diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index e9c6b9f5a3c5a28a09044254b4c3354a7425ed19..7944d32e1d44ac729b5a9114dc5ad1a85a00f5f1 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -91,19 +91,11 @@ stages:
intel_19_serial:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/intel:19
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -115,17 +107,9 @@ intel_19_serial:
intel_19_mpionly:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/intel:19
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -137,16 +121,8 @@ intel_19_mpionly:
intel_19_hybrid:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/intel:19
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- cuda
- docker
@@ -155,20 +131,12 @@ intel_19_hybrid:
intel_19_serial_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/intel:19
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- cuda
- docker
@@ -177,18 +145,10 @@ intel_19_serial_dbg:
intel_19_mpionly_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/intel:19
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- cuda
- docker
@@ -197,17 +157,9 @@ intel_19_mpionly_dbg:
intel_19_hybrid_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/intel:19
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- cuda
- docker
@@ -230,19 +182,11 @@ intel_19_hybrid_dbg_sp:
gcc_7_serial:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/gcc:7
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -253,17 +197,9 @@ gcc_7_serial:
gcc_7_mpionly:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/gcc:7
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -274,16 +210,8 @@ gcc_7_mpionly:
gcc_7_hybrid:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/gcc:7
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -294,12 +222,6 @@ gcc_7_hybrid:
gcc_7_serial_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/gcc:7
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
@@ -307,8 +229,6 @@ gcc_7_serial_dbg:
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
CMAKE_BUILD_TYPE: "DebugOptimized"
WALBERLA_ENABLE_GUI: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -319,18 +239,10 @@ gcc_7_serial_dbg:
gcc_7_mpionly_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/gcc:7
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -341,17 +253,9 @@ gcc_7_mpionly_dbg:
gcc_7_hybrid_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/gcc:7
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- cuda
- docker
@@ -375,19 +279,11 @@ gcc_7_hybrid_dbg_sp:
gcc_8_serial:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/gcc:8
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -398,17 +294,9 @@ gcc_8_serial:
gcc_8_mpionly:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/gcc:8
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -419,16 +307,8 @@ gcc_8_mpionly:
gcc_8_hybrid:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/gcc:8
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -439,20 +319,12 @@ gcc_8_hybrid:
gcc_8_serial_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/gcc:8
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -463,18 +335,10 @@ gcc_8_serial_dbg:
gcc_8_mpionly_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/gcc:8
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -485,17 +349,9 @@ gcc_8_mpionly_dbg:
gcc_8_hybrid_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/gcc:8
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -910,19 +766,11 @@ gcc_11_hybrid_dbg_sp:
clang_6.0_serial:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:6.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -933,17 +781,9 @@ clang_6.0_serial:
clang_6.0_mpionly:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:6.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -954,16 +794,8 @@ clang_6.0_mpionly:
clang_6.0_hybrid:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:6.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -974,20 +806,12 @@ clang_6.0_hybrid:
clang_6.0_serial_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:6.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -998,18 +822,10 @@ clang_6.0_serial_dbg:
clang_6.0_mpionly_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:6.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1020,17 +836,9 @@ clang_6.0_mpionly_dbg:
clang_6.0_hybrid_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:6.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- cuda
- docker
@@ -1054,19 +862,11 @@ clang_6.0_hybrid_dbg_sp:
clang_7.0_serial:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:7.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1077,17 +877,9 @@ clang_7.0_serial:
clang_7.0_mpionly:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:7.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1098,16 +890,8 @@ clang_7.0_mpionly:
clang_7.0_hybrid:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:7.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1118,20 +902,12 @@ clang_7.0_hybrid:
clang_7.0_serial_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:7.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1142,18 +918,10 @@ clang_7.0_serial_dbg:
clang_7.0_mpionly_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:7.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1164,17 +932,9 @@ clang_7.0_mpionly_dbg:
clang_7.0_hybrid_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:7.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1201,19 +961,11 @@ clang_7.0_hybrid_dbg_sp:
clang_8.0_serial:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:8.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1224,17 +976,9 @@ clang_8.0_serial:
clang_8.0_mpionly:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:8.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1245,16 +989,8 @@ clang_8.0_mpionly:
clang_8.0_hybrid:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:8.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1265,20 +1001,12 @@ clang_8.0_hybrid:
clang_8.0_serial_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:8.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1289,18 +1017,10 @@ clang_8.0_serial_dbg:
clang_8.0_mpionly_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:8.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1311,17 +1031,9 @@ clang_8.0_mpionly_dbg:
clang_8.0_hybrid_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:8.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1348,19 +1060,11 @@ clang_8.0_hybrid_dbg_sp:
clang_9.0_serial:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:9.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1371,17 +1075,9 @@ clang_9.0_serial:
clang_9.0_mpionly:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:9.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1392,16 +1088,8 @@ clang_9.0_mpionly:
clang_9.0_hybrid:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:9.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1412,20 +1100,12 @@ clang_9.0_hybrid:
clang_9.0_serial_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:9.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1436,18 +1116,10 @@ clang_9.0_serial_dbg:
clang_9.0_mpionly_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:9.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1458,17 +1130,9 @@ clang_9.0_mpionly_dbg:
clang_9.0_hybrid_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:9.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1495,19 +1159,11 @@ clang_9.0_hybrid_dbg_sp:
clang_10.0_serial:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:10.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1518,17 +1174,9 @@ clang_10.0_serial:
clang_10.0_mpionly:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:10.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1539,16 +1187,8 @@ clang_10.0_mpionly:
clang_10.0_hybrid:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:10.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- cuda
- docker
@@ -1556,20 +1196,12 @@ clang_10.0_hybrid:
clang_10.0_serial_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:10.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- cuda
- docker
@@ -1577,18 +1209,10 @@ clang_10.0_serial_dbg:
clang_10.0_mpionly_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:10.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- cuda
- docker
@@ -1596,17 +1220,9 @@ clang_10.0_mpionly_dbg:
clang_10.0_hybrid_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/clang:10.0
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
- - CC=gcc CXX=g++ pip3 install pycuda
variables:
WALBERLA_BUILD_WITH_CUDA: "ON"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- cuda
- docker
@@ -1628,18 +1244,11 @@ clang_10.0_hybrid_dbg_sp:
inteloneapi_21.3_serial:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/inteloneapi:21.3
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
variables:
WALBERLA_BUILD_WITH_CUDA: "OFF"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1649,16 +1258,9 @@ inteloneapi_21.3_serial:
inteloneapi_21.3_mpionly:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/inteloneapi:21.3
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
variables:
WALBERLA_BUILD_WITH_CUDA: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
only:
variables:
- $ENABLE_NIGHTLY_BUILDS
@@ -1668,67 +1270,39 @@ inteloneapi_21.3_mpionly:
inteloneapi_21.3_hybrid:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/inteloneapi:21.3
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
variables:
WALBERLA_BUILD_WITH_CUDA: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- docker
inteloneapi_21.3_serial_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/inteloneapi:21.3
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
variables:
WALBERLA_BUILD_WITH_CUDA: "OFF"
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PARMETIS: "OFF"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- docker
inteloneapi_21.3_mpionly_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/inteloneapi:21.3
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
variables:
WALBERLA_BUILD_WITH_CUDA: "OFF"
CMAKE_BUILD_TYPE: "DebugOptimized"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- docker
inteloneapi_21.3_hybrid_dbg:
extends: .build_template
image: i10git.cs.fau.de:5005/walberla/buildenvs/inteloneapi:21.3
- before_script:
- - pip3 install lbmpy jinja2 pytest
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
variables:
WALBERLA_BUILD_WITH_CUDA: "OFF"
CMAKE_BUILD_TYPE: "DebugOptimized"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
- WALBERLA_BUILD_WITH_PYTHON: "ON"
tags:
- docker
@@ -2098,11 +1672,6 @@ msvc-14.2_mpionly:
- cmake . -LA
- make -j $NUM_BUILD_CORES -l $NUM_CORES
- ctest -LE "$CTEST_EXCLUDE_LABELS|cuda" -C $CMAKE_BUILD_TYPE --output-on-failure -j $NUM_CORES -T Test
- before_script:
- - pip3 install --user lbmpy jinja2 pytest-cov lxml
- - cd python
- - python3 -m pytest --junitxml=report.xml pystencils_walberla lbmpy_walberla
- - cd ..
after_script:
- pip3 install lxml
- python3 cmake/ctest2junit.py build > report.xml
@@ -2123,7 +1692,7 @@ mac_Serial_Dbg:
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PYTHON: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
+ WALBERLA_BUILD_WITH_CODEGEN: "OFF"
mac_Serial:
<<: *mac_build_definition
@@ -2133,7 +1702,7 @@ mac_Serial:
WALBERLA_BUILD_WITH_MPI: "OFF"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PYTHON: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
+ WALBERLA_BUILD_WITH_CODEGEN: "OFF"
mac_MpiOnly_Dbg:
<<: *mac_build_definition
@@ -2143,7 +1712,7 @@ mac_MpiOnly_Dbg:
WALBERLA_BUILD_WITH_MPI: "ON"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PYTHON: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
+ WALBERLA_BUILD_WITH_CODEGEN: "OFF"
OMPI_MCA_btl: "self,tcp"
mac_MpiOnly:
@@ -2154,7 +1723,7 @@ mac_MpiOnly:
WALBERLA_BUILD_WITH_MPI: "ON"
WALBERLA_BUILD_WITH_OPENMP: "OFF"
WALBERLA_BUILD_WITH_PYTHON: "ON"
- WALBERLA_BUILD_WITH_CODEGEN: "ON"
+ WALBERLA_BUILD_WITH_CODEGEN: "OFF"
OMPI_MCA_btl: "self,tcp"
###############################################################################
diff --git a/apps/benchmarks/FlowAroundSphereCodeGen/FlowAroundSphereCodeGen.py b/apps/benchmarks/FlowAroundSphereCodeGen/FlowAroundSphereCodeGen.py
index c565baa589f21dc6681bdd40826aef643b0afd2a..675a3766ef0e46bec0581e830f12b77b27394219 100644
--- a/apps/benchmarks/FlowAroundSphereCodeGen/FlowAroundSphereCodeGen.py
+++ b/apps/benchmarks/FlowAroundSphereCodeGen/FlowAroundSphereCodeGen.py
@@ -1,24 +1,25 @@
+from pystencils import Target
from pystencils.field import fields
+from lbmpy import LBMConfig, LBMOptimisation, LBStencil, Method, Stencil
from lbmpy.advanced_streaming.utility import get_timesteps
from lbmpy.macroscopic_value_kernels import macroscopic_values_setter
-from lbmpy.stencils import get_stencil
from lbmpy.creationfunctions import create_lb_collision_rule
from lbmpy.boundaries import NoSlip, UBB, ExtrapolationOutflow
from pystencils_walberla import CodeGeneration, generate_sweep, generate_info_header
from lbmpy_walberla.additional_data_handler import UBBAdditionalDataHandler, OutflowAdditionalDataHandler
-from lbmpy_walberla import generate_boundary, generate_lb_pack_info
+from lbmpy_walberla import generate_lb_pack_info
from lbmpy_walberla import generate_alternating_lbm_sweep, generate_alternating_lbm_boundary
import sympy as sp
with CodeGeneration() as ctx:
data_type = "float64" if ctx.double_accuracy else "float32"
- stencil = get_stencil("D3Q27")
- q = len(stencil)
- dim = len(stencil[0])
+ stencil = LBStencil(Stencil.D3Q27)
+ q = stencil.Q
+ dim = stencil.D
streaming_pattern = 'esotwist'
timesteps = get_timesteps(streaming_pattern)
@@ -32,21 +33,12 @@ with CodeGeneration() as ctx:
'velocity': velocity_field
}
- opt = {'symbolic_field': pdfs,
- 'cse_global': False,
- 'cse_pdfs': False,
- 'double_precision': True if ctx.double_accuracy else False}
-
- method_params = {'method': 'cumulant',
- 'stencil': stencil,
- 'relaxation_rate': omega,
- 'galilean_correction': True,
- 'field_name': 'pdfs',
- 'streaming_pattern': streaming_pattern,
- 'output': output,
- 'optimization': opt}
-
- collision_rule = create_lb_collision_rule(**method_params)
+ lbm_config = LBMConfig(stencil=stencil, method=Method.CUMULANT, relaxation_rate=omega, galilean_correction=True,
+ field_name='pdfs', streaming_pattern=streaming_pattern, output=output)
+
+ lbm_optimisation = LBMOptimisation(symbolic_field=pdfs, cse_global=False, cse_pdfs=False)
+
+ collision_rule = create_lb_collision_rule(lbm_config=lbm_config, lbm_optimisation=lbm_optimisation)
lb_method = collision_rule.method
# getter & setter
@@ -63,15 +55,14 @@ with CodeGeneration() as ctx:
'ScalarField_T': density_field}
if ctx.cuda:
- target = 'gpu'
+ target = Target.GPU
else:
- target = 'cpu'
-
- opt['target'] = target
+ target = Target.CPU
# sweeps
generate_alternating_lbm_sweep(ctx, 'FlowAroundSphereCodeGen_LbSweep',
- collision_rule, streaming_pattern, optimization=opt)
+ collision_rule, lbm_config=lbm_config, lbm_optimisation=lbm_optimisation,
+ target=target)
generate_sweep(ctx, 'FlowAroundSphereCodeGen_MacroSetter', setter_assignments, target=target)
# boundaries
diff --git a/apps/benchmarks/PhaseFieldAllenCahn/multiphase_codegen.py b/apps/benchmarks/PhaseFieldAllenCahn/multiphase_codegen.py
index 9be9c174b29b7d9452202f70d77d21ad497a8db2..f74db05e940e693ead49990d4d51542ae53a30f9 100644
--- a/apps/benchmarks/PhaseFieldAllenCahn/multiphase_codegen.py
+++ b/apps/benchmarks/PhaseFieldAllenCahn/multiphase_codegen.py
@@ -1,8 +1,8 @@
-from pystencils import fields, TypedSymbol
+from pystencils import fields, Target, TypedSymbol
from pystencils.simp import sympy_cse
+from lbmpy import LBMConfig, LBStencil, Method, Stencil
from lbmpy.creationfunctions import create_lb_method
-from lbmpy.stencils import get_stencil
from pystencils_walberla import CodeGeneration, generate_sweep, generate_pack_info_for_field, generate_info_header
from lbmpy_walberla import generate_lb_pack_info
@@ -18,16 +18,9 @@ import numpy as np
with CodeGeneration() as ctx:
field_type = "float64" if ctx.double_accuracy else "float32"
- stencil_phase_name = "D3Q15"
- stencil_hydro_name = "D3Q27"
-
- stencil_phase = get_stencil(stencil_phase_name, "walberla")
- stencil_hydro = get_stencil(stencil_hydro_name, "walberla")
- q_phase = len(stencil_phase)
- q_hydro = len(stencil_hydro)
-
- assert (len(stencil_phase[0]) == len(stencil_hydro[0]))
- dimensions = len(stencil_hydro[0])
+ stencil_phase = LBStencil(Stencil.D3Q15)
+ stencil_hydro = LBStencil(Stencil.D3Q27)
+ assert (stencil_phase.D == stencil_hydro.D)
########################
# PARAMETER DEFINITION #
@@ -54,17 +47,17 @@ with CodeGeneration() as ctx:
########################
# velocity field
- u = fields(f"vel_field({dimensions}): {field_type}[{dimensions}D]", layout='fzyx')
+ u = fields(f"vel_field({stencil_hydro.D}): {field_type}[{stencil_hydro.D}D]", layout='fzyx')
# phase-field
- C = fields(f"phase_field: {field_type}[{dimensions}D]", layout='fzyx')
- C_tmp = fields(f"phase_field_tmp: {field_type}[{dimensions}D]", layout='fzyx')
+ C = fields(f"phase_field: {field_type}[{stencil_hydro.D}D]", layout='fzyx')
+ C_tmp = fields(f"phase_field_tmp: {field_type}[{stencil_hydro.D}D]", layout='fzyx')
# phase-field distribution functions
- h = fields(f"lb_phase_field({q_phase}): {field_type}[{dimensions}D]", layout='fzyx')
- h_tmp = fields(f"lb_phase_field_tmp({q_phase}): {field_type}[{dimensions}D]", layout='fzyx')
+ h = fields(f"lb_phase_field({stencil_phase.Q}): {field_type}[{stencil_phase.D}D]", layout='fzyx')
+ h_tmp = fields(f"lb_phase_field_tmp({stencil_phase.Q}): {field_type}[{stencil_phase.D}D]", layout='fzyx')
# hydrodynamic distribution functions
- g = fields(f"lb_velocity_field({q_hydro}): {field_type}[{dimensions}D]", layout='fzyx')
- g_tmp = fields(f"lb_velocity_field_tmp({q_hydro}): {field_type}[{dimensions}D]", layout='fzyx')
+ g = fields(f"lb_velocity_field({stencil_hydro.Q}): {field_type}[{stencil_hydro.D}D]", layout='fzyx')
+ g_tmp = fields(f"lb_velocity_field_tmp({stencil_hydro.Q}): {field_type}[{stencil_hydro.D}D]", layout='fzyx')
########################################
# RELAXATION RATES AND EXTERNAL FORCES #
@@ -83,21 +76,23 @@ with CodeGeneration() as ctx:
# LBM METHODS #
###############
- method_phase = create_lb_method(stencil=stencil_phase, method='srt', relaxation_rate=w_c, compressible=True)
+ lbm_config_phase = LBMConfig(stencil=stencil_phase, method=Method.SRT, relaxation_rate=w_c, compressible=True)
+ method_phase = create_lb_method(lbm_config=lbm_config_phase)
- method_hydro = create_lb_method(stencil=stencil_hydro, method="mrt", weighted=True,
- relaxation_rates=[relaxation_rate, 1, 1, 1, 1, 1])
+ lbm_config_hydro = LBMConfig(stencil=stencil_hydro, method=Method.MRT, weighted=True,
+ relaxation_rates=[relaxation_rate, 1, 1, 1, 1, 1])
+ method_hydro = create_lb_method(lbm_config=lbm_config_hydro)
# create the kernels for the initialization of the g and h field
h_updates = initializer_kernel_phase_field_lb(h, C, u, method_phase, W)
g_updates = initializer_kernel_hydro_lb(g, u, method_hydro)
- force_h = [f / 3 for f in interface_tracking_force(C, stencil_phase, W, fd_stencil=get_stencil("D3Q27"))]
+ force_h = [f / 3 for f in interface_tracking_force(C, stencil_phase, W, fd_stencil=LBStencil(Stencil.D3Q27))]
force_model_h = MultiphaseForceModel(force=force_h)
force_g = hydrodynamic_force(g, C, method_hydro, relaxation_time, density_liquid, density_gas, kappa, beta,
body_force,
- fd_stencil=get_stencil("D3Q27"))
+ fd_stencil=LBStencil(Stencil.D3Q27))
force_model_g = MultiphaseForceModel(force=force_g, rho=density)
@@ -150,64 +145,64 @@ with CodeGeneration() as ctx:
'PhaseField_T': C}
additional_code = f"""
- const char * StencilNamePhase = "{stencil_phase_name}";
- const char * StencilNameHydro = "{stencil_hydro_name}";
+ const char * StencilNamePhase = "{stencil_phase.name}";
+ const char * StencilNameHydro = "{stencil_hydro.name}";
"""
if not ctx.cuda:
if not ctx.optimize_for_localhost:
cpu_vec = {'instruction_set': None}
- generate_sweep(ctx, 'initialize_phase_field_distributions', h_updates)
- generate_sweep(ctx, 'initialize_velocity_based_distributions', g_updates)
+ generate_sweep(ctx, 'initialize_phase_field_distributions', h_updates, target=Target.CPU)
+ generate_sweep(ctx, 'initialize_velocity_based_distributions', g_updates, target=Target.CPU)
generate_sweep(ctx, 'phase_field_LB_step', phase_field_LB_step,
field_swaps=[(h, h_tmp), (C, C_tmp)],
inner_outer_split=True,
- cpu_vectorize_info=cpu_vec)
+ cpu_vectorize_info=cpu_vec,
+ target=Target.CPU)
generate_sweep(ctx, 'hydro_LB_step', hydro_LB_step,
field_swaps=[(g, g_tmp)],
inner_outer_split=True,
- cpu_vectorize_info=cpu_vec)
+ cpu_vectorize_info=cpu_vec,
+ target=Target.CPU)
# communication
generate_lb_pack_info(ctx, 'PackInfo_phase_field_distributions', stencil_phase, h,
- streaming_pattern='pull', target='cpu')
+ streaming_pattern='pull', target=Target.CPU)
generate_lb_pack_info(ctx, 'PackInfo_velocity_based_distributions', stencil_hydro, g,
- streaming_pattern='push', target='cpu')
+ streaming_pattern='push', target=Target.CPU)
- generate_pack_info_for_field(ctx, 'PackInfo_phase_field', C, target='cpu')
+ generate_pack_info_for_field(ctx, 'PackInfo_phase_field', C, target=Target.CPU)
if ctx.cuda:
generate_sweep(ctx, 'initialize_phase_field_distributions',
- h_updates, target='gpu')
+ h_updates, target=Target.GPU)
generate_sweep(ctx, 'initialize_velocity_based_distributions',
- g_updates, target='gpu')
+ g_updates, target=Target.GPU)
generate_sweep(ctx, 'phase_field_LB_step', phase_field_LB_step,
field_swaps=[(h, h_tmp), (C, C_tmp)],
- target='gpu',
+ target=Target.GPU,
gpu_indexing_params=sweep_params,
varying_parameters=vp)
generate_sweep(ctx, 'hydro_LB_step', hydro_LB_step,
field_swaps=[(g, g_tmp)],
- target='gpu',
+ target=Target.GPU,
gpu_indexing_params=sweep_params,
varying_parameters=vp)
# communication
generate_lb_pack_info(ctx, 'PackInfo_phase_field_distributions', stencil_phase, h,
- streaming_pattern='pull', target='gpu')
+ streaming_pattern='pull', target=Target.GPU)
generate_lb_pack_info(ctx, 'PackInfo_velocity_based_distributions', stencil_hydro, g,
- streaming_pattern='push', target='gpu')
+ streaming_pattern='push', target=Target.GPU)
- generate_pack_info_for_field(ctx, 'PackInfo_phase_field', C, target='gpu')
+ generate_pack_info_for_field(ctx, 'PackInfo_phase_field', C, target=Target.GPU)
# Info header containing correct template definitions for stencil and field
generate_info_header(ctx, 'GenDefines', stencil_typedefs=stencil_typedefs, field_typedefs=field_typedefs,
additional_code=additional_code)
-
-print("finished code generation successfully")
diff --git a/apps/benchmarks/UniformGridGPU/UniformGridGPU.py b/apps/benchmarks/UniformGridGPU/UniformGridGPU.py
index b615be83289620e218ce160d57f7588ba0886647..86f5ae3b73357f0217b24285a882d4426e58b70a 100644
--- a/apps/benchmarks/UniformGridGPU/UniformGridGPU.py
+++ b/apps/benchmarks/UniformGridGPU/UniformGridGPU.py
@@ -2,15 +2,17 @@ import sympy as sp
import numpy as np
import pystencils as ps
+from dataclasses import replace
+
from pystencils.data_types import TypedSymbol
from pystencils.fast_approximation import insert_fast_sqrts, insert_fast_divisions
+from lbmpy import LBMConfig, LBMOptimisation, LBStencil, Method, Stencil
from lbmpy.advanced_streaming import Timestep, is_inplace
from lbmpy.advanced_streaming.utility import streaming_patterns
from lbmpy.boundaries import NoSlip, UBB
from lbmpy.creationfunctions import create_lb_collision_rule
from lbmpy.macroscopic_value_kernels import macroscopic_values_setter
-from lbmpy.stencils import get_stencil
from lbmpy.updatekernels import create_stream_only_kernel
from lbmpy.fieldaccess import *
@@ -32,40 +34,40 @@ gpu_indexing_params = {'block_size': sweep_block_size}
options_dict = {
'srt': {
- 'method': 'srt',
+ 'method': Method.SRT,
'relaxation_rate': omega,
'compressible': False,
},
'trt': {
- 'method': 'trt',
+ 'method': Method.TRT,
'relaxation_rate': omega,
},
'mrt': {
- 'method': 'mrt',
+ 'method': Method.MRT,
'relaxation_rates': [omega, 1, 1, 1, 1, 1, 1],
},
'mrt-overrelax': {
- 'method': 'mrt',
+ 'method': Method.MRT,
'relaxation_rates': [omega] + [1 + x * 1e-2 for x in range(1, 11)],
},
'cumulant': {
- 'method': 'cumulant',
+ 'method': Method.CUMULANT,
'relaxation_rate': omega,
'compressible': True,
},
'cumulant-overrelax': {
- 'method': 'cumulant',
+ 'method': Method.CUMULANT,
'relaxation_rates': [omega] + [1 + x * 1e-2 for x in range(1, 11)],
'compressible': True,
},
'entropic': {
- 'method': 'mrt',
+ 'method': Method.MRT,
'compressible': True,
'relaxation_rates': [omega, omega] + [omega_free] * 6,
'entropic': True,
},
'smagorinsky': {
- 'method': 'srt',
+ 'method': Method.SRT,
'smagorinsky': True,
'relaxation_rate': omega,
}
@@ -94,34 +96,26 @@ with CodeGeneration() as ctx:
if len(config_tokens) >= 4:
optimize = (config_tokens[3] != 'noopt')
- stencil = get_stencil(stencil_str)
+ if stencil_str == "D3Q27":
+ stencil = LBStencil(Stencil.D3Q27)
+ else:
+ stencil = LBStencil(Stencil.D3Q19)
+
assert streaming_pattern in streaming_patterns, f"Invalid streaming pattern: {streaming_pattern}"
options = options_dict[collision_setup]
- q = len(stencil)
- dim = len(stencil[0])
+ q = stencil.Q
+ dim = stencil.D
assert dim == 3, "This app supports only three-dimensional stencils"
pdfs, pdfs_tmp, velocity_field = ps.fields(f"pdfs({q}), pdfs_tmp({q}), velocity(3) : {field_type}[3D]",
layout='fzyx')
- common_options = {
- 'stencil': stencil,
- 'field_name': pdfs.name,
- 'optimization': {
- 'target': 'gpu',
- 'cse_global': True,
- 'cse_pdfs': False,
- 'symbolic_field': pdfs,
- 'field_layout': 'fzyx',
- 'gpu_indexing_params': gpu_indexing_params
- }
- }
-
- options.update(common_options)
+ lbm_config = LBMConfig(stencil=stencil, field_name=pdfs.name, streaming_pattern=streaming_pattern, **options)
+ lbm_opt = LBMOptimisation(cse_global=True, cse_pdfs=False, symbolic_field=pdfs, field_layout='fzyx')
if not is_inplace(streaming_pattern):
- options['optimization']['symbolic_temporary_field'] = pdfs_tmp
+ lbm_opt = replace(lbm_opt, symbolic_temporary_field=pdfs_tmp)
field_swaps = [(pdfs, pdfs_tmp)]
else:
field_swaps = []
@@ -141,7 +135,7 @@ with CodeGeneration() as ctx:
stream_only_kernel = create_stream_only_kernel(stencil, pdfs, pdfs_tmp, accessor=accessor)
# LB Sweep
- collision_rule = create_lb_collision_rule(**options)
+ collision_rule = create_lb_collision_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
if optimize:
collision_rule = insert_fast_divisions(collision_rule)
@@ -149,8 +143,8 @@ with CodeGeneration() as ctx:
lb_method = collision_rule.method
- generate_alternating_lbm_sweep(ctx, 'UniformGridGPU_LbKernel', collision_rule, streaming_pattern,
- optimization=options['optimization'],
+ generate_alternating_lbm_sweep(ctx, 'UniformGridGPU_LbKernel', collision_rule, lbm_config=lbm_config,
+ lbm_optimisation=lbm_opt, target=ps.Target.GPU,
inner_outer_split=True, varying_parameters=vp, field_swaps=field_swaps)
# getter & setter
@@ -158,32 +152,32 @@ with CodeGeneration() as ctx:
pdfs=pdfs,
streaming_pattern=streaming_pattern,
previous_timestep=Timestep.EVEN)
- generate_sweep(ctx, 'UniformGridGPU_MacroSetter', setter_assignments, target='gpu')
+ generate_sweep(ctx, 'UniformGridGPU_MacroSetter', setter_assignments, target=ps.Target.GPU)
# Stream only kernel
generate_sweep(ctx, 'UniformGridGPU_StreamOnlyKernel', stream_only_kernel, field_swaps=field_swaps_stream_only,
- gpu_indexing_params=gpu_indexing_params, varying_parameters=vp, target='gpu')
+ gpu_indexing_params=gpu_indexing_params, varying_parameters=vp, target=ps.Target.GPU)
# Boundaries
noslip = NoSlip()
ubb = UBB((0.05, 0, 0))
generate_alternating_lbm_boundary(ctx, 'UniformGridGPU_NoSlip', noslip, lb_method, field_name=pdfs.name,
- streaming_pattern=streaming_pattern, target='gpu')
+ streaming_pattern=streaming_pattern, target=ps.Target.GPU)
generate_alternating_lbm_boundary(ctx, 'UniformGridGPU_UBB', ubb, lb_method, field_name=pdfs.name,
- streaming_pattern=streaming_pattern, target='gpu')
+ streaming_pattern=streaming_pattern, target=ps.Target.GPU)
# communication
generate_lb_pack_info(ctx, 'UniformGridGPU_PackInfo', stencil, pdfs,
- streaming_pattern=streaming_pattern, target='gpu',
+ streaming_pattern=streaming_pattern, target=ps.Target.GPU,
always_generate_separate_classes=True)
infoHeaderParams = {
'stencil': stencil_str,
'streaming_pattern': streaming_pattern,
'collision_setup': collision_setup,
- 'cse_global': int(options['optimization']['cse_global']),
- 'cse_pdfs': int(options['optimization']['cse_pdfs']),
+ 'cse_global': int(lbm_opt.cse_global),
+ 'cse_pdfs': int(lbm_opt.cse_pdfs),
}
stencil_typedefs = {'Stencil_T': stencil,
diff --git a/apps/benchmarks/UniformGridGPU/simulation_setup/benchmark_configs.py b/apps/benchmarks/UniformGridGPU/simulation_setup/benchmark_configs.py
index 8b503fedaabc15bd0b8bc465ac6ed7a1f6d1047d..a1816eba1569722be2edf70c04a70078bbabf4f0 100755
--- a/apps/benchmarks/UniformGridGPU/simulation_setup/benchmark_configs.py
+++ b/apps/benchmarks/UniformGridGPU/simulation_setup/benchmark_configs.py
@@ -128,10 +128,11 @@ class Scenario:
sequenceValuesToScalars(result)
num_tries = 4
# check multiple times e.g. may fail when multiple benchmark processes are running
+ table_name = f"runs_{data['stencil']}_{data['streamingPattern']}_{data['collisionSetup']}_{prod(self.blocks)}"
for num_try in range(num_tries):
try:
- checkAndUpdateSchema(result, "runs", DB_FILE)
- storeSingle(result, "runs", DB_FILE)
+ checkAndUpdateSchema(result, table_name, DB_FILE)
+ storeSingle(result, table_name, DB_FILE)
break
except sqlite3.OperationalError as e:
wlb.log_warning(f"Sqlite DB writing failed: try {num_try + 1}/{num_tries} {str(e)}")
@@ -220,23 +221,18 @@ def single_gpu_benchmark():
job_script_header = """
#!/bin/bash -l
#SBATCH --job-name=scaling
-#SBATCH --time=0:30:00
+#SBATCH --time=01:00:00
#SBATCH --nodes={nodes}
#SBATCH -o out_scaling_{nodes}_%j.txt
#SBATCH -e err_scaling_{nodes}_%j.txt
#SBATCH --ntasks-per-core=1
-#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=1
#SBATCH --partition=normal
#SBATCH --constraint=gpu
-#SBATCH --account=d105
-
-cd {folder}
+#SBATCH --account=s1042
source ~/env.sh
-module load daint-gpu
-module load craype-accel-nvidia60
export MPICH_RDMA_ENABLED_CUDA=1 # allow GPU-GPU data transfer
export CRAY_CUDA_MPS=1 # allow GPU sharing
export MPICH_G2G_PIPELINE=256 # adapt maximum number of concurrent in-flight messages
@@ -247,7 +243,7 @@ export CRAY_CUDA_MPS=1
export MPICH_RANK_REORDER_METHOD=3
export PMI_MMAP_SYNC_WAIT_TIME=300
-
+cd {folder}
# grid_order -R -H -c 1,1,8 -g 16,16,8
ulimit -c 0
@@ -262,10 +258,18 @@ do
done
"""
-all_executables = ('UniformGridBenchmarkGPU_mrt_d3q27',
- 'UniformGridBenchmarkGPU_smagorinsky_d3q27',
- 'UniformGridBenchmarkGPU_cumulant'
- 'UniformGridBenchmarkGPU_cumulant_d3q27')
+streaming_patterns = ['pull', 'push', 'aa', 'esotwist']
+stencils = ['d3q27', 'd3q19']
+methods = ['srt', 'mrt', 'cumulant', 'entropic']
+
+all_executables = []
+
+for stencil in stencils:
+ for streaming_pattern in streaming_patterns:
+ for method in methods:
+ all_executables.append(f"UniformGridGPU_{stencil}_{streaming_pattern}_{method}")
+
+all_executables = tuple(all_executables)
def generate_jobscripts(exe_names=all_executables):
diff --git a/apps/benchmarks/UniformGridGenerated/CMakeLists.txt b/apps/benchmarks/UniformGridGenerated/CMakeLists.txt
index f964f242a7967c4440643aee9ed7769d18c7ebbd..d0b16d6e20b4c3fd5068608c921d793f8633f8b3 100644
--- a/apps/benchmarks/UniformGridGenerated/CMakeLists.txt
+++ b/apps/benchmarks/UniformGridGenerated/CMakeLists.txt
@@ -2,7 +2,7 @@ waLBerla_link_files_to_builddir( "*.prm" )
waLBerla_link_files_to_builddir( "*.py" )
-foreach(config trt smagorinsky mrt entropic_kbc_n4 cumulant )
+foreach(config srt trt smagorinsky mrt entropic_kbc_n4 cumulant )
waLBerla_generate_target_from_python(NAME UniformGridGenerated_${config}
CODEGEN_CFG ${config}
FILE UniformGridGenerated.py
diff --git a/apps/benchmarks/UniformGridGenerated/UniformGridGenerated.cpp b/apps/benchmarks/UniformGridGenerated/UniformGridGenerated.cpp
index 844ae03d6871d9e8f617bf1fd6fc7e1a6beab8a8..e27ee3e61d6b2bbf6e57d9ca7353d190978f6c22 100644
--- a/apps/benchmarks/UniformGridGenerated/UniformGridGenerated.cpp
+++ b/apps/benchmarks/UniformGridGenerated/UniformGridGenerated.cpp
@@ -20,29 +20,11 @@
#include "lbm/lattice_model/D3Q19.h"
#include "GenDefines.h"
-#include "GenMacroGetter.h"
-#include "GenMacroSetter.h"
-
-#include "GenLbKernel.h"
-#include "GenLbKernelAAEven.h"
-#include "GenLbKernelAAOdd.h"
-
-#include "GenPackInfo.h"
-#include "GenPackInfoAAPush.h"
-#include "GenPackInfoAAPull.h"
-#include "GenMpiDtypeInfo.h"
-#include "GenMpiDtypeInfoAAPull.h"
-#include "GenMpiDtypeInfoAAPush.h"
-
#include <iomanip>
using namespace walberla;
-using PdfField_T = GhostLayerField< real_t, Stencil_T::Q >;
-using VelocityField_T = GhostLayerField< real_t, 3 >;
-
-
int main( int argc, char **argv )
{
mpi::Environment env( argc, argv );
diff --git a/apps/benchmarks/UniformGridGenerated/UniformGridGenerated.py b/apps/benchmarks/UniformGridGenerated/UniformGridGenerated.py
index 3b489ab1a943a6aeb77a7d5f50907f152ba22fa5..01b00e4a53fb6c3a8d463c9b15e86f9a34eb25fe 100644
--- a/apps/benchmarks/UniformGridGenerated/UniformGridGenerated.py
+++ b/apps/benchmarks/UniformGridGenerated/UniformGridGenerated.py
@@ -2,7 +2,9 @@ import sympy as sp
import pystencils as ps
from lbmpy.creationfunctions import create_lb_update_rule, create_lb_collision_rule
from pystencils_walberla import CodeGeneration, generate_pack_info_from_kernel, generate_sweep,\
- generate_mpidtype_info_from_kernel
+ generate_mpidtype_info_from_kernel, generate_info_header
+
+from lbmpy import LBMConfig, LBMOptimisation, LBStencil, Method, Stencil
from lbmpy.macroscopic_value_kernels import macroscopic_values_getter, macroscopic_values_setter
from lbmpy.fieldaccess import AAEvenTimeStepAccessor, AAOddTimeStepAccessor
@@ -42,7 +44,7 @@ options_dict = {
'entropic': True,
},
'entropic_kbc_n4': {
- 'method': 'trt-kbc-n4',
+ 'method': 'trt_kbc_n4',
'stencil': 'D3Q27',
'compressible': True,
'relaxation_rates': [omega, omega_free],
@@ -62,13 +64,6 @@ options_dict = {
},
}
-info_header = """
-#include "stencil/D3Q{q}.h"\nusing Stencil_T = walberla::stencil::D3Q{q};
-const char * infoStencil = "{stencil}";
-const char * infoConfigName = "{configName}";
-const char * optimizationDict = "{optimizationDict}";
-"""
-
with CodeGeneration() as ctx:
common_options = {
'field_name': 'pdfs',
@@ -106,18 +101,20 @@ with CodeGeneration() as ctx:
options = options.copy()
if d3q27:
- options['stencil'] = 'D3Q27'
+ stencil = LBStencil(Stencil.D3Q27)
+ options['stencil'] = stencil
+ else:
+ stencil = LBStencil(options['stencil'])
dtype_string = 'float64' if ctx.double_accuracy else 'float32'
- stencil_str = options['stencil']
- q = int(stencil_str[stencil_str.find('Q') + 1:])
- pdfs, velocity_field = ps.fields(f'pdfs({q}), velocity(3) : {dtype_string}[3D]', layout='fzyx')
+ pdfs, velocity_field = ps.fields(f'pdfs({stencil.Q}), velocity(3) : {dtype_string}[3D]', layout='fzyx')
- update_rule_two_field = create_lb_update_rule(optimization={'symbolic_field': pdfs,
- 'split': opts['two_field_split'],
- 'cse_global': opts['two_field_cse_global'],
- 'cse_pdfs': opts['two_field_cse_pdfs']}, **options)
+ lbm_config = LBMConfig(**options)
+ lbm_optimisation = LBMOptimisation(symbolic_field=pdfs, split=opts['two_field_split'],
+ cse_global=opts['two_field_cse_global'], cse_pdfs=opts['two_field_cse_pdfs'])
+
+ update_rule_two_field = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_optimisation)
if opts['compiled_in_boundaries']:
from lbmpy.boundaries import NoSlip, UBB
@@ -131,27 +128,32 @@ with CodeGeneration() as ctx:
((0, 0, 1), UBB([0.05, 0, 0])),
((0, 0, -1), NoSlip()),
))
- cr_even = create_lb_collision_rule(stencil='D3Q19', compressible=False,
- optimization={'cse_global': opts['aa_even_cse_global'],
- 'cse_pdfs': opts['aa_even_cse_pdfs']})
- cr_odd = create_lb_collision_rule(stencil='D3Q19', compressible=False,
- optimization={'cse_global': opts['aa_odd_cse_global'],
- 'cse_pdfs': opts['aa_odd_cse_pdfs']})
+ cr_even = create_lb_collision_rule(lbm_config=LBMConfig(stencil=LBStencil(Stencil.D3Q19), compressible=False),
+ lbm_optimisation=LBMOptimisation(cse_global=opts['aa_even_cse_global'],
+ cse_pdfs=opts['aa_even_cse_pdfs']))
+
+ cr_odd = create_lb_collision_rule(lbm_config=LBMConfig(stencil=LBStencil(Stencil.D3Q19), compressible=False),
+ lbm_optimisation=LBMOptimisation(cse_global=opts['aa_odd_cse_global'],
+ cse_pdfs=opts['aa_odd_cse_pdfs']))
+
update_rule_aa_even = update_rule_with_push_boundaries(cr_even, pdfs, boundaries,
AAEvenTimeStepAccessor, AAOddTimeStepAccessor.read)
update_rule_aa_odd = update_rule_with_push_boundaries(cr_odd, pdfs, boundaries,
AAOddTimeStepAccessor, AAEvenTimeStepAccessor.read)
else:
- update_rule_aa_even = create_lb_update_rule(kernel_type=AAEvenTimeStepAccessor(),
- optimization={'symbolic_field': pdfs,
- 'split': opts['aa_even_split'],
- 'cse_global': opts['aa_even_cse_global'],
- 'cse_pdfs': opts['aa_even_cse_pdfs']}, **options)
- update_rule_aa_odd = create_lb_update_rule(kernel_type=AAOddTimeStepAccessor(),
- optimization={'symbolic_field': pdfs,
- 'split': opts['aa_odd_split'],
- 'cse_global': opts['aa_odd_cse_global'],
- 'cse_pdfs': opts['aa_odd_cse_pdfs']}, **options)
+ lbm_opt_even = LBMOptimisation(symbolic_field=pdfs, split=opts['aa_even_split'],
+ cse_global=opts['aa_even_cse_global'], cse_pdfs=opts['aa_even_cse_pdfs'])
+
+ update_rule_aa_even = create_lb_update_rule(lbm_config=LBMConfig(**options,
+ kernel_type=AAEvenTimeStepAccessor()),
+ lbm_optimisation=lbm_opt_even)
+
+ lbm_opt_odd = LBMOptimisation(symbolic_field=pdfs, split=opts['aa_odd_split'],
+ cse_global=opts['aa_odd_cse_global'], cse_pdfs=opts['aa_odd_cse_pdfs'])
+
+ update_rule_aa_odd = create_lb_update_rule(lbm_config=LBMConfig(**options,
+ kernel_type=AAOddTimeStepAccessor()),
+ lbm_optimisation=lbm_opt_odd)
vec = {'assume_aligned': True, 'assume_inner_stride_one': True}
@@ -191,11 +193,16 @@ with CodeGeneration() as ctx:
generate_mpidtype_info_from_kernel(ctx, 'GenMpiDtypeInfoAAPull', update_rule_aa_odd, kind='pull')
generate_mpidtype_info_from_kernel(ctx, 'GenMpiDtypeInfoAAPush', update_rule_aa_odd, kind='push')
- # Info Header
- infoHeaderParams = {
- 'stencil': stencil_str,
- 'q': q,
- 'configName': ctx.config,
- 'optimizationDict': str(opts),
- }
- ctx.write_file('GenDefines.h', info_header.format(**infoHeaderParams))
+ additional_code = f"""
+ const char * infoStencil = "{stencil.name}";
+ const char * infoConfigName = "{ctx.config}";
+ const char * optimizationDict = "{str(opts)}";
+ """
+
+ stencil_typedefs = {'Stencil_T': stencil,
+ 'CommunicationStencil_T': stencil}
+ field_typedefs = {'PdfField_T': pdfs,
+ 'VelocityField_T': velocity_field}
+
+ generate_info_header(ctx, "GenDefines.h", stencil_typedefs=stencil_typedefs, field_typedefs=field_typedefs,
+ additional_code=additional_code)
diff --git a/apps/showcases/PhaseFieldAllenCahn/CPU/multiphase.cpp b/apps/showcases/PhaseFieldAllenCahn/CPU/multiphase.cpp
index 07c9542cf3c83de774e3cf39f8cfafed8b9ec597..e9f21f0f0688932f9a7a59bb2b34779cf64a60f1 100644
--- a/apps/showcases/PhaseFieldAllenCahn/CPU/multiphase.cpp
+++ b/apps/showcases/PhaseFieldAllenCahn/CPU/multiphase.cpp
@@ -39,21 +39,7 @@
#include "InitializerFunctions.h"
#include "PythonExports.h"
-//////////////////////////////
-// INCLUDE GENERATED FILES //
-////////////////////////////
-
#include "GenDefines.h"
-#include "hydro_LB_NoSlip.h"
-#include "hydro_LB_step.h"
-#include "initialize_phase_field_distributions.h"
-#include "initialize_velocity_based_distributions.h"
-#include "phase_field_LB_NoSlip.h"
-#include "phase_field_LB_step.h"
-#include "ContactAngle.h"
-#include "PackInfo_phase_field_distributions.h"
-#include "PackInfo_velocity_based_distributions.h"
-#include "PackInfo_phase_field.h"
////////////
// USING //
@@ -281,8 +267,8 @@ int main(int argc, char** argv)
{
callback.data().exposeValue("blocks", blocks);
callback.data().exposeValue( "timeStep", timeLoop->getCurrentTimeStep());
- callback.data().exposeValue("stencil_phase", stencil_phase_name);
- callback.data().exposeValue("stencil_hydro", stencil_hydro_name);
+ callback.data().exposeValue("stencil_phase", StencilNamePhase);
+ callback.data().exposeValue("stencil_hydro", StencilNameHydro);
callback();
}
}
diff --git a/apps/showcases/PhaseFieldAllenCahn/CPU/multiphase_codegen.py b/apps/showcases/PhaseFieldAllenCahn/CPU/multiphase_codegen.py
index 607fc161686c9bec98be1a103d6c70a47e6d0095..df777117839320c77eb9081d0eadcca929c81024 100644
--- a/apps/showcases/PhaseFieldAllenCahn/CPU/multiphase_codegen.py
+++ b/apps/showcases/PhaseFieldAllenCahn/CPU/multiphase_codegen.py
@@ -1,203 +1,201 @@
-from lbmpy.phasefield_allen_cahn.contact_angle import ContactAngle
-from pystencils import fields
+import numpy as np
+import sympy as sp
+
+from pystencils import Assignment, fields, TypedSymbol, Target
+from pystencils.astnodes import Block, Conditional
from pystencils.simp import sympy_cse
-from lbmpy.boundaries import NoSlip
+from lbmpy import LBMConfig, LBStencil, Method, Stencil
from lbmpy.creationfunctions import create_lb_method
-from lbmpy.stencils import get_stencil
-
-import pystencils_walberla
-from pystencils_walberla import CodeGeneration, generate_sweep, generate_pack_info_for_field
-from lbmpy_walberla import generate_boundary, generate_lb_pack_info
+from lbmpy.boundaries import NoSlip
+from lbmpy.phasefield_allen_cahn.contact_angle import ContactAngle
+from lbmpy.phasefield_allen_cahn.force_model import MultiphaseForceModel
from lbmpy.phasefield_allen_cahn.kernel_equations import initializer_kernel_phase_field_lb, \
initializer_kernel_hydro_lb, interface_tracking_force, hydrodynamic_force, get_collision_assignments_hydro, \
get_collision_assignments_phase
-from lbmpy.phasefield_allen_cahn.force_model import MultiphaseForceModel
-
-import numpy as np
-import sympy as sp
-
-stencil_phase_name = "D3Q27"
-stencil_hydro_name = "D3Q27"
-
-contact_angle_in_degrees = 22
-
-stencil_phase = get_stencil(stencil_phase_name)
-stencil_hydro = get_stencil(stencil_hydro_name)
-q_phase = len(stencil_phase)
-q_hydro = len(stencil_hydro)
-
-assert (len(stencil_phase[0]) == len(stencil_hydro[0]))
-dimensions = len(stencil_hydro[0])
-
-########################
-# PARAMETER DEFINITION #
-########################
+import pystencils_walberla
+from pystencils_walberla import CodeGeneration, generate_sweep, generate_pack_info_for_field, generate_info_header
+from lbmpy_walberla import generate_boundary, generate_lb_pack_info
-density_liquid = sp.Symbol("rho_H")
-density_gas = sp.Symbol("rho_L")
-
-surface_tension = sp.Symbol("sigma")
-mobility = sp.Symbol("mobility")
-
-gravitational_acceleration = sp.Symbol("gravity")
-
-relaxation_time_liquid = sp.Symbol("tau_H")
-relaxation_time_gas = sp.Symbol("tau_L")
-
-# phase-field parameter
-drho3 = (density_liquid - density_gas) / 3
-# interface thickness
-W = sp.Symbol("interface_thickness")
-# coefficients related to surface tension
-beta = 12.0 * (surface_tension / W)
-kappa = 1.5 * surface_tension * W
-
-########################
-# FIELDS #
-########################
-
-# velocity field
-u = fields(f"vel_field({dimensions}): [{dimensions}D]", layout='fzyx')
-# phase-field
-C = fields(f"phase_field: [{dimensions}D]", layout='fzyx')
-C_tmp = fields(f"phase_field_tmp: [{dimensions}D]", layout='fzyx')
-
-flag = fields(f"flag_field: uint8[{dimensions}D]", layout='fzyx')
-# phase-field distribution functions
-h = fields(f"lb_phase_field({q_phase}): [{dimensions}D]", layout='fzyx')
-h_tmp = fields(f"lb_phase_field_tmp({q_phase}): [{dimensions}D]", layout='fzyx')
-# hydrodynamic distribution functions
-g = fields(f"lb_velocity_field({q_hydro}): [{dimensions}D]", layout='fzyx')
-g_tmp = fields(f"lb_velocity_field_tmp({q_hydro}): [{dimensions}D]", layout='fzyx')
-
-########################################
-# RELAXATION RATES AND EXTERNAL FORCES #
-########################################
-
-# relaxation rate for interface tracking LB step
-relaxation_rate_allen_cahn = 1.0 / (0.5 + (3.0 * mobility))
-# calculate the relaxation rate for hydrodynamic LB step
-density = density_gas + C.center * (density_liquid - density_gas)
-# force acting on all phases of the model
-body_force = np.array([0, gravitational_acceleration * density, 0])
-# calculation of the relaxation time via viscosities
-# viscosity = viscosity_gas * viscosity_liquid + C.center\
-# * (density_liquid*viscosity_liquid - viscosity_liquid*viscosity_gas)
-# relaxation_time = 3 * viscosity / density + 0.5
-
-relaxation_time = 0.5 + relaxation_time_gas + C.center * (relaxation_time_liquid - relaxation_time_gas)
-# calculate the relaxation time if the phase-field has values over one
-# relaxation_rate = 1.0 / relaxation_time
-relaxation_rate = sp.Symbol("s8")
-relaxation_rate_cutoff = sp.Piecewise((1 / (0.5 + relaxation_time_liquid), C.center > 0.999), # True value
- (1 / relaxation_time, True)) # Else value
-
-###############
-# LBM METHODS #
-###############
-
-# method_phase = create_lb_method(stencil=stencil_phase, method="mrt", compressible=True, weighted=True,
-# relaxation_rates=[1, 1.5, 1, 1.5, 1, 1.5])
-method_phase = create_lb_method(stencil=stencil_phase, method="mrt", compressible=True, weighted=True,
- relaxation_rates=[1, 1, 1, 1, 1, 1])
-
-method_phase.set_conserved_moments_relaxation_rate(relaxation_rate_allen_cahn)
-
-method_hydro = create_lb_method(stencil=stencil_hydro, method="mrt", weighted=True,
- relaxation_rates=[relaxation_rate, 1, 1, 1, 1, 1])
-
-
-# create the kernels for the initialization of the g and h field
-h_updates = initializer_kernel_phase_field_lb(h, C, u, method_phase, W, fd_stencil=get_stencil("D3Q27"))
-g_updates = initializer_kernel_hydro_lb(g, u, method_hydro)
-
-force_h = [f / 3 for f in interface_tracking_force(C, stencil_phase, W, fd_stencil=get_stencil("D3Q27"))]
-force_model_h = MultiphaseForceModel(force=force_h)
-
-force_g = hydrodynamic_force(g, C, method_hydro, relaxation_time, density_liquid, density_gas, kappa, beta, body_force,
- fd_stencil=get_stencil("D3Q27"))
-
-force_model_g = MultiphaseForceModel(force=force_g, rho=density)
-
-####################
-# LBM UPDATE RULES #
-####################
-
-phase_field_LB_step = get_collision_assignments_phase(lb_method=method_phase,
- velocity_input=u,
- output={'density': C_tmp},
- force_model=force_model_h,
- symbolic_fields={"symbolic_field": h,
- "symbolic_temporary_field": h_tmp},
- kernel_type='stream_pull_collide')
-
-phase_field_LB_step = sympy_cse(phase_field_LB_step)
-
-hydro_LB_step = get_collision_assignments_hydro(lb_method=method_hydro,
- density=density,
- velocity_input=u,
- force_model=force_model_g,
- sub_iterations=2,
- symbolic_fields={"symbolic_field": g,
- "symbolic_temporary_field": g_tmp},
- kernel_type='collide_stream_push')
-
-hydro_LB_step.set_sub_expressions_from_dict({**{relaxation_rate: relaxation_rate_cutoff},
- **hydro_LB_step.subexpressions_dict})
-
-contact_angle = ContactAngle(contact_angle_in_degrees, W)
-
-
-###################
-# GENERATE SWEEPS #
-###################
-info_header = f"""
-using namespace walberla;
-#include "stencil/D3Q{q_phase}.h"\nusing Stencil_phase_T = walberla::stencil::D3Q{q_phase};
-#include "stencil/D3Q{q_hydro}.h"\nusing Stencil_hydro_T = walberla::stencil::D3Q{q_hydro};
-using PdfField_phase_T = GhostLayerField<real_t, {q_phase}>;
-using PdfField_hydro_T = GhostLayerField<real_t, {q_hydro}>;
-using VelocityField_T = GhostLayerField<real_t, {dimensions}>;
-using PhaseField_T = GhostLayerField<real_t, 1>;
-#ifndef UTIL_H
-#define UTIL_H
-const char * stencil_phase_name = "{stencil_phase_name}";
-const char * stencil_hydro_name = "{stencil_hydro_name}";
-#endif
-"""
with CodeGeneration() as ctx:
- generate_sweep(ctx, 'initialize_phase_field_distributions', h_updates, target='cpu')
- generate_sweep(ctx, 'initialize_velocity_based_distributions', g_updates, target='cpu')
+ field_type = "float64" if ctx.double_accuracy else "float32"
+
+ contact_angle_in_degrees = 22
+
+ stencil_phase = LBStencil(Stencil.D3Q27)
+ stencil_hydro = LBStencil(Stencil.D3Q27)
+ assert (stencil_phase.D == stencil_hydro.D)
+
+ ########################
+ # PARAMETER DEFINITION #
+ ########################
+
+ density_liquid = sp.Symbol("rho_H")
+ density_gas = sp.Symbol("rho_L")
+
+ surface_tension = sp.Symbol("sigma")
+ mobility = sp.Symbol("mobility")
+
+ gravitational_acceleration = sp.Symbol("gravity")
+
+ relaxation_time_liquid = sp.Symbol("tau_H")
+ relaxation_time_gas = sp.Symbol("tau_L")
+
+ # phase-field parameter
+ drho3 = (density_liquid - density_gas) / 3
+ # interface thickness
+ W = sp.Symbol("interface_thickness")
+ # coefficients related to surface tension
+ beta = 12.0 * (surface_tension / W)
+ kappa = 1.5 * surface_tension * W
+
+ ########################
+ # FIELDS #
+ ########################
+
+ # velocity field
+ u = fields(f"vel_field({stencil_hydro.D}): {field_type}[{stencil_hydro.D}D]", layout='fzyx')
+ # phase-field
+ C = fields(f"phase_field: {field_type}[{stencil_hydro.D}D]", layout='fzyx')
+ C_tmp = fields(f"phase_field_tmp: {field_type}[{stencil_hydro.D}D]", layout='fzyx')
+
+ # phase-field distribution functions
+ h = fields(f"lb_phase_field({stencil_phase.Q}): {field_type}[{stencil_phase.D}D]", layout='fzyx')
+ h_tmp = fields(f"lb_phase_field_tmp({stencil_phase.Q}): {field_type}[{stencil_phase.D}D]", layout='fzyx')
+ # hydrodynamic distribution functions
+ g = fields(f"lb_velocity_field({stencil_hydro.Q}): {field_type}[{stencil_hydro.D}D]", layout='fzyx')
+ g_tmp = fields(f"lb_velocity_field_tmp({stencil_hydro.Q}): {field_type}[{stencil_hydro.D}D]", layout='fzyx')
+
+ ########################################
+ # RELAXATION RATES AND EXTERNAL FORCES #
+ ########################################
+
+ # relaxation rate for interface tracking LB step
+ relaxation_rate_allen_cahn = 1.0 / (0.5 + (3.0 * mobility))
+ # calculate the relaxation rate for hydrodynamic LB step
+ density = density_gas + C.center * (density_liquid - density_gas)
+ # force acting on all phases of the model
+ body_force = np.array([0, gravitational_acceleration * density, 0])
+ # calculation of the relaxation time via viscosities
+ # viscosity = viscosity_gas * viscosity_liquid + C.center\
+ # * (density_liquid*viscosity_liquid - viscosity_liquid*viscosity_gas)
+ # relaxation_time = 3 * viscosity / density + 0.5
+
+ relaxation_time = 0.5 + relaxation_time_gas + C.center * (relaxation_time_liquid - relaxation_time_gas)
+ # calculate the relaxation time if the phase-field has values over one
+ # relaxation_rate = 1.0 / relaxation_time
+ relaxation_rate = sp.Symbol("s8")
+ relaxation_rate_cutoff = sp.Piecewise((1 / (0.5 + relaxation_time_liquid), C.center > 0.999), # True value
+ (1 / relaxation_time, True)) # Else value
+
+ ###############
+ # LBM METHODS #
+ ###############
+ lbm_config_phase = LBMConfig(stencil=stencil_phase, method=Method.MRT, compressible=True, weighted=True,
+ relaxation_rates=[1, 1, 1, 1, 1, 1])
+ method_phase = create_lb_method(lbm_config=lbm_config_phase)
+ method_phase.set_first_moment_relaxation_rate(relaxation_rate_allen_cahn)
+
+ lbm_config_hydro = LBMConfig(stencil=stencil_hydro, method=Method.MRT, weighted=True,
+ relaxation_rates=[relaxation_rate, 1, 1, 1, 1, 1])
+ method_hydro = create_lb_method(lbm_config=lbm_config_hydro)
+
+ # create the kernels for the initialization of the g and h field
+ h_updates = initializer_kernel_phase_field_lb(h, C, u, method_phase, W)
+ g_updates = initializer_kernel_hydro_lb(g, u, method_hydro)
+
+ force_h = [f / 3 for f in interface_tracking_force(C, stencil_phase, W)]
+ force_model_h = MultiphaseForceModel(force=force_h)
+
+ force_g = hydrodynamic_force(g, C, method_hydro, relaxation_time,
+ density_liquid, density_gas, kappa, beta, body_force)
+ force_model_g = MultiphaseForceModel(force=force_g, rho=density)
+
+ ####################
+ # LBM UPDATE RULES #
+ ####################
+
+ phase_field_LB_step = get_collision_assignments_phase(lb_method=method_phase,
+ velocity_input=u,
+ output={'density': C_tmp},
+ force_model=force_model_h,
+ symbolic_fields={"symbolic_field": h,
+ "symbolic_temporary_field": h_tmp},
+ kernel_type='stream_pull_collide')
+
+ phase_field_LB_step = sympy_cse(phase_field_LB_step)
+ # ---------------------------------------------------------------------------------------------------------
+ hydro_LB_step = get_collision_assignments_hydro(lb_method=method_hydro,
+ density=density,
+ velocity_input=u,
+ force_model=force_model_g,
+ sub_iterations=2,
+ symbolic_fields={"symbolic_field": g,
+ "symbolic_temporary_field": g_tmp},
+ kernel_type='collide_stream_push')
+
+ hydro_LB_step.set_sub_expressions_from_dict({**{relaxation_rate: relaxation_rate_cutoff},
+ **hydro_LB_step.subexpressions_dict})
+
+ contact_angle = ContactAngle(contact_angle_in_degrees, W)
+
+ ###################
+ # GENERATE SWEEPS #
+ ###################
+
+ vp = [('int32_t', 'cudaBlockSize0'),
+ ('int32_t', 'cudaBlockSize1'),
+ ('int32_t', 'cudaBlockSize2')]
+
+ sweep_block_size = (TypedSymbol("cudaBlockSize0", np.int32),
+ TypedSymbol("cudaBlockSize1", np.int32),
+ TypedSymbol("cudaBlockSize2", np.int32))
+
+ sweep_params = {'block_size': sweep_block_size}
+
+ stencil_typedefs = {'Stencil_phase_T': stencil_phase,
+ 'Stencil_hydro_T': stencil_hydro}
+ field_typedefs = {'PdfField_phase_T': h,
+ 'PdfField_hydro_T': g,
+ 'VelocityField_T': u,
+ 'PhaseField_T': C}
+
+ additional_code = f"""
+ const char * StencilNamePhase = "{stencil_phase.name}";
+ const char * StencilNameHydro = "{stencil_hydro.name}";
+ """
+
+ generate_sweep(ctx, 'initialize_phase_field_distributions', h_updates, target=Target.CPU)
+ generate_sweep(ctx, 'initialize_velocity_based_distributions', g_updates, target=Target.CPU)
generate_sweep(ctx, 'phase_field_LB_step', phase_field_LB_step,
field_swaps=[(h, h_tmp), (C, C_tmp)],
inner_outer_split=True,
- target='cpu')
- generate_boundary(ctx, 'phase_field_LB_NoSlip', NoSlip(), method_phase, target='cpu', streaming_pattern='pull')
+ target=Target.CPU)
+ generate_boundary(ctx, 'phase_field_LB_NoSlip', NoSlip(), method_phase, target=Target.CPU, streaming_pattern='pull')
generate_sweep(ctx, 'hydro_LB_step', hydro_LB_step,
field_swaps=[(g, g_tmp)],
inner_outer_split=True,
- target='cpu')
- generate_boundary(ctx, 'hydro_LB_NoSlip', NoSlip(), method_hydro, target='cpu', streaming_pattern='push')
+ target=Target.CPU)
+ generate_boundary(ctx, 'hydro_LB_NoSlip', NoSlip(), method_hydro, target=Target.CPU, streaming_pattern='push')
# communication
generate_lb_pack_info(ctx, 'PackInfo_phase_field_distributions', stencil_phase, h,
- streaming_pattern='pull', target='cpu')
+ streaming_pattern='pull', target=Target.CPU)
generate_lb_pack_info(ctx, 'PackInfo_velocity_based_distributions', stencil_hydro, g,
- streaming_pattern='push', target='cpu')
+ streaming_pattern='push', target=Target.CPU)
- generate_pack_info_for_field(ctx, 'PackInfo_phase_field', C, target='cpu')
+ generate_pack_info_for_field(ctx, 'PackInfo_phase_field', C, target=Target.CPU)
pystencils_walberla.boundary.generate_boundary(ctx, 'ContactAngle', contact_angle,
- C.name, stencil_hydro, index_shape=[], target='cpu')
+ C.name, stencil_hydro, index_shape=[], target=Target.CPU)
- ctx.write_file("GenDefines.h", info_header)
+ generate_info_header(ctx, 'GenDefines', stencil_typedefs=stencil_typedefs, field_typedefs=field_typedefs,
+ additional_code=additional_code)
-print("finished code generation successfully")
diff --git a/apps/showcases/PhaseFieldAllenCahn/GPU/multiphase_codegen.py b/apps/showcases/PhaseFieldAllenCahn/GPU/multiphase_codegen.py
index ae42819a4b97579616086e14ba0d70391ef1839a..a41a6939b590c0e96aa0bc6f459ee5b5d9677838 100644
--- a/apps/showcases/PhaseFieldAllenCahn/GPU/multiphase_codegen.py
+++ b/apps/showcases/PhaseFieldAllenCahn/GPU/multiphase_codegen.py
@@ -1,41 +1,33 @@
-from lbmpy.phasefield_allen_cahn.contact_angle import ContactAngle
-from pystencils import fields, TypedSymbol
-from pystencils.simp import sympy_cse
-from pystencils import Assignment
+import numpy as np
+import sympy as sp
+
+from pystencils import Assignment, fields, TypedSymbol, Target
from pystencils.astnodes import Block, Conditional
+from pystencils.simp import sympy_cse
-from lbmpy.boundaries import NoSlip
+from lbmpy import LBMConfig, LBStencil, Method, Stencil
from lbmpy.creationfunctions import create_lb_method
-from lbmpy.stencils import get_stencil
-
-import pystencils_walberla
-from pystencils_walberla import CodeGeneration, generate_sweep, generate_pack_info_for_field, generate_info_header
-from lbmpy_walberla import generate_boundary, generate_lb_pack_info
+from lbmpy.boundaries import NoSlip
+from lbmpy.phasefield_allen_cahn.contact_angle import ContactAngle
+from lbmpy.phasefield_allen_cahn.force_model import MultiphaseForceModel
from lbmpy.phasefield_allen_cahn.kernel_equations import initializer_kernel_phase_field_lb, \
initializer_kernel_hydro_lb, interface_tracking_force, hydrodynamic_force, get_collision_assignments_hydro, \
get_collision_assignments_phase
-from lbmpy.phasefield_allen_cahn.force_model import MultiphaseForceModel
+import pystencils_walberla
+from pystencils_walberla import CodeGeneration, generate_sweep, generate_pack_info_for_field, generate_info_header
+from lbmpy_walberla import generate_boundary, generate_lb_pack_info
-import numpy as np
-import sympy as sp
with CodeGeneration() as ctx:
field_type = "float64" if ctx.double_accuracy else "float32"
- stencil_phase_name = "D3Q27"
- stencil_hydro_name = "D3Q27"
-
contact_angle_in_degrees = 22
- stencil_phase = get_stencil(stencil_phase_name)
- stencil_hydro = get_stencil(stencil_hydro_name)
- q_phase = len(stencil_phase)
- q_hydro = len(stencil_hydro)
-
- assert (len(stencil_phase[0]) == len(stencil_hydro[0]))
- dimensions = len(stencil_hydro[0])
+ stencil_phase = LBStencil(Stencil.D3Q27)
+ stencil_hydro = LBStencil(Stencil.D3Q27)
+ assert (stencil_phase.D == stencil_hydro.D)
########################
# PARAMETER DEFINITION #
@@ -65,18 +57,18 @@ with CodeGeneration() as ctx:
########################
# velocity field
- u = fields(f"vel_field({dimensions}): {field_type}[{dimensions}D]", layout='fzyx')
+ u = fields(f"vel_field({stencil_hydro.D}): {field_type}[{stencil_hydro.D}D]", layout='fzyx')
# phase-field
- C = fields(f"phase_field: {field_type}[{dimensions}D]", layout='fzyx')
- C_tmp = fields(f"phase_field_tmp: {field_type}[{dimensions}D]", layout='fzyx')
+ C = fields(f"phase_field: {field_type}[{stencil_hydro.D}D]", layout='fzyx')
+ C_tmp = fields(f"phase_field_tmp: {field_type}[{stencil_hydro.D}D]", layout='fzyx')
- flag = fields(f"flag_field: uint8[{dimensions}D]", layout='fzyx')
+ flag = fields(f"flag_field: uint8[{stencil_hydro.D}D]", layout='fzyx')
# phase-field distribution functions
- h = fields(f"lb_phase_field({q_phase}): {field_type}[{dimensions}D]", layout='fzyx')
- h_tmp = fields(f"lb_phase_field_tmp({q_phase}): {field_type}[{dimensions}D]", layout='fzyx')
+ h = fields(f"lb_phase_field({stencil_phase.Q}): {field_type}[{stencil_phase.D}D]", layout='fzyx')
+ h_tmp = fields(f"lb_phase_field_tmp({stencil_phase.Q}): {field_type}[{stencil_phase.D}D]", layout='fzyx')
# hydrodynamic distribution functions
- g = fields(f"lb_velocity_field({q_hydro}): {field_type}[{dimensions}D]", layout='fzyx')
- g_tmp = fields(f"lb_velocity_field_tmp({q_hydro}): {field_type}[{dimensions}D]", layout='fzyx')
+ g = fields(f"lb_velocity_field({stencil_hydro.Q}): {field_type}[{stencil_hydro.D}D]", layout='fzyx')
+ g_tmp = fields(f"lb_velocity_field_tmp({stencil_hydro.Q}): {field_type}[{stencil_hydro.D}D]", layout='fzyx')
########################################
# RELAXATION RATES AND EXTERNAL FORCES #
@@ -103,25 +95,24 @@ with CodeGeneration() as ctx:
###############
# LBM METHODS #
###############
- method_phase = create_lb_method(stencil=stencil_phase, method="mrt", compressible=True, weighted=True,
- relaxation_rates=[1, 1, 1, 1, 1, 1])
-
+ lbm_config_phase = LBMConfig(stencil=stencil_phase, method=Method.MRT, compressible=True, weighted=True,
+ relaxation_rates=[1, 1, 1, 1, 1, 1])
+ method_phase = create_lb_method(lbm_config=lbm_config_phase)
method_phase.set_first_moment_relaxation_rate(relaxation_rate_allen_cahn)
- method_hydro = create_lb_method(stencil=stencil_hydro, method="mrt", weighted=True,
- relaxation_rates=[relaxation_rate, 1, 1, 1, 1, 1])
+ lbm_config_hydro = LBMConfig(stencil=stencil_hydro, method=Method.MRT, weighted=True,
+ relaxation_rates=[relaxation_rate, 1, 1, 1, 1, 1])
+ method_hydro = create_lb_method(lbm_config=lbm_config_hydro)
# create the kernels for the initialization of the g and h field
- h_updates = initializer_kernel_phase_field_lb(h, C, u, method_phase, W, fd_stencil=get_stencil("D3Q27"))
+ h_updates = initializer_kernel_phase_field_lb(h, C, u, method_phase, W)
g_updates = initializer_kernel_hydro_lb(g, u, method_hydro)
- force_h = [f / 3 for f in interface_tracking_force(C, stencil_phase, W, fd_stencil=get_stencil("D3Q27"))]
+ force_h = [f / 3 for f in interface_tracking_force(C, stencil_phase, W)]
force_model_h = MultiphaseForceModel(force=force_h)
- force_g = hydrodynamic_force(g, C, method_hydro, relaxation_time, density_liquid, density_gas, kappa, beta,
- body_force,
- fd_stencil=get_stencil("D3Q27"))
-
+ force_g = hydrodynamic_force(g, C, method_hydro, relaxation_time,
+ density_liquid, density_gas, kappa, beta, body_force)
force_model_g = MultiphaseForceModel(force=force_g, rho=density)
####################
@@ -180,41 +171,39 @@ with CodeGeneration() as ctx:
'PhaseField_T': C}
additional_code = f"""
- const char * StencilNamePhase = "{stencil_phase_name}";
- const char * StencilNameHydro = "{stencil_hydro_name}";
+ const char * StencilNamePhase = "{stencil_phase.name}";
+ const char * StencilNameHydro = "{stencil_hydro.name}";
"""
- generate_sweep(ctx, 'initialize_phase_field_distributions', h_updates, target='gpu')
- generate_sweep(ctx, 'initialize_velocity_based_distributions', g_updates, target='gpu')
+ generate_sweep(ctx, 'initialize_phase_field_distributions', h_updates, target=Target.GPU)
+ generate_sweep(ctx, 'initialize_velocity_based_distributions', g_updates, target=Target.GPU)
generate_sweep(ctx, 'phase_field_LB_step', phase_field_LB_step,
field_swaps=[(h, h_tmp), (C, C_tmp)],
- target='gpu',
+ target=Target.GPU,
gpu_indexing_params=sweep_params,
varying_parameters=vp)
- generate_boundary(ctx, 'phase_field_LB_NoSlip', NoSlip(), method_phase, target='gpu', streaming_pattern='pull')
+ generate_boundary(ctx, 'phase_field_LB_NoSlip', NoSlip(), method_phase, target=Target.GPU, streaming_pattern='pull')
generate_sweep(ctx, 'hydro_LB_step', hydro_LB_step,
field_swaps=[(g, g_tmp)],
- target='gpu',
+ target=Target.GPU,
gpu_indexing_params=sweep_params,
varying_parameters=vp)
- generate_boundary(ctx, 'hydro_LB_NoSlip', NoSlip(), method_hydro, target='gpu', streaming_pattern='push')
+ generate_boundary(ctx, 'hydro_LB_NoSlip', NoSlip(), method_hydro, target=Target.GPU, streaming_pattern='push')
# communication
generate_lb_pack_info(ctx, 'PackInfo_phase_field_distributions', stencil_phase, h,
- streaming_pattern='pull', target='gpu')
+ streaming_pattern='pull', target=Target.GPU)
generate_lb_pack_info(ctx, 'PackInfo_velocity_based_distributions', stencil_hydro, g,
- streaming_pattern='push', target='gpu')
+ streaming_pattern='push', target=Target.GPU)
- generate_pack_info_for_field(ctx, 'PackInfo_phase_field', C, target='gpu')
+ generate_pack_info_for_field(ctx, 'PackInfo_phase_field', C, target=Target.GPU)
pystencils_walberla.boundary.generate_boundary(ctx, 'ContactAngle', contact_angle,
- C.name, stencil_hydro, index_shape=[], target='gpu')
+ C.name, stencil_hydro, index_shape=[], target=Target.GPU)
generate_info_header(ctx, 'GenDefines', stencil_typedefs=stencil_typedefs, field_typedefs=field_typedefs,
additional_code=additional_code)
-
-print("finished code generation successfully")
diff --git a/apps/tutorials/codegen/02_LBMLatticeModelGeneration.dox b/apps/tutorials/codegen/02_LBMLatticeModelGeneration.dox
index 95f27cda46a056e83e80f20b03a86190998fd24c..7e0309c4d2be4cabd28d1175047b96ff38f96864 100644
--- a/apps/tutorials/codegen/02_LBMLatticeModelGeneration.dox
+++ b/apps/tutorials/codegen/02_LBMLatticeModelGeneration.dox
@@ -25,6 +25,7 @@ From the `lbmpy.creationfunctions` we require the functions to create collision
\code{.py}
import sympy as sp
+from lbmpy import LBMConfig, LBMOptimisation, LBStencil, Method, Stencil
from lbmpy.creationfunctions import create_lb_collision_rule, create_lb_update_rule
from pystencils_walberla import CodeGeneration, generate_pack_info_from_kernel
@@ -33,12 +34,12 @@ from lbmpy_walberla import generate_lattice_model
First, we define a few general parameters. These include the stencil (D2Q9) and the memory layout (`fzyx`, see \ref tutorial_codegen01 ). We define a SymPy symbol for the relaxation rate \f$ \omega \f$. This means we can later set it to a specific value from the waLBerla code. A dictionary with optimization parameters is also set up. Here, we enable global common subexpression elimination (`cse_global`) and set the PDF field's memory layout.
\code{.py}
-stencil = 'D2Q9'
+stencil = LBStencil(Stencil.D2Q9)
omega = sp.Symbol('omega')
layout = 'fzyx'
-# Optimization
-optimizations = {'target': 'cpu', 'cse_global': True, 'field_layout': layout}
+# Optimizations for the LBM Method
+lbm_opt = LBMOptimisation(cse_global=True, field_layout=layout)
\endcode
Next, we set the parameters for the SRT method in a dictionary and create both the collision and update rules by calling the respective lbmpy functions. They both return an `AssignmentCollection` containing all necessary equations. The only parameters needed for SRT are the stencil and the relaxation rate. For generating the lattice model, we only require the collision rule's equations since `generate_lattice_model` adds the two-fields pull scheme for the streaming step internally. At this point, the lattice model generation is limited to the standard stream-pull-collide scheme.
@@ -46,12 +47,10 @@ Next, we set the parameters for the SRT method in a dictionary and create both t
The update rule is still needed in the code generation process; namely for the pack info generation. The collision step only acts within one cell. Thus, the collision rule's equations contain no neighbour accesses. Calling `create_lb_update_rule` inserts the two-fields pull scheme as `generate_lattice_model`, and resulting update rule contains exactly those neighbour accesses which are required for `generate_pack_info_from_kernel` to build the optimized pack info.
\code{.py}
-srt_params = {'stencil': stencil,
- 'method': 'srt',
- 'relaxation_rate': omega}
+lbm_config = LBMConfig(stencil=stencil, method=Method.SRT, relaxation_rate=omega)
-srt_collision_rule = create_lb_collision_rule(optimization=optimizations, **srt_params)
-srt_update_rule = create_lb_update_rule(collision_rule=srt_collision_rule, optimization=optimizations)
+srt_collision_rule = create_lb_collision_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
+srt_update_rule = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
\endcode
Finally, we create the code generation context and call the respective functions for generating the lattice model and the pack info. Both require the context and a class name as parameters. To `generate_lattice_model`, we also pass the collision rule and the field layout; `generate_pack_info_from_kernel` receives the update rule.
diff --git a/apps/tutorials/codegen/02_LBMLatticeModelGeneration.py b/apps/tutorials/codegen/02_LBMLatticeModelGeneration.py
index 39deba33e739693965d7bc8119e013265342dbdf..224ab742a9fe382ad8e07df3eba8eae731ceab4a 100644
--- a/apps/tutorials/codegen/02_LBMLatticeModelGeneration.py
+++ b/apps/tutorials/codegen/02_LBMLatticeModelGeneration.py
@@ -1,5 +1,6 @@
import sympy as sp
+from lbmpy import LBMConfig, LBMOptimisation, LBStencil, Method, Stencil
from lbmpy.creationfunctions import create_lb_collision_rule, create_lb_update_rule
from pystencils_walberla import CodeGeneration, generate_pack_info_from_kernel
@@ -9,23 +10,21 @@ from lbmpy_walberla import generate_lattice_model
# General Parameters
# ========================
-stencil = 'D2Q9'
+stencil = LBStencil(Stencil.D2Q9)
omega = sp.Symbol('omega')
layout = 'fzyx'
-# Optimizations to be used by the code generator
-optimizations = {'cse_global': True, 'field_layout': layout}
+# Optimizations for the LBM Method
+lbm_opt = LBMOptimisation(cse_global=True, field_layout=layout)
# ===========================
# SRT Method Definition
# ===========================
-srt_params = {'stencil': stencil,
- 'method': 'srt',
- 'relaxation_rate': omega}
+lbm_config = LBMConfig(stencil=stencil, method=Method.SRT, relaxation_rate=omega)
-srt_collision_rule = create_lb_collision_rule(optimization=optimizations, **srt_params)
-srt_update_rule = create_lb_update_rule(collision_rule=srt_collision_rule, optimization=optimizations)
+srt_collision_rule = create_lb_collision_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
+srt_update_rule = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
# =====================
# Code Generation
diff --git a/apps/tutorials/codegen/03_AdvancedLBMCodegen.dox b/apps/tutorials/codegen/03_AdvancedLBMCodegen.dox
index f5a74030f87f38d6368ffbe48cf891ad04cb9385..f3882b26c5f1cebe8a426661d5ddbd61b9bc1a15 100644
--- a/apps/tutorials/codegen/03_AdvancedLBMCodegen.dox
+++ b/apps/tutorials/codegen/03_AdvancedLBMCodegen.dox
@@ -20,36 +20,35 @@ For the stream-pull-collide type kernel, we need two PDF fields which we set up
For VTK output and the initial velocity setup, we define a velocity vector field as an output field for the LB method.
\code{.py}
-stencil = 'D2Q9'
+stencil = LBStencil(Stencil.D2Q9)
omega = sp.Symbol('omega')
layout = 'fzyx'
# PDF Fields
-pdfs, pdfs_tmp = ps.fields('pdfs(9), pdfs_tmp(9): [2D]', layout=layout)
+pdfs, pdfs_tmp = ps.fields(f'pdfs({stencil.Q}), pdfs_tmp({stencil.Q}): [2D]', layout=layout)
# Velocity Output Field
-velocity = ps.fields("velocity(2): [2D]", layout=layout)
+velocity = ps.fields(f"velocity({stencil.D}): [2D]", layout=layout)
output = {'velocity': velocity}
# Optimization
-optimization = {'cse_global': True,
- 'symbolic_field': pdfs,
- 'symbolic_temporary_field': pdfs_tmp,
- 'field_layout': layout}
+lbm_opt = LBMOptimisation(cse_global=True,
+ symbolic_field=pdfs,
+ symbolic_temporary_field=pdfs_tmp,
+ field_layout=layout)
\endcode
We set up the cumulant-based MRT method with relaxation rates as described above. We use `generate_lb_update_rule` from lbmpy to derive the set of equations describing the collision operator together with the *pull* streaming pattern. These equations define the entire LBM sweep.
\code{.py}
-lbm_params = {'stencil': stencil,
- 'method': 'mrt_raw',
- 'relaxation_rates': [0, 0, 0, omega, omega, omega, 1, 1, 1],
- 'cumulant': True,
- 'compressible': True}
+lbm_config = LBMConfig(stencil=stencil,
+ method=Method.CUMULANT,
+ relaxation_rate=omega,
+ compressible=True,
+ output=output)
+
+lbm_update_rule = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
-lbm_update_rule = create_lb_update_rule(optimization=optimization,
- output=output,
- **lbm_params)
lbm_method = lbm_update_rule.method
\endcode
@@ -77,9 +76,9 @@ Several functions from `pystencils_walberla` and `lbmpy_walberla` are called to
\code{.py}
with CodeGeneration() as ctx:
if ctx.cuda:
- target = 'gpu'
+ target = ps.Target.GPU
else:
- target = 'cpu'
+ target = ps.Target.CPU
# LBM Sweep
generate_sweep(ctx, "CumulantMRTSweep", lbm_update_rule, field_swaps=[(pdfs, pdfs_tmp)], target=target)
diff --git a/apps/tutorials/codegen/03_AdvancedLBMCodegen.py b/apps/tutorials/codegen/03_AdvancedLBMCodegen.py
index 65a0602f2a4ff2916c87661a38bc5b90c262cec6..c94eb81bab1f584ccfaf2ec4b9ebf2545c4434f9 100644
--- a/apps/tutorials/codegen/03_AdvancedLBMCodegen.py
+++ b/apps/tutorials/codegen/03_AdvancedLBMCodegen.py
@@ -1,6 +1,8 @@
import sympy as sp
import pystencils as ps
+from lbmpy import LBMConfig, LBMOptimisation, LBStencil, Method, Stencil
+
from lbmpy.creationfunctions import create_lb_update_rule
from lbmpy.macroscopic_value_kernels import macroscopic_values_setter
from lbmpy.boundaries import NoSlip
@@ -13,36 +15,35 @@ from lbmpy_walberla import generate_boundary
# General Parameters
# ========================
-stencil = 'D2Q9'
+stencil = LBStencil(Stencil.D2Q9)
omega = sp.Symbol('omega')
layout = 'fzyx'
# PDF Fields
-pdfs, pdfs_tmp = ps.fields('pdfs(9), pdfs_tmp(9): [2D]', layout=layout)
+pdfs, pdfs_tmp = ps.fields(f'pdfs({stencil.Q}), pdfs_tmp({stencil.Q}): [2D]', layout=layout)
# Velocity Output Field
-velocity = ps.fields("velocity(2): [2D]", layout=layout)
+velocity = ps.fields(f"velocity({stencil.D}): [2D]", layout=layout)
output = {'velocity': velocity}
-# Optimization
-optimization = {'cse_global': True,
- 'symbolic_field': pdfs,
- 'symbolic_temporary_field': pdfs_tmp,
- 'field_layout': layout}
+# LBM Optimisation
+lbm_opt = LBMOptimisation(cse_global=True,
+ symbolic_field=pdfs,
+ symbolic_temporary_field=pdfs_tmp,
+ field_layout=layout)
# ==================
# Method Setup
# ==================
-lbm_params = {'stencil': stencil,
- 'method': 'cumulant',
- 'relaxation_rate': omega,
- 'compressible': True}
+lbm_config = LBMConfig(stencil=stencil,
+ method=Method.CUMULANT,
+ relaxation_rate=omega,
+ compressible=True,
+ output=output)
-lbm_update_rule = create_lb_update_rule(optimization=optimization,
- output=output,
- **lbm_params)
+lbm_update_rule = create_lb_update_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
lbm_method = lbm_update_rule.method
@@ -63,9 +64,9 @@ pdfs_setter = macroscopic_values_setter(lbm_method,
with CodeGeneration() as ctx:
if ctx.cuda:
- target = 'gpu'
+ target = ps.Target.GPU
else:
- target = 'cpu'
+ target = ps.Target.CPU
# LBM Sweep
generate_sweep(ctx, "CumulantMRTSweep", lbm_update_rule, field_swaps=[(pdfs, pdfs_tmp)], target=target)
diff --git a/apps/tutorials/codegen/Heat Equation Kernel.ipynb b/apps/tutorials/codegen/Heat Equation Kernel.ipynb
index e5ccffbb761fc98c722e28d2fdc81ba6b2e82641..d506358314d4d4e0e9cd4c0b4a7265d503378b25 100644
--- a/apps/tutorials/codegen/Heat Equation Kernel.ipynb
+++ b/apps/tutorials/codegen/Heat Equation Kernel.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -35,7 +35,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -54,18 +54,22 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
- "output_type": "execute_result",
"data": {
- "text/plain": "-κ⋅(D(D(u[0,0])) + D(D(u[0,0]))) + Transient(u_C)",
"image/png": "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\n",
- "text/latex": "$\\displaystyle - \\kappa \\left({\\partial_{0} {\\partial_{0} {{u}_{(0,0)}}}} + {\\partial_{1} {\\partial_{1} {{u}_{(0,0)}}}}\\right) + \\partial_t u_{C}$"
+ "text/latex": [
+ "$\\displaystyle - \\kappa \\left({\\partial_{0} {\\partial_{0} {{u}_{(0,0)}}}} + {\\partial_{1} {\\partial_{1} {{u}_{(0,0)}}}}\\right) + \\partial_t u_{C}$"
+ ],
+ "text/plain": [
+ "-κ⋅(D(D(u[0,0])) + D(D(u[0,0]))) + Transient(u_C)"
+ ]
},
+ "execution_count": 3,
"metadata": {},
- "execution_count": 5
+ "output_type": "execute_result"
}
],
"source": [
@@ -82,18 +86,25 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
- "output_type": "execute_result",
"data": {
- "text/plain": " ⎛-2⋅u_C + u_E + u_W -2⋅u_C + u_N + u_S⎞\nu_C + dt⋅κ⋅⎜────────────────── + ──────────────────⎟\n ⎜ 2 2 ⎟\n ⎠dx dx ⎠",
"image/png": "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\n",
- "text/latex": "$\\displaystyle {{u}_{(0,0)}} + dt \\kappa \\left(\\frac{- 2 {{u}_{(0,0)}} + {{u}_{(1,0)}} + {{u}_{(-1,0)}}}{dx^{2}} + \\frac{- 2 {{u}_{(0,0)}} + {{u}_{(0,1)}} + {{u}_{(0,-1)}}}{dx^{2}}\\right)$"
+ "text/latex": [
+ "$\\displaystyle {{u}_{(0,0)}} + dt \\kappa \\left(\\frac{- 2 {{u}_{(0,0)}} + {{u}_{(1,0)}} + {{u}_{(-1,0)}}}{dx^{2}} + \\frac{- 2 {{u}_{(0,0)}} + {{u}_{(0,1)}} + {{u}_{(0,-1)}}}{dx^{2}}\\right)$"
+ ],
+ "text/plain": [
+ " ⎛-2⋅u_C + u_E + u_W -2⋅u_C + u_N + u_S⎞\n",
+ "u_C + dt⋅κ⋅⎜────────────────── + ──────────────────⎟\n",
+ " ⎜ 2 2 ⎟\n",
+ " ⎠dx dx ⎠"
+ ]
},
+ "execution_count": 4,
"metadata": {},
- "execution_count": 6
+ "output_type": "execute_result"
}
],
"source": [
@@ -111,18 +122,26 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
- "output_type": "execute_result",
"data": {
- "text/plain": " 2 \nu_C⋅dx + dt⋅κ⋅(-4⋅u_C + u_E + u_N + u_S + u_W)\n───────────────────────────────────────────────\n 2 \n dx ",
"image/png": "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\n",
- "text/latex": "$\\displaystyle \\frac{{{u}_{(0,0)}} dx^{2} + dt \\kappa \\left(- 4 {{u}_{(0,0)}} + {{u}_{(1,0)}} + {{u}_{(0,1)}} + {{u}_{(0,-1)}} + {{u}_{(-1,0)}}\\right)}{dx^{2}}$"
+ "text/latex": [
+ "$\\displaystyle \\frac{{{u}_{(0,0)}} dx^{2} + dt \\kappa \\left(- 4 {{u}_{(0,0)}} + {{u}_{(1,0)}} + {{u}_{(0,1)}} + {{u}_{(0,-1)}} + {{u}_{(-1,0)}}\\right)}{dx^{2}}$"
+ ],
+ "text/plain": [
+ " 2 \n",
+ "u_C⋅dx + dt⋅κ⋅(-4⋅u_C + u_E + u_N + u_S + u_W)\n",
+ "───────────────────────────────────────────────\n",
+ " 2 \n",
+ " dx "
+ ]
},
+ "execution_count": 5,
"metadata": {},
- "execution_count": 7
+ "output_type": "execute_result"
}
],
"source": [
@@ -140,7 +159,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -156,7 +175,7 @@
" dx "
]
},
- "execution_count": 53,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -175,7 +194,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -184,10 +203,10 @@
"<div>Subexpressions:</div><table style=\"border:none; width: 100%; \"><tr style=\"border:none\"> <td style=\"border:none\">$$\\xi_{0} \\leftarrow \\frac{1}{dx^{2}}$$</td> </tr> </table><div>Main Assignments:</div><table style=\"border:none; width: 100%; \"><tr style=\"border:none\"> <td style=\"border:none\">$${{u_tmp}_{(0,0)}} \\leftarrow {{u}_{(0,0)}} + dt \\kappa \\xi_{0} \\left(- 4 {{u}_{(0,0)}} + {{u}_{(1,0)}} + {{u}_{(0,1)}} + {{u}_{(0,-1)}} + {{u}_{(-1,0)}}\\right)$$</td> </tr> </table>"
],
"text/plain": [
- "AssignmentCollection: u_tmp_C, <- f(u_S, u_N, u_E, u_W, dt, u_C, kappa, dx)"
+ "AssignmentCollection: u_tmp_C, <- f(u_W, dt, kappa, u_C, u_E, u_N, dx, u_S)"
]
},
- "execution_count": 54,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -211,7 +230,7 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -302,13 +321,13 @@
" <span class=\"cp\">#pragma omp parallel num_threads(4)</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"cp\">#pragma omp for schedule(static)</span>\n",
- " <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"kt\">int</span> <span class=\"n\">ctr_1</span> <span class=\"o\">=</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_1</span> <span class=\"o\"><</span> <span class=\"n\">_size_u_1</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_1</span> <span class=\"o\">+=</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+ " <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"kt\">int64_t</span> <span class=\"n\">ctr_1</span> <span class=\"o\">=</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_1</span> <span class=\"o\"><</span> <span class=\"n\">_size_u_1</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_1</span> <span class=\"o\">+=</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_u_tmp_10</span> <span class=\"o\">=</span> <span class=\"n\">_data_u_tmp</span> <span class=\"o\">+</span> <span class=\"n\">_stride_u_tmp_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">;</span>\n",
" <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_u_10</span> <span class=\"o\">=</span> <span class=\"n\">_data_u</span> <span class=\"o\">+</span> <span class=\"n\">_stride_u_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span><span class=\"p\">;</span>\n",
" <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_u_11</span> <span class=\"o\">=</span> <span class=\"n\">_data_u</span> <span class=\"o\">+</span> <span class=\"n\">_stride_u_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">+</span> <span class=\"n\">_stride_u_1</span><span class=\"p\">;</span>\n",
" <span class=\"kt\">double</span> <span class=\"o\">*</span> <span class=\"n\">RESTRICT</span> <span class=\"n\">_data_u_1m1</span> <span class=\"o\">=</span> <span class=\"n\">_data_u</span> <span class=\"o\">+</span> <span class=\"n\">_stride_u_1</span><span class=\"o\">*</span><span class=\"n\">ctr_1</span> <span class=\"o\">-</span> <span class=\"n\">_stride_u_1</span><span class=\"p\">;</span>\n",
- " <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"kt\">int</span> <span class=\"n\">ctr_0</span> <span class=\"o\">=</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_0</span> <span class=\"o\"><</span> <span class=\"n\">_size_u_0</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_0</span> <span class=\"o\">+=</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n",
+ " <span class=\"k\">for</span> <span class=\"p\">(</span><span class=\"kt\">int64_t</span> <span class=\"n\">ctr_0</span> <span class=\"o\">=</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_0</span> <span class=\"o\"><</span> <span class=\"n\">_size_u_0</span> <span class=\"o\">-</span> <span class=\"mi\">1</span><span class=\"p\">;</span> <span class=\"n\">ctr_0</span> <span class=\"o\">+=</span> <span class=\"mi\">1</span><span class=\"p\">)</span>\n",
" <span class=\"p\">{</span>\n",
" <span class=\"n\">_data_u_tmp_10</span><span class=\"p\">[</span><span class=\"n\">_stride_u_tmp_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">dt</span><span class=\"o\">*</span><span class=\"n\">kappa</span><span class=\"o\">*</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"mf\">4.0</span><span class=\"o\">*</span><span class=\"n\">_data_u_10</span><span class=\"p\">[</span><span class=\"n\">_stride_u_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">_data_u_10</span><span class=\"p\">[</span><span class=\"n\">_stride_u_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">+</span> <span class=\"n\">_stride_u_0</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">_data_u_10</span><span class=\"p\">[</span><span class=\"n\">_stride_u_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span> <span class=\"o\">-</span> <span class=\"n\">_stride_u_0</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">_data_u_11</span><span class=\"p\">[</span><span class=\"n\">_stride_u_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">_data_u_1m1</span><span class=\"p\">[</span><span class=\"n\">_stride_u_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">])</span><span class=\"o\">/</span><span class=\"p\">(</span><span class=\"n\">dx</span><span class=\"o\">*</span><span class=\"n\">dx</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"n\">_data_u_10</span><span class=\"p\">[</span><span class=\"n\">_stride_u_0</span><span class=\"o\">*</span><span class=\"n\">ctr_0</span><span class=\"p\">];</span>\n",
" <span class=\"p\">}</span>\n",
@@ -323,13 +342,13 @@
" #pragma omp parallel num_threads(4)\n",
" {\n",
" #pragma omp for schedule(static)\n",
- " for (int ctr_1 = 1; ctr_1 < _size_u_1 - 1; ctr_1 += 1)\n",
+ " for (int64_t ctr_1 = 1; ctr_1 < _size_u_1 - 1; ctr_1 += 1)\n",
" {\n",
" double * RESTRICT _data_u_tmp_10 = _data_u_tmp + _stride_u_tmp_1*ctr_1;\n",
" double * RESTRICT _data_u_10 = _data_u + _stride_u_1*ctr_1;\n",
" double * RESTRICT _data_u_11 = _data_u + _stride_u_1*ctr_1 + _stride_u_1;\n",
" double * RESTRICT _data_u_1m1 = _data_u + _stride_u_1*ctr_1 - _stride_u_1;\n",
- " for (int ctr_0 = 1; ctr_0 < _size_u_0 - 1; ctr_0 += 1)\n",
+ " for (int64_t ctr_0 = 1; ctr_0 < _size_u_0 - 1; ctr_0 += 1)\n",
" {\n",
" _data_u_tmp_10[_stride_u_tmp_0*ctr_0] = dt*kappa*(-4.0*_data_u_10[_stride_u_0*ctr_0] + _data_u_10[_stride_u_0*ctr_0 + _stride_u_0] + _data_u_10[_stride_u_0*ctr_0 - _stride_u_0] + _data_u_11[_stride_u_0*ctr_0] + _data_u_1m1[_stride_u_0*ctr_0])/(dx*dx) + _data_u_10[_stride_u_0*ctr_0];\n",
" }\n",
@@ -343,7 +362,8 @@
}
],
"source": [
- "kernel_ast = ps.create_kernel(update, cpu_openmp = 4)\n",
+ "config = ps.CreateKernelConfig(cpu_openmp=4)\n",
+ "kernel_ast = ps.create_kernel(update, config=config)\n",
"kernel_func = kernel_ast.compile()\n",
"\n",
"ps.show_code(kernel_ast)"
@@ -360,7 +380,7 @@
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -384,7 +404,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -414,22 +434,22 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7ffb64712510>"
+ "<mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x7f3c79f92310>"
]
},
- "execution_count": 58,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1152x432 with 1 Axes>"
]
@@ -455,7 +475,7 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -471,7 +491,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -484,7 +504,7 @@
"2.0"
]
},
- "execution_count": 63,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -504,14 +524,14 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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LiFxC4hcQvYUi4uqi4uqqTNmMzp1ELiFxC/iRJPJ5JcB4wAAAEtEGaYCfAJ5gfPgVP6DnsYouO7Xy4xTf2vlVvAv/0X34I+q+90f34K6qqqtVmxe/BVVda1r6toEnVf+w8iv8BAAnzebzfY+wpVeT06qqp077wRo0EVVVf4xCjY34mtZPwZj2INIpFq5/f4BGKKmUQpbjFXvAJ0lixijjQdWMVexizJ4HH4GetZPegHj/XLwNQGdZ9OBpuCQWovUqOvr/qBxx/c+98WzkX79Xx8QXNfr8C5X+IVQaXAy/oR7EKLxir7GLfGLbCCKln/t4xXv4CHVOvBITVYvToIRIaBTDIA9+99fQuCKqqrPCAH1Ff4EFFc+DtGcxmNKwjN6g0vBIEK17TU+CITqvZtx//QR/xC4hcQuMXWMUV1rv1+uX0utf/aP/H4YGWoyHtn99Hq+EJRnkVHtt+mAoetW+CVWxiIWj4hYNK0EO6i+q6+vr60NY66wRUV+gDNnVPS+1Z4M6KzoAjIHBW2ugNPhKrVUDP8DH8DGiMQaV6wBvgyE1cxCrELiFwgpev7eMXbGK9sYortXJ4GkngL4DHhFQIRU4yN/vwhl5ln9tt1ov+tfHWvjGqGmWaDT/AGAFxC4xCLmIXGLbGbtjOZGXAMYBr8Aj78Df/RUdy/d/jFdsZKFlRdDexil7eNq/p2IXO8Gf5fr9GfEKsQrxCrGK7YhcYrtjFdsYQr2xihZ5NhDdAP5db+BRV9K9Z0vUOWxiFg0/9YvtYortX8MvGL98ar/Eo74xG8i8i/xykJMYxbkGn/IqVRCq8uhitRixil6RRi2oQrPgFKAt4xH9jkY928BI8QuuBnhXELneDP/ACCQG9EY8AKoAM3GLfGK7YhcQuMW36viEXsAeAAJx71t+r4hXjO2MV0yiFxek51AJmvYhcQud4M/oRI1ELjFuYzplGLbGK7YhcZOK07jJC9sYtsYorTKO9zoAVsAZnEIpc4BBgGfjJC42MRT42MRLmIXO8Gf4RcYa0bXtt8Yt8QuMX2IXEK8YtsYtsYr1jFL2xiv7GK9sYi2xCIXocQpb7CGKzIb+23xiyqxiuZMYrlTO8GnAZtDFC6ySKMW5jFL/GMW5jFekUYrufAZ6t4CC+Bdr14F2rG/1aXdbEGvCKsY/DTLN/bbiFxC4xfYxbYtRprVi8NMtWM3asBPpWMIdMv224hWo1Q0yyRRvTqfDTLRnTqI87wZi2Gr4thlKmUQuIXELiNOMe5iF+HVfGdhEK8YtsSpciN4xHpFELjFtjOmxalxsZ3Yhc7wZHhHO/gGAAKCgokUYt8QuIXGKXpsQuIXOpMnybGS0iiPGdsX3Z1LnAWIBOaESLPKXIjxEjzvBmLYat4CAAo42H7sQuIXEeeNzxjz4rjO2dH8CwAaGxHiYgviFxC4jxH8Azq1neDM74tj6O5/P54x5oh//h4a/iPEeL6Pz+Lji9sRH5/EeeMJ4iNz+LiBRjuxCE4hc653gz8E4YNxTGqF7pLGXf8aYXUXF1FxcXWMmZ2YzNGq/Pp9iFxC4hcQuIXELiF/G1UXF1UXFyYsZdmRadfn1qIQ5YhcQrxC4hcQuIX8Eo1VVVVVRqohcQuIX8EIknk9PBrwxEcMTCEHYOviPgQPiPgkgAAASYQZqAJ8AnZf/lBIGqzY3gKriFWMX7wIle1rqdPAqV7GKL9r5fH/wTFVVVVr78KFWqqqqqqqqqlV+CKqqvs3w8fsFe+DwL4u2J/4t23rgekLuroEhq1feCUNgywa+QJVrwC4RZdV1WEFdzpp/GKvIoxV9iFX6juzf4/0Gzr1MR2P6SXA2/Ao0a/6+8DQBnBCJe+d84KOq6r78EVa/MXhVWBArE1BKLWta/+eCQQtVfwMVF2MfITYmX1+DqskcciK5BpxIJBValsYikzxiFxi3MYq5jGLbGLf8Eda/eAgV+sWu9Tp4GwDK7389H/hCUA96M4ZGV+94zBnwZC70AkVTKsCTCFFy+BtXuoHnEIQlEcGnH4SRBBJFVPp9/jFa9iFxiu+IXGK9YxXvjFFGPrX/gYgN6P/oDj0sfq3YgCirPA0AwQlGKjFt1qlUQuIWDTgp6gaQRDDkjkuLEK8QuIXEK8YusYorrwED7gbb9HeYchZ8sYonELBp/Wh0fGqGmW7EKO+iFQOMXWMW2MV2oYivFvA58ElV+/RH8I7HIo9+MYQJ1/brwRb3YxhJcbELBpUCz4AwCjdiFxCvELjO3UDj1+AkUr+GUj/6A1AacZiXEp0cDfjMVoqUZiHmRT4GdW+NVvibHd0Gn4xELjmIXELjFuYhegDQAYcYrsYxbdT4xFBjfYxiujeOXb4Gn21bwhr1fwM/EE4hc7wacDKreAJ4FqxjFuYhcYtsYqbGMV2xi7bwHMtWMYu74GlWvbRWKGLbEL9Ij4xVrGL7O8GZ3/VjwCAVYpfOr4xbeBIAz0MUVtjFe2MUvjYxeaxiOe+xihRWjfgWwMKsa+F/0R8Yq9jlPSoNOBlQYfwCAOsAgLkwJr8AwAFmsMhH7QVfGbti9JMuAYQBo/Hq9YCX4xFc6YxRWRWMWdZBp/gC/gOqJ2IXELiFxiu2IX7V/hWhipvjFVsctvAw/BRRXMR+AdoC7jMuJlELiPGKfGg0HsNV+vwP4rEIL4hcQrxi2xC+AaADOrFBDu+23xil7YhXjFtYhfiaGK7YhcQsGlQDQ+AUADLjFvjFdsQuIV1gYdYCByCF8B0/CnwDAmrtFYrgNGDQ76+H1bH4aZZN/bbiFxi6xqhplmxC4z9+qVcBg1Y8Be/AcvFeN3bEYRXRGLbGL7GLcxnfFyuzFq7QaHfwF6GEIfEeM98YtsQrxCrFK+oDtxc5cixnb4ZRKxHjO2dUozabwCdgOmhHjO2I4NRbKXLYjxnTYz9jP7Gf7HdNiFxkhcbEeLQmkUWiFz2MkdsQuMl9nlxchctjF/xi7Yxf4NRbitsQuIXELiFJsYpCTYjeMw+yyRT+LxX7GYQehNiPFs+WLmL/Yjxnb2AaGhj+xCwanY/P4uKFG2IQvELn8/njni48UBjsfi44vNNiI/FzR2zozzsaDr88YXxCG51xXi0aj86waeFwkVlYvJmgE/EYV+FDKqqqqskzcRcR6ItRC4hcQuIXELiFxC/gqqqqqqpiLM0aKiFxC4hcQuIXELiF/G1UXF1UXUcZLGXrGSxl6dfn0+k8SJJ5PCTSvgPGAAAAEhkGaoCfAJ5gfKDUX6+6A7LX34PKkTwIH1X3gZeT3cDH+94/gnPWtT67B3fhQqqutVzrUd2vbX36/U9sMAJZHPP6Qc6r4+8v+EER/17eWMqS8uDTtBD2MVfYhcv/+MVexiOL8fjEb2MVLKq0WLaeBFgivu5F/giOqqvv17N8oR/Y/vLn7grz19G4f/sERaeWXGuGab7G+X0v8ISA6ul/24QUuV/aaBqMiBTFYXGLsYhcYq9hBUlLpd/jF5YxS5Xh3pcGHGYMJqBnUfr2mgMrAz0LrwidXxbiHDIyi8bVFSi2UQ45i5S4ZPAgUI7GY8puYhc7wZ3Ay5sfj/QKhD22/4hSEohcYusQuMUvrGLrX26ro3H/Dx3fu78ExFW3/7BHWvsYQ4QTrUs3H/hw0V773oLNs/2z++gPNXYvGfGlHUBYkqv2BrxCLiFxC53gz4O+4HFDCpjFNTsQuMV6xC4xXrwC96WAoesXgc/gc/kosXQEf0CWv8QSX9BGQQrg0/CKgPaHLRf224hcILWn/ELjFdsYr1jFe2MV28DLxil8Tfq/xdj8NMtTf221gJ52LUNMs0GtBADOvda/6+vGK7GM9MtQM694CZq54CB+BffgT14JeM3yZcCaK3/eAvBKufE4zmRkDW4GHvxC4xXbEK8YiWxi6oYhZex4D/q1CEZ/q/6OlUcuir7kGIloNDv3CqsYhTZELiFailLSMV28A9gGlUqjFtjFFdtf+AygM6seAVH4CleEJwsrQST22+MUvaxC1wNsGuAQADShDFiFeIXwJuxi2Nfq+OU9t9K94HEDCisa60X/gP34FXjESxjFuQacCf8tCFxC4heoGfGIljwJvGK9YzdMozuYjfgClAGjiFxiu2MRba78R4hcZ0yBp/2iPjO2MW5iFxi2xGfPSv+r4hcdpIL+CMDXjFxSYD/JW0r4hcZ2g0/COGMtRH+23xi6xC4hXjFtiFxiu2IXGLbGKK6RQhKIemXtt5RC+AI2A3qlUYrt1AJqtKohYNcAOjAGhV8QuIXELjFtiFeOV28BtANFWJKgNHVd4xS5WIJr1rGL7ELiFzvBoPfQV/ttzri1tnUNMtEKXxi2xi2MYtMohXjsNMqsZ3MXn8WfeKw0y0X+PzqGmWjnuYhXiFPSIXO8GmAFfgDPqxjO5iF+A4cQuIV58ucAbUAbdc2Mz5HMYvLPpRmK2xCvwCAAFBrYixeXDpiODQ7DRl4xhsuo2vAIyta/W5iN4onGL9iFz5cjO2My92IX4BT18fCCsYjxeTEy8J4jxEuIXGL6DQ74ty9s651z+LxWkUR4z2xayp4DQANmrVgWAK/wb4uW2dEL4hcX+zrnXF5e2IWDQ75gD//YIhr8fH4ziqCXELiPFxF2dCcWhgh7Yhc0Q//oEQ1+O2OjprER+Mmo/PM884Ovxcns8/gPoAQeiJ8QhOI8/Bp4XDCi41SUPRPS/rG//BUZVVVUXVV0aqIXELiFxC4hcQteCmqqqqqjjJmdOoQQyv7bcQsy/Q18QhyxC4hfwyFyeT0B3wu3Nf2A8YAAABKZBmsAnwCd+CINVqXv19jFX2MVfYQRRcYDG9e9/onfrzfr79fbUDLwRFWudvcEFffhc9VVVX15t/8TWvNivo5/H3Wn8FHVdVirtEfeWavL+jf/VPBn2CQVWsugDtV7EKsYrUbwEZRexiq2MRPZv8f6BaPYPmRFn0+5DRDX0O9tf8tH19gkKtV78KVVVWtVqtffr3UCX+r/AgIv8QS/0R/CSg18EQytS3gHqPiEXGKvsQuMWxjFuZv8P6BWJzXG0Ve1U23wKNCIuva+CQtalvA0AZQT1Va1y+wQnVcaqMfg26gSVm973jHFsyMovN3MW98WylxzGZvkyiJCZGLqDW4GdCCI6m3H/9EiFxi+xi8sYr1jEVxldusCC9d30BhJ3A5gkOtZUVfXghxmbTTYtRXb2BhxiOGBlqxC4hYNOP7ie6QQixC4hcQuMUV14BTfgJ+ryjCCwJXhxmOgFTWLwL9bvQGGuICg3d93d930i+hjYpc7wZ/3LiFxC4hcQsgxR3qbGqAe0h0AnNQxdGVHkhplvfu3QRw0yzD9tt4D6ForFXAZqMlW+DP/AGAAXURsQuMW51A09fXjFPlqwtRWPAQtbeG6v+qVRmSqZcDPVvCgGGxm6Khdq5BpwLOX/6xiJfELiFxCvGLtjFdvAIy/lR3rA+1rGKK0ijldH7/oQorXCeIJ6gINbQafl+79ESqMW5iFxi2xC4hXjFtr76AOwAzcYipseD+v6GLIroAUDRYur61ruA20Rihi2MQsGn+AKC1gEB1gKXQxXbGKK68AQCTX2CHqrqoASfV8YukWgGH8ASABjV9fPv6V8cvsYusYv2d4M/8ATmsQuIXEL4Cp2MktQj8B9Aac6pRnc+VHexi7GMXzKOXWIXO8Gf42RJFfwNavYhfBvV6qA0VfrxC/q/gUdDF9Q5b+AgwM6vjFtiFeM1aDQ74QUYa069tviFxC4hcQvxKvQ1Qwyz/A5q/gJEXjFdsYrthDr7bfGK7YzL6xi2wgj0y9tviFeIRC+MW0GuAHTgDT4xb4hcQuMXXcAmuMJPiVcAW0Ax8QuMUvbEL4HABo3xX6viCSZGKK2MYtzELBpwGjeAWir4xbfAaOPldf7cWrplCONNUy/txC4xEti1DWXbOuL3TKvgxRXzkIGmWjOzGdMozpXOivOrg08werWNYdLiCuM7Yzu+E8ZJcxHjF2+Aw6GS3xuTXMR/Ag/Bz4BIQLFCM2RC4hcQud4NBblxBfEeI/pUYqJUmRi2xC5/F5cbGKXufAKLiPwW1ljOAy8XV/AWAGVb4jxnKrELiFzvBpoAn9ClORiJ8X2M8uLinbPiuLxXWM+H6viPF+LwDdANFb43+zouLXWuAXmCVXxHjORM7waGKH/9kiELzrn8/i4wvbERusAnAB8YPcXx2CeLQ4vNNnj8XFezRD//BELfj5MXFS/OxeLjBXWIjc65/FzUfneDPwThIy6xeTNAJ+NIqi4uqqqxksZfEl8ar8+n2IXELiFxC4hcQuIX8PVUXVVVSYsZdETX59EuxC4hcQuIXELiFxC/hSqqqqqkxmbMWMujVRC34k5PHEGRtVXwItfX1wFVAAABFVBmuAnwCeYH5INdWBwrWMVX1ray1r6/Vv19Tz8EZ9V9r7MXaa+CSqzZ34Ita+YvEnlJTqGATHWta361v9HJ4/VNP0L6qtd52SrwbDEOJnG1GKrFYHysWMVTqwgji47v9L6N7wMNb+Wvb+gRl1Uqs0PxhAOGz11Btslwi+B+hAwgat0FVyxeMKzfgo6rVVHb8E9a1rYzeH/4Jex2EWyC/L7o74zp1FuXNz9EfGL/Bp5hla8AsD391zovfryL2vIva+8Cn1+uYxmDkfD8sbA0113ICI7vesfEgbtAElCpXve8XMM1NnZS7Ozifi5ZmTwIFCnxGbxCwaD2Egi5ytv+EEJAclcsIyKvTT4hcQuIXELjFe+MUVmXZuIQDw6CxcV94MleAzAM5Kr8te2oaAxj+MlBvUF+/Rf+EAEai3+BhxC4hcQhCg04Z7gZ0bsQuIXELiFeMUQ+/gT/QAhHQxFLzXjD01rRX6gcfhBWqotHfr68QsGneEY4D2mpX9tv4hXjF1iFxivahKhpluHdDEcLKpuwBBYGasBigZvAwgYP0Xk+1bwNCRfHgIjjSCA0y3YhYNODnqBh6+vr60Sur6+tbdfXdQMOv/0JbHb1rsBNVfGSUyjFtiFg0uD/qdEfqfqJV8QuMV2xC4xXbrxCPb7pe6n67FL8DLQxaZA1wBQwGnELiFxiJbEK8Yr2xC4xXt+ivW6/9VexiKekb4v5/lWP86IxiFcGvAvoiRRC4hcQuMV2xi2xivan/QxaRbgNGuOR0qjEeKu/Rny/X3jEW/zL/XAi6MxBpUBn9R3X1oiReBp/VrGK7UMWnxy28Dbxnc+PR0qhCWv7eNkSbeAgUAwavjFpFFqXGxi3g0r6hHEL91yUIXwGevjUR6EZ88WrHgZy4xdYzdNjFtiPGbplELiFzvBmd/QG9f4Q9vtt8YtsYu2IXEK8YtqEqGMtCGX6b7bfELjFdsYtsYrtjF14HIBsovqH4QXRb9vjES2IWDTgLvGKK3xC4hcQuIXGKX2xi2xC4xRW2MW3wGarUMJt8Bh2/8Qiv4DXxvdBqLcUdG1ELiFxC4xbYhcfhpll/bbhLt/t+AzbXx+IV5/F9sR4jeIw0y0Z2xCwa5gHCrziF+Gf1fFeMW2Ly42IXEZcjM+NiFxalxsZ3Yzug0uAIbxblw0F8R4gnF5cbEE4hcQvgE4Ayq+IfFz2xZDO3wCW4jx27Yvd3wCiqx+r0LR6VTrBpf4AoAAzNYBAeeJxccX2xH8Cqr50fFyF7Yzt4FgDbi5bZ1+AZHXAkw7i5acZ5c65/4BsYNPxbzz4hcXGCu2IQ3EefzrnfF4Ovo/PGF88uf8D0DtWMQhOfz+f8B0ACGcZ0in4NPBEGDcXOpln4KiKqqqqqro1UQhyUQuIXELiFxC4hfwUj1VVVVUmLGXRqohcQuIXELJ42qi4uqi6pJZMi06/Pp9QQQ4mP/bbiF/CwVJ5PD0sP6+874DxgAABHlBmwAnwCeeg1HPpSoyXQJy1rWpVeB4qSNVweV+xii/e6J7oI0Vmvte/BMdVVVX9+FNaqqqqqq1+vQpHb9e/Rnp4uKovQa+go7oA31FjmIVqMWxjEURoNVa18YjL7GI9+gL/sCTBFveLV0yKdz8EZVr97giqq+RQ24IVVbf6aenxZev/Xdfove6/x+aRoBP9uFFKS/009NPBr2CQZqvsQqxipMYzUomGEFmTLv82P8foFYvKSo8sJhZtNvGIj06VBftPtGuVzgoGqqqqqpV/on87jNUWzm98Izx2Z+98b5M9AIBoQ/wc47Scg1CEUI00sVYq9NPjF1iFxCkJRivWMRd8YoUFTJVaJ2bj/h0bve7vwZV2Bxr2YAfCAfyArF6eKeM0f8IwT1rWv/UDXpf3gWY+j948P538BAgjxCwa2ALKA19gIH2Agf6CHsQq8AgfL//0AQf5t4P6Es3X/S+y//6/R/ngjHq2IJpcEdInQaHfoCMkYiYxf8YvsYp8rGLaT1/Ivi+gyfGSl7dAfAGn1BAhP+gH6BIg3oBJ0MYxn7GLMmMV0yiOTi0d97V9wPPhEYv/0R/CJEV+gz6+kLRWMZumS9ag0O/QB66FP1SviFxi2xivWMW3pr+hiOFlUfjFL6bGIj2x25lY3JiZaEUdvDoGhf2MXXwmiNiFg1wBQAGvEK1EKlGK02MUZVsYusYr29nXugM4G1FY8B+1d4Iq8q4W4hetf9Arq8ojg1L//3AZ6IxiFxiu2MW+MUvbGLtiF8Bv/BH6AK0AzetXxhCjrGNhBHFHm/2+B9CGMRPvpEY+THIt8YtoNekZvAMBV/A81f3V8R/Ax/IuY+Bnxi68DClf2dX7gE1xmfEyj5y5TL+3wdvGSXPASPEeIXEcGnoMcv6CrGIXELjM+OfKr/J+rnwn1Aaqv1ovYze74HFXxizKoblxghHnUuRi0yjFsQahHDDLaHf9tvjF9jFtiFxCiuMW2EO32/CCiu322+MV2xiivdiF8A+ADBxiitsYtvByJxC4xbYhRXEeIXO8GZ38AXcAy1fGLSLgH4AYa8q4CDqxiCS+MW2MXWMW3wGGrUOJPneAfEDArYon5fAhjkV8YtsQvwGrZ3gzO+JkDTLRHi1tnleEFdf22/AaN8aiNQ6U+JX9WxC4iQvhCXTL9v4Dh+fELi93zq4NDv8B4ycJ16v8Kq+L7nwRq+MW3UA4uMk2EWpcbGd36/xGSkZ06jFpFGdoNdAxxndiCcQudc6E4tGdsWpcrEfwerF+r47kWYzticuRHiZfgEhRHxROIXELBqdi8/nlz/gCAADcoZ36vjPWL/vAIAAgVIvGZe5iPP53Fc8bi44vt8Dqr54vP5/PwarEAGBEAG7RUmv9f4hcWhHWdCcWhX3hABA53L4hi8/i4oVtni8XHF5psRH5/P4uItnQnOsGnhcMVqaSxl0An4vKlr8WSJLSS4hcQuIXELiFxC4hfwVVVVVVUmMzTqIXELYxDiY+IXEL+CkeqqqqqqujRUQhyxC4hYDygAABHRBmyAnwCdl/+dBpvtfV0vMmX/+sJ9/ovt+SCYq1rNjTSao4r/6/vUEWtXzfEP4gghFB/vjPsCQJxCfiHe2vEiE6dZPBn4JxTa1qv3gIDQxV79H94HzjERexiPq3gj4Itazj4Iaqv3o4vquqx7pZevzuWMW5u/YGWjsxiKbPQbi2FA48KkAg8A+VFKmIVYxbYxfYxdj6Rx2ZRi6sIYHROoX0AiXf9VYGWCGteY/BYdV1rqq++CWhbKF6YkvheiMzsbjJqL1ETEyMJJidA09BLsIEEhS0K23pp8YvsQuIXELhBCRn18plOP4xEDApg/4xEFY/+BH5uIQDw6C3d94iUW9CP0CUSq6r/x7hAugWa3/QyUGXzGLlplFuGBlrYhcZJbELBpUNgkCVa93A0oS/f34xRXfEL4CBaKMU+kT2u+lgVuYrvqgN++BJRWfotdMBs9at1AuL2IWDUIoYB7TUap22/iFeMXWIXGK9UIXXz41CQD2oOisYoPfBOboCjRO7AV/TgsgKzp/+CD9HtYxS+2NR6VA1wBhAGnwPfGId7ELjFesYuu4G9ekXc3gM4DTdQWoTehcpcIhtebV3A6oz4z8o6gN2DT8YiHJfxi6xiu2IXGLttL1bqq6V7GTiuBAVgMYBtovdgQcmz9aDjwZi3t9oKvjFVsQuMUV1iFxil7YhfA38IIXBLj7baacYjF9G8Yo6xbwESKVsdltNjJQ8yqC/dLyr34E9q2MW+MRNsYvoNcAUABnRGMYt8YtsQuOVm2m4DJj1bu8Yr0i8DCipF9WNcCfFq3wLuv1i9tYvpWOoGul+rFDFvBrgGAA20MRLYhe9XxH8DXXKrHyq9cf+r4zTTLWr0I8aily2MX2IWDS4BKdX3jJKZRC4hfgbVabAIABjRXK9U/gS/kq9CFxC4xZlYhT4DUIsgYDLUaFe23xC4hfnVjGe2MUvbELjO2EFdv2+MUvt+v/A80djCWMtCZe23+JU/whJt9vz/gEDAwq+IXES4jg1wBgADRRP4hcYtsYu+MV2xCv4HVXxi28BdAM9YvA/1ehC+Aizb7AXdHY8GPELiFxC4xaRRCwa3ANjnXFolsWoaZZ8Z6+BbWsX03gLvi/rFqGmWbEK/AcAHXF98X0yjMNMs54GcBr0LW+dc653g04DZVihHiFrjfY341XxuXGxeTps6lyM5kZRC4zFZFYtbZ0fP/AJyjNb/gzO+Ly47EeIJxC4tSa2IXELiZ8WjUyjMuWzri8uN8AouefFzO3wCkq+MJ1i56XOTjHtneDM755BX4BMVYsS+efP/AKDiFxcQXt8AzKti5bGd+oAm1WMZ6zouL47H51zrn87wZnfPPn8651xcRdnQnFoYIfbPG4uML2xEbn864uOdsXP1nicXER3Z/4Ahn6g18NBArKjVJS/khLfxtVVVVVVdV+Imvz6fYhcQuIXELiFxC4hfwUkVVVVVSZFo1UQuIXELiFxC34UqqqqqpiLiOsZMznR01/+CY44g2eSHdAiiUG+vrgKqAAAARUQZtAJ8AnfVYGzl/7tBbv15l6UqvAif0ZvAq68Ewuta19+CPqo7vwyVVVRinyPkf+CkiqqrWq/TjFvBqMQpGOOxCtRiq2MRSYO3gfuMRBf2MV79AX+q9Hy3huq/fgn1qq/vaXvnJWter9MHVKsClQlzFyGl/GLyg17Qj2MVWxi/GMUX9iFebH4f0CIuz5jGIz3/Rb9ie/1Or3Xx9oT3wX4uRsph1XjuEYmZGfv/q/gKSqRcBSg9xk20GnBmhjZvx/8FZd3AGWKsVemnxClpELiFIRjEd74xEGerGIr3qvf6N3YGf5aE93Avr2Mwf+GxbLtj4kMUxG0/e+fxclvglVsZJdBoX/9BSuwBMAGnu0L7sBd8QqxCvsBkAbcYiggzrKpL65dalboTWL3Rf9wmCStf/grX/hACirnhloTXyYpET1A6onYhYNPQUl1obFiFaiFxivfGK9YhXi1BW5apaA6pCf/D9BHCK6Kb/bjEUV2qoF1WxahplkiiFzvBn+T1/+hC9wNeIXGIUIfbGK1XgI/3A0+FfQCjq3QFOr4ycOstRkPG/H6eAl4xHOo7gSVt8DBi8nNs/Bp9CEMeIXGK7YxdeAdvdgee2v60V+gM/CBCny7/fx94iivQhCHiFg0/L/3eMW+MW3UDer9a+OnV8YofZZvAI21Y8AgFHYxiMHmRqwJ1X3/8Yj//RYq2IrvGTU3wEHBn+X6/REqjlZ93AZ6pFGK7ncDHjFtjFFdvpf4xdqf8lQLuMQ1Uir7Qh/1SqMW2IWDQ7DBv7QV/iFxC92r4xbYhegOgDR61aTAv8Z3PhlcqGS4sYvNny5CC0y/bxCwa4D4A20Mz5bGL7GdAPGSFy7rxm7CM2rY3GVRd+v8apccoZ3YtFJjngOYBkr1jMuJsQsGh3/VjGLf4HFWMYtzuATlEfGZaNjJbY5ERufA5q+Iz5E4w1jO2L6ZeBvX+MwxlmxEofZbwnVQCcq+M7QaC2HLfaDr4hf1fGE2xi3eAUgBqq+MW2IV+A3QkrGMW7wCUgYVbwLDxi0ijMvIrGLMrEL+v/tf+A5+IWDT0HGY9h2mX7bc651eLxRtiPGLTKLw0yzY5dYvtR1xUoRLoiHzri5Ns64xXbOudc6wa6Acat8+I/gM2zr8Bw4ztjMQ98Z2xazIy4CBAcPxdDOmxH8Ag6EpUhnTQai3p1Fyaxazpn86I/ASAHXGE2zqXIjxaKfmRl4BR/gacT+AgAK+fxfbP/BniFxCwa4A4AA2OM6RRC4hcYts8hfEeLl2zri5C9sRP+iPiFxct2dfgGhxC52fP5+DUW9Hc/i5KPzo+eML4iNxcVtni88YXxDG5/O+fz+Lji+2dD865/P5/O8GfgnDFajZQszpT8KVVVVVVmOI7iLiOdCLUQs/jaqqqqqYjOOMnEfxFr8LssnZcQuIXELYxBJU6iF/Co9a61RFr8+n2IXELiF/Od/g1dz4EXiIEj4Ej4cgAAAA+ZBm2AnwCd+cNO/EsIlhwFFWPWBSo95ngv4JK1anTVHAlF1qtb9H/go6rVRf+9AWHUsm+weV6DUewoaRSAjX61/jEX2IVYxV94EB0MX36+p4R6K3uCjVV1Uqe0vu4GmtAXvEgi8cK/RXz4yoNfQ5N4BhNCEc2L177Re8CrX36J79ewhKDhXhfgES7/+CWq6qq+/BQWtVqvIt2+7oe69/XwnxkTfEdDJmk7Br6Fezcf/0CLv7ELiFxC4xHLSsIKGBTB8d+mn6AYPsBr0SPm4hh/oLd+K4iKX9R2yfbfXghE1ri73Ves+1fFuf1nfFy2z4r+qMUDbiUM7CCqrTX8YvsQr8B119eAgtWAxaLr8Emte7voZ+BtRbeI/L5fwNOIUvjPUGvoR2IU2RCyP3kwvxiEvYx+ESdFGlft9QS+gktfCSP1YKNC0TUGtwNqEd3a936a4pcsYupRkpfpRqrgZ+oGdX60V8ZkqiovGdcqqBlrbxnqDXpCP4hViFfX1fXqv8BB/dCf4xGNW2M3TLgair1DCHc1jVCgra8E/Gd8XPcg1qBtxCED3uAYADOvYhfAXWhCiuv0TvAs0V6f69+vYxdfAz2KRcZ2xC4hYNK/AMAMoQuIWxC9QMaMx4CX7/RXxih5kRVQ5WoxXq9Dd03wOKL3yIiVRC/A9wZ8C18XiFxC0IXETPwMAGvGLq8AntH8eAVvi8uI38Nr4xZJcc+GVfwN734CR4jxHBod6GIhsf5VfEL8DXfAyq+M6ZRivXx+IloZvbwGgBZ8B0AWcR4zueAQcDbiFg1L/+r4hSZ4tX+XGdqEL8f+v8Zl9sZ2+1sYtfMovFdXxeEO32/FqX1iODXQHdXr1fGLbELjFe36viF8BwhKsAlNWxC+wOKpVGLfXdL/GLrwIKrDLxkofZZIgaC2CC+LQd8ynXOufxfbFqIe2fxe/s/Ql8Xu2d8WtMovJ7Z1xD4zplOudYNDv6Aa6kTELiFxC4zt8Bm/Hq9cBL/ARv6vjPG1wGrAiouWM6b4BOcdnxsQiFyM6RRC4hYNBbDlsZDpcbEE4hcYtsQrxaLbFoj1rgzj8Rl8Z3xfbFouvgtxHnxlfAKTnJzrnXF8iIgaC2CC7FwS0rnXOuL6ZTxBfGesZLbP58/i+bZ0XxYF3O+eYV+BRV9f543P5/Pwai3o/Oudc8Y8XGg6+PsXEXZ4nPFCji4uj8/i4wQ9sZPR+eZ4v2zobn8/4FkA/aHPQyPoqg08NBitUv6zf4Kqqqqqqq/GqQxBImPiFxC4hcQuIX8FQR1XVVXOhqohbGIIK+IW/CgRVVVaqq8maNVELiFxC/spPJ4DxgAAABEdBm4AnwCe9oMdS+198q9jFHPNjFX3WvqL4ua3YJzqqrU+1d+CeqrVVX2+sMFydaqvry8kfXRuq+il1VeiW1x3BvgEA4hf0M99L7oBBVi/X2IQ0a9wr7ApwRCr3KzPqpd+Co61VVWqqo7swf4YdhDMziHF6lzEGQwUuPK/dOhbJ/8FiE6V/V/QKlbe4ysfPlz2ifg1L9/oQVWIUXjFnViFxC4hFdUBdrFqpZFzH6+zQHgAfyBouo77mdRLipk6/uCOq/UL3hn6vjfMx0/JnPITIxRW5BqPY6+3f4QRQHpOWY+mnxCvELiFxi74xXsYxXv4IPUuMUGlqDKl7Aw1ix7EgPSDlhtLbf8a4EOnGJY3B/2xbm9dAZf6O+IXEaxCqDTgp7gbUMIvGKK8sQuMW+MV6xihRWvAQuq8YoP+b6SAxqNRe89f4QRQ0KYCcb3/9L/GMpejOX/9W+B5QlHcZMXtiFxCwafhFDgHtOWKumm238YtzEK8YusYorrGLrGK9UvhVCaqgRitfCa86hLO63+8fjTUyh/bnIV9QHCsf4YVixCwaHfuBrQp9PgCNFiF7gb+/wCG/ARfx+TAt4gREwa4Au7VSdfWhFurRXKEK5fV8bOTYFGv1/fF4pcQvUBhwaUAbV4hcQuMRsXVr/rxC+A0fgOWjsYSw0jNAM+23xiM9eBlo+sJZnN9tvjFPejfwM6/qoHFE/XorQbVSJyqIVqIV4xXtiFEPjFdpfV3690AIVqxYiWxXiFg09BpL4BcAMKCrFDPrEeMlPlMoxELjYhX4LavjOmU+Tfor9AXa2KE5ci8muYz+9ilfFqXGxi3xi2g141EfELiFxi2xCl8Z0yjCXrGLbwDI1Y8Bu74HXELjcuN4G8K+AkgONDFtiFLgNDv3AJn8Dqj8ijF1iFxCM8Z7YiXEbxeKyzL69jF1r/EfgGhq+8X9/rFeGXi57QaHfEPjES3gEAAZtYBAQMioxUYTbEL4CDAwqx4FsDDjFJleA2gM/wM/hUDOr+ASADqr4xeb4F3ELiFxC4xRnmg14C5RGPnrhRH4xUXhplqRRiJbFK8b6ZRf2xa/mL9sX3xf9i3uYtb4vtnRc6hplohcZ2g1F8Es2LWkU6+ARkO0MkpvD4DVXsZ2xC47emUTlzgZwG6r4jx0p8TKIRH8AnNC0fFnXxIBO4NdAE5RmxHiCcX2z5ciCcX2xfrPlyMy9sR+UGqK9cF+X+9VjfFoiVRHiOhc8qQaiXxHiPGSazo+LRPvgE7RHMRnyfxbn7s6E+ByAROIXF9MouYvfOz5/F9H50fPwaC2Cq+LYK4+zrnXP4uaOx+LiC+s6H5/FxztnnxfdiiT+fzshfEIbi4i2dCc/i+Ps6waeCcMGXmXUwZZml/DtVVVVViXyNV+F2WTsuIXELiFl8FOtVVVUxHRqohcQuIXELQxBImJ1EL+CkI6qqqqSWdDVRCwHpAAAARKQZugL8AnfgkD1a9r5Uf2MVfFBBRTGAoc9NPyl/+1ivwRHVVXpfX8HHoV7GISFfnMYhX2MUXHYyoxUuxiqdX6+8CfR7/onZvwjHCClrv2uD3nAoBMBIVyaTe38E1V1r8/XkROCzpAuo+kDUexRpAHAR7/wENQn1+iexii/sQvgEB16+whKDh/CtoCpl1u//BCVa/e4J61VV+x7ja59a/F6wFTj8Xc+/iZwzTaGvS/on8bkx0GuB3oV2bj/+gRd7YhcIL623/GLrGKjvVApo7dBKTe9r/nqO34KC1rWva0sENV+/RP4t7MWzh9TKxcsvM65/CChjLL7beDTiUFuxCEmyIXwHvX2MRSEHvASNfeBx1mSv2A0a669q9rLxQF9Fr8Ela//Fq+MU26b3XjuMZbPgJGhBMGoRjAPaajFme238YrvjEJDrLViFxiEPWMRRDzeTpb9a/7AaNFtjcJpot+v/gcfgXawmCqhKEwa3Az4hcQhR7jER74hT5GI71jJhR5yVpfjJwjs5RvF5dMjLxlc/yq+M6ZRHBpUD74AujjEb2MQnHxBLxBCCuMUvrGK9MtwMvfiFPT4I61vfAzrljJbdwGqi93aJ3wjQhYNPQW7oBAFQT7MMf/6JqBr61fqRXOtF/jEQV2xivW0uSvX9DYkaTIAWtjoKVbwRhlGbGIuv18fIr4xbYhc7wZ+gw+MQV+xiLfGIlsYprbrX+uojuAy1rv7tWMYihwZSdxiJt2AgQGb2AMgANL3rXg0dYGk2uFvEI+IWDXgb0ItiFxiLbwGABlxHdwuCPqvdx6v0AOBatpr+4V7+9axf9WFeIXGZe7Ez9wC1waF/+0KaXi1fw7rARATxi8mUZu2M3c7+g+A4Ed8YTTJDOaxBOMRi42MW+/4NNGXvte+fEI+MRLYhS/gZSrYrAs8XhhlnxcqploAcDVGKXrdvgWPGdjF6plH9H/b8RLiFg100If9XxK/AuoveAia94OeMW1YN/gvq2MW/jf2r9wHWr+NE9APgA0OIXXAaN4hcQsGh3zsud8R50R4tXTKIV4vv4GVK+IXGIhfbELi0JDTLNR5ifgEHefP0Ll1nUNMtELBod/QDLxC47PjYhcZJb4DRV8Z28BA1f4bVz6Rx2/RJ8XLc+GUZ99avi5cTLwN6IRMZzpiPPH5+DQ74yOLmmUZ9Yz6xn1+vPQzuxc9Kp3xcxcqhXi5dImJ8/8AqKsfqxjO6DQ74ti5ZM651+BlxcVK2dC8/n/gE1Qx9/r/F+s6P4AoMMI1Z/PG4uMdv1fOi5yc6waHfMAf/7BEJfo/ELiFxC5/FxF2eJ1wI8CChT52LxXnJxcV98DDnYvP+EwFuhj50fOz5/PwaeGgwq/h9Y7NfBL1XVedEGIJLzqIXELjF1+HQiqqqqqpL86/Pp9LgJHeEp/Ovx+WvloYggv8Qv4WCpPHEGg9/nPnhH+wIN8CR8CR8EEAAAEUEGbwC/AJ36D0q5EX3jGra+sntqBEgm9eBEr2MUX4/wN6X35y+5On94fkgu1rWo7H/ghrWVGSHuENVMJVrWv6BGfVeuhxgU1VVWtV36CMEmtX35oJKryxiMTK/P1O+88LwaYCESEe/RSqxir6xiIvvtW6+wIsERb3Ijq/BBX5+i8yl/d8Feta1WuL5gRa1KmL4rBWvr/9FKooWyEU9nzSjlvBqMiIM5PiQR9VKrCCr7+23GKSmWMWxiFqgzRe01g2m3vf4IarlT9e7MBpBCV7xmO12v36v9Ip1+BxWL55/gR1cxyJYg1CBBAYMsim23pp8QuIXELjFcyYxd6rEFVbrd9e7wIPNDgH/QKy7isGaLb3r+c4IdU/YRwIdOMARb3+L30znc3iSFxfbEMuIUMZaI4NbgYcOIcQkmnpp/+rbf8QuMW/gNWveAg+X/9H9472B57a/swGsvd1UDP1r/GSl8J4TAQ69is/i118DTiEWDQ796HRd+MU9xfr68F/wt7ATPCMoFa6CA7bf6A3+gPfCM4ELTjGVv/rX/giBFi0UNMs3wMaI69gYYNbARA9CO7j17ul7Gel8E9exi6rBFkoD0Bpxcpcou8fVq9CedRfSCDc79RXWhlYxCRXGxiEPnrA8/ATeximrqhkSE/oYP1fL9/otYySVKE5MiCYNcAQABrRCJQhcIerbf/AN4BprAQv9f4zVugDi+gWNa+WTaQm/6M+Ml1iFxi2g1wCAhJSKMQpaRC4hcQv0jcdwgQTt2/8AkdEbGK9dAOkiI36kSTYIFfXUFqK/gW+O6CfwF2tfAXed4MzvfP8SiP3AaOMQnuxC1UMoz165SC8uJsZ3UIWhcTig150KfGS2HjVc+XwH+sZKfJKfoj/r/4UVvAgper1KvGE3MV9AFAAMv155fgSkR8QTiFg1wgBhV8Z6RRC4hf1/1AcOIU+fX9YPCosfwUtGfF9vgZUV8X7davjO2MW+Lltn8/8BtwaDWYXo0qMWx8DWv8Yu2IXGLbGKK7YhdYEsnjFL+oZyIijO3gewM+MW3wGSr3UA0PWrEGuAgAGqtsQvtLyqdcWoaZasUpqz+ff6OlUQuuBXh1Wzr4HQDnjIgvTYhlxfbOi51z/xSI8Gpf/1Yxn7GesZ2z6xEuMWxjEYuJlEfgQdCP4X8H9WMXPRd8AnP6MxRfu/zzwai3uZ1z+eJz+LjC9vgFHxctysozO/gLUPInYueWXFrTL2hD4uelxfbP/aIVMQTBqdjc/n8/1AED9aGP+isZ/4GnExP6GPnlxfb2BnROxb2zo/wDY4jzz5+DU75/4B6fgJxUdz+fzrnjnj2Ppf1rFxxemzofnXOxj3gQgNMDTi5afP5/PLi5Os6EwaeCcMVN5UxHFTFv4UqqqqqskzWMmZzoaqIXELiFl8EkmJZ0NVEIJLELiFxC4hcQteFAiqqqqqjjL5MWMu+IWDuvr64m+68Qg/AVUAAABCFBm+ArwCel/+0Fvmv1irNVz6UqpHh+Iiy1rqvwprVaqqquq9+COqrbDbhTT3v97xK+tfXo7HU1aWkuU1VwbeggVWMVe/XsYov7wcV9+vvAQtWusEZ9VGIY+CyqqtVVVVffgj1r2PcA7WZwFf6+qgmR38K8Yqufq8G/aGexikw6jEKvtfYhViFSv9e+0X3YGHtrwSa1LvwprVa1VVWv32i+x7gNzM4Ct7H64GTL/Akf9oufPk6G5c1Bp6CBdjEcMGW9jF9jF5YxbGEF1t9PjFe+MRRDQbX2AkYiq6761fa+Ivfu/wQ1X9j3Apx0DSu23/CGAe9GcW9/v/ESk8/4F3jGeexChjLRCwacWgtKuRCfYhTUiFfge+IXwGn7An+tW2vr2EEcEiutDde/9oDT9r3hmus8uLJ28fWugGyB1XsYt8QsGn4QQgDGpY6+238QuIXEK8Yr1jFe/4s677voBV0aqdYEH3WLnDTLWEW4aZauwGT+B1Ws7K6p1Z0CMYr4xbQaLg3+4GvvQxjuu/sHdFjF0GK9MOlc7IHhLXAu/gZD+BT50Jz8GuAKAAzodFjF9iEU+bAW9e7Aw/Az0R2I+gH+ToBVOXgY5DxZc8Bk4jg09BiWX/9BHsQi4QRcVv/rxCEPqBnRexi6pcIOtvtX/X+MxW3QBMa1jO6uPR+xj27A0AGpVjvRKxi/QapsAUAAtYLV7ELiFxC45T0rwBHNXqgFzX9eifxnu+Bfoci02dFT4Hn3+BpAJwvj9exCwa4B/gN+IXELiEIeM+WM7eDWi+MXlymVfrHxeXM5Z1LkZ6ZeIoZ/mM9dwDIq+I4NDvtJAXfGKTHxncxmfHMYrpFGKXLYxT41jO2M6ZReRtMuBhA23xdev/aV8Z00Ggthq7zAc0G2qoDT+BnV64lFY1wPv4Py4yZ2xavXgIMDTjPdi92sZPr2lfGe2LWVM/BqLcUdsR2IXGI1tf+BxAzor0MXHoYi68EL6AZ4HHrxfYzr1ANL1+PAasGtBAAoKENjFv4CHMiviJcZNbPk8X0yiN+BgAz4zlbE6fARAHPOz4jxk1zF4rdiEfFxNIlyfAYcGgtipkdT+dfgOG8N6G8pZ1Ln1fP/AcKI/gSUr4ifPE9QBEOI8XGE5zZ0Nx7BRX3vBoNYdJjYwnWIJzy55C+MyY2fs6PiJ/A60Ofwa86P8ApKM+MW2eJzo+IjC/wCo53g0Fs8ffAJjiPEeMkpFPF/AjoY+dlzxOf6AETgGxxcVbPF/Auod/4FnPCOf7gDV0O/jO2eNzvBoaIf/2CKvR+fz+fz+Lji81i57Z4nP4uOL02Ij8754x6wOAG2Gc8bn/gCAbP53g08EQa1V5BiCBMfELiFrwUhFVVVVVVLfELNgJvrwE1V8QuIXEL+CEpP9PAjX4hBuAqoAAAD5UGaACfAJ34XDTat27dYjCIw/2vvtFb9e8LVaUv/2iuWX/7Rer0V+oYBFVV9pYchFdituDYWwpKY+kX36+ximxsYjhT4aKGUxCEL3R/eP+eCWq9axvwQlWvcfClVVVVa1qtevpF99KwINUuPBp6CRd9o/eAhqt9qVWMVcvtffr7wQ1bXwki+rpe/XvdF9j3LmWtfXB0v6GT0tHkDj0QsGlQQocXYQRwzlirLbf8QuEFt9NPiFxC4xXv9r6rAx17CChOEdAMlLp//BUdV1XVVUdvPUdvPXsW6vnfGouvF8awjPYjz8GlwL/eg1fvW/dK/cq38B3te8Br/AlpF76W/WCOq8WkojXf5V92YDf0IA74ycHvsIXZwxTK1+tfZn/A1/Ao6GsloNbzDN3jVAe05aLGLq+lrwY0LqsMJYsIxAFa6Bv9tv9AaSdCEQr36/1/8C77r/4IMYxNsQoaZaI4NcAYABnxiHL2IWsCuqGK9eG9Y+i6koDLQn/oDGvfAkq+LcuJ1wMYGdXoWiypBrUDCh1YhT0iFeIWhC+Al6LFdgFd6v6FxJmwOmeQuEpgE3g0r8AQBy/F/4xEV+oG3CHq23/GKX1ruF/Ff0f9eveD4DD4CBAuV6vWndcGn/aNFjFsYxbmMXWMV2+HF7w36CIFlHiVV4Dnav4KeLRA0y1Y72+BdV/YpH8VYDUANP3/q/UIYhYNDv4BgdClxX1AQPgIF3gxoRXyr94forHwMqI+Ml3Pi/BjV+9XMR43LjdQriPO8GnolyUd8mSwK7rATdHdjM+GW+lf4RV+oC9VOtQ2rner4wm2f6SUqYztBrx6Ix8erGIWsHA3GPdjN68BtAZ8dzJQiLxqNbwMISROobPT4tEds+K4z1i8vbEcGh3+RWP1fEKcjWB9wqr2Ml0VYgnoAgIDR2vJjFpFqATmqgNv4DR8RXqqAaHELnfuAJdg02MV8Z0yiPEeIy+PWmX9udQ0y0ZJbwCAgNBFexkj5Y38y4N+J8R0efPE4hYNRbFUbU/n/gNmhmOrf4DRUq/gNnF9sZuRG91/WAat4j8DSA+VvjJ5WPzy5/P5+DUWxxctiCcQufoZ+xftn8XlxsZnyvAO0ATnPL8Favv+jsXn8/1AFA4hYNRrGWxH8Am69jPSKefFrTZ/FzCtsXEx9Qn/Qh7EIbn/gGZQx/1fPG5+DU75/OuL5LP5ycXGCHtnnzsxfGRsFMWZ/P0fz+fzvn8XEXZ0Jg08LhhtalQzs/JCWvw7VVVVVRxl86Os+n3Pp9jEEr4hcQsghV+gidHSGIIK+IXELiFxC4hfwRhGOMvxqohb85V8fub04DxgAAAQsQZogL8AnnaC3dWjNIupQRHVfN9I3vwQnVf3i+/wRa19+CEqkXXa19Ahqqr36PXqCWq6rzn5yd/NptXq8G2A/khHYhcQjKhii/sYsaq/RR3dAb9PyV76BGVawQ6n5aN2LnE/rrBJ1XtJcGCK58Fi1v9exEhuDXh/wC4NEi8BAvwEA0TsYtsQqxCvrX3ej5ZPp7/whg0LqFbQCCXW//hStVVVVa1VVHd6qVP17O+fJ+GQx8FqElX8BBL/G5ddjO6DRfgkCVauwhOA9XLClrbemnxi3xC4hcIK97bf8Ypeb4xSEn6+9dZPdP/BFVfLNDGH/ICs+KxWRc/Ny9n/w+E6d+qxrgPaNMrFu3KmkBBgn+BxAy52Lr0Qq4NfBAaTPVdVHHT6ff4hWcQuMRteAWoDLiF8BW0V+vrXW05PvRYux1Sp1A49gYfWv87PR/O+dYNBbC2+EEPAcmRywi0VbbemnELjER6xC4xXrGLvWBE+DDW5DveqF+gT8X2zuK/otqPvPuDSoGHuBzxCIsQjCuMV6xi7+B56+RYqwW8ZiHlF2AQf19Ki5YvS0y8DPi5S5d1AVOIXEKK4hYNaAHAAM9EliFNSIRHjFNlYxFL6v16UWylz1jeduvFPi/bOQsGh36lQrsYo80djEWRMQvUDavVyr3gNIDT4HWCG79YzDzJGCGZyRkkHSnx2KJeLW2Ly+u2AaGDWgECZGrGL7CCFyJTT/pKIhm8lEY6AJWI6Ar+gKS18Mr/WBR69Q3L0g8IUT+M9vA0gX8Qi4zuxCwa1CaNFjFvjPWMle3gZfgJfWDYIK94H4DOvUNUuW6gOvF+2dFLnloXyoqi+mQNaAYvGRF8YtsYusXKXGk5aGbt0AJ3eMz4ZMZl6ZxHYzv8Bo4hc7waC3FczGcXb4GuhmWjYz53gEEAw9QPqsdAUasdfgKJL0mAyfgagNKK5i+x8XiPGSag14GvuA3VevV/A/kV8Yv3xat+rVwMSv4FH4Fv4G+qRRC4jL/ovKoz0r3AEwqx3Fq3Z8Ggtgg91Aa+dB3FrbFrf5cZ98X458mI8ZKXtiOng19cBor/GesQuL5kTOuI4NDv6AbNivF9Mp1LkR5/F9MuB/Aau8LfEeN7Yvp3F62MX26AIWAbHELiF9gEtg1FvTKcnOvwGjiPP4vti+7x5FuWLe2L7mLxXWdF7gC7c/nnz+fg14BNUMYoR50Lz+Lmt8AnOIXFyF5bFvNZ0LzvnYoVzsT8A0KFPR4/PGF+4A1lXxCwanY3Oufz+LiLs8TnjFnjcXGWzy4vt4B++fxbMXtnQ/P5/4BsVR3P5+DTwRBitf/gpqqrqscZfjVEGIJL1jFJnOvh4IqqrqqjjL/59PufW4hbwH9kL+B/gPzr1DEEFfEL+GwqHMn194/vB9f4BAvwhiEG8QuIWBD+BI+COAAAAD4UGaQCvAJ5geOMQYXr3X0hf+6R+3hGEv4JKr9+C6qqq1X1cyKar1/bwQsHkSWta17oZ0GuBs16J7GKvv19K8NwRNF+ov+uC2qqq1+3xfS/kfgQBfnHov4Ne0K9jJQ7lhWxaohfnRO+194H2vdAGBa5jwnRyqy//gu1qtft4Rggr73X2PcC5zOCJIq1/O6EvBGJVVX/6lSQZysrBr6GdIMVfeDfm4/D9Ai7vfoDf6DFl3e//cEhVVVHdj3BFzqAo+23/COAOXS4w+3v8Xm+WLduv0V+gETxkbR3ELn4NV8JoLNjEVfa+PVsQtYLKN3QPfX19gXa7OwNgGVCc+2tgaV1v4Gehef1i2X3gcRuefP5/HsNBimJ/veDQIxID2nLDFl7bfxi3xivcxC4xRXfCCvX+mqwRnVfuvrXXT9Li8Iroqhb2xbPrWxn4aAgqVdev4NbA2Aa0MY70cyjvRGMYr14DJVCFtK/jHLzJVwGaixUMy9yu0XvpF/0BpAY+dC4NODHqK6A28QuOQWLS/yovYhfA65MDj8E9evAy0W8mJBWr4jg0/oBADcQjPELjF19L3QG4DaixYxQ+y1eTQxSV1vsC2+gH2sV/A2rfGuX+xktOkI86l/gM+DQ7Cye4DNQRviFeIXGEisideMUvrEIXjJhXW8Cj7wPAHP5lY/V8b43h4DPipT5G+0G3oPFvAMABlQZfGLbGIv/UqJ0nAz9ACb61jOmXgY0WqHSU1YCAAz517gCBauARODU7/r/ELjO3gJA2IWuSUXlx8Rul7r/wP+Dkv/69iFxCE9QGiiRfq/gIH9r2Od6/XP0AUGr4Qlr7fnmFegBOPwJJVfF9rPE4vptf53g0eGgMOjMfa+Orxi+8BkiK6V/A41eSrX+MngEXgQALvx+JXuAJt+A4URihCwai2S2L+2dc64jz6cXOGmWbEf0jfv1fsAKrrOuMW2LlFdYiXPM/gc1diHx7Dn6954Xg0FsEIo7mM5VmImLkR4zt8Bk4uWRXwOqJF0LAc+LnpR0AN55/FslMojWIjfgFBv0ISqdYNDvi2YuWxn2xZPs5OLW1CJcXyJi+zwJIDRzv4B+VjP2di8XHO36K+L/Yvp1P5yYNDvi3ti3l2fxfb4HHP54vFxVv1fFs9L8BZ0dFxcYXpsZ6zxfwDU5/P5+DQ7495Zta3vO+f+1Sqdc/i5rZ/Oi4uS7PE0djHi40HX0mcnP53z+Lji9NiEPg18E4aVaxxl/yjEECYnUQtegijVRCCCrAdVXxCyeFwjWpiLiPk62z7n0+xC37KTxxBg4EINzUANT9dcMfEfEXX1wIXwJHwQQAAA9RBmmAnwCeYCAJ8iCzSr5ULKlr6BWStdVXUUzKvlBH2mqk8EgnU2I7+GOo97XeXeX82m1r5YNbwRBate6AIMWvR+/XvVfYxV9jET47Az0Jbb8oIe7iifeF6SzdWCrWq16r79fY9mOpXr64KEPr4LlYrCQMMbMHfdBtUSCg2q6qVXzorfOiHTCCqkWmn8QuIXwFi+tFv3qRe/wQ6178EetfULcJFnCfF4vlfDyLFjM3/ni86waC2GLsITgek1MiptvTT4xVrELhBdKm2/4hXWBSXWhPbT9EizcS/h0Fu/FcQRRLv16j/6E/+Dhf58nnhHPwacL9oDH+g52IVYhfAS6uvr1fHghLVV18Si++dR2+BbxfgxT4yXXgcwIK1i2LpM8UXxHBoLYUe/zoX2EVAe05Zf004hXfrVVgjKq/deNRAiI6BV/nMdV5yQj9TPdezy1igP9HZYNbAQQG3v7AQNBB7wELRa6AJ2q9a7AcMEfVYvAvjOgK+TAi0dnwECrlcD3iODQ7Cj7tDfYhViFPnnR/VhD1OUi1lFylzL4tcrGz2oZ0zR1g1J6SVf4hcQvQBpgNvXVUp08bQjsRl8Zl96wOPGTu3xK9jf4aYBQVcI655c8TBqMQQNj4hcYilysQv6I1CEUPMiwM/GIxe2M3zeBgAu3QUEWJQnqAZvEKH2WBrgGAA0/oR2MXWIXEKXxi6xiu3xPgY9UAZujtiOsD8B1V/ArasDgAa/4DKzrnWDUW5cMqhC4hfgaUasQiivQGABn9eLy42IXoBete6pW/V8b2z5c7EyCuDQ7+AgRa3xn1jN9WIXrXsdOHctWMmL2xizXgFC42XFQlwg9Eb7Z5bFokiZ4uDQ7/oY/wLauYztYxbdAGBWO6RcCqBhXzwCW1dWATHjFPRsYuuoBmVejs+dc6+cAnMGh3zszxau2dXilzq8XvnMXyKMYi7mIRH+qehsvXwGjQnxDv4BQVYg40A10aLEeM9MoxbYpFPkR4hS54DVxkhcbFqTmTGS3OgAhS6E/wCc0dnxclzOudf0CD8GmgCIoP5ScCsrGMkuZ5fkUq85OM6ZcAQC11R3o7Pi5LZ/P/ANDiCf0CD8Gg9gp82tb3n9dwV0LYi7PLniHi/bPLi2Yvqz+dC8XJbPE5+j+f/QKPwaYUAMCgQsYh88+IXF9Jnii+Ii874uKL2xDG5+uE8/nJz/x+fz+f/QKPwZ+FwjVtZM5AJ+seWv1ydRC4hcQuIWTwU6qqqscZLGX56GIJXxC4hcQuMW2IXXwDXII/xC35yuiWH8HB0G5VwhxN/fB/8CR8EcAAAA+ZBmoAnwCd9oNffo7eqOVPA0JCk6hBY7umnttr0cqUX/9Sq/D2tVrye33WrL/3osVeQqqq/BDVVXr4pZj9eg2GIYbBih+MR51enjEIX2MVQU7GKqRL0XvPvzdVj3Xta/4LF1+jlSsVx+Lx/14vpc6OXAacL/CSCHsZjXgDimhir2MX7GKlljFelXdF96lqvCGDAL4Ay4BEut//BPVaqqr3x4KOq6r1C8X/fix6rVVVUJkXZwPMGn4QjgxltKm29NPjF3xC4hcIK6tt/xiwX+MUVv43QxFBpaiCol/Fgp1aqutfvwS1XVfvi17Fub66BXRfvgXC1rv+vQTR3ELBr6C3vAelE9jFX2MW+MXXgLvl/ATv0VjwI/X65bbAScCNWKTLXvDPPPiFxc9M54vPwahJBgDG5Y6u238QqBwghZiT9BILTTiEMeMRy8fKoxS80vgj9Az+PrXX1q8h5Sszv4GHR2Jz8GtwNOIQo+RiNrEK/AbvEKqrRSpVfQK/Qar2/LxGbYt31nnD6mfXsZPShP+A0QGSjdBrehXd69jFai/Xqf9Y6veGeNlEdgrDHEYaUzwmvdQF2j/P1/+v/gNFUyiHxPBoLYWmuvEII9QM63xCitDFxeAl/gda1l//8XoUT4F0JIrPAkhJXxsu5QvlZFFIp/Fyaz8GtUiNjFv3AaKIRMYorrGZeavDnHK9SjIkvoqxK9sA4qlWNgaw58Bo0IQuDXAKkBloZHGwosQuMRHrEKXxH4Hd+BL2MlV/b8BuAM1Yv17GemROA0YNedEfEeI8Z68BAAa/B2cEh1XZ0BgAZ+vBPUqUMk94GX4HNLWM8Ver4uJxizk51g1yAYfh+xkmLwPfEev0Ts/+i/8Db/Uq+oDhX+LXXgf6vQlf1/jPbEcGuzr3w8vYxbYzv1AatCF+l/r7V8YpNbX62eWvGK4DkAaOIcvjO3TANDjF2oQuIWDUW71nV51edX4CRq+MlzYyS/UBsqkUXvnYt6bX+I3n864v2zy/A4ojYx9Z0NzrBod/gNHGRFjGLcxC5/XAbeiPjs+N1r/G8zqEeKz5WAIB34EDQhaOxufz8Ggtgo16oOtjntiPEeMz5X6vjuVuoOVfO+fzxMgyIyoWxNL8A0NHJg1HsscGfWr3xcVrP4v1i3L02dcX7Z18AmNXz+fL2LZNZ0Pzri5qPzz5/Pwai2aPs8ufzoU8XGg6+kxcVNNni8/i4i7P3hviWJz+fzvn8/n4NPC4WGqTqXfWIwt/8b1XVSZOOMv//j8t5k+AZHrhMAyE5O/nMn2IXELiFxCyeFKqqqqqq6rvQxBBXxC/gmC5PJ48UvvAeMAAAA6BBmqAvwCe+gt68G1Yv1mxihOiOsugOKp4mavvzl87m1Yv9F6r8EOtffCG/w2eX9z+0bVWCgmqrW/0CEqqvToG3oLS/RzqoYq94HypEoYtMl6L79eZXhGoJNVUd34I61X2vsE1VXWvq4ay/4n1UFiE8iQvGkr8G/aGd4H6vpfRfavAuhYEPVcx7qdTL9/gmqutbfYIqrX2LZL1TApIb47hhSr/U6/AgNUqjOVv16DX0M9iFWIXEL4H2vsYu/QBH/2j+7YXVzNx/w6C3e8UcGBaJPNDwD/ICvxXHn66fj3CCdTCz2/8IuAOXS4xLWv9BNfboFsM/gk+BYo7CedYNPBIHq1LvhNCYvA/1iw4ij3k09NP/1aa/iFd2AaX0Xpr8YiA0jURUuT20wLMMiN8ntpipPo8x+o7dgZVicvi9cqwPoR8CDzsIwa3AnoLPjFKSviFoQrq/rRZ3X0FeT00v/pcfx9vf4red8XzO8DDzsTn88XBpf3oMP3aPXeiP9L3gTfk0lwJ3rX2LlB7wy6sC79AXewG38Db8J0cli/SZ3L4yNpM/Brc+MQ62IXGIzv4EHQhaoDfWKRf+BkAw1uj1iWXF42rYvmTOTBrQBAAMq9jEI1iFq4G+u16JEMX0AT3iydvgPHOuG2GgxTFr9a/d97waVoIlTEo4aZbUDKjFTEL4N+MRxD8vAz17oAQmTwLdGevR+sZl+sR47PjYzkq4Yg1uAh/AMBxC4xFpVFoxcrwC5AZUTLELjFe9jFL66AKC/A517FspdrE+L7fAaKsg4O9DJOsR/Kp0xHjJxD68Bc4jgLmDpc8C74BaclQGn1rXgLfQyRODFzzio/i8MDLJPhNEqhM+eJz+fg10LQzsYusZJ1JyYxHxeCv4EcIYgnwGjVsUp81AM2iNewONCEXELBrQD2AJrjO36I9YC5+AueeP+BfxC/HoZ5iH+CBf0v1/iHoS9iHg1rXsZ6dIXG0V5+hH+hR14t7+B9qyzxedfYHnEPnjC5Ey9QBPOfg10AQnPE4hfgLHEefxaFWxa38MgzXpD/QA8cIKxnZfgGl7gDO0RjOTni8/+gUOgzFv749B3qGL7Fy0uLXli+7PP0AckA0PgUefrAu63VJ4B8eIfFslJnnxcRdn4NTsTnXP4vj7FxF2eJzxgh8RLT/s/0AIZ2eJz+L6TPwa+gwdHRQggkv/7bcQuIXW4AhoGGIXELiFr7L/gHw8CL8n1XAhffwIvwQQAAADiEGawC/AeoxB76BJ5bsCNQuK/X1L6Xj/wT1VVVfyqhAp9VJ69BwMQo2e6AQlEcxi/frEy+vYQQlKv9L6IVP16vBJWq9ZPVYEz/oX/R48gxbQa4CSoV7GavjF5jGLyxi2N8+vsYu+ucDKHkXL9fYQwcL8DLgPmXf/de1+61oXmzvgmMetflrWvXsc81nQRx7BIGKYn+94NC/96F9Y5S37CCpLb/jFDAyr9AIWi90Cyr9a3/BJrU6n4I619XBLi8310Cv8DBQyLviPPG53g09Bj2MUX9iF8D7XvAmurAW/9X7Cf0l7sDKauBxR6oXk+Xui94F3R4/PwaO+BZwggwUl3bb+MX2MV6xiLfGKXjfqnqvCOERdAB/tt/vBDWv2LcI/Uxk616j/QHPIeE87waXAxoLMYhe4Gla7+9b0l6RcxI6fN1WLxaICXH88J5/P53g0vQWbGItzukRsQs+Dyj90AwYIru+8x4Rz+fzvBpgCAAMuXwPe/QxuqRnOte8H9e8DjxivfwW/A91qrAKjRa6dU7eTjZZB8754vFxhemxHBod6rUqYhevoDfqgC7/VEqhmFlaxm9WNnpDP4z28AgPwLOjvneDTAMBQqsR4QQuufW/4Dg9aN03Az/GYyV68BkusBYANGhcu5n8653g0wDAAdcQuIXXD8d1oV3QDuA4oSdPAXdEi/XpOfEEnyI/YDJqgVAGxRaxD4jxHneDSwC4AE1xC9wCarFjJhXliJX9rqhC9ADCaxUI/kV87G+Gv6GWxcTKzrgFNoR2Ll1n8/neDTMBpsQvQEcBo4hcZLIXgGzoR3694LRHgEpFdADEQhQ3pvCBcQvUAQH14hcQsGovenz7z7z7x0l2LldK50UNMshC54nP0dfB6NQxsZ3zvn/gEpo6G/iQTE8nk8ng0SQCIAJzAg4hAnxiFFysQTjFNteARsDziFkwDUvFvTqIyYi+H/Wr0LlmnGefP5/HsE1fO+d8GlMFaDddUr558/4BGgMvwMuefP2dfAMjQis5OOxtWoR54nP/ANDQ9gmv97waC2H5bP/AKSiMWeNz/6GdXoqfxfwr6ACEPF+s/nic/1AGk53z+d4M+ATHoBIACCevOufz+fz+fxfFln8/1fQIOLivZ2Lz/gCEedc/n87wZr/X+IWfwWBjVUlxxkzPzoQuP1/8dfnLznD7lZ4Ti8HIhBnELF/AkfAkfDkAAAANMQZrgL8B81wIXaD036ElWXeCLWpZpnnml6r8EZdVjpVzUQSq/VkWsHGAhN+hnv19W6+8bXN+Xqt4dklLqvwR619XaJ1WLgjOtf/wUFtVaaVc1Xgjqqr0G+A9OIQg2L0TxjFXNiFy/d+cvIvzdsnr7HuLQslWv/r74Ml1VQIyFxIq+Bcg48Eo5V1X9hBWlbb/iFxC4hcYu/YBG/YCmiDquu/bUqs2/j+GO93ijvwJSAQEVYq9NPjyQBf0scap/dSfye3kAwnml6/ROrwRnWvUglBODbAR0FAY1XVexCs4hcQvgM3VdUBjWOJYou4GNFTqT11zdao7m+gd/SrA64N+VE6hiOvXgUvX2AxzIhE6+vFkhEjo7xFY90DX4Fn4T4xBGRMZFF/QanfwBRQF3uBh70IquXXxFYz1r3X1yC4nFZ4/EeMQ4uU6Bp4JwtWta93aH93aP7u0bsYupUv0l9F7rXsa4RWcqn/0B546Xlni8XFF5qDW/rxC4pELcYr1JQBk9PwVV6xcr1ivOTnfOz4ua3gPHnj4NRiCAv/GLbpgaegLfELrA158YoQ2croIEWpBSK12BxWLqATXayAUfUAqvgYQMuv88bn4NcAwAHehCCC6pYpKhPrxeXKoYuvAZYHLw/rBgBno7COf+AU2DXjuoGfELjIQcyYrMR4FuvVgMA1Dcm11AaeOlv5Mgl/ALD+ATmDWoDF8BBtSpiFxkj1iMVrmRcvH/AbYySgCY8Ru6AkAGhxC4uXUGx3oQTiFxiEPXgFUAw4iWxC9ACca9LT42ePs6/DvxeI87waHZheI3iN53xe7fAyyDvWIWsGPFqTtjPWeXqAIRg4tAGnRnxnNMohcR/q/QGMD6hJ0x2fDXgY/ge9YDZ5+xfSLwCc5/FokyZ118CPBpb4uLiyGTPP4uOLlsQTQuPovmwEj/QhHcR55HnlxfrPE5/XCfBrhAAjNHfP51o/4QA+2fzvnfFyUngCCQxR2XPGF/pf55YNTsTiPP5/wD+AGBQ7s7H5+R+AuNH+gBxPEefz/YAZF5/PwaeCcLVqYosZf/4frWsZRdImf+fT7tn1CEFFiFxC2MUmOa4EEAQwBAt/yp4EsAsGBB+hCBfN8CB8/wIPwSQAAAA19BmwAvwCeeg16l8ulBlpCdlWDSsTojx8dX0novfr+DsYhS+xiJ9jFUd369jFXv17sDfQm5q5hxFMh5m/CMMIQ5fg95WER2BJJ4XZZOy8KZZ3fbrX7BQTVVVV6T0f/6x74tFSoG3aFHVjFVsYifGMXliFJfBDsn1V9Ui+xbgWOM0IJIS17U6t+f8ci9dQMiK3WtvB1g3HIUD+/YhcQuMVA8rrR+8MMEW92ZuJfw8Nd+K4XYgUgq29NP9AnqutfUPfWtfi2Ze+Bc/quhcWL/g0FsITXgIAaj9eAkfg3+BF2X3/yfp+BxgR0i5fOvv179e9te/BHWvvhKxCF5+DT0GPY1CxpqONWAgX4G0YreBh0NUCBpxq8VRe6kUi8fhELoN+3/QGet+iVJ6F7Z7F61WcCSi94GEbR2Pg1wBPAGvGILexi2P18eBB0MU+MeevdXjMvN6wLR0d8I4RWcW/8Zg3qZM+H1M2A0R9DYRi2LKPwa3A39/fiF13oLdve8YivfwINYrGYYFMrO5ci8uD88C/WVHny//4vxUeNg1wBAAGuqquWhkJPrsARW0VzwQ8IYXszW3/wLtZyhe9Ufzk72PDvwOvEIfn4NaAEBrrQS/+vYxVqb11PQDfCyPFR2fPwa1fWhnda9jF14EdL3geUi9WPFL3YAnOrVgXANOsE/kwR17Fo+sQuLmLlMoz9BrYCCAz9+I8R0MXF4GX+tdAS3jGQvO2InPnAaPFedcW5NyxmfIugDd8R543OsGo2MXWM11jNddAgAZ/69jO/gIHWDasXgdOK3ILRT+LrRuzsXnlz+fg14GVBB6GLY6lXtfeIQvELWH3v7ROsXv3gYfsIUdi8/n/gGhg0O8mAgR+IjC+eXwGqCCXA0ggkEoT8AoPgWdneDTFBnP5/4KsR4iIDT3BXoQvgPGteBxyCInqAJ52rfZ3g10Au6XA0+fzxz8AvgDRzz+An8x4nrxPQtaVTrn4NTvn8/n/gS8/n6wyE5T/gGh7/xa0qi4i2dc6wanYnP5/Xwb5/P2d8/v+j/hjjOCWo/n8/n4NPBEFlU3nM/BVrVVVVXOjpegp+8AnNetYEeAcCgn+IWATYHoBOII44yi74hb8LFHEGzyb187514Nq+vr6+u/xCDOIWhCwJPwJHw/AAAA2tBmyArwHt6DBVQI/aF/LXz/gnMta1ze6Ee8C7rwUDaq1Vffgk6qL8rwWErWta175mJ1X4JOPe2/X9eCLWr/gqqq1VVVcsa4hDF+CGT19BmG2Y38z5n//LXv8D9r0J9EdAnKq1qq9JWTK69Tp1o6VRsgOroCoNR7CAK9MAOMff6+Su0fvtexCqhCu/KVVqvXtf1cPAjrXFqsM0J/Xr+hbCNHGDR3wNPgJJoNdiFX69+vYxV14EHm4//oPd+wEDR37DEK93vci8vv/VE9mh4B/kBWXdxTT9a3v4RXqeWq9+vVi/sDB8BBQbXAyo18Yr1jEVyLxiJesGOm2799O+BxBGWtdd69j9yD73+fN+wNynq6R+8E+De5EKviVDTLRC4xXvJXk91/0UvZPVQOf/9fYtwupgfYxw0KYPuwPdFrP9AZfR9DI8MZY6YhYNfQW9jFXsQuMV38BC7rVzrxuI6ZP0XugMNer1rP4ufWLlmZnOjE87H5+DQ79AG2AzoLdiFxC4xXvM/+gn68ZO9eGQgrvDHPLnZc/nYnPwa0AhS4xBT2MR8X6N7EKT6AJv67df4U+J9aP2Iy9H87P4DJ0eJg0FsEilugKaQR7ELiF6ALUAycYipxdAc309iMPMnE5Dyn8Xiu1CI3P5+DS4CByev/odWIXEL4CQAz0MRPeAq610AYfQvLhqQXO/Yh87+q9nY3P4uMLlQaHf5MQvyoR1jkdMqXgSa9jMvrwLasYt/AcOhcsrewCc0eLg1GwgGMsK7ELiFxnr5V7EL4I8hfffxXnRbGzzNR/P5+DWoDN6+vGTStjFLzWMXfGYrqsPuqALK1/167hFFrwLNYs/XDfxng/4hYNRZCOnzq8+/AICBxV69az/gPn4J/gQapZDy9ACcQQZ4vqATOz8Gh3xrHFwbTELiEXELXEa+nWvgx0I8TyHZ+lkPwaHfO+fz9n9f5/wFLz/gcxFcFeI/ANjs75/PwaHf4BG6Eefz+LjC+qwHjvBVzstHl1/R0fP5/PwaC2CjWdgpz+fz+LjC+sRG51vCujvn/CWj+fz+fz8GnhoLVrOZIQhbn/XsQgoqEKsQtDFvS4EEA6koTVLYhcQuIWsBW9cCOAJKhyo4y659bwuy3mohaGIJExOgb33XT/qvEIF/xFcVAg/X38CH8vwRQAAAA5lBm0AnwCe4GYld7oCAYHCQQqggiC/umn6SWdgkPltrx+Caqi6qvGpfOrfgqrWta19XlPqOd9bVqHNat/Nq3uDyWqrXlGJyeDT1KrELjFX2MRFGqvA+UWKhiIvdgZ6PfzwzrXqvf/glqta1dXQJdaqq/Z2ZdQYITr4KkUivwS1XVd71BERa+/X8GoyQRwVge8QqX18YxEX2MXliFJfA/1zHdAjPqrnY5gj7uP+bH/h4a73itwrZBKmXf/19j3B/mY6/1o3UT0u/0NqvV64CAg2wP1DPZuP/6CPsQrwgqrb/iFGWhUCn3iDquu/Ascw/h/w134o4EigUgq29NP+CGq/uoHFexbr1cCMtfA4dMDxR2Pg3EL4CPF+BT+xv6CXdga/Va+NQnsYjAueUIJRP+vuoK8W71QuVS0nA4fA4Qa3AqoxEdRKhplohfivAj/CXwEJqwM9H7HyBoGWIAtNtv5yQQ06mv4CNzymXi36xsvLwOPGIbMmMji/8G1wziFxiJYvB8/Ag0Tuvx3uBt7vrR4tvDOuVC/0giEcQsGt6Cz4hcQtYEX4P8gvLhlmLZS4ZR1qO2EJQ0phkZXv6FufmrwLPPH5+DS/GECQ61jmIXEL+CPqrXsSsUta9Wki+Z+zvnj8/BrUDLiFxC3g0oIdjF14ZfhnjFBtrrxfsAnYHCx0uLwzzvio0HX5/Pwa1aCndRefLmwBJABM8UvgLviFf0i9jF1eBdH+CNrnobL1SwLPzxufz8GtACEANeIXELjI4V0qQjxhIrG06AEUi1r0deoai++BVRnxqPbsAQLROye//qVKEROf8AotCOdR3MkGtAJFr2I8Z6xm9dQGP4BmRa18LdRSL3QLfQC00J+wLOsEdEyxLPn6FxMVWfg1qJxC/Au1gSfxVYFB2Iin2ATXWD9onYmJzvR58/n4NTsYfxfrPE/G54hyYHBSi42lU/R/PGPF5fWIQ2DXQDZoZFUqi+2MW/TAbtH/Ax+gBMvP53zoX4BS/YAu3n+wB2IElDOxkbH2I8/BqLYqlUWi068BgoQ+M7mMy+xR4+j+fz/QAxH4a0f8A0Oj+fz8Gq0ARmAUHELn/AQIDZxC5+j+eML4z10E9Hejxtn8/n4NTvn/glz+fz+eMWeNz+L5mzvn870fo/n8/n4NfBKGFVVVVU6YhcQsvoJf/DATMRXJhiOdfbPufR+WYQtLwDQV7ELBxXVeIQdn4jr+ImELAVUAAAANGQZtgJ8AnnPQQQ6VXTT/VrLL/8qi28CNDN778T36f9+CXWqqq99Amqqqq/fgjrW9eivB56Gd+vsYony7GIiSREL4T8uhYtM/BNXVa41+i9Su9YoOcD1rA1ZvRip+CTWopH69Q91/rX/BR3AtIpW1fqxq61Y0vA5Qbl/+0b2IUtxC4hcILW3/GIhSDfrX3uhPuwK6WzNxL+HhrvxRwuyAQRVir00/4JqrrX3UCitewT4tkFPt1/UMdWhdVwOGIWDQ7/oZ7CCEimVJ9PpNZqafGIQ9YhfAJz8DJW/QGuvdDEzu/J6af/8qJ3cDCsXoHqii84/O9YZCCPF4jQhcQsGpf/0I9iVDTLRC1X0BhrfrWLoJ+v4v4KqqA08ZKm9nRV4FmvUePg0uBrxkKGwo+MWlV/o/qrRu6+wCW/1rS9Ll1fTor9QKPQvi/rHa5Y2XKhkbFkuIQ4vBtgCOgNPcC/hBT0VNP/lr26/GL1JXuvKS91z9gMH2G6t4Z+E9CITxC/sEidODTAMgNQTImkuEetb9dUARGCM+q5sIKCRnWAnHGf3+60TsYSHmSt/S1iPF5/VCYv4CBo8+fg1qO6+tDKxCIfFT7XAn0XpMC7V/gT1irA87F/s7G5/PwaHfqfq8R4xBbWv/B5rnR4/gVy0L+vAaIErGd6OjkyL5k8B4gwzsXn8/BqLjiYe6gzRH60V8cuv17wc16+nd/gTaJrEIp8QtworaxHR2PzxxfHe0Gh3+Bpvga/i17XGUi9hDDAplbf6x+8K/H8U+Ii6wKIMUKirA988XBr1T+sR+AugLKGdrvXsZ6sYi68P0bqPvFyn9YvEPrELR0Lzrn4NRbGC9Yh3iFeLWZPi/BWLxC4hfAQPPk8X68C7sRG2fz+fg1qATnr6+vP2LjLz0AdXR43P4uOLlvgCCYNUogGKAXeePz+f7gCEUKrwGSRWeAbEDsiNiOTPkPH5/oAajg3O+f+A0UMajouL6cWeJo/8M53z9H+gJGDga8fYhcR4jzvi44vpsQh+d/Ba875+j+fo/Z/PwaeCcLVrJn34U6rqrMZnEYj9eAkaCXYxb4xbYxdYhdcCSAVgCViFxC4hcQs/r36Cp3oN78QgzEiFgLCAAAA0RBm4AnwCf1yP+/BGMrUjq9WrUedVqqqvNTeJqq1rvf0SP9K8G9APzjEKaixipHVIMR+evRG3uIqeRfBZVV1qqqvse4FfUGQxWb/JsiP2vQLFrX6uULUuPBr6GN+rV2vsYq9+vsYu9vH6R9b8IQRVr9fBUuv64KUV6F5v2IQTg1wEAdBpvnVv199L75/Ag/Bj7Au0ev0R/HV6Tf2Ba6gZOuzwrBqX/9BD36J7wECXwHD+bL//Tf6GKC5cgMqD/516uBdxb+xcq5VwNN4lQaD2OKuaX9ax6GgPacsNCiLbb004xXfELjF1jFEPMvsB61iqtEaghEgqTGBCRw22/2Br9moz0dzdYTCnQF9/AQGIQVxC/iQSJ06dODQZCYngM5MYsqjGIj14Dvq1NL/BTWtYED+J4xFEdNL1q94T4vnS8DLo8Jwa3A0oLd3SE93EXWvpAjgUo6DVv/o2zu8YSXvjfl4E3iI/ELjo70Gh36tYu5UT2MXesEwlYr9e8CqIvj1qhs5frFvboBB/gIPPG5/Pwa0qCUXXjFeKxiEuRFxqgQNHGv4v+pU1lz0X698e4aUxb3+MUvNYzv5aN2LnmbOxefz8GtaCXutexCr4tYvi6GfLw/xnyvAz/lz6UWoUVrFxMyYuQuVnj8/n4NcAgAGdBJ8Z6xivWIWhHikXwKr1gr9DNqvboWvsW+vAZfEz54nPGF8dzJBqEZgxlmLbf8Yu2IRcQuL0/+ATui9Jgd+vXsAQSsS/gNHiPxPPF5/sAaDz8Gh36gNFBB+pOtF74GfEKXxksUt4G3v9El0AW1YjLycIeCB4iJz/gExeeXPwapJYK86/od2LXWIUNMtGKX1JhDKNj4rvAKDo/n8/BroBtodWMWVKEfgMkCSvb/xEsx/fgQe8GMXo8Xn8/n87waDWMJnYjxfrPL4CAAROv88TQtirM/4Fnnlo89n8/8AUr8EkGotmj7OuefP0d/Ac+sM/CesBU8/n7P1w9BqdgobnYKc/n/AZIOc65/O+d8/r+j+d5D+Ojv4NvBEIbmXkF+CWq6rU6YhcQuIWxCCirw2E1S+4/Lr/+IW/tcHoBQII5MJn/ELiFrzlxx+5/wb1ERXwJHwJHwQQAAA3pBm6ArwCdj2Gm0u8/6wghIaZYyzpJaacQvQFCu/YerySZalSYv8PQtiBK1qv3da0HFCVR197/fS6ppzQmh9Wg67Q7369T8s4vj/5NJutCB3eeCHWvS116E3rJBJqqpUtjVaDX0M7EKvtfV6tRsfh/QKxuz0qeTSbtt/Rh3dF7ofpY3z/CJiaqSpkNerBIBoXtrA0cG3aFDsyiFGmUQuIX7BDVf1eCKq4tvdH7bQFsQqNOzbiX8PBYXeH0AICKtvFtPj3DAMsNoYptvTT/C4IiKv2LdS0tV1FdCcGvOi/MQq+0b34ISpJfvoEfVe8F/L/4EOub77svVfSnt+BpAQCsxbwVr4CIRf/A4dvdeF2EK1+tf6g09DRqrELjF3xi68CPXqwLvr61vWBpA0KVXwKuJcaTPdeg79E98iEnViF8Be0IyrmqtYutFuYvB/hRFOmgNsDLx2XKxbiuWd8ZKTnTwmFFKnnr1LhLxCH49gkJ++94NVdwLqC6RVc+p1Zf//0T3l1gRvQUqVZdgba9i8Q5f9ezy7wI8D3RX8DWAm8Z+g3uPQrsQqxiK7a8C37ga0SLsE5EJixihDM6G08CPp0G+hHdgdSEK7+hjXsb4OgGzWs7E53xcnNi2NksQuIWDS/qTJ6pf+IQSS2Dv2BfWMQkm6WKwiGOgCe87F5/Pwa1oJN1q3X1PjEXH8Bi17G4N/jLsZInBJk55bPH5/PwaHfFxxGpUkvomVcC+r+ASV4tFLlUIU2XgPH4zJ8v16hfavXs7E55/AMDxHBqOhIhIrYhb9FbGLfwc+gDE6oBY9q+vUJ6PlZR2PrDHPwa6A1oVu+Ze9mXKxRCGyIxX4he8EHGSl9da91eI2uBXe/voAg4R6A4ivBho8XBod/QHH4bxcY7+BhLiH8DuBlXvgZ5BcIyN4DR+ufxC0d8/n8/Br/wR9wCcoIRYuXl+idjPXgMWuvAu6PH4uOLlZ/oASj6Aoc8bZ/P5+DXgQaOT4HICbiFzxhehf7OxvgFB1Whj43eFnY2sM6P5/P53g0Oxx/4RxC5/PitCI3wDE873iN4EjZ/P5/O8GlIAQDn8/ninnjc5OfxcRM1HQnPzH8/n8/neDRL8mAlaBV3gffgHR4hdcCWAUACUYJqvELiFk56L/gE45qrrAVmvDJSeOINfefwcCEGYj4EH5/gQfn+CGAAAA3dBm8AnwCieCActarUn9MldMv8TWitBt6CEvwUC9V1X5jF+xiqdETGI1v0eNS1by+/pr1i2QSWP7WCz/BGfVSpXr+/V4NBbCXfGRopQjsjiFxC+B6aJ2MVWxivl6aP3YGciv+vvVfb3nBXWtarqvqy0btVRAdITqi+P4uoJxKrrW3QHHBuPYUBt0NL7/CCEhS1pU23pp8QtYH1IjYQW+30+MV6VbBC/0J7xNE6uLBKVV1XdmwwD/sJEL/35GvXYoDgQ7v77x+DXnQj3tL7wEDy//r2MVbOgGz8O17sCz8ZRexig5PiCFEuvRPYhxpMxeKf/0WNTSdbEI8GvoV7EKsYr3xC9fQJKK+MVx92C32CbhGQIidBAHe39+i9ndvwEh8Fnw0i18BQfASGIQ+DW4GfGQs4B/GKkVP1bwEDqn7wSFWsqdL1rF+vuoHX4EXsDP8IV7Fz0yBxf3SEZYxXfEL4CR7wd+sPf0W50Xk+q//H64Oc/R2XwOIC7sQhsGuAZADPjEEvdwNqxYhfAj/199kK76r6JXsnqv+iRb/xuGlMvjvXwEzjGfLO+MlmaDd/goEarqvsYuLGLy8C3rCmfBkBxxuX6zy9ANn0E+djcdHeg1FsMFZ7wDAAXcQgniFxaKlXgXSr2EonkY/0+gMQHGXAYYGzwM/EPn8753zsTiPGIcXKg1G5MPmMW2MW+MXWX4T/XvRvB3rVe14IK6kF5fFjJRlTJjelU752LxccXms8fBqNhAMZYV/GLbGLfoAjoG1WkEfgKHv/Ez0N5U8Bi8/nfHEn8s+XxnJZ4uDWgCEANXELiF8BkAZUKi33oksYi6xHJ6N3QDP4lCaE/gE7A0Yv6zqfzxOfg1Fxj9nV4txW+dFxChplvAQaN2//CHP43J6xvrwGTo89HfwDR7OxMGh36gE560O7r7gGn71rvWLFyzJ0ALx4lfAKD8DjRO8DrvOAgd+Bi+Bi2dj4NDvjWOJlsYvsYus8vwGtR58/QtCi/VnifAQNGdieQ8bn4NTvnX4DRxHi4jrPL4bAJ34Bb8lAE553xbFUudj6O+fz+fg14BO8R5/P4vkvBOsRy4ji5qO5/wN+jz5/P5+DTwThatQ3yfOmvwxVdSZ9wuyydl/ELiFxC4hcQtrgtAKBF0lzEUtCFmwGVDdTEdc+t+D7/AKBl+BI+BI+CGAAAA0JBm+AnwChegk3WqP/Rfb3UL1VVrXuLf/ghrX0/or/q0G3oR6T0T1ehPWX33JVvwREVa99AhrX6vN1WHZwJJlRq19s3q23gErww9G+n8Eda/FC8FYkPwb6EYhaGIUvUIX9fWbGGA/4YLXFYrwraCaa0BBov76ost4J+va3Dsx61SXhwFFV1XioWzGz0G4hfAfvEIQQlwa1bw3/R/UEcA9IItAMkAoQVbeLaf8Elar7fu6rxrIA9rTO+BiXXYYAwdiMlQPkGnKhXXyoT7CCOqtNf6AKPrAn0a/xPxOMRQaH8IJRL/X2/LXqFvl4Qrrvg5uBhBR3fdx/18J4xdda9VAS6t4OPQGURXF4/GmpkO3v+Pd79f+Bdxbif7F4j8sv/+OyUl2A1/wEhBrfQxFDmW9jEYuUql//oYilzS+DP19ZDXvGYM6io+gLOQXE+3gaYHH7rXXn8Qh8Ggtgi38AyAE9D38X7gZVrv8CTkS6qx0EZLv+RH68C6A3L4CBoXi/Y79Bq/+7X2MXUlAf8i/rh/O+f/XqGy0mMWnUQsGh36AfwG1GfCCEnILlNP+MQh7yV+kvUMUHvjd7a9XH1gcdUAweeJzsTn4NaAXvELiIUeMWZKwCaAb+gGDsZl5rFs+vAwgWsXpV4GXZ2XP52Lz8Gh33yaCWXyrXUBCr146jxVYAo97f8ZzJ0AUP7onYrOwxE/gMfnfOxefg0O9ehkT/qRMXiu+Mwxl9YzL6VIIc1t/7jvQGRW6NoXoVYiXFo3KvR3Z/OxOfg1+uT9Dv+BH4hCc/4DBFo3eBqHr3gXH2AIJHLroBrtexnrwGi7ES4vo7nic/Brzod32vYv1nXP4vL74haOuLlms+IfE94GX4Hvi++eLz+LjtQa8FGIXELjF1rgIGG/AXPwNKXsbLJeAVn4YyHi7P5/4AoHELBqL6VT+fzr8CjQnkXgw5+U70fz8Gh3zvn8/4H+hkfwJIUXqwGjrBBkFxsVxbn8R4jzvnfP5+DQ753z/UAQChTZyC8XHaxEfZ/P1iOfz+f8AoPwNLzvn8/Bp4JwtU3mTMgvwU1VVVVWIxHOk31gFYr1fMsPwCgQUUl0kuPiFg9/wCsZPgSPgSPgigAAANPQZoAJ8AoBf+e/0ECxvGKLtrio1Fyrz/fWb6+1wrwTa1rX9dHOpnyRT/5uq/Rug7CCEC4xVr+hiMrVYFdIbfL/gj57fda/BZrXVaqopL9E9fl6qjcPhAACgWi2nuNwTve+Q0RdQYwa0qFMfaJyKMVTpiFvDaXLvQm7b+CQi1ibvpeR+l6hjMAe9Zz3zAhFqqr1aaL+Q7HwahBBIMGWYlMlM6afELiFxC2bj/+gRd9+y/cEJlX9EjEJF/4hcQvfBpcCyhQ+O/HgoKq6rlV8gKKrquVZ+vvAk17sAmPr61vfCII6qqxY921Wv/A+59fCCLLqBwVvA1+oHDEIuIWDX0MfEIudQ0y0YoaZasYoh96Sr1jRa9j8NDLAGS9tv/r74E9arJVv0XvjFSKLnDuWliPP54mDUZCyPLwDdAaUJfELjER7+CHfqVOwL8EdVVexmCpPikuX/477W9WBp2eU2RC4nNsQsGvox1YxCdYxcXgZ/gi3YGX13UahHYyeZs74vOpKWJ2ov5Z4n4FpG7Gd4NbARQHFeZb+gEABtrA2+gCuwRXfrHIhaU63a+78X3+JXvgRcW4rvi3d8U/QDZAQHTo8vArgV86LnWDXtDPdRnQH/jFe/xC9VAffgbFWBlovvgUb4Zz+d6EIb4Dh5+DWvr68ZGd/iS9VVdCF+Btx3ehG8WpMNeAgedlo8XiFxCwa1HoJd3A34j8G9exi68DrrA21rwMvX692AaUDJ3/ATKP2L9YrxDPR3zz5+DUXHd8QuIWTAsaESh9ltgNc3YBBQLiI3gNEAhWJ87l8ZLrPF9AFB5/P5+DQ764F2F0MZiFxC4zFd3gPIDDr9EixEX8VVg6C6J72X9e+BpRKkPH5/P5+DUWwgGmWMtju/wOOIXGor1i8n1JgYirFXBH8DpQhaPG5/FxhfWIQ2DUWxxcMi0LcmHTEL8BIo508AkNTpYvNyC8Bo/gSfgSaP2eJ8A0vwSc7wai4iezrn85OuG/w0BpzoXiXrASGY/Z/PwanfP5+j+f8A0NBDNVACGeee6AMzz9HYmQ/Bqd/gGho/n8/i4zns8ucmjvi5pqjx9n8/Br6DHp/y/4BQPELgRwGhk8pQTaSXweiYZxCz/AhfL8CH8EUAAAAvxBmiAvwCe+gp6b0WK/BJVcoK9bQe+jexiIvfqdXgfsnovV4KOq6qwvXBRWuterhL1QnpH4EDjFEcOjqdhZQaeh51YxTZFXKjN9ovsIKuXTT/6+k8lVVb9tezfKH+wXFrjHa1rW1gSHr0er9E/BuNRBhqMU+WsCKugJf3X1BBQjJnBEol73rX4Jqr1qffr7Fu9Z360XWT9f9cc3AQUGY9hCbP9foT7wOdH986J6Uv++pVdgT636VT3zr1+vVTA/QcXAo/LhFHAe05bqabTXxC1+T6r/6BD6cX3fd9aJWPxpqQBittv/Li3Fd87q6X2BgQn/wLnUBQYhYNP7gZUMvjPfGfP4Dh1gt/o8fVAbM3Q+Te6sDSO7pEi+BdVqESp8Ih6xX8CxBp/6J7EKsQtevXWr9avi8m5YtlCg1GXjnbvj+ZN7+jvi/lrwM/4Eqw2wt/1r/V7/BmdlwggmGMtlbb/iF7ga+7XvAQ+sRXutfdfwLNUBn53P9AafV2ePxC4hYNRpAgO+i7kWLELYxFT1a/WvgUF2dBWvdga69i5XsvYGnxde8DPo8XBod+oGlBLuqxqpRY5Zl/oXrL//LSDaN2N9Ufechz+fzvBwl4G/EfcBj/KhXeQsgxnL88ojL53JuwDX8Sy4hCYNxsIBjLFYs7hPEL8Mq2Ml7+A0eL1y8DP1v2L/WL5rP52fPPBvUBjoc+IWuFsQshfAqe+hMvQBNefxfO2cnP4sguSz8Gx3+BxxHiF8DMBl3/eahndAEfBAvdPtL8/iJfAZOzxPgFZ5/Pwai2OLhkXELiEe8Gb8CfqgY6r3gabqVPAz7O9UASHnic/BqLY4uViCcQiy4Ezl/98Yoh5v4EHdgCEQY4nqvPF5/PwanfP/oZ2fzsueXO+eXO+fxcZLeC/2wKOfs7543P5+DU7/oZ2eNz+d+wDbgH97+/oAT28/n/NxnH2foXyWd8/n8/Bp4IgsNUnXvwT6qo4yWMvOkQlgT+hCzYDQgkqv10AOXwf3wJPwJHwRQAAAAydBmkAvwCfL6Q4qJXonN+u03QJ6qqqqqNd+vTeHD6qVf3ppg5whxiFG5HvcEVV/YxEfPJ2c65en6CLgOBKRUzz1rXxyPWT6ff74pZZo5Q/iEL1+v+vi4NfQpMvr79fV6+uj0fqNj8P8Fl4o4U2gPmXS7m3zQ/8A2C/3vhjCh5+te/UrfgkqqqVNdAUeIUuJJA8gRMG1WieximzxiFxC4xE14G9+4IyrX3YFut6FqA9IOWEEohf4KarqutfSXgmE6qqqvdwPncasXXBx6JzKuwH+fX6EZVYCxS909Y+jthJR5DEBaJPpp/uBhW/cI+cBAr36+egMHwMtDlXoN/ROP/R+OcILX9NPgIFVYH34FujPqvBGXd8tfq/4Ir3nu/xgCIRO+19ncT/HAIKqKBkR+rAg4Nu18YxbmIXdfeCrt/RoVXfXQ/mXvf9gd6srCuxiEmyJ1GLyg1wBHAGfCC+pJbTXGImpKD9E79WrLRdx5l94+vsW79i/369jWX2NWRPAzis8bnWDW4GvvoYghiuwHDXugP9e6BJXsIYL3IZdv/oDT6ra1+E/6L2IfwMwCAxC4hYNqmRq8AyAGFC45jEZ3xSI/Aj8v/9WAx/UbjGcQ+l9Vn68I7rf+Ldf/onZ2LxPn8/iFg1qkO7qJ6nmsBs0XpT+K87F+Bd0LnjtnWDQ79QzjIQNj4yLmS6AKC0I7oEHsAmNarDeXF87E5+js+fg1EsIBjLRC4hcYorviFL9Qz1NYnTIfzy52fP+A6dHng1uA1UO74GX4jL//Yj8Di/Ay9/fgZcp0XO+I88Xn4NToY/hnELR318Mr2MXWLd2fBBeBx4jefzkLrAafz+eJz+fx7BMGKYevf4MzvjIIY+O5+sBAn8DOJzzLF5cr4IaoB788fIfo/n8/n87wZnf4DRxHnfw/8DDrAbfO/gbef8wQo72IXwCg8/n8/neDM753+A2cQufz/gPN3gVsouKnqOxNnfP53g00AUFSpn8/njH4MQEPn88br7z+fs65/wM+xfBLZ/P53g1ELiFxC4hcQuIXELfzYBOKDHYhb8EJRxBz6D/6vgRvgSPgigAAAMKQZpgL8AoPoIc6J4I+qxk/l6qDcewoXv7/7Qn0Rif4Jhaqq1qe9yVrfr36K9PAiwELX/xS+34EjByMiFKKlVxSJ7GLvXgiOtfbxsIVcIWyJGS1/fosVC5xPCoqBr2hXpPXV7o/v0Tt5Y5Lr8XrVa9QYr0wxHFzpjF9jJeUGvod6lx0avsII9/0+MRnvjEUvv4fq3QGWt/wR9V7f6jGSuF6L//XordsDMvUKRcQsGo+KDTLFYue23ppxi+qgEDX3QEf0BQ7rwSHVeVeie3Xr7FuJWL9ICfj++9/iNNCUfwLPwMtEehCLj2FCfr3+DTAEaAY8YhvvjPAu5MC6ZFp1sDDxcgP9FS6rXs752c30BnAVXQFaida8DJg2uBdUyjvWx3resLUTrr6VFImLxDgB/wNYGBe8jxrhpTHzsud5Bn7GIsiQa2huIXEL4CZ3loz1Tgj6r2L3IE+l97VHfxfsDr8M7PE4thwn6DS6xSCuIXw/XqXwzjlZ36BHUqYwkPDJXwMMmGsmCP4GX+i9BrUDajPiF6gaV7qJ6+vwE/RYtOAQX7UBMx1ergY/pezuJez9YE3iI/EeG2CYMUxa/Wvz+9/g0qRBVuvrxy3x0S5kr0IrwbZxeXcq9DR2xHnmJ543O8GmAgALdCiBAtIxFevAy8ZiH3/X3YAgnJhWvVhuvWdnzxOfz+d4NPQQirA71i+Bd9gYUWukA0au/1e8fz+fxb+z+d88biFxC53g0uA76y/Aw/i0MioZ+kF+XjOtUCXw9ROxkus/i4uCtn8/njHBrdXgXdDd6zr4CfrXgcfgZ/gWdC39jvXgMfiI3O+fxccXmsQh8Gv5/P5/Fx1jwDc/ATblwNO8bzsfnfP5/PwanfP5/P+ByAz+BoA11g11jMgvoozrn87+AaHR+DQ753z+f8BjAJPOtYYXhvR3o/R/P+A0efs/Bp4XC1ah6S1Yy8VNi8dif+CmqqqqqrGTM41RFELiEMZxC4hZC/4DY+IWQQtrg9AaEEYtUvKD0Qg7WASDYlZ64EL5fgQ/gigAAAAs1BmoAvwCg+glUnrlB+ORDWidRi3PtWoYiifGMbSGIy5fovor0W8G46ZRrGfiV91KvsQqxiIS/fa+rTQn36IVGS9ckRF/VPByIRFiFxC4hewCF194Zovq3X2EZQD0gnoC7INTBVt6afHuGABljIEttvT9fozYt193ATgIju/s7wS7rd+oOrh1G9iFa+vqGLy91O1aovr4/X9UQauu4Ryft/6Ll0BxolQccWp0/BRVdVzq/RO8BwiMaoaZZLW/YGmixfonq/HvJVr9bAQC9V9DInLGLvjF9Br6FeMcvvASPwJvwIu7mRXNX69jMGWpSVeBBRK3qWj+r17xv9a8DTRarBz6gJCDX0K7EKTIhbwKfGK9eBMPQuUuCofCfl4t/YvJ6sXKbDNfS9BrcDP34hbw3/Rm7AWPvW/jMguU37wOICBWs7qzoovEcGl/eiRYhcQt0Bf+Bz9AXCrLvXsZgvclULdusX36AQOQQhvYBkefzvBpUDP8MocdWLy4/g1fgz3gm+I4tQy8arAZoC+zu+gQUXsYh8iVgWaGVYyNjtBqMhImb4jxi7yYR9AZ2CM61xULy8oTwNNeobiHCAxfMi543Evn860LiJUg0O/gIAC3jEa+M+vA5/ARtXqgGerGT4sWxD6o7E53zxOd/AbOjrn4NSfX/+AgALaHRfAurFiPXxawVUSilTsAUi+4LugCY+nRH/XpDy53o8Xn4NRbHaxn7O+dc+XvA/UWLxfFyUq19fWts7H5yewBBPw7z+fz8GvAQOMjpuYxaVeB78DSBv+W8VWVHz+LlkuvPF53o/n8/n4NRcVSKdcWiyJnfX9Hllwrz/gNHZ/P5/Pwan8/n7O/gWcu3YhaP5/P5+DU8ufs/n8Wx3s7Ln5DxNH6P5/P5+DXwQhitcpcBI8QteCbqo4yZnOmIW8BZ5MBoT+5zI/L9YAh3iFgQBCDsCT8CR8EMAAAL4QZqgL8AnnoKevAkEOUiM8vT8nr2vuvBHWrTfavJ6KW/NzUUHdAasnoxVeJn8AFTwW/vxEE9Vqq1On0CaqrWv0vgkPqpUr0boN8msDdvB7vU9fda3+iFSSpASFVVXvwTVrVV9+CU6rqudKuDrV8EUG3goGKqqqqvYhUohcQt+Sq+wLsER+XjPkX8irAq8n6X+uVDJfYhYNuRCu+0JOngOEiEPWDl+6v4MPzq3Wu36+w2zh8pm/3v99a/N//DYLN7lta/9e7gJQl39xqLF+vQdD1Ae0csnX6aawM+U34f4Q4Kf7yAX2/i3j475a9ne/BJ1XvYa8DL/Qv2IWDUXFFw8nwj4Hd/y+vv10nmr3wI65Y/pVf+LcR+q4CAR6y//r2I8d+g1uBh7lQiq9H9dAJ/1q9UBH4vJsEgvnToDjWq4GbsBugUPgUMQsGt334QQl4umn5K6xNCPb+E1Kpi8/rFvrGYaUx1V+qL3gbwNdYPQxiFg1qkR8QuIWhiOe3rLrAp0SugN9e7AaPvx+K773+Lf2d8QgjWwdoZ2cgvOsGtDn14gmsC+7FwhgoXhRW/gNADrn8W/WfzvWIASHi+eU2RHBrV9S9S9esukTpeB1V6L//jcNKZB4ElZ3zwjWBnAn5/Pwa8Sgh2M9YQnDGWTptv+Mw+y1b/ROxnrG712AIN2MlkShsg0mYqFvrOx/h3n875+DQ7+AgALKCDFY9r3wMq98DL8iL0h1p+C+vYv1n+wDY88bQj8AmPPwanYx0L3rXwz8X869YuNovxcUXml8DSBmXsX6xTn+gCc88Xn875+j8GugKKHHS8DKHsR/r7Fs+T+SrZ3xbEFwyWdifAY+jxOd8/ivPwanfP/aHdiPFyzfOvgN34d+Bb4tEms/QufVnic75/P5+DWoBTUM9iCc8+fxbyV5tn87+XZ5/AJ3z+fz8Gp3z+fz+d87yvDEb8fo/R+j+fz8GvoMem+lgjgNCvYhZsBg8vgx4BavQhYPL9v90AuMCT8CR8EUAAAAzFBmsAvwCfLC2VBD0/rHfq8vq9ccCE6qtBUBx6GNjFFx3GqsBAUfVDEKX155zr9XW/BBBH1XmSDbhSv1qnf50VrX5N64pa/RZa+DeDagG1kwItCPHbr6vRWN/ghqtckWtffaN4k7oZEm5co7CMGuAgKC0Vm4//oFYneVEXpp8QtWBroR4/p04yiju+NX2PcP0yVa/7r79ew7KBILKjVr9a/qM4jbD8HXOhH8LIoniaemn/8Uv6N7wNfsCXXLsZWuywRHl/R23mx9evr9Fy7A1/A18UuMROUGgtjr16F+wihID2nLPT/8C7qwKPrRu/RfmPJCJOgsv6p5vw/whC/77wJYYrbb+PZA0yw2vt/R3+AkKqAoO5ii9VBvcCrjIo1+xixFLxCyetdARat0Nq+LwZPIyLfopedgXfwPVUT1+Cauu4HBSpBr6EdiFWIWTAyUVun68XiHDS9gXff34vbyvhWxmbOg0FscvdQM6H3xi45iFk7xxCluLrROxeKxv4v5eBgr7x/O+J0/JWCU+IQvOsGtRyNHO4GVe7ZF7warwLPwM9dY5T5XQI/YHvXAo4t74xw0piXEOX8DHWqxYY8C2AkKN//8PD14NahFBLuuhi4q7RdngWvWsbt17tfhWh+77/oX9U8Hs+xebOg14GfwGxVjGfLGLqsCluuhvvQh/ga8T0Ij/A5gJHELn4NfQQi7gdVrpgccQr1/jZcY6AJj6A0ilivAzg8V8W9rOQtHicQuIQx+AaHBry1xWMJ3xil+51E9ROqwOHoAoIGSQTvO+dlo8Tn6ELBod8Wxjv3AcPzyDPXQCBBB1ef+Bh+Bhs7LR4mz+PYJKsrL4NLhlBcqYhc7LWBJ4vP68AkPw1W/XIfo8tHns/neDTMAucR4jxK+DHiEOV1169iEXO9Hi6P2fzvBoLeO2d/gN3EE4mOL3g+Ccp3xaP1niaP2fzvBod/j8/n8/LgbwK3gdwJFUAaXn8/neQ/neDPwRBZiUrMlir8KVVVVVUwXnJjMzoiiFxC4hb9DOxC68BkBJCzpiFxC6+GOuhC08A0HsQv4ZKTyfqDV3NOtr+Djee+YSgzB38CR8FMAAAC2kGa4C/AKCvlusEISVVVsn6/1a+Yxar8YC7Wrv6l8ElV5YhCCZ89Xnh8Gpf/69HlYxEfxN6986Lq8tbQbj2EJs/12AXqhZVYxVclsBT1pVxNF78FFVVcn9l/EF8ElVVSqHy1r8GKGVi2JE+Kda6g6/XoN/Qr2ESDQxllbb00+IXELJ/692Ngm1rWvpwhEgSNONlL0/+HZOqxCwbC2FFvk9JuH+C3/R2rAu6EIvYF34irdajtjFBcPkBhFEvHkisj9a/7X1cDJ0QDgQ7v1/4c78HPwMuvQQ6DS9Hp1Go4D2nLRUIXwMvfgaNWBhq+33WeuLXsW+/nqyqKDyR4FuAseIQmDX0MzGIX7XvAu6sDn6++ngl+LdIy+oO+tRjtfyCEXPmyI8JsIEXWv1r8GZ37AKwBlxiH3MQslOuuj6F42pRncDfj3devx84dphkVfX2BlAy9wKGmnexnKnUDJBrcSje+19jEJVj27wJFCG6PxmnfF4h4/9AYfYG2tdef66wNPEK8ZEzNiFg1bXondwNeIU9H6NrsBu8cxJsv2A0d19QEF0E6914yedLELiEeDTgcfAMAMQS7r7gbcQsw3BnW+1CfRWxb8qP4uQnyzxdCEJ8DSArMQsGn5f/0Evda9T/WUuTYvLmeQ8Xipj7Ox+fz8Gn+AUYfjI7rf+/0TqGyl+p7AaIEuzz+A8dniYNP7gdUOixi8qGK/fan2M7+BkAyr0SdCYOPz9C5L9wCgrXejVnRX0AaH4uvSi/2L5mo8b4v4GXBsd/h5DmxnfGesXLL/AKL8Z3T+L5vneTOBpz5c4GUCTZ3g3FsWo7HfgZcUuI/A4AaaOxT8BwuqAcPPF9ANfQjs/neDekAaPP5/P+gMPYHsAw90BZ9dHfzBPP2fzvBud8/n/AYwHnwEBxvTyH+gCc879AFh5+z+d4OBC4hcQuIXELiFxC+A4EUMKlsQtCFsv+AyPAjCEHYCvgAAALrQZsAL8AnvEoc5EN3DkEla+d7wzrXtM2+eJd+e1kXsvy+r08EIKNdQbF//QhsQouxir6b16/BIdppVLfgjrUn6sKUTvigSCa1iRxcge8MUcAdDEKNnq9F+X4IyqqqAKYCVEEquQn7/6/xE4QCqIzlTCbC3/WvwZj3Tv7+l/WA4e/wQi1X7wZ17sBO0WvJp4R/XdwPgIrv1mxHHAPw0enT1nSM4jv/wQ7rj6X17CGHssqaf8Qum4HiEYNggiVaa0kuIWq/Br6b/X0u6J3pI/VcDQi13olUIXELBrgfKM+MW2IXr8EH9E13ovfr6q18sQ4apiqB8RX7g0/BJ1XoOOH/0arw7rAkcv4K+DP0Pq1OngbV7O6Ue+Xr/getL+l76YV3+i9jPLELBrxVcOInSevdAXePy5fe/qwNPFky/9H9Z36XEeLyfuoFjFsKeg04UQR9ilRr16vXq9HfxdG79e0kBT+d3nZegMe+B9xj+xGXg1O/aAtr87pSrO9EKnhXRPqv+sC5RfVkonV695gEb2A0fevVuj9iENxC4hYNfQ6NnUucBR8Yia9OrAJ78CzobpmzPKTNAZa9nZc7538CTxnyo8Xn4NcAgQGfqBrQSfql7GeDwRvGS2Yty4ZCnX6I5iZa4HlXxb68DLo7+E+LnlvAa6g35Ua+I86LYhfUEpFXVfqwOPO9HRc7F0fxC2IXEIY4NeBnRO6gb1KmMWZP16sCrXvbvA28X4M7L59Hc/njbP5+DX0M75V7Pv4GXF++IXwc/E7GdLQt/WdjZD8Gv0MipqtUTtYC5/g73wM/gUfQC/Wfzvr4P87H0d8/n4NDvnfOufkL//QuM9nfr68WpcfP4t/Z42jvnXPwa44Bo0MjL5/47wgL18P+BnyjSC5kxcZrF+s7H5yc75/OsGp3xfHbP54x52No72L5IJc73YAgnnJz+d875/Pwa+CkLLVa1JnnSf8v+A0Oe5z638YglefAaFBHoEP4Ej4Ej4IoAAAC4EGbIC/AJ7gZBCGRfhDqqrqvCChKkcGMF9Uyc+j67hxQvX/0ktJIYxEvb06aDCIGgMt/6SWklxldwTVXVdtZ9Ai27ZVn4Jq1VV0ler14ISqvKXwRVXmQbF//r0KTL6OVP0b7EL+jt3onU8sVBDVV+m9HevXoORkZz4xFXMfr7GIivXoT6nlhCrd4JqrVayp8J0X/g8fvNVcHnYIhCr9iFoYpCVW8bwUHrVa+x+AfpB1sF2QFW23pp8e4Ee3GFkqN21p+/dfYRcB7Wme1r9669VYIT3WVIOfQrvi6wLuWwIP6BCWteT2EV6uBAJWsq+laDf0RuqXugELQl5HYbgfa1r49c3Z6pBr42vQjq2DCuOMJ1VWBlwbHf4VRq/V/16R2G/2GdC8yoA6Yv/9DEWRIN7gR1Mirep1FevjwED8CH6175Fjn6J0uLAQGfJ+wKHwMlVAydcGh37hPuEfDr8DO7sDa16xmLeejv+i98BAb/vhKq4NRxJ6PiCcYialoDSNR3PAlmxmDz5pcWzPfFSif+vv8Tk6oDjg3O/SAx9desbwYUTpReXTfsBm+/Oy73Ao87435Zff/EI+ImLwbcDOiXxCuhitNJoVd6K2LJvi+ZeLj4si/O8gjxCwa+gh79fYj8BI/Al/lVq4jP+Ge8M9a8LZBKP4jQuWWg2qA0UKv8C3jid9d+M8OsMYgZFwAF4GkI+FfgZQIOfzo8Go2Mc/zq8+8UtYOWvpD9iENo74uM1QhDcZHF/Z+DUYx9JiP4EnEeLiqYqgCcrrzohM8DmtULl88h1o8XiPELBrwGyh1Yjzy4vmRVsAnIBP/Aorr65TxeLjr0IY+j+fz8Gh3zrnX4DJz+d8/1zH8XHTWLfWdj6P5/PwaHfF8fHc65/PGPwESBx8GIGTPG0dcV53o/i46+L+Wdj6P5/PwaeFwsbrUwMZnlkhNnr0O61/iF6+u8D/mXB+Bk4hYEEv4T/AkfAkfBHAAAAMRQZtAL8Anvdr6Q78ngk6qxI+AgIWBPWtXyX69a16afwbF//p/36FdW6OXe6lRlf0CPVVhH4JK1+rggRIvwRlaqsX4JC6qx+vfrHg4GRUXySCF/XMUvAmhBCTL/DPmp6rpP95ZIISLX369fFMqry/4vgnquqqv/1fGZcMJhBwEkQhJKm29NPiFxC0IXrBCIVdqvQvjn0kTW8I9FL1F//uuvEdV1UHPaN3UWj9VgHH7f650TpOE8W4v1+j99L3gWa9jF1iFg19G6hi27AWFHfrRrK9H7sDDo34f+C8j/eKg4bf4txI8feWrfgnrWtfq4aBGXVfdQwp066xeDb0IOoxykzuotfdferG3+1XB2j5jF4UFQZIOG4GVEYo7k8XhevfAQS11fXeAQHBvgPmr92vu7VLrvpLBtrljHMyZRjMHvmlqnBFVfs71w78BA+X5NevQa3AzosVDERruv9W7Aa6Xu7XugM4SR+6+nRGdICf7r791XiMn8DJWQDFYhHg1HEJ7EKende7IBnX/cCuvzr6m6+vWBk/Xi/fEPn/Ay/CYCGWKuAkMRl4NqYGnpVfGZ8rqE+otYuvwEtROzv4ZF4vTy3/0Bx9CvT52XG+XgWXQifGLKkGh36+vr6vrxkR7GssbTL/+jdiNop5874v1Qj/Xs8bBrXQ2OJZF9atYxXrsAlvvXqqAgf19jJZLFsl8QtYX54/ELiEMcGutVH1gEBAudACORHQBYF13694GUDgveBkFr7sAifOxfQDlCWLiMvAc+jxcGotjHq8DCJs74t9YvvnfwMuQ7H538DNQotiEJxkcX9n4NRuXBX87/BAvXgSDYuXfsATj8DeBxz7+B9z9njc70fxHiFg14CxxH1AWuf1/VAOnVgEzD1ifwNvP5/O+IXO+fz8Gp3z+T6/+hcZS+P536AI2BSz+Okpaz8754/P53z+d8/n4NTv8CLR/FsVNZ43O/V5/O9H875/P53z+d8/n4NPQY78K1WqqqnRFj8tfL8wharXuu8BF5FgWwWgO6WOMljL6/xCwIF8CR8CR8EcAAALMQZtgL8Anvoc9+rSeCPqbPl+CPmxeZPwRlx72lRPFVX1UG2AyKFFcxCmxei+v0T1eFCrNSduPWq17TeCHNadEF5olnCMRZ0g17QjLGKTGK2lY+0TMVh+j+r19vC8RBHVfyh7I2Zuv4bZRPPvf73GKvWujfjh/BWeXtKvylhLD/dXPdYoN/QSZIIQusGvrRfFYnvcNUT1BtzqN/vf79Er36wT7rWvsfKE6FQUCpMvv/16ThKDYYiKO+8Drqwe+gJP/oTVzaZTiAMqm/YkUt9O/+vu2AiEXXqvkHQRiA0yxWTtt6acQuvhP9Far++ul6niOpquB8719k/f10XvzE1W/6ELjEXlBrxdDFXMXWje6BP+kX1YiidTby19XA81UDV1fVI8XVwbXAjoQ53ap1uvA21r+7Au7FspdbHu961+tgfFasM/DjuoGSDVN3DOIXEI8t6J2LcV18Wia7Ay+9F7s/snXfWHNNPqDbAMABjxCy2B5ovdh2jdiCQsrF5lMPwMgFtXKP53rDISvhjEIXmD//sPBon4NvQ/pfRa1XfUDfi+9evsX36AQPaQVgp9H1wIkGwueVMW9iTnvCNCKxfOosXLvR2Nzv4G/XEwbcDSghfwEgBpxmcixi1DDLXPA37HLehkvXcBzonZ3xbL7GczUIXX+NjZbELiEMcGo174yXrL9/o3sQut/wLfxO8EVe8EevWuwGjo8I1gWwOOfz8GwhcQuMQQvGHj6GPLeATHnjFBrwKOL7/A6r3wONCN16+iDxvgZ+fxC4hYNeBxxkdpVwEjvAs6Oviti39IePs/n4NRbx2TOuddf54wvrvov7+vZ+Q75+jxtn8/Bqd8/QhcXHazx9ne7AJ/zvi469Hj7P5+DTwRBYy8y8gvwSUl+xCCiUQuIW/QnqXAggYpRpMHGXiFkX9rgx8QuIWvE8cQaD3wdf2Bi3rAkfAkfBHAAAAJwQZuAL8Any+eXP9UOqvBLrVRdV9+tQfejHV0B2SLqvQhv1aJ3keCGGvxcG3LXr8/X03giKqqrQIQhZH+J1qq1OECHAFtPjjKXp/oYhC9XovQa7q2MW2MW9YHP+hCSrAziEJvXgjqtfPYVWUljwgrMnu3/ovdgbfgx4hcYvKDcahAaZZ8YorvjFevwR1XPqxNH7sC+EAREve+EcIgugVAtW30/17fsDi9avhqmljaE1iUYdar1ig14sEpNV1XsYuvn7AfofRWOgFjV+tGr/0kV+lX1C3PzLoI40mVvf/Bwvfr1cN2IWDQWxydfIh5URRi6dIYv3YCn1YHOsWr/oDHWuwLPt/19i31XAnfAnSCFg1HEr3yovVgZdYyiJ1oJetFrrRr4uUV0vfi33nwIPOh8GtwL9DEOZuV67VX4UrXg8r2Ny8bl787vEOGlMjMvy8DLWviPgfPgZPgZMQsGt/fiF1g56t+vXQT0LkGV2VQGnnnEvKM/fAkYhYNR3NmM6YVgQe/7oX7AWv4GHGv7pOvXXXiENoU+fz8Gh36AQAHnEL0CYDyjJ0sLbyUIlT/7AJyBymwMuhC4hYNa+oQV4k/8DPYhC6EL4G3R5YNfUdj+vELiH1+hjvAo4gX0jnj6P4haFxytBrUGuef4GXGIRej9CHmOy542j9CFxCwa8C78Dri4rFnXwEL8Bk6FvrxezrZ4uj9H8/Bqd8/n6wOGzrn8U/i+fxbHZUePo/R/PwanfP+DjR/Ef6HdnZ8/Ifo7E9AE55/P5/PwaeQMVVfgh6qdVP+xC3gYIIa1nTwlnwMm6AE44OPkwMG64EH5/gQfn+COAAACj0GboC/AKB4JxyqqqL87sHBEZ5fR79atB+nA4D1XCK9G9flPqqXpBsrTSqv1JuDflxkUS4Fxi0ifaJ6K6Qs6WlweV6DbwSmVdV+NqIXELiF+LuwJ8EJd3o4+fm8v4eGvfFcLsgUgq28W0/4IiKu0wQIULrQ9NP9BDnBTT/iFg39G94CDorFYNUhD9hH5qE91OTdd0+pajsh14QcD2kz719+j17osoNzv+hnXQDhFd691r3QUoTOarwSVqnFNcBAKxk/f/k9exirlBuMyYdroE9E5LoJAaVvi5EgDlrdeu+h0+XwISxQcDETljEJkXVeMRifesMNcuwOJsXlwgFTx/9T1ei9VgNGrQb3AzokXclei90Btd392jVfr7sDL+Bk+BoXqqB0rB76gZoNrrvQuLwW/ARFCO6At1q6Ak8fLKJw2/+Bbv6+hvB/Qb39/Xr/9GrrRexnFTVWr7X1ejvnevXmXhiuBkWXwLmIQ+DX0Ejq6iVOmM9eAgfk0MV6quj/gaQNWv6EMTXBx4GUO7+FMQsGp/OsmX+iezvY33xb+zvi+ZnzsvhkMXgRQpjOVEQNcBnAad0sDX1KiOYhX4JwgvY70vYAiHIfoT53rA07PG55YNRbHOT1gNH8C6va//X3cX2A8CKVKF9M9AMmvdfT5/Ohuv5D8Go14+z9n+gDN87GLoB4mXvAsgWlyxfSqLfWd+vP543X8HNwCO0f8Bw6wUAWOwDn0O13+qL3YBoeeYuagYc8vgZ+eN1/4CNwcHfP5+Un6X/V/X12fo7wcHZc/R/FPeBYodVHfOT5ed6PF53zvn4N/BaFlVVJg4y/YhdfAua/+T5PkpcCCA4OIWagDj4PPkoBwbvgQfn+BB+f4I4AAAAKqQZvAL8AnxfX+LfWCgZdVx73UHwxCC3zJdgiqv1YuCQqpqm9eCS2tQETv5Saq9oEJ1XOknSKn7DFiL3Bt6jFXzrqhiMTDoiIsHsL0LGUftMFFVVVr518ERFWpmbrBXWutVqvyh7gqpNFWev+gSVXKnSovZf+aZexnFFjMmcq+tQcv9E91FonuwFrVv15lxi+kXvwQ1X6vRGo3hh/w0fXi8a4pl/82A4B/sFfk4IGpp/g66RB3YxE1Jv3oveK+ei+rwSVVfr9e+KBF1X7pfnXoOONRHxahplqxC36t2XY/GmsPtt/qCkEdV9ULcXLfrzK783VYRlCXUGbv/69WBB4hYNeLBKZarqrYxSUesC76AXdb+H67O/9XXTazY9yZ1a/S4CQ/g2kEK16XoNbgZ1viFfyq3to7Vg8aENp8Ff9HKmLcuGRVpAQcLudn/vf3/XqzgJL4MlrX/cBEULf0HGAgdV1hmra/U6dgae3fRPdgZfb43DSmCz58nJYDR/o/YhYNDv3Az9/tSYI0pU6+/f/hHt8CtROxb+zvXClXXUDJiEWDUa5MMiqMXU1DekuG8ZIX6XwgBbzvnfFub94GeixeE9X6XvEcGtQM6MdXX1UnrF+seqA06PPnfFvJYv1+vpBCwa1MrVWyqvr8PrFzCu8lPR+jvnJxDP4F0C1Z54NanxC1gg+BJc2BjoU/QDNA9L2d8753zstVwbneTDavPz850J8J4OOAgc62LipBMCG9LWfI/COUQhuMhAvOCDbgLHELnnzvn6wR8/4rMIQmhcVKrOxuI8QsGp3z9HXwNb8Cy/D+jvIfo70d875/PwanfP53z+d8/nY5Ud5D9HjaO+d8/n4NfOFki/JpNeAWHLgWK9r+dvgfatiFg6sAoOTAsb1mrg7+f4EH5/gjgAAAAm5Bm+AvwCedoIRMhV4JOq3k8x9T2Dn0FG/Qs6Xisno8TLSgk2lim3iXq1ehXqN4Bx4Qh0WbKxPHuASCHPAmpHv+Ccqyqqqqma3RsrFoIxikG3oLelfgr12jtmxtCPhw50kn4RZoJSNJva69G9Is56Qmq9cN3UDQvYz5QcF//RL3YO9aEq2b+H/DXfvDsQAgCKtvFtP1r79ewg4A6T8uMMf6+cYov/HsYT9eeFlPCDTk/Qnvjf19+vr4lFl5xC3346idL6OzqBORak4hSpneDTwTjNVVdrOoaZbQG3Xo/zsH9SL/X28sZoNudS9/vf6rXdlda14b/i+q6rwM9a8CDxC4TY6ZiZj5mJmPg2wOtD81X96t1XVemiexcp+dlUWzZUP1Xv6r7gaEeLvg45K4n9GfvV8QpfaQIf5KK3wl5B9ZQcvWsfJW/69H7r6pHioQuIWDQWwx186CPsYt8v3fVV1fWbi90uLz8yZ3F7bAsw3vgZNJ39fWuoOPkrXutF919wED8DOsVHfsC77H6wOIF+hC/A4QbCFkw2+uqN+OsXJMvPyi1NiZeBkg1qAgcdPN5bAcYlXqlRPUKdJPxfPr3Xv1KmO+VP+Dj8RP47VxHWhXUL3vi5z96O/gZ/gZ+fg4O/gIAC6r4hcQt5N4HHXr7wMdWlF/vAo8Qj4hYNeBpp/A42I+gHXRpSevSiENo75/Pwa6DNiFuuJP0dc/i5pUg10BJo62f6uXgRs75+jz+DjnfOufg1O+f/Qzs7G53z8Ufs/n4Nz+f8DToR0IWXCOj9n8/Bz8mBinCy7Z9+v58BEHg8rvA0b1gQvm+BC+b4I4AAACqUGaAC/AKJqcd36JyxXr6/Xq6BP1VV3g59G9+pUu5V9a5tHy9Vy3liKILxfFQf1OvYhCl+jlVOb+H+wWHivGKMuvv16T0WKDUIxAYZYrFttvTT4xdYhZbBCjnTKWEsPm4hAA/kDV4r47TGCubhtqGdvLyd5+yKvevMNRwHW6WrGI3/gICCG79Qb8/2idRf/+gFvWvBjRW8FHrVmEMFwPGCYCkb/xbn9GvuvpsCz1/BsPkDTLb9tvTTiEWStGqQfKGmWBlAx7fTx5IXFMER/X3wICv3AxItXf1wbDZBaPFH6jurAy17f/hiia8JUe9YQr3nrmPYM1li2a7fhOQt7z4Wrd/IIXELBod+4FdUqjF5V90BLq/695a+peq9iyXlnfF/VVAyfo/QdDET7rX3Wia68cikdXgZa37Ap8XrAqi3Q6/Xq4GT+qgXOvqbr64NPXs6hploharx21fsBg+9fd692HdZgEDXr2L7F1Bt8b1EL0Guh6J1XA22T6//TfouutZYtkM2yuBpoRhpTNAXa1B3mBmhN++rgebpvovrrrPMlo/rsXL6DgIzBjLatt/xi+whEj/iB22/36Jrp1bFu9+vFyzotC96zvvwM/wN/dGgNPfLBtwM/t1h19QNN19RNHf3Xs75+8/Oufg1O/y/F5+q8XJeSwCg+7Ws/nZZMDXqgDQ86Fwar+hC538CS0O7Ecotz/v0XvAyg4s8hs5/T54/PwanfEfwOKHdjPfP9AG5dn6F+sXLT52Lz9CFquDk6+A4dU/XQhc/i2M9i31nY3P0fo74hcQsGp3z+fz8mfKfz+fz/QBQXn8/n4NPBIFjxX5vwQ1VZ0k+RcFYGqaOMvxCzrgQQNXr8A2dBJvAEZ1b8EIVJ6c5QcX3gat/Ef4G/Bz83xHwdfN8R8NwAAAnJBmiAvwChBBBAlPdNP4cRQ0Apn1r9ZONMYO7Y3hoNlaku4l9n6/l0xz4I9a1frVLpVaD0YhS+q5V78ER1n9YbcBBy8J/pp6aeIOgNmyh/z9ww9Gmvi6rrWD7nQjpLAmkk7BEWtffuqqvwSFrVoPvQju9er17w7QnreG9r3xt8gIeqn5YhRX9a/XoNRbHPrqBfQsf/0RqfF6P3VyevN+p6+kbt8DdoXLuBYWvmXoOxCjbQsDKtqBj/htIzv19LUD93q/6L2m4Fiyluu3fCVaq79+Dv4HvBwMRFar69fpMm/q4etfwcYHWrHj/S9d2G1jYgvyk/vq4FjvQnvpexi6g1XDNqVMdltyggTr3/X1+GqN36FzHQFnJ6N7/55MC3g24GdaNU8DDtVh2i5jp0Zli904Ts4X0i2S/YF32/wFJ8DN4FniEXELBod+oGnETOvR+kSr9eeYn8DPV6t2Ga1R/X0hNfAuQb0h6Jl1qZeflxPp+uhedcndatILlmaxRMGo3DGWBnzGPlLYDd/o3872dH/RvSYGfYuJlSDXgYUTq76g9rF74Gf5aP2Il8DLrAzhij8GovlbF9KuG11BzeK4uQ/vIsX/gKWj+daEIXiFxCwaHf4GnEed/DfwLexHi4zBRyc/nfEIb4GUCRIfz8Gh3zv1AbKHdjF1i4+CGxaHF99/2/6Oz9gE553kELR4nPwa0vwIWdc/4M+fxfM0gyK5Z3kEef8DmBJg3Oxefz/xefxcdNVi9C+azofnfyASc/nJz9DOWKoNvBEFKtqZvwv1UcZOI86ufT78QuIXELf1gYII5MXOqYQtLg9Aa2D6/wMG64CzgAAAmtBmkAvwCheghFfr36/sv/8Hpv/x6BJSS/T3giquNU/U6VxK9B36CEu7Q/2IUlNAcMqy9FKjLqSZmn3r34IyLXFB/6IdK7R/fonvA61ZI6L0XmCPcCPZxgVjBTba00/w2id+CStfoPRi/Yxd+wCw1Y711fr6vXZYxwHtNM6uNR+rpe8Dj6AfPGL6DWtDOvAw0JfodXXgatYnj8N46AZBA7tn9Uyfy49w0yws/bb/uvsfzPb3rWLkF8xJfWA0cG760Jfp5PRux+Icsb3+L0hdFOLe/6J3qjn0v0IWDX0T2IJ6CdHboDrRK2v/q1WBhot+9GfJ73/4t3r7X2d/F/0f3QGOsUgxdQb4HcSisdd4IfbojUNy83x8Q5l3/xL1kA0fAxVcDRpr6GIq9BtcC6t+9Hckf6lV2qJGdQLKKW6tE1+vq4HDzUTq1R66+va9QbiFxC0IXsAotE1VAZQLHUI4vLjOwNPO+NZ5bFyC+t5vVfX8C58DJBr6xYhfA+0VIuBd4hdp/vwS/BBxcyWcRe5v2vevZ2fP1YDRDqIRLPPi+VMewwTqP3Nfc/Bpqgn368ijO+EPr3/gZatR4nO9COzv0Bl9efs6Pn4NhC4hZPQzvA0+oDNunzvJgcVfA6QbHRc64xEvGZ9YHGvQbi2Ppc6/AzoId8DP4GP0A0dYLcUIQ/ELiF/BMCJa1r6DTgZ7PC9ify4ngJCjxuf/BMCJa1r6DM752CPOufvAjZvQgjTnYnPwaHfO+fz9n5MucQvQBpefz8GnnCjGLy18n0IW1wVgap6/k1OIWdLgZMHf0dBmsDVu3nErB18/wIPz/BHAAACm0GaYC/AJ52gkNFWIUXT7DkEItVVTyT9I0TL4KK65sWKr1ZXhstarm1PweYaod0voT6x7MHKbOirX71KLqu8M6BPVVXVZM2byhH/DdfrGDL/TgQrwSErVvpe/WL9Yq9YIOvW8nr79fSD3D4GWBD8nr6fsQitmxHG0PwV09cJeLb/J6I0HRPr/0SWEFCGzg3GN7/a+VZXwLQJy3ve9QeD5A0yx87bfxC16tYQRX0Y3TP61k6bH4Q+nCoAR2+ni3cy7uBg1XgrqnTl+7+UHJ3+F0Y6IoxVc6roDPR36br1iZeF79GOjXX3VevQd1o7nX1ojfr76X1DJQcnzS3XV6+q4FJYu77rV1o3QajZYf5cQtUB7369+r2/gIGhrhpTP1X1XwI9WBACC6g1FsIewggmLmX22/3Ay18lga1JwEGv8fqZN7+QSQtVA+dcGtgIA+IWu7/tcfEde/oWuqHyqt7+TYOq4N8QsG4hZMCNxfmP0b2NybnsfOq3v5eHoNvQS93ARueFc60/BB8E/9X90TugZ8/+vs7sU8odpkZJ7sBo+++BkxCwa8DPjEXFQhcYiF7KL/+tY4nFIJnzv35376EL4GnBtwOOIXEL+reBLojdALv19DPXjPnHgcewGbXs755c70I64jEIT+CEES19BoX/+joN/At3YDP9aJWJ7vzvi55pc74hHg5O9H/gZ/I0O6QXLS2/CvP52XO9iJ8RwbHfP0fz+L6VM74yOLzNi8nejsudllPE4j/BMCRa1r6DQ7BP+gkeo8fi46l8EPER/k0d+gGzoZyWd85Of8DSB1o/n8NsEUzEzHzMTMf2nYnY+DPwTj61VL78FNVVVVVpGB+dRMIWsCNP7h9p6F5RC2uC3LJhM6/gSfgSPgSPhuAAAAKuQZqAL8An3od6J8/L61ZKtfIP1xPeovWuq81em9e/V4P8HNEf9fS+CStby1opBeDypKDX0O9hGLDGW0ttvTT4hcQsocIUIyzi+tfrWPKZDLUJqcJhqsbR1heZEa9wRH1WLHKDRmhKt6J7X6pION194GXIX/9e7OdfY/DQMJAGQEBb6fe6/86JtV6S6g8CClJd/TTXo3JjEU2iqLfl6DNXp4RhCv2LdzJ7av6r6qgYEWu9ew7gkaqfWv18umn68EYjVeg4wO9CXuwO/sG1EaxeXBiJaFsxmE3691/Az9wKSFxd3vw7XsYjag2uDX4K1viEQtNgEno7dBCsXXjFPg369UyQ9z9/749/9a/EMgaUxu/kuBk2/nFKd88Pg04WrA80EX6+vGIxfs7ASqQkidgwfeiaxcjgvLQXlb3+9/sZ3+AjuoCCu4Gjv74NeCxSqxC4QQnFy6l+vvxkUX1jEcIbOUq3rF1olY3L4dYni39JhDfA1ItY9hap4Tw+DbA66sDfu37/gXf0Eezv4vi5V5df/N4v8DJYthT0GZ3xjCZMyxj8sYuK8Djwjlwyd/q/PvFvfFvrxX4Ja4ED4IoOBrKGMs/wOK3xC+FMmO53+Bd6A11fomWwG+WsfoWwx6DRfaDJU9isYie8D6Kpbtorv16uBxquUQu0v8CuFK47FsNWg04GVBnsW89v+Ud06Yt/fAurV0Ax/gVvwIOIjcRGF4NuBlxC18nAor2fxfMmLfXYDR0dDZRHiODU75/9CGzsXnfP+AiZRS18N6FvS53zvR4mvQ71YF3R4+Dc6+A4dYY4jzvWCOjEsWyezvnfPF0I6wU4ODoudcfCVb3+dj6P53zvRyegGjz+fzvn/Azglz+IXELBp6CkFjEEl6XAQPweUEWKrmwGhgQsBwYEn4Ej4IoAAAIrQZqgL8AnvExnoY6DzkBEbVfX6/XqjxfolTei1+rQeDIoXFZMYtIi+it+uYnwUYOsDLROxirb9dSG4/4eGj73iuF9A5MFW3pp74q9YOedE749XmxNFr8EdayL/WD3X1erfqkm9ag10ELFoQGmWfGIpeNy30itJ6J6/RekGEr2IVQajJpkVL0VsYpyL/o3SXOj++12V6J3wPm/0WKubuBgWKhktNiFx7CgYph6didj4M+CvGIbfEf6L7GE8x+p08J1c8N1ckFuXX/Wb33/X99cUvfFwaC2OsxcIxpX8G6vVAN99AeF1yDcvoCOoCZQh66p6z90hfquBm7qDawPwGNYuwKVEcrAw7w0173U6sn1/69Q7Ky3YGkCdt9ZK0durRu+Ev0XoNDv1ogymMX2reBlk1XvCeTdWxjhpTOrgnuqg3bf9/XJ/iggvZ/9YYn/19i3kpDy7/ROg2O/oDEvdfUI3Qv0tcC7n3j2frv9HcO0zgZwxiOQZN74GKDWuWwGj7fvsZLfO9nJrAw6EcG4jxH4H30FudF76P0fzvIIQmsC7z+I4NRfDO4bFnWSu6gO3rz+d5D9WAbHpr4NuBlxCyYHzWBxAuV6GGuwGzkOhfga9n8QuG2CKZiZj5mJmP7TsTsfBodgjz+frA6BejvILjrxJ4+DU75+ueTAzbrz+d8/n8753zvn4NvQW9iFrmQztf6/t4GDob2IWqAKPl9eg6wGoEKrvAwYEP4Ej4K4AAACVkGawC/AKCvliCfX/yehSeDn0b1es2MRlqqUEgutYusWTVdVl9/UEfVXJawSH1UiV4KBWq6qVIOfXx9go6rqvzGalMUMXkqfglKq61+v0I7vBIJWsq/wR1VVb7BUetVXVVU6fomWMUmV8sHgQRi/fb/hBFDDKGmMG95Omt+6rkrBF1XkXUEJdVHZYPLjUbvBH8DT3fqxXr6T19fghKtZnVYGmtVWid1wa0qxsILy/pprBFVu17++biu4CQWpuaDUYpMyMVELVgfav2/ejPXSP6vWdi2QuZ87q6gZkJvqvr0SLXxWIRYO6DvsCT8J8XIKwDlvf5e/O9WBn0X/9SpBrwcrWMW+MR7kpgwkBf5At39MjP2Bp4uRyAXFOVhCHDSmRbNa7r5cYj+xi6xbCSHqDTl8DDiPQR9LwKFdor9gfeMkL9QbfSwPOShir3YH+iNq/R2OwNvv7+/FsnsayuW7A65eBQXvgZINRuXBXmM74whbEldX7T/snFs2W7/oZ6+vp8QtcJQajWUMZasR16+8Hvwe/JXuiVdVAs9+LZOrpc7/AuQbnf4GlHixhOqwLuXA0Abc/nfwMuzr8MVwlQjg1xwHHELi5Lya9gYRdWA0dV0IXwNNerA48QhuIXELhNgimYmY+ZiZj4NF/3AaPUBw9wHDNQ310/BD6AWO9uzwRwanfP5/wEDl2sXFTXXR4uZeAoeIJg1O9nlxcRSyWA4QOGfzvR5ZX/R5c7wbH6Ovl5+j+fzvR3z+cnP53z+fz8HAxAkXpcDNoQsohYPRKBLeBmwI3wJHwUQAAAINQZrgL8Anvoc9+vpPVpPJrX4Ia1VPB68CN1rGKKY1VJ69K+6BH1VrrX8HoxCLHui+lvX34LarWtUim8Sh/sGBX8XwUr8X+6vWKnn6xfr0HmBjdBBQQZ1oaYwb3k6a3VgmqvWp7uH1jnbgrNWtarX3WCStfX6P+BDrqoUX1er1QGmtdcHNatL6zu0LXvwRkVdnfKuDyGke1iCYOMDrsZMXlJV8ENa+r0Q6YuUukA4eGJPX3wQ/qdLELBrw2sc+G8n9314hTZsGdE7v/R/UPcKKmUfrxeXAdUEOLZG3VwEB3Ayd/awc8NLx/68ZvAy/B/U14do/fonSXaJ7dfd8gxF1BuEEJcqKtJL194GPnYgvi8/S539192BZ9hGiml61901VQNHVdcGtwM94GXVfcWiZdfS36+zkyfV2vQbX3k1gZN06LWd+gKv9ZdAZQMtnfP9cHOB5zjZC9Lpfo8vgYe9+rA4l+Bi+CTEcGvEYhFkxfGyaxb/9AVch/285NcIVwKmIX4CAg0xWsfvA1/J/V5DvVclgFB2LYc6g0FsE8li+jsQLjuXgafidV4hD7ELiEXEL8BBQacDjiFlO/YDd9gWPXIfz9HXO+fg3P5+avFeI7P5+j8HB3z+fzv+hCSQ72dboBo+vPLi0alo8XBp6DE4L8+BXyl/wMUs/Nz614PBKDN4GTIIWBC+f4EH5/ghgAAAB7kGbAC/AKAT0v/iPBEMVdyV64rWta+nVVUGwuQuACAnV+BSqnTrXcC74OB7HKtf7wCQQHBQusZyiKbGLzFYfrUT6LlB34Jarqv6X0UjO7xN0QqMgIfG10smUIK404u0/1YuiN1r6h7ratf/RXgQw2SBj0d+tfrXncsG1eNP9ax+GmWD8C02/TJ4I+q9Xhk6rlS8v/8131xUHSvAo0ZrWBZ6IRHV+JqSfF5dMjCsurgWFir0fqGI2qEIsG+B1iy6rqvA4awc0Iy/LWtZ/6KKxi3Lp1rqBKlGIylSDcYi/67/Au+gW+uWwNICARfa+AgNf+TVQMyvIIWDQ7/onv19/V8q+0V/QCDxb5S1A0L0HIhfDp5MO1i6Av0SqG+D4E2/XpRCpg2GuTDJiOTAo79es71wOmf9gaEXvYGbE58ivELBrUDCvYgmXL8ta/RGo/nYmT/Ss7P8BAQacDTXoV369JWva+LXs74shL53kOj+LwbnfO/wM+v5K8/8DPn5BC+BpAtUdfhjEIbiFg1sIANmx6CFe/kX9esVjI2ZoPB7xm1r6PyH9eBk+BX53s/YhaOsG53z9H870Lmo7QjzvnJo7PRyc/R/OudYOMDDs/R/O+d6O+d6P53z+fz8HHoE3Qj4ZCa1+hduf8G/wJHwJHwVQAAACbEGbIC/AKCvtDIpC+CGvZ6odoal/8/KnjJv/Pi6Zt9eEpP9VB5hTJtI3eBxr4mf4Ii1r+/V4P1xMceJkOnza188N61FPNEpYfWuG9arqeH3hFI4JKqq98oKdarVV1yr1a6HzVX+WtYOAjIGMsfLbemnxi2mrRe8lE83fYIi1fqb17CGKwGVNP9G4//oL9VX9B36Jq/XvQRkNHOEP8NHNnmzOiNMZXf31gmJqTrH+g9sENFbFogD2nLbyagpqqqtar9d4U1qqqtVVVVfY1kAHbtcbqq0f1a9gceMUXTKIWDX0I5ErAz+wGDQljoC/VqVw3BSqVVb65ivQjWLf9i3d8IYaUzLe/kx/vRcqGL6DzPRPEh3X6+kqEPA06EKewb0AQGcXzps+/l4EFXMayJ1b/xDhqm/Azfr36t2dora94OawTdVVc6li4jH7gY/gQOoGPFyztLwNVWBR9aI3XBsNlnzOvX1dYOXS316qgUbsCv7HJdZ+sX68YvsYuoN8DrmsCvXqFqb/qxdZYvy+AgP1qsCzvgIKDb6sAo+S2sXyldAXeeRdgNH31YHHiEfELBsIXEL3qW7der0TsXLnlP4t6XP9xWJXsGvEI+IWDc6/o1+/v76O9C++L5mzvnejxcHB318Hef7gNGq/AcHp64GnP4uKv4GXIfzyGydD8651g14GejvVd0Bn9BDQjkELWBr2d4Nfo/Vda53zvR+xC+Bl/4jxkc/QbnYRz+fp+tnfO9H7P2f6AKDg284WbJCOzv/BX1VV1XOr9BLsQqmWBvAigZt/KuBBAyUEe64OsBzZMDNkEINwIHxPwf/E/BDAAAACUkGbQC/AKL6HFv19Xq8ngk1r+n1r+Dha694cq79E5BNuvvwSa1bf6La/X2EEVa1/B1YHChDEq20g4da2P8auPwSdVivwT1XVVU6flqqqDnhuxiN7GLqT0VMk8/WVmfiH/sLdfUYp1rVhCUMAywHVotp/xCwb+hAxQ5zR//0HqX+vogW5/Rrj3pv91k/MKO00qqvCBISR14+l6wcj5g0ywM8XPbb+MWmQmtfXUJfMrwdiFJi9Fcv1f569CPFC3d1ei5SjFNkWIWDUbkwVwGxi8vdfHQGn1/onsIRYQc5AVz/r9Aaa0aKk/XH/RmCCOXkPf+gioD2mmbb39XAga6CtF9IOk9Br4Jarqv11ZzqfW/9Xtf7gY1rvRr+CaCGq/sXPKThL//neuNveDgWiBplssciSJdV013ooyngx2aGH/2TQ+p7sDLUnxeT5VQGX4Gv4GXXS+g1uBlXLEE+B1/6avxeXHxjv1XAo1RgGf34t3q617z4Nzv2qxzGdBDItwQfAy16xfKXiPepU70Sq9e60LMlv+DZ/Au19UvXR3s798tcHXfp/FexVXAY/wMOd87yiFquDfQHHFzXzoXJehkWdlv0RpBCLVedC8651g1X9HXO/h57S9Du716T1axC1wcVwI8Gx39gaaPFyjJLs75/sBo6EIXYha4F7EeIWDU72fo6+BpAsVYDR4uOpe+jx9V5/z6O+fg14CBvAv6wR7O+d+7o/R3z+d6P5+Dj74isDRlwEjvAyawGDxC/ghCydPqDZcIeIQZzrdAaM3wIHxHwIHxHwQQAAAjpBm2AvwCeYHAqHNjFvJ69F+tq9bQai2OluwHwkJfGLcxiOW+Mr6siX+v7rBIVVVSp4WwfdKCkUvbRO3tGgjqqrphQbZP+9/y5ILvIbH8G+gVn7iqvf7GZuAixCwbiFmWlKjO/BDVc4XevXetTCFJgN/X369iFNt4G3onViYIDrXWq1jBWq9/Edfr1jlXswf/9grGGU9WOx/BsNQnf9F/SbA4371IhquIS9PcPL33+CM91UqQdCFvB3Qh+whUyGKVhGCPqvs3/4eCPvFSFqVQPy9+iFVVYIyrX1DJRRlqfFcHd6s/X94uiGFi3vfS98ZB3w6tyvQlMkm8W/0R5MIAIWhbFL14KaFy6de32DHBwMRPY5ddAe6L0QLc36SwMdXvwSE1X0HGB3d8XXovvP6gQUR87LJetfa1dAMn8DR8DtBpquSpadWAUkQvdAcRSnSqgQaGSW7gFBr17v+BIz9tX4NTv0KAw4iW6l66qCHF+BvgfaFvl3yCF7ASODepVi61e6ArqUXyiq/r617r6P67rg2ELiFs/2A0fb9+d6FreQ/1wcHYvP3gs1f0Bs0LYQLhkzss2BE4hDfAy4Nzkyn6rs60f7AUesCjg3O9Hl8D4AmboDj8DNQS7zeu6Ay6rzx8HQ9lq9/z+frD+t6rs/VefoQvgKXnfPwaeFwwbqVnjjL/fvJta4KarqtXEUvxfypYEEDFFxxkzOo4y8Qt/JgZIYqupiOv5NTB2IQZkwMWX7ELB78R8CB8R8EMAAAAIMQZuAL8AnmEKHPP4Jq1qqP6J9f16/g25K01eKa+upfBQXVWqqVIO+Lk7RKiNxZ61VVXgQLNe4PMDj3hyBfnKaCH5OmslQ3dzAjIqqrZoCGAB/gr8vMyoZxG2H4Pn/1oZrt0W8zWBgcULtq2ra9Up2g9oDvlqlKsx+CPukHaAUCrtn9ZPN/+GEOE37yiCk229Oxbm/X4KC6rqo7UNQgO5ZzGZedINhuLXSVovX6+rwQ611J6J1LCevLg4GoiXqEIb+hHS4vi8uEAtHf8Ela+k9fUMzZ7GcqQbjkct+JQ0//+tRn9Quve6IeS8X717oF9a6XsDb/RfdQNC37gaFr8oQMpmUwbUA+aCcUno3U/BHxfKPK1oP0djwJmhH3SEV8JQa+pUxi3LsBo+wMG+BbRX/Xaxb2xeI86dwFZd+I4NxiJ5EsDL8DJWpC//6+CPOz16J7uJXuuqgZOuDnA76sBo5PxEuLIId6sCTvlg6wPNCcviEVMtdDeUXQHHn5frhWU6warFRQOc89YW518C3s/Id74PEK74PL4GiDWoGXEc3Ao0L5RSDENpVoBogJGT4NjvnWXCkEYpaz1C0WlU75ybPPnfsBogTdu4v0A+8HP3gWuI7oDL66OuLiKcN+efoEXP+A0ViFg2PLn7O+fs/10d8753z9n8/Bx84hZcBN0CZ4PBCBP1xfFQe/AkfBTAAAAIBQZugL8Antci4rQx34JCKvl+KKta0bl89v1j1CeCGq9q9bQfvE1BCRVzxRPui1AQmBJsyrk1BJrX2EcA9IE9AX0B8229Poe5vP/ZQIXSy0lxKKOOb8XVtuDvgkItd3zmqqqb0V9+CX2Bxwf+ry+CPqvdwXLB0Gal8HQxE1Xr1+qZFFuL82EcB7TTO3vXLhOpUocpCDyIGtQJKsSceorq9UZWnUqrFvfHvd6+ujVWrGRMH8r2B3wdVoR67177RekFudSXK4Klb9E6/Rb/r79eg1EKHctEL+vU1A+wa/A41NLVQMKt1r+hcnLFk030i+1h3B91ASiv8DUve0id2Bp4hFxC49hIn7sfwaehubGZMY90Xugp/RTjOvoDjWuwk+rsU98DWjd3ATXgZQmi9dcGoviLlzr+jHSTEfAsZBTLjZZm1+iV83w9BzwMt/IdnzvQ/1e/53louhnOiKIQmDQ7/A09gjA4/yW9VAXed8Wxk1ne+BK6SXoOcQ6wK7uvO+Lk5Yt9Z3xb6xffwM/sBl6F+rwMwCYxCHwanetq68XGX+Bbk9TqP7EIb5cHHAyoZ0p+j8v1YDRA42IQ3ELiFg1FvH2fz9iFs/eL0d6yZD8Gh3x7LV7/n5D/YBQQLVH+wGD1/R3y//5+jvZ+DfA6UCbokQsvob4xC4hYORCDcCT8CR8FMAAACI0GbwC/AJ74KhFaqqqq4tj/JRGSXgkIq+UCW9x1WB6dFqDrtGcgSC//r34Iar9P69+u36+3paL+D2wPawgt/09vDfUnVj6v1AjretpGb+tKDkexVf6xiGzrLGqGmWj+BH6/XevRfdQIy+sWzG/6engkLqtfYLCqvqruq4fgk6q1cUCGqqrHgQaI/QBBavBqOmSO/7XvA8ur0XtuCuBHor/rGkO96BwtdsHGr4MFyVbA41q8HWDYWohzrsEde7aXfw3+l78EJK16vRG8lH92wJ3cE2rgT/J+2F/9X0MyZ2G2ECLrX61+ueHwZi3XsIQiGGWMgQ/TT9zgjELX48CdR3O+YRl8UzlYi2bfv71bvxeteBG9+IRYNvXjnGLfwnqta6ergaV+d0v8XIXjf/RSpfArfqa7gal7qBkg6/r61xKw1Xutexcmxjnmsey0v169E7F+u/vg7Oi/Sii6+nXqFqZtfAx6+v0Q6RAyX74EiDYQuIW67sDuBg2792BnA7KlsQvUDhVwNEG4wmmWoGnyfT6625b+/4Gr2CBX68QtcHMG9XVE9fT9aJ2dlo79yZ3tf1wJXwLGIWDY8Xn5MDJQz3X8COvfA+53zvYhD64uz8GuqCFYgmRV+NlkSxc86Z3zvNgXQEHBz8h5ewGiBGugNPO9HWxC5+Dk/ef0vT2fzvnfP5ycQi+Bx4hYOMDT8DNQJs0y8DR1/1wIIhAnkf8CL8CR8FMAAAAHyQZvgL8An2fRfsGnwXDFrXU+UYcPCAYAgnr8hJLzoVrWb1vfBDWtoEPBpRiPJXiaYJC6qLU/BHVu24T/WUlurwdjEbyJcSCateq9Xo50p56giquXfQIarnxXqVcCCEFCD0bLbemnvwV61VarW+8fGNevwSa1c/W0HuXFdL2/JX34I6qvUHQ+QM47ph229NOvi6wNKnNDD/wYcKqr3iyeu2/dwMSjfk9X6AQtc+NUO5aNBsMRlSonorWvHpb5v/w8EfeUmEhf/16UQsHXrX6zfq1+iciT8fRaWWggoOcBAusDPRWJvVi/Rvfq9XAyI9WMXsg39WOgGT60arCO9+/+Iqxv7RbYuQv3lvg5O+LUuWxiPOirgYu/0SXWiS8FOhbvX69Z3xP1ATnfdwKncd3wajZSYeXGLeifpf8m69QyQvfExNcD5JfV8HH9gR8wsnq/rl/BFd9t/1f3wbDCbnYCsA02JXEOGBTPA09Aced7++BVRm6gwWuuDdYiIBHi4+goaoBw+vwOPoDPmO9CF7AaP4Jb4GODUWxxcMmInrAw6v65zoiv4ODsbn+oC9kouZ3gJH2D3QiOPjrX2dTYDU7Cd/MLiKTO9HZc70dFoQuI8/Bx9V4js753o753kP5/Pwb1oEmaIwEDibAP/g4EIEvf1wFxAAAACFEGaAC/AJ9vJuh3Vqvfr0vr/9X/X9ev4PDf4w+h9elVdVzHOaOa/5/t5YeXiF1J2rd8HeoIiKv03QJCqq/lF//VoET1bvXpt31UHPCq9Zf+JiZ90Rq9Hu/BN1VV5eqlrwY8YjCH5YxcUGx1DTLFNwH8MAQbF393NIi9N9tU9iaN03a+/X1WG6y8FVWzf+P4Kpbpp+DYbILWlm9Tp39nqVqHv//fs+31osVZq9XNBqPYQr/X2hp0rgxr16TF1zGLlEDyBP0T1KoEYJ8vwNHArd06POdA5wc4Hn0BnyC5t/AugakVzGsgPfG/JVr3QMf6L3YGoDR0CnjF1jF9BqN4fpmqLR0iJvWBlATWLZhLyWd2yXA0WIXGTF/Yjg1uBdV6XgeRdYGmjv4FWuvDPxwMNcM6JFJwPn6x+r6rS9wbnf1UEt8QijFXgeRS8i0t4a9gXwdd6suuuCNeq/4fq4FaDaoGn7RvVm9v34+Jp7/52LrAzAY8WhVL0BlAQEl6NXVdQVbvqDaq7BSBrkxfsDH8d8Xo7E9d8G+1/5OvqWDU7zX1jdDmI91rXX0f0s3A0Qa5MlPJfv+moz0eWhCH+JwcaFzUE8p+zrXoJe+EsRCefg1O+f7AwAFHqu65D+v6P1gXcHB3z8h/O+d872fzvR/O+fzvn4NvQaOqvibAIL7++/BVVVVVVTEZzqg6vrirwMGBD+BI+CuAAAAHLQZogL8AoRP1/4n0Mb9ar1tBx6tNuiFVJWuvwR1X+SXwcDJFKOD+i+svv+idS9sEJ1VV6DvVCBir9R3fr6NMEQwCDfQKxergvKnv+ECFEA9TGU2/g45EK9jFNjyXPFBAgsEOMMLaTUvwd4uhkV4OaO6sR14+rJvW/4KOq6r1XozwcDFnTGKlSrgcaK/eteDivSC8RaG9MLr6/1ywjOEPOS7/By+Li0IfCC2pJfqtEYifQn0lvQxfQcU8gvP7+2rfqxfoR6qgaJfQnoNuKxCLQxadcCTRuuhdekEv8D59qz9ddQEpWBn4hcYvKDYZ3qwGb71sqwP8EJb3p0U70L90jTwV1rGIhf0HHoscvDPsLJa0n/2uStD3r61+Si//rF1A0dXYzlWYhYNjrYj7L679Cj1fIdn+BcoQi4hcewsGKZq88Pg0zcYQL06l//ROxH4HH069N698knAv4hfAy4NqgcaqBxql6Xr9qzsTLwe38Gx2Wj81gbeLiprOzzboV3wEBiEE8QsG+B5dV3X3oId+rZ2JmELB18uwLHZtH7EItnl8DOG4Oc7kpeuz/QGkDPnfP9AaQ7nXO+IXsAnODj0CboEj5aAzYEP4Ej4K4AAACMkGaQC/AMavtDoGD6sFzLKT1examwqQajZCYCUEtH8FB61VVUd2suwxqq45TZ/PqzAAQwGH4c5r3wgquCUsu82+y//ojz8UCUqrqudINfBKZV1Xyn40EB1XrVayrI3LVy//4ISKuxEbwcet5PQj1vCKxo2tdVrVVWWqWS/9o9WmlfxHSKdL2kK6DfAy7FqA9py2usEJ1rZ5IJtaqux43j5C8CWAq9vp2McB7TTBxyvX3lRDqqrsCi4Pa0I7w/RZ+00Tpa98HHWMoSACg59CEyMEIk5IaZiNr9a70nCOXr1qtZNUM79Hqr12wgjhgFMRop/7ELBsEZKS23pp6L/xN1gZ/up5PTBFVfvET+tn//1cxuD/pUtQRa1ZeDGt4OhpO9hBlmQIe23VMnWBF/qO2HiQjY6+tf8l6LyPF+slEargJTuC211Gwa3A0rEcboDP7D/wI1EljJQwKYb9gZ61QuSUTh9fS3ovV6lSDmn61rour1rvxbJdE4GfBvm1gecvDtHnzvQyYNKZ3wOnwNCO3wLkHQwmnSGk6sR537A49v9VycIeBh1g94zjtneDQ7E5+sDT6AdPwOPsDT7fsRk+TA0+wETrh6DW2A2e7ur70M7wRaPyfiENg7sVz81h/jI7WeWrA07wYjoOjsfJiPf1+Bg518DT66fgh+BJ53zrYhcQsG3yHJz9H7O+fzvddH8/Bt8/oNe1+h3RIxTUOleCMIhNqz7INrWIOgzAgfE/B/8T8EUAAAAGmQZpgL8ApPmGaj3gIHohVSeuFGAOH/sbX18/nXWSHe+mWtd2rH6tB6EIiAcBGXf/ghKtbX6vNngkKqqqVOoolVwbF//sII1e/xi48nq5vH9FTj9EaVdcHeB5q1F//VnWiarqWtCe/XrL//B1a9r1oQ0noS+6DPUg6/BOS97raX0X36J0HN+T3f/tVgUaLSonoR7GM4D2mmfeXJ4I6r+g39W+1+U1wp61ubX9qH70EscoZFV79TVAoItfqxWF8G64Ts9fSJvCC1Pp9/Vv0+/64lcrz4PtBhWxhOJ1wEiEOuxihgFMygQxC9d3/aI3iK9neU/4LcgxfYxdQceoxVfr1WBl16tQQUNKZW/6wWYEQ7LXARnwNUmBZwc0AQHiEZ0Jl7gYfFaFzav6OQT8DNBzwONH4r6wYksQhOIXELBvgd90BH0d5KH9eBl+Bp53xcROCDvA8ujxOcmj/sBA3XR3o/QhFxC4omDY/n7ELXoId4GXddiFg60Aic8I0fzvn6oDLkO/YGfR/wNPOsG/oNGbX5/c+iXfIIW/8DJP7n0++YQsHYhBuAvIAAAHHQZqAL8An/gvEVrqo7F2ptZ5fcpf++J8VWsy8rlB16Eer1evBEXVZvwS1VVWZi36wOLNhhiAw739drxPMPD54wvmfgSAaa4FACTg96VyS4SBRVVVa4voXWuqr6F1VVVVUHRf/4l0+CTWsiqxbIHaZ6lwhZCu+BAL/y6xk/a1B+EVDWX2faap/RGUPJAe05bssG2x00z2H5D1r91rXol4N/RvFDEW/4IeqgtoinX35N7riliusEdV/fgoquq/YhEMSB+OZysXdKiemwVpFfT0DnB3YF/KPf1a/J6+8M6fhx+H8G3r03r+IGYh4t5buve1wdXo3dg5oSfSC5KVMIYaUw6b38gzlvAs6GLqDUbkzSLwcyWPyC2bUo3J+7g87CAKINzv1Aw3gZ/Yd4iLx/Mip+tc7P7oZ6/UYpNt+LwbVfVVrk9NO/0XXeutV9+vZ38DLs63/xUG4jr6Fok1n+wMfvXWLZZqsJ/Ay+gNO8DTg4Oxuda3u/v7T75K7rrg2xCwa8DP8DPW9UEPX2/gYPgYOM6VK+hC9Cch+DXW67r75N++64OTvR+sDBs7yH6EL15+DsIIEi+7bfjRC+BanGrn0fl/GIIegPSAAAAIOQZqgL8ApFaHfv1e/X8G3aIVGTWBIgZqLfXG6tfr/9f/r1+v69e/Xv1frg6X2tX6710v5F1r1evfr1bqVIN9CET03o7Tj5AnhUFAwpl92/NAQwCH8NH5frBTKgZ8F22HrB/g5/gjrWNFD7NHDDGUMNUvn8MmgNmH1vT8eyC42hr1+6v9eov/9DET0HW8mqK2PwiCOgEwBB2+n8G6M57gm1rWvpNKvRfYSyW7pp/ELj2ODFMXf4NRiG832itOLcuAv7rBES9yq/NrX0CM61tl+/4Ou0asYi/G//hBe8bk7RE2d69e/X2X7+gSHe9isXBRrVayp+rQfcepU/RPF1r9jSVljnB9260VqEIoyg4F9+BBwbejRyhCLNwMvUDL6Az9aGdfIhbGqCsDSVbGX//wICg39GyMpYDt+B59AadXAlo7a35Bk3xJYFP2Wj1YxeUG9dWvgX/QGfqk6+Bm/qwLNELYl32Bl9/T2O5wYhcewgGKZq88Pg1GIKx266r6fr6XO9nezk1gYdCFxCwaHfzawNFE6qBA+38DQ5qAy+vr1/T8Gvwc8QsGv1gadWT19cSdHugHvg2+SvrugMtCHksDPoQh/YCV/oxUZcHf4CYxCFwanevvAzZBD53nrg3ta+zvn7P53sQvgZeIWhc0dj/1PJBweWj+d8/Z/O8p+j+dc6wcCFoQuEECKu235PXoD7gAAAYRBmsAvwCkXod+/Vq9fwd+rxHraW+DqrBFrXK8XUjFSAj4Nebj8P0N7vvvgvFN23rW3uavSbk6rW6YkuqrX1g7ELhBQ0pmze/V+61rw2Sq/fdausFgjVa1VVX3XXSF1B7YGnHejXoQiwbDETmmvR5Yty5kYKN1B56xybDNTt8FslwcInfr+DvlxCzcFJS6rH6Yugh+n1f8CJ9dXWETwceiZi+kV3gS6tQlQo0LwOnf3q7qgTVrWtjuBirAsrEIhciFg2GLKkTQI6JVV3wI/6P1YGmvYxE2g3wO9Hir1Kq7A0rO9/fAiyjOdIN8DvqwGjksDjxfOn4Jq1rWe/7Ay7qBog7wNPrsR9iO1wOm/v6wOOhnHbGLYg1OxOfivlGTU/gZdCFg14GnEd4ElVhuje6Az0Wv1a/oQhPYGfR1g5P3Qd9dnfFsVPV9W8noSWbOTBsdn/QS9JQFTWT+pbOy0dl7+llPF/ow7HwbfVffnesLcR53s753zvB2IXwLGBI+BI+BI+CuAAAAB2UGa4C/AKQvhZDrF+rwf7rEyN6/gQvU6K10rfrtRfX3VpsvXgRK9jFE8SqG2EAxTFr8zEzH9Xnh2UHmegixvzV2UE2KXf73+df5v//RAa4CAo7TVs618/3PWX2X81UTU9cRqqquqeDv0I6Z/aElTeEFSxeqnqND/HwY+7f33iarX6NhEfAMLBHvjtN6sg2G4jgM+SDMPZA+2D8T8CPRNv1/Yt5I1k9f1tLlB566Rv5K8a4bpvycYov6DfnkeH8MmOqqvNUqfMiPvwhXq4G7uB+7wRXfNPMMX0Gh3x6hpljJ22/+je6Ebwc0V8v/6sxZLpruAg7vvAr0JT3zQar4N0RKnxaI5WCKr1wLtj9syXv/j9HxPksCyqELBqOkJh6T0f8n1YGkBGVwUy2LcG/r0R9P+6gYOu8DPg3Xxd4GfQle4HGjvi5rxXEwa2KA40IjaoCX6BHq3Ruv5xCH9ANHBqd7wPP/pegT63Q7rvq9EKngZwI8HuCvVhnWG+dHv6wMvr68Qhch1g2P9aCDUfzx9nevuvqGawOODg7Ln7P5/wNPO+d6/EPnfrz/gacHHhoNVrB+eF/gn6qkl+r5lgZQIIEqev5NTfoR1iFpeBowdCECOXAlYET4Ej4KoAAABwUGbAC/AKX6GNfraDcYjFtonwSdVir1avR6/Wv1aDvpYxP4IiKqqkQj1qD7VXMfhBHQF3AUTFW3pp8e4awsWT2/6HuHzLFWaf4PvXpevNFFxynuvn29CsR1VaqD0WjAPacs5d6L+l8Wr/r36vXrPa4EbzYY+HAIbLT9VDUcrLCWCa7a83VfoR0G+RovYxdfrGPAw1vVAW63rdU2T9d716bVbfr2GNP61/xP2MmLhP9l6rELBrwW/a8iS8GPrsfFiHACPvLDmgTgZEK74pHd8OKRP12q4U/X54FHBqd/gZ1vl+v18p/rF4Paxb/V79WxfSrSqzeLkqWri4PMD++9e7Aw+376y5BbzUH2B73YGKjdLwMt1AWHcBISiEJxCwbeiVWEv61KL+qeL+Pet+uD0QiyjedM72d+xeD461S9e1L5PVmd5Tr4v4Gf8SrQanYaVXA4yV1QFvnYXxcnLO+chcZ5eBn/AgUdHoQheIWDWhQFHP1XvwNGhcIcpREbVdCFu9EKjKdYN/iTy9dcDhWBx0eXPwb4v11gced8/Ifs/8D5B2fqvro/IflEfgccHAhAqU2DnPgJXB58n9A4wIfwJHwVwAAABnUGbIC/AKN4LBy1Wq1X78E0ns+OvpPV7rW0CD0CLTp+v179e/V4O6pSp6oR0noS+7vWKBD9GPpS/+CCzquBC9en0X9ezfwD+wWFnzBD32Vl+J6qqqv0TqL//B1i6uV62xihplqCnNKH4hgEFpd3AMjQQeDvxVrJ028fHERIPddUutekrMXVQcejVXrlYxRD9+gM9HH34V1qtVEWiL49joXEFwXRPeJq/wXLr9F/T4h1ZBxUFswxFDFMTrithGIL89+vVRSK6vV/Cuu1sQb+veCOjav0Jy/VjGKGBTLlvwhur+JgQQgigSecYbl79ZqS+aBAsDjQh7FyXmoGODoaxJMyO/oR09i8wuNmZUngW4GvBxaAwzD5Z69/oWxGlt4vq0o3mbfg196EdBtcvfNzyYuhabsDLoQslaM0HFwNcghbP0dn7eD/Aw+gJPoFPa+iRWLkvi2Wezst8DBS3g0zoTB5X08gjzvZ/4CAg7P53s/IfzvQhbEfgZcGwtgiGqFmlPBLn5T+d6P4onEL59WAUHAi4H34Gqg53fBxwxAXkAAABeEGbQC/AKX6GNd62gQe0S9+i1+tfq0HfqVUvq3eiTwIXr0CIPYpta/+CdC4/6+r0W5Nf9q0ngn6qq8oP+KUdqxA/9WlfBlgk6rKzePx/DZd7/gzcWxb+NQgB1NSzwfdIsckFsQbCpNxSP9BwMy2UeahCX3epUseyG9fe92DGhJEv0ToOLgYVvVgafb966+19Nevq9ewhPAYSIvT/AjcVjMQ8AkqYxG14GmvQcHWsCTRK/R2vF0TkRxKOENHEMRJ1Gv4NswjELYhfA411j3q/XnPiefBGVayoy0Bp0IXELjIg+dBsMR47WdZDv+p8muHOgNesD77gaIN+P8N+wJfs+cv//wER8DJ8VQhd313UHFff3xH/DfwSUdYOT/f1oz1krl7r7FsTMyr6IeSQ/KdD86waHYJDedglz/wMuf34IOft+CXnfO9iPwYk7A+6XgZcCAMjqVTvId88fQhcQuIWDg72fs753qwKPsL87yH8/Ag1zCFgQn/AWMAAAAFUQZtgL8AxC6wTjK1rVFWMQr0CL6+/Ql4EXRog3EAh/oNXvxXEEoOXiWEVxeLyfb//xC9B8EFlVaa/fr+X17ePhhL32v5Fq3YhGFYO/RfyWJost3hCCaqqqrnaY3Ae2OAQWl1hpPF5enr4Uwd+jek9e7VEynvBQdarqsVeCGq9kHHojT4rR3ldvMKOq6qqg6X2ie8Xv0SUrxeAkUd04heuhbCBP0G1gecwuNP4fwM82F9dL0G/qdL9ckXqh88Vp3+qgY7xf9E9JQHH8UrkHIhf0TvtcquBrxb+75sWtfwd4v+hYNdi5p2YXfL6EdZ0Pg79CPS/34uWnA4lawI/EIsGo9ia+98/VAZ98DXAhcDLiOsDD6A064qKwM/wNODk/VLR/S7zegkVLwK3VYH3iEX4FCDQ752PrEAZbP2d872/BD+B+vEYOj/T9ch3zvAi+gQdVcB+wAAAAUpBm4AvwDEZNY/AkF8IcfzDFVVAi+fVtuvv3XcnrUT6J0GwpavWp/VpO5RipZQbaqxiFn9fK83VfgirX637/o40UsYjGzoN8Du63RDg7wSCdVaYexSffe+PZTdv1yL6R3UMRvYzmFBvi6+Lwe+8Ex1rWs+jMCXxCweDmUa4kq+gh0vo/SYGfjF1jF/g8xIax88ol3vWq0BpvCsmtbwSLBDWtoOlw7iOq6rELP6uX6M03qxjJbYxZU/BMElrWvUG2IF0MhUR52Ydayo79wErZ3x8hsj+/nEIsG+BnzjOASs79AZ7u/p9r8H/A4+TneBEsDD74kQtbD1einkg1O94v/4rZ+YQvYGX3o3Qd/4rKLZZqQZ04b+/ERMHR+XDua/r66oDjg4O+fo/0Bn5/O9H5K+/P0d4OPBCGlVVysn0v+iZWIWBF+BI+BI+CqAAAAGWQZugL8Awyw1wQiq1HaM9E79f/q8CL4I6rXUHgxG1HGxHG0PwVi+oF9B/xbf6ELB1ejf/QhN+hLVxILz0l1qmXurqM2kIig1wEBWOSejleSWfm8ocfCC7u5RAq9tvTT+CTqrqCOByZJkR0VRCxCjStX65iDn0YaoiJ0ilIC/SsvdEikFsoSqOCvSer/r0H3rPJ2vSrx8WWtVrYhYNvRL43xTD2JmXbZZffFxROUQEShGJCfUDbNp/peNj9arWta5UI75VtBxYd9gSe39Ea84Yrga8Wy2ksNhgJC1VVffjNYCDo1Yhcwf/9grCBlP1O+d/wacWh+fGLi66b4GT+vUdkdi5cnACDf7P3rKqE1y8Xxmt/0IOpB96E9NQgO+KxNi/YF3Bv6lVZ1oQsgvpUD8Wsyfop7Oj3YHHFYV2dFzrBqLejjWBp3UDP1zDEIp1wMuvQqva8DDoXKbCpBroDPZ5c7Pi4q/X2Bxz8ChVAX9CEPz8G+B11T3fnexCyYV1XByfo7zn5Po7wc4H/G/rwpg6+BI+BI+CuAAAAWVBm8AvwCgehjt+Mqy/PrZJmj4E3wpVdVVdVrVj9ddf6tB16N0Vy69hRFl+CGqa8IErEfBRg5FL0H/QEevVk+qKVIkOKGhQbb29frWjshkhn9jEZI6QIepj6rrgReJRHrhRW/Xq9ag5sDvV5a1n/XsXhygIy6L66rJ69+vrGLeDb193q8gR9GDfk9cv/83q314V68RupVtB1QFurVZ5zjBmSlhfyZ6JrCGCtph0VXvWsRKD3/o36L//rA04KP176XoNDvi8uGX9rmrB7kswFG/U9fr2LzZXwjBz6nREX11XrFYt9WNy9Klwgvfr3QFmvQIIvn818HHq+MIWnXusCPUk+flFvSSiFg6Ot4GXeBd4tk3xcnZn8W69usG/wMvoDTrA0/Au4ODoJxIj8DL68/8C52BnAhZ3viYNhBN8DT56GN0Bxy/fwfHi+nmP390BpDMHx3kxWhC+BnAm3XB8IWYYgUegPiAAAAFYQZvgL8AoOsb6HfgQ/V69f9atB6IWI9dfrBB0EcMMsH+Ztt6afELjESdIutFa90UdunROxihjLZQInr0q2kH4NbAQ1Y5jFNmYusEZFXa70c6p/L1X6sQdDEQmZRAvYhWzEnqWZagkgQxeHBQMi3wIC5KtSourS4+Qq7UIIj3+23/WcxiypBv6tFV1VKyUQsGvaJ6reIHxsVocb3rW8T/Qq2+uDni8Qvgeax4xbsIr0nS9jFpEDYYtIoiXwOOY/+idSwMPVOnWvbV9E1+tQdYGn4GmimsfPJKm/8eyyT3vWucmxcR1B8LZLL84vVN7/wNH2EPQd+LkoDPsQvxJa19isQsHWBh2I69DPZ4mjv2Bn3XB3i/gXfgWfgccgvpegNPO8tAceIQvwMuDj6t+wjlrv0OARQe/4vni6PLII7EfgacHn5/O+d5T8ohaELB1gf8CR8CR8CR8FUAAAAEtQZoAL8AoPod0V6vAkVr/9X/Xt+KSwQIfrKQv8Zgk68MYO/XNEXq0BCesV2BpW3L7HuEfqYbQr3bb000LeT49mF+rX6rR+t+EeMUvr9eg/eFetTetV69B+ngxVau4CE1fmKtdtQMjyYKKJ369Bv63iBmDk+dllwhuuhCwa+r9a+98IIoaUwFub3rUT0KrWtYPvRMqfg1rlJcGa/xeT5WMcNKYVPg0VoO1dwIqtEevIvrkxVcl69Xr0G9wSxA9y839/YrlTwI34Ja9jFLzZBtarFpur/UqRQ/re/xjyzdQPEHgtbzYvR3lqGvFawETg4wNOYWyFy+LYmliRCF4hYNTvS/o/KfnwMPwMuDfQGmJP53lELB+fs753x7GVe/y0L0IQ3ELB/gNHE4BCcB5QAAAAS1BmiAvwCg9RXoY1+toEX1t+rfrW8bwR1WuECB0icyAh+seni9goLWqk65Yuv0Txb31eBC1Oc6fSK+yL4U6qtarrWegRN4lYXXBuLZiYZFRL16XJXse+7++d4PdUI6vQ1pvROgQF8q+67wM8gl3/oQevLixCwa1q8nX6Olm9E6BB9CGRLagfMwvVdQEBYjJoOBa0qJ6IWZV2Bh1WvIvrFXAjL3/cK99DIuWaDe+rgcbEePk0u3/8lV1j91B1nfP2dYONVM1YvXog10m3JwER1A7L0vop5INhcXRxo62IWf64HD4Gjp/A06rFjNV1UGov5Kj2wJOd5DssHp4mQ/+CYYta1soQiLOzycZXE3QDRwbiFnPPneT0RJLXiFg9PPnex70373nfO8CF6DXoD9gAAABIEGaQC/AKdWh1oEX1/+r9a1vUVXoED1q/XUCB65ohfq0CNU36+lGKT1BwMXkiP654niP1qDU7CClFoJg5PCtILcKKmah77v716ug7FsIDtDS/Bahd5N19eLrsTeYTVV+WpOuD/i0I1XovWLe/6+oXKbgEnD4Ia1npRCwa316J0mD2tS+tQfiEWhiNjOwOPweV1FiEUlQc9onIkM6cd9V9a1PUDN4Q0Mxf2Mz50Gl94vKLY2Sxck6AUYtmmr9Wl9axi+xCwb0B3xfrUHh4+XDvy4mgLODjXFoZOlniaL+BF/FHXwOODk/8DP8DT2B553oWxFLE0Bp4hcQsGp2Nz/i9n87zHeY/n4NTvZ16A05TvPYHHBx5w5yL8mp1/r/rgECgAAAASFBmmAvwCl+hz3etoEX1t+rfr0HHaIMdAlYGGitQQUMMoN6MbbemnkL4vi5Ku/VMnUqtAj+t5cM4OPWKsDPlrV4jwR9VioYu8HOBpJfr36t7anQor16+S+l42gcZKI9eIF6SWklGIZe1wiCWjvErwS4PBf3vuT1brX0QIWDf1q8Gv9el9etqDHCQIjqqqWgQK0M6q0dJeP/SjVLqBmoQsG/q8z4MdFAJIrp9G7vWrxdWmGfL9b/rUGl8ghDcXMXmlvuIELB4IWri6wMOI4rwNOjoudYOB6NNb/8DTxeGmW1AjYGnJYHHMMRaZbA0+6gQbAz5b8754mBBvkte37erA4i4ODvn5D+d6O8h3zvn++D3A958C/gQfn+BB+f4EH5/gigAAABJkGagC/AKP6CTSeitfr+BF9f/q/61AiesoPvV8Qv6IfSPc1FZJ6vAgBBY3GM+8mp8IKGAFMGmMG961t7kqXXWt4PfXKPrBH1WKvXoNxi0qrA8i4q8GtaWvV4EH0Rq9FesCPW/YEVLeX1Lr9ZUv4PMEDX2sCND/sCE8IThimFEEDP23k6azcDQYl7utHLMkIWhCwajUaO66rroIThgFMBMF48/+X0apOlb9WxBKxil+lg29GOqsUWuwRidVF9TsNwEDqtGaIsDTg49FYo6yin776rqT0SKDa/zasDvp+w/KeWwhLLb//Rj0H2xWEEeNy+n6E/YGn2+3+BBPL91uhnRIhFsQheMjC/oOBazpAlHQ3FdHeXA05RizpByfgSMXjcRgRcDvgPmAAAAU1BmqAvwCl+hzwJHrb9W/XoET11AiPcZV0CP6t+r3et4PPRDoYUKHs+d9+ulddYJOqud4I+qxoPd8fg/wA0BF714l4Vw8Sq/xFVVVrByEJr22/9P4Gn6f69mgPAMPkDYvlx/G023+tDwRk1Vq9Ee/Rdx9WHiDa1+p1EHIxEV6tZx84OrmQCr3rXuAhIgQsGvJiF9xPVVX2Bm9cmPXsDPVclUidQgn4Ug5oDC0cyKiZfRgM93AUiM0CBYf9gScnKt+kCZcKvnELiFg0v826/2VV35Kr107quVeBHgl6rqqV8DPd3+BpwfH8738gvmvAzCr4rf8GwvDQZY6S+bquvviMK6/PwbC2Lo4y4Nfg1z+hCRlGIxsKjKIWDX1Oqjx+fo/+ghyIq8CvXr9SpVgcRdHhHP49gkqeE8Pg7Ow3IfkwvzvB0fiT8oiL7Ay4BPIAAAADjQZrAL8Apfoc0CR6//V/wRa1vAiesoCGwMLiOl79fQInr7FuGmWvNeCTqvd65Qe06987Ktay8XMXADICJsmBBqC9FuH6M1E90v/9F6Dvj1ivnnyV6IELBryUsDzCtXv1esT6gaarg/GKI5+Y7LLWIqqqtYENLAzv+ie/WCD6/zY6oGaxiLSwcrbfCDhpTD29/fFQIYhZ8DD3+jPIIQvsDT77EI+IWDgX0FDLgWKHPZ5DZwNOjvnhOsDLrlg5sDPr0EqlOi9/f8CwiESD47CefmxfxWQR9AZ8Hx3kP0dcXEUqAJ/AAAAC2QZrgL8Apfoc8CR62/VvVfQb+iFVAkar79TpKaUfHD0NOt333wTgob9eBO9agRK1aV/MrV63IEKtEZAh3cCNXN6+ixCwa8lF/90Vr9WlO8CLitiO8TJrUCF6tZ+BIFvS36wQINQhIfluBq1f3xUG+Ly4F3XoyRTxP6FdXoaku/tl/g3OhM9Ogg9Hj+gM++BgQqL376qP+Jg29TqJTobIfk+rWDs/Ofs/Jw3B2fz8p+f4PMD7gEAgAAADrQZsAL8Apfoc0CR6//V4OPROifUqsW4NtCDZSoCJqzrd17Iq5vBJWv3vAQXrxlGfujlSmouEKI9+v/teqtagQMTXpfXuoSr16DvH1evXr3BQWtVrLfon59KBAdhACvArxghYNeSvV79XmDT1r9a/9XS+lwegwRUqiFg3xfCHX+ovp1rfReq8H+BGxfwLrg6F47QoGEbk1g+wbLgWYGWT1BDN1CCnoriasDHg6CC17/FrAWWU/VAafXI7d4Ox6ztv/wJPwOO7fOyOX16qgYIEg754uDk8sh+U7wIGBnxB3kELQhYEb0Crr9egPOAAAARhBmyAvwCl+hzwJHrb9Wg6GL6XPUqyvRHn9F6D71f1XqL55ImKyeiHv0V4EBckXW2vSev4EIIqAL01cYE/e/29yVa/W1+vQIta9qq69WoZk0s+MUNPUqBx6I1ej19K5frabmt+CWCGq51HgZ8CMsKwMNcxr3fVeAkcG3Jb8P/A0979VyetVUKUNlAk9RgCRX4GgKYxEo7jF5YlYNVgfnB6uYvshb30Bg36MdJhmXmb9T0HJ31wU60Yo/r+ZcFWvQIIuecsWxGoELBG8ZH06I/jZbAy0OeD+wO+QRLKL6Vg/OjzC4ouZRIlBGDc7/oUe/U6IjiEefYFDqBog+y6PLMEEMkTT/B2eNkP2fmP53z+IXELB8IQKFAfkAAAA5UGbQC/AMOb/j+gVDiobX3yL7DfVY35oFMviI9f/q8HeAiq+k1BLrVU1TOhMgJHS1Ak+tSVr+BCfurX6vfr+BL9X9vwzV4O61qStbRPq8HPr01ur4vYgDlk9e1j4K5C6qvRP+q98TBtyVeiPKPYsNMsZfbbrUj8FPCDgPaaYOA29/1A8EJe5BizpBvrE7gi1q6Tag+wMOSuvROuoETFyh7x2gQBPEeCGq50g8EL4GnE+jNYhHgIrHuBDFsbGNi4qlgVqAVfELhdhgMUzX61+yDvLlfgh4yEy/pTwjAkVoFWUH/DEBaQAAADAQZtgL8AyfoIWgIYYiTZ3o/5/BP1VVVffgm6rWsaBG9bQJHraD4Qsb6tXq0Hfr0l62l9fkCH6+iPWoOOSX0VEVXc2TJwpBv6J/GdLEneD715lCC6Kf6zev4EG4GVevxBVXVco+QR+Z71q7F1GKQe5uMX0XwNHg39AafhXB9gRaOZIg/4Um6qY7DRFwd3AkzjEHadEEI+IWDY/IdbELneX0ISa8DDg59Tpj1pE23+J9Ukp4mBG20OqU8fAkWAIxwHnAAAAw0GbgC/AMn6CH4PfRmm9FwgRK0X0R654Eb1/+CfVVVVX29rBD1XaAkfV4EH1/L62L4QV4ECtfS+rXWtQcaEL6hCLHLFpwS61WstBx6JaqA01aJFvecQuIWDy9bfBKr5+D47+ateCPYyWnU72LmkCiMDPrwQlVc+QIPrKvVvi+wM/WBmVwcHY2bDeJoaBovhzELBqLji4eiFeBpzehKVTvB/h3neUv/h3fFQcnf9EPUeLzvIfzvYvmbHR3pBCHwfn87wCiQAAAJxBm6AvwCfehkUCTvGeiWgIf1qf1vAj+tv1v+s8CNerQJAxwVtMPQfYGX+is/Wv1aUW516gRPUdi6r0ZpQhOF/zpp9vd4Iq11B1iFJ67IzhTELAgC2S8HwyXUZ1B4dYEgQ9PsELxmX6X25q1lVatJQGH08CNTwcacj8HeM+BAFvLS7AoLV3wenjYgXHUtnZYEk8XB74JQ9quq5oD/gAAADjQZvAL8An1aGRQKvo3V6vAafr3WrwEcLf89QSVrKsv0Vr9E7EKTAc1otfq91q2Lw4pgBwEdQPi9Rvh/7Bdr96/J+//Al+vQcc10BhrVevQIfr+vV69YGkF5uUsbi2ZxQt76zzClb9eTeL6xV6/IPheGVMg/X9iH3i+pUr17WKAldCEeDrF5PRGlEuRSI9E9BvQGctbc96z9cHp0EYEgZHUqQt563h2BhmO98736pIEA/+iHVJ6sTejdKIRcQsGx4uU7xR3s8sGp3/Qo9R4vO+fnHoICPPb39C4RonawNOBIX8B6wAAACyQZvgL8AQuMQS+/X0pvwD/gsveBKgKttvT4CP9egSRe5pgVQEwryeivB7etS+ridyfeidLYn+saBAL/4zOT1rfdHw/WoEA3h/+EZRLB/62k9cJMSrFsl7qBmr1sQIld8DRAgb+Jq+LelsJTj/xZOP1qBC5fTROkrX/cKQfYviJR2gEfF6FvAdRIvD3ipQ/Nkr/wIHonS3AgXaB1B369Md5PROkwNNCSNByeLlO81AacAoMAAAAIlBmgAvwCfeh34DZGIn0TurHxUCL69S1qDxfC6L0eNUJaooxQOe0bJEJF81Ah+i9OLfaAhvX1evQIvr+StcOgIuYW95X7r8/X0HmBdz31wNEgxTY2MW8Hu6tFHR86weehFSejtLgaRNCFxCwc+pRQKXoqT2JgQzvQQRjZF7+BExeSgNOjoXXEwDCwAAAGdBmiAvwCfeghaA2Ri2iPRugIWtH+V6+juIXoCLFvaIGKbCpB8LzqM8CL69AhetpBik3p/VpfWx0BhwdjPexb3jBi3g64b/X8CTYF33wIno4lzfox9ASR2WKEIbByfjuEUO6BAO8AosAAAAYkGaQCvAJ8IIEFAEAi3t+CStccgMz1ehi+jvXoOhizIqFejtB9gaap1EfgZ7u+vW0SIWD86/EQIZ0WMGLSpiEfGRhf0HGBnxQuQ2FSD/0V8XG0XxA+Ore/gQ+WBJP3gacAoUAAAAQEGaYCfAF3+gp0BI7BmjxQElhvL6lSBI9e/V4EKtbQJWwhApetQd4EX4GXAinesZi91KkHvfg453kELB+eeAUuAAAASjbW9vdgAAAGxtdmhkAAAAAAAAAAAAAAAAAAAD6AAADQYAAQAAAQAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgAAA810cmFrAAAAXHRraGQAAAADAAAAAAAAAAAAAAABAAAAAAAADQYAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAABAAAAABIAAAAGwAAAAAAAkZWR0cwAAABxlbHN0AAAAAAAAAAEAAA0GAAAAAAABAAAAAANFbWRpYQAAACBtZGhkAAAAAAAAAAAAAAAAAAA8AAAAyABVxAAAAAAALWhkbHIAAAAAAAAAAHZpZGUAAAAAAAAAAAAAAABWaWRlb0hhbmRsZXIAAAAC8G1pbmYAAAAUdm1oZAAAAAEAAAAAAAAAAAAAACRkaW5mAAAAHGRyZWYAAAAAAAAAAQAAAAx1cmwgAAAAAQAAArBzdGJsAAAAqHN0c2QAAAAAAAAAAQAAAJhhdmMxAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAABIABsABIAAAASAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGP//AAAALmF2Y0MBQsAe/+EAFmdCwB7ZAEgN6EAAAAMAQAAADwPFi5IBAAVoy4PLIAAAABRidHJ0AAAAAAACigQAAooEAAAAGHN0dHMAAAAAAAAAAQAAAGQAAAIAAAAAFHN0c3MAAAAAAAAAAQAAAAEAAAAcc3RzYwAAAAAAAAABAAAAAQAAAGQAAAABAAABpHN0c3oAAAAAAAAAAAAAAGQAABQWAAAEZwAABKsAAAS4AAAEnAAABIoAAASqAAAEWQAABH0AAAR4AAAEWAAAA+oAAARLAAAETgAABFQAAAQlAAAD6QAABDAAAAPlAAAD2AAAA+oAAAOkAAADjAAAA1AAAANjAAADbwAAA50AAANKAAADSAAAA34AAAN7AAADRgAAA1MAAAMAAAADKwAAAw4AAALRAAAC/AAAAzUAAALeAAAC7wAAAuQAAAMVAAAC0AAAAnQAAAKTAAACrgAAAnIAAAKtAAACdgAAAm8AAAKfAAACsgAAAi8AAAJaAAACEQAAAfIAAAJwAAACVgAAAj4AAAIQAAACBQAAAicAAAH2AAACGAAAAc8AAAI2AAABqgAAAcsAAAISAAABiAAAAd0AAAHFAAABoQAAAXwAAAFYAAABTgAAAZoAAAFpAAABXAAAATEAAAExAAABJAAAASUAAAEqAAABUQAAAOcAAAC6AAAA7wAAARwAAADpAAAAxAAAAMcAAACgAAAA5wAAALYAAACNAAAAawAAAGYAAABEAAAAFHN0Y28AAAAAAAAAAQAAADAAAABidWR0YQAAAFptZXRhAAAAAAAAACFoZGxyAAAAAAAAAABtZGlyYXBwbAAAAAAAAAAAAAAAAC1pbHN0AAAAJal0b28AAAAdZGF0YQAAAAEAAAAATGF2ZjU4Ljc2LjEwMA==\" type=\"video/mp4\">\n",
" Your browser does not support the video tag.\n",
"</video>"
],
@@ -519,7 +539,7 @@
"<IPython.core.display.HTML object>"
]
},
- "execution_count": 60,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -534,14 +554,14 @@
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<video controls width=\"80%\">\n",
- " <source 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ZZbh62i/Ei8mhTL///0GiTEu4LE4l20////0FocZb8uaRfby/Qaw0y3+8iK/MZXS/4YB/oNCcDV8sJFzmaqblhKeb8EREbWva9g9WH9DRnR9DncA6q6FXuuiZW9BN//rBV4dnsBZuuOJF6eJ11zv4ix9PiO/Yiq2VVxfAI9fbKg/3ubz+INY99LISw33o+DN6fxVHlht8W+KFhD8BEFMaIjBX7W9jbDgKLDjYieGUnHLAq+taiIZPAALQdGg0eWedT+eg0GvvGAIfGAIQlNBBdi4RF/X8CbjZuv/8wVDiDDpQKVglpS1wMk/ICHHztUs/2FTYQLs3CIJ/X8AuphmGvXj//4b1XzDkWl5QLMxgLEnf/wPAIsvHaKKIlMwUDum4mOza9J1dzahR5QhL/81/P3Cd9f8PBviQhCgmyfmerz4Cjp/ht0vwh0w7dlwjfuf+A//CQcmjdBs90fhKA+fXgEBAf/Ew3qnqFJ52iPrt+GXIS6AB4/QjrsvzIthwNMyUxzqcRcL/+JEWRg0VWyCe8TClgwQh+cIJgbQSEuJCgJQKxRvQs6o+8Ogqw874OCNSSU//tAT7EwAKhZysCz1FoGKE/9MBFpSYGAAwfYwy1p3l0Y4/QGAAEAgLAXCALBm5wJfF04WlC++AMS0ICZd7ZTddrK+CqWr+xqP/uH9f7/4T4DcxWr1mblf/6/1D4EJAuz0ExiNmjFaFn9v+odrrrrrrrrrrrrrrrrrrrrrrrr+H/8SMigDk8/GExjw/yLvFjqYjhPzCxOJ+YF98HgCTmUY5URn9a11DzYe/Ly70K0sarOb8/8Hu/GeAkHVvwAkOYUKn4C/nwKxP5wfxgxM39UHM+5v+4YvDDxPKFud8BYO2ea4ArTMWV+AcooPz0o7/Mwr/i7k7oH/FRBM5C784dFMlf+O/CBk7MT2RswBHNmTVj/y1iBcihPySCH3/9PhwoCyVeXwMbTIh6+pv/i8HeFRfFIuIsk2JyWNyAR7wGWTf1ShNsRtM/xBxv4zgCuZiivwUzOQlT+cCAzIWawF0lc35/p98cef7uDv+nE5pG6sjf+4Vm+nAJOZDN/MEufOJ138ZymWc/vo4X//CcNaVA0v/4/3/fhN6enFM+4dPAD///CgUhYjxPzavw6Uz/4vuEKjsKJJ6wFVejMQIVXMjGs39b+o/Zru4bDCBIcewhf9T4lJ/v28gJs38tbZZcCiYGRtDx9ZSCIs1rRrP0///Bdx/1i+BG+238ssSd/+AYAg72htlqfLWiN0vb/+Aa2C0utZffzb/2+LYUP0x0FG+eJAdbcsmcAwx9iKg+aOidrhOiIRLBoNX9pe1sMBLlDOgHSwgji6/////2JBwR1h0iQ7/62Fo8f6KJDqJD/kFvHvj4iNVXOq/6DQSXcpI/8PwqiID9eHEZXr+ysfWRLVY8/huRA/cE3giD3eCX5gMZlXbZ8R0IQ/P/n9ggC/P//ZjhwKcJH9c1EgoBv3aEq+/CxQWIpiZl58FlkOSAgVgdA9ghpx69j4jJlHr//nM/YLRIcgyPhzdgVNhCZFwQkjj18AdeMZDHr4cMP+JlzXPAANTL+JBdzPgqon+L/ef//8JGvss/OA05U9Q9TX///wlKMXJQUXPnyIDcdPX/X/8JQ9zWrPZrkUZ0PJf+H8SXA05H+w05KeuqLkHh3H3XfkA4AA/xJGr4jYjtHWiU82aDT1zVuT8PA3L/VfEQgHF8d9hZdTk7Lea+egN6p6//wDAOFahqUBxc+UFFyaNPX/+AYLwqUNMdPUPc18iPUNwFlzoNnuj/4BgEK948A3lT1w5KajB0fd/8ExCLNCEcn5hReT8l/gPZ8uORBqQq5zYLn0AJ7Ck9HOo3ex/6/CcGgBsWCcwORClxaf7gOZAMWLXv9/K4J43Ckcn1BmJenf/rgEKkwALQ3Rk//KYx//QC7IaK+VNmv///hNpOYdMYr9oCYxY8M6tC8l/yglrrrrrrrrrrrrrrrrrrrrrrrrr+APwp8JByAEQv3JPeuvEgk2ezOAXkrm/P+4//8VP7VAa7uJjwuSRPzimJZweOoSnUL3//3wxgYjat5qhC7gJOxKb+aMHiUnHiMzWDklvjhl+/8Xd34VKA2tkm/BBZ0Er85/r/fgYsZlT1CK3N+T/vuD/4ngCb5hR00B/HE/YzEBILU34MwaLtf47dnafgL/cXu9P+EpQ72X+Q8HEU1AXA9JzkSZI/58A//4T1qPOIHXtM+BcLQn51DYDrbTP//wQh7AQJii9g1HiTNs7/93fi4No/xut5w+kyEj1vwUaTjDnh2uaiC7ucCx4qBfF/wd3/ifAQpii9GcBsJy9ZsUN/QGbduAa/uDu5APpwlASDq34LO2p8rj3V8EGZU9Wn5GzP7jH/+Poswjn1w+pmvp8DZDwXjncb6BNLl/ICslPBmAfC3u86iIRFYJMUJra8A//0Ghw/5e189w+aRT7k0KZf//+g0QMsswlx1ktaPOhKXs1P1+v+g0Uf58+Al6uBHQH8XS7mMgddL/H1/0GvUAbucxnyQJHqDoD9JE3aaA/cv5L/1HguEHq+ZAIPb8m/9V+sFs2du23iDok79pbAvw2N4b9gDGS5SBmyIbfDw0/hEJbPthf+1vYiwXBpeAcCp35PURDYoDDyhoN1Va6n9eGg5CK5d+H09WQA1DPBbjFeP9rep1KeN8CyYgoPQ/xuvz/jYAhMVdZ2TBK3PDoOvAQwX56TP58OBp7rZYahgcwjBl9W94FllLUv3h7zEA2PjsvWpnAVGjVqfapp9TS5Pw8w9zV2Y/4qAb0j74WWjb/64YgwrSi7yy/2agufShr/8AwGFein1KCi54gmC59f/ANcK9U1ne4If//4TCgDpRkpwdFOSnGl5IYUXk/P//+C43AvIHscA/D33DI+8MAIB+STDjxIPt5JfvfELQjIPRu34z5u+nwMext9hsuB7mjcln8CFgXQ1/M02bCJuZpJmyQzhGHD/YIMtMFDMLJ4/xsBHuDCY9ETavS4tTOV4keJJgMSNT/YFUU8HrJCX/+Hs9O2hAG7ptWNepg/xRwoQf4SiWAkPBdinYAFDi7AAliXoVLVzP5/+1FiEhs0prf76qGjPvLH/rzwbPoEQ6Favtf+MVRM9khe9f/DGURSM3sbKGvygnDXXXXXXXXXXXXXXXXXXXXXXXXX//8IIQQYCQWlvwSTOLnqf/d4q0ZHZTAWmtPzwQHxJAsD0nORAG+I2U3phqLiIRtnQn0P6fdFg3fwB3K/vwR7xhlk39QIDMhZrTksbkShPYjc/zb+Li7kP+EsAVzMUV+AcxQ1npX2rFMzkJU/nG6sjf+/7//+KwrN9OGc7+10yfwsTRPzavBNa/MP/+Ewif/gNBHoV497hP/+++FQTYEIlOYSDNkZiCPeK5Q39HBzewf/3ARd+6TvOxfwIRKOYHPRbdXQkZE+ZypLklngppz0b7rxe7xH+Ei5iBcRNP5ycljcgEe8AwtreDqpuW/ff/8JQcHEU1APSc5F33z4//8J1GiWfGDZIZRQkeOoX//jvhUOYCqU6AMeSZedAhVcysYb+q73V+xFchIvG+IrybrEfsdfxcQpA83J94UdwaWqXQf9f5cYq+41L+XcXwSsCvz6F2Xk0KZY1ZUNraZGZe4yYyKVRLRl6W0KoUiwPmBhyxyWXeUyKLMz71HzQgbmlnQVFA0d//ytQcCHN6WAcBHvyfhUQ/6DRp+TAn8A4lIBxwlbD4IyvY311X1X4aBWKdnAzMDnKtLNfsFB/fk366wWCo42hxvPoNGMM+ER8CgaYCzL/8Vf6UcybnkJXs/ybK2VB3ze+BkyCSEeJeN9w8UQe+/4r/6DVffRqoyU+sPON51P/U6wVEyRj3ebmJ5PCIPz8iyWXIEM2X24vw0bQPCkleBizG2Q1FHoXrY67mNJp8+CIr818ZQHX/9Jh7BAP3uGf8P/hIEmEfXgdm7op///wioDTlT1DTlT18YOj7geOw+75fAY6zyCgAB/8SZlPiNugOm0z48yWe5imTOuBCNiL5A1wXXLG90xoHs8cDUwOLVRxP1/D/f//aw54IfdpuIJIv4MAyAg+K9Yg+0Ooh/n4walquT5gatyydlvAigHf/x9hU4acqeoacqes4x4+4HjuPu/H8AAGCNiitiR68eCEryUwuBih6kqvkOajXkzwCXIjEKy0rupC5BIjj/vvBFchj1dmsFx+pi/wrUSctumRjivzigASb5Cyl7M6cgJhZ//H5pCvSjEc5lVEGvffvWgACQ+QOeAVCeDL3UABcLx2HTWIIk3/n6w7XaNR/Zcff/1BTXXXXXXXXXXXXXXXXXXXXXXXXX+b/h4fDQwhJ/tcg9fl0D/4UI88DF0bD3cNwLkUJ+YeUmSQ3///vBCHMAbWyTfggnzYT8fp7vd8Kk6pp6i7ywECxL/mKLL0Zj+/7u7gI++em/gJFlN+Yi+bE/DedWb+YI+utnv0n3Uul/4f+Du7l/4SmkCEUN4MlXvW8DBZeRmBGU7fl/3/h/h4e3rfPwP8OH/4Th2kxhacT84bCfE/OFiaJ+f//3ghGYI94BC+4D//d4PhXgg8VAoIHm4AJHYoqaDi4XM1h/93IQiPZCrwPcmQkWpvzEXzZPxHOo39QzGnRo+/f63gJ+c+MX68Hd3L/xM0gjnAZeuW8EFepAzPgS/fuPa3lIYQJzfgbT5N1vsDLQCN47/p2Av8/hsIkDX7/4w0YNTCW/t36WlpaRbWLQqKnE+g1cvwZUvi0aBmYH6rCjL8YXy+KVIGZxiBtS/UFWXxfAOD0/Do0/hTL2L/HXBb3kRzlgsQO5ZYnoWYn/5t+/BL7Z41/FKoOIoMR5+f8Y4nzw7FEGPj1X/agXAkviWQldgqw94LHHmUf/PgqiUAiYOh/4bjcn/pincDsgBcqMY8W4tFgZMQHJBz8b7s452dYG0B3xv72+nnnU/iND88PvvBeXn59//gFQBVh0gNMXQV9O3lBi5PkZ12+n/4BgsWFYDc6epIvv2USg1bX/4Bg8Zq0xb9f5KE/3A1XvIJ1OlQ09FvJyIn/G/xzO9vfuB5DAyYULUA53CqgCr3L/p+EQyGvQZKPsJbN8g0KcIRkipf/N4HWgbKeoVUG6jUcQWdh2OKNcPw/4UC0Ao8TD0faaZFUifwqC3J+bWwBvlxQi+pn8AVo2iOPO1E2JI8O0P4zx5JW9YdvKHv/kDkY5Ean+/Hin/w+NgBdBPKx8pFeP//8gxL/4SEceM/DFZaVUo2//93kIRE1MsjZgcOgAwUyG/CAuHa7/qCOuuuuuuuuuuuuuuuuuuuuuuuuv9IfDwQgi4AYdRjjj7/3e4QjBEooSMCAfllUBRJvWAqr0ZgP6Hnq3DObvNMfJ+O77uP+JLxN/qeARtU9EBJ2JTfzEEbx54H+7lf/8JFHE8vRngO0q7/zn1+vmf/JBOWAAZ///hMEgDG0S9weO4lOWigWmifn//4vCpuBCJT4Y1eJM2zoxlm/r/7g7uWFYCR1b8F96nxPvdXiBG1T0GIvHf6bu4YeJ8AGT+pk+BBH7iXv2gQiU5gflXN+H9qz4rBtne74P/8JSQZsjMJ6xuXnQc95Xnkgl/+n+EwRFZOyHaYnUzF3TK+n/ycmlCAjhx7Aqp04CqnozE0mysJ9HhW0AjwhF/XJ5EXy/w13C3wqgQDggqsy/4x3+MF/yFGFz4z3mhcYrhdGHq/nNNbbe23nREfRLvJvUdf/oFc18RaWy72vq+o/6DXlrukdEUm9r9cf/BUQA4CJd/8PqGAaDtPr0yz6fen/1DwWHSs0f3AFcSOii9NP/4BqqCG77o2eay4YuTRHf+P0+yfy2L8EYm7xdFr4f/0CIJiviSQia+o/kLUCwMcvKTHDo0/wt/6wXH/oNE1fqBmyJp8H29Z9+f64rwXe53PEu9PIv9dTh0u/eRc/zh1tacTDoOJ4BQDgcUf2yL6hi/uK+7vgZZAI43w33NXzjqv+gVkLm4GZtSXt//rr4LvfY8VfT/l64iQF+73cl5xINz1/FxYdBlg/QOx7f/6/+giIb//9Bow4yn1bXiOott6afv3h/oeXVyv1Xg/RlT/C6E59//wHQAJVcurlNLbv/a3tKggIrD4e4b8vwKmDtrWMyuE8HqFLBp075lJPr8Ykp2GjIr5//zmPr/8JTQaeqpr5RvYu8sNW5b/H4cV0s/2G9U9dbLEkiNe+ONMwovJ+eAbhDF+KN7EQAyXnkRu7QwSYjn2vaTBUpP92kiqAQ0JYPWeJoMs6f79fE/oMVyz4AHN8iRj6MoBZkGUiqiDXvvxqAktHPFoAIvYOeAHPO197/t5hGIiog9179/2G+WL/gAI4qTw6RaEwkAc+1N1vvaoV2JT8VEf/qrRK1snOqT1GhFZ268Zm79vDqCeuuuuuuuuuuuuuuuuuuuuuuuuv+MP+FB3BTHMx/6fPh80NbvyivTP/j+LMtu5RdMw9/wmAFSePgJB1b8/+77vCpMoX3fAWD0kgNAJHYoqaCAukeH/uD4PxnASCym/BN7Yp4JXhcz1k9Vm/r/pb0CP/kDv0j4mFicT8pv4I/VUXnfRnmPxNALWmb9QMHyjPQC/dpmdM41Npl3KP/4egep3Iv5IK///8WC6D0vA2recFGhI+BAsj/zf/d4vhXlFy4zMJynz0gIRKcwo8ky87vn+7uG/svASDq35iy21PgO2j/ZvVm6h12/T8/+f7iD8PIcVCPqbqX+LVxbS5rIDci/yLwPpMwJ4mH3pEghOwiZkAjwUF7HJ7mUVx/kG/S7oz1fvhpJgHDLscl7j7k9v7///+GvXJd7bf/rL/6C3lzNkyev/V+CW4131/49fDt1mI7mvXbb//r/QIeS1//60HS4573NZU6Sf8P5/hcIv94qjUf/4dCDd9k7+p0GwHBd3lhzGKiuC/+hwIIz3n3eK1GJd5N4hDUP9BqXL90YGbImxa2mvqH+ClgwJlxY7JwD8u/8ilxXXw6V1H+ObIHxLoq9NP/9eLBb5mTiHf8JciKPQ074dghXe73guff4TQTFAfw67T/4wKHdR1/tKgWBrpAzoiPoovTT5a6x4rwV9KKl6///Hn4dzU+S359FXpp/Llrxjfe/mt79v/D/+gRBmeIZGVR//0CwLWfmM+9P1h/+gVcrZd/6/+Ghvo91vnUaX8v1/0GvNfcRaIva/AP8JDBh0sAwWnFmE4chKTAOXcE9FHrj/SIuUPXj+c0Gnq0l8f8DrblgO+Myf////8KlzV59Bpyp65TNSgOLn//8KmygouQ3dPXNBp6//1UG4w8KJsn5CbpUceY77Bl6/zrWnD//tLZvgJBcuPAckBjxKf7txelHvjaP4hY9WMXlSVvWf3BLf666666666666666666666666664+GP1//+73/sVwEnYlN/NlFl6MwH5V2/AIbGv+YEuj9/4vd8P8TARqTaJpxiFyLT+c0H81eA19BuSq3LeoNEhpJPOSHzwABAqqf8Q/3eLBFXCYhcqJ+ZTJmDHlpLgWtlm4McHyjPR/93F+FTQIRKfBAnndkrKzM6Bvfnf/vu7jj4kuAkFqb8EXzYn4gj3iuUN/VUXYy/dz1OfB3B3H/xJAS64DHHVrBxzkTJAfkd/O5mIVyQXngAGP/8ncIQBrwCrpOnAHHgkHK0BCqf2D33CIbmCUngNzfAx25Mc5R1iIiqJf4amFRBppeUI9s7+5CQRJEDrQIE3Hr/k//Xr5EWKPzEdy5KCkr9Q4jgGj6hCzyl9vyZmT/wDFbwzDGW+DeytUB19C6hnFP82m3b67bf/8KhqCDvISfMRMBIvT/5OW6jrRS8oHIAN3wjunVdCaFlv//xvr4hqBDIfoF4wYaUd5nfv23/BcMP9ArlXarffla/9QxD2jIO7kJYNOyaS78raX/D8NQ4KdykcflRev/BcN4dnJdnuu23+RfGo9UGbxp9mbzrplJVCjHigP/4S/nw0h6aM/3ISIiLowjkAJAC9qvMvFRqsf9BqCmlkjJ6qK3OKnE+ir6f66j+FBgRly7vOrx/WHx+96Ps+/t//14VBAl9+/8vnlL3Ff3HiIu9U6FiG4tQ1Av+ggMLjP7v8tv+uGAf6C1/kvmPUMA8uQV46y1TL3+v4YLwU19Hnb/wDCWHSOvAqsJkE5/xsyEpMA/CYr65/kUuP+CsXUaGSQhYzgRCBPIo9f8IRf1/zIEYoABNjgxpTXHwh/4Qtz/+OIh/4oMMravvhG7Ryw6dOnmuUzxuZLOVOJHgGKiP/EmAV7nJmuoCmZD3/xC/+eQJaZOgAEYIip5gE41F9b8/+CCBSFJewdBRSU+Dpw1EMV8NcuRf/oAeHa1G+GRJfnel2ReKJF2s7gGCaWN/zWnXSbjam/82b7/wnAB72BXQbaf3wPLEr6e17wyL4xzJJ/4/r+cQAyQcu7rYCuv3//+/f6gprrrrrrrrrrrrrrrrrrrrrrrrrr4YB/8JjBXJ+v/p93iwoFpon52rx2gKs3+I9WNv5DN/y4scqgMQF8jv/d7vxngQiU5gBJ2KKn8Di8vIzwD+I1dvy7/3e4P/wlyQU2RmF+DcgEjq34L71Pgt/7q/fuX/wnBG1T0GGfn1873UQLXE/MLXE/Lh3cI//0zePBrGkzavN7j9MBF+8LFN0/RvWAd4Dcbk6f93J34oT4ELXZAJJsSOmwTeLHKwLcAIXYWL9EwuBIG1zX3ykgcRsPHTBBN+Bj7k45OMd3lUPFfjjxkkZ99n83KS82DYbwiSIJ3135//4Auu4uWhNy34d0iDovPDsKZ3wEYr/oJTAruQRMjykuK/3x1y5y2zw3DQmAYOwpg8iV/ciIvXFdHzATJg2SPhxPV6hPDfv/D266w6nLIzYvOhh6uT/cuOUVxp+kQDlu+qhoRJqa5qPyP/68UILDvvjbWFkoELA6dH6443H8aQ6SGSJ4ks+gm5gV71t/656h/QSvv+Ql/WJFcR13A7zRuHhlaJB0Idg6Xd3mT14R6rwT71RhpRJqf/wXC4IO6YaZatCp2mka9v/APqGoL7v3sIpBx5uNXnF6fx/4LvQZE57fDs9Qn1m9FT544j9DeCb2K0CFFiaDOnoPeCFoDrPv+Yz7/UcIL/oNGUyv8hLtL/gGr9/o2UfKS2aLHQId/l4frq/PvyV2DNfX/qvgEKykvpI+Qlwr6o5YdghJbfHWWqZbwmjl/8Jvz/w3EkxYrj8PoFQjLb/r9f+gtf6N+9ev/m5iL813fm8/+sKBLRv3//gRFYQJwSPYZrANw1ZwdjJ1xutc0eEJEwr7HvC1GXH+yJwsriX84CbfDUUh74MfeKLPE2kLrKmMcl7n+u9PvPf/6gNW5bhCPlClECckUev+DV98ehIPV/0ydeo0NJPXhwAjRLBKwE1z+PzkHtdsoM+Q1jV8/DTANL5g4YFyn1f/+sfwraSgdm46KaOAacUs8wclNf//wreDz0fcHjxc4bnT1DTHT1//eDBGQotk/OFAXpPz4Eh2pp/BQSjYRgezzqBN+nbb/7N8JiIHJAzzp/sB5IDHi4S4AkXLjy26Saw8TQtR4oid/6+Omdj/uyglFAA3111111111111111111111111111/d3wD+EgQQEgspvwAYevvH+/t+Dk+bEZ4nnRv/YfdoF/+JpVt614k9YvIn/f+MdEpjAbCUuHb/p9PMLDXgr0zBVjf5+6cUzAM4ivuWqa9C47JNL/4Lu8OhDcRALOSNxeWdPkjuekFbRVwSR2x0/9weo8gQLgEkzBIdPithnIAUtmASD/F6GiF6meD8MFfD3TDKNV8Z90+zubkuPWEdAJJmEjps2Gc+1Xr739WS4dnqChUwbHCqbo/p//iDuDjQQF8IWUhIBHvjb4MnHfzzR6M+6Crqex9v/EnlFsV6JNiFDFoHkcKd/9CD4LNLfd1xfzH08bJHcRHu+BU1cxE0Z+oJXijk3S7wYAV3cV6iRw/mjw6hpAWVLGrAanFvBtoc0NJZb3Jnz4e6FfgZVLRYHearS4fBH436mA3+ag73fvo8tDw35Pf5Ifjfq/nPyIvqOo6cJHH1f37yWQnjzX//ALlehnzYUkUeCVxS+owDQNL8RuONR1p8InB0wI8KS79L7/GGvnJvr19eRXYSDafZ8Ifc3xl9w4dnqBRgdPLuC73Bx/lwy3CVgNRgkmChJZG/G9Hf6x//6FbrX0DvUtleXL78JVCkuvF7kQ++iXjKqNhUZLn5CX1HhCeUFXx5rQXGmX5f/guCoKxr3gxwqEAKVhH5peLrEYh6eHYUwNwp3fsZp/cPg9ks5sa9UdX9fdGqyR8pIsfIAeXCu6yGAaE+EbA8RDcB1rmobhTv4RVAecyf0zI01tt/5/D+qydRxHhHhSQw1MftlLTZbC+/14x4rrqjw7CmZ7dtJ9RhrF/+B7ggzzJ59EPxnrNAiPSN/dSjyFeFDeGIDz0H+1lt/wXwD/jspLf8dAhVtv/qGoQePl7iJKyPB5BIAb9cF8PDoLCXdIJ6lTRkX+HZl5/yJwKwgM4BJ2YxgxkCYJZ2+Am55fcJ7OiWdFED/CEEQcDCxQGOlwCNxI2YOjfH/Iil/+PFgEYb2g+vqLGYxLBxMayw3o/34acWnqqLnwgBOcoRRE2SRErf83fn+H8AGIcKjQQgPms0ho0f68DplgJ56QaBjyev/8F8AhWA46I8n544yMcV+NUzjfmJSeVPWB0CWiVPX+HguDwrxvgxQEkezHXxhZwGvQM3F+8HSwrRK//z/eIXi8YI8Ee8AKBqy63/AAVq+zChb4eAoRTU2dAwRU2Wsdi/vsJuSP2lv/+4MGrd23ZeSv0tqAvFBUrs4Z6af/LJAFhohFrxN9k7i5vyf3wE2bVjwQ7UWb6j/X+/AvxmiLT+cKylc0/lSNho3WkIqexioIS+J7mkjnfrB0wITtQs6X7bQcigLmzPCIxo/0+/Sre8PiJQu/Tc56//LjWA+63IH/8JRuwMPhViVwOcRv+cYHjkG64nEbW6rNS13/W+aAJ65wFqbCsJ6Kv+13oSNbRbI/3g/u//gJDF6NhgBvnAy+Fb5oiKEMjZ8wi2d/f8BErXHoBR+St6TAHAlKEX63KPPEquDOL8AAQBpQGgTBTpezFWT7HKtwxvD8sUOE+Q4N3GJ/xj+CAgG8HaHtNk6Ww/fjBmFu7U6rC8/+eobrrrrrrrrrrrrrrrrrrrrrrrrrrr40+H8WEL969L/7pgEWImoxPzCxOJ+YcnJn7gnFueZE4tPX/gGGsZ3fRTWDwdx92sLIimci/9wV3Hit3u4BJ+seACbNpY8JiwyVfcQ34NXuC8XIQvRacAkmBHAsG+nPIIhqQBHArbvkJ0beecu93u4/6kxVmM0VPhvYtPwyXqx4J7G3kcN3oQf8DTpzfg4PTnlgY6U34lhvAHJghgwLT8CCjtjgTyZTT93Edeo78s40brZ9+Nulur/zQVAZWXUCRUT85N1emEFmIDrRHfsfx/3zG7+uggcCtVOMfiBGTU+sIbkNwzIELJO5v4vt+Ef07fr2/zF7hiuIV4x0fEOnNdNwn/xvi2y3+o+EjU34Ezd1MbW4z2K3/iOfwd3EUyayHGj9IDMpaCLxsMa6fddVMR8l7oNyTxnjyPEet8+PADx5dQGeMH7JfYPfS3hvLKyy9W+mvjS7F/0Kf7FmlvBI9g0cblhKZyhOT/X/4pLXhDgS7jf6zFkCaqcTdK/psL79v+A1HFeHcJTTW8ke+0J/j+Ayx0eEjT2QAIAHd3cQuOl1T5jg8ykR7EO3CSEzH4P+NTwd6lucXu4/1vzXi0yS1BI8bUk8NivmI3JeB7mieCzS3G44B4Xe/deRUeW2jwO2fDs9UNIbEjv5eEcdr9u2NtTDcILwyOxKX6h6kLUv98fHHp0KGLq/PriNHlvaPA7ZDs9VR163u7ssNZr8ebn5gj8b9UEfjfqXFlpjAb8gO9Sy6X63EVHuONOUkuovYT1PQ6V/eMhsy20eB2yVX0fXuN+BlUtFgd01fXw7PVh2epcWR79d2csehPGmhoHhKwxQJ89p/MWRb93/X1sgzGGt8xF5gVF+AYXAC0I+G5qFJDwQJdnxD5Ly9xccdetw7pGKQvX+f6j+pye5SUR1X60IrhL53XsvbbXw/CAlkG5qQ/oMtx/hbhMYvlILxmsalLg0u1qlT5ZfHn+wxW6PLOOa4JvYLK3NT9rzcoQiPDs9SeG9JF5yEhiv87Wr52+LkPwHE9H0vu+zHNcE3sV+ofW8YyOxDCP3CwIlx2zhqURtmkPW+WzEdQsAYxDqsbJGasBqZh99/0eWaSw7ltbf9gz1/J47GvfwxloEdX2wAObS0gNxLZf8pEEpYB+BCLCR8cGP3fJydyjyfgCLYWcpDYAEevtg3yPcEOeGhdo9kGVSgajAdYUy7o/0JAxRc8IoLjUQQveAN3URPoky7j1z99I5f/+cIgAAEt4wABrH3AiFiyevgiFipPUI6+fm1dzfgQKlcKUL/qENQhXl9tUxxX5dn+8A6AkkqrlnocQilwazf1P/gAuYwqSAGUrMws98B7lFGdL/JsH1YTsfAefM2QWV784QYk5vy0hXuO76xYK8/7+sYFOpVhsQc92ve73VXJ33/wAfpT4wUPfQkBxPdAAjberHyges+nofO9m1MAUXxKZsiYOBMHYY2XCnhnnfuAyDhQIFLQhjZPBHfsvdeGUAKWBVYgLxQCr2zgRZN//91iIAoEqlW3l3w1n3fMneX8TEs1I7572t+35gPmA1m3VLM3b9gHI4Co+Z4RGX+9UjIEjUkmK137MBWOENH+8Q+f/87iJQnK7Ioq7//hg5PvqVb3h/u5Rf/4S8YHjjEKuJzvy9XwAbYFLht0H7kNGHLxr897M3mPq/d18OWOuhMVtkDgAN0xKER9c0WQw3wUp62EhetdVPnOIRboItyVwfETM4RmKFXyGtGDINewgBsKXkv77SRgMHUNjBBjOof7aA+OQl+w9vFAiL/9JCGmwsxdCgjqz9Y3HklrF9TrV/v/cMguuWff6666666666666666666666666666+WAf/CQUgAuZcAHHBJMJP9Z8RBAHnSJM5QqABBmaLxEOPnf/1H/ULINgQTpPw39YfZkIqs/PD5kIp0K/ITbme5HkOLS34EcAAQBSPCABskea5+AAgjnMIr934Jgwo//8YyCoS3EozGX8h5d//hLgSfMTp/zAStiebyC7EPQwNrgg6KC/EBQSjFiDEnb8jgAQC+6QHpDUHN3Q39dx4qXD0y5/CGjOOYKgAQGZYTiIcfOmL4jaPuZdkJU8Gs9IUHgxJTdIPwynyZpj78KBsQKbxNeHJmzN+r///6ciU1HwdQ+72AFC5FoWfT18BOACARtjtRDj5whQV4RBnr/zgABAAKkyQHpAP8AD3Mb+7//+q6iiLmoRFjlfnT6jgyw535FnG/Iev5a7P9iAQnSTkvzooAYLhv6/44ewzF6e8cFEGLSX4IGhvHmALYFZeHn3//61MVOub8GvnIF0SwEYDj6U35OzoYFFzia8SIS02nrBELmk9fAFGyEIm4qKa9/QSM3oE8l4B+jpRLO77fU9y9YvVZCmU4y4skcdPXWX4BimPzOIox0YFc5Axw9WPpv5MCC1dM/OJVXmZz7jWb0/MvgP0dAOzrmVBkv6X/8Y9fgVDZH/xHi//O2tKt+ENtFXz0WMt+xyI4jwP0dK5Y8zPp/U7//k8DHnosrGkHZ6v9nNS+S4Ik+ZwLJzofd8ZYJImdp4xL/eKv/61DXnrmr75r5AaSdzGhIipqGKudBilztsSLFEQM6+d0Qdz6fY9avXAArYQc2Fu0ZOcIwZwa30w9zXo34GMx2ZwGdRx6WHTpeklvuKj/rjRGvdj7Z4WZzgNrnMcFT3IAl65pAZ0VbTQH7l/1GC5KJuoOfkL+WJiUOzzQNbC+7Wy+QoZ1+8boTT/wBXAXjCQ9eEjT6aLofysl+6TCXgOupbmTd8+AJEXdqo9xXXNfriIjyz9RX/8DbpZcXPt/XFx77jTR8a9kP5v1KQwkV4y438CWKaWPfIDrqWxu4mO6fcN+WhhWeW5caLa5r9e1B71LdSXQ26W69dFcn7Lxl9w+pb4PejUXjgBaz/SNf4e/xO/y8V8B+41CyNHG1/h7LfvG/XrGnT38i9fZgOtUtyF8bzLFGRPm7VOxT1xzgt9yUUyLiNd71b7S5ZTtc4YZQaEPGGz+4tDm0a6vG/V/T5gbhrNQ/fyviA7zXykvGvYCu4dwVyu9w+D8llIgOzpbWlmwGjOyJ5GyXgAlcnHrp38JWGKKsDb3qPfOT6liuPqzp08NiPH5L4PyPDL/M7l0V6li94gnnJ7lJKlhSHEQ6hi+BJP3sJDRc5OWKjAeKco8HYYLLGw35L3UOM6+K8Ot3p+Nl346XXA26WRzULJYGD/ByFb9x0+nzpwMqyWIm95zXXuX4PhZ0PKwe/Bkms8DrTVPPqAMnfK9X6R2UiAakX0FjeAkm0g84z/DDdbi5RIEDcAIi+U3XwtR5CgEO+6YdVQfEL8T+gS+GL/RXb06ueb8DgFsGPWwGPLk6LWP3/B3IuPX5QA4PCIroLGnSN6Szf+AB/1Bp6uJY/7F1yy8kBCHRQNiE5gkfH/H+//9xu6QyPeAViEWbSC3gPcFrq9RWABPKzMkY+BayH5M6mvjgnMWhwzDlggFpKyBCl3+rv8Xdxc1J7JwAmrmYWeu9hfH6hMqfzhRVu08DelWb+tvv7iRRBLYKr1/vWAe4UdaMU9ZS96pDINFrJMZ89xgK5YDvuMFeo//2CWkwANoAdY0t/wPd1eTu5fuKiewoVPYTgyNjfUBQOHTQi44cI02/abObNFXeLaSmYt/+7BWcB/uSgyX//+6hCMZMyi4m29nMUMx1xPUnPv6/bb74PwP+7yd//hLgdIBNLmK927fsA5HBUbMThkf/j8yqeB8pRDK6ZTl6/8GHu88VnMY7h8yEU6FfgfgANkY1xCZzuQj/GQ6bh4yRGNp4vuawAsuygHBkicZhAARBr2MUuzqyEcgAAQDACBKZ5goLQ2ktRdYx23eD0Bv2eUUcmiTxvsKycW/q8Ozj+nwt+Ey79943+oxOmKaMN6kcHsD0gqySYaaTr1Cd/rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr4U2/8JBSoDgIo+hdkAHDCL9RLLlfEQ/eQDwAFFQC4fiARCCGO6UYbRecGoVrrv/wgRF8JhbwAEyIZXQRmJeljE+RmIjKc6kGU9avIzERlOfsMha+8WDJwckR26CSA1lPoh8RqUV9MXbtaEhTg57msGNVpG/fw/8PlD5iIcXlvwOQNiBT+Jrz////CRBQAkLTap/OG4AgZdjMAa+v/HQCWLmpnOOAAQD/BAD1//r+HQSgJRxV2qfzigAgZVi9DfX/hwDbMWztxQ8GInqYMv1tY+chow5ONfk8eP+gl5kaOzDKb+YbgCIuw1EDX0uIcCtDYaNZQADHAGskB53+s4OOn4mgAfNTNIBsAIEVY/AGvrILeBDTOcwJjBOmxOITvniI/3GijAAEACgPgPW1B5dNSaHcGUuzit375yNGOLjX5/4q63/6d3LMSIS3PMqNxUhHnvj/X+/A6UaBWL/gPdSm6X+B/UHpEOPSx9FgCCLszpDj53+KwQZNiKRT+ccAAgvwoB6/+OH/rhJ5sml+HShyTzc+A5tHb/YPhxlHj358QE45hwAIBfIkzSA4CIq31PX4DgAgZVjcRR85zsiH0fnh9kJXRX4cXBGV7P//Uf1FTQGegB0DYnivzcij1z0HTlGXkAgAPt2lvCKmrAouZQrs7xwUhlzvwcUev/zvEiAJ6JU9YNOizLgP0dGXwJAy/Pol314Q/1nBSRzAh6IGk9zCi5Rzp69xRpqERYcV+eCBD3CprCMB16G/r97EiAmouT1kBeNQOcjoHe6AQY17H0vgGAan6kyJbHLQO+Tt+c6co55AcX1XPXrlvHzI5bDTlT1xFziFVzAP0dK5YfuX9L4A7qWL3+p3dhaDgOkWYgiUczcevW9hi54oesvOoeegG4es8xCsy9d/UdhCRGAdCaf/3XF8a/XoMt7D2W4HujDnrBvnTz3c0dQNKc5gB68q5lU09eAwDJVgypZSKXz7jerljXdeUkW1RHDcdljQGjnUoMeCnImxFc0toc1OBNgYdR3GQOtS+XcY4j/rMXGd9DrrN/yUf4BisfmcRgZyLA065vwJdV4AzoYrOTQHny9Im+PWu4qrn4O1jpda7wlvncAl83MP7jUEQ7LHfg3lDo3FPHpDSWwBbTBMZeDty76qK98X4rCR8eaPxuZY06RDcsxZfgZSlv3HEe7NvWnahhHxAd5o4Di5Sxu87GvcqP7jUB/qWE3t+/OOD16XhDAqZ8dvcuZj2awkfkLMtKiiz2Q3ERGfzG2tijL/8GVLIFWzcSe6cdR5o09CBDQjxklr3Iey3Bi7Uwa3PnEj8/EprDen/FYVxi5Ff4y+/ljIj2AF6f8b+JDeEqGLN8/q/94vdsR746WRiWN4gl7lRnYYvn6keb/IwWXu/4hMBd/YnmfwMlS0+TjMAIi+Sbr4N5biwcjD2hPBhee0/5ghIjAPswECzBZ/nBRxwfXe73KJJh5q8lykoAosiH0fk60b9QBGogfInwOZDt3+AX2w+Uz8/hd5YDgUQFsVYCi62JpKjrT+ruTkPr34CMpufg30HUMj/T+gSD+ln+wWPM3bMnl0GPU1gcAUCgbkRoWsf9v/977o5JD+vAG02Sb8B/j5DUi/wAeGvCFnpAj6Q39BPFGDYRX6MHkwAWlUCupN9HFvfn5kUFIrkxO/VAABiTKts8Mu2oKmDV01w1n8txV8HcXNXuTvpYB3dmFFXIVlFO0/kN6UAahIpmcEkqt3bBYCYSWknMWOcwK5aDt2MGco//12gAEUFjLQmt4BkHMU5vZcEj/+vFVJ+B3Ayn0KcqrfAv25O5P/8LQwFg+ZCV5nDhGH+5uhgjCLjqTyf/Z9/977/+grwC7MlBkP+/3alCJSJkcWv74DDffiM/hSKvjVPwP4YVzbeu1/gAVBIUVQmNpM8KNG/9MAR6cmQAuDwrR1ZW8oug/vvX8i+E/oRS5m+cFkw539ADG8IOaQ+mSVM2xcAAIAIAz6HGxQ4AAgHqDULs8wCcqoDO+WitP0xpodgYhSIVy//+yhcUAb7/XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXf666//+HD4WgAPkeBRYDENaeBgT/+2DGlVFuss6ib6KsQf/h70kBtmBrPLQgUJf3sdAbX2Wk0Iy+jGX0IHW7heiM4dQNd5FAFaiiWHhBK+p//9Qvff///hCgUwEX6+x9+AAkCKGt43S6LFPGMiMwiDOTPv5pDxJHI0YV+NfgCtSQIK+Z1EuP/3h8Y8tDTMTiC+N3P8wxn3bzCFo/4MO1AECte71DOEb4FsgDHImXTd3mzADBk1TlMLXduSIB7M5pCQVP7//6CVEAEAvuA+4RIAHeamcr8oAhFWegb6xUCCdJeSn87/1x4f0EofMyCOpH5l8HzIRxcW/BwHIcWlvz5UBGTZGDfWJAQZNiKT1///QS8EACF3pmkDAAgPsEAPXwHQSE50v+cUAQIqz+G+v1x4fTKEVnN+RBOcyonxRtD6qX9DI9f4WkxaeqIe2nqC4VssSuinyFDYg5fE15/hyPPden84VgAgM2wjUQ4+d//v9LvTj47j7h8HWPu9dM/2DeDaSz+IRJOJxnOAQFgQA9VFvkVSv+tIyMSzs6p+6EIdfxiGjL/cHfFCpq8JgGXt/6/b1Um+FOeu+vHUwDgxDLFfgodYOdNg79y9GCOzp3zg9F4z534DrC8i5EWfJ8BUYZxm65+QasAHPOl0W9lKDTOLTzkNEHfi34H440YcfoKgIMmTKG+sDnI6AH/IvYCcRrh59C7L/jX/1JEwH2WKn8FB1TeBNa05qDubFFfmXZbEQ3HT143QL0TDf1iQILsImZ3DCAPsM7gOCZqLU9YHOR0Z+5AgzL2PymfuP/qK+GYDWBYDgyw53546eE878FoXbz0c0boAcEBJQuT/nDi0jZPxV+WR6p6un3gc5CLRyiA83L6Qtf31/1kXzcw/tGoInrgb8B8dqaK/M4bPbHKG/FbUsRU1p6a/YBxco509fA5yOgfH0jgHeYyJ78LjuGAf/tw4/sJceM2A4yMOK/O9t5g5X4YJyNejZc6BseCBZuAfDWnDyKXz7cbgru5eokxsuib4A5Z4mL0PhlBSI5x57xkAIS7EwfyL4MI9bfffDBLw3kp6qCP5CpwAJX4Xy/eCDGUFwBRPWwaha4PFli0j+OO7lIfcXeOUIiKJOWf/+B//4I94gCa9ijp7CSysyHz0YE38F87tHgxIalJbwVuL07rA0C+dR2ywx0t/wIARUvCqQFJESngfw+j/93AXPeT6RcBwPWa0zyHrSDcgHaMxbvwLPm0JFT+cTrb35+S0FotKRGpxUBA7TGR3m4UKs/UgBEDTCF8s/pfvj/PaQABADDB0/r7tSQsuTYpcXvv/t3eAuMv0WIMU7fl93AAI4LOTC1P0AkHMdRJuJp0VYCwTCEknKe/APAXz0OTLhCSb//7qRueQpyrx8CvFVJ+B/rdxx/9hIunIwAEMdcKhqEv+z77/9/fB/dzLxKqPfbijg0mOW//n/H64yK1MgeAAmZDI4ZTcQ8aU0/+SMTyciulnofBMBFCDd8IEE2qf/kAUEtcQNzOlrkEMJ7RbKJaTtMJfb9CY0hklUjitPUL33hrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr/t/8PhSAHG49AZWP6ReMlu3fi3//D3jrBipqKpech4EJk4jJ3Mba6gMNQrXX//y4UCnAAaoiCHIZ3KHneGfgWBPJe5AXRDSbjg3D6LhYv4Tw+AicAxPdA83e1qAj//ew6AbSdNWJKbJpxlphA71UPQK06oNT3+5CgBWowtwkoYvyv//8Nwrv///8JBTw5GxDi8uvPh8NGHLx78EgAhG2Rk9f///4qAnABARthGIhx85EACF9Azu/AwAAgAF+DJAecNQHE5WN/X/8ABEH6wy09SW8oh/1q/fCnFQAIDMsJ5EOPnBInxHuH3hlPkzTH1Uv4MSfr4FMeEq3/jE33BWjr5G0ozF43rIlEwMwcXpc//j+hHGoag1/fvxva/4BYT8QG9jaWmnNkcH/mdIzK2lGw5uQu1/9BLnCZ9IUd9+hriUmH39cWACGbeAu4cfOYUFeMgz1v93780rZkCaUrkR//4QTnPliNs7v/aQsSPGSHHhPfXeX/8JagAAhz4npeRfrzAOY9CRcTjJy3wZKfgLLAAN23gtWwHUrfQywt9f9y/+dkCfNfHFdToP4ACDcKtbPEScaMbTuE9RBc6Pol0aE5v3gmCqW/hE56M9SJI88BT9gmSjsNPxIVvvrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr///h8IQ4ADRQ0BSkmqPCMNlH7I4pb/wkYCQTQCi8x+gxGNUvmoB5QMmu9Jxaf20gHFCgqdYQNYhP69HMWA1bU8Qfg3/Bm4SnNGFWHz/bZ+3//waheu+uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuvAAABE8QZo4E+AULmFcPUwCLP33rCSKMBC1PLZ9juK+dedc48S33hLwp4INy4PJ++zRCLkHvPqbsMAhGwkYCP8O0Xst+Tm4W53YShNgiw9zXtwm2Z1/wSmlvNhs3y+CXcmIFg1mTKchJ9J5mUcZOv2/l+uIwTD4070kXuBNtf2f/ilya0XhKGxmACDRrZMja9fx3/hTwpgw5B8AUX/UP5/P298+puJhPgmCOwchAMg0FAmwaKiCpJkBapn/m6VxIroQJBEQFHcl7u3C4WBHyWgbaEQaEN3fopcDSalhZpapkwQhEFcG//vl/ENZDDDg6M5dSNgsHu7u97u7wsv4OAN54FgBXihEBpdj9++DeH7TRb/wRThlcnT+X+IxBzByQLB2/7uEetahLsJwl4S8KeFPCXYThaWDiiISjAu5+FPCnhLx2uUo4J+AWPImj117wQb5/P3mZDIIo8PlpubwgJ4oTvn0CYWAgPOtAkVhQRNK9u7mS27u7u2aIf/0CkMiv+ADb7q9f//Q7H5v+LflCFZe73MBb+LbCiEin//w8vwTDZnHK30EhGxJvXM4//6CxJ7x9kv97Zv//QaMnZZ989wbZcijJvJvQaCwKYU3d93d3d3d22AmQiAhxA8sOyYf/D4y270DUIALOE+b6duAiLNG/Py+biQkEQgEbAtIb35/pSDTd93rJR21Ri2s5xZKw+xeuaD4/gxE+cWcq8CH919YU182YJtnhevcR9iAwDMX/wONNVhZ/dWIEEgbHn+s4eOnr/aDZCUOrsWL40WmcgwDlZZ5+j63mfHaN8P/hQ8CxHCfmFgvifmHJyZ08ICeKE8PiCCICIbTNOrbTKwpdl6Y9MapnxnFm5xeX+jsSDIEBrgPeuKA70JfMCYI/Lu7eIqXxqW/Nj1j/grNpGW8gI+7fgVAYBS7u7933d3d/Zv8eAaHl33x1oSMt8RtfzmsDIDAKXd3e3e7u7u5VfCl3d3Fbit3d3c5K2y/+GYUu7u73dxtru7u2PglFABhnOBiYvyli7N9i+EcFAelDPzd8agQ0YrAIOUE4avKli5uXuIZiRTAQj7z8u///8H8Hm68KBJ3d93d3d3d+P8hbu+/qYlOTp4IPBF0HRIFHsOIFokSUq9GGmWsvwTeCTr9YFMT5RfMNAFTdf5alx9QZZyM99cpYFxeT87d6rN/Ew27Otub+i/42Y5sykD/L94DxQneFfCsmCvTPYUg44iCPh/rDwnh/w/ICSEp68raWHhIUgkJd3iAeYpf/4Kx4dOX/CWwwgohAgxt8And7ef+MV2MWorcxHiFxihvzGMUesRqsAqMKDCXd3d3d3d3eOx+wfQ9X2gUQgJBF3f3AYQIApd3KEHd3Z93fKrN/6r7BaLwhuetVAFw7H4Ctr2eSFCO7itxW7uKxWdl3t+MKjYIxQACcEYQsDG4QCgJnu+5y7u7vb25xhYzLnxbDctuWdBp/PwYieC8TwMG4PfByJXuliYIdchARYJq7F+Rw+6MOK/Qbi//+1/1zQ23CAneY4rlXyjx78fpuLEXBDy/xbEl7KuoeVRM3gjM5iYCbHL+gRZsBZNTvmrj/9DhNc5L9cgfo+g1cvjEspKX8yj//oFRBFoirEJnjEzPMv6/6BVEWhKQtClbQorhBSkhFoDnRjImZymRCZ5tV+PXgqIBlwSk+feTYQVkVAzLbFXjEsu5v9Y/QLi57cDmMDOWkKVj0ssVN5f88FuqOK5lhZl+GTT8ZC4fABlg9yub/lYuw0EL+3MgK/Wq41B2jasAiMB9g38EAJQSPe7u4wUOehwUu7u7u6vu9/fBDd3f2HUN/CFuf/Ksp9tvxAcQzmViRMUsHVFfyeXxO+J0hOIJn+bNVV/avsRideYkDp6ZhFequ+LiYTL5ciXhGG3nXYjEw/x/L/jYvuJKJ0xICE1ROCcnG8vxY8SOKP8by+WD4EAGIuCsEXDj23cVufLBmRVN/jCvh07+9QJYPfn35vL/6DterN/c5lvJpNm/1h9Au8lidGW2XdM2zf+v0HffqMUZaRN7cNsb/8K5f65d/FeIQRxiCUXfCKyL598m+ASEoKRWq6qqqo4x+5ZV9qVNRijKwwEdHBywCBouHYOjQq16Dg4DAQMIqofTDXA15LH56AWbo/Iici2zl4Ir6krwiv1Pt/kqGpTZ8EOYMxOX/BNTMXh0X9qUnyBsXqNeFkYrBty/Bipfm9COLFamyDo8WiTPcnDliexPiaYMMRThccJ4wTl9iRpVosYLT6gqIx4E7/Z/1AhDwWBSqi+OmCxqLisVmcZvj4/zr1+kGMtuII81AAHOIdLunm/8FwoO+Qwyc2PNvxcMigw74YzxC41EpVMP/8qDgoY8wj75Sx+X/m9R/9ArFEw5LOiJ+iryanzQrgH+g179QMzIiGha0/gG9Ggoqqqqqo0URcgKKxcX1UaKvQJNVjuzRD+f2CoOiQ4i6wydRvfxILgpqtVUFu+FC6rqqqqqqr7gPkOoVHNep1Gb/OK64LS7yoywWGwbuLfoF1VVVVVHHeIBX1VVVVVVGCriQkCOqqoBjaD3BH1Ud3xfLiWa+F4LbWtVlWeQEJjZ5VbvBDZjnvHMIIJh8yx0hfLODvBnlnO/CYJARDoEetx/2g9+DMI5fBMLQ+VjCZw0YfaSpBGNCIgEcPD550sp/AEWpu3586jT8+FQRYkkBg6emf5iwD8JEsSJ/f/w8/4LAj4Ry/GhEkdspV5S4hF8ZxMFuX5FkhsSHOMH9QmbGcDYDHgz6A2AxAVQNwoGINXSe/JqRbn0uRVNC0lFwAowBkGDbd3d3d33e8Sqwwh4oxjv//9+AE5gJwIQhGPftyAUxavsCKJDvQ8gCdrl5goRVVVVVWq1WdOAKohS+7u77u7983/iuJoOlzEX0q6Ynvzf5F/BQJDsEJZb78lwgg3EJIaqn/QA3oE2CIPJGv/oHJo4R591XAYYdBGa1WLwhu8FI13d3d3d8BFsTAI+J0A7QVyoQ/AeoLQSkd934/AcII+AQsIo5E4BAR6lWaAcPXs0evw9BuD/K9fbf8II06tvTT6CQ+YFnjXlVZcwXXLA6Y1lu4FYFLhdX+KE0+MoRBriOHI8SUGQAq1TN+f3scXEyLzrHHmLDPedsaOF6of33QCwBiCTwD3WKN23JCgqDqYg6mN3Xsl3SSYS+AE5AEBhEcCMchsWGFCVYIZ8IuPWR0MPyjP+LgBTICxl8K5OgRmqvuBdEr3AnizVJHsE8OBHfYqqqqqvGwV1VVVetewzGi//hL+fDsMJEvAFAEBCR3fffJ8EJDZe+x+FQSXR+XrgDmA5mj/p+CUI8InB08FLLm9XLIi9AvGEJCZH7mTPv9OA74ISarlph0GqNhz98IBAFF2VVVVe8wutpHUtFrLAQYnMMKj/6DRX8hLi6af4BDQ/uohG7L4ZQEAEIAkxTAsqpcxRijPmLk9GLTTM5bb/EbyaegiTiotQiwkxEDiWmaUk/T/PBXDf1CTElBBbg7r43Sj4U1Bh4iCvUJAbgRiwZQV6Z2mUnewPAKyRnjrV7tyYsZfgx2vwAlkAiAUCb1qEOGFxZvu/FcVT3h2CEIAfD8N49vIKoufCH3NM6xH4jxTx+V1DVWXX+WOYkNhUl8EMO9sFuobIUzUYX+4CBCK9oCWLCcEROa+0CUCCG+4EIeghVVVcOwQir8Lt6rsSgCHgzBQfl3P8W+3wu+9hYxCc25C/KXjXsAI+g5HkUhJZDdlfqqqtQR1twWAlFkqtVVdgjLVVXAJKCc2kpCWApQqLvlea4YIcUP4RfD5dSy6Mh6j8GLCBp1DsEJPOqrDCOE6qRTXEOl3/+4EIIDBSqM+bipJGmBxU5a0q7OlZTs5tHy+FmBBBEBDCysWBFe/a8V7wsUchoKDmYPjIdsTpUEmaReReJgjtYCSBiLBVhwZblGSHHksDcCEIxPhCqdujdYykuZLLBYGQoJUFpd3d+W7yiiTm8GAgTd3KSMPb8e8XNR5swrCPw3DQpsDZNRB5WUx2ViI/DepLJX9344yvCMIUi0/CRU+tZIRHqPCVUvTe0XwhoMaJqNRjJVBHegWwFqGS8OwqpoPs/hG5L7m21BDCG+/KSLarO+YKVqtVVVVVVV7biTAr6qqqq1XtRFDK1qqqqqqqq1M4Uqqqqqq1UmTK9sDyPATIRL3am+Piu4oM+Nhumg4Yh7msXBygpVRdZ1yrJTmwX7MPUcP3jq8bb7+AR6+2MxszAg+vQCOttwGDkucb7DYaDsXFMVh1PkI+Cl2/tmr/63Zf+CAQCMe750rlLwGLqdlzCOAELqv4+9wJExCMCgZCOxnUHC5I1GovMwejTBWR+ZmxjZICNBwGpsqOMq2mimvoMSwVlQf/9aWa5l6BMEcFx+BK1uPU376FmLBOYDKpYxeA31q+gAmIAmDQp5quoJWUiVdeVvoJJwSwSPUfkPoFqPIMVY7jOtlU9G0czwLOUAILo3KAkewaqGKSNY+hjA+hEJaUnqD09XprhfoBMhEBABAElVBHaN+q3CkRcRGExj3ZY814Wt5nsb+KMe6FQxH02bFbX4CNnqTckI9VWTW/x33hqCLquXCjNdgw7PUslYBKQTi5xcpLxr3yeSXkLrS+hl+Tw9CcYX+5jnYRyXjvtZKvEpI49YNEo/molw1Vah0y00TEvVfhTqqqvVVVVy7jKqq1VVqqqq+MqqqqrlH1nNRZoBsh8CyJCFZ9VVVWwBfgEgPCApFbqpzhJazKqFccb4Z3KSPi0QgLLnbFoLN0uwUQI4lBeJ1vBNxAwAQpT2n4Hy8+J+f9HTr5JRIewBssiafnx0D+EKsh+CJFAICHPzv9YfZYBlzp4+PtQCWBZOw/0aogZ8DhaNQSoWnrlAcXJUHFzRuI0gRUOT1BznTVKjnqC01c1CqsmBROpw36t+S/pz2E+HGWcPFLgYZSz1YATJAkhDfV9wPKa5TEY4ojQTiIuCDVF5iMPc1aq32q9XiJsX/JZB6hmxh8PZb4by2YsqWFoMjUUB5e7llc4GVH5YT4V+xj38dCVmc1PioRhvJG4F4dy38MZbwCcgnCEOJ5Z+Hst421cgoJcJeXMt/CfJNkMe/qagblGeX/wUSw6y7o+jXHlQMNSgT5zfx3CnSg1DIkZ463brEOc14zNI5404hloc97FIGSCkuOhF5yKUoErxR0/RvH+7d3d+WXwwfAFdoAVGhcY98cZA7qna0KW4NsAJSg5wpXXIJATNoyO3Bobun0V0v+7lv1osZHwloYl+oRafS4uZlVUFrqVSh1jIfpoeb6Uy0yVyw3XPVNpKaZkbe6rn7nChFVZkgxVVTGWcR54utcsv8tgCGAWAqKqg/1IXqoZPj+IWWM/PRF5bAEIA0EoKEUXlVZhNwcFYPW5RnU1+W5cwzvl8UXBOUFdnTvf8CWJgsEQ78oWAAVk+9fLgw7eDSSCR5+qR26/wVbL4KkLXkg545vAefKxTVS9ctQAShAk4gUr19K95Yb0f7UPE9QYYqerCi5oAJUgScRCn59VFiiJo09cPc1bKgAlKBJxBMa9+HNNL4AqET6j7sAP+oDPv/gLzlR4MK96xMIxj3zuEzD2WTw3/6FSxM1O+/ROPlv4x78PX+sAUeJCh+A71O/AP3VP4P+lnZxr32XwBVYk0sI31sa26BAIGRchcea+sc7y+ALlEgkE42M8M8YpIBndPWrbr95Xl/JKJQlyI8Sfqc0XxprK13iD6kAECA4BXnSkJAxi541o+qpo2ZWgBBgLiQUmISApqdsrGoO9S2zJHiV7mgBCgLhMSc2qBc/5q4TwdzWJCXQWGl+7hLhIy/gm2b8TReQhyUg/3IQJvPR8lREYa7rGdDEdWnflgjE4AHOd13u7/vqBBxEneAFCT29FOvgE+gAAA9FQZpUBPgGHxff2YNQ7PNGC/3OX4elKG+b28Pc1/vWxfZrgPVSx5BfD3w1kxPeqwd4vv7NOPiIV7Qj2fDGf6hXXogLxkGOXQIvgQvgl8KXX5Uo6Rf5IoVwDd7Ng/2BsriDeW8darWve8n22inCVCDmNgPLpavahT1zrWWf3dYJ7MOMRNfSChjgYAlEg4xENL67gGG+56B8S+7XyIT2syoEx4eh3lNdcUg5B/elrWX71JMFCTz5w9KLwARc16aefw94GWDGCPvY4SKTgemX4ceLvDC1itYeCO0YEWICIiiGvMUTXCP9gFtoSTumpKyNxdLCmLzEnsGN79BQxFAo7jPtuO2TzYWwKTzd3qooJBZd3MDJRttJSffgIKBCe8HYoMApNw7lusMCIlBssvggB+CIIQ9DrP3A665K+TwVhDw+VjIe/7lN+b9Pqr+sEHbKHog46RleXT780fh74e8DLk9fwx32GpYOMSMO/D3w98Pd/wRdmElXwCP3SeMEfe8XFMggzhklj7XweE1hDzE97ef70OFgwYnDA9wQApCm0JGC1lt3d3t3d3heAsA8CcW7u7u4biTWwojDK1oYX/k//DCmz//huSTsws3yL9FxX75wt7YYRSX//w6hp2zDYr5f0XCOgpCgf+2sfB//3+MCgxxloeyd8vY3bu7u7/aQdggAIXBKU2eAETw/Sj39mS5Tru0irCwrd4aHr8gQ3eSygm5Bd7flflFbwEADAGwEQYGVVVVVVVVVVVl/IvQac8Pifl34MpRqZFy9B1JDwrXyInmEBoDn2rinBxRILSz8n1c8mIMBkL0u/9Rdq4FcDVJq6vEGqZ4dX2LF4Ex+g3Kdnf3zVvPnwWz/V4uN+N5PoaggXmJAbSn+pVv92vdfBjT/+f4n+uIqICnew+CEGwUFcsdu8NuQbKzu3dyoXeArgkFIrdyXu727u7uVDa+FO77u+7u7lQ2psev/QIid+8CiEAT9u73eJl8KFdxW7u7u7u4ho9lQ/wxCl3d3t3fn3Fd/a8IQoV3d3d3dzkrd3f2b7Fspl4LxsMoPmBisPV/cAO6+t/bGkH5Jk/EsL98I0CcPO7z+aLthmLgnfG/r//+Zgz7wJ8QLFBLd3d/iwXZgyMR3u7nuiv1K/633k9QG74dE8AhvxPxPAbvghE8Bs+CETppRZ5XhL/5Q3p4BHOiP5vZSdXLzwUw2yUYVgog3l17QQiBXuljIwT0I+IE+tiYfy/HceICJQREXyj3FBHgOIICYCX8wWstKxTBSDoFxM9Matpm74CJlw6DYKHt3d3d3fd8cY5zfYj4+g7xVLloXImXE97cxf9fsEAnaxCkyIR8XEG9jGRNB2Jh//sCBcaL9/9bwCGhENd4E0Iw6hTngEODAKru09u77uVDa4kQCq7u7u7u7/bxEkFJXd3e93e2NhcUBghg475yoMe9kBPWuqV80Q//sFYdFPif1rda+VB58nv6OFxJZ18WDqwqEeAsb4rz0Jvr6RjL0uYX8Rr8QTsy/tKZB+0T+X0J/1xPDcwzrFWJ8UJ964rEQT+C8Rk/PhE0SCIi9CmUwLNTKjrJcUJ34h+JHOAnWe5r/xEM5v/2YihQMcOPSRy/UEMPqZ/HBjhxqFypiF8BHAyBEYf8v944AxwD+CURU1nzjjHPygiu+MFDnL5deCUrue6ndqMFDnwCjgxBTd3e91XGKH/KCG7vKrfLKi+8CNU0G/ACaQjk9f6BEWk7se4JTHGX7vaKhtX8Qie8ggFxd3e78ijY2IqDl/CALQgq7vxa11v7rT8A3h3fAFHBD4bEm8FbTLw4gqEXx8tRzEuKHgh4mSIXf0wSFjm+PjXUoPDlocv68NhHXhpCzA614+U2EoHBLUyCT5noBdkB+q+l197xQsQJL4L+U3kTl/ECYb/fzYmHcRProF5sn1ZAzH5QQAWqmc0GsG/fCWuGAQhPwI6y/wvCJ0JJlXg9ryC+XziPBtzO7njL+QNQJLWb9r0+S6+++Cyc/PtNSb+wUVVVVVUYq/BQVVVVVVGChzhBVGqIqfb/gomyq6qNFDCvgj6qOMYV5lGihz4BUwVoaODHDhHmQx3/jITg4PT7BEGFrGIXwj8vgO4II3sex4rXCJueEv5+NBIKqq+1vqO7L/+cn+mW9s1+Rer0ciOugiCQRV3v4U6/0hoLBU5hsrMGA4S0vjlo7tNpCn0QlfxnxX8OaPl/zCZxROZcw8wdlX5h+9fy/Bd8BPAQeICHcwmYIlxsM16+Xn3woCYKcAlAIKNfUAGYuLEvd7v4AXuApGzXWb/WPUUC4u4y1KnpfwAkgCYXvF5rjY0XBAKMoxG6xqHyId4ALEAsMFLu9YAmMHOiN4AgMCD4Elm6rL/xFcA54IRqqqgOAkxiCNH+A2wXgkCl3cgHDgKEMgm7u94kSERrsJAmrWtVGirwmgT11VVnCHkChVVVVVVVVVVVGqvPBQZVqupVNfXwxpll3sRCQgyN8ttcvhQQGgt4gp6HofH4UuY69frCIUJ4FRTXgmBL7H6RAEkKs0THwUwXf09CxGBf09eArZQs8PfdQA19PWTpP9+84kSEfhGG/44S098QEfe5hNDxMq/518/l+ghwcCOnYQDmCPfAEI9YxMYiAB4AO8DbZlxzqAFgDkD8EQQ4Li0ye+OaPV9X+Alwz4RH4RQbiGRlpp/wAi8Dzw6vBDBGGK17eCNZrWvA0hHeGMyO2gAMoAmDItDYqWDBfglG6wB8APNDGPAFJCV7e4AtKC0ST+5YtukGXwBTRgBNg2LAg1H/eBHCYVo07XUgKKqq6r3APsWCjqubIpVwF8areA9QiFD7vu7u93fc1xwN/yArMkl6rWpU1xAwMgrNqq1BxRLLZSWtcwIiYuCUUABidsR79hL2EviYdRBGungiEgmA9SZ2hGP7/nxEGO5BTCQeVe9rhX4T5PooXdVEhDiUrYpV6XDIEgxpYVk+IKOMl4SK4WI7wwhYhz6ZP5M/fL4CLCcnq/hL5SEMq9eCu61+jdVwAg4Il8+U4AfXAs4ZhH//wR7P72H6NV2mMVr7dY4Z9gk1XF4A48P+AOJBH4Ax8b4DxHq/wusXyeCdorn2vbgzANeC1eye8gVBElVL+sH3L48SgzsPEEGXlK5fIGbhSEBBosCECPguwcWxvC/p71sazwWw4qxIEf4Z9BnL5ZLDGwSRUUDcwPZeHBfABLoWnK5GeLwTf8+8MAkPBIElVVSqT4vrBKXEcE6c2PzKCg4XWrjcyCnms69owzBDqGoDW/UCTDsKYMBYf7tSpyYJPGgoX4ul2LBJqoAe2Bz1AIGMhG+rtSnMsPBKES8o0OsqQA3ABmNFnqqrVeAZEHOvkMaqr5BeLr1W+w3BFVVXvCsKVVVVVqsYaFVmf8nwHWALnAqhYd69ivYgFfVV1VVXs2o9f+Frx9s8D/a9fEAorVTUWV72QFFaqtWu+QVVfCbUvACmwIfQiF8dY/gSkzCMyMXgQjqDcb6sAvBx4QChIjWKMt4Tl3UBxU5aAVa6Z/qSUtpngyfMIv4JRYBJKIk2/5l5wL933z8vfffcNnhv0Gcn8WSw1sEUr4N0GqGPzF4ANdFZmRHfcJB4WM0npIyjwhNe8iIUFkekLzR+MeZdVV+ZwoLiNRZdcyqrVVr/sFddQ9PLyqqq5+LCmpiMqqrNVWt8nhYY+CWqqqqt9Bn9i/DkzL14A+QDQ7huDUjYVP33oCHyeuAECgODYTljHnAOZphAs2teabODSF7+tbxuU2qhNhobQBiQawS3pVkxLLgDAAh4VLHGbh7ye//wARWBVxIIo00ffr4qMR3+0J1VVVfmCNVxpl6mxZXABFQFWsCoGgBvgEgIlkvVQ1lkWWVZy+AOAAiIJVghgfflPpMsM4wxwaCO4J6EAibNmSANASNROoU+1EvzrOBkrz1zwexJ7EkO+P6q1vbUqvXOWCSq1fcI40bw2dQa/vzLZXMqshz8smRUbZd/J4eIkgoJIdzizJsVmPk+gF6DBQRQlCOE2mlqgl0xtLzK9iiIJCYeyyRca+gJRLF9HNRgzgbdLfACcACIBaUw/NZbPvy8VF3JcZ3j2MnyT6oskGEJVWcUMDTCv4A+gDQEySX5aB/OS+WWJhSL6FPPQYron5V68IaEdyEs+HJS+T5wgJK+/Gd8OR2tVMCiZnOMn4AXuBRBDD0XF/XhSCjKDSD+ceykYuGHHSj4H+pwNqms3+P416gWcsEnDsvV1k8KFTZgS8skCyw5ceCTqYec41XMnhQ/7h2FN/nNhCw2u3qEcaA6ygp5LzYKr3+7v40H7LlZHfz5PHG8DiDAvHlFhKWk8+MDMtF7TBJ6MCXgq0JBOeAKlsRuR+J9vAROtPzQeOmvcEojlIBoGkk9QSUFp6rBIdyTWHQbFydNWxIeJgio1PUOQ41yKOsBs5Ub71IaJv8n4NeuFa3D4QGEL1YPPHlMZNv9ZM4JSLUPzePY40TjNOFBAzwlpfqqX9kd+RByU1xTHlDjJrwAjuDkZGPaOG5NGTWYeeKfGuj6h481wzunoHSysYYkIRkNFkvhLl+BMfWOAEqQIILCqqjhYQ6T1Gel0veIdbfsQE19Sa65t8nyIMAPhA4hgQCU4g+Q9XAV3NXKr3ZlgogMqVHwvaEeW+LnFkXx7b7KoU+yi/g7ksxwH5HOXmOh3LLFRcl+9+eEcGbb7kn2ES1suhQ2JFWFykvSGvcBLg4F9wMAt6m0WodfNyfJeLlmqq6XEReqqqr4Ao0TpsvQSCToJBbyefgeQYBWCOErxDSP+awXFJBQES0DL/itw4zevixe8Gnho2Cb2PLD+j/UCPjjBe1TmcTie5h0vKnrgJ8VZBgY0nqRNFz+EnB8n1E4gm+RwcRDgn+8ORxqZNAlJ+Yj4JeMtGZ5HMEerXfSAgQcBaCqAPGU6eaA/m+v+ZGsf+Ntvc1uLgsJGPcPoiMxf7OSD1wiEtzSOEFi9UHYiYit1ge5rTmiateymkF9QifDfk3gby0D/C8v3pNT3/6QQSwSlcoNNGz006xuq5k0zDILOBHumfAZW50MaPl4NaXM78WTxGnCQkRAT4OBlZoq0fw3DfgGPT3avGIJ9hNv7kJUhATj5Kt7JXfc5BXUArd33bnBT3d3FQbuVByc23bJMclPVElgk8mPk8wdoYJAQoOCxRDnAV0C8d9+sssEh+OrF4+jtB/4ggGUpYw0Br02WT3F8CRwhAKFAAAAOy0GaYCfAMO3x/uJ7L7JFBjnXnXKR9/f+QNns5La8T3nrB3j/cTt1G6e47rvCQehshaPgl2aXv22v36cyiBFGSkdbjEgcYkrKu5Pb8Ej9kjTX35oJRIRipqA4uUtsC7MJffJ7Sj+pJhPBI924sESL713urGcOIu7L/kixIKDQ8SU/z244IpROGs8/BF8FmDGw8EfH92sT31URngh14Z5f5AzGGBQCjgFR0mEBxMovvj8yrochd7vu/kBJyWS3N08MC+77va1IXu+jhDXUxuHGW7xxl8PArFDaeL68gXkFdYj+EQRAn1mH3cLhuC5zF+FJB9ZVVVZVVVVU6Zo//6BEE15Eye20CMSsVhAOPNa1qqrq8mrie/wSfBF8EXtidXr4JPgk+CTvXgiXNOYJiFeRSVJx/x/d4fEd9Yz4HgHJeEjpfBLyfd8M7+DoI61cwSzSe8FXGfHe3d3d3d3eGov//OLozfF+FIvQUI+7273t3d39l8AhH4nxOvCh3d3t3d3d3d394GMSFDO73Fbvu7u4diQZ4XBkIIHxlXb27vL48GXhfl8IB+6EEBT0zfVVff2+sKVVVVVWqSBKqr2u8KVVVXIFlVVyf2YuPw/BEEwx65jRQiNW3mXYRCe2i4Rye/4JXUh43qpMvTcTEZPb9ZtggCLg/7x0l+374bV7EmGhiTGuTiJ8vXbxI343pxgkZITU69ahDWO+BMBCXjtXuGfOECEg26Y9mCInfUzrn/k+n/+wScn9/gZAKpRuBq2mdYTDignHbu7v98cCrd3d33v7wQCAVbv3d3/fMFO5rd93vd3dvAbogKXd97vu7u/Mq8bBR3e7vG1+FL3mCTu73d3d4vCggKXd3d3d3e7vbeFSuMEu7it7e7s3d7zRD/+yfBPdy9gWGgMreFrrGyyxX4mJGO97u/oFtVre/Zv/U/YLeF2VyKV+CvH+QE5VVQ9W1hKuW7lHEBWSsqqquq307hcbk/SnDAmM2dGDiiPoeEewThnwMYR8nVxIPBInmXgxQltIiyif+giEel8C6Ee4Zy/6E4k97gie/N8C9xU54IO37dYi17EmJGxIQ4rTeFAj4JfglkJBLX+emnBJfYHQda+Jh/EeuGIR9hGnwTBMEQJQTIM7B7Iz1BZfU3g6iM1EKGC7d4H4JAqG27u7u7h1luCFDT4FgIAhEPd/eCmCu97vu+3gIkaFBLu7u93ct1O7v7GQiKAyzOLhA+sY1DaZTfH8iKgWCu3QEf68Ah4LUS/gOsEYggNURJ6Swj69igV33u9329iNcIEgsK+93d3d+zfH/4IgqK+gv8gJAxd7fQIeqnHxsNwSwcU/gkD2q1pcJBDoAlnEQR4jxH8dhaEj5//wIXoNXg8ICQVVVSqvXt9OSgwIM1fvACKlSeme7yLmL4n/J7qv7KOCrLpvzUbS2/suT/Ew4r2GTQe1/VQQixHbCOT7sThHQYJeuVfoIYiCXEevYR9hGl8difXBIYqBIAiLvlBCcPpIfGAKHDjJfJiFxiq/zIw7H+Agwqje+wRXfKrfLYJSvPbVy0sYq9NWF89brwWlP/WrMIoMyJTT/l//BEFnd47GkPgiM+/MuNgku+VXyKw5PhsL+EBiMKlyrj69DxPDbGb/73TBJVVV/lCh1VaqqrWtdHfJpeosh1424eEOJ8gGKjSWDwF16ZsQCnvhu/b6C1CIf0uD3rlB0Ecn0seQEPVE+3XQzoT6+F8vwuQMkwVGBIEIJwG/wiGAoJrVVVVXVccd4AYCAqeBB+AkQz4HE6J3gJEKr30CPqvvAnj0cd2PixTDms568Y7jIRgwePdBQpngio18vy1/yAj6ryKvtF4/0kTJPkXsn1/6JBpfXP1oWRf6INxjveJBGI5cz6/zsP79hDVYSBWCsNEX/JGtd/TCglRdVVVVVVVVVGwGCHTBMZa1rp/QQ9hDX2QVRNWAWtaflimVT7ODM1/hyvXsf3BPeglngj8M9UihYMlBENUWpOKrK34eCPk7yhYgKGQE7+P+Ze7ATgFHttXrBOlrwAl4CwFB8l93v+CoqrqtVVVGCrGwvDGcDqOMQWLxdeAZME6JKvV98usc0ljwoCEqqs9jYVhu7X4IwlVffohU348QveQQvv0f3tfCyFd3BMjlTwjBEK5aX8CAwS6OWmWhLvk/UFBQn9ykhpr3J+20BEFecbH+Me/WYofYaEcaaiUhFoR1PEut+wTB9w+9/36rBWINikXJ+lyK4s3HLQF0CtHvnBxXcqtfR6Dk/X+J1/DdRO7Lgq6+gMwGESC3FcV/YXBjk9txwd0FgTAsKCYvCLQfgT/36+ZPeQEHYHwCzk/rx8WBYNqGyASLO8oLiQV4fHeDZMi6qqqsHgBfICV4FU9WAEfQM5a17hEo3AiV8z9/d9ysU76eAkw2AwQ341Otd90ivm9OX8g0vGVSf9/L/XsALEAw8JwTG1VVX76BRWtVVfeFYJPLT30vtfDaL72REa8FRl7fYEEd4MwkvUsaEA0E0SvBICHXBl4iCHygmh7XV6BTk9poSBLEgiScoIiAVAwdl/gkCG64LPDQ7EQ7rwj3hgaCQaw9YoqKqwyBQBOTgFRyI6Fu/5S+LsBNGChQBDI4ve/z5lj08qq8qqqqr/wAsEBSMVhCwd+BjEhMmHYITjvn+BbElqve/CKKW74RQnH6af8AdwBiBCbt3v8mnAo4oR2ms2Fpv7JF9VjOXk9tsF3DcPrbYIdgkE8ZXvih1Vwty+EeAcf6wSCfwHCCtGg7ATwOwS3fd3e+rA6gjzXdhFIy+DXQ2WCsqqutVU3kYppsAYuBjwRFVcqfDQJhGqqq+1cgA3EHIIb1Vj3Fz3rdV8gKIe+y8X1X3gaxILKr6rVV9l/qSwh5oL11W/kF9V1Am+rbiCChGMn+qTP7wTB+GvKGqwQh/d4gCCKlX5CRB/pufFiBCPgHT5TN6BHx8IfPf+4jvhv+4NmGw+9/h0EHhN08aQQHxISiooOy5QWwelX4Le/GAk3ixAuMJx2nK1oUlmVVfjCtKqqqqqqqqLrShcCjQworFbisVit3qqqqrWBSgBlQDN4HQMC/DsEIG8tF+sGUNxfhF7qRma/hGbnm/Mwhd33vd3mFcV/9Cs16B/mBVeUUvfQJM8fL8EVVh2CE6i/+EosuwYby2jnl5Pu4WBSgl8vkOEhPRvGJysMhEKixHknDsEMYye5XQWECfLGq/F/AdMvUo/1BOWHffKDS5/xT8ulgMsH4X9AHZBmU2q0tAGsBgEuqqq5fmkxRwUVWqqoBsmaN6+y/Qg9ngjIJwDHMFvKgXvnxL7P6z398OUEwYl8CHvcWkMKLIJ8EnpXosFse+1k+/gg/0Eg5wJ+5fvhwn0Aj/dVeEj8/gyDHiowmqiW2ppBTp5Xk+vATQEkTNCAkKfTKqoYynTVQQdVcPH6UsLwU8oNdwn8/j2gnszPxqgg8YSVqq7vd3HQqfbCOpAY2w7Cmfu73gIEfuTP3k4vUI6FJaW9UB5gBG0BEgrKH7CY4GdMl+7u98t4CcEBOCjwSOjalDaHuJt322pdk98BIBkCxAS3BLF1FxcKwoaudPCQsfxj3mIjpY+Mht4Q+Fu8rCjBRGvbRGlnuLwAr2BZV/DPy+gB+4Hk3h6/hl+U48aOZvChtcgVgovD7FXj+NNHFl+TIUHsdhuDgnZN5KuNe6w/AgGy+FcR4JfPsoKhvLdr99Ey3woYuFUNxgV1eAXofpwF0EQH0CoEfUAXPdTxFpo4YyEBAtyO/8Tgo4gFBMA4SySgGLqL0giaAxEHp6oioSyOL4J9z+oR42/hdE/z9V+JjoV+maqqqv8Ex1VSWsqufywpmVWZVamyY1GLL+SFKqqz2qW9pNVyqx8n1WH5IIhAmuVVVWqATI88lrD8iVQV4JeimbKMyj/LVAJEIlmwEdLjfBRf+AuQyXgDQuI/gIObwLen1zIX21B3JLePhrKSwpETZC4T+fXfwpx1qtCpthu3dBlmm98teERIJPG47vxfyXx5ol/CEdJ1isIiMtdclyfVDY6Fvk/S/b1YsSAYYSES4JXijtQjwzrnamVq3LBRVVVVVeyftgRSgieSCwgx71VVVUvvVcvwQ+Me78FuUFGuar/3gsqtaqq1ARb6L+8DyDAJDwU1VVXMAwm1x/fsH1FPQWl8WJyVGlgCttE5+AztHF7gDwjxOKnrIEAYhc3H/L9PMddXEWkGpy37/rJxArRgioenqCAYWWno4Iz1iC5GlOrCLyGGmuwfarzT1Npt/l/xIkRCmtJdYZj/BmIxwcRiyeliJvi+A78fUNJ3O2Y3PeUV6lJByJCWOtfVt9YwQCQguYGjhCOPxcpI5esIGDpRYk4uv4JHscICBekBvtxl1+NkeawgiR28PQ7tIJNPP6e5PysvkkgnhubjzIN7T6mhwgZXWpi5KhamH0eW/DuWuXwAjqDEOcNwS9w+B/Mg8T1f65aqJwUYYy1w87/Fk+sCLFoC1BBHebDVamGIaF+sUEAeiQnHiy/uEeG/esG4gJRXgBhGv+32igruEuXgigl723Hg04cefTh7LVh+AYgI5PbbId7CbGFVVUc84Ho5fzCOQ6K+Pxpo9NuOgiqvFk/cnNDEE5M1mBQenq/2vE9UoBGAwWqqGyeXigwyYDRVznJ7WBUeN8GuahsGvxBAF7lRicT3MROVPWg/hgY0nqLnymusAJagScQau65YOIhI09cOTmqXUqwAlqBJwS1VV7UEj2JA1hzSgBIUCDgliyg38htv/f/5h414eHzgs4/tcYIg+pa/w7khjVhIPU1i1hcSLYyHqa/A7z6++6xqWg8pIKr7pQxUIQmw0P/hjXvAlaur8BmBgI8JtdR1gFv0/MR8L2/zSreEf6wl2eYeAGzbp+/zWG4qLhjFfnHxtp9ZeOksDlbReOKy6AXPOrN+N+iwH+lMEcT8Zq9UeT28eJHickR0nzkgktx6UQESCAS72QaCFYzU37kG8xrziQUEhjFfnLjRE4tYDgBAoLCSUxMkQ1XJl9KWWxITVEjf1/68bg/8RJYcsrrmifW/KT7/d9O/AMHAAAA3/QZqAJ8Aw5neP/8JCkb9e6H4naVCXtJxXVYv0BKyLxfL/i/B3j/USQVyL6WnuEOuXKIhvk8ntYS/6Xo5l8pku8n3Ydh0X7NkwmUqOExQVHiwZrGGuDsRYMKIYQde5ZfLpTPeZhXrsKQrIMgcaa99gcra9fPpzXeLQra1xHjwOXnvkgrJA7yZhx5MxZi0f8D1ND+Cr4KOJglgxfj/j+64npax2rhveHgjRgSBHthc1/0vfi+77v3NyX1ECy7jrJbQWYjanUgvu/Fg+dC+gY204PPUtyfpeGRBnl/6kKJ4bi/pXpgjgrFHBufhxydayARZSrZBPgo9CROT1TEQVYz8FXdYsTk/TBJyQNnoSJ1VCPYGz398EmP6xv6iel7/HawgEcv8SNuK8q8hS/CAmvX21AEEMAhDyetACCHCXoS34I+714BAGy0CDymAnwFgY2BH+H+DekxL9V7vb+JHLr3BKKGGu5HmGJB/7eNYurZfr3rsEmtXEbD4oAAgJRoABNAsAAzk93wQ/BWCJaqqqqqqvtwXghiSic8nnhvfNyH5MFYnwUdJRMQIDCMJcUuvDmCPdFhKl4gQICM18e9OJTn46yWCrlusOk/r54kWNmXx+njcXhH4R6UcJCEhD7GqPZr/EQR/FdAJQHJQRZ/q8BWgoL3fQVCJCQMSaZNvFJP1YsagoFt+Jn4z7ce1jBWX/2IFicF3TPhJ1vkHhgFgy7ve3d3f3iRAUO7u73d3d3d394BBRAUM93u93d3u7eNgj7v6sBUwS3d3d3f3iYJi3e77nk+q9mGH/9h4Jv/IC0dd+79jYsUcgz0/TBEEKqr/hSqqqqqqtVVVf4sKVVVVVVVVVqr/QJcj/kH4iS0qrDAlcvhkaJElFbONMwQuui1DXgWQiz578CuEeguGeny/8WCQSUQOlcOTmv6DYZ6XonEw7D1RO1whxM9eYOcAMb0m0Tjgj44I9gewgQkAxtYdflLgbE80TM148fFiOFKefuGKeFMJ9QzqmFVy+EWEB6BVBQCURMgMKX2cnfMqwQQWHCg13cVu7u7u7u7jsf8gKhV3d3d3f9r8KXd3d3d33f9r8FRXd3ffd232xYJhb3vu3gRxPgEBE7/BEId35ePoS28Jf8EQx9+8bCfd3d3Rv//hEOo1H4Ig4ks8HN7dAqqs1VVVapcbDMEMF8v6gQ6aDj687KLyruhMN4jxH8Vr4Kcv+xceg8xpT16Jy+hcTERAgw8XrSXAfnRYB5RzW4d/wTtk7dDRJxAMzLNGHRtOn4FDrvvs9psmAf1lF2b3XD+ICMOL480HtfCLwUccEfHBHL48S3oUSD8evTxaJTK18RBH95f9hUaE/EBH4R04gEIwIbtBD+0CT3yghO944xzjZcWMWdMYiLWMR9/AdoRQxvBsEUWLwQghWDfcrJV0vrEyQ2GYIY+tGBQFHfffvKkZyr0Lv4PIIhWq336glqqqqqkUThsgIwQ2r8cBiCXt+/wQBHd6BiaQkQlL+gsEOPpnxUHg7Ev1BHA97Q837Mv/mOCYSq9aj4112GCdwRmPu9dRIsYde9e4DRPhHIuJlpxMFefhuiBkgJsAnbkpPtceEiQAwjzMPvfBtf8TBPiPWiASXq5DgrKCw68i5SXGhHxvXjgk4KMBzYS8TBbS+GygyKvKv4LgU+AGTANjEgXkLY/EoT4AWGAsYQQqdJ9b0k/gOcIr1bLv9cugFiJQk4MXCsOJQHb6QW5EuREc+pFyFq/yIl8bGlwIkkEnvgcwJRDQK2LY+j0QwlU7DdN9hTyQSBStaX3Ch3VVWqqqqqqo0wCB8iGT7XJ04hAjCBJCXGT+nfIYCfOLYZRe3y+uybrrurb174cwgAkjCeBDZPqbwFX4QCPg/DG8CJ3AS9mdfN/4mCHP5f9hsWGyggDtMevgNXcfdgYAMPgjFa2gRAgFCOHHsqt8FASCIIyqvBvH47eDs8Tl+TkBX8Pil6vRezOP+YewuGV+8sf+JHKvU0PhkIAixj3rJ/UOxXgkMupBeSCjVVWq96gj6r7eJ6+EbCooGCAGP9oFIQVVVa6rPq2QEZtVaXbDUlgaWu9Zbp/SwX4gVwqsvHfLfUmsEAJfEO+1LIe8Mm8IBs5ASnz1NHCmLd/mBCdbuP5rNCA1GBGdccJc//IJyf34aDIgCHqsQBHxMGuI4b2E+w2HyA1plPWRXpnCIaoTBTl+e2FRYRKCjMPBG8zngjE+CH6DDgDuiZl50K2+pv9YSgkJWvfgoqqqqr70wUFVaqq+8AMIgW0TLoHaMWa1mT0vAr4muwA4MDgxdma7f1bMIhH/6CITR3yyBJVXwkXyB0Em938B2CgR9V78EdVPdXvsEJ1VYvLGoIzo69AmCmqqv30CXUt6ritPoFNVqqquq99Ahqq8PmR+/II2n5KxVk1cIL7ul7XJMiEShEFOI9p/pvnEAynLzcBulM8GAZLPXf/oQDHJ6b/8N2u6CUXyfpXCKuUSgKG4ef4QBD4QP6DjKBOv5n/iIKc/riWGSgumXMPNBttBIF0LAogVUc+hIeN+PbF7/l5eL94RjIU+marHmhVVVlVl8Je8KFVZVVU3XDsEIsyvfhSqyuqqtVVVOn4U6rqqrVQtAgFfvCxC1Xv3ghdAkLWvt3hAwJvDsEIvVnc2sLlhgJVUOwQj9w3AefN/BRznw/+DsEI2H+6AIaGQSdV7fWCThndjY99gu1XqveF4JOq9l/NDHBHUOQQL+8yLVeX8DL0vIR1DsEJ/QJBfCLAz58vkQzq5KyfyCOA9M5a7BEHwMwMWfOj/f4oENJYNxPVbCAsVD8kAPeXEHvx6Xjjuf5CzRQ/7UT1l4xM4bPBbpX/CIez/UCDk/q4ZKJQKSgmlZlX5xOqLEA41oFjjiBO6/5ZV4dy3ADHgFoQJA97W+W+9dhoYI4U+UMapc0TWAGWANEj8M5xpLQAVsApAoxjResHGI17yPsASZF+HL3fE9cFANARgnKE+G8X8t+7AagY8YGWRcoGT2+ifSl94Jy1rKATDQe9rF4DdBOJzQDsEJ9C98B0gnJHmn99yO7/FwV61VdVqdPxecuNYlA6ryVfeI0sVYdKbWt/RcOwpgZhpPvDiC/VYDpC5uiXglgjvX3kQUqqq1VcGM1ZLGvAlOp27L6q54JjaqaDQbuPsFIIivrhymBByelRxeYFAgUXwVctLX5e1YEAQCYOXgCc+yTX+6RFC9ZS8Z/99KLEcrqsv9F4suqjTRytcuFBOah9lvqtuqtxb8QQZISUozIbQpfyqrgec1d72wfixeoV+P5BH0b9QI2RR36mHcMamGt0ykaIjhkD7+uLUBr/oUINjHrQPo8FnUclTeBlWVXtc4gRvMIcEnmuLfl68v2wQ8B3qWRwa/Lw0kR8vlheA6AQBCYoevhv0kiBp7w7NJISf5NZIJIjiqveT7Zs4FhnOS/64UKpwOQDaqqyuFflWJf/hIiljrXw0Yd4R4HBmweOXXXWXvAua+CAwI4Yy0ClcvAaIZ8FCBN1VVgfTR2X+4LrBLnCJp7ENeyP9wCb/QIqSwgEzAoET4B7mis5Iw+MtGY2Gpzn4dmxVXnlY1XOv7+kTf6hbqqrrmMm3/iqqutL40Sq61rILDj05UyKsul3+4UqqrMrVVVVVX8AK4gXxlFCohXFmea1DPyzKieJcnJGVVVXUNnpwYmqgb4LL4UzD0lFnVZ1Vf+lPPCmVVVVVXVVq+XxnAf4fAIaCEs4NJnr83LIAkzdL/dXSQRIuYBx0MOQjzbDa/3kt4D7BGFl0AIPByCWqrVQ/lb+Iidz6ihVEj/xflzCF1NWGKcdCJXd3caCn94YfS/WPljJgH3ISe7vu734n54i8dCpZQU/4D7BS971mEuTNleAQME4so3j9yEr4D9BOCgnHWXjTWLwH2CfWFkuvxnD2WYdwPcyvVVlVWXwNArqzRjyDNv35GCwlaSdivyX7msjE63grXE4IhcAVtibo/H8gtYjh03FgHRQqT1A8KwsnrRBjLFFR5OJQQyGA3TIEbOltTL/1hMuFRA+IM+wQ8+KwsLRnWIm/1hCoVqS0ZkzPHF6Yybf6xnC/Mw9AF/E1SZ8xyU1Pp9+ssFsbWZNJqtfheRh5prq32q9HJdbyGvrC0kmtaxpgQEGdVwrlCP12cG5/i20t2hj5/v8ri6UFzWFBAJCArLyNvHJtrjmVqY0Rs3+uY1QowAR9Agi/KrVeOEEwSHix5P6BlLGfSQL5YRrUwMmBhC+8exnf4lc/l1uKOP+/w9M3wjzF2o6WRt4D+41vrLAfwYeYHnBt/CXmy8bDfL5r4+M7mBsOoGZgnnHjpLifl/wjDcXXdcCL8ZeXLGY/7yA8ty1wDlA6jwG4F62brHBPS8+wLtX+JPpcBrhEKQ3BNIPz/ugVaZnb9V/2sEcAjgICmXCLikpfIXx7E1UbJOwkMFz+jsJ7uDYvh0SCET40RNP7A4E2agLjliWvzDK/4wF/JwNqJdPv9YdE4gRlg4iCa4z1w5OauOVgBKMCTiCYdy3GvYVUAEqwJISuG9wzKOwz7wkiwV9TWMlyq9V85Iw+5qhMKzaUJGn1WoD6BAL+SnGI2X9/CReNtMotd14D4BAy7z5b5YKMxF+5B/i9yWR2o+X8/wXFEIMIKdPV+Hs78tZajN5I7ZPiWYE19MIS4t1M+XwCIAgEcMwS8oMA/mkA//Fb+5d8mXctmNeARQEAsQeIx74bn0fgEEBBrd+3QloP8REEDGWUe/BL6+/4BAgQCDY2pnoB+tmJZPJUw99wQnJZJfQDCQAAAC7BBmqAvwDEJrieonr6fE8i8f3lrB3hGYMbvJ/Xj8T0xPhHteKyel/K/WhXdKcy+XS76pEfJ6bdxM2FR+A++WByvlgPOajkyViBFr5dLvSgiE4ZIO++vg/Mn3ruIBKcpcAo8szqPfYDT7n13FYjsWKaW78lb/kuHst79QQinUypXy/fahwhJY0XK/w7PrwWdfxmTBNQcCPX0HxAo9uZeDqXlZ2DAM1jtHgj3jgrRAVTfrwn9q09B/viKFFhpBlfjLR149yeFBKrVVVVrVVV8YhMiawWlvWT+Cz1E6T4Q8F3wXewPXqJ1XlGcHWvwXfBd8GGCTCPwjyelUS7oYTBnEvXr+FOkI8d8YEd/HmjpZrXf+OCP5hGMra1nzwgWGYn3d3d3d+47u+7ve3yyK/yeIXjwje9ngh7qSkJ6PEByJrw9KY6g09SXEMwBVa5G88lVTS30Ja+uG1XEix8y8O0w/p1MaM6BD69KHhIIJDcfTLwh+CUoTwEIqNx79hIGOthEIiN6WJC2G09C0E9f1jQl6l4/T4LwICwxwW1bwY/BbFXu7v1gxwIRN3euT+Twj8yCW985IpGNYZj4++/thrSXmq/lFq+sxMFOQvUQj4lH+qquXUbpVhpiQOWkztXfX19L0+qBessDFZL8wTBv3dVrqvzwzXEQ7T7cIwgaSwfAx21hoEOsF/IGoGJNM9KHxgLr23YwdfpmhMEPxxQ8D0vIv4RCOti/eh4IAVgpEXP4IhrGo1bZB4BdobwYFp4Xx6JlwQLwW0WLeBKEB2CiWnfS9TwG6GQCMgdUf2b6MUvyBPf61xkM+CcOdgLtopZvE/LrpCmU8RKT2QE5G61VVwCEjYTgyTFx4TBGFlVVdr4iZVUGelWE0fLS/0XpdhgQQq8rOAWJ5GlxwfdU1ocX/8nptr+xZwLWyu9zLV8rATynP3d//zxMOK8SBLJL/xQ/WC/5PVsEIRjbhokG21Batn3yfhfXk9vd/KTA1bTOJgrp3wLpQUYhDXxPYfBjk/phUYJQY2K4qCWEA0ixZf/0Vv198qG+xk3LwGry/Jf+A5QiiP4DNDKv4Cggi7vfTwJOCi0Wm7/friEbCooDBTK9YkKO73d7y0S/xKLX2CUyrVVXfL/a4K6rVVWu98nt2YGImTgmpymLPd8PBcE+4O+17zH4NulVaCBTH/2sRqzL6+ICoKRaqqqqqqrHxoK2OBarsKsI//+OcHqOGdJ5gwQLDOCA8sf8YAl7ebT/EQT5f/9uDUEYkIZf4lfwqEcvzinC2wVCiJyDjx6/rEwT7vgVfAgAx+RAi9nRyeIXfJo0bwPo34R+VGypVyISYChyaW4fovTE+//Qiq2DRHH/5UQf9/KCO9+YPKGNKmOBCilGPlUfHHdLOCIinJZI16E9OIBzBQTd+nfIKDtMBcT9Px6076f9b8I/CMOWJAl9g2Xf4gM5fEinEhjZZx4HZT66J7aiQLP/QFET4LQjrYdBYGhBso+YFyj0CtGy4AZEAQQZGgFkhYh9n/eAu+wvjHf4TYJCar34JtV6r3lr3gfR65Zf/16sBzB5Cx3a6BpBF3eLGoXTpUAhQih1/BF1+uG8VavVYumIXMdr5RKJXxQJ+7u/fftol9YHoRFAuJhl7j7RzJl8CkrB9CdTIqvRhoSBQUlu+7uZFXkBadd3vo7rkFDwXEVfOSBxiREdVqCImeCfsQBLh5/C/YkIEBNeX22uJkJPw+pm9f4YxEFO3EgVxAusV8aQcCiYeB8POPAzhnWhf+LFKGeGib4MP8Kaqqqqquq1KmX6Agn9a8CSJBVVVqtaqveAFbAJhsd0KI+wA/gBYNrPfAzhFe3isoKxeqrfqvY2WCf3gPEoFcpCSav4gFBNV1XsvqCXuCjVQ7BCB2CE/WLoJ1v4UMCqqqqqqqv7L47ya9vDSaQXHY/6MFqqtawRAo1qq8X19dE9fxAIt7xe0CW7u+7kX4JBhiO9+BaCC38HZlv8y385EZ/k2v1cveINMSgRPb8HXmgxt/qTpf28DNDPwzDhgD//ggBXjFO1EhOCH4IabxGwQBplnj/0M1ZwJuT3fBkIBsBwUpuKfyFVV9MEPUZa9Xuq8n36UgleeCUqqtZ+9sEZa19SxgXYRBL59aOf8HQDxBf0ERJiHNkz0E0XqtpJg1Em4asnN3LlX6KdZACK6+84uwDug7BR3d76+QIEWq6qq/ZV73BJWvvvJ9LhPtgjFrWz8gp6yUCvpKcmkkfMLGRnTmK6CltH8EgX9CfOJqq+TPBjn4cVEBD9jQXsEw6U6fbeLidf5fDcXwkCgorgj3jhQFAUwIlbcAuuW8exGahVeXyuq7CsvML4dgZOoIsgBPWX8TBRVVVVVX14iMIqpKuuq1VV4KCrjgUJQas+8VeaBAbovhMsnhKpJ49GfrCwQC/J6VCO+TqloNiRG77vqsLRN77uHYUzYBsQYghOLi4I8gDj+7AIeCEViEW+EeFJ9MQScoHVfWFz0TWvxlVVVVQ5BAkDGiCuhQezef2AdkHoiaHd3d/QKiquq1qaE9+ySVX5YK71WtVr68v1CfMBHqnW0hEc+pF/pWbEYmP5CpR/pUsQIOPgGmrn3Huw70FDnP9exlvUviAUw2eC/sob3UxvXqg38viAUcF0w8FARhcBjYuan8fp8Aslf2MBaS/iQYNUcyyWe//hQT1VV1VVqqx/cKHWuqqGsrlVVV7L/eIhC0eRMmVqtfLBRqUeSIUqrr34UqtVVVVVVVVe8AKkAUDE1CFhinACuAFjwArQBYy/hGNYQgoLhNQb0YQs+oyWfYsvhOSuJwUqWIF/y3WKh34NI7lqURUMeiyZ+grNJZB7vAdQYc0gzvrV9OAhQiAdAHQLCxj3wB/auhsYahRTg/fuJETJ58cSWhpsPQweP1/DJQSeAmvoD3tlk9tsAQWD8JaKjYx7254VY/e6quqrsPoFG6yt8WT6aTAZIZ5RNV3e8nri/iIKNarX3kmIqqtWNgCBwYrl6QvDUGBO4iwdUvWCdOLEelOv4EjWQwRICgRjTRhjLYJ5qXEc9M6LfjII4DB9C9WF/kOa/BZVVUg+Cf2Pyv2tyQrqq1qubSmvl8dFRMgkKFVVVVVVg3v1hFR/zs5ay4gFNV1Xmqv+CjDeWzjXjAmYsv74UhLVfUY9WAFTQKIi9V1gEHBCAEXAJBeGc15fAICCELYQG34e/1rm6hkQxeixUf8oa7QVjDfNUCqW6VPO/SX2lBGJwifdz4cul2/VBQwRhCCq9HtT/u73u8n2uJipR5e54W1gEYDwKcoNIFrCGgZ1/d3fLsSx/jPuVvGgpbUJZQMG4UuSyEr6byzbuls8FGiaMRGiJuGSkotN4mCHPzA77WE5hBKr8IIRVSmk8LQq/uE/DuWjHv1OZcNRIfgvWDM+ME7gNMA0nqCB+PLrw8jEU8piJHERrQ2Dki419QWjyPWHy4gUO5c009fcPHR9mXBawp3AXXU81jOc1hi+XwIIkgRBHxmbA9PUYdb6+OFkHnNdW+wo6k4ASVAojLCXVarpctUo00esAJOgUQohvhn8tLDCONJp67E88ianm0GerzDS5jJt+T0kgArCBZxEZBRVeqqDqH8lXKesr9y556zzCYbXnh+Gx96cI4ch9F73d460eX4cBCbwnMMv/gC2X0/NqJHXd3ctu4GuW+2jNpFjoIZ0g2RLZmWT02nGQ3Fwnd8xcfIRjXabE4vu7jzR/gH8BwbxuLx0ZhP6O+IQan/wxiu0aA7z6vcW3Uvlc+vrHsZ8lCHHeDbAdwYBENNMDTghJQ+PnTWA5A9iBHwcYiFkuscmSHpVd+snRIugrCPmwA9RnAknGbvqRPemjiBEfVeAx+mp/GvCL6/zUl4vr5Uyht6iHJPXx3cEZa1g1hDglPjL9W+ODfm/5YYxf69xEf8oa+Ga56FmX8sRM8Bxv3rWX6y1J4CPfm939RH1p5rryCo3v17sSR38f28H6/ESYS4RcWoOMvqRgGKgAAAMHUGawCvAMRUSKDmVeVf6yny0GGuvwYZcTdTZy/4uvrB1hPk/rkCeJ6YnJ9YTl17VYjSXQLxEda+UDyBZfhN56frP749mQe99E9aFPpTcKjeFFjolnHokviZou9qEIXhXLebOvg9mfL9mfhWDLLqAuBxtyoLr59Oa6+vEUP9RfybxAww+HbG/H479iyHHhsO37IBo9AXODL0BgwZCYI+gR90E8SKDUVFB2UzqPjBNWEvfnghy/3da/CAKA9Ea+MtDC/QNJ39FmJDXrxS18npKDMSDET4m+PDBzWl/ovdAykE6VyiPBh8GHoC96ApidVjZRO4lX4MPgy54ZgksKifCffykDWBi4M/YP7GBGsJaEwQ6rAuzAggOwipkjRODxAR34weCQRd3fxg9a16YIx+71rzIKXcI+FJbu727u7u/diIy7u7u7u4rFbhlAJH9y/WFuyudgv68QJ+QKZxRO3d3d3d3d26woR3d3d3d3d3d2tPksVhvpOCVeSr/ObVVk+uq8YFxTFxTFMUxTFMUxTFMUxTFMUxTFPQYGMOLXsUJKHcEvx/n88fX2f77sBGehwnSY2PECA9EIanUcItc1J+IMA9iUn+DdUj6o3bFg4jiRH4dfc4TH8q4/TNXcsAtjWb+3hP4T5PaoKiQl0IIalB1r2r7KSecRD/X4S+KCPhUSQEkB5kaZFbTJfY8QEUsSTvXCXzsHGbP6e4kIdCQxl+IFhDwUbu73dzoYGAUXu93t4JQgTd34ZjLu7u/e738MwVXd+773bSiBGLu7u7u/ElBZu7kvucl27eJhS7u7u7lJdu73b8EN3d2u1qrEEygxVrm/wn9gkPrT+GxIUqqqqqqqqqteyfejlDMkoZJuAQ0y0vFcgC+GZYX7dQ2Ecvx4VCIUHTCBbkZGwtFQcXXly8XAjhHwJIR8MhGievXHhkIiZV+DqXgEnv670gGx91/nhmj8PUUTk9KtSyCCBx6/iYavCS+CMwR4AZtvVj7iAh4M5B0BjEZ5jk+uPFixQzdfXkN8zAHw9wlxMEPx2f1nFBUXvjGQLAozngJZycBGBllaSV1fvz2/XUQCUru5bd7cqvBWwQkd3t+Csr3jbX5b7eGYLt73v7XhSCMr5bbN8my/h/3jUF6RV+CQJN1oCZvHh/QKzhlByb4xQg6TT/+gh1f5+NhsUAx8HTSeuCoOKq6qqqs0uaPPD+g0EV8IFAwHlQjpMBF5bb7YCVtzb94cwugo/wl4YGYiCfEeIn6jqWGQiHOsE4R/DIR1xoYCAgFGZcDjprAexdMDjzabmeZ6+NEiQeIjJmHIAAgCSgWqj8DCImWTz//sHreeaclA7X+EYbWS/iBHhgI6wW/L5gnWcJEwe1wY4Zw9Mp1iAh119+FxuuEfL7DZgytb3fJ/fBhBYCgJ+AgqJmOrXj8XHnYHY3jEY3RT+vugCp1OqnhfaENINhmWVrCHqf9BqlXIkejj8oKjVqtVVaVHHyCRuqqT5VVVVXo74z/j/vCfQGwO9fX4TDJvCUuPJPX8JHC4kvZxprwcAvzN//tBgcsb4JHu2sRkJa/rW2q3h9NsFLWT31rjiQInv9ucf8t5ru4b+xZt4EL9/p/eX/uDvwQAh35ArvLcSFDKYdksuJHK+Ve+GBJyr9OnrC0i1kgeoJS1VVVVHbEr+j9vA2llQjvAdId9h/wG5Qhj+hcLwQCGrLQiUmqGMdSLX0r/SmXr3BFd7j/52Cu93d3d7uCIC0JUMZP1hYI4PSUV/SBLD7LfXUFwECX8oKeqwHu5bqqqMxr4PFk+8IiTAxzhonl+6FCDLguCpPwIXt/MdU5KgbXVpsMtKbR7/ZD+0yzEj9euP1//fDd/wh8FJg3wAh63q77hgI9nBjrQZYYYYAzxHcLMoLOhMFvwhl99w78N9NoEDQUBgVf4I94bEBHVGXkTzLH/XE7rP5Xsv8wLm4IRoAleckln/94P2Ceuqqv2uQALdARkTMeCOL6rVfA7hVTqKffl/+kXvDFF1Wkf2N//ka/X+yoz1bgjK7u4+O/oxF5fgg6wT73d7Q3/lChru+Nru7vd3v4CYEC5ce3cpJ/gqFgoLisV3dzJ4KxSIZN4lSAoK73d3cFwUl+Zhu7u4+NVLsn/y1aG9xOJgyhy2G9t77dfw2IxHq8SBf8gIfQId5YcCIkGe4wmZUw3/QRBD0DAHQJDNKqxb4bkCAks1qtV1WYT+vlKtL8aAy6r/rf+v/BIRcsv/5s1/CpVfwVER2xs+/zCNaqc2vJ+kH9AOnsx/9A7YLScvVVXt9Da9WRIrB5KJ1UjV6v9Al3u99fYJLve/gQAgCLavWYR+of0H7vwgqrS/8wUu6STvu7u73roSwUmd93u72r7bBcD8Vd37aPA0BfeGRIT7o8QBREeKg8Jic7CESnMk/bvpDQgbW3LBDZbfmUGgzl6m+LGbdTG+nr/EAh8QCGHHjI30JBr5RNN4h6EwX5fh7x34KDAuVV9MKC1F1UpLlVVVVVXvoZVVVVVVVVVVWYRHqMYD0Eo6BDbyNPKSS7EAsIq1VVVar1Y+Ciq9Q9779nc+83j/9ApEv+EXB1nvyGMQTf4iJ1WqrvF7BHqvugEiGVvpLConVJYQLDEB57vd3f69NomspKq/sE5eo8oi74YVz63irFX/99XcaCsQ73wjz4lLaMrbQDqTloteD4L9AUOeCHEfcvXD3BCULARupXf+X3WCLZAe14CX1narUFtv/QoGPZg0LNgmMczgKaHX5wIzOg0emzlngaNIQEbVVL1mq/bGCaqqOqqtdVvrClVVVUgG1VVlVU6azcKEVVVVVVVVVfvcFNa1VdSAf7wAsEBY6APSDXSShqyqvdWAR0PgiK78VYQQK9Vqq12u3m2UiII6FJL6H17sAg4IS3w6ghpPaSCrwjCXWEWEpDzU/TJKSrz1YlzaoYb6CHd3Lc9uUFPBjCXBATScQCGiRas5n+M1/r/X+v9LLkNjycwvJ61cFYkCUkEBGaxrwR9Q0VIx5Tz/P7/EifiYdf/hv4T5Ppx9wgDgLAmCgmBiTTK2nX0wF17o/8OP+UXjQLAUIEZcBhazU8UWerdirqq7/hQ/yuZVVXOSeS5b+goVVVVVVaQzmJTvrodGVVaqqquq2fTGaNVVJVWSY0RJAYo2q9W+CKzryon9BWKjEKEarcEfVYtJAn5aqYH+ggJ0ljfQyJ3GdVQULM/kgkLTgQXxfjLsJzSFy57cEMfmf76oJoBzBgWW7h6HaO6jd0swGCCXKDCYx4cQJ/+y+FW/EZfxFiKZOWPLn18uCfYKdbBSgUMVOGijy0p165fDAJjZ2UPcQj5bMPFHl5LnAN6JTOR/HLY2zv37rDQoFPVZVclUZ94Yj4ZrwpC05KcfLr7nJpjPy+WHPiZiQaj6r8kXSRhPoEsB955Z9YVeFD4dypzWsqqyqrmOg6gpDH2+zMyqqqhKqBJVAf7cnroRqqi8OQpVV0gG5zpjOhSQC3cvWkxPVzPOvvx8JFu8iN5fHf/NeOFxMOMUpQ7hEIVVV0sMFOq3kiYyq0if1quq6ADtoChyeklj+HonyaN/dH1QD6DD+78+b32QSejRRIje7u71YB1gIAYEFKA3PgyCrxX18n7+bYkpQV1fesDCxkI9VxpqKcJVTgz8ZITGGrB6sol947lxlo6f5shsxGQaYTEhcoE9c4HQNoxEufn+eXOqH9BVcv/pfEDPhH9upiwIW5x/SiKhXcsa/+/nlazGZ9+svCtV1xsulNpTX/gOkEIKZhkFmPUksb7MMJQyzMtWZVmsB3hEkL+XB/yokeuQ0TfpckIzEsg3N6gUyHngCzyP/Szetm6ulDUGokEMJON3Saer0q4DeDCvRPpXDdXFl41k7uBbr0cSEpbvzEX1gluUZfd3KRbTooB9gIBih9naDJ9aiZbB4OMnl9wjLJ9yZcs5fzy8kMX9+5S/4vk6r8xlZ4OFgPgEGGhqgEty4eEHvWF2X/rAcgIMQK+DiiIdn1xhcfX0IiiQOrLT8CFVzKwSQHkCDSgPQEBYKMu4x5RKYS3NeJm0mZh+8TXoB8gh0niYgXMDTiht7rBG2t4f78kXUMp612iw6FH2kA/wQqXb8nr5bmrhXkmD+WwIfhWQkGMt8CZqh/KfW/wDFQAAAU/kGa4CfAMQq4kwWKvmF9GE5P0vzvCsf/8Z78uL+4sRlvmyDvCfdcWKJIvIvNUIvPfxW4oIhP4R73CRTeAJvmKOmj4LDbZMxwfRQy9dY0KaZWfaQrhomqxfLoP99KCQi1XuhlGc0mWPgvCOALXW4D/F9qQAV9/g6jQFv4ekxnDJMf9r7dKZ7ULkGMKlByyQQ+evCg8M715C9fG2b98v5OT6obffrrVwVjOAm161zzVVVe12mGISjz6Cf876yf+cGgKlTM/cH8tmpwZcRDsXWYIZCRaAmrrCfdcWKEWKKiFEEQe+ME1hKL4I94Bk2EV4NggNxCzUAcKLhvX+U3/u7uWFYArTMWV+DhM5C5nnN9/mBisUyrT+DDmc/8/5uO7uP8SXAGQ+MSFr/wP5juG039cAVo2Sb8FcWieJ/Pdvme34MZ/7BSMF9kc+gcYjNT8QJ7cm2/p+f/NFhXQaQiCqqqqrfuCci1U1+/4Rqqqq6r0+RAqKqqqqsoGu+XcRq/7kvlMetdYUKqqqqqqqqqqv+mcKVVVnlVVVVVVe2kpofEEAL4dOSXxcPXUZeu/w9EschFodKSFSevryeko+OhER+DT4NPgy9hkI5P0viwkintmHw55z4NPgywS2YI9+T0mhImLWKJwR/kwdxylOv8x8AVKMZs78FJG+Qwqfzpvh7u/jCQFrtPyFrifnFYkxSVP4MWZ32ADIfGaFX/kFsebV0+AVSpMm/Fxfcu/wBtbJN+YkPeGeekCA9pIDCuJJnIn86P9n4869GAXnNu7v/8JYAbbxkhV2APpbm4Bw/DifkMwIcUSU0G/6vKCkJkKOYdfh/4P2e8wR8vqh+btwTAxBNC0wrYJ3Y6l9u75vgG5+YS/w3neorrEQNdEbJ+teKQJ6rVV5a/LXXuCfqLqqqxv2goFQ+RaQvLMUxTFMUxTFMUxcUxTFMUxTRwd5wiSFAxAdU5ZZHvAMSOW9VVdQryfMK/j/YkWjPLfEUboNnuj0wGnFJ63/m//+EuGnKnqGD6mvB47D7nEdY/gGtCeeYCTmKOOY+MjywKK3RgsnHR5v8f8Kw69qNOj/fhvJT1U++b//4VjAbj7igMLnw3OnqGmNT1+b/9fCpGkgr9Tstcam5a5v/EfhXhKC59D0SwYouSgoud4s+AohyNq6/SLCLPrBuGYs8wIYZHp3y8FFbo1g09BITpQiwmIEYgQ0DrJPXwcxIBueB5aX2IAcsXJ/wyx0D/2pEAYTzXoy64bfU4TH3uP0xd0wBbJ6t+cJ/CfJ6TiRJniBBDF4ansD5YsLP94TPefBHvE3/uO7wqIxJMXN+xxaBKZrgDa2Sb8AN5KXa/xvrPfn+ICPgQzEvUwk5wRZ6QIDMQs1PwMJTDL8dHX7j/mCL3eRB+J4fx+MmNs5eSDcgR7wBhbL/gcYzNiNyHYl/wsERBygD0Eh5mcJA4mFfPS/HVmY+r8D/VHgv70jNis/p+cxuCPeACT+mKqEnrS54iLJj2IzUB6Mz1AQiU5hR5Iy86e/j+cJAjLI7nl9x9yX4nlBDvdng2EIe2T+68J/RQVBBam6rKqLw81RKE3/n9ggG67PrgiEB0KGVVVZVVVVVVV9YgEJGCs2oaMRlVzbv4Te7CiMCkSLHaXg3tQrdyE/PM5f9v39+k/1YM6hLmfXlDMDAGURHMP/D8JEEs3dD74BvVzzBpyp6/N//8JURPqMHR93lAYXIaY6evzfX/4SKGlGp6h9MvExPUz4gnHMFs306b//4S5rp6hoa0nrh5JqCR3WaXxBsMEUedgKilGaB1gLz6ABMKMCZcjg4RRn4QJ1BSyikQ03DlobdX4NaM0b8dAOwU2iX5/gF39vb28MPNxwsKWOt8ADC9VvrW4kH8YpClvvwAGfEgSvUQOdpdRPUGhzf+tbTD1rH9oEB37gEzS5mjKMS6EjuK9SeIIuEX4Du2yYCQOlmvGOy6KXAVKTEdxb4w9rG3n+1QkOO2Fzwy3f+9N/5fggCdqojDaIcrnhw07nMEdqJYSKQYkX9674v2ovwEgtJvzEnOCLPSBAZiFmpN/7u7jhUsAJlcxmxa4B8lnNwDQmGiqYsAdYo1Ny/64yxxMEe8AwmwjvBsEBuIeagDpi0Mte0UcZa/D/zJcXf/4SKAK0YyZ34KSN5iOn85/v+/AycSYob+oYGoxz3z/5Ai6/fx/4NYrANnCX8ufH/esgRBkNE1UzTMaI3TLgj+O3r+6M//j3i6/BHvACbPwnT/mzgChi0y4BFCRKc/fmo4IiOaQCQWSb8HJuRDM85/v+ODJ8TGZnQhHV6P+bF93cv4ngFraGbgxQXlozygJBZRvwRlOb8hu+L/sSIjQXIhmecZw0TGn87/bYjq8A/+EgRkHqvxXtVj9v1/QRf8Eox33Ll28nfkeT3/n83+H+CIMihqsfByH+CINKtX9QtVVJ613/UcRO/CyCg9VVTjmVVVVVVRzHHxBAVjMAbXqb8USctyF26FwdMayxHEbzzfLyhAWUAynvjxtw8CyllvzEAlmoGAkzzt83+P+FThaanqCIWXT18ozUTF6mcQTnMm/1/wr1yIGm5zLpP94IhcmnqUprN//8Kmxg6PuKAMLnNFT1DTHT1mj/+oQTHv4e5qRB1iXyhoLuO2Zlv8mleBQDOqHhH+PGEqCL9efAeYlTA7lgvxtXT7OXlUwARdNO5udS3hkvvL/wxYOu5PzWAhAjYkoC1sa8rx7w4sTMfnqAP+EbX1bPZ/6ghhvxXAFaZiivwUzOQip/Oji9zA/+YRyqSC5TM5oYwym/mASMxPXzFE0FjmZw3f8gRZxWQmyHHT+c7FmM/K/P+/ME97wiKJEK4ReiSXHd5ePXe82s1ZMJfmLwAdpfDGht9Tf+L44VJiJnoEpnrCeJcu74AK0zFlfggmOQjvzmtaafk9IzB9wdwH4kgLlE/43wCT5hOn/McHQSmawoz0Sh99mq++xgUJH5sHd+H8TgDa2SN+AhKM4Fx32/OEE+Ngn4J1ZRv68/9fw6EXaZh1/4D/L4cHQvOmUgqKMltpZMOAiJCSeQFrwvj0ML0L6/Q1v0aBh1IiN5dVo3pMp1vXR52X9TU+oKCqqqqrv+Coi1WtVWt/KPBXVVVVVVrGY1+GQhB+pg5amIlQY5fk3HUS+gEICHpfOGd3iAMoguRRnRwdGV5YGuMpZ5v3iCGonPoHCneW8MirzsGCKPO3zD/+zYqS4oaZ09fHskGf03uigHn7woiwQiVnp+PjgPpiBDUDadIMEUedsHBTPLGwRz6mx6/5sXw9zV2Y/7g6zctf8ICyBKLn0Dkmct4BnieQNfF+7A+Ef3jZRM6/5sPgf8KiRS5eTT+cBWmZeydb7kPwAEmbK2EQKXDhC9SHySj/LmH+PC2Feg2IcMUQ1QlikK34AcuQ0X77dP5ZgSSc/iWeja/kY2Ig1zdklKOBIfHoAAQAZOWgSJhU+DDdnBTk93dotDiSshvvgG+heucoEZ//7vCp+AFZdhiQtcsPOHCKpDMAvaC1iWe+TfT3d34VFAJB1b8EnOEs9Jvi/meCDMqeoge3H9/iOAhWUfQSsSGZnQEgtTfg7JSMZnnP8We35Ndyd//hISICpZDO/OOg5Wz/UF8BAVTHFvgL/h0I+HBIkQOMp4cEFXhy0mfxOtc3BHvAHOcJ56XhYXwJRos3HGgFjc3BgIFif/MUXCjmZ3/fNgpEcCFVzOsTubSWc+q7r/a8BADUE2/Vsaj0d3gOv1h/+gk5fL3oT67N+1o7k9L/AgUR+yVv+o+O/gk3dyAcL5ZAoRbvd3d3d3eC5/h8pbbd3d3cEZuhz2CILwlNfm/1x9hXwDsN5Tnj2A05U9Rm5ab//4V4YPKaygOLnNBp6huOnrMPr9dcE2BE2n4/h/NRinLS+4jwsTHaeT2sJAkBGENBfNWRxtL8EUpId9NS8r2uLpRmNHsOAvyfTwyIAm6mnHgylco4pelkxYjBjF1wC+ydO9GLVVX1QRcln6XcIngAY2na2tDyxum6WuuoYEbUM50wpJ6yb4JNnDV1Vdr/+EUzHkhXO7hhw35hMATHPBQ6f8f8IBE2AOcxR+ASe/SQ3794eDiFNQElhlgN89/Afm3d+AfxJYA2tpN+BbwpElT+Z2+Z+/BwN8vT/MLAZjeagfz/XwyygNAtFHabx0fXvx5/9hnXDexhQ1BxSne/l//Fn4ApmYo/Zh/NwkKbZxmCohlT+dXDAcizBCq6wHLfEP/zGfs2xwMqqLGZnQbGv+b/m/d3/xPAgWJf8xQeFOZnQIRKcwXlOe52+4v/BCIlFyQy84rpMZed/xxPOZ/5Pt4thohYSBGTBGUy8yuXFXJWA4wz8M//KhNdgCIQIK18eCEWtZd1IFBqqqqqqqqqqqjsf7JIN4vm1yGoKV5UCXd7v3PPW/0Cjd3e+/0CK79fMCUl7u7uRHXiAVXd7vaLlu9ZjOedX51j74fccLr+jGQOA13gsKxF+Df3BsGQ3xllEu3Uv14RgqJr/camcsHTP38ea9rwQxsNzOdgckzlvKEyIGqmVtwXb3d35db/wiC6GsjnYqT993e+b/HzLBccY8YLT/MscI6k5c+CXgFVS34HgLI8thrCefQNxhPPM7Niv/HYsm4lH/C7y3r/GoLn1/YYFwcKdZYMlnO3hiUc7BopGBW/wwLIPPAAbYksUUjG33wLMstPgzF+/zT1t+DikZxvx0AzDlw9+fFifgJ2Zmxv1KruoYAGF5K3pa3CEbtONFdVvoIu6B4DiZ9c+mDS3kFjZkIL/KQGfV/WAKkC9sQHGTXJ2TrEpPYsrnHI31lqYsPDfDJhfD3Sv2GRRoQ8lnnj1boLOxmN84f6mLwR7xN/793hXgYTZf8B4ChlNSAJvjNCpoA8k0ciD6DAREYCRat+CLdEb84MY9JZ3DMVRjT+d/t8XuZ/5t3F3/8JHgj3gVqZU0/hs280RgLZkZv9BwH2Nai/Of5Qi4gxWGV+c/x378/5fZoJ/KaD5ed6J9u/oExBAFLc+n7+5Ba69QRS3td0BjOCQla9l/+RFYsvwQfgjK95bGzi5ULdaCGC6taqq97QISLr2T+r8xlja1Lk00SDVEJa1eei3/BIIu+vZAS93d3evAZQW8CURb4QRzxoZdJLa8Yvs2Xx9YwYdd8GdFp//ICUz3d7uAk64QCYIbvKRg9i/ghy/BDCOSKzr9CB7X5PSUQBJtimCgWZ2oQ3XgTv17kjhqsIAIk/WFAETsoErem0I+I+v7Q7Df2m6Ik9/f4bxPNh/u48WLfwCS9WPAAtxSRlhV0hPWbfbwqObnJ/AsBiGWagL31jhHAoiV62BVihmZwilRjfnAlZnAa+9827u//xJwBWmZI34KZnIXT+c339+cGTpZZv6IVm+pv/v8oId6lBCJNOnIvB8vrzcEe8A9ic9W0DAWShuGUSSxKC8BI6mb2JPd+CYVykGH3Xysv5O/lkF7Sy+n+CLhJ6PLeBjGyAo8OwQh7XXLwG2G9/fWCTmv3iKE98gISSXXvEyV1+CLWuXn/i+4/Lh2CGpWt7tE1YpjOX5Oq6Gd4WESiN3d3d6wFFoxHv4CPL4DMCGX8FcCjB+CEE+1d3d75sfX/ICsmpLq3/tYRUFRHd3u75sR38EAZBKV3Df8NTnRjUzliLM55uzYjj/4W6hqUORMdREOCf7rwUAIHwUAIHxQGUIzLzLx4umkuKNC+C06FqLMHcsuE8GM255g3dtilf+/+qBJj6kAqP3/r2cxeAopyxX8vAFWAVofa0mUAvfeGxEFfnBDk91hookoIcoJhnmj/Xiy8Ee8YHJ2EWRwIDMQs1KtAgIDApoabf+0DDTeKmNaPc+LBJVVsPhASe2ZrCto6hv0fwjUoKWVWqqvkrXgaRoLeq1Xl4HUIrl4yXjypDRoMwS1XVfugrChVqtVrrDUGUMBOPugHCCEBghkEtU6Rzj9/FvSQJ3BYC3UNWVwzB0f3l5f3Fcs1+snR8JbLYhb9++/oJwS3fe+vsRd3d7vvGbu73d33f3Y+7u7u7vvwNIRBZu73e7vrwYxkljzW7eSp27uW7d4R+emvNHCGe8+YWYxO9/4Q6+vr1X9hMBRd/cRmcH//w9Ai0E2VY5DuiAw4crFieAfsTz6wQZCeaggsbm4NucIk4CqU6AIFkf+YexGan4JkZz6nv4+bvcXEQ/CRcBfPiTNs6Mazf1ASC1N+CL5sn4X49PR9O8CDpqPQgFAjgi3I7CdT7L9aU87ITF1k9f/CZ1UK/RjHmHHZ1uCCCgodxCf8zJmq9rOsFHVdUv+4yTPOSXwGO0LUzD6hvFEU9eiECkM5x1zqqntVSYpKyqvqgNoRwUVVVVQ7DRJfJ6qDFEDIdYWDDqtarBOusdVdVjoUtVSzSCewDgg5155i8NQZ+T9cggYIYkXLIeiHg3f/VYaEb6UFhZ7XzDI9qUGuhTRxT2Yns3h1sQ733MLygswPzESke3vLlv0C/tX2SS7MEyT/VxftJfXAjTgl4Dq8/xLgjAswysb1xf496hiN5MvR+qwxZYvsG5Fl6nr1y1+OUp9/L4MxJYdEuO8TwDpzQuLGZGPNI+sMcFVaRo4WyZlVqtLvwplaqqQWx4zqaDT6CmsXp80fq/lglLmC56ps6BN5/0neLZnPkqqiQrBRUhJZVGqvk+l7wGuHwW1ISWVNazD0tpjqG6hwKmZVV483WltjK61WqmtREHBi6yentlEdiOGr/7R6ody3yzV35fZLj+2EvXhaXmwwx1k8N5XfiuT6rDdoEEpwVYGpCb9GzX8n14XiXrFk9fLETQl4Tfb5qkhL9fiI8/PyIMafaV8a67ruvLpdk+sFqUM8EpMCX042LwHVkfeQfs6awxwyYTYnx7/r59b6wQiV8BUhkEsMCa8+2E9NPV9zWBDEkgkxxl3PUFHgj3gIRqZnnZtmy8zzXtagOQEBvDc16AfoIQiXjzwctpWje+q1JNiXSY6xARzU1Niys168VCXgH/QrMOzzezl7xW2WlkJVj2aLrKPQhW0ya6v1io+COrnP954Ugg0/8fNV1X4Em80db5fPURxdeZw+mWIEBYR4/wcYiDbLqY2HnNZ7t1riBGHf3CLv/v1jbu8fhcvi+p+XycbEy8xExHi4QhB/T+cisZ3QbtvU2vBQNSeGYak1LAS8cgR/BKRJcPZb/a34BiYAAAAzkQZsAJ8AxHOYMJyrg9j1+DSUuPhhKOX90BXExeeF++TE96r2bB1hPk/vwjjzBaRSMF0vn7MEdUE8MZPdMwus4Q0sqYLTcHxKYDBI0+vYW0Fz4IJyUGsXTbVVWZVY82tYP9Ma6UJKKQp9L6I+lExJguEceITCUz/+Y0JmulFcMVXVf82p6QH3L/sEsw8D8ljRdFWLu/7W9bsVUEI2Hod9YP+/IQ8hsuvWuHRGyh5FyPDsMFXn/ix1f97YkT0BowaYT5PVPs4mNMcKoZwL64DX9/8UJ99YQYSFm4Jbjf1cX4WH0T++ziQiKBEKHi5Emv/U7iDBMbXhQwIcN/AB//bwJL94NKuHSCE/L/3KCTqv8v+BYOCY694gSrC+robBPFUZF6eT6yl27YJC0H2tBDSa/BpIN3fY8IiC7vmH3tKJDUSKoYOKIvAU6/o/wbfBpMJLmclsaEcn7XjEGBJRFsHok9fg054ZglsgR79vykC2BKbZv+zVuPmDoLBHJ9r+E+9xs2JsjBfkyvDAnwsd8Z/y+dRmxJicZqk9L83yevBCExGI6tmAyBtyyOBji/oeIR+urvyYEi7mfvsR8R1ynYJhWOnbRFl0LYdfL39BQJh6TRMqqrqqqqrWX4sNiQjlBgFDiO7KtfDvY8cKCqD4m/LyfWE3hOEpRYRjnZ9tX6XXwQicnpf5uny8n9R2FxeWndeCL0Bq9LpsIhHyfdzwjNiMy8Cx8ExZcGAO/5AO/pPb6ThDYgAXMyvS78qhxyVDvJDZZbN74bL9/OYIh9TNfRxvhX4V5PpoUJO9ChBt3TDOOgh+Z4BMLr5vo608EDm8ba3iS4okq8q4OKIhhSp6+sKCbLXlp9JIKhAKhnWGZonnXgF2zcbQmsWyMHtCgjtcvxgnoIhilhL1hzeIWX/xkpHbfCLDIoGWXWv9vGxguZ/nP6KCMdqrCEWH6ve/vB/tBisv2DJdo2YaBmGcv+hIRElGgPLz5tGNFXwRiewJIKfD4nS/+OIXm/oE1/HWSHC6+ZfwSiaVeyh3LKvfVBaF+uLxgidcaavXzZz2VfF3lgHgo9S1+d0jnVLFKUgK4Ky+GT+C4LEk9T2pH0u5eBFvA7vv8TDVoI5PTTFsUL4QIJTSf4clJnt6BQEdf7xgkQxWRcCXbN/3SYrg+WinDX3LH1a8J2JFTJfWIEfo4kUBq2mYutM3+D0vRPpViMJ8n1lCHKPIUhF4dgt58X4IPh0IEJBW0yiUzwg4isz7zf8qwccSJe93KRyWTa838Lkt1VVVL+pqcbDsfCIwY+sH1nBCCBV3+UFmtVKBKqqrfzIKD1VVWtVVVVVBEHHWwUlEizDZdhwdWU6wsxyyXk+uO0wRSwA2/uvttb02ql8xcxIO5alivVE5S4Jf46r3d+X5RZxQ7YghKLF4IRPQMwz40T40Tl+4hFRwhKG4OpeAESpyrPW37htwG7xjYA+7HHStaet/jg5ADuq38VsQ5aoCTvKlg3Xp7k6hmG8SJNAM1ueX5P1cQ8QIJA6tMtOMHvhR9DwjrMcjFFdIk4yln8jBeP72RE16vTwlmy/X00vGCcvsIMItj0UzQBlPn+pl/4/WylIDsvGj9o+lgoCKDRCF4SFGvqQ4v38clxp9aF9EjYuWCRpfQIgsq9/lRn9QSFWqoxVYNgVYIi1XR38FRFWq1VaqPjvlOC6qqqlks31sJLr/V4PQc0X+NwRhAhR4uhyZL1gmCO4IUx6WGIzv/OsOiRL8PCAU84+uq1qPjvg0iSFyKpqaDV/7YuUfBgMLnvHwbZdetjExcRtcXy91ugxYAcH5CWD7jDT1Hr8AoGIeuzrf9/USA8Urrffal/0XKcfNctsAK1iTOzAGCHMwn1FQ7TF9FNasJOOSXgpRP+Xk+1/CPSsc+1wIQZKQAeOhR5GA5igyz0vS8I8vjghjBMSEOVeD9eEvrzNf2Egc0X/RQjKQq8q/goCP2hvs6/o7eCEqENEP5cv/6FdfSLXyAiqtR//BFrWjVfBITd334QEBy93yVEtvPaf1BJbuA3OnqSfJ6d+Cr9r36wRiYYIeCtpnrMGYZ+JBDwmaevRirwIIcBKJBFWuglPPrhJzZQ3AxslnZgZixXhUhnweVn6wSH7J6ye/5BJOv4bxLMP4B0ludXiUVijWhqBoq34KjBnKOU/+8M9/y8YcUJZfHmMn9BG/Bz0cF+lEgWSBgpAXGfn/ihOq/L6DGyhOUkfTI6Whm8z9YoT+KLl9yB5h5QRmwMdU3L9De8Ul75jk60ff1tLmIjT8KL9BlHd+MoKP5iIl/1r5gSXd3Mm/wWXfd3d3d79gawZdgSQgK1ufmt7l8FZQWE1ZGEn0rBlgU2ea6y+DAMooIyDTAnEWIHxLixbeRgm4OMR93Mi+zITRoqsCIHwXH1/rYYAliTYDgW5UyQixqeJPfyhgSkhZuOxIAjGKI0T0BKAAU14v/fcoFO6YCmeL7XPbJ6/pLDmQTu3ODHbXDJTz2dR6Jyemr+PDGT10FtlECS5Hgo4b+QBcsL+ME+KL2cCKzEd/H9E+kgjQKPBEbBBbnmfHvwVjeqqqqqr+fj+q6mAi9VyAkIuvdAGrBiuEnU/KYr38sJX33fUoIibvW1EZQWHu73d3d3NKuoU7u073d3d3e/qCUzvu+/4I+77+AQgEgJC5blTXAmZqTu9XIAY2CUzu+PNXsi9YZ4U27u5Le3d7vcf+wD/gmBcd3fqsW35AUEqu79mXzHxQk4kEO7tR2vHfDokwvc+XR4QBKYRlEgbfrSuUJE8BwUhnzvz9VvjX7lJ6pfggAe7OA3f/3/gw9fEw3RQzpYLBwQye/LmCg4IFgNBllbhbsZ+2hX+OCGsVG8n7nDWJAhiZSRYusf6bWKA1CyTL+E3Vu6wkTVdV1hQWqNVWqqqi6qq9+FK1VVWqqqqq9S+gVdTWuYdghPseF2vaX9cn+EBeqqvqEuBCvU25CKv0X0vbKa9Umg5iflCd3730ARAFFP4nwJYRBLd935eCweIu73f2DWLIq1Wq8A+QbCJn3d3wSt0GX+sMBUHAVZXf4KKP3gm6/3QJxIYAc+k5AyA5BAjAaj0//9/rvP2TQz/dQVcOCILdePsOAE49qccr/2o8EYRlEJyj5gXy6b0KE+EerCMOAuMKmxZ2IA5BSY0S69Uqq1VVVX6wWCYw0LKqHMkqpqffjsNeOe+bOpghdf6rqusZqvVVIB0plVW8OE9fi64enrND08RXe7so/7zg3jXl8W9CPS7+sl0rFZjAhqSX3etd342yPvayl+b4FEIm1S8BBhHwIIsVlv4b4Vsw8ICDNNSXdGYluMZQDu21/7+/v+CDWDQAguTH2vrDADnxAh5cANEUvkt3flrXYOXP17PgjaKt4csSBBy+KD2QSIElwc6ke28zx/x/sUBwMQCL8F/SXdAsJqtVVVW/U48WqlJa11E89WFKrVa11Wq9k9b8KGiAVFlVWVVRcXUmd1hQkqqqqqqzVSZ9k9JfLsdVVqqqqpLXkO667DogZVVqq1VVVVWT+vwoYIFGI//quq8MIXSpc9jHtAHlAgrFrHwlLxr3Wbkh2CGv6CPJ6Xh6GI+EyqtR9l+ujZbURDD127u73YnQwj3d3d3fzlzD1ajjO4/70kLjq14BCgUdhcSIve7u9NQQgdwiEfNDDsKZlJX8BRx9pLfd3ftsHIm/QgQYl56DMZ/28SIgnhx5QUiPmBQwTCMT3w/6SoUoV5R8fLGQfD+ZbkZmqv8n9BIThcSCWCusxHDWJdf78XeCUiqtaq5k97wo/eqrbcRj61Wq1WYjTWNEh4SqhSXDKl4uLqtclW2XfqgG6CMZBRaVVhf3i2kSWFKrqwkCIPRELetX6xkg2sqqqqqTJVV0lkgsqqkxa1mr+A9wR+eaH2K/k/rVRcsg+Yly+4dkmvTHy09Y+EoS8P6MsQJ/Rg4r0F5s5IEfjfqYUFLiosoHeeqwv/da9eJso4H/5if3WEZYo3BDs/RwGtg6n+T1onC3cD/JAdtl/SQc+kgEOEchIbhTCb3gbxId71LLHmi+smC1a4IBs0uXZwPyzILCFuf6UbwrlgAIVzTJ16XkWYC6NbmANBm89/+EVFl50v2v+khMgkKjjUdZu6/zD1V9o+8n0kGRP56+2XelJEiQxrZqb8Uppuff6q5k+6LQgdLBRRQR7wQ6c82G0WL0T7VCpblGN+hPr2nxASOtVmJOtjKsfDUJcImGagFm3yjbzgPLObgVdRgS+rHIG4kRKDfhjFb3kgm87JZrlKCDc9a9tnl+s//JQOpge16kuYDnB2i/i7+T98JcR1uJ+E/qhbDrBssfxAzBxiIPyWA87llO5PT1H/0or5fBGJcRfTgOUEKmFY01+X5OsRLY3IcAVa6AJBfJXxGjNWog8HWe/AMVAAAANl0GbICfAMRz7bjzsoUwyhn/N4U6/gmLP/BW5bM/E96rB2IhnJ+n0cTCJg5Tjc3sgRye/hUy+7X7wWG5qcbwHkGKnlcenbhAEU5I6460EXcoi04+JkFc10phesWT0uizPBSN2grb86SBjBaXU4Gdk9cZC/w14/5fEzXl8J7XEZcwCWtqN9W4DjXLfyfeJhPZKE9qjSMEx7c6ZBbIP45+Q0O5o8v620DAQjyzs6qqr4ZbT+X7EbOcNzg0wN1g/hmK9I+3OwSwaYV5P78UJhEweh2CM0PeZPSSCgR/7IJyem/CXZsylFHeEIKAj4KAiTnm8nrjWjRRRAaFAZc8oe4LLuAtr6YH71P+dOGUsWrSEtxlI/3/6sskElV/+IKCPnJGIv0jghIq/6WqZuYkW65Sbucw2GyyS/2FKqqqqrKqqqq/0kz2NKqqqh26hZVVVVVRqo/mvhx5PqmcNCVFigsKHWS5sWVVfTzL14OPg2+CETk9tuEeEPwcfBx8GnsaEdXjJRNuBq7fcHWCa4R8K93wiK8IZge+OKUwvEjTqDj35Pv4QCds2EVngMcf9HTc3+X3+nuFTEIGCG/ens4Icn74X8pjA4w0QGfSDJbgRPQEu9fDKcT+rJCguCF4e7DAPnqqqqqqrfqQEfVa84hXq3CmQGDoH/MqsrVVVX1ikNOzKv13Zk7EFz20uCOkpgls6wpVVVVVarVVX/4KSqqqqqqqq//Cg8MwYBmVUXUXF1VVVV/4NxY4IFpxwus6P1lV4W7yQcicnv+jBhCxIOYkNkQq/Dfp/0/6f9Pl9whr/sSEfBx7EhHTYiICAjA4rTNwb5iQBR+/5knt2qTGYQEAHmLwn/Rle44DXxwxW43XjyKp35w6+r8K9+Fe/nFC2IPr6QaUP/ce8i+1yfb/goCOT2/hDC5jEDsN/ETWXq++COX3P/CXJ/YlLhMP6wqEEcsGvTfyD/CPVhG5CQZbvDk5M7DwkXx+nxnLsyuOON83m5fH/y730GtLxlX9v2vlNrWGYRFAMBO/6x+v///GRB0zuQJVgm/LlL/qaCqq+tVXVXxsPxOzPs4UCyqqqGbKWVVVVVV96hsSGzRyJWUrx/v9grBPvZQQiRI0aTLGWhvAG2a+u+buW2XfnBGJ8EYnuBZ3knkMP+/L4RYaEhERuQkDhhIy+VPhti4J25MATpdKT/craUTHHiPgEQQOXKG/5vJp4lS5T6lEAYWgP3+aL9A69V66f+v3fBu6fa+JhpPCPp/FCwTtq/eHJSZ+X5Paa/QRYlRj34Jgjk915xQ2hQyDMmaOWhAyoM0zJPHwkXWpeqCo4D6EMvhcIBXCKlujA9Sb8nSYNZeX2EOcf3hGP/kNAIhrXDr9QgCWL7Ic+gQaE81OcFOZvBu/03lEu70/9+Mo79CxKGPW2E6qqqvyVrMwhnD/QaCK+CTzwOMChopPiR0VffE4LApqqqqqqzL9IFOtVr1XQEaxBlBcNVetR8FiahsXw1wXkLI788zSMn2sIagqCJZ16Fh3p/pWH/4kTl9CTiSBDEQJRrRc70KsL578EInwQifBCJ8p9cUaLMIU3OGWEoGkGwcuAeXoHb8V+y+xWhIgSUr2aINezWCLlg3Tr8pM8YbL/BjQkSY4ElM4249+XxIlssQIIQHUufgGPWZ9kVlvwp14V6wmOxR7y+BNu7ppMKpWEeUmpSNXpmiem1+H5hPG9aqEAEUCkoooZniES/hH4R6UYGQgLEmY514OMp8nr+l+CgIgvJAnpJXfvcyKtxLvEd+cb3G5df/XJEd/1ygj1VUYqb//4IgmvJMl4Igwq77+wVVVVVVVVrf8E1VVVWoyBw6S0d+UEddaOPlFqSAo1sOERYiv6/8GXdZAzpVICjSm7mUzqOn8zBYfd3e7viJIYq+wWSiIPyXTuBRE6phAwZFyIIdUjHaAhgIbifl2UJh8+T3V94kovpv4E3uoVfiyYACYNjiPWhKMdRhYmzQBBWxaWcxt9gCygUFG6T6pBB6pkMN8I78LxU3OvANf/8MzsGogptcJBHrsNCRMavTN4bgF/jdKvOrBG49+PhPSsIDhgOfGCaL7RZAlYkQVz5V/uCJei8lCTt8nyojj63wix+jvCJueEv53+hxR/wQ1XvhBlNgFyAEcHb8W/tGo7l/kgRsL9JxW7uNxjpHp/1C1DiuKxL3LYDePd/+b4LvYIwc6b3FH4GXN58y+/yHBaW7uYiM9wdUvooISO7xh/gXBHaBjp+8nrf8Crq4nhvR9NQgJCZsC4ll3l0sm6T38UJ/xQnL7+P6sWGBAOSzLw3F1+xAOS3fpJBjyfuQGGQNBWySL/FCfMJy+yw8gQKIEZB848EZOfQkrb87/pThTCgTv6aZVVVVWqqqjRV4uCk2q6h2CEVVDsEJ8pYd6+vkXvpev1zWssdWvkQXH2X1n9BiD9b/gj7uRf4KCaTu/DwgobGmGVz0FvRt+wPIMARHvetZTzBQzvd7vy47tzGV6UUCGJBNwgfq7uHXjC6XWHwUenQdzYBEVeaM9HkmaZyHBR4COPtxuZcCj4F4eflICP2XBhXqlCwJVIO6CgIe/vER/zx7hIYEymOgXRe8Qcqxe/VrpuDSIhvyFgQtRuP/02JBuJMK+ZfLg8+7eQIymOvTS3jyTbiAJQwFFvOnEkDTKVh14KLF7C4EHsUByBQQCFVzDGqtpsjr9wC8qq96glFquQlXFl/9QWV1VVWte+QIGXqqqq1/LVVWvC9WPH0VjwnBJVfDC8b/8Jfz8rGq7RCKYR3a1Q5gmJVazXrrQm/yoc4+Ci76W77/BTe73e+9/Mwod3fd3u7u7uC/zKsXqP+gVkf9pf7AIkC/TeARUCACYiqqW0W+23wQAlPdctV7bg9hAIAqMuquHZqPdJ31l8GGK8Kk7vuS6tl3+uKAR8Nd3qXW/+ccCou7lpfd7064RGAq3bvdu7vkXrJBIGQQ3t3AD+FBul/dh0FESQoDAt3nYKmHAXyek2GgCArNiDZYw7T4GAPBPiBbe9Ln2+JBRDmy9nse7T6zBiCEooHAcph2Tj8UJ8gnVnRQUOKgjlYivwj8T1yigQBMRYpl+EfG9HgoBDBRM9V/vvyyCTX/QUqtVXVVVVVey//gjqq+8N/Ci7iAVlWuqqqr7wlCh1XVVXVVVSgrt9SjCSqhqDKDLX9V9VVV5YLJbqq1VVVe6cpFXJVAhK78quUERLvIvpFWPtXUKX3u9373rzwpd3e07u7vd3r8FhHfd3d3fffgLoED3ulxsSEa73u7u/AcSBLd934vUFN3u73fevBJBFd3evCBAVGd3e73DekWQ6eETWIg+Cvgq2T6oRiBOJIcEK6e7XRaSuhBiUAt6CyAXteb+oq4dvj49R1x4pR9Q0LSAf78T8TDmrCOAn2k/2+3CIHMJ2YIlj69H/vwj8b1ajAQMRB7ZgcS0zXhHpvEAwBRlJDBaVjxcqumSi0khA6C3lc1v1hQq1WqrVVWq96l4cxT6QiG8tyq69p8MZb4VL+L6kiUFX5WJKLKuscwGoVXfoRnjsAS5wfi2varGIRrVTQSsvfQUJ1JqhUNEBLpO+7xbSPGQV93d3d3vXkhS5cdoGsQ5dvbu7u9bTAJYDAA+QELoCN6CHwd1rwOI0FhLu97u7vXQbhS7u7u7u7u7u9aoKjgVggCRtTEnf8YGYB88eL/UMR/9L/WCz6wUfWcTk518npcqfiCYRDT1ArD8h99+Q2WeHlYR7CLwBk/yyR9uuqxj2IDc8rx7Byl+P+Eem2OoJml8vAMXd+c6aogkJzDOqrWlGxsFImVU0FWVWVV8n9YT7ljHjvmap8XVdddJBTKqqkguq6qvtLgNUEIJa1UOz6OVy0uCESCWqqqqq5ZPS8voRWq1Ta4DpBD2ALLArmvjHqxgoonL4ReJhaCctVVTAMkfsnvYgQIrgu6qq/ao85gUVWq6v60snKXHHFgTZfJLcV8V3jypCzcaI7vH1bXajL7u7u7rYzd3eqDNRF33d+1JcwK63BL8BwhkI7vtKTCipiXmy+0V3p4bhTvb/GGu7he1pIJw5HGe73d3ez/jAgInJOu1bQeiv+IxIiCmHNgpy+6EoFCBRr/ShwFDhoFRJ1VSojb/J9caPooVItaTwwIBLWuTNzoT6JPXNrX6wXFVdV8uiTVqusFdV6quq91giqu/ndE+kicPz+i+wwI1i46UuQoH7xcVBPSgdpn9VYiEtDOT01xvoXw1CyVAP+CR6iT11CQgnco+99gP8EJCO/ssRd979rtvHiAl8snmBT0mJECsF5Prwu/BAPGGoNmcZQBA63zD9x48fg7Ec6Ih+We7/k/UScIvEWEBjsLf+vyVqZ1YClDOPh2epFM3qPfml6CdEc0oyCSCisEe8G13Nmixdl/+6+jw2fAruhb6/Lrfyeqa5IT6xsRFlMPFJary+NqpYSjbf+oD5UVmsksRz2QlhlPNeXl8bxVI0InmtdjxGWFMmZIgde0+UGR9p8bZ9l9wpFd3GQDnfUenD8f3lEP1wbrH8R8HYiUlge5q25PvUZGRnTr79x4jhvLfDs8zkJDBdViKl5aDOP8TNrGcViZISLh5fyILxn9BvDMvDM8lrUWLCXT1YS5R8Z9zb1qrwf+IIPSRdV5fLdV1n8AxEAAAN5kGbQCfAMPR7kvyfXycSYKFXlX7KEc3hP/w+Jn9sy7EwRdz4+X/PUpMtuDwn9+KEwjq4lfCfduSKgluPHf252abVQSClVfZPbyzYW1fTfRRC11QG8IzAjItV9pIYLoOj8ZavnjrIWupdThaJ3+TSba0FZ6+Jmv4hhWHcs4eXtcMz/IKKszrtfKZBV5d+8Ssh5L+aFhOUCh6ttApp5t/8x/DC2NF/J1EDpbt4fQp37DETBgbnufkA4BD7rZ+TTI/BoJgj8K8n9+KEwiYNOE3CYVie4R7XJ7+whwibB2UxRFwoEdpII+3MEYoJDAJfqMJrzrvsL+W/4Ayd6XlX2C6my3zWux0WYi5yV0xlZPpv5L7qi/eHlC+7lDAMaL/9/aCLt4EHKCKt5BtctrZ7BUIK4ujo/LwJBxiXi44qcODj4OPh8Tk+7lAUmtlwOXNaLamK78n7+4PPg69BoTk/b9BE8uB6kzSKE8bBK/sxsq5Y7hHwr7hEUI4FRsPQjilMeEjShIIwhl/X8v8ws9m4Sljf0SiGeuFwkfwlEF3fhI4zLJ6eE8I2FevS3QTgsDOT7/J6WSarViR1rG8giPtfbhkFQz4rrsSwUECDv7iFfR8rOLzDylKtFqJ+CTDciECDOtdqFBYFHQcVJeLi6A6QvMqouLqLrny+DkSGxPYIRnUQgt/L8HIneDgwYQkTjtIMB73W/3OmnT56r+sEImjfH/5L4QBx05Qj+Dj4OPh0Tk9ttBHYQcRiiIwyCw1p6f5rk92+EdQgIAOTF4T/eySA66j1e+pgiOTaj3++WAASKpPwCrz0Pn//E/Ew5hX0Cnk9NLfBRIJoKCG2tj6a/J/f4FAGOT+/FiRQITEgE205/F5/J68E4SCPlL4w1P9F/jOyFfH6wmGBRyiA+pn+EfuKJMmUfavzevjtZR1bUFrCAjxwhe38jmv8JJk3XhDdSo8WaIf/4IgyKfMh32XfRdeCgOToG5VdhCLD9a1e/s4UCyqqqGIDhdVVVVVSr4YIp0ayD2S+/8Yx8ry/EaEnE69dcRBjr9nGPfusEIZyffFgQQQx24Ajn9RnX2H36/wbewIIIfBF7OCHVwgIiyFJglHlsPBXplimOJAEanff8smZqS7riELKQGU3t0b/eHiRqWj5d2jr5hTM353EfCGGrYR06oICBOYFnLgh/OvyAtkzpLgmCJu73ewQirg4FF4D5ZP5ARwD55lQHH+1pYoUKE6wmOyzJwBm3Eof/PnQGcUJp7hGUULFFi6AoAx8IyYGKaZW0k+kmE68EvkX5f+BH+v+AIle48P14T7y635fjdVihN7dV9EWvEUatWs4JMtN67BJqvRauZITR30f1BWMWtVXVb73ND41VVVVVWqgEQFB+t76QZKfAT/9Y5WwNrY6FyyqYOHqa4MPf4nVIKwgzAn2lmwqUjy5FsJEiyA0Yp64C3KPgGnBpTtMn0Hx23D1Uib/VOxDl/wJb6Sr+G7hHeNEhQWKsUJPPDBQbjPc1f5/uF8n1/tsIkHh4sPrBQgmjCoRc8DsN63K06COUhTJlUGH7fei/CGzhGWUMwdlIZb3wn8f0uELFAy0xlSaY6SoEb2ofKHkG+EXlYikJ1n7WTk0+FPy16KRKGw3Fs+nQ3QYvvrBDi/f7RKdfBFVd/wRHVVUYGOOEWXR3nrxnu6YYJBMbnVVVVJAV0eJmDRCQOZfBlCX/gy+DL4MPghEmLJsB5kaZJ7amAh7DQLAZCYivBjn+YPQy9F/QgM+oKN3u+IKRVL/gyg2lgqCXDeDPDInWwgGhAR/neDUxLyHa1bgHwBhdLvMCFAMPFw+sl+HNCYRhzicn34kEupsFyQFeQDSf7uEf2Jwg89HfhWTgS9Ye/VIcEAyUUC6yf4nl47Ev8J/H8vhA+EMSJgNzgP0nW/k61fwl/8x+v4gT2UHOT62J0FvJ6Woxwx+TQhBGb5LKEUFn/V8awnR37BIOu739QUXd33ccwU/o4JMaKvAl8E4qteXL/KFKqqqtZcctuKxWKxteX5QhyQTRl9uOQsV3ed4MPgu+DDvApiWxR6JTIryfheXX4qLjlrsawWFiX3zD4MoM77cRUvxYLCO77u73jDGuvFL0xRQavxUT9g5dplobgqMT+nICthIQKztVkXRfAnAAJUmuc8U/5LH1ff/82+uPN70ly3uJZ0AKrRn8/+JhvyDy8+W5P74QC8oLzCokQ0EvF2hjUdvD4gT4gTrCfyekkYFWJAiiwQ+IEl5c+Jy+2eIDGxAaUxhRy3e44T5OX2JBQEygigiKIApZz6fnrHhLYRJ+TSQjvCde8mX66RXwigqKA06p//BIGLu9Zf/wpu7u7u7u93ciYblAQ+9+f+TTb8RdxDvT+goR3d62T73dyAcJf2DDwQ1LnfN65/8NFf3y9p/zazmdeCqpr50uOM19UbthFrCboLJbHhZQfYFsCSG/Evrx7v+120BPEhgEOX8eGg+ec2DnUntDBdLxAfwSby4UX4UE93d3e4763u45jBUvu4UDeCm72xn/3vAAn8d3amBySHE8pdkrevTaG0IEBD6cEdfPzyTubgyj6NHtd86ccdVH/DYPc3Ln5eniC5Sqr/V/m0Oc5DwDe7M9fJ94x+eTmpdp+eT3f2EQnKTDFvBEbnmk+ksgIS12uP7zBbZAAxPJCq3hPiYzM7/4wT5BOnEgQShh51517xl9ZgwMCxRGC1U2bestBVz6Vi8mtUZ/lV/IkZ/cFW7u7u733/Cl3d3d3u934sv9RFhQt2+le7u7u75fk/Cl73fd3d3e9agiMtLlmF8f/oFEl37yJvCAE4hwVbbd9wi8+3u99YZEwNAJDar2lDQR/BGEAW6MUH33rxYwKCYiW9vbu7tXd439YkUKFhUlu7u731HDH+ygQe9jTULnTZwicJEJOuMtBtrsptBGUQIgmu/MARmpra5Bgm/+c3m3ezkn09fZ/iYb5dYmkxtwTfMCPk9pawiE9wYUqMF9bDcv8SGKbiQIexWIRcg9W4R+Pm3fJ7b7AwnAjmAzGMCd/HvJ77wRVYJfAMdJ4AVZHPSvm/QQMMF6qq1quuWMnr+RWKlJQ7CmMOarWo4lTEetdVl+/rL/vlLSVa89ey/5f5Mjyf7BGV7vVPrMZ3f8Rd3u/X4+973fMA3rw4QFPd3d33ffsBxAo3+FLu7vd3e7u7334CgDwI7u710AUYECxdgELBECGtV7sFLBdLj+q13gru71qpoI5WLugR46y8WT22vkAZExlUI7J4+HAmCWKxLjvx/8hxwptpBthpEKVe+T6TrpAhFkALDG8b5hYt/BX6aSyVDy5D1lz/r64bEQX64l8ntUwiMFIEWwTFXHQ0Qg41ixQvwj8/J6ScE4p/SSJMCg4pctD36VSTAsEqq1WtSgqYtpBGeCqqquq1r32auQGfFakBmqr0C2MpLqutVVV8B6ghF4fyu8eKR+A6QQ70wG6HwUFxxmEe7WOtjoEOoBSstF4iIqHWBV2T/FxVdaw7CmVliILeG89PtUvlIsUS6xyIR3d9so+7u93e768IE8Eokhbu8nr9eyVr8Rd93vwCAAoBJNAk+LwQXmgvwMIRCd382eBxCIUu+W7vu7veuw2EAV1UXVVWqqL9rAIgCkWKECHc1N3aak+2scvEYZGWdtBde6B7p+qBp9UCj6oEon1QIxOLx/3GrcsVX76SDXFwfNywPW5bxe4oDqnLfJ9d4kCUUEBYGq4BBX/IBqcW83FIprw64MCdfw2eC/TeNA07SGnYnx/wjMCrgCbNpMeUlFiALAswF81MrF2z78TVs0Sr7NefQoFAjhFxdhBfzK+vJECcOMswvjTVsdYUrla1qqqqq/WEaqqqq9V1goqtSgtdYusKVVV1VV1Wvawmwx8FyJwH/S3J+l4Xqbw7PM0JlozF4umj/s9FfxUkoKwnfFD++KgzD3xdkjIDc5et9EplvrGJt3fl/C/GRBOWm78V87NKDZC7+hOQk/tPWFX9ZLv1Xi5L5yT/AaIIRl3d3d7ufWgEeXvwVwhPn3fLmXywJIkFrZwQXfykndymuvD3D6wgLGiB5CkmueJr4/78RiF/DtRfUWL8LKZxKJzgd8znmrHw8CDSriIQicBny0l5UmUr4IiKoa1WKP0WCLWsc6w91XVVXXJqf6x2qrWqr6wTFrVVXmOtFx9YUqsalKbJlZVdV7wH6CNxUEGf/5Jc4ycX6p8Eu9d3zHR/Xk9NvUI/svtQSlcsBYLOPu5SOW1H1FVVVOcOkpx+X/UXE1qpB8/NYY38iwhcJ7u8lyA3hGLIQ4PNH4bnk/YBAgRGLw/lUoEEboneAQIEnZJt5QrbReE/Lc+eseV7d3d3B3JOH/nTFbvL7WMmnOoTad//8Rivgk8NDwI90wQG0QqWTP/pEY/L5NJvWJWCIJDhctJfLsAS2BLEVZZOz5svVgQBG4ZzpmeT7oRBnxvokWTBHvAj0353QXcxu62moR6bLHQRnajXk3r6bHTxBcIcKFjoYbvTa4R3ef88nkJSe22SL4QhKUeEyRh73H2iT228d4kRHSw/fd0tT9eSYuo4Gg/L5BJ+QrUskk85Ld+Sq1WAQMEIvAt+n90TXAnK7ZSgOAMYo3DsKZs48PyMviRASjO4SPNP70oZj/qjQdrGfBWX8b8FQ3I3sXXwH501S5aeUUy1r6cV8vgPAEGI5hmQkS5fNMiy7WSWIGq4JPwYmntjK/cn81rfXkswY1f+sb8CR8CR8CR8CR8CR8CR8ZAAADixBm2AnwDD5Obw//CgoFrJ/oe84wu3nH6/Uebu83hOH+H8/n88fLnnGJF+J6bb1WDxXwj2gj4Z5Pu+EeECCBUEQGdNPMYQVleT271hCja125RC1IAasEIkFkFEtkJLK1xzJ7SUIoWVUCAXsEA81ywYMtMjYEpy8ypVn9Mm2ohBeCviF6fj+VVVVVKnmYdhpPNIvrns+4dAsd2aIx8F+cJcBlvTSf/Py+T3gmGyJ1MFsg/dv13tqOGAbTPV4bfvLd8q3tkhuMkRg/qx/D+TBnXWFerwoETHmUyr7hHv2o0RCHhII+CYI5P6EiRC6CIyN/AO8lNdC58H/e+kxfq1CzDlV8/w2kr/bQSMxQzWqrqus5bpsKVlaqvISFg8WDGmjttgIkRj6qqqqqq+8KVlVVVVVWq/+0gU1VVVVqtTo63dXXy/saHZf7/sNYfgn/g/ghbt3/ONMYvTDiiRG+Vy8Dq2v8My6Abk/P+p5QfVg06OSVeLHg4+Dr2BZDO0osOIpR9qDv8Pfr6J6zC8bZXr1eHxJTQOVJngnPDvYP+3WcUHuH6TC5DdnL/+KE78KiNYUhQWKLL+VcFeeaAmZ3Se57wkEVL7LANJsJhFukkE4cDPzdhNb5O7te8SEsnp8wT+hj7vOJ8UTVUNEgoyfVVGiWlHsteNFocALy8XFzVJ3AuXi4uLi4uLi4po7+CR35+sHo+UICPHfC6rMXLzf0A6/lfwQG8CqJyfuDgLZATh4eUuC3J/eg0J8vEw7iI3exgPJQTZV+0EdWDrsS7/B57QRye3fsdsVFRRKGquJwgQBVK0/3Dgt0zgf6D4+ft0HWFSecPEG6/VQ764dhSHML/C/J7bbCPoIEEuMwlAhVZQfBQEaSYsQCkImEQHtaZ+IElE8Ed/z4VWnYXyfopl6wmEBQn+i+E4R4rk9d9cg6P+6wsuwUn6BMroPC+4nWiRfSgkPqr/IFBaqqrKqqqqqq/2pngsCHGApKqqsqr/YJ6qqqtXx7BDX3vvAgw6UaFBcXFxcU4uHTlC4uLi4uXi4u+BD7X2/D0vz/KGIBM83B3lv7wXGG+f3fER9h8E76TfAg1Lzz6J+pQloMxIkXy+DOInBGXnEL0RBD6kBMezF9YNRuT+5w4ozEwRbN8gHU1LAQCq+b0pfPN+X3r3COT+64tAhFigj9zebYbk/g1Pmk6T7f4RUpBgABEfW849bybuuIYRhuyhHTqQJCBuYZIPgu5P6wuVlL7RPVv4KQkIy/4kSKFmIHE/24vg4pfTCPXk9r0CKwQyC+XyetfEwRlEDpww8pM/ECbwn1v5PtqcWO8EYwJRdjfg8m8io32LaTdgvHyADXjoEPv141jY7fodn3eq1pxGyCMkX6KCKqqr/gsqqqq0lVVf1ClVVVWqqttVV/wodVVVPFZVVVVVX9MKClUXVVVVWqrv8oKaqqqqqqqsP0g+N9VVVVaqCIAoMrX/S8FZRuAMTrn58vvwhsUOy/+J8Pib4kTA5tJyhxI5ZFBRZP/h0T4dE/i+77ulcQYHIyO0/c+dIQMwAS5S+DNXsI8Nq8yGnrT99cIAoD+vxEJGHqCZC/fWZvLcNk90+3YRNeATKr39W+8aJCgsUQHUvKlA6stACD0sdrjb7T2iHYSEUT3XQhhljzHgr0z1xOUyCmf7CFL2cSUbUEc8coZiRT8I9YRvpIYGTBxjgc6mVTRPVvFBgNCJPSrKv0Efl92T+/439CLMbCsUTpeCUMKtVWpkO5v/U/YKoDQBcFmH4K8f3BTVVVa1VZv8i3xcbAFz2FwVCNh8ZLqzhx59lVF1+bJIryelkDIyhVAhqmnHJL+8GnwZfBp6B6CE3gGPEw6qhACYILALo67pc9VNpnoCCCExeBMuvj7yFsFEvGWju4kncQSQnU2H1t9gtFgBqPJTc17RZMa8DwLoqwTco6vDTlc3j/9ghC/ADMZvSZkn3iQJjDE9CATebLnR+QBMMH3ud29ERhqD390CcIApsbxkQfWQVJyhTDaVYNSBfDTosv6MUTEmCUwagXNh6nxPiOtSWcvLDk9ttBCDESBFhHsHPwmbL6inDGxgLqLQj8eNvB99+P9gUQQ5P6YTKJMspAmLGX2gtL3npQJQORAY8EVDezyiOIv/hujd+hXb/Xq7oXDMOmgWJ9BC/hCiX+X1WlCNhWCGO+6Cl/SUwf6GjIDj5xaHDa/IUEFcO+WY0FHQ1ust2PjR46zrvJt8odElI9/Bh8F3oDByfa4EQFeBACxhMgaKMiiwW2lwsggdOrP7/zX6FhfcNNhJ2qu3AlQpxCo/7EjduK3d3bFYrd3vAQAgkSzinxXwYDMn22ogHvGkgcHVoj7ntf02+IFaknIvhP7xgFp4N6i2aAT/q7lh0PWau8ot+EH+iO+auHN8v6CHmFw4k7j2vxQnzCegUicnttkqUK4kgBJ5U/584wtIPZLPcv+KE/E7xDCMcYvIuQ60f57G7qdTMd11jC5P0owMHKHHBGbAPUbgy+1v6wTkBGCEEg8OwQn2+gWG1XVaqve4I6rXq1XK/R8+bDX/0CoaIZKaf+5zBK7+EK12cSt8n0v+Cfd+76xiCoOAFaI0FVyRzCm7/W/yo5F/gkFXd61yYLKTu7u73dx/zT9T/4aPf3wQMyy/+u8FYp3d3d3e7gievOXwNoRon9ZQ0WIBqYbOGoivL+Jj4V+dAmMPtb3e8cx39QbB+wZC9tgURNkLI8KpdDjl6bloebMSAECSbU35hjlWNF99sa75FoeaiM8K/P29iBJZOt7X+H/+IEw2nKDH0/kEu1eUN01sT4oTRPemPzAg2YwahMeeNg8sUJ+cT5F4sUga5OT63lgi90WHTgoOqqq1X1bIINVarUn+WhbHnq5b/BCIveqvBIW97/hS7u+7u7u7u9b8gRBQZ97u9eSCbd733/W/4I9J3r4gFVN93d33rwxBVd3d3d30iL20AjwZAdwXL7SYEXgq8HXimy7nvrxwgFZA68z26V27u7uAXHGCAwSk7lJbFa59b/xAuzu93fiChS580ahyLjaCsV3u3cyjyIKCTEqBf2K7isVu4rdxWKxWOY7v1BSTd8Vnx3FcBCAbeU42j+UlCDGTFBK2mw2o/ccKr9LJhE2f0UM0AKz2Xl77q7MyAI+uPrsNwU1byHDN+9//91UlUeDk/f+J+JKcp+YABKFw3cm3YFUeGX3e3nxNCBthmOPMOGU978frYkSInTiK8Hy+l43l9Ag2U4KBZJl0CYVDWJglyOBIQdmT/mmkCZQUUqZTjTQq64b3lBEJkJV7zROa6nBv/XeatV4D1BDl/+qL/hHyLBwDkV8vL4T/goLd3d3d+qBbhvLvu+/5DcvT/BJfevwVld+7u93vXKQ138KRnSe7u7Tu++gDKBHN1xfKXIXv0ElLn7oAiYOAnVLo1WT0m/vBJs1xdB8RtPA+gQtvYIQwCI2EN0FLIkc6AiApYkE7+P+vwSdpQUk9YvnNgA5flSzf3v4sAyrK3V9qI64c2CEo3ABAZ/3V9dBWpw+E4kQZfYx0MwR9pYKb1799eE+mkCo4KfH9bigUCh3AJL1Y84mX0+q8FA1ZCTcOQ9iwf2T9dfCJAxmmfqUk61WV4DdDZuGX9HCmffPYvwm/CS8XZQzALP34qXw8l4O4TLOBSgbWvuEJiVVVVVary/xZGiElla+N6xsR1WUT5ZSXfwgT2jbuS6XDSChXHQIUu3e73d31rExwIS93l98RxZHvd/4Jiu7u7u9/CZfAJACgTtGh4y0fAzhHwLIR8DCJBLu42122W75Pe8AhwIREBsCUKYW0MYHgFIjUB2XLAqqOgJ3X/r7AjgQO/v0kSJAkaav1iQQQjr+HF4VBCLCs/hIn48uaizCh0kX9evjg4I4BRZI5i8b76SzAUBJJV/1Ba00wYUEuI4qr6SFieSqrK6aGVqnUozK1qqrW4ZObw7PVpoFHldVj+Nbx0NIevn9YXYUWnCAjBLzYtQxf4Zy8WzeEjDazkRZy/cGyLLUhVXIX+vER5i8X5zpb5fSN4nL+E42Oju7uUGmB/u+yCRl3d3t4rfd/bjO77u7mLke4zcuQD22WXrSn4Ks/ex0ulZYbt3d68BwAoMTh2FM4BAAUBOuzkvrwhBJ3eLfgI8eCjac9KRL4vBiwjJru7vd3veDETjCO7vu7vu/WPMPECJb2YfuIkfQYExCf88E8OPglIXSViAJXYgCRpPdgq5Lk9NPD+JJEeGcRWn0oJda6rHPBBDl3+6RN/RIR6qq6r0oiGRIjqqzWT06EQzD0THHVVWq1qvAdYI1c8B/gj0mCGA+AUBLw7PUutuOEWCEq2XfsggEkY8Zefv3yftiJOKlyGc566CLN4d1Blm+88dEeSwygznT/NkzpvBAIFwQXK+S55fb/CAhwn21f6UB2AgAeQIPNJ2+9QfiTblBqoKVpAERBSeMu7u7lB9u9vZLe68EsXd3d7vxYgJCHd3h5i2uvNgstiTj6w3H/396v+GjwBMxpOHPipqn3+T6ykGZZQQhLxshftYDiBATpQDqgQfwmJRIsntX5I1l474+StskIdeFEEj8M61gVbnrVVBCGb5t8/l/E1EIFB0eDqHg4xVxJgN3Mviaufr/WeKiubJeP5cj3JJJu8v4RiebDUsLyfeEYZ46Xnn0+WbwArtuVj/nbJ7dFEhCPhwQLJ8BAdc0DxZfpovEbvvvJ7vi5oKhGsFXg48NHAySliAHUh/R+W/f9ZexnDuW6/y/lm4jzXwkaeisUsXzETEcpIHslShPhOfPLch+svCXIXI0aI2XyF/J9iz2Qd15Jhf2nT99evrB/4iObQZb1BZLmv/YpcQlTR4BiYAAAOTkGbgCfAMSE8/M+x/Y7H+bwn/4fGT+2d55IJuQkS+O+eDxXUImJIpNFFL+1e/hXp4RCkkEjz/JAs3o399/J7bW9TbbckFBhdVVpVWP2oKBRSLUqUiW9X1YbEhyLtFsXL2ousntJQjE+CAXgcnNahTFxdWeZUqz82xz94ZhOI4ayeamXx+xzxHgE5t8h3wSLp60SeoEfJ6tBP/k+s3vEjbHxsJJh+Fp+oQGQ8vYXBmo0Dlqqre2mCOJgcNw2+WA1wDXCvJ9N+wiooTTvHhKKPKT/iBPQKRGncFEk489mVmXxXwl6BQCE0N4rfk/sSJxsQxQ7Ay55QEr7Kbvb0xF1Uh4yF+U0NPRKqq8rqvJ+l4Zh6FK16qq9SlpffUouqlJSqmp1XgoqomRWNVWRxo/l9eaRay/hLaP2FgQ+IHgiFw6kl71U/4y0KKqqqqqqqqq6nG1lVUAUv+vzy8qqqqqsvwJW4m78n1s4IBK2FiMsXWWgGpAKsxwWmadvt7RfrY3d9fhkT3CIk3FRHJPXnX3+Dz4OPh0Trw5KegIsBxa4KMK927CIokDDslm9g6UKtE/o0SyWeKE+KkK79YkwlhOfPMzLG8JDxAkpMdqvgp5PTcQGYR4KO/J7p/goDZhWNS+T239fyGCglVVVVVVVVVXf8ENUigf/3OFKqq9VVdVX3facPhj9GYwTHyjfaizjFpLsVbVVXICouHw9TBfDGC8MAxGBqtVVaqqrVZfr6GiVVVU4AV1VVVVWA7hx3f/2HyYfe5BtF0slDF0cj85RYcb0zq48JNv4IPg1E7sGhoRLwLGhn7UlHoHnweafhnpuJjmeEI5Pzente18HnwyJIfly7DCQRGCImu61q+5CQzdowWFbr9X4sgBB1ZEPZK7roePc6IWCE/wC73qViO3f/Dxf+CkK/CvfzijmXgBmr6/f91R8MDpdyL8EwR99WFoSBCbAeZNMLhDU05cUJL5g/XWKEuQuGk+XL4tPnRCAUXDf+Lq1P39YTMIEidd4EdcDV6ZrxM+4R6xQqD2vBRiWfGqUvBBp2rEEAHvFHM4EpGc+vjr+Z/1hREKHuN9YWQjWodghP7H+xlX7+pSllCxR5VG0khEowMi6qqqqqqqqqvZkGnHpCXy+M+5Byr3izqFKrtqqmFzfmtmNghggY/L+EAltBQKKqqQDZqqqqqv94RC4NJeoWSclxUQUSZeZfw+J8DeEfDonwKI/dg0MBYEiTYDnKhYPCmP9uX/QOvX2CAI+DUTq5gbBEWaH0n4CFqwaOPuAj30ewgzUbL/91V6lMNAAS6f572F720m1JU/EmPlCxOG25QiIElLL+X5+EpBBbgFZ1m/oFpPxewPLygWu80apjy7V4mNAu3KL9eIE5P75okQCExobb3EaHqNvzwgEcntYSBCCb+3wmECCQCde/Hgf5fCY7YRLKQFxZP9wa8J/XVP7dwqCDSs3k9UpdAixI7OujAR6m/e/ZsqvDSQvvC+1jOiPS+VEzDxKvT9MKHWq1VVVVWq0aqX/7BUIrVVVVWqUfHheta16YeP99aYggIRq6qARGtLiYSZQMVN7rUz9ZF1iZVMTThAcH5LiIx/efkEyF9hK4nCHgrpjR/j9Tzypfw3bCOT2/KDUSGBzFbmXwBBzydm16N4mxv63+HFw4JpYwQKFmwRCmn5oq6ophmkz8v4T75P3d8vL6uQIbKFoGOUDTz8J/CcWTJPMSveEjmYkQZTYjwCispD3hrevk+q2cEElUqJVIf8rJq4nwtQxKvKr+4Iyu375vH/8EQXXiiBkX+g5f9X9UV94E3n0Y+GXY4e7F/hWq61j44/wiokv8bG4nMvxIdE+FhDwJd/+sgEta81KpmAL4z3by+gyFxMOW6FAfKmMoxc8GnwafBp8MiTacAkvie+l+O5wuJLYejqkcI/w2J9wxyMu0W3syKs/EJn6+9ZL60JhIRBfE/xTE44Dq8GeCWy8De3yJTEbAEIHv5fQRhHpnIJzVaD1/4RzwSw1m6WmJFAk3n8TDQS+B2wBLX5RNLaLJLdFHaXkCBbgBquzNmfP/wn8J8ntpsGKkAhbEA4DlMShry/CvL8VcWLRSA9rjpaAg3Nr+CIIkEVKRxOHJxa47/+LQZmT5ASBJ3d4PClGre5K39TzB/HO/GQzQUDl/n6OGL6z/5f/0SjEI2FYZNQaZ9BAfHH6G1XWvmtTjwv5ZayHx3WJmvvRQ+JC1inUU6mBxtljXXDPM/4fE9AJII+H+T9cMBSClCQiEfyr4Jf44K2SzGAApSWa4eN1hYmCoRG2XMxOdgd9zRltMVu4MoB7Jv/7NYc8AvsaZ2yJOJfhKIwDFXVUzibjiX8HCZAF3/t/tuC80efPZjcy5aEBTvlavBf7xTecCAIFHDexjNiAEODe2yPACdNWWG39CNlTc23qWMNf74bfuKGxpru978gQIQB1Mxurvz/YuBcBD4oTu84MdPmAgMgMMJe96SXjBOT3c4IxAmFCRJnl8NJ5jQZg61+CYI69/hopMB1kR6tdAiFqTOKIyUJf1Qwpear1yooDxSdERxz/lBIMu7v5ECy+73e7zAdQxGhda//b22wYVLv4wEg7d764iBMBL5cpO+PM5Vz/OcFcbyfLcKREDrR6f/Zw+Sz97d3du5iM6ymv0uLeJgwyevry5fDAg5sNhggLANqPqjTgP07g1J9UEBAJwzhYQHPEOd4SPz6yiAKgCXBYR3d03t3d3AcIAD40O3d3rtAcVtRW7yT7IGp/wsTJ73ByhoUBogVGOBpUfUKVjrf6uTFirBHDh+/XGMy0IIAaWSnnq/21HxBSiMzz9/3+m7S6huxIEHbbqQtMwLWrTfE6XCwIOg+GduJAojBLIZe6aXmE7ePCMT516EOhacOcfyfWPwSQQZBwNvyRNT+qCcH1+CQ1a9+vfgo2q6r1V0u/2+in8ZJd37FiQUld3d7u77/KC2lfd3Z4ur14U3d3d3vcb8W7kTN8fVfgov94q59hQjpO8t0ne7u75fYU3Su93d3Su9dgI8HS9usDWCYJXd9Unqhg6CwEt3cbIh8tuU63kjwiCgQ7u46y7fLWUSE4KOCA3QSPuw4y1aUEwfBAIDBeIxHLQZaFzGi79YZPIEScP29Gvdvu9VgKECgCUuGFEp629u4C/yiApd3d3cD35u7ufhVmv31hQNnG68ko8w8MyvTXBCL57H2s7JOl0ThDPJ7pTCQKDqLEQb8QgFzcJUUsGraZqgAja8oFnXm/58EcOUVkC8v7dBEoMO4kSEAxJ/tCpB0ynuie2vwyFAhp9QgI514HKkz7f4j5Om6EAYvgiBGICOhRfYX+CYfPfhX7je0UkgHWeaFNbVVISWtdfa8sEteYkvNfU8J6BNrWte10Lcr/22GxJa1payb35jAuvd3vrxMTd93v8Kbu93e7u73r2xl3d3d3dyXe6PkYLMw093d3d3rsB1Ai7AQwdMRSl3ubvf3XGRVxWk73d/GmjDwHcCIE3Vd2e7A/ji1VewnWugFaBGFnltXZj2noIk0nIBcE+HQoPGNNSXw6RgVznYepOW+sUUCICFiRuV/4LPgklG5iRJyemvPE3pq/8kPoOy0NOKJ7J9k0OcMXc2GxEP+QGGT0lm4ogKiggLyihw6rP+E9E+nQe4IBAJ/CfVBOEAUBIJcAxaTwDV+/J6umTWCs4ELkf2at5qz3jeXOqqqr7yQgetdkMN+8GjjLqCiuGGsrbFe/BLHGXNa+/JyROS415f6r1hSJf5i48GfMuYRvCBOilFmd5e/dYko/u73fd/jLvu7iu7u7u+gDxAQAnJnwP5l+KhEuVXWqmsMf3kq7DzCZOqWW/D4RERtovu/tjgWdVVVVVVe7JCGrVarqsnt34G4H4JAmUhiKV7ew5CpsajAdVjAxzo/rCDc/lSv6X+vrhz9rFihIsKxYo6Wm0ea4/rCMOAg1hAolGCla8gIjVpZPSUnxscLVVVVVVVeiwhVaqqrVdLhkSE6zWqhI09msF1Zy7UkX6CkRMbDc1F2Z5PTT5gjyV13hTqq1qqqqqvdpi+qkzJgVVxBUCOPPjX4tcw8TrHwlLxpr+hhQj1NuBXhjFaqq6r3F9VWpwPeT0tcnk2X8QvJD25frGLV+xokYTd93u7u7+OMXu+wH4CbvJKKle18vi/xGsrl8PQnMA98t34DjBCFLu727vbnxLCmNFBTN61gI8I5bu/gQxILLv7vlu/kh4juS+41r583MTS/0oQCIzBKlOtj7wBg3e+lLX4gEGsaTEBKOl06/mjKsJVVj+IqrB1Uhf1glqvqscyekt3Q0qudYJKrOS7S4VOSueesKiQ9BQfAlupnz3vrD0N8ntVjOAIDAkiKqqrVZPTb38XVda5PbWtScntJm0JZ4sqrrXJ65Be4+Lzl6rVZfwn8Enh7NnZfFvkGS9pID7JCeeSLqqq2MNvazcnhu7r7lOXAo6CsITxV3d4Qaj7XTqF+sMwxNVUXgEGBQ4ay/S8FokJ3f4I2ijuA2Q2P5be5gfYYxZm87fdAiQMxI+0A3e33d3k9V9xYgWKLb+e+sSIEWdIGZqGYaT9iMF6wisRzEtg/zkElzZX+lCPYgaIkX4RE5Pby/ES+SdOSXvCMZHC9VxxxR4qRGG/n8vrHRsExcRZ2yU1j9Vfe9C4DvU7+NFk4k0XQa9l8800sJTDw01HA36nQJ080M0p5pfJPLLfOPVrPFS1rI3UdES4YHl1kRjf5PSo+xMQvEcvm+AQMEIu+4WfbpJVgPUEOOM7u73HSyz3rAa4ZFRBR3AIp9R/cl1hZD5BF7yetKaJERDu/VlEPBuX1G/INIRg/JFuvyDobmv1i+6iNv1i/5Nr3IN5b+WeIYxXk3EeOtHjvu5RU+fyXEb69fJgHJgAAAO/UGboCfAMP5RPLjN4T/8PhSf254Vm3BQFst8da7flPgS+png7V8I6vfwryfeIEwjxWT3Tw2JvMOKAx9qs7hEFlzXl6i4u00Li8fvDGHzGlSEHEDHubd6BoL4lX4lNHNbUMGmJLI60GwXNQvL45/aHpdNbEBg4daaEuK8vs+f7+80asnpZhJsRnv9MTNeT+h+7w74CPyl//mHgDL7nm/+NB62PwywH9BGQo/7/UEwmULuQfMrv5w3ffqQwCd/b8/9athQ3BN2uq8O3DvZr1WW9tMEcZCk7c3rOwTwaH8n99CBMImD0yyRIlPiBPiOncSIJhP902s1hSOCOvJy/E2LUgodtgEjuscAIKptP7QtN/rkBURWF0D9nFCqqq4cgmXqwhWEUA6rqqr5i8O5aaXs3NnhgE6PvT6sxQ0v8f03JH/L+FCiFLI9SVVVlVVVVX+woRZVVAhd4H9pryqqqqqvl9sJiVUbE910Ny0ZoQ57fDfN7Btis8PxOuH8aPtH3PwdegO0g3mvhsTk/a+EQQCSiCFwFV3N6p0lnYCw9AdvX4dEwU4X6vCgRNKPASrodn2QJaZvECRQmn4kR0GgztwP4gFMpZo9+ETEGQNV2ycfD1Fpydbs+8Wb8FvxCrEjOxJDCeHYerbUnpzn+3Y4pEo32kJRqvk/W9XBJqt8wD/j+CIOrxfA4OJ7CgYWuqqta6vl/XxolVVcgEWVVVVVW/8PyV+9lDIkFEMWQaNPJlgG4SPtm9YMxOm4YICksO0xi7V1Kc68vmv+T204RNhDchISrzp7Xv7/DInVxIaEhAsDq0zHYNwSmlbcCoUsqXHCwBWfod5r/q/FiAlG9Q8mjt771/CPWFSf+Atl6nX/f/8Tv+HH4Z+F+/nFCTcfoOloAn//ovQLi8b9PxFe8Egv/ZxuT9aEBYaK7vAr4n+BC1/do9LR7HECSm6VU+Fe6wchAlwEpPCf3U2Yn+Se1gl6Dkrt2XunY9fpnJ6f8KxmsFAlxQiX8OIVPL4ThG4rvCBziBAqUeOPYIDWPovhz/0CVr21xAvuJ8Y5NwVC615vWrGYvX/4WKBC/Drc/kzMw1B/ghHKq3HsaXVVXVUS1VX/jiDjYdg3gl3thMJBQKar1PJVVVldHd4RDYHKCMSCL/bSQVW+IYd3lBE/5A0ceGWjrL6A0ghyeqo4eEMKbIBwrEzowEv+uLpeaWnr/wZidNMIAwEixBoh7kCU6G6tJ7dntQDJ0O1sV7VxCGlgGAvCq1myE+Nv8jcFvn9q9lht8SIE5fnn5hAnAJD36fzD5mgz8JP/A7ZaLCgawWv+BPhfvBFfL/MKEjBpiQOrQAPNYX4Z0l+/+YqwQwZAlMcPX+v8IF7C4RFeozLJx5ozM3hEIBQM9CAz2wU62NQaEmOtMAE4NIvkXpeE/ocUUCUHKSM6mIy4UXnaG3kpnQx9qsoIuq4Kpv/U/YLetfwl/Ou8KHVVVa1VSeq4HDpgiqvR39CL7wfgnULlqqrVb/4KqJ4rWgsJaEjDOnVVFkGXGyT6hNyxfvoQCHJ9YkIgbQQwhsoa7x76+vpSlCd56GR3qt8vxGcSQSUgFyoOfYU6J0r/cNiS934bE+CLiIf3w0xmX6f2CSBNdcLhitXO2dM/5YbdaCeqkcxwAa6tgnkp/3l+/0H91tWqk3x04QCOXyBH/e8Zk/fxIFc8xN34VlLu6J9duQJbg5JcdlBNwwYoaetKsL9wj2okUDOJeerwIhvF/rywcv2uT9cNCiDMgoE7+PkukE+pfybL4cDuNj4JMGj90HH+wSlqtVVTACFQ6z//Hu/XXmR+oIhCsq3/BDyeNgCEhWJ/DQ+//J7diwQ4SAhKsivskFZwNANUlnHAA9TAnQpT9Nc4/uAXDFQf3QGjqv7Argp7U2E3SgyOkX8qmEgKQfGDSSJ22wt3d/dL1tu7vEQ7ryvWH7sFQT65frMEfWgnCQgwsUAg33DfIDixUn5mQE8wROkO723MCcRZeqATZ5WAbiMN1WqCISEg1MJ4hY4V0qFCSyZfBvpL5OQdsgBluwOO2El4sn/wj8JzTXfL85nqJMBrZIYsxLLUAnyZ3w/QULte+YIUT7s4a5YRKYHLXlXBINmey7BYGEBADZtUlxn/+3BMEaquq9Nc8m0qYXqhVLjIbFAMHFAXEf6DVGv0CMj3cwev0PHMcewEjOIMyvL1+b0mH0jDBdvHzG5MvMELFD6mY9e0zWF0cR/KPg4MJcNLVYeDNE9tcCkIxIEAh6WTjGgE+3nfk915dQvCRp9gWO5mebrcmCoT2PCdsA8YZfTNbNNSFOnaGz2K7cVit3d3Fb3jhglX54t5YW4gF4PEGYQjk47MRUMUXw++/J94VOIBiG4UQrGqFzGGpm6MoKEuk0GaFiKyXwSBNKfkOPOoNCWT/v7QRLCFfR+8//98NqmEcgvgBQfpXzyT9IsnyEMRApuwN6Usg4/k+qLxGGWPY2X+IE64Z9NCwYwUCQgBgBjks4RBXDud+K3bbY5/1iBOT94SxIOxrKZOYkCR8u+4oT4LRmX6QKEEOTeS6RavlrwTXd3d+OZfX8I7u93d35fy/Cd373+Uju+vwS9N3d3vm8V/9AqFx+mntt88FA53e+98vy/AsAt5ce7vfeEBtBTMRfcuPb3d3uP+9IMw+dAkVljBErH9aKOqnmCjLHcPrgxmeGZJPHUsSqNHJ69/ZB7v35Lu+ifbhawjh8LEFQMFe8sHRXQWBQHRCy3z4UEZh8vt7e7u73Gw8v8+GhIJru7u7xmfwQcn9uwsHogHJhN4+TquswgGWJHCszBrOHboIiQUQ5sM9jQYihcDBCdMgNG8sJtKZ5P6QlWGN06maVN/xgNVBsGPYwCDZP1GBRRIEmJ8okw7hF+toLBIlagHYty4gJ5vN/3ED9Vaqvpm1UViCF3d7y/L/+Cw93c0L3d3Ae9wTk3e7vc/Cl73fd33dzL+QFHd90WH6KKv8oJDO7uQP8FOXLu7u6TuYPsBNg6BEWWX2qAUIJA4NMSc2/MpxU1vrWqCBMAMN/Vfvd3icuB9sn8vYLFSjCvUtqjve8PiQUiQWk3cbXsV4aoOiYKewmETDeCPeJPvh8QBLEEKQFAq63y0hBN9chbc7PJj2UfWCGJGC4+H93e/UEBbu4rd3cBaerBTS8XTDYHXD3Y+vQkMnFc/qGPv/8OidJCQQCAJphuZ+kuSi/3E8N961HophL4l462SekkLBSwQpEhCOiRxzqYAZNf////mHH/MAFIsL/Ulh/aXk3Pfab+T2+XjTA87QR7XJ7u+UqBF6BEgo+/wgPWqqtVVQiwM+dKTL8t6r04kuq1SkyQSiLu7u7kT7ChXd3d3d33d3v7hS73Ma3d3d3d6/CU1d3e7/BER76+UF273fv5YUK73d3d3IPPt3qKCr7/BMR7vd9fu935Jibv8I6Tu77vemgKoOAC4AoEHd93a8BbAwBQTVJd4ugOYH5+TKoJAwwVEcKWr3jzV73ciawiSOBTt3dz27u9yL6AV4EreSFhoUu93d3dw0jB0k5YNtXl+5ghsDmDgMnLiJzfUPTo//Gwawxjt4FOuT6z4guCAgKgJhue6L5HouliMQIAGOruEfOmxI5LG+fEn3y85IEVakX5v2f+I+Iw2IhvSXC4kwJluTXqacdPSTCbGFHDtEFM0CXu1E9fb+PAgGFcAm/X/mwgBmKK5Fuelye/N+ILxdV9zBQWqqqqqqqm6rK/7cKVqqyqqqi6qTC/+8e1BFmvY/CFcbDBOB5VVVV4uCvqjVVVVVf7dmxdRhiYD7wacv1mn7xsQi+Cb7xzJ+4s1feb5YJMMPXku10KhTMBCgG7d3t3D1vfXbKFLu93u+931+II7uSb9+ECAr7u+73evLFX3u5yU3cVH1zkKV8JcCF4JsZpVfPR5bh7mrYmtNgO4EmSW9avFQQk3erfgXhwI6Tu+W/Cw3XYHEI9gJUFBuq84rVBAIhCIEcCFyZlmzIVfm/v/7CHqIHwFhOOjg6M5ZYioyzzf8RDQLKWdgYcKlmd4JMKFnYNAWQs7f4IBEjIizzA8Ziy3goJZ0YLLnQDoyllqpHxBMOvarS/IGoOZuD3C+mCARkFAAGL2QAD9m4I7DeV+PeYsDcbuKQSUFCG0/nfxIn4mHKcQNlljL7/J6arxe4Q+Cd9QjA7uLXnjQIeT6XBDwkBAMEgJKzLzjdPdH9Kosgg0FbTLe7z1SENanwwCIRBazUFFnq537/WNphYbwxlvqv4Yj/8n3+IYTgl4by2o4WEJ1T65yyf2QThuUQKrVRdV7wpVVWVVRpk4GTyu+mycKa1VVVVVVWr+A+QR+MghqqO+nHLNPIY7Mn94zjBC/rJL4ySTZCOcu0iYFdYwguS7u/H/CkfCPCkrt3t7vemxUk24dy3J7SQ7S+kwsyC/AIACgs9/4zd3d3cpK3u/8Zd3clvbu4ZZZpx5zg20cEbGXeUFUbve3d38G4sKXd3d3u7u7vryQRCL2otNFCIyxofGT4n4cTbBi5ju/SuKA4aX9KCgCRiApHf9mL04bJhWq61qbWv6UpAj8O/GKsB2gjD4vSLkdjTjJ0ax8v+ZfLrfyelvk75r3W+T0khcZVelJ3ekxU9ctWaMljxZX+Jm8Je5W8lRUbb2x5QfDuW7yomRfky33lhCIvNAYjQjYUp/rFQnNBN7CCeb8n1huG4iFpefeT2sn/J+oVeGoehTquq8JHp6kBv7zfBKJCd5y/LIOwMkn6WIl4Qu+7936yQQx/d3u3d7v2goKd3d7u7n3g6yXhrVmi/gkoVyfWA8ARBWIYiCE5QWkvn0irAcAIH0q7EYR1LkmST1yRAnClY5k9OhMnBBBRwm6m13NmC7mN3Xu102P2USG0NMXBJ9MmyViIuC4ThKfLJctquHkhHj8bxCCx0sZejNurUGw7tb7JVVrUXE6rPnb80Xvd3eyf2T4JRJu5J7AICCkIEGWj/A7TPSp6B5zTTuIMOK7u9293vJ7aviJRAgQ9731gk7477rENUEWAz4Ky/v4dzPgOXcsatQsgDt8mp9b+sT9Z+EwtzXJes/fuU5R58hfyfYko0Yl1iuTwEWvdtxt/w4xWn/x6rWN+BI+BI+BI+BI+BI+BI+MgAAD5FBm8AvwDIvLJQSeDxVxevBUERZc1hJ5bt9CROT014RxAolOdIi2oeFDpcsH+jBvaHwlZ0PuZAR/6hRFjTblhTvJnCS73FItIl5eGE+vyvawZQYlDBAe7A+bXyyDA59r1yann0+00wiEShujyypl/KsmpZ9PtNYQhuG5nfeYCO/iU1pwXQyEPsRx0v1JajzaK3LbL54g7gG/P/xoIAyJBBrDwd18+wwtjRfVW8KiuAl3dLuyzvYeYuGYkj+w3CFweIae6/xKEi8GyvhEwmDGKG1JuEe/T8IEh3M4u54WOymnEYId6ggEq+8PiJhQS4SM4YAu2SN/Xf+sv+EkCZbUOiP2QQWGYOhXlVW6bofVdVUwGXXJ6S+KVAhEgH/QP1P/88MAn8QP8cEVZJyhqCOo/xrx39+GCK1+T3yhEGXsLcA+uAzeGIsTK+1OcHPwYfBl7hHvEn4OLXGy6Eabzr2chemeDD0Bu9fZwjBThHq6HBE2CO4DTHGgpSWeMElrYj/xndcIb8MSlwRf7cv/+T+zic8wQIShgCxlHZozEDpu4YE6rhHakJgmHWqqqr26wRQRDVWv5XeExXYr5jAoHVrVVfzQR1rY12VgjhueqvsLwQeuwoNN5VVIEllVVVVVffYJxII4wBk7JFGqTvcWXPnd0T34QxIuES5+Dbd8lm6fhBwfkgGidhOqdJ/v7+/v1eouYZDpLXjAKUl9QcEG63Sq9xZgHgMqdOs/nplgAGDqcAhbrf8U//xPxMOPwj0u/bCIo7EQcMuGP+sigsWe7g1vpaEBAT0HAyXht0evWEZJMFb6Vukn9MRxW7yPjxZjjr9rCI4SUS79+GUKHSryrwCb3z/4UCE/meYYuT68I8koj4/Tk/UeEBoQrnoJe7BYJrVTAdZVavjYZFADFEV/KCYNLWq7/q9L7BGaEjhUGS+zHQ3CpMVfYKwksqqqqqSCy4r7R2y/EG4nl9xiYrdvA4ntTL+wbgh8EomrOEd8SOiZfUQ2Bv02v8xBvxfPj8ZaEnYQHxcEO/jw2AIm3d9pueWO7lgJi3BKG56uIymAEzY3sf358XX5IT/w3bCJRN7yemmLEsI6HBKA1N5P+sFzWszz/76WEoQFmw87yC16Qjxk8DO3bwiKE0s8PyR0uEYk0zW0k+s3m3grb1Pz07wxECdYkM5sEPpWP9YgJiTCTAGvhlTWC4EnVPzmLdbd0s4WE/EhCIugSamhMGnXQhOr1r8ElV7D2j/1lyoj6+gzrW/2xkx/OFKqLqqqqqtVVTDCsvG1VVVVVVVV1fxtB8v48uoUOoOsfV+GJUdb9VusBiBC6wRifJ91OLCMdsoHVpnBEVkAjxTl1sWEe/XgjE+FxOX4jnCoksFji6OAaJL5d3evEf4JxOeEaEeviBAW/P4y/TtQT6OtlTmLsyif8ntKYM/vDBl07rP8JHH007zRD4cthHJ6t6EamOAcAvdTw33RB2sW75oFAIeveVIYSAbvvewcH+T1tLmgr8cJ3bwU5P1nM4Q3A5KTLR4kBF+GdT/5WP5Pv4O7EoogHGUwqIdimDNqGdZ6DBBUFrpgmnsXx7f3xHoTf9XxkPigAYMIoVgQOnvgkBEq98OsJ//CJufrdX/4KwkutarXf8F1V61Hxjm+k/+ESpcntpFBrwwWGBaupEjvuA0Dd4n/yff8JhRAjlIgOTXLL0zga1r/ocIyfV3gqUCjp8MTHgGJc/3avwsENZPl9wqJCEDpHEPj7v4CL1146qwSiffhUI648KBAQIA59BYEJu492j4ODgAFDqQA3C+n+7v5wLH40D2y81/31ZP1n/fKicEs/TBCUr+v/1By8gtyR+QEG8HwIfXXtUGY8mAqJOlYDwxdISQPNKWE44QQDE+GsbsYrru398+dY/k90hIFUTMGcSTMvB7XOlAcKypn3flp3eX8viAh4gSUQVeVfeCAIpgjJpiXmFr77wRj9Vc7+/vkrRr4uHYzGvWg1Th9GMut0UXA7i4ZhiAxADICfyINEtazAnNd3d82ZvVZ/8FYRvg4iJjXeO9/gjFPvR35QSH4acgKwFBrhixNTS+DrXtx3hLk9coZ+ECFBwBQiCPTmYopO9gLkCi6+7/k9eCldoL8Dymo7Edwj5NC2ds69/4sKDeGhbLYrFYaMWKyxisVisVisLInRXRjAocctYX6fJ6SgXwz4WDAZhI7M1pvvrv6hj7/8npeYPlhOaDgKERoWkoDhLqth4WZxIoXy6AIGJgAQKjV78JKqS7BDForLJJHFlVV+1ICGJ+JhumCEgvjeKc4RQICEcBQ9nLmHLGJ9+7KsLqn9BII+LCPTDIkQB8hPxgDqSGAY2xySX7949rJ/66WICAgSLINUf878I/PYNVhnOkCHwj6hYQMAj6btw9kZqfjJmepO//tUPi6k+kaLsP+qra6F7rNu79Rd3d3v+tfgkLu9/wU3fe93d7/gw7pUr3l1ORH/wRk3eL9Ca8fBIIrLmIeNBQak7u7vf2YKeK+K3d3cVitxXfL+E62CYWER7psHjZNl5Yz8UZY5/B1ILjvvd44HPgovu8n04TEhYM4SCgrA2BwnkD3NU6cT/wktzDX7URCYnxRwQDLd3ER903d3cF4ADhkW4/rDAO7Cl3d3vd3P2Pgde1JFZwXBjdgTRJgamFl58AMzXM69miT1azgoCIFxAj18Tr9kHWj4byhEULvenCU2fSiQdnlI44X/20BDD6juCPRv3jTb9fDGYwCC3G7rX90FIXVNPTT/+T60CLYTNEh5v/BOfrXFCfUkueumEBJDCx1eD0vIv8PmMTgDMc2SOcqg4EATBjeCgwPZwj0mk3/hDXJVyId3jjb+0Nc778Egx37+1vrEnfZXd68G1me7rwRXd3vhmNFAfwm/P/geNgef4THd3u7/GT7u9u+73fGzyDvA0g2BGRqsiZf7iJAoQ+Fu73IL7uWBj/bZbvrDokNDC0L2Zr6Wha4JvgpE5Pt8gE2MaDBR33l4FHSCP+t2mdj+SDIxii8FvT/XlICwRu8l+9u7mU3sxIUO7u7u7u7uFn1lsP3aH/WpNE+3hogE3KyBFBiq1P75Pa9hroQIB1tywGCh8mM9floB4rdyP0ZLf/C7sp7lFgYq0p+fSrK5k+V/iMN/uiRIIRQ17u7uIcl8wLuJQkpAOAARJUGAUfpHWtd/GR8gnyd5QxsQAKrMzZvz/8cJ1ifk/VhM4lCiAg6wShAB2RmoKLPV75fvrBeEI++qzULy9LdqFe/RuhK6vXj/lIbpkH/LXhO793f4zd7u93d90vgoPd3d71+Cw00S5d3e3d8vlCkVu7u7u7u7pO9aUD1xl3efGlV3d3coK9gIEGgmtVSmspKkwVAg3JV2iDOgejwUbSq1VYtUPE4JRF299yJpIEIIZA8Q17fcV7uRJ9b/qvBKWa3s1vYrImqCIiUEpS5dv+7kXtQECBEJBbd3fjWMN5ZIi7oK996hb4PROT27HDx7qEBARz5A4lpnCMb5HaZxdU2liDQDkaWEsA09yM9YEzbwBxgswiJ989m1CAHmoHmmbea3h8ThwRC+6idjgAq9a60zws/yfrC2YLj76/+KE+KE7wibEjooiHS/KMCfYnAcu0yqwiEQ54BYGsymxeCL9j6oIgoEA4l1BFNnf2ia1vWgSVqvVR/XqwhfVl783vJF/CRDc8SD1dBkuXxv5DeOypfF+EMI5gJe9u7u/LH3d3d3d3/hS7u7u7vcdCo7sJdXsgy72nd30nclvfFQpHQqXb27vb+7x+gDbDTalJKC47FjATdVHJWq9PdjpOps2oslVkBi13i+q2lWmrCQRF1y1jRZIF1QiWPNwR7xmM6deC65byfWBgBrEBJUICoAhcEJMLN8CZ4KCzO+waADDEFnmBFoCFn191g6E78WtYfE5bluclWCbjC5rITyki4lFgo88DgMwrYW/Pa6weAvxc6DxTXzzAHpP4DgUc51OAr7MBeCJpZ+/QkpgFQszvaFarmFiACJAAHbJE+ngRLZovzz9+gFOsIRBZfmELOeWl21tma/6GwABQepN1svxvxEuM9ZlflkH4cQefDlBAwkSFtM+wYdAB9TNGYnNue9/8nq1MURCNCIEX4sA5/Lxlr/+/Iadyfjn6YgZiz2HT55fcCFBbA22PGlZl3VcFQsEWBCKnMh1MvOj2xVqfr8nj8j6aQMBphJLV8z89FWD3vCnVV5WqqqqquasXwVixjvi4uLidA5cRMrF6gq5R8I9PvaZDeL6qlUn6ZeCOQQCvXWqqo9yy+T+9SIYIGVWuqS50f1WuhOtxHNF0pLteNj+qrWqqv3Vdpz9e12EPY6L4hHwkeVhVXczb36ooR3d73hLjLvd97vd+q8297wzo2YBjvny6vad/hC7ku3d3d388xHfrzy3fW4Rum7u7tO7vSxzgmqtXd3y1gcgIOURPnV4j4rOv4deUPkYsN8AL991j498Sj3vJ7SYgCUOCI8mEeXuA5B/P5gBderlfH/2bkdpltx1zH1v/48vl/1YIOMwbfhsh6WfMBg0358iPduQZ3e9JnJhkNF8tOA+pNJv8n7Y8SL/394IdVVzvdVXvNVen0cFJVXVa5B8DbpbP+MGqpr1GPLOqlIsJa1zYKIy0fMPGsoLURpUy+JqS4SNHSxriy1/v4TusaC1flu171TyE+qHQzcOTca97cReUHnu5CXvEFd93d94Uu7u7joeAS2cPND73bu7mksIybt3rCMJIXeAu1v3AP3RYdXJ4Rvc2XenCJLku+33esLxgjyQlfcf9dvXhGCs4yGHu7u7vkVvUlBBh1Ic+cX8EPobFInBqIwoeN8GFBEMseR3ZpALsiMPO/PN1g/zCl9G5TlD8z+NjVjYMgjqz84aVM2/y+WXEl6rCNYr8EIRlHjFPMeoI5LlOGuL3EmrUsf5dk+/D/9OH7k8NzXL7MEEO+s0K9ZGGpYenq/J61cwQglm8Oz1Uo2eEd5gb4fZbRfK2VNiIZjDuH2RRvvd9vu79YJYLvhb0WhLAZ8FfEhokAQi6mEtT8/mMGrl/6/YS+DVSijerL9ON/4KODKIxwcRw+T6jScfWL8XwTPqYor2Ub2Tcg0NGBSIvKO+XwhJxcRE7993f8pR+X3C93ITIZH2mX3BJXEH009YIb+5wEI/I3qX28t1hrix0mcNp9H2CDVcb8GWxIVCcAQi6mEtT86jxmADQYIi0n4CwnJpuCCB73+BmZEAPaN2wXBLc/JojvX4gLJHhn6w8pqoI8cURHiOtcRw85q0ljXqqz/eqYKIHuaj2S5KEofH5Fr58n3uf6CuXicf8CR8CR8CR8CR8CR8ZAAALYkGb4C/AOi9cwYyAdZfP1wUbSraWlT8FFm9R6MnXsvv+Ciqqqqq9r6BbLnUoP3V5RZgtD0W/34YEFCxwqrjuHYN578ZMfg79HbL/WkYJATbnG8v1fer8TVdV6uvVq/bhQ4agynqqqqrVVE8P53yAmqqqqlFeIjqeZ/EC0PYb/zhNfDH30vBtQY78FVZrUcD1VVXvwTcz0CyAlZhwdLxHXEVrqXb0As4JiVVVVVu8CbVujhAENQ5BJO/ydy7br076EOafH8npv+jAi1qwjYdnij8EgYrXt9YUE1VVaVVVVVVV/y1qoMfMeOn+s5P6vwRBnsNggYUwHYjNR/4OB3c3QVMSq+wl8I+0IVjSf64ikJixsO0Nh+OMCcNccBUyuvcKFVZVVVVXVe+leUEgQXV/kBYeqqtaqq/xcNyC/CgSVVU8pfVVVVVX/BGJw97eL3F+OtHZYAr4gwjhN7l4ggo5/cvEIr/MCMTjXvq0LVI+xSNf2Q5F8mm38isb/Ra+KBRqutaYa6g2BFqu+/LBOTVVVVz68iBT1VdVVVf84sWSoMrc+vjPcOu++HS/iPzPf/IflwvmHcOPcOBFGAzwP15EPv8iEPYyG5mdb0EEq3SFPXMr+je6318agQjmOP4aBFVd/y+vz3+OGj0+2hdHf0Mv9Amqnvd7/o4+O/nF4fxD/16J9w3lvgq4aFhAFVVJ1Oq74RrkQm8nJW4cK93Mm5/X8IoRAJOMFWH4Lcf44ILc1+CPu98XDcPlBgZr1BIGOK765kwTEXd7uYPN4n4fgtFvwdETGu//BGIe7jn/oSMMa0eFZfC4Q4TfxMJ9F54xUAI3hC7cf8KRA8UZ4xQYoxA0FGWMsGKDFBijFBhVoKN8RgGNfz6+P6fBA9eJL/8PZPqtAgfEBThweBQTPV/Rf66BGPwX+N+wigmKAATjkjtJIWxb/Q4PCAoq9V8pPmRm+RWPk+TGw7KwMof0GL/EAiu/n9kBFd+/iSAju96zeK/+gRC99/kBQMd+7vXoUCrSTd7u07xceGYsV/L//AoY64bDAKxC3u73U12ZR4YhQQ7vezSu93uPvFIKd3KSGmt293d73/DYkVn4rLYDYD3blfzeN8vd7eKw0iQLYrcVuC5yT9//BTFYrcVivbuKxWG3hKngpweBExsF/cjCa+HkTClqooXqq1zR6/+gRnhhleOdfyXj/yeq3+IQp/arRATlu73vfNHr/6BUVZ96f0YE4h37u9fIFN2ne7uk72net5MoUFu7u7u7uXKbu7jKfgrGbvu7u73y//hSyft3d7vu9+gEqBUR+6AohwFBMTpmtq+15BAUI7vP4rP7d3e3etet+Cg/AG0zJN+YSxvpI77xPrM0JjIq4sQCwVpPft3d3y3kpDRJbFbit3dx1kt8Wy2Ww2Pg20vGFHXCPQl/ECQyJFHQx+nBDxbT/4KC/6BIgRBIKcC9NZl61omzhOVPVg/1fSQnsvybJ/SxWlCgx+v5PSp/J0INS1Ft7u8v/0FBLu7uSL27u7u5TlfMIFPd3fRf/sRd3u/8KFt3MaKIoNXe9u7u7v9gp293dIw07vvrBtIwgR93uNBSy28n1hH+Epqfd/EBQrivFYrd8sb3es35+9+PJlJcaI7HclBYf72/qsFIGAQQuO7isBE69+P3fgthQmX3b7u7ny3cQ0LeWXwWQpgRIEAK93DjLRW8U+tfyfVAJMEADlAgdAuGgICRKfgi84m5e9bwlNCgx3d3d3d88vIm/iGV3a8TgmXkBJrEAgwRGq1x+UwKhqwp9NQvaJlaT6eMxFVpKq66DIn8XKSGtrqvec5WkG2bxplL/heaCTUoK+3k/QT7VKTyryoE5VXmNGO8VclE9dGxEnpiDbu/IvHGZN35Ca8JEBDd31k/vy8Fd7u7u7isdLCW6+gjeS3MA3t7d3+FL27u7u7u7u717jLu7u7u7uG4U59vXIOlJjPrrDhIVEbfd/YCVAgFqvvCZzD2fH1WT228sZASAFkWMWtz5ye6R44T4jgj3gcjwNJbbApqf/w+X0JiXwsFwDDVDJo/I88+ofcf4x30X39TXzjwJElZAZBgLYI90COpvzq8OlW0bfppA0NBVl8Oz5E/rq+X+5DkBWLDEN+5qSitLmwE71HJGdSfuUhZhASgswUqWvHie0QKV1XsnrRBAIPmIqqEtQFVAhE0LqrQYqZFGVw7lqUOBAGAgQXPY9OaPJNKEIRhPVVBSpYhfmv9JhmSMqtVlL1WqrJ6SSgihCNi6rqvWG4MBYK665SRjmEw1O+8MIRwSvnxVhIaChnOP1hF5rQw19ZIYMWZtiWEL66fqIBHykhoKHqy5MsEvdc1OXQ3rDcVF8aD7mNv6iB1yA1/DH/u/WFYciNvu/eEjYUI7kveOsru73d8vCsKHCC8Y9luK3d3FbuWl29aTCMdGEt3d3dzklbK7QryEI/XdUDAOBmEMOoUpM7Fbu7u/B+JFmx1ktaHf8EAXH3cVnx33kvqrBG0E8oKFgIEFGCsr5fuE480wG+57BBqbjJAdrpwNqXl64fa6af74t3P/8pVL4EGBfGgWg6BcDoII2zECOeYAldZen892G2Wz/5kv6LyuL5RnL3z+FcwPdFFtH2J6TWbWO4gNeaVBdOz+CMLn1/eDUSCflcl7nRBAuWwzphj38PPxtdzaES8wXc3rBIc49XlpIJ8FwupQW3CX4tVCQ2U0Afv64+0OBdWq1r22yxUtdabJFzVHMl6TERXL+GIjYJOQhCbz17L4RhOF4aQLPBH436oI/G/Ujhiu/GvffglLhreMIH3yxgdcQmHu1j7WX80kIwUQm9w0z3LfFl8sTEy8vj+MiviNpZDxBN3u+lskfd93d3u/CAgZd9zi73u7u71juLJu+585xAJyYWfIWTF1y3i1h4JkBCGRYEvY5U8fi/5QZjX/6+uC7w0PwFqMyF0fn7sP6I7Gfmj/j9D/PwB4hLSaesGoXNH+uDkD7H3A5AOsfcr9Pv+b//qggOgCikY8r8/jcz/YaY1PXZOsm/0r+lGQEmgiOwrlTsgm9zYTdTBdzV3WlJAQ4sXNruYwXc35u6/2va9iZj8d9J7tlyYiMG1IMh2nmnn+v1w1/Ji86scjsFBAjeapt7ltGQ97S5A8U4sn22GcdQ5BIhhog+NEToq1QeNhOErjFUnD9oo9VAhhFZPbwsILDl9NOF5u0By0R7xfQMpg9nHykqbxvVhmMglK7v3eukIZHeYGyeqX5oJMt9bzVFiMfj+fOXwdAhBsCEKCBwhnG2gz9w8e+qIEQaiUFXAODax41PBJ+XIAoDIU8K/BaJak94ZiC9wcQn/rYMDiBLRKnrByP4+7AWkQyiYt+dm/8XV3oKwQI9zk1g6uPKFh8K6EM6fzgXsTJZllvgDWZgSH+AeX9ODEVT2t/vw8tXTk4Kg0CROnm3mTyIPZLgtzWHwyPIfrl+04pQgECGvq63A3fxWmuTX0Oq7azxcHVQN5Zb73YNe9Ooo4vyYHstuoG8tJ6VBL9i+7Bryx1YKd0LhowQ8kzl94/zW+gkICYkJLnq85+GoYuT00jcMzswkJdNJkd6bCUKCAWw+/GnF38kn/ZPdrDUCPFiAmIzXjvtR5kaSEcxBDQB1TlvJ71rwQieJ0Ae9Sw94Kly3l/h8axbBQGcQ4Ed3ymkEH/2d+OuD9Y0FOGiAEV07+A9CVwx1lPzvzpgkXbP9UGE4iC1kH7kCTd8ViQM0EB3CLj5gmwtBYACdz8P9LqCgfJ2cYAJCCyyKgjz6pIvGUQE6EnYDioHxJnDyGIuWEeYq8wHAJXJLC/AZIEv/tJpFrJ9pBQSviApwUqWO5gyvE1lJ6dqBDOgVgagRiOHWmh1C4AV25wMdS2b1UDwWPBDl8SETgh+AYSAAAAt+QZoAL8A6Piwxwi4PGJ7CD/oer9ago1UwHNiyvj3F9VNmV+W0e6ZF9BYbIH4JdD+6MP+CT7+tfD7fvov/WCQYqqr/l6qDx6pBEVy3wR7wHp5Qe9D3/blEA2ECeVWVrV4N4LOqqtVXfpiAR1XffEMwnqqqgeX65P5DFMB4dyyXLS/JhQllE63zY03JIrKL/XmEVr8KDW1UXVVVVVVVVf8KVVVDsCRSlVVVVVWi1fKRaqDpfEZPW2O/D5uf8a9qqqoenE/83/+RQUjV8OxJhQN9IT9e/CBFVVVVVdfQUqqrMqqqqqqrWs+goSZVVrVVVVVX6PECK11XWCzmzWsquL6BRDMNJAFKqqr/HsM0n9foKBRVVVVVVVVVVX/CgkXVVUnWVVVVVWf8EcNe5vBwX/xj3imSxmIRfykrCesL/kfLbK/xFV1VV9DCqqrVVWiWq41glFAAJRwb5AoHq1Wpgkqqq5sU+MzeP/wRHCdg/yfOhiQH8gKQlqqrqqqxv6BKVVzBLXguNhuZGd7WCsIKqqqqqh+JEsoJf0doNl8cYVTl+0CDv79Pf5TIQ/Q4MIr/JjYIRQAMM9Fh1BOGl1qq3zFx+H0CIbfEOhFr0CkJLVarVVW/pAlypayqqZFV9Y0qqqqqqq1VVV+PdH/wpVVVVVVVVWsA/vdEG1rVa1qtVGY7jXf73wUmVVVV9Vx5fgut1C5VVaydwDjv+G5PPw+JF4LbC7d3u8C5H+BP79L/CzgGq8OCcnpuDYPiF4Ihg8XP308mjx2T0v5fqRGrqRFqvRclV9NIXR35kEr/MCKq9ejAlqtVVVf5kXHi4dFADDAUsM57wINJBy/0CGq9/aCh1VVVVVV1VVH/6BSIVV2S1qr+kDDkze4Dngi7MJfCXDKP830n/wsV/fN/w2Je5QhFko4VoP1XN5eyLgK35uT9fBWBIgk7EhDfPL1FZP0/9G2aahrQRMngxSCWZ8iI/xCv9Lf9Hv+t/dY+X/9Gf4pax7CYoM0ad7/4gEgQu7kBvwfA2C1d3u+AARCk/whEgsuWHUFOv/ggQjA7+FDvbvn93d7u4BEEs/gn8visvLYuvxpRDhYyxisVisUYrFYrFYrFYSrJgUa+g4Y47UCTyXqf8EMIB58+VIKS//DmtB62EgIeRvnD2Wam7Wfy3z/JVw4jva9ULi35oKDXd93s9zEvflIW7/LBFd3e+Yf4f4RCoNGjPqgpD9QRd3pQhmf/5f8MnRb8IgkM55vcPwwEQWGeXN3e3d7+KQUK4aZaa2qtoR4s8ZY33cPDxY8dUfz8FZYoXBxljDrgWDLHwRf9dTeNLwi4UBWKy2K5yw5bLYrFYrBeAAj6D7OFOn8OkdxW75/FYrdwEJ+8W4NX8FOFAgUZhXKWSeO+hYvVdV5Yvque1+CPqPNHdwTAlOqqGoOhVZX0mXfgiqYi9xdEDzNb+nH3fd33d+MJl//KNw0y18RCg53e++7vd7/It/wUHe77vf8FhqpPd3e7iyy/J+FKT7vd3d3u5k6AWYFwEZ82NRbSCIaB2FtejBQU4rd7r2/IrENHs5wfjLuteRAj8KvjI8+pW8IaDBZrpGyBg/QzNY4SwlF4cGfeJ7ykBQYvCiVl23Ht6VXkkhQ7u7u7u7isVhYWVmriT9HddYIRIrkD+KCfw4OwCjpOY3lBID/AB3gPjY+OdJ9+YCVBFhUnHylHfkN8g+tt9/+VBAXMrVaqvzS1X9AtqqqtfeyAlqqrKqvfglrrrXtYY4QLVVVaqtVyIndwUdlfeitWJq/cR3sl3e8LzCASHx0NLbi1yBaCyk4rdJ7d3d6zLFcVw+g1Fd3zEEpJ9P/kIFDO9u7u7vKCnH5y7r5Apfd3d7u93euwGUBIZR9f7BgQEZMoKfLJ9Jh7BJGyiYutaquglBbDeWq+q90EAmOu+7tCG1KPcnqljPglJ1Xd3eLVI0EAJSRDR7LhSV3XhqFJcDgy123AvLK7d3d3d68GoIAVCwyrxn42jadVVvctnDdeIICkU7u7u7u/fL/Xsrnz5cEpf/FAgyfSWgJC/WCXF6n/Kq+qbJBQXaXVe1RJIIda9rGrEaqq61tSE40FDvLddfQKeq6rqvSrCd+4joJ+2I1wwCIIk3u/LCfd3d/mGXe+UFO97715Yy93d3d3d3d9PZIRLqr56OUFMte07BoIHZJzCCYDCI/3tKJ6NzbEgq1O7u73uYerDSIBVFW32i7w6iiyox564T1MHgev5abIEhQQBXUaIn9VWqvpodBSYIZ811lBobnncntp+Tj8tw7PU7Saer417pvCsRSfdykVbWmhgIRpxYqaM+AFHXmeNDn6wdB8LsKixWCOeY5gIbcjeOD7filL9NIk5nLnzGl+M+vUTS/+yhMIlIt0ct4JliwUAsBQCgMBzJkPeCHTnmmsiHZZ08foi9FFwRncgWg/s4Sic+hW4pwyUFEcLC0fVzlgtTuZHVqHnNe6wWFML1VYec1ENrfnqt0yxL9n0o2JBSFBKqqqouqiW9SIPYD2S6zfxzzlpIskKVVQmU/qVfUVJyXqLqq9pKiQhtVNnK4uUe1Yypr1VeTQ3leNetIvBLVSfS1y1QRqLxj31rVCq5PpcNAoy7eJ5sPXjsBkv65ZchHx8b9e/k0T2v9wXXd7WHJPddYIiO92y/JiCY+2MEnrWP8tvSjQ9IKu+73k9axTegldzZbkk902Igv9KLltym+cGvcnpJngohDj5yQ7GdgNZN7u+saBJCEZfj93fd3d5pVjOM3d3d33e7vXhGFDhtNRT4t324rFe7jZdXb1TaCYWAXwG1jhjyC+OP0BISDPQTeW+62CVe+CENcBOHqYS1V58gxgsun/zbuLuUf0N8gIxs2Sh8AIGbF6Q7/3eE+sal97wkAufEWeoE497cSW+4sRn9/q80c1iOiO8zj//oJCBxkZxvxO6ev6Ie0nqNtVmeb/hWCUIF3QHUnLce8DpP3b7Eaantf5b8GWisERAJKyel94FSLJAUeeObVdf6URE9KX7T0IBaLqD2SaPRkumnY+T1b8KyRBVhENyfmmLnuDAXMPM4/eynDNye2n6EiY7qupQfjGqxlTWLE6ofGdUKvqhMkJwMeFe/8saoXnCXZEhgfss0Om+CKUZ65bsXQKMn7WThYFBZqtu9ai7l768Rd3uMhif6iNu7u7vxMTd3cpGzg3veIEQZTVSWfkXVGNl9YMYn2AgwPAgUqjkQzMudgEcA/68CSEfAkiYLsDECFBllLXNw/odcIobGFItiet1/WFwUhYEIIgRB2rfzafaIaawm60rF24ZBi/QstpKK+lCP7CIgIicjhtvDs8xDDOX5qC0/sZECZh4J9eay8vKs+rCfBZXUo+2G4xaw3I0cHeR9Td1iyekkgUO42Um+xj0npt4oQE7uUGEBtKklPyfpBCaKvk/SkLLL1RbhHwl40TvZ7fTgyBkGYwoHdqRD5u3x1V/jsCVdqR9N/b9u7u4WWt3iIj5IRHXUmHHx/3F1yw689OSBIDKCPeAigEjB0sMhFRomUfh0hv4S93S4yYWUkqIbwaaCJJOms8SrtfcPIs1BhSuepdlRQauXLkR2Yr3H4+sAjSm0jQd98oADWEjjM4Pw0JSzmvoF8smZY8/8HLAnNQoA4LOB0Wp9PWGgYXT1BaLQJ67FfWl9YZBjBHk9PEiROIkQc7J7f4MTAoHacRAQASCBctxj38d8bXNST0v8kuFnwos9X07EfZZvNlW5O6CggUCgXDLLA9blvHugDqnLSjek9cHIaBFBKCGCFC8sv8EfofUHdfX39/f8I0XwMIMQOIMVXN48f8XXgNA0knqDkH2Pu8HgdQ+4Dokryev5v/1CCDMEQWLJ6hHXz82ru35rlqI3ivwQp6t+cdM3/gGoIK8SgvTSesE+qa8BLziEUPkAuzN6AR4l51RIoBhIAAAApnQZogL8A6Ky1MGgjYAVPfl6q14kIa/19CRuWJwsUjklfhMRy/PkHj9kFjOE3SHCN1/qtkE8YD9yveBJpG8eaK+YfVa6qqxlo6QSrVdYy9RDh336qq0lhd6vCARMLAxoP7/V+g2ij5IPX8UcES/wgeHT6K/2LG5bx0DIOwQn9QTYYQfv9V/+Cg2qzARd+wCBhBapcvTvhDSjSF5PXJ2V/F/pGff4UE1VReouqqqi6rf8Edak7EHBf/GTDh/3+K6TQSdBX41KCIdnvfWydbe/tX1wciapV6rtHf1R3+kEn1+CwqqqnBQb4eVVWEbDcyBDvBfwoOWVUOWxl4e8qqouzi5eZf5T4esOC/ydzAv7x2VzL0O1odJ1D3hgtdIKMeCF/Jl/x/9Iovmgt0Z/kPy+W2+v1fXdq/yAmLWq1o754KiLrVVVVV9dmQ0qqqqqqqqqp9HfDLsf8Nn3ckGXGSt/Df3g1XhwTvDgmQMipdPHFEX/J7dct8ntoTZrJY35EZ/o9/k02/k8kEdV7Cdhv6Qav9AiqtTL3rKio79Bw1KKwDjj4Zdj/QLb3d3dx8dzfSf/BeVzpN+aMee/9CcFQMy/+YJeGw9dQx8l8R8iGZVxG97BGe9rfeEIToXWlhaGNeCyggRfyAjE5cj/+CQ13e++oYBbd3d3uYPsDGAikJGu22CkE4Owd/hbe97g8UHWTeHT68d4/hfy+Kwi5ELGAfwi7dZblWhag+FIUbhbLYoxRijFYrFGKMUYoxRn4rwUBP85SDgIMfP+CDwny08vErwaBEgY4TddGBkygI9kbt3/KqmtBdvk+T8wSu9dLfx0Ue97u/LBMR33frywSbvy+NBJd++/tCR/+QFYp3u+7u73HiApuXN37d33es3/queNLvv74u3+uAiQIQPAUEveY0K7rwO0FhHd3u93d76xSLGlDTLXEHnnG28sZbFbdFZbNhgHMtt1dLIlp+/5f+8LFIz+duGrLGEyYUCxljFlD0sj/m8afl5hP0LGeMUZ4GF9F57LYrFYrBZPj/f/gpMK3dz+/3dwkd3Mngo8xuASTZEabPD3y+0va/9k+bfuCM/Ncb4p1Wq5PmZLv2FgeLXyBE934767u/kLd3dcgTNe77+ile7/BTd3zXdk7634LAgCmfC3d33fr5Qpd3d3d3e7u71+MI7vdJ3d935niuJ4a8ERRklXxkPoBNgYgUTF6q+8XwgFCO/du1Hfaohnz3WuhBRhEQreK7jatx7cMNFfztd88MRNdFP/bZYnHLFrPVriVL9vHaBRlwhE7ZbKgInZcY9mTxYgFmqQzoXHt3t3MmX5cpCRpbctu7uK3ENBWWzmhtc9DRHwQeZw0RJLrKl6hkSHRyu3y5Qk0t/H++CdfBALCgKadm13Mv08idl5jd1+T0tAQpV0XDL/1SE9l+b8I891rVa9IXqqqtfQutaqqnsebxAgXd93+PgsK73d7vcbWvGxxne+73ussUV3d7u/oKXe93e93d3v9C+73d+JIEibu7/k3kINhMrv8IuKNxhAjzAp3Ll3bu9LGYK6yjy93IPd8N5MgQu7uel275B7QCfAyDo/FCzdHlsVhyF8TcqfRr+bfURj+h3aSVtVZZKcCJO/H8+dZb/vECEYKCHtu7jZJ7e3vd3IveG0C8QFNJ3Lghhdu7u7u9OtANMBEDDr17Cy0SpbFazrs6oCiDAXKIxjPl8R/OdRj//wS+HPAFaMZkd+Lwkfn15gQGEarWQEGCGEIuA32FZKgoFquq8q8I9VVdV+WFOq6qtVVVXvaXq2T5PkNIDKrXlr2sYbyffIZYU0X8OwvCU0NzU/SkBB6xsgR6ggCF993u76JCm7u7u7u7u7vWqkLF3fe/4i7vchufF4u9hO7vrLEwjfd93e7+QZ3dx1rt3d73vLmGbeQaco9kL3d3t34QQQvn93d9y5xMFGQuNhvcanH3uLTgLMD0EIzmHh4spx6/t3d37DAUCZGhDfcZmgZem8Gp48DcBvGEd3d3GYquU4e/xq2ZnJ3a0y59RNsvO/9gN4BAh0JgCL9JgGbZwUqngaim0ZBTkqTj9iNDq4agCyFT8/vyIi7h8sGOrMzM34H8AKCehDe/wu//vKee/nK756AlVoEQKgOAVCwaZZoLUZfx9qlmaKFma23/SYQBRmMEyzzMl3MpaKbAhXF9a0JBlsrfhW5aT98J9IX1vjMuAwdy3W0106rCgKA3DczT1za19V5RKqusboWLj+w7DN8xbhM6rbxfGP61GwgRdToKuONC/wphzIs9VlQ/4EyqmVSii4TbC0u1uIhDqqqDTnRZ6W6BGQmsfL/ClVqq15vUaI3CL9oCQCWqqqgUyoX6lmr5dLPwl4dhV5m+eIl94pJP1iImI3Pe7u8nryQQn7Je/GCBE487u7u9YXhWMvd3d7uEr3VePPYPgkfopWCAKQhe/t3vrCMMx5Xfd37vxkKXd3d3OccMT/fHGIpU9PWvDcIX29vu76+wWXf2w7Cmw3NZpcuprobxsYR3d33fey2729YX4zdu73d3e79PGKCIRwo9FtIBnAI8ZCpxRgiOZlziPyEzeDf1GP//WgrB2BNE+TBN61+rleCWwG+q4A2Wiafn8sw/w/oLR5AfIfcKADgzmcDiCSi5PWGhxdPXyqM1d3w/oNCBMXqZW+PiC5zBDW7mWAKCiDPivxGyp6oh6SeqwR72f6cCOD7EBL8Z3LAqf5jcXCdKa5Pbr/CZM4kUgO5siibL+JhD45/Ed7ma0+HGWWsv4mLlf4/fqP+X+rFLFlyi2SEZbFbPoSDLV+L5X+Fbltb/rGwggXjeG/RwtQG3QsKep+XW/rNCLBPx+P+qxZf8XJF9TmsAdMlRYreny+XpYvKNJcTYXRVsbK1F5J1KPnH2i/f9am3e1l48jvd7mBV7y+aFPiI2zUkHtRv9YSEObe/CQgZw3PNC36fd41fbvZLe/EBTt/bupwnZV3YbZHbStdOQU+Oa3gmCoEQIglEiHDj9BHJIATubMzHP/h4r4EcTBXXObx+H4RBWCNrAQ8D0GQUBaZAYfA8jozOAl/0JHW/0JuXmZhac3BtSqYeIxFxF30IgOMhlmfgljOOgPSIZTIV+BYfilQhip/OCyyDKjGKn84D3NBaxLO98/lxbbe34C/8e2CUNZMhTTuBtvzq7qV7OfnTxERNfVfn+P6skC4BYN4L3TcfF8Ws1PjkqsfCCBGfGNha5k1mhNs+AR+6X58v5+KQvwBRl/V+0JvdU1y+j/3+LgPVXxv6z2jfl9BivaPjrKvCe3ubrdyDuOlnJ9+QCBoEIk9oYmU1OkcF3lvwIwZBQGWjj+N+VtH14EcSC2796JgDvghDRIC0TuIR6n58UjAou5/mj+EF9AtLgCi0J+j87yloQqFoJBIJBTf/+gWBYBFbm/GqY4r8/4sNaf/XwmhGWsbElCZQ5Z96fjrKsTguFkg2/Cjnq8Ws1Fcr5fwp/wrNgkHUy94Ykmj/iq/fL4OgyDYMg1EhCcXlDmXr/1gZoEcSCgKiUfB7JXBXyzJPgZpztN4EcEKJ0BGXwCfQAAAlYQZpAL8A6L1w2GLzApfxsrsL/BRVVU2LKxflrUNxbgovvrgoqq9VjD4J+bB1r1WL8T8PfLmrT62LAyqWSSsfXgjFVr0Hq6NFipRl8JGkQ4p9VQ0wKskfVVX7oooENVX2T9Kvs1V6rU0oHXr5vkFVrxYMWDq6EDNPNlF41GuF2X6/GCTZK1VVVVVVVfgrJVVVVWqr34f44vKqL5sumRxtE/B0X+YViMnqkPDHE6EMbwiIFdVhgPgjH6rFM8kcYgStLutm6wS+brmxpc4nxIQDh61X+Gy3FfB0GRJvKrTj8MW+v4OX4oTvsn82URDsEJrL8I0Ofxf2t/WvrdNfj+8PN91niuoZhTllV4gat87DooH8Ew5VVVVVFAg3L7+IEewh5CfIhnVUIK3X0Tta6Vcvucrj5rr6BLL111cXti+8NTHC9dVqqAj8c5n3ySLfL/6guqutVMAAt/QULquqqqqpNJt3F9uCYovD25v927gXSDXDgne4LKFRz5V7CKzoiSS2mvyK/yWLhumepEEmfJ1Cn0CIy1v8v0igHFj5zIxA/1v+Cvqqqtd61zQ8CWLhh5O7d3wzfSf+gWDXD6JDgFYdPvHe/w3eK5nDi/jqJiC/6RZeYeNgK3wkb4bDXxHcGyCHdy+1XoXrseCpFrquprMX/D8IjQPHAJIPyAkC13w/RKzQSbu9/mWsMxv5f/4dqLegPIOkISL4Kzvd3d3d3cGkJ/BP4akXU3HtMPxsvLZbdy4Ky3bisVufit7xhX/hR0+CEoJexUu+kPsAU4NgiQMQZKlv2QGwUF9Vqqqqqqqvu8EZFqveVVyL1bIhPS0YOecQiv8m+SRirvRf/wW33lx7vvkwRXe9fir3d3d/grvd33KNd4NbPhQj3Lm7vd3e9eiBQpSN2VICbueCPtT2W3sVu4rcYAhrL/9BYvDYiCZbPy2WzC49j83hQTw0kw728wt2QZU9lsVisVg6pB+NM7uKw46z7isVth933CjP4Z7T+Cgv/oOfDn2FBC1VVVVVVVa97jq1VV1qqyEBRqqrX34uq+q/BR1Wq+9r5AVFrXVVXbwgrxRPbk5Pk+T5ARmd+t+WCYS933evoKXd3e7u8w8/evkChru73d3IPW7u9bxHClzwd0wQ/H9HnCle7g4v6DDjeS2XWvFxZAm9IxPysr0Ea95T4K+WiRf5R2QMTDxrIRZYV+WloYrml2uXSmfl8nkgkBYTHSJbjQ+WG7U+u3zG+kgoW3d3d3EjlltwotCuWM7C+/vl+u+CYv/2HBGATF9P/EECwcUADvCQfnyem1iQM0kCALxPzKqq0kwSB6MtaqqqtVWvmQw6qtaqtVVa1uRBHISWa1Wq3gn4Tytaqt/jKrqvVV1XZQfBSqqqqqqqqqquXyAqrWtV1XuoeBZVVXVVVV7rGVVVVVWqhq8KrPQhgk1r3cHRa68QQX1Wq1YkHRS4YyzQXyL5CXf6N6Mvaxsr16XxAi7u7u76YYCl33fCP0193d4q6BLe/d6+IBPfe9yJrmxmUzMDbXsnj1vb7e9YcCGMo8svL+4ru7u7u7v4gFQi3dyXtz57nh0QIjzVtLsZ9fl+oQoCCCAFZ7ve4S8bIYLMjOR1rBmHAIwI/EXCRcBn3wSryAgBQKDgyxgu5vI7+OVfL+gQeEYK2eoWs1OS3ldV6jIqumqlfVMllV1UlJ+FBMqtVVVVR5oVde8sZVVqqqq1Va/BXVddVVVfywhrWqqqqvmGRoiTKqqqqqqqq/GdVWqqq1VVvsDCPGVVVDUKr2aqq6rTYDFBEDntsF4l+/TYD5Ey9EEawrQS7IYtdabXye7llzCq9vS/EylVf5t1WX9LwhlGbu5KNH35SXUktvpZZr5/rkL1iTfhXtYYCKCV3cpyezg261BMYfd4o8WUPlJFzq4gBIgIAWM4a0Qm8BYrZ6rcBAgIhCw6ezkq4WfA0M7sWrIdqslEPeI1ZmZ08d0RY6rf/YkMjwA97HGl2X3v2PHy2W9o0D8B1DO7n/P5sP5OHyff+EZAvABB92vs/TQGIDgCoDgMDnUqqI4PLJlV1VcnrkBFsELBCIHIsnprB6wIzwGqsD6GghUmC1mohZXgbnLJBltuFJVfArBAmV5cLmOkR+Sfp/BFAjBHVYnfkJEH/5eNe0vLCg9VVVVaqo00Kshs9n3jNArqq4w0zOpBSX/BYRVqtVVa9k+3w0IG8IdVVdVIXmmw3CcvDuW7SOCE3ood00A0x2TjkejmpJ9pBAaMEG+mhggJplVVXcQEarhj3rIPrMvqE4QEEECuq1U5asIsSwp1WqquUzA14NSsv9uw0igQH2QYxXam8D7DNJ7t5NUK6qTfk/rFwvgg1Q2Tk9cWHixH8b8RFzENKaXrCAgI6WOC5YgVpBhktEP5gKiPAa3FQEcAkQQhEARKmUH+Q8BxCmoJX/z0d8i+hZwQUXr0uUEIawBCErjEap+SNNVgM/c/ye1QCOA4AbQdA/grBWCX8DkCoTyZnAch8JRMzsDQACcG6XvAUHKpNzyugVgrGBDI+Yllgr2oLrlli4l/gsudHB0xrLG4uA30opgJAGPQYQRGclg2/Lffx/xVfpP0i/xfQjLIRlttA4MttCBfHvAVuWs1vuklE8nptYaBQNda4IRcw8bZf4vSyXBERV31hH9QRBHOtVWqjxYaaMIL02hAV6sbDHSbEerCgjcPT1fpMIggE8npNipYzr2lCNhnShLgiEYzwOqctF4EoIhkeAFImjuB1jV9P+SgFJJKef/+QMwa+htZhV3URj/QSgDxBLTJPWAtGBJm+SZgAVcaC1iWV744ttmzfgapDQN8Le4B6wVadYq/wP6wHkCjBKKFiGvFmgFphFnm8Ew2LTgXG3cGfUSw3i0d4D6BQICXKr8/MB63LNpWfiOBu5ZuYK3LIcX3BRBdcs/OGRlinaMXumiy//QuN3LA7LnS6zUz+HngNL98n1X+LrWW/AXXLYepqMyP91wQlZvGiJywUjVWqr1SOXv6ghqvH9xZONe3C4/5LwrpOJ4gRw7/0jLFb9J9qDYNwJQCMCIWQIQmDOGHMRU9W2L28wIUL6DjwSGkiYDZBOiYBABCLlQyF4MADIJOEEeASwauX5N+ARUFIKgVGfX5CS+BCJT4Q5QzM46acMASACJCPEWOXDSepaRE9QfBl8G/y31YCQBGIoN94CLCKL+AcuAAACkdBmmAvwDovXCYYRPvH85N5ckjA/XlFnCMeL7Rf+uDfy+aLeu56dvVmBAEuCzAhJrPZvYDoAXlxk/8X4e7sZ/uj0xAJ8qqhW0T+1X6qvpiDVX0WvbXZhfOSHmvlpVEnW0oiynxry3y5FeD0v4iIiRCNiEviwh5KL1PLl7QQrxh3d33d3u7joEN+wpew8ixLe3e3d3d37pEBXd3u7u7v906EZdYIYby36/WEOX9QdP8ME4ceyjMnYv8N6evigTCbv4173qFDu93u937u2NV3MYrsdhXWShXUX/yVYdQoJd3d7u7yjzvet76JWNhOACBMfrXugtiHTg44QBR5b3fmXSClld7t3d3d3d+PxiFn1/wRCGe/MthgFC+7Ax0T3hevqx9H96ovZoh//giDYoPiiBkIq9Ai7X95/6E6Rg2w4J8YPOQecsxb4q+v19mjwh/oFdr+y70/yHIVX0zb3e6+P15F9ffoWODHD9qNYz1CSEe9pfU+S0JHd7SIO3UYiRyuRX+RYq8EY8gZvTiJrgqL86DCC2CQLVVe7ODwKCVVVWqqqqqqo47vCnVdVVVVVVRx3eCgiqqqqqO75AUVVVVVUd3QCbgo6rqvdgMEUvd30A6wvRfL/Xu4KF99Ivs3/r+ERoJGZcv9Du01hddRisr0J7xtE6J8EgvconlBTg2CPgwCILApAtpL7pKprdeITR6+o/DY0sB/WFFWAcGff4QRh7Q6bfT5lj6/6BX35IqX8IIhpHTT/0DeC6q+q+zf49egzr9+7AowQ6r7uQKDVVVVVVV1VY47oKGYjVdfcQCiqqq6+7ZAR6r7DMaKAf/giV7PwUsH9XBQhndv8vWsV6MCKWykn/b23co+B3mur5S4I/bfgvXoNGCFVXiordVVXfgC/gUeBTEkGhz8du74KovqtVX0JJq0q8vxXgLOEr36rwESFSXf4owIy6r3yL3YDLBD5Qj19ND+7u7u7+sKFd3d3d3feeRKxeAYoLsnb9c2vdxC92zAo1VVWvdxK92IXfk9JuFvhkEJ9V7pPaUqgiEO/ll8SuREBPwpCS0/M5015ll/zGyx/w1blnWt9yTbwCy0m4l/xmQXcd97CQivnGwL/L5OIxQLfLg4R/OLXxIvH/WkrPjSWCfA9AhFiMuazUrIGhAkJaqu0OtdAKgHfQVkEvfa4E+E976rwLLF9WtV4BiQUgquMNSgrLoPZZ7vevALqDMdt3e7u73vLAcoIwQ3frwHeCERvd3fX0CLWveA4w/2H13fgCkBPhWXqr5ugDFApJVa6IgoVVVdVVVBNp/Zeppe7Y4FxlrrXu0KBQVVVVqvdEFhSq616qSqyvaegEgBXye17MX6eYIiMntWKHHCHEZPVS6BXFkXsvxER46sqYlqa0nDLTrk+qDWI9mPn8TDB8e2Rgx7UAHN0shqS+wEH/dvARS5amSfWcQQQCUk2bT8XmhkaG5n+kQYz3/wSeYtJgwCWvX/lcqDwLApuBcxqCIzLOIF88wcTt3yzJY1oPOFINVoH0uPz9YNS9ZLyYdxVWeL3y+Dzguj4QF4dy3KhfYLxHA3+Ly+XwJMFAugUwoTzY0jVETXNjiRfNVV7WLUFILOqmz1UC+TTKCF3qLqNSk5Dcc5FmqaJ5QiL1GvSqr4DhDIKK1CXQBWmA37wHeEV7WA+wzm8PZsrBaIx1arquu74DFDLvUcZaAIqLF9VtKaWgsbTUwyIrrqsntpxAdysxqr7CHT5gc95rv8jN1XiIJC1X3cNd7r69gGMCqJ2kwDDBckEtV6r7sB7BAERVWuXeE6rVzye6yxUIcxp7fd3fihIQ7u7ufX2N9xAnTaBEJCALpgbbCvxpAppb6ye9+D4PzCxmOMv4K2eqT9wkca5DscJLZCQcmQOa5uBApv7Gk5c2jm3bNgHkPeAa0RmakMXvhy2NaFj7+n/BMn/wZjwSCVlqcfc8CaIChIBoRYIb+QNjlI4e2eRhfZxxHjr+Gnc/0cxP5gIz4VNvg1YEf2YJ/ewsk/8oQuJr5nAEixVgPMGwEwMghDHGcvU0toPZawGmD7BVUc9y8X9x/3ZPSUDyAkCah4ZFxfLy9RJsHFWYa4os9X5PS7gKWGI8Jy8vP8vj/vWEQLMCSLsJEvSJnwESBQGVkXWVVZXVYYzqqhcoIAoXJnVSzEaKuO4XimWYpvqwSMGIdBKLUNw0IXJfkMMJQpeZy14QCQUJyqqqqouNMhcvkwhD5PMkqeEIwiNg1WzWjn2bbByrqG9W66xgiEApaSrqqrKqq+y/fOF0bWtLIKlkgsrTgPEMlm8N6JfCgMWwugnp3RhRijJZLGczwH6CkJZpUsJvAPTYA4MGI4IEnx+nD8AT8DkFM2VXVctv2nFcRwkeelSTpgeS/dPTrF2RBLOSlBvPdNmWCWOkRpLjYUfH/drER/xd+W5f8ISdeWYYuMeu3NHeMtGOToCLnPNyleWMtgIwFYJQ6AC8km7GEp98PAAWCEmpV/7/Y+DAIiLJQwsvvoP9QVL9WJksCRhDgDwMHKl9ADnQyV345/WauE/nEK918w47vHGNLwBCRDKZCvwLLIMuYxU/nQA3iqlZingHucFLEsr3yRbpmewcIQncjK/PUKYBWTO/RQOoM7lEJKeT1+VNOBhAWGIDVM8+kIyweywZveXwEyBAAS4ECCGCAfkXy8XpYPdAHVOWrATIFACXGZmTJnF8e8BW5bW+6ssWBCHkVdVVZL+AmQJAyqqqqql6hrKF/NVUpcXpsHQRAHjgg7AHmggBcNXWqq+T3b3sWi6rqhKC4ORklkHs5c2YT6et936sIwCVhhx0Mzb9OAR0FIibqtYApESGYu0qrRqsnttgLMPNAC0xIAuE5uq1YI54+qquodnqXrSlDxBAR6pLVa1pR6DkXVV1Jn2IF1VVWqy+0FYw0gQlGV9VzUNkn1XDxNaobg4ECjZjLRPjaJBluBDBxr38BD4NjKL6//CwdARQBD3H7dPr9KISUunr/hfTiWHdyau7fjURvHfhF5ggAf/QS9lywgcQEtEqevIiSM1g5AfY+7ajw0lB1Tlh7ykCtyy32/OnaAF4BjTQkQEYsg60JGW9uBplvsSIBRuP+X+q/ELLRauICMX20DjLKLtwr/VYKYZlhszXy+6sOsKwVlUmaqQlqqgxLz6cGsAT4BJGCVVVVVVVBi1RnXhxCXriwzNYFsDCIQy7mHmj5ammF2/qq1WmwRghA3gKgFBhdRcXFxdbGXwQxUDqBIA5gSB3VViW1FyZ8DOC4RVQKZSnEmD3lJahlBnqwJImD4QgvmH//FCgsERZzfg40NY78/xaTynvA6BLRKnr/AB+iuMMTF1wPPhv2GxL3503/u4uAJxZ+U1g5APsfd5sKnqJECSi5PXgI231j6AC8im7GEp98IA5su78gx4ACwRJqWq1Uznff6BKAwBAaUU5Zb45s9VQTl2EheOGpuWLXmsQStULLNoqSRllrLgRAyGQmThd2oxXLax6GN1/94iEkhrvAiiVaAcuAAAAoxQZqAL8A6Piwxwn4DkM1fzL6/N4dcuvoSLwm+3tWHz7g/eIdAjCQdnmlwT4+GqKaZUg6iOnF1u+71VEgkrkt/YhayfpexV9RG0+aukVkHpf5opIxN30xBe76iDb3pccJChXd3d3d3d3d3bJ9fVqFDu7u7uK3d4RuN4vxulCl3d3t3d93+tYE9Jgkyge9ax3BCIEMLK5vxbNd/UQsISeuuJr1JvrQ3KDmg4JDAQoFDfnl4X+v/ie/Ch73d3d3eSu6BvyK/MwoV3d3d7x1q23e39jcUfsYqv6QIjO/zLcILzL4IryXb3X2uyfSRW9sFO7u794dRINl+S/QdRz1IgQOb/XoOXwkhike/BDe3+xsW5WD3jFLdeBRgpFXd7u7u7+3+Cu73d3d3d+ZSelwh+Cvd3d3d3fxlWvr7x8FW93d3d/29cFR3d3fd3v7NH//giDYo+CBjn3BWGHd3d3d1+M/o3SrXg2w0JKbGV5i//8LDV9cOagZI4hUuxCIMVeAhRKJ3gMEO4S2hWwKvn3k218EN3edEP0XjK+vH9AK0Mot/oERnf4DHyLyJecvIrlp/5D+qa/9h/G3wU3mNwDHJG50EgUEKv9VrmzFx+K64bE269KBg9g2fb/N6v/6BVzGff/MCUmq6qO74gFVV1XVVUd3yL2uH9e9Amzf+o54aEr14wUKDCzL824RUIruAdKCHPz//VIznQEEesHzAj6r7f6OO72kb369+vay/uEO7ivQ2KCmgNgIRYU4cepHOdWDAEILBJPVavLCglVVVVVd7u+OmPwpVVVVVVXVcYq8AaElp1wBrgQRO8BShdfeB9C65b8DYMLpKteAMgDSLF4IDghqua6AIyCPwgUEm7uQPwBPRPAMQH+wHOCEFHd73v8gK+qqqqqqoxT6BHaqo7u0FgSVVV78FFVVVVVHbuOBHWsd3TgkJqvezL2+U+IL/VWhLoJ14IjZPqsOCQQhoVKEIZ7JZ0IEssw/s9NdmK+vAFpPsAR6Cl3f3GrXgG8iLT7v1gjwXKdPkFz39VWFWYq6vAFDDly9kL1WroAUaFgRdV7sCKgSz5fd+LaQD7BTKCW73d3e+T9L/CldVVVd9xX5jyQpVVVaqouLRMFxeV98gKzaqq1VVX3gIoCCCsp8+77vWnAyD5BZ9x0hXym9jIJzLXR170YFE9tT1Uqa6CMKEnNTEsM3lnVcEXqF/teGEGPVcI8MzWDy59Lv1+LguuWGpuW81efQcLRr9+kawc0QQ/ovFOgUaUNTRKPUt960TKiMMXXL7Nqovxwv2naS9mbGhHYdGfBK/FBjWB4BCBqBmCgKR7vK/a0TGF1WYismyraPgx7mUe3xvCLOw1/CZU9DhibC4+/gFQBAJu5sZnLfgL4EAuq1Wq8MxdVrquhAkXqq6rVAR5yl4nBZPbTAFYgz5MJZ81qtJgOYOr0kLrrqvZhdarqreCk7+WbqvwTFWuUks98wK6qq1rVRcqdRgUItV1WqqqqvaWK9PwjpqIL7SHlafd/mjqak1CMcC7VVVVNypqiSQpVSYzFVU0TPK9V7L/8wUqbCXmegSZczRquq9rCsLIKaDVVdcyLqqh0nqVZXvZAV1ntXDM9RdS2x8JArqTGDkXWVVVCuV3uGLUiGPAFtMzcGBCZzwARf2qQ39/zqCwEweHG1RxGPl9GRKwrkINKCLZRyXcEdepPnd//ySCZ89CcEdZsAGWnaent+s4cBAQEwUzYcuqdWXm0JeOeg8CqF/HfMkwXlQ1lp+WuFQUnCPVRcc90H71mk638Vitz9A2jPLeBwUjS7H9T3CuX5dzPXDgr70PPfVhS1KSXFyoJZDGmE317f8An4LwiQmdwxG2qzjXlqrAECgQcX5piM8xBNfFhMI3eweG81qxr30nd/jYI4x7178VSGPd35f/xFLmhcRPJVqc3+MEiLivFbu9eWCXqqr5eIMCjquq98msEgghBGeeSbrrAFPicJ9parrmAEJDwU1VVVVUTp/eAQMGOuEVNeQWarY31yCaKZN5ELQKLB1XVe1iD+lBDLJqvQBiAiCWqqNNCrPL2EXHSy/WPCOLy37kvgIIG5CGyesCcD3BVS480chW0Lqaj5f5iGJChFU/SVlWC4FHS4hHlMLQjXZfDQRtvDwk1DcrHgp/uCyyIBu8SsN3pKOcr3hQk4e0GNLUvr/BRgUT+CYMnGuwLPYf6wU8KQCyHGGS57cQOY1rzwEt/Pta3Xzkv+DzHwDp2p7uNSMzJRZoACye7QBAQKAIwwTh2DN1IW8r8PvfActMtvz/h+iR3+qAhhTIGIey34CLAnCMqEl04wXPpV/AQIE4EuZVZlV7XAglhPbu8Y96xwzGVVeq5r5WDG9wEWBOClVVVUXVVdkqo03Js3P4DfDYSF7jrQpgaA9sTxpnPAXYsRejmpEHZ6k09X1gNkLgvEhGPtX55b3h/01cYGQUjJSRYrOH9CSGcIdnq+yVu+gC5AaxFzA3dcWRbfhbrAIOCMdNQKceXQAvECuFKrqSBRn1XVf6SIJESXfrAQoRHTa1v4jxog3UEjxtUqwrBcCkWRzD2SVpPesIGBCI8AxgXLVfgGSDO8vvUEnhmGCfJ+l4COBmLQIzZcFi1WWI58CRc7ZiXNQpZE7oM9Bka73fHqEE4AfxOXIbp/OD2Yqkp2Iv8Jd1mex8CXwDoLF09QHHk9Pzarc35rlqmOK/Banqe+sDIBqGgh1gQAMRZA0ov1YG4DUA0wQCIuvKqtWA+QUqFKrVVqSxTS3zz06k9Xg1rDb6DPeAdQGAeQLReG8jCg1KM9LuX7AH3gWwoeoWkvDUzfzxXWquawEQBBGDgUE1MPWhkN3Fk+2wBTokAM3gaSIUYT1XAPVVDB6mwAziBBB2JEXMDN4p2+rAe4IQSdJgBH0HJ4IsCurm/awGSGQdwgRDDCIPejr1WscIGoZ1XhzV1N5yuXyCIgO8I82Kqqsqq1zGgoIqrUYlV5Zvl8tmkgQAlwj2PMuMUwgTnr9thUCAFoiC1fAZko/yfc9dZqMr9+/J6TcHGWsG/sMyZo0R//oVwMAagWoHEJKLU9eCoYXP9QHHSHUfmOMjHFfn5tf/6CAoELeAHAdnPnfn6Zbpwb0p6+GhrSeoJeuaWA2vYRTezHFgjr2f6UAIFnUgSVelGwBN4EkRIR4R3PqVC351ye3RYvglBLqwB9wGgAWwB4F8d9yZ03EBeLjBHFyYKfTRTA7BCEM2Y0R/4ey1NRAbhGuvjUXkCYnlMrmAYwEAZVVVa65PajDX8DyBIBLVVhT6bDIyz5PayA6Eg28EIVAF0nSMHmmW/0lBi5Om/XwEeEUJeD47CeIly+AMWDG7++YVdxff+gkGAeyQGJOWACYKvZ0tvjjj0eKbgBDqz133SQnWSK5lcaAAae40GQRcmnrM74//Qage5xXLt7447b9vxGBpEnqPIH2Pu6D8Hg7j7gOi1fT1rb/0Aigg/eMmviAQqyAcyAAAAm8QZqgL8A6K9TBocChY/kL/rlzl5QIRfQkbkFxyaBHs+fPePwfl/SUJhgFAS4K2CEGPHud/zoogFcO++93e7sLoGeIN3eqw5F1u+76iAUd3MBN3On01Mpu76iDctNNTeNhuLIK6+u5gQiBsQH+pveNnHgu826xBf5oj0rEUFAg9u73d3u7u/sv+qhS7u7u93e93atTb3mJiKP/w0EzBIthi7m347EGXj/f1IFBTu7u7lCzu7u8DPfusKXe3d3e3d3d3b5Qpd3e3d3d3e7tuvBWZ37u7u79tcWykd3eT669/E4O8RBRAP/p662rCS768kxj6BUUZxd3d75yXvwS3u7u/34UEjoEN27u7u5bt3d/YYUeX+/v38AgIn4RIKd+qBxxF13HfeqkwS7d3d3ftchIUu93d3d3d7v78FF7vu/b/KXd42CEUAAQA4yL5ASAkd3dvk+WDjjASXdfz1BFe3fj98mCo7u7vb7u/H45HN3PAoiUI94PSonvBiHVi8LhFb7wRZVfd2CCCIrqt8XDscYv9woGHd3d3d3dyw+ffgiM7/MvIvqqRH90uX/WQ9eGHc8I8MqQa+YRw49L+HBIcE6Jll8n6Q0Fihz8Wo4xz4CBoowFDnf6Fe/Uqv1HZF3V/tXP1v2NCSC47H9nCKC0DhVva53tAqK7u7u7u/wBPz/fqT37X36+90RAT5j9R/v/waLwkI9BIFhs1bVVqsqeoKyrWq0arUaqzf+PWkVf33NR9qVXgf4KDqqqqqo4DG1wFKG0I94BGQT+AT0F/yVygiLe4Ln1MsTrWjQdsikR1u0cZQ1XwSEqq9+Ciqqq1UYq/BH1X3kgk1r48ktVryQQiVXigzwbBEEgSvay8aKChdVVfVdV7sDYBD02HOixeAPgBEvdgFCByt+wStb/N+tVQDfCKLrsA5IJVi6ZtNBQKSrl0OrH+RQHbsAg4JwT2npXvXsgKTVqqqq1XvcF1VWq196grEmyLqqqqqq+/ClVqqqqqqqqR89+CYy16q2sJYEgIcsjyWdp+X5BvxEoHZ73/KXAj8jTjzvXgnXhIIIZ2X9BaIxvgXKNd2LlVlVWKN9/9AJwCKQ7vfQ+Iu73f4AiAG3yAo6rqsXzF6rXGiaLFpsB7hWtWuCHu9UvB8Qt9UT75cGn8AU0KV/F91JELrqJ1VRAIeq9quNBFVa5ZP0v8KVVV1V33d3y6ARgN62YEnU1O/BXVaRs5VVVe9oIVUo9qPTKhuer1l8wi/FGwL9T9VTrXikCiQYJoz4Z1P1l/0aYWQf8v978B5EkffIKQuSzEQbfit+MRrfd9yAoLmHyDxh4xdmbGX/ObJ3euhEOXCd168Y//WCVeQNCzSL1qq1gegQgdgagjkzr28QLAMQCsFZxte3GPT9SZFrALcDnBQVp5Uk8j3otdY6WQ2R/Pnyvx133fwzmhdFl+SYBSwHL8B4gjEXve7yelgEaBj8gvquq0od4it33fgHwBoLqs1rh9AOSCHdfbwVuYJ3f1XQB5h4uff1XYHoOgo6rqbO7EBMFs+P5cuasb31Wrr/BFqq94Ao0J5PriY7V3fk9L/nBDw0y3lk9L5OwhVLd93fSzEi7u7u79ZqrXgbgXhDmzkvU1PgbAUhDJfGvfMPBjLTVxAHUSCmqqqqqsz/ZP3moH54EMFVVNSZ7WR9Zle+QKST7lZMSWVVVCtoaXme+QKVqqqpqOuZVZHvT3xAQ+qqpCUqq7w2QxxldVPbU1CssgyJ6vIxnM4CS/bsv+hB6+kffiECEePsn3+gkvJ4AWr8pkYOvYckCgY7szMIheWEvFT1IWPEyFmWdQXc313X+/lhSLqXi6qqzL7M/tYLAJoIShPmyov14EQUELPUXWCM3Qq1ruYZ+VwCygyClVVVlVNkqPRKqr2bfrioVg0LqajJh2GEibf1yXnboF4GsXGclzVKT1bUxtrqk9a8CIF5BUxEpJfJfgENDoQ0dccZOvGmlt8AhYfCW5SXmh4BBQ39ismZb9YZCYXZeM6fALiCARVa8eaPhOq+qr8KVWtRoiaqq9V7qICMljBZb1V3nL8B/BovDLN/8KXcNIZlmHrvbu7314BoxAuqquq8ojWONhOiX5b8R8ZrmRli8EQ8TJZyXz5w0Pyen19COq8a98BGRcY80241nLkL91YCBCo6SjezD8zcpFvpOwBFgFHJ+3MHBa481VyoW+cQ+Ctnq7GglEgKf2Cz/RFEQd9QnWe5ngIj7zZpf+xBWXwSghCYn0Py1gcxLr2qHFlEBKN5DQX3C6Nqgrg0f1gGHB1iBHM6r8D6BIEVXlY4y4CdAkAlzMrMqvaSBKBBAdICWFxjzM4jgW+9LgWQEgFOq6rKhAunaqS2Z2T0qvwCch8FJ6U5F3txcvHrEWWY1TMQIovarwpVVCsiKNSTue5pRlix/LwCFgpE1xZX9WuAQOCW7qe3fa7wDLxF3uOll34Apljvd5Me5SVa4hoJXeUZlGV5fIImAIaCcAi4JwQ169rmiAnVVxho9cQCoSIqvWtYBjgyF4SrqW/SQAiACONNTywhNESRB/4iEbkvMRkOQeyXrNBDBLcotuG8t21lOCCCQVamztVgJ4HILfA1blgXo9X4taYCJBcCUeUHwa4de4AdQrHjmYmJCysTzUD7oUhX5roBEguQvoMlwQubgdc0G+nyb/x+grwOgvSSesN6QxeDkD6H3Bo2p614dBCC0dJH81tr8QMkvSXl/AmgowEHCdV474MjOs++PgopBljBc+v33+6AEVg5J1WsfEwRlyX7wzF611XgDQAUixqqsD+IT7meAZwJijKEGnbvVaxQksX1GstDmx4/WC2orw7PVxpo7woIKZ+Q1lcB/hcXVVWqrWUQeFKqq1UPT1VVaryz2sBchEIwVw7M0/6rqARqTZDzvBIIBQK4rU4rHuYIuNOUjLOh7qV3V8vhgQxENAYQwO8a9mgwzJN/PYkGWsier+1Agg6AR4MAqFwz3QBWI+sCh7ZosHSm8BCiQsbXNxIfP2TwH4vo/3/4EIRBd4c5Q+H4qVY3/+NdOAnAFwCg8JfPRwdeZ/scPI+usXBhy5HEiXAOq+gG9plnbMrAHXhoJMgaZmswLecrAEJm3IMjjL6wBGqxFZPnM9KAMxAkgX4Is1VzaQZhOP1UmCNAql18X6qYKxmaqtVHGsWCPeBPCX6XmXmH5U14GQDMFIdy2Xi6qI0Cip84kei81fT1/sp6v/zwIALhFVB1Wai/wD9M9Uzss74D4EULAhDYQQXQyi6u+D/fxdXVYEcTB3arFqvo3j/+TgCagJFL6EQOWmhFlewunksCOJVu9bO+AcmAAAAk0QZrAL8A6L1zBjH5zXi/DSDIlC94GrzRkDr6X0O8NQeh3x3g0NwOvXiRcNSGV4HnDEX814IxWb4oPXiHQLRBV225ThR5gusv+CWCuuXgxmlET0e/mxr30xAKPd1ad26wUd3pO/dKCzDsSHSLj2XNv2T1S/xZN33eqrBQflzlxgl8mX/CnQRI0HhfzYhCNeOoJN4fgk3kuLoHgQ3oRYSu7mAl31yEghEjoEPf3yhHe93d32X7E/BLkld7d+3nKJKCQQ7/ZPTqThD0uuun3+x4dRIvg5J7S4cErDQsIRpk/b/CMFB5be7vbeGYdgju+3gQBCthSJCBd9H8oP0wwhOhgJ/jTjVeLMPr/kKDBoETafj9Q7BCeXgLsLghIW5I30+d/1auQFN3u7u5R6/vEMFRXd3e73d/MbBCKAARholjU+xwJN33ICnd3+W2uDl8IRwIsvuVC7L4NAn+CAu7u+7/j+TU5v/Fd1Qd5yS58pl/b+Cg+W33fj8Yo61SrgJEShXeAtQy421/wlV9+AgQv7W8e9DUc+WC0ju/d/fgrIab7u7u7uVXlQIiO7v75gS93d/30hLWX/7OV493qM98GnmHcJJ4uHBK5jL/+CoS++7u+OxlegQ3fOiLloo7H/GIg4Mc/HAitFvGChtfRh3a9qqAv1wzRD//BEG34ZZQwBN/KkFvb9QSyw93vmPz/fyan7XxfIid7SuQaLwkb0EkT36KO79fawEGL0X3gEHD/gGbBr+ierAQIb3XOiiwaSgul2sGeid+ve6L7J6/qSHy6quqqqhPmMhPQ9f/IH+Lqqqqqq8y8f7/mBGTVfeqnV6r79HOkGXhwVwCzkhmxeN99JgIIGICsBnqxYiiEVfYCQB+ikTV8mrAGsfwBYS8DuL8DwI8DiEaoBei/0Jrr01J6oA2wZn7AIGF2aq6t8vd9gPcFIJbvu+ReT95ObBDqlw+RffIvb9IFBK1VVXvKYEda4vaBSUD7aj0SlwHGC/SzUYK9RdYvy8Nz1YLfMTgj3in0GNJAJ4GMQINBVdY+slf9TCBJyRyXPfwBMAELJ6aWAmfrLTWHq9quTwGCF8v8ssPghqv2+8FGqqqrivaBJyXrwBUAj5vGmNqq6igR9Vi6EUWLJ7aV36NlpqMvvL1WrevARAPSHVVXgagUgrrWTFzZkHu+IChFXVV1VVVV74gKVXVVVVVaqvbxDoKE6qZJMuN5qiyVVXlSku1kmMGIjhPO9VXLZTP146GCD/hq3LTr4wckS67Zd+X/iBSFwMlH1GKR9fLQd1l/nFaFldJlGS/cM+ulD3lzMaMSXqsQ7/WzYvmwaaMeyUEq8PBDL/DUUSCgRqqqLr2sDwHwFIBH1gJoCThAa85LmwPTUUoz/glu7u7u9eA8A0CjquHZ6pi8gkuyPZakT6wnivE71zZ+CzkpVVWppV7wN4RF9V1X2ErVVWtLoCGV9V6m1r83VeAKNi+0fLv4wlPkAE2hEFNV3frXq5Sb31QQ5sVJVVVVdCII57+K8PDRBamIyuq34HwF4J8mZsEyX2uScFJJVVWq+MeOrfxAUrUeaFmayq1r3YDnAkjONez2pa/UeLLGCE/P2QKZMSWVZqsjjQLl8mtS/vRApVVqqrKiOSqkA/2X8SIDZjCwpVVVVmsTttMFROm/ll8KMY/dopMADvq+k3g9gSBDCYE7/07/ryYb1fBEl8EGAEjIdjVnvP7txlkYprHe/0qBtglDl3LTwt8iDLG3WXTnmc/BTlahc1KIXXzL+XgM8CECWLqlVVGmjteGSgqzVKqjWH80Mv9rokEmoE5twY/u1wV4K6rqorcsftViJnZsuKlFbjcTjzX417hJOioeLK/Bwzp/+Aa8FYw+0aJgdJ6mZ8lF2jkfgHbBmMJjHvjzR3qt68B/hkFnnuceJSg6yku+UtetcIyl6jRE4Agg4vPtdVl+WJfCcKV/ebPMCOqyj7e9AnqtbSr3QAusC+CLd3i8B4glHXd3JZyV58/gPEEvgGNXhf4lAqrqqr1XsvjN+CzLa+pQXUpL78Rd1GyXKMs35l5pLkv95fucv1yipObDj9JgIUKg9A/jy1qouqnJRfEEBSMlVVaqo00L9l8LFCgmgiJBSUK2iSe0apAnOrKreHgUdLB7GghEgKroRqFT+c1CYNSvj/aCnhdC2Vg6CaxeDoJrWXxDfxHAFGmc358daBngr9FAeo8vLx/zf/+g1C3ACl3Vk28SiHlj8v8PCPUTVS/BAKY74sz/AW4A78i55+CR7Z+CQC0IDBd4Cp5bn7Uf8NW5bwIgJaqjZ0PRIXWGFrXLwGSDoFWViHPC5fIvPn2vLBJUX+1wRgWAgLquq/AMaCAXVQ2RyqqvAfYJwoPhaWEpYWaWWS7/Mi6qqriPAIWCcFBGdRoiFJGHvFl+RGoB/gpBTMQarqvVe1gPE+5h5L8AUmcnd7ywH2CEJXDy+aWevrkGwVccZLPVXeYv2uIcEvVclF7wDFBMKdgg1kkBq1pZ1lcz5dACHAIIKKrqqq+kp6BZmIVw3lplVqvaWgEyDYFkcInnOVqvXtYfv2ARwCybCnyzJ+2IyeEeqyqrVQly1dOYVhn8JH5+dVAUmAkgckmuP+F1y0zxXu7/yhnBEXsnvAPdGAK9DuPyw/6QAKlPwMHgX0NkO/AFrEiSOb+tvj+/P/wgVfhSkDVe4I+cfKSfXBB+A+AUCwxhN1NruaE3UwXPqu6/CNVAvtSEZlHG13MYLn1/Xdf+gBhoGFnXYTyfno0Wvo1V6rAFzgWdVjYwTKMmIzjb7FmlsO5X5pBHp76poLsWVQxijRXM3mv+gAxiDcZBm8qL+3Dw4mSBNeZpeYF6qW86yj4gSTdeq1hUJifgJACCP3e+58/jIIyEJa1vEywWm5aK/Fk+sTIEehGDLZe8f8Vf1WAaEBEAyhUNgAiUQSYzEI3feGPznoBd0iCcfn/B5gCBgbL1NIKQRQS4RNPRxr4OGMNR7ZPXGCABQwE4xzQSk6gynl4nTzfhhy6/OrXX2AFdwED2LQgJGz+PPjTQcMBUDwJCD/hq3Le+qAJaAiBURJiqE1DPxNQei3tMk3K2AhwPQgrBqkPYmaj8CMufRE7LO/4BHwNCDPoPvXKhkfFcFDiF8ATUBInrQQqBArgGKgAACYtBmuAvwDo+LDHAibygwe57+l6zL6YvGqKhXA/XRILAlxuODsztG18Z937TUQMMH5d/3vu6+H3b/dApxHTf5oZg7ffubGve0oi+ggtNSBFm49K9X9AkPOyUH7EHpfxGIiA4Z3d4vx0ihA/19ahSM27u7u7u7u7vSjeMu7u7u73fd5PfEcRwhd/d93SzRiRa3rIFDu7u93fcgBu3tm//9B6Ejg2hLZb6oYR3u933dwl49DczORR/9BY4R2H/gqJlPF3fnx/GsEowlnL3MS3rXikbu9fwdvFNQX5pB7m2UfDyGhlhr8IH+m/qCW7u7u7+8Ox93d3u7v+QKbu7u7u+7394RhQS7u7u7J3ct3d/ZvH/8EUdAhu7N+ARmCa7u7u47EJNYDHCOCEQS577w5BN6kBde8lkHtvE/GEBGd5btjYIRQACMHC8Diy8gIQSO+5l+T9WAOF4S/GH8wfH+/B4IRTq8tfeAkQivvAICCFCe6AJCEUb3jYIhDvt4CXDKLfw41boSQEN3e3hGCcS7u7u7t4wIBAju73eeP8iJ2X+WTBdd7u+2uXCd73fh1CV+AS9bT6P/5Epp4l0f71R4ntxVQ5uYx4Y/clfqnYNPMMmiBufGK8XDwlSmP0caoZXAS41ffqMUP8BKBJHHY/50Zvi+gGaNXvnV+ok85FC/TNvBBQ0cY51+hgvkfJXmBerfYIiu+MUZakRO+QLXd3d8pjjfPR45zEfkc/4NC/8JQoCcVw49lZUyfbcJAIgGvghsWVMvuJBOd3fu4yvwBuAIli8AfcCjfFaKKd+pUZOpUT3QCFa1vXQkd2uT1cQA8+rlYJUTMfIvvSX36+8YgSVqpwvUFBVVVWqlWQZvxyBIKwECZX+OwD/g4CYKCaqtVi7DhAViSb1XXWLuli+I8CuJXvAX4ZV96QAo74BBwRr14Ggc63y/88694BlAr4BqBe6C7hHuOBJd+u4RRb+qN78EWqr2GSCQ0zv00/T+VnXoFBlWj1Xt/EDKqvMNKMkrldV8wU6rCn3rVWl9rLJDNf76plv/5eA16B0zOulBMW77yCkoJi/+OCBhwKLPV7VeoIOBfNQoYDWbFk/lVB+2Cjj/0kAsgbh0czh2FsfD+g6ONvWkou3Vr4BFQUghKe33N0CuAKHAniN7T7VUDf5P0rBj8u0q8AIZg73yh2veEOX/y/AEQFvsEhFVVizFEf9R5NT6Si/VTaLlqtifAQAOQQ3d8XgIoG5CqteAggWglJVarX3yBSsXWtVrWqnV7QJeiWe/vIYUbMDAi0tyZr0gRwzHPY1+LIJSIGpuW5OyhyIl/QYo0gHpuWQssYjD/54+ZO2Xfl/xWT6HH40Rxryzee/l/xUkI3d9Rr2T/kwR+bwEbVvyvQYFm+TebBeegwFCBO6/1VVVVVV7J/QHoF94DIA8hQW2I5haymVCtomVLf+zOOo+t66KUl+bK1NPucne3yghu5M+zcRr774vyksdLLlGfgE9DYKIutR5o5Xt+A0Q2JpX6rWA3w2xQLN1WqNgyZ/tfgiu6td4AmeCius2L9WGEXNnwEOETd2lgCiYJKqo0RO+YXVnClJKvhmCGq/Zf/wS9VVaPvAFChP5Ald8l+T6+TsZd3da7yY1YPaXsI9Vd3d3d9IcL1qqqusFGH86WaaJVi11fyl8t8D8DM0k6rSgP0E9BGH0+T7maqqqqzVH1CGI6jOj8/JZyeTBsuO6xwuYvmCnUN5a1jRZLLPrKr2vLCk5JeQfrqqqqr3lFAkMs4W9rCIpjQiXlwEwYcjiJMZn17/9YkRkjDL6suCNKCX5PT4CAGA4fBYLEuYArRmLK/BwKvoYUzzgm9yAW6qeb+l+gm9z1/304mkCUUFb7B8RTUnxYzM5qWbNQMsFX9QINCtz/v4EMggkrzZ8G4SEEleco/wbhIfU2MzVVUhZLiABb6s4TjuCqhcCaEBlR761XJgxJywr5YfYVr31UNjILK6R3BfFxcxKezb/x63KtR5r5SXAVqcgaejSmWwBXBRKnjy74B2wdiz48XOFLJNlv4SBCP1lGRj0C3ltZ+FImRaS+Ne+fVtC5qd4QgoqrCOTP34jmoSapb8AQoUFFqq40RJiy/vGIUC0lV1X2vte8Bwgj3ygO8EfgGdFCyvVrGGho/L/ENYTnrzDymyfhKspKq8v/EC1vmsXKSPq48SN8gZmteClDrlIrqgof1n3LVRr0y/jNNi+0uq6ATALwUaqqmxfvwUkHmj6r8mdqrAsgTwVeS6r5b7J+pICcBTuCSteX44iqqqq1VLeXGjT0hzzIgFBDMTnUxnoglHT+c9v/Amvpn4ZvmdU58Dc7j/3D5yiZg+fh/kOTD9TASNlh/Agxh6JZ6RMwXP5A0LAYANJZpgLEdsDTz8/AJYJBds8c/9BUGAJYlywEFnmHgRyiZbxzoMlK/PHGQyx34PARy3y3aV4bEBjjrwB63LEwmBTudlc5aXwC6h6g4gDVAqBLw1mp6mul2GR5HrE2M7VWAUYCUM5/DZjXsDVuWHXnBOt3MV+n1XILh2NoFrnXiofEgzCs+v4G0eGLsj/rxElXJqf8B7i+gBd4F8YLykij2Ne5h6tkteusKVqqryqqtV7WAKHE5uGbxTwCClEEvJbv65Mvd6wV31iRWCLGmj9rG78AywRF1VdVXQAg4H4uHoaJMqtV5CDsBEzjzR6qvIYFHVVVfZPS/8KdVrVVqq17xkJE5sVVWqEoBgARzeBN9fapP0sRAshjEYDbLF26UR6/w1Ny3OP60Fag1CKC+MUMCv226wCjAQAFkJBQEsA4kVz0gH/nXuPIxsAeJF3POHi5SJm3udWfP+2NKP4wcIkvOPQCiZI5gfzcSDNs5UFs04pf+cv/+nYiCM3Qks59T38Ivv0E6NFrcDSEdZdG8Eejda8EsGqBGWt8+T9IhLAFqgU6BEJrXLVAZwMIEESYmHp6ulADEIOwqICNxv3NlV1Wl8eW3q7kn8CK9OJf8ZARgO0M7wEgBBWD8QPADsUn1HVo0ATezHtHwQYNEpwNYcaZwlmcOgejK8twCRgaIOX/SwBAAOPoAQADZDe68npWAE1wEiSepIbtRrCPk9dQg/EYTcbo9WvfpQfgq8npYV1/YApECWQLY171QVATQyCUki5L1XrShsC4cNiIuHDKDvvRadyFritmbYNAIAJQzgBm22pj4GgAsJLzLoivbaeYSB58I+ekAWNmLIZR3vzg/fwCRgaFvAgCGEcR+AJqAkT+gh0CBfAMVAAAAIBkGbAC/AOit1MGB7o+ZeIE6+ey/9cHq2QkFojhMcCWoQtPUNrqPrJ/fwVBMQE+l/b33h2po8PFxG71EAjmIpW/bXKY09L6rsXNZr0jEbyf1/EC5r26T3vBcIBSOFk3fd/IUuF1k8edVB4X8iiILgW8F+Aq26V3y/8YEBGtgl/AboXF5by37oEvNdzkgddS1LhCMu7u7u7vd3f5goJd3d3cOoIR3cNwfp7t2zF//4ejIGV9/hS93vd3d3mAlIksX9ciiRF3u/qT6g6eKeLmI3d0nesgkLwifd7u7u71lDoqCi7u7u7+8IBgXuWjL481rwjRm+P8AhIRMLeq1QxaN2T6SsAmIJechC2/XUUCK+/eFS/RT8vl+CQWrCInfvByvCXxkF93d3u8Mfan3gmCAUu7u7u+7s3+PxiIXEy+FLu7u7u7pXf9+CXdu73trAUMBQglQ9MuER6HX8Ew9X3SLDKK/xIISXf34JhLu73v34IiXf7e9rzLqCgju77v36++kN97SGer2PwjwCWDR+KE/oc4hGdoO9CKPPvJsIomOe30+oIru86GV9ePwigiKZUImiant2/Am0EHF/av86v3CS4hfJo/sv/4W3u9/H/mov8EtX32gNmC+uBNkRPfghu7uLQgfXj/pffJ8i9BovQSBOK4UHJZ456HI47vmXvAG2BrfWsvAFiJb+AKeo9/AIqFbSXN1az6T/sB7h1Ec91HZF2RfD4KCquq47fonx9fDYHIGqO/6+gyXwkCgdgxXuW9jwQQSVX7sBIA5BCJJfy23gkl3ftwRXfIngSfgfwn4OXk/oEPkA975VfoCTBH1Xugulw6va3Sy+YEdar78ERVX94XgkM9V7XgZJNV9Uf2n/5kI75gRFGPf7W0oKYI158tX4dmoQD/HiJ8/LwQvATmz+UvD3gTLwE8DEwQNgT1jvwNQIRODFU/NjVKB4BXDIIRcr5dZa1XX1+A7QR/gk6r3gKMK+AEHwZ+F2vfutfD7+/AFIDLqbrLXWT1X1y3dewHmH+1WvA/A3WLwEIDEER6yZ2voFRK1XXVe/BRaF1VVGPT3lhT1Va1VVVVXsvlkN40068lqq16xYYkk/1likXVVl/xRphZh3id99QMzX19goqGriPA7zV1qWqNkY3jFRSwjah498vm8xTELMKjfIWW6Qb41GUFHOFbP8Ehf/QW19Bc3AVRR4BHOe+XWv6yBbCkE7+KL1aVVVSZKqq94NBIUGrWq61JhKIFr2Yfu98+hRPUMZZ5HcpL5hfvGvm5HNflJNZb+Emno65WYfLd4rfWvyiLucl3vXCebaS8AgoX8AdqCVe/BHrXqXiTm6r8mqquXWs3gCfRPy12CK73rsB7hUEnVYtJXEAmqvkp7aV44rT1d/NleE96kp5o6688wqPEbsn8l5LMRwYzVg/FquDcFNVnKqLi4enqVe86tVEAUY4larVVX8wLNVWqrqvZfrwFiEwU1VaqpKKqh7LdvmKKClRklys8vLlQ0SXrKpLSS70IGE1VZlVVTDPJhwty+GRwVFFhYIglO61lJVflrKsLFSGmgAQvrZgd0f2+oBCPVOWtUv/k+uCa/glwKPL4FGBTgTgqNjhMAfGJNNSyAFku8Bm+zC3eAOsvwHy9L4pdsxFPg3CAVEeOVdmB9Mv2Imv/giCCr5asHYQA/goClVgR5YUr/lp+Avs6B7zLNx1vHnj+CK6R0XKSx/3CUmAcal/+rwFODQFHJeSxMigX3gMcCECWtV1Xt/gpOqqupqKTPvAK0CFiX5b+CMmqlT8FlpVJnJfJveCBOqX4DnBD4DpDfgGcCZZs/AMgGfALsHSZx5r9CyhT7mcfQNRySuiykfLXAMEFSUn+I+f44/jIJ5b8aIhC/34Q5JlFpkO5bVe+fF9V1XzC480OHp4xp6wEcgwgW1XVen94CABmCUi6NVrll/BAIiKBLXUK2ibJ9ayCQUsKnLwFryN1AGUpUZ/GA0OQPlnr1/AOqcDgpng4xHGqBxefV+xqKRILPILyZt6gsCIawBsuTn5+qYQtz/NEf/6DXmALQS00r84WgXJp64IQP1prByAdQ+46Il8t5nhTLJMv6cC2BaxAYwdk5YrfhjxSvDFOW1ysOJWTjXvgXwiJ5rHZQ/773MXhl/0AEiQIKEv1gkjz7/F4BB2S1GQw9cx4IamNfawHmCE9YtcUEoiq9VXgCkRZJyQ2y9YIYRQIcr9riMEWPFh/bxGhdVVRckFnjIRrNirVVVcvgt0AkAdgIAHI4oWth+ij+4W1Vt/mQkYbOq1yf0FWWCcIAJwCDk/WgPoKMBDgmDwRAV4hS9IHIZiy3mI09VrXM/hFvLc6iMvV9f+heCv0Jcn8mCb0HLgHwBwr5PXAPGDkA4QEiEMkAe5jDkzzCUBcgpc82oCTfgG7AkhUUCRMpZ2Aqhau8f6LFumBMlz78W6fSmf+pAhuQl8RweiXB2I+BtCKxa9o2PMfWNR1rIIBiVey+CXMCA2LE4emlJRlZ0kAERAEbFhHDGaPVVWq0mDgWASQDSYmp8k9cCGJC0VLBETkuLWEIRkKA6pywnK28f1gIsFDhUKgpjz2EEDuFB/QKS7b/9cjUQUcL4SbMAIDVh/D4Kaj/AyhiD7AE3ARi85IPwaVhBBeIDkRR+XyaFMv0oAj8B8aDx0yfpACfQGxkEKQcq8ntpFEMSpYVCIAVm6KgzxEAWQ87fhvCPAYeQzFlvAdoKaev/wCbgaEe0AQpAAAAHykGbIC/AOj5Qxwi40peXw3B/ie/GwP1YRBAFhYCDVfGgC+ALat4E89/3fCNyRsD0H60iQXhKQHzEWh0MIdQdD/fjpFCS4dfl+vD9+Ote/kLBma/v8XMRLT3fTEC+7s79e7miASQzeR27CvBD1exAvkvmvXj5QnhyDX9VUnckHa8Ry+shuCcJQ7BCGvd3f3hYQCgru7u7v2vzCN344QCwru77u7u7tVQGoIAqvd3e93trwkli39lLjrR1yw6CcW93d3fvFCxZH3y4+kIBZd3d3d3d39vLNCgq3uXPu7u7jeft0ISIWYJd9L9b/g4rDheAMC9ac/Zv8En+XpsgLcMPNK/DQWeu64pRleiAjgl3bb3d28DSJH3d773d1gURLI7v4BAwQgiGu/30Cm73u5QNdyovfQJdv4IRnj5tYoEGIGW++9Ji4CPgluVCS3Xbq2i33m4Uu7u7u93d3d28JEGHd3d7HQIbdw7NbE7e3hiHwxlk09NP/wcgHGp7hQEDu7vd3d3d3Db3dSPTJLBwvCH4wEX2UjgAmXkReP3+jd+vvApiV94CVBCQW7/yEJb69E9k+28HXB2EVSDzwoV3d3d3d93d/fhS7u7lg+7vu7+/Cm7dxiOaMbt7vd395UCUzu7u7v71e7v1Q1qak0iZQav0yjOBuOj5zxz8T3+i++0TkUerXz28mp/X2EUd+Zc29v6I9evMHJXkUn9ATfCtqWb9R3frzLtAqvvu7T/vVfBl/9URj6WKDMv/oIa/BYSHHgNx0EO1qvlYtV4A2YJAhEjKu/9/AG8DFIjqtsAiNclXAFhUR8v/wgs7L/+vuwDoBVHr3Ul8qJlrrXMdANEP9aKLsbX1Hu9wR9VBKX4J9VVVx3Y1XGqIvQI61UEpa/RsLXwdoW8SX/5EXKCeswrgFHU5kn9fCQEiQZwm63QKhIHYG6Lh4AsgIFFmzXgp0/0bvBCbwavqE/BK0WpKCqqg+L1XIp0+gSeXB3VYCGBr3GbfkFk2ltKvZAUVVVVVVvmBXUmLKqqqqqmV5Lgu2SqpwCvF+UuEOB4gJbuvPgs8GBsq/BGZ4CoHCigAd4Jr9n8DcCEEYi57mxpIBYAz0VzqIMJVSV9ZZM+te6D9e6JyfpSAj/4C1C/gDsQW1hpWl18ASkI6jl7oSbJ61/okGmgCLlBL6ARgNdOhUq5eh/xoUEquovXJfKSMc97QKjarquq96ICmq61VVVffhTqqk6yqqq1+3+CcgbiwXNV+y+3heGYLuCpOW7RSMvrwjFmEtguuW5wPU183+PjoO1RdNH+kDeyXXQSC+T6vx2KKeKuuqy+zUKKSbg9kuxEMjw6sr6hij/8r8HFzD6HHnYPXmEQGWnS8v6C0LYKRWE3UhgY8gBanTiBbnm+edM5a4YUFU3Hp66qZ5mqqH9BOSF3gKEGAUyqqqqqr1WveAxAcgrFvqqgmGKjlVVe/BZUKfdVVVVV7wYSbUmS/deTeMerwW/yd34AnGC6q9pV6sEn8FFVVakz6lyF+ESL2X/8RzZpX8S77+wUdVx73F85e0/ZfAMME6S4w4u7u/PrwEIDFewziDZd5r0xjk8el/KaLuWkgPoFXc+bSXUYMkzqNNHvVHXWEcQCnKqsqSL8JP/eCgYCohrlhKcxJVU10F2T+q8wgE3NeQgVFPll8KhAOmPDARBCUxJTXFhk+s5Ir2UIlA15McMJJD9f4nBOX3IgmEwswTjZyTxtvlMeAxQcAiCUv20oD5AkAyCIJSMsgpW64PMSpmmTte1gPUHGCqUkvVZCUY9+8Bkg2GGVetV4VsnZgkeobdYGxVvAE0mClF7wxNWTF0AasDyLG2hj+ckvL4G3gbOCrw9PUbArJHlJFHlnvAbIf38nQAvECz4TICIir94d5f/oTUoz5h8pLhUhs4xXl/vCgwslmLrPDogEM1/vmF+NNHjXlxGX/qgV5L8PTy/JbfIEiUq1jzRXOSUqqYnPIQIeNe51JTK1WGQIIJchJcmMH94CuSFdrAmCQMYKgQlVUYjfZfGFY2UfBKJA4EXmoCeo7IM8cRdzBCzMPvM3J/wgySgC1AHRh4bgD0B4whM8wD2LGEJfnYgDYOVL5iCW6NCRh8tdn8PAOxjmH04DIArevxAa4K2epVNC5qPzk+SscqgBaYFfwzFiVXU+ddVrkwRY00fuwAn+AkScl9lEk3d5P3oAUaNCW2VZrwBU4sXvXnvQASPAkgltFGOW3rXgMQGoK/Ru7sx6OKaX1+EcOz1dVVWlVeAmAfhEkl5L8xGELKT8uAmJCOUl8BMRE18E26ANci94/k9VAhQEWCgBlgoUIBUPdeBUV+DwikNlv8rkanAXsfg5dZoVlJgBj80WtYi8Md6lD7ghIQke8z0v0tiCR7Z/7wBWYFcBoghECm8W/fn/JQDtMh/zCXAKKDE45Uib/xM9TGTb/eJpLF5oukclsw2Zb1WAKJAlm8RuqDpA6hfA42yLTwVaaj8laQBGQqAScJgjLjbNV4yKrSADugOR6MRQGXS+liwCAgVhHluaPTgdBIZhUIgjEhPKGPX8rQ4edYEdEPomg6SWeuP//wCRgaIOn7xNBIUQI4AoKQy535+XwcCcbEwqIo/F/A9YbWF+dRDca+X/3lgE7A0BmCf1AB31iS6O/AdQ6Zy/+AIVgAAAHm0GbQC/AOjrP5gtwQtH/z+CMSJg/94Pr8nvfRixAKAljpWW77mnmmVuphd3u93qugSXd3rWuLg6iJiPLD/F933eT3X4iQ0pI19LhsvcQXksxGnxomD1fEbzWQFulG2t3ueFDK2nyfBBNveuJzd34WEdZuW9YIOhdfMKu7lC72UAf4IZk49Dvt61k6FXPp8KSyp9rXrBz4cLhN1Amqs/XFb453G+rxkFEbCnfu7u5Yu/BRd3d3eVPDNW8BWhFGbwCYgh3gYBADrD4JBLy2/eAREGILC8OwQm4dghA7BCf86/AcYRW/h+CkQld93d3dvEJW8QqXHXdcG68JdZhDgkvd/n0FDu7u7uW3d3e3cd34ISO/98q9Xgk7u3gfgsCIrv+6XacuCIl7+31u59PsXBKJVvscV3d33eU1rrBLu7u728vL+tYIiO+wmiGcP+Fgu/nhiFsIsBHtWTQYZ9fofKDR9EgjCGHZLWWYlP0UTrMIKPtToibb0/CC8kVPt/1HZFwPolfeAkCHLmO2XfVaJ7oEo1YYWD0KorvBBBDd3fAoXo8qfP0/iaJ75AQ3d+L9HGsZ+0I9Xi61VawZr4QNhN19YcEqCny5Gfnz1qVZ4IAgjju8CzUd2EFVaX/bRveAKAq/gPMJosbfyF3vrX51Oid+vdALcNosc8DUElOEJf/Aq1i9pfjqCPVV96gjPqoxV7SEe6DwlRehQo2LDvg6g48BEVreDXSt+XgrctjPF8P4J+VoI38JZf4SXMFNV4cHovt/SP7oANVF8BCi192AQoEJKr7A6j1rwBM4b+W9Nb5f/+orwV/iF78EVVVe+QKVVVVVWqqq/svghBD4Iw6sHYCMBv3HAkLtFttPxJhFVXgI0QCLWpU+YE51U4K61Km8lwWkD6er669+Gy4BTcjSdsXXGe/O+teglfY5hgRpBNfVwtR44ovxkAjhlGjcf+BOCOsC8GSAn8A/0ERzU/tYAkLvmp+CTVfeBxGr3QBTQI+qATBorr6lBFd+Ksn9e+Ve0qyL3QCtDvgLMOq/QBAQQ9YJ+5aarF2A+QyLqqrqtNgNtZe0W9ITW6+16QUqqqqqUe1XVToi+CrVaSWqqvfgryEqS5LqqlT6ChPHnxXcBaQSYZVURorsp09psvteGYf6af6DFbXaQa1rKXaf5f8FbkFmPgXWXJWyIH2ymfQuKlyxUv3i/lqfl/x2QWV9pVPc1qEijS475xJw3wYS5lcqpy6ksh//kiaCt/WB+eiM+mGwSLwKIYIJ4AibySb8rHBbHjATjFc4Ur7aizzDal4u4GpuW94uBZBEFK4usyq4cktwSpodeH8gBLXgrYKarVVya6/mPA6ggClVVdVVdVUmezfxxX2C8+pbww0euGdP/wC9grClV0lXVVVVVe6AUoF0FHU2DXvJcXgF1BavWv8vz8n0AL7Ao5PWq/XKsfBb1VUq9vZ5POSssVJnd/ylu9mielUv5uq0uAY4N/KCi0vqsXYBCQQku5buvwRVX7f4UquueKrrKMxHugKoF8EvSHmvK94OwIYLIx7zYqpRj09tZzBWCquVpVVL7wgYYI1OS1KRNcqku+qiGMBTVZ1XN/LL6IcjExIgSqpYknGWUvhkQCz4ZKE+HtzAPZRY2oEb0bnT//p4Jy/wTFCd+E6F1pIDKHgMgfGke4BaMZ6fkHCKQ1Mfg+0OvgEz6a6df/7jOsIyiqfnzM3u5rumWL/jKXy6+TU+sBpg4BoEQUiipRdGHnbya1IfIz1/datrAVIETClLqjnrJyXlJGjDn4ZI+r3gKUCICoguLjDKVNin6HBi1edNcEoHAJCqSV5M+AIJHIneAJpOi984wTu48WX435ZtGLi+hEFBKaz7rF1gjo38vkBXpVVdar3h8oKuNNGtakyvZf8QKC8mH5pJfDbL1WbFdz+5LUa97C0jEamGDGUZ237oAy4LQS82MJZbfLJ+vhXIbKSX6irlJWsY9FXiBxaqq3za9RPXk+QKSXWqqo00T1r77BRW1qia76Bb1VdTpl9cGsGkJ1UXVa6wIvFCOHGrimlvwOQEIQc1BegoXylMX+LEhk4CqP3jQUPAcqopKH3+EvwAhGDyMB/FvgzWAsgxgrPgShHGmfvYbZ1wLb2b7WPv5tYGwLEjTTxBHvAk44Qs9JhJAbmVFIVKZnGHmHXTQgvb0//vTSKQ1Pv1klsURTvGH4GcI6wKo8sxD3jXtACzQK/SlrCa1Nrubh1IuWljMFBVVAqmvKngCJBITz3mzyelyfICapKeS2D7wEIC/sAJxgJF3c2egBh4EN0hj34DtBCbkowfAVANQlrSx5o7wrESm4byXlgNACCsc8BNAQRBwzlOM8KgWsuDoIzX+AiwUQWegixHL4QBDeQFFKuZPqvwJcRI/2Vf5cBL9/r/AKaDEgwCRdzB6Mry3sN1gE/BjsYwk+a+AINMbScxKsAQ2YZ01EACawJIKCD8f+Hcn7SQbh2L8xEpKbAYzX4Al0wvjXh9TUJmo4CcAnAlrVdV7L4u4MM5yqbUv5fkASIKCCyCwQhcA0ADTCkzzCSTAXHmd4vI8BHgogQfwignEUnbb/gCvgJEnoIeg+9YoB1YAAAB7JBm2AvwDovXOGFz2S8NRJWhIy+q8EwvOLuHEPm2D9oPvMTgAYXqv6auiQkO3Ae/lynCAs9IITp2Ixd3d939C7hnK6j35f/oEk5I5L7VcQa7u+4gX3dK77xcxHb7vvyftfihBcwHvS433MhLj1B2vm3iJTMQEDwJfvUGsEPjLQ0k54IBAJbu7vHQIdvBAYxL3v80tH9xAKN3d3u2X+8IRh3d3d3d3ec/d5i/6/giFh9B/v8bXlChHd3d3d3d7u7YdRxWBNezs1///+NV7bffmhQY933d3d3d/7L/9BMTWsNuHvrg48EhsCtNkA7u20sFjAPXQyCCUvul7u+30Yf4/6QIbu7t1EI3vAeoZVvDNWy//gmO93d9vwod3e7vd3fd+PwzPDKHK//+goHw2UUOdA3d1XeSjAqxzC9ve93eEYy73e7vfu9pChSYJiu7u7LYRsPwQMEn5ECBtdZq7g4Xjpibv4oFJ49HvffbpO7uMUC5ywS3zknflV4CNCO/0b3gSRJrv/BEV3+8okEp3d3d/viEb3hmCK+zcbAxsEt7VVq/uCjd3d3lVrCkOEBQV7Tu7uVWbxX/0CzIDdz7bP+Cs26Tu97/UNh2GToUwIvpBxwvDI9FID+vhu/x+DR81AjHXCegPu/frEy9ovMr/XmX0TtfX6/OgELR+KFoAtoMVeuRRTF3YCVD6kR1J7/x2sTqv15l5AS3e073KBg5KJ4X7oXxUpff8EIh3448gau7zqyWVLz2ZL+lcgzXwgYRqtYeE2vvwwLVZLVdLqabbf8DkFlHHfqO7XFAaaJ7L++Aha98q9VAEXBGj9k9eX9E92B3qMUZfXkX199AiLqo47yUTkXJBFqvs3/qvwXnr6UHtgZXl3/oEghdXEvr4IpdV9pCep/Nvly8I+SZLlhHosUE68gQMENRfoIBSVf5sF2hcXWVVSq1qALeglKLqTOtTp4ArwoIbu+LuDUEfVSp3gj6r3f3ErXgUzr2t/wNI/VcYpUy//qMVeDep09sFHVdV7oAgIJVeleA+/qo1TsBDA1BHPnMLuUWdVVVqugEEC1a+QEghVX2+iQUVVVVVXvIIBMdUfi69rK7BIbVe/KXMBwlweCCt+whryLwEsDHfYCqAiuCPK03+AVYIgiyXwZfAXoX+gR3tLLwCCh8Ft39ykovmBDevtKoMYJK1WLrF1qstr6Cj31fg9E1gQwisG8H0MtcNYPRYCNBO7v6QzJ6r/l6roBvh1e6n1XIvupi93v6/BLNRda+8BFAvBLVVqqr3i0FKqq1Jmq8qouO3hNhSqU2VVVlVVVUYp7QUrXqqqqqqRB7tZhFgqMsr1GPJde8JobzJU9CfKzkkUk/HEvntWYpd0/rxsWSfFpxEl31IGIM0WKYNU1dfXOaPv0lSIatVrUKQofCK4EqCrPnPfUJfhH1sgRhjpNN5rfsqMuf8mCN+BRDBBcBILJN+SaznMl8SvwSjvyH541UlzOZf9xsFVVVVVV4Z/drsFJQS9VWq9rhnLEyJM648KIFFcJC10Z77MmAu+IrHY0ThjzSSWoa9wDsp0JTLCPnCuX5d8AoYJRdpGTIpiK97RQiWQm6rwG+ERFp93v7BH5bi8Uc0mP8AsQX+ZE6sSZe+QFBdV1XvJ11ErHKJ9egRYNyuk7vSxwMvQZ1yC+W8sV/KUnJflXvA0AnBJrXvAWIZBDVfugP4FEEXVe6EiAUbSqqieT2atyEo+/RQp9DihpPIVPmeuPtBr5zIj2Cu3gqMQQFBBp7r5WqrJiXO4eiudNUhBBQJeqrKR5ZPrsEISYmJEHa3QMu8vh0Qvhk6SL/4a3A/n1AEr90nZ//2X28E+F0hOXh4IhqA/5CqTBAbxirdM78AhFrgHYbzD/WDUIOHf8abAAYblLd99Cg3K+cN3phMzDwb8AuHhHWz/6wFSBAwqKKrr/9s+D+kFrLtZpS8+/WAoQfYKaz08XEnDzStPGywgjdv9+g8dXgNEHggyqLcM6DtNaya+AIrEeAJ3P0ALRBvpLBaLBQXJW6OvZPSSwVfJVe1qQFnVdTYur2X/oXBRVdY177sAJfgIfwyKEVXXXhGCEmq94Rgkqo16e8RrAMuFDdGb/BXXWqqubO6AIYBl8BIA1LGPf4CiB6CONNH+6YEcvJfgEQBACkmq6rqva8DSCp1qugVCAqcKlR5YzccLIuZK/Kc+b/8aJDJwEDSSSvn/6E72L4x7/wWxu74ZdRlNtMQZk9LFBgQGNwUnFbiGlisAJlBKvQrO0cHLZu4aa7hkJLr5j+AdgGAgUIO7EV+cLFIwcuDwZXuYoPPn13/mGca9T2A0ARdgrEltQSvRZ7GfBLBOJVda8vAK0Bpy/glgl4K6NtdZsVZ94DPDYjLfm68As4b8CyBBL40RK8phGS7UPZZr4yENWqr5S6OT6XwBWgMwOYMvAwgoBCRYvy0oYEAxghGhqDk38vBO0eoPMApoMSdwW814BU4J4IW8A6YZZb4/7nV4BS+sO11hlC7DGEd05Iy3AVrBRwcYiHZ9Rj0kTmv2vBDLwY3ht0txACABLVc2KtzJ9YjxOQo1qOvPwXwyFQAckVJEkPv/mhOPz4wvwINgCagJFDYTjabrAFRASJPQQvB965QDqwAAABr5Bm4AvwDol8vXFhhPCPAKQJNn1v+SsvGQ/FeMi8vHQVNeCMWCR4+evpaL/1lFT8wWweamPwIBVz1L+qgRoSHFXPw83K2uf3TlEASenyjDSXvV+Cjd3e9qqIF93u71flIld6vzd33Ed/VIb8Py8BNvhuQcrxE9f4e7foICD5uOyrt3d34IhAzb3e7u7u7u9OAyAxgsx0CHb3d3d2FOASri93d3d9UPJe77lJX8MCGW76xIZctw28fOujzCd34kw67u7vu77+ENYREjf0CgQ7vd3du5sv/1ByX/0w4WGpPgBy+jMtW8PeHuxwD7f9diAW0nfvcqOe4Uu7u7u7u7u7v7wFqERBHfvfh2Pu7u7u7u7/BXd3d93v6niYTE/oI9rw3IMvfwgCa++7+/BRd7u9/fYLL37J33bGwQigAMMwONreCwPPd7u7u6D99Am4IvPX9BV+3ZMG68JQRG4ceKjLoQC4vbu/xtfCm7vdLe73fmXAToRBXecld7u7vzLgIEIgj3v34Ib3+8BYh1feA8QT9gPEMgiOWnc2mAScFIDvBCjPt09XbxewS3d3d3f3i4Ib3dvSR432hDeRgmu7uWf+xsOwTgYERNViaDjmT7b+ImDgm94v44V1hFBoX/0zeAYK7Ml6Hf55UZeIdB71r+iFQ/8EV3xir9E9l//XvA/jV7f6vpYUvvBEW7+YN6v6r7wvXEK/ChXu+7u93d3BLGlX3onMHTBDu/kV+WhPvVG9+iciBovBUJ8PCwSkuHBZlT+qv2B+BWhMTreiXP1GKvLUd3yr36nSyfbX+vdS9v3XcqE37wSCLu4qRV5AR9V9Xr3xAIq1+rlR+3hz5f8by8AiL1OvPBevYS9BAEwwq/XXvAFuBpHjnUE61pqvwBPnwBNEvVdgLgFvd+Gd1r3UvX1l6rafgju/XcQCIrv12zghI7vIvoDsC9SL9IEJar9+CTtD3u+YFBFWVXX3xAIarXvmUqb9wp84EqfnGavVYYgkpr42X/rL1OBjPjbk65Fgk4Sy/kCSgICURVV4CaAh+AnAIISFu982fByCHlvHPjVv4YXUUvdIu0gII8H4nwGIFgR9Vi8BzDL7J3fgbggr+WCSq5U/BD1XLS4PRvX0vWCLe8fJ6X6UvYBAQS9zF1XwPgL171BZVc2KouqqvdgF7jrzkpMJd+psq+MEXkp1muvaGG6qs2HvVaqtYT4J+UkWa6rFr7MQ+BzTf0CON3LOI6mRVXQqEfNFgyk6parzQQHhANuL7yYAlxbqu7m0OoW+0nLsv1RJGWo4RT5Fw+CR+BRMGhsATh8boVfn6x3v9YJjYIRljxCEnaYErG8Bt77XQFIJgpquq0Dg9L0LLwGaD4FRFXVZr0YyWba6EQpnK6qYeaP9pdhL7XYLCAkqv7XjZar/BGVYGkbSwXtU4AVv7Re+l79e8AbiCX6XugBgIFNcvovJSvBKSqqq/eD8TfEmLqvBAQtg6rWCf+AZAb55qryekvxQIWCnquqrr3YCRB2L5J8wxKSpwgBJiC+W72TF6SqHstPmGNV48H1jbXu6WNNHpFv+1geQMcID+S+a6r8QQKVUNZUm3nKjTR1Uw/41hFGZRAfy0z6MFCpKpBiq8/RJNP4fRb//l8SYaZiYkFRUlpPc9IfQYo4W8NY0Rso8H/dCaOPxv9wUF+wfxZK8UE0evw1BN7ADp2Eamfn7iCRds/1gNMCFggnsBxGV5b7RSVAw7fxIrRRJDk2c1ak2sBigQFCpoYw16gjKsXO8jmPtn38BkgQBBnXWhaORdV0fgQASmqS+s1O13ZpMS9A2Qvu+78M8vxXzI/eAuwyW76w2jca9vC5MLfJvHpftEL6Zd+AdkL+4J4x7+o0RO/BHXF7L/gO4E4DYBTrBccCuPov48GYdWCGHstM+8AhEEohV617wQAodVrwscNDwOAtWgF+uevtfH/x/jRMFl6E1M7UFQ3J6WBQG/nvk2ff4BPgQAkmv21gNXiIAmANPuEJT+cIQPMNZJmd/giSarDCy++/9YZ+XxnoGeugUBrCbqaXToRegwFz67i8EaXtVgEhDO8EZAStC8x4BXANb7vL/9E2lWX+vBR1GPcr3gJAHJa5SV+AZYIsvGvcAzQRMI4DFynbgEeBErngZQVOJ4m+8JsE8MhUAJH0XFPTFgAXI95xQnF8//BHXoLPVyEFgFbBj9ACswMOT0t0JUAJ1AJAE5J7wc5EHw/yRUv5PSwUCnX2kCV1pQRu/AdIGIQFFXUcZe/ARIKEVsvgJMFALfoJ7+GcCGsv1vgiz5t4N6v+vfr6AITgAAAGRUGboC/AOi9enq8vmC3AS76u8r6YnDLNLAsD57khwmBs0/yBeqz4F/hrAtdYSHTAqPFyYe58dOUQD3k9/+YvMBHpyS+yiBe93d3SbmoEZN3Y7rV/tqa+5kNcA8XiO8ROIBCIAge3H/hjggEa+DDwVwQ3f7wUII3NAOwQm7K/d+GIKL3fd+6y7kvpLGzH3fyqylnL6elRmNf5f67/Y/JGDjwUCuASfrHtAI9W0xE22gOLGAfiRAJ5iW93/fq3gK8I+DoQie1hfm3vwCcgpyfSgEp8waBSEiu7u7u8bFlvgp6woZ3d7u7u7uv2T0sR/BCacLXbxpaL46E6GhMK3kwbrx0EXgtdSnbyCMFhfe+77v7eEOCKW7/eEYIru/2X7/BETd/HgExBSrFYIIITu+kNNgFRGgXAqCsbu7u7u+3hGggzxIl3XrlcEl728kE997u7CGYXFD+G1+X/wwrIUwIXjYUC27vu7u7u73HwRXvxCNh0UAYOkBFgI/kFhx3d3u4NX8YY+AgHOJ+ff6EFC+OR+Pwgpf6n2/8Ed3+NqX/8EpHfd/2/1ZXrf9Hv1MiGQtSz1Y8EFe1+ijuF+oIjO9yjPkxsIwkR4BgRL+CIc73b9930jL0GXmNwL5rXhpAnJweearF4eEgmPnxdVzfKj+/RO8DyPUqeAJ4Lrl19+vrrXPfosfX6P6i//oxBfSKNVeB2ByhAIxrr9cugFQEc6G7w5ky/4nlNjrJuAuXodr6BOEOBfNcrKnocCopV/rlVXX3gC8Ah4ArJEE8+cAS8JWLwBTQqsAbeG/AeYXBDVf3xK9SXvqIXqXgZ694bgi6qdN4NMUveGaL75OgEkDHScXl6rwoIBIS9a1xACLBoCzqbDYsrVcXh8QFNVquq1VVxawRwUxPVWqrL5ryaN34IymjCahvIkluEAVO+uCNeQIa9QSE1XvQQBWTKvVVquVX6+8DQBBQv3g8Ag+AIPB2sXgKM694DhCa94BCwuCXLe7qvVgP8FNZO94qsCUvAthnsAqYL+gGaFzVX1YKOqufFnugRdVUXk9P/X1/BLVetTp4CCBSCqqrVdVr79E9+FC6rWqr6r75AV1Z6qlTr7WQ3rCv19ixCqW5UDKUsLzfJRTKGXOX/C4iYh+Db9L4jRzDpyrN6f9QxDftY/KeD2Sxz6aM+9fcEeBVEeDmCU4/4aty3m4sWkizT9ZfqmF40EuAw3JbjlX+NY+DcUhHawEiDzJVa1gSYNiFxUXXjymIq9eHygk5h9fdQNIs+BsxSyb3U1wHKHxZVRrvXhWCPqvZfom8flpu/zX8XVWUzU9fN7IvfZK6ov/hfeAXQNgivfF8t5K98R8WCPLedMv+C3Akgi1r2sAvcAmIaF8t5L+Ac2XMQUuvDgQBNtLdr8a6AOAEQWErHWlVhe1Wf2X2pB0RBSSVk1oGsqqS+L5azEQ4EpzEFWjyaD3Kv7L+fExPmEhkSH3D6hFhhf8FixJNHbL7YCjBwaaN4Fracz+fUHFopb4G5hn+I24zopwoXmRRV/6AXIEgEopQ1We2i6UxB+nSKlrBKKldVzp4CUDQIaqqt9/gk8IwuA3fl4k3uP4BAgVoTF2AE+QEiC7VVlvfwRwTy2/mLmvvB+l7wTwSRj3r3xAKKrVar3gnfQArFgo1GmUqqr3VAsqvqqqq94CcBm8l/IhdrVVJSeAiAbgr5M9TUNi/vAJgDv8MEJdVHmvIEhB8zgqvCPc+uFwDhMplX/AOodzYvxhwShMA5fVyYMP/wcBsSbzYuet2cSLjRMFl6LURgPoLL+lgFPF64ZPSsAUWr4nwj1QRlDQsNZog3tEJupgLgN3XLQI5tc7GDz6+OdACkQJ+T0vwCbAgCc51za18n0lo2AYEH4ROtaqYjJM/gQQ30AbECGCuS/vNmamLwOiBLlv4wy7wHCCPVYAiQCCLEVNbrm/wG2BiHai6kzNmfdHgoBcxIeDLMz0lgSxKHy8EbWoLPViEl/rBrwwQDiW1TT/fm3kA+R7kco9QynREfW/6oaSUcFHzF5rGC001wv9bDjK441/sAOggc3x/3Siu6yZmFQAf/wsHRiNAORpcf2dyv5YtvBgOIEtEyevwgErnCkguCNo0oEMbCMyUX1xdfADjwDQQQaAIVgAAABitBm8AvwDo+UMYe/2lutF/1y8PQW7S+n4S8PEXsXKGihVodXsVw1BvAePyTFwUYD5NShet70CMYaPfHbTTMJHAuL3KSd9tPRev9fYsmYCO7+5tXpdxBiFJXfeLOH8jtt4XJKe4Wl4n6+kfoPF4iiJVyQWEbfu7u7u7u3gqEBQu77u7u7u/26xAkxHe+hMFF3d3d/aURBh0uBNEiLvu/rF9yXx5rS4U9YoTd3d3eGUWEKxyv///zrcQCoRd3d3e7mCXV0WQGXIDfmPZB2JDByX+iRUFAjKSAjqfUQl3+2M17cy/zeFO7jDbdvbu73dsw9f/o8xL7+qv7w7BPve7v78Ze73d3d3d3rwRykr+oOsaKAP/4RNz/z9azD+3lKgXmopZcN+NAQf6PWKMjdyW9u+sKXfvu933f19orPqDheOhoRhx45gvz+n4gFQs5z7eK+7uOyBWExGFJyVu7vd773GKvwUkfd73v9r8FN3d3fe/3gMEM+ARkFNBmLFf/hZl/nENR+QQCUVe7u9v1auV73rh6Fcv1Xq59KxmiH/+CIPivjcMARtqqOGFzpjZF/6PKDMv/RhRRHAvtehgLTXdyf8XHwVFe/d3fHdhBWbUv/AF0ViL/AFhVc8AVNVzfJrDF10rHWi5eCUSGelfvtrf0pUNr68Bj7Bdu930ZXw4W90YfxLveBBDL/IhXvtC/e0hG5r5D3M4+e//JBl5jYF81D0elYcEg6CJCbvL8LV4IRYPWfx3eALyS3xiyLO4ENRlHXAu1I3YAlkCeo5jveo0ocfAMTW/UylSrAdYXVtNXrmPwR9V96o47qsBQhdGHDGipf/0Ud19ovfm8N6vTEb6ykxkPikeQLl6CGX+EKwUDlXVVUqMuggCs5V6qqqqqscd+CIq6jjvAFWOr+6WLsCfW/gE5Bd6EVgZjK+6DI0EJu9fdAkrHOpfde/XvoEnVfeaveqL3yI3fEL77XmUv/6+1lZP5ZbhBwajkFS9BD0EAUE1VpVWL8E5tVVVXvAawOxI8ApSimc/Wu+QDgBF8BdA713gj1X3gCGwdrF8vVE6rpfAIOCH9E6ugR9378nd6X0X3QZIp1daI3SrF0E/V9fViLvvafgI4GPYD/D4SK0/qtXeCvqubKrXttgOcGagqu+fD15b4+3D+QFUtfqqqzNSXyBSq4fmadaoowQpZjM7XQKwbAlEdVMRLn1vGGDQgMVVVXHGiK2Xf+H4uvN6S78CvRmMv9BSxBbSrl80qOYJMIi4R/Zh9ryuCXoDyCe1Fl96FSQ7893kH4cWRg4az2JkfwS2AowQZP6sSjDwNcFIkAN3jbNgwteme+03ecl/xlMZIqwG2DowRBKIiXVeugCHg+BKRV1XOo0rYFBgpi6w3J+qystFnVrxJ19qsBygRASEVftV4KKrAR9TjyrxJFT8EYmoG2pb7fFWsVYbrlWH34Esq9WMLXku+8AvgVMWUlVYCYBiLJxporK6cEhdV7S8+l9E7J6/y4JPGvd0AOHTu/rFbTvarWeaFK1GPLzNkuouirwSCbtN7WHhTo2WsEAgWJDJcZaHgdWz7/WHxWFTmGIYzX8/1aw9333ihYeorDYvBWT69/R2PATIMjwCRcQLCdh4K1T+sBNgywqQB3shVn4KgDoY1MvzUe80RQ0gBwB6tGuHkvAwAw4fKVEHLFQGXHnHRS/QMwdAlHGyU3Qeapby8XxFoi4C0DCNll+4uSvUEnhGXAbvy8HZM1H59CTJtsAIFge/vye7/7y3+T8Exct6OsXgPoE694BmQsCLxj3fMCTVV7J+l8CH9gqARPsgJI0RJ/dAGdBiCHjEQ8l3gGQGgqMtVyq1VLvA2gqBTqqqqqI3P7wocEMXNTy2oOo0SCEJmv4oK/BFleUG7169EYBAgstqwHSCFYt5IIROsRAeHoAUsBQQQ7VFkkJmtdAE1Da92AEGQMJqr7CfsL+7rAewIA4XJQBaep4AReEZufXB6MAQRTa33VAEeApAVIISqqpduiMFBkL7wRtCdeCFr0Fnq5CaWTVsnpYEYi7gp4b1N43AFS18zMfAPHpyt3O7r6dfawDNgXthCsudv7ADHYFHxQCC8FbgSK0PvAEKQAAAG40Gb4C/AOivUwYPPry+GYqknl4fg/el9CRcxIoNYfCbg7t/B8/JDhsETxv6iWrL1l4b4/Dae+usNDMNIIR5KDMlZXlSqyPraLCpcPZbykg3lmHjHfu05hdzAS7u7/CBCAfLe3d5LvV8YW7mAlyfa/CUQa78n0/SJi7h1BCbzEXunw3FzETEbt76TogguQAiu5P2vXKLy4Ue+CSNh20cFc8Pzi18Pvvn8vnwF3k4SNPWmZeI7xHCoUAgXbj/qgqK3S387+S3o/huTjDX4IBAIoZQ8D/266BIR3e2lwUXu/hgoJN7tpqg3Flsnu5L9FQKbu7u7u7u7eCQSOO7+5gI7uUDJY265u5gRkTe/e/0hLEHJf5HiAwI4CrfPQy+/fxvviRAIcu/zWQRDIISu+34LCbu7u7u7t4IYU7vu7u7u/7wCxgpVjwCagp8Ag4KfAIoChC/eEa+8gQBMKCjGdu7u/vDYjqIBVvcNIIS93f7MP8P8IhcPuGKF3MFAk7u54t273d7skneCHMBknGeg383gXKa14QgiEQ48XCi5THxAJhfdu72jhfgsu/d3u/mXw9e8tHu735l6RN+AQkEP3T8MQUYneq5kVcBoAgReP8VBMIe93/a0UHsEW9/fQUu7u7u7u73d/eoJL7+EbD4oAMQKsDIF9RAIg872uZWvO7u+aIf/4Igu/KwwST5EFu19lPmjBovMI+MQj3uvIuB6EfSP77X3gP4OL75UTMZPS//ona+jzsv1L6ju3g/H4Iru78i+CG7/sbwUsDJfGzmcelNVHa0oIb2vvdC/aV9CCoyl+X8/v0yX34ECi9Bl4KBXAvmsJrUPR6V1qZMv4IBY4Rgh8C+axzJ/X4AT4oSCUH+AL4qnVeB3qdR+vdABH6vfS+/UYq+lp0r1v3J3omXwgikZ36X3gjqLSHVRis9MFVVVVrWvvVG79ep8siF91EIV2voMC84CDeK+Cbz/i4N8v8Fy9hAEgQltZ72EHCEE4Df8wJTrPM51eAkgdopEdaAHXBHwJa/XvwSdVKrwNJVbsDMDVYu+sCYKV8n6/eCTu8HddfQPkvda37AbIdV+8Efd8/gdgW9A6Bv6IvekCiqrVajt6gp6qqrWqlX7hTqupsVeqmI9l/trRtflLsgT+A6SO+sInw3Ird3Cf8caf4I18Ib0EGEjDFn0Fkj1dQU9VqtVAws5TxxDELwG8DlDYugCPAZ65fAEbgt8A4INwQ93i8AI9g56AMKDVE7S6+B6N4BXwl8tJd17rBJd/un6Biza31r2nqM66wfV7sB7h3TQCPBd6r3y38wJOq97QI61Gmj3kMCIxAMYeX7wkIBZ5qVmou8fA/mXawRwz1wiBFE5f6ZpDk7pk35fEaxkNnwm87Cs5oW0f/y/WqhHnyHZ6txHg/Jaqm0P1BKX4+DjlC1F34gGN9JoP5eRVi9IGrqP/dAzBaAW0DgIEOP+9kvqgGGDzIRVhtqnAS4EIdVQwRwae4A2V6CitbyrhLFmZ6Ll8BPgRvwSmVfjXpy14HcIm4DEp0COozM6rwD6goBRULfJJnPpFfx0LxdWv2uNkBRVfSSf8FAtarVV7wBRYta9hL7N1Xtr7wKb9PwlNWW/DlcvBGzbyjPxd+jSe/Mc29+WCzu7v6q/zgkJqveAsgbr3hFIrdAbTgl5byZ9l/H/8B2gpJfOShUkd943L6ykikf+vHMKRj0szNRnVSYuouq9rDsSQEgg1gSvQGHTZc6Oy/Qybgpqp6pJiqsH2W9z+dNYE4Qc4ipqUnAYc9F6yqwRC8/eL7gq2ETn78c/yE+8AvgKADCgo8EUBywIKagDgADIJ0vLz7lk9vUQfwSmipQ5h3jMeZ34fdGCrhllkiLuYIGhlhZlasB2BwBCBgQEHZ8l4GelEuVxEEvHURNeB5zWcvD0F3GvfCMuA3z7BRwdkzUFV0xr/qeuCwTqq1XVcvAWgM9/JXhEvLeXK9eC9eExAJK1X3gLIGIJar5L92AEwSk8tza/8VulSfxj23JfYMaInF+CzKSqpqar+y//gt8lrkzvAL8EQRiMN6sKq7m7WEpOXykB58KiZL35+sMR/78aJBCPuvOQW+tSXrFJgNoMIn7WBuDXl+CCob0Ky1g+AuAMwFHh4FAggGNy058VV/odYVUngFDBejReArg8vZf8OwV17eCXovZfwYhOX8AQ4BBF1VVGmiQsZ0C5MzcAlAaBbNi0axW5a+8gYdZJnTmBweYLu/oBJiUTWlX94K/ViE8D5V/AJUGARYE+5x+4k9vAC8oC5VcREX9RnKH1wbp/hH1+w56BABgyevgre/glgk43Kyt3wZeI5h8wTqcG0gn6v3reD/1igHVgAAAF5EGaAC/AOj5Qxl44Hs6+rL/1wePyTcFmf5SQJ9/QRETAO4S5f7ve/JHFjzkg9nfYPZK7d6wR8EWpQOgfOa8GIgEPG2pZLlm1mYKihHDUGf7mAj3u9flJu8nvZOzBAQa7u8v+H4Rq+X72hcI8DfB6uUkNgd/f3e8TPg9Xmm5cfpCIzGXu0k+l3qYYXd93e7vfSuDqyXfoTBLd93e3YHwIAou97u7dSAou7u7u7aVQ1CV5gI7mgYCXw0tYcUEeNjYKbWrCAszu+7/TIUevl/z3Le7p9cHC5hUFHA9zV1Mba8X0CK7u7eCsr3fwtBDd9zwIInwj8BughRGN/lLVVuh/Cgl3d3d3d7u7um8EMKXd3e5QM7vIANu7PCMEQh93Pcf3fd3f4hKw5etTUdvp7mF8G5f/HdfgtFS4FmMf5J/FxfESBQbfvbu70ruclVxn0QFd3d33y39+Cwm7u+75Vfgpve93d/1YC9BCjMavByIyekl+CTk9JQCV8IgpAekhSXb65QcyXv5YI7v+3yYJr3Pp9i8NcJQqguj8v/1BxxiDBUZXp68i+inRF3U6v0X3jaIMVZfX9Riqq+vdJaKNx3sJ1p1b9IkMPoEPd2PmUqpMHU58ITvz4QG5/6LeDHwUBDGhwFL0LgYH4Lu199YNAiGvgDCBH6P79fY3Lh3PVfdwJa0ar0vMtNK5V/c3f9L79eRelxDYCKAjqPjVfBGXVToi+jd+CKqr79CfZf8F8FcJ9VXK3UxxHdIu/8WJwCn9L/3cT1cIwHEeYswbh7gCUv/oIGCAJ38fr7RovQ4E0IRcBvVVr3QDMBih8XVdJK/Uiv4Angy+8ATZX27Bm/VXrlk+v/Xutfda919a11aysv/0vpdkRO8pAXFGGj6rXtfKCOtZ7W5pRC6/KUPex2GIJvFaj+c64R6EvwRr4Q9jgSDOCyjN71BRWAkzOocOPE55s2LGdH36L7wGsC9Hi+wUdV1UqvwR116sGJzVX4ARrBn4Ay0Pgk5sXvAIDBFXXvAdu+XS+CSq/eM6WvS/1aJ3YBFALqLFWAggW+BmDq98q+PlBH1Xv175AR1VV97QUqqqp7WVWqqq9l8P0TwWCCBooLJcqq9VVzWE9Am6qtVc1h/QS8d8uvB7Fk1XVbwKWQI+PeqzNa7L1klgsExp39IbZPvB5WDAFC5droYUIWbxpoR//5rgnwbhAw/hA86rAf4KnIISIQwi+EnpWASkHWDYI4JZDJc8wx+xSn2uasVYNqhQ3dVY80yz0BsT+A0wZAlGKuq/eAQ8EQLqrXAYlOgRyjMzu14GaGIMELQxqpfiL84VtS2hEpvDFGWKsu/xYQBR9uqv7eBAFTq0mDc/4IhK394Ke/l38Ybu/TBDy27lYH4M3gMQCGvYUxiL/6DOa43L4EQGuuXBJqq9r2gSVX7wD6/CCBQSo00TVfb7FMbVVUxInmVCEVdaUQPh6LCO2Ug8yF/vdMCBri+sRIgQkNfKWWsCAIGiQSlVZgMox79bw6IIVCags9CfAmAuhwVlGgzq+AjnzILkuFbQAFQb8f4VxgKYNEOX4g713iAtw8GZrkAPTOM1S68EhLERr3czc68hFa8IQXdTZWEZcBvauwDThkXBkyIHGNo/HTGCAZsjnm97LKCjmy/GGpUZX2FKxvATwN/dCypWjAh8mdvwDDhDoAU+JXuoVBRVdcY92Fvpk0pHnNec1ymuGQJ5eps4FsCv8DCCSpsmztVsBMApEVrqousXvwQ1VSZ2sYfEDUeaBjM/344skEunr+POhP68goELZ1yfgr9C+k9ekwG0FlaQw/w/oFQtf226wfAWBUKjJ85LxqiIS6KvJqfrAZgJFIKHGhmZQZwGMHEIy18nlS5fS5eAYMHJuY4Yel/5ogldVrBzAFJAQBejiEfJiQ6iXwYAIoFNVVVXwV8t5eF2xIFGnzRfgpP2PEobqC31chTAdQ5fCXwBNgEY/8nttgBLUD+5AmwYrWsNA2g58FLq+BFrV/19B/65QDqwAAAGOUGaIC/AOiX/XDgYDLh9fw1FTyeXmHh8DyX/5Py8Z00vpi8I+O2dg+1DgjgC20kfZy/wkfnaQqLfYTsuP4TZaXw/wpjAW5Qaefe9b6mfMSDuWl+sSaOpZM2vCp9eOiyFJBLw3Z75evyicHYjvuT6Y0E78+T68orh+6kHK8ROIXiaXhwRLN5pIUjJZfkeaO7u+7t4fYIpbvbWDWAg4IpADd28HAgFBN3e9zwVfBCJye6XxGCsrvvvu7ZP0vc03d9GMa9/N1r9SBOLBXu90T9v+RCXg5eIiRGX+I8Pmo3d3u7398f//4N4LMtLu793bwIggE+7u939+Cm77vd3d/awQwH3XvLd368s15LrBRL3KI6oEV3umV4RjYJPN/aSkJBTfd73a7DRCFE5ov8E5HOMG93dhdaQQuWTvEnt3RXg3L/47r8EorgX2sIrFJIBqnipx8QFBtu7u7uW7u7/vAkiQpd3d933dxtlbZnXFfFcJBtIhreSXETvLcPOI6+l8CaJBKS33f94CtBDvwrXq9feERAKD2qV7+/Chh/S9zEi5d3u7u7eIgou7u97awhwQ+HYIRvwRic0fdFgkFXf3WCru93u/7Nhj6fhEIlYardFlghHPd230py9/Hvcv1+CE8DVo+v1Bk94z4xDpfFKO78EN3/fr7GooWWpVVgNsEKuVRfV+Fmvv198pLvLesNbu/v7ZN1hQ7u7u7u77u781aIhHdaEju/QjmG+SlY/R+/R2gw8wzhN1r0wUEY3E+f+T7g777HgmE9gNIGKE063rfwBVlXPtE5FFr+S2Cmvd6L72lYbAZ4dRRlCLUn7/6nRF9EHd+vq1RG6A9A7WGdaP3hava8asv3l4JOAm9+z8oLS/+hwsVw8/Anfx/rywRE4PeRzoBTAxRY50vWjudUtda11q+T0k5P+tXv1e6foJ+l70WP2CGsTqr78BAApXvwUVVV1NnvsWStVF1XgeQXAs1VVVVVVUqvkBTVeq1qvvEwVVXNlVWjVta0PNmz6mA+uoh9ZhZF+CNehxPAVRRaJfhKOwfnBcM5FyX8vUFmqrgCTM6Q4cXoU/UK/Sb4mZqJ3anV+FDrVdVVeqrGir8E1a1rHd8SCQqr+8BoAxBFqtpEuAcWp00vqdMnpf/eSCTesXhj0CmidpfKXd9X1fgF2DfymqW1nykIuuwDM+wYG71jnY+ivpMBKgk/OgUSUrqq95UCoyqs2IFqqqLU8fBVz2FVr1Eqaz9h/KJKxbwjBHKZcrwdwUdV4ReiLL4a7ETE8daKxFsEAmAR3rhn0DuHUEojevSqeoYDOk9TCwgex7Nk2fZf1EcI8w8PtC3Ei35c6obKCbBuvBugRY6vrL7P+HSAgxiEuYBx8obXrn53jUSaYAmRHx8r6/mXxFoSkJdCjL/8Blg6Zp35ifWATkFQkImFRj3vAzQGaCoaeoLKlp1VSSljUo9MGvAxsoiwdLeRY/GKH8+irxbTrwCDhkWTu+714CeDBtQjC6cNpy140JaE5dgCKQN+T9/5Va8P69X8ZV/k8AzYjwE8Dv78ArfwjWt7wgCW770ta59e/BJrWLNUfD/QWlv5L5awJcDVRu8EhgpWvdk1wGmHJ6pYIc1XMYW9Tm22X97WCIQc4Iqr9pRBsEJQj4PwvkCDhrF9Hc6ReuCqtCfApojrx/QaNhLp6giFzaesBE6yfiKax8HUPu8T7yb+AJEAQQJQgykydTYj1KSaX7WPgNgLMuNe66cFFQPV7grLKAWkZv7x0WRg8J0xpodmj4QYusZIz+YSyuYY1TXgtPFAjLlFNAP+lgDvAUwEFCb0+E6XvaBH400d0ALdC9YBWkCQmq94D4Aq/Bb8HYc4GzKWA/fhYA+YB+BVKw/gEIBECMlVTW2pKC4xAsCEJgF3Nm4/8fznWoOlgEWBBhfANYsoPejAw3EP6A6Y5fpgypf2CIGAVEA4SNpYMllAPDBMgU7AXagX534GBjiUjwdBWdz5dm3k9UgGICgJfBCMc2qpr506AISE0Q6ZPpKvDte8A0of8AyYb8AUKQ1da8AUkBELXKP1gEQArCmEhMY98Fblrl8AiQFqr2LKZp78Pvw60eUFvqxCmAJqAiLcPC78FOBHvV4ED1igHVgAABjNBmkAvwDo+UMYS8bCs6+T9i8eAkQRZteUVnbB49yTcCjwzq8U1IC0RJjqGrvbxzeSGYKz/MDTVWvn3e7tZZfD+g5yEymy58EXGvKvPrKYnWkUQCXmzx3za8pVi24aEDyo0Wu8FB4CtyPSjfBmWPbsLxLLg9XyZPrkkJwnBR6l7u/qQKb3e7u7ve7aXwU3d3u73d+8FvoCoEASXe7ngtEglu7vu/ZPtdcOQjd3iv7vpxfd931iiu7kAz7vDMWWYnfz//P///i2G3l/8iBKKd3d0r1rqgtHA3JYt82MN718MZO+jCC33B1ogKDZzaTvua6HEBLu93d/fQKTu7uE66O7v+8EFEc8GIlW9QRbu7fglu993bxF3f0Ebvffc4Kiu93d2V3dt1EYUmAj27u7u7u7u7eJgrJu7u7u7q3kT3cwES2W/rqoNi/+Om5ce/IwUk4SXjgSbmH5vljMZvT8wdwSDS3+RfXkSwbia9Ga8RZ3f4xfKCUzvu9+9R93eyd3d+1kwoNd3d3d3ct27u/vwUind3e7u7v7L63Jgh3Gw0Pr50Ni9wSDHu8MSf19SQZrwl9CFKhd8QCWLEIYP2/x+KU1eB0rmPiV94HU69+vpe0X18i++QERnd/v19XJrJkWKDLzcOPCk/r7oxCX16GjFXqjd2AgAV+q9+vv1HYyvr6/X2PVDKry70/a+90cqvtfa7wSEqqs9Je6peRcsEhlVV8OkjjtjpRT7wtMRdqX/rKTEJFZf8vBGJwh8hC77PBaX/zhAWOy4Ck/gJ3+Z/6wKQZA2Ap6AUwL+so3Jb6rq1i61frWnWtEf3X2IXoArYJVOnV9a9Zf/17X6+/X2/15F7X34Iiqqr7wEMC1XPmRvfMvz8FhdVXXVfeiL7WuCURP1kBS/flKCLxvpaXwJW4E5AVeKPwC8E8szYy3rQ4CGIBEMmyvZf+PjwVlmVUBPdE8UHF76RVF4VSiXjjvA+giBT1Wq0M+s/kXLBXXVV1XHdrAcQO7BHVcqe4Iz6qVPwR9VK1YI/gCL/gElCKtl+W/Vvj1roDeJXMZPT+FPWD2Re6AbIVXutSp0690HRPXWApAb/r4/BJJa17VYBF4Is2Z0/BRVarVSq+IX30vsvyC/gmNzR6r7WE9Bjqe5SSz0Skf9YbyAkIupVZPXDQhGLH17XThTgrcs0lIu7ima0vORIS4tZow4KDxlkBr8tlA6CRu//t0IOljsR7fkgpjGd+aS4ey24l1rosEI0YA//qCbD5vDZvDQkKwGRVKAEktBVvV/KgIacYDgyKYa53gy0RBdli6O5+DOpYX+AuQfMcJHQosztB/1gEhBgNH+ApQYB4qwHtgpYHUy0ljZiljWqDYrHQCUnH4LFUT1Ns+/wDEoWb7vu9eAW4G3g6KCPgCKte35/b+XsApoGHrR+8GSBFd+vAlhXXU1YKll/ALl9ayfX/7+l96giqv78ENZs90AqAKvgNME+IVYVVA5N/p85r4sgK+q6qtVXvDxwoZarqqrJir8sv5kKKc4U6qe3U2UPTM+IqlD//NaeGZS4byfhLp9R/1m4ITgWZDaxksRB7PQmCzdDbjf1qx4arqsB1BgK+Zx7AFfibs/P5sS7xHeT3oASOCECUEPBKO7cBwWzyxsEOfQB1qKhc0fsaxskF3jXvgHvE8BstYHwIANYMHt/TJt5Cg/YJiZsggTMwGS/fHNeoI+MN7RpvkDHAPsymodzMQVKnMZNv8Egr9H79e8WKBJ2l7oARqDE3NhspICXg90oAivZM+fAbwGcst/YAyEBA68ESChlquq6mJSYBxW1UxHawSMsFNRdVVVqPYr5azp5IIYnk+WnBYIcMhVGwefch9H/+E3Bb61G4D+DAJMwNIPS3gPgMBUgSMoMOEYhZ3gyIJLJgMBwQqfXgYYwP0BZdgDpjlHjKlDr+wEwBCECiMJBzzCVWb8OF/S86DtgsnOgejK8tVgnWCUYNebf8Y8zMvALKCsEvORfd4tctfMbhGW6YEGicC+EiAk5MA9TX5qsAVcBOBEXgkHVHaoKAIgkEIlV0zzGT78CEa5hIRPl7v8Ebgu9EchTAE/ASF8KyCPN4/X6DYl/lyvk0m8Qj+CPAj+gg0AQrAAAAX6QZpgL8A6K9RYYygYzhsumIOXzeGUP/fiRcwHKF8FGFudO6WD7UwrBObcAe9yzJy9XBEIoBDMRuJfwz9n4GVS3eFpSBDlpe93d0nOIkITDkHZ6dOVYtumou7lANvd68LxZLu9310EIKCw7FidX//bzsuD1eabcZLlt9kDs+Ht/9yT3tO3xCl/DMEN7/eCmCyHYIS97vvduhAgKbu+7u7u7u7dYzd8mvmAj27u78EJdJCpRAR3e5AFe3d+SM3fd3u935fwpUdBNd3d86FqL+4PY4oJD7uwl+vkCZB0LD7u7wd8xyRvy0KlZ61CCFld99+DGLva7vwfCAS33d3cqteCuCXu77+/BVtHzu7vfHK8XaQO7u735YLLu7u7u7u7eFJSu5bvtIneJiu7vhuycQQFE033u9t5uPK7u7u7u7v6GXd3d3d33vqunlA7bwCDcv/jpTcC+1l9Rz5Arwm6mk4FAlML1/5iapLxvn36/HHd3d3dzkt39Aqu7u+97+/X3gxE9AK8I+SCUju77u1eCy7u7u93f7wTQVFd3d37v94Vgju/zLqCXkvx3fFhxBETsv/ehPtZb1yqdVeG+E+B1nX+HIkoM34U79CEI5le6rwCN/giLe1zL0CW+W3e34/CCuv9Lyghu+w0CWvMrqfBFu9um9X6+63d/4J6V33dq1BDev1bghJu+XyAiu7u3WCoS7u7u7u928kSI3d3+SvfQIRvVegxXkZhmUkH81rijjARwY1TRn/ugJARXv0f32pVdwbrF3oj/S+NL6KdEVfrE6l+r/9Uq+t65Fw9Jffonj3X3qilV+ie9QR6qpwvAQU4nkQ/3xjv/VtVxCEe8pASdsKfd+Cg+hINeCryNwrCH3vPmILPMM4Nv0v/jQkCjmDmhxeB9p5mYWsCkGQEUCl934CkGghOBB/Nwb/90BeBH0CYEvV9OtdOt+9fnf4BKQ3Xoseq8Z4HCX7/Bb1Wq9Zf5vBFlz78FFV9V92A8Q73Rrv6AKMAg0UA8egV+BtoZ30C4tVVV/e69l+pacFFVzZ+1vZjQ/afrzMWXD0jmLh2GwZ3rgvO3KJY8VPX1PAwzgj8mE3WB2A3suK0M8vvxpRYYMBDVRo8sgdr6IsrAllcy4N25LjKxLoq/rg5DJteNona4JQfiQRn4A6xLaWNr9YmXkBIXVYvkF9Vev4JOqxfLRf/67RMqX34CjCvgK8N+A6Q2tdemvXvAL4EasAxIQXtNeXVe9W7wR9Vi7cEW74vmClarVarVVX3ijAqIpyNakl6r2XwnCOIMIEWdV1WsN8WRVqbPpQkI8nrg112Tx5rL4jRyWGDyko4B+BHWwNQqWJU+nefQk15/euHcpKU2XHfPOXYcOS1bGnrX+ChYsRof3gaIIsCMIqkwiDzMik9qgagwBQEvQisnpf4iQQkt6wFODYHATBSdVJNi9YFga0kjY/KOiLQA9sF4s273d68Bhg2BIQ1F+1/r8pVqrwy/lrAkusdyfX++lhv6IEEVvnrd3f6Ruoby3AewIgR+V77X3gkOxCry/QQZCnOCAqqoav5gNdKfTC/3gI96zI207i+sZyEUmdY54kWAb6xpfwA56r39NtIsh661fzHgr9C/BJ11bt113q/hoSr5oj//RorgYDUC1A6JKLU9eCoBhc/1AcdR6fm1TON+fOrWFWA7AwxSOuVLdYZbokvBeJE+BAsS/5gClY1Nwb/2kAWkEv13gvgkqq+rbrAIcBb6ArgW98RgkLyT7XgEcBAYVg7EvBQUpcNMt3RGOMreH36oI9Bb69HLAIIGsmCvfG9BWkAW0FdhUgIhR7PSDgViSwNNe/+V3d2gc9ihfoAeHKl7ON8B91ZnsHAEkQl0SKXEdFv1ICUdsQVuz/YcLZqNNDsHwyOChE6ZPVIEiIS7BKZbiIMplossXnTwDJgvfd+AZGCQuCFJvBgcYSmuLwCMg2FkmOb1kJSf1gF4AjAChgIwMDI7aoAkAadCzqM0f/+CIFYoD4OAAZMBABAghjh/Bd6BExCmAJ+AkLUCXWrQIHrlAOrAAAFaEGagC/AOj5Qxhi+PMvWBAe5JtGG5931FCJ++93vwrCB+FLUIKrfl97vDSDqS+GaQW4IuDnIiwbm/2TEJJ+ou93d3fcQCi7u7u7tro5h3NizgeGmWu+vFiV7XioRzgwgHw9lyKfA/1ima+u+7v1+Yuf75KRZ4PC/qInEazUcSKGSw3+bOCP+8t/BH1gyN6U0CCURvd3fVOMhC7u7mI3e78WJMXd9DjApu7vd3dyjykDC3SetSfpzi4zOfl/W/0LeDd1jJyrwy7vhrAtc2CMJRnTfa8ILwf/wUFd7vd+ZcTLu9eL3d3v5IT3tS0+6+8LQoXd7u7y3u7+2kkIIFN273e7u7u79rFxkJmve7/JB0X/x0vhwalL6kyUCqU4G5rcUHPUmvn9Wk7+9oFB93vfjKm+uK4rru69yC1XMS9z/Tv8KXd3d3d33c5Kr34Lr3fd+y/4KOvfonn5t77AW4ZNu/0CIto0Nhwqwp3ucg3e993+OjBS7uQed7MS3d743uCEl7t+jtr8EhHa+SRfwZr4ryjFKt8gIb3ju1vghu+VGXkRR3a+l9+CSW32xiI5VV6lSvd7XYDfD6LQSJuVEMgUryKULqRffr77Xi5e0XvpG6DN9BBehAIYqKKi8y6qNUMrto40UMqNXERl50T3yovH49ZU/a/ryKX/9ffr79cED6pg3J3rShuRb6v0RvdfeSFCrVbrVVWqr7wIwT+T7Rc36+8WT617YY4Im7iyvTsO97c2YqzU8FnmGYLk6YpPSX/44FnjuUCpjkIq388Tv7jn1SjwKgZfbngKYNoX3gwBL0AtAbr7oEQpE7r0t2r9audaueA9QWrfGLr8EdV/f/qVXWixzpJGfsJCu/6Xq6Re9kXq5Ap1VV1VVVVlT2gQkWq+vxc4BkqAh1R9nOfXBgq8h3AXlDuIH5CrUS+xwsVhlgiHTPmNaHBoSHhPqMn+tHUGFe41V9KTSbwN4IAV1rxMZyDy/a9pEy14eQKC8i6r+0uHSgkMqqve2hZVdAIQHuquFVeuXL/1rfoAy4JUQ6dgDBQJfdLXxK90691a919OtVYBAw+vd693/gk1Ve6AIuCkEJXfp1sHoFsJ5b9V+FPNlVrjzROoV+98QidrCQiMFEmvVax+NF4fZbri5Zjx/rD+9YO4iUy57l+Smt8t6zbCgnUe9ALNdCfNZaDuAOPLwHl/g7EZeSFODfNmXMPT1fxpq64oJ79WnofF4aE633Ds+bAZ78B4gu1hogaEgnEVgMI6ilLaF5CWSKsB5gwwSGkv7wHmDAEpK1VVVtcEeCSq7nnFRNYIxOq94Nkr1g/rF9rFS/8DTX3gOEEIcNwHnqULS8PR/wQHBVqqqqrFQf2s0PsO1quAFGWnXT7wKZLFz22ff66wQzX7HooJjgAjv/t+7B/3evuCwv/uhdwlvq/arrvV/DlW8AjQIFfWDYRgqJaGvZLJfRhx5wNzGvwuaa+Eqp+uufRLv17gl8ATZsUKn8YgXCld40LxdAFJBT19D/hn9qlVdwive4LebKkp94Ia+wrjxf5TXLvb/N76/95Lfh7LWYS+cl/gIEOG4EkdRwvCPc+X/AI8HACQBwFBFXE0eB1bX9qgEVBgIBadSZ3vkiv0bw+0Pf9ag1rV58B5BgNQE7LXwNiUlfHAfgE4gUXc/yfSYcQBTQT+FSIpzdVFAtTeCMyK5hPNCfOYCuRFoiPdLumbesAw4IaEBKyy1uJY60d+AZUGLu/fgGZBiCQuCFI3gwaDiK+tzeECEi8FaPPYDH4UYfzrwPKa0kATQCYHIKaqtVyO/j+dEUn6gEqATQQl7CKRYPxCBD8EOC70HnIF/1eBA9YoB1YAAAAVYQZqgL8A6Jf9ei/5uUNDrQtaPMvpjcMZ+wDg+fkhwVlBYwWS/wj4b03+GhAbpjp9fhtl/+IPnHgxluzEZfDPrCWUDGA9O7v2gWEzWXHd3u3uX0CXiZIxIgPOFgvcGW+LL/zDf0Lh/MhhLuNRr94ll9yAiE8/f6g7sOCQwI4Zf7i6pYopkw/j37pWYZGSV7ku7u4/Jbu01oyCk5J3d7u7vfdzoIRmWGUFXd3d934JW7u/QWEAh7mAjeGYIb3trNXS0v2a7vhmERRjHf//OjOlE8aId3d3d3d3csSy2/PtfgiNlFyg3qqT8EI04HkQWg3fiJvDlq3WJGAoCEpK+7vB+FCvbvdu5bu3dyWyXvBLBVt3d3fd7ZPXwh5QVd3e73u3gSRIKb3d3d3fb8FVynO93d3jnlgt3d7v9eGIIy4RqgMqj2N7jesXEQRGd/qqYpd3VcG6+IBQYF1y3Bbu7b6wSzD4F90BHYzM7+W/fEAlO/fd/ecQCHd/fr7wWifJVvwS3fd9irAX4bBJu9+8EV60qk9/CcG3BEW7vEXu/kRO9GBZu7u7vd/sww/qHwVSoy2+bfoTd/d6eTBHd/3UoIxO0/dCYJhDve9+rzi5Rqn/BkvCn4gE4zd3f+OoKLvu/34J7vu7v78FBXd3d3fmXiV9+vugGuEV5lJ61L+vzsA74N+rUpOG5AQ3ffu1evBFd3cqOfgqu7u7u7v8i8gISO9/D4ISjcr5QfKqRLJBMR3eq16DGswX4TdaXOO+KQY96o/vkXkX18b/X32vvwS3fd/3Wp0MrWvqqRcOi1v3rmK3X36JmJeRH7L/9L1bWX/L1YiC/r0Gj3vjJxzF/BI9PBQO3ArjoytkQdeVqrAewEcsAlup2+f/gVgz8oIRa1++192AUYC6sTrdolOt606k/b/1zH6+7gQ1TpE/Sl/Vqr7qi//ryL697r34KK66r7yV94CGB2uXgLQCGbqtagL8Dwie+RF+fgoquq/a9iAXdVWvuqCgjVYQ7HejVdV9qsQYpZQM1UauCTyC4BoGbCsa/XHesCqCLBKZYPa+shM6vDqURajXBz6eHQ0RSprw2UEYhV4tPdgjPWvutFi0/r3XVAELBSvVyrF+sVYC2Bj4CQBj0A9Qz1rH0vq/gJ4M+HQoveAswRfYI+q++RW9198gLNTZqqrqvtYIYPQYF82VtwgCEkhBWcQl8fG4oMCATiNVISmvawvCLBWXU1MlFqa3WVXQeEAvJzYqyTOl1L+qobLVeqNDMaJd3lHjntLMBigYNjjA/CQ1i4DmJPXDOaaYr+/UcQcZ/lHl0GW9iUEtZAiHVovr1esNCd/SvvBHoEUf9fdrAKSBgDQkKGgvi0my4iWRJrAWnqiYUdoWcM7g1keSKsAsYF7QjL4YBDVc6awFJVe8CKEd/ddosc6pR2qsEklP69s1V+AXVeAx61+vvAcId8B4gn8B8gr8DeFQQ3r7XQC2FAkEce37wKAOwR5My37L4mnOU4IuCvFq3RZrIv0JgqfhTl//r0E/B+suwwJV/C1X8AjQIFbWBAEa9rBITBGIJbZhnY0oBgQX4JY6yNi59dlb9h1MjD3kmugCyhHJ6pX/4S0X/8ojAjEo5HqgCZgVfxZV11XYAxkCusfvBDd7Ufb4BDARIwptYwLCUvawF0KFwQlVRG1BfnTfizBkWCDc9T/iJd/6EtFeDa9XnwCCBg9j8c/78AvoLwz8BUiuob9RrvndT+BfiAsP+X+9+BkZb1gQ/l8AwoKaEwBOgEjShDgoyXhjLfKmT9IAIIAcMHAjRzqNUuGR4eQz9e+/AdRApf/F4CO9WgCFYAAAFpEGawC/AOj5QxhPxWUCI9ctYbg/+T2LlGzgRBKYRf+uDzUwrAj63031qpBEtu/L8MwXH/ArVc3DqCH+ZfCPDH08Rk7lyXwJszJgsJnA5QVryEOBIJ/Ad2C81TuEevGQUR5qkmLQNqyz+xvPLg9TQcExAc3H2i8Q/JZ7k93LKaGJQTZde7v7w78N1+eCWvu4gFlk7u7u7u7t4bhC9nd3d3cgB3EAmu7n9790KEL3aEBK73tUuI6+4O14r8wrZJO9/sE173Le35b0n42FC3uS973d79fiCO+7/3d/iPQuKLd3d3e1xsKFd3dy0e3d3d3f2XwjG8IQoZ3d93d3d3f7uIBZd3u7u939uy9FaltUFoYQqmAD3fXxxB8G6+IBaI4TdQTrarb3Ks1l4KYb1VUR7+nbykn3b8FJbuW3fd7e34Kbv7u7/vwVXd3d3d3e34+7vd+9/hSS3d3d3d7u7v78E17u7/vCMEl3d3PwR3u7ZPr+YLQRlMClu36+6wS3d3d/2X/k2R7T/R+QO0Ckm7u7u7+307vt9ZyvwRfGx6LDsVH/R6gyXoR8QCEZu4pL8Eu7u9/vsE3d3f98qlV8gJC7vx/geQiuXgkgjy3+8K9eBMC77voBqh1Eesh69F9XSJi3+it8uX2r85Nds9/0svyfoa0GGFJvG9ehCHd7QIZl5l4xV5qEjFD8SrPYDuBOrnfXq2MU9nUYzRioFpscBDRBlDvWt9V/YC1Da5xsG9b9r2Wt9v6j3dUjjsf+je9JSq/BMJWvVSpWcgsQq9a7KY1V/gjPgMd4zUtaMIK9PX6FZZfpGkywXoCKfL7UnBSi+7AJgBxR7/aju/Uqv0Qqv199r7oCwDda61InT9akR1rWnXtX61ud6ud6ud6udqvdyLXgJAG/YBdgPKK9L8UatV14GMFlZSq34KDKulXjMv/0LLquYSMcLeh7lF/qrgj2hEuI80ABQB1hUM/e/w6Iy/JMxbFghEKoOWvi3HgtQLqru+dEXD6Rjq85AR5g1Fi94IUje1+j+/BGWqx3dOpUrdYrsHIIVi9Pql7wC6Besm9wSVVbnYAmcCYCguq1W5k9vhj9XNXgPAHYiqrVfYDDBqC2q+q1r8E8pFeq++QFZNVR66r74gFdaPqq1X3nMCwiKddVa/ax8IIExFjjWLDTFVmLWF4Vgl5rZ8t9rospMc15Pb2IZhO9ZIWOCsXmtIC5nOweTaPAdqr//KsVO3/b6w5wfksGXn3/pobmIJt/foNCdr6uovnEBFChMSHSQaYSjcfqSLq4+NUp/TQdf1gbwfKCI1ZRbvA3g+BDVedR4G8HwIa194BaQsWS/XgMwEGlghzVVV83618v69WCavfgu8lqv7oASv3ySrFXL0AUEE/gNMFaxeAQEF6lTfkOCA2q4Z/OIqGzwwafJoDRijLNojX+FijKqqqtcWDmJEH1jX6zsKsK1VcgxAe/FiQJY/n/rEdXeOEwVl//60Puda+BMMCVbwxVrW/rDSw6TgA7KfGCh76xYA1DCzUAaBacVZvHGLe1VIm/1QyARcEIggFG6fz38dV6AE5idVhP4Bbwx4BgwsU2AqincAaslszFdtIvfCy9+vVwfr2b//V4dNwFv09KiRwvwDTLosAfMA+bRNL2+A0gSDaqqrDP5CZoAN/lI6nQvrG/PkZTx/GIWbS6e79YBBgTGhUSoExepgHt+Iz0CNVT8ibbI0agmb0Rn1+DiaEpBzjoAcf/6QZGhoYiSKX+d/BawChr75/BTD/t1qC71qIL/7ojROAZUGJIEi5zB4jK8t/L45B1A0irHJss6nrBCgGwGFfJ+kAWEE4BgWAJ5Akm+A8AIQVKqgHOPaXAHrXRSX4AOqtl9qHC3skZ5wfiLREFb1U///hkaD1r4RMy5DcPnJIXX8/gzL8GHqxAv+rwIHrlAOrAAAAWeQZrgL8A6K9Q4GAzOhkev7Z7MvkLMDAx1vXsXkB5BZexXNgPNRfhlmiE3lGfnr38giErUeX8L/Z3bPmXw3V8nA7p9fwXUspLhuD9OeUgS2AWYqd733iDZrHfbr18QTxr14Ti5bv+giUwHkBkCMf3gW/0/v3qNHH7u8Syy9y0ryJHmQeJoOCYgEggoKfx6pcKXvt3d3u7u7aoEfGbu7v7tbvdYYEAou7u7u6ZcEMt3vTgWeCW8lx0CHu37wq17VEYJBAKJQI93fbx8Xd3fd7aHCKLu9E/S8uEB4t77u9xW9fB8xAbQzDtAz03iZaV3By2sJTCd3vRNDHCL/yYKCu+72DbwgSvH3rvd7v8EXG2XyJZ4LLu7vd3d3bocIH33d3u/8h4w19YlrrFKmZ3Gyy028QQIHvfd3n3X6uK8uDW8xOE3WviAWiOIcH/f3bcHQRCxAVnpHlO7veVZ54JLu7/HkBRpO7u7tvidfHgQROtoGFFb8E8ueu+Y8Rp0HCYId3+HsEl32y+Nf4u7vfdrXFSA0oN33vXL3d9AoLdty/2/RegyVZAgghEy8QCEuk/uwXAhW/6++UEpXd3d9MlyO8t+CmCK77H2CG77D4KLu7vdyoyXICTu8TLpr1eCje+7xz0gSb1b8E3dz59hf4I+7xU+TRGVyIW8GGGPoUhUTLuvvlBEV3/Zf/tffrxlRitd+i+r1jn6+/Rjp8yufr75V79UyXr30sXy16/PdCfUvkQjt+1+LPj4Boy4MXkWs2SxHorEEd0YZwX59FehXxSlV4FQEP2hIxViVWMV++D1EIvFkq/6+xKKTPr6i//r3gOUb0AnQ6vdNrE68Svj9EKiJ+CStfZf/179ffr78ENVyoy4F0FngZQ0CLPmRPUEZNV7W9FJLfergrOXePBU1bVCTLMzT0mV2lFK33rwRltFi2UEEdA5Ha8Otey/rFxYJeXjCZVZ+8OwUY8WXUxn8vDtCOy+FuZNAmPqsqmNoDq14TaFdr9Hw1wVgrFgj1gI9ctfKngKgGKP2KJ7AIEBhUqzvWLwF8HfD7WP6Sv7rei//onvVffovvAYgM0SLwWFX3yL75V8e6+6gdUWulWsnr/JgnqtVXi+gSkkzNT96MCnM/kzUlPvDQgExtTEiEgq3h/aw3BLBRw1mgeiXHmraaiA5DhFXWXW6/qqGw141ypF3+nHiLD4vQHsHrcsD1uW8x91RSpb8JTJzUM/fJ74qojjoTVR+4yGH3Eo+2/nGxyP9/wR+WMsb7vV8n03r65eGhO/6WBEGBoSN2bEjLiwxliPCYkH04hpF47ZuEtbRZr5K/0xbrAYYOMFogYI/OSZPsa7wRVV3bL/1QJM+feAaUQLgnHePpFJdVgcA8hHSYDL+AtQz0AakF6PXgENrfwEhXsv/698i96gj6r7oAmYJqwKICMX3kgiqte+cFZtVwTNUCNXcId6UapcPGKMq9QRzPUA9VLf2sLssEUHseMMgCN1+vLm2nGFZuS99ZD4IRZgPcZD52lDfBJjQVzvnoJvlrRbn61ELk1c1+HcZaIBGij4Y0NvrMPnAAawjzNQUASScd6e3Hu+xfOlsu/0oDjDOxABTjZ/43n97pf8GtGi8IBb0FAUcCESj4Q4s5mdh9LXt+f3gExS94BBQLfgCPwKaEkTDKqU15d9v5N7L/+aqw2e4CqBkCgVjpYBrF5QdWONHSXvQkFvB0sBqtAv0HrOUDBe+FRYE31MNuYU80YiiaPBVOEwcC8UTrxFoiSP89i39+9CbQVfJ69H4D4DCoupYBgQYrrALCGH1g3EZhnP6wG6E8ZNfrMWNnyD5CXWAY4GThWoFhykGIkPUupnGuL6WGKQ3zzoiJZUv/sMjzBUqmf9j4x3366gt9XP1yr1chLy+WgOPV0/khI81fv1qI9EaAIVgAAABVNBmwAvwDseYLcBb7H/y+USMgd/wfPejCsASr65ftR+36giEFXa+1lqzgnbYX7PH4S481KsXJQO/8QTU+8pIpKT6SAyxIiTKVzg1+X2STPITZlz4ve7u78Iwh2gWVLPbvd3l/881xnX3lv+KE8JcPQKB/Dll9dDsHariBQjgjpy5P7vL5MJAshkS4wX/34I/huTd/D93d8n9BuIXgi3c5KLShwvk+lCpTEiDSXd3r8Et3d3d3buIHXd3d3u7vxAnX6F5byIJ2Icgy+i//UHGIhw+7rxOvCa530ShTjcQFLu7u7u7u93f32Ckru7u+WlL9VAv5P1/AR/3BNuk7u75nhVm3d0qCEKGLd+lgo0N9pRAIfxOug0a91+3D3Y9QbeYnB7y/LBabSGdIWZQIm83lRlL7xF4JTh2ebf3371X34Iu7+y/92Wcld7/F13vflq35Lv/BRfd7u47givfyLiCAk7u30FD7u7u7u93uw4uzPf5Ky9LWvfo7eWjNXhkXk3Xx2x8GJf/C3L/EIRoJd6EAiLd/H+b+FBJL5yV3d9343YBBAVgllIjq+2hL6dW2BoUgIqqq0aqr5ETMX6+r1+/BFu7+9wS3d7v+9FX3uvUX6/X3VL7J99/R6/pkvcx/pdPuPfUfr9SISK8gwdRALqJF6EIxU914/9F9htFOV6fOa/Lv300+X/9R2ZeLXu5FjnyIl+9Y5fSxNrrX2v1Y/Uqv19+vh3X6yev/1VL75ERvwx1UuZrtH35f98Nic9uuNzrB3FtwV3mNwGXHRL/xUUCM2Fv4vtaFAIKCESDDH//fSKOx/gQA2pU+1Hd4OgI633+s7uBFX3698qlT9erwR9VKrw9X3/6+8DGVToi2AK3D64julIhqtyIl/dfe6+91Or8EnVfC/wUVVVVVXvcEddSq9wR637yERDqN/nIsag0/5f+jwUHnDZhpWS9RO/TVwSpMDaD4DWIBEGYR9eWyf2Ag/FgawsCEJEs/3MgzUy1hsoYFgi7kI+MvgXQ1XoQdR7QIi6r2vDfX4syRY/H/XgdQfAUxAKD+AIFkSTce/CFl7DMSa8pr+lj6eLs8AhNE7EEo+A1RevpEy/X36t+vqwVj69Y/QDdDaxzoM1c6vrUidBcikT5AR9V75ASVXKnzL3QGMFnswJDaqVZrF8KbUpI2fWuqzmsJyxZlteNLJ7axFhSQr6rePNQjquQklWtjBdwlehtmLlJ8HK3LFFyvCbtuc8s0t/L9Ll610PmIJK6vVtvxII8bx8KdfWGpCRTS3+FahEksKBaW3lwQYyFcxFjaepsC/OqfW/+GxAJSIX/5rNedNc2CEi6WbWBJDKmz58BKCPAXIZ3k69+iju8GVE9VAG3/qVZRPX/5OgUdV1X3gMUGAJNV9rzBAENVVfHxYICKtVUAx7plGqUgCg5Klkc/GKumDP/5P27wJCUE3VbGWjDOoy+ImcSc4VpIaw0ijTDT3g9rFrNDaKBMPN9f6wp/HnBGJcwr1rLuCv0XxFYeglwFZao4KEFWmAYKwiTzeK0RViDYgiVPsXtlfEF769f3QBMwQixmBCJT4Q5RmZ2LyM59L41Yn0CQBAoT3Vr219e+LBQbE6As0VAoOlCrlLvxijKsoQw75swjacEjyfmCplcYP+dXFX/gMACWFRYC2GSeYEscaXfhPQomzQazzOA6AEIB+L//6Tro/eevQeYCqCCpQZPb4Fj8EWEfXvrAMyBQ9cmQdzS4DWAiBUSAsR9qAOaxknqLNJ4TuKM61jV6CmB3YIodZi7j89g+/1Qlgy/v8Fla1JWrkCoT6S/4vdWQEZrAMNAAAFnkGbIC/AOiX/XKGJR9nlL/7NAgvyQ5lAzZGLVzl/grw9W/wWCJzrW7++7wZfoO4R2Vt79RHGWh4tmIh7LV5P4KN7u7vWZ4//0DC9n6xIvaa76VkxCSZ8XfaLddElw1EgC/Bsf5ZT8lg9XpCicBZpgdJbf6D93/kt8ea7Hwhed/9YVgtfgji7u97+COTd33EApvdygp73e53hS7u73d3d7vbTWFIKbvu7u97G8Ivy/iupcNQS+kmml+CQbKB5JdvIsFYgIXGnv3d3d/dFrF+Y8gMDUSGDjKI1yYLBm7u7vu5QqwUs13fVgMmDIIAoK7u7u7xz8Et3d3d/eIgs3d93e/vx93fc5K3rdBAQCvu7vu7u2GYTFDGO/2f/g00GCi9xAkJO73e+q6wTd3d0vtrdBQS97u7u73DKAwZnOzq7khD3eQDwTeYRwsyq+YEBMJuqnwJ3aVvvd3b8b4+/FHChw95t/u6Tvd3d0y9gpu5Lt3ZtW+mXwQkffusKXd3d3dK+Ujds3l3HXoJ+qZb9AphQu73d3d3d5b4/rEXd3d3/BDrcqMr4nBFe/tKJ4m7vXf4KDu97v9rGicFoh3e7/d0Cm7W97v+pKLfIX/rR3/R+gxL/4U5f4iI0EnNYzgjO9/OP196Mi9v8El9e8GNW/BHvf34JSXd3v5F8EhXf92AoQivttxbXdLfuUENVXTryojD5Cu7/Egpu7u7u73fMfghI9/tNEIBIB6CES74jIH6fUGPEfEIQVGXoEMgQ3f3yo/VYCaBr+vuwEYDFU63q/dAilvv8SCK7/MlcIq/X3/r71Uo36I36OUBct6J7b+id4H6coDiABEKXi34ycSHBQxek+RTiO7Z99L3BQfchenKddBX5jR30OjrT4oEZNQMsymZ4x9F6glOGO8/nvHdv7BIdV/fa+wurr8m8m8xm3uvsOL6f+Teun/6++wR114yvontL6+r1cutSI6e6+rtSrPwQ7qpQvm/BFVc4P178EnSXq2YERFa2+gTkhFp9U0ElJTr34YKqh7LJYXBmmFigXrc8FFyph1768I5b9dJwSYfsEv58y+1xMSCYOGXjtEFMbGBFFGA/vD8Etxloe4YcZbnUeG0IM3AV8t+7y+HFNHyqdPlBQIJYx7swPZb94bEAjqWi/3iUjju8ekX3wzl//Xvde6gTVrtgQeqXur6pYulUqdgCZ2sXxqt6ryL6lVRf/197EL3lEAi6r3yAo1VaqvvwW1rVcqvwREJn98wLMPP2dVWtta4UvtT2ZkKuvNcsMZa6j/ZYukWVxdqwvDAQ1QM4aEFFKNNWfVb5P3JoWIsFQvOXDuWjXcgnH9FiP8vUK2D0WVLZcmNe+kVKn90POoAm+bwS4FZVOMP66CREpKArCNPr/MZfBGGiphFCWIXylcT9YX6wjeLQMAgjdpeted6VdfVyorn6vWA0698EIJKmzKr9RirG4nTL51Hd4DpBCsXgPMEC94BOQwvvEUT3kOCyq8CjpRHOJOflVnYHRijKsRw1U1k3Wmff6zXBbAIb+nxXnYsu8OqEk3KO84tuMkYvc4K3d/Hj4M+JQ1/iVua31Y8QJDWBGRXTPzg3B17/WAzA1glIJYokZ2AJH+oqvn5zFsz2hsd07edXYBswWkFcY9sP+/bnsPAo4EIii8Ci0UMzOBiTJPz/l3gEHDOX/BUOB8OXvBMOBQJqqrr7oA94GsEc+e2/AdgdBKZV1VfvAfAIxpFqtVVQYl41KkZNQ2KgN5Vcmt/WASwKuHiqqs8CYvXBGRjF79B7ceo+LF9sW0+zhUSA5YyGoCLQTQG0JegybxeA7HyckevRa616D+gApSAn1fJ6r+CceCcjnzZw7C9VQX5xSgHAA/aN2sBeAowqcC2YzWCLwmgMZWDgbyBrDR7nUQzxPv/rR2AL71qAJ1gAAATrQZtAL8A6Piwxu5c8hf9eRfTFzAUOpO2BUHxf/UWK47lMDSDssbmeoSEB/or+CzS3X7KONnnvkgl4YxdeaghH3NP4T4OKIgdtld9fk421VNHwQ9U0NJVl8HOvJzVAO19FYmNM+ly0/BRAhkp8+u1n6VvGSj18W899XihJjkA0fH91ekcInFEv4+m/XlCGG5PsHKaDQmIBIKu7sKqTExuVS77yfawxEYXDIS3d7v4dgt3d3d7eH77vd8QCct3d3d28NwUXvvNDteoLicda3u3jYst3d3fsYIGXd3dygS3d3t3eYoY//CIRQdSm//yJAuNAB97S4ZRH86CGx+IDoSk/4L1scWYEIgo+NAqFEdyvBOfhvS49ObgHFCQgYlb+QLiL3d930Y//6wUd3e9jwSiQRlIXu7H6tepO78n8Fl3d3e7u7XvFSX3SyzSXd39G3v0tfoSyDfw4IwL5qLWai/xvq+YF5MuGGR/1/7TJf/iCgtOW238t9rwESERF8l7nJVgIgKAYfgkEAm7vFp39eWrHYC5C6v3Yve7tfCRHu9Z5tb9JYY+Ci0++7ayt1Z1ICi7u93dpPQuoMS/+EOX9iIjQSjfQIi3u/vnBce72e/j9fdAgCCju8CBX3gfwivdAN8Mrl1yevIvqcLWuCPd39+rViIIju/F+Cozu7u7u77Zf/wpe73e776pVyUSOQY7FLzLxC9+o7vwVH3d79391ELxz+rfgj7u3gQK8iXErfr6iVf4mxu0QBetgOEMq/0iYv0f36MdWX/8EfVfb8te+gSCdV97EInU6kpE8fiz57CXhqmlT0wV+Y0HvIGUUt0YEZMGT2RnqRpMzUb6RfYZjQ2Mtt/mvIQp/L9onHP4Irv+woT/OaYP+XfMoxVl//X30CTyZ79E9l//X3TasfS3oYtKvr79fd/eCTqsc7sEWtcnB5KJ1cgIuqtrrBKVarqvvZETvcEnVe1+zdVl/6PKdw1ay8Xry5vOdZHBKwyL/glL8SPDXgiLeXy1hnjtp/B7yJQZy+HUG/iBBaPzEg1fy6L4T4biaM3zgokwLfeJfNrgtiEfvYhE9nR8v/6+urBH3fuuu1OrrXtLr1axdWtdOtzrUidSKReq7U6fr79e/RO+YFFVVVqvviAQkWr7VEQ6CrkkOtVXjTRQepU1YXjhyEdrBDBZ3g44SLz2QkQly+SosU4wTnnOPyGRrxLuYDB9iX+sksEsINa/2wNZTBhxpfr9j08Gwfgmr6+uTyYEZLUz8vhsRMtM3j/vWG6q54DgBACEyqfEYzrMeAwAwjd+ve690A7Qyj9e4JOq1P6++wRa17fIFhILCLXWqyEhlp98WH6rhk+Tn5JpfDWqgTf51//dIS79YcF7qvw3BLDZliHLAOngQXGG7j425v6QVheCESCHZ9tf6DRY8T+DqiuVh5BWGYyKSKZ3gzpJ/tA4xliuPAIYn0VeTU+lHBLQg6jwZCPL/BQTAhEo+BRaKGZnQIRFF4CzEN59T38dvwrBGSZ+j/2vAJwCjX6L3gMhglE5sqv3hYESEdrkQsFGHyPYndE/O0z3Hdl+Qgklw6XJ1BivEBanqKNNOz7+gR1UyW9P+oVEgg7E0+hBlOP0j8TvyAGwcdf//Ahr9e1kBBo2byHDJwCETeIifzjSpY/AdJ8mp8n6a/369BZ69J6vAE4wAAATEQZtgL8A6Jf9cNhiWOuGsn68vKFxsPYETU2XAkeaslfP0fBeIKrMi/GvL44a8e8qJHnMSNH4Yyz3f4U6DLMJYdZx17u927tl9L8KePNXEcxF7t3t5gI2snfLlP6HcaaJk0GcuiHuQu/jeX8QJo8EcwsBCvxL766X85V+akisHfm8Ee8ckFArt5b4taghhOEDwL++/BHNW78L/DMERX3Y35ZN5L4V680E3GS/NmATJYIT/H8by/jIzVeYXMEhsD/votiBmQ14jT73fVaRzs/w5NUHGI7/Qyc8I/DvoEAgFBS273e7eWCK+reIkveskEl3d3KxhFYJYnq59Fu7ulSqCQXLy+3GsFZjge727u93bs7g18EhsuBAuBJKMv5lNgt4XvrCcWEtf4YXJ4dr1+CO7/vLZXfWSCTVbi9cJ31d38TBSV3d3d3uclt1MjN9E3f7BLd3d3d+9wU3d7u93f30CK7u7eoUEu7u7u7vd3Fbtv8Fd0nd3d3DEEM9xFm3kVYP0XoMV8RrXBEEsuCGHeIgrOXHve73f1VCIIirf34nJb3er9Ea+VWH0VMl4T3fd3rB0sFZd3d3d3d45rl6wk17f6J6k2TAhAlu7U+arBpOgNcEQvV88F5P3/DXTTwkhxUZeIUqAf9FGqH/r75gRXf79e/BDd7PmSGK/v179fVyL6/Xvl/X36KVX6JpH9E7f6tJ0usv++GxPDEFvXIPm/gr8xoFw+zt64oNiARkJx8+n70wQlBTE/v+/R+P8ErRfemiDu/X32vsJktbf/Sa/r79eZfX36+/RPfPWqL76RPFE9f/Re9190B3ohVdAawWoolIar6JyLQBAw6o+O/MCHqvdgfwWL3QHkPLWuWEATVqqr+1rFgjELX2vsp4RYF5CU5rxbm8p4y++CXiUWDw2gRWgXXLfL6EGfg2y7aBnEeX5KYR1LFWug7Qj2v1zHiRC+17r7X6OO3YAvMCyjp1vRL9636gQVrqRX7gT1Is71jncnqpVfr78EPVe/BIXVSp+id9r7oBtgrWPeBXBM9olPw0QhL/0T2rxQ4GHHn2g1RVCbz0ZUpYtfVi5YJBCgbZS3i+ic966D5kXLWSHhIKROD8kO+kiR1N3o36Ye/w9QzIS3Z6tTaPPX+Cd/oa/TlwTfmb1xGrt/0X4tC6r0JX+sM8EWBdnR+1xOCUieeJDFTnbwdXyv/3hqWCUmDCvGVTlVdTqHTJ9YI/hHpctCO17q2vzea3UD34G0JoX3gM0I/KCiutV++0TvAf/wcpYvjV95a9+LNquAOnLm/CkjPol+BJEFqgQFWqg7YFver38d3LV/rCnXLJ75IsIvhqv54/ziRRj9oPbOrJsMG+DChbHgyqw4cgiwCVaJrs/lpUBW0btKAIHN+DYdqpgBBoEkxBjyKfL6rFmLHmCyEuqgR6yfpfWie6AIuC/wCPCgpqq1qq6gX2v9rASgXiQUSC6oTH7zDL/4l8BMAjPfBUJVLCNWON9NJk13P93Iph4VqPArAjDokQEkUz9E9jb9EHu0vzPiNe/WoEBUllzEp5yfpBscquCzPngHy+fxfkM4EH804f9C+XsSHoCQkLJ2fqPOQdRRdxticYBOPz//V4MPV4F/1eBA9YoB1YAAAS/QZuAL8A6HmCHD1bL5RExEojcv/r315RYyB18Hz1JFiuEjz31dQHupXoJCDApNp/461l88vEQye118MG6/r98lhBrqy/QKer4lJHJdR3HGvOBjEbaB3eugU8D7atIlA0uDtA17mT6wmwn9k3f4uMeYMeceDWT3lvrpSCZh9/1B0X/604aEqcUs9p/ye9OoK4oQCrb+7u7/eGbvbvwzBFu7/PCsEZbv7uICm7uUFbd3u7u/vDNW9oZykg7BMnddne3e7vxbVtNhDEGu768X39GF5QITfbEIjUMBLrty3d3T60LeDeghMMmNPfZET48wIu7t+Yr3rzbu/LBZe7u7u7u/ZlHP+RPSnNLmX+i41KVeixcfd2l3gSwjku7vsTHXd3cwE3u6yQScOwQm46Qi7yjG7t9zCru7u7/iRL37v9EcA28FBuaUOPBJeO3wwmUv2ZolBTy5Ll6ru78LoaIChTWPNbu7u7nJbu017obBL3ctv/daJ6qAwiTbu/woW7u7u93u/1LkiV9WoJe2q3f73XvIhl3d3u07+78RBR3fd+pbr+rheQuGIITXo9QYr0bL+EIqBUghCXHtueKoT7wKgJQRVXv2AngdrTrc4Iqrj41W5lo71gmo33d391glu+76RaVYmWwDECfyZb747BJ3cqpNpfb/X3qhKZPVCIvRENaC8v/hihj/EInfS95YUO7vfd3e7/GVsDcCsEu7u77Ver9ur94Iar37LW96EqZFXARNU7XqcAD/Uqv1b9Y55K+9V9k/SXwHhVItGgsrWtarX30vdYJuqqv3lomYrxYnCJ56tvBHt/1grw2IMQw+CCZzzeuI0Zv19+j+/RxgobXcEZHf9tL1+S863v0RivX43BX1D6xOtT1uvfaK54HAEK9XgjrqVWX/8ENV/eeCPqvfWt8EQrmy2vxZ4atZ+JR4I/N2vFuZ9ZTywuCMv7/mf//oFQfEWkWha/1EoNQb4kN1l4ZiI7w1blvF1viBns55vrUNCwqY08CvZl+S8rbdfrAxgqpCMx4HMFQJS46yW+1vvJFXhqva90TugGGCIEheHdWO77R2rdE+V6/lL/+p0/X30vv19+vfgo6rWvugCGgQOv4gEfVe91Km+pgSG1XteIhTSLhcHqaillQPJQCFu+33Je1hPgoEKL8cLC7P3pDsa9/PZd9dbLnseZfCokuOA9pywOWuWc+EEzsS7vtLD2VQ8b6yXESR0ntVHPISDiangnrQ16rV0r8CkElc1gZhTglIAEC5lIrVoM/AOMroNvPr0i4HgQCUgKqnQt9svj/iqy6SK8Nw4I14RoR3jhG/KFl73BGJxr379fV6+/BJrX34I9VlToAQf6hD8Efmz3gJ3+CTw373jBIJBWQkQl7XYtjRKr4QqURn1fQQiUCjM/uS7wSqza2+v11gwqt3LeS/IvbbJrNl81HfBP6CyvlHTq6Jlb9XButAwwRCQJBc5g9MN5b+54fCHgt65gdQTiNVDGWGSUt/a8LiETvAitfVkr7L/hCSCSkAcx3T1/awFkxLC1VwqylwU0visuKtv5fAWAsS3cFokFUmZIMqOlKPmH/3ujx0vkFEC8soPy/Jg+YPmQTwAzG20mPumrEBIerQBCsAAAEekGboC/AOivUoYluMRpfN4bg/+8tAGgg/2vYvMKDMQD7UOCrwyzVeDFAq8Ookcvr4bkEKYFvfccd1rXb5T3L3e/JfkuX8FPJfjzRL4OdrgixoKTuvq8R3IX6Bge00cTy/9Hgjl0YLT90eLtXIPbor14O/Dh+HB5f4CPXtuSGBnblJFJdfkvVL1qHYYLu+7r896Xh34ViZJvnu9+FfuSab/HyE3f0I3QMo9jrV13hF+sRUFp5gIURuaQ6BDbBvpokovc4M5ZuYDkA/zHw3Aef0VoN3iMiGMK5JAWkKCr3fb93eW/1hP+CQ+XLSLhBQWbu59An6t7/9u7sNzBQ9u7u7u7u7u7trLOIBSZyD13u7u7s3pUCvDUEvMXYM3e7u7UsGPQnX0yBG4P23gbg28MCOEV0R3yEfq7qVPFv5fxWTDdKpJ5H/vuVKnHxJEVvAhnBPLR93G/W2sBYhEBjhdYvAIOCFUqv8Ek5LdjzVbeFY0oI6rsK8C2G/N9IIc9aTu73fqLK93u+uf3k6MmS8Fu77vb6BCV3d+/Qlgl9HIuE3p/hvLsRfBCXHw/fQYr4gw7d78dBMTlwV3t0GILBN3u+739v9FcvDYlEv4D2BB+738noBkghV+rW+T9L+vCZQS7u7u7sda++wRl3fmVP0CMjvwjWE4G6th7Ch93d/d7u7b/KTd6ynwR3rwgxXzexCNHP19+CESf/v15l4V+J11q3xKueFa5vVfngYxuX/9W7C3uSvWAD/ojFborUX/+l97+IVvOgSEqvumsv++CM7y98oK1hLmIAi/b8ZEy6/l8MYh3BGZ5I951fgj4OpedX6+3iOhPvwS3f3fj+wCBAV1Ks/X36+p/U+oIbu7pF9Y5WmvjXwarn916+IXvwR1X74j6ROy//ICExs5riy/+WCM4emKeR2cqsv+N8Fa4vQvD4lYsvh2G/kgWbuBqblr68NDwyYNGIxij1866+mTeuGpkbxqwXhQGoQXMfCAI4E4nnM3X+dEXQQX295QScaaCHavv7L/+CM9Z+y//r32vfr4v193Ak6v17qBFV+l7gX17T/+Cuq666r7yV9+Ciq6r+/XuwC30SL6X3xQIdaxZPVOIFxCECAUGVYe/Vxj3tYThRBwhSRR665TJ9+8JIkXITKR8999UEi0IeauskX5fNq8QfAe05YEu+hSDwe5r3ke8tjTR1roIpYJr0R5uJW362PArCArAIm6rfqQiS5vwMnZEZ5kR0p2/zT63/fgVBQJRAbep0Gd09fdih0XzGpuWOngSjAlJuHwHN8t4Ool1YT5f/wSG4CiU2B2vAmDgRicRbyxz3WvSVBPpa17wPYFPEaxK/MveFGINqq19MFJ9VqGzLJbUAnyxY9/tYYF+T11Fv1YKtlDEG6/7g3Wlo7HghAleCECVqg2BKBAIQrsn6/jAJwJuFfvCLz2ftfSN2vVe1+va8OFXtYYhFgjKCY1IRZx/svlxaqFxIF0SP6lHvOgODdjEteLcm/RcyBFpwR8vsQY+rQL/q8CB65QDqwAAABIRBm8AvwDoeYIcBPvr7cvlEYEzWdp7X1+xeYqQmD7UWKy2YGzAd3jD6ChnnFx4sinLWNfe0k9zEusvlj+IghK6/lr8R4Yy1mgxX+Tx5orokR3kh5o3ETEfonfhIpQPAgfuxbyxgVfeGYt5fT6PLgQezIv3luev6ZtrxQuDsv/yGKe8wNl9yTaCIpAh4s/Ht+IpJ7rw6wTFvd6bv28OwSQ3CnXYrroEsKZAZd3e7ve6O3WC3e7u+vEQSxtoV5+LKlu37wrN2lk+6eIFYwEvjTV+Fn3zXi69+LlswEmm9oL6GCPEoULveUFIA38QYfDYrAZcCvh7V00T9el4OM1Gc/BX4817u7u7+H47lJPe9u/4y7ve7ku7nJdv8KXd3d3e7u7v90JiLu7u/wQyFu/ubGFS/hAk2L93e78sEd928YteJes7iDFfcnsoavoqcAXAcG3hgRx2VLiuVpl/DY499E7wmIDdF6vBLNnd/34IiO92k8EpxxlAfd3f2r8FJHePNXfd728R/BUV3d3d3d7TeyCiO/u/HFChS25L3dp3d3dJ3cU+4KJxnbu6VIr5KNu8Gy+QphYt5fxUgjBCThZld46CUTulb/ugUJffr7oGteRPlXvJXpF/9Gu/L/+CIp8v9+CUjvu7v71EXd58/0JcpfJ6SN30hJVEF6+IKMOvHRI/KIUqz0l96Oi+/BJkv6uE9fq2/h1WdgOwEN+otUPopUrAj0R+gTApWvdTqP18FWCW7u7u7pFrBDe/w9LHOsERVXa3vguIq46ypzwgsWGrmh3xSvfWFkoLDQ77pjLDqpwYpoUl/7GX71UEZQj99fL91/IvaJ36+/R/fq34It7t3Atq/wUq/UMq/Wr9Ur/r79ffr8+Vffr4fX3yr6vVvC9W/X3zL6vVqy9b4IjHAIdgOl+y/6R5SoBBHwX2Yq94K+dC4KJ+7OHRHq5vDQmIZADdKLnEB3G4L+tCA/1g96MxrcNME3Dv+wiWe+jdf7+cHyJ7eWHWvvDEEZ8Am9bHr/eD0ai9v9Eb9W/UdvVffr0/r70Che/XvA4gqRb5f/wUE1XVW6giBHVeN+C3KRKRU8q/uhQgIiFBm9njPshjb65fyw1rWC/gjID8j8vxBc9ANGf2HU/Fb9L5P4VE4v5ZAPxh/YCL31dqzzIm8HpF2OfvaUO8vkdgiX481aSXy63wT+hbW/64noolbda2PwRQD0WVBCkU9L4/g8CAgwIzlcxoKmpsOBD3p+Nw2r4/78LQSmw57jYGaltgyDsRza5cFXMPAYrlsg/o8951fJ4EkpicBVKNyX/jASDLwf0fvgIVb4n8CXX36+1+vsQq16a9vAQgKQkwUEXR4PRIRHzp5GCk+awKN0/FVGoEzVS4ClffxWo1hh4TqS+81/hWflDt+7D2VX68JlKXzg3IWVerIN3gvgxRMYa9LHQcQSkVdVzqOwUhAEtVVVXGqIuEtLz0K7L/juCSUkCrTX+1hZ2CwSqWg/cLhG89n5l8BCArCZXcERwg2H847osdAi+rkGPq8C/WrwIHrFAOrAAAEfkGb4C/AOh5ghw0xkv+uhEUQX/rEi8oXIDSA3ei/9ZRTyB6DxdqCTHZQKO/1/a/FGq2v416sb/UKH4c/ZTNVLRt37e56gqknvjzX3d2fgl481WPSQDpssmyzmtLEGx2IjjXy0+GS4cZZgAr8N6f3j4n+C2UDw/B4s5wMZOx71WgudIOi//WX8kmtaghaDDFF9R8Lwj8OyXf9EmJOjy+GZf/iOYmHZ9HmzieX8REIXCXMBHMTMBJd0MEU+nZXPj/de16X4IRJ5d4N2kOuhzn4avOSZ++XU/4JL7ub5ARy5L1jfqMOUFbu7u7u7u78JxxN3d93rxsF2yu7vd5YJLv94gi916UMiQrIUtH/IC68wKHQobu/SbUGpf9SQwbDDQSOgTdUjTd5Vv4335Arymi/d/fLU99rCAIYULd93d3u+lt4JYIc5J7bqIBFd/ja4JSu79eB3Hu9fwWXfe93/SVICq7R593d30y5Ym793e/HHBbZ3vZerxV93ez9jru/dp3pA1vN4I94p+ZEKm/FRJMJuqnS7+aGRLb/59m2voEt33f9WGEvtfovMvgiu1f2X7D3yEd/4JLvf5dYIrvuV4Jt2u7t8tevdaE/K85tINz7HZjH9ehOMgvXzFGHX/KIUqF3hA4Uyxvt7vvf78EhXd/uwMAEladaAgAjXL4V/X2OUfa9Xq17a5Xxq/Caxf8SCIrv+snrgq/qO79e+ZG70izEVry1y/BQcEH5lgIu++91+f877rNvPrqCtcRmJBF6j1hLgsNSeo8U3vXNA1qW/Mfh0rv7uBgmpNDO/+SL8mp8KIFW73fd/kXiET3bBihMTrVAi2bvzK/hNe9Vb4LVTq/g3Wu4EH9Uy+req9fq1+vv1Kp+vVtL7zwRdV954IdV98gIeqnV8iI2vwREqwzBAuy/+eLKpozErz3XlzF8ulUx+ewS86Lhb+YEsCUqXQOOZm2nnH+5LWLWD8FE5xTfJqfVAdQRasbwWQbA8BDwnygV97jMa8GonaQKwaCKN7wQGBGfAHU6X+d1e/R43waq/uiePpSpVsHat396xdwUrF3gk6rF4CBrF0Az/WsXwKi98gIda9+vvkXvggLWvzAp6qSPkJLX2T1aIIoICBUaI5LpL5dwgzqv3iEz9YbjoRNqprHWjvGI/Ly+sP7CJeaz2P+8dZL7wTfW1glFmH0CMShmN1fLwPGF4h4Zxlo9R1f+C3iUL/pqf/BFgKy1R8vBBEEMArHA64pbzeUBgTHqxm/+GIJTFGLRV7UNeU3ljI6jwlBKTdQpLGmifHVgogavlnD/Ac3y3/a8LQUR1o/VIElRnb2ksaBCX2q9SqvAnA8R/Y1VfF+WM0dfr74TX3tIneEjgozEg+y3hIeah0f95DhUqwhNc6po0xlmROqbY//+nDB982+JZPh2GlWJsoRODln16ug3J+rDAbE/qg+DrU6jWCUHQLK5tYH8uQ2AOeMS9TfoH5kI7wfwRar7X6t4YQLi6rB7JSXlrDQgJsERd3bL+QSuUTnPm0CNWrkBIerQBCsAAAR1QZoAL8A6HmCHDMKd84hfx16vLARl6go4Z+NONRuLjjOL1Em3MCvB7JS+4+G4S34g/D8UW5F8N9BDzR0vxnMRD8PikBVj8l3e7vxOnnv1ReUUMB6WD7YXAnf/t/+Drw4FHwR0ZtSXhri+X9UphQoB/x5Xjzcs5K+vDZxBRm3Wd297vdhuDGE93dJ3e/C8Kbu7u7u+7394Rjru7ve7v0CgQCySe73d3d3PCrCO73d3c+kAPL+/jOcuQBZR4xK7fdu/Exm93d3u7u7vxF3u6sSOMLmAj3V3ICsxKPHN3d7vc7ECAV8EnzvQPxsDr936vQn0G+aCgdy2728W8eIwV93d3u7u57MCO7v78Ry33c3iL3fd9iBC+21+ssR8lFcbEQTXd5+7228bgsE3d3d7mAjLb9kIBjgdwEwbeGBGEfpxtc1DWYnXde0y0yX98ii7u77usOiTXvLcQCYru/d+rwUdpVXY8QvwSd3b6Vr5PmDN318Y/9F+R8/fSo6Vgx8xOC901ejAoMRPvd3SjfioZ5cBAzw32z7+QERan2/H3fez7v8FG73d/v179evwRd3bsAgIKf3d9eCO7/Iva91guK93u/q5PlX3cilC8kEmte9QV7nvlVXVY5BgX/miPCEoi7vx1fe4Iiu/6uN7gQ1fvWnVXE6318SvfDOOJv+vzvW/epkVfWNQxRVZj0JUqyvXx+pVlrB/6fJS+Mv++c67Rr8Fa5l1hrgrEYLNLeTMGV5mfMbw3iARw9OSYvO/nRF4hfHxC++gR8a9+8BQA1BEdV458JrfuDFb+wcLWn9Xsv8H/ql718d6p1uEl+N6v3axzulfte9YnW6UiOtyKZfuCita1Uqz8EdVX3zK31l+VeYFnJiqqrK17fEevoMEKMTAdZKPblFya39edl8dam8p5QOUCYJfRYLfgI8HimE6rASYPAW0Ki1gvAje8F2EAS8JBU1HG8dImxrvRGOg2FET2X/DAYQcKJw9PNR5PQIy417+VWtdfXVWsc/V+/rVzvWOd33mqvvWOeA063/UqeXXIiRaqjIablIqlMS1hyH0XWro8XLUQmfk/UK/oWZVykT310mOLGmS7SVId8/GDIh+0IujeX0Y391rR8Ki8xcOM2QIMroKQT2rnLJoZ+pzJiz/1kiYJfI63uM4/CCp/3o6F2r1i8MBYKxGbpkAlk/GhfmFaJ+cnGU62BV9mlWy7/J9YYDAR+xAfxYia+eOA/pQnXVAmS9BWY3AhEUXhJ614BFAhgorztBDHcGpaID9qvXvA6AVUe/636gSVisZ/V4Ieq9+CQi1BI9c/3mOCyq4dMtOdrjnot/fOCUuCzn0ksiQEj3L36FtTbB86jShB4cK7N6tn3/nE78ZBHMNTuRW93R5+vVkG5o//6BWGxXzrJ9bzGjGi63ZPbwsBiq8EQYSJd2NPgIZAiIq50RcAhwwEvAO2SuYaMpbIoyLyNrmqdRpqWChCjqNJwJ4GQIaT6XvAbhQR4Haff7WA5Di2hOsv0Q/nOrVt/AF1wAABB9BmiAvwDoeYIcADK9V/XfKI1P1m1LOX/6/Ei5qqwBjZE+D7UwyEfGZF5KX18NwQEHpotyd2/d9x4aipvSz1jYYgr9O7vaLbvu34g+eA6GlHRYsZVLaJfD1tF16hTqsgGHmq5rd3u3e+T2uImxcxruOgQ317wzPy+XkQmsXv+UTjV1oWB2X/0jTxMz9QkKjpCYW93WG5i7vwrBDai4vCtWPF9YTxRL3/EceavF9lJfE/EIXKPb5B4w1Xi3T6LBKWxC/n0D/e/X6E3g3zfUg64x74biLuclu7/gn7u5Le4vwpvd3d3d3d9v3d38ny/wR73cy+P4K+CQpL1cViIiE+7vf3GFd3e7d3e7u79xl3d3u7u59kA6f6rqDdeoIxGEXgM/Coy8nyG7vagZeCYqT5r7/OgwICl3d3u7u7u7/eJghvd+rGQS7u739Vgtgou93v7X0Iu73d+jGrUEfV2HwV6T3fd7lSDfzcJuqQaavmBEThN1TK/NBXzS8cvX3d3fyeaUrvrwS3fd3ab5AS3d3d3t8SCW++7t4Zgl3u939+CG9/eWL3u96f+qktfen8it+itJqunAGN5vBGZ0UuiAkEbv7whUqeEJu738IiTu1u76/BDd/tr4I7v+rpe/X3gxr67AY4X71jldr8+N3wjrkqXq365q1RWrZjkXbPv3+CWq61+PLLMPkJVEl/+kJcgjVyA3CBBHBjsqusEJPLeSK+eIBHDkrJg98x8QGrv5fyan4gEN3+/F8CGen5aoMMvfg/R069r7wJo3qCdW/Xj/193BurncFaI/br7uX518V6L0vqVXxy+8tW/XtfICLqvfSlTfeKEZrVVJnL/5aLFrz4K/QvCy+EcNfrMBG9YMVkEO/eCjFAlu4S1cykf7za57XZm6DAMEbkWgbiV5FXgwQKC4SaXh6yx6Zs9+i+/X3Wrfr369l9//tXrpfeA+akX4BG63/X3QD/CCt6q3yL7oBLgmWLJ6pfX+CPqvvkRPdGEFNhxpJIelWE4UQswcZb8ba+uk2XjTWXyr8nAdVyyRlpfJrxAvBxU5YEGvoGPZDTMJnrMfeoJdHey2W/rL8m9sJj8q7gs9CP165eDCSBUkqcFZFPr5PrBhK/ECAO/VcJZ6flvk9cSxwjJBETHmic3QDPCIT6rJfwkIBRHGTiDwzmhpL9r3F6wJHuZz38A9fLeYFBPB1EsxI1tXoT7wGlX3yr7E48Wi9ffr7S4QEo3eSCytampsz5Zf0vBKeo00G9Y5SOMaXSmbWEi7LpJcrk9XQcYRglLD7Lde3uH+dNYIYQgiIt/ZfwSVoKakvqAxcp2ASvVSf2iFE9W2d/N8wJRFfVVU1t+iepeMOCzqsca/GmuJtZ2LgkEyX5Zfx7/vQkykCGtBvgz9WgX/V4ED1ygHVgAAABAZBmkAvwDoecIL/Dc7sCPqHBWNkFc1X8cNWkupjZSTy+JlhWMouz8QXH/epKfBd1GWjhLx2J3rS80QTD2WcReej5h5F89csT4BC9e/4T6WUA33BHmi99B54cPhx4y4GomX8BXbFEv5JNBIUNFk4Z8ci/WSDV+E/4SO775yXHxfmgvfj4jCF88bCp7Dnffv6iLuck0KPD3tXHOLhDlvJfyy9C5B8oEaAd849XlYw7u7u7u7sH7d+T9+tIZd3d3d3d3dx0CH+GYaXblgG8pu//evRWg2L/4r5oKAhmH2fLeHRxAIt3t4RyeFD7u7u7u7u7v78FN7u92noJJvd0ZoL8s19axnLUkby/413reuiEdzAF99BMoQ8fbL3fflwaXm8CVVuL06FiulcKrZv3VG+D6iseH9Z6tWJqx+CS73bTgfQjgjK98crGQTdXd3f3RYKt7u7u7zU5FxCBDd3dj6Vz8Efd27iET36LAyI9dCagw8wrLgLWanwWG5cvd7yptwcBEUnwo6Wl4KqL6sL177XrsBRhtFfxdWKsBDjwSbvc/BLd3u7tZvxF2u7v7Fd3d7XwUXfpX9WWyvd5PX+8pHv50qRdEr0L6C0v/za/QQi8IVbxlfeBQBDVB4F/yovv1b9WpfBCuY6VfFVr7ttfFciL1UBtqRHXlBX3d33f98bXr34Jaqq6r7217L/8wJKr5vLDFqlbtOtNMLMX5f983DyKixHo7EEa0b1o2CIU73Oo11gs7uAm9S97aHPkf1F7Lfl8QCEj38i8QCbu7v+1404vqAkf53OyJ/tV5+j+y//r38la8yXq36+on9/6++Ve+VF4uy//qdX2vmvA2+gO4JOlWvdWvwUGDsaOf52kbxHHu14vBX6FweNErlawOYINEc1gcQQf4gmDN4z877wYA/1LcJCEY6dMQiHRFeGJkUTjx2pw90Xr3X3RK++Vffa+rtfV6xOvB5XBd8LIjnwtrkDI3aQQEFgjMUi1JntdEgoJHmvcNBvLby8iGc15ST9RySLyX+hB8G9p4cxT/BCdXrqWiyLy+l+/PQFvEoXaq1y8MSYCstWPwxEGMip9AJ9Xa+Px96liC6nm/haCUyr3kzCnyYyzGuiXwsyoOb4WgogzeZnCfTsS2j/eoKKpkLNLf4Yy3q3BHwV8t+6A5j0X1eveBra96r3gX694DdrF4UBOje+FwRzYTP3oWFBOBVqdhbGYGK8AYe7+nn/5p5xigbFXjWCoXkyX87r/uDnMQQfBC3jgiZ50Lyw/SgrJglIDlblofAdVyyx9/lvimKBLJbXiBSpbMmmsGD/P8nvEmBCbDN87QHlrnr50I7wGgCcFJVXVdgxxo51GsArQJz0djx9nJO8BrerQBCsAAAEBUGaYC/AOh5ghwm6y/69LyaeuUQaREyhLV5bz+BA1F8da3wj8079wWmGRZ9/B3qW8tY/ouXqIju/fjDXu+oasJN9KI5yRx/BOauO8NxsJ3yknd8n3uIEqWCnjXngQwM3X+++n1PXTNv/KXBE1jB2X/6Dk9sRTlpR49761UPCuP6IEcevISfX9s99bQTYJDu728fCm3+GYaZ99vrfngmybdvu28bG1rdjoKYUhN5z3d3d3d3d3bJ9JhWGfDALu7vMBLat8EvKZDuLDKD2+w8Qfc8813+KhG7vHQtOz8liEF14nT92XJuDkv/iPmoUw+CHu/eCOrfgoK7u7u/mXHwRd39evk+oi9+71iPrydakkLyx+Cjd3cYLLxqtJFcA283gj3RfyAkGGtLiZXp4Iru7+8GTHl3fdykat55u79wUbu93/PwS3d3d/NfiO75L3omC4ucXfd4qyEBFhteQ1sPglsF372m/BNd3ePkf78ERLvYvL15EYSNkfwY+CQ2CtnqV5JRr4ohuOyukCWDcRlu7/f3YD4BWj37lWjvdLTret/AQIV8B0ghW+sBvh/W/4JLvtS4S196Kva/XvkVy+gS3fd3f0xPr/0NggtL/4kRrxEEYQOvuw4Qgou993c6ASwMfb763R361p15FY63d/qr965fr79er19Xq11r79YmS0170qy/wRlfCd39TwVrm9c2zXPfL8suUgIbvCy1Oo8HNEOml3/F8PZavGixbXgplPgbNSzb4C19QrfJ1Sv1rSrQIK36BC1p1rW+r/9ffr79fU//1by6sBEgw9kXupgR9VbXxCN2X9aPBGWq3HcXykWuDf43lPhncsEvouEiwH2CjWPrA0go/BIEAqIGvcgEy65mmymq+q+LafwFEGEZIvmJwjCcBuAkAkCgSHvNu/f5UFpd8VjJugu1cyet3/u75PRffr6vX1WAZf3akXXBUCHqvVgbQmsXuCOSn72QERI01/ZP0ujEcPm8PZbAYnuEPn0taPXCJ8Z+/6FkMV0EqXXr2sB+ur+gqcpLKSwn07MJKfW8mp8vmtXiD4BzL6cKLlvD0H06kkJNYLfQv9esVYD8DwggD1wwbqix5qq5EQdTO+lBHJYjHWjyerYVEqHhC9Ajm8AkdhR00V4D+BACiagVsvxD9dX5/3QBGwQq511gvfgEBosUnr2vHFRu8jBTVV4dy0CUmX7L4pp+FRM2B6aUQngR4P6HnSkXf6wkXBMW94w+0e/lZBBw3KnB55BZ7a6y8QQHokMkThg1zga1zt/BdBLELXMhCW5n/8sgNTcsxvLAJdBKar5D4lWDqJdmxXEfdeCcmOl0t9BE9PLpcQSCOYiTPtZKoS3pwO/o7wL/q8CB6xQDqwAAABE9BmoAvwDseYLcHcA/Ei4cQa4o/huH7/B6vgh9TDsYa9alQMCcMPZC4hJKzJbf/zcEfizutREYJCXUdh99KDzR184/bvbu/wpyWS8KLRd27284EMn4jisoHKF+PNHUgjOSD2W+ILvJDzRh3mig6z5P3wS3Wm+6vr0gifhmDo4CzWb9VhE49D+/LhtAohv3g4L/9GvLwm42o6gnGGtItOS/qXYV+P+EZijoVO3vCUbBKU5e8EGn/j/FrExn8hONNHE6e/rSXrIcaDZeLAVW4g3dQcPNJMKz2Ul6CMt/8t7vwnk8EZ3vbwjCV3h2FVqlsEsFm97u7u7xfgou7u9pe1jKjLvu7uy3d3emsXBGV7v7Xmguve77b6shMbLL4mbu+1Me7vf4JpAcYFPu7v8g18OeBI6ZuDL/CR+XpyBsZfv385S65IQ878CuJFHkzvJe5hN73355N6Vfvu5PJd7XJVzzQQ3vZeerG+IJNvf0FCu93d3d3d3dyrG8KFd3d3d2n3d378Fd3d3d3d3f34b3KMY3xt7befmLBvPrB8GPhgVwRm8Zp5chE9RhMv4q79QREwm6qdEyrax0ljZYVcrv393WQQCE7vtb/N3fk1yr8/BLu7u/68bN3f4KN7vd+/X1dF7v41H99glI77u79r5Hd3cGBf/FiC+CPeJf+YRRo54yreMqdPhdFc8FnxfoC8DlX6KdXyev/5f/1bwGqGVesDyJWJ1GLsfq2vwSF3dz8FPd7u7vtqvBER30y+Cgu77vzL69k/X//PFNbHvp6ZLb+ki+rSRmy/9RS6y/+XBYuT1yZ/Ii6Xc2m3l9tcWQO8yACK9OHe3qvtT/PX2p98GvwR9+DGGCXgC9jyved7LLC09/TNvkX3waotOvrTpXAh0X+vW/2tfSuSeCMrv+krROm3BJVfqfvl9VHwq5PMJ4I94BP6wSLr1h0GX6BkCUwGzpbi4KVLEG4z0R/DAOkI70GgSeCX+GNco5iyhvrSivfdzGhj3A0Bhe135f/zFu83a+91+fr6vX36+rwSdV9WCta8MGROyf30OhAQLMeYSfEUH66hTM8ej5PScFaBgQNFEnBHISOS8vJHZiTh22RiVuPw9Bwbv6/ECcD1tCFliD/q2SVA0y+IruGZ7juSqmOM//U4Vf8Y74LKtCv165dAg+CCIwLVPWIS8Bo5/mwHi5S3/HWKphvLetyddOaMeZnr8XBZUUdThvLi1xf8VBRVe4hG8c8LNFt9go4o0UVMsoryr3utFGKugEjUqrF6/PAKDV9+LJ4qYQ7RCV9MFvMSMRPNL5eEmCUSo140pj6HNAE/uquv8WsEgvRW8Just0UH9aKZgoDvwSwK1U4U018h9TCxl/eIglIkT/Y9kvGvV/svEMEpOKr+NHnbMfNRxFIvglJhJ7m8aa/usFBANflvgc21YP9tKhWCM45kyXOrS+Q+evt9aO6B29WgX61eBA9coB1YAAABDZBmqAvwDoeHAhwJWtwv8MsnyiPIS8pf/qBBT6i+0Uvjm0Gb6vBQIBEYu7j/lXiDjXva5L+I4x5BlthIhJ6hHgd01DXqfYDq8EJdvdu/PBGR3d2y/5xAuE+W4wW/7UbLF5o/DK9L5ROJbqZ4Oy//R6/wm8ycv5JMQKGFHhUF0n0kSCmJOMZ/u70pYd62OEeg7NDsKZFb11ZI2yaS9KFZP0CrjD8eSIBnoIpF7d68sZd+7vd38bzrFaCF3eCB+4QoO//eenylx8iXxHL7X4zqKhDY2wMwZ7twe3t93vW+W93BwX/xHuIBIKd8qzyT18f//4diS8lvf8t3d9Yy73e93d3b+FZru78ZBaV33vZv8kPZb95SCWCGUiF5dfvLBVd7u79SZsaydWP9aiKs+nHi/tgHteIK7u77g289vCPc+uiUMb6c6+79lJfl/+ERF3u7/AaId8BighXJVsBsh1Ffu1f8E13d3aX4+gQ49GE3cLwpd7uQZpYxn3d8OpAR936vDdymjj2v1JWsGq9Q4SXjpEfhU8W/+Cwm7lUPu9/tuBuBDM41KJ8Z65Ufr+vW/f3Grkq9Auu17u57d4cghLd3YV+CIj7XSZK+AMC/+zF8FfLV5IIicJuvvGQz7dfbPvL47/0wQ36OmXA2ifAW4XV9L4Id35j5/PXJVq1vVgOEN90qdb1vk/X/Xq6X2+G9Xfq5fr4ryz21y/74Iys8PoOCNBWshvWjYIjPL+WX2UryxF7MN+2kgF764vRgW0ZIuCmCLu6RfBQUtw5+R4Q7sygsx/1Bj0vrVAImjxzvX3Wt67X1XrF3axOt6sd0t+9SIar65j9fdgaaxzuDZXO9b/goquq8b3Vt8RhIQGIITSZohVo5f9I9nxqN689YteJ35hPASPW/PlJzv5sE3ouEj8PiDF3etifeHIGgLBUUGzLBKZ8N+w3thIotKfW/+AgA9uwZiQFoHkY6jdAFWCwJQ2CMoe0keWeX8fQbYIzbvXgIMKo7Evr7v717p0WJ1rV+rVOtauSciJ7L/+vvEkBDz3boIGBWISrZmOSIjEgbi6dvlrCyKwlWlPcfrMlFy5eNsv/EcRJDBdFJTXz4PiUvk14gTgetoB8uKDHv1JgSbytr08Lb6ZzEnOn2xtnwW3oX+vWKsRJAi7+pmKlf80QKTZZ1OJ4Ne9YfqI8CUuUz9VrkwSYx7+NfgoswPZbuoOtPboBphkEnSJjngOILAo480EvTdhs7q0XpeDnfixCzeEGCgRNeGmW/7yFCheHctjsSCep9Exl7L+j5F0UEpQz+Sk9WDflFERLJ6/KD9KM4JSmmB6vcwNAYGrKUx+WXwgwHUFvBLxYPAhvT85Walu/3gOYLAlMq9qQzmdXXsso/mPAdwWy+EjzySawPSAbAYBGQY99jWB2X4/+hrIEStYoMvV4F+9XgQPWKAdWAAAEHEGawC/AOj4KAxu4+x/1eXhFxmy0XAhq+CH1NnBgaZ2gy/UpvGi54RmzDwR4UpraL+E+SEz31zhC9a4U4y0Xu9u7u7u3RIsju7u7vftE4+1+X++HctLL4z/B14JD4Eipm4FwQPD3qCkRHSyESW0ZHIo4WNu9vG1qFYgu7vdu9eoy9u7uYVs+vndk7vWQRhS7u927vd3e/vGxl3dyW93d0gTfA9677fYKBATjrZ+96obBfCV37u/x14x94UemztpJkb+XrpSzDRz5Ntv3EHjAlFBOwEcYF33g4X0HPCUt/Uv8NYF5v0MFVd/bb273dy33+M7vffdLd+oJe73KRuKsnWAg+LO97u70o8SSC3x7R2tjfEjat56osKzSQRyZew4nrLEQT+WWSN6ywUbvdzjO/klLu4NtMEnHfC3iiK3fJSGOfr7pmBUV3d3d33fkWhEFd393e/3htgt3u9/p/ChTYle97u7u/3hiFN7u7vKRe23f6I1gxL/8gsm4GKM9QKrpnz6uvmBETGiyQtNHF6kr7zGBEe7+/BH3dutfdYIe7+3+veA3w2rxHgiKkt2XYGgCCtHHf4bu+v7Q4ZrIgzd/3w9uf96OUKC4v/iRBRWE3UzufXwWE5cd3venfGQUXfd/3g2FoS3QCsDKudAhD6v1ol/179fVgLEPrebiUeLX/YDLDaIZDFF0kdv0TJEk3xi7Mnut8hZY+1fHgsXJ65ML32556+TU/4d4dtQ6lwMueQoJfMvvLtOLZt+COhWY8CqK3h4koKC8EfjebZg4Scxk9V8BVg+A6g+/BHxr2/QLgIOT9L/+JRcuqWutX/XutfVWvr0I/X32vv1avrlX3698i/P0Twr6DZY8HbxfR1Jtedl4lnrz4KvQmCVYCxBtirvu/wRQqYDHpavh3LOnj/nPi0n8pArd+76tl3/YXEIRki4DIDAKz7vNTGmgbdLfmHq8CD/Xv16vX36t7oqReVfexKlSTgkBGTVe+GFf9eyfS5DPgi6svaouwwIIRCiDfgbx8EXqPX7Zr6yIzBZqa3El/vj7ZD+GsiUEVgx1rt8v1+I5SRbl9/hUThllg9bQU1UtDB5Ww3Sn+snJhkal5fX3OF8IfUf/gsJ95C/oZar1y8IVrxkEWBGS1D9eOiBD3DUw3lnZ5fB9DLHR3WEeubeGJhAKIFPUEmV6bLGYw38/3kiyI8sq96r3kz4KOTB5o3UVvkRWv17oBn19k9f/X34JNa95A6YxSSRCX7DZQx+85LLtn3+2FDiNAh06sSm+ukBLqow/yXzAtM4fyBbpvVkHOpIx5R7+oJZrVzJst4PepZGZb39EBKQWVLLffwZXh9nfglJgO8pYqoiYe5oRmW8ZPXHauwXdY3FpjDUU0t/a6Vc2sY/z0LYANb1aAIVgAAAENkGa4C/AOh5ghwCB+uar1MIVrEXlGhrPPwfamGcly+ruFY42a/Kyf3KP3jeGC7lJZQWIbY+B9S/9XDuWNcvcvrJ/4gvAIN9m/2GFetEa8d4bijrr2kO81e/nsz7yfe9SFb8Mt3csFBSkn8JPfq2PylwJm7qbcOvy+SIOC//Sxa/BKK4bWZcXsLXc/BQUlkvcxF3ArL80IfOvpYR6see8clHBvWPsQ7v/fGGqwB7iZvDfuZC4fv4qSFuDfuQel/zvy/yCJEYXIBr/ZPDGjt9aK8Gxf/N3ZsQGB1Il7vsPksZesfVXeHWS8l6wj0X5vwjBHe/vE3d3HQpd/cI31qsmevN1WsZ1d54Ki3u9u7u7+1kiI27u73Pru46FD251ftyXeRFgrjsa0E5B5933eGQceeVP8KP66NDAiXAf3u7uuMf/+QFF3e77lYTd+Ej5yTvfy78Ekmbv5YJcmfKPO5VIgIe7+/CnU7ve7u7u7xz7BZd3dyDz33bbopHd/1SqkkVyDLwUCMCFVzNhqDblBVIXzL43wR7xoB+FF1Izcsz/27kF+Kv6gqjsowTx3d27vd+Y7AoA3W/t169NheETpXe93d+wFaGQXard++T3//wYBG8INE6n+imUdZ6yWn+DAv/ixAbJjvjS5cv5dbrxUERNIdlFV4z4Fij96DKxa3zeTNQIKvqvViuJXv163+CPe/v194JK9Jg7aKPJzrBKR93d3atV9+i9XVZf4Yvj5ZaGD278davL+CtZCeuTORT63/fKPiOGOkDrlLPWeaC75AEDBKR37vftfghI937XgohgpsBF/v9tuMeUeXPreTSbS7Rbuwe+CECAitXr7/9er1aqhvogXW/YBGwj3Ir+BLrmNLwJKmQnWtfY1WrH698ixeq9WBFrf8J6nuqpM6+YhPDkfwXFzJk7OP1CLT52vF17L/j/KvUxXzkgS+i4SrX/BKQDWUsAxe/jzu6HfwLXK4/vrOs5F9M29UNhUQQzv8OQUHCTJFVNHo8saPTdsfXRyL7V8C72BjDvWrHqqdb7a+inVXr79eRfX14EGiOZf/I9e4iq6z8Yg+Y2FJjvq8JtF0TXtILpvZTHCO4F/1hpHY3U1qmo+y++PwxBOupNST/eNkk4y0VehHKcDaaykikqF/EHx/1WfA6gafvqPKPAt9C/16xdhBrWqC3/HmBm8kzrwuyuZGZb4+y9YX7EVFvrhL1gNYRgoPGPdmDhJ994FAQBAILIrZzzZ9HYvgYaPlODqIK++7RRoof+vv19+vfr30vsv9hKaCQlZCXsvgVJueCouW9QdRFR7E796KIKbAjVLeLUZv1QICaK/tkFOY4PGuPfgIQcQTBSpb/BLKG8CM0rm8j9TAe00e3uCUypoMmtWnKr3jHm+wSkzthzOt1GeWXyabsEPNhsy1iJYI5Jb9l97tPp0LYAC64AAAOdQZsAL8A669YECiQ4GJjUGU1XHO8yMIXDNKWfhGXC55SRSUNoODu8h+XcjNUfxHIRJeMeUNfiOoa+QsUgryTATD2WvH3RGF/0/oJCbvwRNmyqHcA7L/qSHJzQGo8SyYD68K3r/UFQrIPOkPFkWTbe9sv+Hb0vDdFcy/tAl0XlvJ9J4T/rExH1EZQsNd4DvUs4e5/m2+nBCfNnBkXwrzSx/0X0Gxf/NBITl7Hk7+Q4pRj//4Tm7v8FZXa3e7u7t+CbeW3u03hqWkcl8n1E3d3lNL89ymibpZq6/vLroFxwlwP8/zgp7qDV+kCThAue9MsYwtcmcUvyWe++89fH//8hSu/w7J3f4Uu7vd3d932/Ebvd9b3v+6qTdLkGVYHb1wYl/80McdlC1moMyPL73xfy618ERJpDpIv2/kcdlfVsCSCU+qmp9He0QFklEvmoqzv+CS7u7fBGEPLe71fqRE9XgoLe7v+6wQ3d2m8KV6vBL3d32PBYE1fy/wVd3vZ3fHPwUXfe9utFbf69Bh5vCbrL/8gbNj3gN5ETLNqf/BETHfKdJvEV69KrpHIs71v3rfrX0R0remrlF//BFd/wXorfaJ7L/whr369XgjKYekBLTAFa2T1kJhomX18mp8v1+CXxjTTGWPR9zxKXvBTQjL8FBQ3lvh3LeGs6zG/D8EfZE5JumZfd/f3otj9Y5+CK7/vgjV9fgk7u3YcBX+vq7Vr9ffr36xv0U6vdUi6SnUeWCTqsX4ISKv1F/0QsuCb5u/9efr+/BIJ4IzeK/KTRJT8E3osEqw3/DUEpAtZAQf+c1X4/6lhVpb45tw+BIgRGa36sEMOkBDvc6eEIKCkgC2iXGspZWG78xl/60I7sWCpF9TX/dF9N6+k8EPVffQIeq+/VvkX1E9bCgjTmBQID2ChGeh+/bwlcSZ6DwCb1uvvDf5jebiOO+TM6tQ3rSwQ1QMhJhJz8RMSdrz3L5v4g+C7y1v58braxHH9PDIP8vr/BZ6F2qtYute617xwIgSkgyvdBqS5o8izWFa6wr0IY11gkKMeZn9r3BRDCf5Q4p6pkLKlv8dgFvBP4CXHyDslfYYX2v+fd9eEK96hMlamol1qWCU6xfDP5YaRX8roc6dTf2jtBZ4nLZL72Q31jgmu8BKBBVi/BKQMZZR7zXU5c0e/4JaPLCT8b6sehIWp9QLNLfv7glEdUscZLPN+CHyG8sxrrVjXqLFrdyF7794AuuAAABBpBmyAvwDoeYIcBM9aMCfZcPQ7VpkHy3UozMKPL6rhuQ29lvL+Xmh8Td34CL9G0Pt5rVl8Y7/x2FlrNODxfMXcN8a99QVeS+TM8LmvxHGPMuYHkJMJdRAjGWi93WJ5PTcIxAkWOcExc1gI98vOT7gPc3luXd4PF6iysENkT48mfWqiBGQk5R59UnUO9dDvqESkshL5NKCnaS+WMj6kB4xfvYi93e7u71i5ozZ33d3e5gJe8RLE87xhre/5ru5UVPqEt3lBYchbfk9uuj2dHbtwRFu4Jvn78KAlb163g2L/5pyP49/5fyeSvECJb+S+E6xylhSX4/5YssdDSce4rvzwp3fd3d3d3dzyxl3d93d3d2mSXxO8MoZmYx78svd61GSbw37pBWUeGQM/7uvk3bRf1JpFhgGz8n5N/jRE489u793d3dRha//1bw7VrrBDXfq8EJc1Fc88Ee723+C2+tV5fgiu73En9g8/ICPd3bfJu99dYJTjQrWj3yWz73ZMbLL9C70qV99atBn5vCbrL/4iLEZn59K5QR9f9eOQJfBWz1R3lpEX7OnqCGgfv+1cqK5S/rwS3d7v5u8ENyXtWySF//RYOsFfd93d/2+W/TPX0z/9b31rfydqrkF3m8FfLV8gIyAou5U/lTXpAnJlwQ0e/vGQQ5r3PJBSe73ffdvi5PXvVerLBDLb/vwS3fev11q368wfBJaXOorkRZUv/kRPS9ZfvfMXLSCtaN63wzGWz6tH3/soV4wahWlAbFtLAY7qLwRcbllr45Lj/CpEZvBTRvHuES7vAifov7YLNLerw2IBRt93/eGa+18Cejv+rfr3UCOCKq9/ghWu4PS93S/8bXv1jnk16+p8EuuXqvpMtffgjqu2+TBEYMQQmwf+/FlmGqx33rz9effoT35SYdvvwTei4TeFSApVMJX6e3+1A3lu/2zX34IbMrfar31hmCCrZf8IR0FBYKVLcmtzx+LrV8FGm8XRvarCAhHKrrBFd/3wKq0d+VW8BCrrX36+/X36+/Vv19+vv17+n8i91EInZfwqhEVG1XZnsaaxBN59IotkR/CdVeP58eXmz9U4fXeR/WEkds2NNfqHS58PhB9g30fQxfMD8Uutf+grYObNPtWR6+N1aPv/xBTEo/5f4932yl9fz1HJcX+X/+Cy9C/1WuXWvfErWq8EslhoxfKbwIzlc3xap8gyW+qEgVPJ+s3jmEz5bMSJgIjRQ/GMFBGDw80dpIN5o7e5uTA9lr5A33+vvtffa+r1byJW1hSmCiq6RCK50/BEWbBj3rfyiI9ob6fXmGX5OvuDWtFjguauX4JcDKUtxSjMDrlLe34JSOoG80c281SX729MQRLltljHtmB7LVgheCXg7Gpb5s9rFd9Vr3KJ558uALrgAAARuQZtAL8A6flDGYLlxl/9YEF65pcc+cvquLlJgpovl/PJzHmDwTcSO/gshvU+w+/O5993d+CzjDVlvd9zA/71EcYaqGgwHsslp+oIrAMPZYHepZzeEfSXc/v4iR/Lw7cg0Dg7L/9BjIcgj3h+IKVr/BtvWX9SccI5GjJdLCNSSEuWSvrwRwgW7b7vetfi5xYab+7Cy0PdcQFII+7u37z5342Ikn586f0CXxhqYFV7TfaxN94hYuckcHv7jDXuU7utZX1MWD4DCwD37hkjD6CaX18tSJf5icPQ/mX/6/Qn0Gxf/JMThxNHJ6xwouZmu+7u5yVPYNYQ7u+j3fiIJSuYFXSe/dYKrvfd8mWNUMhGCje73dvEwR3u8cy/8n4mzgkeNqhhqX8vWZuKIYFO7u/ywnd3d7u6LBH3di/KfDF1mDR5Jpicd70CQQ7uWyp6X4Q8tvvcl9XxAKCu93d3+fhLJe936m3d+aC277y27fglu93d3Y3Xgk3u1eCTu7DuCsodhVRd3d3d3i34yCOld2K8ERR3Fvj0y+CUju7vd3HURvd3wal/80FAjGdEKepy0/Mr9wRZrPlbfgrnHyD3d93/dBD1o7dYju8tvfA0j3vX4I7u79fk3f8fd9373eEoIrvXwJmrHJaRAVCar1etegt8xuIcr2QNmPj9f2z78O+E3XJmUpjHfj2mD4h34yCKS/7f6L3YHgGKxdgiq/Yb16tWBfCPm78CnV+wG+CFX71v3rR3V+t6vVMtYKD3u94BN2hKIdRfa9dYI6r+/DGe846QX2bpJ59CP7OCxdZCx9o9fiCW+78v1+Ihx1asdiWqDLFK9wihBLh9tG7/Bb8EEFEDrTQ0S4D/Us7PmNJ4bEAjlt/3greuD/RU63Aik7vuRW9LwONWMnpr/qzvW53q/lrR3b3rO+E16rVSoyJ2vvaX3UgIeq++T5KL+rngmooCZun6/nSZr3DnNdfzmjbX96hw/DTLV4awL4J/WCVf+GIdgppYAz//XfrzhTpCRmoqCqiJlmUG7UAgTs9d9wI7z637BaBIECrMS7v1bHYIbv94QgrO8O//HN0F/E0oYYi6g/Y34EaCMyFnapl7G7sE8t5L03WCS7vjnb9/eiv8ipE/XkS9e/X50CVL3QHMEyxda361i9LWTghIq7a2lBXKbOSCb3LWuMg1jYeiD7Mva93rfLWkJYLjaRyDCHAmf33mkiC1sxtl2Pte5JiWH2W+FS4uuWI4/R7PFfxvvL7S+8baPoHUSGT58Ev8YLPQ61esVUHEteA8AQBqDK8U3vck1P+XwuyoLQt0smhT6XxRblu4Zj9d4KKCeaVZoiye0KmX5j44WZ2DXs9GS+G6+6YhHHY/9ffq32o7v197Midr5QRw5lfyRS+Rp+CUuNM1RKJMppks6fYIShHp0dv7EnuUfzg0HngijHhTU3v+CWCM5U5OY49Pf/M2ub3/BKIVeVe/ksY8xl9oFK8F3keaJL8tc2CHeW8sv1LqGRN3r+mTesAXXAAADmEGbYC/AOh4cCHDTEl453D27xJf+uD7VCIqWJ5eOtH1DuHct53x1qa5V/Nqa8QXJiUgaJiDLJerJuYB8TFE5c5ZS/+WGS8a8vm3bXlLhDxmdg7XqCiPFnfjyZHSJcvUQKlOT0SMffPvcbZca9/LbOX7yx/WFY5BLw7CmaCD2Wl8kfkxBQg07/z56wj/wS5QYeLLD8Gx1ZIN9PqOeWHPDEB5E682596gs5JWw5Av/NHcP9Kjej3g2L/5KI3RoRGbuUeLb+8lrDdFqsI6xbBd7u7/vEwkV3aWQXvL+L3iSnJDEz+71icmslFCBMtybvu/P14qXeUFE6DJQm4NgX51847m4NXkm7+t/hkQ9zkm+e0/hWrV4ISu/71CNykdLOSflJdwhHJXve93d54Jt7u/34Ib3fDzdfICW7u7v99CLvu0UR5fkLvR/Scioqy+XWoc7ltn+HcAv9WQYeYnBUuWl/8QwUCNaRLDwyofi14IECXhF8RI5aksO0nlLyWTwRVSv377usFMFxVXd+Ofgmvfu11VQI+7/C3Vq9ffgj3a9+Fd758a782t4L/NwIVTmUy59V7ICMgbGmffv7XmgqJy5lofN37xUT3d3+4IT2+0v+qtdAOEEKvJ6KdP0Rt/FqVQfrhXaK356/pk1b+4JNuW9fi5pfjUdgrWE1kLn935fVu78rCplA26W9UmBjLOfSHJf41Bkj0l/p5JfdrXwQ/wV33SCVrl3UGmXsb8Lw3dnX8mp8MwWd3d93+y/9YI77t/Va+utfXuzqvwf1sZPrg3XUEV3+q9fd0vMvr6t1cupkTtfMIIuanVWvG4KvQ3CbwS4AZn2rh93C2CUNZS3xzsWIOIyn1vJqfWJhb4QguLmiBt0sCmllHvy3/vx39E7rX0nr6T1b9F98q+/U6vwSV19VYI6r++gREJHIXOmT+grSGIJBAOz3wO01zVoFUU9kEzy36omHwkiLAzfn9iY4al/PDs1OXwtIQaUtCG11iCyU1s/wS460O4o2nH/RfglLLadll/nvs48RILvQv9euVWIrXiIIsB6SluxrH/XXrH/WXgoMQ4O59NllWn+PUFBkeWdnGveubXlXh/Xovfq36++lbWTKCiU4Hssj2Z+9HJNi/sEJw9pkGT5yBA8EU0gMqlu34VlN4Hepb1QYgZf0x7S63/8QIVMlXvVe8Y9L/XvlN5JnVYr+T7/oW6BE9coMvVoF/1eBA9coB1YAAAD2EGbgC/AOiX/XnX0YLbh+DmvwTeCPx8FXkglwMJ7EHnhjpjoQQttUx+NXMFRg1+tRMpuSEvmLgL3xnZUR/CUt7XI61XgpLw5AluYNQgywQuDCf3OqECNhIhLISPleoRKYCT8ealAzu9278sWR3d3d6eW/5RObCjIOy//QvwR7oPP+tcQKyToFGmn3EAhL4Nv3a4hnLG4v6+7eL/5N5yXLBVd8o8+342BwXFYioKSu7h1BgJ2+eaeLdR7Dl+SY5ZFBQ2Gzv3EEGw0Jth0r2A99eXyy+i+g2L/5KI55KGdrkDNF1v5cnu4IN+IK5gVkN5ySL4W8msXjf4I887sLxUT/BJmBQ5lvrEenqVCt7RgVf6co1jev/WDbN6kCRt3d331+VArPcpHfd3d/nQgwIbtfvl8fHUtqU0ce0qVZ+/JCfNCa7v3L3e/UFuUealpd98he/WvBCciAhfvXICI13+/ZSCt4M/DncI7n0v8JH5L/4qGBUJvJad5f6/r+Wv8b46RL2ck9738BcY/y6e/kjT3e77uW93vgUHwX05+M+lqtffI7v6BTVusEm7v48IRd7vu/wRXe/S+yqTPeex/iO7kRGH17f5Cu/8+uXWvWoLpb669Bd4c7IJjzUTL+Eg+HwUCIefr24945v1CvlzHSI/Gxb/4ire5iu7v8E29G737rVsv//WCru+S7v+7JX369VYIru/38vJfO93yfX/gh3e3pAhK7/r6RPe1Rf98pX3BWsN/WG+S5SP8EsE1yfp2cD3e8MZYi8vi4X/4X/hgwx5Pjj3UDeW+f+2Xe4X7uW7pZjn1v+EZ6/m026EwR3u5UZbYEPuJWu+/Fd3d9E+n/0X0nr79W/Vr6Xsv/6lWfCBK3rfL/lR4Q4bggW2QLbG82aB142XjTKg5f8bLvwSH4aZb8E/rBN4Vw7o4yVLGRppevn1v7iBFM4/tFvrCdVbWECYJCwZXv3YsQjHRFvBHd/3ar74lF79fe6lRl8Ed3/V6+/Vvtffr7f6sXxq9WkbWaPNBF4y194yOJdUybyWoXDXkAbPX/MQPzzTBN3zf8GBvD5tI2X/eXUv5fSE8RBCU5ExLc/BLNdnMRHWj3/CpXEsPss1r/PipTQ2H//f4jhjLMIh2epn4LfQuq9Yq9epZxGxWNe661bWJ+sI8FBY/G9mB7LfL18N0bvDf7R+ZExFE96gojrRPJvSKtSxBagc21UeNg9o424l2wQlTT1f2K8ggoF2gfehL/glgpUszOHp5p0v8/4gwbyzs4dyyr3y+83snkysZzFISfr39/0JZA7erwL9avAgesUA6sAAAA/5Bm6AvwDoeYJcEe2wq9RZijJyRwCwVO5eBBXwQ+poRtPr9eJsmNhQ3eEeKKUkEep/mAw00NL3q5fNmyRHPnUY98Epdl8lkt73YjHGTiMv/lr1PegXiYcg+GgQeLcBMf1b0vyC5fg7XqLmGQIvb8v+E4w5Z38aIm0hc14OmOwStnF9u+7v09k3/4kvNj271mli8xF7OPFIfxMEndH28fRFdk9W3CeInEQxlvj7X8n9njeI7xCwVzRcJONR2993rL5fqCi7u7o9svr5florHqYuOyr+FCdxN73Sd3d7umWnKRzgbXoTeDYv/SSMlXJBIKLd3qvCR3LfmH3+MK27d7jLRu7vbu/E6yQUSkr+ePnuPvfmwclEveltmgqu/e7stmtRMTdwJbqZ3Y9/l+T+CLbsFcVWWFLu7u7uQ1e7vb9Wg2zQ5lNPEvFv+lv8SQ839y38RfKSd9ruIvvd6xc13/gppO7vkt92vcVvd6++8VcQcpwm97rJFmPPu7WkiEhEeJe/cg89u/aFkd3eNlk+bzn74ezv4L/BIbAha5mHzZf/NDBo7khZ161eKVOLf67UM8OJ0X3z8hb6gtK7lI93aOmGNA8u+mUj/oIfcFV7e5Lvd/vy93VA9Egku/34Jbve728Ka8EN9+/UqyugT+S93wy/4WuCEp7+JkvBORp6T7t0oKN3fd28GwL0ejVcn9Ewgv8OcefYb7k5R49/68yKQEFuWr0HtfICUmCPeOEfxnfX3iK9+j++gSbvTJWBzq1XB+r9YJbuW9z59L4JO79W6+8+vX1eS7/65F7X1T6LXvylHSo+CtYJ/rD7/PBLKSAZ3TtzJU7Ccunj9zfglhU3uEGoTsVd2ZvbLv/BDBER38vLBAdK8NY0R/evvZZ1NGx0zb9/IUgbyyPFf4Z1a1jaK57DPcH63+JXu4NVy+WvXvwSd39JXjOnRfRPWvF79DbflJhtmnl/pc9fwy7GCX0XCbw1DviTK96Qt/39MQmy36vGPSjjDIKiu/e8Ya/LTUWGUYjlpyek38CME+m4IcEndKOdasSeCW777+vVWutF91a/KvX30CGtfvohJyS8vhthBXh3VAwCjpOZR6kjPz2gFNt/TBA/Tfn65qDvuzGgp//WHrghETXEy23hVYKS0njLIP9o8PZZ496/BLsuXcuPuZfJ/CpeP+KP17Jx9YYzv/WvBf6E5SLFk9c3rr1+LJJmMeNdc39RZnUNTNg61/BLVvDNfb/R/dOvv190zL3jKJll/GTzyxx9vH/8EJZbCHzU9jJ9Jpy1iCjHvyHC5YEHwSyWCmluQtEpCnr+L8QR1A3lu7AWaW/wQkVe8lseTry/P/Qt0Dt6tAv3q8CB65QDqwAAAA2dBm8AvwDrr1Ya4aXCAgamqMRL38P7jOnclhgxfY0Rr469V7yi/l65q4efiD8mFDWSZVV/smIkn7gk8IePxcsvliBKuCbuAnr677DAfu/BsDvtMsuDzwUeHHrpEKZpl1CpJh4wvhHpfE0k+kL44akiXP9wVFd7LbvbuXNtai4U3bu96l9d3e7a4g8RVhUu3u79xl3dx/127jYaHdzkhtj3ERhqybAbuclrcRlJD7XGe0Gb3kqyziMvr5ZYxOf2LyPrg3X1VFICgnJe5x/LxPwhBKct/GGv3n/iCc0t37iLu+7+eXND54gpRoMda/b9Z4z4isXeutZYiS+G+D8XvGZo+f9wl4yGwD+X/fg0zT1/D2613+hEH4RK97vd3LevLe/h2re9eJ7u7S2/e9K8n6CYkwD3vu/Pd/DUSPBh4JCY742uahhFbl/8RBQbVyl/gyu1/TK/1zVqCsvLb7u5Le2/F5re92uGeX/9fX6+/Bb3d3R2l8QWld3d/gqu7u7u7T+3YCABiCIk0t+7W/6P3konVkwXeCTgMeU9487i14iCMwED2476sX4V8EneJcMFwJdwvl0+/GSfd6/IV3s1aB/3m7vuYEUlxcc+Wq1avVpfBLd3d7/fq54v+vtfr3RKL8/X36I32rFLGLDhQjd8ftxqdYl/565i5v4LFgl/4VgTbhy5bMt+u8G31Hx49Lq4PjQtfFxBm38mHJcN0Tsn31+Cg8l5MAaup2+WvUXVaNJcEj1Db9c4bohU6Eg+RfdsHqt2Uar+QCOt69fSF//RffqdVYEutxCOW8lX6f0CS7/v1KsvaBF1VvKIXvkBERa+peeCHluVNefr1gq9CYJvBFUDJKW6QL+gSmZgHaJPLg/PrGgRr1jgJXvlbXMb6CHf5bvqtfV6+v19Xr7wS19+vvtWy//q5XRNa11gqmzx/z78v+y+kO3gpJkJAD69Hc5aR6eoW7xDVuW/5fSM+X0u1BEVxll33k7h/tEX+Qp7szz+8siZgzWf639fsupyXwXc2Aj3glkzSLqCggey08aZOz+N4I9L3hH4Z+SrV6L7zUTvRQW82NHKEvy+xBZsPFhcM506nAhYUiCxj3C1U7MHvwpEQHXUsLManAYPfSzslpet8EojDv71XtYq/icDx6FvAv+rQIHrFAOrAAAADwUGb4C/AOh4JAlw1B6FvymzbxZgRvNGdavL+ouLgqynHseOSHmt27vbXjIzlZduYfKSu3cxFw89pEHslfTjIJv3JxLyg+JjI/7wTd3JxLhLl+r57/Ch7jTFJBaXsslmnHKO7e7d2/HCMYaqGuyyErt6WX298p+GdwDteoJCQ/J2/hr1EE3D6/hmGspJ+Uo21v9y5h6SL8RLYagz9hJ4Xs1XuUuGU9OX9a97TiLm7i7dyvOGl5pgPJsG6+g5ygglgv6YSXY/NBIMlv78KHOPEJPdmIvdu5Ld8gq2sbDt3QN3+Imz5L8/zwUd3d7ueeW98v+XhErmNoKKG4U7duQvfLClJ3fd3e7u7t5Ynu7u7VZJYZ4RcUru/bPvywT73e9vUPbvkHn93b8eX/CkEnz5zt9yg13d3t3jQcZO74hCHNfYIi3S98gje+Ui9/j+je7uS/qssl3d+ebu/JVvwRXd2mPJBF3fusEN3d/eW7v3mWEDlBW7u7u7u8v+8gK/LjxPd9xyRBN2nMCvn12CG93zEGnhzg3ylpE7xPfl/foFBIaT1Xvx9xeuFeGUtPvJbET3/6BIXRu1cgIrv90C0oIru79v8JS3d3fVdbW/zd3WDGCO7+Y10Bku7u1z0XvGVY/BCRV3eSiuXyIjQX+bwZcX4KMdlE63ZWyJd+ZfBUbLiOXGvd/fghu/604GwCPo71h/N2vqwnXupJvRY1bWl5F81+esm0/7rCNfmw5czR05BYsN8mfP4JYdxwLMang99LGNTn3N+dBUws0t/Je58TNJ/BDy/J+H+7wReo6j09j+fW+4KJqFJclPMeJhy85Ku2XfvFIJTXf1ovvgR1v9ghyU/fr6qA1jVcvlXr9SrJfXkXjLxggF5FTdKb4GEVdYdnc/5f9z36Oer+CMprkIlJPrxe/BIfjvvwT+uE+FoITApRr/HNKPS6UFBsM8I96NIsfNu3L/ir5f/FUbt4qOgjLkn79fnQGWvzrXq9F90A9wyqdF618Ehuq+EPAqhJa9HRO9Ey+bvh3nJAaZnyQOYWMNYF36NxrmFLUuhjR/e+FTLKRKQTXUAQvxvm028v0TqCIpcfc3l0IlIL5rKHTg3+Ilx/nw/l+vxBQra/HyV8Ga295MHd9dZN31m+XyaJUJ8N5YQ++ppL+oI9nLebwn8L/CNffgj7v34Iu7+/VvGBdfaxgXF18fYjjHs8xyV/bOgj+T3Da2Thfg98kmBD3/3/iKbLJst7qAM/Tt5fcL12Ikt9Yov4nA8F8v9C2gX/V4ED1ygHVgAAAOLQZoAL8A6HoJPE+z5QOQHg+1DgyHct1/CJwyPR3+Fs4uUH+8hJQ3gOB/0vck2DXu1ieCTcwatz8RxhqH018e7765oXNEvl+WCHxmZirwRlw3PYaBmIO16iyBLp6+YGw9tP6kIUljTK+g3CZduGJZQaY1WpIuHYUw2oae7t315oKO73e34Rzz8pclnBvMNX0NiMlc+esJwh/ES2y+QkUarE617YShxBg28Dw1BrjbTfMOdqlqng4L/5Je47jqURG2u68f8J+w4QEIktstusEu93u6Rcl3eY1xf3y+dPuivrLkp74Id7sfhLw3BtbtXqTe/wQzh9e4Bq82jEe/rl+vC8Estbd3kt9n4KLu7u7u36tv8t39yArveld939+CK7/V4JN3dyvBZOSPuqV93YLPXLeuXd30MECRMdJD7u7/MYg8+vBCUPxcT7lv1DdqeNjh1Ej/Bh4YJxqN0SepS0Rf4cG5y/6yBsxrdf7Kn89vL/XFr2qQiwgV6Tu/kmW+jAtvvu/ysXrxe730tgK0MrY7cEnd37CYkEl2lv+CG7vwkL67uLK7v3el18msn5K6gu8OcA6uc9wPtmtf4G5z1XpBI3CbqFWI9by/r4LSSDR83fzHm/gkO739+vv1TLgjr369WNgn5cvd27/l7Dp1frV+gZ1v4b+eUu76wT72krux2B+BOiRa/BaW73v6vBFLniZG8tawWrDL/cEsCbd3F7LfmrT0A+fF++/oEpqg7/6334vCPWkOh4uW93cY8KY9Xrk1P5f9XBQRNlnU5x9rfwj8QTrRfeGPX3qx076ry1v2/4Id6lVXEr34Ibvxz9YV+rfr6tterCtYtfX4czEhqaPSW5HzLzmvrxOT0cyvykxnLBN6LBO6yoKkGPIPePh8w/uiOtZ9Ia6wjCE5l8mp9YQWGivaqXdP/YK+W5B4PNT5XMnU7GvcEhi3gbunrdALQMYIy5hqDLz7xDZ69fICHu+V+i+6iFHdXr6/X2T+vwPde3xFInUXwhrwUzkoDr6UtIOYOMIhXSON3x39jXWEzbpymTl/4JSlIIhJY/EX4q8nj5aA0L+vo7msZzYyz9b6uKzEx/UpLGveHP3+CgjMy2zGmj3qh1feGf6t8QL7vu6ydZJZYT6fX6wtxBZsIS1SuBC8hQ3m9kf8RUeaO6gby3l+LDP2IqNnWI4kt2qNA3a2XA8ejvAv1q8CB6xQDqwAAA0RBmiAvwDoeCQJcCf2eqmmL/5MCD4KDXLllHi33NeN6xcdCOannJDzbfGmiX18LwQy53/dq7/BEcwUjT6UNXPxAjiJeSyWTLNGt/+U+bDvB34YJwVs9UuErKtfyWHEU1+NJkIuXDEl5BaSt73d1JzEv/wgXyxe3d3atYkzm7ZiPxfG2ny3WoiueOMe0vfy+jYOl9GzDxx6+ShEW/Hyn3fniZyV+k5MTNd69opSg2y/cEd5btvPonH2hpBpVO3zZT0v3BDhPh+p7nZZe7g3yT+ZftjL1rpQUCHve/vxBzj17v/d8xHhPXgno93ku542Cald93bV/r/w/BRd99JisZRflZJCXu8lcINNQUeXMuCXGy//ICggcebl+7wt9KCnjk1Qt33dJvUEpXku73b1BVe99Hd3jdYI73dugSwSd3b8FG7vu7dgNMMgo3dp3+/BJkv618M9gLMMgqKG5mmA617unWsFRN3d3d2f1ZIg4e1L/Layf/lvz40VX8F3hwmD8iDFtV4ma+vaBYThppnOQXEsg+neeyq9QrP5ve2EDnc4A7K5w5zu/RPUv+aCvd3u7u079+O7u776X3d/9eXu/cEN32JuFJlgJXzr3/BGd45hgEYgsWE3+4VkDYey3kN5bxUzbirB+Z6xfEGdn8MZbhXroKQ4UujHvmPtWz7xcFBMeaPDHvfMdAxr7wggQ73bNn16/or5bftNW08l7yY3JSAjrRqvBypkVLZVe+evXq6p8nr6BRVvJI5fbXnYI+a8WvPPXJqf1/S9QUHnvPfevKSejLBL6LhN5+vw3H71EIQQ0ChvcpgzfrxDRGy+EEX7Kk85KX/GcXJE41kv3pDJhBsLffyHKv4tm3sit11qCLu79wLqxz5Fauv19XoqZFTcQaOyU+fEiX1ikVglNh1naIzmoDGjnuZawgnZMy2N9QUlLj3ZkvkJByDY9a9goBLLQpBmLmsOPMHuLL7Xe69pS/fWuCIrO/oMy/aQyMkKS96xIOPVDwcDoKd6N30aVpj4OAS3moUk7turLS6hAzvksmY80f4Iat4/4X16++RfeIwIngiEwynq7n4ibfvMtPrzWqclmp9YX7EcO/uvzFcpLAbHorQBCsAAAAzdBmkAvwDo6mDA6z+y/+svlGjgN/wfamGVu/Lw3irYrwyfhuHtL5jRjVNPpSGMBIQeejrP8vv7gr5SRQPe5x97fCD0v/0afSjKL6hXAl9bBh97EdR6ej9fbFTS19CONNPjXv4vDD74819ZoR7/L2Qw1rxsMzhUOELXr59b6xsd/ER1r/GGrB6sS/eIWLhqDqfmi99x5Xd3d3d3d/jiu7u7u7u5/fuyGA5xR4H/mIcVjJwwc6oRB+Ejkt8t/j5rvfWMKWIdyz3cZaFDV3Lfdu8v4R9ifjzV7lBBr6H9pIJWGKRL/bL/JBDe/xriMKXd3eYFQj435PHvd7GsbwQzGne/5r715YKIy0Xbu7u5k9f8ssaLS7de4cne+DjoEmUfvX0CwRZXd+93FaqCE5/t21hvKCPlt4vwWb3e7u7v78Et3u9/Iq/LtX4mvdBAlZJN78tX8kEp2EfHt3IPPH0il//ChHe3d3e9q0vbX4Jru5TQ5K+3l14ItymtgDTwUeE3XbQIHn05eozyXuYjmu+enhtr3i4vJelarAiia8Rfe7V+S9KvXMfgku/8/RYmXxPd0r36t+iNfJr8IC3e7Tqm7vBZ4JBEvCTz3JV9PMLX0GzBC75r8daufxVfgp8+Dvrp6Tv7xGrA7ArVy+IXry1a60VvaXv1jl+rnyK3YBdRij44y3MqReVSpBcvyn3f4JSAIN1a6dWX5rVOdT3N/QJTDTQm96Vqv38M9LMGIKiu+7u8Y9+LF8FZHu6jy5r7iI217FPELLz35dWCfNxNei+/X36+/X026+1+idr8EJc2evylwK90a1Vq9WgsWM2CWYFhjLdBPUuaPIjqvrL4xfhm8p630zbyeli142jZvETElvv3KXBJ6YXtdBmrVtq/waotfgi7u1YFkqsYjFb8Efd/fr6ulbWCekqZV8utcKmswY+gDg/3vzIVtCNnFSLv/xBOz21fTfmhUrrfd3q0bf73cEMtBlozX7mvg1W/ra/JEH2ZszEgWaWvceUFGTPDuW2PJpf3q7tfNRG1kngp8a95MJn5vsQUO5ZHlkGp+b65lC+BC9FY1vglhjLMznJB7LdzWF+zcC91OwBd0AAADR0GaYC/AOh4cCXCHv6k6/jagSFqL/izZrB2I5Dg217kw3FevfZITr277lBj/wkcgEyYPgbf+QRxhr99R7J5S5R5Qd+GPALPTAdqdALQf11HnrhG98fxBMve5y/W+7ykt+SE97n0cZdL6CJbw3Dw3Jfc5Jebd36hTGwE2AX05LSe3e3d+ZS+X+Lu7u7uYCV7hLJLRkBt5FvwcPyQXx/3864LWGhLxNL+iUKg10G2CUSl35I+SPjYb7uvt5t73nrrzTc5cpLuFCpHx7t3d33u7trF8KXd3d3vcdCk5Qf/fglI7kH8ZA6+35S8l10aCK4a2nvvy3eLj4//X/0Xd9+W5wW+DV4iaY1X+kRvoJntvvdLAnCPwr/Jckvn+Ogk3v86CsFepCPb3d39WXeT0N7UvBKXd7u8HmhiFGL93v35tJdsv5e1Bj4Y8d9mxc1CA3Iioo8K3rjPyZfN9qC8RzRfvfgN/DKP72sEV7/fghK7v77/BLvd3d2woSD/PT25TWU/F8IwUbvd3OS6XyXr45S6Rrv/BQd3e+5VX2idBf4cwha6x195yNf49+voFpOOyn4ce/fhXkLZSlcpyT7HxViq+Ir78Ee7u1eCW77vdu4OVftHRb5Pf/wlyX3eT99/BJd3dvwSXf6bwT7u7zX9Xgku+35rvrBXV/wUXfd3dpNJXOy1+QXeFYIPDwszh3NVzR7/Prf8EpEeWaR8t7nj6J78EonGmXjXuvcIkSvJi4T6dK2f7EAQET29gQMEZXf6vRfVYMa9Xgj7v0T6+/X30rfq36+/LVfuQkNblYL/BMW6Kei6Lsnwa8XL5br+Cr0eCd+OhWXQT+23YPB7Ldz4l1vr1yf2EF/4QhM20HHhjzL94diJjGxe/opS3kBq8KCXpO93u7hd9du3Xl//RPfiL7818CzV9fq9V/r6TJXvr6BPljIRjl394qFTYFdyBxm0UDDV0B9xh6si/Np//Hk32clNeMtH/jipPh9lm07lJ/wSy07lzi/cuGvrJwSlGvfcIfi9QZrfZ+elYX+sI/W+FSas8tGl82m3l8J4IQhJLkv2CCici4JKseGeT9fpUX3ya617zURiBF7BKJSDGW6jzyXY1hcuzYd/YAu6AAAADI0GagC/AEL2SHAwegxpth8nnLffGfP9Z+XlHzm68bEzVLEoNsIPPR96hPksl7nBnwS3wxltBl2D3bwrfL/04jhuaqOfG7Hd5bweF/9fUR0mxojeDE2Xf5P26CtziHykS5V4LIRvuOBmu3d3evCMIbvd3d3f8XNG93688ZZXcMYvt3egbu71i5oKd7u7u7uOhUb9yxLd7y0VjL/IbIYrvg6X0cy/hH7aLQVa197j4RyT932jz4uCLw1Cqi9R0bahdqev78ej5brG5BBHfd3dfvEL8+n1+X5IIy5h52lVZcGmT1IcRC+jEKVP0SFBJpI+7u7zl3ffLfcgK+S7h5Ky2727V/jYJ9J3d9svy/jLu7vu792vxPcYa8lXfjr3anGK6paX8tSAmOHltPd7v6vPXhxxm82V6ojkGHgj8EP3DCZLpCMzuYKFvd5Le7pXf0vgl7vu/RHgiK7+WvkBVvd7uPrVzqmtr0kU+H0UrQWeYRArnQEQ89dqV7QIDYyqJPVbwHfmYljcWsutd+oKi5g5ufYmaCS5byW+Ld4j+pU+ybnJaf0CzJTJnOSvck+rwQ5b/fgjvLfqwnrwXXf3fmXwR936T0Xvw13c6Ivtm2P6IdRW0a76rgt8OlDWYyAVTzeZafQauU7eivjpNT/+FTSNNHaPB3LciRVNZDQtfCPXQQqdPw3JYjb+Y4jptxVl3hHl/7sOeW2/m1rYgQvvH5vXr8FBXe7u9vwUXfd9ojZKdZYU7RB+WmrR2G8rOtIXcV7l3OXcSry9efT8kEhQdyXeCf1wm7BFjuvlS/8RCpLyY7vJU1bLv9/QVu4by27l9sfNtvxEwgpI2d+4KCjzLxvbv8x42vgrwR3fi60X1UA74I8+Pv4F/l//VMievv191IpVda9k/XJ8mO+ulL5q+IMzMg6nODtOaM5oQel8ITITwtkpPk0v5B7Mn3/4JcxKs4+u/4Vxlr1LXeguRMy1/8RCXzs0g85FrBsnD//BKfD2WWe3iwZny73fWtfgRtYRonqX6K1PGVRMtZpaseeIKNeXaezOBD8EpZLwZvJst7lLCH7G4AuyAAAADBEGaoC/AOm9eBFXnhINDpEr+71niIsvDV4s287fUKSkn4151/ncpK3uciz3ClV7z5nx+M7vvbfqy4/OZnvgjJwCI/8HqPB2vUMTDIIH5HPiC1gTtSXf5VnHv/16onpPBcV3d0cMaq8kwRfE1yfyc5wg9Wepu5gJWpYRzhZ7DUHQsFO9ziWTcXnUb5QL6CWX+Ng94Ny/vk9CxBxDn4hS6xe8bEHx0KkQ2S2T5/JnBsoH7hAsPwbBSt7u74N36XqCg27x2I2VPwju+rd8l/IUt/4i9Il3MXiXj73fdz2uLL/EXcdLLaMa+J15O7ov/Wbd3vkGwVlZmJXd3d3d27kBZy53KaJZi8V92IKCwjv3e77HljSkJPd3d8tu7u/MvbJv8Fd3IDWj127vKaHJR5h6ghI73r9HjyetQX+LNxossHazUvpAjMzSuVumSvBYV93u9926xN3e7+ogEt35L+8I78EJWjGtrxVWrlRG+QFx7vd7lWk83moDHw5lwGLLoNXU7V4c5/r6BabDersJV2N/nb6UEscL8zaa+1sq7dx8fe3d5K7d2b9Fgh3f0lat+vfr88I/wUd33fqy5V1hMr73dL77uvDm943zbbbZP1Ah/1irz2+bc+8tWO+C3wrkzJi9M/D+iv/BKQ5IJWnbqS/q7+EeughBF4dy0GX6/LUY910C2731diWgFeGQSldrKMy3Ijql9XutfaoDbVWugFylMiq19XZf/69fe2vfV+DDqfnGkCdVMjmqYyylq3Xn6/vzlbwm+z+Cf1gnwhDUFau0j+svIv6n3r/L4QR/oneOhnjTQZ6mUu0/rwwQt/Jlc+t/wjXuqR+qtE79fVWuesBI1fL/+rT5oVJhbFAqqv8LoO9a1CzLrh057y+EO+aGU+X76JBbH2V9SXueoiWj4y0GH3j/vhD529bWyzENwaLDf1hH0sI1NGPMz8lEav6CUFp3fwj7hv3691QJN7NqeuI5bDOaN1db4iHJ/jPJiPKv4PvBEUY8YaA3vk+tsFbXZhrKWdTgC7oAAAAMlQZrAL8A6HgkCXBLs0UogR9ZvIIwxlv4jkJJYx66z+D9a4JeYWOP5yRx9zWfgl81eW3KWo+Yr7XwUS2YG7IhzNjL9DY3ghny/5XiJR5b8ly+J/vpPL69OL3e93B54KPDSeqMkFg98y6giFbu4rofECRnF27pO94VeEL3fRu+9tAh/WM5L4ey3lNJd3+Ci7u4ru8XYRlvd+Ol3kumx99YvEayKJEcwMPJ3t9iWCor3u7u7h99ffgp3u4awK+8t3rLBJxn1sQb5O/8vkl+EJ737mL7MWDv1EE5LxuLFrHdykuW3vToMQjLu78Tyf3/gqlu93nJPb3eeCspbQw0jJRW7u7u7juCi92pbX1r1BFc4x/DiPiNZe8n80m3f9FygzeuGzYZzW1/D7cI3H+cRBm1L/uY2778Esx5jV14T8tyXrL6yXf1go7u77Vj9bgoO93u7UVZIIzXu/6P1+CGOdF+/BJd0fvwTE3d32Pkgv8OY7Krmjv8M4Dr6BBwludjS6crlXY2yu7BvaGza28zHYl2sM1+Nw4jI3zVWduWtClo/u6j0cl2/Gh7c4upmnV/wQ94+2FVNkbl/LuQuXcu/QYEBC7u7u93Zs9gTK+rwl3d75uK+6+KWW/jDQ19nJfSeCyt7vu7u5G+rmuuC/xBZLolHjI0+v+CUybLTyc2bnhGiMacCeICEPHdx23y0MSjT650l1KTfpQS8FHGGXZgy+ex2EBCEMVno7eTLZQY6+BPVjph26wRFvdjoBrgmW+v1YEy73BEaHkHl/rXl5f8b4MM0NC4d92UxHf6/1hBe+xyZuNtbwuwhBIY1A3lvmNPCOLLlveS1r30vghK9bn4JLv8yvgQdcfl9Da+8Dn/X1YDxErR3wHmH1vXr7tiFKr0ukQFmB7mmV5dO/WnjpF8KmiiOvfg1lZlzPB+3lbWMsN6P/JEUDGmr8x/hc6ycRcca9Iy314iXOfeDVdesTznXyan80K8Y8HXNGZ4XuR5arZdp/Si+akTOuCkJ0R9cH+Lo33f4Tghu/7wlKXQxr3AdAhW8ZBF3f2X/kUEhCkQ5pv0COs3ZXfAF2wAAAs5BmuAvwBC3m8NNL4sNduNNfy+uWEauPiy7ZR+UHmIbrx8TKTSwheAfvLUEM5Et+W//xBNV5MZfL8sRykg1BzDAej1PXlLkH1B2vUFE49gjrNy3D8lnQV21+yc+IvjJOF5Z7JmskWU2e4w1Ulili/+Iy2UFU+GMVby315JS8cA/vpwQyhJeGzvByX/pTkrhvjf3q+vzS25hX4/4mE8cgW+XF4JeahJ3v2X/E8fe7+73a/Eb3coN34Q5tu73u/ct7vyTXf734Lphfd7ypBo28d9TEcH5L5Iawg4mb6nsm//EFfUpLh3LfqCrvJeQkcm9lu/n4K7SX3d3e51i7kvpO/EfLJd38t734jWWSlXywSlD8N/Xr9vIPPrxERd1pdbvd714Ir7t+Ciivzgp+9wT3vvaPZf+uDDzkKrh6P+9aQjvwR738fjjuW77vkt3n+WrlZcvSt8hMbJe14srpcoy97JBHu7lRkTcN93iy6Tf+TBd5oI94M/PrWMq9IEZH5+WvcEOkXvM8iWXeC2C21vu7XxIK+kk73d+L8Et3d3d39+CG5Le2lumXd3WCK77dUvIieCK73zdIy+6s9S6Y1/a3gh8aauZf98XzFyAbCBxhYLfBFDGWZnf8EJFNe7oO6L4/QQjocjHvr+fW1RCBu8+1ymS7/F68Et7vu/vwQ3d/pflX/xCL78End/VusTL0vvUEVJ/1L2gnzZlGTlj86rPBRwku5nEb2steeW9HJ5eNr16q8FmIgiLDt7v61v5fEJ/OTL5NT60sR44X+qIXxHL/iKQshsGve3+EYIfLjeWCMuW/q9WrLmrVr8EfPL7WR2uWsyxBkTLePEbVDPIVEKsVclLWX6XIvwSxxq2njuRB7v+6lvvg2Xf5GCE+TCZFrCNQSZL8x5dY3eI+SsI1iOS8ffRqaevtw3lljL8CH4IijDIUqW9tYf7NwNtS294AuuAAAACwkGbAC/AELF/9bL+N+LC2MtfHmrh5eEuP+feW78O8O8EMMt0GWYSQoNgP/LE9/Xq4vLdYvxfUhseA/+DteoKJhlqbI2HY8t/UQSPkohLaMwT+oWM5fwj8Jl8D3NGOhiv5blrsv4mJ7hhBK6+n5IjHmX5zhLl8s/KIy/7llsiC+vIXh1B7wdP1DBNwwhgr26/jP37pQUCHu+7tusEcFYme8Hsk9vLjW3QzvcJwRUw+/0ixEqBb3e72Hx1K8xF98tlflwcZPr7gmnz8lkLt7jrvV3TlHsQksn1/4iYeePdv88Ed29isT3qoI46WX2D6CW0lS3Nkgj3e2/wYCXHwpPu7uvt5uDPziHP6Qy+r+gSGd9MpfoR4sENje+SV4UPo3ySd37e/dYUpWUlbuW73fb/qxMFXd93d6Tfgi3ktpm8Xqx+Ca9ru7fghKSek3qrF5P0CIjvfvwQ3u03WGStNc+MLwpiv3guL/6hyCPdDjSjyCeqvB+Z+X+6EwWm3dzkn3/CoOCAoV75yVyWjdz0it/eNYKCbvu/Tc9+vX4JC3f71BT3d33fb8Et73dq1PjdXtfgh7u36+gy8kc2y/8EpFzVc0e82rz9xx9E7XQ6c6+bWuusFl3cozJTMMLtoPfL8F5t3Hy6+v6RNWeCs7u+5SO7u7H4KLu7vfHL9ek8EZXtWrwQ3Lf8tX9+Cjz5uMtDk689e15deCTx2U8F6y/L4hfhncNu61Prf/Ed4+aYQPNH1iavfEop0keApf+vvtffovv17rRGsn0ul69+FSGlKPjhdagv9R2Ww//5ILsInoZcsevUFNgwdiNJwusuUuwlxb1wS7LTGWsB+KvCpS2jzacl9SGJr+DQvxAR61uGucfKSXyan9/F/F0RuizXfuuQEZ3lJh3LNWTvSy3vrUv8kMR/Ah6gnLJfJHfL617MS/VEv0fAF2QAAALSQZsgL8A63mDXB2AgEF65rnM9aidLUsQUpId96g/nX6grqvOz4S645QMUH7h+O8IsCZdlo3t9YnhXx1ltp74qF/iOGb0DgqQLUVzf83NIzQeeCjnmHGqGkZDVt+yZcdeEy4EvqZ5L9ePm8Oz1X+LkmaO+CL1EPuSk/L4mLpRAS8seHEE5fveUDXluW+4Oy/vRKvl9fzCDEX1ifvrHxX8hw3Cnf+fWWHDu718+t9YuUQK7uYDNBT6z1fXQiTeEjj274IYCN+jNqtiiOFvT2sV5YmFMk/3DcEBng0aWbVEoV3aIFDmHyktx1r9J70m8/J/Xf+J60Q8End+6Hwhd97veYY8ZrPNKSNnL/+ErkFc0P3BCUE2jT+7eviPnrXuK7vGdjcTEDZbQihTj7u+vBCd4x7i8TV/xF3d3d3/+CW77u/vsKbzj76fd3d37J9/1hSz3e3d3d3f7DCm/2spku4h3pVzwRS3+kxm3ycnovMgXeHCJOS2Zr/FQq9IF/LjlRHy7hZtLvLpt5f02TG55gkvK33oKxuVV8xJ3fzfPpkusM1c+QFt390frrVug3BRaS7vbrrwRXfYrCO8v3V9+C2CKqW5+Hy7u7o7u39/TFtagiubP5fIHJ816bbX1lgh5R9+94LvDWGDLeKmI7/8KkUeTZbeckw94nQtm3FWMy/xPXYj+C7nIltJex7SN3qveJaL3uQu76/ebpfVqvMt69rwc6rk3FcmdVr4SRsvwXFwh8/XG2V5fteLrFBliIIjw77mGFf5fCCJ/whEGw9llxE2gslOEIKMl44ydTuOIhiA99LTkHjXyWx/Fr+TwUXd3ff0V1+Ky3kv/EEpDrQG1sXbQL5F/q2iwniwa1rwS+nK8v42TuONf/Jd3/BDLg018EG2IiDsxtr22W/iIjclnbfJC9Yuqwxx9GzeJ+EdehPvNBRu77TtXiCZsZ2j3gRfdSZgC7oAAAAKUQZtAL8A6HmCnBHuXJl99eBF8hjDNB52/zFlBQZhuCtaH4IS58fpvROg78MdJy8ZiFrXX9sba9RBJh6ceKNJ5ySL/ki5ylT+fW3luDHA7vryeGMiL/5deYtwn4PwG79wpuCbY4u3d37d3d3dvcWZ3dyg7bg5110Ngk3MPeXjYje9ymnj2h+sv/WIpeWCE5x4l7y+CLbkHrbyx5Hd3d3d3+o67u7u93mBQNV9ebrqQQbccI41e9bkK9LeSeCErwdyXvcXc3xslxXk9cwj8Fvd3DLOfDxMEdLJPvLCPHyVEYeHi53uK30EYIZsD2mZHTL4I4xt5La7f4R6TpP3fflhQp0993eW3d3dzyxZHeUZOZKb7WXXkq+X6/IfSaBnqLFZo8xp+SYm73lXvXBQXkt9/VidZZd3cR6xa5JYj0L9Bb5hExINYky69IL9wi4xTboG79uba9wXYyygXeO+73Eb3Zd359agovcpJ3stth3HyJlIUm/+nx/p9ai+77v8Enct+m1BDkzcqwEWF1ff4KCu7u936vBJ3dz8Efd2/X4XrqstYoL/Py/Bq5f+CIhyQStOvB4/+TqG8tWGbhyYZCDTnXTJv/RMxqlRBF37vvH/9CTq6wR3f8fkJekJ9FbwE2CJb+AkwKAJOqjKGqXryL6+PcEUmfpPBITNQ99rz1rXvBov1z7xEyBJBH+x93jevy3frARuNRb4Yx31q3bb/z7pmr1b1X347Nmq5cfp8e1fqIXq9Eb9fdyL7v9I2Mg//4ITY6RELLVKtcEJzYYl5QaL31xi/uW8MZauvx9E6lwY6udRIKzu/gLejOkjFco3m334Qu+77vetQSSkyX5Iuorj7R2/3cNzUWSvAiF968Esl+clvUAXdAAADDEGbYC/AO2X/rg7L/6+pg0NNF9F8T/Flxhvp5r/C1coVfgfzQvxmWny/34nLflusneWpOGMsgy/z1TNv8v/RYjlJHqcA03tPfL4fi4gO/F5B4dwkTUNZwYvl38YTKOveYjbiHLe3esXJGnffKTd3d7dyD+HwU1x/47jXqrhlBrt9vh66E8v/uLtmA09kGeMNZfo8/WL80oed36hSEPnmUkcVsFXwj1qu7u9vMtrE3BZy93f3fll/apsE3coHOCRF/f5vxe3J4vhJxr/ecVg3L/0SrHROvcKWA2+33KSe3mBqTN425iO/eXhSU44We30dl27u93tv8T3GGr2YH+v816+IgsLaKD8eC+0td24yWFaycryXchJo/L/xA27ojR56WT2Jq/rfmnbwbZPrl+q6CktHdyknt3wxmr7YPteCYgjuH2jM9daDF9Vt+EvLG0QG6ywWb3d97O2vEXd3fkkvd/glu7vdu9Iu4i7u05gZiKLBKe73fygz8EQ7d++glJpce3vl+I7yH3fmI7v1t6t1QKN3d0nf0SX5P75P0VMl4fu/LeNkf6/k1v5yOfnPn3Bf4c8MZZf4ay9e4I7nOZyTGX1daCsfr+7uW0rSEKX+/CPwzBHcbZd34ruS3vWy4awq+k3nNc5ptpG3pfwS7KSe7tevLBH3dzrBT3e7vd/vwU3e7u73NT8Zf/wS3dy27/1c69Bz4IiwkZfF6glIU3kmdemy0Lv4/+I45WnxxyUksTFkHmj421+88EIlpq/fgkmxAty/BJR7+qgJMEMt9zoBSoEd6rer1SItRiseWrSPa9fQLiajvjGxh8/VLz4Mll+siyXfr8K8OZ063dNirsf4tie974JJMD2W8I/BHkNZPl+UmpyVL0jvLoTXq36tJ4JOW2s51go3qjrYyfXKiEThrA7bQ7Tywhef/+lvBKIdg01aDGEmSORHoeQr+UWsIXDM1vup9a/rpSdEHcsvEY3p07S+DUvyd2CEW7/fhmcku5aPv/wRE0hksXvG5MR155cba/rAi+8Y96163gC64AAAAn5Bm4AvwDol8vXmXrXiR0wGOTx8D34EFeXWqJYgQV6lMk9KvwrrG2Q0+u6+XQ3Ekrm8cWPNe8fKS8tjQUgIHjBG40FKNhQ3ku7nJGI3y/hNehP/cFkfaKDcDXg2k4z6+2bniYZmBQdifbtfLqXWJjJO5a9wREDcKd9zz6ywRnyknaToFucep3DSCNXYfBDHqKSNjt68vnkDV+TWphW7t/kO7uNtHJBLe7d3d2MvxH4JbvcwMe/r8EJSnA3L8fF4mCjeebsovZf18pNyXflghOSR57F5YI7yD/vevRWfhS++7kBt97xQZUkUZlNh7Oub9BmPC2iuvgquX+XBjBQUpF8da34vcFfd3fPhRnY/BN4y+uXMvEyd3l/Bb6XN6ibv5L7wSybZL3f7ugVXu7u7nJT+9IF27W7v1bXrJ4I5M7DZP6PeC8n9fUgWEVdm7xxv989plNfcs+fly4RzeESu/d3d3eGlAr1WDr1/8pr6VOr8a1y/BFvXMVky9Hr+XW6+6flgm6MuZ64oL/BCQhJe543l/HxjxF3W7vrH8Rd2rvHovxBH29J39Bjdq9ffyanL/6Ru4K3Ulm9D/qC0973lJfJawV3vSu+7sesTzfkMHIPF4duoS88pdRYNLxOn5IJCj7XzRaC9f6yLDMBA39brWn18mmM99iqI5l/3ERW7uS3kviILO7lmNte0QuzY8TRPfo/utffr3gjz6Ef/KsX0uVexGOkhS0fJcca9aiJ8LTgSe3l8G6xf8VBCcO5b1c1hTk8t8I1Y8X8bRO/BIfaDuWqskEt3d7u/fOM7vujaMP7SFg/y+MO3uEsalD+eLP+wQ1zRF3IEQvqEfvJeqeALsgAAApBBm6AvwBC/iw1jQUs4MZu9ahH+ESvvktAso+Yj8Jykjm2hrx331BbLefLh3Lb/gmLh9lql4kP/K8QbJZM0gx/rP6eW8Hup6nslnz9eEr3ee/4icD37u8v5Pk27vL+Nn40rwc9uk773d3fL7Z9Tjrc0R3cub93NUo+QD68EcoiRfYg8L/0osmU6UGXd0X+wrwRld3tT8smW90J7xHNyX54IYevn99Z4q7vrLg5fm9E+SKEQ77y3pZJfiLPwO81XmlJb8svdH3828sEJwxPN+5rE5NcmCM27/JK4Mn5PkhvhvLa6YWtf5PuyEZv+Cgu75LeL9W7DMJ3f0nXi777tLybSSB/BQXd3e0VHJS/14UyXHZRTTlt3vd+3USMkH9u7zjLu58e7/BTfbu93P9pPXvwRXS49dIvoLfDgiUFxjy8OM0/L69BmG75+/LrZN/go5SLWOtfLd8R4/42ENG939J+CWCW73t/qx38El33PGwWd3d7u5SW5tKNwldy3bu1+Iu73lGetY5WTf1UQi+rwS9VLNewXgju+xXq3XXiyRraoep6uZwXeFZMJeEdTtQXi+fW/+XxSxX1hT70sEcJ1z332N4pf4a3ZpL+bWl4Jb3vekXwTX2kd39/+CIrvfL8EMl79b8GNXPwhvfd3f0Igou+9/Ita+/XmX196gjlp/V0CTua2+lavEGyua2vPgyedde+TB5ol/8k1V+SaQovyz12y75fBGd48XP1+L8ZaPLiuKWKvXr8F/UASPdde6TPMYIPuf/hkUSfWg2yWs+6ZN/iI76Ly05frzQTeO+G/fq/Zw7lnZwbeIzZ4x7eX+L3Je+z+CPjnLSxOrJ+/yYKi8tu/kv95Gren+GiFvr6ZNwBmcAAAAntBm8AvwDql/14EFevrRff11qJigoOFKv8bx8EMO5ZLlvb8Fvd7vsfgtJxhre+V++ErTtry+PZA78Mcai24abQ0q+v431XcRw5C1yjMg8tY2SrZfycJ3z6Yer/8FGbLkBt5wltXlLct/xEbbHAbjX+yXL5ZeUQrn4I46Br9zy1eDzUoiS9LxNnjDX1lu4++/1jY7+I2iggUDEDYZn53/BLd+70ivXBLd3d32K8291uCMr7t6r2t8EMAj/+HPv0foftrL9fuGRE+XwbaoVlvwjCZxUGcf5h5K6/EZbvnmMNU+h0u1fl+Jz+CG7vYm85XH8N6cGOSjFTyQREl2lF+CwpyRyXe5ST79Wi79wUFfadoxr96l7v94bmp/jrlJblu+eJL+CXdswLmHjEeL/rBROSnHpRkl3frBPa3vdz8FF931b8Ty3dHic1Wgu80N+cS8v+kLiCc0co8cvL+tC/4Ky5bykjbd7+8KwSb3KmvxW7u6JJyeLyT+S/iJb6T/wTb3u+x+/LfWs2yerf/6q0iwyTBEV34b8EN3d2quq4Li/f4LppHjkJNbn4ZNJmpNT/5KN3jIVO8zksPZbXxyXH6xPy/+EfVIjG+sXuclvKRZ17QJjyjN3dp43hGW5yH4Rmu93hWtVuvfr6K9e/RPD4cnJRvtRnv/Xif5u09eJ3XBd+XwgtfwhBL1SzGhplFWaWPNE/nhq76/pm3Vet+CQ+XNd6+/X34Irv+/X36pk7CFfV4ItObeL8QKMSjbJCnqSNb8eCayXJLTSvxEZfH8P7jg1SzZBbv1hP/ghIbPWuvwz8TXxrxNW/Nw3lqlkq/qEr7UbZcCMX8L/1mvAF2wAAAAnlBm+AvwDoeYKcJON4gI/mMQWNX+CTd+Y/BQV3GG++Hs7791GGb5PDXGGhhJfHJa2V4P16i4dn+1e9pLM2q8RanL8pJ/vx7P5r43HV6i7jrX7eMNJ/MUwGD0GC/4JcNY7zfD7f7wcl/8lG5l1/BIUD3NO0Zvl8tXcFshINz482JcW9xMENYzYX7l+bG8x0KnL+TlgjtHhiJ7fglvfu7fie7u762hNYK89fxw01g483cIvBlo0xjETEb6cEWab+b3BIV929zX3+Ci8tu+YFdrxogEPct9rJiTbveky7vL/l0CScuEjjM/mTfqC24Yy/TWov1l9/1Y/e91QnT0tEaDN+TWoIzPfBvawQlnHrtvwzBde7pPteEYLiu9yX21/Xu8xuL8OFxtrr/FsGJf/U5F/h7c73wwS5L72axbPtf61x8/lBfKSNepb4v4uCYuUkk+7ZPdL+IBXe7vlGS5/vCMEdTj234Jb2ek1bbyaseT5P5eQbJjoM1aq178FPd9y478asuT0WOVqidr47r1lr+WYpD117BEF2IiJCQYy3Zg8WPr9CMsvikLf6xHvwSF4K+W7XkgjLqvb3YRq3WTu/194Tr6J9e/X5IvcISH4AhuvjXO2Re4x7oymv4KOWwha6eraOtefJeeuCvP5//VoLVi60qfCvGGpB8z1wu8/9GklbEO/3hGEocjDVBpvy5NT/4KCSzyjwmS8t+aa7/NF3c+bv/R/dyr79e/BRy04779JUFhe7q9ffgku+1dInt5Or5fX6ZqrxgE00vJISPn1c+cHHhU8t+TCDyn0+5NJvSydLxc1pr43WGdeCSD4l2PxHZgde1UaaP+8rgDMoAAAIwQZoAL8AQ2X+qsF4U3fMSNF/i134IodRAvJjleQ2E/mvB9rRf15LL+dCejxaxMRCXllmyy/yZYvu73g88EZMuBNYxSLok5Fha0/7/Col3d3e6RfJ7eXxdcXHXu4QemvtX1+rGsTGQl5vnll916ITlt5deS7v5YKzvd3fd3dj8FN7vPF3AR4G+La6RfBDDE9xg/3KeuL4d/5h6TYNX5J6/w9ut/gkETkn3PUX3d3d+4IDuUl3cf02Xf8upx10JhLdqPFz7vyZI+hMEu7u779YS5gUTDUD1xdkosQUMQyn5+/1tKWTJ4IuUDkA7AGOb5O8sk4hiMf//hGEd3a3dJ+YVjf/Ci8ozDs9Wy/nJOX4Ii3d+/BHd5CXVR9bl3uTe+QEJTFr+vaRM1ej371cAsL/7QJDAj+XqoNN/U+iwQkaKIbseNiCu+9/3JfXhHlH2dKclb34Rgr2jD/bvu/59EyXp5ffi8t+7T5AQ33crxG9p9SPpwQ3f7J6/6orgnlJVYMPPx/Pp9+X+CHyUi1l5yL9OfepY/GqJX+g5lzbn1v+6seoIikv5qrhDpWlXiderwYLreQfa5fb/J42zl/f8v/uUhr78JHe7uW9erSv8kl/ga63619XmvrfX+CImoJt3Hsv7+IM/IXT8gzshseXfiIIasiL2PvXglnw5fLT1V4IjjoYXj3ZBwX/moEt3Lfd7eJu76f0CPgd5oeLsVRqtvJz8vtybAjF9Q3/J6/ll9lwBdkAAAAKFQZogL8AQyXyxPNMFs5I5InhPu8oXMF3hdF/P6XK/BHw4huTBWKDtepSXlJaV5Tj9YuOour8ElDur34iUkUV3y5oG3Bdw8h5FvkAzwceHJrL31/G1aR+CgzjDXsJHJffgoEmIvLIpJ7ufhGluW93dJ78sIQ/mv3c5K3dzEqxMkFnc4Ed9yjN27mt4QCnCNQ9u0pSW3d7d3TKX/CKct3d/lx15PaWJrvNwS46VWAl4b/9Jll3f1g21OaMPxCl9Vd5dF/C/2XJd5YJL6TfZLu15IgruQeePu7STJ9wU3e3d3eSde/Dt3eWIcvElor++bUbdeEeW+0QGjtv/JJ6Oyi//UGD8mtSDt3l+v/cJld/ksuL+Igu7pZbpl8EJQmac+mXJrxHkvOSKS+I3u7v+C2MPx5fy386VSL1etVqtfnxfNier1vBd0cxBx8xYclxy/+Pnttl3/5S0CLfwjVz6Jct6L7//go7vZu7fgjjnu3boIwS556W7ZP7/wT+W2u/tfgokve7v78spLbg+8K4Zj6jzI9ykTf14a4x5c0XI3Lr/1iecil3T/qHI80PGfl+2ff0M3fe9o+a/uFN3u/eclxCSrS9L10CGCw+73OSt3aeP+Jy03mNS+CKk7/fglu+9/v3d/6xpfBVXWq1XcrwRGPurJrwSFy4G5/t+uC4vw9xKfiJ6tH3/iIV4w1iye/xyXH6xvNjHv8EhDULex4R+Edvvl8x8d9rAU41YnRPISG8tcB5fr8MmoOsisxWz3/L/T4IaRiO5l+lyQ1chLfFv9eCHDLNHl+zls2YNVhPrX7qHct+4Z480a/NttYn0Fa9S41195BgJY/77v9+CW797Xa1xF9y34AzOAAABmkGaQC/AELKtfWvKFtRkM1eLiZSQ2S9nzy++WkH+4gnPG4PRJo+vZcxwt1ki5vCRxtz8029b0sv/fLaC5atB2vUsHX5/6nM58llI20X/yzH3cz8uDx+Ta33ZDzXrxF33fv6BPuaNvd3PxN7u934mXz6XPmrBCLD6GafcgzyTDKRb+oL8td3v8/LqevMUwy70nBQIxe8oxfulqGbtP8TNjz6rmtf3lzeCKSTBbdYJz5xl3d3jfgopHGXq5MhSevMG9XALPBIaCe603rL6+NgjJ5L7f0veN+J2X8K/rHxZTD73PDb8nyVfyS5L+X+CPya8pf5PivFyDJj7Xju8F668v0X6vW4IySXsVkhzwj9zLtl3+arfgoEu1dmk79+ECHJP7vuS/q1Y0ixVerRXJa9oF3UZkjhEKZgOLXnYI/G2UPxr9ekvgv8Es4y+9ttvjmt8Evek+OPzrX/klJHmj3i/N4JD7u5J6/LxGlk/iJBVI+YO/Ict0ZIrG63Pb+mLfwxx5r5SJs7+mba8M1iov9Llw4n0++xcmNkvgDM4AAACOUGaYC/AOwvECYEHzBbh7LJbqEody1X5ba9ScY8gyyL4sRyFsnDUGoPeDzxfcwL3HWreuCs/HGhz3HtwJXp/1fuvwUT27u99t74KIyX43MHm9hueyW2uhMRMD75L6zxUI+fbmJZQNf8kxLOPJe9vp1aDsv1ifrxcEs+cMZav9rfEYSXVnVtb/xF33effiLjpZNFtoWjRJ+fl83SF9ZZISuYj5x5yawcP1PXhvj/1BQbd3f71DPd++OPKr/BIWYGuwkx5eXxn/WSTWXeWuUnir3d35P6kL8Ex3d3ue7bL/8gKybu7y2469Pbf0vfoSVSDJ+b6+T2yDxm7kJPbuUl9wSl3cpIpK/teE4IvJbVhGTektyd34iS9AjEU8L5C5h6+hMFkw8PRe7vvcqZfP/XvzUtyagh3lGXg1qhpDA28sLlJO9A3v77CJpt+f0EYIi7vBpyEwSd3bJ/fJ4Ju5Ly09l9fxHJcoyk/467vfeS6XV3v+teJl8lWt8EV3s26wS3ezevq1BJa36S3X3yG7u9LyGBHWvx7r3uvQX5JIja/GfJITUxdL6BL3cuEu7dvCHDt3d93KRq1/JqfEQSeO+7yS9a11jZCvSv6zZvRW/Xk+z4mX59bELxeDDVXFdZMI+v6ya1Wbu7xt3f+rfq36+/BDd/36pEsDPRH8VXvoEmNJnsb/BLW7mHyF/l+CkRjbI1exmRr+jQfr3rwRSEj5uZfX8EM+PxQb+zpJaWdLWEcmqsSdwIZfeb4Au6AAAACJkGagC/AEMLUIyBwxoYa9+LE5J5g4QDXX4Ieew+zr0CCX/19RBpbDE+vhj/1ibgmLpPu7PEZ/E8cxrQJWXy+XjX3AcG5f+ifUxiX6yRtCWPcFEo+je+lVLPJLw7lvu/P/m0QNsMP8mYGXS3JJ5H31glzhYPwHrRgklfLccun0W7kF9awcF/DPUuW+rw/BTKSuYeLe5jVvjasncoKkv1Nmn0koVk3vTQIBGL3rLFn9ycbJfrfmu99a1yFlhiev8vi/zsoY1W/WVoUYm73smL3e963V5V9BLu1HSJwYamI9FJ4gstuyTmGP+C67uweW7RG+/J1yfyq2X/9cvwQ3lu1einH8lG6DB+q9vqgU8Oz1I5qaeFO99NqwjBDe9y8uXLpcRgpKQiEz0nu3d3fb8KXu+S3d0t7sVUi8i+CPyXmPwRc1CTbL9fr88mslY52HSLBJlqRPz3AXZ3yEPgv8EuUuUdyUDGL6Qb68v7+FTFv5Lr5NT6x/BFfR9Xgi7uxfkEyXrxOk/cxHeCSe/yJeE421eb+SrPXRf/wllz3deCfd6V3c/XKvW1aaI1br36J0Gy6/wQxr3kyLL9Vd/i5MNfGY38usuucICd3Ld7v/U6fr4m7BFH/faTxAzGplOy2+e/oJx1k8g8INRC9+onQOO+GGrM/yYy2eX1/DNS1+rQxK/g3yRBw3lqvmHyD3z1bLqcf6CsEPLfqob/BH1CfT21Le4N0pm8AZlAAAAGWQZqgL8AQsvX8wcMPFJa8pclkIvUEpdygWcLHy3m8pNRzQHZf9cXD08vzgkkd+OLuWVzJ5c+X/E9X/BZzCpwac2b75blLvrz/y+V8v0/iJSgegTyAbQIeV8CFvXkjodtpIk15ZSRiXy4PH5Pk+SQRJfyRB3vu/ua7vfmK6B3+bkysTBXVPd3d3d/ZffkUFF58lH2dypv2iUZAYTMq31cPPRWgy18msK0Zz3y+ePjOCLu7/gl7vcoz9SwjS/ctvvL9wnTvd+91utcoNH6hyIR4KO/Xgs9z9deeCq73d8lv90WCG9AiX35pbMPdeEYLeU0zNfbxesXBHMMv92WEO7u93cxOpZCXjYI97ue4vu+78+ifX/otU/iFYA2ySESGPf/k1+9L34yY2ahSXwkV3+Ne+CUrvd7vmkrXvJWLvifBH46y8kXlmNmoUjXubqa6/WKDIv439+K5byXr/L3+jpEStWLxNXk8EMtO1ZJCRtlSLy80EIjvx4pVrwarH9CYvyQ7lvODXia5dhXv6J3fdL3k15JsXAGZwAAAAU5BmsAvwBDRf1LH8v76sLS3kWXwI5fEz9iIPnrgiNd+at8v05fl8TdViHry5cG/iBN6ecePnLBXor3e7xslpleHZoS5bMPcTrL36hLLdyjRI5Mu8tFeDV+I68un1oRl4n6sr7ugrBDMPGI3g6JF3yWoenqu6onL7vsmtyR4M9C4r179fkF66E0Z6L/+LLOSLbbd+fExBWij+S3f8KXvdxtrbteY1du4gFW5aEiyd7dzkv1eXkyX16/VIvgm7u77fgjujzp9q1PaVW/JulBiuuV/gjJzY/4IyntgtKq/X0/gl7vu7fvu/179SpeWuby/16I8Oc1vDH8M7mDHwQmHWvktyvFyWYg5qfnq/lyeUTjWmWtW/VqyafX+IHLIPHsuJdeykJEJfUK3fG+QSe/ciOp//EbS8OMXwb+ylIr1n/hHVDcngt8aaA1kUtP4QBmkAAABqUGa4C/AEN+EgtGvfmwCEX/1MacLs/4nu/GvdYvyLy4EF+ppL9eEYKBK7WNkttHy+E6QQj4SksZJHhj7cvkv7qy6WM/+Tw0Q3fTiMoHbucUfvPJNGS0eLdWJ0vE16Dp+Sr1r9C7mJd38sEXSdzL5Ooi40FK/1J5t+WQk/39dfauf+bSyeTwRw3PI+w9fo7wZalGbv1BJdJ3avCZ4815J+uhv7VvwUXd8YavbeSCPbu7leKljGGriN3/BRd3va+rW+T8E5Xpct3q8FBN3e/59Bk5z96+fcu9tWl9WALPMIGWg9vq5BtUn3vh1nV5S79IapGNfz/E/xBXfKSf0X6vMPJXsXXL3JuUZiN4JewYby1qK1dE3v55Pd35fk/V5PV/z19PPoMCe/X6EPSUX9+EZY+WkXrwl9zeDf74w8xpg7ykne4pd9YRrHPdel8EhbviZJfrm/VInkr36x5fYjNQeJS8Oct1xEz8GRfxf9dcuJy+C4TdqS/8/UqyrH5evziFhE+l/+GTFJT4PslQtzq/g+xOvCAnd8Y80GZB8pL5uW9eInr+bUxC64Ay2AAABpUGbAC/AOh5gxx2UBH1ov+qm3QJX4JSlvPQoF0z21akNpEteFOG8pU2nAGi0bQNu/d3PcXSu+7g88vMDRl9EXnmKYjmBteSWNMlYe4Wli6l5ZfiIby3y3nivFSgZ3coMMWnp/cZd3d3d7fd3e8nCl3d3fd3c4ucG9vwQ3IF94OS/9Ex+XBvZMy8XDJbSX37Z95fy/BR46WTljuxWWCGNkVRv3rxcMX+P8lmIlfpK3qUm7gzfrS1yG0b7KQEZbv+eJ/gsLd7vu/34u7vd8maW76eGXICbd5J0rHrJjYIy7vm9USL1/EHd+7/h67u7u73KP7/x72DIvk9BmHBjvr9sm6oX8IwVHzES4Ye2939+Ci78bcf1+sV4vVBduaH5l56vJqi10RAiu7u1+e3624tlW/BfipDQxlv1ZE2W/xG19V5T5aFt3r83kEYJO7t5Iuld936l7v0Lug/iNLL7X6M0i8Xgz1JyYQvT+CXmrd9jxkEO78x5/iaLF6ghLe8X6xpPBD3dIn0sXlqdPG16/IbKRCG6EsIeTwQzZ8IOC//VY+I3vd3AGcQAAAGVQZsgL8AQ0vExwWlJTg0uee/LWyYYxXB94vlx3IbKS+pJRmS68UXJWe+vF93OR2yL4njMWk4CML4PS+vEdeTJXJ4i73d3esksTd3cgNr15O5Jp5a17jEH8G79Vb1MaMNfsn0hB582DLT6ZdyEspfEya5LLBaXd7u/vyk48WXJg3omKaqoJSpO4PZJ6F9dx7BFvd37kt+/8lY45ZppJehEEUOc/0qfgh7vaDDw4QpE96/wrmnl1lgtK9348WVa/H9z5gx4vd9XQZklBb3+Cq7u93jpc/Or8TvaJvy/14S8Z/yW9wXSjRLfu1cF8Em9+7CMFuS3t0neCqwQ8uel89MvNrX8F3nybNoMi+SX+l+3i6hIRyW78v/iZzr+Fcv+CSkl94nZfr/1BJd/1eCUt7To/N/42CO7+V+vVepU+na1Zf/6XnlEamIl83NkGGrjXvrw3GPfX8S6lz3u9YR/5fr9H6J95abL4i/iBmMhwV+y4OyvB0v/JEHGtFbX6hr3rrqwjrwSdtct4/V+CXLZyWQuPNfFAGZwAAABckGbQC/AEMeYPFv1hHgjPpEvKBHWpIT5rzx2X8fL4EHUJky2XCESEuuIcEgl5Qfal42bzZeP4Q3d4R8b8gfgr2X61yXcpK5f8aRXfdZdeI5AaQWOF7DfvrokJZRWcDSg0pJhknUHGo7uYGzZvpZfxvJCfjDXLe/CC1hCPdea8o8jXQT6MuDM00FcmvnorQavzayaxMxCw/y+S9BWL81dG66y/2pmIucluUllWNdEvuDh+poQ+fK/XQ39GNlq1xNFd7otfr3uWPFz+T1yf3zFzl1hH49yeCO8lvlXgiyX1B56EOZfSLX1l/75o1ivkmLmzS5P5CzZ/BFd0ex+CXb3d9joI3dzD3fJicpN+W/4JryXel9P4Ib3dpPVMu8Gq2vWVaM9+aHs6oLvwQld3nV1EAhvv30CIt3t3glvu7v91giuj2PDdWK9e/U6fie7n3/q35DTEXv4jSd93+Ge6Nh8I8Z08vYgxIxfB5QTouXfJkwBmsAAAAEfQZtgL8AQtrAleIC3DFtNLnXWSLm8JHG9fUXO5/kp08vF9sNQMr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\" type=\"video/mp4\">\n",
" Your browser does not support the video tag.\n",
"</video>"
],
@@ -549,7 +569,7 @@
"<IPython.core.display.HTML object>"
]
},
- "execution_count": 62,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -584,4 +604,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/python/lbmpy_walberla/additional_data_handler.py b/python/lbmpy_walberla/additional_data_handler.py
index ac0dcd81233a5f387eed5912a1fe2338985869d1..964b6469c7e6707ac967ec43c819ab624d21ef2e 100644
--- a/python/lbmpy_walberla/additional_data_handler.py
+++ b/python/lbmpy_walberla/additional_data_handler.py
@@ -1,3 +1,4 @@
+from pystencils import Target
from pystencils.stencil import inverse_direction
from lbmpy.advanced_streaming import AccessPdfValues, numeric_offsets, numeric_index
@@ -7,7 +8,7 @@ from lbmpy.boundaries import ExtrapolationOutflow, UBB
from pystencils_walberla.additional_data_handler import AdditionalDataHandler
-def default_additional_data_handler(boundary_obj: LbBoundary, lb_method, field_name, target='cpu'):
+def default_additional_data_handler(boundary_obj: LbBoundary, lb_method, field_name, target=Target.CPU):
if not boundary_obj.additional_data:
return None
@@ -58,7 +59,7 @@ class UBBAdditionalDataHandler(AdditionalDataHandler):
class OutflowAdditionalDataHandler(AdditionalDataHandler):
- def __init__(self, stencil, boundary_object, target='cpu', field_name='pdfs'):
+ def __init__(self, stencil, boundary_object, target=Target.CPU, field_name='pdfs'):
assert isinstance(boundary_object, ExtrapolationOutflow)
self._boundary_object = boundary_object
self._stencil = boundary_object.stencil
@@ -73,15 +74,15 @@ class OutflowAdditionalDataHandler(AdditionalDataHandler):
@property
def constructor_arguments(self):
- return f", BlockDataID {self._field_name}CPUID_" if self._target == 'gpu' else ""
+ return f", BlockDataID {self._field_name}CPUID_" if self._target == Target.GPU else ""
@property
def initialiser_list(self):
- return f"{self._field_name}CPUID({self._field_name}CPUID_)," if self._target == 'gpu' else ""
+ return f"{self._field_name}CPUID({self._field_name}CPUID_)," if self._target == Target.GPU else ""
@property
def additional_field_data(self):
- identifier = "CPU" if self._target == "gpu" else ""
+ identifier = "CPU" if self._target == Target.GPU else ""
return f"auto {self._field_name} = block->getData< field::GhostLayerField<real_t, " \
f"{len(self._stencil)}> >({self._field_name}{identifier}ID); "
diff --git a/python/lbmpy_walberla/alternating_sweeps.py b/python/lbmpy_walberla/alternating_sweeps.py
index 4bafc5dc800020fea52674695146cb20e001d0e2..587217690cb464a8e8f6dbaa2a535aa0c1835f0b 100644
--- a/python/lbmpy_walberla/alternating_sweeps.py
+++ b/python/lbmpy_walberla/alternating_sweeps.py
@@ -1,8 +1,11 @@
+from dataclasses import replace
+
import numpy as np
-from pystencils_walberla.codegen import generate_selective_sweep, get_vectorize_instruction_set
+
+from pystencils_walberla.codegen import generate_selective_sweep, config_from_context
from pystencils_walberla.kernel_selection import (
AbstractInterfaceArgumentMapping, AbstractConditionNode, KernelCallNode)
-from pystencils import TypedSymbol
+from pystencils import Target, TypedSymbol
from lbmpy.creationfunctions import create_lb_ast
from lbmpy.advanced_streaming import Timestep, is_inplace
@@ -43,8 +46,8 @@ class TimestepTrackerMapping(AbstractInterfaceArgumentMapping):
def __init__(self, low_level_arg: TypedSymbol, tracker_identifier='tracker'):
self.tracker_symbol = TypedSymbol(tracker_identifier, 'std::shared_ptr<lbm::TimestepTracker> &')
- self.high_level_args = (self.tracker_symbol,)
- self.low_level_arg = low_level_arg
+ super(TimestepTrackerMapping, self).__init__(high_level_args=(self.tracker_symbol,),
+ low_level_arg=low_level_arg)
@property
def mapping_code(self):
@@ -55,10 +58,13 @@ class TimestepTrackerMapping(AbstractInterfaceArgumentMapping):
return {'"lbm/inplace_streaming/TimestepTracker.h"'}
-def generate_alternating_lbm_sweep(generation_context, class_name, collision_rule, streaming_pattern,
+def generate_alternating_lbm_sweep(generation_context, class_name, collision_rule,
+ lbm_config, lbm_optimisation=None,
namespace='lbm', field_swaps=(), varying_parameters=(),
- inner_outer_split=False, ghost_layers_to_include=0, optimization=None,
- **create_ast_params):
+ inner_outer_split=False, ghost_layers_to_include=0,
+ target=Target.CPU, data_type=None,
+ cpu_openmp=None, cpu_vectorize_info=None,
+ **kernel_parameters):
"""Generates an Alternating lattice Boltzmann sweep class. This is in particular meant for
in-place streaming patterns, but can of course also be used with two-fields patterns (why make it
simple if you can make it complicated?). From a collision rule, two kernel implementations are
@@ -70,29 +76,34 @@ def generate_alternating_lbm_sweep(generation_context, class_name, collision_rul
generation_context: See documentation of `pystencils_walberla.generate_sweep`
class_name: Name of the generated class
collision_rule: LbCollisionRule as returned by `lbmpy.create_lb_collision_rule`.
- streaming_pattern: Name of the streaming pattern; see `lbmpy.advanced_streaming`
+ lbm_config: configuration of the LB method. See lbmpy.LBMConfig
+ lbm_optimisation: configuration of the optimisations of the LB method. See lbmpy.LBMOptimisation
namespace: see documentation of `generate_sweep`
field_swaps: see documentation of `generate_sweep`
varying_parameters: see documentation of `generate_sweep`
inner_outer_split: see documentation of `generate_sweep`
ghost_layers_to_include: see documentation of `generate_sweep`
- optimization: dictionary containing optimization parameters, c.f. `lbmpy.creationfunctions`
- create_ast_params: Further parameters passed to `create_lb_ast`
+ target: An pystencils Target to define cpu or gpu code generation. See pystencils.Target
+ data_type: default datatype for the kernel creation. Default is double
+ cpu_openmp: if loops should use openMP or not.
+ cpu_vectorize_info: dictionary containing necessary information for the usage of a SIMD instruction set.
+ kernel_parameters: other parameters passed to the creation of a pystencils.CreateKernelConfig
"""
- optimization = default_lbm_optimization_parameters(generation_context, optimization)
+ config = config_from_context(generation_context, target=target, data_type=data_type, cpu_openmp=cpu_openmp,
+ cpu_vectorize_info=cpu_vectorize_info, **kernel_parameters)
- target = optimization['target']
- if not generation_context.cuda and target == 'gpu':
- return
+ # Add the lbm collision rule to the config
+ lbm_config = replace(lbm_config, collision_rule=collision_rule)
+ even_lbm_config = replace(lbm_config, timestep=Timestep.EVEN)
- ast_even = create_lb_ast(collision_rule=collision_rule, streaming_pattern=streaming_pattern,
- timestep=Timestep.EVEN, optimization=optimization, **create_ast_params)
+ ast_even = create_lb_ast(lbm_config=even_lbm_config, lbm_optimisation=lbm_optimisation, config=config)
ast_even.function_name = 'even'
kernel_even = KernelCallNode(ast_even)
- if is_inplace(streaming_pattern):
- ast_odd = create_lb_ast(collision_rule=collision_rule, streaming_pattern=streaming_pattern,
- timestep=Timestep.ODD, optimization=optimization, **create_ast_params)
+ if is_inplace(lbm_config.streaming_pattern):
+ odd_lbm_config = replace(lbm_config, timestep=Timestep.ODD)
+
+ ast_odd = create_lb_ast(lbm_config=odd_lbm_config, lbm_optimisation=lbm_optimisation, config=config)
ast_odd.function_name = 'odd'
kernel_odd = KernelCallNode(ast_odd)
else:
@@ -101,8 +112,8 @@ def generate_alternating_lbm_sweep(generation_context, class_name, collision_rul
tree = EvenIntegerCondition('timestep', kernel_even, kernel_odd, np.uint8)
interface_mappings = [TimestepTrackerMapping(tree.parameter_symbol)]
- vec_info = optimization['vectorization']
- openmp = optimization['openmp']
+ vec_info = config.cpu_vectorize_info
+ openmp = config.cpu_openmp
generate_selective_sweep(generation_context, class_name, tree,
interface_mappings=interface_mappings,
@@ -110,32 +121,3 @@ def generate_alternating_lbm_sweep(generation_context, class_name, collision_rul
field_swaps=field_swaps, varying_parameters=varying_parameters,
inner_outer_split=inner_outer_split, ghost_layers_to_include=ghost_layers_to_include,
cpu_vectorize_info=vec_info, cpu_openmp=openmp)
-
-
-# ---------------------------------- Internal --------------------------------------------------------------------------
-
-
-def default_lbm_optimization_parameters(generation_context, params):
- if params is None:
- params = dict()
-
- params['target'] = params.get('target', 'cpu')
- params['double_precision'] = params.get('double_precision', generation_context.double_accuracy)
- params['openmp'] = params.get('cpu_openmp', generation_context.openmp)
- params['vectorization'] = params.get('vectorization', {})
-
- if isinstance(params['vectorization'], bool):
- do_vectorization = params['vectorization']
- params['vectorization'] = dict()
- else:
- do_vectorization = True
-
- vec = params['vectorization']
- if isinstance(vec, dict):
- default_vec_is = get_vectorize_instruction_set(generation_context) if do_vectorization else None
-
- vec['instruction_set'] = vec.get('instruction_set', default_vec_is)
- vec['assume_inner_stride_one'] = vec.get('assume_inner_stride_one', True)
- vec['assume_aligned'] = vec.get('assume_aligned', False)
- vec['nontemporal'] = vec.get('nontemporal', False)
- return params
diff --git a/python/lbmpy_walberla/boundary.py b/python/lbmpy_walberla/boundary.py
index 795d8565616b8b2335f1c50ec960a170d638542b..fcbcb921d47bf3fe41a13473a82f8f6b3b45275d 100644
--- a/python/lbmpy_walberla/boundary.py
+++ b/python/lbmpy_walberla/boundary.py
@@ -6,7 +6,7 @@ from pystencils_walberla.kernel_selection import KernelCallNode
from lbmpy_walberla.alternating_sweeps import EvenIntegerCondition, OddIntegerCondition, TimestepTrackerMapping
from lbmpy_walberla.additional_data_handler import default_additional_data_handler
-from pystencils.data_types import TypedSymbol
+from pystencils import Target, TypedSymbol
import numpy as np
@@ -22,10 +22,10 @@ def generate_boundary(generation_context,
namespace='lbm',
**create_kernel_params):
if boundary_object.additional_data and additional_data_handler is None:
- target = create_kernel_params.get('target', 'cpu')
+ target = create_kernel_params.get('target', Target.CPU)
additional_data_handler = default_additional_data_handler(boundary_object, lb_method, field_name, target=target)
- def boundary_creation_function(field, index_field, stencil, boundary_functor, target='cpu', **kwargs):
+ def boundary_creation_function(field, index_field, stencil, boundary_functor, target=Target.CPU, **kwargs):
return create_lattice_boltzmann_boundary_kernel(field, index_field, lb_method, boundary_functor,
streaming_pattern=streaming_pattern,
prev_timestep=prev_timestep,
@@ -55,14 +55,14 @@ def generate_alternating_lbm_boundary(generation_context,
namespace='lbm',
**create_kernel_params):
if boundary_object.additional_data and additional_data_handler is None:
- target = create_kernel_params.get('target', 'cpu')
+ target = create_kernel_params.get('target', Target.CPU)
additional_data_handler = default_additional_data_handler(boundary_object, lb_method, field_name, target=target)
timestep_param_name = 'timestep'
timestep_param_dtype = np.uint8
timestep_param = TypedSymbol(timestep_param_name, timestep_param_dtype)
- def boundary_creation_function(field, index_field, stencil, boundary_functor, target='cpu', **kwargs):
+ def boundary_creation_function(field, index_field, stencil, boundary_functor, target=Target.CPU, **kwargs):
pargs = (field, index_field, lb_method, boundary_functor)
kwargs = {'target': target, **kwargs}
ast_even = create_lattice_boltzmann_boundary_kernel(*pargs,
@@ -94,7 +94,7 @@ def generate_alternating_lbm_boundary(generation_context,
boundary_object,
field_name=field_name,
neighbor_stencil=lb_method.stencil,
- index_shape=[len(lb_method.stencil)],
+ index_shape=[lb_method.stencil.Q],
kernel_creation_function=boundary_creation_function,
namespace=namespace,
additional_data_handler=additional_data_handler,
diff --git a/python/lbmpy_walberla/packinfo.py b/python/lbmpy_walberla/packinfo.py
index d6a0ab23c6d1a01dc5f0ba9c866781eba4cf2dba..fef1c0026c7c0ed6acd95331a65ca354fed0bd5c 100644
--- a/python/lbmpy_walberla/packinfo.py
+++ b/python/lbmpy_walberla/packinfo.py
@@ -1,10 +1,10 @@
from collections import defaultdict
-from lbmpy.stencils import get_stencil
+from lbmpy import LBStencil, Stencil
from lbmpy.advanced_streaming.utility import Timestep, get_accessor, get_timesteps
from lbmpy.advanced_streaming.communication import _extend_dir
from pystencils.stencil import inverse_direction
from pystencils_walberla.codegen import comm_directions, generate_pack_info
-from pystencils import Assignment, Field
+from pystencils import Assignment, Field, Target
def generate_lb_pack_info(generation_context,
@@ -15,6 +15,9 @@ def generate_lb_pack_info(generation_context,
lb_collision_rule=None,
always_generate_separate_classes=False,
namespace='lbm',
+ target=Target.CPU,
+ data_type=None,
+ cpu_openmp=None,
**create_kernel_params):
"""Generates waLBerla MPI PackInfos for an LBM kernel, based on a given method
and streaming pattern. For in-place streaming patterns, two PackInfos are generated;
@@ -24,7 +27,7 @@ def generate_lb_pack_info(generation_context,
generation_context: see documentation of `generate_sweep`
class_name_prefix: Prefix of the desired class name which will be extended with
'Even' or 'Odd' for in-place kernels
- stencil: The tuple of directions specifying the employed LB stencil.
+ stencil: Instance of class LBStencil
pdf_field: pdf field for which the pack info is created
streaming_pattern: The employed streaming pattern.
lb_collision_rule: Optional. The collision rule defining the lattice boltzmann kernel, as returned
@@ -37,6 +40,9 @@ def generate_lb_pack_info(generation_context,
kernels, only one PackInfo class will be generated without a
suffix to its name.
namespace: inner namespace of the generated class
+ target: An pystencils Target to define cpu or gpu code generation. See pystencils.Target
+ data_type: default datatype for the kernel creation. Default is double
+ cpu_openmp: if loops should use openMP or not.
**create_kernel_params: remaining keyword arguments are passed to `pystencils.create_kernel`
"""
timesteps = [Timestep.EVEN, Timestep.ODD] \
@@ -60,7 +66,7 @@ def generate_lb_pack_info(generation_context,
for comm_dir in comm_directions(comm_direction):
common_spec[(comm_dir,)].add(fa.field.center(*fa.index))
- full_stencil = get_stencil('D3Q27') if len(stencil[0]) == 3 else get_stencil('D2Q9')
+ full_stencil = LBStencil(Stencil.D3Q27) if stencil.D == 3 else LBStencil(Stencil.D2Q9)
for t in timesteps:
spec = common_spec.copy()
@@ -82,4 +88,5 @@ def generate_lb_pack_info(generation_context,
class_name_suffix = ''
class_name = class_name_prefix + class_name_suffix
- generate_pack_info(generation_context, class_name, spec, namespace=namespace, **create_kernel_params)
+ generate_pack_info(generation_context, class_name, spec, namespace=namespace,
+ target=target, data_type=data_type, cpu_openmp=cpu_openmp, **create_kernel_params)
diff --git a/python/lbmpy_walberla/tests/test_walberla_codegen.py b/python/lbmpy_walberla/tests/test_walberla_codegen.py
index e38880acadeae832ae377a24d366e6e263ae34b8..c36bf659f4b983d44acdcd847f9d5920940b32e4 100644
--- a/python/lbmpy_walberla/tests/test_walberla_codegen.py
+++ b/python/lbmpy_walberla/tests/test_walberla_codegen.py
@@ -3,6 +3,7 @@ import unittest
import sympy as sp
import pystencils as ps
+from lbmpy import ForceModel, LBMConfig, LBMOptimisation, LBStencil, Method, Stencil
from lbmpy.boundaries import UBB, NoSlip
from lbmpy.creationfunctions import create_lb_method, create_lb_update_rule, create_lb_collision_rule
from lbmpy_walberla import RefinementScaling, generate_boundary, generate_lattice_model
@@ -19,8 +20,11 @@ class WalberlaLbmpyCodegenTest(unittest.TestCase):
force_field = ps.fields("force(3): [3D]", layout='fzyx')
omega = sp.Symbol("omega")
- cr = create_lb_collision_rule(stencil='D3Q19', method='srt', relaxation_rates=[omega], compressible=True,
- force_model='guo', force=force_field.center_vector)
+ stencil = LBStencil(Stencil.D3Q19)
+ lbm_config = LBMConfig(stencil=stencil, method=Method.SRT, relaxation_rates=[omega], compressible=True,
+ force_model=ForceModel.GUO, force=force_field.center_vector)
+
+ cr = create_lb_collision_rule(lbm_config=lbm_config)
scaling = RefinementScaling()
scaling.add_standard_relaxation_rate_scaling(omega)
@@ -49,7 +53,10 @@ class WalberlaLbmpyCodegenTest(unittest.TestCase):
f = ps.fields("f(9): [3D]")
generate_pack_info_for_field(ctx, 'MyPackInfo1', f)
- lb_assignments = create_lb_update_rule(stencil='D3Q19', method='srt').main_assignments
+ stencil = LBStencil(stencil=Stencil.D3Q19)
+ lbm_config = LBMConfig(stencil=stencil, method=Method.SRT)
+
+ lb_assignments = create_lb_update_rule(lbm_config=lbm_config).main_assignments
generate_pack_info_from_kernel(ctx, 'MyPackInfo2', lb_assignments)
@staticmethod
@@ -57,7 +64,10 @@ class WalberlaLbmpyCodegenTest(unittest.TestCase):
with ManualCodeGenerationContext() as ctx:
omega = sp.Symbol("omega")
- cr = create_lb_collision_rule(stencil='D3Q19', method='srt', relaxation_rates=[omega], compressible=False)
+ stencil = LBStencil(stencil=Stencil.D3Q19)
+ lbm_config = LBMConfig(stencil=stencil, method=Method.SRT, relaxation_rates=[omega], compressible=False)
+
+ cr = create_lb_collision_rule(lbm_config=lbm_config)
generate_lattice_model(ctx, 'Model', cr)
assert 'static const bool compressible = false;' in ctx.files['Model.h']
@@ -65,14 +75,14 @@ class WalberlaLbmpyCodegenTest(unittest.TestCase):
def test_output_field():
with ManualCodeGenerationContext(openmp=True, double_accuracy=True) as ctx:
omega_field = ps.fields("omega_out: [3D]", layout='fzyx')
- parameters = {
- 'stencil': 'D3Q27',
- 'method': 'trt-kbc-n1',
- 'compressible': True,
- 'entropic': True,
- 'omega_output_field': omega_field,
- }
- cr = create_lb_collision_rule(**parameters)
+
+ stencil = LBStencil(stencil=Stencil.D3Q27)
+ lbm_config = LBMConfig(stencil=stencil, method=Method.TRT_KBC_N1,
+ relaxation_rates=[1.5, sp.Symbol('omega_free')],
+ compressible=True, entropic=True,
+ omega_output_field=omega_field)
+
+ cr = create_lb_collision_rule(lbm_config=lbm_config)
generate_lattice_model(ctx, 'Model', cr)
@staticmethod
@@ -81,26 +91,34 @@ class WalberlaLbmpyCodegenTest(unittest.TestCase):
omega_shear = sp.symbols("omega")
force_field, vel_field = ps.fields("force(3), velocity(3): [3D]", layout='fzyx')
+ stencil = LBStencil(stencil=Stencil.D3Q19)
+ lbm_config = LBMConfig(stencil=stencil, method=Method.MRT, compressible=True,
+ fluctuating={'seed': 0, 'temperature': 1e-6}, force_model=ForceModel.GUO,
+ relaxation_rates=[omega_shear] * 19, force=force_field.center_vector)
+
+ lbm_opt = LBMOptimisation(cse_global=True)
+
# the collision rule of the LB method where the some advanced features
- collision_rule = create_lb_collision_rule(
- stencil='D3Q19', compressible=True, fluctuating={'seed': 0, 'temperature': 1e-6},
- method='mrt', relaxation_rates=[omega_shear] * 19,
- force_model='schiller', force=force_field.center_vector,
- optimization={'cse_global': False}
- )
+ collision_rule = create_lb_collision_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
generate_lattice_model(ctx, 'FluctuatingMRT', collision_rule)
@staticmethod
def test_boundary_3D():
- with ManualCodeGenerationContext(openmp=True, double_accuracy=True) as ctx:
- lb_method = create_lb_method(stencil='D3Q19', method='srt')
- generate_boundary(ctx, 'Boundary', NoSlip(), lb_method, target='gpu')
+ with ManualCodeGenerationContext(openmp=True, double_accuracy=True, cuda=True) as ctx:
+ stencil = LBStencil(stencil=Stencil.D3Q19)
+ lbm_config = LBMConfig(stencil=stencil, method=Method.SRT)
+
+ lb_method = create_lb_method(lbm_config=lbm_config)
+ generate_boundary(ctx, 'Boundary', NoSlip(), lb_method, target=ps.Target.GPU)
@staticmethod
def test_boundary_2D():
- with ManualCodeGenerationContext(openmp=True, double_accuracy=True) as ctx:
- lb_method = create_lb_method(stencil='D2Q9', method='srt')
- generate_boundary(ctx, 'Boundary', NoSlip(), lb_method, target='gpu')
+ with ManualCodeGenerationContext(openmp=True, double_accuracy=True, cuda=True) as ctx:
+ stencil = LBStencil(stencil=Stencil.D2Q9)
+ lbm_config = LBMConfig(stencil=stencil, method=Method.SRT)
+
+ lb_method = create_lb_method(lbm_config=lbm_config)
+ generate_boundary(ctx, 'Boundary', NoSlip(), lb_method, target=ps.Target.GPU)
if __name__ == '__main__':
diff --git a/python/lbmpy_walberla/walberla_lbm_generation.py b/python/lbmpy_walberla/walberla_lbm_generation.py
index 21463f79ca7df3e1fc56c63f6025da7291fe1cd3..109e4ff21920ebc8376d61d9ccf7e774461ebebf 100644
--- a/python/lbmpy_walberla/walberla_lbm_generation.py
+++ b/python/lbmpy_walberla/walberla_lbm_generation.py
@@ -8,17 +8,16 @@ from sympy.tensor import IndexedBase
import pystencils as ps
from lbmpy.fieldaccess import CollideOnlyInplaceAccessor, StreamPullTwoFieldsAccessor
from lbmpy.relaxationrates import relaxation_rate_scaling
-from lbmpy.stencils import get_stencil
from lbmpy.updatekernels import create_lbm_kernel, create_stream_only_kernel
-from pystencils import AssignmentCollection, create_kernel
+from pystencils import AssignmentCollection, create_kernel, Target
from pystencils.astnodes import SympyAssignment
from pystencils.backends.cbackend import CBackend, CustomSympyPrinter, get_headers
from pystencils.data_types import TypedSymbol, type_all_numbers, cast_func
from pystencils.field import Field
-from pystencils.stencil import have_same_entries, offset_to_direction_string
+from pystencils.stencil import offset_to_direction_string
from pystencils.sympyextensions import get_symmetric_part
from pystencils.transformations import add_types
-from pystencils_walberla.codegen import KernelInfo, default_create_kernel_parameters
+from pystencils_walberla.codegen import KernelInfo, config_from_context
from pystencils_walberla.jinja_filters import add_pystencils_filters_to_jinja_env
cpp_printer = CustomSympyPrinter()
@@ -27,7 +26,7 @@ REFINEMENT_SCALE_FACTOR = sp.Symbol("level_scale_factor")
def __lattice_model(generation_context, class_name, lb_method, stream_collide_ast, collide_ast, stream_ast,
refinement_scaling):
- stencil_name = get_stencil_name(lb_method.stencil)
+ stencil_name = lb_method.stencil.name
if not stencil_name:
raise ValueError("lb_method uses a stencil that is not supported in waLBerla")
@@ -37,7 +36,7 @@ def __lattice_model(generation_context, class_name, lb_method, stream_collide_as
vel_symbols = lb_method.conserved_quantity_computation.first_order_moment_symbols
rho_sym = sp.Symbol('rho')
- pdfs_sym = sp.symbols(f'f_:{len(lb_method.stencil)}')
+ pdfs_sym = sp.symbols(f'f_:{lb_method.stencil.Q}')
vel_arr_symbols = [IndexedBase(sp.Symbol('u'), shape=(1,))[i] for i in range(len(vel_symbols))]
momentum_density_symbols = sp.symbols(f'md_:{len(vel_symbols)}')
@@ -54,10 +53,12 @@ def __lattice_model(generation_context, class_name, lb_method, stream_collide_as
macroscopic_velocity_shift = None
if force_model:
if hasattr(force_model, 'macroscopic_velocity_shift'):
+ macroscopic_velocity_shift = [e.subs(force_model.subs_dict_force)
+ for e in force_model.macroscopic_velocity_shift(rho_sym)]
macroscopic_velocity_shift = [expression_to_code(e.subs(sp.Rational(1, 2), cast_func(sp.Rational(1, 2),
dtype_string)),
"lm.", ['rho'], dtype=dtype_string)
- for e in force_model.macroscopic_velocity_shift(rho_sym)]
+ for e in macroscopic_velocity_shift]
cqc = lb_method.conserved_quantity_computation
@@ -89,8 +90,8 @@ def __lattice_model(generation_context, class_name, lb_method, stream_collide_as
'class_name': class_name,
'stencil_name': stencil_name,
'communication_stencil_name': communication_stencil_name,
- 'D': lb_method.dim,
- 'Q': len(lb_method.stencil),
+ 'D': lb_method.stencil.D,
+ 'Q': lb_method.stencil.Q,
'compressible': lb_method.conserved_quantity_computation.compressible,
'weights': ",".join(str(w.evalf()) + constant_suffix for w in lb_method.weights),
'inverse_weights': ",".join(str((1 / w).evalf()) + constant_suffix for w in lb_method.weights),
@@ -131,44 +132,47 @@ def __lattice_model(generation_context, class_name, lb_method, stream_collide_as
def generate_lattice_model(generation_context, class_name, collision_rule, field_layout='zyxf', refinement_scaling=None,
+ target=Target.CPU, data_type=None, cpu_openmp=None, cpu_vectorize_info=None,
**create_kernel_params):
+
+ config = config_from_context(generation_context, target=target, data_type=data_type,
+ cpu_openmp=cpu_openmp, cpu_vectorize_info=cpu_vectorize_info, **create_kernel_params)
+
# usually a numpy layout is chosen by default i.e. xyzf - which is bad for waLBerla where at least the spatial
# coordinates should be ordered in reverse direction i.e. zyx
- is_float = not generation_context.double_accuracy
- dtype = np.float32 if is_float else np.float64
+ dtype = np.float64 if config.data_type == "float64" else np.float32
lb_method = collision_rule.method
- q = len(lb_method.stencil)
- dim = lb_method.dim
+ q = lb_method.stencil.Q
+ dim = lb_method.stencil.D
- create_kernel_params = default_create_kernel_parameters(generation_context, create_kernel_params)
- if create_kernel_params['target'] == 'gpu':
+ if config.target == Target.GPU:
raise ValueError("Lattice Models can only be generated for CPUs. To generate LBM on GPUs use sweeps directly")
if field_layout == 'fzyx':
- create_kernel_params['cpu_vectorize_info']['assume_inner_stride_one'] = True
+ config.cpu_vectorize_info['assume_inner_stride_one'] = True
elif field_layout == 'zyxf':
- create_kernel_params['cpu_vectorize_info']['assume_inner_stride_one'] = False
+ config.cpu_vectorize_info['assume_inner_stride_one'] = False
src_field = ps.Field.create_generic('pdfs', dim, dtype, index_dimensions=1, layout=field_layout, index_shape=(q,))
dst_field = ps.Field.create_generic('pdfs_tmp', dim, dtype, index_dimensions=1, layout=field_layout,
index_shape=(q,))
stream_collide_update_rule = create_lbm_kernel(collision_rule, src_field, dst_field, StreamPullTwoFieldsAccessor())
- stream_collide_ast = create_kernel(stream_collide_update_rule, **create_kernel_params)
+ stream_collide_ast = create_kernel(stream_collide_update_rule, config=config)
stream_collide_ast.function_name = 'kernel_streamCollide'
- stream_collide_ast.assumed_inner_stride_one = create_kernel_params['cpu_vectorize_info']['assume_inner_stride_one']
+ stream_collide_ast.assumed_inner_stride_one = config.cpu_vectorize_info['assume_inner_stride_one']
collide_update_rule = create_lbm_kernel(collision_rule, src_field, dst_field, CollideOnlyInplaceAccessor())
- collide_ast = create_kernel(collide_update_rule, **create_kernel_params)
+ collide_ast = create_kernel(collide_update_rule, config=config)
collide_ast.function_name = 'kernel_collide'
- collide_ast.assumed_inner_stride_one = create_kernel_params['cpu_vectorize_info']['assume_inner_stride_one']
+ collide_ast.assumed_inner_stride_one = config.cpu_vectorize_info['assume_inner_stride_one']
stream_update_rule = create_stream_only_kernel(lb_method.stencil, src_field, dst_field,
accessor=StreamPullTwoFieldsAccessor())
- stream_ast = create_kernel(stream_update_rule, **create_kernel_params)
+ stream_ast = create_kernel(stream_update_rule, config=config)
stream_ast.function_name = 'kernel_stream'
- stream_ast.assumed_inner_stride_one = create_kernel_params['cpu_vectorize_info']['assume_inner_stride_one']
+ stream_ast.assumed_inner_stride_one = config.cpu_vectorize_info['assume_inner_stride_one']
__lattice_model(generation_context, class_name, lb_method, stream_collide_ast, collide_ast, stream_ast,
refinement_scaling)
@@ -314,9 +318,3 @@ def equations_to_code(equations, variable_prefix="lm.", variables_without_prefix
dtype=dtype))
result.append(c_backend(assignment))
return "\n".join(result)
-
-
-def get_stencil_name(stencil):
- for name in ('D2Q9', 'D3Q15', 'D3Q19', 'D3Q27'):
- if have_same_entries(stencil, get_stencil(name, 'walberla')):
- return name
diff --git a/python/pystencils_walberla/__init__.py b/python/pystencils_walberla/__init__.py
index 2c7892f9164feb57949aca3bfd9c976b1a1ae019..0ea2d02cb4b93fc880f0addc38058e0363e39c8c 100644
--- a/python/pystencils_walberla/__init__.py
+++ b/python/pystencils_walberla/__init__.py
@@ -2,11 +2,12 @@ from .boundary import generate_staggered_boundary, generate_staggered_flux_bound
from .cmake_integration import CodeGeneration
from .codegen import (
generate_pack_info, generate_pack_info_for_field, generate_pack_info_from_kernel,
- generate_mpidtype_info_from_kernel, generate_sweep, get_vectorize_instruction_set)
+ generate_mpidtype_info_from_kernel, generate_sweep, get_vectorize_instruction_set, generate_selective_sweep,
+ config_from_context)
from .utility import generate_info_header
__all__ = ['CodeGeneration',
'generate_sweep', 'generate_pack_info_from_kernel', 'generate_pack_info_for_field', 'generate_pack_info',
'generate_mpidtype_info_from_kernel', 'generate_staggered_boundary', 'generate_staggered_flux_boundary',
- 'get_vectorize_instruction_set',
+ 'get_vectorize_instruction_set', 'generate_selective_sweep', 'config_from_context',
'generate_info_header']
diff --git a/python/pystencils_walberla/additional_data_handler.py b/python/pystencils_walberla/additional_data_handler.py
index 7abe7d5aa0365622fc3b870787c64810c52159e4..4d4d82d5256e48b4eb6a8c284dabc9b512311c70 100644
--- a/python/pystencils_walberla/additional_data_handler.py
+++ b/python/pystencils_walberla/additional_data_handler.py
@@ -5,7 +5,7 @@ class AdditionalDataHandler:
"""Base class that defines how to handle boundary conditions holding additional data."""
def __init__(self, stencil):
- self._dim = len(stencil[0])
+ self._dim = stencil.D
# waLBerla is a 3D framework. Therefore, a zero for the z index has to be added if we work in 2D
if self._dim == 2:
diff --git a/python/pystencils_walberla/boundary.py b/python/pystencils_walberla/boundary.py
index 22d3635dbb1143b7c52c0c4e1dc7339aeae16507..c8bf214def82c5dcaf531208f114ea2cb9601077 100644
--- a/python/pystencils_walberla/boundary.py
+++ b/python/pystencils_walberla/boundary.py
@@ -1,12 +1,12 @@
import numpy as np
from jinja2 import Environment, PackageLoader, StrictUndefined
-from pystencils import Field, FieldType
+from pystencils import Field, FieldType, Target
from pystencils.boundaries.boundaryhandling import create_boundary_kernel
from pystencils.boundaries.createindexlist import (
boundary_index_array_coordinate_names, direction_member_name,
numpy_data_type_for_boundary_object)
from pystencils.data_types import TypedSymbol, create_type
-from pystencils_walberla.codegen import default_create_kernel_parameters
+from pystencils_walberla.codegen import config_from_context
from pystencils_walberla.jinja_filters import add_pystencils_filters_to_jinja_env
from pystencils_walberla.additional_data_handler import AdditionalDataHandler
from pystencils_walberla.kernel_selection import (
@@ -22,7 +22,9 @@ def generate_boundary(generation_context,
index_shape,
field_type=FieldType.GENERIC,
kernel_creation_function=None,
- target='cpu',
+ target=Target.CPU,
+ data_type=None,
+ cpu_openmp=None,
namespace='pystencils',
additional_data_handler=None,
interface_mappings=(),
@@ -34,18 +36,20 @@ def generate_boundary(generation_context,
struct_name = "IndexInfo"
boundary_object.name = class_name
- dim = len(neighbor_stencil[0])
+ dim = neighbor_stencil.D
- create_kernel_params = default_create_kernel_parameters(generation_context, create_kernel_params)
- create_kernel_params["target"] = target
- del create_kernel_params["cpu_vectorize_info"]
+ config = config_from_context(generation_context, target=target, data_type=data_type, cpu_openmp=cpu_openmp,
+ **create_kernel_params)
+ create_kernel_params = config.__dict__
+ del create_kernel_params['target']
+ del create_kernel_params['index_fields']
+
+ field_data_type = np.float64 if config.data_type == "float64" else np.float32
- if not create_kernel_params["data_type"]:
- create_kernel_params["data_type"] = 'double' if generation_context.double_accuracy else 'float32'
index_struct_dtype = numpy_data_type_for_boundary_object(boundary_object, dim)
field = Field.create_generic(field_name, dim,
- np.float64 if generation_context.double_accuracy else np.float32,
+ field_data_type,
index_dimensions=len(index_shape), layout='fzyx', index_shape=index_shape,
field_type=field_type)
@@ -55,10 +59,11 @@ def generate_boundary(generation_context,
if not kernel_creation_function:
kernel_creation_function = create_boundary_kernel
- kernel = kernel_creation_function(field, index_field, neighbor_stencil, boundary_object, **create_kernel_params)
+ kernel = kernel_creation_function(field, index_field, neighbor_stencil, boundary_object,
+ target=target, **create_kernel_params)
if isinstance(kernel, KernelFunction):
- kernel.function_name = "boundary_" + boundary_object.name
+ kernel.function_name = f"boundary_{boundary_object.name}"
selection_tree = KernelCallNode(kernel)
elif isinstance(kernel, AbstractKernelSelectionNode):
selection_tree = kernel
@@ -79,7 +84,7 @@ def generate_boundary(generation_context,
'StructName': struct_name,
'StructDeclaration': struct_from_numpy_dtype(struct_name, index_struct_dtype),
'dim': dim,
- 'target': target,
+ 'target': target.name.lower(),
'namespace': namespace,
'inner_or_boundary': boundary_object.inner_or_boundary,
'single_link': boundary_object.single_link,
@@ -92,20 +97,20 @@ def generate_boundary(generation_context,
header = env.get_template('Boundary.tmpl.h').render(**context)
source = env.get_template('Boundary.tmpl.cpp').render(**context)
- source_extension = "cpp" if target == "cpu" else "cu"
+ source_extension = "cpp" if target == Target.CPU else "cu"
generation_context.write_file(f"{class_name}.h", header)
generation_context.write_file(f"{class_name}.{source_extension}", source)
def generate_staggered_boundary(generation_context, class_name, boundary_object,
- dim, neighbor_stencil, index_shape, target='cpu', **kwargs):
+ dim, neighbor_stencil, index_shape, target=Target.CPU, **kwargs):
assert dim == len(neighbor_stencil[0])
generate_boundary(generation_context, class_name, boundary_object, 'field', neighbor_stencil, index_shape,
FieldType.STAGGERED, target=target, **kwargs)
def generate_staggered_flux_boundary(generation_context, class_name, boundary_object,
- dim, neighbor_stencil, index_shape, target='cpu', **kwargs):
+ dim, neighbor_stencil, index_shape, target=Target.CPU, **kwargs):
assert dim == len(neighbor_stencil[0])
generate_boundary(generation_context, class_name, boundary_object, 'flux', neighbor_stencil, index_shape,
FieldType.STAGGERED_FLUX, target=target, **kwargs)
diff --git a/python/pystencils_walberla/cmake_integration.py b/python/pystencils_walberla/cmake_integration.py
index 8533b21359a91fc52fa865185e5cf7242d8dad0c..3e5a4ebad844ac2d83be0948770e155257a9f85f 100644
--- a/python/pystencils_walberla/cmake_integration.py
+++ b/python/pystencils_walberla/cmake_integration.py
@@ -64,9 +64,9 @@ def parse_json_args():
expected_files = parsed['EXPECTED_FILES']
cmake_vars = {}
for key, value in parsed['CMAKE_VARS'].items():
- if value in ("ON", "1", "YES", "TRUE"):
+ if value.lower() in ("on", "1", "yes", "true"):
value = True
- elif value in ("OFF", "0", "NO", "FALSE"):
+ elif value.lower() in ("off", "0", "no", "false"):
value = False
cmake_vars[key] = value
return expected_files, cmake_vars
@@ -94,13 +94,13 @@ class ManualCodeGenerationContext:
to constructor instead of getting them from CMake
"""
- def __init__(self, openmp=False, optimize_for_localhost=False, mpi=True, double_accuracy=True):
+ def __init__(self, openmp=False, optimize_for_localhost=False, mpi=True, double_accuracy=True, cuda=False):
self.openmp = openmp
self.optimize_for_localhost = optimize_for_localhost
self.mpi = mpi
self.double_accuracy = double_accuracy
self.files = dict()
- self.cuda = False
+ self.cuda = cuda
self.config = ""
def write_file(self, name, content):
diff --git a/python/pystencils_walberla/codegen.py b/python/pystencils_walberla/codegen.py
index fea8c04b0a97cb161f1d34cbdb0858a9de490ec0..c0f3c3ed31441b98f99c5c32042ac52b5a36ef0d 100644
--- a/python/pystencils_walberla/codegen.py
+++ b/python/pystencils_walberla/codegen.py
@@ -1,30 +1,35 @@
import warnings
from collections import OrderedDict, defaultdict
+from dataclasses import replace
from itertools import product
from typing import Dict, Optional, Sequence, Tuple
from jinja2 import Environment, PackageLoader, StrictUndefined
-from pystencils import (
- Assignment, AssignmentCollection, Field, FieldType, create_kernel, create_staggered_kernel)
+from pystencils import Target, CreateKernelConfig
+from pystencils import (Assignment, AssignmentCollection, Field, FieldType, create_kernel, create_staggered_kernel)
from pystencils.astnodes import KernelFunction
from pystencils.backends.cbackend import get_headers
from pystencils.backends.simd_instruction_sets import get_supported_instruction_sets
from pystencils.stencil import inverse_direction, offset_to_direction_string
-from pystencils_walberla.jinja_filters import add_pystencils_filters_to_jinja_env
-from pystencils_walberla.kernel_selection import KernelCallNode, KernelFamily, HighLevelInterfaceSpec
+
from pystencils.backends.cuda_backend import CudaSympyPrinter
from pystencils.kernelparameters import SHAPE_DTYPE
from pystencils.data_types import TypedSymbol
+from pystencils_walberla.jinja_filters import add_pystencils_filters_to_jinja_env
+from pystencils_walberla.kernel_selection import KernelCallNode, KernelFamily, HighLevelInterfaceSpec
+
+
__all__ = ['generate_sweep', 'generate_pack_info', 'generate_pack_info_for_field', 'generate_pack_info_from_kernel',
- 'generate_mpidtype_info_from_kernel', 'default_create_kernel_parameters', 'KernelInfo',
- 'get_vectorize_instruction_set']
+ 'generate_mpidtype_info_from_kernel', 'KernelInfo',
+ 'get_vectorize_instruction_set', 'config_from_context', 'generate_selective_sweep']
def generate_sweep(generation_context, class_name, assignments,
namespace='pystencils', field_swaps=(), staggered=False, varying_parameters=(),
inner_outer_split=False, ghost_layers_to_include=0,
+ target=Target.CPU, data_type=None, cpu_openmp=None, cpu_vectorize_info=None,
**create_kernel_params):
"""Generates a waLBerla sweep from a pystencils representation.
@@ -50,21 +55,23 @@ def generate_sweep(generation_context, class_name, assignments,
to allow for communication hiding.
ghost_layers_to_include: determines how many ghost layers should be included for the Sweep.
This is relevant if a setter kernel should also set correct values to the ghost layers.
+ target: An pystencils Target to define cpu or gpu code generation. See pystencils.Target
+ data_type: default datatype for the kernel creation. Default is double
+ cpu_openmp: if loops should use openMP or not.
+ cpu_vectorize_info: dictionary containing necessary information for the usage of a SIMD instruction set.
**create_kernel_params: remaining keyword arguments are passed to `pystencils.create_kernel`
"""
- create_kernel_params = default_create_kernel_parameters(generation_context, create_kernel_params)
-
- target = create_kernel_params['target']
- if not generation_context.cuda and target == 'gpu':
- return
+ config = config_from_context(generation_context, target=target, data_type=data_type, cpu_openmp=cpu_openmp,
+ cpu_vectorize_info=cpu_vectorize_info, **create_kernel_params)
if isinstance(assignments, KernelFunction):
ast = assignments
target = ast.target
elif not staggered:
- ast = create_kernel(assignments, **create_kernel_params)
+ ast = create_kernel(assignments, config=config)
else:
- ast = create_staggered_kernel(assignments, **create_kernel_params)
+ # This should not be necessary but create_staggered_kernel does not take a config at the moment ...
+ ast = create_staggered_kernel(assignments, **config.__dict__)
ast.function_name = class_name.lower()
@@ -72,8 +79,8 @@ def generate_sweep(generation_context, class_name, assignments,
generate_selective_sweep(generation_context, class_name, selection_tree, target=target, namespace=namespace,
field_swaps=field_swaps, varying_parameters=varying_parameters,
inner_outer_split=inner_outer_split, ghost_layers_to_include=ghost_layers_to_include,
- cpu_vectorize_info=create_kernel_params['cpu_vectorize_info'],
- cpu_openmp=create_kernel_params['cpu_openmp'])
+ cpu_vectorize_info=config.cpu_vectorize_info,
+ cpu_openmp=config.cpu_openmp)
def generate_selective_sweep(generation_context, class_name, selection_tree, interface_mappings=(), target=None,
@@ -89,7 +96,7 @@ def generate_selective_sweep(generation_context, class_name, selection_tree, int
selection_tree: Instance of `AbstractKernelSelectionNode`, root of the selection tree
interface_mappings: sequence of `AbstractInterfaceArgumentMapping` instances for selection arguments of
the selection tree
- target: `None`, `'cpu'` or `'gpu'`; inferred from kernels if `None` is given.
+ target: `None`, `Target.CPU` or `Target.GPU`; inferred from kernels if `None` is given.
namespace: see documentation of `generate_sweep`
field_swaps: see documentation of `generate_sweep`
varying_parameters: see documentation of `generate_sweep`
@@ -112,7 +119,7 @@ def generate_selective_sweep(generation_context, class_name, selection_tree, int
elif target != kernel_family.get_ast_attr('target'):
raise ValueError('Mismatch between target parameter and AST targets.')
- if not generation_context.cuda and target == 'gpu':
+ if not generation_context.cuda and target == Target.GPU:
return
representative_field = {p.field_name for p in kernel_family.parameters if p.is_field_parameter}
@@ -127,7 +134,7 @@ def generate_selective_sweep(generation_context, class_name, selection_tree, int
'kernel': kernel_family,
'namespace': namespace,
'class_name': class_name,
- 'target': target,
+ 'target': target.name.lower(),
'field': representative_field,
'ghost_layers_to_include': ghost_layers_to_include,
'inner_outer_split': inner_outer_split,
@@ -139,15 +146,15 @@ def generate_selective_sweep(generation_context, class_name, selection_tree, int
header = env.get_template("Sweep.tmpl.h").render(**jinja_context)
source = env.get_template("Sweep.tmpl.cpp").render(**jinja_context)
- source_extension = "cpp" if target == "cpu" else "cu"
- generation_context.write_file("{}.h".format(class_name), header)
- generation_context.write_file("{}.{}".format(class_name, source_extension), source)
+ source_extension = "cpp" if target == Target.CPU else "cu"
+ generation_context.write_file(f"{class_name}.h", header)
+ generation_context.write_file(f"{class_name}.{source_extension}", source)
def generate_pack_info_for_field(generation_context, class_name: str, field: Field,
direction_subset: Optional[Tuple[Tuple[int, int, int]]] = None,
- operator=None,
- gl_to_inner=False,
+ operator=None, gl_to_inner=False,
+ target=Target.CPU, data_type=None, cpu_openmp=None,
**create_kernel_params):
"""Creates a pack info for a pystencils field assuming a pull-type stencil, packing all cell elements.
@@ -159,6 +166,9 @@ def generate_pack_info_for_field(generation_context, class_name: str, field: Fie
otherwise a D3Q27 stencil is assumed
operator: optional operator for, e.g., reduction pack infos
gl_to_inner: communicates values from ghost layers of sender to interior of receiver
+ target: An pystencils Target to define cpu or gpu code generation. See pystencils.Target
+ data_type: default datatype for the kernel creation. Default is double
+ cpu_openmp: if loops should use openMP or not.
**create_kernel_params: remaining keyword arguments are passed to `pystencils.create_kernel`
"""
@@ -167,11 +177,13 @@ def generate_pack_info_for_field(generation_context, class_name: str, field: Fie
all_index_accesses = [field(*ind) for ind in product(*[range(s) for s in field.index_shape])]
return generate_pack_info(generation_context, class_name, {direction_subset: all_index_accesses}, operator=operator,
- gl_to_inner=gl_to_inner, **create_kernel_params)
+ gl_to_inner=gl_to_inner, target=target, data_type=data_type, cpu_openmp=cpu_openmp,
+ **create_kernel_params)
def generate_pack_info_from_kernel(generation_context, class_name: str, assignments: Sequence[Assignment],
- kind='pull', operator=None, **create_kernel_params):
+ kind='pull', operator=None, target=Target.CPU, data_type=None, cpu_openmp=None,
+ **create_kernel_params):
"""Generates a waLBerla GPU PackInfo from a (pull) kernel.
Args:
@@ -181,6 +193,9 @@ def generate_pack_info_from_kernel(generation_context, class_name: str, assignme
i.e. the kernel is expected to only write to the center
kind: can either be pull or push
operator: optional operator for, e.g., reduction pack infos
+ target: An pystencils Target to define cpu or gpu code generation. See pystencils.Target
+ data_type: default datatype for the kernel creation. Default is double
+ cpu_openmp: if loops should use openMP or not.
**create_kernel_params: remaining keyword arguments are passed to `pystencils.create_kernel`
"""
assert kind in ('push', 'pull')
@@ -213,12 +228,14 @@ def generate_pack_info_from_kernel(generation_context, class_name: str, assignme
spec[(comm_dir,)].add(fa)
else:
raise ValueError("Invalid 'kind' parameter")
- return generate_pack_info(generation_context, class_name, spec, operator=operator, **create_kernel_params)
+ return generate_pack_info(generation_context, class_name, spec, operator=operator,
+ target=target, data_type=data_type, cpu_openmp=cpu_openmp, **create_kernel_params)
def generate_pack_info(generation_context, class_name: str,
directions_to_pack_terms: Dict[Tuple[Tuple], Sequence[Field.Access]],
namespace='pystencils', operator=None, gl_to_inner=False,
+ target=Target.CPU, data_type=None, cpu_openmp=None,
**create_kernel_params):
"""Generates a waLBerla GPU PackInfo
@@ -230,22 +247,26 @@ def generate_pack_info(generation_context, class_name: str,
namespace: inner namespace of the generated class
operator: optional operator for, e.g., reduction pack infos
gl_to_inner: communicates values from ghost layers of sender to interior of receiver
+ target: An pystencils Target to define cpu or gpu code generation. See pystencils.Target
+ data_type: default datatype for the kernel creation. Default is double
+ cpu_openmp: if loops should use openMP or not.
**create_kernel_params: remaining keyword arguments are passed to `pystencils.create_kernel`
"""
items = [(e[0], sorted(e[1], key=lambda x: str(x))) for e in directions_to_pack_terms.items()]
items = sorted(items, key=lambda e: e[0])
directions_to_pack_terms = OrderedDict(items)
- if 'cpu_vectorize_info' in create_kernel_params:
- vec_params = create_kernel_params['cpu_vectorize_info']
- if 'instruction_set' in vec_params and vec_params['instruction_set'] is not None:
- raise NotImplementedError("Vectorisation of the pack info is not implemented.")
+ config = config_from_context(generation_context, target=target, data_type=data_type, cpu_openmp=cpu_openmp,
+ **create_kernel_params)
- create_kernel_params = default_create_kernel_parameters(generation_context, create_kernel_params)
- target = create_kernel_params.get('target', 'cpu')
- create_kernel_params['cpu_vectorize_info']['instruction_set'] = None
+ config_zero_gl = config_from_context(generation_context, target=target, data_type=data_type, cpu_openmp=cpu_openmp,
+ ghost_layers=0, **create_kernel_params)
- template_name = "CpuPackInfo.tmpl" if target == 'cpu' else 'GpuPackInfo.tmpl'
+ # Vectorisation of the pack info is not implemented.
+ config = replace(config, cpu_vectorize_info=None)
+ config_zero_gl = replace(config_zero_gl, cpu_vectorize_info=None)
+
+ template_name = "CpuPackInfo.tmpl" if config.target == Target.CPU else 'GpuPackInfo.tmpl'
fields_accessed = set()
for terms in directions_to_pack_terms.values():
@@ -279,22 +300,19 @@ def generate_pack_info(generation_context, class_name: str,
all_accesses.update(terms)
pack_assignments = [Assignment(buffer(i), term) for i, term in enumerate(terms)]
- pack_ast = create_kernel(pack_assignments, **create_kernel_params, ghost_layers=0)
+ pack_ast = create_kernel(pack_assignments, config=config_zero_gl)
pack_ast.function_name = 'pack_{}'.format("_".join(direction_strings))
- pack_ast.assumed_inner_stride_one = create_kernel_params['cpu_vectorize_info']['assume_inner_stride_one']
if operator is None:
unpack_assignments = [Assignment(term, buffer(i)) for i, term in enumerate(terms)]
else:
unpack_assignments = [Assignment(term, operator(term, buffer(i))) for i, term in enumerate(terms)]
- unpack_ast = create_kernel(unpack_assignments, **create_kernel_params, ghost_layers=0)
+ unpack_ast = create_kernel(unpack_assignments, config=config_zero_gl)
unpack_ast.function_name = 'unpack_{}'.format("_".join(direction_strings))
- unpack_ast.assumed_inner_stride_one = create_kernel_params['cpu_vectorize_info']['assume_inner_stride_one']
pack_kernels[direction_strings] = KernelInfo(pack_ast)
unpack_kernels[direction_strings] = KernelInfo(unpack_ast)
elements_per_cell[direction_strings] = len(terms)
- fused_kernel = create_kernel([Assignment(buffer.center, t) for t in all_accesses], **create_kernel_params)
- fused_kernel.assumed_inner_stride_one = create_kernel_params['cpu_vectorize_info']['assume_inner_stride_one']
+ fused_kernel = create_kernel([Assignment(buffer.center, t) for t in all_accesses], config=config)
jinja_context = {
'class_name': class_name,
@@ -303,7 +321,7 @@ def generate_pack_info(generation_context, class_name: str,
'fused_kernel': KernelInfo(fused_kernel),
'elements_per_cell': elements_per_cell,
'headers': get_headers(fused_kernel),
- 'target': target,
+ 'target': config.target.name.lower(),
'dtype': dtype,
'field_name': field_names.pop(),
'namespace': namespace,
@@ -314,13 +332,13 @@ def generate_pack_info(generation_context, class_name: str,
header = env.get_template(template_name + ".h").render(**jinja_context)
source = env.get_template(template_name + ".cpp").render(**jinja_context)
- source_extension = "cpp" if target == "cpu" else "cu"
- generation_context.write_file("{}.h".format(class_name), header)
- generation_context.write_file("{}.{}".format(class_name, source_extension), source)
+ source_extension = "cpp" if config.target == Target.CPU else "cu"
+ generation_context.write_file(f"{class_name}.h", header)
+ generation_context.write_file(f"{class_name}.{source_extension}", source)
def generate_mpidtype_info_from_kernel(generation_context, class_name: str,
- assignments: Sequence[Assignment], kind='pull', namespace='pystencils', ):
+ assignments: Sequence[Assignment], kind='pull', namespace='pystencils'):
assert kind in ('push', 'pull')
reads = set()
writes = set()
@@ -372,7 +390,7 @@ def generate_mpidtype_info_from_kernel(generation_context, class_name: str,
}
env = Environment(loader=PackageLoader('pystencils_walberla'), undefined=StrictUndefined)
header = env.get_template("MpiDtypeInfo.tmpl.h").render(**jinja_context)
- generation_context.write_file("{}.h".format(class_name), header)
+ generation_context.write_file(f"{class_name}.h", header)
# ---------------------------------- Internal --------------------------------------------------------------------------
@@ -398,7 +416,7 @@ class KernelInfo:
def generate_kernel_invocation_code(self, **kwargs):
ast = self.ast
ast_params = self.parameters
- is_cpu = self.ast.target == 'cpu'
+ is_cpu = self.ast.target == Target.CPU
call_parameters = ", ".join([p.symbol.name for p in ast_params])
if not is_cpu:
@@ -436,29 +454,48 @@ def get_vectorize_instruction_set(generation_context):
if supported_instruction_sets:
return supported_instruction_sets[-1]
else: # if cpuinfo package is not installed
- warnings.warn("Could not obtain supported vectorization instruction sets - defaulting to sse. "\
- "This problem can probably be fixed by installing py-cpuinfo. This package can "\
- "gather the needed hardware information.")
+ warnings.warn("Could not obtain supported vectorization instruction sets - defaulting to sse. "
+ "This problem can probably be fixed by installing py-cpuinfo. This package can "
+ "gather the needed hardware information.")
return 'sse'
else:
return None
-def default_create_kernel_parameters(generation_context, params):
- default_dtype = "float64" if generation_context.double_accuracy else 'float32'
+def config_from_context(generation_context, target=Target.CPU, data_type=None,
+ cpu_openmp=None, cpu_vectorize_info=None, **kwargs):
+
+ if target == Target.GPU and not generation_context.cuda:
+ raise ValueError("can not generate cuda code if waLBerla is not build with CUDA. Please use "
+ "-DWALBERLA_BUILD_WITH_CUDA=1 for configuring cmake")
+
+ default_dtype = "float64" if generation_context.double_accuracy else "float32"
+ if data_type is None:
+ data_type = default_dtype
+
+ if cpu_openmp and not generation_context.openmp:
+ warnings.warn("Code is generated with OpenMP pragmas but waLBerla is not build with OpenMP. "
+ "The compilation might not work due to wrong compiler flags. "
+ "Please use -DWALBERLA_BUILD_WITH_OPENMP=1 for configuring cmake")
- params['target'] = params.get('target', 'cpu')
- params['data_type'] = params.get('data_type', default_dtype)
- params['cpu_openmp'] = params.get('cpu_openmp', generation_context.openmp)
- params['cpu_vectorize_info'] = params.get('cpu_vectorize_info', {})
+ if cpu_openmp is None:
+ cpu_openmp = generation_context.openmp
+
+ if cpu_vectorize_info is None:
+ cpu_vectorize_info = {}
default_vec_is = get_vectorize_instruction_set(generation_context)
- vec = params['cpu_vectorize_info']
- vec['instruction_set'] = vec.get('instruction_set', default_vec_is)
- vec['assume_inner_stride_one'] = vec.get('assume_inner_stride_one', True)
- vec['assume_aligned'] = vec.get('assume_aligned', False)
- vec['nontemporal'] = vec.get('nontemporal', False)
- return params
+
+ cpu_vectorize_info['instruction_set'] = cpu_vectorize_info.get('instruction_set', default_vec_is)
+ cpu_vectorize_info['assume_inner_stride_one'] = cpu_vectorize_info.get('assume_inner_stride_one', True)
+ cpu_vectorize_info['assume_aligned'] = cpu_vectorize_info.get('assume_aligned', False)
+ cpu_vectorize_info['nontemporal'] = cpu_vectorize_info.get('nontemporal', False)
+
+ config = CreateKernelConfig(target=target, data_type=data_type,
+ cpu_openmp=cpu_openmp, cpu_vectorize_info=cpu_vectorize_info,
+ **kwargs)
+
+ return config
def comm_directions(direction):
diff --git a/python/pystencils_walberla/jinja_filters.py b/python/pystencils_walberla/jinja_filters.py
index 00fb7279e989838a4fdfc6f2d733596bdcfec79c..c615243a7a2c17b90d7eb3cd924ecdcfeb10a5a7 100644
--- a/python/pystencils_walberla/jinja_filters.py
+++ b/python/pystencils_walberla/jinja_filters.py
@@ -1,7 +1,14 @@
import jinja2
+
+# For backward compatibility with version < 3.0.0
+try:
+ from jinja2 import pass_context as jinja2_context_decorator
+except ImportError:
+ from jinja2 import contextfilter as jinja2_context_decorator
+
import sympy as sp
-# import re
+from pystencils import Target, Backend
from pystencils.backends.cbackend import generate_c
from pystencils.data_types import TypedSymbol, get_base_type
from pystencils.field import FieldType
@@ -39,9 +46,9 @@ delete_loop = """
def make_field_type(dtype, f_size, is_gpu):
if is_gpu:
- return "cuda::GPUField<%s>" % (dtype,)
+ return f"cuda::GPUField<{dtype}>"
else:
- return "field::GhostLayerField<%s, %d>" % (dtype, f_size)
+ return f"field::GhostLayerField<{dtype}, {f_size}>"
def get_field_fsize(field):
@@ -49,7 +56,7 @@ def get_field_fsize(field):
pystencils fields with multiple index dimensions are linearized to a single index dimension.
"""
assert field.has_fixed_index_shape, \
- "All Fields have to be created with fixed index coordinate shape using index_shape=(q,) " + str(field.name)
+ f"All Fields have to be created with fixed index coordinate shape using index_shape=(q,) {str(field.name)}"
if field.index_dimensions == 0:
return 1
@@ -61,7 +68,7 @@ def get_field_stride(param):
field = param.fields[0]
type_str = get_base_type(param.symbol.dtype).base_name
stride_names = ['xStride()', 'yStride()', 'zStride()', 'fStride()']
- stride_names = ["%s(%s->%s)" % (type_str, param.field_name, e) for e in stride_names]
+ stride_names = [f"{type_str}({param.field_name}->{e})" for e in stride_names]
strides = stride_names[:field.spatial_dimensions]
if field.index_dimensions > 0:
additional_strides = [1]
@@ -69,39 +76,39 @@ def get_field_stride(param):
additional_strides.append(additional_strides[-1] * shape)
assert len(additional_strides) == field.index_dimensions
f_stride_name = stride_names[-1]
- strides.extend(["%s(%d * %s)" % (type_str, e, f_stride_name) for e in reversed(additional_strides)])
+ strides.extend([f"{type_str}({e} * {f_stride_name})" for e in reversed(additional_strides)])
return strides[param.symbol.coordinate]
-def generate_declaration(kernel_info, target='cpu'):
+def generate_declaration(kernel_info, target=Target.CPU):
"""Generates the declaration of the kernel function"""
ast = kernel_info.ast
- result = generate_c(ast, signature_only=True, dialect='cuda' if target == 'gpu' else 'c') + ";"
+ result = generate_c(ast, signature_only=True, dialect=Backend.CUDA if target == Target.GPU else Backend.C) + ";"
result = "namespace internal_%s {\n%s\n}" % (ast.function_name, result,)
return result
-def generate_definition(kernel_info, target='cpu'):
+def generate_definition(kernel_info, target=Target.CPU):
"""Generates the definition (i.e. implementation) of the kernel function"""
ast = kernel_info.ast
- result = generate_c(ast, dialect='cuda' if target == 'gpu' else 'c')
+ result = generate_c(ast, dialect=Backend.CUDA if target == Target.GPU else Backend.C)
result = "namespace internal_%s {\nstatic %s\n}" % (ast.function_name, result)
return result
-def generate_declarations(kernel_family, target='cpu'):
+def generate_declarations(kernel_family, target=Target.CPU):
declarations = []
for ast in kernel_family.all_asts:
- code = generate_c(ast, signature_only=True, dialect='cuda' if target == 'gpu' else 'c') + ";"
+ code = generate_c(ast, signature_only=True, dialect=Backend.CUDA if target == Target.GPU else Backend.C) + ";"
code = "namespace internal_%s {\n%s\n}\n" % (ast.function_name, code,)
declarations.append(code)
return "\n".join(declarations)
-def generate_definitions(kernel_family, target='cpu'):
+def generate_definitions(kernel_family, target=Target.CPU):
definitions = []
for ast in kernel_family.all_asts:
- code = generate_c(ast, dialect='cuda' if target == 'gpu' else 'c')
+ code = generate_c(ast, dialect=Backend.CUDA if target == Target.GPU else Backend.C)
code = "namespace internal_%s {\nstatic %s\n}\n" % (ast.function_name, code)
definitions.append(code)
return "\n".join(definitions)
@@ -122,8 +129,7 @@ def field_extraction_code(field, is_temporary, declaration_only=False,
is_gpu: if the field is a GhostLayerField or a GpuField
update_member: specify if function is used inside a constructor; add _ to members
"""
-
-# Determine size of f coordinate which is a template parameter
+ # Determine size of f coordinate which is a template parameter
f_size = get_field_fsize(field)
field_name = field.name
dtype = get_base_type(field.dtype)
@@ -133,26 +139,26 @@ def field_extraction_code(field, is_temporary, declaration_only=False,
dtype = get_base_type(field.dtype)
field_type = make_field_type(dtype, f_size, is_gpu)
if declaration_only:
- return "%s * %s_;" % (field_type, field_name)
+ return f"{field_type} * {field_name}_;"
else:
prefix = "" if no_declaration else "auto "
if update_member:
- return "%s%s_ = block->getData< %s >(%sID);" % (prefix, field_name, field_type, field_name)
+ return f"{prefix}{field_name}_ = block->getData< {field_type} >({field_name}ID);"
else:
- return "%s%s = block->getData< %s >(%sID);" % (prefix, field_name, field_type, field_name)
+ return f"{prefix}{field_name} = block->getData< {field_type} >({field_name}ID);"
else:
assert field_name.endswith('_tmp')
original_field_name = field_name[:-len('_tmp')]
if declaration_only:
- return "%s * %s_;" % (field_type, field_name)
+ return f"{field_type} * {field_name}_;"
else:
- declaration = "{type} * {tmp_field_name};".format(type=field_type, tmp_field_name=field_name)
+ declaration = f"{field_type} * {field_name};"
tmp_field_str = temporary_fieldTemplate.format(original_field_name=original_field_name,
tmp_field_name=field_name, type=field_type)
return tmp_field_str if no_declaration else declaration + tmp_field_str
-@jinja2.contextfilter
+@jinja2_context_decorator
def generate_block_data_to_field_extraction(ctx, kernel_info, parameters_to_ignore=(), parameters=None,
declarations_only=False, no_declarations=False, update_member=False):
"""Generates code that extracts all required fields of a kernel from a walberla block storage."""
@@ -192,13 +198,14 @@ def generate_refs_for_kernel_parameters(kernel_info, prefix, parameters_to_ignor
return "\n".join("auto & %s = %s%s_;" % (s, prefix, s) for s in symbols)
-@jinja2.contextfilter
+@jinja2_context_decorator
def generate_call(ctx, kernel, ghost_layers_to_include=0, cell_interval=None, stream='0',
spatial_shape_symbols=()):
"""Generates the function call to a pystencils kernel
Args:
- kernel_info:
+ ctx: code generation context
+ kernel: pystencils kernel
ghost_layers_to_include: if left to 0, only the inner part of the ghost layer field is looped over
a CHECK is inserted that the field has as many ghost layers as the pystencils AST
needs. This parameter specifies how many ghost layers the kernel should view as
@@ -219,9 +226,12 @@ def generate_call(ctx, kernel, ghost_layers_to_include=0, cell_interval=None, st
instruction_set = kernel.get_ast_attr('instruction_set')
if vec_info:
assume_inner_stride_one = vec_info['assume_inner_stride_one']
+ assume_aligned = vec_info['assume_aligned']
nontemporal = vec_info['nontemporal']
else:
assume_inner_stride_one = nontemporal = False
+ assume_aligned = False
+
cpu_openmp = ctx.get('cpu_openmp', False)
kernel_ghost_layers = kernel.get_ast_attr('ghost_layers')
@@ -246,11 +256,10 @@ def generate_call(ctx, kernel, ghost_layers_to_include=0, cell_interval=None, st
if cell_interval is None:
shape_names = ['xSize()', 'ySize()', 'zSize()'][:field_object.spatial_dimensions]
offset = 2 * ghost_layers_to_include + 2 * required_ghost_layers
- return ["cell_idx_c(%s->%s) + %s" % (field_object.name, e, offset) for e in shape_names]
+ return [f"cell_idx_c({field_object.name}->{e}) + {offset}" for e in shape_names]
else:
assert ghost_layers_to_include == 0
- return ["cell_idx_c({ci}.{coord}Size()) + {gl}".format(coord=coord_name, ci=cell_interval,
- gl=2 * required_ghost_layers)
+ return [f"cell_idx_c({cell_interval}.{coord_name}Size()) + {2 * required_ghost_layers}"
for coord_name in ('x', 'y', 'z')]
for param in ast_params:
@@ -275,7 +284,7 @@ def generate_call(ctx, kernel, ghost_layers_to_include=0, cell_interval=None, st
f"({coordinates[0]}, {coordinates[1]}, {coordinates[2]}, {coordinates[3]});")
if assume_inner_stride_one and field.index_dimensions > 0:
kernel_call_lines.append(f"WALBERLA_ASSERT_EQUAL({param.field_name}->layout(), field::fzyx);")
- if instruction_set and assume_inner_stride_one:
+ if instruction_set and assume_aligned:
if nontemporal and cpu_openmp and 'cachelineZero' in instruction_set:
kernel_call_lines.append(f"WALBERLA_ASSERT_EQUAL((uintptr_t) {field.name}->dataAt(0, 0, 0, 0) %"
f"{instruction_set['cachelineSize']}, 0);")
@@ -297,7 +306,7 @@ def generate_call(ctx, kernel, ghost_layers_to_include=0, cell_interval=None, st
kernel_call_lines.append(f"const {type_str} {param.symbol.name} = {shape};")
if assume_inner_stride_one and field.index_dimensions > 0:
kernel_call_lines.append(f"WALBERLA_ASSERT_EQUAL({field.name}->layout(), field::fzyx);")
- if instruction_set and assume_inner_stride_one:
+ if instruction_set and assume_aligned:
if nontemporal and cpu_openmp and 'cachelineZero' in instruction_set:
kernel_call_lines.append(f"WALBERLA_ASSERT_EQUAL((uintptr_t) {field.name}->dataAt(0, 0, 0, 0) %"
f"{instruction_set['cachelineSize']}, 0);")
@@ -315,7 +324,7 @@ def generate_swaps(kernel_info):
"""Generates code to swap main fields with temporary fields"""
swaps = ""
for src, dst in kernel_info.field_swaps:
- swaps += "%s->swapDataPointers(%s);\n" % (src, dst)
+ swaps += f"{src}->swapDataPointers({dst});\n"
return swaps
@@ -328,9 +337,9 @@ def generate_constructor_initializer_list(kernel_info, parameters_to_ignore=None
parameter_initializer_list = []
for param in kernel_info.parameters:
if param.is_field_pointer and param.field_name not in parameters_to_ignore:
- parameter_initializer_list.append("%sID(%sID_)" % (param.field_name, param.field_name))
+ parameter_initializer_list.append(f"{param.field_name}ID({param.field_name}ID_)")
elif not param.is_field_parameter and param.symbol.name not in parameters_to_ignore:
- parameter_initializer_list.append("%s_(%s)" % (param.symbol.name, param.symbol.name))
+ parameter_initializer_list.append(f"{param.symbol.name}_({param.symbol.name})")
return ", ".join(parameter_initializer_list)
@@ -347,9 +356,9 @@ def generate_constructor_parameters(kernel_info, parameters_to_ignore=None):
parameter_list = []
for param in kernel_info.parameters:
if param.is_field_pointer and param.field_name not in parameters_to_ignore:
- parameter_list.append("BlockDataID %sID_" % (param.field_name, ))
+ parameter_list.append(f"BlockDataID {param.field_name}ID_")
elif not param.is_field_parameter and param.symbol.name not in parameters_to_ignore:
- parameter_list.append("%s %s" % (param.symbol.dtype, param.symbol.name,))
+ parameter_list.append(f"{param.symbol.dtype} {param.symbol.name}")
varying_parameters = ["%s %s" % e for e in varying_parameters]
return ", ".join(parameter_list + varying_parameters)
@@ -367,14 +376,14 @@ def generate_constructor_call_arguments(kernel_info, parameters_to_ignore=None):
parameter_list = []
for param in kernel_info.parameters:
if param.is_field_pointer and param.field_name not in parameters_to_ignore:
- parameter_list.append("%sID" % (param.field_name, ))
+ parameter_list.append(f"{param.field_name}ID")
elif not param.is_field_parameter and param.symbol.name not in parameters_to_ignore:
parameter_list.append(f'{param.symbol.name}_')
- varying_parameters = ["%s_" % e for e in varying_parameter_names]
+ varying_parameters = [f"{e}_" for e in varying_parameter_names]
return ", ".join(parameter_list + varying_parameters)
-@jinja2.contextfilter
+@jinja2_context_decorator
def generate_members(ctx, kernel_info, parameters_to_ignore=(), only_fields=False):
fields = {f.name: f for f in kernel_info.fields_accessed}
@@ -387,9 +396,9 @@ def generate_members(ctx, kernel_info, parameters_to_ignore=(), only_fields=Fals
if only_fields and not param.is_field_parameter:
continue
if param.is_field_pointer and param.field_name not in params_to_skip:
- result.append("BlockDataID %sID;" % (param.field_name, ))
+ result.append(f"BlockDataID {param.field_name}ID;")
elif not param.is_field_parameter and param.symbol.name not in params_to_skip:
- result.append("%s %s_;" % (param.symbol.dtype, param.symbol.name,))
+ result.append(f"{param.symbol.dtype} {param.symbol.name}_;")
for field_name in kernel_info.temporary_fields:
f = fields[field_name]
@@ -417,13 +426,13 @@ def generate_destructor(kernel_info, class_name):
return temporary_constructor.format(contents=contents, class_name=class_name)
-@jinja2.contextfilter
+@jinja2_context_decorator
def nested_class_method_definition_prefix(ctx, nested_class_name):
outer_class = ctx['class_name']
if len(nested_class_name) == 0:
return outer_class
else:
- return outer_class + '::' + nested_class_name
+ return f"{outer_class}::{nested_class_name}"
def generate_list_of_expressions(expressions, prepend=''):
@@ -440,8 +449,8 @@ def type_identifier_list(nested_arg_list):
"""
result = []
- def recursive_flatten(list):
- for s in list:
+ def recursive_flatten(arg_list):
+ for s in arg_list:
if isinstance(s, str):
result.append(s)
elif isinstance(s, TypedSymbol):
@@ -460,8 +469,8 @@ def identifier_list(nested_arg_list):
"""
result = []
- def recursive_flatten(list):
- for s in list:
+ def recursive_flatten(arg_list):
+ for s in arg_list:
if isinstance(s, str):
result.append(s)
elif isinstance(s, TypedSymbol):
diff --git a/python/pystencils_walberla/kernel_selection.py b/python/pystencils_walberla/kernel_selection.py
index 316c701c1ec837c399a8a39f2d802ad1151fb42d..21498b0846d32d2c9955a10b70418eeefdcc89ba 100644
--- a/python/pystencils_walberla/kernel_selection.py
+++ b/python/pystencils_walberla/kernel_selection.py
@@ -1,8 +1,9 @@
+from abc import ABC
from typing import Sequence, Set
from collections import OrderedDict
from functools import reduce
from jinja2.filters import do_indent
-from pystencils import TypedSymbol
+from pystencils import Target, TypedSymbol
from pystencils.backends.cbackend import get_headers
from pystencils.backends.cuda_backend import CudaSympyPrinter
from pystencils.kernelparameters import SHAPE_DTYPE
@@ -88,7 +89,7 @@ class AbstractKernelSelectionNode:
raise NotImplementedError()
-class AbstractConditionNode(AbstractKernelSelectionNode):
+class AbstractConditionNode(AbstractKernelSelectionNode, ABC):
def __init__(self, branch_true, branch_false):
self.branch_true = branch_true
self.branch_false = branch_false
@@ -134,7 +135,7 @@ class KernelCallNode(AbstractKernelSelectionNode):
def get_code(self, **kwargs):
ast = self.ast
ast_params = self.parameters
- is_cpu = self.ast.target == 'cpu'
+ is_cpu = self.ast.target == Target.CPU
call_parameters = ", ".join([p.symbol.name for p in ast_params])
if not is_cpu:
@@ -262,8 +263,7 @@ class AbstractInterfaceArgumentMapping:
class IdentityMapping(AbstractInterfaceArgumentMapping):
def __init__(self, low_level_arg: TypedSymbol):
- self.high_level_args = (low_level_arg,)
- self.low_level_arg = low_level_arg
+ super(IdentityMapping, self).__init__(high_level_args=(low_level_arg,), low_level_arg=low_level_arg)
@property
def mapping_code(self):
@@ -316,24 +316,24 @@ def merge_sorted_lists(lx, ly, sort_key=lambda x: x, identity_check_key=None):
nx = len(lx)
ny = len(ly)
- def recursive_merge(lx, ly, ix, iy):
- if ix == nx:
- return ly[iy:]
- if iy == ny:
- return lx[ix:]
- x = lx[ix]
- y = ly[iy]
+ def recursive_merge(lx_intern, ly_intern, ix_intern, iy_intern):
+ if ix_intern == nx:
+ return ly_intern[iy_intern:]
+ if iy_intern == ny:
+ return lx_intern[ix_intern:]
+ x = lx_intern[ix_intern]
+ y = ly_intern[iy_intern]
skx = sort_key(x)
sky = sort_key(y)
if skx == sky:
if identity_check_key(x) == identity_check_key(y):
- return [x] + recursive_merge(lx, ly, ix + 1, iy + 1)
+ return [x] + recursive_merge(lx_intern, ly_intern, ix_intern + 1, iy_intern + 1)
else:
raise ValueError(f'Elements <{x}> and <{y}> with equal sort key where not identical!')
elif skx < sky:
- return [x] + recursive_merge(lx, ly, ix + 1, iy)
+ return [x] + recursive_merge(lx_intern, ly_intern, ix_intern + 1, iy_intern)
else:
- return [y] + recursive_merge(lx, ly, ix, iy + 1)
+ return [y] + recursive_merge(lx_intern, ly_intern, ix_intern, iy_intern + 1)
return recursive_merge(lx, ly, 0, 0)
diff --git a/python/pystencils_walberla/tests/test_packinfo_generation.py b/python/pystencils_walberla/tests/test_packinfo_generation.py
index 78ebdd92a9ed6a6569b5e18db53e55d97f54abf4..c3fc2f50fb6c76a6db1dbcc609a049475f95f589 100644
--- a/python/pystencils_walberla/tests/test_packinfo_generation.py
+++ b/python/pystencils_walberla/tests/test_packinfo_generation.py
@@ -13,10 +13,10 @@ class PackinfoGenTest(unittest.TestCase):
for da in (False, True):
with ManualCodeGenerationContext(openmp=openmp, double_accuracy=da) as ctx:
dtype = "float64" if ctx.double_accuracy else "float32"
- f, g = ps.fields("f, g(4): {}[3D]".format(dtype))
+ f, g = ps.fields(f"f, g(4): {dtype}[3D]")
generate_pack_info_for_field(ctx, 'PI1', f)
- src, dst = ps.fields("src, src_tmp: {}[2D]".format(dtype))
+ src, dst = ps.fields(f"src, src_tmp: {dtype}[2D]")
stencil = [[0, -1, 0],
[-1, 4, -1],
[0, -1, 0]]
diff --git a/python/pystencils_walberla/tests/test_walberla_gen.py b/python/pystencils_walberla/tests/test_walberla_gen.py
index 939d7af55b173a8436da0ad7dd379bbd8eb8a41e..ced44f3ec49bb56dd71ca92758652af7cff1414f 100644
--- a/python/pystencils_walberla/tests/test_walberla_gen.py
+++ b/python/pystencils_walberla/tests/test_walberla_gen.py
@@ -19,7 +19,7 @@ class CodegenTest(unittest.TestCase):
dtype = "float64" if ctx.double_accuracy else "float32"
# ----- Jacobi 2D - created by specifying weights in nested list --------------------------
- src, dst = ps.fields("src, src_tmp: {}[2D]".format(dtype))
+ src, dst = ps.fields(f"src, src_tmp: {dtype}[2D]")
stencil = [[0, -1, 0],
[-1, 4, -1],
[0, -1, 0]]
@@ -27,7 +27,7 @@ class CodegenTest(unittest.TestCase):
generate_sweep(ctx, 'JacobiKernel2D', assignments, field_swaps=[(src, dst)])
# ----- Jacobi 3D - created by using kernel_decorator with assignments in '@=' format -----
- src, dst = ps.fields("src, src_tmp: {}[3D]".format(dtype))
+ src, dst = ps.fields(f"src, src_tmp: {dtype}[3D]")
@ps.kernel
def kernel_func():
diff --git a/python/pystencils_walberla/utility.py b/python/pystencils_walberla/utility.py
index 3283e84c044ca24d28b1c6aac690d19787e23b5c..8800130bcab6fbb9e03a5c3a3d5d2be951a74fb6 100644
--- a/python/pystencils_walberla/utility.py
+++ b/python/pystencils_walberla/utility.py
@@ -2,6 +2,8 @@ from os import path
from pystencils.data_types import get_base_type
from pystencils_walberla.cmake_integration import CodeGenerationContext
+from lbmpy import LBStencil
+
HEADER_EXTENSIONS = {'.h', '.hpp'}
@@ -74,6 +76,8 @@ def _stencil_inclusion_code(stencil_typedefs):
dim = len(stencil[0])
q = len(stencil)
stencil = f"D{dim}Q{q}"
+ elif isinstance(stencil, LBStencil):
+ stencil = stencil.name
elif not isinstance(stencil, str):
raise ValueError(f'Invalid stencil: Do not know what to make of {stencil}')
diff --git a/tests/cuda/codegen/CodegenJacobiGPU.cpp b/tests/cuda/codegen/CodegenJacobiGPU.cpp
index 824ba1f644be17d545762be270f03fa1471e1f68..7842220f6838177176a29111cdca9f95c020ca9f 100644
--- a/tests/cuda/codegen/CodegenJacobiGPU.cpp
+++ b/tests/cuda/codegen/CodegenJacobiGPU.cpp
@@ -29,10 +29,8 @@
#include "core/Environment.h"
#include "core/debug/TestSubsystem.h"
-#include "cuda/HostFieldAllocator.h"
#include "cuda/FieldCopy.h"
#include "cuda/GPUField.h"
-#include "cuda/Kernel.h"
#include "cuda/AddGPUFieldToStorage.h"
#include "cuda/communication/GPUPackInfo.h"
#include "cuda/FieldIndexing.h"
@@ -43,8 +41,6 @@
#include "geometry/initializer/ScalarFieldFromGrayScaleImage.h"
-#include "gui/Gui.h"
-
#include "stencil/D2Q9.h"
#include "stencil/D3Q7.h"
diff --git a/tests/cuda/codegen/CudaJacobiKernel.py b/tests/cuda/codegen/CudaJacobiKernel.py
index 24a48238b4548ad08f05457b6f50afa5548115e2..79c2d767cf0d9b41a4bd52cac27b5aa780169137 100644
--- a/tests/cuda/codegen/CudaJacobiKernel.py
+++ b/tests/cuda/codegen/CudaJacobiKernel.py
@@ -10,7 +10,7 @@ with CodeGeneration() as ctx:
[4.44, 5.55, 6.66],
[7.77, 8.88, 9.99]]
assignments = ps.assignment_from_stencil(stencil, src, dst, normalization_factor=1 / np.sum(stencil))
- generate_sweep(ctx, 'CudaJacobiKernel2D', assignments, field_swaps=[(src, dst)], target="gpu")
+ generate_sweep(ctx, 'CudaJacobiKernel2D', assignments, field_swaps=[(src, dst)], target=ps.Target.GPU)
# ----- Stencil 3D - created by using kernel_decorator with assignments in '@=' format -----
src, dst = ps.fields("src, src_tmp: [3D]")
@@ -21,4 +21,4 @@ with CodeGeneration() as ctx:
+ 5 * src[0, 1, 0] + 6 * src[0, -1, 0]
+ 7 * src[0, 0, 1] + 8 * src[0, 0, -1]) / 33
- generate_sweep(ctx, 'CudaJacobiKernel3D', kernel_func, field_swaps=[(src, dst)], target="gpu")
+ generate_sweep(ctx, 'CudaJacobiKernel3D', kernel_func, field_swaps=[(src, dst)], target=ps.Target.GPU)
diff --git a/tests/cuda/codegen/CudaPoisson.py b/tests/cuda/codegen/CudaPoisson.py
index e14cbdea8906cae2029bdf2b1c74b5db3ffb3787..eedff3609fd0790a72134aed3063357468cbf633 100644
--- a/tests/cuda/codegen/CudaPoisson.py
+++ b/tests/cuda/codegen/CudaPoisson.py
@@ -15,4 +15,4 @@ with CodeGeneration() as ctx:
+ (dx**2 * (src[0, 1] + src[0, -1]))
- (rhs[0, 0] * dx**2 * dy**2)) / (2 * (dx**2 + dy**2))
- generate_sweep(ctx, 'PoissonGPU', kernel_func, target='gpu')
+ generate_sweep(ctx, 'PoissonGPU', kernel_func, target=ps.Target.GPU)
diff --git a/tests/cuda/codegen/GeneratedFieldPackInfoTestGPU.py b/tests/cuda/codegen/GeneratedFieldPackInfoTestGPU.py
index da0cd947461a7b9f3d65af04eec9be987df536f0..bac0b89da710fd8fcbb79d3a4859a0b641104b8c 100644
--- a/tests/cuda/codegen/GeneratedFieldPackInfoTestGPU.py
+++ b/tests/cuda/codegen/GeneratedFieldPackInfoTestGPU.py
@@ -9,6 +9,6 @@ with CodeGeneration() as ctx:
field = ps.fields("field: int32[3D]", layout=layout)
# communication
- generate_pack_info_for_field(ctx, 'ScalarFieldCommunicationGPU', field, target='gpu')
- generate_pack_info_for_field(ctx, 'ScalarFieldPullReductionGPU', field, target='gpu', operator=op.add,
+ generate_pack_info_for_field(ctx, 'ScalarFieldCommunicationGPU', field, target=ps.Target.GPU)
+ generate_pack_info_for_field(ctx, 'ScalarFieldPullReductionGPU', field, target=ps.Target.GPU, operator=op.add,
gl_to_inner=True)
diff --git a/tests/cuda/codegen/MicroBenchmarkGpuLbm.py b/tests/cuda/codegen/MicroBenchmarkGpuLbm.py
index 45bdc303c9f9983c7c4120748e4b1fba1f23ecf7..c461df018aea8a419828757d53b5984f23a73d81 100644
--- a/tests/cuda/codegen/MicroBenchmarkGpuLbm.py
+++ b/tests/cuda/codegen/MicroBenchmarkGpuLbm.py
@@ -1,6 +1,6 @@
import pystencils as ps
from lbmpy.updatekernels import create_stream_only_kernel
-from lbmpy.stencils import get_stencil
+from lbmpy import LBStencil, Stencil
from pystencils_walberla import CodeGeneration, generate_sweep
with CodeGeneration() as ctx:
@@ -12,10 +12,10 @@ with CodeGeneration() as ctx:
copy_only = [ps.Assignment(dst(i), src(i)) for i in range(f_size)]
generate_sweep(ctx, 'MicroBenchmarkCopyKernel', copy_only,
- target='gpu', gpu_indexing_params={'block_size': (128, 1, 1)})
+ target=ps.Target.GPU, gpu_indexing_params={'block_size': (128, 1, 1)})
# Stream-only sweep
- stencil = get_stencil("D3Q19")
+ stencil = LBStencil(Stencil.D3Q19)
stream_only = create_stream_only_kernel(stencil, src_field=src, dst_field=dst)
generate_sweep(ctx, 'MicroBenchmarkStreamKernel', stream_only,
- target='gpu', gpu_indexing_params={'block_size': (128, 1, 1)})
+ target=ps.Target.GPU, gpu_indexing_params={'block_size': (128, 1, 1)})
diff --git a/tests/field/codegen/CodegenJacobiCPU.cpp b/tests/field/codegen/CodegenJacobiCPU.cpp
index 20bc8b06116b9437de17e5967bf44e83df4100fb..d0a9693ae55cf507e5d724b18086209b84e98e12 100644
--- a/tests/field/codegen/CodegenJacobiCPU.cpp
+++ b/tests/field/codegen/CodegenJacobiCPU.cpp
@@ -30,8 +30,6 @@
#include "field/AddToStorage.h"
#include "field/communication/PackInfo.h"
-#include "gui/Gui.h"
-
#include "stencil/D2Q9.h"
#include "stencil/D3Q7.h"
diff --git a/tests/field/codegen/CodegenPoissonCPU.cpp b/tests/field/codegen/CodegenPoissonCPU.cpp
index f5137375a14eecb4bf79e007bef72bc1bbf089f3..4ecbab91e582551e45f738eb1aff6032a2d949bd 100644
--- a/tests/field/codegen/CodegenPoissonCPU.cpp
+++ b/tests/field/codegen/CodegenPoissonCPU.cpp
@@ -28,9 +28,6 @@
#include "field/AddToStorage.h"
#include "field/communication/PackInfo.h"
-#include "field/vtk/VTKWriter.h"
-
-#include "gui/Gui.h"
#include "stencil/D2Q9.h"
#include "timeloop/SweepTimeloop.h"
diff --git a/tests/field/codegen/GeneratedFieldPackInfoTest.py b/tests/field/codegen/GeneratedFieldPackInfoTest.py
index f63151b6e47c7225a929707a8723d567814859bd..8124060c37a37c03e1e578f76b89d90aef684d15 100644
--- a/tests/field/codegen/GeneratedFieldPackInfoTest.py
+++ b/tests/field/codegen/GeneratedFieldPackInfoTest.py
@@ -9,6 +9,6 @@ with CodeGeneration() as ctx:
field = ps.fields("field: int32[3D]", layout=layout)
# communication
- generate_pack_info_for_field(ctx, 'ScalarFieldCommunication', field, target='cpu')
- generate_pack_info_for_field(ctx, 'ScalarFieldPullReduction', field, target='cpu', operator=op.add,
+ generate_pack_info_for_field(ctx, 'ScalarFieldCommunication', field, target=ps.Target.CPU)
+ generate_pack_info_for_field(ctx, 'ScalarFieldPullReduction', field, target=ps.Target.CPU, operator=op.add,
gl_to_inner=True)
diff --git a/tests/lbm/CMakeLists.txt b/tests/lbm/CMakeLists.txt
index 5b1301a872ba56ee1f8d5c11e258c4b661c651e2..a8cc3e9f91a2a3090a299f314abcbe84a8f6c707 100644
--- a/tests/lbm/CMakeLists.txt
+++ b/tests/lbm/CMakeLists.txt
@@ -87,7 +87,7 @@ waLBerla_generate_target_from_python(NAME GeneratedOutflowBCGenerated
GeneratedOutflowBC_NoSlip.cpp GeneratedOutflowBC_NoSlip.h
GeneratedOutflowBC_Outflow.cpp GeneratedOutflowBC_Outflow.h
GeneratedOutflowBC_PackInfo.cpp GeneratedOutflowBC_PackInfo.h
- GeneratedOutflowBC_InfoHeader.h)
+ GeneratedOutflowBC.h)
waLBerla_compile_test( FILES codegen/GeneratedOutflowBC.cpp DEPENDS GeneratedOutflowBCGenerated)
waLBerla_execute_test( NAME GeneratedOutflowBC COMMAND $<TARGET_FILE:GeneratedOutflowBC> ${CMAKE_CURRENT_SOURCE_DIR}/codegen/GeneratedOutflowBC.prm )
@@ -96,7 +96,8 @@ waLBerla_generate_target_from_python(NAME LbCodeGenerationExampleGenerated
FILE codegen/LbCodeGenerationExample.py
OUT_FILES LbCodeGenerationExample_LatticeModel.cpp LbCodeGenerationExample_LatticeModel.h
LbCodeGenerationExample_NoSlip.cpp LbCodeGenerationExample_NoSlip.h
- LbCodeGenerationExample_UBB.cpp LbCodeGenerationExample_UBB.h )
+ LbCodeGenerationExample_UBB.cpp LbCodeGenerationExample_UBB.h
+ LbCodeGenerationExample.h)
waLBerla_compile_test( FILES codegen/LbCodeGenerationExample.cpp DEPENDS LbCodeGenerationExampleGenerated)
waLBerla_generate_target_from_python(NAME FluctuatingMRTGenerated FILE codegen/FluctuatingMRT.py
@@ -109,7 +110,9 @@ waLBerla_generate_target_from_python(NAME FieldLayoutAndVectorizationTestGenerat
FieldLayoutAndVectorizationTest_FZYX_NoVec_LatticeModel.cpp FieldLayoutAndVectorizationTest_FZYX_NoVec_LatticeModel.h
FieldLayoutAndVectorizationTest_ZYXF_Vec_LatticeModel.cpp FieldLayoutAndVectorizationTest_ZYXF_Vec_LatticeModel.h
FieldLayoutAndVectorizationTest_ZYXF_NoVec_LatticeModel.cpp FieldLayoutAndVectorizationTest_ZYXF_NoVec_LatticeModel.h )
+
waLBerla_compile_test( FILES codegen/FieldLayoutAndVectorizationTest.cpp DEPENDS FieldLayoutAndVectorizationTestGenerated)
+waLBerla_execute_test( NAME FieldLayoutAndVectorizationTest )
waLBerla_generate_target_from_python(NAME LbmPackInfoGenerationTestCodegen FILE codegen/LbmPackInfoGenerationTest.py
OUT_FILES AccessorBasedPackInfoEven.cpp AccessorBasedPackInfoEven.h
diff --git a/tests/lbm/codegen/FieldLayoutAndVectorizationTest.cpp b/tests/lbm/codegen/FieldLayoutAndVectorizationTest.cpp
index 09ff983b2325f63dd4401216c43658b5bb1fce30..db86a8d31c9470707230dd2e24560aa1d91a92ba 100644
--- a/tests/lbm/codegen/FieldLayoutAndVectorizationTest.cpp
+++ b/tests/lbm/codegen/FieldLayoutAndVectorizationTest.cpp
@@ -64,9 +64,6 @@ void checkEquivalence(const shared_ptr<StructuredBlockStorage> & blocks, BlockDa
int main(int argc, char **argv) {
debug::enterTestMode();
-
- mpi::Environment env( argc, argv );
-
walberla::Environment walberlaEnv(argc, argv);
auto blocks = blockforest::createUniformBlockGrid( 1, 1, 1,
diff --git a/tests/lbm/codegen/FieldLayoutAndVectorizationTest.py b/tests/lbm/codegen/FieldLayoutAndVectorizationTest.py
index 58c88d426d0e6f8f0c67d40ceb40cb42a97842fb..f04e357bc26d626521058946a8616e3897ed89e9 100644
--- a/tests/lbm/codegen/FieldLayoutAndVectorizationTest.py
+++ b/tests/lbm/codegen/FieldLayoutAndVectorizationTest.py
@@ -1,4 +1,5 @@
import sympy as sp
+from lbmpy import LBMConfig, LBStencil, Method, Stencil
from lbmpy.creationfunctions import create_lb_collision_rule
from pystencils_walberla import CodeGeneration
from lbmpy_walberla import generate_lattice_model
@@ -8,7 +9,9 @@ from collections import namedtuple
with CodeGeneration() as ctx:
omega_shear = sp.symbols("omega")
- collision_rule = create_lb_collision_rule(stencil='D2Q9', compressible=False, method='srt')
+
+ lbm_config = LBMConfig(stencil=LBStencil(Stencil.D2Q9), compressible=False, method=Method.SRT)
+ collision_rule = create_lb_collision_rule(lbm_config=lbm_config)
SetupDefinition = namedtuple('SetupDefinition', ['name', 'field_layout', 'vectorization_dict'])
@@ -17,7 +20,7 @@ with CodeGeneration() as ctx:
configurations = [SetupDefinition('FZYX_Vec', 'fzyx', {'instruction_set': default_vectorize_instruction_set}),
SetupDefinition('FZYX_NoVec', 'fzyx', {'instruction_set': None}),
SetupDefinition('ZYXF_Vec', 'zyxf', {'instruction_set': default_vectorize_instruction_set}),
- # does/should not vectorize, but instead yield warning
+ # does/should not vectorize, but instead yield warning except for AVX512 due to scatter intrinsics
SetupDefinition('ZYXF_NoVec', 'zyxf', {'instruction_set': None})]
for conf in configurations:
diff --git a/tests/lbm/codegen/FluctuatingMRT.py b/tests/lbm/codegen/FluctuatingMRT.py
index 6adba1df0d9e7bf7d71c8e901a406faed522006e..66185e171ec1542a64f11bcb32c9ec7bd573429c 100644
--- a/tests/lbm/codegen/FluctuatingMRT.py
+++ b/tests/lbm/codegen/FluctuatingMRT.py
@@ -1,8 +1,9 @@
import sympy as sp
import pystencils as ps
-from lbmpy.creationfunctions import create_lb_collision_rule, create_mrt_orthogonal, force_model_from_string
+from lbmpy.creationfunctions import create_lb_collision_rule, create_mrt_orthogonal
from lbmpy.moments import is_bulk_moment, is_shear_moment, get_order
-from lbmpy.stencils import get_stencil
+from lbmpy.forcemodels import Guo
+from lbmpy import LBMConfig, LBMOptimisation, LBStencil, Stencil
from pystencils_walberla import CodeGeneration
from lbmpy_walberla import generate_lattice_model
@@ -19,7 +20,7 @@ with CodeGeneration() as ctx:
order = order[0]
if order < 2:
- return 0
+ return 0.0
elif any(is_bulk):
assert all(is_bulk)
return sp.Symbol("omega_bulk")
@@ -33,22 +34,22 @@ with CodeGeneration() as ctx:
return sp.Symbol("omega_odd")
method = create_mrt_orthogonal(
- stencil=get_stencil('D3Q19'),
+ stencil=LBStencil(Stencil.D3Q19),
compressible=True,
weighted=True,
relaxation_rate_getter=rr_getter,
- force_model=force_model_from_string('schiller', force_field.center_vector)
- )
- collision_rule = create_lb_collision_rule(
- method,
- fluctuating={
- 'temperature': temperature,
- 'block_offsets': 'walberla',
- 'rng_node': ps.rng.PhiloxTwoDoubles if ctx.double_accuracy else ps.rng.PhiloxFourFloats,
- },
- optimization={'cse_global': True}
+ force_model=Guo(force_field.center_vector)
)
+ fluctuating = {'temperature': temperature,
+ 'block_offsets': 'walberla',
+ 'rng_node': ps.rng.PhiloxTwoDoubles if ctx.double_accuracy else ps.rng.PhiloxFourFloats}
+
+ lbm_config = LBMConfig(fluctuating=fluctuating)
+ lbm_opt = LBMOptimisation(cse_global=True)
+
+ collision_rule = create_lb_collision_rule(lb_method=method, lbm_config=lbm_config, lbm_optimisation=lbm_opt)
+
params = {}
if ctx.optimize_for_localhost:
params['cpu_vectorize_info'] = {'assume_inner_stride_one': True, 'assume_aligned': True}
diff --git a/tests/lbm/codegen/GeneratedOutflowBC.cpp b/tests/lbm/codegen/GeneratedOutflowBC.cpp
index 66311c7ebd98866bbb553e92be406fb4b8abc3ef..dba8bdbf8d844f47fe0c60d9e5bfdc578264dff0 100644
--- a/tests/lbm/codegen/GeneratedOutflowBC.cpp
+++ b/tests/lbm/codegen/GeneratedOutflowBC.cpp
@@ -34,14 +34,7 @@
#include "timeloop/SweepTimeloop.h"
// Generated Files
-#include "GeneratedOutflowBC_Dynamic_UBB.h"
-#include "GeneratedOutflowBC_InfoHeader.h"
-#include "GeneratedOutflowBC_MacroSetter.h"
-#include "GeneratedOutflowBC_NoSlip.h"
-#include "GeneratedOutflowBC_Outflow.h"
-#include "GeneratedOutflowBC_PackInfo.h"
-#include "GeneratedOutflowBC_Static_UBB.h"
-#include "GeneratedOutflowBC_Sweep.h"
+#include "GeneratedOutflowBC.h"
using namespace walberla;
diff --git a/tests/lbm/codegen/GeneratedOutflowBC.py b/tests/lbm/codegen/GeneratedOutflowBC.py
index 0aa6927d004953d829a897103d4dceea02f9f48a..3fae265e1c3cbe49ce2703adda2d7d78cc95fd97 100644
--- a/tests/lbm/codegen/GeneratedOutflowBC.py
+++ b/tests/lbm/codegen/GeneratedOutflowBC.py
@@ -1,20 +1,18 @@
from pystencils.field import fields
from lbmpy.macroscopic_value_kernels import macroscopic_values_setter
-from lbmpy.stencils import get_stencil
+from lbmpy import LBMConfig, LBMOptimisation, LBStencil, Stencil, Method
from lbmpy.creationfunctions import create_lb_method, create_lb_update_rule
from lbmpy.boundaries import NoSlip, UBB, ExtrapolationOutflow
from lbmpy_walberla.additional_data_handler import UBBAdditionalDataHandler, OutflowAdditionalDataHandler
-from pystencils_walberla import CodeGeneration, generate_sweep
-from lbmpy_walberla import RefinementScaling, generate_boundary, generate_lb_pack_info
+from pystencils_walberla import CodeGeneration, generate_sweep, generate_info_header
+from lbmpy_walberla import generate_boundary, generate_lb_pack_info
import sympy as sp
-stencil = get_stencil("D2Q9")
-q = len(stencil)
-dim = len(stencil[0])
+stencil = LBStencil(Stencil.D2Q9)
-pdfs, pdfs_tmp = fields(f"pdfs({q}), pdfs_tmp({q}): double[{dim}D]", layout='fzyx')
-velocity_field, density_field = fields(f"velocity({dim}), density(1) : double[{dim}D]", layout='fzyx')
+pdfs, pdfs_tmp = fields(f"pdfs({stencil.Q}), pdfs_tmp({stencil.Q}): double[{stencil.D}D]", layout='fzyx')
+velocity_field, density_field = fields(f"velocity({stencil.D}), density(1) : double[{stencil.D}D]", layout='fzyx')
omega = sp.Symbol("omega")
u_max = sp.Symbol("u_max")
@@ -23,35 +21,24 @@ output = {
'velocity': velocity_field
}
-options = {'method': 'cumulant',
- 'stencil': stencil,
- 'relaxation_rate': omega,
- 'galilean_correction': len(stencil) == 27,
- 'field_name': 'pdfs',
- 'output': output,
- 'optimization': {'symbolic_field': pdfs,
- 'symbolic_temporary_field': pdfs_tmp,
- 'cse_global': False,
- 'cse_pdfs': False}}
+lbm_config = LBMConfig(method=Method.CUMULANT, stencil=stencil, relaxation_rate=omega,
+ galilean_correction=stencil.Q == 27, field_name='pdfs', output=output)
-method = create_lb_method(**options)
+lbm_opt = LBMOptimisation(symbolic_field=pdfs, symbolic_temporary_field=pdfs_tmp,
+ cse_global=False, cse_pdfs=False)
+
+method = create_lb_method(lbm_config=lbm_config)
# getter & setter
setter_assignments = macroscopic_values_setter(method, velocity=velocity_field.center_vector,
pdfs=pdfs, density=1.0)
-update_rule = create_lb_update_rule(lb_method=method, **options)
-
-info_header = f"""
-using namespace walberla;
-#include "stencil/D{dim}Q{q}.h"
-using Stencil_T = walberla::stencil::D{dim}Q{q};
-using PdfField_T = GhostLayerField<real_t, {q}>;
-using VelocityField_T = GhostLayerField<real_t, {dim}>;
-using ScalarField_T = GhostLayerField<real_t, 1>;
- """
+update_rule = create_lb_update_rule(lb_method=method, lbm_config=lbm_config, lbm_optimisation=lbm_opt)
-stencil = method.stencil
+stencil_typedefs = {'Stencil_T': stencil}
+field_typedefs = {'PdfField_T': pdfs,
+ 'VelocityField_T': velocity_field,
+ 'ScalarField_T': density_field}
with CodeGeneration() as ctx:
# sweeps
@@ -59,10 +46,10 @@ with CodeGeneration() as ctx:
generate_sweep(ctx, 'GeneratedOutflowBC_MacroSetter', setter_assignments)
# boundaries
- ubb_dynamic = UBB(lambda *args: None, dim=dim)
+ ubb_dynamic = UBB(lambda *args: None, dim=stencil.D)
ubb_data_handler = UBBAdditionalDataHandler(stencil, ubb_dynamic)
- if dim == 2:
+ if stencil.D == 2:
ubb_static = UBB([sp.Symbol("u_max"), 0])
else:
ubb_static = UBB([sp.Symbol("u_max"), 0, 0])
@@ -87,4 +74,5 @@ with CodeGeneration() as ctx:
generate_lb_pack_info(ctx, 'GeneratedOutflowBC_PackInfo', stencil, pdfs)
# Info header containing correct template definitions for stencil and field
- ctx.write_file("GeneratedOutflowBC_InfoHeader.h", info_header)
+ generate_info_header(ctx, "GeneratedOutflowBC.h",
+ stencil_typedefs=stencil_typedefs, field_typedefs=field_typedefs)
diff --git a/tests/lbm/codegen/InplaceStreamingCodegen.py b/tests/lbm/codegen/InplaceStreamingCodegen.py
index 7fcef0f4215805cfa40f68d6fe82f6d3cfbba1c6..099ad4617c93e73ff41a03747bb5417805b54f62 100644
--- a/tests/lbm/codegen/InplaceStreamingCodegen.py
+++ b/tests/lbm/codegen/InplaceStreamingCodegen.py
@@ -1,57 +1,50 @@
+from dataclasses import replace
+
from lbmpy_walberla import generate_alternating_lbm_sweep, generate_boundary, generate_alternating_lbm_boundary
-from lbmpy_walberla.additional_data_handler import OutflowAdditionalDataHandler
from pystencils_walberla import CodeGeneration, generate_sweep, generate_info_header
+from pystencils import Target, CreateKernelConfig
+from lbmpy import LBMConfig, LBMOptimisation, LBStencil, Method, Stencil
from lbmpy.creationfunctions import create_lb_collision_rule, create_lb_ast
from lbmpy.macroscopic_value_kernels import macroscopic_values_setter
from lbmpy.boundaries import NoSlip, UBB, ExtrapolationOutflow
from lbmpy.advanced_streaming import Timestep
from pystencils import Field
-from lbmpy.stencils import get_stencil
# Common Setup
-stencil = get_stencil('D3Q27')
-dim = len(stencil[0])
-q = len(stencil)
-target = 'cpu'
+stencil = LBStencil(Stencil.D3Q27)
+target = Target.CPU
inplace_pattern = 'aa'
two_fields_pattern = 'pull'
namespace = 'lbmpy'
-f_field = Field.create_generic('f', dim, index_shape=(q,), layout='fzyx')
-f_field_tmp = Field.create_generic('f_tmp', dim, index_shape=(q,), layout='fzyx')
-u_field = Field.create_generic('u', dim, index_shape=(dim,), layout='fzyx')
+f_field = Field.create_generic('f', stencil.D, index_shape=(stencil.Q,), layout='fzyx')
+f_field_tmp = Field.create_generic('f_tmp', stencil.D, index_shape=(stencil.Q,), layout='fzyx')
+u_field = Field.create_generic('u', stencil.D, index_shape=(stencil.D,), layout='fzyx')
output = {
'velocity': u_field
}
-method_params = {
- 'stencil': stencil,
- 'method': 'srt',
- 'relaxation_rate': 1.5,
- 'output': output
-}
+lbm_config = LBMConfig(stencil=stencil, method=Method.SRT, relaxation_rate=1.5, output=output)
+lbm_opt = LBMOptimisation(symbolic_field=f_field,
+ symbolic_temporary_field=f_field_tmp)
-optimization = {
- 'target': target,
- 'symbolic_field': f_field,
- 'symbolic_temporary_field': f_field_tmp
-}
+config = CreateKernelConfig(target=target)
-collision_rule = create_lb_collision_rule(**method_params)
+collision_rule = create_lb_collision_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt, config=config)
lb_method = collision_rule.method
noslip = NoSlip()
-ubb = UBB((0.05,) + (0,) * (dim - 1))
+ubb = UBB((0.05,) + (0,) * (stencil.D - 1))
-outflow_normal = (1,) + (0,) * (dim - 1)
+outflow_normal = (1,) + (0,) * (stencil.D - 1)
outflow_pull = ExtrapolationOutflow(outflow_normal, lb_method, streaming_pattern=two_fields_pattern)
outflow_inplace = ExtrapolationOutflow(outflow_normal, lb_method, streaming_pattern=inplace_pattern)
-init_velocity = (0, ) * dim
+init_velocity = (0,) * stencil.D
init_kernel_pull = macroscopic_values_setter(lb_method, 1, init_velocity, f_field, streaming_pattern=two_fields_pattern)
init_kernel_inplace = macroscopic_values_setter(
@@ -63,7 +56,7 @@ field_typedefs = {'PdfField_T': f_field, 'VelocityField_T': u_field}
with CodeGeneration() as ctx:
# Pull-Pattern classes
ast_pull = create_lb_ast(collision_rule=collision_rule,
- streaming_pattern=two_fields_pattern, optimization=optimization)
+ streaming_pattern=two_fields_pattern, lbm_optimisation=lbm_opt)
generate_sweep(ctx, 'PullSweep', ast_pull, field_swaps=[(f_field, f_field_tmp)], namespace=namespace)
generate_boundary(ctx, 'PullNoSlip', noslip, lb_method,
@@ -76,14 +69,16 @@ with CodeGeneration() as ctx:
generate_sweep(ctx, 'PullInit', init_kernel_pull, target=target, namespace=namespace)
# Inplace Pattern classes
- generate_alternating_lbm_sweep(ctx, 'InPlaceSweep', collision_rule, inplace_pattern,
- optimization=optimization, namespace=namespace)
+ inplace_lbm_config = replace(lbm_config, streaming_pattern=inplace_pattern)
+ generate_alternating_lbm_sweep(ctx, 'InPlaceSweep', collision_rule,
+ lbm_config=inplace_lbm_config, namespace=namespace)
generate_alternating_lbm_boundary(ctx, 'InPlaceNoSlip', noslip, lb_method, streaming_pattern=inplace_pattern,
after_collision=True, target=target, namespace=namespace)
generate_alternating_lbm_boundary(ctx, 'InPlaceUBB', ubb, lb_method, streaming_pattern=inplace_pattern,
after_collision=True, target=target, namespace=namespace)
- generate_alternating_lbm_boundary(ctx, 'InPlaceOutflow', outflow_inplace, lb_method, streaming_pattern=inplace_pattern,
+ generate_alternating_lbm_boundary(ctx, 'InPlaceOutflow', outflow_inplace, lb_method,
+ streaming_pattern=inplace_pattern,
after_collision=True, target=target, namespace=namespace)
generate_sweep(ctx, 'InPlaceInit', init_kernel_inplace, target=target, namespace=namespace)
diff --git a/tests/lbm/codegen/InplaceStreamingCodegen2D.prm b/tests/lbm/codegen/InplaceStreamingCodegen2D.prm
deleted file mode 100644
index 08b9584d69ea211bc4cbbac0c85b70980843ade9..0000000000000000000000000000000000000000
--- a/tests/lbm/codegen/InplaceStreamingCodegen2D.prm
+++ /dev/null
@@ -1,33 +0,0 @@
-
-Parameters
-{
- timesteps 50;
- vtkWriteFrequency 0;
-}
-
-DomainSetup
-{
- blocks < 1, 1, 1 >;
- cellsPerBlock < 30, 30, 1 >;
- periodic < 0, 0, 0 >;
-}
-
-Boundaries
-{
- Border { direction W;
- walldistance -1;
- flag UBB;
- ghostLayersToInitialize 0; }
- Border { direction E;
- walldistance -1;
- flag Outflow;
- ghostLayersToInitialize 0; }
- Border { direction N;
- walldistance -1;
- flag NoSlip;
- ghostLayersToInitialize 1; }
- Border { direction S;
- walldistance -1;
- flag NoSlip;
- ghostLayersToInitialize 1; }
-}
diff --git a/tests/lbm/codegen/LbCodeGenerationExample.cpp b/tests/lbm/codegen/LbCodeGenerationExample.cpp
index 665292f1bdfc0b340c10897f08b920afc78c1b30..8bb6f96f7f5f9510fad1b81eced900bfab009dd6 100644
--- a/tests/lbm/codegen/LbCodeGenerationExample.cpp
+++ b/tests/lbm/codegen/LbCodeGenerationExample.cpp
@@ -19,112 +19,117 @@
//======================================================================================================================
#include "blockforest/all.h"
+
#include "core/all.h"
+
#include "domain_decomposition/all.h"
+
#include "field/all.h"
+
#include "geometry/all.h"
+
#include "gui/all.h"
-#include "timeloop/all.h"
-#include "lbm/field/PdfField.h"
-#include "lbm/field/AddToStorage.h"
#include "lbm/communication/PdfFieldPackInfo.h"
+#include "lbm/field/AddToStorage.h"
+#include "lbm/field/PdfField.h"
#include "lbm/gui/Connection.h"
#include "lbm/vtk/VTKOutput.h"
-#include "LbCodeGenerationExample_UBB.h"
-#include "LbCodeGenerationExample_NoSlip.h"
-#include "LbCodeGenerationExample_LatticeModel.h"
+#include "timeloop/all.h"
+// include the generated header file. It includes all generated classes
+#include "LbCodeGenerationExample.h"
using namespace walberla;
typedef lbm::LbCodeGenerationExample_LatticeModel LatticeModel_T;
-typedef LatticeModel_T::Stencil Stencil_T;
-typedef LatticeModel_T::CommunicationStencil CommunicationStencil_T;
-typedef lbm::PdfField< LatticeModel_T > PdfField_T;
+typedef LatticeModel_T::Stencil Stencil_T;
+typedef LatticeModel_T::CommunicationStencil CommunicationStencil_T;
+typedef lbm::PdfField< LatticeModel_T > PdfField_T;
typedef GhostLayerField< real_t, LatticeModel_T::Stencil::D > VectorField_T;
typedef GhostLayerField< real_t, 1 > ScalarField_T;
-typedef walberla::uint8_t flag_t;
-typedef FlagField< flag_t > FlagField_T;
+typedef walberla::uint8_t flag_t;
+typedef FlagField< flag_t > FlagField_T;
-
-
-int main( int argc, char ** argv )
+int main(int argc, char** argv)
{
- walberla::Environment walberlaEnv( argc, argv );
+ walberla::Environment walberlaEnv(argc, argv);
- auto blocks = blockforest::createUniformBlockGridFromConfig( walberlaEnv.config() );
+ auto blocks = blockforest::createUniformBlockGridFromConfig(walberlaEnv.config());
// read parameters
- auto parameters = walberlaEnv.config()->getOneBlock( "Parameters" );
+ auto parameters = walberlaEnv.config()->getOneBlock("Parameters");
- const real_t omega = parameters.getParameter< real_t > ( "omega", real_c( 1.4 ) );
- const Vector3<real_t> initialVelocity = parameters.getParameter< Vector3<real_t> >( "initialVelocity", Vector3<real_t>() );
- const uint_t timesteps = parameters.getParameter< uint_t > ( "timesteps", uint_c( 10 ) );
+ const real_t omega = parameters.getParameter< real_t >("omega", real_c(1.4));
+ const Vector3< real_t > initialVelocity =
+ parameters.getParameter< Vector3< real_t > >("initialVelocity", Vector3< real_t >());
+ const uint_t timesteps = parameters.getParameter< uint_t >("timesteps", uint_c(10));
- const double remainingTimeLoggerFrequency = parameters.getParameter< double >( "remainingTimeLoggerFrequency", 3.0 ); // in seconds
+ const double remainingTimeLoggerFrequency =
+ parameters.getParameter< double >("remainingTimeLoggerFrequency", 3.0); // in seconds
// create fields
- BlockDataID forceFieldId = field::addToStorage<VectorField_T>( blocks, "Force", real_t( 0.0 ));
- BlockDataID velFieldId = field::addToStorage<VectorField_T>( blocks, "Velocity", real_t( 0.0 ));
- BlockDataID omegaFieldId = field::addToStorage<ScalarField_T>( blocks, "Omega", real_t( 0.0 ));
+ BlockDataID forceFieldId = field::addToStorage< VectorField_T >(blocks, "Force", real_t(0.0), field::fzyx);
+ BlockDataID velFieldId = field::addToStorage< VectorField_T >(blocks, "Velocity", real_t(0.0), field::fzyx);
+ BlockDataID omegaFieldId = field::addToStorage< ScalarField_T >(blocks, "Omega", real_t(0.0), field::fzyx);
- LatticeModel_T latticeModel = LatticeModel_T( forceFieldId, omegaFieldId, velFieldId, omega );
- BlockDataID pdfFieldId = lbm::addPdfFieldToStorage( blocks, "pdf field", latticeModel, initialVelocity, real_t(1) );
- BlockDataID flagFieldId = field::addFlagFieldToStorage< FlagField_T >( blocks, "flag field" );
+ LatticeModel_T latticeModel = LatticeModel_T(forceFieldId, omegaFieldId, velFieldId, omega);
+ BlockDataID pdfFieldId =
+ lbm::addPdfFieldToStorage(blocks, "pdf field", latticeModel, initialVelocity, real_t(1), field::fzyx);
+ BlockDataID flagFieldId = field::addFlagFieldToStorage< FlagField_T >(blocks, "flag field");
// create and initialize boundary handling
- const FlagUID fluidFlagUID( "Fluid" );
-
+ const FlagUID fluidFlagUID("Fluid");
- auto boundariesConfig = walberlaEnv.config()->getOneBlock( "Boundaries" );
+ auto boundariesConfig = walberlaEnv.config()->getOneBlock("Boundaries");
lbm::LbCodeGenerationExample_UBB ubb(blocks, pdfFieldId);
lbm::LbCodeGenerationExample_NoSlip noSlip(blocks, pdfFieldId);
- geometry::initBoundaryHandling<FlagField_T>(*blocks, flagFieldId, boundariesConfig);
- geometry::setNonBoundaryCellsToDomain<FlagField_T>(*blocks, flagFieldId, fluidFlagUID);
+ geometry::initBoundaryHandling< FlagField_T >(*blocks, flagFieldId, boundariesConfig);
+ geometry::setNonBoundaryCellsToDomain< FlagField_T >(*blocks, flagFieldId, fluidFlagUID);
- ubb.fillFromFlagField<FlagField_T>( blocks, flagFieldId, FlagUID("UBB"), fluidFlagUID );
- noSlip.fillFromFlagField<FlagField_T>( blocks, flagFieldId, FlagUID("NoSlip"), fluidFlagUID );
+ ubb.fillFromFlagField< FlagField_T >(blocks, flagFieldId, FlagUID("UBB"), fluidFlagUID);
+ noSlip.fillFromFlagField< FlagField_T >(blocks, flagFieldId, FlagUID("NoSlip"), fluidFlagUID);
// create time loop
- SweepTimeloop timeloop( blocks->getBlockStorage(), timesteps );
+ SweepTimeloop timeloop(blocks->getBlockStorage(), timesteps);
// create communication for PdfField
- blockforest::communication::UniformBufferedScheme< CommunicationStencil_T > communication( blocks );
- communication.addPackInfo( make_shared< lbm::PdfFieldPackInfo< LatticeModel_T > >( pdfFieldId ) );
+ blockforest::communication::UniformBufferedScheme< CommunicationStencil_T > communication(blocks);
+ communication.addPackInfo(make_shared< lbm::PdfFieldPackInfo< LatticeModel_T > >(pdfFieldId));
// add LBM sweep and communication to time loop
- timeloop.add() << BeforeFunction( communication, "communication" )
- << Sweep( noSlip, "noSlip boundary" );
- timeloop.add() << Sweep( ubb, "ubb boundary" );
- timeloop.add() << Sweep( LatticeModel_T::Sweep( pdfFieldId ), "LB stream & collide" );
+ timeloop.add() << BeforeFunction(communication, "communication") << Sweep(noSlip, "noSlip boundary");
+ timeloop.add() << Sweep(ubb, "ubb boundary");
+ timeloop.add() << Sweep(LatticeModel_T::Sweep(pdfFieldId), "LB stream & collide");
// LBM stability check
- timeloop.addFuncAfterTimeStep( makeSharedFunctor( field::makeStabilityChecker< PdfField_T, FlagField_T >( walberlaEnv.config(), blocks, pdfFieldId,
- flagFieldId, fluidFlagUID ) ),
- "LBM stability check" );
+ timeloop.addFuncAfterTimeStep(makeSharedFunctor(field::makeStabilityChecker< PdfField_T, FlagField_T >(
+ walberlaEnv.config(), blocks, pdfFieldId, flagFieldId, fluidFlagUID)),
+ "LBM stability check");
// log remaining time
- timeloop.addFuncAfterTimeStep( timing::RemainingTimeLogger( timeloop.getNrOfTimeSteps(), remainingTimeLoggerFrequency ), "remaining time logger" );
+ timeloop.addFuncAfterTimeStep(timing::RemainingTimeLogger(timeloop.getNrOfTimeSteps(), remainingTimeLoggerFrequency),
+ "remaining time logger");
// add VTK output to time loop
- lbm::VTKOutput< LatticeModel_T, FlagField_T >::addToTimeloop( timeloop, blocks, walberlaEnv.config(), pdfFieldId, flagFieldId, fluidFlagUID );
+ lbm::VTKOutput< LatticeModel_T, FlagField_T >::addToTimeloop(timeloop, blocks, walberlaEnv.config(), pdfFieldId,
+ flagFieldId, fluidFlagUID);
// create adaptors, so that the GUI also displays density and velocity
// adaptors are like fields with the difference that they do not store values
// but calculate the values based on other fields ( here the PdfField )
- field::addFieldAdaptor<lbm::Adaptor<LatticeModel_T>::Density> ( blocks, pdfFieldId, "DensityAdaptor" );
- field::addFieldAdaptor<lbm::Adaptor<LatticeModel_T>::VelocityVector>( blocks, pdfFieldId, "VelocityAdaptor" );
+ field::addFieldAdaptor< lbm::Adaptor< LatticeModel_T >::Density >(blocks, pdfFieldId, "DensityAdaptor");
+ field::addFieldAdaptor< lbm::Adaptor< LatticeModel_T >::VelocityVector >(blocks, pdfFieldId, "VelocityAdaptor");
- if( parameters.getParameter<bool>( "useGui", false ) )
+ if (parameters.getParameter< bool >("useGui", false))
{
- GUI gui ( timeloop, blocks, argc, argv );
- lbm::connectToGui<LatticeModel_T> ( gui );
+ GUI gui(timeloop, blocks, argc, argv);
+ lbm::connectToGui< LatticeModel_T >(gui);
gui.run();
}
else
diff --git a/tests/lbm/codegen/LbCodeGenerationExample.py b/tests/lbm/codegen/LbCodeGenerationExample.py
index bdf992612d5521c4b9e24e4eeded2d6f84b3e145..182a501d466dbd9cce0f1b8f1a77e96ce43076e1 100644
--- a/tests/lbm/codegen/LbCodeGenerationExample.py
+++ b/tests/lbm/codegen/LbCodeGenerationExample.py
@@ -2,27 +2,24 @@ import sympy as sp
import pystencils as ps
from lbmpy.creationfunctions import create_lb_collision_rule
from lbmpy.boundaries import NoSlip, UBB
-from pystencils_walberla import CodeGeneration
+from lbmpy import LBMConfig, LBMOptimisation, Stencil, Method, LBStencil
+from pystencils_walberla import CodeGeneration, generate_info_header
from lbmpy_walberla import RefinementScaling, generate_boundary, generate_lattice_model
with CodeGeneration() as ctx:
omega, omega_free = sp.symbols("omega, omega_free")
- force_field, vel_field, omega_out = ps.fields("force(3), velocity(3), omega_out: [3D]", layout='zyxf')
+ force_field, vel_field, omega_out = ps.fields("force(3), velocity(3), omega_out: [3D]", layout='fzyx')
+
+ stencil = LBStencil(Stencil.D3Q19)
+ lbm_config = LBMConfig(stencil=stencil, method=Method.MRT, entropic=True,
+ compressible=True, omega_output_field=omega_out,
+ force=force_field.center_vector, output={'velocity': vel_field},
+ relaxation_rates=[omega, omega, omega_free, omega_free, omega_free, omega_free])
+
+ lbm_opt = LBMOptimisation(cse_global=True)
# the collision rule of the LB method where the some advanced features
- collision_rule = create_lb_collision_rule(
- stencil='D3Q19', compressible=True,
- method='mrt', relaxation_rates=[omega, omega, omega_free, omega_free, omega_free, omega_free],
- entropic=True, # entropic method where second omega is chosen s.t. entropy condition
- omega_output_field=omega_out, # scalar field where automatically chosen omega of entropic or
- # Smagorinsky method is written to
- force=force_field.center_vector, # read forces for each lattice cell from an external force field
- # that is initialized and changed in C++ app
- output={'velocity': vel_field}, # write macroscopic velocity to field in every time step
- # useful for coupling multiple LB methods,
- # e.g. hydrodynamic to advection/diffusion LBM
- optimization={'cse_global': True}
- )
+ collision_rule = create_lb_collision_rule(lbm_config=lbm_config, lbm_optimisation=lbm_opt)
# the refinement scaling object describes how certain parameters are scaled across grid scales
# there are two default scaling behaviors available for relaxation rates and forces:
@@ -32,6 +29,10 @@ with CodeGeneration() as ctx:
# generate lattice model and (optionally) boundary conditions
# for CPU simulations waLBerla's internal boundary handling can be used as well
- generate_lattice_model(ctx, 'LbCodeGenerationExample_LatticeModel', collision_rule, refinement_scaling=scaling)
+ # If the field layout 'fzyx' is chosen vectorisation is usually possible. The default layout 'zyxf' allows only
+ # for vectorisation on AVX512 due to scatter and gather intrinsics
+ generate_lattice_model(ctx, 'LbCodeGenerationExample_LatticeModel', collision_rule,
+ field_layout='fzyx', refinement_scaling=scaling)
generate_boundary(ctx, 'LbCodeGenerationExample_UBB', UBB([0.05, 0, 0]), collision_rule.method)
generate_boundary(ctx, 'LbCodeGenerationExample_NoSlip', NoSlip(), collision_rule.method)
+ generate_info_header(ctx, 'LbCodeGenerationExample')