW66PmJnbxm8uECY6IBh83qe.uUtbUbcpwAgdVhCvQImyUFX4KbgmY1yblmH7282+2+E+G+9e+85AsHdSvY5.ljdrFuSVyBrRAXs9wqgIuaf2QqVspUsZ0EZ0pkOFCqVo5AsZ05l0pUauFMZrW8502qYyl6VqVs8fh2nYyma2ZOVsaz54Z8hUqV8FIAOYWmycKyrChO+WLB5OcVmE.js.iBb63AKI9qEwCGdtdNOyrQ2eRlBL.JND5ERInXW7RZGexLapb4P5DND5e.vsHt72fjWGAIO+oCbxPNNS6Rq+5jxro2JWl6qWNo1sgiO+mkpjkAjw0aT6eSk0HKmA1NOkHe4tLSm3.mL4zUnbYO5.PmzQQMj3xPY.xU1Lu3KDfCItrwAi8XxdGp+Ko8sxCfNCoHQk5QT2ie8MLtbucjy4FX1pCfsFO3c2qsMN9.Iwc4wIS5tMHZSzO0Egfct8fUD+49SiGGQ.CIfMGc92NttnxAPmLTlrE6Pldw0i4Sb+qBMyhhqqpnKo9u3.mcbY3fRwqAEYX7AVKs9lBI2Quz56hK6bvAGLLe97gPgHneDkvUpax59zzYIWwhPOyAcGOKRGutM02rW4lNPG290wtwFdb4a5EuPjO5wl1Wduk.uqO4fNdbvhKPFb3456lH.vIkAMnpGzJfUIawMIeuIKGEax9+m11WZ++ykb8utj6O8ZeiCZREhvk72TmQYKWZcnokel98BPYwzq4teDvji6PXIxRWxSbmBy6btfpl4ZOpC2ELneFJPdWOWV.yLany4NLYAzKofyxCfsGtNDdkIqLRMfMowqvHfRIq+AiMBhKC11i9.ToPnKT.OWOmuUy7n0DoFsCvUq16JrQi+zAOrYCZ.GsNL3J2dVKj77tg2cXJg706.l3wFoqcDGmRukA5L50ScGzvnHAtttrVIKC8F8XGMpCISuAqhYVGfxf0434BsiR3nK3bNOyJ5C88SFkUeyLefLasUOukWtPXYyNn6nQ8sXDzKfRjw0w4C3R5b2AlYiBXHT1AcnLPamKrjYC5MdzmWlzTh2yrJ9PGbNWnshM9E3prL49mICLY7my7cNmuYU8g1AIkYxXlMZt26bNuj5+R5jdAenedJxbtttYAxZlYIcxeT.SfxYgNy3btb.YJaVvKbzQteyeyeyvu6u6u6g6s2dClat4FBDYV4Hnajy4H9+aoMtNDXPA3n9itfjhGB8FREX6+rsskeGK6c7T6Y7NtVJz45bTEyNpCb3ZvQW81G4+2JWWbZPky1.lqJrRKnhy4pbgKbgkOy4OyLzgA+D+D+DW+m5m5S2A52DnOUXOZygbxS6I+30dgFyRUVkV72fx7HCdgAuq82e+uoc2c2Z.mBvCrg0pU8flMadKfaZvMcvsLysa0p0287W776dlm3L6dzQGcirYe3a.cugy41AXWyraQbGuBArBLZ6L1fJdNWqLVEKuqsK2W9K+ky8deuu2I1YH94+4+489A+A+AC.7KYVPOveyM2LyW4q7U7dGui2Q3pqt5gTlC68m16nBEJLzrJNnSXR1LElbA1d+k+k+k7M9M9MNrpY2rMbCfcgB6C8in.d+p+u9ql4wdrGKyZqsFIWjS5.mL.pODZL8.l7V8xbe8vzYT43Rufgbvp4ct9YRCpQMH5C9w9Xtm4Ydl3GSUxRKxEmEHUmAZmy4bYAxYlkCH2Mtw0ybpS8PT1rgcGkAck7ft4nH4ccGMEBOx4b6mz+uihecUNKzI+30+8+6e0ek6EewWbvi9nO5Q+O9S7TG9u7+4OwQ.CLqxPnywYS2wkiNhimNFIAiaoAv0GP7hR7Imo.uwX.fdynouH1TiWOXb4mkHqaaWVaUKCagex.QMVeGq4CMCJ.A8bN+xlEzcrr1jJjw0xkAHvrJAPmjckIRCv2nmyjLjK4hNKG4bswJaAtNtfW7EewfkVZozKjdz.JlzWNqhYtNvQkgi5LdPOJQHcILtdqhQP+wq+KM6Ow4bQ0LKr0nLHnx.n8Km9loxbmrw5O1FdIqeLr95qyUtxUfwFDzEAucFUdbQGriQU7bMc9VAyOMfbL1LiHInFdULiNPX7LsdrrRoHdzC+3oyUoLPufo6++st0s7lYlYFlz++8.N.J4ftAThbtNtLDWdMz4bGYlkF33CgBCg9QkfnNN2vxlcT2imEFQrNQwKYd.rNIqed7deuu2nuzW5Kood3qgteEvDeVi.tJ4XUlkMYNmykOYQxKJo.Q575Jn.LyWYyMyu5pqlMoxxzzR+.f8WANbqIlWWqMDt5K0HC7fZgiIiP5JDvViVvpFcgawYFQ7nKGmlqq5AalbQeVPud87JVrnADkzIizQkdenvQP+SLKSVC7tJ.qAb0WyRe+6TZxd6o54JjgslHnQVRjgGEc4hPldTNGzIWRmxxYlkOIMfylDfD.b+J+J+J78888Oy24Z6e8qecuqe8qya6s89iftNmyQYyhGg+hXziLTjb27u5l4lc1Yyr2d6EdpScpa4btzKjY..kffNwuumNhHiW9+nUgvMSSmOyF.kNB5lN5qQPERxbgzND.UHj1LjJbDsusQRSd4a5QKarQgX4.Xaeh2cQ75FGrxrzkbTfYnOokk7H973fjNR4CjuHbptN2B.ySbYuzNsEQbP9xzINfy4iC.So.mqiiQS4ghG4bcGBLrjYg8fPJQDcIjhLjdSLMGOx4b6WoRk86zoygDGDkn9DeozIe1v24b1W6q803s81daQEgA8hKKljY.ENL4y+i2P7akaXNs9jr.yCkWE5T8S9IuP4m5odxUbNWdfit10t11eaqsVm1PSJQujoDyzALI83klF4yUAJzlxeCm+Sdl24O9G6G+cEDD7M0pUqpwOW3avvJUqdPqVstEwmCtEwAccOyrce1m8R6dly7j6ArWqVstQ0pUuAkYG5vtTl8oSb8LI+MDjVGOIYWRQXlW3vCymKWtYRtP2QSAQyLuxPP63LULIiEKmw453QbcJGXVk8gNG3btiJa1ftvgTlinCQTFiNjw4b1+1O+mev+vOvGXOmyssY10S9a3n3NfVxG5FjT9ahol6hvvclXpfsdTxNPkx3zW4FOnuFrtGbkwyBfzxmyTFx2YhoM3p.alNUbxBkxBcyt+96m8aXlYx1AxQIxSWlihLuqqadhmxNdIY61AlYCGNbnWPPPtRP9Nwk67HtNswy.NWxn+mar5+7Sp+6n3.qTdemq8A.GVwrAcfAIk+NhhrO8lXJtdTR+OSyfoiVBFd83Kdc51HM.aCRFBnIGnNUV6tapxWSj4HL1OKSbfnKjE5mKMinG6wakIdZClzuUenXFnWFmykyLKOTN2uyuyuTvuvuvyF7u8+8OseGvmh3SO7iS92IxlEemy4BCCCCBBBAnD32YrLwlwFTVyLqL32N941EOXtkNz45dHwk+FBEG.8F3btQCpvn9cE2Nb5eug.Ct4Mu49yM2bGPR8aa.gW9dqbkpm61kVNwCvO45O7nDFcwXEL1ZhrMOM6jF0etkAusohOz1OsO+GczQ4xlMadhWXpCdz+K+u197+led2i7HORzy+78v455+U+peUu29a+s6CE8fd9NmyqrY9ciyJyz9+m8m6+oet7ezO5GMnSmNCqToxMcN2MLytIwkG7KBY6Fu14j19WZV3sOv9EgC6ACR523gPoCgtistjsbDrsCvMZ.QpPzXYi9ak6a1qqBdoeH2UGWI4UShR2lUxboKcwLlUHSRG0hGEzUIhMIjUIp+lDt5pq5JEGQtjQ0nTdn6LPkY1h1oYaRxHEb0oWeStaWH3ChEPN9hX1pTDz0AK6AdYct9yXVkrTBO5RHUv0uM.aZki6PSFfrEK9NyR7na65RmCINpnIoZVeORxvg5PTCvw533JqYWkqlNUURCHAb6oi4qTSeQqmzOe7FpCXqSG.WyGV0Gx46bM7MqrGEvm93QYB50gbPm7kf7lYyBLOTZNyrYfhY5PuzQ8L566666C.+abiajY4kW1Odj765nLNyJE0pUKWkJUbIUHmgdkmY1YmMexEYDFO5YU1ixrGc3v+h+h+hv29a+salYdkhe+2Q77sMYzwJc3lz8nO8m9SmF04iftiFwrxvvNzNjUIjMKF0K90paT6AsKYISCtw6zxChel3UiSJkO8.BVFB1lsCnL9zoj0ktwML2MYzY6WX1Kcoe14MqbtjN7EYVkgIMn4QQx2qGKXl8PkgGpCbpCO7vbYyl0C.yJ6010If3rOIcwb0OIMiSFwzdGYksgzsT5b0NL4hNCu3+hKN3IdhmHMnJC.NzrRGP4dwWTRQNreOF.8GV73LBv2rpdwSeuhg8n2Aou1ABSRqYOhaf10GBYiQa6huUbZ5L0Hw2IIXBQVw3uAmyYCFDYsSKqz8d73VFu1cvC53ctm5LdO4Yexj5JMmgy4.mCrVsZ4UsZUOyLqYyltZ0p4ZzngCvcly7jzrYSCv6Ye1mMYjtr3oBamxFzIBJ66bsyAj2rJ4nH4nGYoHY60iYxkKW9RvLlY4+pe0uZl+V+s9O2h+inj0ItLc.PlVsZE.cSGMrHmycHzYeJwAlU9f3+O6mTWUHcJ44bcxXVIbttGE2YwJoWPSVJw9s6x.nqqZ7wL483B.8Mnl+NzLMfuIs2eko2VZUPSd4435vh2ty8gq3uBXaM58uULXqLPsLcnYR1ijdgriBVRZPbyQYxNyL+Mxvwq2HyBrvK9UewE.Vzrxy4bc7iqapzQThgAAAly4x74+7e9blYYKAd++0qmy4boCXvQlUNpSbv4Fu9OOyrnm+4e9CJA62kN2zJY6SuxGxwWXQbpu2q3sfd6AkOHInJGYV0CoHGQONhRbz06xQP6CWFNZ6QkqVC3pFTwtb7J54zYS28q9z7VQS2doe76mP76oa3X8KabEBXI7abc7g99Effj5sxlr3k6AE85POuO+m+y6kT2P.zKKvr6u+9yUDlqGcx+s+8+s6SuxoKfvAI8iMyMu599yMyL9ICxPFffgCGRlGNSb8Ikv8c7c7Q7R5+kuYl8A+99f749U9chhm9LksNw026A39re1O6gPuCR6K1nfuUj8MqzM+s+s+s28a+a+a+lICNx.5V14bsMypfy0dnYFyM223PhWPriXY3xaiGP3FPzkeoG.3GTK6c2FL+je1FbUtrkzWk3yYaU2CZ3AE8gPemay3c0t3rBwn.118wCZ6ULNifyCLW1rO7b.yBEy0ld9s9B+6HoNGGw0+jl84wYQRQ7Mqjs4laxJqrx38+O+G8i9QmwrxANWmgNm6llUddJwMoKC9Y+Y+Y4G6G6GKvLKnbb4e+c1YG6niNJbokV5fffp2pG8t0uvuvuv91nrNu69DW+6fZvvlrcXbVNUIpOsSl9Yo8UKdjq4sl8K60cuZxvjwtPh5YfF4Hdqha1ja4HoRGfAkgCSRqMXzH2UZ9s29qLyxK+ME3bcgjEArjzyb+jT07PX4Cqv1G1NcTSiSIoSpiSOnFQsiCXPxnDALGvB.y0ue+7uyBEB5EOZ2Qoos3+7+4+ys+U+q9WkAJk245LCPtFMZX0qW+PyraTnPgazue+a.k1aU5dvlooVaIBq2MIvI0wnQcfFNt+t.QdRo34zMbXwc.bMO3po+sOZcbgiS0trEfr8IMMhs7DuX0MiY17PwEbttKBL+1u3Klc4G5gblYCJWtbXmNcv4bAoomNwYrBDOcZfhDQuRQIAoHHcTPRdMDQxH0+G7G7Gbq22668MZwXL9keQqWum2Ur3ify0Mc8L4fjx+2xEudDbHT9vhz4ndwMXenUzNnReNpMLjBDQ+RIM1eaK3cuTAWTtciOBFIetZMuJbUu1Ie9pSmNAuyxk82B7oLdzY0rvl4IdJUr.vBNma1jxfNfnqe8qyRKsjOwi757.ODTZ4O6m8hOzG9C+gmiiGQ2wmOsiusXFALLLLbP7n1FF56mIBvs0VaEsxJqDADEEEMDX3m6y84N5C8g9Poiv5AkL6fdI0o9o+ze58+Q+Q+Q2OYjeCgR3bc3y7Y9ey8C+C+OcPR4tcKVr3t8506lvJGLwBd2hLrzNSryC7VooKYZ8MAqrxJ41ZqslGJtZQ5U5oN+4K8jO4StrU1xWrKC+sd9m+5Ox24izl1kZBc6A20LLIMiUNEwQG3uATZCmqy6rUq1+Mme94pr2d6sfCBLHBGGUsV0a0pUq8.6F3b2.iaLyL426fC1+lOV05G7kZ17fpUqtuYkN.5cKRBpcR.riCfBLW61sm8QqTYl1IYSGwWLZt81au7YxjI2y+7Oel2y648X99oqAiSr32MJS8XxEo3CANbXzvCx3W+fjQ.KLcTgSZm4.hmNNW+e3G3Cr8+O+692sCvtDmwLGkj5wNnn6vCegnb4xklMVoKZwIo3d8gKQivqe6qQS2uyjw2JZ7.9NwB860t103zm9z.m1AWKMCSl04byYlMSkJUx0tc6.nPlRzOW2j92kjgUYMyxPIxP2x4fNyAbJmysnY1BO+y+7y8NemuyfiN5Hmuu+Pee+zyUSTtJJJBOOOWTTzPOOuA.tnnHyyya5cqmPfCihht0gGd3s9Flc186FGrjglUdny04nO4m7Sdqm9oe5wGQ2z0NmCKAG0Ms8yQYzxx6CaeHr3PXmQ8cLY.HBWe80CuxUthVOvdoMd.lSWuiFeP8o.32+3Ep0zsE5bP67Pw7NW2bI2ePxE+ldgvYghyTh++Yu2znjkypyz8YG4bVYMWYFYFYIcPnCBiP5nIDPC5BHLM3llkuH6Fuv1fa4FDvRX2XijLr.YnoYRBvfW9B80VB41zxs3ZlQFvfvskwfwcaPBgDVFPGA5nSkQjC0TV4PkSQru+3KhrhpTI.g8Oz.eqUbp5TUVUEYD6X+s2u6286t47Mf4UUmsYylYNlssUC.UU41u8aO4S9I+jyjHQhrISlLM6+uSRL22lB.an8Ub1v.6+9q0N6riXYYoEJTXDvvxhLrAL5q9U+JiunK5YM.S6OtM1rEMJuMTOx2uZNszfof3A65.Cc2uN53CE8gVAgsJdbaq3r37wh1dGLl+n0CbO0UIAqsbRXio9TlBpQLMyoDjt4drsORKbRY7aYOmp0m6rO6iN6G8i9YxbVm0YkPDQKUpTvm6y843BuvKzprHoarWK6DAhKTB8AI9+Dg6isKPWCPuwi8GKvNwvg2ehLYNEKUanXre5KhzAnaXbX6B16VhFCZFAXWUYTIWF2DlvxLgMJ4CMiG2+CVwENrqi+r0Cg0OsLLIdREIWh0RsYnhASYRScR.kEU8Hxo42Y80YkUVIJnqviFr3hKZUlloDQRXaC0qqYssISiFS6G1gTdyAd0iILlGeeitoCFr9iEQiMZILOIoMYfx4f54AlYkUVISSHYqVshDNM+0Wec+v6GIKSiLhHynple0UWMY4xxjoURBRwJMRt95zEylXCEQz0ht1uF5YxZZHEV+W6J+EupXGjsDlu2ci0RbhDaFI3vNjx3rrXBnUBLIHjukA.oYBqDVtu6286l2vnDlyllyiQXGmcoEVH00cc+wZTaTzoSG9M9O8qa0HzI4EdgWjkuuujHQhHfShrmmlTjF1CjpQk1m1tM999C+4+4+4Geq25sN4FtgOr9JekWFEKVjRzDQj.aaFWuttqsM8ZzfNgA8MjR0G1rI6F5.saiFM5Xaaa1ToE9KSifMhNGNUf6GENhBmHNqe9Qsdr5yKQqCxjo3IXXwRmHo2l60mqkKWdu.xpSJaVOaiH.JKy7pmNmHkyGV4eK.YwEWzJbi1zD9Z2c2cWLWtbKPXq4v9SPH9jPBBC5KQhDgaHZE355pNNNr7xKOErXKKKefIuzW5Kc7K8k9RGGDDLBXz2cyMGs3hKN3S7I9D8eMulWSeftG6Buvt.CKQC+wiG6+leyupIqu95CpTQRpSE3XDb1HUKWylvWy0bM9uw23abTiG30r3OG7Hc6IAP1XiML2CJ0jlMM96joBJnnNNNA3gZSC8GAASNHKkD1qMAAPcbpDryN6D333D355p.hXIVtttIpVspUM2ZTsZ0nf98UUCpCTsZ0j.4rgLW4688N6UcUWkRYrtnm6y0BJkFZlEXlYlc1Ytk67NmYmc1IStb4RmJUpTg9nRAj57O+yOIf35VScbpFcNmH1Qz4sR7oOBLpY8liTsdDcyC.SRL20ccWgrqS5.rvM+49bKIhrCFU3dGftgBB63JzLHSlLSJWlIhHiJWlQ0qSDvI9r7ZI2Zio+citVN4oSyL...B.IQTPTEMUohWIse1Z+q31cSSd.iOnjm5odpBftB2OqCV11jpQCxUtrjSUsfoXBjC6V4pWWyIhjSUMOFlYlEHyG5s7gR8xe4u7LyN6r4w3WaVU0B.4mLYRxzoSKAAAGrsWlBLrkkkElOYZrbgeo3OuD88FALLWtb69C50aX974m.3qZ8f1saOZbv3coL8.ZC1cfFcw.ZxvPcTYbXwH5AzA1rCvtUo8vZvjxkwudcBVXAwGPCG7.GzO2A2S8Q5959W5Z+wWD1OShbJw+dQfkjgv1D27QuLarwFYeRKubdQj7kKSla6erVpRkHQylSqpeNraNaiFrjp5h2wcbmydtm6wx356ikkUvINwI3EdAWPx5FP7x+FdSugLW665Zi9abvIwkOfVudcbbbhN+D.000EGGmn6sxbyMGtttZgBEFCLptpiCBBFOXvfw.CBBB1AXSZPdapmsgwm1tThwZCcrH1Ceiuw2n+0bMWSBLCX.M77IZexIgs4p+7rVPaPWZIzM2D01lfFMdLKCmN39kVgxzQ7BhuWNFqgVjMzVgE4fxg.4ZjbfHf+y1z.1atvbZBAWodJfB1zXAfEtq653yAjw1dJf+5S8o9Tk68duWq5pl5K7W8ER+B+28ByLZznToSm1RMI.D22Vzf2HJ9+CNELm.3eW20cEb1m8YqhHR5zosroYBQDsbYF64o8KUhNMaR2v3+Gfcica1vj+qHR+v8UMrOYCFUllQZriEyiParBGLDOX5NGw9Z+r0Cg0OsLLIdBEoIzXDSU6yHhSRvSt4O4mT+E+k+kCt669tm7g9PenQenOzGZB11AmmiSxm94e94elW7EO6u9u9ud9ve1Tus2w6N4+ke+2TT6PDlno8PUqOPDoeQnWqUn2pqyv0Bql1K4k7R7+3e7OdbC2GqhFaBfz1vLMXk4Us07hHyCjuDjrgp7W9W8WM4e2+1+sSlLYxj2y648L4S8+3Swa889VS8Beguv7e7O9Ge9W1K6kMOPtK5htH4y9Y+rCVYkU57I9De5s+k+kewaJhrcIncyRzawlLXq8OBrNbmY+K68DXD0oDvcaAXYCRCynITZ8.qB+TAaMRHvHTy.rg4qq57.yKkKWPqWOuHR9PFlLqp57850agYlYlYFNbXpLYxne+u+22+LNiyH1jAXZUVimPaz6ynMbS345lnhiydBDaXer545MthSkwtddSbpTIpRGXYYE4PKHHHX7jISFjNc5dhHgiHTi8OP2xhztQI1nbS1p9dUyNhII9Xp31HQNxH39iOa2+wcu3QKrC3g55fUw3ffkjDN0jv8mDHUEHoWQRUtEo8L.JF42qP3wblJrVdVnQ9a6a7MReF+b+bo9A+fePxicriEoIDohk3wbXXbPdfLttdIbbpbvpqHQ.iPXvViFMJHc5z66dU3qQCBBBZ0Z8.e+ISbbbl.365VariS0IAAAirrr1c73w8NwO7G18nmwYzU1SrEi.1qGlf+BSr0taHkPMTeuhL1tNCZDaplwidp5e76+gA3uzLvlK9G7G7Gr768Jthh0KwRZCMGf+O3DmXqS+w83pC3wO1ojyYlDt6Lr7xyxFaTDC2Y+4TUeRdddGUUsR0pUm6889deouhq3Jr5zoyjNc5rqiiSWWW2sbbb11VjsaVhdkaxt2tq6DGGGBA1JN.HI60qWxISljJUpTYxmOeNee+7MazLeEmJwmlIGDPDXuDSm9LPPPPTRsQ1gJfuqqquiiiOvXWWWiMfpAHBkKWVAlXACvxZ2M1X8dKt3R8rrr5hwtZ6PvS5vdASFI5c8BCHLTylJ5Cs7+9e+u+3K5LNiwMWkwEWiQs1OK5dr1d9+jrlZKelf0cSkjfWJrsyn0qGlTw97yDEKW9icriU3Nuy6bN0rm4b6t6tylOedy9kkkbZcMGPdee+rIRjHJI33LLNG6M4bhXY7gcOZJvIiFNh02Xc0wwgv8RiNuh9+A9999IRjX7nQiFs95qOY14lye1BEBvr+6XGGmgDDzGKqcZ1rYWaa6tpp8qHxPOSqiMb73w8RkJUaQjsUU2Jbe1H1YEo6IwNNyIvceXL07fIv9XM6uCauyvONUHNs.rlGR0lhYgV4TUyLZznzYxjIZzTGAz1Lg++HcMIIPlO8m9SOyYbFmwhqt5pKe764dV5LexO4YylMapv+VSBYKcB0vdtbg+NhCVR7BNL8dnaMWbp5bv7ehhiSBeMJhgYJNNNSB84MAiOqt.a1e29qOS9Y1DiesPVAX20zVXrCP+hvvlF8nSCKhVj3refVOz.pSqVs7KVrXb1z8Xo7ZNP7WEsLspI5pf+Z6u6Bj8dcDwZ77hXmAZNMu.aH+m7q80J7LelOy49TepO0L+R+R+RYDQxDxFjHVwM+m7S8Yl44dwO6zVIRXMS97Z+98kYmcVASqakRM5tTbM+BL1LAg1LwYwGLEXLF644MoRkJSp44MoZkJQf2AfDx3IBes61qWudEJTnOvt20ccWCOqy5rF.zyVjtMsoS4FzIL9+d2zMcS8+09090lNkcLw+WcB3FE6evQ.8DbTENdblK8u1E29wDqeZ.LIdB5QFp4wPUybhHIGNbHYVMS3HoshenxPOFXxJPv5gpwdoRjuYyPQFqHYdlG44l4S84+noJUpTBQDsQiFAkJUZ7ccW20ficri0Gnitm.ZFSDb12H8BdrkAPr.7mOKzdNfkvlEoAE9i+i+iSs5pq5+hdQunw.Cq.i7ButUtLTuNoJBEZYd8KAL2nQix.DjNc5c+u9e8s19s7V9+cCn45.afYiACyG1yQO7i9guGp2ON.EOqjTU285SwCToLl5jxNYnPBlRJIonEYgRyAMWVUcYQjEKAEt2Ncxee228kKQBI2YbF+bE912wcL64eAWfoZZ6QgyozyL1gbHGGLHpCN0gBBcnNsO76zoSPmNcBLRg.p.JhDjLQxIS7mLpRkJ6Bzud856VrXwQIRjnOvNhHqiQnAVGSxFQfVE97UoAPycA5yoxtb+SC.7fmiwuu7ul.c8HoU7.8N3813.vkRDmzfW5XZEQFL97lUUcVLfdLKvr25sdqydAWvEL6e9M8mm+U9e5UlMSlijQUuz.ou268dSc5m9oGkXR7DLhBxKAfr1ZqwpqtZ7yQVe800UVYESvddtpSEmo2mhRvv00CGmJAgUFKna2tAfD.pOPfiiyXWW2Q.CTn27yMWeKKqcEQFjKWtQG+dN9tG8IbztwXBPaC81s6BMiWYCiMF6SbEOrwO9izrkd..lbsW60l+M7F9CmC7VDXYU0Ee+u+2e9q3Jthf67NuysO1wNVSf5.qCGoWndacH.lX1yaYX12zevePwq3JthiXayOW855SxqVsmvLyMakd6zaNUzL.VEJTvua2tCJVrX2VsVuMXlBNsZ0Z2y8bO2QgU.y5889deIuxq7Jyc8W+0m823RuzrIsrxD1NCQIMDOg13i90CZ2GAtaPrjFlBnhqqqDlDqFDDn0qW2vvl81+0ue+9Z61soRkJpqq6DSqiwHPGtvBKLbmc1Y2xkK2AnsHR6v8x6ppFkbQaQjn11Y.Sa4hhSf0GCL70+5e8id+u+2eD.e+roA1guhYGezDvwiX1V5PAcMKP5hEwpYSkRhHsJSxeq+u+sx7A+S9fQIvNGvBppKdxSdx4toa5lx+ZdMulb228ce4N2y8bmIhEIDZeUtb4T0qWOxNKpB+GVKOD.DTy0EADQjDnpkJX4Tw.VBPn+LWI7y0PlWoNNN9tt07mat4CJTnPvfACzs1ZKsRkJ9ttdicbpLLRrjSlL4tKu7x6lHQhg20+zcM7Xm0wFTF5dS+M+Ma9betO2MJUhVkJcVaeW20csi3H6ptlQxM6MkcBAM4n9g5nyCVrMOR0e2C15GGCZNXAFNXdDBmIB2MVvxofMx.Ly0e8We92xkcY471aXDjKjYyQ.ljWDIyZqsVpW6q60l4y7I9L4.l6Nuy6b4icrisBvBgutTDyeU3e+H.XllPanNPYXPhmKg6YZ1C00EQ.UCs0LeeB+dhiiCCFLP60qWvxKubTLbA.9MZ1Xr+D+9VVVcBBBZ633ztc61sme942IbuysoDaRS1BpzE7hFY1A1feXqYDo8NiKCS7h0JhrWdMwKDwC18hGMsN39ug6gUITe01WKmrOVc.jCJV.ZUPUM+E8btnz+8ek+9jgscS9PPQVzFl+6s81yrvBKj627272H20e8+oyjHQhYGLXvLYylMK6GLjH+WIhc9DsuZ78MOH641GHcg.sMsk42Zqslzev.+ozL03+CBvGwzV9NNNwisZHPOQjcTU2NDr2HastgG8.69gsF6HJxXZwX3TCf6eJ.IU.eu8.m6wp4L+S85gJfISMnOJj33KSZ1vnWF2vMbC4dEuhWQlJfU3XDdTjZ+x9ErUEvBJlBZkdEHy56EPWdfY9ve3ObtW4q7UNUk1+Vequ0nm+4cd8aQocfl6.zwA56VjgFihGSBXRLVXrW0gVAlecXEU0ElLYRNee+frYytKlGphF4jiCmk7RYHccSxdKCTLDXgY888SbIWxkL4lu4atip5FUDoUcpzD71DnSXO4MkF17i9Z9C0.JN36sjKCI1vTs+TX5IwLMKSV0SSeu268l7nG8nQnImpQn8jMj+BdguvEt4a9lWNQhDqDDDr327a9Mm8o9TepYGOdb1zoSGUo+PAexniDRHRFb.5CyCLPg3UCC.7bcEEjxkKSi50UES.eiGONnUqVZDs6iBBbJnIpnpnANNNiAFExFfgiFMZzNc5r6f98a+Q9ebist5q9M2.XiRPml1LRqq9XnZ7.L8J4NMLI614Tg92+dZpv9NuCmzNAgBO1iE6U1Cit5GFfbY.xZ7SUJSn.zksLTntMyo004DQl81tsaalmzS5IMSlLYlYznQExlMajVNEAHRTOZm.HQnnfEOAi3Ix9ftZ1roVpTI.3jqsFmxpqtOavXr.X5gwNaZ0sl333LBXnqq6.LZF0nJUpLDXvm6y+458w9K9Xctwa7FaqlQU6VhHaaCa2fxcf58TU64HRWuJzWc0AgrA3vRf8QZIRbP.yhzAjBUf48nzhPyETUyYKh9J+8eS67td6uqVXXWxlDaRevCBfILUCSJ83fl+bppmIvS.vASRpYbccsvbcbjiiS+1c5z8IN2bc+Vtt6533LZs0Vy2xxRcNWmjZCM0fACxlMa1npqltW2dIlovLwYPxA0CmCxhonyWeLU+TUyziHNfI333HgI5Fw3I000M.y+Wc8bQTTB8k0Ymc7mct4lXRx0cR30lcWZok5lMa1t20ccWcN6y9r6fgwIa9Y9Lel0ewu3W7F.QSTmvJlICKBCaQ0APsgXlzNi8NSlvc+XtJv9iaMcuyyDRb26Ms7xxdLANen9QjvFnQXBJ59YM2LutW2qa1+y+m+OO+i+w+3mmPeYgI3VfP8Qi8.hK9wTveiCzFwhSSDaU0FQ1XVt0bsPPhuWZ7UHPJpBZBKq.MHPUAbpX1GMh4SdttSpD5eiPVi355NxwwYXPPv.fNVVVaJR4VPiF1vF0MIgzAiM2TcOgXrN4Hv3SvYFDM+bNSf61z9IOZZuS4.e9CFCZN3dmS+4NSHp8rsrgDgwhEJr9LCFaqzPkzfW9RvLMLs+0Lm8S3Ij6N+9e+re8u9WO8y3Y7LxJhLCFcKYYLwmNW3uqHFlbv1gNNyRlddUudcrrrXxjIZb6qHv4.CnIdttDFuV78TU.0y0UUDEz..+v8PG35512wwoGPugiF1QCzs8882rPgBqWB1noAH3oSHmvONHr3VCJCi7zo6gNJ1qKtPW+nAaqeRVw2+MEP5a8Vu0jOkmxSIwoO6rzDmIfqOfeQPaYlHNVrWtiyY1mlYUUyTVjT0ME4JOv7.Ke8W+0uzkdoW5bISlblwiGmKUpT4wHnvYykKWj1jHppR61ssle94iw1ERVy0MY08XP99h8Zh+DRlH4TedFaFh7ME366GDDDLo8N63WbkmbPsZ2dXtvHhJRX7+QSLmoEgpYyliVd4k6u0Va04S9o+za+ZdUup1kfsm8obr1+k+O9+ameieiei1eyu42bGL4k0qw90so3ZYXDiWl.UlDaDW+Xo7l+Wz5mF.SBMfpjD7hpRZzXDNEfdC23MN7U7xe4gUhb0gKwZS1Da+PgoDpffGVv7Ig1QavlgoU1n77MZ7sKXaamM7u4Xf9+s+s+scdNOme01f2NPwtPqn.TOLGKOZ9l+AqFdpSERe+lqeK.rjmm2bkKWNoHUFVj5sagcanggYHkYB0QgxBTOJH9UTUKCTRD6EUsQ1OvG38y4dtWP+m6y84r0sdq2ZqK9hu3FP40g5aCK0C1Lda4bXmivCdK67i68GreAvLLAlkyn55YMZDQ87g8RsocI1CA3LrG87V3+8W+yrzwN2ycou62+6tv4cNm2L.Y1d6sSuvBKLkcTD1VXQWWCor4C.rjNc5H6rSGpFiNmtt0Hr2+ibfJgUDikWdExjIMtttJBZHQBUTnbYG0vDOTWOWEkPcLvaBpNAgIfLAzANNNcFNb3VYxjYiWvK5Es0s74+l8Us9nPMoYnpZuvpytQ3wl.6.U5uHdi2hkTXSy47h.agFBZ1Am.EQ2ydz75f.xEOoxjPwjPqnJVEWffyhAzjLf8LiFcxYAl8U+peEE9B+2uwbMBs4.JDoIPXBTLBzj3.i7.DVyXIWH.QsaAiFMhzoM1PyM2bzt8NHhIvtff.pW2CPhGnWTEYCWpBRjcWrMMiBNSG63Tcrqq6Xaa6cSjHQ+67NtytG6bOVafMDQ1PUccQjsXJn0U1A7hDvygyCiau+.8djH6kh6SMNnYQrIZAQpLmpd4DwV+E+Euvt27M+42DXiEgs2ZOVOdH.lD1RNvbTjhzhSCJ8jTswYB7Db8bcJLSg451saFPjxksCrrrF455NnZ0p6VqVsggLDJvwwQa2tsL+7yGY2NULW4AlvvgwljCaMsJpCFLT2byMDGGGKWW2DCFLPxlMiDYiUqlKkJUDQDRlLo55VCPhZmh8wj.QDsRkJAtttAH3S.iwhgnrKH8rsK0QDYqDIRrAFfmVGr2DZrClDXiezGJMDZNFVdLrQbAt6mAZxgTnAX0rKwZ41vHB0y.TfxTP8zreouzechegeg+sQ+boIzO2IOYsLtqsVlm9y3oG0tgypFgfcFn7rp5MG6k7Zj1KE4W6frXBfH6hnpzGUzfowSFxlDIxGFJ3T8Av3Dv3VDQP26qopfnUL98BTvWBGA6lp3qiU0zh2EJTn6byM210qWe8xkK2TDIBrycBOhWo1cicD1hXqpvZRXAGh.Y7QC1eOfhAEtNr2SGr8CrBGuowesg9OWN6JrwLqyJETskQ23JSZ0SSIhcdn4LSlLoP2tcm43G+34dJOkmRznNOmHRAUUCioMGyw9iSK94kEfU+98sxmOufoUGnQiFRkJU.P60qGs2tMHfhhLcOSS7a6wTyPEGYuA+nF6KD5aS7gfn8MiXEPeWW2NJrUUGmMA1Pjx6XSi9W9a6cr6a4s7lGHhzmxzU8zthTpGzJBjtnhYNDVcLr1Aa2vGoZW8PYcXErXJ3qFVUVQg5Z3yeQ1eYKAEZZ.EYAfBpQSaRJRkjkndtu2VaM+BKrvx+9+9+9q71e6u8E.JDlCQ7135f.FdvVW8P8sEDDPmNcX94mG2PP2DL9rBicScc8TDMPBDeUz.EMnpImAK.KOOO.HbuRe.eQz.Uk.fwNNNCccc6uzRK0Ia1r6LXvfcxlMaDKSZGx7jH8AqCPuq65tt9upW0aYzJTeRq8ljhiwlQzfQPkQfWbBG7XAar+EsdnH5qGfB6dgFPkSA02SUhKxjWwK+kG.NiA2QvZi1Dl.M1KwZisg0Qo83iO0XzYTEbC7fDP8LarwF4wzahIEobBUqGHhLtXQF1pkYrXBDDl82nvysn9BSic99nMif3n6mHjoNYt+vJgSERqtpkHU7eUupewIP8dsfcBC.sOvXpatNsD0Sro45SRn7tkEYPcUGAM8AjW+q+JRpplCrGewuzKdWU0tNNROOO1MDrjfPwEZuqwGEkiiD90i9pw6YO3g78jUgkVSXSRXauQ5vpfEJfqkyB0S6pZJee+TIRjHEP1FMZj63G+3y749b+oy8zeFOi4v3DMr5FjHa1rG353TGhV.hSUGHVhGQ.gL6ryRmN6DlnZj3gsmeVGGG1Ymc.c5uCbccoToRzrYSy68v28VVl.IylMKNUbh5Eae.KmpNILizSMfoi2SRIB49RetO2BDlb1e0e0e03ie7iO.n667c9t25ptpqHe5zoy.kRAMSBdIMIxsoOf98+9eeMTaVBT0H1rwroNXPHOZbcXzI1RU0xQjjdPZUalVbjr3Q1SuTob2aSxKkkb6ryNomc1YS666m9Zu12Wl69tu6Tmy4bNI+ve3+rjI9ueio.RI1RJZRxW+q6pRJlwh4AlL.6Kg13LaQh5c5omngBlcHamP.oPgBTnPA777HZS1Hv577pOktww8.53T0J7qG2uH.rxJqXs95qmz00Ms.S1XiMx366muvbElIHHH+jISxBkxH1lVQ5i9Qe+o+U+UeYVp5FX.qyvTs1QOCsHSXKl.GwGNwCV0We3rMl45ypHgtuTXYeVYiwhHirsYTXqApEJL+HnzXno+V+nCpUh0S+InkAjr2+662KwjISRzpUqDnjna2tg1AUrb8bAcpHxotdtIeeuu2Svq+0eEBfDV4q8U8K1usUz6k316nGX5U.SsKl9yjMaFS1D6YSJdddBphmmKVVv5qudr2aSyrHFnIBYylQFLX.tttIDQrTUSfPBTCKDVbwER0nQiTkKWNgpZh64dtmjmwYbFYUs9LhszllrMvVpphTTBXc7cbZpFRtrQ30S6IgSnfn3KdrBnuwWGH1Lr1qfVqkaSydkQ.eLqHxr.4dAufmWzT7hnjTe0u5KK20cce3zTZ5DeHioUWIYnurzG3HxlKNPvGjESQUgcJSkhDXyPPhmZmAP4xkUKKq3rJQL9zTPPpVsp555NsnDNNUkM1XSSK7HHhFVwVyaMoRkpQfxjnWudVc61UVXgEhtVEMtjiXPSGUq2cvfA8xkKW3T0XUEmZJtvxKultwFHp5pl8NOhF5m6QC1a6ArpokZfG7j1CesKm3+1+s2dxK+xuby85SEk6eJCxyBajacn.r9LhY5gjVDIoHRx+nOvGH0y949bS+rdVOqze8u98lwlFQraJqXKQS5wY.xIkkLZcMIPh+la8+Uhm6E+yGsm19Dv0s2daIDvDZzXeRwsryN6fXYNMcp3Lc+SQrvyauATgHBHPkvVzoW+9R6s2Vip6f48snfjJjMfIEQRswFalwwwIqqqaVWW2bppETsdTQE1UDo+G6i8w57G8G8G0VDocYHYcy41DvdjMMFYNiW6QC1R+zrhdeGDNITCE46JfsmkHh0Jqfr95j.7B2uqbJndtll36W.XdQj7hTN063c7aaopWBLhTcFU0Lu829aOp3BYUsdTAwhZEGIF6JE.qm+EewV2xsdq6SuubcqINNUk3f4N+7yO8ymF+EDUnK.TmJNVdF6k.IzPx00URkJUDndXZUeIQHvIQfyk1yyKCPls1ZyrUp3Lylat071EK0UUs6IO4I2Y3vQsAZSI1RanaIRk1upW0qZGf9qaXQbn.YqQwuHKhmFF2Rz.TI98fe15PVOTXXR7DLStHj4qbm2Y1icriEVg9JYAuDg8kW+kXotaxl8fiL.NQzMjniClzRzFW4AVnHT7e33G29nG8nKpplwVjfSNbR+SISxNMB664xPm560G8C.6wPiCaTp9nMCAg8S21PDRWIW3nMMR7cs9O7e3EO9S9I+5cro01M1quviBrTvlDzXZETWBvtc61U9d+yeuxO0m9ScALARD7U9JektOmmyyY8xP8aqVsFUqVcikg1aX98EducYfMlVU0EYQqsXKfkCpvF9dG9HH8A6829n7YQHUKHiCj2kxEf5yZCEZXzMmL.IEwNIzL0IO4IScJmxoj8U7a9Jx8g+S+vyJh871zboPQesPnFTHX.iKZhkDwBfC1ZDSChy0ySbBcpY1nUmtgZzJFJw344QjSvISlvnQiHe97zpYKc7Dyz0N7AAMLRvoh9UHE1iSO9.Pl33TYXMW2ABLzwwYjqq6jEVXgw4yme2O+m+Kz8E8h922VUcigCG1LSlLgUOydyxzna880S1gLLXIlvl+DMFxdj55Ay+1AAKKNfF4B6y+b.4twa7Fy9xdYurrhHosgTunW0qM4S+BO2Tu4K6xx1bud8ORKSl49tu6alS6zNsYTUmIHHHehDN4h1X9i9Q+noOsG2ok5o+u4oGO4hC1pWSWwsgb87voREb87PXOarnUkJUhWceMYhj5D+IQ9ZkHqsvVBKd+QOoZ0pA0pUap8G6w9jcme946LyLyz9Nty6byegy4b1ptgoIqWFZVeuJytKTbDzJx9ZzRrz3MYSe3H9mImH3t2OSzd3H3ISqNenu0npIkJrsQyuBL25Td12y640m889686Qkicr924cdmaCrErTGXyX9CI10938YMK.XCbZXySbRsIOoFMZbTGGmx0bcm8idS2T5eseseMIZTqBLQfIKt7xAYyjA1ucygcbXrH4PeNPUMpW9UGmp7c9NeG8rNqyR+zepOMOsm9SK5myT0eOOIUhDTrToo9+.YpcnZP0Sw.ZhumqmeEmJJFPfid8FFNEJjhHQLMgdpp8Vd4k2Ia1rc9c9s+c14O7+m+v1.aJhzpDrdSXyJPGOJO.pOpBL5n+e8+03u5W8qF5WaJEiezTKR7iacXwQYAjbdlOUaZmCVovJrdgV6ATxreyu42b1K3BtfrhsXQSystv8Eyopl6u9u9uN8y6487REB3aTLE4sEYlFF+bQhUcTaFFG7tD999VIRj3vXp.XrGznufI0TUrrRfePvzJyFyWFoRljQSlPUGGh0RXwYQmFGq+lMaFToREeee+3Zqj+hKt3jb4xMdxjI861sa2d862fvLzBA..f.PRDEDUY4kVZmjIStSpTohXXRaancCn83wiamJUpsJB6zB5az+MGEb0SEz6eOek+jFayCWWwYlj0QfDmXuKnG1ySRnOxn8LSAqj.VeZr7kob15TOWkJOgYTu6Yl5lXISJhXcC2vMP850sdy+Qu4rzfBppyGDDLWhDIlUUsPYQxU2vHpBwZ+qYYuVaMtNS7.700ueexmOO.zoSGlc1YOz2zQ6kFEKmqmGEWYk3fAC.yLyLZmtcQ.M+LyDzuWOeQT+vp+GdcYpVgMxwwYWOWutUbpzEnumq6tKr3hCxkKWeQjtCGNrclLY1Jj0lafQS51hv1l21LUmdv7k8HQ6qGJq8kmIPxSERc+PpkgjaX.HIRvVi9XVnTAn4bg1KYtka4VjWvK3EHfchxzHWcif7ufHxRpIWfHcmKN6xsdPNOrbc8rbbpLskV87bkeRi+2yyi4meNc6saGQso39+h+YA.9kKW1ud85GHGVIvwoxDWWuwfNJjwI6BrqkkUuxkK24C8g9P67Zesu1sA1nhTYia9e7Su4S8odI6XS890MS2zI.C++7+4+yfm1S6oMfxrK0m1tXwKp8i1sw9odY8i+kb3qs.N1wNFfYDIBdQZXi.vlKuYny1SDiFabvOe+AoOuIX6Vv3idzi52tca.rZnpUlLISzLVRMeKOunISQgkVhYfF4VhklNK24ARcpGJfC8v00zGhu68nrVtxkofpsJThRyD90TwQF8I+je19PqdJrKr7H.+iBJGILH9Fj.brXok.XBkY77yO+jAiF3iop6Y.x8re1O6YTUmYlS+zmoZ0p4AxuwdNZxVoBYdqu0eqTmJmZz09ja4rUx2065ckD1H4HHAbjCsxSOHu+BSxX0jrLoaE1hMm8K3EjGpOKvr0MAtkWDIissjb3vSJkor9E+heQEH3F9B2PHbwMRT2TYrLDNxeCqTVBQjDhT1ZxjIGLfyCl3wTvR.iCwHmkau81.fmmKUpTQqToh5VyDHmaHK.RlLI4ymGWW2PvRTHTrWcbbTDqoS4FB6KVP5AReEc2e2e2e2Qf5655JBjRDIqqqadf7au814bccyedm24THTXqVISlLkkxRUfUgFUUvFJufpZdaHMECeteS.p.m4Y9nMGjO.P23vu2JvpBKsj40U0T46vf6xAkx8xupWd5uzW5KICFLHXswiGeCW2GZ3kcYW1vl6sICm3DmHEPNawd1G2i6wMWHMhm0xxZlPvRRIR4D+p+p+pVO8+MO8G.yRbccwyyS.1GHHQKOSRBlpfwgBVh55VKtuU+IA9gAwK9BD.FaLOW2IfNFXbxjIGALoVsZwEatX5MkX0tc6rt0pM2oeZm1J0U0909ZesU.pTGJCTBX4hEYIUatP366B.yrYwMCaCoSjdKHIbz39je3peYAhTo.3Zu1qEVEVObeqVpF.0CrrrzVfdy27Mq.pw84l5YxYdveWBfbzvJwBjrxdicvTzfjIRjvBQjNc5PUGG8JuxqL.Hnb4xANNNrvBKHUbbrxlISTqqFo6MwEWyHZKeXU3+A85qHBUqVE+.U.3rNqyRZ2ts0k7KcIV.QBlHdttxwum6gI99SAGQUyGBCLTiEikF09ghHQ.+NYgEVz.9iXZALfwnLILYljqt5pY2XqMlqVsZK91dGusU.JdMWy6onpZwlFz3W1U0kf5KBrfqpK7U+pe04.lckUXFvKaQJFWDv+wpGPOBecPPeSVgJQ6yks8JsC0pj0y+C1YmYDoxLX.CN8S4E8TR.f1P0M2byf2zUe0JDg4EVmwYbzTFVbZW.SREEDQx+C60aehpISYDhDc9..IRXFRDsa2NteJoVsZfA.NKGGGoZ3GymeFBBAKAHxWlosGD7GOYhuXlLSARH.hdddQ5AQXaLDDMokFUpXow9AAiTXDHCOupUGBLbqs1ZnsHiZ0pwjd85QUGmTat4lEt+SdxkTUK8O9O9OZqpVpgQK2VJUppyQEloU3T+wCx.towz9ySiy7HG4vYUyivVS8GeBLLQCVU3HGNvr2cLPxVZIR8O+O+UyTjh4wXuLe8h0WTUcYOu6YYOUWTUc1u7W9KmqDj7M8tdExa9M+lMxWO1hToRRKKqbQfiDVbqEhkXaTqeEWHgevNlBVB.oRkZpMXDqL87BYFPkJgI0ZDRNAXyM2j34Kpppc6zAIRXq60aBvXU2SyQDgw0pcxH.NDuZdIUgrhHyHhLqUhDymLYxEcccWQUszcbm2QELZVkyYe1WnM11qnpt.Un.PtFSAFpRJN5Cve1ijswdHtNpRQ39CyGX0y4bRC0ygwezb.KJhrP4xr.zbVU0bW20ccot+6+9sdAu7WP30nFhmphoFEDnpNc.jD1N6v9Yp4AE20j.IbbpL85umm2g.VhaXdAUvy0Xu454gqqqVoREMe9YLuPw.PLJhPDCNCAKwTHgw0qWe.60lV6hpCAc78ce2Wfo3pHtttIXuAGvLtttyeIWxkr73wiKBTpd45Eu7e2qbYn9B0UcVQrmAHmMj8o8hdZlhrWmLfSZrsOHyT+YqGj0CkKPwXHxYFGnCeQjfs2daMTrLi3O9OtpIpG3Hf1LAVdbwPGQyM2biIT88iNeMTCubj1SLKvratIyATXS1LZb1klU2mP28iq+sejvJV.RG05I9DehSm1Fu+2+MkEHWSZlwyyKIf9ott+xQD1+sMggPtI.AGGDNQwjmJjlxgA.r4lFjSqiB15uxy5YY4qZ5+8u3+84FLXPAQJWPDovW4q7UlgxLKXGuJSE77H+a6s81xd+b+6ELkh7e7+3+QAvZCPLsU7OxDkhmfgww0otVV1f7P4B.y9k9Reo4oDyMd73YwTMrz.xm3S724ewW7EOtN0GcYW1kM5+x+k2xjPmOv9YQvTwuSUMmpZVUqmJYxjSC.rVXuTygr4zjISldxFs46t6tKttt5t6NPcccYqs1xTHUUUz.M50dfpgEfPTubG3Tohuss8HfcUQ5IvNftMhtEJs+.efOPGGGmcAFU87qNdvfgSG6gUqV0jXrJ344kbmc1I+N6ryhZc0FnJvp0UsBvJhHy0.xRKmTkgDLOVfmvce2O..h9Iwf7goq8UsLld++nwZagil7HPRpPRXsjr4lludMRA1osinadklInNboW9k6mMa1gmxobJ6hMCfxi.7emuy2I.IO0S8TSCjqg1XFLOSL8YCBC1V05oN4IO4gkTKNNNSojYzGUU2CXDQl90AyFwQKUUSE7UYJXIhnSP0IW1k8aNYqs1Lte5IgIQL.XvD+ICAFIpLF0HFcUqdd8EnmJRePMBFrkUxNc5j200c9q9pu5kUUKFp2QU.pzpE1hHEEQVBSORNGvL850KGPVyTQncrfPN5gED3CGVJPDrG5a387Ff0PgULYDJBTD8Jux2Sfpp+W7K9WG.nato4m6t4tOLfGsNNXAAV.I7.q2266OTTUMvMAfpZmNcBbcc8qYFWvS.lLYxD+74yql+z6aBgcviC5a8Ar9a+a+JQsD3zUThDe1O0mg66GdeBfzsWOfPQPzyS9S9S9SDDgmvS3IDBThtOQE9Pr+Ba2O0uVsZ9DpkDqu9FipVs5.EF.xtXnn9f4latQNNN9tq4hXp2Rh4me9T+S+S+SY+898txBqs1ZyC1q.TTDoDknnp5JhHKigUjKt95LOvrsnUThUwSn+gK1V+q45.EVvT7DO71SXWkoBYd9Ymc1bPP5+7OxGIQcHP8zgspWu2W+q+06rzRK08c+NemQix4g.iuvibZbO2y8jR05QwXMGPg74ymCHiHkiCPmTutdnfzM+7yO0uUsZ0vxxRHDfX.Y80WG2ZtrvBKPxDIU.cgEVPssKariDM.EeDw2wwwWDwLcQL9u5Cz+s9V+uFlXgDoED8PnGvtatw58qV0o+2pVsdfzUDo62pVscTU5XYY0SJKiAHalLYpUq1rWvEbAK.rTYXYQjkvt473wbXJDUNU0rXSFnbDXkoARchS7nh3LmFexQ.DQTprlvIPBK1k483QIAbj3ZLQlM2jrOomzSJeKZMKvbeuu22aNRrGyKEQl4C7A9ix77e9O+jMApeb0uro.c9pVGpWOw2467cRCjWD6YwnEEQ6gFMtgS828282EsuwOQwo34YhGKdAFLBltD98MwugB08pippNw2WKUrjVoREcJq4Lssbj82P0Hv98qVsZef9iGOY2pUWcX0pUGALVEcx0bsWaPsZ0nVsZVA99Iu3K94moZ0p4877l6rexm8Rpp1.Utq65aTgFMJKhTDOVBrWzN74MJ6MCGmrf8AAo7mH.weD3J98UKpb7v1WsXZfre6u82NOUHOTIupZdU0bu6286Ne85lQQ8G7C9A4U+pe09G4HGYr1PGioET3nkKKpVG.UDIPjx9kEw2vb6o186q8Va2t8TvR3.wrUYeLMO7il3uCO62e7+tttGPS4DIZjV6VyUcbbBVbwEmfxP03CqKlIF21.sQjtNNN8SmN8tUO+pCAIRPpGUsZ0IAAAJfkHR50We87c5zdV0SW3a92+2uHvBhHySnNT0.lgVkxWLbp1BtYnQiCiY8+r0grdndgIdUMhplQXK4TJOzLop5jhm2ou652wOn6RPuM2+3m8AqR1BPhJPZEloNrBP4986WJe97yBXYCSZXT77ggJTcT0QGZYYMrRkysOzn2Jvf0Ca+fJvDu8O9jdjJ01l5bLFcwmJRtlMkJmGZjpDDzvP+pdgTIcGJR+voID.IfSMCb+YTUSKkkDzXkjv54rgkqqZUfSUDYUee+kZ0pURee+9me0pqeKe6uc8m+4bNsZ.as1Zq0Y0UWcOgpp3oOjV26nhFFB4CnNf5BJKhuQWC9Qp72B6uUix.UyA0xBjuDjOrOEiD50DevO3GjBEJ3eoW5kNFJoPyDkfLeiSbhBG4HGYtRvRMTcYorTTqqKgYi3H52EWQ+i2O1wmjD.60NDwWat4FLbvHp3TQcccIPU0BQQlR9bAPTIRN.PCoiW.hDDJLE5m9y7o0K4EeI9pHCsf9mmiS2FPOic94E.MRTqVsThHIAPUMjFv5XPlnpNQDYb0pm+DngeYXxe+O3GL4wejSaT.A8srr14K8k+xa7K77e9s.ZBrITdGnt4d2pLh0lJfhGVKs8HkU7MaE1iZwVTDKZE+0sLvFlMAKSRpubJXizUBGQ5tFAdMSsZ0R7Y+reV8xu7KOvFBZ.REHsmYyl4CSZyVUs33wiWIUpTQ1XyxzoAv9Rn0pYqVVkJVbeB7ZD8Mu2iebxOyLSOQiHp4dT2L9RYg4WT2Z6MAj.PBDTeM138U.Q2ekClnvHKQFFlTdPsZ0DPBTzIurekekA2zG6iMzoZ0Q0pUy+27xtb8V9BeVCPh0pEDFvX+zoS2Ie976jJUp1+C+C+CseVOqmUWnb2SdxuQuK7TNk90CEoyxP+5vtrBCY83i.9kCfMd33XSL99aIA6DPiz.YKB4aYD277gTAVEQ1kPZ7GJB4CY+sdjIHLaRQiosjybPkhf2i6Zeuu2y326JuxmHviGnnqqaNQDq74yy7yO+ACJ9ASTM+oJ3FOOOTfc2sOm9i+ziQOcypRr1+xve38Z4FyqvLPzWXw4ks2tMJnVg.1EDxB.TLBWsH9X7aMcDnWsZ0Iq455u544n0t8ZAe7O9Ge7K4k7RFd9UqtaCCK610w4bGryNGuW+AC5erRk51D5qp1WDIZRlr6d+e6dPidkf9M2abWePgS7QCqCZSDVLfJoAuz1P55plw11NciFMxIhjmxji5j8a7+9+chzEJv4bVOOEpadtqDPSyn27O389dm+ZupqZgFpt7wN1wJdm24ctLF+Y4wvrwH1Qcvo9UjsnEfUMWWo5Azio3qFMaRfuOAFZL.lxlpxz6QloQxW7V9xAuheyKMXsZ0hdc9QB4Jni.YewRTsZUtkuzWhy7rOaUBz.QXRfniErFKlw35nX9+FiwulkpRBHfKX0UG65622xxp663c7N15pu5qtkHRSfVkg10gcCioL3q809Z9WzEcQQi7yggBn3C27m8PYE22W38yiHvILIIhQ5AsAIbpJEU7on3+yAMyTBR+BdEWZpa7F9yRaCouz2zaH6U76bEo9m+m+mkm8y9YOAXHTdnpdHhjSUcQyuVpPHiEYOAENxNae1ZQ5dyAeC345QEmJOf+ez9qQ97h9Amt+ZHxHxde4nOIzlT7CYEWLFMwHCXdFIHVMhzjngShDQkQH5n.zQhocqC.TUj.QU+e3O7GN7bO2ys6a92+Mu0e36+OrgHRiRldZpcCy9m6BqL.VeWfAKCC2XOQU+f4w7HwbZN3JdgtB8ocpog6OKFlQjuto8.C0UIicwvgCSccW20k32929smHL+yjQ5oopZpe8K8WO2+y+r+mE1c2cmMLWx4z8ZqvniBrmvuNsvCAAAXYlJCxO3duWd7m9ouOatH6qFMZXZmvPin.0XHL+Byqa2daUhYYoBRL6u.QHPLwwGN8jnW0pU6WqVscqV878Mw+6ld3fcSlIaVQ.eDYhp53P1xLQDYR3qchML51bc60qSmNmwYbFs888amrZxNzvvVEU0AhTY.TuGPWVgNr9Jcg06uJLZs8mCP3o4OaEsdnRAm3AM4isYFQ6.Ca29dFfQP77W+N9AJf0l6ILc+jDXm5AA0i5e9hL71tsaajssc.PhFkHkpZFGGmLgBfSVrIqiiS9ysRk7Pi7tttYCGQwIeiuw2XBukW9fZEvCWoB9OIKANyD2Mj.VxjreYS.SkDIAzHAkwpIHuq206J.XbqJUL82cK7OyHC+kIQ3n4MiHRla6KbaYg0yhMYaXDGorhH4eku5WYgd85MqsscAQjBeK20l8bNmm+bMf4Was0Vb0UWc40VasU.VtBrTs63uaNpP9V6oZ9h6pqZrU1BJFYCblO.lLD+XuVMpJ4FL3dKfIfs4eO+Ye34TShroTUCti63NF9a81+s5doW5k1QUsGzbWU0wMANxQNRBU0TMssSIhXs82a63U+L9l8QLRJdfe.P8F0mdgOt1kDcL+7KPEmJ3VyHnSUJWN.KSBCUcb7QHvopSfrmxpqH3ivDzoa9N5RdwWxHQjgB5tADz+1qUqasZ053bdN6Tq1s2tVsZsA1Q0fNNNmWWfcpV872VU1p54WcaQj1mW0pcbcust0pUq+sUq1nbYypWwUcEosrrlsa2tK+29272Tx1DLRQJyxQzZGXNViBvF4Y9CM.3GIrNrJtH.Vmv11TwfVjZ0HP3pPFXiHg9JO0IOrQdJStO+se6Y7LB3FkOZY+M2bywW9ke4iA7aXCPoDlIwjcFfLFFJooARmNc5zRYItXHdvVlHIPhREKl.3A.VB.4lIhJwlGUKaaSkJUBs+1+9VphVyqFnRf.AfNQMATMjPccphiy.fAgUNdWf9Wy64c2UUsSMicV3zGQ6Inc+y+X+EcPj1qs1ZaU87qt4+8q++1l999at1ZqssJ5NXzZhgqrxSV2d6sSaYYM6y7Y9LW54+bdNq.0KM+7yWLDr6kUUWrdoRFviVmYpDEXcQxAa7vYaMyypmIAPio.ezZErLjbP.pXD71JlffKAJwPjKbsmOtFnWy0bMQ6YNB7F.r6a3ptpnD6G.rqiiSukVZoNyO+7cvTkoHMQIJo++EWUw38Ys.73OsGOvdzS+K7E9hTtbY7hCRrpZHUfiZUhIf3iE9aucaeIbpjnv3nDRAFDVo+NnZj9PL8v00siEz6lu9atGP2WxK4+vN+u9e8k251qUaSW201z3+6aMna2tT20Mqmu+7pYj2urp5RXnr+b.y9c+te2YfF4UUy17AB78ilV6GrjUMIV3.otka4ijlRjsgY5hjsYylYkRkRAjP8T0FFOybys6477NqtlobGaBroQXcq2QUs+UbUW0vlFcyx5tZcWoGMZTzT9Z1fffYUUiXsRTxEwYw6zjYqFazstupvF9w.e+ouYHzlpZ3zyIL4z.UU8e2K34o0pUKpFDQfkLpZ0yeWP5W87q1UToy4WsZafMq4Vaiy5rO6VVPq16zdCE1RTos.6b9NN6.zd0yu51NNNa6551Fn8uxuxuxNhncrrn2sUasA0q2v+Vtkaw5pu5qNRSWlAnv2x6jy.ky4Y76m5htnKJopZRfTkgTfWpv6GORlZ66ikXwEyVWUsfkSz.RZFI0jCmoSPoYCaIhYaBybi2veVZ.9cd2u6wej+zOxtkJUZvy5Y8rLCIfRHPcqHgmTDwRDIgTVRFpiNSODQR+C+g+vGP6FF01+Qqo9zhCVh2dfmDWSIh+CFAVh4yMTBv79UMS3qoigUcHn6FxhoNO6m8ufwOlEsqVc01BzVTssBaeu268tEvFXw5pHqKHQSsvsv755JhL3zNsSK3u3u3uH068Zeuy.r3YcgGckFpZ2.JqpZSIrUs0JDxXyMJxLG4HmSXKwcDS7+m4Cvd6Q5961CrjUICb+Q9elqQIhZA+zm8YegBf+se629v69tu69uie6e6dPydTNBnICX4R4x5M8QtIKQjj4xkKElVxeZ6rJlo55CZKdEwHN.4we5mtbvV6phio8aBBBL6eFZuIl+Qa2tcPHXY9FfNvWj8jfhvhJLRUcu8JQ1w0cssqd9U2tVsaa60Vas1f1NStrcqV875pPWGmyqip5NqdAqtiHxNme0pcccusd0pUqecU2UTc7S7I9DAH8sca2VgW2K80sPYXoy5rNqUDaoHTuXud8L1Vqybv5EXExu1gmKzizso9W00OMWLh9YRXdv8DFgG01NOMZjAyEZeLA60uHraq852+eTUt1JbzKlESxbk.pnpVBnfsHIZZy3S9MO4fS4TNkvw2X4IP8wtttibN+yuO0quCgBmT3e+IEA+VPvpft1daF7fI.gObbsGx+Ggjbh3iN2h4gVETUKH1140FMR.LVDoKPafsWF5sg45e.lGDx.ja2c2Metb4xhYC+z0g7P4kg5mhp5iC3TAVdznQIRmNceaQ17F97+kMeQuvWzlhHcMT30dDzbHPeJUZGZ1LZjVM85+oB92O3+Vequ0f21a6s8fwvjvpksZRXsHgrcNLnAWPDIeIHcyxXM9ji0mwy3YL9a7M9FCgxCg5l9jw1NMMZjGisyxppK8FdCugEu1q8ZmCXtxhLecUW3BunKZ9uwW6qEGQ4CS.N2WRIQHM644wngC4HOtGWbgN7+et6cObIor5bweWc026888tqp5upmYfgAPGDAlQwnjDEMwyCAyiQMnPPwaGhWHXjXv3QMlbdBdINhPf3i5OdxObRL3ENZLdvfADU7FZDF.Qmi.CWlY5p5tpt28du66cWWVm+36q5t2aFLlKFGN0yy1MNyd5ceYUqu05c899t.fj0HhRkBUa2BP.I33GGFfRfHDQAQDGj.HPXYEUwtRDAJhHZj58rt0p401vnXG.LzxxJ9yrj111IUeFNhHZfjkU5g.dxUoHyQVVV7C+vOLUnPAMgPjF.Z6e+6O5RtjKoG.ViHpAy7p.XchntP1.buhD0uAPOfx8.pDS2uiEKfNd65IC3swrDHdhCLywEoowLGuUuRxLmzfnTd.IedOumu1266cmZ21scazK9E+hmdJtIKAj1Qt5zyCfBmzIcRy8HORmk.bW4m9S+oqbpm5otBQzhwMwAYLbbCEietFEEQIRjXSaqjexO9mfS6YbZ.3IZbX.3XRwjwzEdpo1CfgPZjlirrrBUdFfFXPTBDt+8+2N70bIWR+xVVibbbhhPDRfDfYN7Zu1qc3e3e3UzC.8KWVLv11NNFHAwjFR.HNKACWDToRkH.vkKuG9HG4tC2111V.jNwdOl4A.nCQTa.ztDPqGe3vtaOSlgt.idsuo2j+y5Y7LFc4W9kOYRcGeYXhwwNwx3KK.x+Q9HejBui2w6X7ZLG.QPZ.osAv5nXwNn9SjgIJFyoUDPq9DiecQHyudpgggOstc5dhs6zdIgPjD.wV0eR0DXi8MhoYEWR7yA3ISCFGvlm352+6+8wK6k8xF+2444gvvPDDDhjI0.Cf+ouxWA+V+V+VwLiiAgPhQHOY5Swwywwg9.v+1u8ae3K9272b.CzaOVV8OPEaeHKTLhIJprPDB.11V56NwLWhYtikkUuJUpLBIRvkEhwTjds0ViVXgEhiy5.fN.FsAbayL2hz0ag50a+Y9G9LsunW1EEW37zwV.GeDe8u2qsxrjjxAmzTxPRCirv0MKKMAwzw48L.zpwbhu025aA0D9i8nnXIqlx.nfKvB.l5.0LTxsyB.l+0+0+0K8G7G7GD6MZSyxoD.Pqa2tTgBE15PnH.ro7WwMWvzzS0mAAJhjLbKTxrD.4f8Ag350XDRDTxITBtHyb+xk26Pa6CLhHZHy7n21a6s4ecW20EB.XYYQ.fL.vArsC2ikkuKvHaa6AVRFzE..D239kdoWZBMsr3S7ItdVHDgUpTYjkkUGEs3W2.XCWCzAtiaHaDvJ9.Mj+2kKODUprEOf539yO250VOOcrL.KBjn9hPCqMgcypZyjFlutd1G567cRdxm7ICHm58ncsqcE14PGRqFPVCfLJujimx6HxAfEYlM888KkJUJCLggIEvjF3FWm1nQinzoSON9ZS421hQaN9ZJpZxDPCOOTTWeqFkdDFObqopqS50RwMzF+0.l4Qemuy2M3BuvWEaaayV6cuv9dtGlIxurPDOfhA5.9dPZ70QQQZkKWNlUyIsssIlY+CdvC16E8hdQszzDc.biYMWe.zwvvXCWW2Vq.zpwxnKVc5EsPoPfpw42dpP8ZOYWpXscqAbvoUtPAVtNyyqCjwSGD7juVufK3BBu4a9aFOnhDPWOM77RqFpZV02KnjD1BrbC5D+UrTuJfXILNkG4DuIuhexwRSwdS0lIMKc4OhuuORkJEJUpDrcbhYCWLnagrLlmQjTXtR83N9yrXqSHN1pG.5wDMTcNo1i9nOZproSmV9ukBAvnq7JuxQ2zM80BLLnvCbfCDAfn8u+8GdQWzEEjNcZeKKqP.DZ.DtuO0eavq409ZBHhBul+p+J+29a6skW8swi...H.jDQAQEeF55c5zo4IMyLq6BrAfUW.6oxu8+yb14+oc8uWzil9f63FbyCY.Xp2xa6sfG8Adzg+yei+YolRKgAn53l1i9Y7XpY.jwUlvTG.hq9puZwq5U8pVJQhDoEBQHybexf5a5YN7u61+67+M9M9MBmpgy1.nELQaTC8g7vqHrDXzbbyEacif.b7cvP760ikOBybZAIxTEUiW8ZypnJdB.LDlXCTCqAf0Q7pDVdECXhxvVMx0oyijdlYlIm5vOccfs6x7IzqWuskHQhEZ1rYBgPzmHZCaa6Usrr1.Fnmoq4n614tCPDFUtb4t111qSD0THDqCSzB0PLso8UaikoSrOcSEiatExh2x5BT3LNiyX96+9u+X.SxxLm3q9U+pQ+I+I+Ii9Q09Q808z64IAqIT8XjiYd1QiFsb5zoKB4jtWB.yPlzLvU5R1P1rRbCso.PhW0q5Uk3y849bD.nG6QezDm3N2ozDNUE5McBS0dUOtHP0FfX7qqHKKqHaGaY6Ewe1QfYPgzXlkP9P5n5w+6FRD0SHDcejG9g6jMe9AVVV9.H5c+te2zkcYWV78o9DQ8EmoXvg+gGdTxjIiDBArss4DHQjnrxaALQRm61IS+g8yjKWNsCbeGH779ucd8zzzZAfVrbSS0CPuKfW2WzK5E08Nti6nM.ZUFnSE0JKFG+eH7VKrS9msK.bnozjp.ZvYSMapZ9zPCvUiYV6u3u3uHw66889nK7U7Jvm8K7EhKwZ56WlQc3cAo1OQAhn4YlW9FtgaXkK8RuzkwjXqXcWOcisS+bd70zzH1opCno+qO1ZwQoOV.l3HKgUfiiSrDEG.f9Vmk0.6601mjZuFrTqt9S8yDXYYwe0u5WEaaaaiMMMGs7xKO..8u0a8VGrzRKM347bdNwRZfqToBsvBKfYlYF1wwIRblBX5YpUkqpUsZUsnnnDVVVQQQQgZZZAv.C4Zb2w.2pi1va7zeFD+7DXo9.M+4Qxl+W40z.lDapZwleozrzzQJ1kiN7gOb+S3DNgVPN8v3sP1zqnO4i0tQBbvw43hGHPY.8SILr5ojHQhc.fEa2ts1tlaN1SBpWV0u23FGdBMOfmD.SlhJw+LupVsJRpkDE0KhpUqhLYxfgCGN4GfAXvbPPPz1291ibbbFK4f8XsGTiqQiy+wXDHLzxxZfsicWvnKjrIoi0drFZe.6wMxJAxioG5gdX5bO2yMxwwYfPH5Bft5RO2JFn3z.HiRRhIXliTxwsG.53551xzzLl0JsG+kA5pZr8IqnuiGhy9250j7bkPpEqtXp0vZw9HwzLlLMJhTn933BV+jzi7dDuX.sBgLEBQjHIj0PLGjMppyJuU.SjHwRPFCtUcterh+F+eOcdswRhvo5DZkvQx4IvHJgVhvnnnHKKK1w1F7jGaljdMPLPOx7a6wpuyAb5xfiMFwt.nukkk7bKCDU4dpjXvfAZ4xkKwW7K8E4W1K8kIYnBw9IPhPgPDBXv.tQLyQDIAqwDf9ak.ly.xo+pZdURgcftnH5tR8U52.MF75e8u9g23MdiiXoLnGKUbb7+YmOYWSCZxzdlT7YmogAxY3ZTnFWaFHm5eVkTsHhnHe1eXJJUbdcMXhBnlTN0pGKlHJwG9C+gy8Nemuykvj3twLp.GalLQXKrxb5qo2dWa8rSFXxxOW8mvRnfgvRD433Dc3ibjvSX6aWJOGhXNhCOZkiNXaaaacEBQahnVNNNcDmknutq9vCXe.eRIKLhona8e9VCeiuw23nu3W5K1+reVmcO.ziHZj0drX6CXSVVVofLeVtO0m5Fy7ddOWeJ.Wx111+Fuwab36487d7MHJ3tOxQF0sa2gO8m9SuCy7FPNfq0fLOWWXB+x0JGVAUdx1RSOUIlK9Si352RscfLGYxYty.InIYAfVPPPPpToF8o96+TCtxK9JGVG0iuGKIj0+mmHZFniYXWdFhn410t10BO7C+vKpFl0hJvSTwslYYtVld85kIe97oWs4pZKuzxIvTwXNNNrhMviMle.LAD3sLTqoyqoFRPn0YYEU4dskLVRJAGUNMRwdIYtEKKq9F.CuWGmQJICFB.XaaG+9SDQTfXOhQN2syHl3frYyFMb3vP0.Sk43MPn8Ar4d85QLyI98ure+De5a7SmvvvHwW5K+k3e2W9ua.QTel4MHhZBLtWwVF.smHIrmvfSepRb0uvt92K0A2p94jgKFPiYNyG+5934+m+F+yy.gPlzq53h79WiNwraLpt.CgAFlNc5fJUpfRkJkhYNqIQ4XWNWMTK2YcVmUNhnwf07g+ne37l.4PMEsbKURlruIzdrG6wl1XtdplIWRRqHrHI.RPqPZUQ0TF.o95e8udJH0WbRnCsO3eweAPMUi3ae6aMI53oRyLGhRtgyLyLLFKSE8btLm6RtvKNW974yjMa1TBgHoSUmTNNNYDBwr111K.fkqgZ5IhfAQjosssgkkUQwYJV1DXATCy433jCRVNjPAV0TT87IBVxh.oALx5pP789u+6OGKmneFXfjWvEbgz4cdu1HuezOJDIPfG7BzmxuF.PDoSXaYxDO0HMxzTdPuKRd0W8UO9y9pUqtIVkDCVB.nSbmRJpWpTIXVxD..0pUSYrSx8pdLERbrcPbxTgPPx+LaRTRAbgxrv.i.R40H.XzC+vOjb55DFadXLycrss6dRm7IOvxxx2wwIz11N7xtrKaDHzyxZOaPL07LEhUQBzHYxjM.vpDQMIhZJJKVClXUGGmFNGvwSTV3lMa15QQQMOqy3rZ655NjYNQPPPt3F8gtWQl4h2wcbGqTBXQ.iYqLAHoetMWseIcsU1jj.kgVYfD3Pp4IUpj78emwwbwduTZl4znna71RBW2668EwLG7Y+BegISfUGj5mM+G5CsuYIxH149miHZ13oeboW5kpzw8lmTwVdNNNeyC7.Ov3WDxCccTufHk5G.bqUClkLAiIFA6zpqVXIXhI1woJC.VwFo.gP36beNirrr5yL2lYdM.TmHx0xxpF.7rrrVE.MO8S+zW8zNsSqd+98qYBT011t54cdmWMKKKO.3AcTC.Nk2a4JyLyIczJUpbzyRHp.B1Gv4.UMk56uQ4xkWG.8+p21WMx.HEWiKr1ZqsHLMWgYdE3IKBVUvbrobpg4a9jxpqeIeIiolWwxjRHsTFVF4LkFpYV.jNJJZyfWr6mviiLu2A2DyFiabcHz85lHQhV.XcWW2lyM2I2zk40TEI2ES4CGDY9ycQKOYfkLMkhcTEDJAKQF+sjbc+L92.qhFSlLIbbpFQLw+fu+2OBDhtmJ2C633DKih.PvGD5aaa2AL1.xUko6a6s8VpBWXaYYYSD4XaaWyxxpNSIZbtW341v11ttPHpaaaWGPeUu3lBT9Ulss8fUVYk9BwY08V9xekNkj9Wxvy4bNGeCCi3b+xOyJhDEka+M4mGkKOsQ49TYpqO99ic.ngQPaMrVRNdaKE6CWlpMvTcj..7nQi7+jexquu2i30ARvjhioBIhHXTMlAUEXlmae6aeyAfYIhhYRUr7ll1.DOVFb536cmVdDSaN0RyqFRS1DPMOA4+pnnHh.fiiCEyJSRlNTsAFA85e8WJ.Hl.X660NJhiBAigVVVcIh1vxxZUXf51110rOfcsxkKWMa97UYlq8xdouLW.T2xxp49uw8ugPH5.fd11GnC.ZQDslIjmcVCX0S6zNMYyo5X3e9e9eNXozKmCvXIOOuEgFluAZTnHPla7Fuw3yPRfsiDkN99LyedtlN+h7y0hHAv10.zSYBjAIPFW3lQPTRRtnG7Ih5SFTmu5W8q1NEkRxxaSSY9qZfg78nLjbywLG.V7c9QemKwLuDy7h6xzbVHkSVLvbOAodE+j5XAVhb61.TpjP92S.ZISBcESRn3WMiysMI9ywQJ6qSXG6.BKKFLhXliPBDtssss.q8XMzwwomSEmNmkPz9w+gO9F2q88t9drJ2DxyScYhcdi+Odi1ezOxGoxy4Y+bpTtb4Jk2ydpXYYUAt3nVVVGE.UpTohskkU0W6q804Bc25UpTYUKKqMdCug2POhLBbYNgttd1m1S6oMCzw7DQK.f4kKZgh40Ax18Q5lpBpj7HG4HGqsN2SEtdB0tUFHIvhoNhZKecjibjLJ4yjhJQZDI3FMZDX.L308G855VG06XNIm1..LhLoHc..OnQDk9HG4H4e3G9gKnXA0r.Xlvvv3gPji4ZY.Pp74yqAfDKuzxwC..kJUhO7QN7XvRh+yXUAZwLla7emXxFWpPgBSxhA.660lIIXG9fQelY4f8gTppVVVsjLx0nyArs6yL66XaGo5AXHHzwxZOqCfUOKgn9Me82bclXO.Te4kW1Cxba0gA7rss8rOfsmkkUi74yuZgBE13l1+M0gHZPpToBeNO6miVPPPNl4YoRzhLyq.fh5pMClqzBDjC7y3Iz29Skhw9Ex0+QdCHdBroPYjCUvrrzHDmExlLB20t1U2G4QdjM.LZA31EOwI9r0GuXlTDSa4R.XaO1i8XV4mYlELJVLM.BTn9OB5vGdHzwohOhRzmItkkk0Z.XMcf1JyeKDvLBnlTOhFH.taZhlGuyzjoQ5OMjq7zrDQEPIT.Uwrv.yAWjQMI41DQqVBnQUorbhm5o7yqkQZr53ImF+0bPhtuEy7IPDcB6e+627RtjKYFGGGXYYMphsc+DDOPHJOz1112xxJ7Vu0aM7Y9Leli.g1VBqUAP8G7AevUO0S8TaZBzLXkUVuQiFs.VtGvpGKYdLtA13mKwToCpI0ypMgCQTXkJUBJWtbrwa0GqfQnAhPIngpX5IkYXBnWCXEVZvSynZvcNLgRdwL.XbQz1Sze8lYAvXSCyA.zlMDQVoued7luQRyb.1qdctXwhQJJsG.Fir1q0H3hg1119PV35nG7AevAm5odpcDBQeaa6QLyA6cu6M5.G3.A.Xfkk0fCcnC0Ke9r8DhxwL2YDL.CWow0YYYAaa6XTlgNPRuwdogQxG5g91zobJmBMXv.JHHflYlY.jFgWeHO3o48e+2e8y7LOyUgLtIlYRGKSF63gqIrbaWHANDzTLAhA.uK.9Piu2YwT.qI8rDoAHFKcPfmbVOkd0UWM2xKu77PRa3EU.MMCjd8SAXfY4Z7BjAsL6xKgIq.wmvp2DPAvlk5.XFnZsMuBgYH8Rhoarc7VyA.T7+qJnilnwZehSLjItukkUOGGmtHJpuPZJy8.L553bu8DBwHG4VXAO7C+vb974CTzYuqkkUL6O7cbbFEEEMpb4xCgJFPEaw..NNNIDBQRniTvCYfAxAW45sqYyl4SkJU5YlYF5RuzKMnWudctoa5qs9a5M8xZ9I+jex0fAZCW0jZUqMucu6cO5fG7fGOHKmoKhKNmTAl4ETe1W.xO60.vnO9M7wa+Vtz2xZPflvAsf79xXFlL87LiKpMIlGYvFXFHmd+1FLXvNZznQIKKq4fz+bzfLO3BPlKaALwT5NVd+xO2mgu1ZqgEWbwiIU1mc1YQK45yD.3XPWcDRDGJDkCsssYFfZznNJtRQFD7sDVCrss6XYY0xwwYsyRHV0UBZxZJCYUxxQSngZR5nqdtGtwFaLb26d2CAfussM52uexcsqck4Zu1qMY1rYS7leyuYFxXvfXfAqVspeoynz.3gAvDC3p7.kLCaCf1BfdNiyeUhAptU439ToIlMIW2zlTtERCaI3upAGkkkxMDu6286dzG3C7ATxdSeDfmR5pPSUqPFHOWLtnYchHcVZf0KmJUpXFZN+e292e9Wyq80dr7bnmTFkXXX.WWW4eo5NgstNzYl4QiFwoSmdqSFeZD+hAXTsEur5+XO1i0MUlLsHvqYIrVywoRSgnbqJUpzob4x8AfuNPj23mql.nFCozHRHDhD27Meyz4bNmSnpY+XSvlAfliiShnnnjkKWNE.R+Nd6u8zu+OzGJwQNxiGcJmxSa.jwXafRXcTEswJnKZLl8bGKYF9Tk3r3qwCxBXZIfi3XrbDQYgIRgZfPIDhpikEZ.jRAfcAR9betO2720c8XKvb0kfLWVAEnH4fjYzKhoxyMECNiy2kD.Z852mxmK2SHW2zrWZ53uovFYRl3sx5DdSRuIB.gequ02he9O+mu5HV3C4YTavL2rb4xMAv5111sAPWq8Z0E0fby44hX4XMBRo3LP0+wHkzYj81XhrnFx433jikxmSSHDwmOj111NapToRoqqm.RFN0oUqVqO+7yG6EJaf35+LQ.pAeX.eUOMOUHlaqfrNkwUiIdKmZ.Tpbazm+y+4CdkuxWYLSujKrfhHB0gFJhbnNlS0+4hLyyWhnkpx7xPsR5gLWlz29Tqo5G+we7TmvIbBwfAer7dnwa2FUkWD.QYylECFNjl9rxMydNl+61+94Wyq80JO2joPlFapq8rrr5633LjkFgtOjriqOjm+EA.njT8HEyy6CfAJ4DFKk4gPGAvCQ+UW+Gk+Cu7+nnJUpDUtb4..DoCvdieeUO8+m+O2Y1m9S+oWv11Netb4xL+7ym3M+ley92vMbCcGMZTmToRsdPPvZoRkR5sUx3rX.12Ze.GuFa8K7q+iBXRbCu4fZ+qqJzq.QDUsZ09kJURZvXyiVXiwET9jUXb7MPY.JNKP8h.Xal.aW5aYXNUAkApC5hYXvHaa6dVVVs.vp.EWEndbSeibccCLNSi.UR8ooK4VY8vwiABiARpHP55pIBoCLmm7l+Ef7fmjarwFiVXgEVCnnGP8FPNstoksxXZNiXvRJh7ntwB.tFLyaiLocFZGriDZZ5NNN4a2tM+7eZOs.W.eGa6fHlCIhB9y9e7d7+e9Ae+9DQCXoo9spkkUC.zvwwooPHZfwELiNXWXHNzlZlPBBTQjF0MxC3V..y.CjGtn.fQNe+Jo+te2uK8JeAufPOf.Sfvu9AOX3t28t8efG3AF8LdFOiQpIbjTUv3bLyKSjoNy0L.PQHWqVynZzMFo4XZtmLJJJQrSXO062iADYqE4EeMSgBnS2tX94mmWe80QwhE450qGEFFx.faznQzC8POTzEdgWXjsssDLEl8gZkfYYY4+7ddOufa9+0MOBL02xRHABw.ibNfc.K8pj9LysHhZIDh15.8dWezOZ+K5htnAmQoRi7ja4jDVVVwI7Uwz5QxEhCzpToRxxkKmTsoNRCfrCFLHaxjISqooA.LhHyt.tMgjUA0gGVCX6c.NxwZSSL88H+x59ki0AtSA.xth.NzzxpIMVA4PC4VNARC+hhOvhHg+Xs.uBRv04jDULKPiBPZtjKAfkZ1r4BKu7xyAo7blU0D87PdO37W9ke4Et9q+5OVLLYSWiFMBqt5p..nTIS33TCFF5nQiFX1YmEarwFvvv.arwFHSlLXiM1P8yVhA.ZznA7G4yLXNe97g862OfYVYdXzfG+wdrt+p+p+p8rssayL2tb4xsfT+8wzEmcbbBhhh5WdOk6.O8V.d8.vvJUp3WtbYe.8Q1126nnnH+m811VPMoePwJqMMgIf1O7nGM41111RCf7W3Edgyb0W8UOukk0b+3e7OdlS6zNsrJYTLD.snRzpnlrfOVJILolc2N5iiTZHP0iGj.1TwTFoAbyCfYVAXgFx3fBjbC4.ShFrym6ys8ccW20ZPVfwVkjyzkmGGGFq++7PNP.yO7U+gK8NeGeTi8su2wr+w+w+wRiC1Eyvxh8LHhVQAz6lYmywXphGKppq.QbS+Lw.y0qWOr1ZMAkPCyO2bHa1rnd85nXwhv0yCIHhylMKxlIaTM2ZgYRmdr+kPLQgbHRjHQDIM70A.nSXX3Fae6O60.bkrTBnw8du26Fm0+syZPkCTI..T4xkieelcbpDJD6I.vS9YuNzbtOmTrzPMIl4HWWW+8t285aBDt6W3KD25sdqXGYxvG02OLUpTLjKlf.HWUwsADsAb5hIE5EhkPHZ9DnuNvwmm4O80XP2T9gSJHMdzzzDSxLCjMxpFtfY.fa7p2UVvqAhfqbyQnXORd.LGzwxvS2fYWC.nSFzxvSZZyXxvEldiesIl5zrYyILSBS1nbqs95XP+9HUpTfYFAgA.pcQR974Q1rYw5quNGFF.FDaIDb850QPP.z00Q2t8n1sawLyQc5zInVsZ9mxobJC61sa+BEJz80eAu5V2126azTIS33B7UlkrQe.2g.HzwwITHDQPGL7FOPtjxsiCGV9YUdDpYLBvMNuHIafUOoiy8kF.41iPT3u5y84x8JdEuhTZZZQ850qe974aojIw5S98J2bS.FiT.D9To3r3qsBNWlU.x1.HmNPAOkTYzAR4ov7hHZ5Xs..SFnVREnbyRDsj5LzkqWu9bElc1YxmM6r.X921UbYKccWyGa5gMjA.Y9oO3Cl4ocpm5OSiAe55xLMMQsZRprjKaNL2byhUWcUjHQBEXvsPfuOhG6vxKsD788Q1rYYWOOl.h50qWT5zoYgP.Ou5QPtYb5Sf1PXIV011dU.zzxxZsC8POzZ4JTXC.zNtA3nnHeYCq59.dS6mMAPtlgn27a9Mm7S7I9DiM11pUqnUpzdRB3IkqoIJL3wGj+H11o+Z+S+S7a8xu7AF.spw7ZkHpYMYbdGAvHmIO9S98b7MnIalQvx5+0P8smB3Hw4vxJyOYjFvME.nQiFEkNc5gl.8qo7GQ01phfLdYFHOedAhnk.Lmm4pKQDs7W6q80V9E8hdQw.lL+vgCmMSlLwC05XN7AGGGv.rkPDEEEwIRjfULbJ94NQfHP.EKVD999na2tv22W9FdLZaiFEQDEkJUpPHu2nGjRuI9dkHaG6HlgeY0JoF5Hx4.UBYIaz6PDsAy7FW0UcUs+G93e7dug286dvkcYW1f8ZYMrFfekJU3xkKCro5+MCUDD.Ht9WcjGdS5+4xu7+vLefOvUQyLyLADYLfY2NDQqASrJWkaPTw0AZzF.CL.F4d78vS+urq+S.vjckbwEOT10VSFzVBXwpRfSxPR2xeCEaGZZ.z0cRC7OY.lDircd.rLfoEPsSLHHX6ttd5VVhB111INqy5WO79tuucvcdmeqQ+5+5+Z8srr5.f0uyu9WuwgqTY0K4RtDYi5.8s.FYCLZ6.9GQ0.XIH2JOXyqcX7j775WlWGqh3mGxBtWhYdQSSpPsZL9g+veXme6y9rq6BTCRvJ5fXebQQ6MkSHmU4UH4YoGgDylmSfYdm.X6NNNqHDhrNNUYc8hgdddgpV.TS0VxxCKq810w4.aDEE0jHZ0dc5rZ9YlYUDSSLfFKAzZKqW5weNuHPl0jMfN2AO3Am6zNsSKFPiz+M+M+Mza7M9dB.poPp2H75u92K+xe4u7PKKK0ADFIJUxMsiCWfHZASfkpxbQHWUc5XJekvjnB0jMLu0B.AvDMJFCRxRKuD52a.xjIMVe80wJqTDMZTG4ymGiFMBAAxg1EONBBfEBA633DsxJqDkNc5nFMZvoSkJpSmt7i+3OVP1b47Ih7CCC8+betOm+UbEWwHKKIkOihh5SDMpWudCymOuB.PiF.tpIKX1U8dg58CDBTJggQ0TW609YR97ddOOnooEUtb4.GGmHEcBSToREsxkKmRGHmGvLNNNyztc6Bu9W+qO8i789dbMIisVGRGzJFjKUCfiW4vSCr3urYk0T2SrqDkJcHs3gJMk4NCnhwJAjoJPtR.ETqL3jl.bM.eS.+ZSJ3.Xy9xzLLyyIODFKwLujp3uXyCaQ.Lmuu+rqs1ZEz002pQBON+ZbiECGNDoSmF0pI2BSKu7xXvf9na2da5EnjESROMY7jvlPyDHDBD6eD555gISlLnc619yN6rCWas05mJUpdtttcVa80Z8RdVO61t.cL.F5JYPhO.5JDh00AZ5ArliiSWhnAkJUJtPO0WBe.G08ZkX4xkbZssalrToZ4pVEy.f4cbblWHDyedm24M69129xeZm1oAee+doSmtoIP8pLuNQTaVZrcwdVQrdY+ksYINcyBoAP9q4ZtlBWwUbEK.f4129tlBL6m9c9Nemrhkis.L2XkUpsdiFnM17Ya7TOdD.zJAjrJPpq8Zu1rW0a+sOWCfkL.J5JY22bPs4kT.woC.AjiGeQLEHu3eirKgYFiFMBN11X3nQXW6ZWXznQXiM1PwjDaN9ghHhlBzkXOZRcOOEA4Fj.ZZZDyLRjHAOZzvfnHdTud85M2ry19q+M9Fqctm64V+du+6u1u84e90rssaXYY0RUvafssczU+W9WF9QutqKpDPXUIvu..Ity67NS968686E+ZLBvzGnlrXRYiXSCPpFybpRknT0pATqVM+mooYWuIT0dD.7enG5gBNkS4T72Nvnin9yvwWFM7OqqohIKmDnR7z9U.kTJcwhUyTutb6zY.D5pljo5qgKBDrF.I.zbT41TM+tvu8u0uUw+gu7W1HYxjlr7byomF6BXhwHlB.ICBBzRlL433uXP5BBBPiFM.GEgRBwXv6bp5f4madTnPALXv.r1ZqsosVhbBtLvXCeUdIDBX6XCsDZQQQQQDQALiQoRkbnuueO.p8O3G78W+k+xeKqYZ5tVsZXCaa6MHl2PTtbqJUpzVw1jgUO5QGUZaaKRsRb0d6u82t1e+0dsnNPXb9+pUqFBLVtGSkCvLOPsY.vrWzq5Rl4l9r6OCjzquOy75UqVcsy3LDa34IAAdYf9qNgoIOUDzjoYzbF.yrLWMOMw.Mm4FtgaHykdoWpFKWEpc90909UZ+c9N+f1XxZWlvjyPmUGXIWlWwjnhtw0rRzR0l56W1kc4y7w9XW+zx.KkSUGMQIwwLWWsZ0.yLjC8gwhKsLZswFX4kWdS.GGal0InDvvzX7+9XS6OQhDHJJjwTayPUcSQQQQA0p4N3PG5gauicri0OgS34rps8AZDWWK.VEPecccu1ddxZxhYAmpQ1H.DyL5.CfHEmqHkIfqYBnUCHkiiSJ.j6U7JdEybW20cU35u1qOW+f9ZW463JCUwZcHxnEfWrjEUfyaEnqa664IY2xt.7OzjyQAN9IlaZ.GzpGUxrI...B.IQTPTIBjptzyBS4tEFLA4+eB.XznQAoSmVwNCyA.0F6sZP5Cc4KVzc950k8Coh0VP0SyhPxN3k9J2xWYgy+kb9yBI3Jw0+uUeYZ5qHojSoPc8hPIKUszoSkHUpTIzzzvfACHeeIwIiOre75qlYVHDrsiMWbkhgMp2XT1rY5MX3vtVVV8N5QNxnK7htH9l96+6o1cZGt3hKEuEvBd7G+wGchm3I1SHDqqp+uA.VGPuiZvVwrYaDfNWrnWh50kxViYlsrrBO5QOZ31111h..YaamxxxJmNvLd.y9HOxiLy4bRmT9u1O9Gmdm6bmZ4ymmUOdsXlaRDo98g1.E6ATW86Z2A.G7XMTqiWhw9E90+dALYSA+P5+E4A7lWNwBikYtVA.DQjnEP05Pf5RZKu69.Gb5anOVO1wli27PVz3IEDDrSOOucTpTokcbbxVsZUHLM8EkKOrRkJsa0p056d26tIzQi8uuOciW6q8Uq9.2rKPs9XEL.MvPfkB.ZFce228wm4YdlakN9GOiJqF.RqB5WBlnHWkKpnfVVHYjvZnDphpEqATuIjELIaDbWPCGBY.LxB3FKEmX.SVARWw+DU.lrMHS1jQIImHGmJQPtQMXH8eit6QX04y9M9FsO4S8TWur3rVGvsIzQS3Y1zw4.dBgvCvrAPsMvtP+oXXBvlog2rP0.ppA0YTrFIDxjCwSuIPGfuOGmPwdEgEqhn5Jv0TSgMVdDqPDYvRVIsDjwQwIJmlZ6a5vXWWWXXXfNc5frYyhjIS9DjiyzWwS7eJSVLl1uioxtB.E.BrnjHRMwq3oAD333L51u8aez49BO29+Yuu+rAepO0mZH.5aaa2RwXGGHA.q4kbIWRmO3G7C1OQhDCKc5k70afPO4SpTG8n0RtssYRxIIVJ3nG8eIXaaaaLfNC3QnDRYe214rr16rUpb2yVtb4YUTeMd66zCSLWr0m587XJFG.fvcADN+d2az8bO2ySFqS9E80Vm3pRikFDfKicfHb3c.fCqxMUNEPkrRlTgb.kRCTE7TdJClTTabgdYwDWTON1bYl4kMHR2UN8+kf7dD4VB3Iwf5heR2qWOLb3.rvBKNdh+GKFAro3LVZrv.fcbrQbQcJvRHfw.mDIjqky.njTStb4F1ue+dDScWt3xcqXWo626t9tCO2m+KbDQTWKKq0Afq5qX1nEucqhapb5IWEA.7RdIuD5VtkaIAJhDqTGTiXi.D54+Nemu3L+p+t+pyiZFK.3t3a8M+Vm+ZutqKS5zI8+J25WYiy+7N+UAJsFP03XqdS88AS86KFTN06B+W10VALI1erlERV7kUsQkXTBCuyO6c184+7e9JSG0nKf6zxNL9RNMMorwRBrXRf0RqdbmGJJCyRoCF6IWK.4Fhq7Ue0Ws063c7NVFx3r3U19Spl0mNlZslqgEWZQDDDnxkMkjBcpB0H+ACf1sawyM6bPHDi0wcbUfxbX1QP4gDr7cJRTRD+SEAf.ee+AG3dOPu4ma9VdddM10t1UM.XaYYU8Yt2mY8a8KeqsrrdV8ApNcNkfI+lFyBmDpFNhmdZrgwSG8nGMollVJwYJRa3gLti2jBFoA7XXhgnlYWfZpXpUFAzXDJhgnN5Cr8dJlyc7.il9W6Z55rRBfTJveyfhHqYcjo13yxJlDndDy7PhJ1Eng5drh9.0iPQj.0kzyFvK1f9WDRVXZvLaRDshss8xBgXI.DKAwBCFLHShDIRmNc53Oeh2XInUqVzxKu7SfES..fk9oSTTzljjyzaxj3yPw3yNswz4+DVBF.QUqUMji3PBvujPLvwwoGAzY4UVokqqa6UWcsV99CW+reIm85vynYkJ2ypkKWdCfhsAp2ElXzm7+40G9ldSe.xw4dvT4KmlwwSeEyd5rerO1GqvkcYW17.XwQiFMWpToxPkH1rF5pl3eK.y1.05.A5BGQO.G0Tj2kOvgll0Y3X7653kqsFuE2DabNv43XIoZPvzC8qIqUXcfU1.nQerLBwpKQ.MU43LlEvcQniUfGL.LMXtZwK6xt7U9XerqeZv4hyuM8YnRC3zoJEK2lVarAxlKGBBBl.36zq.cr43OfwmgBGGaHDV..v11I1hIXFfWes03uzW5Kwum2y6YbtQCCiHWW2QBgnW6NsamfRr1G78+9a79+fev5vDtl0P8ZikISwN.06KkIgrlqJUp.MMsvRkJMBlXzQ9WNxnsu8mcfZc0GmyaZYoGy7qBNNN4DBQ5fffDISljAf+O3G7CF7a+bdN88.5gRXndU36MlgIhg.N8QQL.028HUisGOAHr505t0.NXLq3Fal4m9dO8LOv87.YYlSefCb.s2+6+8G9E+hewwF8r56iD.ANxyeSrTUjoobnVKPj4JNN26xhyTrD6xwl757.X9y4rOm4+dG46MKWaSqE83e2aZ.D99iXe+.122m888CIhhVYkUX0hbHteWB.T7lzgmHEb..L84mlkL4Z0pEBBAhRhANNU6KDkhA8A999TiFMvsc62V3y64977+Jekaw+Jth+n9NNNsEBQSr4ZzlHGrmkUe8pXjxfzS7XOVEsS7DKSXE.zPOzw49Bj43DLfiFJgr12sc950aVnXwkxaYYkG.Y+G+e+Ol6y+Y+7o+ze5OcDjLzbcLwDX6vL2UwfrA.veW.gG5Xy37imOG8+zt9OC.Sh04XVn7PBl4ULHZ9eTsZzW6q8059pe0u55nH7D0wZNRzQiY8vOKVljF5XN3oanCuc98ezG8TNwS7DOYHareNGGGMkl76A4GtqBfFLy0KWtbSCfVt.scbb5JDhtnD5YVE8qoZPpTIDUs5S0XXRL8yLVQI4DcUhgj.nKQTcHav1cIfMZJCxkMBZfzBWj0QBtPdgfJTsp7fPSfhUYda.3DAvNAfvw1YAgkHkiSUPf4RxF+4G9ge3vSdWm7.PRyJx11dc.rtkk05111qqZDqoAPCWIkrqCf0wNPOb3IEGuicfjGVthjyUDX15vbQfZwSuelez88izNiy6LF8u7k+W57RN6ytk2lLAQi..2XYFsU.WVRQiccHozdQE8hGSs3vvvTZxQSjv00k788gttA.XjNc5wF75z2cXZZNtnPGaGlI.qXvP1BZqBg.UcbT12oj4IkDBDEEwtUciXhiDhwfm3W0wwmAFjKWtgO5i9nCJLagd+ieg+wMdMutWi20dMWWk8uu+xJdx2KU5Jr3Pf5wMmEGaDyTlP.DpCD3Mcgf5HooGxTSABftNl0yCyvLmyfnjtxMEfDbJczdeW495bk66J6Auh8En9PmM0Dc4.Hcn8sZhua88heQb8DjiCKWQuzT+tok.zZNYxEYgAxUxEYpJeeJvzDCqUSFOcAWvED9deuuW5LNiyPVjXITfc3YnRzLnlwL5vcAOYdshJf3VA.K466uPpTohoQ7zFj3XiXaZIQvQQnVM2s17.633.lAnoyFOI9aL3apuhAIg1TysxodFpjYzXoJBkdWEBw..LpWudCVe80aem24Wuwu2u2qoJ.rMAbqAil.tRPSLPe3hgvD9n179KiMBWcSM0VNAPkoMPy3s0QdnnGqAvxt.KNXvfBarwFjwyznmtKV2SECqqi9G3.UFTtb49R+lnTbizAX2HDG7IsY1eQFesY.SDHKbrxIfcAmMwJMcFvK389deu8upq5ph2dF8vjF64od7j2aVBZKWEZqp.EvxBYssGed4RDYLqpQ1bpbXkXYN4xPx.koAL4ma1kTsZUTrXQPDAMMswZsNNtS8bkii+FybIFzVh+hAqiUMTPSwxIlHJrToR9wa7lVsZsdmNcbu3K9hs+leyuoMzQUSOznlxTWWYEzuQCYQvxIupS.dwuWQnJBk9MlHXE3DzP9xQ0bwJoAZLVVI2xsbKye9m+4W.RyOOhYdPIhF7Y+ley9ufWvKPp26Rn2JUQmFpMcxt1EFbnCcb01Y5XcQ.Hwt.RdnMIkVQd.mwLkjk9VR3AO3AGt6cu6XOboiyDIhAXfjy6hTanjDltNl86+8ezE24N2YQ0Yk5.XYhnkgAV9K8I9RK9R+cdowLLICQTrLoR1ndCMsTIoEWXAZJVYFGOAgPPa0qRDBAbrUfgPahgbwweLQp3LB73rMLwp5OTS7EgBgHvQtIIF533DC3Z2hEK157O+yesa+1u8UM.7TdnibZokPaipXfarGaXf.WouSDJi+L.fqLhuHHTGIjaWshoMP8rpM23RPd+5Bp2ShTLkSslqQ2q4Ztl1WwUbEsMAZWaBS.dx7.fi2h6lN+mxukJlkYu7jzaBiANNC.htpq5p57m9m9mtF17VBKlMyYpDK6KfEgNJZ5AypLaPDoaZhhUqJk5JlLPqwCcvw1IgvRL9LlMKi0IfjbrjK8XpXxi8nDlAfkP.Gamo.AVk6BHhkrmSFWVcb9O4JsFXTlrY6szRK0V8ZcUGGmXllrpggwptttqCYtsA.HrHTK71XC9dEzuTCLrp5rYU+GL.Hc.MOXkBvVVqRIjEUQVXfLv0LCyUSSDEuw8B.LGxb0gDQi9I+jeh+t28tGJDhAUqVc7ZOFG+AH7z.hmckUPt50GuBfSQjYFlqklHJk7VQLRduUwtVnQO6oj6ppduXutbN04kEgzyBWA.KYPzhe+G8QW3DOwSLNtMOj9OVF0.ly.fzNNNoDBglssCYYInnnHtVsZ.LhPBDAlBIvnjj0QRCH11gXBj0lygM9ZK41.ADURHBcrsCfbSZEHDhvopgiAnPHk.1nO399fCzB0Z+xufWdyEleAuW7YbF0bm.ZRSfwRnV4Qmapd.F.Ql.A0l74eBniTp5+yBfLFFHqq63s+3LPdeWHQTWXhMN7O3vs1wd2QWzPzqDb5WEi25rA.kh.pFWS5z0ld7Rr1uvt926VxYSWWvEbAr.hXvG7Ih78LMCLLLvEewWrjIA0QJm+UlNl5ZRCBdk8A7F3Azam6bmwz1FPM8uG6wd77W7EewwnDRVVVb4xkgsscBWc8j111YDBgDIwpHUsIEaxoqtii0GxGu8gMsouVV8cC2omnSH.FcO+K2yzSrcTSCi3aVj.P4BMGER1DQYbb33oGTnlTmoyB4gaEBBBxAJFAVNCCjw1wIC.ReJ65TRRInDOzC8PTrIi9Y9beFeGGmPKKqnO8m9SG+9+zSiRCGdxVoA.I6b3kiQUOScfL.0RCYAuoHxH4C7StOsJ2cEsy9rOaxC.UpTgQQ0dKunqhxqE8gbi+.cnK2LNROtgfjc4Dy7VMurjwfkr95qiEVXAnoogzoSMFrDcc8sxPXPDAmpUQylMYgkfs1RAbDn.ZBqQBfjcLQBgHhkrNIpVsZbIqRrPHfqqKwLzbbbRwpF662uel8t28l6ocJOsBurWwuyL851at8su+x47hS3WTwJlhpihwJLTMFWDEmdWt2wSWu6T++6BOzVMMrl.nA7z8fb6orlqDE4PHO.YF1kW3JuxqbQ3YtHP84skITKfkTLzYwJJvA10wZqS8ycyb+G3ZSnZSDgRnzzSrIYyRkRi3sgCPFtFm1gYMlYdu6cugKOyoMFLga9luY+y3LNi3IMB1gI.jfqxZLWKoKyoTEIlA.YdfexCjF.oRkJ0z4x1Twvc5zAc5zgI05ntZ0pnlq6X2T2slKhh3wrQJtYAgPHoDLMd3+w4UCVdkkC.OVRMi77p6SD4+E9G9B9BgHrToRQBgH5N+leKPDow.ouo+9aJGjxvZdGGmEVe80WD.KcQWzqd4e7O9mT7zO8SWuFPQIFqXN.jCtBIv.0LAvFQqFyBOC0AikqndIKX0ysQ5Pe.jwZsAvFtEK1rc61MWc0UWWWWuCWi8cka7hT.HC4YDuFTyu7oe54ApFazaYwAKFC9zuLhuHfcK+c3Xl.vlb.38su8EfhRYHxrae.L3ptpOwXlWUFk+YA9uFl.Vh70hs03en8su8gq9p+SFmajHSM.n8Q9HezXP.+YxjqpUqhff.YWmLipNUGuIb.CTudc344MdSkDeIDhwwW4xkMN2UH.EBBg.T78DgKt3hALfeXXnuPTxWHD9sa2NPHDgBgHFntwRgqSmNoEBQga4VtkEN2y4bVDdXwZSLbuTnwJ3htnKJ.RYh0GvKF3o1n5xRV63JZA3zRAxQGLuJWlUCkbSJN..CeIujWhussM+fO3ClD.4Was0l6u61u84eAufWvB29se6K.f4YGdt621dbSYsOjw+lk1z+EeMdnTGZB.dx0sogy7xg.XrD.VhHZl0We8L6d26FPUClyRKsYYG4BdC.vx6A0z7Jkbm6bmi2vdj7BCFLf4ZL+R+cdoSyf3DLyIZ0tMU0oJVo3JXwEVf..LLL3s.9lbfBLyQLyJFjHyyQDKrDQYxjIxwwIh4IdHGQxywDBwPQIwP.dHXLDfGxxMlzn4leN+O4G+SFpxYpAfLBgXl25k+VWPHDqjJUJya61tMqFMZX8ttlqoLjrSVG.yipHqDpZLB.cS3VJFjiNx0noarDlFf5aeH.F.mR8Ap2wM9bScoYHRFTe.f23a7MkkHZd.rhILMXl0uhq3JJBfkqoqOMqV2peIb731ZZpgftK4m6aTNgrDAB.HB5xOinRROK4O8O8uN1.NGrDVZ5FzSTQtgBG6uNP1rV52065cmx11VqZ0Mc91VqEmiq+pe+9DyLZr5pRlIwSL00ouJURdt57yOO.OwXzgJFiT9YCTq50UWc0Xl95y.i.3QPsYt.iQDgQfUx2WHvRKsTRaGm3gysrPHLBCCsN5QOZ4e2W4uqkALLwDoUlqtoYxidziFUoREo70kfkDu1o8GTcwwFDqTRp18fLdbCT0nI.ZryBmdcfZqpjvpOKqeIOyUmSF2YN6ocZm1LNNNE7q5Gug9jmct6ce7bdsDoaHhqIOMQTF.2zJPYS.WC7s+1e6PhLGAzXncLqWMjf.QKSpXE4BZPUmOUudcM.n0tc6TtLm8m9fOXNH8SyXVkjVwb8w2+kKWNzrYSXoLieWWInoBKAAFD.qwp7f1NNjmmGIrDvRH31saGW+eHAJjTmU533DppmVd1nTdOLHBBgPyzzbrrJcbbja3TvIAPJgPj95ulqO667c8NKr28r24uu669V5h+i9iV9QezGcIXp.VzvSArnYbuNgFvH1TY6Cfd0LMi6ET1Wfr9+0gDzEuDtkpAIy0cIhVExsE1.l4HTyPaG6XGo45bZ.GIiFAxBc02EUUJTXwokP2+lFjySUu9ONCSjR8XZyrScXNlG.YLMM82647b13e5K9kaTDnYcYRgo0g1S1ierrbVD.ay.3Tpw7SG.mLj9sQ9pNNAh8X019.1ddMZT8rNiyvC.qpCz59bb5KDhdkJUp6cbG2Q2Ymc11m011VmFSlD3wZB4GOAXxlljdY.sJxDL4gjMEEa0p0JG7mdvY9UN6ekne+2xao0+ee7Odc.3UFX8JxWmg6d26lN3AOXR.jcIfb+vG4QJbRmzIMijFyzbAAAKpzu7NFLXvIjMa1s633nqjESrVBY0zqCHhFwL2Axa9Zpoo07a8s9VMekuxW4FNNNsAP2yTHZeOG8nq8.OvCT+M7FdCd0pUaM.zsLPfpUqIS9WNAqXuhXte3O7GN2Ed9WX1u888sI0DjZKDhV5.scYtuhdXCAPTQfT0KhYQcrzq++9+8U9++FtgXOlXIEyRhM90kg7frr.H0ZquVh985S.DIDallvAAAXznQHe97nd8Fv2eDuE5lLtIC0TUiANY5XG4zW2xzLTzYOBSwEhACFDsycty.EKS7gZ0vB4meqUqVsZ+ze5OsxEdtmaUEichkKy.Cfg2yXS5DAdddA555gBfPGUr8RKsD2rYSF.Xd.sMTrA6y7Y9L4tnK5hlA.yqqqO+8ce22bBgHGTLv.5nG6xchmdlNPus79+nR.9U2EBwgdBM08KZ1ZMI+ytQBbvoZLU5UDoppznpPHx3HKzIsAPhm+q7U5+4+7e9dXxtlO..Pou+3bXyACLObw7PIaBc.c2IShcQ.LmAQy3JARI9v9j.Py22OYpToHGaGhRH8DB4jvjwQwZ2OdppZZZbTXHywutjyD.JIbEQDGxLEBPgVVB111VBxhRiDS8tLSDwmkPf60wIdpuQW5a6sF9+9y+ECqUqZfPX4GDDzud85shhhZZYY03ltoax6Jt3K1ySNTrUAvZUpTY8xkK2Fp7k6.H5vp2iO7gObxcricj3vG9vQ63YtCFaf3FXRAfrl.EpJk52r555y344kiYNoAQQtRy5NtvwgRIDPCT9aQrCvO.KgQn4lXh3zwV+hHe8wX5pi2hXYkSAbx6yPl+YX8kv.zbSRJJ94mjc.6ZWZG5PGJ0G5C8gztt206Joyl7G.LKjSEaIShVvUlGrfh0fEg7bNCHyeEuYulVJgnW+dHet7nYylX3vgiYtjllFz00GaxqD1r9pgZp9VVhvojzEWsZUoKUk.HJhU.4wQDnPFTjBXOvLSD.YHMaQlAGRfBMLLBTzXefiiSGSSy0SjHQCGGGu8XY44BTW4oIqholLc7YCG4HGAae6ammxjVCA.uK.5PaVp.YE.EtaGm4DBwBPZ5eycW20ckaG6XGjZagMRswmFVtb4tk.1npDv33bna0TqAN9oFf33wjPl6J+McG2wruvW3KLdUiNGybta6NtMsW7K5EOX+6e+sdcutWWS.zrDPqpS1xYprIH01ARejILxbQl4kI4lwYrDCUZ+eko9ylIJJJM.RVqlqF.mHQhDIhhhjMVHmlJyfYBDRlLEBB7wTm+E6qWSRUEyfD.3U2iEkDQAAASLnWFQKs7Rb1rYQPPPTmNc3985wLHXYInVsZQskazIlkOVLKiQGoqq2utqaqRVVq444U+tu66147O+y2F.1F.tta1fliqCD6F.GbJFgUudcpXwhwG5qsLPtUkmEXToREyUWcUihEKNWIIv7..ipVsZWgPrgsscy33aSSyV0pUqOjLox2chDvOda5+iG1..RhxHMpHA54jNohY57H0y5JMkyL..DQiLA5VyBsf8X1+FA.MUbVAn5EfHZYU7zJPlKaQZ75xEKnjNwXvk52uepLYxjD.iMj+oYRRbdrUVYEzngj6YrhRHEJTf6zoC..WHegnd86ExR1yxLGwS2xyT4+BDBQbytie+XJVexjzXq8I4VkqO.5SL2sjkUq986u12869cW827272rA.V0Dn08Xa21xxpittdGOOuN.nuxnVCaznQzJqrxzarKx.HgKPxibjins8suckYn9+k6dyiRRtptS3e2H2yZeIyHy2K6pTK0ffVHg5pwMFPfQHjvijLFc.iMLZPryXOF8ABDlEO1vLiMxZAKrFi4.1CFIKLivrHwmLHZr3XKP.B5tAYo1nVkPzcmuWjQtVUlUtmQb+9iWDYkUKgwiQ.Z9dmSepp6tpHhLha7d2289aYrgZLWFfEtjW6aXgOwm3uLcvF9G95dMuldeh65l6BWzlYt8m9S+oa9pdUupVyAr0laiHCuvOt+zGl7S0H7cqvOSSCCcJSSDkvFHgqI1C.X.xisfCZBy6pcJ.Lr31zlNB.huHP554vbnDVBl77yh.Kx00r94xHHWMr8ZmQ.PzACFDsZ0pwDBQz.myhPPb1byNGuwlafsQ8137+2QywH.e6b47JUpjev7eleHiNfwZklOk4+7YCRfY..ee.O+g7J6ZEOGs1iAFEKVL+gCGN4d.ZBfZ+CG7fUdwWzEUFaizjF4.ZdXCCJ5CfAA4+Op.fWv8Jdx7+WDfpeJH8AAnyOGvbe4u22K04dtmqE.FhbnGJMtfKcO4IOYucsqcsCMlaI.uZ4vPTZbg.mjdi+hNd6mIie5QXx5i+NFXYOXzRiNDQsHxtUoRk57e37uvg.43J6rBT+jJViOPdOjwzQ.Wfl+Q+Q+2aQD0wwwYnYQZXoNrB.f22y7Y5CX6A.+inTvHbS4nCcnCQ6cu6k20tViq9X6V2SVQWxjEjJJLh0ZH2NIr7xd.n+ryNa6QCF0D.a9w9K9K1DvtE.5VbbhR6kN5QOZvKGYiUGH1YbFmQTDTUVjEQhDIRbX3Ne5jISlzwwItPHhBli.PiQER974I.F9FqK1mHxSJkd4xka348bet8e4+5u7ABw9FHDhQGQo3csqcYcwW7qMZoRkBttWIZwcZKho93exOdnpMOCQTZl4D+R+RWZjGo7i.SGKyNRbthg.vqbvlAA.AikqEoBPzfEUR7I9K+KGy+See+o.vTDkaG1SW2tci..pSmtjPHfPjersgY5HqFQiFEoSmFZslMEKAf4wZGQXgRFAiku0Uq0aQAVYI.05s+1e6sAPGsV2EjoZuZstM.sECzhA2hYzhHZKPncpTo5.fdLg9fvPobsvEEhcIWxudZl4YeguvW3xZOOaX1.U9.9Csz25QezYKTnPRXtu5mM64X5R73IslipWudv6Y19aFzQC.z9U8pdUsPFrI.1rb4xsymO+3NX.fYu8O5maAhxs3q7xekyCiiLMcf9Jj33G+3wfQ.KiFTrzfmwq9iyd19Ywvr44iNYQOy..DwAHloXeKGWq0AU.21xEfe5O8m915hvBaKto5wzMx1C.d4bgOPV.ayFVJCjv22O4gNzghS11QAPj.DSDR8FZznQPq0T4JkMZLBMgkyAffhkvpsQUhO.7777FwD7.AOBF6olX3o0NAwYT+HQhzGvefRo5GTzptfns.GD6YhoZCfNGVo5wLOPqMh05e4G9uXToRk7.He.fXwhEct4lOsTJWPq0Ye0u5Ws7ddnGZ0uzW5KsJP1B.vdsBEV.X4oB5pPjiiwk4iWMP6mVc0U8OybmYXx+i.VYv6487d5Uxnt6a.joZ4xkKCfpDY27D85MHnckwYlSvFA3MM.lN.8AypLn.HsccjDXkjXowhyVXxNgcx3IxwDEm1zc07ahn.4ChiPzxaS2pHHSlnvHZcwQ8+06tx5quN..+te2+IPCfq65ttsKtWlwz2JRIy4IN.RdcW20EVTjPaGbrcchIRXgYF8602HP0ANUhiiCN1CeL344scwZJo4C...f.PRDEDUQ7YlYlCo.V3BcDY57OQjOQjmRq7XlGM2ByMDF2uvXe6f5v.clYlo63CzQru80gHpMCzpToRMAvlDnl.TqnQi1gA2mHxG.QbccSq054ylMalGtYybLy4dYurWVV.rDxFfnIy5aD.7WYk8MD.CJTGCMhLmYykquMMyFhEWb..5++9dtmtBgnELE.oAQ42747bdNcxjIyHyyhbwDBQhBEJjhYNky1qEXhmxLwyzGKJldxwXu..fbAnK3Bt.KhnnHWt3AwkQNy8bl70cMWyvW6q801y3NLnmy1EBX7iZfLzI14FiibO2y8DGFXparI1.D4EbriAiKxE0xxJpkkUTgHuU5zoIe+wxOW35m9DLEzczngga7zGAtXnixYH.Fv.8Yv8XdbgQGjMS1AiFMx72IzUJWqCHzoQ85aQTtlkKWdyNc5zHuPrIfeSkR0dqs1pM.5v.8.anAmEaQDQIJWt7z9DVrd851dddxK9hu3c8M+FeycAjMuqYy5gTayBELeF.f2QGWDi83A.uyISFOf7iPACkJpE3vE.XyBOqm0lmy4bNsBDH6nDQSq05EDhyc40We8kjR4BEKVbt63NtioKUpTftIjIo61Zzw+trE7eFOB1HUln.HwREQR.6TXYj5QdjJgalkIh5SFzq1rDPSnljJh4s.PzSLtwCYmoToRyAf4Hxdlf7GhA.pd8MFiHNLARQCGkJUhKUpzX8fHDAIgU5EvHj5iu3CPUxVasEXivT60tSmQLyCAPelQOXbnPCJR.0mrB+dzmHpKQT2HQhzNSlLsAvVDgPTH0hMhjdOl4gx0VC.HZjXwR1sa2YRkJ0BunWzKJyIO4Iseaus2lcIfEkR4L.1wKWtLYt2X6EjWl2xl728vdFidTufBoMbkU1+..zawJnCvJagkLm+J.cTpGcvM8Q9HVv1NE.l8Sby27rFGkDoHhh+pdU++DC.Q2bm5xwS1Pyz1ywZiHATqIT3WivLiNc5LJqCF.H5gULhmbwIW+yHlt90A7QI3uM5Fy5yL6Wd6XowEbFFDbmD.IqWud73wiGSHDQBc+FsVCkVw4ymmSOUZyEJEV3LhPPSPYyw1GlBn0OnXnsILlVdcXl69nO5iFl+eWirQPsYf1VVVs8YtM.5XYgNQiDsimmWGlPGPnW1rm8.gP3444wDkKR+98S.foufK7BWrb4x1e069tkv1V..6CoTKJDhoC97YkM6yvG.9EGOu+37+Yf7d02lh1lFFtBBW6biR.atmm1d5ZbXGDIaIDmYNIxYzUmcsqcYbwTfocbblA.ybOG8nSiRSpeeq9jw40dBc7DxFaBpDOYJ7k8HfbAB0Y4VjM04s9V+iG.ThWBfV7e6EMgAb7PEL.BzE4yu0u+u+ePKl41Ly8EBguPHHhHq0VaMpXwhV.tV+5+5uhHjOEjXcIRreACjwGnhuMf+hOlywN95SlFlEvVGwvJHAxkKLQuHnZUFAPK87NuyayezO5GEHROtaATnuQiI.CbTKrDhZD5xxID.Ixt8joHqokoQe+u+2+XQXJLQLFvhAaYJjJHsiC.HtfTx.vmYeekR4oTpQ6a+6ezm81+rCTpC2GHaexmFbGe96vCnLPFXYfN1IhtBPrbaKjXS86eEuoY9u9A9.yBylkl4G7C9Aod4u7yKJ.XoTN7j5uWu22a5c2CYPOjO+PGGGSlZkKGJTVishrZ0pE.we6zVVVSyLOMykl9tN3ckZzHu3kKWNZiFMrzZM0uaen0ZzndivVsFffSC8Izpwduto6plM15GzIVurYyN.f6FrA0lroSoM.3Muwa7FaJDhsfw9vZ0eP+lfwl.bi0jxFVfZP.MXla.Fa5ybKsV2VJjckh05yboAZsdjVqwcdm2dzDIhkF.yGQHrYl20G8i7QOMTt7oA.4t28ty.fYxkKWbyyzRLvb9.KDTR7MsvJvBXQBvkWEve0PN0BzIaErEvxsfYCt8A.+i9Q+nDLyy7RurKad.2E+S+S9SWD1XAjIyXa+a0UWMr3cQAP74latfM6c7nAa37m0a7Xx2amn6vUHfbQXlilgxDGnZbjGwAri9+3+wakxCL5i+A9.FDL.LHeCLDXOSHpoB+.swwygYOlcY3BhYN5ev6+8G2xxJ192+9ixttQu9q+5B27dD.DsUqVQJW1nACRgjfo6TAu2DbwxrAoQl9q5S.dbvlJdSuw2znF02viAaRlhnQFXBy8.POOe+d.TehoA9Fq5sSvlE2jHpAHrAy7lFzeQgvKuiPH5.fNRorK.5+M+leygJkhO8Se2Q.PRFXNhnkmd5oy8q9q9qJ+hew+pB.HeYfL.UmGkwTHORfLYBKJfOZzXrUF9POzCMpPgBAEL4Di9fevORef7AcFoRcrLpBjoJP4MZ2tcmgCG5A.Khn34IJoQrvwzJkZ1idziNqTJmE.S6BLEvIRiZHEDHAVXgvXteVFeYArtE.rLZcii0MbC2fgVe4yaEffnDnRkz.Xp69dtmz.H0xKub3lfl75YBDwTvGngeA.95u5qN37fn4pfX.4CngHEKja0W8Ue0wIhrBRLaHkiBKXxNPxViFMvBKrvXN8+Q+neT..7T2ySErugTDAztY7zbmR7mO.4yL6oTJOBViHhFTsZ0ABorO.2EfMIWQT6Ymc11x74aW7vGdKl4lfPCgPT6QdjGIvwH3FJkpoTH6vLOPJk99v2BfRFMZzYeJyN6RNJk8G9CeS4APtrkQF.6PcxHNvJDPMiyR.LZBgKbx+3g50GBfAO+m+yuK.ZkISlFlyeoZDkeiACFzE.dJ0gh.ajPqKkzwwIoMPxhEKlDlD7RJpfD.KFVLtIKbBvSNR3ivQAPFvv1NbtNe1wIrn88NsS6zZcC23MFH7vtaATnGPgIoGAArJATgD.zRaersdkufWPzfBjjzT7xboOmy4bRmKWtzUqVM4HOu3kJ4DE.QpVspkVqsle94oHQiB.lme94Yaaa+f3Te.1CfLPTmog.9CAssi87e6O7OrCApCYP2WHJ+ZCfN0pUqsTrVak5vFZYQTSk5vMHhqAfZTtb0.nM.iM8CZPgTJaJDhsDBQGl3AAabhHXEqWudgBA+x6Z0UsGMRm2FvN61n0J0hEQrf0q.F+955l4+AFA3LBEwHrWLB4yO.gc7sToFDQaPD0N.4sQEBwT.km8LNiyXVfryTnPgoeouzW5zvFSCXOEPkz2xsbKIJrcASdxP7EvNJhV9H.UR.fTUYdJ.2o3JbZjGIQtbwBJTwPToR3yNC5CWc0.cTvIBPt3.1Iu3K7hSCTd57ma9YZ2t8rL6NcVfj1Ay0s3hyGPGrbw.PribjiDgMzmlZznAA.Ja1rHb5NGGGzsaWPLfSoRfYe1yyiyjICmOed12HSIAyqYED+w8Yh5APcjRQGBns2nQcDBQWoTziXzkA5dMWy0zQoTcTJUaOOu1uvW3KbKoT1B.MkR4lAqy1hHpsPH5vkJ0SHD8SkJ0vToR4WrXwHNttIiDIxrW8Ue0K9MtmuwR.Xd.2ogMRBrbL.Wq4.f8jyks9DyoA3YJPbkg.XfYysmnOpgNPf1HSlsN3Au6t+Nuk2xHtTonFp4vy.fYsssCbxpxIM56zxm57Y+htXviO21SfvgLtiaJQb.DkY1RoT9oSmdXYf9.5A3DXH16NZTfOJV7TQvPvwubD.D48+G7eaxOugnlJdrXwh6n0w60qWLsVGE9vZGPNBDzZMOQ9+.fIozHtqZslkBg+7yOenKg1Al3hMXiXotI.ZIDhsRDOQaoTFPQYpI.u49jxMXe+MHfMCxUaS.ZSWW2lRgbKoXs1LWpqVq6655Nj4RiRjHACfXT97yjISlLWv4e9hG4du2cAfBRozF.KjKWtf0OKGAHOATH3y8lQBx+2BvA6Eic3zQ.XPgSfdFQP23pbSm7L5xryn1saStF6mOA6vofMlB4yGpELyl278yt28t2Y.vz1lB3k.33wBno+SVla6I7w+d+fsyfwEQbTOeb.mD1.wbWFwPTDEkLvGN.Rb8QfvXgGK7ke7FgPWNEBnkC.NSl4yB.OU.XGz831LwkKH1uqRc3J6WJ2nTVS.PwCWrYgBEBU72PWfHDFtmJ7gdxRQSlDZ3gEEHAaBfi..ZYfnQ.hWxLQCQ4nAv0tMf6VY.5VYOXHhCFGcLrqBg.Wx.N7EBq4E.ffY9zxQzYTxHxf4zZ07fnjvGQB5qLCBCO9wO9fUWc0dHnCxBgnF.pp05JBw4VEn7F4.ZUxFcK98J1odk5sNmy4bBu22Elm2gzJZF.L+i9nO5r6d26d5r.Is.hVBfKVr3v+l+1+lNu6q9c2BA9NO.FDXOafYlxQTLWymoEPNrLJkcYfxK2oS6LO7irdly4YbNY9W9W9WV9LOyybtM1Xio50qWhvJJ6yLrHvAjZHbusg26gg4LDSf7C5VAC.9y+4+77kcYWlWPrbmfBizwHzg6yGvMhRohQDE0flOxOfFSC8YeOvvmHxWJk.Aw2ZcQKlIqfSL0sWOqjISDI37OjA2SJk8.P+FMZzuSmNcylMaqq5scU0to+7axE.kfMJA2L0.pzZNf9at.XLCrNwW+DD.vJqrRXmpOUGoHdPbwR4.Duneieiccq21sYS4oYACKXDEuV5h5FhBhZLyUIh1HCPSOft0CQxTdvm3aeB+UVYkQE1Y2.9Y86US7tRgn.ESrLPppYPZXgjvcLuUs9JekuxnWxK4kzElEGBcokAg2OrAhEAHoFXJXiYgElGNX4ie7imakUVQ..AYD70EggNNS4xbZee+DtttwYFQgo.iVXBnYFdgN27ywazXif6GjYyELFAC288MQhgI2v.DMB9X.YQ8C3ZsuRoB5VEF8e7U9er2sda2ZeoTNPoTduoW6uM82ev6HhMPzCqTQIhrBndgO.7u1a3Zw65c7t..rX.KxDi6AvCXB8lYpY5N6rytkRoZXYgxh8UPCWnfABn0w1TXvaOATtZ8c77cuDvQsJ.PEsgEFAKTaa64EF3WOGy7bG37dQo9B21eSzf2CXlY+uy25aM5W647bF3BzEYwVfQWTYLr4G709Zes9m+4e9SB8ymnDYrvmQgttTLr.hh3HQP7SB.j7C8g9PItpq5phCfDY.hUlYpQiF8VbwEahbnNJM2l.aFRwK+IN1guqMI55R9m9mdMy71e6u6P5OrbdhVjAlKfVNogg60yvF5sFj.9XgQLpVqMnDDLxmWLVLWQ.+DNk6MLCFKL+BzFargoKYiSTmGAiVkLwZhlM9RfFwfGJkROkR4IkRVq07m81tMuW9q7UNHDJvl+u8AfxQUJU7u22+6Ecem69rBnDlW.7hGbK2xsz+BtfKniTJa9e+O5CT4+566OzAFep1MCPMKiPs2E6ACv5+Xs8Wq8BDooghpFzSZi3v0NMf67YAV1k4kHaxTrOSxxdvj2QHGtCoBT3b.CWBXXJCsfNUKT+WDicfDDrSXrOadfYu5q8Ck5pdWWECfdHCZhJ1aB3tUdfNNamaEv1vuNZwhEiUnPgv47WlYV.inBmOz8upUq1hKrvBKZYYYJboqaRuQdQAgSovBi+d.B9eku7W06085uBVoJxloxfOYrIyIchrwqIHkRbWe46Bm84bVrOCeKPiPvOOQ7PFz.3iAL3AAEj0RYlWKNXNx4JkzQTJHkxHJsJ923dt2DOuy64FaMoLxgUJeD3JEv7buNLwYJhnSBiv3WFl3fsviKhbdLCiQD.LUdfE8Ax4BrK1jyjM.lxwwwe5omdyVsZUQtlrBbQcGGml4ymOr.1gEYHDUF+h1F0ABhy1CPzZ.Ia.LEDXFL.oQUC5BYiHa5OXvf9wiGuaNf1DPamPZerJ.NtcD.2DRfzEYdZxlV.kMysQFQ3e1G3AdfoN6y9rCoe87e6u02d9C7rOvbXBA4Wq0QEBQDsiNB7AQVAj.bblY7Ni8B+OM4nxDfGCLDFMHYLRlBnLHC.hMhdAAF9fvHl3gVLM.DMhY1OL+OD7NjTJgxDqQeguvWv++0G6i48W7w+3ijR4H.LToUCkh0FA3NToTcYha85dSut5G7NOnI+rcNmyjBx5igJ2mx2SX67kW.FKlWxFplOC.fiipqXeE1PcXUcoT1HCPiJ1XSXgsfyXat+TkefedGuE9YIbeGgNtzrDQg6KIxq+JthQeha9lairXCDAa.mwyQO4ZpVqBDsIPhFlBsOOxgLnjcd.WAanM8xTNZY3hk1ZqsVNUpTK3TxYJoPFy00MxHOOh.SgjZlA34maddyM2Dl6KLEDSQiwUKEp0RzHv7.N.o1RoriRo5Jkq4A3FQqTw61qWrjISBrcAJB0ZQuIx+G..SLeIvXMLirXvf7oQLwijRou4mU4CfdBgn4O5G9ipbgujKTu95q6frnDJmuFfyFKCzsZdLD.P8cUvyyyOH++Sc9Ffs2CvTKCLmEvR+K0pM+hKt3TTNJBbMnuKXs+QAW+d+fG7A8+UNqyx+88m8mM3W9W9WtyAt3Cz9o3+La+v0+9cxCzeh0edxrfp+uqwO0HLo..g5fXVaA.qRLSnJXTBd.4FZPbBXjyvi1b+aGRhguTOBYP+7ltVt0sbK2xDNR.w.HhErRB3NsTJm6l9Le14PYLKJizLywti63NLeFyA..ZksSd8TEf1mLUUrcVzjbHQFJSxu6286lD.IphbQcAXhxMPPTG3NVzx5WAqNBqCeitNjwJni6wfMBqj6jUeNhcPQYTiFkv22O.xnTDvSTQZKvDHr5pqN95iYNhRohaGTLmhEObD.3cHkZ.bQ2B4Kz6bNmW7f.gQBXIyjAAHXIdP2TS8r28tmhYN8i1tcx69nGMhVqYKKqAG6e4XgPfsEVFssgcmBEJzG.iHRPF3slcJafYXGdFfxyvLOS5z6dlm4Y+LmA.yblm4YltTI2jyN6rwDBgkVqIvLmOWNeDHrgBgzifk2DBfnuYCsgalXrsVN3xtrKa..5wL5BezonR0VoTMEBQSk5vapTpMAnVLysjBYKl4Mkx0ZvLWuvZEpSDUe+RYckRUWoT0TJUU.T6Ftg+zFYssaN27y29zO8SuqTVnKQTegTLzwwgKpKFSWTOc2tcWTHD1kKWN+e1+y+L4eym5SIyBjCtXYfJyBfjah7QQC.bBvqrx4wqrxYGHTmS1w6wP8ePAi0S1tDvVep+wOSG.LTcHUjO+G8yOkRolmYdIwZhEUJ0bgKtUA1ILNPyRQy.DY86YcqUVYEK.PECv47OGFS1wDKfhQ.PrpvNApfjvEoAxmFvNA.rdIWwKwG.CVZm1jbvD56Et.jNuwUafKLoWCPqt5pQHaJdNJWPWXoj.HgimWhQiFEqToRwXliXam0RHDVBg7wTrjfN6G1U+QLYt2SVg10F0KuwEa5YJFG0EL5bsWyGbKl4VJsZKsROYGY25V+eeqsXfMzEK1PJk0+3+0+EU877pdXktJ.pvLWF.tR49bAP42063cUUJkMHl2j.ZFFmt7xY1JW1bc2ZqsFzqWO1xxJNyzLbIdgW5kbIFcLHKlKOxOEBfx95XOX8siiBdm4ndHbCmtX.pg9.q1CHi48XSQr2xlntetO8M6IDhH.HUVfoIxdlC7bdNy4lMfVNGQMGpfY.vzOzC8PIgMh8ad9+l+rTTwH.fitGS5Qe32+Glg6DtLzxn+G7p9fifY8hDkM5LxzVVwSkEHAJgn.adphY9jIl5GB+5BA7v9s+1e2Q.P7a3FtgDDkOoCyobsQpq+5u9j2vMbCgBY9jelI.iaQ.XzNh67u+Kib4Bzfo.a2j8m77R9AECwiHxaiM1XDALhlXNMhrBQbUaPnELwFMAPSl4l.noVqaRDs0cbG2wVLyMe4uxWYiC9UNX0hZsqVoJIkxRJ0Qpn055DSadtOyyssPH5533zUJWqiVq6Q4nQu3W7K1RHDIKp0y85esuwLEKVL2q+0+exFvd4J.y4ZGHHrq+XDF9ISv2+nFDnLDXuCVEnGbQW.21.nUYX2hHp2H8H+ie7iGSoToKVr3TLySci23MlxdaaRcJl4oxBLEfLcMfjEWXr.18jgtwFVrjnXNDMPTyA.7zL2+pdWWUa.6lYA1.UvlFzkftNFZLMgN5rGywaAPEJT.YL+6iEm+fe1HkMEha5DIRLS.MRRoU5XdddQ.M99.i.gL2bLHeBvm8Y9kbQW.TJECNbsSLfYzWJWqC.ZKWS1hHp09BbQuhEKV8rOmytBfUkla1pFaPc4lDQMEhBMAiMkEjaTnPgMJpK1HKxV+23232nN.pxfpdDkpB.UwwwohTHqbdOumSU.T6HE00EBwFfMMHinbCCtWNEQz7rwEMx.i9rLaNfT.4Gi5Vr8ZjSjOv3+LD.8bvhsLBAa1lG7fe41vPCcNc5zTylMME0wEIAxNU97OyYAvrEKVbZ.jN+iU.X+E4Xbr15.QZj2nAUrhSipXr6hP4n3YPF567c9N9.XTIr7.CsuJ3sG.FGGDfaT.DWgLIHhRfxHAfAUzA47EMHewjv7t2LG3YefYfYSughGYHMIrD4EzNtJw3hkXdtvA5AAn.DYZhoCn6UWCEInV+J+J+pMgQPK2TJkaR.aPLuI.13Q9gORC.TyBVU3sWyrL.E5tiUAPMkR03qbWekMeyu42byCbfCz71uy6bi27q6MW2yyqlVqqKExl5hGpyvgCGQLEE9XpCdmGbgd85s7u4u4qNK.VF4w7.XJwXmVy7QBm55D6LtyfbykP+Ewhg4C21wwo2m9S8o4C9O7OD+bEER+Uu0u5LRobFl4o+BeiuwTvEogCRCXmBnfon+1H9d1yd9EoPWS.vZkIPlL.hmCHNfcDhH9o9ze5CAPWTFcgyNzXncrw6iiBTC.K.oUVfHlFy6lHnQCyPDMKbMMdHUpTIJUpTLoPFQq0Q7FMxRFlmFAJnwj7latoePyq71dMyfhMYgQDFSC+dLnwHGQq0FGBs3g1nXwhaxLZlLYxVRobK.pkTtVSl4MkqI2fHZi0BbSzhEK1fYtN.USJWqgPH1LRjHaM8zS2QHD8.idhBh9RobjRqXkREgHJoPHlA.KbZm9os7+3+3+n8Mey2rswttcVF.KTEYlBNHAbfkT9rvJqbdl3oUeLwZL.72CvHfE5WEna4rn8RKsTW.L7rV9rH.j7o7TdJyJWStvK8RdoKEnuUy9zNqyepin0Itxq7OJ1K8.GHp990Qd35e+n.HpCV7wihq++aFOAfvjBw.JF2FHgqwNrhwrgPoHXC+4M5c.CylUlrCuS1I5ebmiXBfo0FA8Y2HG1K6v6cvfAqVqVs4CD.p1qIDMOrVu4a6s8117FuwarA.pJDhx.nTFfZULHUnmRoFFHHbmRW0dRCJSlDFYIAPptc6lNUpTFspHCr.AFkMI97b2+ys+8dn6s6J.cOQAzCEGihfvpGFXauFKjM.cIghN3h.XEl4SG.mA.1kiiyR4ymeJsVaPGjok4lp1usFXzAFt50rRkJ0yjISYhHG49kNnDphsQySG.z0Fniq42GXr0Hi4BN+yyLO0AO3AidQWzEMJ33GZeaa.fMVBncMyuOgEQLXgznJlmMB6j8cdm2o8a3RuzrkLhXWlQiFsL.V7Dm3Dys6cu6TNZcbe.KXb3UeXR5yyITp1MPA0ZhpIyf2N9XeRo2QLBsoGCLj.2AfZ6551x11tM.5ueozujMh.WDSoTQfYR9ADy8VqPgAAV7EBEHVgPficr0wTSkJpTJC4LdLkREG.Qt0a8Voq9puZlHhUJUjkVZoX0pUKpPHXGGUelolBgnV+98KkLYxSlE3jksgCbQif66iioCDApSUn4Be+LALThJK.V49u+6+zN6y9rKzpUqklYlYhq05A.ntTJKA.8gNzgJu+Kc+a.mPDGr3Pf5ixC34rsPcdph04SziI1Pydi.bzXBfj57XJ3Lt6EoHhhA.1zA.oQA52N1L7ZbRz1DpCNyvLOOQjM.JvLuJ.VEFMjYAOOuoGLXPpFMZj.fhID4in05waxaGEKw2mAQLGTrDrMRe5SFATNrC9gu2Sff2M+Iu4AulWyqoqTJGXbtDyw1mYu+rO7Mz+JuxqpC.5VnvZ8TpiDlXgEQTDeeeqBEJvvfJElHhBJPQrfN3EUoTb5zo8VXgE1gFYn0Zu74y2ywwo4vgCqs5pqVMGPkRghkYAzFdnGb1QWDl74xo98Qx.DuRfH.BfkN+y+7ybvCdvEhFM5LvzUQR655s+y8b6SFqstsVq2RHDsN4IOYy8uqcsU4PQ5cULHvdxO0y8+dGiQXB1YgzGS2p7.obBPDGy772vMbsS+Nem+dQ9g+ve3Vm9o+rqATor.ntdUzFG+wfbxSEwfgBc8B.HCarYSa1HJhyiIT0eNPXtCPYxbsa2N8TSMUbW2RQ78YqQiFgnQM5j2e+cdm3+vEewSlLTf.Zx9Awef88QfHJFDGRC+pe0CN3BuvKrGybWoT1UoTi69IQjuPH7rA3CGHzvLQCsfeGlosjRYmhE0C.7w9KTHlqMRAWLkRoRFHld9.vaMozKvgRrBmarRkJcylMai74y6d228cqufK3BzXmnYJTzB+w0E1IKpQjk.hWaYLMphkgA0jEbbbxIDhYUJEIkx1Ly0HhpwLuQ.cJCSJu2C9fOX2y5rNqwtLGdbRT+mSiSEYIwsMb7OzoBRAKDGk.8O8O8O0+E7Bd4sApr0R.cpsWzeB63FgGmLF81YxFEEhZWA.1M.Ni50qeZKrvBRsVurPHlUq0o.PLFHhocqAwUgcZMzFuLNIAQFK1cxNpN.l6k8.yckEJLPoTCBlyava8s9VGdS2zM4CCZSFO2YP2LGBCxk5EhfN.y7p9.V+W9ses.HE9y+y+yoBE1eDs9HFWLiPbvicLQykGy9NNNCymOeWhnVZst9W7K9Eq7VdKukRvTV7JvjmQnsYdpyoL46wVXUDCCwTPiEAPA.6cyboUzZcFgPDiL5K0l.n4e2e2e2VuhWwqnqiiS674yGhV1IQ67jBy4uHx+bxbMSfsmWZg.5LEiHxOKP2CcxS1dW6ZWag+3Ryw...H.jDQAQkbnErvVPitlq68.f0iI.RnEHMzlhgDr4pkXlWvlnEJaVSdN.rz8ce22RG3.GXY.Luuu+TkJUJTaWlj9HfHhJWtLxlMan.lyL.SjQXfAFqqclhoPXH3w4mZPyCy8HfA+Se8uwneqeqeSVoTb.hQXhngAnjyDmF77OP2TrDBgELMUL5gUpnvfGAOhoADYP9YwhZeoLuUNhh4ZiXvEwzFAWuO.Z544UqPznUJYlaKTT0ahchxdfsyI6TGVqBD63lmMKhbHO7QA1ksCVaHBQTOafltYQcTF0fIFqEL62Z3x.CqNgC8fe9iroIW+KDsoyGfvjzA6WbDLy6uwDW+StFv1Gm7HFbPxG3AdfoeFOiyeQfJ4JUpzJ111mFLHlytXwhKVnPg4.vLZkZJPVIXliLZzPq3wiSS3rWg4Qbpq0PgSpQa++OfA5RfaiIn8LQT+8IDdt1HxO7a9CiGOd7XAG.O.L3ce0W8fa4S8oFAX6oTG1G.7m7S9I8+s9sdUdISlfCDb1n.HgVqSvfiBFzBKr.mNcZRoTQEBQBGGmX9L6G3fSMEBwFOymxyr1W5e7KUcMorraNTNX+WgHmqO.FkGXjyN0.MfctGfXX67yVD.KcnCcn42+92+b.HEybj.WPsmVq6H1mnGbQmCbfCz49tuSzBnT6r.8JuD5YZT13Xscn4Z+zEB8jiw+dqv8DIlUzG.dtB3AmwIZALwjwNYxj73G+3SCaLctc525+jPXB.fuQPFs6lCXKTBMKUpTypUq1Me97iHhh3Cj96pKNO.V51tsaaYl4E888CsvvjUrsMBGXdD6bjxvJ7ep776IaUCi..VFFquBHG1ZqsrPEDgcGKJaV26gtW..+SrH7PwG2D7nJYyx.fobDa5CIhvF0NOM.RSDMkmmWJDX6VvrXQ3CQiytXTu9wS1JkRPVTjrYyFiHJpbexHOqBOqvDq7Af2a7JtBF.vMSFyBg4gUlf6yLynWud9Lydewa+18t669qNg3QN97LHuQv05CrX+EA5g5nOpNdglH.H4kdoW5Lt4P3BzKRDMmqq6TVDkPq0QY.KoPPDMdAVesV4OgcXNBfGF74aH7GCkytDQsOhpnQDv.ZEzc9lRor0LyMSG.zWJkCKYiQumW+6ouRo5RAE7gHplnPgZe6ie7pJkpNybc362v22uA.p8Tep6opTJKqTpxOxirdEsVWSJk0.i5W9ke4Mbbb1vyyaKoT1819Lel9zXWzo.dfG3Ah633LShDIVjYNS4rXY3h4QnscNQmD+wPQlvmSgEopM.ZcNmy4rE.587ddOO9889dewykKWpRkJMkVqSaCjd+6e+oyi7oO4IOYPWgpGA.ji.Lp+ykIFmrCrQANZLDlrloXIgNayr.HMybDoQjR6ikPuEe7gesE1YxZDYTh9H16rPJSAfopToR5FMZjDDhAvQTAEKIbDdgxLybP7FYb+nIK3Xal4sjRYKkR0DlE4pCfpjOW907ZdMkAPEsRWSHDM992+82XiFMpmJYxJW609gJWnPgJ2+Cb+U91e6uXUoTVQteoqTJc7guZ94m+j.3jZs9jRo7jhy8bKZ.4hVwLqUJUQe+QEWXgEN4e8M+WeBsVeBstXQlYGgPTE.aM8zS6s5pqFmYdp+tu9WeNf7yaCLKJhTvYmN0xDiSsaY9.vqxd1yPXb9l1YAZ+09ZesdQhDgYi9ILsPHl6Ysu8Me61sm6RtjKYNl4YDBQZ.DescsKKiTKkyeIS2LmLF6Ih4qO02G1w7PyALz.wTwnftya8Nem+dQAPzs1ZqH.Ur..oE.33+DNS4wj3uhA.ttOz0Al4vhrQH.EdLyIIhRwLmB.wqUqVzNs6DA.Vdd9ThDIFWrDF.W7kbI..Xj2HNzwG.vHPzP4ZqMTJDCshXEtI1N.nkTJ17BuvKrNLIx6nTJkTJKBfh.PKDBWkRU8vph0O1wN1FFMXBUEhBUjRYU.TqPAwFEJTnkqMZCWzVoUsHh1zwwoNybcoTV6Cea2VckR0fYdy.MmnelLY7C1f4TOsm1SaNX1n17XmtAziGphdbiwpU.iP0waRuKQT+jISxJkJVf6eMEkilIqoCjyAfETJ0BAm24Nqy5rlZhy6iWr8OOFmxbaHVFfDkXNEvxoxBjBkCQzDvK3E7B7.pL..CpU.iBD.6GybvUrsYlYeLG7E6rPii2r7byMW37aow1hSZHUQ8AYPShEY4AFgVIcnqIMNWMhnQz1MVomTJ6Bh5nTpVjgm+UYlK+49beNGoTpjRoRq0p0We8RJkp7m6y84ppKVLLdooVqaKkx1R4ZasOgnoLe9Z29s+Upb629sWtPgBU9JekaorPHJK2mzQJjJhHkPHT.PWrXwR.nh3bO2F.nsRoFwLG6xtrKape6e62xbX63sPwHcR92ep2GM+8iCOnGGm0BvcS.rkPH55aTC2Xvji67uhWwqX4QiFsrmm2xvrYWybnl6yaqIc+hI+ySEISS5bgog4dRBl4HkA3csqCLLCPOTB8fdLkRXf0I.DQmAwXEGZSrwQ.xRHhh5xbD.Dkro34IJ0ANvAlFl0QSscwR3w2GHr85ng5XhVqAXvRgvCLFd7ie79fPOl49Ly8Agdm7DmrKLhy5lRorA.pKKTntXs0p87e9mWMkRUl.b0ZsyW9K+kTBgn3m6K74NQwhEOdwhEeTkR8CAvOjY9QEBwwAfRWT6919f+QUkx0pslTVC9nlTJqJDh5.Xiq+5u1sHh54lC8gKFoTJu.WvKpVqSEMZzYcXdwa4VtkkfMVvd642lDsGgiGOT.vGGvCHS+k.ZiRXSTFM95e8u9lLy8Hh.ybhRLOMJiYqUq1rHnfDAec1OwW7KNM.Ri7HwR+7WCc1tv11vxNnnsSzTcF.depO0mZRawsO9WSxFLtJs0y3Y7Lr.pDA.wrssiSFQEd5QiFMaznQmUq0Sq05TvxJlPjOBQvJd73D.fVaLKjfB.OFM4Rob.ssC90kBPzKQTKBvfPIxpgTJ2HPma5JDhAt1X.bQuDIRrEQzlDQMHhpKkx5+IW+0WWoT0qU6nMHhpSDU+89deuUO8Se2UDBQYsVWY80OV0icriU8U+pe00.iFDQatwFazTqzsu1q8C1867c9NcO3W9t5HEhtRgr+e6e6equuueru+C+8m408FeiK+ParQ9q+cd8gVa8LvL2BA.Om8rmSs3+SdOMLmmcT3v0VasH.XpG7AevEAPlFMZrjMvbBgXJ8QzwUJUz669tOxv3LfxY.U76WbL8Owie78+W+3mVHAtcRlZSBlrgypitoa5lXXfQZbToR5omd5o4R7LkBff2J+ay6lmnaYt8KYBdaN6ry1b5omdK.LrQiFVDyodO+dumYYlWPoTKBfEJr+8OOPtYAvLv00HJRNHQsfhkj8wufIO4YrGvXQvUC39FPI+omdZFSjLEybrkCuGV+TdYXO6AFpuCFkK6CfQbIdn1zEnH4IJAy7TLyyLZznoJWtbpACFDBIYKS0W4I2DwHPlIT.vvO1G6iMhCfCoTJ8UGQ4ekW4UFx0Nf74s9K+jeYyBwUpXJXkChUI33mmHjLYROhnguzW1Kq+0bMWa++4+4+YC5XxB333vHKXmwm+5ipO1wHVbH.FQ4H+bDEkYNE6vyBfEJUpz7kJUZF.jJV7DiWbPo0PHDLyruYCEzHkREZeWC.n9fPOhHi5VaTs5sN1wd3V.zlLQav.MjRYc.TWq0a7TdNOkVqEzUV0gUi9c+c+cG.f1BobShnFBw4VC.UiFMpQ35HpNSTiBEJTOmApmUTJkqTJcSkJsKyrawhEcAAWw9Dt.9UhDIRM.z3kbQWzFLvFZstIf8VWzEcQ8E6S..jzwwY166+26KbCGFW.Xt4h.TXxMU73tXL1ldNgh+zl0qWeq6+9u+9+w+u9iQoRkhs1ZqkTHDocARWrXwobb3o10t10THKRlIzG10+LWXwl7XaRxyFQKDJhvZLELHCY1O3G7CNCYamlYNZ61s8QkJFpUTCCpuyNIDVg8IcKCi3yYiHHKhVh4XW20ccIAPRee+jdddICDY2PaD1xsTIJe97DXL1dpCgOLYPyzH1Xwo8fQ.VaSlD6Zq05sjRYSXJVRYlYGlHsbMoC.bERQE.T6bN6ytxdOqyxsa2tN.4zJkx4heMWr6tjRCLhKAW.nKHJTb5om9jEKV7jBwZmD.J0QNhlHR466qjRYQgPTb+6Z0h.n3E8huHkPHzBQAGhHW.TkxQat1yastAcgKwt28tm947BOyYcMKDOEPVyFK26Nl27GW7kOVecCGZqidkC1PqMQC+9e+uukiiSRsVOC.lc5omd167NuyoaznQZkREGYPzJiWipDpsy3fmnG6XC3XOvCEf2li+2z9U..YzFlH.HxW8q9UijK75QO9Z6G+w2AbwftdGH9eidWui2kwAQL1q7P.3c8enqmfI9JI.RNZznDKszRwxjMiUfp9iEWbQNbfweEbzHQ4Pjc7s9Ve6QDQiJdnCYh8XiSevABEbwhEqxLWlY1gHRekW0Up.PQ49jJhHsxP2lJDYU87O+yu5gUpJBgnhVqqBf5vFMsCzEL8Qz8UJUmkWZ4lBgn1W7NuyxEJre2OzG5CU9EbdmWYl4xDQUzZcEhn5kJUpEy7.lYRJkoTJUXh9yhLXZjGIQ9+OHoqhmRweykqyBKrPnUmFkYNEbwLtLOO.VxFXYoTlA.KoTpIKTSTj+WnhjHg8.KrDhfbHVEfDFZ.VMU4.KtlYN16889dIjC9KEVfuhOtED27GWWiUVtIXcPQ4r21B0mgYdl.mOHM.Rpczw.Aqd86BDF+yrGHxy222CFds6CfPTkD5TEiXlGbEu1WqwAu.0RoTAz7BaHDhZLykKTnPorlBzoKVrnVHD5zoSqYhKcYW1k45agJx8Iq+IukOwlg5DlRc3sbMTqnAPtZZstJxgpm8d2aU.TFkQo2+ev62QHD5fXWcgBq47Y+reVG8266UlHplPHZIkxQYxjI1G4i7QCQ45jEv3mzFIClinfINKO1JiQzGaUud8dkzk3fmOyq05kuu669xFMZzrEJTHC.V9G8s9QK5BLmRolNfZXiy2Zhy6OO2H6oVrDCc0xhTjMkfronDQ7fACF.3zqh4cq9AhJrO1KP9vFMTAQCPzow.AxkKNQ1wYlMTzwTruobXdJ.jtV0poxjISBgPDC.QhFMlkssMIDhsI.VPnLyf4fM2533LBfFr5pq1StO41BHLiN6ZW6psTJap05MKp0Mjx0pe7ie75pibj5RorJybYgT5vLqeOug2Xwq+5u9hO6eomcwBEJTrPgBECJV7Ik6WVD1Po0EcDEDtWwU75p.3V4vJUkBEJTVoTUPv7ee5a7F6jMa1A5CqGMb3vgBgnOQTOhnAR4Zb.RsSe4W9kOKbwbAqgltv1EK6mzy6fXtJipYV6rIfc8m+y+42.FZSNzwwIpVqSCf4e5O8m9h111KBa6EYlW3du26cteseseMy51NHYse95RSSFSaAWDIvskhlynGiV.FjXboW5kFRMz9XAL.64eMzIXa9R1wEfIZPQ5RzoSmTVVVoMBgpwUGE4yGUErtI.P974YgvXdESkdpf7eoPTsMfMN1UG.NDUX0Ylqw.0jRYMeeTC.MJp0sHh5o05gpCqFoTpdvX1CM.PMgXeU9LelOSHUuprzRKUF.UDBQYafxJkxUoTtBgvMYxzkld5oKcq25sVhItjXehJc61otPJ13C+g+etgnPgFWwq+00nXwhM.r23c7NdGsijOx.GGGq65u+uOc61sW324242IzJkmG4xkFHqgdoqu9olSV3XxFOrMEjsg2ccW2UDjEo51s6b.Xgy5rNq4cMqUjVHN2jRoLd9y3LhlMLu4JHRgBEBe+OFVXgn.ENUJE++0O9o8CwNmzcEDGmXrMPl.FNaNqCvrqu95INiy3LFRDsIxiJvA0C7Z6Ssium5wmVEHd.jzBokySmc3mF.1sV6rL.mJ35XHLPQplgW0J09kR8gTJGoTVAHaSfxcdGui2wvq7Juxgq9Ku5f.gQ5TEkreQNF2gok.hWCHg.HkNPY+APBjAwQEParwF8me9m9V.NFgQcaH4FBwq3YAR4FzsRfLIApLELdWdVjEEPYral4UcbbjLyKEXSUwBRLmw1uH0Cf6RjUWw9D8TG1L4.LcFu7ke4WdoO4eymzYE4J0AvVZsdfPH7xBLp7Xn.Zy.tQfMR.WjB4PJTBoN+y+7Sdq25sFy22ORgBE7O1wNV6idzitwa5k8xpVAn1B.MaXp.JCiH+MEbQF.rBP1mJytOUXvFpvQqWLuPLsiVG2mQToTPgeVHPd9j+HhogjwVvFOobkJU3LYx3SD466ydVVT+8IDccA5FXkqCXlG7vOzC0+oblm41TkvGz91+9nibjivrgdE8+gqud2joS1QHJzIGPuRiEWTaRoNrkbeRT7vE8J7rJ3gRXxhfEG.Q+A+fiE4o8zdpQTJUDoTl3XG6XImZpoRvLGSJkQnbjEWhogCGRUpTcnPju0C9fOXkmwE7LzvEEgA9mafsEWtebP9MLVKDRdY.vt.xsmgCO4YTwshv2BIsLUQu1Ye1mg6+7+7iTA1XC0gUaX1nucKGmuWm74y2Cqfd3Dig84SjPZemK7tWXgiFJflKmDn5TMZzXlEdZKr.bw7LyoIhHl4N4HptqgKx0AVrEP8PQelBNNicHJLtCa4lBnzhLyBhncaCrGsm2tsrrDNZ8bLgTvmiJjRKsRCxhn.KaE.gEKgAyjuEAO1vw8dLysYvcHh5QL5KVSNPeDcOee+tEJTHryJ8JVT2uPAwPcQ8HQAAd3G9goolZJel4A.nqbextnrA1i5SpG5S9CJTnPeXigv0rYFowMq3.5HDZs3QAJGE1vBt.HG7MZM0NolTPRtwxADojYyP8cbb15HG4H0ujK4RpibnAJ8XDvtebnKZLxI.VJAPs4PdXCGrBan5TNX1zRDGGmd28ce2st7K+c1PqObk0DhJk.ptLvFU2llFCvdvHr9iKcMdhHFKLQxH4Crn5L.opXtFWB.YYyltid1m8Y25AdfJkAbMB6WAzFECsXycfBlf2yJDCnXBrLRgpXFjEKd8uqqe42467ctbvwdI1PMmkAvx850agM1Xi4ykK2XTYp0ZqYlcFp0lMYXz7UHjhP62jo.GVJXtngJkZHQzvJkKOb4LY5yD2qv9JzUeDcaw9DsUGVEVvtPw0tOPVOfxTNiHbGQoTg2S7Zs4l8eZun81GkvPkRgtc6FYO6YOItwa7FikLcRq+yu4+yL.FoTpQRozKKfWYy8CKfbQ05CmPrOQR8gJFWr+BQO428jd6ZW6pOyby67Nuy5W5kdoU.xTAnR8f48+IQMmsQGVFDCUP5f6k6B.mtmm2JkJUZQh4nfr5Ijh1vFcUG1XO2Bgnya3M7F13u5u5upJFqaAK0BnVnfINIEM9YYtAOl3jL.IqXPIWJXrNzzrQOH7Ih5.fM.xtAP41.iE3wv2COUDDXArXLf5oPdr.bf.Ywddn64gNyy3LNimpqq6pvL++z.TbgHO4ncXFreHcaFLX.GOdbeXnr5jmCfs6RYfyQvsDhBa8nO5i1Nd73aQD0TH1WSk5vsjqI6nNrpubM4v.gEbRJvAfrLfwZ1EBQjO6m4yPOmm2yyiYdHQz.w9Dg+dT.UFIoTRHK.JCqfeuPjrNFsf.1w1yYOS70qrdjK4Y7hG9AtlqYym0y5YEpXUUv1Tk3es4yrvdQTbTjD.K.jU.Td2Ly6F.Ebz5E.QwOxg9t7Eeo+Z8APWGGmlv2uAfUUQAgKxCW3fZXQzD02gQD7yKZfE9baLssAvzO2m6yc968du2ECleKIL5bSqb.UCnjoQ792KFFflIK.6X.tg5RxzDQyBfY62u+hIRjHb9rkHivu9+G68lGjjcVdlu+9N4dUUWUWK4xYo6VRcqVPK.0coF7XYgD1XMBD3.KCRvEY.4wN3Z6qXUBOXrIb3QDgwHIjXwbGSXFVjAT.CvXaPHYtiwHKCCKc0RHodvVqc0m0LyJyrxrx8LOe2+36bxJ6RKtMHLybi6ITFk5tq7r9dd+dWddedVAXk1cZu7ryL67.4bccSJhDyf3SNoDhwoYzHQLIoNAzSFUz864dtmAWxK9RBi9lgQwn0TnDjfF.a4Za2QJDQDmegdP4ATfQTlQThA3Sbh5SwYFkDfexRPFeHmiiSVgPjJLLTXYYERAFSYDNNNY.xYZZN6C9fOXlM1XiDW5kdohuvcbGbUu1WKBgfhP3Qu7KevW8ttqlCFLXiLYx3SQJS.0XY1JZDFhGi9mtm6S+rZNzYI7nDfQmNcLxjIyhZZZYDEEijAxVBgnoss8VVVVsKBcCh3Bv0We8F6cu6sEKSmni6NGQims1lNdsohwzJKXOGEXAYfbdQQwLx.olPHFv1iiylPw1Pvzizwz6qDJR9lY.lGJTPJCLA1unn3bkAxyw000THjKEFpMyL4xjtaudSP.bD5eTMyRvXPL7S9I+j8u1q8ZmD2pTRnPv.Ah9FlFccbb5FYa0Gnq4plcIfgtttHknEkiwXIx9lFlJoq11tugkU+BP+xS3LuBg21s8dBe6+Iuco6wcGabXiwT9zHm6T.Y9e9+7eN8y84dvzNNNoLMMy555lRWWOkmmSBccSoiiCQibsv01US2TWqd85i61s6VUpToxgO7giQIZvBvlad5Dm6SUN.B1lXwWDzMAuyoRPkyYkBqX544Miqqa+idzi1.XCaW65Zn0zvvXSPugs82ukkkUOxynO5ezGc30ccWmBQwbZ4V+uEnO+m5aOaP5TJmYGfQrNCXum1rxM1UJGKkxvO6m8yBQu7TziDvJIv6Lh3W4ja2EbELH8K1.XSGGusLLzGZXX.SwCABgX1nNVsnOrTiFMVDXdnbNfT+k2xsnsu8cQZQG+3Ep+e4pDVTGUwEio+qETIORoTd7iebLvSYHlGImH5eep.kJq30fn6yUD.IJ4SNnvBx.4Rnfj77wP+N56ILLLhFEHkgtholEikR4Pm0b6IkxHIMUVGn927DeyM0jZsojJ.WCCCdnG5gzJCot+6+9SKkxzPPZoTlVOPQzSROo7i+I93C2v1tuggwvH1vGOOuD+p+p+pZUhdVTmEmbd.HInT7BnTfxwrWeNfYjBx455lVBIQflqqiXokWVBDJQNVHiHuID8A5KQ1Gn+gO7g6aZZ1SJkcEBZKCkaslsSSGGm5llWX0Wyq40DHDB+C7bNnmo4pAn3HmFRgr4ZqsVKohHw1vwwo74bfCDXXXU9Vtsaqxw8rqhJg8JNNqU0zzrVoxzvx5nMwW04MTAiTw000Gv6ReNGzEvKh2P7O2y8bcymOuskk0oDBwo9v+AeTGgPDTsZ0pqZZrIJnOKk9xTTfbrc2qNS5X1NQYxVEvuUxjI6napORHkIz00mAX9G3Adzk788W42++vevxupW0qZQf4KRvr555YQmTrNIYomT2Kd158osC9+D5wHBIkTpPuzhOmESS.IUwWQ3Farw.gPzMXatzoOTKNnT01I1tPUR0HpkAHSd7yfxORNfb9a+ukT2vHIRgFBgv00ECSCgttNqt5pnqqOEK9KBEBFiPFi7t9.8rLs5tpgYWIz08Xtc+Q+y+nNVVVa433roiiSMfMV0xnJvFFVF0.1HWtYqHkReSSSWSSSWJWzCv211t7p6wnrkkUEGGmpm5GbppThJlllAnJZVYyHDnnrsJ6A3+5uzqtrss8F3OYVgiCZe.vvhBw.aa6AGywYD.BQoDFFFouhq3JTcfzuzDHaa8jk52ctMkM1Fi.5WxiNfdGgPzqZ0pgn5PTFcc8bWy0bM4JfeVCCirGywIKP1pJDsj8E9BuTE7seD8eZ1grIKpGo6LhJrh.fOvG3VAfa9luU.DejOxGSqHApyCcTgn7LtojCwBUIDJLhxz+FtganiTJaeS2zM0FEo+NrjPHEBQhrYylNLLLtKzIhQWRqlsBQHjkJURhXaBEFHTJh51ufwNNNi.Y+Por8JJo2shPJBbVywWJkAq+8WOdzZpgpfIade2280nDka3dJ2M9A11UApXZtZfooouPH7eNG5PUwuXcTcRqyLyjoqiiS6q5ptpMekWwqr5wN1w7AbMMMcunWxKwc8d8hUBGWGmi4ZXX3VL.eCqKrJAz3AOwC1AX3cdm2IuhWwqH46487dxTjJYQoBB+KAe7s8wrODTAIVLhIiKAM0zz5XXXL9wdhmHogowtbssWtX.EPweLKKkxc+e5+z+o4rssi8YlZA1XZ6qc5K6m1wGHrhLjJq7kgPTJNo+3IUXBpO0o7N4lpc1AwoHm4Z.jXEOxIkxEnLKevCdv7IRjXIlZTnLLz0bcbiTeZjG9vWhDPdVm0YIAvzX0niiHTnHJw9HhF0KAaAroggUMGGmpoSmt7q8W+WOPMdWqEXZZVtP.aXZdg0HfFnZ3xF.Ubbb7A7JP4.TchsFP8+cWzE0HLLrgooYcCCi5DTpAPCGGm5G0zrloo4FNNNUcNtSUJo5f6O5g+QA.9BgvCvUJkdPPka9Fu4Ft+.6Ne7O8md7EdgWXha38bCwib4SEZOdp1jQEKXDPuBphU098+9e+8DBQ3s8Q+Po00020wt+6a2+6ekuxEEBwBRobdoPLmgkwL9NNYK5Q5ScpSkhZSFM7yjt9+SC6uIwJJkRs+w+w+woK90fpUq1GnqOEigpuJwusG8KJRfZ+nGYbV.gTJ0xjIilToHiYPwedSFSjrYxNqmmmRAdPpMYIy3SJAJYQOb6hkDMlgCkQEkyzzr0kbIWZ8UsL2PHDa.rQXX3FJj8ZT200sgiiSMCKqM5zoSYaaaea60bAbsWy10000CeTHEQUHHEGYAczwuMPye0eqe65.UMMORfggQfllV.PUJWpNJYsdqkVZo1111sVbwEabIWxkrgsmcvU+5dc9BgnriiSsi6Z25qc22cWfQoSmlG3AdfT5AjAJlgMlXyclPn4aifNukZQTwE92+u+mGWAGtH...B.IQTPToShDIjs61NKkYwvvvBtttkrrrJBTHPU73EA10d26dmAH8BaPBXeOU90d1v15zat0zjWM1oAxTpbz6akIkPEGUHwna.FtOBdlKbXfBgIkKWVSmxIEBQFQIQNYfbpQISjRHjI61qqlLpXI.wEKA.oTRXnLL723Zu1PSSywQba4PgfdBonMRE5RLMWspggQLI.WmfBwxGbKgHbKaa6sjRYSjT2wwoxuyuyuSfgkk++w2y6w+3JeZ9.d11q481e6u8xEBnpgwgqS4Sy+W4neO2Wxy8fd.AQwtEHkRe.GCCqSIDBGTqmVtZ0pUW0xntPH1ZwEWbnPJRdAWvEDyEIK.L6l+KibtchxjgEwa.vnwiGKARnqqmQWWONuhbVFGM2fACxYa6kKOd4rrrRSQRREDW2Mdch0We8+sb8x+Mc6YqWPlfJBX4TvFogkxBYmCb2ETbWTJHmzSJul2z0z9y8Y9b0ApsDzp11cP5YpCkwECY9U.8pv9eaW+a647G+d+iOucu6cuWGamkmaWykaqs1BIzS.sVasiW6U9JeEUbbb7srV00089bLLLBnH0HP048HzODMRFGZPjRO7yBhda5q03q2Xh3JFYI4jRYlRBQl.HIEHjxqzCpFC401rsLopA4SCIxA94fUxRgpYoL4PmciGF2xsdq66c91e6mCvdcccKYXXrjmm2rKu7xoqVsZbgzhYF934aKpSQFSRtKJf6p6e+6eiG8Qez5aWc37xO5G8OR6JuxqDSyiLhRkGiOBxSJYYYVgPjtHnELwgZojNNGKUylMEKrvB8i5PPU1ljr5CHiH.u4nH4Inv4.kOzW6q80N+W1K6kcPMMMSfEbccyr7xqjXiMpBQA1gPNFICEBQeoT1yzzbfqiyHYzyZEfDjiAwPSSi9NNNwinRShVbzwwoypll8OlsaegPFJDhDc5zIYtb4RDIwbcht96.E5CZC.+9Pogj2eLUPihj5yeae9jWzEcQjHQhwVVVQx10QA7h6p1jwtJRJHiCjSq.jXMa2zll5YDhRYjR+jBEOYzGn4C+vO7Fm64dtUfRUgzMg06vSe0kis4z.8Lf2tfBEgx6+k+xe4Om+h+h+hC.TTHDoz0060oSmFmyryVO.pSQpRPwpPPLIS07q+s95se4W5+m8f0mdFT+IsCFmVA.KBICTvlOEdlYAmbDwsHpNbULGTNgTJGHD5MA+MfkqwxazZptp.flNjxSsHvrQnRIGjOGTYNoh.g2yuvuvuvYcu268d1ttt60vvHumq6bgRRYZZjvyySL4JSnPVhDjKt6cKazXynDYjSyaIsLW0bKm0bhI7u3NxtkTJZYYYDIQclw2+FYaaORSSangREc5V.5cettCjR4XSyiLFJGSNwQ79S9QPkQfdz8aOMnfF4KqQERPDwzEYakzxxJ4C8POTpy+7eoocbNVZMMsz555wiqXJGGGMCCCoHRV3K.sJCMQmlp.2p8uFYhOIKRNpyR.lkf86IkmsPHLbccmUWWOTHJ0ABpST29uhq3Jpdm24cVGnkmm2VWftdmxmN4uF+77mTe1Su.e76eIuka4VRc8W+0mEUBkKW.xWFVjBjr7CtwVEJrbETxTZU1VhReZPXBIYQxPcE72KB6JHhHbgBEfxkjRotPHLkRodDAbtfmmWtHjEH.vvvP554JQoHhwGCIBBkRFIlBUf850qc1r4ZBxFqs1Za9q7a8qzx8XtcLrL5633z+u6u+uavu3k9K169u+6u6q3U7Jl19aDfz11Vdy27MKusa61j.iK.gG2wA.su025ak70+5e8w9lhVqnPen7PHezykJgPAIEKKIfDTjrDPNaa6YS.yzrc6zG7fGTHDkFAAckRYqW+q+0W+y+4+7JRF9zI.1oiQX56qZurW1KSbW20cEihhYgB4Aw9tnKZ+G3Nti6Xu6YO6YYgPjywwQHPLR2PumtPrUTmyCdausq26C8gtEO0i2Jax11z6DNyOUEl3Yismp09iGalb.4dkuxWYtu7W9Km3M8a9aN3ye229VT1nA3FiJhmYT3b.RxiPVJwtwGSfCdW20ccnK+xu7mKvY455VvvvXNWW2zS88lVRRkn3hoP4N45GIiLsLG535zQhnoFrggwgqAhF11Gaya3F98ZcGeyOWSBXKJPWJyvH9DIzxxJz00MzvXUI3GZaaGZYYMoqzu829aO4sca2V74xvBvv62yarTJkFFqB3KO8q0hBUshKBHjpbPJJHePJpPNJvt5ep9ylISlLtttiz002THDUfUJCUqCVsA6oQM2S8yoCPJdDqYA6U.1KvAkR4AANqWyq40r7G9C+QRF46eKSyUq43rVEf.yKzzEe7A1XEnU0Ib1vR8gZOURMrXGG6cVPreb11IBSlAJNKDLOnOuT5NqPIN.8PE+SbAUiQr5N6Jd1ScpSMyd1ydlEJsKozaAfkEhRkFNz1LYxjVsa21LWtbE8882MRlEwjD4zl9pzvv3zRpkss0F9XO1i08bNmyYKoTzPHja9DOwSr0YcVmUefwemuy2Q9y+y+yOjnho.EZ53b7llllsghsnXvVDPGxy.pnd1FgJTwoN0oXO64BAJO111djkk0NIZzInzpHj3X11IzzzRe0W8+G4t268ak8Ceqe3Lu02waUnNOKLDJKgRofvLiF4lx22Wy5nVivmtThsvWUbZSXSGUb66b8ym13z1mh.Xmg7rDUJYB9myvgCO6jISpexSdx4RlNcnkgQGnXKGm0ZIDhlggg0UiVonL4opRUsn6AfQOxScwV+Iw21T9kOjFbhDvxIQeiL3wrTfEnrZzwK.oCTnFqETrFDTaJz88z4uWYyVjYIPeIvyD3b.NOoTdPWWu8AxhBgXWRoLclLYRLXvfoPujThRNo2V1ykzGgXPz6q8iH2z102r9lKtvhwbTTOGGmAMp0X3tWZ2CEJRpU9XO1iocVm0YgkkUTCGKzVg3uR8AYeUtPkFPd+gTAI4QiJJTvIkRooo43ScpSMdO64EFB9Baa6DVVVw48k4QezGM092+9ieuWTDzVywIgh+uJkTJ8yD0XuY.Raa6NzzTutPn6BRaE5WWrAGpdWNwSad1w13YfB6BJqCr+O3s8AOu2wa6cbN.K444IVZokZlISlZBgnAEnIkKto5Y1jhN1AJNPcMa0CF1eJjB8+OBSh1l9ksPXi3tYDBtQAtEzufO8EBwvO2m4yAQNZqg0zUW8YZK7.wU8FZSA17NtkOT8EVXgFRobKIh9as0ViLMMkBIBSSyDG4HGNoPHRoookFJmNLLTMOWAp8Gf7G56i5Xur.NwOKma4o2lDrPQEY0lALxDwUDoBTJkCd2u2Xn5HJwvnYpcGN3qj.7SpP1Q0LEKGQlWdJEpYtYxtDvRgggKBh4bccyHkxjoSmVAm0HSakLVJPDUARoT1e80Wu8Cb+2eCSSyMJbfBaXa6V+QezGMR4iBhPXTkgW20ccCDBQOn7f79QKHTYa4ENfBQIATHD7GZZZ1a94muqggQWGGmdkJUJJvkkkGH5ZK.zXIRppRe4rTjbWwUbE4zzzlHEmFFFhM1nJEKFMqiHkuq2w6LDEevDMqh18LLWs+67c9NGfhCAFHDx9frqiiSafsVc0UacG2wcroo4p0KpJNTk.HvZUifUMM8MLL7OvuvAhq.bTW8C7ABdSuoWdEOuu+FtttM.+MKTgl.MIflW7EewM2291WKKqKrEPqvvvlfWCfZFFFa.rgss8FkJUJBl348yCtW7EewNkAWqUMbKJD9W9u3gCD5hpPwFW8U+ZZ6551+.G3.iu8a++hD7Ev5mI1xQu65Egxjx8A590+5e8dQIpKBkxrttt6pQiFKcbW6UbcsW1831K53r1tt0OxsNqZTuHyK+Re4of0mVMF9I8coIcq3PQAsD.IwfjK6QZvIMEi5Vihr4Dn53Z++x+x+xNn5TTGXi9Va7jYAcOhHiXkr1oTZApLgsvKTnvR28ce2KVud84QRNWW2TRPSnPtzjQvITJkRYnBlmRB2rQiwpNuJG.Lne+9CVUkDZ+u2ey2qqPH1RHjMkRQCSSkbyYYYnjxUEbZa533r4W3K7EpATwv3v95ptJ3UF7MV0nxKzzrJTdCJPMnXDTVYSnRSfs.u1uxW4EFgtlxcVohh7xHPoRY+C+C+CssrNbWfAm+4+KEJkdB.Qz0TnTJCKVr3XExxHLpKfoKCy7ley+G1UQOlGpMG4IqpH4mQ7QUH0i5HMz0mRsA59C+g+vggggBWW2LE2+byMd73c6Z6tTgBEV9Nuy6bo+ium2yh4g4000mqLjCcRm+jS5F7yFqg8T0ULwRKsj35u9qGXIgdz0VYhJLVfTt1ZeeIEQpfv8YzwPPc0Ouoa5lHfRQIcVL7CbK2ffnjOjR4LsZ0JmHlzpmL7Mp6gddtiERFKjgQ7FgHlzpmPXc.8DBY2rYytkoowlqZZtwuxuxuR.A3aXYDXaaWwz7H0eCWyankk0E18vG9v8cccGXZZ1a+ufWPGuS40TWWutkkUsa6197UKo5.V4xPUyiZtwEZZV60+5e80HOUsssiGqAWnrOPYnxFtt2eMWW2MgxapGDYeFP8G3Adf5VVVanacgabdm24UywwoQjr31SHDi+7e9OuFPRcc8n3ClzMzmtM4ccW2Uz8mZJ9xgx8ff1e6u82dq8rm8zEkMbRfbRjy8e9+7Gati4nTBFGGmDenOzsDUnhJwiTb5kWd4HBh2J49dpk44eptUrXwnXpzCWAB+pe0u53zoSO3y++ys2ekxzCbGP9mww3XRBPG3Q..M7iSPl4u7K+xW.XdWW2XN7HoDzjP7HsD8tfPCo5ZNZGNV.CQJ6JDhsPHZ53XuIR1THk0UqgUthiyZk0zzpbG2wmqZIk5s0jxz9a8s9VsMMM6XYsZL5+ZC9s.1zxxZSfF+4+4+4MxmOes2065cUEVoRAk8WkxvF5Oe85u.CiFfecxSCHebmZa7Q9H+gQ9BC176889qahhXVamuBaArEkoYlLYZJkxlFFq19a9M+lCO+y+BApF2E7yD+XviPHXOhRzGx2EnSXX3.fvOxG9Cm.jY.xZZZl1wYsjQHDJliDE.IpRoT23MdiofURB0dpP2xN8KI1wuyyF1fRvZLDD4W1S8LEZ9I9Dehs.5.K0awSezK296ZgDVHbO6YOQEs2efPH5qFyhfQoRkRHDhzyN6r4788yAjgHxRe94WPDcUD8tqT535FWPtQBEmNM.D8.ZeNmy4zRHjMzzj0u7KY0MNqy5rpXdDSeSyU89y9y9fNBgvQJktt11AP4pBQXcf5PPi7J6u5Tg3D7ZnqejV.c2yddgi77teoqqa7nNLZ80WuejBH1DzaTR8cpE.0rNpUiCaXT+du2uUMzo5q409Z7.b.rgx1.tfumTFDzrYyJVVV0K4yV.ivmjREOtrKGEGKNKrPbrqOSqkIgHBfcYFZVgdfeWJPujISN100MQpTolwY8Ss6a8CdSK63r1Jlllq.rjoo4tKpFUpYnxDxFN4ifdjM+gd1hGG2g85IhhCbin30HiLPlE0nXksLEiPAcIPgpjwvFOcMYa67LKBEBP.dInDYfBy8w9X+YyOd73c0qa2YEJDCm.jZ862e6cfLhSFESxkQYeIDCQQ5qiQMNoCBCC69R+Eeoc52ueaCiizpHroooYiy+4e9M.peDCiZFFFUu3esKNF4QkApBkiPro+F11GqlqqacvuQoJQiaTEZ93O9i2RwmRWvV.akHQhVS4+qAPcOOuZOvC7.UylMa.n6UHx9J.bMW0zWWHBrsOVfPWD.kpHkx5c5zosFD9FulWa5a7F++ZFHh6NodJqSblxOSkiiOq2G38+A5CLx00KwQLLxJDhcIDhcSQ1sywc1sq6wWv00cd0HrqlNgHU.ZLXOFB9YI3C9ox1yVK5uc0+rHI1jVp3MiIJKAptjjTJkiO3AOX6G9ge33f7epl+1mp8erL6t.J4v6.2y8bOOmW7K9EetnlW4EiHXoQRXKMgPAAYgHXrT5PXnskkkKJDKzlsqj+3EgA0mLmY+LUh2D.IJVjzAApBbHkxYDED4hbzESJki+g+veXuK3Btf1.MWBZWa66gQAdajCbmEEbHi6R0bBgHuqq6dz00Ovcc22847K8K9RL1n5FKJgY.YJPjnToR366KAFKDhgRj8DH1JZrSpp3CF7PUjfMJBaF.s+A+fePmi9xOZOpPeUGFqLcmnDnd9sqnOYkRo3IdhmXz+ty9rGcbW2QRoLTQJazes0N0V6YO6QEviNcviQLoJnr6H9k3f862+7SlL4gBBB1uTJyifYkRRIT+tSfpNaiTlNn5BQOaW2gBYnpSsBB60qyv8u+ysmqqcaCCqVNNNaZZZ1fhro6wc27i8m+wZ999ieewnCXbz4SxRf3XNNiusa615cS29M0QMWrkFA9gSc7kw+9LYVsU2ahP5zXCCBccAccDddS5diF5.dS5CSLWmLCvLEJTXlG7AevTJ1jm9TjlJnNWpdjS3XDl7LwOOZ.IVZIxUqFKWrH6KHfCIkxmKpp2unqqqlooYeGGmMCCCq82eO2SvK3487bufK3BBJ.UKGgxDVhNTitP9AJjNLIf9ebbdtcGRiJx5q5U8pR8G8G8GkZ0UWMt6qyJUxupRoCJhf.FbEWwqp8cdm+UwRTWbWpiuGbZ2Gijc64u268dW3hu3KdoBEXkxkQGkjauGWWGcPrhDlSnr+Tv77zOWiWLdrpK+hgfL1lqGPGgTr4nvQMzzzZXYY0Dnya3MbMc9au8Oa6.ncwhz4u7S+M5dnm+g5G0keETnySWpbZy59ow345P3DxQdYBYCjph.OYzBlNPrmTGr0gY7fbEKRlf.R355pYXXj3Vu0aM463c7NRRID3S3W7K+EGeU+ZW03G5gdn9e4+5+51u2e+eeUQ9fMc2Vplel5La73yEyGU6sRkJ6eokV5r0zzJ544Lmttovyys2G5C8ga9m9m9mV6ge3Gtx49hO2p3OIPWkc1Ydm4NS1lt6UZ55jH58O06qKQVpo3vDoTthPH1MPBaa6VVVVUKAd9OyHLYmjq3zpQwBEfBARoUoRhyx2WtO.KWW2BkJUZAMMsrCFNHQ5ToEJTknryjfzxzT5DI2uekuxWI7JuxqbX+A8G7pt5W6f65+1+sXTM0.XiibjiTob4xUi7oEOSyxeyeyqU6q9I9Tx.XPoRz02msz0YKOuIxr5HLXLtmFGeMEuXfXEXb0XTNsBioJRXYsH6uchbmsIFxnQgHBoWZkJgz2mQRor2wO9was5pqpJxrNcijw5cxQGvNSfbejfSRFnztAeCfy4s+Nemmyu2MbC60vvHuiiyrFFFBWW2dlllMbbbBd3G9gceIujWhWgBDTtL0xmmVUpLQsFFyhDR8oH+7e5v0DSrAsrHos8D6jbThbO924wyd1m8YmVJkiEJo2tEaK8l6zlam623666BEzK1Ovyyww44IDxCJkBcf4UI4KhWaZ6lfojw0wDkDaLWJEFF1QSSq6q5xu7d+0+s+s8jR4VRgr9McK2ZkW4K6UT4xtreoIxa4W4q7U5ekW4UFGi0DYLGXbIXjOLpXQFFDvXCCjttS7aIoDR7eRxh4jm44AhGeWVFAafFnqoPW2ooZCoO5QOZ1evO3GDeuUCkra1wC1zvfFttz5.GftOxyLGIEu9QFf40AcO3.862+7CCCeN0pUaOFFFyMd73wIRjnoPHJiBEZwimVrTF2FnisscWKKqd4yS2JUdRy7+zEIQvzOW9w2m2oglo8BYWGxkOOyToBynqSJOODqu95826d2aaJQK7eR7g1DTysDjtVDJnoDyhOyCEWAB1iTJ2OvAZ1r49le94KnRvhnwwIpVIp3ujBAQwhICkxIiI7.P1EDsQPSAhMehm3IZbQWzE0.n0q9U+p67O7k9RCJGKkoJDL0y11qmkkdL2jzAkcXGcc8dJhiMNNYxlGxTARoqizyiAqrB8pVcxZsiPmvoh+J9coIiRkJ7rnQZPmQpQ8ekzP03wXXFhPLvW7K9ESdUW0UEhZs8MAZrDznFz5.Gf9mA1cwqgNOQuK2qWumSlLY1OfNPtVsaOd94Nmttt2WSoT13c86+6W4y8Y9LQEzVgtI1dspwVvX6SmCG+I0tRnJByIRFMVVwxI7tjR4hBkjSOaz3S2ofPrQEnhtN077lfT9mN91XxzFfpRKG31u8a+49q+q+q+77771utpiOy655kAjIEhs0c5oQVBR4.ohWF6KPLPBi9a+a+5i78pz+M9ldisMMMiFGey5QqYt0Mey2bma3Ftg9exO4mr+uw69cOfffAO+m+yOL3Ad.Vy1NrZ0pCO7ke3dDPOXk9P0HQp3z7aMc7XRfvH+eiWZIj0pgnXQREDL03nt85uv1wsFq1qYJByErsTMOSzwqQ73HhpXNwnz+oK9LsCcHRdhSvL4yyxUpvYA7bkR44ArOWW2cgfwF5FaIDhl.M+m+m+m27fG7fUK.9kisqJwlJeEK2C1XmHl6+se6YityM8lbe1HgklnO5.HkRsHF6MiPWO2Zqs1Lrs70clxD9gVvHJQ+RQDDzkbIWRi9862x00sqqqaz71KDBUGRjFFFgiGOd3EZXLxx5npEbMLllD2DtttT+m8nJIdS.nEDLI35zBQwLxxxz.oJAIoDhhBQ3EbAWPLrqGVapEwNfhb2RlO+vIAlVPHxHDEUrsegBYijYwrurK+xyjNclzEKUJEPBCCSM.7THuQhpnrgHYrTJGspo4.gPLvwwoussc7XCLN.v11V6nG8nZ5QvMKpXIC.5UD5C4Gx1cpVCH4latYhy9rOas.0Lp12zzbKfMqTgFQEKoCrz.7Tuvc.PxBJXoE4DNyi+3OdVee+rQPVWSHmjHqL9+h6NqTHBEBQnooYnqiSnPJGaZZM.gZbIxlclMUxYnHhMpo9ZqsVCBnw8e+2ei+h+322ltttMJFsPGQx.qOT8a7M9FUuoa5lpshOM.8Ff+lnBnssIzCLTKnWr3zKf2FXqxpe110U0ssnEM1R88WpEdJTAXBaQQ1x11NB4Dzub4xCxmO+jpiqGv3HD6LcBaS+ympMIfrVM0h9AAwApvvn4KMgooYFGGmbHXFiRF4dCWy0j4BtfKHKP10Ty++r.ytRMlALmgHdHf+k43hy3McP9WcO+UxqX0U29DWsnqlTplEZoubrTJGdm24e0DhbS+I6zd5D3zpDkL6EewWbNfEJWl7862uz27a9MKBrLHlOLLLqFjLVts62qmhYWO8hxMVNNbj.E7NEJtkoOPu+qew+qcC0nydeguvshJVRCf5u+2+GnwZNN0cbbpGDPsK6kcYwi4VE0m7UnhZ1YWD1jko0xwJCPDbt8lndTLRgjFqSqSMJbvXnNOUPGMdLg5CzyC51rYyN+f6yaqu3W7K1xvvnEPqkWd4s9ve3Oba7oiTJ6ec+ZW03+o+o+Iseoy+7y9tugaHtX34bOc9x4YpyrRNzgNM9nJe97M0zz1BXfggk.HqTxtdqu025ht1tKetm6uvJtq4tBvREgc644sqUNcEl3mT6qc1E23hkD8ojF0T66RPha91tYsa4ltEA.wDqq+Yzg4PpigttlZ8vIEcHcfTlSHDyEDvBc61cAGkpAMiumeZfDUqTEWW2PAhI7QgPoTIiQnRh8JuxqrOP+9c608Pmy4F6eoIJdlZy669tuVu923ars4QNRah727E97eg1ehOwmZqiYa2xwwo0wNlSSaa6lddSRFeKnTabo80bMWS6hPaJQ6hD0s9nQVrJQnmCZsbU1BVtMrQL5A5sBLHRYyFRwhwRx5V.stnK5hZdi+E+EMAZ64I6IUPzd7pp2wUwM38LpjHSWL1vCcRBUEq2e.P2uw23ar0c7A+fcbcc6MXvfwlqtpv00MYwhESezm2Qy.j4k7RdIYAxVtLyBL2Zqcp3F7jExmw6DdY.RuDjj88SeEzw1VseUqUVTfOZm8Ye1ZEA9nezOZnw1Ec3Lkn5EPQULEkJkCXtgCGNOvtjRQNCCizFFFIjnPAgL96Hi9pphzMV.ijHGb6e5auKP68rm8zxzzrwO3AevZnJXXvEZX4+68Nem9W1k8KED82UuHz5JuxemdNNNCoPgXeUwERt6G3y7Y5BzMHPwgDQqCFMNrVwb8Uq7p0+ZmeadopGPuJp8Uefd6cC5BFcAuIieYI0HJmDP7CN0oFGE6RbAnCcUpdUFWWkbJ+HOxynuro+6k.i8h7mlISldISlb.HBGLXfV850S455l568c+tIcbbRBjz00Msqqqp3BQx2rkkRMIpT4IYWMMh2RrOPi88jN29w0FbRQSVOZ7KqTQkjlmR1VGu28t2gf4foJV0SocVsoNWK5iFjWCBRhB0mY61oSt1sam0wyKMwD7pTAWICCSUA4fwRoXLBFEJmr9TaProPnsAPf.gmwpFdm0dOqxpN2SyuzW5KsUYkHCzDXiRkoJTphkk9Fqu95aBzd80WuOTbDvXuAChe2IN9fdUfdddd8N1wb6BzsZ0IEgb.r2gQEqcPdnGKQu8NAUTJerdvlG3.GnNPiE7nIr2MgpQH9jVEfNP9Qm5TmJwU8VdKYkR4bRob2e2+wu6h.KTKR4PejGgj7L6WQhJN3SqAfYyls8nQi5EkCjHWlLo6z4IxBgyXZZNye2m4yLiuu+L111yftdtK4Rtj3XyRUqVsD1O0qe9iqcUjMxI.PJDBohAFM.PHJT.oTJ2XiMBAF0rYygUhhewy6IQx1OkaEO8BWkqc61yEFFNmLLblF0qmw00KIH0hi+mHz+x1igy.hGQZSqFxnFwbYW9Ur4a7M8FiV2i1+nS7i53551wzzryMdi23V27MbCsbcca7t+M9MZPPPMfMdfG3ApVFpXYYswCc+OzlkBXqG8QWuMTMdc1t5v.PWU7MEp4m36CnieTrb0po9yAJT.GkiQ9Z3RcfMKBMYYZpPjYLA4Ryfn04kR4l5BQbQzkDUPTCHIrz+RnHRdhSn7mUoxDaqHDl3BPhE8Cy...f.PRDEDUFjrKfEcbbV1wwYka8i7Qx655l+3NNKipfMyj2mTpwhbiorE9+6r8rUASlTUxSFONN5DCk6HBKqbpu7W9Km4s9pe0410t10L.4VYEEIioeFRNk1vX7Yf+1RfZiLYxzzzzriPHFFIarZQDIj.Hzx5BGe8efOvPveDPng5gebG00LLLz1KHhxp3mUaSm.WbGfS7nO5il.EolpAns9fAB7QZqb5eZjqKfDNj3QfDPsTURTIpCdExTARKkAoARWrb4ze5O8mNiPICWobccS566oYXXHbclnMlwOOi5nlX38du26f0bb5KkxAQ70gz11VfREERXYYkvwwIgGnYXXHhf+lDJNN.FCUj.hRBQh27a9MmPJkZG7f6VywwQpCiIOSbhw1J1P2n45cLf7QP8uDrcklSYYYkNRZ5z.Q7P8GE.sHDgXxLW2tUqwRobjiiy3PXjPHGFIEXsLuPyXxWppoo4FVVV0LMMaTpTolPwVu7W9KuoOzzvvnUDqi2111N9bs90dsWacvngJ4AusXAhbXZzyAF.tiVe80GsTPv.fdAAA8JURkv6dgAr3jjd2gC0Zc+xe4ubOfdNPOBnmkk0zUtch8iTJ07fDP4jjOV1ryOQ913L3crn60C.5+09ZesAO5i9viADNNNo.RKQjIQpDo+N+iem3NDOqkk07111K.E2cUXAvYWTj4.xsC3l9u1jLjS8SEJJpyX+o5xqPQPln9niPHBO4IO4PJF+9whC8dxA6I.D6cupBsTRghmTREwGO+Mdi23xoSmtvO2O2OWdfcChY0zzRKkjPIOcRxlIqjHI0j3hk.iDZZC.4.yUM6KgdQbgS6WyU+ZZIjxlNqs1l.Mrss2rXQZYZZ1ZUSysLW0bhsDLMZJpzPotOzoNziMn+FaSPqi.FE0wrwv9h5TjcHVp2Ad7G+wC8fvSdxuSHP3h11wcIenooJvToT1Y98Oeq8nqu4UcUW0D3s+FeiuwFuk2xaYSPukPHZG.8Nuy67F5KkxToRk3O3c+GrsLxAIgC7uzyXIm3DwjKV274UDDYQUGK5DcsPXXXpjISlsc+1yNdr2BFFGY2Robw0bbVPWWeWUI11ZRA49IkDXm96o89e+u+X+vI.+DPoDnTLHsa3seChq+cc8.vccW20N1MVOCGhSnJ5hmWLAzkrTIEBeJJDySIV7O6i7w1ctb4VP.yYXXjEAIiP6i.TbWRTUfi8wMF4DnEOzzb0AyuvBC9fevap6a928M2Vo9MhMMLrZIkxs9belOSW2669ljb4081t5t.s0zzZYtpYSSSyMiJlWKhJJmm2w6B589re1Oau.nO9zOHx2zIO4I6RjhUn9bfdJayM5iUb.WKLPg9jZiO4IO4XyffIxnrgA891e6ucu26u0u0.Jv.gPLTHDit5q9pmBMdkSCFwbpyzwILcWPm3y+DPHTYL5pjhtrK6ZZ6Ca8betO2tUpTYf6wNVnPHz7CBR+Wc2+UYAlo.LKEYNGGmc455tq8rm8DIe1jCpjUW+noARUCRxIOiFg3eb2lNg4H6j.MnPBoTlH.zdKuk2Bta6y4LoycQ6ufj.YJ56OKv7ISlbdT1Y4.R454lPHUGTsHBRLtrMQGmQFll8+jepOc62v09FZgTQ5pNNN0N4IOYEoRNzcB.aSSSGJP.P067N+FaFnF4ltBgnud4xCt669t6aaa2uPAkMx63M9F6SgIwxri0+r6B6qKwEFIfAUN8XeFUoRknjfyObc0ZsCvJpYNAARePqb4GRSJkhRAAg+1+1+1CAFVr3DThltYylwbE2zj+5j6BOM2WI59eT7Xk5mLYxAFF5iqVsJAAAZ.IO34cdojBYZfrG1vXFT9ulqXQlw11NcdHAEdRGm33AItXqu0a4VDbxS6X+ShuNMckrcmxDxfN4hQhrTJSs+m+9E.gfyXXooQT0Na5vTwstRJEAoWYFJvb.y+c+te24q2nwbRoLqotdJgp3TBCSCRkNo7q+0+5gB053iQJGIkxgZJUSIhKRj0jRYfoooqgggC93c3eQixRor9k8Rtr1epO0mpqqqaWCCi1NNNs7g5feMzo9d26d2z11t8d26d665d7w.xU1XiIw0ZYoV+y00sm9En2wvvHtIDwMSXXkJGaTTwVFUAFQMhrwn+i+3Odruu1Oxi7Hc.qtaB8f06wRw9Dy2qLLBpfkkUBomWJgPjEH2K5hdQyALmNQxn9oSx0Osqg9HSS.rQEtIYxjsMLL5JDhwISlTqd85ojRQZnXl0bcyTRUnzYz87l4dtm6IGPlUVgTKszRIWRMY.SxyXGOa+wNdM0mhgJ+Utg.gEpTITHDiWd4kGUrnX3BKrvf7v.LeRHH7o0uVvoWvjLunWzKJmuueVDhLc51MIH0LLLDBglPoH0BoggQX+98GQTwRBEhlBAaXaaWV.AlqZ5KjxIE4867c+Na9RurWZKHrsiiS626688tkuhnWaFDM9ym5TmJtwo0AZbMuoqeSeXq8u+81AiX6n7C7fQfWnssszz2eLvPee+9kJo7+s.LjEYZtnaB21AU1RwKhzI.5xFzMBgnS9Hkx1.aIDEa5qjd8NnViEnXBWUNgmIix2De8DYi+.Ov8OTUZSRgjYcccWvzzbIfU9+9i7Qx+C+g+vBlllE.VAzWnBLCDjlEeVYDu9e41dVGgIDmzkGiMhRnAUxbItxq7JS9g+y9yRUud8zCFHSWsJoWVAG2yjYCN9g4PTcsZKJo5rkTJaKkx9tttiEfrYylZgggBGGGgq6w426262KdeHbi5J40ccWWxZ0HwJf15f.uel8v8opXIo.Rs+8u+DnhgQpCgoRkJDEaaOr.LfRriDmOg1hQvLDe037.kyEMdTYAxE.y7ldSuoY9deuuW1Nc5jpToRIDBMMWGWQzcfsCFO5EXIxAu0W6qseud8GHiJPiFf0EZk.HUDZORXZZJJEeQIDQy8evzWWICfje7O9GOA.kKi7HllgufK+xG68C8llbYi+riwj5.w2yzJnBxH4ryNabvzZFF5RgXRWXGKfwyN6LiA4na9lu4gyM2bSBJSfXjTJFVqVsdlllagOMLMMq9k9JeoJNNNSTNBCCiVmx69ZoCaAElT8Yaa61V+bVS2I2lf6VKG24qMoudbvappzNZu6cuCqEAyzhEuvA99LbAX75fLlaCht9lvX0.C909090FrrpKsSN+KAi9u+e++93FMZDJTLVaBgPjtTDIlRElU8+WIt6+mIuqOsyxdttt8ewu3Kc327u+uWJhTxDT7YRte0ege9YbbblCEzHWvxxZ2NNqsPQ0edWOze2CMaQHGr4Yp5C7zsMMJNhsIGUBFQwI1FQ+zebQX3YcVm0PBTDDHT+oJXO.cw5qivRHD9PB7HoPHxUqVsc8G9G9GtafEqWu9BtttyYXnmUnTcIAQEhSHXrPaaaMfQBQDxbjz6O325OnqPELSKSSyFHUEA4y849b0.Znoos42467XMu4a9l25X11sIfIc9ms6feDy8WaaEJXJnryAT+TWWOJ3jSNoS6XirHDd1m8YGBDtu8suvy67Nuw0mb9t2QutW20OXIEuQrEJN1ItngwepIDhZddqUOuRt+Z0nQi1BkDIO588m79fs8WkDdjyH9nRcMr29UpP6hJRjsEPGWW29RobbhDIBKTnfXlryjLQhDYu6uwe4bNNN6xz7Hwiy2Lkfbrzj4w9LQkINS2ju6286F.toa5lh7K6mHOj.JngBMShh.G5PGZGe0mQYxIFMJREpnJkv2mrEgcUFVT5IW52859cVzUgtjbQiWZBCCCMWWWg.vwwASCC.gTnFKmQnj9yg.CccO9vUMMGJjh9e7O1GusiiSSSkJZsooo4V111cLLL59DOwSzwwwo8wN1o1pjxGWcBHljUmpX0zSWWuO3MHOLDzmTT2hvv88h1WbhqQ1lOR76BiwlQ6CFAaNBXz8bO2y38su8MxYx47xCbcUn+xCFQ4sGykuvW3K.pmmQEk0M6dgzf0S0y3mpBmLFOFVD5Ak6.zYlYloqoo4PzzjRoLgk4poMhRdsLLu6wbWvzzbdCCi4We80misQ.aJvI429a+sS.F+zBYI6Dw.63SYs64dtmjfdBJUJAfVdPP9y38aLhUyEnd+YAT9om000MiqqaBBQf3IccoZ5PzZBNNtct1q8Zagj5BMQMyUM2PJkUEBQYSSSOKCCaOaaa.OJSYcn9UbEWVSf1999cMLL54A8t7K+x6YYcz9kKS+7vfMfX40bm95ir2N4oMBOrC+e4ymOZMfJi1211eiK.iKVrXnTJCKTnPnqqanOL5q9U+pCgRC88kgQbSTp4me9IDsHO8pyzjmQGPc+e50qGB98a1rYLxakm+4e9Z.IWXgERaYXkCX10rsm2vvX2PoEBBXtUsrxVARR4SiiR1QhqmPrOPd8W+sN0ykS6mmoaSEq49R34Qp7PFGHGdLqmm2b.yJDhr+O969ejX6mE01IIzp9bnSGALRYkTm7jmLKvbTlc+89deuEeAufWvtylM6bFFFYcccSFArDQXXHCGNRd3CeARDBkJ3HXLBEO4Az0wwIlvYK6333BEccbb79V+0+OqbDSy5epO6mp4u7u7ubaoT19G8i9QszzzZpGykWdrYAnokkUafdFFFi777BqN45X4PaaFsWnuggQGJOYs23QwaHvHksUP3ANchNdrNL5r+2c1SPhq5icrc5HpwHERvpDBHcbbHp4NgRoLTHJJ.R9XO1ik0Sc+emEL4YZSpCiHO8iNma827272zzyyqsiiSeg59oPHDIcbVK4FarQxG7AevjVVVo9911QM4POU0pjpHjnFng8oYysSer+q0e2TuGGLcwcCCjxn2UKNLHP1+BuvWP+Jv.b9Wbrwi2TMd.RPIUrGG4HGIcDJ4TwYJUnbWWOl36kRWW2wm8Ye1ijpmssERYSojZWnkUYCSSO6iY6XZZ5XZZ588+9e+f8sm8sAPCCCqlRgnUgn3+e7G+waBzx00cq8r5dhQYYDZ37acpScJEu+3ROCEWNNBHb80WWZYYE5DEqXoRGcnuOCWDFu4oG+ebA8hssFrMuPdfIq2VBFvRp+egRxi6Ak6VHx9UHDQivyj7uNS3TvcVvjd1198PMZ4fPjtToRy333rKfEdfG7AV7E7BdAK633j+K7E9B4AukxqVeI2AyevyDfP7+1s8rYASdRvS2EFA5iJ.iEB8wQNKj6d26VjNsHIjO4FO4ND9LsEBGXLvvpn2Ee1RHDsFMZTLOnLBgPtqcsKgkkUxSbhSjxvX0TEgTPoTO1i8XpY+pDo+nezOZZHe5pmdv1+rppXSGTSZkhkLo6ofJnxA.Cde+Quu9.CJqfJ4NUlBs5aOm7yHDhYkR4r5BwrPo4PIevKPQl+E9BegyMyLyjUSSKkTJ0RlNIp8iLDDiPF+BqnulPzaMGm94xkcfk4ENx11MzvZUbVyQCPXXXfiiy326688NzOJ3YOOuwd.PdsUfjPgznHiojUqVMxwPowqYaGd228cGFkzWjyzEiSLbGiRwi.XoAjnLkRQYR666mFUx7BTIiLVp3dkQs6zYT61sGIPL3c7NdGCPnpnq4QLGXZZLPHDCVbwkhQAvl.0d0W4qdCSyirAJT.zDXq8nq21Kh7Lgk5.z0x5E1EmHoTD5TLpHOS08+gpJKaMZYXngw4ucxEKw.vd.jezlO4YaTDMDEwctZHvvMfgJYwcog.C7I+fW5K8kNXgEVHdgFAPFuHBESJk6xGlEVN6RPJEik+z5vbGApVZ.Pu27a9M2CXv4cvCJ0MLzz00SKfrFFFybbW2cYZZtvse629BNNNwDG3LAQD504e9me1iYamFVJ0BaSbi+jrEc9cnPfPeLBIXax6Jl.u7UcPX.v.UmcrdxnK4PH.OAXgy1nyIAPpkVZoIbiBQnXPEnmTC1dbujHBQxX0BSpf.d++Iu+Alll8GMdTme2e2e2lRnAHp9s+1+iULMORYSSyxW8Ue0UoH0MLLZdNmyEs0MbC2PaKKq3fzF.L3085dc8m5OOMz62tfHPHOxj+twG5z65bHpQkaLnOpXTxt+S+S+SJaEcFAqO7Vtk+K8qE0gLhf0o5mVS24j5555Mp.Mgfl6d2mabQC6GUL7D.oW4LqnESYms9Hvn+Tx9bWoT1WJkCMLNx3nQmS333j54cnCk1xxJiiywy8o9DeprTnPVexmw+D9SHvth+3WTto6nUz8tkCAjuq206hn8YxJPJnbRgnTBgPjvWJ0FOdvjq0ROc68mz0MiEVBI3mDxmyWJm+lto+zEu4a8lWDUhrJzkfhXDiPJoPpFCwXjSJkQiHgoo4.oT1uamN8kR4f0bb5GRXWfs1byMa.EpWDZXa60zxxZKf1m0O2Y01zzr0d1yKZyu+oN0jmyLguexOsJNM.XfB1zdSJXW.GXDAOoQC4zrAOILFrFuLL5RtjeksQOfEQJBxRCbUu2N59u+6ex3kDMlcojRY1VsZkCEGKjFrSF4G6L443n.nOrR2RPm6+9u+t1t1Cz00CMLLD1NGKkqqW1tc6tq21a6srfgkwtA1sqq6B6cuunoPXBoCBBRbQWzEgZbvK9SSXFG8dyxOoD0uzK8R0jR2j3GlDHYEVVKh.02YmC24Gs7JHYmI5ZZ9gCGtfqq67FFFyDgNyDaWrDYrRRDcuTNVUPNYWP15D+vGrgoo4F.UIPgHy8t28VtfZF180srh6RaCOnIX1tHzoTom+1D75xzA76BF8prs8yTm+SV+amHo4zeO8Q1tX0GJxF7jaG2vPEuVrb+BBQenT+IclUmdfeegn3.WUBbBNMt0I+yjuLA.OBG.pvDhjrXzwrc61wIMG555JLMORROOmrttty.rKKKq4cccm2w4X6x11dlxaipkDaeLs1Y7nQH21dbz5e6rHgmIaaaib.R.mLETJcEH6K6k8xxALqggwLsa2NqTJSkOe9oSdamHYRi+e4t28njjq5677yuHeVO5p55UFYFQ1cIgZdnRFiTIAFM3EYOyvNVlY.6iWXVarGav3mXyrHj.6cXGNLX7.5AL1fAOCCiN9r90ZViQ.dEXFaCdGrEVp6V.lFAzHT2cFQjYVO5tdkui329G2ajUVU2p6RHI.u2yoNY20i3wMtwu6uGe+88KK4vo10uYWHiHR9q5ptpwwRZ5O+m+yethEKNSmNclLLLpvBKrPFrDXcT8H0rsKnZRJWLEipCvZWCCJS1v22+72xsr7ZPi078ug0u1q8ZOeSJeAKh31v22+B2xy44rQkJUrq4LsvUSisrs7gVfWmJUpX7WdFRLhSQ0Tzhj5GmEU.KND8R16+3Su67PLPbDKMp8uKEWsYQVQ4jEfXe+k6qp1SUsiTQ5.M6JhaxUe0WcJmjLburqfuZ.nQrXBqLr0h1909xdYaWoRkVSO8zc+FeiuQbXXHIIINuhWwqJ6ryNa1YlYlr.YqVsZNCZgixCKjqwvhcLb8WV3XirV7ao8SS+TgE0YAEVX22eKAPCEPO9w+hCayT1663WtgTGWGpuawkiBCGFGoZuhssRhJHIhHwgQg8A54eC9c888aAxVML660rZ0pQAAAA+J+J+x0dAufWPnmmWivvv0.1npmmss4atyUe0ees.1wya4cr7JWKfcNpsXWG4HGYXqRGB8giFWFhO5Quwg1p3nzGpM.lK976lDxQRTpg38Y+IHlSGCGa.vf5bzA+qdQ+qRs00yyfJqdMM9CmfwegrP47kg71Vx7x1RNLz1pWJJy67C+C+C0w1BSIflod8FEUkw88Wdx4lclC8vO7COsmm2gas81y.b3ufgihF+q809ZEh1csz++Fzl7TMBSfgS5UrFSh59W+k+xc52+bcv3jceKJ.nBqLRuhW8JMolFzrMa9QV3nUZqrYylBg3tUpTouHRRPPfrzRKkIL7D4Z.4CBNdgmwy3YXpfPcCgpEF9EFAN4SmYwmdpdzkaLRF+qj4nCIRrnhThB5PojbgXVfdhHcde+meK1duLv1pJGauAbWxj3EU0boDuacXRn9T.GFXFstdXfIiBCGKzXnwYP+AHPBHwfNvy2qWlLY5BZGUM85opZOW25CDQiglpuueRsZgIuy246ruuue229a+s2gEL+twwwVNKoYlUgBPywwjDmhyO+74DQjxTWqVsZBFmKz4G9R64S3XWRilRkJ0rP0tdgW5K8kVrb4xEBCCyoJNggQ3uruJPrlnCFe7wsHjgdG8nGsKpEl3MoaPPPuq2yyJ8Wz9u7u7udG2Tz.PyKDDDrwcdm241.sN24p2gxl.H77V2N+GY+rZWfdM1KZXFFzJTafIYGmOMiwcYcyuaUVY3FEm8rmkc0u7HXoQOFCcBbP0pqaONqzAn8u7u7quEV0k.Hya623sMlp5jhTw5z+ZEWGxYYr7K6Z6kF5fT891pWzMLLrmmmWbXTnDEDjUDJFFVabfIpUq1j+f+f+ylv22u3q5U8yjSDICkHiIS+tYqVsZNX8ra.NvYdx7t0v0BKszor+qP62urJhDKhz+wdrGq2q9m+UaSXhu0QlZ6u5XvoLuyU1nHBN.Nh35jlzDLPTOGPNSu8aZ2Kzg6kq.puuugievb9+0+0+06DDDrc1rYOOPSU0HPCtpq5pqEDbx.f5Kt3hqZql+Fkn9loUjHHHnSPPPOf9+w+w+wCrpew9cB6RWMcH9TWrSG1JED0uwdC1cfk.kGTkym1q3sgJsqNzwwZswcz9zd9svksTU2pToU1BXGQjN.Iu1W6uXNfBql53e0CDZlR.hKYPeUGfVwwws7775HhzsVsi2GWRBBBbTUynhjixj222O2K3luwbq8U9J4fUxUtb4r110vndXWLbhehLFY9bsAL6tH2687ddOYrs3VdUqW.Cp5xt81syBkx.H0OvIijDBPAxTxvyOG5Nti27gu82vsOMvjflVoQGa94DEjImbRwjHggq+R7886GDD1Cn6XiOQGPZYe1rUqVstvRKsz4CBN44+rOxibgpUqroshsahUwZVfnKbjibDKpRpt0bCCZXkQaOhcqZ5dcda+eu8GHmccap8uMRUumdTKsJYq2SLN50+68686MkujPDICkM7evgNzgFCbKhmInxEW7TGDmuTnpc8+p8pCcu9q+566WwO11sIYDjbddUJN93iOwO7O7+xod9O++IGtLb3jjjof5GhR6lvDW2qygYMG2JUZbPbn+akQpRf4.qIm8rm0dePLTZfZBrWUsg0eg0F0uo8Ck9Q4Hmbq.EMRsLSAb3rYydXfCEFFN1ZqsVtz+VQFgGqkcQ4gpzFjsQ0Mutm60sNvZdddqBrxlat4JAAAq1zjjj0XgTRYtpUgUBZ0.1oQi+gTjysCqYVmMm48+9.IAAApAbJfgWT2i8t8+U5O+RY+y98VxVkz0Zuhokf11p9c6PjIPmEnYawHW5wPYms1ZKafqqjmouRphzoU.DwvMTMrIL4rm8r12WDEvIH3j4erG6rE877lTUcxfffISRRl.XbGGmQZAH2rkGF3ZMmWwq3UL557QRbzotTIv3fNLqMLbzRNepWPUsvm7S9IKBkJnplehIlHyFargR4gUZNFVbzyWJpWrqwlwArpWHjkRLFvTf6Lsa2dHh477pjakUVQDCy3jHHwBoszpjhjoXPF.zKLLrKP6vvvsculm6l+M+MmbSfMKUpoMg802rToRW3du26ccf0ZXPD4EnTZ6DtfMX1UZG.sfvNAAAFjibdyZlkn1HUxeotKMrh9mYTaa6mjmsuSdpQ+YWh.9Olc8Y8AMUs227a9.cDQ1QDYGpaPxRYZ1CPOwINQFnRd2gEc3xZiyd7Oi84igS7r7fWqM1bitWy0bMC777PDw4u8u8yj022ufuueZQXK366mGHePvCmRbsYgplVyAx.mNikqlNHswwUXbFVGDXEqeVRFZtKsCr6wsrV8JmrDYognZpg8XTNWRRRVECY0mxgk1ihhAIlwppC7p30GnW3CG1MHHnyM360xx8MWHHHX0az2uw66889i777piKM777VKHHXiff.ivS.sWXgl10U0s6UtPGfNmMkSbpjx+MUs1AN6f5vfO8m9ObWDibVSgvpxZCKH7H9+CDs+jCOx+9z12SN6fO9G+iO3wdrGqGPOaA0GlrDnrU0GqWrNT.ZbPZ2K6bVXJEIzAnyLyLSuolZpXTU77pjUDJDFdxhUp3O1y6487FOLLb7Wx+henwqUKbBuk8l.XbXAKOyc5KiMz+w23oqVxIYQxaSrwr89mccWWmb4x0Fn0ZqsVaftppwOl1UY3l60NHN6ld7GLOovQq4VsZ0ZKP1VDosHR+JUpjVUYIIgrkfBppo8nZQLUMNu2yyK+i7HOh0nwFYNy2lkMv8NhbNqUtccg7ZCMsUFjRrRBqPOOnya7+8e6TduvVYlSaCDbQigjllq80WecQDw4O4O4OIq8dexOxG4iLElr+eHfIp34UvjERy8pZCxRP5GFF1qb4x87775truWGUktK6626DmHXfkGPFDFF1CR57U+pe01XjHzswHeosuop2TWfAh3SEiSAiggYrGCSBcj5ksYhOLLwyySW0NSLKHb5KtpNUAIJx0AHmKj+c7NdGi.IeiLhEbh..zBEKLrsVDQ5GDDXj6NU5Z23r6IMvWrsuue6myy4Y01Vs6cJCa666u8u8u8u810pUq0xGobapS2RPuvPCBON1vMZqsmJQvEGz5vqC1avGCpszt+MG8nGM4nG8nI.IUAkScQUXKAHtVMCoYVw1+pefOv605Xpam669tu325a4s5.juDIEe2u62UQnbgRGrpuqmxdM6BCvvV+87775GEEEiJfHYRTxppTP0jhUqVsnHZtff.me+e+6U877hsPqWqetuf8cHWw6ov2kN0oLsAgGXIEw5VGWpz8ptpqp808rtt1.cbYPJ22bIpNlIXh5yOZ6jzLq8cgr.47p3kcs0LpLifUlMEQLpqIhmWEIsBF.wpRu+1+1+1cTUOuHRSQjvpUqdVe+kO6xddmSDMUcDlmA+SZ...H.jDQAQUOE4RMKwNG4HGYGf1999oDDauYLy+6mnZGcr+fF1+Fqit1YzpTrmjvUaOqKi5UazfjaLTdZaOMq1lFzxWj1MaNeafNKXNFY9fevOfsx0kMaPV6.KmzIM81UhgylMaZh+F3fCXI9ZQj72nmWgZmHLmKjcok9mmYt4lywcHGWDloQiFYfi9joRF6Yd6XPBqOb9Rdmug2PFVgbu665tJHhaAU07t1joY4XJwHXAGnyQ5yImlkovce22cJmFLwpqtZQPxGEFsm6EAjs2dGyxOSx4hEXPPPv.LRWcGe+aXGP2VDYCU0y+8+L+9WGXce+a372xy44bAfKjjjjRDga.rwJlVZ0FfQsTzwMZvB629ydQ3zdue3R74kx92vupOynRfrz0yyqOPxK4G7k3P8gI8O21a+MxQHY8fLm4LUOHqsziQQ65buAU18bFGTKf33XGee+rggg4hiiK9C7C7CLwG8i9gOzwCBlRD4PAAASFbxfI.F6m7m7mLeEVMCqCGEznnpOUmvjg2Kmx9dyLfbzidzz4u9dzrmMv9zmOp6vpAWy5+xwFUMXxVwfR0hPkwcMqulFXVfYWas0l1yyaBOOuBc61MCFzyYkG8877sOPmjj3c.YqkqV8BpQ8AOeTsZq+67676b9kVZoKXJxvBl0QqXJrvBz0F74B8lE5355lxMDC4Gh0VbH5RF366GWsZ0XfjEuxID3wy92H1.OkMX3JcmwhhNee+zVtvvIJya9TDoupQL4jSlCnv7PA1XHWl73YOydNirm2ER.heguvW3.fXW2RpmmmCP9EWbwwCBBlHLLbRfwqVs5X9994UUGp1Em8rOX151moyBY9ve3O79sksea7iNObPFoIRaHRlCVfBhH1.oalCS6FpO6Ce3ATeWzMtjI378brL9hEgo.8fQzyHi1PKXH0z5Se9yu9TG5PGZBOOuTEIBEh877FHhLHYDegr1wRsGzMIIIkTL2guwWZaOOuc9U+k+ka+fO3Y5fgHMac7ie7sd0u5Wsk2u7t.VYqFn6Rrf83sXWW6wzhZkgH.5T6YuwSM3T6hn4KBsHr28HtT66N5yCcIxaSrha+xhz9pu5qdavTzAfs+w+w+waU20z5D2vMbCBDkqgcsf6UFcGijrvFVDxTxDHuRGqu4JF9ELu0m6Itsa61rAyZRDr+M3mCH6YO6Yy.0bV2tNOJJR3LWr+2eqMpJGcus5is8szQQQEP8KeCsZGV++DO66JW3BORVGGmrhkLgWXASeJ546oX3uDSLMh1KJJpKJcUU6kjjz6jl3.5fw1vl0ssj7Bvp0Nds0.Nuuu+lWuu+N+edm+ws7f1e+e+upN.cN1robz0JoEJ0rGSzv1yxttt5fEf9ujWxKYzhPL.Ht1w1ce0Q7+WW7Rmb38T3kp1y4UcUWk8cl4Gl7YQDwEMKPgM1XiBt61lgGDPInyBIrxtsD43iOd+ImbxDDQBLEXOGplWDIepOy992PtpU8xGdBCgV6his8FmI2wdxWLquqY7zABS.POCmwNgutk.aJsipZqklat13Z13ufT.XdGVvRLkKcvbzFHY0cgR2VWy0LwlSM0g1tRkJsBBB5EFFF6eC9Z4xkEQzLBj624C7eo.PwxPQJSAOOubrBYeNOmaIKkJYbzXVxr3hK9s+9tZokL22tHhHYRqVpHtNTFml166Pn+c75e88.5d1cMtKKtHNvYxLMSm97LdtklKFH9U9Jekfw.UgezezezwKaj54wRRRJ.jUUbrMZPhEkBCTR5CR+ffvdgQg8NQsZ8DQ61v1ubhH888864440qZ0pc9jex6sETu07LeZUo2otacaaFDFiA6k4UUK7+yG+iW.WKafWmjG3AdfXum2yKtVsZIrfohtqOz35Ri9BtTCDaO44z.x9re1OaSvsddYLAuXM.qHc60SspXQhpZhci53kq5M.SO40AnkpxN35Zp7TIZctyctV0s8EJL+VUWt51+cm9zs.5Xf365Cv.QyKELLuTUB6wOv0SQBrTLUqZL35ZLlV6RCQQqwRiifQiPLTUpv1pVu0O2K+mqiHx..ZVtYla61dy4f54F.YMx74AI6xnMFd8VdPnQtmiwP03piH1M4jrAAAYVd4kcVdYe022O1E2gIpr7xkcdfG3AbfF1yW0Kyo8JNF4Z1PdlglJhl97sGD0tBUZcG2wcrCP6FS2HcirK1oRWyl128u1cmEJmiRCaWr7.4ihBxGFFkagEVHSXTnybysKD4E6bXXXnsh+n.862ua6a9lu4MEQVyyyqg2M3E.TCZTKAh77tol.qWA1788e98s8O32+OnsuocaMT0iVv.kyyuGhb9wspKWJGmuTqAuTId6waMY7999C3XLXCqSsFHlt5.f3ULuSmSDY7s2d6If5i6h6nD769dtcIt1CIFJkpbTcN8id5dppIdU8xTqVXAe+kGa1Ymcr6+DmnPUOu7mLLLSPvIRe+enyWW605lANqiKtOY1Pd3b0oG9tWUvCRqd5a31u8b1.KxUW0LyM2bN.35BPCp73yb3CsEr.nLmctoNYu8a+1KnpVLNNtXu98xO0TSkQQ260uA35IVTFXZAsQRF7xK6283G+Ou0x9Kuop54+O8e5+zpMJ0vTweZttCb9fffKbjibjMJCahqsR+0oErXaVZogJdFWdaYWp+OboWidor+M5Zq9bd5Ay1gxkSaUht.C9K9q9K.vQjJ4jxR9ImbRCAkaRPvUpnFJ.mdoSaW2GNHA5qpNHIIIFAsYy5hZTpnrG4H2Tt74yW.Xb+k8m3VtkaYBee+I788GOHHn3u+e4ue9H6Z5yBxUfmZ9VcXtWLTzkb9g2GUGfK8BsANZqPcGLsamfs8aO5Qo.b5hyxropuxDQtQ1jjDMi.yCtk.JopNW2tcmNLvftTQDmzoQYDaEl8Qjd.cxHRKeeusOQPvVU871NHHXqJUqt0q60851DXyvvvcfUZufIH0A.IqTYE6y5U5eHSEW6Az8XiVzfyfAh4UpXPChc+uybwqAuTiCh8uXHp+TPWvHwrK.cXFakdWk9v7Cv0Mwhtlb.EWcHgRWMOtCaYgQBtauWCttnvJIknTb56nYxjIIHLzADKpgkh999lfHLGyb999YgRYYgExdzidzr3aR105CSTiqCGa3Z7Gu2IOHizigiKjAOagBVgbpp4w0MMwPHhLnwvhA41GWhO0Em.KsFnVNhHoRETnIKvBN.EDiOlS544OwVascwvvvrggQhmQgJGDFF1SUsqX7mbWdqSFlTqcbbbFpDbVRlt6688+96s3hK1Gn+Ihh5Za+gsmcV1FB2w0f1h9.CNkwOoX3LClC5aaylzBccws25i+dmOdqs3R7yFNNEojaditpofVaAromHappt8Q8NZKZPWnbLoDXZISa4z3fuGZLP+RkLb0z+v+v+PaqZSN.SQdxAL1i9nO5j26u2u2T21scaS8Q+nezCgqQliCNYPNvKyQu4a1bNlwbbsOWUKYCO584Szg.0jDP9Zesul.HU.mnnnQVSuPByOu47U8xd+ZG65+Gfb3CeXIJJH0WLYkUVATzvvPUMn7xntWpSWOOuNhizFnsiiSm2467c1yy6F5gqaWfNm4LmoEvNq.aUsZ0s.2snLaexZ014m5m5+0VgPm+r+r+ft.8N856sks3h8mx9UsASaV+0Gn2Ri92bZKR31m++WA6eJPRsz8PgdddzEVsyBrPWQjtpp8qq0U.Y7wGOaBjIU7GN1UFwP55CeunbLkM2aQQQJf3644HPVExFDDjwtOpTq1wkRTJimmWtvyEVrQ4FVanmO2VW5Vy4eTlzjmtZImQx.J8paz651hHsqqZGZrPuxhz2Df7pBqXaMmScfx.Fr6hk1.aWuNaN4jStYTTTawpVN3fSlLYxppluAT7+363+x3Q0pMdcXLpuPgfffrQQQNPCGZ1zj480IyYNSu8Cu0mtGBmx1lAMPfRZILxwEtMUpm1lr14SW5a43i3z+9yXXt+LavFCSXBwjxqCwhHHRYGLjtZdfbNNNYCCCyLwDiaa0.RaufAK6WMVQiQT8Te4Sk3W8lR788iCBBL8g4tU6x.GLaVUWkUGVg.5uakjrZTO.YeourWVVZTxrgPIzW3K7ElvJqjTsZUkUPf4bpllQxEO0nOKLiEfTgR8C8gtWBCC0nnHIJHxwyyyQDwAwTwrffZCWGJPRXPPbcHFb5Khz0eY+VUq5scsie7s888aQSZS1rcJmtAt2psnI6bri8BRkfv9rzdHmpqjCLWtMg2MAH0pYLX1XOFguRIfIkjb6DEQaQj1MzF6VwrHM409Z+Y.PLIfRGc87kYc8BpW50Vo5wppCvjDlTNHJQA788AfSdxSJm3DABfzvjLqr.4B+BgYeguvWnC.gWrCFeqLLIQqJNvLiZ7MMQeciHZX0CYbd7PXBzvkJfb620smQ0nrzrYdfhhHiswFaMlpRAOuJ4RRRxfhr1ZqsmqCc2pkf832MWt7aiohDq.zL7jgM.ZBrZSXcHdiG7Aevshfsemu86cmG4+we8N+I+I+IsfF6XIltVlpyVIcS0zmyWowSl0e6OoJW72+znVdavb7cAK4mlQDIupZwIlXhh.4aXThiCjsyk.kYPglwXTNpdG6YbrA99KKpp4md5CMVsviO15qudw+1G3AJDFFlWUMu+M5mCJkx2.4CBBxs95FGLsq+dpylc0Z1N+pD.b228cqpppHFGJ+TepOUBf1ngYtIx.k1GugBFZOf0lixfPIx7tuy6N6u3O2uXtLYxjyqhW1ImbxLdddCUFG.luToTxrdf+x98zck7utppcazftttka+59O751x22+B28ce2mu1WH57QQm6B.aDAa36e8a.rYcXGZLW6EGZ+9LC3Tm5fj72KkiaWo.2tbq0rATrdOpW2TouH5Bk5iAB0Bt0yPCxAUFVIdSqRbrq7y4cQn2.Sh9K20fHRFbnCMsZQGFAAmPdf+tGvAkL0NdsrelOyeSNaapZHy7HxYkjViMG2mF6G6SCfqceXhYlZwl8DbSUCicXdZCyGem24clx8Aic1yZjC40Y8z1tcVL6TVFvuNbDPqBTILLbNOOuI878xKh3XsvnnoOSLnWJIIoOn8DQ53U8FaakWy13R6O4m9SmZmsUsZ0Z6440An+WL5KlZuRHY21I4Ll.G5CLXeEZvzZWQQonZa+6+cPFWp0g6wGzyXaMQf9q.C37KDWATisrUgFMDfLc61MGrPACYkyXLWshzfBvbiRb4i5ShI4sMrepMTQjj986GGEEk364oXPuSFPyFDDjAHS3ICyDDDj48967dyFU6KjkUVwXOKfBW2M8+TgERWqOaiLb5KYRZ9VYX2ilLDtPNU0bTh7hH4nQibpQcIUU0X2zVGnZiAz3w0WjgIAMJxXyXEVQrUbtPbbrUVt0T9YgvvvXf9992fAt+BsHIUMPXST1nVX3Fhpaln519K62xr1pTZ0y68U9JektP4NhHCEG.qbr1qAMtT9NYQRhoMaN8SL6bGz8VuTe+gw+zDZCxN.aGA6HRoV2687t5X7kutJhj4QdjGIOMMxXt2ieB5tTWCwMaZZehm6y841MIIoupp5uruiHR9d85M9y3Y7Ll7m4ey+loqVs5guoa5lllFkltDLo+06OFDlCy5Rgya1qewgyMm5RgdlmviZf7rdV2hCfDAZ4xkGT1PT48fU5ypqZJr0t4h9w2+gkPgiNbt+c9NuyjJU7UQkDOOOc1YmUsMjiJPLnwJzGztgAAsTU2w9rn868C+d6BM5G9PmzfriEWLAVXPEK+cPoFsoN6Ts50OL4cLKc2scathIZSwT.lz0e6u0ouR9+u+wnG2zVBqWXnINfUXkg7Am3ZRrdtb4zUFo.Cm9.3a1bomqx0MLSEPkJUvyyivnPG0zZUN.xEtvFFz+WspzjlYBCCy6cDuhAGOXX2bX3ImcdxvKNeWy3oMDlvtOT6aj3sU5ggCS5AN8aXf8Cat4lNP4rXHtuCpZ4jdr6h0XaiUVYSRXGKwNI0dnZ4BqENV0kqNAtbHUilpR0p19DWJ566m85ewuXw3K7B7A9.e.oJHtDc.Bp7o5wwD.bA0klIVh6omVeXBORRSZRkFjrOk+vACTJy.jY9zq6UQKKRxwdtGyfzVstHhj4cc22c1tc6lKLLLqmmmyNs1wVz7zWf03SDDFKPhpjbcW20QsZGWcMNw1aYe+NU8757k9Reoz.RUS1GKk.DOc5KxqS+YSIsRW5IhLPDI1bsz.QDnIJtjXCHQlGb7YMmZfyq407ZDLvudzg5tBIPTLP+1s2omp5fJUpDmHpFFDJUpTwQQj25+9+8BpnpnwXQNS+ACh+6enGJ12+F5qp1kFzhRriiiy1XZEm1KWoR25oPCMrrEBwq2c1TNJ4TWRijWowUxn59yL8kayXX2MiSgBXOX9NhHcbGAxyW609bMqSlE0P17WwgLGqLL68zrDNNNpZcvv5HuV0vcGlEjIIhuueFaPrEKCEdnG9gxIwhyBoqollDn1SJG8vhlf4pQV37CgJ8tISzMFne0Tm8htnMzFY3nQfRCTojj9tSAWXrolZxw777FKLLLuldNrvTMsG+GtnTIACZkZqhron54CBBVG37ppaTNscGJSqZ0NYqm+y+4a66UsUDz9U9JeksnDsgpsAZWE5tDQCFYSUt3q8mxFOAVC2fpf7te2uaYgF3j1FJppx1aus82w.E1CJNhNE.mGEORrx1Xef3vvSJLTlOcFCn3OxK+kW3F77J366W3+kuuWdQn4v1qz1a14f4yB7TEOTYlSpcTavqMiKCCti63N5CzS0nNhHc9d9d9dFgTdqbPRt0vQcPnIhlSx769A+cy.jIJLLSTXnDFFRXXX5CEc0UVMQMjgXufSDzQfcDQGk6a5Jhz86+E8hZ6ZQGW0JU15FpbDCRRJwNvJ65zGyz6L6s5XOQrk8TwX2JLtDIV9NafOzuLMGX4jnDsthpZVHpPY6y6xaPtCfZLo.5BoIkoLcgFs+He3+zV.sspk1.ERPPu4+I+HpJVu.E0w1JEY51sqsEWVI666889xB3rPimVJjhBnKAZUZjXSNVBmetTX2Onj0Nu6pL.VUeSuo2TVrx59u5u5u5Tf6L.yUNMQIMnZYXQfqlRbUqrxW9Hppk877lILLb7nnnrI.pXgstXpDKn80Qp7+YNyYZGDbx1nZmZ0p0M73058y9y7yLjXs2Uh6KmToRElKssOZfyh6NOsejs8s59eOgmS22weX.fQfCMLJsgYMF4JTnPg+9+9+7hMLILYbVaAa6KrVgYsIwn5tjjY5m1j4UNqHhiKn4xkKYgEVHNsUisI+zQUMquuedOukyCT3W808qVnxxUJ.UJBTzEFqwC8+awUfBMZzHGqezCZ6Md4Fi5OsM4M4yJhjiljix6dsqppt6hvjATalQeVL575k.wXtJf3ZPyb9l0alOJLbDB4WAEUEINH7gME6QoiJ5NhHa466u4x99aHptwNs6tcUe+V0NdnsEzZFGDDj.D+CdsWaOndmxFToMzWmQH2cYeWqilf1Q+J8m+zwXuA0Vk9P8cKlHYLDReChMEioT1m8y9YWnBTzkUJFd.kW3Qt2F.t8.5433DGDDnAmHHSTXXt74ymOLLrXTTzX.S9284+7S69Lm9vMgYXElxf1j4yCjYFPpPC8L6dbepXH3AVd4v1dLk61vx4JF+epL.HoZ5u+kovdFJr6rCma+090dSw.Cp3WIIJLLYs0Va3ZRQM98ig2L644620z5yK2QDoK0oOkHw6HKK.YJAN0pcRMBFbu2681klKz9+3a8s1BVcGaQT6Tc8gsayAAYROdq8dxX+a+IjyFGvLcAZUxxgYzjcpHR62xa8sziRj.qbPWqKqkN2Wur.HIIIRTPfwGXEbccM0MUQO7gmFSxSJkxclEv3WVw4SIx3xjChFMIf+iVTl7zUBSfQevViAyaMBab7qQ5KiYNzgNTNndNlmbi.EsqzDoYwhG8.ZUF1rboRa3VwcqVsZ0od85HhjGGlPanSSClIJHX1G6wdrC+4dfO2TPywBBBx13q+0UZRb85eojeo2xujVCng43O54+o6GpBbZgYQZ.RcSDfwRYwp.GlMAjED.OhvjEX.MEBuSap1aVlmrqtaeLmoQoRYN8W5zYEQx4JRdfBuo23arPlLYR68rLnHddURuKU.022KAgXTM4c8tdWwfN3DgA8EQ51.5DDDzc1YmMlRHe8u9WOyw+7edSu76grwHFIV20MFnGMn6BjRFR6IPVo1wqYB.qBNO327alIv97++1G8+19g+rBKpFk+nbeft2wccGc8886FEF1WPS778HLLT787bdaus2lff9y+Z+4i+De7Odef9YylsuekJCfFC50qW+vvvtzjVddd6TKp1NGOHXmURkUNW5cly74GRTSqW8RBY8mriKkg0qjyh69yqZ1.pBzW0UFvBzuAz6C8g9PcEQ599u260LeuNIyc4udG5T0ZoNUUlLPyLUpTQp54kddAfff.sWudl7QIhDVqV1+p+p+3hTlwOQX33ye34KT4HUxrxBKX1LXCRV5RgziC9PXIbrppUd6F74YAxIhjYAPTstxBCgp3kBYI5teFs6lMqPxi9nOpCkH+PGkMAhjyQFUYbzTnds6yKgXP5IPmpdd63ciU2122eaLr6e25kJM3U7JdECnN8uISfEcwkNPyzdmsCMoMTqCGid0fuckrjmnCoVEbtsa6dxXpVwBf4Zr2i9nOpMohqzCWFT6I16G5K6ldYwPi9Tl9ekuxWd.fFEEjwyyKuekJ4788yIPtSDFjKLLL++2228UrVsZECBBJ9Y+re1z.MJ.qlmJj8LOUwCUUA3roH6necW5bO2y8z1zVDk2An8m9S+o6.zedCz+UV7.bO6BvZCCf41usaW.brUtQrhHA.Xg9WhXkaSLsHQafc77ptsJ5NufWvKnyx9989C+C+C68bt1qsaCi7LuCvNmHLzjfjlzd5zfKVh9VBa86TIJYuHP5T12Gmgj.HtdZed6NTA3b.J7QenGXLnzX0g7vrGHjLshQ40FX4epV+aus+21YgEVHkqb5IF95JNL7jIUW1GEDOeemOym4yjkRj8pu5qN28e+2eNfb+Juiekrv710+Kwk679DbLztzoXD6WUIFVa36RMKSBLWRCShqAHCKPAfI9+589dmFZLKkXg5P4+n+n+H+69tu6i7U2XiEUUOp1POR+tcKGEEMqTVlDnfmmWFw.i8DPGHNRe0TPqgJ+FPqibjizx22q0u6G782pZ0ap0cd22canjAsNKXR5lgOipqTZj8OfLmoREm8cedPBr3o50jiX2G0Tg5URSfQFpaPRzK8k8RxIhj+4+7e94ulq4ZJRIFS0lEs7+Vg0KSA3nEpAEfiZRX6BFjnLOLlpQEvkbM.m986qMa1LAQhCCCSr7IgTsZ0LAAAYg54EQxGFFV3O8C7mVDhJhKi+PQmahUspyj606lGN6SkUi07thGNPvvBrQcxzoyi4XAQ2fGqUKSRfmiAv42exEz88uS.TSmbzPvcHBlKnhVPgbyO+7YTUcDPPLYY22zZNwVNEoaRRR62x+G+56XChdmm4y7Z1ILLrc0pd8N24NWbPPf5eC9IPEq8g4MIdnjofbtXKHywFMoPWTbDWp0bOcORi+IYtz02UHApmd9k4latLPyrgMBy+e9i8wxOT0Zl8JFGjvhledIHAZDSIiu0hHRiFMxlnZNaa4jWUsPXX33una9lm5je1O6r.yEFFNGM3vvpShKEOOjKJMn1ktn8P+VecXHpuMw.ppcUsdJ4juCMoKDMfiB0tXxq9hlOM7r2ronse.P+ye9yOHLLbPEOuQRf.8q3WoOPewF2YsZ0FPY5GDb7tIIIcCBB5Gbx.00UkvvP4D0pgUDJF7peyu5dvJc90eaus1.cpOmQ8jpsWDxAW90QWIadeqZ+a++cCfy2mJz0R9uaoptUjpst1m001ilDuvtHzdzmq6ON2zuxvBjEpmRJvYTGwoho26zFMZl.x.DhEUR.jffSjCWJnpV3Vu0asPTTT9UccMHyqNY9leyuo4Y6wdJX8z2AGOclvDXjLgk012pejOxGYX6Y.jWDofKTjUo.TYz9E8w8kF6mwXHfy10gMw08BNNNaL93i2RUcP4xkyVoRkIhBCm9gdvGZlanZ0Y+leyu4LunW3KZJfw888sJPCIWe4xwrFIVndM53oyGpCWfVAbXcx.ymB0IrRlZ50i7Z9W7pD7RIYRKS8e5iAfrAX3AlUGJMd4.xoMZjxKCE.FSUcr0We8wxlMaAU0bgggFXxEFAJpsX5wAAAwpx.Qjdu427a1TsIcWIz0RPkCnoqFEE47u7G4Gwg4mWLvWG60HIznQxQsnLJytD5U+2+6+8OrchpVsZlxP1nSDk8pu5WX5yck0uHCHvRmw98paRjQyxcCCC6pPeDm3vvvT33kNR9u9A+f8+E9E9ELDHlRa0jvmdm6bmqmmmW2vvvNkgVUqTcae+aZGf1QQQcnA8VbwckWtkp83zdGO4GOdFPux+c0VTAzHPkxkSXEhu+O882+m8m8msKPm+t+p+pNKXl2iyNRRpr+862vYFnRZEyJR8gRpaNKThc777bt2+q+Wc.xTnPAmpdKmw22OiW0p4e1O6m8XT2HMgW0UcUE.xvJqvr16mS8jadSV7T3vzqjEHupqX5E7UJmGiJPjQJW1gUdbmOSue2+FMw.I270bMBMI2G5CcuEZzHs2KImpFXGhBpdQ+s8MnkitJzNHHnEMnUsnZsA5FDDDSylI+Me3ObBP7CdtyYVy1fgUmEavgKACFQhL+ttjk.HtQ3fWfzoSGAi5N0SjxsddOum2NhHaiqaa681AEN8JPxG6i8whcg9TmdKsz+LKgc6PXXnSXXX1fffras0V4V1yOup5XAAAiUsZ0I788m3VtkW43.icxvvhL+74I5IjD0e4G01qCIKzftuw67M1RUc665tt8c.Zce22mnKP+UgDRxU8VO...B.IQTPToVU4LGfi6tHTHEkNoqwjcaCGyRNrJGglnCPoGh1Ai82V.a6fy1AAA6bxvvcdUupW0PHp6440x9YWK756WLspwm56Xqw1ektG9LZQvxcjKnVNfnGMLnQ0htxLee232WdJ2zRx2qevdFGEgq4dbfis8E60qW6z97VEM1xqBDdxPw2e4LgggY+A9A9AxQSCxAt0a8VyCj2MhrL6p1q6S8zQAUF0o4XpsakvUU0x0AleMEHMId76c2+dNpp4aZHS8o0F57.t2y8bOku8a+1qL0TSU9O+i+wcequk25bpHSSBiq007tttYBBBDC5RHcMVOfNIlVjnkZq.7sdq25Ng0B25W3m6WdiyctGbi2za5MsQsZmXKOOu1gegvtQQQCVARvyKglnesu1Wi50qOb9ej6sQ+b+22OcGL6tm+SM76khXPQU04Sbe+ENP4L.Y+FeiuQNZRAQjhhHEAFqRcFiYOqUt4O6D.SxJyeHfoVElRbcmjFFxz7QezG0wyyCuJUzk88S.R1byMIHHvAH2wO9wyu1ZqUvyyq3O1O1O1XkfInAG5HUtwIqUq13.EH5.o1XOQGBgl0qofkTUUuphEi0FZeQb6M93iaZUu0tHdzZz4x878NO3v7ymyz9R11YBJ533ja0UW07dpf344gJhDFXHl+29u4uo566mHhD+a71+ON.JmpLNsSLnmqclLY5JhzqbSFTq1CZQh3pFaYM8F.nM.nZUw19ROdAc+s6jCum4LG64VCGQp8LI9DUUwuruyK9E+h2kG9V+w0txt1QMHv1oIHTtrZIb+3CcnCgqqqCP1xVR4TDx444Uzyy6PppyDFFNumm27QQQyTFNDMXbqziaBx8T6YuzqDep73NpXuue3ULbZjXQ6A65+S7rfvYwAl8R01F6+bpv513KJO.n+LyLyPxTUMsOdewnzR8Dne6VsGHBIN3nAG2r1qZ0pI999Iat4lIzvTl7pUuQaxGnKMSUOPSgFN1ZCUOoQ8u4ftV5xs16Ii8uc+8O1whsDNaJIp2QDo2s8pdUI.N4RU.oZWRBXM8+mpdQ4YER4Cqw52ueQTxEFE5XKdnBj36ubBNXQApjmFTrZ0pEu+6+9KjjjjmFMxVwV.qa4puZye5o2y09+na7sCDlnrHw0sY86G6G6GafosLTGnbdfwOWudVnOFUXlCnhdX9Z8AX0gbZzXiG3AdfKHhr0TSOU250qy63c7Nxqv3ysvbSdhffIelOym4jAAASTqVnUxiVHC.0Gd7NK1LwI2xsbKe6HCXBfDMTwGVM276B0ygHE425252Jy+s+f+.GaBITphtDKwHq9D0xCKppYSk.SrIjRUcrFpNVRRx3EJVvJkcZFvDPnt66+psplwUMDGUOU0t999se8u9WeKU0THd2tVsZcN0o9q6ericLChQVc0cStww18F7r.TAM84OP+W2q60Ea1rnrCfyIGRBTMT+8BMs8lzDiyNITh3RTpOTu2TSMUuxkK22yPbbInosHj0QPXPPXPOee+N9U86r4FazQDo6K9E+h6bm24c1lX1oto0I1Bh1xC1oRkJongYXOEtOhO66RdY+L6d8zvjDsm2087Rnro2FKUpT+UfXpPRi8FnxnesqQRhFypnBSY+ZxCe3COF6BQTmW8q80lAgrA0BxVK3DYAx8XO1ik2VItwCCqMVsZ0RI+SYcXXSQNx3fV8+gF0OC3vF6xv+knTQndQLUOIKMZrKL8O1U93Y9uyC.Msjt1q407yTLNNNUpG20gUYOajECzWP5lPxt8LpJFYSOQ5QYh888Sdqu02pg.cKyfibjiLzIgyblTYKr5kpuV+1oScGjgfgjUEBQJVrnRYhmm46dty8PswzRjsnQigJ3wtbE2kbLx8noZNMFxGOM5EEFNnRkJ5m4y7YRWel8VtkaI+IBBFCShtmDWlrDklDZLQXX3XdddE9u7a9atqpVr3SRzkbwWuwqX5w3NhHcti65N5VhR8eCugW+tO2FJIpW1wdRXv8bO2SlxR4LgggY.bBCCEwR3qxnI0SrImSwz69tzJHHXGU0s8882RUcKU0sdjG4Q1NHHXaJaB18085dccZ.cYF523hQc02oViIuhWwqv9OWJ8ca67xJT1PjyopmTWLIlTeOum2SFpaJD.kIaEp734ivdd12.TpPR59OIIICknWGb3l9dtoLppYUUyAMxqpV3rm8rEBCCKTqVsB0pUqPPPfoxuVtxwviRO4BjXeiQelrenamHhn0AkUwjPIWhmm4G7S+S+SmHhP+98yJhLlHxjppS8fO3CNMF62G5VeouzI9O7g9OLluueQupd4BCCy1z3qf3Xt7SCRnuuuemUZVuiuue6p99cPoy8e+2eaupd6366u4QNxQtfmm2EpdSU2rLk21yyqakJUFbW20ckfQRnSdVOqmUb4an72IPuzAcLRxxmOVUMVDYPPPPLTeHxmgg9PUDXrHXRVmo1byMmBWltDkl9BW3zoyySswW+qOkp5D.ExkK2PkG7DAAJf9bdNOGQTICP1a7Fuw7yN6rE.2wBBBFuoUkrflimISlBAobJwEOG9jctTARN6YOqsJ7y2CnajpcDQZ0qWsVkRQX6dQB7kaXHrzUWs.FzYNwNs2YbfhyLyL4DQxffnJ7U+peUwAPsxk9q4m9mVBqkxkPAI0BenAK6620yyqkX70bKOOus877ZottcpVsZua8Vu0A3QrKtwm4L+c69dhw9qDDDHlLv9cMUxVAzUreZ4Ilzh0LTwvTUGb3Ce3DKsgu+q8K5dXIVxwR.1l0J0qm.LX0UWs+Vas0.TTOOO4DAAYts631xnJYCBBxCXsSDO0W7K9EmpRkJG5DF0aZLX8caihciC4ISagYZ84oQWXgEF0ll8dZgr.4rjbb1eoeo+0YV3hCl+hGKf5ieLTeXKoaUXs99UpLPDI1yD+x.EhKNdwDOOeGupCEFBw0PE.CJVr3fFPeOOudAAmnm6tJ4UZ6dYH40KMmk7cKCkSe5c26vaHJaRpqpPExFZkTZt3msoyIYwzxgEwfxsowvGVSu5pqNAPATxpfCBhfHPCw19pYEUxGFFlOHHHOtjyQcxBjIBDlC4re2471S3wS2HLAfDNiohrXCXv1ixYTMpHvj4ymeJW3Pv7iedyC0CRRSRNlkTkvDzxl27O5MuQ2tc2ZxIlrMP+W8q9Um3444jOW9rhHErx413Uq5MVsZ0Jh6JCcxdNSE1yrh0QnO6m8ydkew8I+XHDBm2Hov4Wc2E1EnjYwcgIJjAJKfuNMnjA8Tbp8DWnHhvB3TQjLrhw.jToRVU07epO0mpfHx3NNNiswE1n31aucdPxvHpwfHhwXNn999ZXPPhaY2Xee+9m7KbxN+o+o+OZWs5M1tjEkIUqVs0gO7g2wyyqUPPPaf9LOwyApMONBUpXN9QCCzrmHR2VsZYpdQYSknpTohtbkJwv7CBtzpOS5PmETDRZRy9kftSN4jcbbb5DFF1Gw72Z62+AZJa7qC2Xp2RKsTG60bqehepehc7Nh21.a6AaCysS3tYUtmExeeWavr6Ku.ppZxyyya.0s22ksNtFsmrGua+Wu3tNE5BS.tS0vPXfyCLeiUZL64uvENjUV.20.qpYQH+e4m5SW31u82bwq5ptphUqVsXXXXAUk7NNNlDrTwFTyZWDZV1OJWt7ikPfEbfxNtPForjqg1Heo8tIPFlEmY.YjrXmN1GRZryEdqlNWjRzeiCTLLLLuU4kD6Ca6lQRrHx.D5pnsjDYaP1DX6DmjV3R2k88GPchqUqVxq8W7WzrFt9dkU0EWbQy5piU6oKXn+T6XF.Jwm8S9Y0RPBNDuJq1+EbjirWxOqDxrfik7tuxDVm49OFXPIHFJEO6byk.vOwOwOgCPVee+7G+3Gu.vXOve2e+DtvgPY5FZioJAGxyyabfB+7+7+74vESEMNyPm79VEoIl0JSa97ttq6BnRLTdfKz+t90tqAMoYx0ccW2deds3Srywa7c8Fy74N8mKyLyLyPhd0T5Q67iLLX1ddd2PWT5hpoNwklrpsTU1v22ei+o+3+S2bYe+snN67Reouz1+NejOhQ0HN+2UYCS+ve3Or4eYZHcAv4nf.yKM.8K+k+xFUSorQBHoLwug2vaPfJYKCYIlLQDckIW3JlvOFsptG5PGB.GEx5c8d4uuO08YH7Yy68EAJlISlhdddEqtb0BUqVsf+x94A27vBlye28TktCDIGePlW12WIF9cgDliDSxEKGWRjX5S+UY0tUrITJWtbw0pUCJY7kHLLzQUUjxk050qqAGOPCLAtO5SdsRZKKY2WNLLraIW2NA0B5DFDzCGip3EFF1Ab2AJsYYXChYy5TemELqA6Y41mALG8qrqzZNfkV56VSbhweD2UGjVs6O3u6uaWvs+W5K8kRvtOgZT1uhThIfRSAKLy0L0TyhCyzjly7rN7gO7FarwgAldpol5PRYYBWn3JFRb0AfLYxfmmmAQGNHn3DDDjUEIGkZjVnghQ0pU79tu6KWkJUbrDpdZK29TYxRTfjidziZBVu7pcEQZKhriKrS9796zbzVmdoqXBSR2OMGvXTlIusW+q+PiUXrIDQJr15qmqRkJNnl2ONzgNDppBn344IhHhJl+u.IhJCNYXno.nvl92f+E.t.kXyG9ge3c.2tG+9u+Azk3FzH9FVbw8ZWqJ52mueJR+9NchgGcLRR5XfXI7TwyvSPhHc9v+Q+Q8.23nc+aFpZVWhimbJNkLm0tyEtvE.SKpz64tvBc8775gPbnAgWx64teONhHY788yFTKHmpZdUxje94mu.PdOOu7lhbUtPZ7Gggg4MDrMNl1g9ITrP60N1F6AcyYvRV00q+kFGqLG6A4+.efOZlUtxmCkrjDPPbYK4r1oSm1gggcBCC6EYU0wfffD+kWNAzDTHHHPBBBxXSZIMZPRsnSz+pu5qt6a4s9V5VB536eSCUjIfdbr8PBqOU1J9OUOLyYyhLOvPj965J.Y928Z92kl32wG4qwrszYdfByZkf9ZlD.ahAnLk9hewu3rhHSCxXkcKmKUMRq3UIym3i8wyFDDjCjbpn48tAu7hp4oAYiINCttFesW663Is7orw2NZImQMVz2x6HwXhwOup53.S9q71e6SBqZfi3UVVsT.8zoUGwynk1Tmsbbb1JHHXG1Mv2NhJ8UUiCqUSpEVKWXXXwpUqNtEBkiAT7eHJJODMZ121uy1OU9Pe+AvkcUCq7m2tIcAU0Bzj7AAAYeQeeuHwz2iAIaXR.k49OMhYO6mqfDYf3sSIHC0qmSDI+OzOzOcJ7pJBTXyM2LKlVrX38jpZpbhQPPfnfzndCAHoz7khgF8BBNQ2lP6fSFrSXX3ldWu2F.ajjjrMPaVk9qYgr1RvPH4Z49gXqyucGarw5IhDScR6u2A+O9FeidXTYm8y.0vHFfWGRngoBjMTs0m+y+42te+96.zxqh2vrBK1u7775gQJ65GDDzKHHnSXX3N2y8bOaUsRUChYf1gPWXsTT.XLR136JMNNbXe9qFGpMIjZECpf5opNf50saRM+vDCvQYHgYxYLRPIvTMfYfFKfgkEpryN6TdPuAyU02eJQjwTHSXXfEQJRVUjB+y+g9edr69teWSDDFX1zyyKkabb95ekuhPTjMv4oGElr6+S3x+tkvoPL8cdcibx1fLetO2mKWSWS0+DWo3s9O+Vyy53b9K+wxgJiT8vvgPHdhFl4gT9KIaTTjSXXXZk9sN5jLPUs+EN+E5phri+M5uouu2Fhna9e+S9euU3IC6ZQKQ+apZ03iTohEoTk1kfdcGQVMO820uQL.JmmDnY7K9ewKNtIDajCXRhl29p8B1pL1j7qagz6wNHsFSZhUAoIHPSINNVvf1BGQjbAAA4EQJN0TSU7EdyO+wOdsvICNYvzAQQy7R9odUSALQX34LHFrA4rR.a1pW4V67warKJP1vj3m63NtCAh.pqM.81eC2tBve1e1el42eA6w+LGfi9tnsRnIxW4q7UbN+4OuSXXnSPXn4baNZpfj344MPDoesfSzCgdpscUX2Dlbgp2n24erG6wtP3ICuPCCZ4Z82+m+m2wp9HoDx32oG60IZPsJYC.xYAGXUAPutq65R4dj9hHCnNJfCDkotQNTScj+x+b0t+iH9oemg62JP9fSFTzyyaLq8sw777FCKg0g4mmOHHHKMHasZGOCrRl5fCqPZ+Xeof27ScySmxlvj0HApEC0im4YciCXcq5z4ZqDZE53662QancEQ55440ILLrUvINQZ6a0AneXsZw92ne7G6i8wF.z2pZPCUaI0jLtdux+0ux9JzesUWafp5.OOuAPi9Pyd0gtF07hNqXjA5dehOwmvjL30nWjsnXKA84Tm5JEv82NFWpD0aVCZ7inGP221uwuQWnQum6y84FCfpZFQj7hHiSSlBZNyFab54VAlWC04.loYYN7zSO8TppSJhLgVWKVW0bKu7xNVhaVcccS.zff.0yyCwQb788ERRbBNYfyC+vOblvvvrIhj4k+xe4o97t+1x5op4PkYGdLGPcqZf.sZ.6nZyTzkX3OnScYOuVdEirV4vcBpygtmeqeqCUud8ITUKhpYCCCSSXhIuHBhuu+tsdNPsZAId99I9998SRR57u8e6+eT2ad7V1YYc998YsGOy0YXOrVqcUGzTDgBRnxo.QtLXPBw.RqBJQafnfcS3Jhh1p3.nHHbQHBBpMpneDPHsZ3JefVMgtwlzhfbUSUIg.kzPEMmSsF1Cm4g87d8b+i20Ze10ImToR.BIu4y9yopJm8Z3csdedeF98762qeOWW2soNaBrE0YWaa6VPst0gArlY+m0KDeub73e5wfQ3vruQhLmGtiCZya.16+NGgLrMJu9W1KqqpUGvnE049qPRWvwdMydIIswXOpQmpPmc2c2tNNN8i64GIIgU9d9DmD9AhZZGQOCxbRUoRkLP0bDGifiiSV1JtHY6W.jGNn1QgBIH2R9HejORFU0w.lrb4xS+Y9Lelo.lvf.hvGLx71LLpxT+pEoCPykWd4ccbb1ESqDNrsf8OyY.DbLbvmfhbed2WRq3oUrqL.n2a6s71Z+l9c+caAgiPhvGp+Yeq1V1EaHrNVqZ3lIqRfnUMB8wa6s81xhw21onDCQeHMXJhQ215vTP3QvTrzx.N9m12dgEVnfiiyLfNlUJqLgggoPIcPPPlq5otTFfLhDkUUMC0HqSkJYe+u+2e1mZkJYoVs3BzVdejJc7uoCBguoNdj.gIfoZIlfFB1Of3W2q60k5u6u6uK64N24F6M8ldSil0qLPkKkpTZLBELDkI6t6t6tsiiy1NNN6333raPPvtNUbZphzQEFTwohkpZt+z+z+zwAljRLITdBaaaSxZVHtG9LP98f8C42HePabJeNRQgEMAyB4jhRNyF0kyhoBqVO+q3JTfAEOHrpSpj99bGhX1HpP5ZplAHmw3TsIpVudbaOQVER433XYjVwjD+BAAAx0ccWmo0dfTUCCs788shhhv22OZo31zw00s0obb1IdCsMO5QO51wy+CMVc1QLxHhDgio+EoJ8jRk5AL3c9NempySwIBJ0+xtrKyjrhYePynaDPuhlLyuy202020VM2auMDQ1hj1CvX3roqqaKee+NNNN8btJmjp72NB1687ddOFV5tDchCn0bNKjbdKk7LRNwCUDQ7H03bwaDuF8UU6HhXRPnTpSLgAigb2VMKtwYX1HAkS.LEvzr.GAJr.wIJgh37w9K+Ks2byMKBLmp5jA9A4DShRRNyVmxwIMJ4788yKQ5X9994.REA33bR8w+DehifpksRQoK.9ei1arOPymCWusX7ZkEFoEh9geVOqrwI7b7d98xea+c21HLK+g1SNliWnwIOMd8AkXLn3D+z+z+zSNXvfIDQx633joXwhV.TnPgn3.WGnpzWDo2QNxQ5XEQSpw1ddda53TYqmzU7j10wwoEPWnX26vyq64M7VRen9.CYuQTrLMdPVV+QqCEHxNNgQ1lJi0CnuCLfUMFOV9NVNMLZ+qam+bizFWb31PMueTlzwslYVfrat4lYCCBRqFBCzHuqHY2d6sysjakwEQmTDY5mpiyLyL0Ly.LkiyQSrsY31FJm06BO+OLV2VPv37gTL9dnbryHhHVEAqK6xtLy8ViGBUd6BTnZjWzK5EIpHVkJUxx0wQhctCPTaa6nfffApp8q35l.c6j9Ttkqq6d.6DbmAa83dbOtsTUMpxDkZ0fQ4SlZb.GUdj1FVx63QG3SLIbZHw7a+1u8nXm+6CEFtOPAHpe+9I78RJHFIYm8Pcf+fAJoPfFSpkR+98S0qWuLwnlKWPXPNaa6DIpNqHRt.OubPwbtttoEQR466aUoREqx6iNsTuzW5KMEm6Bro8Mmjl.QUh8yY0UWs+W8qd5d.8b1mOjZSHMMjQrrCvNhXusqq6VtttarxJqroiiy1.6phzx+L9cd9W601Fy9j6gvdpHsbccaeS2zM01cI212xsbKslbxIasvBKzRDoUPPPGOOudkMEdvXK.FPH8CCC68hdQungnG174D8Oj1L7aEiCtu88OwIkPghC9hewuXbaRZ3MFQJMTEhJCSqpN2kOyLyqptfHRghvBTk4TUmQDYRfw+ReouTdQjLYydzTwqiiBBBFDOmEEDXTJNOeeArX6s2lWz+oWj33rj355ZAHwAvFAy8Mm4u8EKOAPYAF.NFDOHRWlOg6RpbwBTzLGd138iM78S91saONv3wbkQlX35KI+mXoIHrwz5LlGACpT4nFE.y2uSkSUoy66889ZAka5440DnYAy6pctq65tFVw+4LD1twus84+qCqUtezw3Dw9pEdApaXOCw0VrGPzW3K7EjX+jx.ktXEbv7Lwj3n9wb3QBQx2YxImrSPPPefAkKWdfhFEoZjaE29wDs6P94nxRU5EWzRy4sHY877xNRPtoJw8yN2k5Hd9uAwEUH0MbC2PFQj7IEJ2Jq0DfcLEIPJl4hmjHRPIEzOlmQ19I7DdBafgEr1Nl+t5kvMU.DDDf6U4pHD83N1iKRUM5q809ZC.56440EJ14m4m4mwfbyRIEL8D2eNT7QuiQsyYIhM0XHWWYJ1PYxigqZRjc94.l03+uyrjHE8kLIK4u7u7i49Jt9qurp5bgAgS.jcHxEMI6LEZLh0UIUbwsx366m809Z+MxVExDFl..gpoe5eOOcyu649FFhL+Vx3QpDl.mEszHvRq.L326262K5ZtlqQN9wOdFQDiC2MHdwiWRefdwlbU33CpXTvjV.67Dmc1sA1LHHXSfsbcc2sDrWEGmNfz222GQjL+J+D+DiCLcwZLMTcxf.Cul3cWdwFJBuXvt8qmG16WASHs85jkFKOTypoAYLjnSsT.V+o+oeDsZ77VcrGkvghuFJfiCr2d6E+2vBZjIlrxlPDYZU0oWXt4lJLHXbQjLtNNoBRplY79lhHhiii7o9TeJAPDvprokZTKKqHWW2n6zKHABusCiaCJvY6xkY234+DmzG0PSTAHh.FrPBJipWuup5fidziFUrAQPs82DYiKZuyNDIEwrA8FPwFybjiTKBZ.rtiiSRhS1y22uUkJU5FDDz2+L9II6oSEGmVkhudCtyfHndRv8iD7SMILLDvVN6iNWfGO+ZOXlYnabxRZZZYp5IYIGwnhLYNYtSN1Bv3as0VS7c7c7cLYYSBSNRwUYAnQwenenWZ4Se5SWx+N8K9Le1O6EbccOhiiyTAAgiY6XmrVLoRIbFSeVmAjbpHYUQRGDDHK43nP83JBa3FkBPFpQJ6QB3Xw6euw9.l3jki+8ZXT0AAHUXLYF+1e6u87oSmNNIqjpBHv4Nzi0I1+7H27MeyFRQqFYuwa76er2467cNdpToFSUMaPPPp50pK.znwppsicrTeJ8GFvpE6466usHxlEKxVNNN6DDDzrDzNH3t5ToRkNG0zxJCrAkPnvgUk88eV9n0QTX751pL6PBq8ratYRxSF7ze5KBPl+r+r+rwrgIfvDj5jzxi2OdyIVdNyYWkwfpIIvaRGGmwUHmER55MpmBHshlFHaMSPr4UUGKT0wdiuw23XddAiUtrIQM+w+w+wiYCikvsMbgxy3CkQLhlPDohU8XmIqRgrw6SkstAERofhxBK7PZeAEPsGwNtEQjJUJ.iyc.p.pef+PR.MNnqAu3W7KtePPPhZlrGTd2kVxYWfc9PenOzN1vdvH7ICHyCVOJvQk6GZAYHJSpEAL349bet8ECo80GZDmvjhQ0UUyjICU8pBw6c9ffjrKX3XPyiUQvJc5zoVc0gshWVzj2SjLtttYUUyuyd6MVPvckCHiigXqs.rpRoguC+w9Xer3pmUHckJWPu9+MxgBnIUMegEdRCUAhf3VLc0UWsksM6Ar84O+42rHrgu+o23Zu1qccf0O1wN15kEYcQkMWx0c6O7G9Cuc9b41Qfsccc2BksEMZGe+fc+E+E+E2K3NC1SUcmYlYlsTU2TDYaU0c6zoS6pTruiiyfye9yqgwAkZauzfEVfjjML3Dv.3rOXshyijItKwWqTGej8eh2KJcwZjBpackW4UBlfLDfzpVyfZBJOUnpy.bjppNmHx7ppKbts2t.v7hT5Has0VSAL9S9I+jyEyENVwAgNPMxNerOMVXaaaUw005FeU+3VOwm3STJUCApZAjxyyypRkJwyIIY133e8LOcAILpxHIpNV9eSYuJVwUbavcbG2wPza.dWJsiiETLcwhFa44ymOIw2YBBBR4GFZoDumcDhssaJhUzQIN1iqxsx.Hpmuue2ffftA2YPbf+Ui+Y41MhQJ9IO42S2vXTys9ElXjClbmGss2pxYQiS7YjpZja7e9+168+1.ndD.OimwyH0U+Du5XkIoVbacchGnjTD6K778JUZehjOlju6GihrAUqVMxwwIRL6gz222uCPykbcaFDDz16LdChQZdZfb+Q+l+Q4pToRVnZrMM6z0FlrjS7vIFHs..MPc.51sqZ6xsGC..f.PRDEDU.Hat4lo777R+c+L+ty.g6yoFacIwekQqXr+0DJuUoRrZ8UWsliiSiZUqtIvdAAAcUnuhFIv.+6zuqHRBmL1Y7wGuKFj.2Cp24tu661zFN0RJX6YitHWCOZaLrfDyCCTMr+HEVoKv.MTkxPtVsZM4sca21LuieyeyYAls3pLODr.Pg0We8hWYoqrjpZoNsaU3idK2xQbbblv1wNS7YIZpolZfgCdjg9ubUK4lx000HhKplCpmyyyKmsscFOOuL1Pl+oOy+T5JWXh2fGcFW0Ec7HUBSDNAhgKhsUhIQu39GMwwtTu9W+qOQQWFkyDtHIKAENWjmG8.m1.6VsHaAroiiyVhH656Gz7zddc7886455N.D0wwI8o87F64+7e9S+OdtycDfoS5E9JUpbP1gNE7vNCqOvyGwATFNhB1.jWUMmHxPG8+g+g+AfgFGCirSfDxwQpXRNhDDfNwDFE9wqSGKn3PXuopNMvzoRkZBaGm7pANURLAcm.WMTLRqEfLrGZgAelOymouqqaeu.uANUbF7Bt1qcfuueeJQGGnEDzrZUZAKdPICNdiqRCZDKAXMTsWbkC6Khz+Ztlqw.we12gKt+a9cfm2w7VSA1qHrATu1O1OwOQfqsc.PsvvvMbbb1MQIe78ChbccUGGG077uTWOOujVmHJNao6yqGiDHusssXfh+i5xx7Hyug82ZK5By1FnU8EnEX29O+O+OO9Ygiz.ReWMuqLMTM6Ly73y8U9JekwpFStq0TcVee+4+q9q9XyWtb44cbbNhiiyT999ieyezaNKnoiQhTLYmZ3fn3yukqqikJhkEfiiSzc56O.JpAAARPPPZnXlFPFNFoLjabIKeeeYYy8wk5luwAuJR430khsjZyM2L6u5u5uZVy5kBoADuJvC.YRDi5oRQ.CdEuhWQD.UgTefOveR1wFarrwbWRZfDG8.TB7CTee+HPSfSaqHMZuqx0cGWW2sqWmsbcWZGfl0JRaGmqZHYgAKz+e97mOBmDhe6DG7c6GM8d0niQuNiIntM5NeLD9Oxkcj1wn7pc0pL.JYcC2vMjOrLSBklhX4wDl2DP5wS3OGCZ57LPDcxPCCoLOv7sa2d1vP+IcbbxonojXUJx7S0x2+Ns788SIhHAAAoVx0MkHZ5SeZ+rPwbu5W8qNWXAxe9ye9XjlPFnzCmVyY35KU8H96mEZLVYXbU0wnH491+1+1SC0Ss5pHf8k3wt.PrUk3QDVRPXfDDDHSO8TwW.pHpQJKbccSPdRzm7S7I5GEE0w4jNsC87ZAU26zm1eWOOuseiuw23Ng1rW7dgCL7VQEiTuV5PQ5yiziCA8Gizh.6yyOCKLfp0PLDAaT4JkiVHY+gYtXb67vg.krBLUXKUbhurrsss50qWJQjTKuxxVAAAobccR666mY94mO6ke4WdVU0LwR.K.wq+qoNI6c6PJSae0HsmGogE+lEJSFlzLnV+ES1qzl9yAcWXgE5DFRSvdmidzit8G91tssbcO0VenOzGZKfMgRqWCV0YImF+Zu+e25W1k83WEXUEVCXcU0MAYKQLDetiiy1hHa566ulqq6pNNNq455t8kcYWVSnd2hPzQO5QiXgjBfENX0UY.LSDPzYu310Nr.t9l06hW3weQrNGjJln+SE25do921cWq68duWKXnhUY3iCSBbmEpNOvBhHKHhrP4X98ZpolZAU04g5yM8zSOip5j2y8bOiIkkbppYEwvUBBRjqqaj6RtnnVAAAo788S+G+g9PoWYkUREGLZp68du2TwemC3244d39d0Ed+eBDOPfiYR9mpotu669RENR.LO0m5Scz22df1WZ30ho3b0spWmTP4TMa1LEf01auiEf3ZaKDYTGGDizoa3tP0RATUG78+Z9O2SUSgrdZOsmVGGGmt+F+F+l8vHw0chSbR7m067zssMIfZwCQT.Nb6KOZYndfByGIhD4Ges+xdYurQK.Zpa+1u8gh8.f0HJx09nh5B1Wdsd0pQuXTpzuVYy7x+g+C+Gh84cI9deteuVDipbW2qRd4uxWYTMHxwwQqToxPNEAH+q407ZRJ9TRgisVd4kG8Z4g750FwH4J.hxl8XC.FL6ryNv3O99EN3252525R0ePkEouiIwZaUqFqVpvUTEnZjpqVpToscccaIPOAq9Ntt8MsleTKUk87BBRZSwN2gmWWftOkmxSYnp8vicPVxHCChXVqD8EwNo.psfhMEQZKkjdgppiM1iK80ccWW9ekesesI.lolpyda21sMOv7yM2by8o9T25QVYkUl94cMOuwEUxDFFRPPPeD5hP6c2Ym30hZWId95LmwW88CLH2VjLP4rhHYAxToxSy32uCV+Um8eRf4+Vo+GecO9lcBS1eRoKhQAZBUf9EKNDBYsJYfZr7deuuWCL4chq5i8kTBJhMbDXZKm5riHxNgFdLoiHDUoREQEwpLjx00IMP1+9+9aera61tsIGarwlpHLITHAVXoY9XiUEvBJkpx8GV4W381Cs4i3fwGxkB4sgwEo7DppSHhjOtmPSqpJKu7xQk1OgBQFGsWT3bIa.lLVC.IWtbog54KIxDppSBL4W8q9UmfXtY.iSApkkwfPBOMjOWdcvfAJFmCSXv6teOeOeOcJBcq3Tomuuezs8+7+o55dRkZDEbAI6X4CiEoihqdXe5kP5uM5rxJqzVUsSgBE5BzqZ0pC.mK0dELBNdeZPq5vV.M9HevOXfHhOl3PVUDYGee+NwA4J+e9+7+IUPXfUICxQTKKqnhEMHkne+9VgggogxIZNdZlINoIE.nhNhne7s5pkcvQRvr8gML8E6pzAB68y8e7+Xefn+1+1+3gWyehOwmPf5VhHYrsMvMtZ0pS7Sdi23jddAS355NtHRdJQF.qm6y64BFhNsGHl9jGZq5977huueTb+vNH98lHnNK43jJJJJCT2rlpGoLjTVMbcc0EWb30+n+7fCANgTZj43pfg3LphdjibjjJ1nPCy6OdvEgLIzDzLEWUzA.TJ1AUGCxqrFBkXhQfkLRh5flhJ6JHaeWAAaQLZ19m+m+q2VUcGpSSnVqhwIL4M+l+o5czidz9jl3jcd1GKjrjjwnAsM.n+ZPOnPGViV0iUHHftPMUDIye9uye9XPsIJMT4GVyztLMXrYfwfkm.XpBE3H.EArKAtO+m+Of8Zqs1B11tyDDDLFJoEKQbcWBr.QrF5vnpZjqq6fy36G455ptttRPvcYrm1frG8nGMWo3V7IAod7vHYIbLhDYdMtULxppN1+k206Z728652YLpS1+5+5+5DxcTtvTf7.NFhbEa682Gw01FGaGbbbX6s2YzeaAvx222JVRREDYPkJmpG0oSTbK34551rRkJ6ArGgzDB5Twn5IC.O8SeK2BvkTBFdjZbv.bF.Ge+jlWBCwaVBKQJIEGwN2pv.vc.aQz4dvsGaA0LsXZnZUBREEEYEDDX0nQcQUUV7XKJ.Rud8D.q0Vasz9g9YhaGGKnL+9uu2WbU0PCLIcUH.KOH0W4q7UhSH2xeipXJG1bUx5uAKmLGER+0GptTzBBaBr2K3E7B1Ept6SwwY2vvvsWYk+kM.V0+N8q9R99eIA+H+Huz.U0pW0U4V222e0JUprdqlM2LJR2x00caLPaeUU0ZAAAUgh0KBq644sCPq5PWn7.VknZPT850i22dqQC76A5Sx++KEdr5aD6qt+wd.hgvQaXAXM+7Fe5lXhIjm0kcYD2pcoAF6i+I+3SKhTfRTBJaiom9KATHT0EDobALINo.ljoLmHxLW4UdkSqU0I.xaaamSDIcbkWS6cF+zDoYYeNxIWrh3jIHHHc974kX0KJtMEIiYe34S3CpGt9aZ7cc6DgMXEK.oPAw5a6a6aKEP5XzljDKfFu96AMfwDB5zLuVUFe7wE.1cWiMrlMahaEWBBBLkiNRMLnAnf1WDo8a8M+VaZYYsqmWvdddds.5bi23+oN.ccWxsKrPmEFQsRVA5BK1ikOTU+5Rw+wGoGCulNADAqM.neoRjnVWQkA8xtrSQL5lR3YMKCoienVsOjjoV2LeX36I000Mduiyj9CcyevLNNN4TUyG3cW49e+o+z4JW1TnVee+wMw+Tb72467cNVXXXlDUaixXAErdlKt3WOqEMWqVDUB5qZXWfNkKSaQjNhH8KaPRn9K+K+Ka9F1OnGuHVlAFdGjcAVEpFJh3WnPgvUWc0FA99aAzDztA9987CC6cRmJcpTwoSEGm1m5TtcCBB5UoRk3yeg8eeZex18RIVuGMj..MFQL8MsoY0NwHneuq+5u5cwvqYwzUPst.QEKRZf7hHS9i+BdASAklx22eBGmJ4W76bwz.nh1WU5HPST1EXWE1AkcUXOM12uImbx9hPjqqK6t6txa5M8pjqJFUlPLOFpH+fm3oKwwo9no0mOjFORfvDyKRmyzyDwNdzOccmN.MEQZU0v+BC9JekuhIC+AEMILIb9KEmcUfAUnROLIfYuNc5rWIa6NXBlJkpZVQ072gQh.y8LelOyrO2m6yKW0p0yCL1crxJiAMFCH6oO8oSyZwOraLmETS909i9iFlk9K3d5gy7P7lX8CKLjOFBTMipUyEC267TuTNfz862mq3JthDY2b.yF+hVkkE.4Fuwab3bv7F1DbX1hqWr3D.SIhLwke4WddLvjzxw0QcbbTTYHbFKUtbT61sGjJUpgL3MP6efefWZKf10M8W5.2kbi.TOu6L1f8BWrMnF0ndeV2vD+.sO1wNVaawtS74Jpb4xLJQrbQFInJJN3UmcwrBLrHEOu+4OuusscMSUynMlrnm5xu7KOifj8K0vnJRQQQVTun.vQO5QsrssSGCAwXXAVw7NWiB.db1C+96QxpkcXiKHwTmfSLZO71u5by0WUs+22222m4eqJQu3W7KNIi9RTXICjbssS+e+u8uM08bO2s0sca2l344ITqD.CPoKncTUMjSVoj.j0VXz58tXZyo9AdA8bcc64Z34lAUKWdeG..KBQVajM4mX4SbPmxd.LhdVoVxwvAAPEaYfgfBKOTVesw9vP3zAmqFF3QpUKkrAI0NPPVwsEQxWUKWtbjB8PLs8mJ5lhHa9C+C+x1DSeytyK567EsaLAK1Bna8xk6Az6s7VdKl0tqvfvGs0W0WZiCj.T5ywaDGn1LCYTdari9.u+2u7i9i9il4tu66NesxkMbDk4yTrESukQp5lEXAZTnLfKvw9Jarww9ze5OoKPgfffo.IqiqiE.99mQUU0Hy9FQttt8UQS5C61wPLt2IcNYT34OODm7q637m+fxl2kx5xK78jUlMBVOpp46lEH+a3M7FF6m+M7ykCHyq407ZFdbsu3d4c+BdTCKe+rUDDDP1rYYj+886W33flbbbTJWKVFXupN.s7882qHE2EF1Zjc7LIitOP+m+0e8CnF54dzW.Eib8bt8WaTKduwZEsf5TS0App8jxwbnyB9w1wtH5W8IFBebChzDwpFHVVIIdyPFfIHrrd85IeSQTKq+q+W+8S43bRApxq60+5ipTox.bhC2qXMAv5S9I+6rdBOgmfIAXOHd4+vbLpswQa+fX67mXXgMJPggqI.Z1nPg8JWt7NG6XGaKf0eNOmmSCmJUBA7EQ7+re1uRULtjs1we7O90Eg0877VOHHXUW2kpWoRkZNNmrNTes6JHXyJUpr64CCaCz87m+eIA8hZwhEE.4XFjKj5.eFsM7jQ+6mfSbX7X0gkbE3huu5EK4L6O7HY+HAvRWaNydIhDExvVwIKvjWyy8ZmSUsjVUcUMrhHRks1ZKGU0x.EUs5BhHyCLup5BppyqpNGF6aSs81aOQPPvX1114887y666mWfwQjweyu42x3ppS7Q9vejIbccy655lv0GI9FCUKZJbXM67vZYO2CcBr9fyaV3g0Zi7cytpSxyiT0LsISxwWol8kn8gBJ.hHTFi8r0WecKL1ojM2bSB7C1+BRDUEi++hX0IJhlNNN6nptckJNw7uzPwZnC0VnKrZ2USPdVkD9Ob4QQWB7nG6YWrgd1QJ7PlZUR70teUn+4N2crOulEtfwV3Vy.Ghj+kb7F4mpM6+LKIovAAAY.xZ63jOHHXhfffoTQmw22+HZ0Ry566OKvr0gY78uyI+k9k9kxYaamxwYIyApZYfFLrvrG+gz77n6yDQHCpwPRFdOpVdOU0l.sVoa2tP888cKrxkBJgF.GOtsbXyhTrNf+N6riWoRkBscbVSUcm1sa211woun5PzrGDDzOpZw9w7bhZzpyF6m.2yYePt16vV2cP6TeqJwIGnvCl43ZkJ0TUcua4VtkcvPbxaJhrIv1hH6YUubGU0Auo2zah6JHP78Oi08bO2S75+x8EQ635tTKWWm8t6u3WbWE1wcI2sA1BK1LetbaIhriqq6t6s2dMIhV999smZpoZ+Z++9mt6c5626Fdkux8aqoPHb+8wdzZxMePGORjvjgu7aaZABSOYaPDR66y+9ZJhzrHz8I7DdBQ28ce2of5YLYYesQ44fC6EwgN35gWehq1xQykq0N6rS21saig.UkIbccmRDYJfI+7e9O+DNNN4qTwImqqadKKq7kLHvXrScpSkC7yXHeo0s.307ZdMrHLJq9+P8gbx09PGFZXpzQxlLhsHV+E+E+EoR3ujOwm3SHYx3l3PTuxPe1vDrKdlu2e8G3CLT0EVi0LPhR0wJAS98+c8cMMvTkJUZBf7gggYbbbrDQjvvPTysf533LnZ0p8RRlAlrItGB69I+jer8bccaED304pbc6QMiboVohwf1BrpgpfdfMRjrHtGPRVOaBzpJU6BwURDrJcoSPiCCbqDAsoHaCrZcpGT4XGyamc1IrQ85qArmqqa+xkKK9AAYTUy8jKTHqpZFWW2z0M7VxvJjXHG0DG87rty67NkXinG1ywGpUK6alCELxLc7eNpBLf0WuuHROaCDVSHJvgjhVMpo.5K7G7EN.n2O9K3Ez8Lm4LcMahWqKPGUokqqayX3M1x+LAsvPltlDCDE0VEsiuuem+1+G2VGf1+5+5+FcBBB5R0p8LsDk+PmarGwY+yZ5y8GnDHbAA6.jp.jl.LD8PU5Khzxlp6HNxVevO3Gb6PB2iKjCctnyY0n1vykoBYj3nGnjDLkBnUqVsukHcbrcZBrqqq61ppa8u8O9+d2q7TW4t.6Um564662Dn8JqrROpVsOvfhEKlzpA8OwCb6l8Xgw9IS3bI7P0V8w1buERX+a709ZGHhnW6S4ojRMD90XEJTXRLrx9r1lpxVBvoAMN5evevezwTUW7O7O7O7nppkEQlEXbTM8W6beMhhz305Zj.Qsa2oOPOQkt.c1byMa4dJ21dddsqS8tZpT8AhJB51auM.x9bB.vk95x3y6FCgLbQv5c+te2F4ZWDKUUN1wNlRYy7RnA4jOPuKCfr3HuOW0vaAV862+Br40saWPQmbxIUUSPOkQKN62uejuu+ffyDzGnWn2Y55440100sUcpOLPiJPWZzHQBq2WUldzWu8mLF8ZwfBfBHpVCHljuEoK0hCXZ0jfkN6C78vYOK.L+7yGKVDlW7hhhTDvwwQ88ChBCCGdLbccku6u2uaKPs9AewuXKOuyLrp6kM7vkY9qt440q9G3Z.6Gzm+e8NNXRkG4yYGpPHMLb9ReftgggsoQi8DQ1sb4x6.r48du26ZEgZ.Attt9SN4jA+N+Nuupuge02PcLINYUKKqFe3OxGtNTqNkoQPvcsFl1admRPyiZa2Bn6QO5Q6CLvk.k4P.RuBqLj3lKXPpYFfLG+3G+fItzBH0Y4rIAjbXIIw5P9bXIE49kHFtv8guf4vRwEK.PVm0S9ch.FTFPM7h0T++dK2x7RIIIgtUTUqL8zS6ho12kDQJnptPIQVf3VzASxSlUDY5omd5IAFOvOXbDlPGh1Nch2xa4MOQPXv32vO9MLlmWP934INkq6fkVZo38spmxP7lgIx9Ypi+PucvE.NQbhCCCCicUyXaKffHUUR16aDDlvHsg7gMF99XAZj3+oDaOK8jSN4EljZY+CT7eL5t9h2UOU01fZjOXW2sKA6XYYsGPaGGmte4u7WtGrZRKRLnDninZPOZ0N1Cz39UvFO7RrK2ajOwbQxp8MImcqGr6sgOKB2GgiV+9+t+tVyLyLoJTnPZfL9ggwRJqNMlBVTnF0J555VPUcdee+ou268dyi48jAP09r.8gpQUMJsXTAPiEYfGp14F5udYCwB2BnYUptGPytc61Ia1r8rufmodWJGSSgSqXPYRcpuFP3byM24um64d71ZqsBpToxp4ymemXgenuuuWjppN+7yyc5emfYMRJU0LkXeI1EByPgg7u1gQp2Gl++GVhdejbLLYb1PWpUqiHRKaa68vfvjsrwHRG.aEpgaCr2a6s81Z533zVDo80ccWWGiDxWscDzx2+LMAZdEWwUz7Ttt6EbmA6Jhriqi61Ku7xapptoWf2Vppaoht4cbG2wlNNNa81eq+Fa6551b9ibjt111Ch2iUq7X3DkjLdj3AaxKRIFRSSROys.iypCqB4XXd4smHx1K.arJk2D6p6Q3nL9+gd7iQVQgogFk.91t9q+5u726688dYppN+a26+1LOqm8yJcPPPuXkSYWP1Vkncp3TY6fffMbbbV2Kza0J1KsFTei4gsWii0hBqzaHabe+6YxGNyAINJjkBjmFLMvQvXHap3MsUf8DwYCJEtN0XSLIav3zagBPiFIm+39qmIgxEfptppK1rYyEmXhu8ic629egyUe0W8BlJ2RdEMChkknXgnfReQj1JZqa81t08dgW2KbWWW2cBBB1JFoFanprNDs94O+xq+LdFOyMBCC2v11dKhkvR1WJdOr9eM99tRZvKGFtKXZLLO+XXptSKLsWyVPg8fFcF448EKf5D04XRf4gh1P8ippdzff.6YlYlElXhIl1vkFzwwwYCQjPLvYYUU0siO2swTc1cAm8ffNGC5sByM.606S3CHxEF0X4naJ9HkAgQMTmBrSGSVwwumUNETMVYObSCAo888y455NFv3TjwusO5sM1K3ZeAiCLoWf2zhJGQU8HVhLoFK0tpJYEQu.xZJFxmC788aArgqqaCee+ZtttqArouuebkiprEv1vL6Ba0lQThfQliN3b092SkHK0H+69c+ty+y+y+ya1TqToTZ0pIRSXRV0aAU1C7ZwA3Ag62wL4clhTf57sA7j51q6UTuQ8KWTwEXJAxpHpiicOflAAA67Zesu50+jexasAEKVk508Ap466ulkk01111Mghsf5sA5TqVstkJsTGvOFNwK18PZasGKLNLm.Fc8WdfITUmpa2timKWt7Xb.efHkihkJwThXmqWuym+pu1qdxO+s+4mAXVU0BXB.onuueADlUTYBEMS74IBg9tNtc7882cvfAakJUpMdNOmmyp+8+8+8qVoRkF.qhwIfcfhIDdbbkrlqEr9PYUkG38PN3HNvqR4nTsInFE.JqpVDXZob4zTqVqO0m5Ss90ccWWcf5.qCK1DV1HImWPbBHvIRCmMCF6eyBrHE4D876cE0qW+DHbTU0iXgUVEkolbx96r6dcPTSBrgsdqu027p+5+5ukP.eWW2f3y6ZlyMaBGaWXkj0XG797fNp7nk2+FccYF3X4Tc47EEY75plWbjbDRZ.UUssHxdTpztTq1nIHcz6qQNdymGVaRfBPoiA0dhCFL3DQpd40qUyFi8MwwwomHRu63z2QGmxNsTzMAp45tzJdAm9euhSkUvjrAy9vKrv.Vc0QQ6QRvNWLBK+azyYv99RjAH2m6yc57Oqm0oR3+MKvAHP9Rm4KY8jW56wTHJiOGFetJyjAmIXbGGmL25m5V4jW4I633bxcf56TB1qFE5PoF8oF8+PenOTmW4q7UZVaM+78Ys0zkWdYYwEWLIIEJL+f3VNH9YxwhfUh+6KN.VdXBKTUQdbhxx2OeFD.43f04XQgiurNTM3tv2cOXRSD33JbtCtGb7uywsfyk36YVLsXWBpdmBSBObDo7wZ2d4i1oSG2omd54v7NR134YEHZmc1IZpol5PViOzWrgDeJfQVWkgW2wUEWVCz.QDeUUeWW2p.qCk212+z60pUql+C+C+S68p94e46v5rKFaXWpuecvBzkxn5igVruM6IUUGO99GF5CT4cgpWfhGdHmuQ2GcFLun8cr7xKeEoSm9IqhdYhJEAYBPyDe+mvSQsTXqJtt0CBB7e1euO6v68dt2Z999a555tKTZOn1t1vlgTZSbpsEAjrVeTdt6wRnKAt+qYSywHGqPBRLmvv8M18gp6MOr0ZL+Nbh05vYGZO+vdNj77bNn7iCp9jO8oO8UtzRK8DAp.Lk48PIBztKrvBMsrr1oQiF6nptEl8Np4555C3CEBgFaVBZVi46P405Q0Kv91AKH0Ea9ej0EKlFVNa7053.iyBjiUIU7wLlDycZBAGzGtKVL.InpeRn77wvhoxK4k7RN563c7Nc+a9a9uW3G8G4GcRDTGGmlhHIsb3p.q8ze1O8M+m+b+y6vv1Vo7dP0NNPu.lsOk1neLQvdXw1jr9Zz4huUUTrCDK.Vwq4i+2lyBVe+XugwnLiSURDbj7.S566OEvrWkq6b2UPvrJ5LtNtS344kavfARpTozJm5TQTqVhc9dAAAsIhccprzV99mdCWW2X+Qr2DBi8+2YGHHAAYi5iziUV+B7HCBSfQxt5h6mU0NZCssYABMMPr2NgDNytJjCplgPREq26WLjGDe7aDS9q1stka4VZaaa2Gv5Y8reV4BLJDyDAAAS.LlpQ4OkSkb9994bbbFCXhJ1mZBn93.4WCxBqjhFPbUbd3tHXnQiiCV16mzHqBMf3dmqmpZWnXOQjdhHc62ueGHrM0nMknGyN6.LNMBMZL5wMUwhEiWHTMGTZruzW7KM43iO9zpVcpq9pu5ICBBGiXRPzBIknpEnhDMT4O5hRqW8+o2TyO9G+iuGvdwDm5tttt6ZYwdUN0oZ8LdFOFIr2s...H.jDQAQEyt.8ssGB+OKfTyO+7CgL9g7LJ9YiWz7vfB6uPQ777RAjy11NdAqSVnwkhhCj7bX.P24latXEyodMnfmHkO+VarkelLoqGmLrlKrvB8N2W8qp+z+j+jCSdmsHoghYu0a8ViyrbwrPPFfzq.Vv5FYncQr3Dm3fU5ZnyHwDm02LjWxGhiP0.q24ichuZWNFcEwsKDzcNn6U451c4kWtKPm20q+s27Ebsuxci23byJNmZqmyy44rckJU1ww7reGQzsEQ2EQ16U7JdEMcccaqhzIHvqsuuemeqeq2U6DzI45511jzfxMccc2qRkJ6QIZAk6.acXHr3Ae8TM.G3m+c+tSpBlRsZChOm6Ara7y+lfW2XXp+.gvD43i7bag5jvcMoGzefkqsqE.hJwT.tRPfuQV5VZI9C9C9SDeOeg50sBB7RAjx0005jm7jwOqqqPwHXgAkJUZ.3GYpTTgHX4GKitjCq5JP7ZvxkK2UDoctb4ZZR3ocykWd4NPsdhsfHESCUGa80We5O2m4yMafWvr999yFDDLyJm+7SGy0RioJYUTKTTWWWicCE022GDgzoSKtttxm8y9Yku7W9dRV+2OV4D5+re1eGCLy0kipVsZDrdDyRDr3CmphATSoFJ1LfRkF7ddOum9.8zpFEb3u5S7I5.zqToRwNTs7CTh+rfyZMKjd93faAxVtNYRmNc5YmcVKTDSrUFT0ryt61GzNnz1wwok6RK05m6m6WnsqqaWWW29efOvePx648KWtbL5BVY.b7QqB6AqH6i1RVxnCEpnvJQhH6SJ7gzkEnG11ChI+UgZ0fKI6qqEQATCQOWSKCpkkkVyT0TMYxNHHvx22OkcY6zJZpOxG4ij5q809ZV99mwphSEKOOOKJUR.ja+1ucX0UU.ckULICv119RMQbeiZL5yQMd+3nm0y5TC.2dwsoVKHnI1z7Iuzyq4m6y8wat.raoFrMlfjZXWkZNNmLLHHH7EdcuvPGmSVCpuJvF0fsgF6EdWg6gMMek+R+RCa+QVasNXPuz..JWtrnplV0UyB14.mX4EekwnD4gikEVNcEvJVoVPDgEWFhaqpQ2SMEPFirjubFNGYvlzb7iOZq9jz9rY.xN6ryF6+v4xAkxXBNaTDmbBANm0bPpxweOU0zEDQL3NBTUEQrSqZ07QQQSNsg8kmA3H6ryNyPbQd.ldpolZJhU0KFRt0C+jGHWfePFGGmzHjFC2njVRtlUxHB4.x63bU4cccycq25slAJYAUEWWW43G+3xq5U8xEVGXFDN9CIFH5Bd+vPHjgCp.CleDdmqrHCLzyBwIRYgzwJ1yCIjrTpToT.Y777x533jUTqzl1LRkQWTD+PVEkHee+App8u26Ym9AAACLsxaoAPsnvyGpgf.0DBPvFKlatGHzC8XwgBvLqfByODAFh3zCLHScMCemfA3vW7wbyMW7ZmpVPIqmzS5IkJHHHUPf+v1iBCI1mZsUWMa850GuToRSBLgq6ox+4+7e9TF99qTm3hU1tFz8Lm4S2ipzm4Y.Kt3nIo5fno3hEWV7mkGXC8mwj3kN.cJsJcgEF.n2xsbKVFBZOHYs8kBULn.CNwINQbRHqtATtJT57e7O9G+9JVbg66m8m8m02w0ogiiyNXTQH0yyyRDIUIQx7O+4VNumm2De4u7WdJCY0WcJfI7UcLXi7TibLOY4BECjK.UaI9+e7ie7CCMbORMF02x303gwI55Dcg06TAZMuwm4c.1pXUVGJutpZbQWr2v00cKQksqC6pptmisSSee+1hHc9C+C+C6VoRkd9m4L8BB75FySjMupq5p11ohyFP00+d+d+A2.JEqTogseG+1uidfy.HPOFnKd+rM8XqwinsjCPzx6avtmgIeCZAzpjHcNyY9ai.RWsZ0bw5FsgwlO6EcwyHaJP+RDzkxgsn3vdgrugzozzppYUUy9y7e4WLaTTTlZPFWW2L9994B77xGDbm4Axey2xMmkBinXJl1H7qWX.JmCrBikyMfTFRypgBDYKxfS8jK1SUsqpZ6LGMig2HfNTidrwFCvzRSLOHyNhiC0SWOKTJKPtWy+4uu7Ww0dEiIkkI.lra2ti63XmWQyBjVgTfX5YVTEzAJZ2kbc6D3c5VujWxKo063c8+SSee+8B7BRXN+jJ50FC7UU6QLZbcurqShUIhK5yn0HQoPPuwW6MxQO5QSIF4nMaYHaABNHY+9fYvLBn+5ysdGX1cAVuHMpRwZm+DWyIV4U+puwy633D.znwZM1drIln0O1OwOwfXHZm5+4W7Klc4k+WF6E9BegSXCSVf5SBLNkHGEFxT3oYYxvYO6npnyEXT77m+7BT4Pp30iHFLu.CPGUjHrWa.UhSL4JzELR025Pm5P6EWbwV.MeCuw23dPscDQ1pHrYYps4m8y9Y2z22eKeCoYs0uyuy6aKWW2MQ0s+nezO5t999sNkiSGTqthHcdouzen1NmzoYfWPSCWJTeWe+SuCELpt.0nYLS22+rO7VCoDfRPPjHRjpZzBvfh62lA8VOy58fYuXxA2vmGmajJ7rJjQUij1YYYkNLHvrgmUDHfiiiHhk7+5S++JUvcdmoUUyfPt.e+bpJIP3LaMoVVihZfUQpqTX0HrI5K7E9BCLv3uwi41XHdbvDkL56zCW+UsS0tlDkQyBzXWVHb2G2+WONS0DpR2XB4U50qWZQjrRJIqqqaVGGmLKdrikxOzOEPB4RDgE88C76cye3adXfnN11IjwaJfTO4m7UNrcIruJaEJy+v+v+fr.MDJWkxOkxlquMPOjDYboL12AjPhnVs9CFLnqHR6RhzVUsy+5W5K0EnWsT0F.ycwpXh.XsAj9W9ltIib1B4pFSx3quw5o.rldxIM+xhFgPeQktlVhLXO+ybl8ld5oa9pdUupNkfd+Z23O4.fn+0+0+UspU0nX9DH5DlJr+f84QkiiajxTisqER3q.FvpnDFlnhIVE.q4efsudg2qMX.TaPud8FTMw9ikklvcIUbcsTwHa0RLx.tgeraH0U+ib0oDURAjtRkJooVsz.odtO2mqUA.aH5XG6XQ.CByFdXUe7a1ig1QMm+4G.zuH98bOoaWRHU+PZC0Z+S8K7S0dUSfPMA1ILLbCvdUndMU0p1PshTuNvFTjcnLMIli4HjlTq1vV9Zdn6rr+ymvvvHQJiHhUAByPgfbesu1WKOPdsplCS65jwCRMBQ0qKCjn7FGmDUGhLLCYwgbas0VFzZDRFN24FhbiBP14hUWPfw2H0FiCyONvXNTKOysbVrYeRb2frqLqCYqFmrRQjLqBoRHE5RhjpDUyHhj8ldOuqji8X.4mZpoR7EM4ynUkcneBau81Y1YmsM9HXQpff.C5RHI86wnqQvJga8fZY888ydxSdxLEolIPwxHl7XD+E2Bky8.xkEOXuiDEKyyC7LAhmTvxt0fdpFNHt0brrYUSBolmQ4quK1P.jZQ0RAES2rYyLCFLHCFk0yBvHyWRb1IwjfRcez1.TWUUiBBBhJRMC+RXE0+2988aueRHCga8i9QOry8iUFGz2PK.YKvJgDLKAQEHbfp5.JPDEujrUK.rttt.EMEOkZodau82lEBhiiaxuWTbAHTMN4XUqVMinRFe+SackW4UFcJW29kolI.aa5QY5tzRKYViuF8Y4kiNv4cz6G3h+7PAzPXvVijvtppN.VUO0y9TVutq+5S6PMyZbGxLj3auDRHyYO6YG.mnCvtEn55TtVUrYkYmc166o+ze52GfWPPvphH6ELXvfXYhOacXLn1jUpTY5SbhSbjxTaVfYn.SINhIV.XLVi734kmX6OGGRehQRXhw+ea4bm6bViTPtuUlzjCv0UmsOPeOn2ZljU0DX25vNP0sDQ1pDrcQB2FXKmkb1lRrsnxVggIHDgcecutWWKOOudK451WUY.PeU0N0nVy.ufc7771t9W5eYGOuSGWnT59q7K7qzekU9+a.PzJl3+StNeL43QRDlL5CyDXz20nlDE5TG5sz0sjppl5HG4H4hgJjIgIktTPbvYi.FTi45RUZQc18y+4+76ARyxkK2Yr7iM.LAA869dtI4nG8nR61sGxeAqswFVQQQo.R8xu9Wt0O00+Ss+byI95NqXIa.IyDqC8D6bvBPZvNUUPN8peIDQFHhz8xm9x2m7qJPuJTYHgctFyjZCHMTLmKjm.xoZ0r.Y+i9S9axoU0bZUM++1+1+d9UWcsbAAAYDQR433HNNNTpbIy8hQ8OhDjnZv.0HWr89wd4+3IDzpgyQJRSOOu19998.hpRY4Kbe2mETVJCby+d2Lbw6gaIge9JF+O7Ad+e.EPKCxcbG2gUnpoaXbxIEgOD4yjyQeXi1.6Tm4Vi5DPUV9O6O6+w+9OzOzO58Ab9JWUkZttta8hdZOsNF9ciLW4Udki8ct3hSppdjPXtFkXNfiPMlTqqCMPhCYOFGy7mMRiZ702hhMF4tE7hMTdnYY9aVFMO36kQdbhADRe7RfJ9IRHLst.sO197HydqrxJ6ZaZkgcpCaWs.6jOe9DjEs2m7S9I280+5e8aSI1FXaU0ccccacFe+1JZGGmS193G+3MoN6ohtqqq6tm97g6355tCMRZQhYen1NDG79Jh4neIJ0Cn2xKub+Uo3.CEMVRrgTTCKXiKIaYII56lu4aNEPFaQxBEys5pqlQGxOEhfhDFDfsss0y6ZedVnl0qhHYTSEJy3EDjsHjkpjUhhx.jtNErnAPH5y3Y7CpvLOlHX0KkwMdi23AQWhAl0aPu3VQp4ey+z+zdrJ6YfQcoVfSGftWwS6J5YYY0qHzy11tmuue2ffftkgtwbRRWD5533zAkNnz4k+i+xMv.VHpe+9366K999B.ggg5a8s+NnHPvYB.pJ.o9hAAVTEwPQMkTChS95ZtWo.QknTu2va3Mz4ltoapccJ2QDo2m6y80FTHIgdr9E+nTxXG3c8K9KlTY7L2zMcSoARYYTka1Y2cELE6VEjHUTC4tdUtc1am8ZGEE09C9A+fcNsmWuS64EAkzolZJ0vW1M.fydgDg5iod26blhnGgGQw7ThhI.xzWy280joWudYAR2f4RYHvxKBouBJrXTr5qEkwIyvjZ3ZaSPP.HXED3mRTqzXJlfYeYkTTiTNUbR644kgRjwlgDAHMnnFZTVg9UfAwJ1w2JlmMmukIBVKBXPcpziFzi5zElqCLaGfN28x2cGftKu7xsAZZaauSHgat.rtq6Sc0PX051rgmm210tmZ6QUZAzpb4xsnLslYeBdt2ZTo+FPeX191CkY9ZQ.RCbRSCx93e7O9blVdIl.+MJ+R5BGXuQiBDVJy4fbdULI.gsXbBX7YlYFCxXKQNarSRVwXMn7XqCS.NSTDljUYRUWcbU0wBXt7rN4HjbvL4fiYRrQQywdgDYAOFIHkgrPor0gb0JSVU0r0BpmrW+nEI4fR9ax+dZQjThHold5oSM0TSmBvJV0qTQjnQ+333D4Z3FKDQj+w+wOepXeyReWlV3I04+WNeJpNRfWyfrHK9P0Gh6muA.CNwHDCOP+hhLPDCJSBKEeOtFVb1GTeVDXdwFrnAof5oulq4ZRmJUpgDHqAYCwHJJ9vIir9v00Uh8GL5k+xe48qWx3ihqqa6q3IbEIsVYepPzM7BugCt15Q81yhGi5Cn0wuv1xWLEuqXzxsaO3+et6cOJI6r77d+8tqq88q0k8koaMRsFjFIYqYjjkQiyIQANFC1APN9jSrkcriiMgDrOb.YAIfCwFYHKLHv1wdYvKaehuErMKaAlv0.XxxKIvflQhXkAjzDFlYp8dW6p5aUes5pqZ+d9iu8t5paMynYD1Hj+Vqdpdptp8ku82k2KOuOOMKm7roIwzmCpu72mGEDVAvvIeDop7.u8GPPEILLTBCC0ff.LHHAULAMQbccshIV.za3F9GEWG5UO87GROp2OgTc4n6y1MKLD17EizluXs8sGTkC3LeYH9j+UAbpZ0x7g9ROrIYBAjivDeAd18QcvfBtSSlYSpyxDRcn7EdzG87eCQjy633TWUc0nvvcDQX6s2NOvHUgIfJS+a9a9aNS8xLMvTzjIz.ME8XiPEFYZl1D7DnvYnTtSa7aypRe6+MpAyYL6Sb0f37+11GgKURRtXkPZaizLOS6HX6FkXKU0soAaRDqohthiiyRt2l6hXLvYcOOusNkueGSEQvNVhzlH1FK1xyyaqFTcaOOucds2yOytP0tPYShEl0L9YgW3Lu8h19VU.Sf8+vqe8OYjotlFClavtu829aOtPgBVj.2RfbSGcE5.sMwvxcIodL+d9d9dZ43XuVTTzlSO8zcJTnPrHBtttVhHVEKVjvvP000s6sbK2xtqr1J8k1veieiei9FZezS2+5+4dybLRhpb+5NN2hPNHLmpZVMTs.henG5g18odpmJE5Z650jd0LYeClBKp1JGvPPig8Y1gAJVQjBTl7pFkSLZfc9q8ZObdGGayBOpH.ZPPPbmc14fNQH999hqqKZhjc533zywyoWPPP2JMnqmmWOWWWkpHggOlbMWy0HPcpaXm63Etz0jl4YlYCXoQxjW0HoZc+ZqtZ263NtiXyZNoPC8hhjiKUaOG2Vf1vxqShx4.Qm6O+O+O4qWUju9a7du+ye+2+aN5wCCWCyBFV.EiLvpcxOxG8iNiVWmgDtjQDIcAxh+im66t34zyYLXaBxSoykbMdtLgTQ.nDHTAK3LCBa3uUUlNCDHxSOnZ4j9+6RMCwhc98Xp7smat41ND1lTBirIaOyLyrcsZ0ZCryq5U8p1wyya6JQroqq6lVVVatmR4vV0pcpMA17oe5mdii65tNUX8CcH6AjvrFsgU1cg8CkStH+9AaChhAsxxzKBixfbMm3Z1kJMTLYGLefpEvX3aVS++kuONgZzj68duWSlVf7+d+du67pHEbbbxYaaagPBBrPBBB.EKMIuXwI80VVVpmiCOdPf0S8TOkUrHYnJVu427O4.WCQ.s96F8y34g1u0u0u0Ama2etmMrKSSm67Nuy9R.og7fC1sLr6m7C+I2wwwY6Fvlppa333rtqq6F0SJqJUjMA1HHHXSLD17VXp281nrSiFM18q9U+p8Pn6YO6Wu2se62du21a8eeuFTomiiiFDDnWn9EhS+cy0VTJ5ddtEj6j6wxMINhntpp6d+um6eWJWuKf9W9W9GSSi1iKSCWN+2Mk1CFD18fO3CBfb+2+8O3ZB6ab6HiLhI.n998Bd7fcGYrQ1QDoCUnq2w8RlOEY44cLKLHtvhowJMi8OGtee9tIbl86bAP1c1Ym710Yn+6e9+6CkOe9BTlbXubRBTN8yx844THPKk5DBDGGGqgggXPqjjw11ICnYJWt793noDobNqmmWtfGKHWHg4RdenZij0Za1qloD.e9LfTJf5AwThtKt3iO.QRt7tvJcJA6RnwH4DR.NMYHarHrNlLKtFgrtmm2lUpTIMYMsAZOcc1oEsLbZwBDy70RteWINj45Az0AhYVrRfTedRBtLzLWEHG0Mnwq49CDQtZlr7NBvXTauxdgD9yBX3pQLTHgCu7xKOBUYDJWOojXBl3IWYiI.FurHiZPU6xCm78FAZMJb9Qq.iQCywcQXBQjILeeFqNLBDMpp5HTmQDQF5G+m3GOkvGubNDJj3XjobjzmgSjIiyTU033DkdJHHX2fffcA5JhDeW20IzjikkiiSFfrG5PGJiuue+9ImVj4bFYq9psjTdFNNc5C3.USHlDBfeOdP+YskLGcIqPpjAHSkAr2IIIjI3HQSNeRrpRLhDKIIOw22OtgwVvt+k+k+k6PDalT5Dq8xdYur9J90L0X2kXoA4OiACFz2N2Fb8rL.YOydi+MnpTDAZnEKVrGM5GjhKGg3uuVpBNVFTJChH5YO6YUGGarssw11VbbbPAbccU0DzjXee+XSfNo2e1e1ugAARmp1f7Byd9oc59+egEvBuSOXPAtj6gcflBnG9696tO4jJhzsAzS0fXOOOqS7cchz0N1KYkWYpb2.1+tTpTCmTh9M7q.WPDI3zm9zMrMD1emkWdYAnvq707ZFU05S9u4ey+loutxW2LXjwhz0GFAXjG+S83i9R9+5kX38oYXXbZZRjOgYhnj.Hy.BSQVL6SjhFtA8AXv9mClL0+tH4pWp.mru4+AzOX6wzj369tu6z.prUhLyuT05zv00Dzje4G78zREccL1nsYrJa566u0wcb1lJrcPvoZCry0e8GNo7xZXHr4jDf7bBmbeaT6akALAt3AMoODAA14M9Fei6.z8QezGELZDe1kMY0+YeRSngQ8AZ+9deuu0AVsa2tq5brisNv16ryN6566G666q990zZ0pE633zgDoYblImYSf12y2+8zm.2pr2lLey2p.NlnJaQxFsLP.BDQDUU8dtm6ouQOyXfRYLr.ULQSNahwGEAF5jm7SUDnXipTfFjWDofpZgNc5jeqs1JmeJwigpAgApiiS7JqrROP6kJqVI8qRPPPRzRM8m999bqNNDAJTUKCp+I8STtgzmgg8.hOyEOfIFCeSq+3ovpr485Ihrqp5NSN4jsMLlNce0u5WsIihCvyKbUizj9D35x.0AtPTkJm689de2ma5RS5WpTolppanp180+5e8V.E+gd0u5Qek+.+.oDQ4jXLXaTfgsgQ9bewu3vhTdDaXHZwPzjTNgIODkCHayoHCQ6q1pOXfeFbQyuYWn7h8cRWDbvnIO3umlgoAXi88QjW8mCJhztVsZ6TqVsNUfNOVPscfJsuUGmsCdrfs9u9e8+5F.a344sgiiyFW+0e8aDUgMHpudumV9V6BDeFS8WakvEQWMF8IK.I1wMQbUnG9nFDkPdRIOLnX8YHGQWRjn0eb4.L0cpSY49Q9Q9QJHplOgbfyLPgWqBhoOLQcu7bb5555lFg8tNNNwG4HGQ7NtmPcrdWuq2U5ws+31P6WvFxjmsMc6u4a3BzikeFF7A.MpTI100sya39eCaWAVWDYMQj0.VKHHnEvphpqfZj9NWW209C+C+CW200ccw.Gz0+4eausMN5MbzMQYqumum+AaEDDrMP6ffGqCPGma0oygpdnN.61qWuTUG3alRkH0QTZ.w+xuu2WOaQ5RjI6eppxm4y+YrHQsyVdJjKR8lueCTli3pPu669tudpp8IRtjfizO30hJxlato.FHsqZbrq6wiOlqaOhH98+1e+344kLFqYFfrKYSVV9pV9Qe9tcPCHGf2ILj42gJTnXHLjHUGVUsHMH+.HPbviwkpoMKYJWIpr25.AA9BnVAAAVNNNRiFMFzljX0nbZY+re1OadGGmBfctRPVWWWg56crSTpmmucbSqcT5QS5M6rytWlgStWZ54YlONQhBpse04HUc7RKA39RENvtkfcWF5BdpmInVY3bCt214s.rBpPFVjre9O+mOOP9G4QdjTGFxEYaW.n3xKubwY1qTWLAIwrW6zXzakY.ltUqVSb9ye9wUUGsLLRcXLnx32vzSOA0YRZXHIeU0ImZpQm.Xrl1LJQLBl8sGGXhDtGYxnRklX0UWcBrYpRvTsZ0ZJU0ovP19SRhCRppi.LzMeS27fALIcroBnR+hLYe1oLXPfxBXEDDHppRMeebbbTWGmdIxHaeNbPUcmZA01UUsasZ056jruue+yQIHS.jAJYczqLGSubsA4vqA+4fsK234AmqlIw9mbQkMiG51sqUBxRHHHHIOcnm+7mSEQ6kjnrdhno2uo8KF4l0rd+ZX3YgMKA6rzdk+0yGk91eazL8USSt41OpkDRS1Wx7wj9hqz.lzOvQMpfRCy2yoZ03fffXv7LHHLTDfjwUFGkC7S6668O8e5+zX.cu8T1aLw.4.vL14LXQMrtu669N39LWIiI0E+hew8c+hQ4bRe1Z867676z22FfBkt5CZRWLyw1BiPRzLx1tNf+QO5QCu8a4VZr5pqtpiiSaUU8C7A9.YEQFBXry72blISWWPDYbfwpBieq25sN1G5C8WMdIXbVhQInuHkzmyEWZNxxJ8EzjTzvYVG4nG8fINMs+Zv96ql9x+1H3JGbMrr.Y97e9Ou3e9y2y22emfff0q.K+n0psHTYwi45t7O5O7OxpdNdsDQZArtmmyFtttaUuJaSjgu0.149tu6quuEOlYrnxLnG8Edyc2W6a0ALA1uS0GbP91iM1XskJxt292+sCUHaT+HMdtmMh.ZviU62v65csATs0K9E+hao0quguue6QFYjt.wNNNZinlXYYkZ3ZGWW2cRBdxtOzW9ghoJJ1PzeaFAvHj..QDojI5xoCVEQDTU68o9TeptTgcoDcvltK0uTFNS50hETMa61sy80+5e8bG+3G2DQy58qo2gDQFpYylEVc0UyIl.wX433ffnUqVMVgdfzKHHn+Byqu95oY3HiuuuUPPfk6wbsd7ffj645ZCnmqqaWGmuSyhckn6zCXjIOSx1zb8dlyXlXtBYZjrIgsYgxsTU2TDYKfc9ve3Obue7e7ebKlkb3QNlgKlgKWp1fO+2Aa6Mg4VEnIQQA.0dKu42he1rYiDQVFXiG7AevNppw+YejORpC3CIhLBUYLJy3ThwCMp4y3PywCgwO7gO7XPYCgu4xHylRzaqXVj7i+w+3lEK87J.SMXjlO3BiGbwR3JegxK2mcv4VOCGaS3QjdkFvf5SdxSl9raWfcbuM21.aKhz9wBB53330Ah14w78a633rw8du265tttqVKHXkG7c+tWsVsZstvidg0XvfkXa2AVvL18Lmwb+d5qnMF1q+YAxbl9YxnUl58WbupQskr6mExhrD43JXbRhf0IyZ9LYAxctyct7pp4cbbxBHI4GKFgdJpouQ09vYz22eWWW2coL6VA5VqVsd0NUZVWSN+UpX8G7G7GXtdBC+lesimeaGLXb5A9aJm4.ALsREKv0b+GEECz488teeaEkP3X0pUa0SbhSrJl.atnqqaSWW2F.K566u7ce228x999KgQEbZ9.OvCrnimSB4j0QGnJH...B.IQTPTUY0d85sluu+FG2wwf3oF8c3aqCcnC0lJU1El9fpRzU2l0yC0RVW6M8FdCwug206JFnuTb9+7zOUF6zwbq.7rs2z4oa8DiDqXLLdWfdiO93w.5niMpgaMr.aaaATKWW2jCaDmpVM.jwFarL999YnJYeu+x+x6wyC6YL9KDCZBPReqMFhdrLYq0oSdU0hPz.YcrjwYi4e1t+l2zGzro40H3u3u3uf33XQDKS.oDwfhLnGhwPdUztdG2K163dV23Mdi49BeguPdHLWy9N5T1z+ZSZMqe053vea01KXbmdvZVu+Z6l+dsjU8ZA0Rdu4LuyA+NlwjSQWlhdLMpoPuPfZYpYFeUDpZB5wrLzzob4QjgaO9G9O7eXQU0gtqevevg.6gAFVCBFgRkFc5omdrkRHR0plfjLKFVG0VU0SU0EvdhiLQ449tlaFohLSi9ARIZ1HipZUBrKALqTQlVU0Dri.cLy9zLIvLToR4wFagx.knYyYmbxImYsmbsoaBSO93iOisHy.LcRBRRIv0gUUK9o9zep7.YVa80rVas01WP8z8jfdKfLgAAoy65mQ7jfvY433XIIAOAPUU64551WFYccc65430000smkkU+jZ3db2TtrPO44Oex4qo0o+laNs.XclDUpzdOmRKhgSWx1+XOMCVF5G7Xz+XQYLnGBxSCyZOMZzXf4ChJIiOmat45OFyMgCDDQ58U+pe0dtttcSHpXSf6lgswi1ThNMuzAK4EJNdsm8LKSl+lVsr.xjjBEMEo0rW.D5wrzi4HFuq38rTh1KoXewuzWZPjVJnpjFoujiW7Ovc7c0UUsaiFM1EiMLw.5EtvEDbPvCKrQN8d2CYNS+w8yX8fO3CJXibz896O6WiFDFDCzap874qSsZ0RUjI4m7m7mLO1LLUYTpxHM2imftR8AHMv26B1aCrFggKBTWDw+gezGs1TSMUXEQZ9Y+re1VrmpsHjlDtJLFUYbpx30gw1XiMFW0vwZZ7EXBnrIgpNLb4z.ib99jIcdU07LyLE.aydym9zCFfrCVVe6Ko4b42G4hsOyykjsZUYff8Vud8zfTU.Hq6cNGhHcbNtyVQlxvoEDs5opUaEGGmUBBBV100cEee+Udv286tUsZ0VK3TAa566mh971ToxNIOW6533X7ObIzSu2yoWP1d9HfIos8g1jjL1ZL5sgcGhPefelGHKPAGdVcbdPXFYPYP85a.0W6BO5itFv5Vhr8latYGQjtRUItbkxwPRjJ16XZJUlF14oNElIzrIn8U1f4qfVIyKSCM2ay2zVOQjNEKVrMQkZSS5Lw9ky1zyq7Veq2KEKVjq8ZuVDwNsLVFRUcTU0w.F011dHGGmbXPxhDZx3gdxScxXQ5GUeCLugtiM1XwIQf1x083lrF83AxS+zOMUg3Oym4yzsVsZ6.z9q809qLv0sIcVF6CdMN30JKjLA0buZx7.tzKD1gYYKQjMt669tSkwN989898xxhUJRMFtxRlnL6sWTluxWvLLrCb9smg9pBPCvIDHDahDQVLqa1U.V6oe5mdyj9BEHmFpiPClrRSlFX5JhLs4oFSe1yd1ooZioUUmFelZQp1OCUTggeEuhWgwHjZ0JBqTzYvHMuWseZ9YgmQ8Qe4t+NXjoG70Cd+ewVPZeANoYRFG+m+O+ed7sca2V+4Nuu226qM0s2zyyaii65tQLrkuu+1u6286dKU0M788Wy4XNqBrhmiyx+e+C+CurHxJG5PGZMbF.YIgg8fyDmxs+m+7mGX9Cdeb4Zh8YLyGc1yfTSPAqVeXU0QHLIXIoiO7thPuhPUjEgLI0Uetq65ttbIjObhLPJfhJHwNNNoNezCvXDhjXbRCz28+e+g344odddJUSydcfEQQxO1O1Olk8ysLH7sSsCFbjzWO3XLiito2uQQY9U9UteqR.ppcUU2wHYjrN1rlkkUqG9oe3kcbbVDnQPPP8fff5ppQfzDn4iepGuoqqaCQjner68daDDTqAvRm8rewkOzgNTKSI8TMcS5sSHZLyF1QQcfk68MSFMV3bl6S2Di0eyu42L.hXKVhHYO2S9jYBo5dOau3BYw.6MYaLjqJ61v759bDXi02fff.MNNo+DgZ99HhH999VhHYCpUK289Ft2bttt4oNEdiuoGrHvPenOzGpHtWw088211LklYUgPjRMPxmOee0Pgz0QmsoYL14tjFUl754Nv7uJxK8k9RM+uAJafO8m9Sq.8bcb2Enimyw6D73AcCdr.MFrd0u3Wb+xLAHGS2vbMER1ApY8mOCPU552CFrjX.cvLDetycNy7SGrNOXgGVOzC8PC5HE.BqLmEqPFOChkx6sGAqNJvXTp93sa2dBVjIVlJonzXJpvjhHSJhLA0qOIDNUUXFQjYoYyRPkJ.UoBN0AOU04ANLv0IhbchHWGv0PT0CQcbq1.G.2xF4Q0SDwCvkJg1ppk0HcVQpNsp5ThHSKUkz.v315odJOHxSU0I48pL93KTRUsjXKkBUcVU0YDyd6SHhLJlDlj+U8JeU4.xrw5aXsggWg5mM1PydD8S1gcRYzPRfRvTZMVAA9V99AYDShwrBSBZx.OqzDdknWPPPWGGmtIbDWGhr6344sa4lDO2byIybkiz1KWyJg60xwzTLzvMCoxYawDzNaN1KO8kScVjDjhlgFjMhR4TU2WfZMAHR0jIYw4xkKEAqcUzt+9+A+989U+U9UhcNlidi23MpAAAwttt6M1cox8nF87ZtOjV7bOv2eaSKILk1ngfhC8KKlzeTU6whkTNOpWsqnC5d8I1l8Rzd8hIIwvBFdazRUKW2iKJJhHZcU6Jhzob4xcN6YOaGOOut.bnCcHKBpjgZXUxTh7Vyuem7sXlkLm4v9bl0ULRX.TlGcEHN45s2wN1wRSzZVQjgz.cLpy3kqaJMOmAsw6pJnIg6ZarGMoL8sCKVrXMpxEZ.0do26KM3C8g9PQpQkXVaokVpsHBq70VIO0YXMTGCXrEFczwSJiuIDQlD6FSppNIALdCJsG4vVghhH4EQJvRKU.BKVcu8MxBKXlibzjfUTZeAZ8JoD.Se8hY++UZxVElGqHvJ092pUq1GQLW3BWvhvx8DQZWstgqCKmnrNVVVqEDDrZh8+q555t5O7O781xyyacGGmMbuc28Jkynn1dddoAMIInmdWNEr7EDsmu1fevHsMXcqMDvXkfwZnZwtc6p4xkaCnzJPyVPe8X+hQfjlACGkrbZFByl3t.Knp9hBCCuFaa6oC88y7K9K7Kz4C7a+WrQPsGeMGOmV.qTqVsU877VGXiJPqn8AMvI1FZsCGLyMWY2mv.PLFnvzPwkozPPSCSxqZAQjbppwhHa6.qEXfS1FI2uwIGiDtKwYHHnHPwpPw5v3Tlxzf4TUu1d85c3LYx3hI6LilTpAHhzSUsq.6pPGT5fEcbcb6DDDrCF9En0u06+8uxu3C7.KAzz22ugq6wVDZrhoufsgpaA02FX6EfNmgKZfcF79dekphppkHdY.+AMFoeTVumu+ue4g9XerThAtM3sMTKElvWI8+G77mxYL4AJNKLzhL6nvhS.L4W9K+kG+Iexmb368du2r.wUDoSjp6lBUvtc6FmISl3kVZotFHOWsah7u00F5FB6hMFIvzltjDnqs1ZqtCO7v8ynDl9IsBHlZerYpQToA5avETt3iuuHvDl8aLwkqMvwX9rv4RcBYeJ.PIX3lvH+GdK+GF4AdmOvPFEXxvf+O1i8XxwN1KSqU6T6Jhz93ttqGAqREVgHVkoXCVgz4JoOCFzAtChFoK1849lunplSDIUcBFApNFTezj5FusHRKJwhzjUXd1hysOcdev66L.4KCi1fJkfnqQU8n.2LvM.3FDDLt.4rcbz.+fttd8koys.ZIhzPUMz00sNvhUfU+3m7jabauhaaiy+kO+FycmysAgIpa0dPaeOBr6pa8iucus23oEHCmgbLG4376qNsyj7YSQeS1xvPmpVsQ777RkqyTn4OBvP+0+0+04uy67N6O2HHHnWRVJRjR5JsfnU.VMHHXMGGmM+5e8u91W60ds6Lv7uAcd7JtlvOv8UVfhvziAKOop5zhHS8ddOumgtu6693U8p9A23K9W7PK0.ZvbrBmmsvLO+fOeSGSmCiAfyFP0CC0uo1cZeKK0boavwwwCX7DB5NVUsMJqIVxxNNGaEHpkuueKScEWoUPvisZbbbZIL0hRrLMoENrFA8uN9Vsb29bocPaAxZCEBMFgNEvrI86ETU2RDYIvNBBWj8rG3hMWueY6k78mC3npp2BvMFDDNGpNgZoYEU5Rh5w.rpHxJppsbccaUqVskN+4OezccW2UHP8KbgKr7gNzssQBIGuyzokrxQoKm9xt98eW1jKyuOXYijEHSYPZPYLxfd+.sDydYeLacUSIuSKnRZoWjrGQ4bPi9bWQDjmxTjFTjJTjHRSTiljDh3Daa.vpLjogYOkhXbXOUZdsVas05cjIlXm5p1QDoqpZOQrUUC2mspoG2xPbCnGUoG0QnBYz5ZNQjLppLfCohpZVQjBUfbel+l+F4VtkaI8dOSx0wzXBtRIRrcJ4d1JAERjptRo8ylR6J4RSAGWyeOHHPUPsfXcurouijveLJrFJK554F5666qpF5440.XkJvFQ6sewNfy1PfQ0wt5mWuu89.FtLL1E1YmIVas0lb1YmcXS2orElDKszTvZqXNWcY+qkkZ6PN3bEAFuJTNT04kJxMnQ5MBbjff.WGGGyZYfZ63r623a7M1Ie97skDtyww8XqTq1oh777t.v2.3BXJg5UJAa1z33UpyVuPsTbFruuu8sS.zBy+x9s+MipJUDoWi8JUtAsY3hc7y.TDGlh.lixbSum2z64679tu66lBBBNjS0pS3WudNIA0E+2+ze51u4+k+K25T9AqpZbjmmWMfy8vO7CW6Dm3DMAVtDzpow2ijwad.0rhhhjJUpXYxc3xw.wG0n9gWIjdc+0kmGxdNlIGrTBh0XXR3THfQJC49odSukcem+xuyM.VEbVEBV+Jn+3hd9X+Abu3rPwEo7PXTEy9m2yctyMzbyMW1d85QlLY5kX+euZ0B545Z2KYsjcUU2QDYafssgsCMI+nG0IFaRIJOUM9PjZ6WpuKwk.qlLUFXEChlMqSltFwfIQAt36q8bw9+Cz+Oc9ffmnfiiyv.i7jO4SN5XiMVQGGGqpPb88VmQ.6bP3v.CWqVshddGOqu+iEaYYs6sZa2NB19Tm5Tac7ie7swlsIru8+Grb+uZJ2rusr87Qln5GgvitunEVMYPRkXib6Rtb4xkjYilWIQXzLf4zoNeVZGnzl.q8a+a+61ZlYlYsfffMTKY6Ovu8u8t99OlhnYqUqVQU0wTUlDXFee+YNYsZy566OElIRCCsRiB3UaD8169cgj+uCbgM2DnII74gkwvjpISZJ0IHgrWY+CtRgRaOHvH8XTQSJSghZjNJFjNLwYO6YGKHHXXfBIYMOCForyBvRAbcciEgcccb6DTKncb7dYm8W7Adf1AAAsCBBZ6551NgTP2FncEXan9NLAc.utm4hO3+.Yhd9Xy0KwTkXQjXvua48wiFkvjkipC+PerO13c5zYJHQ0Zn1XrGQPc0Dk48pWxilTiryPqEgUfEWrpgdLBui63NB+Q+QeiQhHK+jO4Stdjp69k9ReIIAdcCmMa1wDQlX1YmcZU0YTMbVJyrOxi7HyFjjoJMPmBXbBsGQUcHnZwgGdtBppEfYxS0j.SLGEhfBPyhUY.kfBuKWcZt+wRPFlueFitZPtv.apctX6Ajl2KbgKzAncsZ01rYhiCOv67AVFXoZ0N4RAAAKCrx22wN1pPiU8Nt2ptt25RQFB1cYhnEvVrh2fK5umA4Gsug4Ctv9UzhlevO3GLM.J4dculWWgnnuRQpRAQrSiJOzL4Xct4u7GrRHM.4ge3+b1igyIEV9.vTSOcejjjT1Z8W7WMjhrFDDnu+2+uk9W8TOkZW1tGQzat4lqGg18OQW3BWH42mHE8EOelE5+tsYHrSN2e04RdiYi+ze5OchA+k1Ngrf2jYMxYmmm2ZkgVepO6mZUfVlffXd8Nuy6b0fffU.VAHEFnq366uZEn0EtvisFkYCnxlu5W8qdaf1W60dslMjCmHYrWIMQpySaOW660DEvwRDISUH2O2O2OWAwVJd+2+arPiTzGd9qvmuNXD0FpKUASgeADDDpA6Ic1l4HBpppku+oxfgDMK366WnVsGKmywbx54c7TTQY8Y9feFiysAUuTqg7Bj1zRJwLSxZEc61Mta2tJPhZqFhgIk8tTGjj0U7T.DojlHOaZbbbBGQnFL7nhTtbYAL7Eiq6wALJ3AIOyCLkcQdfB29gNTQnQApPdnTtG6bmyrt8oedc98AWS8.FYWxh4RBB8Lj8jW3B4fF4cGD0LXm75LEiLHsXTRKuEhlEnBPUU0ppFYSUrgJN0U0UUcNMRmWUcdhpNup57ppGVD6qSUcAn5QDa4EAbippGMR0a5O5O58dzpvQa0p0M.7h.NBvQFe7wu95pdDQjinpdDfWjpguHQjiHhb8.KHhbsTlqQUc9Ff47VmCCbsZc85AtdU0iHR0qWUcg0VasEnLWqHx0r1ZqcnH3P27MeydppdXP5hCTorHxThTcDfhgggYCCCSKmFIsDa.De+fAdeWbbbL+XBVR+wAtNNRxnr9RhtNfZLIVhkuuOttt877N9tlrwVc2n81mT788sffKFWGb0LNSX9Dm1mgbMf7EJTH+2QoR4Ei.AjCJkf10pVqr2Xm8eL5+y4LkrAUk5fAUXIp5RPPflVNHkKWNVgdAAgcymO+tXRTWWTQ88eLwRDKeeeqZ0pIXHOeAJI+uZzP341842t1hSTZltTgcaAcgVcmde1NWF.QjYowUeBUTBRrupQkt228cecCBB6BRuvnnTXra8k+xe4L+e9x9dybJeeKPkAc96Dm3dD.qa5lt8L+we1O6ADrfZ.nUpXjQRXYXFDvlmKkI14.EVR6uOG.TMQlsqNTCXj2w65cjVhciAAiPBJSVXeWWWw9.rKGksA1joo0hvxPiEKaBPW.PMnbs4medeQp13M8ldSq.r0hKtXWUUwyyImHRZvcMI2oJSBUlLP0oUUm5tt16xfx7PaCWaQ47h3j2X+uaAJmXquGEaBEfUJLaRPbLkC2bIyuO5AG2ev4BeSa+uo+e43uKSRn5Bz8E8hNwtu829auSsZ05TeONspMP6ffS1122eSf0ucOu0fFsbOt6Z11emqFAqBrxwO9waArY3oBSBzY4NfSRxBqzgEdFIU+EjsmOgtq0oMD4pkYvR8jG7QV.Y9Q+g+QyppV3odpmpHXWnJOC3FeoFbj3jbyNPys.V+m9m9eUq74y2BXyDX2hqqadEYHOOuwDo5jVVlZp000cVKKqobccmnZ0piBT7M7FdK4l6Jeh5fWaoQDzZ9TE.H.Mgj+3B6rifIpxYf5RqVsTnYe34M+7yOXTaUf3yCJyhpMTAhxREJBUGVDYze0e0e0w.FcgEVXXGGmBAA96UJQ59g6kuuuZ63zy22uCVrskEapZ7FPBwcFyF25sdqa.rwe5e5e5lkSl.EA6fGcJ0htXTtmKWzMSVv5bovqrK0o6C9fO3t.61fzxcXZEZJFT1DNDv34ymep+G+O9ejTJLkmnpIaOWMPyK87afm2oSBbxRoYol0pS4kAZBkiLLpMMuga3FVVDYs67Nuys6zoyt.5latY1qiqaHfwfdSJUjYpzfYuq65tL0RsXmBs2ofvIDQFCpOBr3PlEZWZnp0SHnzyuG6+GpZwW2q60UvXrZsCFvjKlAKlWmGN+e04E3bjHW0WrO+yV+RbngiQ1ElnygNzg1AXGOOusS5eVuToRq9DOwSrhm2wV93NNKArRDrhuu+p+G+oeqq.MWAaVAlNE8W6.0NXvRL+rW1WuRIyL011NFH9G4G4+WCSaCxuwu0ugU4xkspVGQ0PQbDMIXqoAB5xerMAVQOwINgFQk9ysbLR8npfVrXw8tPLg+OF0veO.cCBB5opF+Zesul3i7+wQ55bnasKP2m9oe5XHL4ZoodnCcH.Dnkj.u0+9lgfC1T.RVypGr3teueueuIaX1r8LQIAjcQRGesYCJu4K6k7x1JLLbaHZaee+sqU6jaBrtp55999qUqVs0fpq+Q+vez0bMnYZ8LYXiRMXSHZquz49RaCylhlmcgVcAhCC+epIRc9y41QO5QAP.WojorByUGx+fO3ubAMTKL4jSlmJji8jb1m88EB5mUPqet2y60pPgBItsalaDqZrBoDxMXJQx7A0BJXLDiBd2lS9JQjMH3wk63exc.TlW5K8klLlpdx07K3FmkrW4x8g9c4xkAHNWN2dtYy1EnWcHlYI1IDEpco1yI40ZIqAsXbkFl0c9q+q+xOiuP1rYELIRvpVvikRF64pToRtuxW4qj6G5G5eatZ0pkACZJx95dcutrkhHKzzZ9SLuzuTae9G4XWBz50Li84SbXdIxcnCcnB.C4yrIDvpcQHLAguKM787O6e1X.SVsZ0YApnp5TwfVW2eoeoeIOopbnx0YNHZ9S9Xm7ZEw95DQVPDYgNsO+B.WuHxBu025+pq+Idhm3Has0W+HpeuaX80W+FAtQfa7k7RdI2vI8CNxniN5B.G9TO1olKNNdNL0s40npds.WWxOWqp5gUUObxe6ZzH8ZR98Cm72utSdxStPbb70Abcm5Tm5v85Ebs.W6XiM10t4Y27ZUUulwFar42XiMNDvg.lSUcN.OUqaqpN6idxO93.CYaamy11Niiii3GDHA98Cnt355fiiiD1me21qEFDRXn4yZPeBBzm2Sx3551ujbiiQTUigx6FD73c7775jfbUsVsZRoRkRRlVEqpO2lG2eM.uykLVXIy+eyM2jPJoFT3XKp1HYco5ChHzK94zClqFppgfMJTt+97NNNDFDP4xkoQiF.nNN1wG20M1M4GrLiS0zqMuaSfHiTeSSob4xV.VyLyLuPtTVgAsc+zI1+FsGmrrLyjf5xpVIH1xBVp+9HyO+yRxe5eNrGH4fQ695++4mYGP2Ah6nIjK95qutbG2wcHnoqInVZeNTrRVnQNfrexO4Ck4k7RdIV.VyM2bI82yqymj3qkgXlA8W+W7WWgPtDkg5yd+ADKxzIiaJoI6aYoZXh+PCV4AkJVIIgomAxlTRKWcbZxoSP40xzlDzD1.mV.KCkZBMRBdRTv6889diDQV7lu4adss2d6sSJoXI4ZpnTQFsRcFGhlVpJyJhcoG4QdjDToENkgvXaLJDNhHxvf+vkajHQw01i28ZpZtG3AdfLPIANex8xoGL3nWLaJDlGzugd0Z++fATOFnWMlIgzusaCKt0uvuvaYSOOuMS5eZO6ry19q7U9JscbN112pq6l.qWGZEDDz58b+umUgFsnLqBylxegaYaamjvzFcgfDj0GsKm4Yj7+WP19V8BPomuAKAirUg70g7kJQwlMYhxkYpFFYfK6G4i7Q19U8S+pVhFzDSFGS46hKkiWICrmoHrzD.N.GVU85BBBNjqq6r999CioVS6JhryG6i9w19U7C7J1LLzeaUk0AVd80Wewa3FtgHflXyxDxFrmxeLHjvtXFoj95AG7mVZHIkVfolfKAEanp53HaGFxpttrpuOCBAMcf9rzu6XXPTxrppkANjHx0npN21aus6pqt5rIPcMmBVIWToQTrs.aE0r45G6Vu0M788W200csJUXsnHV8S9I+3qdK2x24R.McccahMKQnobbBBBRIG2qjRLX+QE8hyWGYlAJrDLhsMiEDnSTohLVTjV.fW+q+0u8u1e5u1ZTmUXuxTpuBrvUNDnO3ykTXJWrJLbcXXaaF5G5U8yl+C8g+OmutgDcKBLRkEXn5OsNLPwO1G6Sj6G3G3UjMAtuw.cKC6Do51O9i+3acrW9wZSc1FJ2NE11IWq8F.VxosXfNy.sWB1x1lNgg6qbRF7dJs+JcbTZ6YqDWtT8E8OV1PlPHqmGYqUiLyLC4VZoAfwcExlp.PgggY.v11tyK+k+x25S7I9DaNGr042OpntTiEF78uTWmG79LGdjmZ8gs4DIYdXXQD8s9Veqa969NdGqDZHGzVr.ayYtjkEQJrjGECgCdM.2jp5MGDDbCNNNtAAAi633jKgvz5Bz183ta849fet0OxMbCqZAMcbbhvjSsl999KebW2VQlf.rEkXmZOVs1dd2dandGfcO6YOa2Ce3CmZnzeuXiiAZC9rJCkHy22s88Y8I+jeIfk2yfk8Fe1uTIH44YIXplv3e3O7e1v2wc7cm6XGyUZzneo3DSYhoA8BBB5.rchzStQTTz5UpTYaOncsTHu5QRBw7Hwg4uYleXVu0gIHfYUUqJhTVUcLQDoa2tqlMa15UA+5PS1eITdfr72+9dLLdXeXfaZ2c28nYylcg0VaM6wGe7QSPy.IqwtkHxVAA01Br15i+o9Dq+S8u7e0pAAAMUUa3551DXoJvJmrVsU777ViprN0YS1+bwuccb1f8MYmdZxu7xjWUMeRF8FsSmNSj+P4GWqq4vT9cq.6yVfKVlqROtIkvJSig.AtoM2byuigGd3aJLHXtXXxJkKmqYTyXUz1fttqq2ZAA0VKwNf0.Vz8Xt0qcpZ9dddQ.qTud80qVsZZf+11F5DdUJGn+sT6RY+1f64lCnPiFMJTtb4AUugbI6g0sJzstY+oLhHCUsJiT2ve.i+HOxiL7K9E+hKHhTTUsXPPPQWW2hIOixCLTmc1Y3b4yO7N6ryPEJTHAwjWRhNePa.1GgGFFFH11N8m2mnzGZpZ94GDD6ZBrc+.x2saWsQiFpiiijP1pY8CBx3lnTKIe19pFBXPKRPPfjbbiCMkOijP52EwfL2hIb.mEfDFFJ5.c3wIdP433PxdEo+tjV1NIueriiSbXXX2DH5uqppgLJgPWW2yVA95QvERhwvFI1ADCnAAAcbNlyVDYbHgqt40C1utJHVX...f.PRDEDUmCS4eOrIgNFkDBXjpfUnpaWohrRiFzzyiUpUq+4JsudeyUS5mFCXlxvbMRPODvKJHHvKojbxlbstG+DlPL2hHshglVDe9a0w6bMfK7O9e7sU+y84N4ZyLCaszROCk76Ex6Ydv.X221FU0rkDovhPtJUHyW6qshtvBS0Yok5mg+mMapr7f70LOKroJGQC0aF3l.NbPPPIwxZXMNNSxwIMggsTXQT02BtfJRsieb2F+4+4O7R+f+fmnUTDoNAuSR4FN38QZ.05Szrbks+5yz1NnfMLRnQ0qlBXbaaoP85nXTRpM+.+Nef0ds+Tu1zqmsY+9dbw3OwK04tee1.m+bUfbQPtpUIa85juREJDE02OqQnJCWkpCEFFVnWudYO0oNUl63NtCIor.0Oym5S06y94+7cemuy24tREYWZPGnxNPTpzr2gD9oYfRDLsea2YfNKA6L8zr6xKSrsMwIy+OXBoOXfTTt56+gC3OZRIvVnRExEEgU4xPCihKYZUvJQAJyFFFl0111BPe6u82dm21a6ssyrvNKlVJxNv49BmKd94OVOX4TjY+rkX8Wvzddsjb.DU0T0uHy+6+2qkvz1jUUMipZ1W4q7UlkFlMUq.YF.Z6WtVLFcaeGfMqBq+cb8eGaN7vC2w.wQJ333LRPPvn999i78+O46enfff7eoG8TYRfhK2vMbC366KvrBgly2.hC5kJp2WrfkHdf7ZdMuF.TlatAG7mCnXBSPWHznzAY78eFSLD.7.AOrpZ1vpvYuvEFFXzjM.GEX3Se5SWbkUVIupZFArPQjjwnJHjTGw1NNRkYKShdru6q+m40uSTjAJVeeeeuhsbcc2z00cKnzVI0jVmRPWmuamtktx2vdvHJaBVy7ymxH38WvawDolKLjdhHwQQJXT2mB+Z+Z+Iibuuj6cLfIp.igCi3s+xy4JE0O6K51LfbVm.AssBCYi+yu++yqUuNop2wJP0Uq+z5F.sqHR2a61Nlnpl6Iexmr3G8i8wF9tu6SLxWa4kGSDYhicriMS05LqpZo0W++8rjvv+UfogpFxuy1dFL0l+DjvYCKkTa3gOSkt3hcuoGkK5hovU9hQ6qeHLoenVMCuarzRzNQ1V2Bb2LwXs0mEVy931qca11s.V6S7I9Da.r84u37Jzf82bfWubWmCdsECd8nlA1fkR3JlphzQLjU7tdddcCGjjCOyyRefm2fm+ClEdEHkc4A0747Okud228cGKpQVDSP6h9u3m3m.WW2DY21fJrv+mgc87NVeNtAn6se3C2C796AjV2kr0+9e9lz8S9Iehcgk6KafLHWhbzilZvvtjTJXMgcd3G9gae624cssqq6lm5T0VCnkiiSqZ0p0hFrVPPvZ2piyZN2ly5ylDbpJUprCPmZCrdxQqQO3n8fZ8IpWt5leb.CamJEUHVUEICU1yAukVxjMv5W50eFX+fiZLPY1YMkEoweq9iG1XiMhEQzO3G7CBXHNXSvSpjWUiypqs3xEnJYIFKWWWRHMxtQP2LYxzEnmWc5kTJJuPZrl.Xs7xFi3DSlOy.H4yOuRDc61samphzAa109Y2Ap8sViY9pAx+EKVr+yIWGGZznApQWrr9Fm6BYBBBxEqRdfBhJ4DQxV6T0DOOOirlWgdwww6iebBe9giEtXAgXv8CSe+rUfrkK+cXxVqgyJFGXh1saOAvj0SJkWQLkkb85TkDRY8ttq6Y9D9ewqpHNNNN1sVokSud8bTix13kKedO.2BEJ3fwLIaLUOUULkxS5OkAJEDDjt23TAAAFnrCiXa6LBvnNNNiALtiiyDNNNSfY+xojDhWO46NSPPvrMZ1njHRIfRNNNyFFDLsqiyjAAA6QFsvzBhQscDY5fffoDXxfffo.lx1wYRQkICBBGmjr+533jMXfRuw11t+jYaaa00wA0rWQJxJF70j8PLkbPXPPrAIIn1116asnfffz8OhO0oLJURXHZsZ0RB1xs2iHy9ad6eN8U4XMO.TY1YOncPwgp4P0ngYbSsZWz0x5+8VXP64fdQI1ukb7TGGGsc61HfHpXI6MVzxHZ5RGaa6s8bb17e2+t2xVOdhDw+49bmbWftKsDwOwS7DIyml+p617aOaC1euumchHYVLwOf500hSM0TEVZomAJpuT11p.Zs8bZtSk5rsHU2NHHX6vffNtttc+e8272DKHFTzJhAkOJXohHhjUEIqqqa1nHrNwINgUTzfm2JRRvR5uG+Q2qDkO35dWIiIGzI+tXB17NXBh15hHqFFpoIGcSfcds+TuVEC2mUjYXH6DIGlmcw.4h8L3fm+c+pKu7N.aVutoTzOxQ9GjpNeKBUVRC0UCCCWuhHal0M6Nuxuquq3d85koSmNCopN5K8k8xl7+z+o+SyHhTtbCppp5nZ8z0AsKCNPEaQjpXfM4zXVyaHf7KkDz3kW1v0JggCJKwyOnu.vQgE1m8wWU66j94NX++1.aFYryeyFMXq50qmDrmJaSjAw4y.qYea1spjvumus21aacfMWj9b1WmYBny7ye66BKeP0S8EJ1hbYaOefvj9alWpD4Z1j7yBEVrBEpFsvv04LiiIZiiYpsKZCUVFhZhwI1TDFb4bZ2Jg7WGFJOMzXNfinpdjfffCKhTJV0hVB8.Y6MVe80FYzQWw00c0ZlL0sDFtYH4bVYEHZc1KK5WtAARx4OQFU8DnV+H6MGj67lMk6Shf+PuxWYg26u9ud26+M7ues+j+r+nkvj8rAHco8EfkQvXfyzXxTYk2w63cX+VequUWnrmpQNAAAkR9LEcbbxjj0CSMxCcUiSLaJhrliiyp999KKhrRbb7JhHK2sa2kylM6htttKZ5GlsEr3Vrexq7pIiZGLB6lHi6gUsZj0AxG3vvDvfY9XTf7INVryi7HOxFm3DmXM1CkICFA9q1LPH.VG8nj4zml7NPAeSFyFjmZRI4tQA6wgv8TCGXDee+b4ym2Z1YmEfdezO5Gs2q7U9ZTUCMJPTEZuxWaksmZpabGUC6jDY431sa2qPgB8RxzTaQjsp.aEUgMIZeDc1AIqR8.W+v92D34xBRWpLebfeJaAMFDptCFvoA4ZmmKafd4ttRtdlIOrT+ZHMI.gC+w+3ebqWwq3UzFyXhkMkFzxChJqC1mMX1XJWtLWaiFbSpp2TPPvQvrA2nXd1i49R2AjMAZ455truueSLxf6R999K455lDXMZ8e7W5WZ8ewe9edipsTk1TmNPktPzfYA3u2r4w.sK17a3YNtbeY6zwgQBBXzJv3QUX3DoIUXf0WdnG5gz64d92RR40jF.l8jut8VS9RUirWMAL4fYfIYMfpiC0mQUM0Avw.3IdhmXka4Vtk5.9.MAuMfZoHLgCbrRU7swBLnaZtJU3FpWWuwPe+ErccsC8CG210NePPfL5niFuwFajTRSRaPSH.OVTEMzywKDHLHHngiob4ZASuNr7dJU029Wyv6gDBaxQH4UCAnmhtugAFAJOjpQRBQ6kLWm0f9jKW7E4XmzmOwvPqYv340MqFRe8nAAAygY877BDq6MtZcWW2M9BeguvFyM2bqJhF43b60f5eCL069xzm7yeFYA9aE82CNGaPEaQLPyOoj.mGgyQVvKOTaHRxVpp5nhHinpVnREQhhzd1hracaTLpB33ppyhI3FSg4YPl1saKEKVrOwgt45aVbjwFYHRUHhAjkRdlIxP.HNNVrrrNXxljf.eYxImhgGdXf97fQRIZXxyCnoumVoREhhhzDjbn.rwFavZqsNNN1xdG2.F76hI3EXmbrEQhsssiCB7icbbACRRxXaamIHHHSJmkzmeqTCotFD3qNNt6KnCCbtrlbxIsVc0UwXyUXOGG6dAgA6hxthgGO1AXcQnQbrdti6480aTkKPchvL1ZvfL2FlcyD6uR2a6pYb1fnBIeYX3Fl83lnSmNikKWtQqHR1HisHqgwQwk4hij6AG2kZK5rXlacTU0aF3FBBBNDl4V4.QAsKv1kJUZyEa1bUGW2kpEDz31NlS3oNkesay0sVcapSHKQJpF1aM8mK128sqsA6+xNwDTnUKFdVXjEKwHzj7XtW2h8ryMcMtKsuNlmsCCklEZdXRPMKFB30AXLDxhRWRP3iqq65999KCT+3tt0hJyEpcpZ0877VN4budx0QaX9Nv4tTJbxykDRj9pEP1ofBqLCivR8WKI0N7ji4rwvh6KImreR0+hw2iWtyu.HI1+mwFjvJHDgx9GemrtY0wf5iY9cRI+zgsvZnM2dyBKrvBYAx769e4225m7m3egkHBpp69pe0u5NejOxecGUCSEPhN6ryNamOe9MDQ1rTI1rYS1xA1NvXuXO.8q9U+p5Mdi2X58aGva2jD.cv9v9Hs6RbOeo5G125uGExbZOxPsmQP2S5KmNNgG2jC72RsqMoueAfyXN1KflTFNoetK20yKXZeqFgICX.8Q0lMIdBHdQnmVW6UmyzCn2i7HOhgfnRp+t6+e+OoTFx.NOaRuzdmmSSbISsTkpvEa0sa21.6Fqp553XARNaa6rG4E8hx355J99987775jnJFcBCCSFjFkna0S0WB4N59GXk11yQuSShjFVaeFNb99NAVJqHRdU0b+9+w+wYO0W9T7m7m8GoXxNQ5wq+857fELceFuWUMsl1G9s7VdKCqpNhpQCuyN6jBI1LR+yqll07XsuSahpppURFz633raqUVoiHRm4me9NtttCfBjEiO6YOaOXtAk.sq1Hadvnh1qVM54AcCfcInOrM2RUcCL7XvVm+7meWQDq64Dmn3scamHkXamrDLNy1mWSFDsIWIbI..5oOMpMDG.8DQ5Vud8TlIuMvV+29u8eaSfMfv0qZtd13KexStAvF+A+W9urYqVs1VDosHxt29se6r6tWHGvH850aJhX1ImbxJP8p+q+W+S6.3r81aaWrXwJ.yBkm5m8m8mcRU0IifIIpe1yRQeRBgCat+7fbIQaNClE4tXNodkfzlK2ykAI3zAT2mFIDRmSZ8etUIXar6irD3.np5p3Z3YqIvRRYvBpjVFT8p.ce4u7W9A1jbYN5k+XwdJ0WEhhTzT4EUAQjzLDNv3bQKUpjJPLTomZPYRruQtWs.xTqVMK.9E+4+46UwzmsC0YGOnCDcwJEm+9V6Yj0RtHDVM6yvpIhCBX2xvNQvF9mxuUXX3x.K466aHRXXk64dtmUfvVkg0BCCSqu1clE1EmK443fYzCtx62OXfCyB0srAQDaMIvmcsEo6m3y9oO.JVdF5.49xzOPtf9YDqrUTDDGGqiN93Rf+++r26dXR1U44896qt0cOc2SO8kp10dsqo6AoAj8nqyLBDXYcrwlPbBIX7iisCXmfAbbvOFSHHguQN1gaFLRB6ClX7AGH1wFysjiv9jiQjfSxSvx151LFgzXPL.pmo1qcco6o6d5a0s8967Gq8t5dFMilQBICBm0ySMcOcU095ZuVeq2u2u2WaNDMm0ZyIhj+C8g9PCoLrhlMVdRPPvfO9ezGefsd89Tg96p7H6Amoe4m7Kn5aMZQti2cWxhohBXBzZPUG.yCAns7SbfwBjYo8qsa5Gm6tu66Nk0.Bm5TmxQ+xTpUKfh3Vf7K5E8hT2XM4TnQRkzm+Ic7w4fXl+u0WD2v3KV.JxrLB6TdtSBQSZRYsHKxHyCEg5Y195H3JiwIbfN6u2u1Wai85IxTQpNMQLyO8q60MGfmHh+Jqrhe+988+090908hiimazQGcZQptWbLZc7wmb7c6vDS.Lt0ZS0DkgfmLLSoqs1ZkxkK2tsPyBsZ0p.PdiIH2pqr54T5xo.Vj2XLELF+gtLhBEa1rYAfhgVagzx9H+YO6YyYL94.xkxNqz64ZdPGV5O9tDHkCDw22Wx5KP58QU0r3uTq0lXs1DM88LAYkCjjDYsw1Haeq01yZs8LFSu8M8T8MFS7pqtZrwXRhr1XPisV6.T2B8TnmwX5IhLHIQ0b4jbsfhzfhomK4.znnn3vvvz4hWJdVHANzS09YCWbSqcDu79kJUZPEQbVb9kW4MLbb0EfDpcQchrgOO4n2riBKsa2NQg3vvvt0LGdqicrvMNbPvVMfNDcQA38oRbMeqdS.ubqsFE7fQVBFm1tDElUtw7TxfCZOHM9i9c61MCjE.UL9tRTakUVQCBBRBCck6lHjzDRnEZMG6akSe5Su6xkKOrXtKv96o57qm+2OdEm1BlUhiYIE8ruhWwqHUW7VJdNGqLG49tu6abfImClDexrz2r0.b9f09Dt+OwIFxv5cqsLo55wPsNYSnwFUg060qmSeGgMOZsZaaNpo6y849b6KhD2nQC8e7K6ePAfw1ZislBn7m9S+oMPi4ANvwO9weNppyOxHiXdauy2lG3MaqV5zppSYgonAYIjcpuyuyuyLmCL08vpORYnD3mUl74qA43fmiVCcgVCvEquy4bO3DPFKt2crpYuF.mYvBPOXgrqKaWC5vAOmD7N.N4PVUyIunt8yypaeynjb.PgSj.KjrV5EXQjgHG9c8c8C2cW1wT768W6WSaA4.a9T2D4xZAwsgXvquSvRqrwq+m60uQkJU1JvMgUxDSLgXs1BVa8h.ECBBxAnAAGIAH4ge3Glz8U9lPQXkhvzE.Jbhy0UStPGOhUUAJKkAwIrOoAtUgBP6BP47OzC8PxXiMV7q3G9ULrCqwbiIbtK.M+hPN3L4ASdfBh3ULEvkQDQxBTYrkWd4QMFSIbJx9tb+Cw446JIHjjNIl9f0swf1OLLr6UesW61FioC30yZsmSl6dNOmmiBmJIszBdpTdAWvEUUemEo2Enip5lh3sNv5hHquvBKrkpZ+Vf7.Ove9Hp5bCn13uOVZHvBiUdmrac9kyzS3wTztr8ppUqltviZcA59O5ez+ngCPzvwHm0e9G8e3Z0qWekew25+Wm4fG7fK8k9ReokCCCW8HFylsa2teTTjDEEMhp5jqu95SCdU9S9P+6qppZFarw7ApJUkJPqxefOvmbtT5D6AT8k+xe4oTYtpmGdygC.ko.lnNLFrnywcfRmvM34tlT6BlUuKqmSt.2aNe5zkTqVsDvN.J2GnWaNX+T6aUAn1EtDh9FIHmyYwlN2HoYAGaWPZhG.IG+3GWAzfzuzItb1xUQglpHUyr8xLGIQihBSRe9OVDM1XLwsa2NFQSr1iGWq1QiqUq1.UkXiwn0qWWt268u.+zqcM2E6Hpet0p5S1Lw7rs142GZ2+sy8ys.JrVb85062J0lICBBNquu+p.qFDb3TmywYq62xsbKq2JUnXgpc.58C7S7SDicXFgxbbsy+X4hcLbgZ6hUSGLKfwgkHWDHPiXvqGPmF304gdf6cnt8Xtv6GANj3.aepgL1RUMmSBb7T.Ve80AAQgbhn4TE4U8JeUCeF1sPdc.Pu67Ntyt21scacUQ54pUZ5EEE0KHMqzse1HXIOt4EBR.RDQh60q2fd85MnwPadetXfj1oW6dBZxIAfZLGH862W.jW7K9Em5pI9bi23MpphpZhhfagw5NYsKEbzA+E+E+EwGOLLwwTLuDq0lrDkUNka+.Ot.Welns6wCKsH01CKyT3JOEuxtxeoh08+mBX7SAiAUG0Hxnf2X3rX48HUkwUMZ7Il3JmrkqTblFX1G9u4uYtG4Q9hyopN6Zqs1LEKVbe+x+x+BSkOe985.ZowtYSxn.ilxFnQ.JkF2wv4jrVadG3Eg4lZpobZAhMJW2tcyYir4xbmnnHqL4dmThhhjkWdIwXLhfHQQVIc9zbt0eiHfnJhwXHXWZEh.IQQgWnj4HYeFajKoQHjXL9wYNfVpV1MH62A5aLlzEAHCjz+t0ZGXsQCLFSOE5gROiwz0XLc8Do6dFaO8rVaeiwDas1DeiQMFSxQBBRmOQhCBBFHdxfDMINWtbpwDj2ZqWxZsijE2F36J6mffgmOK6hY9oR+lcONnatnYnuHROU0Nmm88ldsq1k5YKcQNnR8cdtMqLUsVKpHpwXRTGvLCDj3Ymc1XDzff.0E+YyAehOwmpWKnaXXX19FfbvbmeIl83XqzyhahygSZV.lsTS2ySSnpNkp5995e8u9dA+ww8L0k8Zclcm3OR.RJVrnZsVJTnPNiwIy.Figo22zj5RXJN75GNukyshP1+9eA4wkHngIHdWGGWfje7Td9lr9joLofNN88Y1sA57o+ze5t.8eiuw2XxRf70+5e8hufWvKaLfw+u7W8WMIQjArvD96.bxtMGjmn9KmexctPuFdb0.1pToRadpScpMrV65Mo55zj0sV6Y60q25G12eiJUprs0ZGrxZqj+U+pe06AWkRT4Q+ReIyMbC2P.Ngy17u8e2+1pPSOQpVQDY2kqXZoJVdFU0ygQ6sgwbBycsQ.FoNSWhSdN.DcgdcohCO6914jHan74uFujEgjTVF0GnecneJnHCNuu+SmLM+a4ZeSDvDzCwhYWXG.l9yj14Dh5fGcR0ofAhTkSbBm5A66W+hosCOtsuaa2LMn7Vq+g+c9vmsPgBqCrUXX3fm6d2q.TTUYzvvvQsggkpTgBggGKGUP96826um.T3y7Y9yJ4rQPJZLqTXokVZ2LG4hzpgHh.sk1PdG.IoAf6zjkb+Kecub85u9qu+JqrRWnRGugzuy5DZmgTCqbAl0MAR0p1z5euEpphHUy8nO5iVTDoTRRRIjLjVkcc7IZZxSzfZAImZwSM75iH5fffZc94949WuUXX3V.aAM6jjjz+O4O4OIsSuWBUcODbvCV+7eX3Ia6Bs.8AfW+ZPp+l2ZC7XcRcrGQLaqpNvc8zUlLqu9ilVixdSALY6LpA6cYAbx4CNv.nVOugT8qdelZHCK5BU6npto0ZO6+w+iumUqUq1RPqVqrxJs9N9N9daFDDzpArrwXVy22e6Z0pEGFFUXu6cu64Dm3+w9Nc+9yATVUshpZEZREU0xUnkGtx.IPUc9Owm3Srvq608SNOznVyxMM3BBtLTcFpv9dIujWxTyN6xoHOGkh97T6xZheBAO4Iy8kDNztyfvzJyj4bSsSQf9jof.bP.j+hS4V8vSQW6Y2sceLm9LfWA7nnpZQe+Voz0roHhv7oZBTX129RQwDPog69dUZFCD2ue+g8m88CHL7XIhHwpJ8ihh5qPeUkA999Cr1ikpcJl920ccWw0pciI+v+v+nDkssmYn1q7Dw7gucsoWje+b+LKhBDec0p0mgYxox5kSqMVn0lTkL1jr8m+y+k1l4XKee+NkK2nKP++v+v+vz9ey.PtSbweF+IaSfSlionvu4u4uYJfIUKPUj26688lTsZyd296616.M29c+tuyNo.nLv93oqbZe3SHMAd2u6ewgAbdm24cl.j3QS0snIS5bEH99AordX2iMI8SfNAAAa+du8acq50qu8NITnROee+9gYK1o1yp5esqmINThW54qp06mxdsdkJUpWoR6umTUF.DWt7RomeMuL2E0YIfhEcUY2HiLBwwt0l0pUKDQzJU7RDjjiDDnm5TKpKt3hIg1v3fff9hHCVXgEhCNbfBfmWSLFi.sSGaZ5KmrZ9MZa3XgkKmwpj56CnB3U6Aev+SKz1IhvKfG0.7fJo.mzXhHXbn4dTUGSDYLsgNpHxndzbO+x+x+xSDEEs2UW8rSudy0m4PG5Z1mHxdOvANv4.NxK9k9h2iywGXrTlsNBtRlc2y6r64ayIoMiIPhhhb8uM9LxHifjdoJJJBeeCarwF366Sud8HIII8Jo6yn.FSfXCCw8rh5..wZSLFSrwXFjNFc+TggtOH6BveIwXLwBouuRWq01QDIiMocTU6DYsampcWaairoum1QS+LtjIocrVaGbhO81VaTmScpSs8wCscr1ntFioKv.QkjnnH0ZsRiLVLl1BOdnJHI.3XRSsQLlaXnSf.Q4Ri2ZPqVsF.yGi+2PrYZ2y8DyYbIHxyEe8PgUctg.6WWuzRGxIS+4bBPtlY26cHZoc5zc.jBLCza4kWd.JZXXXliMJ+5u42jBN2mqd85wyA58bO2i.KkiJCYkWAvOOG5Yzmu9ai1NwzrnqTT7X4R.i8U9JekIDwaRfI2Zq0GGhF049Zmiqf9D1VN6W7c8O1d6tXLFoRkJh0ZydAhC3jfffgeWmVMVI2QpUKWTzoyk1+KWsZYIEdFgC9z17q696m8yDXg3YGl37kGvr6rP72+6+Sn3CWwUbEx202U07.ibS2zMkpES96CX5nLvELrGfQfCtafdN+XfOePD0y62ckTxv0GLSeU0N.acS2z7aXLlMfFqGFFd1jjjUKUpzxMb2BVwyyaifffdu6286VDQFQDYxImZp8s7xKOMvr860aVR04POZ5gKFeemtmnF.OncYbfmLCv9fp6kJNfgLl5iqptGXkTcbY1yg0drP1XwG5xcNoyG3KEZm7A9.efc8+O3EZMaWJ18Bei2G4a4ZeyBvD.3DtE2jdCvFelTzqJC89O7d9Oz8QdjGoGvfpzTOzgNTNCTLJZth3JIlcmUmKTyM4.k6G3lvacfURRRV4ttq6ZciwzsgiofYtVyXJL1wOd3nGMHnDsbBvCPoW6+fu+QnIiTEJYskKN2byku7EmpS.n0F9mlJuIs90YmfLJ.v+2+W9vCTU6NyLyrMzpSZ1oUOHOMGt+KNEsKvxj2Cx0ngmHhndtLvknZCddOumWtzsYQiuovYO6Yy43Ypd9CRnggg57yOeh0ZiQX3BV9s9s9M1LHHXCq0tYUnyO4O4OYuW9K+kOnQiFwUoYBMHwGhO4IeJwtjK18mcgxayAoNcQmYgNzjTvaXKehxJCjth3mnpVXzQGcO862eJn4z.SCloA1KMYBfw7v67qm5KDHa65g+5CZlxvmZv.VamRSoBM5Jhr0QMl0+m+O+WbEfy3CKO8zS2FZjINTKArr0ZWEXCQzdehOwmPNzgdwkJTnvdrV6dO8oO8951s6zppy.LWqJNKaTUslHx7iM1Xy+g+v+dKnpNOsY+ouW0pznBsn7e8m6yM6xKWYVfYliLJ7sVJ.JlyQHxpKds...H.jDQAQErN3NSXbo.W7BcOQ4D6fT7G3C7Nh4LjTChWbwCcdnOeREPme9aw0W3DOshpr.j6S9I+sxQSJTQjRVql4vC4.X1YmM83Xlr8+k9bK87pguaP+0VaM03xFnXsVQPjzxAH1XL8yA8UwAdhwbj9.8ipWu+OzOzOz.nQBf9XO1i41+mwcs1m3ucHaXOUZOQ222IncH9LKLrVj2BZsY6TWGYZnCM1ghr0ncOVhd0ftsaOUGfdyjlQz1s+xYSp+z1IfOHrFxa5M8lxCj2iF490+4ee566m+mueiFU2NmlaqJvlelOymYanYpdCL84uflLf.TeP+k9k9kFlAma61tsXf3ltEEoVqEeeevAlh566OTP5.5IncEU55Acdy216qasZ05eXSlkr2Jc6V1se2QvFe1TeOENg1LEvj8KRehF12n2bzrOMIta2tZ61tySuK6McMlaWO6mjjPylMYvfA5icpEAfBEJnpp5CZsIyO+BIuvW3KLFk9ggg8LFSuCGDLfVjTEnYSO.xYFlLiURCVcgKGQZ7oRaHXIyBiztc4I.lIkQI6GZ9bRRzC9Zdcutm6q407ZNn1PeN.6uLpOPYve5WzK3ELEvjo.dLhHRQU0h+I2+8V5085dc6w22ex8su8N0CcxGZe.6spiJ3YYqsHvn+2+r+22SJ6NmrZ0piuwFqeNTgua2tCO+SAGgomd5r90C+YjMZ3+2F498g1vaTDfPylMw22e3qA86Sn0pHBgVaxHiLZrj97QVIwHhzKHHnWJSkG.5fomdZGCQDou0F1SUsCpi5+hHaj3J82MsV6l.anN516.sU4roZ5wYyAqasQab5Se5M.YcQjMlZpo1TDYKP2tPgBcBBN71fts0Z6DEE0SEc.jPpi7j2XLE888KlFmVN.wbXC.RXXXtvviW7Ar1R3bsi7NWVkjJUpDOCmJlnmxIo5hFebKuyMivKkNu97.r3S71bgg8KWJE3vptXMbr+QUMI1XL8UkdGIHnGNcLQEUxYLlhgggEePqMqOiVqVsjkfja9luYM.jG7y7fCYYxrDk2oGfeaCKSxAjugCrwRO2m6ysDzZDfRW8UecEq5h++Iy4o3HVFjYPEat45YLqBIc6jBThXs1TVlfVqVMbLquU9VPAe+8muJHz.IJ84yY3L6fOlq8z0hf2ULYKlrb5hv8gjL6uFHmGMgHhqBCtm64ghUUEOmCcMdiFGee.yBUb15q0wH6on8X.kfCcoVCvtON1osy4ahGmouHRWOXqnHyl.aT1o0UmUDY0q3Jthy.UVF3LEJTHM9eo6W9K+2LHLLT888ktc6l2ZsE9De7O4nMZzXbfo9Jm8ryzsa2Jpy88LhHFbhDqmHxrppSqptuJzXezx4lfVak8IhL0bCKGxkS0UkzDmtnaMi9rxS13+GdunFnug2vaXXb9+.+.G7w8Yt.utPelusq8MK.Sb27NA.9BkK69qUcAS2FF7ZdMul3q9p+9hWZokzFoBvmEJAKUBn3It7PeUg1CRfNP00AV0Oe9U99+9+9WyZsa4J6DIyN4JIhLZPPvXMf83pG2JiQEF89qWeDfQZ.kRYJRt1Ot8y4960qUGf7vZEEXLvaO.i4CiHdRA.zF5.Qp1COudG6XGKN0FdJ1L0+wCCCGgxTbszy0l.p1HAHt4NKZU77bAu0nQiBVqsvlquYNEUbVttS0xcrLgTsXvsPPTGXIVqcyfffMviMEQ1tAr8m6y845BU6U8vU62HsDChdpUJNWh6O6BzjEbfTr7vrdTtqp5VQtAf1vsfpFcDQRJTnPghEKNNvzUpvbfsrO9kIEU1lkaloAHkV3wSSuyulLOGZ4sSoTLyf27a9M2uUlK5.qCMOKvZ22oOcVICbVOO2e2CuUEQNCvppHm8lukaY6vvi2Oy1Bme94yu7xKWxZs64M8FeS6c4+lkmFGRxdppUUU8SEVReUUeQ7LhmX9OeO2iOf+RfupMqBTYoTDnUU2mwvTfcx4XtTVmvXmblSNTKa3IGnI655xgTpQR5fmwN0X+D6FvjcQiuEeFnzSpASA+n+n+n4.x2FJHtIJKJNmzfG4QdjjpUIANSxrL6kb+cHPY9zisH.ppwwwXsVwFYEiwjSwAFiHRBdL.UG7neouTeU0AggGKFHVykK4ZtlqI42+2+2ONJJJ4.G3.IUoJjsngxQOaOiXeiztPYY37ypy.VLETfCcHW4XtfCv7UF1+ZJk4gTW.XfyMbVqOP+yjNlT4xkiqUaHvcv23iKoQCOdmSu8a+10lPxuva9MOnAzU0nstsa611ropa8S+S+S2En+LyPLrhdnGuFCnvIzwckF5.f9y.8wKELnTQdSEQihbE8yq7U9JSxJUfffiLvXL8+i9Xer9.8ZB8+EdKu43vvvjl.29se6JP5zmsYJlBd7iu8rk9fJGZXYZlxtuY6ybLXIW4wnNGqscZI4cNmWOAmiY5JSE.HWtbf.EJTPNv7K..VqUDQjZG1Um+gggwfzOH3HcCCC6d7vvdVqMtAnPSAHm0QY8B.Ele9g04+S2WqyFyNOvHKCS.smAvusyljeNum2y68JDgqXp8rmq3E77eAWgHxATUmusW6f68du2pPzbe0669lRUcOppkTUycza9Zzewe9ewjvSGJW4U9hJ9Y+u8eany4HNMMYbfQEwqjTUbtqyNZkxDMZzX7IlXxyAf9QFYjrEjLDbjQGcT.ne+9.orIwrCvIR5m022L7jM66GEEM7Ug740L6DVfjdc6Fqo8QRYgbOU0d1PaeiwLPTYvfACFblUVoOncQ0NJhKALBaDbjfMzDcCA1HH3HqiHaDEEsQvQBNKvZhnqljvp999qXLlUUXEPWIe97q.5p862es0VasyFFFtAvlhvVggGaSwIN3aqp1wXLCRTQs1nbBTHzZKZs1RQVaQDxCP3wBSLlCmMmYt8eXSAfhGKLLETESBFhOyENatOYZCKs64gBLaZ4F1Lk8xUq5FuvEFtdpKi8QJdJBtRLLOzXnyVAnqrxJIVqsePfo2wCC6B45MyLyDinh0ZKDDDTRiiKAdEvUZVJUbmigfdzidTfxR850kke1yXXWdM20YwwpvxBUpjkflju3W7KF2XXbTd5kAiASu1rr3g2v3aqTohDNjUWFAPN0oNUpl8jVGhpFas13vvvj50qS8HG68SWuk36eCfG5YdleAvtXBVvc+OZ3bXyU3V+kt07oi0mzHkAJhHCZ5XsUopUqNop5Luo2zOV4JToBtqtyrV00RcgxSLlOTZWLN4Bw7hKx4mm9U+pe0jzDo1oIrE3.W8O6gdnMHEzj0W+qsFzZ0pTcEq0tZ850WUU8riO9ja.xFO5i9napparguuWx2Wu3jXUDova+s+1GqToRS.rukVZooUUmQUcllNfRlVjp6S7koallf01vbp1rLP4k1gAJSWoB6EVax4Xt8.L5C+vObonYhNeF1bot9O79P8gqEZgDNHI28ce2Ocutum019lJCSbzmOR+.+p+p.nz.EBR7Sq8Q7a1et4lKNsPiK.Th4nDT4xYgfCylYDzC2jYq1BVYu6cuqBrgi9jZrMzp.4MFSovvv834r1tInRq8T+X0GsVsiNhME8emsRV8RGbT+cp23PXTPF6u99tuQifQnkeAOHmXDEZl7R9NuV8HG4HPDE.+QpjRCViwTh1C2e.nRfnY4W6fG7ZyEGGmuYSJt4lalkMn79A94LFijUiuQVap3aQBhj.xvZyKHHXqjAIa97ulm+Vzjs88ugs8fNmNJZ6nniusyRgqloZ6YKP9oy1NflrHIf+fofAqu958vztqHx1oh.qKyOdrAToiicMZQf8zrotu65ttq4hHpBY0CXtg0x8jKB6AlMKaXY8ct.pA84ABf2Yheeuu2WOn1NB.kOaBdat+8u+LUquWyltqkMo4FFiYMq0tbsCaNSMi4LftpIHXMANaXX3lhHcBBNxfegeg2hzoSmRhH64+we1+iIrV6D.SJheVfqSqZy4nEd27M+C4CX.LKVewp5Nt0QYf4rV2.mKwR6CWfuieSO2aZ2Azt6ZP8IASSNQBNaZ8hJramy0qGesK9TosyfxKTWYM2BWSeu7pp49y+y+yyAfpZRbb7fFMb8MWlkuj66S.volRKCJkQ+n+A24v8WpvnI.hSbSTI7XgnhjbUW0UoAAADbj.A7xYLF4+5+0+q7pe0uZ022O9Nuy6LtAMhARNUV4I4Ze6PFwdp1t..HL7m6.T4INgqu0htIo8fj1samvzqobJRdzG8Q2o7TNHCR6OtiVwT+xRvBubNVU.8fYGW9K0+s7VdK8UUyX6v1RUoC32IMS1w.5YNiaCbhGO8lTfjSBIvnNV0.wzrZRYHgJo6yjjgG8JtNKAAA5Zm8QShhhhekuxW4.f363NtCEOj+vO1mHOP9W0q5UU.He61twxdnEenyue1yl52otKet6+.wL2xJKAPYQDImpZNlCIUOvtraKsCaKkjjDPEhR01qS80WTp3VvhDdrPHU6TBBL8wq4ffff3Se5SqtxvgbTlbvbRTTjTqVMoUqVxoN0SI.ouTscAVhWQbf+OMdTEpt+2869magOym4CM+2y2yB0.7+G9O9Fplq359.ACFLnl1PCtoa5lpBLWSU2qH9i7a+a+ayq+0+56e5ux1c98t8e8t2vMbC8gVxK8k7RF0IDrRlnSNAvnp1rj1Py.LI0whF5TDiBTx5XIvPvRxXWRV1oAXokVJszaRAK47.NIxFM7IVM88G9frpJhjYeuwHLHIErjfffdppcEQ5txJqzWcZw1.Uz9EJTnmfNbdaA13K7E9BqGDDrd3wBOqHb1fiDbVa8iuNI55FiYiviEtQPvg2PUYi8u+f0kpxFhHmMH3HqIhrhHxJAGIXkEV3Erlp55CFDudPPvlII5lwwwaZBLaFDDLzkpDzDi67LmfjIZs4DDUfAHzyV+3cCBB5GDDDGdrPApjYq14vXA6iq7NepzGJGTq.PoSAivxkGqBLFUXLUaMJMZ3hIpM.95T678eh1eCmqLU7yyAjywRyP4Q+JeEEHILLbfl5nIqblyzMEnKrggExmOeQnYQbh+aNZAPPldbnTocpPjdoSDxypZs8zpPBkIQ0Vwzp0fG4QdjdoFHPmpYtDjoYbpNwboZBPtlzLaMG4RRRxQRhHhHQNWxLWwBExqplOH3n4xAT6n0R788iCBBhyQNsleMApluLT3qd5SmGuloNGi22HkC1kpsSrjKhBypUAmX7O2Rxc9tuygr8EnGUnCTsq3DAbvEe6Duy246desnUl6dUAYHfB6MBl.5sKFwcAYe8iG.kZMStxq7JGpae.cwmNPPmq65ttsIcrk1sc5qVCZrtwXVEX4fiDrTsZ0V5K+k+Rsupq5pZVq1gaBzJII4LAlirdXX31u427adfHBhH4Z1rYQq0Nzc3DQl3ZuwJ6kFLsHUKiiQgFwSxJcGefpppdsZ4.IZIVZJfItlq4ZFiRmSY5e4xNqyK4wKNH0oaN+3p+1qmGeRz9loFl3V8RM3M7FdGtEr.5LDpQYYfLpRlfvlfqzHK3sDkfVmu+a+DseRnF8gnN3nb4pW2Mdiqgi0BcLFy.bhQUNaXXIf8zDl7c9Nem6M73giWqVsQflEudio3W9K+kKVtIEfFWJ5NIzDoZpfU1oSmBe0u58T35e9O+zNvIEZBEHh73g7G7w9CDf7kghMN88U5u1ANSAQBR2GMjzR7QpZyDLJJcxS9EGsPgB6QUcLMCfEmE.lyFZIEHnD0AtPBPhwXhwQOxdJzEO5Ta9ar68+v2euO5G8i12ml8aB81uueWe+qKsNdazegm4s1MEHYAhRVCFL4jS1Ga0N37m8MCb261nZS1.ZsopZmACFLvoiKxHuhWwqXBU0o61sqSDUahmWlPJUg8AKmEH3tAO4hU+4t9MMOX5hxp2a5TQo8W8m9WsCzLq1m6Vud89QQQ8g45ArcUXMiwrLMoYXXXjwXr0covLBnowXVNL7Xqs15quUPPPuvvv3q567pRLFCQgg5xK+H.Hc5zo3W5K8n6QUcpG9g+eNmGTQU0agZK38q7q7qLTnnDQpnpVFpLGvL9vzO1i8X68du26chYS84cGHemCirjmfWrqelNQ0ItPho24yX.8h79OUaJKtP51YIWYRjFXv28282s.nhHIW20ccofzT8BURDWzVaPoM5O9s9iqddNAjLxZI0BtyIplGUxGDDj+q80+54OZPPtvvvbgGKLm0dbWfIICAaK9Vu06X.vfxP77yOeBsQOz+aT4uPM8B7Z38tCwhIMgjxkKqrxrI.IOum2yyQSWHgSRxt5O9T00tdBO9N4v4el1URLhzsbpvH52jAoVbr7XO1iML.UX5KTPWY+LgczqKEDsM3z8Ufff.RxMrCtnfXs1bar9loaOI4vAA5sca2F1iay8O6U8OMOPgfffcIJeyHKrvBR4K7wvyFZxAAoFKlwdDgklU.xoZqbdoiQO2R.z9hAD24zFRj34f98c.jjwvjzNdx9eNyKEJTHGPNA3zm5TJNm7Ht9CZiARdguvWnFkJHhkai.Ko999I3JYhz6qW9EIzShVp3C2br4b.gOWklX9Pen+k6+E+hegA+E2ywp7ddO+6m5y+me5wiZ1cO23M7bl7C+691l93egiOmHhmmS2r1m3K64G4G4Vje1e1e19+N+N+Na0p05a1D19JthqXfpZNQ7GEXRU0oHEvcxl63beMrrhIMXbiwbNyq366Sq1sGBNB3XLhmmGQ1ncUFNNPQ7MtQ1y.PY2S.466qEJTPUGyRbBNnyZd6AzqSmNcA5ZLltSO8Lc+3ezOVOft8GLnKJaiKqtqqNKgesq+5u90pasmEQVCQVq9wBWSE8rHrQXX3l+9ejOxVdzZ6fffssV610Otcyvvv0CCO1ZCFL3LFi4L1iEtRX3wVoVsZqVnP9UCCCWUDY074yuFv5m5TmZSbBYcOiIHNUDTkT6NFbX.EqPu.SPmjbIc.utVqcvQBBnJsxkx347XyRTVc3oFXIt9PGjbP8rR7dTH2XMUcLsoNl3KiBUGQDovbPNehXsmBia3zUORm+Lfm2y84JBHNQd0A.oBc2byMcNDjp5RKsTAmlyUwIZvPQeBysLn2y8bOwzxOcr8kiOzS+ra9usa65XuXRCHg1DK9RLTt+Ue0WcmpvVWy0bMa1v0+oO1oNeF6d9sc83x7BP9JoLIJWtbRNGHyhhy4nzzDEFZOd9DfSdOmLV7jAfW+9w8i8.IJ534aCEtx8u+7zrR51+bzKpmot1qGBTOVNoQJn0ae5siAFbe22Wne57ucmqEcfFaArsHR2TsMjwGe7RppiqptOq0NGQT12kPwxTk4fSkpGHLIzIqjCGgCdNfJr60CmP8CkVZrsGrPZ4wNaDcgPmQPznwVOxi7Ho1Ku+l.q6Cm4ccGumlzDaylMO8K9E+hO0G42626TggGawfff50pUKxZOd6fffUMFy5VqcSq0t8gNzg5d3ff9UEIIIIQTUK9P2+Csmuqidz8AMK6kpyIQegnZ+V+V+V0.1Ov9EQp81dauMCT1CXVe243doAiWIqrJW3IzTHtvfEsqR0iyKFsmFum+rt12L.L4bCjrNIPyDlkDKot8LnhmDCsxDdyAhQRvUVJERYXRA7uLE+05DOk6AtsviM1NLb874yuYXXXWq0NvXLfG42tSmR.iEFFN9q407Z1SPPvPADssO4tpq56MeanPp8FmOUXVun.1zvySdqu02JiN5n7htxqzIredjGZVRc1B7HzjR999EO8oOcw1Pwp6e+EiiiKvbjCrCudU2f.ky4JKnJiopNAUbznEXxyt1Z6AXDiwTXzQGUPPImyBVQ0rZ9cPnMLCszdBzilL.ZDSEzumuuumLwqzUJBzNSPv5u3NKJ4Yh1vISVLKSx9zEZzgTqFNrFaAk21MYRksDQ1r39KlQA19hHhTUFoc61SppNa850qzrREe.eZgueFiLJyLvxoCbNrVsKkNv44oF3mzsvIn+J9zGJ26s81dacybQGlkd0pUq205622ZentejOxGYqFNsx4L.MCN5QshHmplwr3uy+t+cKFDDbZ7vFDDz59u+6+L.qopttwX1TpJaYpUaqNc5rkHR2QWXzAOum2AkjjjQt5q96bxW6a8WXZQpNqHR42w63CUAG5xF.eOQ7UsompZ4HXlCbfCrOfIN9oNkS.rZRwZW5ALSyD047YtPKtky6mOQ+suQZRJwecYrzfl5pHIe1O6elae3D3LsBnPCE+mfsVV6P.r1NSJzhjjjjjTU8Giwf0FldsHoPXXXgq347bJ9Gee+UEwkkvhFygKBjOIWRt50qqNwpLZ.9zu8trlsS7j..m+NX6BBbxIxxvwBL.VdHSRhN3EjgSm+j5v2XWm2Y6dHhgUxzQj31dj.dRja9mQ.F8t+b28X.iTwokE4YgKZvG67xC8NtiaM68E.Y80WWlbhwkDzbtr0p4ckEVlFToZqJn29semppZxG8i9wz50qSl0oVAxCmIG0RAB7YOYCZm.9OD4NIjqd1XPFxAKmGHe+98y2rL4EwHKAjUdMWhldxCdP.TVBMnXwD.8y9Y+rpweWKNWcKxCmKEI6e9EzLAQTDU.xEFFl2+H0xeO2y8lVJtkUfAT18b9zNM35oyfIyFKNON.JFeoprOnZk2367M3ejibsd2+82Xlq7f2zX+T+T+qxM1HioiVbF8QOYm7W20eMic7G7yMEvzMTceOvC7.S7y7C8yT3y+o9TI.cjpx1PqTwkGQDo3i8X+UigKYBCsxxzRwY2rwLu0FsKg37Bmg1nnHhGLfnnHZzzsPqnnng.Us6GNDXHyShhrn65QXUU0ZC0ACFjL1niNPQ6BRJiQjsA1d4yr71nrsMLbaP29e5OwqbafsKVnvVl.yFKu5JmMHHXsie7iuhMLbEfUDzUL99qZLlUQ0UCBBNKv5AGIXqW8q80143Va2vvvdJZ2jA85LXvfsBBBNqCPDuUTXEfUBCCOCvxw8iaCrjwXNSXX3ZyO+7aLXvfsA5YihhEQRPG5DIoIrxwNFfthJ8fl8MG1LnIjbLqUpWudN7IOz3xwfCtT8iDNI4qk55WNV41rnHhiQxMnnpQETUyuDHQYemKGIgpp6Gd.hys4.RedBx4aL4bh7ppUfAAAA8Fehw6ZLld0qWOd1YmUrG2VDZMp0ZGyBiDkFu8M+CeyJDMDP7S7zGf3eytoP8cl6pACf18kpR2FNP.5.zk.5Cqc4bNKtaUmRXNHsTUX6s2VTmEs6XAzNimjGUyIhHiM1XJsHFZFuvyegjlPNee+hKt3holbQqB72hrT7DPRyLfQqRuwFarN.aeS2zMrc6.5.yzospaCdaALzXHTU2TDoqTUzuxW4qTx22eRU0YhpV0YpBMv36XncUpPEn9r3.VXBNYJa4pcNIPM8b9DCY15hLTt.5CyzCnS0qtZmq9pu5N9PmvvGXKJy5QvY9f+lefl.gdW60d5+5+5+5Ees+j+jKdzffSCTGOZXLlkvMNxpppq466ul3ImsErwws1sxmOeOwSTfR+29e8+ZbU0oanZ4+z+z61y22u5a7M9tBTU2up5B.K7a+q9qteUaEnp5G4lbbVf80pbY234Kxn9ma44e4poKWr39+6rsuYVRNCmD4fNQ9QSsyrrEx3JKmT5WVIhXwSzW6q80lK8A4BDM64m07K19IoTl001jsNYiFa444ssJZO.MJJJW3wByO5niVJEjjQMFyHgtZIUBCC0xQjTu98q.7U+pedWmrlkehn4jRylI+GdWuqDfXICrAmXtNlHx3as0VSbi23MNdEXO6e+6eTveDfh6e+6OGKgTud8gaLOK4wucZFBZsWQjYzl5b.yljjLEv3okMT9zIr.GqRbNO.z+K9E+hcQoyG6O7ObafNChi6Vud8AgggJsfZ9GUxDCJZdNVq04uPkmoZYK9Y.QrCM3ftd0oKS0tCv1p1z4W6M7VIkprqRkJa7X26i0ua2t4DQFqVsZ6Sa1r7Farg+M+7u4fXnFPMZS.N5rkMvxTdvjbRFCJegJgEEH4PQL.mCwzCp6.LY4Zc.5tDzwbC2PmW6q80tMorXJJJZYZznQYHD3Ten206ZQfECOV3oBCCCeIujWRT850aWqVskihhV4T2+oVAmFnrVXX3YoIajOe9tMa1TsVawete1+UipZiwi62euPyYUU8vYSY0ZA0jpUMhHUwkUwY.lZ94mehxN5SOxVmqK5j6B+5PWH67K6ZvST1cd5LyOmOSWD.lyhpZiXQjj+9+8+9UfbD4U.nXKn.dd4RE7rm3I3SqZha4VtkrfVhWasr.SD0YC2hpfXLA4.JHpTnVv9KDbjTfRoY950qWnl4n4qUqVtlfPkJZpv7cgVD++61kWa38jT8MYmWm7bJIvK1j4Occ8VSEu3g5D0bMoH9MGUUcbn0DppS9cbvuiwAFqETDe+br3kjsiJMQusa61fcsPyMVeCAEQPDee+bhjKGoNIgppJpjXOV8j2xseqwhpCdKukaMtVsZIFyQDf7sf734keWB9Z196YCM2w7I.nVVlpEr6n2TkJUJu1RygWT53RstTAw6N2O4I0ob+d17uw27MeyZj0p.Z1BzqToR5uJfS+hxsK16TJHHnT0VT3leE2T57Bsc8Qa6O.HdEHgZWRqX8Iy0iL.SJhioG6sZClipMpbi230V9ttqOy9Ve8sGoZUedOum2cuW+OyOSmer2zap+q7U9Oi++t6+mEuga3pG+23Nuy8NXvfoZ0p03evO3Gbjzsm5nXOR14Vbb7nKrvBiAL1VasUlcAWRari3jlcfkxRhKFaD20EdGqRzjDhhhXt4lyUhNJmCySxt9GkJ1qBR56Kouin.wc1tSeP5hntxhE04fVprsjS5nPmfCe3Nn398ffs9S9i+i2bl8suMrggm8vG9vqpvJAAAqhxphHqFFFtZsZ0xXHx5gGKbCRckK21lMKTnz5EJTXMfUDQNSX3wVNHHXYQjyDDDrbPvQZO+Q3wlkI...B.IQTPTAluUPPPqnnnkEQVCXyyd1y5JoBmsTmXLlrRJRIM64gggCHE3jvvvd1iaG.UiARpUqlPzvE1T.H+AO26EWtKhUR+d4iO2EHo.ZJHGBPJPs6pTyO4EYKt6a0M7U.sIjfm2vwlcZyA4RKWqB.4OdXHgggwp5RnPwhEiCBBDiwTzZsiYLl8.dYL+MWln2xEdL+ms21Mf+wpp8zFZ2G4QdjLlK2mvKaVSpmDf4mWYoc.tZrwFSQyTwPv2XjgkUX57JYGGgggPKxAUK.TpToRkRWmxEh80OS.fx4b8.nOMXX4zArkIjsw+Lcc.J0zEmcSyphHmQDYY77Vs9CVei+O+29+YOw0FSihlZs0VqLf++fW6OQ.v9oE6G2ZAFtFf.XJpyjv7oNryiq5ENuiuy3VWxxyuMNsMb6fidzsoMa.rFvxGrZ0FzpU3K8vGNDHLwk86V0ev5KEUu9JVqc0ey2+6esfffU8EYUZwZf2Y8882rd85cnE8EQnd85khBiFyZsie3Ce86UUcep1XVb3TZTUq0D1u34USDoFNG2znp5Q61yUMEXHYGGOa2tpyEx5tuP2W91km69Ft8M2RxIsS3IG1YrdRylMyFDIVjgVCm1BfVj6C+g+vEnJELXJllApKGwVSam8fXp.d99de2w16au6qmntx8AGEmKFFFVTUsjHRofffh3Bdh1NgmKAH4JukqT8wGn8EKvgrNXwVHlxjzz8+yCLhKnalZ7wuh8VdlxS0JUL0BCefwptSG5b0pUaXvSMg7DQQU08PU1a61smAXtNc5LaiFM1GoBbp0ZyYbZWxvGxCNRPeftW60dscPXy+o+3+y1LHHXyB4y2QDYPPPPxu6u6uaBzLoSmNIQQ6frO+s2h+x196VSLFtfol63bM8k4jsqPkMflqgCo1yPqVqbfCbfydEWwUzY6s2VUUKAL4DSLwrexO8mz6XVavwN1w1OvARes.3UCvuYpPQAs2q+NzQ9bF37D.NMVgXNHCpQs9P8Tavltzp0P1vvLrguu+Z.moMzlpznADAXCBBrAAAViwDUq1QZ7td6uqVppsme94W9K+k+esxgMlUCBBVCGSU1xXL8BBBRRRRxKhLxC8vO73oTmdt65SeWdoBEqu1nwPsMQpJCskr1vTTlIOSZ+C1sEjwA2M.IEfSTn749Ydln17ex1z68duWcogVNGCnBZUplW0FE+HejOh6XsYycCx0S31CP+7e9OulxXE5zoiFFFpFiuJPxd1ydTAzG5K7PhwXDUzb.R3wBIHHfvvPMG43O5S7aHevO3GzEXaqV6Lwi22zul8rw14C7wtpk1GGXsWrLf7zcSH0MTVBFkHFWDYRp5X02byM83jMNQzSnH+dg.0QfJBPNEM2FargnnRj0Njd6SLwDtwBygZpUKN73gCL0p0mJz0CudV6whO8oOsVudcglM2oO2k1Zs+Vo1tt2UmJ6JqnjN1Tud8JHhTfloiaMC4mkYubdFSWywVxrLWlLwDSj3aLpHpJHpnRx8e+2e58EGiR9K+K+KchhITJLLr3e4e4eYgGnd871uf0EuTURpR0XHJdokVx0+rd8mIXXRl1kr2FvLzf416d269toWzO3dN5Qd94dGui2Qu64dtmN28ce2cznnA850i+e9O8IKpZmwlatxS7ReoeuS9xdYurIoJ6oQptnAL5OxOxO9Xoh.6dxmO+n.kZznQwel2vaXWyMPAQj7IIIOQfvMzIMxb+FiuOkFozv+O3zvjru3NZahhjUaTC+K5v2+d+q9qxPTQQby6GXbrxH3HAcEjdAAl9ppCBBBRBO9w0fffjZAAIgggI23y+4GWqVsAlffdAGInaPPv10qWeqfffspW2tUPPvVgggaEDDrswX5zue+tAAA89Y9o9o5DDDrAvJjPaQjlgggsLFSqfffVgggsTUaFFF1rd8GrIt5UnswXVwXLqGFVe6q8kbsYZ91vRpHKlLGVBtSWq0lDFFFKhz2XL8gF8SRRF.Dacf5MjU.m7wmDiKU+GAPNIHLyPliPZL08wITtYrgLws94FZ0Ky9vKvBjVZhIppw3haOY5omV888kG4gejTWjhhHTLHHn3C8Ednb4DQrVqdzidTMkgbEMFSp1MzbzFN8TIK1C3aeytsByLLNWQjdW8Ue08SSR7ka716bsIsTBwMd2v9c4KTPQbtkiMzhaaKI4xQRJfjfS.qKBMJ9xeYurhIIIE1k1Dcg.I8Yh1v0LACSVZVL0crP2TmSqGSQGClMA6Ywwl6knYy10pUa4O9G8iuFdrU50w7u829aeOm5TmZeui2wudk50q6OXvf8Cr.vA.uE.pE5XeRE3TyLqqjD2MvIm+ZLUf3CxA6AmpyviwFMxzzj0oFqcxFMVFncan0wN1wZ0xY6vqVqVsy5Wq1lFiYq2za7csIvFOXX35.msd8GbcOQ1nVsZaBz4V+Weq8tpq5phM0LfSefJDFFVRpJ6YiM1Xu.S2oSm4TU8zlM8UUqALu3KyKhre.SCnBUXFqqRDRkjfr4Blcnncyku.w92oaeq.CSTfjEfDX1XOOuL+aePpWzqpp4.+BP0hUDoDCXDK1h9W95XRV.3NVl.aeq25aYqs1dqN9A9CpTohL891Wdvq.PoOxG52sTXX3HTIsdcqRNnB6e+G0IVeBZDQLy4VFCm+9LgolxkIqVZVFcFCXBQjo9Me+u+8cG+FukY9v+de3oAl4i9G7GLcPvQ26wr1rfvSEXVJP.Efpk.uQqJx3nr2xkKOCvriLxHSiCvkwPnf.h0ZoToRJNzlSp+f0yNu2TRj0EQOaXX35AGIXqffftVa89+a9W7uX.To2UdkWY2ACF3xPh+4vtjryqmIam+hfNuL1WKdZX.EoeKZ00Oa.JmfvtFdbVOQ17.G3.cEQToZ0RVa8wMFyz+n+S9mT9vG9vlTprcfSe5SeEuzW10+bvAdx9cT1aNunT5rALIUSGfYVFw0+agbGDDJgVm5I01ATG2.7UoWUnGmw0GipU2DXcZvYwAryR3Tt.WfVUZ07s9q7VaAz968696d4ImbxUNt0tFv5VqcCq0tYXX31uyes2Y+b4xk.d7Cb3CmOIIoDvXeueOeuSBru50qOyq+M75mSpJkUU8noSLnvyyogKswY4x6tDjfhT9jCCPtLT.ltX6crRxBrvkEXjOy0Vv0O3l9AuoXW12pNvCFb2+Gu63H0oiDG3.GXmiuxOI29dNJiO4jSRPP.VqU8MFcqM2REPu9W50mXsV0ogRRBHw150GDbzfXSMSx+5erWk9C9C9ClsvtBTlhUfBz7oryD820aWHfP9aKvQ1cyceqb5BVliR29se6inpNVEXbMRGWDY7O9ezmJSmGbyCctxvv4u8N2sMHTsUlPIlS.wUGktO6G+i8wzM1bCMHHP+i9X+QwgggwAAGcf0Z6bG+72w1MoYGIV5t+8u+A0pUyM9bUbZXxIt3L.3awZmGPRKnokTjad0xtEKVZ9R4UUKnpVrLTfQH+xrrarXW6Byvyre17wWG199AfPhJptvBymH6jY7Bn5HVqczrjlL+7ymuVsilyXNBkS2tMnASA5byM2yT8KSKKCJc+2+mbO2+8+edhG399OOdohEG6q+0NY9u1W+qEex64d5Czct41bvG+S9w0ZGpV9ek+Mu+RIZxHeGGZrQeu2wadrG3A9j649++8SkUtM6EXpO0m5ilomWiopNBPgpUqJ+9ejORV7L4AJnplOWtb4jptU4uxYV4h1WpznkFB1wryLK9F+T1hjxUjr+I8pjlcKKKE333XR5axK7E9Bytql9QEE7ziDDP3wCQQEOHePPPwTl0NRnMbDuT8VQDozO3O1OVAqsdd6ws4ulq4ZxWqVs7.4EQK.TH3HAY.iU7EbfCT.pH+w+o+oCr0saFDDbFyQMMMFSjS+w7ZBdMORPPyfiDzRUs0QqUaIbKX6r3JQfsEjd1iaGXs1XW7WpZcZiE3XqbNU0bJjybCFIHHHwXLCrVaWnZ2Z0p0EnupZBlzmCLjGJmCN3tiy7x+45y3BhlchWwUR1Ujd3h0NE3j4RZbYNN6hrnVigLlYnnumyIPubK+ebKo8izRnt3nutq+5JopVv3JUmb.R850yYs0yGEEU.7JTsZ0czSho9V5wt9FsoL6YTfXQjAkgAP43cUZSBydYG+farMORpBwoi2kXLFcvfAYuuhPBJIfljnhFbTStW1K6kUHHHnzQpUaDq0VHNcgyFiQSkkIgoIGrvkqvg9MZa2qUyIICGjAPsXvefGDSIFXw9+O68lGkjcUclu+12XHmyrxJyLh3dOQlYAUooRRkTURXrjX507vVXL7ZYnke.1FZ7.Xr6Vu1cwfAZFbyfsjM1X2O2sw7vV7nM1psaimZKvd85EvBICVpJPhtPfJ4VUUw8bigbNhLyX7te+w4FQFUIIPX.QYZNqUrxbkUTQbu2yz97s+1eesmcu3+2.Xc7YceQ1jJT+RtjKYGoPgN+a+29uQWbwEyXLlILFyroRkJupZQU0kih9BOMfmNvSq.rDTvrpi0I6GXZ7SRd5bLJ3mEVNygSNq2o4zwG97A2oMGlVGBZQIm0kyRKUGXqicri4VeHgsL3blzcHWkc.10XL6d8G4H634403+9+i+G0ApGFFV+G5k7C0HLLbGx4JUq2za3M275JVrc3IB6Ihvm3+1ec5Nc5NFvjerO1Ga+RAI2W4K+kCzHsnpZQxm27deuu2BTcuxzgDIb.XbJrpSGWfrtyReng0xkmJ5q+mbsKFdXL7lz8QkdfZA+K8K8KM9a+s+16iJVZU0dkJUpwhKt3Fr.ajPCpV7XszzgaIhmFiQNlipb.fCqpdkgggGzXLyCjNLJp8O1q3UryK+k+xqu81auxOxOxOh8.G3.kvQmpUvQ2p9pfdalgNrYxD5Sed.K3sLj9LCriO+ofnYvYCryAL2K8k9Rm41u8aejCcnC4AzMIaGM.1nToRqWrXwMu669t25lu4adGbKdjF2fcefC9G9G9GdoO6myy9RB7CJZsgyEDXFGHS+xwQfXDompZaAYGD15G7Ze1a9fxirIUXyvvv5FiYyRkJsQwhEWsToRqTrXwUwcv99Sv66DLeif582paCmsQmSIkbf9el+u9YjO3uwGTvmzDwn3BHbVfYsV69hiimtXwhS7Y9TelrO6m6yN6OvM87R8e7i7+i2XiMF999cDQ18gdnGpw4N24Z7BdAuf53V7c6jW6R+9Y23q1Lj9T.n3ii5ryP5y9fm0aokVpeHgw6GhWK4Z+y7Y9Lxy9Y+rcKDMGYXUGvYgggSYLloYOFsLb.QowAvwLkJUZ5hEKNwe9e9GO60cceeoEgQigwDUGIHHvqWud8.Z95dcutc+PenOztppaCrkHx5ppq.rpjX2wDvNXcyWTU6GryfLM3.mDUDo+FVmWlx9VVu5W6V+0D5GP6n3dFMwPuFIgEZ6P.qikMvMlsMOw5sS+0YFG2FHG.3pTUORXX3kiHFOQlbzQGM0t6ta+Ms2EmvAtVQioVXXXMiwrNvVVqcKU0MMWmY82xq4sr56889d6eMradmMv1mgTOUA332q8MWa3xgHMI5GAIqqnpteQj4UU2GPpO0m5S03487ddkYANK0nLt0OFdun96u1OHjrjiIoJyC7zti63Ntxie7ieUVq8xABTXZwMd2o2DhTOHHXivvvUTUqHhT1XL82KZiyEctsVzewso.0orabG8oz8icM6KVG6M7y79qw2e99j3d1OoS6EnsHxVjjsNND6NToZ8XX+Pxm2nf+9gnkAtpd85cDOOuqDXIq0NSx6QMFS2vvv9Y0rtwXVMLLrOqBhTUsEKVLBGX2qCrSx9+coH8btI12RDe3ArZJ49eAfEeM+Tulm1DiOwS+k7hu1Bv7isxZM69xusezlTld28c+WOxO3O3MOy8cemXe2y87ec7K6xVx6tu6uT6q3JtpsVekUVQi8p8leau4UeM+Duls+89D+dwcscylNc5owcvf4UUmSDYN04PNiyd069EdM83Id3DEYGXMv6oII6wvj9yDdL+8gZVqk4medxlMK850itc6pYylMNNNtq0Za869g+v69+9+aOuVO6m8ys8JqTK925292N947rtItwa3FE7PVo5JdekuxWgm0y5YIqt5pZTXXuic8We6yd1yt8q8G40tcyoZ17M7Kb71W40dUceV23yp6McS2TOiwnkJUJ0Ue0WcJ.uu+a3FhujCcnFevO3Gb0OxG4irBtwY8mSGasmKNHXw922dgggdhHoCt1fwJchRS2sa2ENvANP.PQaXXfBKDDDLUjMJCBJp1VE1Fk0TUKWrXwPbJ5Z4vvv0MFS+3O5WNBN8aKLb2DMOYX2J7q09wWvbpEx.0bwMMGoX09h+JSppNIPJQjV3huse7eCG22E1bqONCiylLG4YYp31GE3JvIHkSmvRg96g1X+6e+MF6.i0H7DgMTU2nXwhqCrRXXXMU0ZEGB.p65ttqsu0a8VaReGi46NDcx98K82aYbU0IEQlD25cdppsEQ1FWeQC1qe3I5du+m2n3h88..WYud8tlJUpbk.Kow59DOIsFqcQXGiwrg0ZqEDDzOdlUA1pj0tUwffshhh1v22ecb.QrEtwh86G9Vo.q+D0FFH7gSJ84Wp79.Q3wRjgyNX+hIAlr.LQY2uOAjeJnxj0qWe7oNzTidm29cl5m3m3mPEQ5pp1pWud6VoRkFIL6dijW0IQ7lYuX+GNd3dbHTN8iAfQEPNL3cpAqYFjEri.L9W9K+kG6JthqXBfQCCCy7Y+re1T25sdqCeN79LKrerGSFFFNowXFKLLbDUkQg3ww4jNiDDDjRJH7n26ipAECnZ0pwFioIvVWxkbIq9vO7CWoc61qLxHirAPCU095jSeGFqOfO8u2Fd8k+oP7COk1tXAvDn+DhhjhRjEVXLn1D.SUvQI0IUUyjPovF4f0p5Vbu+.6uVGpenfmXe3VP+x50q2UUakZWV2dw9Fe+wrVar4XllZYc6O0m5Su9kbIGprwXByCkNQXXz0YLql6HGYqG3Adfcu268daeC2vMzePV+ER5Mz22f5OFXlG8bO5bGXwCr.NMlXAQj8ALVIaIOUkNdtEGWybTSsvSFVaiMZr9UdkW1V.MmGhWwc.wYSpSsCopdo.GDHHIvugNvsnAA9p0ZcVwErsBa5I5ZAAEWOLLbCiwT2ZsaFKwaPOVsXwh0VXgEVsVpZq6XDQwcgRMG5d66TaVM.vDeHSTBfIyCocB.H8ot5H3FiLCvrhHyBrOq0Nsuu+XRAYTpP1RkJkwXLoAHJJpmuueSQxuaoRmXmwFarFycEy0fpz.xsCTcWflEfcJC6r.zrVB3IEgtI9U9vKpK.wvr8f0O+whK.T671rzcv+EXRpkaryct6ejEWbwzjCuvSFJFywx.UFGXpvvvovAPRFUjTFiYjbv3eAqcrs2d6rWxkbIDFF1apolpW1rY689e++5seNOmm812zMcSaIhrJvpppqHhze9xNzO3q.55Ti8MAW1rDmUFNWWX0K7fWOUCXRJfrEfwJCiW.FubBcIUUSm.pyNz2xo269p+30Kr0+fv8AL4oQNtJshdDq0dYAAA9VqcxfffTgVaOouPQ61.cUfpFiY0RkJs4wJVbqSl.5HvFVqc8fffs.19rm8r6tzRK0eSnmpz+muW6at1E.tgeFHZDfITmkqtu7vbUbk519TUS+fO3CV+HG4HQ.mEdL.lv484sLo3LjEW1clG3oQeP6s1K0DDDXs1oEQxfpwJRKPqCrlHRk+f+f+fHQ0vezW9KOrXwh0V.1r1dyk2AXmRkJ0rXwhsgk6.m4IpTltXr0esyzLGYc.JmeBUKOgHxj4cNWW5jCSrEvZKAadV288SB.SXV7YYh3pTUuFfqDXotc6Nc0pURkKWdsZ0p8oFeKfsiiYUQzxFiI5du26szMbC2fcAnRMGCA2ZAXmZ6c.1gWm7aE.l3AKmANy3.6u.D7C9pdUK+LelOiCjISiBG8nW5jO7Wcad4uhe796+L9+lie7YVs54l9e0+pWV1O4m3yFu3RGa60VasM2YmcVUUcsCbfCT+U7JdEsEmyYLpHxTppyJ4k8eauxiO6uw6+N5aI88Au+wyU0NOlyMrUACPmNcn1JqLfHIBfmmGKrvBr4laxXiMFc61EOOOMNVoa2NL4TSR0p0jfg9bhhhH1UWZw3XvPGU01AAAsA5ZsVU.0eOMYvKIQQR+RewZswhHs888aBran01F23mdIBuZef.REZsdILLp4LyLyZ+W9u7e4bu5W8a5bPEKv5O5i9nsNvANfFEEEmb+JK.opsGX9S.LSXX3Bqs1ZAfr3r6e1hlff7IyoyFCHN6IeWE1PfphHgAGKnDkyYgpq.rUTTz19996BKrKTaGfc8gcifVK6De+mr6Ge9fPteRyZjNYeyrzOADKv3ZUMcx39sXd1fUFbP8udIdXLbftsLvU0oSmqoRkJGVDukTzYR.+smHRyXU2VPa749be9FOym4yrAvFFiYMxSMpjuBToLPsyctys5hKt3VNg8rR+DV8chXP91Qa34Qi.LF4YRpjaJUqLtHR5DCLXavrADVm8..+qEfI8WmKounvUpZz0DEEcU.KopNiBoEzdfrCvlfrJnqhPMTVyXLqas10d8u9W+Ze7O9GecnvFP45.aW.ZV1Eya6RO0Bd04AXRQH0mKLTLFitLnmAzm6y84JepO0m57.fBXBQjI.lnToRSVrXwoHGyPUlJLLbBU0wDQxTnPAOOOODoPm+5+5e+ct5q9pqO6ryt9DSLw5Pt5Iw+2JOzrBzLvUZPMAZk7rXOvSdr5ry4mn2.FAKic1yd1QWZoAZkRlvvvTFy0SX384DabejnSDk12+niBkm.XRaI63AECFMOjoRx8YNXhSTpzXEKVLKPpvvvzhHY1rdcuK+Ruzt3.dcyj3+WiBroFoMDQFDu.NSPYWsr1RDGayXA5QM5AGJFN82K10Knk96zW.r2fKGkAc5bZWnVGbTSrq3rEWEPdkuxWo2G8i9QSMPkuwOsueTpnnutcrZQnWxD9cJ.M9a9a9ap+7ddOusGYjQ5PhFODdhPQDAanMQY1yu6Ismb6fffcfB6V9AdfV.ctga3F5BjBlIdgE1LUsZm2jk9HhlFHSdXzCr3AFOOL4s8deuyHhrue42y6Y1e7W8qdhiEXRchvv1AAAdhH6hSKUfAaTUvaEJmBXLQ7mDX5jLAME6oKE8QdUkDp2Ys191wWeQmsqpR2vvvNOzC8PsymXStWmewlUbKBzrVsZ8qIu1PoK5NrWTBjxppCoJ64zEVnZbsZN5wUPj1UFRrXCBBZYKUxyd+VOUTBBBPDwKLLLCP1mwQNxnpVYhtc61Z6s2dZshtCvNu825au4K4k9RZ9Ltte3lkohyu0I+1PksA1oTesKg46kK2JwUcVDpdpScJ8vG9vCXZBfaw8ZtnJNSR+57f7kJWVulBEh+adf+1VKt3hiXs1LhHduk2xaIETIcRFOcAYo5H3.KwCXjS5V3eBQjQeSuo2PJiw30qWOoR4xd+b+budld5oSKhLpp5TEDomHRbd56xTjYdXmUflKXQp4.Kw0Oa6qcDq93sIvS0MAlSJypd.oJStztDsKCw.l7cymuRbkJOouFEbA7J0.OsRhHShLf9ghHZXXX+9uNppsKVrXSaIaKRnv7O8a8s20XLcfbsKTnZq9AyOvVgepuLR9ds+w2tPlfjB56HHAIUpAwUnPWnb2jZ+Wuq65t5.E5lKW43pUeb6mEvI7hm9LjN.xXSJAtBfWYPZ0pkJnp0Fo.5DSLAMZzPbKki.BppwG+3GuKTn8+12vanMP6ZCxJTgN4yWtakJzqXwhwK.ZMNyvAZNL6ljGmqwKVZtq2U6mUtJIrYHmTVqDmLeePIhd1uAtOB.rQNS8HN1YZIppTsZU.QpVsphyC67TUSAjVDRqhlBxwMbC2fZsVMH3Z064d93d23MdiItkGwvRZ97msWkJeKI4SCQA5ynt6WSyxDV+Nuy6bs67N+DSbu26eZpSdx+1NG8nWwnu628qO8ezezmYjQllrG7.Sm4G8kcK7ENwC19ydOe0c+We827F+3+3+3qIhrVNndUn0q3U7JDfL+o+o+oinpNtHx3ppiuxJqLFvHIGXaHMsxc8ToREIe97BfjX65.vjSNIfCfiwFeb5ztMhpL0zSyniNJUqUiEVXApToRefUT.Zzngt0VaQxmiD36qQQQCd90GDlPanXBLRBSExj78mJHHPG5ZIUhts4t9BCIoTjUee+QBsgiXBLiYBB5mg3dCI.qdytu8kx39+1gDV097e9OeOnRGfsA+sNvANvttOui1+dPpk.leXXXmq+5M5e+Ih7L99YCCCmLHHnAvtVqscPPfqrNU2NLISB8R.SXL68UZLUjwLFyX.s888SFiWqSXXnmw7LHBmHEeFNbLbpuQ1SQwYpB8N8ZvrtA4BPx5Dn4pgJNlH6bzmUdR8YOD.r9wEJDokKSb5zoiMFiZs1AEbkJpmnjVfLBRlzoSm0XLYCCCGMLLbDbhpbef45s3hKlLOuRxygugum+mDs7fVAHWEjpT0SDmC78k9ReojyODl7L4P7jQAdSZpOnQ6oXtJZr5gnIBO7fCxKndAFSpj3f6OGxcFLRbNSfidzav6tu6Odp74y6UBeAh9Vw5beizFz2WhANXllDGs9o9T0jhEoWoRN1QjSj9NUXafLEKVrYXXXFQkr9F+z3XSkWdH0IBCyBjQ0xis95qO5ryN6n3RF2jhH69U+pe0VW5k9raWgJs.11xBaC0Z.znjKI8sfEZGDTqi0599WF5clyGz73hPuRV53CRRrgw9P6SDEk9H99o.vXL54N24XwEWT788SCzpToRc.ZKojsCCCyJpj9K9fewLuvW3KbhSFFNgmm23gggiJhlcsM1LyryLyHSOwDY9y+y+y0+9+9+9Nuq+8+6oUqVYWbjQlpZYRkWjQA14gdnGpwy8xu7zU.s9GNWA..f.PRDEDUoPkAh+L.B0nKfGb5tC87mGme++krcw.CS52FNn0AHFhCXf8opN01au8nGbxIkJPyibcGoQs6+A1LxkY4sgC0LoS9IJCOITcZowgyt.NpqcEppWIvAsVatfffwRVToII1CqpZYQjR2xs9BO2m+y9.g9PsHXS1O6leM574OyY5r7xK2C76BQc4v.mZYO3LYAlDJrOUiV.HuHhuppOPAq0NGN8LI0JqrxNyO+7qDGGGVrXwyfyYUVwceQuvvvLIktQdU0kEQNTOs2S2CuEsV6bAAAi6r7OUQH1Dbz3vvS5V7SoMB0EQVOHHX0vP6ZfttwX5W5Oq8o+ze5UOzgNzZAAA8oi4dkczSMTv6qU67PpcVH65PZq05crf.Rp4Vu+h+h+hLu3W7KdLlmoXk7ynZ48ce228M8y3Y7hm788dtswdY+e9xxdIG7Rx.4yFFdhQMFy3ppi7kenublYldFXnLYYLlNgggcBBB5BzoSmNM+gukadmO4e0+eMxC0q.Mn.08Ky1QCdNk2sI+7zkU76.QIYcrXLTJlCi5bBhCIvoyPRsVSRMDxdZgfSSQVXgz0pUa32WJ.uBP5xvHTfIKTlIJ6rA6LFyQy.USQdRaOo06889de8t8a+1a2qWulSN4j6ppVGngH4qCU2RUstHR+xtpuMS16w402I.L6BKKhwUUmBX7BhLREHEKfjuFcS.6qATnNTdXFlbgYFK4y7PogSOAtr7e.bkjyUYirWtijm5DfWJP6hxter+vOV8ie7i2W+YVMOrQEXi69tu6M+It4adipvZm6bmasEW76aCHpAArK1EZA0F9442UEr22E1FdMlgK8uQnel.SXuluHyF4.r1qbXXc+hEqBDA90fnswM9aPoY5bdpS0OCui.4m7W8W8Mr+333kdiuw23k2oSmqHc5zW5V02JnQ8Fy39dE.sMtCvshwXrVaoyEDT7b4AaEmPRuwu5w+Uq2qWucdSuo2ztPgcgxsIOcoRw3DqqTgCqI1i3EM.eeAsge1ObF66K9180bI.ZmCZTE1fYYqCsNMO8Sb1WS9LWXTn1.FlzqWuq4BJImzJnIhEZOP6.x13JM.qwXJgiAQg.U7g0hfsIOMq9fUakKWtN.sfC09qS7GOYdNz+m8yfeeVpNCNNJ5W.xWFl6+zG78N00cMW4DwzZZUimYi0qO4IN4Cj9G5E8S1rb4Ja72cu26Juy246bEH2lP0co.PYxB4lZkUN0ryM2byiikc6myWP.GreiMz5EXBD.r1PBBLB.0qWWlZponWudToRUxkaARmdubuMnrbRD9UUGXWGZPfQCsV03NbFVq0KAKgj6ekf.2ghHgoH3zigdVqsq.872CvDOUIkHjJANBoeZiBB7Ufd1PaOjDF.IzEktAAA5N6riL93imJALFvkbkM.N2C+vO7W4RuzK8KC7H3JAqcR5Wo+gZHOoluBYWAF8bQQisn+0LETcVxggp4NnpUNnMztLt8YFCmI.0wDbrlggmngprQwhlUBCCiLFSDPMbLUrQPPfyE.cz9re4QObIQ7MTxAvwvszbFRCEyBk5WdqCJED1Kl2gKEjmnuKAH0Bvn0NeVMbsQQQWoprLnyDDDj0ADriYM.6XLGcm+j+q+m196+Y9Lq+fO3Ct1Mey2bkRkJcVfykThRUStFtv8xuXbsq+wzFvbVby2lAGq2mFGK5Z1mEc3dN7jjgICuNWgqR0nqA3Jsg1kPXZfTIZXxtjb1FiwrVXX3ppHqVLHXMbO6qAr1Mey27l28ce26FEE0x22uECRP34wf2mJ5ONOFbeXPN0ikIGof4xBqNJ4YBpjeh1sO23Yylcbnv3P4IIOSQEl.xOp0dxwBBBlPUcRq0NtBo+ku82m9K9F+E6.zxkHLmwi.Das1VoSmdm74yWOGrYUXKxSibUXmpIIaFx2Dpzh.Zic1tv5IiaKpPIHOdNssqXZnzvBa7E59UCWRN8Oav.6cOgkIiSdlL7DgSXLlj8GymEpjk7jN7DghpZuhEK1sd85ce6us2du2+uw6uu1E0TDogp5lhH8K4psYu0W5bO2y8z4Fuwab3RP56ll+8MU6hAFlzuc9LMA5vRzhyt+T9rVpjr700QovBwew66K5kPsvQ.tvfUd75XSxX7Y6BratbrU0pr1ce2ex0t4a9GXgfffYrV6nAAAohhhRqwZF0Sy5Idi1sa2Q+7e1GXzRkJkMUpTo88utTrlUp.r7xKmLgNxsP3oPgy3UDRWBRCkyHhjkBLZRVclDXpvvRSKh2jIYtAU0QKVrXl65ttqL25+5aMCkIC4IKUJnWuypimBXZQjo2b8MmxCuIrV6H.oiii8RxHohBggmfAOGDhEnavQC5DFF1Az1Fios0VpUPPwl.Mu0myyoUYx0eQYIYxsGGB4IO31eauo.wqCcgYIHHXX89Pewu3WrGPOVgtPkN9hzrrCXk3fffl+hefewTOzC8PomZpoRaLlwihhlLJJZxK6Rurw8SkJy09C7+g2O6O6qNysbK2RZUUGpq4PJcBarHZme+e2OZafchiiab7a6MV+O7O4OXqH2FOIfNTwsv4JzBhZs.zxkE3RtLlrMwGBzS6df1agEncBqX5lCZVk8mFVyATRlZoqYqklEHM07SUfHuxIKlVFRC4FixUm3y9HOxDiLxHiXLlr3bHozppYBBBRkGze1e1eV8e3e3eHVUM926idmi7V9we0BtMkSmOuLndP8Art6Y2h7Ghdb5GSf+O0mc5CixolSWfUGvnHWeSNgZ07JqZ5a61tsL+l+l+lYvwBqKbymGmq2S6AHAAHIIBy8dTQSrMSOaj0KvOP1d6skW9K+kK+Ce0up2a8c7NxfKaXi.4yd0W8UmtZRc9u3hKtmqNYoKT6aU5Yv2q8s+1viWbAeMKoXcRw9wSWUEeQnbhcfBzRjBaCUzO9G+i2WH2ZAQWHHcIetmR.3C7N9.79tsaixTI93G+3w4x4.SKc5z8hr1dSL4T6ARondSNwjRiFM7.RYCCSGD3lieRm.WlIHHH0wO9wGhIakciAqPLThhfTBjgxNqbA+7hwl.HILHLgIg4o.U0xzetunfQY8P8zOotOpM32JfqDQhhh3rm8QYwEWtuUaJkJEBNGj.RdlphpggV8rm8LbC2vMPXXHFiQfBBUJK6t6tCkvoukrQoKf4CiGmBOWYbVrmOkZGkPw5xPljmOc9ve3Ob7ZqUK6y648zG+t+DOPqicrmQyy7nmcqG7A+B09semuyDsVoZCbA8lwUkKnhHoxkirUpniFGGOlmmWew+qufe6DiXGXIB.AAFpWutL4jSxDSLAsZ2lF0qSPvimtjjrjJpBPfIvA9QXnqTYf3nHqln6IdBjJoKW.gjjVE6x3sDGD32mUHw9AA8DIupZEIHHfDVlHINtBn3tlrVPDIvDPj05ECdF+.ObYXFUUwZsYBBBRGYsphDiydcYkUVwMWZA5Ps7sJPkcKmTlcKt3hBTviJkSsRx70E886e3hXpxnP08CzP7jlZhqmnfmmhFFdBMHHfnnPuRkJkFGKUFAXLiwzRUsaXXXWQjLAWafGUCXFrN2d5ebGbw89mfXGqFJAfTn.ohhbrIxWDIR6q4w3fQa0u9ISs1ve9NVMn.3HDtqeTDwwMbfiZLdgkBSIdRZU0zG4HGISXXnS.dMGse7bw3SOhdbsT3uqokOOTohC7DU0z.YdjG4QRIhjl4QXEbJRx5GVS7b8uNsZ.HDg.kINNFOOOBLAZj0FqBhnzS6y3bQSDRbSOGPw4hs1u..RvQCj69tuaOJf36esLOnq7c19fj83Rbqxy+um7yUcumJzCpzwjMa6yctykJc5zdIL1HksTodysvBMGYjQ1MedZEEE0EPOlwLZUxm9+vG3+PJbImSrgVuybly3kNcVDgNc61s4t6taiUWc0s9q9K9qqe6+1+lMdjJO3.QbkDVnikcg0asen8ZPGnjaO8NDuef0bmGn2RKg2YOqa+t..6dr2IM9jgH5RAZS47oxQEwQfcRUAx.EFmJkaHhLVXX3nt3+qjoToRoEQRaLFIm686s4lal5m9m9mLCP57hLVU29.oJ3Dx69rcQinf.k8.ja7VtQ2yR27vKlHUw2waemzkbd7Z8mT5Dbwy52BVa2H2gR2h4otHxN3WoqHRp2xa4czW7pFYVl8IiMnFSh0BKUyWGX8W3K7Gb0Nc5rNv1EJTni0Zw222yTzjpXvwR8G+G+GmJUpToARW75Kl122OEErIHCtvfZDcFlY.6..RUx22CmHN4VTLRSKhLxC+vO73m5TmZBQ7lHNlwaznwnpyBbyDFFNxsdq25XTtvD.SQkbSAkm708tdWSgqTb1mp59l4JlYJfwCBBxBjpb4xRPP.xPA2Lzy.UAsz8WpmwX5ZLlNO7C+vsUUZCzxZsMKCsgp8.3C7A9.onxbN5Pe5hWLnVxCOlvcnzhqmTpJ4GnSDNadzo.7pp6d+gga+g+HejMSXMyJq8PqU8xu7KurwXp.T4Z8u1Z999qmJUp5mLLb2O4m7OqysbK2BppoDovHppiqUzILF+oAlM1INdELFyh+Z+l2wATUe5+o+o+YGD3fufWzK3oWfBKAXvEW9B0xmeO21AlfyvXmdOVjHoq42WTQaVE1AVqAP8Z0pUG69aDFFVmZ42Bh1nbeqSaAGKGJU5Dq7leyu4pG7f2X4hEKFUpTonBTHJgMTQ.kqjOekCe3CuxO7O7O7l+yeQunce4urez362QAwotsa611mW0ByRRlEifQS.ezAhxoOT+m8mG597T6X.0A93pw05KFU4SXAStZJfHhe5DwfL89Y+esrU3K7PwdXOeq2FThhhbAdq3Ys1Tat4VoLFS52563cLnluKUpz3u1et+4iYLlQRnyZFfTADbgVO2i2O+dsKNam2Zb+5u8ec2uu19423232PK2eLhOcTUadGu+iWGXqa+1+MGHHd6m8egYca3ep21scaZ4gbUKupEFjAG+fftMZTef1JHHZiF06y5hr9AAiBLdIm95LYv0FLJt.mFX8lKw4WFXkJVjesesesgu+F9mWr1T.UDC.HxB.UkxfWmNc75zoi652Dl71eR6exBQHIfNK999xy7YdChptR7vZso77j9.tpfz0XLcEU5JBcWbwE6Anli5ttvurBnKu70O3Y9g3POAe0OIu916mdbJmatPsYSAk7RfiPofa+BIuTGXiWyq4Wb8+x+x+5M.YyCeEW8Vqu95q8qe6u+Zus21auR0b4phS6p1RUsIkoqHBhHd.oqTQy.jMUpTOtYxjglSj.LjL93iiHBUpTgQxlk1saSb7iUhK78CvUBSAf5XIh0Z6EXLcc1mKsUk1VqcfXCJxfCG2KHHnqHZGmywnMsVaKcPlbokpUZYs1VVqsIIwvHHCmA719AAsUUaas1NJzwDDzwZscO6YOW2vvvtQ1n3ff.mKLhCjkG7AePUUMd1Ym0MOsV9tPkNk6Whu4ck4KyVte7GN2vC1ddluuaczmgF6np1IYNsJNPS5yZF8nAWGhCMgTIGxIMtCyjwXttzAAAdTMmfCrju4Or5oPpLz9eZ47C1Su74u9mvpy8j7CcOaoq.E5+qpFKpHRrBwZrN.H4SFFh34J80O6m4yJwwPRIVHP0899iJbw95TeS2hqrvf9BQjzhHoO3AOnKt6U7c2+qOKO4.KAIwl0gKXuLan04VlJwAFSuAIECuXiwLHwNunWzyvAtYPfPk7h0Zgx9JTo2J8S9yxeGoDsGdezg+9OujSrfarnhiYHcp462ZwEWrYhFFsSo6uTC77pmH9oqoUxsVtb41vXL0OYXXSnRObmWKqHEFWQmZ4kWdZiweZQjY.lcs0VaAiwD7Q9i98W7zOvCb.q0dvOym4ybnWxK8kbI4I+gv43NG.Xw0xmu+YAlGXerFStVhwk.H0O6r8OKZaqKgK8W+pEQ4ZctyctlTtvNPksp12EfBXUfUs1STCnRPv0T1XLQVqMJO4iDQhRDE9np4yWAXkhEKtwgupqpQXXXyJp1687ddeoDQFsbYcbbrlchHXbUiFoerzTYA2ZCQ6oqTeulqcw3BSCeHsAzzM.Fy5nvVeGyIylatY2YNzL6PsATWbGNeZKdgsgEqpYAJBbYppGF3RAJZs1Imc1Y0u3W7Kt8RKszFFioxYNyYBSmNcoD54VA2.3cBf118SLqgxPkzvL.a5B.YBee+YihhxCrnp5ADQNfppwZs6+9tu6erWxK4EGGEEs0QCBpTAhTUiDQpYs1Mc5lBJvH4g8WA72ZqsVZ7wGeos2da+sazXV+ffwsVaZEUlceyFuwFa1Cz9AezEn4a6W5cr464c7KsxQCBpcRm5XuJvpus21aa02869cuJvV4gcqrGs61kBrKkGnRzesTo6mpZOlCueHPNcQ7nz.JNNBvnIhD5H.YWXgERUqVMO.RzMf9tjxLkJUZ1hEKNaoRkl5dtm6Yha8Vu0wTUyZs1L.dFiwKLzEb9wLFNQXXLPuwGe7tau81stthEachvvcCBB14dtm6Y6a5ltoF.MJ.aWNInoBEJrSYmGs2Lvs33vpQc+wowN0YYuROhBjhxjpb4xZgBEzBflDXS+xzYjbP1pNzn6+2bHICoSBNJUgBExTtb4QAFUUcremememLu1W6qsaekXWUcMQjMV.1pFCbZiNrLwblG2MqdpxwW5SWwL.iZfIBc8a8ooelOwe4eo2bKVn80eMWeC1SQ2e7TU9gG6jEmPdteQjkwUVdWEvgihhJppNMJowitIzWcafFSO8zM1Zqs1TDYifff0dqu025Jen2y6Y0SZsUBBBpwPtnkOzJZOwQ66wxjKta82CbXWZIMAjA6fLtORNXrpv32wcbGi7FdCugT.c7gchlm5rx4Qq0g26o+XNOfT6Gxr1dtYvhjTRnas0VW11MZTLvXlILLbDTRgKI3MEQqC5pfTUDwFGGWRDojwXhxCq7F9U+U233G+38yzUKfdIU8t.vxKuLm4LCD.1KFcqogma1ujbFm4YhVgslXjQFYbfQyAT0c+02h125vvtm5IduIAH8byM2nqt5pyBbfBvUFtWI4rbXncehPVb0yQrBsO6YOaikVZo0vIvyQkJUpTwhEstDBSsDASuAPy7PyJKQGNKOdzW9erOC5aouoEQFPc+4gYt6669l8O5O5OZx63Ntir3zFswqjm8ee+U20bexO4oGWUsya8s9VWOGT4Nty6b0W0q5UsiuSePxlnkUyVV04wEHuONKzbdbkCvv5glGNl9Lf0AOQtaCv4I7qm8rmkzoS2mhIwiL5HwsZ0J4fYZOP5kH7pZoRVouE+N6rylZ80WWDQbGx1iNZLcEgthncUUFDSShdLf.hl.viSqLDADOAkNc6R5zoUQj3omd53ImbxXaXXOEhMFCN68krhLfQMMEQV022+Q+ROvW5TW8OvUeJpvWEHBn9gfNmdnwU.oBfzgp5k.BUFbIGoXd3JNo0d0wwwWgwXJZs1obeGZGPZgv1BRCU0MLFSMfp4fUNYX35hHaFDDT2ZsqE3JSh0S99ac5uwGeM73pz6Gxt1i0w4FCPRD1x9f9TmCSKN0Wy4VtRxIfYw5D80DAU9vVqcwfffosVaJQj3XU6H6oqDMA1IwQfVMLLrBv4NpwbtpNGCpJvFLGaypCJU3uSG642pZ6whwDSJ.m6qMeAQlorioI6HhrFEnFkYC1yPKdh5G5Odr+YZNPd3J+erxJGocy1G123WLLJZRiuum0F1AGvhaArtwXV0ZsqFDDrVx5ZqkH5tqw7rNqPiyblyr8xKu7PLl96nLm8BOu5EdNwL.YR1mMCP1b4xMZ0pUGGXzRkJk9487ddxoO8oSi6Y+9AVHLLb150qO4TSM0XAAAYsg1LpmlpXPQuRkJEKB8tNSwd2ensGncwIN3cOZPPuSXC6F3GzFX2JUprcgBE1NGrS0DWtJI9+swoal6VduyXMri6zWSgf9wfjmTk+hkkBEJDi6LkwatWecFfQBfr1bLBUOuR5OUNvqZx6qPgBiVtb4w.F8O4O4tF8Y9LuwrFigFMZzdxImrQR4esYRottmXvVjlT5oTmQ5eRztXigIWP6PPdGpg1jLyop1g74iUUY5omNE0FbXwzP9udYBeO1q.MIG0AV+HG4HqYs1MCiB2An65quNe+e+e+oLli4EFF5kNcZwQEWWPcOvC7.YAF0BiwZLJtIii3ruMRsYxyUUUMJJJYPVgT.Y+O967ebDq0lEjLW+0ecodj+gGwSUM0m9q7U5Gr3j+7+7+7ydrff4UUWfBr.vBOz5qOOvbSM0T6KUpTSM8zSOl5rQ3T.hnBarwlpHn5..SbAY7t+28thUUiek+B+B8TU64JMG57t+fu61VqsCPbEfRkJkBxmNJJJUtx3AyIIIw6hAf0tPllz6zLvNGiA59K7K7KzAnU4jC4BE1pVsZaU9rk2DVnd48Jgls.1PbtFy5dddaciOqab6vvvVVqsqHRuiZL8BCscQniwX5Twk4KBBBRO6ke4iYLlYNQX37AAA9.KcS2zM8zrV6gTUujHUujO5G8idoP9CUtb4CB7zf7Kac.zsGpyyyjjPE5nAyEy2CnyrkoMrTqBWVgVI2S8qizcnPgF.NjmiXcbLPYMXgUsV6pUfUa2t85VqcixkKm.lXtsEQZduet6s6wO9wSopNN4YZw4VSSWaP8LmeDfQl8LLBL6nr+A0RY+rO9T0ZFt96kc84g6oGHcoPgd.89hm5T8t9q45Gh5ty80ZA8yKi+Ikb04YaZwtySJiOw3B5dG10XLo1ZqsRAZpDQFTd8u9Wu2IBC8hiiSCj9m5m5mJCjOCP5nGmrz9sxGLeu12Ram23B3P8.5MqktPPGVvEXSUn6uxuxuRm2va3Mzh742AXm64+4+ycYkGiKNbge1IiMObu01KvC2q7t+OMZz.+f.JE5T77jQKwhzmL6tCN+e9+7GcrhEKNl45ttQvUKydIkkSRyIrlmdv26giS.K4wi4KWj1lQAhWXEhGYjhwsa2V6zoiVMoTc.TLFEP+5j6UAfUWc0j4wEjxI.ADFFJVaDIXA3JCH2uqKuzx8RbLfcg7aetyctsCCC2IIYBchii6wBKDSxdlb1KH67eSOe2UhuKKhGj2AbGjcEH60e8We5WvK3Eve2e2eWGfFUfUoBU.uHfHOuzkApTEV4U8pdUa.9MhhhZ5bdiBwU.Ymc1IUh.SlZnWdWvKoamNRe1iXSzhDee+yCbj9+snnHhrQDEEQ5zo0DwiLFQbfknzAjNh30VQaqp1NLz1o30EzUDoa2tc6t95q6zBFklHrinriHzvbLSi3XoAPi91taTTTCiwrk5bAhMHNdCiuYcmFjnqGitQ5zo2.XiNc5r4lat4Vgg1FZxgABCC2AjsEg9kT2NhHM888aEEF09pulqtagJzCVHlEbyc1aNEP97J.V29H6w7UVnCP2JI5efmmmBn8KsHPhMFSOUINQWWbeNVqV0AjS2ffi1AnUbbbGnPOly8d9lrfuDnnrlKC58Gep36ttUU6UV0j0ubrK4vOIH1PsjGBC05KrnwMa1LgIQZKOUZM2byMPiDv4zQcBsgcMFSmO+m+y2spSqHDHeJfTytJdN8d66J26zce4HniJhDeOOxi3pIMwwL87kQfYDNzWWVyNjPkuPZbksQp4laNuXbBirjzmGDX79W9S+uTRmNsi8IVWmWoRkFvzmEW7YH.rvJnP9tK+8sbWft0t3PK19ZwtE8QdjGIFnyZCrkayNUqVcmG39efcfBaWrXwFm9zmtNtyArUXX3Vggg0A1YxomrkJZ6u5W8q1V7jc8vaaq0tkHxVFSw5tyTnMCBBhMG6Xo788G8jV6TA9AyA3KhrbudwGTU8xpn5UbxSdxCelyT5JJWt7kCbYP9CV1w9j8NCvBLKNvpmnVRR.gk7.zfJzqPgquCy3.Zby8LxhcX1Y2FXKKrIUou8OuNjaiRkJsQUXqRkJUOJJpw8e+2ehtDUX8W5K8VW607ZeMqehSbhF0pUqkTPTRXwZ0jjsBAo9k+k+k87KgGrP+weeu3XSZWrBXRRmyocBkiOoX+62CbRYOUpnIzKM0688d6oymOeRovTIEb3uVzOt+jst.sVnJ6.427AevGbiBEJrkw2rchaWPbbr2m6y+WjFzzliZRS978EhmQOxQNReltLNv3EJTHoNxpk1QSZmPoIh.9HP9TWxQlJSbb7H+P27OzHet68ym0XBRKPpQGcLOfTSN4jYf7iJhLwu0u0u0z2eX3rhHy+S7C7JxQNxuu8suEvcP6Ywc31QCBB5mcjj.9TUUMtn45R1DOt6m8d9rcv401c9nu+2e2ilP0Xq01oPUZGDbsCXjSwhWKPEw22Ool4VsOi.uXIH6gWvbvAONzgbGx48+9e+sghMg7ILCn71jmsKrzUuKTqYdn8B6svy1GyX1LLLb8fff0KFTbCUksLFSchowWvZanZbCSfY6Pa3tgggMEQZKhzK7DmPEQRIhjMLLbBfYJEFtfppIJJ5.hj+P+X+X+XWZX3It750qeEWR9q9JfJWdA3RK3rB5C.TbgUHOL+bLHCeKk0MFFVuud6rYRon3TOemyvTtbep.uCvN6e+6eaf5PssBBNxlP95Yylc6fffcwmlIVjbiG7Aev528u2c1viLs.PKqiB4mVUce.y.KLMTYRfIVGl.VeBViIHOiwRKkrf94Ysje6ucFT1OJ4xE+w9XeLmPbUtbOfduw23ar2q7U9J6tmMQtp9DPS+AGVb4DZaBzKeU5A4iAhcLIxiff.7DOvk8RATOxgqBfQTaIaufffdFiI1XLZwhEEee+zenOzGJKTICr+rPw9h504YCme68A02q8MYKYMkSGCz0oURVmE64SLyOeu2za5M4NTWkJs.Z8I+jex1.cFxUj3I3mJbJMOPggx5a9JCFa3YsVOAOuImbROzA++bAoJf4nG0SDuTgggoBu+6e.XbgNaVLATfHIY7exZimZX.ZtXYM7uFsh.ap.wt.jthPhb...H.jDQAQ0q0Ka1r8xrXldc5zYOcAx4fUOYteDlAOHmGTNUdvyyyy6bm8bBnI5WhaiSTTPhUzXMV6YLl1PklKt3h6ZtNSKJTnCjTdN0bkDHI.NrzRKcgy0+lncF.jyBBTIEVRSdR+nO5ilBfWvK3EzczQGcfnzCT8E8h94hhEIT0dVHWUnvZppaAQaSdZJR9NpVtWdfwGebAF3VJOlXkZ2tM.jNSFRkJEQQVb4DfA.ir2vS2eSUUicwd3J4jPaR7GZWE5fHcLlfVnZKOwq0FquQaPaaOQXGUoc5zo2a+LGHFM9BewuXciwTO7Dk1R7XKywN1VVqsNJ0CBBpGFF1vXLMTUabrq+5qKhT2c3lisom5slnx5FiY8k+9VdSUk5c60owwLlsEQ1tWmdMLlf5GyX1REYqtc6V222ug0FsSPwfV2wcbGcJ6bpwXpQL9WvgzpT47N.w..hnlBEhgBwAAApuuuFZsZTXjlT4OwppwhlXWxwRbIqMVUMNLLrKjui0dx1.sKVrXGnbOVEXIRgyMM9lXujRxRfrfiMLBf3GAvBtACN.57fUE3wnUDeMaCChVPPfFDDDmJUpt.sMG0z5y84+7sVc0UaeTioiHtxcSUsKJcBCC6bK2xszEnWPvwz+r+rOH.dq6bpiuabOyAicluFRePtO3AOXraFjKAqNFBu4SDRYCybHu74ymvLxZYfBYfbo2c2cSIh54z5OwyZsohrVue+e2eOub4x4YLlznZ5ffqM0OyOyOSh3GmOUoR2m2K7E9BoFDCU5Qkj3OO7guPVFbwPeiBnG9vGVAhO3AOXBnkykDieXSLz7HW2+rcgx6jC1Yg8had6iYL0UQ1vXLaTLn3Feku7CuwkcYW15ILvulpZUiwTKLLbUq0tgwXpKhznzIO4tuzW5+71ppwVqMUTTz3ggg6CzBevO3u6RhT3oGdtvKYokLWtp5UDFFcEe3O766JxAWVNWkLbPfmV9ZrHjO.HOvbPgogyNFPZKHPIkMoGEoGty33h+e80Ou3+me9429K8k9RaCUaTr30tMjemhEK1z22uk45LMgbaCk2r.r5W7u5SV85ttqq1q60851TKq6BEhUU8b5nS9zfM8a9M+lSG4FOklSSJNziAT8+W13YuX6lduEBNDo3zCXOxvNVvH4gwDXzxPpUWc01yc4y0fZ9qCQacHX2uNTWrefNihSiIL.Wpp5UVPjK6DggEKTH+jkKWIVP1xFYq366GZLFaXXXYf0NpwzHEz7yety0IUpTcCtlftTa9cfUbzzMfdzkzTkIwAxQQfC8k+xe4CM8zSe.btJ1TfjNHHnanMbaSfYsnvv0TQVGgsMG0zQKqJtCdMtHx99DehOw7O+m+yedQj8644MoSzW0Tty0ghPWPc.jnCn.4N3PfbEbNtPsvvvULFyJI+s0A1sToRcRmNcutc61t3ynnaxXzfRx4hAzkuv1vA6kBNjGb5TK.d0lkTLAodG+juizuq206ZfyWjHZuY7886W9JN5eCSVpTooMFyzQQQSnpNhHRlfffTgggopWudlK+xu7rIZVQZmPwE4syt63s3hK5s5pqlx2EPS5lMaJiN5npMx1Ek1hHsxjISy4me9cBCC2od85Mthq3J5qF8ak7pd9jR3oBzhBzgrzkyNfMMmW4krPR5pVMIyB+29z+2R8C8bd0dPUY+Pp0xSFRwHXcyW9I+I+Iy9Ne2uyLK5uXeG+Xheme2emw9W7R+Wjc+6e+w20ccW67yeq25VhyAX1hBzDgXh.U09V54trWcj28w455aG8uCrl6jq6w.FaAXrTPlxf7bdN2PmO8ot2FrRtsfpWXYQnWvm2ERe0kAtRU0iDEEck.KopNCnoAoGJsPXGf5hHanptlwXV0VpTMUj0LFyF+b+butM++9O4+zlTdgMgZaEEE0v+Z72kNzj0OeZW9swmUeu127s8NDzgIEqS5jRcq+5D8qwWOeQztP2U5WywyRSVe4VvY5WFVOQi6xhiF76GXIfKWctz1UXs1hBrOEx.JHRaQkcCLAaaKY2REG88CCCKAb1iZLkp1u7PKv1TN+tPklKCsOy4Sg6guVtXb72f87WFReFHqp5nRfLJQ6UBtIkGRafsVHQmHps2b8GOGwRv02MNt8fWF3J60q2U644c3tc6tX5zomww1SWRTDQZ5.ZfZ.kLFyYANGNEsbs68du2Mtga3FZjC1MMzwBw2288ohu9e3maWJu+VvZ8Kgfmry0GdOr8nW9bjgQHqFpiJNqfbbbhQ3n0qWmCM8zsUX2DscJK9L9q94+Sk0Kar9W9g+vMqlisIEsSDruQvoUUyiyk8J7I+a+a8u1ib0ExkKeNbTJXZfwqu0VYmZ5oS0qWOoZ0pN9OHI2Ih6fw80ljjrSq8+GCBBzpUqp851MVctwQWfdINOWm4lat1qt5pcLFSuRVq9w9C++kWwO5OVpNc53kISFAPEnqBsMGyzN7jgsIl1HzAktHme4jcme36j2x+t2x.vFIFTwwr1d850comwRDdhP.jjRqs+y5XPTiIfyblyjNUpToMFSlHqsGhTWUMZ80W+Qtp+YW0CSU9ehaNV8j9U.RMGj4+9C7.oOxQNhSyaJBTpPJn7TEfhkKvUFd+gGIHH3v3J8tICsgHHsMFSyvvvcLFy1gkBWGm7dV45bZqVsxvp3ylzgFrx.JxOr9rL7duOYFe0Od29qiMpp5nhHiMzumRUsSdQF3.ULGM4ze8bfpjxKzmkIJ2UpZkqA3xa1rYvZqs1n.pwX5FFF1dOwE0Ux2FWxpVEHxXLkx4J8op+1+w+wq+xtsWVCZPC17wDqw+TtM7b7zrWrmy.LaAXlH29KMEeYcTpQkAkjyE1Or2ZE7+O68tGkjjUWuue1Q9pdzUUc8HyHi8N5tFFZFX5dXX5tmYt73f58dTDPWHKvAOqAOGW3R.e.h3vfJiG3NJxUEPEudv6QTj6UYPXT.uGDgiO.zkWv4PWMy.SOBqVGptyXGYjupJqm4yXe+icDYkUMc2zyviyLn60JWY0UmUDYDw9wu8ueeePVli7zdgogVKZLlkEkEGOXkfS.7Tw5p5yjfL19RoriVGtAXZqTplI6EnkKr9JAAqAzPcRUDPMpQKr88+ZMu1+y5YyX2SOtCbNwxfX0kAFhfJibYwIpVsZ9lMal4Dm3Do26J.bne5e5ex49s+se2yGEEMaoRkl7889deYedOumGpSofHHofDY.xFDDjUHDY877xVVHxEYG+LkVqmVJkSBLQXXPNOOkSXXXbhfO2MSlL6355tMv1wwwad+2+WbyScpaZarnaaafsKYi+eyZv1TjcIGcQShVIMZOXoiCRcKH6yeexQE2rPTFEjIvkbHXBpZcXtKdwKJHKli3cjzYzyAT3K+k+xEttq65xJDh3hPurvtgvNTlcvvtDMxoT6O14+RIDyOQer4Uc6wqHLANucvv+z+z+jCKPlO4m7SNpZb+Zuu2GUAVas0bNwhKlk5jEByAj873e0Tkmzp20qDrKTZqelelelMOqVusPH5TsZzPoTJLFSla9lu4bFgovK4E8RJ7I+jexBJkZhUpToPHT3HG4Vx6LbXVpi.ZH.LQ3BZDTCGvKGTdBiwLMtbnq+5u9CIkxIKUpTdW2xYxjIiXmc1FkTQXXHxS6CfP5IcHh7BgXZf4DkEKbWuo6Zgm2y64c3LYxLShp1my.NPZkhR5yZDFgc+zCA5AhNJkpC1Eo5nqTomwh3jAW7hWreQKTR64662q7MTtquueOBoGgOBKr6alaL9wBjlGev5P37CA5Wmi0m0nOUn2ce2+1cAe6lZVjd9998d5dd80Zc226688tK1DUzBJW222OT3IpHkxK9te2u6KJkx.fHkR079tu6acWb2xXL6HkxcEhx6HUxslXpo13HEJzVJkaPBWSmXhIrZjigYAVzXnb+9880g5qQoTW6ryN6wBBBdJZs959G+G+GeJkgmBT5IGAWSjG9.tTkE4BLGobL9njedleDpEpyhhl.vBliBlW32wKbHTa.PuVH6PDcPSmJUpzoZ0pc9C9C9C14Hm5HaBrlWRhyd0uxW85+3+3+36ZLFtsa61lnFLSTIlqd85yQUliPl0XLyHDhYLFyzEo3TLhZNKevpc8MSH6YvEiD4HDEUmhl26G+iy+O2y8HN2e2mEZfHQz3txGm8dejdrTNousmmGoUH0BtDPjT6sjqqzf+5XDhcsiob6uzRKEGd1PGK5xHqm2MlkZjk0HKrXlwfU7+pMq7OAos27bmi3TWZ3s81daFVjXWbGJDhABgXPUVZfUH7jFWvvZPBx.tbsTns5rn0UD1GUHBBBERozZuphDGV0x9DaeVGPffJUrVErRcJpY2BqSIHKUImEcSjcU7tbhO7i6CpY0kARo4P3dBjKkommvKMvwA0o7v5esudDvw.7Swd49FCVqVsDW7XOwDOI.2gqt5pbgKbgzMjjIHHvoLkcdVOqmk.PTihBM3.dYFLnPFphCz5Qak2FOYI1J2crj2ahCZxlPclbTDmxTN95ttqa2Ymc1MqQ410g1fZcOnAgD899i+8q9deuu2p0XoFTi1DxNPotV6tmgThglDZW779t+tw0sr.P3lf3f986yLyNKZslLYxbI+lljrDqU+l33FBabFC0Z8fACFzuvDS1CSZ0OEaYLlsA1pYylaCrUPkJahwrws+C8CuAP674y2VJkq2rYyVxSoZb1yd15AqDTGC0AZJkxlpSqZIDVDiXLlMTpSt0OxO5Ox1AAA6p05NFioyfA81cvfAaqTmbiLYxzlHVCawfVKHHnEPigCGVW.0kRuFAAAsVd4kaEGGulPTdMCr9byM2lJkZm+g+g+gtTaLt66a6Wl9pItw23MdiwvQsTqoBFvJDvUoLTcDUaPq0FsVmDSFwZaRCFp059Hn+IUpdJ0o5clffdmIHnGP+J+OpLfFiRPTVVhrGkidoVy8pu4Z+7KXmiYjgDHsHKICPlZESlSZMbRi+9JePKhDro5fZitG0pUqQqUGDnGBDGnChEBwPgwLvXL8CrWqcNoR0oRkvN0ft07n2O3O3O3.BXHs+ldbm+Oyl.rZDjEwgEoZx8KgPLjpDSzk85drjD3mYdHGsIOzp.PAgvq.QjGrE7CDiudfvR6IiCfi0UULYA2bqjjL.kRkM3rAYn1nTkFywurEA3QfPsGq2KtDutZaIwyctg.CWEFvpLfJL.VtOrPOfNkOQ4Nm3DmniGzwRIO1BXsOzu86tF3pcccq7E9hegK9JdEuhKpTp.hrIvSoTsnLqWhRsEBw5dddqIDkWKBVqhVuVY67Rsiii2FnqmmxfcM9B.SAhYGNb3BgggkFNbnrdTzxttEuViwbLiw7T+c+c+ub8ulWyO80WCdp0Jww.tFpiBMkvVTuYvkomm4SEl6LvwSP.1xlhfwdsF0Cna.xcIxp+jutW2qamKbgp6bjibjcNxMcjs.1XI6bhMAZ8TepO01tBwNFiYPcvIrD18pVkYHh4vlLuYYQlwCuTsCMOr7kTbver83+IVsGucQdvrvlCH+hPgl1J7UPTRT.GJPjkyWIU.eGIrttDqSM1h8KNQWpVpHcMM1JubM.G2XLWuVqexHXIomLWB+eacJkpZTRFvwkVHXiDAQsCTrCTe2JUprs+s5uCZ5lbNRE2OkKbsUMlmRsZMdxCG12KgBDSjTol9JkZqJAAMEVDfrN1A0hjiwLB3vHDy644kPcBlLoxXocXsSZHX.FKkSTJU2fffswX17T99sNSEcSe+QhHV5fl5Xg06VrWEL5yiTXh9l0BWWpIcubPa+JcLF+Xk1+YTfnK.YZwnrMml3gQ++gggY877FgfojWSToRkI.lNIwUynTpIqnqj2LbXlibjkMUpTYH.9994bgIOqVOqwXl4AevGbxumumumbgggY9mdnGR7zt9qmXiIV.CxlMa2gCG1wyyaafsa1r4V850ayjjtLtJ6mXWwrqKzMJoO8a6s81F7l90dSCocZe64bf1LGPaq.DC6csV.HmGjIbu6iohd7r.y+4+7e9Yttq65xMyLyL7k+e5kuy87GcOau6t61cxImLcQxAImeKse7XaFPGpSOtxYZ9q29Kid978+8+8m6i8w9XohA4THXJpSA.we1e1eVuW5K8ktcQnc8u1BE29qLF6IVcAggmPXLGE63qrX4Td+fffcdq+R+Ra7K9ley0UJUUbILXkfHkR0FXGvsCD0407ZdMa867m96rIUYSfseKuk2xt28ce2i1j2k3d0+V6wesCt9SVf7KAEZjpSVKkL2RiTsvZwdPyc8gNU1SneuTUCLsphyfEgIGAKBSNNvSaiM13HyN6rykH3zwX62rKV8UXi2+6+8u1cdm2YMJSELbQhPCz.717c7NticdGug2vtUcwFrz9QdwSD5us+JllfnzzJgact.gvXL8Edhswgsr1239P+3AOdiPXxRvRM1CgI2niiy0OXv.+rYyNqVqyBLz.c8UpNZsdaiwz9BW3B0N5QOZfRop3BgQPifff0T2jZCLrq095IFjCAc+4fNs8oKUthhN+3e+fGo1gjFD59PTmmUWj.nOKwtTfcIHs56yAzNaIHSMqx3l9L2AaEPmQJDK7vc5rzDSLQpXupLFiDvc6s2dwIlXhCEEEMIPtkVZImFMZHDBgvyyCcnElJoIS1.FgfgXHVJkwIZVQZ7GCSQUowXFGQD8AQOr5WReee+gAAAFgP3HkRwG8i9QM2xsbKCEBQWfcMX5pjp9AAACTJ0PsVmdtbhINivXcUjwR7n8qnvz2W52Sq0cMFSrRoPWohyI88IZrJzVoREgPHxoTpbewu3WL2MbC2Pdg0156XLlFe9O+m+B2xsbKOLVzE0bYXmUsissOyVlLrZZLDKHfVHgbZWlgHNJ1XIuQsVeboT5q05oAQrwX5JDiDR7MAZlJ5qUz5Z9Vp.zBaL.6VoRkdYxjoeud85t7xKONJStZQ243HLI0QISEN8zM.MAVQFtmPH1BFoIBoHa3RMGxXqiJmGzKWBNQj8Z9ogUiFlH4uK84+.qV5vtJkZqfffVJkplVq0RoTiEIOMgQBnc506iUgT9waswWaIkd+SCLW0pUmy008PIH8oiPHVm8tWbPQec74JyCxBFSvjRg3PgdrDgrLvSKHH33.OYoT5p05oLV66tuTJ2Uq0I53mXcvJ5+RorM1m8MvpLMZrYAaiiCcGSbsuT6Y7wJRC9ZE++iFT5co9YGRPDVS65JoyyB6slb9xkKOYpvnxdq6msRkJ4MNNEbLlBe3O7GN+q809ZyPIbvgrTkrFioPPP3j99xC4By9Y9J+SyMyzyLCvjRoLWPPfC.m12O9LAAwhDG.Sq08jR4.RSbJzsToRac1yd10u0a8VSG+0N40ltvlIweuKyQWxy.pmF+eQGntXQvZtU1JoNZMTfbEAQ88bnugI2aRQ65zehOwmXpu2u2u2IRRPOLZLqEQ4IBBsc7nKcovHgNebzS+3QAk+a3sGugvjwypbLvv4gAMgAfbPIgX.0Y.Q6UMHrqk6ncIK01WVufK8faF63OfTgzIQPPkR4NDSea7YlLRoL+JAASFn0S8hdduvoHhooJSo05IAJjTYYG+awWfdeSlkyXLS.L8WrQiYa2t8rkJszLdddSIkxBI1HmiPHDAAA7.2+8O90uPHDYTJUAoTNoTolxyyap333ozZcdsVmc5om1wxA6w5gZ16dWB7GGfPzekffABgYHkvDDDfVqEUpTYTkyStOlRgmCpLxeypMdBNxbfW6Kq3eMNNGrOyHQgkjA9sVdY6FWOJcJsm3olHLTrkmm2FjZcu1EtqBn888CuYe+PkREoTmr1a6teqMEFQqibjkW6K+k+xq466uluueKflQtrlTJaqTpMuga3F1VHD6ZLlt+u8u+e+.qtxnbjRYtACFLgmm2zBgX9vvvRKt3hpCe3CeMAAAOYsVerfffigkiiWKv0TBNZD3AEKtDL2a5M8lll1TfQI8oswXLwsWjgzZeW2oOK6FVt7tFiYGVfsTIBdK1EGacy27Muwe5G7CtCvf64O5db.JL4jSlBE9IMFyDQP9G7AevbKA4IjbVDcsOM53f7a7p441USy.vG6i8wDfTHAgotwg56UosWxK4k..0KBz7w7j0Fmjt5RojhEsV92ccW2kPfP7K9leyFDhAAAAcCVIXGkRsK1jZ1GhLAAAN+NevemrTcu6I28ce2OdKYz+asqt13ymXNJXZ.Fvy7q+q+qCMvgF1DR9o9TepIfl4AxUw8pP+JlGVvtd09BvbyM2js1ZqQhv2G7C7AfwRXnwXbt8W9KGcEs4seGucSvJ14vIQ2Rt8a+1EmMLTPz23uY7sv1n07IwEwDBwfh12GBkLdBgwNy7nO+U5Xk9toQoR10xbI99tu6KFvTqVMFLXfAHtToRCDPufffdFqidXN5QOZFfBAAASFASDFFV3jJUdpSNZrWk0t3E+GM.wsWfXKRCtpaGL4bib4M1aCsS+xeYu7I0FS9G3Ad.QYnOMnCAiSOx1c.1tlKakTrnwSXFBgHSnqatIlXhBThBk2aSxSzqWuI5zoS9nnnQZgViFMDhD4vMbTxRhsiGLVZyfktuc0g5cA19y949bagfsDH1DAaXLl1JkZsDTgzxHDqoTx0.ZcJe+VZstoRoZHkxZAAAQ2xsbKQJkJRJkQRorpRpBqTohNwxLiR9bMtIorguzu4vgCaIkxVupehWUSkRUCHxHLgBiHrhtRUoTVSoT0BBBpYDhnUBBhpToRDkHpLT222ukRoZq05Me5O8m9VBKUb15AdfGX6UWc0suka4VRoAS+4sUsd+1Y5pihWpOzpOvP8QGgJfQ8AFgFGgHFLw9mVMTLpusoOvvJ5Jw.3mDKG6MNXfuu+fS64MX4kWNFNpw6q2MiHGgzp8Em0dSG8XoYGLVq7iXypBkRkpuRBvf+oTFf3JUpLxwb9TepO0tV6ideIm4RUDrmnud5kJN0Xf3xkKmJdvidO8O5JXb5IycnyJDhbgEo.ginbUA.KkxsqSfHA8Ju3uuuuA.CrhObrA.oTl4E9BedYpToRtjDHOpuwh.m6Rzu61tsa6q2mGWo3+ez7L+fErae6eDXPyicLq6L4RmDMLIU+O1Dnc0pUSKdbsUVYk5XS6v5999adKR4NJkZ2W6q8stSIX6fyFrEUYyJUprgPHZ66KWGn0+w23an4S65dZMUJUKf0DBQaGGms7882YEstmuRMTJkTVHxHkxBBg3PZs9v.kjRopVsZKKkxqMHH3Xc618o7W7W7W7T.ttRvSNBtFvyuD3RaVf5LCiDI15N0qWmlEwPy8E6e59a1t9RKsc0pU2AO18niogKIW+a+7e9O+NRoHNoXMyXLlE.JZLlRZstn+oeFEA2EKAySDGlKvLXWe5flAw2NLN8J1d7VBSRaoPAKQ78nOn6W2p518.2tBgnCEsYa200EhFE3wA231kqMdBS1FXi+xO4e4F.aIUxtAVgkKCvDJkZZLlY+c+CeOGF3v+Q+Q2ybRobFsVOYwzNMg6kYRf735NgPTdJfCUZokl4e4e4eYVfo0Z8jAZcdiwjMLLL6gO7gyBj8E7BdAYUJkky7kHuwXJ77999tlPHDS827272LIvjNNNoZuQls2daQZ.1IWnFgQbvjFDu1ZqY1XiMPoTBpQFgc.qyO2q+myAPbwKdQQ5m+i+w+3VgE0ae7k6alUEOcxxTzDYecrGQhStZFHd4Sbh0oHFvEnesDNAVdOtAlJfRoSfzFOViRzjxTupMK60fZ0eSukew5.Me1O6+WaMyLyrlVqWuDztRkJsCVInstht8a8s9V2vXLaR4xaoTmZqgCGtkTJ24+9e8ecGgPzKIQewAAANRobBfYmbxIW.njmmmBqNZ7jZ1r4w9IdMu5qqF7T.tVn9QaXqbyBPQ6DldjaNax0Lzb+KByQOZbpi6P0p8DBQWZQmfjq0R6k3jV+nuxW45CFLXariGxfqs5tFiovpqtZNfrm3DmHacqs2kHrXVwHzm80u+aFBcZxwP6nsIXzA7DFiwj3xH1mw0ezUkC096qXLFgQJk.Hr6K.wO4O0OovlCRhsUGSzUoTcsPcm9pSoFFFFZGaUGmfff8bQnxkIw0dN3q+s1iua19aGGCtvEF86Bcdiuw2XBxs7lDX5+pO8mdpx1M2linqBGjZMnkMnXS4w1D1lat49N+u963Nra33jJG.mOvG3C3fwHj9Rty67NA.oTJvEyRPrTJG5448D4J8r+4s8YHyy.Jxv5iFeFYpN5iaGmdUdbioVM67DQDe5Se5zMlXpUaDMBhEBwPkRYojyEtfQHDYTmRkWoTSn05I777JrmSB3l4BW3BBbwbSG4H1uystpGiePjPlZivoZLwzTlYnLyfGG58+gd+SAj+FuwaTTMgFwIhZXZE9rabOZz5Y1MdJkjP0h7DEM4Fas0zCzCloJL6fAClse+9yjOe9oVbwEKHkxrV36mbOczc18.IkALHRCH25hPBDaKfMdVOymYaLrtwXVGShy035ttTdxVAAAqILl0vZiosNaPPyXKZcpQRwILFS0W5K8kVshtR0fffna3Vtgp92reDknFI5rlRoZWKopqG8n25Z.M+K9G+Kpo0ZsRotfuzeUkRUQXDVzJ35FoTmJRoT0LFSMee+FTiVUSPvoKrSbbbmfffN.c0Zc2m9S+o24ZtlqoKkoKdzkEn+Z6GsPOhMiQZUVuv9JtjArNFiTJMjPEpfUBLRkxnTJyIkV5W6K8yBjWq0ERzHMGVhXJReTzSOJNkKLH7qWjVnGQGmL850KMtKmDz0rWrCyggkuJNdKhvKsubUD+cel+NgTJGQiUiw3DDDjHtxHRn4g39tu6SbRqSWE+xe4u7XoTlduyIJJJCKRF7IKdeKTb4+VWy.D6tWQ8Fh0UkrH3xXfhXmhad3bWoq+EfhI2mSJhUdfB0pUq.VGzzQYc1w3jywf28648L.nuVq6+pdUup9FiYHP76487GJRP39OTfOF...B.IQTPTQ1fffLTFGjHZdYhi6du268aDquboh+OKG+wDUOtTwZY6We9yammLhdIVibuimp+X6O1+MN0oN0FTjsnD6fK6TE1Uq06BQ6VyJqA6di23MusiiyVewyctMJCqq050dgO+WXyfffTcgrwO2uvuPyj4+ZO2ryt0G+i+w24G4G4GoyJAAcAFXFNznTpLtttEvhh84vgkjRoW974O5y3jOimrwXttybwK9z.dZP30UyVDUenXIfCyhLsGjuXwhYrVV0XE+d4k6lX9E6PiF6Vtb4NDR2Kjb8O+9u12NLjNqu45lG5gdn76ryNGxXLGV3JVLWtbKclyb+KdQ8YW79CCW.3vf6r.SSQlbtwry3GCOydBW6wqWX6q5KIPpJkCW4nH4RrS3I.27FSUiPH5fKaPDahMCZWInwZO9GibbdlF6TTWCVHRe8AAgWqPXJlb95M0DSr416taKee+0.ZWQWYMeo+5.spTIbMeeu1ehOwmXim+O7yemDn5lEKVYK6BW6e3G6iccOum+y+XQQQGQJkKp05IRt1heKuk2Ru69tu6sDBQq4latlqu95anTmpePvJYSRJybe0G9gO7y947bR0zhTXi4n05z7wmt.deAzUZEVrcwpDzaEAs0AAsMPaivrFBZwPZ3eZ+ZDQcrnMXKRppBbr9IZBx2LnQv3UOXbpWkNfanci9QCONDetGoKOb09cY791IIQyKGDlc0UWMyxKurCGkXtvn.fD.h+9+9+dwy849C4.Zmj9cSVDNzWHHXFkRMCvjX897L1uKkDPMK8tJwbAmM3vJkZ5+a+29yycpScyoIQHGIY7WHDoAR4XLFqf0Aj3zK8sTz5TCz5y1Oe9786zq2NyMyLaLyLyrNVTvzjhzh5oOy71EBsPedYhSjRgzyY1DOYOAZrKL.ZYXNxPaJDFFNkmm2gv55RSuwFaL8ryN6D0qWOWwhEEtP+HX2DekeOZBsH6lP8kgvBwPKaeDWLDsOwg5qmDELZCEEg702S3CmVHEERRPYbBkXRoKzNDNhpPWJzQYWf1kBDw7TjiRcNtwXdFZs9FvBa+4MFQdgvDKPzKFylNBQi333J999W.H.WZjfzj9PYSPvYF99e+u+Nuwey23VDNRLe2Fnywgdm6IVzi3es1FmlDB1acm7JXxe1246bh63NtiIIA0UBgHypqtZ+kW9l2BpagP6xrKqdIqTZBLrmu.r1T3xBDwQnDOMSj4FzZ80KkxijTwobfv.l9.ckR4NgZc62+8bO0uy67NqpqToh7z9ADQXPPPjRcx0gZ107VfNz5IzT.KYL+wxBmOuKLYDbHWXpHHa2tc6WnPgcviMsZzwkkRNoGqrKBSzzR+tqAJeBiI7YDFFdBOOuiNXvfYqUqVljiyNJkZmfffcUpS0IHXksDHZJUxnfff.K8AtXs3XwZ9mxeKpkRYgk5AMrtmzwoKm6JNVe79CN3SVprOgseBr8ulRHDS4ASbg98Anatb41.65zswmcSn9yAcshwfpOSCb3xfaUPZLFegv0e3vPYrI1KalrkzZ8gkR4TiSuWK0m.ozZWvwVHIb.D4Z5nT96BraPftCXFXEy0JCEFQOkRsaPPv1.6JLl9FgXPx61DNWldTkQTtAaADhSnqyt1DRWq+89mbuba+GtsrkgrUsZtfiuueFqilgYvfA8Wd4asCDYQnPYrHPBDZ8EQJOBtfHBbBBBbRJVTgfffoFLXvgV9VWdpTpcCfwX1MLLrtTdxKBQWnDTqlciTi2O6fHnLcSeSfGGNgRDG2XL2.vSSq0JgPLMFi.DcMX1EAaKLrEBwVRobCsVutTJqCT0RQkSWEBWKYc2dKZQY83Pp+Qy5poEPLG3OIT4P3xrlploAlPHDYvVPfNITxoML+FvZoBt5kiRNITLr7BP0qAK0Ve5IWydEKVbxZ0ZXr5isMwdBD8kJ4NejOxGYia8Vu0ZpSpzTiJjHpxPZr6t6BQ6LOzYs8SKmuYh34uU0R6ujCISglCCLehdwky9bnTan9ZyCsWaTL46g9b169edvcJH5PFiYdQYgzT07jDBwS0XLOkf.suRImWq04EBQLF5Zvrs5TpMCVIXCgvrgwHZmLu2NXst6lP4.n5E8fpgv5GG5LFkbRuFRaOZiKO8u+RE+O.CA+APkwel+0yZXWNDOev8gLFcPWr.zbpG5gdnIu9q+5m1ElXkffb+C+C+CYdYurWlfxPx7LYfRYgZ1DcWhY0eA8zRobpfff7FiofiiiUJILlbdJUVgP37U9JeEw0ccWWdiwTH4YSlDmlZXR7+wFS0AgAg8MBytYylcqBEJr9q9U+i17C9A+v0oD0oFMvZWvaAUsnJzmdV8LYz8KmkAmUGcstrAV0vxHXUbtsa61xcu268NA13+SQUxR.yHDhBk.SjwzqrPzIJI4JFiYagqX629c9127Nuy6bKnztPM67uGm9btKonv9sMsGOivjQYHrIEGOC3CntWJx.5rDQ8DBwPiwHHhL3dUAqK6w97DicxncvtIm0.V2wIQSOrkWN6Nc5Lguu+LUpTY9fff4EFwbI1IaAgHNyC9fOn34+e74KnAYXIK+iMFyg.lKBN7q8W3WXtNc5LiTJmJTqyasCXQNgQj6tu66NuvxW6ot1q8ZmQHDyVlnYykK2L5.8TRobhm8y44juUqVYa0pUFcn1Qq0iPWBhDgpzpL8CM1AcCQXWb8r5JwUBBD+M+s+sBkRI7k9wmxyefu+o5u58s53UpZrWm+aFc5G+YwAgZovXLNUpTwAHyBVq0M64djzzAtzOOuTsKAT8BiAhuIKDWGOYIoACN349bet8KhtKP2lISRTmhaoTpMHoBWW7LWbzlh05ufUI6sPhdSgPrdPPPqSd5S2DntRopqTplFiYsye9yuNvFFiYyfffMEBw1+W+C9utCvtBqkEGKkxrtDMcbb7Bc61srvXN5gNzgtViwrGccp6dTrnMYdOBmhDgAjUQjTYHCfIHHv7Ez5QWaPqA.C7ZSOX9TcTI85ZqSdxS1cvfAbCEKlyXL4iRRPYjEYI6UEflkyk.i6r9zJKP1G9ge3rDQVJNJayeCKiy0o3nqIgPLznMiDowxDYSPiDCgWcKd6FAvhwT2lbmd85M.XvgO7gGBDKDlgRobnwXFHf9FKu7GoV3UNSEiRoLZs1ThpCUJ0fCcnCMfPFhKwUpTYz3my8soKb7sYs8sQ1ikz+Mg2y4BnT963NtiT9+OsPTdFfYNyYNyTtT2RONINWFcecr9+qAfv1+qLTChiiIAYSi97BLBL1x5q0Zy6+d9fla+1uczZMRe+T2+vnTpXOpYg29bziVOBGY5Ij86NNm2.oVqINUSl+oPgBVDjFV9pEp9ll6aMfpDGa2y0fACHa1rBCFgHIX4fffbJkJaPvJY.bLLZMojjZeD788gZkFcBVhF1evhc8ql44RBPe4LTYequMRy19E+4+4KXLl7gPlrYyZxkSZSHSplfT43WtmwB7FoAJE.ltJLqwXVPHDEgZkbbbJlMS1E50q2LXXBcndDcbDBgnb4xHDfNPa777LBv74+7mYDhJDBQegvoqVq6DDn6HDlcUJ0NThc7k96.rSPPv1IV96FRe+MTV66schtOsdvYBVSq0sRbpu5XEWwZ9990CBBZUlZqAz919ObaaBr4YBB1Rq0a+q7q7qtEvFNNNsUJUqb4xUuLQQ.grDgTkpqt5p0.ZHDYaBzLJQfCUJUaoTtMIBMZtb457k9u+k57E+hewt.89pe0uZWfNwwwcJkj.lZWZsB4RWI6E.BQPhC6FDFXBBCiASrUYXQXvjU.4EFJDaDSlrwpoINdZsVOUkJUxKkxLdDJ.HoPGzjEGuuyi10RGCULUFBD6lHnnVffX+LITAIFHdAV6pFsTtT0N9vEm6+9u+LgggYjRYl50qmUHLYEPtDjSm2fofVqm3Vu0acBfI+vu6O7D.EpToRNfbetO2mKKPNIQ4Axu1nMRe7Gsw9838lAV1f9.nBNoULAt.qckOFw.w9DYo3UQQLQXDtBwK4k7RbzgZgRISEp4gFiI1fIVHDwAqDD+Jek+DwlXwPkRECLToTCEBQesV2uDUGBXRzMIw4rBW+A99+Hhu9wRaew+aLFGfL2na+CZesWtWWMsKOxS1O88SnXGckzrKPmomd5cA1IhxaqTpcdNOmmiU3pOSvljfF8vvyX0ZjxzlZrVrH15zPJUCee+5RorQBsDWWHDsA1X5omdyf.8VIInbKiwrkPH1QHDckRIkHJmNPeHfExlMqWoRktl4latmxexexe1SsQqFOUpU9ofs.+ROptHvLThInB4X4QIdxPpH3N5ZaUa7AqxP3XCu268dS0ojT5IsoPHRAafoFjse+94qZLoTD8PZsdZp4N4zSOcAfBRpYAwvbjiyQVN9WGBS8S.ZOdMgIGnUOEtZwEggPXeTzCJ1qAz2XrdNHPVhVLYCaG6QIsbb2DXcgn7ZdddskR4NHneRvr4CBBNjuu+bB3vRobV0oTSBkypTJNwINQbxbbNznTBDqbmEbWvXLK749a+TyO8zSOiVqmvXgJWVozKCBSVgPjyXE.1oWYkUlQJuo49BUqN6fAClVpjSHDhb.YlbxIc51sqivPp2IjLnWXGzaXHNio1+Va8quT5OP.Cd523SePkJU5o05N0fcfZamMa1T9N2ElebKq6azUC+fvP1ArVi0nOfHIXTjNs1e.jozr5fIb4fG2K2y5wmjbH9LzBy1KL7q7U9JisX0xIUv4X8qumdtjjXt56TsZ0s.13AdfGX8ibjirtVqaewKFtwccW20F.aDrRv5AAAMMFSjwXBEFQfRcx..sVqCcbbhDBQ82va7MzToTqUtb40CBBV+YcKOq0TJ0ZZsdciwroVq6T0JjwYSrpr4BCCKFDDnjR40XLlq8y72+m9jnLGAJ4FZqd5zIhfZVV0KYL87Fk5YMzJtTyODuQOaGDBCf05snMgPIHGoz1+y+y+ycyjIiopwj8FuwarPBRnlTXs1xIMFSBEzpVv8lbyCKkqRRRTdROomjMYJ0OZBzJ8y.Geb8D5wZSXioFiwXFlnoACnHwBQoTX5KXerS6J1LQfAZF6krXR9746Czuc61ifXsVqGfvtPiPv.kREGDDfKjVkSGo7TjDTcuG3Ad.6FZhVpmuueeX9gf++FpR1q80afOeytk7c4XNm+fTDzhfrBXQ3zgfnYnDGpToRSFkpb85uFWK91++2467cJh.GnpSoj40rI.OTj90vSJ22cla+1+gPoT.HbSSdbYDPQBSWGn8i.YWOgsct8ViHKP9jJulyXLYVxduSf6nO9Uw0pzf2XqCDCQ0qiVqEBDYLFxBh7BnPPfNUenb.v22eeaNnDXfZFJhA7LmyRqmTw16J0NvZVq5L+XEDHYyBNP4L+J+Z+ZowkM3i7Q9Hcg5o7tuqcNqycoSVB3P3BinM1W9K+kmlDw81XLKZLlE0Z8B.yznQiIkJYdLjAydwAVsZUKu.DX0eCX3MeymdOMwxX5ZLltFLcA5XL13GBNaP2.cPmjumao05199mdcfV+r+rugVBgXsW7K9EuVhXe1JVHZVoRkFXo7ZzEuXXTXXXck5laV0RUzMpToxF.abwKdw1Rob865t94WqDzxyyqwpqtZco7T0qZWbnAMnIPykW9VVCXcOuS0F7RDNQ213wFUpTYSJwlqToxlRobi4WZ9Mtga3F1vXLar7xKuIvl9927101SvSuRim1+89VvMdq2nCgkc.bTdJgxyynT9wBaAsb.xIOopfAlRHLS6ZQAzTFDELFSVKcYJIB2OJBxBMG4PdGe+TN+peSiGOYdhkXXj0tmMk2qeyXND2hlVWEIJw917hzjZRDYld5oyXL3jP2mL.4jJUtf.c9O8m4SW.qkgWPJk4MFS9986lOHHHuuueNnryy7Y9LyDDDjQCY+y+y+yGqvKm6wSqS7Mn1pl41+F2SJHjG0uZJ1KXfiMrR5FhaXK7oIxD+S8S8SEK8jF.dkuxWoIs3pJkxR2XCh2y642kS4qHHHHVWQO.b6Qbbm333NeAsNEQUX+Nb9C9c4fIe3q6lPrrwtEtiJdfnnCpKdWMIO4qYxyuBuFqnoKOHgFbcN5QOZha5Tcyvvv1JkZihvFJkZiKdwKtATbCOuirAP6Je9JqWoRkVPlZAAAQZsV+W9W9WpAKRPylMaMsV2HHHn45qudKvzTq0MOyYNScf5I+eqGFFtUjwzCAwdJurCFLXptc6NuVqcAN5hyu30ZLgO4OvG7C7jgRKGBJfhTiCCLEqtvizLPtjuN+viumdmrKnRDB3har1ZqsswX5YLl374ymILLLGvDCFLXxSoTS.QE9I+e+mLG3lUC49nezOZNZmDqz47+1Z2y4w6ILIoCse77I7LtlwLjxLnT.8g58wiA+m+O+KaXDTTalPcmyO9FsuBGaFBy2EhRPYRz5.sa2t8V.cEBAlD01Wq0yznYyCEFFLk9LUxCUcnDfEAL.jUq+BIpecsCaLUW7du26cwolZxCCbHoTV.Hmqq0F27jxLFa0yxq05IkR4rUzeg4FNb3rRob5G5gdnBZsN6YO6WHSgBEbLFiXwkVJov.FK+XELzXD1M6aXfvH5qTpdBgnqPX5lvQ2cWZwk11wwYSiwrwu+u+ueafMUpmQhZjWtOr12rT73wG3rOwcZuLWWLs5GlDQDKCP1uzW5Kkrno2ACT3fG2C96tTM6jGU1i2nW208bS94iMDVM4mO+n++icrikLgxQ67zKWdGfMuwa7F2.WZ+Ljx1G4Hds+C+3+gaBW6FJkZckR0PoTU888qnTpJPs.fPoTFcSRYsicriU+0+5d8M.ZkISllJ0oZ7LdFOiFu1W6qqtTJqKDhVJkZCsVusTJ6Ttb4AXikIWi5MRgMW4icsWqudE8QfZRJhKrzhXCLdxD60V.qMDtPRlkWq+wB22FpFBzuIGKIgPk2EpsK196C2Ymcb9j+kexItoa5lNDVQfZtxvbBgX15vLTjoVJjBPi7nHGrvXat7BIuWIKbtGqYa9.+MEw2N9eP8DM1oXcLPcmtc6lAJ8XQuahCo3.fdkK+T5JkxdFiYO+l2vvScpSMDHVJOIAAAYTJU9ypqT.Wl3TJkc7uKFvsewhGNYyBMRDvt0FrWe5+US6pIfluVAA8s513yc3.mejPFWrXQmeyeyeyLu829aOSIa+6Idmuy24gnDGxDYl5Ftgan.tjEJ+0L47KmHHn2wcbGomKQMJ63333HkRgT5kfzDCinZoEEIFPDqCBLwwwhUBBbTmTkwsJYd6u82nkq4rzk5d2SD62MdejbkJUp.vjP4IKKDEDBQtF1pYmfRrq1qQMdgi8Oc.wd1LrCBQVvT3qt5pELXxCjY0UW0XEaVFpTmdXplyb+gWzPYhKU2hXw3333wRL5UyFMGcdakTQUPlIwgBx.UEXq3e+xkK24c8tdWaCxsWxtI9dg6GZ7G7XlAZkPU4vodpO0m5gvttvbwwwyOb3v4kR4rZsdZoTle2c20FXaROGiI1DaRhqvXQymPH5afdXrUhz.aIDrMF1Qojc.5d1yd+8DBSWr1I7VBgosTJWChZ9G+G+G232323cz7teyu4lezO5GsoEx+zv2yqou+ISbEFuVG4HdM877ZBgqAzVq0ab5Se5M.Z+Lele+qsDzz22uQMnNtT6Ys7x0gpMYIVCjoH9bSn1F1eNbyKdw6KoX.QasTHa466uo9KnamPs5FJ0IqKDhFBgWSfVBgXciIL0YR5svBKbUN+8wAv4AtuGvAplAKcoSSDg8Yjw97I3rAIh6qovJAA4EBQFDvdZnSszyoimmWBEAJl2KAsmmi8UE2qll86+4Rh+owdwADZLlpUqB3N17Gesy72ds0fhHfRhs2dat+6+9QJ8DMZTOUqTxZ0kES9uquyuqb.Yuu66+giVqc788c928c7cjbcTx.US0YiX.yOvOvOfXokVJYchk+1tMdAHrdaH.EGg1GiIbz05byM2k4O09QgyGiaJE1GIT1CKUrnIg1Z7ddOuGG.GoTlIHHHCFx9pd0upr.YhrO2Q5exgUpbldRe+N999cjRYOrhQaxbw9eiZ84KShWJFCWHYNzKjtOgrI8+ylT3szhnNNhyublMvi1umik3jUG4PLyM2bIILYwMNsmWaf0qCswkMt4ibjMg5agGaCE2122eCee+V9ddMREv5WvK3ETEnpRohh62utTJqqTp5m3DmngRopKkmr5Mey2bnwXzRoLToT0MFyZZsdi4O7763551AXXylsbFNX3jZsd10We8kBCC89NetO2ir81O7x3xQgx9.t.K.sND1h6LdwJujIH5bin4mWOHHo3o02XgEVHU7vG.HjxSl6m809yNQ1rYm5AazXRJQduZjChxgjbu3W7qNUaMyAUNnHv9sUsGumvD.LbrJwoh+pPH5SUFTKklAgD++0a8MyEu3EsAJTlD2qYtQPMkuFah10lrfNddrIv5wwwsmat4RsZWiPXCV100cxEWbwILFQAoue9OzG5CkiZjyk5o7PdB4ojSALK3dXgPL+N6ryBSM0TyALkNvxU3nnnwl.xjQrmMBN887+8e7r.yFDDbnie7iOoTJKbxSdSYcbbxf.QiFM.vHkJzZcr.wPgvLPHD8A5ZbL8BBB5XLz4E8hdw6BrcBcRVWJkqoTp09w9w9wZCrIzXGftyQ0KU0T9FMBSF2xHSpXx4y.HRgfnTZ+bEsz5H6MbC2fsRuKDl8b6exxK0jlWoMMueTlLhhJQIIIZDEjFOgQCO+4OuEpddWna8DG0AXChX85vZEKVrMQz1k+k1XqJVcJm5iCtAXsh5p.Q+iO7CGoqniTJUDPsvJUpGDrRMoTF8g9+7cU8AevGLJLrRsu7W9KW+TmR0LHHX8UVYk1JkZKfcK4VZPPXXlO7G9iNkPHNrmmWwd854Qc7JSCOr5vybTjIs2acSuV6Cz+76+ZKMKyC.5OOU6Az0yyxW3O9G+iIdM+zulB0pU6P.y+UuvWcgp1j0LuVqmk5bnFopjc.405uzdB95RjYkUVICL+2HD9U6eew5TYbg1Kj35.FiIagBExIsH.vlfziuuENOX6.KZTe.P2G7A+bcA5JDl9kJUx12PP7JqrRL.A5UxbO2y8juhVOgT5OEQL4YshzWVh.OhF7K+K+qZS9ToTnyu7AC19aGy39USRQFerY5b.i+5xk7juU1D.briYe+c8tdWB.pWut40+5e8w2467Nol0sZx8FdCug7lHSAf7yM2bYHBgGUubaVdT+sUOvFpeGui2gHkHzgZMarwFIeQD1DmHre9fffgBQbbrwX7884dtmOnSvYCxEAEty67NKPUx6QiwCR4IppUe52WmEWzVc5DwKbBiIbhpFSp3xIHBwiBCAR.jV0dbbbvyyiKbgUwfU7bEXx.j8Ztlky5VpTF.wy9Y+rikR4fO6m8y16+uO2GdvYrtN2v+k+kvXpRbZ7GkKWdv7ITcfGkUbMUbLKi1pUSEsA4VrHwFioWXX3NelOymYaT5sarmVFb430uc7UQKcbLFyTX4l9LBgXVGGmYxjIygRJLSdfrsVaMGvPFmLi5mJfXqytv.iwz2XL8DI5tgwVc01FCq+S9S+S2NHHXKfseAm7YryC+vqtsPH1.XMozuYkJ5F.M9g+g+ga.T+s7K8KUqRkJ0vhHj5.MNyY9Ds.VuDgqCrtTZsSSkhMjR4lQQQVQX2swZMR0uKnAQzRmpmKMXKOz64ZPtot.wQ24HG4Hi98MRnWqTJScDu5PsHfHiIrlPHZVtLsEBw1ttV84pUqVWMzNPr73EFnDoTKvQq0YDVqS1QJkYvPFoTlEHq.QNiP3.wlSpT8iii635RGfdEKxvKbgKPXXnnRkJNW3BmIS33w8btG0vd+xUQcS4xkwkHGKcrrGuEV3ppfC1VcLtTKd5omN9E8hdQw5PswHbDEKVLCPVK8yswFHLBwsdq2Bic9GpTpgUprx.fdKsDc8886VrH89q9q9qFznQCa+7kWce2uuJtdeBRqsCfXIpKLFiimUjac77rOGZ2t8UJVF6y9HDtV6Qy9LoL7zuwaDkR4n05QIZvhpcaeueueueuL.NUzmUXLFitxYFHDhtttryK9E9h2IINvg1jogCtU95cyuWt3CD.3ZYP.RocNLWWq30FFFZK9cwyk5fXoZ8zdBDKj83ORG14xcO6J0Ferw.fdsa215fpKzbKcBkUvF++lQo54WH6Tj5ohF65TJ0oMKFYeOQXqcbBCBBphcQ+pZsNrh9rZoTV4z99U.Bz5JgBgH5u+u+uq1TSOUiyd1yt1K6k8xZKDwamIalt.wau81YAlpToxGtc61kzmUqd3G9y5iEoIt3x7.GBVXLmz7QbMl9yw.CONgC.5tzR183TpDa8JdEuhNBgX.fCkhJ7ybm+LSALc2tcOD0X5vTaIWSg1s+J6smtilFi2xeaIJSd7bBS1qC74saTxG5iG8b2SM36CLrAvQNxQx7a8a8aUvqJSBEm.ZOtPhdEOOQ1Mh0MzJjbalISlMhii2JopylDHqlc6s2NGPNAhrAAA4eYurW1j.SGAyf80gt6eh6dFvcVio5bFiYtu6u6u6YzZ8TUqVMOBx555tmpgis1BdVUpOePX3ju7+Su7ojR4gjR4gLFyzAAASPBruEFRsRXgVqMXQePr0hvLCvX5SBDYUJY2+e+y+y67J9geEa4BsEBgMXCW2VX2b+lIBCa21ORQB7aTIKYrIH8sbq12OcxuTKoJW8DHvq01IDqWmBFa.x4gh4nkcRxhPN7I6w1axwC5NKWoMMbv.FRQTykxQfF+yMjPK2F8fcoHaOucxxMqWu9F.adVs1NQJrdopzBba.QMAZVoRkl+m+4+4q+jdROyHourZkvvHrvPttwXpCTOBpehSbh5dd90e8u9WesUVInluueciwzPq0MTJUKoT11A14Vel2bbhiJLS974W5bm6KIeNuzWpBqtpWxqNG11+OJC6O3nCFj8nFOCjXB..f.PRDEDUDHkjLxdgVASse1r4M+5+5+54pToxzAAAGtPtBK9vO7CujKr3t6tqMoI1fwmBKcxR2LiypmYUwoN0KjT8Z3QaGF1+BdBvWjp.3RHlEIdI.iwjIAp9406sHZlictqpInGk3ra9lu4dKszR8.5YLh90pYqvWRUVs8cMj81u8aOuCLItLYYK8jxmHXd7hd0u5QIyQViAf2PX0wE9yCtvwSzW.4JkjjLVQ6bOJsrLjE7sIUdwEGe7eZ.PGL4IomiuU0L.b9yaGS75dcutwbAik5SUKbmKKDwFiQ7a7N9MDBgfO8m9SCfI7pJIyG2NdaQqyI8FdCug8cxmc1YAf64CbOlj41SFyJhMFQriiiwE3Ne6+rNInQXjMzFt28xrG6I1U1Q.HZ1bbmiobVfrCFLHimkJRNEu5t9FMdqLHvJtDi9cKezkQXGeK7RVSM1fSsZ0D.we1O6mcPPPPum0y5E2+n9GsuRoFTBF9b9At0AE2iW38.5s1d5.1URTJubvAWTMYbSsZlLFiQTuNCSzzpcgx6PfcizWgywnwfx5jyXLEDBwTXiIYtM1XiYC0gGJvlrjBZsNqNP6HrfevLLdXR+cmw0xrQV+pR4uCvVNvFZsd8SoTq8e4c8tVCXMivr9YBBV647bdNsN6YNaiSoT0fRQ21s8RqAzH7hgM8fFR4Ia3662.orA1DVr9oO8o2DXqZVAxdGs15XCAAX0ELXGn71DYWucNXSVfMKZ2bxVIeltgiGOXD8fk6AWH0EL54C8.WqqPXqdZpd00hDjk.rY0pVgDNJ5phZaitmu5dICNSwZ3HDtiRRRPPPVsVmMTGlAAYBCCc.xX.6rIFwfZPWee+NqrRPWf90qS7QO5QM.Fe++WLI+LW3BW.VjqJCr4xzLXcLkXiwLLQ6+FFAC2d6sMUS32ZqVW0GuXfgQI8WxkKWeLLnbI2XqCTYbzA5LfktQtdtB0oNE.nqnMIz.J122e.vfFMrz9pdc5887878jzWedX0usacyQ8W.x1.q6Up0FG.Rbya.LvwtbGiQq4FMVrutUw4K8k9RYhiiGKIBVMfRq04RSfkvHbHNV366aj99CUJUuUVIn6G8i+Q6DFROsVOv5TndPzWWa5c7mciecOJ98nj3DRh+ehnHlvX0MiI.2IntULrcgonLSIIsn3108N2Xw+wd6A3qSjlLpXicokUuLWF1Ae10iws0c5txEuXGRbZUYMVGTqA0aBzTq0M7fZR4yHRoTUCBBh.pYLlHeorJP0njhpJk9UkRY0+ceGemiRrxG7C9AqUsZs520ccWMaznQ6+p+5+psLFSuLYx3Hkxo+LelO8BRorbkJUj.dtQTBJeXn0zjRW3G48g8sFTJRSZz.qE0Wiseeuu22tFiYnwXxPMlvwwYZsVOqqq6rkSPsH12mdt4laxjyUV8mSmElKCr5AQ9y2Vzd7bBSfCrA2J1pK2OZb6bKIHBiwvq6085xrRXXVndNV5Rp8EWti+PVf9rmlNrAVd3lpT3rxJq3LyLy3XDhLRkLmRoJXLloAlKgWvK.kW3s7VdKK.QGVTVb3yctyMagBSLsTJmnb4x4jRoSlLYF88ILLTHkRgVG3.jQ44kCXh333I0Z8z.SIkxQ9csmT5Dn0BoTRxl4.fDKPdnToFQGmffftFnyuz+G+R6DAa9c889c0NHHX8xQQibviSdRUWrIh5aAbeuh.HylOzCk8bm6b4AxWpj023WzNQ3zX2D9gvZyVSCLETeJPNIvT0gIoBSb98uYqwd4NpZFbkQZR7XuevDEcoSZhU6O5Qc5t1dSVtKvtRobGfcpVs510Rf.LtjvI5S29W9W8WcsjDnz3TddMf++4t283srqp5786Xsecdepyi8q0ZWmxj5vqBBwTkoCH1FDtn2lV4J4izeBzsbArwGMh5MRLpejqWaaEaBgtQA61VaZ.oAe8I1pbSatwOnHn.ITU.kTHzURnN0d8ZuOuesO6Wqw8Oly09rOmppjpxCZpdVeVe1m5r2m0dMmq4ZLGyeiw32OZVsVskO4IqsLvJEgUohw4s669tuU777Z566Gelu3Yh9Tep+xHnbruuey29O1ae8i5czc9bO3msmkaalrToJy8C7C7CXMXVoRHLGzbJJyHrOyi+D0+ziYHM0AjC0q809Z0ie7imUDYbOOui35dSy8Q+nezhwPwie7iOuqq6rXLXNAvHV8a2Ab0icrik.QIL6SKv2F5dV8A0Wc.nrBrrI3roaJee0yB2Lm6JyHsBjTrH89BeguP2RPmfvfAR9rqqqJhHhiHepO0ekim2Iy355ly00Mu+Y7yeZe+rv.vK0eqeqeKa+zMwTLEg1rzFGKg0cXf7tVtc4b.JETjBv4FgEXTfQgEF87vnP8QAF866a+aerm+y+4ONvXULYC3HyBEnLC+r62HINrKJhKbfMMtbWnRGfNw1MFeGui6nOPxO9+xe7CY+3xtUFENK.57qrx.6JUpLHp9J.A9A75e8udrx7oBjHRRhm2Mknplbmum2iV+KFfqqqSP8fbAAAEBBBFB74ExdtuwN18Lc6.fvYR.iHEHIa1rIgPBQnMu3O+k57XecFmHPfvAO6466KUccw0PJhDD3KHXF0sk92BKrfEvf3N0pcxdXHAzt+M+I+Mcadv.1jBVxUp5kL3d6zSa9bejOxGAJa5uhHpZj5yN.6AQ6AydEp7QSKVoWOOkXbU0ofxGYxImbZEcBAFwVO5YTAw0y0LGCLjDuojD6.rmpZKU0cA11lIIapJa355tdLrV+d8WyyyasZt0Vyyya050qu7McpapQLD+g9Pu63O6i8YWFX0pG8EuZHrFDuJvZECBrY2J6TqlAviEfNLyfwzTB82xgXQcr8+NaX27Ry8e+gApZnrD8783DoOCWrecnOD2WUsa0UW0D0XX2YmkcA1oHrKknspZO6ZMWp4VG9mG19mEv2JYZBNPCILLLiMB+YAxphlAHyM45t+Z.BpmmWuf506REFnXPb.6P9cgx8.5uvBKjvJnGHeKtJatF9rPEQRJWl9hH8qB82c2cuZWq9.QiGXuy9O7OrGPmFMh6hPRgBif3HBHhh5DEEkobrgP++GN2WK6oNUsr.NU.ghGXSpCcecMEp8znG+MMsCutoy7yamanZVJRFYeV3cn6EmCt36KomiAAl3c8tdW4fx4hgbufWvKHaRRRZFkjw00KCBYbccSmS53Vy04TmplDDDfuueBkouQ0+Li+ttt14igpMakGtO7Tou6.jo1Pja8zSa.7eNXLJaxFNLk.9DV++GGhGCpLNvDwvDZnNQfk6ev.bxH1iBvbWNIt8p459v98m5OP2yC8nNcCOns+NG8nGscTTTanRq.XGveaVv.xqq6MrdHrJzbYfkOkojDWwyya0JFe9WqDrNUHUELaVy0M5O5d+iBVc0UCdyu4+E9m7j+u6+q7q7qDN+7yG8leSu4U7771TDYu50qq29s+5yu7xKOgmm2LO3C9fEighPzb3lVh9UuJnlBRIW78.5zue+Dy3Y4w777lz00cp333ibZe+YvvchS666Oo89PdnbNybtMLRC9019hbIaeyNfIvEOANAn+hFPNFNZH8DQRpToB.BKiyrWbssc4O+qZxxDr.l333rEvNAAgc.5exSdR8QezGSDUc9pe0uZ1xlGRm3G+G+GeFWW24TUKBQEKAyu1ZqMGwLyjSLwjyO+bix9nedQSbCCCkb4xi88x355l8y7Y9L4s7cxH6ryNoaHLiUBZkff.Ay+br0JpEY5vjRFRvsSXXXaf1G8nGsU85024+wW9+wNddd6DYIONvqSiFzqnAHpmMxtjz1APCexImL6INwIxCkGsQCFuHL4JvTDYPr7QdjGIE4xzr1YJHXZU0IgxoY0vnULx3W9G3Adfg3Oi3by8DLVaaGFv.3x2uOLvICugptbrAFXZCrWkJUZOS5+Ol8V.1EhSAfacf0ZXipETbkG9g8WsLrtemNaZYd6zO2pdddMeMeuul3a+1eCQPbzo77Z799.+FM888W6u9u5Sui04tLyM2bie629sOqmmWokV5AqTBJALCwL9BWdDlOP+67fRv.Clc.59q9q9qhHRdfI78OyL+B+B+By566O2OyO+O+r.yTBl9U7O4UMouu+n1nVHP.kSGmVkjiseFVb01Nv8EQDcNH0wIGfrEYPjTSWvLODjs7SNnD16mypMaR+xXJuuREK02wwIw00UCBBjO3G7CJppxsdqeWTO3gIz3XA.5e2e2eW+S540ixlLb6c9Nem1q2.Y.ccdVijtAm8voK5+qPzxDfLr3AJyt7P8BUM2KFikXhpvjvRSx9.gNwe1e1e1D+C+C+CSppNYTDS5BiuJjJumC6vySjs6mMVD9vOiOzQT+RPBTJQDQum64dnBH2+m49Oz0wS3VYTfjksQ2EpjDEYr6pfIqRrLcP5l1777PUQ88eX8jddIu9W+qOAPeOum2Cmrlq3dStoo3u0Q3kbpdsoyIGNpoBVm35zoSGQjVUEokp5.IFs7k+bM3bZd9asCWBmhk.cIHH.QfRkJa4rCFD8cOOu9ddd8CBBR78e39kf9PwdurW1KqGPuogdT6.Yo3Uy5mJbLciMPKAIuw23abfbrai7+vY3QWq5lcEvmFaPYyXWVMVGspHSoZzQ.l100cBLkpiI6FL8eUDRp551CkNhLPwD1VDYKQjMDQ1Tsp6fWsaZi5AAqegKbg0ylK6ZTg0.Vqd85qVqVskcccaVFZ7ldSuoUHl0bgMt+6+ilpBaaArUSSvZZAk2qdc5LGz4zMa1i0Nfr4N338+9e+8gU6erCpjeGlfiu3fdbVTC7kMGrVhHRR3f0umoypqRmJP2Fp1mFjJb.WpRD7R0DKWhLj8un7ks1uTUcbc2+4STS.7Niuu0maCY86662GGm9XsEXC90vfQzEh6NMzCl6opBXMvNZvf9TIhiQKB8Cg9Yyl8pUFWUpMrnIPqUVoYKAZoIZGT50tc69FaYJXyR6y36mGH+y+49byely3m+O+O+OO2u48duYo4AJe.ieImOMKMq+rXf79FRKseMTVXtP1kWlrkfbhHY0FpCC.I9xZKYfMxZPFld+4d+b+b+bEf3B21q40THIIoPiFMxEFDL.7.2pthkWcD.w2OPh2utF0xwkSvTNdCopJo.WE+TYN2v86A.lTevyKUFYiMXrxvDq.SQLGALGhHo9+a4fonoLf+N+zou27vDTlwrYhhMnAqjatKcY9N7X2Ux5iWpfldQkrOGa+LwqRkJsgn1kS8+eI1qLrKrxvJQ4ZQVkXEJs9W3KTeiJvVmod8cBNSvNVfoWGX4efa6GH5ttq6x++x+kO1EfFW3jddW39u+622VVOq366uINNsDQ5aABaT2ZtSeWui2wb.yS.yXHU5vgKMmmn8AbvrpA5bm24cpurW12U1ktvWXjfffwUUmvyyaJU0oKCSqpN0uyuyuy3Lv+63Ldoq0tBYK+M9fe8rd6ZoNvgQ32RxqLNvTEgIZZdvoO6KSvaCGaO37o096k5A9zajomqRPkqChdAc5z4E7I9Dehq+1tsaqjue8w.Qwrf+JddmbEHdUU0MCBB16Tddc9teiuQ4i7Q9HinpdDQjJc61sZ1rYKiwPPJvIWxINgggTsZUMHvWcc8z.+.00ykjjDVe80k81qkX8yAUG7SIfzCz1friH5FpJq644sNlzmcCfl2jqaXLUBgnkA1HNNdqxkKuKLytvZCqNNOSBXxfEINAj4rTLOzzD044YDVlbPwrPygR+Z6hGCD8nAHetOK8Sk1lnNUoGF4OyHUdfZUWl8Az3JJpbOk5So+7.dZnFH022Q+z2WfYyr.qlYIplABMKfUgrDQFX9LX3efznqayThxY+6+h+E4tgu0aHOPNee+Q877Fud85SUq1olx2+Li64cpB99mNqmmmfwI+VhHqeu268Fda21sUGvGnITdSLxj3kxg6z4hYvXzaF.WU0quc61KlKWtEbbblOIIoPlLY5YLnKq3441LHHngqq6JXV.XyJvVQlmM1W9KgdvI5AmMcgFtBuOb.P1X+noT.pLADMCTdFHdBUUc80Wemm2LyrRCSMtuo8Z3IJ80yfA88YfvuEfWnp5MB7BBBB7.l.EAg1frKna544stuu+JhHwtttwlr94LqZ4GncBCC21JSy6Lz2OPYw2+Lpm2snVRf8pcyUeyTan6KKlwRr1YAxTAxDYuG84+7e972xsbK4UUcDojThlIMnDPCGU0LRUIig9Np1GB6.zVUcOQD6bmpcfvgGqtb2GGd76o6X4kvo1z4bLNVBWFpTFhlFJIpFulHRHfeEnYjAr8Nbv0ZFdtrYMFWlk.V.Jch98CeQQQQm.gihxzXTElDU01ttt6FDDLLHpKCkWAhWIHHX0jjjUqUq1x.qVw3LVJYsM70v0BywFNZ84A2BPPZTDGAi8wDybjpaCQaCUaYm6bo3JH1+bUYbWhlMfRK.MNgp5K5y849bm3k7RdIKDDDbjRkJUnQiFXxvBZA5FfzvyyKDCCjGADUud83Z0p0DleEX4MgYZAqkZW8pYsyg5qEyCMGYHfeKfYy1p8Yggsmb47gQ.bVDxdNiOFyfod1eNsa29EjMa1mOv2RTTTQfIQo.BNl5MTra3U634Uqikf36.zRTYu+k+vuo89s+s+Ps77baUud8scbb1x08l1zDDfxs78OSm81autG+3GeO66uw2+2+2+5O3C9foDmZWnTWWZzKfY5MfX4WD0D37iI1T2FNfMwEwpLGou2vaX4Iy14vOukEJlcZZlcCby.1RcaN5a31T2714YoQ1NmArpxaYI++MwDs0Th1c3..jtlTpBxMgHxrXP0+4pp9B.dd16ESEDDly0spF3GzCgV.a.xxfF344sTPPP8jjD+Z0pECkVEZr09igCmAMUSvv4.WMqiLzyVLNEYxse7smXhINdZo61yZ+0xECk2AhG9YqK04O0d1D.kKAKdra9lugG7AevaXiM1Xwomd5xAAAiYFiTAjD6yX68fOzCt4Mey2biZdmpND+3.OJvEvTpxaBy2FVdXe3ROtVxlVZ6vfAmkYIGqxHP4wg3oUUmVDYby5kxtv7qAKuBlMYmN+KYnySVnXNqsiwEQlrBLaDbTfmip5KHLLbwpUq5FDDLo.4TDEztnrSkpU1LJJZEfkUUiEQh777hBBBBM91TYEHZSfceUupWUmG3AdfgEDhqlw+KxeAU0bREIOwFESRDYTfw9TepO0Hememem4EQxnGzO9DFxWv8ATtZaHrM1.DWSjtOTPPaKg0dXNY7vWyOc7a3h.ABi++T+ff2Z+LKJU4bYBY1rVB4N+W9Q+x4dQG+EkElOq0++gyXdGCILGOvm2G5gdnwu4a9lGy22eLOuSNZ85mdzZ0N4Hat44JL0TSkEfxlRiqsp5Fqu95MmYlYB.Bf4ZTjU1zlUdC6G9k59TNfwfhyAMOJv0qpdc.UEQlbokVx4nG8ns788W2yyaUQjk888Wd0UWck+2tgaX8XCn3sdaus2VmOvG3CXyPvZcg5WpmcuV5Y3AsqExvjz1kJi.RfZ8.5XmPzUUM43G+3BT15L+SZsTkdN6OKokkSzVThMxkK2F2xsbKaWOHXODomqo9Ow000od8GNquuetffvQ777FOT0i7g+ve34TUKJhT7O4O9Oc1rYyNE66vWZVfjRbqX++.P0pUIHH.WWOoQiFxLyNiSRRhCfSq81yw00yjaiBhiSZeQDPb.ICPFUcbvPVf8++4+6eoNtttVcDmcMo1KcBCC6Vtb49.8mi0NL+V7LEnBGXCuFYhr49QidYiCFUo4j.SnpN1Mdi2RgRlLZHmHRAU0QO9wO9XppiqpNAkLN0Ttbzjsa2dBHxVpRtVF+FGQjLTMMZhUuTHL+zscoPdtOokKFzkSjZT5X1wzU6uDzCB6Bz4u99tuNDYhj2br7NX.2qETpSUn8OzOzabuvvu312v2yMXHQVH1y6lhpWudTsZ0he8u9WVCOOukUMbcfsVe80aUud8dgggYTUG609ZesSCLMTzlpbwCykOGFvmg5WkSl2xN3hHsF4XizwwwQAx433Llp5juq+M+al52727e+z.SYx3Glv22ebfQiRSQxxooGYJYuc1mpLl8PH6unRQ.voXwnb.4+a9at27ofrMyLyjsQJnSybEkUY.ntDN7BboKjHCQ5lIf1Gj999987775opZSU93Vddds7886.zuZ0p.3XKEvrvr1HcDKFPspe3910xMwRXy4TUK.LVjATgIoDSdK2xsLMT8HlMPzb1XUmCZLKvLhHyPDynpNaIBmCXNyuuxzVY1bDHrfk7JubjBqygdEd5+78AWHu1.f0LNmVwjEapFl+tum6tfpwEhiiya4XiLQOwqubP6EAF.pKSi9NNN5DSLghZ9ysxXOHHoxAIF11ne85068deu2YWfdttt8qUqV+vvvDfjnzyesKZMxuYuM395wFbuNvwP4HXKMkx6RE1UDo0bykRP4gOQ7ExPyUhDeUcfFN.RRRhrvBKLXrsQiFlwIkjkVZo9fzGP8C7cBBBx566mEpXk7Uv0cY64cM67uS7TYMFEPqRyDRkIcp10Z+suHReaIJXu+s3SD3..HmKs+VJcdao7+g+g+gi.LZTTz9YukQSbD4fKEn9998.Z644sCvlpit18ce++sJnq366uh3Hqljjr1oO8mvBjP7VdddaM5nitQPPvZ0pUaMWW20dvkdvM.1FJZqy+FcBFnZX10FOmwt9IL77TpR0kbhA1hOmxhCu95ItTkN6UXqo7o+696Dq56YF6WwbNpY.PwBBRQmphXIr0XG7dROw166SKujWxKI02lb1RKLmue3gJwP0IHHvg8G30S54ljJW8tteqbpZ0ra.qQVJNbVQsfvLluupF4d5oWzZahL93i6nZz.6mhToe4A2KhuR2XSBLcOfNMf8dnG5gZgQcQ5F3GzCHwjMWBKszRNnF6o+it4+QYPISf+YxDDD3366mAplYdHy8du2aFXYGJOb1lbYm+esRan0RHKqZVaSUihJYhNnQoh1Zq0DXYwZ+6vmigBhTyr.4DoXdfQrq+NNkYbrbrFFtQQro3i0VC8ihhR.3TddRsZ0bdqu02piuuuyIMkXLPjRQybgG3AdfmpY0TZy92bN.LpmTrAnwxFdVZLfwdzG8QG8K+k+xi.TPDo.Unfp5HfMKRpvX.iWohLoIaSBsYgRwQEQx6axNeGlNcLJ8YoKoh57zwugCuddOft0u3reyB3y45aT1rUGTtguni+h1CnkmQzMr9+Oe6xvdggWnUPvCuEUYMr9+ey272WfpZfmmWT4xwMpUq1J99O7Zat4lq666uwpqt5NmtdPGa+Z7ibjibDnjsr4WY7lGLSyur82SLnO0rOUMfUkjjHAAA4d7G+wGcgEVXzf.+wpUq1XVftFwy6jEtga3FJDa4SMpvnefOvGXDKoyl2nVlWxRR+Zoj0XP6ZI.SRaCaHOwF01d159UEQx7nO5iVnJN1MtMStEOnC2WtyotpYB+dyCaSCVONNd8RkJsYkRk1MiS116t6t8sorOmplK.hqaUGfbhTYLfoDQl8m+m+me11c1aZFrAf84SkpUqR2tcurWDIIITpTIYzQGUbbbHJJxj9rggBhP0pt.xPd7XnaAiCcpw0On2O7O7ascPP8cUU2ojkjhBBB5TsZ0AoZ2JUeFm6RNL5qNvhNEGptEIMk8gQCKwH1n5H+H+H+e1243GuyoO8o6np14ttq6p6YNyY5+XO1io.x64m4eWFU0BwwkGqPgBST0XHs.DXS2LizZpAF4hrLICuAqCe7LQ6RmtdF46iZz298TlhCYj867U+pGjwLq3ZRkUf8pW+LsOSXX6+y+m+H68sVs5tDyVXxdiUJQil0pUqYQH9i+w+aipWudbPPPCOOuUNxQNxF0pUampUq10VxVinpNdwAy8pjq1ESllWBiVw8W1VKxUgcI1Dcqff.BCBxGDDL5ez+g+Ci8i8u5mbLee+w.Fqd85i544YIWvxlxmJlI.F8y7Y9CroBXwLm3peb+.a56DbNklHEgLMaVMKPNe+ymUDIiMhDY.iTcyZW4jFZf2AtGpIIIokjyfOimmGsZsKJHkA77Nk5662qBz8U7JdEcr08aOKQc5vxlEoWfIrKRLmT8hAZa3930ZMAPJZiVlHhU5WKapA4FoQqMXxa+1esSAkltrHSqpllxsSaec1FkYNU04t0a86XVH9HVxyd74gQn4AJOmKBD1S7rGuvHm.DpahdjpZVWZlkHaslCYeG2w6HKPlLYxjAR2zQomr43CCvZef9wULy61YmsTW2pnXJShAeZa6jdtnIhdpZ0z63Nd2Z850S788S.TKPcJ3ZlaU+ZFfRtn14suppBgnVRmqy7D2gHZ6BcWYkx8.RV7J1A9RRk8KaUGGGmA97nG7dR2EVXgthH8DD0y0KipZdOOuBe1O6ebdOOurUgrAAUx.jsxfrB7pFP3A1bBWbX9xIr+x10PpB5u7Owu7f+fEMa13x0ry6pZ5iML1eJij8k+xe44ihhxWpTobXH9QG1m6yD8h.Mm9999sEjc8b81NHHXSOOuMNom2ZdtdqcpZ0V6Tm56c8G+we7M9c9O8eZSfsbcc2z00cCn7F.aPjIKmpQAaVFNa+h66iwP9E.m8.o494RNq85ZQP3bCxfrD3rWMkJxgFqKmbqu3WbZoVc.fWpuf40G4QdDEZJglH7aHUX+K6ZVGx98F749beNDopYCZwjSUMmm29bGvPkBQp+YI.8Oiueu50SUXoFDCR850cpBNW3LWXvF9bYIGVCoYylDFdw6j9Jab3fGhH7W+W+WODnDwZrcro1U1ZT1y0F1MMV1FQY53662CA000MsLVkEV3nlw.a+WDoWhH81d6ssRIbntLH21scaNtfyPDMp7jL++alaCulkQzCF5PbkAyQTMBQDchINhAqjvKxm0A9SC33N..lkyaJKECOenQ5D.iJH4s9ChqtHWD3...H.jDQAQkqaeP6VoRkNpZJyud85kbZeew22249tu6KimmWluP85N.NUAn4AxVhmlfkbf+1AkwViJCD2Am2xa4sjbu+I+Ic+ReouT2O3G7C18c9Vem81d6s6ea21sk79e+ueUCU9jexOYl+n+nOcAQjwKYJa+ofliCT.JY3quMLi6koqcLulywtXk07oZP7Nbe6h8++hGyRNzwfrk220vAh.s9I+I+mu6CszRspV8nsNoq6tDx11dyJUHpoHRiJPbbbkF0qWuop5x0pUaEOOu0lc1Y2zyq5d0qWOwVF8SpZr0Wq4mXt82C5ST+UNqctVYPIzb8e9yedw00MatbEJXJ+duQqWu9nAA0s7mVr0+upFvuhLbWWQxXAouVthWZEQ7Zx10h.lLbSAz4m2fZPESz9FUUc7vpgiCLJrVgsFBvhmfyUBFGX5rrkwiqT4FWuc61aDGEsS4xkZu95q2WDIAPOiueeOOu9hHI999hu+YxGDDLV850m3s8u5sM4q6e1qaB1uLbN.fMVmb4v+rmqKVo7h25a8GERi1sp3Z+bgggbjibjzS0f9ipIpXJYnd999cRbnUBN6544YQvrZaWW2NkgtTxhDZ3SHq9+TsYLtehTCSmK2nPNKhiFTiqX2LTCjs2du9Tpzdus21aa23cezsN0oN0FhHa7te2u6Mmd5m2VG+kc7sEQ18c7N9+piHht0VmKKPgPXDqwgrF4HaYJNjd1GWNN8ZY3HQ+LM5lWjiHoG0qUmeweweQfXLHYMmBjLMjvzVGkCnKTqKEK1oVsZsq9hq1FXuXqDHBytMywlMLFNWsIrbPvYZVqVsXOOuF.M788a566ulHx1mz6jcrWW4aVtoEg2nbsuz7mwgGST60U6PCp26DEEsmqqa+t85I.4Nsu+H077Fsa61i344MRsZ0FAXLee+I88OyQVZoklAKYv9c7Z+Nr0VZyrad4Ig2mrwV.KcYBRSHCDlQU04e1+r2vveVGHNSQJd0XPVwGEJ0mxk62oSm9NNNIAAApMJ+BHNTlLKt3h47bcyeFe+BAAmIummW1HP9jexOI.ZwhF4Icdl24S+o+zN.YVxcI60wJjAzghR50psCL2o49QLy5vV7XppiFFFlFM6b+d+d+w4TMN+G9+9+8BhTwJQr5nKu7xiJhLtFoSALym5q70lCXlu5W8qNMvDKOHBYKjixWTcv5.j4r663yy3OWe1AeOUcDOwIv5fkkTNGjUY+w+w+wF9znLIPCsF0tbNUNvww4fDpXcjJJ0dgC.pmqq9w9XeLU0jDPrGjDCIe7O9+0jGNHv78TqFVd3P.nTI.BXZl9Yht++yqcL.yl4rappRhTVRr.IXH8YiDZllQEWt1PN32.ATnh.HIIIXIqYJWpjtzRWPwjUG8vjgpcSzjDeeeAHquue9W5K8klGHWHjEhxszRKUHZHxl9XG6ojC3pISKp0momNk+RT.d781i2467cBLOLCWtsKNXN+hKhCDloDkRAXLWbo3bddd4xlMWtlMZjUQLWehIKUY.ZIx9afPL76hqmahuueOW2u0NAAA6cFe+c9O9e7+3lwv5u226cs90ccW2FeWuxW4lXJYksJWlsf3sbw0xOIzo9r060rYy9vpIYMkRB.RMP3XGZcmT6hFhxV1m3hWb36kWo.MenMqD2OKzGlqmHR+xlrvxrolkPgY0W3q3UL37ppJMGXSYZgEeRCzVxbygBQX.tmrVBYOWPPPJuygAeJTQrDrqEnLQj9dm5TJf366K0pUSBAN5QOp.F+ASguuXwhpgDNeJuIVsFzmxF6W25sdqcsfRZ1rmmYbo9SxI4P8esREy3bEpzuQiFIdddJ.19ti.NVb55u7xM6fvdtttsp45t6y849czp1IOooDVsYU0CYd1yAbcXtgmObM4FsL.vSYGLkdddpZK85PxBkcnbYrkgYhkmcr2amNMSGGJaINWFfLAGrrQyCQinFoDeTfBJZ1LYy5nppAAA8muXoNNNNsEGYOuS50IWlbIX1eQVJWNMytyAjMbv3cQ3DGXN1Si.OtHX1LtiM6Qy+tui2cNQp5PkJ8AZ8K999E15Fuwabi2xa4sr4u7u7u7VSN4haeuel6c629a+suiHRqWwq3UrWkJU52qWuLu2O5Gcj64dtmI.lrTIFCZjaNlafc334hcTUEntSmgx3jZPFN1ULOE8j0NPf8tDGGt+qESKIPnKAzAVnMkJ05889desV3TKXDOBRkF8h6PI1JZe9OY0G5g9yVtVsZMLktGwAAAM+DehOwJhHadpZmpspJg9g4azngUV4WdrrPAX5K2df2eLnlYbIdfeUkkq+kb8NMa1LqmW0B.iIhLtiHi65VabWW2wCBBlnd85iCgS366OtMXpi9kB9RV5End1N1.mBj4DWimkIWKCXxfIq1JbISDQ4UUGUDYhM+palRPnEhgrVjYgKeTCrNvLWWWyD1MoR75iO93q6U6TaikWB7C70u3W5ue3GJDfLddd4bccGoVsZi93e8GOkHhRSEJG.Is7aRec3VXf42klR1+5+5+61+ME4.+Mqs95G7hWUEURTLrbummWqZmzc2ZttaCrSCn0ce22QaRU5gFt8lCRFRTGdlXB7A2L9l6in9RPAHaALfbjinxNe9O+mO4M7FeCsmbxQ2gFMLZXdHqAkVsngKJZR0nFDwxPoz54b2u9W+q2SUUdrG6wRyZkLAAlu6GaqGaezcSN7BMm3YBTkubM8P+rRFzeoeoeIEPiAExl.jrtp8Yi4Lo+ZY5C06Qyllz0qIsgp6opZTfG2U2iUXOnxtX3mfMbuI2UfxMAhWc0Ua3440zyv71qGoQa+9deuuNhHIev20GLEI+rlM2t3vkwf43DG.0cXCR.2dVhqp0XiMVqtc61Natb8wt35Ox+pejBc50KknUGEXbOOuIUUmdgEVXZrYI.YHkroxT+osyNKtuQcSp5p1nE1GP+ZesulcS7MuRWDTqBJySBznOwwcu628c2Yqs1pqZ13DpIisxt041Juuu+HVUqxdTZXVZuPSa1PrLKm4e7+3+wlwyDbd7G+wELNfpvYO7hqWK1FNZ1obtyHhHir4laNhHRgpUqlAfO1u+ueu81auNhHcTU69a7a7Kz+28282MIEvYUU4F91tg7O94d7wnQioTUm4487ddGApNYIiJYMJkVJuI8cqs+ytKh.0DiyPOq.B59B4X0PTeEnR5l55op1VjJs.Zce228aroZkgz57DtUCEPWAfnpZU.KvI53iON.r4laxq+M7FzeuO1GOQERvjF0FhgLMB4kIodPPhMKS56662+m8m88l.jrAab34VWKrIi8WK87j.kSVHMKbbi6SCRL7XsYyrTDGX5mn64C2+SKWojtcuPBf94+7edBBBUAnQiFbritf555lnIZhmmWOOuS1yR3qootYFnTt50qaHVvxTXgEt4CHI1m+7OkcBTg5JargB1zUmxYN1HiXluO2xhQc1W7x82K.x4LYigDqwNjFAawb80qW2bJjwQjgtFkAnk.pfhHpjAkL99AYd0upWUNUjrTtQFWWW77NUuezezez8.19N92dGaBk133G+3afgmz15ge3fsA1Iff8lcVqh9TfdEKVrOfcCfYT.9799BmulbhK0XwYAnlrzRKY9Mm3bGFjjqF6lCxjilPWXhtfUgEquXxwfDlkDX0DhiGvUGh3pym9byBajloKWzX9v++UVw3CnrubemGHmqq6vDtdBPWE5fJsA1y6jdlwpnnjf5ovhTJsZLT7BzJUpnvB6GA6EuHhY8JcLwDHGHg3Y65BcdrG6w5HhzVDompZB9.TSpdv4xOYym0HKvuQDkTpTojvPeEi+BN.Yp55JFPKo27EK1RP1JLHXCee+0gFa4+vO7t.soIcgRI2rEvEpD3vJ3XAaX38p7M61zNPy..e2TvzxRXwbUfbTjLpFIZTTJnscUU6VJk6XldCak7drz9uU8Ql1lUJl0.skKXNL7e0.I1sboRztc6DWW29K2rY2fffNtttc8Oie+aplqJhXJ6vybl7.E777xe9ye9rkR8Ib1lNb1Ai0WJeqdxtOLTPN1w.jKjmlUxWFx+S8S8SkU0Pknn8.1hlrF3tx7vx.KSw3UnAq.UsBk.a7bdYOmsyl0syq6085xbG2wcT.Xr3XcLfQVgU1WvGR1WPLBAASFeKOTXnCmupyh66K7S21UB.lJjWAz+tvPkUJmTa.oguTGZzvP11MYGX9chhhLA4d5l6QC1CVXf++27q9lWEJsLFoINx00M568686sIvJO34evMRRR1spW0dkJUx4O3O3OHOl8+lGFaX.StT1zxPcxBkxU0N+Q0HQiUFczQcrJgog7WgIeiuw23TXH90iTqVsY.Nhmm2TdddiCTne+9CtWrAH+a+29ND.4rG767Zt10p.lb3EKRQvMGlrXXzfffQKmJ2QtjEN2UxlkSpwJ8BLaXbGhJs0+G+S+mtkpQ6FDDzclYlQEEmW8+jumLdddCWlIE9t9te0iHhLxe4m7Sl+k9ReoCW+8C9NqVspZe07kkre.mq5VkvvvAe1QGcT7CBHHHfzzlH8uyriwD0ldiFGCDoqnRmS54s6i+3O9V+5+7+5a366uouu+1TgV24cdmFhia946CNFG2OOBK9LJ586aHsNNOvC7.l6ItjCZjWUMmMs14k7RdI89XejOVKpvNPks.1zE1nFMVuooTTVkvxqBrBzvH8Vv1uxa3F1awEWr+0ccWmvAR0q4jImbxTmMDZhSwTvRpRF3rOakkIosCZ377jbhTG+WzHqgjVe5jueLjPLIlnpXbHZdn27D1UDwPbUAyZI8rntUMyI2sZLaBwqArxLyLyxlwGVUUccQjs9I+I+Iac++E2eu2xa4sPozwm8m+aGCrRc6YIC0HS5Bxy.hGAZrk6.ld5myd4xkqsa0p8Dffffbu+226uvXiM1nX.OXBee+wusa61F+1+W7lGKndvn999ibgvvBDQtvvPiCike5NVeNlw9SUhMoyqMhv8UUSdtO2mKPU465a6aWpbENWNDTVl9TjNP48tyel6r0jSN4dhp850qm5.Y.I+DSLwHUpTYTL.jLNpNEzHsrRlNH3BSBL58du2aALp2iYLNBN40ccl4BK9zJsV+e1sgcRxldqgoaLvT2pvHSM0h4AbJA5K8U8c04e9se66drQFYKfsd0uoW81u829aemevevevc9e7XO1tyO+KpkHR6u7o+x8ttEuNm67NuqQ.FuDLUEBmpQJnaMJaAdtd1Z1EeO14Pf5olDOLPkOizNW54Kz0lsCQZYH4dtm6oeYQ5Bw6Ihr2XEJjRFm8GTRLO4MEDMDTCw2B6ryN5latoN0TSoBn29a3Mn0L7kUeS5TK89m+FdCFdOIFslqq53n8Mav2q2cbG2QWvr4zSbf0Dup13y+yto.ZMhSVJ8YkfzL8pQZIf4LeSDXiqj9ldrz0Fmmjb4xk.jbi23MpttUsR2ApJlr5PETeeeBBdXUDoe850GTlu+Me1+DmSVqVVf7UiIODmsd85YuvEtvS+0Nqg.Kjll8YKRb1Gc6sMqosxr1y6ksjDFZ91LhHRFqytiXUcpQvtwcUUA8R7.i.hiHpnFmlQy967g+vYEUyU+gCxDDDP8fyzOHHnMvtkiYanwl.aFFFtoqq61ttt6Bk1CnyXqtpYMqPqRFZV+KwpzIpI6Cxnm8xDU1iQccgEVv7+O6INTe7JtcnrLwJ0vC3Xfy0+7fxpnThDX9A.RnZfZYXNgkpMb.FNbz0cF5HKTMGPgxPguze+ee9P+vrg9gYLtnQeP6JpzQg8PLxZr+Y7a4cRu19998bq4lbSddZIZngoW29yYutVpe0zwxy8zhb9s+cY5G.ct9q+5aSEZCU5Jhj3ATk5NgGrDduBOuUGbMUpTEDUF7bQPXHesu1WKAnSXTTKW2aZaWOusTU2lxrim2o1CniKzqBM5+4VZICfPQU0UWcUf3THCuVvN1E2VDL62GLbjiZHH8ljUJIYDoBX7ko2O1a8s1qQ57zMlwFP1yKkAGUUmYWYf8uz0.ErfmHC3fGbDUjvf.xWHO.IttC3KGEH4L99pqqaZ.ecpWutyRe8udla4XGKSizRgc0oG.TyINXorb0D3Q6dLF3yvHfLZLjOWtbN29O3s2GJtGv1yAaTlf0V13++Zzz07Jgq4YF.WoZCVGZrcgBE5X624MDlc4Q.JLaJWMsFYlN85rHBDltGJARNbVK8r47J6yomE.sZ0pIPtj5PB0oOmftvwZCzZVX2oY4VUpTwP91aT1ReBK0YVC4+taolrEzXMfkEQZhgmSZBkW9XG6XqDEEsFv1qrxJs+N91+NTfrFdUJb3xaFtX6XYcg7UoQgPiec4DoriHhyDSLQZ.xFoZ0piWtb4I90909UmBCHIy366OKT4H.S566OBP1idziJkR6+SidW20ccXaVWK5K70r.lLbSmGTHlVsZwW7K9EcDQx77e9O+7wUqZP6OfbkexePeeD3gdLOsgF69mde2Wqs2d68Pn2OwOwOEpQBGyuyN6Lhuue5FoF6Sd+++Nhuuetb4ymomoDFRWrd3izuG.HsbpGl3WsflH.ha0plXbwEUFOpMJQFjogdPR2DmjVmtd8cttq6515m3m3mXKuSdxc777ZQD6UE5Tud8tjY49P8zqEGN2yZjwi7pdyuYSJCFP9+QuvWXAQjBREImkY1UftFBPMpEyRq.nUca4fPM1wJIuaCrUixrMTd6FPqG8Q2sGfXIhw7yC4gUxxbjMbHsou4zSmEHiFnNU+FWpfMvIsA0n84FhDnVj9Co.HIlZEcInB5xPZ5m2i4nmUNI6Az6QV8Q6BKrWHrCTbKfMDWYcf0KAF4eT0sEQ1869U9c2EPZTspYQp.1Wt0NANvYE.4O8O8OUnNBbdGJRl0fr9oFUqfBM5Az4rm8r8TA0y6lxznQiBfyX0qWe75A0G2yyareieieiB+9+W+P4cOoaNOOubGsZ0b.Yqdi2nYLOlm1s0lEEluenp8LpnBVm8RcBNT+KC9a4If7MG99y9NR2jNP7tEVnvtIIIsbcca2nQi9FV3TyFGGmMJJJmHZNPxu9FaLZPPvT999yPElw08nGoLL4sca21n9994ZNTF6r1LyX99N2UTTH9lw1vOu3.Kl47PFKg9ZKEGF4QdjGIOzHipZxOzO2OW6O6C7WtCvlwyO+F.qQCV2B34FOmW50uokA92Fyy5ce2u6eM1c2cy2.FOzPlvSZH443TURoP84HKTMy4MkJi45xjgbOSMddv4MEggHJRhKg7S+Se2z.z2y648zGn6G7C+A6AjXV+IfgxXuKe6Pm2z1TSMkr4laZs8qhs1+ILLLwyyMw8jd8+XereujXn+ce22SeW2i1kRF4LuLzAZ1kpzOs703fND8MB6dOSz1ub.lGGHvxOQjoYQDQphYyrSO7yQWpmoT.87KtnBzmkMxGNP+wFarAJujkD20ff.UPcTTGUww22WqcpZ87775np14aYgE52nDxe6e6ealPHCLuSsZ03nujiBXyVsq9l4dScx.KkkRTH.FoILx3iOdAiStq9D4e1f9uYZ2ZBUwIzFzn+h+h+hQCBBRKOtLhojavy0EUUMSlLHfhhpppgQ9pZzeOERLYagpIpp8p45108jtsCBBZEC6.U1FXqpeqU2FXaCGo0nMUoa8g3nmycvM4aNNA8gyeXEdYvw42mK.5a4tjmp1MG1N+gI298uGz.GXYGJYhHsMpzYnHYsDVnyEernwGiYG1+ivBppEhgQdwunWTgpdUypNH1xjtu.cUQ2yQjVdVx32yya2fGNXOQj10Cp24L0q2uQZVQEEovJC5CgG6.W+WjujWwsYQglIppFtTHhtPTeJB9PlvzMKerq7ME6AJDpTxbsEGGqpn5N6ri8pT0m2y841200sa0JUZGD7v6AzpVsZsIlt99mtGUII.zHH4Tm5T8ohgXmmc1YU.8bm3Zx0OA65+Vd7IQpIIpZj5ZUUgl.DqTlDQj9ex+5Oq44j4QM3F..NwygiHy5rpY82z4nV9JhbhH4oB452ue1Nc53nhJFslTjff.wVdTCmk0I9998RypEGG5uv2x2hFkBLRYxBaL.fjyViL1L87JsLX2e8myQlpF6PippNNDNFFxc042+i96m.M6vrr2JPKi.UvtTicg.ydAfs8mgsfRaFBaXT9QRy5+Dyd6hyQIJr5PY92FF+eyXCdp45rH.wGd8wmsZCuFkwl1In+PpkXeNqEL2SP2Ug1ajpxjGitP7.Rj8U95dccgx60.1ElYKfMnjQ87p.q.wqbgKbgk877VQDY84me9VteamLACuJk2K845SbIAJJCyP1.H+K+0+5K.j+e+6+eeNnQF.mVsZ4HhlASYFNRbb73yLybS7E+Rewo7q6Osn5T0q+ElHHHXja8Vu0TtRQaT1HU0rAI0tXNdY3wmqYZWKCXxfA8kgDpVs2niNZ2a5ltot1HCwe0G+imsjcyyc1OiOfm3EBLSrWdfdTu2DSLQaAo2uz+5eQroc4nW+0e8SJhLMvzYylchfvvQcccyM93iS974M0n1AkEs9.I9AAWjQ+JUpL3mM.inzOIY+LPQMfpDDDnppZbbrljjjfgQ+6uW61cEwosnRKbb1Aiwjcpe5SuaUS4E0tOzoVsZcIde4Y69u+6GHszgeRGWdxZG1IVndcwRNU4V5QdjbppoxIVtie7imw9Y6u.zkUOf1u2k5jRhXsAZOaL6w7w6Y9+g8AbHpTAfwWFFmpUGiUX..V.iwFaLhqYwDmjm8QRd+98k1QsCCdx.mAmFT+S6qAAAojBUeV4fZ99KX1i2ihKYmS1zvwIgdokb0NpQVgaAzwjEK37kefGHeQ6FNGOMaSN69FLeMulWi.3LmYQEqT0YPWlHbrWe8edOmmWeAQ88eXGAxCIi444MdM2SN9u8u8u8npp48N0oxPCxTud88IXqFMb1el8SqwTs3pjPkk6YJwi.SIKEUoEFU8oKkJ0mfqJCvJlw51kfVDyNc61cGe+v8bcc6YydKRRRDQE8lbqoHpzoSmLekuxWo.vX9m1eRfIOc85iSkJi544YF6RcLYs0jp6+ccM2hC1l.jYQHCkOmELxfB.iJREiLgC4oREm0We8j2065cYUZL1gkWdq4MRM2VwpZ.+L1XaByb1cEQZWVjjwFarrP4QDQlPUcBMVG+89deuC.Lo5JTfoCsDAanYN74O1guNG90mp8UyQyCB5PwFjQ0PGLQ5S.jO4m7SA.KCBtt6yZoOQslW5u2vvP1d6swfOtH5AJgBzxwvcd2+z366ycdm+zIggg8nQkN.sigNLyL8rbRUZTCOrBA7MyfkbvMwBYXYiTyaqqdmhMAUCsajdig2H8kuctyoybHawl0MM+r0daeSjVEtvRWHc7Rqe55cBBB10yyaWQj8pel5891+9+1S.zO6m8Oyr9teot.8Bu5bB7fNpB47fBzfQfRiopNlHxHDefx48Ircd.VXAgPRyx1Qt0a8VG000c.fIX4sjT+O52u+f0VDjtUq30wQb1yyyskpxNppaArop5l0qWeqJQr8oO8osxbbj403J6.raCnEUq1gv88y4xbX2fvkD7B8.etK8m4pscIAkw9doQMMWIHOMHOTLU40xZVOb+mgNw9kvZN3blrqa0xi.LxxlrZtfXT2u7hHYCBBx3Vsp.jfRO03WSGqJq0QDoS850635519lbc63fSuZ091RARhJUpbfM0br8ssb0tYiCt4HahNX4IH.nhY8+bopNGPVN+EkYMW1wXePoTo9z3.JDRxFarQhqqqhRBhzOHHnGVE9JkbqCBBjuMOOGB2WYCa1rIDc.BxNkL8uVciVIERCDlOCxN1TdKoBnZjpPU8q80dDSeaegzzAvYgUHKUWyd+IvL+qYoQAlHFljJUlPC0wylM6H4ymOGP1wFcTC.fpgWcN+4OuoT6r.l3440d4kWt0IqUaWvoEToKF6hBwGX8CoZcjkV5uM8Z5JcsVwJ3FY6XALQLxm7X.E9c+3ebGf9EM9+2YXhCdH++6.z4XqQalsQKfsg3s.15BW3BaKhzJR0dpphFqYY+rdsfFDjur0erj8WWe39vy1YcdZaeaam8h7+O82mtOQy9eN+AUbmG4O7OrOKFaIL10LhEQi41EX6HXKJWdyidziZyHGC8Engg82d6sc.x0Kc+um8.8aGL.tkg0HC3k425252JKPtepe7epzr2OyniNpCpjQDIqXyr3elelelQ+Vuwu6wDGYBUjw+pe0u5XtttE9q9z+UY.HJJRINtuEnj9iruc8qk8E9ZZ.Sf8mv0ivv1ppoxzzdppIu7W9KOSCHOEI+Z6S9pWIQsIciqc.16C+g+v6op1AkDU0rtttidlyblIcccmNJJZpd85Mga0pEDQbtga3FRjJx.vVrGVFim9dlzfa3uqADUJ.sa2FPnQbLMhaX+.fkP3Tfj9I86mwISOUR5AzoPgBsUUaAraMW2cojQ4UpUqV6G7BWnKUnaiCBfSefjumumenDfzzi8.WSOMZC6XhD.N+l+l+lYiL0W4.iY+29u8eKURNkk1+u6hcvxdrJziksf8TDopTMupwiqpNEvzDFNMlZpaZJRJWZLZ.jmEHS7231vvkJ5mWtHco.5FlEu56551alAxQ1Ab9neLzyTiuzkZzYNlqM32FqbZKhz4we7GuCFdV.rJXRSXDlmBKcH9zwb4UT.jUL+tbUu9Iyqpl2n9PjyFcWIa9rTsZUmjjjrJTvwwYjJRkwfFi9pe0u5B0pUKGQQY.a8zVY+nwEABkeZOllzD5SjAfCorrWYJ2BhZIhzRUs8O6O3ao6fwzxOoK.ZueTrOFIQrEvNEJTXGGQaw9Oulnp1WEs2Y786hRmhEK1+U9FdkbRuSlQEMmuuedGGm7DEkVFdYMQmzr.UHHbhuodypWt1ARYyyAYI1VBhkIeQJV.hGUUcjW3K7ElinHYlYlIAnKU2Grya6G9GtMkosHRa6uGXwYvL...B.IQTPToKFx0riHk6.zqAnc61MqpQiT1B1oHxH1ZTdDfQBgQXCqjUWi7yxrYOjjweoNdZ0+eeuu22.mJZBYtm64dxppl4c7NdGYJSYmBikc+uGixJcU9cdP3DUTACXLNhpNn3nplw22OyY78y3+v9l9ZY3lpdS.Q6aeYs0R+9SKyx8q68uw3T3Sm1fqsx6OmKErjbX3gIYPYXMG5hWdd83.s0.nbJORg533nAAAZoRkRJUpTOfdggg8.RN5BG0T2JfVqVstttt6Arqqq6d0pUqS3WJrGPuW5q8k1cVlsCzXv55b0s4dKXIylCHuOLxW4q7UFEZXHrQixQ7DUy4G9bIrzRC3w.JyHe8G6qWHHHHOP1ff.GyRBndttoAZoGH8PoqhtGvNerO9GeKee+01byMW1Rr3QNNNMpUq1xQv5eeeeeeaArKko0zL8dPzdupW0qxTVZlwvKalivAW26v.Lc3.Mbo9LOcZGFPNmSbhSjAJmEHerY8tBp1HkaZxBj4C8g9PCdN5rlRX0DPgRLlKtS.wSfozTGUDIeJXIppYbcccrpsVhqmaeOOOiuKhI6PRRRjZ0NI.ZrIam566e5jx6C34vO+5b9qr4BOYsCYernCPtngJuxhV9WgCl99Wt192aaXxF0nnn1tttCJUwfffDDRRTUM7gimDDDjw.NSkBtttFa6V4ju399oCW77lq0ZCl+Wee+5F.BfpZmW9K6k2KBRL.XEh02MXe.UyBTXIXDBskE77LQIJMEzXVfhppEIJpnHxQTUmX6s2dDWW2rG4HGwjgdNFvmylIadwnVVhmmWufff1u3W7Kd2FvNttt6Vu9WncQnuU3INP1IFB5BKrvv8qge8vsA9NbNHCtjaEy7qQAF8tu6+ciBj+VN0ob.n4.e8iubfs1+7POCnJzlprSIJs0QNxQ1FnksDsEK+sLJkMiShHiFaddM+JCGHKPt669tApc3q4mMZOQ11NrMvKUeWARNKjXC15P.qrRZ.kaQb71O5i9naCrkcev8.jwGe7rTjrwGPhySsqbrrvvumelm6TSkxMNYTUynplIIIISoxkyTsZ0LJjIJNJ2O6c8yV.ZLhp5HhHi7Jekux7.4N4MVKCkwIEv25V+UN2ECX80hOO++R.XRZ1brmTT1oBrsHRKKQV4.jmljF42TzUeBQLm8KKm8TU28M8y9l1w0jFkcAjvP+BtttS3W2ep98SlxyyarG4Qdjb9A9RlLY5SrAnEXeYiBFT9.Gv3+vj4ZXXHEJTX+qDAJWtLUqVUmbxI0++4t27vkjzp57+yIhH2y7tmYjQDYUEBEnT8dUMLJBB3Fz.5f7CXTjGPAQEF0gF5FGWFWQTnra7wAlQw9mHLzsiHfr9SPPVjl9Qn6tptoaZFkpcnpJiH2t2aU28kLy376OdiHuYsWcyVUdpm74du0MuQD4a7Fumy62y2y2CJwBLHSlL8UzsrTYSP1.j0UUW+V9Stk0qAa7fe5Gz3vpVsg6ZW6J9zPqemMiuuliGn227mHadnw407ZdMoZ8R1zLwToRkrTq8HV+r2yLaSmVvVMTvS8AAab9322GufmHS.LMvb.yQUlyUj49S+u8mNaylMmhZ0p.jmicJfkc5av5aU14JyVmdPjCA5anUe09mXjlHbpLLgwuuMACWfEF5eZKt9XdLOlXWS1ijACF3ru8sOSfPyOJHnSkhwyzKMKaYCfbs92VNuHRAIoGqK0EyyM0vYkUVy1xxxoTgBYTUyc3nCmOpYTdUk7IGa6lQMs.jl2SSqlMaZgWRqtryi5LbO1X0digp88f9jgs6PmsplrAbQjg+Q2xeDLGRMy46h4dqhWOyF7gMbSn9oBqmTu9CRXpyPDFDDr+9fLJXm6s48NX+9MhCBBv22mlMaBdPylME5g0wO9wER5FGos4mKSsD0SeZGUUmYfLLjr8nWta6O+1xt95qmpKQB0QmChSX5PLyLS763c7NFRmQykQSADPD4Jex0r.ru5q+pytqrYK.TpSpfuB4deev2mgds0I2W4q7UJ.tE7g7rI4VjEyLCmRVfO8WeC+L9+k2z+ESPiUwFbctoa5O1otHNhm3zgNNO8m5S+TxB2oF+0EgU2HhIkJUBOOODDJUtLnIW3VxH.aBB1uCfy9CBrCOTn08z7dD2cxRtYbnJ16wPAZAPtka4VNcPRtTDrjSw5L18MUU6s1ZqDpm6YC0sbAAE4HW3VsKr2Dwhtyottuuuu533D633DGDDLT03gBL73G63wJnhJwQggCw0vJ.bYqVsZssm2U2uNrMCY6EYw9ytyZxOZx1uM9Klxnu7+.OwmXNn9oBTR0jOG64BdrLA.WGm5PV5PtRUJky22OS0pUsccckj+h3nnnghPeCiGzsQL0F+W3NuyUtoW+a4jhHKLwDSzAnEtD4662FWl+49be9mDptRMXc1lsVhk1dOP+O4m7SlF.+oW9LmO+ebZ+em9OetdOeyxjG5gdHalsiCIIxw.1gWpORa.qele8eFy7lYwxzkG1c14fBnTNhnIRRViQukfhhmj20b7x.X666KIso9cZowJRXXniHRlvvCmMLLzAvtFHAAGPtuwJI6lMaZ99cikAbi8N987G09SWd4kMioy0yJYs6rlM93kuWht2.3bQVVN6DCdc13O4Vu0MVd4k2.XSee+zDzEu8VaEGEEQXXnsuueVf7QQGJOP9uz89kJXXBqatO8C7.6zN4mN4y49Fcdtb0RmGONnIaIUks+b20mqOljbE+vO7Cqus21aK8uwxTVLjCOJByUApM4bvTXyLco6b0gZpp0A7TUqopNyC+vObkM1XiBIhzosuuuEZBvKBNDicBxywpp8CCCMs31vvsaz3562Ch2+92u47OWx3uG.6Qg8FuuK79E1wWy91mYsonQ.+3npl4lu4azFv5cc6uKCCrHo6QxdOcvDNiXkqB8YHa0kta9DlXhz8TIX.JtXcnBcXhnnnJTspowezMIQKi0wRu4a9lgFOB5GTeiYmu01tPuNUlnbpLvKc9Tefsdb+GdbaVyDObLfHFA3OyXrkabPXcXlilnoLoZ+RM61IwlAXKthSREU3zoSGSKN222ota8LUqUMq6N6oKaXXXFpQl669hxjjXsLTspYcjNeSoMNeIg8uG.LIlFF.SXdVusYiOa7beVOKS2tP0Lvb6j4f8bFYb6rcLMN.lmMEQVk1FZkC5lfDqporbd22w6tBnUBCaV5JuxqLWc25VG5PGBFCDGfMEwcbVlbJ.lb1Zwvm7jmbz2mlQrkWYkXDFpP+9a2eKE1PEV+du26YMQhWsQiFqt7IVd8CGFt4UdkW410fAst+6e3m+y+4GBysCaE16XLW3LoG12zLW.Z2V.j+9+9+dIA7JawUrTUsusa61rLc7QiMRnEOmVSfVRDXQaxr6cu6hscYJfp3Rc.e5gWWn9O6O6OasCznwrzs6j.kf5ocsnL3MZwiuYlM5KV6rsf3PfAcf9PuzLvmNO4LPZlcnmZhlHh7k9ReIy2HBsMYnvtgiyNajhp1ppVOA7rXlwVzbQFQgwPnHLeZmfonpZA5fIyaczrNNVYAxr1FalAHWrp4+7egOewFM7Kb6+0+ux+Y+ze1LM7aXEDDPiFMzqqQCAinuZCS+MZ1wT3HwPugsf9DReU0A8pwPLNzMN.lmLIBVl0dO+kemYru0HGNa1AVSDYsJUprVRlk2NoNuG9VO3ebLzIVj33fffggGJbPiFM199hh1JLLbylsZtUiFM1lVzuQiFC5zoyvqYW6RSXcP546xQybOqIVvIrEQxrnA.tb.Y+49s94x1oSmLRcwLOqM1yaJYEyFaWbwj4ZtoNoyH0kzrYV3A+ROXAfR2+ce+kamncICGNbTVaegO+WnIPm1T3Jthqn.zovW73GOOyabRuneRf8t6naQrCvfmd6H9BYm4ylcLe8sdyuEfNbK2xqS5.h1xHrd+C+i+Co58i43ewD+U0wFaauy0VTTzNbjWfxUpniwpPZFdHQD0pCXEDDXIh3zoZRlgmKIfvd3jlI5F.u9W+qe7Oai+0KksQWiau81Rtb4romQiHf1Y6.YXwKp6qVbjij.nzbiVKLNN1BPhLBptFFFFCxP+ffg6d261j8ehE+f.I7PglqmNLLNNteqVGZ61ltYVefAK38nlhwlmGhF0Vty1Cx.sS2Li471K4XezKvQy.rh8JesUbZmL+2Ko98yjIiXaaK.pjjHHU09RZlISzLrW7+o+SqDFdnSBrPPPPOftzgd.y27dadhO1G6CtLza8tvVbBCn9G8LA1+BMVb5fm7n887n0Fm4FBKvNhjoHVIkb2NyohR9aV.anWF3XYmGJbiuzarLvD+b+h+bSALI0XJfIzVZkNP4jlNP1j1JLBRrQmbFwZfL.EBBBRKe37GpYyrPaaOSRFD.Zzvf.6Q+7GESs7cdAI77Y677+rnSLwDlZ8d9ctdLr+sc5FKMc3tidd2jyNanqgg4mzl0eyG7fqTtb4U7882IwCHCykKmhgkX1G6XGKEjpb.4Z30HaOHaX3gxbUW0UsiP9ehjy+C8s0Xz9Vgsy09L.oa1c9c.bUDQebOtGG+x+x+xJoySLkkWAZQIU6UQ0NS0S0YnCyATqsKtXB0tpHxr.St5pqVpe+94AxLb3PSasVMq2HFcLJc8OACSSDL9T.Zm5KxbsNex0cKT3nwvQhenKNF+Xte8POTx8sZRJyYt+G79UQD8s7VdK5G9C7gGslwRlX7NWGuQqIzCRK8q31tI.kTix.SSGlsMLmp5bGv2eF50aBfxP8hsZ0xD+uO1iNpeaCujy7ywE464rAtx3uOy8BuDVmLOoZfjnpZqsZkvpF2c.KYlQITJKKNh0OEWDxCcSYgiMfk1QseFOimgSBSyGsuAQjLcme9bGJLLePPPdDxoplmtTv22uDPo5PY50KoS05NJopb48ywWVCXxNShZNBfhjxfYts9XehOwfjfOx.ymi4R1v7QOu0E7Xn5McZs3uhKbx67Nuyk.YUeeus888kVQQYeY+zurB999E78Cxqplcqs1x9.G3.RRftCvf12Vp1IcivmMFcbF1TSMEQILOIYwqQA6rxJqrMvlhxFA9AqEDDrhpxRgggK+q9q9qtZPPvFfa+tIKD+zdZOMElWwM4y0QNEzJGGAS37+f7iHaLc9Ttga3FPpKJfpcTEH9M9Feilw.WTX2miixoZIDX2BHyCd+OXgnCGMQqVspRG7A1kp5t.BpT4w49K7a8aMGF2SSBsKiGEvPqww2P03Nh+1sS4SGzjwecAy1lG.l1or7jexO4QNjLy4ckVIABZd0SDOQ9WokUxlMrwP4tr3RdndY.SmeoF6zZfSZctQQQ4NwINQBKUzL.Y2ePPgm1S6oUDn7y7o+CU7Y7C9LxFFFljwf5wGtYycb7yIrR5w6eiL9F+r26dGwTJOQhS.by9icaerrppocEhbfelibwgnsxzL.psElMNrZ974SDiTYaee+AhH5Au0a0DnsJ1ggg1AAA3Zz9f9AAAaxvQrHaSfsbcutsW7TA95xYSlMgIC0Gi510fbZaM6i8w9XyPGb909090bN5QOZRqSrtSmw5fXPm7T0PGWssVRUsLl4XlRnClre+9S.LgsmcEU0JIyIKATxKA.OOnvt10txAjyWjLDkbN5LJH+j6+yb1nU9ij4dJTczymuo2vaPATINcocSWI3C729ALqYz9Qvwu2YcMGw22GAHUnDWYkUwDDKr+f.wRDArD.qnnHqCDDXSOSIBbve0Cl94OICZ6IU7xOeqmbooYJeuXvMNWtb51auscRYBTLo12yCjw8LW+N0NSlWv7Yn93cTMDXr5fEQCCC4XG6XTqVMKCCAbyFDDjMJJxoUyVRPPP79810nxuoA.sNsMfuy4+7YmBKSTUsEwaDqnTUUCy1pOxG8dO+2yFo2NUpTIkYNiNGIB9nZ3kiFCLHHHXaPRX.qtNHqFFFtbPPvR9Wm+RQQQKA0WFi9Cs501nwZ.a5sSLL59N6f+eopM97gSoT0j5BPc50qGhHHFx3jvnCCPrIYRMWRWWrzS3I7DJWGl719ytso.litTUUslHRULwbLAlxzww222xy2SB1ef3m.zIPVPRaZ.EfZ4MLNIzgZHlNYR0QO+ds6YOoyCfG4i0mZ7CKbp5LhTUHMYVOvC7.N+S+S+SIfNO2YKFoy1wNllljNfQ67V1xxZ4nVsVMUS0778FBnAAAhppisssiuuelff.6jXFLwFpplzAwApxQO5QAPt3J9tKIscdVOsLIW7TjE.0rdmqpppOtG2iaHykFuPUGLq0UpNTVDYh5hLwu0u0u0T.y3Byt3WcwYvzs9l3e3e3enDPgq4Ztlbf3.X0oSGYs0VSH4eZxbeUUKeeemfffrMiZlqYynTemYnNNVCGZC0GsIY2cVe6rso8yo8re1O6juqqJ0jXfAW6Ucs8ey+w+w8eCug2vfG3AdfXi315cwNedz6y2.nT9nnnIzN5b.dCFLH.HPDWu6MJpFvr.SpZqxdWmWQfbIfTuSxT16k7fwcgYfRqSY+bBTKk4bIsY5NNTKsyGsCaetwa7FyRUxu4laNB3VpaXidp9g8Y9LeF60VaMKeeeqVsZY80+5ecaU0LhpYemuy2YtnnnbRrlO4uubXX3jf6z2aX3zXX++jPmRTm7bwWloWxZWNCXRpktoeifRA8+6969KF.LbgEVPDQx3B4YdJ3mhb9YFjyY43chDm.UWoCbh+r+r+rEmbxIOYTTzZ.88Lzs2INNNaqVQY.rJTn.ITub3pqt530a1oKFZm2EHZ0xDI13rOAP888GVoRk9AAAaKVxlQQQqUud8UDQVNHHXohEKtRXX3FPm9.CMsvJzpIYK06bGjy2pB3QAFppNjNLnNz+U9Jek8AFTOYyjtcPgicQctauSM+a8Xe7O1r999k877lILLz6P2280.XWQMa5Ccc+4+4+4mEXJU0IbgxzhRFZ8abLz3TEEwSOazemfwImg1lv449yNs9upVddFbRRh2PFLHbGE+uFRU.ZiTamLbYWKQY085P923a7WpBlwpYzN5L.SIhLgpZILAIlCvw22O84FmCGFkEnPXXTQyWCyEDDXapA61w23Mdi5wLLLxxCroYhv48niZdJf9wOxQFEDY6jwCUUmmyy44jWDoHPw5PdH5TpW0yywLt5IXXhVDrNvpYxjYUyy35lgQgCFLXnZz..MCpjUDwILLz5daFk.Jp6V6e+MR0pnMMs+4nwZ2rWxuYhKfsWVHYdSKin.lCHeWHeRc6modcruga3Fr1yd1SRq7tyn2GPAOnD8nTMSfekeNOmmyD0MA5MJfurYyVJNNtXyC0rTTTTYUaY5VNTu78FEURDoTK7JRx7wjVeWVX1ryY.CO+m5S8oJLCTDVrvt28tOUA38h2Rte0K4qdZRMVquta9VLapx7Rd6u82NfKioY2mO6T1He8wVuY0UWUhhZMZytRhxuFFFZ466a2wPEV663N9ea2rYSw222pMXYDX45NujWxKYD.Qy.VIx.5YigZWJZmJ3QFj1UnSL.YyFX2yzcEJKheELAlkqyEFLrQ.lnpZSarSFysZ0pk366MBnJPkff.Y26d2Vc610AHaTzgKBTz2+ZKDKwN.Rqc.Qyp4HlBT0oQiSIlhKJalju5IhEzNkoCfYiiwP6DvVmVOx4cMj8wrylNlglTRlitu6mpYZJpmWfBLLLLrOnaYRthrFnqopr5G4i7QVIQTlWGZuIXJEo4SzcnVr6gom0G5r6m7Rw4Xidlau6nIDo.tYWqCBzVqVsJpp7NeyuyDfR5kCnHLmQTMgJcnVEfJu5W8qdh1FejyB0pUuNd.9.9pp0vrlVQOOuLgQFcGJ7PgVggg1ppNAAAiR7fnZtvvCmMHHHaPPfCcw1j0+dIIRYxAmHIFxi7MVxsFOFi3jt1hN27jpgdVW4Udk1EJTHAXw4SV2bumOlZBf55xPLIqbMWXoUWc0kP0ke0+R+Ranh1OJJZnsssl.Nh.XEFFRXXX70EDLHozc1pQiF8oapeydw6YO6IF7hOK5evkSlrOSYR5LYBCHu0a8Vc7R1zZsNXkz0Vza+1uck4S2zaOC6Rndw1PwO0m5SkuC0y+68686U.nRaUmX5omdBQpWFH+O5O5O5X95L.lpfTrTQ.CboryZhNtPlu3W7Kl2RkRhnkAJ8Q9Hejhzlb020txBsSlG3loyni69FWaYtf1G+i+wAP8fX5wfZIBe7u5McS8UUGd7ieb0adjjV96EaLgljFXFKK644MSqVsbUUC5zoSinlMCftd.o.lLkqHSPaJWOIAf.4ZjxDuibFBi9kcajeO6Yz2JUAKU6jlPTaUU65fCcwo9XLvsJ37VequUm58HStb4x9ley2pgsIsMI1RDovcbG2QdQpmsb4xNQQQVdddVYyl0ZlYlwod85Ye9O+meNU07W2AZT.nXXX3DhpSAclMHHXVfYNdqiOUMnBsovt2QentrE2gKauvSrwVH0zBAilkg+D+Du5g.wyN6rhpZ11plmZTLxr.Tp3Wd9dvHE.lsfdqBbxa+1u8EJUpzIxkIyJl+eF566Ke9O+cZ444KCFLHdqs1Zf3IocVmjRvwcHfFGGe1Vv+LV3Y3vgi.Joe+9366iuuOHl1eXx01PU0An51AAAaDq5pf6p.qGbffsgZixp88ce2G8.EOzVm447ak.kjbsVe3G+i+wGPM52F5+W9W9Q6Khz+m709ZGBD24h67mb7ZG6k.7ze069uRTUyDF1pRlLYlc+W6yxc3vgtpkTMLLbZQjIBCCqHhW4NPYpS4q3JthhfaAfbM2QbyNEZnw4uTN9VgctPN97YByZVbD5QqVnppCEQFz0kA111wplvn+t0zdfPUr6ZxbTVfbcSxZVKn3u4u4u4DppyHhTc3v9UUUmES.gU.J366mEA6VsZsSaWDM6DSLQ9Z0pVLFJEDr+h.E5Tmbfal+1+1+1L6td8LvbYaAYXZbdnGc.SMd.hw6MMnuYXXMylCDL0FatUVYk7smibf2o+L94LXud6T9ba.r5sbKGbEfU8882HvOn+t28t3q9U9p1QQgYTQy555lMHHvQD0pNnQQGdX2tLD7F.Lr0rL.7GiIY6I87eYmiXicDHIHEQDm69tu6r.4UUyWOAHsVsT6efefWr.PWUA7NkZiOR0BppE+8+y+eTDpU4O6u3uXxVpN8FarwThQGhJpplavfAYCBBxGDDTJo0MOIzdRee+IAl.ZUNJJxjIDWxMGjCVH+7ySdnZge3e3e3BKVkBfagicrik.XRiGs5YRLPrpQikQ11CukCdvgjTVCpp8gNCZ2lgf+4CThwlGNoEfU6SUuUrP1gU.phkZDmuLQQQ4BaFlKLrY1a5ldcNhH1Ma1TvEY+MZvAO3qWBBBrf4D.qEmFIYNWpuhKGXWxom8eqc9+5kse+9EEQpnZzD0LrNJGMvYOWX.QAlSEQne+9iZ43iS4qTpoGFENBfkDwIuPXXXYnawfffDJE2yoVZaqeFxVCxA8x0rIYf8bwNOSAzESt+z1nk.ocsDUDIEvij6am3BbO6gzEVXDinF1oN8gZ8+LelOyHAo022O1nkFgpuuerJx.Uksihh1.XMUjUazvesq65ttM.1122ejtrbpIs4XxLo2WZf09t7YMMg8kHb0r6r.Y6jD7dWvZ6s2NsrbrOzgt6z55uDvDv7o.6NKzclDfdmRUcJiext0Z0R8.BTU8.pFGGOouue9nvHaQkcZoqhYtShtkjIHHHiJhSPPfSXXXJXNiqASZRGg5aztFzoGmrl1rPlemm8DQD6ekekWyottDG4BMeNtSGFB9aCrdGWVoRk8tzryN6Ju8+z+z0C7B1FHd3vgRPPf0W9K+.VJvG8i9+mBLr6NLCeKfsZ2t83kk7Pn0kKqgc51H.JdHidr3rDjAlI6q6085x0Bx8i8i8iksqIa9VhHx26262a5mMaL++4TsUdU0r+v+v+vYf1oyMKJ0kR0EonpsygI9mzXyD+DsSSTLtULPvHO7+1Ca.PVDmCEFlawEWrnBkCBBlHLLbherererRMa1LsyZkDabmwJy0G5h0O5Xy2b0VI6+vTtHUiAiH2VtbYo0YBVw46Xm10Aswir0MfeLYLTUj50CBB7TQpGFFVCXtlMaNcXX3zcSXNcaXBLfdVpo4uMeqScS7Wtrd13lbzih.9BfzqJhHd5ccW2kBve9e9sIsAabIaaHG3kGHWuj0ZZa7247FdC2XZBtJkjnzRujWxKo.zIukkU1Z0p4DEEYIf0hKtnU61ssmd5oy.j8i9wtm7MZbfhAAAkTQp7W7W7WT48+9e+S75esu9JxPmxcgR3SgiAYMZA0kufSc4NfIolZz3.hYAhg1CqB5m7S9ISnWqQ0jmCJkTmlN6aemyaZoOrODW1FlacOSaZZQQjEmsZ0khhZsNP+nnn3m9S+GPa0p0PGGm94ymeCskl1VeWGXKUaODPs8sunlbXaaO5pX94mmjZs9TbS7o9jeZSvvhzOJJZyW7K5EsNzYsfffMMcRjtZylMkVsZYcsW60Z9LpiVrY7Omey1FcLaLFHG2vMbCCwhAtPenaeU0AOkmxSYT8aZZ8imShWldLiAhaY.gZ6eye8eyA.pHpyryNaga+1u0x111UPonuue9qyjwlLQQGJGPgnCEUrNTF5TBnX0pjx1jbyfYS9vi7LE9c.Sf8AK.8RxFop5.QDihY2YjJrO3s+1e6Cgt.XgCYpB4f4K.TzyiR.kAlnNLsH0mSU00wIqqTWbwTKnSk7dxM2ry4366aZopIhH1RKsT1LYxj2BJFFdnRgggknMEgNIh3F4g4MeMGYm8LUp6jOOWPaz7JS1llMlEQ6lLGKEzjJUpjwnuFsRYPzEUMX2.5CdaBrxMcSugk2XqMVFXsHiFkL7G5G4GR.CPQVVVVggg1A6e+1sAqDV2HQQ2q47r..QL4n.QO5oSW+KGbRL103dYlcF+st9q+5c.2rhH4R5jZoFtA..f.PRDEDUtBNlw+j5RQDAZ4TOoiXkJhvhqT7+1uvqoLzch8znwT0qKyTnPgYRliURDubYylMqHRtvnvBAAAkBttfJetO2maxZvTu6286dJfIiiiq.0KRGx+.sZkMLLzHLrlLxkM59ixd7ieOY7GADZyG0LZhoIVjZCmKICuppCdc2zMssp5VhHaDFFNl1TEct1LS541xrA+kr.rqV8znFrJ6jAPQMrWPHGH4Qn.XDh4fffLhHVzA5Bw27Meyw.wG+3G1bMeBT3nOZ.g86D1XOWr2QkJnQjWwg5jA7x0HSlhIAwUrKjG7xPSrN5Ewy1v7w.CxrqLCqk3uwhQk5ZBXSlw+j0QDE09n+eOZdfR20c8OWJoyMk6ttq6JyG7ttKGvKCKRttPtNc5j323nWr0ms.P0pi.zZnuHC62u+HgAcj.gFfByjn4kmWSgdC+.efOv.ZyVP2M.1LJJZSf9shhhCihTTznvHUTc3ANfeeU0s.13.99azrYzlMZzXaLLPwrEKOrgZ1UGCHqEAq21a6sIzD4xJsrdajY.a3XNppYqUij0MHairYyB0yppl6M7F90xiqArjDPQl4jm7jyhQX4q1R04.2Yw.hxbCGNrlHRpVRLW+98m1xxpTqnnb9A91AMBLkAAXgZXUfuuuCFVVH.oUgh.XGFFlEujMrNKNvLoBh+2j.M3HJ.UMZoj709ZesjNyRUAv5ttqujynxWyCanw4ymU50wPHxjzgNrBzYob4xsjkk0Z.aKf566KQggVW8UcUVVhH+1+1upQGWymeOEHtd85w3lvJ6FeqSi89VrcJrIDvl7jQUMKrnojVqQgOxG4ijGHquHNc5rX562l4LkZ4W3K7EJHha95hjqd854vTlEEf5E01Zo1pV.HWpHCqpZm.NmHfjbEHHfpH6865wZIp3Hplc+6OH+MbC2PQfRQQQkCB1eYfhVVV4ntoyZYt+6M5XxivX0LVGy3gZDjeU6J.VttXuXhFmYVeowEkO5Vf.yJzBq1P1gCGlOvyqrpsmPUcpomd5oDQm7578mrQiFSEDDLMvzMu+lSGEEMEzcBfxG+3G2ThNPh3wumuSvv7uQL4TeEYAf10vNwWv2+2+1.a+K7K7yM..rHSUn.zpHPgpUIEXLGCayqmCCHwUDWYBU0JppkSJ08Lc6101222RAADQ2AT0LW+ANP1vvCkqYyl4+W9W9Wx+pdUupbeueueu4tk+jaIaiFdYihhxvvzw4dWNCN0kzaL7QiMJqZ8pUS+Q9Q9QXvfANhH4u6O1cWZ9z5zBxr7C03BEfSLc16.X9sZYDR1knFmjXV122a8nnns888GzqWuASM0TCNwINwV.qKhrhHxxX5VOahIXaz1JWfy2HqUqV346ct905S7J9tURXYhp5Vuu226ayZTaiVMatUylMGzrYSswANf3k1kR.g1Hs9V+hAiN1MabJsLBk1l1mWJSH9M9U9MFI.tmv.30ExgnxLIrAnZms.17C8g9Pa666G+676+6X8LeFOyQ0ndTTj1QUsYTj366mACKIJe2G+3UvTewUnW0xjPOuEgrIHoe50X2kJOTepWK66g.PAughHCEQ56gWZKFaSQjMEOuMihh5ShHPQKb5pZ9jMdTItk6jjDzWanJzwEntpp21GeaWLxS4zX.LI+7yOuyHAxamt2gSylMS0OjhAAAkZ1rYYfR+D+7+7E+G+betczUj1iZsaOZAMYmwhYVH486NJPROwSSpceK.qliIddW.KtIL.ZsAFfNOomq2RXpe+M888GdG2wcn0pUyjnFQrFNbnUzgOrcRsh5DFFZec9W23elrVZ26Vle94OMmZWVXmF3NGgEM+rR8D.Qb6XopNpLBTU05lxkyDvG3zwyajdm.jmtT3Jdl+nkL5SBS2tsN6Jqrxrqu95SIhTd3vvBhH41XiMxgJEhhZVht0q73dbOtI6BS+xdYurYZFEMciFMlj5sKAjyy6pxZn2NYqQMm6+9ueaeeeYW6ZW7kZ9nRQ2FeiIwbB2APuAympGU9FPIk5x5.q8o9Tep0vvLo9yvLmuxuR.jiNFqRr6U25fu4CZgAzMI8Oy22mD5Saa1fkNZicMihxX.qK.f3Cdv25nx97.65.mM8JAtzeiFoyyDL0WuM8lwPo3VpCzJSaSa1M+vgCMrFxsUxyZdmsmoFeSk6TVrsYPWXX+98i87L0LupiJ6IKPIQfZzi90Ol7888+84.RtGyiY2Et2jrt9TdAOEmmxS4o3faKG.m1sa63555.XUMoEsy4947S4+2pm6H.SZACyjIyvwKkF.kvYTXwKTS1RAh2M6dvK3E7BR6Neq+ze5O8zN621JLPLIXIFwrIzCe3n9AAA8A52A52nge+nnnA2+8e+wAAWmzrYSKZU2pYyCY0K8yURHI+R+t+RIm28M90vkxlvQPVDjmzS5oaC3X0s9nRFrKTDZW7e6e6eqzt10tJSmZU.l7U7JdUSIhLyTSM0b+LupWUMREYS2NUEQlUj5SaYYM8fAClljRKLSlLEAx446aCXE0LBeeeIHobVSZ2vB.pZt2kp2bQQQVVVVYnUs7.EXA2bvho5wUp8MxX8nmMjd0z67Nuy3G+i+wiHhnZ2TweMKsMYglVykAZdwniIpmggkagwG5Rqu95K444sRqvVa546O3C7A9.nfEhYi8G5PQNAAAYwTdmELh5bRWrriq431buWtwtjS2e+nRXlVoBqqQGbztUKiIa94aAYbcmIQaaHOyWsHPom5S8EUD5VrCTnc61ERzvoRQsOTp9dktel70Ma3MCPlnvHaDwzZWMiXh.hJhkJFled3CGkqYyvbXXKZtvvCk8q9U+plxttcc7884+6cEBzhDFqHz3QTLLmheHskR2w.PR6Tyd1Ym0AvtGXAMu3iQZ1ERFqqI111N+W+0+ulqUqVE.JTnPgBDaU3PggEaFEUAXhlMaNcCuFS+09ZesT84q30uqqOGP1Nc5XunIwVWtDeFbV.jyEi3KYz+J1pspqKhrdBishoE1ule6e6bow+mr+mzjPTB5TdkUVwz0Q65NiHxzjLVgYdocXBaLQTKKQr.wwjT5nb.4dvG7Ay7C9C9C5.XEr+8SBv6wppJcfEOSFjd4Tbw.+6C.SNiLoUEh0NlZfNIq3Yu5q9pKTamZXKayyelGSNVGYTaGsJrJcYYrXYL.nrYbb71UqVcaaa6s9B20csgp5Z.qbeG9vKKhrxfACRaEwor.9bNIQUkEWbQSaFVgkWd4wnCqYmffDCLTPFDDDLPDoOPeee+s6R2sesu9WeeKKqAWeiFwzoiBiJiG.T2GkCvOJLgllwWOOSshqphmgJdCTU6eS+t2z3Z6hdAxhl49whI.lzy.Lvy64871RpKC98+c98UEUZ0pkDDDnWWPfJhPCee6Dj4KAL4SZW6ZZfoBCCmrG8JAjuYylYAbdWuq20khs9pwmqry2+PoAI2ZHyX1PPKZ0mD5sppt4y8.GXq2za5MssqqaLlOWY8EIuHRQU0I5PmoAppp5pp58y+Jek9uzW5K06K8k9R0yjIScU04XL.S.xdxSdRGRxjQPPfs.NVVVNXnOZ1vvv7VVV4qC4eGui2Qtm3i+wmsYylN.N+F+F+FNt6.VxYCzjK9wjEMiE0oi999Puugppa+O+0+m2Bn+7yexAUgX1occe9teltlwfpiEr2INwINYTTzxAAAaDEE0+k7RdI5W6q80nQP.KrvBr6cuaq26688ZWmtNgggVAAAV2ywumzLj6LG3vwNl8byMmU0pUuTZ9z4yNaA5Mx41rPJIRPaqHhfaRoCrvBKnsMJruspZFWHmFEkUUMqIqZF0X+S+o+DixbKvLkKWdlhEKNsZD90x.EVXgEx666U3AevGpDzdhFMZLUXX3LgggyLr+vo.pDcnnB.Yqy7orQvpKcstlq4Z.H1EFznQiA.CaznwiFQ2MYyrcF019pAaqQ5FhHqos0UAV8u487dVi4XCfsWjEOasW1we10Z1wFSaSa4l+Uu4zykAnDRJQD0jWPUO06C+uui+Z4Nti6fvCYBD4sby23PbYvAO3AGzktiqSVWpxnjyosGyWDCsxWzl5XKhLpz.bEQRXeYpNmfGsN6GLio.5jSN4HPSbgg111wCFND.Kvb7ihhb.i1QDDDvd1ytoYTS.UN5QOp0wO9QsZ1rYp39pzwz9vup50ELZ0qzidWrrKwBvpiQ2BfD.R7RRlfpZrHRhXnu3E7yWxqgGiiktg00qCqYaauVTTz5sZ0Js0aNTRAiQL5JVXX3.QjggggCCCCi8880q4ZtFML7vZiCzfuxW4eTazngBnc61M1zsLHldof57PeKQv3+VfofwY1ce2eNQDwpsQiFxqpVTUsjKT9w9XerU.lP0NSfKS7Nem21DXRvxjuy2w6XFssNmHRUssNKvzqt5COAPYaa6QwUxXkUSTTjEVXKhXYxRuJQQQRhXNqhvvjM3LX+AAC888wyyKSylGp.dTB5L5Xtuu4UpvIfzzcvS8o9TSiYXvfACTRJs0MV4eKOdjGl+z0bfy04NtEsFoiIdvIKUpzI.NoWf2pcme9sSJ2DqDwdMuuueAU0RgggUhhhJ6BocxDGnivrPR4fd4xZYmdbZV.Vtti5PbN0gbpgUHk6QuJppUDQJATntQmlJBLAzaZpyLPqowHL5S3BURzUtI8q6OgHxD24cdmkIQPzaqsK.j222OiX.kRZ2ps3G3OZvSRY6RR7ZMZDjESIYXAjtA2gP6ALGC9t9tBFUJXyZD22Gw.wm.NcrHhVESBmTUc5R2LKrvBiDVXuKd1qnrf4q0nqt95qKu4+v2rkiiSlvvvbhH4ttF94788KPbboZ0pM4m8e5yNIvj6cuO1IHojNu2l2qCf35Z1UTiy4o7RNaTbZ6yTpMN.Y5XR3qMl7W0WDYKfstlq4Z1Fy3l8e9u6uadQjRppSr6Cr6owjvzYTUmwElsb4xUAppZ64TUm9o9TelUDQxCjw222Jv22x22W787SDVb0No7BcTT64latQZSTy68dGDDDz200MEX9g2+8e+5o843xN6eO.XxXVCEHt2nLK4Mna2tJfU1rYyzcjPAlPW68cA2fbLlVG318LkXyJOvC7.q.rhuu+Zsa2dcf0ylM6pOum6ycULYkdkq85ttUAV6k8x9Y2fj9hc2tcgyChZhHr4VaB.yN2rr1ZqQTTjZDsMQQjXQhGpvfW8q9UuUTTzVITpcT8d92727YG366OnMz2HradCFNbXRfzdZmu8ROaA7jVsZIef+5OfHhu1BFt3hKNPDo+e0e0s2G+cZyxOzE9ZJMibCnVss.1v11dyvCEtcTylw.RbLVgggVcSP0OJJJ6gNzgJfoU+Mwa9c8tlDXRQDC6RpS1FMZX+rdVOK4k+xe4Imi87srAjGg1orYKNSv1hOFLjEM26UUGB0GBLTDo+G8i9Q2Fp0uSmNwFl.3loURMJl33cVbo1e8eyesmHh2e9sca0eOum2Ssm7S9Im1cgRKGmhIHH6L0TS4nh3TsZU6vvP6uvccW1IrLvtQiF1hH1ppNsGqTCRaMh+A+A++RmcXff42u2K9ZW8Tds6caAHsAdg+GegCDQ15AevGbSQ72Xt4lZqdi133EkavXfg8LkVwZPskZznwI788WJLLbMD1JJJZvjSNYrppNyLyPTTj7hdQuHq6oYSqff.a.mcsqc4.Uyd0W8Umc9QLoogUud8tb.E8Setl8d.GvywPKbrSD9UTUQpWOMynwhH5sdvCZAjApWTDobmDwcUDuxINnKqpNgHxThHSKhLCINq+fenO3T.SXYYUILJrbh9kT5JuhqnRXXzT3xrAAAyALqssLEPkTZh1FroGRylMkie7iC3FiIi4IkkV0AMMqM7HADgwVibewLqAj1tv1hHadvCdvMj50WGXiO8m+yu83sFRN6adLYrcur.vevexeP5uOlZ6HBiQshT0fThh.9d9pXxO3v2ycbGCBBBF7S8S8S1+m5m9mdPvANPLF+bPG3lu4aNYswce55bvkCazPAFqy4ZXpjaaDnJXH10vnACR8yMf50iAL0F+44XB6SWZokRtu3pc.0xxBGSa10RrzQcLfZ0pYGFEZGEZn17A7a.H522y+6KdW656ZXiFM1QGIpWOtQiFw8fXhNk4KmqOim0MSkvXCEHNxv7R0TcGHFwfz2hIuf06c587AI5HwlsMIzYMfM7771DXvzSOSb0Z0hAz.+fzNvj.lxRJAbJKnNAAAZzgiFbEWwyrO0MB9ZsZ01Q.626dOWrY5a21omQ+yy5r6MgIqnXXxTFbIuYcJYh1pNwq3W7UT4tu66tBPE2NTVUsTBfJkqWu9DjHL5X1jwTuhWwqXBwH13mq1lonpZEFFZkfEn366qFlLIwpnC888GDEE0uyHeV0rrrrxFdOg4AuB3ZzIpG5a7j4rCy4R0sqZiZsza433LH426jub9rUaQdXtr3cA02gwNty1GX8VlRXegvvVKDEEsTs4laCPFlvtlLMCCKDEEUNJJZxfffohiimrCTw6o5U.byAjYxEvFZbofVG7HIS3i7eZd4Y2oSmjmqbsaaVqIGFPNJiI9pR.EaoZYQjIWYkUlAXtZsopp5bpQa4ltspSppNwW4q7UlPUcBU0JOsm1SqRhNyUFnzvgCKDEEkUMhDtEhozCS5W8FIMA0RS.hNLJDfgu+2+6e6fffM+d122ylf6V.a6NOaCt8opw+1BmYSI37YiVSrUqVI+cAwIhmtHhXMXv.6+6+O+uOJNwyKz2m9w1rN1ftvfhEKNPUMte+9hHhipZ1NiJEXqxG9vGtxK8k7RmLLLbhffcUBHWylMcZzngLl+2gMOyFtvkh139ObdHHSRqmNYesF1NZprTi9Q9I9DehDhSVy1ngITRDYpu3W7KVkZ3B3Ih30VUeQDOLRXeUfoty67yTVMs9aCa4hBIJJxjT+wYOE3HH1W+0+bkfff3id7il3ur1vNc5jDezbCulq4ZFO1jKx0suzx92C.lL1fcS0DzneB6KZ0+ce6u6AhTOFiyK6ZioSE68gtHx9bKFhO8wyaCfUu5q9pWBiCgk888WI46OIvhe5O8m9DppKC0VUUcsa+1e2afIi+CpUq145AQEf0Wec.gpUqxByuvneQTTHHoZUgLPfs+PenOzVppahIH9wN1chMkgQ8sBBB1JJ5d2bWOocsMDzGZMXOe6sVPUvnFoufep+yJzJ1Eh+tmYlA.Ctq65yMXtHhgZOR1DiIvvtc2FXymzS8otEPeuf.UA6f.ur27M+qUHg1Yk888q7deeu2IfZS9A+vevIdtO2m6DgggU788KCTnZax.0r9DehOAlisaroey+c7ENO8MvZw9123hA2orHSsZ0v79ZaA0k986qhHwarwQAWrEwKGzonGTtVRFy.lwsCy8R9IuwpCGNr5C+vO7r+A+gu4ow76KCTb3vg4a0JJSRMW6DEEYKpZ2c94c.bdJOkmhMf892+9sRo1dPv9svPOP022OFWR.xocrKnM.tm64dD.YOG4Q7BklO2G6XosQ4XLAyu4y6487VCZsF3uILSBqtZdw3XezwA72.5tLvIVe80Oguu+xnLhkXQQQHhvC7.Of.X0nQCafL0qWOwoUube4ub6720ccuIfx1zlYPtDBDtS2NCpcl7x4nPFnUFVfLTiLTudpyXQa2VSL.r9KeWuqb.kg1SAtynplPQ81SPMlTD2oEQloFLSMylMRZ2bT44+e74WFnRbbbYeO+xhHUBBBlvOHXJUimgNLmp5r999SaYYMEPkm8y9YWBFUCt1u7W9KW10t1kZ5rJtC.2jLm1abvLdjZJ7PJKPLtDybyMDXvMey27.LrWbzwrd85WH+H.Ggpf9m9Z+MRWGK1s6XLBQQAQULsc8nnnXf9hna+R+oeIadva4VVGX8F99aFdu26fjis0sbK2h0Nm+iwXfDdoZPeiaiCrShf.1NVUMtCDC8hoK8k5xVN99IsAW1h1sGAz9433lLm9gDlzPcani3lrlYXXnQuRTrzDQrta2tNphyW+neca.q+t65thUgAgGJb6FGvuOF.CLykZ2djOhidzitSmn47W7LBrCjJpp32BEBF04uL9e5hppUWvFhrYIrXOm2MqBi1Dbz.yFdpuAvFIkjyV.8OwIVbXud8TAzjr.Jsa21x2+Zchhhb788cLYKrsfo65L.5tMsYSpwVL8z6HFmG4HiKBoiec7sK6zC19zAd+rDL9QL0ep4mcDQxUqCkA2IwHv4S9L99elUdROomT4s2d6RsUsnHh4UcoTGSF9mDCQUlFX526688lxLtQqEIRcilVEFI999hN55SETURzjNUDhshshg56nYM3pQQ2mDGGmTtJsxQGxRcb3huDSuPVBKBn+t6x1PvlXJi2sRZPAVhHY6Yz6rbzhLor36Bb9igEF.02DbWAXwFM7mud85KFF1ZEeeusSzMnrVhTPUs7a4O7OrBTq7ryNaovvvB7v0yCcxBjYoT+memi0uiGi0oC34Y68l708YkHz31PKaCfmthpsoJX4Y.qHKPg5hT526M9GVgD1i.0l509ZesyppVsioaKU8s7V9iliZLiTu9zhHScEWwUjH.5TIQmIRALonsscNPxfhcTXj.pooQD3qus+GuMc4kVREDrLO0pRrL.Xye4e4e4UBCCW9m4U9ed4nnCuJvFcfsfNa+t9ieW8YFFhgolORiIVYunvzJDp0.bS.Qzwww5sdv2pvNc9RtHSvkR2toB0+l21scaa1pUqs888iAr+E+EeM4BCiJ9d9e8dJED3WAnxwN1wKKhTJLLrnKjuQiFN.VtcQAuK2XjYZRGG0IBcccyqplW01YAuLfmippMz195tgqyVDWGnaVWCi5JALkss8btcwSUM.y.ehnU2tNF8ZZps1ZqRhH4hiichZ0xRDKAPhSZa8ILSRpUqVhe0CEC0Ftmcsm9ggg8a17PCLfR4ECyqyA5t24yv3qaCem4Y7Gw1kEWjW.6TB3eFH2hyRYxvTzlTw4pbRz8qHhzEiRXdRLrF47E7U5lHJjbb1Mv2ip5SrUqVOFOOuIA5Ktxhz0sCzY9ZvZcqik1RKKhTCvyLQjZX1jPALS1OiIKsZ0J4mU.golZJ8Dm3Di0wdXYfSJhbx866uz8zr4xhHK0ue+EylM67999KTCV5s89euq7heMu30nCoBPaZs1mpb9eqZAhz6E13QFZYpQXLkBUQU0bhHNIu2MSttVFXs8Aa9Pm+qsz6EEcM2K1SGy8h8A73aE1plJZFP2LX+MVJ7PgmHHHXonnn07882BX6nnns788WCCWm6ALOvI.VYZXiRv1M4rlw3ucZmty4SOnAcO.aB1IJ8eAlixL+bkf4KUCJz0kr.Bc1o2rqplMgdcEUS1LlJorabAb2byMmKe97U.xK0kLZacDHI9991I0csBnBLTG0YYzUEwZQMV690O5WO5E78+8GcuQMaiv7M7Zr.lmyVgDQPlD1e8i7ib0C+jexubJc4uPNsF+Y7L6FxuJTbQymk7RUwg4qpPu9UgM6MCaRQ1flrIW316qA7CHaenRGnFt7XP4Inczuaf8DEFMKBY.1TUc482nwh.c6BKzrYy4ar+Fmjt0V43G+dW8520tVqSMVGk0oGqatUwkZsY3S2Ik09.qkA6lfMShCkv9e8y9uZ8DdBOgTsAJsyQLYMSqdYx1P4CbfqJ+8bOe4rhT2R01.DK0kXssFm3PMahS5IWYkUlsRkJoqIOMoc7Df+k+O+eF7c+878rUTTzV2wey6Yqa5FeCaiQXsGHprMVrtp5IBBB57w9Xeri+y9betQcMOCuJywVsdfVa+E9Begs+u7BegaD5yFLjMoynw7Go.EO9ydiJoua8Vu0IdcutWW4pP1tpN3G+G+Ge8OxG4ibRpxIoW0UgdoyuG+d73GqjVEISiwWx9TUupvvv8YIxt878mJJJJKBZ4xk6u5xqtNBKALePPP2vvv1.c2ePv7JbhtFv5MZqU25KAsWwC1rkYc9w2P6kx1nwGOHSKHKyggUf8zIFLXvDNNNESnx85hHmrNL+FvRKwHvLO8moFOnxRj3u.XeCGN7JiUceN116JJLZpjmqAXPhlistIoGrXPPP2nnnl999GuYylMaznQmlMatzSpQiMG.86YV6ZaZvVzzcKnSJKXNaWOvoAJIi8bkGLwQ2d6xYylMKtL3q9Y+pq7DehOwSh4d75Xtmdt.+azb0YfoVzkF.Ow9M6eUNNNWQTTziAXxYmcV6b4xssQz50UEUVyug+ZgggKQhOQU0N11z06ZaziNr.F+iqgYMrz0p+NIKlFerzL2YeHIko5o+5T7af4Y4JXd9qJP8u7W9K6dUW0UM2wN1wlnUXXl+Ceeeewhqrk1Q2LojmIgQaUv.rxTIrCHEjjczooDVlbO2y8HW+0e8LVWMz32Tjgpp8Mk5YySRrUO+F9QggggWWPvBBrbGXabY6V2Wq0hiiWJHHH8dvpri+yuQ7gLN33YwDa1jtvLsUcZQjxppHdxpzl4wDizI4b+rVpkF+cgEqyzLjFzimfp5UFEE88nn6QPlQDIiojAzkzXo6fgCZZmw9que+FGSfvNPu2za529D+52xu6prvH+mWLwH7MS6bAPx3m+S+4aALst5i3gsFo1hIU+mx3LvzppyIhLopZgDlco0.qNownUi7HjSaqYv.fkElxnofHxjtvjsUMMwVoMu.i3ZhZCh366KI.ToX..MNJJZHl0Q1.SLY8BBBBCCCO90EDbLKH7dCC6Dr+fEwlkIZz87wKe9K16CiXBQx02DXVGtJlxLJCvFhHKBzwCVzFVq4N9tNWywblDJlElqmYM8u6gCGtu1sa+ckbryiPLJaEDDrYXX35.qFDDbRfNMa1LpQiFFwYwHQ+KCrwdg9G4R2RZc707rbgrYLc5Sy9qbIOclKKLedWnPGWJos0B.NunWzKJ9889deCRJ0yLXzMmIRVGaD6jpKRwDQDtvvgCyZaamQDI27yOe9YmMmEVFg...H.jDQAQU1hQQQoIoxwbAIwJZZGtZkJUprvJqrRDPXvABh.5QaVLLLbwjw9kwrFVZL4oymtTcL+rZWtyvD4z9dYQlAVfgltESsTkfOEE8sqel5Ix4yL2DcY3bL2VXBb3j.K544sXTTzh.KRWVD5bBU0k5Rs0oMac0W8SpOTafoTIX3RKszvOzG5CkN43LlnDEEkJ5bitrNwhmH8GUAI8uYfu+01+PMi52nwAFr+f.8w7XdL.PTTj1k58ew++7h2jNrNTacnpoSNrmus3zYmfUZQLL8PXNy3cUDQDGWS.FoheUdyO23hgxooG6gcXls6.a.0WKNNdcfM8B7LKHnRovCENEvrQQQy56ecSEFFVFWJt7xKm2ExEEEkOIHnbldS9L1m.jlMFcd9NgctyPVCjO7G9COxo7QopSGHCTKqKji4IOL+++T26dXVxUY89+4sp8k99tus20tpZO8DfNgv.HYlDhBQdDPOh5Q4lhdTDOOpXHJGMBIA+QPO.GQ7mZBGPEMFQdziXPOnbS+8fBHWTHQfvLQEF4xDgYlcU0t16998deod+8Gqp18t6YxLSBgDx54YmtSOcWWV0pVq2022uue+Nhp5XMo5DDy3+Vu5aabvYbvYBfo9y+K9ymQUshCTQDoLodTOl.AGcngFpucxkBVReMFIy5MyD+KESwBnhQL1MMkq65tNNdPft0FakTysVOvoWZIQ.9l6wrbH7Q9H+aOXFGtu.UNCS2aIiaI00SjNZKsMzpCPRKl05Y7DeF4nN4goxr6yK57b0gj34HSo+WilrLvRgggqN53it4w7OVaOOOUDw5xutmStnd8JBUF9pqUajv6Kbz50O9HG5PGZjXbFglLDsnva+s+1ycop96OB0NXVYSCbdd6SB10mI0xTWk7DRgq3JtBy6pkonCNY5QR9lTsPjpEo5SXzOym4DSHhL8W8q9oJC3HR0pZCspHhWbbKevolHRsm924S2ezQGMK6EY.GmUi7EFehIFJLJbDfwt4W0qYhvvvRgggSJhTRszRnL9oO8om.nzt61oTSiEANpKjmE.WW2d+H+H+HcCX1dDRhQmKNxAu2en0eMC1N3j+U+pe0CALRKpNlXrOuQmEFlVjGZcwDQbSfqm6l5TKKKUSYUhHROT5Nw3SzVP5u1UEprquueaee+Nw3n22YCsBBBriN6YyQSxYXWFVQF1c8Xor1zm8fQymtt3BzkEniHxt4ymeaoprUmNc1TDYKfcZvzcWE5AG4AB7fzONVPECvIU2SaIxYaKgggBBoBZnfQTm098W999ZXPfoTGqPuqo10zCHoVsZZDylzBz50qmTFTSs8GeglOSOv2mkk+dtFG5pcDzofegdppp1PkmzS5IsWoK5btrJ7A3bjrDNcHlcHlM61MYSLfsryYO6Y6s3hKpgggV99d1.48p4UHHHHOFFWX4eLeoVsZnpklB1t4yTjTyTtyCB.vAuudjrkN+071tPdNYlEntuxGIc9siXd9OsYdlY2qjHFGnzS8o9TmBnzzSO8neGunmYQQj7ZrlmTA3TUcDopj4nbYVt4HPeWlaz669tur4xxCXeMWy0XktY0DRiayyyqqpZOw3BQIpZJImzMw1qIz8DggcAZGb7f1IIIc888Mf9LM1SwTObx1h9y83ZJCndwPWohjEabNMRyhQqHUIuKtWr0QSG+UqCMXaZwZPkkDo5B4ymeYQj0A1USRRY6sXaYaYkOusTy6XRSP96N9myFH2sdquw7rXe.EyJc9Goa6MF5H6Su0tP8+xonlEQY5Bxb1fQreYVy3upvvokw0nMZzXr2467cLlp53MgwqWu9XppisyY1YLsgNZp9lLtZbqooDQlVUcxFFM+pOSfAFpa2t4877rybasAAKAnWXXXmfffcQ0c08rw4cBBB1x2+Xq2DVqQYVy22echYKBIyA35v7OnAKYf17l9xpl9OUU1byM41tsaKQDIoLj.yRDH0cuzliaUJ2sErC3rdEXEKKqkA1PUssmmmfRAOOugCBBFAXjUWc0QBCCGILLbzqtVsQek+R+R8AYZFHOTJ2o9VO8K7fs9IVHloxUGJ7RdI+jEcfgda25aanT2ubHo5kODwFiV3c9NemE+q+q+my.ycTfRFGwT565WYeZnZVhrl7q809Zim96O7LyLSlLVjCiFDgmmGpwasxLsZci0VSATe+i0qRRkdzfdP081qs4Yqky.BeOoIpiGCEuxi0ALA1CYWKbwBVJqSuGzrc2tc2Y6s2dSQjMA1twdnjm82dwdHkb3X5s.KXz3.WVUpJK0tc6E777VXs0VaQU0kq.qZbOglsA58u8u84TUiSxD0qRkJ08E7BdAcwb9OGTa2iVu66NKcPojnjZ4fhzCml8TQ6EDb7jOe8vzEf0Dui50AZrCvVwwwaYrWvV6TCZyo6Og2iDszIUWVSohkPKxpuvgUUG6du26cLfQiggf5Y0+6EGzjZzipK0FXKnwZppqDFFtNv1ppJxdYQ5u8u8ucpv5e9RGy2ez5e9vBiM1X4ZnZNU07hHEN0oNUAi0ntj47W+RZ7v2LZGLiF6SvMcqi77e9OeyOeFroTqTEUu4vw3LJvXUgRhiLMzXlO2m6yU9W4W4lKCwkg3xUfJurW5KqpHR0W6a8s5ppVQUcFQjRoKJmov582TAfcRRhsldML6ryRXXno7eDUQPEUS.oml57QohHXmq3Jth1.seuu2+vt0pUKYV.BLGy5GfsLG3yEqkvQHAmkRYlRbmW0u9ucGQjtUgDphnZqb2y8bOoY8a4BvoyDLuKdcXeFxDttMvvjqE877VZ7wFesSDdhcBBB5AX8+887tJ7Y+re1gBCuugNQX3Pug2van30TqVwvvvBPbg50qmGH2q7U9Jsf58KEfGka6iIdrurbeJCPIlfTyml8mrMMTvoEC0PaLjHxP3vPTswHu427adTZb+i+YtmOSIU0o9eea2VZs82XFRydakJy5qZiZpp09bepOmqkkUYLfzMZ5wdvEOyiYg8rrYLlmm2XppidLO+QTzQO7gO7nAAAi2s61k.lnd8iO7wihxAvrPOilHsPxd2mm7giMYHdKhcLw4usa61JRUF9s7VdUCqpNxe2e2e2PKfSAfbyYzjmK17GR3Ad+ta2tB.JnXDiytHzAnsJ5N99969WdW2UmlzrWXXXBUPwIVw179h6gtF.znnHLZhQ72.2pOp1TNUlVbXt+IMv9JwrS9742EnMdzkxYBr6IuPabWfXKnosppMM1SnoyboDfTcByUia1LAMUQ+ERBBBTOee8qelyzyoI897Aedy50Nz8S8od+YfmjzxnaN5Cnm1cv6w8+IIB5AQl64VljZHhHunWzKp+XjxwmShgN+G24IAh6RJ6MGd3hqCrt.aU6PGp8+m+OuqDOOOBBBrVYkUru669ewdmc1w9Tm5ThpZFHI8777RRE.R.rlaYrpabwhAO+OZw9xAlG6T1QLcNfbSC4vYeymjNmxIKVCFhkXXfgWv.VxnhHiqpVRDYhM1XiwVe80GRan4UUyIhj+ttq6ZHfQeGui2wXZCc7TcOaTopjIvqFmjAJdUW0Uk4jU4HSrWMWipuuW1FVMfAZDJ6zjeIc877xFq2NQR1AXGee+s888yXSh3tD1Kyxeyn7TR0AnlFfwZ0m8JCxHmhyzf7QDcwzSjzwz0GXMzlq.wKLyLyzRUCSd8782AnqmmWhpp54UKIH3D8.zm9wd5.Xe1yd1LvRxAXe3S9PRb3en1NGF9ld9OX4PevOoZxV8r0UyCmIODVvAFhELVUcCC6llVUcZWW2o9Y9Yd4YN2xn0pUaDfgKVr3vhorkFIIIYrphLAFlojwLyRQQQ8c4QfbMa1zL9PPR2TaevR.5.xtdd96hHaaIx1999aigoIaCwlu1hsYOFwNPI38Mh0NeJYNPnAF8OSDFczQStka4VR.zV.vBlmuQWRisUNbqz43hWuIUWVDYwolZpkDQVOJJpsHhXhEig788G9G64+7GIIgQ+G+G+3iGCS71+c+cmHsOejEggfUOn6M9n09.NX6bStkC1TaYafbui2waOeLyV3FuwarniAXygih9JCSJnbMa1bDHdr+KO6mcIw35MyDYzEmYUUmsc61SiA.kLwsdLfQle94GFCXwCkjjLTZeoMfDDFJgFsuAATO+iYFSXYk1iESylM0ie7i2KL738788My8EQxYO6Y0rnSd+u+2Ov9rl9uUn+9h1drLfICNwl0opgEQ8Grq.89LelOyt4ymeygmat0A1nUqVaoplQ4qCdLNeMEfSC8fo63BaQDqRLKTnPgl.MKUpzh.qzrBanMzc.5jVSpHh.FWJIql6ZCzIIIYe.lDFFlDFZBPQUiYy0Wf.PTUSxNFIhpZvwCDeeeKeeeoVMOyByGyuWJEz2EXWGGm9kBP8ibjGoo8z9nE6BttVpp1REICg8QeZOsm13evO3GLCg7rfMtPOOLGu5ziFzgT19jKWtE877V.X0zZ1lzi2HW+0e8iqhL1wCBFtVMuB0t5Z4hRc0DfbyO+74MzXre1gdzzivGbyr82f+ggTufnrLCHrH1rJ4dBOgmPQU0gUswn.SDo5TZrNS850KeEWwU3npVUU0UU0qoiimHhqpZ0a7FuQGLrLYZU0I9pe0uZeaCK679F+Ue81.Vqt5poTHQYokMRlWylMUzAYIk1y22OAQ546620T66FPAewu3WbO.cAWWqFMZXiG4bNW.Sdv0NIIYA1Cz807q8Z5.zowrzK0EKr08blkB.4OYsKpEhmcszCn8G5C8g1DCMnW3e3C8OzxxxZYWW2M8886566a445kat4lqfmmWdWW2b24cdmVMbvxyyy9K9E+h10167YVBI0FSeTpsuf5Nxd.kjEjeF3H1W+0e81Xn.b+emJPwFpNjHdiTEFkXlPizIdcutWWIU0IdlemOyIDQF+seG2wXjUK1IISMP4d4l90Aso5hozC0BP50qmXaaagZ1jijdsktPcwimo9899CCLxy7Y9rFMHHXzZ0pMrqqad.YgxnD1+9874DSOjBBZp9fb3ZYkjXqQZtjD67.4e4u7WddHNGf8YpgvEVSdDbPb16c78MWi.InofEjXJePfcCCB67e6m3GOILLDRvJ79BsHFR2vQuzMH2y8ntYtXx2xSq0KPaPVLzCnWEi39k.U5oppDhRqGb2ihiiIeXNHVVGHjGA9Wtm6QEHw222HZ1l42zff.dbG5vDaxbVhCjPL89N+N+NGfgnF8rIp1kz0zAY3U13k92uhHJf79deuOaLk3PpsadjK97W6A3zNXnb+J.KO4Lyr13iM1V+T+2+I6FYJSDYpoJIW1kMGO9G+iOY94mO6duap14noqQZ6.4NigEl4N7CfFZ8nPK6bao5hBf0u4+2+Hg3AYMW+M7ORcOOCaPpv3kSEjZ03PIimVdpC655VTDIa9vgdouzW5XppS7q8y8yMYZ4SLNvnep+lOUVxENH3L8O2dotIAfDDDH9G8nfAPzjqxyqGjBLJZafcSAGYGIYOFkQZoIDEEIQfMT01Y+yi8MZ+eevF2ZqsR.5pp1wTtLRO.1d6ssbgbKB4fR4l+hOOZ16tokqKq.rvMdS2T7TSNYCOOu9k.QXXXGSe.cSLrNQeOum2C.xgNzg5O+cMidZ8v488kRqebnGF3qqp.Glib9O+le17Xyo5mjggXOVHMd7ryNIvLTAmzxyu5W3K7uU4u+u+ueZ03XbSfYb4noex.0aTKKqwRK+lRoel.XTWW2LFAmKYOffALf.mVNXFcjSjtf1t1U6uiHx1.aEDDrIvV6t6t67k9Reo8.GY+reevOOTYUldlRkTfDwQ5U0r+n1X9Z2ud8u9ASd8E54rY8ASxeSEo+FKCzZ3gGtkmm2xdddapp14c8tdWZyFMrBBBxcO+q+qErrXnW1M+RGIHHXzvvvLw1cXfhm9zmdPAa9RY70ijiASmW4vFWlLlbT277tToRVp1xFnPCUGVizQDiH.O9ce228Du1W6qcRfY9H+G+GU.boxdwhIhLcgBElTDY7d850uu.nfHRwSbhSTDnnkkUQQz7HhMJV9oLxL6ZLH33hBxoO8os787sCBBrArcbbvyLWWGuq5p5.z8PG8P87RGa8BeguvCFqxiIha4wx.l.CNvsNYBAI.JSOcmu8u8ePiNYzp05.aTtb4cDQ5hIiPGbx+KBx4K0MxrP1pPkEtwa7Fi.Ba2tcrHxhzj0SoL7t.sEW2NppcN68du8yTFFQZsikkUBoNLAlI0xN8lrF54lZyjZpH.JpZBhII6RMLLTdWuq2kUPPf366qDSu50q22ZYoZ01opVdWN4IenJ7geizTnbZ44DY9ZKjHUs1c2cymKWthO+m+yu+KoboxvDi3n0AXKJyJUflhHM9JekuRyvvvU7771NcQBaQpVPDYuMmECqt95IAAFAnBCEyRyFvdzNiuA1f0En8.sPvACh1hZXiK1TCqS2+5ok0h8ybg+P2+8e+CKtxnNoYePDYZQjYum64dJWpToJ.NOmmyyySDwWazvm8uw0YRo3Yo4me9Qwnp38Y4yq+M8Fs.r1d6sjhEKBHzqWurqbMabq+w7S.R50qWOOOuj84HIUqhQ1dvhnHqpUqZSHV+I+c+cVjdwj1dvVVNJLuYCFtzclLfGWHEPQQrDiZomGS1+yS85WJYNnevde++Le+aiKqgCK988C78s.l56e0nnnc.5cG2weD.DDDfqIigIDSxq7FdkIO4m7S1jk5JUTnp4daOKN9Q514jghSlNFp1d.lzmcQ24cdmlf+cHGtTPUcnlvHhHic4OkYlnAL8S4o7TlVD2Y9LelOyThiLAvXufWvKXvrtNhXaOFo0q8.exrzugvnqI8elDGGK850q+lHc87RAsQs+reVCMsUUyeBS4CjO8c5BTgBUydms0dYDLNN11HRsk5aagOj58NhA0LSKReU2xsnhHby27uChHxm3S7IxrCQg5WBGuXjXv9+2a+1yrX6bVVV1tttL93imjJbc8vht9GyuKB8778R.QTUyohl2yyKuoTSPqcM05VIiwhMdDuN+e3pctfHLEBU.p.mc2cE.Q03L6uzBl8fqS7.DbcYy6zMalHhjPLIG+3GWg8rwYT363Y7LTL.jn.IpwchT+i5yoO6oSOmwVw8+KLtDEUH4Lm4Ll2+qeA62O38n8zmKnYJl7jnppxW9K+ksoL4WpOvem7RAH+8z6rxkWGptzlat4hKsvBKMwDSrNJ6nF8mJ4nd0RTU6FEE09q7U9J69E+hew1XXWhFFFJ3fMUoPb+RncoBm9Q2DJLXyzO6fNS5ZQuhezWwfavvl4lq.L6v.iQXXIU0IoIkZYX31jNhLIPoa61tswxmO+Ho56UwM2bygTi63LNvjQ8EvZy7WW20cc8ALQpJCBTxfwMXEFE0GHkv669r.vy0K4DFVJ1IUeTFjkw8788M.lVlN3POph1sa2ziaCq3G962UfjQFYjtoWSsEQ1kJzNNNt6vyMLQ8Gmtp8kXYKXlCqTocfJqCz52+s9VC2YmcpuxJqDBrfZJOmcRRR59ZesuV8PG5P.HOym4yzbuVEaph8ezezezAYj5iTs9fCbZ.S7NmlCjM7AWWMWJXIEw.Tx3ToRIvcJfYYgEp.3owZMfC0qWOeKqbNOum2yaFw3ZbkTCnHiqFGkabfwwgIj8bAmA2jeevR50qm01ato344kUxzTsZUxbWyvvvDmTWkgXZqptShoL81x22eqgFZnctxq7JaCz6e4e4ewTBXkwXqyy2uu3gLXI.JqtpIgwMo88u4l6HhryW5K8k1UDo6g8O7AkHgKkDaY.kykspZz3oEDQZljjzJHHXYfM+IeYur1ULtolr5JqX655lO33AEAJ9t+q9qJFFFVHJJJOf8gO7gOe6E7fWKG7m8MS.712ZE.4gSWHtuVIMsYuJNX6XzDxho.sUBXxa8Vu0oelu3W7r268duFMzrggkuZbeszbZ0TVWiqpNpkk0HhH8K8FU0bG8nGMOP9jjjbtt9FQjULk5UFXvhQEXsDvZt4lyJndfURRhMfk+QOpjJhzcnYSy9eWf1go.yc3yUSbfGCD2xi0ALYeHTIU5C5PBKsTWn0tNFQbK0l8l0PyQWjHvhouj1jNvAxbS4lK769696F849betfBEpEhQrzVlJrNvFhHapQQaBrYsZ01VLhI2tXPwu+.jd85IIII8O2dddR0pUIHHTMnEKYTqSEnOMY8OpmnpZ8betOWoa2DMHHP+nezOpV6pqsWl+aznGr3AYVxijCHUnUhaJEq+i+i+iaCNsEQ5V7PESvTOiClo6K0M2jvQhMYvnEq1jxs.Buhq3JB777hCCCWJLLbSzjNp1.GGGaU0bgAAVfihEIpklzDTbPBBBxn.5.0A87ObBZx4CTtK3Dx5YUKmDGwotYA4o1eFyFFufQAlfFTpIUmTMA0MyK+Fd4S+LelOyoRyZwze7O9+PYU0pc5zopppCvroTyaRLBuUVsJNTZYXrOm3Y3gFQlYlYF3B2X95pln.IAGOHAEMNN1rYCKvXghXe++S+S4fl4ql1m9g9PeHKvQ9A+A+AUlpukf9Pa73QNk4c7HRVvnQPlMs3sGqlDQxAw4md+kZz46Y5fuaXxbQT0sIh0JGyR.MqZDJ5kxmO+5pp6bC2vqninZOee+dQp1Gnna8MbqYYIt2Yt26UgF3t+m0WrLn7vY6faRKWpsykGOx+y+a7ajmYnv.keSV+Tdh8JPDCWwTtVkpBS+U+Begx.U9m+m+mc50Kn70dsW6TZrNlHNi79e+u+h.EEWYHCqmzQHUnmS+LXPd6qzu51sqL4jSlcMq.DFlAvjv0dsO88sYDiEnphmmmMMIWCpjGHWPPfMNleGGGGI5DmPfUyJGpGR82GwDgr5mtIfrxqjpw8.Rld5o0n8f9StHlgj.SaAX8+yu4MYzfHm8X015qutHR55WpPvwCPLRpgknpsuueVIKULL7ya..tA5uxse66kQvReqe.GO.sACNLGKWyllXUtYZIo.16ABJ4X5EtDKOfVFVgzOqoU58TepO0DDznzrwB8EacA.eeezDiQbVsIVyM2b1+K28cueFK4zvbdahN2bemOXWaUXFr9OWYECn3m67RVh3l+m+m+munSKifhV4AWBELkDQqVa.MV5wO1XM888aEFFtT0pU2.XWQn6IBB53eL+cuu6691947bdNaWpTocCBB5BPRhjiXJTtAEN6YOawO9G+iuu5X+hbM7My1f8yIDSxRTdOP5mt+3n7blyTDVXja9lu4wAJ8Kb8W+TPkY.lgpLarpyr81aO4y5Y8rlnPgBilNu0v+T+T+TihgobSxdkOQFfui.TTpJ4AxoMzCFmP+OdFWsIqDGsArRsiydhH8DnmHpx..El9umPqJIgmHLoZCTMeds7d26Ob1GlVBW8KAtLAOdSZ5tU0pUauxWcEsx7ya6s+0GtPu6s25nqtZan4FUMBqY3LyLyY+QeQ+nmAHDRVDXSKKqt+l+l+lR5wtfuuuYbViJ4zH09+4q3UXAHkejY8xGv6kibtrrPnerRGNK1wh3wHXLXhIz33oTMblJlMn5hQE2pA3sxJq3bjibjYYuwVSgojax99IAlTaXrR3FMZLlrm8Uuu2Csssk0VaMIJJJytWoQiFDFFvsbK+JJXhOS.MSzW+m9jehc7882LHHXSOOus.1MJJpy2w2w2gY7XqYrXQrRcwvGp86Gjsfc.mcFczQ2VUcqq7JuxsopwkOOP+5E6blcb6RD61.+0wjPq3+8u3+dCOOOCSSbc2zyyqsHRhuuur4lalIdtV2zu4qxxyyy18o4dv4dEN2w2G76OH3nvCuiMO34xdp8JWYiAZTdIilIESglTMSSlJ8b9u7blFnrETVihpb0W8UuOWv4cbmuCGQjowjLqQG3yvpQSG6Ge1MbC2fMfskkkc61ss7MIvGv3bqdddhppQOCytlEv11l50qq0u26MAhSpt27KcfYLLY5Hz4z6mESOlItkGKCXxfnRkv7jPKRXl8IppchMr5HkhiKzEP0PUfYsYoyIXkKzh.YTMbSZ4sDP70dsWanCMit+6+9aJtxRzjUTUWAXYQjUDQVa4EWbcLpAclJ2S54z1xxx1xxxZfZdkFMZXFBplyoghrR+AU999RvIBjm2y64odddIc61Nw22O464m36oGwjf691.3iFCD22FPiRWL95u9qeaHdKfszXcWQb5ktAgAY3wEiI.li6IIACk71vkVKAzPbjyBbVOOuT0uV1HHJpSbiF7q95e8Vd994fX6mzS7IYIIh.UsHFaee+7G5PGJCzlB.EJyoFjhd7.bsbozN3jeWnIj6OgcEQj33Xq3zwIKSM6zMKLDvXDVYJLh1ZYGZTAnLUY1+ve2+vo2byMm7G9G9Gtz.A7MS974mkTAcRUc7jjjwN0o9OGsQiFYYs3bVHtamtxN6rCg6swBCSmDxFOZ9XzcASM86US7OpucXXXwmvUbECCLbiT2C36+6+62JUXDSlYYRRYIxCkwo5.NhPReFd3ROBoGNjr6t6p.npZuz.0BMW3f86WlOyXzAnMaAKqp1JFmF2zMcSwyN6rKBrdXXXaOiM6gqHV0pUKGNX6W02hxli+byMm.XsWF5NGpam8r+g61AGOkA1V9TKarvrgT3085d6EXQC8KY+t8vvtDNJv3MUcJU0YaPUG.uff.+ff.OKKqxo.uMtpwYfgLjFoCmVi+ifgYJCJFhYAbuu9hEWZQYkUVAFXbffRkxUv22ee2K+u9e8ane1O6msmpRxa8s9V0ff.qpzLGPN+i4aSLRp3BiqqK.x2HJ5QZFE0.lIg9znmtZj1SUsmqqaRYhLiiqgxouXOSVR.rqtfgh8+Nu5eGaKKK6Hy7+VAAAV20ccW1fZO1XikSw3TUt994BBBJ7te2+UEAF1y6XCkpSNV+12zMYNCyhIWaWRNy32xzN33TafbUM5.fcKPJVrHsHsjNMYSylkbujoOc7dqce2puPG...B.IQTPTk.MUaaaUTTEAzTQeU6uwWqff.wRT4zm9zRCCvq10l6wsGXhP93+s39qMThyXN9G4RZcWAPldQjIm7oHDgnptu6AQDKGZj6i8w9XEu021aaDfgahaFqFtz1rZe5pyxMSYfommWqFMZrh.agxtJzt9mu9tOsq5osSud81oVsZc788ILLL2wp4V.XnVvvG5PGZnmyy4+1A03pGo.88fsCd9TGZY1j2bXwRFlwwdhJ+nWwU73GGXp+f67NmEZVAnpFoU2Zqsp7R+wdoy7s+s+smw7sQDQF4u4u4uYLmT8MgGH1wk5fbLv5JGPWbjvvPKOOOaAxoFQ00VUU7884nddFQ.SEq5g0yEDDTnd85EbcMiqcnYhpZuFP24pVsaKpNn6D8vUSS0lBiSOkBXhHxlu9W+0uMPmImbRs4oNUtPpLXRsdvjfwsaXlUpIP8Oxm3ibFfyN1Hi0PUYEU01pp1gggFl.AiYr90lCIhjONcrVKJmcbuPIe3ajXzdfNFI.Imb+88Vv780ojo3zlwZtLJgLFUYbQjwEQJ8q8FdCS0zLybkkVZIGfJqs5pyL8zSOoHUm.nzu7s7KWZi0WeZfYDQlIcCsYZUxj.kpVs5XpwAS1GKn2bysjFMhjz05xVyq+s0a8s8VHaI0a9VtEQUU888S9Xe3ORavYafMCCC2nd85a355tCPmyd1ylLCKBf0oe3APfr4j5.w6.rUEQ1Fb1VizcA58Rd9OeX+fPbwNe82OlGAlR+xgVW021UE0sa2HLtm2JhHamJ1x5G8i9QACK80fSDXXIXy8MOce.Jl+7WJuC.TxQtTEA3GLsCBZS+DHrLd49E+E+EK.L7t6t6HzpRV4dMhCMFSUcRopL6G6C+wbnBU+e9FdCUEQphwyly9T9ke8u7oUUmnpIQXYIxJ6Se.SpJRt63Nti95wzBKrP50kjtFinFqQWzTPe6kB7UGui500xxpWs4lKAPOdZogYddsnIF6S1OV6GSwtD3w1.lj0LOPNU5CgEc6Aj7G7G7GzCbSWPnbO7Smva1rMPrP9p6eiTWrVBPmZvNP35TlkfpKDCK7DdBOgknAq.rpHtFqdjpq.Nq9ttq6ZSU0cTUyr3RROe1I85YBRTwZ.a.ybOI6sANQ1i1lAFk6u6+v+v+P6vvvsGd3g2rd85aEdeg6bO2y8zgnxohdnmUJ0sezHvFy8PePlpsC9rIv5Tl0A1rSmfsUU6kFvXdvIMfrZWJOORWPt7NQYKH2j.QjS2sa29flrwZqutBseSuw2HggglE4bxBzoQtT1OTHNNtHLaJc2loXq8pQ4Chh7CkEoMS.djzMDbj9aL3.fnbjziWYo0AlvDpmOs9pG4M+leykflynFga0MFbEQbW7KtXkyb1yLyke4W9ju2266chT2wYv5dcbR2Hqss8vyO+iejpUq1W353.LwHW9b354lc+0ei.B6Sz55.rqnFFTcG2wczo98EhoLmpT.pT7Nuy6L+u1u1ulUJPd8XN5tHzEN02HHKqbXRfY50HairQlqIsg1qXwhIKszRHRUS1FY5hTKci6G47VqpCNocuE2qFrWWDYAHN5s7VdKgas0VM1c2cWDX85g0aCUzHS1CyGdhPywuU0zmYylClMEHvpEX5z924ufflboLt5RInwAW3MGLUd7RADbFxu.jCByWduMAVDpNLvnfy3QokSiHxr+.+.+.UgF09S+S+SOTmNcp8jexOY20WeyJ3XzijTPQ567U6dl1ibS2zMkk0hAq2+8QYc.YkUVQlY5YTLhdcOw3pDIJn14rIHHXeOid8u9e0jenq8Z656624k7RdIcU0J43l4LELBioRvLoA25l.kRCxcetkykZaf9xEAylmIsT9RjpR2Ymc1tsxV3u9EYr77oGqpHMLTr291+UtE6c2cWKUwVDwVfb27Mey4.JrwFajGnPPPPgfffBppE9w+w+wJFFFVLH3DEsrrJ.UxkQS+2xs9VLmm5O1Bwjzlj5lTVppVM16ciz4chztc6JppxrfEDITBouCLbdOdvdE9mqjtmKs++AELTLVzT.aR0tGaOeeqCe3CaEDDXkjjH0p4JYLmyEx433jipXCUjU2OH9W74ypYRGJTGW1q7FyJeUUUodmN4TUG5FuwaLE7wn9VV6Cv86fsz0FKsM3rJvBfSCfFdddK554sFpwZGu5Z054642y11NwTslX64cz7mHHH684hAAAEf39uCOyCeLu770tPy+s+41lGKvwNNSOlNiwJpIxYbfIfxS.tSb8W+uvTfyrhi3.3SEpsxJq38ptkaoxeyG3uYxjjj9VyZ5lRGI1Xav8sbSRchD1O3u6a8RKKKAf.Ck0UOOORGaYYIhsqgwIRPXfbh5gBnVGy2OWMuZC466OLXmtokJ1w.oZJS6FMZrKznMmq059MxFLNHqJ6789898lBZR0cdiuw2Xes36W9W9WVZ17KzGbH2K9FaG33Nep376tBtzDHPpJmsToRg+6+6+qK566usHhkqq6nW208rmJHHXZe+iMU7dwpjBPPqyWhMen.dxCzu64qrpN38C.bXvBNUdy540JtbldkDkls9FUG4i8w9Xi.NC+q+FdCiggIuSO0TSM0+4+4+YoIJUZLfQTswH.i7+3U7+X7QGarR.S1qWuL1A2OlsO4m7S9.lTqBExKUq5RTJiLihhFXPgncZ2QytE+ctseG.HHLH4M9ldScqGdhcA11yyaqZ0psETdaf1G5PGKYw85qd3.P.y9.bSYZfKsaA6BwcRS1k064C9AykF+edn7AKK9GnioBzKLi0+wr.PT979mEHHLLbgfff0R0KE8HG4Hlxvzw.ZBzfe5W1Kafm+No6Kb5bm5biYoe73G1Tdj1bXrgibwR19Eq8.MN7.LdOL2u2u2uWNf7EJTXn27u8MOJNLN3LYrAnsJ+kus+xpuxeoWoai+sFUeiuw2XEvoeRRY.suAXj38zqjA+zWS6ZXX9et1saa644YAjYQ5pjE6uIQoIXbArtpps888aSbk1dddcM+9USKemJR5RwCR9fGsSr+Co1i0ALYO5Y1+STBPxuvuvuP12qPKbCRop4BTTDYnYmkgZfeZ.8WzMo2+AccnCGlcJ2hMfFqQUVCbx7X5MgFok+Sisf3s+k9k9k1IkcIY.lj.PRRhfHxVaskUZfajRqDU2CgdiaInrqzWCTzc+0+0eC6.r0Q8N55dG0asZ0N1Z.a9LdFOi1PqT.SBsLtDvEkAMeynM3KFcg5scCXGLkQylhHalOe9sEwss4W2IWV1ug5WL1.LvBxsZyzrI3sLPCfylOe9uNvYt5q9pabkW4SbIfM8775lh9dNsgVvyyq.PwO5W3KTDXHGmm5vvBlrjqKzG7fCe3CevIMevrP89lHLUo0ywIOGghy1TBPmL89t0dBtYkJoSl4VzyLI2X25sdqSppVtpHtc610WU0+M+l+splKWtxO9G+ieJRK0FUabvf85OwX5FYGTzO2WedRRBQgQR5jjnlIJSyxdRhHz6X99cQS1UDYGuZGcaee+ctga3F5TyyqWXXcMH3DTu9w0q+5u9jumumumdkiRGOeluQrntAlf8zzCVrKyPGQNbl18zIEo6dSO8zRZf9CCKMhecCcp4jmSVRO3wtuN.L2bysAvxvrwUg5iN5nm8O4O4ONrZ0pK565uwevu+anqIyhN42byMKppNjpQoA9tvv6taPJ58MJVcoTPoN04E.gKDnGGbrzAWb87I9fY+No+rkyoAokyvhXu81aaCHMMf8jSUs.zHs18aLtpZo63Ntiocbbpbm24cVcs0W26oe0OcuK6xtLWfJiO9nSqMTikT6zutpGQDYjBExOxse62dV1KJ.jOErxy4dcxImjlMapdddIgggIl48LzTOyJqAjzwe8TU6bhv56hC6bTe+cDIYWuqxqKTNkIdjjVFhc+Zes6tKrZ53rS9fYbV+yaZsMaAd1UR6uiUUEWoW0X592+2+22FW5.Uunimm+ToeSC31u8aGUUsAUY2c2EOeSIVN5XiYGDFlWgbYyQ466W.QJbM0pk+ttq6JupZA+i5mVJUMyQYrAWqW8q9UmNV4RQLU9VtllccKhjI34ZESs2qPYsV97J.K.BNHysJBbpKvgDALkZipglB2DjOvG3uEX.MLw7isv.ViMf8ce22ctTQOMs4noWSlR4sJVQmHRflvznvgGDv0Kz3Lc.sNIIhrRnsRuz0sUQDxmOu0q3k+JJjsAdn7v9l4tuXrjaf0FWsMDmJd0wMcDoQXXXLvRd9da1oSm1MLkGrDDDX2qmQ7y61Mn3ce228P0qWenJUpTv22u.3Ze7iebCiv.gC+vdLEWpaB1Le2gIW4SQdHtHyxHfy.ZlTroran0LPzr.kUsgiFq9.GpWTuZSN4jt+Qu829rhTsjkkkYcgzOo..OX4DNTXXTALZmzfLKYeLLoZ0p.n9ddIggAohtoI69ppVYIrw2yOuquaNQzbGOHLuAPppCA8RW2nY1yYc1Ymsa0pU6.tcJue.Sd3pktd2789ve3ObpoDzHSzY2Enya8s9VSpT4oIyll7lHv1.V0E74d53vSkx1onsJGwJ.MIl.Qjy988888EEFFtxW6q805BT7S8o93S366OSpKqMCoLq.XXnZgxC1meDxcjC3neb9W+6RbL0Q5yXDN+kua+e2SC4nD4zE0hP8hTkg.2gUUGw7IZ3m6y84VDhyD90wEQJEEDLwryNaVBFFZ0UWcHfgu7K+xyrm5RVVV6ysR.F965656JCnjCpwdrPqVC.RhxTSMkJobqETUjLyhP6klnKUST022Wq44k36ezTShn7NPqc.5.s590a1TAvww4hN.5hz5Gu1giRS.rINvdl0+brvTxZCAwowLz5fLY5BMWWONBcfo2BibiECMCDQp6440PDc4nnns.5d4W9kqgggnMTQUUBCqK2467clFiT49ruU0Eyh+OesZ0RGyc3b0RGWbZv9ltoeRK2SiEbxyWLaWr1EKVtL11WbuDZ5mub56ehHEds2xsLZkXlDhm0Tp8NUebOtGW0eu25umiiiS42za5MMipM5q6RrGCRFh8J+lg1c2c6G+ep4Wj+e8e8esOPMKt3hVof9120kzrRS1vp7T1kKc8886FDDzIH3DY6yUgFo2SMsqFcdeV9XFfRxZORsA5uY21av2Qv1rozYxAKlMIaeKqTUcnzZYqCFPNVeFXyES0XDtDnTK6eiJ4JAVql8yllBrDiAkmEZ4opdHf4vPV5LQ2YRfQBBCKHnVnhXdRnJHIhPOUMVMGvNHrsum+1AgAafxphwtSWHHHnIPqq12e4FlILVmprAMXqnnnsbccGzdvdjVP.GruJGl.RxVbXBLOKxKhjztc6cKTnv5NNrdbLa33v1wwjUBSOPWyYG67kfgV0Dvzr3f2Je4U7JUpjGF5PNdPTjsuqam21+621V+vuje3MNzgNz5XbQf0A179u+6eyBEJr40bMGZi3XVyyiMCCIS.e6xQHYPE+JscvfjOeTKavwJ4TUsRKCoAqcOwArhA64lC6ybFxM6rjagEFXwaGJPLihgllN.dppthHU50q2zeOOqm0DerO8mNydfyVvevRfPDQxn+c1FosF3q6iIMppznQCbccMA9IzCkDE5JohJrBaIFUvewi56uvIBBVz22ek5g0WolWsUgpq7O9O9Wr52828285XTF+0vPS7s4arwiCtPiMfcYC88MiwpxPzfQLLFZgB.xK5E8hZ+9deuusw77dCLADN30v463mED0P3x3DwzXd2sZylMmoQiFi8s8s8skOstf6544sKvlou+sATdSnUJ3orAFaud2LpKx9Ui9yW6f8MOfA8UCj5Y+MyixoPg4E3T809F0XWlYAhLX.h4cfBwvPUqxvMZvnNNLYbLy.37I9DehJyO+7y566OElL+MT5eaV1ISpJBoYknuUDy9cRhr2C1282VaskNxHiza0UVIYxolRCCC.UrvTyDYG+1HrMJqpPSTMTDoNPnuueSfEcfkiMLMaSbYGSPYyjogSC1OewFuk8NvfAuLLUXBZxjXpy7QwD3RWLioW1CVJzb92gycMjrmS4vD3RILiidb.OoNc57j1dqstbEb2Xi0GWvJGFLC10yyaqvvvMQXSAYCU0U888WJHHH122OBiAZ0nBrXSy6haPM1g5OpLe+Ck1f824bbHWbL477HWXX56dvnsa2d7BEJLjpZWQjTPLMtsAm6FIGbcmr0blF3v.Ooc1YmmRwhEORTX3gRfIEnHJBBcEgcPkMUzUvPqnXee+5TgyPSpigp2qGDDr0f1x47P6Scg2P6AiaH26+8+9sdguvWn.HdPtPXzJUXx3XcFWWYpq8Ze9C+A9.efjeieiei09U+89UWfXZh4Y7Vmm64yy45H4fSNDlXMbAdbewu3W7IL0TScY.ydC2vKu3e3e3ebOfMOpu+F2WX3luq20e11ulWyqcqJUXilMY8nnnUbccWEGVgXVEy7X6st3CbMnO3yhKk14C7ky2beYymLnajL7rvHsL5lzPqs1ZEKUpTQnxnpFOgHxLppkAlsc61yjKWtIsrrx1PZ9ziehHRZFSwNIIofkk0AY0SewoLsbarHsTb777389deu7hewu388NW1+VJXtYNXng4hvpJrjuu+hg0CWDKV3ptJuVMaxBNNrTbL6kDt4XKNy4rl0CGuWuu05JUhhqtJCqFGZIK9LTiy4rElm8aydwTdgra1AGumoyBSHhLCotm1W9K+kqdEWwULSTX3ntFgjbaQjUTUWDHVDIFnEUYYZvl7.vxFG5W5cJGoOk+yddob9GekcMZ6.1wf8TSgr7xj35RunnANFySlCnM.iL2aistvv+0e5O8nW20cciRUFApNBMXLnwj.Ud2+k+kNe6W60V4w+3e7SmpabYqgN3ZiCtmBqA92N3lw6+0nnn9kgSTTTeflI0xpw3BSYe1lTmk7du26coq4ZtlFpHA077pWAp2DVnbY1rUK1sREZ2r49rV3KTb3Wr1frlHCDow95e8u9XW1kcYi.TvARhgccbXs3XVat4Xiybl9ysegLrBAvZdH2oLqsNAtTlHpAUtLUiurvvPOOOuR.Rud81HWtbs.BR+z.XIGXiXX2JUnWylzc1YoyBKze+GILMYzBLquef94GTL+Zvmg8e1dDPNIHttXGEQ9pUoPiF8igxhYwFy9ALZWnYN8owr+FmO+m+y69zdZOsJ111Sk96LXLXVXh+uuyqRVENjjjyxx5fjEXeycmNGlFFFljtOft999ZPPP16G6Hhr5W5K8kZ9DehOwP+i4GRLwfyBP7h0qWuUsZ0Vjr3SXf90GC1drNCSxZ6MX8jY5ivhI.IpQ0arfYyopVPbcGQSob47yN+X.Cu390OiKkySxfeVcfEM7WBAbESrUn.ccEYWLKFzW7WWes0TeiRVimumIM9lZDKIegh641Mvt9d96FDDr869O+css+w72PUcUfkDQVv22ewFlWmWGX664u7SzAbS1e8L9nRq+yDinYUtGPmT6xbGLun0FPKTnPNfQhiMHqGG2ei+WLV+j.zcUS+z5.K5FS3jSdkmUD4LXnlW7U64shHU25FeU2X2C8ze5Z850sZDFZ+I+3ex7ppEdBOgmPwCcnCULN1D.VX3fzTa5BbxLsMgBTN8qWDsYXvOtfMLsgx2yM2AnaG1wPtyblyj6LmwrH7BKvPoYrXbvYJhMSL5.t3feEi.h4E0ngqkkUkO1m9SOClMhM1ApOw9zHtufIB4gj9WuQQQ6KSJc61knFMPUUCCLzKFEcOgpS5JHcEyDe6BraSniuueufffdGyqVmvvvcfFa8c+c+cuAvFNvlPUiNBYr35KkEWtPO222BVs1ysA550fDnJo1sVQU0Qduu2263PUSPxFV6bgxfwfGaS4jEwZ.KTAhvgy7sUoxY.BTUa444sQ9746fISs40HcHnxHp1b3u9W+qOTTTTgpPAvIuHRdbcGLSVGbbyA0ak8QGzi.1Ti7t6EzVQfh0GfAQlLu5lCNU+iaYvRDey4nb4AAKo.vvwofX1nASALabLUd1O6mc03n3pOqm0yxoVsZUvr.cF0Nynr93.i0vLeZ+LxxdYCaeYDav96EWbQYkkWgvvPYys1hff.TUDkLAl.PSY6iA73csTscsqtV6i462N0JN2En8u+648XTd8xGtaoH5Bk6BKdPKR7Rc71A.DubNZRta+1u8b21scaVhiX4JxfA7Hg682cAZGAfDLNcV+qmb4xwFatISLwDhhHJpUJvl1CjUZa.4n99DDDn+Uu6+JRuFy8VdK+1EZN..UoBE8iURDxffL2KN1vBsvv9Ap2AlsS9742KveG55u+Mpe91r99.yNsB+APJVrX+9F4.aqWUAEMAn6YNyYZCrSXX31286+t2AX2xPmFMZz4pLtYR1XK8TW5yiobjin.IuvW3KrOXdgoyi0rIct+6+9a2nAc9fevOXOQDdcutWmMwl2kbMu+eIFixI6qkIyBKANMdxO4mbva5M+lqOyLyD+A9.++slmm2tdddRSnfqqawWyq40VLHHnvINQPd.6d85YAtRz8EIjsNVUxM04ojT3BmY+GHVZdNyww4vfi46OW0TF.wGwOMi7jpQWK.y9S+S+SOqHRkqnTI2W9K+5qAMmCShplqpH01byM8KTnfqkkUYLykkU1GCgYchhoNkyPVVV8sj9AtGyAjKLJz1yyyFvJvrYBAPdwu3WL+4+Y+4.n+W+u9B.LrXJLHTDPDj8MGuBV9ouOiEPBVG+3Q4qWudg3XJl4fGvzvY9lJE1yhip2pqZh4LEbjMLfSVcqz3znJjmJTXt8KR+WLFOkTC5BSsiHx5Tt7B.g.m8I9Deh0AhEKqUwLV0NErlR2+8e+SqpNM3LIMLkmxzvnLKiVdOlyNBlRLH66KV9jTHUHVOPRgl2LdpVsAKop7.4hSA9X4kMYgOJhAd96jwJzBdPQlggqjtoeLiglHBl35ttqqDPImFLIMzoTMZZfY5zoyzO+enenIebOtG2Do66XPc8ZvXAOXLkGLVx88N0xKuLJv1aucewd0PNSTPRf91V8NGa.vcEQ18E7BdAcADRRxAjuY54qUK3Lm4LIMa9vlfbNv3hZlue1YU.trK6x.H2pqt5PMLfHUJN13FUm4L8KGqKowWmZ.C4vIhEMkfXy5wwwmcpolJHJJXgvvv0hhh1MHHHod85YfeU.XnzwOi0roIdlEVfAhgt7HrjQmObLy+Tv6bie6frcZvmWmOV.uuXvNY5y9nHy3vFMX3W+q+0OpY7R4IXAl.nTEnDUY5pPk0VaMOfC8o+z+y0Fe7wc+HejOxr.kpJx9rLY1K9+9mutc6lGXPvRjvfv8MWbXXnDFDlAZhwdp0z0qBBMrKQjrRyo2S7I9DUeeeI7Dg1PkbPbtvvPaQDaiwGT4RX3x252drRfUWps8PLuF4nN4.2ho0.7X.kJCkZYVfr25qu95SLwDKVFVpUY1fVXbQmKL5WmS1hREjNaSc3EW.X7+i+i+iotxq7Jc.pJh3B3pp5A3tyN6TtWudkVasUKpJ4.U.QQIAgTpQZ.Uv22emffvc.cy0Vcs0dRG4IsPPPPz.YXbQLfErE31FhL+8UYGZvtvgaCm9aFhF1kR6bxV+LF.pFUUcDGGY3lMYnJPt22m9SmbcW20kZayrZ58TVFauTxbWdn1vP8wAllxTlVTQUc1d85MQtb4FxPEuvt999sA1QUcaQjsq.aEabRosRyfYFy.RyjV0DCXzmSFvxzyizu2sGDY99ifxIQ.WaHp+DqU.oYZPO0pQu50AOPBcvl39KZlgD+nl.HpNADOcZlxb+4tgeF221a42ub2tcmbhIlXPlkb9B.8fuimMYtEFT2w11V.XkUVgwFaLZ0pE111Z2tcSKWhndf1w22uSPPvtftCHqixxhkrjp5x.q7k+pe0UFajQV3Zu1qsIUoEMXYnxFPyMYOZ99v0XwCxzj7PshP8QAF+9tu6a7q5ptpQgpE+y9y9sRxkK2luzW5KcUeX4fKNKSF73aYB.6zYYqdPsgozpqt5XkJUZDfBoVtlQ+SbXiEO4hqOyLOk0gn0Ic7zLjogKzapon6xKShCjDmllLNWPYkT1hLHifN3ByVl+lp8fFlwjygvYR2vuglqYrIwNNcgyT11MRJqIFGnTpXtN6ZqsV4Owm7ST9G5G7GZZFvgHXurV.6O6OGL3fAYUx9FC1saWha1TEP877RhBCSTAp5TkFMZv.G6cA1z2+XaDDb7U.Vv22uA6kgnXRs7Yf0AmMSEVt1orB6fA88fkcIE.FFbm.hlTUcRwDPRNU0te7O9Ge8m6y84tDvB.KCyuMbpNbtapIK.shy.SrnIonOtJU3IGGqO4nnfKWUwCkwQHG6Iv3aC5Vf05fthnxhIVrv69c+Wz5k9i8RWDXwDRVJMCNKCyrJr3Fr+2y9VYFl.6ueeeqqRJCSLkbQbQLiI1h8Xq1CDqPGXNA2QSKMiCCbDU0mRXX3SB3PXxTWwJUpPqVs5PBaohlp6GzDHTDstm2UWGhivLVKaticG3qWrrsmcMMHHAx7yCm5THNfcrKCSDiigESSJFgG0Z2c2cmgFZtUflKfIKcaB6i8kOPsz9QmBP7XXxFoqCT6KuxJ9ppUlbxIGOLLL2w780FptKvNQQQa344sNFAudYQjUfJqBM2.yltxtey9zG3nC7I845Qxntt4+CzSl9ueji.m7jv7fbpZXQ8yIoUp4+ulcZo5lAT7vat4liN5niNtp5XouSNBTYDn43oYweBQjIa2t8j4ymeJLyWON6sg0rwaCxBsr9srykMz2JM22yxvfPwy27yR+20fvvj16tqVrXQCjuZ+6EEw.7qmm2NgggYN6QKee+VAAAK3eT+kqe75qTqVsUAVIHHXEee+U.VElZcX4r98Gtemdv0QyhS4.Z0RYank0O6O6Oq9m7m7mLnHwlwBgGHFHbv3+xUEFpgYczobcoRTDUUUcttmy0M0mONG8...f.PRDEDUG9C9gGdsMViT1Zt5Ow+8ehEtq+r6pE3t.DsAvtpp8DQNe.l1ClsCrfAr0Zzg5jXz4n9qcZOGXcFyuexTSAKuLVNFcaHOfUplBkwbfd.bkW4SW9ReoOW1XhgIM90zxqIU6uXLnx3pFOlHxXuo27aZh+z24e5jm5TqOspMxzRhrX0Fj8HCx.iygsuG7gUFIAZD0.WO29fkjVyfYZLQ268yeu6bMW80rku+w1LH33aohtoEVahoD02JHHXsd85rzbycYMp7+O281GjjbWdmmedx58p526pqrxLqo6QRCFwHMBzHgj4sEVCXIaeK1guf07xtAdiXEgu8Nr8YPvZv64f337a7RXSrarQX10ducCAdwKWvZ6EFv9PmDfFiPZDVBM.qdcloyLqrp98Wptq2xm6O9kY0U2ZjzHC3U3eQTQ+V0UkUl+xmW+978KrbqZzjVrNG19VpzW+218bi+YLKl7tJJll5MY85L8S+z6OYgBEJ84+7ed8m6m6mqiHx5Xt23xYq64x1ZFnQNX4x.yPMpQKbgZMTMpNlXXxVWj8hLHTbUU0UEQ1xF1qogiIiEibem1Ll9ImZSjV5C0n7TaFiEOqcLDY9dS7+LVraBfjDlmEPFWWxDDfTGjl1HDQFL26MRcAUUyeK2xqK+27a90JhQFfmWDwVU09a+s+10t9q+5qhYu0Db33yNZCme1Pq7ynwgsZ0hACFj7+nJXMTDFnw5.CRL0XMVLHNwrWYKQjUiiIxxhVX7ethqqaavoED1lC7cdk365Esq+9BBSN5JlkYn8AbZxnf5ao5H4rcpolJ+BP91PNZeHm0OeERJE9eJlaxRdOhh2c2cUfgurW1KaPcQ5KhzWM2LNheEJVrXbkJUTGmTmvhotvhgD3xjIyvDR+ZX5rvJf7xN4a.eee0yyafMziZiBZKYSXXpLJFSSzEfX3B+vnqDWoqil72fDB0rmHN8hhz90AMR0rW5RWpxuyG4ijPXabzh.7bUg4zW+9vx6snIX5UnMg1vxhHW5O4O4OI7u4u4uosHxlttt6Az+IexmTEQrpAYaYfXeNQj7ppE+3e7OdpSvJ.kUMrhswg3DXLZMgMLA0ML6NipFcX4ES61+4ovbPZw5FwF0sLFAKC0Ku7xTddnX.THoXIERla8zNnMuHhipMOlMbUelOyexU++4G4ib7+s+q+C8JWtbsolZpzjYGeNEOJxWNZUsOjS4zhk.vzSOMsaaF1+ImbR.RTIGUEQhSjcxAfzSDom2M40y00cfgDsfeh2vaHNoXT6QS5TE1CZkVXhiBcwePrNTmjgkwK46eEuhWgZCwarw2M9c8tdW563c7ND.K+QmSrGOw9K29qw1acg9m.1GlOMP2V0fPbHb5omtkp5Fpp6UsZ0g1fkpZNhnvKa94K.giTeFfxqBk5zoSIvqz5qSYaCBOJlb8OaBOJLdPT4fGuvzPAvs.l8H4mGxiG4M7OhoCBpFlagzjJtHEcfxP6zwfaRfoapZBTNsWPDolsoftthHMDQVrYylKc1yd1klbxIa7O5+o+Q1X5FaZASJhovPo6uNJogktm6nnJ4PqVsam9KSIcSKTjnlMEjQO+3D6kc88O2dXPjV+kCBh8884BW3BhuuO.wgggIIqFEuP5dry+L1qckhtjzuJe7O9GOYuQXhz1VOczBK6HRkpUqlVDoj8TO9y1doz2ec0Dagpp5cdmezjiOKSBYBoXqgOym4yj72jXPGJPeUnaCW28ty26c18Fa3Nv8lbkj4rN4Zvpoc+5GUQYxQ8WjDTZzfZFRnL0NRhu8myqs5RflT.acu81SAHNNNI4WAWWWDAsUqVwZrFewK9zCEQ56kffIOOutttMFjruRMDIr4XqJnIJRzQQSwy2m0QetdbiJkLLxHQloIgtiHx1InHc6hEK14i769d6VCFBNiWbxmu2ujD+h5ioHSqCzLBtzLyLyS+O+W7e9k1e+8aMwDSr0CEDruHxPQD0wwIMoLIY7QEUix3XPIWIbcqnpVwCJi8gHOvb.4VZrttZ9eNeZC.JBT37i5j9B4O+4Mn07wgbrr4+OwdV5yOoS9KOYUXZvNcr3l6ZpToJ0nFlVWVSUslpQ1pp0qK0850qWi1sa6kKWNGfEvfFkw6vedfr862e7lTL5yxX7tjUfAElVwwwBoigimKc1qi.HIESQ7bcMHXRwhDh7O8ZjqyAEWI42Y444I0fXOOugzh9MZzn+xKu7QKBs.qKK8buu56m0y.wlbPw.SRbt8v82eeq+8+6+KRa73jtPEZL559ymezzB.2q4nBd5tRXHA1vkDQtv89WcuKOHdPKGGms2au8Fr1Zqk8t9ObWU.lABmuFLO3LuHxbppy3.SyBlXwHgWFTsc9po9gVFiORd7wUMoxmakURRFcgRquNEW.JDY3mlLhHo6+R8YWDn3246b+krgxP8IIgDWsEYgs2d65XP66wTUWT0nEqKxRsa2doepa6m5XO1i8Xdp1L0GZJhlFcNKgZ.FOdrmyhk.PXylllJHv5ar9g9aBIbMmxfa9lt4Q9NEQ2QLpRzFtttaArqHR+LYxguuel+lvvbz5PIX+Cx3zNz9qjBRzCb59DOwtcOVwh8EQzy+HmOsXTGkjauRP9exw7x8cLE0cSZQKa3RPqmRD4o1d6suDPqlptyxAACMnffR1PkHnhHRkfffxppURZfTZbSS.LQMXRvdRLxFsAkS1GRZdKCQkNR7+EgGOkSjpPx6k468lHHfIUUmpILAQlhjXxMfzFXMqHRsyd1++bsgEg5WkHxUqpdU862eoq+5u9FXr+cYsswyN+9L55xEu3ENTgTTUkgCGJtttXd3oIkiKFQFpv.Uk9hPW8.aD66551qQC2g2nqaLwntttIisS3y2nm+iTq+9VASNTWNhfXX534AEbSKtwPf3uxW4qDqpR6mYGauRf7p4qGzAygym3roRkJoHDoWDzEp2CCKBOJ.uACFjVAuQnnqXohlCZMVGNLNcFwF43TAqkW9AyHhXs7xARjQVrhSbvNz22e3evevevvHX3S8TO0Pfgs+ACr59AwJ0oog+FpROnYOQpMno4ZRl21a6sU3C7g9PIFJbRMX974LN80djS4KZLXtEvpQPDrP368ex+jfa3FtgnACFrpHx1TidW8Ue0JTKSjpYRUjBf7hHkduu2euwG4foDQlJBlAZMKTeVQjYiXgooISd1yd1IAlrVhg9KNlQ10LFYmxDrW8zwYXJwVlBZNMXO8pGLhCSfQ0QlApmNmudX5D5wifi2oytK9g9feP2rYyt.vLAA9ommFEvJWdnROxobBz6jfv.ILLbz4zd85gjnm0NNNr9ZqyXaazj8uCA5qp4QvCMJ3NQDQ9U9U9UzT18Gn2JP+vvvAydPv9+fZe3QCpPRF.M8eym+ymtOqWSU2elYt18bNXtqUfLTkDkenwURQShYDrOWsK3rCvFsX9UIj1.sEQVc3vgar+9624gBB5mLun4ZAkfZS5Xt9mxV4SV9pJOA3OITcxDEAXz9cCR0lKs3ViPazlPEHnD3VTDovpXW.exKhjCHiqHVhHVi3xEnbHL4FarwLNvbvB0vnnRMvlEgniC0OdSUWBS22WpWudKkOe9kt5q9DdX1+MOl8moIFkEHk4zsvL1HiCY8KGY4MZs0VaYH4TGGPLIZnJhkkkjTyXPSJRflJiuReOOut2wc7OqaPPv.KP777xtzR2RVOOynp333fShcf1W9tbdktR9eOoBnu2266czqUavR0l4.6BppkBUsX850GE7qyUdByJ1FhS6NuyO1Pf3JUpnAAA5DSLAXF+M8ceGu6XOOuXPF.zSg8+ze56pCTqCPmGJHnq+476CnKu7xVehOwmHKPtfm40fWrutLc6ZoidrqQplzXB6X.M5.6IOq1Tt.nPCsJDWpzRJfZYYQPngf0CBBzDtxIFKFbrkVZfp5.MQlzwTPuL.YM2WElAbw22WWgpvxGFwHi844xsFu3NWtNUlpLW6BtaIhroHxV.69a7A9.caYZHBvBWoWaGkjxIfdvb6fAMpgTmK849u94tPoRG+RSM0TMcbbVy22e6kWd48EQFHRcMA15VXZlPgPnBDMEACmQDYFermlnQJISI2D+PW3fhmVHzn5KUlCl.7RsyUFGJAsKA0K09vE5OYrXpml3zT.S+.OvCL+JPUHZAn9BhHKzD6ZzB6tc65np5VWDOw7v8rO4YcymOuS0pUswfrloYLarjTT2fffL4xkKEMYow.jKHHHahMNAfa9ltIAflMaZJ9QRgPJWpLgggDFFHQFjwItttVEKULkiSDWW2CcgJQpzGY67gLpuTruueefdMZznKTqehezQw+cgq7hx8210XwQsz36OU.oXwh4ttqqZoDz6Lc.1SwxOCDSb4dMYrWKC5TVhNPvVv7qFkLdNYyl8hyLyL9.sJU53a80+pe89XtNUQUMQddCqB1yIhLSH1SQaiJsopl2QjrhHYWYLtEYUnHXWZdnLXWAXhpUqNItLAzdBnVk1GNNSKCOeUcbT9VVDYhHXJn4rP8phH0ZQs5+p+p+xdlBknKAb7emememkt02xaYwYmc1Fm9zm1kC7glt+aj+hfffLpQRksBBBNTdGas0VOiykggAnpxTSNIpZHj082aOyIYMN4ZmAkIHiEqMr+M51XWUksRPrzV9996zrUy89y9y9K5444MzwwQSZ9Px0rEuB2x77tNpMuTkxYeHrSkJW8tsS3DoOz+GeH8K7E9B4LMLzIsHDGsnIOu4.DZ9L2AXiHnET8R0fmZpodIO8c9Atyk61sa674yt6t69TCEwNSDj+q7U9KyCj2yyKuHRQQrKyAw+OoHxTsfognYfZyHhLSapNEQL4JqrxjXJnREtrw+We5EfYfpow+MMtLM3OCTc1DD8NJ1PQjTEtbVU0p.NYxjYwH3pUM7pUUup333EykKmaPPPJ5RF+b0ylB+XAX0oSGAPCBBhCBBiWbwkF4CMNNV2e+8wwwg.eyfFmTfWET0yyMVLE3suhzUP26a8s9Vc.53662IHX4NOTPvdtMb2+S9I+jc8775C1CLE62cbe0+nRrIOi0ORdP+7rdFv.61tsaq7YNyYlPDIEVlEAvF1Oxjb8Ff81KPTGijWcEAAL3vUF1T3koIGaRYSBxMWPU0SDogp5h.Kt2d64UnPgZVVVSGFDTTSHkSHANch1WUIknW2OHHnug.X0877719y849rst0a8UuLvEaznwxPsVIvk0fzj4oOqROXtdvZWN4n6+QrF+5QJA7VpNLQyDRLJoSAYvLVLqgKqRPJr7NYujYx9JExyYf4J.qUhjhdPB+K7POzCM4q3U7JFMijhgr25JhS2ZzreqjNYJNROMLkjNqC1MynM0L11hzpEwpg.BGB18gnAO7C+v82au8Fdq+r2pRSTnt.MSgXW9jw1xRUUS6rvYNyY31u8aOInYGoFgEaASopNmTWpRDKnpN+C+ve6pUqN27IjW03iGwHD3DDDP97EnZ04G+7wy14oCsTUYiM1fYmcVBCC.jCH7UHFj9H59nzQD1y00aWe+fcEgsccuwc88O2tpJa1ngaarYYh3RPsHn0l.ct8a+16dlyblQcGlu+1KdHT.vgG+nbXSAhnD3VTU+7l.fFA85XNf6U1eVXu0uxIE4iZWIYbMVXBn8Lj3zSUsRPPPAOuazBZoIel6kL9Wc5zoydkKWtGInLPDYvi9nOZ2q65dC6OOs2aUywSLNXQXZwOpVP01VhHTsJwqrh43w.UzEhgUR6LnRxncopVRDohuu+DdddSgYeUZ..ocxnjHR4s2d6JVVVSVtb4QrzePXPYWG2B.YWc00xL+7yMJf8f.ebc8jmkyUOq9TFNbHsZ0B.iS4DopybtWhEzXM4mccc6FDDrKv5JrdiazaqkO2x6XYw1.q451HDvGbhfv0dmuy+4acW20+tNXSWhNjMuqzQxY7qwoizYB7XqOK1MqoM0ZNNxbggZQL6YVSj5Mgn.LvPcWt7vlOAxvjCSvTK.bba35Zp5oBBBdofz.zovr2Z3jSNY2s2d6cA1xy6zq46et1dddQ11zNJxPv2Ku7x6znwo2CZsSPPvVtttaRM1hViFWkueuW6uKVitGdIHyERuedZxxliN+WV0vbhHCrsY2nnQjO5yE7dSuOsLljVVB35t++5+5a3Udq25IANVPPvzW7hWL2Mey2bbqVs6JncTzsTk0abSMV0+b9q344Ece228Ed7ie7.WW2l0qyZMMDPYeee+9ddWeeX8wssckzfhidOhfona4BS6F9BTh1jy72VnuyUmsW3SFt+ryR20WmT+5WIWeOpMqxX7ENmMrvuxu0u0b+5+5+5S2sa2hKVrX1Gx2GWW29RcYOsotsHxtfcWrih0lJNNRbylzWUceQj8.68gntZJgVWiXZgZJrS6Te84Ll5FQ30w.xd6smTpToDjM3p1DjIJgusjZRdZmZisdYUCKIhjx0HkUUmFXFQpOspMmRDoxS9jOU4q5pN93RB73cz+PHe6QdjGgScpSkdN5YTj0jwrQ.HHLDWGGBCBHegBzqWW.g50qiHB6u+9r1ZqovH0jvjvgL50N100sePPv3Dv41.qHhD355tLTKv2+gZ644sguu+1pp6znQijQCdwNz3h+vjLmOpuzrLGYYMRPaQ0xTckIXElTMJrgZ1WvZvBa.sSGCvmOR5mwdexLOje0QnJn5z1rxLQlDEmZyM2r7LyLSNiOt5CUsY54tthH6qlQHqqH0RU3EyXTTigzxzR7DHRkt2unZHwVK.50q2Pu74G1dzXk4p0IvpogGUrjZhnsReMpmi5Mqng5zNNxrMax7ppII6ZOCDMkp5Deuu22q7K8k9RK0ue+B4xkaDpp3vMwhidNZLRDV1auNRoRkOzItCStqA333NZTbF2Opqq6vvvv9ppcSFEmMAV0yya0fffUSPWRWee+dIiW8tppaXYY0x08zsqRyMVIcjbroWhezwG4w+1rma7bkFebWKZCSDYPGwbP8oroY9HXvq5U8p19rO0YWyLR2rErPGNY6db9WHhxQibvxE3fBuNpnDXPjTFRP7hw1k8fZzhVX5NnTWFpM0ghHZPPf3ditYRr+oMax.0PDx8Si++Lm4LClat4FdKukaI9xD+e1jsi5i7HOR7oN0oxRhZUJhj47m+7CO4IeCCSd+KnpNo3HyQSlyLBN0mamcdhYqToxQkH3RbPA3rpWutXYYM94hwWGpP8hHpZ3JMKfLwwX0ueWJTnfbgKbAVZokFO9+gflBDftf1Ej8d8u9WemG+w2Nce1Zdddq7W9+6e4pu423adsG8QezMttq65VGSQ5GwwlbkMhUunc822PXR5ZrKDyxC9k9RIcO+fpbppNHxNoiyPdHJe6Cyb0WIu9i5hCovYbSyqeUFL.XfHNCTS2wT.JUpjzrYSibMk.+bKKw.tD.vBAQPAeee000cnHL3qb22Seee+9YyUbfkk0vatQijigVIetrMUueUS28cnvKD1a9uKVieC6flMa1qosQpgUU6HFBwsOfXC4IXzXLTzlyOdUSe9px7PfAmf4LpVBNa3bvLo25Fuwarc2tcWa80Weq01XiNpp8LWaZl8Se22cAfIbDYZZxrhHyALWMZNazCGMKvLQQ5L.yZOpxvQy.L0MbC21T25sdqSQSlVUc15lNaUCpaqpVCSRRyKFVhu5G8i9Qsu8ewauAl.4ONDd7VvwAtp65+zcc0ZS8ZdfG3AtZfieC2v02PMcqXNLN.NTWQvzIKoZ04OD7vCO.EII+b3k8bmpJyN6rDDFN5zaRkkAPEAEUTAhccOcruuugdDUMiu+4xBjSDMmuue1TBJrJwIGa1x8elyL90mePrFqvXoNhc.HlHixpXyvtl.5c6T+.k4AU07+7+7+7EAJsdsC2wwwN2cYOMwXHY5jlBTtKzdSfUMEtjVhHqN0TSs0G8idm8MiIiNgpZRPALa4xkS4+jYDGYNf4utq65lCZO0pIcf0CJS3nwxZtZrxb.yZCSuxJNSBTYASwNJjnDPYMRiG4MEKhIbDmoAl2y6lrqAd862eIfqQD4DO1i8X+X.uj333Snpd0SLwDKUtb4CgpDWG2zhJlc94maTGJBBCkjhkjdc3xt+JMftzuFGGikkE10LRUXPPfpG5bpNLN4dWAoePPvHGzVhzE4Z5dSMZzSUYfpxPeee8RW5RjNxE20c8+iYuUjYuwI3DGEtyuPViK+qZcZpDY99vPUEQFZKR+68du2dPTRxqK9BIQlXfgQ0HUgkh4fhEA.au81i5Jmu+45CLvFzycNeAvpFHMZzPgVC.5Ob3PSf+sdQAhBuRWGBcIWHof5Mf7r4AcJtJMKHhjeAHaTzBYv3i3Joi6JLWr8Xj+6q3zF6WAAAn.KsTZG1T00yS.IqkkUI+y4OIvT999SuzRKMoqqaYaHeyl1V.r.nddd.qmXK5Dierbkh9iwej1cztKB6Qa1UUsCP2Ens9O3U9SZMGja80cxAjcoqbjDMd2cS7Ix5.shbv+C9A+fWRD4REKVLHR0U77715a+ve6dDgHhjGnbMhlJQFwmMLTmGXd6DeiPzr.SKhwtDsnzi9nOZoEFAq75SqpNy29a+sGOX+o.ltzwJM8A+tfISHe5I+te2u6Lzl4ApC3VilM.N1m5S8oNlHNKhgHWOlHxwTsYiu025a4pp5bUW0wqatz7LFivQIUjd95Tm5TGx1Ef3GDHAAFeltttRpsKWGGBBCww0kt85AHrvBKfHBgggTrXQMYrbzyctyguuexUDkTDlDF5CIHPASiZxphlRF6Y78eH0yPlv6eyddcZznw9.8Z1r4fY4hw+cfZgOxm5bLpXI4AxWmUxwJjqFjWD6h.kO249lIn.ncx42kd9had785CAFr5IGsebCXk1QFHT4WSjfa9lu4Vppab1yd18sIRIo4mutehaqHlQhrjAMRsSJHwBlBB1xvoH1h8j.SCNyTKw2axiY.lNe97S2tNSmT3soffIZlNtQhLEsY5jFrNyBzbNZxB.0CCMxS8xKu7wTUanZSuu62865.X+Reouzp.yjKWtwSn8xUvtiLpD5HeTGsXI.GRIbNnXI5neW5JHHHUIThCBdnj64k9A99cccc2+ttq6JUkl5.rk2o81nQiFqaJjRycWIgSnV6Idh3D9T6GDqwQYTZic5CzMZACh5TU2U0v8+p+2+uOPUUdhyd1Bqc90RGkkRtzt.mmWH4.DCKmfXu41pVJx5LZW+xXP0T669du6M52u+NhHcgVwsLu1ErEoBQLoHxj.SbZW2IzlFjUEFZJTqsHyfQFymEX5a+1+mM0sbK2xTzjYTUmar3+qopZa9dpdpScppjntM.t.MN4IOYCnkWKChxMnJuIGCXw6+9u+Eu264ytXkJURUY0E3YxYIYbccsrrrNj8rc1YmCESVPPvHeEIEKARZ.lkErxpqx5quNKszRLddCBpnhXIfkBVfP7fACtm64d556etcMiPprMv1u423adKfctu6695.z8S9I+2NHQbpGeu+OJg.1Cs96iEL4HcSbcTP.SEbUUgZnhHwlfqWH2YNyYLNGpen4+945h43+siB6rXf3UrWIo5rMSHGM6gIEMQlbxIMSqt4+TiiSjCLwz0Y0Huv.Rref+fO2m6ys+a7m3Mz4i+w+3aey2zMs85qtZmliUot50IFhF3fSZRF8CMj+5K1lcL8DvPVfA0qWuGQrOrvtRsZoR7aGQjAQi5Ln8DTmJQGLadWwFLebd7j.DC6zA1BptFP6pPqhWUwn4latVyO6rq7e6+1Y1PUc2m9oe5duo21+P49u+6OenYNFmVUcVrYtVv7111yIhLa61smAX5HUmQUcVU0YpUiYfvoqaF6lYOyYNS0l0aZewKdw5PSG.GQbrwLWD0nN1uu226ySC0kTUuFU0eLfWJ17iA7R1cusultc6d02zMcSKgw.5BYrrRMRVDH2e1e1e13iRxk6ANNt333bHmpfgY02byMG8yQQQDFFN1I0wx2RLaPAM49nHIwvYFOOOKOOOKQPDQk66qeeVKu7xFk9o1JIPyNJi4+qwOHLNd4PWRFGGDHTVfEFELVz7QIcjJn2.Cw8BPVGQJN4jEMA60hRP8BrvBGsPoOW6uhAhO+AjRZmEWjMgVq4haaf1+T+T+Tqcme76b6u3W7K1se+9vXnpxFJScpLXvfozPcFU04oVs4AlCaS.c9ijLt51TG6HUsEQrifEfv4AlokpiPLR85LEzd55TeVLE7Xg+zu5eZc.WnYiHUWLWtFG+q+0+5Wkp5UYW293m6AO2RVVVGCy9q56t6tGEdmGEZmBXRd3nqThZcbk4ZLtPv7bhhPDgnVQlSi5nSxFwgAhabZOyHeQ7nYn+d+p2aWWW2dD8D8SF2o9c5zYfmmW7wN1w.PpWGf0nggTID.qGuwiO9w8UZhrGYMGTCsYp7Fyn.71NR0seOum2yN.cbcoGbwgmzLJOGccn2+5PRs8PSKtQPXn555hWxbCa1lkTfeiOggdddZDHhHY788y1xLRAw.8sso6wN1w55g2kCQg+nPwSL2KOGVppYWdDr5qkmZTnspE.JzR07InVHSzUbSMVSsfXnthYTbUOOOULE.VAHa1rJ.9A9BPVUiKPB+Y3cZuI.J666Wz3WJR.z1igjjEV.R3ulzOKuPWiG6vv8VfDNOq5.pAsgL+m+O++c9q8U+pKlvIREtvUdgdG+023SzIII0PZCNATu9kd4u7WtuTWBu669tWswhM1dvfA8UUs94+E94K1xLpnSKhLy4N24lAX1HUmSSdTqFyBMmwAmo.l55ttqap1UamzU2lyBL2oN0aZNLnZYVpyLppSqs0YUUmi50mGXdroJvBurW1KKgaHrWTUcoHUOtHxwui63NtJn4Us4lad7K1s6hCGNrAf6q3U7JpSRBI7Lmm+T0lRpToh.jfhxCVMihD.YpImTbcckKmBCJXryIXrskMaVv78ZPP.AA9JfdZsArRV...H.jDQAQ0SeZ0yySSl+eBBBvxxBUMIyTWDAwvU.hZ5hmuuuppNjZFdrILQQSfpcqey06u9Obii6fXFNABP10FgD354TUy1LYOVjpzr4CaAj8zm9UlxyGEpUi7vExdRN4URxPGjT64S1OtzRFdMw1vOXsgvG+we7PQbZ8M+leyUap5lequ02piHRuu1c+kU.qPUymf1kR0pQEn8D0nVJRhm8q+Xe84wLFOKDoZ0jQoYVU0YIoPI69D6ZTZIy9upTmp.UCLjq+B862uF1TuswhcCQjEwzfqEmbxIO127a9M8.p+ReouzTNxYJL7gwQKTxkaTUGqf9hLF21LZYF2qPhiieFwvc4.PfXz8KE03yPEYnHL3F87FrbPvv2467cNvF5+E9RegN.as7Ct7ZfypXauNv1T2f.f4tlqIY+1H+Ye+5C4PEFlzhlzltfydhs8thH69RdIuj8AFzBjYmcVCmIUmxAGVwWdA03TXs8EXGXwM.VYAHhZzTDo4Owq+mnctbKtN0qusp5dIiioDoZ1KbgKTXvfAknNUZZr+kLtLLCvLQlX+mSUct50YVHbVarmEXtMVYiEZZ2r9SeoK4.McApKhsMPs5vBXyBpp0A7TUOVxHccUTmq5M7FdCWsHxU8s9VO7wUUW5jm7jMdc+C9G3DDDjpTgSBT5u3u3uHkmRNZbNit2ahIlHM9+C82Vas0FId.sZ0ZTCvrDQLptzgrOJppVhpVJjQRdurxlU8775644sOvtVVwaWiZohaPm63CcG6Az6W9W9+kXSndUy.j0FxvI+QJNV6Pqej7f9JXkZPZLl12cRHXRU0hhTOmpMEotf1T6iIQ8sA1DVXWn8yE7BubcWc7+VFfh3xjpuYFzDQV7q809ZWyq407ZNQbb7RVVV0AlLHLn.wXgwZ2PQjgwpNvRjTH7uqqq6N9AAahpa344sF1zlnZsCB9Vq355tRXX3Zm6bmaielelelTXJeTER3EKAMmdtJCrTFatPlHy3KkFbpAFs1WcQs4SXALTrk8oE6.1aCQFV5+jz+J.Zdi+9krOX9bvpIN4qVdAVoT6ZThVFB95TWyoJ9.m+AJmISlJm+Qdjh2vMdiYoFx1Ow1xjS9RrfllQfvQTZVWgliL9m.G4C3HCHO0nHsFwJ3Y+NemuidsW60FaP5jcdahpDUiIzHsDXjvwLYxjCnRPPvTIieyj.kBBBFMahoP27.DmdXHa1ueexkK2y3jQ5yY7uVnPAlZponcKCQu535LB9w9AAFBD6fNBjlv39LZV6YaQjsccc2Y4kWdCQj0777ZAD8nO5i1NARdaZdtN6wAEw6us6IG+dtL.Y9Reouj0sca2132ClpbUYSFGmDxIcghl4lm7ppZcQ5FUmcnYpBSMR0bFO3zW.6uFw2HFkzoNyPSl827272bp+U+q9WU.fLYxz2rOolVmV4ZViBZjlACgP2AXGXgtNzVBgBXSYsoVVDmB6t6SPkJUFPc5QyZ8gVovyN8Q5mcyXnYyzDYf6pp5b6tam42e+8ld94mehgCGVZiM1H+7yO+kQ9EeFb4zUjOhvfvQOyomdZJW1zorNc5vf98Y2NcNzyWUUccc0.+fXLJHQeD56551KHHXeE5HvNppad22y8r8Owa3MrimmWGrYmkenfMZ35tBPKee+1dddqgYzJ6jbMLNgbrGG8eWI1BO70zYo.qSwjwtxDLtMS768d+DYd+u+est+b+bu0M+7e9+zU4.F8eb+FWtWy7XlI+4rEYwV03jZjd8AAAmjDUa4t9L+I4dmu821PUj8DU2xyya8fffUUUW6S+o+zaJYjMd6+Bu8MEQVK09uiiyX2m8LTipWLX6+YaM98xI2C4jS0fDI31fzp986OQ1rYyJhzkZrIs7VC72hCTQsmM+zFewvbfyRP30u4ladC6t6tWuiiyhm8u9ryb7ic7rKXuPbqVs5644069N6Y6d7EWb+XQ1QPW2y0q8YO6Yuzq5U8pdZfmhZ3SK20gf8.FtxJqLrZ0piqpF+scjIFctXzn4LGkYMJa3MmlhHxvu5W8qt+q608ysugOkFMNgWoic6n8hMfbKOG4XsCHjZfIgZSViVS9GelyT91dSuoBYxjImH0yCQ4TUys6d6JST9DCgliFswCFOmQDPuRMxQKJoFUroHXKAAOTeiJhUeXcZRSy3CZMXv.Ma1rIWupUpFslHR0oj5xTZScJQrqnZTolMaVXgEVnPlLYJM1wbo0We8ByN6riK0mo1wF4u3x4+ywwgtc6RgBEFY+Zt4lmBExOxOX55Pi.QXHyO2br1ZqQ0pU0b4xkZGK0Nbx6qJffqqqFDDN3.XsK6A51hHq355F.bgfkCdZ2FtKC0alLZD6hKcIf9Pi9vxiqtb+f7d5i3Gat799ORdOOu7pp4EQxl7UCguZyjZSMuHx.U0sEQRUzjzwB+niJ1Ux9wwrArXg44hkV0HgvSvJTApUBZUPUM2vgCylISlLIA+LdB3C9G+O9er9Y+reVymCaxRznh9mau81K6ce22s0O8O8OcLTanMsFNlxzMp4KarwFY+wlYlrs.KpiPSxA1kfnYVe80melYlwzbiiv8XA9AYb8bG2m4Q8kdzbEzUVYEpVsJgAgRph2jt2Lsj44xki4ladhhZNZ7aT.OWWBCCk4laNsPgBLxOZRSbPjcP0TkXZUvd0m7IO6lEKVbaU0M+E+m9Ocs+xy+UVkliTYtjwIswfSvxwO9gKh6OnQGr45sM4SH6zDz.wjfSIUCr.FHNRWZxtfytPXGfcwl8I5j8uBFQ+weurRT5vDeLUKXyJEhpRNVY7wqlI1e+8mnPgBk+re1Oatege4egLFkqodNnYdU0bRcwhHaECpmTXz34zK43wBi7aWfVjdOTlc1YmgUpTYfHRLTKWcZMYSalVapkvzHDc3vgxN6rSgolXhIwxJEMdSDDDTFibmmqd85OCR0e73+Miv1yLjsDoAN8qZpxeMXv.Ia1rRfe.10sknVsH8FK2zwxWjgSNwDC2d6sMpKJrqX1yrhpZ663Nti1ewu3Wr8G3C7AV828282ccfM8882wyyaWfNggg663b88Nvm0R8GS4VewR9oWQqr+O5CfeXtNAviCJUQO+89WoWy0bMIDGW8gxA6phEwL2o0.TZS6m+W5TChiaDTSdO0GGhIfA0EwP7q0Xvq809ZUUUoYylY.rlXhIrRrJpfDqBCQ0AVIiKDlaDMIqp59XPfw50iXsGX4ysoqq6t.8bbbRbPUaHzZ3ILDT4K1JVR5JIAlKXHj2MIl4Hl0rUSgeAM5IACQaUne+9YMjxVjUhuM047ngGTA4zWyms2KXDCsuZZPs8fb621.eyhTmBzzozi7DORkkJTnuB5E61kM1XiRSO8zE.xu+9OctBEJjQDAaH9O9K7Go+z+z+zxW7K9Eka+1u8D07vtODgZ3pjh69T6Vrb4xEEQJRMxcsW60ZP0Dv8e++449JekuxDu4exexTlSO0H3HB6LHHnhqqaQf7tttGJI1vvPwwwgvfPbbcvwwggCGpYxjgb4xQPXnLdvdoD4T5yOLLjYlYFJTrHVhLpPIgIAIFXJVRpL0oZBlQUvZuNcrJYRFNgOPh6fQVw1TTYqq+DmX+uz8bOCshsrpBFRXylLDE9CppJOp86.ba21sk7cyKvpLKv5XHNy4.VyD3SFMrUpJHkWDCHA6dgtCJ3UnOqL+PX03z4Q47GruI02wy1wQ5yqObxX37Ca.CVF5SS5UC59g+ve3Ne3+se3Rzhr+QepOkl.Y1L+G+O9eL665c8tx.T7s+1emkTUKYKREg1CCMEDqneXXoO0m5SUR0v70EQ1c2ciKWtbxryNJQQTUsDotEDkyFJ0zvx6if99v33oqTo7TUpTdBfRYxjIWud8NprycHaZAAAjpSFGsiqGclpAydnC8bBBoRkxTpbYJWtL61oynjMR1iYdhVlw8REUsDINzfhj8Q0Nu9a+mc264L+W2+e567c1Ov2e3ccW20v+2emuy9Mbc6466OvyySOfDEcEHf4wf+VOOOcIPuvy.wgWQKic80mV98+8+v7q9q9qNrNzsIriSDCd+u+eMKf9OwC+cGSBeWZHbgmsWuwOFTQVvXWtUp8YgIlnB6ryN7O4s+1vw0UBBBjIlXRAP9O8o+L7weeuW4N+neT9X+ZuOQGpbm24cBf533jXWyYHDFu.DeE3+5EaKEPmGhKQX53qZQncVHpPtb4JXa31B0Ujbg3+7odZGd4hRfYzslZpoXpolhACFvq7lek.Hsa21Bvx222RDgKbgKnKt3hwfjRFhVIxwclEZgUaN.ZyUqVM43OIZiu+NG.Lx+lvZNYfvbPTpL3N7s95dcvynC0XwAbCxyURDirYsLzm0HlEHl1MF.K2qFzEZsWKXuela+1m.pOw8bO+mKs0VOlN4jSZAj4ZJOQ1Deb49O7G8ePdW+ydW4RJHxDPTum5we79W0INgRKx8c9NemxhHSPMJoQMsDCg62GhG1DfHxfMYyl0LlJF4cMpnHxDqs1ZS9zeyKNUmNcpnZTYfB0qWOOFU5Juqqa5WyM6rydTRN+Pn5Zb6UiaKKXLTUdT6WxX+8wc3jhDy0VaUzXz1saofnHFNXBi+xzQuYr8nFT0454FGDDjxqU8L7fyMF61vkZfHzzJJ8+KvEHHFV9uSRr3DftIqkP1zLTj4SkZ035f1DxXGQVwPp4RBefjzPhp8gUTaPRP+0URiGR+LMp3GKwEiuPZbZq3sG3uMzpTcnnHRgZPg23a+sWnYyl4sssyce228k8U+pe0YWe80k4laNKaHayDkiijQJUrkrkJUJaBhGi6z4oGVtbYkHrnNYoocdUalEHmsHYaAY5zoiUoRkxbK2vMj89e3Gt31au8TSN4jy.LcPPvjtttk.JDD3my00Kiqm6QsEIA9AVpnVdtdR+98kizHKomA8o335vfACNzdyT+tarwFzLpInpl5ybTBGwpt5pq.HnlV+q.wJLzy0sefu+fe++0+AwXi5etyodddCWd4k61nwMs6xK+fa2nQisvlcHxsCDz8DvfG+fhk7Cq7GNHtoH5y7rOqZKIw+ODB6kLJfVfskpMKKhjwFxFAhSDRnIBswO1d9r2MLQsPS7UJ8hf8XExRUxwJ1Efnd0ggKUr3vHyyq3W7K+kKd6u42bFfr228ceE9deuuWA6HxzzT7Z4Kcluj0O4s8ShItd6gPTrIVLI2884uuBupW0qJuHRdrISkJURU8uXPx8T6s2DYxjYp68qcukesu5Walj8lVSM0T4+Fei6u7wNViIbccKCTz008PnIIHHvLxfGN9exjIyAcQcrUXXHtttR+980wUtqN6sG4ykyDKlkHsZ0xvUSI137CBPRPrz1au8H4WWfghnCI1ZnWCu9m3Dmn6xKGreiFtcqAcO2xK2yyyqqsQ3SF3bSNCAzuw23avsdq25ORCRiej9f+4XY1TbxSlkye97.kSkOJbHuFnYGqB0FR+rtrKFxjKkXZtbctJ87kwY3RjgKfLVf4oIYYjHqpUmkUVwA3D1vKqopWaPPvU655t.lJGlVvpgf12yqQeee+dhn8TjtnzQLLk+Jt2naHQ0B78enHOuaZsyctuvVKrvB6bricrsbgsCfcwg8Ijue6h+OLWWNz4XpvqAsGGTo45TVC0Leuu22q+0dsW6NXyFD4sA3uCrz9u.qP4AUZ9jXw4G0QyToQsD0nxgd+sYBhLDGmpZoe+O4mH6u7+a+pDGGqIP3NsaVRbbrZYYE+HOxiXcpScpzwun3fACJkMa1ThYR52ueblLYXqs1J6d6sWoOym4yLwuzuzuT4xkKmEv5FtgWh0C+vOVZEueFPPLspvomGSqZ7XKM84APghEkdc6xBKrvHHDGFFhkkE4ymm82e+C8upi9hBHIAMICTU6+5u8SO3dNyC0Ez8AYaOO20888aCrxo87V8b99q5cZuMIhzwqZWVXgch91e6ssss2YdnypGz46ue1adv0xSPFdbrrASPllrkSCRMCUqlWa2tnHR45P4PUKI1RdMRAnWRWQMctXA12.QzQGiWoD333GSiqtMkXgEpP61U.Jkz0XqTRYEXhUVYkome94mne+94srrjm3IdB8ZtlqgLYxjUpKEIhRIDCVArwRapvX7wfXKhFo4Z1rY150qm8g9adnb23K+FK.TNNNtRrpUxlIShLJZH.wfvf7njYDgFlzwgzOHAAgVPrkqqmDD3KhXIGTbjPN52m1Y1iN5WVVVXaaO52UpTI1aDq9qGDzDLLNNdfkkUeEceAoimm21IjX5V.6366uu2o816i7d9+Z2ewew20lIHYZ8Owm7Srwu1u7u1Fggga533rIMXWVdLTdbxSpb9y+BoKYGbczgbDNRlQKRUxyJ14fn7+t+teB4C7A90hwk8HfcWD18hF+Fi2c0i95ZRtXQJwEYNLRQv0opdJee+qyyyaQ+.+Y7b8xY5pC6gpaCxF.q3cSdqQS60Wd4Gb8FMtoMfnU+XerOV622668sRcXslv13RGBHkbD+9AsC+c05.+omfr73iYS1lRDYHqu986OUNub4zHcOQj0gZsgVavRzgKbYIY2zW6TDlLqCrTHb8ppu7vvvq2wwYwfffYcccy8W+W+WyhKdr9pPWQkcAcGuS2XK+ys7FfrppZfHxkTUuPiFMB.60fncYQ5yEYfCLL7EtMimsyGFDJZPWyT.S9.OvCT5l+otYKZaOvHSvFhFjZziVzsAr2xP2DzW9BryqI9wNfjcSrcQEZa3zAU0Ij5Rku1m6qU7G+G+GO23RQ+Kw9kHOVziYAX0tcasVsZCIMFHa6JZylS.TttHYaZTZsAX7qfZHdwb.YGLXPtgCGl+Lm4KW7m8m8eTpzZVlw3AhD0qI0ualfvfLtNiZlvnXJBBBPD4xNZMiiTjze1X65.xzzn5HBqt5JTs5BR5eGjwscYVJwhPbkIlX3t6tabR2akjOaonbQcccGDDDjxgDIBMfga0DQZ5ditADQSLbs1FTisSHu4wiG8GV2OONhHL9vVjrbww3c.GxSnSYHbJU0JhH4vl9ZSsScQ1MB1i4XeVajOzWHHeJ8X.Ris1P51GndQ0IOMqV.VoXBhOJQcJ8G+Q9iK7Ze8u1Bm3DmHGPt+7+7+7BW60dsEum64dJcG+F+FEIJJeJOwHRBSWZZbkUxuuHPwuxc+Wk+TW+KOW+98S2ikAHye5e5eZ125a8sV.nzfACpjMa1x.ECBBx455lMHHHSgBEj4medBBCTTv00k0WecY1YmM875nlbEGGSTzADDxgKfW.BF9wISlLr5pqP+9CR1yoP5H2L94rQUySiEwpmp5dXP6y1hHanpt9oO8oWmnnUNmueauS60lHh.Zio2BFjYdR5kX63xUDheX3+3vny0Xuu7i7HORoScpSUDC5aKHFodVkZx9zt1VPqsfFaCKONhfuRZJx34bb385Pd7n.9iPsV4kWd4JMZzXBfoFLXvTY8xNw+yu1egh+W9u7mjcmc1QlXhIRsCMhFG1ZqsXpolxBHaPPPwEpUKeFKqBVVVY3.NbQa0pU1rYyVd+82ehjBukES6oR2SlJC1oE9KUUujwi2er7ANzuijWrms31TU0YlYlTT.KobhiYqjIsXGGW0LlgxXMZlt.644c5N99maKfU51saqe7q9pa9fW5RQG6lN1pg+MgqGGGuomm21TsZGVYkt.CpACaA8SDkjWn1FdQy5ueWvjTnmBEpBkVAJ9jO4Sl+pu5qNClt3phHwK.8Mpiiy9PX5MhWNHMeDHDtjkpOso1Km3Dw2zzSG+fO3CBG.M9Yc.uP3k9u4ey+5q6s+1dGmb14l83gAAKnPYEMifnBx.EMYCozEzdJz8ceG2w9epO0mZaLRWpu6qv8hzh.LrN7NPscfV6BrCyOeGVc0i5X8EqqwKbRlFP1kghf6Dp5OkHxTu6286dx+v+v+vBppLXvf8NVtba1rJavJifd+QSJ.d9MXl90z.AxBjeAnXanLTqBzZBLZs9DhHSztc6IxmOekc2c2Be9+rOu7u3W5eQRG8kB999EbccMLutssUvC8PYcbbJjLCqk788KIPYTo.VHDi534nI7PPgzJHyg4OCqjBoMxPYR2pFYfL0nXJz5vDPFvnpBKX5xECGNTVYkUHNNFTHS1LTsZURYS6iF.4XJWx3xcYxL0KcQ08TCb7VEjlddtFmu0quBMatAvtu+2+6eueueueu8CBB1+ztt61D5r.rW6m8BQ9BYMdfUG37y11RaZp7exeOCPNWnX.T9S+o+zkeGui2QZwqzwfQY2yblyr+se62dOnVWnUJrxGGBzbEb7dzjPxATzCJ3CEbgrAG3ftDFzeLKvL0fxsLb1PVUix7a8a8ak6e4+x+kEsrrJNXvfhYylMcdnsF63Hs6dEWe80ymBIc+ff7BjWfhNttoEcK8Qtj442JkjB4fhVnQQQ333XEDDLtzAO947CAaXfmw2Cvt6tKC52mNIEHIMnNmCF0K.TEMVPFeO19frqHr0a4s9VW++5m8ytAGvt560oSmcpToxltttIifRss+ve3+W2527272ba771Be+wUqgKGJ6tRtFlFLUZQKKBj+i8w9X4deer2WVZNZlzUijw2zLJUNNcHL7YS0VFu3vkRttuDlBlbCgggWGvhppyBRdLcptKl.e2DkUPXUuS6sFQr1G8i9QW6Nuy6b0kWd4VMZzXUf0wwYG+G3A534482ljU9eTqwuWNCXmyH22iFSyo62u+LYylcJGQxEZFS00vDr+5vRcfK7bVvjDa6iJXxW3Lm4k+Jtga35cbbVxOveFAIufPh+2Tjbtkmm2FAAAq655tluueyaxyKnoMWJIo10A5.N8RFyv9bxSNfyeEAS7mqU5dubjLZepQ83JUSDq1FEnIFfc2cWsRkSLLIdkcwwYWBCSsc8BRQ4bfLgfELaFX8wPMfgGW.lnNToYcJRSxmLtiVfAAPqu95ExjISoImbxbIc5LSx+eph0TAy8SvgsoZs+96mqXwh4hiiyaYYkGHebbbAKKqhqrxJE60qWAWW2Qj1ZpcojjGrZ2tsrvBKLpqoAggib.LdxnCGNj1saSrpHXJf6LyLyn+dZhD.Ttb4C82Fek3Cdje2jGCEiZ6MLwTmk.YTyHzJobMDGP5ta344sNl33VMX4fUvhVttmtcPv4ZOb3vMN1wN1VvnwEMkzx+gUivNZhjGEoNVNPtPWJQ.SdwKdwJG6XGKO.RpxGUqVeZ0J8dnzGuPTyoK2wxn3zlFxtYcxQyQi9ZQndYnoonZ0pU7u5S+oK9ldSuox.S7G8u6e2D23McSUt9q+5KATHqoiQVc5zQJVrnkkkUZwRJCTZkUVoPud8xlTDjwKZRpu7QxNMlDhyTpTYqYmcFqwZbkBGPtltttRbbrr+96ylatojppRG0e4QWau81zqWOle9QJdnt81aqau81whAASokI4.jTIxPTsulLJqdddaArYvxKutJx56u+9sdzG8QidKuk2RSfvW+q+029dtm6YcfcNAr+ief+hwW+vzuwnqyKAYu.T.rK.QopKznhlpplEXfXK6PK1.SQd1Mw96KjQ.6PuuIOxNKjacHGTMWBI5m99OIvLAKGLawxEmZ1Yms3q4U9Jy80+leSqm7IexbWy07ZJpZXw2y648T7W+C9AyhpYbccy7k+xe4721scaECBBJlvyNYEQUhshc7bzu18duYt5SbhBIHHOUsKG0vsfff7pIFtb.YqWutkkkkbYh+mff.011lLYxnvyH9+Qnqqa2tr1pqgJvzSME4ymmb4xcYiiCLR3njVjGkdhPW2S6sev472SgNlwwyZUUia2nQiHrsaRTzJXJBmgSbf8t1q8Z68c+te2gtvPma5lF7fO3C9BUQ4dQ05uuVvDXbG.NjMoagiqS0hgnhifCp9W+ogdadvrAeTC8GtJ3i0AAFOPfkHKWfRXl0wkvlW1vfgmpYXyWFBKBLmqqawjjWhEizVk5rYOOOud.87W1u6e3m5Ob62869c2F3hdddOMfONrZ0vp6rBqrKFGpGkCFdwbPxiuF2IcAfxK.S19fwInTRwI52qWucxmO+V.agCclOb9tqxpiy4DWoIFM10vSlAiB7jJWgk1d6sKM4jSVx22ujmmWpdvW4a7M9FEN1wNl0oO8osZpMyYGYW4b9mqhmmWAU0r999Y877J7a7a7gJ81e6uiJW+0e8UTizsVoe+9EyjISllMaBBpmqWlgCGlqYylovsyJsPEBfiYDbF0Iszi4wKNRfef554Nd.bi9LO4jSZM4jSZEDDLp.B0qWmnlQLwjSv1auMi74l9EUUkQDGloSfFdknGFzXzWMR4WGfMNs2oWIhnlXXe7V999s7771.nC0nKsFEf3drH6yEGs+7GDIwczhlbXh.yAHbTBpEvlxIpGSpyojBBYO.hF9Feiuwg+1+1+1Ctka4V5kL5EOaIc9BwYbZhO41ZqsxN0TSMNGgjTvjZy9POvWZl9w8m3Vtkaoz297muva7jmLeSybuVZqs1p3q407ZJ8vO7CWFHWRg1rbccy7RbdI4ervGqT5naE3GjEw75mjfQtfffrYxjI67UmOS1LYMA5Y5JaZgPhC9+m7dyiRRtJuSzeeQtU6UWK4RbuQ2ExzHK0VRntEVxR.OIFVF8NbNOaC1FPxCGr8w1Xv3wFPH7XvbX.6YFvRLdW9v3mOfAg7fELiGCOa4Ay1XwwBT2MHTYVZTqp53dibOqbeIxH9d+wMhLypTIg.jrkv2C4IEcWckQFw2869s762uOsNzLItnPWkKjBokuuehToRkXr+XqACGPsa29P6Vq1SCgsHFNn..X3vgnW+dXPuA66zkM1XCtZkpw8DCl39rBIf.FrezXNb.AzItfbRorpqqaCGGmNZstaud85b7ie7Vn.ZksX11UPEC5fh5nFlpgSG1gwOdCVe1BlLOlDb9j8iIQdjJeo7zewm4uXzMbC2P2nO6YnmygZqD+6cNLofIEtLl8tBqPIjfN...B.IQTPTMOueHl4iwfWyBTZF.24cdmitoa5l5JDhldddUCCCq8Q9HejZ.n5s76bK0iF4sUgIoq3BKYNGXKLB67zlBlru8KGCH0tSSVOVj8VNJnYCBSxgJNkc1yEtSzrF7XgvDAVCZrE.trG5gdnm8EcQWzko05sh9cmN5e6HXR1nEaB3qQj13TWq0kDBQQDMcEvlno6Yc6633DqsSGlVh7c68iX+FK.Sg6WjLiS2DwnxfDTJVyIO6YOKN4IOYbR3w1f8moawOZWKGnwOHQDEKnnHhl56LJ4gOym4yL2MbC2fIFp7Ho5zly6.vBO3C9fK8K+y+Kuxe++ve+h+E+Eejzuxe0aJCJh4hJ1yJHZhNnUZKP.RojXli65dRLIIANkEnT1Q9uvTTVZPThIQTLCbzmhpDkl.APDHaaSyC777v7yOOVYkUXlYjHQhIc5OdECqc.vJsBVf34WXA9HG4HwiF3CFirY+j4bSFl32B3PdLlpgIwmem.lDWBTZ8HhQePnCQTSl4FR4o1S6dlFgHrNQTMoTVOGP0x.64551zwwoUzyzGK+ZOQrlbe7D.I1d+ZjPbx4wwnMODXgb5bYJixSzIluxW4qfK+xu7.hnQHO5iRShI861lOL600rwam.NH0ltaloJpNq96D+9RHOVs3Wt3pEJTXk+w+w+wktlezqYd2SqSmayMSkNc5jQ9Rx.fE7TdKXKsm+U7JdEou822smTJjw94SGDDjLHHHY0pUiafvrES5f2iLB3sVCPvBgvJhlNVu9ekWO9C+89CoYoD8r5nCfofdkJWBKtvhHSlLnQiF.LfsvNtHLSDEZP.jYNENKsDiEl7dfPaDhlfvdHpnbLykrrrJF4GqH.pBTnUTA++WpQ85iHVo7.oKMEgEKE4CYtxkKib4x0mHpMxh1aVYydUQ0Yat8iVSRdz9bm8yNllgID.V5ohz+RHZpJoTpUXly333jjY1p.UHcITZ9c1YmkRlL4h.XdgPjlHJcbtCQw+O2YO6YSckW4URDQbj1pYoTpj.H0rECN9ZIJmfYyUcRCSw293+oLykg1X8M1W7+LLB1e2tcwhKt3DDyMEgRFmZzTTzwfQHnYhi2retG.5eR4IaeZ0oa7u+W4ee869tu6xJkpnTJKAfxHKZfJSZzk+m+q74Cd9WwyehVCcHOqdZy56q0vDD6f1j7TrnbNFwN6xVhPEiS3B.IKBgUSnm0IHi86jO17Z1tvSy7YY962A..Vv1NE77lCkvhVVVKBx.yJgPjPOcbOwLGFRj0r7Z0jfJ3wG8nGMPdJYf68qCzZ83SJD9kBwnpn5POOuQ111OZNLd5xhA.af0rieE3FW3nTHuYpPf7HLUpTbV7rR.7MSWwBg0PMZM.qF6+69iG34MyyvsmTnLIQDvlX4kWN..ijRYeXidvCcPNL20bM+noA3D4PojHGlWMVMRlLYfqqddsVmxwwIExgvy+OddqzoSmQoTAJsNzh3PlovI5PBCKkRknXwhoDBQhXX1IDB1yyK.XxgiHJ.YfIHBfXBbn1y3rzSab5o0dFMf.V.Las7xKCsVSL.ANjEBI444gM1XCTqZsI701yyPmhjISBee+YKVhAFdVXDXS2hLEziFRbXWlntmVc5t.n2G6i8w5+xdYurdRorGJfd4KhdkJGk.wFaLB0pMB69HJj22q1mwOCiEYqHJX.bhsA11TnbB11L77hG0rLyruRoRXFGqfy9LC4JeKfO0m5SQuzq9ps.xmp3Tw7BvA.tG5AwOV1V.w9LN9wAN243UVYkwX+InD8b9rjPHBYlGxLOOQ1YTJUl++9Deh49o9I9IFtxyZkETmVE7.OvCDd4W9kmY2cuPR.XQ4oTbINQzAnIzZcR1hSPfrxtY13hqwfnvfffwISjbB+mm72YREfEBQnVq3nQXd7OGQ.V1BAuTpkn1saarUlb3JCaaAHlvnQiP5zog1yLMH1X8Mvf9ClbVbrl3jNcZSBFlSkY.hAwL.EBFgJkZB8uHlFvD2UoTs.PCjC6IDWYG.zMGP2xgnSETomqqae.zOZDb5CaLFdOBcb36dasB.+m+U+OS25sdqVDUHAPPx7nZ5RVHcQtn0u4u9uoEL9e5CCsRnGCYr3fEzkQghFeeQhqlEYEFO0Mtoa5lLOGHhdu29605Vdi+Nv08LrkEBeU27qJPJjbN.Td1DIxAZ8x.024wks5SoVFE.IK1gKOQz5l.e97Hjn7ALWJn.PPQBgtvMLRuv91+8aFQ+uZ0p3XG6XG1OESDEHDBesqdDP3PkVM.L5uyN6zWq0C1Ymc7eYW60F5kBVNNNIk.rZloaClsiue2eeO1GrOxgAQGKMlH6D.4RZSTFXizQ5wTx2xa4+Pl2y6421vu8bfJTFnnQDlls.NG10y9hk4nDAfsBKY35O.vXjEiQE3irX3MbC2TJfbo1DkSVkPRoTlLOvbk.Vz11dvm9d+zATAJPcZ0hrGSDQo18BWHHYhDigYzYmDDHhojJkJ9bOqHXnmPHDI777rXlIsRaleLDYPDBARXaN+KS5Tr1aBTzszZOBfsXhAAhrsELfAl4gLy850EG4HGYplIwwI0FoOSBCcaXF3HqdDte+9gAiCXOslmHG+SdlRrQ8N.KDB1yH.mgLyiAMINDTnPgjVVVP64QfYRGKf5DBjRYrOVKjuDIbLIUqTJKfbVmUe1DBgHgiiShKbgKXczidTKrNHTeKKfch0ssmzVQ52k46qCLiW8iiPbtsBA5wNnRn6HLtLJmNKPpJFsEJcD0IRoTJKy31F.YyRnRk36eiQ7Sf8+9gsNXgtii0N3D.VaCLtJp56.LzMR3gAv7n.FjuH7KUBgEJT.+92wcPWy0bMw9Q3O1G6iQvPMGKkRkPdJokuqOo0J9+5668wVIRDp0ZFFcyzxVHRlHQhIzgH5rq..lEBI6o0rsgZqgMZzHre+9gISjfykOukVqSVoZUK+QCoei25ugYeFAJlBqwZ4UbQSRjHAV6HqgjIShpUqN4oPDhAfPHfm1iAXHtRYn5LJh.BYPzd60fW8HqN1xPImgfw.xh5GBzwBgsDBmFJkZum2M97ZBfNW3BWn+QO5QGgiUbL18Iz3x9tYw.HbKffcvFVkLMA0XyXCKhHB1HDdERvrGW.HUQKLeUTkhPsabys7wVXL1YR7ZOd0OGF.gBhXf7AZyTPy36Tf.nQHxCeo7T8Anz4AR7C+79gsJgRoK.rvVas0RiFMZ4JUprHUfVHOvhBgftvEtPZsm2XNLLHWtbV.v588699HhoXjhvLyiiPmjkPHrhZfNqUZhHXAiuw3hDiucw+aFVkf1X8MRDINv6K9+RkJgM2Xynu7zD6u3iBlTa3nSFDRQnRqCn3g9fwtZ.PX+ynNyPBXzce22cfRohhmAr59UikRo44QgBiQwhAO+q34Oa9YOcLG0IqueFgI.GnKJvL4CRfF6mRFHhirQNVMUo0z0lCihKGr52wq3+dK.jNGvxkMyxzK9m8l9Yu728uy69xrsseVZsNOLUtLVAnFCiC+tQulTw2nDH5BfpRo7BW208Ce968d+hW..UO6YOaqq7kdk8fB8MJG8j.jd5lwXbkdmT0+ntpsXylMmekUVIYjH.5C.++z+z+zQefOvGv+y849b9Xc3i5vOhK4we+O3yqGs6EGryEyhni8owIdddosssiq3aFXPcxpZsdEl44kWkLkQM0w7e9O+meokWd4i7re1O6kIhV7du26cws1ZKCz6XjBVHovVLAdmZsNwce22M+xe4u7..LVJkglfmLznHJPDK..Sxs5IhoVgBEnhEKR.LDBISDQZsNwL7m1B.z7yOOsvBKf50pOoCG.ORZ3v.LM0tKhuhxtJkpmTJGnTtC.n9Qv8rNxiJt2uaImmiSYTDUfoS28.vnc1YmQas0VQcecqQOIoJ1OZ6Ei+yhd1tVZfFlNUtNrPcfnqAJtyBQ2uXl4wjQTUmENwy95wqs0L1WGmhF6nDxCqHMLYdX7CrHlhjgoZlQT2xTJ0hQHcJCybx74eVIJW9bYfYZqrpmm2p.gKXaKi0kIKkRkPHDIeaus2VheqeqeKKsq1hINIwTZaocJ.jzyyiXlonhjDKVg7uvO+Ou0m3S7IhGEmwz2gLBf3z1QLSiIPpTIQ5zYP2tcm.qcgPfRkJQ4ymOtCYlNVXgfn9yFeuKtyXw9.i41umTJ8foaXMhTc89.n+8ce22fq9pu5AXSL.UwvH+eGjq+OVOedzVwO2lU47i6bYl2w63cj4c9NemYhRPv5y9Y+rCu9q+5aiBnNJhlXpNl7ni3grXNTYJkb788uhxkKGOkbVC.Yh3ZevRKsTuNc5zjYtBrrJ6HDwH4pF.p644U+C9A+fstoa5lZczidzNXZ2ne5D0LeDcYDS2CrDLZXxRFw+FCfA8GlISzww.bN3iC2mxTDl.wZ.5s.vk466+rSlL4k444crG9ge3ir0VaklHxTzRhZSLu2N6rS8q65tt5ZstpHZZLALg2+MPjtRn05wBgXDbvvHsy460N0N68iYoAP7YSY.lL4YVB4wbnDRn05QQh.eqnWc1.nesGaZqN6m0r9Om13m8y0+YOSwb9nYRPr.xhUhroW6AdfGX4WzK5EsbwhEWhHZQ.rr1UujvQLmxUMm7TxLbIdBcSi7YkPoTI.PBoTlPoTIjRYFkREitqTLQjzTLwPXD1RRZfbdBFbBgsvJpKsDLn5XRGYiRzEfAh55+9z4DsVyEJT.VVVg.HrYylAc61c16SSPjWjsRXzYkQE2h8AHeoTNVq0fYNUrnzRDkfM7.ZDAzWbJYe0oUcHhZwL2.Fec0fAQc0gw9ZOXPY2rzxINwvmLR33vPbTbgKms.zyFqTpnIgzbvFyCOLQq1.v37.CKY1mzC3XC.18fzy46TezydVu4bUCxwSg7HCWjioB8RdddqDFFdDoTtJ.V9C8A+PK+BdgufEkR4RZsdAl4452u+bG+3Gedl4LZsNC.RIkxjvLE8xn053yjSEkLaz0JA.yHV0hHKaaaRqUgfo.oiLN4QqXp8.fjfgEHPl+29ohimmGVXgEvpqt5DQFdV5QDedJCDR.A.THQHLDlfWhtEFPr0HXgdLycjRYSkRUG.kMc8OWIyzZF0Pdz.klPqkXT.8uTmU7ngzi8qufXhskE.329a+sG7tdWuqwnfQb8gMFAuI9em0N6wJVsChdo3+r32mf5cSyrnXZQOU2SL18Ki7X4H8NbY.rhRoVUHDGgHZo74ym4zm9zoti63Nr9k9E+krXK1ZFcWJ1+Ghh4OgTJSOieu4hh+OAPbyslF+e1rYoJUpDG+OdtO2mK8W9W9WZEgfpDesu1Wy5RtjKADHZ0irJHhv7yOO777lfJ38YqE8EOxlKV6R5CftLQcbDhdZstK.5IDhtJkpsTJaoTp5iFMp7EcQWTb7Z0WGnS8IHtM6XfJyFC8SqZlyrKqu8+Heewhwwid.0.6+fv7Hog6bQblKW7nBSDK3lGFjLieO7.uGsxZUNV3WAV7VeG25RglwEaLk.r.Pj5Jyi4nDFjRYWxLdi6HkmpWTRqS5NeoysS7lb9EekWY.TQEInz9Tj7mNtl1EAAhzVBwvUWc0ADQ8sifipMf0O2O2OWlO2m6ysTVfUQcyLQG6h3oMSjSsSLKUVl80AWy1cPCzyriOje8XX9274XauG.1ydlfXxCzNLLr4e6e6e6dnnAB2.n9y6487pekW4+1ZZstlVqq+LdFOioABQnkTH6oz5A.Xnmm2.Pn+a3M7F59.OvCzQHDsUJUGgPzkiBRJhpV9BgXjmQz3L7ClwfhEKNHWtbCIP9ZsNPoTggbTgki9BZYYg0VaMTqdc.hmLx5.lneISN0klFPX.Gc+PoT8Il5.fVDnl.XOsV2ToTsUmVYDkthGLQgB7VasU780.fc9NgVKemrNr8hw+2SQJCZLBaDMNjquOJrMjxSCADiAPvm7S9IAkmhnigXdDQGKais0b1y.Ob7XaWM60T.v4hO7wOpvlCu7K+40G.sQdrGfcc.T8hu3KtJrQ0986WCHWc.T+ttq6pdNfZc5zoZdhpd5S+Ypp05pZWcUsVaB1loFZst4G+i+waqTp1.niVq691dausdLy8DNhdBgnKSgc0ZcekmWeaa6g.XTsZUmPqs4maN79e+u+nK+oeshUM8Y+yH1PyqzoSiLoyfUVYE111lm01JHHfGOdb7nPLDDB.iw7L5hSDjOiKXRGhnlDQMjxSU+qs810.P8B.0kRYi7.Mccc6b0W8U24YgbcQ0nmkkdDzQbVaiuaVLf8jDEhBu05O9c9NS.fjDQIxBPQ5bPHJ9HPOxi9Z5HrgA.rrrX.vKu7xy1AFv.T2tcS.fDVjE4HDgELETcjVqGT.Xfss8na8VuU+jISdffCO92Ce0+m0E+HdsZ79F6Y1+hw0pUKH+L6wWC.3bOdeFqMzzC.epO0mBJOOryN6fs1ZKPDGFZDizvnY0NecW20ACccMF7wet4L+JH.PYMIkS6t6tVv8aqufuSueLqlWza8HZ2lMpgJDQ8YlGfRv222msssS827272Dy49kAxsTsHeVXpVXcX9rNnuxYsihgPc70wfsl1HoNqCzFkQS.rGpfI5wwZqsV8kVJWChnFZstQ850qIbDUc0t0Yv6gxnsxyqG.Fn05gZs6Pl49+8+8+88DBQOkR0SHDCJVrzPgP3mHBgJRa6wdlBCMXt4lafTH5WqVsg111iD1B+nDIB3n6gJslylManPHLi6WBAfLTPTMiXo666iHs9hA.7773EWbQVJO4j6IDQiizpiwLy9RobjkglpF5pBZjTJG7q8q8q0Kj4t.zDwZkYN.DBEBAX.K0oUIXfTLG8bg.IkmLTJkAvPQxn7VxOIgNmnjqNwrzN8I90r9uXhdFG7rzYiSZjMv.TN5LUuIEygAPBfrYJx7bau81QEad24gXRCAl8LzCVrtucWeSiSDHHZ.GLBkP7XstK.ZYaeEMjRY02za5MU100s7O8q9mtLQTM.rGybauRk59LelOyN.nkVq2iYpA.1SoTs9m9m9mZCfdFDEXhQPq0i.vn+uN4IGCvikR4XKfw111AkKWNDfB60u2XkRMRHDCEBwvnhXNNx9iY.L+ByYZdPTBpdddXkUVACGNDX+2D3P1r..V6HqAZRx8rwMU3jjZGBP89e8I9e0gYtMLiz0F.n1O0O0OU0c1Ymx.kKiBn11aucyHpRMsvaa8u3MMe1mqgTgXQFcBcGGHh1KUv78Mw65c8txvLuX0uZUCkM8hxCvP+uEAVK1u2iPPnOjO6Y88Md5qSLQygxYxIqUtow+2JKPK2unqQ7lKMI9+FJkZOlol228ceM0Zcyyd1y1TJk68Z+kds6wD2TJjchJ5PeOOu9ZsaWl4tRoriPH5p05tBgnGGY6YYY4e1ydVegP3OS7+8AP+JUpLfHZHAZjVq8+nezOZPT7+LCfK4RtDF.X4UVF60rI1auFSh+OlB0dd58kP7D53XPad..7IPCbDh9ZstKybmvvvN4MMmoyC+vObW.z6K9E+hyPAurA022y0Jg336qvqOsrXI.+qGDlXAyXqKYwnNJanfCRmEX9+Oe8u9hW7EewK.SE+FgnDkAPGfSLDX6GMja7H5p8I.RtsoPIa.fmA.Nguu+UjLYxS344cTaa60zZcFxXQFP.8Yiw2dDQ6IDhN.Xj1UClXBlCca533TZmGZmceo++7Rc+pe0uZQXBPoMP99m3Dk72d68UzjmNYLdvtpE2cw4PTW0MBLGr.rSB3kDFz.QDQ7K+k+J8+7e96pe4xlf4NFvfcml7ziGdMdvhpbfNYrQRfZQcbtvbJ08OuTJiQFvB.4Rq0mMoPHRo05zmRHVrngy8Kq77VxQHVHOv76Nb3bUqVcgwiGuvwN1wl2SqmmmhxnfHMBYL.fTJSAf4c0tKPLEIZTTnsscfVq8iF6eTbUoAfESjEwbxuv+v+Ppq849bSlNc5joRm1Z0UVg777PlzYv5ar99D1IfXGj7DGjuw23ab7se629HXbJ2A.MkRYSsV2VHNYWs9LceMulWSm64dtGCJSPt5EJTtQQSG16bgKbgdG8nGcVge6etDfxGMeYS5hvV.V6D8LNZ7GZcLfj6Fglm8aqE+xliD1wA.hA.5C1AiGstXPOF+2IrAR5YPhVlx6uaxw19Q1W4WPqOybBgH0LceIMLid5kEhSdDs9LqzqWuEO+4Oe5e5W7KNYIfjkKWNsuuerXhMq1ljA.o7z5jyuvBI60qmUlLosFNzm.XqHnwmDLRZKrS..KuX5CxXFDJQv1t.5zoCZ0pEGK.YvzZx3ebBXht3LgxMRoLHl1YFadyjWBFcinJ.Jp0ZOgP3AjuLPoHzajaHvDgELZZLjczVaUY7NOwoYGwcWJhGyqu.P8Xwma9YzSB9s+1e68eWuq2eyM1n3d0pg13wVCSlAwCSDgzeHl4KuUqVWZmNcOpTJNhRoliHJwRKtD2tS6g.nkPHJo0ZWo7TtJ0o024cdmktka4VLnr.Y6HDU5q0lfn1.XvgfrfmJedvrc46f6CV.nvx.EWJpqYiHhZByYeMwzti9Xd+V.rlFXqB.W18ZzvjeH.bLsVuJLIuEk3A5Af1RoroVoavfqxLUY2ce3hW20cckArq54c5F111wc7eDvF9BQsgZ89708DgM3A69ZB.mL.twHuYIaa6E87FO+MeyuvTe3O7cAl4QWVtb8dvJU5fonUc3F.ipAL9D.Aa+H8YM6m4i107AOWL95JIvwRKk6tnRgUtvEtvJW0QO5xkAVQq0KFBrniPrXAfENe+9Kznd8kDR4RLyKbgKbg4RjHQj+1PDFZYQDm..wZixhtJ2EIPKD0sdDMoYhO+CQnRIIQHUHiTDPRxH1pTjXqZR92fHkXZ7jPq0VykIiU5LYPy81iHKhEBIfofEw9oBAPvBKrH50qKL2ynvrY2bbkJUF+m7m79Cesu1eAlYNjHLNjwPKhFxgggRGmTJkZwnmSyEgPgYE25dDQsDBQEsR4ILHoqrI4ctkTJa6440x11tMfnCftO.FBb7QQEe+ISTlLaBlyVzD9P94RAbrLQO+Wpa2tK9LVbw4JazctXjCNbSfgUMuOpZjd+XCL16v637i2uSS7a33fjttFTujEHckYDyU.rXdfkKAbDl40.vpdddKbRgXt620MEQTJ.jNxlaNl440Z8hvPsh4.PFsVmLUpTI78GaID1wHYJZOIYIL5LhO.FoUp.oiiEybJsVOG.xP.IsinecLpl777PgBE.yLrrr1mnCGsXFLMiD5DqoD6uAefF9.OvWt+ke4WQWhnNmTHZUxn6RUYlKJkxhE.prX1m4dsp7s5UIp3maBLnJP+HZM8jExkd7rNHROlE8+otm64+SlWxK44EU3sByATbeMcFHO9u7e4MFbq25sNB.82Dna0YZHFL98B29wWd.G7+ehYy4.HaJW2yjxwwIViQx.ja9ekekW4Buk2xaYQhnktJobkh.qxLupRoVhHJ8ojRqy3pIl3jQZd3bZsdtHjMCi6IqXzNmQHDKBfEUJ0BDQyEgvjPgP3e.+eV.TBDg3IBHo+vgoRkISJ.jjAr1X80oX8v4QOaeFAAgSZdC.BiFDICAnN.7dsa2t4kbIWeKk5zcjRY2O1G6i24k8x9waAjusVel5BgnB.plGndISdCC1d6s8OwINguCfu690wnmJGOxi45eMfvDiQvI.WBHjnbg.fKBvc61kq.fevevePCYwIJIalg6yTI7serpp+iXC31GGDfHF9rKBje4RkJsjmm27Lyo877LSnmnJkyQirIhnQQHJYH.FxDOPJk8enG5g5emen6bnRoFmZtT3YeYWl4Cp.BOFP.Povs29o0Fg6qByX+U50OhdDiAP.y5PlYKl4jTNZdl4Eu669tV99tuGdYfrKAfk1ccSRlXBMGxaPcxINztZbXOWOv0QsXX9MDn3vSZDj2A228ce8xBzBn7dBgnIPglLyMKlGM.xUmHplUHp8i9Re40OiqtV0pUqJkxJISlr54O+4aHLBjZSoT1RHDsDBQKoT1F.sipjaOhon.kvPaa6QDQCgo.Z9JkZbTPIARoLLdi709betHSlLjkkE51tiQT6rswvQCiD5IfgiFgQiFMyWXZxyga+1us3hmDJkxfd85EnbU9gHrOPoNetO2mq4a8s9VahbnsRo5ATdv8e+JefbA.f+ze5OMCr9LcK+DGrxxOYsdjcq9.1U6Lsio9abheRe.3u6TXbNJpKhDL68WjYdUl0qAi3MuFfd0MPDZl1.Ybdz6d6rAadXWagQAK5W1fhm3hKEOli64440A.sTpS2PHD0YlqQDUKuQ.2phbn7UJDEAJ4IDhROzC8P0tzK8R2qTdzJHHnsuu+D3bKkx9xSI6s2dM5RTgNZstCCzcs0VqK.5Ob3nAvLkP70ZcfAx4LqUZVq0LGFIL+zzwR8pGYE..zpU6Ip0tPHBgEEJDByXOlQHwj44OgPlCGCfQJkZ.y7.1LBDGvFZd0Kh9WcN9wu7dBgHhhMkhKN0Xs9r94hed4fg4AFBTw+IvhkLqsTjca8.l4f26688NlHxOa7meVL7O9c8tF.TbTsZXLP9Gm5lRV.XTJYDYqrxJqPfXRq0zRKsDwLrZ2ocLjcszJskIuuR7Ikxw2zMcSiPNLrf46+PsF9.qG..t1F..acXA+8T409tuMEE5H.nXbm9F566OJebfWqCbhGW+pyZjvDaCVgO5QO5relgvnuFSnJvN6rikRoRABoIlx33HhKf+7.dycJC0LSfrfcccCApEn0HzTX0mv7uMqeqY5.p63BF+W8wlniVqaATo0G9CeWcpZFei3qVtbZfrKBS2VWEH+J0.VFBrv1.yAXePTxM6m4re1GlOz80M1S.3Cr6HkB8Ar67te2u6VkKf8.x2PHDMPXXCf70KBT+241+cpKjx5ZsdOkmWyicri0RJksEBQGaaYWGGQWoT1Ef6oTpgLyiHP9v3SxWHDihoJIy74G2dTC..f.PRDEDUPkVEGqzHgP5G8yNFDMdiM1HXJz0oPFfkmRxZOMdauseSlAvQVaMtUqVvJQBHDRn0Zn0ZbjirZ78iP.DzqWWifGRnGQnaESwnZ8K9K9y2DLuG.ZwL5P.8BCC6KuJmA.4mYzdSrVYzrDoTZn6hg5AVJkhER4XWW2QZsdnPHFJkR+KbAuvoz1PS5notRDsNexbcvmyGVCAl7yrEPHvtgJEB.1LXwEWLnLyy13tjLyYpZPt8JUAVEH2p.XYODij6IBw9AmNOOdVg.Hz0EiA1xG.CqrlwVDFZMUCnPkR.kQNT9Feo+aJST9J.nZIfJNNNUjRY4a7FuwJZsthVqq64o1SJk6QDUWqz0zZcM.rmu+n1.b+hEKN..CEBgINclGpz5QZs2HsV6KuJmQJkdnVq8IlFC1f5ISw6L2Fi0sDhHTpTIn2WwR3IEnJ5rSCkx.yBovzABivu5S.CXFctwa7+6lvfdlFmVop65paHDhFRorIPtVEA57s3uU+J.i9DehOQ..P0nhT39jKpkd7tNL+KA.H3k7RddSNWmYOhYSOYPd.St.EScq25stvC8POzJ.3HUyi0.xeDXhSaI.GieO68gro8Oj.ldMbvWAQSgmn3+qL7pbbFBfgEKVL572x8+89898553bUcXlaULO1CHWCxLM2pAfpmVoJKtJQIoTVx0UW8u5u9uduHee8DBw.aaY7dei8jROHNWv32EBwvXDswLORoU9.vOtATTjMSpLY.h5X0JKuLZTe1hkXLwrsswrklKjAOsXIbH.EvfCts2ysMFf8AvnK4RtjAJ0o6AfNeouzWp8K609i25u6u6uqEPoFgDUGH+d.nUobn2N6ryH.DbhSbh.frgtemWLzmxt998BlL0Q+1H73.gwboZ0Uw3EWbww.v2DDR9XA0JAJDMFNyiT.4OrMWOZKKbNj.PmxNp51EK9kW1hnkBYdNxfLhH53.fHmDDPnPbxPozbvMxAlXZLxiAOym4yr+s8VeK8.PegPL78ba21XfrAnHB1c+haDl4Z7IB3A+O2qoEp3XHnToRiQAD.jMXyoPiMBdr1g9ZeFQUhdqs1J8W9K++1DTacrDPokKLAdxkLcpuHlK+DJ67HNf1B3DydXMM60ywmApdkAF8NdG+1Ct5q9p6VAncmNch3MdwVgggsQIzBnbq7.sENhN+O+D2cGgink7TxFWd970kRYsLYxTSqca.flZstkVqaCTnK.5XYY0kYtiVo6PD0Ef5Af9ZsdfRo7APfV6wL.rssogCGRJkx507ZdMDQj0Fato0nQCoACFP4xmahU.CXv4YHyOis1hSkJYLjO4UWd43hjXdmLPTWoTgyO+hiOoizmXZ..57Jekux1WxkbIs0mU2QJk8.vPCMANa..Bd0u5Wc.P8f0lbn21+KMUwNX.+g.Hb6s+nSNbdCfw.1wA5QHqYJUP4okAvp4Lv8bM.rVMfi.jaUTCq3NMnuIiaPL0FZVeFGr3I6KwiKSJ8ADi.vHWW2AHK5Yaa2w00skTdUMxATKBRwMJAzjnB6gxn9YTpx.njRoJ+RdIujZKu7xMUmV0tXQuV+z+z+Ts1c2cimbFccOiaud852EnTGee+1.bakYL71k.5KkmbH.7SlL4X.DDAk3YtjM+errrPlLyg81qYrfgsuQrovVDnMBEVPLL3IfPlQPTRMCAv.hlTbntDPWkR08ttq6pK.5++3u5iLPoTiPNDn0ZNa7uagHrb7zMxEiKc3nH6It0FHDHenMQiuka4V7AvnJ.iHpvPtLOHtao.XLPoGOWCDPEB.VEJLUOHzZcBNDIXlSzsaWKXPVnE.rXFV20c8Qrty67iXo0ZpL.KkmJDkQPw8I100C2c2cCQM.fIT27fHC3oxqIAUQ1DfYeH.xGXaJl4vzoSOpDvXfMCQcva+35W6DQmjJ.POvW9A..3wiGaDTVf.sVOVJOU..3s1ZKqnFmjhINEP9z+3W20kw00MsmmWZOSmFITAvwwYFeJdOQa+cvjTA.BKBLFH+PTEcIhLPBGn4latYGhnQDQz+o+Suw49I+I+wVF.GAnzZ.XM3iiHAVAvaA.LmiygBYcfC2N4PKfx1SatwH.u9+I+I+IcPQzFnTq7.scbbZCTpE.1qT0pMxAzPHD0QXXcs1sgqqaSkREc9W9t.nW1r45QD0WoT8Ag9G37ug.vWqz9LanoJLEbdTiF08ymOuOyreDMCiJhpoc.pyn.w.+5+52JkvxBVVVPHDHSlLQEoahvJFOhNiQHQ+SIkc.iVro.I0IlpIkxJrgfcUgAN9cHhFfRS7E...gvl.AKFvRqTVHB+cjA07AJkZzU43LPHlRAjid01FTLJPHffuJg3eNR13fmOM66G7uG.f2YlFK8m8m8d8goAWiHhFkOp.ju5W8q1LwmRfUcccWCn75.X8M2DqAfUAvRGCXAfrypQDGLVrucWubDseCQCLFva3pQnECnnYRwj.0N6m7SWEnbYgPT100szN6rSIkRUYNlqHDWYcgPTOLD0c0tUsssKCBkXlJcJorBQV0+C9C9CZFFF1VHDsTZcqu427a1RJEcIfd.7PhngpSqFQTng9VjgNVu6+i+GmTB3ToRwKu7xvySOsvIXB5mX.JLhBWAvBA.T.HDNZjenVqiZvJ4CFCYfdNNhNQzvskTJaIkxVDwMyAzx7mWtqVq6gTneVfguzW5K0WoTA+p+p+BOUI40CZ2MsvIqaN+cFpXFZxAHmOJYlbijYbdSWzEcQotm64dVXm6amU.Js5lQwqA3ZnpS.V1Yl30N9weLKdR70Sref3FsMpDvPfBCKTnvfhlwqazT5qTKGGmVnDZCTtUNflNNN6IkxFRorFXTo.PUGGQsmyoNUCs1skRo5nTpdZsd.P9XMXYH.OPq08APe.pOlw+GQjuVoGC1DiFQHdx6fnuCzBKLG..Vd4kgsvFLGBdluQdddfHFQr9hISOJh1KSwEIY3a5VeSCAng2we7e7PXjLh9tttcEEDc90928lZ+hewu31.nkiscqH+7cQYLXqs1ZzpShKZh1k78EEM462KXR7hA.etoAX6OWy7SFIfEA7AJMlM77vp3YKZffUo0SAT5vRB5vdMKrhmyy1NtKOKy.KR.ywylPESSpxmPJIs9LVJkJIybR0YTViC8Y8Yz9Bgn28651QJksQAzY73w8PgJwhoyrb1+vRT6o5AIevkYC0tqGjOe9.Tb8w.UFWgMafwlltv+CbYKz6XG6X8Ipv.lYehnvWzy9EYgHgB7qb+ekiTz1dC.rILTiZMTGqVxTDkEAv7YMGPGmnapnwKbj35c7YEBJbtXm3qZbb9Ne++GhFyVY6tzRK0A.syhrcN5QOZmG5gdnt.nao746pTptJkp8q609ZacaukaauGnTo5JkplTJqgPqZRorAybK.zUot+dLyCrss6IkmpKHz9M7F9UaCvsAPW1TkY++r+a+Yg.L4HkIzZcxLQvu6C9A9fIYlspUspkssf..JUtL3PlmgpDLHDIbdTHyl8CsZ2dL.Ok+lb7ADjOQr+m7K8kFBf9memc5BfNhqTzwHxf45eO2y8zG.CuRwUF6v2G.9Mxm+6zQx6SlqGsNlx.Hr1zDdLvvrRgjLyI3Rbxd85ktDyyyLub+98WC.ah7kirq1bMX1iuvZFdydXiCtCtmb1qm..32.vGP6CfgNNNCPkM6Af1NNWUSfh6UNRH.0ZcS.z1y6rc.PKo7phG+t0UJUi69i9QaIkx1RoS6O81mq0wN1wZRL0RoTcrfUW.zgYt0oO8oa9M9FeyV.nqTJ6y.iTpy3SDEjOe9.yDaRwCFLz7LiLZhybyOOBCBQ+98l7kYZfd.dZChTjRIChlD3isPDP.i4n.OjR4nSJNULhZhE65tO+m+yuuTJGbYm3eyHh3wnb9.gPDdFWWF.ge568Sa7eWHdRFs026VFO5KF0xGBTZbwXUhWDMQFxVZnoa2SQlfCbh+2cXA4eP5UjLrXtT.H0C9fOXJgPjzxhhEawDfoYNWlwq5ltI5VtkaIg3JEIbccSfbkh7OUvRoTD.nMwl6e5ubhSbvyq.dp44AOx8lEMn0fKyg.kF6EC+77QcsOa0n8pG+Q6r3GILu8fUQ.quxW8ArzZMJWtLGFxyDfdIF.wCOERJkD.n68d+37G+duW1wwAiYl8tfGibUh92rwD+ZQO+ex79Ki7fYlCvwJEQQvBcfgS8MKfBwh33325a8sZ8Q+L+CYXlWpc61GA.afJXSU7Yg.KCWm3wr9rE58vhy4vuVl9d..72.arODxUJe9du7Wwqnq9B5t.n8ev662sYYCG+a333rGyzdNNNMiDP7dZ8Y5wL2Oc5z8EhS1gHzBL1y2eTrHn1VHDFpPYAeGi.qNQKjVes08O6YO6H.L5c7a7t7gA8tg..gggVar9FIxWnfkkkEEFFRQE4Equ95..LYn5.uvBKDNdbP..6+POz46+G8G8G04zJ0dLy0AgJ.nLSbI.TZu81qjTJq.f5eiu12nkTJGpTpv27a7VIgPjjARFFFZIDBKxzQBRHE..ALOs3wk.5EMdW63551w8K5Zz3f62aHf12KlBnm3I8ySOXg8O3e1g8yGBfwu4el2bLUI6CfAkxlcH.B9y+y+yIl4z965ufiiypLyqCfropZmC.4.vl6BrFPkXcnaN.mCpyIea05jSLEuYL.BalO+riC0tvCsdQ27qYuVsZUG.0Nkyops0VaUSJk09he0uZiG9guuF.ntiiSMKXUgHpjTdxhNNhhW5MbCkBYtxq+W9WtJ.po055NRYim0y5Y0zfxKCEPh0rDgvgkm7jSRR8s8a9aZJJhThQiFg1saC.S7+GfJNl60LYPHESglN9ifzoSGq0gCAwwISOPq0CjRY+SJOYunFfzF.sOiQjz6BSiVG.OLLhNNCkRo+c8e8u5fi54+kbc3wlUOaHPofRwnvOOMjYtO1rRjVJUHVSmFXS1Am9zml1ZqsRyLub074WCF3blC.4QYroqAovGA.KM3bNwhs+g0D0Ys0ldMc7n3hWuXjcU99llZh1BHZCfNdddcAPux4x0C.cN8oO8dtttMJXUndQfZJkptTJ2iYpoTJiEo8AZ8YF0nQigkJUpuTdxt.n0fACaxbXS.zlMOGGcG+g2Q.HynYWq0VwTHWq0IxmOuEQDcjirFIDBpZ0pvS6Axxx7kgH.S0QhApyjl2QTbgumrmoOXpK.2+09K8KMPoT8c05tWy0bMcEmRz48ca2VW.SNN.nSgnmEHpnuMssiot9Skry9dd8Tw.ndxXcvtsMawMlG.KkEX0JFsmHEQjO1DcPUrGLcns+wA7iJ3Bv9O.I92YLu2WJOvlk.dF.3RYluLsV+CB.I.eDgPlIBhkgLiQDgd.nCAzpaudsSjHQmLYxzSZTScCep0tUuRgSoxlNZDqT+srA56cBLFaOSKfejID9zkJ5MaBEovpHEZhzQ75iVGfpOkaioAPlB.oKlGIZ9MZRqt5pw+6SlCHcICOZ474y6WpTog.XTNfgkmv87II2G..9X.3yd9yyWzEcQL.FisP.1YxlbxFHg2T90m..VqCP0kffZ5D0A.ykGXo620cIX3v77DQIjRoUjt4LmVqWnYylKs7xKufkk0be7O9GO0O1O1OlEhdtIkRKWsaZhoXdPuHALmvnsIoTJUpFMZjY80WOECNIXjfAaQfRfHNMB.qXqBFFE8mLjkzLVWMVESRp0fH.JfYdLXLDD5Jkx1Jkpd4xkpdim7TkJM01qiqqaW.zy4TNcPYzE.8uvEtvfidziNbcfg0ejSpimJYGdvDXSYCLmWzjogYdQxllGEmfdjDOqBER7MKVD.f+Feiuw3K9hu3gYA5TIK5hJ6Ss48OFPvtQA4d7ie7fycty8nEn6jjS1xnuJSmZSGCVX2o5WPAfzEiz2jc2c2zutW2qK48+W+WmzKGlCkwZtttaFDDrVlLYV7JKTH48alzDIiTo8z20ccWIeUupWgkP3XEMEJlSoTFgtNx9xVHlSq0ogQc1SyLmB.IVas0n81aOZyM2bxHOLFx3gggbwhEACNj.Y5HlIHONWtbb4xkAlHdXX.al7RC0Z8nH3jNPoTcOkT15zJ0d+L+7+L6869d9cqcounKs5m5C+YZboW5EumPH5H.5pmHpXlWaA3uyS71XlmIm.Iv1SzTiTqCjtl4rgzv.0bjkH+pBL.5IO+OLgmcx4C4ARWxreNK.9AfYJ4bYISl7GTq0BXJ9VZvffEB3PLjHzBl8c567Nuqcuka4McAXFTtUxAznbdzNRH+lXC5.DDCC1SbhSvau81+yEs39tcEueLRvkQFrIxvULiqTDQgA.vQzxnO.5mGXXoS.e7Hoj5jyBDBw7ZsdC.rUNfeHuffKyxx5h0ZsHWtbKUtbYKX7+EKFwwSIu8jRYEWW2RVVnzUJbJ8+7K7EJesW60VElD36tAfes7XbjPNO64IOQQQ1IwqbBfDaeftgtA.UaUj.MMZZDhfgddf4KZljSI788sRkJEcy27MG728g+vCKaJNYLxyh2OEjEHnxiTzWOrDnOrqwoSNBf4yBr3CTr3BEJTXgBEJj4K9E+hIO5QOZ5b.yU1H98K8S7S7Sr3+8+6+kySDOmTJmSq0oih8JgPHRpTpTvngYYHBoAnLesu12HyK7E9BRpTpDDQVmTHnR4QR0oUoYvoIPoDBQx7Dk3LJUJvH0pGY0jYxjwJYxjjVqnHsJgzZM77JxEJjGDQ7RKsD2oSmPgPDr6Cu6vjYR1kC4NNNNwBocOoT1SoT8u9q+5GdtycN+b.b47HAJgzOzC8PyM2bysHy7Jeiu9W+H2vK3ErlVqWAFe1Vvfj3Qrw1pI.pvLtfiibG.rimqqmsiSiIOWxignTrNMgQyD+4SU1Ga1icbjDmaRrOoYlSminjkMwsktPgByWrXwEgY5NkNRCtrpVsZvlatY7dYy3h2XaZzdh0fOZ7H1OcP6wYiqOA.rTJE8bjR3EkflssskmmWF.rvl.KVc+hgbJsVmIxGwB111Kn054jRY5ye9ym7htnKJE.xn0tK9M+5eyEt9WvKXgH6xjRoLcdf4JkGy+0+be8LKszRQ5EFYQDRaaaOuVoWHjCmOa1roSkJUBKKKK.yHuF.Fkii.hGq7XZAHCkmTF5dZMrr.LCkIZ1DZGTqVs9atwFcEmR1QcZUKXro1SJk0K.T6K45V2wwodgBEZTrXwl.nqMvHu8ihi3DZehzm02qqYedlBQ1TRhxnM9Wh8+wB.KcdjReF8bBgHdpFkVoTIi7iPeguvWfu1q8ZGCfgE.5WLtX.Sz.MSAWWEXby8OUDOrX01Wg3WGHQ8igDXWjDSmpaq9ddOumUt4a9lW71u8aO0a9M+l4vvPeoTFFE++hJk5HLyqBfkrrrlWHDYzJUJFH4C7Ue.5F+2diIh8+AfEHhmiYZN4z3+S.fTmRJSdZsJIXLSydoT.ro.3LHxxLhQMiPXMXdx2ovHQxIHBkN9Q5n3PvXzM+ydyC9S+i++s+y7Y9CzY6s2t4pqt5rSxq1tttcRjHQmfffFNNN0QTQsAv.GfwtG991mJXe88z5eMfvjC1Y232Cg8TnwWY+aTrxWEo.JLGjlQ844lN9gSi8I5OSDnt4izQiUJYpj4l.E1Tq0GA.KtvBKjwjLqoSC.TLsbH.XwDrlew4oefefqkAPXgRXLQjuq1suP3z4LQS+B.zGarwP.D3AvX6o+N.1JNAqmp2UwGs0jNVjuI7AVaHcTZHLIfOQcnYl6AftEA5fRn2pqt5Py+tBIYlWrrApma.jKawhEy+A+y9f1.4sKalUB1vLtmyAAVGle1k2EXwK5htnE.xNO.V3X6f4AVeNj0fX.uoUfFEKVjAPPc.+nIUzHCbgy1G.8KAzob4xscbdNMcbbZHkxFdddM9U9s+sa.fFggg0uzK8Rq4bUN0EBQyW+q+02kHZnTJGG6754HbFJkW0P4ojiXlGKjRVoTIbibjt1Zqk111NsvVjLc5zINxpGIA.QBgMwfmZYAvDiP.ZLHxmAOh4nNkSSFkqc.QsYlaQfZCxzkhyd1ubqd850La1bMugWwqnsqqqQLAymuCylfIQYDONS6dzidM8idVcXA57Tx0Mdi2H..4EsmwHheje9hX.fcOjG8FLXvnuYwhfYd9ye9yu5E+7e9YYly8sZ0JOpfbvj.bVfbYAP1cQrcU9kN24NWT.Z1yRamCBCzYf27ViAvH6cwPf7CvFFa9YNnevwdNOmA2wcbG88.5EUrp1NNNsSlLYmBEJz69Up9RorqVq6.f1DSsekuxWUKgvokRoZIkxtLyCjmRNNBJmVrQXgSRDkrZ0pVfYqb4xQ..yO+7..HUpTSdP544AOOOTpTISyKXhLg+wTzYxAkKWwGlNu0GfFvQA+qTpwLCe.LPoTcO4IOY6m4K35ZwD25d9je4VenOxGpUghn8K7E9JZKtJQG.zQOMHG+ye9yGDcO6IZ6pI9MO91f.xCrlwmTcf3wMsO1bywDQgUXlfdRQbSZDHt8A02YFQ44SWZlQkK.VCEvQRlL4xQhCXRPQ1Dl1QyDQQSsJhkRIcy27qHgqVmz00MA.R7leOuGqhe4hyTn70Mb5ex2EGqs2dhFb8zgyCX.mP.DjuJBHxI.4mPGyvntokB4xkA.yUBHM1dBhtRG0Y5YNaVDKfiKAjaoxS6nXRgPfxkKGinDK1jDTpKr6ElQaOxE5bUNiEBmgkAFbsW601C.8Q1rifAYZ3K7+3KLCRVN9Sz2amDuxLh0Zv0e8WuYxAA3ilSKfHfXDyreon6W.4oToRk5AevGb9OvG3CrbIlW6Lm4LqCCRS1.EhgsNNRkHzvB3Dg.ynN8e7Ink6vFuvGf1gaDBfwU.FMd73g.45UrXwno2Q1VeYOul.Xue+e+e+F268du64bUhlQ51U2PD1WJk8Xl6xL27M75dcUkxS44bUNJ.xSJkUtjK4h2SoT8HhFYYYEbFsNTcZESDwTTXUDQ3dO24nrYyRyM+7zhKtHkLYRK.XIDRRoUjxjbZ3UcUmJPJkiAv3Nc6LtPgBiAv3TYRE.FANNWkOxa53Zz0YKoT15y9Y+rcbcc6dZW2tnD5occ6kISlgBwICjRIcMW6ORJ.jJc5zIEBgkPHH.vfn.gP3KDhQRobHYMQDXGa63DkPrX..5mqD5CbrAHqowCOEqXIwKFmaBkEFBbrADQ8qDMIm.x1uXwh8Yl6yLOx3KiSxLuvC9f+Sq455l8C8g9P1.PLM1rMyAfMrafUAxsLN1jBbLCZfmTftCXGZSRSwRht+bhPOOuP.3CXOrJPuc2c2N.n0EtvEZhBnkPHZAC5kZSD0RJOUK.z7Y7LdFs.x0F4QGgvo8cdW2USXDk7FBgnoVqae+tt8TmVMb4kVxGLBd+u++ajTJRBBo0Zc5ir1ZoJTnPhLYxXYYYMAUSwMaHJ9+3weMLS+ZB4xki0mUCh3vy8sNW.LTYc..2KZRZ14JthqnKCz8r+smsK.5xL2oVsZMUJ0dEA1ywwoIPtNm4LmI5bSwPunhsf3hEbh8Mlmep1hANd.xhwDQihZVROLkFL8z.cQIzMZPYzEvd.LZ5WRivolasejejejrLyEN+4OurHvQAvwhd4fbP.y3PKayIwrUXI.rvF.yAjcNjGyhDciMmsMA.tNP.1cJccLhROFby27M2G.ctsa6C2111NRaBy2oXDRZYla633z14TNQH0FC3HpseEW9ULjYt+IkxNR4o5HO0++j2adTRxU4ch96KhHibu1yk3dypajnYqQMnt5FjzHKFPxHaDFKFaL1xRXCO+XS1FX.jsAKaVL7NOuAC1i0vlMLHwhDFCBLOPBqQXawhPpqVr03GzRPWcbuQFYsuj6YFeyebiHqrKI3w6LhA0x2yoNUUcUcVQl4Mtea+Vj6vL0QJkCTJEoTpD8wL8w05zSTbhTYxjw11xN9dfDbMgQmLSiPxjoeIbr1NEe9SOl41fPSlYiqmQX0m5S3PqjMalk+C9C+CV4O6O6crBLBstowHUqtokk01ddd6TqVsQ4kATaH.X+yT6idj1YV+uz5QxIO8vwZ7jD2Kj9n8CXeJLcZf0yyLWD.EHhxbW20cYcwW7EOfHxL4NA5f9k5ikW9gZRVzryNq8pqtpK.J.TcFfHIPiysSmNOtUVasGKEwRgTLcryn3Dex3PvnGSnMArMQzlBw4uoVeeaJDhcTJUyXNytRsiVKDjWCDDrJF4dOnELIJEcfCb.bxSdRqkVZIru8cILP9HfSb1Huw16j+MACKABnDvxKOVwI6R+gxkK61nQiDzAL4W5K8Um3m4m4hxxLaqpq3ZdGcnRcrASN4jCBCC6+XerO1tvLgxlgnbmXm2XnQGaBS579tNsyrfAOCg0Va7DDGcnvLyLi0ZlelCvbo.rRCzHM.RiJvEgkRArrSY.WBHcXLJSNrPTXQkpfTJmPq04DhEbApm72vAlD7Kd6egau3DEmn3QNxQx1nQi3jG3TDYY644QAAAnb4xTXXHIDhXQhDDHFLH1L4etOAzGDMfi3gfvPhoALgdvH3mC.ngRoXnuueWKKqlBgXCkRsxy3m+Yr58Gt8p+pOqm0Z27Meyac5Se5sme94SbhgcpToR6vvv3GiZCA72KL7dj19uw1mc.afSlB.YdSuo2Tl2xa4sjFFsFxNd51oQEjCb4IQiFS7abU+F4+u+Q9u6F2XqVDQsdGui2Q6W6q80F+bt7..qt.0MM3nD5fkEcAz8D.CZCLX83Wa7.3.TiA7iuG8..3jieMBX1+agr6KEVZoDAnLAxxN.vsRkJSDFFNC.l02WWjH1kXhD0D1nBR4eLsasZBmJwEFde95LhZhIz99SGYYMCw7DZsNWq1sSevmzSJU1rYSkOedGkVaQfiaDRLpNAQLAPiAdhXwvgID2XNl6CxLML1XKc8IxXImuvm+yevs7o9T8APm+oa+1a9yd4+baAvaHkxMTJUhSgsJLBl1F.XKLKZgzhtXzlyfUt...H.jDQAQkPN9fDGtGt1ms2IV5vLaSSQ1nXMK366f3IdEu2fN2y87G1pU8d0qWOF4ZyL.vNBXYy0RIPX4osAV2sBPABXt3D2db862+I333b.sVKEBwLZcPV.ih3G6TGsA3sHxZM133AJsu1OBQpZ0psLhcKlJUprSXXX7zYqMvnqjmh9K+K+Kwq6u50w3TG7rg3AiPEB1EIelBjpfTvV3BCJDriEm4DgRtmPHFtqcrV0BHxBngYpekPQrbox.Kuup.OtE05GGhv9Xhm8vRYtiqzNBiyOEwFWJqCLwXWgIpNhh7IhTxibj.Tudh6z0Dw1nZ850iN7gO7vfffjo+NNhmd350kwiKl7uk73aM6ry5t5pqlD+qHQkyCrrQPMKiznQEWs93olc1YIaa69NNNc.PmJ.cBQo9.KGGuqROfvQTUFInDbFLD1khvxKuWzlv.f2+92ON0oNU76chT.cSCrp48NArgtpEPcmXaYOa.P9u3W7Kl+W6Y9LyeuZcdDEUnQiFYWXgmiKPHAfn68du2gG8nGkAfCpfr5iqmTHDS466OEQT9Nc5jd00Vy4o+zdZopToh6m5S8obOuy67R0saOmtc65.lSIjhTJs1dVS7YpToRbpTohojJFRLEEQ7PKhXl4Dm9JBL5+k+Je4lKrvBakIS5MXBaPLkPgwN.X3ce22czEbAW..fkuuepZ0pkG.SexSdx4.n4xlMyz.bAPTJy7K3Q5gxh9p0AgkOrTFz.U7UpEWRJkA.UaDq4FiYQm+Hm58OsW6MeMafYsAFXCroAg.UPVDVIOPXgx.Es.lnNPwSbhSj6YdvClJzPGk9.nCQUaCD1BwCfI9i1PhtnWoAX4keHcmtRkJgkWNwu1KA.mDJ1Fg8CBmphMPXpJ.tN.tp3XmUARUGyk4Vu0+1bW4Udk4UJUVoTl4ltoax8Ztlqw022OikEx.XkkYNM.Rm3lNvfTkItwa7CW7RuzmYNoTlftA2xkKmYqs1J6LyLSZcfNEhfsPJF41bdddnSmNX80VGQfimtEXNVOCietML96iaFUL0hXzlIz1h3NOietmSya78992d+6e9MhhhV847bdNq949betUpU6vqCr7VAAA63c9dMQXkl.gc1OPu73LbLFfGYFSX7Xv1vCVn2r1X0UenLsAmJUpjNLLLGLLDXBh7l7iey+0S7Bdguf7p.U5pkqZEFFBgPD0sa2ANNN8bbb5TFncCTtMPi3lwTsUbdaiiVzHTBLnxLZzHoA.i2D.J1ErhcUuJ4Anr.0MngoDXrbI.rr807q8qk91+Xer7MLCMo3y8Y+rK9d+fevBRorfRoxHkG1Enw3rfvEwBl7+5+vcMw4dNmSgxkKmays1xsamNNffkvSjPqKB.VBgvVqCb.yNfXhAQVwJVR7iaLEbLNVHCz0hnN.nUDPye0ekekl2xMeKMAYt+KdvaaIN7g2Dgga.isU2RJksUJUSoTtSoRk1Y4kWtI.5ZhgTbvOAcyqept92CMLYDr2wYRaEfwnkSUyAf4pCjEUPJvfdaux21fq+sb8IPsMJ4wnZ7ic8jGaOjBAkxBr7DUAJUuLpwg79AviQq0R.ThIt.wTJyeSJhnn9LScAPal3srXZcFXchnM.h1gYpIwzlhZhUwthK1l.XaoQnDa4aNHcHN.Xbx8S.mB6G.m5LCpL9y2yFV6MAwwO.w.yZfT8.bWIAxtkoLX4R4AVdxx.S0nJl7ldG2T1q4W+Zb..oTJDOIcVq0C8775Ob3vt2y8bOstnK5h5.fdULvxNpNPDJigvB8Q8Gjk3B.vk.FZALHL4mMGHrhf.zVU.rCKAGXCGTeTR+oG6iDaiMKLGbNA.lTq0SvLmgY1o1B0rug2xM37h+MewYVe80Srv3Bw++bEBgCLhEIYzahfHDAvlw7aymIxw33mC8Xl6VqVsd9Jko4I.8.3NRYstULPRe3h99CIh5xDucMQs0u7K+xW8C7A9.qFWP6lRobmxFJjsSXRRMyfNXsZ8A72KjtS96+Hs036orAPp8A31AvsAPJTE1fgEBgCL6wJRDMM.lgYdBkRk0xxBKszRctvK7B6gJnOWmA.r+betOKth+O9ELPMt9Hq8b7DfGmamiJ5eZSxQ7Z.LlM9pa0y35bDUz788ScjZ0RElr2pLJhFUmRoN1zGVJK9o+Ze0zW3uvEZiFl+OJkJsTJSESEGiH7ALkRolQoTSIkxIDBQl0WaM2omYFacf1V3Ir52uOs7xKi74ySMa1jfI4fQSDyrhqSMtIvDw8YXNWCrg9MG6du29G4nGcnRoF.fARoreL2W2BwSsawEWb8JUproTJ2DkwZvBa77dJOuM9L29mIg5.ir9vC.vtm4T2e3Jn7tudanjyXhB8tTFwCHSefzqXd8mfGFhfx8.ZzeVfgqVBQf.iFidLSn94Tvfts8666e.oT9Xihhlud85k.PwXqIm.gHvnGXzFD15pegW85+B+m9EBupq5pzRozuBfOADVGXs2467+6s9O+m+GrCzhV.5N6GX3oN.XzGDN0nmWmMDOX2jjqAGzBo3U4TwP3OMhEQc.3FDDvGxyq2JI6KJiHznL.ZXI.r0UPJXAWDX1mCSUTyu5pqt+Nc5rO.TcpolbxMWeqrLwNwzYjIyYfF53rfrgZQkZokVx+htnKxGFiMJD.q6AzpmAgGIHUcHBlsarip8vcCSF+0lGJwIjPEjBgdYABJVFnPixHOHj4D24IRcvCdPW.3d7ie7TG9vGNE.rh0pMtUqVC+9e+u+vm7S9IykAFPlXZ8P0XzbVeDptNCapdNyERTij8T0.CeS7u4.bVQ.GvfPvYnIJIMBKax6k5SqyKlWT..EhsPyzLy1xiHY8h5AWh3Rv8i62AlyqlFwZvhRoJdXoL6G5y+4ScnCcHm4latTMBajxx1x0.GcxFDrAuKR9hsn033Rz.fnA.z.h3ANob4O++OeNbc+l+lzw0ZvLOPHDsWckU19q+M9FacYW1kcFCopBPzG+K9uDcWe46Buw23ajPEXivJ49pe0acxCcnCMatb4lSq0SKDh7Zs1I90sdvTz6NLQaZgnUEhZM7880DQm9HRodHP3x.qAA1ALZifG7q8OLuu5gi0Yju1A.rNYMXg96F+D6lqSrPDio9re1Oawq3JthrDQIFg.CSih6d62ws25xu5Keai.BOhlNizqlJlNxLPmb+2bfwJki.ZDI.hz6CQXaDg0Oi77GCsei95j8i4KGS84Fl7+SCFoQiQ6USPllqRoxrfTlcQkp..lTJkEe4W6KO6e7e3aJybyMqqqqqKLVQb53yyc.fECFVjEA.DwLSHdDCTxvFHi3aBLjHNhYZPr0t16qc2es1O8K3o2D.6DSKLCR2.1gHdSw4WaUzn5J.0M15ZUXDfy5ix6X7lu8PsO5Qh6oR973wfGQEdFfVO4bEIRCkWAffIJAL0xUvTfwj+UW+eUgW0q5UMlimBGeeeKXYEI875Wud8Nau81cN4IOY2WxUbEsAPyPflnBZgHzEKeF5uwngzLGv.KiAPXdsrBHDVxFXY2R.tKWEtHBNnwYXQxI47aLgBf7OvC7.4t4a9iV3M7F9CK9s+1e6bSO4joYhrkKHsTKprt1W705dyelaN+O3A9AEOmG64TbkUVIO.xJDh35HA.PjNPODLEughsQLMVAuacuL3XT3ggLQCHl6A1nGNLP6iHqsCCr88o0aINrXS+i6uUMQss0ZcKl4ch0akc.vNU.Zy.sajjaqDsgxqyZq8s6+jlYlAgG.CvIw.7Hylw8+RqGM2vjyrP68CabJjzPgjB5nZ.19XlT.qkFBjAZSAs6ryNoJTnf0K5E8hvM9EtQpb8x1MPiX3.VwxLIjxVUQiTwZKPd.L427a9MKedm244YZTB4Af4.3hfPFvvtZkJvx1dnVq6AfNKHkst5W60s4q608ZVStfbUDZ3GsVqaYrqVrNphUKWu75MPisui63NZdYW1udSfvl.y1Y+X09m5LETmjMnORM.6ONqGJjAkf5BG.gKfNMP4LUQiL02Ep6SvLOEQTwq3J9Ey7o+z+CNgggVRoz1W66TSTyVoTjPH33oT1y22uOQzfq4ZtF9NOwcRuu216CuzW5KMFFmweTFCqznRTnA8ICAJ22fJkx8qfFCCOiq6xV.MhObuhC.4BTOiYRKHaUfL02swI4KCLw64SdqS99deumhu62y6Nmskcpb4x4L0TSYqU5zfPFgPjMPGjgAGCKPC0G.Hl.GAhXgPv23Mdiz0bMWikVqsF6V6H.d.HzcP+Acd56e+cNlR0whntBgniVqZwL5FyewA.XvZqsV6Ymc1sYlWWJkqipXix0KuUCzHNowRMAVdGfYaMIVs0lO3Iqt2IQ9Hw0XEgreGfSkB0PJ3OJIafj.cygIJuR4YWzewYOwINwDW9keM4.ZXcm24cN3pdVOqg0APrdejvM5ngCG1667c9+s4gNzk1FHrMJi1nA5UAna3tEgzuJP+5Xt9.qL.UQDpO50vcuGnJRN+ItoIykBXEW.O2xHHywiQoD.lT6qKJpIhuNpjRoVLiTJyd5SeZ24medmC.394O4IyUoRkI1ZqslrXwhSr816jGfcEBgytVXooMHr4KHXLaBq3FlPvDAN4hjqVsJWud8glfvlogIkKriRsXahn9hCKhTKpFPL2OBnCQzVDQqsfPrVcTY863N9HadYW0ksMZfsCBB1xyyKwVjaBOz6fAGbvIvI16dqeRrWa7hSMIbreP0NUMxG9wMXuTFfkywLm4lu4aN025a8svw9pe0nO2+z+zPS+PpGe8TE.rEPXZ3g7H.yTEPbi2wcr+C9DehOlrYyN+0e8ugJW+0+lljYNCnXzGZRLqKAzhA1FlIbuhPJB.fOPEeS+Rpt5bn9lqraS45BbfA.mLAR5.69Z1iziGr2oUmffpTPfrU0UKTG0KxLmcmc1wpXwhC.pzqJBGVe26Wb.7RADDWjV4B.VSsxJeqRSO8zdAAAdVDUoT4xyznQiB3LszT.B8AiV.XMl450pUyG.mVoT9Lygwn5Yi3IQ1GnTTErbThnDB7P1f8GtdsYrlOb.J98X9f.zIvnBSyyLWfHJ++7+7+b5CcnCk5Buv+iNe2u62zwzTEjoLPlPlyDqEOVLyze6e6Gv52525kf+o67ehe1W5ydH1E96lF95gNUBpzON9WjwBkCh.pLPfv95c01MK.OKff3BfqhX.gG2TA3VFHcL0nxAfbU.JDhxEAZTHNFYpEWbQZgEVHY.UoPETPsnZx2065cMyq9U+pmD.4KVrXld8Fjpa21NDQoVYkUblc1YcLSXU+PMIZFlflCyWnvfc1YmdF2cSMfMEafw9+LD.cHfsSmIy1qr5JMIl5r3wWrWXX3f2+6+lhdau0qO59WZon+rW8uK9R2+8a84+re1L+e9xe4S355NsVqSDWxb.jCLZ5TBBSZsnRk3tQqdDor9wTJkTJ0nJBQcjfh3wEQwQMK9g48UObs9gju19sA1IEvpYPUjK33AEdJddS1HNGMkRUXAoLWHpj02+XYpcjZo9ju6OIddOumWWaa6lgggaeIWxkz76s82qW45kGz.MhGxS0d.0G.TYXUDZFx0n68hikBLDPD+ZklPEXgPPk.rVdrAkTAHa79uI0ZcAgPjPAnzJkJKyb1ZKTKakFURunZwzRoLKP4be6u0clmAJVtbo7yN6rYrrrFWv2So0AN.rMQVVLGsacVLCgTNRyRHBLuq3tNt1hzm.5ywnK4vxCuSCzX6idzi17S+o+zMQDZJpc3sKivMZXFlZLhC81.HvPceTsCP2d.qmjK6izaZ93q8tmZLTmWxx39YSaIv5NekScpz6e+6u..lpe+9SkJUJivViJY2byua5ImbRWCBNj4TJUVgPXSFo8nuRo5yLMPJ85u7xK2trQrViikZPvOpfnpgUGVG0GBf9vfJu9.dC7P.aTjljquxN.MhuNqXaZfaiLnLxiFHuRoRPiTF.jsLPtFnZw+q+Wu9r+N+I+NY+ZelulSsZ0hE+c3lj+uVqGiZZjU73QiHiMdOPJkQvz9M6fffT7nlEy6t2yLTtg7th+a63lu0B.6vDuIwzZw5Tx5vLPq1nLZWsQ010Q8NAAAs77NeSNsXt1BrRG8tmQkfv7GtQ96iXVOZugID.nXASyxLXESSdOxQNRzwN1wRRzJIAsjhdx.fLc610Mc5ztd.o0LmcvfAYcbbRDdHKyFSNCQTgEDhIVTqmRHDywLWJHHXNl4YHlKxDkEFw3IIvcrBdSc.3l.XCln0pIDqpTpMRT2Zee+sqUq1FUAV+d8823zm9z6bQO+KpYv8EzzyyKtKy6uGvo1KbMOaon0eTqw2atamZmAoPZ3h.SPMfJYXtdZAQ4+5q1nXV2rE2byMyeq25s59a+a+a6vLaWknzGyWmQJ8RmH5Xi03D..xWqsIvohmJEyLOHc5z8qWud+G+i+wOLc5z76889didJm+4O3O3262t6W7KdOw71q5Pf5bU.pdUPn9C5.d2p.Y9u7wtkr21sc6Yeiuwe+zarwFtG7fGzciM1HK.xmJUphkJUJ+fACxtxJqjJlmuiumLEL7t2F.jNPCXrALlHJxyyKJdp+DQDESSmDXFGEn0Ci.5BFsIBsIhZGEE0Nl1Ms.Pm226+826x+4d181+76uC.ZdG2wcr4S3I7D1pVsZaUFXq2x+s+asdkuxWYqS789dsO3y3w0RcupVFaEtTGfkGeJX.mcrmaunLIo.swmhqMLh36jgloaNCphIAPVTG1lI05Af5NwB.WQ0hiBHOToTcHl61e3vdUpTo+kbIWZ+64d9xibGArqviMNxShF65y.gW.qfjyoJCGXAGT2fXIsVmQHD4q.TLrBJhPDCuSYZ.j9U+676j4Z9M9MxdtO1C3BPo51scJl4LwIFlWq0IS1M44dxJRHDQZslI.xy.6TKFfL56OA.D+YNQPgGPD0gYdjZ8iQTGjFxbTel4t1116vLugTJWEI7h0Dbt4EbAWP669tu6wQlS6CBz+DOXtV+SpIj8Pk7ex2agRHEbPlXzKjKt4I1UAn5ljxsioMRxqk1UARGXJjcFee+pRorF.lWqCp.vSiXjS.yq+7G8i8QGdU+ZWUm3oItSr8wtJ.ZHVPn8uW+SWqVs5.XY.wV.5l0.Z4aDJxjDiG+0kyVhGrKJSFSHQk.4T.SzqWuIbm2MGXXiFHpUqVCxk6bYlCvm8VuU6m6UdkIn4KK.Jn05IDBwze2u62ctBEJTx11YtpUqL8W4q7UJtu8suLisu2lA.YnIaSPXUvPSDsjPHNcE.cHPC.rlVq2PbXQaDZteUoTCkR4vJ.8hEf1GtEPwy7bp8CKLDD7SZB1AAvIrlAvcsRHGVF4vXZ0ByrUYhrW1DCIG.Jd7ie7IJWtbdgPjNtwIt999In4gHh5cq25s19hu3Kt4S4o7TZUwnKWIEOX1KElX6td8.BFTAfCKCq+0Ow+JcIWxk.LVS5p.fvwDwwO3G7Cl9E+hewIStOGLnrrPbwDoAf8q407Zn65ttK6m8O+Ou60+FdC41byMmnRkJEsrrxqCzY.iTF6eF1Bg2d0zBB.TLBLgVqAav.GG6FDwEdScIBCDBQjuRQDYDJw3edKgPrCQTyS8C9Ac2292+PsVyF5eohDBi1mDDXbWMl4zxEjEQHlVoTibOM.XSDwLid.bhKBsCYzLrM.vxDQABgH..gAAAq344kbd3N.n0AA5dhGYILm+vVzd9rE.blEH0p69dcQ.T71u8au3ke4WdBBamvWqm.HpfErbYliZ2tcmCbfCzA.chQELehSbhgO4K6I2GQnOZjbNW4HfFCqBzq9tBk63ZnFJAPKu6003WaIhk7DvzfqXpQKhysDo8UpLGQJy7WdS2T5K3Btfz0le9LDP1zoSmE.4z5fLDAWvrCGuGLtocl8gLSfHXYYigCGlX3bIHcJBfSJtLAwoChGdUO.zqcmNcxkNcaXY0jMNp3NxCK2Qee5c.vNBwg2ToVbsiJkqFTAqiPLx0dPr1RbPfAmkr+Yuq8hzDa3AaDABgI2muOafkbKATXYOLk+83OU974mbpolJ2EbAWP569tu6T2+8e+tG3.GHOybwW0q5UU7M7FdCoYlsEBA9Begu.t7K+xsUJEGSSkdvLPmtddd81byMi99e+ueT4xkit1q8ZGtuG6io+e8G9c0ONN6PfxLPCpLfUipvB0OCT0YCfTUAxdo+5+F4unK5om2yqZl4medWgP37JdE+Nt+0+0uyLoSmNe4xky1X4FYDdhTc5zwds0Vyghs2dBvk2EY4V5.chsMDAfABgXXfVGwlAaYA.aKKK6nnHRHDHPqAChM5yGh0CmQNZV6wxyXCgPrhVqaHL0htoTJ6TAn+hJUeoT16z0Oc64Oz7swxnsuue6Z0NZGf58p.LHbWix3gaj+9Hl0i1aXBv3v4Z+.6+T.mZObfDOXXil.eprUAxWGn.PkhLWOOLNHQRBcoYlK.fICBBl1yyapFMZL8ryN6jMZzXBl4BUqVMaPPPZhH63I0FmnQ7TbHzj.sEybRm81P6qaIpI5n05chhh1jHZcobgMqfvDZPLpXBjPKmG5hUezvl0QMfX+.NmZWMDvc80W2c5omN8oO8oSO+QmOKBMPc6tu66NasZ0bdFG4HN2e85tnRk7pEWLubAYVtNmRq0NBgH00e8Wepq5ptpzm24cdtIbTse+9t111V0qWOB.CWPJGpGNLJLLDdddQ.XfVq6RD0gA2W3IFlb80rYSZ3vgVSLwDPq0DQjsmmmiJHHszyKSbwsozZsCQrCyT5hEKloXwhYiE9QW.3TsZUq1saas4laZE2jDSC5zZjNSFLyLyDEDDLLtQIQwb3c7jARlRczXecePn8BBYqEMzgn0oO8oadgW3E1D.s788aYYYrdVgPjTrayJ.sNlue6Z0NbGOrb6fcg3YBWOGOAky1NfbuEnYGFFRUpTYzYGwN6PQfJybe26mc5dQQEd5O8md1u025a4bdm24YwF2XxcyM2L+EewW7Deyu42rH.bIhHee+9RojqdfCv0O4IQPP.444MLHHnKQT2VsZ044+Be9cOz49D68Q93e7waXxHDNTFvpA.ATkph5V0GKPL.Re0W8Um4C+g+vYOzQOT9O8G+Sm+6dxua9sVeyLWwy84lNSlLt.HikkUlfffTdddNCGLv11wIEfQX5hebREyC23.rALGamgGVJ4iqTDHhHlsheyki0vEFFTnvjAN08YvsAise6u829V2vMbCa4q0Mqc3C2Qs3h8IxPUmXg1aMXZBv5ZsdyXwOqM.5d5Se5tyO+78.Pm8Cz6T+vmZ+Oo1qQODe83IAkTTdNl4Luvq9pc+3+O9HNb.mH.3tDQoTJkU4xkSkRJSW+a7MxW8oVcZ0hp4.PEh3J.VywfKBFYA3TXWshYHQTOXD3tcjR41ZsdSDgUXhC9XerOl5085dcZXlp3F+E+E+EMe8uiWeKnGapXmcFOXuwrclFvccf7.dSTAASTm4rXTQnlIfvLaQkIarrA4dLy4IhlnSmNSsxZqMSsCe3YTKt3ryM2bSs5JqTfMvZ1Uq0oVe80SkMaVmLYxj75dSv7ZLfRJkKUAXoiqOsVHdZqTF0W6S+U+pacgW3ElneX6VrSELVRz+jQCS1yfeFG4PwzxwTj2cdm2Y5m0y5YMxM2pWutU0pUcQUj+6bmemIekuxW4L24cdmS9w9XerbW0UcU4fwASRZfgK.XkR0iHpyBRYm5L2QEDzu5byE8Ut66lusa61X.D8bedO2dWzEbQFzEZ.V03nya2brpVEndcKTANUCga8cEL+z2xsbKY+vevOXg+n25aM+29a+sydgW3Eld9Z0brrssWas0b777FQim.sNqmP3BCcTsDBQRSQr0A5yfBNwWC7XP0iggpawnAh6AP8fY5rrRo..fPHh.PWs1XGxGVJaeeZ+AQvhrLm+wewu3Wb3Ue0WcOkRkneNVUqVMc850KJDhoAvzZsdR.jiArIyqCcMBgZBxchZwLssTJWG.g9990srrBEBwJ999qax4St0z.MWeWDb9P4RiORbclmYt+86fScJWTFYqz.4BiclMXZdxj0qWelpUqNyce228jWvEbAY0991kpTIJUpTCYlSNGyVq0PHDQ+p+p+pCdmuy24.WWWdvfA7exa+syu5e2e29OgmvSHIu5jXolWmpTAHLjPYPka.tg4eOIe+b.Xx2065+xjYxjchm4y7Yl6bNmyI8C7.OPpie7i6doW5k5RDbGLXnqmmmK.bGLXfaiFMbIhb877REEE4TOrtEXXkMaVq1saOJWLdOi3OdvBCMMJgFtayQ39w6G6SF2Txf.JktCCtCHp487Ut6MeZW3Se6Ymc1syjIyNUOP0V2ycdO6XYYsk7HxMPcSS1VZokZsu8su8la1YqEvNJ++CBXcB.63btR9YFTeZPv4jvzzqhLyYjRoQCx77Ri50yepScpISYaOArrx4444RD4pTJWoTlIFgvV228ce74e9meehnAJkp+BRYzhJUDSTjzj+e+fffArAYGbrfNOB8Kc610Zmc1g50qGA.KOOOGcPPFgmWNUfJqzS5FDDXGSCMmzYR6N6LylQqUoMCUGNfgsPJRNiaTtoAZM4ID..I5vTDL6mXZrZYymKGZ1pEPx64FILgAF4TWc.PqFMZzrb4xsiQZx1vfpjU.PnTJWA.aUFn2wN8oile9iFUBg8WdWyGHo9SCxJMzvYuB8JvYe629QtdzbCS.NS3bMdAkie3AumeOGLMbgSLuKW1zA73OxGWXsCQjCJibUZfINlu+LVVVyDSGjI777xGDDjiYNC.ROyLy3t1ZqEa6XHAxqiBdxLuEQzFuo+j2zZu4q+MuIrPqs1XqNm3e6DMeA+RufsfYibhEG2BPzBP2FkPmCtL5chyrgI.O5ZS5dQrQJf8kBXoTXFjp5Zvo9XV5KRfsVoRovxVo.ByNXvfhNNNSnTpbLyt0pUKUEfz28O3GjKUpXBhxFE...B.IQTPTUIAuSRXLytTSXT5VjPHnff.344M..8zZcRvnwC9Zicosf4he2ll3RbBkZfEYDfyTBgvMHHXjEtJDBSGjSddymw91QcUF.C.QQjgRriqyBIMHYfTtvPkZwHhnnvFM5UtToQthygkxVK56uSbSRZJDhsdEW6qXy28M7t2HHHXKuC60DQna7jbRDzvtujWxKoyG3CbqcAVqCJgtGXYz+j+jAF5+ui03nHHIvDAu3e5.XgkgK.JrT85StupUmfYNO.RSDkxOHv4qu3hN+c+c+cY9DehOQAsudhUVakINzgNTZeeeaKKaHkBal4TJsNMYJTfXlGPD0qToRcSkJU+fff9LyCXhGJ8jrJPQ111N7vgNyLybVqt5pINVCYYYQqu95Vc5zw100MEyb5986mw.UXjUq0YSt9h2S4JDFwOrzbyY455ZES6lXmiXzzvR9.toc4d85YNSwfjIDWYJgw3xabSS1sobL5gXaRWtvBaoVbwshsl5VwhkXaXNCay+k+k+k0dFOimwFeiuw2XymxS4ojnO.l6opg9nEFfbnO7OCk7++ct+ZuwFG6LnRo.VNMjHaIERu7ttkVVXhQjipVMiZwEcjRYJl4zuzW9KO26+89dm.FMXXF.LiTJmVY3Buwkb10gyXPX.XLBkIRobKee+U+nezOZ8q659K0.00uk2xaY4a3M8l1HbW8UXuEKb1XRK61zDiNxXD9tD69lnz.ksAZPnDHukgcvtnQIM.x366Wr1QNxDHjl12+dmSJkyRDMiRoJBSQrtV.NBit9jblbxd6VKszRqA.0EckWzR56SuzfACT6ae6aWQHFU6.TuGpfghPLTuqHgONr2+IABSh2+se.bJfChnXKUFX2lnZd9L2bNXkHKf0FuIe4We80mb5omd1x.SceZcQOOuBDQ4O0oNUdGGmBvzLImXJCjH9vCDBwvjlTA.DifwdwE7EQDYkfVmIlXBZmc1Ixwwc3TSMA2ueejNcZq1sa6355lZiM1vYP+9tdBQBJ4LvMmPZgmHkVqSzfjQSYkIJkzyyIltM..QfHlL9joMQjcLpLiE7bJ1PHXVHDITfHBFZNzyxT3P+pUqN767c9NQSO8ziGasEHrs7vxcPH5.fgwEoYG+zefVq6JWP1sZHFVGvNVvPmToTyTsZ0oCCCmLtITVwOti6xGsVnVstGWo5.fsXlWIFwX0888Wt1gqsJrvFvFaAM1Am8NThwisN59S.jsBPtv3Fl.fYtga3Fl4Zu1qcJl47TUJy+v69ev9W5U7K4fvQhpapd85YuxJq.lY1xxhihh.QjUTTjkTJGBChe5EEE0yxxZPbrIpUqVzNsZYUoTI5zm9zQOgmvSHJJJBau81Nat4lYd7O9Gews2d6I52uewXzW5FOTKalIGDit23Askneb1vruyZO4eYP0TP.ahcx7ryNKVc0UAFK+MZWaLu6wN1w57K9K9K1QoTcIh5xf6IOrz39hKp5rvBKzNLLb6XzmuEP4s9S+Secs98eG+9cKGhNM.Z8I9Dehc9k+k+kShCzF6aecvR86BDb139lwW6EIvV.0H.efZ.d9fB10fDJD+QNTspKpy1G8nyacu268lDCYhd85MYiFMJrPsZ4ZXF3QAkRUfHJWhd5HDB9c8W8th9UdAufH.BRojYlizZcjPHFBfn3ySrHls43bsRPu9dx+2xyyKY3ToSdNP.Dax22I9mkrOKI+LB6h3s8V653h.Mvt0J.PiUe.n9.734BDgXqM+HFpA1jhE7Ul4sIh13J+UtxUtm6+dVFLVCFWHc..3q5E8hh9n23MNHlRNs.BaiIQGuMQufGLc7OaLuierV+6gFlj74wet9C6MTBFKfLEPX1J.ECAljYdB.TjHxjXaY37s9e70Scdm2SMu1WOknlXta9lu4Yt3K4hmzhoBLPVh3zBQszZkJMLP+MAx0CAPGoT1FUPSDhVJkZaoTtA.Vybn3Bs.B6DqBwa4ArYfIYsl.nUXXXqJUpLtOhe17Ah+3rNyhZKAGrbRAtUsApaBHWFtpiqbkR4HwVsDPgkAlToTS.frRoLU7z3xdW20cUnPgBS7TepO0hDQ4+ReouTty4bNmLwuWMNMXRBRhO4m3SL3+zu7ubeh4thZ0FpTJDmTmKHjhXXylNOyJUPjkE.yrU0pUcBCCchOPlHhrhsHrTvz.NKdjfomTyDEihXyarwTjXHHzuaq18O2CbfgJklgwYMrheLhOvj6APCHCmE6SD0CQncjUzNu+226u4a9M8laEKnSIhI15vL0+0AprUkJnsUX3f6w2OZ6s2dvS5I8jRnRRW3gdH3AQijyV2+MdC4hmN5AAvInZ.V9.oJCjswtSEKK7PZDjTbR4z.MxBfhZstnPHJByTtcHpJUFgtMLSvMefNnPDhbkBISDMDUv.0hpHgPD8G8G8Gwus21aKJdRmVLytwBzpcfVayiqeHFn9Z8xd4ubm+w+w+wTZs1MldAtISNONYuQSmfHh.CJhhXhI9s8Veq70+G+Gy.iT48wR5i..GQDMLxP.6QABMI5wC.ndRobvW6t+ZCi3nn4me9gfPWhQagTtiR4ucqVs25hebOtsZ.rsRoZ9Q9HejlW20cc6DinjMQErEBenJ1+LbqpeZy65wSXybFTE3fvDmbQ5BnL7W2LkqX6YE4fgi7oBioNE.JHDhIXlmfHZJsVOYTTTAKKqQB4lRorXlnf.EKEx9BonsRoZt1Zqs4gNzgVE.g9ZecMQBkbJuARzVnYPar1iptmzF.N0.x5atGJCQTJrKzmsM2mEOYtxHMGxYIhJBfIeaus+ul8u95eikNtVOmPHlUq0Eh3nLDQ1RgzdryeSA.JFoAsAn0AX8BR4Rg.Ko0ZUryMYFZg.cf1fvjScpSMb+6e+wba+mXM26LG7yAAgS7fRNkvAfMN4YX0pV6CfVJlZn.kxAr7DnLlEMvzeyu42bhK6xtrIBCCKPDMR7UkxCmVoNdpEjRm5lhAM+ALS0kUZEAFrTJGpTpHoTRJkJEQTB0DHgPDoT5AjIAdRHDVA5.mHDkRJjNlynPJlwnyrhe+1lYXQDrDBgEhQSBLm+YYbDhHFLFJqUKJtPFSwTDbHFVwi4fiXJhnQMzkIPQQHpGQTWoP1Sq08YliDBA+C9A+fny4bNmAJkpyy+4e06boW5Er8G7O8Ocm5L2A.C0ZskTJs..TJ0v33+cigjeJ.jWoUSIExYTJ0zDvDQLbsrHKl4gDQcAi1BonEpfNHDCzZUunHdG.rZbCSB.PCfRqAr7Fd.6D.zZVftq9f0GmyVtudrFMWIEPnKybZhnrnBx6eL+Irssm1yyaJ.L4G5C8gJdYW1kkSJk4z95LQVHc2lMcerOtGmKyrSPPfSDhrpIpQvb+ep38NVvj20vwP.PBZMsiKb0REnhHlFJkxHkRQ.HsPHxo0ZSg1ioqQwza9Ln3k4qYKvfN0RKgGy9dLDS7nBbiUCGlHdXTDMLd++X.cxTPqTJ68G9G9G18Zu1qsM.ho2LZo05tLy8hsQVi3waxOaSee+0pUq1lnL1FMPG.L3XG6XCNxQNhQj0mCsO8wOc64me9wo56Yi6Y165g57u30AHfSZNeaNjkWlyZZpt48vp.N0GQ8pxEAZTDFpKWDkwDpiqlTHDSRDkuRkJoO9wOt8eyey6gt1q8kQDHxS3Mp1QhnH.Do09LyjkTJcTJsKQFym3GU9+kJUJ0JKuhCCCUftzeleFqa5VtkjAs57u8u8uY8DehOQhHhhhX1xJ9vVd27+KWtL2nQiHPXvoO0RCuvK5hXkRSw4+mbM1mYtKEKLqDQ8DBQjuuOaQVQQHp2e+e+ee6Wyq90zLwsazZc6XTkuI.V6a+s+1q9jexO4sLtHDFVEfWzzrn9X27LhY2vA6Cbh8NjfyV2m8+mqGs2vDfGL7p+QAQYB.10.R4GaoqvzvjBDQYgwtQsHppMy0cIhJvLOsVqmStfbZ0wTSHOhLmZQUZPHCCJMwQYXPYocUp4H.zKQGIfEZQLsEQz5wIksdEiCjz122uUsZ01An519926V0pUKlJNdsABdzRAq+3r1yT1NfEvII.XMRDu1GrwRHElENX0QVN7Hmnw22u3c7E9Bo+MeIujTkAR0.URyb8hDQSD2Lk7.HuTJypTpz.HiTJSqTpzwIzYC.HDhge9O+s06m+m+mquVqGFO4gws6UagPv5.8PlMNuCCPVDYWpTI6FMZbFp9MC1g.YGCs7wgy1n2KqToBGFFBLp6xTeSCQLPzUJk1ZsNogKQ.nuTJ6+29A9.8+sdIuj9999c.PBkaZdXgnUHJ2V6eeshrhZEqF1qKDh0.v5k.1dYydq3qg4FFKjZw64lrOvlIST8rMsKYuqeXMUM46cdpO0mZ5u9W+quK5kPE2G6isf68e+2ua61syjMa1bnBJ9m+5+yKbc+EWW9ct+cRWnPAaes1lLMeKOQjwx6LS3N08bO2KdZG8nfIlrHKZpolxJa1rwLegsCTAoXvoAAm3oPXkISFqtc6RLCJSlzVc5zwJ9ms6jkSzgk3oSDOICpZ0pnd85IPBd..MXhIJNbqs2BwEY3DOAUiT9yTDiX6m1Lk4joZDCcXt6a9M+VG7ley+QC.ngLwCHl5JOhrs5XplfvNLnsrhh1.VVaIDhs8882oUqVMujG+ie6PTcGf5iKrgiZ96AwYX8g+zdu0danVrXtEtqsCSTJTAoQHxgRXJtAOQ850KDEEkJtoWoeYurWV526688lGFs0n30cc+AS7g+venoTZUAJhyxwSmRJkTx8xDSQLwcYlZRDGa0sb8ZhEpCznA.VsDvlKCrCJglXYzA0PW3+HdQh7Gm0tH9LAQOl831.RBPYCS7XSg5UgKpaDpQoTVD65pJUTJ0bweedhH2CKD1+i2ywHgWUaXgTfQRAWQDQcN0oN0lHB0unK9hNM.VBnrFnwpXjtRH6Bn5Cfg6CX3R.CQsehhFpeTC9g2yOauhc5tvVONdXEfoCQooB7+FSd9Ww4OQ32HrfRoxmISl7yN6r40ZcVl4rwwBSDCZK..gPfa5l9vz0bMWcBhKoFMZ3Vtb4L0qWOSTTTp3F6NTHDCVas0FNyLyPqt5pVyN6rwBhoNV+QXGFriEH64JUxJUpTThVbMZxpzHzcN54cbbxg.zvLYRGM6ryRJkxFFiHwNgpf.XXbSc33vqQDPuCKkct5W6qu8q609etKHD0sa2nzoSGAf9268dusN5QO51LyaWqVsVejOxGoyUcUW0fXJVRdddv11KZ3vf9111CXls9BeguP5CddGrPMQsoN4Cbxo8p3M4latY9XD2LJlLabotNJkpK.M.f6BB6.FqxLGRDEHkxkqBrdcCkXiKJYecAVJId6Yan47LQuMPJS90ykFXkDWCo3q+0+5m3085dcE877JpTpIjR4DJkpPPPP1ibji3RD4p05TLXGvlGmM2bS2mzS5IkgHJqVqSwLaGmgOuvyXA9X+qGmqIDVJkxVHD1MZzvZvfAQsa0pet74G344w.HUfVmwynoWYiik5jKWNpcy1FYL1yKAgRHFsRirx3olZJrwFaL54Xx9LPX.ynOs64AI+NLAD6jbbaPnIwXGPzNvfz2NJkZfTJGp808Wd0kacsW6qYquzW5NW89tu6asy+7O+MAJ2zX5.H5zm9zQyO+7CPIzEKitvniKiWavYiZWxdW+vN+K4eyBdvAAv0Xi5mQtPo.f6oO8oyN+7ymG.EJCTnApVToN1jLwSqWxeR47yWv111sZ0pNZslXlskRoiVqc7LHaCvzHjHhpBlqaGSy4DT8l011N8wV7X1Wwy4JXcfdvdy+GlF7YicueHI++wQG9CJt8XHVIgJzwZdCw.r8pqtp8byMmEGwLrPWvnkTJaFqAlsia7VDQTDyb+EjxNgnRSkZwVLwcpIp0ToTMiOyayx.a0Xj1ygHee+nZ0pkj+eafpsAp+noFx8i85eOzvjj0CEBSdn9cRRtHCLvqtHQTdee+z0pUyxCfBLnbvEKWsHP8oAvr999SWqVs7995r0pIRalFV0b.gYApjQqOtg1ElaKh.P2Ejxtg.stsO2ss448TNzJG+X2aiegewW1xZ8hIAKaKDh1.nYPPPSOOu1yAzdEf1lo7evA.m3QCGH9iyZuIAR.GjfoUyiEPdeN.K4B3k12+dxVqVsB.UKfp0yh5vMQ+RNsVm9oID4qa7t8hAAAEDBQdfJY51coLqrxJYGLXPt8su8kKd5vFAwCneDi9jEF.lXoT3BfLJkJGHjgYXSlCVGn059F0qtBTpEMP50LcBaFv5q9k+xNWz+g+C61cXCZPFhXA2D.HIPM18.ogQQQ8m+ny2WsnZH.vfACrbbbRNvMQ.w5yL0mHt6a8s912487otgsPnYR+.nMST2EDh1M.Zq05sEhCu4K4Ec4q+AtwaayG2iaxleuu22aP7eeJ9ZYH.FrOf9KAL.0vfeJRWhGtW6sQIi+0iDeR.3NCPp0.Rg4fKVYD5RxgXzmn05rBgHkYx2d1.0cApjSqOdAgPLoRolnc61YefevOH007y9yZ2.vtQXCm9C5OJPZ7zvRnYlqVqcxlMqS61ssRmNsU2tcs.AKgmvNHHvgYdLnBGXQDrXFPH7hCzRLLXWOB.CqToxf5ggcs.FHLSU2TzuY5vizf.hnAEJTn+N6rSel49DQ8AycYCe76wLOHAJz.n+2+6+8635515Juxqr4sdq25NDQaIDhM.ptIJWeaz.MQIzBKaZRREfNgiZVRodFkm+L1O8S6IV7ffCbr.7ZgYAgUm1BX8jBARCf7.UlDHbBC8Q7bAp6.T0UqWL9mi7BgXBesdxiJDSUGnXbCZcP7yy986StttV..Ly8a2tcyO4m7StA.V8u35ttv2+m4yD979sddqnNtZMaa6MqVsZy50q2rZ0pc.p0Cv+QKMLYroSalX3j.VaBPnJbP8YSArpK.RWudc2pUqlAnZVf5EAJOcmNKM6ZqsV41saOSlrYlFLxQD4FEEYQF3R3XlRnJ4rctcmNcxjIy1LyMN1wNl5JuxqLwRgWKHHXKhnVUqVsKJiAng2PiawLhRN+j77veTC94g52arOePK3cBSAEyf7XMLwoCBl3HddEa.LQnNL+PLLuI9W4bsaepbqt5pEpUqVAl4rKszRocbbb1yisEyjcsZBW.jUoT4PrkWtq3nFzWJECAp.kdQavHEHSCpXhbrXNA4bIMJAIOuh0Ffw+2S96RrA1kwZ.AhPD.SLkOedqlMaQvHJ0QkJM2fkWd4Aum2y6K5k+xeow74O1ttAZR.sERY+lMaFswFaDAfdR4BsTpE2Flo52lYtKYbTuHee+Q7qo1QqwndUBndJ.j022uXsZ0lToTSPDUvyyKcLpTHfJjRsXzK7E9B6eK2xsziXpKSb2iHks+ytoOzl+rOye1UE0N+F.MBUJ0pRob63gkk37RIedbsL4ro6s2sgyF2pzAFja51vjmcN.ub.A4ApV.n9TLySAfI0ZcNoTlItgHNDQtBgvEkQFNjyp05DqVMM.RozZa2Tor52efkP3QwM5yAFJdQBgfQL84zZ8PoTZyLmNonW.JkP3YmruKtAII4+DEW.LG25CFfoXDVYg3JXiaRROCJLEC.pDoTKl7Z.f48v1GVJa0.XKkRsE.sEwQ6Hpcjt.gCzZcDybel4cpUq1FW0UcUq7Q+nez08.1NvfljgXk4fQxIF4RWcAlN1UbFUavdikd155GVNZIeebLhYb.VKEPYmSe5ikZ94m2EvyEkBxfkQ1W0q5Uk626262KOQTgCKkEa.LEy7jAAA4Mn5cAKs93DyriTJSyLmIdfninBHyfrLMlMsTJyAXb+F.jgMzsgEBQ+8l+OLCxxFlATY2uWOaWW2379LNdYb9+Ci2mh8b9GHfnM2d6gOo+iOwgpEUwzBhriQ4TDLMhqoTtv1J0haYrC3xsuu6616e9m+4OLFUcIN1U6Pf1FlLb3V.MZBTtoRc7VRorG.F7.OvCDctm64lffxdyAzYEjzXtC1+eGU+I.92WML4Gm03cBOc4xky1nQib.UyVB0cWNIItpvtTc3dgWwyq3m4d+Lyvg7LAApoDhZ4fQHXSSDkQoT4Ihxztc6zYyjwkAREmfFQDOjYqt.bKXznjFRoLFl0F6bpZ0psqWudGfxsKgFsWNQvMKgNwZGwYyh4z++c8PzvD.blSXauB2aN.j6q809ZYetO8mt6xi45LFwdZgr.gE.JWPquuhuhWwKM+M7teeYHCcHxpTp7c61sv4dtWTNsZQWgTZzeBOZPk5XvhF53j1zkYUd.JKLz3gSDMJgPDEDD.X7tvXX7ohKBfr.FA4YF.C6OXvvGy92+PkREsvBKvKt3hQLQ7QDBdQkhAvPhvPl4A.zP4gkPcbEfo6yCXlG7Buxetge7O8sOjYtePPP6pUqt4QpUaiPCj6ZAin10SHVnCP81.UapTGaaC+Xw1vrOKQLYAPY1CMFF.L769c+tCe7Wxie3ABwfGEt+6gJPLAb.mJ3jNgl8MNxmpLk2JHU.PZTFYz2mNiPHxpTpzRozMNwNGXPfPJhnLwb1dR.L0sca21DG5PO4rBwQcGNT4Tud8Tw7m0VJk1.vZ80W24I9DehtKt3hoXfTGQJcVTorgwpBsAy1LyNvhhm3F6X1OMher.6Nwhw+HNYNtqTdj9+M+M+w7y+4+7Go+AbbgiXDZRPWobgdZ+i2W7+j8d2iRttpty+Om68VO6Vpk5GUU2Gc29QC1HhAoVFXBwXyCClL+RBIqLvLCXlEIP94fIrRvjj4gcviAmjYHfAlfIX.y.+BPxXxjgPHS.VNI717PpaYOLBrsjk6tu2a8naIYY8n6tpacO+9iy4V8sK0RVFrczi620pVsTWUWcW2y9tO6yd+c+c64zNHveU2o8VsRSZuGe+nXHVHL5nd+bWF335VL7X.Gw22+HdddOtld4IBErtZotcdmuy+Mse+u+OQa3HIGJ3etaAmznmuFsfaZJkRgnlPTsIzDfQvfCNgELehPvtYcR1Ku+8u+7WxkbIVBgHudbhmzi0CEDDLDvPZVJVP0NcwRWWOI.A9Aho24zLyLyzFDmXZWmGqopk4VJLLboc33bnlvgTs0TsiAMROJDOeopO8yRBUk37PLhOVGTk3xBrTs7PiB.EpWudIaa6M8fO3CNzfCN3vttti9i9Q+nsr3hKNzUe0W8.56QsBC8Mbbl1b+6+9xUpTIi33XgmmWreX3xBo7ntttKEDDzPHD0cb1YqZT+PMRzJlQnSkCR2VfDG5R3ViRM5Ne59vrOQE9YC8gose6UHn27a9MOvce2+cCBMFDpNnu+tK644M.P428se6kdW2xsrovvvMALfiyNJUil4ajH3r0DFUah4LpDMUxwwYfvvfAAQYT6+gd+uDQQEgVytbccyultwHLFYjgEG7fGRp+nHAj5CfxccW2k3Ftga.PHm10QLipEJTMWnjXoT1EEM4WWhUEJ8MoqTJSR1PRkdk5jerrTFeLW2cdLn4pAAAI2uzF33ZeWGOHHXUWW2NPk1UoUTSkdggnlvrZSLl02OmTHJ533LPPPvl877FJzObPoPVTyrLQPPf3vG9vlacqaUn+r0Q01WbbgPbz333C44syCBMWBkHXmz50qZaaG889deutyN6rQu8Wyqo87qMM0NWLgIv5zgGLgsZAGNGLZNXoDV5NHvVdku7W4Vu+e38OzryN6.ttSWrBMy0BxcEWwkl+q7U9lEg3xDy.N6za.+cGVRHjJMhPRdgfbRHudRgX533zqsVQcMKBUxw5pObbATwJlvRXCHgrmxXIDKjz00yMVJkw0qWOd6NNxY88kHDnS3WJVBHibc8VUwjnd1eni+SDDDzEXYPbTPdXgP7X+R+q9kd7+lO+eywzGTsquue7zSOc6YlYlG2ya5CWiVGpQR7aUoSslHa.BpBzzMBBRrMR6++7g8.5GazYV2njqmv517MZzH+1qUqPi0zPmx999k8714fPyMC01ru+tFPGylv00UhhIbk28t28f+h+h+FCTkF4aBB88+hpMwZlff7HnrisyfgqM1eMTrPQzII9+vvvdhStqqqYPPf19ecw+GKDxtwRQWOU6hkD+uDDLsqCZ+eZ1xE2EDR2c3J7mI.kFfKz5Hm7Df3XRo7HdddGIHH3n5jizIg8RAAAscc2wJPqkWXgEN93iO9I.V929c9NW9C89e+sA59U9Jek3q65ttXvoaEB6zBZGDDrh6U3116Pzw+byj29SExRXx5gJ3hsg0d2apQLrllWAAABcerlTw4MAr0Jvv6ILby.kc1gSNZR9G9ge3hCLv.kkRYAoZptXoCVHuqqatvf.jp.bOZivFG567c+NMeOum2dyVs3fiMFO9hK1SMhWgwXkG5a+Pq7re1O69UA7yGcHdpP+ANmf0OsiT8wcNvtHTu3sdq2Zwa61ts7.VG6vGybvsNXNfb0WXgB1iOdIfAzZUylpTgAZ0h7Uqh0l1zTE2291WYkfPIGTJEEjBgoPJiccci.51rYShhh5I1i.kDBgULfXsVXHFTzp6.G3.FEJTv7M8ldSVe5O0mRz6OZoHFCPJiieSugeM4m9y8o5Jk8D1otnT2ePfTFKkdJsSoKJwnSoGFIAArC2N96IrqmyNhCBloCvIjBwQ7bbNbXX3iqYrTTXXXGGGm1.qXCmnNbrwFiis3hbBaaZWudJapIHl42vCdetd637Dg9oSbhvbZAjSKFc4qAE1kuedOOOKpPNZ0SGjRZCu75Cps4fffs555N7QNxQF7XG6XEArt9e4WWt+vOzeRtK5hlLmiiaNcKYX455l6.G3.l4ymOYy+bBgvxwwwLHHvzxxxJJJJokcziGUoo5LEpjk333DEDF1UnpZgRCbzZZS2tc6XXX.RrPPt+C+GdGV+4+42iPe.hDeMIsKiRj5jx15Je1NHHHJNNtqggQGgPrrTJOVXX3QsccOpmy1OJzJYZKcLkXgYmz1MITItKPmgg1GZ8hl4H6NnQC..f.PRDEDUYK9z1n.8OoD0NBXcPH+7yOewI9WLwfDRYXrRvh8zRIoTVRHDCFDDLnqq6VBBBFRJDad+O7CM33td418ryJ93e7On7q9U+1x.UhQku025aM9C7A9.qVrTwiKP73NNNGILL7wbbbNLpfmOLvQFFN1gRnu+SOi11+4D8SG6jGIAEWXXH+gzL.SuW8.vXaBVbnfffs355tYTL4YPfx+F+F+FE93e7OtkqVzWERgkimC0pQmFM33gggOFvRRorkqq6hgggGxww4HnRXxJjZBbrqcsq3W4UdkcN7Z1umsE.YZa3j3VRZuvA.FnFLXCXfZ0nTiFT5c7N9sK8A9.enAA1juu+.XPYhofPkbVCWWWK.q1samawEWrrNoeChZevb5escQI3fISSBCsOpbG9vGN2ke4WtktMSSFI4pYDkT.BogTl59L0oME5p2Z7m8m8mE+Vequ001eLU664Nsar+LAcE51VUHDq533zNHHHF.MszWUHkGywy6n.Is.Qb0pztYSNd0pbrlMUTR+K7E9BseguvWXac+6Go+aJGPt21a6sU3Nuy6r7G7NtiM86bS2zlCBBFxcZ2ABlIH+sbK2p4se62lv000X94mOmoooYylMoZ0pQ5DK2apTTAVpEbnJU3wZ0hGuZUVtYydsDqjpzUOAlRRpb+h574Bn+325o0NCAlGALGcTxuzRTdzQYyKsjZL+BLfuuegtc6lyxxJ+0cc+xE9g+vePY82e.T5RWgvvv7Rorf2N8J5OaX467O8NK9a8aci4bbbLBBCM22C+vlWy0bMI5gSRw.5hxlpfDJKDT58ba2d9+fa8OHgceoaKzXcbXwr93tj5BTHzeJkRIQBUaqtBvJRoriggQ7wN1wLFXfALtq65th+MugaXYIbDgPdvWhyzGZ+z5wqVki2roh4KRorqqq6p.GkpbDZxQt5q8pO123d+Fqp+6AeeegmmmrQiFcpUqlx9XJ5v9NuqPVOQHsONMaSpZAMS72oJFj5bX8RZB5QbcEXSsfx0pgUiFDWoBwsZgEJMcZyBgbyRonHBR7+Y.XoS7aIgpMqU5eipUnSRJWuynoaqFC.ScKzK5oYgRhQn8e45FGFFDCh3T5vCnrwLLDBCGGGoePPrPaWJjxXoxVrq6ztcBlIX0a7Fe6G+i7Q9SOhPHNrTJeLs36uphwKjzxjcXs3+OQsZrbiFrhsMQ0qmJt9JzM0veHwGT+9gtPvNKKgI8gDm5IUbMOTMu9fO4FCLVbTLpsDV+fEVH+G6S9IG78bq25V.1x91291DPwqZpor1s5FoBZgqJ218bLaQUKe+cm2vvnnTJKt5JqHZTu4pu3q5E+3.GLHHnkqqauJLrvBKr73iO9xvnKCKsLvJCAcNhRvMe5PM9OaFq6fKSBlys1ykdyA8FxdVfeAzszPUvpIiY.KZPsZVznQdfBOxi7HktjK4RFDUvzaBn76487drdVOqmkw67c9Nyo5G9oGT2NEkzNHEBDw1N1c0h.kYPPPg29a+sW5O8O8Osnho.X53Xyeze36g2zu1aVNsqKy5GJkBoAZE8mXPhL10ys6BKrPWECw0D90vHVnlLAQOzC8PQW1kcYwAAAxc55J97eyuo7RujKM1w0oKP2fvftHoidy0UTIBYGQ996tqPH5.bhocce7q+24c93u+O36+3TsZGZ1rKJJFG444sJ8F0gjLZWi.jGZ+GhguzojvghAh8.o+TDus8gbum+VAizXc1cSAF6yFyQpi4AWKAJoeXZCV0oRtvv8j2wwoX8Epm64OtsUKpkGZTlJUFNX1YG4q72+2uke82xaoTxOWPPPNoPjWHk4kfIJ1lX7W+W++xbqacK4trK6xxOsqa9YBBxIPjShpZERozHLLzZ4kWNWoRkRD4UC8e7cQJhde+I+W67696+uuarPs4JpMl6zpUqt5ouiUfePNogvPn5++UO5QO5xaZSa53tt6X4ffYa655FE56GKEhHgTzVJjcTUektgggq333bBfGuJ73Mo1QCCm43NpQE7IpBK2jwVAVrMiLRWVahAjPO5zUNMs5ue1.RGnufsgXa6E165Y1Vuw7rCTLjQKt5pAEJTnPdT9eJ9s229JO0TSMXPPvlbcc2x92+92rTJ2zTSMUITLWxPWfRoVPBSlzUq.MOtuu+wtROui2nFGKX2AG000M4.WIGjOomiOeMQ5qqcPnmPl1aBEoDoavJTIDyk+w+3e7.W9ke4CVAFbOggC533jjzjAbccKD5GVzwyo.fkuuu3G+POzJa6xu7ir8su8C1pUqV.KAUOLz7wANlMrb8JrpcK5VGj2zsdqh631tsmoZImeZvZ1vpQ7ZR69kzJgC9Zesu1xenOzGpjiZr8VDnrxVc5ABBlY.WW2hUgbyDDjWHDV1114zhsawfffR.CzoSmhpI2kZ+u5ggRjh3c34HmMLDc+6agR2HLrcbj25sdqcusa615BflcaIqs333HqWut7nG8nhAGbvjD9nodtRmtBBChDHh2+92e2ux+6uR2a7seiQR8THS29fqLsqam+o8t2taaaaSDDDHevG7A67xe4u7Sna6kDFuE6GFtpAbrD+V999s87dAc9Q+n+w1OmmyyocUnSypHraR922m8yV3M7FdCIG7ZPfg12912lKWrXYoPjSHDlNJ8yvZZWWquy92uYwhEERgnqPJWckUV4wuzK8RODpdpXofffGKNN9HiO93IsisN4L1.0k.Q1Pm5mclTtyDrQILIMSgS7iVDXv23a7Mt467NuyA27lurxP87.4q.4aUkRO526QKeQWzEUFnbXXXAsfYmCnvq7k9RK+o9re1x.kEPdIXLsqq4rAg4hQOISjRbcUrEQnzVhBA9AkPzKge5FtQHu4+f2U7se621ZLXRPrPJ5pD9bhDqMo3zcBlHVmrvN+i+C26pu7Ww0t7NbcWoEUi782snSmNl4ymm+hO6mcke2e+e+GC05+gBCCOBvIRRLWPPPjRvNm9DPiiAb7pvJMcIZ3.jGZsqY8ZWBN61GzS2n+8G5w1jI.q4olEzHG0pkiFMJBLP8EpuY6wsSDo8ByLyLhZ0pEs8su8tsZ0xRUbicrkffYGx00cfPe+7ZsFyrb4xl6+QdDqqbm6LePPPgUVYkBEKVrfhIcBbbrk2165OP9VtgeS4N87XV+PjBo.gv.ozLo.ntttcme946ZYXIiMzm.Po0Rc25V2ZzhKtX2ImbRBCCM1tiStYSZ8KorqmmWTPPPjTJ6n0XjNemuy2oye0e0ec6a5l9cV1vfGONlCOsm2i0hpONzbUpTQRqVlnX+VWMilRFQvIeMIFBATS36uqXOOuNdPa+wn8jKRzbqOVsKXryxRXx5Q+UTN2W9KeO4d0u5eqbPKKeeeCOOuTISQkkxjCaKkFE77rUGdRUoqh5ppHzJ9d9jd8UJDFexOwGak2065+7iGFFdHmocNDM3HnNz5J6d26d4ctycpMhqbBnUZ5Ve9dk86GoqrnIfw7yOuXhIlHI6+8OhsrXLxoFIrUxAs5IDdZUyNGPAaaJVuNCVu9BC1sqHY7dZ5440ay6fffxG8nGcfMsoMUFnve8W3KX8q9q7ZDRoHVJkQdddxff.CgPjWpGizBgHmy1cxQKL0JwN5L6lHni8DPVgPzIz2uqyN8jzDiff.zUunil9tcbcmVFDLSxmMIB5JhEckBY2jfCcccWAprRPvrsEBQjyNb5nD+qZGWKtlGGncMH91tq6ha3FtA4XiQ2EWjNMZL+x0thINAKpr8HUETUecxXXt9GaXx0+ZNuEazFwqS3dW6e2SLPyiCEIjhyO+74uwa7Fs9ReoujIPoZvVlILbzNc5r074yOfsssUPPfg6ztVzj7ggg4+K+K+KM+W+u9ecudi10cG4BC2SQYrrHBJHDRKaaWi.UKgIPkDNKojbfTMwkPJPRWPD455zSbdCCBjwRYrNobwB0F34jfkPJDRgrKvxZZoqGIvS2IHXlXPYGGF52INldLMILL7DezO5G8Xu628G8wgVONvInBqPKVEptJzr2D8pBHaoulVoBxVsnyXP6E2FsOKkYD8yvgM5401AiXo0Ti7ZQfM+0e8+54+LelOYxHKev599CZ64MDpopz.5DwZoW+MBCCkR0T0nitx3KqmZZKqSL0J908W1y163AAAGIUazcBXp1v9NWSTHexfMhsDEZ0pUtJUpXA0LpWeVgsscNpRdZRQs1HzSbIqBaZFMSSPM4cJYXPAv.hYYoPdXWW2Vn53JsPuZehvvcuriiSxdv82FHIL76r4J6ld+SKf7UpPwVsXf50qW1d61koIEVXgEz88uhAJgggC9u5e0qef+p+pOWIoTVTUMe+7RoHGppklnALEjPdAjWJkEdnG5grd4u7WtUnJQIRMM2Eg99lRgvRBFtpVWsaXneWmcza+uD+oxXhiMvP55NsQPvLILJRHDxHaa2U0STjNhjpq5NcWMqJ63Ns6pgyFt5Ue0W2p6ae+eR7+QMP9cdjGIJe97qZZxx11dqF56GggQ2333UMMMOtsschXT2VU.hmeGXwjD5ZrvBKje7wGuHpjNUPecZSwwrou5W8KW9W+W+WufRicv7tu6+6VW208JShoPnEf1HoTdLgP9XNNdG7Fdyu4CcW+c28iQiDQEt2dv50ssJgCmZbgdN4AU5OFNSMisYRHdtIAlCCVKt5joRWAvNOTOmN4HkBCCKqEayhttt4ejG4QrtjK4RrfpECBlozC+vOb4W5+lWZofcGXo0Ymj30K9fO3ClavAGzLo5Z51fMQCSJfhEmha9ccyw29sc6wFFBYbrdJ3nFbgwnF46sERwpp1TMn+BXJihhhlbxIaGDDdBPtr9foxCbfCXkKWNgPHZKDxG2ww6PUfC0RMw3Vds1.qWRXaCUW8ttq28J2vMbC8RV1byMGSN4jx0+5VmlFBmaYe7TE5qMNmxPOEcLoWAsTIkqZUF5c7N9ur423a7MNvc7gtCq24uy6TFGGmj7AKfABC82hTJFBURQSFDDVBov79ef6me9+k+7F5VrOwFyTHDlNa2wjVXni+OoMeHHHnW7+111cMMMiBC8iz9+D999hDFiqi+uqq6zBe+caIDh7RgvzfXjRQuDkomDNsuy+r6p8u7uzuPm+g+g+g1+69c+2sLs3XPsGCZbDTwHztFH9tO5iZc228cadW206gsu8WUzm7S9IWwaGdK++8q8+83O2m6ycY86qDPbO2y8Hdcut2dLzLwNK8fd3BN6rrDlrdze.YIAWXUCLavXlvhV.4bf7giQIVjjfgKfhlVFZp9VPJkE777LCBBv0003U8pdU49nezOZoK4Rtjx5W2pAqMRgOhuu+wO7gO7JkKWt8kdoW5pZVlbBTUoscS5IxNWPQCJ1.pwOIfdS19CRM40XMLja14lybxImzH06i4byMm4jS9ByCMKLFT5qN6rCricriRn6eUeeewm9S+oMu4a9lyGFFVzwwobEnbKpTBZUHLLzZ6NNLaPPjiiSj1AWRBwJpEorjwSrAHoc6Nw4ymWBXnb5oRXRXXPjiiaTnenDidN56IZTNNNcCBBjS65JZRUCphLXlYh1gqaTKEUciT8in6J.qVEVsoth8UfkmQUEsD8MH9c+te27betOWwu5u5uk.pGqY2xpdddIYXdEN4DxI4jcNdgPxRRvF2V.pwaW5oRgwVAyCqCNaTnzRPtEVXAqwGe7jJhODvHAAAC655NXXXXNGGG788Mdo6bmVeiYm0RGDmz22GOOOSoTVnpPTpEUK6GNaQgTlamtthcGDHccc04WSXhViUDRgoimi324s+1i+.+29uEGDDjbnhd2mzocG4EcwWDZAe0LNN1vvvPp66+kANpqq6w78CW1yyIpJD2jpBnIe0u5Wsya7U8pZu6vvUINdkCbfCb7WxK4kbTfi1XgENZswGeEfNOvC7.cedOumWR6ekrYqDv3du26UbsW60J0hHbR0wNaLgIv52m7jZIGRkzjw.qE0T.1ExGrVKPT5Vu0aszsca2VRqNLnmmWYs+kbg9gVPrYrA34nTj9fffUbccWILLbkocbVoAUZCs536629E34sR8w3XrXuCYomnFm0dM7oJHPw1Kq8MB44fTPqYPl.hgA4gz6aaC4CUzvt3UcUWUou025aMf959lfJCBsJGDDTRqgAHDhkARRXxhnXvywAVUMZWq0AZzFnaUP1zFI064a7r4jkjfTI2qZNnYQWnz8M+7EmXhIJBXM2byYM4jSlqd85ErssK+e7+3sT9O9O91GrFTtA0JCMJGFFVXZGGqcGDHbbb307ZdMxu3W7KZ.XFFDj2w0sjuueIMaZM09Uh0NpLzSOFScBgSFOwI6cK.DW+0e8w+4+4+4QdddxtRoYy50y63r879AyZHfN6z0c0FUYYZxp5hFzEHJLHnii5voqVEZOaXXjRuunqJYugckRYGW2czAZsZPPPmTUns8B0W3D+ZuwesSbu268llwVQtPT.CKgCoz2NnXKaxsv2egbiO93I5Wz.ZQ+t.pXAxsxJqXUpTAgiiGggg7pe0uZwC7.O.5CFezJvi0RYm8XnaeQVaOX.DMa1jmW0pxlLUDruyEry1HzWLbSZBygJDtdedRKf1klat4JL4jSVX9FMrdQ0pkuNiU.VrDP4pPol0n.MHQLXygdc4W9s7+agO1m3ikuFXVWJMCBBxaXXTDUBRKHEBq69i8wMt0+y2pQ850shiiyIjhBRgLu1ORRR9hCBBD2569ca72dW2k4LAghO7e1GQ9a8Veqcl10ckcqz6nU.VMoMsCTiYa0nmVyvWH9D25sdaq9w+3eb4+z+z+j4K6k8xL88Ci77bRR58ioDdemj3zRyTjt.QUgnl3FAAw.xw.4hUINUqZc1dBaelD8GulQpupsurG.puYee+MIkxh53yR2pVV.kqWu9lsssGRMYcpVBZVJLLzZZGGycE5Kcsc6JTBHRRa5jHj34zLbxDPDGGKMLTD+sQyFl10rQ0hzqK9ePydj0JVpabPPf4Nccy2fp4+q9e9QL+W7hdQxc54E0rBcnEcBCCWU2d8qVCZ2P4+X4pvwmML7wkJM45D5OaB0fuX64fVhG7Aev3u7W9K2929292Nc7+II.r24QFA5dP7h.+9Yv5ET1YYIL4jw5B.N0W0IOYrbpdSer75QcXATJ9sQq094xAj+i7Q9HEtwa7caUgFxVTSnlXBTPqmAF999c777NNpMIS1nrMTqsNvrUR8n+CTbAkgJmjyusIf8l9v7oO.SJweZXysxgDGlshJtDLvACB6UQ3DJcmn2DFPMCnQR0NxWud8hIiOLGmsWNHX1htt6vDZk3fMox48nnrVzXK9HOxiXN3.CH2wz+7wPyXPkk4jfEkRYWCC5533IALBC8ENNSKgVQnDoottttDF5KN7gOBaYKaI100MJLLLxwwoquueWOOuN088aGKDqd8W+0u5+eetO2pG3ge3Ut5q9pWFFc4ZrzJMXrNpy.ffQQvRH.GfvjOCI1YIa9lNgHaTxQtPy9CV+FwazWSRVWBk2KBUxOJsrVhZ5IlCCRUFllrUe+vAEBYN.bccA05ugiyUJfFR.odC3Bgg9kAix.EWHHHmmiigNoJc877DgAABoRjYSN7nA.+F+F2HehO9Goqi6zwAAynpD6Nckz.9XerOg3xu7msnRkwXSaZyRftO5idfNW7EeIKSLmPJjGWmT2tG+3GWNv.CnKJmLxya5NZVusBJ5BerlJeXGm0UE9ZRWZzMfwhfEUa1ZiA0AeeeYBUR4bCplmd+RYeeuz9cRwBwgyWkCkuI0JBMTT32lAoNk09IJpSZVNTUcEW2c1QO19VYgEVX0wGe7dLzQq2Bc788W0yya4a5ltoieG2wcbB8DG5bQAg7IKzWm2lIr2jp5kaTvbI0ymT7X8Zvn4pxR4ZhcAndo8u+8W9R+YuzAoECBUJCsJBX8i+w+XYqVsV9pu5q9HPsCuvB+fCO93imrmbxgXZCizANXDLULru92+4r8q6oYIWNXq4fCmLtlWajjqnudh1bU9q809ZC7Reouzx999kEBw.0a1rvUN8OedsrG28G7C9AQ++7BdAc0w+jOHHX.WW2ACBBJKkhbsW4DrzgNb7K7E9BhADAgAVtNt478CsDpo6.IUfUkj3cFefC7cit3K9h6DFFF633XED3mGD4ALaszRcpL5nq355tbPPvJutW2qoym+y+ESZEmNwwwqpYcYWnpbWeuuX7U9h9E6Bshle94WchWvDcTGzrZGnY+SglUfQW4RcFZk8Gt+DeRozgkN4fEKBNEfPqjqm0f7ZQkrjdhJV7G8i9QEdNOmWlUMZPCpI09zMdvG7AEW1kcYcCCCW1ww4XZgwNoJvGWyJudILQ+09YS6Y61ZaDNcwvk591jXr6ESVO8J5K9E+hk9k9k9kJqigtvm4y7Yxc8W+0mSqgI40L8MIgbFPUqJzrPKpT5y7YuiRurW5KKu6zt4nIV228ceFiO9DlKu7wMKWd.KgPX0pUKywFqBJsdEDBCAHMEBoUbL5llPFs28t2UdUupW4I.Vw22e4c540ooN9b89ZwZ1AubEZtbKprBzJFvHLLzPqmXq333brfffi455lzNzqBi1FVJBHVOwoheaus2V267NuyXnZLzTpG0q82NfWnd1fSE1nBcYpYedITI.NoPoIs2TWvV9ZesWUtO+m+ymHBwa5Zu1W1.e5O8morq6zk88mMmm2NLglIL7IpJHap2+OQa6BBBJdu26+Xtq8Ze4Ftt6Dngx+2oN9ecBjmNFZ1FUqYI0BEadcRAMbbbj5yEDoSpRmfffU050zp999m.XYUQPqbb0.CwdEndG.XTLYIrfpF5yjDgx2m19qZj962uumzOR99WPgrDlbxn+.fEvTFXuOSpikiC4tm64aU3ptpqpv8e+2e9m+y+UkagE1kw3iOtxQUEDZQeLOzLe850srssS+9Ygpmo4J87hlsd8UrssWFprrCsVMLYyac1CAZCS04b3JK7TENUTieitdzmZruNQhs2Ac1JXb3dLIZTSWVxLHYcpJFzLYi5QKPkkJRKJUsJCr6c6W1vvHG.NN6HJLb1N5w.lEppdTBpVDZVP6bDf3uzW5KE8K7K7KHA035c6NNh8n1PDnhrBsL1SnuvwwiDGhnOzYX3LRf3sucmtsZslJnqqnwZ8wZEVkVrhdVo2eRPRe8Q3.DpdehfIZay7qVe8T9M80q9+2WHiSUqYj5PyCmGNTg4latbSN4jIGHIGJ5Fu4fffg1gq6.+seuuWtWzK5EATiZzfcoDxMB6YWfETqPX3LEkRYwom1sPylj+G9C2q4V1xlkFFFwNNNha3FtQtq65iXQULoIFPEgu+LBOOOCee+X.YROupq.FnnJJ.cgpQ0nYmFI5IRUVklpCJ9714OW7e+W7d5dM6XG73zJdlYlKxyaxn50q21194uZPvrKqC36D3vxDRGnhDZIt8a+1E2xsbKIssv5zmjsBwGds1Y3b4.91fjlT0BZlSmLjjf+SpDcYee+R6zyqXScB2gJV0nk4LgKHcbFuyrOvrqrim2NzZ6RsUOvAtu1W7EewsqTgN56+Snq8J5VdJ8TF5bwqgmonuVKYq4USZCslHLF57BOp.VxDvZ26d2V6bm6LI4Jk.F3ge3GdfRkJUxyySKRoU5VgVqzJIoeiwwYwDemtcpWeWssssaqED6SUU1Na+Z95RpmVrh06OZaTi5hFIwnTkbpVZhRvXk78msjmmWopUo7ry5WRJE4EBgvwYGcCCmsy1290zoUqGFpQNZv.P0MAMKGFFlW6GSp2qRhpRrEDPADhbZAgUhJYrwqMEGb01y0LCCmIuTJyM8ztFMaRzm7S9oV85tN0AVS0BCQTg1zh1PkN5CnJ9Zesul7a+s+1w27Mey8DM+pUqFE2rYmE6ceTsUqRijjN1lgnCGIYc1Shsuf5XZaSg50oXiFMx2oSGywGeZIrnjjDzYSApSwj8+0GdR0JsqYeX366GekddczUC9Xf8wbo9wBzsjMaitr2jjHrMPIbRmqqaXmtX3Rfx9rJVzLI9MaSFsdNVhBZgIVIvlPdnhEzJw+ZdpfIsP.UHLbOF5wbc9tc6VZ7wGu389U+p4u1W0qJuuuukPHRlJcBnpnBMMZgpMJRJp.fgeXno.oERLmdZWlYlftnStlq6NVw2elUzSeottS6FSSjPktPqNIrDj0mLawANvAh+WbwWb6ljLbGFckprT6lI1iCSGNTx5cUI1MioNwCMDwG4HoY8qW2TU8+7YlE9SC52tyBHmGj2W2FMvvLJGJVmzcCcK0oEG6JkezG86W9u3y7YJ+A+SukR+i+i+eyOzPCaAcwya5nffY6555F+29292J9Eey+hBZgo97eEBTC4CK.dnG5ghe1O6mc+w+i5rCUnBsXVMi6zEDUGydEC+5yXIhEV6XGthVsTq8999wO5iNe2q5pdw8lng5Vg93f8xP8jj82+fBQ.XVCDMT1KQPsNCSiUOz5KbUZzegoufzNKKgIaL1nLSZn5+sgxCGIuCTHDx666a444ALpDVJop.lUoZtlzLGTyJLbFgiii.f5KTWXOts.UkVinNI5.vpIzVeBHZ90N354yiHrmrnepwuQGlO8AWM.Oyp3azbsW+Z23OIBl6jnsmXqf3voXohCjKDJTEJ1TSk6ZPgFfoVso6pOfn4C+vOb91saW79m49y+5eiudKnpgu+tk5pAzUmHj0wzDTUcPDCFdNNF999bvCdP4V1xVjSN4jIYStaMHpAU536OSWS.aOujOyweyu48E8RdI+rsQmoXee+UTB55HsqxAiZtVVgSfgN4doqRwoRA9uP1l6LEqikI1XmqN0yA14Fk54VR2lF59vtLqQe6jpaPROcO+7yyDSLgnVsZhFMZjbnaqJfUKpkKHX24DBokiiWxXRjvvPhiiM777D27Meybi23Mp6c1cXFFtGdGui2t7+w+i+mcQMxBwyyyfpH72sebhVjfdbSuCW5R9zg..PG1lDQAQEmnMM0TQe8u9WOx00sqpcbHpBD2hpcglcsg35qY2rJL5JIhSMPzPLDGgiH.GCaBE0WeUJT+aaho95Zmgy0C3qmujIYRq4XNKXHqpbj7M0AzefCbf7W7EewEXMlFlG89HBgvv00UlZBV0FH5Nti6X0a5lto1iBqtjh8gQUfnVqq5hqq+0OW9Z3YBRRXRRatkbvodUDqQiFTqVMCv0rFAlMRILujnOBUof+t8MzrbZUfUt268dW4MbsW6pMgNMZzHpVsZQi.QGDZqllSdczGPo+fJOa+ZdZ+SpqcigIKdxsXldpkzS2Gz60kuBTLgV5UfBs.y8rm8Du8su8NPsHMCJx466W5C+e6COvU77uhRug2vMYEDLSRhYi0smrRnMCBJIDhBNNN4zIvMVSA8Hs1jzQe+fgiii0a4M8lL9Dep+dBCmMRJkIsg5J.cBUsdSGsVQrZXXXz8ce2G+p+p2nYUZJz6+0AXkW3K7E146+8+9QLJcq++odGaa6UXsVZqsMDUe8skJ.BU7eST.lufNl.sJsSL3JffdSgnpTsXSZVDkX+a3o1qNVyXF.j+3e7Ot6ke4WdDJljchwGe7iCbhIgUl6j+8mrFct9AVNcwvk78RyDJiw.yEAKkMmx9SqiL8XeR850yEGGaoimBck4Mfp4qQyBMVajA2aRURUrBlIvPmDOo9mioccEynrGMbccMpAldu3Wp4BemulwtCCEdNNw+G+2+604O9+5eRp31U1+ZM6I93O9w69reNO6NPsU7820pdddc.hVXgE5N93iGCDsP85cFesIGWz7yOe2IlXhn4me9NSLwD8XMxVA4ggX09v1RnNdfzOqp+OYw58A5gI9qax2AfXBvbdvZ94mO+DSLQdanPcU6dURa+UnJjqoJwmcUw1qXP123a7M3pu5q1nBUrZQqbPk7gg6wTe9ujBblvzjjotpQXXHcAw3pooS7INwI5dcW200c+6e+B8zPRTADsnBAAyJgdrxKFUxnizLLYYfSnkygNvXcpxhQMWu9GotFLFBVLIgIqK9+MJFhy0867TBxRXxoGaDcqS1TL4gY0pHZ1TkwudBF5nXon8DlUAF3J1o3a+U+RxsaaKaZSWpmX.6FwXsiXwEiXHh3HmTRRNWgtuOShSU0IR+7IAUaXClyTutv11VNIDO2FmA0MHIYdBFy2jEw7ZtlqI2W+q+0STE+xKrvBE0aXyXiQ7e4e4+.W9ke4ltttp.MUUnyDfJfbFeeoooY2ossipWgHTYIFeeeCuq7JsnQCSc0QRXDi.pA0ZHoAIi40dikUTNbw00UXai76+8WHxvvnsqdZ4fKqRfxFyCh+1yMmbxImTNDvQl.Ayqqjh6KPxVWNhCe3NrMhNKU3MOWAqsg7TXw9TAyoBzdzb996wZZOubsrIO0I4Py4cmdZKZ1TPELTUGKw1spIUZZQqdLjRRJAuLLLzzwwgpUQL6rKDClBGGGY3Bg37BbfFp2mDgGVJkwWomWbipXDLSfoPHLzUHd026688txG7u3CthRnfSrApEGDr6XWW2nOwm3Sz4s7VdK85S550qisssDnakJz4lu4OT6+3e6+3UanZkjH0vcnmttXbXFRbnCc.4yZ3giOnx+W+92NeIfuzA6aAXNAjadpj+FtgeEquvccW4ZNFVrXpDn87e94YwEyQUDzrWBLifZcYrFQrXuQ7buVyg0NPU5jMc9x0vmHjd+3bSLA4+te2PKgPHzr4ThReQL.LFAL1yBKXL93iaMJjeown7m4C7YF3U7JdE4ss2NAAyz1008DTiUngZDZCDCCKa05GGWoRkzATl9Zu5204FXs801FlrWL2yd1iw1290Ad4hw2Olsgj8llIrd49bet2atW+q+0mtsHJATRmrub.xZ0n6t1keD.ZF6ThJThVT.vrBvrpCfJ+T28cyM+geWFzR09pgggE+O+G9Gl+icm2ofp.qEGUWnZWp1T24OPPPPrTJiSZiuDAxrRE5LyL9Q5DrnRznZTXl1+mzvvnyz11cpWgn8ee6WVtb4Xa6qnMN4WgvvkYDVkCdJ0RozSlo7MZzvpVsZ.DaairdcDsZ0xpRkJpVwbLJxhJcsyFD+.e+XSSyt6v1tai0RTrbe6aecm5k7Rhnd8UXLVgE6k3ljX.Sv4RIm6LAmpX35ONrzrENYjvlnQLFtttFUpfYqVXo2OTkDF08+lnRlR93337RoL+U54kqQEJ3OieACCi7NNaWDDLab2tc6ZjyH1y1St1u6pFAAyX355ZDFFlTzSAfTWnrHfNUpPzLy3GCnJDAHRMJnWsBz9d9Zesnm0y5Y0YZW21MGkNrjhQfWwUbkQ+eZsPLMal72bRh8ZCiDg8AS1q7Twlsym167YBbxcPv11lf8tWXaf1+m9bdSXRs4snQOeeILTrnNoaZwak3csq.oTJkIh6L0vjFp83qBF61utvvPFuSGmt0U9lRhg2zcm6zhFMLzw+k.ITUp8+krtutjnpi+WVoBx6+9Wn6W6q8sZ+u8e6+VUxeULMOBPN+7yGGWrX7EUoRWOHx2CvOIgMu.ICc7tbjizgsQGc7+mpyZdAu8UVBSNyvZNumBS1WUqjQMLqMdzRLxL.LqWutosscROtB3HfPc.tNcFkvnkroCqQuW8MCSEC6q+pslkcumbX88rnhoIBvu+dwai1nt+2CCXRCXNkSyQnDGbrRZ8qQSm60s1mC0gYsDhtl11iqe9pn6K4HnVmQoQ2kFCotBeVKrvBlFFFVtt6zrJMLalRE4+t6ZWQG+HGYkWwq3UrhpUspJgloGMeIYItGSAnFcnQ5.+5ogKRXqhI3vFyqzxkzYX9bUE3+rIjNPuTGznlkVWbL.WypDX0bTxwRj+09Zds49P+YeHS.CGmcXLFMMWT01d49te2uq0O6O6OqLNNNZ7wGOBajO723.VOqm0EW.v589duCye+e+aB5Owp0.ZzKXSATA5sabMqpzvpoZC5tnGobWxkbIq7s9VeqU2oiS7Ov2W5sCOopBDUiz8TaGbnKgtRHHc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2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"bang\", \"bang\", \"int\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 1341.0, 72.0, 40.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"uzi 7\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"bubbleside\" : 3,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-63\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 4,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 316.0, 466.0, 187.0, 64.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"As the decoder has to reconstruct the phase, you can select the phase reconstruction mode.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-61\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 542.0, 507.0, 160.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"prepend set inversion_mode\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-62\",\n\t\t\t\t\t\t\t\t\t\"items\" : [ \"random\", \",\", \"griffin_lim\", \",\", \"pghi\", \",\", \"sinebank\" ],\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"umenu\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 3,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"int\", \"\", \"\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 542.0, 466.0, 100.0, 22.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"bubble\" : 1,\n\t\t\t\t\t\t\t\t\t\"bubbleside\" : 3,\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-59\",\n\t\t\t\t\t\t\t\t\t\"linecount\" : 7,\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 325.5, 331.0, 194.0, 104.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"You can explore several latent geometries by setting the input coordinate system of the decoder. In spherical, you control the radius with the first dimension, and the angles with the others.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-57\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 536.0, 404.0, 112.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"prepend set dimred\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-55\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 750.0, 581.0, 42.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.+~\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-52\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 606.0, 554.0, 128.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.noise~ @chans 16\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-49\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 609.0, 581.0, 66.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.*~ 0.05\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-34\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 1126.0, 555.0, 150.0, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"< stereo jitter\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-16\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 989.0, 581.0, 66.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.*~ 0.05\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-14\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 922.0, 587.0, 42.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.+~\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-13\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"multichannelsignal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 989.0, 554.0, 128.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"mc.noise~ @chans 16\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-11\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 922.0, 716.0, 102.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"s~ audio.output_r\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-9\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 166.0, 436.0, 100.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"r~ audio.output_r\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-5\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 922.0, 682.0, 34.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"*~ 0.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-4\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"newobj\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"signal\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 922.0, 649.0, 65.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"cverb~ 10.\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-31\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"message\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 2,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"\" ],\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 993.0, 151.0, 90.0, 22.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"faders_all set 0\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-30\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"comment\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 1020.0, 115.0, 150.0, 20.0 ],\n\t\t\t\t\t\t\t\t\t\"text\" : \"RESET\"\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-28\",\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"button\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"outlettype\" : [ \"bang\" ],\n\t\t\t\t\t\t\t\t\t\"parameter_enable\" : 0,\n\t\t\t\t\t\t\t\t\t\"patching_rect\" : [ 993.0, 113.0, 24.0, 24.0 ]\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n, \t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\"box\" : \t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\"id\" : \"obj-25\",\n\t\t\t\t\t\t\t\t\t\"items\" : [ \"none\", \",\", \"PCA\", \",\", \"spherical\" ],\n\t\t\t\t\t\t\t\t\t\"maxclass\" : \"umenu\",\n\t\t\t\t\t\t\t\t\t\"numinlets\" : 1,\n\t\t\t\t\t\t\t\t\t\"numoutlets\" : 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\"live.toggle[31]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-18::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[16]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-18::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[16]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-18::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[14]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-18::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[14]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-18::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[16]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-18::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[14]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-18::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[32]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-18::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[16]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[15]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[15]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[15]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[15]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[15]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[29]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[15]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[15]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[15]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[13]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[15]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[15]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[30]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-21::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[15]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-24::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[27]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[26]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-27::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[25]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[24]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-30::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[23]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[22]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-34::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[20]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[21]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-37::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[19]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[18]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-40::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[17]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[16]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-43::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[14]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[15]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-46::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[13]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[12]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-49::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[10]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[3]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[11]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-52::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[3]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[3]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[3]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[3]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[3]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[9]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[3]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[3]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[3]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[2]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[3]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[3]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[8]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-58::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[3]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[2]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[2]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[2]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[2]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[2]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[6]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[2]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[2]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[2]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-59\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"latent_slider[1]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[2]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[2]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[7]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-61::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[2]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-117\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_max[1]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-118\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"range_min[1]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-119\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"freq[1]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-120\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"phase[1]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-121\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"amp[1]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-137\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[4]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-235\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"free[1]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-47\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.numbox[1]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-58\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"input_scale[1]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-90\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"scale[1]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-91\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"mode[1]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-96\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"live.toggle[5]\"\n\t\t\t\t}\n,\n\t\t\t\t\"obj-2::obj-3::obj-64::obj-3::obj-99\" : \t\t\t\t{\n\t\t\t\t\t\"parameter_longname\" : \"clip[1]\"\n\t\t\t\t}\n\n\t\t\t}\n,\n\t\t\t\"inherited_shortname\" : 1\n\t\t}\n,\n\t\t\"dependency_cache\" : [ \t\t\t{\n\t\t\t\t\"name\" : \"M4L.latent_remote.js\",\n\t\t\t\t\"bootpath\" : \"~/Documents/Max 9/Packages/nn_tilde/patchers/latent_remote\",\n\t\t\t\t\"patcherrelativepath\" : \"../../../../Documents/Max 9/Packages/nn_tilde/patchers/latent_remote\",\n\t\t\t\t\"type\" : \"TEXT\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"M4L.latent_remote.maxpat\",\n\t\t\t\t\"bootpath\" : \"~/Documents/Max 9/Packages/nn_tilde/patchers/latent_remote\",\n\t\t\t\t\"patcherrelativepath\" : \"../../../../Documents/Max 9/Packages/nn_tilde/patchers/latent_remote\",\n\t\t\t\t\"type\" : \"JSON\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"M4L.latent_slider.maxpat\",\n\t\t\t\t\"bootpath\" : \"~/Documents/Max 9/Packages/nn_tilde/patchers/latent_remote\",\n\t\t\t\t\"patcherrelativepath\" : \"../../../../Documents/Max 9/Packages/nn_tilde/patchers/latent_remote\",\n\t\t\t\t\"type\" : \"JSON\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"M4L.latent_slider_component.maxpat\",\n\t\t\t\t\"bootpath\" : \"~/Documents/Max 9/Packages/nn_tilde/patchers/latent_remote\",\n\t\t\t\t\"patcherrelativepath\" : \"../../../../Documents/Max 9/Packages/nn_tilde/patchers/latent_remote\",\n\t\t\t\t\"type\" : \"JSON\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"cherokee.aif\",\n\t\t\t\t\"bootpath\" : \"C74:/media/msp\",\n\t\t\t\t\"type\" : \"AIFF\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"ierf.gendsp\",\n\t\t\t\t\"bootpath\" : \"~/Documents/Max 9/Packages/nn_tilde/patchers/latent_remote\",\n\t\t\t\t\"patcherrelativepath\" : \"../../../../Documents/Max 9/Packages/nn_tilde/patchers/latent_remote\",\n\t\t\t\t\"type\" : \"gDSP\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"jongly.aif\",\n\t\t\t\t\"bootpath\" : \"C74:/media/msp\",\n\t\t\t\t\"type\" : \"AIFF\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"latent_remote.js\",\n\t\t\t\t\"bootpath\" : \"~/Documents/Max 9/Packages/nn_tilde/patchers/latent_remote\",\n\t\t\t\t\"patcherrelativepath\" : \"../../../../Documents/Max 9/Packages/nn_tilde/patchers/latent_remote\",\n\t\t\t\t\"type\" : \"TEXT\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"mcs.nn~.mxo\",\n\t\t\t\t\"type\" : \"iLaX\"\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"nn.info.mxo\",\n\t\t\t\t\"type\" : \"iLaX\"\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"nn~.mxo\",\n\t\t\t\t\"type\" : \"iLaX\"\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"thru.maxpat\",\n\t\t\t\t\"bootpath\" : \"C74:/patchers/m4l/Pluggo for Live resources/patches\",\n\t\t\t\t\"type\" : \"JSON\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n, \t\t\t{\n\t\t\t\t\"name\" : \"vschaos.png\",\n\t\t\t\t\"bootpath\" : \"~/Documents/Max 9/Packages/nn_tilde/misc\",\n\t\t\t\t\"patcherrelativepath\" : \"../../../../Documents/Max 9/Packages/nn_tilde/misc\",\n\t\t\t\t\"type\" : \"PNG\",\n\t\t\t\t\"implicit\" : 1\n\t\t\t}\n ],\n\t\t\"autosave\" : 0\n\t}\n\n}\n"
  },
  {
    "path": "src/shared/circular_buffer.h",
    "content": "#pragma once\n#include <memory>\n\ntemplate <class in_type, class out_type> class circular_buffer {\npublic:\n  circular_buffer();\n  void initialize(size_t size);\n  bool empty();\n  bool full();\n  void put(in_type *input_array, int N);\n  void get(out_type *output_array, int N);\n  void fill(out_type val); \n  void reset();\n  size_t max_size() { return _max_size; } \n\nprotected:\n  std::unique_ptr<out_type[]> _buffer;\n  size_t _max_size = 0;\n\n  int _head = 0;\n  int _tail = 0;\n  int _count = 0;\n  bool _full = false;\n};\n\ntemplate <class in_type, class out_type>\ncircular_buffer<in_type, out_type>::circular_buffer() {}\n\ntemplate <class in_type, class out_type>\nvoid circular_buffer<in_type, out_type>::initialize(size_t size) {\n  _buffer = std::make_unique<out_type[]>(size);\n  auto zero_val = static_cast<out_type>(0.0f);\n  std::fill(_buffer.get(), _buffer.get() + size, zero_val);\n  _max_size = size;\n}\n\ntemplate <class in_type, class out_type>\nbool circular_buffer<in_type, out_type>::empty() {\n  return (!_full && _head == _tail);\n}\n\ntemplate <class in_type, class out_type>\nbool circular_buffer<in_type, out_type>::full() {\n  return _full;\n}\n\n\ntemplate <class in_type, class out_type>\nvoid circular_buffer<in_type, out_type>::fill(out_type value) {\n  // if (_max_size > 0) {\n  //   std::fill(_buffer.get(), _buffer.get() + _max_size, value);\n  // }\n}\n\n\ntemplate <class in_type, class out_type>\nvoid circular_buffer<in_type, out_type>::put(in_type *input_array, int N) {\n  if (!_max_size)\n    return;\n\n  while (N--) {\n    _buffer[_head] = out_type(*(input_array++));\n    _head = (_head + 1) % _max_size;\n    if (_full)\n      _tail = (_tail + 1) % _max_size;\n    _full = _head == _tail;\n  }\n}\n\ntemplate <class in_type, class out_type>\nvoid circular_buffer<in_type, out_type>::get(out_type *output_array, int N) {\n  if (!_max_size)\n    return;\n\n  while (N--) {\n    if (empty()) {\n      *(output_array++) = out_type();\n    } else {\n      *(output_array++) = _buffer[_tail];\n      _tail = (_tail + 1) % _max_size;\n      _full = false;\n    }\n  }\n}\n\ntemplate <class in_type, class out_type>\nvoid circular_buffer<in_type, out_type>::reset() {\n  _head = _tail;\n  _count = 0;\n  _full = false;\n}"
  },
  {
    "path": "src/shared/model_download.h",
    "content": "#pragma once\n\n#include <cstdio>\n#include <filesystem>\n#include <iostream>\n#include <string>\n#include <curl/curl.h>\n#include <nlohmann/json.hpp>\n#include <thread>\n\n#ifndef MAX_DOWNLOADS\n#define MAX_DOWNLOADS 2\n#endif\n\nnamespace fs = std::filesystem;\nusing json = nlohmann::json;\nusing DownloadTask = std::function<void()>;\n\n\n// Callback function for handling the downloaded data\nsize_t JSONWriteCallback(void* contents, size_t size, size_t nmemb, std::string* userp) {\n    size_t totalSize = size * nmemb;\n    userp->append(static_cast<char*>(contents), totalSize);\n    return totalSize;\n}\n\nsize_t ModelWriteCallback(void* ptr, size_t size, size_t nmemb, FILE* stream) {\n    return fwrite(ptr, size, nmemb, stream);\n}\n\nbool is_file_empty(const fs::path filePath) {\n    try {\n        return fs::is_regular_file(filePath) && fs::file_size(filePath) == 0;\n    } catch (const fs::filesystem_error& e) {\n        std::cerr << \"Filesystem error: \" << e.what() << std::endl;\n        return false;\n    }\n}\n\n\n\nclass ModelDownloader {\n\nprotected:\n    fs::path d_path, d_cert_path;\n    bool _is_ready = false;\n    static bool create_path(const fs::path& path);\n    // api root\n    std::string _api_root;\n    // available models callbacks\n    std::string get_string_from_api_callback(std::string &adress);\n    json d_available_models;\n    // downloading attributes\n    std::vector<std::thread> d_threads;\n    std::queue<DownloadTask> tasks;\n    std::mutex mutex;\n    std::condition_variable condition; \n    bool stop;\n    // helpers\n\n\npublic: \n    ModelDownloader() = default; \n    ModelDownloader(fs::path download_location); \n    ~ModelDownloader(); \n    int init_downloader(bool force_refresh = false);\n    void init_threads();\n    bool is_ready();\n    bool has_valid_certificate();\n    bool has_model(const std::string &model_card);\n    std::string get_api_root();\n    bool update_available_models(); \n    std::vector<std::string> get_available_models(); \n    fs::path get_download_path() { return d_path; }\n    void download(const std::string &model_name, const std::string &custom_name = \"\"); \n    void remove(const std::string &model_name); \n    void enqueue_download_task(DownloadTask task);\n    void print_available_models();\n    fs::path target_path_from_model(const std::string model_name, const std::string custom_name = \"\");\n    fs::path get_certificate_path() { return d_cert_path; }\n    void reload();\n    void worker(); \n\n    virtual void fill_dict(void* dict_to_fill) = 0;\n    virtual void print_to_parent(const std::string &message, const std::string &canal) = 0;\n    virtual fs::path cert_path_from_path(fs::path path) = 0;\n\n    std::string string_id() {\n        std::stringstream str_id; \n        str_id << this; \n        return str_id.str();\n    }\n\n    void find_and_replace_char(std::string& str, char oldChar, char newChar) {\n        for (size_t pos = 0; (pos = str.find(oldChar, pos)) != std::string::npos; pos++) {\n            str[pos] = newChar;\n        }\n    }\n};\n\nModelDownloader::ModelDownloader(fs::path download_location): d_path(download_location / \"..\") {}\n\nModelDownloader::~ModelDownloader() {\n    {\n        std::unique_lock<std::mutex> lock(mutex);\n        stop = true;\n    }\n    condition.notify_all();\n    for (std::thread &thread : d_threads) {\n        thread.join();\n    }\n}\n\nvoid ModelDownloader::init_threads() {\n    for (size_t i(0); i < MAX_DOWNLOADS; ++i) {\n        d_threads.emplace_back(&ModelDownloader::worker, this);\n    }\n}\n\nbool ModelDownloader::has_valid_certificate() {\n    if (d_cert_path == \"\") {\n        return false;\n    } else if (!fs::exists(d_cert_path)) {\n        #if defined(_WIN32) || defined(_WIN64)\n            std::string error_message = \"Could not find certificate at \" + d_cert_path.string();\n        #elif defined(__APPLE__) || defined(__MACH__)\n            std::string error_message = \"Could not find certificate in external bundle\";\n        #elif defined(__linux__)\n            std::string error_message = \"Could not find certificate at \" + d_cert_path.string() + \"; did you install ca-certificates?\";\n        #else\n            std::string error_message =\"Could not find certificate at \" + d_cert_path.string() + \"; wrong compilation platform\";\n        #endif\n        \n        print_to_parent(error_message, \"cwarn\");\n        return false;\n    }\n    return true;\n}\n\nint ModelDownloader::init_downloader(bool force_refresh) {\n    curl_global_init(CURL_GLOBAL_DEFAULT);\n    bool is_path_ok = create_path(d_path);\n    bool is_certificate_ok = has_valid_certificate(); \n    bool is_model_list_available = update_available_models();\n    init_threads();\n    _is_ready = (is_path_ok && is_model_list_available);\n    return _is_ready;\n}\n\nbool ModelDownloader::is_ready() {\n    if (!_is_ready) {\n        return init_downloader();\n    } else {\n        return true;\n    }\n}\n\nbool ModelDownloader::update_available_models() {\n    _api_root = get_api_root();\n    auto url = _api_root + \"available_models\";\n    auto model_as_string = get_string_from_api_callback(url);\n    d_available_models = nlohmann::json::parse(model_as_string);\n    return true;\n}\n\nstd::string ModelDownloader::get_string_from_api_callback(std::string &address) {\n    CURL* curl; \n    CURLcode res; \n    std::string readBuffer; \n\n    curl = curl_easy_init();\n    if (curl) {\n        curl_easy_setopt(curl, CURLOPT_URL, address.c_str());\n        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, JSONWriteCallback);\n        curl_easy_setopt(curl, CURLOPT_WRITEDATA, &readBuffer);\n        curl_easy_setopt(curl, CURLOPT_CAINFO, d_cert_path.string().c_str());\n        res = curl_easy_perform(curl);\n        if (res != CURLE_OK) {\n            throw std::string(\"could not fetch available models from API. Code from API: \") + std::to_string(res);\n        }\n        // Clean up\n        curl_easy_cleanup(curl);\n    } else {\n        throw std::string(\"curl is not available ; cannot download models\");\n    }\n    return readBuffer; \n}\n\nvoid ModelDownloader::reload() {\n    update_available_models(); \n}\n\nstd::string ModelDownloader::get_api_root() {\nunsigned char b[] = {104, 116, 116, 112, 115, 58,  47,  47, 112, 108, 97,\n                    121, 46,  102, 111, 114, 117, 109, 46, 105, 114, 99,\n                    97,  109, 46,  102, 114, 47,  114, 97, 118, 101, 45,\n                    118, 115, 116, 45,  97,  112, 105, 47};\nchar c[sizeof(b) + 1];\nstd::memcpy(c, b, sizeof(b));\nc[sizeof(b)] = '\\0';\nreturn std::string(c);\n}\n\nbool ModelDownloader::create_path(const fs::path& path) {\n    if (fs::exists(path)) {\n        return true;\n    } else {\n        if (fs::create_directories(path)) {\n            return true;\n        } else {\n            return false;\n        }\n    }\n}\n\nstd::vector<std::string> ModelDownloader::get_available_models() {\n    if (!_is_ready) {\n        throw std::string(\"model downloader has not been initialised, or did not manage to fetch available content\");\n    }\n    auto models_keys = std::vector<std::string>();\n    for (const auto& source : d_available_models.items()) {\n        auto current_source = source.key(); \n        for (const auto& model: source.value().items()) {\n            auto current_model = model.key(); \n            for (const auto& model_name: model.value().items()) {\n                std::stringstream model_card(\"\"); \n                model_card << current_source << \"/\" << current_model << \"/\" << model_name.key(); \n                models_keys.push_back(model_card.str()); \n            }\n        }\n    }\n    return models_keys;\n}\n\nvoid ModelDownloader::print_available_models() {\n    auto available_models = get_available_models();\n    for (const auto& model_name: available_models) {\n        print_to_parent(model_name, \"cout\");\n    }\n}\n\n\nvoid ModelDownloader::worker() {\n    while (true) {\n        DownloadTask task;\n        {\n            std::unique_lock<std::mutex> lock(mutex);\n            condition.wait(lock, [this]() { return stop || !tasks.empty(); });\n\n            if (stop && tasks.empty()) {\n                return;\n            }\n            task = std::move(tasks.front());\n            tasks.pop();\n        }\n        task();\n    }\n}\n\nvoid ModelDownloader::enqueue_download_task(DownloadTask task) {\n    {\n        std::unique_lock<std::mutex> lock(mutex);\n        tasks.push(task);\n    }\n    condition.notify_one();   \n}\n\nstd::vector<std::string> split_model_card(std::string model_name) {\n    auto ss = std::stringstream(model_name);\n    std::string segment;\n    std::vector<std::string> segments;\n    while (std::getline(ss, segment, '/')) {\n        segments.push_back(segment);\n    }\n    return segments;\n}\n\nfs::path ModelDownloader::target_path_from_model(const std::string model_name, const std::string custom_name) {\n    std::string name_to_write;\n    if (custom_name == \"\") {\n        auto segments = split_model_card(model_name);\n        name_to_write = segments[segments.size() - 1];\n    } else {\n        name_to_write = custom_name; \n    }\n    return d_path / (name_to_write + \".ts\");\n}\n\nstd::string lock_path_from_target(std::string target_path, std::string model_name) {\n    fs::path target_path_fs = target_path;\n    std::stringstream lock_name(\".\"); \n    for (auto p: split_model_card(model_name)) {\n        lock_name << p << '_' ; \n    }\n    lock_name << \"lock\";\n    std::string lock_path = fs::absolute(target_path_fs.parent_path() / lock_name.str()).string(); \n    return lock_path;\n}\n\nvoid download_thread(ModelDownloader *parent, std::string model_name, std::string target_path, int dl_idx)\n{\n    parent->print_to_parent(\"downloading model \" + model_name + \"...\", \"cout\");\n    // create lock file, to not download two times the same model\n    std::string lock_path = lock_path_from_target(target_path, model_name);\n    FILE* lock_file = fopen(lock_path.c_str(), \"w\");\n    fputc('l', lock_file);\n    if (!lock_file) {\n        parent->print_to_parent(std::string(\"could not open file for writing : \") + target_path, \"cerr\"); \n        return;\n    }\n\n    try {\n        CURL* curl;\n        FILE* file;\n        CURLcode res;\n\n        curl = curl_easy_init();\n        std::string url = (parent->get_api_root()) + \"download_model?model=\" + model_name; \n        if (curl) {\n            file = fopen(target_path.c_str(), \"wb\");\n            if (!file) {\n                curl_easy_cleanup(curl);\n                parent->print_to_parent(std::string(\"could not open file for writing : \") + target_path, \"cerr\"); \n                fclose(lock_file);\n                fs::remove(lock_path);\n                return;\n            }\n\n            curl_easy_setopt(curl, CURLOPT_URL, url.c_str());\n            curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, ModelWriteCallback);\n            curl_easy_setopt(curl, CURLOPT_WRITEDATA, file);\n            curl_easy_setopt(curl, CURLOPT_CAINFO, parent->get_certificate_path().string().c_str());\n            \n            // Perform the request\n            res = curl_easy_perform(curl);\n            if (res != CURLE_OK) {\n                parent->print_to_parent(\"error during download : \" + std::string(curl_easy_strerror(res)), \"cerr\");\n                fclose(file);\n                fclose(lock_file);\n                fs::remove(lock_path);\n                curl_easy_cleanup(curl);\n                return;\n            } \n\n            // Clean up\n            fclose(file);\n            curl_easy_cleanup(curl);\n            if (is_file_empty(fs::path(target_path))) {\n                parent->print_to_parent(\"failed to download  \" + model_name, \"cerr\");\n                fs::remove(target_path);\n            } else {\n                parent->print_to_parent(\"model \" + model_name + \" downloaded at \" + target_path, \"cout\");\n            }\n            fclose(lock_file);\n            fs::remove(lock_path);\n            return; \n        }\n    } catch (...) {\n        parent->print_to_parent(\"failed to download  \" + model_name, \"cerr\");\n        fclose(lock_file);\n        fs::remove(lock_path);\n        return ; \n    }\n    \n}\n\nvoid ModelDownloader::remove(const std::string &model_name) {\n    auto target_path = target_path_from_model(model_name);\n    if (fs::exists(target_path)) {\n        fs::remove(target_path);\n        print_to_parent(\"model \" + model_name + \" deleted.\", \"cwarn\");\n    } else {\n        print_to_parent(\"could not find model \" + model_name , \"cerr\");\n    }\n}\n\nbool ModelDownloader::has_model(const std::string &model_name) {\n    auto parts = split_model_card(model_name);\n    auto models_json = d_available_models; \n    for (auto p: parts) {\n        if (models_json.contains(p)) {\n            models_json = models_json[p];\n        } else {\n            return false;\n        } \n    }\n    return true; \n}\n\nvoid ModelDownloader::download(const std::string &model_name, const std::string &custom_name) {\n    if (!_is_ready) {\n        throw std::string(\"model downloader has not been initialised, or did not manage to fetch available content\");\n    }\n    if (!has_model(model_name)) {\n        throw std::string(\"model name \" + model_name + \" not available.\");\n    }\n    auto target_path = target_path_from_model(model_name, custom_name);\n    if (std::filesystem::exists(target_path)) {\n        if (!is_file_empty(target_path)) {\n            print_to_parent(\"model \" + target_path.string() + \" seems to be already downloaded.\", \"cwarn\");\n            return;\n        } else {\n            std::filesystem::remove(target_path);\n        }\n    }\n    if (std::filesystem::exists(lock_path_from_target(target_path.string(), model_name))) {\n        print_to_parent(\"model \" + model_name + \" is already downloading.\", \"cwarn\");\n        return; \n    }\n    int download_id = d_threads.size();\n    enqueue_download_task([this, model_name, target_path, download_id]() { download_thread(this, model_name, target_path.string(), download_id); });\n}\n"
  },
  {
    "path": "src/shared/static_buffer.h",
    "content": "#pragma once\n#include <vector>\n#include <torch/torch.h>\n\n\ntemplate <typename data_type> \nclass StaticBuffer {\n\npublic:\n    using BufferData = std::vector<data_type>;\n    using BufferShape = std::array<size_t, 2>;\n\nprivate:\n    std::shared_ptr<BufferData> _data; \n    BufferShape _dims;\n    double _samplingRate; \n    \npublic: \n\n    StaticBuffer() {\n        _dims = BufferShape({0, 0});\n    };\n\n    StaticBuffer(const size_t dim1, const size_t dim2, const double samplingRate = -1) {\n        _dims = BufferShape({dim1, dim2});\n        _data = std::make_shared<BufferData>(get_empty_buffer(dim1, dim2));\n        _samplingRate = samplingRate;\n    };\n\n    StaticBuffer(BufferData data, const double samplingRate) {\n        _dims = BufferShape({data.dims(), data[0].dims()});\n        _data = std::make_shared(data);\n        _samplingRate = samplingRate;\n    };\n\n    StaticBuffer(const StaticBuffer &buffer) {\n        _dims = buffer._dims; \n        _data = buffer._data;\n        _samplingRate = buffer._samplingRate;\n    }\n\n    BufferShape dims () {\n        return _dims;\n    }\n\n    double sr() { return _samplingRate; }\n\n    static BufferData get_empty_buffer(const size_t dim1, const size_t dim2) {\n        return BufferData(dim1 * dim2, 0.f);\n    };\n\n    void clear() {\n        _data.get()->clear();\n    }\n\n    void reset() {\n        std::fill(_data.get()->begin(), _data.get()->end(), 0.0f);\n    };\n\n    torch::Tensor to_tensor() {\n        auto obj = torch::from_blob(_data.get()->data(), {(long long)_dims[0], (long long)_dims[1]}, torch::kFloat);\n        return obj;\n    };\n\n    float& at(const size_t dim1, const size_t dim2) {\n        return _data.get()->at(dim1 * _dims[0] + dim2);\n    }\n\n    void put(data_type data, const size_t dim1, const size_t dim2) {\n        at(dim1, dim2) = data;\n    }\n};"
  },
  {
    "path": "src/source/attributes.py",
    "content": "try: \n    import nn_tilde\nexcept ImportError: \n    import os, sys\n    sys.path.append(os.path.join(os.path.dirname(__file__) , \"..\" , \"..\"))\n    import python_tools as nn_tilde\n\nfrom typing import List\nimport torch\n\n\nclass AttributeFoo(nn_tilde.Module):\n    def __init__(self):\n        super().__init__()\n        self._valid_animals_ = torch.jit.Attribute([\"horse\", \"goose\", \"chicken\", \"pig\", \"dog\", \"cat\"], List[str])\n        self.register_attribute(\"attr_int\", 0)\n        self.register_attribute(\"attr_float\", 0.)\n        self.register_attribute(\"attr_str\", \"apple\")\n        self.register_attribute(\"attr_enum\", \"horse\")\n        self.register_attribute(\"attr_bool\", False)\n        self.register_attribute(\"attr_list\", [0, \"christophe\", 1., True])\n        self.register_method(\"forward\", 1, 1, 2, 1, test_method=False)\n        self.finish()\n\n    @torch.jit.export\n    def set_attr_enum(self, animal: str) -> int:\n        # a custom setter can accept only given values of an incoming symbol. \n        # a setter function should return 0 if value is accepted, or -1 if refused.\n        if animal not in self._valid_animals_:\n            return -1\n        self.attr_enum = (animal,)\n        return 0\n\n    @torch.jit.export \n    def set_attr_list(self, val1: int, val2: str, val3: float, val4: bool):\n        # when defining a custom setter for a list, all values must be unfolded in the function\n        # signature (this is a TorchScript constraint).\n        self.attr_list = (val1, val2, val3, val4)\n        return 0\n\n    @torch.jit.export\n    def forward(self, x: torch.Tensor):\n        x = torch.zeros(x.shape[:-2] + (2, x.shape[-1]))\n        x[..., 0, :] = self.attr_int[0]\n        x[..., 1, :] = self.attr_float[0]\n        return x\n\n\nif __name__ == '__main__':\n    # Create your target class\n    model = AttributeFoo()\n    # Export it to a torchscript model\n    model.export_to_ts('src/models/demo_attributes.ts')\n\n    "
  },
  {
    "path": "src/source/buffers.py",
    "content": "try: \n    import nn_tilde\nexcept ImportError: \n    import os, sys\n    sys.path.append(os.path.join(os.path.dirname(__file__) , \"..\" , \"..\"))\n    import python_tools as nn_tilde\n\nfrom typing import List, Tuple\nimport torch\n\n\nclass BufferFoo(nn_tilde.Module):\n    buffer: Tuple[nn_tilde.Buffer]\n    def __init__(self, test_method: bool = False):\n        super().__init__()\n        # access to max buffers are registered with nn_tilde.Buffer, that can be optionally \n        # given an optional minimum and/or maximum buffer length.\n        self.register_attribute(\"buf\", (nn_tilde.Buffer(None, 64, 2048)))\n        self.register_method('loudness', 1, 1, 1, 1, test_method=test_method)\n        self.register_method('shape', 1, 1, 2, 1, test_method=test_method)\n        self.register_method('get_sr', 1, 1, 1, 1, test_method=test_method)\n        self.finish()\n\n    def get_loudness(self, x: torch.Tensor) -> float:\n        return x.pow(2).mean().sqrt().item()\n\n    @torch.jit.export\n    def loudness(self, x: torch.Tensor):\n        buffer = self.buf[0]\n        if buffer.has_value:\n            loudness = self.get_loudness(buffer.value)\n            return torch.full_like(x, fill_value=loudness)\n        else:\n            return torch.zeros_like(x)\n\n    @torch.jit.export\n    def shape(self, x: torch.Tensor):\n        is_batched = x.ndim > 2\n        if not is_batched:\n            x = x[None]\n        buffer = self.buf[0]\n        if buffer.has_value:\n            out = torch.zeros(x.shape[0], 2, x.shape[-1])\n            out[:, 0, :] = buffer.value.shape[0]\n            out[:, 1, :] = buffer.value.shape[1]\n            if not is_batched:\n                out = out[0]\n        else:\n            out = torch.zeros_like(x)\n        return out\n\n    @torch.jit.export\n    def get_sr(self, x: torch.Tensor):\n        buffer = self.buf[0]\n        if buffer.has_value:\n            if self.buf[0].sr is None:\n                sr = -1\n            else:\n                sr = buffer.sr\n            return torch.full_like(x, fill_value=sr)\n        else:\n            return torch.zeros_like(x)\n\nif __name__ == '__main__':\n    # Create your target class\n    model = BufferFoo()\n    # Export it to a torchscript model\n    model.export_to_ts('src/models/demo_buffers.ts')\n\n"
  },
  {
    "path": "src/source/effects.py",
    "content": "try: \n    import nn_tilde\nexcept ImportError: \n    import os, sys\n    sys.path.append(os.path.join(os.path.dirname(__file__) , \"..\" , \"..\"))\n    import python_tools as nn_tilde\n\n\nfrom typing import List, Tuple\n\nimport torch\nimport torch.nn as nn\n\n\nclass AudioUtils(nn_tilde.Module):\n\n    def __init__(self):\n        super().__init__()\n        # REGISTER ATTRIBUTES\n        self.register_attribute('gain_factor', 1.)\n        self.register_attribute('polynomial_factors', (1., 0., 0., 0.))\n        self.register_attribute('saturate_mode', 'tanh')\n        self.register_attribute('invert_signal', False)\n        self.register_attribute('fractal', (2, 0.))\n\n        # REGISTER METHODS\n        self.register_method(\n            'thru',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) input signal'],\n            output_labels=['(signal) output signal'],\n        )\n        self.register_method(\n            'invert',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) input signal'],\n            output_labels=['(signal) output signal'],\n        )\n        self.register_method(\n            'add',\n            in_channels=2,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) first signal', '(signal) second signal'],\n            output_labels=['(signal) output signal'],\n        )\n        self.register_method(\n            'saturate',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) signal to saturate'],\n            output_labels=['(signal) saturated signal'],\n        )\n        self.register_method(\n            'midside',\n            in_channels=2,\n            in_ratio=1,\n            out_channels=2,\n            out_ratio=1,\n            input_labels=['(signal) L channel', '(signal) R channel'],\n            output_labels=['(signal) Mid channel', '(signal) Side channel'],\n        )\n        self.register_method(\n            'rms',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1024,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['(signal) rms value'],\n        )\n        self.register_method(\n            'polynomial',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1,\n            input_labels=['(signal) signal to distort'],\n            output_labels=['(signal) distorted signal'],\n        )\n\n        self.register_method(\n            'fractalize',\n            in_channels=1,\n            in_ratio=512,\n            out_channels=1,\n            out_ratio=512,\n            input_labels=['(signal) signal to replicate'],\n            output_labels=['(signal) fractalized signal'],\n        )\n\n    @torch.jit.export\n    def thru(self, x: torch.Tensor):\n        return x\n\n    # defining main methods\n    @torch.jit.export\n    def invert(self, x: torch.Tensor):\n        if self.invert_signal[0]:\n            return x\n        else:\n            return -x\n\n    @torch.jit.export\n    def add(self, x: torch.Tensor):\n        return x.sum(-2, keepdim=True) / 2\n\n    @torch.jit.export\n    def fractalize(self, x: torch.Tensor):\n        fractal_order = int(self.fractal[0])\n        fractal_amount = float(self.fractal[1])\n        downsampled_signal = x[..., ::fractal_order]\n        return x\n\n    @torch.jit.export\n    def polynomial(self, x: torch.Tensor):\n        out = torch.zeros_like(x)\n        for i in range(4):\n            out += self.polynomial_factors[i] * x.pow(i + 1)\n        return out\n\n    @torch.jit.export\n    def saturate(self, x: torch.Tensor):\n        saturate_mode = self.saturate_mode[0]\n        if saturate_mode == 'tanh':\n            return torch.tanh(x * self.gain_factor[0])\n        elif saturate_mode == 'clip':\n            return torch.clamp(x * self.gain_factor[0], -1, 1)\n\n    @torch.jit.export\n    def midside(self, x: torch.Tensor):\n        l, r = x[..., 0, :], x[..., 1, :]\n        return torch.stack([(l + r) / 2, (l - r) / 2], dim=-2)\n\n    @torch.jit.export\n    def rms(self, x: torch.Tensor):\n        x = x.reshape(x.shape[0], x.shape[1], 1024, -1)\n        rms = x.pow(2).sum(-2).sqrt() / x.size(-1)\n        return rms\n\n    # defining attribute getters\n    # WARNING : typing the function's ouptut is mandatory\n    @torch.jit.export\n    def get_gain_factor(self) -> float:\n        return float(self.gain_factor[0])\n\n    @torch.jit.export\n    def get_polynomial_factors(self) -> List[float]:\n        polynomial_factors: List[float] = []\n        for p in self.polynomial_factors:\n            polynomial_factors.append(float(p))\n        return polynomial_factors\n\n    @torch.jit.export\n    def get_saturate_mode(self) -> str:\n        return self.saturate_mode[0]\n\n    @torch.jit.export\n    def get_invert_signal(self) -> bool:\n        return self.invert_signal[0]\n\n    @torch.jit.export\n    def get_fractal(self) -> Tuple[int, float]:\n        return (int(self.fractal[0]), float(self.fractal[1]))\n\n    # defining attribute setter\n    # setters must return an error code :\n    # return 0 if the attribute has been adequately set,\n    # return -1 if the attribute was wrong.\n    @torch.jit.export\n    def set_gain_factor(self, x: float) -> int:\n        self.gain_factor = (x, )\n        return 0\n\n    @torch.jit.export\n    def set_polynomial_factors(self, factor1: float, factor2: float,\n                               factor3: float, factor4: float) -> int:\n        factors = (factor1, factor2, factor3, factor4)\n        self.polynomial_factors = factors\n        return 0\n\n    @torch.jit.export\n    def set_saturate_mode(self, x: str):\n        if (x == 'tanh') or (x == 'clip'):\n            self.saturate_mode = (x, )\n            return 0\n        else:\n            return -1\n\n    @torch.jit.export\n    def set_invert_signal(self, x: bool):\n        self.invert_signal = (x, )\n        return 0\n\n    @torch.jit.export\n    def set_fractal(self, factor: int, amount: float):\n        if factor <= 0:\n            return -1\n        elif factor % 2 != 0:\n            return -1\n        self.fractal = (factor, float(amount))\n        return 0\n\n\nif __name__ == '__main__':\n    # Create your target class\n    model = AudioUtils()\n    # Export it to a torchscript model\n    model.export_to_ts('src/models/effects.ts')\n"
  },
  {
    "path": "src/source/features.py",
    "content": "#\n# NN~ - Scripting library\n# features.py : Simple scripting example for waveform-to-float case.\n#\n# We demonstrate the basic mecanisms for using the nn~ environment.\n# In this case, any function from Python can be used to wrap it inside a nn~ model.\n#\n# ACIDS - IRCAM : Philippe Esling, Axel Chemla--Romeu-Santos, Antoine Caillon\n#\n\nfrom typing import List, Tuple\nimport numpy as np\nimport librosa\n# Pytorch audio operations\nimport torch\nimport torchaudio.functional as F\nfrom torchaudio.transforms import Spectrogram\n# Import the nn~ library\ntry: \n    import nn_tilde\nexcept ImportError: \n    import os, sys\n    sys.path.append(os.path.join(os.path.dirname(__file__) , \"..\" , \"..\"))\n    import python_tools as nn_tilde \n\n\nclass AudioFeatures(nn_tilde.Module):\n\n    def __init__(self,\n                 nfft=1024,\n                 hop_size=256,\n                 skip_features=None):\n        super().__init__(sr=44100)\n        self.nfft = nfft\n        self.hop_size = hop_size\n        transform = Spectrogram(n_fft=nfft,\n                                win_length=nfft,\n                                hop_length=hop_size,\n                                center=False,\n                                normalized=True)\n        self.transform = transform\n        self.skip_features = skip_features\n        # -----------------\n        # Register attributes\n        # -----------------\n        # self.register_attribute('sr', 44100)\n        self.register_buffer('audio_buffer', torch.zeros((1, 1, nfft - hop_size)))\n        # Pre-compute frequency bins\n        self.freq = torch.fft.rfftfreq(n = nfft, d = 1.0 / self.sr[0])\n\n        # -----------------\n        # Register methods\n        # -----------------\n        self.register_method(\n            'rms',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=1024,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['(signal) rms value'],\n        )\n\n        # REGISTER METHODS\n        self.register_method(\n            'centroid',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=self.hop_size,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['(signal) spectral centroid value'],\n        )\n\n        # REGISTER METHODS\n        self.register_method(\n            'flatness',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=self.hop_size,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['(signal) flatness value'],\n        )\n\n        # REGISTER METHODS\n        self.register_method(\n            'bandwidth',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=1,\n            out_ratio=self.hop_size,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['(signal) bandwidth value'],\n        )\n\n    def _compute_spectrogram(self, x: torch.Tensor):\n        # X : B x hop_size\n        if self.audio_buffer.shape[0] != x.shape[0]:\n            print(\"Resizing and resetting buffer - the batch size has changed\")\n            self.audio_buffer = torch.zeros((x.shape[0], 1, self.nfft - self.hop_size)).to(x)\n            self.freq = torch.fft.rfftfreq(n = self.nfft, d = 1.0 / self.get_sample_rate())\n        # Using the previous buffer information\n        x = torch.cat([self.audio_buffer, x], dim=-1)\n        # Compute the transform\n        spec = self.transform(x)[:, 0]\n        self.audio_buffer = x[..., -(self.nfft - self.hop_size):]\n        if self.skip_features is not None:\n            spec = spec[:, :self.skip_features]\n        return spec\n\n    @torch.jit.export\n    def rms(self, x: torch.Tensor):\n        x = x.reshape(x.shape[0], x.shape[1], 1024, -1)\n        rms = x.pow(2).sum(-2).sqrt() / x.size(-1)\n        return rms\n    \n    @torch.jit.export\n    def centroid(self, x: torch.Tensor):\n        # Compute the current spectrogram\n        spectro = self._compute_spectrogram(x)\n        # Compute the center frequencies of each bin\n        if self.freq is None:\n            self.freq = torch.fft.rfftfreq(n = self.nfft, d = 1.0 / self.sr[0])\n        if len(self.freq.shape) == 1:\n            self.freq = self.freq[None, :, None].expand_as(spectro)\n        # Column-normalize S\n        centroid = torch.sum(self.freq * torch.nn.functional.normalize(spectro, p=1.0, dim=-2), dim=-2)\n        return centroid[:, None, :]\n\n    @torch.jit.export\n    def bandwidth(self, x: torch.Tensor, amin: float = 1e-10, power: float = 2.0, p: float = 2.0):\n        # Compute the current spectrogram\n        spectro = self._compute_spectrogram(x)\n        # Compute the center frequencies of each bin\n        if self.freq is None:\n            self.freq = torch.fft.rfftfreq(n = self.nfft, d = 1.0 / self.sr[0])\n        if len(self.freq.shape) == 1:\n            self.freq = self.freq[None, :, None].expand_as(spectro)\n        # Normalize spectro\n        spectro_normed = torch.nn.functional.normalize(spectro, p=1.0, dim=-2)\n        # Compute centroid\n        centroid = torch.sum(self.freq * spectro_normed, dim=-2)[:, None, :]\n        # Compute the deviation\n        deviation = torch.abs(self.freq - centroid)\n        # Compute bandwidth\n        bandwidth = torch.sum(spectro_normed * deviation**p, dim=-2, keepdim=True) ** (1.0 / p)\n        return bandwidth\n\n    @torch.jit.export\n    def flatness(self, x: torch.Tensor, amin: float = 1e-10, power: float = 2.0):\n        # Compute the current spectrogram\n        spectro = self._compute_spectrogram(x)\n        S_thresh = torch.maximum(spectro**power, torch.zeros(1) + amin)\n        gmean = torch.exp(torch.mean(torch.log(S_thresh), dim=-2, keepdim=True))\n        amean = torch.mean(S_thresh, dim=-2, keepdim=True)\n        flatness = gmean / amean\n        return flatness\n\n\nif __name__ == '__main__':\n    # Create your target class\n    model = AudioFeatures()\n    # Export it to a torchscript model\n    model.export_to_ts('src/models/features.ts')"
  },
  {
    "path": "src/source/unmix.py",
    "content": "#\n# NN~ - Scripting library\n# unmix.py : Advanced scripting example for integrating a deep waveform-to-waveform model.\n#\n# We provide here a simple example of how to use nn~ in order to transform incoming audio.\n# In this example, we do not rely on any ML model, but simply apply effects on input buffers.\n#\n# ACIDS - IRCAM : Philippe Esling, Axel Chemla--Romeu-Santos, Antoine Caillon\n#\n\n# System imports\nfrom typing import List, Tuple\nimport os\nimport math\n# Pytorch imports\nimport torch\nimport torch.nn as nn\nimport torch\nimport torchaudio\n# NN~ imports\nimport nn_tilde\n\nclass Unmix(nn_tilde.Module):\n\n    def __init__(self, \n                 pretrained):\n        super().__init__()\n        # REGISTER ATTRIBUTES\n        self.register_attribute('sr', 44100)\n        self.pretrained = pretrained\n\n        # REGISTER METHODS\n        self.register_method(\n            'forward',\n            in_channels=1,\n            in_ratio=1,\n            out_channels=4,\n            out_ratio=1,\n            input_labels=['(signal) signal to monitor'],\n            output_labels=['drums', 'bass', 'vocals', 'others'],\n        )\n\n    @torch.jit.export\n    def forward(self, input: torch.Tensor):\n        # Preprocess the input buffer (representation)\n        in_r = preprocess(input, int(self.sr[0]), int(self.pretrained.sample_rate))\n        # Pass through the deep audio separation\n        out = self.pretrained(in_r)\n        # Return the separated channels\n        return out.mean(dim=2)\n\n    # defining attribute getters\n    # WARNING : typing the function's ouptut is mandatory\n    @torch.jit.export\n    def get_sr(self) -> int:\n        return int(self.sr[0])\n\n    # defining attribute setter\n    # setters must return an error code :\n    # return 0 if the attribute has been adequately set,\n    # return -1 if the attribute was wrong.\n    @torch.jit.export\n    def set_sr(self, x: int) -> int:\n        self.sr = (x, )\n        return 0\n\ndef preprocess(\n    audio: torch.Tensor,\n    rate: int,\n    model_rate: int,\n) -> torch.Tensor:\n    \"\"\"                                                                                                                                                                                                                                              \n    From an input tensor, convert it to a tensor of shape                                                                                                                                                                                            \n    shape=(nb_samples, nb_channels, nb_timesteps). This includes:                                                                                                                                                                                    \n    -  if input is 1D, adding the samples and channels dimensions.                                                                                                                                                                                   \n    -  if input is 2D                                                                                                                                                                                                                                \n        o and the smallest dimension is 1 or 2, adding the samples one.                                                                                                                                                                              \n        o and all dimensions are > 2, assuming the smallest is the samples                                                                                                                                                                           \n          one, and adding the channel one                                                                                                                                                                                                            \n    - at the end, if the number of channels is greater than the number                                                                                                                                                                               \n      of time steps, swap those two.                                                                                                                                                                                                                 \n    - resampling to target rate if necessary                                                                                                                                                                                                         \n                                                                                                                                                                                                                                                     \n    Args:                                                                                                                                                                                                                                            \n        audio (Tensor): input waveform                                                                                                                                                                                                               \n        rate (float): sample rate for the audio                                                                                                                                                                                                      \n        model_rate (float): sample rate for the model                                                                                                                                                                                                \n                                                                                                                                                                                                                                                     \n    Returns:                                                                                                                                                                                                                                         \n        Tensor: [shape=(nb_samples, nb_channels=2, nb_timesteps)]                                                                                                                                                                                    \n    \"\"\"\n    shape = torch.as_tensor(audio.shape, device=audio.device)\n\n    if len(shape) == 1:\n        # assuming only time dimension is provided.                                                                                                                                                                                                  \n        audio = audio[None, None, ...]\n    elif len(shape) == 2:\n        if shape.min() <= 2:\n            # assuming sample dimension is missing                                                                                                                                                                                                   \n            audio = audio[None, ...]\n        else:\n            # assuming channel dimension is missing                                                                                                                                                                                                  \n            audio = audio[:, None, ...]\n    if audio.shape[1] > audio.shape[2]:\n        # swapping channel and time                                                                                                                                                                                                                  \n        audio = audio.transpose(1, 2)\n    if audio.shape[1] > 2:\n        audio = audio[..., :2]\n\n    if audio.shape[1] == 1:\n        # if we have mono, we duplicate it to get stereo                                                                                                                                                                                             \n        audio = torch.repeat_interleave(audio, 2, dim=1)\n\n    if rate != model_rate:\n        # we have to resample to model samplerate if needed                                                                                                                                                                                          \n        # this makes sure we resample input only once                                                                                                                                                                                                \n        audio = torchaudio.functional.resample(audio,\n            orig_freq=rate, new_freq=model_rate, resampling_method=\"sinc_interpolation\"\n        ).to(audio.device)\n    return audio\n\nif __name__ == '__main__':\n    pretrained = torch.jit.load(\"unmix.pt\")  # Pretrained weights\n    model = Unmix(pretrained)\n    model.export_to_ts('unmix.ts')"
  }
]