diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 6c1361b46..e57994633 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -32,108 +32,61 @@ on: release: types: [published] - -env: - HOMEBREW_NO_ANALYTICS: "ON" # Make Homebrew installation a little quicker - HOMEBREW_NO_AUTO_UPDATE: "ON" - HOMEBREW_NO_BOTTLE_SOURCE_FALLBACK: "ON" - HOMEBREW_NO_GITHUB_API: "ON" - HOMEBREW_NO_INSTALL_CLEANUP: "ON" - CIBW_SKIP: "pp* *i686*" # skip building for PyPy - CIBW_ARCHS_MACOS: x86_64 - CIBW_ARCHS_LINUX: x86_64 # ppc64le # uncomment to enable powerPC build - CIBW_ENVIRONMENT_MACOS: PATH="$(brew --prefix)/opt/make/libexec/gnubin:$PATH" - MACOSX_DEPLOYMENT_TARGET: "10.09" - - jobs: - build_wheels: - name: Build wheels on ${{ matrix.os }} - runs-on: ${{ matrix.os }} - strategy: - fail-fast: false - matrix: - os: [ubuntu-22.04, macos-12] - + build_dists: + name: Build Distributions + runs-on: ubuntu-22.04 steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 + with: + python-version: '3.9' - - name: Install cibuildwheel - run: python -m pip install cibuildwheel>=2.12.3 + - name: Install build + run: python -m pip install 'build>=1.2.2,<2' - name: Install build-essentials - if: contains(matrix.os, 'ubuntu') run: | sudo add-apt-repository ppa:ubuntu-toolchain-r/test sudo apt-get update - sudo apt-get install -y build-essential - sudo apt-get install -y wget + sudo apt-get install -y build-essential wget - - name: Install GNU make for MacOS - if: contains(matrix.os, 'macos') - run: brew install make || true + - name: Build Distributions + run: python -m build . - - name: list target wheels - run: | - python -m cibuildwheel . --print-build-identifiers - - - name: Build wheels - run: python -m cibuildwheel --output-dir wheelhouse - env: - CIBW_ENVIRONMENT_MACOS: PATH="$(brew --prefix)/opt/make/libexec/gnubin:$PATH" - MACOSX_DEPLOYMENT_TARGET: "10.09" - - - uses: actions/upload-artifact@v2 + - uses: actions/upload-artifact@v3 with: - path: ./wheelhouse/*.whl - - - build_sdist: - name: Build source distribution - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - - uses: actions/setup-python@v5 - name: Install Python - with: - python-version: '3.9' - - - name: Build sdist - run: | - python -m pip install cmake>=3.13 - python setup.py sdist - - - uses: actions/upload-artifact@v2 - with: - path: dist/*.tar.gz + name: distributables + path: ./dist/* upload_pypi: - needs: [build_wheels, build_sdist] - runs-on: ubuntu-latest + needs: [build_dists] + runs-on: ubuntu-22.04 steps: - - uses: actions/download-artifact@v2 + - uses: actions/download-artifact@v3 with: - name: artifact + name: distributables path: dist - uses: pypa/gh-action-pypi-publish@release/v1 with: user: __token__ password: ${{ secrets.PYPI }} - #repository_url: https://test.pypi.org/legacy/ - + # repository-url: https://test.pypi.org/legacy/ createPullRequest: - runs-on: ubuntu-latest + needs: [upload_pypi] + runs-on: ubuntu-22.04 steps: - name: Checkout code uses: actions/checkout@v4 - name: Create pull request run: | - gh pr create -B develop -H master --title 'Merge master into develop' --body 'This PR brings develop up to date with master for release.' + gh pr create -B develop \ + -H master \ + --title 'Merge master into develop' \ + --body 'This PR brings develop up to date with master for release.' env: GH_TOKEN: ${{ github.token }} diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml index 2e3463e5b..e3c808410 100644 --- a/.github/workflows/run_tests.yml +++ b/.github/workflows/run_tests.yml @@ -45,10 +45,11 @@ env: HOMEBREW_NO_GITHUB_API: "ON" HOMEBREW_NO_INSTALL_CLEANUP: "ON" DEBIAN_FRONTEND: "noninteractive" # Disable interactive apt install sessions + GIT_CLONE_PROTECTION_ACTIVE: false jobs: run_tests: - name: Run tests ${{ matrix.subset }} with ${{ matrix.os }}, Python ${{ matrix.py_v}}, RedisAI ${{ matrix.rai }} + name: Run tests ${{ matrix.subset }} with ${{ matrix.os }}, Python ${{ matrix.py_v}} runs-on: ${{ matrix.os }} strategy: fail-fast: false @@ -62,9 +63,6 @@ jobs: - os: macos-14 py_v: "3.9" - env: - SMARTSIM_REDISAI: ${{ matrix.rai }} - steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 @@ -108,19 +106,13 @@ jobs: - name: Install SmartSim (with ML backends) run: | python -m pip install git+https://github.com/CrayLabs/SmartRedis.git@develop#egg=smartredis - python -m pip install .[dev,ml] - - - name: Install ML Runtimes with Smart (with pt, tf, and onnx support) - if: contains( matrix.os, 'ubuntu' ) || contains( matrix.os, 'macos-12') - run: smart build --device cpu --onnx -v + python -m pip install .[dev,mypy] - - name: Install ML Runtimes with Smart (no ONNX,TF on Apple Silicon) - if: contains( matrix.os, 'macos-14' ) - run: smart build --device cpu --no_tf -v + - name: Install ML Runtimes + run: smart build --device cpu -v - name: Run mypy run: | - python -m pip install .[mypy] make check-mypy - name: Run Pylint @@ -164,7 +156,7 @@ jobs: retention-days: 5 - name: Upload Pytest coverage to Codecov - uses: codecov/codecov-action@v3.1.4 + uses: codecov/codecov-action@v4.5.0 with: fail_ci_if_error: false files: ./coverage.xml diff --git a/.gitignore b/.gitignore index 77b91d586..97132aff7 100644 --- a/.gitignore +++ b/.gitignore @@ -12,6 +12,7 @@ tests/test_output # Dependencies smartsim/_core/.third-party smartsim/_core/.dragon +smartsim/_core/build # Docs _build diff --git a/.readthedocs.yaml b/.readthedocs.yaml index cecdfe3bf..88f270ba7 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -23,7 +23,7 @@ build: - git clone --depth 1 https://github.com/CrayLabs/SmartRedis.git smartredis - git clone --depth 1 https://github.com/CrayLabs/SmartDashboard.git smartdashboard post_create_environment: - - python -m pip install .[dev] + - python -m pip install .[dev,docs] - cd smartredis; python -m pip install . - cd smartredis/doc; doxygen Doxyfile_c; doxygen Doxyfile_cpp; doxygen Doxyfile_fortran - ln -s smartredis/examples ./examples @@ -37,7 +37,3 @@ build: sphinx: configuration: doc/conf.py fail_on_warning: true - -python: - install: - - requirements: doc/requirements-doc.txt \ No newline at end of file diff --git a/.wci.yml b/.wci.yml index 6194f1939..cf53334c3 100644 --- a/.wci.yml +++ b/.wci.yml @@ -22,8 +22,8 @@ language: Python release: - version: 0.7.0 - date: 2024-05-14 + version: 0.8.0 + date: 2024-09-25 documentation: general: https://www.craylabs.org/docs/overview.html diff --git a/Makefile b/Makefile index bddbda722..457bb040a 100644 --- a/Makefile +++ b/Makefile @@ -150,11 +150,11 @@ tutorials-dev: @docker compose build tutorials-dev @docker run -p 8888:8888 smartsim-tutorials:dev-latest -# help: tutorials-prod - Build and start a docker container to run the tutorials (v0.7.0) +# help: tutorials-prod - Build and start a docker container to run the tutorials (v0.8.0) .PHONY: tutorials-prod tutorials-prod: @docker compose build tutorials-prod - @docker run -p 8888:8888 smartsim-tutorials:v0.7.0 + @docker run -p 8888:8888 smartsim-tutorials:v0.8.0 # help: diff --git a/README.md b/README.md index c0986042e..610d6608c 100644 --- a/README.md +++ b/README.md @@ -643,11 +643,11 @@ from C, C++, Fortran and Python with the SmartRedis Clients: 1.2.7 PyTorch - 2.0.1 + 2.1.0 TensorFlow\Keras - 2.13.1 + 2.15.0 ONNX diff --git a/conftest.py b/conftest.py index b0457522c..991c0d17b 100644 --- a/conftest.py +++ b/conftest.py @@ -120,7 +120,7 @@ def print_test_configuration() -> None: def pytest_configure() -> None: pytest.test_launcher = test_launcher - pytest.wlm_options = ["slurm", "pbs", "lsf", "pals", "dragon"] + pytest.wlm_options = ["slurm", "pbs", "lsf", "pals", "dragon", "sge"] account = get_account() pytest.test_account = account pytest.test_device = test_device diff --git a/doc/_static/version_names.json b/doc/_static/version_names.json index bc095f84a..8b127e586 100644 --- a/doc/_static/version_names.json +++ b/doc/_static/version_names.json @@ -1,7 +1,8 @@ { "version_names":[ "develop (unstable)", - "0.7.0 (stable)", + "0.8.0 (stable)", + "0.7.0", "0.6.2", "0.6.1", "0.6.0", @@ -15,6 +16,7 @@ "version_urls": [ "https://www.craylabs.org/develop/overview.html", "https://www.craylabs.org/docs/overview.html", + "https://www.craylabs.org/docs/versions/0.7.0/overview.html", "https://www.craylabs.org/docs/versions/0.6.2/overview.html", "https://www.craylabs.org/docs/versions/0.6.1/overview.html", "https://www.craylabs.org/docs/versions/0.6.0/overview.html", diff --git a/doc/changelog.md b/doc/changelog.md index 73ea36511..179f4cf26 100644 --- a/doc/changelog.md +++ b/doc/changelog.md @@ -9,12 +9,128 @@ Jump to: ## SmartSim +### 0.8.0 + +Released on 27 September, 2024 + +Description + +- Add instructions for Frontier to set the MIOPEN cache +- Refine Frontier documentation for proper use of miniforge3 +- Refactor to the RedisAI build to allow more flexibility in versions + and sources of ML backends +- Add Dockerfiles with GPU support +- Fine grain build support for GPUs +- Update Torch to 2.1.0, Tensorflow to 2.15.0 +- Better error messages in build process +- Allow specifying Model and Ensemble parameters with + number-like types (e.g. numpy types) +- Pin watchdog to 4.x +- Update codecov to 4.5.0 +- Remove build of Redis from setup.py +- Mitigate dependency installation issues +- Fix internal host name representation for Dragon backend +- Make dependencies more discoverable in setup.py +- Add hardware pinning capability when using dragon +- Pin NumPy version to 1.x +- New launcher support for SGE (and similar derivatives) +- Fix test outputs being created in incorrect directory +- Improve support for building SmartSim without ML backends +- Update packaging dependency +- Remove broken oss.redis.com URI blocking documentation generation + +Detailed Notes + +- On Frontier, the MIOPEN cache may need to be set prior to using + RedisAI in the ``smart validate``. The instructions for Frontier + have been updated accordingly. + ([SmartSim-PR727](https://github.com/CrayLabs/SmartSim/pull/727)) +- On Frontier, the recommended way to activate conda environments is + to go through source activate. This also means that ``conda init`` + is not needed. The instructions for Frontier have been updated to + reflect this. + ([SmartSim-PR719](https://github.com/CrayLabs/SmartSim/pull/719)) +- The RedisAIBuilder class was completely overhauled to allow users to + express a wider range of support for hardware/software stacks. This + will be extended to support ROCm, CUDA-11, and CUDA-12. + ([SmartSim-PR669](https://github.com/CrayLabs/SmartSim/pull/669)) +- Versions for each of these packages are no longer specified in an + internal class. Instead a default set of JSON files specifies the + sources and versions. Users can specify their own custom specifications + at smart build time. + ([SmartSim-PR669](https://github.com/CrayLabs/SmartSim/pull/669)) +- Because all build configuration has been moved to static files and all + backends are compiled during `smart build`, SmartSim can now be shipped as a + pure python wheel. + ([SmartSim-PR728](https://github.com/CrayLabs/SmartSim/pull/728)) +- Two new Dockerfiles are now provided (one each for 11.8 and 12.1) that + can be used to build a container to run the tutorials. No HPC support + should be expected at this time + ([SmartSim-PR669](https://github.com/CrayLabs/SmartSim/pull/669)) +- As a result of the previous change, SmartSim now requires C++17 and a + minimum Cuda version of 11.8 in order to build Torch 2.1.0. + ([SmartSim-PR669](https://github.com/CrayLabs/SmartSim/pull/669)) +- Error messages were not being interpolated correctly. This has been + addressed to provide more context when exposing error messages to users. + ([SmartSim-PR669](https://github.com/CrayLabs/SmartSim/pull/669)) +- The serializer would fail if a parameter for a Model or Ensemble + was specified as a numpy dtype. The constructors for these + methods now validate that the input is number-like and convert + them to strings + ([SmartSim-PR676](https://github.com/CrayLabs/SmartSim/pull/676)) +- Pin watchdog to 4.x because v5 introduces new types and requires + updates to the type-checking + ([SmartSim-PR690](https://github.com/CrayLabs/SmartSim/pull/690)) +- Update codecov to 4.5.0 to mitigate GitHub action failure + ([SmartSim-PR657](https://github.com/CrayLabs/SmartSim/pull/657)) +- The builder module was included in setup.py to allow us to ship the + main Redis binaries (not RedisAI) with installs from PyPI. To + allow easier maintenance of this file and enable future complexity + this has been removed. The Redis binaries will thus be built + by users during the `smart build` step +- Installation of mypy or dragon in separate build actions caused + some dependencies (typing_extensions, numpy) to be upgraded and + caused runtime failures. The build actions were tweaked to include + all optional dependencies to be considered by pip during resolution. + Additionally, the numpy version was capped on dragon installations. + ([SmartSim-PR653](https://github.com/CrayLabs/SmartSim/pull/653)) +- setup.py used to define dependencies in a way that was not amenable + to code scanning tools. Direct dependencies now appear directly + in the setup call and the definition of the SmartRedis version + has been removed + ([SmartSim-PR635](https://github.com/CrayLabs/SmartSim/pull/635)) +- The separate definition of dependencies for the docs in + requirements-doc.txt is now defined as an extra. + ([SmartSim-PR635](https://github.com/CrayLabs/SmartSim/pull/635)) +- The new major version release of Numpy is incompatible with modules + compiled against Numpy 1.x. For both SmartSim and SmartRedis we + request a 1.x version of numpy. This is needed in SmartSim because + some of the downstream dependencies request NumPy + ([SmartSim-PR623](https://github.com/CrayLabs/SmartSim/pull/623)) +- SGE is now a supported launcher for SmartSim. Users can now define + BatchSettings which will be monitored by the TaskManager. Additionally, + if the MPI implementation was built with SGE support, Orchestrators can + use `mpirun` without needing to specify the hosts + ([SmartSim-PR610](https://github.com/CrayLabs/SmartSim/pull/610)) +- Ensure outputs from tests are written to temporary `tests/test_output` directory +- Fix an error that would prevent ``smart build`` from moving a successfully + compiled RedisAI shared object to the install location expected by SmartSim + if no ML backend installations were found. Previously, this would effectively + require users to build and install an ML backend to use the SmartSim + orchestrator even if it was not necessary for their workflow. Users can + install SmartSim without ML backends by running + ``smart build --no_tf --no_pt`` and the RedisAI shared object will now be + placed in the expected location. + ([SmartSim-PR601](https://github.com/CrayLabs/SmartSim/pull/601)) +- Fix packaging failures due to deprecated `pkg_resources`. ([SmartSim-PR598](https://github.com/CrayLabs/SmartSim/pull/598)) + ### 0.7.0 Released on 14 May, 2024 Description +- Update tutorials and tutorial containers - Improve Dragon server shutdown - Add dragon runtime installer - Add launcher based on Dragon @@ -64,6 +180,8 @@ Description Detailed Notes +- The tutorials are up-to date with SmartSim and SmartRedis APIs. Additionally, + the tutorial containers' Docker files are updated. ([SmartSim-PR589](https://github.com/CrayLabs/SmartSim/pull/589)) - The Dragon server will now terminate any process which is still running when a request of an immediate shutdown is sent. ([SmartSim-PR582](https://github.com/CrayLabs/SmartSim/pull/582)) - Add `--dragon` option to `smart build`. Install appropriate Dragon diff --git a/doc/conf.py b/doc/conf.py index 932bce013..8f3a9ca63 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -29,7 +29,7 @@ import smartsim version = smartsim.__version__ except ImportError: - version = "0.7.0" + version = "0.8.0" # The full version, including alpha/beta/rc tags release = version diff --git a/doc/dragon.rst b/doc/dragon.rst index 0bf6a8ea3..e19b40e4b 100644 --- a/doc/dragon.rst +++ b/doc/dragon.rst @@ -65,6 +65,34 @@ In the next sections, we detail how Dragon is integrated into SmartSim. For more information on HPC launchers, visit the :ref:`Run Settings` page. +Hardware Pinning +================ + +Dragon also enables users to specify hardware constraints using ``DragonRunSettings``. CPU +and GPU affinity can be specified using the ``DragonRunSettings`` object. The following +example demonstrates how to specify CPU affinity and GPU affinities simultaneously. Note +that affinities are passed as a list of device indices. + +.. code-block:: python + + # Because "dragon" was specified as the launcher during Experiment initialization, + # create_run_settings will return a DragonRunSettings object + rs = exp.create_run_settings(exe="mpi_app", + exe_args=["--option", "value"], + env_vars={"MYVAR": "VALUE"}) + + # Request the first 8 CPUs for this job + rs.set_cpu_affinity(list(range(9))) + + # Request the first two GPUs on the node for this job + rs.set_gpu_affinity([0, 1]) + +.. note:: + + SmartSim launches jobs in the order they are received on the first available + host in a round-robin pattern. To ensure a process is launched on a node with + specific features, configure a hostname constraint. + ================= The Dragon Server ================= diff --git a/doc/installation_instructions/basic.rst b/doc/installation_instructions/basic.rst index 02c17e1fd..226ccb085 100644 --- a/doc/installation_instructions/basic.rst +++ b/doc/installation_instructions/basic.rst @@ -18,7 +18,7 @@ Prerequisites Basic ===== -The base prerequisites to install SmartSim and SmartRedis are: +The base prerequisites to install SmartSim and SmartRedis wtih CPU-only support are: - Python 3.9-3.11 - Pip @@ -27,13 +27,11 @@ The base prerequisites to install SmartSim and SmartRedis are: - C++ compiler - GNU Make > 4.0 - git - - `git-lfs`_ - -.. _git-lfs: https://github.com/git-lfs/git-lfs?utm_source=gitlfs_site&utm_medium=installation_link&utm_campaign=gitlfs .. note:: - GCC 5-9, 11, and 12 is recommended. There are known bugs with GCC 10. + GCC 9, 11-13 is recommended (here are known issues compiling with GCC 10). For + CUDA 11.8, GCC 9 or 11 must be used. .. warning:: @@ -43,66 +41,146 @@ The base prerequisites to install SmartSim and SmartRedis are: `which gcc g++` do not point to Apple Clang. -GPU Support -=========== +ML Library Support +================== -The machine-learning backends have additional requirements in order to -use GPUs for inference +We currently support both Nvidia and AMD GPUs when using RedisAI for GPU inference. The support +for these GPUs often depends on the version of the CUDA or ROCm stack that is availble on your +machine. In _most_ cases, the versions backwards compatible. If you encounter problems, please +contact us and we can build the backend libraries for your desired version of CUDA and ROCm. - - `CUDA Toolkit 11 (tested with 11.8) `_ - - `cuDNN 8 (tested with 8.9.1) `_ - - OS: Linux - - GPU: Nvidia +CPU backends are provided for Apple (both Intel and Apple Silicon) and Linux (x86_64). -Be sure to reference the :ref:`installation notes ` for helpful +Be sure to reference the table below to find which versions of the ML libraries are supported for +your particular platform. Additional, see :ref:`installation notes ` for helpful information regarding various system types before installation. -================== -Supported Versions -================== +Linux +----- +.. tabs:: -.. list-table:: Supported System for Pre-built Wheels - :widths: 50 50 50 50 - :header-rows: 1 - :align: center + .. group-tab:: CUDA 11 + + Additional requirements: + + * GCC <= 11 + * CUDA Toolkit 11.7 or 11.8 + * cuDNN 8.9 + + .. list-table:: Nvidia CUDA 11 + :widths: 50 50 50 50 + :header-rows: 1 + :align: center + + * - Python Versions + - Torch + - Tensorflow + - ONNX Runtime + * - 3.9-3.11 + - 2.3.1 + - 2.14.1 + - 1.17.3 + + .. group-tab:: CUDA 12 + + Additional requirements: + + * CUDA Toolkit 12 + * cuDNN 8.9 + + .. list-table:: Nvidia CUDA 12 + :widths: 50 50 50 50 + :header-rows: 1 + :align: center + + * - Python Versions + - Torch + - Tensorflow + - ONNX Runtime + * - 3.9-3.11 + - 2.3.1 + - 2.17 + - 1.17.3 + + .. group-tab:: ROCm 6 + + .. list-table:: AMD ROCm 6.1 + :widths: 50 50 50 50 + :header-rows: 1 + :align: center + + * - Python Versions + - Torch + - Tensorflow + - ONNX Runtime + * - 3.9-3.11 + - 2.4.1 + - N/A + - N/A + + .. group-tab:: CPU + + .. list-table:: CPU-only + :widths: 50 50 50 50 + :header-rows: 1 + :align: center + + * - Python Versions + - Torch + - Tensorflow + - ONNX Runtime + * - 3.9-3.11 + - 2.4.0 + - 2.15 + - 1.17.3 + +MacOSX +------ - * - Platform - - CPU - - GPU - - Python Versions - * - MacOS - - x86_64, aarch64 - - Not supported - - 3.9 - 3.11 - * - Linux - - x86_64 - - Nvidia - - 3.9 - 3.11 +.. tabs:: + .. group-tab:: Apple Silicon -.. note:: + .. list-table:: Apple Silicon ARM64 (no Metal support) + :widths: 50 50 50 50 + :header-rows: 1 + :align: center - Users have succesfully run SmartSim on Windows using Windows Subsystem for Linux - with Nvidia support. Generally, users should follow the Linux instructions here, - however we make no guarantee or offer of support. + * - Python Versions + - Torch + - Tensorflow + - ONNX Runtime + * - 3.9-3.11 + - 2.4.0 + - 2.17 + - 1.17.3 + .. group-tab:: Intel Mac (x86) -Native support for various machine learning libraries and their -versions is dictated by our dependency on RedisAI_ 1.2.7. + .. list-table:: CPU-only + :widths: 50 50 50 50 + :header-rows: 1 + :align: center -+------------------+----------+-------------+---------------+ -| RedisAI | PyTorch | Tensorflow | ONNX Runtime | -+==================+==========+=============+===============+ -| 1.2.7 (default) | 2.0.1 | 2.13.1 | 1.16.3 | -+------------------+----------+-------------+---------------+ + * - Python Versions + - Torch + - Tensorflow + - ONNX Runtime + * - 3.9-3.11 + - 2.2.0 + - 2.15 + - 1.17.3 -.. warning:: - On Apple Silicon, only the PyTorch backend is supported for now. Please contact us - if you need support for other backends +.. note:: -TensorFlow_ 2.0 and Keras_ are supported through `graph freezing`_. + Users have succesfully run SmartSim on Windows using Windows Subsystem for Linux + with Nvidia support. Generally, users should follow the Linux instructions here, + however we make no guarantee or offer of support. + + +TensorFlow_ and Keras_ are supported through `graph freezing`_. ScikitLearn_ and Spark_ models are supported by SmartSim as well through the use of the ONNX_ runtime (which is not built by @@ -167,21 +245,8 @@ and install SmartSim from PyPI with the following command: pip install smartsim -If you would like SmartSim to also install python machine learning libraries -that can be used outside SmartSim to build SmartSim-compatible models, you -can request their installation through the ``[ml]`` optional dependencies, -as follows: - -.. code-block:: bash - - # For bash - pip install smartsim[ml] - # For zsh - pip install smartsim\[ml\] - -At this point, SmartSim is installed and can be used for more basic features. -If you want to use the machine learning features of SmartSim, you will need -to install the ML backends in the section below. +At this point, SmartSim can be used for describing and launching experiments, but +without any database/feature store functionality which allows for ML-enabled workflows. Step 2: Build SmartSim @@ -198,19 +263,19 @@ To see all the installation options: smart --help -CPU Install ------------ - -To install the default ML backends for CPU, run - .. code-block:: bash # run one of the following - smart build --device cpu # install PT and TF for cpu - smart build --device cpu --onnx # install all backends (PT, TF, ONNX) on cpu + smart build --device cpu # For unaccelerated AI/ML loads + smart build --device cuda118 # Nvidia Accelerator with CUDA 11.8 + smart build --device cuda125 # Nvidia Accelerator with CUDA 12.5 + smart build --device rocm57 # AMD Accelerator with ROCm 5.7.0 -By default, ``smart`` will install PyTorch and TensorFlow backends -for use in SmartSim. +By default, ``smart`` will install all backends available for the specified accelerator +_and_ the compatible versions of the Python packages associated with the backends. To +disable support for a specific backend, ``smart build`` accepts the flags +``--skip-torch``, ``--skip-tensorflow``, ``--skip-onnx`` which can also be used in +combination. .. note:: @@ -218,19 +283,6 @@ for use in SmartSim. all of the previous installs for the ML backends and ``smart clobber`` will remove all pre-built dependencies as well as the ML backends. - -GPU Install ------------ - -With the proper environment setup (see :ref:`GPU support`) the only difference -to building SmartSim with GPU support is to specify a different ``device`` - -.. code-block:: bash - - # run one of the following - smart build --device gpu # install PT and TF for gpu - smart build --device gpu --onnx # install all backends (PT, TF, ONNX) on gpu - .. note:: GPU builds can be troublesome due to the way that RedisAI and the ML-package @@ -251,9 +303,7 @@ For example, to install dragon alongside the RedisAI CPU backends, you can run .. code-block:: bash - # run one of the following smart build --device cpu --dragon # install Dragon, PT and TF for cpu - smart build --device cpu --onnx --dragon # install Dragon and all backends (PT, TF, ONNX) on cpu .. note:: Dragon is only supported on Linux systems. For further information, you @@ -319,35 +369,11 @@ source remains at the site of the clone instead of in site-packages. .. code-block:: bash cd smartsim - pip install -e .[dev,ml] # for bash users - pip install -e .\[dev,ml\] # for zsh users - -Use the now installed ``smart`` cli to install the machine learning runtimes and dragon. - -.. tabs:: - - .. tab:: Linux - - .. code-block:: bash - - # run one of the following - smart build --device cpu --onnx --dragon # install with cpu-only support - smart build --device gpu --onnx --dragon # install with both cpu and gpu support - - - .. tab:: MacOS (Intel x64) - - .. code-block:: bash - - smart build --device cpu --onnx # install all backends (PT, TF, ONNX) on gpu - - - .. tab:: MacOS (Apple Silicon) - - .. code-block:: bash - - smart build --device cpu --no_tf # Only install PyTorch (TF/ONNX unsupported) + pip install -e .[dev] # for bash users + pip install -e ".[dev]" # for zsh users +Use the now installed ``smart`` cli to install the machine learning runtimes and +dragon. Referring to "Step 2: Build SmartSim above". Build the SmartRedis library ============================ diff --git a/doc/installation_instructions/platform.rst b/doc/installation_instructions/platform.rst index 086fc2951..057a25d87 100644 --- a/doc/installation_instructions/platform.rst +++ b/doc/installation_instructions/platform.rst @@ -12,6 +12,8 @@ that SmartSim may be used on. .. include:: platform/frontier.rst +.. include:: platform/perlmutter.rst + .. include:: platform/cray.rst .. include:: platform/ncar-cheyenne.rst diff --git a/doc/installation_instructions/platform/frontier.rst b/doc/installation_instructions/platform/frontier.rst index e23856155..9b05061fe 100644 --- a/doc/installation_instructions/platform/frontier.rst +++ b/doc/installation_instructions/platform/frontier.rst @@ -1,23 +1,15 @@ OLCF Frontier ============= -Summary -------- - -Frontier is an AMD CPU/AMD GPU system. - -As of 2023-07-06, users can use the following instructions, however we -anticipate that all the SmartSim dependencies will be available system-wide via -the modules system. - Known limitations ----------------- We are continually working on getting all the features of SmartSim working on Frontier, however we do have some known limitations: -* For now, only Torch models are supported. We are working to find a recipe to - install Tensorflow with ROCm support from scratch +* For now, only Torch models are supported. If you need Tensorflow or ONNX + support please contact us +* All SmartSim experiments must be run from Lustre, _not_ your home directory * The colocated database will fail without specifying ``custom_pinning``. This is because the default pinning assumes that processor 0 is available, but the 'low-noise' default on Frontier reserves the processor on each NUMA node. @@ -30,8 +22,8 @@ Frontier, however we do have some known limitations: Please raise an issue in the SmartSim Github or contact the developers if the above issues are affecting your workflow or if you find any other problems. -Build process -------------- +One-time Setup +-------------- To install the SmartRedis and SmartSim python packages on Frontier, please follow these instructions, being sure to set the following variables @@ -39,25 +31,20 @@ these instructions, being sure to set the following variables .. code:: bash export PROJECT_NAME=CHANGE_ME - export VENV_NAME=CHANGE_ME -Then continue with the install: +**Step 1:** Create and activate a virtual environment for SmartSim: .. code:: bash - module load PrgEnv-gnu-amd git-lfs cmake cray-python - module unload xalt amd-mixed - module load rocm/4.5.2 - export CC=gcc - export CXX=g++ + module load PrgEnv-gnu miniforge3 rocm/6.1.3 export SCRATCH=/lustre/orion/$PROJECT_NAME/scratch/$USER/ - export VENV_HOME=$SCRATCH/$VENV_NAME/ + conda create -n smartsim python=3.11 + source activate smartsim - python3 -m venv $VENV_HOME - source $VENV_HOME/bin/activate - pip install torch==1.11.0+rocm4.5.2 torchvision==0.12.0+rocm4.5.2 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/rocm4.5.2 +**Step 2:** Build the SmartRedis C++ and Fortran libraries: +.. code:: bash cd $SCRATCH git clone https://github.com/CrayLabs/SmartRedis.git @@ -65,57 +52,61 @@ Then continue with the install: make lib-with-fortran pip install . - # Download SmartSim and site-specific files +**Step 3:** Install SmartSim in the conda environment: + +.. code:: bash + cd $SCRATCH - git clone https://github.com/CrayLabs/site-deployments.git - git clone https://github.com/CrayLabs/SmartSim.git - cd SmartSim - pip install -e .[dev] + pip install git+https://github.com/CrayLabs/SmartSim.git -Next to finish the compilation, we need to manually modify one of the auxiliary -cmake files that comes packaged with Torch +**Step 4:** Build Redis, RedisAI, the backends, and all the Python packages: .. code:: bash - export TORCH_CMAKE_DIR=$(python -c 'import torch;print(torch.utils.cmake_prefix_path)') - # Manual step: modify all references to the 'rocm' directory to rocm-4.5.2 - vim $TORCH_CMAKE_DIR/Caffe2/Caffe2Targets.cmake + smart build --device=rocm-6 -Finally, build Redis (or keydb for a more performant solution), RedisAI, and the -machine-learning backends using: +**Step 5:** Check that SmartSim has been installed and built correctly: .. code:: bash - KEYDB_FLAG="" # set this to --keydb if desired - smart build --device gpu --torch_dir $TORCH_CMAKE_DIR --no_tf -v $(KEYDB_FLAG) + # Optimizations for inference + export MIOPEN_USER_DB_PATH="/tmp/${USER}/my-miopen-cache" + export MIOPEN_CUSTOM_CACHE_DIR=$MIOPEN_USER_DB_PATH + rm -rf $MIOPEN_USER_DB_PATH + mkdir -p $MIOPEN_USER_DB_PATH + + # Run the install validation utility + smart validate --device gpu -Set up environment ------------------- +The following output indicates a successful install: + +.. code:: bash + + [SmartSim] INFO Verifying Tensor Transfer + [SmartSim] INFO Verifying Torch Backend + 16:26:35 login SmartSim[557020:MainThread] INFO Success! + +Post-installation +----------------- Before running SmartSim, the environment should match the one used to -build, and some variables should be set to work around some ROCm PyTorch -issues: +build, and some variables should be set to optimize performance: .. code:: bash # Set these to the same values that were used for install export PROJECT_NAME=CHANGE_ME - export VENV_NAME=CHANGE_ME .. code:: bash - module load PrgEnv-gnu-amd git-lfs cmake cray-python - module unload xalt amd-mixed - module load rocm/4.5.2 + module load PrgEnv-gnu miniforge3 rocm/6.1.3 + source activate smartsim - export SCRATCH=/lustre/orion/$PROJECT_NAME/scratch/$USER/ - export MIOPEN_USER_DB_PATH=/tmp/miopendb/ - export MIOPEN_SYSTEM_DB_PATH=$MIOPEN_USER_DB_PATH - mkdir -p $MIOPEN_USER_DB_PATH - export MIOPEN_DISABLE_CACHE=1 - export VENV_HOME=$SCRATCH/$VENV_NAME/ - source $VENV_HOME/bin/activate - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$VENV_HOME/lib/python3.9/site-packages/torch/lib + # Optimizations for inference + export MIOPEN_USER_DB_PATH="/tmp/${USER}/my-miopen-cache" + export MIOPEN_CUSTOM_CACHE_DIR=${MIOPEN_USER_DB_PATH} + rm -rf ${MIOPEN_USER_DB_PATH} + mkdir -p ${MIOPEN_USER_DB_PATH} Binding DBs to Slingshot ------------------------ @@ -129,17 +120,3 @@ following way: exp = Experiment("my_exp", launcher="slurm") orc = exp.create_database(db_nodes=3, interface=["hsn0","hsn1","hsn2","hsn3"], single_cmd=True) - -Running tests -------------- - -The same environment set to run SmartSim must be set to run tests. The -environment variables needed to run the test suite are the following: - -.. code:: bash - - export SMARTSIM_TEST_ACCOUNT=PROJECT_NAME # Change this to above - export SMARTSIM_TEST_LAUNCHER=slurm - export SMARTSIM_TEST_DEVICE=gpu - export SMARTSIM_TEST_PORT=6789 - export SMARTSIM_TEST_INTERFACE="hsn0,hsn1,hsn2,hsn3" diff --git a/doc/installation_instructions/platform/olcf-summit.rst b/doc/installation_instructions/platform/olcf-summit.rst index 236d15054..07be24eec 100644 --- a/doc/installation_instructions/platform/olcf-summit.rst +++ b/doc/installation_instructions/platform/olcf-summit.rst @@ -6,10 +6,10 @@ Since SmartSim does not have a built PowerPC build, the build steps for an IBM system are slightly different than other systems. Luckily for us, a conda channel with all relevant packages is maintained as part -of the `OpenCE `_ initiative. Users can follow these -instructions to get a working SmartSim build with PyTorch and TensorFlow for GPU -on Summit. Note that SmartSim and SmartRedis will be downloaded to the working -directory from which these instructions are executed. +of the `OpenCE `_ +initiative. Users can follow these instructions to get a working SmartSim build +with PyTorch and TensorFlow for GPU on Summit. Note that SmartSim and SmartRedis +will be downloaded to the working directory from which these instructions are executed. Note that the available PyTorch version (1.10.2) does not match the one expected by RedisAI 1.2.7 (1.11): it is still compatible and should @@ -19,7 +19,7 @@ into problems. .. code-block:: bash # setup Python and build environment - export ENV_NAME=smartsim-0.7.0 + export ENV_NAME=smartsim-0.8.0 git clone https://github.com/CrayLabs/SmartRedis.git smartredis git clone https://github.com/CrayLabs/SmartSim.git smartsim conda config --prepend channels https://ftp.osuosl.org/pub/open-ce/1.6.1/ diff --git a/doc/installation_instructions/platform/perlmutter.rst b/doc/installation_instructions/platform/perlmutter.rst new file mode 100644 index 000000000..71f97a4dc --- /dev/null +++ b/doc/installation_instructions/platform/perlmutter.rst @@ -0,0 +1,64 @@ +NERSC Perlmutter +================ + +One-time Setup +-------------- + +To install SmartSim on Perlmutter, follow these steps: + +**Step 1:** Create and activate a conda environment for SmartSim: + +.. code:: bash + + module load conda cudatoolkit/12.2 cudnn/8.9.3_cuda12 PrgEnv-gnu + conda create -n smartsim python=3.11 + conda activate smartsim + +**Step 2:** Build the SmartRedis C++ and Fortran libraries: + +.. code:: bash + + git clone https://github.com/CrayLabs/SmartRedis.git + cd SmartRedis + make lib-with-fortran + pip install . + cd .. + +**Step 3:** Install SmartSim in the conda environment: + +.. code:: bash + + pip install git+https://github.com/CrayLabs/SmartSim.git + +**Step 4:** Build Redis, RedisAI, the backends, and all the Python packages: + +.. code:: bash + + smart build --device=cuda-12 + +**Step 5:** Check that SmartSim has been installed and built correctly: + +.. code:: bash + + smart validate --device gpu + +The following output indicates a successful install: + +.. code:: bash + + [SmartSim] INFO Verifying Tensor Transfer + [SmartSim] INFO Verifying Torch Backend + [SmartSim] INFO Verifying ONNX Backend + [SmartSim] INFO Verifying TensorFlow Backend + 16:26:35 login SmartSim[557020:MainThread] INFO Success! + +Post-installation +----------------- + +After completing the above steps to install SmartSim in a conda environment, you +can reload the conda environment by running the following commands: + +.. code:: bash + + module load conda cudatoolkit/12.2 cudnn/8.9.3_cuda12 PrgEnv-gnu + conda activate smartsim diff --git a/doc/installation_instructions/site-install.rst b/doc/installation_instructions/site-install.rst index 26ecd6c13..53e0ff8bf 100644 --- a/doc/installation_instructions/site-install.rst +++ b/doc/installation_instructions/site-install.rst @@ -11,5 +11,5 @@ from source with the following steps replacing ``COMPILER_VERSION`` and module use -a /lus/scratch/smartsim/local/modulefiles module load cudatoolkit/11.8 cudnn smartsim-deps/COMPILER_VERSION/SMARTSIM_VERSION - pip install smartsim[ml] - smart build --only_python_packages --device gpu [--onnx] + pip install smartsim + smart build --skip-backends --device gpu [--onnx] diff --git a/doc/requirements-doc.txt b/doc/requirements-doc.txt deleted file mode 100644 index 696881bef..000000000 --- a/doc/requirements-doc.txt +++ /dev/null @@ -1,18 +0,0 @@ -Sphinx==6.2.1 -breathe==4.35.0 -sphinx-fortran==1.1.1 -sphinx-book-theme==1.0.1 -sphinx-copybutton==0.5.2 -sphinx-tabs==3.4.4 -nbsphinx==0.9.3 -docutils==0.18.1 -torch==2.0.1 -tensorflow==2.13.1 -ipython -jinja2==3.1.2 -protobuf -numpy -sphinx-design -pypandoc -sphinx-autodoc-typehints -myst_parser diff --git a/doc/tutorials/getting_started/getting_started.ipynb b/doc/tutorials/getting_started/getting_started.ipynb index 0a5230b0f..e2caf0070 100644 --- a/doc/tutorials/getting_started/getting_started.ipynb +++ b/doc/tutorials/getting_started/getting_started.ipynb @@ -24,7 +24,8 @@ "metadata": {}, "outputs": [], "source": [ - "from smartsim import Experiment" + "import os\n", + "from smartsim import Experiment\n" ] }, { @@ -38,6 +39,7 @@ " * `pbs`\n", " * `lsf`\n", " * `local` (single node/laptops)\n", + " * `dragon`\n", " * `auto`\n", "\n", "If `launcher=\"auto\"` is used, the experiment will attempt to find a launcher on the system, and use the first one it encounters. If a launcher cannot be found or no launcher parameter is provided, the default value of `launcher=\"local\"` will be used. \n", @@ -52,7 +54,7 @@ "outputs": [], "source": [ "# Init Experiment and specify to launch locally\n", - "exp = Experiment(name=\"getting-started\", launcher=\"local\")" + "exp = Experiment(name=\"getting-started\", launcher=\"local\")\n" ] }, { @@ -78,7 +80,7 @@ "settings = exp.create_run_settings(exe=\"echo\", exe_args=\"hello!\", run_command=None)\n", "\n", "# create the simple model instance so we can run it.\n", - "M1 = exp.create_model(name=\"tutorial-model\", run_settings=settings)" + "M1 = exp.create_model(name=\"tutorial-model\", run_settings=settings)\n" ] }, { @@ -101,7 +103,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "00:18:27 e3fbeabfdb3e SmartSim[1408] INFO \n", + "19:17:29 HPE-C02YR4ANLVCJ SmartSim[97173:MainThread] INFO \n", "\n", "=== Launch Summary ===\n", "Experiment: getting-started\n", @@ -112,37 +114,24 @@ "\n", "=== Models ===\n", "tutorial-model\n", - "Executable: /usr/bin/echo\n", + "Executable: /bin/echo\n", "Executable Arguments: hello!\n", "\n", "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "00:18:39 e3fbeabfdb3e SmartSim[1408] INFO tutorial-model(1428): Completed\n" + "\n", + "19:17:32 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO tutorial-model(97213): SmartSimStatus.STATUS_COMPLETED\n" ] } ], "source": [ - "exp.start(M1, block=True, summary=True)" + "exp.start(M1, block=True, summary=True)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The model has completed. Let's look at the content of the current working directory. We can see that two files, `tutorial-model.out` and `tutorial-model.err` have been created." + "The model has completed. Let's look at the content of the current working directory. Two files, `tutorial-model.out` and `tutorial-model.err` have been created in the `Model` path. To make their inspection easier, we can define a helper function." ] }, { @@ -163,15 +152,21 @@ } ], "source": [ - "outputfile = './tutorial-model.out'\n", - "errorfile = './tutorial-model.err'\n", + "def get_files(model):\n", + " \"\"\"Get output and error file of a Model\"\"\"\n", + " outputfile = os.path.join(model.path, model.name+\".out\")\n", + " errorfile = os.path.join(model.path, model.name+\".err\")\n", + "\n", + " return outputfile, errorfile\n", + "\n", + "outputfile, errorfile = get_files(M1)\n", "\n", "print(\"Content of tutorial-model.out:\")\n", "with open(outputfile, 'r') as fin:\n", " print(fin.read())\n", "print(\"Content of tutorial-model.err:\")\n", "with open(errorfile, 'r') as fin:\n", - " print(fin.read())" + " print(fin.read())\n" ] }, { @@ -192,9 +187,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "00:18:45 e3fbeabfdb3e SmartSim[1408] INFO tutorial-model-1(1431): Completed\n", - "00:18:48 e3fbeabfdb3e SmartSim[1408] INFO tutorial-model-2(1432): Running\n", - "00:18:49 e3fbeabfdb3e SmartSim[1408] INFO tutorial-model-2(1432): Completed\n" + "19:17:37 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO tutorial-model-1(97239): SmartSimStatus.STATUS_COMPLETED\n", + "19:17:40 HPE-C02YR4ANLVCJ SmartSim[97173:MainThread] INFO tutorial-model-2(97250): SmartSimStatus.STATUS_RUNNING\n", + "19:17:41 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO tutorial-model-2(97250): SmartSimStatus.STATUS_COMPLETED\n" ] } ], @@ -203,7 +198,7 @@ "run_settings_2 = exp.create_run_settings(exe=\"sleep\", exe_args=\"5\", run_command=None)\n", "model_1 = exp.create_model(\"tutorial-model-1\", run_settings_1)\n", "model_2 = exp.create_model(\"tutorial-model-2\", run_settings_2)\n", - "exp.start(model_1, model_2)" + "exp.start(model_1, model_2)\n" ] }, { @@ -224,7 +219,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "00:18:53 e3fbeabfdb3e SmartSim[1408] INFO \n", + "19:17:45 HPE-C02YR4ANLVCJ SmartSim[97173:MainThread] INFO \n", "\n", "=== Launch Summary ===\n", "Experiment: getting-started\n", @@ -235,28 +230,15 @@ "\n", "=== Models ===\n", "tutorial-model-mpirun\n", - "Executable: /usr/bin/echo\n", + "Executable: /bin/echo\n", "Executable Arguments: hello world!\n", - "Run Command: mpirun\n", + "Run Command: /usr/local/bin/mpirun\n", "Run Arguments:\n", "\tn = 2\n", "\n", "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "00:19:05 e3fbeabfdb3e SmartSim[1408] INFO tutorial-model-mpirun(1435): Completed\n" + "\n", + "19:17:47 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO tutorial-model-mpirun(97310): SmartSimStatus.STATUS_COMPLETED\n" ] } ], @@ -269,7 +251,7 @@ "\n", "# create and start the MPI model\n", "ompi_model = exp.create_model(\"tutorial-model-mpirun\", openmpi_settings)\n", - "exp.start(ompi_model, summary=True)" + "exp.start(ompi_model, summary=True)\n" ] }, { @@ -296,7 +278,7 @@ } ], "source": [ - "outputfile = './tutorial-model-mpirun.out'\n", + "outputfile, _ = get_files(ompi_model)\n", "\n", "print(\"Content of tutorial-model-mpirun.out:\")\n", "with open(outputfile, 'r') as fin:\n", @@ -320,7 +302,7 @@ "source": [ "# define how we want each ensemble member to execute\n", "# in this case we create settings to execute \"sleep 3\"\n", - "ens_settings = exp.create_run_settings(exe=\"sleep\", exe_args=\"3\")" + "ens_settings = exp.create_run_settings(exe=\"sleep\", exe_args=\"3\")\n" ] }, { @@ -339,41 +321,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "00:19:08 e3fbeabfdb3e SmartSim[1408] INFO \n", + "19:17:50 HPE-C02YR4ANLVCJ SmartSim[97173:MainThread] INFO \n", "\n", "=== Launch Summary ===\n", "Experiment: getting-started\n", "Experiment Path: /home/craylabs/tutorials/getting_started/getting-started\n", "Launcher: local\n", - "Ensembles: 1\n", "Database Status: inactive\n", "\n", "=== Ensembles ===\n", "ensemble-replica\n", "Members: 4\n", - "Batch Launch: False\n", + "Batch Launch: None\n", "\n", "\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "00:19:24 e3fbeabfdb3e SmartSim[1408] INFO ensemble-replica_0(1443): Completed\n", - "00:19:24 e3fbeabfdb3e SmartSim[1408] INFO ensemble-replica_2(1445): Completed\n", - "00:19:24 e3fbeabfdb3e SmartSim[1408] INFO ensemble-replica_1(1444): Completed\n", - "00:19:25 e3fbeabfdb3e SmartSim[1408] INFO ensemble-replica_3(1446): Completed\n", - "00:19:26 e3fbeabfdb3e SmartSim[1408] INFO ensemble-replica_1(1444): Completed\n", - "00:19:26 e3fbeabfdb3e SmartSim[1408] INFO ensemble-replica_3(1446): Completed\n" + "\n", + "19:17:55 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO ensemble-replica_0(97347): SmartSimStatus.STATUS_COMPLETED\n", + "19:17:56 HPE-C02YR4ANLVCJ SmartSim[97173:MainThread] INFO ensemble-replica_1(97348): SmartSimStatus.STATUS_COMPLETED\n", + "19:17:56 HPE-C02YR4ANLVCJ SmartSim[97173:MainThread] INFO ensemble-replica_2(97349): SmartSimStatus.STATUS_COMPLETED\n", + "19:17:56 HPE-C02YR4ANLVCJ SmartSim[97173:MainThread] INFO ensemble-replica_3(97350): SmartSimStatus.STATUS_COMPLETED\n", + "19:17:57 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO ensemble-replica_1(97348): SmartSimStatus.STATUS_COMPLETED\n", + "19:17:57 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO ensemble-replica_2(97349): SmartSimStatus.STATUS_COMPLETED\n", + "19:17:57 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO ensemble-replica_3(97350): SmartSimStatus.STATUS_COMPLETED\n" ] } ], @@ -382,7 +351,7 @@ " replicas=4,\n", " run_settings=ens_settings)\n", "\n", - "exp.start(ensemble, summary=True)" + "exp.start(ensemble, summary=True)\n" ] }, { @@ -420,7 +389,7 @@ "metadata": {}, "outputs": [], "source": [ - "rs = exp.create_run_settings(exe=\"python\", exe_args=\"output_my_parameter.py\")" + "rs = exp.create_run_settings(exe=\"python\", exe_args=\"output_my_parameter.py\")\n" ] }, { @@ -446,12 +415,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "00:19:30 e3fbeabfdb3e SmartSim[1408] INFO Working in previously created experiment\n", - "00:19:34 e3fbeabfdb3e SmartSim[1408] INFO ensemble_0(1449): Completed\n", - "00:19:34 e3fbeabfdb3e SmartSim[1408] INFO ensemble_1(1450): Completed\n", - "00:19:34 e3fbeabfdb3e SmartSim[1408] INFO ensemble_2(1451): Completed\n", - "00:19:35 e3fbeabfdb3e SmartSim[1408] INFO ensemble_3(1452): Completed\n", - "00:19:36 e3fbeabfdb3e SmartSim[1408] INFO ensemble_3(1452): Completed\n" + "19:18:06 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO ensemble_0(97408): SmartSimStatus.STATUS_COMPLETED\n", + "19:18:06 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO ensemble_1(97409): SmartSimStatus.STATUS_COMPLETED\n", + "19:18:06 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO ensemble_3(97421): SmartSimStatus.STATUS_COMPLETED\n", + "19:18:07 HPE-C02YR4ANLVCJ SmartSim[97173:MainThread] INFO ensemble_2(97410): SmartSimStatus.STATUS_COMPLETED\n", + "19:18:08 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO ensemble_2(97410): SmartSimStatus.STATUS_COMPLETED\n" ] } ], @@ -467,7 +435,7 @@ "ensemble.attach_generator_files(to_configure=config_file)\n", "\n", "exp.generate(ensemble, overwrite=True)\n", - "exp.start(ensemble)" + "exp.start(ensemble)\n" ] }, { @@ -486,16 +454,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Content of getting-started/ensemble/ensemble_0/ensemble_0.out:\n", + "Content of /home/craylabs/tutorials/getting_started/getting-started/ensemble/ensemble_0/ensemble_0.out:\n", "Hello, my name is Ellie and my parameter is 2\n", "\n", - "Content of getting-started/ensemble/ensemble_1/ensemble_1.out:\n", + "Content of /home/craylabs/tutorials/getting_started/getting-started/ensemble/ensemble_1/ensemble_1.out:\n", "Hello, my name is Ellie and my parameter is 11\n", "\n", - "Content of getting-started/ensemble/ensemble_2/ensemble_2.out:\n", + "Content of /home/craylabs/tutorials/getting_started/getting-started/ensemble/ensemble_2/ensemble_2.out:\n", "Hello, my name is John and my parameter is 2\n", "\n", - "Content of getting-started/ensemble/ensemble_3/ensemble_3.out:\n", + "Content of /home/craylabs/tutorials/getting_started/getting-started/ensemble/ensemble_3/ensemble_3.out:\n", "Hello, my name is John and my parameter is 11\n", "\n" ] @@ -503,7 +471,7 @@ ], "source": [ "for id in range(4):\n", - " outputfile = f\"getting-started/ensemble/ensemble_{id}/ensemble_{id}.out\"\n", + " outputfile, _ = get_files(ensemble.entities[id])\n", "\n", " print(f\"Content of {outputfile}:\")\n", " with open(outputfile, 'r') as fin:\n", @@ -526,9 +494,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "00:19:40 e3fbeabfdb3e SmartSim[1408] INFO Working in previously created experiment\n", - "00:19:45 e3fbeabfdb3e SmartSim[1408] INFO ensemble_0(1455): Completed\n", - "00:19:45 e3fbeabfdb3e SmartSim[1408] INFO ensemble_1(1456): Completed\n" + "19:18:17 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO param_ensemble_0(97484): SmartSimStatus.STATUS_COMPLETED\n", + "19:18:17 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO param_ensemble_1(97495): SmartSimStatus.STATUS_COMPLETED\n" ] } ], @@ -537,12 +504,12 @@ " \"tutorial_name\": [\"Ellie\", \"John\"],\n", " \"tutorial_parameter\": [2, 11]\n", "}\n", - "ensemble = exp.create_ensemble(\"ensemble\", params=params, run_settings=rs, perm_strategy=\"random\", n_models=2)\n", + "ensemble = exp.create_ensemble(\"param_ensemble\", params=params, run_settings=rs, perm_strategy=\"random\", n_models=2)\n", "config_file = \"./output_my_parameter.py\"\n", "ensemble.attach_generator_files(to_configure=config_file)\n", "\n", "exp.generate(ensemble, overwrite=True)\n", - "exp.start(ensemble)" + "exp.start(ensemble)\n" ] }, { @@ -574,12 +541,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "00:19:46 e3fbeabfdb3e SmartSim[1408] INFO Working in previously created experiment\n", - "00:19:51 e3fbeabfdb3e SmartSim[1408] INFO ensemble_new_tag_0(1459): Completed\n", - "00:19:51 e3fbeabfdb3e SmartSim[1408] INFO ensemble_new_tag_1(1460): Completed\n", - "00:19:51 e3fbeabfdb3e SmartSim[1408] INFO ensemble_new_tag_2(1461): Completed\n", - "00:19:52 e3fbeabfdb3e SmartSim[1408] INFO ensemble_new_tag_3(1462): Completed\n", - "00:19:53 e3fbeabfdb3e SmartSim[1408] INFO ensemble_new_tag_3(1462): Completed\n" + "19:18:23 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO ensemble_new_tag_0(97520): SmartSimStatus.STATUS_COMPLETED\n", + "19:18:23 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO ensemble_new_tag_1(97521): SmartSimStatus.STATUS_COMPLETED\n", + "19:18:23 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO ensemble_new_tag_3(97523): SmartSimStatus.STATUS_COMPLETED\n", + "19:18:24 HPE-C02YR4ANLVCJ SmartSim[97173:MainThread] INFO ensemble_new_tag_2(97522): SmartSimStatus.STATUS_COMPLETED\n", + "19:18:25 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO ensemble_new_tag_2(97522): SmartSimStatus.STATUS_COMPLETED\n" ] } ], @@ -598,7 +564,7 @@ "ensemble.attach_generator_files(to_configure=config_file)\n", "\n", "exp.generate(ensemble, overwrite=True, tag='@')\n", - "exp.start(ensemble)" + "exp.start(ensemble)\n" ] }, { @@ -617,31 +583,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "| | Name | Entity-Type | JobID | RunID | Time | Status | Returncode |\n", - "|----|-----------------------|---------------|---------|---------|---------|-----------|--------------|\n", - "| 0 | tutorial-model | Model | 1428 | 0 | 2.00734 | Completed | 0 |\n", - "| 1 | tutorial-model-1 | Model | 1431 | 0 | 2.22411 | Completed | 0 |\n", - "| 2 | tutorial-model-2 | Model | 1432 | 0 | 5.98942 | Completed | 0 |\n", - "| 3 | tutorial-model-mpirun | Model | 1435 | 0 | 2.00939 | Completed | 0 |\n", - "| 4 | ensemble-replica_0 | Model | 1443 | 0 | 4.64557 | Completed | 0 |\n", - "| 5 | ensemble-replica_2 | Model | 1445 | 0 | 4.2261 | Completed | 0 |\n", - "| 6 | ensemble-replica_1 | Model | 1444 | 0 | 6.44562 | Completed | 0 |\n", - "| 7 | ensemble-replica_3 | Model | 1446 | 0 | 6.02451 | Completed | 0 |\n", - "| 8 | ensemble_2 | Model | 1451 | 0 | 4.22712 | Completed | 0 |\n", - "| 9 | ensemble_3 | Model | 1452 | 0 | 6.02064 | Completed | 0 |\n", - "| 10 | ensemble_0 | Model | 1449 | 0 | 4.64088 | Completed | 0 |\n", - "| 11 | ensemble_0 | Model | 1455 | 1 | 4.21892 | Completed | 0 |\n", - "| 12 | ensemble_1 | Model | 1450 | 0 | 4.43377 | Completed | 0 |\n", - "| 13 | ensemble_1 | Model | 1456 | 1 | 4.00995 | Completed | 0 |\n", - "| 14 | ensemble_new_tag_0 | Model | 1459 | 0 | 4.60659 | Completed | 0 |\n", - "| 15 | ensemble_new_tag_1 | Model | 1460 | 0 | 4.39902 | Completed | 0 |\n", - "| 16 | ensemble_new_tag_2 | Model | 1461 | 0 | 4.19067 | Completed | 0 |\n", - "| 17 | ensemble_new_tag_3 | Model | 1462 | 0 | 5.9866 | Completed | 0 |\n" + "| | Name | Entity-Type | JobID | RunID | Time | Status | Returncode |\n", + "|----|-----------------------|---------------|---------|---------|--------|---------------------------------|--------------|\n", + "| 0 | tutorial-model | Model | 97213 | 0 | 2.0073 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 1 | tutorial-model-1 | Model | 97239 | 0 | 2.2181 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 2 | tutorial-model-2 | Model | 97250 | 0 | 6.0111 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 3 | tutorial-model-mpirun | Model | 97310 | 0 | 2.0072 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 4 | ensemble-replica_0 | Model | 97347 | 0 | 4.6530 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 5 | ensemble-replica_1 | Model | 97348 | 0 | 6.4457 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 6 | ensemble-replica_2 | Model | 97349 | 0 | 6.2330 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 7 | ensemble-replica_3 | Model | 97350 | 0 | 6.0211 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 8 | ensemble_0 | Model | 97408 | 0 | 4.6442 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 9 | ensemble_1 | Model | 97409 | 0 | 4.4313 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 10 | ensemble_3 | Model | 97421 | 0 | 4.0064 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 11 | ensemble_2 | Model | 97410 | 0 | 6.2264 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 12 | param_ensemble_0 | Model | 97484 | 0 | 4.2159 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 13 | param_ensemble_1 | Model | 97495 | 0 | 4.0068 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 14 | ensemble_new_tag_0 | Model | 97520 | 0 | 4.6525 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 15 | ensemble_new_tag_1 | Model | 97521 | 0 | 4.4403 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 16 | ensemble_new_tag_3 | Model | 97523 | 0 | 4.0074 | SmartSimStatus.STATUS_COMPLETED | 0 |\n", + "| 17 | ensemble_new_tag_2 | Model | 97522 | 0 | 6.2288 | SmartSimStatus.STATUS_COMPLETED | 0 |\n" ] } ], "source": [ - "print(exp.summary())" + "print(exp.summary())\n" ] }, { @@ -655,7 +621,7 @@ "of an experiment and across multiple workloads. In order to stream data into or receive data from the Orchestrator,\n", "one of the SmartSim clients (SmartRedis) has to be used within your workload. \n", "\n", - "\"orchestrator-overview\"\n", + "
\"orchestrator-overview\"
\n", "\n", "The Orchestrator is capable of hosting and executing AI models written in Python on CPU or GPU.\n", "The Orchestrator supports models written with TensorFlow, Pytorch, or models saved in an ONNX format (e.g. scikit-learn).\n", @@ -664,7 +630,7 @@ "\n", "Orchestrators can either be deployed on a single host, or many hosts as shown in the diagram below. \n", "\n", - "\"orchestrator-cluster\"\n", + "
\"orchestrator-cluster\"
\n", "\n", "In this tutorial, a single-host host Orchestrator is deployed locally (as we specified `local` for the Experiment launcher)\n", "and used to demonstrate how to use the SmartRedis Python client within a workload." @@ -679,22 +645,14 @@ "from smartredis import Client\n", "import numpy as np\n", "\n", - "REDIS_PORT=6899" + "REDIS_PORT=6899\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "00:19:57 e3fbeabfdb3e SmartSim[1408] INFO Working in previously created experiment\n" - ] - } - ], + "outputs": [], "source": [ "# start a new Experiment for this section\n", "exp = Experiment(\"tutorial-smartredis\", launcher=\"local\")\n", @@ -707,7 +665,7 @@ "exp.generate(db)\n", "\n", "# start the database\n", - "exp.start(db)" + "exp.start(db)\n" ] }, { @@ -721,12 +679,21 @@ "cell_type": "code", "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SmartRedis Library@19-18-39:WARNING: Environment variable SR_LOG_FILE is not set. Defaulting to stdout\n", + "SmartRedis Library@19-18-39:WARNING: Environment variable SR_LOG_LEVEL is not set. Defaulting to INFO\n" + ] + } + ], "source": [ "# connect a SmartRedis client at the address supplied by the launched\n", "# Orchestrator instance.\n", "# Cluster=False as the Orchestrator was deployed on a single compute host (local)\n", - "client = Client(address=db.get_address()[0], cluster=False)" + "client = Client(address=db.get_address()[0], cluster=False)\n" ] }, { @@ -772,7 +739,7 @@ "\n", "receive_tensor = client.get_tensor(\"tutorial_tensor_1\")\n", "\n", - "print('Receive tensor:\\n\\n', receive_tensor)" + "print('Receive tensor:\\n\\n', receive_tensor)\n" ] }, { @@ -808,7 +775,7 @@ "module = torch.jit.trace(net, example_forward_input)\n", "\n", "# Save the traced model to a file\n", - "torch.jit.save(module, \"./torch_cnn.pt\")" + "torch.jit.save(module, \"./torch_cnn.pt\")\n" ] }, { @@ -822,10 +789,18 @@ "cell_type": "code", "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default@19-18-41:ERROR: Redis IO error when executing command: Failed to get reply: Resource temporarily unavailable\n" + ] + } + ], "source": [ "# Set the model in the Redis database from the file\n", - "client.set_model_from_file(\"tutorial-cnn\", \"./torch_cnn.pt\", \"TORCH\", \"CPU\")" + "client.set_model_from_file(\"tutorial-cnn\", \"./torch_cnn.pt\", \"TORCH\", \"CPU\")\n" ] }, { @@ -840,7 +815,7 @@ "\n", "# Run model and retrieve the output\n", "client.run_model(\"tutorial-cnn\", inputs=[\"torch_cnn_input\"], outputs=[\"torch_cnn_output\"])\n", - "out_data = client.get_tensor(\"torch_cnn_output\")" + "out_data = client.get_tensor(\"torch_cnn_output\")\n" ] }, { @@ -877,7 +852,7 @@ "sample_array_1 = np.array([np.arange(9.)])\n", "print(sample_array_1)\n", "print(\"Max:\")\n", - "print(max_of_tensor(sample_array_1))" + "print(max_of_tensor(sample_array_1))\n" ] }, { @@ -893,7 +868,7 @@ "metadata": {}, "outputs": [], "source": [ - "client.set_function(\"max-of-tensor\", max_of_tensor)" + "client.set_function(\"max-of-tensor\", max_of_tensor)\n" ] }, { @@ -927,7 +902,7 @@ "\n", "out = client.get_tensor(\"script-output\")\n", "\n", - "print(out)" + "print(out)\n" ] }, { @@ -939,11 +914,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "exp.stop(db)" + "exp.stop(db)\n" ] }, { @@ -963,7 +938,7 @@ "metadata": {}, "outputs": [], "source": [ - "exp.start(db)" + "exp.start(db)\n" ] }, { @@ -982,7 +957,7 @@ "rs_prod = exp.create_run_settings(\"python\", f\"producer.py --redis-port {REDIS_PORT}\")\n", "ensemble = exp.create_ensemble(name=\"producer\",\n", " replicas=2,\n", - " run_settings=rs_prod)" + " run_settings=rs_prod)\n" ] }, { @@ -999,7 +974,7 @@ "outputs": [], "source": [ "rs_consumer = exp.create_run_settings(\"python\", f\"consumer.py --redis-port {REDIS_PORT}\")\n", - "consumer = exp.create_model(\"consumer\", run_settings=rs_consumer)" + "consumer = exp.create_model(\"consumer\", run_settings=rs_consumer)\n" ] }, { @@ -1016,7 +991,7 @@ "outputs": [], "source": [ "consumer.register_incoming_entity(ensemble.models[0])\n", - "consumer.register_incoming_entity(ensemble.models[1])" + "consumer.register_incoming_entity(ensemble.models[1])\n" ] }, { @@ -1035,46 +1010,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "00:20:48 e3fbeabfdb3e SmartSim[1408] INFO Working in previously created experiment\n", - "00:20:48 e3fbeabfdb3e SmartSim[1408] INFO Working in previously created experiment\n", - "00:20:48 e3fbeabfdb3e SmartSim[1408] INFO \n", + "19:18:53 HPE-C02YR4ANLVCJ SmartSim[97173:MainThread] INFO \n", "\n", "=== Launch Summary ===\n", "Experiment: tutorial-smartredis\n", "Experiment Path: /home/craylabs/tutorials/getting_started/tutorial-smartredis\n", "Launcher: local\n", - "Ensembles: 1\n", "Models: 1\n", "Database Status: active\n", "\n", "=== Ensembles ===\n", "producer\n", "Members: 2\n", - "Batch Launch: False\n", + "Batch Launch: None\n", "\n", "=== Models ===\n", "consumer\n", - "Executable: /usr/bin/python\n", + "Executable: /usr/local/anaconda3/envs/ss-py3.10/bin/python\n", "Executable Arguments: consumer.py --redis-port 6899\n", "\n", "\n", "\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "00:21:02 e3fbeabfdb3e SmartSim[1408] INFO producer_0(1500): Completed\n", - "00:21:02 e3fbeabfdb3e SmartSim[1408] INFO producer_1(1505): Completed\n", - "00:21:02 e3fbeabfdb3e SmartSim[1408] INFO consumer(1510): Completed\n" + "19:18:58 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO producer_0(97711): SmartSimStatus.STATUS_COMPLETED\n", + "19:18:58 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO producer_1(97712): SmartSimStatus.STATUS_COMPLETED\n", + "19:18:58 HPE-C02YR4ANLVCJ SmartSim[97173:JobManager] INFO consumer(97713): SmartSimStatus.STATUS_COMPLETED\n" ] } ], @@ -1085,7 +1050,7 @@ "exp.generate(consumer, overwrite=True)\n", "\n", "# start the models\n", - "exp.start(ensemble, consumer, summary=True)" + "exp.start(ensemble, consumer, summary=True)\n" ] }, { @@ -1104,21 +1069,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "Tensor for producer_0 is: [[[[0.16503988 0.12075829 0.3565984 ]\n", - " [0.72577718 0.09396099 0.1618377 ]\n", - " [0.33099621 0.55506376 0.69916534]]]]\n", - "Tensor for producer_1 is: [[[[0.68450198 0.27678731 0.65711464]\n", - " [0.74589422 0.45886442 0.52484735]\n", - " [0.5394516 0.20950066 0.96127311]]]]\n", + "SmartRedis Library@19-18-54:WARNING: Environment variable SR_LOG_FILE is not set. Defaulting to stdout\n", + "SmartRedis Library@19-18-54:WARNING: Environment variable SR_LOG_LEVEL is not set. Defaulting to INFO\n", + "Tensor for producer_0 is: [[[[0.40963388 0.66147363 0.88239209]\n", + " [0.67788696 0.66730329 0.26504813]\n", + " [0.80848382 0.96430444 0.75951969]]]]\n", + "Tensor for producer_1 is: [[[[0.67515573 0.28582205 0.79349604]\n", + " [0.78848592 0.67902375 0.54826283]\n", + " [0.01769311 0.55995054 0.47818324]]]]\n", "\n" ] } ], "source": [ - "outputfile = './tutorial-smartredis/consumer/consumer.out'\n", + "outputfile, _ = get_files(consumer)\n", "\n", "with open(outputfile, 'r') as fin:\n", - " print(fin.read())" + " print(fin.read())\n" ] }, { @@ -1130,11 +1097,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "exp.stop(db)" + "exp.stop(db)\n" ] } ], @@ -1154,7 +1121,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/doc/tutorials/ml_inference/Inference-in-SmartSim.ipynb b/doc/tutorials/ml_inference/Inference-in-SmartSim.ipynb index 711ae999c..2b5f0a3a5 100644 --- a/doc/tutorials/ml_inference/Inference-in-SmartSim.ipynb +++ b/doc/tutorials/ml_inference/Inference-in-SmartSim.ipynb @@ -38,14 +38,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'torch'}\n" + "{'tensorflow', 'torch'}\n" ] } ], "source": [ "## Installing the ML backends\n", "from smartsim._core.utils.helpers import installed_redisai_backends\n", - "print(installed_redisai_backends())" + "print(installed_redisai_backends())\n" ] }, { @@ -68,16 +68,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "usage: smart build [-h] [-v] [--device {cpu,gpu}] [--only_python_packages]\n", - " [--no_pt] [--no_tf] [--onnx] [--torch_dir TORCH_DIR]\n", + "usage: smart build [-h] [-v] [--device {cpu,gpu}] [--dragon]\n", + " [--only_python_packages] [--no_pt] [--no_tf] [--onnx]\n", + " [--torch_dir TORCH_DIR]\n", " [--libtensorflow_dir LIBTENSORFLOW_DIR] [--keydb]\n", + " [--no_torch_with_mkl]\n", "\n", - "Build SmartSim dependencies (Redis, RedisAI, ML runtimes)\n", + "Build SmartSim dependencies (Redis, RedisAI, Dragon, ML runtimes)\n", "\n", "options:\n", " -h, --help show this help message and exit\n", " -v Enable verbose build process\n", " --device {cpu,gpu} Device to build ML runtimes for\n", + " --dragon Install the dragon runtime\n", " --only_python_packages\n", " Only evaluate the python packages (i.e. skip building\n", " backends)\n", @@ -90,12 +93,13 @@ " --libtensorflow_dir LIBTENSORFLOW_DIR\n", " Path to custom libtensorflow directory (ONLY USE IF\n", " NEEDED)\n", - " --keydb Build KeyDB instead of Redis\n" + " --keydb Build KeyDB instead of Redis\n", + " --no_torch_with_mkl Do not build Torch with Intel MKL\n" ] } ], "source": [ - "!smart build --help" + "!smart build --help\n" ] }, { @@ -124,12 +128,11 @@ "\u001b[34m[SmartSim]\u001b[0m \u001b[1;30mINFO\u001b[0m Successfully removed ML runtimes\n", "\u001b[34m[SmartSim]\u001b[0m \u001b[1;30mINFO\u001b[0m Running SmartSim build process...\n", "\u001b[34m[SmartSim]\u001b[0m \u001b[1;30mINFO\u001b[0m Checking requested versions...\n", - "\u001b[34m[SmartSim]\u001b[0m \u001b[1;30mINFO\u001b[0m Checking for build tools...\n", "\u001b[34m[SmartSim]\u001b[0m \u001b[1;30mINFO\u001b[0m Redis build complete!\n", "\n", "ML Backends Requested\n", "╒════════════╤════════╤══════╕\n", - "│ PyTorch │ 2.0.1 │ \u001b[32mTrue\u001b[0m │\n", + "│ PyTorch │ 2.1.0 │ \u001b[32mTrue\u001b[0m │\n", "│ TensorFlow │ 2.13.1 │ \u001b[32mTrue\u001b[0m │\n", "│ ONNX │ 1.14.1 │ \u001b[32mTrue\u001b[0m │\n", "╘════════════╧════════╧══════╛\n", @@ -144,7 +147,7 @@ } ], "source": [ - "!smart clean && smart build --device cpu --onnx" + "!smart clean && smart build --device cpu --onnx\n" ] }, { @@ -198,7 +201,7 @@ "\n", "# import smartsim and smartredis\n", "from smartredis import Client\n", - "from smartsim import Experiment" + "from smartsim import Experiment\n" ] }, { @@ -210,7 +213,7 @@ }, "outputs": [], "source": [ - "exp = Experiment(\"Inference-Tutorial\", launcher=\"local\")" + "exp = Experiment(\"Inference-Tutorial\", launcher=\"local\")\n" ] }, { @@ -223,7 +226,7 @@ "outputs": [], "source": [ "db = exp.create_database(port=6780, interface=\"lo\")\n", - "exp.start(db)" + "exp.start(db)\n" ] }, { @@ -321,7 +324,7 @@ " torch.jit.save(module, model_buffer)\n", " return model_buffer.getvalue()\n", "\n", - "traced_cnn = create_torch_model(n, example_forward_input)" + "traced_cnn = create_torch_model(n, example_forward_input)\n" ] }, { @@ -351,46 +354,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "Prediction: [[-2.1860428 -2.3318565 -2.2773128 -2.2742267 -2.2679536 -2.304159\n", - " -2.423439 -2.3406057 -2.2474668 -2.3950338]\n", - " [-2.1803837 -2.3286302 -2.2805855 -2.2874444 -2.261593 -2.3145547\n", - " -2.4357762 -2.3169715 -2.2618299 -2.3798223]\n", - " [-2.1833746 -2.3249795 -2.28497 -2.2851245 -2.2555952 -2.308204\n", - " -2.4274755 -2.3441646 -2.2553194 -2.3779805]\n", - " [-2.1843016 -2.3395848 -2.2619352 -2.294549 -2.2571433 -2.312943\n", - " -2.4161577 -2.338785 -2.2538524 -2.3881512]\n", - " [-2.1936755 -2.3315516 -2.2739122 -2.2832148 -2.2666094 -2.3038912\n", - " -2.4211216 -2.3300066 -2.2564852 -2.3846986]\n", - " [-2.1709712 -2.3271346 -2.280365 -2.286064 -2.2617233 -2.3227994\n", - " -2.4253702 -2.3313646 -2.2593162 -2.383301 ]\n", - " [-2.1948013 -2.3318067 -2.2713811 -2.2844 -2.2526758 -2.3178148\n", - " -2.4255004 -2.3233378 -2.2388031 -2.4088087]\n", - " [-2.17515 -2.3240736 -2.2818787 -2.2857373 -2.259629 -2.3184\n", - " -2.425821 -2.3519678 -2.2413275 -2.385761 ]\n", - " [-2.187554 -2.3335872 -2.2767708 -2.2818003 -2.2654893 -2.3097534\n", - " -2.4182632 -2.3376188 -2.2509694 -2.384327 ]\n", - " [-2.1793714 -2.340681 -2.271785 -2.287751 -2.2620957 -2.3163543\n", - " -2.4111845 -2.3468175 -2.2472064 -2.3842056]\n", - " [-2.1906679 -2.3483853 -2.2580595 -2.2923894 -2.25718 -2.2951608\n", - " -2.431815 -2.3487022 -2.2326546 -2.3963163]\n", - " [-2.1882055 -2.3293467 -2.2767649 -2.279892 -2.2527165 -2.3220086\n", - " -2.4226239 -2.3364902 -2.2455037 -2.394776 ]\n", - " [-2.1756573 -2.3318045 -2.2690601 -2.2737868 -2.264148 -2.3212118\n", - " -2.4243867 -2.3421402 -2.2562728 -2.390894 ]\n", - " [-2.1824148 -2.3317673 -2.2749603 -2.291667 -2.2524009 -2.3026595\n", - " -2.42986 -2.3290846 -2.265264 -2.387787 ]\n", - " [-2.1871543 -2.3408008 -2.2773213 -2.283908 -2.249834 -2.3159058\n", - " -2.4251873 -2.339211 -2.245001 -2.3839695]\n", - " [-2.1855574 -2.3216138 -2.2722392 -2.2826352 -2.2573392 -2.308948\n", - " -2.4348576 -2.3421624 -2.2397952 -2.4060655]\n", - " [-2.1876159 -2.330091 -2.2779942 -2.2849102 -2.2582757 -2.3122754\n", - " -2.4250498 -2.333003 -2.250753 -2.3871331]\n", - " [-2.182653 -2.3381891 -2.2795184 -2.287199 -2.2628696 -2.303869\n", - " -2.413879 -2.3404965 -2.26254 -2.3739154]\n", - " [-2.1733668 -2.3377435 -2.2724369 -2.28559 -2.2537165 -2.3127556\n", - " -2.4249415 -2.3484716 -2.2515364 -2.3897333]\n", - " [-2.1839535 -2.336417 -2.2839231 -2.285238 -2.2608624 -2.3198016\n", - " -2.424396 -2.3165755 -2.2433887 -2.3935702]]\n" + "Prediction: [[-2.2239347 -2.256488 -2.3910825 -2.2572591 -2.2663934 -2.3775585\n", + " -2.257742 -2.3160243 -2.391289 -2.3055189]\n", + " [-2.2149696 -2.2576108 -2.3899908 -2.2715292 -2.2628417 -2.3693023\n", + " -2.260772 -2.3166935 -2.3967428 -2.3028378]\n", + " [-2.2214003 -2.2581112 -2.3854284 -2.2616909 -2.2745335 -2.3779867\n", + " -2.2570336 -2.3125517 -2.391247 -2.302534 ]\n", + " [-2.214657 -2.2598932 -2.3800194 -2.2612374 -2.2718334 -2.3784144\n", + " -2.2596886 -2.318937 -2.3904119 -2.3075597]\n", + " [-2.2034936 -2.2570574 -2.4026587 -2.2698882 -2.2597382 -2.3796346\n", + " -2.2662714 -2.3141642 -2.3986044 -2.2949069]\n", + " [-2.2162325 -2.2635622 -2.3800213 -2.2569213 -2.264393 -2.3763664\n", + " -2.2658355 -2.3211577 -2.3904028 -2.307555 ]\n", + " [-2.2084794 -2.258525 -2.393487 -2.26341 -2.2674217 -2.3792422\n", + " -2.264515 -2.3262923 -2.3823283 -2.300095 ]\n", + " [-2.2175536 -2.2577217 -2.3975415 -2.2582505 -2.269493 -2.365971\n", + " -2.2619228 -2.3258338 -2.3984828 -2.291332 ]\n", + " [-2.2151139 -2.2522063 -2.3931108 -2.2577128 -2.270789 -2.371976\n", + " -2.2567465 -2.32229 -2.395818 -2.308673 ]\n", + " [-2.2141316 -2.2494154 -2.3948152 -2.2606037 -2.2732735 -2.3758345\n", + " -2.2620056 -2.3184063 -2.385798 -2.3094575]\n", + " [-2.221041 -2.2519057 -2.398841 -2.259931 -2.2686832 -2.3660865\n", + " -2.2632158 -2.322879 -2.3970191 -2.2942836]\n", + " [-2.2142313 -2.2578502 -2.393603 -2.2673647 -2.2553272 -2.37376\n", + " -2.2617526 -2.3199627 -2.399065 -2.301728 ]\n", + " [-2.2082942 -2.2571995 -2.3889875 -2.266007 -2.257706 -2.37675\n", + " -2.266374 -2.3223817 -2.3961644 -2.304737 ]\n", + " [-2.2229445 -2.2658186 -2.399095 -2.2566628 -2.266294 -2.3742397\n", + " -2.2578638 -2.3047974 -2.3973055 -2.2988966]\n", + " [-2.215887 -2.2676513 -2.3889093 -2.246127 -2.266115 -2.3842902\n", + " -2.2586591 -2.3106883 -2.396018 -2.3104343]\n", + " [-2.2099977 -2.2719226 -2.391469 -2.255561 -2.266949 -2.371345\n", + " -2.2596216 -2.324484 -2.3890057 -2.3031068]\n", + " [-2.214121 -2.2561312 -2.391877 -2.261881 -2.2639613 -2.3679278\n", + " -2.269122 -2.3139405 -2.4036062 -2.3015296]\n", + " [-2.22871 -2.256755 -2.3881361 -2.2651346 -2.2651856 -2.3733103\n", + " -2.2641761 -2.3182902 -2.3855858 -2.2960906]\n", + " [-2.2103846 -2.2450664 -2.3848588 -2.2795632 -2.2658024 -2.3679922\n", + " -2.2666745 -2.3190453 -2.3987417 -2.3054008]\n", + " [-2.2175698 -2.2573788 -2.391653 -2.2519581 -2.2637622 -2.3839104\n", + " -2.265371 -2.3158426 -2.3929882 -2.3040662]]\n" ] } ], @@ -407,7 +410,7 @@ "\n", "# get the output\n", "output = client.get_tensor(\"output\")\n", - "print(f\"Prediction: {output}\")" + "print(f\"Prediction: {output}\")\n" ] }, { @@ -451,7 +454,7 @@ "source": [ "def calc_svd(input_tensor):\n", " # svd function from TorchScript API\n", - " return input_tensor.svd()" + " return input_tensor.svd()\n" ] }, { @@ -466,46 +469,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "U: [[[-0.31189808 0.86989427]\n", - " [-0.48122275 -0.49140105]\n", - " [-0.81923395 -0.0425336 ]]\n", + "U: [[[-0.50057614 0.2622205 ]\n", + " [-0.47629714 -0.8792326 ]\n", + " [-0.7228863 0.39773142]]\n", "\n", - " [[-0.5889101 -0.29554686]\n", - " [-0.43949458 -0.66398275]\n", - " [-0.6782547 0.68686163]]\n", + " [[-0.45728168 0.88121146]\n", + " [-0.37974676 -0.31532544]\n", + " [-0.80416775 -0.35218775]]\n", "\n", - " [[-0.61623317 0.05853765]\n", - " [-0.6667615 -0.5695148 ]\n", - " [-0.4191489 0.81989413]]\n", + " [[-0.4667158 0.8836199 ]\n", + " [-0.47055572 -0.21237665]\n", + " [-0.7488349 -0.4172673 ]]\n", "\n", - " [[-0.5424681 0.8400398 ]\n", - " [-0.31990844 -0.2152339 ]\n", - " [-0.77678 -0.49800384]]\n", + " [[-0.32159734 0.92966324]\n", + " [-0.6941528 -0.10238242]\n", + " [-0.64399314 -0.35389856]]\n", "\n", - " [[-0.43667376 0.8088193 ]\n", - " [-0.70812154 -0.57906115]\n", - " [-0.5548693 0.10246649]]]\n", + " [[-0.6984835 0.4685579 ]\n", + " [-0.55331963 0.12572214]\n", + " [-0.45382637 -0.8744412 ]]]\n", "\n", - ", S: [[137.10924 25.710997]\n", - " [131.49983 37.79937 ]\n", - " [178.72423 24.792084]\n", - " [125.13014 49.733784]\n", - " [137.48834 53.57199 ]]\n", + ", S: [[164.58028 49.682358 ]\n", + " [120.11677 66.62553 ]\n", + " [130.01929 17.520935 ]\n", + " [198.615 22.047113 ]\n", + " [154.67653 2.6773496]]\n", "\n", - ", V: [[[-0.8333395 0.5527615 ]\n", - " [-0.5527615 -0.8333395 ]]\n", + ", V: [[[-0.7275351 -0.68607044]\n", + " [-0.68607044 0.7275351 ]]\n", "\n", - " [[-0.5085228 -0.8610485 ]\n", - " [-0.8610485 0.5085228 ]]\n", + " [[-0.6071297 0.79460275]\n", + " [-0.79460275 -0.6071297 ]]\n", "\n", - " [[-0.8650402 0.5017025 ]\n", - " [-0.5017025 -0.8650402 ]]\n", + " [[-0.604189 0.7968411 ]\n", + " [-0.7968411 -0.604189 ]]\n", "\n", - " [[-0.56953645 0.8219661 ]\n", - " [-0.8219661 -0.56953645]]\n", + " [[-0.69911253 -0.7150117 ]\n", + " [-0.7150117 0.69911253]]\n", "\n", - " [[-0.6115895 0.79117525]\n", - " [-0.79117525 -0.6115895 ]]]\n", + " [[-0.8665945 -0.499013 ]\n", + " [-0.499013 0.8665945 ]]]\n", "\n" ] } @@ -522,7 +525,7 @@ "U = client.get_tensor(\"U\")\n", "S = client.get_tensor(\"S\")\n", "V = client.get_tensor(\"V\")\n", - "print(f\"U: {U}\\n\\n, S: {S}\\n\\n, V: {V}\\n\")" + "print(f\"U: {U}\\n\\n, S: {S}\\n\\n, V: {V}\\n\")\n" ] }, { @@ -553,7 +556,7 @@ "# Compile model with optimizer\n", "model.compile(optimizer=\"adam\",\n", " loss=\"sparse_categorical_crossentropy\",\n", - " metrics=[\"accuracy\"])" + " metrics=[\"accuracy\"])\n" ] }, { @@ -592,8 +595,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[0.05032112 0.06484107 0.03512685 0.14747524 0.14440396 0.02395445\n", - " 0.03395916 0.06222691 0.26738793 0.1703033 ]]\n" + "[[0.06595241 0.11921222 0.02889561 0.20963618 0.08950416 0.11298887\n", + " 0.05179482 0.09778847 0.14826407 0.07596324]]\n" ] } ], @@ -604,7 +607,7 @@ "model_path, inputs, outputs = freeze_model(model, os.getcwd(), \"fcn.pb\")\n", "\n", "# use the same client we used for PyTorch to set the TensorFlow model\n", - "# this time the method for setting a model from a saved file is shown. \n", + "# this time the method for setting a model from a saved file is shown.\n", "# TensorFlow backed requires named inputs and outputs on graph\n", "# this differs from PyTorch and ONNX.\n", "client.set_model_from_file(\n", @@ -621,7 +624,7 @@ "\n", "# get the result of the inference\n", "pred = client.get_tensor(\"output\")\n", - "print(pred)" + "print(pred)\n" ] }, { @@ -689,7 +692,7 @@ "outputs": [], "source": [ "from skl2onnx import to_onnx\n", - "from sklearn.cluster import KMeans" + "from sklearn.cluster import KMeans\n" ] }, { @@ -704,7 +707,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[1 1 1 1 1 0 0 0 0 0]\n" + "Default@[0 0 0 0 0 1 1 1 1 1]\n" ] } ], @@ -726,7 +729,7 @@ "client.set_model(\"kmeans\", model, \"ONNX\", device=\"CPU\")\n", "client.run_model(\"kmeans\", inputs=\"input\", outputs=[\"labels\", \"transform\"])\n", "\n", - "print(client.get_tensor(\"labels\"))" + "print(client.get_tensor(\"labels\"))\n" ] }, { @@ -753,7 +756,7 @@ "source": [ "from sklearn.datasets import load_iris\n", "from sklearn.ensemble import RandomForestRegressor\n", - "from sklearn.model_selection import train_test_split" + "from sklearn.model_selection import train_test_split\n" ] }, { @@ -787,7 +790,7 @@ "client.put_tensor(\"input\", sample)\n", "client.set_model(\"rf_regressor\", model, \"ONNX\", device=\"CPU\")\n", "client.run_model(\"rf_regressor\", inputs=\"input\", outputs=\"output\")\n", - "print(client.get_tensor(\"output\"))" + "print(client.get_tensor(\"output\"))\n" ] }, { @@ -799,7 +802,7 @@ }, "outputs": [], "source": [ - "exp.stop(db)" + "exp.stop(db)\n" ] }, { @@ -815,15 +818,15 @@ "text/html": [ "\n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n", "
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\n" ] }, { @@ -874,7 +877,7 @@ " db_cpus=1,\n", " debug=False,\n", " ifname=\"lo\"\n", - ")" + ")\n" ] }, { @@ -889,29 +892,40 @@ "name": "stdout", "output_type": "stream", "text": [ - "21:18:06 C02G13RYMD6N SmartSim[30945] INFO \n", + "19:30:35 HPE-C02YR4ANLVCJ SmartSim[1187:MainThread] INFO \n", "\n", "=== Launch Summary ===\n", "Experiment: Inference-Tutorial\n", - "Experiment Path: /Users/smartsim/smartsim/tutorials/ml_inference/Inference-Tutorial\n", + "Experiment Path: /home/craylabs/tutorials/ml_inference/Inference-Tutorial\n", "Launcher: local\n", "Models: 1\n", "Database Status: inactive\n", "\n", "=== Models ===\n", "colocated_model\n", - "Executable: /Users/smartsim/venv/bin/python\n", + "Executable: /usr/local/anaconda3/envs/ss-py3.10/bin/python\n", "Executable Arguments: ./colo-db-torch-example.py\n", "Co-located Database: True\n", "\n", "\n", - "\n", - "21:18:09 C02G13RYMD6N SmartSim[30945] INFO colocated_model(31865): Completed\n" + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "19:30:38 HPE-C02YR4ANLVCJ SmartSim[1187:JobManager] WARNING colocated_model(3199): SmartSimStatus.STATUS_FAILED\n", + "19:30:38 HPE-C02YR4ANLVCJ SmartSim[1187:JobManager] WARNING colocated_model failed. See below for details \n", + "Job status at failure: SmartSimStatus.STATUS_FAILED \n", + "Launcher status at failure: Failed \n", + "Job returncode: 2 \n", + "Error and output file located at: /home/craylabs/tutorials/ml_inference/Inference-Tutorial/colocated_model\n" ] } ], "source": [ - "exp.start(colo_model, summary=True)" + "exp.start(colo_model, summary=True)\n" ] }, { @@ -927,16 +941,16 @@ "text/html": [ "\n", "\n", - "\n", + "\n", "\n", "\n", - "\n", - "\n", + "\n", + "\n", "\n", "
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1 colocated_modelModel 3199 0 3.1599 SmartSimStatus.STATUS_FAILED 2
'" ] }, "execution_count": 22, @@ -945,7 +959,7 @@ } ], "source": [ - "exp.summary(style=\"html\")" + "exp.summary(style=\"html\")\n" ] } ], diff --git a/doc/tutorials/ml_training/surrogate/fd_sim.py b/doc/tutorials/ml_training/surrogate/fd_sim.py index db68b24b2..7732f13d8 100644 --- a/doc/tutorials/ml_training/surrogate/fd_sim.py +++ b/doc/tutorials/ml_training/surrogate/fd_sim.py @@ -9,8 +9,8 @@ def augment_batch(samples, targets): """Augment samples and targets - - by exploiting rotational and axial symmetry. Each sample is + + by exploiting rotational and axial symmetry. Each sample is rotated and reflected to obtain 8 valid samples. The same transformations are applied to targets. @@ -76,7 +76,7 @@ def augment_batch(samples, targets): def simulate(steps, size): """Run multiple simulations and upload results - + both as tensors and as augmented samples for training. :param steps: Number of simulations to run @@ -85,13 +85,13 @@ def simulate(steps, size): batch_size = 50 samples = np.zeros((batch_size,size,size,1)).astype(np.single) targets = np.zeros_like(samples).astype(np.single) - client = Client(None, False) + client = Client(address=None, cluster=False) training_data_uploader = TrainingDataUploader(cluster=False, verbose=True) training_data_uploader.publish_info() for i in tqdm(range(steps)): - + u_init, u_steady = fd2d_heat_steady_test01(samples.shape[1], samples.shape[2]) u_init = u_init.astype(np.single) u_steady = u_steady.astype(np.single) diff --git a/doc/tutorials/ml_training/surrogate/tf_training.py b/doc/tutorials/ml_training/surrogate/tf_training.py index 932cb2df3..a7aaf3ebf 100644 --- a/doc/tutorials/ml_training/surrogate/tf_training.py +++ b/doc/tutorials/ml_training/surrogate/tf_training.py @@ -20,7 +20,7 @@ def create_dataset(idx, F): def store_model(model, idx): serialized_model, inputs, outputs = serialize_model(model) - client = Client(None, False) + client = Client(address=None, cluster=False) client.set_model(f"{model.name}_{idx}", serialized_model, "TF", "CPU", inputs=inputs, outputs=outputs) def train_model(model, epochs): @@ -43,7 +43,7 @@ def train_model(model, epochs): for epoch in range(epochs): print(f"Epoch {epoch+1}") - model.fit(training_generator, steps_per_epoch=None, + model.fit(training_generator, steps_per_epoch=None, epochs=epoch+1, initial_epoch=epoch, batch_size=training_generator.batch_size, verbose=2) if (epoch+1)%10 == 0: @@ -68,11 +68,11 @@ def upload_inference_examples(model, num_examples): if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="Finite Difference Simulation") - parser.add_argument('--depth', type=int, default=4, + parser.add_argument('--depth', type=int, default=4, help="Half depth of residual network") - parser.add_argument('--epochs', type=int, default=100, + parser.add_argument('--epochs', type=int, default=100, help="Number of epochs to train the NN for") - parser.add_argument('--delay', type=int, default=0, + parser.add_argument('--delay', type=int, default=0, help="Seconds to wait before training") parser.add_argument('--size', type=int, default=100, help='Size of sample side, each sample will be a (size, size, 1) image') diff --git a/doc/tutorials/ml_training/surrogate/train_surrogate.ipynb b/doc/tutorials/ml_training/surrogate/train_surrogate.ipynb index c811d1205..5625b86b9 100644 --- a/doc/tutorials/ml_training/surrogate/train_surrogate.ipynb +++ b/doc/tutorials/ml_training/surrogate/train_surrogate.ipynb @@ -25,7 +25,7 @@ "\n", "The problem can be solved using a finite difference scheme. To this end, a modified version of the code\n", "written by John Burkardt will be used. Its original version is licensed under LGPL, and so is this example.\n", - "The code was downloaded from [this page](https://people.sc.fsu.edu/~jburkardt/py_src/fd2d_heat_steady/fd2d_heat_steady.html),\n", + "The code was downloaded from [this page](https://github.com/johannesgerer/jburkardt-m/tree/master/fd2d_heat_steady),\n", "which explains how the problem is discretized and solved.\n", "\n", "In the modified version of the code which will be used, a random number (between 1 and 5) of heat sources is placed.\n", @@ -35,80 +35,68 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "id": "6a49acfb-2585-4423-9de9-3b26bd679a90", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -223,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "id": "253f9c3e-95c9-49ad-b2d4-4fa409aeb36f", "metadata": {}, "outputs": [], @@ -233,12 +221,12 @@ "# Initialize an Experiment with the local launcher\n", "# This will be the name of the output directory that holds\n", "# the output from our simulation and SmartSim\n", - "exp = Experiment(\"surrogate_training\", launcher=\"local\")" + "exp = Experiment(\"surrogate_training\", launcher=\"local\")\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "id": "38c45f55-e7a4-4141-a445-85a6158eb12b", "metadata": {}, "outputs": [ @@ -246,18 +234,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "12:21:18 C02YR4ANLVCJ SmartSim[68607] INFO Working in previously created experiment\n", "Database started at address: ['127.0.0.1:6780']\n" ] } ], "source": [ - "# create an Orchestrator database reference, \n", + "# create an Orchestrator database reference,\n", "# generate its output directory, and launch it locally\n", "db = exp.create_database(port=6780, interface=\"lo\")\n", "exp.generate(db, overwrite=True)\n", "exp.start(db)\n", - "print(f\"Database started at address: {db.get_address()}\")" + "print(f\"Database started at address: {db.get_address()}\")\n" ] }, { @@ -281,18 +268,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "id": "537a1489-b4c3-4736-a628-b7af433a9cbf", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "12:21:38 C02YR4ANLVCJ SmartSim[68607] INFO Working in previously created experiment\n" - ] - } - ], + "outputs": [], "source": [ "# set simulation parameters we can pass as executable arguments\n", "# Number of simulations to run in each replica\n", @@ -308,11 +287,11 @@ "\n", "# Create the ensemble reference to our simulation and\n", "# attach needed files to be copied, configured, or symlinked into\n", - "# the ensemble directories at runtime. \n", + "# the ensemble directories at runtime.\n", "ensemble = exp.create_ensemble(\"fd_simulation\", run_settings=settings, replicas=2)\n", "ensemble.attach_generator_files(to_copy=[\"fd_sim.py\", \"steady_state.py\"])\n", "ensemble.enable_key_prefixing()\n", - "exp.generate(ensemble, overwrite=True)" + "exp.generate(ensemble, overwrite=True)\n" ] }, { @@ -376,26 +355,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "id": "5ce5c68d-38f3-40a5-a0c8-a7297036022f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "12:21:38 C02YR4ANLVCJ SmartSim[68607] INFO Working in previously created experiment\n" - ] - } - ], + "outputs": [], "source": [ "nn_depth = 4\n", "epochs = 40\n", "\n", "ml_settings = exp.create_run_settings(\"python\",\n", - " exe_args=[\"tf_training.py\", \n", - " f\"--depth={nn_depth}\", \n", - " f\"--epochs={epochs}\", \n", + " exe_args=[\"tf_training.py\",\n", + " f\"--depth={nn_depth}\",\n", + " f\"--epochs={epochs}\",\n", " f\"--size={size}\"],\n", " env_vars={\"OMP_NUM_THREADS\": \"16\"})\n", "\n", @@ -403,7 +374,7 @@ "ml_model.attach_generator_files(to_copy=[\"tf_training.py\", \"tf_model.py\"])\n", "for sim in ensemble.entities:\n", " ml_model.register_incoming_entity(sim)\n", - "exp.generate(ml_model, overwrite=True)" + "exp.generate(ml_model, overwrite=True)\n" ] }, { @@ -421,12 +392,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "id": "8a2b2061-5de8-4039-a431-c895e0a8940b", "metadata": {}, "outputs": [], "source": [ - "exp.start(ensemble, ml_model, block=False, summary=False)" + "exp.start(ensemble, ml_model, block=False, summary=False)\n" ] }, { @@ -447,136 +418,139 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "id": "eb96f840-0a52-47d4-b5e4-3f2f2a3a2ebf", "metadata": {}, "outputs": [ { "data": { - 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAA4AAAAOCAYAAAAfSC3RAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAGUlEQVR4nGP8//8/AzmAiSxdoxpHNQ4hjQB59QMZfQJbWQAAAABJRU5ErkJggg==", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAAIklEQVR4nGP8////fwYqAiZqGjZq4KiBowaOGjhq4FAyEACzFQQkwb2h5QAAAABJRU5ErkJggg==", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default@20-19-36:ERROR: Redis IO error when executing command: Failed to get reply: Resource temporarily unavailable\n", + "Default@20-19-37:ERROR: Redis IO error when executing command: Failed to get reply: Resource temporarily unavailable\n" + ] + }, { "data": { - "image/png": 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20:20:52 HPE-C02YR4ANLVCJ SmartSim[15001:JobManager] INFO fd_simulation_0(18881): SmartSimStatus.STATUS_COMPLETED\n", + "20:22:06 HPE-C02YR4ANLVCJ SmartSim[15001:JobManager] INFO fd_simulation_1(18882): SmartSimStatus.STATUS_COMPLETED\n", + "20:23:28 HPE-C02YR4ANLVCJ SmartSim[15001:JobManager] INFO tf_training(18887): SmartSimStatus.STATUS_COMPLETED\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default@20-20-52:ERROR: Redis IO error when executing command: Failed to get reply: Resource temporarily unavailable\n" + ] + }, { "data": { - "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAA4AAAAOCAYAAAAfSC3RAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAGUlEQVR4nGP8//8/AzmAiSxdoxpHNQ4hjQB59QMZfQJbWQAAAABJRU5ErkJggg==", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAYAAACNiR0NAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAAIklEQVR4nGP8////fwYqAiZqGjZq4KiBowaOGjhq4FAyEACzFQQkwb2h5QAAAABJRU5ErkJggg==", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "12:25:30 C02YR4ANLVCJ SmartSim[68607] INFO fd_simulation_0(68661): Completed\n", - "12:25:30 C02YR4ANLVCJ SmartSim[68607] INFO fd_simulation_1(68662): Completed\n" + "Default@20-22-07:ERROR: Redis IO error when executing command: Failed to get reply: Resource temporarily unavailable\n" ] }, { "data": { - "image/png": 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -598,7 +572,7 @@ " u_steady_name = ensemble.entities[0].name + \".{sim_data_\" + str(sample_idx)+ \"}.u_steady\"\n", " client.poll_key(u_steady_name, 300, 1000)\n", " samples.append(client.get_tensor(u_steady_name).squeeze())\n", - " \n", + "\n", "pcolor_list(samples, \"Simulation\")\n", "\n", "for i in range(0, epochs//10):\n", @@ -615,7 +589,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "id": "7bbce88c-6f63-407a-8912-5787139f015b", "metadata": { "tags": [] @@ -623,58 +597,51 @@ "outputs": [], "source": [ "# Optionally clear the database\n", - "client.flush_db(db.get_address())" + "client.flush_db(db.get_address())\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "id": "7d9f2669-4efb-4f38-97e9-869a070ab79c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "12:26:24 C02YR4ANLVCJ SmartSim[68607] INFO tf_training(68664): Completed\n", - "12:26:28 C02YR4ANLVCJ SmartSim[68607] INFO Stopping model orchestrator_0 with job name orchestrator_0-CR7RNSKODOYG\n" - ] - } - ], + "outputs": [], "source": [ "# Use the Experiment API to wait until the model\n", "# is finished and then terminate the database and\n", "# release it's resources\n", "while not all([exp.finished(ensemble), exp.finished(ml_model)]):\n", " time.sleep(5)\n", - " \n", - "exp.stop(db)" + "\n", + "exp.stop(db)\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 17, "id": "2bca8a25-6e1b-4540-9d1e-932eb52d7b1e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['Completed', 'Completed', 'Completed']" + "[,\n", + " ,\n", + " ]" ] }, - "execution_count": 10, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "exp.get_status(ensemble, ml_model)" + "exp.get_status(ensemble, ml_model)\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 18, "id": "50b42065-6356-4a5a-b742-daca17b8bd6e", "metadata": {}, "outputs": [ @@ -683,35 +650,43 @@ "text/html": [ "\n", "\n", - "\n", + "\n", "\n", "\n", - "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", + "\n", "\n", "
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'" ] }, - "execution_count": 11, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "exp.summary(format=\"html\")" + "exp.summary(style=\"html\")\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d11562b1", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "smartsim", + "display_name": "ss-py3.10", "language": "python", - "name": "smartsim" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -723,7 +698,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/doc/tutorials/online_analysis/lattice/online_analysis.ipynb b/doc/tutorials/online_analysis/lattice/online_analysis.ipynb index 3389b1190..c5f58fa97 100644 --- a/doc/tutorials/online_analysis/lattice/online_analysis.ipynb +++ b/doc/tutorials/online_analysis/lattice/online_analysis.ipynb @@ -90,7 +90,7 @@ "\n", "from smartredis import Client\n", "from smartsim import Experiment\n", - "from vishelpers import plot_lattice_vorticity, plot_lattice_norm, plot_lattice_probes" + "from vishelpers import plot_lattice_vorticity, plot_lattice_norm, plot_lattice_probes\n" ] }, { @@ -121,7 +121,7 @@ "# Initialize an Experiment with the local launcher\n", "# This will be the name of the output directory that holds\n", "# the output from our simulation and SmartSim\n", - "exp = Experiment(\"finite_volume_simulation\", launcher=\"local\")" + "exp = Experiment(\"finite_volume_simulation\", launcher=\"local\")\n" ] }, { @@ -144,7 +144,7 @@ "db = exp.create_database(port=6780, interface=\"lo\")\n", "exp.generate(db, overwrite=True)\n", "exp.start(db)\n", - "print(f\"Database started at address: {db.get_address()}\")" + "print(f\"Database started at address: {db.get_address()}\")\n" ] }, { @@ -188,7 +188,7 @@ "# the Model directory at runtime.\n", "model = exp.create_model(\"fv_simulation\", settings)\n", "model.attach_generator_files(to_copy=\"fv_sim.py\")\n", - "exp.generate(model, overwrite=True)" + "exp.generate(model, overwrite=True)\n" ] }, { @@ -201,11 +201,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "19:49:59 C02YR4ANLVCJ SmartSim[54122] INFO \n", + "20:36:32 HPE-C02YR4ANLVCJ SmartSim[25938:MainThread] INFO \n", "\n", "=== Launch Summary ===\n", "Experiment: finite_volume_simulation\n", - "Experiment Path: /Users/arigazzi/Documents/DeepLearning/smartsim-dev/SmartSim/tutorials/online_analysis/lattice/finite_volume_simulation\n", + "Experiment Path: /home/craylabs/tutorials/online_analysis/lattice/finite_volume_simulation\n", "Launcher: local\n", "Models: 1\n", "Database Status: active\n", @@ -253,13 +253,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "SmartRedis Library@19-49-59:WARNING: Environment variable SR_LOG_FILE is not set. Defaulting to stdout\n", - "SmartRedis Library@19-49-59:WARNING: Environment variable SR_LOG_LEVEL is not set. Defaulting to INFO\n" + "SmartRedis Library@20-36-32:WARNING: Environment variable SR_LOG_FILE is not set. Defaulting to stdout\n", + "SmartRedis Library@20-36-32:WARNING: Environment variable SR_LOG_LEVEL is not set. Defaulting to INFO\n" ] }, { "data": { - "image/png": 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Zy1jGZ9BYFPxlLGMZy1jGMpaxjGUs4zNoLAr+MpaxjGUsYxnLWMYylvEZNBYFfxnLWMYylrGMZSxjGcv4DBqLgr+MZSxjGctYxjKWsYxlfAaNRcFfxjKWsYxlLGMZy1jGMj6DxqLgL2MZy1jGMpaxjGUsYxmfQWNR8JexjGUsYxnLWMYylrGMz6CxKPjLWMYylrGMZSxjGctYxmfQWBT8ZSxjGctYxjKWsYxlLOMzaCwK/jKWsYxlLGMZy1jGMpbxGTQWBX8Zy1jGMpaxjGUsYxnL+Awai4K/jGUsYxnLWMYylrGMZXwGjUXBX8YylrGMZSxjGctYxjI+g8ai4C9jGctYxjKWsYxlLGMZn0FjUfCXsYxlLGMZy1jGMpaxjM+gsSj4y1jGMpaxjGUsYxnLWMZn0Ihv9YHVaoUXX3wR0wRUFYBpkj+A/KCqAJQ/Kv+Thz3D/2D2ueI98zF7no2Uyt9XVZ5nmNkw+twJed4h3De5N5n3mw1971TJeyu4/QoBKb3J+7iOEO5uDX/gnuU/87bn+HY+qJ8pPupedu/Pi8k+Z3B9fI6eAedfPOZNaOitXnfvs3Teb3eqz9ungj7f9gTe+j3PuVbl82bPSumezz5vavfswZ0pzuiPz+cVum9ZNrUpf/fOHGa0w9/Zd3QRb4c052t+Lt0UC5uNt7jrz/3q8549OzzS9fx59z4zpTf/wH2HPJvkm/KK2Xdt6bO7+KYbP1v3mzz+Dn+987t7vyS/m1CVc7vnc2/2iDcd95z5W9Lb83j0jHfd+xX/3dndK87J8XP+6M5zZ4t+3h7c+e49Z/w8sWhyaRjkH+4Dz70Ts/150/18Dg296T3TX3IZfH6oprwndZ3n7r/v783z9n52f+z3z9FLnjvfNxnPpYf7HvacPQJUj5h/h+us6+LxybPi58ltN7k3kxd3ruI933kuXZPQfqGbds+4l/XewwffDj0950f5F8/ROd7OMu6Tr3fm/Db3o6CdOw+S8alPfQqn0+m5z3hLBf/FF1/ERz/6cRwOwGoFNMNBmEDfA5sNpm6NYZDP8qI1wwEIAWNcAZCfDQNwOgFNA7QtUA1n+Js5hgYpyefaFqj7g/w+JSBGUTraFYZB/gso0acEXF/DXhSjPKDv5WG7Hc6pNgZQH2+AEHCAzLuqdF3plBlbCIWQ7ntdV38DxIgT8rr4Ny9Ck07AMOAUt0gJWEP3S+fy+r7BZgOswhmIEWOq7BnV/hkA4Nxd2J7GCDRhzHuu49xd4HiUpfp58N/cpwoTxiQXtcFZnuG/BGCEMMnTCVivde+7DodjhXU3Fe8+t7KutgWqpPPiOXUdTkNd/g7A1Mp+Vftn8rm+B3Y7HLC2OfAxXSfztvPvOjsPbiMAjKOeW3TEr/R0OMqeti1Qhwnoe0ztCjc3SgO1PNbP0Z+9zbFtMaKWZ6SE01DjfAZ2jZzxtNkazfIRTZzyOYWAKTaFzKzSKHcjVfLc4xHoOuxvKpzP8qyuk7m3rdK4Pm9qV/b/Ma7w9KnswXoNm+OhrzEMwGaTf2Z/2hb7m0zXdS1/2xnf3gIh4Nxd4HQCnj2T5zzYjRhR43DIW73dytz4vN35dVnX7oGRS9sCq1bn3/f23HGUd+/GN2xe6DoceqGdOp3tPVNs7G6RRvn+lIDDQZ4Vo9K3rsHfI1sfD0qJY0Sd9wIjkBLOaO7oOCFkOsLxKC/jnxCA/T4rRjHa3Sdd6xHLmWA0JjehQnV7k/lgCJi6ta21Tnpfuw5TqIV2eJYAECOmUBtdN43u9/F450LxjgNKo0+eFPxyalcFuzWFgnfB0dAhrTCOIneMTsnPdS3kkat2yvPl/m02GOMKfS+0sFsJ4Z/jWvb6Vvig3f1QG68/9DWOR2C3k3l62ghB91d/MMUGfQ+s4pj3EZXMFSJzQtC5x4hTn3lxE0YjYnv/MMhZty3209b2PEahj/MgPGrdjpkBkwhixP5Ql7yHMiolYLfDiLogUZsHiUjvyf6mwuEge9C2ec3nVNv51RiBp0/z5ux2mLq1kcbFTtZD/sQ9rX7uozKXqxdsHuRxXI7xsv4k+zbUhezmteB3AaAeTpm4YsT+prK9MHr3/ErvPrfciVBctCeZ8O0t8PLLeP1pZXIjRj3P116TB19emlziMJ6kCzmnvO8xZv2FCxmTnOv5LPw2xsxKKH9OfSX7ns5Gs1zKHbnrX+Y3mX/rvR1Tle+Iyhzb3BBkD/Z74NEjPDs2cn7DGc+Osm8PL8e8Tj0nm6MO7nETxkIf47ve2Nd25rsdUF2/gXH3APt9ltUUdwWfpTzZ7TB224KmK0zZQO5PthdTqI3VAJm2KXebJm9bHSZ5foymF1K/tN97XukeOqaqkBvG349H4RubbeYbIRitrjv3zBhtDV7Eepua21hVjjfrnIvLNGNid+SSo9UxNHbPKkx45bM/G282qml6ns0h45VXXsFHP/rxYiGkfZ7jutVJcNL6oblw5uFQeEyo7EB50W1iVGxub4HLS1NcgJI58dl24F5KxYhzWFGm5EMHCqExhdqEcNWfjGCaOGXBPQzCMLsO+/oBxiyns5BLozHZVRSmexpqU/y4Xg7eJXuPEo0nEidXUfUnjHGFw6Ek9hAgikIIOMe1nUvbyjzIbGz9FCp6kNzbvhcGRuWtMMBSaTly7sU5XV0ZszUjTbkyFfQmTrKPbYux2+JwECWN69jtZpdzv5dfXF7Ke1TAmuKizNfPq0mnQpk4BGEw45iV4QmVzP36GkgJ4+XDgu/WEKXW8Uf7/Sqc83+6Dj//aoUQMv/2PLgQhinhHNdZCJOptC3OqTbll+u4wDP5/dWVnNNRhIgZo45eqCuQXB/sSvrzsoTMkMKKyhnp/vZWPkdh3YRyL/x95rNpOHN957PudTrbPPtefr5bZwWITN2MuhgxxUb2S42ovlfmSLp1ylLbqvKg50hlBl2Hw9CURvz1p+X5lw9MYRtDY+BFjOU5nU5CqlREdjug2b9u+zhdPcTNjQpfz/88IalCZoLneCMb/PgxDsfKZPY6nLIit9mY8RtC1q93u0yXIYiwRdvijX5t50Aexr31gidGoN6/YXRLg69pSgSU92faXWSgII2yx3yJnqkZAjwbNVwA952uwymsTbk4o8F+n+2Qz37FCb4QcEbm61yXKcRUuNXY8QrqZgPU//BjYkBcvYDbWxMfWHcTXn8q+/34se6R8pJTe2HHxVGHzJMB4DzImpog/PT6GnbnzcDRA3t2KzR9dSXPIcBCHX0Vzllg6+8MgBpE4TEDKZwxxQbX10KH6/Ud3CsrSbr3xuuvrzOvnAliymbjtcdjcenPQ5XBCu6BKsKcK3ni8Yg7PIa8kHMdhmzY8/urtpwj9+J0EuORzyNPJJ0WCpkzGM66rXUN7E6fzpeG97VbY7/P+pXJCZ5FK3eOPOtwrIp1mTE4DBjbdVbeMTMWvVHKe6Fnej6LfAXy9BvIGdNQnybgIh5Mpt4xzmdnOYYGt7eqF/EwVBm09aghEyNQHTOA13XOOAdwDqvijM9nB6D0N2I4vetd+NRr2agyGTCIXDoMjd0NDm/jE5yzuTjAeD+un6tbUb+bLh8UoBp5yvW18AAzNFLCCQJcrKOc02Fo7HspqfG0Vf6jCv746EXc3madlOfkn4u2xXmoCiDI32OCBjQOQlCdcL8X/hSarB9RX95dZIM4nQWE0btxjkK7TaN8oBX+98rnfi4+/vGP43njLRF8boZXooAStZxCjapty9scAqoYEUJWwPjrOqBQZAtlcf4CpwSEmbeHlqA8p0LddZnj6INJaGRkZh3pPHA8ouo6hCDMKyraHCPuWn8qFFfx7n48b1DZSSkzC26RXzuAO8x+vj/8BwnGlEe4S8+9YWiFfod7BNSoUaLW4yifb1t5tsjRynwY3ptRYzSUXL5eoSHHHAZcXjaFIWU3KdT2rEqRuTpMWK3uMRz8WshBvIXYtmbczS9Y8SD9BRm0KdXDAMRGhJizLL1ACrEGZoBGfqTT+FNC15WKVEqyj6uVOx83HxNQlNIo0UgzuWdS0+igMKSz0I3R3Y8QTKmO0QmGYUBwnjV6FBACqjSiiQFdVxVbz33wr+UwxVufwT2sa/3bIR0hyJ7Y/HTu57M+20uW2X7Nrd6mqY25A8hKjIMRR9V7zNOjPycPQkqoY6ZBMUDlIKgo8AwpzEAeMwyyX42DgeacXnnibusuuT5sQlXyM294p4QQM++oKqcYZfYqavAwYLMpyYLCZ3AGmd87hOwNJa2ZEu1QHBpkQq8hI+p69wqUX9dGHgYATcz7kfjq2CAkEcSUK2NyvDsEpKGcrhh4wS6ocYwQUGFCjFWmVaILyDwNkHfQyLIF65l5nmt3IsHuELcEAMZQY+jdHW9nvO7OhstdrwMQNpWeoWdYOcTEf9XI3c3L8/1ieP7oh4fSZw83vTNWcrb6GQ/KjakC4sp42KDK2bqTc6fRo2RhaK4HRTjfYSgR0xBwJxyoDpPpC94gWK8zHyVQBFQIzttFOuadxRjLc4nxrsz2k/TbqDzLFFcDxvK95pkikS7L5wovAfzhhiQKmkew/WFPXoxzXscj6hiRwqp4vv09DKUyHYJDBB4YDwaASFnk1me/1xfHkB8BOERcPZzks11Xo6rkjEy/0nUWnhSTC6Vuw2XXPH9db+PvYeV1xvxF3nl73pAQWzn3tkWhXAX/shDM/iHNGh2678yvUpaDTh8cBjQzQDYEZ+jrZ7bbxtYwhRqVMj7qUSb4ld9yTXV0+9K2SEPJA+7jM/eNt6XgV5gwDKWCSt2kiRNOfYXzucZujTsmfRUjoJdSvaRAzEzTW3kTqhw2Qc1kt8PNjUV15M/bxtTmtp+mGptNjTpmV1adzqiRgEE2bL/PdLruYFZh3FzgcBBA4eoqW6PGnULAqXuggvWMppN18SxJhJRpZAaknYMYzbjozggxH3pKyghCMAeE92Z4xWqKDQJKgwUzYuTWkCg9ckomeNEFO8AxOQuzFTczoxx42SooMwOA/S3Qtri+FoYjBmaN9WYDXF+jVguVhMnJizKgzDmuReE6HtGkhGYczEVdKA1BrFrzEoSA6fKBeGefCD2soiilmfHjjlFAlAUAcBTJNqDB8QgcFSnbuK/MPSCnoTb6o2VudNH3eLCJGENjXuGkCgyAAmlMSeS1F25nNMJ4hzNWbUTbVri5UdRq0xUCaNWKYDj12dU6pByyUKcz0Mpnz0NtaHzbAuvQ23zrywgw9KgfMEZBOZqjhFCtjPNl6KpKCQ1gLkL7yNOn8tzLSyBGo1cqEPQ6EKlSLc/CJ5KiKCkBtUqVimvWy34fQzM3+34PXF7iGS7QbbK3YegzKTT9DdDDPElpyAo+afDcbgUX6GqEIHvTddkzU12/AQRBolcbRZP6HqsYcU4NhiG7slcqPfY3lXgrPvlJQ2LokYip9Pjg6bEQIG0rbPR8FloyFAtO+ELoqOlvMjO5vMQb15XtvxkmutxaJ0k+yDkbon08WljD8VbOUYyyCkMQ1HK8ocsZhTCYUKE/ZlmZ2grOnjPP0W49oj7usWpbYBNx6GX/LnZyAf28zmcaGRWaQnnZCe8azqhSwjoGIAVMj1+Udw+iGG63LrxDQ4HGtBb6180Zjg4NH84ZalelkIYW9974dgPzRtKz0PfZY2RDvUs1IJ/bbO099W6HAdUdUGQky1LDq+vqwsPEw51CfeduACpLKSxTsrm1bY0KwOmQFeimqbDSDfBeOoKa6zWwWjnv85Mnck6XL4qcGA4i724HRA3foAFEb12rIWDmrd7fmny3cTyiGga0mwuMY0bu69tnJg+qrkNKDU6nbHxyvk2j72M4DIm+bfHGPnsy+Z0pNIAaq0LhYmDQcDHw4vZonxk3F+h7YJ1uhJZ2D0z5srNLlYSLELTQc2pC9nZVISCE2hTUChOqKgMNZzRodjvjr81LLxk46YGbGio7aGzEKOczDAiXD/Q8hb6qo4QeMWS2bXk3KqQkz14Pz1AD6JN4tR5cOj7bdcBLLwHDgIt2wCEJSFAP6n1Uodf0B5GzPIeuQxWCKOkxrzEloCZEr+fbIh8d7+0wAIlAosowe96QFGxJ2DUB6PPdGFEjQPnfvle9JYsWCwel+9sRE40LflaAQWfp0/149RCHg9yRChOM+Dcbmdf1Neq2Rb3b4XCscHvb4NEjDVcmD9OIguO1swc3NZrNpggvo1EC0LjOoTzPG29LwZ9QmTDiJRiGjIaG0KCuHZLPYdBHbVYTrcEhSVwVY4kY/7hqnUarGletwcIW9mGzj0CoMTpjyCytILFMIdaFxcx1tBROOimiyXWt69L3T6EGWo1tNIsvT8GIJAFIopxMqCyngKiqCfKQUSdDUZRoL3Ybiw/zyFoB2IYZP+d/9PRpYHhFOcbayw5xJ3aNfcZ5or0H3owUb6XW+p7LyzKcxDaVCBoVfJ2fRwxSUtTOWa8IgtzVyd3qEJC6LQD5LEOpiruoD60ABHoJQo0qhKxlc4+8RZmyIsqPVJgQ2mx8UgFetQExVobEmxucqHHfo47A1VWZRyJeIBgqYALOGVd8P5WXKiVstxJmdU5Cv174VyHIfoQJ2N+i2Wyw2SjqFhpDWmKUvdhuVREhIeme7LY5BCvr8+48YhTjg0iMvr+OE9q2sv3iuVsMt9tPQ4X7Xhi+0sZ5ECXb01wTJ6MZCjJno4nCQgXPD/0Qj5VhBIwRH0fQDrcYaaFbVRj1GU2cEDb57EOQ815FNaLVAD3uAXQ1VqqYTKgwHLPbGdCcFirG/tKqAsWwkSqNYkQPzqrXjfOGjyl2Ot/NRhn7kL0JtnfOYAdgKNt2q2ehl5sYAj83xRWqTg9PPWT06HF4tmzhB3ov/fXyZES+RON0GPQBm42TD6Vndt1NBc8xj5ZDl3IoirPK29act/y4B0go/I1/Kn+i4CS6ic3Gwpt4dKu2RK0BF7cMAV+OR4s0yKAMPZYeFeMmKtFGIr0p2T3KYZIhvwuinJmnBe4cQsCpr/z1LcR/hQmrll7iGuuc/qQyTRbazgC8ttW59CdsNiuVV53yGIbG9nfWs9nonT6KRVS3LUKoMt8nwcxh9SDo5nabZRaS887pe0MQvsbPmOwnP2IYlxriu90azxs+ciClHL5ovMZ5Uet0xroNQNgIPdPY1lh8htPU9+heo3rxK91gMbay0r/dNka/KQFT26C6vMy8V+UAZYBgJU2mB+6l8niGwwClwefXG1Xhtnuja3VanAwlqjFVQlfDgNiu7PmFcCfxzOOuAfPYzsMCuejKPSeq8RUj6Rz5fBkG6L5bvDeljJIDxpgePRJewyMdQ4M66qVR+RSQebnxR+SIBq9sVv0JuzXpOe+98SqeSd+j61YWftSo/DBdE5lnmiKvstDnfhDkYd7LW423peADTomFKBkx1uaPi7uGeg6AGmC8pSZKhlC7QxKBlLA2VAQpoe/JUCuxbFsXw6asqu4PYtHyNu925l0gMzJlJEYcbnjRMlNm7BKOuvEkwmEQNDlA0D5FPXzEz26HgrmOo+7La6+VruEYcXtbEWiywwNy7H+NEYiCqli8+PGIum1x6i9MSeTIikcZkkNBN6QGSBrn5S8aRBGlGxsAbm5EaO52WekeR3lJlZKgAX0q3p1DxQRxXuMANDmR2ofeUJmkItfOuAXPvW5jsQbA7QWVlk4SFpOisvMcFfH6JBUi3qVe4XSqZ27h2rwZ82QpLrTqe9QxIsQme5KGwdA3j+odzhVWqxqNfm4FqHSjQIJpCBQEfCETh8js4rZG1d8C+z2q3YCmbfGsl0TER4+UueiehFDLHl1fC91uNjiFNW5vgQc7GELUBKBpBjVUGwNiHj0CqqevG82SafiDGlHjyWvCry5ah1wMgyhV/ZAZaghFArO5aI8HE7Doe+Ad78Cpr8zBtwpyV2vulcIq+d4WslWU7rlCoIx01Yn3iXkMXvBjkotDD5kZVauseKPvZR5tLhqAlITfbDZ4o8+5LcMA9G1loTP2PBXyRVJWn/KGxIjjrdw/iadPxlwY7346AbXecyuSgRE+q60+PhMAo9sitCs5S2jc9oCcmxAmi/OvjsKLmfDWJOWTvSBvz/YVdrstqiB3uj+WOYEp3ZPsH4LFDpsrP51lH5UuxrC2ePAqRjRtizE1OJwaMTKPxyzoeThKv3XXIbQ5znVCJd6dlFF+zoOHwJQd5/jKXmFdSJUkjv6cBOFr0gkQ35rmwmTtbL3W/b/ey75RgPt8L2e4HQ5qvN3eiKGACiPDXHjPAAOOxDua0UECgA+Cota7ncorSTI49jViVC8TE7Z1MzglhpXV3kLROzi0UpzhQefyRqicp4SqP6FuW6SUPW/VreSNNJrZPG2EhoLyA0tg7taA2qrNcACub4v3VzHicKixWlXifaLSMEetbm9RpaSIb8QpSI5JE5IdtyQWJ6CNOXF6SBjjCsOQvRUrPfeaa2X4XpqFyKTsZbWETgZfewF2K26tZ63keJgxp88/aG6QAY2OUPnfRv9ROYABw4DqeMR6twP1FZFVIl93cDQcI4Csc7VtbTJQXFUOjCiulnp+GUaCrBmF0IhcXgsdMCLO5q/71vfAWtcbN9vyHpIRKpr/xm2Drst8HgCOx9qnld0dpGdF5QW1d/sYczK8GJgOvNP3iufUnV8IVqDiAs+AKgChs8IRu20rOUeahN51OU/PHyG9nwXytN9nq75tLeY/JiBGF7p+PIqXYdspkl8Jn7AwJucBDmcA4t0f+pyPYvkKAFK8uIN13TfeloJfpbFAHFdR3T26y0Sk/Rn5Ta9bYZ5jqoDQAG2DdZiwjoOh4ZuNJo4dDyLkjzViFJTNdOcUhXu6GHsgK9FyxxpETcLwMYtmYPR6STSLYj+KZV9U95mtyxAeRdEOaYVRFZoxNKgfP86bpVx245I2jSEFVk+RTQqtrHnqtqIcq+az0VPJ1RYGVToZ610J8WAyjwp5uQ1/8/oeTQg49Y25kG0/AIu5liScWtZJpFWJiJ4WKgy1Y8qF8IoSrtIfM3qwgriVGTNJ6/ishlmVBLOt1WpF2xrj530nMsjqGd7y9ZeOjqsQZ7HphlpUVr2rDhNSm5lpjLUqm0MOE3F+MSKzduE1zhxdlw0TPkyt75Qgcqgl/SVD9G0etUNXqVUpHdCNGkKNWumiCRr4+I53mIBtVQ8wdyOT1gJj1Ues2oDVI5jiYIl2rHCh72Yc9XbLs3IoqWdwTpFjOJeFkhxF4E2PXhAFQWk7BgfsFFBvHoY2OwXlcKwkIUpdnzYP50Vp2hYWBgaHvmgSZevQ3RCAU2pUrVPmnZLEAbc0OGuERy/I64YcKuON7eDWcx4qO1cAJaKm2lLbAhcX+qvYIO4aeESNXgcCURZTqj8QgACmVIYA8SYEifHlmkX5S2jbRgV1uSeF0Ezi8RkGIKqSdq/XcH+blUH9YUqz2HCyHd2kEJv8kCihWUiiOBv96HdGKLATHL3FxhBVu/MxWihQo2E9VJIePcp5HW0LC0uYa/3k7T5595Rkr4rwGp2XeQq4gVS8oQmrR/neaqWkHrdIM3pb6Z6yspmAOJXJ1wkVKpX3fL55I5URrFsY8FQzIUMJgQihoaMh56+tVEldteKBQ4ru0PIk6SFohqzwNEXAcmZzxT2GyM6gqOs5rhGu1mVhh2HAaqVe1ssHee+dcnZOdQ7j0fsjCg/sDKXCCawaQOX4UN22gI/P9lpk2+KMBklfKQZ41pL4nYeXY+aHrRRA4GiU1/v8DtuDEMpqKYg5LNDdqSmubb/ECNaQ3SB8pusyiHc+SwlMu/eavxNTRsNDyLlDiNEKNnj7gcaAFD/I+WtMGua5HdSAbBgp4cJsz6mW8N1dg9XVFao0Kk3L2k+9GOPNJlePO5+BdtuYkf3gMmC3c+UqTYmmglujDs6YIdKp3smUgPMpr8tCh9UQmyDgit13vaMSmlPhwhsEGv3BXDyya861UnnCEPC6Fv4QAywypCjfhLznlBF1jBngUuNDnqW86XjEefcQJwUGDExoW6DdFvbl1K1NP21nV/d5460V/EkuG7OrJTyxQuXiFEFrGzkhMyVFEChEdAPPZxFgD9qjWD+qsVkcl3KPvpdsakE2T2KBbzYav5gvP1LCKsoFYDktICemkacbss/vKhR847xwux0EvY65QgQTD+2GdJ3Fca3X0NKBTU7kIxK/2SCou7VCzvhPYWXxvxQygn7W2K07ZeLKFBU1MuW1bZFcJYMQRDjQZVdQlh/KRM/1A9zeigLlUbO2rQ09FGNE3Ilk5FXlrXB107aZdLy7np6Ts+PJmOT9ksxcFQzHu6eRkpVdbTqh4PONI9bo9pIJHR6qc2hWFYVZFMZHyKEyQY2sthXmyhKQtVN6EMqSVXR5+hAIQAR8RdriRENA7wS8oZ86D3Ntco8UQToeZWvrMKEezqhVEU0JYijrxo3tGk+eAI8e1aiPR1RpRNvWwK3QN0us8VjWMWUjZLfDKTXiqeKa1ShgGEUIGsYDoEwKRLl4ZaRhcEYwlY6rKzx9Clxebs0tH0IOd5hQAaqA8HcNwYOZZdf3sgfjWpVARWEFcVHvRwhi4Ctfp0K4vxGjjnoKSxre3spZ1jU0hjsZ0sh7xjtARlvfPkPddZhal2iqHxq09K5dP93IqVuboUdZcDp5XlVZyIRXmC0hfJAvjqjRH4F1Jxy/J0DaidJMHcyXr627iNBWQJ8V2+IskdenQFMRZsO7YspYjIZM+7WG4J6tyvZ9dGLVylzVGSr4ZrAwTqTvgdjku6KTYQzyMMxc5ymhPt5I6BzqHE/Pg/dx2Sll1FMVfIYMkz6RklVFMwUfMKWZCbKjVn3pOqHfN66rOyWM6zor+JyCj6P24XpVBS31Wysr0bCWYVAP304VopTvSUrodvMrGoyGrRiCIonYbHIJYJ2QGQMR8p4YMQwNzrFGdMoFdfKuQ7mvCoyEUJnjrusqQdIVKYqK3UhxpVryfVQmj6i1yEiFGBvUIWVZrZb0FDRkgag5CYEJeoOAYTFWWcF3htjttTdys3HEH9ZhAl57YgoBlVruabOTtdZpVtJc6TIEyIY/fQo8emQ5SP5+WGnhlXzvdp+XcnWlHiU907reuuTLei5i7ibmpmT5CTHm+Xl68/x2pXIw6BHSQ/5g0EW7mObTTZaTfVvhopOHTpstSRMhAJeXAvClJPLSaEr3uwYs/pxzy0o0sGaOIP8oWEUPVaZvNxRYnOdDcMN4hhdXwW2YwxG6xsBMo4u2tTVwjpQD1L8aFgdwr/RTNw+D8kPmYrYtJLfjeMRRr/auPuTSp7udKfjVcMaIRqfUqI42ob4TR3V3vO0ymay160uvEX01JqMXhWXOaLH60Bx0nVhMsxJVBfNVxaoQLMOAURNhrSydZiTTsvPAXoECpGQ1y5k8e0o5uUaYbEbYPc/wblWEgPNQ5XJM6caSICzpj3vBMhEcXCsZtSOKc1x7b5Ig3vwMv6NIgpUuI5Hql6wcp4sLtc3wwtwZYvu9MIBd40xiKoGXl1ZSyr2mCEu08+e5qZfHK5YxIicBOuTPhq5z2mxNb09JrFkyRa90mluabmFX9aFm2IGHLpxb0+rI++EEpF/s5JKvOLyhYMN/18GeRNlK7wGy0ufLqOlgycZdczIPE+filS2WBRPl2ZXdQ66F7Y8fYD5BbwqP7UVK5mmYI2c8QO9p8WudIMZpXWvIVgg4h7s9IrjFJuh0HzlXusStIknKNczNW8P38mHKS17fixvYSvX2PXB5iVNfmaJWVnaZsiRqWxyGxsKWWKIQgCUes7ayGbhciFMcKD3GzUXpNRrOZrRxULllvX0qr6wU4snQaiDTzay81+fotK3yi5DL4pkywX3i3XMHY/HABZpML1NvxjbgwBESYJyVN8V4Z26WE6HhcE1/Yyg5eSXjzZk3QiSTJYXd1RB5oQnVXCfgvE+6Nuu5wbtFrd1LXt3PHBPteJj/jgMPuD6CO3TK+L4W3E8Li1KQyRBZr1npHwJiGV2VXx2POY8kxhl973bG70Nwe+DCTXlHfS31aZIiD5Z57+UuYPd5vdZn6vqf7cVgefHRCByPGDVs8l75rt9hLgzXzjs4w0+y11zlOfNkAIewK6GxT8Idg5My1e3ts73wrDVyzt7Yri0nmh6yogIdJ0hh7AxZvpfyif1tWNqQIR3z/WN+iu2T22v/Sv5hTh+LGXCPSTqW8DkLk/PRVnfkoXM7MqRutdKiBzFXgkkpK8IPLieTY3Mj3vJfCMpqQusTtYse7s4FOHZHtqg+5veVzxVvk+PRIVjJYnqmPF+IMRdWYPlw8yKnXEb3dMp3kUavgTau34ifM9fMEtCjhm+ydKjRr5ZHL/RP3FOaVL36RWhdkIgQQO+48hrOIYM8WX/2itgrn//5b1omc24H3RkTaUkZH2vuMvTIKMrBORTuVNgmVPniHI9y2DOlfgo1RtQYQ4NJ4wsL5RrZIvQuEY8kmUIZnTKj2iYtWH7oyRNh1qt2EkJV+J+f05Ko+cWQZhY8RLHABKaItE4pJefKPSnRaztu30IQYUBedq/CSeudE+Sf2Z7bf/TPmCqMqIVoCJ30PSpMxviLRTsjZa7HegI2PdAJ/nkN/7ZFPmsvYOHeqe/ipeOP7BL3ParhXDzXr9HxjGLd/O7cmDJEKEmingl0lozQc7rzPn7XT9I/P0gYyamvTGE2RWsYNJiqsn0wocbn6Zgm9x5dp4Rhue3T9fFnZoykZP1f+Hsp56ZJ5rMQBc7dRxfZPZ25rodBQhX4UioOZ10G9+J0yiBESjB+cd99IrM0iMFxVT6DKK0tjPuttM+5TaHO+6Y/J9+pw5Q99V6Ju70tadlJR5IQBUaR3c3J++He08TJEjb5OirzVPS848mPGmMZfqaToUEYguyZ77c1v6+2dr84J3ApSPs+hxUZTTiavq/GvQ95MFvafZZz4x+fD+H31R8FCdBfXRpXphw4fkejwBQY93BjiXOr0ms+jlcNgxiaCCHzAO4BYO8dBjFeWNGtUDL7vpBXVByYl2B33Q+l1SqNBVusw2TPUed5mcjoNul0UkR17nKhEoUcqzzR0cTzc3zRK5v8lclmXbvnD3U6o4mTvU7KaTrtMiU0ccqemr43HnN2GIwpa85gZh6L3Xu3Zh6pDwc9pQZvXFcCKtIQ1rMdx5Lg/D7z3WOqciUiL8OdF7+JkjNXDWejbzKoajjbfU0p60tjXNmdZSSAP3sv00gvjP3m+vte+TdG+39BP7qpFaaCz9RhkrvharsjJZmv8mPyDfICghJUXE99VYBFKelZXl9LREUoDaOCl+nc6pT3xs7SnQXPgbJoru9xUtxXkgkfw4RV6ka8J96QOh7lnmy3ZSTt7a1644dTwfPqIHoReU8IEEG036NO5zv5hByUe5y2l4d9DzO+63TOPKZtMcaVrYs68PW18JliT6mjeEByLoPuGW8Lwf/4xz52hwnYZB1CQgHiO+txGKPTjZx2kuzDmrrP9pUxks1Gk4D4MqL+WoFkTnh0K1mTJL8JuuMnrGikISXg1VdFqb660lCEV1+VeLnN1soUWokoogK3UvuVyWGW+Mb4+WEArq6sq523Nh24a397q30u7At01zH1/Y3EhF1dIdcA17nFKGWu7IVdh2f7yvi/ETOHVoug3mLPS8kaaln29u1NZn5tW3Rl5Hw9+gJocwl+BygrG+j8yIwPh1xT17wDRFdCyIis61RyRmNNUZRMbB9DQFlSrm0lX2Iom/iwdCORP0C/p8mV4+YiMyEOXjQXm+p7NcxtGcCh015B9JeUyojjIMyUt/3w61HmZ906WdItlg2ILC4ylpVJTHEahgLlAO6pfXzPvJ+lrYUhtK3c4zFV1iC1bdUDdnuDc7vFfg88vMrhLIgRz46NPbrrMspL9MqaZc0bkwHA5aV503Y7YJUOmXFeXRXeGvuuU3QpHzn/6tbV4o0x71sSJG5/qDPqRcH56BHOaMx1/LA7FFCbdWYN93ST1jF224JHeC8UPTqW6KmDnblZUYwhQHam3h2o49DXRaUf3hE2ziL6ZegT761TjIks8a5bNaDbHEfHTtf0Kh20pGuTTtZNmgnUBdrl4Up3n1ZxlLJ/ymctb0SbxNBr4pVGkzXqfSpAAORrS5TtPpTSECzuoxLKp58Ib3+wcQ2NevW4bTbWX4DnEaM2WAOEaXM/KbB1XeZFGyQZ+hS3poys1+K+P4U1Xn1V9vzy0oXDbTZFDxWjlZDLB88BC3prfEww0dB540fGPxs/0LPan5q7nXmJUvaHwlAar16w/1ZV2bCrODOVPbYXgCG457OWtw65hPWhr8tkV2T+RnqoMdpen5G9ofQQ+C7OxDdYf7/GWMStMazEmhTpxT3HtSG1U7e2vnuANsgLuQkmWfwc0R8G9cB7pVyf/3ovfEJKls5KP/MZysftbqtH0njhfp9/6WWNMwrRtoUsKzzQNLLnBqW7WPQG+kcCqks5VJ5CzSfMmtHsFVi3x/tpa2HEnL55eN0+8no5Ww31k08Jon75gvH+3Q7S+I9CYKaE+YZ9OZfFySDuZQjYT1ucz5q/oXJmio3VhGH4numJKeG8eSBlg7Wi3aF31SLppfHMKyVMj16wIhUA8N73vvKLQ/BJAHMmWVhwMZrQ9KjuHJHxQofnN6aqCLGZA67caPuZ/oNIwDiWNFauLhNSSihKsdFaHUfXyClJXDy7hRXakL7aEFav9Lh98u4uP6/5HD0SB8glqoYzqjQasuY9FXzAOIoy3PeK3PpNno15Fri9mD9UdNzigfV9FFL8eOGCnR3QfXtv0yZRqmWaEoq5ehSN7yLq7+N5LbZxvqnPmYP9f4Z4hZB/Nv8MEUd7gF4uW7+erVVfcM+eKwhzure5zZh2AUl4pqZK6Pk823J/CYkAKnpjzycScu+FKI+PqIrkgshdICJ/Bxzwc3d3mAx0rkj4u+a+cu+BVR7cCdl70jSZzwyDK1EYQhGzbM901tWdUCwuHqUDDHCKEhVZuA6F+lwfM++lV0qiO7CK0Px3/Jk08XGb4RCjwvh3+5JS5nOe9o2NKlx9h2f6e6K0RHRrTqeYfW0YcPcXSuMcc0+d/3ITxpwYrPcnBBgfI8+9U2LXKwyAeSsKGeNCkDxP5j05z3Qe8nXuj3kt/OccrXivRqEt8iF9L8KXsuE5d+y5w6MP3DPdN6OD2TPtKFLC+Zwr5Zi3ZeZpS0kMMX9eQBlVxvs9707Oddreu58VjXlm9FHkXoRQyuqZdeGbtvnHzO+K34t5ZSxPxHdymdx6zTZ1+93EqdhTf/6kEaLBxftCDsOypk4zbxYA847P8Rt+wLGHO3tTDE5m9nuLuZ8LmmEoeJ7pW6r72KboHAEXWTFTIomkG53dw/vv/B+Q3EOM9z1ytkn5/17s2dzmupXKOnq0+B0jRfcQ7gHRfaAMc+GWmRKt75pCXeiBlKMVJtNtjW/MFe/5uakMn+sk85GS6pMzXuLnZc8E7Cy9nH6r8ZYI/jvf+Qr+9//942a4XV7eXYvXd6vK1fB1wytznjZIc4ccKmdl3vgZfp4L9zWKhyHHRWoxCYsJJSo5jg7dZ/xC25qFZQ0yuNE8QCZxKiL2qb0gch6IUdoTRIWVhYJW2hlzW+qU3PsVmhlf+iy8+mquzsEYrE890aYTa7XuPvlJ8S6o18OF390Ja+e8/H6u1yU6XxCOQ2LWraB5DNE0FNeja04bn2LDbu9ljPJMwfh0f0FgNYdQFbdaWmSbNeve4RFAvrO6vbF4XLYfHwbg4eZUwLJvXFeGLnOwTCMRH06TlvbD3TkzVy8VyVEUzaUA8ICExYy6+Amua7vNLk7EiNfTAwBCe4w/N5RXE3Kl5JvMLQTg8WNFOGjCK4I/DA4Z1kQ05TG5bwTjMBXW4Dn3PfDCI5n3p/cC775wlaXbGBp210YDiQ3lFhs9c2/a1rxc5gVwLloTFkRAeGFDKJSxlFwjK937U9wWbd65lNtbeZehbc6j6J8JlIgtecN26+4t9ykE2/RD+8DYwW4HrIabfNH0jH/+1cp4QNc5L5omknlSKhr5OZqy89EzZAxxjDpvQqox5iS9Jt8JJ1cQQqaHsdv6LTHS8dWAiCz5MnGMKyWNmxevbbE/NWXOhGfqtJIvLyUpeMarqtubwhADgIMmJ1+0OZbcn51P9mcjI8bDuvxOew/pw+L2Hz0qWsbPjRzG8NKxyH0aBi0pezxYsypLDvKj67C/qYwG5wqBla2ce8RizHTgFxBjLrc3izDq+1lseko5pCTmggFE1ue6mZ96hSwPWfrSwBznnpvHz/MZxsdTMlngcQufd7A/ryQHYJMZ5x2k2OfzkQ60eIbpFfQUdJ2hv4B68Poek1bQo7gqclF0A0iXvBMsIWt5eupxMz6HEYe+tjtepxy7buFUzKlST8Ghr+27XNPYru0+xKiNBdvWYrBNNnrF3t0Fnm3TaN6NAqtEeylrmMdB0IHFEnLt+NwXqOpLrz4beR2PqhfVh+I8zmhywQg9OEkIz3xv3FzY9Pn3KuZzs7KmuugxVVnGPP1URtTVI0Cv+9xbV3SR5t9khBqdYLRO7/fcuHbr4jDaffrUzsCDrV6W8a6TRz0Iz+T9l5dG3yS/rnPFNhw9rFsnE0KwvM5PfQp48UVgdft6WWEPObTyrRD8+NzfuPE8I9P/nMo9SweyUQw3xDMIuoo9w1mtyuf5c+PnrNkNSkbdtjPEMOTYbK+IFYKo71EBiFGUW7koKOEeSkHlsgxH8bRxdnpt7Rj2OK3gTSezIt0c6zBho+3Lfbwg92Sa3Jp1TRSUTJoqmCOASZFyvpsd2awKw9zyjxH14JA0p3OIQYVCCauIrOj+VVWTCcCb407pG25RIIgSbhXyfAAE9aj4M74D8fif67+t3J2rZoDZP33IyvMs6UL4cZP5Sxc6gBCKygXFd+cSMCVblwiOvEf8J5/lG+hwD1nyzSO8rA19R1rrey1Xg9Nwe2xGQaiLhlQctGX8WoesY4PVMtlYrthMFRIx3tNQZhik8hUXcg86Wunc/b328wBgNec9CXt+HWItjeYCK59M+joiM0ozWhnLyiT6+RQPzAhh4cH1njVkbyD3xm+q36+UUNZSni9gNnjUGO7u2TShCEO68yjtyufJxc+J30kJRm8suTvfCn6HpXGLnIn55aEASvVcZ80KHDdT+UTs7iw9K2bIyerWPEh5oW+UyNb1946U0DR1gVHcobGEsiGh+1ztDf355iTx7FnPgijPCiE3YjJjm0eoPGVMldxxj5Qp3+EcfPEHAIV3pKChJHk9dYyo6+efYQilgsc1FT9zPzfvKe4aC8UPUMpgOftKS9cCON/16vE5nFM+C323K1QwZ3VA2c8EtzIXeU5+kaDutcgtXUQdA7rONzScyYwkvXu8PJjLB+7V3FNC2jb5OeT9qYOUKD0cyvXcWZsjQq+US0nIezbx3oe4Kj9ElK0iXFV+/A7TncnC+c+5GWrkmBdFFSQ70zRiYq8fT6fuQ1yf7a97No2stVbVqjEitHUh0p5LnP49wL0ApV9UGoopIoSqaLrl74O8v0Kt37uX7wyDVfIDUOonOhdTM/gzhzAUdovqUZKgK4zGNy99s/G2EPyf/umPm0Lu9+ZOBroeOA0RH88HiKAzpM2bYu77rEXsgVO/8X5wP9bpRggi5goeDLmIETlunodLrgtYSMrTpxorl06WiEqr6dSKRbru35A5xq3NiwK8iFVPCc9wYVZb20LabXNhrdSYb47PBGF66SWrigDkhjksseXjV23/+GHGg3PhIQgCeJszsUMo94CIH6uPsY/AMKiF6aEYxl8670mVRmnu1baYrh4amLTZuNjEJDFmPIcQBP1Q20Dis5++ntEnDQuiUcZ4YLOmOVmvbHOoUcXMfAqFZjgIin6Pde4rq4Qwc9fN4iEPYWv0QVTMT8EYmsb9nvpKPBrOFcJSl0Q5mAPg/zCdozoeyvhfMvvom5vcX6mBTcwuNmP2Jughn4a6CIO0OF59j0fqUiorDbQtcjfVEHLIFff0k5+QO/joRVO+rJrEfp8R6LY1lAjQdezfyA/SGHwOsol5iKQvk8kzoFDd34jQebBxCU0xYtw9MMGD62tBaF56CYewnYebm+fD4uiHEi33fMmjciEowheCNTPzYQS7Xf69V9yKeuF6plSIp1byh+glYz4RScR7Vec800qOallEnv9m40AHPmy3y92LkfMlDljbnfQ8hco1hgGn1JjCBUjYPCAIeBPGjIYBmB69gKdP1Vt2fV2UNSk8PTw3dmh2e+HBHX/3mHO0imM5WXe4zA1jXgqvCGUWK8UwtptlVWnreznE/3vRUlRnCcEQTotV13W4cFzJOZvX1kwpI8JhmxFe5oBoRSGjHSbAXF2VRq7zztDzViC4QSrfFXxQL9WEyvjRuHtQ5Bl5BN8uoX7HX3nG3M+H3UVH9/6S7w9yTrt1zukLIXtJbm9VjjDEDjAZkBKskWDBw+hF3e+l8Z6rpnefN4j7x7WRzhmlMM9xMt1HZXih46jn8tQ9EFkQXZI477waOswdMXS3bQta9XePc+N3eMZW2ela9BY2ubO91ypfKWmlmL435JnVobi+GF0O5cx7Qhz0E58QI/zxY9WjnjyRanyedlW3Ol+9WJQgtjuj+gnlGKB3kHkUKZnn905Xac2JnHd7BmDnTE85018anO/ksRVnTI+5ei4JMtA7QwSfr1qnmxLgdH0QSO8nbcR20YmX9RTlXFZHOadnaVvcGeqzejzZ+wHglc/93F8cgs8XSYhHKC6grcoj03DWGO6iK3Jh7uHQ+gzvWiOaNH8lBVDbMjY7mpVNIum6pgS95rCNZ6Jw/D/GHL9B10wq5+D/TeZhwpIWfA9rhlGHCXP4P8a6nI/bRnbs6zqWUgyaG8D14s537aLvdnJBXawjP8o24TyTmXGuz549N2mPA52coeu6N17Y289VApMxzAFS8yq4sm4VIM24xqYoQ2WC0iPqbVskMvOaz21GS+Lts/InCpUkjNXIRiknV8QXO0LmFngXqC/hCQgS5J/lBZZVi1FuWKURbC7EkBabt9+4O3tfFwojQiiuX9OUv5uvwdmYmWHrHkiceGXMlZ+1uuBOmwntrFKQZi7WYQIbzaQEQSiJUjpiLKbmrSZkhcqDlHfiEnVQ8eRWsdHJNCEvlEwDDrHz+6ujQP+IpHjvFjI/47OAMgRMFhcN+eR5FPWq3TqMdojkKJ17AVXQIxHXqjhaUwzqALDxXUqwBl78uu8FYD90F9QUE+4BADYj5L30HqUQtLtsj8Jjya0Vnlq+g7zMJuSsyiqEjLrqA+qUy/jyo34OAKznSteJBLZyd6gRnVxASlK7GpAclhgBl3jZdEH3cbIGgD5W3YsRXk2v4xY1+/Vwu86V7gy5lCdZmlWGo6EDlGEawceaKyruYEzrBeAIwuQn6cXRrUfqrbiAF+GOj6UE62FBBNrzPpMHzrJMCdZM0T2yDA3xIbxegLi7UTQcCwHWRE1HVKd0bFfW4ZjeuVNfmTI271vi31fcIVVqhyEr7XZB9e924/pfJOkhk5KEfno7JcbKzrsgkJS9t2NoULNsL4DKdWI3uam6EvsQ+XvFfjp8dtO27u7noiS89POjQtBGfwEF7fmysW3rSzYGo+u2zUAHSbXQN1hloW0RvP6B+4c1pGtbe1aRpzO4S6/0WRjXMQsUm2sIhr5wXj6aZBiApg3F8fB86poAV41Gv+xlK/WwsjEigKP7QIxmhJicvGekpCCxnkF1yvlnhX7hPJhvd7ylgo9pEoTN1YadKw7cIaKwJiS0W2ITQnbVEDHSQWuGCrm1UR6E2YvSVBcoCRWTlVL7GdIKvCbqdHuLahjQKHJlQp5zDUF+rv+nsZCSzAdxC2gVg9S7+P0kTKEJ2fXUhLEoX3TYSAvrqytndbYtPr1fYbXSmDZtxQ2gKKlJi7brpElLRSRdGfoqyJGZhe0lnSJhpyRdFa26gau/KtteYR1OqIYBG9dqusKEmv4mcpcY7yb66O/PG0Ehkjo6qtsbjHGL159WaJqVZvqPWMeEsMnVUoBcQaHHWioisKYigLH7VYUDgZDIKTWilB4POBwrvPZaRtTXqiQMveZbbEWR//STynSIOggjTd2FVHSJtxbHX7Wtofz+7kyopDmXeidoRe9WZ2lSAaBqWxz7lTQDW9VmDI6ogW5rzxsVfDh3KzRaDajqOgxDk3MY1C0Z2pXMSZlDMxyNbus2ZBf8IHH63C+LUR4GIMwqA6SEJgZcXla5VBc5W9cJU0wSKnM6CSdZrVR5VffFKTVYBeHoVYyIUZsJJeC0eSj3d/8MdYxoNUb62b7CbnchXQGVlup0llCGvgcQsT+vMr0OZ0ViLqxKi8QtpoIOyWvoPav7g1Xbok0hQneF9aXc22RKTIWa97/rAFYlGKRJzoQKp0GapjDEr20bi3Vlo71aW8Lj6dPslmqltn4IiiLHYEL3fHbeS0DukXaqpPJFx2Hl3Se8O1TGhzz1QnAok6yGAfVmI0qOMzQrTFj1okxQoI+ogVgDcSVeCoeos5oRwwEpeOmZzTy5MqWOSsXVVf79eajQ0E2odLtrEs5phSFssY6n7EWIEfvbRrGKlXhEX3sNePTIPD/jmJFNqyGNBNzeijBWDyYfeXEB7La4mxynMN7YPLT1xZgbQjVAjpN11d64Dxz00hTGFuVcSqjofdxsLKdltxPUdN22mLoGY6qRNLxNPMG3BS+enKOl6xq0lw+EZ/vQFi+b4RQ11YQbAJOeKWU018PrQJ5TissGITZobm+k4aTS1NBuAWi4pX6YChS7+prMVnlV0Sqi+0nR4L4HLnZTUQGq0Qs7aVUdLhODhNm0bWXOjPW6RtPUWGmTqNjKXtJbVsjM3c5Q4IZ5OxRQbYvjsZY4d7YipharQNSqbYFeDaf+FnXb4tUnK2uCmPsvqjFEwaH7RsX0yRNgva6wIy+OEUnl9BqHXPWmbfFUyeFXPRKgVbwZlegRdJNvNqjbFreD0OwLO1nXflxrr5KzKvq1feXqSmT3KUpVtAc7l3OXkuggXYcBKwkx03tTAYiX4qFnespuJzxAEkAr9P0FNjHn9g2DAj5Q+usqO2588jWhhcsXC3tP9lg8NFZxjF6S4YA6RvSpyeFuSvs0uNbK14Ma2d5jzjtMcuw6MZQlETtXhHthpwZnzLmd3stYpRz85OkIbYunr2Vbh79n2I7dI2VLp2aNGHJVHfQDoHLO8jkQCk/PW423VvCVwIqsV/1jbkCikv1JGOz8sXqxDVGgVdm2uXsYZlais4Ao2+zQh7w5E/LvC0WUc/aoCIdDwjxawaVxiuZyV/TwrAw46B9x6aSMRCHbQBZ/5/eOLyGCpMRIVxr5sx0eCZaSw821eF6MOfNX52DWu/7MFM0RYu1CwobqGDGGNQTVdhvN9/q5AyYcT6dau85lZEyqH1SGqPr4Qz8HfwfIKPk+2wMtUR/1YoIoQYyI0HCqpjxaaxShBiGf1baqrO92CMqIcJvPbUSOSXekZ+70GFcW75uSdq7dBEPRLnb5svnqLp7MiG5EMkndS/M0DdA9lDMuKlw4FNrHxKJtjZwMwQ410ObwGUNwjd4bmf8uFtyuhpu0/tU0yFDHMCC20jH1vvvE9uF1zN407v0cZbPvq/JSu73nfJvhgBeu9MtJG08hlzWlB8R4+ozO+bx5lQ1Db1KNFC8Qh3wdp9iAdau999BfwTl6YvxvtneCvon0ajTMrZgI19mUsZS2tc67ILXxIUqgNk6ZOR80XrVG29Yyn77HKmX+k5FTHeYeyS7mdRvt0KZQI+g0fFfqtoXUwu46BHbodftERahWcCZ2opxZwxcfkqHhNutdGefSttK99vpauiBXG1GQiSDbfsXcQRlHB62nMka1abKxbsqaO4ftNkezJdK3bgrXRcV3jkrzLFLyiK821eMvVLmbYmN8ZhjUg5kSquNBvZmVKRurzSavJ0bzBmevpWs4pMm71piMysc9jMgbZbLXM7rDna/kQX6hHm4uT2i7BoIAHHxPQRfc3JnsIxpsQ0P0ZD/VKBjOqEEvjYRcEhW+T7xzqiEAtf6jivJOH8ZroT2cpCZ1xqi8z8svWtM8gBnwxeNyjhV5B5vE8UM62RhzyUtU2TDjnbO8G/05ty2/s84HRxDTzaXrAAT5/nitvyg8b7JGehlMToYaVesYi25w8LSgc6qGM9ZdxDhWOSwUsNBE41EhoIYg3RiyC6KJAQOjOx4/lvfAlZP0G9m2BgxR7yS9dzOeydmPI4BOBESdzqaENzrnus586cGl3CeG99QhGK+xkJ++R9W2SKm2aAjbUN41B9iOqWLVZS1/nJcTIzQsuyxkUDQx0zO1KJCUPRD1c4P/y/HWCn5VWfxejLkSBdumi3UhgqV6+rr84PHj7DbkoOWil2Zs17i9za48Go01hXUIVvGk61ydb8c5GDMcY+mCNa3HboUMi5GDxmeT6aZ8xwlaswKOxGAJ2vDaaxIDyGoZcAi7VTMJI5owAEd56DmsMBzdJGLEOeXYYWfrWPy+aS3OIp2Xpqsd8U+o0GvVBbsgnBuFSBoB1Dl0oe+l9n/bon/82WrIILuV9Fkp5c7FIcBajp9OypxYEUWJvOua0rUZggm1nBDo6pLzzHQTmv5GXKxhjfM5VwJgwhpCgxjEnikGIZEQ0F++CMDFOB4lY/36uhLPyu0zIzh6kKhDM+TDqjXc3qK+igBq4/EypxrrtXale/VVY7R116F3Lbh5TAzBsbAY3a8mnk25z8ZvqThNKpRSApDcXEODGhPWcbD9PgVBbxj2UyMVSkaCVGI4nytBAttG9oNJFCj5qs2p7zMarPfKXNAhWAjSqstosUelpRJGjbqu0VLox9JrYgr+MAhdsWzFZoPrW0EyXnwcDcqsQsC6u6uheGO2bKIl95ePf+014JVXgItwY52UBcWZCqWhyDUqtH0dvMD6GQt/YokpwBKm5wv2zkU+usJUGtaqNPdREOwHu/FOKa2+h3XpbNsKF+koC3zPe/BsX5kRaMyWmkgrIXGnk/DxWnn74UaQpBoJ01TbFtfpLPR+dSVKfsihVDECeHqbkfIYUV1dIbQr2wprQ9/3SOolHVGLsFQlcH0pVWlee00e8wIvey9t7mtkVNeE4kyZpXJxPufttjjc4HgOJEZ5tdngiEY/X6OJUMRW6JhVyXwVpxCywVIPA/DkWrwC73oXbg41pqlGVdXYreVgPVDc98D5XGPXDIbU1m2LXpOTU2wyqQ2uQ2d/AhRiGpQ315D13JwaTBOw26ns9QHz7hrQExCCq2pD0Mx1kK9jKHKVzPrQcmhM6j0cq6LrLmna97HAa9elbHPGT5USVsIQMIbGUuAeP9bCF/SQaxDy/rhG08hZrMIZGHpMm22RzHm8VW/T2BtN+E62VQXsqqHIDeI6DJNz4BP1lZioBzkiSAkPLnNvCCLD1XDO5ZgAHIYG/a0okzUSdiv5PisYARq+FhwCpuu5YJjkMQsVuXcNhgScp5Ugv7e3qPdvoAYkZyKnIAmcHXLYoeVFxCh6SxcU+a4EuAvBemwULI9Wrd7X3W4ne/FJkYPh0a8qwqvGJF7jahjKyjgpIWqPg+uD9BG4vIQ8l6XjQhDvHWqJJOl7VJeXqFvpSWFREW0ryjr5J8TokFDeRr5Ludu22Gm389tb5aevPUH1+DH2NxV2a7k7XbcyHWS1qtFQhvKOhgri3YJ4UlLCs1v5XdOoB5y9gFxVKgIVT5/Kr7pOaEZ6LvS5Z0NYZZ6rZgvFcU0e+xbjbSH4Xh4gyA/q4Yh1jMBGlKSbG8hBD4NLHHOlsKDfb7NFdbHJ9ZVzy2+9HXqR6Nowg0EZMzOsOb8YK/EeeG0h5TJG/DHjSBv9AbPT61YfdBzQOgXLwzSUuWSOKyJCw4AqRoRQ2WaxPN14zq3bm0aeF4PoUj5RrqjmoesoyjylEbWGZMj7JU61GU7CIF3TqAkVKi0TJfxLQinaVpj3mCqEzRbVyy8DyLrE5OpM02uRFA23BDFlMDEqI6tL9JKuXxpOHrD1azHIjLd6pjQRmWFC8eGoNYjhwjUoxXlYl5fi5jRazbTAYzSl1MVgVhUKg6tpkMOG9HxXq3wWjMWrU046sncNA9ZdXjBdhbkZzJAXSOJV1IrW+zDMYv21+yAVbD5TwlM0pEDfvwoTVqTlEHAaVkAAVpdCHyFl5Ij7wXbr3Ery7yblRjpoW2sIw5CRCpPRKHWw89CYh4vDG4jjmM+QSB3fKbQTswXtGNhm4+oQR5eQ5g2jGBGc+3K79THUuUIPz+Oll9Sm6YOF6VFh611SlofmpthIuJEurInKs0KNWpvPGI2RrlXjqR0ia95Pq44ho6huosY7q2itO5njOdVAXBeJfOsuoHs5N3VCuzOPT9vqWR4zyst51sMZ63VjOReIklfjK1r5JHzEKFYRIHWvY5RwK9I/adH9XSFXCwMEeRviGmnISoZ4lXZiLCpavtvp+agSdg65IVFwuquh80DmQ9qgrG0dnWhviXa7zTHkbLynxpaxIZ55FOPeh+SkBAOnmqbGqlWrUSc8hdoMejOSAcQoD2GakkSoxSL+vOtqT25o4mReQUt83mwMJEoJCJsVQgRwcLKEG+jvR0powojLy1zhqHgZIAgppBt3ALQralaMoOGMqQdW6gkmf2eYkhOZZW4az4egFRufEbhw37WjTMjxQ/qcTcjrnGKDpECH8L28dP1Xpqnkfw4AMRvnMWI85VC0lFwcd9vavTKPwRAM2ANgHkUge2un4DylKYHqj+27Cv8U62zYw6HodH8CZtDz/qr9aYan3VcHDJD1PH7MZ+cYfwMh6KXQM7E7itwwys4kBHMXj6lC2DUlsKpCoChpzbunDzH+RgMPIk8Z3hSC8ofH6yyvofLEu7BSwqNHerf7PHd5ZPa2Gbl5710IwPGIVYxYXUaRyco/1mvYXFeqiBsbUAbBK23FQoaz/S4eM7hmk+Dfmw0OxwpdBOrhhN1ODAgrAJP03c7TkxIQ3D4Og8jBpnlbqvvbL5PJPwgwZNN2cPNQEd0GsctuyDpKAuB+r9bMWtCa0NaWmV9ROdKNP4xCwKuVc9VobC33jMo9UQNaTM02Xyb+GZQRknkzXnK9CeUCaRGpGyY45doLSvLDvgfaXVN05w2xsffPGyF6HbBKI+qUxPozlMZpwkr4HpSqjSGsCvCuQW+Xv4rRYojPqcbpkKsINMgJIWwhX9cSI76alWILasQJZUsFDauXS0HAxCV1A1qMo8LhVFKBrKgWHUUJ615dYWzXd8qIco8I/Y3TVlGVvkT9iUhCKp4Mw6y1u+4lFTj7uTMo6loYJfWvzQa5ak8Q4d1s2sKLEgIsDr9IoC6sYQAQ5LlA7gEUxBQCbvfAZqOlt5Irc8o9dQbN+Zzr8EvSY2Ul0GpW4FFpdH280DPIoVM1RhHag6zveq9xn+uMFLYtgCd7Y0xTbHIjMLdkc4XfSk1n5iP4RGQyXEOCdH6pa/yRA4NUUDgOEvMbqJj2PVZxBBO+aLjGWEmcrrMgfGmzSic3ICc10jBqW4355QI0rMRoiTXMW3euStcx1sK3lI4HKKNeh8KLOGoccBqAJsB4Gec/jsBunVAQFfmO/cx1Bbq9Ra08chzF2yAIT5KE0dtbiXWPa+mCHLaI1K2e3gIAjlpzPiWht+Z4RN1JXDcAFbz56oRQWZ+ARvnw7bCVag5PX79XoUcI1o2UiFoTmLgH3BwEELq8zPk4su0SAkZvkDlhn4h36XibrwJp1a6ZVyCdfKqclE9JPJjigSNaW2OtD3F6dvGPGbuAbrcZrCFUFvs/psrAqaIiTUpSxjWNwO0RrXqMRJlrbcOr/iQ8QGUDhkFDAarMN3c72wven7p2CdCqkUgVtlpoj0MRc0s07h1PsktaZ518GDKCrrEfxz3vRlas8l5k0Ix32jRK0peO4xF4/fVs44QgBjU9f1Tu2NEbqBEHzW2hjExSTefBJTJgBFjVqzMaDL3ZRViFXDkGiKaIep5bjBgtf438i2AkQrDOzOuYw5BDyIUCTqcKTSNhmATmjV+p3E5B+FON5Moca/4VFWGl52Na2T4BVREKY8og+V/SVPMnT0WHuXxgR209CjxhI/+Xd9KnAk2oAK04Y0UjUOGilfeNmnNGvlBtHKJCi8+/KAQL/fH5RPubCq+/TttLwlxqjKVXM6UyRxHGxgt2aiCWKylse+9DYTTGSrhvMD5ShYBIBVvXEIaMT+52APrsQeJdsDvBUB4F5q6vlacdj1jtBICwJAbqMxpSGTK+iBhFN7B9dwbSm423LJP5yiuv4OMf+1guuUTCYFyDXgAzAJAJzlcZiVGUxDGucDiUCKJtOlA0CdluYSWe/MKJULBSgs1LqXGKTY711J323XJDyGXUDAl0p2NeBYbMaIk5NhBZd7m82rqbcrKWyxHnRTEFiI2m+IuQu3GKm3Ewk/vTT+RyM5au2ucSmywTZ3vs5mj76Nbg5IsJg4IhzISXtV1X6fpsX2nCzqkoF4bNxqxxKvMUuFSyaQT4e2kKPkfM7eQtLAplWbpifi721MuOEHI4z3Z7d71Fwy614O0F7tAK1IHKOv/My9LNYnmLm63Dl8jy58EzbHC2PeDv2YBqu1VDV9vXkwaY/CiWfLkEuj65TiLFnhfEOIsNbUvjBXCxgLP7zc/QI2H3h9q/h0HJJ3Y7HI5Vka8BACdNjreQrr7Hud3iyZMcVrrCKbvRuf5UlQ1f3NkNQzYKyJ+sLKImh1pztpiTv6wRSwH7oLCyfWOeEBxNzfgSAHsP5U89nIpSr2y7zmTEhpVi9FnD4JreOPr0Hpx1VyqQBY3MmhOxEtIb17lkY9veTcLncqtKXd8aFsF3+EZ4xb2+lfJw0+WDIjyZe+V5fVE2DpOFRsyrfpGc6v5gDzoMTVH+2EJAuAfe08IQCZ0s94TAA/lVE0Rh83egTucSTLi6KipiSMJbPheGfxZhKXPllptLXu/+FJ4+ehcwWZGGU2oyH5sNhgVQTK7D3QZobeuKPgyDACsuL8TX2+c2WtI75aJDdQHNeXIK3IiMRFvTvuj2IUYr3zzvKVOg4yh5DTEciqU1XRV6F85nbfqmIX0TKvN68v4zd5x/ijKK7kVTqE1ppw3vZRtz26xKU3L0EEKxpwUYBKXj++jB0QE9M6yCZ55f5R2HoKBcyCWZ5zK8qDhGkEwNUBvKI+rhZJqqLzs806VNPjMMy99ThsKOUfi56SvO6Oa6djtnICFXqJrvFc/f9EAt6Wm4Y3Ry0Sm7p7A2QNn4ql4s3w/H9t9dnEIf8/pg66pgzYfTWwt659mSiG5vMe4e4LXXsl5nPJEJ9R706zo8O6p3qLpHzusZv/Lud//iG11Z/C0zeFWIWw3VIDFlh2GmcCdpNtAECFIwDECUbHMS8XqtrltVSGtMQKwyWjjT5PoeWEGelcsspoLRSFe0WuKP6R7TsJscjuLLP6FgiBbHRQZP5Rm6yfs9qs0Gw6DJFu52HQ5ah7yV8IZm7hq6x4WG3gln5M68q5Wbh3K/IpSACb7eKFHCIpupk4RCnJz7kUwjxFL5M6GkQpEEPwy4Y+ljkIorbGjjL/+cb8l8XCMVne/xmFENbrUgyFnhp861bjMDJZDldZoQcmMPJskczk6ARRiMI03NAooH6flUbSt1p4dGYpJjzH0RUgJD1O5o7TTAnJLAC18j4TzkvWarafM6KV1RQaDyznbbDTdVuWodxRMzZ+wh5HNiLfn6eNDQnlVx1sah1aKooEl//oFKA3M7xo8iPh0ouuza/g6aSMd4VEpy2nn+MJHJ3QuZOVphsIQzrsgqGqJk+jubd0rGZzg4xRWRz1YSTE1B0AfME7EB5NCUQapreO3EK0Jdh7w+/TPFbeHoaepMNEmNDsaBey2ZQEuMKBEoVcBoQBP1zR6t0otie6Jz5nWwWuk1zECzkMukKHSVa4xTKV/pgr1z1zc2kvfK/KrhLF4Hm0h9Bz0taK1tDTJrdw+Mv1hYkR/K5G9ugN02Fg+rMapXWQwKHteIXP2qmIBnTElyyKgP4HqfP9d1SKEpex44ei5q+/vNVuP3nLJxJbqG83QqUQcqN6jFGHX9T2L3sDCq+H7mXfFK1kye1A/S2BFFsqxUx98zJ2O4B520D+nLa6dA8lkx5hAz4/khs4AGZ0xtU+RU1kE8AOzYzHMyWp6GQpmu67zfuYdJsElEV+qRvKW4fG6fq7bFNDV2F7xck5BMGK9JyZUQTvICFknwWIEN51m1TfSTihJeajTmx5zZ63xXbQ57ySSnZWapkN5jNU2xweEA7FZODoSyBHMIolgH9d46FauQ835ePi/LPhwCTgeHVThQxL+Lz2/ihLo/Wvw/1+GNQcmVm1kDwwB0koNmfZuod7atrM8BH23b5GqG7nhWbamP3Rl8lyrivnGk/fy+luHuPSlpJS0ynnvowZMoGynGyBC8MoT7eeNtIfgf+9jHM5Ku5ddoFAIZbeOBWwUXICdMdJ2U2kNOuDLFbRjMpesbRhhi4SmLm5hc1QDX5AS43+LhuXBw8/wj77xPLavz5QtlTPTg0HgKVkVEGSfK1tmcN+Och0FRR985iZ/bbHAeKvaQwgsbQSqsRKRrNMT3E8EgArarNShPFRU+2tBxj9wqEbOygEeH5mglX0mmd6cR1T1oryEl+qDRlTs7ncQYohue3ylQ3hixP9SF18QD6jxrJkPz3SE4BDqEonGSGZ/6AEPsrYQGZB0usfV0goZEuNhFOCWa++YI7JSkHB5b3ZNZ2WedsVLsY8puWh/iBGQ5YE1JCIkw1tbNwRIK42h0Yu/RcKFzXFuzmJrl4oAC6eFd5KMptOphVoGCoRkuzMsQVH7GhSKQxgCHpinD3N9UljCGzaZotIYg8bRE5C52U94LPSB6f4z+vLdGPSB++xivPYU63w+PyodgijWQz8nTG+CQM2Um1pjHo7kcul+vP62M2dsd1++y6ZLt106SbFfhbLTL+dAQ41bbd+nFGMrwsjkqaoqKG1amVSsXcQ/ZFMnuUkpmvL2BBwXCf9EKEVu9dbhwP05GJ8RmcNye+XsL7xqH/3fISOEbb0hI8G6Vm8KYUkw6JD0QeR5cSUXOkQz00SOck3hlmgbYjW/k53YdxrgqS6DqmkkfhsJ6VH+zwaGvc1nldM5nqmFRnjmyxOblpXpMuH41zOmpmvOSuRPI7m+SynB+z1lu9nDMvHa3K71JlHEhuIIO3E//sBjtORc7mY9vVnXnXt3eZE9gCBgvHxY0zSiAORxvfMSBSJ7WfRz4mCpZ4+0t8OiRlejkVtt9Be56OPQMqCuEoBEGQIGAH4/iTX7xRWDVP8tnfnlpoY4h6O9CKBo0uo+i6k9WDvj2Ns+x0FOUZ/rGUxyrJAg3G1zVfU5G5TwfPcKd5pS8IpR9TeNykXgXkavFNWEszt03/wLKbrz80qevZR8fPZoVBdH5+b6W627K99ARstcnrAHcZoP9TZWBB0ZH6KGdwrog1WFQGXk8Yj9t8exZPlsrdU4C8Qql8tS5jsmaI5eXmS8bDerY34jO9vAqR51w34DSwUPa5xJsL3SSr3z2Z78pgv8cEyUPU/+pVGvcF3kfCdJbdoWy4WGBGO1XbNTiD43/91UsRtTC9Lz7wj6cvzimyies26u98eQt6ueBsMUXdHf9f4sHKdEZTx+GEtlwE+EBWaLgXOCHMgHGUAbkkI1isjo86jtN5e9Syu+raxVgntvrpPjYKUj7JyJKZk2nsxmWx6Nc+rOzEzxSW2MshT8nou+hfjdN5VEylKGgmb4v42zd8lh9yUp9pVQ+H1WekzLsYsuVWPteEmGLBJ7ZVp9OwLNnbs2OLvz/70wS9yDc3Ku+R43RnulzE6rhLFUNmlyRgVsyDPncipiK2d3g2Y+oS6U7jQVN2iOeh1bc86sQ3Bz0B5OiI8Uez5X6UJZ5ZUnKOYKx1ioGfP4w6Hm67xcIsd8ct65iz91lnwNMlLjDANsb88TM5+z2j4/m4zlHzinGmbucDJLuGlVm5myy2OgZbRmPGoaCVQHZM2Tr8HSig6+vMNlHUkL+HGt2D0NhZPphyg2c0OdZu2lXFe4KZg6/aH03n2tLngmXAgHkXD2TDwGnU+aF45iNoOJzXKObSvmP2d47Lwd515wX23zdegv54wxbf28sl4Y9FngnHB1Swhev9Exztn8pqXKqX/AALoEVz5P9kj0hFqQzR1bg3jOHqh3/nm9rE6fC4DV+riWJPU140g9hZhjOhHtK2SCtMBX30p+5l/HP02X4Hyr7HvjyMtVC8nSSKeW+GYWcd+4Zkt75nGnMLxtQWZ2ysUjZW/A0r8jMtqOQyymVPN69z86G2q6ToRxcj5dbfKd91t0V7inXQj5anC9m6/ELTNn7eYdtzHiUZyG+OAGB2DuyWdfnz87OAZlnHI9yR2zP5hsbQiHr7M66dR2PmR6HIf+bOk3efJhnll5Ri2aIQnvVcC5l8PyQ3mS8vRj8j37ULA1aTcOQ3VdW993HEnEiupBzWLkybi62jIO7RZO96zCGxkCPYQBefDxZO2IjyhDwxnWJTlFX84CCt1IRI05xm61MmshEuyAhLZbdrKbUCdlitNjQ6GL+h8HayF9053IvgDIfYRaHlpLzfKjCwfKkXLK1bCaUG3LsJtdOxPbenAkljLEV1MbHdk/dGp/8pMYmuxJXu/VordtJuCHksFwSollXKZnb2QQ96w3r3o/aBEk91BmNml1GowdANm2300ZaGufaHwqUskDMZi5utK2hL2TEkwsdj1HjO9ON0R/P7OYmh9TUdUZTXnjkkjRDKOIhLbZR13XuLsQC92Wzug4HrIWWWJHHKaI2yDyJYHICgCSgp1qrpEgMKsu1cQvbVsvhaRM4XkBWZKLX5IyyZCwFm29qRFpbH18Xunjps3A4OA+SQ0zPkJJ3q5Ur86dx+0D2SJknJOT8Cg6pH5xw0kRWJl82T35eDu3yEv5SFu3Lh4xgccvrWu81Lxb399EjyRMIuXIQ82zcVguttq1L1Cv4fjaiwj3ejLlLTBFOQOkCKPIr5h04V3Es6M3Q0JRLF/O71n1c7w/3gEi9j7Vet2NG0Jyw5//f2Nfc3kyrijgxXOQQpTHZC5fOa8MxjyF3PNU2FsjyA5D77Oq6H45V2fjs+trQS36W4UEhwLoZp6RomveiXb8OQJqzkaZ4/y2xU5Fdi5VVz04Rj0ui0kPOpVZL5Y8N966ukMun6vnzO4VnjHvvlQtd62nIbnnO3eKaOZxR6o/SeznnseTMcxjjyoyNQ1rh+lqqsJjHDsC53dozKa74LqOR403mzc64tff5vAn9w/w8k7uKFJs3/9bFdocc8kM5Qv7hh8k/dVVNXU4y97Lc7ij3WuWJ3VuNXDDeqDl1n3oqvItJ39wT0x/IY1QuMoclJaHRpsm5QLe36ulgiEPbGqpLr91haCyMZredrGTtIa1MplllF73Th3hReN1puBShkXNPKXkVgVU9S+815Aay7j0Tfq14gdLs/sYVeNDnM2/E7gTjkjcbTK6sbuE58IigysPinL2hoGUwCzACKFEUx3+5p76DLhVxv2eUrUbn4a43NyVZToxSEnVMlXlKG+RcAt7BZ7fy3Qe7nFtanI/qL/TsehXhcz/3lV+CGHzdDI9kMHeAl6RoW82hMxnjCidXpcQ2/D5lhh9ISUp/uUfxHRb7rw+s6zpbZlXZVfCOkutQJpv3nXfP1qTCKIVVEaPGhxjxBNeoYqa8+/nItGtznxiz4WEqg+l7WKwoGz/VVkRcPt+6pkZ8gTckLA+BwlDzGMYRaFbRfufLfMmeOkQHmcip5NKdHUKd90m52xQkZtNsmwLWLUCxjODO9rQ4ePdzovY2N6+R8rxnKCtv+R1EwJ0HlX5MyfaQlvlq5WI94cjWPZDIhc2Nwl+lB6c4xSY3wFF6CQGw8pl+D2YTttAwB3VOrrkM503hQdI1pJvPVeQvwMWHu+00QK6nMlkmlIWQn0G01PbHMdDQSihHEccaQhkbGnJdZn/kBBBYmSYqLRWoz4w27mydTtYDMFbukBamxogVDcRssbmcYKU9+nzU433HlBIsJ6cwVOaG64wP+e/bXrr/j6PmCei+TdBeFQFgqbug5faKluZKazYX+0cO57DP8g8P2tDB2vZuGCRnqVCkQiiRPmeE8IyLZbqfFzTrzxTunrmv2PAojpNL/Azn6s+AU1vrh3wH2tFspzLEg+ff6Ny88VbbRcnzzey7zvcV2UBuzFUpE67SaDy8oC8942pG455fvdkocp7cvhu/cf/3sngYgNBWqIYBbbfKPNzxWPc4TvXOsVuDPQoi7oRX7P2DQjBke57XZq92NErQAXD0jrw3voTsfP7UA0lCRbUjfw/8Q5F5I5sOcb/GMYdR1kGa5Jl88Chjkg68VZWVSuYPhOg5S34vGyGZwo2m/IwZglkhNfnnZM98KwqmiAw+1i2K+RahdDq8vsNR1zOv/nNoxU/beOo9e04Pvc3Dz0k/S3orEs/vuRzyX5Xhs8/FWJsMo6FHmp9tEdiR3OguZOOD58c8n8T+EXqGmVXkezyXu/xHzbK7z7noz1Ob7xtvS8GfQo3klb/hBOz3kkCqCKLQwRohurgnTWrcK6qy3eZkAbv8tloYukslrUojdjt5dqMVKEO3xf5aPv6gFSG9IxNxrcD5HkNGifh8zueIZaeMUpoYNGjUuqax1ARNYiWRhwBoLJu5WMhtSWkxorm9EWHQ7azyzuTCpnxp4jvC6/aYn9W2uFXkflfnuEhBEVbSYnsYUA3HzExDsHJNzCTfbTMlnOMa++uMuKzXTU4+3e/x4mVntYm7ThFWZWTVcMYqAKuql305yp7Xmw0m1NLQK8mvhqeZv1jMJCABdxqmVWNE3d8C7Q77cX0npt8S0/SHh75GB2lKJGQrMaxEHlICGqItfB9R/+Pa9rrrXPWRlHBqM2rdtpCSfKBckhjsJg1oqgGTejGsabDjmB51LVBKTZmve0l2/flXVzifK6xWW6alyB75ah+M83RJpnWYpJFX22LcSD5HUrKXKiCi6PBK7VZK9xFA2+LQ14hxjeaxlDo83WjlCQbhx4io+2AeF70MycWsP7wU9HS6eihOm16Y8eUl44wH+27dDXi466zyiQjVStA9AIewNYN5tQLqNCCFxsD14xGoX66xbltr3pJaiRFtXnqp5B16aPUwGFL7rBe09WIzommBcaxzd16ljVP3AE+eAI9b8q1k97oKkpDpmb41R3LlS4PSBT8zr6Qh+Tu1JCEy3s4MVqE/ejFoO9HT4Z06I2og1sb/bM/1WVUa0QSgcUJ6jBLD22tRg5XWpeuH2mhnCjX69iKjXVRKdK5dtzIEEBDPDJT2plCj2mywBZWcWVy0IstWJjOop0Hp1yOLNduB6oIlzljpIwKrDYU88Hq/JbCKNgArSIOgpkuYYoObmxkqH6Pxl6mT+zOpXKIB7CN+rq7EWzOp54O/4JmQPzQux4EgFr0IF1omcLOpzfV/quTuczRhQt2fUDPpUEMxWVlovWYjnVzxibpQ9hQEMzoASKGKJGVi6xBQq7el76vsBVbDil60aSOofEg5nKK+fYaHbQDCBufUIGjHWRrkVX9CPQxYq+fw032TFbMUMszJC5RSLgmoP5u6NW5u5I6YcaCeJ8am23BhulWUlYrna8AU1jgP2ePWbFJpdWh1HbLsqgJWxzfksB4/xhQb9PSOBOnlEXmB9Z1Rm48Buc8Ll7de677eHlGFYD1Lpm6d+anyi9VqhdMpJzhjOKLaBAxDjf1NhfXmQvbgeBQdKwAsxBHVo9TgDCDifPkCrxVWww2Y+DXGFfZHqWrD2v/eGzmiBtq1yaqijDQZgYtsmDZbVMNZPJh6UPSq0uPSXYnceraX8qxtuwaSenCDq5gRguXU9T2QQo2GnlgFG5tGSyLTyOX+6fymUOOpiEM8aJ0nQvl60TkcuV9A02g+o5aTbkJA07U54sHlytSXl5iiA3kgxlvTqNfz6bUloFa3NwJQqMwMWu0IKaE6HrHuewACsLIICGUNS9zannv9hYer3hxepcIofZPxthT80oq555dqFxEtbLdNrketCl0IObM5BBSC1N8Uq6GvXyLh8XspZfdrkY3ghjEKZ2mzzimTzWhpM7IktLMEJbgETM2+8Qq5HF4+hNy4RgZjtNbrPD0KEgqae8+Hv3SIDY6iELRtDhGaQi1MzhN2CIbEx6juplRJF039ohkTfpAJDtKwq2nq7IImk55bk4qcUZB7uiiMcY9Y3TMmVIYAh5DRpIQaIVRW0xdWWSTmOcdcQcCsYzX0/HzpdYjRJYvpJGPI55MStONozP93NMq9tXk6F1OxZt5CZk7OjnceemGfn3kaOC87L/0HaZe/92iWfXYoD8VIShEFYyj6x7v0DVHRO+g9SXOirWYgoUdm+QwfsZdSVuSCzt/QX22cU1U5NKJYe0qWc2glaR1Z+uZ1IVTl3FLCalVnw8xdhOIsZqisryyXEqSKzAzBYmdCfm2OW+QuzLXQl3tgHXLHUH7HKpAFqavNYejnDD0v/j07Hx6DvdbuT95zB4pl5JfV0gCE2bP8a7kXrH0+4R4kMoQ7yWbz54iRU0tlojn0HtzP6BHgfHUrvLe1woS6zvQ8oZJGhLPt4p7kmt5uHxQFNc+m3qf5tKxRELJcsjmFGhWyt5t0MacPTrSoxNSXyDRCyB43N3w1OLK1Osz2T+88wNAM4feVO1C7Q3HKstl933t3s2c25EuakvW6qTHme6R/2/ncI/TImxmy4jfH02VBLHDyHSVeWFVOPjrm7uvUNw0Ern4TONR4oZ6J1w04WGDP9AE3RyY4r2YywPNqARzk+ZRT9JLZh0Owgze95pb5DU2+3wMK/jX3AnIpNTJ4UMesjFRu30uB5vbfrSXoO3i/JHeyKvbExHCA6RO++gtfMaIW3kq5TE+Jvywk/ra1bTE5xs+0rYXL8EcVJqzXZcia14mED5fP8HsSHc8vvAp+cLEquOIw86TO7pMPUVyt3OPMI5Oy98vJZwvjJvj8FuNtxeB/5CMfN3QydzTNgYNEHBlbxpjbGDOgut1qPPftbS5fwQQh5Mx8ANnC8oqGoqE+636ejT52WztAZkbzsJi5TKLY1RI//+yYu2laRY22tbgpRrZ4ncW7ZiZt/JCShhQocrrXhl276ZlMdrPJjI7VRRg/ai7qHLfP9282QP3005nw1GPC5De+7xwFpWYsJevls8GH74hoBleryu6TJ3njmSChEmncXOR5+AsXc3xkCPJ81rP3BkTBoPXArF7ucMYZuTKExZ8CeT1PPw3EiFOXm3SsW43B3e2yEYac0EMPzq5xGYJEALgBjx5ZTXsfa31P+kiOp+dilO4PgzBX5j2w224IORF5t6M3SPaNeRxzpXc+UsqIIjPybR2Ml+46nJEbUFWV0oNCm/MeFHaPnBEDoECK7f4xLvLyEp962uQckOFkQa+kJ79vjmcW9e0xDBh3D3A4ZDTlxceyJp+MRP5m8eNq+FulE63mtZ+2ppRxWZeXuYsiAOuUa0zxno1mPCTPjSAR8wWIYB3SqqCL4jNkAMoorIoEUWtInOp+L/Rg9457EyPGzYWsz5fnjLEMRThKrflD+8Dijud7bnOar1UXuT+vLFyK66HQtzvPLtwuLtWMY2qS7su++tA5iMfA1+gfUePJE2SP1axHCSuBcV6sq11YHgyxvLqyuGQvN0k7zItipSQfimE1sUmsKsPo6amqWYNFr1zon3PIdFCHqaiCMQ/HJF01vXis6F1lLojVy3fZk0RF58UF7E7w7oeAc7u1ctNAzhPanT6d3cWkc73jzXC408+EBliNWS6GJy5P/DxXbeRWVHRRup4evYDr66zkWA4Oq87wHW0ul0hU1XfytHNSmXanR4k+59DXBf+gLWh5ZnynU+xY4tnXAeDwCuJc3hfKI6sdMS8AlfGEU1hbYyOKViCH5V/sstxiGPEYVxKLvxlz3kBs8PSpvO/BTvUalX+n9kL4idfHmOegnpo3ris76jqdpYLf5SUOaWXHYF6GudwgYh8lN5Gx5MZnAOwP8r7dWnkhaWi3s+882GXP782Nq0iYcm8STxZ2B1mjX6sPEUQmTwqhNFiyp1hj+FnTnjSv/SzoPWlbWF4j83h4HS/ioeQB3ijg4CT0S896ke96re72rQghe3FJ5/pMyiKqhs3xmdwPraSUkuq6Tq995Qu+4Bcfg8918RKEoJ0cdTfqTTAEnGsGUCB/TYM71tjhWKHr9HvZoCkQMlPgHAMlQwksf6hU4fdeLPjMeMlojKkeYYigR1c8qkd3I4nKJ59OoUbS2tiGfrjwuPzve9BrWqMx17UW5bm0KDebKndw84h58qhgRhjMMgUKtDklWAJNHR134npJjbOEKEBc++DRRY07RVbKDDAYBjQxgl0IKZ9NOZmBJEKwoiBfXrqLoPHQDRm5zo33wNase0JXeX4mssuLBqTPpHWCg4xbckcqi8ON0Yc+VKZMm1DUF9nalbl2XU5A5+tk32o0rEHd5/XTkK1jNMHhQErEmJUsE4IU3KpoNTGh2URLdDKlC1VB14whNmadct5KQEanrAlRbFBdXWEKNS4vndKvQtI35gFKxXgYWH2lEo+LEuHhID9vW4dupIQmlF6I4NEw1ZKshrdOmh1Q3ZJU+LtSrY6n2IEYMaNAaOf0ap2H7xlZiUZx/zj4TuvuPAxo29qumVQeWSG0K4uxtgRB/6yUUHl0SfeS15TvsrvDuEP+wjNE/dK8g3pWSLPxSo8E75PREGF/EqnjNfetn3tdtwFdVxVKgV8nSWSYn5feN5EBMEWUYJP7SC7zSVlATzDFywA0XWvIqkeEvQywLSPP9YgIcknVEbXwwLg2ZaSO0fhjkSzu+LNnt6SFFFZGv2HIvN3zAkOrycNCQBNGxF3uimx755JQcz+aSfoq9M5oCpKozVA/k0te7nLzPHKj/NQnd4eo8laVBc8buPZhgIQ0ORr1gJ0HT8i/xLDJdCw6gPJTZwDyq+sugwYhOGL0VrD+OwRXCAIT6Bm7Y9PQWzwLmUsJiC4R3P7Wd7Rdjs1nVR3qFdZnAsi5i7oO2zen18Sohhy1XFUOBbnPAofrFrkkz9/tcilUf8atkrf0jMjRENK9tS7uKkvYUrH2noxCF3FniGFA00XUdSWJo5eXQgOzNAJPIxn3dHl9jimwBDq3R/Ybxe/dFSktPsDCnm5vc+RA24pntQhTAoCYkXx7oRcSfK4DZQcNzxMwAYXHY0yVdDWnLsF7ZQc2Eyeq3LPIivC47OkvmeX9460V/GlCdXuD2GZ0XOiplgbwWtGjalusSGi93O6jduJarXSd+95ux7N9hTfeEGTf60/eWkYIyksqAFkhQS9E3l5KPO6kSIKXP2QmviNq2yoiDBi60fQ3aLoOB1UKzqjRBGnGsl7n2GOuA8gJKbe3WZnMiq5MYKUxZudhldcF0oYYB8ORiYRjEZfLBdTMXN9dyPIGWGMofnREDbhGC6dUG5JVdx2qKncfXKfbouoDrq7s4rE7aF0LURCB5ruYJb9eZ5S8aTj3wdCbCgC6DkwOIjP0ycwhCIp7c6MVGq5fN4H6bC97+7AT+Prcat6DNj8Zk6S+Vl1nHgMOejV4TlLlI2SLV5mioSyF8BZaNTf3wHjBRhRGl3C5Vhd49ChvCKi6AfVmgz7lOrwZRawKPnM+653QHhHV5aXQRYJTCmfNqQCkVOF4bAQJ3e+NSaxefhnns5Q5rdtgvMPQAC5MlRMW4gnBVV3Q80vtVpF2Ue5Xt68D+vnx8iGePsnfZfWX29vGKVqVK68oAvl8rvHsmezFK6/QWxWMq1dtixBWWdjSiCMkM4PX6uGk7nNFRNQNNHZb7PdK7+ssDHxfB2uCo/esiiLsCUACwLq/zah8jBZOBCjapXD81K4wKO00RDJdyGIThQiqYcA6BExxbcAUADzctIZ6VjF33RXFPZVoa9dZ98kawFnb1hfVgMhMZ0jTSRutWfymbkat94Fudl6CGgC0ypLdsdQXwu3UV2WVCzjjhvdd79jFTrXooyQ2s4Z0BSkFVyMhdqxznwUX4+lXqwaxa9Afc9I24IxxvYNjtzXQhdvBuyAeNip1ClakhGY4IHZrCw+fUGFINVKqcwM0CvcnTwRY2L1gQOpLL1VYsbuX0q3spRjd7VYZUpK9r/ujTe72ttbGjEBdV1iv5TEe0QwBmTF5T/D+Gaq+R3v1gvHuugbQ7Yz+pGkWSu+lU8S45kePsrGREpCOwDgKx95toxlGcqORmUvbYmgv1AMjdz0l5RvabZWVyMYRaLZdodiY5yVMheJCZS9GYO0sPxbE2WwqSZbW55tStN/LmWrM/R2lzFlxdRcloVgVWS+zSMsMVR1DY55HAFZthcVfrq5cMzNdX3V7g10TcUiCytMIMzT/iR4yjcmus5wvhFhU/DF7+jVBx6dHL0ie4jFPihVeiio6KaFujwL8ab5cpQBRnc647ZmzWKHrVvK8lHIeThKvxZNPCumxWpHfKIZvTagkrM8rYX2P7XalOVW1BW4URh3KPBjSfeM+yPAfr9t6TyVBuqbJ0SOsepNSjXZ3kb3SAPp+be8RsE0ayFV9j5XS1eFYA6jN4BIyrCUUkR6xJGtkFTE+k3xifPSiXTnqAl1XI7Zr0XsYYhGjysDKbIIpShUr4TEAbm9xbrc4nV3BhbcYb+9jzlJge3VBLXDX/+RgBxcyJUOV6gkVuqG4z5I04mP/9HB5AEQyQ4BBLlIvPGBUNLLBGYzjHVFbkw0ARXKRabx8eZIYQ7MI9VbVcULXlbG1MYpyEmNjMYN33OJ6OXySHqKPFc8fAxTpo0cEEloQYo60ZTWGJiVD+Xn5+UyGiexv2MZekHCPdKLtCgs7l8rT+C7fjhtZSeWxAlkBahpVpNVnd263ZXw7mAiUAEQgwZIfmXhj++EgrRZ62Sl8Y0YKgIx00g3plWbO10IJiL4oczujwaAAyMUuFK5x0mJRpaXvJZ61bVF0QNW5ycXVs5tZ1EQRiNyYYoVcenMYgGa3sxfXXYcUKjsjro2MwztaLDfEWVGmzCIn4/Duje0D+2hyLswi7Erdsxiy+zJGGEIhyUOF579YM/8ZoybNui2ra1EiQtBQk5Rw7i6QUo24afT+sryahHpIWcRtdh8D+h29hxjz5lxeCvqMEV1XZ2UHOem1MMKJZKkQ6sPaQickLlY27hwlUawAS2Jr5dwKAEZRrKM7r7twoAxD6Vy4FFFfW9t8swdJIraXqgFYF5OD0cPkjFKjGUpQJ7Uq/ax5iZRo6jRi3QXzkCF0WbHT5Ygnr0Gjin6dznIzaRR4GFc9VQVSzgfpGswwCLlT43abEcPo+GgGhMr9TQlFmIt0wR4sHMT0XFfeuBrOeHCpSs0g3mEBj5ps+PAdMdq9tibnvMchGE1FDTPMSPQZgJQkroYzcHuLi80GbVtb5Op6LWvtOtfvgQYvFa52JYb77gJVGpFSGdJDVJxhk6dTLUne3Bw1XKdQ2z2tB2a55jABKktcd5F7QkMyRhD894+X/4cijO++mGTKU9xmQ5bhVYDKfcrT4wHrtgUgsprKfUrIZTxVb7AmbaoP2LMZijQMRgsNaOiLB6b33aL1QtbpjN0KBf2GkGWOrFEMSLRrhG5tOkHnHONZMYVkcju6LRJK29YUQuIMAISJwiHoM+9hltkh56JRUR4GIDYaQlgjxpp1KtD30NCZdS55HqXEJo7AxYVGPwzSfKsfaqy02otnPxMqQL0aACwx92K3Qdt6L17+UoUJ2232CHOfJlRIobEQmuhCVQAILyTRRXrz73oYbH4xWrOxVtWUqsrrYiIzvzBPHfShnuLlqIBEb0W+EvI9sWRYUrNJJ6y7Fm0rYc2HA7Djoeqk542uuB+UxQgif81j8jbG21PwnYvRIva9T16R8oZWH5W1NhP05BTuYZAuXs1whJUK2d/m51E7QbawWFIqBE3IaGuLD0vKSH3XR+weFMDWboeyFKEJyfwe31yCn6kArGIo4sQaiFCs61oIdlZ1xLt1xjELWRNIYMhKZesDKnOfEynYblc5xpfP14dVbYuUXGWjo1zIcVxjmmA1YGuMCJoJPqJG0DgzumVp6zAEKUZo6EdE7PLzebG8fmDdMrtOG/NWWEdX9o7oq1L9jcbq8T1NELqwmHxMpWuNzA7u/ykBmvdApmllPynkyBVJC61Uprnd519d7BwNAGjaydz/xaI12Dl0QmPnsxpkwSWEz5J6edZVGlHXUkGjxijoJdER9lTYbPJ7hgEhNJbkyME9p65njMcp+B7F4hW0mvldh08/zegUmbrF0xtiIWFKPJ9xzPSKGLVb54RVnHGXEIxZkvnR0+PRMIsfVIZ8qnJptdUK2NVHO/NTvbK4081mhVr5zdOneQ27XY2K3W67rQnuVSvMl6RzR8kFrIb2WoXUuZKpXV3p/iZJBts/lb3wevaYKqBdY3DC18eVE8zd7QCfhOnPhnMRRaQuDNkQIArPvCvj0JcQV+u+MOP4VKxI7w825xzTGiPOcS08T6Vi5Qx+QgtNGiz0DiHgnBoJjeAZBp9rUoky5XuhRNfNV6vAkLdZlbMwK7npNoGxtJaPFaOhhVnYuk11Cr6XF3h6DRbRr7oO0H4PGZHT/7j69GHjPXoMowx2V7SargEV7A+TBmCVeg1PyB2GYwRqvYs35xq7FeR9w4BV1+F6WOf66EhZMZ1L8pQbucnc6kIpynMWOZVSg2eaBrZbR+N5I6SUsYWsPtmLAb+TOuucc3sfjek+nFKDQINUUd1Jw914z4YBBU+dl9MWud3nZi9tW3jSLImSSMcwYL3bCciTchhUzbjml14yzy6VRLCPR092XqPCYOK04fnrGIYm6wOcpFpg1W6HRj1pKeXQMNZT55r5nlVIVoHOEiNVT2DuB89OjJ1kxsf5nK8aWf3tUea2Zn6PV/CHWfiLKpCWv6T5PQQyUgJWkPyysX6AmxuWi6yw0jv16U8JjbxwNRrRJc0TiLFCjVKZZj4bvWwXm2CehFXbWoiqzVvPthoG1ClpqVzZDHpNditRUohw230yxEr+jGNdhgGGeYnQCv3QYDyVXoR6OOXC9Q5sYa+DKbhu95yzGoQGRCgoJjH/SfIPtKqT6FSCLNYq+08nYPtonRPPQ7BwaK/gr9oJK/RAEnCEqpHPkXqz8baSbD/60Y/bC6nDsq02LyKRpxBg7kKWKKPC5/lwEd9HLTM4q7OAxlCgX1YObBCCPatr3yc0zEuKcUN267FwL55Ctjb5Cp8UCQjzBjKqZigAJXmMOXGCPihd7KGvjdgZe7eOZ0uSOYeVhRS89BJytz4Vwg5wyQl4KVmzKM7bN61Cy7KILKHoknsVAfLKl/+M5+FFGIsnZjhPhyr4p9QUlyml3EDJJqpeBVNAh7NdYvMKUSPxVK4CiXObW+jP9vLMB63swSFIWI8vh8mmaXQF7ppTmSDnGrjF6BJEPRGRKVESq2SlEsbSXx5lsCQfZ2wgBEOCpISiJgfqYLKPV9gBp9Qguwj5xxpZ3Zcx5uZYoPZEjlgBikne3HPXCrygQ9cRedrIXpur2BtmaljZ/I8ao/PoUVHGjHRj9DYMwGaDw7HK89UQjDfUtcr8pGo4WwKWzc3xjXkiOOc2qvtTK/3aETGG99m+IsnKe3ypO0XvPbrmFXy7ezjn9ex2hbJOXlmiu65yV0pF2V7b85QswZnrWrXuLm422B9qe0/B02ZGqN2NbounT2VPuf6C8HSSZzSZzypKejoJX32wcQ2clI8TFCGyxddbyTxVlDyLKe637ifjin0jo3mH6Dt8iRsbYw7z4mHyuSjzXjxfmTdc4+OYxG1lTUN+Z9EgkbEkJFY9iDE0ePJE7vxuO+WiE48e4dQ7hNNn/ceoANpkd3xEfQfYMKWUyspmg3OqfQ/Hgr+HACtZO222sH4xbYvDsQyHsL1FWQa2DpMp5yw6YHPBdKdc4Qx0LRLRLUF3d4G+V945kwUMteMoUfGEw6CNp9qxWCz3ixERlrMB3G0Wyd+RODxg5HMzdAJTbFjBGBdtztGYN6X0lcqGIZdT9TkZc7qm/kBDkmHBu22pV83PlfKVNMfBz67azFMRcslLwOkoIeDZrSjNK5yK3CvbYw+IOg8Z5zLXk6yHET0uTq+zCYaZgr/OSbD+avvwwDE0BT83WUJwYsh7b14j1Wfss4yj510/PpMv+K6eTrYVjbY8yAvYofqGn2RDm43QPZO83fYVojsl1euePBHkSd2Ffo9/0Y2uWMOd3gQ7D0VYGCLqS9KxgQiFRaHYD2dB0hlPOvuANbnwFsGMC1vzBz6UH41l3LcJviGh70Ugr1aaoKPSkTTuc3+ariQ6Qwl6UQj7tMK6BSx4kl/2yuAMeeF/pc15MqYYulztRtCEkDvZXa5xPsOE6Ha7MiabBpSD2oLOaawuCuPehx5VMaIO0oxBEBQ5REG3KuNJVtfV+STvNDNT5ruKY1HdB4C6ApucnNf3UqrMKTEJ2Z0OIDNTZ00jBAwzQ7IIsVJFCIMo3req4EvMXkbbCy/F0XFE3CU3USoahK7JCZc8REMwlTmeakW7cvgAt20FDdRlGI/+Le2nG8vmX/svdeuC5Kn8WWzyMABtbehp3wMXXcqX0UtyZbpct4/ZNAWLaJmiDLp5qNsWw1CZkuYTY/k5uxtU7ljPWOcSuqb4HACraPXwilVZnDLmLPJ1DADkfvPnlPVN1BwXDbtiqU0LV6GnK0gztuKQdTIeKTRy0zt8PMp3LnZThkt5UWME2pxxPxfKVnv9mGmGOS6sHjHfE/7HGik5g9YbVPS0UHbx2Vz3FGoLo/SKFveQlaN8rWWyDfFc5P3JXSszsxG+K4n+gKMJegectuLlHeCu6232JCQ0VkHH70sdYILH7Nb2fuWesigETQTuHSLs49q4YbwnXeNJVcO6KqMFXy/e7mMnPLhmEytOI+XGjNYEy2ow6/u7jkCeJaHvapVF19daTlEFrSdMyOctnn4YUIeEGD1cq8Md5rS7sNKaKWU5kpIrVKOLv5m2aJoKK31v163sLDIvysUMbGq8RBpSwhA3nmHR2CoE9M6zJIrOYCFHa5Wjluz4VFzm7OXAHKVhcImqSg/SW6G233lPIVopqzjObVzl6cfjymigbYFVcv8hEXt55F1jqnQZIKN0zfwOf//n9oK/E1TuLYk4CdDX7S4KowiQu0KapFjyfSps2Evy+TM+Hcdj7oHSttYDxe6rrtWq591mUGS9VoPDE4fTC0pjBUWYHrdzrbHrhUU4n3twHpyZ3pFltCR4U4axUEaDNDvs3AFWjFKxeisNVUNb36nS03jDzut1+kx/hqa98wd675uQMGlvJobmXF4CuN5jaB8YVhgjq4cBcOCBSzoxWmM+wtsZb4ngv+Mdr+Cnf/rjd0qymdU8DEUsKg8xBIeEe9cGh7ow+GMrtXXPYdqF4iXTF7xxKxtRlBGcDR8DCLh4eWWCz4a1XOqQ/YhnCLpqgtotmojgKo7WeMgT8CHJXly0UpLtAEEczar0t7vrLJbc01GDs1l/w1C6Yjxaw60BFFnyh+AujjWC8qg0JwUUhHmGWMKrlWtQ4Z9LVEoF5hSbnHS42WBs1+auZWKfoSneAlZYb149he41fte72i0cSjdsbNfmLQpBzyQlacSD8j5yO6zk5eWl0R8AC5HwP+O5rMK5SHak9+QOWkJFXtHn41GVWNZbBey9HuUIYVYW7PIyt6tOGcml4bUaboDNBq8/zcrIuhXU7xS3EjJRHwwJzwlBJ+C114SpM/CSELEiJgByuVL9HUvYpqRGu07Eo5x3vFuu3bp9Wc9/UqE1t93tvc7A4x3w3kBfcrUQDKH0QlnYAKuJ0JpXw8YM2VCWeE0pJzlaWUve9c2mRNaZ4JkSJk36pyBdIaO9tA92u+z99PLHI+xjXFlZSe9K5t91fyjBBBcsehrqO6X5DBH2gtgZU3OU1zwPbVuEJlLw0YNr+0l+EIJV2yC4w9KxpjBTilI5UrS4CLFzgwLZK0h+L3hHWVnHvG/uvnKvTenQBREpZqIkjdimUV7ivAx8r3n4HJLP6lBWptUZ1wihQD1tftBwldubsmy0D32dDQt99B64kJFhemiRkqCtQy20q8YO5zpNTnHTxU+7i/LesMBDEHqaV9YsPB5esSHo0VdG80D+juRlIV+0lGxeVMCtqtrMOvReT1/YIgRYEu+Dy5mM0zXQSDRE2ZWpfv24Nl5ghSMK61vma8UZaBUChRe3aUo9wp8F5zCOOdk2K8+CcE8KGhiv6vtSFiAbCt7msEaJm01RxpNHMi+9e9ZCHL6ZnvcOF8Px7mmzxfV1Lrttd1lpcdy5PK+Z7Lay5SrzbR/5XYbp+lxG8iflzdSDdqu7ngnjLVpGnF+nTDlH0cManAvZdkqZN/vy7l4X4fPMQ+Y8A740ru21/4F634h/Wfnu21srGfpMqrzjV11KBMK02Wa5ogbqHYanPPSVz//8X3yZTD8KlEW1jkpRdS877kzGHRg/6OmpjqWFVLhc/XfdF8k87iA6brLujt0RHpyrIMLOTG4bsyeamN93TtkKtC87ATemCv1eP65oGFEdQ4HJaRTJDfq7OuUa0uwY6F/hEQiOKo0wdJKJg1SofIyHLqGIf/Sar0d8+Vnd0inWqNrZd1Ky+Yu1DFP6aiqAKSFoiFGhobhLjSCJWz4O1zduITOcJmdsFch9rp0dIzAmqSjU3IO82VeUIc1difehELYlM6OTwoigvPU1cN4Onos1GtPhGXRVZTnuqwzJ3tU2gWHISH1VAatZwzR7fNsaQmj9huZE77UkWg36mcPYSNzkrs3KfwhoNu0dQ5n/J4mx3B4AyY/x8UXe+xFzQxT+yPZ5xtg5xWzfVxkI8F92NFmwAf2PL13HvdbtymfvKt9YU6/53KKUI+Uonqsf9HeHAZPmIalhjYZkf+5pWJVSsWXeCOLHakokr+FoWAArXxVz58JmggeQ+xYw6w0Xyi/zq75MsPEYTkp7MvjwCe6xLY8xyH6Cg+Sd+OGPtm1lAnWMSLMOwff9W2i0klJ07kxIq75cb8E/58NvOkoebFcq5A7Whsy5fT4nQRZ5b6zLKz2mXmBSgdRHs/qb5Y2kBGh4Ste5CkWAJUXzvT60zsuC0DVW/cXoXumAeyE6kmswqACMD98wLygH5YejyZQau7fj6O/aUMyVm1nXoribEYw8N9uvGBFCdacpWTGcwokQyj4SYRLvEXmbzsFfiaJxkr7Ey5GGpWi5Zv0sy2FavLZnlqAek+m3qlQpZQW6GIurYSMl0Y10ETWAEGv16DjQUPUKz1eM5mcMxCIgZmW+C5r3DFAnRs9KjLPP6gYWMhNltasVvzNI1bIpiKxmQr3pWwElf0jJaIn7lo03xzeGwQCH0OZQnnWcTcqdq0w48wfStRlTKKsqld/jD4V/MLdot47lvrivmRNRdaIKE5rGlTWdMzVFthjyaQb1/KzeZLytGPyPf+xj+YHelKe1npoSNSTcT4RHL72vy2txvzPLFHBWKS+hNnvg8CC4oSncxbdatDJDi0ln/AHRE40Rlhr9LrQlBLx+LUaMeQxSstg+8ozdzqFVXvh7hmZJyVO5GIcWhTDzaui4g3g/+XR+cdtif2pMwaAdwbh+i21s8xlweKHqXXlcl6ENT56IELl8aMsy9JXr8wqdrreIhQ6ScMcIJ3osiuEYawjuHDz90e/16BHGdm1JmQ/jszv7TUvavBje6ADuepPU1co6tL7Bh8WFezd6CGVsKuMvnRI2pmzJ73ZlnLEh08OQ4xRD9ipw6QXC5NZxDiuLkCDiSQHLSgE8L4vp9vcr1HjtNZn748ezOOCUxCvR1+Yp4GadY2amTs/OSL/zEHBYDo1W07GNV0XRYu8xWn3/eUy5nVWYCmMNUHryMdBXV5ku+UXn2bI9SaMZ8T4h1/ieY7a+sZvlmsSy+Zv30htz5kaRJ7qYcobxADOknHxWG0LtmlO5liBNgNhYyHdGtHX45Nf5cB+22GH1zM7ZqRm0/I+O/aG2xm4xQktbAs3+dfmA8livfNo5OW+N0zllH3jP6ALnfXIeREBDz2K8l/9x2HnOYiTYvKpA6Dj0jp3CWssEl9t2OqF4nygp8gyude4dDMF5QLquaGRDI4L9Ii6irIsylsBC0SzNoVe8x12XvcgArDTpjO3hRkLwhaeg7N1QAFjIMpphmLmnzNE8BpR99HI9fSrGwcWFTJNldT1PrAf1AD96hP1NJVWT0mjG9Gq4MRry3i1GXxWGtt5BouWA0uFwKAypuS5r/E55Gn9P3rbZZA8xkgPjQpZ/Uytz22yAdXCouReiHrFjroTqNxRtnJPlN9CF6eZv9EzexDhqd78NJNWwP+ZVeHlLJZb8iZVcLP8hiFf/yRMp4NPsX7/Lz5mjePss87auw/5Q2914sDlnVxkgeUShwauvGnnKHt++LvvhK/HMcj/mAMYw6HxJ+G2LQ5ByyVdX+XynUOdcie6ckaPNRkIX09n2mh5e0lDTuJh/f1k3G0umTalUfdtWC0twolrJjzzDKjt6YovRmmFyeLlHTy9z237RMfgAMsrnGR8PMkZYW+25MktidpcqBEGR+bF6JrCL4RR2v2DGbBJB9XGQbzl0buKWKhEwW1/fozXhpvGtztL380tDjvvihRfFTD9MJUM/XyS4eumvQ76rjNhX4NC1+fb1c8aGGAEXXVHETKd0B0WYD9LtfcqU3ztvrds5esXBIzoqgLwBMcXamhClpKXo5pZpSmX8tNvDYmLOACp+5Q0jBzWPqcrl8fi7EMCKRNaqXcd8z4rM9ZmS5RU7Yx66HzTMQhCFu6jQMFtfvkJlIip5vN1FH7amg6jQoElT81AYIrwNv2e0AfiGTzWVqJkhYXt7z7j3/t75Yf56ES9PRRWOiffSIMp/z79alIkEOKNXvnzPHP169I+ve00kLGr+gymxHKpE2ZJSkt0eXcy120e/zkIJcfMxo242imRouMNjnS2vJOj/bU/rfKwZpKxyXWvSygxh5BnUbSjoP4RMqwYI3HcHUf7XvBiFm2x2htE9R/fzDsl4xsFnecsJznACUNeNIdR8jk+cBVB6MYGiglEhv/zFmdlFnFbRZp4/13XZz3Vvg/NOGlXHWHrGxjIfgV8IQc6WcFzh8ZvNa/5eAixApkUCACfFZaatKPZz482HpdQhoI6SuwXkEDjulb03KeocK7M9rKKRM0SM11BW9D3W61Xx+/v2AVAEPLhzc4ZzSiWfnrOgGchfVqrT78+fYQ9y+kjhzRnEK5eS8ASEe77rD2iuwc2G6R53f3iXZ/BOz+i6Uh7DiknZWZsZ0/yOFHdTnz0cZzTlDT937oWnICWsVuJpkDvh5A2f6+LmS75abk+tMDv1sLmib6i4KYQlwHLv3noPzDCgjjDDfw4Y3Rme9yYpsV5Vd36cdTwSnHrRmNdqlR19WF6MCCmrT56f2/1JqTA+3my8tYLP2VCZ2O0K1/6YqowS7fNuTqhwTCuMZwAOESbhMsyBLh+LzQPADmBjqtDrJBtXQYHJJEYUFNw+1t95DQCnfGvVgBS3kuyXHkiJ5F5qxxP5q3GLN8JDzW+QUmQPwjNd81YMk9tbxM3WrDeTn8ejoQArUmHb4pxqPH3NVVCAaG3P9mItvnApiBbLsRHJFdrIMdnmnqVlHwIOaYXxJiO1x6MIH6sqoh6KVQiS/Afn2u2lBvvTfRYZjLNbEwEj6qUmanV7gyZGDFjpVCpXLahGXddYJ0EWKEx8dYIGCU2bcMIKr74q4RcxAg8uQ8GBk7rb+r5C2zZYdU7oPn5srmwMwMMNayF3dvYW1qMXse9FyV2tNA5RL1c/SNlAenTGThJ1AwQJuwiDId0SwlvhIobiLgAzY4TzHKTcahMCmk5/thdE+Fm/yjkg+vkU14aSkcabMAKvvirnqLWQaTyNcYUGozz7eg8AiJdZwedz2lbiF89o8MY1q5fUeDFeA/s9Hr70klWHOZwrjKPG5Ncr4Aa5+yJdBc5wI6hjiDM09hwN1ulkDb3QthgGQfeu+7WeuYtJTqPQzVPhOVXbSvKhIiUEgSwO9Ok1qs0GIaws6Xi1Q6bVmGuvSwWKGrut0FA1nNGo2x+3t8DtLarjMZdD5FDkMEBRPNJwzOU4U6rQ9MIIarXErPoLVOI7oWho/CrTeg1gV+uGHiUWc39bS1m/4WyK0mmoEUKNphWeeKNnw+oMuD0ibLZWghSQpMkKk5Xza9usYK3oleEm6RyNG7CR226Hc6pxfS3103fr0dDD3W6Hcd1Y4yTbPl/rOSU06WAxyYdjjXFcY0V9ITnvrR88D0UGjdi8YqT/tnyfvgdSAIZo3hrqgG3LPiOqoGq+yBhXuROt0kwDWV+7y1eOr2vCmJOVAauuNKYKCI0pMOxiTSEtIALM8D9p8nUTJ4RQeTYLQGRSs39dyjNu1vaserYPRNi3Ww2nOibzOnWderp1Trf9ysK8OXa7Gs3TJ8LnNE8mUfbEzJdXlAXXEpY57h4YYGPeT4gB//CyKzUf1RkON1l5sXM9HlHHAYewZSqS5hvkM64wYb0WlJ8u4HOqxfTV+dUxYr2WuyeNm6RpFJVY8lTLT5kBNfQYbkUEZO/ascK6EyBwFXO3Z0PaU0LXXYgh1q5ybLzmXRVx7mRiepisq+7tStJUYa0XKJI8Z2pXSHGVDS63noQa//AfikF0caHP7VwUQxrVwBr0K+KpOmvDsqPy24sLfSWLijhPgt1CFlgIQWn204iPXsAnPylVyTa7B1apjQ0tHz1y3grvqUBe8roLJW/SWH9692pNFn4WH5qHfTWcseoGSBn2YLTzwlVS3lNhHFeYsMJFO8Lc/zFqU65cMtnwLV3baajRsvjG7S2qYcDu6irrInCFs0KXc+uGwegOcLSnL9ifV6iTeLmaLmIYtHGX8xY3CvBxjm813lrBZ6AwufZM+BXolFMYJRYsx/z54dGI6Jg7kDeRzIrxe7wEbOLjrXvpiljnmqLeknTtpYGMNKfyTgNwDaf0ggQo8Xn0YWZtV5hQVZXJHKIdPLfVTgQcrU7LQAcKoSOn4aC3IcfpEnWty1cX4R/0ovC/dk+Gez7PfXYKKIYBcZag4+tTV46hADCibN2UfWMs+TsWFj4Ry4mxd32P2K3ovUdVuTyBGSpE0rK26JiKesfyvpDd1SE3ZplaHzwMqwGcEqxZRqGQKLpQHLcy1ypGxCgoob1LN4Boc0oQRcEhCmSIlWfw7hG+NXxImUZSYvnQ4S5tKzcbBggCMYMdfCKaxf/2A2KXy9mZgayKmKC1dRGr6xtWeeOZ++ZAPFicNYDQrnSfYqGUeXQlhHw3ZK3ZIJ4n6MbYlPTFByTJdWGSlH1AXa2+4ktd5+oLdcxozx3Ix2/Q7M57mEa8kZWd8RzZAYAJOTSRdy202xzvS+Lj7x3tk480MTcUIs/gvhWJ97r/VRqxXmfeR2BlzotTUr7n99qveQaWUJFrGpShLkkaA1JBtagRKih+33RtzLHx2+3D1oo/VPAZpuAQXna2Lhbl3klEjtvMsC6TG5w/G9Fp2I8pn46k+DdzKe780r3evDCaH8VHWdUj/Y7xzeTq36siN8XmTuUy/lfOX3IoKkxZxvF5em7mmXXPoVh37Cn/w/FdW+vce099wHmPJF/CCWZ/DnxYEs+ZlxWm4Bv9oRxe9qSErXobuIAYkKui6XutEp/O2zxYagAEegZnn5sPKmF1K02KrONzEqDNYvJnTb+GAdKJVemP95VenApTee+VX5D+w9wr6ZJZCrk8R/O9YhSC9buhTtC2sJCqBAEKvEdrFn1jV8+KhOiZe2WWpOx7DtT6Ul/xJSVYBb1Kmhxb2NMUmnxGEJpLyksKnSAlhDl96MO9c7LhCzmcolVF6U8EqM4xa1YZUxbdc/0QUD1vnhs6SL8Q7ot9jxs421irOule4hvBTaiKr8x1kRjre6Mt5+MtFfxpksow7U7LZinql1KNLrjMfQD7s3xmd/gUAGD9+HFRV9SUlWFA20rDBqseMVMcfbiIJcd0HdgAKIQcV2W1YddtRhj50l1O0EgJ1laeVRtYT5ZAbMIaUQ+304O2qgdxmxVDKhgpoWlqixtkhYbbQRIvjsdc6WS3A5qnnwKqDRA3BA0lpnIFjZFbSQMwXWRoVznhlYSeci17Vrh4cMlwHdnDxtf90meNqCFd0mXvrdGRVnm5vHpoblseReZ7ed8BIMQ1mjihvr1B3XU4jboHbEoDjVsdgSbl5yVtJrXd1tLcYjjh6mplMesT1jijQdOJQjxIKDyaXirHfPoJ3XOVKZ+MgX52FANFKpWMhvA83TdomlyikAx4GDRhcRiwfrzBaajxqaeiRFJZNGZFIdD3WG82QBexv1FPRRDPQa3Idj80OI01pmltMfGs1b/bNagZEpASVv0bwG6H15/WSGrUPn4snqZzuxVmxfa7VGI0wO/cXeCknrDVDkU8Lo+eX0kpx/1WAHa7HOO4v9li6ra4aCdjoF6nqnqJL725AcZ2hZodP1NCPRyEMryA1n2qLgOARuuybxES2FBUkLg2WZUOo62uQtO2eH0vaPArr6jX7XhEFXp0HUugAr4EW53Osv/cJ60yNAw5DrPB2eqQxwgpC8j+C5R+rseBJRanrLsXioPjsCkBp7ASG1MN/er2xio1tG0lTaWur4HbWzQvRzGAIMlmgbyUz3UlFYW/Vdg1J9R9j6G9KJyq65Zae8hncH2NGkCtyJJHaguDDYoEhS3WG4mnZr1pIo7sHnxW1PXyElLJxxee1k2o9eHrKBL8U69Jx8yHV1kR8fXzgVn/CycdGWc69EDXrUUBUDSeyYE8juOxtvrgcw2AIuFBe8jJiG2L47FBVWlHyWEwfnFzqOU5bi8icmlWqTW/l/e4sqA0Ohhju7p93SqhIARU2kL6cMidZasknZdNYWDFLZZjffSC/G11LXNI0uHUuLyHHFZginvX4dRn2dZ1WRbutrOQMQ6dKxVDqebh7pW3cnY7qcKiorm6fiMHU88SWUcaIn2PCsDagVOnvkI/rKWXx+1twbNSqhAY462IbXX9BLi6wrN+hV1LlD6VCjPpyRuh3Fc9f6mGVmHlUFQggwQW+qWujjoEjN0Wh0OFXTMUIZiH9gFCANZaLlpyrSrU9RorvdLMC5Dns5sszMC0LrbDgGqzAVDbHh6w9nUPTOm2ppve8te9JQBgXWiffjoLvqsrDFpxbZoqdF2D2MoZ1ikBl5eoY0Dcaa7i073xpf1NlUNvkPNueH1li2usLy8xoTJgP29zlXtFvPYUiBHHzQsYxwqr1co8Y81mA0TSbIOmlYdUaUStSnBF5XkYsE4S93I4VuI181oxXYsmFGuLqz8cK9wetwbm7tajCCox1wA10Fh5txly4z3zwiiTYYinGQWxAWKDSrvEeyN8CnXRJdvY0EE8pKt0AAZ5Tx2ClAlWen54Fe/0mLhvvDWCPxveOLd/6H+shS4bA9CSjpUx2d2uQhMCaknvAEJJjLUy8TDM+LRawxUAH5dNBkkExExVd7hUVICC54h8vr1FFSMaTXz1eZNWInIgsdQZ0SCxYJaQpwfeHvNUTFFKY8GoqR94IDglFGZbhQmbTZWTzRx6dR9/LkaMRaJcYVnyMMkpYkSVRsSY42ArZESOViuJ9HAQ5rtSZlLXpSeHDL1pCscOgNyOnXtl5RHhEUv5TNsyMazN4WBVpjXGQIfYFNYufupvA9/0TcA04eHDh0J8h4O54BtuwrNnIo13O6yaBi9ut/LQwyFPZo5CtS3wjndg9/3fj9PuhWydq8+s6yR5zRhaW1mMHpEOc6kiIwO27+raDarMFoKL748RSNkTNqJGvdsZEVVJCrCOyVWRcF4Fb5g3jYTzEBHkXHyMbYUJq5UqC6lG29YS5jCPVefLdM7mhkd5nydUGJLUU/dRHMMAHFJj+2hzcCN32qzFczFTDEl/5L2e3rl9de0qXyjyhFCLYHXvI+37eN8pNkisVYzyThF4uBcRnBnclMkhzJSUmJm3T+zGUda4aidT6EIoK0+Rf9qeJFfRabyr3PPn0ySKd9N1phM1JcBoZ2kNBV29RF+ez/Kx+t7qqVtnbbePHpHydEOeNQeNa/2HINFlfgq9Mxab7pCzM3O5KRfUOtrt6O7JyiZCyF6gmCs+MQ+Bypgx9RmMFoKEdlSYgOvgDg8mnJumzugeMl8FBb4jJvudeiGD26+myflfnvyznS2808pOHiXHZBhQKr5u7dzsJsjb2LXb9pNCzR2gnaMSq1UOc8+0z9zDDPjPETXqzcYUYb8VgNInFTQPM+u7izwnrk/nPXXrAoG+My/OP0ak3v3cIx3eAJjxZJ9O4rd07tX2LODmhgUthJ9aaJp/kN5legaYf2hXxocieEXHzcOSoWl4qSCWkNmqsK3rWZ6SeZy55yFgtUIRmeH1F36UfMkdgekUxdrcWdKzjhhM32sYEhkCgNbySTLLqBG6rYBXWnSjbVflRitgYzpbjNi25Vw4z6ZBDrulrhAlRMtCYhyQRsR+pcKLOhPDJrnuenakthdhRjjzw9PfEYRuHG+vn/c9N95Swadrf/6s4rCUE682kj3/+rXUNH3QiqVTty36vrIqHQ+vgh3G9OiFIuN9vW4Ax6yqNGJCjcQs5+MRtVpDdEevMMBalseyK2lHRf1W4mMbbXNttbRd17/QCWJKZQy3e4uhE2aPbIrqe+owycVUxftGM8et6sx+D/ScTIdD2CIGoMGEVRyxYkZFglVzKBgokWFS9OWlr2yYLUWH7tvoOlxfF0Y7Vu2krkIAGtJi8YnDoKh6dnnUXSdKg1rAVdui7joctGpGe5UrUPBFrKu92+neH2XCvcab18cbiT8/NoLaPH1ahJtYKTgtH4oglZqQ3L72PUbGix9yYqIpWP/VfwX8rb/1VuT9ixvf/M1o3/ebtQZyjZUykep4QF2vcXOT+QoNEgpsIbsKu12uqc8a3HTt1FdXiLEWN71DvIhsej1DgL8Km40gpvXxRvYca7tbKVVoN9vcOEk5++pKytyygktRHcMxyiaKJ8I3cN5sNNwHsLAr4+hQZZo1rUNpnLKAlVX7uB0wtBf45Cflyl1dIdeD1vVTKeYcuq4WZYTvbXOXzzXESJvaC1mYKky7nTOY1bDnnJh3sG1nd8zVqr65EeHMcFSLqaSB63tFqPAHkN14bi6WUM8YUA381J56uQrWPu8B9ntB510NboB3oBYh7WJz6hgwjs+vNc80KwAYYhb46w7KT7LRFyOsScy0u7B139zkhnUS/y0PvNhIdZynTzX3aCVlMQtDg7SoCqKGM99rjHCiQZEyzstXt6FSxHwSlph947pC264kx+N4RKXu01N7AUDuTYDwyAmVyJzhLJ6X+aapQkmy8/IK19c5i65tNWcihw1YLpJTpgbr96DNDN3vEnKfAt+9s1UxQcWDuBq3c79Xb+BrPy+/SAnVo4DYroSefFUiV1bUBMZmgyHVolQHbb42ANDO8dwRTnXACulIQ1IaBZo3i0UF4t0qMESyxXmWu3vSK85a7ccjcKoqrLoLpL4Mczj0ddGwbDc5Btl16umuUVVr+Mg4S7AOUk2JZU1NT/YoLYQvhrbChBpos+elGdikKBS83OjChapUtzeC+h8kf20cgQcPgIs4AyoSa7DXmqOYsFoF65zr7wMA6y2UY/3lArHC3QFrtLt1rl729Cnaq4d234dB7pXPWyOPHNu1raORTkFa0xKi3+idFOPqBISIU1/bPls+AofW98fugXzXGfPnoUJo19lLqPtxiBecjhk748hG0A0aJZK6Y3O0WkKAlJat2WAE6us3MoNFnmfbArg+2neQkjyzbQHt5E3vGqvnDAMQdtpANSUg1FbbnvkwdciAqTV75UIYNq7XL5dZzgp+Tn2rJSysDK947nhLBX+aiKhO5uISwdIULhJvVZknT9F5xhM9eiRET6QIQZDEdQSgLhUfj8vYvyr1Qqp62ewzkGQp6/Sp82DM7dzIByDPU2YVujWqK+RNToWhmhsCcTjLFjFify0eiVovQxUC6nqV9523hoymbdGr7JcQkzJvgMaZNbbwCVxdZx39JElU5kjQlq4vU87VCKrrTAcpiSEwT8apPArqDBfvtjQBoPO9uiq9KA2D1l0tcotT1dvDbnncYDsflzgXBmGOIjgbNDjZWRalKSHnvsYA/P/+BeAv/+VMsHizWkG/hOOrvxoT1MVPv/zv/t3Af/AfYP0oYtyKsujdqqS/pqnN69FE4MEul471Xp4msnRZhZWiY96g4fBZ/IbkAhg1YcxiKHkeNCL1TjchACvRkuzsFamgQSgeFfka7wjDuczlrxeITUgEeRVBwXKATRhRB1HOAU1I014Cq82Ix481KTCMQApZi44RGLJxwYiX1arGjtngSnwhwPjAvNELUMuaFBUlQuPIuwAdhiHn7lTDGet1Y/lD/nxZIcwYt34g6GdOpwqrlXoBVeGxuFs98zGucpx7nMomaVrqMHbIQngY0DS5sZsZ7lyI3l9We5kDAyaAq4w2+nB2hICV7g9DBFh9LCvYlaHehsI5T4gHus9obH3m+UxtDo8aBlzshMczWirTEYxvDZqIbs2tWKSBjUf0hSHkJpCs5W6L1qTY26f5x6KYudrU7hz9po2pEmMg5Jb3cvdqkW1ceAhYhbH4PxVcOQ85f1Zjk5+V/UrqmBMwJ1QYIB6kFSaswoBVJ5s0puytLcBRb4HouYXdRQa53Fnb3nWd5Ls5UqiDKCi8G7wDnidwXd7j60s7E7xjWVN2Vl6HHti1RXlCGgAW697lMp302PDFjJs2OksXWU9OOfxhcLRnwxE86b+w50LIVtNmA/YjkOdWucRmW5ap9oVFkuMvRF4vL2Gd6gFkoM3zMA7me7RS2IBqycjEVZWLBrSlJN7NEIzv8qjXbWsKfJVGtG1tyrK8t0JsV2+OMBNp4b9jNGO3oQFKA0jlyNSuxLjRg7BwR8B6/3hdxSrzWVficgpznsbwMtJ4SshofAgIsTF9lp7OEbXhLpwOZeecIKQ/w2gXy7MG7js9mRcXrmGeWL9AjJl/uuHzGfgs4z3kNc+jy7cYb0vB73tgHYeMeKeEWuOrRtS43Svjb1xM9OzxDNXMYQsN6k3MdV65ss1FPhwgx/INA/DokdVpBXKZ/QcbWeypz4yZay9cQrRIY8RgrYsV+mE8a5cv2KGvMQy11Cz3HdaSJM48e6ZhBI5zxI1T8Dl/pQRWZiBh8OK3rSj6tPCs2YjzLkztCq9+Mis3VPCro8RAD7oOIt5jEEFaJDNxOHTP5qhjBDthSlzePFGV+9D0NxpKsJL17/dS27ZdZxcn90Up05hqFEPMhE/X2d4AQkdEY5pOJk8Xs8WmalBjRdjql2lUQCkkf/AHgR/8QUyvvSaZ9UlKhVmOhu77eq3SiTR5e4tqs0GthlwKKzRal/d8XqndkgWcj68HcqgOH8lLMOnRWl8JFVJnNILMARZ33jyW6ibFnVPGLWhX1rVZW3nP3IK10pIi6NfXDs3V93LuTa15D7udCfQVs7X3e1x0nXnnqKn4bqQ09g7HCq++qp6iRmtwR0G+hOEqI+D5UOFuBRE8HLRShiI0QcuEkpx1OWIYt40J8roVz4W391OSz/pwiqz4y7rZRTRGYN3KB6yJnCr4+2tYLfvCwxgjTr33nlRYqwXDOdo9n134CRnVY+dsxrEmzWXxCg8NvAkNEGrhMTFiGEQIs8SqL3UbQlky0ir9pDOqENC24kFgvlTTwDxJ51ZymywWuu9RbzYYhjI53jZ1GAzosJKSvZ4xDUxXSs868zZSIYQLPmGF43WuBQ/IGqbJxbE7pd5ArAF2p1mBp9psMGiIZ7NxsZdA2RZZB+lMaFlpbMo/MyXfKUv8HnO3PusluS98fnr5swv0eZ4YCXduIsYrvPCoK5HAkKvcWEMr3kUFJ/o+K7j+0eQJhlIOA9DlPhk8Dwk3k8D9w9hIJSmtslXHiG4jnvRCPoVgYbS+NrkAgQFNEKCE8uHJ08YU+7ZlTkhj4rgazuZNSM4jGoICC4Ah8hMqCfnRs06xNp2zo5dL30vklmdQDWcM2i2VoaVEJ1bhhNW2xTRVRiZSTa5GDJg1spQNtGIdrfQJePIEuLxssFL0v6I8IbHEiJtTY2GNIm4b8UrpZ1ddh/O5svtUVRkwbF1oWJFY6olYeVe/p7ENoxXz6qPBfg883LVZDvEZAKo2qdKcPbxjqAVM1kpdYVM6OJowAmr8pgScsLJQqUGNRKTBdL4qSmWaqgJWTTSFu23lnOgZ9wr+hAo9+zuoV9wUfGRl3BdXEXmoG/7UXM22XCurrIPYIEEWr+BTPlLlKc7AWwXPGW/Z6Ood73gFH/7wx80dXe2f2dvoCudo21msKnJHtQba6EA1E7YIvmhP2VUYIw5DrmDQhLFsea8LI6Nl06CLzWgKflRE/4zGulEzOQpAUed3VNcn38f532mwNFuPb0qz2055XUHcfCk5V6oLHZjXV2X1pMtL17gkBLxxK66gi+7uXH3Sr3F7AKf2IjMclMoFregxNE6AuL0gcq/NSegG8yFsdJvvdixP+FS+s7nIBM59Yia9xtaP7bqYj8Weu6QT7+jw81+lQ3mrYwQ+8AHgT/9pPYtfmcOo5vu/H/uv/QZpr+0vi67ljFzRhsPi4/Xz++qi6NhrwoNoT0p3WpRz/5p0KiQxKw+RL1soTpKyjBYuFDJdT11u/NLgnI2sKA14qNwWdKBz6vt72pSznbeihEWFGWehW0IjaGw7+teOMs+0vKk3dtj/TCMkUL32KZnr5qFthUf3ABfrT+N+EKFoSbiubTj3sOvuyUHQu+qXk435/DHzSCltswmSPff2JtOJ8lpvIMQINMdnxWfOrJOPrHCdNeTBykeSeNSjwjzZVXRFvRVJny4fZO+tH2oo3N6WyL/+Su5tHMsFK5Gc+tzJ+WI32TxSQg4doAKeGts/33AQgK3LswUrckCCMOVMjFtP39xrP0VPD2zUdtJ+ErvtVCB0GY3NnpRps8XNDSyBcPTJod4C40tUgPs58b76O8fzvM+grDHmBELNdhw15KAow6e0SxlGfv6gY2lh8ZDtbyozNK6uNHH28hKnvsoyTeetPQ8ZwZpLMtOoHgYLG/EKcdsqfTvADABweSmlKNuxOGu+05eNLugCTjart47NgnjXa+RQH561P3fANf3a5mcab3II/hQbs9msYRIAtP9/9v6dx7YtSw8DvzXnXI/9iH3inHvzZmZVVgGlEggCoohutqCWIZQhiyYNWQIoQIBktCMIMrsNmfwPcimhAYGQ0SDQbLdbBg1BhlxSIFmVYOXj1rlxInbsx9przdXGGN+YY64dJ++llMwSxTOBg3sjYu+15mPM8fjGqyuN8EKRh2zWx5Ad48luIWyoaEYAVmv0jer4HZTqUJarUZPXHZ0b33FhcQTX1teVz7DzIl/u+rumXJU+EcSjxTh0p7oVfsu7ruvx8kvxLuz3qhcp8Vw1Kdi8xao33iD5dwyVBg0Lt8dMlmcTT3reLFpC77bpqvQ66DraVnURf8ZOD7u50DLjzZzDdoszNuU7XtkJAS9anpz6nt8L09984Ri36T/7oz/6jY2uvh/jR9n0ywUl8BQ1jXYdSicwoCK2nPUBWkECpxNu2shvjS5M06qVNmCbihAkiz7P6i6UPy8hVko558B9ZGe711dUAi6G5Y6w/boYNsD1kFfSpbLboeY2XXenrGGQToUUbC1uRhjjCMtGl5wCeQFp93htcc2tKQxdx3yCU4mRWy3AhVYDcELv6QkxLBaPOo6oNZ0QcD4LKmaIDBY7P+7fNLm56kumSRSwJYh/XmLnSyc87zDwe83nM052XTLPznIcxaBQza35r/9rSNDK/34H59f8p/+pKPfffitKKTdDLyyRZNa1zRnyGZ7xNFWxyCEIs20uGjCvm9pMN0lc13tBEN7unCqR/JHbOuemyn/geUgt72DPJs/ENJWJZoknZsj3NMHuKJ6eTNguIRrMbEqA3sOquyvDcHSul0tJILX1wO3P8YiH/YLNsEicrwr7y6WgnONY9sjvsRdI46g07QR51znlnsxvmioDyVzzvFBvMRMdKUGTY8uxWRWEnHG9Fj2M9ZUBCMo5SlUOojwVj+F5KN/g3KZJvHF8rqcVP0/jFesv65dyFn63oClr1TtNXj3PbyzbHxjp/XKpQANJLKXnEgVV0A+R3zWXs/HZGdFi2kkugPCmGfHuLHwDPUFE5b4x36TrRJnfdDP6cMNm0DPSM7cQCt4znSuVlfNFk9Qul9pbHEqZ09vUmOekUmrVgC/N55ayx/XR2rOpF2wG4Z3XKRaZrIRvSgEvOgG5XAAqGmaeFhY01jsj50LfDJcyej0eEceziQ87e8/gV8x+rXSa2+npyZSp80XeY/Tiv6RfJOswg84939N53wlfkHDAxehMeg0Uus+57C1peZEdqtcVQilQoPvXNCj6zrDBEqTTd5uvJQ9jNWz9nDC9tw4o4LwYumPvp+E0jmZ8tLgZ4MhHkr55Fch7+lDLZV4g0hT5kyHIcPvj7hTpks4j4yGnk1Se0hCm87nw4hAk2oCs2wgnJQOCeH7cFgupczyhSoLV+Xhatcn4F6ck+RtXl4Ok+1npav583LCYeIg88qRJRni9Vi1Eyvzc+00PJvNcM03lkeQxtvdEgvnvDbr63PheBP9nP/sZ/uzPfm4Wokf4rIFRcOhkSngZeyk/tpmtDN52q5arbux1ihbSFsNiCNZ6kx1N43BAUWwAnLGprCbfBptERrfGp2e5AI+PKkBd+AuRTV4sujZ9QmAIasCkhE/PjaBe+VxBSAuagr46tMQ2m4iKMjNDYICK4TN+3qzddQMLLjiEwsT1shJRrdBHbiCDMvXB19xWj/IMk+hXNX9edP0SiZ3nxH+WVKkP9uWc1u+5TlGbiam1roRPVC8EoPlv/q7Etts8/tUa1QX7n/4nzH/9/yx75BA8NlcJAehx79WqzpPwkYcvU102i4DzZvxUtIL9Hp+eSyz142Mdknad6u6RFdrDQRokGq+ubpYvPRwUAXl6EjreSiUg/1yja9KJD7vSNVGRNq+gQkZWinfnDAMHBBDpnmctl8p8Ar23lNPWpMiXefTrc543m/Rqj9d07vOQQlAPlj8nhywZau2rBIUSZmiJcjkbsuSNAzelqhwnDWWGwVBIWuL0G/fXFCYNmSHSzcH3xJN4bz267JcFOPTL0yYfsv7SiscwxIN7YEqcEgIbhwGQBLxpwrx9MPsrJUXBlJ9Lgr9jUFr6lPtnc13TH+kgC2o4juptyLlunufPFsWr4BUkfsSTFuD4ss7J6ECfe9bkeAvpGmr5a0q8VxK8hsFnDQOW7a7SlSujWh9mqP+qk609K2ejL99v5Q6590SDwlM2WRFbRfSfnuR+bsZPBoKti3bYpJ0coRislHw3NwuFdKj6ghLmEDEbYGZlYPXcyDPf7eeiRCoPmEMrHvvm1cqpouvwMuo91rC6dnytL01K5jk3+Ugl2/MY0iwBBTdvYh9SLvxkiD/vDHUdRgHcQl/RofEhvYtrvYbKtylMjnCXrrf9nFNfBVQYoMvzYVJvKBEOVY6MhuNdr9JwETlbQRSfT7PWe00OcfiL5BkQ9y3V4ZPVcy7CK1+0RO5mo/tDJYT5CUo7DJEJodZPrhDv52Yj/+hgORxKhAdpiiW6K88JFU3PLLzlkBJuoS98wg93z372x3/8vx3BZzydR3Srd3Fz9Q9UMP24XiWzeQ5t1TAg57rhEy1sPocIRnXoOomUSrwYgCI89flVzBLeeAbnPY72Tpu3oqsVMOcsxuqBFGL+uagJrIrhh7voPgfBLZqoaYXUcY+9cPQE7p7DZLcFTRFefs3TZGh+ZaU7AvT71VzOlbnO8Cx/zmsbhHP0+s1dYw64LXSWbYMFMd/Q/NN/guY//o8LIv7mt//3Pfzcm7/xNxC//aV5nWIQ1IRu2Zxxp7gDLgzCHwo32ymXWH2kIqKcqx/t7q44Kv9uSD4P1iESdeJciT20+evvmzxXzNY/qvqFtwBW017QGNK1LFrF9NJIIi+/69bB0qxUwOfQ4jo2VcnOuwnB3RUqNW5/59BaslMMSxE4uhdUvrzgrJjHavFVaIFT/PmR69gUj1ieTZkhCk3eeLnA5hxCSTxclmI0kra8Ms3zo07rSYAuck9Pa6FLIySGpSDw/LIzCBc0mLMi2CGUv6swW9OqR5E9Ifi5V0ngbuQs7m3W2beFrZ5hdscbHgs7AFW8+Fme0zTV3lZD4V052srI0/3xj75ea+9Nda/1hVTu18Pr0NWd9BfGw6Eh+KthZ2LfTSU0E0AhKkU/z5emEBtkTsSL2rSUWPuca0AohJpH0KWmZ25ApCq3xg8Y++ENw3APNt3xGpQcBt6bNwnXf0X/fie3+D2n0xCEsd/rP5KyycGVUQLAeHw1AcffFy1YsqZJ6lv81ZybWtfQZ9ky9cMeha94fAiVcm+6CZb7u6Dro9GNrrNlB4J4pBPdPHoNSNPkwymVfTEepZN764i8rc2/VzTr95CH4+45j+hyQdXN2+/FNLn7tZIfaznEYTSoXjL28pimEqpIY+h8Niez8OF1szj/cO73OrzIf46H6rzcNTN4e6Tv/QRfoJvQp8IsycCYIUwX1ruDoA3ni3TbejdcK0ay3sfqZ61LD7XsiPK3+Yo595jCRlqrT5OEu+ySdiQEcIFksh/hiLyUkOPetUTuaT2HAGR1Y2EERpmM80hLy+AI4HLB+61uMMt1Qt2ejtkRbbD76C5XzDP68VQIljGOYaosf8bk9X0jcx5HLIOUX9wROalKXgC9S+ScU6+h8i1SasXDwVjo1OL8rSzjYe/iW8mIDweE/YOdTXx+LgkDqkyEcF8LV6zXCKSImGdJ+kklXjCERux8paGUXOMcrURglt//8D8A/8F/8INI9F+p8ZOfoPnTPwW++UYaEqWE2HVYtjthCKlFYhLnVO8tplwu+Ao5BgoP2e95Fi5pfaqTc9vpLOVjdc/7BKft6cvZIXK71fbewDIBD2lGVHqcprrY1IyI+OFDhboBZQ3nm9yZvnXK1DDgu+dYNSRjuUK+HwAewlx5joyhKHqT4WSrlmo8aV7Pw7bcDXQdboevAOiVyyuUSPmDrXlhQn2pyFOqAgkS2E7yvrkThNv6EnjDXDeCoIbwhwZj7jGf6zAcIrTXSdb77qAKsiZc0gMdDj26ALSXV7Rdh64rLm8rs/ntk8zhwwfMqce3v5IlPqSz8ZtbbjGF1ro4E/23uvC6HvNS5myVfQzB94pZSlb9cxiAyDJy3ItRmgfRm8GOwOzi/PoK7HYNmos0ORMeqOetCdoxLNgMwKaTZ1K5v93ESOoVMZ4mYDPdpCBASrDO6f5COOXmuv8KTx/lirb5CmgfDqZbPSS3lpyRVcGfZ7fnH8XTdt5+ZR642w0Wm7xReqjyvLqCCFM8AUW5KN5cp5XygrHSmdbUvAahw+minvJ8A4KWqdQHs2iFgWGsfjSOVvu7O7SIA9CHGX1XPCxGgC6BMObJjIEGwEAZonQhZR+B3/s9eubki20KuI4R47QBu68/6Nq9LI1hkcZ/nTTxQpIeKH2e0aeSq7NxfKHpOvSa77OggIsLm8wpX3zYyzncphZpaGXHh8F4QEooiT3KZL5yXicAWPYPZsRtCDI8PSGOI+LjI5bU4+UopS8BQZH7UPJ8lu0Ox0uLbQDidAO7d5MO5tQ7OdvgfG6sWo+BY6dXycahYaseuetruc9dpzQaxDPWEr326I/yqfFiagN+9DWBwGwKJ8uLeiOnmSb0OaP0EBI6ieMZm5yBSfaP7MJKx04TpMTmG1AeDUn1ClsuFyRE6ngUhZa6X0oq43LGy7TTHMZ36BLwfqt8fHTyDRucPkoBha4D4vMnqbjYPJiYGYYWTdehzVf87KdFp91vVNM/CfPs+9bulenhug9XSPnebUDlwbWu1QoahCBA1jT1Is/Hq+WuNk18u4zwavwwBZ9ve2NUjUX4L2ewlFQIsNKNsQtAqhNN+djKrakLri0v+eF2A3p2KQPUYnXVXlCXHOLwiSsLGmnN7taQs5bzcxZi07T1Q7zFpe56L4jtJjprleEoVO7HsVTQ4IdMiXGK25wbswoFmZJ28pXV7QwJvi9S6EzSXpsGzjwDcN3d3gQ3nBV8Z8WuvsAa3n6YF0DLeUaHljYhmACPKdy5m238w38o8eopofkP/0P8H3E0APCHf4jlv/vvJEzk3/g3gL/xN6zRmAcUDC2Z3GGRK66Ue7+PvA9Enua5xBvbF/wXPVzi6ZxCQs+r8SCSoyPv2RVkLVbdRqt7AseZHDrB19n8lW4MWafADgFJBV3FQNwj/bA/eSQLorwwqc1o3K2XChufIZ9540D93QjBEvWtN4Wf0Gqe3HYaEVTuANfbAvXPrLblv+/nEoPQkd9HP7+cUVDylfORXnr/XL6n6rXxhjyYZ8DKWNxd7Pv18+9cD1kij7pt6xCd6l1qIEXWDlcNMOdCW0T+7By7N+YDFay4R9hsX508Wsuo4vFZLfeNtYeA2ou0/mPO1RX08qN63vqe6t8revuMvAacQfHGZyQECQbisFJNSK0BWHb31FHjQ9PiipewWpPP2wNWiKYyvJzrUBYSAqdpMlo/H4JrrqYfYBWWioBtv1wjLGdU8S7x5Va5RuWw53ts4lWKdkyqqBZdIWd3l5ObPPe/KX+/u7/+o0F6aOTsw1rvtTr7PC/OCuX2oCp5y7oc5xIimu5tTwlRagB17p8yCu9IMnT+bu9XdKn/73Nk3lTc3Dyi+3/bCfcunhPJTfJQGEoH+4zI1NXcUsJ8K0sLwdGyA11s7goExP0eOTSVsQ8wAbcxueuZqc9ziSi/t/ujPIV77/lQRgH7f3NwvYzvj8H//d/Hz//pP8WnkzQn2e3KJnOSm1THbtII+dWvxOr7+muNfWLgnba4zlkRMc/AlVFfp0LI3GeGhLB8F4mDCH5zkSznK8saBVc9h2UCXYOSlOT9zGDf7yFVggjdf/NN1VSIc2FVHjmgukLIt9+K9f7Tn+raNNZ/0YYS1gBqOluWNtdxHRtTxoYB6C+fDImhe2y7VYRIE88+5YcK+N7vXXIwN05jk/0acoZVQXh8dDGhfv/gEE/Gh3mTVJOfSZNxPFt3lfOlKTGAo+QqsMJQSrVXij9vurmSfv8qhuP8i4zq4jGuPmcs+wfjoykB7cdfGmrJClQpCSK3JKkWtRlKrLmP3eS9+fQJ+OorYPP05+Wdh4MgsIo+svLJHAQ1akapx/lyLGfZXz4JTf/e7+E8xrvqObxbDAcGlN5ZpxywOXL4vCjSg1WoKdCJ/PfpqdCheuJ8x0T/vE26FUILoVRWUQ/A8Sjegt3OxaA63kQetxkWYWbbbaka5eOYlemdIaVqN/m1rFMr1ez3wg9uQSpQPAx1NSIqE0z+tNhVV8HGun2P57InayXN+9F1zVXcuda01yJY1kaey5gm4AEvsjmPj0VpCMFyAbz+4COacmbvglzusc/fcFV0fFfdDWQ957Azr8ZmWIRBceIfPuDlFCsHXwiuGofGs3qPC+fWdUKfRHfNY6llyV60AV+Pa9mE/R63HE3OnC9annBQPvn0BBwO+G7cmeecMdBE6LyhFAJKZ3QyPlYQWYFStjbGXHNzhwG30Fu4Fu8tv8s9pTxmXDHlMq+N9MPQc1H6WEc+WJPG0wm3Dz/G0xPwo8eb7RlC3XCPxiFDwqwqHEOYhofKgCft2B2fJtyGBwtvTwlWpWm/SO3Jc9gJsqryBID1huEzidjSu+4VWd+AkU0zedaVUqr6yHmMlfeuDSUvj++0alH+nybGW9w8aYoEobKZ8tDpmCWvR+O/qzwlvUP2YR3MY+PZkb5vqBsill4R92DO8dpaUm8IpaQ8z5Uh6pthKdUL9DIebz3mucSfk57HEdiEq/FONvrUAIGiwzkeasZ2zlb1iMfCvfbVlKjD8bvG+/NroVUVKL5QCr3O8fjJSjuRT67SGots1Xu7dL3IC/98Mhu6TsnbKbc0H4b06vVmu0+5VFJjcv92q3qR0uq5e2cRKgDwb//bP/stVNHJuWos4Wl5ngUBgUOXY1jM80nlrYItzLJf6rgiB+P7CgmeFtd13RkqVC3K/dwolmYbrxbnOGolBTUsrGYwX641i2OQzPu+K3GmJARDCpylTNTDXC+6EOlk52SyhixdLqWCh1fA7X9yqVRiqA5VQ90gz5verFWb8/pHAMVYyLm4gKn8TBNqRtB15QsOKaBdNo4oZ7g6Az5jbSjRKk4J2Px//z9oUvpXOtb+X3RUsfn7vYQkgYimuw7ManqD0LmfFsuNQgM8Pgnz0qPhg12CjwdW+DMAUzorulSNaQki/GJYqpANHyMO97q3PFaMMfVGLf9VyKVfN3/WF6yVcS8Q3mQMDhkBXNwkJ5rrvAF7hD7U07DNpXI/uvdOkzaS0e8oj7TqG/6fG57HzbmpBLzdS+AuBvY2NXd81qOrOcvvyJ+9PALKFa8XXq+fy/Xsmry8TYt9wAw4t761VwS4R7PZndRXd/KIlv/eWn6YARRrkbLO+7FYZusE5c6NmxBCpYDYdvjNSqniY36PmgZ1LsF6KP9nKdi14uvvoM1V6WeaRFnldIGi3FeyQ9dW5Uj4DQyCRLK6GuOVQ0CV/GZX0Asw90CT+5po2nVaiabrbf48dyciavmJO7ZUKv2QDvgdPsTlSHSdS0yH3IVx1HmEWNGP6SMeEfCGqJtT29Zk4qsohSDnsKT2DoHmo40ve3TdrZfTsFeqckKQpuIx/sPcDJXBjFCYplIBzd9T48F3jBmm3/Fz627XXScKOc91rasx5Ifr9d5n+x99X+mqXm1XPZwF5b/+Ft0ZDeWMZrxWlcrqjS2PrfJD9e/Mh2vDfCdy1qjGWu9DKnlY9juvwLp3eWO0/h9YBTx/vDYIAjlR1a4CTN4abzkL66FZNfuOqExCzsW9DUBjxduiRDw/IwL48QepGfv6CnS7XpoSrZCdl2mDcSzIzKYTptOMJ8RhwITSEdMnzTJ5i3vk629bd0i/S2raXUeZ+8uLMI95bgRhPH5nh3OM7/DyAny91RbUiq62WmeY8WFCfOKyY5wnLV9+5zq8E6X/9Io4TRIbiC3mbgOGtX/zjYTWpFTQcACY04MgDR8/oh8G9I9bZQDZFL6tMjdLiiOj2m4rwhSlJVoX3JQUqQsBVwjC04ZQoa8hNNa04pweMF5Iu1FzFUbEaULY7vDtt8C8abF/fMSCppwVrdeUcNHL6mVExCxxtX/zb34/tf4fffwn/wnwZ3+m+SUcAS/pvSBZnSAwjE2/TSXuV0DbiFYRn17dyJeLMAKrHfz4iDn1Aj4EoMWCOF0Rs9TeBmCVcY7naI2EUlIEa/+AvH3ANCqD8fWZAemKu92C7ect1pPoh4uf36QJMSXt8KpI7ql2VS3q+rb7/fhY0NdQjOqUXElBKgbekg11ghmVwGEAcNFcgtAbApjTzpAvAGZ50FsWugZTjsiK8gKynwBwmzalV8Q0YZMS5rDDd0/Ca/abWdqhE27Kpe65b29Pvf4hjMDzM/LXG42J7iUU4xe/kA8eDsB2i+MxYr+PpYEZ5BnXqzPwjrJv+71Uh2Ehoe1WkVGiUkTvgUrhj5gRL6fye49c5WyeSlHAUWA2BwBZPfBQ0EoAoCe76JYP4skMNwuHBJTfX+Sc5kEr/rjIgWHQuHk2aFLvqp/qPDdYlh4YnYIegqGh+73OT9eVhl0R7F3pDcFULqCEObRYkFJjZYX5XikXG4Gux+urxOJ/8w2A41Ga3FEJBNTj2iCjx20GbhcgXAqvH0fgx4/iIe4+/MiAQOG5wmTnbmMIsnTubEwuzqEFgnpApgnX68Zopg83QV1VsY6Ysd1GvBwb5NzLmsGcldITwu7vNGF+/Eqr50n975tWVGElpknzA5CihO6GgOaiPrAgXoX9RrSZl2NvVxCQbsiTsol3h0XokVql0ppPEpfzaezeiz7f4GE7GM9CzgYr39Bas8Y+3VfbYvfxBdJoLyXgqy2KcENd06JlnA0LVGwf5C6NV0RAOrbmLHJQk5zH7gHPz0Knfd8CXal4VyXFXi5oxhH7lHBtN7bukCKa4wtizojbregMeteIjIt3MgCXCzbDIs01WaVwIvgT0ZxeEX/xC+DDB9z27xFSj9D1FgPfQ74z542Bp+YR475ut5Jof6v3BqmRdwThMba2ywXQevvvDosaPT0yYoVK5wzMqUWcztYbpaVF5hrLUT9j7yT7k+Z2WVnZccReeRj3GycVWCqPxgswDD2aLD1+zufStZgdco+vDWIANqy8o3RLD5WQiTMyeZYhyPz7aEm8S4jIKSKHHsulzL+qeviZ8f0Kvi+8HIobwed28iIldR2acnk8ohkGxKhJZ17p1IU1K4NwRpTYbb2wXbdC5XIhhMiT9qcRQo3YcAP1RH3iWEoO9XDWFi1SCVuZChdXZIqPNBRpygCiIRQhlA2yR18K8/HIg7c2GbPMZL/bJAlEDTMYXVY/AEtSrQxVRTjm3CCjxYQWXZBniifgPl6yWI0S88d8m5QAZJnkeCzepDXyQnpo2zoHwroxqkYftaoHy6rFEID/9v+J5m//7c8Q379eo/nzP8fyt/4W8J//58Cf/Em1v8sCa+uOXNq3U/EpCFC2e0q0QOjvUiVE2e95F9f3Jme0bTEgAKxgDR3+Trt/KcUCvBGJMoRfnxtaIJe8NZzqOzIrM2+wKAqnoTidxgmnBB8Tb7TJEVLlhfCgSYXupmRTb5XheGXS1pnSXXw63DO9e3rOjSTZKvOKYUHbFi9Zw0tGfpZnUQRQ7lAByQRCo55td5eWsk6qIG+hmpeVV1VlF1kSZfsuYbNpasTObQ6rkbHbrlUC8wxwBTmRVxP8aPRZLHHp5cYCUXjpAYohI6aEiyqNjDTqt5LXsN873qXKsD+HiqeepuoPAmDMgMoYusGBOgeD8ddyZ8byUJTPAEATpGOrHzK3Ih+YLFw1JtS7GbV8vQemME1m0EaVb5S3i3NW2Px0Q99CJYvnpkHQruai0EHoWxO1STvJAdhIqQgBpdHNUBLOQ2CC7AUxJSQ2Sgob9IdBfh8WHA6NloQEWsKNqXhjSDoLpJtsVHAP0yQx0koATbPB58ZtauzOGtDQdSZrKF+90Ww8jVqmzolJ8wQvLYnRERnLafdpRgNgu40CdOqLvMcsJQdg+b31g/TACeoh05vhyMZ4jC2IQloXlbqSM0HAroLK9XKMI8xjMwyNFMKYppJf4+YmBkoyOnFXXeZcLrv9L4HfCs2DGoURtZ6k4y4PRgGBEErnVz6fUyQt2nOcu4JhPBZmDHuseJoUJJtzLNEWqXibPM3YD3p2Bl6q3ODd9A08STtS/r1F6MQorcP2mnrP/YtzQemZowTUMo78+TeN71fwuSJl+OPFVQnwIwRRhgEc5w02mxbx5z8Htlt0P/l9Ad3GiIctwML4S9ejRYnZW9Bo17CIXhl4c3pFu93iOjaFAHhJeFu1asCyf6g+45ttncYSqwdI3K3vsmY7py6v/R5WwvIlvbeYrRCcsHTSLAytoZ0NFhyvwtDY1ZDC9eUiCnefZgxDQf8Yu9w0MMaWc6to50bisBg4y8sNdY/njMhW7RqTfH4VL8XTk+RAfPjQIJ5e0IwjwoevME2QNtA5V133Imap1pEzkMUFeb2WklMmoLlxqhg+qEW8jicNoUHuNDaZl+D5VKTVf/af/SAS/Ndm/P2/L3T17//7FYfJmUBSgw2z23JGDEGUnalmzOxUOQwulrfr8O23LgxGczVY+aF18arIGb0mXdF+9h4z8iWpLd/iIZ+Kgp8Sttu+rrtuKNWIkMTgZ03lzfhJqmmoYvFybMBKGvs9gGnCZWqr+9s0wjt4HYjARJfYwdwV2jAMtQUoeEcgSN3k6ysFT0TQVvFAUdpH9Egq2FkitnrWVBS/nKM2jWux37fW1XC3kxjR11dgWXo0TY+9jwen8th1QLcRtHwacQ0bqayxLfJyQUTz4YOcCyKQNZcAC4BCNzHPksil1SKukBju+PwdkBL29G7Q9e7iUSfDE0Rp7eGUqFDqXucMrTqyII9186te0fOLIlfWCT0EnLPEqr8bcpXjM027qk/Nwz5IdbXTCfHxURBWtH6ZNsxj5BKTaOy241GUz8NBUFMUj6kg38rfW/UQPD8DX38tsfjBJfzqopuUzFilOKB4SsnV2T85i7rrMGWp0iExuFNR0kLAqH1MNkE2P3YdYifGBCutbFICBs2JGFrQoeJjl/18/L2hgs9k3GsWI9vb6dexQf/4WGq/hyvw8SM2j48CwFFp1fyy4YOg9d9+C+z3DXa7DfanV0QyoRDQf/ON8g+lHW2M2uQZ1ylqDlyUSl50Uehdbg913wWg0NflAlybFntlSLfQY7o4jyUZ1Tgpryydpo05KMLw/FyfoYWrOa2S6Xn9o/zw0Aktz0EjDdyePwzaD+SNEGSjhxAwXoCUtFKeEiQNJMpdVhYjnVBfOB4jHh9bxOMnxHzDbtfad9rW6Ul6t29TU23vfg+0ypdtuDler0DY9Igao8Ma96I8o1KeaWhaVZ8gOTFM52nzFUm7O5sOpQaOOA4btOpBvKUNsrsak8shXJdgb6CKl/P6svr44RARUzCPzH6v/Pr5iGa/x/EYcThIpa55EK8mRqfnuZia66i9iybJTbuhtbA2yjoaiSQ7zuN6Bd69k/wIH+JtVQUdgdOIZNdn3tcIICpTnlPdq+Rz4wch+Gy5HVBaeONyEbcLUUV32IsSeHx8NKJAcmiHWtm0hIUbiXDc7aJ2JXOzV2RFYid77jcaIpD6GSLDc2gNwyLSn1JjjQ1Iv3cNSxThDJkbG1RZkSneITE6F/5qHSa9LKIUha5HoyETZtCGYOg+LcgHBeoRNEvfCcuui+b+8S+ZIQpJg0vlNQF6A2HYTIxeDF4U6QipYR1AuU0uSY/VOGh5sw45Y0CjC5nyyMW6SoF1ZONF/Ht/D/gv/0s06yYO/5qPBgD+wT/A8od/CPz9v4/r/+n/WiE5gro7eLnrjPH4hDJ6pua5ICDIGfu9nOc8A8vQoukEVW89+s8k2zxrxRpXg1yFe+p6f/XEDUupocaf0b5HKXPWXhcFDTSa1oQ/F2EiVy2JUUygDoChUGYD8QI6hIz3kSRGujdUR7lwA2C3K1Wu5rkoy+sSpDlEA6a8QgWi3VisKZEZAVrSkeUniezmrF660CJug91fhIDYyeYswwZBdZA+qYKZZb9YiIBLFkHjNy4UJcbxjZwh3gUuKgSpVpRaZEV7+dyaV8KSD6kYE80VoVWSu0mDuFzQqMGXMzB1D9Z+3mKbQzJXOUJAu9QgstG60nCbSrOiGBYxYFRReH0FNpsecZuLFyiwelSqgrzNONQW9zfI2q3Dq/4LPuTT0TGVfEEXG8MsijcoFAJ1aGaHe0OMBk7/KCEUeDpVd+J6VRlGrTZnCXnV9QHQ8pUlhGEzLNaoh2NBg0aNrpw1GVYJv+06vEwSxilQsIbhpSQEOE2IeSwXSpU+0nl1Xhb/BoPOiVIvaIocVp7G2H3yIPu+Q43tvPKMdhdWSbRbYLvF5UQ+2SClWIBIBz1n7fDc7rbloTljGIph5MO2DBVWj3lFj/b9MkcbDogjf7pNhQ/EkHWrovEwhmPYGXarKkp6X3OqewlFNdQl50d5PDSU5vRq30uprSoK5ow6ZtajyNAGTSjJE74XiJVHV91mfq3RcXrtDFhJHZJbCv+HtBMC7H5SLDRYcB2bAn7ChfipPkY5A5bbnUpkCAEKzyODViSkPJQ92BnZGmCU3aGGUGRsKB5yNrCS0rM9mukmBpOGQ81tW+mEbMAXyWTJA1LhgW2YgeQahOr9MZ7ela7z3ze+V8FfloK0dR1KvNs4AocDTkeg6xpVVMtKxhHov/7aoA3GAHKCVuaM9eAZ3+bhEPO3yn8Ym1lA7ChKPglTq9mMWgXGJ92Q6OPxU1EG+B1lJtdR6hw/PmqWva4nThIrZ0HkSui+wAGZmyXsuX0AgGkSxaUPUou4uiiUihr8fjt85aOYbIobUp9pNbBucRt5iT0rDr0QX1Y/9AScJ7HueeEYj/bOoe8mQPTiNtNNvCwhAwF4GSVjnnOKXa3c5+yqNOhBNSRQJaTr8A7Df/QffT91/ms8mm+/Bf69fw8fPy54/6hIggpEAEYDbO/e99IvgfknfV/IYRwhqF/OeNgXhim6eG8VmcjdGFPYQ4jQC2ZcRleqsuTHHF8bxNhjsxdmT/4l9zUWEECVzhikVrAINuEJ50tTycQGi8VO9kHDOJIkJ7LrpGdyrPJC5KWZbmhDwJSiIX5mDADlAdOEZrogAhgnQXGsU+6q/J11yzy9lt8r8iPsqkFzekUDSB5EaHF8LQaSCUzdT4KcXRcxDBFNfjYDm30RtltFg4/KK5XZHI+iGBwOZX4h6B32PJH8VTdWEDGt9Ty+Gi1NQfaVBRU23VzqzuvZzYhsUlyAg2nCpPWZvYIfAgD1LMZtxhQ2+PWvgR/9qEGfJSmuDQFLaJHZbCm7KmmjVseA1oc2OG8CGBPvykUFjW2Xih4b69n0/nEpbUqdEWhItAbdn06xNAbzvJQufE9skyPwYQDQWk7BhN5iZw2ECqFUVplKR3ejQYJmXSdhTcqDqaAbUsgYEnMpi6yb57osvYS8nRBTwmYoZR1FMShVp+J0qgA7hHe43USusMLUnCNyiOKJYvmprsN5eI/pVLZkvy+hLQtDS6lZKbjDOu5WCUfR6s2mKRV1UkHFPS1RuTcDx2k51/1XcA4DQZ4DENfVTLKUGrxegWVpkFKPjYKM7APiu1UDqJA76h4stS2NAMscfaljY2ZqZDP/j0BN32XlaUL3yBlTjlZz3rqWCxQtL1EAhUsfhnKfCb5GyB5+Uv7wQJ43TcKX+tZyEXIGbmrUy7zkOwEKjvouuClhGl2FJP3d8bWxko0hCN01w4CL9kHadGKQX8cGfbcg5knuvBroTZ7N8zp7AJe5W92DkQUV9xgW845MaWeFGkIoNEi1w8uUYnyL/hfHM25J8iEPB1WuLw705R1FXfiBV3YTrnJGH76SanZZmaDekc3XP66QdvGU699JTIeDgtexeNtDkAgLzsPp3DfNpfwhZTK/H8FHiWVbt1WXcJa2ID36J/Z3kaziiAxpeuTd57TciUbxIRb3qS2YqdfQouNheQtreXwv39HkwGBI52SxWHdWGREjZ3I7vVnGGpLnjbhcEIcBKdVlnIASF8VyomxIYYn6XYsOLNM1Fkbt5sFyVZtBYuZpsc5hU62jAbDZCDO5hgd0+wc0z5/ku/kVGAvKMGfxHvg66NutIgd6e6YJCKkHDn0pI0bES9e+jnO2S3s6itu76woKo2POmkvAJOsf4Fr6MsowBJ7uWVcmzDMP/i6Exq6Z2chjMiU6JXEVstJE79GbacJup7F9U/APBRu/zJ2GvjjyiNNVmX2JC+x7EQZzbjQWUVGnThFRzl1p36rn+JjV0BuimJIkBcac8T5koBtwRYHD6W2QKhKtlZPsVBk0ulVjRsrBRoQEURbGEcN2J3eNNDvdxLXflbjmBlo3TxWtiIKaVXGkatz2fV3yFwg232GoOxBH1q2EhIk8dEEMoBCwHN7J7zVmfbt1ITLJKaz8RQi47d8LUqslIHsV/ldoQyUHGoS8KpLyhiEUQ8Dj46p8Q87Gvlzknjxj/8EUqz4t+PGPNdQEyYCeCEHhY+fd9UCrylkDaQA1MrwoLCJPQi60EiRUcLdrrHcIx119eBUIM6QhX3jUvh3R5SuEfYEVszQbyzlKEQjU8a9NntGOZzMg2HTppt/piQAGJ0NTwi1HbTZ3Kii5Xqjrh5+qASN/en+wTPpipKg3o9XsbhqSPINluyuywjakrQotmAqgMvhhW+QVw/hGBYYeHx8E+VTZuelmCRW6C8ssypA1GdP5mlhw8p5dy8064TV10X2AC8XwMffK4OgFYF6F3ScqDXrezLvu+1KFxLzgmmAaWGxAw7aYpG3gQc4WkpchIIUAacHC5ULQeP2wQb8V+Un7kqDfooivhfEMg3g3t00JlWLsFC+Y6gr5Uumehc5dXo/pYWlXaHW8on3+NdrtFsPhjfKpekACQGoODQ81Zw3TS1Za3Ksvdu45SPgSwZsRQLdRj6t4LOF5seMzx2ctEd4t9lAC+5ugZWzp9NezD6Gc5br5k8kU1Um6Ts7xeI7YbCIiJrRJ8ntkqaEuNuHmdh5rPr7dAghybxuIrnbLO6DbSeMwaFGCBPQHlxeXumrTeH8ou3sqWcoXFo0AQZLy8XSSV7rqZ8YPUvCrRjCrUSnFSgTNeEUMAbOrwxoTDCWoSls6xruEiFfrtlbcZT6EDXDJXnrBn5+hNfrvlXn/DNOIQuk81/NC6Nq8scJLzWna8epzGL7it4X/L8lF8kPsOuS8Sr4haqI3ZEY0YbscSyx+A6BX1LJka2vc1jRJYhIahkPi3TAUC1KFBUtX9eFWIes9G79cFNnIJXP73aEolCKE9VxcD4CctUJHhxJD+waBMC6RDVL6cLv/3Jfx5iCTBkoiFsnYR5cBZftz1nAOuAoOeo4WQXO5IHQbrbDiHqCKZzWcO+kWeqvMmLPrkKn0drvtqjkJvfUWn8lRJSiSb5CoXIgYfdW0rTdEMFnq5PAj4S86/yYETBrDuKFSihlwd5nv5DNTgrlVyQ645JZzUgNHmqvIfbQks2lCTKr0eUuCbvGuljrWHDBLB0+vIC1dX8LZfKDsMGjsPvCwlfdyrtU5eVduCHh+FjT2Zz9VI92USTWMlD/wKwaeACKEPYPXtW6GEtfuj7ACgQTCxPE1ak6VHGLrQoyqZlW6VzElOz/PfMfcWjGTrmtE0XDoPWlosyl77UvyWRIjz6ws505vxjjiFnpcbyWWm1EpwyDPfy0OHOx3otBgv5ez1XlNYSd9Wzbp3hMQAq5noG1yQc8pxMfGCiS9f68679NzrdH5u6KjbYtiygpaXedifKcJ6FpLCqzKw/KMFfXvGFo3TZjn1kD+lHqEQbupqkLchOLlk3mVvaE82aybjDm51IZslbOsKz0Kba/LnFauOwWVAkRpXzeL82ubplIkarO5u6ZCm/59ek6+9KPXISoNS5XwcSrha+YlPUhXXP97Hl2viDcA6yIcAUya59Pt29LsDwAU3KmSnXWuC6Iq5mo0YUYMwPkSS4+EAKk2M0lcdxrE07X2ugP08hQdJ3IDDwdjJWRPFbjA/WDohyq7bJjFrZO7NlX7PI5qaKdsOqGFTD8dC+jICxuCkd26hDqA4hXSP4hsk5C3toXpUV1XmqWarOkKr0AIxgNoh0rSfjCeHcOC5+dG5WJfvHWUEcOAHFoxal2YHNyWCclGzZsSxpQzENSTzGInP7SKzvc3uvrZz/CP/tHPMU360F/9spyOZutZ0ydro60zVYTmfFaLOddt3l24rltceYQJDF4q1fIFDQTa8bVoGSHY71Mql5QIAg2E5iilIW/drrZcdTK+uQlddZQhFu7iBpsrcK5MoPDhQXRrp1QMN2uz7NA23zhkzcvNaOF7len4/lMpaaMPwB6wXs80FZRjt3Muz67DeSptsWlEzaG1uGT7jj4rBOdGT66rYL6VWGx14dl3/9v/BvjbfxsrJ+iX8ZmxAMD/+D9i+Rv/l5JjgmL8er3FziNnu5c+jtwMsr40bQH0TqgbeAmxGNCAKRx2F3V8ehZmbE3S1MK8pp3RriUlEk2eGs8v7a6TVMwdDBjvWBsv1hhPFYJPl5IH826vJe32+8q9f9d8iRcKpf9DlUAZAj6d2vJMjzLpBbmOjaDirlnVFeJtsCZVuJmQAur7e76U2Gg2EKwaEHkFQpWhuzhwGuCa4Of3irz401EUhQ8fVAi7OnF8pvEsLQtJD43xzKfv5MEKGNwgTXFuN3FrW7iFn4AKp1u3q89A1+MTtm1vlFZejo0Yjh9/bXLmGsSNzu989WGpDEFL/nx6EkOzK6hl1WCK8iKV6kqMsmmeP1le2VF1iR9/o2ESrh4mFRFWImlbDWkLtyq596ZddcmTl2FjxtsybPD6qgbE5YJbEmN7owVjzufigu86oM/nciA0/jyMFyRhlrKAwxoxkobVxe8NQwGczkUebbd4OUV7HffvrqmUS5Ssck5QwuQYNuf18TuFXWnmOsVKfzNP9qbYDp7Eci5K5ocPKnc4QfUE1OioyvHLqzVyW1yOEPM1fF+PZdgYVmZJl7rJd3cS6m3VsFuWI2bIiDXec3Pzusd1KvkUb2xPvX96Vyz/agV62lC+S92cOkgb5hImoiixdxQw3IlnnZJ6bLgZ6g32ZOjzOcwiPBxwxqaObQeKDpeWil+QxuhlatNS8ogc/yAf9rKNQC5DIAFYvsgtycWygiJuoz2N5OyiHNxnblMBUYdB76NWMLnm9s7eM53R68QpVaWamUNAuWhgE3mlfofJ7rebevJcQYaf/Vv/1m+h0RXq/b1j5vV+VasMoVjVd6Y5UB1OznK5oiZ9hoBCLGTOuaDy9mVlYJ5psVU8f2ffcTGYBua49XjjwPHOsj6PeIwjmulWI1frEUqDDTJFQ058IpLO33ssuLzqEHSjPIBDYVwx3K63CjiA7CM/z3g1P0fAxbAxMSmVBiXsrJZzuRCGNKLIHEP1dOILJD6vbYHmT/8Zmi/K/b/QaAA0/86/g+bnf2ab6xEQnnsMrqHbONoZ+UpSOTtvXJCQBi8YDFFwg4J7HOumS3f3XX9B2q2MBFWuPFAoa5PGHlWokS6OBmyDRcNkZI12Z7QK19qbQSKMYbHqT7YBencsvGKaqg68S1cSF4xfrFBf7smabyBnu1enk753zfXdQXDe3uC2v3Moc7eY4LXbRh/gX8OPsNkNjY2cWYe9MBePAK3vLdG2aXIP1RflLDy9kgmeibt13vHOUDwWXuBbk5icC514y3W1Rb6BkHlaQyjKLJyw5LvtUKvpCL1ON5Mx3hFyx2hXRwTI+RmdUVFxFXrWtOMVsNvUWH8F32hqs6lLilbglgs7eWuPvBOnEiLuTHKW97llVefPPbheYR5hfw14Bmzw4+X4NDnDGUsly940UiHK9e1WT/et4VkKcyxs7713T89yTZZm8Cid+I7UtherSRjJOOvGPFi6YOobtudQ+m7krthzOcdxLHrAMNj611jCev7VvinPsb95puK+4HkDmzCzAhafxbPm0td6GXNKPALr1ZfqTnrh1HV2Ps14reaWs/Ap48c5I05XpFTuwoKm8B/GOOmzrfyxYw+2/5VQWf1InTFnO0feB8/v2GuJtOyX5gEDv18x3xDzzeZU7QmE/ivycudkUSoe7dYQJKMhuk5W6/vc+H4E//d/Hz//+c/xFx8bs/78BOdZG7domSEqqBbvrhYk18jN7JOLKUwJP//ziLYFfjx8EkbmEZjxVZC5b74xKzcEZ7EzQUPfP45ysXynL36HzGwzFNcWw1849n0JOVgLd6SEa27FWnWxc7YwMl/9zjWXSfTdIu4xdVsxnoruFkMQnj9VKFslDB2CMOfGUK33ByeRt1v88lclYdGQTVre2oKbyAiTGY9HReSe/qLUUlTCZIt5b9d1HaRxiSI+5rbSDfeMv/2H/z/gT/7ki3L/v3IsAPCP/zHwh39YLCletq7DFeIRe5i+A04nLL/3+8YTOUJwKLH/rp7tphOl/K65FGB3ZN/fzE1raJW/HyHUMbyXc3mfltkzWmZVJR8X4ydLhU2Jbel6/Omfyp8fH2s5Yl4xDz278JNqD5xSueaTVh2H6+YaFZm9TiXBabsVhYGGkVeUJOzneudF83xiCdGQMi/UVeZjmkrZuRBc2UV6DOhZubyWh2y3huSuwQLOq5rHNGEedvj4sSRnkjezmVM8Sl4PttK4kMdGVrf2LNrznaJrDXRyxrLdCXq9cdn63pBSuUAD7notIRfWNt6PJOX4qvNzwNDy4Sstw1jytBjCkpLKF9JhCDVde76ue84f/XmZJ3SSdvKnkxZrCMUTzP/XirUWj3276V44ONTQXaKsqgQy+dXTHbeDyL2PcKvu/TQZn+D8n5/l7+aJ83EIfIDe3094h8tFvE2+IZvnATS2aGdpb8i6MzUHtc3HR5yxKaV79XOMed6kWyFePlzlJ4Glx0eVPc/PMhcmB3FTvBZGwnXGIFKCttmqKtfcpqZKevUyDVDvjDuAc+5NB3l8VM/ISiE1hFYFqq8Bf0Nr9MHl2pwogENB5XnHLfF1fTdCwBkb0wu3W+Dd9mZ7MqfeCnUApSPyJjlr0/EaT0tE29/AQO6GeT9SCc3yZGahNIyh2m7LfkEsUep3pfKi8hhXzZD8aFlpGsaTyKRdOFxKcp8IiO7ba9lYoI6p6jqrKOX1MI54ejE+bHTnJ6dGut83/sm+q/f9PJZiMTREOP2HrZzBz/74j38jgp8++xc/pglBXd6+quHnDlToorFYXm9BUmj1qX7IW/FT9vwVQmE/hoKkA8JoJpSQHH+X+TxzI1ZSNd5nJPvJeMaAYlk2fmGegbjh53EXMwoxRHKWGNGAVSLwairVO93zqzkClVFQbZhuQFJ31foy3H3eHQIrA9wt0X3W9jUErK3e5k/+BF/G//rRAMC/+W9i8e52/y/UiLMgZ3WIi9EWmaMS5+2mBl/KhkgALuckZ4QkFUZMs5umkvgLGCGu3Z3RT0CfU9F4tYYV3d1BV+V1zj4pSqbX+BVNaADAhevkDOtC+cZ1VcbblHC80JaY+RAMbffNXFbsoWyHU/hlL1Ctr8kZXdcW5AjlOT8AnCmxon6/pgkhlfrM5l1cGTo23PxtP9Yb4zNny9e03Kj8TGUzBOVTKsiRawOAo+K364npg7y3kc0HTSlbWS70SFZx5+5xd/SIIixbXRCRxBBwD9gYLZbPFIO5hl09kIpU5BHPZpqEfiwZdkFRXPWXdke8JpRhCcTBFXioeL0bZPnyfumymy81rXqn+pylK7tN3t9Ffb+/R/ZOx1OarkNwhkfFd4A6XM0RXfAhffpg5rhVL3WHGoLwJIdD1dbsW0ONzmnU4gLu2cF7utzSPvOY8j+8sG6P7HseRVbDm0nR6/XIQbVvLrf6RQjFM8+5OGV8zVNJq2bvODR8GlFVwDHPvtdVcgbz597ST/w8/Kure1tt2tv0Wj3E3RWkYNEEPJ9m9ZX1WLOwFtMdXS9ocLvVxnrfA3hr36ld82WphE16I/6zC9NDoJfHH5OJvRXT91eQ3yH7m7faEPZ7xvd/RH0D+31rPKhtZSPo6TqeI/p+hzbfJKljFARw02WdXLSQLE74OkWEtLGff/o1BepeLthUzvqWNkjfbNCMV/TTBei2alk3aJqIPbOOn5/R7/c4IVo5pRY3aTW832NylpPLmkIIdYew89QC3TtX1aOOi+V9zsM7sZwZ3ObcV/RdxafvEPngYcD84Ufy/1kqW/TLCEyipViSoUMfDekcBi1FqJU/lDAe9vSUFFSiuZzx058MZinfpgbYvhO0YRzRpoQ2ATjqHuwf0KYF77ZSvuo8vJel+gsSpGHOWT0SYuHDYr29ckPlxQaTiL6M384I4U7BIQO4De+QDu/QkO40ee98juj7Bq1/BlApLeQ45DPGQFJCM92wCRkzeoxhh02YawRMtW2ffCiAR4+YJkNTm5wFjQ/RGqCJsS5zYBW+r7+O4smigtVJ34xvvhHlyMpYXsRVIJ69FkitlsADHiCxMnG7Rew6az6HlfEZQglvezlFdTa0Ur6NSqzGe7atIkiXC5aw09rj8tQWLtZmgs2dec63qcH1GrEs0dDkfnpFHwPidsOtrgwG1tQXxbscNJHgEIDh6514L+jOQ0HkJCRhRBwGMTBcLUU+o5nFc2cIqoYGsXTeadqgaYCdEzZSwu9kdNMcDpimxuJwWaPfhLtjEDnLGZ7HiNTtrBGYxbl30nzs9KSGRNQ9f3oum6NJak0QxJ2KCRXg2HWCMHbCxw4Hl78BoOk6nE6CluJxhxRco7eccUOLywicnuS5P94KVNyr60iSaa+CQPPyadjY+FEMk1//WuZj+Q8fP6LZbtG2D5hnaQy12SjYd3HSnBdB7wq6zrzgLDlI42dZigzwyZQpAc3pFXEYrPTipAmswwCTBV91MvdF8y6+fZJyre8O3V3s836vJTAXUd7MWzLlcnH3e4T9gy2jCjlQ48fArpRkc6AxzTlVHoyWYX7eJZES5u2D0CAWtFnnqCFxx+YBM4CDAqhFZ4qYpmjGFQD0LSqNtO86LF2Ljx9laiz6ZgbROKKZJuGrOpe/OAqKvdsBbTkuoUHGghvAJvvGfgWtNo7zHaJDqL0jHLcslX6Yp8RzpheRERSnS12+FgB63NAPwENHA0Fi7umZ6F1fib7X9x6dYUk+crrAu59c8IQox/mMGKSBnedBcoQNxjHicNAwbBp8LCrAKIzD+2I46+uXIMZp0p+PR5nRMGyEzqeS6+QTZV2ACNrpUnu9FUBgrkvXRZm/c5+YJ7vLhqp7j7dEryjtq3chuMpVvmgMAeyUNIxnmjB3mwrYia6y46Llb3OGlY8Ooa6WOlUwxtvj+xV8vQ1tmJGGWLmMaRHGFVxieocSB1vt+uQf93H5GN2ygxP6bkyTIi16SG2SlubVh1WIpOQTNOQFdPekpPPVH25Zmkp499s0lXisEKI1CeJ76DK5XgUtYjv2yoRdo4/eleB/5+fPzysBCjNs7sJauA7fqGvp+rtScCwbaEAQ/+b9Q/pulgXknyr3u0eSoFb+UBBbN2UxNDzqdDwCf/fv3h/8l/G/bvz3/z3wt/6WuAYREdX1Sf7Do2o9DJwzYmRJ1FwbCCkh8mhNu3SIAhnnypiw8RnEgkI0Z5QGbTq8i5KvJa2xxCeVtDXqU6F17g9MbqVCKL937vlpQt/XSewcdk1zRtO4RDd3VzxCbWvU+ZbPp3uFxD3fX/OcXWUX4E3eSOVIOqY2AAryRN2PLCeSGauSYOzG8yNOws1rmu5RtrVHj+um8EypqTdR/5tSeZ+vMCaN7sq7Wf6Q3tz2M6irGV9eEePCVnSxpol14727Yg2o78uapktdcP2bt7wAMUh5j1a81BeOsF87qG6eyo9SyWO2+8gyi5EL099nhtWMUjktuypUrJzidGBZr1sU+0DYOZ3cJHLWsy1hGBKmFFabIPMl3lUldrq9aLJ0aQ+B+16ewX2t1reSlaVoh6MvXVhV0MOHE+hnePXX5MRH8fhTQtHEuSDdh5Sk1KrXWxoshYc5WejR6hhRmh759ek7QtogRhivasPqIVkqLbVtXdzjbuSMrouOrssHOcU7b78ndKcnMXSF3ZJtaZz3egJO8Qt317EYMnx+Us9MCPH+OVh5cty5BedNk9/dF1zgXFv3LNIHDf6qYRkFiE6ax+O9sdWd0/dEz59CqdpjXkg39+IBFv1rWco9reQKKrIuHmgdLLtJXdR+r16uEHpPhp8dP6iKzs//0T8SdIjxR15RdBZRdVFDsOziqoqBxtJ6vhBCiTuft2L9M9EWOZeW2Yz/pdB9fF8sZbgseMZDsnSZ1mzNGXh3cLFY2y3+/BcN9nvgAS82mWXYmMchZ4mlY7UJa0SgVWdIxD78KEZg8/xL+YV2+ztnVzZJLbTXV82MflzKy0LALW0qeczOcJ5pVfkHAOb9O+RcDBVR8pZS5cPfDjJG7bDJX337rQAq79JrOcfjsdRlGgYcl13l/u86jYvV6iXHc7SKIEuIaP7xPwL+yl/5Env/WxoLAEyTIc1tqzG541lQ5cf31ZFbZRpyJfUu+Vjy/V7o++Uop2SlL7Wj0TnsDBH58MHFla45bkoV2sA/R9TVdH75K3GNvntXgJF1/fiboo6GUHHo3b+x/vR0xS30eHqSfWCFIOSMTydR6N+nlyqm54ZSdYYMdLcr6HU1FFli91Yf0mt3UNdljWXyfcx6SnIf2WTMOSYKv0rJeCOgZ8cKLi7jkl4DVulhXpKv3EVF3ZqY8XfbnSlIzLsZBkGGGYPq94Iof0oaB0weT4WL9cF95Qe3by95Z99dQjSnj8dAHh/LtpvHMiWcp7ZUxdBg6/nw3sJ0QyiVzVhJyMsUyhhWBzLZQWhW5dGnkzT+YQUbp29Z7e1ZeynQS8PqMDx+23u3B5bDgKJdz6HFt9/KubeteqJOJ5zDzhc1wcNe+Dirrw0Dm50dLT+KhrIgmsCPvl6K3Os6O2vS+DwrCp9vpRLH4SBy941ckz5Jxviyf8Dra0F3ab/202slCK5TlEpC9BK5f6Rra6IXblhSa2qFnbPmfliIE0q+HEtbp6Q8SOP3CQoCmuuRM+ah5PCFIPR8zS2LLFkbi5xV/voEGq/UUsbu99o3A0XHCAGfnusSs5sk66rKqDZatefDB8yhNafP+72LzeOCFSWetfEbPWKm2/jqeyRWn9ChnrB1BbIYFitbeQt9uRN+zaFU8qMtII05awv6fJG8hPeHOoeGczbPwPRa6PGbb/ByaY2fs5JTH26V4U1Umzlc5JmsVgXUCi9DtCg/rrmtAioshFOr/b1cWvOMkgHRm8HPslmeB5NMT0WhxctF8xmOR1y376vcpM30AqSE4yxGXde5XJXgcmycDlehPDxjldU0WvaLNJubP/wI5zPwV//qz34LMfj+sqoVDaA0QeCEyLHZYvoNZAQ5o7mcEbvO4ggBRevcx2iVLqk1xMdOTAdRBxNwYZbvccMGaYRzeiqGm10MvQSlvXZnm0rL3TW/M+s2h0a6oIVQiEe714UgDGyeURIx9MXJW8Y69xjFtWyxj3r47AI6z0RemsIAAxG2WoviPlPBsrWu0Hpxd6mwyaVUVe60NB3jrNcWry62T/fJyx4J2mwYG6pl1v7KX/kBBPZl/NDRAEBKOJ2KXT6Opcsx5a0piWsOFQoazUopANQL0FolJaCcfQDeZFL15Yac/1AQ4BJ7LQJeP1Ihz2QZETWtehlWvYdoCf/U9YBeM6sQ5D46TRB/vbPY075G8tvWzdXNge3Hnc5sQ+jeHUAIOBxU0Tilah3zXBRYKghUuuSuRkPb7t7lPIM8N2O7IVXhcVYDWwWHdbQlShpC4bcQlN3stBCslrhVlAlS33y7dYgkFWOlI8N0yAec5bKEiPxc6KtJQAixAgc9SEg+2+gzDKG8jMaLaGiyiELU+Cd2hw2u83AM0kALgHMx1nSELFVlvBe6Im16Pvkf3fPX1+Ixss96y2KaND4XYFMkfpAyx0TZMCBMYmBYU8NVhZBpAtqh9hS3STy0DJc264loubtDVciWtzBDua+Wa6LbMyMibreYJkWc1YhhUzd0ruIGJ0prhIShmhY/ZiEgDqyqNl5DP+68H4B5heyzqmfw9XaHQ7Dp+Jhxki+dlzy/29QgsemUl5nOO+PLG9qcpgmHQzFceI8AOccCyAW7tCFonLefEIfuF5XfnAsSH7gWL5s9AzUiqHPwqMuQzhAkZBBjAVy9FyUm0aksFr9L1Z74/ISaSZflWD6lz91RfQkoQCzvkz0LcmdzaAxVJ22um9fxd9yGqGfCORhyz6H03rwRDkGvjQECLfsNFV5NWvLeTR9+6IEfW3sIaB1/8F6VKmfsLePSTZ4hVBKFITKZ3Z+/b/wwBZ8b1HUVQpdzA6BFT4ReayxftXUwq7SYi67rBOnVci1N1yGjV3BtU1/8LNU8iE4AehGHTQkXyBlAxK9/LajT+3QqgvzDB5yxwS/+ubzyr/01zVIeBntuSsC77gyEhDn0BdkYR1HipbYVZhREHwC6ri1ovFIdm+DEKJ89xh0QgeVS0K8lRJwvYjg04xWDNgyR840IIdr6SVjTJEDFV18Bm1HQvBxaEeRsHpJZ8i4aIiJegbFi5iU0QttBU1k4HtGnhK+/Zk7EYFnb3WNfuWKtzj6fSytI9yvmC8b0gOdnQea+jH85g9tOupymCCBiq2gi6wxTgJTSjtEMZlagYE3xYSuKL5nPsn8QhpX9XS6S11Aixj4qD2g898sZc7exKlUxluZdLMluQ2Gbdcm8GIox6l3wLE/YdVpVwA9VKqYJ+O45YrvdoD9+C6QkjXk0NtkQdCphDn2dpipkvVJIq8RRltwj3Pr117bfjJVmmEkcR8SccV12liiXM6zbKfUzxpX6agxTFibPKh8vR1HQN0nQdqsfT1RomoDHR9ymRnJu3PEhT2hCxnYrCjER3IgssfUuzCgqon7rdsUYScnWBmjRhCxVOcZReBbpNATYQfUeNRnq+v7ZlNq2kC4JfLuVJmvPMFpaFkizvmmycjCNItJLiCUQd7/HdWxwPIri9KPHW7U+L5zNXU6FSRVU9oCQPArgu+/kaL76UFdcyllDBrxmQNQuREQseBj0fI4ip15OEQ/7BT1K4PA57EyxZom8ELSzKZ+pOS37vaLVH1VYPj5KfxZXEMewMSKqHz7IncrVFO0LJKGcoyHe9ICwY/l3TyKz9vvSvVXik3tshmRn7kPypKNyeZnXV6UeeLStf3dgZRhV/vSOzJCSto16kCyR2y3CfnaVugKKM6yZbtjviodK3pcKoppY7SRiniOik8vmZZomNL/6FWLXYffNj4UnacgvjVgZXaXM73eL0ksEc4+iNlWaQ4vnp6Iok/9ZFaLKqxZln0+nyrNDvYGVaxqlvYtGHLTsvqqmuQABAABJREFUUq9Q+y1H4Q/6xZgSlqXk77UpuNh7BVPb8nnuu+QswZrN3XILdC1y2qELoqjSc7zfiM54Cz2AFm1XaLrtOtUtq4g6H7xRKdhewW+7BXDdwUnTtxyRJ29gZttPTBNawPp77FutxhYGAAHr8CLaRS1KKENzOUvS9iC5WnPWpp7Zg7Kq5GvFoGXYYMrxLkTRd6rvIZ70uN8jDgNeji2mqcX7cJVmqd8zfpiC7yyxwcVtLZ24KS0BZL+3Te17V2XBwxCsxaYIT3AHxsPiATRY0DSNxTpdrzzwFkHRnQBRJPsewJTKqaaEBPFAtq2LSVRluFNCRZB5nF+B/cZNwkFlMSwIXWMCnoI7pR6tJtxxAZuhxE7xc9ME3FJECiqcx8nF1paYX+4DhXtQhMxQNs2wiA6VuIVeAIIA8CPGqH18KMo7/Lv8+Ur3XSgqEq02c9tG9CHX6AZDBny8sf5uPovB1f8X/7cfRF5fxr/4aP4f/3fE/+q/MiOR4Ta8Oy2I5hS0mv/mWT5rgruS7p9pDpNvYjCqpsDmHKYgr5Cv2VVdCCgN0vw7+ErR67UyQi5xqIwbnnNTOkPrhDjb4nFAETadMNm+r0EkNjhatqIQdZ0rrUkkyykIbQIOh7rGd9H5GlN08fgof2Ayec4muCkMK+8hCopnhhOKZ9SewZcScqyPqULfgRJq5Q2DUptb3zcQhZI9Cu5VMU8exSjzVU/ONJL/9siqhFl5PUTEYUBA7WgtHtKhnJEqZk2eS2GBEAC0fotkr7tedqXrgKkcUeWRdcJ6DhK6VcWHZ6m4wk6nc2gRtm1B5OqUCRHCXntwtH29SifY9+8pc6YyX0g4Vrt3iiIPCuXZSG2hnZzxsA9lz/W9m+km0lkVOg902TOJerLik0vS8+FD5gnhPrvmlB4rE0Up2OMZbmp3nHN2n0kJVclOyrwQItKwM3pgUx/zlENodbttCp8IJZ/L9j4U7x/nEFB4iSVsehcZHFrqzjEGYMMKNmqARQBN05qBkVKLJoy2h0C5v6V0ai77oALXz5/riYEItPJEM2CbwlNtzvIZ8lTSt5X0faOyzzQBx9cG+74zBXxJLVighHl1HKVoiBLGdos59bicgJY0qC+gtyToGhqlrQYL+pRVH1BaUi+nrVvD+8axMVnTdTLhPtEor408+1npnaTWOAWZR0wxw/DTqLyRaHg7OUahxmHrv0yd1CtDAPK4yiddDSa6d53zcpI3U9FTeuMZRQKh7hyWJMUgaMi29KDowhrAzm1JrYAWCp7t9w/CVvje7xk/SMGfc4McetlwxnROE5rtFknj2yX0S7Kak1m5YzG19DuG5p0L8/CIGFGct/aZDZd4NtutoE775gRMoXY3pYSQRfY+PqJwO2XWdDPOOWK8QGNPYxE402SEG6cbmpwlcjdJPC7RvcMhyu9J4aeTIPnbrdXQzVmenxJQtWOcJrRdKOhkvsnlU9iwPRyspNZ1bPDdk+QLmOUYAo5HVv0QJrqRcgIANI4PEV26T3SpBBrdk3RrdJ1VXghBu58eUMfKpV4YHtu76XrJFB8evkTd/8sczd/5O8Df+TtYjkf0XYfQt3VTJ4XH8/6dKaY8Pn7kcHD5MSqkgOLy55UR9O4iZ/3hg7kL53nVvRAw+jk+8+o3peeF05ClnF7rjGWUezSOoJeJSP5mPJXi0ikhuDA2WxCVKkWxdgpsGtCx3wNdZ16uTTeX6h9ENJXzRIa5XC6IXYcpbKoGcXJ/BIGT0tsR7w9722DuJRV8E2LKX9owI+2iNZWbU8lLskEEWpUJE8GKNFB3MWXZoZXnMTIFqShXaoQ0KeGsCBP1+GmCVCRhwXh6MHPGokKFdEF9KsbiJpc9bixuliVaBbENpWOormccIUqCK8eXFbkikFIKHfRWcrSKvc8ZVahHCAY0GiCh/xiOxKsBCG16BNkbT4z73e9Cheqez4KCvh/OUsCB5wFgUgV/v9dQFx9bq/eLjePosW2nK1g03+dnbJ4/qpdj0OoapdY3lWlLgnU5Vb63A++vhOXUITM+7CCEYsRSwfe6vOXQrDaJCj7GUQ2iaFtF3Ofx0cX8h4Db8AAgmvIW3XNjCKYkW3WeEKxWPw0hH4pjSPThUOrJg/eoAUJrnWVtU9SL3+hE2+EdxrE0eup1PtTXDAhwaDX5HBtGSbuvxZBkM2JC3Xm8Kh1J96BWj2JuUNdpD43jWIxrd3wc3OvdrjUj+PW18BuSH6db9ctQxfz4TAdZXVqWvIUlICfNP4hZew3QC9RtMF7KulJqxOM3DJimkqey3aqyS1rVNRs+ofvqI0Sa6WZehiYlpCS0YfycgIoqu0vXSy7n01PhYZ5YeVdSkr/zPqEAsjY8M0DR4fqeelpR6GdEMTL1AR6XKXKx0CbvOd/XdVry0q2nAZAzZXqPTRa3aAOgHwZcxxY5/zaq6EAueMyTMFmV+LSyGizY712795wBiiJlbOLmESsnBok99/toLtEQkHOrTG6DBEmsZUITleX9HuYyNarvOueiFiKKqkQDkOSNDPTTiwluWcuuPm8mEet6hOGFInVCsJjaqrETLSrGQZ5OaEPAfr8qhUTTnFbf6VRXC3HM4+Uk5T6J8m23TJw1/yneHw5YtAzpbWrMPcxmQ1Za+XRCTAkxBFMmSNRyOVv0jJ3I2VA4Y+L8sEFnDunSEp5GL99Pd1/Gb2uoQdsmoN2GokSqdI5hga/FzX/WeGScLBylVTQsdnXzjnGE0EbQBPSLi8cncRM9hCLu0dHBWnvSEocs+QUo7+DkQqDNWOZ5yUVqTVJFC11CzorsXKayHw6BAtQIkT9iQVOAUnLhw0H2wCO5vIduPt6TaoiVGs45Ay+niK6L6C+SYRdDEHd31xZkXTXU29QgKxrO5wGrNumeL0wTWM+fsfbxIoG/t9DLz4rgXZzAlWvbSAgQNYJQKkGYQjkswAlFo+HlD8GnNFVzfUs5FrbUFDYxlem3AUYvOUPie/0DHbmkJOc25wbnc0meprLEyhZWV12/1DAW2skFhGCJzDFnHA4Ppq/6V/vuy30KgjQ6pRgh4IOhy6ksXJXMzaED9sKLQ9oIkg8puxyChCg0qsSaUTUUJcTrIzgcnGej/Mh8M2HB9eafL64KCjQBf1iAj0+OkZeRM6xBmD3zNOrPmsTuqrQwAZPIsnijR4udSPsH6+tC76Cg3jDvc4ILcQFsX1nikMUZxGsnvOzdQeSZV/5sjeqVA0QhnEO7JidYTx4a8yEAqZXk3GEDFBtanx2q85gmRVm5UfqPfR6AVUO8JCUWifJGNV6uc2uIttCYUzq7DkBbUGvys1C6egOqiwUgbIt3fxyBXsMCLUEeQHZ74e/EMmxsr1ld7XQCmqaX8KGcV/l+hXRmRER6LHU+9IqUkE25N0xDNLto/1AA4iQey6hK7+xKJhtCzkPQA2+TeFU39DqPQ+0p6/riMmRHY3+wIRT9zjHypgvILin5FnqEoXigY54RUjQDcOnUA0fmmWFJ3VFl6KZLFaJG/uTDTz17X0JEo7KBpUM3vgv3JZXujtOEflh5fD8zfpCCb9q1arUSzyh71XcO1V9xfCrmgl43VW10Lsxid/l9tHZm+z0QTyc0w4C263BtnGvrMlYhKFW1B3JPXuqcMWKDy8WhRmrJ527nGCaKi5fzWpnNCyRkKCW1shm7m1JRsAEL22kPHdJQ4gqjhiZdLsAm4O3OgTqh45O+p3VKmYdXp0msuq4Duge1rQr64A1mW3MICAfJe2BYxzRpeMfQSgzb6WTywJQh1sXV+RtypuEa3rPyAzxHX8Zva5DDr+nIuSRjSsiunKNU47jYI4i4tancw5iSKSJytg3a7dbsV0G/C2q/7nTsE7HZ+Ij3aYbEQ7bjaz1nJ0SbPItCeLqUe6jrIZND1yGEvo6/pXHuEqLIZFnX2IwT7p8mb95uJXTGElb1efx8RDYPBt/bNKJkapEK9G4f0XVVubpbLjkQ9Ej6GsmVJ8RbFdMk8azOs8LY+KwhDEGV+3Wn1JyB7hDRhCLUiMFYoxeiJ1S40GDSuFXO0dfk94qDhS7kjHFszaPou5fmCbCGADkDUCWOdAHA5x8R7Y/DgGVR2h1HBDVGpQV8dNul3lYm+GWYQLUqNoybTgkt+RjKflYNqzhUxjBePY5nO9c5N6Vhkl6iYdjg9VX5qeY3UC7126zrK0nuluCpChcN0yVsCl3njOb0irDdWTdSG07ejpeCnNNANoJwJUG8YsD9226dzEvJbAe/HywgIcabIrUOrhelTJqsHQ5qWOvZWrfsYSn8Sum7VGIS4s7aVDHycCaJ+x/H1tgE908M/N48OhlV1FSRReQZuoHNdmsGkdcBhQ6gHrLoVBOhVXpP1knC0wSErkGj936a1CPJAwGAppShBsSI9Bfamn3xLq4Q4aaB8cLYdQgaZjxNwMtRvPsWcgiR5xU56/2WwlwN4jShbUVxpTNht2uqsLkcSsUxAgLz3FS9PMr7lGZpcF3O0nNnSBbNsFF+VrrUyoacz7H0byDg60AG2+SVx8p0vJTQYMENLSa02ITxPkQuuHAvn1CdEnzMCLdfbEc50yYBQOlY3nURjYaCAqUo1bshlzvBfXQeQOpanBbZO3VDhFCa9XkjmMoYnzuOd01P3xo/rEzmn/2ZbeJVEfbLpZSl4+XwgrbK5B6vWLreyg95ILhq2wzYwR5vvW3EnZtsu4U1GoGEmBBI2O9VcdALvEkuEy+4ttDffiuTU4vvlstl5tw4pXUrbnPxh6sR31XdKWzkYO4WdUUxc5wj51ow2i/1QBcnvMh4PMI0B2EWfbjVz9DvX7OgAdU7+F0Vhm2u9+9ygZWLWz+yz2fb+xkRx6N8liUxkbOhhyJLvoTo/C7GopedbcNNqJJLUbB7rwuFgDKOG4rgCcGF7agSTdcx4OhQk9HN2zRtKuC5Dzdru04038osUgL7C5GSlToEXFjAurQh16W0+DL2aJpVuJ+WkrtqWM2+r0uxMQaZPzZ5tvvvhb3vM2FCQuncy+31f71ia0Mv0/Gs7dD3ytMukjF6SxvTrxlSwHvJGOh1JY+YRTv/9Cyey/YiRQRYntgDVes5etkj6P0J87Bj/YMKDU1pxdO8wOFD9OGfTkXBt5LC3uDMkohLRVUa6Kh0PBzq0p35VXghw3umCXPqcT4X75Cdudtzov5GS6r096ku1+o3goUXuq6UJQYceOXAJIRg8bjek0xFlY8146pzgNCa32+3uGnFMZZiduRSld9k+cW79eicXk6yKZZwTu3P3XXKZu6Xj+22EKpheLPcrc2JIaL6jiW19t2529j5Wew+UMoiev5EuqAhpqEP87AzuucSuG1dp7HXXVcKVrDcIEdKOJ6jAXFdp+EpHNutldz0d6LkCbgLklLlzTR5qnyBpVD9oGdzcSFnfo7UbfZ7J9MVJOk7F04EGPGwaALnSPrj/kyTJts7Y84aNWnpUCo3NxbZuGdP/ppWe96cXsWodV4Bo03WIFdF7TwVo6DvFiv1K8m8dTK07aPyOt5B4w3+vnJPlHZYBtYP6taHA6w0pk0mFI+Ex4w4SK8sS+nTDFNS455zV7plKB9ReTqU2vG1eLe6TsLnhk1VCpd74cuLhqD31/OInLEMAlCb10zX87M/+qP/7WUyDWlJCeOxrHFZ6acSdxmrjTOLtOtLKak3Z5KKQM8Z0Smd1tZbXaHLVhgAoxI90M7nM0Rlw9Jiag1duncAGikrSKIPAQjRygau9haxW092tY4QcLuWi9auTbMQ7tYdAqsuRMlSd0KSzU76NBf0iBPSz5h+9dgiBickHXxutW8Zc6gGmJ2PCmxpiFUuLmsVO9C15vSo9yjmLLG92y1CaNF8++vPHPKX8VsfT0/A4WCMresaS1a14ei8MruUTllNiYCBJVzpL9K2NTowJcBL3Wm6MwgxTQiahA8Ir1gGRZH53VVpGi+jo6sQReFhzXfc4DsZG991isVkQfOWBbUw0WdliKIdwn2+T2V8vyUFUSvPxa0ujJcClEmxVTghSsMsM5RCAPYbbhuQJB6WekQIEB6BFd9R5cOmpZKoG+pSkQBMQSC78142lkekPDocYB4DwCknNHa8lbE6eK8MRs7V89mUajrhpHSxSfl+CMBGQ2Q8sMHvEtF8EyQJrZ0t5ZSUzYtI3Uqh5dxSyWHpurKnVpHIK6O5GIcxiyI3ZeHZXg55e0hCK3Ot+TtaXA++f92wiOUXjSdz00Ow0qFV6Sei5Gt3vj6kwYog9LO8T/4KmPF5KjFbS4gqYiQXLQYpMvGWF3ctMz39hBQtvDDnuoSvT2WwMJgQRNbQ68BhAkvuk+Xo6APmIGW31/qC33R2SOazKp3GW8Y5S4gPSqw6eXDUdZTGqBHxIspTHHoDOuklrKqs8DwcTZtnxxVA4HlYsz3OyZ2n8WseIEqjTtqtEooMfX6sllftq3oZqv164x9fF6NEeBSCjcUz6gqF2Dy1Q3NQh9hmWPEXJysoL3g9YxSwmectmG7/pr6ZM6zbOe0S4+HThJSKh3ZBY6Bl5IVWYNg31eIUxcMBtG2qNzGUsrV2PPqLrHtOWkHnjDulbcPFhpXV/T3jhyP48ErzG1bZNOGGOubLkEA23lBrl5fLMvO5ArdKi2kKxS28tujoYqPs2G71na57hiHdRDt8YN3KBJ67jSX0AA6N90GoZNAcq0YE9Gj4fSBRUdh7NzcJDIDFzm42NSO7ewhHCDiPsf6O7s/50sg5abMivoQEGwLQn76T57Ce5XpPcsYVfd3AIZXSUPQyiKC7lYV/+IDmruzDl/EvYyx/82/i9v/6f9vP5hFTQW0Kom9E5A1QwIzDc/eu+vMayb8LX3OX75p21eNZCtuDn6Y0YS60nLPxh+OtN8G+3Tp3vhoDHpntS85hCV3rSnUfb+t6j64pq520HO8hzaSu+68s/z0l4P32Wtx5QA2VKcJJhaOU8BR+9ulZchD2vXMD87/7PebQChrkBLIkHxaEzidL8j1UAsxAcEmbKRVkk42wAlF+5W0AEJ+/s824ocUvfyleuMPB8Q/v7QihakyYEtAcX8phEqpd1WC/oq8+0hxfyoFttxZLbnN01gybSvF9NFJ8Sc2KB6ek+Q/aXEdh2+vYGKCZEqwYxOOj0raWNb1t390pCm2+Vvx32e4qxxjt001XPNC3UBrmpOTo3FtVjt7JQ/kRkpkflA+2f6q40+MEOMX3+bm+dIcDrlPE05Oc8W5XjsmqtJxeyyHxhYoYe6+C06sq9H+aRFmqPGgeBNAFvYW2cphc10FvpDegq3czvId0p2VUK41dL71vguXycatmfbMzukOAVbFb3lBEK8IMAcfXRhp3jS9GJ+tmSDFqcy8IT2EVL0PWdZFEsXmfSTIebDi+NnVPEu5FKI2mqOvw3vpIgD7NpTCGt8J08fSikpddx8bkOz2X5pl5eqrKkH5KXyHnEjnj4/i5ru225pnczrfCAE3W6IfMI7FqMgbI/u52ta3BuwEU8lrbC9xjH5J90y6xJCWWfI8ff13oTuUhFfoqzBIFEzC9Uz1j9JZPE/Aw3GxTblNjDdC4fynBQkftrrgG2g0W/OwP/uA3Ivg/zAzI2YSLWYrKKUKA+UU+a1ToH5rphoi5bOhbXwgFqWJTjgYLpkndiQ4Znx3/rJgHhfHlYq+ovguJx11CLMjGOGp5Plty+Z81RXgXWiiNNqh00EK8Xu8Jjv/jf0/in6a61GG1nrXVpgtnM6xpQuVKtI+676xb0JOKqlhlj8zmXOn74+i+fLmUZJ6s8c0hoOm6L8r973A0/+AfmLwxJR6wmEdWIlkbiXNu6t9lSaxunLNoCa6TSPmYlvpLVRa2Z5YeaKEgolLg77HdybWwQYlPLkkg8jzGrXt90tMt3/2G/V7erYuxRNs37qQNPuTuD/fX0f8+EoLnPzcJH/LHTbPr7eayfgdHDEuVY+CVYD8fD2zas1brrT5L5dNbR+7v3PP1IVe0RGaGwpeqeTi+YnNYnX/EjBAc7cL1cfATcprLNKkgdH/7nHixfda998qA5Y54Ra7rqqPkV8umws6hQoW9cPIaJJUpxOoMvCLmtzPn1Zr1S1WjOp6dN3zcMohWzsUeeXtjnPylcnUnflaf46jmspabq1/fzcGdh1eU1vthG+34UrWnudHk18/f6Uq+hvDZK/45g4Tf8/KWXyafqzxrHMrLmG9xr7jU7MIrdv6zlVfCfcloyG0Wz5x883O87G7BTia8ZXRWv9QD8w4E6z6ds63F39+V6MH1qusaR9ETnVHv50u5tNZ1YmRFqJpe1st8g2RqI25FD4Ars+5fqHofB+++JEEvtuZbrnXWtzxb/hnke/6IeN6xiK47fe+zz/zeT6AIRQpsTDCLP2YtrziOVjWCq1gQAU1G8Eh+04klOs/azMJLAGVgjTuB86Wx2NC+C0Y4+/3eEDtAFeo2Iu3fiYV+OqE5vqBPCbdpg+s1GgMfR1FmmDWOaQI+fsQ+Z8wffoSnJ1aN7LFJ8gKJS4zSbMQpxxZzdTph+fpHlmvAwaVIbG3p6Ejh9+mTfP4nP3Exa7rZc2jN4O66aEREbwnjm03IX0ZVXnphMtt3ytAKwfRJY+YP74xgcm6AtCmXUxNIEsSipHfh0zFiGCJ6zQDutbygAAlf4u7/Msbtpsglu4t1HU4aSlfVCw/BGkmJoqbuUq2e1I6vaBtgUe+ZNVvSRkEeDU1JkhyPGntPPZwdRZfQSgxk8VED6mYWOmzUqG1wPG4wDKWHA+MgxSMqc3wIr2imCdvtO1yvEkbWdYr4pBY5tMJcx7MkcmmVAc9azGsIqCBRC+HxEcjF0RUCMIceOfS4HLVa1ulYQTS08+HQJijY8ZCotemLvcRRxSAQxVO0L8CF7AwDTpe2dGvVmPzM3JlRQjCeR7mvD9u5lDHd75G2D3UIXxeKB0CrbVAP/NGPHCEpg1i2u1LYw5EOf567DSJLjCapjx8pudSyIq8x+vMNrlICpuImD1sJvVr24sWQykSreVn+gZQybRk/qF4bgEZVQSPSoyBxZKdfbUUm4CTE+hf5PcIIvN8upcLKdotlu8OSpMIK6afFjJiAW4yF7tBo8qigcg/TKzYfP8oeDxtp9JNaiVlfjyxdQ0mjTDWxxMUQgBDx6ZN8/MEpwEtqcX0tNE25YrTGfJUQMAzA11/Ls8/nAiBJ6drirqPXh6Eg5mlGtFKGPl4ckBhnaYokXmRDdnmvNH9nmoDLU6EHXoc2zBZCOncbQ0P3rTTx2ez3pijztTMixiw9ZVIAcrfBFDaCVE7iAVvQ4LjsMI/AdBLgbd/fsAkZmy2AnCwOP18k1yNOk/TxCQE+2ZvJ3CTDSEGaM2IIYI8xpH0xsobBkkYtFwELXo5CK5XOE5x3SHmNR7HNq6ZIeT88OOWvQXAeiqA87DY1yOiRslaeUVk9bx9EKXz8qtrTEErSabl3co/ieDJ6bYYBXderHdmg236FsC/P+Io5kaGHlfvOGe12i7ARbz/zLJqgZVH1efSu7b9Opo81ScLhosWfllxLFUuFp+cMjBnoeiM/H4pk1Q5TNF2syAeRMRPzRHBDzBNu1Ieev5MKYy7k7ZYlNM13vTX+DWAYHsy7lXba42Wa0ADYpAB0AQjyLFYfYnEieqnJc+1eOYvieG3vDb03xg9S8P0w5N0nHZG5ryAOzieSQ1DQr4dHN3yJAH1AZZV5+CRLbBqVdnZ+mybNSHYwnkdKOGJEsYxdTH4MC/pequXMM7RNI8CSb+a6Rl05xD88BFTtkuVzpRW4XyLnb2ikf4h7lr3CJdvx74YKkvlON4TUWjy90y/sjLh0XlBz9etnfOwn61pbQBfPf7WWL+MvaTAuRumdrrw79Avld4XJx1L2C3D3Vr7IZklkZP4KM8F1rZBVDGmlGPOzHuGthNk0IaQNqqFfJFpDwUlBAKzQtFFqc5NuTUEBqthGKkyh6DqfR6x0j2nUO1ZXb6wPGvYPcx8ud0+V03UsuX+3e7x/j5UiXcFSniVzFAS8VpioEI0jbH3cGu+mrt7PH/Ql1bt0AztHX9OEopC7dZHv8bk+36CB8OAQSt33ah4OReRUxMgqhkaDBcOwQv45IYfcW9yzI04vyvTLAKTpXwio6M6Mn8u9d+R+49yGTZPmP8WKnsxYyhJ2ID+WmA16eptGm9axShXfk4risDqqe6VAv8O/+2Z03CqfLEu550MngFhK4vpLEaR60tqTZPuTyr5w6lXOzDRpaefyJd/Lhb0m9KNFIUcdF10N/SBBy3nWR/jD1v96+Wdo6Vrf0WFx+0rENKR8+EfTaPMinlfFPBwNO4PWdCd9J5XWlapV7SENJTmCkpO1Dg/24WSs/ld16F7TLfdiruwT+1jUyVR9CnTPYxcKAOie20B6Zphq6L5TzWO1T9Vj3GaQJihLrNEXeQXKHsvdK7RstEUZRPbNF3mdV/nX50qC82Mm2zzNOLnq1yGei4C+Fx5TnQU/qPvCDtffN36Qgu/lB1Bb0cgAtoIEV9n1qF3oS2rxeovog9xr2xzn11lSi19822IYNAZWBXW/73DrG421nSyWn67/faNZ1Yq4CMAfkdTaZav4ti1uL6th//FJYqCGh0KolzP284jb9h0uF1hFGoYpXkc5VbN+WRpqvzfBRaudWvM4yXdaRyBkQt98w/JWuRgbobQ2fzeUkh3XLIh+368EpAZwzft3gsA/PaF5fMT1Gq2EpWWWK5M7qiX8bls6z6WEEi+kkNJiKFRGv5XQiiveizBeNdD6Mv5yxqdLj4we+wC0WLCPCgOk7k7oNpPUxWHlJyt7pwbC8xMREvoE5W63ywXQeHYousb33IKinakvlVH0TjB2s+8W7ZaqQnYRRNqUdcaAjCPaDwNCaIyn3rBBUAR5083A8SMwBkOGjYH6UmKqQG23aiyotiIx8i1i1K6PeuUM5Zlo6AdstxJ3Nydp9nT8VmO4x1fEYcDslE8rRqB7zVhiaX7UisGk99iHGKUEKetGoZaz4Q2mwIHJcMGQpf34oqCDoKX45hsAwnc2YQQwAlOnd7uy/1Woz8DTszQsTD1ejg3GsbVw5uzY0ZtD9zq6kieMk43HF8SU8Bcn2fvttkHXSUEAKkt9PqsX4IBbjpaU+vgo7dxbbVhzxsZ0nKrpUkpS2z4EPOxLi3gDdy4XPGACtlJkAFkRuOdn4HTCfr8pykroERTxz5eyRDNoVXm3pEGuGb1UMmMCh2uw46tkAKJcIERMIQo/dUUO3h3ESyboXWMy5D2+A6aEZV+QW3Z0B2QbxNMcsVFw7Jpb5EuZ4maQBkwpuYY+3irKuVSZ0d8zd433ljHELA0aQiMdQwF0moydsyp5LtbodisVSfhKKpdAg16FanM5o+vkPObQIu5TCZp3YUfNMCCknckpiRoI1tiNS6LxS+823L2kEhqC7DOyU9q5aG8wKX+Zc4PrFAFE9K66EUJQgL1Bp7kGNg822coZ+17mIb0HIhIR4jyjzRPaaQSCQLYErXMn6DI6WXPz9J3wmG2hB4+PSjhzY0ZgSsCGnVCfntCkhJgSRmzw8iKx5V0H5C4ipYioFcjswFaet5xrxZIgZggl38F6mjw+lpKok/QumVOPKbRVbHobAn76k2Qesa7TYgxZvCTLsEHuNoBOa5NEoal6YpQqEwCivbfpOsSUcB6FRq3PiOo3jatIE0OwAg0A8wqT9ElhPpveY8rDuXswURdp6TtjhNdhw31U3sVCKoCrzqP3qeVFcdXe2NSqV316082ScPs94wcj+JIwhLvJO6MC7VBbpcUi1Wc4PZD3aQltsWZR5YOC0MiiDI+Wr28D3SiDAkR5bbsOU2qq+8nEVz+mCQhdtNqjnMI0FeQyhIKc8/kI0owLKPQUnU/PXzQANn+T7aHEvNPIMbTTowMhqAJQ9w/gvIim8uc7tNAhpinJOtbel3Z2D4Tbdxf3fNc1cRwVuSqVOmiFf8bg/zL+JY++h1UNocuxdXeqOhhHSxSEpPus/Q1IL+7IpRoVv0Sl4I0DlzsRq5ANopQV0hgCUjZPvhoFykj0zjRNSb7iq0XZcNqNamBVuT9uhDLTtsC7VTWRyhPB+/oG7OfBPesMO959rKqgIXyi5pGRCtJUyUydh9N+dG7TVNCyxFKRKxTTC4YKyvPCZvUdUzwyTJB7Xhfqqbw9/KYQfguh5mWOH3pSiWFB6BoxFh2f8nmeJix1ilW1FH9wfLAi6udzg75X5fOtBbh98XPinoTujbAu8k3/j0YGZeLqYdMI5FA6I1MmGP3x884bTL5evdftawQQkzTl8UpW5b0IUg/fHwsRd1YqAUqfB8D5vtyeSwnW0lgt8ve6Rw0WC1m4udKddqH15XFChWbHLCjlpKESCMmUsTBssCzsNNzg3bb2+hjfSO6++vOLThdp8Sbt8RdVTodHZv3Z6AX1yhiNbjP03Bnw717hXv+S51ydsTYSW9/XlY0h9wL14LtsKln6cvg1LyFKHXc+v+sQpqLc+1A8Pt/KxL7B4z2f4LmTtj167+nGb0gIbk68p6pXJO2BEFjVT4HcyJJkq7XnLPesJSKSs4Zx57t32vB6FhX8N1AMljnlVSducD4Dm02L2GXjW/Z8fS7vCc9nRkRMjiDzSk/0k9TD9IUWKs8yD/w3Mmidzvd9gI2QosZ9CheuhQW7s0nGdbTJW1MQZVCboQhYVnc4zhvMc4PDQdqtv+tUck7ZKtSkLIJd4u2jID85Y0w7AK5Lo8Z8dd3GXN4WM6eu25iEuJ6eFKA+vEMz3dCcXhG3W4y5ATqRuiGUxNkQYNV55vjO9iUEeSZ3n8ouD+h4lAux01g9X79WathPRoy+ljnj2rtOy4FS8HfOQ8Bubwi4dfKClpUb9DJbvwFXf/amDWz2O5bQkzjLTSedIlmrexxFoRkGICS9kOopSIf3FbG1X5T7v7TR5ivajXCAT8+N9oNoKwPQSmcqtxCvjHQGPF9KlQPAxTZON2S0Ck42gkLwgV7ZUoaVc4klftgPpcnOJI1qpDqBtD1vGq0yc7kAv/hWnrPdSp+FTrrKxih/Zrx9jECfHfQ9DFKtxUqTqfFLTug8AkSUfETem3Wv+f9OOBBx3O+BePwETIVR5wK62+selDH46iI5w/KOvNDYDKVmtheOETOgwo7Pff8YJHmUpR7prSB0yfOAoKDjBGwUKWu7VVyvfueWNnLWeUbbRlVeFkPGZZ36TF8PfJrAxKhrVk9IANqLCyZPCUN2hhkgitI4CnLWddYnJGLBBhflTz0yIlpFHpvjSzHYHh+tp0iDpWyurufpCXj3DmjzWJTNVIu55fBOjtgpR21arGlg64X9KizTKiZpicjbqxi0jdOS5iy1sc8KzLEBJWOPQ3BavFbE4XvM8ytuBdyGB5sbR7/fI7hSqKXyUolRNqQzsQGjWuppY3W7GepmdzpJ/fj9Tt4Xt1t0Ghs/TVGqflwuAjilhGvaYTzWgPccIoLGlOexZhExLMCv5K5vPnwQT/AY0ecMPD+jORwwTRG/+pU886/9tRZNKAgrNSnRHWrlhrorS7J/9YGx606BC6UrrL/uUtFJIhI8YMizfH4uXkaSoV16yr8s3kH2D6gUMr3wM2LdBdw3J+y0qleS37NB1JvJum68lXDbtlpSswrfj+ZdIO/58KGAi9cspWXbjVj2BTNQxVS9ECGVhlT8Rz1kHdIk3XEj2nZTGVsxLNqHIKKH29AsDby8Nw8AHoap5HLp5i4h4nou3oRr06BtN+jzFfj40WQJN8+udJZ+FZcxShlOeqZcDxbSxk0b1o2jRkxcXpD2D/jlLwVQ6/se+25BO12ANChvUN50OWu8fbIeGywjb5Efo3PFAaX0nLoEyPfJTtn3BCGUYgjfM75XwW8aZc65mKjBJctNU42w+f/SxWDPwlLHZ3UdcC6bGjlxde2zQ2rP7qpa/54l325seMLTY2WfAbbZkTXmdTNYB9fKhLFUp9Y1FYYXrLsllYEGi5Xi6kNB38wa1TVahza1zruusbXLsh3STU6QFNXPJaRJwgPUEh+DIQkxLNhvMlgzmGNiCBDdNg794rxYfzboq5mcZmc2Sqxc35dSXMNQ4nS5T0BJUpFnlJJ3X8ZfwiDMDuBw2Bi5W/ysv3PqKizGIUBvjC/PBYh3LY8OofBxG3rYDMloMSMNpXTfy7HBMGzQPnbuLTPaQZLmTTDRlduV0rheIQyhsQ6zKQE45bKOrkNwPI5Ta9n7Yr8v69Y7xnsLKDKplboKspKrz3PZLO9WEoZLjDPfW0KNdL2uIhd1TZ6NrV+/bE1Z9J6zmZPXTc2DqclsFfLkJChDQoYBd4pQ1cE3JWAqoYSdb9qjz/b8m6VwZ0RTTpEz+jRbIyP+7gahHVNmVmUt2Y6dSpSc26B0UhJYU2oRt6GqXBZCiQlXXFs6hKPBZsM9W6GyHKH0WyCCuqApWo/+M8+lhgRZ6FHg3OTvVKIMQdT5tW1RPkjL7KIZsQAIVh7venVljt8Q2pXnSwmiRWlEZgi9yhvm2AIQ4450nZLJLI+6LiFaPfcdUVUlWMZzN4xTd7TDLsgP++L98s2+gDdStZhJr79MCUCSTOBbFjnCZEOTzR5YCKvylaq0Uxli/YsqJs2dp79/3HMW6qD3E6EX2ji+qBxuuZVomiKjkXPdGRU1ya07bLNHQAiKkA91t+KevT9cKBSfaXxceRoV5TZkK1fNZ/W4ou+DJTjz84x8kI672s1c96fwfMklaD2yzH3WXhF+D0MAMI1F2CRpnBiC0IWP+S+GoAuL4OIoWy4XuUvOgLW7r8TETuh9X55LfQXMJeIfVgQ4QzqJH4/SkXdPoxFzKT6hw1dFXBZg3j4AGfjxj4t31RY3Sb4IaclApiDhYNttMdT4jp7z9NaPky9pcntslwVFKf0B43sVfCxLycagMNL4Nb+H3mLlxTy+llCUEETp5TOWVBPfNEEaSmlN2+NrY6W9oGhRMwzIWdB33t1lAW7JVVWYJLkqKwK+3TYlRgtSbAJQpjRNwCSX9HjUCgaXs6EqNJKIst26nawDM6gll+YWcjFGrTbTBl7aEqogl9wn58p3Xo6CKlgtam2ZbghaSlZV5P1+KrXtueYQMFqMWZlXzDMa3c9pauxyUcgfj3I+VZtySEJc2wFIvJkAWJWHcYPjFU3X4XIpl/UH0tyX8dseDpJqNKbxnB4KGkvjVhGkzRBKF54QLBGpx1VpLxmaF2PRk0Uhhn0PKHpbP10s3v10EhIVAzGWShLaFTRpzXcytLmT73z7pyLc3z8uFnvbDwP6jd5f5qikZEa+Vx5uN2g1D3ENszusfUb5DV3M4whswmTVU7w27TtHxjxJ2M0kysZtanB9LWFyrAbzsJ0rRdYr9CmVqiPVpKnPaZWMWSvgfPwoe7HpBMnnMacU0Zye5aEfPhTlT8eszWKGAdbRtxr6s++ySS9HkxKGoaDLvkJZSpDW6Skhpw0QouRsjCIt0+EdXl9hVs3xWZ79fn8TmnL7Mu/f4fkJ2O8btEl45jTBQivb6ay8JSr7i+KV7USp9wncrHzRdS2Qi5GKNFRr965wC+PQnDHzbjkFkqHf7w6h/s4kcb3HYwm9mSYgDcUDQ0XbjloRueMoiuOmy3ZO1EN3OxRDc3VcOWucsaKbrNEfDwdkXypv0iodIcNChwgBaixGM93qOPn9g4GGXQdsiLg5GVglEzpQar6oEvSLX5jB12y3mNY9Dny1niRGfK8GSUQWxHbamRfo669XuRbOO7KEiNdXoG0lWoAeNPMcqpfB5tp1pgCTP5As9v0NOJ6QDu/sHOh52e+Bje5RPwA3tFWMPy82v7fpnMGyPjzumeYMIAg4IN3gG+x2URRuTYi6TtGeyxr6NOqv2t6zg9LL8zOa/V6KEvCs1Y3RfvMNQhfL1aOMyFk2jHxvGCSURnM0Qmgk+oJ7qP/oHeL+kVT766W4OEPAZVQc8ONHNMOAOAyYcpkH7U1ZSLJIh+02ImpVpZbFVkirZKQ6jxA0AqIruYMGWvk8LOXXPiLncoFFcPR9K/rcOALdxrMBy8kaBpn7t98KT+6ffon28RHH2TVj0fll7f/Rdsnotckz4jgidh1mbbA4z0B3iMK7FUxiz4njSeTuZiNnT5qVRo8adZFrY+Rz4/sVfD2RGVHCNLTNfQ/glnrbx5y1RJ6zLnY7l2iKAGSliJQkiSpntL3s5i1LbdOo8EN0KNeSWinlp5Zm36O0era4xiScIRQ3ql3GXJoX8NKsqbRp1MLsNohY0CZndekXkymwwS5S3O8xIWoZycZQgTlHEYLeileCbXK2OF2EACt/uaqEY5s7jui3W+QhinX84Ud3nTbfDUshtJRUKBRUsusYb1m+N9QysFwkonsOMWrGq8aAurgPrOLPvoy/nLHfl7OjK9W5JBGCJnY5AeSQjYBVkmtQdPEmFari6cUFa4ZKYG42+hh9YcSMh30wzxVQGHp04RIhwJDmnOX+qaypan0btOZc7AAQJ4EPz87AJMovNL5SvB2owMYkXQdR+r1U1jtpJO3n4XhL26rxNE2AlqydoUqvjma6oU0JITR1w7yUHEKM6gJFzNjvo11BhABeY1GWMvD4aKhlSkBrYYiScJiSAghd4Xk9FpmDnm/TBYQgAp3KI7J4diTBMlpBgv1uqYxIAk/XKaJXJbKZbthsWpMFZi95JFWR+7Pmc0qfkEKXw6BCWrWIECSxkABLRQcaP967pksNFsR8AVKnCHtdDIGhpdQXb7lF6kpuFbZbQ/joveTc80TvkHileR36oJs0oW4EpGipjZytQNwS5B2+lsE4KqJHGlEUl/esbTcWUgQUDxNQePpNPXFtruvsLcOmKJDUCnQfm8sZLYD9XsNaT9nOCVNd1O6KHki9re9wUGOLCIAbFkLqBcw0oU/KDJxyklX5kzLYS10zVO/GgkaMuakY1uJRkj035N4bBSlpKIiMti35SvMM81LQIKNdYDKNIR6XC9oBCNtC33Onid88E80Liioz59wIfwsRZCbW+CxL7+J3W/nybYoIoQWGtsoVJUBgRAwYDViTLJdDuMDlMxqqLEBizhI+ZRENDI/b7zGnHvFyllKh3EheEofC0pNJr5wBALsPlQI+DKrLOHcRv8sE8Ye90CwLlpiHlO4bniMgYXm5sdLnG53jghZNnvGAU1X84fjaoFWPVL7oftHjNwtv/clPvKdaQuxWLMbuYj8MSGb8KIGMI/ZhAsIWNyZAY6n4Ecurcv+WEC0/Jqx4A8toxpQwDL6c9CpHoqjQP0jv+sEKPt0rbQhmBqUPYq3Ms2Pm02QuNRMohAc0LmZBIzWoyfxDQNhLXGTUUJkwFgV/mgpjDaG4TtiFr9Ha3udrW7m1i7DPOJ1iSYr1TWhSKZfG2MTdrpGE3ZSAFKSCAlYJEQxu6zogbGzp+43UpB7DTmr6sk7uSsm3DU0lscR7NwylcsQ+bHc4HtmNj9nmyjDZXEpPPudYJSE33jTVOXSdK7fpL5X/rCpVzXSpqV8VAou9z6XT3Jfxux3XsUHPc6GC788lxOoKAqg4hDGB57ESkNOkrb61vjoVU5L+NKkRz6oJpOucBWfqOkOKARSBpz97RKcqfenKDfK/VLx4bTYa4DvPGjoRNRaZWm9KBa2jZy8E+LbzJnA9t1wr+AyF0y/xu8bvxhExtaUpE6sRqWKALHGlGKdVGIgrg8f3AorCTtggA6HDnGNJMtO9Pd56C48QAKPSltCmgNvU+AIW+NHXZR/McNc5VGX7xhG5az1rrgxHEUbiIR1HoGc7+WlCHBJyLmUjzajR99FT6vkt66fTw2HvSol6bN0+VkNxuIxIrWyrbSyfn4HDAZexdE33zk5ADQYA57MoKL0+l6iYeb7c3Gns0LOTktaS//ajXaysymZKrvCCO1sLccqF73K5t5t6gXWSPsEuZ8b8ikFdKWAkypxxuUhuS9uUdxPx3m8cmp+zVcGKFzmQ6OSCl/deX2St8sNBvGTNRQ2J/b7IQx1mUKw1Eec55N5yH1ICTMDlDBwOhlryOjWN7IP1fBmkHvhuR31D+YWG4vou7F2nZ9ALGm9JoMobvAEYAoBuKJV8QkAcyr1lrfL9Zr7jEQCQ0VZnDAAP+1A+xw3tOuTuwY6G/7ZbmWvWakbWiwguft/ucXBk0EioG895moznTRMkIZWHOgyYUy8ei3kslc/U69GswMkYpOxsc3oVo+dwwJxajbZoTWlvWe7Y6Q8xiMF7vapRoGd8mXYFjUfALfRok6vCFiTC4XIBfrRHdSdH9OhDFmhdDZbb1ODlpZCYL6cMiIJvXcadlzKEaA2mQkAp2QyZa+y6UiK364wuFtXJhMc0xo8mzYk07CgEE48huOIt3p2hh9R1sWLVPvcjJdHpmHT/faNZlt9cTfNnP/sZfv7P/pm5FFlWb9PN/q31RJUJc7MsZlRX7OP02ulcozxmistgeSJrF+zbqesL6K4H9AKfTtLIIteJE4AmHvnd02QpLqOKTSaz1fKb5sbMr/VaXTypEZImzDJ+uL0ICnrOvQgYErCG3/g4Y4Y4+Ux/XuprkHAGuk15x+PzdxX6esZGDbJSv9gSxDBXzMhfGoMQtlsTyERXUqq9n+K2L63hxQD70uzqdz3GcZFGa9qH3SvVbAtOHb09faqE65tQgNLDrM2jjPDdveb9ZdM0Gu45o3Qx5PPV+l32D5XiaF4rvQtzKq3a3+1nU9aYVAkUI9srQIA8r8dVhObjoybOlb9x3PGP1R4YKsqmLdrunfwh5sIf6BGgPkLAseqMyC+re5YuVsadO71enn96qc5mRrSCBHQ3U0d6t3UtD7tOEHUioKo1ny+yTw+deFfP2Bhw0vfCH+iRkeZHowl4LoGsnWyi65RHaSyQNHuSz1Yx9w5EWdSb4N3KLM7wsF0V1lYE1Okbdx5LO7+us892HdCPwmdvobfXb4I2XHt8xHVsLMHUSuYBtgeVYZIzbsMDriq+2tYh9rQY3N1gDC+n2PpSio4OiLR7kcnvRMjhHM/RYvi5B6Qt0o4ZLF1JlOQZ+KZUkrxZmqjxd7p9BeHkPEP4jWCNN5CXEKmnok+zq7qmXoUcKxXBvPwpWUy3Kd4ro96H1/ij557c3WUAt04SfGnEeX7Vp9kKZfAcrldV0onQqfyk3mH3yd3HlS4v+/38qfygtLAmVa8m3elF5D/KU9ogc5q7klNld49REV2H46tGDZAvheLdMpRf6e4a6hAUU2RN+783Su2ZSg+e/zIEkDzFwB4HInD/ub3sm7Km2T6Iwv1ybMS4cV7jSpfEUvNoPny7xXkqpV15N8QYVbrjuvjPM29l4NU6+Td16bAZXJsWy52hDmbnontD+8vyb5wu5+kCkCIZ/jIKoHqreMt1inXfgiS0/7M/+iP8/Oc/x+fGDwD5ZUYUTK/SsFUz/6dicXslUQeF9dL11e+5uMsFheppKQPl0MYRzXhFCKvGFf4EUc7HBIu6+HiZWAfewhD8oephhoB7N6YS/jQJgmIlef1tU2vK2rHp32JYkJJrCqEXaJ5RWrs7y/uttu7jKEjB6aTfmSbbLjbhshbQa0GyOj+f7FJtnLsRc2grZMgdg62f/6yNNg2a78n2/zL+5Q2Pss6hlVho/b1njh51rhi6/vd8aXC+NHJfQyhC14XWcFjEDhUkJ0jFPbnyVl0uivyWZ9h9VaKicvPyUhArCngA5Z5NUxVNZLKItByCIdxtWqpYZ0PO3ANmRGtxb3dELyPXZEUL3N7533Medw2+OK8QcD4LAuyZvX++8Za7iyrjNjXV+zy6yH2yhFH9Z6E++rzTSXhK5d3QZVmp0SyNymK+IWK2bfXgKpVbGo+ebdqanQHllRTOybytPCDds+NrMWLIF2+TJINa0q+uj2Es9gil0zYtxWPlqjmlJLxrHN1ZhlLS1NMYcjb5YWUgeWCr9XHvPOJthQm8ZqfnICj/LPW089Xk6xIiZhTlnvcs5lude6CehnmGER9fM44l/I3v8or7+dKY9+R0EpleKT4q03xOQZNnNOMVzeVsZE3Qj0CW73jPEBVWY7Pa/Tx8pYlxVD7gZGdhLvJuztVjETkXmWhtm3MuaoQ+XJoHlSRge+nlYnLX/nY8goiWvTc3bwIh3Crz3K3uPJXRJs+VJ4SkU+lF5IGq77RhNvqyvZ6dzFXCuI5N2Vcn+ysVoGIuMjhn+xUtMLp3VwAq74eFgk1NoZnsu9+6vYTcWc7D9D3So6NZqhwV/w0F/W+mG+J0xTSJjKp4cc6C3KO1yC4vrir5kLPthRQ1ybVSc7kIjfuiFHruvrzpbZKcge++K1vnCwas1S8vg9m7qcnznap2x99Op4qxevr5oeMHI/jcOZ846wzt+pIeDrhNDT5+vAfleU8qlIuzV2FbWbe0tKZJLGtFCOwzIeDTSUJz9ju1tn1dK3XbkHAsnpPoxlTinTadWvhVMf5ySJdLQZ2AUv6yuZzvFCBO8nhtBfmZ6kYiS2oNKZqHXWXh7ncO+fEWOS13RfM8YtTmgjia9cl5aMIUCdGjh6aU+HlnSTw2d3OoUaMQHHKbJb/h40fZ8sfHLwj+73oshAtCwPEspQ5556rKHB4dUPTVKwLjWMenx1Ho+prbNaBjP7++lnwYPoMox1pJvU5ydzc4F068AgVK+3D9jsbLWqLu8ydBpsOuICS8K8775Dn88VWqQm3SrQAA262hfdOkKLLUF5U9cneCHr5qP3O2xDvG7Hp0H0BtQCmq56cYgijby+KqejFMRe84PQg5S2LY9Qp89ZXGq59e5SGK7NNVTDSs8gDC3fFQvHnUUc1zGUoDmTZfiwTrOrwcCx9t21L61y+VivlDkhiGWRPXNNe/ePx8zIwTopRrfA8NAQBVeT6br67T00wblI+TUD0dhoBj8yCeA63yYbkMippWUBkVDeXBLo3CDNSl6002AAUR5tptHZ4+OCcf1poSPp0EhfzwQdf4/CxfPhzK95Q+Fq0qZ2ishgAdz9H2axikGgyVZoY7fP01LN/Cb483UGyf+QsezOGApetNvh8Ozuux3ZrXSEWP7dWCBk9P8vN+X5ZCXuKBVb6fW5SSevtVdgIOZfXxRBo6im+/lYd9+GD0boj/+m6cXlBtJHnSyoPgjsn4Ayv/rHml8YppKqg85PyJNHO/N+FqRqIh4N7ID8Fi/nmW59xXtiORbbJBQPndeiOdLjWnwluoLrzbz7ZHMyKen+Wu73dL4a/7fVUJjcav5SQpv3s5Nnedzj3SjWmye7o8vjcg1Z5Hj6Ke7adnoSs2+exDqerF5/PZ9FANg/M2AtYkdZr0+wzvYVgTIOFrKB5MU+ABAxRoIDFvrOr/wXWlZPlSQJnHLW0qFkPe7Cs99d1ieuzicqm4f9Rp//iPf/ZbQPAdU9lsSuOoeS5nfpua8jnHMWgl3xyfMATIoTZzaCtlo2LO/KwiKmbFVOYZUJ2IM/X9R9YWudOjq+9KV8TGBKEk4ghh8f137bBXKAhCsApCFfQQQrG6p6ma37KUMAGOqryos8hJGDnDGPgdirdSsvgIz0MMcXUISqUYXC53BppnPp4XfRl/SUMZMnCv0wCwXxjK6ZAZ/pvnu4/fne8arflcS3jzUjkjdX1dORajcKkk0KbF+CSN6QrZKVOraZwSz0m56i6tPnd3RdYL1XfRw2fP86hW44T5evgF51zd1/VemBB5Y5PW87QQDIdyrXmY/10IgrYuoXR45HwrpO+NbfC/oLIdQimuBohyFzHbM+2clM8RuLDzWv9TBcx4TqxYtCn4ld6z2iPqMNV6+M/DhDnb8/jOdclSr+CRlqjQMVz0rcHOqX4D+b+GXvMhfoPdJeSvTSlZXy5eDEWnK2B5dXC2F/o7r9RSkVwr1d4mrf7Hy7XMPAx9FnPt3pA3Rs7TZN/xKCRZxPV6L5funuHmU8WHe2OsctehQl8XNBb+RX2+AkDWSL2b8129eZ2Uzc3xhAplfYM5hiD3wf6UXNjyWwxSeYdX/k25xmzKPdUk8+y4sKIlxJoYpzrCgb9eQrzTjZZlRQco+t/t5n7tmNBah+Ex3Xk4dT38FUsLe48Qz5DDKjqtvCreGKXObnqT8xpN6nGqjDnvZXNHZ3uw1skaV7p5dVYmf1QGvdVEsZIBK56fs9NT9RzIjrh/P1Tn+mEI/v/yvxRoBquN6Tp89yyJPD/7cbHiiRQuSUIG+l4sWGtbn4pVReQi5xqoaJMi8vq+qzbyqKxorp6bqpR0vjRW+pJ8+nODF53ulTVqffdZbThgqIBzfzEm9Fe/kh9/+qEg69TNmWCxj2ex0r75BudL3X0357L0rnOxgPyj+qletj+uYlVZMcQ+p0ipr2G7joPzsA3PxyxLmqsrZPM6RSG4k/NMdF1Vsu/L+N2Mp6elINyucQlQmqYdj7XeEjGbYkWhwFA23sF11F3OpXwd6ZmsoKrjntlsy6HMdEXnjGPzYF6GNsxWU3PZP9x59pifQkOZjJFoGLJ4kFxoLfrLJ+B0wvKTn1a5aBaH6S5aKXOr61BkmMgme0DkXBBbJqZZ+V8tt0i0nXoGHYnmHTidgA8fcENr6PiiTYGAlcKhd5EAZc6aQxEc3/Qxm/TupfQm+vTrb2UN33xTaIQNWBg/XcFEIZgG70uSWmzofo+XU5S1ES1PUgbxdhPEb0FjAJkhgyte6cNbLKbWw/H63FvojR96L+ct36eaVQh+Srh1OzptKmXWv6Lr1BPb1WUV9+21oIhdh19/FL73bnAlSHkJyGAfH6W5lDag8iWf93sgfvtLkZH7h7q6ElDK1ujlIyLcpsVodZNfpY4qhRsvqAOnuI387rrijlea6DXpOkV91SCnkTF3G+vLEELx3lpzNsoIukIsS7XksNB7MWsislZDNC8K0dtlKfoWvdLHm1TVsOo5LvaeJXYf9m4euo+VPMdcofHAPbrLvj2Vtw6lz4Dft2+/lVf99CdLpceQX82z0rzWu6W3D1DvFu9ZSoaGL0tJruX+kb7b8bUcaHIN6qaiizD0jiRJzyL55vlc9DD+gSEvdHoa+j/cq4XcG8Z/z5oA7PWWdYdh4535Vu6RrqHylDqjCilZeI+/Gu8OS2UZWrRHLrKMim8/aSz511/jOjZWg2S7lT06nwv9ld5F93eD+0avdgjuDjk5yD2xCVDw6MZazxX/GaXRdV6WxxiMPmmZKM3Q+/JHf/TbQPD5NlLKSoFMSSvb8GaiCJEmz8XScahGZbXkXAEEgF52NBUTJXO/Q7v8nPSgPcoON7U2LXdWlyFLKNbs2mK7G9l5BlYW2N2v3LqJStJSfdNiRy2AbrcaOVyHD/ln2zMdSuut7qZZfemNCb+FbKxjy2wPvUXiP/Bl/M4GEY2cUaGtnrSoB/DclhDvDFgitGsmXYVw6YukY7PGFK4+f4cCvnE/+Gv/d7t3vNPKW/z6qpwTZZaeBG83mKbvlVxDSpxC6fmOCRN3B1JCxej9d+6W5e9Yniu2ZI2h9JchlAd670D1It2PEGpE2wxyx8SqfQsFVTThN5XcHUPpeLaN2xv3b+2pAO6RWuMlbm0pOd6GQlP3h1FG5aH0i3KCzUIR9J08l7XcqDy7q/lX6B2KZ962UmmjyjHiWO3tnXK/Oj9/r9q2ILbTBLuMlRwksVAB0DP2CGLOdXnHihB17lSaYljs0dU9103wYU131zPngoamZMbO2lvHktnV2dLK1odKudCCYEqzp/p99NpRT1iRd0FsV3vsz/Y2NZVc9GvmPht/QH03/BkQsa/WiaU6n7uQVjd4X23Outi7O+6iEqijGKq/uttG006me5oFxIDp+/LuZqW+5IzSV8jfcdSgD+fJnCSuj/Lf88H1WU6T63bt9qq6L7pJpOclxIofe/nlt9ee4f55ZdsXXTA+HgIwjpboymdZ6FoDqzZ2vTqvM2Bx8t4zy7NY51DafqUV43PAKVB7R+54Rr5H5e2s/Ga4tf+Q8b0I/u///s/wj//xz8Wi86b3dnvnT2PMoaFrNAk1e3/Wttc+TNbHUgIwy/BXv9JGL5fvqqQbGyGYdWsKqQY23bIk97C0WN8rGsD4ypRw27+3zey7xZo+nMfCTSpULwSL1/fyoyJA1OgYUCtLRDaHwe0RYJawW5qsKYvHg+ikr1sfJ0EAb90O16tYmSkJ+kpCJI35OTDmFCgIPquuZFqxrtwZ6zE/P8vfDwf9jPRexo2Nr9Q6bVwd8C/jdzOW0wm3tKkrQqh2OHcbOZuPHy0nhSgar6/dRRdnCDghqIjt+dKUBkq+dAwZWQgW+zhNhc75PkPMeHGYIOriIgGHnLqKOKbcQOMv4dDeTipZvb7KnadSScWYd823I1/Hx7Ja1OPjylumOTz0BranTyUhQC8N7495vJRHvuDB7tV2q8jn586QiKlCqTd203QIDqsQEdV6GErMNYX3dqu8jkyEtQ0fHy2mNgSNm6ZwXHUfJTDCPiaGMj09yQdSkuo5rkqZr8BTWVxKSCb4ufek0ZwrhdDQUsLKfIZW9eJXzIBxuVpzaPHxo1Rb27dlD+jRIMBO2nw4/rl85sMH8zQzKZSxtdL8bcS8fagcHMwl8NWBNl25Kz7PiR7fT89F6WzbVVU4X/tdPdBAEXt89zw7JFuroR2vrZUbtbmFYN4Iq1QTCsp4zr29DiiAo9H+0xPw4QOuYYPnZ9mXn/7Uocs5l9LMIQg9oLd8Zs7lobuW0nuqcBGBJIm0uJVKNXweYLLH2w+WBO3yygBx2Iwj8KPHOoqA9ELvANfchrnsOV07ugnzsCu0pYaTyVD9zm37rpy5o3P7rMvNYH+Hm5bO7PO5IsSXacMrVWhJ9S1GLTSN8r1U3kcP3MPgYsm7rqI/fw1JT814xZykQt7hsOLnh4OVVqUiTP5oNOs9+3pOSAnfPTWFt9DTx4tKb51PpHVHXfEFd5/P5xL/b12D6elzjGBOvTWvut0kT2nTzdKELWdcv/kDY099t8h+7fd4mTaVHLSKO9761Rwnl3tf2bEki5yB94e5IO1kOl1nZ78+C8tdBaw5JVBkF7+zCVcrq4oQzPP7fQj+98ZTEDG3Wskq3KrayW+gFp87RR8ulzOsi2aVuQyHrFBL9dq0Pp/1XUMorZ/52hhXCRCXsUJGKMRidGtDJbeVkMtBe0ZD6zGGOvubdeF9Eg4Ac9cagJVhRBSHBKTyjOKhSNVeiEWplrduZEDpC+C3mtslin5xhTUAUvKQL/fedbFLqfJjc485bL8UYbK//VCz8sv47Q5FPCqkRxUocwsqTHPNLfJU2s9Xbkelx+CRCDJnFJqK/pKszpz8nHS+BHlfub5Rfy5NochLKay7DpDuyfKevgvWsMYADa5Vs/WaPGOziVVCl332QpTOs6QGDeROtSlgGJoKLPF7G0JRJCyEwiHZMAU/l/0KAdrMsPasfWbfzCOpz2+dMkYhQySVwp7oY99HbRpVz9u+q0oKzyFGlBrmQZpe5VyHCIbAeTfFc7Cas1e065fTo1CSBtnQyypFdH0VYuk9pta/wx+GosEhNBVyZ+/tOkAFb99D+LY+IyZp6sXHEbH0yFiDBW3bwMNdUrq4Ru5tC3SzvIi75Yig7eyB+0Tzrivx0T4PAKGVO+VewKnROJh0f7w3K6ZQ8Wj7vc8d6DqA5VldMj6y0BEBtqR33vbeaS+0Z8VAcIfuQbeUkMeKhAsC6Q0+/Q5DBGOA3GXPU3RDmUjo0ieskRT3qIT0xYoOfb4B98369bg5V3PTn9dnbPvigDKR5w7J1csgcyyvMFqFox3uiz5/fe88Oq/tPsyoWzNL05EItLg7mlb8x56bib43BUBwOlYTApal7sOQkvYfYGU1f66qk/D5xss82Kef5RG/peAvaJReGb3h+Nw0oVG9s9os3bBprLa05lk5ow839PsCIq/v2op9VWu08DbVhaepeEjuroL+IN1zI9glO7izYIi0rVk3br0sktU8A8vQo9mXP67DDD83vlfBx7KgzVfMuUfOETntEAC0YOxS6RhYmL3bJO5gCMZk4uViNYlLfKkw7kmz0t8fWJJgfz8ntzJ6AZbUWuI8kezm9Ao81Qk3rKt9eirCbppgzSHiJC2FlxSBj0+GfnFT6dL2F6NJCZO6etI+SlMpjaE8XaIuQ9ZntZdDKB6FrM1wgsRbsirPeWpFYE2vwFi0/yYlRUsi3h0WxDyhbcnhkzLBEtcIqOGhN6vlqWtTEIwjgqsEsdm0iJgMCcm5vqs5A1HjFW8KFsVQI6Rfxu9wZHGpb4ZUFAYowuKqcJzHiF/9ojjgLN4TIvBLUhDsMs+5wZh7dFnQo+sUcRobdN0GIQFBvi00nzNCaAsqsd/j03NjCgLvOtGOYYjG7AyRZMky7bLI2PWeTW9osCjy9ue/aPD4GLGZXhABbJygRkiiPHBuigCdNQS275IlrG+GAZsDlYx6e2NYsN/L/f7u1OLxEVLNQtE88VqVhDAmMfTjFUgByyD3QuaPmh85BWNGxPEYBYU/fiq/P7w3DxrgKqyoEtsmIO16A9grCap7QSDfPAk//5UcihaKvt0U2bpcpBFOJzWpAVen3qNSw1C8NGmx5mT0BrAaxqZDQahTQtN1mLuNglERIUTrNklX+pwbhBBlX72AZSgP5+AMGNaofndw1T5opaSEJgR0nVQe6cOtbAb3axzRh4B+G4C9NNc5n5UXdrLfre7ngsa8Cb6Ay8ePQvYfPujdOummKz1suhIrTMXv+CrvORw26PcrBZLQ6TAgY1MbGBz6ixCK8ZDbVptwCUoZ0k726/lZqnqktnTF1cvYarUP8nbSToIizuMInGpNdB529m4CZsy7Yd7Dklo0A0zLvEI8U3F0NdqHQbrtEknWhfKI6fmapogcGqSuRzPd0LhmSOzUTt5QZFnRNT2AhlwqqthcmDuoSaszIup+ZUUZwziiTcmalhE0E5IszYj6LpRQwrW2pfS3UWNngfC3mCcs6DHl4lnrcZU7pgRHNHszLLiODS75AUH1kw0ByOmGGALGMZpeZGFJpxM2KQFHgaWXD19JbqTmBk2T8KzrVavo7FUvYakuF1XB6i/WAVj5wHGMJfxHQ35zLk2cCDBxyLRKOcphoEdptNgbAovkE9ME3F7L57nOKuYfAP75P5c/0hDa76Xi4K0YT6ZWrvgzz2CzucNNdD9zabyYd6bWtS2wV0MghgUT75eraQIA0ApJ3ApvSGbtKyQgf2/y0ntcf9P4fgW/0brYK6PNu/F5uc3luKZkCgZaTnqROGIsBCBWj0MKdBARkg2AKMX5VqxhODRyHRvKoQsIKE1peP5r4ZszEFnLSxNn7fl0CzurNwRRVqrs/hBMMeaBTpN2oNXkGAyDNduK/K5qQoakrqxlj5YIQ4LtLS8bp2Ad/sapTsjiIvVfA2C77ct26c2jq887UWSLZJNZKm9RZPbL+N2PZf9QkFIPU9KI1EFB5yJqipFHGMJDgVk6XG6GYEpT2pYwESsT6xJjY5jlTqrw8uE/XVfiiJlM5xW7BdEQHN656GComBIWRKCLyKnHNLqKth4l91AOf6fjznPHX6bkUBeHvOq98l1wx1Hal3MjrfpXpUGUM/D6tnWkVcZQlNdyPiGg4pn8PsvDWXy0gzYlJ0L3t9sYP2V86za5rZjyXdnFvnd1jx2yVREP/6vhPkS0fMdSPmKzcd8jnyP/9et02+bDeDiYWCqPJyob0XJ/+Hzlh6Ygsruqp2cOWpq+fvMKDvP3BKEtDZpCAJzneK20VyLHx1N1HW7dzuQoFUHmK3Q0hDj4ILWOw1T2yZLk9e7ud6XEKMcSIhpVZkShLUpNlTyfC58HXO6VQYio6dp5PQjs8eP73WIGett12O9bXX4rim7O6HErz/QMCdIcEvsigwZvzISCdFuYH8G/nLEJCzYDgFE0yuniwDhD0H0+TcTIUFXV/pkMnZLrxoySUCrFQVJFUxm1vOX2+HjsRhdEjz8XYkmi6kXh56DGQZsCEjXEKdThD7qO87kkcPs/+zwBhiu2aRE9wJ+vA1DNM5oz2lZ4dOUxMpoohDjnBpXHCsLH6TT2tO5vNxVVea7scZsSYmwslKq6z2pQvFWdx0cwWBEC8hxC7s/PZZE6qWa6YbcrZWLNI+StwRDgHGVFqQdqIMjxj6AYjwDaMneCJYALcwp1dSDS9W1q7HjatKDFhGtufZ+6t9Iw3xzfq+CzHJopt7lko3OvmNiw35XL4PmqAMERcRotnglTfSHIdBvHQGwOrj4oAGMWFiuuREpApqwuWa14+5USVJ8vwiD14uQcRUwrai2XXygnFB5ddRibh50RZwgSHlAFaQHC0HSrcy4NXHoUk5fdFfebXFD9rkPQdsd22qrAcz1MyolU5FIy2WUxtMeXCik5X5yhwn3W4LJ+CyC4slrDBuOltJP3d5uAXnN8sXjclYz8Mn5H49tvga+/jmgcqmn9EMgpc0bXRRwOhUTt73rnLG+EoROT0ypUWYnbLZqmhIRYfeQVYZynFptBO+zmpEIrIwLY7UosdZsW6y45pQ1CaBG2rYtrTtVcptDjepVmWF0nzjUyyzeVe8B+75cCqKLkgIeLMdCm3A3nzg3J1ULvWmS0mBcgEijymjzu+VbOQEvP2DrTUPe9p4JARNMp+Nbng2O7re5v17W4KXtq2wZtW5qEPaRz9d7l8b3wMk0+aAeTxCZ4iteuKMFL1/sCNTJHrQ50vhTBZBWLfHa37k2TZ2kxn5Ipliy5XClWKOA/AUOG/h4OLaJ2lGQYQwiwHgfHW4/dri3dPP3QTWFuh8w5moICSFx4GzKuWTybD2GqvAHT1Ihh+vQkXs5U4qht0Bulcfknje32n7GYYB/T7GjZVzAiLW2GBXg+FuF5PKIB0KpCzwokQeVTVDDphhYXdSq8f9QcFJf/UoWAhJIIGXkQGnpQ8seKcrsZdC+oDEBChU54gDjsI9oUxM0BmKZyQ2tewOdn6aux2yn62Wt1Ko3XtxyCccSy3eHpifZPRHz6DrhcrHJWzq5ggC5QdItocdqWsI8G8xzx9FR6Uhh6THm8fydOqEMsIawQFWJ27IfRBKTlEFQ+cw9RKnRxz9s1fVKpyhkNL7B6MKcMi6AYxwYvL+V6paQK7lQ/zxrK+QYTIVjeye2mCik73IeAvncgkJ8XNUtXTYxl0xlzf4HQrA8Rsn1wtkU19JK37cauQsRcvE5qmHIa/K/xID70eLTqOZJPGZG6Hu3WxRoBJgiaUSI25tAaSCZ5DxH7vYA9DI2+TepB1vzDStY43p8SEJ/+Qt6z3eI6NtYnyLzm6glpvNGiOtz12lr+A/s6dT/5qb3Wqkv+AIXrexV8ulEMMcgZTcji3s4ZmDKGoUXTQN2kLS4ndRVtNHarE5fXMmxMcFtsfBImYQqJ5/K6aQ1gYSeG9IQWcVu4XgOoy0xcngDQpoBpdKiH72jH/yo3yGmHW2gRDu8xXuriAGaNkarUkoiTJuGkFpMS3xxaianXYe3pTye0XYftVsJwFjaZUH4ao8RwpmGDRm8+3x87164Z0DrhZL6r0kuO8KcJCNsHmafeGoZxTBMQug3QAZGKgu5lxFSscwjTo3FHV6QZTaowpPWF/TL+coZy0xxa5NygdWfL89sMarWPoUpwJAPt4RSNYZBW9yxDB1E0d7umvA8o3FyN02mSroPDdndXraK5nBGhscIuptcjYXwkf7lsd8KflY+zdKYgjjBF0oc/WNkx9KZE0CgQgbNYrBKTGg2h4Xos3CfY1Z8dSNS2rtmW7iUNds6x9nJ0lvzI+wwUYwC6ZGThZwgBXSe8dEGDRr19wCoOfYUg+Rbqck4OYlQgAGiRDu9LaBTn4prcmSBWo5HHWDz0HfDhA2aN4edU5tRXSOISIqbs261HBMgZbDqZO4sYNJezvYRTHgZJHu4HB3a43CczClTJ7bkdzg0Rw4LYASDI0pXSyDnDwnxCUMPvckG/DRK7eynCnO+9TQ3aw8E8qqYYMuF4v69cxZx6i9KIrLobmowHAK0aSMElFtvwxjQPIwRJ5HNKkEf559xYuGZKstaUWlO4+qSeCNRN7QT1hclj4f+6p3r151me1z8+Fq+F0llSIIj5HC0RY1Uwr6+FVlMSOi99JuU9FeCQkjSGdOkEAOxLzXTDpgvYJCOAlSJWaNeLzRglMdPfIYuP7joL0wNExgcNu8PFoePOgO0617Sq0yZ3St9kuWSdbZdK2CHnyovL359OiF2HCa2GwMlcPnzQszudgCnd3VNAvUz++bpX0+Sq/XDRel/4XfLjnAGEFti2ZmhFzNjTU0demRImjZwSOr/X5k0xt4iFAkL1OKPfdiXkSj2NzXRDr49ZG0jM75kRRZdJSfoqza5WvdLdWcOV+tWeiJyIyIjouwV9yiX/UteWc4slqZc5pdpL47zfOWv0hx52ypr0a8h9KgDL5VIwfH3mcikdvFvVNZvLGV9tAYQOgCTZ/pDx/SE6cFnTnP3KYmmHhBBKHVbe792uZL03w4DzGM09zxj2pEq/eHkbs2alcoO7BRAlP4SmTCVIDGccz2WyerBybqWih3Ww9eZfziXTO+3WHZ2d5blUiShz6mVPNAbQo+by/aa6xKFrBF2dJosl9u/hdFgjf6/IPpm1F0KDxjPGlMTdN0715eXBplLAaL/vC1I5TYBWOyIYGEJE10WJtaxQS0fAivY2qXwnBBhDeLPRz5fxOxlvCf+sMiPr2bJfQeqioAdPT6Lpan8DIttNA/RRH9hJO3RByhoMQ49e6b7hS6ls+aRJFOE5zxJCQCa5BOdp4FCOn8f6VyHAFBRWMHjYSkDMMLikuhDMq8jolzaVOdDLezhoojozskIw1yevTuX1cMr9nBsECB85vjZehzGQYB52mMai41gvAAp6rcZS1RTX8CTfbZuj1RgFJhDL3yOuZ8a318o9FXsa5P6fhTApwssjyFnQ1eggx5zFOPEhOgtKvpSFPmhlEFYKSW7P+bheXbUZ4vVhETb+M0aUM8L2nbzsdDElh2tqwwwcHWp1OIDJzQDRR1h1FlMgvBboQ3YA8UilhAmldnmMajhyEU5xqWOWVDQlUarJm6X7rMLkHz5UHteOxqMWfGioZaoSuKQWxyf57PtuKp9xCaNNgzuDjpt/ORZlVRKNy5kStU5JnnFbRXmRoM+KHtpynWFOsM0bo4DQmySAN+j7Hu2+oJIhl+RiQAwWGuAMl2O4HpsH9Un2iN6LyHPTffLFe/jfOfVA6kUXKAqF0EkonYh5nzBNaEMAUigdtlPZlNIoLto5tTljDn1FRqxqVMHLuk/e2Mk54qjn8/XXtYJvsUj0sOVskQc9UC5UzgiDeOno6ehxBb59MqQb2y1GrWZEOoijK/+S6spPQClrWyV86tRW10Wn2KDrWtlrjwSk0rDL+CIPSZViql13XmJuiO5BPwwAkngyWZ5X9zgc3t0bvaC8a5G2Lc6vJam6ybNVVnxlI+3HiOg9VigypsdYJ6TrHWCfmJYGm/tI6zyU2fGhrHuxb6+WS8Ccqvd7FM9dKN18/XYADdJ2h+bjXwjz/PprYLutOmf/pvHDGl39k39S3urQE3sI26srusQSSyXzfjJ30GYjLoprbq0hCqapNObRhBqLT3MJB9xANpNYFhcHvK5p661GRwksKWeVaS7OONCDO53qUp4mLHQdz8/l8gjCMRri/fws02Ar8OrE1LL01nHOpVHOFX3V1KcNBf2ip9CaA62HPtS3oOe/TSphRX5/SPQvEmWD9/tb0UweH6uwm00ojXlydu48ZYgM/Unpe/oHfBm/9XG5lJr0/l6a8qMeJDJ3SxDXcLk2yFkSGSHSt26a0oa5tIZn5qzyAso3j+JyVE3t/N3Ue8E7USHSZHx6UQ21ZmM15TUruVqxKd4Txsiz2Y692ylWZJjGS3RQ0PqwTo7mci4wUghWepA5QFZv+fmTwYbnUfiLtVtXBZ/hLYJI3ZcCbPKMl1Os4qcf8FIUUG6CMiwWMDA+p6XllmFj85+mVfm7VTMaC9liszKlF5btZQg7kxL9md81CRpH3EJfuaolnKAIWArFire4c4JTiOduU7H7OF2rOQOFnq5TNN0pJZQyeN5I7TprKhWC7oXTgm7DQwGKHO0y9j1nKqYXe+YZMsft1t1BfZetS5+/7B+q6sTUmViW8uaSE+0u8sUu3IulmLtOk2NV5vjmULRGqIwRxPIllAFdqwFgUvTBkfudcWry/PlZkvovJWGSgwYF79uyuDAl7quTKzbcWVUNJqmPkD88f5LPqmJf8RMK8xW4wNLYVvLyM7oDsjTVu15LozW+15TldE8fnoyBwkNog7TT+fO6weqOICVNxtc7dHkpuoWGzFgupA9D0OcD8jeGcZEcWNrSl+xOCVJy1X3o5RTL+WtzPDZd0u0s+Sr8hd/zlKr8TSuTzrMi8jCOwOOjhXrapIKUBY6xbsblm7Z5vlB5CkLA8VU+Rx3L/516JWXAdRIdtD19kg+T4a3BKUcnBLF9R17j87qPLCf7bj9XxQJY+pLsLk7XEhY3na3cPM96moA//uPfRqMrd1hskMOJTBOKxXy5iCvFtfBlWaNK2RhH3G5SzYKUb3dKT4Y/+zb2nIvfU6L11oqZc6Hkp9mpm3hTGXbVhP250zginX/Mpf23r1fvFSd+/3TS3+v7YlgqBYmuNeOKDgFiAwhB12TOXJfdS1VyaOSY4uQZkJ7J+dLgfGmswYm7TzV3cZZySiUjW9CoVD37rg11zkUuekaMe3Dsy/jdDS9oGfbLBjOVgNV7uISIZdiYYAYA5GyfZ18F0qHlZnuJoEo3G4VQwJPcqY9VoKfS4ZJaETZ6ASz8mJo0F8G61BB3t29FjnEUZOZ2T958Fe9VxFyURv8hfV8zXoWhEuRz0CCRT6KNDRap4HE5lzkqb+HWtK0o8JthKUYAYArVb7onpoippmWhljlXdo/N33vu/L5hdSdXoEcz3YwlLarHLUnPRM+gqlqjfJTsg/8aLPbZClvRD9j7ld+EULwXdpYOrQckVHFJbXmvbsJ11GYyGl5AXewt76E3FkkflvNKwlzJi4r9MUxCf0mRYs1/9GF3jcqgz9eypnZWjkgtKdvlrnA/eSaXi5OPOVfHN01aftlfMJ1TxAr8UbpEzqWbsi6mmW4mKwpiLV8zdNBZzLya1d9yrs7VI60eIKiUKfc3S9SuDgi1OwjamA9SYjXmW6Fz/YwBgTqnW45WWtefV6Usu3GHhnqm4vYyZ7kvxjumGxosuKrN6BPF59x49mBeK8618pKgVlV4b2ZEKXLijBM2mDLZ3nW4QuraXzUc3Q7LIQLs9bHWoeweT5PxO2v05vNILpean6/owvSe47Eg4P5SOcZXbS8NtHVokp4LG0stqRXesOh56Xlyzz1t8dWA3lk9LwIrniz4d68PzYiFlfKSePrxP3vjcy4NxUgrvF9+fSGg3h/dowrk0efnDOMpfPeKnX92/DAE/5/9s9LAYSrNYbguKePo4CLurEMz6O73wJwkuakCre7WEFAhjL7NN4naI9vTVEo59aEoACzFtkaKrpo420NuwjW3oiCxnCC56zhKW3mNG20aYB8lNvQWiotru5UyW2znvUYA74ZTsK9pZw10uq4gZWdNUrGSmrwNOVvSxlpJoFLVH/9CvAnbB/tbHM9VIpy5IpVKGPtKgwlwIQRqpVvLca+ApGTNzbIiWE3FAb6M38VYCKl6pJsuUL1Hf/FUSqWV+GlhiqQnrysASpN5rpmak9Y+32W/mSsGXmmC/I5XsEPAeWqttF/XoeQGuAYp8/6doa9Vd2XVhki7fr4MIbDyd7rol2NjuUGGcKnnrcplAazJDsNpLPzicrGmX5twLTBc1+F4FcnB5MAqDk833dqis/EeGfxqbz3Ctg5/4RaWqhVNmRv/qA26+EhLNtXwP/4tBi2zdyl0Yfus8yfq35xeK6Sbc+IcWQXC1wy3uX38COz3uKadLZlKuPUKUTTyuEh8qSFfhKRTMqFHtIsNjKwRULeiEac1f/dcI/kWPqV35OXYmAj4nAC1fXRG4NL1FjaSc6lasumEmfpGbW37xr0KAcereLTfd69AzviLUfj3V4/zZ9eDpHH87mzpQbfmjm6ePPfqvKiBqgChh5bX1rNzknM/iRfNf7brUDcgIj9ilajPyUSuTb/D8NV5VnnLA1PvV+UR85PzzCvUDfe6TnUDaqIWHwajV2CFJuszPOsiKwlEnnMuzb5UATtqiJPXaau9BnAOO1nfpoTuGb/rNhW5M+yZoWPMlWDOkJuuefqqHB2USng5oySG80vbrcSuK698yXKm7w7lu6aT+Q0NoRhY2lgLQCmRrM+m4UjDgWPf3ypj7oq+uiPuqouOBcfXPX/quup+EVy26Amdq2/8ZaCAg8vZDM8bV5SRFm5I3kJG7O4c97oCzdzZ0EPmyd305yAbc81t8Zqozmz8lPuvDyTN/sEf/DYQ/ODayCvheHlUMRHOfi2N3J/Jo9bIizF9fijnyhqyVtm5IHhEoG43t5s6X07hrdb01Q6j/q4pGaiXQYKwsJ2mfJfzM0HrkIclxFpiOFQGKAghJ20oClsic65q3vNd/p8N9+xKSKlCNs+rg5gm9N1iwnqeC1PxqO0doKEP5++IJizHI4DfaDN+Gb/F8e/+u0tFyxXq6i6BR4fsc8o0/Nl6I8Ba0TvUniEtC1budzJL/3n/YKeUcJB27+jdTcIbndbkxCEf/rFVeUsuhoP3UT8y58Zibav583MaemEJ5twL/Xvl8XLviRH3/M+hWKY0rffJoZcMlfB2Fbe/7wp6d5uaIlD8RijPII/1uRGr7TB+5o9xzk0Vs2zAlT6368S4oNK2BjoZslOVtNP1r5X7u0kpz2c+021yPNARKZFznuftVttT1f47VJm8lr+aEStFdF5tFV/r78vbG1meazx5hWazS67thxIDw4oMkX2LhjkREsY03ZXG5DBPFA+WaKgm2lYpMJ7u3Xv5VY8oG+06mcDfV7krDoV9K8S2onfniuF6uIfrOa3PplLq9Q8zohmvXN7t5rxTb+xtk+faAMm5Cq3hKxiDfWes8GymyWh3fYyeJ5CvvPWM9RT5OdI59ykEec/l4uY1lUtAHrfuBGwEQ1rSqIXP3UWS8C3H+h6uNieGpS6xmuqutcxxIcJdadM8u7nce3qCmwb3YwUcrY/0c/zu7jxIh6jZhTWjw4wYFiPVdYlO/yj/fi8W1kCBF1/23VXkhKdlU+7dAz43j7fG98KtywIcX5uCsqXizus6Z/USkgfAOmpsrX45FYSuDSc8pBHAHgtaa5ADxk7y8HTHmyYagmV7cBqdOa1WXVRFXgVTHouh2WCxHb1YntamJCMBIuwViXnYbo2zWdWRcQSeBeYi8CZIXTm4GNUKG1qL4WwSLISGYxh6qfozvuKrLgObLZYQce0EtfF8OYdGaj47944h+4DFZnJ/lq9/VJQ83s5hwKzNFHY7wGrh6qD1vd2W+7/bKWKnv4hhQQ6NIiMtxqkFJkWqNoqWKuX+z/8z8Nf/+vdR1pfx2xh/7+9BkBL1qllNYq8Q5QyX1C/eq189GTQ8MK+CfSV8+IIK7BtaS5DsuhInvd9RE0BhmN7g326thvQ8y91kWbX9pvbEiTegB9reYiRJs1WDo5RwztIwp53OxSD/8AHXsLHGJ7tdLx4l9cg9KNO6YaPRCg0OhzeqTITSGIqIK759Lm7SccRDymB1EcZDErlbur5UVVD0iHH1jXoGInvAv+HtzLkg0kEdIaeTNpwaR7ARWUZJKF3QYEolvj7oP1vI46MYA7pEIoG3LGUKD4cCrlCA3NKmlsNBeEhCOet2GICh1KePFEZVidAWt82PpCEXUPOmaZJKESFYxZluLICOgTH62dvhKxxPwOMgNJhSrPQNySnRdR8OVQwvKxqxsA3HHGQPHvYLzheJYT8cxKiLISACmHMLIAJa6rKZRKtiMySWaOZ5GXvNGX24ot91OJ9LvD67nV4nycl4f5iB0wnzVmL9v/JNv1Jbdf314Mr1CqDfiDfneEQTAoatNNv5dIrYbiPaSWgrhoDdrje2sIQo9yMEq3B3fS38nA0f1wbP2L2zO991QDxpqWRXqOLxsYS3Nlhkn1Q2xxTqqk37B8tra1uf59KZxyiP8recgbx9J/LWlcCkQnk+i87CLvY+jL9qbjA5r7RaPMte5G8z3dCMV2zyBOnpk8Bqf6ZED4Pk7mjvmK6TdT6MV4tW8Ept27boNYekaWSOMyJiFyrLtMmznJlW5XnoilG7pBafnkW5f9gvyFnKZLYtq7OE1YJVL2HuEStpHQ5yP9g9j+6srsN2pQT3KJ7Sa45A2hUP1LqrN9FllSfIwtOZF+AKKGGB8kilg356RR8DsJNS3s/PcleN96NUWWOvl/OlAZjHEOT+YBR6voUe1xuwU4U/pRqhDylKwRBVEj2New8Mcsa7gxo2F1H85tSXyoQ5I6RoQAtllveu2tk6r8ISolWXlBr5sbTlGAbMQXI9t1stVMA93m7RpFSFgv2m8YPiKd4K4qnQO91wQ/h1lWwtTmYtzHfFAZ0VbT87E4fKgCnqQKV8ULn35Z746Cq+L0icl7cIzTqGdJ6jYEVM5soOwB0KaVbZWIg6hGIsCAMvGyTzrpMfrZau/mO7eL+3/K8gEsIgK7QkZ6RhU4E8QmRN7R0JwQR7ZQ0qc+fdbNMCpKaKEzSGqMaON+ABuGYVxVz9q38VX8bvaPzB783wzK8gBg4mQOWRBsZcXWDW+V3XT/Z3cXKIsihd+pk1fPHGP+pyIcB6N8g8RLBJJ9im8tZZ7WZ9RcQ94lO9U/+f3wUYARO1/nLhGWkVSQigRhBC8P+p6dvHyrj13W71/Wc3S4YcDEO0RjYhQBQH8iRf7UV5jud7lMG2dkPpy/u8PcZ52x0OoXLThwBnXNTNduQB5Z77hDGjF4/KTlKNhMnIUecpjcEc77+VbV7Tlg0711ijX54OpwL+Sifc4k01GbCiETZM6yFrofC9TaUgQdcJwXSuEdTqQtVkR0Aq17JpQVOWNIVKoWQ5abt6ypf9w423quLDrrKmjLtLkVFy0IBGKnm4YdvgaJsN0ewcHQ3z7tja9RxzrkP4+NlKl8x1XHc1Aff8rltvZOFdPnRQ9lL2OKst4HEH+xwJoFJI6j3gHQqhAbyx5OZg+8EFut/zTsrcUb3Lf7SlZy4EgHN37+cHYygof9UdV7/Q+s5Kbg4NFsTYmJcwBEl4tSo+ug/O5lkdiIwZWst/FeJkycvywje/y1/F4Pok8GBC3XAthMLHCVb4e7KgqStJ6YYGTTa2PgbuvTnfI+meXjmPnGu9lbooeaR9Ub24ZOu6FZWCX/ETpeXoDO2cS6igGafUT5WX2TA01odR1etZUmt8QXh1LhPza/4B4wcp+EDpPLikFg2kTuicI8bJ1c1lSNX2QSzKb79FOwzYbh9wOgHffQd8880G/R7CPMYrbqEHAtA+fyeLe3yPnCKCVvtoL68Wd951TbGgczb0uoqpdUKrbRWFPh6Bb77BOJbOq8CKJlKL/z97/9Iqy7ath6Ff9N7jkTly5Bxzrrn3OdpnHxtx4MCVrh9whUFwbWxhZFDZJfsP3IJlXLhgWSXjgnFJYOMfYFxwwbdmsAsqCQzCdklgzK0IyUhb0tHea80115g5cmRG9uhxC619rbceOdbDV0fClnbAYM4xMiOiP1pvj6+9IlYcHoAVO5Rhp22ja724W1DU8PIKdnwjg+nOL+jnubZC3kQ/kSGZMC5RLHqexpyRBrF8GcvYB4nv/Py5ZWx93+HQQ5OapWQm0Ta+I0PqHEdtYWmtz92YbyUiz7V0XT/MiPMsiMbhUC1xCKLTzTNiKYgpIRwe7bOcgUsevZPht9c/ocv6LKgAlgoGwGsZVTkbMQ4r+s/fyhcIa338WOnB++FTUsUnImh+xfWlVruYJo1DZuyhF5YpWeMcKgRdvuFyifjyRWoBkwF2qBU6WBHFhxuwisCXWSoO/OypVNSvFOz2UsP8dB3RT6OVn00FVhXoN5/Fq/EnfndqSrZ15xfsAOl8OUilmsvctlt/PAi68vwsCNThwwcTXj0b7SmTv11VEdb33rT6VB+k2ydjivt+xFgWdPOMa9hh1uodKXWYju9MmHXzFb1LUrNGjWqYMQeIcpWoZc7CfvpyBRDEiNAyqOwHAjhwohRB4JlNpkK/DLXZDPfCYohDgImNlIR5nE7AL34fpxMwHovFOXcpYZ5r2AV1ZTE2IjJ2WFZgF5R/s4PWJAit5CnIgL03GKhhxE9PLk4fAUhTNWBVqZ5n4PXW4dC9wCcbnC+jrccwADidEKeMd8fqyvRNi/jdnGFNllh9BDlbHtYhilfpdniPAmBM8vljPqmBOBn/JXi6hB7h0AMUYZ+eq9a73+OUd0ipx3Toa7jY0EQBYVAv7V6nT8T7lmqzrJhv0jyJyoJuCvPYzFvtLMbrVaqtffVh1dCcvuH9UbsG9/MLjseHWhf+UoxmzhfhC1apRr3SNAxT0mpvz2fzVn97kvdoqwF0+ab9cKrSdC09l8KUQqCuyTzXinek6WGI8H0YrmEHBGCkbvH8LBWjDu91kuKNiFrGjqUbOwiA0CQda0x+2vdWAMCAxiJ8YzdIPDlDpUbX1bqbr9Y8DcOAL2dZH7Ltw/IdJI96wDgMOB6j5ERojgv2e5xn4TlURXype6SEPEPqxZOYVZe6XIDHSX8PI4AoZZXR5mxtw+++/Szn2Rvl1AV204pd0AOk85f9H1X0SKnzkY32csY4BDw9xTakKASUEi0ciewnBNTEctctGxre48NVBWCpOWq3EnE+R+GZ8wvSXjxQfdbCCHweGYBWEZNysxFRq5yxEmHOIlanCcDnZ4n2SI/KX7oaraJGQYTMd78fG0OQtLvfs0pjMR04BfFcWb7Lj1w/ScH3lrU8UxBlWhkboEMV5ur+o15h46HVVgpAY1UZjkf9Q4jS2MXH+zotku+10AKDvNx4MprMZe/qDKE2xfGKNtTFmZIQnw/DsTGgvo8egDuzKtR4KQo43iql46qyzXUqRcfsnpecPN1an+ZV8NMv/qciSp2+YN0gdNYtnGPRNWa504ZJ6I19qkkjpVR3+k+gud9ef4wXUaBtEzdPioaU6P6aouohMd7kEDX+iV0NbW9Tja+MjmkhJdPBPThEPmmJqvxQz2UMgipSZ46xJkENQy3B5jk7K3lZDHauZSapvA6DoGgWEkPOuSFSz7c4FRSpZb3fR3sGkfoQOm3SJfzi4WFXjZNQUdlVlQiGvhF17vS8+jUS/tBVYeWlJPmSWU7t2Pu+IuQ2NZUUrHnNy3iIZyakjQ3PaYx104I3f1PmbuvWTshQxu8z/rl/HslkuAMBI6JmTs4K3+5cgyHP/NyXiFiLUdNuOHsG2D5wTVxomsdEgO8pxMLnhRXj2Ikio/kjIttCu77hPo6WcmgYuhpS6hg5lURD3XUxHXhq60Ia2HpeBHkM9Sb3cutGuvk7Qmi8adwf5mjRYIspWVWZ3a6vC+VkG4AGSFhDNMPT5I+jwb6vKGwpNTQjhRbZ5VC354lj5SOpx3AvLTFSz844ovliYxC7sXdc5JyR1Hi6XoH0oDXzITrJOPZY14pem9czs5+MNvnUpHRL3NUDw+Zx9Po0st+dFcz1HqSE7lb3xs66I+I7enfrtYReopIcjd/nNsXmyBH99p60Zs0cHdzpSPqVdehrwnuRaAEf1bG6UGy/J3KuN8qVP99QvsKIBzMOxSszTSprQs2jxG6oz9kQFvmtrV+GlYQn6TB8r6Edfp9fLOqVG4LpbtR9m2k40M0iuVLXRIj80PWjCn6HVerpnsXqnnN/9x2vgALVhTo+PQGlIM6vipgVYNjjOkeLKypqIA2Hx9oUKyUg9AosRetsR4UVk1hFgYzh68/CMJ7eG//w9elpXVhsJgAcDmja0edcq+fsH5qO8GQMY5E4c8bczrWAhgj8Nwh4noWBXK9a2QAum18fTERuh5soTPqca44IWdBEk3BaHWMJPTBJk55ycUjS6SyMcHqwKdH6l/j5eCfwd4PG42bJH+iGAd+dpHX38aihdtu5XS7oQpAY1RDQPwRTgn57/ZO7WJqsL3NTjYHnBVDelsR1VS41LnQYOqQUxbXM2EB0wFyTAg8HrW6ihE6vnfYvQizV937LHb75Ruj86amOcbx8h3GegaxCSKsuBJ758xn9fo9SxP18eFgtmXBMV+kkmqsQW0PE6VmeHUKdz8NDmwz77iiIy/kMdF2H3W68ky8dVkRUA8PInIasHqDTsrMEMPFmRIz5InHP57OEHh6P0gBlrslhY1oQP39C//EjfvM1c5d67IYFfahxwNqmADvoHu73gqqdIS3TCe2UgnRo9CT05YrhQWrMlwJgGqxUXfnwMw/Oq5cB9t6SgZyjImmwZkN9WmEP/PgRt9zheu2b6ixriMDhEWX/CItD5yKqQJ00Zp2eUMaOUuegzChFkeBSat19ZxFctRDY8eiqLeUMnJ32porXEnq8rj12SRDrmLPwVaDp3/DgUEkA8nCVBeu0axKdqdM9K90NwyY8UQ2xvhQsacRcRiw3VUioJByPZlxT76J86HEznmpjISJcCg4H5qDMNjaOg3vbff5W+Lg2DGP5XPapEAVBQKu+uAo1OaPHZqIbJVMQaFlDywHLGQgJp9eIcdTw28sFMZ8q81H06DG5KixBQ7paMSgKDwO1ARxwMzko29JZR2OuKelH1i9LvloIyGrws6+ONNh7RZNIRA+c6tWHB/mdXkiv5NqBI0Eo/N4fEtLU4+uvhS88qjcDz8/opwn9frpHELRMTi7Ck3MWb5+N7XCwGP7DTpnOoK5P3YwbejF02MdH73u9dNjtVGEsiymSRSG6AMbkB1McPX7w6VMlv5QcIOpkS0nC4+jtORwqAs3LzpTP96Nhq2GJMYhhf7vJ1IchYlT66+arPWsJ/aaplA/VLpYTwbr1HpCVYXcSMFUKotLOEiRPMl7OAKSq0qyyUbrd7jTXCAD7tBQpcBsTrEQ0t4Td0bvTl+b8cMxdWYTmww6piFEhua2dVCzSNeoA9BaOKg9fhxHnZyEb6sLT1N8ZuW9dPwnBB1APRAv2GS8HGuCjreThED5D44O8WqpCdPWhujBcbGNe6p6LqZaIsvJTqk2QaCKKEJb6qXwCRE9XgqdGcgmDslu0qVr9Mr7qElXEMUgSiLNCjJhD2Fi03jxTBtOHVeP3lDJ1s4ehlia1e3NGD2ANtVRpKe5AKYwTw4ow1PbxFCQesTJlRjcupUo0LA1YET8nSfxF6/hyQRwGhEHcqp8/r/jX/3Xgb/yN3za++sdx/dv/9or/+r/2IExo9odx9Sz1NrqkPU9OwiyDJYbxalA7B3vFsGKa/J5aRhxCAR4f23wYIrw2NmUYMQQsxcXHzzOOx9Fioc0wt4cHxwRX7Pc1X4XIup0VPZhESbcArw8XiUr/KfW1cssNOPSlSr2csdN8suKPqIZP8GFraBMuS9EBapiM0y0bZCiEir5KsAEMbbIlcApsl28YU7BmgEjJy5SKmO33zV7LZ8XQKv69MQSLIpl8IA2dlMAcoSVVQynnWqbY+ITjrdLED3eIVimbkpYhWGhYv9+3G5VS2121lOoSZhdhTXalx9XCNmkYen5PJFbf25T4073syoJpqgmmG0C6JsSBCGwlDg92so530HA2UeDr+THveMa9m0ONRrgzgnlGNwxILvm1D4vIO9Q9tYHoXnsbqHk+f3iTC1Imws5rTTWPhOgjoIY10WcOwANCFFKAhPahJntvC2j4UoJR7x2H2kzP6w4oorbGIQCXNg4sJQ0d1ps6egiYYe1oe5rGWrVnszy2ny5pvlnkIF6ipydFXi/FQAx+10rjehBQkfZGN3D6R0PrZHJKeCs6XLUjq1nv+/1d4uXpVRjx4UH0IvJDhlmTx/CiN59XKerx2SyG1OGXCj1Io4U6NYyVl/celAJYc7WqHFOFMP7hDSm3JxYqqDxMFPqIfi/vs07Rnoa1/PoadM6qvFlfFEf3IfQNGs9wJHucG1M3TWZEeiXeaF1lUEqUSzJ33yGcctL4B+dLBVO/SM8Fq7umhFoa90eun6bgq8XTBP6jxnVn9Bb+nhJq/Wmv8DrmlRJwy5I02p9fBJ1SIQitBsLDPAyonR11JXMWl/guiDX27UkO87vjWtFxfS9LznHNjscRaaibB01e7fgyVPmxpVUUQQ/ZidLiCQusUshuEEIgQub14XVlndRerEKoRX1xaIo7SNu6wksapUbq+Yzu40fk7BBz3WwK3y7fJD5/nhGO76xNNufGcUmjmlbgIQQRkE7Je710WJaIvt/VPVYFcgww9KEbBkSNDfyf/qeWYfz2+uO7/j//DXsZsPa9GtDsPjrL2bjdBHVln4c4DIhDUlRDUK9FY66JCh6P8i9J8YpRYv80z6VzsOa2W+e7SQ4uGSsgQgBJkhdRgB00xnH/qGXARFEbw2xhFFeM5kESHqAXFU7nKorThFKiebmW6cGa/vEWGYkkNBEtF88bpBrMPgEac18KcOhyE48Rg9QnjwQNwiiC8lDDlbLyrH4WBDpoM7GMHVJ2dddLwVJ6lBIt3KAvMxCkWsUwAJ2ucx8CAKnTnBGBAIwXCfiMxyMAdV+rXSPVVC7mAZgvlf1aIi2qDIxYRClG9bwKiFralpshYNSY0+dnWEUaNkUDhCeKkt5Xj+Xn35h3lXtg4SlsuqQM6TKLJ7PvO4xjRD+fzTNEpZpGyuk1Ylki3oViY2/qzLsO5eswWoOmGIFdcgpESni99mKgDE7QarUiIv/L+mAK/jTRm5ukClFwRFYKEFw5QMj6SJ8WCuSqVHXk/Sq9hc/K2pYCPJ8i+l5b3dMlmxJ6r9DR1eCME9vklITOUkURzfAk6BakM6rMrSaH+rwNPg4uXPb1tZM9Pn9X564WC6t6bXO/pGJIPX99uQIlVDoudS8PO1VstTrQqMrtCgd8bcPXIHNL+1ahI2DwepE45gAg5lmNdwIPaqAh2hgJHgAdQtrVnjpeWS8F/fPnqrAMUrM/BaAPK2Z6giaJaWcYTw+CDAKyWL8JrLV0Jd+h+3u6jZZvY2fo6Qm3MNaEbd2Kb76hZ0I2rwOsa/c0dehyRhcK+iTywOsrXDYLP6airhsUg/RuWBaVF1ulLVS0HpqzgHlGGWo4Ywi1BnxUoOL10mEYIiJyQ3whuLMCiFf1xKpsUfQV33yQZ+HpCUHnDHTYDak5R2A1s3nGsNdOv+UmBnNK6BTYKAXC+50SRX4AOMPCx4bBGb6lAENvydXR4XFFgQmrsuPHFoIBFmZIz+Ih+inhEj+u4GvwH/mHoeY515hGNF4JILgNBposcU44MTa0Cc6HxX2TmYtQClbCqhRYeABX19CvzeLyItIOwDrOTVNtxgKgVrMorOmqz3OVRZY0Is+1zbyhdgBQiMTIJs/auGE3ifvfAX2V6akLdJ12WkkELaMCqpYVtLuvMwLuYmRLsZbtntkQ8XzVSpaG+GhIRsc9aAgm2mNjWMWFO0ibaGlJLveMQS1qrUtGdKDDisvlt+j9P9ZL96sbBM0tiDV0RmmEpSgNVgYqcjO3tOaNUS9XTOmztqqwQ5U1lM1qICsc52StMa5BDYlb2gEJNQ1dD/ASejPmx3DD7/y8Ks839HZDUgXYkDg9G2vq0al14m1lTt2QtN41RdL3U1mysrk8Pwq739DLuT2fxSDAbE1i2DfC3umAAj7KeJn+MSaJt9w2MLHt8DygFDSpcvRQhGgyJQQXOqEJ0F5HZxMq8mUDbeeMPhS8OyYcDp0JrTXUUMxOX8CiBiHUChcPDxIS4MFbwLESZXwe/KSnlUKMTau43Nu4eq/nMAb98KB7dxa+txsWMz4E0NzVsJd8wzRVj+eaeis1V0rdc8uRgIPqVahT4TZaP4ui04csCmqooZtUtMjza/U3VQbzrPvXG31RuaVhcDqJ6LVSjxddeC1neJeX5tEo70ovRZRoBXFe1wdJBD6MSMOIvkiyaNe1zZFC6BqvnxdL3I/Dw1qtQs04trwsXQMafL2GyjBG22jVjbvLN0RIKW6QBodYja6c0aWkBl9nMfk2qCJyyPdwgIZ9UTFcFvdKKgYb4o2o+WUh1MIcYpxGYIgGmL3bq1JGJqNMYDfJMxjiRF5aCjCqgTmKpQVgMM+qjK2rVfZ46aB9pS4AEr0wTSi5RhNwvwjU2P05NzmO9o6zhPVGSPlKLmUIChQlDQkigq4v9x1hfSWdWxHew+0pBcilR0GP5HBfQPh6CAoYAjVvKo3WfDBikepcZ6ffpXRP8tOuRcJJMwB2w+ZsbPWtzJ4NXR14qvJnnoGJIaVKSzRSxcB366z/0sCx9YQLpZ4LgvKLnjqUKtDe0CNdjOFmZTqR0mZzv//6SQg+mU9KuuGU+vqyAocSaXYyQ3NlvoIgRGfliBXbWX3cCGmexYSXXbpJvfucgePRQud5WWwjFIkLa2WCQ2tgMGSIVTEojE2JVwMmJZfpDLQdQVKSuMqllrDzjTlso1UgzKov79QgGklgQPUy7PdgrgHjC0OIViPZLjJArILQqhIdIW7k5nuEJ6fJ4oF304qYrxjHUWK2azkBJT6Jxe6Ks9hCVfCJogDAmt4LqpH1nvNLdRFCEMNpEsXlfI74H/6HFX/hL8jof3v9cVwrvv4aAJEkNezmue3Sacxbobnb8NCguI07US/ndbbflf+Lgu+QEaRU0UDAhBGRexMQWGtDlQ8DQogWx/zhg36uKO3pWZJax5Ss00l3PCKk3hqfkKmOztCgsiNHL9awB72oqDAEyWrbawAwO6kWRUrkHAsfWbVs4vMzsNt1OOwm+UU1yZJ2BhqYu32QhWR4QwgOFOGVsyBjDkle0VWEZ6PgB00OKwU2OfIeshLG9lbviUdQZSCMG7W9oWF/OlnN9/X4zgqphADZjxCsFJzRSCniJcwZUXOD+MygMqPGK23I2CFCrlS/5mRc6vdCLfHr9TnTsKg9nc/oDwecTp3Fxj899ejySdB4BZU43VI6I+fHgwhSAmdR6ViEryx6d3kVEyslEcb1QRUdfhWAynpFEAnUca7DKMbS8zNwOKCEXtHBWhWGSce0X5+eULssD4MJf8AZDrowBlKpK24pndCRk2M3PODzZ4JcwPs9eUBbZ9rnp0UtY7sByWUPqODv99YpmucghOoVTAetgU+vMLtVe6ZDuaQKVD5+JTJ0SA2vquEcTsnXP7KrKRXsyxwFEdZam74UL733RMsXXYOIxcqDbj1fRT31nz/L7++mYgq+JYLqHjCMgzrZy4t2Ww2lMlZlVtR9bO2Gribzcn2ChPP6i8bx7ab5BpcZQXsaMJ3BUHjdU4blrCGiy5e6hwBY3cYaXSohXi5Mlo9SxjPXBnk5Q/r16H5eTi06XUp11n34IDyJJSt55nb0lE5J832A3S4ihtJWeVN6XzV5nzRFMt95sNIp757PGpjEsCJ+T88BE3N9F17A5QkMtbSmPYN7xLPoDAOWCT4eNUZfrzCMbgGLVeyS9wCHgztzJVtvD6SEZahVE3/o+kkKvjOGpHwlJah+sCuvUnLuLBKhhJ3xPd6XMzCXiGnaWTJtXwoWTQaF1uatoWvBEkoThAmz/bQp08q4oi6uJccAJlAq6iBKwLu9HFZDqFTI9jJAQ8BMk3AHLLg9pCArJRqvMkQwZzwebya0acXxsjUErL6uhSKhGlSkPRoVfVjsWXGWg8nqPdf0ACBiVMuO9bfPZ2A3lJr0Mwy4hZ0UegDdg6p8K5LExCffUIub+TiJm5e8iUkxmGfEaUJKnTKyiA8fgH/lXwH+vX8P+C//y59Cab+9fuz6T/4Tcfl/lyIOh3d6+CutkNf0ZW6QPGOkU2oN9P3eyiw+HhSRO4sm/VpGUwQANAmKPt7cEIxQazAT1IsoEn6nZVWptACt+7JPCU9PolRdy4jxOJh2wEYlXecU8KEmLPJyMgyAU4x0riFJGUH5e1UOA6oHwsr2DaN4oi6vGFPC8ShK8+scsWMinTLzr55cIloJYF7CmOSsnE5AjBEjGxLlLCGPxZXc1MGPozz6tXusZ75I3H1QnsWqGz1u6KdaDcyHRuSsISsJrWeFSN/lxQSFNclRInp5EWXk6YleSeExVD6q8eQWfJ7Rp4RbjiglSggLgFvWNddW7TdVeEzhcJfQaKx5UrqpxhvpdWRNePJqZyh9+FBldU30m82gEoWwNvYjUFMKrEdDHKR6zvUK5KhI9+VLs081J62i/WzSZKj7SRXtvXifrQmYljNk4iBtFKBWTGHoPUvgspkdeXMMq+WWjbrHFjqnG2TeXNJrKdgH4Oc/r2vO0s8Hyq5SzDPUk7F8/izlkT98wHXuTN692081DyUl7J0+SrAvHarsIxna4pNZhFAr8bxxSTnRaIo2y1IKeIWqYISANMh/v/nUNUCtgWRdVT7JM9lkk/KX+Ryko5hqMirVgo8fHfilMrgU4c0xdkhpVyMN9GI1MjtErjxmCG9UKysFN/SV7ApqBSF9bwegTwEpdRZt0M1XLa0rxHV66RBjh90k+ow1peQ4nJ5DXfrwsFbjtRQ87qW05zwDX04dcu7NE2r0qxVTHvMZQALmwYpw7PeikEtCcydFBkpGOowaSjUYHTDsr+81B4D7q2edYagk68ulFgewiISwqcnPuHqtcBMCO6drKXE9M2w+xZKlMUg+Bz1rtn+8vAcrJWtWOuYbYkrIuYJKACrzLMX2aVVFn7lV4Vh7kySCNUyy1hyWWMRn/2PXPZf9kYvo7RL6OtjLRVAJpQ4TsGE1OuJZvlx4gEQo8DNDnMJiAocVMEqRd1Cxj2Gt9fCJhudsdMAfb8DZO85niemzGPxQzT8Xc7vSVUNGri4hr9xzrsYo/CWtMrmXzc88C5q4lE7d1WtjTfJ7bKvOLH++xP7PBZ1na9V+nWv7egeG1HU6n3G9qgvY0DZSQ7AlLQX1P14KaWtrjqvRqrK02mYL7TEt+Gp6wV/5K/9Hqey31/ddf/kvC6Nh89brVX48/TVt3vUAkrRXdA1EuCBWxsX4Rf0yBanJ3SQx/rdSy6T1aW2aqm3PHErBde4sZDAEQTalXn97KLr5ipS0/XruGmYYQu3Y2JUF11kQV86dZNjojRuGQF7U0DXc3/SwcPwrOjtE5D3zrG3vfSLL6STlJ04nO/P2ftTE3ctFhYp6DO0dbtAhyHe/fJHvS3fu0J5T//x5rgI2JVtOHwPOB9Oj6ddgnpUfc3FCsLECaBrj8StdvqErS42ld4tmfEFpyRud38crqdRSqV5DbLRe4436IBOySr9L6bCmHis662D6vQSp6/gW7fLrVAz8ObsbPLVE/uiQmKRn8bSl4JWhw6Q5DQlzuqEk4JZFQp86UTRo3FGpodHGITT83Y3PQlR0vRZEU+QEGVQjIcp6zzNaFNevMWWjulm4TznrOaBXISUtd1vng5w17Gbx9prQeO7qOFU5tg/dmvqpeRquDb5QF6QU28/X16ZZbqUlJXUaf2SFOaORv55mSQ+ebeyGxeLHOS6eOzMOHbAroTEMYZb/XHPENVdPuTf0eFHEXx27tHPnaLtJcuZe6nw4JsvZ8AqR8nTvSbBnOBAD5zO6shg5vLxsvHn8TynVw+l4L+0Iyx/guFn1TBeANG58xBOAGiQcfjdf0SeJ/PDJymuIuM4CNFKk2Rl0i8y9v+XOjAYvA7jpPNdvXnwIZF/YFqS11DalNbnZ+nyjN/1Pn1YDp7qymDGyTjsLv2qI9Aeubl3f6lNbr1/+3u/hV3/rb2njg9btwgXlYKz1c1jvJkjU7+VFFvt4rNYS4IT//CrIUqjJPoyl5MEn+kWUYJt1jBCscYOFLJTKcJESTjeZz26n43eCedHGXH4vmkYs6nbexsCGoGPlqfRcTWEduhBzhrWjH9NSEdUQ8KoNcHZDRcunSZ9NwcbKD6ooLUNtZuKX3gv8caxL1GGtfkaX8U8EfzdpvLGG+vAiAzUk7vJqSUXTpCFcjO1H/a5VSvrt9Y90rQwy1v1a0tjQKsPReCZuYTQeNAyO1hjXMQx3WhcRBTvLSpsLYhMuQC9RSkD//I3crBrWd8+dNZi5aiOod0fRmFZ1IRN5sbKYnz/DNBCGhWi5OCJF8fIi71G0moKGAM8wAPGs2vHTkylGgDPOveTdGEJUOoj0MHzH0NSczZU6Dvosn/A0DDj1760xmAc4/Jk0fjG/GE9CCFa3npVpeF79PVS6GRNPObwbagjkqk1bvM60nbshtcwtUGb5eulsPVOClaxb1avR1N3k4PSZrHC2gyD4dCVzirFUHrZVmL99rk2PpgnYlftymb4xEveloVU2KQqjta+/TY9WYpMhpIB6Rzwd6IOJVLI7cUrOtc6QI/0ux9PQKOWfrjVlJ3uHcPy+XrZpjw59dEPS0oYwo8bLHCLPX84yLyur7GtQ+svtl5Tqk19jbBtBWpNIva5Pv4PzWQ3tWA0pZxd4O7HJcTN58flb+QNljtKMLwFoCaYbY8qeMV+xDqOFMhjPA5oQNJ4V75X29LfNLWBJzdNrrHJZv2d8z1tXEF1hq2dx3+50Dx0Yy2K7Ajgtzejhu5WIr7+WX3/3d4V+Fo1EYE6Njc1QOVjkw/Wq+Q7zd/Wzpyf8gz+ScGA6UZZFk7iJRIea8AwofZ1O0myp9CZr/NoRuLF4RvJxzscnQnJPAQvVokxwenylw9MXYJ6xfvjK8KcQgMdB8gOkTCsrdskaXlNb6txXayJt0hu139e9B1yo2x/9kfz+u3/C+EGjr9GCDAHX6Z15trgeQJ3HHTgDWML2koQ/xHyta8PwCzJDT1yofPVP/slf4le/+tX9w/X6aQi+Qz02fxZLPghC0FjCW9hGrRH+6Xarh5HWrT3fKRtGE/zQoTg5O3TCWx4e6eGttGopyNeqGDSIUQjNY4ieG5rnDiqFLgV5g0J4xr1Bjzg2Q043J8U64+Us3opuszaKMswzbO70AnAtOe9xWJEzzJqdZxUsmizYWPPt0jfz9FMHhNlwPykQvfVPpYCGyH/3360AftCW/O31g9eKX/9aCYbeIaUP6lrDgDbWWxGR2w1v0lAjrBxnJVmYcs+YWO6/0oynZfuP3rw5roKAbBAyMs1bdhAuJR+Vo1tFcrbjl9btbyyVU9o97do59udzA0QwQZyXR+I8WjPPzhvCDXAoIccOiODzujAT/VJya++UhxBqns9mOlVJL2V7WzNGce9u9sftHfMnQnBfSMkq1vgk5bu1ddcKQWJpGNnSvoHC+vfbH0kIl0tDM03ZwoaR3ysXvMfzaO91ul4bkrq77hBRfQc7spoirsp/ZfaOhrc06hajT3JGido358jzX3d+eN1u9/QXwuZ9bizNHDcbaPTq3h0d2E+5Y2fAr/tQG3fRkPHDpkzn15sCFt7T42WOfkavhK2NJwS3Pp6GOqzWayIrOt2W2FwaVLvxZLs93opr6ga23p4GOXY3fu6/W6Zm2Zvz5Z63LJvv8OJ52Fz+OdQzbL6bfWaYy+2m7yGMrcTBeHj7nA91c2um6RYoJV3by6tFaNiY/CJ4d8SWvufZEpD5HX7M2P9uvlYa0vd7VmlXKbVEr3vQdm+/h9237Gyr5LoPyQ/sY79PIdg55SO8A8QvRcOvTQhs3r/98haQ0t+/j5/568cR/F/+Er/6u38XKMUQ+Oh4nE863XbTvGnyUMxXQzu4wG+5o4ypKPF6q5xl1/x60Dpn23IieN35Rdp7v3R1bN6K5hiHAadrrX0a87XpDMWYf645ERi3r2KFqXVrVUBKsUS3pjOls85PL50RprRkn7EeHm1P7dl+Y++0b+B01Xi0qKVJHQJEJHdJo5WyixHYnX5jCKeNa5rwZa4Z9IwlfXkRRvD0hBoWtTFI1sMjvv663tNn9Y8qErsUcV399b8O/IW/8Fsk//+f66//9RX/8r+sVaxclYdYWu/Wlu5Or9EEluRM3GrcweHgC7Djy6nmuKTkvFE5Yz08MjewvrMUae7kkfzLF0lcDaONvc/aYEYRdeZHemHI82WMU2lsObyzeEx/JhqkHZDvKp1TaFnFAkVCvpQHdJ2WG3PVIBoBpIedZ8HyYzSe2SewbhWbw6jrxZqDbFY0S+lOOihSUg/ZVpgPA15zX9/3FlRGBh8CrqF67biOTTiLZ170qinyJ+Xoaiw+kTCpsPJ2TpA9Xzebfy/FVb7RsX259M0eU5b5hEW7NA5zOb6vRgc28+FECUzoIBkbTQfE46GtgGLeLEX1uNykg1Jkuzi+lCqfL0U9yjlbczZiRGO4mbeD4VDm8XAhnCjFBndaH9qzOKztfOi2B8zYotP4cNDzeDphfXpfz6JDb2/DQyNbPZ/Yyt0myT5I0mEItWqKoZie0HVj6Pk5n0WeHHY1pLbHrSbQhmBJoEYf/HGAgvfK+L03BZ1/dMKfe8FmS+RHt8N7e7S/pRQnq314HWBrTu+ZzevB0fT2LCohfMk78UJ2L+0iu/BCnj0CvuZp4zqpvvF6qdWtYr4272UsPpfD76MV00jCl2/To68+K172EBpvLodm/FzXgGWPS2n39jp3lV4dT/G197nu5NELYhvN4XQGRmFs9WlJrpX5s8DBVu/mspg3F7Akb/PobEuP64JZiAsqSyFP4/57meS333IXhqGipcrQv1zEq8viNuRHLOW+hN5wuY8fdexa7cyHO1mVqWGBV6q+zOKx+vix5mUBwC//5J/8Y0Dwufqbi4zNFskdXC8U+B/vfmEsbVcWscYdGkym5C3/1T3evq9eAf7dEAzdoaZVtz8VHJObU864M7W2FiCFyd26hIos+efRw8A4UTKVFZ0hJ2Yhp3T3jLu135qC82zuUlsnXT+PwEgL9TfiyPyauBfTcOMh7/vW+DIGtoErmiV1h5oC6s/9OeCv/JXfIvn/x64V/+P/uOLP/iuLyf/r3LW07hU5tGgPz02D4POiEFY05Y7M3DO7slS3qZeauY3L5c2NsHYeAg/geVIKocanewQohKrc+zFtURx6M/q+yl/77A3e1RxsL+xtAduv8ReOm2AGz8qyaEwyF8PDqaltRJWSG9fm/DVrj7fRZX7fCyI/pjvGFVq0r0FU9fIIfLOupTSv9c+/+/vm1u1QStE12mpyG7Tfs+i3xurHtuW517nGtjdVgly+V0ptib/tWL9vMp4vMuY/JQGeSLKWs+AftnkokefGm6TKx3YQ/DXnGvN+R876h23UlA/z8Syi6YCpaxPL7c74WkpXY+XJfNzBC0Flii6i0fX3XaroNFZfCNo4CeaBbqoDfU+cG/WIhmml1NAbb+HHtvnbxdXnbiMJGv2DnUs3LzBa4kJvPXKhjc//PsScwKF5bDguRd85Jp+DQW9ewxt1fp0/Nro2HBLpJMbWYMc02fOHoTa849jMA7SR7ZwOf+jRu+NHgIVWvsV2AaejOH2IS8Xf3wKHrZzy1uO1MSrp8W7I9Hv2fxP0YQNYSlf1Hx0MQ544tgbt3/DKBrTQOTP5O0aVJcFFpriFok7ZCrnvv35yDL6Py93QJt5N1xovlBKuWkWnsa73e5yufW0nv/W3MAsZtZSZxQzrSln7ZtbfVej+evyZgZL7vcb6uk3nuAFn/TgNg8m80+TiahXVllJj4qJlbDzXvImDC2tDNTRwPHLuYx25WVSizWorBdci0seq8qAiFoassbYcKUoJ7rtnsWJ3eN1QJmrljnJt/EmMAQuhhn71vVrSl5eKxivKMM+SBLjbAV8dxNL+/Nndc/qubkZK1mWPz/77fx/4F//F3yL5P+X6G39jxR/+YRvv+ulTLV5BenidJc70/bHNSfnmuTcDi65PHrD18Ngkohlda+nT1zKKx+C5jeF8vUgVhCbkTWPdGetKhHMpnSAflwuu+/dGtiGIV6gPS53Q4dDkznjyJZrMpDRAPmuQQSYHK+p/2NXcIKAe+S7L2OihW5aaB8BY0L5oiBnjI713ksq7tpTncu/3Os5Pn+RFLh9hv9dneo2Ni65KQFcqwuj54OFQ4+ARAq6l5iCZ0uY8FUgJtzBaDKoXih6I3Apn8jTzjrjYe6BdN6/neDDBo/ohVNSLuSLffSfdjg19zrmJSQZqjDfRdF/+8668q6KILKN6uejz1y+VwbLWPJvAaRMsnwuy3yvf0vh9Xv38YptA5JokME3qsdpeDiH23ZS9cei9DG9dRquQajm+TDTvsTWkN41lgZwcumap4nQ4OO8d1yQEkaHcfDIVpceXF0FU6dHhup2WHdZV6RJOXlJAsQqS36dUG2rpttk1hhuagHo9G6dlJ2fz4BRRv0C6L6+Xzqb11YfV1eZM1SM2v5rnnHl316w9XcZbkwNnCZeoxhDzBI7HTfMur3mSmFRZXqcdXl42lWb83rIZWxB0/euvq2JtOg6rjDw94VaieZv2e+eR0Aczd5Bbez5rjtPY6i+6NE1fj9fpfW3guQPip9/IF49HLGnEp0+aN5lf6n6rAHqdozWZA0TJ3u2A+Pyt6FCs1JUzvrtI07nf+bjUaIkNYVMfOx5ljdZpZ6KGXwsB1sxxCbU5KsnYR1IALuld6ey2f9dMwzwmtG6UzwLAiPoebgl56X6vHpCvv5YB/Pzn8nyXu1VK2+2bERv0ClpjWFV6qT973kC99MtF5vp+Epn6y3/hX/hBBP8NG2pzbaC/rVUcAuofaNV5C8tR9W6nB4YWyRblCAHYPhv1+cMgpZZWdLVUZwgNs4gRFke60W9FmCMipHhXHcKjiDwG2yoR7PjYPBBADG1TAwBWUnOnZYvZgILNplKqCr9VvJhksInj9rFASsQpaekomqF8WErmGZDlTbaQ3tARJCM16+50fSFAKuo+MdmtFVH9EGBZ7dzSnIE4DC06ebkgpoQQehwOwB/+IX57/cTrT/0p+bcUYVopxSZhqBQAQULhmpJw+oVh2BxhInEOISGZWdiIfrCoh9yamOjPMMSq5G4aGZluY020YnMQ/VkDNKFJFTBfV/8OcbzIIJOr/2u8Z4Makob5C6ckIUc/jDD+EADJM965hLEtIMiShjYJ5Rv2mZ/c94yBfDC4Y259DXSAVqcaLu/CHfJt13F7rFM0/VxTqsqMIbluch4NZzOaBgnWF1kTK3R3U2RRAUO3ir6jNCRi7moz3pRg2OTKG0i+1wmHK3vvEqP0LDTouhu2lHOtCcr9fqjPzq0Q8eE7AFpllhcNaBfO5ffAr5P3nG3lFQApJz0MJnsICHn6voMk9SF3a5PdQqcqH2yNk4TRBh1f33c1JtrFs7OcY4P485ka2pqSyFnOlddW9IcAIMOU+zX1Uu5zI3NMAfIErcadPwuNwh2ql4vhXEyUDEM0PrGmvtac32yADxXm+jd15al5Mj7YTxBo48Pb7bmDsKmYrytq2VDHe1kOs+HnTvnZgpnGZ5XfL0s0/UDooQKE3D4rOsLNGQaULH83vYJj0vFT3PO9dr6bPQjmYbFpb5mQ7l0aRjvivXqseN8dz1FeR7nC+ce02Ufucaoe5mY/OJa3YsU0xMo6EsN5QcIKX3KZcsyTqV8TCV8KVu6y2Tx9+HDcNUm95n1yvLd5+A9cP67gQ5BfHnoKbcYbdeeXOzioL9r9LEckLuw8S6tfbaFetO06+TArxARdPItd5+RLQTefEUvBdXqHy6XD/vBekqBOXzACGAfpAvbpk8hYU1hKkU5tSaoGSIULbb4FKUm0CxlIwpyiNtYg0UbdfFb9YYhwVOUV8ww8VXQS0Jj1+YroykXFwwH9/r1YY5cLxnFnBnopMMYY59fq+SDz2PfW3TClzpQMVi4in2Hn0gVahSi3TSZiuRmakrN8Ps9VCTTLniX/aBIrMx3DTa18MThYLWW/F2/F6QTcxh36/a4egL//9wEA4y9+cRcD99vrh6/+9C2u+/eq4GfEMuP9BEMJWCbzq+MNOJ+AdLAKE12nlQYAIAiTumZp/NLlGzqsGIZawvIRJ/Bgr6kHFGhIw4PQhPosY86GLF2HGu85Divi1782BEteW4unkwHzX5Z/W8ojyrnqGR69t7wPReUiRNm1RiXnTamYlGxeVOaJnuCzQ9ZTApQRG58MoRFUzVUKCohARgDR4lcji2ZfIKhhkKZi/fk7YJ7xeJjqxB1KtKIzxanT91u/AFRE/VYi5hyxG3AfIhHU+HFeR6JHHhzjmjOm2wvDaVI0kVJLGdlNGw0JIlZs7F05N+uNEMw4I+Id93tQqHOsDBNktS3GvFJwp9QZyNZ1MG/Ga+5tjETdkzb7s5h4XAEUqYs+TDi9jIhxRBqcYqA0xH4pEStiyYg+A5f8PKXagX0YaknH0wkdgF1KWNMO33zqMY41nr8UzTmZZ5R9b+tXZafz+M4zkEYDfUNQpE5lbFcWq0hyOL43lPLhQdci7KQkMkaEBGuCxEo9zFt7nG7VuFRvqnkTtBsSUdDzs1YVKReMKWE49jWeWQ9LnxV5psGgivmMEZcTmvnI1ZkHkV5qAMCsE9rvJT9CUfh3h0H4DDR8gegrHzxIA7qXF2BVcOLxoPur20jP2zioB+Rcq9d5J0ZXFnz3HJFzxPEoxo3HDUy5UtjvZvnW2mGXMvp4xHXucDqbM1LqmpcMDFMj91gRqWlIF1a8P2Tc0Fv/sGGICIdHoR1F2g8HKXNET9scdl71apTtXbhW3jBNuN0k39A6G6sX9+UFeJhct9RSzPv4zSfxPkqOwWB5TmzcFMsN8XJGDAGv4cF4TYfVwJ/akbU2IVwQpYGa746uikx3PCLpuThr40LzLJNhqJeS+00jlCh7CALUMsriu+cOIUQ8Ho8S2n1+QZ8S+iFVPkW5pgKIttFvvq65EeRjPIM469g/frRmjQDw/knP8ZwF8ERnHpf1+K5NBaHA07Xozi8Y93ucz5JD6ktKj4M2TT3/AEjkrh9V8FlpZky1Jm8IsFbltro86M4qsaYMTmJfc7QQEGsXrwuBEMzq9yiEWdcqYLwCYI0NaASUBfu9oJzms1EUG6jow1trU8Ng5J3Nd0KNXR2DIoEOtnQGrQ21p8vT+Z8tBEinyATfEOoar9POUE2+I4YVj4/iXuzK8iYyb0iHDtxb8tWqTq3l6C5jQNQuHFRMBhX1i3F+QRwG3GJv9cnHURI62blNbu/Qaz0uNvSIYcV/8V+s+Pf/fQDfF2P7z/y14q/9NQDpSwVpiCYqI+wB9A8TYuwayNuBngCC7Z8pEGg7zlpfIYfqlVKT5xu0iA/Xy6PBS+lEsaMGyRuJoOYbxgCgENnpzKD0hrPv6GhdnTk2RT4CX86D5914QIv2qo3TtJNHKwyX0iGkHoHn+FJsLQFIJ1nUvFnqg4I4OsQ7BIAA5OFwjzwCQAjWq2IY+ibh1yNEROb7QdZqDRHdEFAuLn7W74fzKtwlSGZ5b4wVCQZcro2Oi10c9de6/1QQOQ8irmgLESCIVWFhPkocUrM6IU3qpUmjIfecvyCWEmLoUXhTGN6IP7cwxkupcy0FyxJNdpnypzfzayF0Dap3JxhIWyGYomC1BZXGDoduC8QakjvPWoSCY9Rz+PoqXZGjyhjf10u8z64crT485qspCnwHjSLLX1MltKloxj1zsd4uPByBiKD+bp4BDU8tlGMebfUaZQhWRY2hdSR7nmXm6ZEurIOzI7BSp2pevaDK+x3CrvKW+TYydwW9GJc21L+n1DrVeDFUOMaHO8PeAAblIylps8ltXp9mVVKmpiTGqUyvB4YevulmoGx1j7D35ow09WaY0zi0B7vxR11Qjxn4cYcA8aLqXmIYsKe9XeoZ7rBiHLtqRNGNoA80Nk5LudRw45wBpB4xyV6yc7cBC6QP1NKlfZKzsBTJ+4qe+Bn0njOGaTRHQeUtYTNBlRFYQG9gxIpp6qpXU78Xo/CDW5HuxWEa7xP4PR8NtbHpFrWfJue90gXyRnOgZeD4UQiSixkPBzvWfViAyyyAQ4qIHz+abEfOmKa+8l5vMW9k8A9dP0nBn2dgzBdbBGMEpRLQLXf4/HUTmiVZ3UHiL8kADPB+0iQOmqu6emzf6y3eUjoA2sK+FHSnL9IWmbHdudcFEyLdYQZOimhpzDD3cAw3IAA39O1ElSFbDHESpi7hN2r9z8qsWbYnBInDLVUx4B4IstphHB/Q7x2HYx1zXUer35oz8FkQ88/lHUrppPMl667mjEPMIsgA3IaHJlOeMWf1pFdBvhsk3m0ND+olgK2H32cinXGAZW9vWy73I2Ssnz4B04Tx6WeqEM3iSt5HfPpU+WzfA/3Tk3lEhPHM+Iv/r4C/+O8ldD9uhP4zea0XjdfG1MQMA8pcWDf+cGg7q4YAFNQmLGoIvr5ojWstv3HRuMl1dbHjkPhZxlg3IS0Dq0rJfeP0CABYVHleNCR/0K6dRKFFuMn5jedTUzEkpV7QrF/9EbqUMB6PWPdSbYbha+MxNYLn9KIdr3c1pALTZGccqJ67bcWbxY2tD6t1Kowp1fye8SZgA5VBVB19lyTWMk4T4jThVnYa6y8CkT0uKB+/nCOmKVbEkzwjJVxf6hxTioKQh4CLAkgmcJ0H8lZ6XK/igUwJFlt1CyPStENHxfPiYjopnVTpHEdBwH2ZPj+2UmBCl7pvA+SQPp2XyFD5IrkgnhRD6KS1vQaudsOAknZWBWYYUNe7FMQQcDjIvl3ziCEA8fLaeCaaiwggx6brVUot3fu4LzXGe6ONrwy54hpAlUU24OGlMkqqlmnd7bJgvJyq20nlxLWIh6dJ9AYajxsAPDyIcdeXbN4M6w3RXaqGV4qc9WnCeDhgKRHzHLGbhIbNeHl677EkRM3FW1NEF4SJk5fDDc3bOA8P8vl3z1VmliEqjTr03dEM9bLzWfPffvUr8QxME9JO6I05LMJbOvRETJ2BsN+LAiiViQS9BAiSR4xa3/1yqoqW1w2BiFH3PxzqZ11Z0F/OdcJa8Q6fxd2w+91a/N8ryqwotCCacmfPZGw+UWslQdp/LFPJSAfvMcsaQkxlN4SavNrNV+ymmusmXnDYBs2WXlB7AiT1ZljFQBduhhBwSzsEqJcrowE9SX8kii9Z9utwiEihVqW6ZdFxUIdi+tK7o2xEPjudwukiFlocVivvHI7vhG70rIQAjPtk5TxjvtbuO2moCelOybJj//lZ5FbYYUwLOl8AX2l0HHe4XkX8cWgPDx0OvTPq1WtBcCJiAVI0Jw312u75O1tDegx4WfgcFXwzMqJ6PmJtxvfpM5Az5iCRHC8vEbtdxFd7uXc31cImfA8rMDFv6ceuH1XwOzaUHMQEvuUOKYjSuYaIMj3gfJJxsHeFof0pWe1foMajTVNNoDDLSTeuWqgrQvBEvhqqxo0jCsbNXlKPGJbGYPCVb9jOGDlLC3eo8u4ahxyPcqsPYRkH+5aMgXAAIO94aRNMKIApzw1dcPdwroJmtVYpmQQVBWm8EZGG3lCFMtdMefmuugzVxRRo/QGmpEkllFgRw5QQA9CLl7Zm+7vSVzHI3HepCLV4MxZAD4kpo/IZUKsvcg3EJR0QGc6la7CmHv/Zf7biL/2l36L4vA6HFf/VfwUAV8ct5bISdqlHR+3IuxXdeZs1QYhMebdzcbTzjJ0z3oRGq/JUQwpauqzhA7XJkhfCVBzpldrqAiblqLRqzomVa50mO6tklLfcAWlnepnxYw9DKtpRoys6pCS/L0vbvIfXUjpFROUqRc7tmno5R8oQvOd2eupr7G3OSJN8RlSIOgRBMCJ5HD9R4BVdzc0pEj5BY4SsZUGPMEkYChGdNNXmfykBOGvYBMu7TQ+1hCn5ql/znNEPC/qpKrKGHIcRCcwVEm4XiuNfDjljnDbb1+dcvQ2DxjbfodpOOHNohpDycmh5DAEzy68OoqBYTL48sD7XyQS+eJo2FUtcjLSXGYYM6sWSzIeHwTSTNUQUbby42+m4yUO52SFI+3hFeogc+xflDAtntKaAVOwcOr+uAMYagmnnlnKDBhoVCLdufQo4HN7gp3q+Ylix37elM3lRARv3e8xzZ8abyV/nDQSAOKnHyBkJqh2aNhTzTRRU89rBaA+plx/48rsZkyZV0rPHRnlcH1YcsXyPUoCkniGld8Ywy55FNKZhzogJRhMeoSVNrugszAeo9B15Bkps1q7B1oIkyZKWPKBmNF+KRS2U0Iv8prf/fMa0f7A0p6wASQgKMqSE0wsbpo0IpeVxK8TTx8vo0IcYAJWXhRoaSM2GukcpXUWbA1Hz7o52AA1L4fNpYKQEGruldNhxjmh5RM5y9voPH+r9GhYc863Ny1DZNTLBWwc7svyo30znVd1WHEwJQJJ3GHhbCrLu7ah0/7OfdU1ooddhaNw2Ce8ZTdTKig6YhXccHnTMl9zkr9FDNAwwfkFvHnPspGiNGrDpDR77xvWTFXxfcUKEyxl5ktrYtGDeHdVCexYkenFxg0ywopLfnb7UWtxcjJSQ54pOkemZ5eeZeZIGJutaFV2pbKCKriqi15fWC7MlvjLsDH1gpYE11IogpQivbdKnFZFGKZbkw5ACX2aq0c9c+FIpAByDFbdl1eh9Bz1WrFHQXyvuKIONGwbNRQCAaVcrHDhFiEhOczl0LSpSa89Mul5a/mD98BXmGXh+ljCoxyD1XF+0m1xKMMuzrajRoXt+trXg4vyH/+8F/+F/cEE3jfhnPVznw4cVv/51VfoaAiLdloKcdsg5Yll2OAzX2jkwJTtvrpCEeIHgqgTo/nfQBPFhdPuEtqNe2MQMU5PX+GzW5T+7rpDD0OYe2jR88D1gZ57ChcgXIGMehlosigZjY+w7TU1cpn0DWvM1xpRdYhORtK3nU9hCb2h4DCsyOjMeRocUSxvxaHyC76POF2M1DiRu09WTZwK7xjbParz3uFmHXkDO+m5I1io+MXGxOCBjqCzt8ZAsrtnHjlv8qiub1EHzGbTCjShZGXPuTWgxfhaQpD/2tDgeATw/o9vvkYbRQhYjFkSnXDTNwJwCbmC604pq9Z5ZhPsSbciHAxDzXImKi+xhVUdjDBMQHgiL2/bs8K3reoWioR36ULv9GrLuPTyqgNt7XMW4LiXJOXN0Tn2nqS5HwtvvG6WJBt/5BExTL4h3asMy79ZCf49OAaGCsaqi0JUFsYinZE2Vds1otnacu1a518EzxEjq/tdiFhEL+pCFbx0OEtM/A2N2GtBm0bmu+z1qv4Gc0Q0DhiGqvBL0k5EENDpMlnPOGsbGsK753DqwDlwflYPGqBSgA4h8c+36JgXNBqt7WdLOR4DYUAxQOJ8lxvuh1opvSol742wg3+qwSwAuF3QpYVBkns02xxHor8+qS9QKaJYL6UCeUtRLUooZ0VaXnnyDVwgWSPHV4VrDMFBLzkbmYKSk5obkCDS0SPlO5k9vGmrkBtBhGHoLfyHokdWQO587TFPEGIp4ay7AY8ptlUbVT8biiNczYPIFrsUbBpxXokkno+5zVjkyqh56iLq5J6c7KtJPFh6xyBgZ3fGLX4iBp87bvlee/3yyQ87OvEDVs4cBovwrv+C1m9owQzG4f1xf+nEbYJVOfpdLrV1K4dJPNxwOzkXBEallErFYtro/zNZSOAQs0wNeX9W9VFxokf5HvAFCNJbIpfczsYkl0sjrGZ+GnMV9/hDw5aRIvlfw1V3sGQHmGV0pEsAzjbhepQFQSjvsBj2YLLUUJI5U85QAqJHjJAgJaJ4r+sBGDjElQdO2prAisq83ER6Pj9XypCuc9zA5SsYfkZ5+pnOHeClyxjrtJJyHrv9t/BaFx1DdPolhP2RGzmuhYJBZwAjBEEn/JRMm89x6PqipQZHgEvGf/qfAX/7L+Gf6+s//c/dLKUY7IeyQBlizJcALHsjZmB7Yb81sZpKhIGtoN26DXm9JEIAp94JCO8R7ntGnFWHfWSzi8dhWDMkZNTEXAZL7EVGGnZ1PK0FZFk2eDehTW787pcr8GubsXWSQewZFhuPlBThnTJO4gZc0WohAR59dcqUpL+LRGIbOGHZKYvRctQnJMIigHT58VcNnFOFn4t3Hj/Kvr0q0lM7ADQ9CWSUsKrzkxIqG73a1JKNtYs7oIOEca4jonp5km+DkNTddf4zVTQ8t8kxlRZXyaYpCX+czpqf3dWmpMIaAm4YgEVhgKI8l6A6D1miuykzn+LySlCS3poIVUi+7mzXMZqiIKXLGw0M0XEXAm7FWwtlaU/rw6yz5GQmoAIcqxi8aFkU2ROWlV0GOywWH4xHrKorFBTscIOjlBRKaRvcDG0HZeFRhZAKv7LHyOEh4w+OeLgWn6KpSxnKr1INJQ4eD7pl667p5loISIQBpj9v02DS+QahNhFDQKlI8UKnmYdEQKgUIqUenccCPQY3iuYC5Pz6PDagoOiDKY0a08LKYr0I3WiCcitb1qt65IMbqMGxySZjDQ2KBhOMxjEf2a20Zlv6/FKCgR8lVx5xneu3reWMp3pSFTqxB3SyTes098sUbltfKc/SHRVoIMvJcWBM9p3RGMi9V0Na9xPxrugoiVjVOdFE1hLHDisMDLD8pBACr0Mug0yHKH/YPppcFf45Czb/aAjdcU9+FtS5kAZF3MVp2tRSv0my8vEjBBVrrT0/Gu21P5xljyAja8GwLsLM61DjIGtDQwFBLAduNKtgIcr/OO0OyDT03xF7uoX5ndOaai9GAYN4IF3MXXoAUsGCHMEQBo8lXSaNDrR6428Hi623fc5Yw8lGbvYZVzpJDlIj+D4Mab/kVlrieEs6fndLPPeH9moT+Y9f34Bibq5QW+eDLcsY4rAzXrrvHP5SiqFOdtyEFusuvr7UdfCnCMCpqBON07NZ2ucCkWZdvtT61v7x1qhLCSku5sXsm0ViiShxEP1lj37QMDe60WEcstW3z+VzzCorEnd9u1YI1t7FLKPHMGYD9jTHSDw/Vw2zx1280wMhZjGifmI5SMM8VCfXCv1kvdTdxeWyf+AeFZT04lBIaJYF73CwqlQlSsoNiO6wGGv3Fvwh8/fUKaYLFn3/arzrXtRT8u/9uu35UNtkBzyuExjQAMQZfK8jRzVeMw4rdtEp4mW84oorYLXd37d2bK9QGLTwS1ohKlU0f69nNVxHq+VorYHHv6XnIZN7CtUj+zUvmGd3lteEX5vWDE+xJQieuc2fNjSIWQQJPJ+B0suNK96+NiWOmAqjvZXWL2w3Glyj8hkHO4ssLarMe7oM2KomXF0HSeM2zJp/XV5vs1DO4pl7DglplUUqikkq6il4o78xZjGNfb9mOtDvbnJ7EeGqTlC3sWEpFKecZXb7VMC0+RPePCdDcA9OiUGvge+SJv1BJF8WzfsnzqaY1PYTXcQ3I32z87r2cPD2exlJ1bkwstcRAvYXIqA1EBcwwwHgm99HGqu+aZxfKqIu8QLpSGl3rWTHFpsKY9UoScsexRCzo8s1+t54x3DMyg9PJ6v83h0XHRpnarAVlqQvBZLNJTvPLSdF+vofomVumlETJ9qWm/VG3h10udkY5Fmv25HQD24RBKoPZ+DjnywXdfK1ghRHS/eWKIJlyFGjlJImn9yLa9sUJTYrXZi6o+0njjrTpARIzoP0Z4yboC8kH7R2lNlla4dAMXdTu8lo9XqpM8fcYq37B9/FZfm8A1HEonXBI7NVj+pG7qRTZr8sFje5jC+XW7fUiRvTrq5MVpag3o66XVyuMPlXf4ufUlfw8iMhT7+M+ns+u+VNwfQxI74ubmzuDfH8I7pzpeHE6VTblN1g9nqRRKvg5S5QL9vtaWEXn1PRwcEbireLcbeVCwIwvT0rN/XeM9u3rJze6WtJoCz9Nam34RlCKFBOp51gc+ITbTS0VV0t448W0Mo6vl07QanU90W29LOqS2jZk4cP2e7zmmkDL9s22SjqoRWMqLXGISI/G7tJgZJm6UlwMsq764mwo/nlkbUE9pGxKsyw69+Dep1YkPRLbkpfmMWHSnLfguLl0M2o+wjb8tss33NBrXKkQmrmVFXk7XXurPe331ifk0i3LcKhl0dKI528r81QU+Xis8eIAajJgqg0c+EMEgUrDf/vfAn/zb8qt//F//E93yM5f+kuy6H/mzwD/1r+F2gROF4eNWK76p6en1r1ooTMOpbSSdkQqPn4049gEpF5Gs8FpsSpYVg09YUUkKrJsCGU3Hw41KXHLdPZ7vM7RbNmPH5XmFAElz2zamSuy1AjKECyJ1hKPijQx8Ymeu3RrwpWuYVfrXgeix0vV3HhQyDAdxz8tO2PA+z2wm7+rdD7srKFMn1YzHvqwtFqVMpEvl+rdsLOh8aXsU/M4yCbf0Fdmz7Ndao4QzzifxYt8d78nEqTjOB6xoDYYejddq5AeBizDruXrbH6TM67TO2ta0xhYXrnS/18xGu/pOk2CdvvJhj8GUsyzFQqgh4P81fZf0UI2/VoWfa5O1NcoJ//z+rPxwFKb3mxwnSpc9bIcFl2id+kFOJ+xfvyZeKXQJq+TRLsijZN8kzaeNVs71Tx9sjDp0uhdz+11kITwMTgrhGu/3xtvNuWkFAtB82tg6KbKP3oKStHzHCUP7rvLaHM5HCBNijgJnsEixTKOxxqnzqG9vooBSQ+iIfsMT1HjuZG7jvB80QvzDFADV9703bxrZRubvzma/FIEKX5/uFVgioShyfhehOqyNSFvp2tvxS2oaHEoBNqa8ZPY1IDgkKhy8F/fNIv6wOYY2Xd75hIqP71pUYN+fmno4HTt26Zwnv8q07hC6spvPT0ArEEi//Ru73rfOK9+zsp7fQKrJj3zTxa9oA/z/Yj8kPzaNI0KlVc0ciFL6dCclb9zTZKUI2aDLdLDLYzW2K1Pq4WWc895Fnh+TH4SjncXDQU741iNPxgf04ksiM3canVIoSnSPcfCZ5VSmyoarfo94u9DDfXy/OtP/slf/jE0ukpJFiI55qEvFORMhBnHsjF4qpWiZ5j3eWXvx66tTImhNtdpGME0mTLTdQAGp0kCFupTu7yVqhAdDsjo/bkSV4vGlLLRDSvURLdJAK1EN+gQLP6L8els2MPn5Qx5vlr2FNT01DVNbEgdXiFRRSKQccaqLJcibuJQ2qpHdoD0l3Xd7AWZirMw/Xfc9O4ORtnM38ruhdDEo/F+TxcA8O/8O3oQ51f8uT+34l/714B/+mLzV/ydvwP8/i+qADEERml41cQanxQkdA+35rGub84SDuHRog2SSy9WUPQ/a+OXsS/tPpJpLbXpEKBJqEy4VBrhWR49M+I49ns7Wjm7kAlFv+lZaBrH6LmOjjkiJSt7aAvhaDkERQWDOyPTBMw1SdDWzzdA0WesTFD3Bkrc2VKkBOC5Ws5BkwAZsWTLzMXdaJH+TJjioi7e5rzod0VxhYObo/13u7VbpK4UVN+9/sRpgoUAeAQuBANechZDspQOo3v3Wu3HmtDolQgdACuGME/If05l0hRd93c+6nbTGFjuk6OnNFT0l3+rgrYVeim1dFtK5XNMQmUMr5c9PiyslGo08sFdvmnlF51XzuhSAiuiMDfCgxd3Bqx+kOc6xmlyCJ9b10Ym8mFKjK8XMQw/fHCJ824Ont5FPmbz2pGnG+3pHL14sXc6jyHHze9FN7/tv0DrJRA5ITdafgYnqIo3DdhGx9paYvqn24306NZFJ1T8bZ4fuQWllxOpKq8hRAsdNFl9OqM7HDDPXaPgN8/eehNS68Xy9ABnEMVNfAWHx8f2DMv4nuXgF8nfQkCVBW94OEphNaXQfJ5SNHWiGbAuTAcpk5pzV5+v76Zyb/hpXUwxzK/13I+D0HgaxtaJtTE4auXCzp53XXopLfuEhv+kfZ07y5l6bMV4nn9ViCie7/rBbAyM4L4itNm9uQcyGNlQeqiGoasyxX2Vid8h1JLhOGcziICInmvp8qUoL0gnxg9/5PpRBP8Xv/gl/rf/7VfWuMlisIYBr7lv8mS7y2uLBG5Rjo3V7+bP9dUEQ6dwaNJnUxLSM8xS2piUwwHffI610RUq4yulgo+ege73QDx/aaxwr/D6MoVewI5DtejG/NKgh8vhnQnPGNuxdKcvVq6QTXd88t/rK2rWdnao5H6P06uUW7KmVbrrSxIrvfv0jazZIImLTAAGakJkTVJ5vedE9HC4+DugliW8QhuIsFKS7hMtVD7KX03CJ9HJEJo6Z0wq43DGIJ6H//l/Bv7Vf/WfLgX/r//1FX/2D78RhqKl7QwhuVysNTfRu9NtbFvDu4tt2t8/KbriynwB7fmyc6Ncef34Mzw/b8K/nED06A1lvUdMc65g+MePG2OUyFwYLWzs5z+vqFgHpYOU8IqKIh8OepY4jsPB0BGADY0g41NvmxmKinrRk2iIul4+mZhr0yAxW205OC+Jc42xOgaFl4WzbBQKa8bl3CeeH24vGkD0TFgLdR3wK3bmXUwJeL+/1kmEGmvb7CN5Ma0s3nw8WkgL0AqLh4eKstszhgHffOrMID+MN+DXv64G2NNXJgu819O8tiwVWiQG+cVFMnkPi1d0KNDuYq7d4m0BFlsvWhuaF5BzLTe8pLHZA0Px5hnr/qE5E6ZwefST9BdiI4N36VYRzhBwm4QHW+8T3dTTbbRiDO+fHD8MAa/YiadibJsh+X36ovUpfud3hC8TNbZ5+EOxWXN6cb/MIx081mXdX6W4HBoNS/m+qlREI+kForwEgGuoxnBKQPz8jZz54Z189/Slbp566r1+2pTy/fU/EP729N6iQ6wikfKlLyehB1/FjWOkjDXa3hgwd8xBH3R66SzywGJ79AX0LtkYSvV+bg0WylPmDn7+rN7B8zeVpp7eW6POxlOgn1t+BZQmXSYwPUj0OuHpqeE5r6+6/19/Xees83gtIzEZeSctGp0rc2j8RYCGHonDQ+WB19Lj17+WITwOV1m3ywXL7/6e8baUYGFe9GqlJORLAzZeXnBNDzUfwteu90aWr+zAtfQGleYfvJbR6Iq5kDwr1Hk4dc+Tpkm9J/qLVdsab0aIRPytMRlgOUL8/s2x5p893UxmU55RfI7DKqXIOYDDAbcSa/6qXr/8gz/4QQT/DTHzIxeFBFpm3Cw4v5NZaaISv5S2WmvMOhpwoz1koW0gYO7GLbN3cU0Ite0ycm6QGa/Ldh2aMpMe9ohhbQ6mH5MfC0qp4/You7vFI4hE7JsYOxcPxrUxBIGXm5sf0vYXr2Rs9RV+rWly5RmcXyS9kcJ8HGFooPKSGlSrnJvrEsPaKOr+ulNqnEJEBm3jTgk9bvh//t++wX/z3/xfPTa/jv1/+V9W/Nk/9V2TEc9rKRJ/aX/W/WC7au6f7WEpLVmEtuqHVz580hMA2/e36MT2pCyN/PPJ3TQeeRZKESR8HcaGVkliRLu98cHrLWXXS8cGTHRfZhysn39zcDZnkuMmmfPMeSi9Wd/s8gz02VSE3mpB3yxoSjVO2/ND1GRfxt37WGZ+x4ZNPpFzY4PbWmwG0cSxK5/xaBjvsZK4S1Xueda3RuR2/W433PGkiKUJh9ks+x3v4jvJH/22UWYQ7WoMJ7dOmOc3P9r+sVlLlQ8cjskc/YX/9crH3Tw29B3CpiQn0Hz21mVyYSPvGD/Pv3t69dXaQlCPms972PATL2A7rNUjUYp583LWkrFY6s82r83N1TeXMtpVeWP7/8bV7JE+z/bkbpFdzoWLevM6BdCe41vupIR3aj3Y/rGW50JhxYXzAot/1wn7Jk2Ny0034XarlfNYMWrLlzgO0oig1G6N36BXe2aIjaXJOV+v+pnjcXd0qgPgeaYH1j53sj+l6uFtGI3ykK2+ZvQ2z5YkuhTJ36BRaufaKQN8TnMu3C9+T/1ZZEWyW+5EkfYL7PfFHRiva3LP+fyGf4dawYuP9faerQufP8+tzHHr3OSJ6XPvojz8tT2zEL7X5Lu5fK+3Bfb3Xz85Bn8dtILBxm0ADQ9oBkrC8YG/KlS6y2vtJqUW8O3mYjaVMhh/fLtp6+zkrEreq1UdyPh8daYxyVh9lR5j2qHGhzEmy+KIObZhU2IO0tQiBEUYuTlbauAmEb1xDYeuc2dg/DAAO7wKOv/01NY75nM0x0E6H7oseK5xCLVzg2beW1yqIoFvpB/YRUTmelUkIX+5E2CN65hzzdngh9fDzwyZpwHDHysXqEmX9IyUosLg/NIoCrYHAE63UTwfp98AhwP+v//7zobyZ/7M/7UQ/f/1f13xolP9f/zLS9OcZ9V6xzT4S3Hozek7GMyWkjV5Yp35mF1ikCIxtWqTPM+QEqJW84zb4T3+4T8EvvpKG2f4WpSenjWn5btzX6tfEV5j7L3mh9yGB1yvghJNE/A43eqENJzPDH1eHu13RroP4bO4S3qWgvaCcCjhMuwa1LUxZkhsGtoAoI3bPL9UKNAy2VDHS4QxpRbNC8udp48XX83H7spLawm9wdSbM6ZM3WI06YkYaudY/oTQdgIeB/E2LMPOkudiVF5DiA4wZO7bz/Usvd9fBd0jf92/xzxr9Rd92YJovfpScnG5PJwfPti6ADVH4XSSNWe1Lb9uFkNOK1AR7fXwWOnFC/BS5Dul4Pb0MyutaueCHpc36Alo8ZUQ5GyQV/LapZt5mLzCI42u0CK6+z2uuVaQoYITLy/1bLqzcJ07kU/Pz/XZen5/87Xsx8+OsiZE9A3FPn8BDgdDTVnhKQTnZdB9Iv3FS8tnfSwvU3Ue02tFbD1g5nmzKtbXLOGD/bN4Ia2MnPIQH3u9VWxM2SSa7vbXes+8YcsNg5O7QONpSanmiD0e2ljSxgPHB+/3gobm1zYJ0FXwodxt6MrrChu6NF3BJ+6r94jIPYGaePpO3nF4J88vVe6v065pWmXzmmqe3jwL6ex2wKFvEWjTfahhu7VlWs8YblhC7wvi2BL4ngs864cD0J++NcXlWuTe98cF3qX4Xa6VcvZ7Re795RjXsn80Z6LX01mVCNC9dPlS3+WH6iXqakWs/vyd6EqsllZKm20darXGb7+V+392lHUjap4zsAsuQiUly0t5f6z6S8xalpqNQPX+RO+202GZY8U/SankStsWgaHAC3UBTplrw/5NKd2L6j/4gz+OGHy1fFLSrn/+FPofS1nn05NZMR3Wysgd42C1uO3lrbcfMlg8AqV0IHKiQOJlA5oqFoyzsw25kKH1rXJbSsW6ifzofxvUm5/7gQfXxGoQob6iMz5OBWC3r9BVh9WqaZi2N8+Iw4B11dJTG2PKGmi5PcglIqUOXZYawinFFq3iu+CaqkD1C1+8nExyC6/x77rXJlDnGV1KSCk2VZG85RtcnKpHGtk4xw/S4vKV3v7wD+vHf/7Pr/irf5U3/J9V2Zf5/Uf/EfB//9OrCWRDUXX9uEZsbmZ9JoCWzkpB38v6UQ/i2lg4TK7VQt7KxfAXhY2dW3soWo6f0j3a4pCmqH/jLcuinjHSqs43kLaxeRYvR9OknWUB1qlvYhnv7nXeoztww88DVb82JA1rRUg262M0ujk8XNM1RG1spGMrMAPbHxEAdXOBNkwGqOicdzujDT2xS890cF6QQGQxsVxxh3GakOeaFC9DDw1S1aVNM6btmrnL5yiEFJuGMcaDNp4/QyhLaXiQH7etsdfo3P8ZOiV9QpxGzsUt5Y4Vy1c2jQXfuLjkKQExS6zxoKX5bN39w3UCa2VbzZ4OQzR6sfv9pN9aAB9SoFdTbljlY0OCOiYvhquMDKbAt3+vzzOjMUm5WDYmg5+z18z9pS8MSQfps4kdSrsVH/7cm+GsQFs1/O+X507uT1ODzg5DNSS8Z8Fvosj70Nxra+nlPTZKOtoQPB2VWz8057bjsdkwIv+K7X5YtTEyJr18yBov6gZc5mFQGckvlQKoN2dBREjRShjfLWRKlhvI91Ra6s1IiSkgJVe+M0kREswup8+NmzzBQGdPBByD6jZ8r+890Kea02IX+SM20RDeviJv9tXiNvPmMAkCcQBScahr6QHt2lQ9z7kb3bPf4mMEFbhf3rPEW9fQ15yJujW12aELr/ZzAO6LcX3f9eMI/i9/ib/9t39l1Q52O9SaoxozRxTvmh5wu9UYPdL/5QK8OyzAH/2RoJGHg1U9GMtrjWlzQtuynLWqzoqu1p/WBT69Rkv+HAbXQGSesR4ecTop+nQ+4zY94nqth8fQSMawaQ3TNcT6Hq7i4VD/7pBA0kMTs6YfnNaHRtEiyk10jXFoHnX59lkO6FfTS0UfpwnXVOPfmKxy15zoIujGNT0IksR2yu6ANMw7tE0e+JzDwd0bJCaUS2Sf083JNfLSchhwWh+sosIwbKoPeWUmBFsL6lgcqlWPGGq1kWlSetFD9uXU4Xj8P6eC//nz2jQ8Jsp4eJDjdnrpjEkdDkD3+dv65f3+voEOr5yFZo9HfHseBV34+musP/8d6zMSQkV8GG98dWDKblerWpDu5hn4nQ9C99ewaxThu3v0uaYgBWeUEv31KKXOZ+u+vxVNJGYcpHogyGtirFO/q5ylcb9MlrcxOtc3KyhQD2H1mi+nzuiSZ4B8uykNqXO1RVU+NM/iWTysXyoKm5J44hTRCUHW64beYmoPB/UYMKB0GMwrc4iu8s3hgG+ee7O7xnBr46pd6A0BgWV6wK9+Vc8R723mDti+sboQlRcBP1wMdykWp8+rKX+qa0KE0ngv+Qvjyh1f3V4mFLdoAHlKKYKOaxWQrQPE8711/2BD2yqY/nc+3ie59p/+oXygXhqLiaVHkRuaEr5c+rZKkP/xaL+7jGbVYGFVpMtFY+BdHDA+f5Znsbuz45X0MhiazEsPyc31dDG64di0Khqvcf7SVHtrtFDydf/3EAw+ZN4QL06dsprxxsuilVX4LJ27nflhwOuls/kQwefyec8vSWQYqkfCeIrfXE9//PvxiF/9vQ4PD7W6Cg89FfZSXL7csNRnuQIcrEZnngIPdHKOeuhuuauVcLYKJ+/TzaS3n1/xxi/1pNutKrUkU8t50k24hl1TIOx9+iKfPz2ZR4JnnseOkRGnZadJ9vL89+lLXfAkOXavr+JROB5rhIQRmz9sRM6HWoiF43/cL1XfK0XizPlSzTPk4FitzPICdD+/nGs/jia/B6iVCf3ah2CVFXepnsGUYJUE6XWj8QugRnlcxAPLHEwO1wwoz3h0g75ceo+94Xhs2URXFnw5xyY/ZTfomOk5dIkkPBs8V0yp+rN/9h8Vwcc9gBtD/SBnybJGztWlQAs4AEiySLcS0X/8aELSnpk3TFE3NyVNUCvyN0NwHLdmXNZdZ7uctZZzb4cu501FkNTXZwJGlGaJO8Z2K9K8pdexb4x0Uf43aMyuTqUaho4IDNwcolQhmmccDmyS4izEYUBxXsRb7hDSaIiLraMOiO/sD4c3BdCKDgWx6e62mwTRNxe1m/vS5l3auxZEaVXu94VaUtXB23nrgaMw7rCi5NYFurgsdoRa8cISbOZgz3o8DPjyZcUf/IHk+7XXPynFv7WP/41/A/jv/3vX1pqEMsq8OB9a5svyBlKsqLSsQweEsaIdjsanSV1TaknE6Gh8g16w8oQp5FpZYw2xBSRCaJQ+MuecAaTexsCOpZL539XmcnrTMPRCo2rlCd8Id8RUingXfMKi71bJOcktrRs8poSu695EvIB7hdCqM4Dx3Z3F0DbKvTvHq28ao5cB/mf3iyoD0thptHmxjbzFtlMYqvCzUnmhehHW1Ntj5fzAGL4BHaXUihY6ca/cWyM6t8ZA1S3owWFIiSjpqAwrVASSy2GdMH1DGf1OU2EjhBqWot8x487tQbM5/G4IVXHTULZxWMyQ4Gs5D2u2hfsCDn4rQ6ihW9MUjR0BqNKXf9A5lAJpUuQQ6sYxoAqiGUbkmT60SmXXUjq8aklDC6np3XPYxJHQPMfhkru9HhmH0CqYG/oMAe0BKEU6c9o5TXWfqMjC7SP/xuoWIRif4RY7fUYeG6rCzK71/LIvohACEIY20XlDAgpsyDrEISBoEzo/wTfLtvIBzmu2QpR781rq97ax0TyLC8QIaYoG5Ix+SgBpzE/+jT3IGUiTep78QaTS7GQm15IJ6T6RNClgsG0kKesYpRGTjpPor9G1A/RCqJ3mu3yTxoIBYKRDVEWTJWvZ3IxhRgxX4rFeg0ZzcG5vEUSRjslBK13Ns+qBPmRt4/3xz4lhtW7JQK1wc+cRCUOzpzlDGoi6Z3kvDEKoEQOelnRvCZwYbep4JaZf1m2F5AJY6CsvberGdWRYYjdLY9OQYivv4aafSgVYPBibEqBdg/nMbRWm77t+XMFfV3SXV3Td7u6hSxGlsAwRKcXavS5UEzEOA1KK2jNjtMIxscjCLNMDChySrT8xuAPjlfCGu0SLT7L1KnWRxqFgxYgZYxOOWwq0fnbEOD0Kmv/5cwN9UejdrhXJJLENQ1vxQp7dYVm0moHGvMZSgEGVWc8sUHl3zsDjIL/0YTbmZFULXttQC1+j36ozOOV60Y70pxxxOET0rEjCBwQ5rBa/Fhbgj34t7cGfvuJEK3PcgO9UOgU57dD3o9gayiwWZQTjuOm6S+R5v8evf03eIeiN5RfMM+J+L6E8GUAIOD3Ls8bn35iFf4MgWVMQ5emP/kj349MnAMDt+FUriP8xXvPsEARFAiwLP40SM3u5iME1TfjmkzA7bULaGDOGlqAmzi2LoCYhAE9PWspN6Xt3HESo7h8RIGvx8lKT78SArXRjkTgKLbDD8X5PoSJD6c4vxpzjNOF2q6U8j8eILl8cYiVxl9HDQqWgy9IX4+UWEQuFelfNLj2/gAjU1xc5gkTZeI1jfSzgvEE6oXEcDRHBpmaAJFjVGuksrcsjQ8QKUN3Fw4e6J9tqL/SgWet2lhADrOpBeBKUnL08Dt2LCMwi1a5m9IAaM7skyvoNI3KO5k2wiknkdynhu2cRXl8d0QoVFbLTVAUKk4NvYVSQTdaa+qKhYpcLcJDztJtQi+nrM73gu16B3a6Tuet7QyNHo4E/3ojfKqodlfxG4wtGC7/+IxnvV8dJ5vn5M+KHDzhdOuFZacVscfUie3o1zrzC5nGHEGDMs4PQwqQxr9/NO80x0cvoINbSdYM8cAyMw44Gto1jh91O61znmmLz/lCNhlx6/L2/J0v7i1+4MAYAS4kWW/vVk9PQcq79EFTBv1wIiHRtWdxQw/3GJGN8vXSiBIaIfnad8FQTvOUOz5/l13eqc1GWARG7UuRQfviANfV4Pgl9Ho+tQR2DGFf0mFlFlctFDImUrIcN75nnqkwGKppwyv1G6HQhCMAWIl5nMYAjZarz6lvFGM2V6HsgZO0hUjQchGBYe6vNg/pVz3qKzvjrjeBh9IoQERmSUoo9Q/Yi1dh41WmuOQKIGA/JwBIkV5GJ0G/O6A4HpBSNJ6MUYJpwnkfHuyL6UBDzFbsAhEnyGr0c78oi5Rfn3FbU+vABN/RGFtZNFRAPy1x5/8ODeC6o9wGSD2V6iG6ghRrrhDoAadiZMySETjwg+g4zJnXtrGTy5YK+FCDU0ualVG8uLX72GuLSiZ7XVVlZtBu3eiLgVMh5js4jFu3vIXRGD6eXaH0RuDfd4YDTS4dDX5oyga+XWk1qGDSH67m61Trlrd4zalWjwlyrY3grZhjQDQNK6ayy4rheWsPoe64fV/C1T/zojDNoTFsMKx4PgkpKIqhLcnAXzwS9n770myGEURjROLSW4C13tVwXGZoiCiw9yTg+Wvze5eCjcHwFAu+VWEMvbd+dcJcuisIkGN5gij209KYjFiKBZtQWeRYT4FZEdEPlirtdZW5rGu9yG7oQkHPHNgQAGlkvAnfuMLKut67Vy4ssz/uDuo88kpal4+g4jq0uo4O+i2FDLUPmBWeHFfu9ItFhtbCKlFqURMYbrV09HxbcfhiirBzG6tCGmsw2jgBuztNTGl2yKvO6MCkBf/Wvrvjzfx7/2K7f/V3gr/01XSrGd14u6CaAjM/mpad9RWfNibjWFsOtRh0TJWPJSJMYC1++tAKV3fJO1x7Qco0MkTiM/GKw9wxDh8dHdWeWqzBwdwAkxEzKByKosk4kYZ7x8FBdk4ZCK0H05Vrfl5IJiy7fGkCH4Wk+HoZRPfu9nAffUMlXe2rYid/0JC3jLTmeC6Rr6e8vBcgQb8X7Y+0/ACiamlag1FCSOUcT4kRdAeU1w1hRTriEQd3ciAWRy1vQhHxsY1+X0MvS8XdVgnxXWx76xknYuF3kne+PmwVTpY8VOTywKblkEeM0ocOKw6Grm7GZFx/JmvjRWWBdvqEvBauG/vgeIQDd3NGQdh5cezLPvaH6atwwj0PnTh5POhqGna1hCEIXPueoyzcsamxHLMDZVa1yuWJN+kna22RvJVopVFlKVRiUaCTGX8bkewX0SWJ6SwFec4809OjnF4yp4J/758QYjfOrzcu/ZxiAL+eIrtvhkJR/f/xoSlMo9mvNH6ESE4JVHRqG2ETZdB2QHnZtLouiupClM6/JiBswSIgd0iCa8aZOPde8a/a4lsDNWc/JtLPvEB0n29na0u0vqAoDx0y6GQKWpWsf4vaTddTHUatdscTq7AxJPYteD6BSx/MSAiphUJHSogOkx1IqpGAlEstiX5f9qHlnvPgOoKvAaCg4HuUZS+nFYKAhUmruIDdgX0m16hsG9klZZTkz8d4zSZkUAm5hxPWqpXGLSwALWgGmRk9aFaYYAqapNky7zh1C6JuGcSu6JqqBvIdDlIpHfdUNdEzSpCqiM61X9iGkWqBjSzMbh0glGfToQ5UXa4iYzfASOhmGzoCOuN+rcQvb9w7Aw4MYELcSkYZovGscIWfEWbzJkaTJyjfQRjuXSb02EcC0r+5Xv9+qO1gpVu7fT7h+XMGHWKnmwtYNWRARZ3ZtfTTm0veduAI9asz7O419d0HdrJTDKiPpKUozmpwtxna/7wRdcYjPGqJVtwn7h5q9ToLRBaL1+fjYJq90F2GyjEm9qvvUEJCcEaeEEjrjI5Zh7tyltHyZtG2u8RwqkQQN84EchBDamrGnlx63W4fDoUc/VMu3FBmTr5KRUttyfRiiuYCvLgkcX38t7qLDO0OUHydZkP4wICWN33UKiK0dKRBAvLwi0qiiZQGIRaw/adr5s2io/RLEvdjr89b9Q4uYMGlGcwpyhilVNKoMjfTS3FnrHGpKNeG4Kwv+zX+zdpXsLq9WaeX9cYEvv2CxeXitiiPpi30fmDGP+05yAJHaHl0WGC4wvMwJoEXDovqs74ETHoO0aL+qZ2VMooF1+z2Gobd5GqPW+vLf1sIGgr5q/gmKaAKMIY4h4LAL1dpVJmLbrYr3PDCuXudTzmYUCuYEoKSK0BBhLAV4ehJE8Fke/3ioiVym3BM5UiH55YtNRzxJnz7Z2EyBLsUSee3QBYmHBBySs+lOW1JV8Bn/ydD3/vlrdNOEkRJnrvRNFsPH0a3N+OIaxxyNFkzPYsfI2XWeHgbrHeFZEwWRdbKdboghoO+j8g8XpqHVtExolFZRAdTI2XghMQwol+qFA2Sd1xTx+bMqpcceEat2UxykEoXFu8aG1g3IHPpqmCvi1H2QZjjMO9qppMuZ5eci+hSqkeeMNOZBcG1oPxA9LxCPHnkFiw+AyjxWQx1Dquc2Ho/6/rk58xSYRsPqWWQH06DeU3rQqmLbieKhNEZWWYrQGL1jXSc8hdvxs0HesfP8Vek8F/GoPj3JOfnV39Oa4r8QWj7dhHZiViPe9VohIEKKpCFHcNaFIGukza7ml5WCmIIpNAIsBFunNOywoEc41Lr0PnTOd+a9XqOEkJzP6IbBmhmJPixIqHnoz4qEDwOASsO+qs62y/006RzViF9XkaEUuGvqW4O4FCRfsMKdRc/HuTY8as7BXgE0gh0Uour5YJoKabU6qITP01i/3Vp9DUHCXlkcRABP9XpCGNTra4eHBykuQEPCeKAqi3EWr7Gg1JSLuvmpUZXAJFJTpocB6zCaVyVnzSlhPXeWXp3buP9EvU4V3xBqZAQgex3cUOgJAoAeC2LOFprF5dwNNazvmiO++07zR3xJqyxN5cq28IDqI10W3hlS7YD+moVu302ywb4j8eC8LZGHg8obYvNe0ScK4jDgpLlbu0n0jD6I923OveX01OakMifqNeL9rOB2FwL6vmtyPl4vEcvSY0WPbpXv5wzcXlxvEgVD5uK6g/7A9ZOSbH/1d/8uvnvWwezujW0u+rYcYggunmwrKVQq+nAE7llKVQH3iRp0j2+RmKbBiz6f5aRIcPQsWFMQl6TEzU9pk0imN1u7bX6mf2NDKnPx5Gwb6ps7mevLQSBvldpi2U9v8EUsNemCIRyu7Jv3RJAPTZOWFJsmdQfWKwSiuE7TdgjaWw17GknGK6XmhafXWEvH8fQ6NIDuqxBcmIVnnJrEROHEddjlL3Wig5To4rBpcPmEzxDULaYGhbf6S6nuRJ8MKcxPJDlrKdN49Ao6FZ8behvf3XoNg82TJNp8hwbqMFYhSbrw3ykFy/TQ2Ky+5CoZP5UQIo278lKRJh0LQ8zGEbVRhtdM3YHeGjG2zmHTRhxLM59bGBuyMIbmEnO98ciybZawSFrw9OXOy91G6vitmZTfTP0e0Xh+nbbaeHEJ6Ntro3z6c8Ez3yRH0+gPAV8uwnStrKTOQxAuGRrD4w4HzTkhP2KpPHdeXFwUXi+dCALPSx0PjVjahN9puktO9HtpyY6+Vbvuz7rpNumPcXWz6/kclqac5DLsaLNWuidteT4bVpxeJAeCxn5DB5yHotzcxzdlgN9kd++SpG39u+MKn8lvjdZ86cQNKnadO0u69NjHGOr68do2hOKaMwzlEF+r8e2UNABY9o9m5AFVid5pItOXi/C73bDUDkDqEWQpVENfdR53RSBcoYqXl5pgzRw2Fs/gPRZO6tdqe0YccVhBDE2cpbw0Q+Cts6tEYg0aOX6V3VzvzZY229UkGlfEqw2zyS55fb83PuUvGh/ME+Dl9ZwmXA61udNbBUUc6TbRB7e0s2ZHIbjGneSR+tJ1i7rrhK2PDmmOn3OgnKe+3BpaDmvDW7xnbp61YMBDdUMRTAyhLX9t/IJhJE9PDW/jcOiZpb5oHivAPAbbCkXf1z8BgBlp6/Gd8aVhgDX1ZMlNa4ZVbgYujrgarXFtbT/Ja1QW+J5LDe3qmhnv0vNEecBS7kaTb+iO3v6yhGNN1GbzMuqMpvsM1SPGxPDTVRK9tSo6/vl//h81yVb9jxdlNHS1MTQj5xoeQOrLWk1HatN36DwjZbgCXblaai5NraLvFcBweFRe3CGlUeLKc7aOlVzP3mm7lI3vjmuVSAAwCNocU1UapPRcV6vh8HQS6XDMoyoQsW2HPgvz7X7+c2St+8xuafs90PPkW2Z0rMoD4aLpnSlsfa/CxKOu+rKYEpCqIOEVgvOwD/s7guVr3g+5Jk8NgzECEby4U1jZCMUmSwOFSgjqgW3urRCMMYlhAJrisJxEKQipolB2eW0iJdxeKipPmgsfRvME9T0w6n51apETyR+H1UKr2HZ9N1XDL89VN46bfgglC5aIIu3QDbHjyVQmcVMUs5WBndCQU0Llvy45lRyKEMMwGDMwxafAEE+6+nutjkFwfPfUhnqdz1Upl7yLHr3vKQGYUjaGm6A8KQGodZN3QzDlbGMP2PpkXYLu/IJ+v8fp1BmqZ3unhlEAgOK8XRfHtb2iz5c5pd1eroPJBZhLB6B2ATweBcXtBqFTCZkJiFENL+fSt+fzZiJ+uqcdq8CgliDlMWj6gqSEnGV+6yFaHOk2KX91tgjmXEN5PL0PgxgufJHuXXOmOOaUUKBeT785IdyFA3mloUcReir1HVyDrMjcW4htShU4EXYeKx8oBXGaUAL5Tldd7SEA086FgYuySaEciMg6ujSlO9QwByvLyaD1rRFIvpaSPWYpHaJDJo18s1sg0pSR9disHZMMMaiipWvAvgOcSFFvNS95dGo1VUfnMayIsbN8kI8f1Rvj9OEQ0PYa0H0nfxAFIIANCpbu0YCnbr5arX5ojXXf9oHyguQkn0XEWbW0IdZ3+rXi/vg/DbVPg1Wq2V5e09E5GB81a1ST1CFhFDlXL4MhyXBJ304QUhGbZ8rRln84O7ZeWda1OyShWXdOmNwtkeTRYqBT6pvneBKig0U6zV6Mrq79zvKpSAaScNmidJ03rgj2hMrfQoBTHJ3C6oGBeQaSAFz+PHF9hgFWiITnChpmRNEcAsQ7FyT/weS+A+YSCy8ATZNFzo/P4KJ4/YY0F4Li5rN62o5HvKJmFe8A8w6N2oskBNWpAOShis6scjqQTrw+WQq6MldgiO/UZ26LNSzqgSiOzQi9VVq6qYp2PG4MNffalERWlBJtjLYvlwvK1Df5YEZPOaNLusf6IaM0jGf+yPXTGl39nb+Dbz4L43IhmnbdofRvoSr8XV1qZjmOriRlSrhpUmpKijYpN1pCj+dn+drHj3L42Q6dC+ItfKefNqXzfFmsEBTR5Ng2yoQ1LCk3R/ForDObu7PoDKFzyCJLo+WsIQm4tVzBvd9KJLJvgLPK+R5rkqAK8O3wvuHBFirjyutdNdn48eAMshBwDTtbP4tn1sYRfh2JYgL1YNLCfwv5ICrjkRFDALjJvBzt+AQUr1w0++OUWOz3hlYCGmIFNN6LcViNlny5OD/upnTeLJaEHzvHYqUNd9VaX0PEy4sLO9KLY9qltsQrgKZyDGnXxkpp5k17ZZKvcxRE7/NnYYa5utGPRzQl5xgm8P5pbRAgoDZQogeEc+xL26+CdE3kwEqklYLX9Gjv7ntNKFX0dKtUUsFq8nQ8suTi5juG/zm657VFmPi306l2aLdmL35xVbJ9dxJB7WOG++Ka3NEaDgE4HMxj5smvFGeA674wgfqrD2s12Gj4lVgTmt8ocWgIE1r+FDy6qd+9aWLzVteyrrsejdfwuS3Pbkq8baw2JiWbArLh399+7kz3NkVO6eGb/A7nM/Dznysd//rX4kmc3t2Ng/9v6B6bbttumVhlxGSD0qg/e75UMqbJFCPeS6WCS25IHmlLPXuGvOravM5Cm7vy4gyWCV/O0cADT5tcvxBq0jqZ/2mR5lVUDMivADReMsDJEKIzikRjv8frpYImKQHx029EFhy/ar4aGTIGada4qSBpist17rhdMq7hDb7LeYSA07ITQAW1WpgZUaHNpWkQac5nvzcPhaHdXo9wSmUp1ftFL7XJUadAvs6x6fhrXt2tF6IU0x+8unJ33qbJngko0k2+FAK+nGR+dhboRVOQxjzCWPHlJPtF5cyLMpO/GnJ4S7umqpiVAH5+rms0TYZemy7FMJvUM+dfSl4CtcA6UGmJ63ZpywVvZZjF6Z++2P7c9u/MWLQzqbpOKfXM9cHxuy3f1w349lkSWQ/9FesgnrfHw9qG1inDoNeAMvp07et7SKOAjEVBJUsE1r17LaPRHIFqFgWxilallha2s0jAdb9vSg7TA7Jo7iBLa3pw8/vou/G0ADXvlK4QymHfQPAiZW5/+S/9S388ja48CsV9sd/9gPmh/yElO+5eirq6+TedeJgejOh797KYVpTSOd5dk+C2l1fIALypgBpPT+6UeYS+1LbQO5/4614SAbHmSrF8AVNky02QSX3uVZk6D5E1yvAXkWfPcYA6LsfsIr+vXCAc39vcbJkdQSMlhCTbuZROYlhVcIctFeg4/Fl5ax1LqQlWfriVHATJbJR70oESrBkDrt40S/rVagQdRs8N7yYqzy5Bm3eF2JCeWPIVhSLSz6mWUkNcWFOda+ZfJ8/rajUpGoKqJPomOJ6slwX1pLkz4qfjx5JSWwFgS7w2bfdZE0qw8e4ISn5BlxJCcM18UruG9Lr1nn4AFPSuVCWa8Xhwz64ilSFWrUIiAk6E3J2HyC2Yn+5uSK1iiTZ8iDK9V4hGEHOXiBXeOLOqtM9zlBrK6X557azpu/07Q4C2WmeSHyotO1dsszf6U9TAl54BVSHx+0pDgntGHWM3oEHuWQs5BFglEu57l9p3d2VBcEpjnUdo1t93N70zLDbryDNlYQJO+JI/2y36H2+rmZBSgbdqg6lt7DUfzcTV5nI8gmdsXSkckyHecQCy0i+vW2Pb6Pd1UKWIMtOHahDKpYfeG1DDgJyjJsvVTs2lVMX7LkQlJdwuFfTf7YCxuAXTPCULo8F8d048vXXdPZl7fiw0V3lnUuR+6ySTh9Ra22KbdloVL9yfpxCwzEy8rOvxfZcp+l6R0IGSlq2CSmWEco8mzsZYvaPG33je9HmkAz6+8u+uKtC6OD7JWLemXlw8XZe7yzwQNXwMOdccCUDk/rXKDSZV8txswUa/tt6WAjQRE6XNYdEBmy7FK6U2SnYrmLH5WylYltawZMiqr4xk33+DX5dS8/L6oQ0DakKOfKhaM15d44ehRbjnWcJ3S5SCCnDlK7UkpSVF+4Y91OHUszuwgptOsnpi78PJilOk+Z8Q0BJezpoH1NXzE4KxBp+j2OQy+WgWzVuzo6DrEkLf8gxlJsUbCd7T+QPXT250pfsgoRHT1DSoINExodPxoYoie20jSWkjCoIYNU6RRDu4ZjRpsUoDTQvjUi1wizGbpfTf58/VwvcJn6W4rHql/leWSZpfm4Gv6MxYZpL0ls+yEYVfZ9KXIS+ES4jkqWV+OoknwmIEAUNDySvHLBbqDb26+87Wptzec3mpUE0pNVHR13bWNffNHEIA4udvZNCHA5Y04te/FnThcb9Un5f6cs3KDLdKWK7so06vuiZztlhKuq7YKOT11QCO2up+GFzJ0Xr+LewmOc6ua8Lzx7XgsGyP+Ud3KNfjO4v9JtoeQk3kJXrpn8v32L4TxWBOhCZOE8HgczwqQEOMDdCAOn8qKUR6OACrzHAWNICeliaOXtHhvlwbZMnG4WOtPXN1TJAIiUfKGEoyTaix30pnpAUvp32cPteLy75Ltxqn7eEhVbiX6cG8sykp6uNrVqNlH3z2OKxNYu527be6hOVV+IdouTNe1giPydAML8qVzoE2vpzeuYaW0OYA8GI5WD6XeTfkl74BGQ36ppnPRhn3xg5pyVzwrjTvK3a2n4AHGu69m/38UmHSatmaR4/Kq49FjVkM9Gt6aOKQd4OcK5MX+WqoO9clzq+Nl/LVJa0ZSktiUvpjHCuXheAkj4AVc6DbRGmcTfN4/oxfKd1Yg6aD5Basx3dSrpjJw0TwPS17YQ5YaVLb04tD/TX86nzeyADd89cyNsrZu4MzsgytkIvKl9dpSKN3eT8aSkcF12q9P39X6VxzqfxZILrfaJsaimMxyco7fW7YLlyb8Lwrxmas9kz/4xmuPzjey5VqbDkVz3VtMLDKj3iFGmLoi209Tre2zj/5kVZo8k2QuJ5bD4uX/caPlC5ug3hLLHxj44HjXlkUQaPlw/bJg0zGl0gHbtJ+/flRSs6Do/T3Okf7jN+9Q5fVk/76qvxY0XLqUX57jNdwn/w+Orlxyx3683c1jyQlLId3RuskMSYgx9gWRmkiRVLCN59rtajjcXNWODj2DpmvdQ108rfcGYkab8rZioH4dbe9nSbchgfhGXSxqGxu+N78nfyiXkS+x0L6dNArOmlyWQrWD181dNFdXm2hl+kB5zPwGFyMXUr45R/8wQ8i+PcUtbnIRPu03iVCmOXiKR4tX7GN9v8GQSGJli4LKrcNrfXH+6iMWbyet6w23/UK+Pde+gy7f8tcNvPw/NW+5u9/6/IcRymmT6vNuxTUh6rV7dErfqlZR/ffUtAqN37Q/KJb1zfXSyWDWZFQxcTtlw+ZeWuvt1O+own9vZRKT3fGp6LlZNrGePq7bWkQjs0r5G/8TwMloiJCXUXstwKM7/KlBXnZoecE3Jo2VZywoUEv7bdXzoZKNEmJbxCWTcVLcjjm7BdVx9in1er5Nh42b/jo71b2btO7o/n+Zq1DcIqk+9zvZTNmz2TdxYoTfvreBtny7WHAPRGR27vnNU4QvpMMVh8W4/373xpjsweby4SpCz/aCom3ytByjTxP8T/Gw1RJWdFpff/V9Esbas4oxTUm8wu4ue5yXTY8oJmY/niy8+P180qpGhHLIgoKiyBwjMjZZMlb6xlCa3zbF99ArChHtue2MST1fvN+xvb5a4i1WkkkKl1+6LV1bT2S5mRbw6Mdj+cHWn1afnU3pCTvb2jWKSVL6bCUrpGRfNf16sb6xtjsM439N97kABAa176kdKOAp1r16A2R2az3lm94Orfxb/mQ5w/bh+vzvDLv+7QAcsZ8paDtIH9wvHpPoxPg3pO1FSnNKxhOktK9vuD4U3N5+uGloSARC4ZhQw+DVMHZhkL7x1P0W6lm513hGWnEI/fhDWQ4JdiXuUysctfoWZ55eyHglrg5C6l6yGO5oU8rcpY9ZYNDP59GhtRpV687v+h+7prfecMDzsPuBsn5cEmadwfxoDaduXVdmvc4q9tv64runradftRM0elubz7/Bzxm9rUf/YY+C58/A/s9vpt3GOASeBwaFcsNu6HGm88zUIYOo2O06zAa4vTwUDdJ4tyiJaFY86p5Rg9IUhhLXLX6DdiddS4RQwHe7SuyVIoU+OslgglL6QFEC1EZTzKv2/BQhfDlgi5nTNOjJS+GAPTPingTLsrSTXMXtIHNXMOmjkd1CflsCD1Vh13C4UGIb3GIDYn5cHBrCyCksSaXobXKX8ODAzgidr/7uwBqJaNcYEmNW5oKT+/t/7Es+J3pBAwTbnkE0g5lv8PnTzKMX/5Sx6To2+kE4FQ7qjeIjHKXrizVVaVE2nVyDxWbW+7Qu3hSNl0yjwG0bjtQmVsQBIKeAoTBrHEAGAenwKnFzavHikM/WzZ/S+SyTvwvre04Tci5w+fP8thf/OIdYrkZskOEnaUbZT1viHkGSxreksRCHx5qrK7ENxcVuAELery+xlqu1TcQgSC0S+lwLaPxiHj+YolJOB4NEbaYf82UfbmNiDEisbEbk5dDqIS336OU2i3w3YGKu65jidZV2Z5flMmwo2i+IaS+Nm2B1Hr3tdPN86bdliMW7HbRuu3GqPGPn07on55wzbXJyThqZ0BWUHj6qnonqIjm6oFgmbqUIKFfPKSEbbJ03aUsMo9Lknld5w5wMrluRyehM5eLdGZModKcIqZZvSfea7gOI3IYLV50N+mhnGewq+SdRaMHl7/2QQ0ypZvHw17eebkgHhKWxbmNdWK76Qrsk629JcU7+uqKhh3u94ZiAUCvyelMWLP5ABUlPx6lN0qRDse+/GJ9/tKgupge5AyOOwR6g0vRalHF6OuGHv0+2cPo2bX5hYBbHm1epWic7vCA8LFW0poVuX9MmqB/lrn+5pPEge/3SteXE7AEU4qb9WKvFa/A7ve4lupRnCY5u68X9YioRnqdO9xeK01+9UEVIybd5oz+/C36lPBSxJtba5nL/32Sno/yCqFWdGlK2HJipYakeNBm25+gnxKQNL9sfgU+n+xcnfr3Vs56GBQBp9JWpJlQHDR8qOh7TyetuPIgxtO8aYmq/zfvglMKb5Tde+clUTrqk8hPP/+uLMDpXN0f04R/8EfyHe0zaF5x0Q96lKmvHg+dh6/gx3VZ0og5RyR9l1VLUdg2DgOW0uN5fkDfAw8DEOcrHssFmCcbDyC8rU+uIVQGkBK+nDqpAX9+gSUc5oxumhCClqJWg1C2tsNuqp5Xl3pSSxOHgO8uo0RJ9LKGh4fqrWLSsDfyuCcRwGGnNK9r04cbkHrPNiqNKp0sbHj3/AwcDriGHYrKg2X/COwfxUtcgMOg7//6a+B4xOn0gP1e2AkVbAOwqDSrt5mqVeNBUC8Dw46fn2Va71l8gleRRFzqdlLVRvb7Lk+zaNK7Jqz0jDxxhf0D6lELAViK6JPd5RVdvuFwcKWl9QDZGVR9krYPt+E8SxO/YehxZZK/vsDKiv/I9aMKftep8raxOho0amNxl3YdAa0bCshBmqY2ntIrnpwkk6msnrwTctG/y9Gk/VmZcEiuVqgK37Dv7y1JtMYCK2DcGUlbt5gzXDa/tsiblZmK0qFXiaKbJhSNIStFE2mi3ktqSQkJDkVQo8K/16NXZll6a8/vBdqEWa7fNEXrjNjvBwtV2V63Ilnl7CBsxE+rXdepKQOK+t4tSAPUOP4WAa3C6K1Ftj/rO5v1JyOgopU3aJR+n8oAFBUFYGi37bt7n/fGR0UfQuhsPxi3KPHCG4Lgq5VYO+8f5YehjeO3/2wsdYYt9WmtnzvDJ3hUUNeCHWEvFyAdenk/18kppv0QwHhie/+GEJq1180UZKLW5B6GtuJM0EY6HqmQZKmIPiyIYW3qKGMuzfv8cpUCjX9M+no9s/pgdm21dxM1TH2N8eW6hWDNwAwJ0htIB57HeBqwdu1beMXxD0++RKyuV6B/CO2D9b5GWG6UfH69n+5py094mlhdpLQDLgUptfkpa+jb+Fg7nHUNfd3wt97XIHWhemB9+daGnvTfEGr+QCkAUqy1rP0CFndvzrXTpXue5ydb+4hX14odQ1WGoa/J8R5M2PIaoCrj24dvpkc6JJ1wXPzOoMahRzPhaLNRXHV//ByFZ3+PzPEvcXQUUsQdX3qTD4lhYxW+qBC7CIxETZfvckh85FycEW2vUc+S0bm5Mdya20bKPyaTPQOAxPNHt97N3PWH/zWU1hFJcPsSQgXQ7Htuw6hAW1iyXz+3MZ4GrXa9gmJdcEZhCFUGuDND2WKMRyvdBeNj8gJrgFkKGOPtm2LamukaxIjK90pBP0hnVKOZLbBg0DXq526OPvfDDCT9kDmSlDHzuTVGgSrnzGutXxiGqgOxfr9djSCvS78tx+uvO57FZ5j8rmRHENWTYde5h6heQzKxSoIAih5lKcgCsFHnm3wibKIiNvocK1SWUvsU9H3NG6RO8z1Tbq4fR/DXtYn5UXqrwY7uUPia2xz3sgDfPcthTknq+3bPz9YimQvMx1i2+OUCIFlVg5wB7fSsZfEqTTKv5d1hEeWeShQck9Kyal2QJM419NKJ9vgeEYvEVe/3UqFkksH0uKHfSwWFUoD1+A6AvPP1VX6mSVHnsqAvWdpbA0CZqkBKCZdLp9nwPWK5GrpSSm9JQYfrN8A8Y/n5n8Dra8TnzxGPj8C74bVC/IZ+iTdjzDeMEyw2bnMGPK8TRGgBxiLPi4cDisak5gw80iJ9fkZ/OCBrWTA7wCnh61/Ls37ng8ZVa1yatWgN4s0QoE479SlTiZcXxGHAq9ay78NyX12ECJl6IJbUN0nAYuCtknDkiCACeNzL+18vo4QHFADnhk+aMUKljuXvDrvK3DoeSo31T0rLT091HD5WP2MUtP3zZ/FcsKoPolWiMH1ho9B5k50Mha5VeqZer4LqT/rV61XL1WqFgeXDz+Sxs6v7q1n+8wwMAeifvwWenvCb38i4Dwfx/MwX4N0h1BrbIeBwfI9StHNfUsMPra5o49d/a5yy5LTsyotkkg9Si58dClOKKJonxpyoDx+krOXo4/RDMO9XgKKFqHR+mx6NHgTY7WXupxO6YcY4DBgOo9HLUjp8+iR0NQzvtOkeMCr6P6oRtJQeRUNKWLUipoScHqrXchisa/Sy9Mp35jth3+crgCC8RumZ1Q53O01gdG67W+5wOZN3dtXo1gW/XqPW0JbybfMsCZ675ECP81nKxOaWT7DqTJdvgrRCOmhLyLA2cAkwF2SXEtjJm8Lbzpzf/402zZhh7/63TsAFFeJKSVquJ+GvBJxDkDKNgMy1xyYsk0mGHz4I2KDD6FnpjArmUENJeJ6GgbGRKmxOJ2Ce8e5QgC4AOVVmoXSYVLFiVFfa7xCHBU1FBbR8FhClPp5fEAHc8GChAE1tcEU4kRJOLx2WJWKaHoX09RhYHG5K6CDNqOa5xr+THsXD4sJSUsJ1eif8R5MOulLUcxmlIgpWqxDCOXTz1QpGDE/SYf02PTapZO+ObTx60GfbBiaJrT6fgUeFYmOWZnkYpKfI58/8arRGd5JrF817742ZlDrjQ40WzTlvFQldk48fK8+UyIGdxXJ3OSMCtdmlNhXre3HIxSzVgdbU4/wsW/5uf5N9e3rCEnrMkG721IIoBjkG8oo8a7EOHg4/VqewCjsYMX0Y0eUbvlx65ItUQZM1lzNppZMvs5aD7q2XAZclaa8Vq5r3fDJawjBIEy/UnJCcI56e+lq9UA6jRUNQmQ4QGn5VtcTy8nTvT5/lde+0fwdL4XbP35n+Qt7CtTiNX6EH8NUTUSExkKIvcOLKU0lhZAAh4ZZjTZTPWUBUbaJmHbo9/xgGfJlrTsg0yRxuuUfOEbt0Q8wZk1YgY2WcPKOeJ91bzs/0i8/PwoNIeIrYi+omNExPnIVQqYIjRuEi+td+jxiFHq1U9vkMDIe7nNQfun5cwVezkAl/LOXDA5RzTYKaph3muVYpoKwuxcWF7gezBgOqvkNU2WLzlOi7siC5rpQbw9ruNSEQQnX/EVkg0+aOhlAregCwrmNBymF56xchmNFigi4A4yjGjBlfWw7vzNuldFbtIgTYwVlTb66rrkNjJfc98O6dzotFXamAo1aP6Kk8h7VFM+Zcu4+GZHNbFkhZDmeND4N7P2CuwWlyNZ11Phbu79ea49a19QCXIRmOIJZrNRL7YQAmraazAWH8RVRPaoPLF6wPg08CTQkhjE28J+nDkEGlC/bYa6o5eWERAnoXesRwI4bicG1MzjMMJWfprDfEigrwFUHWuGm4oYPssOLhobNOeNVArWXauF8hwNY7hhWZsYZDiwYQAYh6Jh4fW4dACFpP2pVQY91488IoAtUgWDwzrmEY18PHoRPx4GceCX94cB4rChN/jkJtKc967RE3xCTubJaPC4Eu2ooEkWZoXIYgisOWKS6lE++CokAkAylDW5WlxjtUpIshxpoA1yyAY2pLqTXT/d6VAnmvOywp1fPG7/iKUn2pseNArXpk4QJktirAF0RRvri+nj/NMzqsGMcNkuRCJ2ooiNLkpuHeLXfoNTSHHqSIFXEQhYzn3yu93Rst1rfoHpGrda2yZBwqikaeugUZ7WZdL8of7q0h/+RbHz7U+/z+hYqokq+WoXo7kTTE0zE5hjF5A5jd1z04n1JN5uNa+qR1/tsUg3CT5LxYU4HjsF103y3FoYw6iACHBKKi0VaCL9UE4XkGxuMRWWXUOLr8hFL7x+SsYUguDI+0brI01RrsQI1yNZIMQZS66KorbRpP3e2zUwQYstC7JoodeH4qIEil3e93UTr3fW0EGAiG9BrP5dku4hFpvJTBJfemGjpD484uVxCAV4cVu0m8mrWOfF8rW+pczWjWd5AHI9ck1KDrZsg2ZSMn4d5NZbM5K1ysEKwDexWLnQFsu53SjDeySlEgB8bTtt5wlGLgrOkm5+rt48XeNCOV/Le8TTlLRUKEVrC9dTmDpXut+Takvz6sSKkDij6LeEZx9hh/SRVA8CoD37P1UA0OJG3011JfEr3BWorlhjXz1cuf4R+6flzBh3Yfe1Hk4XySmMMcrZJBD0H/umPBOE243aKW22PGU0EYRwU4o8Te6oEmEMLa0aZY0xV6uaAbhqaJlSFJsyhSx2Nf45nVqjJFhIFpHz40NWkbiEnfx4PIw00CM2XRuSH7lPDuOJggYKMICwFQLsk29X3v8hYG6chaFPVk0ucyCHoTIDGm43wGSsI6SIOSlxfg6SliDLeqz07qzmelIhIIkYJ5Bp6e0B2PWBY2fYoYhmhKJwvuXGeXL3G5oJtn9FwjRbWenrh+kAl9+FCVMa2CQybO9exHNZS0wRSgzEgtYWlbnlrmszlcTfDpfo9biXh+Fvr5/V+GCrGFgPRULV8LI5qLWf9IqSKBUepz19CIUpUXOKSuVGLtfv5zERy65jElrMOIz6cehwOkUkAIEje7f8Tra2eIC9EiMtVpEpSeSEjnuoKapO0esa61zCRDWczgu1wQkhjXyxAN9ehCwLqqUB0kd+bdICWMWF0nJQGkgA77vaLU2gm4U0HEev2XywbBSMnanR+PLtn2Us9VzrL+8XzGcnyP11dXnYPxuEUqKF1yDzZfIrNMyYUDliKD3e9xOj1gmrT282W2ainr4fGubjwGyQc59KJUsT9CCLW29jSJt0nNqYpGKiQXi5b9LNFyGHrjS8GQuhCAqJbQl1NV+PZ74Ye7VIAEvGbJU3icBuMXXSgWU1v5fGdoTe1fIYMfR/GE4WwaQW02c5bHPj3BlKWcURs0Qc7U4WEwIX8rEWn/YGj/9eYU7IRasejjRyylU7qJKGUn5R5HoM9yDmPWUEJdPyp3F+XN0wRAy+fFyytiKUiHB5Jzo9sKkKIeD43XflW68xEeXgAa8FQ7a9kX2TH7dNqZZ84DEr6D7RjksI5ako+dK/t+RBpQe6Scz+j2e9svAOhXVTQvr9qcsJfvs1yUeghPJ1k7siYppXq+jz0NwbCq3SR5Sq+XTmiKBoNjmmYkKl2IXFsRUxGaJU8DEA4S8+9tuMsFeH6uhlJtuOeMxCSlIq/XDuu6s3sjVlk71Jwt8r1pUj7kQCEixNOEOvfjpneCB2H4o4vG/Kinpw7DENExIWsYEIcBMWqlp6+/Bp6esO4f7mowkF0wlCsMIq+szvnpBFyyERhL8/qKY3h2Xshp13S+5hxuQTv12vZGREXN06F2ay1Fq7Ewx2Wo4aZRgZnzJdqa43IROjwchN6ofHNTt+UAS7HKQk9PzPuTMbKJKD2KvuldTEXyfUKozaJMbhfsysWe0w+DeuQgC6RyPEJ0rFupTSdzBjDVPWaUxNNThx6o/YFy9dYZw9jvcbr2tQqaVdKLFWRUTxluFSjhkVkhuQ/dPGN9et9UWzKjisbhUPua+KhGyd0SeuYeTtNO8vRc/iKDPJoeHHRpN3TtjB4HXJSy6fPyA9dPUvD7sNSYUTW9xiSEeb0CeDhU/9L5jFHd516BTkkMfEFu1UIJkoxlSAPvCZKQNA5VibKrlFqeaL+v7mcKONQSkh4RptCUA6KKrFfwQ0BCBRFXREsYu930xG+gGEM4PSP25lwIKHN1zYpSN2KuuihSUgR6noEwtRWE9JmXizAJ7y0I8MTZoQw7G0LjYqPUKEU6FLp66TSKu/mKmBJeb6L4W7yjhnp4KWoo+FBLpZn1WupXvUuaz4qDME0mjzGO2pCIEOFrxpqw8ya0IQVVKNpE9Hk8XLudKmsX06ZbOioFu0O1g1KCGDRF6pavKbZ1m3Vy1gCNHED/NUFk8ISMfxw7LIiC/IUVYei2w1D0LDTzoODDq4sfzlm64nLd1LtWXIQIADPA6Tka4Zh3iGZokQZ5tI15cnCAIXVyLjpR/BWd9hUZUoqI1jpQ7u+HVRT+JFUlmtJ7OlgqpcuCtmEJJEZcSlT2CEOPOMnzn57k+9fSY9zrQZgmpUdNzvMxr26xfewtYxtbZh7N47G9coYmIrrF5lzVYl6mWmaNZDempanTTIcigj5HJQo9pSnVvJv04JLanGHZTxBlS/mgpycOn+h9h9VQdUGskm1cHAak1JnN2/e9CZbG6/j01EjF/b4i7evK3AJFJ8kYUBPKJWSvVd4tf8KNUXOv65z18knszClh2GG/qwjeGjQZnAiZnilDVGcada1O7FvFAw7U+R5BKsBD/ZwyZl31vHoZUwoi6Vpdiuu0Q1GvGENTr3OHDO37QZmqm9Z0Ay8FI+uNny6V2JwcZFUo9kFYFoiH0E+A3q75inEYMOseeTB0G95odK//Jy17XSUMNW6+g4RPUtH33hryuHlWOXC0iBkAAHGZSURBVHip4St8XsxXo33yijSgyUvr+96MMwAYp6kqTTlj2j8AJQkgpZOjgvbhA7QqVetJYW+VdUU9WOzqpNdhrM2dbLHcuo6joxVjtsoDz18auYLjEXneGK2UORouwkgHqLE2TS6c0MtTpUcMEd3Txj2mZ4JjNjTY0WuXb+hTwrWT+fOcN3vHDfKeV0cXbMjZX8517nyA6k/50j6iRnBIGBkbSHHoQPVq3sJYm4uVYjl1DGOMUC+uDndFV8PktHyv97r1uneclpVfN71Oeve4SLQaWizMHLe9hHHT4Uh9Mzp5YXvrs6J9JURdn8nxJkCsX2NH/rs/cP24gs8YfDLK/R409funJ1yvHU4vHWLcYZflMPXcrUs2Au6KoldEUpUhFY0nMoVJV9cabZBxeaVDBdw1SdmAMYt2w3jCxtWEutAEgVMChmPbTj5nsfr6MgMQNK5X07LvH3C7MRlUENLacKq0pSKcG8wjcUDtwsdx2J5+/gy2f+yGoVXyU8L5cwN2ACEgojZ/KaVmiwsK0mNnGgRs3fpwQ78LTQdgAPLuacKy7Oo+cI+mCV9OXY3FV8nMOLAvX2TKVt1EGUNK1R3rPR/dBJQiCkv0a6VCyIGuKkCdJkojACK7Dw9ry7C0/jRz10RgXMzCv5VYLXv9iUXadZ/PqihEWRir6OIW/vXSYb4AkU1ovPIPJxTpfVKi61PtsHgYxXXMyi2N/uAMiVvucHpuQKq6J+wbcHj0ziIx7tQt+/ws63Q4KKP6+lkedDjYWeAaE52xagrbMwdV3mON7aQyyO3hv+Zl5Vl21hoVOB/ig1BLgJkA0ThGlILu40fkXGnpnSbs78IV6yQx5Lexw2E34Tp3TXEgq7xiFpSsYUjNsGoZUfjiS7s7HsLlN+N0e6mV+Pm5t7PYdRqjejo3uUydAhy3MApPMX7ojHRlFt00oU8JK3ogRHRkIDyfeWdJ75yq1VsO1eIWQ012yCpdqFbLPWUPkZQknIsG2YoO0KpbPH80IkqRqd1u4gVMabQGSZ6XdfmGOM+IWjWLsj46rZ+J/jGEZo1vJTbeH3bQJlC7PkTbHyqz464qHHSnU1EB2q7s5Bum3HnkzFkaPml4C+6Qf5ns9daD/1C7hJ+ea34CGyPetE/GeBhauYKWjPnejvLQG/3TZGvQlH6FGhJubzoqwgqxT5NWk1O+CJB+L1W51fuX0kkIWL7pfgkwV8GrWBsYXi7oQsBeGzIiVyWQpIyg49CSN4VrqYd6Pb6zIlhihEQJ35hn9H01NCVXokdMsKSXTpXkl2WHsQiWooA3fvZRdBxrrGn7BoTgYiGK5DBoEIPIkq+/BkJA//EjVnSNERrgwIpczMMWoErh11/DXEiHA757liaKFN3LAuBBfrFOzaEaOwYq6PO3lX4q2cUqD/WPS+lMTjLR3eSZ07GA2vvCA4yevy+hNz1Vvivg0u0GvD9A5qndl63rvEPtU1IaA3OglIfll5qnMgwA1UMFKj59EoP1oAROXCFnDSfz+oOeCcl/gnk1Gq/bGJGmHbJGVjRRA3quzidpzv30JD/x9F2tYrff4+uvhUH8iZ8LEDpnDT9OEfRMm8pwVmWQ3dLDzkhtUOAVqDrIOoxWxc4Xg/mh6yc1uvrV3/7brRahO7PuH4y3AMAjvsiAtZzQUrranGCuk2FyLQ9CcROPRSxi2l3bOXRYay3KDYJvTX3YLAaoaKvGGrKMIImViEjMV+OEy7CzBg8p1an7JjS3Eq1fwzRpMtQ8GxP0CkNKqK2LnXuH/PiuMZRrlOMVVgo2n9DnK+H49bL1RGupAm3MLo0Qf4+va0tjwBOmFzR2zxtNnnbTWiUnYMiVb2LWEABgreVpPVsXPF0ENpUCKtr8fSWj7ubDDffuMA03sO/pe9bkko103Cz/6huokcbo3rZSe5zXNDXN0KapXV/uw1Ko+GZLjvX6cYzOoFGC9DGqvJcoF5fcyqAxLECVC+2Zhi7f6hnJVRgjhLa1uTKZ720ipfkzFETTBCn3djoBHz5ISJoKLUPx9UywhGgpKoTYYS5nLD//E5bgF4ImuTk68e3QmeBpKJdfRJ0P0UI9ineXrWdY7z5YEBuPlTVgagLrg+UHNCVRs2uqs/UeUqAmaQhEm7JRIEvBFWPTFp0G7zbaEFCPzfkMPD3hOne2Jncdd92R4FIRsaWAYYMrrwz7oXv+4D0PDU92eRl2cX/0YdckJS0bvqEbxURAC1NKNfSj67RRohlMbg9zDU/imrJpmTX5cgqrJ3crvUeBoRP0NJSSygU9UFfX2ZT0ZfzxjTOorKWumePJKWm4XEo1ZOLSOPjacFPdhO8ruGBzdh5oMyjde7Z0Z2dRw0e83ZIS7pqXeXrozi/tH6gFqvB8LaPR3vun1RR8FjEwPsG1mXbUqauMi8Chvzbny+iO6L9ulj8LbBS2w2uDuJnM0XUyPabUJHKuwZ2hp/zgmmNzhgBYaE7TtBEwD9PppRNeltZ2b7A2RR0AB86Uxdad/NA3h+Ix4z1WwETlmRNBrY6j86ZRPaaqRzS9XYDm/Z5PhODKdaocbErshrZTbt9XRf/ugaGGDTmsRubEUE8qwr5zrt7PZoZWxEXDTJc01lBu6lSqezYN3kINQSaZGYjijGsm5FKPvWYxrrwexrPuG4+WUkMDTc/z8XLbg6XP+v3f/+UPNrr6cQQfrrzchsL8QpcC8Us4bsLM4Uhi1liy8wnWpYw1j2+Qgx5TsAVaU+/DfXUPFXXQhe90YxnblBIqOogK4L7fy/vH47Hp+EYk07ch40Ghq9WIwsW6FlXSSZyHPlSFzynTFvbw+bMx936/B6aqqEo1iAjMVMpl/C8v9RlEGk0o6ibfHXhlBhQe0xQbIpK1natiMoxWhehxugEhuUMXv5dZmDAnA1fNYB1G6wa5S065d8KR4SYryyo6Zhf2ui76+5xj9eSEALZm8wlEM0ZDECjEGT4k++gMCq/JFNf8xhl4KKUiqHxvSghahtKQbv07DycRQjtVoSJUXAK5rSrzZCyGVurzmJsRKlmZ1c5YRBMA7JSbsyW6eQHQKFalJpLyvm5yXpvt5RlLzoDG+huC5ULoumnCy0s0YTECovh8/IjTc6UjNqfZ6RoypNe8FEAzlkZR0cGTDn14TQyS4Nkoju6icUJa8cKRZ4desTsFHK2BO89Avw92jmxv4EIctMrRjBHD0KPbhxpikJwSQQmbEpC9TOmA0JtBTlRM0O/eYjmNj9zqucRF6JyhGrIntewclX3GCDeK/eUVOGVDnMuhKvheEJFO17X2r9iiBRwjeRrd9iYpHQO72FJ0gqY22qwjBvXIdcGVdNXQjnSoCdOAKrs6BgICaaiVY4y+czZku7koeFxgdBwGLEtnHtnHQzK+wtKYpBEuxTD0tSKSe4edUSqWl1d0IeB6E3m4c659/9yqyHVWLcUndXLo4nV1XV03x4Jnq1f+03yXoJw7c5dLbYTF3DSfG+mLPZj3hpoYCYiHcBgaJwU9YwStbJmc8JEk7cpTST6H/UUgXaL/w6Oes1HHuNia8NyNAQAKrHJb8OezM9kUIaGCc453+paEa3R4HKohhJRwu8UaPsq11q+Mesit9065SRI9u3drbkNUfkKekrOwFG/c9Kl6jnxCJlkKl4/fmaY6rz4BN1R6BXQvS2nKZA8DxKs6DLWsqYI9pYiiGwNwnmsyM5XaFRFz2OGmOTPvjmgOxjCMBo6tKzAmhzYQPVUDlvyZe7+b1kpbzrXT6Sb5kN/rVcfku/qqrheRKzo3DEK/wZV+Hup5NYOcvJt/0MWKF9ctPbimd7PQRUG0ZNqXa28V8djYi3lFzaZQ9vrD4hLaf+j6cQT/934Pv/pbf8ssIyqdQBUMJuw/f5aBsLSSIj+Afu4SnqzZjZZsNH+pk+bWOl1RayK1lAtmJJTaPKPWnJc/cZ2ICLNagFcEmRzIJlwU/jbG5JSxEKwJjG/m83hYa2IegHUv4UPd5bXdqO2pgyR9Ne8BgGHAt5/l0DPOz3dPa2Kl9ftANS68O5sGC+PvDTHSCaz7B0OC1mE0w5GyhdsCOJejf7d+yXsmQqiNmoigeYaRsxw6cRnfDFGiQkrEl+e8v3wxBIhMeItG2do45QVoE2pM+IcaQnU3L2/RvPX7RiljcqW3gbc0y7lzHwxZpwXLshh8B2Bn4bWMxrwlkfG1rjkE2Xx3XJsmQq8XRSzolt3A7g0tWXKs87QooTRooirEHg212LDjsam00z1/Z+vzWkbzvJMXh1DP7S1V1+SGpGrOhztD9FAMgzLUacI115KkFAhsg94kJIWKKlOpf4vn8OI2x7kmJ1P+PA7X2nZ9GPDl0nMpAFRgNecaBmVIGb0lKqB+8yyKCIsNGOqFGsrF5zFXjo4Ab8vY2Q41cZFnwp9bxvrb/vvYEv7LB/tYnDfO/Yoav/84XOuzhsES30aIhPWe2e780gj76/SuCV/c5S+GsvH1fVGkloUa+GLuoYtDJk8w+nZ8yjsIjkeXt8Q5qrBnCc+mXDBqmVzmP3bP3xnAYcpSaNfc0+5uWn2sidEd32GosX7nFuoa8F/yBFaS4quMZpWvStjUBiDium/P7edviQwZWk2wq/E4AxbD7VljHxajXeoL66reQufZId14cpLKVRmrVuMjjXenL1WR0z1hWNOXi4CAv/u7KmvJw56eqjzh4GhEMWxIx9icjxCMd/L9EQte51rO2ZB5b7hQsc8uJt7TpR5O8+ahll/0R8vOLxONueiDVMX69a/lcXSuWj+aVPvacAke52/kGR8/2nqbl3qaajES0oUOoOHxpy/NWfdKgXlAJjkTRMmBKuN4decX4HLB+uErO3chqCxLCV8ufQVTNx4x81ZxzQnwamjxl0tfATzqXzz7qn9ZOLeXBVx0XYtmTO7QW3EWlqwtBev+AS8vFWCyppsbXU9KTaNJsnUiimTRnKu3olcMVOSlH/7yD/7gBxH88L2f+EsJn4edP17PadAYXTgu2PUqDNF2/HIxPbd5h5sAy+OFUJ9H5soFsrbkHIjez/96nr+iM+XeBHspVVFOyZ7LQ7AFc/zCAnoAiEaje1vieiFJKUGG4NbJvkt3kBKoNajQtW0ez+/rH7ySS8LbymTetoZoD7OuuaFtieyVINsL/07HrCkMHLAt6IN2Nb1eawJUl28Iwe0farkuDuB2a0CzZvAdVpTStjs3Rq43+L3zLuUVHdYQcZ0lnITPXoPTvnTya4hYU2+t7JvFd0q/rdPlVUpWZteSGrijI5v3WwTGdxAu07NCC9/oXX/I0JdS34dSaklUDs779VEV5zeAbhMoPjGdNNNhRZ/W1gWK6qY14adSZkmjlZP1BqNfOGd3mOJ1Plv57uqGcxa1CVHllLdbS/9AXUJBcoJNhPSQs1vvzSHhnC2G+Y3vNBpVKXYP14ZDzrmiMxx7c4ZQ6+M3+6+feX3M6+A8l24pzWDxnZ23HUJ5dVhr6JAvCcEHenTK//gcAD17LMfqaQKl1AoijrHwEc1545w6p7wq0fBx5uHx52aDMvM9DS1s9pjPf3lpp7wdJ2XePAv/mudKY31am1e+xQ/sZn33NccmpHVbQIKvXlFDQrbP3eRRVp6AdutCgBENZXWDqDv6Iz8FYH+jInI+C1jE3BYLEc25sQObvXfrfL3etQww5TBnRfyxuERFaK7Ohj1SqEDEaMzyYOrXlwvaREXKhA3db0M3bH/nNjwLcI7AEIxnN+tE2lP6Y1x7zmjf65ibeUiU77klr/Pd8oAt33GXofY5SwEHv2ZkgDynG3DL3+v3na8zQ8OFr3zvVYrlp2whYwurlrJbxmpsnebZZJw/S2uIRrsNr97oIKYPQuR0A9LoPTw3d3J+nq3vQ+P49fwO7bP4JeoojQz1hxNvbx23BdgsqS6M334jIy8kv0+xe+P6UQT/937vl/ibf/NXRgxEgf2PEY5LaACcFeXchzFfa1yM1/L9qQqhQZhIEIeDWNOGrAdnZShaZGURGYulyuuylzJgzJ7+7rm6kyxeeBhq++gNsYewQc29deZc/Syb9t1FkByibc2iUwFy1Uj8RYIxRHJTf3pNvViTzsL/ctEGGERK5hnrtMPnz/K3Hu3YAVSUoBTgcMBvnkdMk3gj7tAVeO0Epsl/r7UJ4HTtzeW0zXPg2PZ72cdr6S30w5o8OcFFxHZMiwkfH2fM1tt9D4yf/6EgV4evjHFaqJSGCfEiyTK7nuCA33dbAxW6TJhlsxh7kA6SiVYcu7/svLj4Yvsif0i79P6oW4QZ+r5G9hVSflarqFnsuK+t7bc9osaqj0E4FEvGeUPIj5eNokhuTRm/wwELagLkGCpqSP7AkIzT+oDXV9SSisUljAWHMJUa62pldI+3yjeU+Xs6p+vaPa6Jrgihjs0jdJJAKO5Zoq9UTAAfnifeEZ9LYFWa9MB+mQVlfRyknNq19LjdXBlRomGAGfs+1pbrTsPi4UGMRiKa9PDRM1pLwQULSePcyTPfHV0YVUr49iyegnfTtUqRt6yvIA2oAE3sCwHfnqQs5+PeGdMUeNS09PK5O+SRfC4TMS2enofQZ736M5EzlmFXY/AdBNjEvHLzdSFZCODhQXkuiel4xBJ6i+WmZ4lhoYawK4/2YXb0SpIelmHXnK8mxr+09e7vejegJncbP9d1ZBy98T16qUnQFIga7+35lVX/0L31HlQ7v1To2GGanae0Qd5Jw2h94vauvMh9x6ONm8qIeQz0y8xj4+VDPZt5hcqT70K3Pn40vm6xyeXaJG3SQ7o1+O3wuwQ2xlA3OQ8A/uEnWes/8fFW373f45tn0SceHtociaV0VZY5umcOEtDqSaQdelOZR9ZdxCt4eo21etjp1HhioSHFvLwRTz7IfWDJ33fTtc4jhNYD5zeBGxdC4ynsOl8StQDThNNLJ7k/nz4BxyNu6F0CsYyFRS5Ixk1IrD7rdniP89mVVdb9+TKPIvs//aZ6LKYHi5zxldVes6zfOH8xmt1eVPI55X6WjVmmBy9mZR9VB+Pamvc7XNs18wDuMJhnzHRD1GgMH09v/M/x6dut8gGjX43bP73GNhafeoXTRynnf/n7v/+PjuDnXMNhOGAyiqZ5kGpyjBvbXqWgtZ5UiV5DrPfoZx3/os/3FpkZSd70TekOLTLly6FA/MzJDjmAunl3oS+o32uss1IEySQCBkV4Um8x0u51rUHoFpGEtTUCQnA3N1BRJRb/HC6FoRbKVbbzti966asDMEt0nm3tbO5KCM0+hdCM3RBBpyG/JVT4XSMF7nlXESyuAVEJD4gYcrlB5Tya5emkMUJRz+sd4oF7gP5O0Vc0h3Xp76wB98C3DOwtqnTLm7MSakUhG7y+g796tH7Lv7k/fl6Nwa8fbP/vSePuKtUb16BMen4bBEMf0KAPqmxwf6y5yGZ+dr+iUXcGhxucNW9KtfKMP8+eL3+fx2R7NrwX6A722NC7VVlxC9O0j1fUiIrNHa2k1CiO3XxFLLfGwJhneX5XFrEDnTLc7FOuXq0tcLgd/5v7u+ED/gWG8oXawM48In4+jud4QUSl963L+AgPKiewhcVK9ciZp7ROXdDp1MuPkyHN+P2lz9uIIuOjWxTvjvbfGNf1WlHy5ruUTQS4YmuEl7KhDbdBOYtoud3QErbn4W4M9t7NcyysSFnzUro7pNFbAH4NuE7T9PZz/bvtJn2GJU6Obo2/57LzuaE/oPV++X3h2aA3xWhj44n1/5JsvQf7bl/1nDWosvIwv94cL3mu7W+sFYwIFPjvm0zdvtjTABd+c7uvroV5bjyxJv/4An+udF+2OozXh7h9Vtpahcstb0qF8n3kA/osh0FWOuRlRITa3NAxaeab+bE2fGxDewBqRASN1ao13tkx27PSjKsURCzNWlq4u2OoS+maOd/pwG4b2RvhrSslVvmRe7f01DT82uhSnA89FT92/SQE/3//339lFtDdAdJNYhwmUBWOt2JQeZtH1zg3Pi4xHonWvIa3eFTXW01E+83KdoRwuo14eZFatxYnpQNgPCQ39MMHoPv0TTVUNvHTnlfEfBVrltfPf44v55p4+/Ofu8M4DHjNvTEpKhx83vGImm3vDifRIf9ePpOoK4HU3U6Rhr//92QBDwes+wfm+VXkic8PAddQa+vmXCuUjbl6M7Sbu7SQnme7xypdaAzyMuwqAqPxmyE4N59nMk4jJapCbwVRAI+oN25VH8DHzXPxsqS/rixN1YrmcovqK9FwaaZJ0db9Hq9zlLnqPpqlz86efOEwGHK3Va6BjZXu91iTwFjVgXGeVuEDbehJQ/9EQOYZOB5r5QZFEUPQGPVU82F4vmJYm9KBgKvOomN7zX1FEbwEcBcrQ5ghpbGGV1cq7XCorehLUdSuFLwWmfsY2ipY9hyPDH/+LGPQWuxca39x6L7SCkIwb0Kfa66O1d5mLDSZ1YcPuM6dxR1TTtYysa7KAzdDn8d1MDSWZ2F6uNs/Cv5mzQGL86dHwhBzV0roOr1rz6C+59vnun6PB5eTEUK79kAr5EONh85Z0eJ5xnp8Z3Trp0zw2OdZGFGmNpemOVj7vVXQcnlp9m9//g7GkEJovVvOICql8gX+jeHsdALwPNlYlS79XP2czHDzVd9KqZ5fTejzVYGIxvGH86JnbIuOUpTt8IplEA/m05Pmd3jBGGpt+G++kXKl7/Bd9eLqd3nGDg+b4gu+9OU0mQfocqkKKOdjMei5Vq+jB6k2+9L5sGodPYvcF/J+LWl4C2NTY95X6mqM3VArF/k94PWWPDc6478qANen901+4Dje2wvSE0UYrDR1XBqvoDERX7brcLDIAENit2NKCd+dBHXduXQi0gW9aLe0s3PvFcAQlOfPL9VoYGfe+aUKpRAqr+C4teykKaTYoMY8sNOE33yq3nYaL4f4Cjw/Y/3574jnylUUWkKPT59gXn3GTa4//x18/gzLr/j9X+pnjONifowzgHhuDdXXz6wWP2WM88ha0j9BQ5fvhWnCb77uqOo0PCBG1YXyzTxhlq9H/cHn8kFA2mX/iPMZtXfM+dvqsRrGGnFQag7cHW0OtXFgsxeXV4v136VbrQQZgpW75t4wd8ZyJErNweQ6Ulz96T/9x1BFx6xzr9gDDaF3IWBZuopsAgih256DOyN1w9cagWKUuDG7jEno1SBEIdQSh1uFxJ/2LJ0jU+qb+Dqre+5QcX9rM5SNhc3NbF6rzGPLvO6excm/AXN4Jd/fyGc2w9hY6db8IXfSddMtsDuDZlWGUOfF5jTWZCmE+i6n3GCQpiQxtQTfoNUU3JosJApqsXeQG4b9aOPmnC10g+/brC0AIGerGCSkGiWb3k9Sv9dopKj0xAx9o4PNHjVJe/4Zeqgd37L7OG42UUEud2OX99WtLQV31nnYfC7LHGvviHnGOAz2/ORC3FCKzYvx8JhnBE1epPBaoW3t5QFgnygp4fn2YKxKyqYMIs+6oecpwMhDx2jrZMzS0fpWOnvmkbPNz4c52OUE4DZplp/HEDBN8e7Z22elVPNs7i5nEb55Rh0NNcjw9hmeyRRpxOVD3+3ahMFsayFPk2s85ffSvybAHsoqEVzHELQ2fa5MxW+DZ1GiMN1fpNumpraOfUVnSB3f5+3G3r+I9zqj4a21E37dPoefpeT6fWyUDWDj3eN6bB7U8Fel91KAMNzTldGJF3ApAbkqceLhqeik5zV8B2Pwh6EzNz7S0KwJwxTpNbAO6ps190goySeEN+poc7/dumxphmtnckS1mDgQITiIYnqpyZ9cW892rf/CMAChb8i/LWMa2v1QJdcSkBl7p/Mgcg7Awnr8OQqDygSlDXupbsQaolVgsfnNM3Y7DakhUutRcZ1YjFF7ozha4sVnaTEBX4HIsTSpZKTz2dLtnR5EDbIUhBQbHaCRgf4cuUc2v+j7lgVAV9xzdU86975hkDysvrOpXedOGov5RhKbqxQtaTyEu7/beoUIqDH65gM2noeUor2OcoZGFmV0SPX5nQenQ9B8MgBze5ApD3EJdoZIv1KWOVWGTpeFY4xRdWHqMF4/Ndn2hnfmLTtWAAfVsQGUEi2X8adcP6rgdx1qAkyQuNIUgAiHPNEaXx4ad0hKvdEnk+9WLYtki0mLTmuk9ZwZ4+sUIYQarDKWzSCVsFgF5/kZ+PChR58vOBxWxCjE2Cgqam32w4D0sLMksXg84nWOmE+VVi2unWMDZDAfPwKoda53mLE7KmKWO5TSG2py2NV4tTEC6EOjgISkZdQAY2QBPrQFsE6C0ySuSRS8O9a6zJcLsHMZ84BYss/PggQ9PXXY7XrESRhUf/5iexdDwGEU1P71MmI3iFU+5oyxFGAnVmz0dY2nCd9edihqjKanaGW2YgpSus4Jutvw0IAP09RLe2hnzQY1/pntvpReP+ow52glPYcBWJYd0AGHIDGH3fks3ZH3Wq95/yik6Oym3RAMibu+uvjKywVdSghB6DOkBySoZwG1TbY/cNckDdCW5/bMhoC2lCZQ9xYAUq0aYMykSHfoMQGvczTng9VT90oDagLcV08qwT59kn81Ltfep4fv/Cy/liJzxuWCuA8Yhh7x638o3pmf/z7OZ6FboqEA9ytK4xidDw0a6xwJ54orBREFk5aCHQZ5cQdVChWZHV3/Cq/clwIU9Cihl4YfWAW1KUWQfKCWyr1c5P/7fWX4Z0kavU2PTcUtY8yK0HVcL61LXgqskYihl8lV4Sih7cNwPlsZFcqfDmslBF3ArizIOfqCO1UvIRTjDNeIxTp/zrMmN2tFidutKtYCEmvX3XnGThtp3dJOEq+XUfjOPKNAeNw61RyjLgSrC455Rpe1VOQmttnX8k6uRGvcKh2lYDZDhvHmUgL4epWyqYddrXTRQcrrZa1IszseqqJFD9l+jy/n2HiAzZOi8icOA3Ieq8Ku5zmmhN2QsCI2njp6rUJAbTg0DOiOxzuQpTu/oAewplo5baOPb/PX7WKJzhDUe/zpk3gFw4MZ2jkDYRitqpLlV5w+IwL46iB78e3nHY5HID5/K/x/mnC5dFa4bb/v0XstehhwGyTemB3GrQpJAZawQ86dOUmsPvzpizStSsnkqVcCc4acy6lHR5heofpf/b3Ouou/O9DrdMASenz+JO85HIDdBBFK+z3K/p2xNqvAMzxo0qyUg+200th3p2qwHY8dIl0i+z06rNhNtWnlp08ybLINxj33/Yix3BC1W5XPuxG9okOYHqyiD85nRFdWbj2+07OljQRHWZRDrw/R9ZISrtqBWoVeOt5hS8hZWJroRFVfopcVaWpi+/te194p+N18hYNzmoaPXdfh8CDG9W53X90GJQFPTxa/j8NkZ6Cbr3h60jrxJzXInp6Aecahy1jfSYz8p0/Ch3724UnoHq6ctPLZEKTYwlK62thOwYycKzhPZ8V+X1n14yAbuBzfC53MUpHn/V49cqnGvR/6K/D3fy3jnKYGSAA6FO25cntBzSnY76UBpDrd+vkFOM3A09OdnLPu6WUGPn/G+ovfw6dPom9axEYIWFeZr+mrKhOSyvdcHprKXB4MZx7omBbRoVzUwq17xOurJpq/hbBsrp+E4DdcjOgNmZmdjupRXYNYjh4VYsysxF67UIZc72/gIhXc+Q1XfB19O/wuS/c9y9VS832aajOYwKQijv1yQQdgvxfr2pf+olCpg0UzViIJ5kLS72xDKmLEfZiKEjlr3Tbz938j1XsNciOE7gz8LP0BGP4CiFVLhMHQm61EylmV3K59MOCSiIspJGuId9EvTaIc4ZPGLVOnabkETkIaou96GXBvQ6iNIRjfGQKq4aNrsgGYbBqCUkd0+z2Q5X5RRiKSljUd0NqtMdTSo/LTYRjkACYdot+ercXeuKbcWfGXvCfYi6dp96ZFz4Mfy2II1IKIeDjUcKWt9qHvnKZolXVyrm25AyE7/afvKzPbAlXsxscxA5s25/4DQGPHN0gZv0Opm6Q2cAjOmHI5BGTMIYiXsLGiQhCNwdEOaxgjJdsTA2Q9NOnXKGdDErfLt50Tv369AgeGQlwuGIdBFEl0ABP1iTACgIY+ME6/vie9SRMya+GVlm+AmrjVHF1/lovkBhXG9+tng77GlDw9d9wKb3ix0yTDvbZ8lny+Ke3K6w30LQRHJ463Ve+WxixnMQEF2Ki8cKvcy5lv50x03Hi7S7zj30KoJZLtOXnzh+2Py3lwWEVNZC0FGFLbiEiHJWhup00To5253VSLCMyzJEcedkkVHh2Xy/b3MtMXZmDUhsko7ofy3DTUknwpOY9LKao8tj067Pm6p11Z1MsF81K5RzS9O265s27mDw+wexAEqGLZbiMlnUdDPrq4pcg5ITuLjvYBx5t133yCJKe/cWS35yWlGnPnXt2Kw+Bbs1Za1L3g99fUCzLsvQn0OGWCG/JOzw/JH/phwG7XNfTtm+y9XqpX2Yxbyk0OeMNbJSeh6iD0qllyt48ZD+IN2ilbXBDF08EuqgR3SWxuIJPqSAzyvmrX1qIGpfeEpcGRVojW78h7t/wZay5jOHo5GgVkPimJB8UepufZnzNuPaB5HexIri80eVfkSwxhpVz0SbMIYuzYXLkP+rxOQ9xMJ9QKdBEyVsphub8N7TX9elu2OiX0Om3z7P3I9dMUfEit9Grl6uKYSS+7sjsI8YvBITFuhto5JSzyBAJNzC+SdAKL5WZZ/fM8WoKvXTqzmindC8P9/BkxJTwOAxC0S1m5oVMEi8yu6yJ2O40b/PRJDto+Yz084vm5biTXVc53Vw+eumaEMRf4gDGfYMbN6nsAv/5cN8u3/FYkDXDx4MUpigwu01g7aWleM+SJQEhXzVJPyTyje3rC6SSMd79nrFlGGXYopROXYCntD0M9gCa2OKsRyYZTp2uPmF2meSlYwwO+nKIh7Jc5AojYqceAuiTPAS6zMVurrDFfBZ1QhmLNy84XpEPboZFVRoSSq9bTzVf0w4DnZzEurc716YLr8Ijn5w5PTyKkv5xqHO0wAF/tX9EPAy7KrLxyr93c1VjqMOKKCOdKvjgFWw8klaWUeoSkCpZTWkgGt9yhLwU4n9FdLhhTshbWAFzt9w7xfEaviLXEHT5g92HfJid6yVaK7NOQag3wPGKfgFgWg7kiFjw8RDsDXVkQQ0AIEpPu+w7x0cwVuOl6RQ/F54zu/9fem+5GkiTdoSfcPZZMJlmsqp5F+hoQBEGA3v+BtEBzpdF0T3UVK5lLpIeHfpgdc/MgewHuvYD0IR1odJHMjPDF3JZjW5kbOgKAPs9NzMptHSV2Vd0zHRXnnFE0blWq2aBKgiDIzcuLGGUTBZ/jJTGsmKbOjHuJxY/AUCsp7JLkiMRDQnaGxRsFWkdXFlwuEd++AeNfdugPqDG7f/6L0dI0dehd9atlETSv71G7awK1e7Xrko2g4AHWpksoZbpVlPCf5R8VtEiD8Dsa87HchO96aBNVL4kpWdUZCmx6V32eRcSKXZixhBG/fO0wTbGRvz4Z0ieUMqTF56lQCaHCq84ZDeHtpKkO5OxJO9J1MluOVKd08piUD1ykRvXPP9fKxH0Ra787HAB0Vm/aPMGawyModo8OUg3jegUOI6x0HfdgGFBzQnIG9nv0hwPOF83r2AfjpXEY8PoqCOzz86Ps6ZcvIhdDQNh/xD//CfR9xDgMplhdww4had7IPGOa5Axy2KHMss+P+wVI9X59zzvJAbh807tXENLOYpN3PoRynhFTwvkqCufDg045S03xmNUrOs9Ynz82Zf3sOuz3uOXOvKrPzzrff7xITtqsPAYLdvNRvjMcAARDR7kWQGgjpNG8aGwkttMX7naVxRGkIM2KwiP7vyw1DJzVbiJgMdBSJWXEUFp2bZ4qlSXWiMr3DyhF+L3yru/HHkCHx0k25lYi8sVSFjTnRQXe5SKynnJXy9MeWFErRC3aMVsO3NevTRpibdTk3YDBdUgFxHtVCibt12MVzopr8gTIv/d7yYErZ8SccewetSHVAHMTT9JxeEi16syKDv3ljH7NJtB/+uoq+WGtfYFSQgdgDAHXMpoc4zSGAW3H4zTgpj6JUqS+fowjBj2a46voFebZnmeJQNglICSsnz4beMB7y/QA9iih0r8jEFAEbBVATyJHOH3L6ZtnCee5FNymR3w59nhOmp8ANH2iqIDToyStOzp0pxP6aUL/kPDtRehV08oMfL1cgN1QzMuFpyczNnbTijFk4Hj6lZjPdvxhBZ98gczckB1CuCrQ+5RwOgnjTodYWyMrEazopJsXE202GVfmGtMXHQ4NKIEQIqJexKyt24VBdFUx1WeJ+1O68E6hhtYZkwpJOmRw/qi6t0dh+zLX2OkQpPFKUaQiF3OLu2ZoJsTTs7ZOf36uJriiDtHNFSFs1gk5SEoV/bvPEeCcQ4AIPW9ZaFLyMDhFWP9uDDokVcwdqsBh7t7axTIEN1+NI1ynsfkeycHTHuPrZick7B+K/li9cWq8aRN3G6RiwuFQuwn6HgZU+ICaoHk41HAKxpyEUkFflBqLSTKkkdR11c3Ii384iPHG5NFxV400IgdWTcHVdeaQ38vmPDy87TBs98mh/UErTPEVpUBCMkJovCdL6cQ75QxHoIYW8Yx6LEByPQsG6fZnpFgq2rboPR1xxfAwmoLfVEQ6iUJ9vYkhviNHcYgW529J7s6DhRBwSC0vuOXO4rH7sCA9aMiG9+qol4pnbE24QqwJVTmjC0GUetRj6sMCJCLJ9dxSeufcOE/HOFKq5QP9IWwNe8+8+t4hPSkJ884Z3fMzQupxPLMhFAMs5dw7ZVrdPEvss9dIyDP5THWfi2K5mOcS6GuDF68YlIIPTwouQELW2PWW9wPYVAJJnRp9rdPGjI++uZLGCLpSEIbRyqRK4pjccVaT8XtXSkWIY1kAVmtKoRqP3IfNZLqy4NOnmo/TD6lhSL60YNBcHZ5/KQA0tCZGLcmXJCy1hoiW+j5lRuS1KeGNJ402qVyJiuRD9/Hx0YHJuhHJ0wqkPOSSRsvNs8/qutw/MXJPpgmlYiVvPcA5o+9ln7oiBY93U6gHqrESJJsN2dn0yE/NSFWlycDRglap1AdR1z2fYSWO+yShNoDLQxr29u4mHCqEppszEXL3Cnl3w2irh558rGhpw56f0fOsz3AETWVa18M9Z+heHwQ95z4xeCYMo4R+eKSfjFY/7JOXWa6Ve/j0xONLzfuYsyChLdoZXnkSvQ3VUVEbgfp+BmOAGBU5Y3xCDStSmYYQEErdcx7jVhfhcozPkTCcp4RKOsmBdpOBjYrGGP0D5nlmnlffR6OPEKJ2N5bB9dHzyc7Kh0NbNrPmw6CeqSV/a+Kvovam3zga9rRIF1kXAvq+a8mNO88pkhmofmJhkOrR6QBM0wggWIMy6inGT9Uw+yPjjyn4paCw5rtvZa1MwDrOFqntdrmIUjEMGm9HSh2G6t5+QIMODYOLuyRzKQXx8oo4TbihokuPQRCZWz9aeTIAGPc1JGRFVRpDiIK2zjPifm/uklvu8PVrb3rVNBExqRcdIVToVinjeJF68WORLmzLsMP/+p/A9+/Ajz+q+1YFeGSFlySWMW5ALETNUBUeAFnrGV8uanHHVsG3PR+0o+bsFC5PVYPUYi/F1fgnzKKuQlMcEXFScKXrulqPV2NcwtNobdG90AfkLMRirfH+FETnc3WrkyZp55HGoxo612tNkmGi07azIx/0eAiKLLjwB0U/tI8GPkxQxlXkooB5ChJSJAgojEZT2lRYgsy9T6vEHlLZ1P2/aTzzAnU1KtM+XzpjRodDRCzZEpWcXWTMPKZkSAWga2eyEhXUhKZBGyDoYii1JwDDkSRsKSKlaJU5aqGfzmrqpv2DlSR1vaNqWbsslXfOZRQk4etXdIcDpv2DxhnmOkeltxsk5nA3pVrZR++8MbvT0eJ1F8H65XyPtZ4x616nBxVWGkL3RpHT/Ym42O9nCA08PXUYh2SLC1qNyhKM9fcdxDP5eo613j3zRiiI2ciCB5iSMfdStKNpkPC+4Bj67QagHzFOwguG/caVezxWaO7Q4xcJrcafnuQhxVrIdxhLrh2PyRudoYNSsE47/Pwz8OnTiKhVgfphwLeTKPk//BDRhVK/m1INct3vkdMOt5vc5w8f2rtud1PlHMMzfQQew3kGReqbajSk+WHA9doJMHI8qZTtm+ZPzbJCZ4pIUH5m3gp6MZl4ybhfFZTx9IL49IRLjsDUGpokW1bqokLWp1rJil5jxkf/23+rCqyXT6RJgi9YELPLO1K6eTxIThbTZD4cJsuvSEGrrGy0ApLcEnoxKl5eED8NOJ1cGc6pNCFExr72QieCfFbl4jrrvXB0k4itOaWfAvF86axmee30DQT1NFMpGjGL95KEoMoHQ+54b7ZDQhZE6T6f9WuEO0sRAy8l8RZr5Z9YMqLyltV1gqW3OwZNclR6sD3V9WXXGZXGcNZCG/1a+b9Xznggvvs36YnkrUA3+nyxHIbzHJtWM//mCbUu/yD17wHluQkN4k3HPbdzF64CcKi+xT4Fu53wy+uVcgwmhw4Pa81V0X2kV+Rw6LAbHK9WnmjhPbN85zzHhu1y3XamaowAkOowpQBZ/nYNO9yuwO1YI6L6crXzQ0q43dTg2vWIrJyoRrolQkNBXOX5w7TzDmAwhJMsrTk7/cUYApBqpTbxhAQDEsXogSn4IbgwGMf7SU8EM3wfFATJafNXmWpkzFesGHE8Rxx2QjAxZdEHQs0nk9VIg8ivLxJuvhsWARq5HgBXBbd/b/xumcwf/+Vf8Lf/+l+tyQkbEdFqIaMqRQ84Z9yYyKDF/OPpuzENczWktnHNNVcklfJ1mmCNXiSxReZ0iGer1bYgNjzXjBCi5PO5XqoQmkYH4HkGF2/q/mhNVC6vVcEfBhxvY13X4WBhHqW4pikOCVyTlJsaR00CUZQyMaxAB0vZGVFoiUO68T0wRKXYFFTc6mJSbeZEZOONSUk0IkSLMAC0pGbYGHHcqM1YU4/XV5eQyMZcTvCRGVEWJ7qdncK2FIlTvd2Aj4dbrf0ZAq77j+almSZtbsXb7KAaX6aVJeAaY4QNbAZXWrIULM+fkfOmKQcJguvnc1wTFF//3dAa35BFFRMmK5JBcnv60zfzKhiDIYOjBNLzZOOLXf4uD3FokmdQpBvfCIVC5KFWamyqAPnvSvia3i9eRrppzcWxGWq1HbtHAJq4RHrR8zfFy50X0OaqeIH2Jq/DkZ/dK98FVOdGgToMtYqHIXV8CR+W2rbh06RnRtrTfb6mB0XmlkZJ9Evh/vFI+lRLKBqyyS+pgN6WSz3eRjsncwerIc8ybvZCKvpO8LBzrYXi6Tjn2kAupU0M/mZz2UyKg8UF2MzOr3nLDnh1LC7cr49rp+sttc3mvFD2V88MBSdr+Khtky8uyfbudML66TN+/lkUVElYkz1ksn9V8PWcXDgUUbt/fhHe9PmzyqOvv8h+T482Xyu/6NFKv0EpNXRDPViSF9XQ+/OfpYQezi0PoNuZYZrqUdXHNgguf9dnAZ48eMDvjLhW2ellhCXgVkJmwysr26t5ZCzrZ7KWoWj+4lK+MN9C5RxpieEIZLdspufvUffyTc5LE3F5t3zjR5b1HAYXohekGAigv2eTs0nKOfs9cSKzCfdcQm9lEXdJGjHSHk+p8hQ2FPQY3HbP+X8aSeumApjtpTuDZXqw0Bpb9+bSUQaZHuEIw1ee86VIOXhf93t3V7lh/IDqZTSGOazBHx9KmnfFTloFvFb3skZ1rlRxCEqXTvnxDUe36zIZGhYD9jzo4s/D5zMAqKFMjFsjzbrN+X6Kaogv1cWQEs7YVR5vCeGqgKmSw9LCfKTJlvm18gfP8KgYuf1DSo1uxWMh38255sn9p//022Uyw6/+ZTM0XAw5v20J36AHEAZDJbEU1Bvr/8mDc+1551mAZjZzYwa/f36T9Hq5IOarXTprJ64XKAZF0r98MYKlB8EXrxjTYpfLWhmnZAlQJpj0RVYOqxQjuHGsbrRSJFP6nHtLdKWlbwxdt2BBbDgDCdUIMwSvj9rFWZw+mnOd4xL6toGOCuhz7iW0yFtQm2E0rpf2hr6ekU7iWnr5/elUGSI26D5RmHlGDKvN35ByHq4uKGKp50rU78sX4OXFFGfGIvIcb7nTBufR6JGX2hpSvFkY6tq1pu+WKdtn9Iyuc4dbifV5ujbGa5ZSXcycg4WI6HtLkXPoylKP3x2mNYzRO7GmXgxevdEWhnY8Cj37/SvFvt8o0u7OeTfjPOt79BldvjX2xLqi0iMvvLdOdJHrMAo96Zf7XoTRCmnAcc3R2tETVb2VaAYH512KLIt3fllqLOw8C6L8+ip7bHP3zEbHGqIJoXmW82Bp5ubMKYQ0p4H3qhRXJtFpm3wW26B7ReM9oc598/SxBM3mVjg4Z+D7pce3i1og84zDw2oxxgCsNn9KqEzBG3Y6PxpRpcBK4i6htwMl6zIw3SHLFMYIQRClfG3CSXKWfY35ipiv9nrumad3goDN0WytgMvFqj3RKcpn0H6koib5H841Xortac6o939bBtWdH3nHPGtYzjwDx+N7YLJsKe/25WwbEILsa6OwhGCklDMafrEG4UkEhziXriyI8xl9WqttSpp8ecEKMdDsbLzAJdHNM+LlFX0+o89nccC8dsZ7zDup7906fm3z5tkMQuMZfugd49pLUSVxq7Xxs7qgNVW6236E++LThHg+sdyAn382WUF+wPPgeV2vLmyHssM1KzQeXaRx000xRG8xRiz2HRUBDShoChgaUW1H1aCmpWBMC8a0SK4f1iZHIELPPJ/Rz69YQ8QNzmM1zxYRUQqqfFYvoa/lbzKN/O94RDdfRbbMTkDq+fKuEDzz8pcdxL99U54betzCiFsY6/Mdn/FNu86M9PV8kv/Wn7kHrFQ1DCJX5rk2WdsapQ1hBNd0k8btywuWIiCehfNeLujKYvpVDdeuy2BDqMbQdHQsydC5MjXl+adTvSukb6c2yGc1D+eWuz+mU5G4vVHkB+kvSDI3G8RRTnErTicthfyb0LyM30fwf/wR//2//80uAhOivHK53ztXBQ9aXfx+3mw8QGIDanEZIkwRQhVsA2yIHS07b/VfNKNBXftEn6zRAZvl6HeRkjEZnxjUzVezKlnGU+qd6ry//gIAuO4/2tlwPTf0Tey9KVge3uUBhxonDmxQTfcZaxE/icv461dZz2G31Gfq985zbJAo/hexNAyV6FpKak2WgnN6tL/1PXAYW0veN5ZpmupwDmppeqXSI1WS+JstXMgQNsbShGB7bt8pehY8zB9+aOIRPcq21R+41X7rmQOyTjsrU9iHpfre//pXXOcq7FhHN15exU2t+7uuzkNEuvJrQPf2vaocewSFQN9uFgT/NjzY38ag3gv1TPn1hOCSMz1zCC7BygtfvSseJTK0ccOQ+Rwr0Zcq8uPJ19sN/JnKxXut6KfJeZZQy7fR00ePHskhJUGN4+m7aXvH184ERUrArkgzGHoJud9e0Wa4knmWtsllADBJ+3XGxr++yncsIVsfenzVuu7ODh81Mdd7NehBCkGTHLkgNbqZHL8eHptSi2wo5B1GgPPCuH16bzh2iz6oMX92/E83jusgDyGSGgJqN24lcFaEsbh9JR6PzA3DW+SvSaQOjldx0PA/HHB87UyBse+Uayv8eGZETfTFVgfd8ZzXVw11GNYGGTR01vNavSeL9oFoDGO+VwebVPFzLJtKjzPlhF8vafbD3pXKHQb880sFIg4H5xkuBcvTRynaMN4a3uIb1I1pwzufnmrDJu5PSriWvl23fx42nafhZEZYazaiAjwetbbwXGcgh+AqCrkHbsOhiEDybl01VMb4pMoJbzyazCEfpUcdS63qRqCNP2Np5mBetPlsz+B50/P++dPaKmcqXxgVNwxV2X96ah2Zhpw7RPhWZG/2e32vLmp9/ljn4+v46527XpWWNJSykbWHA6zJGGlUG8ft9042eAtcD+586SrtKiBAFJg6DiMkrJy3Aw+4XzQWLJyRVvrTk3kTTMY44IEYkXka9B5T6XZXW9ZHzyXzBKjEkD9pOVHfxJF6OEOtaeuQ7shS/F22ezVUb3tDf1nKiK9PtZQrvxvzVdb//Nw0aOR6qL7QSdCdXu2PlAG7oY0aWCfxLvm+HPRIMoqF86d39cd/9+/+3ze6cvqxnlbfGD0Wu/rOINIswr4OWnhE7Q4HEo1clGHaKdNFo1h2KeF6jeLuYfJUro1v+M5SIFUjbOJcTHsQhl4oJxJUsSoVdoHVsgPaZLniBElTftN/mKbXfo/LtjuoVzzMy6DCwyefApUh8LMAQnqwuG/AKTu5gIlpDdKPeg7N73pU5u44WEXfNfGR69IyBU34j+ZY8Gz7Ufaiu5ylHKgmfBpKoi8nAxCm5/ZM/9hBY7u5QDf8peP+l1LR+xA6dKhTzBkIQ5T4TieUSMvDoEq+exdbgDPfY8cSX06LkPKiPpmzJmJ5nmtefG0m1NiBG03VzoYKN4W92y9L6t0gL+Zly1L61Gpee81ng8LEYUAIXSOc/T6TYfrrdD47+nGfta11C7TlbS0zVOOqySfJGTH2jXuY33UODPS9EzqlYBii3wrgUqrA2yAtFDrr6ksKEn11HkO4q+qllk5CznrzYVPuelP2uXTuIXMkiLbSIDtfOunZoD5erpWGix8M1cMsseoNZGMH2TfemVydMRLT7u68dVHeJu7Xr8v//d8Lm06JEQC9IzRM5G716AbxenqDkNvUoNWA7bEYqmjfw8+mBASRB9JIxwBO6codbmBJTeuamQWWjU7JYCEAy/fRDWdMMl9Hw3vDogBtNtSVBcuiyc17NAo+46Y/fHDX3DJRUcv8uf+oiN1uwPAcpXeD4xG2b+6yGlq77UAdAs43yWXzhdz452HQwg/UiDaGgO8Y32FFtrPtMAyxdtxEm2jYdG7XyRUNR40sPq5hCu5KKcjXIxb1pky9nUHRJe8mXb8m10bXHKu5k8p42ZdgS89GpKHG2jOPMWc05W2p7IJP95uYM0J6a0TxnnijxH5RpJgCdaWUOqkk5+qfv3lWqkUockaVZ+/8F4LLR1CaCsGVzcQK5tE1FjfcXC8XxGnCOMreiPJZ1035KHHp8rs4DGC53mlyVYr0C7XRWmf83DUCrvPxjKIUpH3fzDUWKY/O7RI94C0A6LbbZEqMMP7i1BiN/pD9YmUcq9rDYaVDqxxkKCzp3UBfxzipzkpSdOWhJu+ZzEWv4+mkFcUq47dQxN8Zf0jB13kBqM07uD5AN2iaqtmy34tr5FzdCMIEe/Tlhl1WOHZIWFc5iD6fa5lB3QTJhEZ1k+glpbvn2I0YR8lO7y4XfBiSxeKKHrPD8cVCDk05GQbgL09sD66/ZEeMywW7aUJKTKCJSE+fxYgm8biYwzEB40EXmCXZLZeIMjCr3sXGqZEgDK9DSrHt/srKN27PG+QeQ7VmVTCMacU4QF0hgNRwHfD90htSxPHzz7KNf/3rA/qw4LFUNCUE1J7P+vx5lsTh81nLS/EzIVjzmYcHZQ2O8LnHlmGq6IagpBHAI/YHF5dmulAHKKrGZhhE92cLT6sITCkOaWw0ZVmPoMcByyBJOYyPj2GtZU01QYcXk4rHmh7QlQW7kIEkayPCZLWPqaVRUSiCakaXVC5GI8CumqasaxOu5Vbd4NccEaZHsNIkmd7hIMzXt1KYJimllUvU8NIeaap7YyiAM8anCbAOWpq9JZVy2soxLhIHPFqGTtxK1Lre9TMhMIYSWNNo/FjC2XeiaA0r4vE7Ys64HT7iegMeIPvx+Skbyny+dAjTB3vGLlyxC7PruiWc09dNJn+JQRDieDgAqRNPQM5WzUFQ44gewjQPUe6/731B7x1pYYdzpStF9oBkqFUpkEosWCURDsBy+GBg9QBg59DmOJ+xKwXIQj+kC+qquEj/j3lWpUnzIEZIxaqcYTGnHw9q/U9Doxizhngaarz2TiWnD2fjss6XiNMpYhylGRVLM7bavConLHlYkjFUQxbnGVLADmJUhQCE0Vz7QnKjJIgzXydnqX6WEm6QS9gX8Twsk8TKl4sKw+JiHbneEIw+qeym1DtDOpvxYgahCi4imbyuu2k1JHW3i4gB5s2gzmsKT87oQ0D/UOPrxYivtfbxosLn+RkrOvPyPu4XAHV9t5tUnyLCurJXi1P8ySOidoudZ4A9B9ZhBAaJl8fFedSP4mXyyGAsbbQXPQ+8A6v2A/E2QwjV+y6/i83dt7EJO+DfGoNUf1lBssGSecdREWxWmzkcsIYe56DM2em7pGtWtGHPnW2H3jGJV4uNBdn7JIROzqEUMWRCMIWalasiM2fTqHJO6YNNwbCKkQvUZPwQELHi47PMbZ12TZWXYYDm30V088lomUnkPCfj0U9P4pXTeOydbrqUzF7wiBMwiEdst1N9QnklGwiG4ApGTBPYyFAqFsql2InlgVveIWup1AA0DeVwuUglryRetPD0wQCJANgeQUMBfQSA9QUh0vXpk3kHGkVcL3IfqvzqSFv6we4iuhvpGpC5Pj2px+TrCbtPnwC4c9LvJjW+6M3fTfqeYTTjL778UpXGacJ6qryZz7mmB3x5GfEc5BnsU8FqVIeD5pqw0ZXmi8wzEIocUffyTd7zww9SIjbomfz9H1K96uljNdwuF+wAXIcH0SEREaY35tCbEX73E/xgy+vt39x3QUAqelhKVai27hEjmFKql5uKq2PadvD+5aG2cabFax82NF/+frs1r2ovEGMc9bkWPwgYcsOpXi4ioMxq8hayd2Wg/ooxa81GheBf8WZw39xSa0JIzuJW33JVt25bqHtth1UQl3xrgLE1xGa/QnAv1bkwNMXmy/NVxYbI9hZR4n9GE1yfA5SuV4jwcaE2flvX1LddBjek0PybH9IX2zIcYuH31Uts/p4x5E3+okdPHCTT0GVwaDn/qErINlfFz5XHtUWItyCm38/fQiQ8rftwDv+8N7G2Oph34ulv+266+t/dfzcZ/w6/Hj/Z69XFstK7Nc/Wwt2Hldn9ahDblt6aoV+0c9YPbUuu2nxCjff0+onRtn+/fnBF9zZErGp4Fifpw0rtve5zKLWxkYVTbB5pDHaetfrVhu79vX+zGXVIubjaCMifK/d8cXZyazBvBhf+Ds00zNb94c2vtjw057rFITT3tvHg8Lu/wnP4c1P1qxQ7/y2fe2+8x9ca5X7zn8+B8fPxXybpNrIOFZ3n+yzMSR+wZfX0kvL+kKS8CDDadxteSg254xXyn/NsztNs17k908E5v6cTAKg5Re/Nf8Oj38vn2+6tX9f2P94RAjVvwl6VF+Qs59rQyuwMRn9wG7rkmvxeNiF0RfIPmBMCyLM5d8aB+/14I1+2P4bQKIXrqvfA3Q2+B6VUr53eDfInfs43zjQWuuUfegffeEz9fXN7MM9V1hio9I6QelfX0egCPtoMdK8veK+C3xyd01anM6DK3Z0mZNrROaD8wcfkYLMvCuh0rRpjU+BeMRTWF0wJYcMvQs0l5R3ke2kINTLrvU3UnCxWwPu1j/rxuzH4//IvP+I//+e/CdNMK5hafsXYoHql4I211OzEMOCao7hMNQ5T6l2rVGEDlh9+sDhBE3waBnLOvZWQbKofaElAn0zDZ3g0rhGkLtYQIVhMKuDi5jTGkYjZ56nG/JvmTwtGCZCIj49kAZpwOyPsEVcL3UFKFu9NtC0EdWu9vACfPuGnL5HAs+zjPBviYjV0NxywSTbVwffTOCTI1R2/1/VoXCBDdAxdYYWOVDPaLXaO577fWw6FrQFS2onr4h55YeMvEUNNn583YVT6wiX0raszBKtoQCSMiTxkcqVoSTpC4SFYO3erwuDiHq8YrZO1j7uUkmW5osp8FulZz/TqSrKRj+xS3T+OrUt7TEuLoOp8zlkQesYBc82+rB9zeH74QfbNcllYycoHex+P8p5Pn5pa5BYv6zseur0nIa/o8Pe/C5P7y18qU/MMyyuubGbGpX083GriUUqyn9OEa4643USoHQ5tTLyfJw1QS868nJvKBrUErzOUU7KcEABWvcnyUxTRpIfq0Ld5PD5u2wyn02ulm2nC3/65Q4zAp0/Kn1DLmYqn8mJ3nt4zVlUiWumj2B7TuTY98fCrJxp335tqURzTZI1YBAmslX3WaWduaEBd0f5y6mFSafCk2eQjcNKOZlnNZItLNHKCfeoV1fO2xZYvkJbIZ43HuFDKxihTBP0WxkqLjmc0VUxc9TW+0z7Pi0Wi9l18NxroomFKvGrcc1aFYxNHDAOWp4/W4AtomnA2V4933J5DZJUEnlJzN6apVqEBxKv08iL3yfIF4KraKI0ejzCk28sroOZIXMPOckd4Pl5O+/1lDsMbtB+bcyvFyisz3M7HaFvMt+dhpeBaehOhw4DaMMl9BoOUpLRciRqLJ58hvyCKymZSOUu1F50XQ5uGAYhffqpu0WnC96JyuNP5a8y/z7kzelJasepog9s33SjKBB8CaYohUGnWeXosSFs9syQPhq5Mk3rOWHZwGOw7bGbGszYvEBtdaWhxCLVWAcNZGbdvMtmV+1yHEa+vrveF3i/K94/PayVw8rBtvpy/CF7Hcag+9++KsYbnsUxrKaZfLs+f7Ss9bvJ7PcOm6WAplkNKz6zPG2De0rJ/ZNocurJYbs4bvqRnaxW6VEd1V8Vk9HHZCa2W6/trVloFgH//73+7is4fDtExd6dZufVvLAtnw0xr9zPeXvBmuNrOMawIQ73cIWgzHiVqJmfZM1XpsOSrnBETpIkP2v3xJQIRgnWP9UIrOuHJBgN+7uYGfGdBJhh0Oyoq1zWfCQGwevu6V35NTXMFh/5Tbo3PlfOWUkNatgyWwydbhVDX/Gbyjov0aQWmrlFu7YVFYuhS6t4vw4n6I1uNJ7c+MhLmOjTnE6R7Kp8hcfRuP0L1hNjF8Uii/aM1bprj+jWa9MRSKimzRnwIQNO4xb1P6vYG80xwMMaxmbO+xyOL9vfLBvrV82+Ql5SapEZfTgsgPW+ataAK9JJhzaTEi9ObMGnoD+xNsJk7JB+Gdc+pPPn1eAHEvhRUfMy+oaAhcSfJZWBMP2mb50uDncpOM1//rFKsx802AXlLqo0iqX9kS3mbqFOW39xTf98cXRnqO2d0QwDQ1QOycB83Qng3xr6ZoIOBxOsYm/J69h6vnSmtEcVcFgBDsM3oyoJx3HgkfHyWG3y998zyhzd04u6QJD9WemcPDkB5lzufeW6VTE9D/O4b9rsR9PZ+tdxkVzYx6Uk6bPa805vHlaLxsB4N4Xlsh3t/HAaUUA0VKsKNh8bRiReXjVxz20h6b2huY6i8mZpDcJwYaR+8Geyw7e8vw8i2fK+5N78y/N8bZWaTa4OcEYbRQpGAmkht8thvjtK134Zmf70xFkL16Mz5zd8oP6jeRL+wEBDDir7vsK7uTlPemrEtr/UV9t4bTT7Jdri7ndUw8mfh5WZdYqz8TfmfB0hDqPlRzKWL/lD0wUSj/bVnEzgAb3UAnmOEnWNk7o5vfgonNxFatBqur4U/s18b7/3Nb0oIlpsRAiqjIh+AhtAGBT7nUuVFzhrnXiNRkukTdcO7oHqmKlDhUBF1NgDkUoQ8ouiKivBZ+FgIyHNNdA4BdtDXkwJ2nil5IigFeCdZ/r3xhxT8EFCztw8H3Zy6KHz5Ikkhnz5hHUaVhVEq7iiilAqbCRW7HClJjBiGAd9nQVh2WlmgmzXudnqwDO/dTq1d1qpWOJt1S6V6Tk1Q6QCkwwdLcA8BwJevIkifJKP9+BWmOOx2RKp1M2dpgbzfj9wJOSQiikTyHdoVi4itsNPv6MGOas0z07u7nCsBKkWMPA3G9THG9fkjAEEE//EPAkAdDoedEZ9Vs1GUwxJasUF7AMQpIAwRP/8sf3960kuhPQLyRRG+l5eaGM0aoOpCX5WxRFfucsvtSYDT1Gv8p1rjuSClvmEaJsg1M//D87NVBAGAcewQQo8w1RrCZAhkGqYcNQr3OwqJe3HEgpg2IRwp4ftF5vfnP1eAzpDg5Cx5J2w7ZKzDiOM8olOES2pkzxiIGrs7y8orwwCrstKUWOEGfvokQqrcAIQ3pVe9EBgv34BS8NPlI3KuSD7vSp5r4vSnTw9yf08niZ0ePjh9sEOXdT1H2amui1Llgx6QUvD5WQXdlxcgJczTB26h7JWi1Zxr3wOH9bvk2zw9Sb3n9UHiN3/+SYyN/WiVrdanD1Kq1JWnY9MbAJWTXjKWYYfzbcRhEgTayJEQqcbi++1lFYlKtPK8sWizr3mU+G/ynDQ2xrItVpHFBRGPjy5h+HKR9z89IUwPOJ4jlmWHkNHU6mcP9V1pHxuC0is7YQNGIy8naQB0iKWiYE9PAEZtdjPi8CDxxN9eWq/KGmIVNqWIkmvEuXE76eekK3BnCYes2pPDDkzIvN2iVgWqyajmPZkmzFrhRclOAfZHQZBVyfIVrfw96AC7u/EodL48CZ33NBJ4f/Z7q3D2BKJySi8uVwyl1CZiWqmEnt91BfqD8PzveWd6fkpA7yrKNHSo800KHAHKS0PALWsXW81jQgiNHWL71YRqdY1nSV8qcfq65vMswp5GEROLb2lnlUi6+YrHKQAhYSk198u6z6aEDisOuwo80CPWpSRGmNYuR67hjNVo2ljNavAFpWPycUuG9w0XlOl0WZIlyYPirF4TNdKW6UFXL7ToAa43BQSU0M9zRIKiueSv+32tmJKBxXdqBnBjMQMAO627P0I7BGvuw/IkFfUYyjuoChDztSb1o92SqvSJDLOY9VxszyhsShLPxKdP6gHP8veXF7lXbJKdMzAiA8cjlufPrNwIAPjTJ/FW9DdZ8z+Po27ng3kWb0ny03im7DPz7dTrZ3fYhQXx5/9lSPfhMBrLSwnSdFIVXgwDfjnV6onWu4ZFJfSjH55k36S6V8SBBRByNqOSV2ub60QPAatF9foyotpsuInTSbx3ucMxP4rs+cf/A4SA7vkZ67TD19OIj/tFkPxhAFhiOGf0L19lAp8+Vb6RUo19T6kBR0rprATp97nmP01TJ7SjtM5KkltjDdODsdycAUxVMBGI5mOu53dCF98Zv6vgk5mzEgFRp4YR0QrSt3tFi0LBrGd3ATusb2NZ3eVEqLF6thj/d90JKnImqKfJFO8YJAEibBAUY747YeRe1zbK1Xc5zKJ+mZacfxhgbh5O1a+3FFfhh9zcrYl727lTaVpLXy54emrDC5gJb6iHSopSnIuNc1A3PafsQ4GAxhDe/MNtmr688YSQBvQ7bPvu8+FiQKMwSOJpb8TsXZd8Zgg1mZNLeA8FI0poneeOs72D3ooQmPCMdq/V+Okcsurf3+Ub+pTEU6FC/VZqg5EQHALhSMGXPkRKtRPdBqqrsYeOYzpUAYD2IwB6dYEYYkV3sCsbR2LY7eqdWdGhcyg452bTVlonjzXGqmUR+a4Y0SIJQGM4cj1vwKucMQ6yhwCAS3CXrdZIHn0slAo6JlOvDhyypjtOM1pTj/niYyI399mtk/NrFBSlW1uA0irZjHWkfpXukc1Z6mYy0Vq7u9dz1u+WUsMLOSVfkpQf97piCBo7nfoaMkXeSAHBh2l8R6CC5NC1aWq9H0K/YsTZIB8infgYPqJ0IWAc2WE2Iw6hQauJFnq9d+uQ8PTL4zYh7pRdEynKZwhiNGEJfpCncs6qSHEOtQqMOzdv/eszPVC2ogNSb3G4ts5p4zF1z2AhCq6HtMIqW/4O8XnDgFoy0p2pV3I4bA/0BQH1fqYES64tYScyikSs8/Ae6nc9NUQa+RkehH438szQqRdRumc3VVKc8RLDinHceBO572Q6OiIWgB4DnqV+Pibf3EnQ5QTXX8vJDj6zIRO+T/MY+FHmn9Cg9FfCvPWbczYFbdo0uvIvLIJq8z7xfU3Iqddl+KCcbe9KUQ+XvnC/r/Lb1heSrYmPaEAL/YJn2yEAmKXij/sIMLSIveh+7mUpVXmanMyuSojxgOo1ga03pb7Z39Gw06rXrVr1R+RZRMw3q4hoBV1CQGGUleqVQocs8FHD1m63WmkNr1XGUh1ZoMUMdHPszvsKJe6Qm2iCIkD1Rgw2+yc07++T21u0JEsSTQlNSVc7Mx3eg/1b4/cR/HVFdzlbBzhx7S9tVZvDI4CaaBGjhiPo4Y9DqpVwFFlJ+nmPjsoCWurs5iuGJtmyE2FLi2qesZuqNFkx4tupx+HQW2vf3RBaylbmk5KrkjFI7OTppFnu084Equ1rSFX4pyR1i3ehMqthsPLqRE4X106bBJAzEFR5KgVIOr2qEMfm3ucM9GUGfv4Zu8MBu097/HIa8f27EG1XFuRMQ6m3Zcb5BPyP/yHo39MTvp8kxvLx0XXVhVQ+Ifg1DMowM1rDjfvHSTIWXr/rlYdJ0QFz7XLxPo51GBD3exRXZ9q71CjwaRFbT3UK+42jU9Z7trJS1BTi4WAX38cxW93fbm4Y7ZJGK0MpOSfiEfGVGU7aHZQXkR6KFR2KImlWFkzDdbqXb9XrpEpUhxVxvtT8FP3bTSuP9MokCCQ8PdWwpZRUuT9+Ew/aX/+KknbGqA54FZoOitAgIs91ChKLKhu/7sVzE1++CSNVhItpMUxADkGRXy+UScTK0bqXb4jDUDtGunOP5Bd8H5aGrGY8YCjAuAmlYPJnCBrDfrxYLtAt7Sxp1+kwiCFUhYN77hLPLGafYxiAEHF87bCuEY+DMGSi1N8uI+aXmjh/6KsWzqpdFsd/+maK/TqMyKHyr92kSCJpfpbDPN9Gi40PqfYKoWyX7wqv2XZHXkKPooIz5IZ9mqE0ImOMwR5q9bOJpHK++hzxiCm6NpbqxoJymFO2s++HAUW7AjMWlYZ0KV119aeEcqm5E4zz9bXZQ9AOkvxBFZt12uHlRe5A5/MDdLDMZUnROsQmX2UiJet1MAzSkbJcBAAYFVG3O4mCKHA/rvNo3l2gpq2EIFWOOnoMUsIVI07H+soQ1NW+U+UYzoOdEm4lGt3GfG3BGO2W7Y7QnjkGx6xDaELeYtG44sMB5bDTuUaZK0M5nNJuNdJJkBQ6XqkbtLPofK1yPATg02fMs7DVhwf1JOn31kHi1pnP0ZsgSHUOKeHbMYKVhz5/WoGXF8TDASFFXOcRIbmqOqWg2+9xUW//MPTosaAfMlCSKXQAzBtORWspHQp6lNCjaLTdblg0cXHXLH1bRazZk2nCde6sBDzTkh4PruqL3+d5ttCPpn+KMzJvYQRLWXYU/JdK/ilF2b+csZOSUvh2Ga1nyxoicHhEUZXscGD1FofWlGIOqxCqdzUekimoyyK8LASNOy9FFFMyTO0LY+oAg/HpXdSNO4y3usYcqmVcCuIUEIfqMe9z7Zruc4RC0MZmC/AhXITIfvgBGEZ8v9C74LykquDTU/M973A8Vj3q8+EqngbN8/p2bCunTVrr3t+341Eqwn1+Fn60snS71+bnGV3OKJqXMqrHdGT8/It4Lm/To3hJ59nKUde8P6GJOAyIAegHqcn/8sLcuq6p1c+1/5Hx+wp+J6WeWK7WXIW/8YZpIiIru3u+dJimUdCiUtCHG1b0lszUPkqm5Jv3dGWpMbEg2hVr7CaTOhhnxY864mZCQ6++LZb18yOWGx73wTLXb7doDY4MedAFrkGSdhqEA5UPSJvpWmJtHGp9/a3lRQSBc9+CKmJFo0mwsxKsauX6cBfTjYZBXPZK1OmiSVaMibNQoNGARrv8RMO4j9xLTm7banpswBZlSjcgAGvoAbjnOcOBVb3MMjd3vAhaX+mI79+2OI+o50IEBCE0DaNCQLOxrDtsYVapVmLwy7Rnun2YZpewBFan6Np4VX0I4+R7rk03yZoQub295Q4IFTVHfYzNyxLJsjsTTfZNCfX36k4sTngZGo2lhpUNtTY77w8VSCb8GVKTM8yTp96XSITHa9chaBwqqjfNKQzSpTQAuaArBft9LeVpg4oFOksglnuQZGJWyLsmgFNInU5A1/UiaFwynZ+mvSO5pmnF1To+CWGzgEAIIkyt0U2u38Vlc6d9+3PnoWwUBv6wje0vEqqwmyqd806JMtYjlYbdNcPjGFZuz6Fr/IAJR/IUFbAe0OZ5sCxsE5bmL4r++41hAc3zyDAFmrksSUEAZPEChFB5pY955UN5J3IGeg2FKgXosSAG4Dq7A0hRPJkQMpHE9wseHqoL3CeJcv7vVQEZ04JRjT2EYCVrbTiDHfPbAhC2HlU+Q4iGEwEbuiEvSMlqvUvH5GrYG8o6DHZP+7KIYc5wSSVSvp9HtYYoORrOYqDxXJIozT2bl/EwVCvs8q39XUpGus/PeicuMHoqei+X0iGw7CfnEILRvafXW+7QMwQYPknS0XBK6G71x5jcAkuRs5In1nkngRM9u7UeO8Ng9Oa7kRvf80qynrOKlubI+DmCKWF6cO8p1Yvl5Sqfre/34Z67IWA31aIdC5+n4cGb+g5S0nkKmCbmqcQKgSkheBYN1qa+XNRA75t+OhhSq9tQXjtRSLiZfANMZCdw4g/XnZGs1RN0/ZPv58O7hDA0dNh1vT1uKQr4QkPhLkLbb1qeeObk9o9/Yl8HAjW+lgGHnK3w4EgPnjLocVgxDJ15yy2MVR9ierOWkLfnOt5svRX0jx6l5z3iljVNBn9j/K6Cv66wCgspwdqFnWep6MKmF74MU+cQVN9hdueytqFd3cSKrvAEa+2KlRKtmUIMxRQmHtqOytzXr2ZhdpCEGJuw7sicibz0gmD+/D9ljhr7CsA8AuGHP+FyqcUd/vxnFaaKSp1nUajZqbWhAgiRElU0WV4KoIbDVsH3XdeAGl9s6MqwipuLSo1+ZjfJZwBncYeAmBIyosRpaidYEm5zVjnbxQ9BUSTSjApRPRYbROK+H2vll2mSuM9F30O673U/O40hpzBkJzoyrKgI3TVHQV4YYF5KW+1Iz58eg91O91WFhVQg6bFTpfx4ruFkzT9yRggSKyz7Jx4lMxiChBSt6FCGGhLF5kBEfvBFlOg8fRCFZZ7RpYRxSGaEGmA2jIKuk6h4+bX3AREL0owvzUVFLiX1Cvz977IWhrRoDWbLDUnJxSJLTDyN5eiYEqYJ50tn9harzsxH9RBwncdLVQroAdGKVh+Z7O1DIwBTrBfNm2gSA722FgJikMTEeW7zIDxtjrgK89YKTQyZfHqqHsV+mrAg4h//0EJdP9Z5UPh6IRch7tnTsd7T+PK1eienyXjXMOi9/PoVyIN5XYjgeaM8DDVOnyFGpHe6f8nDRsfMQ4AgvCp4u/0eOddmdZwLFSrzUCRJTG7uqesYuSAiBofolSIJXLPQwBJ6w0i6TvnaPGNUYSTeLgmTkjhvbUA0wO6kl+EIodIYLYb9HqXUqkdN1a95RgcNRdhWbg5alcbHpqZozsDPkyi1t+7R5lcKMAwjArTKj8LuHYBxGHC89W0zMApZD5PzMPki1QgiEY1hkBrtzrCgEWhJluRhuo9sasYr6mwrMMaaf7TqKciaCBywBMlJuuUOSauTLAvwYVLiYI+Lv/5VlAqijXO2TpgSrtpLM6Kcsa4Si64OMXzeVxAC04Qb+tpdXGltGXbmKB1H7S5NIy71tQJKWi1Egg2Srmf5d68E48O0cgYyauWxOF+qgHGbXJM2NyE0gPG9ddpVQ6IUQPtzWInsecb69KEBFa5Xd4bsI0DGsDFkrcJJSCozg/EYesMlZL3Tu1ZdctfMXgmSo9AYCXyAeq6S6iI//yyfeX7ukQLQa0d3Vm3hd7qcrWOu5fTR6JodeZfKZyRZ9KPdiWWphtiiHjhoeFzvmivyuQKGwjrxHpa5kfdvFPx3ZID/08amACD8lm6TfugtYkBoZpS8E7qcU0KPG56eery8KPCDiGjGnxqyxqOCGqw9vn9X8AML+t4pakGSYmkExdih752+cjqhC5L3AnTVY0Xa1egV38Fblq4Ju6hbEjVMyoc7bhxvVWn8nfG7Cr6vZhACDOkBqDBXIrUTokWiCiJzim7o0U+wpJ9dCiLQlDEAwt7HQRjTih7X9CACId9MuctZq9Sw45dKPLovDB1S6bum3lxeFgr0/NwegEgFWfN8xYcncat5VzgJ0eJGk7j8MKhFdTrh86fKzPlIfr9PK374oSrGtBxDaEvDcf8otM0q5Ad0Hk2YCilAoQUf/20KahYGb/Gb9HiwGYM+e0HE7HQ6APj4VFFfj6Bb2bWTuPvEra9DG2dZvLtevotTqHpdVwexgIl22aZ9+tQgwFTuDWlwB8QqPUh7rOjAxpBiPYcm8ZjMjvRQuxDr2Wo5q1iyoS9Fk2MwhaoUlGJ1ocdxZ/HhUQ8+air+NUfMpcNw+NC4FanEEvHauagC7v0bw0yTnexipRpfH4Lus9IBbVzpoRAbVOl86RoHCbd9TEofF7k/1/3HthziMCArfUvTl762seckhtr8S37lOh/qpl+h+SoFwMW9++uLvYe23rjXwz2dME4TQhBFQIzsYjQQgnhyc5bQFTx9NNlCRPCWXYUOLDgcarMperu4eYPSuSiKpyb8a3BeEQ+mGOMONR/Fb4339Ny0dOyOe07GMAymONOzZt+nsaSX3J89dYMYowggrNZhFCmpQRuxG1B5SamOqhhWgGGSszSu2g2i3FSESWjxVnpcjrWMbt+jIr2er6LSmOdLbwS/DrZ+tzMqBbtJcjhOJ5Hznz4pz53lwbup3b4YnAavHh+CR4z7rYmrzqigweLCivxee8WkAywp9HbTOHom81ZCsOZZvGNe75FkckVf9ntDbC35+1S1MvFK1MpZ5BULRuBpFCQzBFP6DAHXwY7HMaw2gQHVKEpJz4WMP0hVknUYa46S7pY1CcpZkkp1YWTH3tj01a7YUHA51JAI7ot9ngaSgh/+cx1WjElyjhgtBEgIks9hINjBMtLF5Gsna3FykcoiaTheXmEXz50jy0KXAuymoV7KdwYNtz4ssPwq/ex80fypqXqArUSzGsP8POUi1ZWUUD9bioR1cg5Krx2q18dvbpvukJoumH1akQ7VUySPr/kFVh67FOTAQiqdFF7YLRhDQU+NdHeojEhBXtaEsRLQnAhlGER+s+AHDaSIxXLepsOj6EtZzsnAn2EVA5OMUs+lm684HLT0JWnDnxcXqhdS+1rpdoXaYNR0jGioOnuHABFp2Bm/yZeqJwNoeHTOFcghr84Z2Ol87YqVAoRoFXb6VEtP2/Blen9j/K6CzwM2wfIrF8MutDPDeAA9pAve+QyEXS9KDzdbH26IFruSXi7onp5wPHbaclw+zpjFEFCZrXaVJLiSEsQrkLMxHa7DlHOtapLtO7EyMd35w+HBnilEIouMqmzRcr3dgI9PwaxhKZf0weSCuQPnGd08IyoyQiOkTwn9UFupewXfxpYLov0TvSBUChhmwxARr+T3zACjdusoZw2i3FOxul4Fdfu4V5RImQL1NMvH0Eu7rtGU71uJYv3zvJwHhmfok8CsDXvwDV9GqaryInHNBHVsyo6G2BSYcZghMCFTuMVlrihnKWqJMy6WFotTuiORF7sI2iqdpb10U5mkvSwuRphDF1vSoxVxSKnD475VzoxWLueKOGsYApUmM/LUh2iNwJwhJl/tLERBmiNFyzkQYStKHo8thJp42+W2Sg6mCV+/AA8PEQeNC7ZoneJ4AO+jnodvtmX3x/GGW+7MmUHSPhx0v45H4fR6d5uz1r0N4UHpfm7uRocVfb6gLwXH8wNutyovo7ZDTsPYEFEXgrk/WeeeSix1HhxPDZ1zbyNg5R63/JYJbJ5Bm8s+SVgK60F/SJf6gBBwfO0MWJFk+UvdPxVIi3svt+BysegsERCpCrGldM4+qfHd/K4ozIL25gz0TtFDCGK0AnJfBskTYRguaYjEuGglEostVk/vMHQmR5bSWehnCNGMxKwK6jRpSUH1qPTDgHkW9P5Ph7NVJ0JKVv5uoSfDzZsx/hf1gFiMsm7Gun8wFNiDF1LBRpDLxn3ugKyYVpQg3g0x3KvHiOfpwTbzkCCoYXsxdO86d42Xpsu3NiC8FMCdOd3032cN1emFdnnPx03YJhs9+b1heprxho3BVQrPt4YVWMied0mEoCFIVUz5kEczaDRk5njU/VSFpyktqVVlqNjxGWK4QeSoGjvUIwFgGBxfVqVszk6G2l2J8KFzbxL4NW76GnYqv7N9l+BS8y43lqUWJhkGAKcL1r3oEQwfsjWRdzFPTwFJ8aqODoTQUEjZyUp/fvHcIOooDJH0hjk5RgjyHIanKl8lx44h4DT3jUg0pKYUlDT6pq44dELDo/axuJWIfgjmPcnq6SwFGJ41dEgfTg8Yo0HiPANDrYPP71cgqjNn8flMXW9uDbIQ7NJFXQ8yqvxwRoDXV+N8VR6rehEvo949r4/5rZevd3b+QoxyYD7iwLy0gHl2lwVSp7eUCpIBQHDRE/OMoGHUHAKC/H6W7e82uvrxxx/xt//235pmNEQkGEvJGHIKJCIjTeY/rU6nRPEzPCygWvj83eurJvA8aBILOnNNEgmhcsXfjRD31Zv3c8fItVKy5kuAc3tvJXWSCg70AD091eQONok5jDdzISFIQwW/L9Zg4u9/B/78Z5yHDy7hThjIenhsGDe1vuM5mhK537fIDBWpPrtGU5s95qitxttzaj7nrFo2JiqliQ6yjwyDQzuCwHNsjpWzVSW0vd+6s0OoYSkfn5Zari4l3NC3aJxjfpbAqmf8+ipM+nESt9uiSXTW9EQPd2XohB/8u15yX2WI/+9xq5CCC0nojt/lQ4ocbLsVGt0VaQLCI01Jk2N1z7iuXdCmZhSY+4+mPPmSir6ZlNGsO3MqKQARpPBG4TaDHdUTZEb24JJcQ9gkXsLqQXP6Teyy20dfocYPopSefw4DGmSOYyznSmyAWZbfLpu8EazVKtUFsgRczQkSoX987YSfsOFQCFgPj2awA7U/A21gi8fN2UoB74JrVDcM+PYi62ronhs3TRbPDwDj6RdgnnF++gvmua5D6sXDztGjT8x78FUevTfId37mlvN5vlqZBwDOl848N28SyLx2xrP1L0ipSZBtaMetgffAd6/0+AIf1zTLGwZrkkWlsZmfEsl1kAIPY5CDs8oTqkh6urdn8B2KmN5cYQKiwn1f75u/K9ZrxTcLSsnuvgdT2JCuaWgENDyGeQhLGo3uQ1C695ZbquGJW4TcG/f+d8zDHb13aKglfgHncdEH3Eq0im3LsGN1U3v2mxKxXIhOnEDemzwEpYGGeLfD05YaZsbH/XdKaWjJT4HvsyRTv4elGg6+R2UP2QOTDy5xms9gA6WUZG2HXhKOrcCIbwqossDoRifKghQ8ZwLoRgcKoFkCPGq1KCaAG0/W9V+vqhtpFzMJx1ubtRM49Gdo1fnYaFTpYtuUktEURnip5uJYoQrmOul9+OmLhDB/eKp37XgWOf38rM9UOjiHByuBbnJeZQ5lp2+WlpLSg/J3njX5myH1GuKEp6eGB5hx6sIwea8kDPRijMnAHvWAeq9RjK7x5+WC9fDYGKPN2Ssful7lvFTkY8yvlqTczN95iRsZ4O+I/vvH//AffrPRVfjVv/jhNwwbi1oFCF0W16uEA1DAs1KOKdVOAaBw8PzCG6a+zqe4+6UpC5kH0SoK5gbpC9UrYMaF7+yoysBWdjUcid/RDT2dxOAoBdXqzTeJlfJStRQtzylruN10/jnbhN+0g865Kp/+/aXY3l4u7lk6YZsu/+HOqdnMeTYiYbwcP8NyZzZHRZXGtFjEwvUqa/dhIxROAEwB80YA0DI4zye4xHXdnJsnYH/2ybUjzzeLjSWTu1xgjKhR+NxeNAqXpwenHHt65LNtso4754yqoeh+8S681qIPIsxTkmYpaRWjCG4eWynlzovvuVxkHvzz+VINr6Zluv7XYTVmwueSOdJgYic+nE7o5iu6slSEqnQyb4asnV7Nc88yr8MgjNp6LHijMoQ25l7Xt5TOlHvSCpX7lGSxOYveTVrH6QQr7swvhGBu/7Pq/z4HiGfKZ/uSgouW3Gw+nxkrXxtBeVsBEKPkViJu6p42nub23esTb3IOlC5vuu2M2+RdoHNgQbR7vFUa5lnWS9DX5q90LonpC3rcBNFmaI4uIgQRVo1S564d75k9UwUt+Xkz9DMxXzGGm12FpXSyBq7bzd+/yxDtUisp2V7qZpAeuTcrOrn3bOAxTe1e6ODevwnvmK/VO8VfhtDMiefuYa9S6tzmWUEVT9s5I0LuDwGL203OssljorBy+7BorpRX0ocBlYFoiKnv3Ls9t5REcenKIkmI89WAN6MpJ3892doPusk5a7KfexENQpNPvM+hl/BXxwdDqHfI34WmNKR73xt55RR8M0S5b1vD0+8XKr+eZwcu8j3ks7mi8TZUFth988UaqCQ7Z0qMeCPTzFI1L341CLle0jMggI2fFse2Ohx/Z/dTP/z6KttyPjvdAm9tr+09/5U/1buie9vQodsHhhN181XA1NF9WB/IM5CiEYLAkl9Tvhgvd3PxuYHWJwhoZJfpck5387xl23CVyvtVUyaW0jWoAmWp9VlxMpn8ls8nDZMOrFzz6WR8jP8155WzLZWPN9l2PFqvqJxb3U68Kms9J39f/L35jfHHEPz/8l/qARMh2bSY9tYLBzvcEoDrLmeLyfKKou/E1rQ7HgZz9dqG8oshWLKKt5po0V3VrbPfu4MoBbdJUHKiK94SNg8E28kTsVXkm6CNufypqLF5RupljWRGKWHZP9oc4+UV+NvfgB9+sJbJpVQr+fulusWmCRhffhIkfXg0ffRw0Ng9voOC+PDY7IWP2TYljJapok1eqBgSyEHGqIFpUs5MXccWr14q2uFiNM9aRpKKW88EP0WhPHJraDyRilSRbi4PgHUGNiRM+1E3qChEC75p5Zgt+uYFftSGamdt7vR4WA0ZUt1L8gvm1zq3affmTqUEdF9/MWKu5fwc8gS8yZ5vqkp4VJH7WgpuWlHH7ojGwB9vo6EHW3T9EIX+bvsPlba2yhYHv0gBzTP0dOBQm1IczfOArMQU6nsU/fKeATYO0q2oSAXXrXfdLR+lKEqudLiUzjU+a7aq4XmfPqG2Ic/Z4uqPy67tTwBIaVUN6mZIAGlnGPRsNX7IUC96dLhv+t91rrHww6AenknavPstLwX4kF5rTkNKuKaHqqwFpe9pwpJGDZ2ZjYYIfrDiliX88dL5eXGTdK/Xpw/Nve/mK25htPxMaRZYEX6eoT9a1u43WplrWMoy7IykmrwRbIweP/whErkKsfm1CXgV/Lfp0ZL6VIewc/P8HqUYnyAf8t5B8gfKKT8I4hqqC+CWdvXOq0eb+9QosYDdTYKFu8HJC677+dkS7yQ85MV+T145DNUrjRBMflo8Pc+WaLKG+RFNP5+FH7GJEQBDRZkqQbq83fT8X75ZKOA2zJMeAXolgZZmG0/pFsCgHFCPBb1XFuONKgPifIY/KBZnaDzcShi/fBXP8b/5a5WlXVc9/0BV/qhvUG/h8Hyhm9WbqrlOVBS5lN0ktGz5Ivna8lNWD/OKmKLJDHvlSEmfx8oBKeGWJCn6ce9i8/XDPmFbHyuyxuVwqG1o2x5CRYF9NaxmT/RMAeeZZ+C+XkLvIfDRF4ubpq8wZN4Ex0v84J6SdY24wgtg5lBYgyvu7zDg20maUe7Sre79fm98vOk+r8M8saVgTb15/wHVAZSv+5Dvfn6ttKaFKcgb4nw2/Q/DUMu0Do7X6QLXw6ORlHm+Afzzi5zZn36Qn4+vnfF2M2po5D491ZK1peDH//gf/z9A8AF7Wykb1LCULXj3q/ybh1xKDakxpu9QS/9zLDfvXatUMc/GoLnZhgw7VMa+p/8gE1tDNKTxDYrPH/TBG15if15DbLRQs+QpcWbXTdN2vKJpnAstWm5pY1EWQV9ZVaV5ltds3LbREvT77hcYw2rvb9bsLPXmYW7v3oRjbA9Z6cH/2p8bt4Y0YMSrG7yV/155/TVTlOfv99eT1FI6E1LNnIpDYHTw0pnB6rxOjuRt60tp187vb++I3yJAaIfGpSEwQd2pjtACPRbOerd90IkTMVkWvHsGzUFvBS7e+dxm3j65soE5t9/fLhKOxpfNn0qpULSuy5SFtFZa3xgYREg9v2GpP8oF87rQxadrsQoIoZ5xczfcj1u06A1RqpK2pipBNqCUnQ/RmcZrxw9v0aj39nFzt0KoiV6loL7Un+1WoLo9fnPn0d6zZl9Q7wS9pPT2NdW4NheplBZNa9733kVy32UJTA/c2KAx5SpOeV7WvJBE4f+g9/jmdETk6j0lL3lzf35t+H32a9lYC6xMYnvg5B3Dwfz3acg1PMbvkacl/c72fpHOuVfvbRQfa8j3uvmD8mzeY99RuqEl92wP/r0hOD0/rq3xhm2ea3Nwcrj5EM/W83HdWy9bPR3au9yd+1V2pptPz7EV6IDzcvr5NJN38/8V2ejlYPN5r09seTbebmcj4/Er/NbN0eS4u/vv7mPOsndO8QgB9jvqTlTumY/Tp9V4uH93l2/mzfFH6/ei2Yft4OSUHoynbu46SYMe7IaPuI0rpdK78atQ+6XYsje86td0Or+/9nkeUM4Nyt9hFWNvrvec36e3yLbA3yHlX28FxvvjdxH8cRzxpz/96Q897D7u4z7u4z7u4z7u4z7u4z7+/x0//fQTrhaL+3b8roJ/H/dxH/dxH/dxH/dxH/dxH//3jD8eonMf93Ef93Ef93Ef93Ef93Ef/8ePu4J/H/dxH/dxH/dxH/dxH/fxr2jcFfz7uI/7uI/7uI/7uI/7uI9/ReOu4N/HfdzHfdzHfdzHfdzHffwrGncF/z7u4z7u4z7u4z7u4z7u41/RuCv493Ef93Ef93Ef93Ef93Ef/4rG/waceLW0o3E74AAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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" ] @@ -311,7 +311,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "19:50:37 C02YR4ANLVCJ SmartSim[54122] INFO fv_simulation(54161): Completed\n" + "20:37:31 HPE-C02YR4ANLVCJ SmartSim[25938:JobManager] INFO fv_simulation(26039): SmartSimStatus.STATUS_COMPLETED\n" ] } ], @@ -335,7 +335,7 @@ "\n", "# Use the Experiment API to wait until the model is finished\n", "while not exp.finished(model):\n", - " time.sleep(5)" + " time.sleep(5)\n" ] }, { @@ -378,6 +378,7 @@ }, { "cell_type": "code", + "id": "6f3ed63d-e324-443d-9b68-b2cf618d31c7", "execution_count": 7, "metadata": {}, "outputs": [ @@ -385,10 +386,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Default@19-50-39:ERROR: Redis IO error when executing command: Failed to get reply: Resource temporarily unavailable\n", - "Default@19-50-40:ERROR: Redis IO error when executing command: Failed to get reply: Resource temporarily unavailable\n", - "Default@19-50-41:ERROR: Redis IO error when executing command: Failed to get reply: Resource temporarily unavailable\n", - "Default@19-50-43:ERROR: Redis IO error when executing command: Failed to get reply: Resource temporarily unavailable\n" + "Default@20-37-33:ERROR: Redis IO error when executing command: Failed to get reply: Resource temporarily unavailable\n" ] } ], @@ -397,11 +395,12 @@ "\n", "probe_x, probe_y = np.meshgrid(range(20, 400, 20), range(20, 100, 20))\n", "client.put_tensor(\"probe_x\", probe_x)\n", - "client.put_tensor(\"probe_y\", probe_y)" + "client.put_tensor(\"probe_y\", probe_y)\n" ] }, { "cell_type": "markdown", + "id": "96c154fe-5ca8-4d89-91f8-8fd4e75cb80e", "metadata": {}, "source": [ "We then apply the function `probe_points` to the `ux` and `uy` tensors computed in the last time step of the previous simulation. Note that all tensors are already on the DB, thus we can reference them by name. Finally, we download and plot the output (a 2D velocity field), which is stored as `probe_u` on the DB." @@ -409,12 +408,13 @@ }, { "cell_type": "code", + "id": "36e3b415-dcc1-4d25-9cce-52388146a4bb", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -430,11 +430,12 @@ "client.run_script(\"probe\", \"probe_points\", inputs=[ux_name, uy_name , \"probe_x\", \"probe_y\", \"cylinder\"], outputs=[\"probe_u\"])\n", "\n", "probe_u = client.get_tensor(\"probe_u\")\n", - "plot_lattice_probes(time_steps-1, probe_x, probe_y, probe_u)" + "plot_lattice_probes(time_steps-1, probe_x, probe_y, probe_u)\n" ] }, { "cell_type": "markdown", + "id": "9d7e4966-a0de-480c-9556-936197a5a5d2", "metadata": {}, "source": [ "### Uploading a function inline\n", @@ -451,11 +452,12 @@ "import torch\n", "\n", "def compute_norm(ux: torch.Tensor, uy: torch.Tensor):\n", - " return torch.sqrt(ux*ux + uy*uy)" + " return torch.sqrt(ux*ux + uy*uy)\n" ] }, { "cell_type": "markdown", + "id": "1c4daf43-34d0-482a-b9b5-b3b6f1e173c4", "metadata": {}, "source": [ "We then store the function on the DB under the key `norm_function`." @@ -468,11 +470,12 @@ "metadata": {}, "outputs": [], "source": [ - "client.set_function(\"norm_function\", compute_norm)" + "client.set_function(\"norm_function\", compute_norm)\n" ] }, { "cell_type": "markdown", + "id": "19409ac6-e118-44db-a847-2d905fdf0331", "metadata": {}, "source": [ "Note that the key we used identifies a functional unit containing the function itself: this is similar to the key used to store the `probe` script above. When we want to run the function, we just call it with `run_script`, by indicating the `script` key as `\"norm_function\"` and the name of the function itself as `\"compute_norm\"`." @@ -486,7 +489,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -500,7 +503,7 @@ "client.run_script(\"norm_function\", \"compute_norm\", [f\"{{data_{i}}}.uy\", f\"{{data_{i}}}.ux\"], [\"u\"])\n", "u = client.get_tensor(\"u\")\n", "\n", - "plot_lattice_norm(time_steps-1, u, cylinder)" + "plot_lattice_norm(time_steps-1, u, cylinder)\n" ] }, { @@ -511,7 +514,7 @@ "outputs": [], "source": [ "# Optionally clear the database\n", - "client.flush_db(db.get_address())" + "client.flush_db(db.get_address())\n" ] }, { @@ -537,16 +540,16 @@ "text/html": [ "\n", "\n", - "\n", + "\n", "\n", "\n", - "\n", - "\n", + "\n", + "\n", "\n", "
Name Entity-Type JobID RunID Time Status Returncode
Name Entity-Type JobID RunID Time Status Returncode
0 fv_simulation Model 54161 0 38.1561Completed0
1 orchestrator_0DBNode 54134 0 66.5750Cancelled0
0 fv_simulation Model 26039 0 59.2839SmartSimStatus.STATUS_COMPLETED0
1 orchestrator_0DBNode 25963 0 75.2015SmartSimStatus.STATUS_CANCELLED0
" ], "text/plain": [ - "'\\n\\n\\n\\n\\n\\n\\n\\n
Name Entity-Type JobID RunID Time Status Returncode
0 fv_simulation Model 54161 0 38.1561Completed0
1 orchestrator_0DBNode 54134 0 66.5750Cancelled0
'" + "'\\n\\n\\n\\n\\n\\n\\n\\n
Name Entity-Type JobID RunID Time Status Returncode
0 fv_simulation Model 26039 0 59.2839SmartSimStatus.STATUS_COMPLETED0
1 orchestrator_0DBNode 25963 0 75.2015SmartSimStatus.STATUS_CANCELLED0
'" ] }, "execution_count": 14, @@ -555,7 +558,7 @@ } ], "source": [ - "exp.summary(style=\"html\")" + "exp.summary(style=\"html\")\n" ] } ], @@ -575,7 +578,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/doc/tutorials/online_analysis/lattice/vishelpers.py b/doc/tutorials/online_analysis/lattice/vishelpers.py index 725c690fd..782692fac 100644 --- a/doc/tutorials/online_analysis/lattice/vishelpers.py +++ b/doc/tutorials/online_analysis/lattice/vishelpers.py @@ -11,7 +11,7 @@ def plot_lattice_vorticity(timestep, ux, uy, cylinder): np.roll(uy, -1, axis=1) - np.roll(uy, 1, axis=1) ) vorticity[cylinder] = np.nan - cmap = plt.cm.get_cmap("bwr").copy() + cmap = plt.get_cmap("bwr").copy() cmap.set_bad(color="black") plt.imshow(vorticity, cmap=cmap) plt.clim(-0.1, 0.1) @@ -30,7 +30,7 @@ def plot_lattice_norm(timestep, u, cylinder): plt.cla() u[cylinder] = np.nan - cmap = plt.cm.get_cmap("jet").copy() + cmap = plt.get_cmap("jet").copy() cmap.set_bad(color="black") plt.contour(u, cmap=cmap) plt.clim(-0.1, 0.1) @@ -47,7 +47,7 @@ def plot_lattice_probes(timestep, probe_x, probe_y, probe_u): fig = plt.figure(figsize=(12, 6), dpi=80) plt.cla() - cmap = plt.cm.get_cmap("binary").copy() + cmap = plt.get_cmap("binary").copy() cmap.set_bad(color="black") plt.quiver( probe_x, diff --git a/docker-compose.yml b/docker-compose.yml index f5be4e338..e65259162 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -1,7 +1,3 @@ - - -version: '3' - services: docs-dev: image: smartsim-docs:dev-latest @@ -18,9 +14,9 @@ services: - "8888:8888" tutorials-prod: - image: smartsim-tutorials:v0.7.0 + image: smartsim-tutorials:v0.8.0 build: context: . dockerfile: ./docker/prod/Dockerfile ports: - - "8888:8888" \ No newline at end of file + - "8888:8888" diff --git a/docker/dev/Dockerfile b/docker/dev/Dockerfile index 3ab3a37f8..faeeae8f3 100644 --- a/docker/dev/Dockerfile +++ b/docker/dev/Dockerfile @@ -36,9 +36,9 @@ RUN useradd --system --create-home --shell /bin/bash -g root -G sudo craylabs && apt-get update \ && apt-get install --no-install-recommends -y build-essential \ git gcc make git-lfs wget libopenmpi-dev openmpi-bin unzip \ - python3-pip python3.9 python3.9-dev cmake \ + python3-pip python3 python3-dev cmake \ && rm -rf /var/lib/apt/lists/* \ - && ln -s /usr/bin/python3.9 /usr/bin/python + && ln -s /usr/bin/python3 /usr/bin/python WORKDIR /home/craylabs RUN git clone https://github.com/CrayLabs/SmartRedis.git --branch develop --depth=1 smartredis \ @@ -50,11 +50,11 @@ COPY . /home/craylabs/SmartSim RUN chown craylabs:root -R SmartSim USER craylabs -RUN cd SmartSim && SMARTSIM_SUFFIX=dev python -m pip install .[ml] +RUN cd SmartSim && SMARTSIM_SUFFIX=dev python -m pip install . RUN export PATH=/home/craylabs/.local/bin:$PATH && \ echo "export PATH=/home/craylabs/.local/bin:$PATH" >> /home/craylabs/.bashrc && \ - python -m pip install jupyter jupyterlab matplotlib && \ + python -m pip install jupyter jupyterlab "ipython<8" matplotlib && \ smart clobber && \ smart build --device cpu -v && \ chown craylabs:root -R /home/craylabs/.local && \ diff --git a/docker/docs/dev/Dockerfile b/docker/docs/dev/Dockerfile index e9db9c342..dbac524bc 100644 --- a/docker/docs/dev/Dockerfile +++ b/docker/docs/dev/Dockerfile @@ -55,8 +55,7 @@ RUN git clone https://github.com/CrayLabs/SmartDashboard.git --branch develop -- && rm -rf ~/.cache/pip # Install docs dependencies and SmartSim -RUN python -m pip install -r doc/requirements-doc.txt \ - && NO_CHECKS=1 SMARTSIM_SUFFIX=dev python -m pip install . +RUN NO_CHECKS=1 SMARTSIM_SUFFIX=dev python -m pip install .[docs] # Note this is needed to ensure that the Sphinx builds. Can be removed with newer Tensorflow RUN python -m pip install typing_extensions==4.6.1 diff --git a/docker/prod-cuda11/Dockerfile b/docker/prod-cuda11/Dockerfile new file mode 100644 index 000000000..fc2747905 --- /dev/null +++ b/docker/prod-cuda11/Dockerfile @@ -0,0 +1,61 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +FROM ubuntu:22.04 + +LABEL maintainer="Cray Labs" +LABEL org.opencontainers.image.source https://github.com/CrayLabs/SmartSim + +ARG DEBIAN_FRONTEND="noninteractive" +ENV TZ=US/Seattle + +# Make basic dependencies +RUN apt-get update \ + && apt-get install --no-install-recommends -y build-essential \ + git gcc make git-lfs wget libopenmpi-dev openmpi-bin unzip \ + python3-pip python3 python3-dev cmake wget apt-utils + +# # Install Cudatoolkit 11.8 +ENV TERM="xterm" +RUN wget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run && \ + chmod +x ./cuda_11.8.0_520.61.05_linux.run && \ + ./cuda_11.8.0_520.61.05_linux.run --silent --toolkit && \ + rm ./cuda_11.8.0_520.61.05_linux.run + +# Install cuDNN 8.9.7 +RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/libcudnn8_8.9.7.29-1+cuda11.8_amd64.deb && \ + dpkg -i libcudnn8_8.9.7.29-1+cuda11.8_amd64.deb && \ + rm ./libcudnn8_8.9.7.29-1+cuda11.8_amd64.deb + + # Install SmartSim and SmartRedis + RUN pip install git+https://github.com/CrayLabs/SmartRedis.git && \ + pip install "smartsim @ git+https://github.com/CrayLabs/SmartSim.git" + + ENV CUDA_HOME="/usr/local/cuda/" + ENV PATH="${PATH}:${CUDA_HOME}/bin" + + # Build ML Backends + RUN smart build --device=gpu --onnx diff --git a/docker/prod-cuda12/Dockerfile b/docker/prod-cuda12/Dockerfile new file mode 100644 index 000000000..bbdfd3513 --- /dev/null +++ b/docker/prod-cuda12/Dockerfile @@ -0,0 +1,64 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +FROM ubuntu:22.04 + +LABEL maintainer="Cray Labs" +LABEL org.opencontainers.image.source https://github.com/CrayLabs/SmartSim + +ARG DEBIAN_FRONTEND="noninteractive" +ENV TZ=US/Seattle + +# Make basic dependencies +RUN apt-get update \ + && apt-get install --no-install-recommends -y build-essential \ + git gcc make git-lfs wget libopenmpi-dev openmpi-bin unzip \ + python3-pip python3 python3-dev cmake wget + +# Install Cudatoolkit 12.5 +RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb && \ + dpkg -i cuda-keyring_1.1-1_all.deb && \ + apt-get update -y && \ + apt-get install -y cuda-toolkit-12-5 + +# Install cuDNN 8.9.7 +RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb && \ + dpkg -i libcudnn8_8.9.7.29-1+cuda12.2_amd64.deb + +# Install SmartSim and SmartRedis +RUN pip install git+https://github.com/CrayLabs/SmartRedis.git && \ + pip install git+https://github.com/CrayLabs/SmartSim.git@cuda-12-support + +ENV CUDA_HOME="/usr/local/cuda/" +ENV PATH="${PATH}:${CUDA_HOME}/bin" + +# Install machine-learning python packages consistent with RedisAI +# Note: pytorch gets installed in the smart build step +# This step will be deprecated in a future update +RUN pip install tensorflow==2.15.0 + +# Build ML Backends +RUN smart build --device=cuda121 diff --git a/docker/prod/Dockerfile b/docker/prod/Dockerfile index 325ace923..f8560f7bd 100644 --- a/docker/prod/Dockerfile +++ b/docker/prod/Dockerfile @@ -36,19 +36,21 @@ RUN useradd --system --create-home --shell /bin/bash -g root -G sudo craylabs && apt-get update \ && apt-get install --no-install-recommends -y build-essential \ git gcc make git-lfs wget libopenmpi-dev openmpi-bin unzip \ - python3.9 python3.9-dev python3-pip cmake \ + python3-pip python3 python3-dev cmake \ && rm -rf /var/lib/apt/lists/* \ - && ln -s /usr/bin/python3.9 /usr/bin/python + && ln -s /usr/bin/python3 /usr/bin/python WORKDIR /home/craylabs -COPY --chown=craylabs:root ./tutorials/ /home/craylabs/tutorials/ +COPY --chown=craylabs:root ./doc/tutorials/ /home/craylabs/tutorials/ USER craylabs RUN export PATH=/home/craylabs/.local/bin:$PATH && \ echo "export PATH=/home/craylabs/.local/bin:$PATH" >> /home/craylabs/.bashrc && \ - python -m pip install smartsim[ml]==0.7.0 jupyter jupyterlab matplotlib && \ + python -m pip install smartsim==0.8.0 jupyter jupyterlab "ipython<8" matplotlib && \ smart build --device cpu -v && \ chown craylabs:root -R /home/craylabs/.local && \ rm -rf ~/.cache/pip +WORKDIR /home/craylabs/tutorials/ + CMD ["/bin/bash", "-c", "PATH=/home/craylabs/.local/bin:$PATH /home/craylabs/.local/bin/jupyter lab --port 8888 --no-browser --ip=0.0.0.0"] diff --git a/pyproject.toml b/pyproject.toml index 91164a68b..62df92f0c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,7 @@ [build-system] -requires = ["setuptools", "wheel", "cmake>=3.13"] +requires = ["packaging>=24.0", "setuptools>=70.0", "wheel", "cmake>=3.13"] build-backend = "setuptools.build_meta" [tool.black] diff --git a/setup.cfg b/setup.cfg index 742386d2c..1ea8d2518 100644 --- a/setup.cfg +++ b/setup.cfg @@ -51,9 +51,6 @@ classifiers = [options] packages = find: -setup_requires = - setuptools>=39.2 - cmake>=3.13 include_package_data = True python_requires = >=3.9,<3.12 diff --git a/setup.py b/setup.py index 6e46ddef9..571974d28 100644 --- a/setup.py +++ b/setup.py @@ -77,9 +77,6 @@ from pathlib import Path from setuptools import setup -from setuptools.command.build_py import build_py -from setuptools.command.install import install -from setuptools.dist import Distribution # Some necessary evils we have to do to be able to use # the _install tools in smartsim/smartsim/_core/_install @@ -95,12 +92,6 @@ buildenv = importlib.util.module_from_spec(buildenv_spec) buildenv_spec.loader.exec_module(buildenv) -# import builder module -builder_path = _install_dir.joinpath("builder.py") -builder_spec = importlib.util.spec_from_file_location("builder", str(builder_path)) -builder = importlib.util.module_from_spec(builder_spec) -builder_spec.loader.exec_module(builder) - # helper classes for building dependencies that are # also utilized by the Smart CLI build_env = buildenv.BuildEnv(checks=False) @@ -128,60 +119,7 @@ class BuildError(Exception): pass - -# Hacky workaround for solving CI build "purelib" issue -# see https://github.com/google/or-tools/issues/616 -class InstallPlatlib(install): - def finalize_options(self): - super().finalize_options() - if self.distribution.has_ext_modules(): - self.install_lib = self.install_platlib - - -class SmartSimBuild(build_py): - def run(self): - database_builder = builder.DatabaseBuilder( - build_env(), build_env.MALLOC, build_env.JOBS - ) - if not database_builder.is_built: - database_builder.build_from_git(versions.REDIS_URL, versions.REDIS) - - database_builder.cleanup() - - # run original build_py command - super().run() - - -# Tested with wheel v0.29.0 -class BinaryDistribution(Distribution): - """Distribution which always forces a binary package with platform name - - We use this because we want to pre-package Redis for certain - platforms to use. - """ - - def has_ext_modules(_placeholder): - return True - - # Define needed dependencies for the installation -deps = [ - "psutil>=5.7.2", - "coloredlogs>=10.0", - "tabulate>=0.8.9", - "redis>=4.5", - "tqdm>=4.50.2", - "filelock>=3.4.2", - "protobuf~=3.20", - "jinja2>=3.1.2", - "watchdog>=4.0.0", - "pydantic==1.10.14", - "pyzmq>=25.1.2", - "pygithub>=2.3.0", -] - -# Add SmartRedis at specific version -deps.append("smartredis>={}".format(versions.SMARTREDIS)) extras_require = { "dev": [ @@ -199,26 +137,54 @@ def has_ext_modules(_placeholder): "types-redis", "types-tabulate", "types-tqdm", - "types-tensorflow==2.12.0.9", + "types-tensorflow", "types-setuptools", "typing_extensions>=4.1.0", ], - # see smartsim/_core/_install/buildenv.py for more details - **versions.ml_extras_required(), + "docs": [ + "Sphinx==6.2.1", + "breathe==4.35.0", + "sphinx-fortran==1.1.1", + "sphinx-book-theme==1.0.1", + "sphinx-copybutton==0.5.2", + "sphinx-tabs==3.4.4", + "nbsphinx==0.9.3", + "docutils==0.18.1", + "torch==2.0.1", + "tensorflow>=2.14,<3.0", + "ipython", + "jinja2==3.1.2", + "sphinx-design", + "pypandoc", + "sphinx-autodoc-typehints", + "myst_parser", + ], } # rest in setup.cfg setup( version=smartsim_version, - install_requires=deps, - cmdclass={ - "build_py": SmartSimBuild, - "install": InstallPlatlib, - }, + install_requires=[ + "packaging>=24.0", + "psutil>=5.7.2", + "coloredlogs>=10.0", + "tabulate>=0.8.9", + "redis>=4.5", + "tqdm>=4.50.2", + "filelock>=3.4.2", + "GitPython<=3.1.43", + "protobuf<=3.20.3", + "jinja2>=3.1.2", + "watchdog>4,<5", + "pydantic>2", + "pyzmq>=25.1.2", + "pygithub>=2.3.0", + "numpy<2", + "smartredis>=0.6,<0.7", + ], zip_safe=False, extras_require=extras_require, - distclass=BinaryDistribution, entry_points={ "console_scripts": [ "smart = smartsim._core._cli.__main__:main", diff --git a/smartsim/_core/_cli/build.py b/smartsim/_core/_cli/build.py index 951521f17..5d094b72f 100644 --- a/smartsim/_core/_cli/build.py +++ b/smartsim/_core/_cli/build.py @@ -25,26 +25,34 @@ # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import argparse +import importlib.metadata +import operator import os -import platform -import sys +import re +import shutil +import textwrap import typing as t from pathlib import Path from tabulate import tabulate from smartsim._core._cli.scripts.dragon_install import install_dragon -from smartsim._core._cli.utils import SMART_LOGGER_FORMAT, color_bool, pip +from smartsim._core._cli.utils import SMART_LOGGER_FORMAT from smartsim._core._install import builder -from smartsim._core._install.buildenv import ( - BuildEnv, - DbEngine, - SetupError, - Version_, - VersionConflictError, - Versioner, +from smartsim._core._install.buildenv import BuildEnv, DbEngine, Version_, Versioner +from smartsim._core._install.mlpackages import ( + DEFAULT_MLPACKAGE_PATH, + DEFAULT_MLPACKAGES, + MLPackageCollection, + load_platform_configs, ) -from smartsim._core._install.builder import BuildError, Device +from smartsim._core._install.platform import ( + Architecture, + Device, + OperatingSystem, + Platform, +) +from smartsim._core._install.redisaiBuilder import RedisAIBuilder from smartsim._core.config import CONFIG from smartsim._core.utils.helpers import installed_redisai_backends from smartsim.error import SSConfigError @@ -55,25 +63,6 @@ # NOTE: all smartsim modules need full paths as the smart cli # may be installed into a different directory. -_TPinningStr = t.Literal["==", "!=", ">=", ">", "<=", "<", "~="] - - -def check_py_onnx_version(versions: Versioner) -> None: - """Check Python environment for ONNX installation""" - _check_packages_in_python_env( - { - "onnx": Version_(versions.ONNX), - "skl2onnx": Version_(versions.REDISAI.skl2onnx), - "onnxmltools": Version_(versions.REDISAI.onnxmltools), - "scikit-learn": Version_(getattr(versions.REDISAI, "scikit-learn")), - }, - ) - - -def check_py_tf_version(versions: Versioner) -> None: - """Check Python environment for TensorFlow installation""" - _check_packages_in_python_env({"tensorflow": Version_(versions.TENSORFLOW)}) - def check_backends_install() -> bool: """Checks if backends have already been installed. @@ -115,8 +104,6 @@ def build_database( database_builder = builder.DatabaseBuilder( build_env(), jobs=build_env.JOBS, - _os=builder.OperatingSystem.from_str(platform.system()), - architecture=builder.Architecture.from_str(platform.machine()), malloc=build_env.MALLOC, verbose=verbose, ) @@ -125,220 +112,92 @@ def build_database( f"Building {database_name} version {versions.REDIS} " f"from {versions.REDIS_URL}" ) - database_builder.build_from_git(versions.REDIS_URL, versions.REDIS_BRANCH) + database_builder.build_from_git( + versions.REDIS_URL, branch=versions.REDIS_BRANCH + ) database_builder.cleanup() - logger.info(f"{database_name} build complete!") + logger.info(f"{database_name} build complete!") + else: + logger.warning( + f"{database_name} was previously built, run 'smart clobber' to rebuild" + ) def build_redis_ai( + platform: Platform, + mlpackages: MLPackageCollection, build_env: BuildEnv, - versions: Versioner, - device: Device, - use_torch: bool = True, - use_tf: bool = True, - use_onnx: bool = False, - torch_dir: t.Union[str, Path, None] = None, - libtf_dir: t.Union[str, Path, None] = None, - verbose: bool = False, - torch_with_mkl: bool = True, + verbose: bool, ) -> None: - # make sure user isn't trying to do something silly on MacOS - if build_env.PLATFORM == "darwin" and device == Device.GPU: - raise BuildError("SmartSim does not support GPU on MacOS") - - # decide which runtimes to build - print("\nML Backends Requested") - backends_table = [ - ["PyTorch", versions.TORCH, color_bool(use_torch)], - ["TensorFlow", versions.TENSORFLOW, color_bool(use_tf)], - ["ONNX", versions.ONNX, color_bool(use_onnx)], - ] - print(tabulate(backends_table, tablefmt="fancy_outline"), end="\n\n") - print(f"Building for GPU support: {color_bool(device == Device.GPU)}\n") - - if not check_backends_install(): - sys.exit(1) - - # TORCH - if use_torch and torch_dir: - torch_dir = Path(torch_dir).resolve() - if not torch_dir.is_dir(): - raise SetupError( - f"Could not find requested user Torch installation: {torch_dir}" - ) - - # TF - if use_tf and libtf_dir: - libtf_dir = Path(libtf_dir).resolve() - if not libtf_dir.is_dir(): - raise SetupError( - f"Could not find requested user TF installation: {libtf_dir}" - ) - - build_env_dict = build_env() - - rai_builder = builder.RedisAIBuilder( - build_env=build_env_dict, - jobs=build_env.JOBS, - _os=builder.OperatingSystem.from_str(platform.system()), - architecture=builder.Architecture.from_str(platform.machine()), - torch_dir=str(torch_dir) if torch_dir else "", - libtf_dir=str(libtf_dir) if libtf_dir else "", - build_torch=use_torch, - build_tf=use_tf, - build_onnx=use_onnx, - verbose=verbose, - torch_with_mkl=torch_with_mkl, + logger.info("Building RedisAI and backends...") + rai_builder = RedisAIBuilder( + platform, mlpackages, build_env, CONFIG.build_path, verbose ) - - if rai_builder.is_built: - logger.info("RedisAI installed. Run `smart clean` to remove.") - else: - # get the build environment, update with CUDNN env vars - # if present and building for GPU, otherwise warn the user - if device == Device.GPU: - gpu_env = build_env.get_cudnn_env() - cudnn_env_vars = [ - "CUDNN_LIBRARY", - "CUDNN_INCLUDE_DIR", - "CUDNN_INCLUDE_PATH", - "CUDNN_LIBRARY_PATH", - ] - if not gpu_env: - logger.warning( - "CUDNN environment variables not found.\n" - f"Looked for {cudnn_env_vars}" - ) - else: - build_env_dict.update(gpu_env) - # update RAI build env with cudnn env vars - rai_builder.env = build_env_dict - - logger.info( - f"Building RedisAI version {versions.REDISAI}" - f" from {versions.REDISAI_URL}" - ) - - # NOTE: have the option to add other builds here in the future - # like "from_tarball" - rai_builder.build_from_git( - versions.REDISAI_URL, versions.REDISAI_BRANCH, device - ) - logger.info("ML Backends and RedisAI build complete!") - - -def check_py_torch_version(versions: Versioner, device: Device = Device.CPU) -> None: - """Check Python environment for TensorFlow installation""" - if BuildEnv.is_macos(): - if device == Device.GPU: - raise BuildError("SmartSim does not support GPU on MacOS") - device_suffix = "" - else: # linux - if device == Device.CPU: - device_suffix = versions.TORCH_CPU_SUFFIX - elif device == Device.GPU: - device_suffix = versions.TORCH_CUDA_SUFFIX - else: - raise BuildError("Unrecognized device requested") - - torch_deps = { - "torch": Version_(f"{versions.TORCH}{device_suffix}"), - "torchvision": Version_(f"{versions.TORCHVISION}{device_suffix}"), + rai_builder.build() + rai_builder.cleanup_build() + + +def parse_requirement( + requirement: str, +) -> t.Tuple[str, t.Optional[str], t.Callable[[Version_], bool]]: + operators = { + "==": operator.eq, + "<=": operator.le, + ">=": operator.ge, + "<": operator.lt, + ">": operator.gt, } - missing, conflicts = _assess_python_env( - torch_deps, - package_pinning="==", - validate_installed_version=_create_torch_version_validator( - with_suffix=device_suffix - ), + semantic_version_pattern = r"\d+(?:\.\d+(?:\.\d+)?)?([^\s]*)" + pattern = ( + r"^" # Start + r"([a-zA-Z0-9_\-]+)" # Package name + r"(?:\[[a-zA-Z0-9_\-,]+\])?" # Any extras + r"(?:([<>=!~]{1,2})" # Pinning string + rf"({semantic_version_pattern}))?" # A version number + r"$" # End ) + match = re.match(pattern, requirement) + if match is None: + raise ValueError(f"Invalid requirement string: {requirement}") + module_name, cmp_op, version_str, suffix = match.groups() + version = Version_(version_str) if version_str is not None else None + if cmp_op is None: + is_compatible = lambda _: True # pylint: disable=unnecessary-lambda-assignment + elif (cmp := operators.get(cmp_op, None)) is None: + raise ValueError(f"Unrecognized comparison operator: {cmp_op}") + else: - if len(missing) == len(torch_deps) and not conflicts: - # All PyTorch deps are not installed and there are no conflicting - # python packages. We can try to install torch deps into the current env. - logger.info( - "Torch version not found in python environment. " - "Attempting to install via `pip`" - ) - wheel_device = ( - device.value if device == Device.CPU else device_suffix.replace("+", "") - ) - pip( - "install", - "--extra-index-url", - f"https://download.pytorch.org/whl/{wheel_device}", - *(f"{package}=={version}" for package, version in torch_deps.items()), - ) - elif missing or conflicts: - logger.warning(_format_incompatible_python_env_message(missing, conflicts)) - - -def _create_torch_version_validator( - with_suffix: str, -) -> t.Callable[[str, t.Optional[Version_]], bool]: - def check_torch_version(package: str, version: t.Optional[Version_]) -> bool: - if not BuildEnv.check_installed(package, version): - return False - # Default check only looks at major/minor version numbers, - # Torch requires we look at the patch as well - installed = BuildEnv.get_py_package_version(package) - if with_suffix and with_suffix not in installed.patch: - raise VersionConflictError( - package, - installed, - version or Version_(f"X.X.X{with_suffix}"), - msg=( - f"{package}=={installed} does not satisfy device " - f"suffix requirement: {with_suffix}" - ), + def is_compatible(other: Version_) -> bool: + assert version is not None # For type check, always should be true + match_ = re.match(rf"^{semantic_version_pattern}$", other) + return ( + cmp(other, version) and match_ is not None and match_.group(1) == suffix ) - return True - return check_torch_version + return module_name, f"{cmp_op}{version}" if version else None, is_compatible -def _check_packages_in_python_env( - packages: t.Mapping[str, t.Optional[Version_]], - package_pinning: _TPinningStr = "==", - validate_installed_version: t.Optional[ - t.Callable[[str, t.Optional[Version_]], bool] - ] = None, -) -> None: - # TODO: Do not like how the default validation function will always look for - # a `==` pinning. Maybe turn `BuildEnv.check_installed` into a factory - # that takes a pinning and returns an appropriate validation fn? - validate_installed_version = validate_installed_version or BuildEnv.check_installed - missing, conflicts = _assess_python_env( - packages, - package_pinning, - validate_installed_version, - ) +def check_ml_python_packages(packages: MLPackageCollection) -> None: + missing = [] + conflicts = [] + + for package in packages.values(): + for requirement in package.python_packages: + module_name, version_spec, is_compatible = parse_requirement(requirement) + try: + installed = BuildEnv.get_py_package_version(module_name) + if not is_compatible(installed): + conflicts.append( + f"{module_name}: {installed} is installed, " + f"but {version_spec or 'Any'} is required" + ) + except importlib.metadata.PackageNotFoundError: + missing.append(module_name) if missing or conflicts: logger.warning(_format_incompatible_python_env_message(missing, conflicts)) -def _assess_python_env( - packages: t.Mapping[str, t.Optional[Version_]], - package_pinning: _TPinningStr, - validate_installed_version: t.Callable[[str, t.Optional[Version_]], bool], -) -> t.Tuple[t.List[str], t.List[str]]: - missing: t.List[str] = [] - conflicts: t.List[str] = [] - - for name, version in packages.items(): - spec = f"{name}{package_pinning}{version}" if version else name - try: - if not validate_installed_version(name, version): - # Not installed! - missing.append(spec) - except VersionConflictError: - # Incompatible version found - conflicts.append(spec) - - return missing, conflicts - - def _format_incompatible_python_env_message( missing: t.Collection[str], conflicting: t.Collection[str] ) -> str: @@ -349,20 +208,24 @@ def _format_incompatible_python_env_message( missing_str = fmt_list("Missing", missing) conflict_str = fmt_list("Conflicting", conflicting) sep = "\n" if missing_str and conflict_str else "" - return ( - "Python Env Status Warning!\n" - "Requested Packages are Missing or Conflicting:\n\n" - f"{missing_str}{sep}{conflict_str}\n\n" - "Consider installing packages at the requested versions via `pip` or " - "uninstalling them, installing SmartSim with optional ML dependencies " - "(`pip install smartsim[ml]`), and running `smart clean && smart build ...`" - ) + + return textwrap.dedent(f"""\ + Python Package Warning: + + Requested packages are missing or have a version mismatch with + their respective backend: + + {missing_str}{sep}{conflict_str} + + Consider uninstalling any conflicting packages and rerunning + `smart build` if you encounter issues. + """) def _configure_keydb_build(versions: Versioner) -> None: """Configure the redis versions to be used during the build operation""" versions.REDIS = Version_("6.2.0") - versions.REDIS_URL = "https://github.com/EQ-Alpha/KeyDB" + versions.REDIS_URL = "https://github.com/EQ-Alpha/KeyDB.git" versions.REDIS_BRANCH = "v6.2.0" CONFIG.conf_path = Path(CONFIG.core_path, "config", "keydb.conf") @@ -376,14 +239,33 @@ def _configure_keydb_build(versions: Versioner) -> None: def execute( args: argparse.Namespace, _unparsed_args: t.Optional[t.List[str]] = None, / ) -> int: + + # Unpack various arguments verbose = args.v keydb = args.keydb - device = Device(args.device.lower()) + device = Device.from_str(args.device.lower()) is_dragon_requested = args.dragon - # torch and tf build by default - pt = not args.no_pt # pylint: disable=invalid-name - tf = not args.no_tf # pylint: disable=invalid-name - onnx = args.onnx + + if Path(CONFIG.build_path).exists(): + logger.warning(f"Build path already exists, removing: {CONFIG.build_path}") + shutil.rmtree(CONFIG.build_path) + + # The user should never have to specify the OS and Architecture + current_platform = Platform( + OperatingSystem.autodetect(), Architecture.autodetect(), device + ) + + # Configure the ML Packages + configs = load_platform_configs(Path(args.config_dir)) + mlpackages = configs[current_platform] + + # Build all backends by default, pop off the ones that user wants skipped + if args.skip_torch and "libtorch" in mlpackages: + mlpackages.pop("libtorch") + if args.skip_tensorflow and "libtensorflow" in mlpackages: + mlpackages.pop("libtensorflow") + if args.skip_onnx and "onnxruntime" in mlpackages: + mlpackages.pop("onnxruntime") build_env = BuildEnv(checks=True) logger.info("Running SmartSim build process...") @@ -409,6 +291,9 @@ def execute( version_names = list(vers.keys()) print(tabulate(vers, headers=version_names, tablefmt="github"), "\n") + logger.info("ML Packages") + print(mlpackages) + if is_dragon_requested: install_to = CONFIG.core_path / ".dragon" return_code = install_dragon(install_to) @@ -420,42 +305,25 @@ def execute( else: logger.warning("Dragon installation failed") - try: - if not args.only_python_packages: - # REDIS/KeyDB - build_database(build_env, versions, keydb, verbose) - - # REDISAI - build_redis_ai( - build_env, - versions, - device, - pt, - tf, - onnx, - args.torch_dir, - args.libtensorflow_dir, - verbose=verbose, - torch_with_mkl=args.torch_with_mkl, - ) - except (SetupError, BuildError) as e: - logger.error(str(e)) - return os.EX_SOFTWARE + # REDIS/KeyDB + build_database(build_env, versions, keydb, verbose) + + if (CONFIG.lib_path / "redisai.so").exists(): + logger.warning("RedisAI was previously built, run 'smart clean' to rebuild") + elif not args.skip_backends: + build_redis_ai(current_platform, mlpackages, build_env, verbose) + else: + logger.info("Skipping compilation of RedisAI and backends") backends = installed_redisai_backends() backends_str = ", ".join(s.capitalize() for s in backends) if backends else "No" - logger.info(f"{backends_str} backend(s) built") - - try: - if "torch" in backends: - check_py_torch_version(versions, device) - if "tensorflow" in backends: - check_py_tf_version(versions) - if "onnxruntime" in backends: - check_py_onnx_version(versions) - except (SetupError, BuildError) as e: - logger.error(str(e)) - return os.EX_SOFTWARE + logger.info(f"{backends_str} backend(s) available") + + if not args.skip_python_packages: + for package in mlpackages.values(): + logger.info(f"Installing python packages for {package.name}") + package.pip_install(quiet=not verbose) + check_ml_python_packages(mlpackages) logger.info("SmartSim build complete!") return os.EX_OK @@ -463,7 +331,14 @@ def execute( def configure_parser(parser: argparse.ArgumentParser) -> None: """Builds the parser for the command""" - warn_usage = "(ONLY USE IF NEEDED)" + + available_devices = [] + for platform in DEFAULT_MLPACKAGES: + if (platform.operating_system == OperatingSystem.autodetect()) and ( + platform.architecture == Architecture.autodetect() + ): + available_devices.append(platform.device.value) + parser.add_argument( "-v", action="store_true", @@ -474,7 +349,7 @@ def configure_parser(parser: argparse.ArgumentParser) -> None: "--device", type=str.lower, default=Device.CPU.value, - choices=[device.value for device in Device], + choices=available_devices, help="Device to build ML runtimes for", ) parser.add_argument( @@ -484,40 +359,35 @@ def configure_parser(parser: argparse.ArgumentParser) -> None: help="Install the dragon runtime", ) parser.add_argument( - "--only_python_packages", + "--skip-python-packages", action="store_true", - default=False, - help="Only evaluate the python packages (i.e. skip building backends)", + help="Do not install the python packages that match the backends", ) parser.add_argument( - "--no_pt", + "--skip-backends", action="store_true", - default=False, - help="Do not build PyTorch backend", + help="Do not compile RedisAI and the backends", ) parser.add_argument( - "--no_tf", + "--skip-torch", action="store_true", - default=False, - help="Do not build TensorFlow backend", + help="Do not build PyTorch backend", ) parser.add_argument( - "--onnx", + "--skip-tensorflow", action="store_true", - default=False, - help="Build ONNX backend (off by default)", + help="Do not build TensorFlow backend", ) parser.add_argument( - "--torch_dir", - default=None, - type=str, - help=f"Path to custom /torch/share/cmake/Torch/ directory {warn_usage}", + "--skip-onnx", + action="store_true", + help="Do not build the ONNX backend", ) parser.add_argument( - "--libtensorflow_dir", - default=None, + "--config-dir", + default=str(DEFAULT_MLPACKAGE_PATH), type=str, - help=f"Path to custom libtensorflow directory {warn_usage}", + help="Path to directory with JSON files describing platform and packages", ) parser.add_argument( "--keydb", @@ -525,9 +395,3 @@ def configure_parser(parser: argparse.ArgumentParser) -> None: default=False, help="Build KeyDB instead of Redis", ) - parser.add_argument( - "--no_torch_with_mkl", - dest="torch_with_mkl", - action="store_false", - help="Do not build Torch with Intel MKL", - ) diff --git a/smartsim/_core/_cli/scripts/dragon_install.py b/smartsim/_core/_cli/scripts/dragon_install.py index 466c390bd..8028b8ecf 100644 --- a/smartsim/_core/_cli/scripts/dragon_install.py +++ b/smartsim/_core/_cli/scripts/dragon_install.py @@ -7,7 +7,7 @@ from github.GitReleaseAsset import GitReleaseAsset from smartsim._core._cli.utils import pip -from smartsim._core._install.builder import WebTGZ +from smartsim._core._install.utils import retrieve from smartsim._core.config import CONFIG from smartsim._core.utils.helpers import check_platform, is_crayex_platform from smartsim.error.errors import SmartSimCLIActionCancelled @@ -159,8 +159,7 @@ def retrieve_asset(working_dir: pathlib.Path, asset: GitReleaseAsset) -> pathlib if working_dir.exists() and list(working_dir.rglob("*.whl")): return working_dir - archive = WebTGZ(asset.browser_download_url) - archive.extract(working_dir) + retrieve(asset.browser_download_url, working_dir) logger.debug(f"Retrieved {asset.browser_download_url} to {working_dir}") return working_dir @@ -182,7 +181,7 @@ def install_package(asset_dir: pathlib.Path) -> int: logger.info(f"Installing package: {wheel_path.absolute()}") try: - pip("install", "--force-reinstall", str(wheel_path)) + pip("install", "--force-reinstall", str(wheel_path), "numpy<2") wheel_path = next(wheels, None) except Exception: logger.error(f"Unable to install from {asset_dir}") diff --git a/smartsim/_core/_cli/validate.py b/smartsim/_core/_cli/validate.py index 96d46d6ee..b7905b773 100644 --- a/smartsim/_core/_cli/validate.py +++ b/smartsim/_core/_cli/validate.py @@ -27,7 +27,6 @@ import argparse import contextlib import io -import multiprocessing as mp import os import os.path import tempfile @@ -39,7 +38,7 @@ from smartsim import Experiment from smartsim._core._cli.utils import SMART_LOGGER_FORMAT -from smartsim._core._install.builder import Device +from smartsim._core.types import Device from smartsim._core.utils.helpers import installed_redisai_backends from smartsim._core.utils.network import find_free_port from smartsim.log import get_logger @@ -207,25 +206,8 @@ def _make_managed_local_orc( def _test_tf_install(client: Client, tmp_dir: str, device: Device) -> None: - recv_conn, send_conn = mp.Pipe(duplex=False) - # Build the model in a subproc so that keras does not hog the gpu - proc = mp.Process(target=_build_tf_frozen_model, args=(send_conn, tmp_dir)) - proc.start() - - # do not need the sending connection in this proc anymore - send_conn.close() - - proc.join(timeout=120) - if proc.is_alive(): - proc.terminate() - raise Exception("Failed to build a simple keras model within 2 minutes") - try: - model_path, inputs, outputs = recv_conn.recv() - except EOFError as e: - raise Exception( - "Failed to receive serialized model from subprocess. " - "Is the `tensorflow` python package installed?" - ) from e + + model_path, inputs, outputs = _build_tf_frozen_model(tmp_dir) client.set_model_from_file( "keras-fcn", @@ -240,8 +222,9 @@ def _test_tf_install(client: Client, tmp_dir: str, device: Device) -> None: client.get_tensor("keras-output") -def _build_tf_frozen_model(conn: "Connection", tmp_dir: str) -> None: - from tensorflow import keras +def _build_tf_frozen_model(tmp_dir: str) -> t.Tuple[str, t.List[str], t.List[str]]: + + from tensorflow import keras # pylint: disable=no-name-in-module from smartsim.ml.tf import freeze_model @@ -258,7 +241,7 @@ def _build_tf_frozen_model(conn: "Connection", tmp_dir: str) -> None: optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"] ) model_path, inputs, outputs = freeze_model(fcn, tmp_dir, "keras_model.pb") - conn.send((model_path, inputs, outputs)) + return model_path, inputs, outputs def _test_torch_install(client: Client, device: Device) -> None: @@ -283,10 +266,12 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: net.eval() forward_input = torch.rand(1, 1, 3, 3).to(device_) - traced = torch.jit.trace(net, forward_input) # type: ignore[no-untyped-call] + traced = torch.jit.trace( # type: ignore[no-untyped-call, unused-ignore] + net, forward_input + ) buffer = io.BytesIO() - torch.jit.save(traced, buffer) # type: ignore[no-untyped-call] + torch.jit.save(traced, buffer) # type: ignore[no-untyped-call, unused-ignore] model = buffer.getvalue() client.set_model("torch-nn", model, backend="TORCH", device=device.value.upper()) diff --git a/smartsim/_core/_install/buildenv.py b/smartsim/_core/_install/buildenv.py index e0cf5a522..bff421b12 100644 --- a/smartsim/_core/_install/buildenv.py +++ b/smartsim/_core/_install/buildenv.py @@ -35,25 +35,8 @@ from pathlib import Path from typing import Iterable -# NOTE: This will be imported by setup.py and hence no -# smartsim related items or non-standand library -# items should be imported here. +from packaging.version import InvalidVersion, Version, parse -# TODO: pkg_resources has been deprecated by PyPA. Currently we use it for its -# packaging implementation, as we cannot assume a user will have `packaging` -# prior to `pip install` time. We really only use pkg_resources for their -# vendored version of `packaging.version.Version` so we should probably try -# to remove -# https://setuptools.pypa.io/en/latest/pkg_resources.html - -# isort: off -import pkg_resources -from pkg_resources import packaging # type: ignore - -# isort: on - -Version = packaging.version.Version -InvalidVersion = packaging.version.InvalidVersion DbEngine = t.Literal["REDIS", "KEYDB"] @@ -72,30 +55,6 @@ class SetupError(Exception): """ -class VersionConflictError(SetupError): - """An error for when version numbers of some library/package/program/etc - do not match and build may not be able to continue - """ - - def __init__( - self, - name: str, - current_version: "Version_", - target_version: "Version_", - msg: t.Optional[str] = None, - ) -> None: - if msg is None: - msg = ( - f"Incompatible version for {name} detected: " - f"{name} {target_version} requested but {name} {current_version} " - "installed." - ) - super().__init__(msg) - self.name = name - self.current_version = current_version - self.target_version = target_version - - # so as to not conflict with pkg_resources.packaging.version.Version # pylint: disable-next=invalid-name class Version_(str): @@ -105,9 +64,7 @@ class Version_(str): @staticmethod def _convert_to_version( - vers: t.Union[ - str, Iterable[packaging.version.Version], packaging.version.Version - ], + vers: t.Union[str, Iterable[Version], Version], ) -> t.Any: if isinstance(vers, Version): return vers @@ -122,20 +79,20 @@ def _convert_to_version( def major(self) -> int: # Version(self).major doesn't work for all Python distributions # see https://github.com/lebedov/python-pdfbox/issues/28 - return int(pkg_resources.parse_version(self).base_version.split(".")[0]) + return int(parse(self).base_version.split(".", maxsplit=1)[0]) @property def minor(self) -> int: - return int(pkg_resources.parse_version(self).base_version.split(".")[1]) + return int(parse(self).base_version.split(".", maxsplit=2)[1]) @property def micro(self) -> int: - return int(pkg_resources.parse_version(self).base_version.split(".")[2]) + return int(parse(self).base_version.split(".", maxsplit=3)[2]) @property def patch(self) -> str: # return micro with string modifier i.e. 1.2.3+cpu -> 3+cpu - return str(pkg_resources.parse_version(self)).split(".")[2] + return str(parse(self)).split(".")[2] def __gt__(self, cmp: t.Any) -> bool: try: @@ -175,74 +132,6 @@ def get_env(var: str, default: str) -> str: return os.environ.get(var, default) -class RedisAIVersion(Version_): - """A subclass of Version_ that holds the dependency sets for RedisAI - - this class serves two purposes: - - 1. It is used to populate the [ml] ``extras_require`` of the setup.py. - This is because the RedisAI version will determine which ML based - dependencies are required. - - 2. Used to set the default values for PyTorch, TF, and ONNX - given the SMARTSIM_REDISAI env var set by the user. - - NOTE: Torch requires additional information depending on whether - CPU or GPU support is requested - """ - - defaults = { - "1.2.7": { - "tensorflow": "2.13.1", - "onnx": "1.14.1", - "skl2onnx": "1.16.0", - "onnxmltools": "1.12.0", - "scikit-learn": "1.3.2", - "torch": "2.0.1", - "torch_cpu_suffix": "+cpu", - "torch_cuda_suffix": "+cu117", - "torchvision": "0.15.2", - }, - } - - def __init__(self, vers: str) -> None: # pylint: disable=super-init-not-called - min_rai_version = min(Version_(ver) for ver in self.defaults) - if min_rai_version > vers: - raise SetupError( - f"RedisAI version must be greater than or equal to {min_rai_version}" - ) - if vers not in self.defaults: - if vers.startswith("1.2"): - # resolve to latest version for 1.2.x - # the str representation will still be 1.2.x - self.version = "1.2.7" - else: - raise SetupError( - ( - f"Invalid RedisAI version {vers}. Options are " - f"{self.defaults.keys()}" - ) - ) - else: - self.version = vers - - def __getattr__(self, name: str) -> str: - try: - return self.defaults[self.version][name] - except KeyError: - raise AttributeError( - f"'{type(self).__name__}' object has no attribute '{name}'\n\n" - "This is likely a problem with the SmartSim build process;" - "if this problem persists please log a new issue at " - "https://github.com/CrayLabs/SmartSim/issues " - "or get in contact with us at " - "https://www.craylabs.org/docs/community.html" - ) from None - - def get_defaults(self) -> t.Dict[str, str]: - return self.defaults[self.version].copy() - - class Versioner: """Versioner is responsible for managing all the versions within SmartSim including SmartSim itself. @@ -261,77 +150,36 @@ class Versioner: ``smart build`` command to determine which dependency versions to look for and download. - Default versions for SmartSim, SmartRedis, Redis, and RedisAI are - all set here. Setting a default version for RedisAI also dictates - default versions of the machine learning libraries. + Default versions for SmartSim, Redis, and RedisAI are specified here. """ # compatible Python version PYTHON_MIN = Version_("3.9.0") # Versions - SMARTSIM = Version_(get_env("SMARTSIM_VERSION", "0.7.0")) - SMARTREDIS = Version_(get_env("SMARTREDIS_VERSION", "0.5.3")) + SMARTSIM = Version_(get_env("SMARTSIM_VERSION", "0.8.0")) SMARTSIM_SUFFIX = get_env("SMARTSIM_SUFFIX", "") # Redis REDIS = Version_(get_env("SMARTSIM_REDIS", "7.2.4")) - REDIS_URL = get_env("SMARTSIM_REDIS_URL", "https://github.com/redis/redis.git/") + REDIS_URL = get_env("SMARTSIM_REDIS_URL", "https://github.com/redis/redis.git") REDIS_BRANCH = get_env("SMARTSIM_REDIS_BRANCH", REDIS) # RedisAI - REDISAI = RedisAIVersion(get_env("SMARTSIM_REDISAI", "1.2.7")) + REDISAI = "1.2.7" REDISAI_URL = get_env( - "SMARTSIM_REDISAI_URL", "https://github.com/RedisAI/RedisAI.git/" + "SMARTSIM_REDISAI_URL", "https://github.com/RedisAI/RedisAI.git" ) REDISAI_BRANCH = get_env("SMARTSIM_REDISAI_BRANCH", f"v{REDISAI}") - # ML/DL (based on RedisAI version defaults) - # torch can be set by the user because we download that for them - TORCH = Version_(get_env("SMARTSIM_TORCH", REDISAI.torch)) - TORCHVISION = Version_(get_env("SMARTSIM_TORCHVIS", REDISAI.torchvision)) - TORCH_CPU_SUFFIX = Version_(get_env("TORCH_CPU_SUFFIX", REDISAI.torch_cpu_suffix)) - TORCH_CUDA_SUFFIX = Version_( - get_env("TORCH_CUDA_SUFFIX", REDISAI.torch_cuda_suffix) - ) - - # TensorFlow and ONNX only use the defaults, but these are not built into - # the RedisAI package and therefore the user is free to pick other versions. - TENSORFLOW = Version_(REDISAI.tensorflow) - ONNX = Version_(REDISAI.onnx) - def as_dict(self, db_name: DbEngine = "REDIS") -> t.Dict[str, t.Tuple[str, ...]]: pkg_map = { "SMARTSIM": self.SMARTSIM, - "SMARTREDIS": self.SMARTREDIS, db_name: self.REDIS, "REDISAI": self.REDISAI, - "TORCH": self.TORCH, - "TENSORFLOW": self.TENSORFLOW, - "ONNX": self.ONNX, } return {"Packages": tuple(pkg_map), "Versions": tuple(pkg_map.values())} - def ml_extras_required(self) -> t.Dict[str, t.List[str]]: - """Optional ML/DL dependencies we suggest for the user. - - The defaults are based on the RedisAI version - """ - ml_defaults = self.REDISAI.get_defaults() - - # remove torch-related fields as they are subject to change - # by having the user change hardware (cpu/gpu) - _torch_fields = [ - "torch", - "torchvision", - "torch_cpu_suffix", - "torch_cuda_suffix", - ] - for field in _torch_fields: - ml_defaults.pop(field) - - return {"ml": [f"{lib}=={vers}" for lib, vers in ml_defaults.items()]} - @staticmethod def get_sha(setup_py_dir: Path) -> str: """Get the git sha of the current branch""" @@ -406,7 +254,7 @@ def __init__(self, checks: bool = True) -> None: self.check_dependencies() def check_dependencies(self) -> None: - deps = ["git", "git-lfs", "make", "wget", "cmake", self.CC, self.CXX] + deps = ["git", "make", "wget", "cmake", self.CC, self.CXX] if int(self.CHECKS) == 0: for dep in deps: self.check_build_dependency(dep) @@ -519,23 +367,6 @@ def check_build_dependency(command: str) -> None: except OSError: raise SetupError(f"{command} must be installed to build SmartSim") from None - @classmethod - def check_installed( - cls, package: str, version: t.Optional[Version_] = None - ) -> bool: - """Check if a package is installed. If version is provided, check if - it's a compatible version. (major and minor the same) - """ - try: - installed = cls.get_py_package_version(package) - except importlib.metadata.PackageNotFoundError: - return False - if version: - # detect if major or minor versions differ - if installed.major != version.major or installed.minor != version.minor: - raise VersionConflictError(package, installed, version) - return True - @staticmethod def get_py_package_version(package: str) -> Version_: return Version_(importlib.metadata.version(package)) diff --git a/smartsim/_core/_install/builder.py b/smartsim/_core/_install/builder.py index fb8ec5b81..17036e825 100644 --- a/smartsim/_core/_install/builder.py +++ b/smartsim/_core/_install/builder.py @@ -26,98 +26,32 @@ # pylint: disable=too-many-lines -import concurrent.futures -import enum -import fileinput -import itertools import os -import platform import re import shutil import stat import subprocess -import sys -import tarfile -import tempfile import typing as t -import urllib.request -import zipfile -from abc import ABC, abstractmethod -from dataclasses import dataclass from pathlib import Path -from shutil import which from subprocess import SubprocessError -# NOTE: This will be imported by setup.py and hence no smartsim related -# items should be imported into this file. +from smartsim._core._install.utils import retrieve +from smartsim._core.utils import expand_exe_path + +if t.TYPE_CHECKING: + from typing_extensions import Never # TODO: check cmake version and use system if possible to avoid conflicts -TRedisAIBackendStr = t.Literal["tensorflow", "torch", "onnxruntime", "tflite"] _PathLike = t.Union[str, "os.PathLike[str]"] _T = t.TypeVar("_T") _U = t.TypeVar("_U") -def expand_exe_path(exe: str) -> str: - """Takes an executable and returns the full path to that executable - - :param exe: executable or file - :raises TypeError: if file is not an executable - :raises FileNotFoundError: if executable cannot be found - """ - - # which returns none if not found - in_path = which(exe) - if not in_path: - if os.path.isfile(exe) and os.access(exe, os.X_OK): - return os.path.abspath(exe) - if os.path.isfile(exe) and not os.access(exe, os.X_OK): - raise TypeError(f"File, {exe}, is not an executable") - raise FileNotFoundError(f"Could not locate executable {exe}") - return os.path.abspath(in_path) - - class BuildError(Exception): pass -class Architecture(enum.Enum): - X64 = ("x86_64", "amd64") - ARM64 = ("arm64",) - - @classmethod - def from_str(cls, string: str, /) -> "Architecture": - string = string.lower() - for type_ in cls: - if string in type_.value: - return type_ - raise BuildError(f"Unrecognized or unsupported architecture: {string}") - - -class Device(enum.Enum): - CPU = "cpu" - GPU = "gpu" - - -class OperatingSystem(enum.Enum): - LINUX = ("linux", "linux2") - DARWIN = ("darwin",) - - @classmethod - def from_str(cls, string: str, /) -> "OperatingSystem": - string = string.lower() - for type_ in cls: - if string in type_.value: - return type_ - raise BuildError(f"Unrecognized or unsupported operating system: {string}") - - -class Platform(t.NamedTuple): - os: OperatingSystem - architecture: Architecture - - class Builder: """Base class for building third-party libraries""" @@ -135,13 +69,10 @@ def __init__( self, env: t.Dict[str, str], jobs: int = 1, - _os: OperatingSystem = OperatingSystem.from_str(platform.system()), - architecture: Architecture = Architecture.from_str(platform.machine()), verbose: bool = False, ) -> None: # build environment from buildenv self.env = env - self._platform = Platform(_os, architecture) # Find _core directory and set up paths _core_dir = Path(os.path.abspath(__file__)).parent.parent @@ -176,11 +107,6 @@ def out(self) -> t.Optional[int]: def is_built(self) -> bool: raise NotImplementedError - def build_from_git( - self, git_url: str, branch: str, device: Device = Device.CPU - ) -> None: - raise NotImplementedError - @staticmethod def binary_path(binary: str) -> str: binary_ = shutil.which(binary) @@ -256,15 +182,11 @@ def __init__( build_env: t.Optional[t.Dict[str, str]] = None, malloc: str = "libc", jobs: int = 1, - _os: OperatingSystem = OperatingSystem.from_str(platform.system()), - architecture: Architecture = Architecture.from_str(platform.machine()), verbose: bool = False, ) -> None: super().__init__( build_env or {}, jobs=jobs, - _os=_os, - architecture=architecture, verbose=verbose, ) self.malloc = malloc @@ -277,9 +199,7 @@ def is_built(self) -> bool: keydb_files = {"keydb-server", "keydb-cli"} return redis_files.issubset(bin_files) or keydb_files.issubset(bin_files) - def build_from_git( - self, git_url: str, branch: str, device: Device = Device.CPU - ) -> None: + def build_from_git(self, git_url: str, branch: str) -> None: """Build Redis from git :param git_url: url from which to retrieve Redis :param branch: branch to checkout @@ -301,23 +221,7 @@ def build_from_git( if not self.is_valid_url(git_url): raise BuildError(f"Malformed {database_name} URL: {git_url}") - clone_cmd = config_git_command( - self._platform, - [ - self.binary_path("git"), - "clone", - git_url, - "--branch", - branch, - "--depth", - "1", - database_name, - ], - ) - - # clone Redis - self.run_command(clone_cmd, cwd=self.build_dir) - + retrieve(git_url, self.build_dir / database_name, branch=branch, depth=1) # build Redis build_cmd = [ self.binary_path("make"), @@ -354,723 +258,3 @@ def build_from_git( _ = expand_exe_path(str(redis_cli)) except (TypeError, FileNotFoundError) as e: raise BuildError("Installation of redis-cli failed!") from e - - -class _RAIBuildDependency(ABC): - """An interface with a collection of magic methods so that - ``RedisAIBuilder`` can fetch and place its own dependencies - """ - - @property - @abstractmethod - def __rai_dependency_name__(self) -> str: ... - - @abstractmethod - def __place_for_rai__(self, target: _PathLike) -> Path: ... - - @staticmethod - @abstractmethod - def supported_platforms() -> t.Sequence[t.Tuple[OperatingSystem, Architecture]]: ... - - -def _place_rai_dep_at( - target: _PathLike, verbose: bool -) -> t.Callable[[_RAIBuildDependency], Path]: - def _place(dep: _RAIBuildDependency) -> Path: - if verbose: - print(f"Placing: '{dep.__rai_dependency_name__}'") - path = dep.__place_for_rai__(target) - if verbose: - print(f"Placed: '{dep.__rai_dependency_name__}' at '{path}'") - return path - - return _place - - -class RedisAIBuilder(Builder): - """Class to build RedisAI from Source - Supported build method: - - from git - See buildenv.py for buildtime configuration of RedisAI - version and url. - """ - - def __init__( - self, - _os: OperatingSystem = OperatingSystem.from_str(platform.system()), - architecture: Architecture = Architecture.from_str(platform.machine()), - build_env: t.Optional[t.Dict[str, str]] = None, - torch_dir: str = "", - libtf_dir: str = "", - build_torch: bool = True, - build_tf: bool = True, - build_onnx: bool = False, - jobs: int = 1, - verbose: bool = False, - torch_with_mkl: bool = True, - ) -> None: - super().__init__( - build_env or {}, - jobs=jobs, - _os=_os, - architecture=architecture, - verbose=verbose, - ) - - self.rai_install_path: t.Optional[Path] = None - - # convert to int for RAI build script - self._torch = build_torch - self._tf = build_tf - self._onnx = build_onnx - self.libtf_dir = libtf_dir - self.torch_dir = torch_dir - - # extra configuration options - self.torch_with_mkl = torch_with_mkl - - # Sanity checks - self._validate_platform() - - def _validate_platform(self) -> None: - unsupported = [] - if self._platform not in _DLPackRepository.supported_platforms(): - unsupported.append("DLPack") - if self.fetch_tf and (self._platform not in _TFArchive.supported_platforms()): - unsupported.append("Tensorflow") - if self.fetch_onnx and ( - self._platform not in _ORTArchive.supported_platforms() - ): - unsupported.append("ONNX") - if self.fetch_torch and ( - self._platform not in _PTArchive.supported_platforms() - ): - unsupported.append("PyTorch") - if unsupported: - raise BuildError( - f"The {', '.join(unsupported)} backend(s) are not supported " - f"on {self._platform.os} with {self._platform.architecture}" - ) - - @property - def rai_build_path(self) -> Path: - return Path(self.build_dir, "RedisAI") - - @property - def is_built(self) -> bool: - server = self.lib_path.joinpath("backends").is_dir() - cli = self.lib_path.joinpath("redisai.so").is_file() - return server and cli - - @property - def build_torch(self) -> bool: - return self._torch - - @property - def fetch_torch(self) -> bool: - return self.build_torch and not self.torch_dir - - @property - def build_tf(self) -> bool: - return self._tf - - @property - def fetch_tf(self) -> bool: - return self.build_tf and not self.libtf_dir - - @property - def build_onnx(self) -> bool: - return self._onnx - - @property - def fetch_onnx(self) -> bool: - return self.build_onnx - - def get_deps_dir_path_for(self, device: Device) -> Path: - def fail_to_format(reason: str) -> BuildError: # pragma: no cover - return BuildError(f"Failed to format RedisAI dependency path: {reason}") - - _os, architecture = self._platform - if _os == OperatingSystem.DARWIN: - os_ = "macos" - elif _os == OperatingSystem.LINUX: - os_ = "linux" - else: # pragma: no cover - raise fail_to_format(f"Unknown operating system: {_os}") - if architecture == Architecture.X64: - arch = "x64" - elif architecture == Architecture.ARM64: - arch = "arm64v8" - else: # pragma: no cover - raise fail_to_format(f"Unknown architecture: {architecture}") - return self.rai_build_path / f"deps/{os_}-{arch}-{device.value}" - - def _get_deps_to_fetch_for( - self, device: Device - ) -> t.Tuple[_RAIBuildDependency, ...]: - os_, arch = self._platform - # TODO: It would be nice if the backend version numbers were declared - # alongside the python package version numbers so that all of the - # dependency versions were declared in single location. - # Unfortunately importing into this module is non-trivial as it - # is used as script in the SmartSim `setup.py`. - - # DLPack is always required - fetchable_deps: t.List[_RAIBuildDependency] = [_DLPackRepository("v0.5_RAI")] - if self.fetch_torch: - pt_dep = _choose_pt_variant(os_)(arch, device, "2.0.1", self.torch_with_mkl) - fetchable_deps.append(pt_dep) - if self.fetch_tf: - fetchable_deps.append(_TFArchive(os_, arch, device, "2.13.1")) - if self.fetch_onnx: - fetchable_deps.append(_ORTArchive(os_, device, "1.16.3")) - - return tuple(fetchable_deps) - - def symlink_libtf(self, device: Device) -> None: - """Add symbolic link to available libtensorflow in RedisAI deps. - - :param device: cpu or gpu - """ - rai_deps_path = sorted( - self.rai_build_path.glob(os.path.join("deps", f"*{device.value}*")) - ) - if not rai_deps_path: - raise FileNotFoundError("Could not find RedisAI 'deps' directory") - - # There should only be one path for a given device, - # and this should hold even if in the future we use - # an external build of RedisAI - rai_libtf_path = rai_deps_path[0] / "libtensorflow" - rai_libtf_path.resolve() - if rai_libtf_path.is_dir(): - shutil.rmtree(rai_libtf_path) - - os.makedirs(rai_libtf_path) - libtf_path = Path(self.libtf_dir).resolve() - - # Copy include directory to deps/libtensorflow - include_src_path = libtf_path / "include" - if not include_src_path.exists(): - raise FileNotFoundError(f"Could not find include directory in {libtf_path}") - os.symlink(include_src_path, rai_libtf_path / "include") - - # RedisAI expects to find a lib directory, which is only - # available in some distributions. - rai_libtf_lib_dir = rai_libtf_path / "lib" - os.makedirs(rai_libtf_lib_dir) - src_libtf_lib_dir = libtf_path / "lib" - # If the lib directory existed in the libtensorflow distribution, - # copy its content, otherwise gather library files from - # libtensorflow base dir and copy them into destination lib dir - if src_libtf_lib_dir.is_dir(): - library_files = sorted(src_libtf_lib_dir.glob("*")) - if not library_files: - raise FileNotFoundError( - f"Could not find libtensorflow library files in {src_libtf_lib_dir}" - ) - else: - library_files = sorted(libtf_path.glob("lib*.so*")) - if not library_files: - raise FileNotFoundError( - f"Could not find libtensorflow library files in {libtf_path}" - ) - - for src_file in library_files: - dst_file = rai_libtf_lib_dir / src_file.name - if not dst_file.is_file(): - os.symlink(src_file, dst_file) - - def build_from_git( - self, git_url: str, branch: str, device: Device = Device.CPU - ) -> None: - """Build RedisAI from git - - :param git_url: url from which to retrieve RedisAI - :param branch: branch to checkout - :param device: cpu or gpu - """ - # delete previous build dir (should never be there) - if self.rai_build_path.is_dir(): - shutil.rmtree(self.rai_build_path) - - # Check RedisAI URL - if not self.is_valid_url(git_url): - raise BuildError(f"Malformed RedisAI URL: {git_url}") - - # clone RedisAI - clone_cmd = config_git_command( - self._platform, - [ - self.binary_path("env"), - "GIT_LFS_SKIP_SMUDGE=1", - "git", - "clone", - "--recursive", - git_url, - "--branch", - branch, - "--depth=1", - os.fspath(self.rai_build_path), - ], - ) - - self.run_command(clone_cmd, out=subprocess.DEVNULL, cwd=self.build_dir) - self._fetch_deps_for(device) - - if self.libtf_dir and device.value: - self.symlink_libtf(device) - - build_cmd = self._rai_build_env_prefix( - with_pt=self.build_torch, - with_tf=self.build_tf, - with_ort=self.build_onnx, - extra_env={"GPU": "1" if device == Device.GPU else "0"}, - ) - - if self.torch_dir: - self.env["Torch_DIR"] = str(self.torch_dir) - - build_cmd.extend( - [ - self.binary_path("make"), - "-C", - str(self.rai_build_path / "opt"), - "-j", - f"{self.jobs}", - "build", - ] - ) - self.run_command(build_cmd, cwd=self.rai_build_path) - - self._install_backends(device) - if self.user_supplied_backend("torch"): - self._move_torch_libs() - self.cleanup() - - def user_supplied_backend(self, backend: TRedisAIBackendStr) -> bool: - if backend == "torch": - return bool(self.build_torch and not self.fetch_torch) - if backend == "tensorflow": - return bool(self.build_tf and not self.fetch_tf) - if backend == "onnxruntime": - return bool(self.build_onnx and not self.fetch_onnx) - if backend == "tflite": - return False - raise BuildError(f"Unrecognized backend requested {backend}") - - def _rai_build_env_prefix( - self, - with_tf: bool, - with_pt: bool, - with_ort: bool, - extra_env: t.Optional[t.Dict[str, str]] = None, - ) -> t.List[str]: - extra_env = extra_env or {} - return [ - self.binary_path("env"), - f"WITH_PT={1 if with_pt else 0}", - f"WITH_TF={1 if with_tf else 0}", - "WITH_TFLITE=0", # never use TF Lite (for now) - f"WITH_ORT={1 if with_ort else 0}", - *(f"{key}={val}" for key, val in extra_env.items()), - ] - - def _fetch_deps_for(self, device: Device) -> None: - if not self.rai_build_path.is_dir(): - raise BuildError("RedisAI build directory not found") - - deps_dir = self.get_deps_dir_path_for(device) - deps_dir.mkdir(parents=True, exist_ok=True) - if any(deps_dir.iterdir()): - raise BuildError("RAI build dependency directory is not empty") - to_fetch = self._get_deps_to_fetch_for(device) - placed_paths = _threaded_map( - _place_rai_dep_at(deps_dir, self.verbose), to_fetch - ) - unique_placed_paths = {os.fspath(path.resolve()) for path in placed_paths} - if len(unique_placed_paths) != len(to_fetch): - raise BuildError( - f"Expected to place {len(to_fetch)} dependencies, but only " - f"found {len(unique_placed_paths)}" - ) - - def _install_backends(self, device: Device) -> None: - """Move backend libraries to smartsim/_core/lib/ - :param device: cpu or cpu - """ - self.rai_install_path = self.rai_build_path.joinpath( - f"install-{device.value}" - ).resolve() - rai_lib = self.rai_install_path / "redisai.so" - rai_backends = self.rai_install_path / "backends" - - if rai_lib.is_file() and rai_backends.is_dir(): - self.copy_dir(rai_backends, self.lib_path / "backends", set_exe=True) - self.copy_file(rai_lib, self.lib_path / "redisai.so", set_exe=True) - - def _move_torch_libs(self) -> None: - """Move pip install torch libraries - Since we use pip installed torch libraries for building - RedisAI, we need to move them into the LD_runpath of redisai.so - in the smartsim/_core/lib directory. - """ - ss_rai_torch_path = self.lib_path / "backends" / "redisai_torch" - ss_rai_torch_lib_path = ss_rai_torch_path / "lib" - - # retrieve torch shared libraries and copy to the - # smartsim/_core/lib/backends/redisai_torch/lib dir - # self.torch_dir should be /path/to/torch/share/cmake/Torch - # so we take the great grandparent here - pip_torch_path = Path(self.torch_dir).parent.parent.parent - pip_torch_lib_path = pip_torch_path / "lib" - - self.copy_dir(pip_torch_lib_path, ss_rai_torch_lib_path, set_exe=True) - - # also move the openmp files if on a mac - if sys.platform == "darwin": - dylibs = pip_torch_path / ".dylibs" - self.copy_dir(dylibs, ss_rai_torch_path / ".dylibs", set_exe=True) - - -def _threaded_map(fn: t.Callable[[_T], _U], items: t.Iterable[_T]) -> t.Sequence[_U]: - items = tuple(items) - if not items: # No items so no work to do - return () - num_workers = min(len(items), (os.cpu_count() or 4) * 5) - with concurrent.futures.ThreadPoolExecutor(num_workers) as pool: - return tuple(pool.map(fn, items)) - - -class _WebLocation(ABC): - @property - @abstractmethod - def url(self) -> str: ... - - -class _WebGitRepository(_WebLocation): - def clone( - self, - target: _PathLike, - depth: t.Optional[int] = None, - branch: t.Optional[str] = None, - ) -> None: - depth_ = ("--depth", str(depth)) if depth is not None else () - branch_ = ("--branch", branch) if branch is not None else () - _git("clone", "-q", *depth_, *branch_, self.url, os.fspath(target)) - - -@t.final -@dataclass(frozen=True) -class _DLPackRepository(_WebGitRepository, _RAIBuildDependency): - version: str - - @staticmethod - def supported_platforms() -> t.Sequence[t.Tuple[OperatingSystem, Architecture]]: - return ( - (OperatingSystem.LINUX, Architecture.X64), - (OperatingSystem.DARWIN, Architecture.X64), - (OperatingSystem.DARWIN, Architecture.ARM64), - ) - - @property - def url(self) -> str: - return "https://github.com/RedisAI/dlpack.git" - - @property - def __rai_dependency_name__(self) -> str: - return f"dlpack@{self.url}" - - def __place_for_rai__(self, target: _PathLike) -> Path: - target = Path(target) / "dlpack" - self.clone(target, branch=self.version, depth=1) - if not target.is_dir(): - raise BuildError("Failed to place dlpack") - return target - - -class _WebArchive(_WebLocation): - @property - def name(self) -> str: - _, name = self.url.rsplit("/", 1) - return name - - def download(self, target: _PathLike) -> Path: - target = Path(target) - if target.is_dir(): - target = target / self.name - file, _ = urllib.request.urlretrieve(self.url, target) - return Path(file).resolve() - - -class _ExtractableWebArchive(_WebArchive, ABC): - @abstractmethod - def _extract_download(self, download_path: Path, target: _PathLike) -> None: ... - - def extract(self, target: _PathLike) -> None: - with tempfile.TemporaryDirectory() as tmp_dir: - arch_path = self.download(tmp_dir) - self._extract_download(arch_path, target) - - -class _WebTGZ(_ExtractableWebArchive): - def _extract_download(self, download_path: Path, target: _PathLike) -> None: - with tarfile.open(download_path, "r") as tgz_file: - tgz_file.extractall(target) - - -class _WebZip(_ExtractableWebArchive): - def _extract_download(self, download_path: Path, target: _PathLike) -> None: - with zipfile.ZipFile(download_path, "r") as zip_file: - zip_file.extractall(target) - - -class WebTGZ(_WebTGZ): - def __init__(self, url: str) -> None: - self._url = url - - @property - def url(self) -> str: - return self._url - - -@dataclass(frozen=True) -class _PTArchive(_WebZip, _RAIBuildDependency): - architecture: Architecture - device: Device - version: str - with_mkl: bool - - @staticmethod - def supported_platforms() -> t.Sequence[t.Tuple[OperatingSystem, Architecture]]: - # TODO: This will need to be revisited if the inheritance tree gets deeper - return tuple( - itertools.chain.from_iterable( - var.supported_platforms() for var in _PTArchive.__subclasses__() - ) - ) - - @property - def __rai_dependency_name__(self) -> str: - return f"libtorch@{self.url}" - - @staticmethod - def _patch_out_mkl(libtorch_root: Path) -> None: - _modify_source_files( - libtorch_root / "share/cmake/Caffe2/public/mkl.cmake", - r"find_package\(MKL QUIET\)", - "# find_package(MKL QUIET)", - ) - - def extract(self, target: _PathLike) -> None: - super().extract(target) - if not self.with_mkl: - self._patch_out_mkl(Path(target)) - - def __place_for_rai__(self, target: _PathLike) -> Path: - self.extract(target) - target = Path(target) / "libtorch" - if not target.is_dir(): - raise BuildError("Failed to place RAI dependency: `libtorch`") - return target - - -@t.final -class _PTArchiveLinux(_PTArchive): - @staticmethod - def supported_platforms() -> t.Sequence[t.Tuple[OperatingSystem, Architecture]]: - return ((OperatingSystem.LINUX, Architecture.X64),) - - @property - def url(self) -> str: - if self.device == Device.GPU: - pt_build = "cu117" - else: - pt_build = Device.CPU.value - # pylint: disable-next=line-too-long - libtorch_archive = ( - f"libtorch-cxx11-abi-shared-without-deps-{self.version}%2B{pt_build}.zip" - ) - return f"https://download.pytorch.org/libtorch/{pt_build}/{libtorch_archive}" - - -@t.final -class _PTArchiveMacOSX(_PTArchive): - @staticmethod - def supported_platforms() -> t.Sequence[t.Tuple[OperatingSystem, Architecture]]: - return ( - (OperatingSystem.DARWIN, Architecture.ARM64), - (OperatingSystem.DARWIN, Architecture.X64), - ) - - @property - def url(self) -> str: - if self.device == Device.GPU: - raise BuildError("RedisAI does not currently support GPU on Mac OSX") - if self.architecture == Architecture.X64: - pt_build = Device.CPU.value - libtorch_archive = f"libtorch-macos-{self.version}.zip" - root_url = "https://download.pytorch.org/libtorch" - return f"{root_url}/{pt_build}/{libtorch_archive}" - if self.architecture == Architecture.ARM64: - libtorch_archive = f"libtorch-macos-arm64-{self.version}.zip" - # pylint: disable-next=line-too-long - root_url = ( - "https://github.com/CrayLabs/ml_lib_builder/releases/download/v0.1/" - ) - return f"{root_url}/{libtorch_archive}" - - raise BuildError(f"Unsupported architecture for Pytorch: {self.architecture}") - - -def _choose_pt_variant( - os_: OperatingSystem, -) -> t.Union[t.Type[_PTArchiveLinux], t.Type[_PTArchiveMacOSX]]: - if os_ == OperatingSystem.DARWIN: - return _PTArchiveMacOSX - if os_ == OperatingSystem.LINUX: - return _PTArchiveLinux - - raise BuildError(f"Unsupported OS for PyTorch: {os_}") - - -@t.final -@dataclass(frozen=True) -class _TFArchive(_WebTGZ, _RAIBuildDependency): - os_: OperatingSystem - architecture: Architecture - device: Device - version: str - - @staticmethod - def supported_platforms() -> t.Sequence[t.Tuple[OperatingSystem, Architecture]]: - return ( - (OperatingSystem.LINUX, Architecture.X64), - (OperatingSystem.DARWIN, Architecture.X64), - ) - - @property - def url(self) -> str: - if self.architecture == Architecture.X64: - tf_arch = "x86_64" - else: - raise BuildError( - f"Unexpected Architecture for TF Archive: {self.architecture}" - ) - - if self.os_ == OperatingSystem.LINUX: - tf_os = "linux" - tf_device = self.device - elif self.os_ == OperatingSystem.DARWIN: - tf_os = "darwin" - if self.device == Device.GPU: - raise BuildError("RedisAI does not currently support GPU on Macos") - tf_device = Device.CPU - else: - raise BuildError(f"Unexpected OS for TF Archive: {self.os_}") - return ( - "https://storage.googleapis.com/tensorflow/libtensorflow/" - f"libtensorflow-{tf_device.value}-{tf_os}-{tf_arch}-{self.version}.tar.gz" - ) - - @property - def __rai_dependency_name__(self) -> str: - return f"libtensorflow@{self.url}" - - def __place_for_rai__(self, target: _PathLike) -> Path: - target = Path(target) / "libtensorflow" - target.mkdir() - self.extract(target) - return target - - -@t.final -@dataclass(frozen=True) -class _ORTArchive(_WebTGZ, _RAIBuildDependency): - os_: OperatingSystem - device: Device - version: str - - @staticmethod - def supported_platforms() -> t.Sequence[t.Tuple[OperatingSystem, Architecture]]: - return ( - (OperatingSystem.LINUX, Architecture.X64), - (OperatingSystem.DARWIN, Architecture.X64), - ) - - @property - def url(self) -> str: - ort_url_base = ( - "https://github.com/microsoft/onnxruntime/releases/" - f"download/v{self.version}" - ) - if self.os_ == OperatingSystem.LINUX: - ort_os = "linux" - ort_arch = "x64" - ort_build = "-gpu" if self.device == Device.GPU else "" - elif self.os_ == OperatingSystem.DARWIN: - ort_os = "osx" - ort_arch = "x86_64" - ort_build = "" - if self.device == Device.GPU: - raise BuildError("RedisAI does not currently support GPU on Macos") - else: - raise BuildError(f"Unexpected OS for TF Archive: {self.os_}") - ort_archive = f"onnxruntime-{ort_os}-{ort_arch}{ort_build}-{self.version}.tgz" - return f"{ort_url_base}/{ort_archive}" - - @property - def __rai_dependency_name__(self) -> str: - return f"onnxruntime@{self.url}" - - def __place_for_rai__(self, target: _PathLike) -> Path: - target = Path(target).resolve() / "onnxruntime" - self.extract(target) - try: - (extracted_dir,) = target.iterdir() - except ValueError: - raise BuildError( - "Unexpected number of files extracted from ORT archive" - ) from None - for file in extracted_dir.iterdir(): - file.rename(target / file.name) - extracted_dir.rmdir() - return target - - -def _git(*args: str) -> None: - git = Builder.binary_path("git") - cmd = (git,) + args - with subprocess.Popen(cmd) as proc: - proc.wait() - if proc.returncode != 0: - raise BuildError( - f"Command `{' '.join(cmd)}` failed with exit code {proc.returncode}" - ) - - -def config_git_command(plat: Platform, cmd: t.Sequence[str]) -> t.List[str]: - """Modify git commands to include autocrlf when on a platform that needs - autocrlf enabled to behave correctly - """ - cmd = list(cmd) - where = next((i for i, tok in enumerate(cmd) if tok.endswith("git")), len(cmd)) + 2 - if where >= len(cmd): - raise ValueError(f"Failed to locate git command in '{' '.join(cmd)}'") - if plat == Platform(OperatingSystem.DARWIN, Architecture.ARM64): - cmd = ( - cmd[:where] - + ["--config", "core.autocrlf=false", "--config", "core.eol=lf"] - + cmd[where:] - ) - return cmd - - -def _modify_source_files( - files: t.Union[_PathLike, t.Iterable[_PathLike]], regex: str, replacement: str -) -> None: - compiled_regex = re.compile(regex) - with fileinput.input(files=files, inplace=True) as handles: - for line in handles: - line = compiled_regex.sub(replacement, line) - print(line, end="") diff --git a/smartsim/_core/_install/configs/mlpackages/DarwinARM64CPU.json b/smartsim/_core/_install/configs/mlpackages/DarwinARM64CPU.json new file mode 100644 index 000000000..2f49a393e --- /dev/null +++ b/smartsim/_core/_install/configs/mlpackages/DarwinARM64CPU.json @@ -0,0 +1,47 @@ +{ + "platform": { + "operating_system":"darwin", + "architecture":"arm64", + "device":"cpu" + }, + "ml_packages": [ + { + "name": "dlpack", + "version": "v0.5_RAI", + "pip_index": "", + "python_packages": [], + "lib_source": "https://github.com/RedisAI/dlpack.git" + }, + { + "name": "libtorch", + "version": "2.4.0", + "pip_index": "", + "python_packages": [ + "torch==2.4.0", + "torchvision==0.19.0", + "torchaudio==2.4.0" + ], + "lib_source": "https://download.pytorch.org/libtorch/cpu/libtorch-macos-arm64-2.4.0.zip", + "rai_patches": [ + { + "description": "Patch RedisAI module to require C++17 standard instead of C++14", + "source_file": "src/backends/libtorch_c/CMakeLists.txt", + "regex": "set_property\\(TARGET\\storch_c\\sPROPERTY\\sCXX_STANDARD\\s(98|11|14)\\)", + "replacement": "set_property(TARGET torch_c PROPERTY CXX_STANDARD 17)" + } + ] + }, + { + "name": "onnxruntime", + "version": "1.17.3", + "pip_index": "", + "python_packages": [ + "onnx==1.15", + "skl2onnx", + "scikit-learn", + "onnxmltools" + ], + "lib_source": "https://github.com/microsoft/onnxruntime/releases/download/v1.17.3/onnxruntime-osx-arm64-1.17.3.tgz" + } + ] +} diff --git a/smartsim/_core/_install/configs/mlpackages/DarwinX64CPU.json b/smartsim/_core/_install/configs/mlpackages/DarwinX64CPU.json new file mode 100644 index 000000000..e7b67e35b --- /dev/null +++ b/smartsim/_core/_install/configs/mlpackages/DarwinX64CPU.json @@ -0,0 +1,56 @@ +{ + "platform": { + "operating_system":"darwin", + "architecture":"x86_64", + "device":"cpu" + }, + "ml_packages": [ + { + "name": "dlpack", + "version": "v0.5_RAI", + "pip_index": "", + "python_packages": [], + "lib_source": "https://github.com/RedisAI/dlpack.git" + }, + { + "name": "libtorch", + "version": "2.2.2", + "pip_index": "", + "python_packages": [ + "torch==2.2.2", + "torchvision==0.17.2", + "torchaudio==2.2.2" + ], + "lib_source": "https://download.pytorch.org/libtorch/cpu/libtorch-macos-x86_64-2.2.2.zip", + "rai_patches": [ + { + "description": "Patch RedisAI module to require C++17 standard instead of C++14", + "source_file": "src/backends/libtorch_c/CMakeLists.txt", + "regex": "set_property\\(TARGET\\storch_c\\sPROPERTY\\sCXX_STANDARD\\s(98|11|14)\\)", + "replacement": "set_property(TARGET torch_c PROPERTY CXX_STANDARD 17)" + } + ] + }, + { + "name": "libtensorflow", + "version": "2.15", + "pip_index": "", + "python_packages": [ + "tensorflow==2.15" + ], + "lib_source": "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.15.0.tar.gz" + }, + { + "name": "onnxruntime", + "version": "1.17.3", + "pip_index": "", + "python_packages": [ + "onnx==1.15", + "skl2onnx", + "scikit-learn", + "onnxmltools" + ], + "lib_source": "https://github.com/microsoft/onnxruntime/releases/download/v1.17.3/onnxruntime-osx-x86_64-1.17.3.tgz" + } + ] +} diff --git a/smartsim/_core/_install/configs/mlpackages/LinuxX64CPU.json b/smartsim/_core/_install/configs/mlpackages/LinuxX64CPU.json new file mode 100644 index 000000000..cc2f81194 --- /dev/null +++ b/smartsim/_core/_install/configs/mlpackages/LinuxX64CPU.json @@ -0,0 +1,56 @@ +{ + "platform": { + "operating_system":"linux", + "architecture":"x86_64", + "device":"cpu" + }, + "ml_packages": [ + { + "name": "dlpack", + "version": "v0.5_RAI", + "pip_index": "", + "python_packages": [], + "lib_source": "https://github.com/RedisAI/dlpack.git" + }, + { + "name": "libtorch", + "version": "2.4.0", + "pip_index": "https://download.pytorch.org/whl/cpu", + "python_packages": [ + "torch==2.4.0+cpu", + "torchvision==0.19.0+cpu", + "torchaudio==2.4.0+cpu" + ], + "lib_source": "https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-2.4.0%2Bcpu.zip", + "rai_patches": [ + { + "description": "Patch RedisAI module to require C++17 standard instead of C++14", + "source_file": "src/backends/libtorch_c/CMakeLists.txt", + "regex": "set_property\\(TARGET\\storch_c\\sPROPERTY\\sCXX_STANDARD\\s(98|11|14)\\)", + "replacement": "set_property(TARGET torch_c PROPERTY CXX_STANDARD 17)" + } + ] + }, + { + "name": "libtensorflow", + "version": "2.15", + "pip_index": "", + "python_packages": [ + "tensorflow==2.15" + ], + "lib_source": "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.15.0.tar.gz" + }, + { + "name": "onnxruntime", + "version": "1.17.3", + "pip_index": "", + "python_packages": [ + "onnx<=1.15", + "skl2onnx", + "scikit-learn", + "onnxmltools" + ], + "lib_source": "https://github.com/microsoft/onnxruntime/releases/download/v1.17.3/onnxruntime-linux-x64-1.17.3.tgz" + } + ] +} diff --git a/smartsim/_core/_install/configs/mlpackages/LinuxX64CUDA11.json b/smartsim/_core/_install/configs/mlpackages/LinuxX64CUDA11.json new file mode 100644 index 000000000..cf302534c --- /dev/null +++ b/smartsim/_core/_install/configs/mlpackages/LinuxX64CUDA11.json @@ -0,0 +1,56 @@ +{ + "platform": { + "operating_system":"linux", + "architecture":"x86_64", + "device":"cuda-11" + }, + "ml_packages": [ + { + "name": "dlpack", + "version": "v0.5_RAI", + "pip_index": "", + "python_packages": [], + "lib_source": "https://github.com/RedisAI/dlpack.git" + }, + { + "name": "libtorch", + "version": "2.3.1", + "pip_index": "https://download.pytorch.org/whl/cu118", + "python_packages": [ + "torch==2.3.1+cu118", + "torchvision==0.18.1+cu118", + "torchaudio==2.3.1+cu118" + ], + "lib_source": "https://download.pytorch.org/libtorch/cu118/libtorch-cxx11-abi-shared-with-deps-2.3.1%2Bcu118.zip", + "rai_patches": [ + { + "description": "Patch RedisAI module to require C++17 standard instead of C++14", + "source_file": "src/backends/libtorch_c/CMakeLists.txt", + "regex": "set_property\\(TARGET\\storch_c\\sPROPERTY\\sCXX_STANDARD\\s(98|11|14)\\)", + "replacement": "set_property(TARGET torch_c PROPERTY CXX_STANDARD 17)" + } + ] + }, + { + "name": "libtensorflow", + "version": "2.14.1", + "pip_index": "", + "python_packages": [ + "tensorflow==2.14.1" + ], + "lib_source": "https://github.com/CrayLabs/ml_lib_builder/releases/download/v0.2/libtensorflow-2.14.1-linux-x64-cuda-11.8.0.tgz" + }, + { + "name": "onnxruntime", + "version": "1.17.3", + "pip_index": "", + "python_packages": [ + "onnx==1.15", + "skl2onnx", + "scikit-learn", + "onnxmltools" + ], + "lib_source": "https://github.com/microsoft/onnxruntime/releases/download/v1.17.3/onnxruntime-linux-x64-gpu-1.17.3.tgz" + } + ] +} diff --git a/smartsim/_core/_install/configs/mlpackages/LinuxX64CUDA12.json b/smartsim/_core/_install/configs/mlpackages/LinuxX64CUDA12.json new file mode 100644 index 000000000..a415b3103 --- /dev/null +++ b/smartsim/_core/_install/configs/mlpackages/LinuxX64CUDA12.json @@ -0,0 +1,64 @@ +{ + "platform": { + "operating_system":"linux", + "architecture":"x86_64", + "device":"cuda-12" + }, + "ml_packages": [ + { + "name": "dlpack", + "version": "v0.5_RAI", + "pip_index": "", + "python_packages": [], + "lib_source": "https://github.com/RedisAI/dlpack.git" + }, + { + "name": "libtorch", + "version": "2.3.1", + "pip_index": "https://download.pytorch.org/whl/cu121", + "python_packages": [ + "torch==2.3.1+cu121", + "torchvision==0.18.1+cu121", + "torchaudio==2.3.1+cu121" + ], + "lib_source": "https://download.pytorch.org/libtorch/cu121/libtorch-cxx11-abi-shared-with-deps-2.3.1%2Bcu121.zip", + "rai_patches": [ + { + "description": "Patch RedisAI module to require C++17 standard instead of C++14", + "source_file": "src/backends/libtorch_c/CMakeLists.txt", + "regex": "set_property\\(TARGET\\storch_c\\sPROPERTY\\sCXX_STANDARD\\s(98|11|14)\\)", + "replacement": "set_property(TARGET torch_c PROPERTY CXX_STANDARD 17)" + } + ] + }, + { + "name": "libtensorflow", + "version": "2.15", + "pip_index": "", + "python_packages": [ + "tensorflow==2.15" + ], + "lib_source": "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.15.0.tar.gz", + "rai_patches": [ + { + "description": "Patch RedisAI to point to correct tsl directory", + "source_file": "CMakeLists.txt", + "regex": "INCLUDE_DIRECTORIES\\(\\$\\{depsAbs\\}/libtensorflow/include\\)", + "replacement": "INCLUDE_DIRECTORIES(${depsAbs}/libtensorflow/include ${depsAbs}/libtensorflow/include/external/local_tsl)" + } + ] + }, + { + "name": "onnxruntime", + "version": "1.17.3", + "pip_index": "", + "python_packages": [ + "onnx==1.15", + "skl2onnx", + "scikit-learn", + "onnxmltools" + ], + "lib_source": "https://github.com/microsoft/onnxruntime/releases/download/v1.17.3/onnxruntime-linux-x64-gpu-cuda12-1.17.3.tgz" + } + ] +} diff --git a/smartsim/_core/_install/configs/mlpackages/LinuxX64ROCM6.json b/smartsim/_core/_install/configs/mlpackages/LinuxX64ROCM6.json new file mode 100644 index 000000000..b4673e901 --- /dev/null +++ b/smartsim/_core/_install/configs/mlpackages/LinuxX64ROCM6.json @@ -0,0 +1,47 @@ +{ + "platform": { + "operating_system":"linux", + "architecture":"x86_64", + "device":"rocm-6" + }, + "ml_packages": [ + { + "name": "dlpack", + "version": "v0.5_RAI", + "pip_index": "", + "python_packages": [], + "lib_source": "https://github.com/RedisAI/dlpack.git" + }, + { + "name": "libtorch", + "version": "2.4.0", + "pip_index": "https://download.pytorch.org/whl/rocm6.1", + "python_packages": [ + "torch==2.4.0+rocm6.1", + "torchvision==0.19.0+rocm6.1", + "torchaudio==2.4.0+rocm6.1" + ], + "lib_source": "https://download.pytorch.org/libtorch/rocm6.1/libtorch-cxx11-abi-shared-with-deps-2.4.1%2Brocm6.1.zip", + "rai_patches": [ + { + "description": "Patch RedisAI module to require C++17 standard instead of C++14", + "source_file": "src/backends/libtorch_c/CMakeLists.txt", + "regex": "set_property\\(TARGET\\storch_c\\sPROPERTY\\sCXX_STANDARD\\s(98|11|14)\\)", + "replacement": "set_property(TARGET torch_c PROPERTY CXX_STANDARD 17)" + }, + { + "description": "Fix Regex, Load HIP", + "source_file": "../package/libtorch/share/cmake/Caffe2/public/LoadHIP.cmake", + "regex": ".*string.*", + "replacement": "" + }, + { + "description": "Replace `/opt/rocm` with `$ENV{ROCM_PATH}`", + "source_file": "../package/libtorch/share/cmake/Caffe2/Caffe2Targets.cmake", + "regex": "/opt/rocm", + "replacement": "$ENV{ROCM_PATH}" + } + ] + } + ] +} diff --git a/smartsim/_core/_install/mlpackages.py b/smartsim/_core/_install/mlpackages.py new file mode 100644 index 000000000..04e3798d3 --- /dev/null +++ b/smartsim/_core/_install/mlpackages.py @@ -0,0 +1,198 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import json +import os +import pathlib +import re +import subprocess +import sys +import typing as t +from collections.abc import MutableMapping +from dataclasses import dataclass + +from tabulate import tabulate + +from .platform import Platform +from .types import PathLike +from .utils import retrieve + + +class RequireRelativePath(Exception): + pass + + +@dataclass +class RAIPatch: + """Holds information about how to patch a RedisAI source file + + :param description: Human-readable description of the patch's purpose + :param replacement: "The replacement for the line found by the regex" + :param source_file: A relative path to the chosen file + :param regex: A regex pattern to match in the given file + + """ + + description: str + replacement: str + source_file: pathlib.Path + regex: re.Pattern[str] + + def __post_init__(self) -> None: + self.source_file = pathlib.Path(self.source_file) + self.regex = re.compile(self.regex) + + +@dataclass +class MLPackage: + """Describes the python and C/C++ library for an ML package""" + + name: str + version: str + pip_index: str + python_packages: t.List[str] + lib_source: PathLike + rai_patches: t.Tuple[RAIPatch, ...] = () + + def retrieve(self, destination: PathLike) -> None: + """Retrieve an archive and/or repository for the package + + :param destination: Path to place the extracted package or repository + """ + retrieve(self.lib_source, pathlib.Path(destination)) + + def pip_install(self, quiet: bool = False) -> None: + """Install associated python packages + + :param quiet: If True, suppress most of the pip output, defaults to False + """ + if self.python_packages: + install_command = [sys.executable, "-m", "pip", "install"] + if self.pip_index: + install_command += ["--index-url", self.pip_index] + if quiet: + install_command += ["--quiet", "--no-warn-conflicts"] + install_command += self.python_packages + subprocess.check_call(install_command) + + +class MLPackageCollection(MutableMapping[str, MLPackage]): + """Collects multiple MLPackages + + Define a collection of MLPackages available for a specific platform + """ + + def __init__(self, platform: Platform, ml_packages: t.Sequence[MLPackage]): + self.platform = platform + self._ml_packages = {pkg.name: pkg for pkg in ml_packages} + + @classmethod + def from_json_file(cls, json_file: PathLike) -> "MLPackageCollection": + """Create an MLPackageCollection specified from a JSON file + + :param json_file: path to the JSON file + :return: An instance of MLPackageCollection for a platform + """ + with open(json_file, "r", encoding="utf-8") as file_handle: + config_json = json.load(file_handle) + platform = Platform.from_strs(**config_json["platform"]) + + for ml_package in config_json["ml_packages"]: + # Convert the dictionary representation to a RAIPatch + if "rai_patches" in ml_package: + patch_list = ml_package.pop("rai_patches") + ml_package["rai_patches"] = [RAIPatch(**patch) for patch in patch_list] + + ml_packages = [ + MLPackage(**ml_package) for ml_package in config_json["ml_packages"] + ] + return cls(platform, ml_packages) + + def __iter__(self) -> t.Iterator[str]: + """Iterate over the mlpackages in the collection + + :return: Iterator over mlpackages + """ + return iter(self._ml_packages) + + def __getitem__(self, key: str) -> MLPackage: + """Retrieve an MLPackage based on its name + + :param key: Name of the python package (e.g. libtorch) + :return: MLPackage with all requirements + """ + return self._ml_packages[key] + + def __len__(self) -> int: + return len(self._ml_packages) + + def __delitem__(self, key: str) -> None: + del self._ml_packages[key] + + def __setitem__(self, key: t.Any, value: t.Any) -> t.NoReturn: + raise TypeError(f"{type(self).__name__} does not support item assignment") + + def __contains__(self, key: object) -> bool: + return key in self._ml_packages + + def __str__(self, tablefmt: str = "github") -> str: + """Display package names and versions as a table + + :param tablefmt: Tabulate format, defaults to "github" + """ + + return tabulate( + [[k, v.version] for k, v in self._ml_packages.items()], + headers=["Package", "Version"], + tablefmt=tablefmt, + ) + + +def load_platform_configs( + config_file_path: pathlib.Path, +) -> t.Dict[Platform, MLPackageCollection]: + """Create MLPackageCollections from JSON files in directory + + :param config_file_path: Directory with JSON files describing the + configuration by platform + :return: Dictionary whose keys are the supported platform and values + are its associated MLPackageCollection + """ + if not config_file_path.is_dir(): + path = os.fspath(config_file_path) + msg = f"Platform configuration directory `{path}` does not exist" + raise FileNotFoundError(msg) + configs = {} + for config_file in config_file_path.glob("*.json"): + dependencies = MLPackageCollection.from_json_file(config_file) + configs[dependencies.platform] = dependencies + return configs + + +DEFAULT_MLPACKAGE_PATH: t.Final = ( + pathlib.Path(__file__).parent / "configs" / "mlpackages" +) +DEFAULT_MLPACKAGES: t.Final = load_platform_configs(DEFAULT_MLPACKAGE_PATH) diff --git a/smartsim/_core/_install/platform.py b/smartsim/_core/_install/platform.py new file mode 100644 index 000000000..bef13c6a0 --- /dev/null +++ b/smartsim/_core/_install/platform.py @@ -0,0 +1,226 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import enum +import json +import os +import pathlib +import platform +import typing as t +from dataclasses import dataclass + +from typing_extensions import Self + + +class PlatformError(Exception): + pass + + +class UnsupportedError(PlatformError): + pass + + +class Architecture(enum.Enum): + """Identifiers for supported CPU architectures + + :return: An enum representing the CPU architecture + """ + + X64 = "x86_64" + ARM64 = "arm64" + + @classmethod + def from_str(cls, string: str) -> "Architecture": + """Return enum associated with the architecture + + :param string: String representing the architecture, see platform.machine + :return: Enum for a specific architecture + """ + string = string.lower() + return cls(string) + + @classmethod + def autodetect(cls) -> "Architecture": + """Automatically return the architecture of the current machine + + :return: enum of this platform's architecture + """ + return cls.from_str(platform.machine()) + + +class Device(enum.Enum): + """Identifiers for the device stack + + :return: Enum associated with the device stack + """ + + CPU = "cpu" + CUDA11 = "cuda-11" + CUDA12 = "cuda-12" + ROCM5 = "rocm-5" + ROCM6 = "rocm-6" + + @classmethod + def from_str(cls, str_: str) -> "Device": + """Return enum associated with the device + + :param string: String representing the device and version + :return: Enum for a specific device + """ + str_ = str_.lower() + if str_ == "gpu": + # TODO: auto detect which device to use + # currently hard coded to `cuda11` + return cls.CUDA11 + return cls(str_) + + @classmethod + def detect_cuda_version(cls) -> t.Optional["Device"]: + """Find the enum based on environment CUDA + + :return: Enum for the version of CUDA currently available + """ + if cuda_home := os.environ.get("CUDA_HOME"): + cuda_path = pathlib.Path(cuda_home) + with open(cuda_path / "version.json", "r", encoding="utf-8") as file_handle: + cuda_versions = json.load(file_handle) + major = cuda_versions["cuda"]["version"].split(".")[0] + return cls.from_str(f"cuda-{major}") + return None + + @classmethod + def detect_rocm_version(cls) -> t.Optional["Device"]: + """Find the enum based on environment ROCm + + :return: Enum for the version of ROCm currently available + """ + if rocm_home := os.environ.get("ROCM_HOME"): + rocm_path = pathlib.Path(rocm_home) + fname = rocm_path / ".info" / "version" + with open(fname, "r", encoding="utf-8") as file_handle: + major = file_handle.readline().split("-")[0].split(".")[0] + return cls.from_str(f"rocm-{major}") + return None + + def is_gpu(self) -> bool: + """Whether the enum is categorized as a GPU + + :return: True if GPU + """ + return self != type(self).CPU + + def is_cuda(self) -> bool: + """Whether the enum is associated with a CUDA device + + :return: True for any supported CUDA enums + """ + cls = type(self) + return self in cls.cuda_enums() + + def is_rocm(self) -> bool: + """Whether the enum is associated with a ROCm device + + :return: True for any supported ROCm enums + """ + cls = type(self) + return self in cls.rocm_enums() + + @classmethod + def cuda_enums(cls) -> t.Tuple["Device", ...]: + """Detect all CUDA devices supported by SmartSim + + :return: all enums associated with CUDA + """ + return tuple(device for device in cls if "cuda" in device.value) + + @classmethod + def rocm_enums(cls) -> t.Tuple["Device", ...]: + """Detect all ROCm devices supported by SmartSim + + :return: all enums associated with ROCm + """ + return tuple(device for device in cls if "rocm" in device.value) + + +class OperatingSystem(enum.Enum): + """Enum for all supported operating systems""" + + LINUX = "linux" + DARWIN = "darwin" + + @classmethod + def from_str(cls, string: str, /) -> "OperatingSystem": + """Return enum associated with the OS + + :param string: String representing the OS + :return: Enum for a specific OS + """ + string = string.lower() + return cls(string) + + @classmethod + def autodetect(cls) -> "OperatingSystem": + """Automatically return the OS of the current machine + + :return: enum of this platform's OS + """ + return cls.from_str(platform.system()) + + +@dataclass(frozen=True) +class Platform: + """Container describing relevant identifiers for a platform""" + + operating_system: OperatingSystem + architecture: Architecture + device: Device + + @classmethod + def from_strs(cls, operating_system: str, architecture: str, device: str) -> Self: + """Factory method for Platform from string onput + + :param os: String identifier for the OS + :param architecture: String identifier for the architecture + :param device: String identifer for the device and version + :return: Instance of Platform + """ + return cls( + OperatingSystem.from_str(operating_system), + Architecture.from_str(architecture), + Device.from_str(device), + ) + + def __str__(self) -> str: + """Human-readable representation of Platform + + :return: String created from the values of the enums for each property + """ + output = [ + self.operating_system.name, + self.architecture.name, + self.device.name, + ] + return "-".join(output) diff --git a/smartsim/_core/_install/redisaiBuilder.py b/smartsim/_core/_install/redisaiBuilder.py new file mode 100644 index 000000000..1dce6ddb4 --- /dev/null +++ b/smartsim/_core/_install/redisaiBuilder.py @@ -0,0 +1,301 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import fileinput +import os +import pathlib +import shutil +import stat +import subprocess +import typing as t +from collections import deque + +from smartsim._core._cli.utils import SMART_LOGGER_FORMAT +from smartsim._core._install.buildenv import BuildEnv +from smartsim._core._install.mlpackages import MLPackageCollection, RAIPatch +from smartsim._core._install.platform import OperatingSystem, Platform +from smartsim._core._install.utils import retrieve +from smartsim._core.config import CONFIG +from smartsim.log import get_logger + +logger = get_logger("Smart", fmt=SMART_LOGGER_FORMAT) +_SUPPORTED_ROCM_ARCH = "gfx90a" + + +class RedisAIBuildError(Exception): + pass + + +class RedisAIBuilder: + """Class to build RedisAI from Source""" + + def __init__( + self, + platform: Platform, + mlpackages: MLPackageCollection, + build_env: BuildEnv, + main_build_path: pathlib.Path, + verbose: bool = False, + source: t.Union[str, pathlib.Path] = "https://github.com/RedisAI/RedisAI.git", + version: str = "v1.2.7", + ) -> None: + + self.platform = platform + self.mlpackages = mlpackages + self.build_env = build_env + self.verbose = verbose + self.source = source + self.version = version + self._root_path = main_build_path / "RedisAI" + + self.cleanup_build() + + @property + def src_path(self) -> pathlib.Path: + return pathlib.Path(self._root_path / "src") + + @property + def build_path(self) -> pathlib.Path: + return pathlib.Path(self._root_path / "build") + + @property + def package_path(self) -> pathlib.Path: + return pathlib.Path(self._root_path / "package") + + def cleanup_build(self) -> None: + """Removes all directories associated with the build""" + shutil.rmtree(self.src_path, ignore_errors=True) + shutil.rmtree(self.build_path, ignore_errors=True) + shutil.rmtree(self.package_path, ignore_errors=True) + + @property + def is_built(self) -> bool: + """Determine whether RedisAI and backends were built + + :return: True if all backends and RedisAI module are in + the expected location + """ + backend_dir = CONFIG.lib_path / "backends" + rai_exists = [ + (backend_dir / f"redisai_{backend_name}").is_dir() + for backend_name in self.mlpackages + ] + rai_exists.append((CONFIG.lib_path / "redisai.so").is_file()) + return all(rai_exists) + + @property + def build_torch(self) -> bool: + """Whether to build torch backend + + :return: True if torch backend should be built + """ + return "libtorch" in self.mlpackages + + @property + def build_tensorflow(self) -> bool: + """Whether to build tensorflow backend + + :return: True if tensorflow backend should be built + """ + return "libtensorflow" in self.mlpackages + + @property + def build_onnxruntime(self) -> bool: + """Whether to build onnx backend + + :return: True if onnx backend should be built + """ + return "onnxruntime" in self.mlpackages + + def build(self) -> None: + """Build RedisAI + + :param git_url: url from which to retrieve RedisAI + :param branch: branch to checkout + :param device: cpu or gpu + """ + + # Following is needed to make sure that the clone/checkout is not + # impeded by git LFS limits imposed by RedisAI + os.environ["GIT_LFS_SKIP_SMUDGE"] = "1" + + self.src_path.mkdir(parents=True) + self.build_path.mkdir(parents=True) + self.package_path.mkdir(parents=True) + + retrieve(self.source, self.src_path, depth=1, branch=self.version) + + self._prepare_packages() + + for package in self.mlpackages.values(): + self._patch_source_files(package.rai_patches) + cmake_command = self._rai_cmake_cmd() + build_command = self._rai_build_cmd + + if self.platform.device.is_rocm() and "libtorch" in self.mlpackages: + pytorch_rocm_arch = os.environ.get("PYTORCH_ROCM_ARCH") + if not pytorch_rocm_arch: + logger.info( + f"PYTORCH_ROCM_ARCH not set. Defaulting to '{_SUPPORTED_ROCM_ARCH}'" + ) + os.environ["PYTORCH_ROCM_ARCH"] = _SUPPORTED_ROCM_ARCH + elif pytorch_rocm_arch != _SUPPORTED_ROCM_ARCH: + logger.warning( + f"PYTORCH_ROCM_ARCH is not {_SUPPORTED_ROCM_ARCH} which is the " + "only officially supported architecture. This may still work " + "if you are supplying your own version of libtensorflow." + ) + + logger.info("Configuring CMake Build") + if self.verbose: + print(" ".join(cmake_command)) + self.run_command(cmake_command, self.build_path) + + logger.info("Building RedisAI") + if self.verbose: + print(" ".join(build_command)) + self.run_command(build_command, self.build_path) + + if self.platform.operating_system == OperatingSystem.LINUX: + self._set_execute(CONFIG.lib_path / "redisai.so") + + @staticmethod + def _set_execute(target: pathlib.Path) -> None: + """Set execute permissions for file + + :param target: The target file to add execute permission + """ + permissions = os.stat(target).st_mode | stat.S_IXUSR + os.chmod(target, permissions) + + @staticmethod + def _find_closest_object( + start_path: pathlib.Path, target_obj: str + ) -> t.Optional[pathlib.Path]: + queue = deque([start_path]) + while queue: + current_dir = queue.popleft() + current_target = current_dir / target_obj + if current_target.exists(): + return current_target.parent + for sub_dir in current_dir.iterdir(): + if sub_dir.is_dir(): + queue.append(sub_dir) + return None + + def _prepare_packages(self) -> None: + """Ensure that retrieved archives/packages are in the expected location + + RedisAI requires that the root directory of the backend is at + DEP_PATH/example_backend. Due to difficulties in retrieval methods and + naming conventions from different sources, this cannot be standardized. + Instead we try to find the parent of the "include" directory and assume + this is the root. + """ + + for package in self.mlpackages.values(): + logger.info(f"Retrieving package: {package.name} {package.version}") + target_dir = self.package_path / package.name + package.retrieve(target_dir) + # Move actual contents to root of the expected location + actual_root = self._find_closest_object(target_dir, "include") + if actual_root and actual_root != target_dir: + logger.debug( + ( + "Non-standard location found: \n", + f"{actual_root} -> {target_dir}", + ) + ) + for file in actual_root.iterdir(): + file.rename(target_dir / file.name) + + def run_command(self, cmd: t.Union[str, t.List[str]], cwd: pathlib.Path) -> None: + """Executor of commands usedi in the build + + :param cmd: The actual command to execute + :param cwd: The working directory to execute in + """ + stdout = None if self.verbose else subprocess.DEVNULL + stderr = None if self.verbose else subprocess.PIPE + proc = subprocess.run( + cmd, cwd=str(cwd), stdout=stdout, stderr=stderr, check=False + ) + if proc.returncode != 0: + if stderr: + print(proc.stderr.decode("utf-8")) + raise RedisAIBuildError( + f"RedisAI build failed during command: {' '.join(cmd)}" + ) + + def _rai_cmake_cmd(self) -> t.List[str]: + """Build the CMake configuration command + + :return: CMake command with correct options + """ + + def on_off(expression: bool) -> t.Literal["ON", "OFF"]: + return "ON" if expression else "OFF" + + cmake_args = { + "BUILD_TF": on_off(self.build_tensorflow), + "BUILD_ORT": on_off(self.build_onnxruntime), + "BUILD_TORCH": on_off(self.build_torch), + "BUILD_TFLITE": "OFF", + "DEPS_PATH": str(self.package_path), + "DEVICE": "gpu" if self.platform.device.is_gpu() else "cpu", + "INSTALL_PATH": str(CONFIG.lib_path), + "CMAKE_C_COMPILER": self.build_env.CC, + "CMAKE_CXX_COMPILER": self.build_env.CXX, + } + if self.platform.device.is_rocm(): + cmake_args["Torch_DIR"] = str(self.package_path / "libtorch") + cmd = ["cmake"] + cmd += (f"-D{key}={value}" for key, value in cmake_args.items()) + cmd.append(str(self.src_path)) + return cmd + + @property + def _rai_build_cmd(self) -> t.List[str]: + """Shell command to build RedisAI and modules + + With the CMake based install, very little needs to be done here. + "make install" is used to ensure that all resulting RedisAI backends + and their dependencies end up in the same location with the correct + RPATH if applicable. + + :return: Command used to compile RedisAI and backends + """ + return "make install -j VERBOSE=1".split(" ") + + def _patch_source_files(self, patches: t.Tuple[RAIPatch, ...]) -> None: + """Apply specified RedisAI patches""" + for patch in patches: + with fileinput.input( + str(self.src_path / patch.source_file), inplace=True + ) as file_handle: + for line in file_handle: + line = patch.regex.sub(patch.replacement, line) + print(line, end="") diff --git a/smartsim/_core/_install/types.py b/smartsim/_core/_install/types.py new file mode 100644 index 000000000..0266ace34 --- /dev/null +++ b/smartsim/_core/_install/types.py @@ -0,0 +1,30 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import pathlib +import typing as t + +PathLike = t.Union[str, pathlib.Path] diff --git a/smartsim/_core/_install/utils/__init__.py b/smartsim/_core/_install/utils/__init__.py new file mode 100644 index 000000000..4e47cf282 --- /dev/null +++ b/smartsim/_core/_install/utils/__init__.py @@ -0,0 +1,27 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +from .retrieve import retrieve diff --git a/smartsim/_core/_install/utils/retrieve.py b/smartsim/_core/_install/utils/retrieve.py new file mode 100644 index 000000000..fcac565d4 --- /dev/null +++ b/smartsim/_core/_install/utils/retrieve.py @@ -0,0 +1,185 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import os +import pathlib +import shutil +import tarfile +import typing as t +import zipfile +from urllib.parse import urlparse +from urllib.request import urlretrieve + +import git +from tqdm import tqdm + +from smartsim._core._install.platform import Architecture, OperatingSystem +from smartsim._core._install.types import PathLike + + +class UnsupportedArchive(Exception): + pass + + +class _TqdmUpTo(tqdm): # type: ignore[type-arg] + """Provides `update_to(n)` which uses `tqdm.update(delta_n)` + + From tqdm doumentation for progress bar when downloading + """ + + def update_to( + self, num_blocks: int = 1, bsize: int = 1, tsize: t.Optional[int] = None + ) -> t.Optional[bool]: + """Update progress in tqdm-like way + + :param b: number of blocks transferred so far, defaults to 1 + :param bsize: size of each block (in tqdm units), defaults to 1 + :param tsize: total size (in tqdm units), defaults to None + :return: Update + """ + + if tsize is not None: + self.total = tsize + return self.update(num_blocks * bsize - self.n) # also sets self.n = b * bsize + + +def _from_local_archive( + source: PathLike, + destination: pathlib.Path, + **kwargs: t.Any, +) -> None: + """Decompress a local archive + + :param source: Path to the archive on a local system + :param destination: Where to unpack the archive + """ + if tarfile.is_tarfile(source): + with tarfile.open(source) as archive: + archive.extractall(path=destination, **kwargs) + if zipfile.is_zipfile(source): + with zipfile.ZipFile(source) as archive: + archive.extractall(path=destination, **kwargs) + + +def _from_local_directory( + source: PathLike, + destination: pathlib.Path, + **kwargs: t.Any, +) -> None: + """Copy the contents of a directory + + :param source: source directory + :param destination: desitnation directory + """ + shutil.copytree(source, destination, **kwargs) + + +def _from_http( + source: str, + destination: pathlib.Path, + **kwargs: t.Any, +) -> None: + """Download and decompress a package + + :param source: URL to a particular package + :param destination: Where to unpack the archive + """ + with _TqdmUpTo( + unit="B", + unit_scale=True, + unit_divisor=1024, + miniters=1, + desc=source.split("/")[-1], + ) as _t: # all optional kwargs + local_file, _ = urlretrieve(source, reporthook=_t.update_to, **kwargs) + _t.total = _t.n + + _from_local_archive(local_file, destination) + os.remove(local_file) + + +def _from_git(source: str, destination: pathlib.Path, **clone_kwargs: t.Any) -> None: + """Clone a repository + + :param source: Path to the remote (URL or local) repository + :param destination: where to clone the repository + :param clone_kwargs: various options to send to the clone command + """ + is_mac = OperatingSystem.autodetect() == OperatingSystem.DARWIN + is_arm64 = Architecture.autodetect() == Architecture.ARM64 + if is_mac and is_arm64: + config_options = ["--config core.autocrlf=false", "--config core.eol=lf"] + allow_unsafe_options = True + else: + config_options = None + allow_unsafe_options = False + git.Repo.clone_from( + source, + destination, + multi_options=config_options, + allow_unsafe_options=allow_unsafe_options, + **clone_kwargs, + ) + + +def retrieve( + source: PathLike, destination: pathlib.Path, **retrieve_kwargs: t.Any +) -> None: + """Primary method for retrieval + + Automatically choose the correct method based on the extension and/or source + of the archive. If downloaded, this will also decompress the archive and + extract + + :param source: URL or path to find the package + :param destination: where to place the package + :raises UnsupportedArchive: Unknown archive type + :raises FileNotFound: Path to archive does not exist + """ + parsed_url = urlparse(str(source)) + url_scheme = parsed_url.scheme + if parsed_url.path.endswith(".git"): + _from_git(str(source), destination, **retrieve_kwargs) + elif url_scheme == "http": + _from_http(str(source), destination, **retrieve_kwargs) + elif url_scheme == "https": + _from_http(str(source), destination, **retrieve_kwargs) + else: # This is probably a path + source_path = pathlib.Path(source) + if not source_path.exists(): + raise FileNotFoundError(f"Package path or file does not exist: {source}") + if source_path.is_dir(): + _from_local_directory(source, destination, **retrieve_kwargs) + elif source_path.is_file() and source_path.suffix in ( + ".gz", + ".zip", + ".tgz", + ): + _from_local_archive(source, destination, **retrieve_kwargs) + else: + raise UnsupportedArchive( + f"Source ({source}) is not a supported archive or directory " + ) diff --git a/smartsim/_core/config/config.py b/smartsim/_core/config/config.py index 9cf950b21..03c284edb 100644 --- a/smartsim/_core/config/config.py +++ b/smartsim/_core/config/config.py @@ -33,7 +33,7 @@ import psutil from ...error import SSConfigError -from ..utils.helpers import expand_exe_path +from ..utils import expand_exe_path # Configuration Values # @@ -94,13 +94,28 @@ class Config: def __init__(self) -> None: # SmartSim/smartsim/_core self.core_path = Path(os.path.abspath(__file__)).parent.parent + # TODO: Turn this into a property. Need to modify the configuration + # of KeyDB vs Redis at build time + self.conf_dir = self.core_path / "config" + self.conf_path = self.conf_dir / "redis.conf" - dependency_path = os.environ.get("SMARTSIM_DEP_INSTALL_PATH", self.core_path) + @property + def dependency_path(self) -> Path: + return Path( + os.environ.get("SMARTSIM_DEP_INSTALL_PATH", str(self.core_path)) + ).resolve() + + @property + def lib_path(self) -> Path: + return Path(self.dependency_path, "lib") - self.lib_path = Path(dependency_path, "lib").resolve() - self.bin_path = Path(dependency_path, "bin").resolve() - self.conf_path = Path(dependency_path, "config", "redis.conf") - self.conf_dir = Path(self.core_path, "config") + @property + def bin_path(self) -> Path: + return Path(self.dependency_path, "bin") + + @property + def build_path(self) -> Path: + return Path(self.dependency_path, "build") @property def redisai(self) -> str: @@ -157,7 +172,7 @@ def database_file_parse_interval(self) -> int: @property def dragon_dotenv(self) -> Path: """Returns the path to a .env file containing dragon environment variables""" - return self.conf_dir / "dragon" / ".env" + return Path(self.conf_dir / "dragon" / ".env") @property def dragon_server_path(self) -> t.Optional[str]: diff --git a/smartsim/_core/control/controller.py b/smartsim/_core/control/controller.py index 43a218545..0b943ee90 100644 --- a/smartsim/_core/control/controller.py +++ b/smartsim/_core/control/controller.py @@ -72,6 +72,7 @@ LocalLauncher, LSFLauncher, PBSLauncher, + SGELauncher, SlurmLauncher, ) from ..launcher.launcher import Launcher @@ -343,6 +344,7 @@ def init_launcher(self, launcher: str) -> None: "lsf": LSFLauncher, "local": LocalLauncher, "dragon": DragonLauncher, + "sge": SGELauncher, } if launcher is not None: diff --git a/smartsim/_core/launcher/__init__.py b/smartsim/_core/launcher/__init__.py index d78909641..c6584ee3d 100644 --- a/smartsim/_core/launcher/__init__.py +++ b/smartsim/_core/launcher/__init__.py @@ -29,6 +29,7 @@ from .local.local import LocalLauncher from .lsf.lsfLauncher import LSFLauncher from .pbs.pbsLauncher import PBSLauncher +from .sge.sgeLauncher import SGELauncher from .slurm.slurmLauncher import SlurmLauncher __all__ = [ @@ -37,5 +38,6 @@ "LocalLauncher", "LSFLauncher", "PBSLauncher", + "SGELauncher", "SlurmLauncher", ] diff --git a/smartsim/_core/launcher/dragon/dragonBackend.py b/smartsim/_core/launcher/dragon/dragonBackend.py index 245660662..4aba60d55 100644 --- a/smartsim/_core/launcher/dragon/dragonBackend.py +++ b/smartsim/_core/launcher/dragon/dragonBackend.py @@ -210,10 +210,13 @@ def group_infos(self) -> dict[str, ProcessGroupInfo]: def _initialize_hosts(self) -> None: with self._queue_lock: - self._hosts: t.List[str] = sorted( - dragon_machine.Node(node).hostname - for node in dragon_machine.System().nodes - ) + self._nodes = [ + dragon_machine.Node(node) for node in dragon_machine.System().nodes + ] + self._hosts: t.List[str] = sorted(node.hostname for node in self._nodes) + self._cpus = [node.num_cpus for node in self._nodes] + self._gpus = [node.num_gpus for node in self._nodes] + """List of hosts available in allocation""" self._free_hosts: t.Deque[str] = collections.deque(self._hosts) """List of hosts on which steps can be launched""" @@ -285,6 +288,34 @@ def current_time(self) -> float: """Current time for DragonBackend object, in seconds since the Epoch""" return time.time() + def _can_honor_policy( + self, request: DragonRunRequest + ) -> t.Tuple[bool, t.Optional[str]]: + """Check if the policy can be honored with resources available + in the allocation. + :param request: DragonRunRequest containing policy information + :returns: Tuple indicating if the policy can be honored and + an optional error message""" + # ensure the policy can be honored + if request.policy: + if request.policy.cpu_affinity: + # make sure some node has enough CPUs + available = max(self._cpus) + requested = max(request.policy.cpu_affinity) + + if requested >= available: + return False, "Cannot satisfy request, not enough CPUs available" + + if request.policy.gpu_affinity: + # make sure some node has enough GPUs + available = max(self._gpus) + requested = max(request.policy.gpu_affinity) + + if requested >= available: + return False, "Cannot satisfy request, not enough GPUs available" + + return True, None + def _can_honor(self, request: DragonRunRequest) -> t.Tuple[bool, t.Optional[str]]: """Check if request can be honored with resources available in the allocation. @@ -299,6 +330,11 @@ def _can_honor(self, request: DragonRunRequest) -> t.Tuple[bool, t.Optional[str] if self._shutdown_requested: message = "Cannot satisfy request, server is shutting down." return False, message + + honorable, err = self._can_honor_policy(request) + if not honorable: + return False, err + return True, None def _allocate_step( @@ -391,6 +427,50 @@ def _stop_steps(self) -> None: self._group_infos[step_id].status = SmartSimStatus.STATUS_CANCELLED self._group_infos[step_id].return_codes = [-9] + @staticmethod + def create_run_policy( + request: DragonRequest, node_name: str + ) -> "dragon_policy.Policy": + """Create a dragon Policy from the request and node name + :param request: DragonRunRequest containing policy information + :param node_name: Name of the node on which the process will run + :returns: dragon_policy.Policy object mapped from request properties""" + if isinstance(request, DragonRunRequest): + run_request: DragonRunRequest = request + + affinity = dragon_policy.Policy.Affinity.DEFAULT + cpu_affinity: t.List[int] = [] + gpu_affinity: t.List[int] = [] + + # Customize policy only if the client requested it, otherwise use default + if run_request.policy is not None: + # Affinities are not mutually exclusive. If specified, both are used + if run_request.policy.cpu_affinity: + affinity = dragon_policy.Policy.Affinity.SPECIFIC + cpu_affinity = run_request.policy.cpu_affinity + + if run_request.policy.gpu_affinity: + affinity = dragon_policy.Policy.Affinity.SPECIFIC + gpu_affinity = run_request.policy.gpu_affinity + logger.debug( + f"Affinity strategy: {affinity}, " + f"CPU affinity mask: {cpu_affinity}, " + f"GPU affinity mask: {gpu_affinity}" + ) + if affinity != dragon_policy.Policy.Affinity.DEFAULT: + return dragon_policy.Policy( + placement=dragon_policy.Policy.Placement.HOST_NAME, + host_name=node_name, + affinity=affinity, + cpu_affinity=cpu_affinity, + gpu_affinity=gpu_affinity, + ) + + return dragon_policy.Policy( + placement=dragon_policy.Policy.Placement.HOST_NAME, + host_name=node_name, + ) + def _start_steps(self) -> None: self._heartbeat() with self._queue_lock: @@ -412,10 +492,7 @@ def _start_steps(self) -> None: policies = [] for node_name in hosts: - local_policy = dragon_policy.Policy( - placement=dragon_policy.Policy.Placement.HOST_NAME, - host_name=node_name, - ) + local_policy = self.create_run_policy(request, node_name) policies.extend([local_policy] * request.tasks_per_node) tmp_proc = dragon_process.ProcessTemplate( target=request.exe, diff --git a/smartsim/_core/launcher/dragon/dragonLauncher.py b/smartsim/_core/launcher/dragon/dragonLauncher.py index 17b47e309..9078fed54 100644 --- a/smartsim/_core/launcher/dragon/dragonLauncher.py +++ b/smartsim/_core/launcher/dragon/dragonLauncher.py @@ -29,6 +29,8 @@ import os import typing as t +from smartsim._core.schemas.dragonRequests import DragonRunPolicy + from ...._core.launcher.stepMapping import StepMap from ....error import LauncherError, SmartSimError from ....log import get_logger @@ -168,6 +170,9 @@ def run(self, step: Step) -> t.Optional[str]: merged_env = self._connector.merge_persisted_env(os.environ.copy()) nodes = int(run_args.get("nodes", None) or 1) tasks_per_node = int(run_args.get("tasks-per-node", None) or 1) + + policy = DragonRunPolicy.from_run_args(run_args) + response = _assert_schema_type( self._connector.send_request( DragonRunRequest( @@ -181,6 +186,7 @@ def run(self, step: Step) -> t.Optional[str]: current_env=merged_env, output_file=out, error_file=err, + policy=policy, ) ), DragonRunResponse, diff --git a/smartsim/_core/launcher/lsf/lsfCommands.py b/smartsim/_core/launcher/lsf/lsfCommands.py index cb92587c1..0b98abf58 100644 --- a/smartsim/_core/launcher/lsf/lsfCommands.py +++ b/smartsim/_core/launcher/lsf/lsfCommands.py @@ -26,7 +26,7 @@ import typing as t -from ..util.shell import execute_cmd +from ...utils.shell import execute_cmd def bjobs(args: t.List[str]) -> t.Tuple[str, str]: diff --git a/smartsim/_core/launcher/pbs/pbsCommands.py b/smartsim/_core/launcher/pbs/pbsCommands.py index 989af93be..2a8fcf872 100644 --- a/smartsim/_core/launcher/pbs/pbsCommands.py +++ b/smartsim/_core/launcher/pbs/pbsCommands.py @@ -26,7 +26,7 @@ import typing as t -from ..util.shell import execute_cmd +from ...utils.shell import execute_cmd def qstat(args: t.List[str]) -> t.Tuple[str, str]: diff --git a/smartsim/_core/launcher/sge/__init__.py b/smartsim/_core/launcher/sge/__init__.py new file mode 100644 index 000000000..efe03908e --- /dev/null +++ b/smartsim/_core/launcher/sge/__init__.py @@ -0,0 +1,25 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/smartsim/_core/launcher/sge/sgeCommands.py b/smartsim/_core/launcher/sge/sgeCommands.py new file mode 100644 index 000000000..a284ee8db --- /dev/null +++ b/smartsim/_core/launcher/sge/sgeCommands.py @@ -0,0 +1,77 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import typing as t + +from ...utils.shell import execute_cmd + + +def qstat(args: t.List[str]) -> t.Tuple[str, str]: + """Calls SGE qstat with args + + :param args: List of command arguments + :returns: Output and error of qstat + """ + cmd = ["qstat"] + args + _, out, error = execute_cmd(cmd) + return out, error + + +def qsub(args: t.List[str]) -> t.Tuple[str, str]: + """Calls SGE qsub with args + + :param args: List of command arguments + :returns: Output and error of salloc + """ + cmd = ["qsub"] + args + _, out, error = execute_cmd(cmd) + return out, error + + +def qdel(args: t.List[str]) -> t.Tuple[int, str, str]: + """Calls SGE qdel with args. + + returncode is also supplied in this function. + + :param args: list of command arguments + :return: output and error + """ + cmd = ["qdel"] + args + returncode, out, error = execute_cmd(cmd) + return returncode, out, error + + +def qacct(args: t.List[str]) -> t.Tuple[int, str, str]: + """Calls SGE qacct with args. + + returncode is also supplied in this function. + + :param args: list of command arguments + :return: output and error + """ + cmd = ["qacct"] + args + returncode, out, error = execute_cmd(cmd) + return returncode, out, error diff --git a/smartsim/_core/launcher/sge/sgeLauncher.py b/smartsim/_core/launcher/sge/sgeLauncher.py new file mode 100644 index 000000000..af600cf1d --- /dev/null +++ b/smartsim/_core/launcher/sge/sgeLauncher.py @@ -0,0 +1,184 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import time +import typing as t + +from ....error import LauncherError +from ....log import get_logger +from ....settings import ( + MpiexecSettings, + MpirunSettings, + OrterunSettings, + RunSettings, + SettingsBase, + SgeQsubBatchSettings, +) +from ....status import SmartSimStatus +from ...config import CONFIG +from ..launcher import WLMLauncher +from ..step import ( + LocalStep, + MpiexecStep, + MpirunStep, + OrterunStep, + SgeQsubBatchStep, + Step, +) +from ..stepInfo import SGEStepInfo, StepInfo +from .sgeCommands import qacct, qdel, qstat +from .sgeParser import parse_qacct_job_output, parse_qstat_jobid_xml + +logger = get_logger(__name__) + + +class SGELauncher(WLMLauncher): + """This class encapsulates the functionality needed + to launch jobs on systems that use SGE as a workload manager. + + All WLM launchers are capable of launching managed and unmanaged + jobs. Managed jobs are queried through interaction with with WLM, + in this case SGE. Unmanaged jobs are held in the TaskManager + and are managed through references to their launching process ID + i.e. a psutil.Popen object + """ + + # init in WLMLauncher, launcher.py + + @property + def supported_rs(self) -> t.Dict[t.Type[SettingsBase], t.Type[Step]]: + # RunSettings types supported by this launcher + return { + SgeQsubBatchSettings: SgeQsubBatchStep, + MpiexecSettings: MpiexecStep, + MpirunSettings: MpirunStep, + OrterunSettings: OrterunStep, + RunSettings: LocalStep, + } + + def run(self, step: Step) -> t.Optional[str]: + """Run a job step through SGE + + :param step: a job step instance + :raises LauncherError: if launch fails + :return: job step id if job is managed + """ + if not self.task_manager.actively_monitoring: + self.task_manager.start() + + cmd_list = step.get_launch_cmd() + step_id: t.Optional[str] = None + task_id: t.Optional[str] = None + if isinstance(step, SgeQsubBatchStep): + # wait for batch step to submit successfully + return_code, out, err = self.task_manager.start_and_wait(cmd_list, step.cwd) + if return_code != 0: + raise LauncherError(f"Qsub batch submission failed\n {out}\n {err}") + if out: + step_id = out.split(" ")[2] + logger.debug(f"Gleaned batch job id: {step_id} for {step.name}") + else: + # aprun/local doesn't direct output for us. + out, err = step.get_output_files() + + # pylint: disable-next=consider-using-with + output = open(out, "w+", encoding="utf-8") + # pylint: disable-next=consider-using-with + error = open(err, "w+", encoding="utf-8") + task_id = self.task_manager.start_task( + cmd_list, step.cwd, step.env, out=output.fileno(), err=error.fileno() + ) + + self.step_mapping.add(step.name, step_id, task_id, step.managed) + + return step_id + + def stop(self, step_name: str) -> StepInfo: + """Stop/cancel a job step + + :param step_name: name of the job to stop + :return: update for job due to cancel + """ + stepmap = self.step_mapping[step_name] + if stepmap.managed: + qdel_rc, _, err = qdel([str(stepmap.step_id)]) + if qdel_rc != 0: + logger.warning(f"Unable to cancel job step {step_name}\n {err}") + if stepmap.task_id: + self.task_manager.remove_task(str(stepmap.task_id)) + else: + self.task_manager.remove_task(str(stepmap.task_id)) + + _, step_info = self.get_step_update([step_name])[0] + if not step_info: + raise LauncherError(f"Could not get step_info for job step {step_name}") + + step_info.status = ( + SmartSimStatus.STATUS_CANCELLED + ) # set status to cancelled instead of failed + return step_info + + def _get_managed_step_update(self, step_ids: t.List[str]) -> t.List[StepInfo]: + """Get step updates for WLM managed jobs + + :param step_ids: list of job step ids + :return: list of updates for managed jobs + """ + updates: t.List[StepInfo] = [] + + qstat_out, _ = qstat(["-xml"]) + stats = [parse_qstat_jobid_xml(qstat_out, str(step_id)) for step_id in step_ids] + + for stat, step_id in zip(stats, step_ids): + if stat is None: + info = SGEStepInfo("NOTFOUND") + # Attempt to retrieve the historical record + return_code, qacct_output, _ = qacct([f"-j {step_id}"]) + num_trials = 0 + while return_code != 0 and num_trials < CONFIG.wlm_trials: + num_trials += 1 + time.sleep(CONFIG.jm_interval) + return_code, qacct_output, _ = qacct([f"-j {step_id}"]) + + if qacct_output: + failed = bool(int(parse_qacct_job_output(qacct_output, "failed"))) + if failed: + info.status = SmartSimStatus.STATUS_FAILED + info.returncode = 0 + else: + info.status = SmartSimStatus.STATUS_COMPLETED + info.returncode = 0 + else: # Assume if qacct did not find it, that the job completed + info.status = SmartSimStatus.STATUS_COMPLETED + info.returncode = 0 + else: + info = SGEStepInfo(stat) + + updates.append(info) + return updates + + def __str__(self) -> str: + return "SGE" diff --git a/smartsim/_core/launcher/sge/sgeParser.py b/smartsim/_core/launcher/sge/sgeParser.py new file mode 100644 index 000000000..0ee5d5c67 --- /dev/null +++ b/smartsim/_core/launcher/sge/sgeParser.py @@ -0,0 +1,92 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import typing as t +import xml.etree.ElementTree as ET + + +def parse_qsub(output: str) -> str: + """Parse qsub output and return job id. For SGE, the + output is the job id itself. + + :param output: stdout of qsub command + :returns: job id + """ + return output + + +def parse_qsub_error(output: str) -> str: + """Parse and return error output of a failed qsub command. + + :param output: stderr of qsub command + :returns: error message + """ + # look for error first + for line in output.split("\n"): + if line.startswith("qsub:"): + error = line.split(":")[1] + return error.strip() + # if no error line, take first line + for line in output.split("\n"): + return line.strip() + # if neither, present a base error message + base_err = "PBS run error" + return base_err + + +def parse_qstat_jobid_xml(output: str, job_id: str) -> t.Optional[str]: + """Parse and return output of the qstat command run with XML options + to obtain job status. + + :param output: output of the qstat command in XML format + :param job_id: allocation id or job step id + :return: status + """ + + root = ET.fromstring(output) + for job_list in root.findall(".//job_list"): + job_state = job_list.find("state") + # not None construct is needed here, since element with no + # children returns 0, interpreted as False + if (job_number := job_list.find("JB_job_number")) is not None: + if job_number.text == job_id and (job_state is not None): + return job_state.text + + return None + + +def parse_qacct_job_output(output: str, field_name: str) -> t.Union[str, int]: + """Parse the output from qacct for a single job + + :param output: The raw text output from qacct + :param field_name: The name of the field to extract + """ + + for line in output.splitlines(): + if field_name in line: + return line.split()[1] + + return 1 diff --git a/smartsim/_core/launcher/slurm/slurmCommands.py b/smartsim/_core/launcher/slurm/slurmCommands.py index 839826297..e72a87af4 100644 --- a/smartsim/_core/launcher/slurm/slurmCommands.py +++ b/smartsim/_core/launcher/slurm/slurmCommands.py @@ -29,7 +29,7 @@ from ....error import LauncherError from ....log import get_logger from ...utils.helpers import expand_exe_path -from ..util.shell import execute_cmd +from ...utils.shell import execute_cmd logger = get_logger(__name__) diff --git a/smartsim/_core/launcher/step/__init__.py b/smartsim/_core/launcher/step/__init__.py index c492f3e97..8331a18bf 100644 --- a/smartsim/_core/launcher/step/__init__.py +++ b/smartsim/_core/launcher/step/__init__.py @@ -30,5 +30,6 @@ from .lsfStep import BsubBatchStep, JsrunStep from .mpiStep import MpiexecStep, MpirunStep, OrterunStep from .pbsStep import QsubBatchStep +from .sgeStep import SgeQsubBatchStep from .slurmStep import SbatchStep, SrunStep from .step import Step diff --git a/smartsim/_core/launcher/step/dragonStep.py b/smartsim/_core/launcher/step/dragonStep.py index 036a9e565..dd93d7910 100644 --- a/smartsim/_core/launcher/step/dragonStep.py +++ b/smartsim/_core/launcher/step/dragonStep.py @@ -30,7 +30,11 @@ import sys import typing as t -from ...._core.schemas.dragonRequests import DragonRunRequest, request_registry +from ...._core.schemas.dragonRequests import ( + DragonRunPolicy, + DragonRunRequest, + request_registry, +) from ....error.errors import SSUnsupportedError from ....log import get_logger from ....settings import ( @@ -166,8 +170,11 @@ def _write_request_file(self) -> str: nodes = int(run_args.get("nodes", None) or 1) tasks_per_node = int(run_args.get("tasks-per-node", None) or 1) + policy = DragonRunPolicy.from_run_args(run_args) + cmd = step.get_launch_cmd() out, err = step.get_output_files() + request = DragonRunRequest( exe=cmd[0], exe_args=cmd[1:], @@ -179,6 +186,7 @@ def _write_request_file(self) -> str: current_env=os.environ, output_file=out, error_file=err, + policy=policy, ) requests.append(request_registry.to_string(request)) with open(request_file, "w", encoding="utf-8") as script_file: diff --git a/smartsim/_core/launcher/step/mpiStep.py b/smartsim/_core/launcher/step/mpiStep.py index 767486462..9ae3af2fc 100644 --- a/smartsim/_core/launcher/step/mpiStep.py +++ b/smartsim/_core/launcher/step/mpiStep.py @@ -54,7 +54,7 @@ def __init__(self, name: str, cwd: str, run_settings: RunSettings) -> None: self._set_alloc() self.run_settings = run_settings - _supported_launchers = ["PBS", "SLURM", "LSB"] + _supported_launchers = ["PBS", "SLURM", "LSB", "SGE"] @proxyable_launch_cmd def get_launch_cmd(self) -> t.List[str]: @@ -102,7 +102,10 @@ def _set_alloc(self) -> None: environment_keys = os.environ.keys() for launcher in self._supported_launchers: - jobid_field = f"{launcher.upper()}_JOBID" + if launcher == "SGE": + jobid_field = "JOB_ID" + else: + jobid_field = f"{launcher.upper()}_JOBID" if jobid_field in environment_keys: self.alloc = os.environ[jobid_field] logger.debug(f"Running on allocation {self.alloc} from {jobid_field}") diff --git a/smartsim/_core/launcher/step/sgeStep.py b/smartsim/_core/launcher/step/sgeStep.py new file mode 100644 index 000000000..2406b19da --- /dev/null +++ b/smartsim/_core/launcher/step/sgeStep.py @@ -0,0 +1,95 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import typing as t + +from ....log import get_logger +from ....settings import SgeQsubBatchSettings +from .step import Step + +logger = get_logger(__name__) + + +class SgeQsubBatchStep(Step): + def __init__( + self, name: str, cwd: str, batch_settings: SgeQsubBatchSettings + ) -> None: + """Initialize a Sun Grid Engine qsub step + + :param name: name of the entity to launch + :param cwd: path to launch dir + :param batch_settings: batch settings for entity + """ + super().__init__(name, cwd, batch_settings) + self.step_cmds: t.List[t.List[str]] = [] + self.managed = True + self.batch_settings = batch_settings + + def get_launch_cmd(self) -> t.List[str]: + """Get the launch command for the batch + + :return: launch command for the batch + """ + script = self._write_script() + return [self.batch_settings.batch_cmd, script] + + def add_to_batch(self, step: Step) -> None: + """Add a job step to this batch + + :param step: a job step instance e.g. SrunStep + """ + launch_cmd = step.get_launch_cmd() + self.step_cmds.append(launch_cmd) + logger.debug(f"Added step command to batch for {step.name}") + + def _write_script(self) -> str: + """Write the batch script + + :return: batch script path after writing + """ + batch_script = self.get_step_file(ending=".sh") + output, error = self.get_output_files() + with open(batch_script, "w", encoding="utf-8") as script_file: + script_file.write(f"{self.batch_settings.shebang}\n\n") + script_file.write(f"#$ -o {output}\n") + script_file.write(f"#$ -e {error}\n") + script_file.write(f"#$ -N {self.name}\n") + script_file.write("#$ -V\n") + + # add additional sbatch options + for opt in self.batch_settings.format_batch_args(): + script_file.write(f"#$ {opt}\n") + + for cmd in self.batch_settings.preamble: + script_file.write(f"{cmd}\n") + + for i, step_cmd in enumerate(self.step_cmds): + script_file.write("\n") + script_file.write(f"{' '.join((step_cmd))} &\n") + if i == len(self.step_cmds) - 1: + script_file.write("\n") + script_file.write("wait\n") + return batch_script diff --git a/smartsim/_core/launcher/step/step.py b/smartsim/_core/launcher/step/step.py index 2cce6e610..171254e32 100644 --- a/smartsim/_core/launcher/step/step.py +++ b/smartsim/_core/launcher/step/step.py @@ -26,6 +26,7 @@ from __future__ import annotations +import copy import functools import os.path as osp import pathlib @@ -51,7 +52,7 @@ def __init__(self, name: str, cwd: str, step_settings: SettingsBase) -> None: self.entity_name = name self.cwd = cwd self.managed = False - self.step_settings = step_settings + self.step_settings = copy.deepcopy(step_settings) self.meta: t.Dict[str, str] = {} @property diff --git a/smartsim/_core/launcher/stepInfo.py b/smartsim/_core/launcher/stepInfo.py index 875eb0322..b68527cb3 100644 --- a/smartsim/_core/launcher/stepInfo.py +++ b/smartsim/_core/launcher/stepInfo.py @@ -151,7 +151,7 @@ def __init__( class PBSStepInfo(StepInfo): # cov-pbs @property def mapping(self) -> t.Dict[str, SmartSimStatus]: - # pylint: disable=line-too-long + # pylint: disable-next=line-too-long # see http://nusc.nsu.ru/wiki/lib/exe/fetch.php/doc/pbs/PBSReferenceGuide19.2.1.pdf#M11.9.90788.PBSHeading1.81.Job.States return { "R": SmartSimStatus.STATUS_RUNNING, @@ -201,7 +201,7 @@ def __init__( class LSFBatchStepInfo(StepInfo): # cov-lsf @property def mapping(self) -> t.Dict[str, SmartSimStatus]: - # pylint: disable=line-too-long + # pylint: disable-next=line-too-long # see https://www.ibm.com/docs/en/spectrum-lsf/10.1.0?topic=execution-about-job-states return { "RUN": SmartSimStatus.STATUS_RUNNING, @@ -239,7 +239,7 @@ def __init__( class LSFJsrunStepInfo(StepInfo): # cov-lsf @property def mapping(self) -> t.Dict[str, SmartSimStatus]: - # pylint: disable=line-too-long + # pylint: disable-next=line-too-long # see https://www.ibm.com/docs/en/spectrum-lsf/10.1.0?topic=execution-about-job-states return { "Killed": SmartSimStatus.STATUS_COMPLETED, @@ -270,3 +270,77 @@ def __init__( super().__init__( smartsim_status, status, returncode, output=output, error=error ) + + +class SGEStepInfo(StepInfo): # cov-pbs + @property + def mapping(self) -> t.Dict[str, SmartSimStatus]: + # pylint: disable-next=line-too-long + # see https://manpages.ubuntu.com/manpages/jammy/man5/sge_status.5.html + return { + # Running states + "r": SmartSimStatus.STATUS_RUNNING, + "hr": SmartSimStatus.STATUS_RUNNING, + "t": SmartSimStatus.STATUS_RUNNING, + "Rr": SmartSimStatus.STATUS_RUNNING, + "Rt": SmartSimStatus.STATUS_RUNNING, + # Queued states + "qw": SmartSimStatus.STATUS_QUEUED, + "Rq": SmartSimStatus.STATUS_QUEUED, + "hqw": SmartSimStatus.STATUS_QUEUED, + "hRwq": SmartSimStatus.STATUS_QUEUED, + # Paused states + "s": SmartSimStatus.STATUS_PAUSED, + "ts": SmartSimStatus.STATUS_PAUSED, + "S": SmartSimStatus.STATUS_PAUSED, + "tS": SmartSimStatus.STATUS_PAUSED, + "T": SmartSimStatus.STATUS_PAUSED, + "tT": SmartSimStatus.STATUS_PAUSED, + "Rs": SmartSimStatus.STATUS_PAUSED, + "Rts": SmartSimStatus.STATUS_PAUSED, + "RS": SmartSimStatus.STATUS_PAUSED, + "RtS": SmartSimStatus.STATUS_PAUSED, + "RT": SmartSimStatus.STATUS_PAUSED, + "RtT": SmartSimStatus.STATUS_PAUSED, + # Failed states + "Eqw": SmartSimStatus.STATUS_FAILED, + "Ehqw": SmartSimStatus.STATUS_FAILED, + "EhRqw": SmartSimStatus.STATUS_FAILED, + # Finished states + "z": SmartSimStatus.STATUS_COMPLETED, + # Cancelled + "dr": SmartSimStatus.STATUS_CANCELLED, + "dt": SmartSimStatus.STATUS_CANCELLED, + "dRr": SmartSimStatus.STATUS_CANCELLED, + "dRt": SmartSimStatus.STATUS_CANCELLED, + "ds": SmartSimStatus.STATUS_CANCELLED, + "dS": SmartSimStatus.STATUS_CANCELLED, + "dT": SmartSimStatus.STATUS_CANCELLED, + "dRs": SmartSimStatus.STATUS_CANCELLED, + "dRS": SmartSimStatus.STATUS_CANCELLED, + "dRT": SmartSimStatus.STATUS_CANCELLED, + } + + def __init__( + self, + status: str = "", + returncode: t.Optional[int] = None, + output: t.Optional[str] = None, + error: t.Optional[str] = None, + ) -> None: + if status == "NOTFOUND": + if returncode is not None: + smartsim_status = ( + SmartSimStatus.STATUS_COMPLETED + if returncode == 0 + else SmartSimStatus.STATUS_FAILED + ) + else: + # if PBS job history is not available, and job is not in queue + smartsim_status = SmartSimStatus.STATUS_COMPLETED + returncode = 0 + else: + smartsim_status = self._get_smartsim_status(status) + super().__init__( + smartsim_status, status, returncode, output=output, error=error + ) diff --git a/smartsim/_core/launcher/taskManager.py b/smartsim/_core/launcher/taskManager.py index 60f097da6..1bc26d043 100644 --- a/smartsim/_core/launcher/taskManager.py +++ b/smartsim/_core/launcher/taskManager.py @@ -36,7 +36,7 @@ from ...error import LauncherError from ...log import ContextThread, get_logger from ..utils.helpers import check_dev_log_level -from .util.shell import execute_async_cmd, execute_cmd +from ..utils.shell import execute_async_cmd, execute_cmd logger = get_logger(__name__) VERBOSE_TM = check_dev_log_level() # pylint: disable=invalid-name diff --git a/smartsim/_core/schemas/dragonRequests.py b/smartsim/_core/schemas/dragonRequests.py index 3e384f746..487ea915a 100644 --- a/smartsim/_core/schemas/dragonRequests.py +++ b/smartsim/_core/schemas/dragonRequests.py @@ -26,9 +26,10 @@ import typing as t -from pydantic import BaseModel, Field, PositiveInt +from pydantic import BaseModel, Field, NonNegativeInt, PositiveInt, ValidationError import smartsim._core.schemas.utils as _utils +from smartsim.error.errors import SmartSimError # Black and Pylint disagree about where to put the `...` # pylint: disable=multiple-statements @@ -39,6 +40,43 @@ class DragonRequest(BaseModel): ... +class DragonRunPolicy(BaseModel): + """Policy specifying hardware constraints when running a Dragon job""" + + cpu_affinity: t.List[NonNegativeInt] = Field(default_factory=list) + """List of CPU indices to which the job should be pinned""" + gpu_affinity: t.List[NonNegativeInt] = Field(default_factory=list) + """List of GPU indices to which the job should be pinned""" + + @staticmethod + def from_run_args( + run_args: t.Dict[str, t.Union[int, str, float, None]] + ) -> "DragonRunPolicy": + """Create a DragonRunPolicy with hardware constraints passed from + a dictionary of run arguments + :param run_args: Dictionary of run arguments + :returns: DragonRunPolicy instance created from the run arguments""" + gpu_args = "" + if gpu_arg_value := run_args.get("gpu-affinity", None): + gpu_args = str(gpu_arg_value) + + cpu_args = "" + if cpu_arg_value := run_args.get("cpu-affinity", None): + cpu_args = str(cpu_arg_value) + + # run args converted to a string must be split back into a list[int] + gpu_affinity = [int(x.strip()) for x in gpu_args.split(",") if x] + cpu_affinity = [int(x.strip()) for x in cpu_args.split(",") if x] + + try: + return DragonRunPolicy( + cpu_affinity=cpu_affinity, + gpu_affinity=gpu_affinity, + ) + except ValidationError as ex: + raise SmartSimError("Unable to build DragonRunPolicy") from ex + + class DragonRunRequestView(DragonRequest): exe: t.Annotated[str, Field(min_length=1)] exe_args: t.List[t.Annotated[str, Field(min_length=1)]] = [] @@ -57,6 +95,7 @@ class DragonRunRequestView(DragonRequest): @request_registry.register("run") class DragonRunRequest(DragonRunRequestView): current_env: t.Dict[str, t.Optional[str]] = {} + policy: t.Optional[DragonRunPolicy] = None def __str__(self) -> str: return str(DragonRunRequestView.parse_obj(self.dict(exclude={"current_env"}))) diff --git a/smartsim/_core/types.py b/smartsim/_core/types.py new file mode 100644 index 000000000..d3dc029ea --- /dev/null +++ b/smartsim/_core/types.py @@ -0,0 +1,32 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import enum + + +class Device(enum.Enum): + CPU = "cpu" + GPU = "gpu" diff --git a/smartsim/_core/utils/__init__.py b/smartsim/_core/utils/__init__.py index 3ea928797..cddbc4ce9 100644 --- a/smartsim/_core/utils/__init__.py +++ b/smartsim/_core/utils/__init__.py @@ -29,6 +29,7 @@ colorize, delete_elements, execute_platform_cmd, + expand_exe_path, installed_redisai_backends, is_crayex_platform, ) diff --git a/smartsim/_core/utils/helpers.py b/smartsim/_core/utils/helpers.py index df2c016a1..b17be763b 100644 --- a/smartsim/_core/utils/helpers.py +++ b/smartsim/_core/utils/helpers.py @@ -39,12 +39,11 @@ from pathlib import Path from shutil import which -from smartsim._core._install.builder import TRedisAIBackendStr as _TRedisAIBackendStr - if t.TYPE_CHECKING: from types import FrameType +_TRedisAIBackendStr = t.Literal["tensorflow", "torch", "onnxruntime"] _TSignalHandlerFn = t.Callable[[int, t.Optional["FrameType"]], object] @@ -230,7 +229,9 @@ def redis_install_base(backends_path: t.Optional[str] = None) -> Path: # pylint: disable-next=import-outside-toplevel from ..._core.config import CONFIG - base_path = Path(backends_path) if backends_path else CONFIG.lib_path / "backends" + base_path: Path = ( + Path(backends_path) if backends_path else CONFIG.lib_path / "backends" + ) return base_path @@ -255,10 +256,10 @@ def installed_redisai_backends( "tensorflow", "torch", "onnxruntime", - "tflite", } - return {backend for backend in backends if _installed(base_path, backend)} + installed = {backend for backend in backends if _installed(base_path, backend)} + return installed def get_ts_ms() -> int: diff --git a/smartsim/_core/utils/redis.py b/smartsim/_core/utils/redis.py index 7fa59ad83..76ff45cd5 100644 --- a/smartsim/_core/utils/redis.py +++ b/smartsim/_core/utils/redis.py @@ -39,8 +39,8 @@ from ...error import SSInternalError from ...log import get_logger from ..config import CONFIG -from ..launcher.util.shell import execute_cmd from .network import get_ip_from_host +from .shell import execute_cmd logging.getLogger("rediscluster").setLevel(logging.WARNING) logger = get_logger(__name__) diff --git a/smartsim/_core/launcher/util/shell.py b/smartsim/_core/utils/shell.py similarity index 97% rename from smartsim/_core/launcher/util/shell.py rename to smartsim/_core/utils/shell.py index a2b5bc76b..4cfe2998c 100644 --- a/smartsim/_core/launcher/util/shell.py +++ b/smartsim/_core/utils/shell.py @@ -30,9 +30,9 @@ import psutil -from ....error import ShellError -from ....log import get_logger -from ...utils.helpers import check_dev_log_level +from ...error import ShellError +from ...log import get_logger +from .helpers import check_dev_log_level logger = get_logger(__name__) VERBOSE_SHELL = check_dev_log_level() diff --git a/smartsim/database/orchestrator.py b/smartsim/database/orchestrator.py index f6ce0310f..e2549891a 100644 --- a/smartsim/database/orchestrator.py +++ b/smartsim/database/orchestrator.py @@ -28,6 +28,7 @@ import itertools import os.path as osp +import shutil import sys import typing as t from os import environ, getcwd, getenv @@ -41,6 +42,7 @@ from .._core.utils import db_is_active from .._core.utils.helpers import is_valid_cmd, unpack_db_identifier from .._core.utils.network import get_ip_from_host +from .._core.utils.shell import execute_cmd from ..entity import DBNode, EntityList, TelemetryConfiguration from ..error import ( SmartSimError, @@ -75,6 +77,7 @@ "pals": ["mpiexec"], "lsf": ["jsrun"], "local": [""], + "sge": ["mpirun", "mpiexec", "orterun"], } @@ -186,8 +189,6 @@ def __init__( Extra configurations for RedisAI - See https://oss.redis.com/redisai/configuration/ - :param path: path to location of ``Orchestrator`` directory :param port: TCP/IP port :param interface: network interface(s) @@ -280,14 +281,35 @@ def __init__( ) if hosts: self.set_hosts(hosts) - elif not hosts and self.run_command == "mpirun": - raise SmartSimError( - "hosts argument is required when launching Orchestrator with mpirun" - ) + elif not hosts: + mpilike = run_command in ["mpirun", "mpiexec", "orterun"] + if mpilike and not self._mpi_has_sge_support(): + raise SmartSimError( + ( + "hosts argument required when launching ", + "Orchestrator with mpirun", + ) + ) self._reserved_run_args: t.Dict[t.Type[RunSettings], t.List[str]] = {} self._reserved_batch_args: t.Dict[t.Type[BatchSettings], t.List[str]] = {} self._fill_reserved() + def _mpi_has_sge_support(self) -> bool: + """Check if MPI command supports SGE + + If the run command is mpirun, mpiexec, or orterun, there is a possibility + that the user is using OpenMPI with SGE grid support. In this case, hosts + do not need to be set. + + :returns: bool + """ + + if self.run_command in ["mpirun", "orterun", "mpiexec"]: + if shutil.which("ompi_info"): + _, output, _ = execute_cmd(["ompi_info"]) + return "gridengine" in output + return False + @property def db_identifier(self) -> str: """Return the DB identifier, which is common to a DB and all of its nodes diff --git a/smartsim/entity/dbobject.py b/smartsim/entity/dbobject.py index 5cb0d061f..fa9983c50 100644 --- a/smartsim/entity/dbobject.py +++ b/smartsim/entity/dbobject.py @@ -27,7 +27,8 @@ import typing as t from pathlib import Path -from .._core._install.builder import Device +from smartsim._core.types import Device + from ..error import SSUnsupportedError __all__ = ["DBObject", "DBModel", "DBScript"] diff --git a/smartsim/entity/ensemble.py b/smartsim/entity/ensemble.py index cab138685..965b10db7 100644 --- a/smartsim/entity/ensemble.py +++ b/smartsim/entity/ensemble.py @@ -31,7 +31,8 @@ from tabulate import tabulate -from .._core._install.builder import Device +from smartsim._core.types import Device + from ..error import ( EntityExistsError, SmartSimError, diff --git a/smartsim/entity/model.py b/smartsim/entity/model.py index 3f78e042c..3e8baad5c 100644 --- a/smartsim/entity/model.py +++ b/smartsim/entity/model.py @@ -27,6 +27,7 @@ from __future__ import annotations import itertools +import numbers import re import sys import typing as t @@ -34,7 +35,8 @@ from os import getcwd from os import path as osp -from .._core._install.builder import Device +from smartsim._core.types import Device + from .._core.utils.helpers import cat_arg_and_value from ..error import EntityExistsError, SSUnsupportedError from ..log import get_logger @@ -46,6 +48,25 @@ logger = get_logger(__name__) +def _parse_model_parameters(params_dict: t.Dict[str, t.Any]) -> t.Dict[str, str]: + """Convert the values in a params dict to strings + :raises TypeError: if params are of the wrong type + :return: param dictionary with values and keys cast as strings + """ + param_names: t.List[str] = [] + parameters: t.List[str] = [] + for name, val in params_dict.items(): + param_names.append(name) + if isinstance(val, (str, numbers.Number)): + parameters.append(str(val)) + else: + raise TypeError( + "Incorrect type for model parameters\n" + + "Must be numeric value or string." + ) + return dict(zip(param_names, parameters)) + + class Model(SmartSimEntity): def __init__( self, @@ -70,7 +91,7 @@ def __init__( model as a batch job """ super().__init__(name, str(path), run_settings) - self.params = params + self.params = _parse_model_parameters(params) self.params_as_args = params_as_args self.incoming_entities: t.List[SmartSimEntity] = [] self._key_prefixing_enabled = False diff --git a/smartsim/error/__init__.py b/smartsim/error/__init__.py index 3a40548e7..c7122fe42 100644 --- a/smartsim/error/__init__.py +++ b/smartsim/error/__init__.py @@ -28,6 +28,7 @@ AllocationError, EntityExistsError, LauncherError, + LauncherUnsupportedFeature, ParameterWriterError, ShellError, SmartSimError, diff --git a/smartsim/error/errors.py b/smartsim/error/errors.py index 333258a34..0cb38d7e6 100644 --- a/smartsim/error/errors.py +++ b/smartsim/error/errors.py @@ -108,6 +108,10 @@ class LauncherError(SSInternalError): """Raised when there is an error in the launcher""" +class LauncherUnsupportedFeature(LauncherError): + """Raised when the launcher does not support a given method""" + + class AllocationError(LauncherError): """Raised when there is a problem with the user WLM allocation""" diff --git a/smartsim/experiment.py b/smartsim/experiment.py index 6b9d6a1fb..607a90ae1 100644 --- a/smartsim/experiment.py +++ b/smartsim/experiment.py @@ -144,7 +144,7 @@ def __init__( :param name: name for the ``Experiment`` :param exp_path: path to location of ``Experiment`` directory :param launcher: type of launcher being used, options are "slurm", "pbs", - "lsf", or "local". If set to "auto", + "lsf", "sge", or "local". If set to "auto", an attempt will be made to find an available launcher on the system. """ diff --git a/smartsim/ml/tf/__init__.py b/smartsim/ml/tf/__init__.py index 46d89d733..ee791ea98 100644 --- a/smartsim/ml/tf/__init__.py +++ b/smartsim/ml/tf/__init__.py @@ -31,23 +31,12 @@ logger = get_logger(__name__) vers = Versioner() -TF_VERSION = vers.TENSORFLOW try: import tensorflow as tf except ImportError: # pragma: no cover raise ModuleNotFoundError( - f"TensorFlow {TF_VERSION} is not installed. " - "Please install it to use smartsim.ml.tf" - ) from None - -try: - installed_tf = Version_(tf.__version__) - assert installed_tf >= TF_VERSION -except AssertionError: # pragma: no cover - raise SmartSimError( - f"TensorFlow >= {TF_VERSION} is required for smartsim. " - f"tf, you have {tf.__version__}" + f"TensorFlow is not installed. Please install it to use smartsim.ml.tf" ) from None diff --git a/smartsim/ml/tf/utils.py b/smartsim/ml/tf/utils.py index cf69b65e5..4e45f1847 100644 --- a/smartsim/ml/tf/utils.py +++ b/smartsim/ml/tf/utils.py @@ -29,7 +29,7 @@ import keras import tensorflow as tf -from tensorflow.python.framework.convert_to_constants import ( +from tensorflow.python.framework.convert_to_constants import ( # type: ignore[import-not-found,unused-ignore] convert_variables_to_constants_v2, ) @@ -62,7 +62,7 @@ def freeze_model( tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype) ) - frozen_func = convert_variables_to_constants_v2(full_model) + frozen_func = convert_variables_to_constants_v2(full_model) # type: ignore[no-untyped-call,unused-ignore] frozen_func.graph.as_graph_def() input_names = [x.name.split(":")[0] for x in frozen_func.inputs] @@ -97,7 +97,7 @@ def serialize_model(model: keras.Model) -> t.Tuple[str, t.List[str], t.List[str] tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype) ) - frozen_func = convert_variables_to_constants_v2(full_model) + frozen_func = convert_variables_to_constants_v2(full_model) # type: ignore[no-untyped-call,unused-ignore] frozen_func.graph.as_graph_def() input_names = [x.name.split(":")[0] for x in frozen_func.inputs] diff --git a/smartsim/settings/__init__.py b/smartsim/settings/__init__.py index 6e8f0bc96..8052121e2 100644 --- a/smartsim/settings/__init__.py +++ b/smartsim/settings/__init__.py @@ -32,6 +32,7 @@ from .mpiSettings import MpiexecSettings, MpirunSettings, OrterunSettings from .palsSettings import PalsMpiexecSettings from .pbsSettings import QsubBatchSettings +from .sgeSettings import SgeQsubBatchSettings from .slurmSettings import SbatchSettings, SrunSettings __all__ = [ @@ -45,6 +46,7 @@ "RunSettings", "SettingsBase", "SbatchSettings", + "SgeQsubBatchSettings", "SrunSettings", "PalsMpiexecSettings", "DragonRunSettings", diff --git a/smartsim/settings/base.py b/smartsim/settings/base.py index 6373b52fd..da3edb491 100644 --- a/smartsim/settings/base.py +++ b/smartsim/settings/base.py @@ -594,9 +594,13 @@ def __init__( self._batch_cmd = batch_cmd self.batch_args = batch_args or {} self._preamble: t.List[str] = [] - self.set_nodes(kwargs.get("nodes", None)) + nodes = kwargs.get("nodes", None) + if nodes: + self.set_nodes(nodes) + queue = kwargs.get("queue", None) + if queue: + self.set_queue(queue) self.set_walltime(kwargs.get("time", None)) - self.set_queue(kwargs.get("queue", None)) self.set_account(kwargs.get("account", None)) @property diff --git a/smartsim/settings/dragonRunSettings.py b/smartsim/settings/dragonRunSettings.py index b8baa4708..69a91547e 100644 --- a/smartsim/settings/dragonRunSettings.py +++ b/smartsim/settings/dragonRunSettings.py @@ -28,6 +28,8 @@ import typing as t +from typing_extensions import override + from ..log import get_logger from .base import RunSettings @@ -63,6 +65,7 @@ def __init__( **kwargs, ) + @override def set_nodes(self, nodes: int) -> None: """Set the number of nodes @@ -70,9 +73,38 @@ def set_nodes(self, nodes: int) -> None: """ self.run_args["nodes"] = nodes + @override def set_tasks_per_node(self, tasks_per_node: int) -> None: """Set the number of tasks for this job :param tasks_per_node: number of tasks per node """ self.run_args["tasks-per-node"] = tasks_per_node + + @override + def set_node_feature(self, feature_list: t.Union[str, t.List[str]]) -> None: + """Specify the node feature for this job + + :param feature_list: a collection of strings representing the required + node features. Currently supported node features are: "gpu" + """ + if isinstance(feature_list, str): + feature_list = feature_list.strip().split() + elif not all(isinstance(feature, str) for feature in feature_list): + raise TypeError("feature_list must be string or list of strings") + + self.run_args["node-feature"] = ",".join(feature_list) + + def set_cpu_affinity(self, devices: t.List[int]) -> None: + """Set the CPU affinity for this job + + :param devices: list of CPU indices to execute on + """ + self.run_args["cpu-affinity"] = ",".join(str(device) for device in devices) + + def set_gpu_affinity(self, devices: t.List[int]) -> None: + """Set the GPU affinity for this job + + :param devices: list of GPU indices to execute on. + """ + self.run_args["gpu-affinity"] = ",".join(str(device) for device in devices) diff --git a/smartsim/settings/settings.py b/smartsim/settings/settings.py index 5f7fc3fe2..5afd0e192 100644 --- a/smartsim/settings/settings.py +++ b/smartsim/settings/settings.py @@ -41,6 +41,7 @@ QsubBatchSettings, RunSettings, SbatchSettings, + SgeQsubBatchSettings, SrunSettings, base, ) @@ -78,6 +79,7 @@ def create_batch_settings( "slurm": SbatchSettings, "lsf": BsubBatchSettings, "pals": QsubBatchSettings, + "sge": SgeQsubBatchSettings, } if launcher in ["auto", "dragon"]: @@ -153,6 +155,7 @@ def create_run_settings( "pbs": ["aprun", "mpirun", "mpiexec"], "pals": ["mpiexec"], "lsf": ["jsrun", "mpirun", "mpiexec"], + "sge": ["mpirun", "mpiexec"], "local": [""], } diff --git a/smartsim/settings/sgeSettings.py b/smartsim/settings/sgeSettings.py new file mode 100644 index 000000000..a5cd3f2b0 --- /dev/null +++ b/smartsim/settings/sgeSettings.py @@ -0,0 +1,293 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import typing as t + +from ..error import LauncherUnsupportedFeature, SSConfigError +from ..log import get_logger +from .base import BatchSettings + +logger = get_logger(__name__) + + +class SgeQsubBatchSettings(BatchSettings): + def __init__( + self, + time: t.Optional[str] = None, + ncpus: t.Optional[int] = None, + pe_type: t.Optional[str] = None, + account: t.Optional[str] = None, + shebang: str = "#!/bin/bash -l", + resources: t.Optional[t.Dict[str, t.Union[str, int]]] = None, + batch_args: t.Optional[t.Dict[str, t.Optional[str]]] = None, + **kwargs: t.Any, + ): + """Specify SGE batch parameters for a job + + :param time: walltime for batch job + :param ncpus: number of cpus per node + :param pe_type: type of parallel environment + :param queue: queue to run batch in + :param account: account for batch launch + :param resources: overrides for resource arguments + :param batch_args: overrides for SGE batch arguments + """ + + if "nodes" in kwargs: + kwargs["nodes"] = 0 + + self.resources = resources or {} + if ncpus: + self.set_ncpus(ncpus) + if pe_type: + self.set_pe_type(pe_type) + self.set_shebang(shebang) + + # time, queue, nodes, and account set in parent class init + super().__init__( + "qsub", + batch_args=batch_args, + account=account, + time=time, + **kwargs, + ) + + self._context_variables: t.List[str] = [] + self._env_vars: t.Dict[str, str] = {} + + @property + def resources(self) -> t.Dict[str, t.Union[str, int]]: + return self._resources.copy() + + @resources.setter + def resources(self, resources: t.Dict[str, t.Union[str, int]]) -> None: + self._sanity_check_resources(resources) + self._resources = resources.copy() + + def set_hostlist(self, host_list: t.Union[str, t.List[str]]) -> None: + raise LauncherUnsupportedFeature( + "SGE does not support requesting specific hosts in batch jobs" + ) + + def set_queue(self, queue: str) -> None: + raise LauncherUnsupportedFeature("SGE does not support specifying queues") + + def set_shebang(self, shebang: str) -> None: + """Set the shebang (shell) for the batch job + + :param shebang: The shebang used to interpret the rest of script + (e.g. #!/bin/bash) + """ + self.shebang = shebang + + def set_walltime(self, walltime: str) -> None: + """Set the walltime of the job + + format = "HH:MM:SS" + + If a walltime argument is provided in + ``SGEBatchSettings.resources``, then + this value will be overridden + + :param walltime: wall time + """ + if walltime: + self.set_resource("h_rt", walltime) + + def set_nodes(self, num_nodes: t.Optional[int]) -> None: + """Set the number of nodes, invalid for SGE + + :param nodes: Number of nodes, any integer other than 0 is invalid + """ + if num_nodes: + raise LauncherUnsupportedFeature( + "SGE does not support setting the number of nodes" + ) + + def set_ncpus(self, num_cpus: t.Union[int, str]) -> None: + """Set the number of cpus obtained in each node. + + :param num_cpus: number of cpus per node in select + """ + self.set_resource("ncpus", int(num_cpus)) + + def set_ngpus(self, num_gpus: t.Union[int, str]) -> None: + """Set the number of GPUs obtained in each node. + + :param num_gpus: number of GPUs per node in select + """ + self.set_resource("gpu", num_gpus) + + def set_account(self, account: str) -> None: + """Set the account for this batch job + + :param acct: account id + """ + if account: + self.batch_args["A"] = str(account) + + def set_project(self, project: str) -> None: + """Set the project for this batch job + + :param acct: project id + """ + if project: + self.batch_args["P"] = str(project) + + def update_context_variables( + self, + action: t.Literal["ac", "sc", "dc"], + var_name: str, + value: t.Optional[t.Union[int, str]] = None, + ) -> None: + """ + Add, set, or delete context variables + + Configure any context variables using SGE's -ac, -sc, and -dc + qsub switches. These modifications are appended each time this + method is called, so the order does matter + + :param action: Add, set, or delete a context variable (ac, dc, or sc) + :param var_name: The name of the variable to set + :param value: The value of the variable + """ + if action not in ["ac", "sc", "dc"]: + raise ValueError("The action argument must be ac, sc, or dc") + if action == "dc" and value: + raise SSConfigError("When using the 'dc' action, value should not be set") + + command = f"-{action} {var_name}" + if value: + command += f"={value}" + self._context_variables.append(command) + + def set_hyperthreading(self, enable: bool = True) -> None: + """Enable or disable hyperthreading + + :param enable: Enable (True) or disable (False) hypthreading + """ + self.set_resource("threads", int(enable)) + + def set_memory_per_pe(self, memory_spec: str) -> None: + """Set the amount of memory per processing element + + :param memory_spec: The amount of memory per PE (e.g. 2G) + """ + self.set_resource("mem", memory_spec) + + def set_pe_type(self, pe_type: str) -> None: + """Set the parallel environment + + :param pe_type: parallel environment identifier (e.g. mpi or smp) + """ + if pe_type: + self.set_resource("pe_type", pe_type) + + def set_threads_per_pe(self, threads_per_core: int) -> None: + """Sets the number of threads per processing element + + :param threads_per_core: Number of threads per core + """ + + self._env_vars["OMP_NUM_THREADS"] = str(threads_per_core) + + def set_resource(self, resource_name: str, value: t.Union[str, int]) -> None: + """Set a resource value for the SGE batch + + If a select statement is provided, the nodes and ncpus + arguments will be overridden. Likewise for Walltime + + :param resource_name: name of resource, e.g. walltime + :param value: value + """ + updated_dict = self.resources + updated_dict.update({resource_name: value}) + self._sanity_check_resources(updated_dict) + self.resources = updated_dict + + def format_batch_args(self) -> t.List[str]: + """Get the formatted batch arguments for a preview + + :return: batch arguments for SGE + :raises ValueError: if options are supplied without values + """ + opts = self._create_resource_list() + for opt, value in self.batch_args.items(): + prefix = "-" + if not value: + raise ValueError("SGE options without values are not allowed") + opts += [" ".join((prefix + opt, str(value)))] + return opts + + def _sanity_check_resources( + self, resources: t.Optional[t.Dict[str, t.Union[str, int]]] = None + ) -> None: + """Check that resources are correctly formatted""" + # Note: isinstance check here to avoid collision with default + checked_resources = resources if isinstance(resources, dict) else self.resources + + for key, value in checked_resources.items(): + if not isinstance(key, str): + raise TypeError( + f"The type of {key=} is {type(key)}. Only int and str " + "are allowed." + ) + if not isinstance(value, (str, int)): + raise TypeError( + f"The value associated with {key=} is {type(value)}. Only int " + "and str are allowed." + ) + + def _create_resource_list(self) -> t.List[str]: + self._sanity_check_resources() + res = [] + + # Pop off some specific keywords that need to be treated separately + resources = self.resources # Note this is a copy so not modifying original + + # Construct the configuration of the parallel environment + ncpus = resources.pop("ncpus", None) + pe_type = resources.pop("pe_type", None) + if (pe_type is None and ncpus) or (pe_type and ncpus is None): + msg = f"{ncpus=} and {pe_type=} must both be set. " + msg += "Call set_ncpus and/or set_pe_type." + raise SSConfigError(msg) + + if pe_type and ncpus: + res += [f"-pe {pe_type} {ncpus}"] + + # Deal with context variables + for context_variable in self._context_variables: + res += [context_variable] + + # All other "standard" resource specs + for resource, value in resources.items(): + res += [f"-l {resource}={value}"] + + # Set any environment variables + for key, value in self._env_vars.items(): + res += [f"-v {key}={value}"] + return res diff --git a/smartsim/status.py b/smartsim/status.py index e42ef3191..e0d950619 100644 --- a/smartsim/status.py +++ b/smartsim/status.py @@ -35,6 +35,7 @@ class SmartSimStatus(Enum): STATUS_NEW = "New" STATUS_PAUSED = "Paused" STATUS_NEVER_STARTED = "NeverStarted" + STATUS_QUEUED = "Queued" TERMINAL_STATUSES = { diff --git a/tests/backends/run_torch.py b/tests/backends/run_torch.py index 6e9ba2859..b3c0fc964 100644 --- a/tests/backends/run_torch.py +++ b/tests/backends/run_torch.py @@ -74,7 +74,7 @@ def calc_svd(input_tensor): return input_tensor.svd() -def run(device): +def run(device, num_devices): # connect a client to the database client = Client(cluster=False) @@ -92,9 +92,23 @@ def run(device): net = create_torch_model() # 20 samples of "image" data example_forward_input = torch.rand(20, 1, 28, 28) - client.set_model("cnn", net, "TORCH", device=device) client.put_tensor("input", example_forward_input.numpy()) - client.run_model("cnn", inputs=["input"], outputs=["output"]) + if device == "CPU": + client.set_model("cnn", net, "TORCH", device=device) + client.run_model("cnn", inputs=["input"], outputs=["output"]) + else: + client.set_model_multigpu( + "cnn", net, "TORCH", first_gpu=0, num_gpus=num_devices + ) + client.run_model_multigpu( + "cnn", + offset=1, + first_gpu=0, + num_gpus=num_devices, + inputs=["input"], + outputs=["output"], + ) + output = client.get_tensor("output") print(f"Prediction: {output}") @@ -106,5 +120,11 @@ def run(device): parser.add_argument( "--device", type=str, default="CPU", help="device type for model execution" ) + parser.add_argument( + "--num-devices", + type=int, + default=1, + help="Number of devices to set the model on", + ) args = parser.parse_args() - run(args.device) + run(args.device, args.num_devices) diff --git a/tests/backends/test_cli_mini_exp.py b/tests/backends/test_cli_mini_exp.py index 2fde2ff5f..3379bf2ee 100644 --- a/tests/backends/test_cli_mini_exp.py +++ b/tests/backends/test_cli_mini_exp.py @@ -32,6 +32,7 @@ import smartsim._core._cli.validate import smartsim._core._install.builder as build +from smartsim._core._install.platform import Device from smartsim._core.utils.helpers import installed_redisai_backends sklearn_available = True @@ -79,7 +80,7 @@ def _mock_make_managed_local_orc(*a, **kw): location=test_dir, port=db_port, # Always test on CPU, heads don't always have GPU - device=build.Device.CPU, + device=Device.CPU, # Test the backends the dev has installed with_tf="tensorflow" in backends, with_pt="torch" in backends, diff --git a/tests/backends/test_torch.py b/tests/backends/test_torch.py index c995f76ca..6aff6b0ba 100644 --- a/tests/backends/test_torch.py +++ b/tests/backends/test_torch.py @@ -65,9 +65,11 @@ def test_torch_model_and_script( db = prepare_db(single_db).orchestrator wlm_experiment.reconnect_orchestrator(db.checkpoint_file) test_device = mlutils.get_test_device() + test_num_gpus = mlutils.get_test_num_gpus() if pytest.test_device == "GPU" else 1 run_settings = wlm_experiment.create_run_settings( - "python", f"run_torch.py --device={test_device}" + "python", + ["run_torch.py", f"--device={test_device}", f"--num-devices={test_num_gpus}"], ) if wlmutils.get_test_launcher() != "local": run_settings.set_tasks(1) diff --git a/tests/install/test_build.py b/tests/install/test_build.py new file mode 100644 index 000000000..f8a5c4896 --- /dev/null +++ b/tests/install/test_build.py @@ -0,0 +1,148 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import operator + +import pytest + +from smartsim._core._cli.build import parse_requirement +from smartsim._core._install.buildenv import Version_ + +# The tests in this file belong to the group_a group +pytestmark = pytest.mark.group_a + + +_SUPPORTED_OPERATORS = ("==", ">=", ">", "<=", "<") + + +@pytest.mark.parametrize( + "spec, name, pin", + ( + pytest.param("foo", "foo", None, id="Just Name"), + pytest.param("foo==1", "foo", "==1", id="With Major"), + pytest.param("foo==1.2", "foo", "==1.2", id="With Minor"), + pytest.param("foo==1.2.3", "foo", "==1.2.3", id="With Patch"), + pytest.param("foo[with-extras]==1.2.3", "foo", "==1.2.3", id="With Extra"), + pytest.param( + "foo[with,many,extras]==1.2.3", "foo", "==1.2.3", id="With Many Extras" + ), + *( + pytest.param( + f"foo{symbol}1.2.3{tag}", + "foo", + f"{symbol}1.2.3{tag}", + id=f"{symbol=} | {tag=}", + ) + for symbol in _SUPPORTED_OPERATORS + for tag in ("", "+cuda", "+rocm", "+cpu") + ), + ), +) +def test_parse_requirement_name_and_version(spec, name, pin): + p_name, p_pin, _ = parse_requirement(spec) + assert p_name == name + assert p_pin == pin + + +# fmt: off +@pytest.mark.parametrize( + "spec, ver, should_pass", + ( + pytest.param("foo" , Version_("1.2.3") , True, id="No spec"), + # EQ -------------------------------------------------------------------------- + pytest.param("foo==1.2.3" , Version_("1.2.3") , True, id="EQ Spec, EQ Version"), + pytest.param("foo==1.2.3" , Version_("1.2.5") , False, id="EQ Spec, GT Version"), + pytest.param("foo==1.2.3" , Version_("1.2.2") , False, id="EQ Spec, LT Version"), + pytest.param("foo==1.2.3+rocm", Version_("1.2.3+rocm"), True, id="EQ Spec, Compatible Version with suffix"), + pytest.param("foo==1.2.3" , Version_("1.2.3+cuda"), False, id="EQ Spec, Compatible Version, Extra Suffix"), + pytest.param("foo==1.2.3+cuda", Version_("1.2.3") , False, id="EQ Spec, Compatible Version, Missing Suffix"), + pytest.param("foo==1.2.3+cuda", Version_("1.2.3+rocm"), False, id="EQ Spec, Compatible Version, Mismatched Suffix"), + # LT -------------------------------------------------------------------------- + pytest.param("foo<1.2.3" , Version_("1.2.3") , False, id="LT Spec, EQ Version"), + pytest.param("foo<1.2.3" , Version_("1.2.5") , False, id="LT Spec, GT Version"), + pytest.param("foo<1.2.3" , Version_("1.2.2") , True, id="LT Spec, LT Version"), + pytest.param("foo<1.2.3+rocm" , Version_("1.2.2+rocm"), True, id="LT Spec, Compatible Version with suffix"), + pytest.param("foo<1.2.3" , Version_("1.2.2+cuda"), False, id="LT Spec, Compatible Version, Extra Suffix"), + pytest.param("foo<1.2.3+cuda" , Version_("1.2.2") , False, id="LT Spec, Compatible Version, Missing Suffix"), + pytest.param("foo<1.2.3+cuda" , Version_("1.2.2+rocm"), False, id="LT Spec, Compatible Version, Mismatched Suffix"), + # LE -------------------------------------------------------------------------- + pytest.param("foo<=1.2.3" , Version_("1.2.3") , True, id="LE Spec, EQ Version"), + pytest.param("foo<=1.2.3" , Version_("1.2.5") , False, id="LE Spec, GT Version"), + pytest.param("foo<=1.2.3" , Version_("1.2.2") , True, id="LE Spec, LT Version"), + pytest.param("foo<=1.2.3+rocm", Version_("1.2.3+rocm"), True, id="LE Spec, Compatible Version with suffix"), + pytest.param("foo<=1.2.3" , Version_("1.2.3+cuda"), False, id="LE Spec, Compatible Version, Extra Suffix"), + pytest.param("foo<=1.2.3+cuda", Version_("1.2.3") , False, id="LE Spec, Compatible Version, Missing Suffix"), + pytest.param("foo<=1.2.3+cuda", Version_("1.2.3+rocm"), False, id="LE Spec, Compatible Version, Mismatched Suffix"), + # GT -------------------------------------------------------------------------- + pytest.param("foo>1.2.3" , Version_("1.2.3") , False, id="GT Spec, EQ Version"), + pytest.param("foo>1.2.3" , Version_("1.2.5") , True, id="GT Spec, GT Version"), + pytest.param("foo>1.2.3" , Version_("1.2.2") , False, id="GT Spec, LT Version"), + pytest.param("foo>1.2.3+rocm" , Version_("1.2.4+rocm"), True, id="GT Spec, Compatible Version with suffix"), + pytest.param("foo>1.2.3" , Version_("1.2.4+cuda"), False, id="GT Spec, Compatible Version, Extra Suffix"), + pytest.param("foo>1.2.3+cuda" , Version_("1.2.4") , False, id="GT Spec, Compatible Version, Missing Suffix"), + pytest.param("foo>1.2.3+cuda" , Version_("1.2.4+rocm"), False, id="GT Spec, Compatible Version, Mismatched Suffix"), + # GE -------------------------------------------------------------------------- + pytest.param("foo>=1.2.3" , Version_("1.2.3") , True, id="GE Spec, EQ Version"), + pytest.param("foo>=1.2.3" , Version_("1.2.5") , True, id="GE Spec, GT Version"), + pytest.param("foo>=1.2.3" , Version_("1.2.2") , False, id="GE Spec, LT Version"), + pytest.param("foo>=1.2.3+rocm", Version_("1.2.3+rocm"), True, id="GE Spec, Compatible Version with suffix"), + pytest.param("foo>=1.2.3" , Version_("1.2.3+cuda"), False, id="GE Spec, Compatible Version, Extra Suffix"), + pytest.param("foo>=1.2.3+cuda", Version_("1.2.3") , False, id="GE Spec, Compatible Version, Missing Suffix"), + pytest.param("foo>=1.2.3+cuda", Version_("1.2.3+rocm"), False, id="GE Spec, Compatible Version, Mismatched Suffix"), + ) +) +# fmt: on +def test_parse_requirement_comparison_fn(spec, ver, should_pass): + _, _, cmp = parse_requirement(spec) + assert cmp(ver) == should_pass + + +@pytest.mark.parametrize( + "spec, ctx", + ( + *( + pytest.param( + f"thing{symbol}", + pytest.raises(ValueError, match="Invalid requirement string:"), + id=f"No version w/ operator {symbol}", + ) + for symbol in _SUPPORTED_OPERATORS + ), + pytest.param( + "thing>=>1.2.3", + pytest.raises(ValueError, match="Invalid requirement string:"), + id="Operator too long", + ), + pytest.param( + "thing<>1.2.3", + pytest.raises(ValueError, match="Unrecognized comparison operator: <>"), + id="Nonsense operator", + ), + ), +) +def test_parse_requirement_errors_on_invalid_spec(spec, ctx): + with ctx: + parse_requirement(spec) diff --git a/tests/install/test_buildenv.py b/tests/install/test_buildenv.py index 21b9a49b8..a3964d413 100644 --- a/tests/install/test_buildenv.py +++ b/tests/install/test_buildenv.py @@ -25,8 +25,8 @@ # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +import packaging import pytest -from pkg_resources import packaging # type: ignore from smartsim._core._install.buildenv import Version_ @@ -71,19 +71,32 @@ def test_version_equality_ne(): assert v1 != v2 - -def test_version_bad_input(): + # def test_version_bad_input(): """Test behavior when passing an invalid version string""" - v1 = Version_("abcdefg") + version = Version_("1") + assert version.major == 1 + with pytest.raises((IndexError, packaging.version.InvalidVersion)) as ex: + version.minor - # todo: fix behavior to ensure versions are valid. - assert v1 + version = Version_("2.") + with pytest.raises((IndexError, packaging.version.InvalidVersion)) as ex: + version.major + + version = Version_("3.0.") + + with pytest.raises((IndexError, packaging.version.InvalidVersion)) as ex: + version.major + + version = Version_("3.1.a") + assert version.major == 3 + assert version.minor == 1 + with pytest.raises((IndexError, packaging.version.InvalidVersion)) as ex: + version.patch def test_version_bad_parse_fail(): """Test behavior when trying to parse with an invalid input string""" - v1 = Version_("abcdefg") - # todo: ensure we can't take invalid input and have this IndexError occur. + version = Version_("abcdefg") with pytest.raises((IndexError, packaging.version.InvalidVersion)) as ex: - _ = v1.minor + version.major diff --git a/tests/install/test_builder.py b/tests/install/test_builder.py deleted file mode 100644 index feaf7e54f..000000000 --- a/tests/install/test_builder.py +++ /dev/null @@ -1,404 +0,0 @@ -# BSD 2-Clause License -# -# Copyright (c) 2021-2024, Hewlett Packard Enterprise -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# 1. Redistributions of source code must retain the above copyright notice, this -# list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright notice, -# this list of conditions and the following disclaimer in the documentation -# and/or other materials provided with the distribution. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - - -import functools -import pathlib -import textwrap -import time - -import pytest - -import smartsim._core._install.builder as build -from smartsim._core._install.buildenv import RedisAIVersion - -# The tests in this file belong to the group_a group -pytestmark = pytest.mark.group_a - -RAI_VERSIONS = RedisAIVersion("1.2.7") - -for_each_device = pytest.mark.parametrize( - "device", [build.Device.CPU, build.Device.GPU] -) - -_toggle_build_optional_backend = lambda backend: pytest.mark.parametrize( - f"build_{backend}", - [ - pytest.param(switch, id=f"with{'' if switch else 'out'}-{backend}") - for switch in (True, False) - ], -) -toggle_build_tf = _toggle_build_optional_backend("tf") -toggle_build_pt = _toggle_build_optional_backend("pt") -toggle_build_ort = _toggle_build_optional_backend("ort") - - -@pytest.mark.parametrize( - "mock_os", [pytest.param(os_, id=f"os='{os_}'") for os_ in ("Windows", "Java", "")] -) -def test_os_enum_raises_on_unsupported(mock_os): - with pytest.raises(build.BuildError, match="operating system") as err_info: - build.OperatingSystem.from_str(mock_os) - - -@pytest.mark.parametrize( - "mock_arch", - [ - pytest.param(arch_, id=f"arch='{arch_}'") - for arch_ in ("i386", "i686", "i86pc", "aarch64", "armv7l", "") - ], -) -def test_arch_enum_raises_on_unsupported(mock_arch): - with pytest.raises(build.BuildError, match="architecture"): - build.Architecture.from_str(mock_arch) - - -@pytest.fixture -def p_test_dir(test_dir): - yield pathlib.Path(test_dir).resolve() - - -@for_each_device -def test_rai_builder_raises_if_attempting_to_place_deps_when_build_dir_dne( - monkeypatch, p_test_dir, device -): - monkeypatch.setattr(build.RedisAIBuilder, "_validate_platform", lambda a: None) - monkeypatch.setattr( - build.RedisAIBuilder, - "rai_build_path", - property(lambda self: p_test_dir / "path/to/dir/that/dne"), - ) - rai_builder = build.RedisAIBuilder() - with pytest.raises(build.BuildError, match=r"build directory not found"): - rai_builder._fetch_deps_for(device) - - -@for_each_device -def test_rai_builder_raises_if_attempting_to_place_deps_in_nonempty_dir( - monkeypatch, p_test_dir, device -): - (p_test_dir / "some_file.txt").touch() - monkeypatch.setattr(build.RedisAIBuilder, "_validate_platform", lambda a: None) - monkeypatch.setattr( - build.RedisAIBuilder, "rai_build_path", property(lambda self: p_test_dir) - ) - monkeypatch.setattr( - build.RedisAIBuilder, "get_deps_dir_path_for", lambda *a, **kw: p_test_dir - ) - rai_builder = build.RedisAIBuilder() - - with pytest.raises(build.BuildError, match=r"is not empty"): - rai_builder._fetch_deps_for(device) - - -invalid_build_arm64 = [ - dict(build_tf=True, build_onnx=True), - dict(build_tf=False, build_onnx=True), - dict(build_tf=True, build_onnx=False), -] -invalid_build_ids = [ - ",".join([f"{key}={value}" for key, value in d.items()]) - for d in invalid_build_arm64 -] - - -@pytest.mark.parametrize("build_options", invalid_build_arm64, ids=invalid_build_ids) -def test_rai_builder_raises_if_unsupported_deps_on_arm64(build_options): - with pytest.raises(build.BuildError, match=r"are not supported on.*ARM64"): - build.RedisAIBuilder( - _os=build.OperatingSystem.DARWIN, - architecture=build.Architecture.ARM64, - **build_options, - ) - - -def _confirm_inst_presence(type_, should_be_present, seq): - expected_num_occurrences = 1 if should_be_present else 0 - occurrences = filter(lambda item: isinstance(item, type_), seq) - return expected_num_occurrences == len(tuple(occurrences)) - - -# Helper functions to check for the presence (or absence) of a -# ``_RAIBuildDependency`` dependency in a list of dependencies that need to be -# fetched by a ``RedisAIBuilder`` instance -dlpack_dep_presence = functools.partial( - _confirm_inst_presence, build._DLPackRepository, True -) -pt_dep_presence = functools.partial(_confirm_inst_presence, build._PTArchive) -tf_dep_presence = functools.partial(_confirm_inst_presence, build._TFArchive) -ort_dep_presence = functools.partial(_confirm_inst_presence, build._ORTArchive) - - -@for_each_device -@toggle_build_tf -@toggle_build_pt -@toggle_build_ort -def test_rai_builder_will_add_dep_if_backend_requested_wo_duplicates( - monkeypatch, device, build_tf, build_pt, build_ort -): - monkeypatch.setattr(build.RedisAIBuilder, "_validate_platform", lambda a: None) - - rai_builder = build.RedisAIBuilder( - build_tf=build_tf, build_torch=build_pt, build_onnx=build_ort - ) - requested_backends = rai_builder._get_deps_to_fetch_for(build.Device(device)) - assert dlpack_dep_presence(requested_backends) - assert tf_dep_presence(build_tf, requested_backends) - assert pt_dep_presence(build_pt, requested_backends) - assert ort_dep_presence(build_ort, requested_backends) - - -@for_each_device -@toggle_build_tf -@toggle_build_pt -def test_rai_builder_will_not_add_dep_if_custom_dep_path_provided( - monkeypatch, device, p_test_dir, build_tf, build_pt -): - monkeypatch.setattr(build.RedisAIBuilder, "_validate_platform", lambda a: None) - mock_ml_lib = p_test_dir / "some/ml/lib" - mock_ml_lib.mkdir(parents=True) - rai_builder = build.RedisAIBuilder( - build_tf=build_tf, - build_torch=build_pt, - build_onnx=False, - libtf_dir=str(mock_ml_lib if build_tf else ""), - torch_dir=str(mock_ml_lib if build_pt else ""), - ) - requested_backends = rai_builder._get_deps_to_fetch_for(device) - assert dlpack_dep_presence(requested_backends) - assert tf_dep_presence(False, requested_backends) - assert pt_dep_presence(False, requested_backends) - assert ort_dep_presence(False, requested_backends) - assert len(requested_backends) == 1 - - -def test_rai_builder_raises_if_it_fetches_an_unexpected_number_of_ml_deps( - monkeypatch, p_test_dir -): - monkeypatch.setattr(build.RedisAIBuilder, "_validate_platform", lambda a: None) - monkeypatch.setattr( - build.RedisAIBuilder, "rai_build_path", property(lambda self: p_test_dir) - ) - monkeypatch.setattr( - build, - "_place_rai_dep_at", - lambda target, verbose: lambda dep: target - / "whoops_all_ml_deps_extract_to_a_dir_with_this_name", - ) - rai_builder = build.RedisAIBuilder(build_tf=True, build_torch=True, build_onnx=True) - with pytest.raises( - build.BuildError, - match=r"Expected to place \d+ dependencies, but only found \d+", - ): - rai_builder._fetch_deps_for(build.Device.CPU) - - -def test_threaded_map(): - def _some_io_op(x): - return x * x - - assert (0, 1, 4, 9, 16) == tuple(build._threaded_map(_some_io_op, range(5))) - - -def test_threaded_map_returns_early_if_nothing_to_map(): - sleep_duration = 60 - - def _some_long_io_op(_): - time.sleep(sleep_duration) - - start = time.time() - build._threaded_map(_some_long_io_op, []) - end = time.time() - assert end - start < sleep_duration - - -def test_correct_pt_variant_os(): - # Check that all Linux variants return Linux - for linux_variant in build.OperatingSystem.LINUX.value: - os_ = build.OperatingSystem.from_str(linux_variant) - assert build._choose_pt_variant(os_) == build._PTArchiveLinux - - # Check that ARM64 and X86_64 Mac OSX return the Mac variant - all_archs = (build.Architecture.ARM64, build.Architecture.X64) - for arch in all_archs: - os_ = build.OperatingSystem.DARWIN - assert build._choose_pt_variant(os_) == build._PTArchiveMacOSX - - -def test_PTArchiveMacOSX_url(): - arch = build.Architecture.X64 - pt_version = RAI_VERSIONS.torch - - pt_linux_cpu = build._PTArchiveLinux( - build.Architecture.X64, build.Device.CPU, pt_version, False - ) - x64_prefix = "https://download.pytorch.org/libtorch/" - assert x64_prefix in pt_linux_cpu.url - - pt_macosx_cpu = build._PTArchiveMacOSX( - build.Architecture.ARM64, build.Device.CPU, pt_version, False - ) - arm64_prefix = "https://github.com/CrayLabs/ml_lib_builder/releases/download/" - assert arm64_prefix in pt_macosx_cpu.url - - -def test_PTArchiveMacOSX_gpu_error(): - with pytest.raises(build.BuildError, match="support GPU on Mac OSX"): - build._PTArchiveMacOSX( - build.Architecture.ARM64, build.Device.GPU, RAI_VERSIONS.torch, False - ).url - - -def test_valid_platforms(): - assert build.RedisAIBuilder( - _os=build.OperatingSystem.LINUX, - architecture=build.Architecture.X64, - build_tf=True, - build_torch=True, - build_onnx=True, - ) - assert build.RedisAIBuilder( - _os=build.OperatingSystem.DARWIN, - architecture=build.Architecture.X64, - build_tf=True, - build_torch=True, - build_onnx=False, - ) - assert build.RedisAIBuilder( - _os=build.OperatingSystem.DARWIN, - architecture=build.Architecture.X64, - build_tf=False, - build_torch=True, - build_onnx=False, - ) - - -@pytest.mark.parametrize( - "plat,cmd,expected_cmd", - [ - # Bare Word - pytest.param( - build.Platform(build.OperatingSystem.LINUX, build.Architecture.X64), - ["git", "clone", "my-repo"], - ["git", "clone", "my-repo"], - id="git-Linux-X64", - ), - pytest.param( - build.Platform(build.OperatingSystem.LINUX, build.Architecture.ARM64), - ["git", "clone", "my-repo"], - ["git", "clone", "my-repo"], - id="git-Linux-Arm64", - ), - pytest.param( - build.Platform(build.OperatingSystem.DARWIN, build.Architecture.X64), - ["git", "clone", "my-repo"], - ["git", "clone", "my-repo"], - id="git-Darwin-X64", - ), - pytest.param( - build.Platform(build.OperatingSystem.DARWIN, build.Architecture.ARM64), - ["git", "clone", "my-repo"], - [ - "git", - "clone", - "--config", - "core.autocrlf=false", - "--config", - "core.eol=lf", - "my-repo", - ], - id="git-Darwin-Arm64", - ), - # Abs path - pytest.param( - build.Platform(build.OperatingSystem.LINUX, build.Architecture.X64), - ["/path/to/git", "clone", "my-repo"], - ["/path/to/git", "clone", "my-repo"], - id="Abs-Linux-X64", - ), - pytest.param( - build.Platform(build.OperatingSystem.LINUX, build.Architecture.ARM64), - ["/path/to/git", "clone", "my-repo"], - ["/path/to/git", "clone", "my-repo"], - id="Abs-Linux-Arm64", - ), - pytest.param( - build.Platform(build.OperatingSystem.DARWIN, build.Architecture.X64), - ["/path/to/git", "clone", "my-repo"], - ["/path/to/git", "clone", "my-repo"], - id="Abs-Darwin-X64", - ), - pytest.param( - build.Platform(build.OperatingSystem.DARWIN, build.Architecture.ARM64), - ["/path/to/git", "clone", "my-repo"], - [ - "/path/to/git", - "clone", - "--config", - "core.autocrlf=false", - "--config", - "core.eol=lf", - "my-repo", - ], - id="Abs-Darwin-Arm64", - ), - ], -) -def test_git_commands_are_configered_correctly_for_platforms(plat, cmd, expected_cmd): - assert build.config_git_command(plat, cmd) == expected_cmd - - -def test_modify_source_files(p_test_dir): - def make_text_blurb(food): - return textwrap.dedent(f"""\ - My favorite food is {food} - {food} is an important part of a healthy breakfast - {food} {food} {food} {food} - This line should be unchanged! - --> {food} <-- - """) - - original_word = "SPAM" - mutated_word = "EGGS" - - source_files = [] - for i in range(3): - source_file = p_test_dir / f"test_{i}" - source_file.touch() - source_file.write_text(make_text_blurb(original_word)) - source_files.append(source_file) - # Modify a single file - build._modify_source_files(source_files[0], original_word, mutated_word) - assert source_files[0].read_text() == make_text_blurb(mutated_word) - assert source_files[1].read_text() == make_text_blurb(original_word) - assert source_files[2].read_text() == make_text_blurb(original_word) - - # Modify multiple files - build._modify_source_files( - (source_files[1], source_files[2]), original_word, mutated_word - ) - assert source_files[1].read_text() == make_text_blurb(mutated_word) - assert source_files[2].read_text() == make_text_blurb(mutated_word) diff --git a/tests/install/test_mlpackage.py b/tests/install/test_mlpackage.py new file mode 100644 index 000000000..d27e69b2b --- /dev/null +++ b/tests/install/test_mlpackage.py @@ -0,0 +1,122 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import os +import pathlib +from unittest.mock import MagicMock + +import pytest + +from smartsim._core._install.mlpackages import ( + MLPackage, + MLPackageCollection, + RAIPatch, + load_platform_configs, +) +from smartsim._core._install.platform import Platform + +# The tests in this file belong to the group_a group +pytestmark = pytest.mark.group_a + +mock_platform = MagicMock(spec=Platform) + + +@pytest.fixture +def mock_ml_packages(): + foo = MagicMock(spec=MLPackage) + foo.name = "foo" + bar = MagicMock(spec=MLPackage) + bar.name = "bar" + yield [foo, bar] + + +@pytest.mark.parametrize( + "patch", + [MagicMock(spec=RAIPatch), [MagicMock(spec=RAIPatch) for i in range(3)], ()], + ids=["one patch", "multiple patches", "no patch"], +) +def test_mlpackage_constructor(patch): + MLPackage( + "foo", + "0.0.0", + "https://nothing.com", + ["bar==0.1", "baz==0.2"], + pathlib.Path("/nothing/fake"), + patch, + ) + + +def test_mlpackage_collection_constructor(mock_ml_packages): + MLPackageCollection(mock_platform, mock_ml_packages) + + +def test_mlpackage_collection_mutable_mapping_methods(mock_ml_packages): + ml_packages = MLPackageCollection(mock_platform, mock_ml_packages) + for val in ml_packages._ml_packages.values(): + val.version = "0.0.0" + assert ml_packages._ml_packages == ml_packages + + # Test iter + package_names = [pkg.name for pkg in mock_ml_packages] + assert [name for name in ml_packages] == package_names + + # Test get item + for pkg in mock_ml_packages: + assert ml_packages[pkg.name] is pkg + + # Test len + assert len(ml_packages) == len(mock_ml_packages) + + # Test delitem + key = next(iter(mock_ml_packages)).name + del ml_packages[key] + with pytest.raises(KeyError): + ml_packages[key] + assert len(ml_packages) == (len(mock_ml_packages) - 1) + + # Test setitem + with pytest.raises(TypeError): + ml_packages["baz"] = MagicMock(spec=MLPackage) + + # Test contains + name, package = next(iter(ml_packages.items())) + assert name in ml_packages + + # Test str + assert "Package" in str(ml_packages) + assert "Version" in str(ml_packages) + assert package.version in str(ml_packages) + assert name in str(ml_packages) + + +def test_load_configs_raises_when_dir_dne(test_dir): + dne_dir = pathlib.Path(test_dir, "dne") + dir_str = os.fspath(dne_dir) + with pytest.raises( + FileNotFoundError, + match=f"Platform configuration directory `{dir_str}` does not exist", + ): + load_platform_configs(dne_dir) diff --git a/tests/install/test_package_retriever.py b/tests/install/test_package_retriever.py new file mode 100644 index 000000000..d415ae235 --- /dev/null +++ b/tests/install/test_package_retriever.py @@ -0,0 +1,106 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import contextlib +import filecmp +import os +import pathlib +import random +import string +import tarfile +import zipfile + +import pytest + +from smartsim._core._install.utils import retrieve + +# The tests in this file belong to the group_a group +pytestmark = pytest.mark.group_a + + +@contextlib.contextmanager +def temp_cd(path): + original = os.getcwd() + os.chdir(path) + try: + yield + finally: + os.chdir(original) + + +def make_test_file(test_file): + data = "".join(random.choices(string.ascii_letters + string.digits, k=1024)) + with open(test_file, "w") as f: + f.write(data) + + +def test_local_archive_zip(test_dir): + with temp_cd(test_dir): + test_file = "./test.data" + make_test_file(test_file) + + zip_file = "./test.zip" + with zipfile.ZipFile(zip_file, "w") as f: + f.write(test_file) + + retrieve(zip_file, pathlib.Path("./output")) + + assert filecmp.cmp( + test_file, pathlib.Path("./output") / "test.data", shallow=False + ) + + +def test_local_archive_tgz(test_dir): + with temp_cd(test_dir): + test_file = "./test.data" + make_test_file(test_file) + + tgz_file = "./test.tgz" + with tarfile.open(tgz_file, "w:gz") as f: + f.add(test_file) + + retrieve(tgz_file, pathlib.Path("./output")) + + assert filecmp.cmp( + test_file, pathlib.Path("./output") / "test.data", shallow=False + ) + + +def test_git(test_dir): + retrieve( + "https://github.com/CrayLabs/SmartSim.git", + f"{test_dir}/smartsim_git", + branch="master", + ) + assert pathlib.Path(f"{test_dir}/smartsim_git").is_dir() + + +def test_https(test_dir): + output_dir = pathlib.Path(test_dir) / "output" + retrieve( + "https://github.com/CrayLabs/SmartSim/archive/refs/tags/v0.5.0.zip", output_dir + ) + assert output_dir.exists() diff --git a/tests/install/test_platform.py b/tests/install/test_platform.py new file mode 100644 index 000000000..76ff3f76b --- /dev/null +++ b/tests/install/test_platform.py @@ -0,0 +1,89 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import json +import os +import platform + +import pytest + +from smartsim._core._install.platform import Architecture, Device, OperatingSystem + +# The tests in this file belong to the group_a group +pytestmark = pytest.mark.group_a + + +def test_device_cpu(): + cpu_enum = Device.CPU + assert not cpu_enum.is_gpu() + assert not cpu_enum.is_cuda() + assert not cpu_enum.is_rocm() + + +@pytest.mark.parametrize("cuda_device", Device.cuda_enums()) +def test_cuda(monkeypatch, test_dir, cuda_device): + version = cuda_device.value.split("-")[1] + fake_full_version = version + ".8888" ".9999" + monkeypatch.setenv("CUDA_HOME", test_dir) + + mock_version = dict(cuda=dict(version=fake_full_version)) + print(mock_version) + with open(f"{test_dir}/version.json", "w") as outfile: + json.dump(mock_version, outfile) + + assert Device.detect_cuda_version() == cuda_device + assert cuda_device.is_gpu() + assert cuda_device.is_cuda() + assert not cuda_device.is_rocm() + + +@pytest.mark.parametrize("rocm_device", Device.rocm_enums()) +def test_rocm(monkeypatch, test_dir, rocm_device): + version = rocm_device.value.split("-")[1] + fake_full_version = version + ".8888" + "-9999" + monkeypatch.setenv("ROCM_HOME", test_dir) + info_dir = f"{test_dir}/.info" + os.mkdir(info_dir) + + with open(f"{info_dir}/version", "w") as outfile: + outfile.write(fake_full_version) + + assert Device.detect_rocm_version() == rocm_device + assert rocm_device.is_gpu() + assert not rocm_device.is_cuda() + assert rocm_device.is_rocm() + + +@pytest.mark.parametrize("os", ("linux", "darwin")) +def test_operating_system(monkeypatch, os): + monkeypatch.setattr(platform, "system", lambda: os) + assert OperatingSystem.autodetect().value == os + + +@pytest.mark.parametrize("arch", ("x86_64", "arm64")) +def test_architecture(monkeypatch, arch): + monkeypatch.setattr(platform, "machine", lambda: arch) + assert Architecture.autodetect().value == arch diff --git a/tests/install/test_redisai_builder.py b/tests/install/test_redisai_builder.py new file mode 100644 index 000000000..81673a7f1 --- /dev/null +++ b/tests/install/test_redisai_builder.py @@ -0,0 +1,60 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +from pathlib import Path + +import pytest + +from smartsim._core._install.buildenv import BuildEnv +from smartsim._core._install.mlpackages import ( + DEFAULT_MLPACKAGE_PATH, + MLPackage, + load_platform_configs, +) +from smartsim._core._install.platform import Platform +from smartsim._core._install.redisaiBuilder import RedisAIBuilder + +# The tests in this file belong to the group_a group +pytestmark = pytest.mark.group_a + +DEFAULT_MLPACKAGES = load_platform_configs(DEFAULT_MLPACKAGE_PATH) + + +@pytest.mark.parametrize( + "platform", + [platform for platform in DEFAULT_MLPACKAGES], + ids=[str(platform) for platform in DEFAULT_MLPACKAGES], +) +def test_backends_to_be_installed(monkeypatch, test_dir, platform): + mlpackages = DEFAULT_MLPACKAGES[platform] + monkeypatch.setattr(MLPackage, "retrieve", lambda *args, **kwargs: None) + builder = RedisAIBuilder(platform, mlpackages, BuildEnv(), Path(test_dir)) + + BACKENDS = ["libtorch", "libtensorflow", "onnxruntime"] + TOGGLES = ["build_torch", "build_tensorflow", "build_onnxruntime"] + + for backend, toggle in zip(BACKENDS, TOGGLES): + assert getattr(builder, toggle) == (backend in mlpackages) diff --git a/tests/test_batch_settings.py b/tests/test_batch_settings.py index db269a9b5..c4f365c39 100644 --- a/tests/test_batch_settings.py +++ b/tests/test_batch_settings.py @@ -64,7 +64,7 @@ def test_create_sbatch(): assert isinstance(slurm_batch, SbatchSettings) assert slurm_batch.batch_args["partition"] == "default" args = slurm_batch.format_batch_args() - assert args == [ + expected_args = [ "--exclusive", "--oversubscribe", "--nodes=1", @@ -72,6 +72,8 @@ def test_create_sbatch(): "--partition=default", "--account=myproject", ] + assert all(arg in expected_args for arg in args) + assert len(expected_args) == len(args) def test_create_bsub(): diff --git a/tests/test_cli.py b/tests/test_cli.py index 710a9a659..1cead7625 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -436,24 +436,23 @@ def mock_execute(ns: argparse.Namespace, _unparsed: t.Optional[t.List[str]] = No # fmt: off @pytest.mark.parametrize( - "command,mock_location,exp_output,optional_arg,exp_valid,exp_err_msg,check_prop,exp_prop_val", + "command, mock_location, exp_output, optional_arg, exp_valid, exp_err_msg, check_prop, exp_prop_val", [ - pytest.param("build", "build_execute", "verbose mocked-build", "-v", True, "", "v", True, id="verbose 'on'"), - pytest.param("build", "build_execute", "cpu mocked-build", "--device=cpu", True, "", "device", "cpu", id="device 'cpu'"), - pytest.param("build", "build_execute", "gpu mocked-build", "--device=gpu", True, "", "device", "gpu", id="device 'gpu'"), - pytest.param("build", "build_execute", "gpuX mocked-build", "--device=gpux", False, "invalid choice: 'gpux'", "", "", id="set bad device 'gpuX'"), - pytest.param("build", "build_execute", "no tensorflow mocked-build", "--no_tf", True, "", "no_tf", True, id="set no TF"), - pytest.param("build", "build_execute", "no torch mocked-build", "--no_pt", True, "", "no_pt", True, id="set no torch"), - pytest.param("build", "build_execute", "onnx mocked-build", "--onnx", True, "", "onnx", True, id="set w/onnx"), - pytest.param("build", "build_execute", "torch-dir mocked-build", "--torch_dir /foo/bar", True, "", "torch_dir", "/foo/bar", id="set torch dir"), - pytest.param("build", "build_execute", "bad-torch-dir mocked-build", "--torch_dir", False, "error: argument --torch_dir", "", "", id="set torch dir, no path"), - pytest.param("build", "build_execute", "keydb mocked-build", "--keydb", True, "", "keydb", True, id="keydb on"), - pytest.param("clean", "clean_execute", "clobbering mocked-clean", "--clobber", True, "", "clobber", True, id="clean w/clobber"), - pytest.param("validate", "validate_execute", "port mocked-validate", "--port=12345", True, "", "port", 12345, id="validate w/ manual port"), - pytest.param("validate", "validate_execute", "abbrv port mocked-validate", "-p 12345", True, "", "port", 12345, id="validate w/ manual abbreviated port"), - pytest.param("validate", "validate_execute", "cpu mocked-validate", "--device=cpu", True, "", "device", "cpu", id="validate: device 'cpu'"), - pytest.param("validate", "validate_execute", "gpu mocked-validate", "--device=gpu", True, "", "device", "gpu", id="validate: device 'gpu'"), - pytest.param("validate", "validate_execute", "gpuX mocked-validate", "--device=gpux", False, "invalid choice: 'gpux'", "", "", id="validate: set bad device 'gpuX'"), + pytest.param( "build", "build_execute", "verbose mocked-build", "-v", True, "", "v", True, id="verbose 'on'"), + pytest.param( "build", "build_execute", "cpu mocked-build", "--device=cpu", True, "", "device", "cpu", id="device 'cpu'"), + pytest.param( "build", "build_execute", "gpuX mocked-build", "--device=gpux", False, "invalid choice: 'gpux'", "", "", id="set bad device 'gpuX'"), + pytest.param( "build", "build_execute", "no tensorflow mocked-build", "--skip-tensorflow", True, "", "no_tf", True, id="Skip TF"), + pytest.param( "build", "build_execute", "no torch mocked-build", "--skip-torch", True, "", "no_pt", True, id="Skip Torch"), + pytest.param( "build", "build_execute", "onnx mocked-build", "--skip-onnx", True, "", "onnx", True, id="Skip Onnx"), + pytest.param( "build", "build_execute", "config-dir mocked-build", "--config-dir /foo/bar", True, "", "config-dir", "/foo/bar", id="set torch dir"), + pytest.param( "build", "build_execute", "bad-config-dir mocked-build", "--config-dir", False, "error: argument --config-dir", "", "", id="set config dir w/o path"), + pytest.param( "build", "build_execute", "keydb mocked-build", "--keydb", True, "", "keydb", True, id="keydb on"), + pytest.param( "clean", "clean_execute", "clobbering mocked-clean", "--clobber", True, "", "clobber", True, id="clean w/clobber"), + pytest.param("validate", "validate_execute", "port mocked-validate", "--port=12345", True, "", "port", 12345, id="validate w/ manual port"), + pytest.param("validate", "validate_execute", "abbrv port mocked-validate", "-p 12345", True, "", "port", 12345, id="validate w/ manual abbreviated port"), + pytest.param("validate", "validate_execute", "cpu mocked-validate", "--device=cpu", True, "", "device", "cpu", id="validate: device 'cpu'"), + pytest.param("validate", "validate_execute", "gpu mocked-validate", "--device=gpu", True, "", "device", "gpu", id="validate: device 'gpu'"), + pytest.param("validate", "validate_execute", "gpuX mocked-validate", "--device=gpux", False, "invalid choice: 'gpux'", "", "", id="validate: set bad device 'gpuX'"), ] ) # fmt: on @@ -735,15 +734,6 @@ def mock_operation(*args, **kwargs) -> int: monkeypatch.setattr(smartsim._core._cli.build, "tabulate", mock_operation) monkeypatch.setattr(smartsim._core._cli.build, "build_database", mock_operation) monkeypatch.setattr(smartsim._core._cli.build, "build_redis_ai", mock_operation) - monkeypatch.setattr( - smartsim._core._cli.build, "check_py_torch_version", mock_operation - ) - monkeypatch.setattr( - smartsim._core._cli.build, "check_py_tf_version", mock_operation - ) - monkeypatch.setattr( - smartsim._core._cli.build, "check_py_onnx_version", mock_operation - ) command = "build" cfg = MenuItemConfig( diff --git a/tests/test_dragon_client.py b/tests/test_dragon_client.py new file mode 100644 index 000000000..80257b610 --- /dev/null +++ b/tests/test_dragon_client.py @@ -0,0 +1,192 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +import os +import pathlib +import typing as t +from unittest.mock import MagicMock + +import pytest + +from smartsim._core.launcher.step.dragonStep import DragonBatchStep, DragonStep +from smartsim.settings import DragonRunSettings +from smartsim.settings.slurmSettings import SbatchSettings + +# The tests in this file belong to the group_a group +pytestmark = pytest.mark.group_a + + +import smartsim._core.entrypoints.dragon_client as dragon_client +from smartsim._core.schemas.dragonRequests import * +from smartsim._core.schemas.dragonResponses import * + + +@pytest.fixture +def dragon_batch_step(test_dir: str) -> "DragonBatchStep": + """Fixture for creating a default batch of steps for a dragon launcher""" + test_path = pathlib.Path(test_dir) + + batch_step_name = "batch_step" + num_nodes = 4 + batch_settings = SbatchSettings(nodes=num_nodes) + batch_step = DragonBatchStep(batch_step_name, test_dir, batch_settings) + + # ensure the status_dir is set + status_dir = (test_path / ".smartsim" / "logs").as_posix() + batch_step.meta["status_dir"] = status_dir + + # create some steps to verify the requests file output changes + rs0 = DragonRunSettings(exe="sleep", exe_args=["1"]) + rs1 = DragonRunSettings(exe="sleep", exe_args=["2"]) + rs2 = DragonRunSettings(exe="sleep", exe_args=["3"]) + rs3 = DragonRunSettings(exe="sleep", exe_args=["4"]) + + names = "test00", "test01", "test02", "test03" + settings = rs0, rs1, rs2, rs3 + + # create steps with: + # no affinity, cpu affinity only, gpu affinity only, cpu and gpu affinity + cpu_affinities = [[], [0, 1, 2], [], [3, 4, 5, 6]] + gpu_affinities = [[], [], [0, 1, 2], [3, 4, 5, 6]] + + # assign some unique affinities to each run setting instance + for index, rs in enumerate(settings): + if gpu_affinities[index]: + rs.set_node_feature("gpu") + rs.set_cpu_affinity(cpu_affinities[index]) + rs.set_gpu_affinity(gpu_affinities[index]) + + steps = list( + DragonStep(name_, test_dir, rs_) for name_, rs_ in zip(names, settings) + ) + + for index, step in enumerate(steps): + # ensure meta is configured... + step.meta["status_dir"] = status_dir + # ... and put all the steps into the batch + batch_step.add_to_batch(steps[index]) + + return batch_step + + +def get_request_path_from_batch_script(launch_cmd: t.List[str]) -> pathlib.Path: + """Helper method for finding the path to a request file from the launch command""" + script_path = pathlib.Path(launch_cmd[-1]) + batch_script = script_path.read_text(encoding="utf-8") + batch_statements = [line for line in batch_script.split("\n") if line] + entrypoint_cmd = batch_statements[-1] + requests_file = pathlib.Path(entrypoint_cmd.split()[-1]) + return requests_file + + +def test_dragon_client_main_no_arg(monkeypatch: pytest.MonkeyPatch): + """Verify the client fails when the path to a submission file is not provided.""" + with pytest.raises(SystemExit): + dragon_client.cleanup = MagicMock() + dragon_client.main([]) + + # arg parser failures occur before resource allocation and should + # not result in resource cleanup being called + assert not dragon_client.cleanup.called + + +def test_dragon_client_main_empty_arg(test_dir: str): + """Verify the client fails when the path to a submission file is empty.""" + + with pytest.raises(ValueError) as ex: + dragon_client.cleanup = MagicMock() + dragon_client.main(["+submit", ""]) + + # verify it's a value error related to submit argument + assert "file not provided" in ex.value.args[0] + + # arg parser failures occur before resource allocation and should + # not result in resource cleanup being called + assert not dragon_client.cleanup.called + + +def test_dragon_client_main_bad_arg(test_dir: str): + """Verify the client returns a failure code when the path to a submission file is + invalid and does not raise an exception""" + path = pathlib.Path(test_dir) / "nonexistent_file.json" + + dragon_client.cleanup = MagicMock() + return_code = dragon_client.main(["+submit", str(path)]) + + # ensure non-zero return code + assert return_code != 0 + + # ensure failures do not block resource cleanup + assert dragon_client.cleanup.called + + +def test_dragon_client_main( + dragon_batch_step: DragonBatchStep, monkeypatch: pytest.MonkeyPatch +): + """Verify the client returns a failure code when the path to a submission file is + invalid and does not raise an exception""" + launch_cmd = dragon_batch_step.get_launch_cmd() + path = get_request_path_from_batch_script(launch_cmd) + num_requests_in_batch = 4 + num_shutdown_requests = 1 + request_count = num_requests_in_batch + num_shutdown_requests + submit_value = str(path) + + mock_connector = MagicMock() # DragonConnector + mock_connector.is_connected = True + mock_connector.send_request.return_value = DragonRunResponse(step_id="mock_step_id") + # mock can_monitor to exit before the infinite loop checking for shutdown + mock_connector.can_monitor = False + + mock_connector_class = MagicMock() + mock_connector_class.return_value = mock_connector + + # with monkeypatch.context() as ctx: + dragon_client.DragonConnector = mock_connector_class + dragon_client.cleanup = MagicMock() + + return_code = dragon_client.main(["+submit", submit_value]) + + # verify each request in the request file was processed + assert mock_connector.send_request.call_count == request_count + + # we know the batch fixture has a step with no affinity args supplied. skip it + for i in range(1, num_requests_in_batch): + sent_args = mock_connector.send_request.call_args_list[i][0] + request_arg = sent_args[0] + + assert isinstance(request_arg, DragonRunRequest) + + policy = request_arg.policy + + # make sure each policy has been read in correctly with valid affinity indices + assert len(policy.cpu_affinity) == len(set(policy.cpu_affinity)) + assert len(policy.gpu_affinity) == len(set(policy.gpu_affinity)) + + # we get a non-zero due to avoiding the infinite loop. consider refactoring + assert return_code == os.EX_IOERR + + # ensure failures do not block resource cleanup + assert dragon_client.cleanup.called diff --git a/tests/test_dragon_launcher.py b/tests/test_dragon_launcher.py index ee0fcb14b..4bd07e920 100644 --- a/tests/test_dragon_launcher.py +++ b/tests/test_dragon_launcher.py @@ -31,6 +31,7 @@ import sys import time import typing as t +from unittest.mock import MagicMock import pytest import zmq @@ -38,15 +39,74 @@ import smartsim._core.config from smartsim._core._cli.scripts.dragon_install import create_dotenv from smartsim._core.config.config import get_config -from smartsim._core.launcher.dragon.dragonLauncher import DragonConnector +from smartsim._core.launcher.dragon.dragonLauncher import ( + DragonConnector, + DragonLauncher, +) from smartsim._core.launcher.dragon.dragonSockets import ( get_authenticator, get_secure_socket, ) +from smartsim._core.launcher.step.dragonStep import DragonBatchStep, DragonStep from smartsim._core.schemas.dragonRequests import DragonBootstrapRequest -from smartsim._core.schemas.dragonResponses import DragonHandshakeResponse +from smartsim._core.schemas.dragonResponses import ( + DragonHandshakeResponse, + DragonRunResponse, +) from smartsim._core.utils.network import IFConfig, find_free_port from smartsim._core.utils.security import KeyManager +from smartsim.error.errors import LauncherError +from smartsim.settings.dragonRunSettings import DragonRunSettings +from smartsim.settings.slurmSettings import SbatchSettings + + +@pytest.fixture +def dragon_batch_step(test_dir: str) -> DragonBatchStep: + """Fixture for creating a default batch of steps for a dragon launcher""" + test_path = pathlib.Path(test_dir) + + batch_step_name = "batch_step" + num_nodes = 4 + batch_settings = SbatchSettings(nodes=num_nodes) + batch_step = DragonBatchStep(batch_step_name, test_dir, batch_settings) + + # ensure the status_dir is set + status_dir = (test_path / ".smartsim" / "logs").as_posix() + batch_step.meta["status_dir"] = status_dir + + # create some steps to verify the requests file output changes + rs0 = DragonRunSettings(exe="sleep", exe_args=["1"]) + rs1 = DragonRunSettings(exe="sleep", exe_args=["2"]) + rs2 = DragonRunSettings(exe="sleep", exe_args=["3"]) + rs3 = DragonRunSettings(exe="sleep", exe_args=["4"]) + + names = "test00", "test01", "test02", "test03" + settings = rs0, rs1, rs2, rs3 + + # create steps with: + # no affinity, cpu affinity only, gpu affinity only, cpu and gpu affinity + cpu_affinities = [[], [0, 1, 2], [], [3, 4, 5, 6]] + gpu_affinities = [[], [], [0, 1, 2], [3, 4, 5, 6]] + + # assign some unique affinities to each run setting instance + for index, rs in enumerate(settings): + if gpu_affinities[index]: + rs.set_node_feature("gpu") + rs.set_cpu_affinity(cpu_affinities[index]) + rs.set_gpu_affinity(gpu_affinities[index]) + + steps = list( + DragonStep(name_, test_dir, rs_) for name_, rs_ in zip(names, settings) + ) + + for index, step in enumerate(steps): + # ensure meta is configured... + step.meta["status_dir"] = status_dir + # ... and put all the steps into the batch + batch_step.add_to_batch(steps[index]) + + return batch_step + # The tests in this file belong to the group_a group pytestmark = pytest.mark.group_a @@ -521,3 +581,168 @@ def test_merge_env(monkeypatch: pytest.MonkeyPatch, test_dir: str): # any non-dragon keys that didn't exist avoid unnecessary prepending assert merged_env[non_dragon_key] == non_dragon_value + + +def test_run_step_fail(test_dir: str) -> None: + """Verify that the dragon launcher still returns the step id + when the running step fails""" + test_path = pathlib.Path(test_dir) + status_dir = (test_path / ".smartsim" / "logs").as_posix() + + rs = DragonRunSettings(exe="sleep", exe_args=["1"]) + step0 = DragonStep("step0", test_dir, rs) + step0.meta["status_dir"] = status_dir + + mock_connector = MagicMock(spec=DragonConnector) + mock_connector.is_connected = True + mock_connector.send_request = MagicMock( + return_value=DragonRunResponse(step_id=step0.name, error_message="mock fail!") + ) + mock_connector.merge_persisted_env = MagicMock( + return_value={"FOO": "bar", "BAZ": "boop"} + ) + + launcher = DragonLauncher() + launcher._connector = mock_connector + + result = launcher.run(step0) + + # verify the failed step name is in the result + assert step0.name in result + + +def test_run_step_batch_empty(dragon_batch_step: DragonBatchStep) -> None: + """Verify that the dragon launcher behaves when asked to execute + a batch step that has no sub-steps""" + # remove the steps added in the batch fixture + dragon_batch_step.steps.clear() + + mock_step_id = "MOCK-STEPID" + mock_connector = MagicMock() # DragonConnector() + mock_connector.is_connected = True + mock_connector.send_request = MagicMock( + return_value=DragonRunResponse( + step_id=dragon_batch_step.name, error_message="mock fail!" + ) + ) + + launcher = DragonLauncher() + launcher._connector = mock_connector + launcher.task_manager.start_and_wait = MagicMock(return_value=(0, mock_step_id, "")) + + result = launcher.run(dragon_batch_step) + + # verify a step name is returned + assert result + # verify the batch step name is not in the result (renamed to SLURM-*) + assert dragon_batch_step.name not in result + + send_invocation = mock_connector.send_request + + # verify a batch request is not sent through the dragon connector + send_invocation.assert_not_called() + + +def test_run_step_batch_failure(dragon_batch_step: DragonBatchStep) -> None: + """Verify that the dragon launcher sends returns the step id + when the running step fails""" + mock_connector = MagicMock() # DragonConnector() + mock_connector.is_connected = True + mock_connector.send_request = MagicMock( + return_value=DragonRunResponse( + step_id=dragon_batch_step.name, error_message="mock fail!" + ) + ) + + mock_step_id = "MOCK-STEPID" + error_msg = "DOES_NOT_COMPUTE!" + launcher = DragonLauncher() + launcher._connector = mock_connector + launcher.task_manager.start_and_wait = MagicMock( + return_value=(1, mock_step_id, error_msg) + ) + + # a non-zero return code from the batch script should raise an error + with pytest.raises(LauncherError) as ex: + launcher.run(dragon_batch_step) + + # verify the correct error message is in the exception + assert error_msg in ex.value.args[0] + + +def test_run_step_success(test_dir: str) -> None: + """Verify that the dragon launcher sends the correctly formatted request for a step""" + test_path = pathlib.Path(test_dir) + status_dir = (test_path / ".smartsim" / "logs").as_posix() + + rs = DragonRunSettings(exe="sleep", exe_args=["1"]) + step0 = DragonStep("step0", test_dir, rs) + step0.meta["status_dir"] = status_dir + + mock_connector = MagicMock(spec=DragonConnector) + mock_connector.is_connected = True + mock_connector.send_request = MagicMock( + return_value=DragonRunResponse(step_id=step0.name) + ) + + launcher = DragonLauncher() + launcher._connector = mock_connector + mock_connector.merge_persisted_env = MagicMock( + return_value={"FOO": "bar", "BAZ": "boop"} + ) + + result = launcher.run(step0) + + # verify the successfully executed step name is in the result + assert step0.name in result + + # verify the DragonRunRequest sent matches all expectations + send_invocation = mock_connector.send_request + send_invocation.assert_called_once() + + args = send_invocation.call_args[0] # call_args == t.Tuple[args, kwargs] + + dragon_run_request = args[0] + req_name = dragon_run_request.name # name sent to dragon env + assert req_name.startswith(step0.name) + + req_policy_cpu_affinity = dragon_run_request.policy.cpu_affinity + assert not req_policy_cpu_affinity # default should be empty list + + req_policy_gpu_affinity = dragon_run_request.policy.gpu_affinity + assert not req_policy_gpu_affinity # default should be empty list + + +def test_run_step_success_batch( + monkeypatch: pytest.MonkeyPatch, dragon_batch_step: DragonBatchStep +) -> None: + """Verify that the dragon launcher sends the correctly formatted request + for a batch step""" + mock_connector = MagicMock() # DragonConnector() + mock_connector.is_connected = True + mock_connector.send_request = MagicMock( + return_value=DragonRunResponse(step_id=dragon_batch_step.name) + ) + + launcher = DragonLauncher() + launcher._connector = mock_connector + launcher.task_manager.start_and_wait = MagicMock(return_value=(0, "success", "")) + + result = launcher.run(dragon_batch_step) + + # verify the successfully executed step name is in the result + assert dragon_batch_step.name not in result + assert result + + send_invocation = mock_connector.send_request + + # verify a batch request is not sent through the dragon connector + send_invocation.assert_not_called() + launcher.task_manager.start_and_wait.assert_called_once() + + args = launcher.task_manager.start_and_wait.call_args[0] + + # verify the batch script is executed + launch_cmd = dragon_batch_step.get_launch_cmd() + for stmt in launch_cmd: + assert stmt in args[0] # args[0] is the cmd list sent to subprocess.Popen diff --git a/tests/test_dragon_run_policy.py b/tests/test_dragon_run_policy.py new file mode 100644 index 000000000..1d8d069fa --- /dev/null +++ b/tests/test_dragon_run_policy.py @@ -0,0 +1,371 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import pathlib + +import pytest + +from smartsim._core.launcher.step.dragonStep import DragonBatchStep, DragonStep +from smartsim.settings.dragonRunSettings import DragonRunSettings +from smartsim.settings.slurmSettings import SbatchSettings + +try: + from dragon.infrastructure.policy import Policy + + import smartsim._core.entrypoints.dragon as drg + from smartsim._core.launcher.dragon.dragonBackend import DragonBackend + + dragon_loaded = True +except: + dragon_loaded = False + +# The tests in this file belong to the group_b group +pytestmark = pytest.mark.group_b + +from smartsim._core.schemas.dragonRequests import * +from smartsim._core.schemas.dragonResponses import * + + +@pytest.fixture +def dragon_batch_step(test_dir: str) -> "DragonBatchStep": + """Fixture for creating a default batch of steps for a dragon launcher""" + test_path = pathlib.Path(test_dir) + + batch_step_name = "batch_step" + num_nodes = 4 + batch_settings = SbatchSettings(nodes=num_nodes) + batch_step = DragonBatchStep(batch_step_name, test_dir, batch_settings) + + # ensure the status_dir is set + status_dir = (test_path / ".smartsim" / "logs").as_posix() + batch_step.meta["status_dir"] = status_dir + + # create some steps to verify the requests file output changes + rs0 = DragonRunSettings(exe="sleep", exe_args=["1"]) + rs1 = DragonRunSettings(exe="sleep", exe_args=["2"]) + rs2 = DragonRunSettings(exe="sleep", exe_args=["3"]) + rs3 = DragonRunSettings(exe="sleep", exe_args=["4"]) + + names = "test00", "test01", "test02", "test03" + settings = rs0, rs1, rs2, rs3 + + # create steps with: + # no affinity, cpu affinity only, gpu affinity only, cpu and gpu affinity + cpu_affinities = [[], [0, 1, 2], [], [3, 4, 5, 6]] + gpu_affinities = [[], [], [0, 1, 2], [3, 4, 5, 6]] + + # assign some unique affinities to each run setting instance + for index, rs in enumerate(settings): + if gpu_affinities[index]: + rs.set_node_feature("gpu") + rs.set_cpu_affinity(cpu_affinities[index]) + rs.set_gpu_affinity(gpu_affinities[index]) + + steps = list( + DragonStep(name_, test_dir, rs_) for name_, rs_ in zip(names, settings) + ) + + for index, step in enumerate(steps): + # ensure meta is configured... + step.meta["status_dir"] = status_dir + # ... and put all the steps into the batch + batch_step.add_to_batch(steps[index]) + + return batch_step + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +@pytest.mark.parametrize( + "dragon_request", + [ + pytest.param(DragonHandshakeRequest(), id="DragonHandshakeRequest"), + pytest.param(DragonShutdownRequest(), id="DragonShutdownRequest"), + pytest.param( + DragonBootstrapRequest(address="localhost"), id="DragonBootstrapRequest" + ), + ], +) +def test_create_run_policy_non_run_request(dragon_request: DragonRequest) -> None: + """Verify that a default policy is returned when a request is + not attempting to start a new proccess (e.g. a DragonRunRequest)""" + policy = DragonBackend.create_run_policy(dragon_request, "localhost") + + assert policy is not None, "Default policy was not returned" + assert ( + policy.device == Policy.Device.DEFAULT + ), "Default device was not Device.DEFAULT" + assert policy.cpu_affinity == [], "Default cpu affinity was not empty" + assert policy.gpu_affinity == [], "Default gpu affinity was not empty" + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +def test_create_run_policy_run_request_no_run_policy() -> None: + """Verify that a policy specifying no policy is returned with all default + values (no device, empty cpu & gpu affinity)""" + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + # policy= # <--- skipping this + ) + + policy = DragonBackend.create_run_policy(run_req, "localhost") + + assert policy.device == Policy.Device.DEFAULT + assert set(policy.cpu_affinity) == set() + assert policy.gpu_affinity == [] + assert policy.affinity == Policy.Affinity.DEFAULT + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +def test_create_run_policy_run_request_default_run_policy() -> None: + """Verify that a policy specifying no affinity is returned with + default value for device and empty affinity lists""" + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=DragonRunPolicy(), # <--- passing default values + ) + + policy = DragonBackend.create_run_policy(run_req, "localhost") + + assert set(policy.cpu_affinity) == set() + assert set(policy.gpu_affinity) == set() + assert policy.affinity == Policy.Affinity.DEFAULT + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +def test_create_run_policy_run_request_cpu_affinity_no_device() -> None: + """Verify that a input policy specifying a CPU affinity but lacking the device field + produces a Dragon Policy with the CPU device specified""" + affinity = set([0, 2, 4]) + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=DragonRunPolicy(cpu_affinity=list(affinity)), # <-- no device spec + ) + + policy = DragonBackend.create_run_policy(run_req, "localhost") + + assert set(policy.cpu_affinity) == affinity + assert policy.gpu_affinity == [] + assert policy.affinity == Policy.Affinity.SPECIFIC + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +def test_create_run_policy_run_request_cpu_affinity() -> None: + """Verify that a policy specifying CPU affinity is returned as expected""" + affinity = set([0, 2, 4]) + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=DragonRunPolicy(cpu_affinity=list(affinity)), + ) + + policy = DragonBackend.create_run_policy(run_req, "localhost") + + assert set(policy.cpu_affinity) == affinity + assert policy.gpu_affinity == [] + assert policy.affinity == Policy.Affinity.SPECIFIC + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +def test_create_run_policy_run_request_gpu_affinity() -> None: + """Verify that a policy specifying GPU affinity is returned as expected""" + affinity = set([0, 2, 4]) + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=DragonRunPolicy(device="gpu", gpu_affinity=list(affinity)), + ) + + policy = DragonBackend.create_run_policy(run_req, "localhost") + + assert policy.cpu_affinity == [] + assert set(policy.gpu_affinity) == set(affinity) + assert policy.affinity == Policy.Affinity.SPECIFIC + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +def test_dragon_run_policy_from_run_args() -> None: + """Verify that a DragonRunPolicy is created from a dictionary of run arguments""" + run_args = { + "gpu-affinity": "0,1,2", + "cpu-affinity": "3,4,5,6", + } + + policy = DragonRunPolicy.from_run_args(run_args) + + assert policy.cpu_affinity == [3, 4, 5, 6] + assert policy.gpu_affinity == [0, 1, 2] + + +def test_dragon_run_policy_from_run_args_empty() -> None: + """Verify that a DragonRunPolicy is created from an empty + dictionary of run arguments""" + run_args = {} + + policy = DragonRunPolicy.from_run_args(run_args) + + assert policy.cpu_affinity == [] + assert policy.gpu_affinity == [] + + +def test_dragon_run_policy_from_run_args_cpu_affinity() -> None: + """Verify that a DragonRunPolicy is created from a dictionary + of run arguments containing a CPU affinity""" + run_args = { + "cpu-affinity": "3,4,5,6", + } + + policy = DragonRunPolicy.from_run_args(run_args) + + assert policy.cpu_affinity == [3, 4, 5, 6] + assert policy.gpu_affinity == [] + + +def test_dragon_run_policy_from_run_args_gpu_affinity() -> None: + """Verify that a DragonRunPolicy is created from a dictionary + of run arguments containing a GPU affinity""" + run_args = { + "gpu-affinity": "0, 1, 2", + } + + policy = DragonRunPolicy.from_run_args(run_args) + + assert policy.cpu_affinity == [] + assert policy.gpu_affinity == [0, 1, 2] + + +def test_dragon_run_policy_from_run_args_invalid_gpu_affinity() -> None: + """Verify that a DragonRunPolicy is NOT created from a dictionary + of run arguments with an invalid GPU affinity""" + run_args = { + "gpu-affinity": "0,-1,2", + } + + with pytest.raises(SmartSimError) as ex: + DragonRunPolicy.from_run_args(run_args) + + assert "DragonRunPolicy" in ex.value.args[0] + + +def test_dragon_run_policy_from_run_args_invalid_cpu_affinity() -> None: + """Verify that a DragonRunPolicy is NOT created from a dictionary + of run arguments with an invalid CPU affinity""" + run_args = { + "cpu-affinity": "3,4,5,-6", + } + + with pytest.raises(SmartSimError) as ex: + DragonRunPolicy.from_run_args(run_args) + + assert "DragonRunPolicy" in ex.value.args[0] + + +def test_dragon_run_policy_from_run_args_ignore_empties_gpu() -> None: + """Verify that a DragonRunPolicy is created from a dictionary + of run arguments and ignores empty values in the serialized gpu list""" + run_args = { + "gpu-affinity": "0,,2", + } + + policy = DragonRunPolicy.from_run_args(run_args) + + assert policy.cpu_affinity == [] + assert policy.gpu_affinity == [0, 2] + + +def test_dragon_run_policy_from_run_args_ignore_empties_cpu() -> None: + """Verify that a DragonRunPolicy is created from a dictionary + of run arguments and ignores empty values in the serialized cpu list""" + run_args = { + "cpu-affinity": "3,4,,6,", + } + + policy = DragonRunPolicy.from_run_args(run_args) + + assert policy.cpu_affinity == [3, 4, 6] + assert policy.gpu_affinity == [] + + +def test_dragon_run_policy_from_run_args_null_gpu_affinity() -> None: + """Verify that a DragonRunPolicy is created if a null value is encountered + in the gpu-affinity list""" + run_args = { + "gpu-affinity": None, + "cpu-affinity": "3,4,5,6", + } + + policy = DragonRunPolicy.from_run_args(run_args) + + assert policy.cpu_affinity == [3, 4, 5, 6] + assert policy.gpu_affinity == [] + + +def test_dragon_run_policy_from_run_args_null_cpu_affinity() -> None: + """Verify that a DragonRunPolicy is created if a null value is encountered + in the cpu-affinity list""" + run_args = {"gpu-affinity": "0,1,2", "cpu-affinity": None} + + policy = DragonRunPolicy.from_run_args(run_args) + + assert policy.cpu_affinity == [] + assert policy.gpu_affinity == [0, 1, 2] diff --git a/tests/test_dragon_backend.py b/tests/test_dragon_run_request.py similarity index 64% rename from tests/test_dragon_backend.py rename to tests/test_dragon_run_request.py index a510f660a..7514deab1 100644 --- a/tests/test_dragon_backend.py +++ b/tests/test_dragon_run_request.py @@ -31,19 +31,17 @@ from unittest.mock import MagicMock import pytest +from pydantic import ValidationError # The tests in this file belong to the group_b group -pytestmark = pytest.mark.group_a +pytestmark = pytest.mark.group_b try: import dragon -except ImportError: - pass -else: - pytest.skip( - reason="Using dragon as launcher, not running Dragon unit tests", - allow_module_level=True, - ) + + dragon_loaded = True +except: + dragon_loaded = False from smartsim._core.config import CONFIG from smartsim._core.schemas.dragonRequests import * @@ -59,10 +57,36 @@ class NodeMock(MagicMock): + def __init__( + self, name: t.Optional[str] = None, num_gpus: int = 2, num_cpus: int = 8 + ) -> None: + super().__init__() + self._mock_id = name + NodeMock._num_gpus = num_gpus + NodeMock._num_cpus = num_cpus + @property def hostname(self) -> str: + if self._mock_id: + return self._mock_id return create_short_id_str() + @property + def num_cpus(self) -> str: + return NodeMock._num_cpus + + @property + def num_gpus(self) -> str: + return NodeMock._num_gpus + + def _set_id(self, value: str) -> None: + self._mock_id = value + + def gpus(self, parent: t.Any = None) -> t.List[str]: + if self._num_gpus: + return [f"{self.hostname}-gpu{i}" for i in range(NodeMock._num_gpus)] + return [] + class GroupStateMock(MagicMock): def Running(self) -> MagicMock: @@ -78,13 +102,19 @@ class ProcessGroupMock(MagicMock): puids = [121, 122] -def get_mock_backend(monkeypatch: pytest.MonkeyPatch) -> "DragonBackend": +def node_mock() -> NodeMock: + return NodeMock() + + +def get_mock_backend( + monkeypatch: pytest.MonkeyPatch, num_gpus: int = 2 +) -> "DragonBackend": process_mock = MagicMock(returncode=0) process_group_mock = MagicMock(**{"Process.return_value": ProcessGroupMock()}) process_module_mock = MagicMock() process_module_mock.Process = process_mock - node_mock = NodeMock() + node_mock = NodeMock(num_gpus=num_gpus) system_mock = MagicMock(nodes=["node1", "node2", "node3"]) monkeypatch.setitem( sys.modules, @@ -189,6 +219,7 @@ def set_mock_group_infos( return group_infos +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") def test_handshake_request(monkeypatch: pytest.MonkeyPatch) -> None: dragon_backend = get_mock_backend(monkeypatch) @@ -199,6 +230,7 @@ def test_handshake_request(monkeypatch: pytest.MonkeyPatch) -> None: assert handshake_resp.dragon_pid == 99999 +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") def test_run_request(monkeypatch: pytest.MonkeyPatch) -> None: dragon_backend = get_mock_backend(monkeypatch) run_req = DragonRunRequest( @@ -249,6 +281,7 @@ def test_run_request(monkeypatch: pytest.MonkeyPatch) -> None: assert not dragon_backend._running_steps +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") def test_deny_run_request(monkeypatch: pytest.MonkeyPatch) -> None: dragon_backend = get_mock_backend(monkeypatch) @@ -274,6 +307,78 @@ def test_deny_run_request(monkeypatch: pytest.MonkeyPatch) -> None: assert dragon_backend.group_infos[step_id].status == SmartSimStatus.STATUS_FAILED +def test_run_request_with_empty_policy(monkeypatch: pytest.MonkeyPatch) -> None: + """Verify that a policy is applied to a run request""" + dragon_backend = get_mock_backend(monkeypatch) + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=None, + ) + assert run_req.policy is None + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +def test_run_request_with_policy(monkeypatch: pytest.MonkeyPatch) -> None: + """Verify that a policy is applied to a run request""" + dragon_backend = get_mock_backend(monkeypatch) + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=DragonRunPolicy(cpu_affinity=[0, 1]), + ) + + run_resp = dragon_backend.process_request(run_req) + assert isinstance(run_resp, DragonRunResponse) + + step_id = run_resp.step_id + assert dragon_backend._queued_steps[step_id] == run_req + + mock_process_group = MagicMock(puids=[123, 124]) + + dragon_backend._group_infos[step_id].process_group = mock_process_group + dragon_backend._group_infos[step_id].puids = [123, 124] + dragon_backend._start_steps() + + assert dragon_backend._running_steps == [step_id] + assert len(dragon_backend._queued_steps) == 0 + assert len(dragon_backend._free_hosts) == 1 + assert dragon_backend._allocated_hosts[dragon_backend.hosts[0]] == step_id + assert dragon_backend._allocated_hosts[dragon_backend.hosts[1]] == step_id + + monkeypatch.setattr( + dragon_backend._group_infos[step_id].process_group, "status", "Running" + ) + + dragon_backend._update() + + assert dragon_backend._running_steps == [step_id] + assert len(dragon_backend._queued_steps) == 0 + assert len(dragon_backend._free_hosts) == 1 + assert dragon_backend._allocated_hosts[dragon_backend.hosts[0]] == step_id + assert dragon_backend._allocated_hosts[dragon_backend.hosts[1]] == step_id + + dragon_backend._group_infos[step_id].status = SmartSimStatus.STATUS_CANCELLED + + dragon_backend._update() + assert not dragon_backend._running_steps + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") def test_udpate_status_request(monkeypatch: pytest.MonkeyPatch) -> None: dragon_backend = get_mock_backend(monkeypatch) @@ -290,6 +395,7 @@ def test_udpate_status_request(monkeypatch: pytest.MonkeyPatch) -> None: } +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") def test_stop_request(monkeypatch: pytest.MonkeyPatch) -> None: dragon_backend = get_mock_backend(monkeypatch) group_infos = set_mock_group_infos(monkeypatch, dragon_backend) @@ -321,6 +427,7 @@ def test_stop_request(monkeypatch: pytest.MonkeyPatch) -> None: assert len(dragon_backend._free_hosts) == 3 +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") @pytest.mark.parametrize( "immediate, kill_jobs, frontend_shutdown", [ @@ -379,6 +486,7 @@ def test_shutdown_request( assert dragon_backend._has_cooled_down == kill_jobs +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") @pytest.mark.parametrize("telemetry_flag", ["0", "1"]) def test_cooldown_is_set(monkeypatch: pytest.MonkeyPatch, telemetry_flag: str) -> None: monkeypatch.setenv("SMARTSIM_FLAG_TELEMETRY", telemetry_flag) @@ -394,6 +502,7 @@ def test_cooldown_is_set(monkeypatch: pytest.MonkeyPatch, telemetry_flag: str) - assert dragon_backend.cooldown_period == expected_cooldown +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") def test_heartbeat_and_time(monkeypatch: pytest.MonkeyPatch) -> None: dragon_backend = get_mock_backend(monkeypatch) first_heartbeat = dragon_backend.last_heartbeat @@ -402,6 +511,7 @@ def test_heartbeat_and_time(monkeypatch: pytest.MonkeyPatch) -> None: assert dragon_backend.last_heartbeat > first_heartbeat +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") @pytest.mark.parametrize("num_nodes", [1, 3, 100]) def test_can_honor(monkeypatch: pytest.MonkeyPatch, num_nodes: int) -> None: dragon_backend = get_mock_backend(monkeypatch) @@ -422,6 +532,119 @@ def test_can_honor(monkeypatch: pytest.MonkeyPatch, num_nodes: int) -> None: ) +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +@pytest.mark.parametrize("affinity", [[0], [0, 1], list(range(8))]) +def test_can_honor_cpu_affinity( + monkeypatch: pytest.MonkeyPatch, affinity: t.List[int] +) -> None: + """Verify that valid CPU affinities are accepted""" + dragon_backend = get_mock_backend(monkeypatch) + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=DragonRunPolicy(cpu_affinity=affinity), + ) + + assert dragon_backend._can_honor(run_req)[0] + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +def test_can_honor_cpu_affinity_out_of_range(monkeypatch: pytest.MonkeyPatch) -> None: + """Verify that invalid CPU affinities are NOT accepted + NOTE: negative values are captured by the Pydantic schema""" + dragon_backend = get_mock_backend(monkeypatch) + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=DragonRunPolicy(cpu_affinity=list(range(9))), + ) + + assert not dragon_backend._can_honor(run_req)[0] + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +@pytest.mark.parametrize("affinity", [[0], [0, 1]]) +def test_can_honor_gpu_affinity( + monkeypatch: pytest.MonkeyPatch, affinity: t.List[int] +) -> None: + """Verify that valid GPU affinities are accepted""" + dragon_backend = get_mock_backend(monkeypatch) + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=DragonRunPolicy(gpu_affinity=affinity), + ) + + assert dragon_backend._can_honor(run_req)[0] + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +def test_can_honor_gpu_affinity_out_of_range(monkeypatch: pytest.MonkeyPatch) -> None: + """Verify that invalid GPU affinities are NOT accepted + NOTE: negative values are captured by the Pydantic schema""" + dragon_backend = get_mock_backend(monkeypatch) + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=DragonRunPolicy(gpu_affinity=list(range(3))), + ) + + assert not dragon_backend._can_honor(run_req)[0] + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") +def test_can_honor_gpu_device_not_available(monkeypatch: pytest.MonkeyPatch) -> None: + """Verify that a request for a GPU if none exists is not accepted""" + + # create a mock node class that always reports no GPUs available + dragon_backend = get_mock_backend(monkeypatch, num_gpus=0) + + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + # specify GPU device w/no affinity + policy=DragonRunPolicy(gpu_affinity=[0]), + ) + + assert not dragon_backend._can_honor(run_req)[0] + + +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") def test_get_id(monkeypatch: pytest.MonkeyPatch) -> None: dragon_backend = get_mock_backend(monkeypatch) step_id = next(dragon_backend._step_ids) @@ -430,6 +653,7 @@ def test_get_id(monkeypatch: pytest.MonkeyPatch) -> None: assert step_id != next(dragon_backend._step_ids) +@pytest.mark.skipif(not dragon_loaded, reason="Test is only for Dragon WLM systems") def test_view(monkeypatch: pytest.MonkeyPatch) -> None: dragon_backend = get_mock_backend(monkeypatch) set_mock_group_infos(monkeypatch, dragon_backend) @@ -437,17 +661,21 @@ def test_view(monkeypatch: pytest.MonkeyPatch) -> None: expected_message = textwrap.dedent(f"""\ Dragon server backend update - | Host | Status | - |---------|----------| + | Host | Status | + |--------|----------| | {hosts[0]} | Busy | | {hosts[1]} | Free | | {hosts[2]} | Free | | Step | Status | Hosts | Return codes | Num procs | - |----------|--------------|-----------------|----------------|-------------| + |----------|--------------|-------------|----------------|-------------| | abc123-1 | Running | {hosts[0]} | | 1 | | del999-2 | Cancelled | {hosts[1]} | -9 | 1 | | c101vz-3 | Completed | {hosts[1]},{hosts[2]} | 0 | 2 | | 0ghjk1-4 | Failed | {hosts[2]} | -1 | 1 | | ljace0-5 | NeverStarted | | | 0 |""") - assert dragon_backend.status_message == expected_message + # get rid of white space to make the comparison easier + actual_msg = dragon_backend.status_message.replace(" ", "") + expected_message = expected_message.replace(" ", "") + + assert actual_msg == expected_message diff --git a/tests/test_dragon_run_request_nowlm.py b/tests/test_dragon_run_request_nowlm.py new file mode 100644 index 000000000..3dd7099c8 --- /dev/null +++ b/tests/test_dragon_run_request_nowlm.py @@ -0,0 +1,105 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import pytest +from pydantic import ValidationError + +# The tests in this file belong to the group_a group +pytestmark = pytest.mark.group_a + +from smartsim._core.schemas.dragonRequests import * +from smartsim._core.schemas.dragonResponses import * + + +def test_run_request_with_null_policy(monkeypatch: pytest.MonkeyPatch) -> None: + """Verify that an empty policy does not cause an error""" + # dragon_backend = get_mock_backend(monkeypatch) + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=None, + ) + assert run_req.policy is None + + +def test_run_request_with_empty_policy(monkeypatch: pytest.MonkeyPatch) -> None: + """Verify that a non-empty policy is set correctly""" + # dragon_backend = get_mock_backend(monkeypatch) + run_req = DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=DragonRunPolicy(), + ) + assert run_req.policy is not None + assert not run_req.policy.cpu_affinity + assert not run_req.policy.gpu_affinity + + +@pytest.mark.parametrize( + "device,cpu_affinity,gpu_affinity", + [ + pytest.param("cpu", [-1], [], id="cpu_affinity"), + pytest.param("gpu", [], [-1], id="gpu_affinity"), + ], +) +def test_run_request_with_negative_affinity( + device: str, + cpu_affinity: t.List[int], + gpu_affinity: t.List[int], +) -> None: + """Verify that invalid affinity values fail validation""" + with pytest.raises(ValidationError) as ex: + DragonRunRequest( + exe="sleep", + exe_args=["5"], + path="/a/fake/path", + nodes=2, + tasks=1, + tasks_per_node=1, + env={}, + current_env={}, + pmi_enabled=False, + policy=DragonRunPolicy( + cpu_affinity=cpu_affinity, gpu_affinity=gpu_affinity + ), + ) + + assert f"{device}_affinity" in str(ex.value) + assert "greater than or equal to 0" in str(ex.value) diff --git a/tests/test_dragon_runsettings.py b/tests/test_dragon_runsettings.py new file mode 100644 index 000000000..34e8510e8 --- /dev/null +++ b/tests/test_dragon_runsettings.py @@ -0,0 +1,98 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import pytest + +from smartsim.settings import DragonRunSettings + +# The tests in this file belong to the group_b group +pytestmark = pytest.mark.group_a + + +def test_dragon_runsettings_nodes(): + """Verify that node count is set correctly""" + rs = DragonRunSettings(exe="sleep", exe_args=["1"]) + + exp_value = 3 + rs.set_nodes(exp_value) + assert rs.run_args["nodes"] == exp_value + + exp_value = 9 + rs.set_nodes(exp_value) + assert rs.run_args["nodes"] == exp_value + + +def test_dragon_runsettings_tasks_per_node(): + """Verify that tasks per node is set correctly""" + rs = DragonRunSettings(exe="sleep", exe_args=["1"]) + + exp_value = 3 + rs.set_tasks_per_node(exp_value) + assert rs.run_args["tasks-per-node"] == exp_value + + exp_value = 7 + rs.set_tasks_per_node(exp_value) + assert rs.run_args["tasks-per-node"] == exp_value + + +def test_dragon_runsettings_cpu_affinity(): + """Verify that the CPU affinity is set correctly""" + rs = DragonRunSettings(exe="sleep", exe_args=["1"]) + + exp_value = [0, 1, 2, 3] + rs.set_cpu_affinity([0, 1, 2, 3]) + assert rs.run_args["cpu-affinity"] == ",".join(str(val) for val in exp_value) + + # ensure the value is not changed when we extend the list + exp_value.extend([4, 5, 6]) + assert rs.run_args["cpu-affinity"] != ",".join(str(val) for val in exp_value) + + rs.set_cpu_affinity(exp_value) + assert rs.run_args["cpu-affinity"] == ",".join(str(val) for val in exp_value) + + # ensure the value is not changed when we extend the list + rs.run_args["cpu-affinity"] = "7,8,9" + assert rs.run_args["cpu-affinity"] != ",".join(str(val) for val in exp_value) + + +def test_dragon_runsettings_gpu_affinity(): + """Verify that the GPU affinity is set correctly""" + rs = DragonRunSettings(exe="sleep", exe_args=["1"]) + + exp_value = [0, 1, 2, 3] + rs.set_gpu_affinity([0, 1, 2, 3]) + assert rs.run_args["gpu-affinity"] == ",".join(str(val) for val in exp_value) + + # ensure the value is not changed when we extend the list + exp_value.extend([4, 5, 6]) + assert rs.run_args["gpu-affinity"] != ",".join(str(val) for val in exp_value) + + rs.set_gpu_affinity(exp_value) + assert rs.run_args["gpu-affinity"] == ",".join(str(val) for val in exp_value) + + # ensure the value is not changed when we extend the list + rs.run_args["gpu-affinity"] = "7,8,9" + assert rs.run_args["gpu-affinity"] != ",".join(str(val) for val in exp_value) diff --git a/tests/test_dragon_step.py b/tests/test_dragon_step.py new file mode 100644 index 000000000..19f408e0b --- /dev/null +++ b/tests/test_dragon_step.py @@ -0,0 +1,394 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import json +import pathlib +import shutil +import sys +import typing as t + +import pytest + +from smartsim._core.launcher.step.dragonStep import DragonBatchStep, DragonStep +from smartsim.settings import DragonRunSettings +from smartsim.settings.pbsSettings import QsubBatchSettings +from smartsim.settings.slurmSettings import SbatchSettings + +# The tests in this file belong to the group_a group +pytestmark = pytest.mark.group_a + + +from smartsim._core.schemas.dragonRequests import * +from smartsim._core.schemas.dragonResponses import * + + +@pytest.fixture +def dragon_batch_step(test_dir: str) -> DragonBatchStep: + """Fixture for creating a default batch of steps for a dragon launcher""" + test_path = pathlib.Path(test_dir) + + batch_step_name = "batch_step" + num_nodes = 4 + batch_settings = SbatchSettings(nodes=num_nodes) + batch_step = DragonBatchStep(batch_step_name, test_dir, batch_settings) + + # ensure the status_dir is set + status_dir = (test_path / ".smartsim" / "logs").as_posix() + batch_step.meta["status_dir"] = status_dir + + # create some steps to verify the requests file output changes + rs0 = DragonRunSettings(exe="sleep", exe_args=["1"]) + rs1 = DragonRunSettings(exe="sleep", exe_args=["2"]) + rs2 = DragonRunSettings(exe="sleep", exe_args=["3"]) + rs3 = DragonRunSettings(exe="sleep", exe_args=["4"]) + + names = "test00", "test01", "test02", "test03" + settings = rs0, rs1, rs2, rs3 + + # create steps with: + # no affinity, cpu affinity only, gpu affinity only, cpu and gpu affinity + cpu_affinities = [[], [0, 1, 2], [], [3, 4, 5, 6]] + gpu_affinities = [[], [], [0, 1, 2], [3, 4, 5, 6]] + + # assign some unique affinities to each run setting instance + for index, rs in enumerate(settings): + if gpu_affinities[index]: + rs.set_node_feature("gpu") + rs.set_cpu_affinity(cpu_affinities[index]) + rs.set_gpu_affinity(gpu_affinities[index]) + + steps = list( + DragonStep(name_, test_dir, rs_) for name_, rs_ in zip(names, settings) + ) + + for index, step in enumerate(steps): + # ensure meta is configured... + step.meta["status_dir"] = status_dir + # ... and put all the steps into the batch + batch_step.add_to_batch(steps[index]) + + return batch_step + + +def get_request_path_from_batch_script(launch_cmd: t.List[str]) -> pathlib.Path: + """Helper method for finding the path to a request file from the launch command""" + script_path = pathlib.Path(launch_cmd[-1]) + batch_script = script_path.read_text(encoding="utf-8") + batch_statements = [line for line in batch_script.split("\n") if line] + entrypoint_cmd = batch_statements[-1] + requests_file = pathlib.Path(entrypoint_cmd.split()[-1]) + return requests_file + + +def test_dragon_step_creation(test_dir: str) -> None: + """Verify that the step is created with the values provided""" + rs = DragonRunSettings(exe="sleep", exe_args=["1"]) + + original_name = "test" + step = DragonStep(original_name, test_dir, rs) + + # confirm the name has been made unique to avoid conflicts + assert step.name != original_name + assert step.entity_name == original_name + assert step.cwd == test_dir + assert step.step_settings is not None + + +def test_dragon_step_name_uniqueness(test_dir: str) -> None: + """Verify that step name is unique and independent of step content""" + + rs = DragonRunSettings(exe="sleep", exe_args=["1"]) + + original_name = "test" + + num_steps = 100 + steps = [DragonStep(original_name, test_dir, rs) for _ in range(num_steps)] + + # confirm the name has been made unique in each step + step_names = {step.name for step in steps} + assert len(step_names) == num_steps + + +def test_dragon_step_launch_cmd(test_dir: str) -> None: + """Verify the expected launch cmd is generated w/minimal settings""" + exp_exe = "sleep" + exp_exe_args = "1" + rs = DragonRunSettings(exe=exp_exe, exe_args=[exp_exe_args]) + + original_name = "test" + step = DragonStep(original_name, test_dir, rs) + + launch_cmd = step.get_launch_cmd() + assert len(launch_cmd) == 2 + + # we'll verify the exe_args and exe name are handled correctly + exe, args = launch_cmd + assert exp_exe in exe + assert exp_exe_args in args + + # also, verify that a string exe_args param instead of list is handled correctly + exp_exe_args = "1 2 3" + rs = DragonRunSettings(exe=exp_exe, exe_args=exp_exe_args) + step = DragonStep(original_name, test_dir, rs) + launch_cmd = step.get_launch_cmd() + assert len(launch_cmd) == 4 # "/foo/bar/sleep 1 2 3" + + +def test_dragon_step_launch_cmd_multi_arg(test_dir: str) -> None: + """Verify the expected launch cmd is generated when multiple arguments + are passed to run settings""" + exp_exe = "sleep" + arg0, arg1, arg2 = "1", "2", "3" + rs = DragonRunSettings(exe=exp_exe, exe_args=[arg0, arg1, arg2]) + + original_name = "test" + + step = DragonStep(original_name, test_dir, rs) + + launch_cmd = step.get_launch_cmd() + assert len(launch_cmd) == 4 + + exe, *args = launch_cmd + assert exp_exe in exe + assert arg0 in args + assert arg1 in args + assert arg2 in args + + +def test_dragon_step_launch_cmd_no_bash( + test_dir: str, monkeypatch: pytest.MonkeyPatch +) -> None: + """Verify that requirement for bash shell is checked""" + exp_exe = "sleep" + arg0, arg1, arg2 = "1", "2", "3" + rs = DragonRunSettings(exe=exp_exe, exe_args=[arg0, arg1, arg2]) + rs.colocated_db_settings = {"foo": "bar"} # triggers bash lookup + + original_name = "test" + step = DragonStep(original_name, test_dir, rs) + + with pytest.raises(RuntimeError) as ex, monkeypatch.context() as ctx: + ctx.setattr(shutil, "which", lambda _: None) + step.get_launch_cmd() + + # verify the exception thrown is the one we're looking for + assert "Could not find" in ex.value.args[0] + + +def test_dragon_step_colocated_db() -> None: + # todo: implement a test for the branch where bash is found and + # run_settings.colocated_db_settings is set + ... + + +def test_dragon_step_container() -> None: + # todo: implement a test for the branch where run_settings.container + # is an instance of class `Singularity` + ... + + +def test_dragon_step_run_settings_accessor(test_dir: str) -> None: + """Verify the run settings passed to the step are copied correctly and + are not inadvertently modified outside the step""" + exp_exe = "sleep" + arg0, arg1, arg2 = "1", "2", "3" + rs = DragonRunSettings(exe=exp_exe, exe_args=[arg0, arg1, arg2]) + + original_name = "test" + step = DragonStep(original_name, test_dir, rs) + rs_output = step.run_settings + + assert rs.exe == rs_output.exe + assert rs.exe_args == rs_output.exe_args + + # ensure we have a deep copy + rs.exe = "foo" + assert id(step.run_settings) != id(rs) + assert step.run_settings.exe != rs.exe + + +def test_dragon_batch_step_creation(test_dir: str) -> None: + """Verify that the batch step is created with the values provided""" + batch_step_name = "batch_step" + num_nodes = 4 + batch_settings = SbatchSettings(nodes=num_nodes) + batch_step = DragonBatchStep(batch_step_name, test_dir, batch_settings) + + # confirm the name has been made unique to avoid conflicts + assert batch_step.name != batch_step_name + assert batch_step.entity_name == batch_step_name + assert batch_step.cwd == test_dir + assert batch_step.batch_settings is not None + assert batch_step.managed + + +def test_dragon_batch_step_add_to_batch(test_dir: str) -> None: + """Verify that steps are added to the batch correctly""" + rs = DragonRunSettings(exe="sleep", exe_args=["1"]) + + name0, name1, name2 = "test00", "test01", "test02" + step0 = DragonStep(name0, test_dir, rs) + step1 = DragonStep(name1, test_dir, rs) + step2 = DragonStep(name2, test_dir, rs) + + batch_step_name = "batch_step" + num_nodes = 4 + batch_settings = SbatchSettings(nodes=num_nodes) + batch_step = DragonBatchStep(batch_step_name, test_dir, batch_settings) + + assert len(batch_step.steps) == 0 + + batch_step.add_to_batch(step0) + assert len(batch_step.steps) == 1 + assert name0 in ",".join({step.name for step in batch_step.steps}) + + batch_step.add_to_batch(step1) + assert len(batch_step.steps) == 2 + assert name1 in ",".join({step.name for step in batch_step.steps}) + + batch_step.add_to_batch(step2) + assert len(batch_step.steps) == 3 + assert name2 in ",".join({step.name for step in batch_step.steps}) + + +def test_dragon_batch_step_get_launch_command_meta_fail(test_dir: str) -> None: + """Verify that the batch launch command cannot be generated without + having the status directory set in the step metadata""" + batch_step_name = "batch_step" + num_nodes = 4 + batch_settings = SbatchSettings(nodes=num_nodes) + batch_step = DragonBatchStep(batch_step_name, test_dir, batch_settings) + + with pytest.raises(KeyError) as ex: + batch_step.get_launch_cmd() + + +@pytest.mark.parametrize( + "batch_settings_class,batch_exe,batch_header,node_spec_tpl", + [ + pytest.param( + SbatchSettings, "sbatch", "#SBATCH", "#SBATCH --nodes={0}", id="sbatch" + ), + pytest.param(QsubBatchSettings, "qsub", "#PBS", "#PBS -l nodes={0}", id="qsub"), + ], +) +def test_dragon_batch_step_get_launch_command( + test_dir: str, + batch_settings_class: t.Type, + batch_exe: str, + batch_header: str, + node_spec_tpl: str, +) -> None: + """Verify that the batch launch command is properly generated and + the expected side effects are present (writing script file to disk)""" + test_path = pathlib.Path(test_dir) + + batch_step_name = "batch_step" + num_nodes = 4 + batch_settings = batch_settings_class(nodes=num_nodes) + batch_step = DragonBatchStep(batch_step_name, test_dir, batch_settings) + + # ensure the status_dir is set + status_dir = (test_path / ".smartsim" / "logs").as_posix() + batch_step.meta["status_dir"] = status_dir + + launch_cmd = batch_step.get_launch_cmd() + assert launch_cmd + + full_cmd = " ".join(launch_cmd) + assert batch_exe in full_cmd # verify launcher running the batch + assert test_dir in full_cmd # verify outputs are sent to expected directory + assert "batch_step.sh" in full_cmd # verify batch script name is in the command + + # ...verify that the script file is written when getting the launch command + script_path = pathlib.Path(launch_cmd[-1]) + assert script_path.exists() + assert len(script_path.read_bytes()) > 0 + + batch_script = script_path.read_text(encoding="utf-8") + + # ...verify the script file has the expected batch script header content + assert batch_header in batch_script + assert node_spec_tpl.format(num_nodes) in batch_script # verify node count is set + + # ...verify the script has the expected entrypoint command + batch_statements = [line for line in batch_script.split("\n") if line] + python_path = sys.executable + + entrypoint_cmd = batch_statements[-1] + assert python_path in entrypoint_cmd + assert "smartsim._core.entrypoints.dragon_client +submit" in entrypoint_cmd + + +def test_dragon_batch_step_write_request_file_no_steps(test_dir: str) -> None: + """Verify that the batch launch command writes an appropriate request file + if no steps are attached""" + test_path = pathlib.Path(test_dir) + + batch_step_name = "batch_step" + num_nodes = 4 + batch_settings = SbatchSettings(nodes=num_nodes) + batch_step = DragonBatchStep(batch_step_name, test_dir, batch_settings) + + # ensure the status_dir is set + status_dir = (test_path / ".smartsim" / "logs").as_posix() + batch_step.meta["status_dir"] = status_dir + + launch_cmd = batch_step.get_launch_cmd() + requests_file = get_request_path_from_batch_script(launch_cmd) + + # no steps have been added yet, so the requests file should be a serialized, empty list + assert requests_file.read_text(encoding="utf-8") == "[]" + + +def test_dragon_batch_step_write_request_file( + dragon_batch_step: DragonBatchStep, +) -> None: + """Verify that the batch launch command writes an appropriate request file + for the set of attached steps""" + # create steps with: + # no affinity, cpu affinity only, gpu affinity only, cpu and gpu affinity + cpu_affinities = [[], [0, 1, 2], [], [3, 4, 5, 6]] + gpu_affinities = [[], [], [0, 1, 2], [3, 4, 5, 6]] + + launch_cmd = dragon_batch_step.get_launch_cmd() + requests_file = get_request_path_from_batch_script(launch_cmd) + + requests_text = requests_file.read_text(encoding="utf-8") + requests_json: t.List[str] = json.loads(requests_text) + + # verify that there is an item in file for each step added to the batch + assert len(requests_json) == len(dragon_batch_step.steps) + + for index, req in enumerate(requests_json): + req_type, req_data = req.split("|", 1) + # the only steps added are to execute apps, requests should be of type "run" + assert req_type == "run" + + run_request = DragonRunRequest(**json.loads(req_data)) + assert run_request + assert run_request.policy.cpu_affinity == cpu_affinities[index] + assert run_request.policy.gpu_affinity == gpu_affinities[index] diff --git a/tests/test_manifest.py b/tests/test_manifest.py index c26868ebb..f4a1b0afb 100644 --- a/tests/test_manifest.py +++ b/tests/test_manifest.py @@ -26,6 +26,7 @@ import os.path +import typing as t from copy import deepcopy from uuid import uuid4 @@ -40,7 +41,9 @@ from smartsim._core.control.manifest import ( _LaunchedManifestMetadata as LaunchedManifestMetadata, ) +from smartsim._core.launcher.step import Step from smartsim.database import Orchestrator +from smartsim.entity import Ensemble, Model from smartsim.entity.dbobject import DBModel, DBScript from smartsim.error import SmartSimError from smartsim.settings import RunSettings @@ -51,22 +54,33 @@ # ---- create entities for testing -------- -rs = RunSettings("python", "sleep.py") +_EntityResult = t.Tuple[ + Experiment, t.Tuple[Model, Model], Ensemble, Orchestrator, DBModel, DBScript +] -exp = Experiment("util-test", launcher="local") -model = exp.create_model("model_1", run_settings=rs) -model_2 = exp.create_model("model_1", run_settings=rs) -ensemble = exp.create_ensemble("ensemble", run_settings=rs, replicas=1) -orc = Orchestrator() -orc_1 = deepcopy(orc) -orc_1.name = "orc2" +@pytest.fixture +def entities(test_dir: str) -> _EntityResult: + rs = RunSettings("python", "sleep.py") -db_script = DBScript("some-script", "def main():\n print('hello world')\n") -db_model = DBModel("some-model", "TORCH", b"some-model-bytes") + exp = Experiment("util-test", launcher="local", exp_path=test_dir) + model = exp.create_model("model_1", run_settings=rs) + model_2 = exp.create_model("model_1", run_settings=rs) + ensemble = exp.create_ensemble("ensemble", run_settings=rs, replicas=1) + orc = Orchestrator() + orc_1 = deepcopy(orc) + orc_1.name = "orc2" + + db_script = DBScript("some-script", "def main():\n print('hello world')\n") + db_model = DBModel("some-model", "TORCH", b"some-model-bytes") + + return exp, (model, model_2), ensemble, orc, db_model, db_script + + +def test_separate(entities: _EntityResult) -> None: + _, (model, _), ensemble, orc, _, _ = entities -def test_separate(): manifest = Manifest(model, ensemble, orc) assert manifest.models[0] == model assert len(manifest.models) == 1 @@ -75,24 +89,28 @@ def test_separate(): assert manifest.dbs[0] == orc -def test_separate_type(): +def test_separate_type() -> None: with pytest.raises(TypeError): - _ = Manifest([1, 2, 3]) + _ = Manifest([1, 2, 3]) # type: ignore -def test_name_collision(): +def test_name_collision(entities: _EntityResult) -> None: + _, (model, model_2), _, _, _, _ = entities + with pytest.raises(SmartSimError): _ = Manifest(model, model_2) -def test_catch_empty_ensemble(): +def test_catch_empty_ensemble(entities: _EntityResult) -> None: + _, _, ensemble, _, _, _ = entities + e = deepcopy(ensemble) e.entities = [] with pytest.raises(ValueError): _ = Manifest(e) -def test_corner_case(): +def test_corner_case() -> None: """tricky corner case where some variable may have a name attribute """ @@ -102,59 +120,77 @@ class Person: p = Person() with pytest.raises(TypeError): - _ = Manifest(p) + _ = Manifest(p) # type: ignore @pytest.mark.parametrize( - "patch, has_db_objects", + "target_obj, target_prop, target_value, has_db_objects", [ - pytest.param((), False, id="No DB Objects"), - pytest.param((model, "_db_models", [db_model]), True, id="Model w/ DB Model"), - pytest.param( - (model, "_db_scripts", [db_script]), True, id="Model w/ DB Script" - ), - pytest.param( - (ensemble, "_db_models", [db_model]), True, id="Ensemble w/ DB Model" - ), - pytest.param( - (ensemble, "_db_scripts", [db_script]), True, id="Ensemble w/ DB Script" - ), - pytest.param( - (ensemble.entities[0], "_db_models", [db_model]), - True, - id="Ensemble Member w/ DB Model", - ), - pytest.param( - (ensemble.entities[0], "_db_scripts", [db_script]), - True, - id="Ensemble Member w/ DB Script", - ), + pytest.param(None, None, None, False, id="No DB Objects"), + pytest.param("m0", "dbm", "dbm", True, id="Model w/ DB Model"), + pytest.param("m0", "dbs", "dbs", True, id="Model w/ DB Script"), + pytest.param("ens", "dbm", "dbm", True, id="Ensemble w/ DB Model"), + pytest.param("ens", "dbs", "dbs", True, id="Ensemble w/ DB Script"), + pytest.param("ens_0", "dbm", "dbm", True, id="Ensemble Member w/ DB Model"), + pytest.param("ens_0", "dbs", "dbs", True, id="Ensemble Member w/ DB Script"), ], ) -def test_manifest_detects_db_objects(monkeypatch, patch, has_db_objects): - if patch: +def test_manifest_detects_db_objects( + monkeypatch: pytest.MonkeyPatch, + target_obj: str, + target_prop: str, + target_value: str, + has_db_objects: bool, + entities: _EntityResult, +) -> None: + _, (model, _), ensemble, _, db_model, db_script = entities + target_map = { + "m0": model, + "dbm": db_model, + "dbs": db_script, + "ens": ensemble, + "ens_0": ensemble.entities[0], + } + prop_map = { + "dbm": "_db_models", + "dbs": "_db_scripts", + } + if target_obj: + patch = ( + target_map[target_obj], + prop_map[target_prop], + [target_map[target_value]], + ) monkeypatch.setattr(*patch) + assert Manifest(model, ensemble).has_db_objects == has_db_objects -def test_launched_manifest_transform_data(): +def test_launched_manifest_transform_data(entities: _EntityResult) -> None: + _, (model, model_2), ensemble, orc, _, _ = entities + models = [(model, 1), (model_2, 2)] ensembles = [(ensemble, [(m, i) for i, m in enumerate(ensemble.entities)])] dbs = [(orc, [(n, i) for i, n in enumerate(orc.entities)])] - launched = LaunchedManifest( + lmb = LaunchedManifest( metadata=LaunchedManifestMetadata("name", "path", "launcher", "run_id"), - models=models, - ensembles=ensembles, - databases=dbs, + models=models, # type: ignore + ensembles=ensembles, # type: ignore + databases=dbs, # type: ignore ) - transformed = launched.map(lambda x: str(x)) + transformed = lmb.map(lambda x: str(x)) + assert transformed.models == tuple((m, str(i)) for m, i in models) assert transformed.ensembles[0][1] == tuple((m, str(i)) for m, i in ensembles[0][1]) assert transformed.databases[0][1] == tuple((n, str(i)) for n, i in dbs[0][1]) -def test_launched_manifest_builder_correctly_maps_data(): - lmb = LaunchedManifestBuilder("name", "path", "launcher name", str(uuid4())) +def test_launched_manifest_builder_correctly_maps_data(entities: _EntityResult) -> None: + _, (model, model_2), ensemble, orc, _, _ = entities + + lmb = LaunchedManifestBuilder( + "name", "path", "launcher name", str(uuid4()) + ) # type: ignore lmb.add_model(model, 1) lmb.add_model(model_2, 1) lmb.add_ensemble(ensemble, [i for i in range(len(ensemble.entities))]) @@ -166,8 +202,14 @@ def test_launched_manifest_builder_correctly_maps_data(): assert len(manifest.databases) == 1 -def test_launced_manifest_builder_raises_if_lens_do_not_match(): - lmb = LaunchedManifestBuilder("name", "path", "launcher name", str(uuid4())) +def test_launced_manifest_builder_raises_if_lens_do_not_match( + entities: _EntityResult, +) -> None: + _, _, ensemble, orc, _, _ = entities + + lmb = LaunchedManifestBuilder( + "name", "path", "launcher name", str(uuid4()) + ) # type: ignore with pytest.raises(ValueError): lmb.add_ensemble(ensemble, list(range(123))) with pytest.raises(ValueError): @@ -175,17 +217,23 @@ def test_launced_manifest_builder_raises_if_lens_do_not_match(): def test_launched_manifest_builer_raises_if_attaching_data_to_empty_collection( - monkeypatch, -): - lmb = LaunchedManifestBuilder("name", "path", "launcher", str(uuid4())) + monkeypatch: pytest.MonkeyPatch, entities: _EntityResult +) -> None: + _, _, ensemble, _, _, _ = entities + + lmb: LaunchedManifestBuilder[t.Tuple[str, Step]] = LaunchedManifestBuilder( + "name", "path", "launcher", str(uuid4()) + ) monkeypatch.setattr(ensemble, "entities", []) with pytest.raises(ValueError): lmb.add_ensemble(ensemble, []) -def test_lmb_and_launched_manifest_have_same_paths_for_launched_metadata(): +def test_lmb_and_launched_manifest_have_same_paths_for_launched_metadata() -> None: exp_path = "/path/to/some/exp" - lmb = LaunchedManifestBuilder("exp_name", exp_path, "launcher", str(uuid4())) + lmb: LaunchedManifestBuilder[t.Tuple[str, Step]] = LaunchedManifestBuilder( + "exp_name", exp_path, "launcher", str(uuid4()) + ) manifest = lmb.finalize() assert ( lmb.exp_telemetry_subdirectory == manifest.metadata.exp_telemetry_subdirectory diff --git a/tests/test_model.py b/tests/test_model.py index 64a68b299..152ce2058 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -26,12 +26,14 @@ from uuid import uuid4 +import numpy as np import pytest from smartsim import Experiment from smartsim._core.control.manifest import LaunchedManifestBuilder from smartsim._core.launcher.step import SbatchStep, SrunStep from smartsim.entity import Ensemble, Model +from smartsim.entity.model import _parse_model_parameters from smartsim.error import EntityExistsError, SSUnsupportedError from smartsim.settings import RunSettings, SbatchSettings, SrunSettings from smartsim.settings.mpiSettings import _BaseMPISettings @@ -176,3 +178,16 @@ def test_models_batch_settings_are_ignored_in_ensemble( step_cmd = step.step_cmds[0] assert any("srun" in tok for tok in step_cmd) # call the model using run settings assert not any("sbatch" in tok for tok in step_cmd) # no sbatch in sbatch + + +@pytest.mark.parametrize("dtype", [int, np.float32, str]) +def test_good_model_params(dtype): + print(dtype(0.6)) + params = {"foo": dtype(0.6)} + assert all(isinstance(val, str) for val in _parse_model_parameters(params).values()) + + +@pytest.mark.parametrize("bad_val", [["eggs"], {"n": 5}, object]) +def test_bad_model_params(bad_val): + with pytest.raises(TypeError): + _parse_model_parameters({"foo": bad_val}) diff --git a/tests/test_preview.py b/tests/test_preview.py index 3c7bed6fe..a18d10728 100644 --- a/tests/test_preview.py +++ b/tests/test_preview.py @@ -357,7 +357,7 @@ def test_model_preview_properties(test_dir, wlmutils): assert hw_rs == hello_world_model.run_settings.exe_args[0] assert None == hello_world_model.batch_settings assert "port" in list(hello_world_model.params.items())[0] - assert hw_port in list(hello_world_model.params.items())[0] + assert str(hw_port) in list(hello_world_model.params.items())[0] assert "password" in list(hello_world_model.params.items())[1] assert hw_password in list(hello_world_model.params.items())[1] diff --git a/tests/test_sge_batch_settings.py b/tests/test_sge_batch_settings.py new file mode 100644 index 000000000..fa40b4b00 --- /dev/null +++ b/tests/test_sge_batch_settings.py @@ -0,0 +1,158 @@ +# BSD 2-Clause License +# +# Copyright (c) 2021-2024, Hewlett Packard Enterprise +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +import os.path as osp + +import pytest + +from smartsim import Experiment +from smartsim._core.launcher.sge.sgeParser import parse_qstat_jobid_xml +from smartsim.error import SSConfigError +from smartsim.settings import SgeQsubBatchSettings +from smartsim.settings.mpiSettings import _BaseMPISettings + +# The tests in this file belong to the group_b group +pytestmark = pytest.mark.group_b + +qstat_example = """ + + + + 1387693 + 3.50000 + test_1 + user1 + r + 2024-06-06T04:04:21 + example_node1 + 1600 + + + + + 1387695 + 3.48917 + test_2 + user1 + qw + 2024-05-20T16:47:46 + + 1600 + + + +""" + + +@pytest.mark.parametrize("pe_type", ["mpi", "smp"]) +def test_pe_config(pe_type): + settings = SgeQsubBatchSettings(ncpus=8, pe_type=pe_type) + assert settings._create_resource_list() == [f"-pe {pe_type} 8"] + + +def test_walltime(): + settings = SgeQsubBatchSettings(time="01:00:00") + assert settings._create_resource_list() == [ + f"-l h_rt=01:00:00", + ] + + +def test_ngpus(): + settings = SgeQsubBatchSettings() + settings.set_ngpus(1) + assert settings._create_resource_list() == [f"-l gpu=1"] + + +def test_account(): + settings = SgeQsubBatchSettings(account="foo") + assert settings.format_batch_args() == ["-A foo"] + + +def test_project(): + settings = SgeQsubBatchSettings() + settings.set_project("foo") + assert settings.format_batch_args() == ["-P foo"] + + +def test_update_context_variables(): + settings = SgeQsubBatchSettings() + settings.update_context_variables("ac", "foo") + settings.update_context_variables("sc", "foo", "bar") + settings.update_context_variables("dc", "foo") + assert settings._create_resource_list() == ["-ac foo", "-sc foo=bar", "-dc foo"] + + +def test_invalid_dc_and_value_update_context_variables(): + settings = SgeQsubBatchSettings() + with pytest.raises(SSConfigError): + settings.update_context_variables("dc", "foo", "bar") + + +@pytest.mark.parametrize("enable", [True, False]) +def test_set_hyperthreading(enable): + settings = SgeQsubBatchSettings() + settings.set_hyperthreading(enable) + assert settings._create_resource_list() == [f"-l threads={int(enable)}"] + + +def test_default_set_hyperthreading(): + settings = SgeQsubBatchSettings() + settings.set_hyperthreading() + assert settings._create_resource_list() == ["-l threads=1"] + + +def test_resources_is_a_copy(): + settings = SgeQsubBatchSettings() + resources = settings.resources + assert resources is not settings._resources + + +def test_resources_not_set_on_error(): + settings = SgeQsubBatchSettings() + unaltered_resources = settings.resources + with pytest.raises(TypeError): + settings.resources = {"meep": Exception} + + assert unaltered_resources == settings.resources + + +def test_qstat_jobid_xml(): + assert parse_qstat_jobid_xml(qstat_example, "1387693") == "r" + assert parse_qstat_jobid_xml(qstat_example, "1387695") == "qw" + assert parse_qstat_jobid_xml(qstat_example, "9999999") is None + + +def test_sge_launcher_defaults(monkeypatch, fileutils): + + stub_path = osp.join("mpi_impl_stubs", "openmpi4") + stub_path = fileutils.get_test_dir_path(stub_path) + monkeypatch.setenv("PATH", stub_path, prepend=":") + exp = Experiment("test_sge_run_settings", launcher="sge") + + bs = exp.create_batch_settings() + assert isinstance(bs, SgeQsubBatchSettings) + rs = exp.create_run_settings("echo") + assert isinstance(rs, _BaseMPISettings) diff --git a/tests/test_shell_util.py b/tests/test_shell_util.py index 24f6b023c..2c4e19001 100644 --- a/tests/test_shell_util.py +++ b/tests/test_shell_util.py @@ -28,7 +28,7 @@ import psutil import pytest -from smartsim._core.launcher.util.shell import * +from smartsim._core.utils.shell import * # The tests in this file belong to the group_b group pytestmark = pytest.mark.group_b