diff --git a/.gitignore b/.gitignore index 6d92679..0f34a66 100644 --- a/.gitignore +++ b/.gitignore @@ -4,5 +4,5 @@ mazedyn/.ipynb_checkpoints/** mazedyn/__pycache__/** mazedyn/animations/** mazedyn/data/** -reebs/** +mazedyn/figures/** diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..c09f56d --- /dev/null +++ b/Makefile @@ -0,0 +1,57 @@ +PYTHON ?= python + +.PHONY: test +test: + pytest -v + +.PHONY: autoformat +autoformat: + black mazedyn + isort mazedyn + +.PHONY: lint +lint: + $(PYTHON) -m flake8 mazedyn + $(PYTHON) -m black mazedyn --check + # Note that Bandit will look for .bandit file only if it's invoked with -r option. + $(PYTHON) -m bandit -c pyproject.toml -r mazedyn --exit-zero + $(PYTHON) -m mypy --install-types --non-interactive + +.PHONY: clean +clean: + @find . -type f -name '*.py[co]' -delete -o -type d -name __pycache__ -delete + +.PHONY: rmtmp +rmtmp: + sudo rm -r /tmp && sudo mkdir /tmp && sudo chmod -R 777 /tmp + +.PHONY: rmprocessed +rmprocessed: + rm -rf data/processed/* + +.PHONY: rmoutputs +rmoutputs: + rm -rf outputs/* + +.PHONY: conda-osx-64.lock +conda-osx-64.lock: + CONDA_SUBDIR=osx-64 conda-lock -f conda.yaml -p osx-64 + CONDA_SUBDIR=osx-64 conda-lock render -p osx-64 + +.PHONY: conda-linux-64.lock +conda-linux-64.lock: + conda-lock -f conda.yaml -p linux-64 + conda-lock render -p linux-64 + +conda-lock.yml: conda-osx-64.lock conda-linux-64.lock + +.PHONY: conda-osx-arm.lock +conda-osx-arm.lock: + CONDA_SUBDIR=osx-arm64 conda-lock -f conda.yaml -p osx-arm64 + CONDA_SUBDIR=osx-arm64 conda-lock render -p osx-arm64 + +# Clear the cache and rebuild the lock. +.PHONY: poetry.lock +poetry.lock: + poetry cache clear --all . + poetry lock \ No newline at end of file diff --git a/README.md b/README.md index c0afba3..478ad97 100644 --- a/README.md +++ b/README.md @@ -1 +1,11 @@ # mazedyn + +Install from scratch (on linux) + +``` +pip install conda-lock +make conda-linux-64.lock +conda create --name mazedyn-b --file conda-linux-64.lock +conda activate mazedyn-b +make poetry.lock +``` \ No newline at end of file diff --git a/conda-linux-64.lock b/conda-linux-64.lock new file mode 100644 index 0000000..e5cd198 --- /dev/null +++ b/conda-linux-64.lock @@ -0,0 +1,94 @@ +# Generated by conda-lock. +# platform: linux-64 +# input_hash: ce0729fbe2b2aa0ca9bc9b567c14602f68c3a0f3dbf1293a6b5e836c77d889de +@EXPLICIT +https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 +https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda#23ab7665c5f63cfb9f1f6195256daac6 +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_7.conda#b80f2f396ca2c28b8c14c437a4ed1e74 +https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2#9a66894dfd07c4510beb6b3f9672ccc0 +https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda#d786502c97404c94d7d58d258a445a65 +https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 +https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.1.0-h77fa898_0.conda#ae061a5ed5f05818acdf9adab72c146d +https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2#73aaf86a425cc6e73fcf236a5a46396d +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.1.0-h77fa898_0.conda#ca0fad6a41ddaef54a153b78eccb5037 +https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda#62ee74e96c5ebb0af99386de58cf9553 +https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda#e7ba12deb7020dd080c6c70e7b6f6a3d +https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.1.0-hc5f4f2c_0.conda#6456c2620c990cd8dde2428a27ba0bc5 +https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e +https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7 +https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.1.0-hc0a3c3a_0.conda#1cb187a157136398ddbaae90713e2498 +https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b +https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc +https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-h4ab18f5_1.conda#57d7dc60e9325e3de37ff8dffd18e814 +https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4bc722e_2.conda#e1b454497f9f7c1147fdde4b53f1b512 +https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2#2161070d867d1b1204ea749c8eec4ef0 +https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.2-h59595ed_0.conda#53fb86322bdb89496d7579fe3f02fd61 +https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-14.1.0-h69a702a_0.conda#f4ca84fbd6d06b0a052fb2d5b96dde41 +https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.46.0-hde9e2c9_0.conda#18aa975d2094c34aef978060ae7da7d8 +https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-h0f59acf_0.conda#3914f7ac1761dce57102c72ca7c35d01 +https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4 +https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda#d453b98d9c83e71da0741bb0ff4d76bc +https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda#4d056880988120e29d75bfff282e0f45 +https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.3-h8a4344b_1.conda#6ea440297aacee4893f02ad759e6ffbc +https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.27-pthreads_hac2b453_1.conda#ae05ece66d3924ac3d48b4aa3fa96cec +https://conda.anaconda.org/conda-forge/linux-64/python-3.11.9-hb806964_0_cpython.conda#ac68acfa8b558ed406c75e98d3428d7b +https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py311hb755f60_1.conda#cce9e7c3f1c307f2a5fb08a2922d6164 +https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda#24e7fd6ca65997938fff9e5ab6f653e4 +https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.3.2-pyhd8ed1ab_0.conda#7f4a9e3fcff3f6356ae99244a014da6a +https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 +https://conda.anaconda.org/conda-forge/noarch/crashtest-0.4.1-pyhd8ed1ab_0.tar.bz2#709a2295dd907bb34afb57d54320642f +https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2#ecfff944ba3960ecb334b9a2663d708d +https://conda.anaconda.org/conda-forge/noarch/distlib-0.3.8-pyhd8ed1ab_0.conda#db16c66b759a64dc5183d69cc3745a52 +https://conda.anaconda.org/conda-forge/noarch/filelock-3.15.4-pyhd8ed1ab_0.conda#0e7e4388e9d5283e22b35a9443bdbcc9 +https://conda.anaconda.org/conda-forge/noarch/hpack-4.0.0-pyh9f0ad1d_0.tar.bz2#914d6646c4dbb1fd3ff539830a12fd71 +https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_0.tar.bz2#9f765cbfab6870c8435b9eefecd7a1f4 +https://conda.anaconda.org/conda-forge/noarch/idna-3.7-pyhd8ed1ab_0.conda#c0cc1420498b17414d8617d0b9f506ca +https://conda.anaconda.org/conda-forge/noarch/jeepney-0.8.0-pyhd8ed1ab_0.tar.bz2#9800ad1699b42612478755a2d26c722d +https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-23_linux64_openblas.conda#96c8450a40aa2b9733073a9460de972c +https://conda.anaconda.org/conda-forge/noarch/more-itertools-10.3.0-pyhd8ed1ab_0.conda#a57fb23d0260a962a67c7d990ec1c812 +https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.0.8-py311h52f7536_0.conda#f33f59b8130753174992f409a41e112e +https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda#cbe1bb1f21567018ce595d9c2be0f0db +https://conda.anaconda.org/conda-forge/noarch/pkginfo-1.11.1-pyhd8ed1ab_0.conda#6a3e4fb1396215d0d88b3cc2f09de412 +https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.2.2-pyhd8ed1ab_0.conda#6f6cf28bf8e021933869bae3f84b8fc9 +https://conda.anaconda.org/conda-forge/noarch/poetry-core-1.9.0-pyhd8ed1ab_0.conda#f9f26b837a81a128648353803950929e +https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd3deb0d_0.tar.bz2#359eeb6536da0e687af562ed265ec263 +https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda#844d9eb3b43095b031874477f7d70088 +https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2#2a7de29fb590ca14b5243c4c812c8025 +https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.20.0-pyhd8ed1ab_0.conda#b98d2018c01ce9980c03ee2850690fab +https://conda.anaconda.org/conda-forge/noarch/python-installer-0.7.0-pyhd8ed1ab_0.conda#65dea78f903d686c8b0c2feaf0e15e1f +https://conda.anaconda.org/conda-forge/noarch/setuptools-71.0.4-pyhd8ed1ab_0.conda#ee78ac9c720d0d02fcfd420866b82ab1 +https://conda.anaconda.org/conda-forge/noarch/shellingham-1.5.4-pyhd8ed1ab_0.conda#d08db09a552699ee9e7eec56b4eb3899 +https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96 +https://conda.anaconda.org/conda-forge/noarch/tomlkit-0.13.0-pyha770c72_0.conda#810ba6f354ddef812d0ddc4669cc8de6 +https://conda.anaconda.org/conda-forge/noarch/trove-classifiers-2024.7.2-pyhd8ed1ab_0.conda#2b9f52c7ecb8d017e50f91852aead307 +https://conda.anaconda.org/conda-forge/noarch/wheel-0.43.0-pyhd8ed1ab_1.conda#0b5293a157c2b5cd513dd1b03d8d3aae +https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda#49808e59df5535116f6878b2a820d6f4 +https://conda.anaconda.org/conda-forge/linux-64/cffi-1.16.0-py311hb3a22ac_0.conda#b3469563ac5e808b0cd92810d0697043 +https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_0.tar.bz2#b748fbf7060927a6e82df7cb5ee8f097 +https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.1.0-pyha770c72_0.conda#03da20bf8b1f1021102633d2e9cee84e +https://conda.anaconda.org/conda-forge/noarch/jaraco.classes-3.4.0-pyhd8ed1ab_1.conda#7b756504d362cbad9b73a50a5455cafd +https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-23_linux64_openblas.conda#eede29b40efa878cbe5bdcb767e97310 +https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-23_linux64_openblas.conda#2af0879961951987e464722fd00ec1e0 +https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_0.conda#629f3203c99b32e0988910c93e77f3b6 +https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 +https://conda.anaconda.org/conda-forge/noarch/pyproject_hooks-1.1.0-pyhd8ed1ab_0.conda#03736d8ced74deece64e54be348ddd3e +https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.26.3-pyhd8ed1ab_0.conda#284008712816c64c85bf2b7fa9f3b264 +https://conda.anaconda.org/conda-forge/linux-64/cryptography-43.0.0-py311hc6616f6_0.conda#f392b3f7a26db16f37cf82996dcfc84d +https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-8.1.0-hd8ed1ab_0.conda#1e7ab80593a1fdae4225f4df4fd150d0 +https://conda.anaconda.org/conda-forge/linux-64/numpy-2.0.0-py311h1461c94_0.conda#4998996a22ef05d2f486216075a3037f +https://conda.anaconda.org/conda-forge/noarch/python-build-1.2.1-pyhd8ed1ab_0.conda#d657cde3b3943fcedf6038138eea84de +https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py311h5cd10c7_0.conda#8efe4fe2396281627b3450af8357b190 +https://conda.anaconda.org/conda-forge/linux-64/rapidfuzz-3.9.4-py311hf86e51f_0.conda#79a96223ab77f9e21ed48a8b026ff120 +https://conda.anaconda.org/conda-forge/linux-64/secretstorage-3.3.3-py311h38be061_2.conda#30a57eaa8e72cb0c2c84d6d7db32010c +https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.2-pyhd8ed1ab_1.conda#e804c43f58255e977093a2298e442bb8 +https://conda.anaconda.org/conda-forge/noarch/cleo-2.1.0-pyhd8ed1ab_0.conda#69569ea8a6d1465193345a40421d138b +https://conda.anaconda.org/conda-forge/linux-64/dulwich-0.21.7-py311h459d7ec_0.conda#a50b0c00b7498cfde328e63a3e18d9d3 +https://conda.anaconda.org/conda-forge/linux-64/keyring-24.3.1-py311h38be061_0.conda#0cd643c771fd81eec082cca79e52e08f +https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda#5ede4753180c7a550a443c430dc8ab52 +https://conda.anaconda.org/conda-forge/noarch/cachecontrol-0.14.0-pyhd8ed1ab_1.conda#a54e449940b3e4bb2129b8daae0c1f65 +https://conda.anaconda.org/conda-forge/noarch/requests-toolbelt-1.0.0-pyhd8ed1ab_0.conda#99c98318c8646b08cc764f90ce98906e +https://conda.anaconda.org/conda-forge/noarch/cachecontrol-with-filecache-0.14.0-pyhd8ed1ab_1.conda#42a12b0b21d64b36a9ab9a24a04eb910 +https://conda.anaconda.org/conda-forge/noarch/poetry-1.8.3-linux_pyha804496_1.conda#022096679c2d4af6fc27f4a8f9eaecd6 +https://conda.anaconda.org/conda-forge/noarch/poetry-plugin-export-1.8.0-pyhd8ed1ab_0.conda#9435d439db818300e34b28eb6ac324ba diff --git a/conda-lock.yml b/conda-lock.yml new file mode 100644 index 0000000..b90e5aa --- /dev/null +++ b/conda-lock.yml @@ -0,0 +1,1236 @@ +# This lock file was generated by conda-lock (https://github.com/conda/conda-lock). DO NOT EDIT! +# +# A "lock file" contains a concrete list of package versions (with checksums) to be installed. Unlike +# e.g. `conda env create`, the resulting environment will not change as new package versions become +# available, unless you explicitly update the lock file. +# +# Install this environment as "YOURENV" with: +# conda-lock install -n YOURENV conda-lock.yml +# To update a single package to the latest version compatible with the version constraints in the source: +# conda-lock lock --lockfile conda-lock.yml --update PACKAGE +# To re-solve the entire environment, e.g. after changing a version constraint in the source file: +# conda-lock -f conda.yaml --lockfile conda-lock.yml +version: 1 +metadata: + content_hash: + linux-64: ce0729fbe2b2aa0ca9bc9b567c14602f68c3a0f3dbf1293a6b5e836c77d889de + channels: + - url: conda-forge + used_env_vars: [] + platforms: + - linux-64 + sources: + - conda.yaml +package: +- name: _libgcc_mutex + version: '0.1' + manager: conda + platform: linux-64 + dependencies: {} + url: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 + hash: + md5: d7c89558ba9fa0495403155b64376d81 + sha256: fe51de6107f9edc7aa4f786a70f4a883943bc9d39b3bb7307c04c41410990726 + category: main + optional: false +- name: _openmp_mutex + version: '4.5' + manager: conda + platform: linux-64 + dependencies: + _libgcc_mutex: '0.1' + libgomp: '>=7.5.0' + url: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 + hash: + md5: 73aaf86a425cc6e73fcf236a5a46396d + sha256: fbe2c5e56a653bebb982eda4876a9178aedfc2b545f25d0ce9c4c0b508253d22 + category: main + optional: false +- name: brotli-python + version: 1.1.0 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + python: '>=3.11,<3.12.0a0' + python_abi: 3.11.* + url: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py311hb755f60_1.conda + hash: + md5: cce9e7c3f1c307f2a5fb08a2922d6164 + sha256: 559093679e9fdb6061b7b80ca0f9a31fe6ffc213f1dae65bc5c82e2cd1a94107 + category: main + optional: false +- name: bzip2 + version: 1.0.8 + manager: conda + platform: linux-64 + dependencies: + __glibc: '>=2.17,<3.0.a0' + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda + hash: + md5: 62ee74e96c5ebb0af99386de58cf9553 + sha256: 5ced96500d945fb286c9c838e54fa759aa04a7129c59800f0846b4335cee770d + category: main + optional: false +- name: ca-certificates + version: 2024.7.4 + manager: conda + platform: linux-64 + dependencies: {} + url: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda + hash: + md5: 23ab7665c5f63cfb9f1f6195256daac6 + sha256: c1548a3235376f464f9931850b64b02492f379b2f2bb98bc786055329b080446 + category: main + optional: false +- name: cachecontrol + version: 0.14.0 + manager: conda + platform: linux-64 + dependencies: + msgpack-python: '>=0.5.2,<2.0.0' + python: '>=3.7' + requests: '>=2.16.0' + url: https://conda.anaconda.org/conda-forge/noarch/cachecontrol-0.14.0-pyhd8ed1ab_1.conda + hash: + md5: a54e449940b3e4bb2129b8daae0c1f65 + sha256: 8d8dadbea881c690037e432075357ad6629f7b050e129a5944a0402d674fd754 + category: main + optional: false +- name: cachecontrol-with-filecache + version: 0.14.0 + manager: conda + platform: linux-64 + dependencies: + cachecontrol: 0.14.0 + filelock: '>=3.8.0' + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/cachecontrol-with-filecache-0.14.0-pyhd8ed1ab_1.conda + hash: + md5: 42a12b0b21d64b36a9ab9a24a04eb910 + sha256: 482d0f3ce8dad6b881f76620ee18152755c61bb968039dcbc0ad45689c70b0d5 + category: main + optional: false +- name: certifi + version: 2024.7.4 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/certifi-2024.7.4-pyhd8ed1ab_0.conda + hash: + md5: 24e7fd6ca65997938fff9e5ab6f653e4 + sha256: dd3577bb5275062c388c46b075dcb795f47f8dac561da7dd35fe504b936934e5 + category: main + optional: false +- name: cffi + version: 1.16.0 + manager: conda + platform: linux-64 + dependencies: + libffi: '>=3.4,<4.0a0' + libgcc-ng: '>=12' + pycparser: '' + python: '>=3.11,<3.12.0a0' + python_abi: 3.11.* + url: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.16.0-py311hb3a22ac_0.conda + hash: + md5: b3469563ac5e808b0cd92810d0697043 + sha256: b71c94528ca0c35133da4b7ef69b51a0b55eeee570376057f3d2ad60c3ab1444 + category: main + optional: false +- name: charset-normalizer + version: 3.3.2 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.3.2-pyhd8ed1ab_0.conda + hash: + md5: 7f4a9e3fcff3f6356ae99244a014da6a + sha256: 20cae47d31fdd58d99c4d2e65fbdcefa0b0de0c84e455ba9d6356a4bdbc4b5b9 + category: main + optional: false +- name: cleo + version: 2.1.0 + manager: conda + platform: linux-64 + dependencies: + crashtest: '>=0.4.1,<0.5.0' + python: '>=3.7,<4.0' + rapidfuzz: '>=3.0.0,<4.0.0' + url: https://conda.anaconda.org/conda-forge/noarch/cleo-2.1.0-pyhd8ed1ab_0.conda + hash: + md5: 69569ea8a6d1465193345a40421d138b + sha256: eed2d2cb8792b3ae6434ce49bf5fe1ae5d885253f6bd2e56da933c427705fcbc + category: main + optional: false +- name: colorama + version: 0.4.6 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2 + hash: + md5: 3faab06a954c2a04039983f2c4a50d99 + sha256: 2c1b2e9755ce3102bca8d69e8f26e4f087ece73f50418186aee7c74bef8e1698 + category: main + optional: false +- name: crashtest + version: 0.4.1 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.6,<4.0' + url: https://conda.anaconda.org/conda-forge/noarch/crashtest-0.4.1-pyhd8ed1ab_0.tar.bz2 + hash: + md5: 709a2295dd907bb34afb57d54320642f + sha256: 2f05954a3faf0700c14c1deddc085385160ee32abe111699c78d9cb277e915cc + category: main + optional: false +- name: cryptography + version: 43.0.0 + manager: conda + platform: linux-64 + dependencies: + __glibc: '>=2.17,<3.0.a0' + cffi: '>=1.12' + libgcc-ng: '>=12' + openssl: '>=3.3.1,<4.0a0' + python: '>=3.11,<3.12.0a0' + python_abi: 3.11.* + url: https://conda.anaconda.org/conda-forge/linux-64/cryptography-43.0.0-py311hc6616f6_0.conda + hash: + md5: f392b3f7a26db16f37cf82996dcfc84d + sha256: 7d5d5c21ba14290ef5ec9238158f5470561be37e03d33d83692ea92325b61fdb + category: main + optional: false +- name: dbus + version: 1.13.6 + manager: conda + platform: linux-64 + dependencies: + expat: '>=2.4.2,<3.0a0' + libgcc-ng: '>=9.4.0' + libglib: '>=2.70.2,<3.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2 + hash: + md5: ecfff944ba3960ecb334b9a2663d708d + sha256: 8f5f995699a2d9dbdd62c61385bfeeb57c82a681a7c8c5313c395aa0ccab68a5 + category: main + optional: false +- name: distlib + version: 0.3.8 + manager: conda + platform: linux-64 + dependencies: + python: 2.7|>=3.6 + url: https://conda.anaconda.org/conda-forge/noarch/distlib-0.3.8-pyhd8ed1ab_0.conda + hash: + md5: db16c66b759a64dc5183d69cc3745a52 + sha256: 3ff11acdd5cc2f80227682966916e878e45ced94f59c402efb94911a5774e84e + category: main + optional: false +- name: dulwich + version: 0.21.7 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + python: '>=3.11,<3.12.0a0' + python_abi: 3.11.* + urllib3: '>=1.25' + url: https://conda.anaconda.org/conda-forge/linux-64/dulwich-0.21.7-py311h459d7ec_0.conda + hash: + md5: a50b0c00b7498cfde328e63a3e18d9d3 + sha256: 190dbafc9e699f74cf8d287e91246acac1e14afda8ce6aedafac87e392e1bc96 + category: main + optional: false +- name: expat + version: 2.6.2 + manager: conda + platform: linux-64 + dependencies: + libexpat: 2.6.2 + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.2-h59595ed_0.conda + hash: + md5: 53fb86322bdb89496d7579fe3f02fd61 + sha256: 89916c536ae5b85bb8bf0cfa27d751e274ea0911f04e4a928744735c14ef5155 + category: main + optional: false +- name: filelock + version: 3.15.4 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/filelock-3.15.4-pyhd8ed1ab_0.conda + hash: + md5: 0e7e4388e9d5283e22b35a9443bdbcc9 + sha256: f78d9c0be189a77cb0c67d02f33005f71b89037a85531996583fb79ff3fe1a0a + category: main + optional: false +- name: h2 + version: 4.1.0 + manager: conda + platform: linux-64 + dependencies: + hpack: '>=4.0,<5' + hyperframe: '>=6.0,<7' + python: '>=3.6.1' + url: https://conda.anaconda.org/conda-forge/noarch/h2-4.1.0-pyhd8ed1ab_0.tar.bz2 + hash: + md5: b748fbf7060927a6e82df7cb5ee8f097 + sha256: bfc6a23849953647f4e255c782e74a0e18fe16f7e25c7bb0bc57b83bb6762c7a + category: main + optional: false +- name: hpack + version: 4.0.0 + manager: conda + platform: linux-64 + dependencies: + python: '' + url: https://conda.anaconda.org/conda-forge/noarch/hpack-4.0.0-pyh9f0ad1d_0.tar.bz2 + hash: + md5: 914d6646c4dbb1fd3ff539830a12fd71 + sha256: 5dec948932c4f740674b1afb551223ada0c55103f4c7bf86a110454da3d27cb8 + category: main + optional: false +- name: hyperframe + version: 6.0.1 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.6' + url: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.0.1-pyhd8ed1ab_0.tar.bz2 + hash: + md5: 9f765cbfab6870c8435b9eefecd7a1f4 + sha256: e374a9d0f53149328134a8d86f5d72bca4c6dcebed3c0ecfa968c02996289330 + category: main + optional: false +- name: idna + version: '3.7' + manager: conda + platform: linux-64 + dependencies: + python: '>=3.6' + url: https://conda.anaconda.org/conda-forge/noarch/idna-3.7-pyhd8ed1ab_0.conda + hash: + md5: c0cc1420498b17414d8617d0b9f506ca + sha256: 9687ee909ed46169395d4f99a0ee94b80a52f87bed69cd454bb6d37ffeb0ec7b + category: main + optional: false +- name: importlib-metadata + version: 8.1.0 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.8' + zipp: '>=0.5' + url: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.1.0-pyha770c72_0.conda + hash: + md5: 03da20bf8b1f1021102633d2e9cee84e + sha256: b56e95c96d0afb1efd39cca3801bb5dd41506ffe996a02263d1805353f9b248e + category: main + optional: false +- name: importlib_metadata + version: 8.1.0 + manager: conda + platform: linux-64 + dependencies: + importlib-metadata: '>=8.1.0,<8.1.1.0a0' + url: https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-8.1.0-hd8ed1ab_0.conda + hash: + md5: 1e7ab80593a1fdae4225f4df4fd150d0 + sha256: b816a3bbe911adfb9d906bd096e61817a4ea4defffff0485ab5a0c1d94a249ca + category: main + optional: false +- name: jaraco.classes + version: 3.4.0 + manager: conda + platform: linux-64 + dependencies: + more-itertools: '' + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/jaraco.classes-3.4.0-pyhd8ed1ab_1.conda + hash: + md5: 7b756504d362cbad9b73a50a5455cafd + sha256: 538b1c6df537a36c63fd0ed83cb1c1c25b07d8d3b5e401991fdaff261a4b5b4d + category: main + optional: false +- name: jeepney + version: 0.8.0 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/jeepney-0.8.0-pyhd8ed1ab_0.tar.bz2 + hash: + md5: 9800ad1699b42612478755a2d26c722d + sha256: 16639759b811866d63315fe1391f6fb45f5478b823972f4d3d9f0392b7dd80b8 + category: main + optional: false +- name: keyring + version: 24.3.1 + manager: conda + platform: linux-64 + dependencies: + importlib_metadata: '>=4.11.4' + jaraco.classes: '' + jeepney: '>=0.4.2' + python: '>=3.11,<3.12.0a0' + python_abi: 3.11.* + secretstorage: '>=3.2' + url: https://conda.anaconda.org/conda-forge/linux-64/keyring-24.3.1-py311h38be061_0.conda + hash: + md5: 0cd643c771fd81eec082cca79e52e08f + sha256: 9a818c8e26df6e5a6efce101c1836baa4141cd6e09c8913157727cc8e5c073a9 + category: main + optional: false +- name: ld_impl_linux-64 + version: '2.40' + manager: conda + platform: linux-64 + dependencies: {} + url: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_7.conda + hash: + md5: b80f2f396ca2c28b8c14c437a4ed1e74 + sha256: 764b6950aceaaad0c67ef925417594dd14cd2e22fff864aeef455ac259263d15 + category: main + optional: false +- name: libblas + version: 3.9.0 + manager: conda + platform: linux-64 + dependencies: + libopenblas: '>=0.3.27,<1.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-23_linux64_openblas.conda + hash: + md5: 96c8450a40aa2b9733073a9460de972c + sha256: edb1cee5da3ac4936940052dcab6969673ba3874564f90f5110f8c11eed789c2 + category: main + optional: false +- name: libcblas + version: 3.9.0 + manager: conda + platform: linux-64 + dependencies: + libblas: 3.9.0 + url: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-23_linux64_openblas.conda + hash: + md5: eede29b40efa878cbe5bdcb767e97310 + sha256: 3e7a3236e7e03e308e1667d91d0aa70edd0cba96b4b5563ef4adde088e0881a5 + category: main + optional: false +- name: libexpat + version: 2.6.2 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda + hash: + md5: e7ba12deb7020dd080c6c70e7b6f6a3d + sha256: 331bb7c7c05025343ebd79f86ae612b9e1e74d2687b8f3179faec234f986ce19 + category: main + optional: false +- name: libffi + version: 3.4.2 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=9.4.0' + url: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2 + hash: + md5: d645c6d2ac96843a2bfaccd2d62b3ac3 + sha256: ab6e9856c21709b7b517e940ae7028ae0737546122f83c2aa5d692860c3b149e + category: main + optional: false +- name: libgcc-ng + version: 14.1.0 + manager: conda + platform: linux-64 + dependencies: + _libgcc_mutex: '0.1' + _openmp_mutex: '>=4.5' + url: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.1.0-h77fa898_0.conda + hash: + md5: ca0fad6a41ddaef54a153b78eccb5037 + sha256: b8e869ac96591cda2704bf7e77a301025e405227791a0bddf14a3dac65125538 + category: main + optional: false +- name: libgfortran-ng + version: 14.1.0 + manager: conda + platform: linux-64 + dependencies: + libgfortran5: 14.1.0 + url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-14.1.0-h69a702a_0.conda + hash: + md5: f4ca84fbd6d06b0a052fb2d5b96dde41 + sha256: ef624dacacf97b2b0af39110b36e2fd3e39e358a1a6b7b21b85c9ac22d8ffed9 + category: main + optional: false +- name: libgfortran5 + version: 14.1.0 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=14.1.0' + url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.1.0-hc5f4f2c_0.conda + hash: + md5: 6456c2620c990cd8dde2428a27ba0bc5 + sha256: a67d66b1e60a8a9a9e4440cee627c959acb4810cb182e089a4b0729bfdfbdf90 + category: main + optional: false +- name: libglib + version: 2.80.3 + manager: conda + platform: linux-64 + dependencies: + libffi: '>=3.4,<4.0a0' + libgcc-ng: '>=12' + libiconv: '>=1.17,<2.0a0' + libzlib: '>=1.3.1,<2.0a0' + pcre2: '>=10.44,<10.45.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.80.3-h8a4344b_1.conda + hash: + md5: 6ea440297aacee4893f02ad759e6ffbc + sha256: 5f5854a7cee117d115009d8f22a70d5f9e28f09cb6e453e8f1dd712e354ecec9 + category: main + optional: false +- name: libgomp + version: 14.1.0 + manager: conda + platform: linux-64 + dependencies: + _libgcc_mutex: '0.1' + url: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.1.0-h77fa898_0.conda + hash: + md5: ae061a5ed5f05818acdf9adab72c146d + sha256: 7699df61a1f6c644b3576a40f54791561f2845983120477a16116b951c9cdb05 + category: main + optional: false +- name: libiconv + version: '1.17' + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda + hash: + md5: d66573916ffcf376178462f1b61c941e + sha256: 8ac2f6a9f186e76539439e50505d98581472fedb347a20e7d1f36429849f05c9 + category: main + optional: false +- name: liblapack + version: 3.9.0 + manager: conda + platform: linux-64 + dependencies: + libblas: 3.9.0 + url: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-23_linux64_openblas.conda + hash: + md5: 2af0879961951987e464722fd00ec1e0 + sha256: 25c7aef86c8a1d9db0e8ee61aa7462ba3b46b482027a65d66eb83e3e6f949043 + category: main + optional: false +- name: libnsl + version: 2.0.1 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda + hash: + md5: 30fd6e37fe21f86f4bd26d6ee73eeec7 + sha256: 26d77a3bb4dceeedc2a41bd688564fe71bf2d149fdcf117049970bc02ff1add6 + category: main + optional: false +- name: libopenblas + version: 0.3.27 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + libgfortran-ng: '' + libgfortran5: '>=12.3.0' + url: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.27-pthreads_hac2b453_1.conda + hash: + md5: ae05ece66d3924ac3d48b4aa3fa96cec + sha256: 714cb82d7c4620ea2635a92d3df263ab841676c9b183d0c01992767bb2451c39 + category: main + optional: false +- name: libsqlite + version: 3.46.0 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + libzlib: '>=1.2.13,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.46.0-hde9e2c9_0.conda + hash: + md5: 18aa975d2094c34aef978060ae7da7d8 + sha256: daee3f68786231dad457d0dfde3f7f1f9a7f2018adabdbb864226775101341a8 + category: main + optional: false +- name: libstdcxx-ng + version: 14.1.0 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: 14.1.0 + url: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.1.0-hc0a3c3a_0.conda + hash: + md5: 1cb187a157136398ddbaae90713e2498 + sha256: 88c42b388202ffe16adaa337e36cf5022c63cf09b0405cf06fc6aeacccbe6146 + category: main + optional: false +- name: libuuid + version: 2.38.1 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda + hash: + md5: 40b61aab5c7ba9ff276c41cfffe6b80b + sha256: 787eb542f055a2b3de553614b25f09eefb0a0931b0c87dbcce6efdfd92f04f18 + category: main + optional: false +- name: libxcrypt + version: 4.4.36 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda + hash: + md5: 5aa797f8787fe7a17d1b0821485b5adc + sha256: 6ae68e0b86423ef188196fff6207ed0c8195dd84273cb5623b85aa08033a410c + category: main + optional: false +- name: libzlib + version: 1.3.1 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-h4ab18f5_1.conda + hash: + md5: 57d7dc60e9325e3de37ff8dffd18e814 + sha256: adf6096f98b537a11ae3729eaa642b0811478f0ea0402ca67b5108fe2cb0010d + category: main + optional: false +- name: more-itertools + version: 10.3.0 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/more-itertools-10.3.0-pyhd8ed1ab_0.conda + hash: + md5: a57fb23d0260a962a67c7d990ec1c812 + sha256: 9c485cc52dfd646ea584e9055c1bbaac8f27687d806c1ef00f299ec2e642ce04 + category: main + optional: false +- name: msgpack-python + version: 1.0.8 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + python: '>=3.11,<3.12.0a0' + python_abi: 3.11.* + url: https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.0.8-py311h52f7536_0.conda + hash: + md5: f33f59b8130753174992f409a41e112e + sha256: 8b0b4def742cebde399fd3244248e6db5b6843e7db64a94a10d6b649a3f20144 + category: main + optional: false +- name: ncurses + version: '6.5' + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda + hash: + md5: fcea371545eda051b6deafb24889fc69 + sha256: 4fc3b384f4072b68853a0013ea83bdfd3d66b0126e2238e1d6e1560747aa7586 + category: main + optional: false +- name: nomkl + version: '1.0' + manager: conda + platform: linux-64 + dependencies: {} + url: https://conda.anaconda.org/conda-forge/noarch/nomkl-1.0-h5ca1d4c_0.tar.bz2 + hash: + md5: 9a66894dfd07c4510beb6b3f9672ccc0 + sha256: d38542a151a90417065c1a234866f97fd1ea82a81de75ecb725955ab78f88b4b + category: main + optional: false +- name: numpy + version: 2.0.0 + manager: conda + platform: linux-64 + dependencies: + libblas: '>=3.9.0,<4.0a0' + libcblas: '>=3.9.0,<4.0a0' + libgcc-ng: '>=12' + liblapack: '>=3.9.0,<4.0a0' + libstdcxx-ng: '>=12' + python: '>=3.11,<3.12.0a0' + python_abi: 3.11.* + url: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.0.0-py311h1461c94_0.conda + hash: + md5: 4998996a22ef05d2f486216075a3037f + sha256: f5c8070a623a216f999aec9b60b181f0624b7b074cc08189bdb4da6376c01a5d + category: main + optional: false +- name: openssl + version: 3.3.1 + manager: conda + platform: linux-64 + dependencies: + __glibc: '>=2.17,<3.0.a0' + ca-certificates: '' + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4bc722e_2.conda + hash: + md5: e1b454497f9f7c1147fdde4b53f1b512 + sha256: b294b3cc706ad1048cdb514f0db3da9f37ae3fcc0c53a7104083dd0918adb200 + category: main + optional: false +- name: packaging + version: '24.1' + manager: conda + platform: linux-64 + dependencies: + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/packaging-24.1-pyhd8ed1ab_0.conda + hash: + md5: cbe1bb1f21567018ce595d9c2be0f0db + sha256: 36aca948219e2c9fdd6d80728bcc657519e02f06c2703d8db3446aec67f51d81 + category: main + optional: false +- name: pcre2 + version: '10.44' + manager: conda + platform: linux-64 + dependencies: + bzip2: '>=1.0.8,<2.0a0' + libgcc-ng: '>=12' + libzlib: '>=1.3.1,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-h0f59acf_0.conda + hash: + md5: 3914f7ac1761dce57102c72ca7c35d01 + sha256: 90646ad0d8f9d0fd896170c4f3d754e88c4ba0eaf856c24d00842016f644baab + category: main + optional: false +- name: pexpect + version: 4.9.0 + manager: conda + platform: linux-64 + dependencies: + ptyprocess: '>=0.5' + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_0.conda + hash: + md5: 629f3203c99b32e0988910c93e77f3b6 + sha256: 90a09d134a4a43911b716d4d6eb9d169238aff2349056f7323d9db613812667e + category: main + optional: false +- name: pip + version: '24.0' + manager: conda + platform: linux-64 + dependencies: + python: '>=3.7' + setuptools: '' + wheel: '' + url: https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda + hash: + md5: f586ac1e56c8638b64f9c8122a7b8a67 + sha256: b7c1c5d8f13e8cb491c4bd1d0d1896a4cf80fc47de01059ad77509112b664a4a + category: main + optional: false +- name: pkginfo + version: 1.11.1 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/pkginfo-1.11.1-pyhd8ed1ab_0.conda + hash: + md5: 6a3e4fb1396215d0d88b3cc2f09de412 + sha256: 8eb347932cd42fffe9370e82a31cfbabc40b2149c2b049cf087d4a78f5b3b53c + category: main + optional: false +- name: platformdirs + version: 4.2.2 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.2.2-pyhd8ed1ab_0.conda + hash: + md5: 6f6cf28bf8e021933869bae3f84b8fc9 + sha256: adc59384cf0b2fc6dc7362840151e8cb076349197a38f7230278252698a88442 + category: main + optional: false +- name: poetry + version: 1.8.3 + manager: conda + platform: linux-64 + dependencies: + __linux: '' + cachecontrol: '>=0.14.0,<0.15.0' + cachecontrol-with-filecache: '' + cleo: '>=2.1.0,<3.0.0' + crashtest: '>=0.4.1,<0.5.0' + dulwich: '>=0.21.2,<0.22.0' + importlib-metadata: '>=4.4' + keyring: '>=24.0.0,<25.0.0' + packaging: '>=23.1' + pexpect: '>=4.7.0,<5.0.0' + pkginfo: '>=1.10.0,<2.0.0' + platformdirs: '>=3.0.0,<5' + poetry-core: 1.9.0.* + poetry-plugin-export: '>=1.6.0,<2.0.0' + pyproject_hooks: '>=1.0.0,<2.0.0' + python: '>=3.8,<4.0' + python-build: '>=1.0.3,<2.0.0' + python-fastjsonschema: '>=2.18.0,<3.0.0' + python-installer: '>=0.7.0,<0.8.0' + requests: '>=2.26.0,<3.0.0' + requests-toolbelt: '>=1.0.0,<2.0.0' + shellingham: '>=1.5.0,<2.0.0' + tomli: '>=2.0.1,<3.0.0' + tomlkit: '>=0.11.4,<1.0.0' + trove-classifiers: '>=2022.5.19' + virtualenv: '>=20.23.0,<21.0.0' + url: https://conda.anaconda.org/conda-forge/noarch/poetry-1.8.3-linux_pyha804496_1.conda + hash: + md5: 022096679c2d4af6fc27f4a8f9eaecd6 + sha256: 2b2eb6c6fafe28731e0dc91a86ea79fd22db99926e9dd3b6127c3c4c3f89a448 + category: main + optional: false +- name: poetry-core + version: 1.9.0 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.8.0,<4.0.0' + url: https://conda.anaconda.org/conda-forge/noarch/poetry-core-1.9.0-pyhd8ed1ab_0.conda + hash: + md5: f9f26b837a81a128648353803950929e + sha256: eb1a1351bda0157257c3cf6074e7203812736319e664b6bbe78befc863b2d542 + category: main + optional: false +- name: poetry-plugin-export + version: 1.8.0 + manager: conda + platform: linux-64 + dependencies: + poetry: '>=1.8.0,<3.0.0' + poetry-core: '>=1.7.0,<3.0.0' + python: '>=3.8.0,<4.0.0' + url: https://conda.anaconda.org/conda-forge/noarch/poetry-plugin-export-1.8.0-pyhd8ed1ab_0.conda + hash: + md5: 9435d439db818300e34b28eb6ac324ba + sha256: 57759580fa2477599ea730ea3ba0eed0c88c65a4a8476da6ad0e0364f4d7c1f1 + category: main + optional: false +- name: ptyprocess + version: 0.7.0 + manager: conda + platform: linux-64 + dependencies: + python: '' + url: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd3deb0d_0.tar.bz2 + hash: + md5: 359eeb6536da0e687af562ed265ec263 + sha256: fb31e006a25eb2e18f3440eb8d17be44c8ccfae559499199f73584566d0a444a + category: main + optional: false +- name: pycparser + version: '2.22' + manager: conda + platform: linux-64 + dependencies: + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyhd8ed1ab_0.conda + hash: + md5: 844d9eb3b43095b031874477f7d70088 + sha256: 406001ebf017688b1a1554b49127ca3a4ac4626ec0fd51dc75ffa4415b720b64 + category: main + optional: false +- name: pyproject_hooks + version: 1.1.0 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.7' + tomli: '>=1.1.0' + url: https://conda.anaconda.org/conda-forge/noarch/pyproject_hooks-1.1.0-pyhd8ed1ab_0.conda + hash: + md5: 03736d8ced74deece64e54be348ddd3e + sha256: 7431bd1c1273facccae3875218dff1faae5a717a0605326fc0370ea8f780ba40 + category: main + optional: false +- name: pysocks + version: 1.7.1 + manager: conda + platform: linux-64 + dependencies: + __unix: '' + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2 + hash: + md5: 2a7de29fb590ca14b5243c4c812c8025 + sha256: a42f826e958a8d22e65b3394f437af7332610e43ee313393d1cf143f0a2d274b + category: main + optional: false +- name: python + version: 3.11.9 + manager: conda + platform: linux-64 + dependencies: + bzip2: '>=1.0.8,<2.0a0' + ld_impl_linux-64: '>=2.36.1' + libexpat: '>=2.6.2,<3.0a0' + libffi: '>=3.4,<4.0a0' + libgcc-ng: '>=12' + libnsl: '>=2.0.1,<2.1.0a0' + libsqlite: '>=3.45.3,<4.0a0' + libuuid: '>=2.38.1,<3.0a0' + libxcrypt: '>=4.4.36' + libzlib: '>=1.2.13,<2.0.0a0' + ncurses: '>=6.4.20240210,<7.0a0' + openssl: '>=3.2.1,<4.0a0' + readline: '>=8.2,<9.0a0' + tk: '>=8.6.13,<8.7.0a0' + tzdata: '' + xz: '>=5.2.6,<6.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/python-3.11.9-hb806964_0_cpython.conda + hash: + md5: ac68acfa8b558ed406c75e98d3428d7b + sha256: 177f33a1fb8d3476b38f73c37b42f01c0b014fa0e039a701fd9f83d83aae6d40 + category: main + optional: false +- name: python-build + version: 1.2.1 + manager: conda + platform: linux-64 + dependencies: + colorama: '' + importlib-metadata: '>=4.6' + packaging: '>=19.0' + pyproject_hooks: '' + python: '>=3.8' + tomli: '>=1.1.0' + url: https://conda.anaconda.org/conda-forge/noarch/python-build-1.2.1-pyhd8ed1ab_0.conda + hash: + md5: d657cde3b3943fcedf6038138eea84de + sha256: 3104051be7279d1b15f0a4be79f4bfeaf3a42b2900d24a7ad8e980df903fe8db + category: main + optional: false +- name: python-fastjsonschema + version: 2.20.0 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.3' + url: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.20.0-pyhd8ed1ab_0.conda + hash: + md5: b98d2018c01ce9980c03ee2850690fab + sha256: 7d8c931b89c9980434986b4deb22c2917b58d9936c3974139b9c10ae86fdfe60 + category: main + optional: false +- name: python-installer + version: 0.7.0 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/python-installer-0.7.0-pyhd8ed1ab_0.conda + hash: + md5: 65dea78f903d686c8b0c2feaf0e15e1f + sha256: 822f95b7786cfa61a6519153117b21d93194890e02a884b9f66ee4275e4f1c0a + category: main + optional: false +- name: python_abi + version: '3.11' + manager: conda + platform: linux-64 + dependencies: {} + url: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda + hash: + md5: d786502c97404c94d7d58d258a445a65 + sha256: 0be3ac1bf852d64f553220c7e6457e9c047dfb7412da9d22fbaa67e60858b3cf + category: main + optional: false +- name: rapidfuzz + version: 3.9.4 + manager: conda + platform: linux-64 + dependencies: + __glibc: '>=2.17,<3.0.a0' + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + numpy: '' + python: '>=3.11,<3.12.0a0' + python_abi: 3.11.* + url: https://conda.anaconda.org/conda-forge/linux-64/rapidfuzz-3.9.4-py311hf86e51f_0.conda + hash: + md5: 79a96223ab77f9e21ed48a8b026ff120 + sha256: 87b3d10fb72e2d9977df93afce849af92673a2216397a79066552a33ac353cf5 + category: main + optional: false +- name: readline + version: '8.2' + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + ncurses: '>=6.3,<7.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda + hash: + md5: 47d31b792659ce70f470b5c82fdfb7a4 + sha256: 5435cf39d039387fbdc977b0a762357ea909a7694d9528ab40f005e9208744d7 + category: main + optional: false +- name: requests + version: 2.32.3 + manager: conda + platform: linux-64 + dependencies: + certifi: '>=2017.4.17' + charset-normalizer: '>=2,<4' + idna: '>=2.5,<4' + python: '>=3.8' + urllib3: '>=1.21.1,<3' + url: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_0.conda + hash: + md5: 5ede4753180c7a550a443c430dc8ab52 + sha256: 5845ffe82a6fa4d437a2eae1e32a1ad308d7ad349f61e337c0a890fe04c513cc + category: main + optional: false +- name: requests-toolbelt + version: 1.0.0 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.6' + requests: '>=2.0.1,<3.0.0' + url: https://conda.anaconda.org/conda-forge/noarch/requests-toolbelt-1.0.0-pyhd8ed1ab_0.conda + hash: + md5: 99c98318c8646b08cc764f90ce98906e + sha256: 20eaefc5dba74ff6c31e537533dde59b5b20f69e74df49dff19d43be59785fa3 + category: main + optional: false +- name: secretstorage + version: 3.3.3 + manager: conda + platform: linux-64 + dependencies: + cryptography: '' + dbus: '' + jeepney: '>=0.6' + python: '>=3.11,<3.12.0a0' + python_abi: 3.11.* + url: https://conda.anaconda.org/conda-forge/linux-64/secretstorage-3.3.3-py311h38be061_2.conda + hash: + md5: 30a57eaa8e72cb0c2c84d6d7db32010c + sha256: 45e7d85a3663993e8bffdb7c6040561923c848e3262228b163042663caa4485e + category: main + optional: false +- name: setuptools + version: 71.0.4 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/setuptools-71.0.4-pyhd8ed1ab_0.conda + hash: + md5: ee78ac9c720d0d02fcfd420866b82ab1 + sha256: e1b5dd28d2ea2a7ad660fbc8d1f2ef682a2f8460f80240d836d62e56225ac680 + category: main + optional: false +- name: shellingham + version: 1.5.4 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/shellingham-1.5.4-pyhd8ed1ab_0.conda + hash: + md5: d08db09a552699ee9e7eec56b4eb3899 + sha256: 3c49a0a101c41b7cf6ac05a1872d7a1f91f1b6d02eecb4a36b605a19517862bb + category: main + optional: false +- name: tk + version: 8.6.13 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + libzlib: '>=1.2.13,<2.0.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda + hash: + md5: d453b98d9c83e71da0741bb0ff4d76bc + sha256: e0569c9caa68bf476bead1bed3d79650bb080b532c64a4af7d8ca286c08dea4e + category: main + optional: false +- name: tomli + version: 2.0.1 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2 + hash: + md5: 5844808ffab9ebdb694585b50ba02a96 + sha256: 4cd48aba7cd026d17e86886af48d0d2ebc67ed36f87f6534f4b67138f5a5a58f + category: main + optional: false +- name: tomlkit + version: 0.13.0 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/tomlkit-0.13.0-pyha770c72_0.conda + hash: + md5: 810ba6f354ddef812d0ddc4669cc8de6 + sha256: 8e61623213c620776f1328da4bee03f8828dbf2730f1a4fbd9b8af5398f5848e + category: main + optional: false +- name: trove-classifiers + version: 2024.7.2 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.7' + url: https://conda.anaconda.org/conda-forge/noarch/trove-classifiers-2024.7.2-pyhd8ed1ab_0.conda + hash: + md5: 2b9f52c7ecb8d017e50f91852aead307 + sha256: ab5575f5908fcb578ecde73701e1ceb8dde708f93111b3f692c163f11bc119fc + category: main + optional: false +- name: tzdata + version: 2024a + manager: conda + platform: linux-64 + dependencies: {} + url: https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda + hash: + md5: 161081fc7cec0bfda0d86d7cb595f8d8 + sha256: 7b2b69c54ec62a243eb6fba2391b5e443421608c3ae5dbff938ad33ca8db5122 + category: main + optional: false +- name: urllib3 + version: 2.2.2 + manager: conda + platform: linux-64 + dependencies: + brotli-python: '>=1.0.9' + h2: '>=4,<5' + pysocks: '>=1.5.6,<2.0,!=1.5.7' + python: '>=3.8' + zstandard: '>=0.18.0' + url: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.2.2-pyhd8ed1ab_1.conda + hash: + md5: e804c43f58255e977093a2298e442bb8 + sha256: 00c47c602c03137e7396f904eccede8cc64cc6bad63ce1fc355125df8882a748 + category: main + optional: false +- name: virtualenv + version: 20.26.3 + manager: conda + platform: linux-64 + dependencies: + distlib: <1,>=0.3.7 + filelock: <4,>=3.12.2 + platformdirs: <5,>=3.9.1 + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.26.3-pyhd8ed1ab_0.conda + hash: + md5: 284008712816c64c85bf2b7fa9f3b264 + sha256: f78961b194e33eed5fdccb668774651ec9423a043069fa7a4e3e2f853b08aa0c + category: main + optional: false +- name: wheel + version: 0.43.0 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/wheel-0.43.0-pyhd8ed1ab_1.conda + hash: + md5: 0b5293a157c2b5cd513dd1b03d8d3aae + sha256: cb318f066afd6fd64619f14c030569faf3f53e6f50abf743b4c865e7d95b96bc + category: main + optional: false +- name: xz + version: 5.2.6 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2 + hash: + md5: 2161070d867d1b1204ea749c8eec4ef0 + sha256: 03a6d28ded42af8a347345f82f3eebdd6807a08526d47899a42d62d319609162 + category: main + optional: false +- name: zipp + version: 3.19.2 + manager: conda + platform: linux-64 + dependencies: + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda + hash: + md5: 49808e59df5535116f6878b2a820d6f4 + sha256: e3e9c8501f581bfdc4700b83ea283395e237ec6b9b5cbfbedb556e1da6f4fdc9 + category: main + optional: false +- name: zstandard + version: 0.23.0 + manager: conda + platform: linux-64 + dependencies: + __glibc: '>=2.17,<3.0.a0' + cffi: '>=1.11' + libgcc-ng: '>=12' + python: '>=3.11,<3.12.0a0' + python_abi: 3.11.* + zstd: '>=1.5.6,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.23.0-py311h5cd10c7_0.conda + hash: + md5: 8efe4fe2396281627b3450af8357b190 + sha256: ee4e7202ed6d6027eabb9669252b4dfd8144d4fde644435ebe39ab608086e7af + category: main + optional: false +- name: zstd + version: 1.5.6 + manager: conda + platform: linux-64 + dependencies: + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + libzlib: '>=1.2.13,<2.0.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda + hash: + md5: 4d056880988120e29d75bfff282e0f45 + sha256: c558b9cc01d9c1444031bd1ce4b9cff86f9085765f17627a6cd85fc623c8a02b + category: main + optional: false diff --git a/conda.yaml b/conda.yaml new file mode 100644 index 0000000..9806a01 --- /dev/null +++ b/conda.yaml @@ -0,0 +1,9 @@ +name: mazedyn +channels: + - conda-forge +dependencies: + - python=3.11.* + - nomkl + - numpy + - pip + - poetry>=1.5.0 \ No newline at end of file diff --git a/mazedyn/environment.yml b/mazedyn/environment.yml deleted file mode 100644 index 737d34f..0000000 --- a/mazedyn/environment.yml +++ /dev/null @@ -1,11 +0,0 @@ -name: mazedyn -channels: - - conda-forge - - pytorch -dependencies: - - python=3.11 - - joblib - - matplotlib - - pandas - - pytorch - - pydot diff --git a/mazedyn/reeb_clean.ipynb b/mazedyn/reeb_clean.ipynb index 4d5169f..4e53b20 100644 --- a/mazedyn/reeb_clean.ipynb +++ b/mazedyn/reeb_clean.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "f51d8208-23c8-4c49-8a47-4df0e51b3807", "metadata": {}, "outputs": [], @@ -36,6 +36,8 @@ "import pandas as pd\n", "import pydot\n", "\n", + "from reeb_utils import do_reeb_for_start_end, plot_quartile_reebs_for_start_end, do_reeb_for_explore_node, plot_reeb_explore\n", + "\n", "pd.options.mode.chained_assignment = None" ] }, @@ -302,119 +304,6 @@ } ], "source": [ - "def all_traj_same(trajs, which_trajs, max_traj_len):\n", - " if len(set([len(trajs[t]) for t in which_trajs])) != 1: return False\n", - "\n", - " for i in range(max_traj_len):\n", - " if len(set([trajs[t][i] for t in which_trajs])) != 1: return False\n", - " return True\n", - " \n", - "def make_reeb_graph_recursive(start_i, start_node, trajs, which_trajs, G, visited, SUCCESS=\"\", DEBUG=False):\n", - " if DEBUG: print(f\"DO REEB, {start_i}, {start_node}, {which_trajs}\")\n", - " times_before = visited.get(start_node, 0) \n", - " visited[start_node] = times_before\n", - " to_add = start_node + \"_\" + str(times_before)\n", - "\n", - " max_traj_len = max([len(trajs[t]) for t in which_trajs])\n", - "\n", - " ## BASE CASE 1: SINGLETON ##\n", - " ## BASE CASE 2: ALL THE SAME ##\n", - " if len(which_trajs) == 1 or all_traj_same(trajs, which_trajs, max_traj_len):\n", - " end_node = trajs[which_trajs[0]][-1]\n", - " times_before = visited.get(end_node, -1) + 1\n", - " visited[end_node] = times_before\n", - " \n", - " end_node += \"_\" + str(times_before)\n", - " if DEBUG: print(f\"BASE CASE: {which_trajs}, last: {end_node}\")\n", - "\n", - " color = \"green\" if SUCCESS in end_node else \"yellow\"\n", - " G.add_node(end_node, color=color, end=True)\n", - " G.add_edge(to_add, end_node, weight=len(which_trajs))\n", - "\n", - " return\n", - " \n", - "\n", - " ## RECURSIVE STEP ##\n", - " if DEBUG: print(f\"FOR LOOP, {start_i}, {which_trajs}\")\n", - " for step in range(start_i, max_traj_len):\n", - " \n", - " new_gn = {}\n", - " for i in which_trajs:\n", - " traj = trajs[i]\n", - "\n", - " ## STOPPING CONDITION FOR THIS ONE GUY ##\n", - " if step >= len(traj): \n", - " new_gn[\"DONE\"] = new_gn.get(\"DONE\", []).copy() + [i]\n", - " else:\n", - " new_node = traj[step] \n", - " new_gn[new_node] = new_gn.get(new_node, []).copy() + [i]\n", - "\n", - " if DEBUG: print(\"NEW_GN\", new_gn)\n", - " # decide whether we need a reeb node\n", - " \n", - " # people went different ways!!\n", - " if len(new_gn.keys()) > 1:\n", - " times_before = visited.get(start_node, 0)\n", - " sn = start_node + \"_\" + str(times_before)\n", - " \n", - " # the thing happened at the node before this! all the paths in this fn are at the same point there so it doesnt mattter\n", - " old_one = trajs[which_trajs[0]][step-1]\n", - " times_before = visited.get(old_one, -1) + 1\n", - " visited[old_one] = times_before\n", - " \n", - " on = old_one + \"_\" + str(times_before)\n", - " G.add_node(on, color=\"blue\", end=False)\n", - " if DEBUG: print(f\"ADDING NEW BLUE NODE {on}\")\n", - " \n", - " G.add_edge(sn, on, weight=len(which_trajs))\n", - " if DEBUG: print(f\"ADDING NEW EDGE from {sn} to {on}, weight is {len(which_trajs)}\")\n", - "\n", - " \n", - " for k, v in new_gn.items():\n", - " if DEBUG: print(\"RECURSE\")\n", - " if DEBUG and k == \"DONE\":\n", - " print(\"*@#)$*$#()*(#@($\")\n", - " print(new_gn)\n", - " make_reeb_graph_recursive(step+1, old_one, trajs, v, G, visited, SUCCESS=SUCCESS)\n", - "\n", - " return\n", - "\n", - "def collapse_traj(traj):\n", - " '''\n", - " Remove turns so that A A B B G G G becomes A B G.\n", - " '''\n", - " new_traj = [traj[0]]\n", - " for t in traj:\n", - " if t != new_traj[-1]:\n", - " new_traj.append(t)\n", - " return new_traj\n", - " \n", - "def do_reeb_for_start_end(df, start_node, end_node, Q, use_turns=True):\n", - " this_df = df[(df[\"StartAt\"] == start_node) & (df[\"EndAt\"] == end_node) & (df[\"quartile\"] == Q)]\n", - " \n", - " trajs = []\n", - " count = 0\n", - " for _, row in this_df.iterrows():\n", - " path = row[\"e_paths\"] if use_turns else row[\"paths\"]\n", - " if path[-2:] == \"NA\": path = path[:-2] # this happens if the test ends unsuccessfully\n", - " \n", - " states = path.split()\n", - " if not use_turns: states = collapse_traj(states)\n", - " trajs.append(states)\n", - " count += 1\n", - " # if count == 15: break\n", - "\n", - " visited = {}\n", - "\n", - " G = nx.Graph()\n", - " G.add_node(start_node + \"_0\", color=\"red\", end=False)\n", - "\n", - " # print(start_node)\n", - " # for t in trajs:\n", - " # print(t)\n", - " make_reeb_graph_recursive(0, start_node, trajs, [i for i in range(len(trajs))], G, visited, SUCCESS=end_node[0], DEBUG=False)\n", - " return G\n", - "\n", "G = do_reeb_for_start_end(test_df, \"A\", \"W\", 1, use_turns=True)\n", "\n", "print(G.nodes)\n", @@ -439,13 +328,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "id": "a6b4c012", "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -455,7 +344,7 @@ }, { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAADC8AAAbgCAYAAAD6U5gwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9d5zdZZ03/r/OzCSTZNILIYGE0CGAdJAapIliZcWGoO66u4oF9rus7b63+NPdtS+66q737ZqArKg3CLKIoPQSOkgJJYSaAiG9z0wmc35/HBIJOWcyMzlnSvJ8Ph7nkZzruj7X+z2gj4d+PvP6XIVisVgMAAAAAAAAAAAAAABAjdT1dgMAAAAAAAAAAAAAAMD2TXgBAAAAAAAAAAAAAACoKeEFAAAAAAAAAAAAAACgpoQXAAAAAAAAAAAAAACAmhJeAAAAAAAAAAAAAAAAakp4AQAAAAAAAAAAAAAAqCnhBQAAAAAAAAAAAAAAoKaEFwAAAAAAAAAAAAAAgJoSXgAAAAAAAAAAAAAAAGpKeAEAAAAAAAAAAAAAAKgp4QUAAAAAAAAAAAAAAKCmhBcAAAAAAHYwV111VQqFQsXPQQcd1Nst0octXbo0dXV1Zf+zs//++1e93pw5czr8z+uhhx5a9Zp//OMfK9Y74YQTql4PAAAAAAB2BMILAAAAAAA7mOnTp3c4//jjj+eBBx7ooW7ob0aPHl0x4PLUU09l4cKFVa132223dTj/6KOPZvny5T1Wc9q0aVWtBQAAAAAAOwrhBQAAAACAHcjChQvzu9/9bqvrthZw6OumTJlS9q35M2bM6O3WtgsnnXRSxbnbb7+9qrW2Fl5ob2/PHXfc0WM1O/rZAQAAAACAyoQXAAAAAAB2ID/72c/S1ta21XWXX355WlpaeqAj+qOOTh+49dZbq1pra+GFzq7prGKxWDEMMWDAgBx77LFVqwUAAAAAADsS4QUAAAAAgB1IZ08eWLZsWa6++uqa9kL/NW3atBQKhbJz1QwSvPDCC3nppZe2uq6aNWfNmpXFixeXnTvqqKMyZMiQqtUCAAAAAIAdifACAAAAAMAO4r777susWbM6vX769Ok17Ib+bMyYMTnwwAPLzj3xxBMVf/m/qzobSnj44YezcuXKmtc86aSTqlIDAAAAAAB2RMILAAAAAAA7iK6GEf7whz9k3rx5NeqG/m7atGllx4vFYm6//faq1CgXJGhoaNhibMOGDbnzzjtrVnMj4QUAAAAAAOg+4QUAAAAAgB1Ac3NzfvGLX5SdmzRpUtnx9vb2XHrppbVsi36so1/kv/XWW6tSo1yQ4K1vfWvGjh3bqbXdUSl4MWDAgBx77LFVqQEAAAAAADsi4QUAAAAAgB3AVVddleXLl5ed+8Y3vpHdd9+97NyMGTNq1xT92rRp01IoFMrOVSNIMG/evDz33HNl6x5//PE1qfnUU09l4cKFZeeOPPLIDBkyZJtrAAAAAADAjkp4AQAAAABgBzB9+vSy48OHD8973/venHvuuWXnn3nmmdx11121bI1+auzYsZk6dWrZucceeyxLly7dpv0rhRFOPPHEnHDCCVuMP/jgg1m9enVNaiYdnzQBAAAAAABsnfACAAAAAMB2bu7cubnpppvKzr3//e/PoEGDct5551W8vlLwASr9Qn+xWMztt9++TXuXCxI0NTXl8MMPz4knnrjFXFtb2zYHbYQXAAAAAACgdoQXAAAAAAC2c5dcckna29vLzm0MLey555457rjjyq751a9+lbVr19asP/qvadOmVZzrKAjQGeWuP/bYY9PQ0JBDDz00w4YNq3rNSoGLAQMGVPzvBwAAAAAA0DkNvd0AAAAAAAC1NWPGjLLju+++e44//vhN388777yyb65ftWpVrrjiig5PZ+hNs2fPzuzZszcbW7duXdm1jzzySK699tou7X/MMcdkzJgx3e6vWCzmnnvuya233pp77703zz33XObPn581a9Zkw4YNaWpqypgxY7L77rvn4IMPzoknnpjTTjstQ4YM6XbNnlKr8MIrr7yyxb/T19err6/PMccck9///vdVqzlnzpzMnz+/7NyRRx7ZL/59AAAAAABAXya8AAAAAACwHbv99tvz7LPPlp0799xzUygUNn3/wAc+kAsuuCDNzc1brJ0+fXqfDS/8/Oc/z1e+8pVOrb344otz8cUXd2n/W265JSeddFKX+1qxYkV+8IMf5P/8n/+Tl156qcN1K1asyHPPPZebbrop3/3ud9PU1JSzzz47X/jCF7Lffvt1uXZP2WmnnbL//vvnySef3GLukUceyfLlyzNy5Mgu71sphHDiiSdu9vc3hhfuv//+rF27tltBg46CDx2FNAAAAAAAgM6p6+0GAAAAAAConenTp1ecO/fcczf7PmLEiLzrXe8qu/a2227L888/X9XetlfFYjH/9V//ld133z3/+3//7w6DC5WsWbMmM2bMyAEHHJDPfOYzWbVqVQ06rY5KwY729vbccccd3dqzXJBg0KBBOeqoozZ9f32QYaP169fn7rvvrlrNjboTXgEAAAAAADYnvAAAAAAAsJ1avXp1rrjiirJzxx57bPbaa68txj/60Y+WXV8sFnPJJZdUtb/t0Zo1a3L22WfnE5/4RJYtW7bN+7W3t+eHP/xhDjvssDzxxBNV6LD6OvrF/o4CAR0pd93RRx+dxsbGTd+POuqozb5vdOutt1atZpIMGDAgxx13XLf2BAAAAAAA/kR4AQAAAABgO/X//t//y+rVq8vOnXfeeWXH3/rWt2b8+PFl52bMmJFisVi1/rY3q1atyqmnnporr7yy6nvPmTMnxx57bB588MGq772tpk2bVnGuO+GFRYsW5cknn9xi/I0nLTQ2Nm52EsO21HzhhRcqnpBxxBFHpKmpqct7AgAAAAAAmxNeAAAAAADYTk2fPr3seGNjYz7wgQ+Unauvr88555xTdu7FF1/MLbfcUrX+tidtbW1597vfnXvuuafDdU1NTfngBz+Yf/mXf8kll1ySX/3qV/m3f/u3fO5zn8tuu+3W4bUrVqzIaaedljlz5lSz9W02fvz47LfffmXnHn744axcubJL+91+++1lQzLlQhJvDDQkyX333Zfm5uYu1ewo8NDRyRIAAAAAAEDnCS8AAAAAAGyHnn322dxxxx1l5975zndm5MiRFa/96Ec/WnGuUiCiN/3TP/1TisXiZp9KQYDp06dvsXZrn8788vqXvvSlDoMdw4YNyw9+8IO8+uqrufzyy/OlL30p5513Xs4+++xceOGF+d73vpfnn38+N998c6ZOnVpxn2XLluWss87q8i/n11ql0xc2bNiQO++8s0t7lQsSDBgwIMccc8wW4+XCCy0tLVsNkXSm5kbCCwAAAAAAUB3CCwAAAAAA26GOQgbnnXdeh9e+6U1vysEHH1x27sorr+zym/S3d/fdd1+++93vVpw/4ogj8vjjj+fTn/50hgwZUnFdoVDIW97yljz00EP51Kc+VXHdY489lq997Wvb1HO1dfQL/h0FAzq7/ogjjij7z+7YY49NQ0NDTWomSUNDQ4477rgu7QUAAAAAAJQnvAAAAAAAsJ1pb2/PpZdeWnZu3Lhxedvb3rbVPSoFHNatW5df/vKX29Tf9uaCCy5Ie3t72bn9998/119/fSZPntzp/RobG/OjH/0oH/vYxyqu+da3vpWXXnqpq63WTLXCC8uWLctjjz22xXi5ExaSZOjQoTnkkEO2qea8efPy3HPPlZ074ogj0tTU1Om9AAAAAACAyoQXAAAAAAC2MzfeeGPmzp1bdu5DH/pQ2TfVv9E555xTcV1HpzrsaG688cbcc889ZeeGDBmS6667LmPGjOnW3j/5yU/y5je/uexca2trvvGNb3Rr31rYeeeds88++5Sde/DBB7N69epO7XP77benWCxuMT5t2rSK15QLNtx9991paWnpVM2Ogg4dhTIAAAAAAICuEV4AAAAAANjOdBQuqHSiwhuNHz8+p59+etm5u+++O08//XS3etve/OhHP6o49+UvfzlTpkzp9t719fX5wQ9+kLq68rfyL7300qxZs6bb+1dbpV/0b2try1133dWpPcoFCerr63PcccdVvKZceKG5uTn33Xdft2tuJLwAAAAAAADVI7wAAAAAALAdWb58ea6++uqyc1OnTs3hhx/e6b0++tGPVpybMWNGFzvb/ixfvjy//e1vy87tvPPOueiii7a5xuGHH54Pf/jDZedWr16dq666aptrVEtHpyN0FBDY2rpDDjkkw4cPr3jN8ccfn0KhUNWaSdLQ0NBhaAIAAAAAAOga4QUAAAAAgO3I5Zdfnubm5rJznT11YaN3v/vdGTlyZNm5Sy+9NBs2bOhqe9uVG2+8Ma2trWXnPvShD6WxsbEqdf78z/+84tzvfve7qtSoho5OKbj11lu3ev3KlSvzyCOPbDFe7mSF1xszZkymTp26xXhnwguvvPJKZs+eXXbuiCOOyNChQ7e6BwAAAAAA0DnCCwAAAAAA25Hp06eXHa+rq8tHPvKRLu3V2NiY97///WXnFixYkN///vdd7m97csstt1Sc6+o/646cdNJJmTx5ctm5m2++uWp1ttXEiROz9957l5174IEHsnbt2g6vv+OOO8oGYrYWXqi0ZubMmVm/fn2H13UUcOgojAEAAAAAAHSd8AIAAAAAwHZi1qxZuf/++8vOnXzyydlll126vGdHpzVUCkrsKB5++OGy4yNHjsxhhx1WtTqFQiEnn3xy2blXXnklr7zyStVqbatp06aVHV+/fn1mzpzZ4bXlggSFQiEnnHDCVuuWCy+sXbu24n8fOqq5UaWfBQAAAAAA6B7hBQAAAACA7URHYYKOQggdOe6447LXXnuVnbvmmmuydOnSbu27PZg1a1bZ8UMOOaTqtQ499NCKc48//njV63VXR6cVdBQUqDR/wAEHZMyYMVutW+l0hu7UTJKGhoYcf/zxW60LAAAAAAB0nvACAAAAAMB2oK2tLZdddlnZuaFDh+ass87q9t7nnntu2fGWlpb8/Oc/7/a+/dmKFSuycuXKsnO1CC90tOfcuXOrXq+7Ogov3HrrrRXnVq9enYceemiL8c6efjBx4sTsscceW4x3FF5YtGhRnnzyybJzhx9+eIYOHdqp2gAAAAAAQOcILwAAAAAAbAeuu+66LFy4sOzcWWedlaampm7vfd5556VQKJSd6+i0h+3ZK6+8UnFu5513rnq9CRMmVJx7+eWXq16vu3bZZZfsueeeZefuu+++rFu3ruzcXXfdlba2ti3GK52oUE65tZX2TZLbb789xWKx7FxHIQwAAAAAAKB7hBcAAAAAALYDHYUIzjvvvG3ae8qUKRV/ifyhhx7KY489tk3790eVTl1IkuHDh1e93ogRIyrOrVq1qur1tkWlX/xvbW3NPffcU3au0gkJXQkvnHDCCVuMVTrRoaOaifACAAAAAADUgvACAAAAAEA/t2jRovz2t78tOzdp0qS85S1v2eYaHQUgdsTTF1paWirO9XR4obm5uer1tsW0adMqzlUKDJQb32effbp0ikWloMOtt97apV4aGhpy/PHHd7ouAAAAAADQOQ293QAAAAAAANvmsssuy/r168vOvelNb8p11123zTUGDx6cQqGQYrFYtv43vvGNDBgwYJvr9Bfl/jlsVCgUerCTvqejUwvKBQnWrVuXBx54YIvxjkIQ5ey1116ZOHFiFixYsNn4bbfdls9//vObjS1btqziiSGHHXZYhg4d2qXaAAAAAADA1gkvAAAAAAD0cx2dfPDb3/624qkM1bLx5If3vOc9Na3TlwwaNKji3IoVK6per6M9O+qlN0yaNCl77LFHnnvuuS3m7r333rS0tKSxsXHT2MyZM9Pa2rrF2konKXTkhBNOyC9/+cvNxu68885s2LAh9fX1m8Zuv/32igGUjsIXAAAAAABA99X1dgMAAAAAAHTfgw8+WPEN8j2powDF9mj48OEV51auXFn1eh2FF4YNG1b1etuq0qkJzc3Nuffeezcbu+2228qu7W544Y1WrlyZP/7xj52qmQgvAAAAAABArQgvAAAAAAD0Y30lNHDddddl4cKFvd1Gj9l5550rzr3yyitVr9fRnhMmTKh6vW3VUQDg1ltv3ex7uSDBlClTMnny5C7XrRR4eGONSuGF+vr6HH/88V2uCwAAAAAAbJ3wAgAAAABAP9XS0pLLL7+8t9tIkrS1teWyyy7r7TZ6zIgRIyqevvDGt/xXw8MPP1xxrju/5F9rHYUXXh8caGlp2eIkhqR7py4kyYEHHpjRo0d3WHPFihV55JFHyl5/+OGH98mTLAAAAAAAYHsgvAAAAAAA0E/95je/ydKlS3u7jU1mzJjR2y30qAMOOKDseKVfjN8WHQUiDjzwwKrX21aTJ0/OlClTys7dfffdaW1tTZLcc889aWlp2WLNtGnTulW3UCjkuOOO22L8jjvuSHt7e5LkzjvvzIYNG8pe31HoAgAAAAAA2DbCCwAAAAAA/dT06dMrzj344IMpFos1+XzsYx8rW/Pxxx/PAw88UKOftmsKhULZ8WKxWLUahx12WNnxZcuWdXhSQlcVi8XcfPPNZecmTJiQ8ePHV61WNVUKAqxbty73339/ks1PRHi97p68UOnaZcuW5dFHH+2wZiK8AAAAAAAAtSS8AAAAAADQD82fPz+///3vy87tt99+FX+xvhrOOeecinMdBSp60oABA8qONzc3V63GW97ylopzl112WdXq3H777XnxxRfLzp188slVq1NtHZ2ecOuttyYpHySYOHFi9tprr27XrRR82FirUnihvr4+xx9/fLfrAgAAAAAAHRNeAAAAAADohy699NK0t7eXnesoXFANJ598ciZMmFB27vLLL09LS0tN63dGU1NT2fHVq1dXrcapp56axsbGsnOXX355Wltbq1Lnpz/9acW5t7/97VWpUQsdnWJw2223pbW1NXffffcWc9ty6kJSOhGj3L//2267LatXr85DDz1U8bphw4ZtU20AAAAAAKAy4QUAAAAAgH5oxowZFec+/OEP17R2XV1dPvjBD5adW7ZsWa6++uqa1u+McePGlR1/4YUXqlZjxIgROfPMM8vOvfzyy/nud7+7zTUefvjhiqc4DB06NO95z3u2uUatTJkyJbvttlvZuZkzZ2bmzJlZt27dFnMdndjQGQ0NDTnmmGO2GL/99ttz5513pq2trex1HYUtAAAAAACAbSe8AAAAAADQz9x1112ZPXt22bk3v/nN2WOPPWreQ0cBienTp9e8/tZMmTKl7Pgf//jHqtY5//zzK8597Wtfy9y5c7u994YNG/KZz3ym4gkbH/3oRzNkyJBu798TKgUR1qxZk+985ztl57b15IVKeyxZsiQ//OEPK14jvAAAAAAAALUlvAAAAAAA0M90FA4455xzeqSHI444Ivvuu2/ZuT/84Q+ZP39+j/RRyZve9Kay4/fcc09efPHFqtU55ZRTyr7lPyn9gv7b3/72LFu2rFt7f/KTn8zMmTPLzg0cODCf//znu7VvT+ooEHDttdduMTZ27Njsv//+21y3UgCiXM0kqa+vz/HHH7/NdQEAAAAAgMqEFwAAAAAA+pG1a9fmV7/6Vdm5hoaGvP/97++xXiqdvtDe3p5LL720x/oop9Ivr7e3t+cjH/lIXn311arVuvjii1NXV/52++OPP563v/3tXQpztLa25rOf/Wx+8pOfVFzz+c9/PpMnT+5yrz2t0skLlZx44okpFArbXPfoo4/OwIEDO73+sMMOy/Dhw7e5LgAAAAAAUFmhWCwWe7sJAAAAAAA659JLL81HP/rRsnNve9vbct111/VYL3PmzMnee+9ddm7vvffO7Nmze6yXcvbZZ58888wzZecGDx6cM888M0cccUR23XXXDB06NPX19WXXHnPMMRkzZkyHtf7u7/4u3/72tyvOjxgxIl//+tfz0Y9+NIMHDy67plgs5rbbbstnP/vZPP744xX3Ouigg3Lfffdl0KBBHfbUV0yePDlz587t1NqLL744F1xwQVXqHn/88bnrrrs6tfaiiy7Kt771rarUBQAAAAAAyhNeAAAAAADoR97ylrfk1ltvLTv3s5/9LB/5yEd6tJ+jjz469913X9m5O++8M8cdd1yP9vN6//Ef/5Hzzz9/m/e55ZZbctJJJ3W4pq2tLaeffnpuueWWDtcNHTo073rXu3LQQQdll112yeDBg7NgwYI8//zzufrqq/PCCy90eP2oUaNy3333Za+99uriT9F7zj333Fx22WWdWvvwww/nkEMOqUrdL3/5y/nXf/3XTq299tprc+aZZ1alLgAAAAAAUJ7wAgAAAABAP/H8889nzz33TLnbukOGDMnChQszdOjQHu3p+9//fsU35f/FX/xFfvKTn/RoP6+3YcOGHHfccbn33nu3aZ/OhBeSZNWqVTnttNO2uV4lI0aMyI033pgjjjiiJvvXyn/913/lE5/4xFbXjRw5MkuWLEldXV1V6l5//fV529vettV19fX1Wbp0aYYPH16VugAAAAAAQHnVeQIAAAAAAEDNzZgxo2xwIUne/e5393hwIUk+8IEPpL6+vuzcr371q6xdu7aHO/qT+vr6XHPNNTn88MN7pN6wYcNy00035ayzzqr63nvuuWdmzpzZ74ILSToV/EiS448/vmrBhSQ59thjK/5n8/UOPfRQwQUAAAAAAOgBwgsAAAAAAP1AsVjMJZdcUnH+nHPO6cFu/mT8+PE59dRTy86tWrUqV1xxRQ93tLmddtopM2fOzLe+9a1MmDCh5vWamppyxRVX5P/+3/+bUaNGbfN+dXV1+dSnPpWHHnooU6dOrUKHPW/PPffMrrvuutV1J554YlXrDh8+PAcffPBW13U2XAEAAAAAAGwb4QUAAAAAgH7g5ptvzosvvlh2bsyYMTn99NN7uKM/6Sg4MX369B7spLyBAwfmoosuyty5c3PrrbfmH//xH/Pud787Bx54YMaNG5fGxsYUCoWq1SsUCvnEJz6R559/Pl/96lczadKkLu/R1NSUj33sY5k1a1Z+9KMf9fuTAaZNm7bVNdUOL3R2T+EFAAAAAADoGYVipTPGAQAAAACAbVYsFnP33Xfnlltuyf333585c+ZkwYIFWbNmTTZs2JAhQ4Zk7Nix2X333XPIIYfkhBNOyGmnnZampqbebh0AAAAAAKBqhBcAAAAAAAAAAAAAAICaquvtBgAAAAAAAAAAAAAAgO2b8AIAAAAAAAAAAAAAAFBTwgsAAAAAAAAAAAAAAEBNCS8AAAAAAAAAAAAAAAA1JbwAAAAAAAAAAAAAAADUlPACAAAAAAAAAAAAAABQU8ILAAAAAAAAAAAAAABATQkvAAAAAAAAAAAAAAAANSW8AAAAAAAAAAAAAAAA1JTwAgAAAAAAAAAAAAAAUFPCCwAAAAAAAAAAAAAAQE0JLwAAAAAAAAAAAAAAADUlvAAAAAAAAAAAAAAAANSU8AIAAAAAAAAAAAAAAFBTwgsAAAAAAAAAAAAAAEBNCS8AAAAAAAAAAAAAAAA1JbwAAAAAAAAAAAAAAADUlPACAAAAAAAAAAAAAABQU8ILAAAAAAAAAAAAAABATQkvAAAAAAAAAAAAAAAANSW8AAAAAAAAAAAAAAAA1JTwAgAAAAAAAAAAAAAAUFPCCwAAAAAAAAAAAAAAQE0JLwAAAAAAAAAAAAAAADUlvAAAAAAAAAAAAAAAANSU8AIAAAAAAAAAAAAAAFBTwgsAAAAAAAAAAAAAAEBNCS8AAAAAAAAAAAAAAAA1JbwAAAAAAAAAAAAAAADUlPACAAAAAAAAAAAAAABQU8ILAAAAAAAAAAAAAABATQkvAAAAAAAAAAAAAAAANSW8AAAAAAAAAAAAAAAA1JTwAgAAAAAAAAAAAAAAUFPCCwAAAAAAAAAAAAAAQE0JLwAAAAAAAAAAAAAAADUlvAAAAAAAAAAAAAAAANSU8AIAAAAAAAAAAAAAAFBTwgsAAAAAAAAAAAAAAEBNCS8AAAAAAAAAAAAAAAA1JbwAAAAAAAAAAAAAAADUlPACAAAAAAAAAAAAAABQU8ILAAAAAAAAAAAAAABATQkvAAAAAAAAAAAAAAAANSW8AAAAAAAAAAAAAAAA1JTwAgAAAAAAAAAAAAAAUFPCCwAAAAAAAAAAAAAAQE0JLwAAAAAAAAAAAAAAADUlvAAAAAAAAAAAAAAAANSU8AIAAAAAAAAAAAAAAFBTwgsAAAAAAAAAAAAAAEBNCS8AAAAAAAAAAAAAAAA1JbwAAAAAAAAAAAAAAADUlPACAAAAAAAAAAAAAABQU8ILAAAAAAAAAAAAAABATQkvAAAAAAAAAAAAAAAANSW8AAAAAAAAAAAAAAAA1JTwAgAAAAAAAAAAAAAAUFPCCwAAAAAAAAAAAAAAQE0JLwAAAAAAAAAAAAAAADUlvAAAAAAAAAAAAAAAANSU8AIAAAAAAAAAAAAAAFBTwgsAAAAAAAAAAAAAAEBNCS8AAAAAAAAAAAAAAAA1JbwAAAAAAAAAAAAAAADUlPACAAAAAAAAAAAAAABQU8ILAAAAAAAAAAAAAABATQkvAAAAAAAAAAAAAAAANSW8AAAAAAAAAAAAAAAA1JTwAgAAAAAAAAAAAAAAUFPCCwAAAAAAAAAAAAAAQE0JLwAAAAAAAAAAAAAAADUlvAAAAAAAAAAAAAAAANSU8AIAAAAAAAAAAAAAAFBTwgsAAAAAAAAAAAAAAEBNCS8AAAAAAAAAAAAAAAA1JbwAAAAAAAAAAAAAAADUlPACAAAAAAAAAAAAAABQU8ILAAAAAAAAAAAAAABATQkvAAAAAAAAAAAAAAAANSW8AAAAAAAAAAAAAAAA1JTwAgAAAAAAAAAAAAAAUFPCCwAAAAAAAAAAAAAAQE0JLwAAAAAAAAAAAAAAADUlvAAAAAAAAAAAAAAAANSU8AIAAAAAAAAAAAAAAFBTwgsAAAAAAAAAAAAAAEBNCS8AAAAAAAAAAAAAAAA1JbwAAAAAAAAAAAAAAADUlPACAAAAAAAAAAAAAABQU8ILAAAAAAAAAAAAAABATQkvAAAAAAAAAAAAAAAANSW8AAAAAAAAAAAAAAAA1JTwAgAAAAAAAAAAAAAAUFPCCwAAAAAAAAAAAAAAQE0JLwAAAAAAAAAAAAAAADUlvAAAAAAAAAAAAAAAANSU8AIAAAAAAAAAAAAAAFBTwgsAAAAAAAAAAAAAAEBNCS8AAAAAAAAAAAAAAAA1JbwAAAAAAAAAAAAAAADUlPACAAAAAAAAAAAAAABQU8ILAAAAAAAAAAAAAABATQkvAAAAAAAAAAAAAAAANSW8AAAAAAAAAAAAAAAA1FRDbzcAAADVtnbt2jz00EN55plnsnjx4jQ3N2fw4MHZaaedss8+++TQQw9NY2Njb7cJAAAAAABAH9STz5paW1sze/bsPPnkk3nllVeycuXKDBkyJKNGjcp+++3nuRYAANsV4QUAALYLGzZsyK9+9atMnz49t9xyS9ra2iqubWxszFvf+tb8xV/8Rd75znemUCh0q2axWMzTTz+d+++/P/fff38eeOCBPPzww2lubt60Ztq0abn11lu7tT8AAAAAAAA9oyefNT311FO56qqrctNNN2XmzJlZt25dxbUDBw7Mu9/97nzuc5/L8ccf36U6AADQ1xSKxWKxt5sAAIBtcfPNN+dTn/pUZs+e3eVrDzvssPznf/5njjzyyE5fc8UVV+SHP/xhHnzwwaxatarDtcILAAAAAAAAfVtPPms67rjjMnPmzC7XSZKPf/zj+d73vpdhw4Z163oAAOhtwgsAAPRrX/nKV/KVr3wlb/yftYVCIfvvv3923XXXjBkzJosWLcpLL71U9qbzgAED8m//9m/59Kc/3amaF154Yb73ve91aq3wAgAAAAAAQN/V08+axo4dmyVLlmwxvscee2T33XfPuHHj0tzcnNmzZ+eJJ57YYt2xxx6bG264IUOHDu3CTwkAAH1DQ283AAAA3VUuRDBs2LB86UtfyjnnnJPJkydvcc2cOXMyY8aMfPvb305LS0uSZP369fnMZz6T1atX5wtf+EK3+2lqasq4cePywgsvdHsPAAAAAAAAekZvP2s64YQT8vGPfzynn356dtllly3mZ8+enS9+8Yu56qqrNo3NnDkzn/zkJ3PZZZd1ug4AAPQVTl4AAKBfuuSSS/Kxj31ss7Hjjz8+l19+eXbdddetXj979uz82Z/9WR5//PFNY4VCIddff31OP/30Dq+98MIL8+Mf/zgHH3xwjjjiiE2fqVOn5tJLL83HP/7xTWudvAAAAAAAAND39NazpvHjx+e0007L3//932ffffftVK9f+MIX8s1vfnOzsZkzZ+aYY47p1PUAANBXCC8AANDvzJ49O4cffnhWr169aaw7R+S++uqrOemkk/Lkk09uGhs/fnweeeSRjB8/vuJ1ixYtyqhRo9LQsOVBZjNmzBBeAAAAAAAA6MN681nTCy+8kClTpnSp32KxmKOOOioPPPDAprHzzz8/P/zhD7u0DwAA9La63m4AAAC66qKLLtrsZvLIkSNz5ZVXdulmcpLstNNOueKKKzJw4MBNYwsXLsxXvvKVDq8bN25c2eACAAAAAAAAfV9vPmvqanAhKZ3ocP755282dsstt3R5HwAA6G3CCwAA9CtPPfVUrr322s3Gvv71r2fnnXfu1n5Tp07NRRddtNnYjBkzsnjx4m73CAAAAAAAQN/UX581HXrooZt9X7BgQVX3BwCAniC8AABAv/K9730vxWJx0/exY8fm4x//+DbteeGFF2bAgAGbvq9bty4//vGPt2lPAAAAAAAA+p7++qzpjaeCt7a2VnV/AADoCcILAAD0K9dff/1m388777zNjuLtjnHjxuWd73znZmM33HDDNu0JAAAAAABA39NfnzXNmTNns+8TJkyo6v4AANAThBcAAOg35s2blxdeeGGzsdNPP70qe79xn/vuuy8tLS1V2RsAAAAAAIDe15+fNV1xxRWbfT/qqKOqtjcAAPQU4QUAAPqNu+66a4uxI444oip7H3744Zt9b2lpyUMPPVSVvQEAAAAAAOh9/fVZ09y5c3PllVduNvbe9763KnsDAEBPEl4AAKDfmDdv3mbfx48fnzFjxlRl7wMPPHCLsZdffrkqewMAAAAAAND7+uuzpvPPPz/Nzc2bvu+xxx4566yzqrI3AAD0JOEFAAD6jaVLl272fdSoUVXbe9CgQWlsbNxsbMmSJVXbHwAAAAAAgN7VH581XXzxxbn22ms3G/v+97+fhoaGbd4bAAB6mvACAAD9xhtvKI8cObKq+79xvzfWAwAAAAAAoP/qb8+afv/73+fv/u7vNhv7y7/8y5x55pnbtC8AAPQW4QUAAHhNoVDo7RYAAAAAAADop6r5rOnhhx/O2Wefnba2tk1jhx9+eL7//e9XrQYAAPQ04QUAAPqN0aNHb/Z9xYoVVd1/+fLlm30fPnx4VfcHAAAAAACg9/SXZ02zZ8/OGWeckZUrV24a22+//fK73/0ugwYN2pYWAQCgVwkvAADQb7zxhvKyZcuqtndzc3Oam5s3GxszZkzV9gcAAAAAAKB39YdnTc8//3xOOeWUvPrqq5vG9txzz9x0000ZN27cNvcJAAC9SXgBAIB+Y5dddtns+yuvvJIlS5ZUZe9Zs2ZtMTZ16tSq7A0AAAAAAEDv6+vPmubOnZuTTz458+bN2zS222675eabb87EiRO3uUcAAOhtwgsAAPQbxx577BZjDzzwQFX2fuM+AwcOzL777luVvQEAAAAAAOh9fflZ08svv5yTTz45L7zwwqaxXXbZJTfddFMmT55clR4BAKC3CS8AANBvTJ48ObvttttmY7///e+rsvcf/vCHzb6ffPLJGTBgQFX2BgAAAAAAoPf11WdNCxcuzMknn5w5c+ZsGtt5551z8803Z88996xKfwAA0BcILwAA0K+cccYZm33/2c9+lvXr12/TnosWLco111yz2dh73/vebdoTAAAAAACAvqevPWtavHhxTjnllDz11FObxsaNG5ebbrop++yzzzb1BQAAfY3wAgAA/coFF1yQQqGw6fuiRYsyffr0bdrze9/73mY3pUePHp0Pf/jD27QnAAAAAAAAfU9feta0dOnSnHrqqZk1a9amsTFjxuSmm27K1KlTt6knAADoi4QXAADoV/bff/+87W1v22zsC1/4QhYuXNit/Z544ol861vf2mzswgsvzNChQ7vdIwAAAAAAAH1TX3nWtHz58px22ml55JFHNo2NGjUqf/jDH3LQQQd1qxcAAOjrhBcAAOh3vvOd72TIkCGbvi9fvjxnnXVWVq9e3aV9Fi1alPe9731pbW3dNHbAAQfk85//fNV6BQAAAAAAoG/p7WdNq1atyhlnnJGHHnpo09jw4cNzww035NBDD+1SDwAA0J8ILwAA0O/st99++fd///fNxmbOnJm3ve1tmTdvXqf2eOaZZ3LKKafkySef3DQ2bNiw/Pd//3caGxur2i8AAAAAAAB9R28+a1q7dm3OPPPM3HvvvZvGhg4dmuuvvz5HHnlkF38SAADoXxp6uwEAAOiOP//zP89DDz2UH/7wh5vG7rzzzkydOjVf/vKXc84552TSpElbXDdnzpzMmDEj3/72t9PS0rJpfMiQIbn22mtz8MEHd6r+Cy+8UHZ88eLFm31vbm6uuHbkyJEZOXJkp+oBAAAAAABQPb3xrKm1tTXvfve7c8cdd2waq6+vz49+9KNMmDCh4jOlSnbdddc0NPj1LwAA+o9CsVgs9nYTAADQXX//93+ff/7nf84b/2dtoVDI1KlTM2nSpIwaNSpLlizJiy++mKeffnqLPfbZZ5/88pe/zCGHHNLpuoVCYVtbzz/+4z/mn/7pn7Z5HwAAAAAAALqnJ581vfDCC9l9992r1vvzzz+fKVOmVG0/AACoNeEFAAD6vRtvvDHnn39+nnnmmS5d19TUlAsvvDBf/OIXM3To0C5dK7wAAAAAAACwfeipZ03CCwAA7OjqersBAADYVqeeemqeeOKJ/Pd//3dOOeWUTh+Pe8EFF+RrX/tal4MLAAAAAAAAbD88awIAgJ7h5AUAALY7a9asyYMPPpg5c+Zk0aJFaW5uTrFYzLXXXpsHH3xws7U//vGP81d/9Ve91CkAAAAAAAB9jWdNAABQG8ILAADsMFasWJHTTz89991336axurq6/PznP88HPvCBXuwMAAAAAACAvs6zJgAA2DbCCwAA7FBWrFiR0047Lffff/+msQEDBuTqq6/O29/+9l7sDAAAAAAAgL7OsyYAAOg+4QUAAHY4y5cvz2mnnZYHHnhg09jgwYNzww035IQTTujFzgAAAAAAAOjrPGsCAIDuEV4AAGCHVO6m8vDhw3PLLbfksMMO68XOAAAAAAAA6Os8awIAgK4TXgAAAAAAAAAAAAAAAGqqrrcbAAAAAAAAAAAAAAAAtm/CCwAAAAAAAAAAAAAAQE0JLwAAAAAAAAAAAAAAADUlvAAAAAAAAAAAAAAAANSU8AIAAAAAAAAAAAAAAFBTwgsAAAAAAAAAAAAAAEBNCS8AAAAAAAAAAAAAAAA1JbwAAAAAAAAAAAAAAADUlPACAAAAAAAAAAAAAABQU8ILAAAAAAAAAAAAAABATQkvAAAAAAAAAAAAAAAANSW8AAAAAAAAAAAAAAAA1JTwAgAAAAAAAAAAAAAAUFPCCwAAAAAAAAAAAAAAQE0JLwAAAAAAAAAAAAAAADUlvAAAAAAAAAAAAAAAANSU8AIAAAAAAAAAAAAAAFBTwgsAAAAAAAAAAAAAAEBNCS8AAAAAAAAAAAAAAAA1JbwAAAAAAAAAAAAAAADUlPACAAAAAAAAAAAAAABQU8ILAAAAAAAAAAAAAABATQkvAAAAAAAAAAAAAAAANSW8AAAAAAAAAAAAAAAA1JTwAgAAAAAAAAAAAAAAUFPCCwAAAAAAAAAAAAAAQE0JLwAAAAAAAAAAAAAAADUlvAAAAAAAAAAAAAAAANSU8AIAAAAAAAAAAAAAAFBTwgsAAAAAAAAAAAAAAEBNCS8AAAAAAAAAAAAAAAA1JbwAAAAAAAAAAAAAAADUlPACAAAAAAAAAAAAAABQU8ILAAAAAAAAAAAAAABATQkvAAAAAAAAAAAAAAAANSW8AAAAAAAAAAAAAAAA1JTwAgAAAAAAAAAAAAAAUFPCCwAAAAAAAAAAAAAAQE0JLwAAAAAAAAAAAAAAADUlvAAAAAAAAAAAAAAAANSU8AIAAAAAAAAAAAAAAFBTDb3dAAAAQL+3alXy7LPJnDnJM88kTz+drFuXtLQkDQ3JoEHJHnsk++yT7LVX6TNmTFIo9HbnAAAAAAB0V7GYLFpUujc8Z07p3vALL5TuDbe1JY2NSVNTsu++yd57l+4N77lnaQwAAGAHVCgWi8XebgIAAKDfaWtL7r47ueaa5K67kjVrSg+kkmTgwKS+vhROKBaT9vaktbX094aG0oOp/fdP3vve5JRTkqFDe/dnAQAAAACg81auTP7wh+TXvy6FFtasSTZsKM0NHJjU1f3p/vCGDcn69aW5jWGGE05I3vWu5OijS/eSAQAAdhDCCwAAAF2xcmUpsHDllaWHUuvXJ6NGlR44DRrU8WkKxWJp/Zo1ybJlpe8TJpQeUp11VjJpUs/9HAAAAAAAdM3zz5cCC9dem7zySimkMHp0MmRIKbTQkWIxaW5OVq9OVqxIBgxI9tuvdG/4ne90GgMAALBDEF4AAADorJkzk29+s3T094AByU47JYMHd3+/9etLR4qvXl0KMZx/fvJnf+ZNWwAAAAAAfcn69ckvfpH8+Mele7rDhiVjx5buE3fXmjWlvdrakgMPTL7wheSII6rXMwAAQB8kvAAAALA1K1YkP/pRcsUVybp1yeTJ2/ZQ6o3a25P580sPwE46Kfn855Pddqve/gAAAAAAdM+cOaWX2tx1V9LYWHoRTV1d9fZvbU1eeikZOjT58IeTv/orpzAAAADbLeEFAACAjjz3XHLRRcmsWcmYMaUjwAuF2tRavTpZsKD08OsrX0mmTatNHQAAAAAAtu6GG5KvfS159dVk112TIUNqU6dYTBYvTpYvTw4+OPn2t5NJk2pTCwAAoBcJLwAAAFTy5JOl4MKcOcmUKcnAgbWv2d6evPhi6djxf/iH5G1vq31NAAAAAAA2d9VVyb/8S9LSUgoSVPO0hUpaWkr3h/fbL/nud5M996x9TQAAgB4kvAAAAFDOs88mn/lM8vzzyR57JPX1W73kD4sW5fR77+1wzQPHH5/DR47seKNisXRM+ODBpYdjp5zShcYBAAAAANgm116b/NM/JRs2JLvs0qnTeKt2f7itrXQi8L77Jj/8oRMYAACA7UoPxMIBAAD6maVLk7/929IDok4GF5Jkxty5W18zb97WNyoUksmTk7VrS6cvPPJIp+oDAAAAALCN7r03+drXkvXrOx1cSKp4f7ihoXRf+umnSycDr1rVqfoAAAD9gfACAADA6xWLyfe+lzz5ZLL77p0OLqxcvz5XvfLKVtddPn9+Wtvbt75hoZDstluyZEnyr/9aCjIAAAAAAFA7K1cmX/966c9JkzodXKj6/eGGhmTKlNKLbf7jPzrVAwAAQH8gvAAAAPB6t9yS/OY3ybhxyYABnb7slwsWZF0nHjotWb8+/7NwYec23XgCwx//mPz0p53uBQAAAACAbviP/yi92KYLwYWkRveHBw5MRo1KfvnL5J57Ot0LAABAXya8AAAAsNHSpcl3vpO0tZUeCnVBp4773ri2E8eHb9LYmIwYkVx6aSnEAAAAAABA9c2cmfzqV8no0aXgQBfU7P7wmDHJunXJN7+ZrFrVpZ4AAAD6IuEFAACAjX75y+TZZ0tv1eqCZ1avzsxlyzq9/vpFi7KwpaXzBcaNKx1T/qMfJcVil3oDAAAAAGAr2tuTH/4wWbu2FF7ogpreHy4USvern3giufLKLvUFAADQFwkvAAAAJKWHUlddlQwZkjQ0dOnSSm/V2rupKZMHD95ivK1YzGVdeBNXCoVkp52SBx8sHVkOAAAAAED1PPRQMmtWsvPOpfuxXVDz+8MDB5ZO6P31r5PW1i71BgAA0NcILwAAACTJrbcm8+aVTjnogvZiMT+r8KDpQxMn5v0TJpSd68ox4kmS4cOT1auTa6/t2nUAAAAAAHTsf/4nWbcuaWrq0mU9dn943LjkueeSu+7q2nUAAAB9jPACAABAsVg6crtQKL3FqgtuWrw4c5uby859cOLEfGiXXcrOPb5qVR5cvrzzhQqFUoDhuuuSrlwHAAAAAEBlCxcmf/hDMmpUl09d6LH7w4MHJxs2JFdf3aX+AAAA+hrhBQAAgLlzk0cfTUaP7vKlM+bOLTt+yPDh2X/YsBw2YkT2qfC2ri6/XWvs2OTVV5N77ulqmwAAAAAAlDNzZrJsWd+/PzxqVHLvvcmiRV1tEwAAoM8QXgAAAHjmmWTNmmTo0C5dtnL9+lz1yitl5z44ceKmv3/gdX9/vcvnz09re3vnCzY0lE6JmDOnS30CAAAAAFDB7NmlP+vru3RZj98fHjasdB/b/WEAAKAfE14AAAB49tlSKKChoUuX/XLBgqwr83CpkM0fTn2owsOpJevX538WLuxSzdTVJbNmde0aAAAAAADKe+yxZMCALl/W4/eHBwxI2tqEFwAAgH5NeAEAAODJJ5NCocuXVTrW+5hRo7LbkCGbvu8/bFjeNGxY+T0qHCteUVNT8tRTyfr1XbsOAAAAAIDNrVmTPP986b5rF/X4/eGN97A3nhQBAADQDwkvAAAAPPlk8rqHSZ3xzOrVmblsWdm5cm/S+tAuu5Rde/2iRVnY0tL5wkOGJCtXJvPnd/4aAAAAAAC29NJLydq1/ef+8KBBTuYFAAD6NeEFAABgx1Yslt6u1dDQpcsqvVWrvlDI2WUeTn2wwtHgbcViLquwV1kNDcmGDcm6dZ2/BgAAAACALa1dm7S19a/7w2vWdH49AABAHyO8AAAA7NjWr0/a2/905HYntBeL+VmFB0pvGTMm4xsbtxifMmRI3jxyZNlrKj3oKquurtRvV97GBQAAAADAllpbSy+46S/3hwsF94YBAIB+rWvRcQAAgO1NfX2XHkwlye9ffTVzm5vLzrWsXZu/veee8hdWeKj0+KpVufLRR/OeAw9Mfd1WMuYbH6R18U1gAAAAAAC8QX19ly+5afHiiveHly5enI9ee23ZubUV9nt81ao8uHx5Dq8QbthCN3oGAADoK/y2CwAAsGOrry99Wls7fcklHbwJ6461a3PH2kqPoSqb/tJLedfUqVsPL7S3l05fGDCgyzUAAAAAAHidgQP/dNptJ82YO7fi3EOvfbpqxrx5nQsvbNiQDBrUjQoAAAB9w1Z+KwYAAGAHMHFip4/aXrl+fX6zcGHVW7g9SWtnHpA1NyeNjcm4cVXvAQAAAABghzJuXOl+a4WTFN5o5fr1ueqVV6rexuXz53fu/nBLS7LLLlWvDwAA0FOEFwAAAN70pk6fvPDLBQuyrgtv4eqsVUmuffXVrS9cu7b0cGr06Kr3AAAAAACwQ5k4MRkzJlmzplPLa3V/eMn69fmfzrw0Z8OG5MADq14fAACgpwgvAAAA7L136c9icatLZ8ybV7M2Lp0/f+uLmpuTgw6qWQ8AAAAAADuMQqF0v3Xt2k4tr+X94Rlz53a8YGNoYq+9atYDAABArTX0dgMAAAC9bq+9koEDS8GAwYMrLntm9erMXLas7NwHk3y4k+WmJ7mqzPjvFy/OwpaWjG9sLH9hsVj67LNPJysBAAAAANChffdNrr12q8tqfX/4+kWLOr4/vHZt6f71nnt2shIAAEDf4+QFAACAvfdOxo5NKjx42qijt2od34Vylda2FYu5rKM3d61alQwZ4lhwAAAAAIBqOeig0stt1qzpcFmv3x9etiwZPz7ZffcuVAMAAOhbhBcAAACampJ3vjNZvbp0skEZ7cViflbhwdGkJJO7UG7vJDtVmOvw2PFFi5I3van0MA0AAAAAgG13xBGl024XLaq4pNr3hydXOF2h4v3h9vbSycHvfW8paAEAANBPCS8AAAAkyZlnJsOHJ8uXl52+afHizG1uLjt3XDfKVbrm8VWr8mC5HlpbS3++971JodCNigAAAAAAbKG+PvmzP0va2kqfMqp9f/hdY8aUHa94f3jp0mTEiOTtb+9GNQAAgL5DeAEAACBJ9t47OfroZMmSstOXVOlI8I06eqBV9u1aixYlEyYkp57ajWoAAAAAAFR0+unJuHHJ4sVlp6t9f/hdY8dWnNvi/nCxmCxblrzlLcmuu3ajGgAAQN9RKBaLxd5uAgAAoE+4887ks59NmpqSUaMqLlvf1pbrr7++6uXPOOOMDGho2HKipSWZOze58MLkr/+66nUBAAAAAHZ4F1+c/Od/JrvtlgwcuNXlS5cty1133dWtUscdd1xGd3APejOLFyfr1yc//nFy+OHdqgcAANBXOHkBAABgo+OOS9773tIpBxWOB+9xxWLy0kvJoYcm553X290AAAAAAGyf/uIvkqlTSy+S6SvvAW1tLZ268KEPCS4AAADbBeEFAACAjQqF5DOfSfbeuxQY6AtefTUZPjz54heTwYN7uxsAAAAAgO3TsGHJF75Qug+7ZElvd1MKUMydmxxwQPJXf9Xb3QAAAFSF8AIAAMDrjR6dXHRR0thYOoGhN61enaxalXzsY8mb3tS7vQAAAAAAbO+OPrp0ysGyZcnatb3by8KFSVNTKVAxbFjv9gIAAFAlwgsAAABvNG1a8olPlMIDS5f2Tg/r1iULFiRvfWvy53/eOz0AAAAAAOxoPvWp5C1vSebNS5qbe6eHRYuSlpbSScFHHdU7PQAAANSA8AIAAMAbFQqlB1Qf/3iyfHmyeHHP1l+zpvRg7KSTkq99LRk0qGfrAwAAAADsqJqakq9/PTnmmOSll0ovmulJCxeW7hH/9V8n553Xs7UBAABqTHgBAACgnEIh+Zu/KT0gWrMmmTs3aW+vbc1isRSU2Hjiwre/nQwdWtuaAAAAAABsbsSI5OKLSy+YmTcvWbKkdP+2ljZsSF58MWltTS64IDn//NJ9agAAgO2I8AIAAEAldXXJZz+b/MM/JCNHJnPmJKtX16TUgCR1zz+frF+f/NVfJd/4RjJsWE1qAQAAAACwFSNHJt/9bun0g+bmZOP921pYtSp59tlk3Ljkq19N/uIvBBcAAIDtUkNvNwAAANCnFQrJ+96XHHlk8s1vJrfemkJdXQYmaa3C9nVJxiUZnqR9v/1S/7/+V3LUUVXYGQAAAACAbdLUlHz5y8mJJybf+lby1FOlUMOYMdXZv6Ulefnl0t/f/vbk7/4umTixOnsDAAD0QYVisdbn2gEAAGwnNmxIrrwybT/+cZ6++eYkyeIkK7ux1eAkOyUZmGRJkiuSfGX+/Az3YAoAAAAAoO9ZsSL5z/9MfvObZOnSrGlry8xnn01zN7Y68eCDM6K1tfTynClTSqfxvvOdpdOAAQAAtmPCCwAAAF20cuHCfHDnnXNWkkOTNCVpT7I2yZrX/tyQpJikkNLpCoNeW9eUpD7J+iRzklyZ5IaUAgwrVqzI8OHDe/aHAQAAAACg8xYuTK6/Pkt/8pPMv/PONKR0P3jNa5/mlO4Xb7w/XJ/SfeEhr33qkuy6774ZetJJyVlnlU51GDSoN34SAACAHie8AAAA0EUrV67MiBEjkiQHJHlzkn2THJRkdP70AKoupQdU7Sk9sFqZ5Mkks5I8kuSelB5qbSS8AAAAAADQP9xz++35wrRpOTil+8T7JhmepDGle8OFlO4Nb3zxzeIkjyV5OslnL788h37gA6WTFwAAAHYgDb3dAAAAQH8267XPRhOTTE7ppIUBSdqStCZ5JcmLr30HAAAAAKB/Kw4YkNuT3P7a9wFJdksyPqUAw8YTeNeldG/4lddd+5e77Sa4AAAA7JCEFwAAAKpowWsfAAAAAAB2HOuTzHntAwAAQHl1vd0AAAAAAAAAAAAAAACwfXPyAgAAQFU0JdkzyV5JJiUZlNJB4W1JWlM6FHzje7eW9lKPAAAAAAAAAADQO4QXAAAAum3nJG9LcnKS3VMKMDQmKSYpvPYpvu57W5I1SV5OcmeS65I81eNdAwAAAAAAAABATxNeAAAA6IL29uSBB+qTfDXJKUlGpXSywuoki5I0d3D1wJQCDlOS7J/kw0keSHJVkltr1jMAAAAAAAAAAPQ24QUAAIBOmj8/+fa3kxtvHJTkz5IsTzInpZMVOqP1tc+y176PSHJCkuOTPJZHHqnLCSdUt2cAAAAAAAAAAOgL6nq7AQAAgL6uvT256qrkvPOS665LRoxoTym0sDidDy6UsyLJs0nmJzk4F144OP/+78natVVoGgAAAAAAAAAA+hDhBQAAgA6sXJlcdFHyD/+QLF6c7LlnMmxYtau0JJmTQqGYH/4w+fjHk+eeq3YNAAAAAAAAAADoPcILAAAAFSxZklx4YfLb3yZjxiSTJyf19bWrN25cMZMnJ488knz608msWbWrBQAAAAAAAAAAPamhtxsAAADoi5YvT/72b5O7704mTUoGD+7slQuSzEryZJIXkqxOsirJuiSNr31GJpmQZJck+7/2GZokaWxM9tgjef755G/+Jvn3f0/23bdaPxUAAAAAAAAAAPQO4QUAAIA3aGlJvvSlUnBh8uRk0KCtXdGe5P4k1yR5rIN16177LE8p2LBRXZIDMnfu/Oy++ydTX5/svnvy3HOlAMWPf5zssku3fxwAAAAAAAAAAOh1db3dAAAAQF/zs58lt96a7LprZ4ILi5J8Ock/p+PgQkfakzyWWbM+lfb2tiTZFGB45pnkm99M2tu7uTUAAAAAAAAAAPQBwgsAAACvM2tW8l//lQwdmgwZsrXVDyW5IMkTNemloSGZMCG56abk6qtrUgIAAAAAAAAAAHpEQ283AAAA0Fe0tCTf+EaybFmy115bW/10kn9N0tLBmkKSyUn2TTI8ycAkq5MsT/JskgVJih1WGTas1M+//3ty5JHJpEmd+UkAAAAAAAAAAKBvEV4AAAB4zVVXJQ88kOy6a1IoVF7X3Pxykq+mcnChkOT0JGcn2amDiquT3JPkjiR/TKUgw667JnPmJP/xH8m//MtWfggAAAAAAAAAAOiDhBcAAACStLUlV1yR1Ncngwd3vHbOnP9fkpUVZgcmuSjJmztRdWiSU1/7vJQJE+5MoUxqoq4uGT06ufnmZP78ZJddOrE1AAAAAAAAAAD0IXW93QAAAEBfcO+9yezZyU4dHZSQZPXqZzJ//owOVnwynQsuvNHkHHzwpSkU6svOjh6dLF+eXHddN7YGAAAAAAAAAIBeJrwAAACQ5JprktbWZMiQjtctWPCLFIsbKsxOTekUheqrq0sGDUquuippbq5JCQAAAAAAAAAAqBnhBQAAYIe3aFFy223JqFGdWXt9B7PvqFpP5ey0U/LSS8l999W0DAAAAAAAAAAAVJ3wAgAAsMObPTtZvToZPrzjde3trVm+/N4Ks4UkB1e7tc00NiYbNpT6BQAAAAAAAACA/qShtxsAAADobXPmJG1tycCBHa9raVmUYnFDhdlxSYZ1cPW9SeZ2uP9zzz2aurr67LXXFyuuKRSSp57quE8AAAAAAAAAAOhrhBcAAIAd3tNPl0IBW9PauqiD2Y6CC0lye5I7Olyx8USFjsILQ4Ykjz6atLcndc7SAwAAAAAAAACgn/CrLgAAwA7vsceSwYO3vq69fV0HswOq1k9HmpqSZcuSl1/ukXIAAAAAAAAAAFAVwgsAAMAOrVhMVqxIBnQiezBgwJgOZtdWraeOe0ja2pJVq3qkHAAAAAAAAAAAVIXwAgAAsENra0s2bEjqOvH/jgYOHNfB7JIkxWq1VVGhkLS3J62tNS8FAAAAAAAAAABVI7wAAADs0AqF0qczBg4clYEDd6owuybJ3A6u/rsk12z6jBz5qa60CQAAAAAAAAAA/ZrwAgAAsEOrry+dutDe3rn148ad3sHsHZ2uO2rUqE6vfb329lK/jY3duhwAAAAAAAAAAHqF8AIAALBDKxSSMWOS1tbOrd9ppzM7mP1dktXVaKui1tZkwIBk+PCalgEAAAAAAAAAgKoSXgAAAHZ4Bx2UrFvXubUTJvxZhgzZo8LsyiQXJ+nkMQ7dsHZtMnZssvPONSsBAAAAAAAAAABVJ7wAAADs8PbZp3QCQ7G49bV1dQOy775f7WDFfUn+OcmqKnW3ubVrS2GLQqEm2wMAAAAAAAAAQE0ILwAAADu8vfdOGhqS9es7t37ixA9l4sQPdbDi/iR/mWR6kkdTOpGhPcmGJCuSPJTly2d2u9/99uv2pQAAAAAAAAAA0CsaersBAACA3rb33snw4cmKFcm4cVtfXygUcvDB09PcPD9Ll95eYdXaJFe99klK2fHia59k2bKu97luXSlkse++Xb8WAAAAAAAAAAB6k5MXAACAHd7o0cmpp5YCBcVi566pr2/M0Uf/LpMm/Xknq7RnY3Chu159Ndlzz+TII7dpGwAAAAAAAAAA6HHCCwAAAEne+c5k8OBkzZrOX1NfPyQHH/xfOfDAS5NM3OYehg17U6ZO/W7ZuQ0bktbW5KyzkgEDtrkUAAAAAAAAAAD0qIbebgAAAKAvOOyw5IADkj/+MRk6tGvX7rzz+/L448OT3JPk5iRPJFm91evq64dk1KhjM3bsKRk37oyMGHFIxbVLliRjxiRnnNG13gAAAAAAAAAAoC8QXgAAAEhSV5ecfXYpvLBmTdLU1OUdkhz72qeY5MUkC1IKMaxKsiHJ4Nc+Y3LccR/IyJH7pFAobHXntrZk+fLk3HOTnXbqal8AAAAAAAAAAND7hBcAAABe8453JNdfn9xyS7LXXqVAQ/cUkkx57VPe4MGTOxVcSJK5c5Pdd0/++q+72w8AAAAAAAAAAPSubv8qDgAAwPamoSH5/OeT8eOTBQt6u5uS5cuT+vrkb/4mGTeut7sBAAAAAAAAAIDuEV4AAAB4nT32SD71qaS1NVm1qnd7aW1NXn01OfPM5PTTe7cXAAAAAAAAAADYFsILAAAAb3D22cnb3568/HKyZk3v9NDWlrzwQnLwwcn/9/8lhULv9AEAAAAAAAAAANUgvAAAAPAGDQ3JV76SnHZaMn9+snp1z9Zfvz557rlk332T73wnGTu2Z+sDAAAAAAAAAEC1NfR2AwAAAH3RkCHJ17+e1NcnN9yQjBmTjB5d+7pr1pQCEwcemHz3u8mkSbWvCQAAAAAAAAAAtebkBQAAgAqGDk2+8Y3kIx9J1q4tnYawfn1tarW3l0ILCxcm06YlP/hBsttutakFAAAAAAAAAAA9TXgBAACgA4MGJf/rfyUXX5zssUfy/PPJkiVJsVi9GqtXJ3PmJE1NyZe/nPzoR8mECdXbHwAAAAAAAAAAeltDbzcAAADQ1xUKpdMQ3vSm0okI11yTPPNM6WSGceO6t2d7e7J4cbJsWdLYWNr/i18sBSQAAAAAAAAAAGB7I7wAAADQSaNGJX//98kHP5hce23yP/+TvPRS0trakGRCktVJ1ibZUObqQpIhSZqSNOXFFxsyblzyvvcl73xncuSRSZ2z8QAAAAAAAAAA2E4JLwAAAHTR3nsnf/M3ySc+kdx4Y3Lddc159tmlSUYl2em1VcWUAgsb/yykFGxYleThfPKTE3POOaMzYUJv/AQAAAAAAAAAANCzhBcAAAC6adiw5L3vTY48ckV+8pMzkuySZK8kk5M0JhmYpC1Ja5JXksxJ8kKS9fngB9+RCRNG907jAAAAAAAAAADQw4QXAAAAqqKYZN5rHwAAAAAAAAAA4PXqersBAAAAAAAAAAAAAABg++bkBQAAgKoZnGTPJJOSDEoyIMmGJC1JXknybJJlvdYdAAAAAAC1MjrJXknGJxmYpD7J+iTrkryU0v3hll7rDgAAoC8QXgAAAOimtrbk7ruTG24YmuSSlIILTUkaX1tRfO3PQkoPqdYkWZDkkfzhD4Py/vcnTU093TUAAAAAANtqzZr6JO9JckiSNyWZkNL94YZsfm84SZpTuj88J8mjeeyxoTnqqKS+vic7BgAA6H2FYrFY3PoyAAAANlq8OLnhhuTXv05mz07Wrl2f5557IqWHT2tTepPWGw1M6cFVU5JB2WuvvTJ58oC8+93JO96R7LFHD/4AAAAAAAB0WbFYuif8298ml122Ig8/PD+lgMK6/On+cGuZKwfnT/eHB2bPPSflsMOactZZyVvfmowa1WM/AgAAQK8SXgAAAOik5ubk0kuTn/0sWbQoGTAgGT8+KRTW5cYbb+zSXtOmnZpVqwZn9epk+PDkjDOSz30uGTeuRs0DAAAAANBtL7+c/Nu/JTffnKxalRQKq/L003cmaevyXocffkKam0dmw4Zkp52Sj30sOeecZODAqrcNAADQp9T1dgMAAAD9weOPJ5/4RPLd7ybr1pVOSpgyJRk8uHv7DRiQTJyY7L136YHUr36VnHtu6UQHEXMAAAAAgL6hWEz+539K929/85tk0KDSfd2xY9enO8GFJBk0qD277166x7x6dfLNbyZ//dfJ009XtXUAAIA+p6G3GwAAAOjL2tqS6dOTn/wkWbEimTSp9HCqWgqFZPTo0ukLc+cmn/98cvvtpT9HjKheHQAAAAAAumbJkuTrX0+uv750L3fPPZP6+urt39BQesnNunXJ3XcnH/948slPloIShUL16gAAAPQVTl4AAACooLU1+Zd/SS6+OGlvT/baq7rBhddraEh2370UYrjiiuTCC5PFi2tTCwAAAACAjr38cvLZzybXXFN6Ac1uu1U3uPB6gweX7j+3tJROYfj2t0sv1gEAANjeOHkBAACgjLa25KtfLQUJxo5NRo4sjf/xjx/LvHmXVLjq/yYZ34ndv5wbb3xX2Zl3vKOYQYOSmTOTCy5Ivv/9ZMyYbvwAAAAAAAB0y8KFyec+lzz6aCm00NhY+5qFQukUhqVLS6cBt7YmX/xi7QITAAAAvUF4AQAA4A2KxeRb3yoFF3baqXQaQk8aNKj0QOyBB5LPf7508sOwYT3bAwAAAADAjmjZsuRv/7YUXJgyJRk4sLNXLkgyK8mTSV5IsjrJqiTrkjS+9hmZZEJefPHotLaeklGjjsvAgaM222X06FKQ4ec/T5qaSi+5KRSq8ZMBAAD0PuEFAACAN7juuuTyy0snHvR0cGGjxsZk0qTkrruSH/0o+cIXeqcPAAAAAIAdRbFYepnM/feXXjCz9eBCe5L7k1yT5LEO1q177bM8yQuZN+/uzJt3cZK6jBkzLRMnfii77faXm1aPGpVs2JBcckly8MHJW97S/Z8JAACgL6nr7QYAAAD6kldeKT2cSkoPiHrT4MGlHn71q+Tuu3u3FwAAAACA7d3NNye/+U0yblzpBTMdaWmZl+TLSf45HQcXOtKeJUtuyWOP/VXa29s2mxk7NmltTb797WTp0m5uDwAA0McILwAAALymWEy+853kxReTXXft7W5KxoxJ1q1LvvGNZOXK3u4GAAAAAGD7tGRJ8t3vJm1tW3+xzauv3pA//nFakidq2tPkycmcOcn3vle6fw0AANDfCS8AAAC85sYbkxtuSCZMSOrre7ubkkIhmTQpefLJ5Kc/7e1uAAAAAAC2T//5n6WgwKRJHa9btuzePPjgWWlrW97BqkKS3ZKcnuR9ST6c5F1JTkyyy2vzW9fQUDoF4je/Se66q1OXAAAA9GkNvd0AAABAX1AsJr/8ZemtWsOH93Y3mxs4MBk2LLn66uTjH09GjOjtjgAAAAAAth+vvpr89rfJyJGlwEAlzc0v5/7735ENG9ZWWFFIKbBwdpKdKu5z9NEHpLn5tixY8IssWvSHJJWPVRg1qnQqxBVXJMcfv/WfBQAAoC9z8gIAAECSWbOShx8uvcWqLxo3Llm4MPn973u7EwAAAACA7cvvfpcsXZqMGdPxutmz/zGtrYsrzA5M8qUkn05HwYUkaWgYmUmTPp6jj74h06Y9nokTP5xCofJpDGPGlE5eeO65jvsDAADo64QXAAAAUnqr1urVpRMO+qKGhqS+PrnyyqS9vbe7AQAAAADYPqxfn/z616UTcOvrK69bvfqZzJ370w52+mSSN3e5/rBhU3PYYf+dQqFy8ZEjk5UrS/exAQAA+jPhBQAAYIe3alVy3XXJ8OFJBy+36nXjxiVPPVU6IQIAAAAAgG13772lEw126viwhCxY8IsUixsqzE5Ncmq1W9ukUEiampJrrkmam2tWBgAAoOYaersBAACA3jZ7drJsWTJ+/Lbu9JfVaKeipqbk5ZeTJ55IDj+8pqUAAAAAAHYITzyRtLUlgwd3vG7Rous7mH1HVXsqZ+TIZPHi5Pnnk/33r3k5AACAmnDyAgAAsMN75pmktTVpbOztTjpWKJQ+Tz/d250AAAAAAGwfnnhi6yfytre3ZvnyeyvMFpIcXO22tjB4cOnUhWeeqXkpAACAmnHyAgAAsMPb+LBnaw+o+oJBg5JHH02Kxf7RLwAAAABAX7VhQ/L448mQIR2va2lZlGJxQ4XZcUmGdXD1vUnmbjYyb979WbJky6J77fXFirvU1ZXuC8+Z03GvAAAAfZnwAgAAsMN79NFSKKA/aGpKFi4sHQ8+blxvdwMAAAAA0H/Nn58sX16679qR1tZFHcx2FFxIktuT3LHZyIsvll/ZUXghSQYMSB57bCvlAAAA+jDhBQAAYIf36qtJY2M1djorydBOrLshycJuVWhsTNasSZYuFV4AAAAAANgWS5cmra3J6NEdr2tvX9fB7ICq9tSRxsbSy20AAAD6K+EFAABgh9bWVjoavK6uGru9Lcn4Tqx7KN0NL9TVJe3tpQdqAAAAAAB0X0tL6X7r1u4PDxgwpoPZtVXtqSN1daWeAQAA+ivhBQAAYIdWLJY+1bD77rtnwIBdN31ftWp1Fi1alLa29amrq8/AgQPT2Dgwa9cOyPr121arvX0bmwUAAAAA2MF19t7wwIGVj8FtaFiRtrZikkJ1mtoK94YBAID+THgBAADYoTU0lN5W1da27Xs9//zzqfSWrfb2DWluXpfm5nVJKicXlixZnGHDhmXgwMay88ViUigkAwdue78AAAAAADuyAQNK91u3FmIYOHBUBg7cKa2tr24x19a2IsncJJMrXP13r302+nmSX3Sr3/b2pLH8rWMAAIB+QXgBAADYoRUKybBhycKFvd1JycMP/zHNzesycODADBs2LEOHDsuwYX/6rF8/MA0NSVNTb3cKAAAAANC/NTWVXnCzfv3WXxgzbtzpmT//sgqzdyQ5Z5v7mTv3pUyYMCENDQPKzre1le5nAwAA9FfCCwAAwA7vgAOS55/v7S5KTj311LS1rc+qVauzevWqrFq1KgsXvpI5c55Jc3NzGhp2zdChxXz96zNy0EH754ADDsgBBxyQMWPG9HbrAAAAAAD9ypQppQDD2rVbf2HMTjud2UF44XdJ3p1k6Db188gjj+Sxxx7LzjtPyK677ppx48amUKjbNN/cXLqfDQAA0F8JLwAAADu8/fZLrr22t7v4k4aGARk1alRGjRq12Xhb2/o89dT67LHH8xk0qCFXX311/vmf/znz58/P+PHjc8ABB2Tq1KmbAg0HHHBARo8e3Us/BQAAAABA3zZoULL33skDDyTjxnW8dsKEP8vTT++RtWufKzO7MsnFSb6cpK7MfMmBBx6YZ54ZlJaWynXa29uzYMH8LFgwPwMHNmbXXXfJrrvummHDRiRJ9t13Kz8UAABAHya8AAAA7PD22iupqysdud3Qh/9fUkPDgAwaNCDve98B+dSnvrtpfMWKFXniiScya9aszJo1K7/+9a/z1a9+NQsWLMjOO+9cNtTwxmAEAAAAAMCO6KCDkrvv3vq6uroB2Xffr+bhh8+psOK+JP+c5MIkw8qumDJl97S07JZnnulcb62tLXnuuefy3HPPpalpTIYN2zsjRxaSjO3cBgAAAH1MH/61HAAAgJ6x997J0KHJqlVJX/6d/tbWpFAo9ft6I0aMyDHHHJNjjjlms/Hly5dvFmq44oor8pWvfCUvv/xyJkyYUDbUMHLkyJ77gQAAAAAAetnee5fuu3bm5TYTJ34oCxdemwULLq+w4v4kf5nkrUkOTzIlydAkxSSr8+qrN2Tp0ju61eeaNYWsWfNMzjzznTn99CNz3nnn5T3veU+GDBnSrf0AAAB6Q6FYLBZ7uwkAAIDe9pnPJDfdVDqFoSN//OPHMm/eJRVm/2+S8Z2o9uUkj5edecc7Kv9ftAULkpEjk9/8phS26K5ly5ZtFmrY+HnllVcyceLEsqGGESNGdL8gAAAAAEAftXRp8p73JM3NyfhO3N7dsKEl9957epYuvb2TFepSCi9s/ddzDj10bubOnZfFixeXWb9XkmuSfHHTyNChQ3P22WfnvPPOy4knnpi6urpO9gQAANA7hBcAAABSCi587nPJxInJoEGV1/VWeKFYTJ55Jjn//OSCCzpRohuWLl1aNtSwcOHC7LLLLluEGqZOnSrUAAAAAAD0e//6r8mMGX86hWFrNmxYm8cf/2zmzv1pVfvYeH+4ubk58+fPz7x5c7Nq1aokg5OMS/KpJHeXvXby5Mn5yEc+knPPPTf77bdfVfsCAACoFuEFAACAJK2tyVlnJfPmJZMmVV7XW+GFZcuSlpbkF79I9tyzEyWqaMmSJVuEGp544oksXLgwu+66a9lQw/Dhw3u2SQAAAACAbnrsseSjH02GDUu6cmtzwYJf5umn/z5r1jyzTfWHDXtTJk36WPbY42/eMFPMypUrM2vW2qxe/UBaWt6bZMNW9zvqqKNy7rnn5oMf/GDGjh27Tb0BAABUk/ACAADAa3760+Rb30omT04GDuzatW1t63P99dd3ev0ZZ5yRhoYBnVpbLCZz5iSnn558//td66uWFi9eXDbU8Oqrr2bSpEllQw3Dhg3r7bYBAAAAADZTLCZ/+ZfJnXcme+3VudMX/nRte3772/+V5OYkTyRZvdVr6uuHZNSoYzN27CkZN+6MjBhxSMW1zc3J/PnJl7+8IWPG/D6XXnpprr766jQ3N2+1TkNDQ84888ycd955OfPMM9PY2NjpnwsAAKAWhBcAAABes2pV8rGPJU8+meyxR9ceUNUyvLBgQdLYmEyfnuy7b+d76i2LFi3aItQwa9asLF68OJMnTy4bahg6dGhvtw0AAAAA7MAefTT5xCdKfx/fmQN2X+faa//ntb8Vk7yYZEFKIYZVKZ2UMDjJ4Bx99Jlpato3gwdPTqETN6A3vtjmsMNKL98ZNKg0vmLFilxxxRW59NJLc/vtt3eqx1GjRuWDH/xgzj333Lz5zW/uVH0AAIBqE14AAAB4nXvvTT796WTAgKQrp2nXKrywbl3prVpf+EIpWNGfLVq0aItAw6xZs7JkyZLstttu/3/27js+qir///hrJr33ntASOooIdsSOZUEEBQtk1nX96u5319XdteBatrhf67q7bm/uTydiQcGuKPYCdilSE2oapPcymZn7++OSSMhMmPT2fj4e8yC59557z1Auk3vO+3w6hBomT56sUIOIiIiIiIiIiIj0m7/+Ff7yF0hP/zYo4Itvwwudmzdvfpf6c/CgucjOv/8Nxx7r+Zi9e/fyxBNPYLfbyc3N9em8WVlZ2Gw2li1bxtixY7vUJxERERERkZ5QeEFEREREROQIDz9sDgaNGmVWPPBFX4QX3G7YtQtOPRX+8Q8IDPT59EOGYRheQw0VFRWMGTPGY6ghLCxsoLsuIiIiIiIiIiIiw0xjI3z/+/DVV5CZCVarb+36IrzQ2AgFBfCTn8D//u/RjzcMg08//RS73c7TTz9NZWWlT9eZM2cO2dnZLF68mKioKJ/7JyIiIiIi0h0KL4iIiIiIiByhthauvx6+/BLGjjWrMBxNb4cXDAN274aUFDO4MH68z6ceFgzDoKSkxGOooaqqymuoITQ0dKC7LiIiIiIiIiIiIkPYN9+YYYGKChgzxqx8cDS9HV5wOGDvXnNhm7//vWtVIACam5t57bXXsNvtvPrqq7S0tBy1TXBwMAsWLMBmszF37lz8/f27dlEREREREREfKLwgIiIiIiLiQXEx/OhHsGWLbwGG3gwvuN3mwFRsLPz+93DCCV3o+DBnGAYHDx70GGqorq5m7NixHUINkyZNUqhBREREREREREREfPbhh3DbbVBT41uAoTfDC63BheOPhz//GeLjfTq1V2VlZTzzzDPY7XY+++wzn9okJSVx1VVXkZ2dzXHHHYfFlwSHiIiIiIiIDxReEBERERER8WL/fvj5z2HTJsjIgJAQ78f2VnjB6TQHppKS4N57zZW15OgMw+DAgQMeQw01NTWMGzfOY6ghpLM/VBERERERERERERmx3n0X7rrr2woMfn7ej+2t8EJDAxQUwMyZ8PDDZmXe3rRjxw5ycnLIyclh//79PrWZNm0aNpuNpUuXkpqa2rsdEhERERGREUfhBRERERERkU4cOGCusPXZZxAeboYKPC0y1RvhhaoqKCmBzEz4v/+DGTN62HnBMAyKi4s9hhrq6uq8hhqCu1qHXURERERERERERIadTz6Bu++GffsgORkiIz0f19PwgmGY1YAbG2H2bHNhm55WXOiM2+3mgw8+ICcnh2effZba2tqjtrFarZx77rlkZ2ezcOFCwsLC+q6DIiIiIiIybCm8ICIiIiIichTNzWC3w3//C5WVkJ7esQpDT8ILTifk54O/P8yfDzfe2LcDU2KGGoqKijoEGrZu3UpdXR2ZmZkdQg0TJ05UqEFERERERERERGSEOXDArIKwZo25sE16escqDD0JLzQ0QGEhxMXBddfBlVdCYGBv9Nw3DQ0NvPjii9jtdt58803cbvdR24SHh3PppZdis9k488wzsVqt/dBTEREREREZDhReEBERERER8dGWLfDAA/Dll+b38fEQEWEOWHUnvNDSEkBJCTgcMHYs/OxncO65nis7SP8wDIPCwkKPoYb6+nqysrLawgytwYaJEycSFBQ00F0XERERERERERGRPmIY8Npr8Mc/mgvRBAVBQsK3i9x0NbxgGFBdDeXlYLXCSSfB8uUwfnwfvQEfFRcX8+STT5KTk8PGjRt9apORkcGyZcvIzs5m8uTJfdxDEREREREZ6hReEBERERER6YLmZnj7bXjuOdi4EerqIDwcQkJa+PjjtYCrk9YWIBQIY/z4WQQF+ZGVBZdeChddBLGx/fMepOsMw6CgoMBjqKGxsdFjqGHChAkKNYiIiIiIiIiIiAwjpaVmiGHVKti926yqGxUFn332NtAIdDYFx485cy6irs58rhwRAbNmwcKFcNZZ/VttwRcbN24kJyeHFStWcODAAZ/azJo1C5vNxhVXXEFCQkIf91BERERERIYihRdERERERES6wTDMSgwvv2yGGcrLXWzYsOPQXgtmiMF96GvroReYA1h13HDDSSxeHMQpp4C/f//3X3qHYRjk5+d7DDU0NTUxfvx4j6GGwME2EikiIiIiIiIiIiI+czjg44/hpZfgiy/go4++BoIP7XUfehm0fzYMs2ZNISYGzj8fvvMdmDhx8FfidTqdvPXWW9jtdl544QUaGxuP2sbf35+LLroIm83GvHnztMiLiIiIiIi0UXhBRERERESkh9xu2LatlmnTLgEygXFAGBAItGAGFgqAvEOvPVRXlxEZGTlAPZa+5na7vYYaHA6Hx1DD+PHjFWoQEREREREREREZYpqaICRkMpCF+Xw4HQgB/AEHUAfsAnaxf/+7pKWB1er1dINaTU0Nq1atwm6389577/nUJjo6mssvvxybzcYpp5yCZbCnNUREREREpE8pvCAiIiIiItILampqiIqK8vn46upqhRdGILfbzf79+zuEGrZt24bD4WDChAkeQw0BAQED3XURERERERERERHxwtcJ+cNpis6+fftYsWIFjz/+ODt37vSpTWZmJtnZ2WRnZzNu3Lg+7qGIiIiIiAxGCi+IiIiIiIj0AoUXpCfcbjf79u3zGGpwOp0eQw1ZWVkKNYiIiIiIiIiIiAwCIzG80MowDD7//HPsdjtPPfUUFRUVPrWbPXs2NpuNxYsXEx0d3bedFBERERGRQUPhBRERERERkV7wbXghCMg49GsA4MIsDV4M1LQdr/CC+MLtdrN3716PoQaXy8XEiRM9hhr8/f0HuusyIBxAAdAAtAB+mPehBCAO8G0QXUREREREREREumYkhxcO53A4eO2118jJyeHll1+mpaXlqG2CgoJYsGAB2dnZnH/++VqwRURERERkmFN4QUREREREpAeKi+Gzz2DLlibuuONpIAsIBayHXgbgBpowJxVvBHL54otHOf74cHwc0xJpx+VyeQw1bN++Hbfb7THUkJmZqVDDsFMJrAfygG+AHUAdZmjKfegYK2aYKhmYDkwAph36WjcgEREREREREZHeoPBCR+Xl5axcuRK73c4nn3ziU5uEhASuuuoqbDYbM2bM8Pn3VUREREREhg6FF0RERERERLrI7YYvvoCXX4a33oKqKnMyeW7uFqAec9VzF2ZwwYI5eTgEM9QQDMBxx03gpJP8WLgQzjgDgoMH5K3IMONyudizZ4/HUAPgNdTg5+c3wD0X3xnAFuAV4DWg9NB2PyAM8z7jh3nfgW/DU/WHfgXzPjQNWAScC0T0R8dFRERERERERIYthRc6t3PnTp544glycnLYu3evT22mTp1KdnY2S5cuJT09vW87KCIiIiIi/UbhBRERERERER8ZBrz3Hvzzn7B9OzQ1QUwMxMaC293CmjVrfD7XKadcQGVlAFYrjBoFy5bBkiWgitjSF1wuF7t37/YYarBYLEyaNKlDqGHcuHEKNQw6XwF/BTZgVliIAuKArlTUMA61LcUMNqQAlwFXYwYfRERERERERESkqxRe8I3b7eajjz7CbrezcuVKamtrj9rGYrFwzjnnYLPZWLhwIeHh4f3QUxERERER6SsKL4iIiIiIiPigogIeeQRefBEcDkhKgsPHSJzOroUXLrjgAvz9A2huhpIS85ynnQa33grjx/fBGxDxwOl0egw17NixA6vV6jHUMHbsWIUa+l098B/gCaAWSMKsluDboLh3LZghhnpgOrAcmNHDc4qIiIiIiIiIjDwKL3RdY2MjL774Ijk5Obzxxhu4XK6jtgkLC2PRokXYbDbOOussPacUERERERmCFF4QERERERE5ivfeg4cegrw8SEgwqy0cqbvhhVYNDVBQYJ7/+uvhyivBvyuLqYv0IqfTya5duzyGGvz8/Jg8ebLHUIPVah3org9DG4D7gI2YlRYS6Hlo4UjNQD5mIGIZcB0Q3MvXEBEREREREREZvhRe6JkDBw7w1FNPYbfb2bBhg09t0tLSWLZsGdnZ2UydOrVvOygiIiIiIr1G4QUREREREREvDAOefhoefhgaG2HUKO+Bgp6GFwDcbiguNqswXHYZ/OIXEBjYk3cg0rucTid5eXntAg1bt25lx44d+Pv7eww1jBkzRqGGbnsL+CVQDowCgvrwWgZmFYYa4Dzg/zDDDCIiIiIiIiIicjQKL/SezZs3k5OTwxNPPEFxcbFPbWbOnEl2djZXXnkliYmJfdxDERERERHpCYUXREREREREPDAMePxx+OMfwc8PUlKgs/GnxsYS3n77D8BmIBdzAnAt5ormYUAkEAdMAaYxd+5NBAaGeDxXVRWUlcHFF8M99yjAIINfS0uL11BDYGCgx1DD6NGjFWro1GvAr4BGYDRHVlu4+uoNPP54QY+vsmBBEi+8cMJhW+qBQmAO8HsUYBAREREREREROTqFF3qfy+Xi7bffxm638/zzz9PQ0HDUNn5+flx44YXYbDbmz59PcLCqi4qIiIiIDDYKL4iIiIiIiHiwciXcey8EBEBysvfjGhvz2bXrd+Tn/weX6+iDJ61CQsaSmXkLGRnfw8+v4wBKTQ2UlMDll8Odd3qv+CAymLW0tJCbm9sh1LBz506CgoI8hhpGjRqlUAPvAssxgwujODK4AH0ZXuDQdfMxKzA8BHgOWomIiIiIiIiIiEnhhb5VW1vL6tWrsdvtvPvuuz79PkZFRXH55ZeTnZ3Naaed5vOfkYiIiIiI9C2FF0RERERERI7w5Zfwwx+C0wlpad6PKyh4gs2br+9SaOFI4eFTmDVrNeHhEzvsq6qCykpYvhyWLev2JUQGHYfD4TXUEBwc3BZkODLUMDIGGPcCVwNlwBg8BRegr8MLYFZgKAKuBW7u8XVERERERERERIYzhRf6z/79+1mxYgV2u53t27f71GbcuHFkZ2eTnZ1NZmZmH/dQREREREQ6o/CCiIiIiIjIYerr4Xvfg82bITMTvI05bd16M7t3P9wr1/T3j2DmzFUkJJzXYV9hIYSGwmOPQVZWr1xOZNByOBzs3LmzQ6ghNzeXkJAQj6GGjIyMYRRqcAI3AO8AWYD3ChR9H14AM0DhAP4GnNTja4mIiIiIiIiIDFcKL/Q/wzD48ssvsdvtPPnkk5SXl/vU7tRTT8Vms7FkyRJiYmL6uJciIiIiInIkhRdEREREREQO88gj8Pe/w6hREBTk+Zjdux9h69abOjmLH3AM5qrpIUAlsAlzFXPP/P2jmD370w4VGNxuyMuDM86Av/0N/P278GZEhonm5maPoYa8vDxCQ0M9hhrS09OHYKjhGeA3QCIQ3umRnYUXbr01k5iYAJ+uOGFCGIsWpXjZawC7gMnA40CET+cUERERERERERlpFF4YWA6HgzVr1mC323n55ZdxOBxHbRMYGMjFF1+MzWbjggsuICDAt+dpIiIiIiLSMwoviIiIiIiIHLJhA/zP/4CfHyQkeD6msvIT1q2bjWG4vJzlOODHmJOPj7QecwXzao8tw8MnM2fOJqzW9gmFhgYoLoY774SrrvLlnYiMDM3NzezYscNjqCE8PNxjqCEtLW2QhhrygWVADZB+1KM7Cy/s2XM2Y8aE9lK/HMBe4Drg5710ThERERERERGR4UXhhcGjsrKSlStXYrfbWbdunU9t4uPjufLKK7HZbMycOXOQPj8UERERERkeFF4QERERERE55Lbb4MUXISsLvI1NrF9/DuXl73g5wwzgLqCz8gh7gduBeo97p0//LxkZ3+uwff9+SEuD1ashOLiT04sITU1NHkMNu3btIiIiwmOoITU1dYAHJR/BDDdlAdajHt1/4QWAg0AA8ALgJdklIiIiIiIiIjKCKbwwOOXl5ZGTk0NOTg579uzxqc3kyZOx2WwsXbqUjIyMPu6hiIiIiMjIo/CCiIiIiIgIkJ8Pl11mVl2Ii/N8TGXlej7++FSP+/z9I3E6/wzE+HC1V4F/etwTGprJWWfldhjsam6GwkL44x/hvPN8uISIdNDY2Og11BAVFeUx1JCSktIPoYY6YAFQBaT61KJ/wwsuYDdwB5Ddi+cVERERERERERkeFF4Y3AzD4OOPP8Zut7Ny5Uqqqz1XRz6cxWLhrLPOwmazsWjRIiIiIvqhpyIiIiIiw5/CCyIiIiIiIsC//gW//z1kZoLVy6LnW7f+nN27f+9x35gxN7F379k+Xs1FYOB1OBylHvfOmbORyMhjO2zftQvOOAP+9jfvlSFEpOsaGxvZvn17h1DD7t27iYqKags0HB5qSE5O7sVQw4uYFVlGYVY4OLr+DS8A7APGAc/ReXUZEREREREREZGRR+GFoaOxsZGXX34Zu93OmjVrcLlcR20TGhrKokWLsNlsnH322fj5+fVDT0VEREREhieFF0REREREZMRrboZLLoEDByA93ftxH3wwg5qaDR73nXzyx3zySbnP18zIeIX8/H953Ddlyh8ZN+7GDturqqCxEVasgIkTfb6UiHRTQ0ODx1DDnj17iI6O9hhqSEpK6mKowQC+B3yOGQ7wTf+HFxqBg8BfgTm9fG4RERERERERkaFN4YWh6eDBgzz99NPY7Xa++uorn9qkpqaydOlSbDYb06ZN6+MeioiIiIgMPwoviIiIiIjIiPfNN7BsGcTFQaiX+b4uVyOvvx6GOdG4PYslgPPOq+TNN9/x+ZrTph3km2/+x+O+1NQrOf74JztsNwzIy4Pf/AaWLPH5UiLSy+rr672GGmJjYz2GGhITE70MYpcB38GsuBDjcx86Cy90hWHM68LRucCPgBt6fF0RERERERERkeFE4YWh75tvviEnJ4cVK1ZQWFjoU5sZM2Zgs9m48sorSUpK6uMeioiIiIgMD/4D3QEREREREZGBlpdnVl8ICfF+jMNRiqfgAkBo6Fis1sAuXTMsbEIn1yrxuL11/Csvr0uXEpFeFhYWxsyZM5k5c2a77fX19Wzbtq0t0LBmzRoefvhh9u7dS1xcXLtQQ2uwITFxN9AApA7Ie+kaP+Cbge6EiIiIiIiIiIhIr5s2bRoPPPAA9957L++++y52u51Vq1bR0NDgtc3XX3/N119/zc0338z555+PzWbj4osvJqSzwQYRERERkRFO4QURERERERnxWsMAnS2O5XCUe93n7x/V5Wv6+0d2cq0yr/uCgmDTpi5fTkT6QVhYGLNmzWLWrFntttfV1bULNbz22ms89NBD7Nu3jx/8IJzly13U1NQQERFJREQE4eERBAV1LRDVP8KAHUALZqUIERERERERERGR4cXPz49zzz2Xc889l7/97W+sXr0au93OO++847Vyhsvl4rXXXuO1114jMjKSJUuWYLPZOO2007Barf38DkREREREBjeFF0REREREZMTbtAkCjjIP1+Vq9LrPzy+oy9e0Wr23cTrrve4LC4N9+6CmBiK95x9EZBAJDw/nhBNO4IQTTmi3vba2lpqamwgLe436en8OHjxIXl4ejY1NBAYGEhERcegV3vZ1YOBAhhrCgGpgLzB+APshIiIiIiIiIiLS98LDw7HZbNhsNgoKClixYgWPP/4427Zt89qmpqaG//znP/znP/9hzJgxZGdnk52dzfjxep4mIiIiIgIKL4iIiIiIiJCfD8HBnR8TGBjrdZ/TWYeXBZc6aVPrdV9AgPdUQnAwVFXBgQMKL4gMdWYgwQEkEh09qm270+mktraW2to6amtr24UagoLMUENlZZPX8956ayYxMX1RGSEYOAgcQOEFEREREREREREZSdLT07ntttu49dZb+eqrr7Db7Tz11FOUlpZ6bbN3717uuece7rnnHk455RRsNhtLliwhNtb7eIOIiIiIyHCn8IKIiIiIiIxoLhe0tMDRKjcHBMR53VdfX8iHH37Ypes6HAe87gsMjPe6z2oFtxscji5dTkQGrSag/Q3I39+fmJgYYmJi2m1vaXFSV1dLbW0tFstur2ecP9+fadPiiY6O7uW+WgED0A1IRERERERERERGJovFwsyZM5k5cya/+93veOONN7Db7bz00ks0Nzd7bbd+/XrWr1/PjTfeyLx587DZbFx44YUDXGlVRERERKT/HWV6joiIiIiIyPDmcoFhgMXS+XGBgXH4+0d5OUcpDQ3FHbb7+3vPi2/e/IrXfRERx3jd1xpecDo76ayIDCEu4Cg3oEMCAsxQw6hRozoNJpSWlrBnz57e6V47FsAN6AYkIiIiIiIiIiISEBDAvHnzWLlyJcXFxfzzn/9k9uzZnbZxOBysXr2aSy65hNTUVG644QY+//xzjK6WdxYRERERGaIUXhARERERkREtIMAMLnQ+LmBQV1dHUNDxnRyzqcMWp9Pl9WiHY73XfZWVCWzdupU9e3ZTXFxMVVUlTU1NGIaB220GGLQYk8hwEYBZzaD3zJgxgxkzZvTqOU0G5qMk3YBEREREREREREQOFxMTw3XXXceHH35IXl4ev/rVrxg3blynbcrLy/nLX/7CiSeeyOTJk7n33nvZv39/P/VYRERERGRgWAxFd0VEREREZIQ77zyoqoLk5Pbba2trKS4uoqioiLq6OuBl4N9ezjIT+GWHrRaLFcNwH7G1CPghnicsB5GS8hYBAZE0NzfT1NRIY2MTDkczFouFgIBYAgLiOOOMvzBpUiAZGRmkp6eTnp5ORkYGycnJ+Pn5de03QEQG0DXAF8DoLrW6+uoNPP54gcd9e/aczZgxoT3vWgctQAHwL+DUPji/iIiIiIiIiMjQZDlaad9DNEVnZDEMg3Xr1pGTk8MzzzxDVVWVT+3OOussbDYbl156KREREX3bSRERERGRfqbwgoiIiIiIjHg//jG8+y6MG3d4YKGYurraI46sBa4FGr2c6dfADKxWK4mJSaSmphAbG0dFRTlFRUWUlJTgdruA3wBfejnHXODHWK1+JCcnkZKSSmJiIhYLNDU1U1zsxO1uwmbL4cCBfRQUFJCfn09BQQEHDx7EarWSkpLSFmY4PNjQ+nVKSgr+/v698nsnIj11P/A4kNWlVgMTXqjGvP+9AKT1wflFRERERERERIYmhRfkaJqamnjllVew2+28/vrrOJ3Oo7YJCQlh4cKF2Gw2zj33XC1cJCIiIiLDgsILIiIiIiIy4t111wH+8Q8/mpq2eAgsHCkHeNbjHoslkokTn2bMmPM8hgNaWpr58ssfUFb2mJdzBwB/A5LabfXz8yMpyQwyNDYmcfLJVh59tGNrh8NBUVERBQUFba/WYEPr1wcOHMBisbQFHI4MNrR+n5KSQkBAwFF+L0Sk51YBd2CGF3wb5IaBCi8UAfHAGrrSVxERERERERGR4U7hBemK0tJSnn76aex2O1988YVPbVJSUli6dCk2m41jjjmmj3soIiIiItJ3FF4QEREREZERafv27Tz77LOsXLmSb75JBf4I7AbcR2nZANwC5Hvca7EEMGbM/5KWtpTw8Cn4+YXicJRSVvY2u3c/THW1t4oLAN8HFnR6dYtlAscd9yl33RXBBRdcQEhIyFH6215LSwvFxcXtQg1HhhyKi4sxDIPk5GSv1RvS09NJTU0lMDCwS9cXkSNtBGxAAuD7v+fOwgu33ppJTIzv4aOf/nQsQUG+rNqWB8wDHvL53CIiIiIiIiIiI4HCC9JdW7duJScnhyeeeIKCAs/P+440ffp0bDYbV111FcnJyX3cQxERERGR3qXwgoiIiIiIjBg7duxoCyxs3rz5sD1jgZVAHdB55QWr1UpMjIOqqmtwuap8uKqVowciICZmIQEBt1FaWoZheDveCowDbgVeITw8nPnz57NkyRIuuOACgoODfejP0bW0tHDgwAGv1RsKCgooKirCMAySkpK8Vm9oDTgEBQX1Sr9Ehqc6YC7mfSLB51adhRe6qrLyfKKjjxZ2MDDDCzcD1/bKdUVEREREREREhguFF6SnXC4X7733Hjk5OTz33HPU19cftY3VauX888/HZrOxYMGCLi92JCIiIiIyEBReEBERERGRYW3nzp1tgYVNmzZ5OcoC5ADTgT0d91qsJCYmkJqaSlJSEv7+AdTWbuXLLy+lrm57j/uYkXEtxx77DywWP1paWjh48CDFxUWUlJQeEWRIAFzAQqC03TkiIiLaggznn39+rwUZvHE6ne0CDp5CDkVFRbhcrraAg7eQQ1pamgIOMsLdCrwMZPncov/DC9VAPfA4MK1XrisiIiIiIiIiMlwovCC9qb6+nueffx673c5bb73l09+byMhILrvsMmw2G6effjpWq7UfeioiIiIi0nUKL4iIiIiIyLCTm5vbFljYuHGjj60uBe7BDC+42gILKSlmYCEgoOPEXqezjq1bb6ag4P/hdju63M+goCSmTPk9aWlXedzfGmQoKjKDDJAJPAX8ptPzRkREcPHFF7NkyRLmzp3b50EGb1wuFwcPHvRavaGgoIDCwkKcTicJCQleqze0vgbqfYj0vY+AHwKJQKhPLfo/vLALOA34F2bgS0REREREREREWim8IH2lsLCQFStWYLfb2bJli09tRo8eTXZ2NtnZ2UyYMKGPeygiIiIi0jUKL4iIiIiIyLCQl5fXFljYsGFDN84QCbxEVFQqY8eGeA0seNLUVMzevX/m4MGXqa3dAviyCtJ0Tj31Y/z9w3y6RmVlC/n5tYwa9Ws+/fTvtLS0+NQuMjKyXZBhsFU4cLvdbQEHbyGHwsJCWlpaiI+P9xhsaP06LS2N0FDfJn6LDC5O4HJgJzDGpxb9G15wAPnA74CLeuWaIiIiIiIiIiLDicIL0tcMw2DDhg3Y7XaefPJJSkpKfGp30kknYbPZuPzyy4mLi+vjXoqIiIiIHJ3CCyIiIiIiMmTt2rWrLbDw9ddfd+scAQEBzJ07lyVLlrB//2JyckLIzAQ/v+71yeGopLr6c5qbD+JwVOB0VlNQ8DgNDbs7HDt+/N1MnPhrn86blwczZ0JODlRVVfLSSy+xcuVK3nzzTZxOp0/niIyMZMGCBSxZsoTzzjtv0AUZvHG73ZSWlnqt3tD6cjgcxMbGegw2tH6dlpZGWJhvgRGR/rUC+C0wCggc4L4caT+QDLwAhAxsV0REREREREREBiGFF6Q/tbS0sHbtWux2Oy+88ALNzc1HbRMQEMC8efOw2WxcdNFFBAYOtmeQIiIiIjJSKLwgIiIiIiJDyu7du9sCC1999VW3zhEQEMB5553HkiVLWLBgAdHR0QAUFUF2NpSWwujRvdfnhoZ9rFt3Ok1N+R32TZp0P1lZt3XavqICGhvhkUfg9NPb76usrOTFF19k5cqVrF271ucgQ1RUVLsgw1AfqHC73ZSVlXkMNhz+fXNzMzExMV6rN7S+wsPDB/otyYhTC3wX2A6MA3wb8O57dUAJ8Ctg8cB2RURERERERERkkFJ4QQZKVVUVzz33HHa7nQ8//NCnNrGxsVxxxRXYbDZOPPFEn//+ioiIiIj0BoUXRERERERk0NuzZ09bYOHLL7/s1jn8/f3bBRZiYmI8Hvfii3DnnRAXBxERPel1e3V1uaxfP4fm5gMd9k2d+ifGjr3BY7uWFtizB2w2+MUvoLMxhIqKirYgw1tvveVzkCE6OrotyHDuuecO+SCDN4ZhtAUcOgs5NDU1ER0d7bV6Q+vXEb35F0QEgE+AH2FWXogf4L4AuIE84DzgEaCbJWlERERERERERIY5hRdkMNizZw9PPPEEdrudvLw8n9pMmDABm83GsmXLGN2bqzqJiIiIiHih8IKIiIiIiAxKe/fubQssfPHFF906h7+/P+eee25bYCE2Nvaobdxu+PnP4bXXIDMT/Hpxrm5t7VbWrz8Dh6PsiD0Wjj32X4wadW27rYZhBhcyM8Fuh0MFInxSUVHBCy+80BZkcLlcPrWLjo7mkksuYcmSJZxzzjnDNsjgjWEYVFRUdFq9IT8/n8bGRiIjIzut3pCRkUFkZORAvyUZch4E/guMxgwxDKR8IBrIATIGtisiIiIiIiIiIoOYwgsymBiGwSeffILdbufpp5+mqqrKp3ZnnHEGNpuNyy67TM+2RURERKTPKLwgIiIiIiKDxt69e3nuuedYuXIln3/+ebfO4e/vzznnnMOSJUu45JJLfAosHKmwEK65Bvbvh3HjwGrtVlc8qq7ewCefnE1LS+URe6wcd5yd9PSl7frh5wd/+APMnt39a5aXl7cFGd5++22fgwwxMTHtggwBAQHd78QwYhgGlZWVXoMNrb82NDQQERHRafWG9PR0oqKiVJZbDlMLXANsAsYB/gPUjzKgHvglcOkA9UFEREREREREZGhQeEEGq+bmZl599VXsdjuvvvqqTxWbg4ODWbhwITabjXPPPRd//4F6RikiIiIiw5HCCyIiIiIiMqD27dvXFlj47LPPunUOPz+/doGFuLi4Hvdrwwa46SYoKYGxY3s3wOCL4mJwueCOO+Cyy3rvvGVlZW1BhnfeecfnIENsbGxbkOHss89WkOEoDMOgqqqq0+oN+fn51NfXEx4e3mn1hvT0dKKjoxVwGFH2Aj8CdmEGGHqxBIxPyoFq4IeH+qG/eyIiIiIiIiIinVF4QYaCsrIynn76aex2u88LSCUlJbF06VJsNhvTp0/v4x6KiIiIyEig8IKIiIiIiPS7/fv3twUWPv30026dw8/Pj7PPPrstsBAfH9/LvYRPP4XbboODB80Ag18/zB82DLPigsUCP/sZLFvWd9cqKyvj+eefbwsyuN1un9rFxsaycOFClixZwllnnaUgQzcZhkFNTU27UIOnkENtbS2hoaFegw2tX8fExCjgMKzsAH6GGWAYA/TXv7MSzIoL1wA3Af2c3BIRERERERERGYIUXpChZvv27eTk5JCTk0N+fr5PbY499lhsNhtXXXUVKSkpfdxDERERERmuFF4QEREREZF+kZ+f3xZY+OSTT7p1Dj8/P8466yyWLFnCwoUL+ySwcKQNG8wAw969kJwMkZF9d63mZsjPh6gouPVWWLSo7651pNLS0rYgw7vvvutzkCEuLq5dkEHlo3tfTU2N12BD69fV1dWEhIR0Wr0hPT2duLg4BRyGlD3Az4EtQDwQ24fXagHygSDgOuB6VHFBRERERERERMQ3Ci/IUOV2u3n//fex2+0899xz1NXVHbWN1WrlvPPOw2azcckllxAaGtoPPRURERGR4ULhBRERERER6TMFBQVtgYX169d36xxWq7VdYCEhIaGXe3l0RUXwu9/B2rVmRYT09N6twmAYUFICNTVw/PFmWGIgqy+XlJS0BRnee+89n4MM8fHxbUGGM888U0GGflRbW+u1ekPr91VVVQQHB3davSE9PZ34+HgFHAaVSuAvwGrAAWTQ+1UYKoAyYCJwM3A6Ci6IiIiIiIiIiPhO4QUZDhoaGnjhhRew2+2sXbvWp7GB8PBwFi9ejM1mY86cOVitquQqIiIiIp1TeEFERERERHpVYWFhW2Bh3bp13TqH1WrlzDPPbAssJCYm9nIvu87thpdfhj/9CQoKIDwcEhIgoAdziN1uqKgwX1FRYLPB978PISG91++eOnjwYFuQ4f333+9SkGHRokUsWbKEM844Q0GGQaCuro7CwkKv1RsKCgqoqKggKCiItLQ0j8GG1q/j4+M1CNWvDOAD4CEgFwgFEjCrJPTknFWYoYUQ4FLgx0B0D84pIiIiIiIiIjIyKbwgw01RURFPPvkkdrudzZs3+9Rm1KhRLFu2jOzsbCZNmtTHPRQRERGRoUrhBRERERER6bHCwkJWrVrFypUr+fjjj7t1DqvVyhlnnMGSJUtYtGjRoAgseFJUBM88Ay+9BAcOgNUK8fEQFuZbNQa3GxobzcBCYyPExMA558Dll8Oxx/Z9/3vi4MGDrF69ui3I4OuPkwkJCW1Bhjlz5ijIMIjV19dTWFjotXpDQUEB5eXlBAYGkpaW5rV6Q3p6OomJiQo49LpKYCXwArDv0LY4IBzw5d+VATQdOk8dEAmcClwOnIKqLYiIiIiIiIiIdI/CCzKcbdy4EbvdzooVKzh48KBPbU488USys7O54ooriI+P7+MeioiIiMhQovCCiIiIiIh0S1FREc899xzPPvssH330UbfO0RpYWLx4MYsWLSIpKamXe9l3amth7VpYtQp27ID6enO7xQKhoWaQwWo1wwpuNzQ1QUuLeUxICKSmwqJFcOGF5tdDzYEDB9qCDB988IHPg26JiYntggx+viQ+ZFBpbGxsF2o4MuiQn59PWVkZAQEBbQEHbyGHxMRE/R3olibgPWA1sAEziND6bzAU8Dv0MgD3oeNbDn0fjBl4WAB8B8jsv26LiIiIiIiIiAxTCi/ISOB0Olm7di12u50XXniBpqamo7bx9/fnO9/5Djabje985zsEBfWkmqyIiIiIDAcKL4iIiIiIiM+KiopYtWpVW2ChOz9OWCyWdoGF5OTkPuhp/zEM2LcP8vIgNxe2bTPDDI2NZmjBYoHAQBg9Go45BrKyzFdmprl9OCguLm4LMnz44Yc+/71ISkpqCzKcfvrpmsQ+jDQ1NVFYWOi1ekNBQQElJSX4+/uTmprqtXpDeno6ycnJ+rvhlQEUAblAHrAD2ALUA65DxwQCqcCxwHjMsMJ4IKS/OysiIiIiIiIiMmwpvCAjTXV1NatWrcJut/P+++/71CYmJoYrrriC7OxsTj75ZJ//3YiIiIjI8KLwgoiIiIiIdKq4uLgtsNCViemHs1gszJkzh8WLF3PppZcO+cCCLwwDXC6zAsNIev7e+vdl5cqVXQq4JCUlcemll7JkyRJmz56tyeojQFNTE0VFRV6rNxQUFHDw4EH8/PxITU31GGxo/T45ORl/f/+BfkuDjBOz+sIIugGJiIiIiIiIiAwQhRdkJNu7dy9PPPEEdrud3Nxcn9pkZWVhs9lYtmwZY8eO7eMeioiIiMhgovCCiIiIiIh0cODAgbbAwgcffNDtwMLpp5/eFlhISUnpg57KYNZaqWPlypV8/PHHPv89Sk5ObgsynHbaaQoyjGAOh4OioiKPwYbWrw8ePIjVaiUlJcVr9YaMjAxSUlIUcBARERERERERkT6h8IKI+ff7s88+w2638/TTT1NRUeFTuzlz5pCdnc3ixYuJiorq416KiIiIyEBTeEFERERExAPDgOZmaGmBgAAIDASrdaB71bcOHjzYFlh4//33ux1YmD17dltgITU1tQ96KkNRYWFhuyCDr1JSUtoFGazD/R8i5v3H4TDvP35+5v1H+Q3vHA4HxcXFXqs3FBQUUFxcjMViITk52Wv1hvT0dFJTUwkICBjotyQyiBhAC+AArEAgqughIiIiIiIi0lH78MLhP0O3/lxt0hQdGSmam5t57bXXsNvtvPrqq7S0tBy1TXBwMAsWLMBmszF37txhtxiN2/3ts/+AAAgKGlmVu0VERERaKbwgIiIiIiNeSwvs2AF5eeZr0ybYuxecTnMSscViBhdGjYJjj4WsLPM1aRIEBw9073vm4MGDrF69ui2w4Ha7u3WewwMLaWlpvdxLGW4KCgraggzr1q3zuV1qampbkOHUU08dFkEGtxtyc7+9/3zzDezcad6XDr//JCeb958JE769/4SHD3Tvh46Wlpa2gMORwYbW74uLizEMg+TkZI/BhtavU1NTCQwMHOi3JNIHDGAvkAvkAVuAbUAz0Pr5wA+IBY4FJgJZwCQgpp/7KiIiIiIiIjI41NXB9u1wwglXAOMxf2ZOwQwwWDB/pnYCO4EtvPzyHxk/3nzGpwVLZKQoLy/nmWeewW638+mnn/rUJikpiauuuors7GyOO+44n6ubDBZNTea94fCxx/37zTGB1mf//v4wZkz7sceJE81gg4iIiMhwpvCCiIiIiIxYBw/CmjWwejXs22dWWjAMc5XzkBBz4MBiMbe5XOaDRofD/D4oCFJSYOFCuOgiyMgY6Hfju5KSkrbAwnvvvdftwMJpp53WFlhIT0/v5V7KSJGfn98WZFi/fr3P7VJTU7nssstYsmQJp5xyypALMlRVwZtvwqpV5sBFQ4O53c8PQkPNX61W837jdpv3n6Ym85jAQIiPh3nz4DvfMQMNQ2zcZlByOp0cOHDAa/WGgoICioqKcLvdJCUlea3ekJGRQWpqKkFBQQP9lkR8VA+8AzwPbDr0PZiTLEIB/0NfG4deTYdeYIYZooHzgfnAdFSVQUREREREREaCnTvh1Vfh5ZehrAw2b952aE/rz81uzJ+jrZg/P5s/Y0+aNJmQEBg/HhYtgrlzIUZrAsgIsmPHDp544glycnLYt2+fT22mTZuGzWZj6dKlg77id34+vPYaPP88FBebY48Wi/lcPzi449hjY6O5mBGYY4+jR5v3hgsugKSkgX0vIiIiIn1F4QURERERGXF27oQVK2DtWqioMB8GxsWZE4Z9mQBsGOYk4vJyc8JxdDSccQYsXQrTp/d597ultLS0LbDw7rvvdjuwcOqpp7J48WIuu+wyBRak1+Xn5/Pcc8+xcuVKPvnkE5/bpaWltQUZTj755EEdZCgsNO8/r74KJSXmQEV8PISFmWGFozEMM0RVUQG1tRARASeeCFddBaedphBDX3M6nRw8eNBr9YaCggIKCwtxuVwkJia2CzUcGXJIS0sjeKiX75EhrgJ4CngRKMAMHcQB4ZiTKnzhACqBGiAEc3XJy4ELMCdniIiIiIiIiAwfhgHr1sFTT8Enn5jP58LDITYW3nrrZZ/OcdFF82loMAMPTickJpoLlCxdCipqLCOJ2+3mww8/xG638+yzz1JbW3vUNlarlXPPPZfs7GwWLlxIWFhYP/TUNxs3ms/+33/fXLwoNNQcewwO9n3ssaHBHHtsbjbvK+edZ94bJkzo8+6LiIiI9CuFF0RERERkxHA44Mkn4V//Mh/+xcSYD/96UprZ7YbKSvN8UVFgs8E115gPJQdaaWkpzz//fFtgweVydes8p5xySltgIWMolZiQIW3//v1tQQZfy0gDpKentwUZTjrppEETZHC74aWX4M9/NldeiooyQwv+/t0/p2FAdbU50BkcbFaCueEGrdQ20FwuV1vAwVvIobCwEKfTSUJCgtfqDa0Bh5CQkIF+SzLsGMD7wO+APMyVHxOAwB6esw4owQw+nAvcAgzulfBEREREREREfFVVBX/5i1nJuaEBEhLMZ3ytk5JfecW38MK8efPbvnY6zWd7NTVmcOGGG2DBAt8WOREZThoaGnjxxRfJycnhjTfe8GkBrvDwcC699FJsNhtnnnnmgI0FNDTAo49CTo75vD4uznxG35PuuFzmAkaVleb5rr8errzSrN4gIiIiMhwovCAiIiIiI8LOnXD//eZqSCEhkJLSuyuUGwaUlpoPJo87DpYvN3/tb2VlZW2BhXfeeafbgYWTTz65LbAwatSoXu6lSNfs27evLcjw2Wef+dwuIyOjXZDBMkBlCQoL4Xe/g7feMgcs0tJ6FprypKLCHOicOBF+/nOYM0dVGAYzt9tNSUmJ1+oNra+Wlhbi4uI8BhsOf4UOhsScDBGVwJ+B5zGrJowCepCi8qgGOACkAz8B5qMqDCIiIiIiIjJUGQZ8+KH5fG/HDnNBktjYjsd1J7zQyuUynyG63XDOOXDLLarCICNXcXExTz31FHa7nY0bN/rUJiMjg2XLlpGdnc3kyZP7uIff2rDBHHvcsMEMMyUk9P7YY3ExNDbCKafAbbepCoOIiIgMDwoviIiIiMiw9957cNddZrggLa1vqyI0N8P+/RAdDbffbq6S1NfKy8vbAgtvv/12twMLJ510UltgYfTo0b3cS5HesXfv3rYgw+eff+5zu1GjRrUFGU488cR+CzJs2mQONu7da4amIiL67lotLWZVh+Bg+MEP4NprFWAYytxuN6WlpR6DDYd/7XA4iI2N9Vq9ofU1mEqoy0DZB/wM+Aaz0kJflmlxAQWYFRmuAG4FAvrweiIiIiIiIiK9zzDMFdX//ndoaoKMDAjw8uNtT8ILrWprzYnKY8bAAw8MzAJJIoPJxo0bycnJYcWKFRw4cMCnNrNmzcJms3HFFVeQkJDQZ3174QUzuFBVBaNGQVBQn12KhgYz4JSQAPfcA2ee2XfXEhEREekPCi+IiIiIyLC2Zg386ldQV2c+8O+PibyGAQUF5srqt9wCV1zR+9coLy/nhRde4Nlnn+Wtt97qdmDhxBNPbAssjBkzpnc7KdLH9uzZ0xZk+OKLL3xuN3r06LYgwwknnNBnQYbPP4dbb4UDB2Ds2N6vtuDNwYPmSkzf/z7ceKMCDMOZYRjtAg7eQg7Nzc3ExMR4DTa0fh8eHj7Qb0n6TC7w00O/jqX/ggSVQDmwEPgVoNr2IiIiIiIiMjQYBvz5z/Dvf5vVnJOSOj++N8ILYFZh2LPHvN4DD8BJJ/naY5Hhy+l08vbbb2O323n++edpbGw8aht/f38uuugibDYb8+bNI6gX0wVPPWVWY3G5ID29/8Ye9+41F0j65S/hggv6/poiIiIifUXhBREREREZtt5916x+0NBgrnpy+MPDDRuupqDgcZ/OY7WG4O8fRlBQEmFhE4mKmkly8gIiIqZ22q6oyHyYeNddsHBhT96JqaKiol1gwel0dus8J5xwQltgYezYsT3vmMggsHv37rYgw5dffulzu9GjR7N48WKWLFnCrFmzei3IsHmzGRxoDS5YrfD222NobNzXK+f3ZPr0/0dGxtUAlJWZK7X96EdmFQYFGEYuwzAoLy/vtHpDfn4+TU1NREVFdVq9ISMjg4i+LB8ifSQf+BGwk6uvruXxx4s6PXrDhjlMnx7p05k3bKhmxowPOz3mu99N4rHHYoDLgTsBf5/OLSIiIiIiIjJQDMMMLfz5zxAeDvHx3+7rfGzh30DnKYd58+azbt2ZVFS872W/gdttTlJOSIBHHoHp07v1NkSGpZqaGlavXo3dbufdd9/1qU10dDSXX345NpuNU045pUfjAM8/D7/9rfl1auq32zu7N5x99h5CQ8cc9dxHuzcYhln9PTTUrPqgCgwiIiIyVCm8ICIiIiLD0o4dcO21UFnpueJCV8IL3sTHn8fUqY8QETHZ6zEFBWYZ6T//uXsrJFVWVrYFFtauXdvtwMKsWbNYvHgxixcvVmBBhr3du3fz7LPPsnLlSr766iuf240ZM6YtyDBz5sxuD2CUlMB3v2sOMI4bZwYXoH/DCwClpWY5+9/8Bi6+uM8uK8OAYRhUVFR0Wr0hPz+fxsZGIiMjO63ekJ6eTlRU1EC/JWlTD1wDbADGcfXV3/D44wWdtrjmmgwefdS3WRHf+94GHnus8/N997vpPPbYGKACuBG4zqdzi4iIiIiIiAyUV1+FO+6AoCBITGy/rz/CCwBuN+zebS7M9PjjkJzcxTchMgLs27ePFStWYLfb2bFjh09tMjMzyc7OJjs7m3HjxnXpep9+Cj/5CTgcZsWFw/VHeAG+rcAQEwP/+Q9MnNiVdyAiIiIyOCi8ICIiIiLDjsMB118P69bB+PGeVxzvjfACQEBANCee+AYxMSd63G8Y5gDD5Mnw2GNmOdejqays5MUXX2wLLLS0tHSrbzNnzmwLLHT1AazIcLFr1662IMPXX3/tc7uxY8e2BRmOP/54n4MMhgHLl8MLL0BmJvj5fbuvv8MLAPv2mSu05eS0XwVKpKsMw6Cqqspr9YbW7+vr64mIiPAabGj9OioqqtcqnUhnHgH+DowCgrj66g1HDS8EB1vJzz+X+PjATo8rLW0mI+NtmpvdnR5nhheOAw4AVuBRYJrP70BERERERESkPxUXg80GBw/C6NEd9/dXeAHA5YJdu8yFSR58UNVVRbwxDIMvvvgCu93OU089RXl5uU/tZs+ejc1mY/HixURHR3d6bG2tuWjR9u3mokVdWTStN8MLYI5D5OXBKafAP/8JgZ0/xhMREREZdFSnXURERESGnRUr4JNPICOj7x/mt7RUsWFDNnPmbMLPL6jDfovF7Mc335gPEG++2fN5qqqq2gILb775ZrcDC8cff3xbYCEzM7Nb5xAZTjIzM1m+fDnLly8nLy+vLciwYcOGTtvt2bOHBx98kAcffJBx48a1BRlmzJjR6YTr1183X0lJ7YMLAyU93Rzg/N3vzFdrFQiRrrJYLMTExBATE8Oxxx7r8RjDMKiurvYYbFi/fn3b9rq6OsLCwrwGG1q/jo6OVsChR74EngCigI6fUbxpanLzr3/t4xe/GN/pcf/4x76jBhfaSwLygAcwJ3MEd6GtiIiIiIiISN8zDPj972H/fnNhkoHm52dWXFizBs48E77znYHukcjgZLFYOOGEEzjhhBN4+OGHef3117Hb7bz88sudjrd99NFHfPTRR9xwww0sWLCA7Oxszj//fAICAjoc+89/wpYtnqu99zeLxXz2/8kn8OSTcPXVA9sfERERka5SeEFEREREhpUdO+Df/4bQUAgJ6d45MjNvJSAgBgC320Fj4z5KSl6lufmgx+Pr63dSUvIaKSkLPe4PDITYWHj6aTj9dDjpJHN7dXV1W2DhjTfe6HZgYcaMGW2BhaysrG6dQ2QkyMrK4vbbb+f2228nNze3LciwcePGTtvt3r2bBx54gAceeIBx48axZMkSlixZwnHHHdduYnVJCfzxj+YgZ1RUx/NkZt6G01nt9TotLZXs2vWgx31RUceTkrK4035GRc3ssK11gHPtWnj5ZViwoNNTiPSIxWIhOjqa6Ohopk3zvrJ+a8DhyJDD559/3vZ9TU0NoaGhnVZvSE9PJzY2VgEHj+qBB4FaoOufDf72t33cemsm/v6eE08tLW7+/veuVpKxABnAF4AduK7L/RIRERERERHpS6++agYFkpMHx8IkAJGRUFlpPnecOdPsm4h4FxgYyIIFC1iwYAEVFRWsXLkSu93O+vXrvbZpbm5m5cqVrFy5koSEBK666ipsNlvbYkaffmqO8cXGDp4qByEh5ljov/5lVmCYOHGgeyQiIiLiO4UXRERERGRYycmB8nIY3/liwZ0aPfqHHcq3Op31fP75xZSXv+OxTVnZWq/hBYC4OLOE69//3syOHSt57jkzsOBwOLrVx+OOO64tsDC+J29WZIQaP348v/jFL/jFL37Bzp0724IMmzZt6rTd7t27uf/++7n//vvJzMxsCzJMnz6d556zsG+f91XZxoz5YafnbmjY6zW8EBFxDFlZy316b0eKjISKCnj0UbjwwsEzuCIjV1RUFFFRUUydOtXrMTU1Ne0qN7QGHb788su2r6urqwkJCWkLMngLOcTFxY3AgMMbwGZgFGZooGsKC5t47rlirrgizeP+Z54pori4uRv9CgbCgBzgMiC2G+cQERERERER6X0tLebCSGA+TxtMWqurPvcc/PjHA90bkaEjNjaWH/zgB/zgBz8gNzeXnJwccnJy2Lt3r9c2paWlPPLIIzzyyCNMnTqVZcuy2bTpR9TVhTPY1g9LSYHcXHNs9Le/HejeiIiIiPhO4QURERERGTaKi+Gtt8yVT3p7jqK/fxiTJt3Lxx+f7HF/U1Oh17ZOZwsHDhyktLSKxx938Pjj9wHbutyH6dOntwUWJkyY0OX2IuLZhAkTuOOOO7jjjjvYsWNHW5Bh8+bNnbbbtWsX9913H/fddx/jxk3DMFYTFJSGn18I3Zks3JeSk2H3bvj4YzjrrIHujcjRRUZGMmXKFKZMmeL1mNraWgoLC9tVbygoKODrr79u+7qyspKgoKBOqzekp6eTkJAwjAIOBrAK8z4U1O2z/OlPe72GFx55ZE+3zwsJwF7MgMWVPTiPiIiIiIiISO/5+GPz+VlS0kD3pCM/PwgLgxdfhO99z/xaRLpm/Pjx/OY3v+FXv/oVH330ETk5OaxcuZKamhqvbbZs2cLtt9uBc4mO9ickJI7k5GT8/QfHdDuLxRwTfftt+NGPzDCDiIiIyFAwOD5NiYiIiIj0gtdfh6oq76ue91Rk5HSv+9zupnbfO50tHDx4kKKiYkpLS3C73Yf2TADm4Wt44dhjj2XJkiUKLIj0k4kTJ3LnnXdy5513sn379rYgwzfffNNpu927RwEtwAfk5weSkpJKamoqkZERDIYgQ3AwuFzwwgsKL8jwERERwaRJk5g0aZLXY+rq6igsLGxXvaGgoIDNmze3fV1RUUFgYGCn1RtaAw5Wq7Uf32F3fQVsARJ9bnHssRHk5TXQ0OBq27Z+fSWff17FCSdEtzt23boKvviiut220FA/srJC2bSp1oer+R96rQIWo8eTIiIiIiIiMhi8+CI4nRASMtA98SwhAfLz4Z13YP78ge6NyNBltVqZM2cOc+bM4U9/+hMvvfQSdrudN954A5fL5aHFd4Awqqpy2bBhP35+fqSkpByq+Bo/4AuixMaalVlefx2uuWZAuyIiIiLiM40OioiIiMiw4HDA6tXmBN2+m1doeN0TFJSM0+k8FFgoOiKwcLhq4CLgX4e+7uiYY45pCyxMnDixNzouIt0wadIk7rrrLu666y62bdvWFmTYsmWLh6MXYd4jWqivbyEvL5e8vFzCwsJITU0lJWXggwxxcbBunTmQ0VchL5HBJjw8nIkTJ3b6/2lDQ0OHCg75+fls2bKl7evy8nICAwNJS0vzWr0hIyODxMTEQRBweAloBNJ9bhETE0B2dhr//Of+dtv/9Kc95OTMaLfNU9UFmy2dbdt8CS60SgR2Ap8Cp3WhnYiIiIiIiEjva61YGhs70D3xLjDQXGV91SqYN6/3q0+LjEQhISFcfvnlXH755Rw8eJCnnnoKu93O119/feiIKMwxvW+rM7hcrrZniMHBwaSlmc8GB4rVCkFB5hjpsmXmvUJERERksFN4QURERESGha1bzVWHEhL67hrV1V973VdTk8qbb77hJbBwuDJgDHA88G7b1mnTprUFFjpbQVpEBsbkyZO5++67ufvuu9m6dWtbkGHr1q1AMnAMUNGhXX19Pbm5ueTm5hIWFk5qaiqpqSlERPR/kCE62gwufPaZwgsihwsNDWX8+PGMHz/e6zGNjY1tFRwODzmsXbu27evS0lL8/f1JS0vzGGxo/TopKQk/P78+ejdmBRiIpKv3mBtvHMu//rUf47Cs5sqVxTz00GSSk4MBKChoZPXqA+3aWSzwk5+M4Yc/3NyFq4UCDuAzFF4QERERERGRgfbZZ1BbC+PGDXRPOhcba46FFBVBWtpA90ZkeElKSuKmm27ipptuYvPmzeTk5PDf/+6jvDwR2OuxTVNTE7t25bFrVx4BAQf7tb+Hi4+HggLz/nDccQPWDRERERGfKbwgIiIiIsNCXp5ZfSE4uG/O73TWsX37L7zsDaOm5ljgaMEFABfmZMIspk4taQssTJ48udf6KiJ9a8qUKfzyl7/kl7/8JVu2bOHhhz9lxYoYHI68TtvV19eRm7uT3NydhwUZUg8FGfpe62psO3f2y+VEhpWQkBCysrLIysryekxTU1NbwOHwkMO7777b9n1JSQn+/v6kpqZ6rd6Qnp5OcnJyNwMOezFXgovscsvJkyM477wE3nyztG2bw+Hm73/fx69/bVau+Mtf9uJ0tq9ENXduApMnd+c+FgB0JfAgIiIiIiIi0jdyc8EweqOq8/8c9YhXXun+2cPDoaLCXKBE4QWRvnPMMcfw4IMPMm6cm3vvbcDtbqa4uBi32+W1TUuLw+u+d94Z2xfdbBMSAk1N5lipwgsiIiIyFCi8ICIiIiLDQt6hOcO9USp5376/ExAQA4Db7aCxcR8HD76Kw+Fp1RQL8EMg3Kdzh4eHExwcx4UX3ofdHtfzzorIgJo6dSpz5kxl3TqD5OQkioqKKSoqor6+rtN2hwcZwsMjSE1NISampc/7GxwMGzf2+WVERqTg4GAyMzPJ7KS0SXNzM0VFRe2qN+Tn5/P++++3fX/gwAH8/PzaAg6eqjdkZGSQnJyMv/+Rj/Z2AQ2YFWG67sYbx7QLLwD885/7ueOO8bhcBv/+934Pbbo7+BoG7ASagD5Kn4qIiIiIiIj4YNOmvlsYqTf5+4PbbY6HzJkz0L0RGf527LASHh5OZuYMjjnmGIqLiykoKKC8vGygu9ZO69hoXufrK4mIiIgMGgoviIiIiMiwsHEjBAb2zrl27XrQxyPDgf8FZnd61JErrBcXQ3GxOcjQ85WcRGSgbdsGFouFiIhIJk6MZOLECdTU1FJc7FuQoa6ulp07a4G+LysdGmqWla+oMMvMi0j/CgoKYuzYsYwd633Cv8PhoKioqF31hoKCAj766KO27w8cOIDFYiElJaVdsGH+/L3MmlWD01lNSEgwQUHBWK2+JzsvvDCRCRPC2Lmzvm3bwYPNPP10EU1NLioq2oesJkwI44ILErr+GwFAKFAJ7AFUgUpEREREREQGRlUVFBRAWNhA98R327YNdA9Ehj+XCzZvNp+pA/j7+5ORkUFGRgaNjY0UFhaSn59/1Of//SUoSAsXiYiIyNCh8IKIiIiIDHkOB+Tnf/sAse8FAIuBeXiruPBtYCGFiIgIzAoNptBQqK6G0lJISuqP/opIX8rNPXJlNguRkZFERh4eZCg6FGSo93aafhEaCiUl5j1T4QWRwSkwMJAxY8YwZswYr8e0tLS0BRwOr+DQ0PANZWUV7NpVTlNTMxaLGZgIDg4hJCSYysrOB1MtFgs33DCGG27Y0m77I4/sobnZ3eH4n/xkLJZul70KAYqAfBReEBERERERkYGSnw+NjRAfP9A98U1ICOzcOdC9EBn+Skuhpsbz2GNISAhZWVlkZWVSVVVNQUEB+/ZZMYz+7+e3fTLvZw5H7y32JiIiItJXFF4QERERkSGvudlcAaX/qhi0AK8BKcAZbVvDwsJITU0lJSWVyMj2gYXD+fmZ/W1u7o++ikhfa2w0/117dniQYSI1NTUUFRVTXDwwQQbdf0SGh4CAAEaPHs3o0aOP2NMArGfcuDG43W6am5tpbGyiqamRxsYmLBbP4YXKyko+/PBDgoNDOPHEQCIirNTWfhtW+Oqr6g5toqL8ufrq9B68CyvmZyXdkERERERERGTgtI4veH++1xWL8LbgUatJkyazf/+/aGjY060r+PmZzyNFpG/5dm+wEB0dTXR0NC0tqRQWejuu/b0hICCAlJRUQkNDOHwssaf3BpdL4QUREREZGhReEBEREZEhr6UF3G7wb/t0a+B0unA4mmluduBwOHA4mnE4HG3fl5aW9vCqVcDDBAbWMWrUTaSmdh5YOJzFAoZhPkAUkaHP4fA1PGUhMjKKyMgoJk36NshQVFREQ0PnQYbq6mqKigoJD48gPDwMq7V7o6mt95+Wlm41F5FBz4EZCgCr1UpISAghISFADADR0TVAxwBDREQEmZlZbSGHiy8OYcWKzu9L1147irCw3ni0qA9EIiIiIiIiMnBaWsznZb2zONKFQOfllrOy5lNSsqbbE5QtFo0tiPSH1nuDr0VHO69O2v7e4O8fyv79DYSGhpGRkU5aWjqhoaE9ujdYreB06v4gIiIiQ4PCCyIiIiIy6BmGQU1NDaWlpW2vkpKStq+LimrZseNntLQ04XaX43A043a7j3LWpk72/RvzIWITEREV1NauAD70eKTD8W8SEq4iMnJSF96P+bDTX5/GRYYFf38zQNU1HYMM+/Z9zr59no92Op3s3r2b2to6XC4XYWFhREREHHqFExERQVhYONajjLLq/iMy3AUAXa9P7+/vT2pqStv399wzhqeeesfrvc1qhR//eEz3utjx6r10HhEREREREZGu8/c3n5d1/fme76xWK8cffzzJyck9PpdhQEBAL3RKRDrVem8wuv6orYOxY8dy4AA0NjYA0HiofEpDQz07duxgx44dxMbG0dyDkslut/nMTvcHERERGQo0OigiIiIi/c7tdlNVVdUujHDk6/BwQllZGY5OlwoJBK7GrHrQ83rJM2eeQELCFPz9A3A6W1izJhV//xSczpWe3g2bN/+AM87YjMXi20rorQ8Qg4J63FURGQSCg3s6uGkGGTIzs7yGFyIjIznhhNMBg8bGJmpra9teJSUl1NXV4nK5jwg1RBwKNYS1hRpa7z8qGy0yXAUDrh6fZezYUC6+OIkXXjjocf+CBcmMGRPaw6sYh176QCQiIiIiIiIDJyjIfF7Wl+GFoKBgNm3azK5du5kyZUqPzuV2a2xBpD/05r1h7NhMpk4dTUVFJZWVlYSGhvLll18AYLX64Xa7qKgox1PFVF/p2b+IiIgMJQoviIiIiEiPud1uKioqPAYPPL3KyspwOp3dvl5MTAyRkZEEHXpC39zcTFFRBS0t6Z22Cw4OJjzcnMxbVRVLZaXn46KiovD3b780yVlnPcYnn2yhtnZLh+Pr6raRn///GDXqWp/639gI4eEQH+/T4SIyyI0bB7m5/XU1CyEhIYSEhJCYmNi21TAMmpoaDws11FFSUkJtbS2G8W2oISAgnoCACGpry2hpGUeAlmESGWbGAr0z2+LGG8d6DS/ceOPYXrhCE2ZwIeVoB4qIiIiIiIj0meRkc3GSxkbz156zcGRVxKCgIE4++WR27drFJ5+sx8+vtttnb2w0n0eKSN+KjzfH8urrITKyN85oITY2ltjYWACOOeZYNm/ehNvtIjExkaCgIPLzLZ20NzDvL541NZn3BoWbREREZChQeEFEREREOnA6nZSXl/tUFaG0tJTy8nLc3Vx6xGKxEBcXR0JCAomJiSQkJLS9oqOjcTgcVFdXU1JSQkFBAbt27WL37t1UdkgefAlkAeYqRu1XHg8nPDyi3STdDRvCvIYXPPHz82fSpPv4/POLPe7Pzf0/0tO/i9V69InA9fUwc6ZKt4oMF5Mnw5o1A9sHi8VCSEgoISGhJCYmtW03DIPGRjPUUFdXy8GDBk7nNi644HxaWhxMmDCBqVOntntlZWUp1CAyZGUe+tUNWHt0pjPPjGf69Eg2bqxpt/244yI544y4Hp3bVA+E8W2fRURERERERPpfQgIkJkJhIcTEdH6sYRjU19d73Z+WlkZdXRjV1dUd9vn7+zNx4kRGjx7Nhx/e3e3+Gob5PFJE+lZAAEycCB9+2DfnHz16NM3NzezcuYOSkhKyssYTGxtDRYXn499//wPS09NJS0sj2EPSqqkJjjmmb/oqIiIi0tsUXhAREREZAVpaWigrK/O5MkJFRQWGYRz9xB74+fkRHx/fLoTQ+joynJCQkEBsbCwtLS3s2LGDLVu2sGXLFrZu3cpbb73Frl27Og1FpKSktE22ra+fw5tvjmbSpEwC+6gmalLSfCIjZ1BT83WHfY2NeykosDNq1PePeh63G6ZO7YseishAyDJzUzid4D/Ifsq2WCyEhoYSGhpKUlIShgELF47lt7+tZd++fW333S1btvDSSy+xbds2nE6nx1BDZmamQg0ig954zGoGTUBoj892441jueaajR229Y4GYCoQ3kvnExEREREREek6iwWmT4e8vM6PKy0tZdu2rdTUeJlZDEycOJmvviro9DzBwcGEhYXT3Nz1vrpcZn9bn0eKSN+aNg3ef7/vzj9hwngcjmb27t1LXl4uoaHebwy1tTVs27aVbdu2ER8fT0ZGOsnJyfj5+WMYZrBpwoS+66uIiIhIbxpk0ypERERExBfNzc2dhg+ODCdUVVV1+1r+/v6dhg+OfMXExGC1el7pt7m5uS2ksHbtWrZu3cqWLVvIy8vrNKSQnJzcYRLtlClTiDlsGaTPP4dPPjEfzvWl8ePv4ssvF3ncl5d376HqC94/Zrtc5q8aXBAZPrKyICwMGhp6q3x032gdwJg4EaxWK2PHjmXs2LHMmzev7Ri3283evXvbhRpeeOEFtm3bhsvlYuLEiR5DDf6DLbUhMmKNwwwD1NMb4YXvfS+D7Oy0dtv8/XtW0eFbzcCxvXQuERERERERke6bOPHbZ2cWS/t9NTXmhOHS0tJ+6YthGFiO7MQh9fUQGgrjx/dLV0RGvNaxPJcL/Pz64goWpk6dRnOzg+LiIhoavFd2GTcuk8LCApqbmykrK6WsrBQ/P39SUlJITMwgICCW8eM93ztEREREBhvNLhAREREZBBobGzsNHxz5qqmp6fa1goKCvAYPPIUToqKivD4o98Ysc7qzXSWF1pCCq3X2vgdJSUkeQwqxsbFHveaUKWZp5/JySE3tUne7JDn5EiIijqG2dnOHfQ0NuyksfIKMjKu9tq+shOhocyUnERke0tMhMxO2bh3c4YXaWnNwc8YM78dYrVbGjRvHuHHjmD9/ftt2l8vVIdSwevVqtm/fjtvt7hBqmDJlikINIgMiGDgReB1I6JUz9l5Y4XDNgB9wfB+cW0RERERERKRrZswwn5vV1n77fK+pqZHt23dQUJDfr3358MMPmDx5CgkJHX+ur6gwgxYZGf3aJZERa/p0c0yvshLi4/vmGhaLhRkzjqOlxUFZmffjpkyZwuTJkygtLaOgoIADBw7gcjkpKMinoMBJUNA2nnnmLSIirmDSpEl901kRERGRXqJZBCIiIiK9zDAM6uvru1QZob7e+0oaRxMSEuJzZYTExETCw8O7HEbwxuFweAwp5ObmdhpSSExM9BhSiIuL63ZfwsLg4ovhb3/zvDpSb7FYLIwffydffXW5x/25uf9Heno2FovnJVgqK2HxYkhJ6Zv+iUj/s1ph0SLYtAmcThis8/XLyuCEE+CYY7re1s/Pj8zMTDIzM7n44ovbtrtcLvbs2dMu1PDcc8+xfft2AK+hBr++WaZKRABYAKwFGoGQAe6LNyXAaGDOQHdEREREREREhKlT4bjjzOrOISEt7NqVx+7duztUjA4ICCA0NJrq6r7rS1paOl9++SUxMdFMnjyFyENpCqfTfF16aV+tAC8iR0pJgXPOgeee67vwAoDV6sesWSewdq0/nQyvYrFYSUxMJDExEaezheLiYvLzC6ioiKC52c7DD/+Zhx/+NSeccAI2m40rrriC+L7suIiIiEg3DdIpFSIiIiKDh2EY1NbW+lwVobS0lMbGxm5fLzw8vEuVEcLCwnrx3XrmcDjIzc31GFJwOp1e2yUkJHgMKfTVg7LvfAeeeAKqqiAmpk8uAUBKymWEh0+mrm5bh30NDXkUFj5Jenp2h311dRAUBIctZi4iw8TcufDXv5oBgeTkge5NRw6HGey69NLeDXf5+fmRlZVFVlYWCxYsaNvucrnYvXt3u1DDypUr2b59OxaLhUmTJnX4v2HcuHEKNYj0ilOBTGA3ZkBgsHEDTcBCzEoRIiIiIiIiIgPLYoGLL3by2ms15OZuwOlsv+CU1Wpl7NixZGWNZ8uWp/s0vJCZmcmoURnk5uby0UcfkpaWxsSJE6muDiE+Hs4/v++uLSIdzZ8PL79sjvGFh/fddfz9/YmMjKCy0tfjA8jIGEV4+Chqapo577zJvPLKBHbu3Mnnn3/O559/zk9/+lMuuugibDYb8+bNIygoqO/egIiIiEgXKLwgIiIiI45hGFRVVflcFaG0tBSHw9Ht60VGRvpcGSEhIYGQkIFbIbelpcVjSGHnzp2dhhTi4+M9hhQ8lTXuS1lZcPLJsHatWca176ovWMnKuoMNG5Z53J+b+1vS0q7qUH2hpMRc8XzWrL7pl4gMnJgYM0D13/9CYqJZjWEwKSmB1FQ4++z+uZ6fnx/jx49n/PjxXHLJJW3bnU5nu1DD5s2befrpp9mxYwdWq9VjqGHs2LEKNYh0SQCwCLgXcDL4Hv9VAFHARQPdEREREREREREMw2D16tXcdttvOHjwISAB+Da8kJaWzqRJEwkJCe23PgUEBDJlylTGjBnL9u3befvtd4mJOYEf/ziC2FgtBCDSn044ASZPhs2b+za8AOb4Y1cYhrmg0nnnBfHwwz/kd7/7AZ9//jl2u52nnnqKiooKXnrpJV566SViYmK4/PLLsdlsnHzyyVj6ahBVRERExAcWwzCMge6EiIiISE+43W4qKyt9roxQVlZGS0tLt68XExPjc2WE+Pj4QbmKRUtLC3l5eR5DCp393sTFxXWYVDp16lQSExP7sfed+/RT+N//hcDA3inh6nS2sGbNGi644AL8/QO6fZ6aGqishPvvh4s0V09kWNq9G2w2aGiAtLSenau2tpb333+v7fszzjiTiIiIbp2rsREKC+HWW+F73+tZv/qK0+lk165d7So1bNmyhR07duDv7+811GAdbCkRkUGjAlgK5ANjB7gvh2sB9gA24I4B7ouIiIiIiIiMdOvWreOWW25h3bp1h7ZkA8uBIuLiwpgyZQpRUVFdOudHH31EVVX7pdOjo2OYPXt2t/uZm1tPUdEe/P2/z29+k831119PQED3xytEpGtefx1uu81cxCgysu+vV1NTzbp163A6nURERHLqqad6/DdfVmZWXf7b3+Ckk9rvczgcvP7669jtdl5++eV2479ZWVlkZ2ezbNkyxo0b19dvR0RERKQDhRdERERk0HG5XJSXl/tcGaG8vByXy9Wta1ksFmJjY32uihAfHz+kHgg7nU6PIYUdO3Z0GlKIjY31GlIYCitxPPigufr56NFmiKEneiO84HLBrl2wYAE88EDfVYQQkYGXkwP33QcpKRDag8XYeiu8YBiQl2dWpfnXv3p+T+xvR/4/1vrauXMn/v7+TJ48ucP/V2PGjFGoQQSAtcDNQCQQPbBdabMbyATsDJ4+iYiIiIiIyEiTm5vL8uXLWb169RF7AoiOfoawsPM47riwbo2H9HZ4oaEBiovh1lsN4uJe4bbbbsPpdHL//fezcOHCITFmIzLUGYYZXnjxRcjMhP4oFFxeXs6nn36C2+0mJiaWk08+uV2FYocD9u2D738fbrnl6OdauXIldrudTz75pN2+008/HZvNxuLFi7sc1hIRERHpLoUXREREpM85nU7Kysp8roxQXl5Odz+iWK1W4uPjfaqKkJCQQFxcXLsHPUPVkStWHx5ScDgcXtvFxMR4DCkkJSUN6QfeNTXw3e/Cjh0wblzPwgK9EV7Ytw+Sk81JzUlJ3e+LiAx+LS1m9ZcPPoCsLOjuHPreCi8UF5uBhUcfNUtbDxdHVhA6PNQQGBjoMdQwevRohRpkhDGAu4BngXGA/8B2hwqgEfgjMGdguyIiIiIiIiIjUmlpKb/5zW/4xz/+gdPpbLcvJSWFe+65h1NOuZprr/WjuRlSU7t+jd4ML7jd5sJIp50Gf/+7+ZzP6XTy3//+l7vvvpvMzEweeughTj311K53VES65MABs/LygQPm4mn9c81ivvjiS8AgKSmJWbNmYbFYMQyzEvSkSfD449CVoYOdO3fyxBNPkJOTw969e9u2BwUFsWDBAmw2G3Pnzh1Si/mJiIjI0KPwgoiIiHSZw+HoNHxwZDihsrLy6Cf1wt/fvy2McLTKCImJicTExAzriYkul8tjSGH79u2dhhSio6M9hhSSk5OHdEihM+vXw09+Yj7c784AQ6uehhfKyqCxEe69Fy66qPv9EJGhIy8Pvvc9qKoyBzG6c5vtjfBCdTWUl8PPfw7XXNP1PgxFLS0t5Obmdgg15ObmEhQU5DHUMGrUqGH92UFGujLABuzBDDAM1N/1BqAAyAbuAIbn508REREREREZnBoaGvjjH//I/fffT21tbbt94eHh3Hbbbfz0pz8lLCwMgMceg4cegrg46OpC5L0VXjAM2L8fIiPNStMTJrTfX1dXx8MPP8xDDz3EBRdcwH333cf48eO71lkR6ZJXX4U77oCQEIiP759r7t+/j02bNgGQkZHB9OnTKSqyYLXCn/4Ep5zSvfO63W4++ugj7HY7K1eubHdvTExM5KqrriI7O5sZM2YM27FkERERGTgKL4iIiAhNTU2dhg+OfFVXV3f7WoGBgZ2GD47cFh0dPSIfiLhcLnbv3u0xpNDc3Oy1XVRUlMeQQkpKyoj8fXzySXjwQXM1ou5WPOhJeKGiwpy8/MMfwo9+1LMKECIytLz9Ntx+OzQ3Q0ZG1//99zS8UFtrrgC1eDH88pfgP9ALrg8wh8PhNdQQHBzc9v/lkaGGkfh/pwxHG4CbgFJgDP0fYGgE8oGzgd8BYf18fRERERERERmpXC4XOTk53HnnnRQWFrbb5+fnx/XXX8/dd99N0hEDCE4n3HMPPPOMObYQGen7NXsjvGAYUFBgjm383//Beed5P7a4uJhf/epX2O12rr32Wu6++24SEhJ877CI+Mww4C9/gX/8A6KjITa2f66bm7uTHTt2AJCSchyJiRncdhtceWXvnL+xsZEXX3yRnJwc3njjDVwuV9u+qVOnYrPZWLp0KWlpab1zQRERERnxFF4QEREZhurr67tUGaGurq7b1woODu40fHBkOCEiIkITAQ/jcrnYs2ePx5BCU1OT13aRkZEeQwqpqan6/T2MYcCjj8Kf/2xO3O1OBYbuhhfKyqCmBr77Xbj5ZtCi3iIjz8svm4OcTU0walTXAgw9CS9UV0NJCcyfD7/9LQQFdbHjI4jD4WDnzp0dQg15eXmEhIR4DDVkZGTo/1oZgtYDt2FWYhhL/wUY6oFC4DTgD0AXl6sUERERERER6aY33niDW2+9tW3F8sNdcskl3H///UycONFre4cD7roLXnrJXGE9Otq36/Y0vNBacSEoCO68ExYs8O26W7duZfny5bz33nssX76cm266idDQUN8ai4jP3G6zMovdbgab+qcCg8E333zD3r3NgD9XXFHOk0+e0yeLph04cICnnnoKu93Ohg0b2rZbLBbOPfdcsrOzWbhwIeHh4b1/cRERERkxFF4QEREZ5AzDoK6uzueqCKWlpTQ0NHT7emFhYV2qjBAWFqYJfD5wu90eQwrbtm3rNKQQERHhMaSQlpam33cfGYZZgeEPf4DGRnMCcVdWIO9qeMHlMldEsljg2mvNigsKLoiMXG++Cb/6FVRWmhUYfA0SdCe84HZDcbFZ7eGyy8zy1YGB3e/7SNbc3Ow11BAWFtYu1ND6dXp6uv5vlkHuS+BWoABIB/pyAoOBWemhGjgHuA/owjKVIiIiIiIiIt20ceNGbrnlFtauXdth30knncTvfvc7n4MEDodZ+WDVKggIMBdIOtrz/p6EF5qbzeBCbCzcfTdccIFP3Wznvffe45ZbbuHAgQPcc889ZGdn4+fn1/UTiYhXbrdZgeHRR81xyPR06Mt/Zi0tkJ9vUFy8h8rKu4AnefLJJ7myt0oveLF582ZycnJ44oknKC4ubtseFhbGpZdeis1m48wzz9Q9RkRERLpM4QUREZF+ZhgG1dXVPldFKC0tpbm5udvXi4iI8LkyQkJCglZh6SG3283evXs9hhQaGxu9touIiNBEyD72/vvmSig7d0JiIsTE+NauK+GFmho4cMAMSPzkJzBvXtdWWheR4WnDBrjvPti4EaKiICHh6PeGroYXGhrM4FRCAvzgB3DFFV0Laolvmpub2bFjR4dQw65duwgPD/f4f7kChzK47ALuBz4GgoEUer8KQzOQD0QANuD7QEgvX0NERERERESkvfz8fO68805ycnI4chpMZmYm9913H5dddlmXn9M4nfDMM/CPf0BpqRlgCAvzfnx3wguGYZ67uhqmT4fbboPjj+9SN9txu90888wz/OIXvyAyMpIHH3yQ888/v/snFJEODANeeQX+9CczdJScbFZi6G2Vleb9Yfx4+MlPHDz00Hd46623CAgI4JVXXmHu3Lm9f9EjuFwu3n77bex2O88//3y7hRTT09NZtmwZ2dnZTJkypc/7IiIiIsODwgsiIiI95Ha7qaqq6lJlhJaWlm5fLzo62ufKCPHx8QQHB/fiu5VWbrebffv2eQwpdFb5wtvExoyMDE1s7AcVFeZKKM8/b65glJgI4eGdTyL2JbxQXw8lJeZ5LrgAfvYzSEnpozchIkNSfb25CtMTT5hBp4QEM8jg7f7ja3ihqcm8/7S0wOzZ5sBmZmYfvQnxqqmpyWuoITIy0uP//ampqfq/XwZIC/A08E/M6gjxQDQ9DzE4Dp2vAZgO3A4c18NzioiIiIiIiHSuurqa+++/nz/+8Y8dKl3HxcVx991384Mf/IDAHpYo3b0b7r8fPv7YXGE9KQk8DcF1JbxgGOazwpISiIiAZcvMis6dhSO6orm5mb/+9a/89re/ZdasWTz00ENMnz69d04uIoBZDfn3v4c1a8x/04mJPf83bBhQV2feG4KCYOFC+PGPzaostbW1nHXWWXz55ZeEhYXxzjvvcOKJJ/bOm/FBbW0tq1evxm638+6777YLi82cORObzcYVV1xBYmJiv/VJREREhh6FF0RERI7gcrmoqKjwuTJCWVkZLper29eLjY31uSpCfHx8jx+uSte43W7279/vMaRQX1/vtV1YWJjHiYqjRo3SRMUBZhhmFYZHH4VvvoHGRnMCcVyc55Ku3sILbrcZhqiqMstFT5wIV18NF12kagsi4t3XX5urtH35JdTWmoOS8fHmfeRwnYUXDMO895SXm2Xqx46FpUth8WJVWxhsGhsbPYYadu/eTVRUlMfPCikpKfqsIP0kDzPA8AFQBYQCiUBXft4wgFqg7NDXacBiYNmh84mIiIiIiIj0DYfDwT/+8Q9+85vfUF5e3m5fUFAQN910E8uXLyc6OrrXrul0wnPPwYoVsGcPuFzm2EJ09LfjAr6EF5xOcyX12lpzgaWZM+H6681f+0JlZSX33nsvf/3rX1myZAn33HMPGRkZfXMxkRHIMOC11+Cxx2DHDnOxoehoM2xg7cJ6IS6X+dy/uhpCQmDaNPj+9+GMM9qPPZaUlDB79mxyc3OJi4vj448/ZuLEib39to5q//79rFixArvdzvbt29u2+/v7c+GFF5Kdnc38+fO12KKIiIh0oPCCiIgMe06nk/Lycp8rI5SXl+N2u7t1LavVSlxcnE9VERISEoiLi8NfswwHBcMwPIYUtm7d2mlIITQ01GtIwdqVp1HS7wzDnET8yivwxhtmEAHMh4hhYeZDQT8/cLtbeP/9D5g9ew4ORwANDebAApihhzPPhIsvhhNP1KRhEfGNYZgDGK++Ci+/bK6eZBjm4ENYGISGQlNTHZ99th6wAH4ce+xJGEYoLS3msRERcPLJcMklcPrpoGzj0NLY2Mj27ds7hBr27NlDVFRU2+eKwz9bJCcnK9QgfWQf8BrwPFAItP4sFAqEAf6YVRmMQ/uagfpDv7YeNx1YBJx9qI2IiIiIiIhI3zAMg1WrVnH77beTl5fXbp/FYiE7O5t77rmHUaNG9VkfHA746CN44QVYv94MIYD5jG737s3U15cBLsyfpa1ERMQwceLx1Nd/+xwwMRHmzYPvfAcmTeqfRZH27t3LHXfcwfPPP8+NN97I8uXLiYqK6vsLi4wQTid89hm89BK8954ZQgBz/DA01Bx7tFrNl9tthhUaG83Kza3TE2Jj4fzzzfvDjBne7w179uzh1FNP5cCBA4waNYp169aRlpbWL+/zSIZh8OWXX2K323nqqacoKytr2xcVFcXll1+OzWbj1FNP1TNuERERARReEBGRIcjhcFBWVuZzZYTKykq6+9+dn58f8fHxPlVGSExMJCYmBj9PS7fLoGEYBvn5+R5DCnV1dV7bhYaGMnny5A4TCUePHq2QwjBQWmqugp6XB1u2wPbt5mCD2w1Op4vNmzdy3HHTiYjwY8IEc6WTrCzzoeEAPQcUkWGiuhq++MK8/2zdalaEqamB+vpGtm3bjjnAaXDccZM55phQpk2D8ePh2GMhM3Ogey+9raGhwWuoISYmxmOoISkpSQM+0ksagc8xKzJsAzYDFYATM7RgwQwxBAKjgWOB8cCUQy/9PRQREREREZG+9fHHH3PzzTfzySefdNh37rnn8uCDDzJjxox+7dPu3bBpk/l8b9MmeP75TdTVtWD+nGwBDMLCgjnppClMnQpTppjjCyecYC6QNBC++OILbrnlFjZv3szdd9/ND37wA1V+F+llhYXmImp5eeZz/x07zKCC222+WkMMERFmgGnqVPPeMHMmJCT4do1NmzYxZ84cqqurmTZtGh988AExMTF9+8aOwuFwsGbNGux2Oy+//DIOh6Nt37hx47DZbCxbtoxMDXCIiIiMaAoviIjIgGtubu40fHDkq6qqqtvXCggI6DR8cOS26OhoTUwfogzDoKCgwGNIobZ1CRwPQkJCPIYUxowZo78LI0hLCxw4AE1NUFVVx4knzmDz5q8YPz6CoKCB7p2IDGcul3n/2bQpl4suuhhzhTYHn3/+GrNmTRno7skAqa+v9xhq2Lt3L7GxsR5DDYmJiQo1SA8ZwEGgAbPKghUIAmKByAHsl4iIiIiIiIw0O3bs4Pbbb+f555/vsO+YY47hoYceYu7cuYPiWciJJ57J55/vxQz/+wEtHH/8FD777CUG0/pnhmHw2muvceutt+JwOLjvvvu49NJLB8Xvochw1NwMBw+avzqdZjWG4GBIToaAgO6f94MPPmDu3Lk0Nzcze/Zs3nzzTUJCQnqv4z1QWVnJypUrsdvtrFu3rt2+0047DZvNxuLFiwc8cCEiIiL9T+EFERHpdQ0NDZ2GD44MJ3Q2kfxogoKCOg0fHBlOiIyM1EO3YcYwDAoLC9sFFFq/rqmp8douODjYa0hB1TPkcDU1NURFRVFdXU1kpCbqiUj/2LJlC9OmTWv7/ptvvmHq1KkD2CMZjOrr69m2bVuHUMO+ffuIi4trF2po/byTmJg40N0WEREREREREfFJSUkJv/71r/nnP/+Jy+Vqty8tLY177rkHm802qMZ1TjnllA6VIU4++WTWr18/QD3qnNPp5LHHHuPuu+9mzJgxPPTQQ5x22mkD3S0R6YIXX3yRRYsW4Xa7mT9/PqtXr8bf33+gu9VOXl4eOTk55OTksGfPnrbtQUFBzJ8/H5vNxgUXXEBAT5IcIiIiMmQovCAiIp0yDIP6+nqfqyKUlpZSX1/f7euFhoZ2qTJCeHi4wggjhGEYFBUVeQwpVFdXe20XFBTkMaQwduzYQfUwWwYvhRdEZCAovCA9UVdX5zHUsH//fuLj4z1WakjwtRa5iIiIiIiIiEgfa2ho4A9/+AMPPPBAh0XQIiIiWL58OTfddBOhoaED1EPvhlp4oVV9fT0PP/wwDz30EOeddx73338/EyZMGOhuiYiP/vOf//A///M/AFxzzTX85z//GZTzKAzD4OOPP8Zut7Ny5cp24/wJCQlceeWV2Gw2jj/++EHZfxEREekdCi+MUFVNVeRV5JFXkUdueS75Nfk0OZtwuV0E+gUSGRTJhLgJZMZmkhWbxZjoMfhbB1cqV0S6xzAMampqfK6KUFpaSlNTU7evFx4e7lNVhNYXhLFrF+Tmmq+9e6GpCRwOCAyEsDDIyvr2NW4cBAX12m+PDAKGYVBcXOwxpFBVVeW1XVBQEJMmTeowEW/cuHEKKYiPmoDdQN6h1y6gHoejnieffI4rr/weQUGTgPFA5qFX2ID1VkSGD4cD9u2DvDzzlZsL+fmVvPDCa4ATaOKGGy5h9uwkMjPNz0BRUQPdaxmKamtrPYYa8vPzSUhI8BhqiI+PH+huS59yAflALubnn51ANdAMWIFgIBWYAGQdesUCGjgUERERERGR3udyuXj88ce56667KCoqarfP39+f66+/nrvvvntQV5YcquGFVgcOHODXv/41jz32GNdccw2//OUvB/Xvt4h869577+WOO+4AYPny5dx3330D3KPONTY28vLLL5OTk8Prr7/ersLOlClTyM7OZunSpWRkZAxgL0VERKQvKLwwgtQ76nl7z9s8v+15tpdtp76lHofLAUCAXwBWixULFtyGG5fhwu12gwVC/UOJDI5k7ri5zJswj2mJ05RuFRlEDMOgqqqqS5URHA5Ht68XFRXlc1WEhIQEgoODj3pOhwM+/BBeeAG+/BLq66G5GSwW8PcHq9X82jDA5TJfhgEhIRAeDnPmwMUXw6xZoDnqQ4dhGBw4cKBDSGHLli2dhhQCAwO9hhQGW/lLGQpcwKfAS8BHQD3QeGifP+CH02mwZctmpk2bjJ+fARiYE/lCgROAS4DTgMB+7ruIDGWGARs3wiuvwNtvQ3U1NB66/Vit4HQ2sXPnNswJwlbGjp1AUFAwAQFmmHPqVFi4EM46CwbhAnMyxNTU1HgMNRQUFJCYmOgx1BAXFzfQ3ZZuMzBDCq8CrwGVQMOhfRbMz0DWQ8e5gZbDtodhBjgvAeYCSlKJiIiIiIhIzxmGwZo1a7j11lv55ptvOuxftGgR991335CoBDDUwwuttm3bxvLly3n33Xe57bbb+OlPfzooK12IyLcMw+DGG2/kz3/+MwB/+MMfuOmmmwa2Uz4qKSnhqaeewm6389VXX7Vtt1gsnH322dhsNhYtWkR4ePgA9lJERER6i8ILI8D+6v28sP0FXtzxIsW1xVgsFqKDowkLCCPQL7DTIILbcNPQ0kBtcy21jlrCAsI4Lvk4Fk1exNzMuQT6aZKeSG9zu91UVFT4XBmhrKwMp9PZ7evFxMT4XBkhPj6eoF4sc1BaCi+/DKtXmxUWXC6IjjYn5AUHm4EFb9xuc4Jffb052S8oCCZNgksvhYsuMkMNMjgYhsHBgwc9hhQqKyu9tgsMDGTixIkdJsplZmYqpCC9oBZzwt4qYAfmpLxIIBwzmGBtO7KlxcmaNWu44IILCAjwx5zI14QZdKgC/IBxwCJgPqDJnCLiXVMTvPEGPP+8GV5obISICPOzS2ioGVwAc4X8999/r63dGWecSXh4BM3N5uef1pxfejpccgksWABpaf39bmS4q6mp6fD5bcuWLRQWFpKUlOQx1BAbGzvQ3RavnMC7wPPAZ5ifh8KBCMxQZmdJcAPz81Lr5x8XkAR8BzPIML6P+iwiIiIiIiLD3ddff80tt9zC22+/3WHfKaecwkMPPcRpp502AD3rnuESXmj1wQcfcMstt1BQUMA999zDd7/7XVU8FxnE3G43S5cu5emnnwbgiSeeYOnSpQPcq6755ptvyMnJYcWKFRQWFrZtDw0N5dJLLyU7O5uzzz5b9yIREZEhTOGFYczpdvLMN8/wjy//QUl9CeGB4cSHxnc7cGAYBrWOWsrqy7BYLJyQdgLLT1vOxPiJvdxzkeHF5XJRXl7uc2WEsrIys/JJN1gsFuLi4nyujBAXF0dAQEAvv+OjMwx4/XX44x9h/34zqJCQYP7aXfX1ZhjC6YQpU+C22+DEE3uty+IDwzAoKSnxGFKoqKjw2i4gIMBjSCErK0shBekj64AHMUML/kAi5oQ9zzqGF47UBJQAzcBY4GfAuZgrFIuIfOubb+DBB81KU2B+/gkP9xzY9BReiIiIaHeMw2F+/qmvh5QU+N//NYOcel4vfa26utpjqKGoqIjk5GSPoYaYmJiB7vYItxfz88+HmMGDOMyqCd39vOIEyoAaIBa4BrABvRd2FxERERERkeFt//793HnnnTzxxBMcOW0lKyuL+++/n0WLFnW6GONgNNzCC2COAa5cuZLbb7+d8PBwHnzwQc4///wh92cjMlI4HA7mzZvH2rVr8ff355VXXuH8888f6G51mcvl4t1338Vut7Nq1SoaGhra9qWmprJs2TKys7OZNm3aAPZSREREukPhhWFqd+Vu7v/ofj7e/zFB/kGkRKRgtViP3tBHjS2NFNQUEBsSy/8c/z8sPXapqjDIiNHS0kJZWZnPlREqKio6PHDzldVqJT4+3qfKCImJicTGxg76dHlJCTz8sBleMAzIyOjdCXYOB+TnmysXX3klXHeduZqx9K6SkhKPE9bKy8u9tvH3928LKbROYmsNKQxEiEZGohrgb8CzQCOQARz988vRwwutnEA+5qrF84GbgPge9llEhoOmJrDb4dFHzYpRGRlHD236El5o5XZDYSG0tMBZZ8Ett8Do0b34BkR8VFVV5fEzYnFxMSkpKR5DDdHR0QPd7WHOCazG/AxUDKRiVlvoLQZwEKgDTgBuA6b24vlFRERERERkuKmqquK+++7jkUceobm5ud2++Ph47r77bq6//noCA4fm/IPhGF5o1dzczN///nfuueceZsyYwUMPPcSMGTMGulsi4kFtbS1nn302X3zxBWFhYbzzzjucOIRXf6yrq2P16tXY7XbeeeeddnNwZsyYgc1m48orryQpKWkAeykiIiK+UnhhGFqTt4Z7P7yXg/UHSYtIIywwrE+uYxgGxXXFNLQ0MHvUbO4/537iQuP65FoifcnhcHQaPjjyVVlZ2e1r+fv7dxo+OHJbTEwMVmvvBY8G2pdfwi9+AXv3QlISREX1zXUMA8rLobISjjkGHnoIxozpm2sNd6WlpR4noJWVlXlt4+/vz4QJEzqEFMaPH6+QggygPOBWYCvm6sCx+LrSsO/hhVZVmJUYMoF7geO60V8RGS5KSuDWW+HTT80qC0lJnistHKkr4YVWdXVQVGRWYfjlL80gg8hgUFlZ6fEz5YEDB0hNTfUYaojqqx8WRpRa4C5gLWZgMxXoq58vG4ECIAazCtVlqAqViIiIiIiIHM7hcPC3v/2Ne+65p0PF7uDgYH76059y2223DflnAsM5vNCqsrKS++67j7/85S9cdtll/Pa3v2XUqFED3S0ROUJpaSmzZ89m586dxMXF8dFHHzFp0qSB7laPFRQUsGLFCux2O1u3bm3b7ufnxwUXXIDNZmP+/PmEhIQMYC9FRESkMwovDDOrt63mvo/uo9nZTEZURq9WW/CmoaWBguoCZqbO5OG5D5MSkdLn1xTpTGNjo89VEUpLS6mpqen2tQIDAzsNHxwZToiKihqx5TM/+sgMLpSWwtixvVttwRuHwwxKZGXB738PEyf2/TWHqrKyMo8TykpLS7228fPz8xpSGKqr4chwtQX4ObAHGIMv1RYO1/XwApirHO8FkoD7gZO7dE0RGR4KCuBnP4ONG81qC115Tt6d8AKYVRj27YOwMLjrLpg3rxsdF+knFRUVHj+DHjx4kLS0tA6hhilTpgz5CQz9pxKzCsL79H61BW8MoPDQ1z8BrkYBBhERERERETEMg2effZbbb7+d3bt3t9tnsVj47ne/yz333EN6evoA9bB3jYTwQqt9+/Zx5513smrVKm644QZuv/12VdkUGWT27t3LaaedRlFREaNGjeLjjz8eNvdbwzD46quvyMnJ4cknn2w3tyEqKorFixdjs9mYPXv2iJ2nIyIiMlgpvDAMHH/88Xz99dcdticdn8QJN5/QadvcF3LZsXKHx32zfj6L5JnJnbZ/+8a3aSxt7LD9Rz/6EX/5y186bSviq/r6ep+rIpSWllJXV9fta4WEhHSpMkJERIR+yPHBp5/CzTdDRYVZAaG1mMSGDVdTUPC413YWSwBWawBWaxABATEEBMQQHJxBWNg4IiOPJy5uDiEhGZ1e2+mEPXsgMxP+/GcYN64X39gQVF5e7nGCWElJidc2fn5+jB8/vkNIYcKECQopyBCwE7gB2AeMAzpPTlVWOnjnnXLee6+czz+vorTUQXm5g/p6J3FxgcTGBjJqVAinnx7LGWfEccopMfj5eft/wI0ZmEgAfg/M7L23JSKD3sGD8OMfm8GFsWOh9b/Mzj7/nH32HkJDxwDewwvr1p1JRcX7HtvPm2f+eG8YkJ8PQUHw29/C3Lm99rZE+kVnn1nT09M9hhoiIyMHutuDSC1mcPN9IIO9ew2OOeZ96upcHo9+5pnjWbIk1aczr1hRwLJlGzzuCw/345tvzmD06BrAAdwMZHe9+yIiIiIiIjJsfPjhh9x888189tlnHfbNnTuXBx98kOnTpw9Az/rOSAovtPrqq6+45ZZb2LhxI3fddRc//OEPNYYoMohs3ryZOXPmUFVVxdSpU/nggw+IjY0d6G71qpaWFt544w3sdjsvvfQSzc3NbfvGjh1LdnY22dnZZGVlDWAvRUREpJXCC8PATTfdxCOPPNJhe0BYAHP/NbfTidWf3v8ppZs8r6o97jvjmLJ0ite2jeWNvH3D2x735azIYdlVy47ScxmJDMOgtra2S5URGhs7BmR8FRYW5lNVhNZjwsLCevHdCkBuLlx/vTmBb+xYOPyWdLTwwtFZiIk5hbFjbyIl5VIsXqrNuFywezdMngz/+Q/ExfXgkkNEZ6vYeuPn50dWVpbHkEJQUFA/9l6kt5QA12IGGDoPLuTnN/K73+3iP//Jp6HB88Q+T7KyQlm+PIvs7HQCAz3dg1oDDCnAf4CxXei/iAxVDQ3wgx+YAc4xY74NLkD/hBfADDDs3w/h4fCnP8GsWT19VyIDr7NqYRkZGe0CDa2/+lKxZHhxYYYGXgMyALPkyz/+sY8f/nCzxxaJiYFs23YmsbGdTyooK3MwefJ7lJU5PO7/xz+O4frrRx/6rhizEsNvgQu7/jZERERERERkSNu+fTvLly/nxRdf7LDv2GOP5aGHHmLuMF1xYySGF8CcB7BmzRpuvfVWGhoauO+++1i8eLEWAhQZJD788EPmzp1LU1MTp556KmvXriU0NHSgu9UnKisrefbZZ8nJyeGjjz5qt++UU07BZrOxZMmSYRfgEBERGUr8B7oD0nMzT54JHbMLtNS3UJtfS+Qoz6sPGm6DytxKr+et2FHR6XUrtnvfXxxb3GlbGT4Mw6C6urpLlREOTzh3VWRkpM9VERISEggJCenFdytd5XDA/fdDYSFkZbUPLvQOg8rKdVRWriM6+iSOO+4xwsMndTjKz8+cOLh1qzl571e/6ou+DIzKykqPE7gOHDjgtY3VavUYUpg4caJCCjKMGMAfgB0cLbjwxBMFXH/95i6FFlrl5TVw7bWb+Mtf9rJq1UzGjTsyBGfFDCzkAfcDf0UfwUWGv0cfhc8+g1Gj2gcX+pPFYl5/1y7z89hjj5lBBpGhLD4+njlz5jBnzpx220tLS9t9Jrbb7WzZsoWysjJGjRrlMdQQPmz/QawG3sQMTn778/D114/i2WeLeOed8g4tSkoc/PSnW3n88eM6PfNNN23xGlw455x4rrtu1GFbUjArX/0eOO7Q9yIiIiIiIjLcHTx4kF/96lf8+9//xuVq/8w9PT2d3/72tyxbtgw/v86rJMvQY7FYuPDCC5k7dy6PPfYYN910E7///e/53e9+x+zZswe6eyIj3umnn84zzzzDwoULWbduHZdffjmrV68mICBgoLvW62JiYrjuuuu47rrr2LVrF0888QR2u53du3ezfv161q9fz4033sj8+fOx2WxccMEFqhYjIiLSzzRzaogzDINtodu87q/YXuE1vFCzrwZno9Nr2+o91biaXfgFeX5wUL6944A3QFB8EKsLVnNRyUVMTZzaSe9lMHK73VRWVvpcGaGsrIyWlpZuXy86OtqnygiJiYnEx8drYvUQ89RTsH49pKeD1XNRhF5TVfUpH310EjNnriIh4dwO+wMCICEBXnwR5syBc87p2/70tqqqKo8hheJi72Exq9VKZmamx5BCcHBwP/ZeZCC8gbnicBKdfeS9+eatPPzw7h5fbcOGGmbN+ogXX5zF6acfWd7FCqQBHwErgat6fD0RGby+/hpyciAqCgb6v9vWAMM338A//wk///nA9kekryQkJHDGGWdwxhlntNteUlLS7jP0Y489xpYtWygvL2f06NEdQg2TJ08e4qGGfZhByQCgfcUJi8XCo49O55hj3qeurmNg024vYOnSNObOTfB45tdfL2HFikKP+8LD/Xj00WM9rKSYDuzCDDA8CAyTBLmIiIiIiIh0UF9fz+9//3sefPBB6urq2u2LiIjg9ttv56abbtLCcyOAn58f3//+97niiiv4wx/+wIUXXsg555zDAw88wMSJEwe6eyIj2sUXX8y//vUvrr32Wl555RWuu+46/vvf/w7rCimZmZn88pe/5O6772bdunXk5OTwzDPPUFVVxapVq1i1ahVxcXFceeWV2Gw2Zs2aNax/P0RERAYLhReGuA/3f8ia4jWEpITQWNzYYX/59nLGzB3jsa238EErw2VQmVdJ/NR4j/u9VV5ImJxAZVMlD3z8AP+e/2+C/DXZfCC5XC4qKip8qoxQUlJCeXl5h1UwfGWxWIiNjfW5MkJ8fPywTHGLaedOc5JcSAh0p9pgZuatBAT8f/buOzyqMu3j+HcmmUnvjRDSSCghVBVQmiCIiCgKKioQuy7W1aUo6GsFpVhYXV1da7A37ICKIE1QRAEhlAQCIQnpvU077x+HBMLMJJOQMCn357pygfOcOefJbjg5c87zu+8AQMFoLKamJpfi4m2Ul+9DrahuzWQqZfv2Kxk2bCN+foOsxgMCoLgYnn8eBg6EoNPXF7cBJSUlNkMKWVlZdt+j0WjshhTkJrDonHJRuy5YAD+7W7344qEGgwuurhrGjAmib19vjh9Px8cnknXrCjl4sMLm9kVFRq66aju//TbCRgcGL0APvAqcj9oNQgjR0VRUqF0OysogLs7Zs1Hp9eo10IcfwsiRMGSIs2ckxNkTGhpKaGgoo0ePrntNURSrUMNbb73Fnj17KCwsJCYmxmaowcvr9N/tbY0ZWApkA/E2t4iJ8WTp0j7MmrXb5vgdd+xiz54L8fKqf7uwvNzEP/5h+z0Ay5b1ITra1oc+F6ALsBoYBVze+LchhBBCCCGEEKJdMZvNvP322/zf//2fVcEtV1dXZs2axaOPPkpIiO2wvOi4vLy8eOSRR7j99tt54oknGDhwIDfffDOPPfYYYWFhzp6eEJ3WrbfeSm5uLvPnz+edd94hLCyMZ5991tnTanUajYbhw4czfPhwXnzxRb799luSk5NZtWoVBQUFvPzyy7z88sv07t2bpKQkpk+fTlRUVOM7FkIIIUSzSHihHTNZTLz828tUm6oJSQjhaPZRq23sBQwaG6tVkFJgM7xgKDVQnlVu4x0Q2DuQEN8Qtmdt59sD3zK1z9RGjyMcZzKZyM/Pd6grQl5eHgUFBSiK7YXejdFqtQQFBTncGSEwMBBXVzmtCNWrr0JBAfTo0bz3R0fPwtMzxur1ioo00tIWc/ToG9gKMZjNFfz+++WMGvUXer31+SsyElJT4f334b77mje3llBaWmozpJCZabuaKagfqLt3724VUujdu7eEFISoZwWQDthfOfzrr0XMnm2/e9XFFwfzv//1JzraE6PRxOrV6UyYkIhO58rKldn84x+7yc01WL2voEANMOzYMQoXl9OrcnQFUoHXgY5/E1CIzujLL2HXLvV6oy0V5gkKgrQ0ePllePfdtjU3Ic42jUZDWFgYYWFhjBkzpu51RVHIycmpd43+xhtvsGfPHoqLi+2GGjybk9RuFeuBX1CvN+y3vbvzzig+/TSLn3+2Lmhx5EgV8+fvY/nyvvVef/jhfRw9al0wA2Ds2GDuuKOhh3i+QBHwCjAOkM8tQgghhBBCCNERKIrCqlWrmDt3Lnv27LEav/rqq1m0aBE9mvugUHQYYWFhvPLKK9x///089NBDxMfHM3fuXB588MF2UCxCiI7poYceIicnh+XLl7N48WLCwsJ44IEHnD2ts8bd3Z2rr76aq6++mry8PD766COSk5PZvn07+/btY/78+SxYsIDRo0eTlJTE1KlT8fHxaXzHQgghhHCYrDJux37L/I19+fsI9wmnsHchR3+2Di/UFNdQcbwCry7WH/oK99cPL3iGeFKZX1lvLfDp29Qq2F9gr/A5gb0D8dB5oNFo+CLlC6YkTJGWWg0wGAx1YYSGuiLUjhcVFTX7WK6urgQHBzvcGSEgIAAXF5cW/G5FZ5GeDps3Q3Bwyy+O8/KKo3//1wkNncSOHddisdRYbVNdnUlq6jP06fOc1ZirK3h7w1dfwa23QmvfEystLSUlJcUqpHDs2DG779FoNMTGxtoMKbSdxVFCtFWlwDeAD2q1X9sWLNiH2Wz7YuaSS0L45pvB6HS2F/5ddVU48fFejBy5hZISk9X4rl1lfPBBJjNndjttRAsEAuuAY8Dp40KI9sxkgi++UK813NpY8zmNBrp0UYMVf/4J55zj7BkJ0fZoNBq6dOlCly5duOiii+peVxSF48eP1ws1vP766+zZs4eSkpK66/bTQw1nP1y8EjAB3g1updFoePPNAfTr9wvl5dZdF19+OZ3rr4/g/PMDANiypZBXXkm3uS9vbxfefLO/A/d8woGjqNdAExvZVgghhBBCCCFEW/fHH38wZ84c1q1bZzU2bNgwli1bxgUXXOCEmYm2rFevXqxcuZKNGzcyZ84c/vvf//Lkk09y0003yZoEIc4yjUbD888/T15eHh988AEPPvggISEhzJgxw9lTO+tCQkK49957uffee9m7dy8rVqzgvffe49ixY6xbt45169Zx1113MWXKFGbOnMm4cePknCWEEEK0AAkvtGNf7fsKg9mAp84TTYL9B8WF+wqtwgvlWeUYSutXCw4ZEEJRahGl6aV1rxWnFmMxWdC61l+8Z69rg95Xj3e4+qA81CuUPXl72JG9g3O7ntuk7609q66ubjB8cPprJSUlzT6WTqdrMHxwejjB399fgiTirPj+eygpgfj41jtGly5X0Lfvf9i16zab4+npr9C9+2zc3cOtxoKDISMD1q6FK65omfmUlZXZDClkZGQ0+D57IQWpNCJEc/0I5AAxdrfYsqWQdeusqw0D+Pq68s47A+wGF2r16+fLwoW9ueeev22OP/30QRvhBVDDC6nAKuD2Bo8hhGhftm6FAwcgNNTZM7HNywuys+HbbyW8IERTaDQawsPDCQ8PZ+zYsXWvK4pCdnZ2vVDDf//7X/bs2UNpaWldx7RTr/Vbr2PaQWAbEOTQ1jExnixd2odZs3ZbjVkscOutO/nzz1EA3HbbLiwW2/tZtqwP0dGOhKv1qBUwVgKXAnJfQgghhBBCCCHao/T0dB555BHef/99q7EePXqwePFirrzySnkeLRo0cuRIfv31Vz799FMefvhhXnjhBZYsWcKll14qPztCnEVarZa3336bgoIC1qxZw80330xQUBCXXnqps6fmNH369OGZZ57h6aefZv369axYsYLPPvuMiooK3n//fd5//33Cw8OZPn06SUlJ9OvXz9lTFkIIIdotCS+0UxklGaw/sp5Aj0AAPII88Aj2oCq/ymrbgn0FRI6OrPearY4Kgb0D0bpq64UXzDVmStJLCIgPqP9+O+GFwN6BdX/30nmRVZbF1/u/btfhhcrKygbDB6eHE8rLy5t9LHd3d4e7IoSEhODr6ysf4EWbU1kJX36pLpDTNrz294xFRd3KkSOvUFKyw2rMYqnm+PGVxMTcZTWm16t/fvEFXH5507pDlJeX2wwpHD1q3f3mVDExMVYhhYSEBAkpCNGiLMAXqB0O7F/mfv75cbtjt90WRZcu7g4d7c47o3jiiQPk5Rmsxg4cqODvv0vp29f3tBEt4IG6eG8m4NixhBBt39dfg9EIbbVJkkYDfn6wZg384x9tN2QhRHuh0Wjo2rUrXbt2Zdy4cXWvK4pCVlZWvVDDK6+8wp49eygvL7cbanB3P5Nrgu9Qu0/1cPgdd94ZxaefZvHzz9aBzr17y1m48CCKAikptu9xjB0bzB13RDVhjsHADmAvkNiE9wkhhBBCCCGEcLaioiIWLVrEv//9bwyG0wokhoTw2GOPcccdd6DT6Zw0Q9HeaDQarr32Wq688kpeffVVZs6cycCBA1m6dCnnSOUVIc4avV7PZ599xkUXXcTvv//O1Vdfzc8//8zQoUOdPTWncnFxYezYsYwdO5b//Oc/rFy5kuTkZH766Seys7NZtmwZy5YtY+DAgSQlJXH99dfTpUsXZ09bCCGEaFckvNBObTq6iZLqEuIC4+peC+wdSOamTKttbQUNbL0W2EsNLxxefdhq21PDC6YqE6VHSk9/e90+amk0Gvzc/Fh7eC1zh8/FS+/8BbqKolBeXt6kzgiVlZXNPp6np6fDXRFCQkLw8vKSMIJo937/HTIzoZutguOtIDr6LrvdF/Ly1tgML4DafeHvv+HQIYiLsx6vqKiwGVI4cuRII/OJthlS8Pb2bvL3JoRoqoNAChDS4FZr1+bbHZs2zbpbiz2urlqmTg3nv/+1fV5YuzbfRniBE/PLQF3AN8zh4wkh2q6iIti4EQICGt/WmYKC4PBh2LwZrrrK2bMRomPSaDREREQQERHBxRdfXPe6oihkZmbWCzX8/PPP7Nmzh4qKCuLi4qxCDb169XIg1GBB7ejkRVM6Gmg0Gt58cwD9+v1CebnZavyZZ1Ltvtfb24U33+zfxPsXvkAusAEJLwghhBBCCCFE+1BTU8Mrr7zCU089RVFRUb0xDw8PHnzwQebOnYuvr6374EI0Tq/Xc//993PjjTfy7LPPMmLECKZMmcLChQuJjo529vSE6BS8vb357rvvGDFiBAcOHGDixIls2rSJhIQEZ0+tTfDy8mLGjBnMmDGDzMxMPvjgA95991327NnDX3/9xV9//cWcOXMYP348SUlJTJ48uZW67wohhBAdi4QX2qn9BfvRoEGrOVnW3F54oTK3kuqiatwDTj7wLthXv7KeZ4gnHkEeaHXWZdIL9xUSN+nkyt7CA4UoFsXmvIJ6B9X7bx83HwoqCzhUdIh+YS3fLktRFEpLS5vUGaGmpqbZx/Px8WlSZwTPtlp2VYhWdPAgWCwnuxu0tqCg0XbHiop+tTvm7Q15ebB7dzXFxXusQgrp6ekNHjcqKspmSMHHx6eZ34kQ4swdBKoA+wGEqiozu3bZDmHqdBoGDvRr0hGHDPG3G17Ytq3YzrvcASPqfCW8IERHkJoK5eUQ7nj+ySlcXEBR4MABZ89EiM5Ho9HQrVs3unXrxvjx4+teVxSFY8eO1Qs1/PTTT+zdu5eKigri4+Nthhrc3NxO7CETKASa/jkkJsaTpUv7MGvWbqsxo9H2fR+AZcv6EB3d1PsdGsAF2NPE9wkhhBBCCCGEONssFguffPIJ8+fP5/Dh+oUPNRoNN998M0888QTdzlYlM9Hh+fv78+yzz3LXXXfxyCOPkJCQwD333MPDDz9MQFuvGCNEBxASEsIPP/zAsGHDyMrK4pJLLmHz5s1ERkY6e2ptSkREBHPmzGH27Nn89ddfJCcn88EHH5Cbm8uqVatYtWoVvr6+XHPNNcycOZORI0ei1VqvwxNCCCGEhBfarZ05O3HX1a++d3pw4FQFKQVEDIsAoKqgiqq8qnrjgb3Vjgluvm54R3hTnlleN1Z4oBBFUeoq6tnq2gDg6uGKb3T9qgoerh5Um6pJLUx1KLxgsVgoLi52uDNCfn6+VWvKpvDz83O4M0JwcLADFQ+FEPv2wdlsIOLlFYdGo0NRjFZjBkMBimJBo9FiNpspLy+nrKys7quoKJCrrnoR+I/d/UdGRlqFFPr06SMhBSHapNoKwfZvAuXlGVDsrMWLjfVEr2/aDaRevex3lsrJaSgwqQH2N+lYQoi2KzUVjMazF948E3o97LZepyyEcBKNRkNkZCSRkZFccsklda8rikJGRka9UMMPP/zA3r17qaqqqgs1TJrkyZQpWWg0sXh5WXBxadq1zJ13RvHpp1n8/HNB4xsDY8cGc8cdUU06xkmewF7AhNySFEIIIYQQQoi26ZdffmHOnDn8/vvvVmMTJkxgyZIl9OvX8kULhQC1eFxycjIPPPAAc+bMIT4+nkceeYS77rrrlEIOQojWEB0dzZo1axg5ciQZGRlMmDCBjRs3EhgY6OyptTkajYZBgwYxaNAglixZwo8//khycjJffvklpaWlvPnmm7z55ptER0czc+ZMZs6cSc+ePZ09bSGEEKJNkSeF7VBhVSHZZdl46upXufPu6o2bnxs1JdYL5Qr3FdaFFwr3W4cPasMLtX8/NbxgLDdSdqwM30jfun3ZEtAjAI3WesWy2Wxm877NBOYGNtoVIT8/H7PZ7MD/CrYFBgY63BkhODgYfXtYXSREO6IosGsXnO0ueHp9IDU1OTZGLPz2249UVLhSWVlpY9wTUNsdduvWzWZIQVrdCtGe7Kaxy9v8fPuhRz+/pl8a+/npmnUs8AB2AQpqkEEI0Z7VdjI4mwHO5vLygkOHoKJC/bsQom3SaDRERUURFRXFhAkT6l5XFIWjR4/WhRo0mi8oLCwkJSUHi8WMl5cXPj4++Pj44O1d+6eX3QpfGo2GN98cQL9+v1Be3vD9GG9vF958s39dcYum8wJKgKNA92buQwghhBBCCCFEa0hJSWHevHl88803VmMDBw5k6dKljBs3zgkzE53RoEGD+PHHH1mzZg1z587lpZdeYtGiRUybNu0M7ksIIRrTt29fvvnmGy6++GL27t3L5Zdfzo8//oinZ1O7sHYeOp2OiRMnMnHiRIqLi/nss89ITk5m48aNHDlyhKeffpqnn36a888/n6SkJKZNmyaBECGEEAIJL7RLh4sOU2mspIt3F6uxgF4BHP/tuNXrpwYWbIUPAnsF1vv70bVH679/XyG+kb6YakwUpxXbnJcxwMgf2/+gxlCDwWDAUGPAYDSALzy37jme++o5R79FQH2AHhQU5HBnhKCgIFxd5UdaCGc6fhyKi8/OQjiz2UxFhdpJwWSy7rpQKy8vHzgZQHB3d69bxGOxhBIUNIO1a2fg7+/X+pMWQrQiE3AQNZRkX3W1/UV5Te26AODmZv891dWWBt7pCeQBBUBwk48rhGhb9u6F9tKkzdMTCgogPR0SE509GyFEU2k0GqKjo4mOjubSSy8FcoFiYmK6U1VVRVmZ+hmpvLyM3NxcysrKsFiUU0IN3nXhBi8vNdQQE+PJ0qV9mDWr4bYsy5b1ITr6TB5UegI5wGEkvCCEEEIIIYQQbcPx48d5/PHHeeONN6yKDEZGRrJw4UKmT59uNxQvRGvRaDRMmDCBiy++mOTkZP71r3/x/PPPs2zZMkaNGuXs6QnRYY0YMYJPPvmEq666ii1btnDttdeycuVKdDr7Bd2Eyt/fn9tuu43bbruNw4cP895775GcnExqaipbt25l69at3H///UyaNImkpCQmTpwoRXeFEEJ0WrLSux2qNFZisphw1Vr/3xfUO8hmeKHsWBmGcgN6b71V5wW9rx7vrt4A5ObmUultXZ08ZUMK+y37MR4zqmsDbSh2LaY4u9h6wAK4gYuLC8HBwQ51RQgJCSEwMBAXF5dG//cQQrQdlZVgMkFr5Ijy8nIpKCikrKyMsrKyE50UlBOjJXbepSUmph++vn51lUdP/VCdnw9arbqITwjR3tUARhq7vA0MtH8DqLFqw7aUltq5MAICAxu6iecKVANVTT6mEKLtKS1tneuf1uDqCmazet0mhOgISgAXNBoNnp6eeHp6EhYWWjeqKMqJUENZXbAhJyeX8nI11ODtrYYaRo/2ZvhwHzZvLrN5lLFjg7njjqgznKsL6mc4OQEJIYQQQgghhLOVl5fz3HPPsXTpUioqKuqN+fn5MX/+fO699148zna7dSFO4+Liws0338y0adN44YUXmDRpEmPGjOHZZ58lISHB2dMTokO6/PLL+d///sctt9zCd999x+23387bb78tnU+aIDY2lkcffZRHHnmErVu3kpyczEcffURxcTErV65k5cqVBAYGct1115GUlMSQIUPkf18hhBCdSjtZXiFOZTAbUBTF5kVLYG87raUUtftCYK9Ayo7VfxB9ateF1NRUCgsKwQs45R6FOduM2WBWCwTb4gLh/cJx83TDTe+G3k2PXq/HTe9GsaWYCN8IVn+4WioyCNHBGQxgsaiBgDNVXV1FeXlO3QKbqqoqCgryrbbT6fIxGm0vONbrg+nbt7/dY2i16nxrakAC7UK0dwbUxXANn4CCguwHCrKzq5t81OPH7b8nOLihE4sWNeFZ0+RjCiHaHoPB8esfjcb+hkajgYKCAsrKyigoKGjiLBy7qV17/WMwNHH3Qog2qoaGrn/qhxrC6l5XFIXKyirKy8vqAuIjRxrZvNn2fmbMiGihh2ca1Os2IYQQQgghhBDOYDKZeOutt3jsscc4frx+UUSdTsddd93FI488QnCwdAwWbYunpycLFizg9ttv58knn+Scc87hxhtv5PHHH6dLly7Onp4QHc7NN99Mbm4uDz30EO+++y6hoaEsWbLE2dNqdzQaDRdccAEXXHABL774It999x3Jycl89913FBYW8sorr/DKK6/Qs2dPkpKSmDFjBtHR0c6ethBCCNHqZCV5O6TVaO2uS/GN8sXV03YmpXBfodp1Qan/+qmBBx9vH3z9fHGLdKu/USX0694Pf4O/zX0Hxgdy7pBz6du3Lz169iA6Oprw8HACgwJxd3fHw91DggtCdAJaLWg0oCiNb9uYLVu2sGfPXoqKinB3d8NisRAUFERMTCz9+vVn2LDhXHLJJfTubX/hS0DA+Q0eQ1HU+UqTFyE6gtrrjIZPQEFBevz9bQcYcnMNNsMIWq2Wnj172ryW2bXLdnVigPh4rwZmoqBe0MkJSIiOwMXF8esfFxcfu2MbN67jzz93kJNzHKPRCIBW60KPHj3rWgdbLLZDTzqdn0PHl+sfITqa2m4GTaPRaPDyUgMN8fHxDBo0iF69erX89GySE5AQQgghhBBCnG2KovDtt98yYMAA7rzzTqvgwjXXXENKSgovvviiBBdEmxYaGsrLL7/Mzp07ycvLo0ePHjz55JNWHUSEEGdu7ty5/POf/wRg6dKlPPfcc86dUDvn5ubGlClT+PLLL8nOzuall15i8ODBABw4cIBHHnmEmJgYxowZw1tvvUVpaamTZyyEEEK0HllN3g65ubqh1WixKBarMY1WQ2BP290XClIKKNxXaPX6qZ0X+vXvx6hRo+g10vqBtSZPQ/mRcpv7PnUfp7MoFtxd3O2OCyE6Dje3k9V861OoqqoiNzeXtLQ0du78i02bNpGZmWl3X6NHj2bMmDGcd955xMfHU1RUyODBg+nbty/R0dEEBgai0+lJT3/F7j5CQsY3OF9FUecrXReE6Aj0nOxmYJ9Wq2H0aPvXLT/8YN3hxcVFS69ePXFxsb50Xr061+6+xowJamAmFtT5yglIiI7Azc3W9Y/aSaGwsIAjR47w999/8+uvv3L0qPVnslrnn9+fceMuZujQ80lMTATAYjETExODm5saMDeZSmy+V6cLcGiuteEFnf1GNEKIdsWdxq5/2g7lxJdc/wghhBBCCCHE2bR9+3YuuugiLr/8cvbu3VtvbMSIEWzdupVPPvmEuLg4J81QiKbr2bMnn3/+OatXr2bVqlXEx8fzv//9D5PJ5OypCdFhaDQannvuOaZPnw7A7NmzSU5OdvKsOobg4GDuuecefvvtN1JSUpg/fz6RkZEArF+/nltvvZWwsDBuuOEGVq9eLec2IYQQHY6EF9qhII8g3FzcqDZZVwaG+p0UTlV6pJS8XXn1XnP1cMUvxrpCp619pK9Jx1Rl+2LI3jEBakw1dPXpandcCNFxBAYqQA05OUUcOpTGzp072bRpE6tXr2bt2rXs2fM3hYWFuLm5ExsbS2hoiN19abWNV+NMT3+FsrJddt7vTpcuVzX4/upqCAoCV9sNa4QQ7Yo74A/Yrkh+qosvtn/ueeONow4f8eDBcjZssL0I2dVVw4UXNhReMABugP1rKCFE+xEUVENRUUVdSGHr1l/58ccfWLNmDX/8sYPs7Gw0Gg0RERF07z7U7n6qqw/V/d3LywuNRm25V1amdnmxWExUVh6y+V4PjxiH5lpdrYYtpICeEB1FV6C9PLgyADqgoWskIYQQQgghhBAtJT09nRtuuIHBgwezfv36emO9evXiyy+/ZMOGDQwdav9+lRBt3fDhw9myZQsvvfQSixcvZsCAAXz77bcojrbKFUI0SKvV8tZbb3HJJZcAcMstt/D99987eVYdS+/evVm4cCHp6en8/PPP3HTTTXh7e1NdXc2HH37IpZdeSmRkJP/617/YuXOns6crhBBCtAhZrtkOxQbE4q33ptJYibfe22o8qLfth8CKWaEso6zeawE9AtBoNVbbeod74+bvRk3xyQWApUfstKPSQEBP+1U+FUWhT0gfu+NCiPZHURSysrLYs2cPe/bsYe/evXV/lpT8F71+AAEB5fj4+BAcHIyPjzfe3t5WgYS8vOZX3MzK+oy9ex+wOx4dPQt394aDU1VVMGBAs6cghGhTNEA/4HCjW06fHsGCBfspLjZajW3cWMjHH2cxbVrD5w9FUbj//j3Yu/edlNQNP7+GyppXAIMAj0bnK4RoO4qLi+td+9R+ZWdfjYvLPQQEFODj40PXrl3x9vbBx8cbna7+9U5FxQRSU23vPz//B7p1UysYabVavLy8KS8vo7y8jODgYAoK1mOx2A5pBQYOd+h7qKiAgADo1s3x71sI0Zb1QL0OUk782ZZVAF6ocxZCCCGEEEII0VoKCwtZtGgRL730EgaDod5YaGgojz/+OLfddhs6ac0pOgiNRsPVV1/NFVdcwWuvvcZNN91Ev379WLp0Keedd56zpydEu6fX6/nss88YO3Ysv/32G9dccw1r167l/PPPd/bUOhStVsuYMWMYM2YM//nPf/jyyy9JTk7mxx9/5Pjx4zz//PM8//zz9O/fn6SkJG644QbCw8OdPW0hhBCiWaTzQjukd9HTK6gXFYYKm+N+3f1wcWu8Yjk03DGhobFT+Ub5ovO0fWPDZDGh0WiID4x3aF9CiLalNqTw448/8uKLL3LHHXcwfPhwAgICiIyM5K677mL9+vWEhIRw9913s27dOh5/fCqxsQkMHjyY3r17ExERga+vn0OdFBxRXn6QnTtvZceOa7BYDDa3cXPrSnz8w418b+qfPXu2yLSEEG1Cb4e28vPT8c9/xtodv+WWnaxfn2933GxWuO++PaxalWdz3NVVw4IFjV37GID+DsxWCOEMJSUlbNmyhTfeeIMHHniA8ePHExERQUBAAFdffTUff/wxiqJwzTXX8Mknn/Dxxwvp0aMnQ4ZcQGJiX6KiogkMDLQKLgB4ecXj6Rln87hZWR9RVran7r99fHwAtfOCxWLkwIHH7c45JOQSh763ykro2xdcWubSTAjhdHGo3Qwa7z7lfJVAONJ5SgghhBBCCCFaR01NDc899xzx8fE899xz9YILHh4ePPLII6SmpjJr1iwJLogOSa/Xc++995KWlsb555/PqFGjuOGGG0hPT3f21IRo97y9vfnuu+/o1asXlZWVXHbZZaSkpDh7Wh2Wp6cnN9xwA6tXryYjI4OlS5fSr18/AHbt2sXs2bPp1q0bEyZM4IMPPqCystLJMxZCCCGaRjovtFN9Q/uyKWOTzTGtqxb/OH8K9hY0up/AXvYfGAf1DiJ7a3bj+2gg5FBprMRL5yXhBSHaOEVROH78uFUnhT179lBSUkJsbCyJiYkkJiYya9Ys+vTpQ+/evfH09LTaV20lYUUBzRkU/jxy5FV0ugBAoaamEPiTjRvnUlGxr8H3ubh4MWTIN7i5hTS4ndEIOh3Ey+lJiA4kHjWba0RdxGff7NndWbnyODt3WneWqqw0M3bsVm6/PYqbboqkXz8fPD1dyMszsHZtPs89d4g//iixu+8nnuhJ9+5eDRy9tjKynICEcLaSkhL27t1r1UkhMzOTsLCwuuufqVOn8uijj5KYmEhgoPXnnwMHwMNDDQZ4WzfHs9K9+wP8/fc9Vq9bLAZ+/fUievV6mi5dJuPj4012dg2FhRvYuvU+ioo229yfn99gAgNHOPQ9KwokJDi0qRCiXYhH7WZQAbg7eS6NqQak9Z0QQgghhBBCtDSLxcJHH33EggULrBZpa7Vabr75Zp588km6dm2447AQHYWfnx/PPPMMd911F48++igJCQncfffdLFiwgICAAGdPT4h2Kzg4mB9++IFhw4aRmZnJ+PHj2bJlC5GRkc6eWofWtWtXZs+ezezZs9m5cyfJycm8//775OTksGbNGtasWYOPjw9XX301SUlJjBo1Cq1W6lkLIYRo2yS80E4lhiai1WipMdXg5upmNR7YO7DR8EJtyMEeRzsvNBSAKKoqIjYglgjfCIf2JYRoXYqikJOTYzOkUFRUVC+kcOedd5KYmGg3pGBP377g5QVlZeDr2/y5pqUtsXqtwnbDmTqurr6ce+7n+Pmd0+j+i4rA3x96O1aoXQjRLiQAfkAx0HCAycvLla++Oo/BgzeRl2fdxcVigddeO8prrx0FQKtVX2vMtGldmT+/RyNblaAuMuzT+A6FEC2itLTUZkjh2LFjhIaG1l3/XHXVVSxYsIDExESCgoIc3n9MDISFwfHjjoUXIiNvIT39FcrL91qNGQy57N59B7t33wG4AGbKyuzvS6NxISHB+rrJlqoqcHWFPnL6EaID8QN6Ab8Djp+3zj7TiT/7XQ7GRAABAABJREFUOnUWQgghhBBCCNHRrF+/ntmzZ/PHH39YjU2cOJHFixfTt698FhOdU2RkJO+88w7//Oc/mTt3LnFxcSxYsIB77rkHNzfrdTZCiMZFRUWxZs0aRowYwbFjx7jkkkvYuHFjk56piOYbMGAAzz33HIsXL+bHH39kxYoVrFy5krKyMt5++23efvttoqKimDFjBklJSfTq1cvZUxZCCCFskvBCOzUiagTRftFkl2fTzbeb1XhQ7yAOcrDBffh198NF72J33CfSB523DmO5scH92As5mC1mDGYDU3pPQauRRKcQZ5OiKOTm5toMKRQWFhITE1O3SO/222+vCyl4eTVUKdwxvXvDgAGwbduZhReayt//fAYOfAdv78Y/fCkKlJRAUpIaYBBCdBTBwMXARyf+3nD7l+hoTzZtGsaUKX+wZ08DK4NxLLhw113RvPBCogPzLAAuRDovCNHyysrKbIYUMjIyCAkJqbv+mTx5Mg8//DCJiYkEBwef8XH1erjySnj+efV80VhBGxcXD84770s2bz4fo7GwgS3NjR47IWEpwcGjHZpnbi7ExcHQoQ5tLoRoN64EfsOR7lPOkw+EAuOcPREhhBBCCCGE6BD27t3LvHnz+Pbbb63GBg0axLJly7joooucMDMh2p6BAwfyww8/8MMPPzBnzhxefvllFi1axLRp06Q6uRDNkJiYyLfffsu4ceNISUlh0qRJ/PTTTy2y3kQ4xtXVlUsvvZRLL72UkpISPv/8c5KTk/nll184evQoixYtYtGiRQwZMoSkpCSmTZvWIs/DhBBCiJYi4YV2yt3Vnat6X8Vzvz6HRbFYhQMCegSgcdGgmBW7+2iss4JGoyGwVyA5f+TY3cYzzBN3f3ebY4VVhQR6BHJpj0sbPI4Q4szUhhROX6RXUFBAdHR03SK9W2+9tS6k4O1ISeBm0mhg6lTYuhUMBnUxX2sKCBhGbOw/CQ+fisbBoFRZmdod4vLLW3duQghnuAL4CqgAGj/X9ezpzbZtw5kzJ4U338zAYHAgpXCaqCgPnn22N9df70inqRrUUMUUGgtXCCHsKy8vtxlSOHr0KMHBwXXXP5MmTWLevHkkJiYSEtJwR5YzNXEivPWW2t3JkQJD3t49GDlyBzt2TKO4eFuTj+fq6kv//m/Qtes1Dm1vNqvXZlOngq6trm0WQjTTOOAlIA/o6uS52KIApcA1gL9zpyKEEEIIIYQQ7Vx2djaPPfYYb775JpbTqu5ERUWxcOFCbrjhBlmQLYQN48ePZ+zYsaxYsYI5c+bw/PPPs3TpUkaPHu3sqQnR7gwfPpxPP/2UK6+8kq1bt3LNNdfw1VdfoZMHEGedn58ft9xyC7fccgvp6em89957JCcnc/DgQX777Td+++03HnjgASZOnEhSUhKXXXaZdJ8RQgjhdBpFUeyvbhdtWmZpJlM+UbsaBHtapyM3/d8milOL7b5/8JzBhA0Ka/AYad+lkfJ+it3xyAsjGXDnAKvXFUUhtTCVaYnTeGLMEw0eQwjhmLy8PJshhfz8fKKiouoW6dV+JSQktGpIoSGVlWr14bw86GbdHKbOX3/dxLFj79od12hc0Gh0aLXumEx6fH0j8PCIwtOzO35+5xAUNAoPj6gmz+/QIRg8GN5+Ww1bCCE6EgWYAfwFdG/SO7Oyqlm+/DDffpvD3r3lDW7r4aHlggsCmDmzG9OnR6DTOfogKAN1UeFKQG4KCdGYiooKmyGFI0eOEBQUVHfd06dPn7q/h4aGOm2+8+bBV19BfLzj1xiKYiEn52vS0/9DYeEGLBZDg9t7esYTFXULUVF3oNc73oY5NxdcXeHLL6GVcxxCCKdYDrwC9KC5Acl33sng5pt32hx7++0B3HRTZDPnVoIaLF0B9GnmPoQQQgghhBCicysvL2fp0qUsW7aMysrKemN+fn4sWLCAe++9F3d324UHxdlzwQUXsHXr1nqvnX/++fz6669OmpGwpbKykuXLl/Pss88yatQoFi9eTJ8+ct9CiKZ65513uPnmmwGYOXMm77zzjgTo2gBFUfjtt99ITk7mo48+orDwZBfwgIAArrvuOpKSkhg6dCgaWTQjhBDCCSS80M49tu4xPvr7I7oHdsdV23YaaeRV5GFWzLx5xZv0D+vv7OkI0a7k5+fXLcw7daFeXl4ekZGRNkMKPj4+zp62lTfegGXLIDISzvQ+qclkZPXq1UyYMAFX1zNL6peWQmEhLF0KEyac2byEEG3VN8B8IAho3vkxL6+G7dtLyMszUFBgoLLSTECAjqAgPZGR7px3nj96fVNvvFUBWcA84MZmzUuIjqqiooKUlBSrkEJ6ejqBgYF2Qwpt7Ybq77/DP/4Bbm6OdV84ndlcQ2npDioq0jAaizCZSsnKyqOszExoaAIDBlyNm1vTwxlGIxw+DDffDA891PR5CSHag3RgOlBN2+q+YAFSgfHAv5HOU0IIIYQQQgjRNCaTiTfffJPHHnuMnJycemM6nY577rmHBQsWENScm1GiVUh4oX3Jy8vjqaee4n//+x8zZ87kiSeeIDw83NnTEqJdWbJkCfPmzQNg9uzZLF261MkzEqeqqanh+++/Z8WKFXz77bcYjca6sR49ejBz5kxmzJhBbGysE2cphBCis5HwQjuXV5HHzJUzySzNJCYgxtnTAaDGVMPRkqPMOm8W959/v7OnI0SbVVBQYDOkkJubS7du3WyGFHx9fZ09bYdVV8Mtt8COHU2rPmxLS4UXzGZITYXJk2HJEum6IETHZQEeAFYD8UBbqO6hoC7cGwq8jnRdEJ1VZWUl+/btqxdQqA0p+Pv717v2qQ0qhIWFtbmQQkOefVbt7hQbCy3RHXn//v0cPHiAwMAghg0b1qx9HDoE3btDcjIEBJz5nIQQbdW7wGIgHPB08lxqHQN8UecW49ypCCGEEEIIIUQ7oigK33zzDfPmzWPfvn1W49OmTWPRokV07960DsSi9Ul4oX06ePAgDz/8MKtXr2b27NnMnj0bb29vZ09LiHZBURRmz57N888/D8DSpUuZPXu2k2clbCkoKODjjz8mOTmZbdu21RsbNWoUSUlJXH311fj5+TlphkIIIToLCS90AKtTVzPvp3n4ufnh5+7ciwdFUUgtTGVAlwG8dcVbeOm9nDofIdqCwsJCmyGFnJwcIiIirEIKffr0aVchhYbs2gW33w6KAmFhzd9PS4UXjhxR57FiBXTp0vz5CCHag2NAElAARDl5LgDZgA54C0hw8lyEaH1VVVU2QwqHDx/Gz8/PZkihS5cu7SqkYE9JCdx4Ixw4oAYGzvRbysrKYseOP9Dp9FxyySVNfn9hIVRWwosvwoUXntlchBBtnRH4B7AZiMP5Ac5yIBd4DLjWyXMRQgghhBBCiPbj999/Z/bs2WzYsMFqbOTIkSxbtowhQ4Y4YWbCERJeaN+2bNnC7NmzOXToEE888QS33norrq6uzp6WEG2exWLhxhtv5L333gPg3XffJSkpycmzEg3Zv38/7733HitWrODIkSN1r7u7uzN58mSSkpIYP368nAOFEEK0CgkvdACKorDg5wV8vvdzov2jcXN1XiXf7LJstBotr172Kud2Pddp8xDCGYqKimyGFI4fP07Xrl1thhQ6Q1r5lVfgpZfUsEBzi1O0RHihsBDKymDRIpg0qXnzEEK0NyuB/wP8T3w5SzmQAzwI3ObEeQjR8qqqqti/f79VSOHQoUP4+vraDCmEh4d3iJBCQ7ZsgXvvBReXMwtwApSVlfLLL78AMH78ePR6xz/vVVdDRgZMnw6PPCJdp4ToHA4CN6FefzgzwGkEDgPjgH8DLk6cixBCCCGEEEK0D4cOHWL+/Pl8/PHHVmO9e/dm8eLFXH755R3+3lp7J+GF9k9RFFauXMlDDz2Eq6srixcvZtKkSfJvT4hGGI1GJk+ezKpVq3BxceGrr77isssuc/a0RCMsFgsbN24kOTmZTz/9lLKysrqxsLAwbrjhBmbOnMnAgQPlPCiEEKLFSHihgyisKuTeVfeyPWs7sf6x6FyaX5m8ufIq8qg0VjJn2BxmDph51o8vxNlSXFxsM6SQnZ1NeHi4zZCCv7+/s6ftNNXVMG8erF4NkZHg4dH0fZxpeKGkBPLzISlJnYt8nhKis7AAC4EPgFDAGV1tKlG7QFwOLAL0TpiDEGeuurrabkjB29vbZkiha9eunfom5htvwPLl4OMDQUHN34/FYuH7778HFC644AKCgoIdep/BAOnpMGyY2nWhE2RmhRB1vgEeP/H3rk44vhk4hNpt6j9OmoMQQgghhBBCtB8FBQUsXLiQl19+GaPRWG8sLCxMqr+3MxJe6DiMRiOvv/46jz/+OImJiSxdupTBgwc7e1pCtGkVFRWMHTuWbdu24eHhwU8//cSwYcOcPS3hoMrKSr766itWrFjBmjVrsFgsdWN9+/YlKSmJ6dOn07Wr3PMVQghxZiS80IEcLz/Ovd/fy67cXcT4x6B3OXuL43LKc6gyVjFr8CxmnTerUy9SEh1HSUmJzZBCVlYWXbp0sRlSCAgIcPa026SyMvjXv+CXXyAiAry8mvb+MwkvFBVBQQFccw383/+B3NcVorMxonZfWIkaYDibq3fLgSxgLLAUaOLJTwgnqK6u5sCBA1YhhbS0NLy8vGyGFCIiIuT63wZFUUMDb7wBvr4Q7FjmwKZ169ZRUVFO3779iImJaXT76mo4ehTOOUftgHUmxxZCtFcfAksADWp44Gydp2s7LsQDLwOxZ+m4QgghhBBCCNH+VFdX89JLL7Fo0SKKi4vrjXl6ejJ79mxmz56Nj4+PcyYomkXCCx1PaWkpixcv5sUXX+SKK65g0aJFxMbKPQ8h7CkoKGDEiBHs27ePgIAANm7cSGJiorOnJZooOzubDz/8kOTkZHbu3Fn3ularZdy4cSQlJXHllVfi1dQFQEIIIQQSXuhwssuy+dcP/+KPrD/o4tMFX7fWrTBstpjJLMtEg4Z7h9zLLYNukYVLot0pKSmpCyecGlLIzMwkLCzMZkghMDDQ2dNud8rK4OGH4aefIDBQ/XL0dNGc8IKiwPHjUFUFN9wAc+eC7uw3pRFCtAkG1A4MnwGeQBitu4BPAQqAIuBS4GkkuCDampqaGpshhdTUVLy8vOqCCaeGFLp16ybX+k1kscCrr8Jrr4FWq4Y4tdqm72f79u0cP55NdHQM/fr1a3Db4mLIzYULLoClSyEkpHlzF0J0BJ8Bi4EqIBJo7SR3OZAJ9AFeQIILQgghhBBCCGGbxWLhww8/ZMGCBRw5cqTemFar5dZbb+Xxxx+XqsbtlIQXOq5jx47x6KOP8tFHH3HXXXexYMECWTcghB1Hjx5l+PDhHDt2jIiICLZs2UJUVJSzpyWaadeuXaxYsYL33nuP48eP173u7e3N1KlTSUpKYvTo0Wib8xBMCCFEpyThhQ6ooLKAxZsXsyp1FRo0dPPthovWpcWPU1ZTRnZ5NhE+Efzz/H9yec/LZTGTaNNKS0tthhSOHTtGWFhYvUV6tQv1goKCnD3tDqWqCv79b/jkE/XvkZGgd6BJTFPDC1VVcOwYBATA7bfDjTeCS8ufBoUQ7YoJeAd4AyhGXcDn3grHMQAZqCGJ64G7W+k4QjjGYDDYDSl4eHjYDClERkbKdX0LUhT4/HN4+WXIzoauXcHbu2n72L9/PwcPHiAoKIgLLrDdXtlkgowM9Zrn8sthzhzwO5vNZoQQbdTPqB0YDqN2ofJvhWNYUEMLRmAMMB+124MQQgghhBBCiNP9/PPPzJkzhx07dliNTZo0iWeffVaqU7dzEl7o+Hbt2sXcuXPZtm0bCxYs4J577sHdXZ4FCXG6vXv3MmLECIqKiujduzebNm2SNTjtnMlkYu3atSQnJ7Ny5UqqqqrqxiIjI5kxYwYzZ84kISHBibMUQgjRHkh4oYNSFIXvD37P8m3LOVJ8hGCvYALcA1pkEZLBbCC7LBtFURjbfSxzhs0hwjeiBWYtRMsoKyuzGVLIyMggNDTUZkghODjY2dPuVLZsgSVLICUF/P0hKKjhcIGj4QWTSa00XFkJQ4fCvHkgn4mEEPX9jVqBeDtqwCCUlqlCbAbygVIgEZgHDGmB/QrhGIPBwMGDB61CCgcPHsTd3d1mSCEqKkpCCmfRkSPq9c/69eDqCl26OBbiBMjKymTHjh3o9XrGj7+k3pjFAkVFUFAA3bvDgw/CuHGOd7gSQnQGeaidEL5DvWYJp2XClQpQAuSe2OfdwBRAkuNCCCGEEEIIcbq///6buXPnsmrVKquxc889l6VLlzJmzBgnzEy0NAkvdB4//vgjc+bMobi4mIULF3L99ddL1XEhTvPrr78yduxYqqqqGDp0KGvXrsXLSzrWdwSlpaV88cUXJCcns27dunpj5513HklJSVx33XWESItwIYQQNkh4oYM7Xn6c5VuXs/bwWkpqSvDSeRHiFYLexcFVMicoikKZoYz8ynwURSHSL5J/nPsPJveejFYjH76Ec5SXl9sMKRw9epTg4OB6AYXahXpyUdx2lJbCf/8LX34JhYXg5gahoWCrKEVj4YXycsjLA7MZwsIgKQlmzHB8QaAQorOpBpKB91EX27kCIUBzbpRVndiHAQgErgZuB3xaZKZCnM5oNNoNKej1ershBXlg0jaYTPDFF/D665CVpb4WEgI+Pg2HDUpLS9mw4RcAxo8fj17vhsFwMrTp5wcTJsB994FkcoUQtinAD8DLqF0YzEAw4Ac0Ne1kQg1ElKFe8wwD/gVEt9RkhRBCCCGEEKLDyMrK4v/+7/94++23sVgs9caio6NZtGgR1113ndy/60AkvNC5mM1m3n//fRYsWEBoaCjLli2TIJIQp/nuu++YPHkyZrOZSy+9lK+++gqdzn7RStH+HDlyhPfff5/k5GT2799f97qrqysTJ04kKSmJSZMm4ebm5sRZCiGEaEskvNBJHCo6xPcHv+erfV+RWZaJRbHg5uqGl84LT50n7q7u9aqumi1mKowVVBorqTBUYFEseOm8GBg+kKkJUxkdMxpPnacTvyPRmZSXl5OSkmIVUjhy5AhBQUFWIYXExEQJKbQj2dmwerW6kO/wYXVRn04HXl7ql7s7WCwnwwtarY7KSqioUBfrmUxq8CEhAaZOhfHj1QV8QgjRuCLURXxfAPuAGtQggxdqVwZP4NQHRhbUsEIlUAEYT2wfh1pl+FLUTg5CnDmj0UhqaqpVSOHAgQPo9XoSEhKsQgrR0dHykLOdqKiAtWvV659du9RrGhcX8PRUr388Pet3pTKbzaxatQ7wpHv3/uh03mi10K0bXHUVTJwI0bJmWAjhEAOwAVgJbAPKUa93PDl5DeTCyUCDgnqNVHv9U31iLAy4HJgE9KTpAQghhBBCCCGE6NjKyspYunQpzz33HJWVlfXG/P39eeSRR7j77rtxt1XVS7RrEl7onKqqqli+fDnPPPMMI0eOZPHixSQmJjp7WkK0GcnJydx4440AzJgxg3fffVeeaXVAiqKwfft2kpOT+fDDDykoKKgb8/f3Z9q0aSQlJXHBBRdId3ghhOjkJLzQyVQYKlifvp5dObvYlbuLI8VHqDBWUGOqqbedVqPFU+eJn7sfiSGJ9Anpw4ioESSGJMrFg2g1FRUVNkMK6enpBAYG2g0pyM9kx2AwwKZN8NdfsHMnpKWpHRWqq8FiMXPw4AF69OiJi4sLHh5qheLevaFvXxgyBAYPBvlsK4RoHjPwG/A78DewH7WScBXqgr1TeaAu7OsB9APOAYYDUh1ENI/RaCQtLc1mSMHV1dVmSCEmJkZu6HYQiqKGF7Zsgb17Yc8eKCmBqio4rRAf6ekp1NQUMnhwCFOm9GTAABg9Gjw8nDJ1IUSHcBD4BdiLeg1UhBpQMFM/jKBHvf7pBgwAEoGLULs2CCGEEEIIIYQ4ldFo5I033uDxxx8nNze33pher+fee+9l/vz5BAYGOmmGorVJeKFzy8/P56mnnuL1119nxowZPPHEE3Tt2tXZ0xKiTVi2bBlz5swB4MEHH2TZsmWy3qcDMxgMrFq1iuTkZL755huMRmPdWFxcHElJScyYMYPu3bs7cZZCCCGcRcILnVxZTRlpRWnkVeRhMBswWUy4ubrh7upOjH8Mkb6RuGhdGt+REE1QWVlpN6Tg7+9vM6QQGhoqH1o6maoqOHRI7cxQXFzJNddcz6effkhgoCeRkWp1Yb3e2bMUQnRMBiAdyECtNGxEDSfogQggFpBqWKJpTCaTzZDC/v37cXFxqRdSqA0qxMTE4OIi1+KdickEGRmQnq4GOA0GcHVVr3kWL36QtWtf4+67b+bll1929lSFEB2OBcgEDqN2WTCgdmRwAwKBeCSsIIQQQgghhBD2KYrCV199xUMPPcT+/futxq+//noWLlxIbGysE2YnziYJLwiA1NRUHn74YVatWsWDDz7InDlz8PHxcfa0hHC62bNn89xzzwGwePFi5s6d6+QZibOhsLCQTz75hOTkZKvfhyNGjCApKYlrrrkGf39/50xQCCHEWSfhBSFEq6mqqrIZUjh8+DB+fn42QwphYWESUhBWSktL8fPzo6SkBF9fX2dPRwghhLDLZDJx6NAhmyEFrVZL7969rUIKsbGxElIQjXr00Ud5+umnGTNmDD///LOzpyOEEEIIIYQQQgghTti2bRtz5sxh48aNVmMXXnghy5Yt47zzznPCzIQzSHhBnGrr1q3Mnj2bgwcP8sQTT3Dbbbfh6urq7GkJ4TQWi4WbbrqJFStWAPD2229z0003OXdS4qw6ePAgK1asYMWKFaSnp9e97ubmxuTJk5k5cyaXXHIJOp3OeZMUQgjR6iS8IIQ4Y1VVVezbt88qpHDo0CF8fX1thhS6dOkiIQXhMAkvCCGEaGvMZrPdkAJgM6TQvXt3CSmIZvvoo4+4/vrrCQ0NJScnx9nTEUIIIYQQQgghhOj00tLSmD9/Pp988onVWEJCAkuWLOGyyy6TZ6KdjIQXxOkUReHLL7/koYceQqPRsHjxYq644go5N4hOy2g0cuWVV/L999/j4uLCl19+yaRJk5w9LXGWWSwWNm3axIoVK/jkk08oLS2tGwsJCeGGG24gKSmJQYMGyflSCCE6IAkviAaUAKlAGpAH1ABmQA94ADFAPBAFSDK8M6iurrYbUvD29rYZUggPD5eLSNEMFajnnjQgm+rqEh59dD5PPfUM7u5+QCTQA4hFPScJIURLqQEOoV4DHTvx30bUax03oCvq9U93wNNJcxRnk9ls5vDhw1YhhX379qEoit2QglROEi3tjz92cd55U4B4nnvuU7RaH4xGcHEBNzcIDYX4ePVLuo8LIYQQQgghhBCiPVIUyMqC1FQ4dAjKysBgAI0G9Hrw94e4OPX+R0iI+roz5Ofn8/TTT/PKK69gNBrrjXXp0oUnn3ySm2++We4RdjJlZZCWBtdeO5+DB0tQnym4AEZiY7vwyisPERcHMTEgxaQ7J6PRyP/+9z8ef/xxevfuzdKlSxk6dKizpwWYgKOoz+bTgUrAgPrzqwdCUJ+NxQN+zpmi6HAqKioYN24cW7duxcPDg59++olhw4Y1aR+KAoWF6nVDaqr6d6MRLBb1usHLC2Jj1euGbt1Aq22lb0acsaqqKr7++muSk5NZs2YNZrO5biwxMZGZM2cyffp0unXr5sRZtqyaGjh8WP3ZzchQ/9toBFdX9blfeLj6sxsXB56yLEEI0QFJeEGcQgH2AauArUAG6gJiI6A5Ma4A2hN/grpozxdIBMYAF5/4b9GeVVdXs3//fquQQlpaGt7e3vTp08cqpNC1a1cJKYgzdAT1/LMZdeFwBVANaDGbzRw4cICePXueqFitoIaovIFewChgAuqNEyGEaKpc1PPPBuAAUI56/qm9/qmlASyAO+AFxAEjgEtRQ1WiPTObzaSnp9sMKVgsFnr16mUVUoiLi5MHkKJVlZTADz/AunWwZ4+FDRv+BDyJiorC09MLRVEf0ms06s14Nzf1BmZ0NJx/PkycCD16OO9BvhBCCCGEEEIIIURjDAbYuFG9B7JzJxQUQGWleq9Do1EXJsLJv7u4qIsRw8Lg3HNhwgQ47zz19dZWVVXFv//9b5555hlKSkrqjXl5eTFnzhz+9a9/4e3t3fqTEW3CwYPw/ffw669w9ChUVMCBA+lUV1eiPlMAUHB39yAmJhZPT/D1hd69YcwYGD9eDeWIzqW0tJQlS5bwwgsvMGnSJBYtWkRcXNxZnkUZ8BPwM7AHtbhp5Ymx2udhGk4+K9OhPhvrBpyP+mwsgZM/50I0XUFBASNHjiQlJYWAgAA2btxIYmJig+9RFPjzT1i1CrZvh+xs9dxrMqnjtdcLp/7p5aWea/v1g4svhtGjwcOj1b890Uw5OTl8+OGHJCcn8+eff9a9rtFoGDt2LElJSVx11VXt8norNxdWr4YNG2D/fvVnt6rKerva537u7urPb/fuMGIEXHopREWd/XkLIURrkPCCQF2ctx5YCWxHXbBXuyjPC7UigK0PHGbUDy+VJ96jAF2Ay4HLUBcUi7aspqbGZkghNTUVLy8vmyGFiIgICSmIFmQCfgW+Rl00XIx6zqk9/7gDGoxGE6tXr2bChAnodK6oN0tOPf+YgSDgEtRz0EDkRokQomEKsAP1/PMjUIjaXaH2/OOBGti09b4qTp5/jKhVZkYDVwBDkY5UbZvFYrEbUjCZTDZDCvHx8RJSEGeNosC+ffDtt/Ddd5CTo96k9PaGv//eSlVVAf36JRIdHWP1vupq9eF+RYX6dx8fGDoUrrwSRo1Sww1CCCGEEEIIIYQQbUF2trrw8Isv1KqzZrO6OKv2y97tOINBvfdRWal+ubmpC8GnTm29heAWi4X333+fBQsWkJGRUW9Mq9Vy22238fjjjxMeHt7yBxdtTm3gZuVK2LYNSktPLi709ITt2zdRUlJU7z3+/gEMGzai7t5dRYW6KDEsTC1Actll0KePFCHpbDIzM/m///s/PvjgA+68804effRRgoKCWvmoB4FvgW+A4ydeq3025onabeF0Cmqn8krUAoRVqEUGzwWuQn1GJivBRfNkZGQwbNgwjh07RkREBFu2bCHKxurssjL46Sf13Lt7t3oN4Ol58tyr19s+h5pM1Dv3ajTq4u8rr1TPv7IQvG3bvXs3K1as4P333ycrK6vudS8vL6ZOncrMmTMZM2bMiSKobZOiwI4d6nO/H35Qg7qurieveT08bHcFURQ12FBZCeXl6vWHvz9ceCFccYVaxEweXwsh2jMJL3R6vwNLgL2oHziCUBfgNedTsRHIR01o+wJTgLuQtnHOV1NTw4EDB2yGFDw8PGyGFLp16yYhBdHKDgCLgd9QQwyBgD+2FgtbhxdOZwYKUCtCeADjgAcBuUkshLAlE1iGGt6sQj33BGL7hmxjLEARavhBj1ptZh5qVwbhTBaLhSNHjliFFFJSUjCZTPTs2dNmSEEn/bqFExUVwUsvwddfqzfifX0hOPjkzcft23/n+PHjxMTE0Ldvvwb3pShq54aCAvWmZ2IiPPQQDBp0Fr4RIYQQQgghhBBCCDsMBvjwQ/jf/yA/Xw0fhIaqi7+bo6JCrWJrNkNkJNx7L0ya1HKLwH/66SfmzJnDX3/9ZTV2+eWXs3jxYhISElrmYKLN27kTFi9W/7RYIChIXUh46s/bpk2bKC62Di+MGDGi3msmk/pvoLRULUJy2WVw//0QGHgWvhHRpuzevZt58+axZcsW5s+fz3333Yd7c0+KdpUCrwKfoz5T9wZCUDsqNJVyYn/5J/47AZiLWuBLiKZLSUlhxIgRFBYW0qtXLzZt2kRwcDCgPuv4+Wd4/nk17KjRQEiIWvCpOb/ra2ogL09dFB4QADNnwi23NP86RJwdZrOZn3/+meTkZL744gsqKyvrxiIiIpgxYwYzZ85stHPH2ZadDcuWqT/DVVXg56deOzQna2GxqM8Ri4pAp4MhQ2DePLUDuxBCtEcSXui0yoH/Ae+jJqMjUaudtwQFdQFfAeqHlNnAiAbfIVqGwWCwGVI4ePBgXUjh9KBCZGSkhBTEWWYAPgReQz1XdEWt5GBf4+GFWrU3So4D0cB9wCSkC4MQQmVB7bTwEnAMtWOUDy13jigHslBv9v4DuA7pwtD6LBYLR48etRlSMBgMNkMKPXr0kJCCaFMUBX75Rb2BefCgGlgICLC+8b5v3z5SUw8SFBTMBRdc4PD+q6vh2DH1IejMmXDrrWolIiGEEEIIIYQQQoiz6cABePZZ2LpVrTLbpYvtSrPNYTJBZqZ6n2XCBHjwQTiTRgi7du1i3rx5rF692mps8ODBLF26lAsvvPAMZizak6oqeOstePddNWzQrZv6M2yLo+GFWoqiLkTMz4f4eJg9G0aPli4MndHatWuZPXs2hYWFPP3000yfPh1ti5wkf+VkUdNA1KKmLfUDVoP6zM0TuB64A/XZmxBN8+uvvzJ27FiqqqoYMmQIa9eupabGm+XL1YJPJpN67m2px3sWixpiKC2Fc89VF4H3798y+xatq6ysjC+++ILk5GTWrVvHqUtfzz33XGbOnMn1119PaGio0+aoKPDNN7B8ufp8LixMLVjWUr/bKyrU696QELjzTrjuupb7tyGEEGeLhBc6pRTg/4BdQAAQTOss7DUCGaihiGnAA6gVicWZMhgMHDx40GZIwc3NzW5IoWU+2ApxJrKAR1FvkHigdkZo/PzjeHihlhn1JokCXHrimHKTRIjOrQR4AvgRtcNLBM3rtNAYC5ANVKOGN58CwlrhOJ2PxWIhIyPDZkihpqaGHj162Awp6PVy/SnatpoaNbTw2Wdq5cGoKPttXjMzM/nzzx3o9W6MHz++ScdRFPVGfEkJDBgATz8t1ViEEEIIIYQQQghxdigKfPwx/PvfUFgIERGtV1ihtFStchsdDfPnw5gxTXt/ZmYmjz76KO+88w6nL6WIiYnhmWee4dprr5Xnrp1IWho8+ij8+adaHCQ0tOHFh00NL9QymeDoUXXx4VVXqQtppRJ452OxWHj//fdZsGABwcHBLF26lLFjxzZzb0ZgOWphwSrUoqat8cxEQS1uWgj0BZ4E2lb1c9E+fP/991xxxRWYzWaGDr2PgIDnOXTIhZAQteBTa6iuhowMtSL+7berxZ8kPNZ+ZGRk8P7775OcnExKSkrd6y4uLlx66aUkJSVx+eWXt0I3G/tKS+Gpp2D1avVnqVu35nVaaIzFAsePqwHLYcPUY55JcFcIIc42CS90On+itms7CsTQ+mGC2i4MRcAVwOOoC5aFI4xGo82QwoEDB9Dr9TZDClFRUXKzTLRRh4EHUSs6dEOtvuCYpocXatV2YRgBLEWtJCGE6HzygTnAFtTQ1NkIM1Wihqj6A8+j3hAWjlAUxW5IoaqqymZIoWfPnhJSEO1SRYX64PP779U2sY21hC8tLWHDhg0AjB9/SbN+7mtq1IegMTGwdCn069eMiQshhBBCCCGEEEI4SFHg1Vfhv/9VCzZ07dr6iwLNZvX+h5cXLFgAV1zR+HtKS0tZsmQJzz//PFVVVfXGAgICeOSRR7j77rtxc3NrpVmLtmjPHrUTwqFDaiDGkf/7mxteqFXbheGSS2DhQvD2bs7MRXtXXV3Nv//9bxYtWsSwYcNYsmQJffv2bcoeUEMEKwF/Wrbbgj0G4AhqAbHFwHmtfDzREa1YsYKkpHeAJ/H1jeaCCyLQ6Vr3Z1dRICdHXQR+443wwAP2i0yJtklRFP744w+Sk5P58MMPyc/Prxvz8/Nj2rRpzJw5k+HDh6NpxQvR/HyYOxc2b1Y7jPn6ttqh6lRWqt0d+vaF555Tn/8JIUR7IOGFTmUn8E8gB4hFrTp80k03/cW77x4746NMnhzGl18OPu3V0hPHvRxYiHRgqM9oNJKammozpODq6mozpBAdHS0hBdGOHAXuAfajnn9O9itz9Nyj0YCHhwueni4EB+uJi/OkXz8fJkwIZcSIQFxc7H3AqELtAjMMeBHwO7NvRQjRzhQC9wO/AVHA2SxTZADSgQTgP6g3a0UtRVE4duyYVUhh7969VFVVER8fbzOkIA8HRUdwzjnn8Oeff1q9HhZ2OYMHf233fRaLme+/vwl4z+b4eed9RZcuDT+NX7s2lqqqdKvX7777bl5++eUG3yuEEEIIIYQQQgjRFLXBhVdfVYMEISHq63/9dRPHjr1r930ajQ6tVodW64ZOF4BOF4C7eyReXt3x9T2HoKBReHg0XDBGUdSFXK6u8NhjcPnltrczGo28/vrrPPHEE+Tl5dUb0+v13HfffcyfP5+A1ir5LNqsffvg3nvVn6PY2MarJhsMRRQU/Mzu3R9gMPyNukaiDKhBo/HBy6sL7u7dCAwcSVDQhQQEDEOrtb06trwcsrLg4ovVAiQeUh+y0yooKOCpp57itdde44YbbuDJJ58kIqKx500G4FHgK9Tu4CdXzzZlXZCHhxYvL1fCwtzo1cuLc8/1Y/LkLiQmNlQkzIJa1DAUtbjXOQ4dS4ha69bBTTdlcfRoPnCE2NjuHD9+GVVVR1rtmAMGvE1k5E0UFkJxMcycqS5AlyVZ7ZPRaGT16tUkJyfz9ddfYzAY6sa6d+/OzJkzmTlzJnFxcS163OJiuO8+2LYNIiPP7u9uoxEOH4aEBHj5ZbXbgxBCtHUSXug00oBZqAt4u3N6cAFaO7wA6gfz48A01A4Mna/PlslkqgspnBpU2L9/P66uriQkJFiFFGJiYiSkINq5fNTzzy7U80/9m3Atce6Ji/PkmWd6c801Xe1sUY0aoLgIeIGzu3hZCOE8VajBhV+AaMCxRe8HD5azcWMhmzcXsWtXKYWFRgoLjZSVmU65UasnPt6LXr28GTYsgOHDAwgIsBXONKLepB0IvEpn7ACjKAqZmZk2QwoVFRU2Qwq9evWSkILo0O6//5/8+9/LrV7X6QIYP76gwaovq1YNwWz+3eZY9+6z6dNnqd33VlUdY+1a2w/2P/roI6ZNm9bIzIUQQgghhBBCCCEc9/77sHgxeHqeDC5A4+GFxmkICLiA2Nh/Eh4+FY3G9rNURYGMDHB3hyVL4MILTx1TWLlyJQ899BAHDx60eu/06dN5+umniZHytZ1SRgb84x+QlgbduzccXKiqyiAtbRkZGW9gNlc6fAxPz1i6d59DZOTNuLhYP7usqFADDJMnw6JFjYcnRMeWlpbG/Pnz+fbbb3nggQeYO3cuvjZLeiuoBUXfRw0Q1N+mJZ7NX3xxMMuXJ5KQYC/EUBtg6Aq8AvQ8o+OJzmP7dvjnP9VF4FVVezl0KA0AV9dZmEyZrXbc2vACUBdguPtuuOuuVjukOEuKior45JNPSE5OZsuWLfXGhg0bRlJSEtdee+0Zh1Srq9Xgwi+/QFSUeu1Zq6Hr3osuOoynZ0yD+96yZTSFhb/YHJs06eSyX5NJ7RQ1YIAaHA4KavK3IYQQZ5U0OeoUDMDTqO3Z4rAVXDg7fAAz8AUwGJjkpHm0PpPJRFpams2QglarrQspDBgwgBtuuKEupOAidxxEh6OghgV2ATG01q+dtLRKrr12B888U8lDD8Xb2MId6AasB1YAt7fKPIQQbc2bwAYgksaCCxaLwrff5vDCC4dZv77A7nbl5WbKy83k5NSwa1dZ3etaLVx4YRDXX9+V22+PPuUdOtTz31/Av4HH6KgBTkVRyMrKshlSKC8vJy4uri6gMG7cOBITE+nZsyfu7hIoE52PXj8KsA4vGI1FlJX9ja9vP5vvUxQzFsteu/stLNzY4HELCzfYHTv/fMfa1gshhBBCCCGEEEI4Ys8etfKrTlc/uNAyFIqKtlBUtAV//6EMHPgO3t69rbbSaNTKt4cPq4u/ExIgNBR+/fVX5syZw+bNm63eM2bMGJYuXcq5557b0pMW7YTJBM88A6mpjQcXjh17j92772xSaKFWZeVh/v77LtLTX+a8877A27tXvXEvLwgLg2+/hfPOg2uuafIhRAcSFxfHxx9/zLZt25g9ezavv/46jz/+OLfffjs6ne6ULdcAnwJBnB5caCk//pjPsGFbWLNmCEOG2FrwqwVigVTUIMXrOFpgTHReJSXw9NNQUABxcaDRJGAwGDh2LAOTyXjW5hEYqP4eeOst9dw7ZMhZO7RoBQEBAdx5553ceeedpKam8t5775GcnMzhw4fZsmULW7Zs4b777uOKK64gKSmJCRMmnHZOdczbb8PGjWrHA2c9+nZ1hZgY2LkTXnwRnnxSvRYWQoi2SsILncIHwDbUhXvOXhzvD5SgLtQ5Fwh36mzOlNlsthlS2LdvHxqNhoSEBBITE+nXrx/XXXcdiYmJxMbGSkhBdCI/At+iVnWwVZG8ZS1YsI+xY4MZPNjfxqgn4I26mHkYkNjq8xFCONNfQDLqjdmGezIePVrF9Ol/smlTYbOPZrHAunUFrFtXwM03R+LqempYVA+EoLbnvRAY0+zjtAWKopCdnW0zpFBaWlovpHDRRReRmJhIr169JKQgxAkZGbBly0i744WFG+yGF0pLd6IoFXbfW1KyA7O5EhcXT5vjBQW2wws6XXe2bIkgOtrmsBBCCCGEEEIIIUST1NSoHReKiiDeVs2pFlRcvI1Nm4Zy7rmfExIyzmpco1Er4B46BP/3f4Xk59/J559/ZrVdnz59WLJkCRMnTmywK6bo+L74Qq2c3LWruhDQnr17Z3Po0HNnfLzy8r1s2jT4xM/wxfXGfH3VBb3/+Y+6gFbu34mhQ4eyYcMGvv76a+bNm8fy5ctZvHgxkydPRqPJQy0saAHOrIp4Y4qLjcyc+Re7do3Czc3W+hct6hql31CLC97WqvMR7d8rr0BKirr4Wv01rKF///4YDAZyc8/uXEJC1OuGpUvVRene3mf3+KJ1xMfH8/jjj/PYY4+xefNmVqxYwccff0xJSQmfffYZn332GcHBwVx//fUkJSVx7rnnOnRNuGsXvPMO+Pio3cacSa9Xf36/+UbtODbO+tJYCCHaDAkvdHj7UVPMHjS2cK8hc+fGERDgWLKwZ0+vRrbohpqwfh5YQnuoPmw2mzl06JDNkAJA79696xbpXXvttSQmJtK9e3cJKYhOLg/137kZNbjUdLXnHrPZwoEDBwgOjuWvv0r5+WfbVdEtFvjvf4/YCS8AhKGefxajnhtlIa0QHVMl6r/zMtSuU/atWZPL9df/SVFRa1fsCACKgGXAACCwlY935hRF4fjx4zZDCiUlJXTv3r3u+ufuu++uCyl4eDT/mlOIjs5sVm925+eH4OXVm4qKfVbbFBRsICbmbpvvLyhouLOCohgpKtpKcPBFNsftdV7w9h7Jq6/C0KFqNTkhhBBCCCGEEEKIM7FiBfz+u1p9tqk5gLi4ueh0AYCC0VhMTU0uxcXbKC/fh9rx25rJVMr27VcybNhG/PwGWY1bLAbKyo7y2muVqPeNTwoPD+fJJ5/kpptuwrWhleqiU0hPVxfQ6nQNL1Y9dGh5I8EFF6AfamdmD/T6SnS6vVRUHLS5tclUxh9/XMOIEdusOjBERKhdIJYuheXLG+4EIToHjUbD5MmTmThxIm+88QZ33nknzz23jI8+6k5ERDqNPRuz59R1QQaDhSNHqvjuu1xycmpsbn/gQAXff5/LVVfZK1rqAXihFhccDiQ0a16i49u8GT77DIKC1MXXtbRaLeeeey6//DKDysp8NBoNkZFReJ62QtxoLCItbYnNffv5nUN4eMOta/z86ndbqu3ctHs3vPYa/Otfzfu+RNuk0WgYMWIEI0aMYPny5XzzzTckJyezatUq8vPzeemll3jppZdISEggKSmJ6dOnExkZaXNf1dXw7LNq0LC1A7uOCghQA8QvvAADB0JwsLNnJIQQtsmn7w5NQU1VFwJn9hty1qxoYmJaKh7ogtpxYQ0wkbZUfdhsNnP48GGbIQWLxVIXUujTpw9XX311XUhBbmQJYcsbwGGg+Svgas89RqOJ1asPMGFCL3Q6V959N4Obbtpp8z3r19sONqg0qAGq7cDnwPRmz00I0ZZ9itp5IZKGQpLbthUxZcofVFaa7W6j0UDfvj4MHepPSIgbHh5aCguN5OTUsGNHCQcOVKDYfl5mQxRqgOptoO3c5VIUhZycHJshheLiYmJjY+tCCrNmzaoLKZx+Y1AI0bgff4R169SqbdXVo2yGFwoL7QcU7IUPTlVQsMFmeMFgyD/xkN9at24jycmBl15Sb2YKIYQQQgghhBBCNFd6Orz1Fnh5QXPqnERHz8LTM8bq9YqKNNLSFnP06BvYCjGYzRX8/vvljBr1F3p98InX1Ge/qakHMZlMqAvJ7wc24u2tZe7cuTz44IN4eTVWnE90Fi+/DNnZDS9ALCraSkqK/Xv8rq6DMZnuRO1Mr/L0DGDEiBFkZ69k9+5/YDBYlxE3mUrYvv0qRo3ahVZ7cv2BVqveT1y/Hlavhssua853JjoinU7HrFmzmDFjBp98ci8FBckUFwcRExPRrPOarXVBFRUmrrjid7vFBX/8Mb+B8AJAF04WN32d9lDcVJxdRiM8/zxUVUG4jR8lFxcXRo58hs2bt1BeXkZWlo5hw4bh6+tbt01lZbrd8IKPTz/i4x9q8rz0enUR+EcfwYQJkJjY5F2IdsDd3Z1rrrmGa665htzcXD788ENWrFjBH3/8QUpKCg8//DDz589nzJgxJCUlMWXKFHx8fOre/+mn8OefatilLTXuiopSg49vvgnz5jl7NkIIYZusuO7QUoDfUT8Ut6HfkAD4Armoi4fPfnjBYrHYDCmkpKRgsVjo1atXXUhhypQpJCYmEhcXJyEFIRyWD3wP+NEav2puvDGSxx47wJEjVVZj2dnVjbzb48ScPgWuBRzrKiOEaC8MwGeAHnCzu1V2djWTJv1uN7ig0cDtt0cxf3480dH2F+kXFRn48sscPvooix9/zGskyOCKeg30DXArze1K01yKopCbm2szpFBYWFgvpHDnnXeSmJhI7969JaQgRAtRFLVykNmsVm0LDBzF0aOvW21XU5NNRUUqXl7WT0gLCzfV+283t2hqajJQ24DXbmM7/KB2bbB9kgoKGonRCJs2QVoaxDWvMJcQQgghhBBCCCEE330HBQUtX33WyyuO/v1fJzR0Ejt2XIvFYl0JvLo6k9TUZ0hIWMaxY8fYv38f1dWnPjfKBKKZMGEp77wzlbCwsJadpGjXDh+GDRvUCsVarf3t9u1bgKLYfrYQEnIJBsNDlJSU2RwPD78KL694tmwZiclUYjVeXp5CZuYKIiNvrve6tzfk5qr3FydObFsLJIXz+fj4cOutARiNcezdW8P69b8QHR1Fz5490Z9axr4ZvLxcWbSoN+efv9nmeGZmY8/mNUAY8AewG+h/RvMRHc+mTbB/vxrSsndu0+n0nH/+UDZt2kx1dRXbtm1jxIjheHi07jPMoCA4eBC+/lrCC51BaGgo999/P/fffz979uxhxYoVvPfee2RmZvLzzz/z888/c9dddzFlyhSSkpIYMeIiPvvMBb0e3N2dPfv6XF3Bz0+9Lr/9dggMdPaMhBDCmqzE7tC+BcpRuxy0RUHANuAg0KNVjmCxWEhPT7cZUjCZTPVCCldeeSWJiYnEx8dLSEGIM7YGyONMui40JjBQZzO84OXlyL/fUCAN+BUY1cIzE0I410bgENC1wa0ee+wA+fkGm2MeHlo+/PAcJk/u0ujRAgL03HxzJDffHMnevWUsXHgQTYNPDYKBdOAH1ABV67AXUigoKCAmJqYupHD77bfXhRSkupgQrWvPHrX6SkiI+t9BQfavQQoLN1qFF8rL91tVZPPyGomb2x5KS/+se624eCsWixGttn5A016oQa8Pxdu7J4qi3oT/9lu4//6mfGdCCCGEEEIIIYQQqooK+OortetCQ4u/z0SXLlfQt+9/2LXrNpvjhw+/Qm7ucMrLrYtXhYUF4e0dR1hYH0JDbbxZdGrffw8lJY11XfiVgoKfbY65uvoyYMA7bN+e2uBxfH370bv3Qv7++x6b4wcPLqRbt5usnjUEB8POnbB7N/SX9d+inkPAZnS6UAYMCCQ2tjspKSn8/PPPxMfHExsbi4uLS7P3PmCAr92x6mqL3bGTfIDjqGuY5IdX1PfVV2rRp8a6Nbm7e3D++UPZvHkzNTXVbN26jeHDh59xQKchGo26AHzVKrjzTlkA3pkkJiby7LPPsnDhQtatW8eKFSv4/PPPqaio4L333uO9994jKOhqNJqXiI/3Qj3PtS1BQWow84cf4LrrnD0bIYSwJiu0O6xi4DvU6r5tNXbvjxpc+A745xntyWKxcOTIEZshBYPBUC+kcMUVV9SFFHQ6qbguRMszAV+gdjRo/k2QhmRnV5OSUm5zrGdPRxbfegBG4EskvCBER7MStQK5/fIGBw+W89ZbGXbH//Offg4FF07Xp48P779/TiNbuaKeGz8HrgbO7AleXl6eVUBhz5495OfnEx0dXRdSuPXWW+tCCt7e3md0TCFE83z7LZSXn2x77OERiYdHNFVVR6y2LSjYYFVdzVb4QKvtR1BQUL3wgtlcSUnJDgIChp72/g025xUYOBJQb8J7e6sVhG69Vf27EEIIIYQQQgghRFOsWwfHjkFkZOseJyrqVo4ceYWSkh1WY4pSTXn5j8DEutf8/f1JSOhz4j4K7NolC8BFfRUV8OWXjQdvsrM/szsWFXUb7u5dgIbDC+q2d3LgwBMYDHlWY5WVaZSV7cbXt/4PqI8PHD8O33wjP7vidKuAEmqLhvr6+jB06BDy8/PZu3cv6enp9OrVm27dIpq194a6jnfpYr8L+kkawO/EPP8ByApwoTp0CDZvVhdZO8Lb24chQ4by66+/UlFRzrZt27jgggtadY6yALxzc3FxYdy4cYwbN47//Oc/rFy5kuTkZNauXUtBwXCgiPz83/H19aNbt25ERETg5ubIebH1ubqCTgeffw5XX63+txBCtCVyWuqwfgFygRgnz6MhGsAbNV19F9B4GtZisXD06FGbIYWamhp69uxZt0hv0qRJJCYm0qNHDwkpCHFW/Yl6U67lS9YUFRnZtauIuXNT7FZxuP32KAf3FgRsBrJpux1qhBBNkwH8hvrv276PPsrCbLZ9p3XEiEBuvrmVn6wRAuwHdgEDHXpHfn6+zZBCXl4eUVFRddc/N910E4mJiSQkJEhIQYg2pLJSrczj61u/7XFg4CgyM1dYbW8raGArvGA29yIwMJ7Dh5dbbXtqeMFkKqO09C+bc6sNL4DaFSIjQ23TPGFCY9+VEEIIIYQQQgghRH3ff6/+2YpFkOtER99lt/sC7AAm4unpSe/eCXTtGk5twb/aBeA//igLwMVJW7ZAZmbjwZv8fNtdFwDCw6c5fDyt1pXw8KkcOfJfO8dZZxVeqK0AvmaN2jlVHgEIlQn4CvDi9MKmwcHBjBw5kszMLPbtS+HQoUNUVzf9BP3nnyV2x4YNC3BwL0GoncnXAVObPAfRMa1dC6Wl0KOH4+8JCAjgvPPO4/fff6OkpJjt27fTt2/rtVNydVW/vvpKwgudnbe3NzNnzmTmzJn8/nsWU6ZAQcFxqqqgtLSEvXtL2Lt3L6GhIXTr1o2wsC5n1PWmJYSEqF3Xd+6Ec8916lSEEMKKhBc6rH2AQkv9Xxwba/9D+OkUZVIT9uwLFKJ+SOl5yj4UmyGFvXv3UlNTQ48ePeoW6U2cOLEupNCa7cCEEI7aBxhQuxucGetzz9oGt7/00hCmT3e0YoQvcAR1AbGEF4ToGPYBZUBsg1utXm1dyajWfffFtOiMbPNCbY+7n9PDCwUFBTZDCrm5uURGRtZd/yQlJdWFFHx82l4bSiFEfWlpasv506sHBQaOtBleqKw8RHV1Fu7uXeteKyg4PdAQRmWlJ4GBA63eX1i4gbi42af89xYUxWxzbkFBJ8MLOh1YLLB/v4QXhBBCCCGEEEII0TQGA/z9txoOOBv8/IY3MLqfPn0SiYmJRqutv2hMowE3N/jrr1adnmhn9u1Tq8s3tNzAbK6itHSnzTGNRoef38AmHdPff4jd8EJx8Tabr/v6Ql4epKbCwKYdTnRYR4EC1Gff1jQaDd26RRAeHk56+mEKCvY1ae/l5Sbmz7f9Hn9/HdOmdbU5Zq127dL+Jh1fdGy7d4OLS/2iT44IDQ1lwICB/PXXn+Tn57F3b37rTPAEPz9IT1ef8/j5teqhRDtRWtoVHx/o2zecioo4jh3LIDMzE4PBQG5uLrm5ubi6uhIe3pWamhqnzdPTE7Ky1Od+El4QQrQ1El7osHbjSCcDZ1MUT0ymDHbu/Ij16z3rhRSqqqrqhRQmTJhAYmIiPXv2lJCCEG3awRN/NvET5hlwc9Nyzz0xPPVUL3S6Bnq51uOKGvJKBUa32tyEEGdTbStm+xUMDAYL27YV2xzTaGDs2OCWn5b1kTCbTWRk/Mjq1UX1Qgo5OTl069at7vpnxowZdSEFX1/bN56FEG1faipUV4PHadnOoKBRdt9TULCBiAi1jE9V1TGqqtJP26IPNTXVaLV+eHsnUF6eUjdSWLgZRVHQnLjjb6uTA4Crqw++vgPqvebioi40EEIIIYQQQgghhGiKw4ehvLz1F/VZLBaOHj3C/v1HUJ/1mGxsVUZsbAwaje1nRl5ecOgQVFSofxdizx7QNvKI0WDIQ322aM3TMxattmlrGLy8ejVwrFybr7u7Q02NhBfEqVKBSiCswa1cXLTExcURHl4CZNnc5tVXjxAQoAPU52lHjlTx3Xe55ORYL7zVaODVV/vi769rwlz1qF3JhQCzWT33Nvf3cLdu3TAYati7dy+5ucdbdnKn8fSEnBz13CsLwAWoRcsUBVxdNfj5+eHn50dCQh/y8vI4duwYOTnHMZlMZGQcBWz/Tj8bNBr168ABp01BCCHskvBCh1QBHEKt6ts2KApUV1dRVlZ24quc8nL17zExZtas+Q87dowgMTGR8ePH14UU3NzcnD11IUSTKMBOwP2sHtXPzxUvLxeMRgseHk1tuybVHYToOFKAhp8u5OXVYDbbfrgQFeVBYKD9hwtff32cvXvLHZrJQw/FA2AwGOuueWqvgcrKyggJqSEz8xgrV2aQmJjIDTfcUBdS8JOSHUJ0OKknslWnVw/y9u6Fm1sYNTU5Vu8pLDwZXigs3Ghjr4kAlJWVExg4ql54wWgspKxsD76+fev2ZUtAwDA0mvrXTl5eagUWg6HhSnNCCCGEEEIIIYQQp0pLg6oqCG+1ZtcK2dnHSUlJobKy4sRr3kCxjW0tGAwFuLmF2NyTlxfk56sBhn79Wmm6ot0wGtX7YY0toDUYCuyOubo2/b6+Tmf/PQaD7SritYsQa+83CuFIYa9TaRtI6SxZkubQPgICdLz2Wj+uucbRrgu1vIAjqF3Upat4Z5eRoXYyOJMQYffucdTU1JCWZv2MpSW5uam/KyS8IGqlpFg/89NqtYSFhREWFobRaCQ7O4uMjGMUFTlnjrXc3dUuJ0II0dZIeKFDOoyarD4blYOtVVVV1y3QO7lYrxyz2YSnpxc+Pj74+PgQEhKMj48P3t75LFgwAnjNKfMVQrSkQiCHsx2eys018OSTB3nvvUx++GEocXGOHt8TtbqDwtnsFCGEaA0WYA/qv2v78vIMdseCghpepfvBB1l8/LHtajSnu/DCfMrLy6iursHd3a3u+icioive3j74+hoZNEjLpEmfYq+VrxCi4/j7b/tBgICAERw//rnV66cGFmyFF9zdz6O6GsrLywgMHMnRo/U/TxUWbsTXty9mcw3Fxb/bPHZgoHXnB09P9YFBejr07NnANyWEEEIIIYQQQghxirQTa14bq17fHEVFhezdu5ciq9VftgvVAHUdKW1xd1e7ZKalSXhBwNGjUFoKjTU/Npur7I65uDS9KKNWa/89JlOF3TG9XhYhilM1Xtirpbi5aZk/P557740hIKA5lW+8UCuQpwEDW3Ruov1JS4PKSggNPbP9JCQkUFGRzvFWbL6g0ahFeyU4JkD9Wfj7b/V5mj06nY6oqGiioqLZvj3c7s/nrl27iIlxJzQ0tMFw2Znw9ITMTCgqgoCAVjmEEEI0i4QXOqQSwAg0pT1bw+bOjatrDwfqL2KTyYTBUENNTe2XAYOhhrVrfzoRUvDGx8eH4OBgvL198Pb2xsXF1i/aUsB+lQIhRHtSe/7xaJG91Z57zGYLBw4cIDY2nqwsA99/n0tmZrXV9ocOVXLFFb/z55+j0OsdubDXA+WoLYVb7pwphHCGatTwZsM3S6uqLHbH3N1b7oZA165dTwQWvNHpbJ1fylDPP6VIeEGIjq+gAGyeCoCgoFE2wwtlZXswGArR6wOtwgt6fQh+fglUV+dQVlZG9+4XWr2/sHAjMTGzKC7ehsVi3dYbIDBwpNVrer1aQai01IFvTAghhBBCCCGEEOKEkhKw2L/92mx///036emHrV738fGhvLwCxWZ+QYtOF2h3nxqNGrKQ+x8C1J9do9H+/btaer39nymTybGuzfXfY/8HUKez/9xAp4PCwiYfTnRY+Zyt59w1NRZeeeUI8fFe3HBDRDP2oENdSyAnX6H+DlYUcHGsaUgDNCQk9G7V8AKo85RzrwCoqYGKCse7l7u62l+em5+fS37+7+h0eiIiutKtWyT+/v4tM9ET9Hr131tpqYQXhBBti4QXOiQDavXhlluAN2tWNDExnhw8eJCcnFzKy8swGk14eXmeWJgXcqKLQkMhBXu0gO3FNEKI9sZIS55/as89RqOJ1asPMGFCPDqdKzU1Zm6+eScffmhdAX3v3nLefjuDO++MduAI2hPzNSDhBSHaOyOOdFEJCrL/b72kxGTz9ZycHA4fPszx447fkYqOjmpkCw3qfO13ghBCdBwGg/2qg7a6H6gUCgs3ERg4grKyPae9ZyReXj7k5ORgMBjx8OiGh0cMVVXpddvUBh5sdW0AtbKbv/9gG6+rCw0McnoSQgghhBBCCCFEE9TUtHzXBYvFYvXc2c3Nnd69e+HvX8Uvv9i+p6vXB6PRND4Zuf8hQA0uWCyN//zqdEF2x2pqcpp83Opq+ytt9fpgu2NarfrvTQhVDWer8wJATk4N06f/SVZWNbNnxzXx3bXP5uUHWLTs72BHfuefKa0Wquw34BGdiNHYUsEbCA4OpqBAg9FoID09naKiYgYNGoS3t/eZ7/yE2s4hct0rhGhrJLwgmsTT04uYmOgTQQVvXFriN7EQQjRCq9XSs2fPujZpbm4u/Pe//fjyy+M2q6h/8kmWg+EFIURnExJivwTCsWNVKIqCRqPh4MFULCfKhFVXV6HVanFzcwPkrpQQomX5+vbH1dUPk6nEaqywcMOJv9UvIRgYOJLIyDji4+M4fPgwFouZoKBRHDuWXrdNdfUxKisPn7KP+vz9B+Pi4t5S34YQQgghhBBCCCFEi1EUhezsbFJSUqiqqgRAq3WhR48edO8ei4uLK0eOvGb3/QEB55+tqYpORK8Psnsfr6YmG4OhAL0+iMTEPmzevLneuL+/HxaLGa325PqKsrJddo/l49Ov5SYuhIMOH76ImBhPCgur2b49h7feKuDjj62LCQLMm5fCkCH+jBplP9QjhBCi4XBN//4DcXUNJzMzi2PHjuHt7c2GDb/QtWsEPXv2xNPTs6E9t/xkhRDiLDp78VtxFuk5mVhuWWqLom74+fm1YHDBAri10L6EEM6lozXOPy4uWnr16lmvuo6vr46+fX1sbr9zp6OtJmu7RDjYz00I0YbpONnNwL6AAD2hobb/zZeUmNi7V23tnJp6kAMHDnDgwAGOHs0gJyeXe+6p4uuv4euv4fvvXXjssR5nMN/aLhFy/hGiM9Dr1epttmg0WgIDh9scKyjYYLNzQmDgSHQ6PRqNhgMHDmCxWGx2cMjPX0dR0a829x0YONLm67VV5hxtdyuEEEIIIYQQQggB4OZm//5HUxQWFrBp00b27t1DTMzJQlUjRgynR48euLio9RnT01+xu4+QkPEOHUvufwgAne5kN9KGaDRagoLsdVGF/Py1dsdyc/NYt249mZmZKIpy4rXVdrf39x9qd8xiUf+9CaFyo6WfzQcGujN+fDQffXQOc+Z0t7mNxQL/+MduzOaGn8ud9i7UZ/PyAyzO3u/g48ePc/z4cRp7htwYiwU8PFpmTqJ90+nUbgaOXve6uNhe1wRgsRjR692IjY1l5MiRDBo0kAsvHI2iKKxfv47du3djNtsurqjT+Tl0fEVR5yvXvUKItkbCCx2SH+oCPqOzJ+IgIyBpbCE6hrN7/lHsfL4sKbHdJtiaAfBGGhEJ0RG4A56o/64bNn58iN0xexVkWp4R9Xzpe5aOJ4RwpqAgtY2sPbaCBwClpX+Sl1f/Iaarqw9+fgMd2kd6+kuYTGV2jmk7vGAwqDdefeX0JIQQQgghhBBCiCbw81MXgJ+pv//+m65duzJmzEVERETUva4/ZcVVevordqvWa7XudOlyVYPHUBR1wZnc/xCg/uzqdA3fv6sVHDzO7lhGxjt2xwYOHEjPnj1JSdnLpk2bOHbsN7sdU7VaD0JDL7W7L6MRAgMbn6voLIJpzWfzixb1JjHR9sLblJRy3n47owl7k2dj4iRfX3VBtdncusfx9vZm165dbN68mcLCgmbvx2yWc69QubmBl5f6PM0ROl2A3TGTqfi0VzR4eXkxaNAgRo4cRU1NDSUl2U3e76nkuZ8Qoq2S8EKHFIu6eK/C2RNxkBlIdPYkhBAtIhAI42ycf4qLjezebW8xns7BvVQC/ZF2akJ0BFrU64nKRre87LJQu2OvvnqEoiIH7zSckQqgG3KDVojOoW/fhm9i2qvYpigmysr+rvdaQMAwNBrrLnje3j1wcwuv91pp6V92jqglIGCYzZHKSvD2hpgY+/MVQgghhBBCCCGEOF1cnPrnmXZfGDZsGHFx8bi4WN//AMjK+oy9ex+w+/7o6Fm4u3dt8BjV1eDufnLOonOLilIX9FU48HgzImIGrq62F3Ln5a0iL+8Hm2MaDURGRjJmzEWEhYXx1193Y68KeETEdFxdve3OwWCAfv0an6voLBJo6c4Lp3J11fLMM73tji9ceBCj0dHjV6CuZZKTr1B/B3t6OnbuPRPe3t5cdNFFhISEsm3bb/z222+UlpY2aR+1levj41tpkqJd0WjU536VjS9LAMDTM8buWHn5AbtjPj4+nHPOQDSaXJvjHh7293uqykqIiIAAx7IOQghx1kh4oUPyArrTPsILtR9i5ApPiI5BAwwAqlv1KFVVZm67bSc1NbZvhPTsaf+GnrVeLTMpIUQb4NgN2qlTw+ne3dPmWH6+gZtu2onFcmatQxtXg3q+FEJ0BrU3tO11jfLzOw8XF9vnpdPZ65jQ2NipfH37220nW1EBvXpJ+1ghhBBCCCGEEEI0TVwceHg4vpDLHldX2wWqKitT2bnzVnbsuAaLxXaVCDe3rsTHP9zoMSoq1Iq53buf0VRFB6HTqffDHFlAq9cHEhNzr93xHTuuo7z8L7vjWi0YDC8B2+2Mu9GjxwK771cU9UsW0IqTan8YWq98/eWXhzFokO1iXOnpVSQnH3NwTxVANGA7ACQ6l8hItfPNmV43OMLV1ZWePXsyduxFeHl5sWnTRv78808qHTx4TY36u0LOvaJWQoL9Z36nCwwcYXcsP9926LFWQcF6FKXG5pi//wUOHb+6WkKPQoi2ydXZExCtpR/2PvC2LZWABxJeEKIj6XHiT4Uz7Wjw6qtHCAg4eZPaaLRw9GgV336by/Hjti/QAa68MsyBvZtOzE/OP0J0HKfeoLVdlQtAp9Py1FO9mD79T5vjX3+dw/HjcP/94NMq909r72T0aHArIUTHER+vVvOrqlIrCZ1Oq9Xh7z+UgoJ1je6roYBCUNAosrM/OaN9mM1qxRghhBBCCCGEEEKIpoiNVbs51nZ1tMdoNFJSUmJ3/MiRV9HpAgCFqqoC4C/gAFu2ZDR4fBcXL4YM+QY3t5BG51pRAQMGqAEGIQASE+GXXxzbNj7+IY4f/5Ly8r1WY0ZjEbt2TQAmAKOBSMANgyGPzMw1HDr0HCUlf9jdd8+eTzdYobm6GtzcZAGtOFU8ajeDSlozFPDooz2YMsX2z+6iRanceGM3XF0bq59rAPq3+NxE++Tiop57f/rp7B1Tr3cjMTGR7t1j2b//AOvXryM6OpoePXqg17vZfV9lpXrNIOdeUSsuTu3AYDKBayOrb7284vH0jKOyMs1qLCvrI+Li5uLjk2g1ZrEYOXDgcbv7PXo0GA+PdKKiotBqbZ9/a0OPPXs2PEchhHAGCS90WL1RF+WaaNv/N5cCgUCMk+chhGg5vQE9UIV6o6T5liyxvnhvTFSUB3feGe3AlqWoN3Ck84IQHUdv1H/XpUDDfQ+vv74r336bw4cfZtkc/+03uP12uOQSOPdciIlRH7gpCpSXQ3q6wsaNhc2cZwXghpx/hOg84uLUCkKlpbbDCwCBgaMaDS9otXr8/YfYHQ8MHOXQfOyFF4xGtfpbLzk9CSGEEEIIIYQQoon0erUgwvr1EBpqPW42m0lPTyc19SBare3OCQBpaUuafGxXV1/OPfdz/PzOaXRbRVErKA8c2OTDiA6sd291EaLB0HhHUldXHwYP/ppNm4ZgNFo/J1AUI/DNiS8ALb//3njXaL3+Yg4d6odOd4SoqEg0GuuFiKWl6n1GWUArTooCgoB8WjO8cOWVXejXz4fdu8usxg4dquS99zK56abIBvZgOvGn3HwWJ/XrB2vWqL+bNWdWF7NJPDw8GThwIHFx3dm3bx9r1/5MXFx3unePw9XGSvSSEnWufrYbWotOqHdvde1AaSkEBja+fffuD/D33/dYvW6xGPj114vo1etpunSZjJtbKGZzFUVF2zhw4HGKijbb3J+f32Di469j//79pKWl0bNnT7p1i7C6dqisVIuryXM/IURb1JZXtYszciEQivoBpYuT52KPApQDM1EXOgshOoZBqBUeDnC2g0nBwXo+//xcvL0d+fVWAEwEwlt5VkKIsycSGAKspbHwgkaj4e23B5CZWc2GDbZDCJWVsHKl+gXqgt7a6gRgQT2PNEce0BepLiNE5+HpCRMnwltvQViY7ZvwQUEjOXiw4f34+Q3GxcXd7riPT190ukCbD01PZS+8kJcHXbrACPsdbIUQQgghhBBCCCHsmjhRDS+cugBcUSxkZBzjwIH96PVunHPOuWRmhnDsWMsc09//fAYOfAdvb8dWZZWVqdWTL764ZY4vOoZhwyAiQr0/FhHR+PZeXnEMG7aRP/6YSnn5vka2bjy4EBl5G/37v8rx43mkpKRw+PAhEhISCAsLo7bTvaKoC2gnT264u4nobFyBycC/UX/WGut+0DwajYZHHunBtGk7bI4vXHiQmTO74eJibwV6ARAMjGmV+Yn2aexYeP11KC6GgIYf7bYKHx9fBg8eQmFhISkpKaSnp9OjR0+io6PrKtmbTOrX5Mlnf36i7YqIUK8d1qxxLLwQGXkL6emv2OzaZDDksnv3HezefQcajQuKYm5wXxqNCwkJSwgODqdLly5kZmZy4MB+UlNT6dWrF+Hh4WhOPIjMy1ODFhLaFUK0Ra1z1SraAH/gMtTKw4pzp2JXMeCLOk8hRMfhCkwBjEDDF9UtaerULvz22wjOO8/fga2rAB1wZavOSQjhDFehXuJWN7qlm5sLq1YN5ZZbGqoEc5LFUhtcOBMm1HPjFORSXIjO5bLL1IeKZdaFqQAICLgAjUbX4D7shQ5qaTQaAgMbTh54esbj7m4dcK/tLHPFFfLwUwghhBBCCCGEEM0zZgx066YulAKF48ez+eWXX0hNTaVPnz6MHDmSkJCQFjlWQMAwzjnnE4YP3+xwcAEgPx/691crKAtRy8sLrrwSKirUZwGO8PHpw4gRvxMVdSdabfOKNbq5hTFo0PsMGPA/NBpXwsPDGT16NDExMfz11062bPmV4uJi4GTw5vLLm3Uo0aFdCvihrsFpPVdfHU5Cgu2bx6mplXzwQaaddypACeo8HVjlKzqN7t1h+HAoaG69uBYSGBjI8OHDGDBgAEeOHGHdunVkZh5DURQKCtSOUuPHO3eOou2ZPBlcXKCqqvFtXVw8OO+8L9HpGj4HNhZcAEhIWEpw8GhAfS7YrVs3Ro8eQ/fu3dmzZw8bN24kNzcHk0nBaISpU9V5CiFEWyMrpjq0SYA3aoChLSoAhgI9nD0RIUSLuwQIQe3+0rI8PLSEhOjp39+HadO68sILfUhPv4jPPjuP2FhPB/eSC8QBF7T4/IQQzjYS6I7677xxnp4uvPnmAD766Bx69PA646P37+/D88/3aWCLfCAMkDtcQnQ2iYkwaFDtw3trLi6e+Pmd2+A+GgsvqNuManA8KMj2PoqLwdcXJk1q9BBCCCGEEEIIIYQQNnl5qQu58vMr2bhxM7t27SYmJpbRo0fTtWtEXRXYxmg0Lmi17ri6+gOhQBwhIZOIjX2AgQNXMHbsEYYP30zXrteg0Ti+5MFgUAs4TJ1quzOm6NwmTgQ/P/U+maNcXb3p3/+/XHRROvHxD+Pj05faTgmN8fUdwJgxaURE3FDvda1WS0xMLBdddBGBgQFs2bKFHTv+ICvLwIABErwRtnQHhgMNd+Q9U1qthgUL4u2OP/30QcxmW1XAygAv1DVMQtTXlAXgrUtDWFgXLrzwQnr27ElKyj42bNhAVlY5l16qOFRdX3Quw4dDXJz9536n8/buwciRO/D3H9qs47m6+nLOOZ/QvfsDVmNarZbo6GguuugiunWL4M8//2LjxhT0+mIJ3ggh2iyNopx5/VjRVinALGA9EI+jH5LPjlKgCFiOtIUToqN6BngH9WaJq3OnUk8VkAUsAKY7eS5CiNbxLvAsEAm4Ofwui0Xhyy+P8847x1i3Lofy8sbf4+npwrBhAYwdG8yECSEMHOjXwNYm4BBwB/Avh+clhOg4Vq+GOXMgKAh8fM58fyaTkdWrVzNhwgRcXRvu2tAQiwVSU9UHtC+8cObzEkIIIYQQQgghROe0c+dO7r//BTZtmkVYWCQDBoTi4tL8Z0TV1VX89NNPAIwbNw53d48zmt/hwxAZCZ99pgYthDjd7NnwzTcQHw/aZpYCzcs7xLZt76FWwS8DKnF330J1dbrVtj16/B+9ej3R4P6qq6vYvTud3FwLkyf/yptvTiFQVtEKKxuBewFf1C4MbYUCpKIWH3udtrVuSbQFRiNcdx2kpKidGM40XNhyz03M/P13DpmZOQwatJyXXvoH559//plNTnQ4K1bAokVq5zF3d8feoygWcnK+Jj39PxQWbsBiMTS4vadnPFFRtxAVdQd6fZBDx6iuNvHnnyWUly9n1KitLFy4kMGDBzs2QSGEOEva0mpS0eI0wAPALiAb6Orc6dQxo85nMjDauVMRQrSi24BfgAwg1slzqaUAx4AhwFQnz0UI0XquAVYDf6F2WXHsLpdWq2HKlHCmTAnn+++/Jy3NQlYWlJer7ZjNZvDwUL9CQ7XMmDGaqCgPh6uFwVHUQOnNzfiehBAdwfjxsGqVGmLw8mr+A9CWlpUFYWFw773OnokQQgghhBBCCCHao8OHD/Poo4/yxRdfcNddd3HllYm89po3BoN6P7UtKC5WKzvff78EF4R999wD27dDZqYadGkOtWPIoHqvJSbOZc+ey6muzqj3+sGDT+Li4kl8/Dy7+9PrPfDzS2D06HyKir4gLm4OCxYs4J577sHd0ZWSohMYAUwAVqJ2OWgry8GOAwHAg0hwQdii08G//qU+nygsVIs/tQUmkwseHl154olAamqiGDduHOPHj2fhwoUkJCQ4e3qijbjmGvWZ3x9/qMFHR5YNaDRaunS5ki5drsRsrqG0dAcVFWkYjUWYTKVote7odP64uYXj738ebm6hTZ5XdrYrQ4cGsXz5A7z55hJGjx7NxRdfzFNPPUU/aeEkhGgj2shSCdF6eqFW960CKp08l1rHgGjUisPy4USIjisE9SaEC2plkbYgB/AH5gFyM0+IjssT9d+5D5DbrD1oNBpiYmDYMHWx8dSpcO21cPnlMG4cnHOOhuhozyYEF4oAPTAbkIpIQnRWWq1ava1rVzh2zNmzUVVUgMEAs2apVY2EEEIIIYQQQgghHJWbm8t9991Hnz590Ov17Nu3j2XLljFrljeDB6v3PxTF2bMEkwlyc+Gyy+CSS5w9G9GWxcTAXXeplcAd6c7sKHf3SM4/fy1ubl2sxvbte4jDh1+y+97MTAgPhyVLglmz5ns++eQT3nvvPXr37s0HH3yAxWJpuYmKdkyD+mw+BnVNTltQBVQAtwKy2FvYN2wYXH01FBSozyucTVEgIwP69YP773fnySefJC0tja5du3LOOedw2223kZGR0fiORIfn7g4PPQR+fuq1ZlO5uLgREHAB3brNIDb2Xnr0WEBc3L+IirqVsLCJzQouFBWBqys88ADExwfwzDPPkJaWRlRUFEOGDGH69OmkpqY2fbJCCNHCJLzQKdwAnI/6AcXs5LkUo1Y+vx+w/mAuhOhoLgYmoS4edvanzEqgHPXmSKKT5yKEaH0DgSTUlsxVzp0KBiAPuBLpOiWEiIxUK7iZzVBa6ty5mM3qw89Ro9TqMEIIIYQQQgghhBCOKC0t5bHHHiMuLo6jR4+yfft23nrrLaKiogBwc1MXcgUEQHa2c+eqKHD0KMTGqou4HK5HIzqtqVNh9Gi1W6nJ1HL79fbuwfnnr0WvD7Ya27Pnfo4efcPq9dJSNUhx991w4p8XF198MX/88QdPPPEE8+bNY8iQIaxbt67lJirasVDgAdSlYEVOnosFyACGADOdPBfRHtx1FyQkqL+znR18zMsDT0+YMwe8vdXXwsLCePnll/n777+prKykV69ezJkzh8LCQudOVjhdv35w001QVgaVTq4rbTCoP79XXAFjx558vUuXLvz73/9m//79eHh40LdvX+644w4J4QghnErCC52CHliA2u3gMOqHBGcoQ124NwW4zElzEEKcXRrUGyT9gXSgBe/wNUk1aoBrNHJzRIjO5FZgJOrN0RonzcGIev4bCNyLdJ0SQgBcdZXaySUnp2UruDWFxQKHDkFcnLqYwLWtdBEXQgghhBBCCCFEm1VTU8Py5cuJi4tj3bp1rFmzhi+//JLEROuiUX36qAUcjEZ1EZUz1FZO9vaGhx+G/2fvrsOsKtc+jn/3ng4mYYaOKbpBBERQQbCx2wN2HOMcDEQQJAU52EfFOortKyaKgmLRonRN0DUw3bH3fv94GCT2BDAza+L3ua65gJX3vtyzXOtZz33fjRpZE4fULh4eZrwsJga2bTMFQCpLgwYd6NNnAV5eocetcbF27V3s3v3+kSU5OWb88OKL4Yorjo/Rg3/84x9s3bqVq666iuHDh3PxxRezYcOGygtWaqmhwNVACmBV9RwnZm5SS8xcJR+L4pDaJDgYxo6F8HDYvt26BIbUVPPe5tZb4YwzTlwfHR3NBx98wOLFi1m3bh1RUVFMmzaNXKtnrYulRo6EAQNM17H8fGtiKC42vzvdusGDD7pP2G3ZsiVvvPEGa9euJSsri7i4OB566CEOHDhQ3eGKiCh5of6IBmZiuh0kUf0JDJnAfuASzMOJJu6J1B8NgVlAW8z1p6iaz58H7AT6AU8DvtV8fhGxjh/m9/4MYAcmkak6FWIGZ9tjroNh1Xx+Eamp7HYYPx6GDjXVB6s7gcHhMIkLzZvDzJnQqlX1nl9EREREREREaheHw8GcOXNo164db775Jv/73//45Zdf6NevX5n73XAD3H23mYRd3QkMLpeZQOblZSZDDhxYveeX2q1FC/jPf8yfSUmVm8AQHNyNoUNTufhi13E/Dpo3vxEw44V79sCQITBhgkmocMfPz4/Ro0eTkJBAdHQ0vXr14o477mCf1S1PxEI24FHgUuAA1Z/AUJK4EAHMAOKq+fxSm/XqBZMmmaTDHTuqP4EhNRXS0+GWW8z9S1m6d+/O/Pnz+fzzz/niiy+IiYnhtddeo6iouufDSE3g6wszZkCfPqZ7SF5e9Z6/qMjcr7Rvb+5fwsPL3j4uLo4PP/yQZcuWkZSURHR0NE888QRpaVZ37RGR+kTJC/VKF8zEueZAAmZCXVVzYTK6k4HLgImYThAiUr+0AF4AOmIGK6or6zwTU3F9APAfILiazisiNUcY8CwmgWknphNUdcjFdFzoArwINKum84pIbeHrC1OnwkUXmQSG6uosXFBgBjBbtYLnnjNVEEVERERERERE3HG5XMybN4/u3bszbtw4Jk6cyF9//cVFF12EzV051+PYbHDPPeYnL89Mxq6OiYgOh6k86+MDTz5pOmCKnKx27cz4WZs2Zjytuiopp6WZ8cILLoBp08DPr/x9GjVqxPPPP8+6detIT08nNjaW8ePHk5VVXe9EpGbxBp4CrsTM1TmEmbtT1QqBRKAJprhq92o4p9Q155xj3p0EBZlrb3Fx1Z/T5YL9+yEjA0aMgFGjTBGqijjnnHNYtmwZL774Is8++ywdO3bk008/xWVV6wixTEgIPPssnHWW6fyVWU25Y7m5plNUp07w/POmcFlFde3ala+++oqFCxeyfPlyoqKimDJlCtlWtY0XkXpFyQv1TnfMBOIumArEB6m6h5QizCTlAmAkMAlTAVlE6qfWwEvAWcA+YC9Vd/1xYK5xqZjEqWdRxXOR+qwh8BxwESapcgfmOlEVnMAezHVuIOa616KKziUitV1AgBmEv/lmM7iYlGSqo1QFlwuSk03Fl65d4cUXzUCmiIiIiIiIiIg7S5cuZeDAgYwYMYLbbruNLVu2cPPNN+NRWgn4UpQkMDz+uJmEnZBgxkGqSmamOUeTJvD003DppVV3Lqn7OnaEl16CHj1MJ48DB6ouAae42IwP5uTA9deb729g4MkdIyYmhk8//ZQFCxawcOFCYmNjefXVVymujtm/UsP4AhOA2zFJBUlUXYFTFyZBYgemmOELQK8qOpfUB+eeC7Nm/Z08VpXF4PPzzX2D3Q4PPQQPPwyenid3DJvNxpVXXsn69et55JFHeOihhzjjjDP48ccfqyRmqbnCw00Cw6WXmqJlO3ZUbvemozmdJjF43z4YMMDcr5xqp/UzzzyThQsX8tlnn/HNN98QFRXFc889R351ZW6KSL2k5IV6qT3wNnAXZuJeIlCZ/7MpeTDZBsRiHkweRR0XRASaAq8Aj2EGTBKAnEo8vgvIOHzcSGAaMB1oUInnEJHaKRhT5WUy0Ahz/5NJ5SZRZWOuP/7AE8B/MdciEZHS+fjAmDGmiltUlKmOkppauS9B8/MhMdEc85//hLffhtjYyju+iIiIiIiIiNQdGzduZPjw4Zx//vmce+65JCUl8eCDD+Lj43PKx7TZ4LrrzJhE//5mktXevWbSVWUpLjYTxFJT4bLL4N13TfVmkdMVHQ1vvmnG1cBMcs3Lq7zju1zme5uUBK1bmwm7Tz5pOreeqr59+/L777/zyiuvMGvWLDp16sSXX36pSuD1jhcwClNoqy0muaCyuzAUYBIjioA7gP9hEhhETk+fPub/5ddeC9nZ5t1JZRZ/cjpNQtru3dCzJ7z+Otx+u7lnOVWenp7ccccdxMfHc9VVV3HVVVdx/vnns2rVqsoLXGq8oCCYMQOmTIGICPN+LiOjct/75eSY+xF/f5Mg/MorJnH3dJ177rksWbKEt956i//973/Exsby+uuvU1RVlddEpF5T8kK9FQj8CzOprhOmAnoikM6pP6gUHT5OPFAMjADeAfqfXqgiUsd4A//AJFH1xwyQJGCqoZ/qKLUD0/IyATN5+DLgXeBi4DSeLkWkjrHz9/XhQkzyQgLm+nGqJQ+cmOtXyXVsIOb+50bgJMtyiEi9ZbPBwIFmIP6mm6CgAOLjzYv8Uy2K5nJBeroZvNy3D7p0gdmzzUtWf/9KDV9ERERERERE6oCdO3cycuRIevbsScuWLUlISGDChAk0aFB5BaLi4szkqsceM10YEhNNl8jTKeqak2MmNG7fDo0bw7RpZsJYZUzgEinh5wf33Wcmt/boAfv3m3G3tLRTn4xYXGyOEx9vxgOvvx7mzDFJN6czebaEzWbj8ssvZ8OGDTzwwAPccccdDBo0iBUrVpz+waWW6Yt5NzYS8z4sHjO351Qno5YUFEwEdmMKqL6MSZRQUUGpPOHh8NRT8J//mIryO3eaRK+srFO/9hYUmISFhATw8IAHHjAJal26VF7c/v7+PPbYYyQlJdGjRw/OPvtsrr32WuLj4yvvJFKj2Wym+8KcOXDJJeY7Gx9vOqSfaicGpxNSUsxxDh0y3Rbeftt0d/fyqszYbVx88cX8+eefzJw5k5kzZ9K+fXvef/99HFXVRkJE6iWbS6nVQj7wCzAXWAVkYSqiBxz+8cH95F8HkIupmp6DeUBpAlyCmRDYtqoDF5FarxhYBnwJ/IpJoPLBXHv8AT/cX3+cmOtPLiZZwQGEA8Mw16CupewnIlLCBfwJfAP8gEk88KTk/mf+/F8oKnKfUOXnB0FBds44owNmYDcYGIRJjDgDJS2IyOlwuWDzZpg3D775xgxkgmkR7+9vfjw8zLLi4iLmz5/PsGHD8PDwIj8fcnPNS/v8fFPd5Ywz4PLLzSDmaRRIFBEREREREZE6KiUlhalTp/LKK69wxRVXMHHiRKKioqr8vPv2wXffweefm+SD4mIICPj7x9MT8vPzWLhwIQCDBw/Gx8ePoiIz9pGTY6rf+/hAu3Zw5ZVw/vkQElLloUs9V1gIv/1mvrvLl0NmpumS4O9vvrt5eaksWbL4mH369+9PUFAYeXl/f3+dToiMhAsvhIsvhvbtKydpoTSZmZnMmDGDZ599lksuuYSpU6dWy++61DTxwDzga2Df4WUlc4P8AQ83+7gwHRZyMO/n8zAFU3sCV2DekZ1GqxCRCsjKgh9/hLlzYd068y6k5J1JQAB4e5tr6NHvTTw9vSgu/vu9SU6O2aZlS/Pe5MILoUWLqo99z549PPXUU8yZM4cRI0bw5JNP0kRZlvWGywV//WXe+X3/vUlA8PA49r2f3U35cZfL3Ovm5pruI4WF5j530CCTGNGnj7lfrmrFxcXMmTOHp556igYNGjBp0iQuu+wybFV50yIi9YKSF+QoLmAL8C2wHNiJefAowFQqdh3+Kfk7mIeXYKADcC4wBGVSi8ip2Ym5/izBVGnIwSRXHX39sR3+cWESGwIxiVJnAxcADas9ahGpC5KB7zBJVFuBHLZsWY3DYe53bLa/q3fYbGZCcH6+nb59b8J0kLkAqIaRLRGpdzIyYMEC+Okn2LTJvAjNzf17vcPhID5+K3FxcdhsHvj4mEH6li3hzDPhoosgNta6+EVERERERESk5srJyeG5555jxowZ9O/fn2nTptG1a9dqj6NkIvgPP8CaNZCa+vfE7uLiIhISTJXimJhYPD298PQ0k7wiI6FXLxg6FHr3dj/pS6SqxcfDt9/CsmWwY4f57mZk5LJjx3b+LrTmolWr1vj5+ePvbwqOtGtnOixYkXCze/duxo8fzwcffMDdd9/N2LFjCQ8Pr94gpAbIAn4EfgI2YDop5BxeZ8MUEzz63bwXJsGhBdAHU9C0HSooKNXN5YLVq00C5MqVJhkyJ8ckQdps5r3J1q3mvYnd7oHNZt6bhISY7gpDhpjJ374W5Nts3ryZsWPHMn/+fB588EEeffRRgoODqz8QsUxyMsyfb+59t2wxSQl5eX/PR3C5zN9L/l3y3i86Gvr3r76EG3cKCgp4/fXXmTx5Mi1btmTy5MkMGTJESQwicsqUvCBlyAQSDv8cBAoxVdJ9MFnTrYEYoCWqMCwilSsHSMJcf/Zhrj9FmEERX8ygSAzQBvC2KEYRqZsKgG1cc00PwsPz8PExbRaLisxLtD17TCvz5ORAkpOzrA5WROqR4mLzAjQhwfyZlweZmfk8+eTjzJw5lVat/IiJMQOYDZRPLiIiIiIiIiKlKCoq4o033mDixIm0atWK6dOnM3DgQKvDAswkrb17zRhsYiLs2pXO+PGTASeTJo2jVatQYmIgJgYaNqzaKvUiJysry3xv583bwtixL2DeYXoChTz66AOcc040MTHQqpV572C1devW8eijj7J06VLGjBnDAw88gK8Vs3mlBigGdmHezW/HdFYowHx/vYFGQDTm/bwmWkvNkppq3pskJJi/Z2bm88QTY5k+fTLh4b5ERZn7hmbNak6i44oVKxg9ejRr1qxhzJgx3Hfffbr+1kMFBbB9u/nu7tplCigWFZl7BG9vaNrUfHejokzibk2Rk5PDSy+9xPTp0+nSpQtTpkyhf//+VoclIrWQkhdERERERI4TFBREVlbpyQkNGjQgMzOzGiMSETlRZmYmwcHBZGRkEBQUZHU4IiIiIiIiIlKDOZ1OPv30U8aOHYunpydTp05l+PDhNbpa6u7du2lxuLzsrl27aN68ucURiZRv6dKl9OvX75hlS5YsoW/fvhZFVLaFCxfyyCOPkJqaypQpU7jhhhuw15QZviIiJ6m2vDdxuVz88MMPjB49mpSUFJ566iluueUWPDw8rA5NpEIyMjKYNWsWs2bN4uyzz2by5Ml0797d6rBEpBbRE4eIiIiIiIiIiIiIiIiIiEgdtWDBAnr37s2oUaN4/PHHWbduHZdffnmNTlwQkeoxePBgVq1axeTJkxkzZgy9evXixx9/tDosEZE6zWazMXToUFatWsX06dOZPHkyXbp04csvv0R1qKU2CA4O5qmnniIpKYn27dvTv39/rr76ajZt2mR1aCJSSyh5QURERETkKIWF4HTGAOcCFwCXAhcC5wHtAR8LoxOR+qzYWUxSWhKLti3i2/hvmZc4D+Lgpx0/sWb/GrILs60OUURERERERERqkJUrVzJ48GCuvfZarr/+euLj47n11lvx9PS0OjQRqUHsdjs333wzW7Zs4brrruPKK6/kwgsvZP369VaHJiJSp9ntdq6//no2bdrEfffdx1133UX//v359ddfrQ5NpEIaNWrEzJkziY+Pp1GjRvTo0YMRI0awbds2q0MTkRrO5lK6noiIiIjUYw4HrFgBK1fCunWwZQssXrwGp9P7qK1sgAvIB7Lx9NzB9Ok30b079O8P3t7ujy0icjpcLhdrD6xlya4lbDi4gY0HN5JRkEFeUR5OlxOn00n81nhi42Lx8/YjwCuA1iGt6RLZha6RXRnYeiD+Xv5WfwwRERERERERqWZbt27liSee4Ntvv+XBBx/k0UcfJSQkxOqwTtru3btp0aIFALt27aJ58+YWRyRSvqVLl9KvX79jli1ZsoS+fftaFNHJO3ToEJMnT+a1117jxhtv5KmnnqJZs2ZWhyUiUq7MzEyCg4PJyMggKCjI6nBOWnZ2Ns899xwzZsxgwIABTJs2jS5dulgdlkiFJSUl8dRTT/HJJ58wcuRIxo4dS9OmTa0OS0RqIHVeEBEREZF6KTUVPvoIrr8e7roL/vtfWLoUCgoA0oFEIOHwT/zhf6cAnjgc3Zk9G+6/H66+Gt55Bw4csOqTiEhdk1OYw1dbvmLklyMZ8eUInl/+PIu2LyK7MJsG3g1oHdKa2PBYokOiIRWigqNo5N8IFy42HNzA/1b/j1E/jGL4R8N5ZeUrbE/fbvVHEhEREREREZFqsHfvXu666y66du1KeHg48fHxTJ06tVYmLoiIdRo2bMhzzz3H+vXrycrKIi4ujnHjxpGVlWV1aCIidVpgYCBjx44lKSmJtm3b0qdPH26++WZVsZdaIyoqinfeeYdVq1aRnJxMTEwMjzzyCIcOHbI6NBGpYZS8ICIiIiL1Sn4+zJ4Nw4fDhAmwcSM0bAixsdCmDUREgM2Wi+m0cDQXkAccwm7fSWwsNG4M27fDtGlw+eUwaxZo7F5ETpXD6eCTDZ8w/OPhjF44mhV7VhDkE0RMWAxRoVFEBkbSwKcBHnaPY/az2Wz4efnR0L8hLYNbEhseS4vgFhzMPchzy5/jmk+vYdxP4ziYc9CiTyYiIiIiIiIiVSk9PZ3HH3+c2NhY0tLSWLNmDa+++qqqnIrIaYmOjubjjz/mp59+4ueffyY6Opr//ve/FBUVWR2aiEid1rBhQ2bNmsXmzZvx8PCgQ4cOPPjggyQnJ1sdmkiFdOjQgf/7v//j999/Z8OGDURFRTF+/HgyMjKsDk1EagglL4iIiIhIvbF+PdxxBzz7LOTkmGSFNm0gIODUjufnBy1bQlQUFBebpIgRI2DFikoNW0TqgR3pO3jguwd46uenSM5JpkVwC6LDognyCcJms5308bw9vGke1JyYsBg87Z58suETbv78Zr5P+B6X6/jkLBERERERERGpjfLy8njmmWeIiopi5cqV/PLLL3zyySfExcVZHZqI1CF9+vTh119/5fXXX+eFF16gU6dOfPHFFxpnFBGpYq1ateJ///sfK1euZNu2bURHRzNhwgR1wpFao0ePHnz77bd8++23/Pzzz0RFRTFjxgxyc3OtDk1ELKbkBRERERGp84qL4fXX4bbbYOVKaN4cmjYFT8/KOb6HB0RGQqtWsGkT3Hsv/Oc/psuDiEhZXC4X/7fx/7jli1tYuG0hEYERtAxuibeHd6Uc326zE+4fTlRYFLszd/PowkcZ+9NY0vPTK+X4IiIiIiIiIlL9iouLefPNN4mNjeWjjz7i448/ZuHChfTq1cvq0ESkjrLZbFx22WWsW7eOhx56iLvuuouzzz6bZcuWWR2aiEid16lTJ7766iu+++47Fi5cSHR0NC+88AIFBQVWhyZSIWeddRY///wzH374IZ9++inR0dG8/PLLFBYWWh2aiFhEyQsiIiIiUqcVFsKkSabbgtMJMTHg61s15/L2Nl0YvLxMssTjj5sODyIi7rhcLv678r9M/GUiGfkZxITFEOgdWCXn8rR70ia0DUE+Qfzfpv/jwe8e5GDOwSo5l4iIiIiIiIhUDZfLxdy5c+ncuTNPP/00s2bNYuXKlQwZMsTq0ESknvDy8uKee+4hISGBc845h/POO49rrrmGxMREq0MTEanzzjrrLH777TfefPNNZs+eTbt27XjvvfdwOp1WhyZSLpvNxvnnn8+KFSv473//yyuvvEJcXBxvv/02xcXFVocnItXM5lIfNxERERGpowoLYfx4+OILaNQIgoNP3Gb16hHs3v1OKUd4HYg8YamHhycXXHBBmefOzoa9e+G88+CZZyAg4KTDF5E6qkePHvz1118nLI/sEUnvh3uXuW/8F/Fs+WSL23W9RvWicc/GZe7/44M/kncw74Tl9913Hy+99FKZ+4qIiIiIiIiIdX7++WdGjx7Njh07GD9+PLfddhteXl5Wh1Wldu/eTYsWLQDYtWsXzZs3tzgikfItXbqUfv36HbNsyZIl9O3b16KIqtbevXt58sknef/997nzzjsZN24cDRs2tDosEamnMjMzCQ4OJiMjg6CgIKvDqVIOh4P33nuPJ598kpCQEKZNm8YFF1yAzWazOjSRCnE4HHz00Uc8+eSTeHl5MXHiRK666irsdtVjF6kP9JsuIiIiInWS0wnTp5vEhYgI94kLVSkwEJo3h59+giefNIkUIiIAZ599ttvlqVtSKa++QOrm1FNaB5CXkuc2cQFgwIABZe4rIiIiIiIiItZYvXo1F1xwAZdddhmXXnopCQkJ3H333XU+cUFEaoemTZvyxhtvsHLlShISEoiJiWH69Onk5bkfhxQRkcrh4eHBP/7xD7Zu3crIkSO55ZZbGDRoEMuWLbM6NJEK8fDw4MYbb2Tz5s38+9//5t///jc9evRg3rx55b4vFZHaT8kLIiIiIlInffklfPIJNGwIVhXW8PeHJk3gu+/g3XetiUFEap6IDhFulxflFJG1K6vU/VxOF2nxaaWuT91SdvJCWckN/fv3L3NfEREREREREaleSUlJ3HjjjfTr14+OHTuSlJTEmDFjCFCLVxGpgTp16sS8efOYO3cun3zyCW3btuXdd9/F6XRaHZqISJ3m4+PDQw89RFJSEgMHDmTw4MFcfvnlbNq0yerQRCrEy8uLO++8k4SEBEaMGMHIkSPp378/ixYtsjo0EalCSl4QERERkTpn92544QWw2yEkxNpYAgNNEsObb8LGjdbGIiLWyyzI5Dd+K3V9WQkGmTsyKc4rLnV9xrYMHAWOUtenbE5xu9y7oTcJxQml7iciIiIiIiIi1efAgQPcf//9dOzYER8fHzZv3szMmTMJDw+3OjQRkXKde+65rFy5kmnTpjFu3Dh69uzJwoULrQ5LRKTOCwoKYuLEiSQmJtKsWTN69OjBrbfeyq5du6wOTaRCfH19jyTiXHTRRVx++eUMGTKEFStWWB2aiFQBJS+IiIiISJ3idMIzz8DevdC8udXRGI0bQ1oaTJ8OBQVWRyMiVnpl5SskFSYR0NR9lcTSEgzKWwfgcrhISyijM0MpiRF+0X7MXDKT1LyyOzeIiIiIiIiISNXJzMxk/PjxREdHs2vXLv744w/eeustWrZsaXVoIiInxW63c+ONN7JlyxZuuOEGrrrqKoYNG8batWutDk1EpM6LjIzkpZdeYv369eTn59O2bVsefvhhUlLKfsckUlMEBgbyxBNPsG3bNnr37s25557LZZddpvsIkTpGyQsiIiIiUqfMmwc//QRNmpjOCzWBzWYSKVauhI8+sjoaEbHKH3v/4JONnxDuF054O/fVEsvqvFDWuhIpm9wPPhdmFpK9N9vtuhadWxCfEs9rf7xW7vFFREREREREpHIVFBTw3HPPER0dzaJFi/jhhx/44osv6Nixo9WhiYicFl9fXx555BESExPp0KEDffr04dZbb2XPnj1WhyYiUudFR0fzwQcfsHjxYtavX090dDRTp04lJyfH6tBEKiQ0NJSpU6eSmJhI69at6dOnDzfccAPx8fFWhyYilaCGTOcSERERETl9LpdJDnA6oUEDq6M5lp8feHvDxx9Dfr7V0YiIFT7d8Ck5hTmE+YUR1i7M7TYF6QXk7Hc/cJy65djkBb9GfmAre5sSKVtSwOU+robtGxLiF8I3W78hOSe57A8hIiIiIiIiIpXC4XDw7rvv0rZtW9566y3eeecdfvnlF/r162d1aCIilSo8PJxZs2axYcMG8vLyiIuL44knniAzM9Pq0ERE6rzu3bszf/58Pv/8c7788ktiYmJ49dVXKSoqsjo0kQqJjIzk+eefZ8uWLQQEBNC5c2duv/12du7caXVoInIalLwgIiIiInXGn3/Chg0QEWF1JO5FRMCOHfDbb1ZHIiLVbVfGLn7e8TNhfmHYbDbC27vvvADuOyxk782mMLPwmGUNOzeEkGO3S09Ix1nsrNAxAbyDvAlsEki4XzgpeSl8n/B9+R9GRERERERERE6Zy+Xim2++oVu3bowfP57Jkyfz119/ceGFF2Kz2co/gIhILRUVFcWHH37IokWL+P3334mJieHll1/WBFoRkWpwzjnnsGzZMl5++WWee+45OnbsyCeffILTeeI7JZGaqGXLlrz++uusW7eO3Nxc2rZty4MPPsiBAwesDk1EToGSF0RERESkzvjqK9PVICDA6kjc8/Ex3SHmzjV/ikj98V3Cd2TkZxDqFwqAX7gffg393G6bsjnlhGXuOiqExoVCo2OXOQocZGzPOHH/UpIXSjpAeNg98Pbw5rNNn1Hk0MtCERERERERkaqwZMkSzj77bG699VbuuOMONm/ezE033YSHh4fVoYmIVJszzjiDn3/+mTfeeIOXXnqJjh07MnfuXFx6cSIiUqVsNhtXXHEF69ev59FHH+Xf//43Z5xxBgsXLrQ6NJEKi42N5YMPPmDFihXs2LGD6OhoxowZQ1pamtWhichJUPKCiIiIiNQJhw7BggUQEgI1uUBZeDisXAkJCVZHIiLVpchRxOebPsfPyw+77e/H8JLEgeO5SzRwtyysbRi46TRz/LbFecVk7nDfgj2s7d8xRAREkJiayPI9y91uKyIiIiIiIiKnZsOGDVx22WUMHTqU8847j8TERB544AF8fHysDk1ExBI2m41LL72UdevWMWrUKO69914GDBjA0qVLrQ5NRKTO8/T05Pbbbyc+Pp5rrrmGq6++miFDhvDHH39YHZpIhXXu3JkvvviCH3/8kZUrV9KmTRsmT55MVlaW1aGJSAUoeUFERERE6oRNmyAjwyQv1GTBwZCTAxs3Wh2JiFSX7enbOZBzgBDfkGOWl5a8kJucS35a/jHLju/G4N/IH98w3xM6L8CJyQupW1NxOd1XLQtvF37k735efhQ7i9mQvKG0jyIiIiIiIiIiJ2Hnzp2MHDmSXr160apVKxITE5kwYQINGjSwOjQRkRrB09OTu+66i/j4eAYPHsyQIUO46qqriI+Ptzo0EZE6z8/Pj0cffZSkpCR69uzJwIEDueaaa3QNllqlT58+LFiwgC+++ILvvvuO6Ohonn32WfLz88vfWUQso+QFEREREakTEhLA6QQvL6sjKZvNBi6XOi+I1CcJqQnkFeXh7+V/zPKjEweOl7Lp72SFvJQ88g7mHbP+SOKDLwQ0DThmXerW1GNarLvr2gDg6edJUKugY5bZbDY2HlJ2lYiIiIiIiMjpOHToEKNGjaJdu3YUFxezYcMGXnjhBSIi3LRQFBERGjRowIQJE4iPjycsLIwuXbrwwAMPcOjQIatDExGp80JDQ3n66afZunUrISEhdOnShbvvvpt9+/ZZHZpIhQ0aNIjff/+dt99+m3fffZeYmBhmz55NUVGR1aGJiBtKXhARERGROmHzZqsjqDhvb1i71uooRKS6JKQmgA3stmMfwQObBuIT7ON2n6MTDlK3nJh8cHTXhrC2x3ZwKMouImv33y1RS0teCI0NxWa3HbMswDuA9cnrcTgdpXwaERERERERESlNTk4OkydPJjo6mk2bNrF06VLmzJlDVFSU1aGJiNQKTZo0Yfbs2fzxxx9s27aN6Ohopk2bRl5eXvk7i4jIaWnWrBmzZ89m9erVpKSkEBMTw5gxY0hPT7c6NJEKsdlsXHTRRaxatYpnn32WWbNm0b59e9577z0cDr37FKlJlLwgIiIiIrWeywXr1oG/f/nb1gQBAZCYCBprF6kf1h9cj4fNw+260LahbpcfnbDgLvng6ISF0LgTj1Gyj6PIQXpSuttzHJ0AUcLfy5+M/Ax2Ze5yu4+IiIiIiIiInKioqIj//ve/REdHM2/ePL766iu+/fZbunbtanVoIiK1UseOHfn666/54osv+Oyzz4iLi+Odd97RxEMRkWrQtm1bPv30U37++WeWL19OVFQUM2fOJD8/3+rQRCrEbrdz9dVXs379esaOHcvYsWPp2rUrc+fOPaZ7vYhYR8kLIiIiIlLr5edDejr4uC9gXuN4e0NBAaS6L4YuInXMvqx9+Hi6v0CFtwt3uzxrdxaF2YXAiZ0XvIO8CWwaeOTf7pIQSvZJT0jHWeR0ew53+/l6+lLgKCAlN8XtPiIiIiIiIiLyN6fTyUcffUT79u156aWXePXVV1myZAkDBw60OjQRkTrhnHPOYcWKFcyYMYMJEybQs2dPfvjhB6vDEhGpF3r37s3ChQv56KOP+OCDD4iNjeWtt96iuLjY6tBEKsTT05MRI0awZcsW7rnnHu677z7OOOMMvv/+eyUxiFhMyQsiIiIiUusVFoLTCfZacndrt5t4CwutjkREqkN+cT52m/sLlLsEAgBcJgGhMLuQrN1Zx+7T9th9fMN88Wvkd8yyks4Lxyc+lLB72QmJCjlxuc2O0+Wk0KELlIiIiIiIiEhpXC4XP/zwA7169eKRRx5hzJgxrF27luHDh2Oz2awOT0SkTrHb7Vx//fVs3ryZm2++mWuvvZahQ4eyZs0aq0MTEanzbDYb559/Pn/88QfPPPMMU6dOpUuXLnz++eea/C21ho+PD/fddx+JiYlcc8013HDDDQwaNIjff//d6tBE6q1aMr1LRERERKR0zsNFxav6vWCLFi2Ii4sjNjbmtI5js4HLBepuLFI/OF3uOx8ABLUMwtPf0+261M2pJvnguLHfoxMewsLN34/v4JCfmk9uci6pm9wnL4REheDh7eE+KBc4XLpAiYiIiIiIiLizcuVKBg8ezHXXXccNN9zA1q1bufXWW/H0dP98LyIilcPHx4dRo0aRmJhI586dOfPMMxkxYgS7d++2OjQRkTrPbrdz3XXXsXHjRv75z39yzz330K9fP3755RerQxOpMH9/fx555BGSkpI455xzuPDCC7ngggtYtWqV1aGJ1DtKXhARERGRWs/b2yQEOEufH1wpWrZsSVxcW2JiYk/rOCVdInx8KikwEanRvD28S01gsNlthMW5776QsinlSAeFox3deSE1xax318Hh0MZDpCWkuT328d0bSjhdTuw2Oz4eukCJiIiIiIiIHG3Lli1cffXVDBo0iD59+pCUlMTDDz+Mn59f+TuLiEilCQsLY+bMmWzcuJHCwkLatm3LmDFjyMjIsDo0EZE6z9vbm3vvvZeEhAQuuugiLr30Ui688EJ1w5FaJTg4mAkTJpCUlETnzp0ZMGAAV111FRs3brQ6NJF6Q8kLIiIiIlLr+fiAlxcUF1sdScUUF4OHB+i9pkj90MCnAQ5n6Z0M3CUeAGTuyOTg2oPHLPP08yS4dXCFjrH9++0U57m/MJZ2TofTgYfdAz8vXaBEREREREREAPbs2cOdd95Jt27dCA8PJz4+nqlTpxISEmJ1aCIi9VqbNm344IMP+OWXX1iyZAkxMTG8+OKLFBYWWh2aiEidFxgYyNixY0lMTKRdu3aceeaZ3HTTTWzbts3q0EQqrGHDhsyYMYOEhAQiIyPp2bMnt9xyC0lJSVaHJlLnKXlBRERERGo9T0+IjYXcXKsjqZjcXGjUCMLDrY5ERKpDx0YdyS/OL3V9eDv3FwOXw0XWrqxjloXGhmKz207YNrBJID4hx3ZLyNyR6f6ENgiNC3W7KqcoB38vf9qEtCk1XhEREREREZH6IC0tjdGjRxMXF0dGRgZr1qzh1VdfpWnTplaHJiIiR+nVqxeLFi3i7bff5pVXXqFjx4589tlnuFwuq0MTEanzGjZsyKxZs9i8eTOenp506NCBBx54gOTkZKtDE6mwpk2b8vLLL7Nx40bsdjsdO3bknnvuYc+ePVaHJlJnKXlBREREROqEzp1rT+eFvDzo0gVsJ84/FpE6KC48DqDUl2XBUcF4+HhU6FildUwob93RgloG4eXv5XZdblEubULaEOAdUKFjiYiIiIiIiNQ1eXl5zJgxg+joaFatWsUvv/zCxx9/TFxcnNWhiYhIKWw2GxdffDFr167l0Ucf5Z///Cf9+/dnyZIlVocmIlIvtGrViv/973+sXLmSHTt2EB0dzYQJE8jKyip/Z5Eaok2bNvzvf//jzz//JCUlhdjYWB5++GEOHTpkdWgidY6SF0RERESkToiJMX86nZV51O+A/zvys3v3cyQkPF3qj8NRUKGjulzQtm1lxikiNVlMWAxeHl4UOty3K7d72gmJDqnQscLalp6gUFoHhxOOUUaSQ0FxAZ0jO1foOCIiIiIiIiJ1SXFxMW+88QaxsbF88sknfPLJJyxYsIBevXpZHZqIiFSQp6cnd9xxB/Hx8QwdOpTzzz+fK6+8kvj4eKtDExGpFzp16sSXX37J/Pnz+fHHH4mOjuaFF16goKBi79FFaoL27dvzySefsHjxYjZt2kSbNm148sknycjIsDo0kTpDyQsiIiIiUifExoKfH+TkVOZR5wLvHvnZsWMSmzc/XuqP05lX7hHz88HLy8QrIvVDTFgMgd6BZBWWXl2mIl0TyktyqGjnhdISIBxOBzZsRzpFiIiIiIiIiNQHLpeLuXPn0qlTJ6ZPn86sWbNYsWIFgwcPtjo0ERE5RYGBgYwfP56EhAQaNWpEly5duP/++zl48KDVoYmI1Av9+/fn119/5a233uL111+nXbt2zJkzB4fDYXVoIhXWvXt35s2bx/z58/n1119p06YN06dPJ6dyJ6WI1EtKXhARERGROiE2Ftq3h5o+7nzwILRoAT16WB2JiFSXEN8Qzm55Nml5aaVuU5GuCcFRwXh4e5S6vkGLBngFepV7nNKSHFLyUgj1C+WslmeVewwRERERERGRumDRokWceeaZ3HfffTz00ENs3LiRa665Brtdr9FFROqCxo0b8+qrr/Lnn3+yY8cOoqOjmTp1Krm5uVaHJiJS59lsNi6++GJWr17NU089xbhx4+jRowfz5s3D5XJZHZ5IhfXv359Fixbx8ccf89lnnxEdHc2LL76ojiIip0GjLiIiIiJSJ9jtcMUV4HRCcbHV0bjndJrOC5dfDr6+VkcjItXp0raX4u3hTW6R+5diobGh2DxsZR6jvM4KNput1K4KJfwj/fENOfEC5HK5yMjP4Pzo84kIiCjzGCIiIiIiIiK13V9//cWwYcMYPnw4l112GQkJCdx99914eZVfFEBERGqf9u3b89VXX/H111/zxRdfEBcXx9tvv60K4CIi1cDDw4NbbrmFLVu2MHLkSEaMGMHAgQNZunSp1aGJVJjNZmPIkCEsX76cV199ldmzZxMXF8dbb71FcU2doCJSgyl5QURERETqjCFDoHHjmtt9ITUVgoPhwgutjkREqluf5n2IC48jOSfZ7XoPHw+C2wSXeYzyEhOg/ASH0jo85BTl4OvpyyVxl5R7DhEREREREZHaKjExkRtuuIH+/fvTuXNnkpKSGDNmDAEBAVaHJiIi1WDgwIEsW7aMmTNnMmnSJLp37873339vdVgiIvWCj48PDz30EImJiQwaNIghQ4YwfPhwNm7caHVoIhVms9kYPnw4q1evZtq0aUybNo2OHTvy8ccf43Q6rQ5PpNawudSDR0RERETqkP/8B2bPhqgo8PQ89eOkpaWyePHiY5b179+f0NDyJw+743RCQoLpuvD006cel4jUXh+s+4BJv06iRVALfDx9Tvt4xUXFzJ8/n2HDhuHpdeoXPJfLRWJaIr2a9uLd4e9is5XdAUJERERERESktjlw4ACTJk3izTff5IYbbmDChAm0aNHC6rCkHLt37z7y32nXrl00b97c4ohEyrd06VL69et3zLIlS5bQt29fiyKS0hQUFPDf//6XSZMm0atXL2bMmEG3bt2sDktETkNmZibBwcFkZGQQFBRkdThSjgMHDjB58mTeeOMNrr/+ep566indo0utU1RUxP/+9z8mTpxIeHg4kydP5qKLLtL7VpFyqPOCiIiIiNQpN98MrVvD7t1WR3KsvXshIgLuvNPqSETEKsPbDadrZFd2ZeyiJtURSMlLIcArgPvPuF8DaSIiIiIiIlKnZGZm8uSTTxIdHc2ePXtYtWoVb775piZFiYgIPj4+/Otf/yIxMZGuXbvSr18//vGPf7Bz506rQxMRqRciIyN58cUX2bBhAwUFBbRt25aHH36YlJQUq0MTqTAvLy/uuOMO4uPjGTlyJLfddhv9+vXjp59+sjo0kRpNyQsiIiIiUqdERMC//gV2O2RkWB2NkZMDhYVwzz2mI4SI1E/+Xv6MPms0DXwacDD3oNXhAFDoKCQtL43rO13PGc3OsDocERERERERkUpRUFDAc889R3R0NL/88gsLFizg888/p0OHDlaHJiIiNUxoaCjPPPMMGzduxOFw0K5dO0aPHk1GTXnJJCJSx0VFRfH++++zZMkSNmzYQFRUFFOmTCEnJ8fq0EQqzNfXlwcffJDExEQuueQSrrzySs477zyWLVtmdWgiNZKSF0RERESkzhk6FC68EA4cgOJia2NxOmHPHjjrLLjmGmtjERHrdWvcjVu63kJGfgb5xfmWxuJyudiZsZOOER25s6fawoiIiIiIiEjt53A4ePfdd2nbti1vv/0277zzDj///DN9+/a1OjQREanhWrduzXvvvcdvv/3GihUriI6O5oUXXqCwsNDq0ERE6oVu3brx3Xff8eWXX/LVV18RExPDK6+8QlFRkdWhiVRYYGAgY8aMISkpiTPPPJPBgwdz6aWXsnbtWqtDE6lRlLwgIiIiInWOzWa6L7RtC9u2gcNhTRxOpzl/ixYwejR4eloTh4jULLd2v5Uzmp3BzoydFDqsefFVkrgQ6hvK42c9TgOfBpbEISIiIiIiIlIZXC4XX3/9Nd26dWP8+PFMnjyZv/76iwsvvBCbzWZ1eCIiUov07NmTH3/8kXfffZfZs2fToUMHPv30U1wul9WhiYjUC4MGDWLZsmW8/PLLPP/883To0IGPP/4Yp9NpdWgiFRYaGsqUKVNISkoiOjqaM888k+uvv56tW7daHZpIjaDkBRERERGpkyIi4D//gVatICmp+hMYShIXGjWCGTOgTZvqPb+I1Fz+Xv7MPH8mXSK6sD19e7UnMLhcLnZl7sLX05fxA8fTq2mvaj2/iIiIiIiISGVavHgxAwYM4LbbbuPOO+9k8+bN3HTTTdjtehUuIiKnxmazceGFF7J69WpGjx7Ngw8+SN++ffn999+tDk1EpF6w2WxcccUVrF+/nscee4xRo0bRu3dvFixYYHVoIiclIiKCZ599li1bttCgQQO6dOnCbbfdxo4dO6wOTcRSGrERERERkTorNhZmzYLWrSExEaqrs29xsUmYiIgwiQs9elTPeUWk9ogIiODZYc/SNbIr29O2k1eUVy3ndbqcbE/fjq+HL08OfJKhMUOr5bwiIiIiIiIilW39+vVceumlDBs2jCFDhpCYmMj999+Pj4+P1aGJiEgd4enpye233058fDwXXXQRF1xwAZdffjlbtmyxOjQRkXrh6Ovwddddx7XXXsvgwYP5448/rA5N5KS0aNGC2bNns379egoKCmjXrh0PPPAA+/fvtzo0EUsoeUFERERE6rSOHeHll82fO3ZASgpUZWff9HSTuNCmDTz/PPTpU3XnEpHarXlQc1668CX6tujLnqw97M/eX6Wtx7MLs0lITaChf0OmDZ7GxXEXV9m5RERERERERKrKjh07GDFiBL1796ZNmzYkJiYyfvx4GjRoYHVoIiJSRwUEBDBu3DgSEhJo3Lgx3bp147777iM5Odnq0ERE6gU/Pz8eeeQREhMT6d27NwMHDuSaa65h69atVocmclJiYmJ47733WLlyJbt27SI6OprRo0eTmppqdWgi1UrJCyIiIiJS50VHw//+ByNHQkGBSS6o7C4MxcWwbRtkZsLVV8O770LXrpV7DhGpeyICInjt4tf495n/xsPmQUJqAvnF+ZV6DqfLya6MXSTnJDMkagjvDH+Hc9ucW6nnEBEREREREalqhw4d4l//+hft2rXD4XCwceNGnn/+eSIiIqwOTURE6onIyEheeeUV/vrrL/bs2UNMTAyTJ08mNzfX6tBEROqF0NBQpk2bxtatWwkNDaVLly7cdddd7N271+rQRE5Kp06d+Pzzz1m0aBF//vknbdq0YdKkSWRlZVkdmki1UPKCiIiIiNQLQUHw2GPw4ovQti3s3Anbt0NOzukdNy/PHGvbNmjRAmbOhEmToGHDSglbROoBH08f7uh5B29e+iZnNDuDvVl7SUpLIqsg67Q6MRQ6CtmTuYeE1ARCfEMYP3A8zw97nlYhrSoxehEREREREZGqlZ2dzaRJk4iKimLr1q0sX76cOXPm0KZNG6tDExGReqpdu3Z88cUXfPPNN3z99dfExsby1ltv4XA4rA5NRKReaNasGa+99hpr1qwhNTWV2NhYHn/8cdLT060OTeSknHHGGfzwww989dVXfP/990RFRTFr1izy8vKsDk2kSil5QURERETqlX79TFeE8eOhXTs4dAji4yE52SQyOJ1l7+9ymYSFQ4cgIQH274fWreHxx+G992DIELDZquWjiEgd0zGiI69f8jpTz51K76a9ySjIID41nv3Z+8kuzMbpKvsC5XK5yC/OJyU3haS0JHZl7KKhf0Me6vMQ71/xPtd0vAYPu0c1fRoRERERERGR01NYWMjLL79MTEwM3377Ld988w3z5s2jS5cuVocmIiICwNlnn82yZct49tlnmTJlCt26deO77747raI0IiJScW3btuXTTz/l559/ZsWKFURFRfHMM89o4rfUOgMHDuS3337j3Xff5b333iMmJoZXX32VwsJCq0MTqRI2l+6YRURERKSecjhgxQr48ktYvBiysyE/3yQoFBbmsG1bAuDE5PzaadkyGj8/f3x9ISAAeveGyy6D/v3B29viDyMidYrL5WLtgbV8s/UbFiYtJLMgk9ziXFwuFx52DzxsHricLtavX0+Hjh1w2pzgAi8PLwK8A+jYqCNXtL+CQa0H4e/lb/XHEREREREREakwp9PJxx9/zNixY/Hx8WHq1Klcdtll2FQxpN7ZvXs3LVq0AGDXrl00b97c4ohEyrd06VL69et3zLIlS5bQt29fiyKS6lJQUMArr7zCpEmT6N69OzNmzKBHjx5WhyVSr2RmZhIcHExGRgZBQUFWhyPVzOVysWDBAkaPHs3BgweZMGEC//jHP/D09LQ6NJGT4nQ6mTt3LuPGjaOgoICnnnqKG264AQ8PFamTukPJCyIiIiIimKSFbdtMN4WEBPj99/3Mnfsd4A0UAfnce+8FDB7cipgYiIoyCQwiIlWtyFHE9vTtJKYlkpCawNaUrWQXZpOdl82nH3/KiJtGEBsRS0xYzJGfIB8NyouIiIiIiEjt4nK5+OGHH3j88cc5ePAgEydO5JZbbtEEjXpMyQtSGyl5QdLT05k2bRovvvgiV155JZMnT6ZVq1ZWhyVSLyh5QcBM/P7kk08YO3YsXl5eTJ06leHDhysZWmodh8PBe++9x4QJE/D392fixIlcccUV+i5LnaDkBRERERERN/SCQURqOg3Ci4iIiIiISF2xYsUKRo8ezZo1axgzZgz33nsvfn5+VoclFlPygtRGercgJXbs2MG4ceP4v//7P/75z38yZswYQkJCrA5LpE7TexM5WlFREW+88QZPPfUUbdq04emnn2bgwIFWhyVy0goLC3nzzTeZNGkSTZs2ZfLkyQwdOlRJDFKr2a0OQERERERERERERERERERE6p/Nmzdz1VVXcc4559C3b18SExMZNWqUEhdERKTWa9WqFe+++y6LFy/mzz//JDo6mueee47CwkKrQxMRqRe8vLy45557SEhI4OKLL+bSSy/lggsuYPXq1VaHJnJSvL29j3yXr7vuOm666SYGDhzIb7/9ZnVoIqdMyQsiIiIiIicowtv7ADEx0L49xMZCy5Zgs2lAWUSs5gD2APHY7VuIiwObbQeQY3FcIiIiIiIiYqViZzG7M3cTnxLPpoOb2Jqyle3p28kryrM6NLf27NnDHXfcQbdu3WjUqBEJCQlMmTJFFakFAJcLkpNh+3YPoB3Qlh07PEhLszoykfLl59uBVkAc5vsbw8GD3jgc1sYl1unevTsLFizgvffe480336R9+/Z8/PHHuFwuq0M7jhPYCyQAm4AtwDYg28qgRCooA0jCbo+nXTuw2ZKA/UBN+z0TKwQGBvLEE0+QmJhIhw4d6Nu3LzfeeCNJSUlWh+ZWQXEBO9J3sDVlKxsPbiQ+JZ5dGbsochRZHZpYzN/fn4cffpikpCQGDx7MRRddxLBhw/jjjz+sDq0MTsy77aPvL7ajd9tic9W8u2ERERERkWp2EFgFxAPrgS1kZ+9n69Yt2O3gdJqfuLguBAX1AjoD0UAPoJl1YYtIPZAB/IG5Pm0ENgCZgAOHo5i1a9fRpUtXPDwCgCjM9SkW6IK5TomIiIiIiEhdlJaXxh97/yAhNYENBzew8eBGsgqycLgcOF1ObNiw2+34efoRFRpF18iuxITF0LVxV1qHtLYu7rQ0nn76aV588UUuueQSJk+eTGxsrGXxSM1QWAh//AHx8bBlC6xZA4cOQW5uIevXbwCgU6eOBAR407QpdOkCcXGm8EzXrmBXyUaxUFKS+c4mJMC6dfDXX5msXx/P37VEXbRrF0fTpoF07Ph3waTevSE42MrIxQoOh4N33nmHcePG0bx5c2bOnMmAAQMsiiYbWIkZe96EGXtOwxTQcQI2zPfYB2iNGXOOwYxBxx1eL2IFF+Z97kbM93cNsBsoxOEoOuq9iTcQjvnOtsO8O+kN+FoTttQYO3fuZPz48Xz00UfcfvvtjB07lsjISMvi2ZO5hz/3/UliWiJrD6wlPiWevOI8HC4HLpcLu82O3WYn0DuQdg3b0SmiEzFhMfRs0pNGAY0si1usl5KSwowZM3jppZcYNmwYEydOpGPHjhZHlYW5vyhJVlgPpOP+/qINf7/bLvlT9xf1hZIXRERERKSecgF/At8APwCph5d7AP5kZBTzxx9/4XSCzQYeHtCrVwcaNLADxYe3DQIGAZcBZwCe1foJRKSucgGbgXmYa1Ty4WU2IADwBzwoKnLyyy+/MHDgWXh5FWMqVBQd3rYB0AcYDgzADACJiIiIiIhIbeZyudhwcAPfbP2Gb+O/5VDuITOZxW4nwCsAPy8/PGwe2G12XLhwupzkF+eTU5hzpEpnA58G9G/Rn0vbXkr/lv3x9vCulthzc3N58cUXefrpp+nduzfTpk2jZ8+e1XJuqbn27YPvvoPPP4ft26H48LCrnx8EBIDTmc+SJb8D0K/fWYAvubmQn2+28/GBdu3giivg/PNBjTukuhQWwm+/wRdfwLJlkJVl3iN4eYHTmcmGDX9gJme5ADvduvXC0zOY3FyOvHOIiICLLoKLL+ZwlXBrP5NUr5ycHJ577jmmT5/Oueeey9NPP027du2q6ezxmLHnr4F9/D327I8Zf/bATCp0Yb7H+UAuUHh4WQDQE7gC845ME8GlumQBPwJzgXVAHua764P5XnpTVOQ6/N7kbLy8XJj3JrmH9/cAWmDem1x4+O9Sn23YsIExY8bw008/8e9//5tRo0YRFBRULecudhazfPdyvtryFT/v+JnM/EwAPD08CfAKwNfTFw+7BzZsOF1OHC4HeUV55BTl4HQ6wQZhvmEMjRnKxXEX071xd2y6mai39u3bx5QpU3jzzTe56qqrmDBhAtHR1VnkzsWx9xclnW9O5v4iEOgFXA4MRPcXdZ+SF0RERESknnEBvwBvYLK884FgTOUNjyNbpaamsXjx4mP27N+/P2FhoYf/5cRUoEkDvIC2wD+Ai1A2uIicur+AVzHdFrIxSQgNMdeZYxUVFTN//nyGDRuGl9fRyVMuTMeGQ5jrWmvgBuBqt8cRERERERGRmm/57uXMXjWb1ftXk12UTbBPMOH+4XjaK15Mw+lykpGfQUpeCh42D6LDormp801c3v7ykzrOySguLubtt99mwoQJNGnShKeffprBgwdXybmk9tizB157DRYsgLQ0k4QQEQG+x81Pyc/PY+HChQAMHjwYX1+/I+tcLsjJgYMHweGAyEi49FK47TZVs5eqU1wM//d/8P77sG2b+e6Fh5vEmZL5gmlpqW7fLYSGhh1znEOHIDMTAgOhZ0+46y7zp9QvycnJPPXUU7z11luMGDGCCRMmVGH17w3AK8ByTHffQMzY88kkMroO73vo8L9bAtcD16ECOlJ1soF3MEkL+zDvYRthvsPHvpMt/b0JQAFwEDNZNgSTfHMXpqu11GeLFy9m9OjRbN68mbFjx3L33Xfj41M11zSXy8U3W7/hnTXvsDVlK4WOQsL8wgj1C8Vuq3g7MYfTQUpeCun56fh7+tM5sjO397idAS0HKImhHtu+fTsTJ07kgw8+YMSIEYwbN45mzZpV8VnXYd5tL8ckmQVirtEn80665N12Cua63hLzbvtaTu4+RWoTJS+IiIiISD2SCrwEfI4ZoIrEZHmf+ABffvLC0XKBA4ePMwz4N9CkMgMXkTovF5NU9R7m5U8EprtL6QOMZQ/Cl8jHdG4oAvoDj2Fae4uIiIiIiEhtkFWQxexVs/lw/YdkF2YTGRhJA+8Gpz0hJa8oj+ScZBwuBwNbDeTR/o/SOqR15QSNmZQzd+5cnnjiCZxOJ1OmTOHKK6/Ebq/4hBype5xO+PJLeOkl2L0bwsLMT2lfi7KSF4529ETwtm3hkUfgrLNUyV4qV2IiTJ8OixebTs2RkScm3EDFkhdKuFzme5ucDA0awE03we23m84jUr9s2bKFxx9/nAULFvDII48watQoAirti5APvAu8BaRjEhZCOP1CXCUTwQuA3sBooP1pHlPkeCuBGZjJsQGUNyG2Yu9NSgrUpWDeFd+DKf5UNcm8Uju4XC6+/fZbRo8eTXZ2NhMnTuSGG27Aw8Oj/J0raF/WPmYumckPiT/gwkVkYCT+Xv6ndUyXy0V2YTbJOcn4ePhwRfsr+OcZ/yTUz92cBqkvNm/ezPjx4/n666+55557GD16NI0aNarks+Rj7i3ewdxfRGCKhlbG/UUypiNDH8z9RdvTPKbURBodEhEREZF64mfgFuB9zOBWNO4qcpwaf6ANEAZ8dfg8X2MyxEVEyrMaGAn8F3NNiqFyBnfAtNRsiUmo+hUYAcwBiivh2CIiIiIiIlKVVuxZwYgvR/D6n6/j5eFFTFgMQT5BlVJJ08/Lj1YhrYgIiODHbT/yjy/+wcfrP8bhdJz2sX/66Sf69OnD/fffz7/+9S82bNjA1VdfrcSFem7vXhg1Cp580iQaREdDw4alJy6cDE9PaNwY2rQxE8wffBCmToWMjNM/tkhxsem0MGIE/PKLSVpo1cp94sLJstlMp5CYGJMQ8corMHIk/Pnn6R9bape2bdsyd+5cvvvuO7777jtiY2N54403cDhO9//LG4E7gWcBB2bsOZTKGXv2AZoDTTHVlm/FFOgprIRji+Rgvrf3AuuBVpjvWmV0l7YD4ZjfhyxgEvBPYHslHFtqK5vNxkUXXcTq1auZOHEi48aNo3v37nzzzTecbm1wl8vFV1u+4qbPb+Kbrd8Q5h9Gm9A2p524UBJ3A58GRIdF4+/tz3tr3+OWL27h1x2/nvaxpfZq164dH3/8MUuWLGHr1q1ERUUxbtw40tPTK+kM64HbgBcwc2JiqZzESDD3Fy0w1/wlmHfob6H7i7pHI0QiIiIiUse5MJXMRwHbMK0/q6rSQBAmKeIAMBbT5cFZRecSkbrhB8zg+1pMkkEElTOwczx/zEB8LvA08BSmcoWIiIiIiIjURF9t+Yr7v7ufTQc30SqkFQ39G1ZK0sLxAr0DiQmLIbMgk0m/TuLp35+myFF0Ssf666+/GDp0KFdccQWXX345CQkJ3HXXXXh5VcYkM6nNtmyBO++Eb7+F8HBo2dJM1K5sXl4QFQV+fvDuu3D//aaivcipKiyESZNg2jTIyTFJBlXRFcFmg0aNzO/G2rXwz3/C/PmVfx6p+c466yyWLFnCCy+8wNNPP03Xrl359ttvT3Hi7G/A3cAKTJJBY6pm7NkPM/ZcBMwCnsCMQ4ucqjTgX8BrgAfm3at3FZzHzt+/G4swiT5rquA8Upt4eHhw8803s2XLFm677TZGjhzJ2WefzZIlS07peE6XkxeWv8C4n8ZxMOcg0WHRBPkEVXLURpifSYpISkvi39//mw/WfXDaiRdSu3Xr1o2vv/6aH374gd9//52oqCiefvppcnJyTuOoizAda1Zhkgwiqbr7i1hM0sJMYByQVwXnEavYXLpCiYiIiEid5QLeBF7EVOJocsIWI0as5p13drvd+/XXTRWlEv379ycsrKKJD4eATEwXhkdQ3rCInOgbYCKmrWZLSgZ2yrouHc/bG4KCvIiM9KVt2wB69gzmsssa07FjgzL2ysC027wEmIypYCEiIiIiIiJW69GjB3/99dcJyyN7RNL74d5l7hv/RTxbPtnidl2vUb1o3LNxmfv/+OCP5B08cSLAfffdx0svvVTmvomJiYwdO5Yvv/yS++67j9GjRxMeHl7mPlJ/bNwIDz0EO3eazgienmb56tUj2L37nQoexRsvryB8fSMJCGhLcHBPGje+jAYNOpa6R0EB7NgBXbvC889DkxOHhkXKVFgI48bBV1+ZLiEhIfDjj63Jy9tRZefs2vVtmjcfwc6d4OMDY8fCZZdV2emkhissLOTVV19l4sSJdOnShWeeeYaePXtWcO8fMUW2srjxxjQ++GCv263WrDmbLl1ObhLt4sWpnHWW+0m8//xnU158sQEwGJiO6YQucjJSgYcw3TxaYCavGifz7sRmg8BAT4KDPYmK8qdXrxCGDWvE4MGlJQQ7MEXwIjGTZHud3seQOiMzM5P//Oc/zJo1i3PPPZepU6fSsWPp96AlauuzndRdLpeLH3/8kSeeeIIdO3YwZswY7rrrLnx8TuY98Q+YJIIcoDVVk7TgTiamgOhQTJE+v7I3l1pBM6hEREREpA77ENP9wAd3iQtVqyGmNd67wH8xiRQiIiV+xCQuFHB04sLJKiyEQ4eK2LAhi7lz9/PEE1vo1OkXzj9/GZs2ZZWyVzCmktDXmOSF4lM6t4iIiIiIiFSus88+2+3y1C2p5VbMTN2cekrrAPJS8txObgEYMGBAqfvt37+f++67j06dOuHv78+WLVt45plnlLggRyQmwqhRJnEhKurvxIWTV0hR0SGysjawf/9ctmx5gl9+6cSyZeeTlbXJ7R4+PtC6NaxZAw8/DCkpp3puqY+Ki2HKFPjyS4iIMIkL1cVmMx0YiopMDAsWVN+5pWbx9vbmgQceICEhgTPOOIMBAwZw0003sX379nL2XIKZWJgNtGbEiBalbjlnTsUmgh/tvff2lLpuxIgooCmwABiPuv/KyckCHsUkLrTkdCanulyQlVXM7t35/PprKrNmJXH++cuJjl7El1/ud7OHBxCFKfz0GLD+lM8tdUtQUBBPPfUUCQkJtGzZkl69ejFy5Eh27txZ5n6lPUfV1Gc7qftsNhuDBw9m2bJlzJ49mzfeeIO4uDjefPNNiosr8q74N8z/2/M4OnFhxIjV2GzfuP3Zvr38TkyDBi0pdf+/BWHm+3wPTMB0Y5DaTskLIiIiIlJHLQWexQw2RZazbVUJwzxIvQF8Z1EMIlLzJGAGVvIwlYMqvyrFggWH6NdvCStWpJWyRQMgAvgMqGiVQxEREREREalKrbu2dru8KKeIrF2lJaiDy+kiLb605z8zQaYsZU2AOeuss05YlpmZybhx44iJiWHv3r38+eefvPnmm7RoUfrkSKl/cnJgzBjYts0kLnh4VP45Dh1awJIl/UhLW+F2vbc3tGoFf/wBEyeC01n5MUjd9N578H//ZxIXgk6uKH2lsNmgeXPIz4ennoKtW6s/Bqk5QkJCePrpp9m8eTMeHh506NCBRx55hLQ0d//v34VJXMgAWgE2zjuvIS1a+Lo99gcf7MHprHjxrcJCJ5984r6LQ6dODejZMwTTbaEpMA94rcLHlvrOBcwAfse8N3H/nT1d27blMnz4Hzz4oLvkBDvQBtgDPA6Ufn8t9U9kZCQvvvgiGzZsoLCwkLZt2zJq1ChSSsmQ9Y/xd7u8pj3bSf1js9m49NJLWb16NU8//TTTp0+nQ4cOfPTRRzhLfWDaATyJSTI79aJ8pyeQv4vzvWHB+aWyKXlBREREROqgTMwAVw7V33HheA0BJ/AcppWdiNRvRZjrUzJVPbiTnl7EzTevpqDAUcoWwYA/ZoDHfZVCERERERERqR75xfkssS0pdX1Zk1Ayd2RSnFd6pcSMbRk4Sn02hJTN7ifc+DbypcD/74rJ+fn5PPvss0RFRfHbb7+xcOFCPv/8c9q3b1/qsaX+euMN0/WgZcuqSVwoUVSUzurVN+NwuK/u7eMDTZrATz+ZKvoi5dmyBWbPBl9fCA62Lo6SDgyHDsH06aYDq9RvLVu25J133mHJkiWsWbOG6Ohonn32WQoKSq5/TmAmsJujKyLb7TZuvrm522Pu3VvAjz8eqnAM8+YdIDW1yO26ESOOPkcgprjXHODPCh9f6rMfMRNSG3E6HRcq6oUXtjN5crybNXbM789W4EVMUoXI36Kionj//fdZunQpGzduJCoqiilTppCTk3Nkm31Z+1hsW1zqMax4tguIDCCooQUZmVJj2e12rr/+ejZu3Mhjjz3Go48+Svfu3fnqq6+O6w5SjHm3vZeSxEjrNMDcY7wDrLUwDqkMp9yYUURERESk5noF2MjRg7PWag4kAv8BplMzYhIRa3yEqRzUnFOpJ/Doo9GEhnoBkJdXzJIlCaxb582BA+7fXm7dmsO33yZz+eWlJXI1xnSCeBp4HfA+6ZhERERERETk9M1ZM4d1Oevwb+JP7r7cE9anbE6h9fmt3e5b2gSVEi6Hi7SENBp2bOh2fWmTZ7zbeDNj8QyeH/o8H77/IePHjyc0NJQ5c+YwbNgwbDaNcYl7q1aZyvUhISZ54GRFRz+Kl1coxcVFJCRsBpLx9l5LYWGy2+1zcraSnPwtTZpc7nZ9gwaQlgYvvgi9e5uK9iLuFBbC009DairExJy4Pjr6MYqLM0rdv6gojcTEGW7XBQf3oEmTq8s8f3Bwz2P+bbNBs2awdCl8+CH84x/lfwap+7p168YPP/zA999/zyOPPMKLL77I1KlTueYaT+z2HzFFvY4dex4xojlTpya4Pd6cObsZMqRRhc49Z84et8s9PW3cdNPxF9dGmLHn6cBbmI4MIu4cAmZhJsiGnNIRSt6dOBxOtm7dStOmUaxencUPPxwstfPS5Mnx3HhjM9q0Ob5CvhemON3nwMDDPyLH6tatG9999x0///wzo0eP5sUXX+TJJ5/k9ttv5z9L/8MB1wECmgaQszfnhH2teLbzaOXB7FWzGdVvVNkfTOodT09PbrvtNm666SZee+017rjjDtq0acOUKVM477zzgLnAz5iuSjWhVn4Ef7/bfpPqSHiTqqHkBRERERGpY5YDnwBh1JxJuB6YCcLzgUHAhZZGIyJWScK0yfbFdDw4effc04rWrc2+RUXFzJ+fwIABZ3PllX/x00/uBzQXLDhURvKCDZNIsQJ4D7j1lOISERERERGRU7fp4Cbe/OtNAr0Dadi+ITv37Txhm7Kqc5a1rkTKphS3E1wKMwvJ3pvtdp8mHZvw7YZvmffyPLy2eDF16lSuv/567PaaMGFBaqq8PFMlPjsboqNP7RitWt2Dv39r8vPzSEhYCED//n1Zu/ZaUlJ+crvPoUMLSk1eADMBPDERZs6EZ581k8JFjvfRR7B8uUlwcfcdad36njL3z83dXmryQoMGnYmJGX3SMfn7g58fvPYa9OsHsbEnfQipo4YOHcrgwYN59913mTnzX8TE5NCuXQSBgQ1O2DY2NpB+/UJZsiTthHVz5+7nlVeKCQgoewpZamoh8+a5TyIbNqwRkZHHZ6vZMN2H12CSF+6v0OeS+uhFTBG4qFM+Qsm7E/PeZCvDhsXh5eXJ8uVpDB26nIyMEyvZFxQ4efvtXUyc2NbNEcOAdOAZoBumk7XIiQYNGsTSpUv54osvGDNmDJM+noTXMC9aN25Nfrt8t8kLVjzbNWzfkA/Xf8jZrc6md7Pe5Z5D6h8fHx8eeOABbrvtNl544QWuuuoqzj+/PbNn5xEc7IXpeFAT2IAWwF/A28C91oYjp0wjSyIiIiJSx7wH5ADhVgdynCDAAbyLad0rIvXP/wEHMZWvKk9AgCdTp7Yrdf2ePfnlHMHv8M/7mOuniIiIiIiIVKeP1n9EWn4akQGRhLULc7tNQXoBOfvdP7Olbjl2got/I/8TGn8ev02JlC0p4HIf177CfezdtZegAUGsXr+aG2+8UYkLUq6ffoL166FFi8pNEPDwCKBdu6mlrs/Pd18N/O/9ITISfv0V1q2rvLik7sjJMR1DfHxMskBN0qQJpKTAp59aHYnUNB4eHowcOZIlSx6lTRtvfv99GytWrCQr68TJqyNGuG87k5PjYO7c/eWe65NP9lFY6P791ogRLUrZywdoAHwKlD8hV+qjJOA7zHvdyq/B3KdPKNOmlf7+ZP589wk5RgtMde/vKjssqWNsNhuXX345q9espvlFzdm3fx9rVqzBq5mX2+2teLZr0bkF2YXZvL/u/bI/jNR7AQEBPP7442zbto1bbgnkwIE1rFy5l4yMTKtDO0pJocBPgNK7oknNptElEREREalD4oFlmFaeNbF0VgSwCfjD6kBEpNqlAfMwiUyV/yjetWtQqevy8yuSMNUI2Af8WFkhiYiIiIiISAUcyD7AgqQFhPqGYrPZCG9fekEOd1U4s/dmU5hZeMyyRl0bEdTq2OfE9IR0nMUnPh+WWtnTFxrHNubsnmdDCCzft7z8DyP1nssFn31m/u5zfAHuShAU1LXUdU5necUbICjITFD/5pvKjErqikWLYPduiIiwOpIT2WwQHAzffQepmv8tJ8jB2/s7wsNbcs45g/Hz8+PXX39l7dq15OcXHNnq2mub4ufnfmx6zpzd5Z6ltG3Cwry45JLIMvZsCCQDP5R7DqmPvsNMPHWfwFsZLruscanrduzIK2NPr8M/n2EK1ImU7a/kvyhsUEi/bv1o1rQZO3J3lLptdT7beQd506BpAxr6N2TxrsUkpSVV5ONIPRcS4sFFFzlo06Yz/v6BLF78O6tWrSI72313j+oXARxA9xe1l5IXRERERKQO+RbIBEIsjqM0gUAB8LXVgYhItVuAeUFzYhvXyuAqpZIKQOPGFZkt4I1J+ppLqWVZREREREREpNLNT5hPal4qYX5mwpZfuB9+Dd2X/E7ZnHLCMndVN8PahRHe7tgkCEeBg4ztJ1YkLG2CS0SnCOLi4gjwDcDlcjF301xcZT18igAbNsDq1dCoUVWdofTvoI9P6RMTS2gCuJTG5YK5c83fvb2tjaU04eFw6BB8/73VkUjNswjYDTTCx8ebzp07MXDgQAoLC1m06Ce2bNlKcXExQUFeXH65+2vljz8eYu/e0pPAEhNzWLIkze26G25ohrd3WdPPPPl7AnhxBT+T1A85wBdAAFVZlK5Ro9Iv7GlpReXtDWwBVlRmSFJHfbn5SwocBQT5BhEdE815l56HZ7D7jiLV+WxX0t0vxDeEzIJM5m2dV+5nETEF7/bh5dWEjh07cs455+Ll5cUvv/zK6tVryM0tK/mrOngCHpj7i4oU8pOaRskLIiIiIlJHZANfYRIEamLXhRKhwEJMhXMRqR+cmIETT6qi7THAX3+V3hKzX7/QCh6lEbAGWFsZIYmIiIiIiEg5ihxFzN08Fx9PHzzsHkeWl0wuOZ67ySjuloW1DXN7jOO3Lc4rJnNHpttzNWz/d/J9uH84K/euZGvKVvcfROSwb74xnQ0aNKia42dk/FXqutDQfhU6RsOGmgAuJ6r6xJvT5+lpfj77DBwqAC5HuDAFaWyYAjVGYGAAvXr1ok+fPhw8eJCfflrEjh07ueWW5m6P4nTCBx/sKfUs771X+roRI9wf81gRaAK4nGgRsAfzbqLqJCcXlLouNNSrnL0DgCLMO2iR0u3N2sui7YsI9fv7nZyXlxeRnd13pqnOZ7uwtmZ/m81GoHcgX275kpzCnNI/jMgx9xfmOunn50uXLl0YNGggLpeTRYsWsW7dehyW3phGAJuAPyyMQU6VkhdEREREpI7YhKlqHl7ehhYLBdIxE4RFpH7YAyRRVW2Ps7OLGTNms9t1ISFeXHtt0woeKRDIB1ZXUmQiIiIiIiJSlqS0JHZl7DrSdaFEackLucm55KcdWxX5+Iqd/o388Qv3K3eCS3FxMesWrcPldF/J/ujqnsE+wWQXZrN6/+oyP4/Uby4X/PYbBASYDgeVrbg4m82bx7hd5+UVQtOm11boOB6H84RWaP6sHOWvvyA3t+oSbypLWBhs3w67dlkdidQcqcBGSutIHhYWRv/+/encuROJiQl4eW2gWTP3VejnzNld6lnee8/9uk6dGtCzp/tzH8sfKERjz3KsVZjiT1Xb8ubLLw+Uuq51a/8KHKEBsBSTxCDi3ur9q8nIzzipZ7usA1nHLDvVZzuA1K2pFXq2C/MLIzknmc2H3L9XFDEOYpIOTyyQFxAQQPfu3RkwYAD5+fns22dl0c4AdH9Re1VNyUcRERERkWqXgBk0qqE9nY8oqaKXYGkUIlKdEjDtjxuWt2G5Xnllx5FKQHl5xSxZAnfc8SsHDhSesK3NBq+80omQkPIqBx3Z4/Cfm047ThERERERESlfYloieUV5NGnQ5JjlR08uOV7KphSa9WsGQF5KHnkH845ZXzKxxSfIh8BmgWTvyT6yLnVrKg6Hg107d7E1fiv2je7r3Hn6eRLUKujIv22HZ6Kr84KUJTkZDh40yQuna8eOV/DyCqW4uAjYDCSzZMntFBYmu9naRqdOr+DlFVLh4wcEwPr1ptK4XeUeBdiyxYylVUXiTWUKCDC/Z/Hx0Lq11dFIzVAy9lx6ARubDZo0aUJkZCQ7duxkwICNfPTRidutXZvFmjWZdO0adMzypUvTSEjIdXvsinVdKOEBbDiJ7aXuWwP4VekZli9PK7X4E8DQoRXp+hAAZALbgdjKCUzqnIRU8+7fbjv25rKsZ7vf5/5O3JA4WrdpTWF64Uk/27lcriPPau66NsCJz3Y+Hj4UOgqJT42nZ9OeJ/EJpX4pub9oVuoWQUEN6N27FxERKzBFRq1iQ/cXtZOSF0RERESkjtiCeTCp4W8XANMATQ9QIvVHPKa95uk/gs+Ykehm6YmJC6GhXrz2WmeuvrqiXRdK+APrMPHWhuupiIiIiIhI7RWfEg+cOMElsGkgPsE+FGQUnLBP6ubUI8kLqVtOnKBydFXOsHZhx0xwKcouYtHcRXg28qRLly4kLU0in/wTjhEaG4rNfuwzoa+nL2sPrD2JTyf1TXw85OSYyvCnKzFxxgnLCk8c/sDLK5TOnV+jadOrT+r4AQGQlga7d0PLlqcapdQVLhesWQN+VTt/tlJ4eJh4ExJgyBCro5GaIQEoBsovYGO322nTpjXjxoXw0Ue/u91mzpzddO3a4YRl7nh62rjpppNJXgjAdIkoRtPVxHQN2Yt5J3H6Sgo/ORxOtm6FpUvjWb06k++/P4jT6X4fHx87I0e2qMDR/YH9QCJKXpDSrE9ej4fd44TlZT3bhTvD2bN3D9u2baNR3omJNOU922XtziKohUlMKC154fhnu5Jkh5JnURH34gEHFSkc6u1tdXFRf8zcGwd/FxKV2kB3gyIiIiJSR6wFfK0OooICMBXDiqjIgLKI1HabqK5EAB8fO2PGxHD//a0JDT2VwSJ/TCvQA0Djyg1OREREREREjrH+oPsJLgChbUPZv2L/CcuPTlhwN0ElrG3YMX/f+ePOY9aHu8LpNrAbzmInfyb96fbcR0+SKRHgHcDuzN1k5GcQ7Bvs/gNJvZaYaDoZeFXDcKfd7kNMzBhat74fb+/Qk97f399Ur09IUPKCmO9CcrL5XtQGNhtsUuNUOWIrJ1uIpkOHEPr1C2XJkrQT1n3wwR6mT2+Ph4c5XmGhk48/3uv2OMOGNSIy0uckYvXHVK/fCUSdxH5SNyViqno3KW/DCjmx8JO7QlDHeuKJGNq0qcjFvyTROOFkw5J6otBRyJaULQR4u29BVtqzXd7OPM6+92z27d3H2rdOTBQv79kudXMqQS2CcBQ5SE9Kd3tud892SkyX8m2plKPs3r2bli1jsFdpu7sAIB3YDbSqwvNIZVMTRBERERGpAwowE21rS/KCH5ANHLI6EBGpFtuorutTQYGT//53B999d/AUj+AH5GMqHomIiIiIiEhV2pG+Az8v96W+w9uFu12etTuLwmxTgv74zgveQd4ENg088m/PpifWsXMlu7DZbKQnpOMscl+GtrQJLvnF+ezN0vOiuLdvH6VWNq5sTmcBO3b8l4MHvzul/T0P/2rs1ddZgP37IT+/dnReABNnUpLVUUjNkQScTAKBMWKE+44J+/YV8MMPyUf+/e23yaSmFpVyjIpUrD+axp7laPswRd6sqdh9772tGDcu7iT32ln+JlIvHcw5SG5hLr6e7t8FlvVsV5RTRNNmTfHNOnZfz0DPY57twtufeIyS58GTfbbz8/Jjb/Zeihzur+8ip3p/cbxdu3azaNEidu3ajcvlOv2w3Cq5v9hXRceXqqLkBRERERGpAwoBJ7WnDZwdE++J7SFFpC7Kpzofvw8cKODGG/9i5szyKwudqOT6pAFLERERERGRquRyucgvzsduc/+86G6SidnRTFIpzC4ka3fWsfscrsyZk53DH3/8wV+b/8Iz5NgEhpJuDccnPpSwe9kJiQo5cbnNjtPlpNBRWNbHknosP99UhK8uBQUH+OuvG0lMnHnKxyjU11mAggKTeFOlBWErkd1uYhYx8jiVsedrr22Kn5/7/WbN+pM9e/bgcsGcObvdbhMW5sUll0Se5FlLxp518RUw70htVFfX6hKtW/vx2Wc9efnlzie5pwfm903kRAWOApwu52k922XvyT5mlSPMwfLly8nMyATAL9wPv0bHZlrq2U6qTuW82z7zzD7ExbVl69Yt/PzzL+zbVxUJBrq/qK1qyeOXiIiIiEhZijAPJNU7wHXqbJg2vsVWByIi1aKIynr83rbtXFyui8nIGMrixf248MIGpW772GOb+PXXlJM8Q8n1SckLIiIiIiIiVcmFC6fLia2U8ayglkF4+p/YOQHMJJXULanm8e0oJZNisrKz8PHx4Zxzz6Fx58bHbJOfmk9uci6pm9xPcAmJCsHD+8QCIXabHSdOip0azxL3CgsrL3lh4MAEevTYQ3j4j8B/iIi4qtRtN216jJSUX0/pPMX6Ogvme+ByVUfyQuVUm7XZoEhDd3LEqY09BwV5cfnljd2uW7LExa5dB0lPL2TevGS329xwQzO8vU/2vHo3JkdzUNXvdQMDPWjWzJcBA8J46KE2fP99HxITz+WKK5qcwtFs6L2JlKbYWYyT0pMXTuXZLq5/HA0aNCAjM+PIsuM7OJzqs50NGy6XS892UoZiKnqNtttL385ms9GiRXPOOecc2rRpw8GDB9myZSsOR/kt+yr+bKl327WV+6uiiIiIiEit4sXfGdW1gQvzEGVNK1QRqW7eVPb1KSjIi379wpg3byCPPrqRZ545sVe80wl3372OdesG4uFR0RGekuuTV2WGKyIiIiIiIsex2+x42D0ocrp/wW6z2wiLCyN59YmTBlM2uU9UL+m80LhxYxo3NhMSw9qFsfu3Y6smH9p4iLSEtDKPcbySSqJeHnpeFPd8fMwE8Mrw22+/4ePTguLiIho3PpuePf/Fpk2PkZT0jJutnaxbdzcDB67DZju5zrxe+joL5ntgs5mxtKqUmpqGy+XCdppZPi4XeOvVghzhw6mOPY8Y0YIPPth7wvLcXCdbtzZkzZp9FBS4P/aIEc1P4Ywae5ajVe6UxW3bzqV1a/9KPeaxnOi9rpTGy+6FHdPNwJ1TebZr1LHRCV0TKuvZzoW5H9GznZTOi4om3jZoUPozWFGR+Z2w2+20bt2KoqJi5s+fT1RUFB4eJtmntHuN4OCKfj8196a2UucFEREREakDfDDtOmtL8oIDE6+P1YGISLXww/zeV42pU9vRsaP7DgybNmXz9tu7TuJouj6JiIiIiIhUFz9PPxzO0p8XSzopHC9zRyYH1x48ZpmnnyfBrYMrdIzt32+nOM99lc3SzulwOrDb7Ph46HlR3PP1rbxjderUEZsNmjRpSs+ePbHZbLRrN5UGDTq63T47exO7dr19UudwuUzChYiPD3h4gKPqhu8AyM3NYePGDZxuBwaHA/z8KicmqQv8OdWx5/POa0iLFu4v3nPm7GbOnN1u13Xq1ICePUNO4Ywae5ajlSTeVFLmY5VzAgFWByE1lK+nL3Z76ckLUPOe7TxsHnh7aLK3lKbi9xehoaUnGaSnl9/dIyPD/TZlHfdYur+orZS8ICIiIiJ1gDfQAsi1OpAKygWCgUZWByIi1SIOyK+yo3t62pk2rV2p66dMiT9S2aJ8eZhki5aVEZqIiIiIiIiUITYslrzivFLXh7cLd7vc5XCRtSvrmGWhsaHY7CdW8w5sEohPyLEv8TN3ZLo/oQ1C40LdrsorzsPf05/mQadSaVnqgxYtzJ/ld19wkZ6eTnp6eqlbbNmylUaNGtG5c+cjVertdk/atZtW6j7x8VNwltLJ5HhFRWC3Q0sNfwjmu+vnB3mlX44rLDn5QJnrt23bxtatW0/rHHl5EBd3WoeQOiUOqNi173h2u42bb3b///WffjrE4sXuK3mfWtcF+HvsucUp7i91SwvMRNOqe3dS+dpYHYDUUBEBEQT5BJFbVPpchZr0bJdblEvrkNZ42iu3A4rUJXFAYYW2LKvrzdat2WXuW1zsJCnJ/e9N69YVzdbNRfcXtZOSF0RERESkjuhKRR+grJcLdEK34yL1RfsqP8Mll0TSvXuQ23Xbt+fx7rvuq2SdKAdoCrivxiIiIiIiIiKVp0OjDjidpSebB0cF4+HjUaFjlVZVs7x1RwtqGYSXv/vqhjmFOUSFRhHgrYqz4l5sLHh5QWEpQ7RZWVls3ryZn35axLJlS8v87jdsGH5M4kKJyMhLCArq7nafvLzt7N79boVizcmBgACIianQ5lLHhYRA8+bme3E6Dh5MZsOGjeVut3XrVrZtSzqtc7Wv+uFGqTViMZXrT616fWmJCKVdoj09bdx006kmL+QAoUCzU9xf6pYYTCeD07z4VouSquC6cRD3POwedGzUkZzC0r/PNenZrshRRJfILhU6jtRXbano/cVZZ5X+nfzhh0Nl7vvzzykUFLi/6ejfv6LvqnOBhkDjCm4vNYXSp0RERESkjigZMHIBJ1YiOBXffQeBgX//e+XK3fj7p5S6/b/+1QafCg06uKiOycwiUlNEY5KVioCKtrg8eePGxXLFFavcrps6NYF//KM5np7lJU3lAxqwFBERERERqQ4xYTHYbDYcTgce9hPHlOyedkKiQ0jZWPp4VImwtqW/2A9vF86+ZfvKP0YZE2E0wUXKEx1tEgJycsDncEHY3Nxc9u7dw549e8nJySYyMpL27dsTGRnB2rUfk1tKcdq2bdtS2hhvbOw4Vq26wu26hISpNG/+D+zlVJHNzYXGjSEysqKfTuq6Ll1g/fpT3z8tLZU//lhF+/bt2Vhq/sLf3+kNGzbg6elFixYnVyG2uNh0DVHijfwtBlO9vgDwPem9Y2MD6dcvlCVL3HdZON6wYY2IjPQpf0O3coGBqLCXGIFAa2CDxXFURC4m0UIXXyldh0YdWJC0oNT1NeXZzuVy4cJFbFhsuceQ+iwa8067EHOfUbqYmACio/1JTDzx4e6jj/by6KPRdOzY4IR1RUVOJkwovSPZ0KGNKhhrLtCZypojJNVHyQsiIiIiUkfEAN6YibcVbSFXtrlzj1+yo8zt7767VQWSF4oxD04a4BKpP0oqCGVjKktVjeHDG9O5cwPWrcs6YV1SUi7vvbeHESPKeiFaUkFDfedFRERERESqQ0xYDP5e/uQU5RDk476bXli7sHInuJRMhClNRatzljZJxulyHolXpDRhYdCsGWzeXERGxm727NlDRkYGjRo1JDo6msaNG+PpWdHpCaVPPGnceDgNGnQmK2vdCetyc5PYs+c9WrQYUebRc3Ohc2ewaX6LHBZ7eP6e02mSA05GZmYGy5evoEOH9jRqRKnJCw0bhpOSYsPlMhVs16xZg5eXJ40bN6nwubKzTZJQdPTJxSh1WQzgjxl7PvnkBTDdFyqavFD2+HJZXIATFfaSY3UB/rQ6iArIxnSrPtWuI1IfxITFYMNGsbMYz1ISaWvCs11ecR6+nr5Eh+lmQsoSg0kyy6a85AWAf/0rin/+88RM4MJCJ+eeu5TJk9ty2WWNCQ31oKAAfvklhcmTE1m82P39R+/ewWV2dPhbSWeIdhXYVmoapbOKiIiISB3RAWgBlN16znqHgEZAD6sDEZFqE4mp+FCxF0CnymazMXZs6ZVSpkyJx+Eoq71nOtAAOKOSIxMRERERERF3WoW0Ii48jpTc0iewhLcLL/c4wVHBeHiXXlCjQYsGeAWW3wmwtIkwqXmpBPsGc0YzPS+Ke2lpabzxxhusX/8CGzcmsXfvPlq0aMGQIUM444w+NG/e/CQSF8pms9mIjR1b6vr4+Cm4XI5S1xcWmqSFfv0qJRypI/r0gQYNID395PbLyclm2bLlxMRE06pV6zK39fX1o3v37kctcbFq1Z8cOnSwwudLTYUOHaBp05OLU+qyBsCZmLHdU3PttU3x8yt/+lhYmBeXXHKqLWuyMEkWupeQo/XB1F3OszqQMrgwk3fPQdMspSw9mvSgoX/DGv9sdyj3EC2DW9KuoSZ7S1lCgZ5U9N32rbe2oEOHQLfrkpMLufPOdURGLsDP73uuvhqGDFnJL7+kut3ew8PGjBkVTXbMRPcXtZf+ryoiIiIidYQ3cAWm84LT4lhK48I8QF0EhFgbiohUIxtwJeYaUFilZ7rqqia0b+9+cCghIZcPPthTxt4pQF/UGUZERERERKR62G12rmh/BQ6Xg2JnsdttQmNDsXmUXR6+vOqbNput1MqbJfwj/fENcV+xOT0vnUGtB9EsqFmZx5D6JScnhw8//JBLL72Uxo0bM3v2bK66yp+uXdvQvn0/WrVqhbe3d5Wcu0mTqwgMdD+hJTc3gT17Pih130OHzMTv886rktCklmrTBvr3N8kBFZWXl8fSpcto0aI5MTEVG09r2rQZnTt3OfJvl8vJypUrSUsrf2JYYSG4XHDlleoaIse7DPDgVCeABwV5cfnljcvd7oYbmuHtfarTzA4BXTFFfkRK9AeigYoncVW/LExn7UusDkRquDC/MC6IuYCMgowjXZaOZ/WzncPpoKC4gCvaX4G3R9Xcp0tdMhxzf5Ff7pZ+fh588UUvwsLKTqwpu8ie8cwz7Rk0qGHFQuQQJsmiQwW3l5pEyQsiIiIiUodcgEkKOIk3DNUqE9Ne72KrAxGRajcI01I4uUrPYrfbeOKJ0l+WTp5cWveFfMwA1PAqikxERERERETcGRI1hMiASA7muJ+05eHjQXCb4DKPUd7kFSh/EkxpVUBzCnPw8vDi0raXlnsOqfsKCgr46quvuP7664mIiGDy5MmcccYZbNiwgRUrVjBlyu2cc453hSaAFxUVnXIcNpudmJgnSl0fHz/ZbfcFlwuys+GyyyAg4JRPL3XUZZeBpyfkVWD+d2FhAcuWLSMiIoL27dtjipdUTKtWrWjX7u/kG4fDwfLly8nMzCxzv4MHoVkzOPfcCp9K6o3TnwA+cmSLcrcZMaL5KR69EFPY5wpO5ndF6oOSwnQFQOldk6x1COgGdLI4DqkNLml7CQFeAWQVZrldb/WzXWpeKmF+YQyLGVbuOURgANCait5fxMYG8uefA+jTJ+SUzhYU5Mknn/TgX/+KquAeJQUDL0f3F7WTzVVaqpeIiIiISK00FvgUiOV0HlJSU9NYvHjxMcv69+9PWFjoKR7RBSQAZwOvnVZsIlJbvQI8h3mRVHrLV2tsxwxAfYZ5YSAiIiIiIiLV5T9L/sPsVbOJDovGw16znheT0pLo0LADH131UY2LTaqHw+Fg0aJFfPjhh8ydO5eQkBCuu+46rr/+ejp37oztuBLwv/wC//wnNGpUeoJAdnYWS5YspWXLlrRr15bqGitNSYHiYvjoI4iq6JwYqTeKiuDqqyExEVq3Lmu7IpYuXUpgYADdu/c44XegojZt2kRiYsKRf/v4+NCvX38C3PziOBwmrvvvN79fIid6B3gaaAWUXfW4+u0CGgFfYCrYixztACaBoQiItDiW4+UDe4HpqDCdVITL5eK2r25jya4lxITFnPI9QlVwupwkpCZwbcdrmXjORKvDkVrjTeAZoA3gWaE9nE4XX311gJdf3s6vv6ZSWOgsc/uYGH9uvbUld97ZkvDwk3lHvRNoAnwO+J3EflJTVOwbJSIiIiJSa9wM/ATsA5paHMvRUjBdF25DiQsi9dWVmAGUXZhEgZqipALM7ShxQUREREREpPpd1+k65sXPY0/WHloGt7Q6nCPS89PxtHtyR887lLhQz7hcLpYtW8aHH37IJ598AsA111zDt99+y5lnnlnmRKz+/aFvX/j1V4iOBrv92PVZWVksXbqUVq1a0bZtHNU1VlpUBKmpcPPNSlwQ97y84Pbb4fHHITMTgoJO3MbhcLBy5Qp8fHzo1q37aU1KbN++HUVFRezcuQMwnU2WLVtG//798PU9dgLW7t3QqhVcddUpn07qvMswhWkSgZp0kcvBVEYeiRIXxL1I4DrgZSAE8LE0mr+5gN1AD2CwxbFIbWGz2bijxx2sObCG1LxUwv3dd0Cwwv7s/TT0a8jNXW62OhSpVS4H5gI7MAkM5bPbbQwf3pjhwxtTUODgzz8zSUzMIS2tiLS0QrZvj+fMMzvRvLkfvXqFEBFxKtf9bKAYuBUlLtRe9vI3ERERERGpTdoCdwC5QAX6O1eLQiAVM/jWx+JYRMQ6EcC/MC/lMyyOpYQDk+w1BLjE4lhERERERETqp2ZBzbj/jPtxOB1kFWSVv0M1KHYWk5yTzMWxFzMkaojV4Ug1cLlcrFmzhscff5yoqCguvPBCcnNzef/999mzZw8vvPACffv2LXeytqcnjB5tOi/s23fsuqysTJYuXULr1q1o27b6Oi4A7NoFcXFw333VdkqphS66CIYNM99dh+PYdU6nk1Wr/gCgV69e2I/PzDlpNjp37kzTpn8XYcrLy2XZsmUUFhYeWZaZCS4XPPggNG58mqeUOiwEeBhTnCbV2lCOcAJ7MB3Jr7E4FqnZbgO6Ygo/uSyOpUQyEASMBnwtjkVqk74t+nJNh2tIzUul0FFY/g7VILcol7yiPO7seSex4bFWhyO1ShgwClMjP+2k9/bx8aBv31Buuqk599/fhscfj+byy2HkyOZceGHkKSYuODFdcQZhCgdKbaXkBRERERGpg24EzqRmDHK5DsfRCbjL4lhExHrDgAsxrZAd5WxbHXYDzTEDTxoiEBERERERscpl7S7jvDbnsS97Hw6n9c+LuzJ20SakDQ+d+dBpVRaXmi8hIYFJkybRsWNH+vXrx/bt23n++efZv38/b7zxBueddx4eHifXeSMqCu6+GwoKICfHLMvMzGTJkqW0bt2GuLi2VfBJSpeaCj4+8PDDEBJSraeWWsZmg3//23Q52L377+Uul4vVq/8iP7+A3r3POOnfidLPZ6Nbt+5EREQcWZadnc3y5cspLi7C4YD9++GCC+DCCyvllFKnDQCuAA4BRRbHAmZiYSTwKGbSo0hp/DFJAg2AgxbHApCP6Vg9AuhsbShSK93T+x7aN2zProxduFzWzlVwuVzsydxD3+Z9ub7z9ZbGIrXVOZgOTwcx3Q6sthdogrm/UIfI2kwzE0RERESkDvLGDHJFANuxNoFhD2aw7dHDf4pI/WYD/g1EA9sw1SGschAzLPAA0MzCOERERERERMRus/NI/0doEdSC7enbLZ3ksj97P94e3ozqO4pGAY0si0Oqzp49e5g1axa9e/emY8eO/PHHHzz55JMkJyfz4Ycfcumll+LjcypVMP927bVw9tmwZw+kpGSydOlSoqKiiIuLq6RPUTHZ2ZCSAldeCQMGVOuppZZq0gQeegjsdkhOBnCxbt06MjIy6dOnD15eXpV6PrvdTs+evQgLCz+yLCMjneXLV5KU5KR1a5NQoTwyKZ8N+CfQHvNuzMpkyBTMBMd7gSgL45DaoztwCyZpINPCOIqAnUAvYKSFcUhtFuQTxKP9HyXQO5A9WXssi8PlcrE9fTuRAZE8dtZjeHt4WxaL1GY24EEgDvNu28r7i0OHz38f0MrCOKQyKHlBREREROqotsAkIBAzyGTFC9+9mIe5x4E+FpxfRGqmCGAGpiqEVQkMKUA2cCdwqQXnFxERERERkeM1D2rO9MHTaejfkG3p2yxJYEjOSaaguIAH+zzIkOgh1X5+qTqHDh3i1VdfZdCgQbRu3Zpvv/2Wu+++m/379/Pll19y3XXXERAQUGnn8/SEyZMhKiqV5cuTad06ltjY2Eo7fkXk5sLevTBsGIwapcnfUnEXXgj33mu+Q6tW7SQ5OZkzzzzztJN6SuPh4cEZZ/QmKCj48BIbaWnBHDq0nqlTi2jcuEpOK3VSKGbsuRWQhDUTDNOAdEzV+mstOL/UXndhuoccwCQxVLdizDubjsB0wNeCGKSu6NuiL4/1fwwbNvZm7a3287tcLnZk7KCBdwMmnTuJuPDqTSCWuiYMeAZogXXvtlMxyW23AVdZcH6pbEpeEBEREZE6bBAwAdNudDvVl8DgAnYd/vujwPBqOq+I1B6dMC+RIqn+l0jJmIH/24B7MElWIiIiIiIiUhP0bNqTaedNI9wvnKS0JJyu6pkU4HK52Je1j/yifO7tfS+3dL2lWs4rVSsrK4s5c+Zw4YUX0rRpU9555x2uuOIKdu3axcKFC7ntttsIDQ2tsvNv376KpUv706lTETZbFPn5VXaqE2Rlma4PgwebJApfzT+Uk2CzwR13QOvWC0hOziU2tj9+fn5Vek5PTy/OPLMP/v5BmEr1yaSk3M6sWSNwOq3s3iq1TxwwE2iJGXsursZzH8IkL9wEPITGnuXkeAFjMQWXDmCSYKpLIeb3pT3wH6BpNZ5b6qorO1zJqL6jcLlc7M7cXW3J6U6Xk+3p2wn0CmTiORM5u9XZ1XJeqevaYe4vmgOJVP/9RTqmQ8/96P6iblDygoiIiIjUccMw1THCgAQgt4rPV4B5WPMFngSuq+LziUjt1Qt4HvMSKZGqryRUhBl8LwIeQC+PREREREREaqb+Lfsza+gsmjZoSkJqAtmF2VV6vkJHIUlpSdiwMarfKO7qeRc2laivtfLz85k7dy5XX301ERERzJw5k7PPPpstW7awdOlSHnjgARpXQxn3lStXMnjwYJ588g4WLmxP376waxccPAhVOW/L6TRJCwcPwiWXwDPPQGBg1Z1P6q7XXnuV3367hieeCMDLy4+kJCgqqtpzFhT40LhxP3x9DwH/AlbywQcfcP/991vSjUdqs87Ai0AsZkw4o4rPV1KxPg+4A3gM8Kjic0rd5AtMBq7BTFTdSdVW+HZhOlVvB7oBLwNtqvB8Ut/c2OVGxp09Dm8PbxJTEykoLqjS8+UW5ZKQmkC4XzjTh0xXNz2pZF2BF4BozP1FZhWfr+T+Ih9TkO8RNOW97rC59IQjIiIiIvVCPPA0sBTwA5pQ1qTd1NQ0Fi9efMyy/v37ExZWWiUyF6aaeSbQHTMw2+10gxaRemEvplLFAsx1qTmV/2InFVOVIg54GDgbJS6IiIiIiIjUbDszdjJj8QwWbV+El92LZkHNsNsq70W9y+UiJS+F1LxUOjTswCP9H6Ffi36VdnypPkVFRfz44498+OGHfP7550RERHD99ddz/fXX06FDh2qPZ/ny5QwdOpQJEybw0EMPAZCTA2+8Ae+9B9nZ0KIF+PhU7nlzckziQkQE3H03XHsteHpW7jmkfvjwww+58847mT9/Pv369ef3300izJYt0LAhhIVV7vkcDvPddTrhvPPgyiuTuPrqfhw4cODINmPGjGHKlCmVe2KpB5KBZ4FvMRPAW1D5Y8/ph88TBfwbGIzGnuX0OYEvMEk4ezGdECo7G7EI2IVJmLgKuBcIruRziBir969m2u/TWLN/DcG+wTTyb1SpCeNOl5P92fvJK8qjX4t+PNb/MWLDYyvt+CLHOgDMAr47/O+KvdsuKipm/vz5DBs2DC+v8h7U0jH3FzGY+4tz0f1F3aLkBRERERGpRwqBD4HXMFU0QjEdGU58kKp48oIT0wI3BTOgdQtwGyZBQkSkopzA15hqFbuBIKAhcDpv2F2YilqHMIPvl2NaaZaWhCUiIiIiIiI1jcPpYO6muby88mX2Ze8j2CeYcP9wPO2n/rzocrlIz0/nUO4hArwDuKbDNdzT+x6CfIIqMXKpak6nk8WLF/Phhx/y6aef4uPjw7XXXst1111Hr169LOuesWzZMoYNG8bEiRN54IEHTli/ahVMnw7r1oG3t0k08PU9vXPm5EBysuno0L8/PPYYREef3jGl/po3bx7XXHMNc+fOZejQoUeWp6fDyy/DZ59Bbq5JYggJgdP5VSsuhkOHIDMTmjWD+++Hyy4Dux3WrFnDoEGDSE9PP7L9M888w8MPP3zqJ5R6ygV8j0li2I6ZAN4I8DrNY2Zixp69gIsx3UIank6gIm7swhR/+hHzvWuE+Q6fzn1OIWZCbB7QFlPJ+6zTC1OkAnKLcnnzzzeZs3YOGfkZhPuHE+oXeloJ6g6ng5S8FNLy0ogIiOCunndxXafr8PI4nWu8SEW4MMkLzwE7gAaY+4DSv3vlJy+U3F8cBHyAS4CHgPDKDFxqCCUviIiIiEg9tBV4H1PlPBXwxjxI+VMy2FV28oIL05ouBcgFQoBBwA2YVnkiIqdqD+b69C2maoUH5voUQMXaYLqAAkxSVRZmoOgMzPWpP6pIISIiIiIiUjvtzNjJe2vfY37CfA7mHMTTw5OG/g3x9/Kv0GQXl8tFgaOA1LxUsguzCfIJol/zftzQ+Qb6NO9TDZ9AKoPL5eLPP//kww8/5OOPPyYvL4+rrrqK66+/ngEDBmC3V15njlOxZMkSLrjgAqZOncp9991X6nY5OfDxxzB3LmzbZqrNh4dDgwYV75RQVAQZGZCWZjo4tGsHV18Nw4er24Kcul9++YWLLrqIt99+m6uvvvqE9S4XLFkCH34Iy5ebpIPAQNOJwcenYokMTqf5HTh0yHRciIiAiy6CG280CQxHW7JkCUOGDCE3N/fIstmzZ3PHHXec7keVeikZM/b8NbAfM1bcEDMRvKJjz4WYsefMw/v1AK4HzkFjz1J1HJh3Jp8A6zDvaIMx72e9T+IYWZh3uwAtgeGY76+6LUj1Wr1/NR+s+4Bftv9CRkEGfl5+hPuF4+vpW6EEZJfLRW5RLodyD1HkKCLML4wh0UO4sfON6rYgFtgPfMDf9xd2Sru/cJ+8UHJ/kYq5TgcCPTHX50Ho/qLuUvKCiIiIiNRjB4D5wFxgJ2awC8CTrCwn69ZtxOk0VY7sdujcOZqAgJKHKF+gCaaS+QWYVrsiIpUlHZNg9RkQj0mUApPM4H/4TztmQOf/2bvv+Krq+4/jr3tv7s3eCTvMAGEvRRQBFRARVMCqxGrrrFXbWhVxtdbRny1urUodtdZ1UWSIKHspKDgBCTPsmUD2vuv8/jgQwdyEEBJuxvv5eOShOfNzf7/jbc453/f348P8/jr2HXYskHUZMAbojB7siIiIiIiINA7ZJdksSF/AzE0z2Z6znRJ3CQBBtiBCg0KxWW1YLVYMw8Bn+CjxlODyusAAu81OYngiV3S9gjGdx9ApTlPTNxSbN2/G6XQybdo0Dhw4wLhx40hNTWXkyJHY7fVjVtWVK1cyZswYpkyZwu9///tq7eNywZdfwqxZ8M03UFhoDg63WCAsDOx287msYZg/ZWVQYl7yWK0QFQUXXQSXXw5nnQW2ig12Rart+++/56KLLuK5557j5ptvPun227bB3Lnmz+HDZqDGMMxOIiEh5jVqsZhhBa/X7Nbg8ZjLQkOhc2e48koYORJiq2iUumjRIsaMGYPb7QbAYrEwbdo0rr766tr66NLkFACLMd+NbeTnZ89WzEl0fvnsuQxzhnoLZqfgeOBSzG4L3dCzZzlzDOAHYC6wELPztPfoulDMWbotR398gAcoOrqfBfP67gtMwBwQG3rGKhfxZ0/eHuZtm8eszbM4WHiQMk8ZAMFBwYQEhWCz2LBYLPgMHz7DR7G7GI/XA0BIUAjtYtoxodsELkm+hGbhzQL5UUQwg42LMd9tb8bfu22Px2DNmjWcc85ZBAW5Md9tH//3xVjMd9sp6O+Lxk/hBRERERER3MAWYDuQDqwnP38dP/20DqvVfLng80FKysUkJl6IORA4GbOV6Gn2NBcRqZIPM7xw7PtpA2b3GPfRdRbMF0ktgd78/P2UgjkzhYiIiIiIiDRGHp+H9Oz08p+fMn9iW9Y2vD4vPnxYsGC1WGkV2Yo+zfvQOb4zyXHJdI3vSrgjPNDlSzXs2bOHadOm4XQ62bRpE2PGjCE1NZUxY8YQGlq/Btt98cUXjB07lmeeeYbf/e53NTrGvn2wdSukp8OmTbBhgznY2+cz19tsZlihd2+zy0JyMnTtComJtfhBpMnatGkTQ4cO5YEHHuDee+89pX0LC2HLFvPa3bYN1q2DgwfNa9cwzBBDUBB06QI9e5rX7rGf6gZuZsyYwdVXX43v6H8QdrudOXPmcMkll5zqRxU5jsHPz53TgTTMwYYuTnz23Azz2XMXfn72HBWAekWOl415vW7HfMe7/ugyH+a1bcUMJ3THDNkkY74/aYcGxEp9U+opZcuRLWzL3kZ6djrrM9azJ28PPsOHgYEFC0HWIDrEdqB3s94kxyWTHJdMl/gu2G31I8gs8jODn/+2OPZuewvgxuNx88033zJw4CCCglpR8e+LyADVLIGg8IKIiIiIiB9ff/0VF1wwGLvdnA3J5YJVq77i3HPPDXRpItLkGZjhBRfmTBR2zFkrREREREREpCkzDAO3z43L6yLIGoTD5sBqsQa6LDkFGRkZTJ8+HafTyTfffMPw4cNJTU1l3LhxREdHB7o8v1asWMHYsWN5/vnnueWWW2rtuIbx83NZiwUcDnOgt0XjDaWW7dq1i/PPP58bb7yRJ554olaO6fWanRg8HvPatdtP/9p96623TugIERoaysKFCzn//PNPs1qR4+nZszRkHn6e+MmBeQ3rDwdpmHyGD5fXhcfnwW6147A5sOgPYWmwzL8v8vOP0KxZazIzs4mKqqL1mDQJQYEuQERERESkfrLgcpkvx0RE6hcL5oN3R6ALERERERERkXrEYrHgsDlw2HS/2JDk5uYya9YsnE4ny5YtY9CgQfz6179m1qxZNGvWLNDlVWnZsmVcfvnlvPTSS9x44421emyLxRzwbddkslKHDh06xMiRIxk/fjyPP/54rR3XZqt+V4Xquummm8jLy+Oee+4BoKSkhDFjxrB8+XL69etXuyeTJkzPnqUhC0JDIaWxsFqshASFBLoMkVpy7O+LCMrKQMFIAf0vtoiIiIjISdgAb6CLEBERERERERERkUaiuLiYuXPn4nQ6+fzzz+nZsyepqan85z//ISkpKdDlVcuSJUsYN24cL7/8Mr/97W8DXY7IKcvJyWHUqFEMGjSIF198sUHMZnz33XeTk5NT3iEiPz+fUaNG8eWXX9K1a9cAVyciIiIiIlI9FsMwjEAXISIiIiISSF4v7NoF27bB9u2QlgbffZfLt9+uB6yYbezKuOyyXgwf3pzkZEhOhk6dIEhxYBERERERERERETkJl8vFwoULcTqdfPLJJ7Rp04bU1FQmTpzY4AYdL1y4kAkTJvDvf/+b6667LtDliJyyoqIiRo4cSWJiIh9//DH2BtTiwzAM7rrrLv71r3+VL0tKSmLlypW0bds2gJWJiIiIiFQuPz+f6Oho8vLyiIqKCnQ5EmAaaiUiIiIiTVZODixcCDNnQno6FBWZ7citVvD5bJit6wzMNnbBpKVFsHWruU1oKCQlwYQJMHo01PMO7iIiIiIiIiIiInKGeb1evvjiC5xOJzNmzCA8PJyJEyfy5Zdf0rdv3wYx0/svLViwgCuvvJLXX3+da6+9NtDliJyysrIyxo8fT0hICB9++GGDCi4AWCwWXnjhBfLy8njnnXcA2Lt3LyNHjuTLL7+kmV5WiIiIiIhIPafOCyIiIiLS5OzaBR9+CJ99BpmZYLdDfDyEhYHNZm6Tk5PNqlWrTthv8ODBxMbG4fNBcTFkZYHLBXFxMHIkpKZCSsqZ/zwiIiIiIiIiIiJSPxiGwbfffovT6eTDDz/E4/Fw9dVXk5qayrnnnovVag10iTU2b948rrrqKt58800mTpwY6HJETpnH42HixIns2bOHJUuWEBkZGeiSaszj8fCrX/2KTz75pHxZ3759WbZsGTExMYErTERERETED3VekOMpvCAiIiIiTYbHAx9/DFOnwqFDEBUFCQkQ5KcfWVXhheN5vZCdbXZxiIuDW26B668Hh6MuP4mIiIiIiIiIiIjUJxs2bMDpdDJt2jSOHDnChAkTSE1N5aKLLiLI3wPIBmbu3LlMnDiR//73v1x11VWBLkfklPl8Pm655RbWrFnDF198QXx8fKBLOm2lpaWMGTOGpUuXli8bPHgwCxcuJCwsLICViYiIiIicSOEFOV7DndZBREREROQU7NoFf/oTPPEE5OdDcjK0aOE/uHAqbDZITITOncHthmeegd/9DjZtqpWyRUREREREREREpJ7asWMHTz75JL169WLgwIGkp6fzzDPPkJGRwX//+18uvvjiRhFcmDNnDtdccw3/+9//FFyQBskwDCZNmsTy5ctZuHBhowguAISEhDB79mwGDhxYvmzVqlVceeWVuFyuAFYmIiIiIiJSOYUXRERERKTR+/pruPFGWLIEmjWDNm2gtruzWyzQsiW0bg2rV5sdGObPr91ziIiIiIiIiIiISGAdPHiQF154gUGDBpGSksJXX33FAw88QEZGBh9++CHjx48nJCQk0GXWmtmzZ5Oamsp7773HlVdeGehyRGrk73//O06nk0WLFtG6detAl1OrIiMjmTdvHj169ChfNn/+fK677jq8Xm8AKxMREREREfGv4U/zICIiIiJShWXL4C9/gdxcs9tCbYcWfik01DzPnj3w179CSQmMH1+35xQREREREREREZG6k52dzYwZM3A6nXz55Zecf/753HTTTXz22WeNZgZ3f2bOnMn111/PBx98wBVXXBHockRq5KWXXuL555/niy++oFOnToEup07ExcWxcOFChgwZwo4dOwCYPn060dHRvP7661gslgBXKCIiIiIi8jOLYRhGoIsQEREREakLX30F990HeXnQvr3ZHeFkCgu3kZ39JYcOLSUzcw1QCBQAJVitodjtETgczQkPTyYioiuxsecRGzsYhyP2hOMYBuzfD0FB8PjjMHp0HXxAERERERERERERqROFhYXMmTMHp9PJggUL6NevH6mpqVx99dW0atUq0OXVuY8//pgbbrgBp9PJZZddFuhyRGrknXfe4Q9/+AOLFi3inHPOCXQ5dW7nzp0MHjyYgwcPli+bNGkSTz31lAIMIiIiIhJQ+fn5REdHk5eXR1RUVKDLkQBTeEFEREREGqXt2+GWWyAzEzp0qDq4YBg+MjLmsnPn82RlLa/B2azExw+jVatU2rW79bjjmh0YwsPhlVegf/8aHFpERERERERERETOiLKyMubNm8e0adOYM2cOHTt2JDU1lYkTJzbaGdv9+fDDD7npppv46KOPGDNmTKDLEamR2bNnc9111/HJJ58wfPjwQJdzxqSlpTF06FCys7PLl/3f//0fDz30UACrEhEREZGmTuEFOZ7CCyIiIiLS6Hg8cMcdsGIFJCeD1Vr5tiUle/jxx1+Tnb2yVs596aVurNag8t8NA9LToU8feOstM8ggIiIiIiIiIiIi9YPH42HZsmU4nU5mzpxJXFwcEydOJDU1lV69egW6vDPO6XRy6623Mn36dEarnaw0UEuWLOHyyy/n/fffZ9y4cYEu54z75ptvGD58OIWFheXLXnnlFe64444AViUiIiIiTZnCC3K8KoZxiYiIiIg0TB9+CCtXQuvWVQcXMjMX8MUXfWstuOCPxQJt28K6dfDmm3V2GhEREREREREREakmn8/HV199xR//+Edat27Nb37zG6KioliwYAHbt2/nySefbJLBhffff59bb72VGTNmKLggDdaaNWsYP348U6dObZLBBYCBAwcyZ84cgoODy5fdeeedvP/++wGsSkRERERExKTOCyIiIiLSqGzfDr/9LRQXm+GFyuTkrGH16ovweourOJoFaAt0BaJo27YzNlsJZWUZ5OX9QFHRVuDEP6d/2XnhmMxM8Png3/+GAQNq8MFERERERERERESkxgzDYN26dTidTqZNm0ZBQQFXXnklqampDBs2DJvNFugSA+rdd9/l9ttvZ/bs2YwYMSLQ5YjUyIYNGxg6dCiPPfYYf/zjHwNdTsDNmTOHCRMm4PV6AbDZbMyaNYvLLrsswJWJiIiISFOjzgtyvIqjqkREREREGrB33oHDhyE5ufJtSksP8u23Y6sILlho3vw3ZGQMAZqVL01KGkxsbFz57y5XDhkZszlwYBqHDy/il0GG4yUmwrZtZveF/v3NjgwiIiIiIiIiIiJSt7Zt24bT6cTpdLJ3716uuOIKXn75ZUaNGoXD4Qh0efXC22+/zR//+Ec++eQThg8fHuhyRGpk+/btjBw5kj//+c8KLhx1+eWX8/bbb3P99dcD4PV6ueqqq5g/fz4XXHBBYIsTEREREZEmS+EFEREREWk0MjNh0SKIjQWrtfLttm79Gy7XEb/rrNZQ+vd3Ehw8hIyMVVWez+GIJSnpRpKSbqSgYCPbtv0flkpSCRYLNGsG334LW7dC167V/lgiIiIiIiIiIiJyCvbu3ctHH32E0+nkp59+YvTo0Tz66KOMHTuW8PDwQJdXr7z11lvcddddzJkzhwsvvDDQ5YjUyIEDBxg5ciSpqan89a9/DXQ59cp1111HXl4ef/jDHwAoKyvjsssuY+nSpZx99tkBrk5ERERERJqiKoZ0iYiIiIg0LPPmQXY2xMVVvk1h4Tb27n2r0vW9er1CixZXnPK5IyO707//+1gslbeXj46GggL47LNTPryIiIiIiIiIiIhU4fDhw0ydOpWhQ4fSsWNH5s+fzx133EFGRgazZ8/mmmuuUXDhF958803+/Oc/M3fuXAUXpMHKyspi5MiRXHDBBTz77LOVTjDUlN155538/e9/L/+9sLCQSy65hI0bNwawKhERERERaaoUXhARERGRRsHthpkzITgYbJXnBzhwYBqG4fW7Li7ufJKSbqyjCs3uC5GR8OmnkJ9fZ6cRERERERERERFpEvLz83nnnXcYPXo0rVu35r333uPqq69m7969LFq0iJtuuomYmJhAl1kvvfbaa9xzzz189tlnDBs2LNDliNRIQUEBo0ePJiUlhddff13BhSo89NBD3HvvveW/Z2dnM3LkSHbu3BnAqkREREREpClSeEFEREREGoVNm2D3bkhIqHq7w4fnV7quffs/1XJVFSUkQEYGfP99nZ9KRERERERERESk0SkpKeHjjz/myiuvpFmzZjz33HNccMEFbN26lVWrVvGHP/yBFi1aBLrMeu3VV19l8uTJzJs3jyFDhgS6HJEaKS0t5YorriAmJoYPPviAoKCgQJdUr1ksFp5++mluvvnm8mUHDhxgxIgRHDx4MICViYiIiIhIU6PwgoiIiIg0CunpUFYGoaGVb+PzucjNXVPJWgsJCcPrpLbj2e1gGLB9e52fSkREREREREREpFFwu93MmzeP3/zmNzRv3pwHH3yQHj168OOPP7J27Vruv/9+2rdvH+gyG4SXX36ZBx98kHnz5jF48OBAlyNSI263m2uuuYbS0lJmzZpFcHBwoEtqECwWC6+99hpXXXVV+bIdO3Zw8cUXk52dHcDKRERERESkKVH0XEREREQahW3bzFBAVV2hy8oOYxhev+tCQ9vicMRVcYY17Nv3LVlZYSetJTn5gSrXWyxmpwgRERERERERERHxz+fzsXLlSpxOJ9OnTyckJISJEyeybNky+vfvj6WqB4Hi10svvcQjjzzCggULGDRoUKDLEakRn8/HTTfdxK5du1i+fDnh4eGBLqlBsdlsvPfee+Tn57NgwQIANmzYwKWXXsrixYuJiIgIcIUiIiIiItLYKbwgIiIiIo3CTz+Bw1H1Ni7X4UrXORzxJznDF+ze/WW1ajlZeCEszKz3ZGELERERERERERGRpsQwDL7//nucTicffvghpaWlXHXVVcycOZPzzz8fq9Ua6BIbrOeff57HH3+chQsXMnDgwECXI1IjhmHwpz/9idWrV/Pll18SGxsb6JIaJIfDwYwZMxg1ahSrVq0CYM2aNYwbN465c+cSEhIS4ApFRERERKQx09MdEREREWnw3G7YtcsMBVTF5yupdJ3VeuYexoeFQW4uHK48SyEiIiIiIiIiItJkbNq0iUceeYSuXbty0UUXcfjwYd544w0OHjzI1KlTGTp0qIILp+GZZ57hiSeeYNGiRQouSIP2yCOPMHv2bBYvXkyLFi0CXU6DFh4ezty5c+nbt2/5siVLlpCamorH4wlcYSIiIiIi0ujpCY+IiIiINHhlZeD1gs1W9XZ2e+XdFTyevFquqnI2m1lvaekZO6WIiIiIiIiIiEi9snv3bqZMmULfvn3p168fGzdu5B//+AcZGRm88847jB49GrvdHugyG7ynnnqKJ598ksWLF3PWWWcFuhyRGnv22Wf597//zaJFi2jXrl2gy2kUYmJimD9/Pp07dy5fNnv2bG655RZ8Pl8AKxMRERERkcYsKNAFiIiIiIicLrcbDANONvmaw5FY6bqSkn0YhoHFYqnl6iqyWMx6Xa46P5WIiIiIiIiIiEi9kZGRwUcffYTT6eTbb79lxIgR3HPPPYwbN46oqKhAl9fo/OMf/+CZZ55hyZIl9OvXL9DliNTYf/7zHx5//HGWLl1Kt27dAl1Oo9K8eXMWL17M4MGD2bdvHwD/+9//iI6O5oUXXjgj70xERERERKRpUXhBRERERBq8oKCfAwFVcThicTia4XJlVljn8eRRWLiRyMgeAMTGxjFy5MWkp6dTUlKCy/UPbLYgiouLKCoqxmKZhmG8X6N6DcOsN0h/jYuIiIiIiIiISCOXm5vLzJkzcTqdLF++nPPOO4/rr7+eTz75hMTEyicbkdPz97//nRdffJGlS5fSp0+fQJcjUmPTp0/nrrvu4vPPP2fAgAGBLqdRatu2LYsWLWLo0KEcPnwYgJdeeonY2FgeffTRwBYnIiIiIiKNjoZLiYiIiEiDZ7ebXRe83pNvm5h4Mfv3v+d33YEDH9K16+Plv7tcLnbu3FH++7BhFxAZGYnP52XDhpXs2eP/HKtWrSQyMpKIiEgiI82fkJBgwJyhyOcz63U4qv0RRUREREREREREGoyioiLmzp2L0+lk3rx59OrVi9TUVP773//Spk2bQJfX6D322GO8/PLLLF26lF69egW6HJEamz9/PjfccAMfffQRQ4cODXQ5jVpKSgoLFizgggsuID8/HzC/S2JiYvjzn/8c2OJERERERKRRUXhBRERERBq84GBITITdu0++bbNmYyoNL+zePZUOHe7G4Yit8hhWq43g4JBK17dr157CwgKyso6wa9cuiouLCAqylwcZIJHo6BDc7lIMo4XaLouIiIiIiIiISIPncrlYsGABTqeTOXPmkJSURGpqKk899RRdunQJdHlNgmEYPProo0ydOpVly5bRs2fPQJckUmOrVq3iqquu4s0332TMmDGBLqdJ6NevH3PnzuXiiy+mtLQUgLvvvpvo6GhuvPHGAFcnIiIiIiKNhcILIiIiItLgWSzQpw9s3nzybVu2vJItWzpSXLyjwjqX6wjr1t3AWWfNwmKx1rieX84e5/V6KSwspKCggIKCAjIzXeTkLKNDh+uIiYmhR48e9OjRg+7du5f/e/PmzRVqEBERERERERGRes3r9bJixQqcTiczZswgKiqKiRMnsnLlSvr06aPnW2eQYRg88sgjvPHGGyxfvpzu3bsHuiSRGlu7di1jxozhmWeeITU1NdDlNClDhgxh5syZXH755Xg8HgBuueUWoqOjmTBhQoCrExERERGRxkDhBRERERFpFLp0MUMMhmH+szJWq52uXZ/gxx9/7Xd9RsYcvv32Cvr2/R9gr5XabDYb0dHRREdHA2C3w+23t+OWW8axadMm0tLS2LhxI4sWLeKFF15g586dxMXF+Q01NGvWTC99RUREREREREQkYAzDYM2aNTidTj766CN8Ph9XX301c+fOZdCgQVitNZ8URGrGMAwefvhh/vvf/7J8+XJSUlICXZJIjW3dupWLL76YBx54gNtuuy3Q5TRJo0eP5r333iM1NRXDMPD5fKSmpjJ37lxGjhwZ6PJERERERKSBU3hBRERERBqF5GQICgK3GxyOqrdt1SqVjIy5HDjg9Ls+M3MuS5d2oEWLG4BmQHsgAsPwUFZ2mLy8H8jO/rJGdfp8P9cbFhbGgAEDGDBgwAnbFBUVnRBqWLBgAc899xy7du0iPj6+QqDhWKhBRERERERERESkrvz00084nU6mTZtGdnY2EyZM4J133uHCCy8kKEivnQPFMAweeOAB3n33XZYvX07Xrl0DXZJIje3du5cRI0Zw00038cADDwS6nCbtmmuuIT8/n9/97ncAuFwuxo0bx+LFizn33HMDXJ2IiIiIiDRkFsMwjEAXISIiIiJyugoK4LLLoLAQWrY8+fZebxlr1lxMdvYX1TyDFTCO/lRt7NjKt8nKAq8XZs6E1q2reeqjCgsLTwg1pKWlkZaWxu7du0lISPDbqSExMfHUTiIiIiIiIiIiInLU9u3bmTZtGk6nkx07dnD55ZeTmprKJZdcQnBwcKDLa/IMw2Dy5Ml88MEHLFu2jC5dugS6JJEay8zMZOjQoVxwwQVMnTpVHYjriaeffprJkyeX/x4TE8OKFSvo3bt3AKsSERERkYYmPz+f6Oho8vLyiIqKCnQ5EmAKL4iIiIhIo/Hss/D669C5M1TnvYbXW8yGDX9k7963arWOqsIL6elwxRXwz3/W3vkKCgr8hhr27NlDYmKi31BDQkJC7RUgIiIiIiIiIiKNxoEDB/jwww+ZNm0aP/74I6NGjSI1NZXLL7+ciIiIQJcnRxmGwb333sv06dNZtmwZycnJgS5JpMby8vK48MIL6dKlC++//z42my3QJclxHnroIf7xj3+U/968eXNWrlyp7x0RERERqTaFF+R4Ci+IiIiISKOxZQv8+tcQGgoxMdXf78CBD9my5a8UFW07rfNHRvYmKekGOna82+/6oiI4cgT+/W8477zTOlW1FBQUlIcZjg817N27l2bNmvkNNcTHx9d9YSIiIiIiIiIiUq9kZWUxY8YMnE4nK1euZMiQIaSmpnLllVcSFxcX6PLkFwzD4O6772bmzJksW7aMTp06BbokkRorLi5m1KhRREVFMWvWLBwOR6BLkl8wDIM777yTqVOnli9r164dK1eupE2bNgGsTEREREQaCoUX5HgKL4iIiIhIo2EYcMcdsHw5nOqEP4bh49Ch2ezb9zbZ2Stxu3NOuo/NFkZs7HkkJAwnMfESoqP7Vrn9zp3QrRtMmwaBnDgqPz/fb6hh3759NG/e3G+oQS+pRUREREREREQal4KCAubMmYPT6WThwoX079+f1NRUrr76alq2bBno8qQShmHwpz/9iU8//ZRly5bRoUOHQJckUmMul4tx48ZRWFjI/PnzCQsLC3RJUgmfz8f111/PBx98UL6sW7dufPHFF+r0LCIiIiInpfCCHE/hBRERERFpVFauhD/9yey+UNPx9oZhUFCwgSNH1gDgIjEAAQAASURBVLNx42qgAPDSqVMvIiISCQlpTXh4V0JD22KxWKp1zIICyMqCv/8drriiZnXVtby8PL+hhv3799OiRQu/oYbY2NhAly0iIiIiIiIiItVUWlrKvHnzcDqdzJ07l06dOpGamsrEiRPp2LFjoMuTk/D5fPzhD39g3rx5LFu2jPbt2we6JJEa83q9XHvttaSnp7N06VKio6MDXZKchNvtZsKECcydO7d82YABA1i6dKkGoImIiIhIlRRekOMpvCAiIiIijYphwP/9H7z7LnToAHZ7zY9VUFDAihXLy38fNuwCIiMjT/k4Xi9s3w6XXgrPPgtWa81rCoTc3Fy/oYYDBw7QsmVLv6GGmJiYQJctIiIiIiIiIiKAx+Nh6dKlOJ1OZs6cSUJCQnlgoWfPnoEuT6rJ5/Nxxx13sHDhQpYtW0a7du0CXZJIjRmGwW233caXX37JF198QWJiYqBLkmoqKSlh9OjRrFixonzZsGHDmDdvHqGhoQGsTERERETqM4UX5HgKL4iIiIhIo5OTA7/9rRkYOJ0J42orvLBnD8THm4GK1q1rXk99k5OT4zfUcPDgQVq1auU31KDZs0RERERERERE6p7P5+Orr77C6XQyffp0goKCuOaaa0hNTeXss8+udjdRqR98Ph+33XYby5YtY9myZSQlJQW6JJEaMwyD+++/nw8//JCVK1fqem6A8vPzueiii/j+++/Ll40ZM4ZZs2ZhP50ZpURERESk0VJ4QY6n8IKIiIiINEpffAF33w02GzRvXrNj1EZ4ITsbCgrg8cdh3Lia1dHQZGdn+w01HDp0iNatW1cINXTv3l2hBhERERERERGR02QYBmvXrsXpdPLhhx9SUFDAr371K1JTUxk6dCg2my3QJUoN+Hw+br31Vr744guWLVtGmzZtAl2SyGn5xz/+wQsvvMDKlSvp3LlzoMuRGjpy5AhDhw5l06ZN5cuuvfZa3n33XawNrf20iIiIiNQ5hRfkeAoviIiIiEijZBjw3//C889DRAQkJJz6MU43vJCbC1lZcPPNcM890NQntMvOzi4PMhwfasjIyKBNmzZ+Qw26aRURERERERERqdrWrVtxOp04nU727dvHuHHjSE1NZeTIkTgcjkCXJ6fB6/Vyyy238NVXX7F06VJaN6a2rtIkTZ06lYceeojly5fTp0+fQJcjp2nfvn2cf/757N69u3zZ7bffziuvvKIOPyIiIiJyAoUX5HgKL4iIiIhIo2UY8Mor8NprEBp66h0YTie8kJ0NOTmQmgoPPghBQad27qYkKyvLb6ghMzOTpKQkv6GGU+2AISIiIiIiIiLSmOzdu5dp06bhdDpJS0vj0ksvJTU1lbFjxxIWFhbo8qQWeL1ebrzxRr755huWLVtGy5YtA12SyGn54IMPuO2225g/fz6DBw8OdDlSS9LT0zn//PPJyMgoX/bggw/y5JNPBrAqEREREalvFF6Q4ym8ICIiIiKNmmHAf/5jhhg8HkhKAputevvWJLzg88H+/eD1wm9/C3/+s4ILNXXkyBG/oYbDhw/Ttm3bEwINx/49IiIi0GWLiIiIiIiIiNSJw4cPM336dJxOJ6tXr+aiiy5i4sSJjB8/npiYmECXJ7XI4/Fwww038MMPP7B06VJatGgR6JJETsvcuXO55pprmDlzJqNGjQp0OVLL1q9fz7Bhw8jNzS1f9tRTT3HfffcFrigRERERqVcUXpDjKbwgIiIiIo2eYcCCBfDcc7B7t9mBITr65PudanihsBAOHICWLeHOO+FXvwJ1Rq59hw8fLg8yHB9sOHLkCO3atasQaujWrZtCDSIiIiIiIiLSIOXl5TF79mycTidLlizhnHPOITU1lauuuopmzZoFujypAx6Ph+uvv57169ezdOlSmp9qO1mRembFihWMGTOGt99+m1/96leBLkfqyNdff82IESMoLi4uX/b6669z6623BrAqEREREakvFF6Q4ym8ICIiIiJNRmamGWD4/HOzM0KLFhAWVvn21Q0vlJZCRoZ5zAsvhMmToW3bOvgAUqXMzEy/oYasrCzat2/vN9QQHh4e6LJFRERERERERE5QUlLC3LlzcTqdfP7553Tr1o3U1FSuueYa2rVrF+jypA55PB5+/etfs3HjRpYsWaKAijR43333HcOHD+f555/npptuCnQ5UscWLVrE2LFjcblcAFgsFpxOJ9dcc02AKxMRERGRQFN4QY6n8IKIiIiINCnHujD85z+wZQu43RAba/5YrSduW1V4wTAgNxeyssBmgw4d4Le/hfHjzd+lfjAMo9JQQ05OTqWhhrCqUi0iIiIiIiIiIrXM7XazaNEinE4ns2fPplWrVqSmpjJx4kRSUlICXZ6cAW63m2uvvZatW7eyePFiEhMTA12SyGnZuHEjQ4cO5aGHHuKee+4JdDlyhsycOZOrrroKn88HQFBQEHPmzGH06NEBrkxEREREAknhBTmewgsiIiIi0iR5PPDNN/DJJ7B8OeTlgcUCdjuEh0NoKJSUFLJ69SrAAljp0+c8DCOMsjLzGJGRcO65MG4cnH8+OByB+zxyagzDICMjw2+oITc3lw4dOlQINaSkpCjUICIiIiIiIiK1xufz8eWXX+J0Ovn4448JDQ1l4sSJpKam0q9fPywWS6BLlDPE5XKRmprKjh07WLRoEQkJCYEuSeS07Nq1i/PPP58bb7yRJ554ItDlyBn29ttvc+ONN5b/HhoayoIFCxgyZEgAqxIRERGRQFJ4QY6n8IKIiIiINHn79sG338K2bbBhA2zfDiUlUFRUwtat2wAD8NGnT1e6dQujd2/o3Bn69YNOnQJdvdQmwzA4dOiQ31BDXl4eHTt29BtqCA0NDXTpIiIiIiIiItIAGIbBd999h9Pp5MMPP8TlcnHVVVeRmprK4MGDsf6yNag0ei6Xi6uvvpo9e/awaNEi4uPjA12SyGk5ePAgQ4YMYfTo0bz00ksKYjVRL774In/+85/Lf4+KimLZsmX0798/cEWJiIiISMAovCDHU3hBREREROQXioth/35IS0tn/PirAA/gYs2a2Qwc2C3Q5UkAGIbBwYMH/YYaCgoKKg01hISEBLp0EREREREREakHNm7ciNPpZNq0aWRkZDBhwgQmTpzI8OHDsdvtgS5PAqSsrIyrrrqKAwcOsHDhQuLi4gJdkshpyc7O5oILLqBv3768/fbbCmQ1cY8++iiPPfZY+e+JiYl8+eWXdO3aNYBViYiIiEggKLwgx1N4QURERESkEmlpafTs2bP89w0bNtCjR48AViT1jWEYHDhwwG+oobCwkE6dOlUINXTt2lWhBhEREREREZEmYOfOnUybNo1p06axdetWxo4dS2pqKpdeeqmeDQhlZWVceeWVZGZmsmDBAmJjYwNdkshpKSwsZOTIkTRr1oyPP/5YwSzBMAzuvvtuXnzxxfJlSUlJrFy5krZt2wawMhERERE50xRekOMpvCAiIiIiUgmFF6SmDMNg//79fkMNRUVFJCcn+w01BAcHB7p0ERERERERETkNhw4d4qOPPsLpdPL9998zYsQIUlNTueKKK/RyXsqVlpYyYcIEsrOzmT9/PjExMYEuSeS0lJWVMXbsWLxeL59//rkCWlLO5/Nx88038/bbb5cv69y5M19++SXNmzcPXGEiIiIickYpvCDHU3hBRERERATweGDPHti+HdLTYedO2Ls3i2nTZgBuoJgHH7yOIUNakpwM7duDJo6SU2UYBvv27fMbaigpKfEbaujSpYtCDY2eD9gPpAPbj/4UAmVAEBACtAGSj/50PLpMREREREQkkIow71+2Hf05hHkf4wWCgWh+vo9JBpoDloBUWtdycnKYOXMmTqeTFStWMHjwYFJTU7nyyitJSEgIdHlSDW6vm125u9ies5307HR25+2m2FWMy+ci2BZMmD2M9jHtSY5LJjkumbbRbQmyBtXoXCUlJYwfP568vDzmz59PdHR0LX8akZ8VuYrKr+ttWds4WHiQMk8ZXsNLsC2Y6JDo8us6OS6Z5uHNsVhO7bva4/Fw9dVXs3//fhYvXkxkZGQdfRppqI5dI7NmzSpf1qdPH5YvX35K4S23G3bt+vk9zu7dUFwMLhcEB0NYmPnuJjnZ/GnbFoJq9lUtIiIiIjVkGAYHCg6Qnp1OenY623O2U1BWQGFJIW//923uuO0OOsZ3JDkumU5xnegU24lQe2igy5YzTOEFEREREWmyDAPWroVPP4XlyyEvz3zQfYzHU0p6+lbMF+s2OnToRHBwMKGhEBkJ550Hl18OAweCzRaYzyCNg2EY7N27t0KoYePGjZSUlNC5c2e/oQaHwxHo0qXGDGArMBdYCGRjDvw5dotu4+dBPT7MwT8AoUA40B+4AhgC6DoQEREREZEzpQhYCswB0o7+XoZ5/2I9+gPmvc2x+xgr5n1Mc2AMcCnQ9syVXEeKioqYM2cO06ZNY/78+fTu3ZvU1FSuvvpq2rRpE+jypBq8Pi/fHfiOOVvmsGrvKgrKCij2FGMYBlaLtfzHZ/jwGt7y/cLsYUQFRzGs3TAu63IZ/Vv2r/Zg75KSEq644gqKioqYN2+eZpuUOlHkKmLpzqXM2TqHtMw0it3FlHpKsVgs5dc1mM8kj13bVouVcHs4zcObc2nnSxnTZQxto0/+XX1sVv1vv/2WFStWEB8fX6efTRquY905Fi9eXL5s8ODBLFiwgPDw8Er38/ng22/N9zirVkF+/s/vcSwWsFrNH58PvD9/VRMWBlFRMHSo+R6nf39zexERERGpG9uytvHZts9YkL6AIyVHKHIVcWx4us1qw/AZbNy4ka7dupbfQwcHBRPhiKBvi75c0fUKhrYbSnCQJnZsChReEBEREZEmp7gYFi6EmTPhp5/M3yMjISLCfKB9LIhQUFDAihXLy/cbNuwCwsMjKS6GoiLzIbnDASkpMGECjB4NmihNapPP56s01FBWVuY31NC5c2eFGuo1N7AMmAl8BxQAEUd/wjA7LVTGB5RiDg7KwxwA1BEYD4wFmtVZ1SIiIiIi0tTtAT4FZmN2jrMAUZihhBCq7qjgxryPKQBKgBhgKGYge/BJ9q1fysrKWLBgAU6nkzlz5tCuXTtSU1OZOHEinTt3DnR5Uk0FZQXMS5/HjE0z2HxkM2WeMqKCowh3hBNmDysf2O2P1+el2F1MkbuIgrICQoJC6NmsJ1d2u5KLO11MuKPyAbjFxcVcfvnllJWV8fnnn2t2eql1e/L28OmWT5m9ZTb78/cDEB0STbg9nJCgkCpDNm6vu/y6LvWUEh0czdB2Q7ki5QoGJw32u69hGNx9993MmTOHlStX0qpVqzr7bNI4FBYWMnLkSFavXl2+bNSoUcyZM6fCM+2CApg3z3yPs2kTlJWZYYTwcPM9jrXyr2q8Xk54jxMaCj17mu9xRo0yjyEiIiIip8/j87Bi1wpmbZ7Fmn1ryHflE24PJ8IRQbgj/ISOhR63h/nz53PJJZcQZA/CMAxKPCUUuYrIL8vHYrHQLrodE7pNYEznMbSMbBnATyZ1TeEFEREREWlSfvgBpkwxQwsWCyQmmqEFf+9t/IUXfvlSsagIDh8Gjwc6d4b77oPzz9cMPlK3fD4fe/bs8RtqcLlcdOnS5YRAw7FQg91uD3TpTdw2YAqwGjOIEA9EU/OBOqVAJuYsp22AuzBDDPoCEhERERGR2uIGpgGvAYcxwwqJ1LwDnA/Ixew+5wBGAvcC9feFtNfrZdmyZUybNo0ZM2YQExPDxIkTSU1NpVevXtWecV/qh5V7VvLMV8+w5cgWgqxBJIYnVhk4qIphGBS5i8gsysQwDHo268nkwZM5q9VZFbYtKirisssuw+v18tlnnxEREXG6H0WknNvrZtqGabz2/WscLjpMuCOcxPBEHLaafVf7DB+5pblkF2fjCHIwosMIJp03qcLgoccee4zXXnuNlStX0rFjx9r4KNIEZGdnM2zYMDZs2FC+7KqrrsLpdGI7OrPUqlXw9NOwZYs52VSzZjUPHBiG+R4nM9PsztCzJ9x/P5xV8ataRERERE7BzpydPLXqKVbuWYnH8JAQlkB0cHSlz0l+GV74pTJPGZlFmZR6SmkV2Yo/DPwD41LGVTnBgDRcCi+IiIiISJNQVARvvgnvvWfO2NO2LQSfpNtcdcILx7jdsHevecwrr4Q774SYmNqrX6Q6fD4fu3fvPiHUkJaWxqZNm/B4PH5DDcnJyQo11LlfDvZpg9llobZ4gX2AAYyivg/8ERERERGRhmIb8BSwCggFWmB2gKst+cAhIIn6FsY2DIPVq1fjdDr56KOPALj66qtJTU1l0KBBCiw0QHmlebz67at8vPFjSr2lJEUlYbfV3vMQl9fFnrw9RNgjuLbXtfxuwO/KQxGFhYWMHTsWi8XC3LlzCdeU31KL0rPTmbJqCqv2rCIkKISWkS1rdXBPflk+hwoO0Sa6DXedcxeXdbkMi8XCiy++yOOPP86KFSvo2bNnrZ1PmoaDBw8yZMgQtm/fXr7s5ptv5pln3mDqVAsffwwlJeZ7nNp8dO1ywZ495oRW114Lv/udujCIiIiInCqPz8P0tOlM/W4qGUUZtI5sXa1JAU4WXjjGZ/jYl78Pr8/L8A7DuW/wfbSJalObH0HqAYUXRERERKTR27MHJk+GtWshOtrstlCdd8ynEl44JjsbjhyBrl3NDg/dup1e7SK1wefzsWvXLr+hBq/XS9euXf2GGoKCKn9oINWVBTwIrKRuBvsc7/iBP48D59XReUREREREpPH7FLNzXF0EsI93fBh7LPDXOjxX1QzD4KeffsLpdDJt2jRyc3O58sormThxIhdccIHukRuwLUe28MDiB9h4eCPxYfHEhcbVSQDFMAyOFB8htzSX3s1789TIp0gISuDSSy8lKCiITz/9VMEFqVWfbvmUp1Y9RWZxJm2i2hBmr5vvT6/Py778fRiGwZguY+i4tyOT7prE4sWLGThwYJ2cUxq/Xbt2MXjwYA4cOHB0SReSkz/Gau1JfLyFuLi66XBtGOY7nNxc6N0bnnoK2rev/fOIiIiINEZ5pXk8vPRhlu1chiPIQavIVtUOT1c3vHBMQVkBBwsP0jqyNY8Me4QL2l9wmtVLfaLwgoiIiIg0atu2wb33mu2F27U7ebeF49UkvABmF4Zdu6BNG7O1cb9+p163yJng9Xr9hho2b96Mz+fzG2ro1KmTBmxU2yHMLgjfUbeDfY7nBXYD0ZgBhhFn4JwiIiIiItJ4GIATeBbwYN7L1FUA+3j5QAbmPcw/gJM/f6kt6enpTJs2DafTya5du7j88stJTU1l1KhRBJ/KgySpl9YdWsd9i+5jT94e2se0r9VuC5VxeV3syt1F+6j2HH7vMPFGPJ988glhYYEJ5kjjNG3DNJ7+6mk8Pg9totrUareFyuSX5ZN+MJ2M1RnMunMWl118WZ2fUxq3jRs3MnToULKyWgFPA+1ITraTktKpzs/tcpnvcZKT4dlnISWlzk8pIiIi0qAdKT7CpIWT+Hrf17SKbEWEI+KU9j/V8AKYXRh25+4mwhHBI8Me4dLOl9akdKmHFF4QERERkUZr1y64805IT4eOHeHYeOu1a29g377/+d3noot2EhbWHqg6vPDVVxeQnb3C7zHGjjXw+WDnTmjRAl56CdQ5WxoSr9fLzp07/YYaDMMgJSXFb6jBZrMFuvR65AjwJ+B7oB1gDni54Ya1/O9/+yrdy263YLdbCQ62EhtrJzbWTlJSCB07htO/fxRDh8aTlBR6knMbmAGGCOCfwLBa+DwiIiIiItI0fIQZHrACrU5YU9X9zM6dF9G+feUDsw3D4LbbfuKNN/b4Xe9wWJk+vQeXX14GjASeoi4D4Pv37+fDDz/E6XSybt06LrnkElJTU7n88ss1M34j0b9/f3788ccKy5v3b87Zk86uct9ts7ex5aMtftedde9ZtBjQosr9l9y1hJLDJRWW33nnnbz88stV7itSFV3X0hh99NEGrr32MF5vC2AH4CM29j1ycj7yu31Kyj9JTr6/Wsf+5pvLyMyc63ddz54v06bNnezcCR06wMsvQ6e6z0yIiIiINDi6D5G6oOkyRURERKRRys6Ge+6pGFw4U6xW84H3jh0waRK88QYkJZ3ZGkRqymazkZycTHJyMldccUX5cq/Xy44dO04INEyfPp3NmzdjsVj8hho6duzYBEMNpcD9mMGF9oCj2nu63QZut5fiYi85OW4Avv8+r3y9xQLnnhvLn//cgSuvbInV6q93ugUzMLEL+AvwCtC7Rp9ERERERESakiWYoQELvwwunA6v1+Cmm9bxzjv+gw8hIVZmzjyL0aObAUXAYsxOck9Sm10fsrKy+Pjjj3E6naxatYphw4bx+9//nvHjxxMXF1dr55H6of8g/4MrsrdkYxgGFou/++mj22zOrnJdVYMrSrJK/A6sABgyZEgVFYuc3NChQ+vddT34/MFVVCxStf374X//60mrVkXs27cCw/ABkJMzAYdjNS5XxdDj1q2P0KzZaKKiqn7euWfPm5UGFxITL6ZduzuwWMz3R9u3mx2833wTEhJO/3OJiIiINCaDhwyud/chur9u+BReEBEREZFGxzDgxRchLc0MEJzp4MIxxwIM27fDlClmTU1uDLc0Kjabjc6dO9O5c2fGjRtXvtzj8ZwQatiwYQMffvghW7ZswWq1+g01dOjQoRGHGt4BvgKSOJXgQnUYBnz1VQ5ffZXDOefE8PbbfUlJ8deS04IZnEgH/g/4L3U5a6mIiIiIiDR0mcAUzDB2+1o7qsfj4/rr1zJt2gG/68PCbMyZczbDhx8bKRgONAc+A84FrvC7X3UVFBQwe/ZsnE4nixcvZsCAAaSmpjJt2jRatKh6dj9puHyGj0Pxh/yucxe5KdhbQFTbKL/rDZ9BzracSo+dvaXygRcAmWmZla4bPFiDvOX09BnYx+/yur6u9/+4v9J1JS39DyYSORmfD55+2pyAqmfPcFq06M+3336H2VU2DJfrDuAhwPeL/Vz8+OP1DBnyLVar/2evxcW72LjxHr/r7PZYevd+q3yQnc1mBhg2bYIXXoAnnjAnkBERERGRo9r6X1zX9yFVBR/6nt23yn2l/lN4QUREREQanaVL4ZNPIDER7PbA1mKzQatWsGwZzJoFv/pVYOsRqQtBQUF06dKFLl26MH78+PLlHo+H7du3l4ca1q9fj9PpZMuWLdhsNrp16+Y31GC11t7MmmfeBuA/QAQQWqdnWrMml3POWcmMGQMYMSLRzxYWzKdJ647W9Mc6rUdERERERBoqA3gO2A10wryXOH0ul4+JE39g1iz/g8gjI4P47LOzGTIk/hdrooAc4CXgbE61C0RpaSmff/45TqeTuXPn0qVLFyZOnMgrr7xChw4davBJpKH5ZPMnbI/YXun67M3ZlQ6uyN+dj6fEU+m+eTvz8JZ5sQVXnJDB7XKz5YstfvdzJDjYVLaJNrQ5SfUi/hmGwVrH2krX19V1XVRUxNaVW/3u50hw8P6u9xnVbxSto1pX/QFEfuGTT2DJEmjZ0nyP0rx5C/r168ePP/5wdIvuwDhgZoV9CwrWs2XLI3Tr9s8K6wzDx9q1v8XjKfB73p49XyE09MTrNSgImjWDTz+FoUPh4otP77OJiIiINBZbjmxhtW11pevr6j4EIGtzlt/l9gQ7nx76lK4du1ZRudR3Ci+IiIiISKOSlQXPPQceD8TGBroaU0QE5OTAyy/DwIHQtpJkukhjExQURNeuXenatSsTJkwoX+7xeEhPTy8PNaxdu5b333+frVu3EhQU5DfU0L59+wYQaigF/gnkAcmnvPfkyZ2IjbVjGJCb6yYzs4w1a3LZvLkQw/C/T36+h3HjvuPLL8+jX79oP1sEA9HAu8D5QL9TrktERERERBq7BcDnmB0PaqdDXlmZlyuv/J7PPvM/C310dBDz55/DoEGVPbxpA2wHngaeBaq+H/R4PCxZsgSn08msWbNITEwkNTWVxx57jO7du5/GJ5GGZl/+Pv71zb9wRDuIaBVB4YHCCttkbc6i/cXt/e5f2eCIYwyvQU56Dgk9Ek5Y7na5+Xr113gPef3uF9YpjOe+fo5+LfuREJbgdxuRqizYvoDlh5cT2jKUkoMVux3UxXVdWlLK6tWrsWXb8P1i9nuAxG6J7CvYxzNfP8OzFz+L1VLfn91JfbF/P/zrX2b36sjIn5e3bt0at9vNhg0/HV3ya+AHYFeFY2zf/jTNm19GXNyJXW127Hie7Owv/J63Zcurad061e+6mBjzPc5zz0G/fubkWCIiIiJNmcvrYsqqKRQEFRDeKpyiA0UVtqmL+5BjKuu8EN0lGucGJ0PaDuGcNudU/SGk3lJ4QUREREQalffeM9sMd+wY6EpO1Lq1Wdcbb5hth0WasqCgIFJSUkhJSeHKK68sX+52u08INfzwww+8++67bN26FYfD4TfU0K5du3oUapiD+TItiZrMVHr77e1o3z6swvLt24uYMmU7b765x2+IoajIy2WXfcvatUNJSPDXKj0RSMectfStGtUmIiIiIiKNVSnwL8CHGXw+fSUlXq644lsWLTrid31cnJ2FC89hwICYKo5iA1oAS4CVwNAKW/h8PlatWoXT6WT69Ok4HA6uueYaFi9ezFlnnYXFonufpujNH95kf8F+OsV2Ijcl1294obIBECdbd0zWpqwTBle4XC5Wr15NMMHk5+T73adNrzbsyNnB/9b+j3vPu7can0TkZ6WeUv71zb/wGl4SuyWy5+CeCtvU1XUdGxbLgawDfveJT4knNiKWJTuW8OXuLxnWflg1Po0I/Oc/ZoChU6eK69q3b4/b7WbLls2AHbgbuBf45ay9Ptau/Q1Dh64jKCgCgIKCjWzZ8he/5wwObkmvXlOrrCspCXbsgHfegXv1VS0iIiJN3Lxt81izfw1totpQklLiN7xQ2/chx7jyXX7v5wFa9mhJibuEF9e8yHut31OIuoHS/9dEREREpNEoLIQ5c8xOB0H1LKZrtUJcHCxeDAcPBroakfrJbrfTrVs3fvWrX/G3v/2Njz76iLS0NIqLi/n222+5//776dixI9999x2TJ0+mS5cuREVFcfbZZ/Pb3/6Wp556is8++4xdu3bh81WcDa5ueYEZmANsQmr1yJ06hfP6672ZPfssgoP938bv31/KP/6RXskRLJgzqK4DfqpkGxERERERaZpWYM5m3KpWjlZY6GH06DWVBhcSEx0sW3buSYILx0QBbmB2+RLDMPjhhx+47777aN++PePGjcPr9TJ9+nT27NnDc889x9lnn63gQhOVWZTJwu0LiQ2JxWa1EZcS53e7stwyig5VHHQBkL3lxMEVYYlhFeYAOH4bl8vF6q9XExYWRlJwElTSOTGhWwKRwZF8uvVT8sv8BxxEKvPF7i/YlbOLVpGtzsh17XF7WLNmDRGREbS0taz0uo5LiSMyOBK3z83szbOr/XmkacvMhAULzE4HtkoaPnXunEyn8mRDB8B/t4Ti4h1s3GimDHw+N2vXXo/PV+p32z59/oPD4f+/n2OCgsxOEJ9+Cnl51fgwIiIiIo2UYRjM2DQDgFB7aK3ch4QmhlZ5H3K8rC1Zld6HxKfE0zyiOWmH0/juwHdVfAqpzxReEBEREZFGY8kSMxhQX9v5xsWZbYfnzQt0JSINi91up3v37lx11VU8+uijTJ8+nY0bN1JUVMSaNWvKB6188803TJo0ieTk5PJQww033MDTTz/N559/zu7duzH8tS6oFd8CmzG7HNSNyy9vwSuv9Kx0/auv7uLgQf8v5yASKAY+rYvSRERERESkQTKAWZhdF4JP+2j5+W5GjVrDihX+Xzy3aBHM8uXn0rt31CkcNQ5YyfbtS3j00UdJSUlh2LBhHDp0iH//+98cOnSI1157jQsuuABbZSMgpcmYnz6f7JJs4kLNQRXx3eIr3dbfDJCFBwpx5btOWJbYJ5Godides7npufg8PlxlLr7++mvCI8Lp378/OVtz/J7LEeUgomUECWEJZBRlsGj7olP9aNKEGYbBzE0zMTAIDgqu8+va6/XyzbffYLfb6d+vf6WDiY5d1wBxoXGs2ruKnTk7T/XjSRO0YAFkZUF85ZcyYKFbt260bdv26O8TgBS/W+7Z8zqZmfPYtu1x8vJ+8LtNu3a/p1mz0dWqLzERMjJgkb6qRUREpAlbl7GOnzJ+IjHMfPddG/chCb0SIObE7Y7dh1TnmPDzfUiEI4IyTxmfbtG774ZK4QURERERaRQMA2bMAIsF7PZAV+Of1QrBwTBrFrhcJ99eRKrmcDjo0aMHV199NY899hgff/wxmzZtori4mNWrVzNp0iTatm3L6tWrueeee+jUqRNRUVEMHDiQG2+8kWeeeYZ58+axZ8+eWgg1fAKUAeG18Mkqd/PNbenfP9rvutJSH7NmHapkTwvmrKXzgaw6qk5ERERERBqWLZhB7ITTPlJOjpsRI1bz1Vf+B2+3aRPCihXn0r17ZLWPWVJSQnp6Nrt2refFFy9h/fr1/P3vfycjI4N3332XSy+9FHt9fQgkZ5zb62bmppk4bA5sVjPIEhofSmhCqN/tszZXvDf2N0g7LiWO+JQTB2l4y7wc2XqEr7/+msiISPr364/Vaq10cMWxGSrtNjtWi5WZm2biM850x0hpqLZmbeW7A98RH2Zeh3V5XefuyOX777/HMAzOPutsrLaTX9cAMSEx5JXm8fm2z6v9uaRpcrvN9zgOR+VdF35moVev3rRs2Qqz2+3dVNbxdu3a35Ce/k+/68LCOtGt2zPVrjEoyKxt5kw44819RUREROqJT7d8SomnhAiHGViujfuQ2C6xFeYB9JZ5ydtVseVVde5DYkNjWbxjMQcLDlb6OaT+Cgp0ASIiIiIitSE9HdLSIOH037fXqcRE2LULfvgBBg0KdDUijZPD4aBnz5707Hlil4KysjK2bt1KWloaaWlpfPXVV7zxxhukp6cTFhZG9+7d6dGjxwk/bdq0wWKxVHKmY/KAFVSYKqKO3HFHO265Zb3fdQsWHOaOO9pXsmcCsAtYCVxRJ7WJiIiIiEhDsgIoAlqe9pHGjfuOPXtK/K5r1y6UZcvOpUOHsJMep6ysjAMHDnLgwH5ycnJJSEigc+dmPPtsCnb7DMxgtkhF6zLWsTNnJ83Cm52wPC4ljv0r91fY3t9ACH/L4rrGYQ2ysnP+iTPKr120loQhCfTr2w+L1YKnxEP+7ny/tcV1/XlwRWJYIpuObGLLkS10S+xWrc8mTduK3SsodBXSIqJF+bK6uq43LNuAkWJw3rnnYQuyVfu6tlqshDnCmLttLnecfUc1nqVJU7V+PezcWf3u2RaLhX79+uHxeDh8GOAm4NUK27lcRyrZ30bfvu8QFHRqE84kJMDmzeZP9+6ntKuIiIhIg1fiLmHhjoVEBUed8Ld9bdyHsAHYWnHb2OTY8t+rex8SFxrHjuwdfLH7C67pec3JPpbUMwoviIiIiEijsG0bFBdDixYn37YqS5d2qHTdihWnd2yA0FDweMx6FV4QObOCg4Pp1asXvXr1OmF5WVkZW7ZsKQ81rFy5ktdee43t27cTHh7uN9TQunXr4x7WpAOFQPMz8jkuuKDytpxff+1/llOTDTCAbbVdkoiIiIiINEhpmPcJpz/ItLLgQqdOYSxdei5t2/qfnQ/A7XZz6NAh9u/fz5EjWcTGxtC6dWvOOussgoODMQPjOcABoPVp1yqN07asbZR5ywgJOnFW7soGVxRnFlOaU0pI7M/b/3K2yLDEMELjQ7HarRX2tx6xlgcXALK3ZmP4/Hd1PH6G+3BHOIcKD7Ete5vCC1ItGzI3YLVYqzVo6HSv66KdRVx0w0XYHWZXm+pe1wCRjkiOFB/hQMEBWkfpu1r827bN7EodWvmfBRVYrVbOOussVq9eTU7OJcAa4Ptq7dup02Ti4s475TojIiAjw6xX4QURERFparbnbKegrKC8+9sxp3sfEhIXUqHzApjhhU5jO/38ezXvQ6wWKwYGW7O2+t1W6jeFF0RERESkUUhPN/9prfjOpV7aqvsnkXojODiY3r1707t37xOWl5aWnhBq+OKLL5g6dSrbt28nMjKyPNQwfryLYcNysdmaERICdT25XKdO4djtFtzuig9tsrJc+HwGVmtlRdgB/10bRERERESkKfFihhdO3g2hplJSIliyZBCtWoVUWOf1esnIyGD//v1kZh4mMjKCVq1a06dPH0IrjGgMB44A21F4QSqzNWsrFoulwozvvxxgfbysTVm0Ps+8pkqySig5fGIIJy7FnNExOCqYiNYRFO4vLF/nzfCekPvxN6skQFBoEFHtosp/t1rMh5fp2enV+FTS1Hl9XtIOpxHuOHHW+Lq6ri1ZFhwOR/nv1b2uwQzmHCk+Qnp2usILUqmtW8EwTv35qc1mY+DAgXz99Vfk5/8R+CNQUOU+UVF96dLl0RrVeay+7dtrtLuIiIhIg7Y9eztlnjJCg058PlMb9yGEQHircIoOFJWvy96ajWEY5ffzp3IfEhIUwvoMvftuiBReEBEREZFGYcMGsNkCXUX1hITAunU1e0gvImdOSEgIffr0oU+fPicsLykpOSHUkJMzk4yMQ2zcuBe7PYiIiEgiIyOIjIws/wkODqnV/97j4hxkZJRVWO7zmQGGxMTgSvYMxxzwU0xdDlISEREREZH6bi+QC0TW2RnuvrvDCcEFn8/H4cOH2b//ABkZhwgJCaFVq9Z069aNiIiIKo4UxM9d5IbWWb3SsK3LWFeh6wJARKsIgqODKcureA+dvTm7fHBF9paKgyPKB1cA0cnRJwzydhe6KdhXQFRSVPmx/IntHFveneEYu83OTxk/VeNTSVO3N38veaV5FcILtXVdx6XEnXBde4o8Nb6ug6xBGIbBtuxtDGs/rJqfUJqa9evN9yM1YbfbOeecQXz1lZeiohuBl6rcvk+ft7BaHVVuU5WgIPM9joiIiEhTk56djoFRYXKAWrsP6Rp3QnjhdO6vwx3h7MvfR25pLjEhMdX7gFIvKLwgIiIiIg2exwNbtkBYAxmHGx5uthzOyYG4uJNvLyL1S2hoKH379qVv375Hl+wAgmnfvjWFhYUUFBRQUFDAkSNZ7Ny5i+Li4vJQQ3a2q1Zq8FXSKhOo8CDpRGFAFrAb6FYrtYiIiIiISEO0AzPU3LzOznDnnRuIjg5i+PBg9u8/wMGDBwkKstGqVWvOO+88oqKiTzHkva2uSpUGLq80j4MFBwmz+384GNs1lkPfHKqw/PgBFf4GR8R1NR/clZaUcthyuOL+m7OJSorC6/aSuyPX77mPH6BxTLg9nPTsdFxeFw5bzQfWSuO3M2cnxe5imoU3q7DudK9rACO+4vOlml7XAFhgW5a+q8W//HzYv998P1JTwcHBDBp0LsuWPYzPV/W2hw7NIjq6X43PFR5udl5wucChr2oRERFpQtIOp2G32f2uq437kNgusexdtvfE/Wt4HxJmDyOjMIOdOTvp17Lmf/vJmafwgoiIiIg0eGVl5gNku//7p1PSqdNk7PbYo8ctY+fOHeXrOnToSHCwOZv5nj2vU1y8s0bnCAqC0lIoKlJ4QaRxyAfs2Gw2oqOjiY6OPmGt1+stDzXYbJX3Gt+7dy/h4fFHOzUEVzmIJyfH7Xe51QpxcVV9GQYBXqCwim1ERERERKTxOzbDXd21sfR4DK699kcmTbJx3XVtGDjwbGJjY08SuK5MEJBX2yVKI1HkLsJjeAiz+g8vxKfE+x1cUbCvAFehC0eEo8LMkI4oBxGtIigpKeHrr74moVsCB5YfOGGb7C3ZtB/Zntz0XHxu/6No/Q2uCLIG4fa5KfWUKrwgVSpyF+EzfNisFb+rT+e6Bjhw4AD73fsr7F/T6xrMazuvTN/V4l9RkTkR1elOQnX48Pv4fKtPul16+pM0azaW2NiBNTpPUJBZb0mJwgsiIiLStOSV5WG3+n/ffDr3IR63B/B/P3E699den5cid5GfPaQ+U3hBRERERBo8lwsMg1Ocrc+/du1uJyysPQAFBQXs3Lm8fF10dF+aN2+O3e4gM3N+jcMLViv4fGbdItIYlAGVfwEdH2qIjj4CFPjdLisrm/Xr91FcXILdbicpqQ09evSosN3WrYV4PP47LyQkOLBaq/oytAI+wH/4QUREREREmoravScIDrZSVlbx5bLPB88846V79xh69TqdGRwsQOlp7C+Nmcvrwmf4sFqsftdXOku8YQ6QiOsaR8G+E+/Vj80KmZaWRmJiIj179STn4xxKDpeUb3NsNslfDsw4xmq3EtMxpsJyi8WCYRi4vHo4KFVze91YKnnmdDrXtcfjIS0tjbOGnsVPK3+qlesawGqxUurRd7X453KZfxdY/X9VV0tx8U42brynWtsahpe1a69n6NAfsdlOPTGh9zgiIiLSVJV5yiqdeOJ07kOOCYkLITQxtFbuQ6wWKz58ur9ugBReEBEREZEG73QedlfFMH5+6R4SEsqmTZtYu3YtwcHBeL35p3FcM2hRV3WLyJlWOzOV9u3bh/btw/B4vBQWFmD4zyewbFlWpccYNCi2GmeyYIYYRERERESk6aqFGSCO88EH/bj11vVkZ1cMRfh8cOON63C5fNx6a7vTOIvuY8Q/m8VW6QBvgKi2UQSFBeEp9lRYd2yABL+4Bz82IKNv377YbDYsFgvxKfHsO7yvfJvS7FKKM4vJ3uR/cEVMxxhsjsqfGVQWthA5pqpONadzXQcFBXHhhRcSFBRUq9e1YRh+u0SIgPk+xGKh0meeJ2MYPtau/S0ej/+JYfwpKtrKxo2T6dXr5RqcT+9xREREpGmq6l71dO5DrFYrXbp0wWq16v5a9JRPRERERBo+h+PnWXBq97g/9wI+//zBjBx5MaNGjWLAgLMIDq68T3Ba2gb27NlNTk42bnfFl/bHHnqr1bBIY+HA7GZQO4KCbMTExBAbG+N3/auv7qp034svTjzJ0X2Yg5T8t/oUEREREZGmIpgKb5NPQ//+0Sxdei4JCf4fdhgG3HbbT0yduquGZ/ABoTUtTxo5u82OxWLBZ/i/N7dYLcR18T87ZNamrJ8HWBzn2MyQQUFB5QPI/c0weWTjEXLSc/we+5ezSx5zrEuEw6aHg1I1h82BgYHhZ7T36VzXYF7bUHvXNZjXdrAtuNL10rQde49T0/DCjh3Pkp39ZSVrhxIW1t3vmt27X+Xw4UWnfD7DMOvVexwRERFpaoKDguvk/tpqs9KlaxesNmvt3l+j++uGSJ0XRERERKTBCwmBqCg4fLjuz2W3O4iLiyM4OISiIv/bGAYcOHCAgoICysrKCA4OITIysvzH640hPDyMmBj9OS7SOLQAdp2RM7366i7Wr/c/u1hIiJXx41uc5AhlmIOU4mu7NBERERERaVDiMF8TujAD2aevT58oli8/l+HDV5ORUVZhvWHAHXdswOXycdddHU/x6B7Mey+RimJCYgi2BVPmKTNvef2IS4kjc21mheX5u/PxuU8clBEUGkR0+2i/x/ilXQt24SmpOONkZdsDlHnKiA2NJcwe5r9YkaPiQ+OxW+24fW6/g3Hq03UN4PF5aBGh72rxLyYGgoOhrOKfCCeVn7+BLVv+WsnaBOB2evXqyLffDsHnc/1ivcG6dTcxbNhP2O0x1T5nWRlER0OYvqpFRESkiWkZ0ZK0zLRK19en+5AyTxmOIAdxoZXfp0j9pM4LIiIiItLgWSzQqxcUFwe6ElPPnj0ZNOjc4zo19KdlyxYYhsH+/fvZvHkv33zjpGvXNowaNYp77rmH//znP6xevZr8/PxAly8ip6wn5kCauvXxxwe4++6Nla6//fZ2tGoVcpKjFAHRQFJtliYiIiIiIg1OJyAc8x6h9vToEcny5efSsmXlM2//+c8befbZ7adwVAOzg1zn0y1PGqmQoBA6x3WmyF359Ryf4j/Eb3gNCvaeOElAbOdYLFZLhW0jWkYQHHPitZ2/u5JneRaI7RLrd1Wxu5ieiT2xWvSqXqrWKa4TYY4wilz+r+36dF0f6w7RJb6L/32lyQsOhs6dqXRSqMr4fG7Wrr0en89f6sEC3AWEExnZiy5dHvd7jNLSffz0052ndN6iIujRA2y2U6tXREREpKHrntgdo4punfXpPqTIXUSEI4IOMR0qrVfqJz0REREREZFGISUl0BX4Z3ZqiKddu/b07NmTc889l86de/C3v03ggw8+YNy4cbjdbt5//32uuOIKoqOjSUpK4pJLLuHee+/lrbfeYs2aNQo1iNRryZgvyvy3zzxd27YVcvPN67jqqh9wufyfo1WrYB58MLkaRysGegB66yYiIiIi0rQlAM0x7xFqV0pKBCtWnEubNpWHqydN2sQ//pFezSOWAXbMey8R/3o174Xb6650fXTHaGzB1bsXrmpm+arWHS+qbRT2MLvfdQYG3RK7Ves40rTFh8bTIrxFpcGc+nRdu7wuHDYHyXH6rpbK9eoF7sq/qv3auvVv5Oev9bsuKen3QJ/y3zt1uo+4uCF+tz1w4AM2b34TqhiIdzzDgO7dT61WERERkcYgOS4ZCxa8Pq/f9fXpPqTYXUyX+C4EB1U+iYbUT0GBLkBEREREpDZ07mx2YPB4IKge/5VrGOZPnz7hDB06lKFDh56w/siRI6SlpZGWlsbGjRt59913SUtL4/DhwyQlJdGjR48Tfrp160ZkZGSAPo2ImDoDIUAJ5sylNTN16m5iY+0YBuTlucnIKGPNmlw2bSqscr/wcBuffjqQxMTqPJTxAXrrJiIiIiIiFszBftvq5OidO5sBhosuWs3u3SV+t3nooc243T4eeeRks3QXY95raUCsVK5znNmZw2f4/HY0sAZZiekUQ9bGrJMeK65r5QMo4lPiObj64MmPUckgDI/P7NyoAd5SHRaLhT4t+rA1e6vf9fXlugZzxtNwe7iubalS8tHLw+cDazWmWs3J+Zrt25/yuy48vAvJyX9n795V5cssFit9+77DF1/0xuMpqLBPevq9ZGU1p2fPoURHR1d6Xq/3xHpFREREmpLkuGTC7GEUuYuICo6qsL4+3Yd4vB56Net10mNI/VOPh3WJiIiIiFRfSgpER0NuLiQkBLqayuXnQ3h45TP2JCQkMGzYMIYNG3bC8sOHD5eHGtLS0vjf//5HWloaR44coW3btn5DDREREWfgE4kIdAASgSOcTnjhqae2n/I+UVFBzJgxgP79K3/Z9rNSzI4LCi+IiIiIiAhAL+BjwEtddGfr2DG8PMCwY4f/Dg9/+9tW3G6DJ57oWsWRcoEBQHXue6Sp6p7YnQhHBPll+cSExPjdJi4l7qSDK44NwqhMdWeGrGyARl5pHlHBUXRLUOcFqZ6ezXoyPW06Xp8Xm7Xid3V9uK4Bcktz6d+yf6X//YkA9OgBkZHme5KYmKq39XiK+PHH32AYFWf8tVhs9O37DjZbaIV1YWHt6d79Bdavv9nPUfMpK3uGVatstGzZkpSUroSGhlXYKjcXoqLUeUFERESapqSoJFpEtGBv/l6/4QWoH/chLq8Li8VC90T90dYQVSPLLCIiIiJS/zVrBsOHQ05OoCup2pEjMGAAdDnZpIK/kJiYyAUXXMCdd97Jq6++yvLlyzl8+DAZGRm8/fbbXHrppRQWFvLf//6X0aNHExkZSfv27RkzZgyTJ0/mf//7H9999x1FRf5bjIvI6XAA44AizM4GZ8agQTF88835jBiRWM09MoGOwKA6rEpERERERBqO4UACZhC7brRrF8aKFefSuXPlQe+//30b99+/qZK1bsz7rPF1UZ40Ih1jO3J2q7PJKq588ER8SvxJjxPdMRqbo/IwT2RSJPYI+0mPU9kgjOySbC5odwEtI1ue9BgiACM6jiAhLIEjxf6/q+vDde32uvEZPsan6LtaqtahAwwcCFknn6SXTZsmUVyc7nddcvKDxMaeU+m+bdveRPPmV/hdV1z8BV267ABg2bLlbNq0EbfbfcI2OTlwwQXQUl/VIiIi0gTZrDbGp4ynzFOGz/D/7rs+3IdkFmWSFJ3EkLZDTnoMqX8UXhARERGRRuOyyyA4GOrr+HyXy/zn+PFgsdTOMZs1a8aFF17IH/7wB6ZOncqKFSs4fPgwhw4d4q233uKSSy6hoKCA//znP4waNYqIiAg6dOjA2LFjuf/++3nnnXf4/vvvFWoQOW2XYs4CmlvnZzrvvFg++qg/q1YNpmvX6nZY8QJlwATMsIWIiIiIiEg8cAmQDxh1dpY2bUJZseJcUlIqv3956qnt3HNPmp81R4AWwIi6Kk8aCYvFwvhu47FYLJR5yvxuE9s5Fout6odyJ5v50WKxVDn7PEBY8zBCYkIqLC92F+OwObgixf+AWhF/4kLjGN15NHlleRhGxe/qQF/XAEeKj9AivAUjOuq7WqpmscC4ceY/y/x/VQOQmTmf3bv/7XddVFQ/Ond+5KTn6t37DRyOZn7Xbdv2IF26RDN48GDy8vJYunQJO3bswOfzUlwMdjtcfnl1PpGIiIhI4zS682hiQmLILsn2uz7Q9yE+w0eJu4TxKeMJtVfsxiX1X1CgCxARERERqS1nnw3dusGGDeYMPvVNZiYkJZkz9tQli8VC8+bNad68ORdddFH5csMwyMjIIC0tjbS0NDZu3Mgbb7xBWloaubm5tG/fnh49epzwk5KSQlhYxbbJIvJL7YHzgc+B6rW49Mdms2C3WwgJsRETE0RcnIOkpBA6dgyjf/9ohg6Np23bmjyAyT5a1yU1rk1ERERERBqjy4AZQAEQVWdnadkyhOXLz2X48NWkpRX43eb553fidhu89FIPLBYLZqCiAPg1EFlntUnjMbTdUNpFt+NAwQGSopMqrLcF24juEE1uem6lxzjZwAkwB2BkfJ9R6frKZqDMLMqka3xXBrYeeNJziBxvbJexfLzxYwpcBUQFn/hdHejr2jAMClwFXNvr2gq1ifgzdCi0bw/795vvS37J5cpm3bqb/e5rtQbTr9+7WK0nn6E3ODiRPn3e5NtvK6YQvN5C1q79Deed9wWDBg0iM/MwmzZtYufOnSQkDGDgwGjOOaeWZqASERERaYBaRbZieIfhzNg0g/jQ+KPPaX4W6PuQnJIcooOjubTzpSc9h9RPFsNfPF9EREREpIGaNQv++ldITISI6k5IXonS0hIWL14MwIgRIwgJqXliu7QU9u6F++6Dm/0/dw8YwzA4dOjQCaGGY/+el5dHhw4d/IYaQkOVYBc50VfAnUA4EBvgWo7nAXZgDvg5+axkIiIiIiLSlBjA74HlQDL1q2n7QcAOvAd0CnAt0lC8vfZtpqyaQpuoNoQE+Z8lPhCKXEVkFGbw2IWP8avuvwp0OdLAGIbB7Z/dzrJdy0iOS8ZqqT/f1YcKDxFkCeLdCe+SHJcc6HKkgfjf/+Cf/4Q2bSDkNL6qa/MdjmEYbN9+gPT0Qtq1e5u33hrN0KFDa16ciIiISAP3/YHvuW3ubdhtdhLCEgJdTjmvz0t6djq/6v4rnhz+ZKDLkRpS5wURERERaVQuuwwWLYIlSyA5Gaz14D2OYZjBhf794de/DnQ1FVksFlq2bEnLli0ZMeLn1t6GYXDw4METQg1Tp04lLS2N/Px8OnbsWCHU0LVrV4UapAk7FxgPvI85K2h9ueXeC3QE7gh0ISIiIiIiUu9YgElAGnAIaBXYcsqVAEXAvSi4IKdiYs+JLN25lG/2f0NyXHKF2SEDwWf4OFBwgKHthjIuZVygy5EGyGKxMOm8SWzI3MDBgoO0jmod6JIAKHGXUOgq5N5z71VwQU7JNdeY73C++cZ8j1MPvqoxDAs+X2uuv95N+/aRjB07lgsvvJApU6aQkpIS6PJEREREzrgBrQZwdY+reevHt4gKjsJhcwS6JAD25e+jXUw7/jjwj4EuRU5DPRjKJSIiIiJSe4KCYPJkaNECDhwIdDWmzEyIioIHHji9WYTONIvFQqtWrRg5ciR//vOfef3111m1ahU5OTns3buXV199lQsuuIDDhw/zyiuvMGzYMCIiIujcuTPjxo3j4Ycf5oMPPmDdunWUlpYG+uOInAEW4A9AZ2BPgGs5JhczRHEPUH9mxBARERERkfqkM3AbZmCgOMC1gNkNYh8wELg+wLVIQxMSFMLkwZOJDokmoygj0OUAcLDgIInhiTxw/gMEWevLRAfS0CTHJXPbgNso85RR7A78d7VhGOzL38fAVgO5vre+q+XUhITA/fdDTAxk1I+vag4eNDt6P/ywnUceeYj09HSSkpLo168ft99+O4cOHQp0iSIiIiJn3O1n3U73xO7szduLYRiBLof8snwMDP408E+0jGwZ6HLkNCi8ICIiIiKNTvv2cMcd4HZDfn5gaykqgoICuOEG6N07sLXUFovFQuvWrbn44ou5++67eeONN/jqq6/Izc1l9+7dvPzyywwdOpSMjAz+9a9/MWTIEMLDw+nSpQvjx4/nL3/5C06nk/Xr1yvUII1QHOaspcHAkQDXUgZkApcBI06yrYiIiIiING0TgfMwQwOeANeyH4gFHsC8txI5NT2b9eTmfjdT6Cqk0FUY0Fryy/Ip85Tx+wG/p2Nsx4DWIg3fxJ4TGdx2MPvy9uHxBfa7en/BfmJDYrn//PsJDtJ3tZy6Hj3g5pvNdyiFgf2qJj8fysrg9tuh09GGT82aNePll19m3bp1ZGZm0rlzZx5//HGKiooCW6yIiIjIGRQZHMnkwZMJs4eRWZQZ0FpcXheHCg5xSfIljO0yNqC1yOnT1A4iIiIi0ihdeSX89BN8/DFYrRARceZrKCmB/fth5Ei46aYzf/4zzWKx0KZNG9q0acOoUaPKlxuGwb59+0hLSyMtLY2NGzeyePFiNm7cSFFREcnJyfTo0eOEny5duhAcrJde0lANA24E/g3YMAfdnGluYDcwALgbsyuEiIiIiIhIZezAX4E7gXSgI+b9zJl2bFbje4FuATi/NBa/7fNb0jLTmJ8+n6ToJELtoWe8hkJXIRmFGYxPGc81Pa854+eXxsdus/OXoX/hQMEB0rPT6RjbEZv1zH9XZxRmYBgGd597N90Tu5/x80vj8ZvfQFoazJsHSUkQeua/qikqMrs/jBsH1/j5qu7SpQszZsxg1apVTJo0ialTp/L4449z4403EhSkIVciIiLS+A1qM4jbBtzGS2teIqs4i/iw+DNeg8fnYVfOLvq06MPk8yZjsejdd0NnMepDLw8RERERkTpQWgoPPQSffw7Nm0NU1KnuX8LixYsBGDFiBCEh1X9yXlwM+/bBkCHw/PMQGXlq524KDMNg7969J4Qajv2zpKSk0lCDw+EIdOki1eADpgDvYoYX4s7guV3ALqAX8C9ALTNFRERERKS6tgB/BPYAHTiz86AdxAxi/xm4AYWw5XQVlBVw78J7WbFrBa2jWhPuCD+j5z5UeIhRnUYxZeQUQoJCzti5pfHbcmQLf5r3J3bn7aZDbAeCrGfuu/pgwUHcXjd3DbqLG/veqEFDctoKC+Gee2DFCmjdGsJP4av6dN7hgNk1+9AhGDUKpkyBkJN8VRuGwYwZM3jggQcICQlhypQpXHrppfrvQERERBo9wzB4YfULvPnDm0SFRJEQlnDGzu3yutiVu4tuCd341+h/kRSddMbOLXVH4QURERERadRKS+HRR+GTT8zwQmIiVPc5ck0ffGdnQ1YWXHQR/OMfEB1dw+KbKJ/PV2moobS0lM6dO1cINXTu3FmhBqmHPMCLwH+BYMwQQV2/yMrHnKl0APAsCi6IiIiIiMip2wLcg9mBoQ0QVsfn8wJ7Mbs/3A1ch4ILUlsKygp4cMmDLN6xmPiweOJC63ZyAcMwOFJ8hLzSPMZ2HctjFzxGmL2u/xuSpmjLkS3cu/BetmVvo01Umzq/zrw+L/vy9xFkDeLuQXdzXe/rNGBbak1BATz4ICxeDHFx5k91Lq+avsMxDPMdTm4ujB0Ljz0GYafwn5DL5eK1117jscceo3fv3jz99NMMGDCg+gcQERERaYB8ho9XvnmFN354A5vVRqvIVlgt1jo9Z6GrkAP5B+jdvDfPjXpOwYVGROEFEREREWn0XC7497/h3XfNWXySkiA4+OT7neqDb7cb9u4FhwOuvBLuvvvUZgmSqvl8Pvbs2eM31OByuSoNNdjt9kCXLk2aAbwPTAWygNbUzcAfL7Dv6PlGAQ8AZ27GCxERERERaWx2A48DXwOh1F0Y+1gAuw1wL3BpHZxDmroSdwnPr36ejzd+TJm3jKSoJOy22n9e5PK62JO3h3B7OL/u9WvuHHgnDpsm25C6sydvD4+veJyv9n5FqD2UFhEt6mTwUEFZAQcLD9Imsg33nncvl3bWd7XUvpISeOEFmD4dysrM9zgne7Rfk/CCywV79pjvbn79a7jzTvOdTk3k5eUxZcoUXnzxRcaNG8f//d//0b59+5odTERERKQBMAyD6Run869v/kVmUSatI+umy6HP8LEvfx9en5eLOlzEw0MepnlE81o/jwSOwgsiIiIi0mR8/73Z+nf9erMLQ0IC2GyVb1/dB98+n9ltITsbunSB++6DIUOq3+FBTo/P52P37t1+Qw1ut5suXbpUCDUkJycr1CBn2Dbgn5gDf0KA5kBQLRzXAHKBw5iDfe4CLkOzlIqIiIiIyOlzA07gNcwwdgsggtq533ABBzHvaUYCk4BWtXBckcqt3LOSp796ms1HNhMbEkt8WHytDPT2+rwcKT5Cflk+vZr1YvLgyZzd+uxaqFjk5NxeN9M2TOO171/jcPFhWka0JMIRUStdEVxeFwcLDmIYBiM6jeC+8+6jVaS+q6VurVwJTz8NmzdDbCzEx4O1kq/qUwkv+Hxw5Ajk5UHPnnD//XB2LX1V7927l7/+9a98+OGH3HnnnTz88MPExsbWzsFFRERE6qEdOTuYsmoKK3evxBHkoHl481qZJMAwDPLK8sgsyqRVRCv+MPAPjEsZh81axcAeaZAUXhARERGRJqWoCN58Ez780AwbBAdDYiKE+nmmfbIH32VlcPgwFBdDTAxcfrk5S09MTN1/Djk5n8/Hrl27Tgg0pKWlsWnTJjweT6WhhqCg2hhQLuKPC3Pgz3+BDMAGNANqMhuFGzgCFACRwIXA3Wiwj4iIiIiI1L5twNPAt0AxEAPEY97TnAoD8x7m8NHfk4A7gMtRAFvOlLzSPF799lVmb5lNbkkuYY4wEsMSCQ6qRpvWXyj1lJJZlEmZp4y40Diu6n4Vtw64lQhHRB1ULlK1bVnbePqrp/l2/7eUeEqIDokmPjT+lAf5GIZBgauAI0VHMDBIik7ijrPu4LKul9VJVwcRf/Ly4NVX4ZNPIDfXfH+TmFixo3Z1wgulpZCZab7PiY2Fq6+GW2+FiDr4ql67di2TJ0/mu+++4y9/+Qt33nknwdVpAy4iIiLSAHl8Hj5K+4g3fniDQ4WHsFqsNAtvRrg9/JTD1B6fp3xSgAhHBOcnnc+k8yaRFJ1UR9VLoCm8ICIiIiJNUkYGzJ8PM2bAjh3g8ZgPvsPCzHbBISFQVvbzg+/hw0dgsYRSVGSGFUpLzdl+2raF8ePh0kvNNsZS/3m93kpDDV6vl65du1YINXTq1EmhBqlFOcAiYAawGSgD7JghhjAgFDj+ZbCBGVYoOvpTgjmwpwVml4UxQBc02EdEREREROqOAawDPgXmA9lHl4di3suEY97XHM+HGXYoOvpP99Ht+gITMEPYNQlzi5y+vXl7mZc+j1mbZrEnbw9ew0twUDDh9nDCHeEE24JPGGxhGAalnlKK3EUUu4sp85QRZA2iQ0wHJnSbwCXJl9AysmUAP5GIeZ2uy1jHp1s+Zf72+WQXZ4MFQoNCCXeEE24PrzAbqs/wUewupshlXtsen4cwexh9W/RlQrcJXNj+QsId+q6WwNi3Dz7/HGbNgj17wOs13+OEh5s/hlHCkiU/hxeCg0MpLaX8PU5Zmdl9u2NH8z3O6NHQ8gx8VS9cuJD77ruP/Px8nnzySa655hqslbWPEBEREWng8krzWLxjMTM2zWDj4Y2Uekqx2+yE2cMIt4cTag89IQhtGAZun5siVxFF7iJKPaUANA9vzpjOYxjbZSwpCSm10k1O6i+FF0RERESkSXO5YNUqWLMGfvoJdu78+aG2x+MmPX0bYKFTp2TCwuyEh5shhT59YMAAuOACM+ggDZ/X62Xnzp1+Qw2GYVQaarDZ1KJQasoLfAesAn7CnM20CCg9bhsL5oCfY+GGFkAfzME+w4GoM1euiIiIiIgIYHaBWwSsP/pzGPNexot5D2PBDDsYmAHtSCAF6AUMA3qi8LXUF6WeUlbsWsH3B79nfcZ6duftpshVhMvr+nmghAEGRnm4oX1Me3o3783Zrc5mSLshOGyOwH4IET+OFB9h0fZF/JT5E+sy1nG46DBF7iK8Pi8WLFgsFgzDwMAgzB5GZHAkKQkp9EzsybD2w+jVrJcGC0m9UVoKX3wB330H69fD7t3me5yiIjfbt28DIDm5MzabvTzc0L499O4NZ58NQ4aA4wx/VXu9Xt577z3+8pe/0KJFC5555hmGDRt2ZosQEREROYN8ho8fDv7Ayj0r+SnzJ7ZmbaWwrJASTwlWixUDAwsWfIYPu9VOmCOMFuEt6N28N31a9GFExxHEhMQE+mPIGaLwgoiIiIjIcYqKYPt22LsXDhw4wu9+dyfg4a23XqNPnwQ6dYLo6EBXKWeS1+tlx44dFUINmzdvBvAbaujYsaNCDVIDpcBOYNfRf3cBNiAYaAYkA3FokI+IiIiIiNQfBpABpGN2Y3BhBrAdmMGFDkC7o7+L1H/5Zflsz97O/oL9uLwu3F43dpudYFswbaLa0CmuExGOiECXKXJKDMMgsyiTbdnbyC7JxuV1lQ8YCneE0yGmA+1i2imIIw1Gfr75HmfduiPceOPvADuvv/4KrVol0KYNdOoEEfXkq7qkpIQXXniBf/7znwwdOpQpU6bQvXv3QJclIiIiUufKPGXszN3JrtxdlLhLcHld2Kw2HDYHzcKbkRyXTHxovELTTZTCCyIiIiIildi3bx9JSUkA7N27lzZt2gS4IqlPPB5PpaEGi8VCSkpKhVBDhw4dFGoQERERERERERERETlNDekdzuHDh3niiSd44403+M1vfsNjjz1GixYtAl2WiIiIiEhAKLwgIiIiIlKJhvTgW+oPj8fD9u3bK4QatmzZgtVqrTTUYLVaA126iIiIiIiIiIiIiEiD0BDf4Wzbto0HH3yQ+fPnM2nSJCZNmkREfWkTISIiIiJyhii8ICIiIiJylNcLe/dCejrs2wcHD+bw0EOPAB6efvr/6NkzjuRkaNcO7PZAVysNjcfjIT093W+oISgoyG+ooX379go1NBkGcABIB3YDJYAbsAEOoBmQDHQEggNUo4iIiIiIiIiIiEj90BDDC8d8/fXXTJo0iR07dvDYY49x0003ERQUFOiyRERERETOCIUXRERERKRJy82FhQth5UpIS4O8PCguBsMAj8dNevo2AJKTO2Oz2QkPh6goSEmBc8+FSy6BZs0C+xmkYXO73X5DDVu3biUoKIhu3bpVCDW0a9dOoYZGoQhYCnwB/ARkHV1mAJZfbGsAIUAE0AU4B7gEaHumihURERERERERERGpNxpyeAHAMAxmzZrFAw88QFBQEFOmTGHs2LFYLL98NiwiIiIi0rgovCAiIiIiTY5hwMaN8Nln5k9mJlgsEB4OYWHmP202KC0tYfHixQCMGDECuz2UkhIoKjJ/vF6Ij4eRI+Gyy6B/f/M4IrXB7Xazbds2v6EGh8PhN9TQtm1bhRoahB3APGA2sB8zmBAGhB/98TfDlg+zG0MRUIzZlSEaGApcAQyqZD8RERERERERERGRxqehhxeOcbvdvP766zz66KP06NGDZ555hrPOOivQZYmIiIiI1BmFF0RERESkScnMhOefN7stFBaaXRQSEsBfN95fhhdCQkJPWO/1QlaW2a0hJASGDIHJk+Hos3KROuFyufyGGrZt20ZwcHCloQbN1lQfFACvAR8BeZhBhUTAUYNj+YAcIBuwA/2BB4ButVKpiIiIiIiIiIiISH3WWMILx+Tn5zNlyhReeOEFLr/8cp588kk6dOgQ6LJERERERGqdwgsiIiIi0iQYBixYAC+8ADt3QmIixMRU3SnhZOGF449dUACHDkGrVnDnnTB+vNm9QeRMcblcbN261W+oITQ01G+oISkpSaGGM2YNMAVIA2KBeKC2umQUY3ZwiAVuAa4Dgmvp2CIiIiIiIiIiIiL1T2MLLxyzb98+/vrXvzJt2jTuuOMOHn74YeLi4gJdloiIiIhIrVF4QUREREQavcJCePJJ+Owz8PmgTRv/nRZ+qbrhhWN8Pti/HzweuOgi+NvfID6+Nj6BSM2VlZX5DTWkp6cTGhpK9+7dK4Qa2rRpo1BDrXEBLwPvAyVAEjXrtHAyBnAIKAIGAo8D7ergPCIiIiIiIiIiIiKB11jDC8esX7+eyZMns2bNGh5++GH+8Ic/EBISEuiyREREREROm8ILIiIiItKo5eTA/ffDihXQrJnZbaG6TjW8cExhIRw4AAMGwLPPQsuWNShcpI6VlZWxZcuWEwINx0IN4eHhfkMNrVu3VqjhlJQCjwJzgGjMbgt1/X+/EmAv0Bl4Fuhax+cTEREREREREREROfMae3jhmEWLFjF58mRycnJ48sknmThxIlZrbXX1FRERERE58xReEBEREZFGKy8P7r4bVq2CpCQIPS57sHbtDezb979qHsmB3R5FSEhzwsO7Eh09gBYtriAyskele5SVwe7d0KcPvPyyGZwQaQhKS0v9hhq2b99ORESE31BDq1atFGqowAU8DHwKNAeizuC5PcBOoD3wL8wgg4iIiIiIiIiIiEjj0VTCCwA+n4/33nuPv/zlLzRr1oynn36aCy+8MNBliYiIiIjUiMILIiIiItIolZbCvffC4sUVgwtwquEF/xISRtKjx4tERnbzu97lgl274Jxz4F//gujo0zqdSECVlpayefNmv6GGqKgov6GGli1bNtFQgwE8BkwDWgCRJ91j/fp8PvroAN99l8fmzYXk5ropLPTicFgIDbWRmOigVasQOnQIo1u3CHr2jOTss2OIj3dUckQvsAPoAkwFWtfORxMRERERERERERGpB5pSeOGYkpISXnrpJZ588kmGDBnClClT6NGj8om2RERERETqI4UXRERERKRReuMNeO45aNkSwsMrrq+N8AKA3R7DwIELiI0d6Hf9sQ4MN9wADz542qcTqXdKSkr8hhp27NhBdHS031BDixYtGnmoYQ5m14WYoz+V27SpgNtv/4kVK7JrdKbrrmvNu+/2q2StBzPAMAp4HrDV6BwiIiIiIiIiIiIi9U1TDC8cc+TIEZ544glef/11rrvuOh5//HFatmwZ6LJERERERKpF4QURERERaXTS0uCWW8DjgRYt/G9TW+EFgPDwLgwduh6bLdjv+qwsM8Tw4otw/vm1ckqReq+4uNhvqGHnzp3ExMT4DTU0b968EYQaDgDXA4eBdlVuuXjxYS677FtKS301PtuoUYnMn39OFVsUHq3lMeDKGp9HREREREREREREpD5pyuGFY9LT03nooYf4/PPPuffee5k0aRKRkSfvBCwiIiIiEkgKL4iIiIhIo1JaCr/7HXzzDSQnQ2XjoKsKL3TqNBm7PRaPx016+mYgE4djPS5XZqXnHTBgJi1bjve7zjBg506znnfegejoU/1UIo1HcXExmzZt8htqiIuL8xtqaNasWQMJNRjAJGAu0ImqOh3s3FlMz54rKC72ntYZTx5eANgDxAHvAEmndT4RERERERERERGR+kDhhZ+tXr2aSZMmkZ6ezqOPPsott9xCUFBQoMsSEREREfFLf6mKiIiISKMyYwZ89x20aVN5cOFk2rW7nbCw9pSWlpCevhiAwYPPZf36a8jKWup3nyNHFlUaXrBYICkJNm+Gt9+Gu+6qWV0ijUFYWBgDBgxgwIABJywvKio6IdSwYMECnnvuOXbt2kV8fHyloYb6ZQmwEGhBVcEFgAcf3FxpcKFNmxAuvDCetm1DCQ21UVDgITPTxU8/5bNhQ0ENOjW0AdKBfwFPneK+IiIiIiIiIiIiIlKfDRo0iC+//JJPPvmE+++/nxdeeIEpU6Zw+eWXN5CJgURERESkKVF4QUREREQaDbcbpk+HoCAIDa3dY9ts4aSkPMmqVYP8ri8t3V/l/nY7RETA7Nlw440QFVW79Yk0dOHh4Zx11lmcddZZJywvLCw8IdQwb948nnnmGXbv3k1CQgI9evSoEGxITEwMwCcwgOmAB6j6P/DiYi+ffHLI77rHHuvCww93xmbz/0KprMzLihXZzJ2bwQcfVP298zMrkAAsB3YB7au5n4iIiIiIiIiIiIg0BBaLhXHjxjFmzBjeeOMNbr31Vp577jmefvppBg4cGOjyRERERETKKbwgIiIiIo3GqlWwfTs0b143x4+K6lPpOp+v9KT7JybCnj2weDFMmFCblYk0XhEREZx99tmcffbZJywvKCg4IdTw2Wef8dRTT7Fnzx4SExP9hhoSEhLqsNLNwHfAyYMT33+f67d7QqdOYTzySJcq9w0OtnHxxYlcfHEiTz/djXXr8qtZXwxm94V5wO3V3EdEREREREREREREGhK73c4dd9zBddddx9NPP82FF17IZZddxpNPPknHjh0DXZ6IiIiIiMILIiIiItJ4fPIJeDy133XhZ0ala4KDW5x0b7sdLBaYMQPGjzf/XURqJjIykoEDB1aYMaqgoICNGzeWhxo+/fRT/vnPf7J3716aNWvmN9QQHx9fCxV9BhQCLU+65aFDZX6Xh4XZTumMwcE2Bg6MrebWViAMmAX89ui/i4iIiIiIiIiIiEhjFBUVxRNPPMHvf/97/va3v9GjRw9+//vf85e//KWWnomLiIiIiNSMwgsiIiIi0ijs3292XoiLq7tz5OX9WOm62NjzqnWMZs1g40b46Sfo3bu2KhORYyIjIznnnHM455xzTlien59/Qqjhk08+4cknn2Tfvn00b97cb6ghrtpfKCWY4YVI4OSpJKOSHFRaWgGffHKIK644eRiqZpoBe4FVwMg6OoeIiIiIiIiIiIiI1BetW7fmzTff5K677uL++++nU6dOPPTQQ/zpT38iJCQk0OWJiIiISBOk8IKIiIiINAqbN0NBAXToUDfH93gK2bz5Ib/r7PYYWrW6plrHCQ+HgwdhyxaFF0TOpKioKAYNGsSgQYNOWJ6Xl3dCqGHWrFn8/e9/Z//+/bRo0cJvqCE29pfdDrYDOUD1Zqtq2TLY73KfD8aP/47hwxO44ormnHtuLL16ReFwWE/9A/vlAHzAFhReEBEREREREREREWk6evXqxeeff86SJUu47777eOWVV/i///s/rr32WqzW2noGLSIiIiJycgoviIiIiEijkJ5u/tNmO/1j7d49Fbs9Fo/HDWwGMvnqq1twuTL9bG2hZ8+p2O0x1Tq2xWL+bNt2+nWKyOmLjo7m3HPP5dxzzz1heW5uLps2bSItLY20tDRmzJjB448/zoEDB2jZsuUJoYYhQ7Lo0qUYm61Ntc559tkxhIZaKSnxVVhnGLB48REWLz4CgMNhpVevSM4+O4YhQ+K48MJ4WrY8ndmwgoCfTmN/EREREREREREREWmohg8fznfffccHH3zAww8/zHPPPcfTTz/N8OHDA12aiMj/s3fncXZX9f34XzOTTJLJnhCykEBCQghhE4QKCi4QWRRkmdTWaq1LBVwq1IVNactXVARcS60bdatVMcOilAKy+gMUBRQJa4IhZIMkk32bzGTu748PiYbMJDPJ3NxJ8nw+HvcxuZ/lnPfVm3mQcz6vcwDYQ1SVSqVSpYsAAIAddf75yS9/mYwf37Hr//CH92Tu3O/vUJ89ew7OoYd+M6NG/XWn7nvhheSgg5Kf/GSHugcqYOnSpZuFGp544okcf/xD+eu/Xpm5c3ulf//+6devf/r33/jql549e27Rzj/90/Rce+3zne6/ujp5wxuG5kMf2i/19SNTVVXVyRZeSlKX5JdJtqwLAAAAAGBXMHfu3IwZMyZJMmfOnIwe3bHFZfizdevW5Wtf+1o+97nP5bWvfW2uuuqqHHLIIZUuCwCA3ZzwAgAAu7zW1uSkk5Lly5MRIzp2z46EF6qre2XChEszduw/pbZ2cKfvb2wsVle/446kf//tKgHoVv4hLS0PZ8WKQVm5cuXLr1VZuXJlmpqa0rt37/Tv329ToKFfv/5pbe2V173uoTz77Ort7vU1rxmUn/70yOy3X10n7lqVZEWSnyaZsN19AwAAAABUkvBC12lsbMwVV1yRb3zjG3nnO9+Zyy+/PPvss0+lywIAYDdVXekCAABgR61bl6xendTW7pz+WlubMnv217No0f9t1/21tUlzc7JiRRcXBlRIY3r06J0hQ4Zkv/32yyGHHJJjjz0mJ5305px88sl59auPzMiRI1MqlTJv3rw8/PDv8utf353LL2/J4Yf32O5eH3poWV73ugczf/66TtxVm2R9kuXb3S8AAAAAALuPoUOH5stf/nKmT5+eVatWZeLEibnsssuycuXKSpcGAMBuSHgBAIBdXnNzsZNBVdXO67Op6aX8/vfvzHPPXdPpe6uqinqbmspQGFABTWnvn9e1tT1fEWo4NieddFJOPvmkvPnNr86NN07KF76wVw4+ePtCDPPmrcvHPvZkJ+6oTtKaIsAAAAAAAACF8ePH5yc/+Unuvvvu3HfffRk/fny+/vWvp7m5udKlAQCwGxFeAABgl1cqdW17Y8bckT597kx19bTstdf3UlX1+navfeqpi9LY+KtOtb8xvADsLjr/F7q2tjZDhw7JuHFjc+GFx2T69FMyY8ab8u//flDOOmto9tmnZ4fb+tnP5mfx4s6GEfwSAgAAAABgS695zWty33335dvf/na+9rWv5ZBDDslNN92UksktAAC6gPACAAC7vJ49uzYQ0NzcnEMOOSSnnHJmjjnmH3Lqqfdk5MgPt3N1ax5//LyUShs63H5ra1JdndTWdk29QKX1TrGbwY6ZMKFvPvKR8bnhhmMzd+7JWbBgSn72syPz/vePSf/+7e/M0NqaPPDAkg720ppiKMAvIAAAAAAA2lZVVZUzzjgjjz/+eP75n/855557bl7/+tfnN7/5TaVLAwBgFye8AADALq937+LV0V1rW1tbs27dunbPT548OcOHD091dU2SpLq6Okcc8ZX0739wm9evWvVUnnjiK2lt7ViAobm5CFz079+xeoHubmCSrt82e8SI3pk6dVS+853D8/zzJ+Soowa2e+38+e3/Tttcc5KeSfwCAgAAAABg63r27JnzzjsvM2fOzAknnJApU6bk7W9/e5577rlKlwYAwC5KeAEAgF1eTU0yeXKyenX717S2bsjChS/lscf+kF/+8o4sWdLRVcoL1dU9MmnS59s9P3v2Nbnttlvz6KOPZsGCBdmwof0gw5o1yahRycD2n0MGdimHJllf1h6GDKnN5z8/qQtaWp2kb5KxXdAWAAAAAAB7gv79++fyyy/Ps88+m4EDB+aQQw7J+eefn8WLF1e6NAAAdjHCCwAA7BYOPjhpbd382IYNG/Liiwvy+98/mjvuuCN//OPj6dmzZ/7qr/4qo0aN6nQfw4efngEDjmjzXKn0YsaPfz51dXV5+umncvvtt+fhhx/OvHnz0tKy+Yrsa9cmhx/e6e6BbuuAl3+WOnT1Sy815bvfnZPm5tZtX/wXRo7s3e65YcNqO9jKmiQHJunVqb4BAAAAAGDUqFH59re/nYcffjgzZ87MhAkT8oUvfCFr166tdGkAAOwihBcAANgtTJhQ/Gxqasn8+fPzyCOP5I47bs+TTz6V3r375Jhjjs2UKSdm8uSDM3jwkO3u54ADLmv33Lx5X8nEiRPypje9Kccff1wGDOifmTNn5I477sjvfvfbzJkzJ+vXr09VVTJx4naXAHQ7E1KEAdZ16OqVK1vyvvc9lgkT7sk11zyX+fM7dt+Pfzyv3XMHH9y/Q20kLUkO6eC1AAAAAACwpYMPPjj/+7//mxtuuCHXX399DjzwwPzgBz9I6ytXGgMAgFfoUekCAABgRy1fvjxPPHF3Fiw4JM8881L69m3JyJEjc8ABB2TAgP5JqrqsrxEjzkz//odm5crHtzi3Zs2fMm/ef2fMmPekf/8B6d9/QCZOPDCrVq3KggULMmvWrDz22Iz06zc206c/lkWLjsuwYcO6rDagUiYkGZBkRZI+Hb7rhRfW5pOffCoXXfRU3vCGoTnxxL1y7LGDc+CBfbPXXrXp1asma9ZsyGOPrch1172Q666b02Y7Y8f2yUEHdSS80Jzi96H0FAAAAAAAO+6EE07I7373u/zkJz/JpZdemi9/+cu5+uqrM2XKlEqXBgBAN1VVKpVKlS4CAAA6q7GxMTfffHOmTZuWO++8M5MnH5za2u+ksfHgTJ7ce5v3/+EP78ncud9v89wJJ8xKXd3Ydu+dP//6PPro37R5rq5uQt70pqdTVVXT5vlnn12fXr2eT13d+/LIIw/l+OOPT319fc4666yMGjVqm3UD3dUVSf47RZBh64GpmTNX54AD7tlmiz16VKWlZdv/ZP/Wtw7NBz6wXwdqnJ9kYJKbk3R0pwYAAAAAgO5n7ty5GTNmTJJkzpw5GT16dIUrYt26dbn22mvz2c9+Nq95zWty1VVX5bDDDqt0WQAAdDPVlS4AAAA66sUXX8w3vvGNTJkyJSNGjMg3v/nNvPGNb8xTTz2VP/zh9/nMZ16d3r17p6mpvHWMHDk1/fod1Oa5NWtmZt68/2nzXEtLUl1dm0sumZjf/Ob+zJo1K2eeeWauv/767LvvvjnuuOPy5S9/OS+88EI5ywfK4rQUuy6s6rIWOxJceOtb98773rdvB1orpajt9AguAAAAAADQ1Xr37p1PfOITee6553LwwQfnNa95Td73vvdl3rx5lS4NAIBuRHgBAIBube7cufna176W17/+9Rk9enR+9KMf5bTTTstzzz2Xhx56KBdeeGHGjx+fJHnDG5L99ksWLSpvTVVV1Zkw4VPtnp8x44qUShu2ON7YmAwblpx8cvF+9OjR+ehHP5r77rsvc+fOzbve9a7ceuutGT9+fP7qr/4qV111VZ577rlyfQygSx3+8mvhNq/s3bs6/fv32OEe/+EfRmfatFenpmbrOz0UlqcILbx1h/sFAAAAAID2DBkyJF/84hfz5JNPZu3atZk4cWI+9alPZcWKFZUuDQCAbqCqVCpteylHAADYiWbNmpWGhoZMmzYtjzzySF7/+tenvr4+Z511VkaOHLnVe7/zneSaa4oQQ23tTiq4A1pakj/9KXnPe5JLLtn6tY2Njbn55pvT0NCQO++8MwcddFCmTp2a+vr6HHRQ2zs+AN3BL5JckmTvJH23euX69a25997G3Hbbwvz610vz+9+vSFNT6zZ7qKuryRlnDM8HP7hfjj9+aAfrak0yM8mbk1zbwXsAAAAAALqvuXPnZsyYMUmSOXPmZPTo0RWuiPb87ne/yyc/+ck8+eST+dd//decc8456dmzZ6XLAgCgQoQXAADoFp555plNgYXp06fnxBNPTH19fc4444wMGzasw+2sWpW8973J9OnJ+PFJVUcWJN8JZs1KRo9OfvjDYveFjlq2bFluueWWNDQ05Lbbbsu4ceM2BRkOO+ywVHWXDwgkaUnyT0nuTjIhndnssLm5Nc89tybPP78mc+euy8qVLVmzZkOqq6vSv39Nhg6tzSGH9M+kSf3Ss2dnN1Gcn6RPku8mOaCT9wIAAAAAdD/CC7uWUqmUW265JRdeeGE2bNiQK6+8MmeddZZ5LgCAPZDwAgAAFVEqlTJ9+vRNgYWZM2fm5JNPTn19fU4//fQMHjx4u9v+3e+SD30oqanpXFCgXJYtS1asSK66Kjn55O1vZ9WqVbn11lvT0NCQ//3f/82IESM2BRmOOuooA7zQLfwpyXuSrEzSHSbL1iRZkOTiJO+ucC0AAAAAAF1DeGHX1NLSkuuuuy7/+q//mgkTJuTqq6/OscceW+myAADYiTq7XCMAAGy3UqmURx55JJdeemkmTZqUY445Jk888UQ+/elPZ9GiRbn55pvz7ne/e4eCC0ly9NHJ3/1dERpYt65rat9ezc3JwoXJaaclJ520Y23169cvb3/72/PTn/40ixYtyjXXXJN58+ZlypQpGTt2bD72sY/lgQceSGtra9cUD2yH/ZN8MMn6JKsqXMuGJHOTHJvkHRWuBQAAAACAPV2PHj1y7rnnZubMmZkyZUre/OY3Z+rUqZkxY0alSwMAYCex8wIAAGXV2tqahx56KA0NDWloaEhjY2NOP/301NfX55RTTkldXV1Z+l21Kvnwh5Pf/CYZOzaprS1LN1vV0pL86U/JIYck3/xmstde5emnqakpd911VxoaGnLTTTelV69eOfvss1NfX5/jjz8+PXr0KE/HQDtaklyU5JYUuy+U5/fc1rWm2AViTJL/TDK+AjUAAAAAAJSHnRd2DwsWLMi//du/5Qc/+EE+8IEP5LLLLsuw7rCtOgAAZSO8AABAl9uwYUPuv//+NDQ05IYbbsiaNWtyxhlnpL6+PlOmTEnv3r13Sh2LFiUf+Ujyhz8k++2X9Oq1U7pN8ufgwgEHJP/xH0X/O0Nzc3Puu+++Tf/bt7a25qyzzkp9fX1OOOGE9OzZc+cUAnu81Uk+keTu7PwAQ2uSWUn2TvLVJIfvxL4BAAAAAMpPeGH38uSTT+aiiy7Kr371q1x88cW54IIL0qdPn0qXBQBAGQgvAADQJZqbm3PvvfemoaEhN954Y0qlUs4888xMnTo1b3rTmyr20PyCBcnHP5488kiyzz5J377l73Pt2mTOnOSgg5IvfSnZf//y99mWDRs25IEHHti068Xq1as3hUje/OY377QQCey5ViS5JMldKYIEA3dCn+uTzE4RmPhCklfvhD4BAAAAAHYu4YXd07333ptPfOITeemll3LFFVfkXe96V2pqaipdFgAAXUh4AQCA7dbU1JQ777wzDQ0Nufnmm9OrV6+cffbZmTp1ao477rj06NGj0iUmSRobk8suS+67L6mtTUaNSqqru76fUil58cVkzZrk6KOTz3426S5j5a2trfntb3+badOmpaGhIY2NjTnttNNSX1+fU089NXV1O3NVeNiTrEny2SS3pNgRYUyScky0lJI0JlmS5OAkVySZXIZ+AAAAAAAqT3hh99Xa2pqf/vSnufTSSzNw4MBcddVVOemkkypdFgAAXUR4AQCATlm7dm1uu+22NDQ05Be/+EUGDRqU+vr6TJ06Ncccc0yqy5EK6AItLcm0acl//mcRMBg1KunXr+vaX7s2mTcvGTQoef/7k7//+6RXr65rvyuVSqX8/ve/3xRkmDNnTt7ylrekvr4+b33rWzNgwIBKlwi7mVKS25N8Ocnz+fMuDFVd1P76JHOS9Enyt0nOS9K/i9oGAAAAAOh+hBd2f01NTfmP//iPXHHFFTn66KNz1VVX5fDDD690WQAA7CDhBQAAtmnVqlW59dZbM23atNx6660ZOXJk6uvrU19fn6OOOipVVV31AG75Pf988oUvJA88kKxfnwwZkgwevH07MZRKybJlxc4ONTXJq1+dXHRRMnkXWuy8VCrliSee2BRkmDFjRk466aTU19fnbW97WwYPHlzpEmE3sjBFgOH2JKuTDEoyNNu3E0Mpycoki1KEIA5McmGSY7qiUAAAAACAbk14Yc+xdOnSfPazn81//Md/5G/+5m9yxRVX+P8bAGAXJrwAAECbli1bll/84hdpaGjI7bffnv3333/TDguHHnroLhVYeKUNG5Lf/Cb5+c+Te+8tAgi9ehU7MdTVJb17J219vFIpaWpK1qxJVq8udlsYMCA55pjkzDOT449Pamt38ofpYs8880waGhrS0NCQxx9/PCeccELq6+tz5plnZtiwYZUuD3YDpSS/T3JLihBDY5IeSfol6Zti94S2fr+WkjSnCD2sTrLm5esPT3J2khNefg8AAAAAsPsTXtjzzJo1K5/+9Kdz00035fzzz89FF12UgQMHVrosAAA6SXgBAIBNFi9enJtvvjkNDQ258847c/DBB2/aYeGggw6qdHllMWdOcuutyT33JC+8UIQS1q8vzpVKxauqqniVSkU4oW/fZJ99irDCW96SHHBAZT9DucyaNWtTkOHhhx/O8ccfn/r6+px11lkZNWpUpcuD3cCiJLcluTPJc0lWJWl6+Vzp5VdV/hxm6JGkLsnwJMcmeWuSQ9N22AEAAAAAYPclvLDnevjhh/PJT34y06dPz7/8y7/k3HPPTe2uvroYAMAeRHgBAGAP9+KLL+amm27KtGnTct999+XII4/cFFgYP358pcvbqVauTJ57Lpk5swg1NDUlzc1Jjx7FzgyjRiUTJhSvQYMqXe3ONXfu3Nxwww1paGjIAw88kGOOOWbT92TfffetdHmwG1ibIsDwXJLnk6xLsj5FYKE2yd5JJrz82isCCwAAAADAnkx4Yc9WKpVy66235sILL8z69evz+c9/PvX19bv0zvEAAHsK4QUAgD3QxgfRp02blgcffDDHHnts6uvrc/bZZ3sQnW3aGHhpaGjIvffemyOOOGJTkGHChAmVLg8AAAAAANjNCS+QJC0tLfnud7+bf/mXf8m4ceNyzTXX5LWvfW2lywIAYCuEFwAA9hCzZs1KQ0NDpk2blkceeSSvf/3rU19fn7POOisjR46sdHnsohobG3PzzTenoaEhv/zlLzN58uTU19dn6tSpOeiggypdHgAAAAAAsBsSXuAvrVq1Kl/60pdy9dVX56STTsrnP//5TJw4sdJlAQDQBuEFAGCXt6F1Q+asmJO5K+Zm/Yb1aWltSY/qHulV0yv7DNgnYwaMSU11TaXLrIhnnnlmU2Bh+vTpOeGEEzJ16tScccYZGTZsWKXLYzezbNmy3HLLLWloaMhtt92WcePGbQoyHHbYYXvoVr2lJPOSvJCkKUlzkpokvZIMTzI2Sc9KFQcAAAAAALuYliRzsnDh7/O3f1ufHj2SH/3oJxk2bHSSja89c16QYvfwyy+/PN/73vfy/ve/P//yL/+Svffeu9Jl/YUlSf6UZFWKOaOkmDMalGR8kr6VKQsAYCcSXgAAdjmtpdY8Mv+RPDz/4Ty1+Kk8sfCJLGtalnXN69Jaak1rWlOd6lRXVadPzz4Z2HtgDh52cA4ednCOGnVUjhx55G77EHWpVMr06dM3BRZmzpyZk046KVOnTs3pp5+ewYMHV7pE9hCrVq3KrbfemmnTpuXWW2/NiBEjNgUZjjrqqN3272ARVngiyW+SPJPkjykGotcm2fDy+aok1Ul6J+mfZFKSQ5K8KskxSXrs7KIBAAAAAKCbak3ycJJHkjyZYgx+eZqbV2bmzBmpqkrGjx+fnj17JemT4iHwg5NMTnJUkiNTjMuzJ3nqqady8cUX55577slFF12Uf/7nf05dXV0FKlmQ5L4kTyd57OX3q1OEcDY+sledpDZJXZL9kxya4vv7xhTzSAAAuxfhBQBgl7F83fLc8dwdueGpG/LU4qeyrmVdaqprUtezLn179k2fnn1SXVWdqlSllFJaS61Z07wma5rXZPX61WkttaZ3j945ZO9DcvZBZ2fK/lMyoNeASn+sHVYqlfLoo49uCizMmzcvp556aqZOnZq3vvWt6d/foBaVtXbt2tx+++1paGjIz3/+8wwaNCj19fWpr6/Psccem+rq6kqX2AXWJLk3yQ1Jfp9i4Lk6xURJv5d/1qSYICmlmGxZ9/J1q19+3yPJAUnOTnJKkr125gcAAAAAAIBuZHmSO5I0JHkqxe7GNSke8O6btWurctdd96ZUSqZMOSF9+tSmGKtfk2LcvZRiRftDU4y7T4kHwfc89913Xz75yU9m/vz5+cxnPpN3v/vdqakp984crUl+l+QXSe5M8V1OikWt+qb4DvfMn0M1rUnW589zRs0vnxuR5PQkp6WYPwIA2D0ILwAA3d66lnX54WM/zI8e/1FeWv1SaqpqMqzvsPSr7depdkqlUlY3r87C1QvT2tqakf1H5l2HvSvvOuxdqa2pLVP15dHa2pqHHnooDQ0NaWhoSGNjY0477bRMnTo1p5xySoVWDoFta2pqyl133ZWGhobcdNNN6dWrV84+++zU19fn+OOPT48eu9quAxtSTJxcl2ROisHkvVJMgHR2Jae1SRamGKAemmRqkn+MyRQAAAAAAPYc65J8P8mPk7yUIrAwLMVCQX+2du263HnnnUmSKVOmpE+f3q9op5TiQfCFKR4OH5nk3Un+LsUq9+wpWltb87Of/SyXXHJJ+vXrl6uuuionn3xymXYJ/22Sr6TYIaQpyeAkQ1IseNUZzUkWJVmVYp7ouCT/nGS/rioUAKBihBcAgG5t+sLpueqBq/K7eb9LXW1d9u67d3pU7/jDzc0bmrNw9cKsbVmb1+zzmlx83MWZtNekLqi4fDZs2JD7778/DQ0NueGGG7J69eqcccYZmTp1aqZMmZLevV85KAvdW3Nzc+67775MmzYtN954Y1pbW3PWWWelvr4+J5xwQnr27FnpErdhdpKrUuy40CPFCjhdMeGxIcniJCuSHJLkwiR/1QXtAgAAAABAdzY9yeeTPJJihfq9U4y/b2nb4YW/1JwixLA2yTFJLk5yYJdVza6hqakp//mf/5nPfOYzOfLII3P11VfnVa96VRe1virJN1OEblanmDPq3EJ8bSslWZYiyDAyyYeS1KcI9QAA7JqEFwCAbqmltSXf/f13c93vr8uydcsyZuCY9O7R9Q/nr21emzkr5mRon6H5wJEfyLsPf3dqqrvPYE9zc3PuvffeNDQ05MYbb0ypVMqZZ56ZqVOn5k1vetMu8HA3dMyGDRvywAMPZNq0aZuFc+rr6/PmN7+5m4VzSil2W7g2yYIko9I1A9CvtD7Fbg51Sd6R5MMpthQGAAAAAIDdSXOS/3r5tTzJmGxrPLxz4YVNd6UYdx+a5Nwk74qHwPc8S5cuzec///lce+21mTp1aq644orsu+++O9Dio0k+lyJ8MzjF96urd3VoTTIvxd+VNya5NMk+XdwHAMDOIbwAAHQ7TS1NueJXV6ThqYb0re2b4X2Hl2nbzkKpVMqLq17M2ua1+ZtD/iYXH3dxamsqt11sU1NT7rzzzjQ0NOTmm29Or169cvbZZ6e+vj7HH398evTY8Z0noDtrbW3Nb3/720ybNi0NDQ1pbGzMaaedlvr6+px66qmpq6urZHUpQgvfSbHF7z7p/Fa/nVFK0phkaZKTk1yRYntgAAAAAADYHaxL8pkkN6ZYKGjvdOTB7+0LLyTFuPuCl/t9R5KLklgsbE80e/bsfOpTn8oNN9yQj370o7nkkksycODATrZyT5LLUszl7Juu2aF7a1YlmZ9kUpIvJplQ5v4AALqe8AIA0G0ceeSR+f3vf7/F8eFHDs/Rnzh6q/fOuGlGnrn+mTbPHfXxozLi1SO2ev9d59+VtYvWbnH8wx/+cK699tqt3tsV1q5dm9tuuy0NDQ35xS9+kUGDBqW+vj719fU59thjU11dzoejofsqlUp59NFH09DQkIaGhsyZMydvectbUl9fn7e+9a0ZMGDAzqwmyZdSrPw0IMleW1zxnvf8Id///twd7umMM4bnppv+8vfe6hQr6rwhxWC0AAMAAAAAALu6piSfTvKLJMNTjL3/WWfG3Kuqkn79emTgwB7Zf/+6HHXUoJxyyrBMmbJXO4ukLUuyOMnUJP+axOJhe6pHH300n/zkJ/PYY4/lsssuywc/+MHU1nYkhPDLFN/fNXn++WE59NBfZdWqDW1e+dOfHpm3v31Uh+r50Y/m5l3v+kOb5/r1q8n06cdlv/1eTDI2yb8nOaBD7QIAdBeeggMAuo3jjj+uzeNLnlmSbeUtlzy9ZLvOJcnaxrVtBheS5Pjjj9/qvTti1apVuf766/P2t789w4YNy4UXXpjRo0fnzjvvzPPPP58vfelLed3rXie4wB6tqqoqr371q/O5z30uTz/9dH7729/mkEMOyec+97kMGzYsb3vb2/L9738/S5cuLXMlpST/meS7SQalreBCefVNsU32fSkGwtft5P4BAAAAAKArtaTYbfiWJCPzyuBCZ5VKycqVLZk7d11+9asl+dKX/pSTTnoo48ffk5tvfrGNOwal2OVhWpKrU+y8zJ7oyCOPzJ133pkf/vCH+c53vpPJkyfnZz/72Tbmpx9IEXpZm2S/jB3bN1dfPbndq//pn6ZnyZL126xl8eL1ueCCJ9s9f801k7Pffv2T7J/k+SQfTzJnm+0CAHQnnoQDALqNHuPaXtGkeXVzVs5Z2e59pdZSls5o/8HlJc9sPbywtXDDcce1HajYXsuWLcsPf/jDnHnmmRk2bFguv/zyTJo0KQ888ECeffbZXHnllTn66KPbWQEG9mxVVVU55JBD8m//9m95/PHH88c//jHHHHNMvva1r2X48OE55ZRT8u1vfzuLFi0qQ+/3JflOii2rh5Sh/Y7ok2RUkjuSXFehGgAAAAAAoCtcn+TGJMNSjL2Xx6xZa3LmmQ/n/POnt3F2QJKhSf4nyc/LVgPdX1VVVU499dT84Q9/yCWXXJLzzz8/xx57bO6///42rl6Q5N+SLE+yb5JiXvfcc/fNCScMbbP9hQvX55//uf1QwkYXXPBEFi9uO+Rw4ol75Zxz9n35XU2ScUmeebmW5m22DQDQXQgvAADdwvPLns+vq37d7vmtBQxWzF6RlrUt7Z5fPmt5NjS1vUVnkjQ+3djm8d7Deqe1346vsrJ48eJcd911ectb3pK99947X/ziF3PUUUfl0UcfzRNPPJH/9//+Xw4//HCBBeikAw88MJdeemkeeeSRPP3005kyZUr+67/+K6NGjcoJJ5yQ//iP/8j8+fO7oKclSa5JsX31sC5ob0f0S9I/yfeTPFbhWgAAAAAAYHv8KcVuxz2TDNwpPX7ta8/niitmtHFmcIrHp76WZN5OqYXuq6amJu9///szY8aMvPWtb82pp56as846K88888zLV5SSfCnJC0n2y8bgQlIEIK677vD061fTZts/+MHc3HFH+wtw/d//LcyPftT2d7Bfv5pcd91hr5hP7pFkdJJfJ/lxhz8jAECltb28MQDATtTS2pIv3P+FLKlekr6j+mb1/NVbXNP4dGPGnjS2zfvbCx9sVNpQytKZS7PXwXu1eb69YETtuNpc8+A1+fIpX051Vecyny+++GJuuummTJs2Lffdd1+OOOKITJ06NV/72tcyYcKETrUFbNv++++fT3ziE/nEJz6ROXPm5MYbb8z111+f888/P8ccc0zq6+tTX1+ffffdd9uNbaaU5NokzyYZv0M1Xnjh+Awe3LND106c2HcrZ/dO8lySL6TYgaHPDtUFAAAAAAA7T0uSK5Msyo6Mu3/sY/tl8eLZSZIJEyakqSl55JHlueOORWltZ22yK66YkXe+c5+MG1f3ijP7pBh3vybJF2MtWPr27ZvLLrss55xzTi6//PK86lWvynvf+9589rPHZvDg25KMSLH7webGjq3L1VdPzgc/+Hib7Z5zzh/zxBNvSN++mz+yt2pVS847r+17kuSaayZnv/1e+b1NkroU80TfTHJskgM6+AkBACpHeAEAqLibnr4pv5r9q+wzYJ+sm7SuzfDC1nZe2Nq5jRqfamwzvLB+xfqsmr+qzXtGHDwid826K7c8e0veduDbttnH3Llzc8MNN2TatGl58MEHc8wxx2Tq1Kn5r//6r+14YBrYXmPGjMlHP/rRfPSjH90UJGpoaMiFF16YI444YlOQoWNBogdSbFu9d3b0n08f/OB+GTu2rYHlzqpKMibJo0l+kOTcLmgTAAAAAAB2hhtSjL2Pyo6EBD7wgdGZMaMIL0yZMjZ9+vROkjz00NKcfPJDWb58y13bm5pa893vzsn/+38HvuJMTZKRSe5McmuS07a7LnYvw4cPz9e//vWcf/75+exn/zm///23cvDBQzN06Jj0aGfa6Nxz983PfjY/d9+95QJ8s2evzaWXPp2vfvWQzY5fcsnTeeGFtW22d+KJe+Wcc7Y21zwyycwUi159O3+5GwQAQHckKgwAVFRLa0t+PP3Hqa6uTt/avhkyaUib1zUta8rqF7cMNSTJkmc2Dy/UDavbYkzmldds1PhMY7GwehtGHTIqSfI/j/9PWkttL9Eya9asXHPNNTnmmGMybty43HTTTfnbv/3bzJkzJ/fff38uuOACwQWooBEjRuS8887LL3/5yyxYsCDnnXde7rvvvkyePDmvetWr8pnPfCZPPvnkVlr4aZK1KbaN7k56pVhN5/okbf9uBAAAAACA7qUlyf+kCAtsbQfi7fea1wzO5z8/qd3zt922sJ0z/ZO0Jvnxyz/hzw488MD84AfvyDHH7JPnnmvKPffcnRdeeCGl0pYTzVVVVbnuusPTr9+WOzMkybXXPp/f/GbppvcPPrgkX//6821e269fTa677rBUVW0tkFCVIsDwcIqFrwAAujfhBQCgon4z9zd5tvHZ7N137yTJ0IOGtnttWzssrJq/KutXrN/s2LDDh2XAfgM2O7Zs5rK0tmw50Njerg21A2rTb2S/7N137zy1+Kn8bt7vNp175pln8rnPfS5HHnlkDjzwwNx55535x3/8x8yfPz933313PvShD2XkyJHtf2igIvbaa6+8733vy6233pqFCxfmE5/4RB555JEceeSRmTx5ci677LI89thjfzHQPCPJb5JsuWtL9zAsyYIUK0EBAAAAAEB392CS51Lsdlw+Z5wxot1zs2e3vbp9Ye8kTyR5pKtLYpfXnKQhdXUDc+yxx+fggw/OjBkzc999v8rChQvzygzD2LF1ufrqyW221NqavP/9j2X9+tasX9+af/zHP6a1nbzMNddMzn77dWRX775J1iX5eSc+EwBAZbSzgRUAwM5x8zM3p2VDS+p6FoMufYb2SZ+9+mTt4i0HDhufbsyYN47Z7FhbOyoMmTQk1T2qs+L5FZuObWjakOXPL8/gCZuvnt5eeGHjDhB9a/tm/sr5ue7+6/J/T/9fpk2blpkzZ+akk07K+eefn7e97W0ZPLi7rcgObMugQYPyrne9K+9617uyatWq3HrrrZk2bVpe97rXZcSIEamvr8+HP9ycMWNWpKrqgEqX247aFKvp3JDkbbENMAAAAAAA3dtNKXZf6FPWXoYNq2333NKlzVu5s2+KRYN+keToLq6KXdvG4M3wVFUlo0aNyogRI/L887Pz6KO/z8CBAzJ58uQMHDhw0x3nnrtvfvaz+bn77sYtWnvyyVX57GdnpFRKnnpqVZs9nnjiXjnnnH07WF9VkkFJfpnkQ0mGd+rTAQDsTMILAEDFzFsxL796/lcZ3Gfzh/+HTBqSeffP2+L6toIGbR0bcmARXph126wtrv3L8ELL2pasmL3ilbdvamP5suVZsGBB5i6Zm++s/07esuIt+fSnP523vvWt6d+/f4c+I9D99evXL29/+9vz9re/PWvXrs3tt9+en//8J/nTn6Zl2bLq1NQ0Z+TIkRk8ePA2tuWthGFJHn/5dViFawEAAAAAgPbMSXJ/kiFl72nhwqZ2zw0e3HMrd258APzOJP+UYgwekiLQsnnwprq6OvvvPy5jxozOjBkz88ADD2TkyJE58MBJqavrk6qqqlx33eE59ND7smrVhi1a/PznZ7bbW79+NbnuusM6OS81JMmfktyR5O87cR8AwM4lvAAAVMzjCx/PivUrMm7QuM2OtxdeWLNwTdYtXZfeg3tvOtb49OYrVdQNq0ufoX1S3bN6i/uXPL0k408b/+f3zy5JqbW0xXVJ8tyK5/LMr5/J8OHDM2ncpKytXZuPnfaxnDDuhE59RmDX0qdPn5x55pk588yxaW19Io2NPTJvXmN++9vfpaamOiNGjMjIkSMzZMjQVFd3LsgwbtzdHb62VDqtg1f2S/JSkukRXgAAAAAAoPt6PMnKJPuXvaebb36p3XNjx9Zt4+7BSWYneSLJG7uuKHZhzUkeSdL24nY9e/bM5MkHZdy4sXn66adz7733ZOzYcTnggAkZO7YuV189OR/84ONbttrc9jx1klxzzeTst9+2vquvVJOkOsmjEV4AALoz4QUAoGKeW/JckqSmumaz40MnDW33nsanGrPPa/dJkqxtXJu1i9Zudn7IpGK1ll4DeqXfPv2yat6ft9lc8uySlEqlTStUtLVrQ5JU1Vbl0OMPzd7D9051TRGCmNk4MzOXzBRegD3GzFRXt2TYsP0ybNjotLYemsbGxixYsCCPPvpoSqXSy0GGUdlrr6Gprt4yMLVzbAxQPFuh/gEAAAAAoCM2rjJf3vH0hx5amksvfbrd8yefvK3dFHokKaWo941dVxi7sNlJViQZsNWr+vTpkyOOOCL7779/nnzyqdx1192ZOPGA/OM/7pef/Wx+7r67cav3b3TiiXvlnHP23c5a61IseNWacv9dAwDYXsILAEDFPLHoiVRXbTlo0m9Uv/Qa2CtNy7fc0nXJ00s2hReWPLNl+GBjeGHjn/8yvNC8qjkr565Mv336pbGxMXMemdNmXXtN2isjRo3Y7FhVVVWeWvRUxz4YsBvYOIlShAOqq6szbNiwDBs2LIceemiWLFmSBQsW5LHHHsuGDS0ZPrzYkaFUan+VnPLpneSPFegXAAAAAAA66okUK8N3jW9/e24WLy7+/Ic/PJ/166vyyCPLcvvti9La2vY9vXpV573vHdOB1quSPNNVpbLLm5lkdZLhHbp64MCBOeaYY7Jo0cI89dRTmTVrVj772f3z5jcvy6pVG7Z6b79+NbnuusM2LcbXeX2TLEsyL0lHvusAADuf8AIAUBEtrS15atFT6duzb5vnBx84OC/+9sUtjv9lYKGtnROGHDhksz+/cNcLm51//M7Hs2rUqlSVqrJ+wfo2+/7LAMRGdT3rMn3R9LSWWtsMXAC7mz8mqW3zTFVVVYYOHZqhQ4fm4IMPztKly/LiiwvyxBPTM3/+up1bZpJiFZ35SZYk2fL3FwAAAAAAVNb6JE+nGM/uGl/60uy/eDez3ev+0qc+NSHjxnWkhj5JHkuxA8P2PkTO7uO5FN+DjodvqqqSvffeO8OGDcucOXPzzDNP59xze+aLX9x6eOGaayZnv/125O9JXZJFKf5OCC8AAN2T8AIAUBHL1i3LmuY16dWjV5vnh04a2mZ4YeXclVm/an1q+9VusfNC7YDa9BvVb9P7QQcO2uL+9fPX5+gzjk7ri635zYbftNl3W+GF3j16Z2XTyqxsWpmBvQdu7aMBu4X5Sdr+/fSXqqqqMmTI4AwZMjgHHTQ511332yQL27z2wgvHZ/Dgnl1bZpJi54UlKQajhRcAAAAAAOhuliVZm2I8uzI+9KH9ctllEzt4de8kK5OsStK/fEWxi3gpRZCl86qqqrLvvmOyzz6jst9+z+XOO5/NY4+1fe2JJ+6Vc87Zd/vLTFI8ClhKe3NVAADdgfACAFAR6zes3+ouBm0FCJIkpWL3hSEHDsnKuSs3v+flXRcWL16c2bNnZ+FLC1PVryqlVX8eTNqwYEOGDBmSGb+a0Wbz1T2rM2j/QVser6pOa6k1za3NHfh0wK5tQ5KWJJ3bZaWqKqmtbXu3hiQ59thlOeaYcRkxYsSOlbeF6iStSfx+AgAAAACgO1qfYhx75+9uPnZsn3zxi5Nz9tkjO3FXdYp5AuPuJEXwZse+uzU1NZk4cWI+/OGeOeecJ9q85l3v2idVVV2x00dVfHcBgO5MeAEAqIgNrRtSSilV7Wy1OmDfAelR1yMta1q2OLfk6Zd3XHjFAhcbAw9N65rSr1+/HDDhgPxp9p8y9/+bu+madUvWZc3CNVny1Oa7Nmw0aP9BqandcsvPjQNFLa1b1gPsblpTjq2gBw4cmLVr13Vpm4WqFPX6/QQAAAAAQHe0IeUYd3+lfv1qMnBgz+y/f11e/eqBOfXUvTNlyl6pru5sv8bd+Usb0lXf3Z49d9ajeht2Uj8AAJ0nvAAAVERtTW2qq6pTameLzarqqgyZOCQL/7DllpaNTzW2ec/GnRf2Gb3Pn49NGrJZeCFJFj+5OEtnLt1qG6/UWmpNVVVVamvaX1Ud2F30SFKTrp6UGDdubMaOrevSNgsbV6vqVYa2AQAAAABgR9Xmz7sId41Zs04o05h7YtydzfVOV353dw5z2gBA97Xz92MDAEjSp2ef1FTVbHUng407KbzSitkrsuiPizY71qNPjwwcO7BDbTx/+/NpWdt2v+312dLakpqqmvTp0afdeoHdRVWSuuw6Kyq1pAhb9K50IQAAAAAA0IY+Kcaxd5XV4DfEuDt/1i9pZ0G+7qf08st3FwDovoQXAICKGNBrQEb2H5k1zWvavWbopKFtHi9tKGXlnJWbHRt8wOBUtbHla7+R/dJr0OaroqyYvaLtDquSwRMHt3lqTfOa7Ddwv/TpKbwAe4aDkqytdBEdtCZJ/ySjK10IAAAAAAC0YWCS4UlWV7qQDlqdZGzsvEBh7Ms/d4UAw7oUuy6Mq3QhAADtEl4AACrmsOGHZV3zunbPD9x/YGp61XSorfZ2TNjWub80YN8B6VnXs81zTS1NOWz4YR1qB9gdHJRdYxA6KSZRDkrS9u8vAAAAAACorKokh6d4sHpXsD6JeUE2mpBiDmZ9pQvpgDVJ+iYZX+lCAADa1aPSBQAAe66JQycmSUqlUqqqttw1obpHdQaNH5TGJxu32daQA9sPKAydNDQLfrNg2220E3IolYoHmA8YesA22wB2FxNSTKa0pKv+2fSf/zk7gwd3PGDwz/88Lr06FOBqTXLwdtcFAAAAAADlN/Hln6UU4+/d1cb6zAuy0YQUgYDV6f67caxOMjnFjt0AAN2T8AIAUDEHDDkgPWt6Zl3LuvTp2afNa4ZMGrLN8MLGkEN7OrrzQnsBiLUta9O7R+9MGDKhQ+0Au4MDUgxEr0oyqEtavOqq5zp1/Xnn7deB8ELLyz/9fgIAAAAAoDs7IMVjSk1Jele4lq1Zk+IBdePubDQkyT5JZr785+5sXYpdTgAAuq/qShcAAOy5Dh9xePYduG8WrVnU7jVDJw3dZjsD9x+Ymtr2H/DtP6Z/evbb9mrn7YUcFq1elLGDxuaQvQ/ZZhvA7mJ0ii2hl1S6kG1YnGTvJMdUuhAAAAAAANiKVyUZk6T9ecHuYVGS8SlWr4eNTkkRvGmtdCFbsTF4c3ylCwEA2CrhBQCgYmpranP2QWenqaUpraW2B3oGHzA4VTVb3zp2WzsrVFVVtburwkZ1w+vSe9CWq7xsaN2Q9RvWp/6g+vSotmkV7DmqktSn2B56fYVraU8pyYokb01X7Q4BAAAAAADl0SvJ2SlWhu+uD4BvSNKcok7zgvyltyQZmO696NXCJBOTvKbShQAAbJXwAgBQUadOODWDeg/KkrVtD/TU9KrJwHEDt9rGtoIJybYDDu3t8NC4tjFD+wzNKRNO2WYfwO7mjSl2YFhY4TrasyJJvySnVboQAAAAAADogFNTLMbTXR8AX5xkryQnV7oQup19krwpybIK19EewRsAYNdRVSqVSpUuAgDYs3367k/n+ieuzwFDD0h1VffJVraWWjOzcWbeedg78y9v+JdKlwNUxH8m+WqSceleg72lJDOTvD7JN1PsFAEAAAAAAN3dJUluSDIh3WvN1dYU4+7vTvKpCtdC9/Rgkg+l2IFhQIVreaX5SeqS3JSk7UX7AAC6i+70rwAAYA/1viPelxH9RmT+yvmVLmUz81bMy8j+I/PeV7230qUAFfM3ScYneaHShbzCohQD4x+M4AIAAAAAALuOf0wyLMmCShfyCnNTrK7/D5UuhG7r2CRTkryYYqeD7mLty6/3R3ABANgVCC8AABW3/+D9c95R52V9y/qsXr+60uUkSVatX5WW1pZ85K8+kjEDx1S6HKBihiT5eJKeSZZWuJaNmpIsT/L3SY6ocC0AAAAAANAZ45Ocl2RdkjUVrmWjlSkeRv+nJKMrXAvdV1WKOaPRKcIu3UEpRS3HJPm7CtcCANAxwgsAQLfw9oPfnuP3Oz7zVs5La6m1orVsaN2Q+Svn54RxJ+SsSWdVtBagO3hTkjNS7HbQXOFaSil2gXhVihV0AAAAAABgV/O3SY5L8dB1ZecFi9DCghQr6p9R4Vro/kYmuSDFfM2KypaSpPjuDklycZLaCtcCANAxwgsAQLfQo7pHLnrdRRk9YHRmLZ1VsQBDa6k1s5bNyn4D98snX/vJ1FTXVKQOoDupSnJ+kslJnk/ltgIuJZmdZK8klySpq1AdAAAAAACwI3qkeNh6dJJZKca/K6E1yZ+SjE3yyXiMio45Lclbk7yYyu4e0phkfZIPJplYwToAADrHf3UDAN3GuMHj8oUpX8hedXvl+aXP7/QAQ2upNbOWzsrwvsNz9ZuvzpiBY3Zq/0B3NiTJF5OMSzGRsbMDDBt3XKhLcnmSw3dy/wAAAAAA0JX2T3JlkqEpAgw7e2Gz1pf7HZnkmiT77OT+2XVVJbksyQkpdg+pRIBhSZLlSd6X5F0V6B8AYPsJLwAA3cpRo47KlVOuzLC+w/KnpX/Khtad84BwS2tLnlvyXEb0G5Gr33x1Dh/hwWDglfZP8uUk45M8l2I1m52hNcWOD32T/FuSE3dSvwAAAAAAUE5Hpwgw7JUiSLCzFg5qSbFQ0cbgwqE7qV92H31TfHc3BhhW7qR+S0kWpggu/EOSj6YIUwAA7DqqSqVSpfZeAwBo1+8X/D4X33Vxnl/6fIb3G56BvQeWra9l65Zl4eqFGT94fL4w5Qs5dLgBSmBr/pRi++jpKVaEGpLyDQyvSjI/xQTK5UneUKZ+AAAAAACgUh5JcmmKhXxGJBlQxr6WpXj4e0KSLyQ5pIx9sftbnWIXhtuT9Eyxg0e51hJen2ROkj5J3p/kvDL2BQBQPsILAEC39dKql/KlX38p/zfz/1JKKWMGjElNdU2Xtd/S2pI5y+ekpromp088PRccc0H2qtury9oHdmcrkvxnkuuTrE0yJkltF7bfmmRekuYkb0xyYZL9urB9AAAAAADoTl5M8sUkt6VYMGh0kq6bFyx2W5iTpEeS05Ocn2LHB9hRG5I0JPl6kgVJRiXp14Xtl5IsSdKY5KAUc0av7cL2AQB2LuEFAKBbK5VK+b+Z/5ev/OYreWH5C+nTs0+G1Q1Lrx69trvNdS3rsmj1oqxrWZdxg8blY8d+LFP2n5KqKltqAp31YJKrkjyTYkWdvVOseLO91idZnGLHhZFJPpSkPl07QQMAAAAAAN1RKcmtSb6SP68wPyzJ9s8LJutS7LTQlGRcko8nOTHl21GZPdfsFHNGv0oRaNgrxS4i2/tda00RWliapG+Sv04xb1TOnUkAAMpPeAEA2CUsXL0wNz99c258+sbMXjY7pZQyuM/g9O3ZN7U1tVsNHpRKpTRtaMrq9auzdN3S1FTVZL9B++XsSWfnjEln2G0B2EErkvwixao6M1Ks3jQoxUBy72x9ULqUYneF1SkGn0spQgtnJDkrxY4OAAAAAACwJ1mY5KYkNyZ5IcXY+eAU4+612fa4e1P+PO5enSK0cHaKsfeh5SoaUoQW7kwxZ/RIisWq+r/8qkvxfdyaliRrkixL8T0enGRKkjOTvLocBQMA7HTCCwDALmVdy7rc9/x9ufHpG/PYi49lVfOqtGxoSSml9OrRK9VV1alKVUopZUPrhqzfsD5VqUrPmp7p27Nvjhx5ZM466Kwcv+/xO7R7A8CWWpL8JsnNSR5IMTGy/uVzPVPsnlCVYuKk9S/O9UgxYH1QismTE1NMwAAAAAAAwJ5sXZL7UoQY/pBi3L0lxTh7rxQPgm8cd9+QP4+716YYZ391ioWCjn/5GOwspSRPJ/nfFLuJLEuy9uVzNSnmhjYGGTbOGZVSfJ/7JhmR4rt7aopFrwAAdh/CCwDALmvZumWZuWRmZi6ZmWcbn83MJTOzrmVd1m9Yn141vdK7R+8cMPSAHDDkgEwYMiEThkzIwN4DK102sEdYleS5FDsxzEwxQL02xSo5PVPsyDAuycQkE15+DY1tqgEAAAAAoC1LU4y3P5fk2Zf/vC7FQ9+9Uoy7T0xyQJLxKcbdzQvSHaxP8nyK7+zMJE8mWZ5izqgqxfd3eJLJ+fOc0T7Z9i4NAAC7JuEFAAAAAAAAAAAAAACgrEQ0AQAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLLqUekCAACgq61ZsyaPPvpoZsyYkcWLF2fdunXp06dP9t5770ycODFHHHFEevXq1SV9rVixIk8//XTmzJmTBQsWZNWqVdmwYUMGDhyYYcOG5fDDD8/EiRNTXS03DAAAAAAAsCvYmXNNAACwJxFeAABgt7Bhw4Zcf/31+e53v5t77rknLS0t7V7bq1evnHzyyXn/+9+f008/PVVVVR3up1Qq5Stf+Up+/etf57e//W1mz569zXuGDh2ad77znfnoRz+a8ePHd7gvAAAAAAAAdo6dNdfUUa2trTn++OPz4IMPbnb8DW94Q+69994u7w8AAHaGqlKpVKp0EQAAsCPuvvvufPCDH8yzzz7b6XuPPPLIfOMb38jRRx/doetbWlrSs2fPTveTJL17986//uu/5uKLL96u+wEAAAAAAOh6O3OuqaO++tWv5oILLtjiuPACAAC7MuEFAAB2aZdffnkuv/zyvPI/a6uqqnLQQQdl9OjRGTp0aBYtWpQXXnihzUHnnj175stf/nI+/OEPb7O/tsILAwYMyIQJE7LPPvukf//+aWlpyaJFi/LYY49lyZIlW7TxkY98JP/+7//eyU8KAAAAAABAV9vZc00dMWvWrBx66KFZvXr1FueEFwAA2JX1qHQBAACwvS644IJ89atf3exY//79c8kll+Sd73xn9t133y3umTlzZr73ve/lmmuuSVNTU5Kkubk5H/nIR7Jq1apcdNFF2+x34MCBOeWUU3LyySfnda97XSZOnNjmda2trbnrrrty8cUX59FHH910/Nprr83rX//6/PVf/3VnPi4AAAAAAABdqFJzTdvygQ98YFNwoX///lm5cuUOtwkAAN2BnRcAANglff/738973vOezY4dd9xx+fGPf5zRo0dv8/5nn3029fX1mT59+qZjVVVVue2223LSSSdt9d6Wlpb06NHxHHBTU1Pe8pa35O677950bNKkSXnqqac63AYAAAAAAABdp5JzTVvzne98Jx/4wAeSFLt/X3TRRfnUpz616bydFwAA2JUJLwAAsMt59tln8+pXvzqrVq3adOy1r31tbr/99vTr16/D7SxcuDBvfOMbNwsRDB8+PI899liGDx/epTU/99xzmTBhwmbHnnrqqUyaNKlL+wEAAAAAAGDruutc0/z58zN58uQsX748SfL1r389ffr0yXvf+95N1wgvAACwK6uudAEAANBZn/jEJzYbTB40aFAaGho6NZicJHvvvXemTZuW2traTcdeeumlXH755V1W60bjx4/PgQceuNmxmTNndnk/AAAAAAAAbF13nWv64Ac/uCm48LrXvS7nnXfedrUDAADdlfACAAC7lKeffjq33HLLZseuvPLKjBgxYrvamzx5cj7xiU9sdux73/teFi9evN01tmfIkCGbvV+5cmWX9wEAAAAAAED7uutc049//OP8/Oc/T5LU1tbmW9/6VqqqqrarJgAA6K6EFwAA2KV89atfTalU2vR+r7322myr3O1xwQUXpGfPnpver127Nt/85jd3qM22vPDCC5u9HzVqVJf3AQAAAAAAQPu641zT4sWL89GPfnTT+0suuSSTJ0/eoZoAAKA7El4AAGCXctttt232/t3vfvdmW/Fuj2HDhuX000/f7Njtt9++Q22+0l133ZV58+Ztet+3b98cffTRXdoHAAAAAAAAW9cd55r+6Z/+adNODQcddFAuvfTSHaoHAAC6K+EFAAB2GXPnzs3zzz+/2bGTTjqpS9p+ZTu//e1v09TU1CVtP/3001us2POhD30odXV1XdI+AAAAAAAA29Yd55p+8Ytf5Cc/+UmSpKqqKt/61rd2OEwBAADdlfACAAC7jAceeGCLY0cddVSXtP3qV796s/dNTU159NFHt6ut9evX58UXX8wvf/nLfOhDH8qrXvWqzJkzZ9P5o48+OpdffvkO1QsAAAAAAEDndLe5puXLl+e8887b9P7cc8/Ncccd1yX1AABAd9Sj0gUAAEBHzZ07d7P3w4cPz9ChQ7uk7UMOOWSLYwsWLOjQvWeeeWZuvvnmDl3713/91/n2t7+dPn36dKo+AAAAAAAAdkx3m2v6+Mc/nvnz5ydJRo0alSuvvLJLagEAgO7KzgsAAOwylixZstn7wYMHd1nbvXv3Tq9evTY71tjY2CVtV1dX5+///u/zm9/8Jtdff30GDhzYJe0CAAAAAADQcd1prumuu+7Kddddt+n9tddeaw4JAIDdnvACAAC7jFcOKA8aNKhL239le6/sb3u1tramoaEhX/nKV/KHP/yhS9oEAAAAAACgc7rLXNPq1avzgQ98YNP7M888M2eddVaX1gIAAN2R8AIAALysqqpqu+771re+lVmzZm16TZ8+Pffcc0+uueaaHH300UmSNWvW5Cc/+UmOPvrofO5zn+vKsgEAAAAAAOgGOjrXdOmll2bWrFlJkgEDBuTaa68tZ1kAANBt9Kh0AQAA0FFDhgzZ7P3y5cu7tP1ly5Zt9n7AgAEdum/vvfdu8/gb3/jGfPzjH89NN92U97///VmyZElaWlryqU99Khs2bMhll122oyUDAAAAAADQQd1hrunBBx/cLKxw5ZVXZp999unSOgAAoLuy8wIAALuMVw4oL126tMvaXrduXdatW7fZsaFDh3ZJ22eeeWZuv/321NbWbjr2b//2b3n00Ue7pH0AAAAAAAC2rdJzTU1NTXn/+9+f1tbWJMlrX/vanHfeeV1WAwAAdHfCCwAA7DJeuerMiy++mMbGxi5p+4knntji2OTJk7uk7SQ56qij8uEPf3jT+9bW1nzxi1/ssvYBAAAAAADYukrPNV1++eV5+umnkyS1tbX59re/naqqqi7pHwAAdgU9Kl0AAAB01Gtf+9otjj388MM5+eSTd7jthx9+eLP3tbW1OfDAA3e43b/0jne8I1/+8pc3vb/99ttTKpUMSgMAAAAAAOwElZxrWr16da6++upN79/znvekrq4uzz///FbbXbx48Wbv161bt8U9++67b6qrrWELAED3J7wAAMAuY999981+++2X2bNnbzp2xx13dMmA8i9/+cvN3p9wwgnp2bPnDrf7l14ZhmhsbMyyZcsyePDgLu0HAAAAAACALVVyrqm5uTktLS2b3n/rW9/Kt771rU7389BDD2XcuHGbHVu6dGkGDRrU6bYAAGBnE7kFAGCXcsopp2z2/oc//GGam5t3qM1Fixbl5z//+WbHzjrrrB1qsy1thSGampq6vB8AAAAAAADativPNQEAwK5OeAEAgF3K+eefn6qqqk3vFy1alO9+97s71OZXv/rVzQalhwwZkr/7u7/boTbbMnfu3M3eV1dXZ9iwYV3eDwAAAAAAAG3bleeaAABgVye8AADALuWggw7Kqaeeutmxiy66KC+99NJ2tffkk0/m6quv3uzYBRdckH79+m13je254447Nns/bty41NTUdHk/AAAAAAAAtK1Sc02DBg1KqVTq9OuVwYo3vOENW1wzaNCg7aodAAB2NuEFAAB2OV/84hdTV1e36f2yZcty9tlnZ9WqVZ1qZ9GiRZk6dWrWr1+/6djBBx+cCy+8sMtq3WjNmjVbDFyfccYZXd4PAAAAAAAAW7crzjUBAMDuQHgBAIBdzqRJk/Lv//7vmx178MEHc+qpp2bu3LkdamPGjBk58cQT89RTT2061r9///zoRz9Kr1692r3vqquuyrJlyzpV7+rVq3P22Wdn9uzZm47V1tbmAx/4QKfaAQAAAAAAYMdVcq4JAAD2ZMILAADskt73vvflwx/+8GbH7r///kyePDlXXnll5syZ0+Z9M2fOzKc//ekceuihefzxxzcdr6uryy233JLDDz98q/1+7nOfy7hx43Luuefmrrvuytq1a9u9dvny5fnWt76VyZMn5/bbb9/s3EUXXZRJkyZt62MCAAAAAABQBpWaawIAgD1ZValUKlW6CAAA2F6XXXZZPvvZz+aV/1lbVVWVyZMnZ8yYMRk8eHAaGxsze/bsPPPMM1u0MXHixPz0pz/Nq171qm32N2jQoCxfvnzT+5qamhx44IEZM2ZMBg0alJqamqxYsSLPPfdcnn322WzYsGGLNs4555x84xvfSFVVVec/MAAAAAAAAF1mZ881ddb3vve9vPe97930/g1veEPuvffeLu8HAAB2BuEFAAB2eXfeeWc+9KEPZcaMGZ26r2/fvrngggty8cUXp1+/fh2655Xhhc4YPHhwrrzyynzgAx8QXAAAAAAAAOgmduZcU2cJLwAAsDuprnQBAACwo6ZMmZInn3wyP/rRj3LiiSemR48eHbrv/PPPzxVXXNGpweQbb7wxH//4x3PEEUd0qJ+qqqoceeSR+fKXv5wZM2bknHPOEVwAAAAAAADoRnbmXBMAAOzJ7LwAAMBuZ/Xq1XnkkUcyc+bMLFq0KOvWrUupVMott9ySRx55ZLNrv/nNb+acc87Zrn7WrVuXJ554IrNmzcqCBQuyatWqtLa2pn///hk4cGDGjx+fV73qVQasAQAAAAAAdiE7a64JAAD2NMILAADsMZYvX56TTjopv/3tbzcdq66uzv/8z//kb/7mbypYGQAAAAAAAN2duSYAANgxwgsAAOxRli9fnje/+c353e9+t+lYz549c9NNN+Utb3lLBSsDAAAAAACguzPXBAAA2094AQCAPc6yZcvy5je/OQ8//PCmY3369Mntt9+e448/voKVAQAAAAAA0N2ZawIAgO0jvAAAwB6prUHlAQMG5J577smRRx5ZwcoAAAAAAADo7sw1AQBA5wkvAAAAAAAAAAAAAAAAZVVd6QIAAAAAAAAAAAAAAIDdm/ACAAAAAAAAAAAAAABQVsILAAAAAAAAAAAAAABAWQkvAAAAAAAAAAAAAAAAZSW8AAAAAAAAAAAAAAAAlJXwAgAAAAAAAAAAAAAAUFbCCwAAAAAAAAAAAAAAQFkJLwAAAAAAAAAAAAAAAGUlvAAAAAAAAAAAAAAAAJSV8AIAAAAAAAAAAAAAAFBWwgsAAAAAAAAAAAAAAEBZCS8AAAAAAAAAAAAAAABlJbwAAAAAAAAAAAAAAACUlfACAAAAAAAAAAAAAABQVsILAAAAAAAAAAAAAABAWQkvAAAAAAAAAAAAAAAAZSW8AAAAAAAAAAAAAAAAlJXwAgAAAAAAAAAAAAAAUFbCCwAAAAAAAAAAAAAAQFkJLwAAAAAAAAAAAAAAAGUlvAAAAAAAAAAAAAAAAJSV8AIAAAAAAAAAAAAAAFBWwgsAAAAAAAAAAAAAAEBZCS8AAAAAAAAAAAAAAABlJbwAAAAAAAAAAAAAAACUlfACAAAAAAAAAAAAAABQVsILAAAAAAAAAAAAAABAWQkvAAAAAAAAAAAAAAAAZSW8AAAAAAAAAAAAAAAAlJXwAgAAAAAAAAAAAAAAUFbCCwAAAAAAAAAAAAAAQFkJLwAAAAAAAAAAAAAAAGUlvAAAAAAAAAAAAAAAAJSV8AIAAAAAAAAAAAAAAFBWwgsAAAAAAAAAAAAAAEBZCS8AAAAAAAAAAAAAAABlJbwAAAAAAAAAAAAAAACUlfACAAAAAAAAAAAAAABQVsILAAAAAAAAAAAAAABAWQkvAAAAAAAAAAAAAAAAZSW8AAAAAAAAAAAAAAAAlFWPShcAAADdypIlyXPPJTNmJM8+W/x57dqkuTmprU3q6pIDDkgmTkzGj08mTEgGDqx01QAAAAA7T6mUzJ+fzJxZjKE880wyb17S1JRs2JD06lWMlxx0UDF2MmFCsv/+xXEAAAAAYI9VVSqVSpUuAgAAKmrt2uTee5Mbb0z++Mdk9eqkpaU4V1ubVFcXr9bWYgK+ubmYpK+tTfr2TY46KjnzzOT444tjAAAAALujRYuS225LGhqKsMLq1cUYSVXVn8dQkuJYS0vxKpWSPn2SAQOSk05KTj89OfTQ4h4AAAAAYI8ivAAAwJ7rxReTm24qQgtz5hTHBg8uAgm1tVufRC+VitUEV69Oli4tJuf33z8566wiyDBkyM74BAAAAADl9/jjxfjJ7bcnjY3FuMmgQcUYSo9tbPTe2losHLFyZbJiRXHP4YcnZ5+dnHJK0rPnTvkIAAAAAEDlCS8AALDnKZWSW25JvvrVZO7cYvW/vffesV0T1q1LFi4sAg3jxycf+1hywglWEQQAAAB2XatXJ9/5TvLf/12EDwYOTIYOTWpqtq+9UqloZ/HiYszkNa9JLr44OeCArq0bAAAAAOiWhBcAANizLFiQfPGLxUqBVVXJ6NHbP+HelpaWYheHHj2SM85IPvrRYlIfAAAAYFfyyCPJVVcljz1W7LKw115du0jDmjXJvHnFuMm55ybveIddGAAAAABgNye8AADAnuN3v0s+9alk9uxkxIhkwIDy9bV0abJoUbFy4Be+kBx8cPn6AgAAAOgqpVLywx8m115b7JIwZkzSq1d5+mptTV58MVm7NnnDG5LPf74ISgAAAAAAuyXhBQAA9gz/3/+XXHpp0tiYjB3btbsttKelJZk1K9lnn+Tqq5Mjjyx/nwAAAADbq1RKvv715JvfTGpri8UfunK3hfasXl3swnDMMck11yTDhpW/TwAAAABgpxNeAABg9/fQQ8nHP17shjBuXIcm3X+5aFFOeuihrV7z8HHH5dXbWg2wtTX505+SUaOSr341OeSQThQOAAAAsJOUSsm3v538+78nfft2KEDQZeMnSbJuXfLCC8mxxxZjKAMHdrBwAAAAAGBXUV3pAgAAoKxmzEguvjhZsqTDwYUk+d6cOdu+Zu7cbTdUXZ3sv3+xeuAnP5nMn9+h/gEAAAB2qptvLnZdqKvr8M4HXTZ+kiS9eyf77pv8+tfJpz5V7GgJAAAAAOxWhBcAANh9rV+fXHllERjoRHBhRXNzbnzxxW1e9+N587K+tXXbDW4MMPzpT8nVVxe7MQAAAAB0F3PmJF/5SrH7wt57d+iWLh8/SYoAwz77JHffnVx/fcfuAQAAAAB2GcILAADsvn7842K1vtGjiwBBB/10/vys7cCkemNzc37x0ksda7SmJhkxIrnzzuQXv+hwLQAAAABltWFDctVVyYIFxRhKB5Vl/CRJ+vZNevVKvvGNYiEIAAAAAGC3IbwAAMDuacaM5FvfSvr0SerqOnXr9+bO7fi1c+Z0vOEBA4rdH772tWTevE7VBAAAAFAWN96Y3HNPMmpUpxZ/KNv4SVLU8tJLxY6aLS2duxcAAAAA6LaEFwAA2D19+9vJ4sXJyJGdum3GqlV5cOnSDl9/26JFeampqeMdjB6dzJmT/OAHnaoLAAAAoMutXl0s/lBTk/Tr1+Hbyj5+Ul2d7LNP8uCDRbACAAAAANgtCC8AALD7mTMnue++ZOjQYqeDTmhv1cAD+vbNvn36bHG8pVTKf3dipcHU1CSDBiX/93/JkiWdqg0AAACgS911VzJ3bjJiRKduK/v4SZL07Zu0tiY33JCUSp27FwAAAADoloQXAADY/dx6a7JsWTJ4cKduay2V8sN2JtLfMWpU3t7OLg7tTdi3a+jQZOHC5I47OncfAAAAQFcplYpgQJLU1nb4tp02fpIUYyi/+13y7LOdvxcAAAAA6HaEFwAA2L2sW5fceGNSV5dUd+4/d+9avDhz1q1r89zfjhqVd+yzT5vnpq9cmUeWLet4Rz16FK+GhmTDhk7VCAAAANAl/vjH5LHHkmHDOnXbThs/SZKBA5OVK5P//d/O3QcAAAAAdEvCCwAA7F4efTSZN6/TE+9J8r05c9o8/qoBA3JQ//45cuDATOzbt+17O7t64LBhycyZydNPd7ZMAAAAgB13//3J2rVJ//6dum2njp9UVSX9+iW33Za0tnbuXgAAAACg2xFeAABg9zJjRtLSkvTq1anbVjQ358YXX2zz3N+OGrXpz3/zF3/+Sz+eNy/rOzOJ3rdvsUvEzJmdqhMAAACgS0yfntTUFAGBDtrp4ydJEV5YsiTpbPABAAAAAOh2hBcAANi9PPPMdt320/nzs7aNyfOqbD75/o52Jt8bm5vzi5de6niHGx8MEF4AAAAAdrbm5mI3yHZ2SGjPTh8/SZK6umTNGmMoAAAAALAbEF4AAGD3USolf/xj0qdPp2/9Xjur9x07eHD2q6vb9P6g/v1zWP/+bbcxZ07nOu3Zs6gXAAAAYGeaPTtZsaLT4YWKjJ/06FGM+QgvAAAAAMAuT3gBAIDdx9KlyaJFxYp8nTBj1ao8uHRpm+faWinwHfvs0+a1ty1alJeamjrecV1d8qc/JevXd/weAAAAgB01e3axm0EnxlAqNn6yqYAZnb8HAAAAAOhWhBcAANh9rFmTbNhQrMjXCe2tGlhTVZW/bmPy/W/bOJYkLaVS/rudttruoCZpaUm2Z8IeAAAAYHutWVPsZlDd8anCio2fJMVYz6pVnbsHAAAAAOh2hBcAANh9rF9fTLxXVXX4ltZSKT9sZ8L8TUOHZnivXlscH1tXl2MGDWrznvYm8ttUXZ20ttp5AQAAANi51q/fdcZPkqLWtWs7dw8AAAAA0O10bklaAADozmpqOn3LFf/7v5nTzrklixfnH265pc1za9q5Z/rKlXlk2bK8up3J+c1sDFp0cqcIAAAAgB3SyTGUuxYvzpx169o8V/bxk4169uz4tQAAAABAt+QpKQAAdh+1tX/ezaCD7t7KuUdffnXW9+bO7djke2ur8AIAAACw89XWFosqdHAHy+/NaW/ph50wfpIUYyht7O4AAAAAAOxaqitdAAAAdJkhQ5LevZN2VgJ8pRXNzfl1Gcr48bx5Wd+RAEVTUzJ4cFJXV4YqAAAAANoxbFixk8H69du8dEVzc2588cUuL6HD4ydJsmFDMnp0l9cAAAAAAOxcwgsAAOw+evVKJk5M1qzp0OU/nT8/256i77zG5ub84qWXtn3hmjXJoYd2aIVDAAAAgC4zfnzSt2+yevU2L/3p/PlZ24ldLjuqw+MnpVLxc8KELq8BAAAAANi5hBcAANi9HHpo0tLSoUu/N3du2cr43pw5Hbtw0qSy1QAAAADQpiFDklGjOrQARMXHT9atS2prhRcAAAAAYDfQo9IFAABAlxo/vvjZ2ppUt5/VnbFqVR5curTNc3+b5O862N13k9zYxvHbFi3KS01NGd6rV9s3NjcX9Zl4BwAAACrhsMOSJ5/c6iUVHz9JioBF375/HvMBAAAAAHZZdl4AAGD3cuihSf/+yfLlW71sa6sGHteJ7tq7tqVUyn9vbWXCpUuTQYOSyZM70RsAAABAFzn88KSqaqs7WFZ8/CQpxngOPrgY7wEAAAAAdmnCCwAA7F7Gjk2OPTZpbGz3ktZSKT9sZ2J8TJJ9O9HdAUn2budcuxP8pVKybFly8snJ0KGd6A0AAACgi5xwQjJ8eLJoUZunKz5+kiTr1xfjKGee2YneAAAAAIDuSngBAIDdz5lnJjU1ydq1bZ6+a/HizFm3rs1zr9uO7tq7Z/rKlXlk2bItT6xaldTVJaedth29AQAAAHSBgQOT009PVq4sAgKvUPHxk6QIVowenbzpTdvRIwAAAADQ3QgvAACw+3nd65Lx45OFC9s8/f2trOh33PZ0t5Vzba4euHBhcuihyRFHbEdvAAAAAF3ktNOS/v2T5cu3OFXx8ZPW1mT16uSss4pFIAAAAACAXV5VqdTGUioAALCr+8lPks98JhkxYqsT3L+45ZYu7fb0be2msGJFsmRJ8oUvJG95S5f2DQAAANAppVLysY8lt96aTJiQVG973bP7778/S9vbKWEbBg8alOOO62D0Ye7cIljxP/9T7L4AAAAAAOzy7LwAAMDuqb4+ee1ri4nu1tZKV1PYsCF58cXk5JOTU0+tdDUAAADAnq6qKvn4x5NR/z97/x1nd1nnjf+vMy29FyAhJKRA6AhKh4xSFFEUxbWgSBEyuuuitwVQuO/93vdv6+26u/e2CQhSrAgidrFNEKQIKr2kkxAgIT2ZJNPO748DgZCZyUyYk0kmz6eP80hyruv6fN7nD2M8Z17nNa70HsquYsOGpKkp+dSnBBcAAAAAoA8RXgAAoG+qrk4uvzwZMyZ5/vnenqZkyZLSB+6f+1zphwMAAAAAetu++yaf/nTpSxfWrevtaUpfQvHcc8kppyTnntvb0wAAAAAAPUh4AQCAvmvq1GTmzGTz5mTt2t6dZeXKpFhMPvOZZJ99encWAAAAgNd673uTU09Nli4tNR70lmIxWbw42Xvv0pdSVFX13iwAAAAAQI8TXgAAoG/78IeTc85JXnwxWb++d2ZYsyZZtSr56EeTd72rd2YAAAAA6EhFRfLlLydHHJEsXJi0tOz8GYrFUuNC//7JVVclkybt/BkAAAAAgLISXgAAoG+rqip94P3OdybPP7/zGxhWrUqWL08+8IHkc59LCoWde38AAACArhg7NvnqV5Pp05P583duA8MrjQuVlcmVVyann77z7g0AAAAA7DTCCwAA9H39+yd/93elBobly0stDMViee9ZLCZLlyarVyfnn59cfXUpSAEAAACwq5owIfmP/3i1gWFnfAlEc3MpLNG/f/I3f1N6/wYAAAAA6JOEFwAA2DP075/87/+dfPrTpfaDuXOTTZvKc6/GxtL1+/dPLr+89BBcAAAAAHYHEyYk//mfybvfnaxcmSxalLS2ludeK1cmCxYkU6cm//IvybveVZ77AAAAAAC7BOEFAAD2HNXVySc/mVx7bXLUUcnixdknSWUPXb4qSZYsSZ5/PjnxxOSGG5KPfSyp8M9uAAAAYDcyZkzyf/9vqclyzJhk3rxS0KCtrWeu39hYuubGjaXGyptuSk44oWeuDQAAAADssgrFYrHY20MAAMBOt2lTcsMNafjkJ7NXkpYky5I07sClBicZm6SQ5OC3vS35+MeTD3+4FJYAAAAA2J09/3zyb/+W/OY3yZo1eXrJkixobExzNy9TSLLf4ME5fPz4pKYmOfDAUkPmySeXWjIBAAAAgD5PeAEAgD3a8EIhZyR5f5LpSWqStCXZ8PJj48t/Lqb0IXtlkgFJBiUZmFKV2aYkjya5Lckda9cmQ4bs5FcBAAAAUGYLFyY//Wnuv/LKDF67NhVJmvPqeyibU3oPJSm9h1Kd0nsng1J6LyVJ2gYNyiF/+ZfJ2Wcnxx6bVFXt3NcAAAAAAPQq4QUAAPZohZe/2a8iyZuTHJXk4Jcfw1L6cL0ipQ/di0laUwo0rE7yWJInk/whyZ9fvp5/XgMAAAB9We0xx6T/H/6Qg5McnmRqSgGFfim9f1JIKcTQmlKo4cUkDyd5Osn6N70pd/zxj70yNwAAAADQ+3ydCQAApPSh+gMvP5JSw8KEJONTamOoSunbBDcnWfLyQ0wBAAAA2NNsrqzM7CS/ePnP/ZNMTjIypfdQKpI0JWlMsiDJitecPa5fv504KQAAAACwqxFeAACAdrQmWfjyAwAAAID2bUryRG8PAQAAAADsFip6ewAAAAAAAAAAAAAAAKBvE14AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyEFwAAAAAAAAAAAAAAgLISXgAAAAAAAAAAAAAAAMpKeAEAAAAAAAAAAAAAACgr4QUAAAAAAAAAAAAAAKCshBcAAAAAAAAAAAAAAICyEl4AAAAAAAAAAAAAAADKSngBAAAAAAAAAAAAAAAoK+EFAAAAAAAAAAAAAACgrIQXAAAAAAAAAAAAAACAshJeAAAAAAAAAAAAAAAAykp4AQAAAAAAAAAAAAAAKCvhBQAAAAAAAAAAAAAAoKyqensAAADYdVUmGZukJkl1kuYkm5MsS9LWi3MBAAAAAAAAAADsXoQXAABgizFJ3pRkapKDX34MSinEUEhSTNKaZG2SJ15+zE3yxySremFeAAAAAAAAAACA3YPwAgAAe7hCkqOSvCvJGUlGvPx8a5INSRpTalkovry3MsnQJKcmOf3l51ck+WmSn6RYTAqFnTk/AAAAAAAAAADArk94AQCAPVKxmPzqV0lyc5JDkvRPqT1hfkphhc6sfc3vK5OMSvLxJB/IRRclF1+cnHRSz88MAAAAAAAAAACwuxJeAABgj7N8efKv/5r8+MdJcmSSF1NqWdgRrUmWvfwYkvvuSx5+ODn33OQv/zIZNqwnJgYAAAAAAAAAANi9VfT2AAAAsLMUi8mddyYf+1hy663J0KFJqWlhR4MLr7cuU6cmAwYkN96YnH9+cvfdPXRpAAAAAAAAAACA3ZjwAgAAe4RiMbn22uTyy5MlS5LJk5Phw8tzr5Ejk/33T+bOTT772eQ73yndHwAAAAAAAAAAYE8lvAAAQJ9XLCb/7/8l//7vSU1NKVhQVVXee1ZXl+7T1pb8wz8kN9wgwAAAAAAAAAAAAOy5yvwjWwAA0LuKxeSaa5KvfS0ZPDgZPbqrJ5cmeTzJk0kWJlmfZF2SjUn6vfwYnmSfJOOTHPTy41WFQjJuXPLCC8m//VsyaFDyF3/xxl8TAAAAAAAAAADA7kZ4AQCAPu2Xv0zq65OBA7sSXGhL8ockP0zyaCf7Nr78WJ1SsOEVFbn33mszbtyHM3HiJVue3Xvv5Lnnkq98JZk4MTn22B14IQAAAAAAAAAAALuxit4eAAAAymX58uSrX02am5OxY7e7O8mXkvxtOg8udKYtK1b8No8+emna2lq2Whk3Llm/Pvmnf0rWrdvBywMAAAAAAAAAAOymhBcAAOiTisXkX/4lWbAgmTBhe7v/mOSyJE+UbZ5CoTTHY48ls2aV7TYAAAAAAAAAAAC7pKreHgAAAMrhV79KfvKTUuNCVaf/6n06yd8n2dzJnkKS/ZIcmGRokpok65OsTjIvydIkxe3OVFOTjByZfOc7ySmnJMcc05VXAgAAAAAAAAAAsPsTXgAAoM8pFpMbb0yam5Phwzvet2nT80n+TzoOLhSSnJHkA0nGdnLH9UnuS/K7JH9OZ0GGUaOSOXOSb3xDeAEAAAAAAAAAANhzCC8AANDn/PGPyWOPJXvt1fm+Z575X0nWdrBak+TzSY7rwh0HJzktyWmZMWNK5sz52xQKhXZ3FgrJmDHJvfcmc+cmU6d24fIAAAAAAAAAAAC7uYreHgAAAHraD3+YbNqUDBrU8Z716+dk8eLrO7lKXboWXNjakCEH56ijvplCobLDPcOHJ2vXJj/5SbcvDwAAAAAAAAAAsFsSXgAAoE9Ztiz55S9LAYEOyg+SJEuXfifFYmsHqwen1KRQHoVCMnhwKWSxfn3ZbgMAAAAAAAAAALDLEF4AAKBPefjhZNWqZOTIzvctX/7zTlbf1aMztWfUqFLQ4oknyn4rAAAAAAAAAACAXie8AABAnzJ3bunXysqO97S1NWX16vs7WC0kOaKnx9pGTU3S0vLqvAAAAAAAAAAAAH1ZVW8PAAAAPemJJ5KK7UR0N29enmKxtYPVMUmGdHL6/iSLO1ydO/fxLb+fOvWKDvcVCqVfn3mmk1sBAAAAAAAAAAD0EcILAAD0GS0tyeOPJwMHdr6vqWl5J6udBReS5K4kv+tw9amnXv19Z+GFJOnfP3n44e3cDgAAAAAAAAAAoA/YznfSAgDA7mPFimT9+mTAgM73tbVt7GS1ukdn6syAAcmLLyYbOxsHAAAAAAAAAACgDxBeAACgz9i8OWltTSorO99XXT2qk9XGHp2pMxUVSVtb0tS0024JAAAAAAAAAADQK6p6ewAAAOgpLS1d21dTM6bDtYqKVZkyZVoKhcJWzy9btjybN29Osdg/mza9kSlfVSgkxWLX5wYAAAAAAAAAANhdCS8AANBnVFe/GgjoTE3NiNTUjE1T07Jt1tra1mXOnF8n2a+D059++fGKbyX5zg7NWyyW5q2u3qHjAAAAAAAAAAAAu42K3h4AAAB6Sr9+SUVF0ta2/b1jxpzRyervemymzrS2JpWVSU3NTrkdAAAAAAAAAABArxFeAACgzxg9Ohk+PGls3P7esWPP6mT1Z0nW99BUHdu4MRk/Punfv+y3AgAAAAAAAAAA6FXCCwAA9BkVFckhh3QtvLDPPu/PwIGTO1hdm+Rfk3ShwqETbdupgNi0KTniiDd0CwAAAAAAAAAAgN2C8AIAAH3KQQclxeL291VUVOfAA/9PJzseSPK3Sdbt8Cy/+tUv8+STT2TDhg3brBWLSaGQTJu2w5cHAAAAAAAAAADYbQgvAADQp0ybVgoFtLRsf++4cR/OuHEf7mTHH5JckuTrSR5JqZGhLUlrkjVJ/pjkiQ5PH3XUUWls3JiGhobcd9+9Wbp06ZY2hk2bkurqZMqUrr0uAAAAAAAAAACA3VlVbw8AAAA96U1vSsaMSV56Kdl77873FgqFHHHE17N06aNJHutgV2OS219+JKX8b/HlR+dGjx6T0aPHZPPmzVmyZHGeeuqpPPbYo5kwYUJqaqZk4sR+OeSQrr4yAAAAAAAAAACA3ZfmBQAA+pThw5OzzkrWrk2K288XpLKyXyoq/neS07p4h7Z0JbjwWv369cuUKVPz1re+NUcddVQ2bNiYJ59cmIUL/yU//OEtaWpq6tb1AAAAAAAAAAAAdjfCCwAA9DlnnZUMHlwKMHRFodA/yV8n+UKScW/4/kOGHJ6DD/5qO/cpZPToMdl//6Nz1FGTc845/fKlL30p++67by6//PLMnTv3Dd8bAAAAAAAAAABgVyS8AABAn3PwwclRRyXLlnWtfeFVJyf5ryRXJDkmyeAunuuX5IgUChfk5JP/lBkzHs7kyZ9td2exmKxYkZx2WnX+7u8+lWeeeSbf/va3s3DhwhxyyCE57bTTcsst2hgAAAAAAAAAAIC+paq3BwAAgJ5WKCQXXZT88Y+loMDo0d05XZHkhJcfxSSLkixNsj7JuiStSQa8/BiVZHySMUkKqaioyrBhR3Z69RdfTEaMSM4//+W7VVTk1FNPzamnnpply5blhhtuyJe+9KX81V/9VS688MJccsklmTp1andeAAAAAAAAAAAAwC5H8wIAAH3S8ccnf/EXycqVyY6XGBSSTEopyHBGkvcn+Ysk705yWpI3JRn78r7t27QpWb++FKw45JBt18eOHZsvfvGLW9oYFixYsKWN4Xvf+542BgAAAAAAAAAAYLclvAAAQJ/1yU8mBx+cLF6cFIu9O0uxWJrjLW95tXWhI6+0Mdxyyy159tlnc/rpp+fKK6/Mvvvum8svvzxz587dOUMDAAAAAAAAAAD0EOEFAAD6rKFDky9+MRk8OFm6tPfmKBaTZ59NRo0qzdO/f9fP7rXXXrn88su1MQAAAAAAAAAAALs14QUAAPq0448vBQaKxeT553f+/YvFZMmSUmDhb/4mOfTQHbuONgYAAAAAAAAAAGB3JrwAAECfd+65yec/n7S1lYIExeLOue8rjQs1NclVVyWnn94z19XGAAAAAAAAAAAA7G6EFwAA2COcd14pQFBTk8ybl5T75/s3bkzmzk2GDEn+9m+Ts8/u+Xu018ZwxRVXaGMAAAAAAAAAAAB2OcILAADsMd73vuS//is57LBk0aJk+fKeb2EoFpMXXkieey455pjk2mt7rnGhM6+0McyZMyff+ta3trQxnH766doYAAAAAAAAAACAXie8AADAHuXoo5Ovfz2pq0taW5M5c5JicXiSwhu8ciHF4sjMmZNUVSWf+1wpuHDwwT0wdDdUVFTktNNO29LGcNppp+WKK67IhAkTcsUVV2hjAAAAAAAAAAAAeoXwAgAAe5xBg5LPfCapr0/e+c4kqUkyLcm4JP26ebX+SfZNMjVJRd73vuS665JPfCLp191L9bDXtjF885vfzPz587UxAAAAAAAAAAAAvaJQLBaLvT0EAAD0psGDD8uGDackeU+SvZNUv7yyKcnGJG1Jiim1M1QmGZBXQw5NSZYkuT2DBv0u69c/vTNH77YXX3wxN9xwQ6655pqsX78+F154YT7xiU9k6tSpvT0aAAAAsBs4/vjjc9999+3Q2eOOOy733ntvD08EAAAAAOwuNC8AALDHq6hYlOS/krw3yYVJ/r8k30oyN6XQQmVKgYbKJK1JnkpyU5Krk3w8yfuTXJeKiud39ujdpo0BAAAAAAAAAADoDZoXAADY4w0dOjTr1q3rYLU6pZaFqiTNKTUtNLe7c8iQIVm7dm1ZZiyn9toYLrnkkkyZMqW3RwMAAAB2MZoXAAAAAIAdpXkBAAA61ZxkfZLVSTako+DC7uz1bQzz5s3LwQcfrI0BAAAAAAAAAADoMcILAABAkqSioiKnnXZavve97+XZZ5/NaaedliuuuCITJkzIFVdckXnz5vX2iAAAAAAAAAAAwG5KeAEAANiGNgYAAAAAAAAAAKAnCS8AAAAd0sYAAAAAAAAAAAD0BOEFAACgS7QxAAAAAAAAAAAAO0p4AQAA6JbXtzGceuqpW9oYrrzySm0MAAAAAAAAAADANoQXAACAHbbXXnvliiuuyJw5c/KNb3wjc+fO3dLGcOutt2pjAAAAAAAAAAAAkggvAAAAPaCioiKnn376Vm0Ml19+uTYGAAAAAAAAAAAgifACAADQw7QxAAAAAAAAAAAArye8AAAAlIU2BgAAAAAAAAAA4BXCCwAAQNlpYwAAAAAAAAAAgD2b8AIAALDTvLaNYdGiRVvaGPbbbz9tDAAAAAAAAAAA0IcJLwAAAL1i77333tLGcPPNN29pYzjjjDNy6623prm5ubdHBAAAAAAAAAAAeojwAgAA0Kte38bwtre9LZdffnkmTJigjQEAAAAAAAAAAPoI4QUAAGCXoY0BAAAAAAAAAAD6JuEFAABgl9NeG8MXv/hFbQwAAAAAAAAAALCbEl4AAAB2aa+0McydOzc333xz5syZo40BAAAAAAAAAAB2M8ILAADAbuGVNoZbb701ixYtylvf+tat2hjmz5/f2yMCAAAAAAAAAAAdEF4AAAB2O3vvvXeuvPLKrdoYDjrooJxxxhm57bbbtDEAAAAAAAAAAMAuRngBAADYbbXXxvCFL3xBGwMAAAAAAAAAAOxihBcAAIA+QRsDAAAAAAAAAADsuoQXAACAPkUbAwAAAAAAAAAA7HqEFwAAgD7rtW0MN910kzYGAAAAAAAAAADoJcILAABAn1dRUZEzzjij3TaGL33pS9oYAAAAAAAAAACgzIQXAACAPcrr2xieeeaZHHTQQXn729+ujQEAAAAAAAAAAMpEeAEAANgjvb6Noba2VhsDAAAAAAAAAACUifACAACwx9PGAAAAAAAAAAAA5SW8AAAA8LLXtjEsXLhQGwMAAAAAAAAAAPQQ4QUAAIB27LPPPlu1MTz99NOZPn26NgYAAAAAAAAAANgBwgsAAACdeKWN4bbbbsuiRYu2aWNYsGBBb48IAAAAAAAAAAC7POEFAACALmqvjeHAAw/UxgAAAAAAAAAAANshvAAAANBN2hgAAAAAAAAAAKB7hBcAAADeAG0MAAAAAAAAAACwfcILAAAAPeD1bQwzZszI5z//eW0MAAAAAAAAAAAQ4QUAAIAet88+++RLX/pS5s2blxtvvHGrNobvf//72hgAAAAAAAAAANjjCC8AAACUSUVFRd7+9rdv1cbwuc99Lvvtt1++/OUva2MAAAAAAAAAAGCPIbwAAACwE7y2jeGGG27IU089pY0BAAAAAAAAAIA9hvACAADATqSNAQAAAAAAAACAPZHwAgAAQC/RxgAAAAAAAAAAwJ5CeAEAAKCXaWMAAAAAAAAAAKCvE14AAADYhbTXxjB9+vS84x3v0MYAAAAAAAAAAMBuS3gBAABgF/TaNoaFCxfmlFNO0cYAAAAAAAAAAMBuS3gBAABgF6eNAQAAAAAAAACA3Z3wAgAAwG5CGwMAAAAAAAAAALsr4QUAAIDd0OvbGJ588skceOCB2hgAAAAAAAAAANglCS8AAADsxl5pY/j+97+fRYsW5eSTT87/+B//Y0sbw8KFC3t7RAAAAAAAAAAAEF4AAADoK/bZZ598+ctfzvz587e0MRxwwAF5xzvekdtvv10bAwAAAAAAAAAAvUZ4AQAAoI9pr43hs5/9rDYGAAAAAAAAAAB6jfACAABAH6aNAQAAAAAAAACAXYHwAgAAwB7gtW0MCxcu1MYAAAAAAAAAAMBOJbwAAACwhxk3bly+/OUvZ968efn617+ujQEAAAAAAAAAgLITXgAAANhDVVZW5h3veIc2BgAAAAAAAAAAyk54AQAAgA7bGM4880xtDAAAAAAAAAAAvGHCCwAAAGzx+jaGk046KZ/97GczceLEXHXVVdoYAAAAAAAAAADYIcILAAAAtOu1bQzXX399nnjiCW0MAAAAAAAAAADsEOEFAAAAOqWNAQAAAAAAAACAN0p4AQAAgC57fRvD448/ro0BAAAAAAAAAIDtEl4AAACg215pY7j99tu1MQAAAAAAAAAAsF3CCwAAALwh2hgAAAAAAAAAANge4QUAAAB6xOvbGE488cR85jOf0cYAAAAAAAAAAIDwAgAAAD1v3LhxueqqqzJ//vxcd9112hgAAAAAAAAAAPZwwgsAAACUTWVl5ZbAgjYGAAAAAAAAAIA9l/ACAAAAO4U2BgAAAAAAAACAPZfwAgAAADtVZ20MV199tTYGAAAAAAAAAIA+SHgBAACAXvP6NobHHntsSxvDD37wA20MAAAAAAAAAAB9hPACAAAAva69NobLLrtMGwMAAAAAAAAAQB8hvAAAAMAuRRsDAAAAAAAAAEDfI7wAAADALum1bQwLFizQxgAAAAAAAAAAsBsTXgAAAGCXN378+E7bGFpaWnp7RAAAAAAAAAAAOlHV2wMAAMCuaUSSKUnGJemXpDpJc5JNSZ5LMjfJ2l6bDvZUr7QxnHnmmXnuuefy9a9/PZdddlk+9alP5eKLL84nPvGJTJw4sbfHfMPWN63PvJXz8uyaZ7OpZVOa25pTVVGVmsqa7DVor0wbNS2jBoxKoVDo7VEBAAAAAAAAALpEeAEAAJIkA5LUJnlzkiNSCi0MSlKTpO01+wpJmpI0Jnk2ycNJHkjyu504K5C82sZw5ZVX5s4778ysWbMybdq0nHbaabn00kvzrne9K1VVu8f/7W1pa8nvF/8+v1/8+zz64qNZsHpBGpsbs6llUwoppFAopJhiUkyqKqoysGZg9h68d47Y64i8ae835bTJp2VIvyG9/TIAAAAAAAAAADpUKBaLxd4eAgAAesvChcn06f8jmze/I8n4JBUptStsePnR1M6pfikFGwYl6Z+kJcmi9Ov3s8yb9x8ZP36njA6047nnnsv111+fr33ta2lubt7l2xiWb1ien8/9eW578rbMWzUvTa1N6VfZL4NqBmVQ9aD0r+q/VbtCsVhMc1tzNjRtyIbmDdnYvDGFQiF7D9477z7g3Tlr2lk5YNQBGhkAAICyOf7443Pfffft0Nnjjjsu9957bw9PBAAAAADsLoQXAADYI61cmfznfyY/+lHy4IPPpFhcn2RZkuYduFq/JGNSKAzKsccekHPPTS69NBniS9Ch17S2tm5pY/jpT3+6y7UxbGrZlJsevik3PXxTXmp8KTWVNRk7aGwGVA/o9rWaW5vzUuNLWbd5XYb0G5JT9z81nz3+s9l78N5lmBwAANjTCS8AAAAAADuqorcHAACAnalYTBoakvPPT77xjaSyMikU5id5LjsWXEiSzUmWpFCYn7a25NprkwsvTB54oMfGBrqpsrIyZ555Zn7wgx9kwYIFOf7443PZZZdlv/32y9VXX51Fixb12myPLXssl/zwkvzLvf+STS2bMnnE5EwcPnGHggtJUl1ZnX2G7JNpo6alf1X//OCpH+T828/PT575SXxfAQAAAAAAAACwqxBeAABgj7FhQ/J//k/y2c8mCxcmkycno0cnhULP/HBvoVDM2LHJxInJE08kn/pU8tWvJk1NPXJ5YAeNHz8+V199debPn5/rrrsujz76aKZNm5Z3vvOd+cEPfpCWlpadMkdrW2uufejaXPzDi/OHpX/IvsP2zT5D9kllRWWPXL9QKGTEgBGZMnJKXlj/Qq749RW5/FeXZ+XGlT1yfQAAAAAAAACAN0J4AQCAPcKaNcnnP59861vJoEHJ/vsnVVXluVdNTSkYUV1damH48peTxsby3Avouo7aGCZOnFj2Noam1qb87e/+Nv9637+mrdiWqSOnpn9V/7Lcq7KiMhOHT8zIASNzx1N35DM//0yWbVhWlnsBAAAAAAAAAHRVoVgs9szXzAIAwC5q7dpS28Lddyf77psMHFh6/s9/viBLltzYwalrk+zVhat/Kclj7a68613FrFuXvPBC8va3J//4j0n/8vysMrCDWltbc+edd2bWrFn56U9/mtNOOy0zZ87MWWedlaoeSDgdddRR+dOf/rTN83sdtVfe8vm3dHp2zg/m5Olbnm537c2fe3P2PnrvTs//+rJfZ+Pyjds8/5d/+Zf5j//4j07PAgAAdOT444/Pfffdt0NnjzvuuNx77709PBEAAAAAsLvQvAAAQJ+2aVPypS+VggsTJrwaXNhZhgxJ9tkn+cUvkr/5m6SlZefeH+hce20Mf/3Xf91jbQwnn3xyu8+vfHpltvddAiufWrlDa0myccXGdoMLnc0EAAAAAAAAAFBOwgsAAPRp112X/OpXyfjxyYABvTPD4MHJXnslP/pR8t3v9s4MwPaNHz8+V199debPn59rr702jz76aKZNm5Z3vvOdueOOO9KyA+mjwdMGt/t884bmrFu8rsNzxbZiVs1Z1eH6yqc7Dy90Fm448cQTOz0LAAAAAAAAAFAOwgsAAPRZDz+c3HhjMnRoMmhQ784ydGhSU5PU1yfz5/fuLEDnKisr8853vvMNtzG8uP7F3FO4p8P1zgIGaxetTcvGjsMSaxasSevm1g7XVzy1ot3na0bXZH6rv4QAAAAAAAAAgJ1PeAEAgD5p48bkH/8xWbs2GTu2t6cpGTcuWbYs+Yd/SHbgC9yBXrCjbQzFYjFfvfereb7t+Qwa1356qqOAwfbWkqTYWsyquZ00M3QQjBgwZUC+8vuvZPWm1Z1eHwAAAAAAAACgpwkvAADQJ33rW8mf/pRMmJAUCr09TUlFRTJ+fHLPPckPftDb0wDd0V4bw6c//elMnDgx//N//s9t2hh+s+A3+fncn2evwXtl1PRR7V6zs+aFztZeseLJ9gMOTWubsn7p+nbX9j103zz90tO55qFrtnt9AAAAAAAAAICeJLwAAECfs3Fj8r3vJf37lx67kkGDSmGK735X+wLsrl5pY1iwYEGuvfbaPPzww5k2bVrOOuus3HHHHWlubs53H/9umtuaM6z/sIycPrLd62xevTkbXtjQ7trKp7cOLwwcMzApdL7nFSueXpEU2599zMFjMqz/sPzo6R9lRWPn7Q4AAAAAAAAAAD1JeAEAgD6noSF59tlk7NjenqR9Y8cmTz+dPPBAb08CvBGvtDHccccdWbBgQY477rh8+tOfzvgjx+dHD/0oQyqGJElGHdR+80LSfsPC+qXr07S2aavnxhwxJkMnDt3qudVzV6etpa1L10ySmqE1GbzP4IweODovNb6UX8z7xXZfIwAAAAAAAABATxFeAACgTykWk9tvL/2+pqZ3Z+nIoEFJc3Pywx/29iRAT3ltG8O7/8e7s27zujxwzwN54P4HsqZ5TQaMHtDuuRVPbdt+0F6jwsjpIzNq+tYhiNbNrVmzcM225zsIL7zSAFFZUZmqyqp8/8nvp6VNBQwAAAAAAAAAsHMILwAA0Kc880zy0EPJ6NG9PUnnhg8vNUS88EJvTwL0pE2tmzKvcl6m7z89p73ttAwfMTyPPfZYNg/b3O7+9oIG7T038sCRW8IHne1t2diStYvWtnuvkQe+en7soLF5ZsUzeWjpQ52+HgAAAAAAAACAnlLV2wMAAEBPevLJZMOGZJ993uiVLumJcTo0fHiyaFFp3r33LuutgJ1ozso5WbVpVcYMHJP+1f1zwAEHZNq0aXl8/eNZOG/hNvsblzVm06pN6T+i/5bnXt/GMHDMwAwYNSAV1dt+/8DKp1ZmyrumvPrnZ1am2FZsd7bXNjcMrB6Y5rbmPLH8iRy777HdfZkAAAAAAAAAAN2meQEAgD5l7tzSr4VC786xPVVVSbGYzJvX25MAPWnuyrnZ3LI5/ateDSMUCoVMOmZSh2eef/j5Lb/fuGJjNi7fuNX6K40L/Yb2y+Dxg7daW/nMyhSLr4YV2mttSJKqAVUZOnHoNs8/9dJTHb8YAAAAAAAAAIAeJLwAAECf8sgjSU1Nb0/RNYVC8sQTvT0F0JOeWfFMklJg4bUGjxucfsP6tXvm8d8+ngfufyAvvPBCVjy5Ypv1V8ILr/99kjSvb866Jeu2/Lmj8MKIaSNSqNh6poHVA/Poske3Cj8AAAAAAAAAAJSL8AIAAH3Gxo2lJoNBg3p7kq4ZODB57LGkra23JwF6yqPLHk2/qvZDCiMOHNHu84MaB2X4iOF57LHH8shvHtlmfeSBI9v9/SteCSy0Nrdm9fzV7d7j9aGHpBReeKnxpbyw/oV2zwAAAAAAAAAA9KSq3h4AAAB6yqpVSVNTKRTwxr0vyeAu7PtFkhd36A79+iXr1pVCF7tL4ALo3LL1y9Kvsv3wwqjpo/LCA9sGBTYs3ZBJ4yZl2rRp+c3PfpON2bhlrWpwVQbt/epfEKMOGrXN+ZVPr8yk0ydl9dzVaWtuPw3VXnihX2W/rNu8Lis3rsw+Q/bZ7msDAAAAAAAAAHgjhBcAAOgzmppKLQYVPdIvdmaSvbqw74/Z0fBCoZAUi0lz8w4dB3YxxWIxTa1NqSi0/5dQewGC0sFSAGHkgSOz8YWNWy21jWrLr379q0ydOjX7779/BowakAFjBmTj8lf3vdK8sPLple1evqK6IsMnD9/2+UJF2optaW7zlxAAAAAAAAAAUH7CCwAA9BnFYvmu3a9fvwwatG0Tw9q11Wlp2bFrvhJeaGv/i9KB3Uzx5f90ZOh+Q1M1sCotjdv+pfFKAOH1x6efPD2DDh+U1pbWLc+Nmj4qS5Yv2fLnTSs3pXFZY1Y+2X54Yfjk4amsqdzm+UKhkGKKaW1rbecUAAAAAAAAAEDPEl4AAKDPqKl5NRCwPS0tLVm/fn2Xr7158+Zs3ry5nZUd/8byV1oiqqt3+BLALqSiUJHqyuo0tTa1u16oKGTkASOz7M/Ltllb8eSKds+MnD4yw/cavs1zS363ZKvnXnripayau6r9axzYfuNDW7EtlYXK9Kvq1+46AAAAAAAAAEBPEl4AAKDPGDQoqapKmjvJE6xZsyaLFi3Kc88tSWtr+z/ou7O0tCT9+iUDBvTqGEAPGlozNGs2relwfeT09sMLaxetTVvz1jUsVQOqMmzSsHav8XoLf7EwLRvbr4Fpb3+SNLc2p7KiMgOrB3Y4LwAAAAAAAABATxFeAACgzxg2LNl772TRomTka35Wt7W1NUuXPpdFixZl9erVvTbf6zU2JocdVgpcAH3DIWMPyTMrn+lwfdT0Ue0+X2wtZt3idVs9N2LaiBQqCtvsHbzP4PQb3i+bV7/aBrN20dr2b1hIRhwwot2lxubGDK0ZmglDJ3Q4LwAAAAAAAABAT/FjUgAA9BmFQnLEEclTT5X+vG7duixatChLlixJS0sndQy9pKUlOfTQ3p4C6EkHjjowKXa8PmzysFT2q0zr5tbtXqujxoRX1p6/7/ntXmPofkNTPbC63bUNzRty9D5Hp7qy/XUAAAAAAAAAgJ4kvAAAQJ+y//7NWbu2Mffc81hWrVrZ2+N0qK2t9Ou0ab07B9Czpo6cmsqKyjS1NqWmsmab9YqqigyfMjwrnlix3WuNPLDj8MKo6aO6FF7oLADRVmzLoWMlqAAAAAAAAACAnaOitwcAAICeMGfOnHzhC1/IZz7zzjz//MKsWtXY2yN1av36ZODAZOrU3p4E6EnTRk3L4JrBWbd5XYd7OgsUvOKVkMMbuUbScQCiubU5hRQydaS/hAAAAAAAAACAnUN4AQCA3VZzc3Nuu+22nH766TnggAPyla98JWvWzE6yMMnYTs9WVlZl0KDBO2PMdr30UnLIIckBB/TaCEAZjB00NsdPOD6rNq3qcM+o6aO2e51hk4elsqayw/UhE4akenD1dq/TUchheePy7D1475ww4YTtXgMAAAAAAAAAoCcILwAAsNt59tlnc/XVV2e//fbLueeem1/96levWW1O8v0k/dLeP3eHDh2aww47PKeffnpGjBixkybeWnNzUiwm739/Uij0yghAGb33wPemqlCVjc0b210fMW1ECpWd/5d/e80KhUKhw1aFVwzca2D6D++/zfPFYjHrN6/Puw94d4b0G9LpNQAAAAAAAAAAekpVbw8AAABd0dramp/97GeZNWtWfvrTn6atra2T3T9LcmmSUUmWp6KiMuPHj8vEiRMzfPjwJL2bGFi+PNlnn+TUU3t1DKBMTphwQqaMnJL5q+Zn4vCJ26xX9qvMsP2HZfXc1R1eY3vBhKQUcHjxoRc7XO+o4WHN5jUZ0m9I3nXAu7Z7DwAAAAAAAACAniK8AADALu3555/Pddddl2uvvTbPPvtsF08tT3JnqqsvyNSpe2W//canurpmm11HHnlDjjzyhi1/XrduXWbPbujmhH+XGTNqM2RI1769vKUlWb8+Of/8ZPDgbt4K2C1UV1bn/Qe9P393999lc8vm9Kvqt82ek/73SW/4PlPOmpIpZ03p1plisZhlG5bl9MmnZ9qoaW94BgAAAAAAAACArqro7QEAAOD12tra8stf/jLvf//7M2HChFx99dVdDi7U1NTkIx/5SG699Z058cRJqa7ev93gQm959tlk6tTkYx/r7UmAcnrfQe/L4XsdnsVrFqdYLPb2OFu8sP6FjBowKn91zF/19igAAAAAAAAAwB5G8wIAALuM5cuX54YbbsisWbMyb968bp2dMmVKZs6cmQsuuCBjxoxJklRUJFdfnaxbl3SxGKGsVq1KqquTz30uGTmyt6cBymlQzaBcfuLlmfnjmVneuDxjB43t7ZGysXljNjRvyKfe8qlMHz29t8cBAAAAAAAAAPYwwgsAAPSqYrGYu+++O/X19bn11lvT1NTU5bOVlZV573vfm5kzZ+bUU09NRcXWxWLveU/S0JD8/OfJwIFJZWUPD98Nzc3J8uXJhz6UvPWtvTcHsPMctc9R+ehhH81/P/jfGdpvaPpX9e+1WdqKbVm8dnFO2PeEfPTwj/baHAAAAAAAAADAnkt4AQCAXrF69ercdNNNqa+vz5NPPtmtsxMmTMill16aiy66KOPGjetwX0VF8sUvJnPnJvPmJVOmlJ7b2VpakgULkkMPTS67LCkUdv4MQO/4xFGfyEPPP5T7l9yfSSMmpaayZqfPUCwWs2DVgowfMj5XnHRFr8wAAAAAAAAAANALP7oFAMCeqlgs5oEHHtgSOrjsssu6HFwoFAp517velR//+MdZsGBBrrrqqk6DC68YPz75yleSffdN5s9PWlvf6KvonleCC9OmJf/8z8nIkTv3/kDvGlQzKF854ys5Yu8jsnD1wjS1dr1dpie0FduyYPWCjBowKv9w2j/kwNEH7tT7AwAAAAAAAAC8QngBAICyW7duXWbNmpWjjz46xx57bL7+9a9n48aNXTq7995756qrrsqCBQvyox/9KGeddVYqKyu7df+DDkq++tVkwoRSgKFpJ/3s8ObNpfsdcEDyL/+STJq0c+4L7FrGDhqbf3n7v+TIvY/MglUL0tjcuFPu29LWkgWrFmTMwDH5x9P/MceMP2an3BcAAAAAAAAAoD1VvT0AAAB918MPP5z6+vp84xvfyPr167t19rTTTktdXV3OPvvsVFdXv+FZDj88+Y//SC6/PHniiWTUqFILQqHwhi+9jWIxWbYsWbs2Ofro5B//sRScAPZc44eOz3+c+R+58tdX5veLf58B1QOyz+B9UijHX0JJVm9anWXrl2XKyCn5/73t/5ej9jmqLPcBAAAAAAAAAOgq4QUAAHpUY2NjbrnllsyaNSv33Xdft86OGjUqF110US699NJMnTq1x2c74IDkxhuT//7v5JZbSq0IEyYkNTU9d4/Nm5Nnn02GDk3+6q+Siy9OBgzouesDu68xg8bkv876r3zr0W/lmoeuydyVc7Pv0H0zoLrn/pJoaWvJ4jWLU1VRlb845C9y2XGXZfTA0T12fQAAAAAAAACAHSW8AABAj3jyyScza9as3HjjjVm9enW3zp588smpq6vL+973vvTv3788A75s6NBS+8LJJyf/9E/JM88kFRXJmDE7fs1isdSysHx56c9HHplccUXpV4DXqqmsyQVHXpDj9z0+/3DPP+SB5x5IW7EtoweOzrB+w3a4iaGxuTHLNixLc2tz9h+xfz53/Ody6v6nlq3ZAQAAAAAAAACgu4QXAADYYZs3b87tt9+e+vr6zJ49u1tnhw0blo9//OOZOXNmDj744DJN2LETTkhuvjn5xS+S225LnnwyWbOmOsleSTYkaUzS1skVKpMMTDI4zz5bkxEjkmOOSd73vuT007UtAJ07cPSBmfWuWfnNgt/k9qduz4NLH8yy9csyqGZQBtcMzqCaQamq6Pj/srcV29LY3JgNTRuydvPaVFdWZ9rIaXn/Qe/PmdPOzMgBI3fiqwEAAAAAAAAA2D7hBQAAum3+/Pm55pprcv3112f5K3UDXXTMMcekrq4uH/zgBzNw4MAyTdg1Q4Yk555bChw89FBy3XXrMmvW+iTDUgoxFJIUUwoxFF/+c+XLv09KAYfVede7VucTn9grhx+e+JJzoKtqKmvyjqnvyNunvD1Pr3g6P37mx/nl/F9m5caVeanxpRSLxRRTTGVFZSoKFWkrtqWt+HKoqpgMqB6QIf2GZMakGXnPge/Jcfse12ngAQAAAAAAAACgNxWKxWJx+9sAANjTNTc358c//nHq6+tz5513duvsoEGDct5552XmzJk56qijyjThG/f444/n0EOPSLJfkqkvP/ZLMiBJTZLNKQUWFiWZ+/JjSR577JEccsghvTM00KcUi8UsXbc0c1bOydyVc7Nw9cKsb1qfzS2bU11ZnQFVAzJ+6PhMHTk100ZOy8ThE1NTWdPbYwMAAHuQ448/Pvfdd98OnT3uuONy77339vBEAAAAAMDuwlcyAgDQqcWLF+faa6/Nddddl6VLl3br7OGHH55PfvKT+chHPpKhQ4eWacKe1ppkwcuPX/byLMCeplAoZPzQ8Rk/dHxqJ9X29jgAAAAAAAAAAD1GeAEAgG20trbmF7/4Rerr6/OTn/wkbW1tXT7bv3//fPCDH0xdXV2OPfbYFAqFMk4KAAAAAAAAAADA7kB4AQCALV544YVcf/31ueaaa7Jo0aJunZ0+fXrq6urysY99LCNHjizThAAAAAAAAAAAAOyOhBcAAPZwbW1t+e1vf5v6+vr84Ac/SEtLS5fPVldX5/3vf3/q6upyyimnaFkAAAAAAAAAAACgXcILAAB7qJdeeik33nhjZs2alTlz5nTr7OTJkzNz5sxccMEFGTt2bJkmBAAAAAAAAAAAoK8QXgAA2IMUi8Xcc889qa+vz/e+9700NTV1+WxlZWXOPvvs1NXV5bTTTktFRUUZJwUAAAAAAAAAAKAvEV4AANgDrFmzJjfffHPq6+vz+OOPd+vsvvvum0suuSQXX3xxxo8fX6YJAQAAAAAAAAAA6MuEFwAA+rAHH3ww9fX1+fa3v53GxsYunysUCjnzzDNTV1eXM888M1VV/tkIAAAAAAAAAADAjvNTaAAAfcz69evz7W9/O/X19fnjH//YrbN77bVXLr744lxyySWZNGlSeQYEAAAAAAAAAABgjyO8AADQRzzyyCOZNWtWbr755qxbt65bZ0899dTU1dXl7LPPTk1NTZkmBAAAAAAAAAAAYE8lvAAAsBvbuHFjvve976W+vj733ntvt86OHDkyF154YS699NIccMABZZoQAAAAAAAAAAAAhBcAAHZLTz/9dGbNmpUbbrghq1at6tbZE088MXV1dTn33HPTv3//Mk0IAAAAAAAAAAAArxJeAADYTTQ1NeX222/PrFmz8tvf/rZbZ4cOHZrzzz8/M2fOzKGHHlqmCQEAAADYs4xIMvXlx8gkNUkqk2xO0phkQZK5SZYkaeulGQEAAACAXYXwAgDALm7BggW55pprcv3112fZsmXdOvvmN785dXV1+dCHPpRBgwaVaUIAAAAA9gTFYrJhw7QkM5K8Jcm4JIOSVCcpvryr8JrfJ6UQw+okj2bVqiVpbEwGDtx5MwMAAAAAuw7hBQCAXVBLS0t+8pOfpL6+Pr/4xS9SLBa3f+hlAwcOzHnnnZeZM2fm6KOPLuOUAAAAAOwJ1q9Pfv3r5Pbbk3nzvpxSOGFjkg1Jnk/S1MHJyiQDkwxL8vYsWjQg731v8t73Ju98ZzJpUvlnBwAAAAB2HcILAAC7kCVLluRrX/tavva1r+W5557r1tnDDjssdXV1Oe+88zJs2LAyTQgAAADAnqJYTGbPTr761WTu3KRQSCorNyTp6vtWrUnWvfxIamrG5KWX9s+//Vty003JeeclF1+siQEAAAAA9hTCCwAAvaytrS133nln6uvr86Mf/ShtbW1dPtuvX7988IMfzMyZM3P88cenUCiUcVIAAAAA9hSrViX//u+ltoWmpmTChKSmJlm6tHGHr1lR0ZLx45O2tmT58uQ//zO5557kiiuSI4/sudkBAAAAgF2T8AIAQC958cUXc/311+eaa67JwoULu3X2gAMOSF1dXc4///yMGjWqPAMCAAAAsEd66KHkf/2vZM6cZMyYZN99e/b6FRXJXnslw4cnDz+cXHppctFFpV8rKnr2XgAAAADArkN4AQBgJyoWi2loaEh9fX1uv/32NDc3d/lsVVVV3ve+96Wuri61tbVaFgAAAADocXfdlVx1VakZYf/9k+rq8t2rX79kypRk2bJSy8OKFckXv1jeewIAAAAAvUd4AQBgJ1i5cmVuvPHG1NfX55lnnunW2UmTJmXmzJm58MILs9dee5VpQgAAAAD2dHffnVxxRbJ2bSlUsP3vzlif5JEkjyaZk2RtknVJNicZlGRoklFJDk5yaIrF47e5QqFQamFYtSr55jeTYjG58sqksrLnXhcAAAAAsGsQXgAAKJNisZh777039fX1ueWWW7J58+Yun62oqMi73/3u1NXV5YwzzkhFRUUZJwUAAABgT/fnPydf/nIpuDBp0vaCC8uT/CDJnSkFFdqz5uXH4iR/TpKsW7dPFi68OhMmXJjKyv5b7R4xonTPb387GTIkueyyN/BiAAAAAIBdkvACAEAPW7NmTb75zW+mvr4+jz76aLfOjh8/Ppdcckkuvvji7LvvvmWaEAAAAABetX598rd/m7z44vYbF5qafpHk/6bj0ELH2tqez2OPfSoLF/5H3vzm72fw4AO3Wh8+PGluTm66KTn66OSkk7p9CwAAAABgFya8AADQQx566KHU19fnW9/6VhobG7t8rlAo5O1vf3vq6upy1llnparKP9EAAAAA2HmuuSZ59NFk4sSkswLQJ574fBob//kN32/9+idy991vydFH35YxY07fam306GT+/OQrX0kOOywZNuwN3w4AAAAA2EX4yTgAgDdgw4YN+c53vpP6+vo8+OCD3To7ZsyYXHzxxbnkkksyefLkMk0IAAAAAB174IHkW98qtR7U1HS8b/78f8v8+Z0FFyqTHJZkUpIBSVYleSTJ0nZ3t7Ssy0MPfSAnnXT/Vg0MhUKy337Jk08m//VfyZVXdvMFAQAAAAC7LOEFAIAd8Nhjj2XWrFm56aabsnbt2m6dfetb35q6urq8973vTU1nnwgDAAAAQBm1tCT//M/Jhg3JlCkd71u16r48+eTnOrnSkUn+KsnYdtbuTfJfSda0c/81efDBc3LKKY+kouLVjy2rq5NRo5Jbb03OPDM58siuvBoAAAAAYFcnvAAA0EWbNm3Krbfemvr6+txzzz3dOjtixIhccMEFufTSSzN9+vQyTQgAAAAAXXf//aWGg3HjSo0HHXnqqS+nWGztYPVNSa5Oxx87Hp9knyRXJtmwzer69U/mueduzoQJF271/MiRydy5yR13CC8AAAAAQF8hvAAAsB3PPPNMrrnmmnz961/PypUru3X2hBNOSF1dXc4999wMGDCgTBMCAAAAQPf98IdJU1MycGDHe1atujcrVvymg9WBST6T7X/kOCnJR5PMand1zpy/zb77XpDCaxIUhUIybFhy553JJz+ZjG2v1AEAAAAA2K0ILwAAtKOpqSl33HFH6uvr85vfdPThbPuGDBmSj33sY5k5c2YOP/zwMk0IAAAAADtu8eKkoaHUcNCZ55+/tZPV05OM6OId35FC4bspFldvs9LYOC/r1j2aoUO3fi9t1Khk/vzk5z9Pzj+/i7cBAAAAAHZZwgsAAK+xcOHCXHvttbnuuuvy4osvduvsUUcdlbq6unz4wx/O4MGDyzQhAAAAALxxv/lNsmZNMmVK5/teeqmzL/Y4uRt3rEx19Yw0Nd3RwX1+u014obIyqalJ7rgj+djHSm0MAAAAAMDuS3gBANjjtbS05Kc//Wnq6+vz85//PMVisctnBwwYkI985COpq6vLm9/85jJOCQAAAAA957HHSmGAioqO97S2bszatQ93sFqVZP9u3bOy8qAk7YcXVq++v93nhw5NlixJXnopGTOmW7cDAAAAAHYxwgsAwB7rueeey3XXXZdrr702S5Ys6dbZQw45JHV1dfnoRz+a4cOHl2dAAAAAACiDYjF59NFk4MDO9zU1LU/S0Rd97JWkulv3razcr5N7LWv3+UGDkueeS+bOFV4AAAAAgN2d8AIAsEdpa2vLr371q9TX1+eHP/xhWltbu3y2X79++cAHPpC6urqccMIJKeipBwAAAGA3tHRpsmJFKRjQmaamFZ2sbif50I5CYXAn93qp3edrapLW1lJ44fjju31LAAAAAGAXIrwAAOwRli1blq9//eu55pprMn/+/G6dnTZtWmbOnJmPf/zjGT16dJkmBAAAAICdY/78pLEx2d5bXa2tGztZ7V7rwvbOtLRs6PTknDk7cDsAAAAAYJcivAAA9FnFYjF33XVX6uvrc9ttt6W5ubnLZ6uqqnLOOedk5syZeetb35qKiooyTgoAAAAAO8+aNaU2g6rtfFJYUzOyk9XOgg3tKxY7DihUVw/tcK2yMnmp/WIGAAAAAGA3IrwAAPQ5K1euzE033ZRZs2blqaee6tbZiRMn5tJLL81FF12Uvffeu0wTAgAAAEDvaW5OCoXt76uuHtXJ6ppu37dYXNnhWk1NxzUQFRXJ5s3dvh0AAAAAsIsRXgAA+oRisZj7778/9fX1+e53v5tNmzZ1+WxFRUXOOuvi5GPQAADLV0lEQVSs1NXV5e1vf3sqKyvLOCkAAAAA7B5qakalqmpYWlraCyqszJQpY1JZOWLLM8ViMm/e3LS1tb3uOjUZMGBgBgxYkQ0dlC8MGXJYD04OAAAAAOyKhBcAgN3aunXr8s1vfjP19fV5+OGHu3V2n332ySWXXJJPfOITmTBhQpkmBAAAAIBdS3V1KWiwPYVCRUaNOiUvvvijdtfnzbs9yUnbvU5TU1OampqyZs1POtwzfPixHa61tSX9+m33NgAAAADALk54AQDYLf3pT39KfX19vvnNb2ZDR1/X1oEzzjgjdXV1ede73pXq6uoyTQgAAAAAu6Zhw5LKyqSlJanazqeFo0ef1mF4Ifl1uhJeKFma5PEO1vpl+fIJSZ7LyJGj0r9//61WW1uT0aO7eBsAAAAAYJclvAAA7DYaGxvz3e9+N/X19XnggQe6dXbMmDG56KKLcskll2TKlCllmhAAAAAAdn2TJycDByYbNpSCDJ0ZP/6jefrpq9LSsq6d1YeS/CnJm7Zzx2KSa1/+tT0zsnjx8ixevDxJMmjQoIwaNTqjRo3KqFGjkvTPtGnbuQUAAAAAsMsTXgAAdnmPP/54Zs2alZtuuilr1qzp1tkZM2akrq4u55xzTvrplgcAAACAjBuXjBqVLF++/fBCTc3ITJr06cyd+3cd7Pi/Sf53kqkdrLcm+VpKQYdtFQr9Mnnyl7Ju3YCsXLkiLS0t2bBhQzZs2JBnn12UpDo1NZPzve/dloqK/TJjxozsu+++XXmZAAAAAMAuRngBANglbd68Obfddlvq6+vzu9/9rltnhw8fno9//OOZOXNmDjrooDJNCAAAAAC7p0IhOeyw5Mc/7tr+qVOvyAsv/CDr1z/Rzur6JF9McmaS2iQTkvRLsibJw0nuSDK3w2sfdNDfZ/LkGUmSYrGYtWvXZsWKl7JixYqsWLEyLS2D0tS0Kj/60Vfzox+9lCSZMmVKamtrM2PGjNTW1mbChAldeyEAAAAAQK8qFIvFjvpZAQB2ujlz5uSaa67JDTfckJdeeqlbZ4877rjU1dXlAx/4QAYOHFimCenLHn/88Rx66KHdPvfYY4/lkEMOKcNEAAAAAOVx443J3/99MmVKUlGx/f0bNszL3Xcfk+bmlV24ekWStu3uGj/+vBx55M0pFArtrheLxTz99MYMHPhcJk36Qn73u7uyatWqbfbtv//+qa2t3RJomDhxYhdmBAAAAAB2NuEFAKDXNTc354c//GHq6+vzq1/9qltnBw8enI9+9KOZOXNmjjzyyPIMyB5DeAEAAADYUyxenJx7blJZmYwa1bUz69Y9kYceen/Wr3/qDd9/woRP5PDD61MoVHa4p7U1mT8/+dKXkvPPT9ra2vLoo4+moaEhs2fPzuzZs7Ny5bZhikmTJm1pZaitrc2kSZPe8LwAAAAAwBsnvAAA9Jpnn3021157bb72ta/lhRde6NbZI488Mp/85Cfz4Q9/OEOGDCnThOxphBcAAACAPcnllyd33JFMm9b1My0t6/Pww5fl+edvStLS7Xv267dXDj74qxk//iPb3btsWVJVldx+ezJ27LbrbW1tefzxx9PQ0JCGhobcdddd7ba57rfffltaGWpra7P//vt32PYAAAAAAJSP8AIAsFO1trbmZz/7Werr6/Ozn/0sbW3br49/xYABA/KhD30odXV1ectb3uIDRnqc8AIAAACwJ7nnnuSTn0zGjEkGDuz6uXXr1mX27O8n+XGSPyR5Nsn2P3IcNOiwnHzyvamqGrTdvcViMndu8uEPJ//rf3Vtrra2tjzxxBOZPXv2lnaG5cuXb7NvwoQJW4IMM2bMyJQpU7zXCAAAAAA7gfACALBTPP/887nuuutyzTXXZPHixd06e/DBB2fmzJn52Mc+lhEjRpRpQhBeAAAAAPYsLS3JeecljzySTJmSdPXn90vhhYbXPLM+yZwkq5OsS9KY5DdJtm1bnTbtf+bAA/+/7d5jxYpk8+bkuuuSI4/s2lyvVywW8+STT24JMjQ0NGTZsmXb7Bs/fvyWMENtbW2mTp0qzAAAAAAAZSC8AACUTVtbW37961+nvr4+d9xxR1pbW7t8tqamJueee27q6upy0kkn+bCQnUJ4AQAAANjT/OEPpfaFqqpSA0NXbBteaM+yJFckeWmblenT/yFTp17e4cnm5mTBguSCC5Irr+zaTF1RLBbz9NNPp6GhYUug4YUXtg1Y7LPPPltaGWpra3PAAQd4fxIAAAAAeoDwAgDQ45YvX54bbrghs2bNyrx587p1dsqUKZk5c2YuuOCCjOnqp6XQQ4QXAAAAgD3RV7+azJqVTJqU1NRsf3/XwgtJsjTJlUlWbbNyyCH/L/vv/+ltni8Wk/nzkwMOSG68MRk2rAu32UHFYjHPPPPMllaGhoaGPP/889vs23vvvbcEGWbMmJHp06cLMwAAAADADhBeAAB6RLFYzN133536+vrceuutaWpq6vLZysrKvPe9783MmTNz6qmnpqKiooyTQseEFwAAAIA90fr1yYUXJo8+mkyZkmzv7bmuhxeS5NkkX0qy9nXPF3L44ddkv/0+sdWzy5eXmhf+3/9LTjyxi7foIcViMXPnzt3SytDQ0JDnnntum31jx47dEmaora3NQQcdJMwAAAAAAF0gvAAAvCGrV6/OTTfdlPr6+jz55JPdOjthwoRceumlueiiizJu3LgyTQhdJ7wAAAAA7Kn+/OfkssuSl15K9t8/6exn8bsXXkiS+UmuSrL+dc9X5Mgjb8q++56XJFm1Klm5Mpk5szRLbysWi5k/f/6WVoaGhoYsWbJkm31jxozJjBkztgQaDj74YF/QAgAAAADtEF4AALqtWCzmgQceyKxZs/Kd73wnGzdu7PLZQqGQs846K3V1dXnHO96RysrKMk4K3SO8AAAAAOzJ7r47+eIXk7Vrk0mTOg4wdD+8UDJjRm2GDBnS7torwYXzzkuuuCLZFd82LBaLWbBgwZZWhoaGhjz77LPb7Bs9enROOeWU1NbWZsaMGTn00EOFGQAAAAAgwgsAQDesW7cu3/rWt1JfX58///nP3Tq799575xOf+EQ+8YlPZOLEieUZEN4g4QUAAABgT3fXXclVVyXLlycTJybV1dvu6cnwQrGYLFuWbNhQCi584Qvt33NXtXDhwjQ0NGwJNCxcuHCbPSNHjtwSZqitrc1hhx0mzAAAAADAHkl4AQDYrocffjj19fX5xje+kfXrX1/t3rnTTjstdXV1Ofvss1O9O33qyB5JeAEAAAAgeeih5G/+JnnmmWTMmGTEiK3Xeyq8sHlz8uyzydChyUUXJZdemuzuP9O/aNGiLUGG2bNnZ/78+dvsGTFiRE455ZTMmDEjtbW1OfzwwzXUAgAAALBHEF4AANrV2NiYW265JbNmzcp9993XrbOjRo3KRRddlEsvvTRTp04t04TQ84QXAAAAAEpWrUr+/d+T229PmpqSffdNampKa280vNDWVmp2WLs2OfLI5IorSr/2RYsXL94qzDB37txt9gwfPjwnn3xyamtrM2PGjBx55JHCDAAAAAD0ScILAMBWnnzyycyaNSs33nhjVq9e3a2zp5xySmbOnJn3v//96devX3kGhDISXgAAAAB4VbGY3HVX8s//nLzyM/djxiTJutx1V0O3r3fCCW9NY+PgbNyYDB+efPSjpcaFgQN7cOhd3JIlSzJ79uwtgYY5c+Zss2fYsGE5+eSTtzQzHHnkkamqquqFaQEAAACgZwkvAADZvHlzbr/99tTX12f27NndOjts2LB8/OMfz8yZM3PwwQeXaULYOYQXAAAAALa1YUPy618n3/9+8sgjyapVm7Nw4RNJNrz8aOrgZGWSgUkGJRmUyZMnZ8qU/jnnnOTMM5NJk3bO/LuypUuXbtXM8PTTT2+zZ8iQIVuFGY466ihhBgAAAAB2S8ILALAHmzdvXq655pp8/etfz/Lly7t19phjjkldXV0++MEPZuCe9NVo9GnCCwAAAAAdKxZL4YUbbngx//qvdycZl1IwoTpJMUnhlZ2vObUhyeokj+W//ut9+fjHJ+5RTQvd9fzzz+euu+7aEmZ48sknt9kzePDgnHTSSamtrc2MGTNy9NFHp7q6uhemBQAAAIDuEV4AgD1Mc3NzfvzjH6e+vj533nlnt84OGjQo5513XmbOnJmjjjqqTBNC7xFeAAAAANi+V99DGZFkSpKpSUYm6ZekIqUmhsYkC5LMTbIkSZv3UHbAiy++mNmzZ29pZ3jiiSe22TNo0KCcdNJJW5oZ3vzmNwszAAAAALBLEl4AgD3E4sWLc+211+a6667L0qVLu3X28MMPzyc/+cl85CMfydChQ8s0IfQ+4QUAAACA7fMeSu9ZtmzZVs0Mjz322DZ7Bg4cmBNPPHFLM8Nb3vKW1NTU9MK0AAAAALC1qt4eAAAon9bW1vziF79IfX19fvKTn6Stra3LZ/v3758PfvCDqaury7HHHptCobD9QwAAAAAAlM3YsWNz7rnn5txzz02SLF++PL/73e/S0NCQhoaGPProo2lsbMwvf/nL/PKXv0ySDBgwICeccMKWMMMxxxyTfv369ebLAAAAAGAPJbwAAH3QCy+8kOuvvz7XXHNNFi1a1K2z06dPT11dXc4///yMGDGiTBMCAAAAAPBGjRkzJu973/vyvve9L0ny0ksv5Xe/+11mz56dhoaGPPLII9m4cWN+/etf59e//nWS0hfXnHDCCZkxY0Zqa2tz7LHHCjMAAAAAsFMILwBAH9HW1pbf/va3qa+vzw9+8IO0tLR0+Wx1dXXe//73p66uLqeccoqWBQAAAACA3dDo0aNzzjnn5JxzzkmSrFy5ckszw+zZs/PnP/85mzZtym9+85v85je/SVIKMxx33HFbmhmOO+649O/fvzdfBgAAAAB9lPACAOzmXnrppdx4442ZNWtW5syZ062zkydPzsyZM3PBBRdk7NixZZoQAAAAAIDeMHLkyLznPe/Je97zniTJqlWrcvfdd6ehoSENDQ1bwgyv/DlJ+vXrl2OPPXZLmOH444/PgAEDevFVAAAAANBXCC8AwG6oWCzmnnvuSX19fb73ve+lqampy2crKytz9tlnp66uLqeddloqKirKOCkAAAAAALuKESNG5N3vfnfe/e53J0lWr169Jcwwe/bs/PGPf8zmzZtz11135a677kqS1NTU5Nhjj82MGTNSW1ub448/PgMHDuzNlwEAAADAbqpQLBaLvT0EANA1a9asyc0335z6+vo8/vjj3Tq777775pJLLsnFF1+c8ePHl2lC2L09/vjjOfTQQ7t97rHHHsshhxxShokAAAAAdj3eQ+m71qxZk3vuuWdLmOGhhx5Ka2vrVnuqq6tzzDHHbGlmOOGEEzJo0KBemhgAAACA3YnwAgDsBh588MHU19fn29/+dhobG7t8rlAo5Mwzz0xdXV3OPPPMVFUpXYLO+OAdAAAAYPu8h7LnWLt2be65557Mnj07DQ0NefDBB7cJM1RVVeUtb3lLamtrU1tbmxNOOCGDBw/upYkBAAAA2JUJLwDALmr9+vX59re/nfr6+vzxj3/s1tm99torF198cS655JJMmjSpPANCH+SDdwAAAIDt8x7KnmvdunX5/e9/v6WZ4Q9/+ENaWlq22lNVVZU3v/nNmTFjRmpra3PiiSdmyJAhvTQxAAAAALsS4QUA2MU88sgjmTVrVm6++easW7euW2dPPfXU1NXV5eyzz05NTU2ZJoS+ywfvAAAAANvnPRResWHDhq3CDA888ECam5u32lNZWZmjjz46tbW1mTFjRk466aQMHTq0lyYGAAAAoDcJLwDALmDjxo353ve+l/r6+tx7773dOjty5MhceOGFufTSS3PAAQeUaULYM/jgHQAAAGD7vIdCRzZs2JB77703s2fPTkNDQ+6///5twgwVFRU56qijUltbm9ra2px00kkZNmxYL00MAAAAwM4kvAAAvejpp5/OrFmzcsMNN2TVqlXdOnviiSemrq4u5557bvr371+mCWHP4oN3AAAAgO3zHgpd1djYmPvuu29LM8N9992XpqamrfZUVFTkTW96U2bMmJHa2tqcfPLJGT58eO8MDAAAAEBZCS8AsMtrbEzmz0/mzi09Xnwx2bgxaW1N+vdPhg1LJk9Opk4tPcaMSQqF3p66Y01NTbn99tsza9as/Pa3v+3W2aFDh+b888/PzJkzd+jDQaBzpQ/ej0iyX5JpSaYmmZBkYJKaJJuTbEjybJI5SeYlWZzHHnvEB+8AAADAHkN4gR21cePG3H///VvCDPfee282b9681Z5CoZAjjzwytbW1mTFjRk4++eSMHDmylyYGAAAAoCcJLwCwS2psTBoakh/+MHn00WTDhmTTplIooVBIKipKv7a1lR5JUlmZDBqU7LNP8s53JmeemUyY0KsvYysLFizINddck+uvvz7Lli3r1tm3vOUtmTlzZj70oQ9l0KBBZZoQ9lxtbcmDDybXX788s2Y9mmRYSoGFJCkmaXv510KSytc835hkdf7qrw7LxRfvlSOO2LXDUwAAAAA9QXiBnrJp06bcf//9mT17dhoaGnLvvfdm06ZNW+0pFAo5/PDDU1tbu6WZYdSoUb00MQAAAABvhPACALuUJUuSH/0ouf320u+TUrPCwIHJgAGd/1BwU1Mp5LBuXbJ5c+lcbW3ynvckxx/fOz9Q3NLSkp/85Cepr6/PL37xi3Tnf3YHDhyY8847LzNnzszRRx9dxilhz7VuXfLznye33ZY89VSyZs2mLFz4SErtCo0phRY6UplSwGFwJk06LMOH98sRRyTnnJOccUbp7ywAAACAvkh4gXLZvHlzHnjggS3NDL///e+zcePGbfYdfvjhmTFjRmpra3PKKadk9OjRvTAtAAAAAN0lvADALqGlJbnllqS+PnnxxVJYYezYpKZmx67X1pasWpWsXJn071/6QeLPfS7Za6+enbsjS5Ysyde+9rV87Wtfy3PPPdets4cddljq6upy3nnnZdiwYWWaEPj975N/+qfk6adLzS1jxiTF4rrMnt3Q7WudckptCoUhWb48KRaTww9PrrgiOfLIHh8bAAAAoNcJL7CzNDU15Q9/+EMaGhrS0NCQ3//+92lsbNxm36GHHpra2trMmDEjM2bMyJgxY3phWgAAAAC2R3gBgF43f37yj/+Y3H13KawwblxSUdFz11+7NnnhhWTixOSyy5J3vrM8LQxtbW258847U19fnx/96Edpa+vsG9u31q9fv3zwgx/MzJkzc/zxx6fQGzURsIdYuzb57/8uBaY2bkwmTHg1KLVu3Y6FF2bMqM2QIUOSlJpfFi9OhgxJzj8/ueiiUiALAAAAoK8QXqC3NDU15cEHH8zs2bPT0NCQu+++u90wwyGHHLKlmWHGjBkZO3ZsL0wLAAAAwOsJLwDQq3760+Qf/qHUtjB+fDJoUHnu09qaLFlS+kb0s89OrroqGTCgZ6794osv5vrrr88111yThQsXduvsAQcckLq6upx//vkZNWpUzwwEdOjpp0uNCE88kYwalYwcuXWYqSfCC0np75rly5M1a5KjjioFtCZM6IEXAAAAALALEF5gV9Hc3JyHHnooDQ0NmT17du6+++6sX79+m30HHXTQVs0Me++9dy9MCwAAAIDwAgC95pZbkn/6p6SpqfRDvT3ZttCRNWuSZcuSM85I/vZvS9+MviOKxWIaGhpSX1+f22+/Pc3NzV0+W11dnXPOOSd1dXWpra3VsgA7ySOPJJ//fPLss6UmllfaFl6rp8ILr9i8OVm0KDnwwOSf/zmZNm0HBgcAAADYxQgvsKtqaWnJH//4xzQ0NGxpZli3bt02+w488MCtwgzjxo3rhWkBAAAA9jzCCwD0ittuK4UHkmTcuK2/+fzPf74gS5bc2IWrFFJZOSCVlQNTUzM6AwdOyZAhh2Xs2Hdk5MiTUihUtntqw4Zk6dJSgOEf/7F7DQwrV67MjTfemPr6+jzzzDNdP5hk0qRJmTlzZi688MLstdde3ToLvDFPPJH89V8nzz2X7L9/Utn+Xw+vCS+sT/JIkkeTzEmyNsm6JJuTDEoyNMmoJAcnOTSnnFKXoUNHtHvNlpZkwYJkypTkP/8zmTSpR18aAAAAwE4nvMDuoqWlJX/6058ye/bsNDQ05He/+13Wrl27zb4DDjggM2bM2BJoGD9+fC9MCwAAAND3CS8AsNP99rfJF79Y+oHefffddr3r4YWODRw4JdOn/33GjftAu+uvBBjOOSf5u7/bOjzxesViMffee2/q6+tzyy23ZPPmzV2eo6KiIu9+97tTV1eXM844IxU7o14C2MpzzyWXXprMm1cKEHT2X8Ply5/M/fdfnuTOlIIKXdO//6RMnfrFTJhwYSor+2+z3tKSzJ+fHHpocu21yciR3X8dAAAAALsK4QV2V62trfnzn/+choaGzJ49O3fddVfWrFmzzb6pU6du1cwwYcKEXpgWAAAAoO8RXgBgp1q+PPnYx5LFi0vfft5eaKAnwguvmD797zN16hXtrq1dm6xcWWqAOPvsbdfXrFmTb37zm6mvr8+jjz7arfuOHz8+l1xySS6++OLs215CA9gp2tqSz342+fnPS8GFjhoXkmTJkm/kkUdmpq2tcYfvN3jwwXnzm7+fwYMP3GatubnUwPChDyV/8zedh6YAAAAAdmXCC/QVra2teeSRR9LQ0JCGhobcddddWb169Tb7Jk+evCXMUFtbm/3222/nDwsAAADQBwgvALDTFIvJVVclt96aTJ6cVFW1v68nwwtJRU466b4MH/6WdlcXLUrGjk1uvjnZZ5/Scw899FDq6+vzrW99K42NXf8h5kKhkLe//e2pq6vLWWedlaqOXiCw09x+e3L11cno0cmQIR3ve+KJz2f+/H/ukXtWVQ3J0UffljFjTt9mbdWqZP365KtfTd72th65HQAAAMBOJ7xAX9Xa2ppHH300s2fP3tLOsGrVqm327b///luCDDNmzMikSZN2/rAAAAAAuyHhBQB2mjvvTL7whWTo0GT48I73dRZemDLli6muHrHlz01NK7NmzUNZseI3HV5vwoSLcsQR17W71tqazJuXvP3tTTn88Jsza1Z9HnzwwS69nleMGTMmF198cS655JJMnjy5W2eB8lmyJDn//GTFiqSzL0KbP//f8sQTn+nkSpVJDksyKcmAJKuSPJJkaYcnqqqG5aST7m+3gWH+/GTSpFJoauTI7b8OAAAAgF2N8AJ7ira2tjz22GNbggyzZ8/OihUrttk3ceLErZoZJk2alILqVQAAAIBtCC8AsFNs3pyce26yYEGy//6d7+0svPC2ty3IwIGTtnl+8eIb8/DDF7R7ZuDAyXnb2+a1u7Zu3drMmfNCXnihKW1tn0ryu86He423vvWtqaury3vf+97U1NR0+Rywc/zN3yTf/nYyZUpSWdn+nlWr7svvf39SisXWDq5yZJK/SjK2nbV7k/xXkjXtnhw8+KCccsojqajYuoWlpaUUYPjUp5LLLuvKKwEAAADYtQgvsKdqa2vLE088kYaGhi2BhpdeemmbfRMmTNgqzDB58mRhBgAAAIAILwCwk/zyl8lnPpOMG5f079/53h0JLyTJr389KRs3Ltrm+YqKAXnnOxu3/LmtrTVLlz6fRYsWZdWqlS8/OzXJT5J8odPZRowYkQsuuCCXXnpppk+f3vkLAXrNsmXJe9+btLUlY8Z0vO/ee0/tpLnlTUmuTlLVwXqSLExyZZIN7a4eccT1mTDhwm2eX7q01EBzxx3J4P8/e/cdH8V953/8vatVb6iCREf0aqoxCHZcsMEd24kbtkFLbCdO7nIpd7lc8rv4ktwluZS7yyUXY2vBhrh3wNjGZZdeDKb3IiRRhLqE6ko7vz8U1pZVECBpV9Lr+XjwCJr5zsxnH4knsjTveUe1cnoAAAAAAIAARHgBaGCapg4cOCC32+0LNOTn5zdZ169fP1+QwTAMpaWlEWYAAAAAAAA9UmtPYgEA0C5MU3rrrYaHiC8VXLgawcHxzYYXbLZISVJFxQWdOpWtnJxseTyer6wqkjRb0kBJTc8xY8YMPfnkk7rvvvsUHh7e7rMDaF9r1khFRdKQIS2vKS7e3EpwIULSd3Xpb5cHSVog6Zlm9x49+kv167ewyS8ik5Kk7Gzp44+lu+66xCUAAAAAAAAABCSLxaIxY8ZozJgx+ta3viXTNHXo0CFfK4PL5VJeXp5yc3P117/+VX/9618lSampqY2aGYYNG0aYAQAAAAAA9AiEFwAAHe7IEWnbNikxseOuUV19VhcuHGx2X3DwIG3evFmFhU2rm79QrIb2hVsl/Z8kKTo6Wo888oieeOIJjR8/vr1HBtBBamulN9+UQkOloKCW1509+3orZ5kjKa6NV5yr4OA35PE0vcdUVh5XeflexcQ0vocEB0sWi/TGG9Kddzb8HQAAAAAAAEDXZrFYNGrUKI0aNUrf/OY3ZZqmjhw54mtlcLvdOnv2rM6cOaMXX3xRL774oiSpT58+jcIMI0aMIMwAAAAAAAC6JcILAIAO53ZLFy5Iffq0/7lra4tVWrpDBw/+o7ze6mbXVFTMVEVFa8EFSTIlVUm6TRMnbtU3v/mkHnzwQUVFRbX3yAA62O7d0smTUnJy6+sKClpqXZCkWZdxxSAlJt6ls2czW7jOp03CC1JDoGv//oaA14gRl3E5AAAAAAAAAF2CxWLRiBEjNGLECD3xxBMyTVNHjx5t1Mxw5swZnTt3Ti+//LJefvllSVLv3r19QQbDMDRy5EjCDAAAAAAAoFsgvAAA6HD790tWa/u8WfyTTwZf5hGTJdkvucpqDVJSUoTi4w2tXLlDffte0XgAAsCRI5LHI4WFtbymvr5KZWW7W9hrk3R595qYmMkthhdKSrY2uz0qSjp/Xjp2jPACAAAAAAAA0BNYLBYNHz5cw4cP1+OPPy7TNHX8+HFfmOHTTz/V6dOnlZeXp1dffVWvvvqqJCk5OVl2u90XaBg9ejRhBgAAAAAA0CURXgAAdKj6emnfPikysrOvHCzpNkkPq7X/u4uKitagQQPVt28/WSzBOnWq4UFiwgtA13XkiGSarQemamvz1dC40pzeariHtF14+LBWrnW+2e1Wa8N/Hj16WZcCAAAAAAAA0E1YLBYNHTpUQ4cO1eLFi2Wapk6cOOFrZXC5XMrJydH58+f12muv6bXXXpMkJSYm+oIMdrtdY8aMkfXiDxwBAAAAAAACGOEFAECHysmRSkv9EV6IkBQmqb7JHqvVqpSUVA0cOFDx8XGSvnjC2TQbwgv2S5c1AAhApint3i2Fh7e+rra2sJW9EZd9XZstppVrFbRyXEPACwAAAAAAAAAsFovS0tKUlpamjIwMmaaprKwsXzODy+XSqVOnVFBQoDfeeENvvPGGJCkhIUGzZ8+WYRgyDENjx44lzAAAAAAAAAIS4QUAQIc6eVKqrJSSkzv7yqWSXpbkkvS0pBRFRkZqwICB6t+/v0JCQlo88siRzpkQQPsrLZXOnZMiLpE/qK+vamXv5bUuSJLH01KLg1RXV9HivoiIhntOXV1DkAEAAAAAAAAALrJYLBo8eLAGDx6sRYsWSZKysrJ8QQa3262TJ0+qsLBQb731lt566y1JUnx8vGbPnu1rZxg/fjxhBgAAAAAAEBB4RAoA0KEuXJC8XikoqH3Ol5b2jwoOjlNJSbHOnTsnqU5SgaQdkpp7k/o52Wy/1qRJ65SUlCqLxdLMmi/YbFJZWfvMCqDzVVQ0BAHCwlpfFxIS38re1oINzdu9e3OL+4KDW25lsNkkj0eqqpKioy/7sgAAAAAAAAB6mEGDBmnQoEF67LHHJEnZ2dmNwgzHjx9XUVGR3n77bb399tuSpF69evmaGex2uyZMmKCg9vrFDQAAAAAAwGUgvAAA6FAej9SeL/OJiLhPZ8/WqaioSEFBVtXX1128kqT/lrSuyTF1dSdUVbVKFssTlzy/xdLwEDGArqm2VjLNhn+WWxMcnNDK3tIruHJxi3tCQhJb3Ge1NoQtPJ4ruCQAAAAAAACAHm/AgAF65JFH9Mgjj0iScnJy5Ha7fYGGY8eOqaSkRO+++67effddSVJsbKxmzZolwzBkGIauueYawgwAAAAAAKBTEF4AAHQoq7XhQeL2snfvbkm9//aV90t7giU9JWmrpJomx50586oGDrx0eEFqv5YIAJ3vUqGFi0JCEmSzxaqurrmgQpHS0pIUFBSnqqoq5eTktOGMWS3usVoHy+v1NlvLfjFoQWM7AAAAAAAAgPbQv39/LViwQAsWLJAknT59ulEzw5EjR1RaWqpVq1Zp1apVkqSYmBjNmjVLdrtdhmFo4sSJstl4lAAAAAAAALQ/fuIAAOhQISHtG16QLAoPj1BVVaVvS2homAYMGKABAwbos8/Gq7R0e5Ojysp2t+nsXq8UGtpuwwLoZCEhDUEAr7f1dRaLVQkJs5WXt7LZ/bGx2UpNna7i4qI2hhd2trgnLy9WH3/8kfr3b7hPRURE+PZdDC8EB7fhEgAAAAAAAABwmfr27auHHnpIDz30kCTpzJkzWrdunS/McOjQIZWVlWn16tVavXq1JCk6Olrp6ekyDEN2u12TJ08mzAAAAAAAANoFP2EAAHSo+HjJZpNqaxseKm6dqaKiYhUVFbW4Ij09XcePX/CFF+Lj4zV9+nVfeqN580mJ5t+u3tw6qU+fNi0FEIDi4hruNbW1l16bmHhTi+GFnJxlSk39ehuvekbS/hb2hUiarJqaGh07dlTHjh1VUlKSBg4cqOTk3qqpsSo+XgoPb+OlAAAAAAAAAOAqpKam6oEHHtADDzwgSTp37pzcbrevneHgwYMqLy/XmjVrtGbNGklSVFSU0tPTfc0MkydPVjBvZAEAAAAAAFeA8AIAoEOlpUmRkVJFRcvhherqauXm5ionJ1sVFRWSKlo8X0hIqDyeL8INkZGRvuCCx1Oi8vK9zR4XHBx/yVkvvgF9+PBLLgUQoMLDG+47u3ZJiYmtr+3bd4EOH/6J6urKm+zLz1+j/PwPZbNNucQVTUnPqqXglGRIapxMyM/PV35+vkJDwxQVNV7XXBMmqzX2EtcBAAAAAAAAgPbXp08f3X///br//vslSXl5eY2aGfbv368LFy7o/fff1/vvvy+p4XczM2fO9IUZpkyZopBLv8EKAAAAAACA8AIAoGMlJjY0GWRnN7wR/SKv16vz588rJydbeXnn9eUHf0NCQlt9a7rH4/H9PTi44Yfh9fVV2r17sbzemmaPiYq6dCKhtrahJWLo0EsuBRDAxo+Xtm279LqQkHgNGvQdHTv2783u37nzAY0e/XorZ6iX9JykHS3sD5b0tRaPrqmpVk1NkTIzf6/Tp3P1+OOP6/bbb+eNZQAAAAAAAAD8pnfv3vra176mr32t4Web+fn5vjCDy+XSvn37VFFRoQ8//FAffvihJCkiIkIzZsyQYRiy2+2aNm0aYQYAAAAAANAswgsAgA5lsTQ8SHzkSMPXFy6UKzs7R7m5uaqt/SJoEBISqv79+6l//wE6duwN5eY2f75Tp/5PlZWFkhoCDKWlcdq9u0Lnz69STc25Fufo3fvuS85aUSFFRRFeALq6i/8MX2xTaX3tj3Tu3Nu6cOFAk30eT7H27Jkraa4aGhT6SwpV//4xOn/+I9XUvCLpWCtnf1RS71b2B6khuHVM77/v0vvvv6+UlBRlZGRo8eLFGjRoUOvDAwAAAAAAAEAHS0pK0r333qt7771XklRQUKB169bJ7XbL5XJpz549qqys1EcffaSPPvpIkhQeHq4ZM2b4mhmmTZum0NBQf34MAAAAAAAQICymaZqXXgYAwJVbtqxK//zPNaquPqCSksIv7bGod+/eGjCgv5KSkmW1WiVJu3YtVG7u8+12/fDwAbLb98tmi2p13cmT0sSJ0vLl7XZpAH5w+LD00ENSZKQUG3vp9RUVx7VhwzR5PEVtOLtVkrcN6+ySvieptfREwt/Od7ekvEZ7LBaLbrnlFtoYAAAAAABAQNq/f7/Gjh172cft27dPY8aM6YCJAPhLYWGh1q9fL5fLJbfbrd27d+urjyCEhYXpuuuu84UZrr32WoWFhflpYgAAAAAA4E+EFwAAHcI0TW3cuFGZmZl65ZWPVVX1V0khks4rMjJKAwb0V79+/RQa2vSH0+0ZXggJSdS0aWvUq9eUVtfV1UlZWdLPfy7dd1+7XBqAn5im9MQT0oYNUlpa244pLz+gHTvu1YULh9phgpslfVMNzQqtGSrpHUn/3Ooq2hgAAAAAAECgIbwAoCXFxcW+MIPL5dKuXbuahBlCQ0M1ffp0GYYhu92u6dOnKzw83E8TAwAAAACAzkR4AQDQrs6ePasXXnhBTqdTR44c8W0PDv6ZIiIe15gxoYqPj1NrbyNvr/BCnz73avTo/1RExOA2zC1FRUnvvNO2N7UDCGxr1kjf/77Uv78UEtK2Y+rqLujAgR8oN3epvN7ay75maGhv9e//tCorp+js2XMyzdYaGiIkJaoh5LC5TeenjQEAAAAAAAQKwgsA2qqkpETr16+X2+2Wy+XS559/Lq+38c9OQ0JCNH36dF8zw/Tp0xUREeGniQEAAAAAQEcivAAAuGoej0erV69WZmam1qxZo/r6et++9PR0ZWRkaOTI+/XUUxGKjpZiYlo/3+WGF6zWcNlsUQoNTVF09Cj16jVdffrMV0TEwDYdb5rS0aPS4483POwMoOurqpLmz5fy8qR+/S7v2Orqs8rK+qPy8laqvHy/pEt/uxwTM0EzZmyUzRYpSaqtrVFOTq6ys0+poqKimSMGSTok6QFJrYUcmkcbAwAAAAAA8CfCCwCuVGlpqTZs2CCXyyW3260dO3Y0CTMEBwfr2muv9TUzzJgxgzADAAAAAADdBOEFAMAVO3jwoJxOp1544QWdP3/et71Pnz567LHHtGjRIo0YMUJSQ0DgiSckt1saOlSyWq/smiUlJdqwYb3v6+uvv0GRkZFX9TnOnZOCgqTly6Vhw67qVAACSGam9J//2dC+EBZ2ZefIzz+hrVtXSCqRVC6pUmFhm1RdndVk7bBh/08jRjz9la2mCgoKlZ196kttDBGS+kj6V0lvXNlgf0MbAwAAAAAA8AfCCwDaS1lZmTZu3CiXyyWXy6UdO3Y0ekmW1BBmmDp1qgzDkGEYmjFjxlX/bggAAAAAAPgH4QUAwGUpLy/XK6+8oszMTG3ZssW33Waz6fbbb1dGRobmzZsnm83W5NijR6WFC6Xqaik19cqun5+fr61bv7juLbfcouDgkCs7mRpmyc2V/uEfGpoXAHQfVVWSwyHt3NkQmrJYLv8cxcVF2rhxY6NtkycP0P79d6i6OqfJ+pEjf6WhQ/+p2XPV1tYoOztHx49b5fGslfSUpPpm114J2hgAAAAAAEBnIbwAoKOUl5dr48aNcrvdcrlc2r59e5Mwg81m09SpU2W322UYhmbOnKmoqCg/TQwAAAAAAC4H4QUAwCWZpqkNGzbI6XTq1VdfVWVlpW/fyJEj5XA49Mgjj6h3796XPNfzz0u//rWUkiJdScPvmTOntXPnTt/Xt912uyxX8kSyGtogjh2Tpk2Tliy58jezAwhcu3dL3/hGw9/bcItqornwwsyZMxUcXKjNm2erpuZck2PGjPkfDR78nWbPl5srRUWZevLJLVq58r/15ptvyuPxXP5graCNAQAAAAAAdDTCCwA6y4ULF7Rp0ya5XC653W5t27ZNdXV1jdYEBQVpypQpMgxDdrtd6enpio6O9tPEAAAAAACgNYQXAAAtOnv2rJ5//nk5nU4dPXrUtz0qKkr333+/HA6Hpk+fflnhAY9HevJJacMGacgQqZmChladOpWlvXv3SpJstmDNnTv38k7wJadPSyEh0nPPSVfwezYAXcSf/iT98Y8NjS+X2yTeUnghLi5e5eUHtHmzXbW1BV85yqLx45dowIDFjbaWlUkFBdKPfyw9/HDDtvz8fC1btkxLlizRsWPHLvOTXRptDAAAAAAAoCMQXgDgLxUVFdq8ebNcLpdcLpe2bdvW5AUxQUFBmjRpkgzDkGEYSk9PV0xMjJ8mBgAAAAAAX0Z4AQDQiMfj0apVq+R0OrVmzZpGVbzp6enKyMjQ1772tauq383Kkp56Sjp+vCHAEBTU9mOPHTuqQ4cOSZLCwyN04403XtEMeXlSba30z/8s3X//FZ0CQBdRXS394AfS2rVS//5SeHjbj20tvCBJpaW7tGXLDfJ4ir9ypFXXXPOC+vVrSClcuCCdPSvdfbf0i180DW6ZpimXy6VnnnmGNgYAAAAAABDwCC8ACBSVlZXavHmz3G63XC6Xtm7dqtra2kZrrFarJk2aJLvd7gsz9OrVyz8DAwAAAADQwxFeAABIkg4cOCCn06nly5fr/Pnzvu19+vTRY489poyMDA0fPrzdrnfwoPT3fy9lZ19egOHgwQM6fvy4JCkmJlazZ8++7GufOyfV1Eh/93eSwyFdRnEEgC6qrEz6h39oaH3p10+KiGjbcZcKL7RFeXnDfeeWW6Rf/1oKC2t9/fnz5/X888/TxgAAAAAAAAIW4QUAgaqqqkpbtmyRy+WS2+3W5s2bm4QZLBaLJk6cKMMwZLfbNWvWLMXFxflpYgAAAAAAehbCCwDQg5WVlenVV19VZmamtmzZ4ttus9l0++23KyMjQ/PmzZPtq68IbycHDza8Df3Ysba/DX337t3KycmWJCUmJmr69OvafL36eik3tyEo8d3vSo8+SnAB6ElKS6Uf/Uj69FMpMVFqy++iria8YJpSYaFUUiLdfrv09NNtD01Iktfrlcvl0pIlSzq0jeGJJ57QbbfdRhsDAAAAAABoM8ILALqKqqoqbd261dfMsHnzZtXU1DRaY7FYNGHCBBmGIcMwNGvWLMXHt/0FNgAAAAAAoO0ILwBAD2OapjZs2CCn06lXX31VlZWVvn0jR46Uw+HQI488ot69e3fKPFlZDQ/0btnS8FBvSkrrgYLPPvtM586dldTw9vDJk6e06Trl5dLZs1Lfvg1vX7/jjnYYHkCXU1Eh/e530ltvSbW10oABUmv5rCsNL9TWSjk5Dfe1hx6Svv1tKSTkyufujDYGh8Mhh8NBGwMAAAAAALgkwgsAuqrq6mpt27bNF2bYtGmTqqurG62xWCwaP3687Ha7DMPQ7NmzlZCQ4KeJAQAAAADoXggvAEAPcebMGb3wwgtyOp06evSob3tUVJQeeOABZWRkaPr06bL4oYqgtlZ68UVpyRKpqKghwBAZ2XyIYfPmzSosLJAkDRgwUOPHj2/13B5PQ2jB65VuvFH64Q8bAgwAei7TlNxu6be/lY4eleLjG/5YrU3XXm54weuVCgoaWh7GjpX+8R+ladPab3baGAAAAAAAQCAgvACgu6ipqdH27dvlcrnkdru1ceNGVVVVNVk3btw4GYYhu92u2bNnKykpyQ/TAgAAAADQ9RFeAIBurLa2VqtXr5bT6dR7770nr9fr25eeni6Hw6H77rtPUVFRfpzyC4cPS7/5jbRjh1RdLcXGSgkJUlDQF2vWrXOrrKxMkpSWNlSjRo1qch7TlC5ckPLzG/7et6/0rW9Jd93V/MPJAHqm4mLpT3+SVq5sCBuEh0vJyY0bEtoaXqiubrjnVFdLcXHS174mPf641JG3V9oYAAAAAACAvxBeANBd1dbWavv27b5mho0bNzZqMb9ozJgxMgzD18yQnJzsh2kBAAAAAOh6CC8AQDd04MABOZ1OvfDCC8rPz/dtT0lJ0WOPPaZFixZp+PDhfpywZaYp7dwprVolffhhQxODJEVENLQxbNv2qWpqLkiSRo4cpaFDh8rrlaqqpIqKhj8eT8P68eOle+5paFwIkHwGgACUlSWtWSO99ZaUm9vQnhAW1nDP8XiKtX37hkbrZ8yYqYiIeN89p6ZGstmkQYOk+fOlefOk1NTOm582BgAAAAAA0NkILwDoKTwejz777DNfmGHDhg2qqKhosm706NGy2+2+dobevXv7YVoAAAAAAAIf4QUA6CbKysr0yiuvyOl0asuWLb7tNptNt99+uxwOh+bOnSubzebHKS/P+fPS2rXS7t3S3r1SYaG0Y8dBNRRImOrTJ0W9esVJanhjemSkNGKENG6cZLdLEyZIFotfPwKALqSqSvr0U+mzzxruO2fOSPn5FTp+PEfSF801AwcOUmxshCIipIEDG4JS06ZJs2c3bm3wB9oYAAAAAABAZyC8AKCn8ng82rlzp1wul9xut9avX68LFy40WTdy5EhfkMFutyslJcUP0wIAAAAAEHgILwBAF2aapjZs2KDMzEy99tprjWprR44cKYfDoUceeaRbvN3FNKVTpzwaPPhGSQmSQvT3f/89zZp1rSIipMGDG/6Ehvp7UgDdRXGx9Oabe7R48U8lhUqySfLo17/+uW66aaSGDpViYvw8ZAsutjE888wzeuuttzq0jeH222/vUsE4AAAAAABw9QgvAECDuro67dy509fMsH79epWXlzdZN2LEiEbNDKmdWd8LAAAAAEAAIbwAAF3QmTNn9Pzzz8vpdDZ6s3ZUVJQeeOABZWRkaPr06bJ0s9qB/Px8JScn+77+6KOPdOONN/pxIgDd3ebNmzVjxoxG2zZt2qTrrrvOTxNdvs5qY1i8eLEGDhzY7ucHAAAAAACBh/ACADSvrq5Ou3bt8jUzrFu3TmVlZU3WDRs2rFGYoV+/fn6YFgAAAACAzkd4AQC6iNraWq1evVqZmZlas2aNvF6vb9+sWbOUkZGhr33ta4qMjPTjlB3ryJEjGjFihO/rHTt2aNKkSX6cCEB31x3CCxd1RhvD3Llz9fjjj9PGAAAAAABAN0d4AQDapr6+Xrt375bL5fI1M5SUlDRZl5aW5gsyGIah/v37d/6wAAAAAAB0AsILABDgDhw4oMzMTC1fvlz5+fm+7SkpKXrssce0aNEiDR8+3I8Tdp6tW7dq+vTpvq9PnjypQYMG+W8gAN1edwovfBltDAAAAAAA4GoQXgCAK1NfX689e/bI7XbL5XJp3bp1Ki4ubrJuyJAhjZoZ+DkrAAAAAKC7ILwAAAGorKxMr7zyipxOp7Zs2eLbbrPZdPvtt8vhcGju3Lk97q3Wa9as0a233ur7uqSkRLGxsX6cCEB3113DCxfRxgAAAAAAAK4E4QUAaB9er1d79+6Vy+WS2+2W2+1WUVFRk3WDBg3yhRkMw+DlXgAAAACALovwAgAECNM0tX79ejmdTr322muqrKz07Rs1apQcDocWLFig3r17+3FK/3rxxRf18MMPS5KsVqs8Ho+sVqufpwLQnXX38MKXnT9/XsuWLdOSJUt0/Pjxdj8/bQwAAAAAAHQfhBcAoGN4vV7t379fLpfL18xQUFDQZN2AAQN8rQyGYWjw4MGyWCx+mBgAAAAAgMtDeAEA/Oz06dN64YUX5HQ6dezYMd/2qKgoPfDAA3I4HLr22mv5gaOkP/3pT/r2t78tSUpISGj2h7UA0J56UnjhIq/Xq08//VRLliyhjQEAAAAAADSL8AIAdA6v16sDBw7I7Xb72hny8/ObrOvfv78vyGC325WWlsbvFgEAAAAAAYknhQDAD2pra7Vq1So5nU6tWbNGXq/Xt2/WrFlyOBy67777FBkZ6ccpA8+Xa3Lj4uL8OAkAdF9Wq1U33nijbrzxxg5pYzBNU2vWrNGaNWuUmpqqjIwM2hgAAAAAAAAAoBlWq1Vjx47V2LFj9dRTT8k0TR08eNAXZHC5XDp//rxycnK0YsUKrVixQpLUt2/fRs0MQ4cOJcwAAAAAAAgINC8AQCc6cOCAMjMztXz58kZvRUlJSdFjjz2mRYsWafjw4X6cMLB973vf0x/+8AdJ0tSpU7Vt2zY/TwSgu+uJzQvN6aw2hieeeEK33XYbbQwAAAAAAAQ4mhcAIDCYpqnDhw/L5XL5Ag3nzp1rsi4lJaVRmGH48OGEGQAAAAAAfsFTQQDQwcrKyvTKK68oMzNTW7du9W232Wy64447lJGRoblz5/KgZhsUFxf7/h4fH+/HSQCgZ6GNAQAAAAAAAAACj8Vi0ciRIzVy5Eg9+eSTMk1TR44c8bUyuFwunT17VmfPntVLL72kl156SZLUp08fX5DBbrdr5MiRhBkAAAAAAJ2C5gUA6ACmaWr9+vXKzMzUa6+9pqqqKt++UaNGyeFwaMGCBerdu7cfp+x67r77br3zzjuSpAceeMD3A1YA6Cg0L7SMNgYAAAAAAHoumhcAoGswTVPHjh3ztTK4XC6dPn26ybrk5ORGzQyjRo0izAAAAAAA6BA8AQQA7ej06dN64YUX5HQ6dezYMd/26OhoPfDAA8rIyNC1117LD/uuUFFRke/vcXFxfpwEAEAbAwAAAAAAAAAENovFomHDhmnYsGH6xje+IdM0deLECV8rg8vlUm5urs6fP69XX31Vr776qiQpKSmpUTPD6NGjZbVa/fxpAAAAAADdAc0LAHCVamtrtWrVKmVmZur999+X1+v17Zs9e7YyMjJ03333KTIy0o9Tdg/jxo3Tvn37JEk//vGP9ctf/tLPEwHo7mheuDy0MQAAAAAA0DPQvAAA3YNpmjp58qSvlcHlcik7O7vJusTERM2ePdsXZhg7dixhBgAAAADAFeFpHwC4Qvv375fT6dTy5cuVn5/v256SkqKFCxdq0aJFGjZsmB8n7H6Ki4t9f4+Pj/fjJACA5tDGAAAAAAAAAABdh8Vi0ZAhQzRkyBAtWrRIkpSVlSWXy+ULNGRlZamgoEBvvvmm3nzzTUkNv6ez2+2+doZx48YRZgAAAAAAtAnNCwBwGcrKyvTyyy/L6XRq69atvu02m0133HGHHA6HbrnlFt4E3UEiIiJUVVUlScrMzFRGRoafJwLQ3dG8cPU6o41h3rx5evzxx2ljAAAAAACgk9C8AAA9x6lTp3xBBrfbrRMnTjRZExcX16iZYfz48QoKCvLDtAAAAACAQEd4AQAuwTRNrVu3Tk6nU6+99prv4XlJGjVqlBwOhx555BElJyf7ccrur6amRmFhYb6v33zzTc2fP9+PEwHoCQgvtK+OaGP4stTUVDkcDjkcDtoYAAAAAADoQIQXAKDnysnJ8YUZXC5Xsz/r7dWrl2bNmuULM1xzzTWEGQAAAAAAkggvAECLTp8+reeff15Lly7VsWPHfNujo6P1wAMPyOFwaNq0abJYLH6csuc4d+6cUlJSfF+7XC7Z7XY/TgSgJyC80DEutjE888wzeuutt1RXV9eu56eNAQAAAACAjkV4AQBwUW5urtxuty/QcPTo0SZrYmNjNWvWLNntdhmGoWuuuYaf2wIAAABAD0V4AQC+pLa2VqtWrVJmZqbef/99eb1e377Zs2crIyND9913nyIjI/04Zc904MCBRr/U2r17t8aPH+/HiQD0BIQXOl5eXp6WLVumZ599ljYGAAAAAAC6CMILAICWnDlzxhdkcLvdOnz4cJM10dHRjZoZJk2aRJgBAAAAAHoIwgsAoIZftDidTi1fvlz5+fm+7SkpKVq4cKEWLVqkYcOG+XFCbNy4Uenp6b6vs7Oz1b9/fz9OBKAnILzQeWhjAAAAAACg6yC8AABoq7Nnz2rdunVyuVxyuVw6dOhQkzVRUVFKT0+XYRgyDEOTJk1ScHCwH6YFAAAAAHQ0wgsAeqzS0lK98soryszM1LZt23zbbTab7rzzTmVkZOiWW27h4cYAsWrVKt1xxx2+r8vLyxUVFeXHiQD0BIQX/IM2BgAAAAAAAhvhBQDAlTp37pzWrVvna2c4cOBAkzWRkZFKT0+X3W6XYRiaMmUKYQYAAAAA6CYILwDoUUzT1Lp16+R0OvXaa6+pqqrKt2/06NFyOBxasGCBkpOT/TglmrN8+XI9+uijkqTg4GDV1NTIYrH4eSoA3R3hBf+ijQEAAAAAgMBEeAEA0F7Onz/va2Zwu93at29fkzURERGaOXOmDMOQ3W7X1KlTFRIS4odpAQAAAABXi6dzAPQIp0+f1vPPPy+n09noDc7R0dF68MEHlZGRoWnTpvEwfAArKiry/T0uLo7/rgCgB7Barbrxxht14403+toYlixZohMnTrTL+U3T1Hvvvaf33nvP18awePFiDRgwoF3ODwAAAAAAAABoXXJysu677z7dd999kqT8/PxGzQx79+5VZWWl1q5dq7Vr10qSwsPDNWPGDBmGIcMwNHXqVIWGhvrzYwAAAAAA2ojmBQDdVm1trVauXCmn06n3339fXq/Xt2/27NlyOBy69957FRkZ6ccp0VY/+9nP9PTTT0uSRowYoUOHDvl5IgA9Ac0Lgcfr9eqTTz7RkiVLaGMAAAAAAMBPaF4AAHSWgoICrV+/3hdm2LNnj776mEtYWJhmzJghu90uwzB07bXXEmYAAAAAgADFkzgAup39+/crMzNTy5cvV0FBgW97SkqKFi5cqEWLFmnYsGF+nBBXori42Pf3+Ph4P04CAPAnq9Wqm266STfddBNtDAAAAAAAAADQzSUmJmr+/PmaP3++pIa29vXr18vlcsntdmvXrl2qrq7WJ598ok8++URSQ5hh+vTpMgxDdrtd06dPV1hYmD8/BgAAAADgb2heANAtlJaW6uWXX5bT6dS2bdt82202m+68805lZGTolltu4e3JXdgjjzyiFStWSJJuvfVWrV692s8TAegJaF7oGmhjAAAAAACgc9G8AAAIFMXFxY2aGXbt2iWv19toTWhoqK699loZhiHDMDR9+nSFh4f7aWIAAAAA6NkILwDoskzT1Lp165SZmanXX39dVVVVvn2jR4+Ww+HQggULlJyc7Mcp0V5uv/12X2Dh4Ycf9gUZAKAjEV7oejqijeHLaGMAAAAAAIDwAgAgcJWUlGjDhg2+ZoadO3c2CTOEhITo2muvld1ul2EYuu666xQREeGniQEAAACgZyG8AKDLOX36tJ5//nk5nU4dP37ctz06OloPPvigMjIyNG3aNFksFj9OifY2c+ZMbdq0SZL0ne98R//zP//j54kA9ASEF7ou2hgAAAAAAOg4hBcAAF1FaWmpNm7cKJfLJZfLpZ07d6q+vr7RmuDgYE2bNk2GYchut2vGjBmKjIz008QAAAAA0L3xhA2ALqG2tlYrV66U0+nU+++/3+jtGHa7XRkZGbr33nv5IVI3Vlxc7Pt7XFycHycBAHQFVqtVN910k2666aYOaWMwTVPvvfee3nvvPfXt21cOh0MOh4M2BgAAAAAAAAAIILGxsbr11lt16623SpLKysq0ceNGud1uuVwuffbZZ/J4PNq4caM2btyoX/7yl7LZbJo6daoMw5BhGJoxY4aioqL8/EkAAAAAoHugeQFAQNu3b5+cTqeWL1+ugoIC3/bU1FQtXLhQixYt0tChQ/04ITpLnz59lJeXJ0n6wx/+oO9+97v+HQhAj0DzQvdysY3hmWee0dtvv91hbQxPPPGEbr31VtoYAAAAAADdFs0LAIDuory8XJs2bZLL5ZLb7db27dub/OzYZrNpypQpstvtMgxDM2fOVHR0tJ8mBgAAAICujfACgIBTWlqql19+WU6nU9u2bfNtt9lsuvPOO+VwOHTzzTfzQGAPYpqmwsLCVFtbK0latmyZHnvsMT9PBaAnILzQfXVEG8OX0cYAAAAAAOjOCC8AALqriooKX5jB5XJp+/bt8ng8jdYEBQVp8uTJMgxDdrtd6enpiomJ8dPEAAAAANC1EF4AEBBM05Tb7ZbT6dTrr7+uqqoq374xY8bI4XBowYIFSkpK8uOU8JfKykpFRkb6vn733Xd1xx13+HEiAD0F4YXujzYGAAAAAAAuH+EFAEBPUVFRoc2bN8vtdsvlcmnr1q1NwgxWq1WTJk2SYRgyDEPp6emKjY3108QAAAAAENgILwDwq9OnT2vZsmVaunSpjh8/7tseHR2tBx98UA6HQ1OnTpXFYvHjlPC33Nxc9e/f3/f1+vXrlZ6e7seJAPQUhBd6lry8PC1dulTPPvssbQwAAAAAALSC8AIAoKeqrKzUli1b5HK55Ha7tWXLFl97/EVWq1UTJ06U3W6XYRiaNWuWevXq5Z+BAQAAACDAEF4A0Olqa2u1cuVKZWZm6oMPPpDX6/Xts9vtcjgcuvfeexUREeHHKRFI9u7dq/Hjx/u+5hdcADoL4YWeyev16uOPP9aSJUtoYwAAAAAAoBmEFwAAaFBVVaWtW7fK5XLJ5XJpy5YtqqmpabTGYrHommuukWEYstvtmjVrluLj4/00MQAAAAD4F+EFAJ1m3759yszM1IoVK1RQUODbnpqaqoULF2rRokUaOnSoHydEoFq3bp3sdrvv6zNnziglJcWPEwHoKQgvgDYGAAAAAACaIrwAAEDzqqurtXXrVrndbrlcLm3evFnV1dWN1lgsFk2YMMHXzDB79mzCDAAAAAB6DMILADpUaWmpXn75ZWVmZmr79u2+7cHBwbrzzjuVkZGhm2++mTcOo1Vvv/225s+f7/u6qqpKYWFhfpwIQE9BeAEXdXQbg9Vq1bx58/T444/TxgAAAAAACHiEFwAAaJuamhpt27ZNLpdLbrdbmzZtUlVVVZN148eP9zUzzJ49W4mJiX6YFgAAAAA6HuEFAO3ONE253W45nU69/vrrjX74MmbMGDkcDi1YsEBJSUl+nBJdydKlS5WRkSFJCgsLa/YHegDQEQgvoDm0MQAAAAAAejrCCwAAXJmamhpt377d18ywadMmVVZWNlk3duxYGYbha2bgd+sAAAAAugvCCwDaTW5urp5//nktXbpUx48f922PiYnRgw8+qIyMDE2dOlUWi8WPU6Ir+v3vf6/vf//7kqSUlBSdOXPGzxMB6CkIL6A1tDEAAAAAAHoqwgsAALSP2tpaffbZZ74ww4YNG5oNM4wZM0Z2u93XzpCcnOyHaQEAAADg6hFeAHBVamtr9e6778rpdOqDDz6Q1+v17bPb7XI4HLr33nsVERHhxynR1f30pz/VL37xC0kNP5jbt2+fnycC0FMQXkBb0cYAAAAAAOhJCC8AANAxPB6PduzYIZfLJbfbrQ0bNujChQtN1o0aNcoXZLDb7erTp48fpgUAAACAy0d4AcAV2bt3r5xOp1asWKGCggLf9tTUVC1cuFCLFi3S0KFD/TghupOnnnpKf/7znyVJ6enpWr9+vZ8nAtBTEF7A5aKNAQAAAADQExBeAACgc3g8Hu3cubNRM0N5eXmTdSNGjJBhGL5AQ0pKih+mBQAAAIBLI7wAoM1KS0v10ksvyel0avv27b7twcHBuvPOO5WRkaFbbrlFQUFBfpwS3dFDDz2kl156SZJ0xx136N133/XzRAB6CsILuBrnzp3TsmXLOryNYfHixerfv3+7nx8AAAAAgJYQXgAAwD/q6ur0+eef+8IM69evV1lZWZN1w4cPl91u94UZ+vbt64dpAQAAAKApwgsAWuX1erVu3TplZmbq9ddfV3V1tW/fmDFj5HA4tGDBAiUlJflxSnR38+bN0/vvvy9Jeuyxx7Rs2TL/DgSgxyC8gPbQWW0MTzzxhObNm0cbAwAAAACgwxFeAAAgMNTX12vXrl1yuVxyu91at26dSktLm6wbOnSoL8hgt9t5IQ4AAAAAv+GpFgDNys3N1fPPPy+n09noTcExMTF68MEHlZGRoalTp8pisfhxSvQURUVFvr/HxcX5cRIAAC6f1WrVnDlzNGfOnA5pY/B6vVq9erVWr15NGwMAAAAAAAAA9CBBQUGaPHmyJk+erO9///uqr6/X7t27fc0M69atU0lJiY4dO6Zjx47pueeekyQNGTJEhmH4Ag0DBgzw8ycBAAAA0FPQvADAp6amRitXrlRmZqY+/PBDeb1e3z7DMJSRkaF7771XERERfpwSPdHw4cN19OhRSdLTTz+t//f//p+fJwLQU9C8gI5ysY3hmWee0TvvvEMbAwAAAACgS6F5AQCArqG+vl579+71hRncbreKi4ubrBs8eLDsdrsvzDBo0KDOHxYAAABAj0B4AYD27t0rp9Op5cuXq7Cw0Le9b9++WrhwoRYuXKihQ4f6cUL0dElJSSooKJAk/fGPf9S3v/1tP08EoKcgvIDOcO7cOS1dulTPPvusTp482e7np40BAAAAANDeCC8AANA1eb1e7du3zxdkcLvdjZ4RuGjgwIG+IINhGBo0aJAsFosfJgYAAADQ3RBeAHqo0tJSvfTSS3I6ndq+fbtve3BwsO688045HA7dfPPNCgoK8uOUgGSapoKDg1VfXy9JWrFihR5++GE/TwWgpyC8gM5EGwMAAAAAoKsgvAAAQPfg9Xq1f//+Rs0MF18q92X9+/eXYRi+QMOQIUMIMwAAAAC4IoQXgB7E6/XK7XbL6XTq9ddfV3V1tW/f2LFj5XA49PDDDyspKcmPUwKNlZWVKTY21vf16tWrdeutt/pxIgA9CeEF+AttDAAAAACAQEZ4AQCA7sk0TR04cMAXZnC5XMrPz2+yrl+/fr5WBsMwlJaWRpgBAAAAQJsQXgB6gNzcXC1btkxLly7ViRMnfNtjYmL04IMPyuFwaMqUKfwwAQHp1KlTGjRokO9rHhoG0JkIL8DfaGMAAAAAAAQiwgsAAPQMpmnq0KFDvlYGl8ulvLy8JutSU1N9rQyGYWjYsGE8fwAAAACgWYQXgG6qpqZGK1euVGZmpj788EN5vV7fPsMwlJGRoXvvvVcRERF+nBK4tF27dmnixIm+rw8dOqQRI0b4cSIAPQnhBQSSjm5j6NevnxwOhxwOB20MAAAAAIBWEV4AAKBnMk1Thw8f9gUZ3G63zp4922Rdnz59fK0MdrtdI0aMIMwAAAAAQBLhBaDb2bt3rzIzM7VixQoVFhb6tvft21cLFy7UwoULNXToUD9OCFyeTz75RDfeeKPv67y8PCUnJ/txIgA9CeEFBKLOaGO49dZb9fjjj9PGAAAAAABoFuEFAAAgNYQZjh492qiZ4cyZM03W9e7d29fKYBiGRo4cSZgBAAAA6KEILwDdQElJiV5++WVlZmbqs88+820PDg7WXXfdpYyMDN18880KCgry45TAlXnjjTd03333+b6ura1VcHCwHycC0JMQXkCgo40BAAAAAOAPhBcAAEBzTNPU8ePH5XK5fH9Onz7dZF1ycrLsdrsv0DB69GjCDAAAAEAPQXgB6KK8Xq/cbrecTqdef/11VVdX+/aNHTtWDodDDz/8sJKSkvw4JXD1nnvuOX3jG9+QJEVFRam8vNzPEwHoSQgvoKvwer366KOPtGTJEtoYAAAAAAAdjvACAABoC9M0deLECV8rg8vlUk5OTpN1SUlJmj17tgzDkN1u15gxY2S1Wv0wMQAAAICOxhMnQBeTk5Oj559/XkuXLtWJEyd822NiYvTQQw8pIyNDU6ZM4a0E6DaKiop8f4+Li/PjJAAABC6r1aqbb75ZN998c4e0MXi9Xq1atUqrVq2ijQEAAAAAAAAA0CYWi0VpaWlKS0tTRkaGTNNUVlaWXC6XL9Bw6tQp5efn64033tAbb7whSUpISPCFGQzD0NixYwkzAAAAAN0EzQtAF1BTU6N3331XTqdTH3zwgb78j61hGHI4HLrnnnsUERHhxymBjvHP//zP+tWvfiVJGj9+vHbv3u3niQD0JDQvoCujjQEAAAAA0FFoXgAAAO0lKyvLF2Rwu93NvpQnPj5es2fPlt1ul2EYGj9+PGEGAAAAoIvi6RIggO3Zs0dOp1MrVqxQYWGhb3vfvn21cOFCLVq0SGlpaX6cEOh4xcXFvr/TvAAAQNvRxgAAAAAAAAAACHSDBg3SoEGD9Nhjj0mSsrOzG4UZjh8/rqKiIr399tt6++23JTX83njWrFkyDEN2u10TJkxQUFCQHz8FAAAAgLaieQEIMCUlJXrppZfkdDr12Wef+bYHBwfrrrvuksPh0Jw5c/gXb/QY999/v1599VVJ0vz58/Xmm2/6eSIAPQnNC+huLrYxPPPMM3r33XdpYwAAAAAAXBGaFwAAQGfJycmR2+32BRqOHTvWZE1sbKwvzGAYhq655hqeqQAAAAACFE+SAAHA6/XK7XYrMzNTb7zxhqqrq337xo4dK4fDoQULFigxMdGPUwL+UVRU5Ps7zQsAAFydL7cxnD17VsuWLaONAQAAAAAAAAAQsPr3768FCxZowYIFkqTTp083amY4cuSISktLfT+blqSYmBjNmjVLdrtdhmFo4sSJvGwHAAAACBA0LwB+lJOTo+eff15Lly7ViRMnfNtjYmL00EMPyeFwaPLkybJYLH6cEvCvKVOmaMeOHZKk73//+/rtb3/r54kA9CQ0L6AnoI0BAAAAAHC5aF4AAACB4syZM1q3bp0vzHDo0KEma6Kjo5Weni7DMGS32zV58mR+Vg0AAAD4Cd+JA52spqZG7777rjIzM/Xhhx/qy/mh66+/XhkZGbrnnnsUERHhxymBwFFcXOz7e3x8vB8nAQCge6KNAQAAAAAAAADQVaWmpuqBBx7QAw88IEk6d+6c3G63r53h4MGDKi8v15o1a7RmzRpJUlRUlNLT033NDJMnT1ZwcLA/PwYAAADQY9C8AHSSPXv2yOl0asWKFSosLPRt79evnxYuXKiFCxcqLS3NjxMCgSkuLk4lJSWSpD//+c/65je/6d+BAPQoNC+gp6KNAQAAAADQGpoXAABAV5GXl9eomWH//v1N1kRGRmrmzJm+ZoYpU6YoJCTED9MCAAAA3R/hBaADlZSU6KWXXpLT6dRnn33m2x4cHKy7775bGRkZmjNnjoKCgvw4JRC4vF6vbDabr6HkpZde8r0xAwA6A+EFQDp79qyWLl2qZ599VllZWe1+ftoYAAAAAKDrIbwAAAC6qvz8fF+YweVyad++fU3WREREaMaMGb4ww7Rp0wgzAAAAAO2E8ALQzrxer1wul5xOp9544w1VV1f79o0bN04Oh0MPP/ywEhMT/Tgl0DUUFxcrPj7e9/UHH3ygm2++2Y8TAehpCC8AX/B6vVq7dq2WLFmid955R/X19e16/ottDE888YTmzZtHwBcAAAAAAhjhBQAA0F0UFBRo3bp1crvdcrlc2rNnT5M14eHhmjFjhux2uwzD0LRp0xQaGuqHaQEAAICuj/AC0E5ycnK0bNkyLV26VCdPnvRtj42N1UMPPaSMjAxNnjxZFovFj1MCXcuJEyeUlpbm+3rbtm2aOnWqHycC0NMQXgCa1xltDIsXL1ZGRgZtDAAAAAAQgAgvAACA7qqwsFDr16+Xy+WS2+3W7t279dVHq8LCwnTdddf5mhmuvfZahYWF+WliAAAAoGshvABchZqaGr3zzjtyOp368MMPG/0L6/XXXy+Hw6H58+crIiLCj1MCXddnn33WKKxw9OhRDR061I8TAehpCC8AraONAQAAAAB6JsILAACgpyguLvaFGVwul3bt2tUkzBAaGqrp06f7wgzTp09XeHi4nyYGAAAAAhvhBeAK7NmzR5mZmVqxYoWKiop82/v166eFCxdq0aJFGjJkiB8nBLqHtWvX6uabb/Z9XVBQoISEBD9OBKCnIbwAtB1tDAAAAADQcxBeAAAAPVVJSYnWr18vt9stl8ulzz//XF6vt9GakJAQTZ8+XXa7XYZh6LrrriPMAAAAAPwN4QWgjUpKSvTSSy8pMzNTO3bs8G0PDg7W3XffrYyMDM2ZM4e3wQLt6NVXX9X999/v+7quro5/xgB0KsILwOWjjQEAAAAAuj/CCwAAAA1KS0u1YcMGuVwuud1u7dixo9kww7Rp03zNDDNmzFBERISfJgYAAAD8i/AC0Aqv1yuXyyWn06k33nhD1dXVvn3jxo2Tw+HQww8/rMTERD9OCXRff/nLX/TNb35TkhQbG6uSkhL/DgSgxyG8AFwd2hgAAAAAoHsivAAAANC8srIybdiwwdfMsGPHjiYv+QkODtbUqVNlGIYMw9CMGTMUGRnpp4kBAACAzkV4AWhGdna2nn/+eS1dulQnT570bY+NjdVDDz2kjIwMTZ48WRaLxY9TAt3ff/zHf+jHP/6xJGnQoEGN/nkEgM5AeAFoH7QxAAAAAED3QngBAACgbcrLy7Vx40ZfM8P27dub/IzcZrNp6tSpstvtMgxDM2fOVFRUlJ8mBgAAADqWzd8DAIGipqZG77zzjpxOpz788EN9Oddz/fXXy+FwaP78+VT3AZ2ouLjY9/f4+Hg/TgIAAK6G1WrVLbfcoltuuaVD2hi8Xq9WrVqlVatW+doYHA6H+vXr1y7nBwAAAAAAAADgSkRHR2vu3LmaO3euJOnChQvatGmTL8ywbds21dXVafPmzdq8ebN+9atfKSgoSFOmTJFhGLLb7UpPT1d0dLSfPwkAAADQPmheQI+3Z88eZWZmasWKFSoqKvJt79evnxYtWqSFCxdqyJAhfpwQ6LkWL16szMxMSdKNN96ojz76yM8TAehpaF4AOk5ntDHcdtttevzxx2ljAAAAAIB2RvMCAABA+6ioqNCmTZvkdrvlcrm0bds2eTyeRmuCgoI0adIkGYYhwzCUnp6umJgYP00MAAAAXB2aF9AjlZSU6MUXX5TT6dSOHTt824ODg3X33XfL4XDopptu4gEnwM++3LwQFxfnx0kAAEB764w2hpUrV2rlypW0MQAAAAAAAAAAAlJkZKTmzJmjOXPmSJIqKyu1efNmXzPD1q1bVVtbq+3bt2v79u36z//8T1mtVk2aNEl2u90XZujVq5d/PwgAAADQRjQvQKZpqqa+RrX1tar31iskKEShtlDZrN0r2+L1euVyuZSZmak333xT1dXVvn3jx4+Xw+HQQw89pMTERD9OCcA0TdXW16q2vla33nGrNrg3SHXSNxzf0JIlS/w9HoAeZvPmzZoxa4YUpIY/9ZL7E7dmz5zt79GAbuliG8Mzzzyjd999lzYGSR6PVFPT8J82mxQS0vDHYvH3ZAAAAAB6MpoXAAAAOkdVVZW2bNniCzNs3rxZtbW1jdZYrVZdc801MgxDdrtds2bN4uWAAAAACFiEF3qg4qpiHcg/oGNFx3S48LD25O1RUVWRvKZXkmSxWGSz2jQ0bqjG9R6ntLg0DUsYphEJIxRkDfyHe74qOztby5Yt09KlSxu9xTU2NlYPPfSQHA6HJk2aJAtP/wB+UV5T7rsnHSk8ot15u5VfkS+v6dWevXtUWVEpmdLo1NF69JZHNSxhmIbGD9WoxFEKDgr29/gAuplKT2Wje9KnBz6V6zOXZFHDH1MaN3acRvcbrfG9x2tY/N/uSUmjFGYL8/P0QPdy9uxZOZ1OPfvsszp16lS7nz8Q2xg8HungQen4cenIEWnvXikrS6qvl0yzIbBgsUhJSdKECdKIEdLQodLo0VJ0tL+nBwAAANCTEF4AAADwj6qqKm3dulVut1sul0ubN29WTU1NozUWi0UTJkyQYRgyDEOzZs1SfHy8nyYGAAAAGiO80EOYpqk9eXu06sgqvX/sfRVXF6vOWyeLLAoPDleYLUxWi7VhrUzVeetU6amUp94jSQq3hWtYwjDdM+oe3Zx2s+LDA/tfampqavTOO+8oMzNTa9eu1Zf/Z37DDTcoIyND99xzj8LDw/04JdBzmaapI4VHtOrIKq08slKFlYWq89bJlOm7JwVZgrR9+3bV1tRKVqnPgD7qFd9LpkyF2cI0sNdA3TPyHs0bNk/Jkcn+/kgAurgTxSe05ugavX3obeVV5Km2vuGNNd4ar44ePCp5v1g7ZtwYBYUHqaau4QfBobZQ9Y3uq/kj5+vWYbeqb0xff3wEoNvqCW0M589La9ZIb73VEFaorm4IKQQHSxERUlDQF00L9fUN+6uqGr4ODpYSEqQ77pBuu60h0EAuGwAAAEBHI7wAAAAQGKqrq7Vt2zZfM8OmTZtUXV3daI3FYtH48eNlt9tlGIZmz56thIQEP00MAACAno7wQjdnmqY+OvGR/rr3r9qdt1uVnkrFhsYqLjxOIUEhbTqH1/Sq0lOp/Ip81XnrlByZrFuH3apHxj8ScA/n7d69W06nUytWrFBRUZFve79+/bRo0SItXLhQQ4YM8eOEQM9mmqY25mzUij0r9NmZz1ReU67o0GjFh8crJCikSQPKmjVrVF/X8IDiuPHjNHDgQJmmqaq6KuVX5Ku2vlbx4fGaM2SOHp3wqNLi0/zxsQB0YTvO7NCKPSu0MWejSqtLFRkSqYSIBIUGhcpisai4qFgbN25sdMzMmTMVF99QtWuapqrrqlVQWaDqumrFhsXq+kHX69EJj2p00mh/fCSgW+voNob+/fvL4XB0WhvDiRPSCy9IH34oFRVJISFSYmJDYKEtAQTTbGhrKCyUysulqChp6lRpwQJp5kxCDAAAAAA6DuEFAACAwFRTU6Pt27f7wgwbN25U1cW34XzJuHHjZBiG7Ha7Zs+eraSkJD9MCwAAgJ6I8EI3dr7ivH636Xdac2yN6rx1SopMUnRIdJOHgy9HnbdOhZWFKqkuUf+Y/vq7a/9Od4y4w9fa4A/FxcV66aWX5HQ6tWPHDt/2kJAQ3X333crIyNBNN93klzeoAvhCSXWJ/rTtT3rj4Buq8lQpMTJRsaGxLd6TvF6v3lv9nu/ryZMnKyU1pdGaem+9iqqKVFxdrOSIZD055UndP/Z+2ay2Dv0sALq+itoKLdmxRC/ufVHlteVKiEhQXFhck3vSpcILX+Y1vSqqKlJRZZHiwuPkmOjQoxMeVagttEM/C9ATdVYbwxNPPKG5c+e2+79L1NVJr7wiPfOMlJcn9erV0J5wNZcxTam0VCookMLCpHvukb79bSmu6e0KAAAAAK4a4QUAAICuoba2Vtu3b5fb7ZbL5dLGjRtVWVnZZN2YMWNkGIavmSE5OdkP0wIAAKAnILzQDZmmqTXH1ui/tvyXTpWcUu+o3ooNi23Xa9R765VblivTNHVT2k36wXU/6NQWBq/Xq08//VROp1Nvvvlmo8q78ePHy+Fw6OGHH6bmDggQ606t0283/VaHCw4rMTJR8eHxlzymtqZWH374oe/r6dOnKzEpsdm1XtOrs+VnVVNXo5kDZupH6T/SkDhaVgA0b8eZHfr1xl9rT94e9QrrpcSIxBaDVJcTXrjINE3lVeTpQu0FTU2dqn+a+U8ak8wv5YGO0tXaGE6ckH79a2nDhoamhdRUydrOWfCiooYQw4gR0g9+IM2aRQsDAAAAgPZFeAEAAKBrqq2t1Y4dO3zNDBs2bFBFRUWTdaNHj5bdbve1M/Tu3dsP0wIAAKA7IrzQzdR76/XfW/9bz+9+Xl7Tq/4x/RVk7bjGgfKacp29cFaDew3Wb2/+rcYmX/4Pqi9Hdna2li1bpqVLlyorK8u3PTY2Vg899JAcDocmTZp0Ve0SANqPaZpatmuZ/nf7/6raU63+sf0VHBTcpmMrLlTo008/9X09a/Ysxca2HsSqqK3Q6fLTSo1K1b/f+O+6rv91VzU/gO7njQNv6Dcbf6Oy2jINiB2gkKCQVtdfSXjhouq6auWU5ighIkE/s/9Mc9LmXNXsAFpXX1+vtWvXasmSJQHbxrBli/TP/yydOSP17StFRrbriI14PFJOTkMLw7e+JWVkEGAAAAAA0H4ILwAAAHQPHo9HO3fu9IUZ1q9frwsXLjRZN3LkSF+QwW63KyUlxQ/TAgAAoDsgvNCN1Hnr9OsNv9aLe19UbFisEiI6p3Wg3luvk8UnlRqdqt/M+Y0mp05u1/PX1NTonXfeUWZmptauXasv/0/2hhtukMPh0Pz58xUeHt6u1wVwdUzT1J+2/0nP7HhG4bZw9Y66vDcxFBcXa+OGLx4YvuHGGxQREXHJ47ymV1nFWeoV1ku/uOEXun7w9Zc9O4DuacWeFfrd5t9JkvpG921T2PFqwgtSw70wuzRbYbYw/WT2T3TniDsvf3AAl+3MmTNaunRpQLUxuN3Sv/yLVFwsDRrU/m0LLcnLk6qqpMcfl779bQIMAAAAANoH4QUAAIDuqa6uTjt37pTb7ZbL5dL69etVXl7eZN2IESMaNTOkpqb6YVoAAAB0RYQXuoFJkybp888/b7K996TemvqDqa0ee/Ttozr86uFm9035/hT1mdyn1eM//vuPVZVf1WT7U089pf/93/9t9dhL2b17tzIzM/XXv/5VRUVFvu39+/fXwoULtWjRIg0ePPiqrgGgY3TX+xKArulq7kl7Xtyj7FXZze7jngQEvkBpY9i6Vfr+96WSkobggsUiffzxIFVVtX+w4qIJE5aqf/+FkqSCAunCBenv/k5avJgAAwAAAICrR3gBAACgZ6irq9OuXbt8zQzr1q1TWVlZk3XDhg1rFGZo64t/AAAA0PN00rse0ZFmz57d7Paiw0W6VDal6FDRFe2TpKrCqmYfxpOkCdMmtHpsS4qLi/XnP/9ZkydP1jXXXKM//vGPKioqUkhIiL7+9a/rgw8+0MmTJ/Vv//ZvBBeAADZg/IBmt/vzvjRr1qxWjwXQfQ2bOKzZ7W25J5Udb/rDV9/x3JOAgBcUFKS5c+fqzTffVHZ2tn7xi19o4MCB7XZ+r9erlStX6vbbb9fgwYP19NNPKzc3t9Ga7Gzpxz/+onHBH8GBxEQpPFz685+l99/v/OsDAAAAAAAAALomm82mKVOm6Ac/+IFWrlypoqIi7dixQ7/73e90xx13qFevXpKko0eP6rnnntOCBQvUv39/DR06VIsXL9by5cuVk5Pj3w8BAACAgEJ4oRsYMWlEs9s9FR6V5zStbrvI9JoqPlrc4v6iw60/kNfaA3v7Qvdd8mHAi7xerz7++GM99NBDSklJ0VNPPaWdO3dKksaPH6///u//1pkzZ/TKK6/o5ptvbvFtpgACw/mK88qKyWp2nz/vS9NnTG/1WADdU1lNmU7Gnmx2X1vuSeUnW95/NfekKdOntHosgPaXmpqqf/mXf9Hx48e1Zs0azZ8/v13/3SInJ0c/+9nPNHDgQN11111avXq1amvr9ZvfSLm5/gsuXNS7t1RXJ/3hD9L58/6bAwAAAAAAAADQdQUFBWnSpEn63ve+p3fffVcFBQXauXOn/vCHP+iuu+5SXFycJOn48ePKzMzUo48+qgEDBigtLU0ZGRl64YUXdOpUx7USAwAAIPDZ/D0Ark6dt06brJta3F90qEgxA2Ka3Vd2qkx1VXUtHlt6slT1NfUKCm3+gZ7CQ4XNbg9LCpO72K2PTnykOWlzWjx/dna2li1bpqVLlyorK8u3PTY2Vg8//LAyMjI0adIkWfz5hA+Ay2Kapn6/+fcq79PKw75+uC8FJwZra9lWDVT7vWkZQNfwf9v/T4VJzd8bpEvfk+qr61s89mruSR8XfKy0gWmtTA6go1xsY5g7d67OnDmjpUuX6tlnn223X5Z4vV69++67evfdd5WQ8KSs1qc1dGiMrNawdjn/1ejfXzp+XPrd76Rf/cq/YQoAAAAAAAAAQNcXFBSkiRMnauLEifrud78rr9ervXv3yuVyye12y+12q6ioSCdOnNCJEye0dOlSSdKgQYNkGIbsdrsMw9CgQYP8+0EAAADQaQgvdHGv7n9VO0t3KiIlQpVnK5vsLzxUqEE3D2r22JYeqLvIrDdVfKxYiWMSm93f0tuEE0clqs5bp99v+b0mpkxUYsQXx9fU1Ojtt9+W0+nU2rVrG7Uz3HjjjcrIyND8+fMVHh7e6mwAAtMHxz/Qe0ffU9+Uvjqfel4XzlxossYf96WooVH68/Y/69q+12pgLwIMQE+xOWezXj3wqpKTkwPqnhQzLEbOz52a0X+GxiaPbf1DAOhQF9sYfvSjH2nt2rV65plntHLlStXXtxxcart+Kiy8X9Jp5edvU+/eyRowYKCSk5NlsViUlvZPqqsrbfFoj6dYx4//ptl9sbGTlJLytVavHhs7ucm2oKCGBoY1ayTDkObNu5zPAwAAAAAAAABA66xWqyZMmKAJEybo7//+7+X1erV//365XC5foKGwsFBZWVlatmyZli1bJkkaMGBAozDD4MGDedkpAABAN0V4oQurqK3Qsl3LFBwUrMRRico+m91kTUsPzV1q30WFBwubfSCvtqy22QcAJSl+RLxSY1N1svik3j70thZPWqxdu3bJ6XTqr3/9q4qKvrhu//79tWjRIi1cuFCDBw++5DwAAledt07P7XxO9Wa9YsNiFT8yvtn7hD/uS/3G9dPZC2f1yv5X9I8z//GS1wDQ9ZmmKefnTlXUViglPiWg7kmpY1NVXF2s5buX69dzfn3JawDoeB3TxvCgpBRJRyWZysvLU15ensLCwjVgwAANGLBQYWEth7YrK7NaDC9ER4/T0KE/uqKpYmOloiLpueekOXMkGz8VAAAAAAAAAAB0EKvVqnHjxmncuHH6zne+I6/XqwMHDsjtdvvCDPn5+crOztYLL7ygF154QVLD80QXgwyGYWjIkCGEGQAAALoJq78HwJX7NOtT5ZblqndUb8WPjG92TU1JjSrOVTS7r+hw4wfyIpIiJEvray4qPFwomc3uUvzIeNmsNgVbgvWH9/+giVMb6uH++Mc/qqioSCEhIbr//vv1wQcf6OTJk3r66acJLgDdwLbT23Sk8Ij6RPWRpHa5LylS7XJfShiZoJjQGK0+slrFVcUtfgYA3ceB/APaeXankiMb3nAeaPekhPAEuU65lFOa09rHAOAHF9sYjh8/rjVr1ujuu+9WUFDQZZ4lVtKtkkr11ZtBdXWVjhw5rI8++ljbt29TXl5eo0a6ztC7t3T0qLRlS6deFgAAAAAAAADQw1mtVo0dO1ZPPfWUXnvtNeXl5Wn//v3605/+pK9//etKTk6WJOXk5GjFihVavHixhg4dqv79+2vBggV69tlndfTo0U7/uToAAADaD+9Y7KJM09SbB9+UJIUEhShhVEKLa4sOFSmyT2SjbRfOXFBtWW2jbUkTklR8rFhlWWW+bSXHSuSt88pqa5xzaelNxCExIaq2VevIziM6e/6szChT+tvSCRMmyOFw6KGHHlJCQsvzAuia3jn0jmrraxURHCFJ7XJfUoqkQklfyhtcyX0pKiVKYd4wZRVnae2Jtfr6mK+3+XMB6JpWH12tC54LSolOkRR496QIM0LHi45rzbE1enzy423+XAA6z9W1Mdwsqbekk62saa6NoX+rbQztJSJCqq2V3n1XSk/v8MsBAAAAAAAAANAsi8Wi0aNHa/To0frWt74l0zR1+PBhuVwuXzPDuXPndPr0af31r3/VX//6V0lSSkqKDMPwtTMMHz6cZgYAAIAuguaFLmrf+X3adW6XkiKTJEnhCeEKT2z+IZfCQ4VNtjX3luD4kfFKGNn4wb76mnqVZpU2Pb6FB/Lq4uu0ZcsWnTl9RqbHlDXIqjH3jdGOHTv0+eef6zvf+Q7BBaAbyi3LleuUS/HhX7zZvD3uS0r6258vudz70sW3rdusNgVZg/TGgTdU761v4ZMA6A5Kqku0+shqxYTG+H5IGWj3JKvFqvDgcL118C1V11W38mkABILLa2OwSJovqU5S277n8EcbQ3y85HZLORTAAAAAAAAAAAAChMVi0ciRI/Xkk0/q5Zdf1pkzZ3To0CH95S9/0YMPPqiUlIYXl509e1YvvfSSnnzySY0cOVKpqal64IEH9Je//EWHDh2imQEAACCAEV7ooraf2a5KT6WiQ6J92y4+DPdVzT0819y2+BHxzZ7jq2vrqupUdqqsyTpJ8sZ7JUmJiYmaOGmipo6bqqhhURo0ahAJZ6Ab2356u0qrSxUXHtdo+9Xel5T8tz+XWNvafSl+xBczJEYk6ljxMWWVZDW7FkD38PnZz5Vfma/EiMRG2wPtnpQQnqDT5ae1//z+ZtcCCDwX2xjeeustZWdn6+c//7kGDBjwlVWDJA1TQ1XL5WpoY9i+fZs2btx41fO2Ji5OKiuTtm3r0MsAAAAAAAAAAHDFLBaLRowYoSeeeEIvvviiTp8+rSNHjmjJkiV6+OGH1bdvX0nSuXPn9Morr+ib3/ymRo0apZSUFN1///3685//rAMHDhBmAAAACCCEF7qogwUHZbFYGgUCWnogr/J8paqLG7/R96tvGI5IilB4Qnibwgu5u3Jlepv/pr7/lP664cYbNP266erbt69iwmJU4anQ8aLjbfpcALqmo0VHZZFFVkvj/1u5mvuSoiRFqMlbzqWm96WiI0Ut3pe+3CgTERyhytpKHSs61vwHAdAtHC06KlOmbFZbo+1XdU+KVLvfk8JsYfLUe3S8mO+TgK4oNTVVP/nJT3TixAm99957X2pjGKqGG0bFVZ2/trZjW1msVsk0paNHO/QyAAAAAAAAAAC0G4vFomHDhukb3/iGVqxYoZycHB07dkzPPfecFixYoH79+kmS8vLy9Oqrr+qpp57SmDFj1Lt3b33ta1/Tn/70J+3bt09er9fPnwQAAKDnIrzQBZmmqb15exURHNFo+5cfhvuqwoNfPIBXVVilqvyqRvsvPswXGhOqqL5RjfYVHSlSbU2tsk5mad26ddr38b5mr2ELs2n87PGKiPhirpCgEHnqPTwoDHRze/P2KjgouMn2q7kv6eIL08OksN5hjXYVHSlq9GaEZt+QLskWblPMwBjf10HWIMki7klAN3cw/6Asatr4dFX3pIuhhTApvE94o11Xek+yWCwyZepoIU8OA11ZUFCQ5s2b52tjuP32f5DNFiIp8H/xERIi7Wv+X+8AAAAAAAAAAAh4FotFaWlpcjgcWr58ubKzs3X8+HFlZmbq0Ucf9bUn5+fn6/XXX9e3v/1tjRs3Tr1799a9996rP/7xj9q7dy9hBgAAgE5ku/QSBJoz5WdUWFWoyODIRtujUqMUGhuqmtKaJscUHSpS3xkNVWlFh5s+UPflNxHHj4zXhdMXfF97Lni09vW1MmP+9lBefvNzxQ2Pk8Xa+EHBi+0QRwqPtOmzAeh6KmordKL4hCJDIpvsu5r7kpK/+Gvs0FhV533x9mHPBY/Kc8sV0z/Gd67mxA1rel+yWqw6kH/gkp8LQNfkNb3al7+vSchTar97UkxajKrOfRFuuJp7UpgtTLvzdl/ycwHoGlJTUzVwYKqGDTMVHR2jU6dOKS8vT1Jg1lFHRkonTkgVFQ1/BwAAAAAAAACgK7NYLBoyZIiGDBmijIwMSVJWVpZcLpfcbrdcLpeysrJUUFCgN998U2+++aYkKT4+Xna7XXa7XYZhaNy4cbJaeScwAABAR+C7rC4otyxXVZ4qhQeHN9kXNyKu2WO+/BBecw/UxY/4IrwQPTi6yX4zz5Qt2KaB/QbKUtz0TcZS4wDEl4UEhRBeALqxM+VnVFVXpXBb03uSdOX3Jd9bziX1Gt6r6fF/O6beU6+SEyXNXqO5+1K4LVxHi3jLOdBdFVQWqLS6tNnwgtQ+96SYoTFNdl/xPSk4XKfLT6u6rrqZIwB0RUePShERFiUnJ2vq1Km66aabNGLECIWFNf+9kj+Fh0tVVVJurr8nAQAAAAAAAACgYwwaNEgLFy7U0qVLdfLkSWVlZen555/XokWLNGTIEElSUVGR3nrrLX33u9/VNddco8TERN199936r//6L33++eeqr6/386cAAADoPggvdEE19TXyml4FWYKa7EsYmdDsMeW55aq9UCup6duEQ2JCFJUaJa/Xqy1btmh//v4mx8fVxWnOnDlKDUuVWdf8W0NbCi8EWYJUVVfV7D4AXV91XbXqzXoFWZvek6Qruy/ZomzSl54NThyd2OT4i8eUHCuR19N8hWNz96Uga5Cq66plmoH5BmQAV6emruH7JKul+W9zr+SepFA1uifFDo1tcvwV35MsQar31qu2vrbZYwB0LabZEAYI+tK3RWFhYRo2bLhuvPFGTZt2rXr37iOp+UB4ZwsKkrxeqaZpIQ0AAAAAAAAAAN3SwIED9eijj8rpdOr48eM6deqUXnjhBTkcDqWlpUmSiouL9c477+gf/uEfNGnSJCUmJurOO+/U73//e+3cuZMwAwAAwFWw+XsAXD5PvUemacpiafrAS0sBApkND9XFj4hXeW5542P+1rpgtVplek0pQrJEWWRe+OLB3qrsKgUFBTV9mO9vrMFW9RrSq9l9FouFB/KAbszj/ds9qYWH8K7kvhQ3PE5xI+LkqfXI4/Eopk+MwpPCVZX/RRDq4lvOL/e+ZLVY5TW9qjfrZbPwf4NAd+O7JzXzfZJ0ZfekL7cuSFJoXGi73ZMsFotM05Sn3tP8XAC6FK+34U9ztyCLpaGNITk5WdXVVcrJydGpU9mqrr6SoHf7hDCt1oZ5PdyCAAAAAAAAAAA91IABA/TII4/okUcekSTl5ubK7XbL5XLJ7Xbr6NGjKikp0cqVK7Vy5UpJUmxsrGbNmiW73S7DMHTNNdfIZuP5AwAAgLbgu6YuyGa1+R50++qzwjEDYmSLsKmusq7JcRcfqvvqcy5ffohv+Ijh8nq9ys3N1en1p33bq4uqVXm+UkUHm38gr9eQXgoKaf6t66ZpKtga3IZPBqAr8t2TWniI7kruS0ljkjRk+JBG2xJGJig3P9f39ZXel7ymVzaLrdn2GgBdn81qa/WF5ldyTxp9/WgNmdcx96SLQQublW/Lge7Aam34c6mCp7CwcA0bNlxDhw5Tfn6+Tp06pby8PLU1lJCbm6uzZ9fIMAyFh4df8bxeb8O8/D4FAAAAAAAAAIAG/fr108MPP6yHH35YknTmzJlGYYbDhw+rtLRUq1at0qpVqyRJ0dHRmjVrlgzDkN1u16RJkwgzAAAAtIDvkrqgUFuo783hQWr8EJzFalH88Hid33W+yXGFBwubPd/F5gVJSkhIkCRVjaxqFF6QpIIDBSo+VnzJc3yV1/QqzBbW4n4AXVtoUKissqre23wt4tXel3zbRsYrd31uo21Xcl/yml6F2kJbfCs7gK4tNChUQZagLnVPslqsCgkKaXY/gK7FYpHCwqTKyrauv/I2BtP06pNPPlFSUpIGDhyg5ORkWSzWy5r3YnghhFsQAAAAAAAAAADNSk1N1YMPPqgHH3xQknT27Fm53W5foOHQoUMqLy/Xe++9p/fee09SQ5ghPT3d18wwadIkBQfz4lcAAACJ8EKXlBqdqjBbmKrqqhQc1PQb2/iRzT+QV3aqTF6Pt9E2W7hNsYNimz3HV2V9kKW6qqZvKW5p/UU19TVKi09rcT+Ari01OlVhwWGqrqtWZEhks2sC6b5U5anSNb2vaXYfgK4vISJBUSFRKq8tV6ya3kukALsn1VVpUK9BCg++8jenAwgsgwdLZ85c/nFfbWM4cWKzCgpaXh8SEqKZM9OVk5OtvXv3Smqotu7ff0Cb2xiqqhrCFqmplz8vAAAAAAAAAAA9UUpKih544AE98MADkqRz585p3bp1vmaGAwcOqLy8XGvWrNGaNWskSZGRkY3CDFOmTCHMAAAAeizCC11Qv5h+iguLU3F1sWJCY5rsTxiZ0OxxZr2p8pzyRtvihsXJYm369vGolCiF9gpVTUmNb1vZqbLmB7JIccPjWh7YlEYkjGh5P4AuLTo0WgNjB+pgwUElqPn7TyDdl+q99RrXe1zzxwHo8mxWm8YkjdEnWZ+0uCaQ7knVnmpN6D2hxVkBdD1jxkjr1l358RfbGKKiJuiTlm9lkqTw8HANHz5Cw4YN0/nz53XqVLaOHm17G0NFhTRihBTbfNYLAAAAAAAAAABcQp8+ffT1r39dX//61yVJ58+f94UZXC6X9u/fr4qKCn3wwQf64IMPJEkRERGaOXOmDMOQ3W7X1KlTFUJNMgAA6CEIL3RBVotVY5PH6oPjHzS7P3ZIrIJCg1RfU3/Jc7XWmBA/Ml5nt5y95DliBsQoOKL5NHBtfa2CrEEaGj/0kucB0HWN7z1eu87tanF/oNyXvKZXsoh7EtDNjU4arY9OfNTi/kC5J5mmKUkanjD8kucA0HUM/du3GV6vZG05N9CuLBarevfuo969+6iqqqrNbQy1tdIE8lMAAAAAAAAAALSb5ORk3XfffbrvvvskSfn5+Vq3bp3cbrdcLpf27t2ryspKrV27VmvXrpXU8LKimTNn+poZpk6dqtDQUH9+DAAAgA5DeKGLGpU0Su8fe7/ZfVabVb3SeqnwQOElzxM/ouUH8hJGJrTpgbzWHuqrqK1QZHAkDwoD3dyw+GGSGh7EtViavqE8UO5LlZ5KhdvClRaXdslzAOi6hsYPlSwNTStB1qAm+wPlnlRbXytbkI3vk4BuZuhQKTxcqqyUoqI6//qttzEM/Fsbg0V/y09p2LDOnxEAAAAAAAAAgJ4iKSlJ9957r+69915JUkFBgdavXy+XyyW3263du3erqqpKH330kT76qOEFbWFhYZoxY4YvzHDttdcSZgAAAN0G4YUuamKfiQq1hepC7QVFhTR9IiZ+ZPwlH8i7+OBeS1oLJTRa18pDfcVVxZrad6oSIxLbdC4AXdOklEmKDo1WSXWJ4sLjml0TCPelwspCDY4brCFxQ9p0HgBd04Q+ExQXFqeiqiIlRSY1uyYg7klVhUqOSNbopNFtOg+ArmHIEGnAAOnECf+EFy5qvo1hj6SGNoaYmIGKigrTxIn+mxEAAAAAAAAAgJ4mMTFR8+fP1/z58yVJRUVFvjCDy+XS7t27VV1drU8++USffPKJpIYww/Tp02UYhux2u6ZPn66wsDB/fgwAAIArZvX3ALgyk1Mna3TSaOVX5De7P2FkwiXPETskVkEhTd9GfFF0/2gFRwVf8jytvU3YlKl7R93b7JvYAXQfafFpmt5vugqrWn4Q2N/3pXpvvWrra3XPyHsUHHTpcwDoupIjkzVnyByVVJfIvPhq8a/w9z3JNE1dqLmgO0fc2WwQFUDXZbNJ99wj1dZK9fX+nqbBxTaGG2+8UePGjVNJSak+++yk8vNX6fDhVaoPlEEBAAAAAAAAAOhh4uPjddddd+kPf/iDPv/8cxUWFuqdd97R9773PU2aNElWq1XV1dVyuVz62c9+puuvv169evWSYRj613/9V3366aeqqqry98cAAABoM8ILXZTVYtU9o+6R1/SqzlvXZH/csDhZgloPDFzqbcEWi6XVVgVJiugdobBezSd58yvy1Te6r24YfEOr5wDQPcwfOV9BliBV11U3u9/f96WiqiLFh8dr3rB5rR4PoHu4c8SdCrOFqcJT0ex+f9+TSqpLFBMWo9uH397q8QC6prlzpYQEqbD1gpdOd7GNYfz4aRo2LE2zZxfqySef1KBBg/T0008rJyfH3yMCAAAAAAAAANCjxcXF6c4779Tvfvc77dixQ4WFhVq5cqW+//3va8qUKbJaraqpqZHb7da//du/6YYbblCvXr00e/Zs/fSnP9XHH3+syspKf38MAACAFhFe6MLmDJmj5Mhkna8432RfUGiQYgfHtnr8pR62ky790F5Lby32ml5VeCp018i7FBkSecnrAOj60geka3DcYOVdyGt2vz/vS6Zpqri6WDen3azkyORLXgNA1zcpZZLGJo8N2HtSfmW+rut3ndLi0y55DQBdT3KydMstUkmJ1EIBjF/l5UnDh4foueceU1ZWlv785z/rs88+U1pamu644w6tWkUbAwAAAAAAAAAAgaBXr166/fbb9dvf/lbbt29XUVGRVq9erR/+8IeaOnWqgoKCVFtbq/Xr1+sXv/iFbrrpJvXq1Uvp6en6yU9+orVr16qiovkXvgEAAPiDxTQD8VEKtNWSHUv0h81/UL/YfgqzNd+A4A/ZpdlKCE/Q8vnL1Temr7/HAdBJXj/wuv7V9a9KjkxWVEiUv8fxOVt+VsHWYC29e6lGJo709zgAOsna42v1g7U/UExojHqF9fL3OD4FlQXy1Hv059v+rGl9p/l7HAAd5PBhadEiqbZWSkm5snN89NFaVVc332oVFhamm26ac9nnvHBBOn9e+td/lb7+9cb7srOz5XQ69dxzz8lisWjx4sVyOBzq16/flYwPAAAAoBvbv3+/xo4de9nH7du3T2PGjOmAiQAAAICeqaysTBs3bpTb7ZbL5dJnn33W5AVFNptN06ZNk91ul2EYmjFjhqKiAueZDgAA0LPQvNDFPTrhUU1JnaKc0hwFSg6lvKZc9d56fXvatwkuAD3M/JHzdf2g63Wm/Iy8ptff40iSqjxVqvBUyDHJQXAB6GFuGnKTbh92u85XnFedt87f40iSautrVVxVrAfGPkBwAejmRoyQFi+WKiqkqip/T9PA65XOnJHsdumee5ruHzBggH72s5/52hi2b9+uIUOG6M4776SNAQAAAAAAAACAABQTE6N58+bpV7/6lbZs2aLi4mK9//77+tGPfqTrrrtONptNdXV12rRpk/7jP/5Dt9xyi+Li4nTdddfpRz/6kd5//32Vl5f7+2MAAIAehOaFbmBv3l4tXrlYXtOrPlF9/DqL1/TqWNExzU2bqz/M/YOsFvIxQE9zquSUHnv7MZVUl6h/bH+/zmKapo4VHdO1fa/VkjuWKNQW6td5AHS+/Ip8PfLWI8oty9XguMF+ncU0TZ0oPqFRSaO07K5lig6N9us8ADpeba30xBPSpk3SsGGSxXJ5x7d380JurhQbKy1bJg0a1LZjsrOzlZmZqczMTNoYAAAAAPjQvAAAAAB0DRcuXNDmzZvlcrnkcrm0fft2eTyeRmuCgoI0efJkGYYhu92u9PR0xcTE+GliAADQ3fFkeTcwrvc4OSY6VFFbobKaMr/NYZqmsoqz1C+6n34484cEF4AeamCvgXpq6lOqN+tVVFXktzlM01R2abYSwhP0o/QfEVwAeqikyCR977rvKdgarPMV5/06y5nyM4oKidI/zvhHggtADxESIv3TP0mJiVJ2tuTPVwcUFUl1ddK3vtX24ILU0Mbw9NNP08YAAAAAAAAAAEAXFBUVpTlz5uiXv/ylNm7cqOLiYq1du1Y/+clPlJ6eruDgYNXX12vbtm36zW9+o9tuu01xcXGaNm2afvjDH2r16tUqLS3198cAAADdCM0L3YSn3qOfuX6mNw6+oZToFEWFRHXq9U3TVFZJlmJDY/Xbm3+r6/pf16nXBxBYTNPUbzf9Vkt3LVVCRIJ6hfXq9OufLj8tm9Wmn1//c80dOrdTrw8gsJimqWd3Pqs/bv2jokKjlBiR2OkznLtwTp56j34868f6+pivd/r1AfjXBx9IP/1pQxNDv35tb2Bor+aFkhKpsFB67DHphz+UrFeZM6eNAQAAAADNCwAAAED3UFlZqS1btsjlcsntdmvLli2qra1ttMZqtWrixIm+ZoZZs2apV69e/hkYAAB0eYQXupHqumr9y8f/oveOvqfkqGTFhHZOfZfX9PqCC7+44Re6YfANnXJdAIGtzlunf1//73p538uKC49TfHh8p1z3YuNCaFCofjzrx5o/an6nXBdAYDNNU/+z9X/03M7nFBESoeTI5E677tkLZ1XvrdffX/v3WnjNQlna+tQygG7l7belX/5SqqmRBgxoW4ChPcILRUVScbH09a9LP/mJZLNd5uCtqKur05o1a/TMM8/oww8/1Ny5c/X4449r3rx5CgoKar8LAQAAAAg4hBcAAACA7qmqqkpbtmyR2+2Wy+XSli1bVFNT02iNxWLRNddcI8MwZBiGZs2apbi4OD9NDAAAuhrCC91MTV2Nfr7u53rr4FsKtYUqJTpFVstVvlazFVWeKuWW5apPVB/98oZfauaAmR12LQBdT523Tv+15b/0wu4XZLFY1De6r4KsHfcgW01djXLKctQrtJd+av+pbh12a4ddC0DXc7GB4f+2/588Xo/6x/aXzdqOT/F+hafeo5yyHEXYIvT9Gd/X/WPuJ7gA9HBr1kg//3lDmKB/fyk0tPX1VxNeqK+XTp+WTFNasED63vfaN7jwVV9uY7BarXI4HLQxAAAAAN0Y4QUAAACgZ6iurtbWrVt9YYbNmzc3+d2FxWLRhAkTZLfbZRiGZs+erfj4znnBJQAA6HoIL3RDdd46vbzvZf3ls78ovzJf/WL6KSI4ol2vYZqmzl04pwpPha7te61+lP4jjUwc2a7XANA9mKaptw+9rT9u+6NOl59WSlSKokOj2/0a+ZX5Kq0u1YQ+E/RPM/9Jk1Imtes1AHQPpmlq7Ym1+v3m3+tk8UklRyWrV1ivdr9OUVWRCioLNDJxpH4444dKH5De7tcA0DV9/rn0q19Ju3dLsbFSUlLLLQxXGl4oL5fOnpX69pW+/W3p7rsla8dl2hupq6vTe++9pyVLlvjaGJ544gnNnTuXNgYAAACgGyG8AAAAAPRMNTU12rZtm1wul9xutzZt2qSqqqom68aPHy/DMGS32zV79mwlJib6YVoAABCICC90Y8eKjuk3G3+jDdkbZLPalByZrPDg8Ks6p9f0qqS6RAWVBUoIT9DiSYu1YPwChQSFtNPUALqr02Wn9Z+b/lMfn/hYpkz1jup91cEq0zRVWlOq/Ip8RYdGa8G4BVo8abEiQyLbaWoA3VV+Rb7+a8t/adXRVfLUe9Q7qrcigyOvqhnBNE2V15brfMV5hdvCdd/o+/TU1KcUGxbbjpMD6A4qKqTMTGnFCqmsrCHAEBvbNMRwueGFykopL6/hPDfcIP3wh5I/iw9oYwAAAAC6L8ILAAAAAKSGMMP27dt9zQybNm1SZWVlk3Vjx46VYRi+ZoakpCQ/TAsAAAIB4YVuzlPv0RsH39CLe1/UieITqvPWKTEiUbFhsbJa2v7qzdr6WuVX5KvCU6HY0FilD0jXNyZ9Q6OSRnXg9AC6G6/p1crDK7V8z3IdKTwiT71HvcJ7KT48/rLuSZ56jwoqC1ReW67okGhNTZ2qxZMWa3Lq5A6cHkB3Y5qmPjrxkZbtWqb9+ftVXVetXmEN96Qga9vfDl7nrVNhZaFKa0oVGRypa/pco4yJGbQtALiknTul556Ttm1raEuIjpYSE6Xg4Ib9bQkveL1ScbFUVCSFhEjDh0uPPCLdcUfntS1cCm0MAAAAQPdDeAEAAABAc2pra/XZZ5/5wgwbNmxoNswwZswY2e12XztDcnKyH6YFAAD+QHihh6itr9XG7I16+9Db2pS7SRdqLsiUqeCgYEUGRyrUFiqrxSqLLDJlqs5bp0pPpSo9lZIpBVmDlBqdqrtH3q1bh92qQb0G+fsjAejC6rx12nZ6m949/K4+zfpUZdVlkkUKsgQpIjhC4cHhje5J9d563z3JlCmLLOod2Vt3jLhDtw27TcMThl/V29IB9GymaWrn2Z169/C7WntirYqriiWLZLVYFRkc2ew9qaquShW1FTJNUxaLRYkRiZo3dJ5uH367xiaP5Z4EoM1MUzp6VFq1Slq5sqE5wTQb2hOOHdstj6dUUr0krySLJKukcNlssRo6dKQkKSZGMgzprrukadMkm81vH+eSaGMAAAAAugfCCwAAAADawuPxaMeOHXK5XHK73dqwYYMuXLjQZN2oUaN8QQa73a4+ffr4YVoAANAZCC/0QCeLT2rf+X06VnRMe8/v1dHCo6quq5bX9MqUKavFqiBLkBIiEjQueZxGJo7U0Pihmpw6WRHBEf4eH0A3c7rstHbn7daxomPan79fhwoOqdJTKa+34Z5ksVgUZAlSr7BeGtd7nEYljlJaXJomp05WTGiMv8cH0M2crzivXed26WjhUR3IP6AD+Qd0wXOh4fsk84t7UnRotMYkjdGYpDEaGj9UE1MmKj483t/jA+jiysqkHTuk48elgwelP/3JrZqacDUEFqySTDWEGCoVEZGr//iPhzV0qDRhgtS3r19Hv2y0MQAAAABdG+EFAAAAAFfC4/Fo586djZoZysvLm6wbOXJko2aGlJQUP0wLAAA6AuEFqLquWoWVhfJ4Parz1ikkKEThtnAlRiTy1mAAna62vlYFlQWqra/13ZNCg0KVFJkkq8Xq7/EA9DB13jrlV+Srpr5Gdd46BVuDFRIUouTIZAVZebgWQMfq27efzpyplRQmKURSnSSPpHz17Zus3Nxcv87XXr7axrB48WJlZGTQxgAAAAAEMMILAAAAANpDXV2dPv/8c18zw/r161VWVtZk3fDhwxuFGfp2tbc6AQAAH8ILAAAAAAAEoH79+un06dPN7uvbt2+3CS9c9NU2hnnz5unxxx+njQEAAAAIQIQXAAAAAHSE+vp67dq1yxdmWLdunUpLS5usGzp0qC/IYBgGL0QCAKALIbwAAAAAAEAA6mnhhS+72Mbw3HPPKSgoiDYGAAAAIMAQXgAAAADQGerr67V792653W65XC6tW7dOJSUlTdalpaU1amYYMGBA5w8LAADahPACAAAAAAABqCeHFy662MbwzDPPaO3atbQxAAAAAAGC8AIAAAAAf6ivr9fevXt9zQxut1vFxcVN1g0ePLhRmGHQoEGdPywAAGgW4QUAAAAAAAIQ4YXGaGMAAAAAAgfhBQAAAACBwOv1at++fXK5XL5mhsLCwibrBg4c6AsyGIahQYMGyWKx+GFiAABAeAEAAAAAgABEeKF5tDEAAAAA/kd4AQAAAEAg8nq92r9/v9xut6+doaCgoMm6AQMGNGpmGDJkCGEGAAA6CeEFAAAAAAACEOGFS8vOztZzzz2nzMxMXxuDw+FQ3759/T0aAAAA0K0RXgAAAADQFZimqQMHDviCDC6XS/n5+U3W9evXzxdmMAxDaWlphBkAAOgghBcAAAAAAAhAhBfarq6uTqtXr9aSJUtoYwAAAAA6AeEFAAAAAF2RaZo6dOiQXC6XL9CQl5fXZF1qaqqvlcEwDA0bNowwAwAA7YTwAgAAAAAAAYjwwpU5deqUMjMzaWMAAAAAOhDhBQAAAADdgWmaOnz4sK+VweVy6dy5c03WpaSk+IIMdrtdI0aMIMwAAMAVIrwAAAAAAEAAIrxwdWhjAAAAADoO4QUAAAAA3ZFpmjp69KivlcHlcunMmTNN1vXu3btRM8PIkSMJMwAA0EaEFwAAAAAACECEF9oPbQwAAABA+yK8AAAAAKAnME1Tx48f97UyuFyuZn93k5yc3KiZYfTo0YQZAABoAeEFAAAAAAACEOGF9tdcG8MTTzyhW265hTYGAAAA4DIQXgAAAADQE5mmqRMnTvhaGVwul3JycpqsS0pK0uzZs31hhjFjxshqtfphYgAAAg/hBQAAAAAAAhDhhY715TYGm82mxYsXKyMjgzYGAAAAoA0ILwAAAABAQ5ghKytLLpfLF2g4depUk3UJCQmy2+2+doaxY8cSZgAA9FiEFwAAAAAACECEFzoHbQwAAADA5SO8AAAAAADNy8rK8gUZ3G63Tp482WRNfHx8o2aG8ePHE2YAAPQYhBcAAAAAAAhAhBc6H20MAAAAQNsQXgAAAACAtsnOzvaFGVwul06cONFkTVxcnGbNmuULM0yYMIEXLAEAui3CCwAAAAAABCDCC/5DGwMAAADQOsILAAAAAHBlcnJy5Ha7fYGGY8eONVkTGxur2bNny263yzAMXXPNNfx+AgDQbRBeAAAAAAAgABFeCAy0MQAAAABNEV4AAAAAgPZx+vRpX5DB7XbryJEjTdbExMQ0amaYOHGibDabH6YFAODqEV4AAAAAACAAEV4ILF9tY7j11lv1+OOP08YAAACAHonwAgAAAAB0jDNnzmjdunVyuVxyuVw6fPhwkzXR0dFKT0/3hRkmT55MmAEA0GUQXgAAAAAAIAARXghctDEAAACgpyO8AAAAAACd49y5c3K73b52hoMHDzZZExUVpfT0dNntdhmGocmTJys4ONgP0wIAcGmEFwAAAAAACECEFwLfxTaGZ555Rh999BFtDAAAAOgxCC8AAAAAgH/k5eX5mhncbrf279/fZE1kZKRmzpzpa2aYMmWKQkJC/DAtAABNEV4AAAAAACAAEV7oWk6dOqXnnntOmZmZCg4Opo0BAAAA3RrhBQAAAAAIDPn5+b4wg8vl0r59+5qsiYiI0IwZM3xhhmnTphFmAAD4DeEFAAAAAAACEOGFrok2BgAAAPQEhBcAAAAAIDAVFBRo3bp1crvdcrlc2rNnT5M14eHhmjFjhux2uwzD0LRp0xQaGuqHaQEAPRHhBQAAAAAAAhDhha6vuTYGh8Oh1NRUf48GAAAAXBXCCwAAAADQNRQWFmr9+vVyuVxyu93avXu3vvrIaFhYmK677jpfM8O1116rsLAwP00MAOjuCC8AAAAAABCACC90H821MTzxxBO6+eabaWMAAABAl0R4AQAAAAC6puLiYl+YweVyadeuXU3CDKGhoZo+fboMw5BhGJo+fTphBgBAuyG8AAAAAABAACK80D3RxgAAAIDugPACAAAAAHQPxcXF2rBhg6+Z4fPPP5fX6220JiQkRNOnT5fdbpdhGLruuusUHh7up4kBAF0d4QUAAAAAAAIQ4YXura6uTqtWrdKSJUtoYwAAAECXQ3gBAAAAALqn0tLSRmGGHTt2NBtmmDZtmgzDkN1u14wZMxQREeGniQEAXQ3hBQAAAAAAAhDhhZ4jKytLmZmZtDEAAACgyyC8AAAAAAA9Q1lZmTZs2CC32y2Xy6UdO3aovr6+0Zrg4GBNnTpVhmHIMAzNmDFDkZGRfpoYABDoCC8AAAAAABCACC/0PF9tY7jtttv0+OOP08YAAACAgEN4AQAAAAB6pvLycm3cuNHXzLB9+/YmYQabzaapU6fKbrfLMAzNnDlTUVFRfpoYABBoCC8AAAAAABCACC/0bF9uYwgJCdHixYuVkZFBGwMAAAACAuEFAAAAAIAkXbhwQZs2bZLL5ZLL5dL27dtVV1fXaE1QUJCmTJkiwzBkt9uVnp6u6OhoP00MAPA3wgsAAAAAAAQgwguQaGMAAABAYCK8AAAAAABoTkVFhTZt2iS32y2Xy6Vt27bJ4/E0WhMUFKRJkybJMAwZhqH09HTFxMT4aWIAQGcjvAAAAAAAQAAivICvoo0BAAAAgYLwAgAAAACgLSorK7V582a5XC653W5t3bpVtbW1jdZYrVZNmjRJdrvdF2bo1auXfwYGAHQ4wgsAAAAAAAQgwgtoicfj0erVq/XMM8/o448/po0BAAAAnY7wAgAAAADgSlRVVWnLli1yuVxy/f/27jQ6z/K+E//30S7Lq7xhY2PjBdvshUDYjFRKtgkkJUn7z0zSnGxAOmSmnXZSOtOZ05lz0qZNUnIyTdNCKB2aZNpkmpBtmoS0ILETdkJYbQzGeN9lSdb6/F8IC4QWb5If2f58cnRMnvu67/v3vED4uu77e/2amvLAAw8MGWY4++yz09jYmIaGhqxcuTLTpk0rUcUAjDbhBQAAABiHhBc4ELoxAABQCsILAAAAjIb29vY8+OCDaW5uTlNTU+6///50dHQMGFMoFHL22Wf3d2ZYuXJl6uvrS1QxAIdLeAEAAADGIeEFDoZuDAAAHEnCCwAAAIyFvXv35uc//3mamprS3Nyc++67L3v37h0wplAo5Mwzz+zvzHDppZdm+vTpJaoYgIMlvAAAAADjkPACh0o3BgAAxprwAgAAAEdCR0dHHnrooTQ1NaWpqSn33Xdf2tvbB40744wzBoQZZs6cWYJqATgQwgsAAAAwDgkvcLh0YwAAYKwILwAAAFAKnZ2deeihh9Lc3Jympqbce++9aWtrGzTu9NNPT0NDQxobG3PppZdm1qxZJagWgKEILwAAAMA4JLzAaHrppZdy880352//9m9TXV2tGwMAAIdFeAEAAIDxoLOzM4888kiamprS3Nyce+65J62trYPGnXrqqf2dGRoaGjJ79uwSVAtAIrwAAAAA45LwAmOhq6srP/rRj3LTTTf1d2O49tpr87a3vU03BgAADpjwAgAAAONRV1dXHn300TQ1NaWpqSn33HNP9uzZM2jc8uXL09jY2B9oOOGEE0pQLcDxSXgBAAAAxiHhBcbam7sxXH311fnYxz6mGwMAAPslvAAAAMDRoLu7O48++miam5vT1NSUu+++Oy0tLYPGLVu2LA0NDf1hBs9KAMaO8AIAAACMQ8ILHCm6MQAAcLCEFwAAADgadXd35/HHH09TU1Oam5tz1113Zffu3YPGLV26tD/I0NDQkHnz5pWgWoBjk/ACAAAAjEPCC5SCbgwAABwI4QUAAACOBT09PXn88ccHdGbYuXPnoHGLFy9OY2Njf6Bh/vz5R75YgGOE8AIAAACMQ8ILlJJuDAAAjER4AQAAgGNRT09Pnnzyyf4ww1133ZUdO3YMGrdo0aI0NDT0hxkWLFhQgmoBjk7CCwAAADAOCS8wXqxZsyZ/+7d/qxsDAAD9hBcAAAA4HvT29uYXv/hFmpqa0tzcnObm5mzfvn3QuIULF/YHGRobG7Nw4cIjXyzAUUJ4AQAAAMYh4QXGG90YAADYR3gBAACA41Fvb2+eeuqp/s4Mzc3N2bZt26BxJ510UhobG/sDDSeffHIKhUIJKgYYf4QXAAAAYBwSXmA8040BAOD4JrwAAAAAfWGGp59+ekCYYcuWLYPGzZ8/v78rQ2NjYxYtWiTMABy3hBcAAABgHBJe4Gjwxm4Md9xxR9797nfnmmuuydvf/vaUlZWVujwAAMaI8AIAAAAMViwW88wzz/QHGZqamrJ58+ZB40488cT+rgyNjY1ZsmSJMANw3BBeAAAAgHFIeIGjjW4MAADHD+EFAAAA2L9isZhnn312QGeGjRs3Dho3Z86c/q4MDQ0NOeWUU4QZgGOW8AIAAACMQ8ILHK32dWO48cYbc8cdd+SKK67QjQEA4BgjvAAAAAAHr1gs5vnnnx/QmWHDhg2Dxp1wwgn9XRkaGxuzbNkyYQbgmCG8AAAAAOOQ8ALHgjVr1uTmm2/OLbfc0t+N4eMf/3jmzJlT6tIAADgMwgsAAABw+IrFYlatWjUgzDDU88HZs2enoaGhP9CwYsUKYQbgqCW8AAAAAOOQ8ALHkqG6MVx77bV529vephsDAMBRSHgBAAAARl+xWMzq1av7gwxNTU1DPhOcOXNmf5ChoaEhp556quctwFFDeAEAAADGIeEFjlVv7MZQU1OTT37yk7oxAAAcZYQXAAAAYOwVi8WsWbNmQGeGtWvXDho3Y8aMXHrppWlsbExjY2NOO+00YQZg3BJeAAAAgHFIeIFjnW4MAABHL+EFAAAAKI2XXnqpvytDc3NzXnrppUFj6uvr09DQ0N+d4YwzzvDsBRg3hBcAAABgHBJe4HiiGwMAwNFFeAEAAADGh5dffrm/K0Nzc3NefPHFQWOmTZvW35mhoaEhZ555ZsrLy0tQLYDwAgAAAIxLwgscj7q6uvLDH/4wN910k24MAADjmPACAAAAjE9r165Nc3Nzf6Bh9erVg8ZMnTo1K1euTGNjYxobG3PWWWcJMwBHjPACAAAAjEPCCxzvdGMAABi/hBcAAADg6LBu3boBnRleeOGFQWOmTJmSlStXpqGhIY2NjTn77LNTUVFRgmqB44HwAgAAAIxDwgvQ543dGO68885cccUVueaaa3RjAAAoIeEFAAAAODqtX79+QJjhueeeGzRm8uTJueSSS9LY2JiGhoacc845wgzAqBFeAAAAgHFIeAEGe3M3hquvvjof+9jHdGMAADjChBcAAADg2LBhw4Y0Nzf3BxqeffbZQWMmTZqUSy65pL8zwznnnJPKysoSVAscC4QXAAAAYBwSXoDh6cYAAFBawgsAAABwbNq4cWPuuuuu/s4MTz/99KAxdXV1AzozvOUtbxFmAA6Y8AIAAACMQ8ILcGB0YwAAOPKEFwAAAOD4sHnz5v4wQ1NTU375y18OGlNXV5eLLrqoP8xw3nnnpaqqqgTVAkcD4QUAAAAYh4QX4ODoxgAAcOQILwAAAMDxacuWLbnrrrvS3Nycpqam/OIXvxg0pra2NhdffHEaGhrS2NiY8847L9XV1SWoFhiPhBcAAABgHBJegEM3VDeGj3/84znhhBNKXRoAwDFBeAEAAABIkq1bt+buu+9OU1NTmpub88QTTwwaU1NTM6Azw1vf+lZhBjiOCS8AAADAOCS8AIdvXzeGG2+8MU1NTboxAACMEuEFAAAAYCjbt2/vDzM0NTXliSeeyJtfU66pqckFF1zQH2a44IILUlNTU6KKgSNNeAEAAADGIeEFGF26MQAAjB7hBQAAAOBA7NixI3fffXeam5vT1NSUxx57bFCYobq6OhdccEEaGhrS2NiYCy64ILW1tSWqGBhrwgsAAAAwDgkvwNjQjQEA4PAJLwAAAACHYufOnbnnnnvS1NSU5ubmPProo+nt7R0wpqqqKm9961v7OzNceOGFmTBhQokqBkab8AIAAACMQ8ILMPbWrFmTr33ta7nllltSW1urGwMAwAESXgAAAABGw65du3LvvfemqakpTU1NefTRR9PT0zNgTGVlZc4///z+MMNFF12Uurq6ElUMHC7hBQAAABiHhBfgyOnq6soPfvCD3HTTTbnzzjtz5ZVX5tprr83ll1+uGwMAwBCEFwAAAICxsHv37tx7771pbm5OU1NTHn744UFhhoqKipx//vlpaGhIY2NjLrrookycOLFEFQMHS3gBAAAAxiHhBSiNF198MTfffHNuueWWTJgwIVdffXU+9rGP6cYAAPAGwgsAAADAkdDS0pL77rsvTU1NaW5uzkMPPZTu7u4BYyoqKvKWt7ylvzPDxRdfnEmTJpWoYmB/hBcAAABgHBJegNJ6YzeGpqamXHHFFboxAAC8RngBAAAAKIU9e/bk/vvvT1NTU5qamvLQQw+lq6trwJjy8vKce+65aWxsTGNjYy6++OJMnjy5RBUDbya8AAAAAOOQ8AKMH7oxAAAMJLwAAAAAjAetra25//7709zcnKampjz44IODwgxlZWU599xz09DQkMbGxlxyySWZMmVKiSoGhBcAAABgHBJegPFHNwYAgD7CCwAAAMB41NbWlgceeCBNTU1pbm7OAw88kM7OzgFjysrK8iu/8itpbGxMQ0NDVq5cmalTp5amYDgOCS8AAADAOFMsFjN32dxsbN+YVCQpT9KbpCfJnmTuhLl59ZWhgw3AkXFsd2MoJtma5KUkrUk6k5QlqUpSn2RxktpSFQcAjAMDwgsV6fsrQm365i6F9M1dOpPsTNL2+nnCCwAAAMCR1N7engceeKC/M8MDDzyQjo6OAWMKhULOPvvsNDY2prGxMStXrsy0adNKVPGxoVgsZlPrpry88+W0dbWlq7cr5YXyVJVXZWbdzJw89eRUV1SXukxKRHgBAAAASqxYLObpLU/ngXUP5Nmtz+bJzU/mzgfuTFexq+/Fn0L63iUuJulOqlKVj1750Zw+8/ScdcJZuXDehaksryztl4Dj1Ju7MVx55ZW55pprjsJuDK8kuSvJs0meTLIxfW8adqfvl0/yeoBhYvoCDGcmOS3JyiR1R7heAKBUWjtb8427v5FP/fdPJbPTF1yoTF+IIXl9/tKbpCvJniSbkmxNfva3P8vl511eirIBAAAAsnfv3jz44IP9nRnuv//+7N27d8CYQqGQs846Kw0NDWlsbMyll16a+vr6ElV89Fi1fVXuXXtv//PuLa1b0tbVlp5iT/a9ql5WKEt1RXUmVU3K0ulLc8asM3LGrDNyyUmXCDMcR4QXAAAAoETau9rT9FJTbnv2tjyy/pG0drWmUChkQuWEPPXIU+ls7ex74WefQpKKpLKuMqecfkq6e7tTWVaZxfWL8/4V7887l7wzM+tmlurrwHHv6OvG0J3kgSQ/SNKcZFf6ftHUpC+MUJe+txALr43vTdKRvlBDa/reRiwkmZfkvUnenWThEaseADiy1uxYk39+4Z/z/ee+n9VbVufFF1/s67DQlb4uCz15PfOY9HVhqHzDT5LTTzk971rxrrxn2XtywbwLUlFWEQAAAIBS6ejoyM9//vP+MMN9992X9vb2AWMKhULOOOOMNDY2pqGhIZdeemlmzJhRoorHl86eztz98t353rPfywPrHkhLZ0v/8+59PxVlFSm89qypt9ibvd1709rVmrautnT3dKe8rDwLpizIVSuuyruWvCsnTj6xxN+KsSa8AAAAAEdYT29Pbnv2ttz86M1Zu2ttkmTGhBmZXD05hULfws2//OxfBu3ysU9NTU0uf1vfbqXtXe3Z3Lo5nT2dmV47Pe9b8b5cc+41mVQ96ch8GWCQ8d+NoZjk7iR/mb5OC91Jpr32c7D1dSbZkr4ww5QklyX5nSRzRqtYAKDENrRsyJcf/HLuWHNHdnXsSl1lXWqLtbnvrvsO+lpnXXhW9hb2prK8MsunL89/eOt/yMqTVvbPgwAAAABKqaOjIw899FCam5vT1NSU++67L21tbYPGnX766WlsbOzvzDBz5vG1wVyxWMyPV/04f/Pw32T1jtXpLfZmeu30TK2ZetDrPHu792ZL65a0d7dnWs20/Jul/ybXnXddpk+YPkbVU2rCCwAAAHAEvbLrlXzhvi/kjjV3pKxQljmT5qSqvGrQuAMNL+zT09uTrW1bs7tjd06deWr+4OI/yAXzLhiT7wAcuPHXjWFnkq8k+U76ggcnJJkwCtftfe3aW9PXieF3k1yR17s2AABHm2KxmB8+/8N8+cEvZ92udZlRNyNTa6amrFCWlpaWNDc1H/Q1GxobMmnSpLR1tWVDy4ZUV1TnAys+kOvOvy5Ta6aO/pcAAAAAOAydnZ15+OGH+zsz3HPPPUOGGU477bQ0NDT0d2eYNWtWCao9Mja3bs5f3PcX+fGqH6e32Js5k+akpqLmsK/bW+zN9vbt2d6+PUvql+T3Lvi9XHbyZTa9OAYJLwAAAMARUCwW871nv5cvP/jlbNizIXMnzc3EqonDjj/Y8MI+nT2dWbtrbSZUTsgHT/tgrjv/ukyoHI0Xk4HDMT66MdyX5M+SPJdkRpL6MbhHT5J16evu8I4k1yc5vnYbAoBjwebWzfn8vZ/PT1f9NIVCIfMmz0t5WXn/8cMNL+yzvX17trZuzbIZy/KHl/xhLpp/0ajUDwAAADAWurq68sgjjwwIM+zZs2fQuBUrVvQHGRoaGkq4qdXoun317fnifV/MyztfzuyJszOlZsqo36O7tzuv7HolFWUVee+y9+b3Lvy9MbkPpSO8AAAAAGOsWCzmqw99NTc9clNSSOZNnpeywsgvKx9qeGHf/ba1b8uO9h25fNHl+dNf+9NMrp58WN8BGD2l6cbwwySfTdKS5KQklWN4ryTZnWRjkrOS/EWS+WN8PwBgtLyy65X8/u2/n8c3Pp45k+YMOZcYrfBCknT1dGXtrrWZVDUp/+3S/5Yrl115yLUDAAAAHEldXV159NFH09zcnKamptxzzz1paWkZNG758uUDOjPMmTOnBNUeumKxmG/+4pv50v1fSmdPZ+ZPmT9go4uxsKN9R7a0bslF8y/KF97+hcyYMGNM78eRI7wAAAAAY+Scc87JY489Nujz2efMznn/+bwRz/3JF3+S7ke7hzxW+WuVeccn3jHi+f/6O/+a9i3tgz6/7rrr8pWvfGXEc4Ejo7OzMz/84Q9z4403prm5OVdeeWWuvfba/Nqv/dood2O4LcmfJOlOMi/J6+11P/rRx3PrresO6CqFQjJxYkWmTKnIokUT8pa3TM073zkzl18+Y5iWvZ1JXkqyPMlXIsAAAOPXwc5dBoQXfpnkyWEuvDJ9f/14zVDhBXMXAAAA4FjR3d2dxx57rL8zw913353du3cPGnfKKacMCDOceOKJJah2/w7nefcL33shz337uSGPveX335ITzh15Uy9rRseuI9WTHgAAAI47K1euHPLz7c9tz/72Eujd1HtIx5KkfVv7kAs5I9UEHHlVVVV5//vfn9tvvz3PPPNMli5dmg9/+MNZsmRJPve5z2Xjxo2jcJfbk/xpkp68ObhwsIrFpKWlO+vW7c1dd23PDTe8mLe//cEsXnxnvv/9oWqtSrIoybNJfj/J5kO+NwAwti699NIhPz+QucuI/4nfMvKp5i4AAADAsaSioiLnnXdePvOZz+RHP/pRtm/fnocffjhf/OIXc+WVV2bKlClJkueffz5f+9rX8qEPfSjz5s3L0qVLc/XVV+cb3/hG1q07sE2njoTDWTPa/uz2QzqWWDM61gkvAAAAwBiZdeqsIT/vau1KyyuD24XuU+wtpnfzoYcXRlrsueSSS0Y8FyiNRYsW5XOf+1xeeeWVfOELX8idd96ZBQsW5AMf+EB+9rOfpbd35H/vh7YmyWeTdCQ5MYcTXBjxLmva8uu//nB+53eeGuJoRZKFSZ5IX/eHQ/keAMBYu2Tl0POE/c1d0ptk2wgX3k94YaS5y0mnnzTyyQAAAADjXHl5ec4999z8/u//fn7wgx9k27ZteeSRR3LDDTfkPe95T6ZOnZokWbVqVW6++eb81m/9VubPn58lS5bkE5/4RL7+9a9n7dq1Jav/pDOHXp85kOfdO17YMezx7c+NHF4Yac3o4osvHvFcxj/hBQAAABgDu/buyt2Fu4c9PtKCy+6Xdyddw1+7uK2Yno6eYY9ve3bot4eqZ1RnU9mm4S8MlNzodWPoTvL5JJuSzM9YBRfe6H/9r5fy2c++MMSRqiRzkvxrku+PeR0AwMFrOWH4h80j7oS3MyPOXbIjfX8tGcZwc5fK6ZX5+zV/n+7eEU4GAAAAOMqUl5fnnHPOyX/6T/8p3//+97N169Y89thj+dKXvpRf//Vfz7Rp05Ikq1evzi233JKPfOQjWbBgQRYtWpSPfexjufXWW/Pyyy8fkVr3du/NPbln2OP7e97d3T78us6uNbsO6Xl31YyqvNA11LMojibCCwAAADDKisVivvLzr2RN55rUza0bcsxwCy77O5Yk6U12rBphp4phFoqqF1Xnz+/987R2to58fWBcOLxuDP83SXP6Oi4c2hLgH/zB4nzuc8v7f/7oj5bkne+cmbIRLvfZz76QNWvahjgyKUl5kr9MMn7aHQMAySu7Xsnfr/r7VJ9QPeTxEecn++mskN4kW4c/PNzcZfry6bnr5bvyT0//035uAAAAAHD0Ki8vz9lnn53f/d3fzW233ZatW7fmiSeeyJe//OVcddVVmT59epJkzZo1+d//+3/nox/9aBYuXJiFCxfmox/9aP7u7/4ua9asSbFYHPXabnnsljzT9kwmzJkw5PHDed5d7Cke0vPu2sW1+eL9X8z29pE7NzC+CS8AAADAKHvw1QfznWe+kxkTZmT68ulDjhlpJ4oRdzZ9zbZnhl7w6dzdmT3r9wx5bN4Z8/LExidy6xO37vf6wPgxXDeGpUuX5s/+7M+G6MbwSpK/TlKdZOgA1YH47d9ekD/8wyX9P5/97PL8+MdvzX33XZwpUyqGPKejozd/93evDHPFE5O8muQvkoz+IjoAcPCKxWJuuP+GrG9Zn1mnzhpyzIjzk80HcJNhAg4jzV1mnTorVRVV+epDX80ru4b7uwUAAADAsaWsrCxnnnlm/uN//I/57ne/m82bN+fJJ5/MX/7lX+b9739/ZsyYkSR5+eWXc+utt+bjH/94Fi1alIULF+YjH/lIbrnllqxevfqwwwxPb3k6tz5+ayZVT8qMFTOGHFOK593zz5ifVdtW5asPfXW/12f8El4AAACAUfatp76V9u721NfWp355/ZBjOnZ2pHXj0B0Qtj/3psWcuiSF/Yx5zbbntg37TvCsU2elrqou//fp/5uWjpaRvgIwTr2xG8Of//mf54477siCBQvyG7/xG2/oxvDDJJuSzB2TGt761mn53OeWD3v8Jz8Z7i3G8iSzktyd5PkxqAwAOFjPb3s+d6+9O7PqZg0bvB5p7jIomDDE3GW48MJIc5f65fWZO2luNrVuyo+e/9Fw5QMAAAAc08rKynLGGWfk05/+dP7pn/4pmzZtylNPPZWvfOUr+Y3f+I3MnDkzSbJ27dp8/etfzyc+8YksWbIkJ510Un7rt34rN998c1atWnXQYYbvPP2d7OzYmVl1s0blefeEmRNG5Xn3jBUzMq12Wv75hX/OhpYNI38Jxi3hBQAAABhFL+54Mfe+cm/qa/sWcaavGPoFoGToHSf2rN+Tzt2dAz+ck2TqwI92rtqZ3u7eA7pmklRNrsrEORMzc8LMbNyzMT978Wcjfg9gfKuqqsoHPvCB3H777Xn66aezZMmSfPjDH86ZZy7OmjVfSmdnVcZy6e+97z1h2GMvv9w+wplTkrQk8RIiAIwHP3r+R2npaMmU6ikHPXfJ7iQdb/psiLlLtiYZPHXZ79ylrFCWusq63PbsbWnrahv+SwAAAAAcJ8rKynLaaafluuuuy7e//e1s2rQpTz/9dL761a/mN3/zNzN79uwkybp16/KNb3wjV199dZYuXZp58+blQx/6UG666aY8//zzI4YZtrZtzU9W/yRTa6amUCiMyvPumWfNzOQFkwd8dqjPu6dPmJ7t7dvz41U/HrYuxjfhBQAAABhF//zCP2fX3l2ZVjMtSVI7vTa1M2qHHLvt2cGtMIfcYWLmaz9v0NPRk10v7Rp8/jCLOft2xKgsr0xZoSzfeeY7h90uFBgfFi9e3N+N4cYbP5iysg1panoqDz/8SLZs2TIm/67PnFk17LEdO7pGOLOQZFL6ukPsHuWqAICDsWvvrvzw+R9mUvWkFAqFg567DNlRYYi5S3qSDDFN2d/cJUlm1s3Mut3rcueaO4ccCwAAAHA8KxQKWbFiRX77t3873/rWt7Jhw4Y888wz+eu//ut88IMfzAkn9G1GtX79+vyf//N/cu2112bZsmU58cQT82//7b/NjTfemOeee27As6SfrPpJtrVty/TavtDCaDzvrl9eP6jr56E+7y4rlKW6ojq3PXNbOns6hxzL+Ca8AAAAAKNkb/fefP+576euqi6Fwut9L4drpTnUwsuQizGzXvvZz9ju9u7sfnnol4Hrl71ew6y6Wfnl5l/m8Y2PDzkWODpVVVXl4ou3ZMGCk3Lxxb+auroJeeyxx3LHHXdm1apV2bv3zVsjH7rNm4e/1rRplfs5e0aSzUn+ddTqAQAO3h1r7sim1k2ZWfd62uBg5i7ZPMTAYeYubw46HOjcpaq8KoVCId977ntDjgUAAADgdYVCIcuXL8+nPvWp/MM//EPWr1+f5557LjfeeGP+3b/7d5k7d26SZMOGDfnHf/zHfOpTn8ry5cszZ86cfPCDH8xfffWvcuvPb01VWVXKy8r7r3u4z7vrl9UPeY3Ded69Zuea3Lv23iHHMr4JLwAAAMAoeWHbC9nSuiXTaqcN+Hy4xZy2zW3Zu2PvgM8G7U5Rl2RCBu9emsGLOduf355i79A7rL9xJ4u6yrp0dHfkqc1PDf1FgKNUS5JfJpmaurq6rFixIpdffnlWrFiRrVu35l//9V9GrRvD97+/adhjCxdO2M/ZlUmKSZ48rBoAgMPzxKYnUiwWU1FW0f/ZwcxdBnVeGGHu8uaxBzp3SZIp1VPyy82/TEtHy5DjAQAAABhaoVDIKaeckmuuuSbf/OY3s27durzwwgv52te+lg996EM58cQTkySbNm3Kt771rXz6v3w6//Lzf8kzjz+TRx55JC+99FJaWloGBAfe6ECed0+YOSG102sPKLxwoGtGNRU16e7t9rz7KFWx/yEAAADAgVi1fVX2du9NbcXAtplvfvnmjbY9sy0nXtS3KNS+rT3tW9oHDtj34k9NkslJ3rDRxPbnt6dYLPZ3eRiuhWZFbUUmL5jc//8LhUJSSJ7b9twBfS/gaLE6SVve+MZgWVlZ5s6dk7lz56S1tTVr167NY489lvLyiixYcFJ6enoO+i4PPrgj//W/Pjvs8Xe8Y6g3Ft+sJsILAFBaT256MrWVhzZ36djRkbS+acAIc5dsSV928TUHOndJkgmVE7KlbUtW71ids084e9j6AAAAABhZoVDIkiVLsmTJknzyk59MsVjMiy++mKampjQ3N+cnz/0kW6q2pHtHdzbs2ZAN6zckSSrbh++6vb/n3ftCC9WTqzPxxInZ8+qe/mOH+rw7ScrLyvPUFuGFo5HwAgAAAIySVdtXpVAo9C+u7DNx7sRUT6lOx66OQedsf3Z7/2LO9ueGWIyZ9aZ/fsMLQF17utKyriWT50/uv9ZQpi2dlkLZwJpqKmry5KYnBywGAUe7VUk60vfG4GD7ujEsW7YsGzduyssvv5wNG7YOe7W//uuXM23a64vRra09eeSRnfnpT7ekt3foc6qry/Kxj80/gFonJHk1yY4k0/YzFgAYbdvbt2d9y/pMqBzYMelA5y67Xtg1+KIjzF3SmeQNpxzs3KWzuzOrtq8SXgAAAAAYRYVCIYsXL87ixYvziU98Il975Gv587v/PBM7Jmbb9m3ZtnVb2tvb01Xb1ff4ae/ga2x8cmPmXjg3hUJhyOfdb+y4UL+8fkB44XCed9dV1uW5rc+lq6crleXDhysYf4QXAAAAYJT8YvMvUlk29MLItGXTsvHnGwd9/sYFnCEXY2a+6Z9Xven8Z7dn8vzJ6enqyc4Xdw5576FacNZV1mXTnk3Z2rY1M+sOZJd0YPx74bU/Rw4kvbEbwwknPJxk8O+mJPn851cfdAV/9EdLcvLJE/Y/MHVJNqTvl9p5B30fAODwrN6+Oq1drZkzcc6gYwcyd9m1aojwwn7mLtnS98fBzl32ha1XbX/zBQEAAAAYTc9uezZVlVWZP3t+5p/Ut1lVW1tbtm3blucfeT7tz7cPOmfDExty++23p76+Pp0/7xx0vH5Z/YB/XvuvawccP+Tn3VV12bV3V17a+VKWTl96MF+TEisrdQEAAABwrFjfsj7VFdVDHpu+fPqQn7esa0nnnr5FnEE7UVQneWP3y1kZZN85O1ftTG/X0FuhD7WYU1NRk46ejmxp2zLkOcDR6NUc7F4lFRWjt7fJv//3C/Lf//spBzi6KklX+t9iBACOqC1tW9LV05Wq8qpBxw5k7jKo88IBzF32/Wf/YOcuSVJeVp51u9cNeQwAAACA0bFu17pBz7snTJiQ+fPnZ9EFi4Y+aVfS1dKVTRs3ZcdzOwYcKq8rT09dT4rFYpJk+orB606edx9/hBcAAABgFBSLxXR0d6SsMPRUe7iXcFLsW5Dp3NOZlnUtA4+9uSHChPRtVv4G+7o1DNWCM0nKKssyddHUwZ8XytJb7E1nz+DdL4CjVXtKsdy3cGFtvvOdc/NXf3XGQZxVeO2na4yqAgBG0tnTmUIK/V0N3uhA5i6tG1oHHjuAucu+8MLBzl2SvvlLR3fH0HUBAAAAMCr2du89+OfdSU6edHLmzpybvGm/i55pPbn7rrvT1NSUJKmdXpvambUDxhzq8+5CCikWi+nq8azpaDN6W6sBAADAcaz42v8KGfzyT5JMPmlyKiZUpLute9CxfQsyKQ78/NRfPTWL3jVwB4vH1z2edXe/vuPo3u1707a5LdufGXoxZ+qiqSmvKh/0eaFQSDHF9PT2jPS1gKPK2P/7PHFieaZMqcyiRRNy7rlT8q53zcrll89IWdnQv/v2b/DvRABg7I00DziUucvM02Zm2inTBny2ccnG7H5i9+sftCU9u3oOeu6yT1evB9EAAAAAY6mneGhrRoWthcydMTfrs37A51XzqtKZzkye/HrLzunLp2fdllF63l0sprvXs6ajjfACAAAAjIKyQlkqyiqG7WRQKCuk/pT6bH5886Bj257ZNuQ59csG715Rv7x+QHghSbY+vTU7Vu0YNHa4ayRJb7E3ZYWyVJVXDXkcOBrVZNCbhIdhzZrLsnDhhFG73tD8DgKAUqgqr0pxmL83HMrcZdnKZYN2wKs5vyZPPvHkgM92r9p90HOXpC8sXltRO+xxAAAAAA5fdXl1isXRWzM6/4rzUzO3Jj3dr4ciRut5d7FYTKFQSHVF9ZDHGb+OfB95AAAAOEZNrJo44m4Uw7XS3P3y7mx5csuAzypqKzJl4ZQDusZLP30p3e1D7ygx3D27e7tTUahIbaUXgODYMSlHTyeDfb8rxzocAQAMZULlhBRSSG+xd8jj42nukvTNXyZWTxz2OAAAAACHb2L1xBE7GRzKmlFNTU3qJtaNeI1Dfd5dXlZuw4ujkPACAAAAjJIVM1ekratt2OPTl08f8vNiTzEtr7QM+Gza0mkplBUGjZ04Z2Kqpw7cPWL3y7uHvmEhmXbKtCEPtXW1pa6qLvMnzx+2XuBos7TUBRyE9vQFFxaWuA4AOD4tmLogEyonDDt/GU9zl32W1h9Nf9cBAAAAOPqsmLEiXb1dwx4fT2tGbV1tmVA5IQunLhy2XsYn4QUAAAAYJcunLx/x+JRFU1JeXX5A1xpp19GRjr3R5JMmp3JC5ZDHWjtbs2z6Mm004Ziy+LU/h+8AM360JpmcZEGpCwGA49KCKQsyuXpyWjtbhzw+nuYu3b3dKaSQJfVLDuhaAAAAAByapfVLUywWUywWhzw+ntaMWrtaM2finNTXHti1GD+EFwAAAGCULKlfkrKUDdtKs6yiLFMXTz2ga9UvG36RZbgdLQZdY4RFn55iT06bddoBXQc4WixJUpe+YMB415pkRZKKUhcCAMelyvLKLJ+xPK1dQ/+9YTzNXfbtoie8AAAAADC2ltQvSXVFdfZ27x3y+HhaM2rvas9Zs89KoTC4uwPjm/ACAAAAjJIl9UtSV1WXlo6WYcccyC4S+1v0OdCdKIZbENq3c+nS+qUHdB3gaDEvSX2SPaUuZD+K6esOIUAFAKV0+qzT09PbM+xOeuNh7pIkezr3pL62PvMmzzugawEAAABwaBbXL87EqonZ0zn8s6bxsGbUW+xNkpwy/ZQDug7ji/ACAAAAjJK5k+bmV+b8Sra1bxt2zIHsIjFl0ZSUVw3fbnPS/EmpnDh0e8w3Gm7RZ1vbtkyvnZ4L51+432sAR5PyJO9IX3hh6JcQx4eWJBOSrCx1IQBwXLvkpEtSW1mbls6hw9fjYe5SLBazp3NP3rnknSkreKwJAAAAMJYmVk1M44LG7Ny7c9gx42HNaOfenZlcPTmXnHTJfq/B+GOVDwAAAEZJoVDIVcuvSlnK0tHdMeSYaUunpVA+cuvK/e00USgURtyZNEkmzJ6Qmqk1gz4vFovZ1bEr71r6rtTXHtiOFsDR5Iokk5LsKnUhI9ia5MwkZ5S6EAA4rp05+8ycNfusbGndMuTxUs9dkmRXx65MqpqUdy9994jXAAAAAGB0vGfZe1JdUZ3WztYhj4+HNaNtbdvSsKAhC6YuGPEajE/CCwAAADCKGhc25qQpJ2VL29AvAJVXl2fKyVNGvMb+FmqS/S/4DLfjRUtnS+oq63LFKVfs9x7A0eiUJG9JMnwHmNLqTF9XiPcnGXlhGwAYW4VCIe9b8b4kSWdP56DjpZ67JH0Pos8/8fycMv2U/d4HAAAAgMP3lrlvyfLpy4fd8KLUa0btXe2pLK/Me5a9Z7/3YHwqFIvF8dxDHgAAAI46Nz1yU264/4YsnLowleX7b3d5pBSLxazavioXzr8wt7znlhQKXhyGY9PtSX4vyawkdSWu5c3WJpmd5LaMv9oA4PjT2tmaq751VTa1bspJU04qdTkDtHa2ZnPr5nzpHV/K2xa/rdTlAAAAABw3vv3Lb+d/NP2PzJs8L9UV1aUuZ4AXd7yYZdOX5du/8e1UlFWUuhwOgc4LAAAAMMp+87TfzCnTT8kru14pdSkDbG3bmklVk/Lp8z4tuADHtMuSXJTk1SS9Ja7ljfYk6UlyTQQXAGB8qKuqyzXnXpOe3p7s6dxT6nL69RZ782rLq7n4pIvzqyf/aqnLAQAAADiuXHnKlTlr9ll5ZfcrGU975O9o35HKsspcd951ggtHMeEFAAAAGGVTa6bmP1/0n1NdUZ3t7dtLXU6SpLOnMzv37syHzvxQzp17bqnLAcZURZI/TF/nhfUlrmWf3vTVclmSq0pcCwDwRlctvyqXnXxZ1resT29xfAQf17esz+y62bn+4us9iAYAAAA4wmora3P9JddnUtWkbG7dXOpykiTdvd3Z0rYl71323lx28mWlLofDILwAAAAAY2DlSSvzvhXvy9bWrenq6SppLcViMWt3rc2Zs8/M1edcXdJagCNlUZJPJelM0lriWpJkXZK5ST6TpLzEtQAAb1ReVp7PXPSZzJk4J+t2ryt1OWntbE1Hd0c+9ZZPZdG0RaUuBwAAAOC4dPYJZ+cjZ30kuzt2Z2/33pLWsu9595L6JfmdC34nhUKhpPVweIQXAAAAYAwUCoV8+vxP57RZp2XNzjXp7u0uSR37FnKm1UzLH17yh6mrqitJHUAp/H9JfjXJq0lKuai8KUkhye8mmV/COgCA4cyfMj+/e8HvppBCNu3ZVLI69nbvzastr+ayky/Lb572myWrAwAAAIDkE7/yiZx/4vlZu2ttOns6S1bH+pb1mVA5IX9w0R+kvra+ZHUwOoQXAAAAYIxMrZmaG95xQ5bUL8maHUc+wFAsFvPK7ldSU1GT/9n4P3POnHOO6P2BUqtI8idJLkyyNqUJMGxJ0p7k00neU4L7AwAH6r3L3pvrzrsu7d3t2dK65Yjff2/33qzduTYXzrswf3LZn6SirOKI1wAAAADA62ora/OFt30hZ846My/teKkkAYb1LetTTDHXX3x9GhY2HPH7M/qEFwAAAGAMLZy6MDe8/YYsrl+cF3e8eMQWdHqLvXlp50upKa/JHzf8cd62+G1H5L7AeDMlyReSXJC+AEPrEbpvMcmGJG1JPpXkE+nrvgAAjFeFQiGfPOeT+dS5n0pbV1s2tGxIsVg8Ivdu7WzN2l1rc8G8C/LFt38xU2qmHJH7AgAAADCy2RNn54Z33JAzZ5+Zl3a+lL3dR2azrGKxmFd2vZIk+cxFn8kHTv3AEbkvY69QPFKrjgAAAHAce2nnS/nM7Z/Jk5ufTH1tfabXTk+hMDYv8rZ2tubVlldzQt0J+ePGP85lJ182JvcBjiY7k/yXJM1JapPMydiFCTrSF5SYnOQ/JPnwGN4LABhtxWIxX3/y6/nKz7+Sls6WzJ88P9UV1WNyr95ibzbu2Zj2rvY0LGjI5y7/XKbWTB2TewEAAABw6Dbt2ZTP/OwzeejVhzKpelJm1c0as+fd7V3tWbd7Xepr63P9xdfnvcvfOyb3oTSEFwAAAOAIaeloyd88/Df5x1/+Y9q72jN/yvxUlVeN2vV7i71Z37I+nd2duXTBpbn+kuuzcOrCUbs+cLTrSvIPSW5Msi3JiUkmjOL1i0m2JNmV5Kwk1yc5ZxSvDwAcSY+sfySfv+/zeWLjE5lSMyUzJ8wc1QfSbV1tWbd7XWZOmJlrz702Hzz9g6ksrxy16wMAAAAwutq72vN3j/9dbn381uzq2JX5U+anpqJm1K5fLBazcc/GtHa15q0nvjXXX3x9VsxcMWrXZ3wQXgAAAIAj7IF1D+Tz934+z2x5JhXlFZlVNysTKg/9BeKunq5saduSPZ17ckLdCfnt8347Hzj1A6koqxjFqoFjxwtJ/izJg+kLHMxIMimH3h2hJ31hiF3p67bw4SSfSFJ32JUCAKXV2tmamx+9Od/8xTezu2N3ptRMyfTa6SkvKz+k6xWLxbR0tmRr69YUCoVcMO+CXH/x9Vk6fekoVw4AAADAWHly05P5s3v+LI9vfDxlhbLMrJuZiVUTD/l63b3d2dq2Nbs7dqe+tj5Xn3N1Pnzmh0d1I0DGD+EFAAAAKIGWjpb84Lkf5LZnb8vz255PV29XplRPycSqiampqBlxR9NisZiu3q60drZmx94dKRaLmTNxTt6z7D25asVVOWnKSUfwmwBHp64kP0ny3SRPJGlNX/BgUpLaJGX7Ob/7tXN2JulMMj3JO5K8L8npY1IxAFA6T21+Kt995rv56aqfZlv7tlSVV2VqzdTUVdXtNzTdW+xNe1d7Wjpbsrtjd+oq63LW7LPyvhXvyzuXvFO3BQAAAICjUHtXe/7fC/8v33n6O3lm6zPZ2703U2r6nnfXVtTut4NnV09XWrtas6N9R3p6ezJr4qy8e+m7c9Xyq2x0cYwTXgAAAIAS6u7tzoPrHsz3n/t+7ll7T1q7WtPR3ZEkqSqvSnlZeQoppJhieou96ezpTIpJeVl56qrqsmz6srx/xftz2cmXZVL1pBJ/G+DoU0zyiyQ/SvLTJLuT7H3tWHmSyvR1ZCi+9tPx2rFC+jorzEtfYOGdSWYesaoBgNLY0rolP1n1k3z32e9m3e51ae1sTbFYTApJdXl1CoVC//ylq6crPb09SZKaippMrpmcdyx+R6445YqcMeuM/T7ABgAAAGD86y325pH1j+SHz/8wd6y5I3s692Rvd9+zpsryylSUVQx+3p2krFCWusq6nDzt5Lx/xfvz9sVvz9SaqSX8JhwpwgsAAAAwTuzp3JMXd7yYF7a9kBe2v5Dntz2f1s7WdPR0pKKsIjUVNTl56sk5ZfopWTp9aZbUL8n02ule+gFGSUeSNUleSLI6yTNJdqWvs0JZkuokc5MsT7LktZ+56QsyAADHk2KxmPUt67Nq+6qs2r4qz257Nut3r09HT0d6i72pKq/KlOopWTFzRRZPW5yl05fm5Kknp7qiutSlAwAAADBG2rvas3rH6qzevjqrtq/KM1ufSUtHS//z7ury6iyYuiDLpi/L4vrFWVK/JLPrZnvefZwRXgAAAAAAAAAAAAAAAMZUWakLAAAAAAAAAAAAAAAAjm3CCwAAAAAAAAAAAAAAwJgSXgAAAAAAAAAAAAAAAMaU8AIAAAAAAAAAAAAAADCmhBcAAAAAAAAAAAAAAIAxJbwAAAAAAAAAAAAAAACMKeEFAAAAAAAAAAAAAABgTAkvAAAAAAAAAAAAAAAAY0p4AQAAAAAAAAAAAAAAGFPCCwAAAAAAAAAAAAAAwJgSXgAAAAAAAAAAAAAAAMaU8AIAAAAAAAAAAAAAADCmhBcAAAAAAAAAAAAAAIAxJbwAAAAAAAAAAAAAAACMKeEFAAAAAAAAAAAAAABgTAkvAAAAAAAAAAAAAAAAY0p4AQAAAAAAAAAAAAAAGFPCCwAAAAAAAAAAAAAAwJgSXgAAAAAAAAAAAAAAAMaU8AIAAAAAAAAAAAAAADCmhBcAAAAAAAAAAAAAAIAxJbwAAAAAAAAAAAAAAACMKeEFAAAAAAAAAAAAAABgTAkvAAAAAAAAAAAAAAAAY0p4AQAAAAAAAAAAAAAAGFPCCwAAAAAAAAAAAAAAwJgSXgAAAAAAAAAAAAAAAMaU8AIAAAAAAAAAAAAAADCmhBcAAAAAAAAAAAAAAIAxJbwAAAAAAAAAAAAAAACMKeEFAAAAAAAAAAAAAABgTAkvAAAAAAAAAAAAAAAAY0p4AQAAAAAAAAAAAAAAGFPCCwAAAAAAAAAAAAAAwJgSXgAAAAAAAAAAAAAAAMaU8AIAAAAAAAAAAAAAADCmhBcAAAAAAAAAAAAAAIAxJbwAAAAAAAAAAAAAAACMKeEFAAAAAAAAAAAAAABgTAkvAAAAAAAAAAAAAAAAY0p4AQAAAAAAAAAAAAAAGFPCCwAAAAAAAAAAAAAAwJgSXgAAAAAAAAAAAAAAAMaU8AIAAAAAAAAAAAAAADCmhBcAAAAAAAAAAAAAAIAxJbwAAAAAAAAAAAAAAACMKeEFAAAAAAAAAAAAAABgTP3/9MGybaBlY1wAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -465,48 +354,6 @@ } ], "source": [ - "\n", - "def plot_quartile_reebs_for_start_end(test_df, start_node, end_node, save=True, use_turns=True):\n", - " fig, axs = plt.subplots(2, 2, figsize=(40,20))\n", - " for q in (1, 2, 3, 4):\n", - " this_ax = axs[(q-1) // 2][(q-1) % 2]\n", - " \n", - " G = do_reeb_for_start_end(test_df, start_node, end_node, q, use_turns=use_turns)\n", - " # print(G.nodes)\n", - " # print(G.edges)\n", - " # pos = nx.spectral_layout(G)\n", - " # pos = nx.shell_layout(G)\n", - " # pos = nx.spring_layout(G)\n", - " # pos = nx.planar_layout(G)\n", - " pos = graphviz_layout(G, prog=\"dot\")\n", - " \n", - " edges = G.edges()\n", - " # colors = [G[u][v]['color'] for u,v in edges]\n", - " weights = [G[u][v]['weight'] * .8 for u,v in edges]\n", - " \n", - " nodes = G.nodes()\n", - " # for n in nodes:\n", - " # print(n, G.nodes[n])\n", - " n_labels = {n: n[:-2] for n in G}\n", - " n_colors = [G.nodes[n].get(\"color\", \"blue\") for n in nodes]\n", - " # print(n_colors)\n", - " # print ((q-1) // 2, (q-1) % 2)\n", - " nx.draw_networkx_nodes(G, pos, node_color=n_colors, node_size=1000, alpha=0.7, ax=this_ax)\n", - " nx.draw_networkx_edges(G, pos, edgelist=edges, width=weights, ax=this_ax)\n", - " nx.draw_networkx_labels(G, pos, labels=n_labels, font_size=26, font_weight=\"bold\", ax=this_ax)\n", - "\n", - " \n", - " this_ax.axis(\"off\")\n", - " this_ax.set_title(f\"Q{q}\", fontsize=30)\n", - " \n", - " fig.suptitle(f\"{start_node} to {end_node}\", fontsize=50)\n", - " \n", - " if save:\n", - " fig.savefig(f'animations/test_by_start-end/{start_node+end_node}/reeb_by_quartile-{\"YES\" if use_turns else \"NO\"}TURN.png')\n", - " plt.close()\n", - " else:\n", - " plt.show()\n", - "\n", "plot_quartile_reebs_for_start_end(test_df, \"A\", \"W\", save=False, use_turns=True)\n", "plot_quartile_reebs_for_start_end(test_df, \"A\", \"W\", save=False, use_turns=False)" ] @@ -521,11 +368,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "fb6ee1bc", "metadata": {}, "outputs": [], "source": [ + "if not os.path.exists(\"figures/testpath-reeb-by-quartile/\"):\n", + " # Create the directory\n", + " os.makedirs(\"figures/testpath-reeb-by-quartile/\")\n", + " \n", "# With turns\n", "# This makes reeb graph based on decisions, egocentric\n", "for start_node, end_node in test_df.groupby([\"StartAt\", \"EndAt\"]).groups.keys():\n", @@ -562,30 +413,6 @@ } ], "source": [ - "## EXPLORE PHASE ONE PERSON???????\n", - "\n", - "def do_reeb_for_explore_node(path, salient_node):\n", - " \n", - " trajs = []\n", - " if path[-2:] == \"NA\": path = path[:-2] # this happens if the test ends unsuccessfully\n", - " \n", - " states = path.split()\n", - " if not use_turns: states = collapse_traj(states)\n", - " for i, s in enumerate(states): \n", - " if salient_node in s:\n", - " trajs.append(states[i: i+10])\n", - "\n", - " G = nx.Graph()\n", - " G.add_node(salient_node + \"_0\", color=\"red\", end=False)\n", - " \n", - " if len(trajs) == 0: return G, 0\n", - "\n", - " # for t in trajs:\n", - " # print(t)\n", - " visited = {}\n", - " make_reeb_graph_recursive(0, salient_node, trajs, [i for i in range(len(trajs))], G, visited, SUCCESS=\"IMPOSSIBLE\")\n", - " return G, len(trajs)\n", - "\n", "explore_df = df[~df[\"eprocs\"].str.contains(\"Test\")]\n", "\n", "salient_node = \"A\"\n", @@ -617,7 +444,7 @@ "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -627,7 +454,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAABYcAAAIeCAYAAADgRrDqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1hT1/8H8HfYSwREFFFBEARF3IqzuPe2zrpqbbWOam2r1llrbd2jam3rrHtUW62r1lVU3AMXsiniQEQ2MsL9/XF/5OtlJpAQIO/X8+TRu875JLkk535y7jkyQRAEEBEREREREREREZFO0dN2AERERERERERERERU8pgcJiIiIiIiIiIiItJBTA4TERERERERERER6SAmh4mIiIiIiIiIiIh0EJPDRERERERERERERDqIyWEiIiIiIiIiIiIiHcTkMBEREREREREREZEOYnKYiIiIiIiIiIiISAcxOUxERERERERERESkgwy0HQARkSrGjBmDHTt2KJbDwsLg5OSkvYBIp2zfvh1jx45VLG/btg1jxozRXkCkM3x8fHDx4kXFsiAIWoyGqGjS0tKwb98+/Pnnn7h9+zZiYmKQnJys2P7ee+/hwoUL2guQSEekpqbi8ePHCAgIwKtXr5CUlAQLCwvY2NjA09MT9evXh4GBdlMFQUFBqF+/PtLS0qCvr4979+6hXr16RS7v1atXuHPnDkJDQxEfH4+MjAxYWFjAwcEBHh4eqFevHmQymdrif/HiBQIDAxEZGYlXr14hOTkZBgYGqFixIhwcHNCoUSNUr15d5XKvXLmC1q1bAwDMzc0REBBQpHKIiN7F5DCRFjk5OSEiIkItZR05cgT9+vVTS1lERERUuinThtDT00OFChVQsWJFuLq6omHDhujZsyd8fHzUmgRRxpMnT9CvXz8EBASUaL1EJLp9+zb++OMPnDt3DtevX0dGRka++5qbm2PIkCH47LPP4OXlVYJR/s+kSZOQlpYGAPjwww+LlBhOSEjAL7/8gt27d+Pu3bsF7mtjY4MBAwbg008/RaNGjVSu68WLF/j555/h5+eHW7duISYmptBj3NzcMGbMGEyaNAmWlpZK1dOqVSsMHDgQv//+O5KTkzF9+nQcPHhQ5XiJiN7FYSWIiEhnjBkzBjKZTPEIDw/Xdkg6Kzw8XPJelOce2Lr0XHVRdqI1+1GaZGVlIT4+Hv/99x/Onj2LlStXokOHDnB1dcWJEydKLI7ExET07NmTiWEiLXj79i1cXFzQpEkTfPvtt7h8+XKBiWEASE5OxtatW9G4cWPMmjWr0P3V7fDhwzhz5gwAwMTEBN98841KxwuCgI0bN8LR0RFffvlloYlhAIiNjcXmzZvRuHFjDB48GM+fP1epzgcPHmDhwoU4ffq0UolhAAgMDMTXX38NDw8PlT6Tv//+e+jr6wMADh06hHPnzqkUKxFRTuw5TERERESkQ0JCQtCzZ08sXLgQCxYs0Hh9v/76K0JCQhTLderUwdy5c9GoUSOYm5sr1puYmGg8FiJdk5mZidDQ0FzrZTIZ6tSpg5o1a8LW1hZJSUl48OCBZF+5XI6lS5ciKCgI+/fvL5GhJuRyOb7++mvF8rhx42Bvb6/08UlJSRg+fDiOHTuWa5uRkRG8vLxgb28PMzMzPH/+HEFBQbkSwQcPHsTFixfx+++/o02bNkV+LnZ2dnB1dYWdnR3Mzc2RkpKCqKgo+Pv7IzU1VbHfs2fP0LdvX+zatQtDhgwptFxXV1cMHjwYe/fuBQDMmjUL169fL3KcRERMDhOVInv37oW3t3eRjrWzs1NzNERERFRW5NWGkMvliI+Px8OHD3H48GEcPXoUWVlZiu0LFy6Em5sbhg0bptHY3r3l2cjICGfPnoWDg4NG6ySi3PT19dGlSxeMHj0aHTt2hK2tba59bt26hc8//xz//vuvYt3hw4excOFCLF68WOMxbt++HU+ePFHE++WXXyp9bHJyMrp27YorV65I1js7O2PBggXo27cvKlasKNkmCAL8/Pzw448/Yt++fYr10dHR6NKlC06cOAEfHx+l6nd0dESvXr3QoUMHtG7dGlWqVMlzv5SUFOzbtw9ff/01Xr58CUBM4o8bNw4tWrRQaj6Vr7/+WpEcvnHjBg4fPowBAwYoFScRUU5MDhOVIlWrVuXkakRElAsn6aLCFNSGaNy4MUaOHInz58+jT58+SEpKUmybOXMmBgwYAGNjY43EJZfLcefOHcWyt7c3E8NEJczY2BgfffQRZs2aVejkZU2aNMG5c+cwcuRIRfIRAJYvX47x48fD0dFRY3FmZWVh6dKliuVevXqpVN/kyZNzJYYnTpyIlStXwtTUNM9jZDIZWrVqhVatWmH06NEYMmQIEhISAIgT9w0ePBj37t0rtPeyj4+P0sOVmZmZ4cMPP0THjh3RsmVLRc/l5ORkLF26FD/99FOhZXh6eqJdu3aKJP4PP/zA5DARFRnHHCYiIiIi0gHt27fH+vXrJesiIyM1Ol5lfHy8YlIpAIUmpohIvUxMTBAcHIz169cr/fenr6+PLVu2oEaNGop16enpOHDggKbCBAAcPXoUQUFBiuUJEyYofeyePXuwfft2ybqpU6di48aN+SaGc+rWrRtOnjwJCwsLxbpXr15hxIgRkrsu8lKUITccHR1z9cY+cuSI0se/+/rcuHFD0tubiEgVTA4TEREREemIkSNH5rqVXJPJ4Xd7KQOAoaGhxuoiotwMDAyK9KOMqakpxo4dK1l3/vx5dYWVp3d7zNrZ2aFz585KHff27VtMnz5dsq5Vq1ZYs2aNyjG0atUKq1evlqw7f/48Dh06pHJZyujRo4dk+eXLl7k+N/PTr18/ybjtyvQ4JiLKC4eVICKNy8rKwtWrVxEaGopnz57BxMQEDg4OaNeuHSpXrlzi8cTGxuLSpUt48eIFXr9+DXNzc1SuXBkNGjRA3bp11V5fdHQ0rl27hufPnyMmJgYWFhbo1q0b3NzcCjzu7du38PPzw3///YdXr15BEARUrlwZtWvXhre3d4lMClKYFy9e4Pr164iKikJ8fDyqVKkCd3d3eHt7QyaTFbncrKwsPHnyBE+ePMHTp0+RmJgIAwMDWFtbo2bNmmjRokWuMeNKA0EQcP36dQQFBeHZs2cwMjJC1apV0aZNm2L1lnv79i38/f3x6NEjxMbGIjk5GcbGxqhQoQJq1KgBV1dXuLm5Fes1L4ux5PTy5Us8evQIISEhiIuLQ3p6OqysrGBra4smTZrAxcVF7XVmZWXh+vXrCA0NxYsXL5CWlgZHR0cMHz5c7XWpi6bO02y3b9/GgwcP8OLFC5iamsLBwQGNGjVCrVq11BB90WVkZODx48d48uQJnj9/jsTERJiYmMDa2hrOzs5o3rw5zMzMtBpjSdDT00OzZs1w8uRJxbrIyEiN1VdYbztVlZXv8OJKTU2Fr68vnj59iujoaFhbW8PR0RE+Pj5qm7jv+fPnuHbtGqKjo/H69WtYWFjAzs4OzZo1g7Ozs1rqyFaUz8o3b97g7t27CAoKUvRANzU1hZWVFRwdHeHh4VGsIUpCQ0Nx8+ZNvHr1CvHx8bC2tkaVKlXQsmVLlSZAU1ZgYCDu3buHp0+fIjMzE5UrV0aTJk1Qv359tdelLo0aNZIsP3v2TGN1RUVF4Z9//lEsDxgwAPr6+kodu2PHDkRHRyuWDQwM8MsvvxS5LTJu3Djs2LEDly5dUqxbvnw5Bg8eXKTyCmJjY5NrXWJioqT3cn5MTU3Rq1cv7N+/HwDwxx9/ID4+vlS2kYmolBOISGscHR0FAIrH+fPn1Vr+X3/9JchkMkX5ZmZmwv3795U+ftq0aZL46tevLyQnJ+fa7/z585L9FixYIAiCIKSmpgqLFi0SatSoIdme/dDX1xe6d+8uPHz4UOmYRo8eLSkjLCxM6WOPHz8utG7dWtDT08szHgBCzZo1hW+//VZISkpSutx330dHR0fF+n///Vfo1KmToK+vn6ue1atX51ve1atXhd69ewumpqb5xmlpaSl88sknQlRUlNJxFsV7770nqffdGHv27JnncwMgVKtWTVixYoWQkZGhdF2xsbHCli1bhD59+ghWVlb5PncAgp6entCuXTvh2LFjhZa7bdu2AsvK7/Hue5lfWdu2bRMEQRDkcrmwcuVKoVatWvmW17ZtW+Hq1atKvx6CIAjBwcHC6NGjhQoVKhQab8WKFYW+ffsKf/zxh0p1lHQsOT/3lH1kv9bvyszMFM6ePStMnDhRcHV1LbSM6tWrC0uWLBHi4uKUft75vecpKSnC7NmzhWrVquX5/NX9XPP7W1QlZk2dp9llb9q0Kd/nLJPJhHbt2gn//POP4pj8PjvV6dmzZ8K6deuEzp07C+bm5gW+7gYGBkKvXr2Ef//9t9ByFyxYUKT39r333lPbcytOG2L48OGSYzt37qy2uAQh9/mqjtemrHyHKyvnOZT9/kVFRQnjx4/P97PWwsJCmDBhgvD69esi1Zueni789NNPQv369Qt8P1xdXYWff/5Z6e/x4nxW5nTu3DmhS5cu+bYx3n04ODgIH330kXD37l2l4kxLSxN+/PHHQr8zGjVqJOzZs0fIyspSqtywsDDJ8aNHj1ZsO3bsmODt7Z1vXc7OzsKuXbuUqqekHTt2TBKrh4eHxupauXKlpK7jx48rfay7u7vk2D59+hQ7niNHjuR6ry5dulTscnMKDAyU1GFkZCSkp6crffzu3bslx2/dulXtMRJR+cfkMJEWaTo5LAiCMGfOHEkdbm5uQkJCQqHHHTx4UHKcpaWl8OTJkzz3zSs5/Pz5c6Fhw4ZKXRAaGhoKP/30k1LPpyjJ4YSEBKFHjx4qXaTa29srnSDJ68Jy3rx5BV7A5nVhmZycLAwbNkylOM3MzISDBw8qFWdR5JWQ2rBhQ4HP7d1HkyZNhOjoaKXqKuxCNb/H0KFDC0wEaDo5/PLlS6UTIYaGhsLu3buVej127dolGBsbqxx369atlSpfFeqMRZ0J05wXkso+nJychFu3bin13PN6zx89epTrQvTdR2lMDmvqPBUEQYiPjxfatWunVNl6enrCwoULc70+mkoOK/NjRl6P6dOnF5gUK+vJ4V69ekmO7d27t9riEgT1JofLyne4qvJKDl++fFmwsbFR6jlWrlxZ5R9ybt68KTg7O6v0WjZs2FB4+vRpoWUX57MyW1ZWljB16tQi/W3NmTOn0BgfP36s1A+J7z5atmwpvHjxotCy80oOZ2ZmCpMnT1a6rkmTJimdjC4pq1evlsTYoUMHjdXVoUMHRT36+vpKXa8IgiCEh4fnei2PHj1a7HgyMjKEKlWqSMqdN29escvNae7cuZI62rdvr9Lxz549kxw/YMAAtcdIROWf9u9JJiKNWrRoEa5du6a4TSswMBDjxo0rcEKJJ0+e4MMPP5Ss27p1q9K3UKalpaFnz564e/cuAPEW1iZNmsDJyQnp6el4/PgxAgMDFftnZGRg4sSJkMlk+OSTT1R8hgWLi4tD+/btFbFkMzY2hre3N+zt7ZGQkIB79+4hKipKsf358+do3749/vzzT6XHO8u2Zs0afPvtt4plR0dHeHp6wtLSEi9fvpTM2p7t1atX6NatG27fvi1Zb2pqikaNGqFatWrQ19dHZGQkbty4gYyMDABASkoKBg8ejM2bN+d6zzTh4MGDmDx5MgRBACCOB9e4cWNYWVnh5cuXuHr1KlJTUxX737p1C+3bt4evry+sra0LLDvnrcc2NjaoW7cubG1tYWFhgeTkZISHh+PBgweK5w8A+/btQ1JSEo4ePVriQxmkpqaiV69euHHjBgDxvGratCkcHBwgl8vx+PFjPHr0SLF/RkYGxowZA09PT3h5eeVbrq+vL0aOHKl4nbO5ubnB1dUVFStWhFwuR0JCAoKDgxESEqL2W7dLYyw55azHyMgI7u7uqF69uiKuV69e4d69e4iNjVXsFx4ejg4dOuDWrVsqDzURGxuLXr16ITQ0VFFns2bN4ODggPT0dISGhuK///4r/pNTI02dp9lld+rUSVF2NmNjY7Ro0QL29vZITk7Gw4cPERYWhqysLCxcuBDVqlVT/xPNQ85zpEqVKvDw8ICNjQ3MzMyQmJiI4OBgBAQEQC6XK/ZbvXo10tPTc03eVl7k/K5R9xAC6lJWvsPVITg4GF9++SXi4uIAiN//3t7eqFKlCuLi4nD79m3JbfOvXr1Cly5dcPbsWTRt2rTQ8v/66y8MGTIEKSkpkvX29vZo0KABbGxskJycjEePHkkmBLt79y5atGiBq1evqjTsTFE+K5csWYJ169ZJ1hkYGMDLywuOjo4wNzdHamoq3rx5g4CAAJWGOLh9+zY6d+4s+S4AgEqVKqFp06awsbFRDCHy7nivfn5+aNmyJS5evCiZnE0Zn332GTZs2AAAkMlk8PLygrOzM4yNjREREYEbN24gMzNTsf+GDRtQr149TJw4UaV6NCnnOLvNmzfXSD3Jycnw9fVVLDds2BAVKlRQ6tick7Dp6+ujY8eOxY7JwMAA7du3x759+xTr3o1RHY4cOYJly5ZJ1n3xxRcqlWFvb4/atWsjODgYAHDmzBnI5XKlh+QgIgIA9hwm0qKS6DksCIIQHR0tVK9eXaleL8nJyYKnp6dk388//7zA8nP2HLa2tlb8f9iwYXn2OLl+/brQpEkTyXFGRkbCgwcPCqxL1Z7D77//vmR/Q0NDYd68ebl6I2RlZQl//fVXrvfE1ta20KEb3j3G1NRUMDAwUPQ2yatXz9u3byVlyuVyoWPHjpJ6q1WrJmzdulV4+/ZtruPfvHkjzJ49W9KrycTEROlbKlWRs/dX9ntbpUoVYf/+/UJmZqZk/6SkJGHRokWCkZGR5LgRI0YUWpenp6fQpk0bYf369UJoaGi++8XGxgrLly/P1SMwv97niYmJQlhYmBAWFiYMHDhQcoyvr69iW85HZGRkrrJy9oyytbVVvO/Lli3LswfztWvXhNq1a6vU8ybn7aeDBw8WQkJC8t0/MTFR+OOPP4QRI0aovVePumOJjIwUwsLCBF9fX0m5AwcOzPe9CAsLExITE3OVtXz5cqFq1arCzJkzBV9f33x7esrlcuH06dNCo0aNJHW2aNGi0Oef8z3PPu9MTEyEJUuW5BlX9vmrzudanJ7DmjpPBSH38EMGBgZ5fsYKgiBcunRJ8PLyUrx+7w7zoMmew926dRO2bNkiPHv2LN/9nj17Jnz99de5PrtOnjyZ5/5v3rxRvF8tWrTI9b2U3+P58+dqe25FbUP8+eefuXrZHTlyRG1xCYIgPH/+XPGclT3/83ptysJ3eFHl7Dmc/f1qZGSU59AYmZmZwr59+3L1ZHR1dRVSUlIKrOvhw4e5hlXp1q2bcO3atTz3v337ttC2bVvJ/m3atMn1nf+u4nxWCoIgxMXFCSYmJorj9fX1hYULFwpv3rzJt85nz54JW7duFXx8fIS5c+fmu19CQoLg4uIiic/Ozk7YtWtXru+N1NRUYc2aNYKZmZlk/7Zt2xb4/HP2HM7+3AUgfPTRR3m2KZ4+fZqrV7ylpaVKw6Jo0vXr13N9VuR3zhTXv//+K6ln3LhxSh87YcIEybGenp5qi2v58uWSsk1NTQW5XF7k8lJSUoTQ0FDhwIEDQu/evXO9vpMmTSpSuYMGDZKUc+/evSLHSES6iclhIi3KeRGzd+/eAi8q83u8fPmy0Lr8/PwEQ0NDyQXW5cuXc+33wQcf5LoYKGy8uZzJ4ezHjBkzCjwuOTk5z4uPgqiSHM55Aayvry8cPny4wPKfPXuWK0EyaNCgAo/J69bxXr16CWlpaQUel23ZsmWSYxs3bizExMQUetzBgwclY0qrehuaMvK6Nbhq1apCUFBQgccdOXIk11iB7441mhdVxo8WBEG4e/euULFiRUX5zs7OhTbYizNmtSDkPUSFubm5cP369QKPi4iIkCSzZTJZvq/hy5cvJeX7+PiodJtpYUkCVWgyloLGZ1RWVFSUSuPyvX37Vujatauk3sISanm950ZGRir9mKeO51qc5LAmzlNBEBNO7/5IpaenJxw6dKjAOhISEnIlUwHNJYdV/Rs/c+aMIjkIKDcMhCrvjToVJTn86NGjXMnFmjVrKv19VRRFPf/Lynd4UeU1NIm+vn6hifrAwMBc72FBiVG5XJ7rR//soV0KkpGRIQwYMEBy3I4dO/Ldv7iflfv375ccmz1/hbIK+r6ZPn16rnZMYGBggeVdvHgx19wP69evz3f/nOd59mPjxo0F1pORkZGrs8TmzZsLfrIlID09PdcPqm3bttVYfatWrZLUtWbNGqWPzTlMzpAhQ9QW18mTJ3O9p6qM9/1uO7Wgh6GhobBo0aIiDyuyaNEiSXm//vprkcohIt3F5DCRFhV1PMqcj759+ypV37p16yTHOTg4SMaD3bBhg2R7lSpVlOodk1dyuEmTJkr9sh4ZGSlYWFhIji1oLFBVkns5x8AsLFmd7fr167kSHgX1ZM35PlauXFnphmNKSopQuXJlxbEVK1YssHdbTp9++qnSr11R5JUcVnbCs5zjBqp7TEtBEIQVK1ZI6ihsohBNJId/+eUXpY794osvlDouZ0+dH3/8UaUY1UmTsagjYVoUz549k/xQ9tFHHxW4f17v+aJFi1SqszQkh9V9ngpC7s+fiRMnKlVHcHBwrqSLppLDRZFzjNDCxlstzclhuVwuvH79WvD19RWmT5+eqzeknp6eSpM+FUVRz/+y8B1eHHklh6dNm6bUsYcPH5YcZ2trm+edRoKQew6JwYMHKx1jYmKiYGdnpzi2fv36+e5b3M/KnD+UqzKBckHi4+Nz3WmkzGS2gpC7jeHi4pJv2zav5PDw4cOVquevv/4q0nGa9Nlnn0liMjQ0FO7cuaOx+j755BNJfarczdCqVSvJsRMmTFBbXH5+frne18J+WHhXYcnhChUqCLNnzxYiIiKKFeeOHTsk5X755ZfFKo+IdI8eiEhnTJkyBUOHDlUsR0VFYdiwYcjKysKNGzcwffp0xTZ9fX3s3bu3yONCLly4EHp6hX/EVK9eHR9//LFk3fbt24tU57vCw8MlY5CZmppi3rx5Sh3brFkzDBgwQLGclZWFnTt3Kl33xx9/DBsbG6X23bt3L169eqVYnjZtGuzt7ZWua8aMGZLlo0ePKn1sUTRp0gR9+/ZVat/58+fDyMhIsXz8+HHJc1WHgQMHSpavXLmi1vIL4+DggLFjxyq1b69evSTLyo5b+e4Yk9pWmmIpKnt7e7Rq1UqxrOo5Y2ZmhqlTp6o7LI3SxHkqCIJkHEY9PT18/fXXStXh4uKC4cOHK7WvNmj7c6Wo2rdvD5lMJnno6+ujUqVKaNu2LVavXi0Zb9bU1BQ7d+5Ejx49tBh13srKd7g6GRkZYe7cuUrt279/fzRu3FixHBMTg+PHj+e577tj+MpkMvzwww9Kx2RhYSGZC+L+/fsIDw9X6tjiflaq6/vm8OHDSExMVCy3bNky1+dcfj777DNJmywkJASXLl1Sum5lz9kuXbpI2kuaGtdaWVu3bsXatWsl6xYuXIiGDRtqrM6c55Wq41u/y8rKSg0R5V9WzvqKIzExEZs3b8aPP/6Ily9fFrmcnK+Xsn+nRETZmBwm0jGbN29G3bp1Fctnz57FtGnTMGjQIKSnpyvWf/fdd2jfvn2R6rC2tka3bt2U3j9nkkCVhnd+cpbRq1cvVKxYUenjR40aVeSY+vXrp/S+Z86ckSwPGTJE6WMBcRKhmjVrKpbVPVFGTqokdCpVqoQuXboolrOysuDn56dynXK5HPHx8Xj69CnCw8Mlj3cnkAKAx48fq1x+cXTt2hUGBsrN7erh4SFZzu/C19XVVTKJyE8//YSAgICiB1kMpSkWVaWkpODly5eIiIjIdd68OzliQEBArsn2CtK+fXuVPktKA02cp0+ePJFcILds2VKli/nBgwcrva8mZGZmIi4uDpGRkbnOj5wTW5b054qmVaxYERMnTsTjx49LbZK+rHyHq1PXrl1RqVIlpfdXpu2UnJyMq1evKpabNWuGWrVqqRRXzragsu0MVT8r3d3dJctz5syRTAxXVDlflw8++EDpYw0MDDBs2LACy8uPs7NzrueUH0NDQ8nkqNr8IfbUqVOYMGGCZF2vXr0we/Zsjdab8zkXNolxSSnuRMf+/v4ICwtDWFgYQkNDcffuXfz111+YM2eOov3+6tUrrFixAnXr1sXJkyeLVE/O16s8/JhPRCVLuSsFIioR58+fh4+Pj0brMDc3x++//45mzZopGt0//vijZJ8+ffrgq6++KnIdTZo0UToRAQANGjSAiYkJ3r59C0BsSKWnp0t6Uajq5s2bkuV3ewoqI+f+N27cUOo4fX19eHl5KV3PuxcZRkZGMDY2VvnXfhsbG8WM3yEhISodq6oWLVqovP9ff/2lWL5x4wb69OlT4DFv3rzBkSNHcPz4cfj7+yM0NBRZWVlK1ffmzRuV4iuud39oKUzOhnt8fHye+1lZWaF79+6K1y0mJgaNGjXCiBEjMHjwYLRr1w4mJiZFD1oFpSmWgmRlZeHChQs4dOgQbty4gUePHkl6SBZ2bEJCgtJJjEaNGhUnVK3QxHl67949yXLTpk1ViknV/YvrxYsXOHjwIE6fPo379+8rPjOVUdKfK5qWkpICPT09VKlSRduh5KusfIerU1G+X9+V13O8evUqMjIyFMvOzs4qtzFyfv8q285Q9bOyY8eOsLOzUyS1rl69CldXV4wbNw79+/dHo0aNlLojLSd1nEurVq1SLCt7LqnyuQtIP3vz+9zVtMuXL2PgwIGSc6ZNmzbYv39/sZOkhcn5nW1qaqr0sTl7+qvz9YuLi8u1ztLSUunj3+3Aka1Bgwbo2bMnFi5ciKVLl2L+/PnIyspCbGws+vbti5MnT6Jjx44qxWlmZiZZTk5OVul4IiImh4l0kLu7OzZv3iwZYiKbi4sLduzYUaxGoJubm0r7GxgYwMnJSdEjUS6X4/Xr1yoNr5BTzl/MVY2pUqVKsLW1RUxMDACxcZiRkQFDQ8MCj6tYsaLSSe2srCw8e/ZMsZyeni7pOVIU6rzVLS+qvo6urq6S5YJ6MsjlcixfvhzfffddkXsLlfQFlSo9W3KeO+9efOW0Zs0aXL16VXH+vX37Flu2bMGWLVtgbGyMpk2bwtvbG23btkXbtm01egt0aYolL76+vpg8eTL8/f2LXEZ8fLzSyWE7O7si16MtmjhPs8+HbI6OjirFZGNjAwsLC7X0DCxISkoKFi5ciLVr10rujlGFthI1qtq7dy+8vb0l65KTkxEaGopTp05h27ZtSE1NRUZGBjZs2ID79+/j1KlTKiVhSkpZ+A5XN018v0ZGRkqW9+3bJxkOpiiUbWeo+llpZmaGTZs2YdCgQYqE9IsXL/Ddd9/hu+++g5WVFVq2bImWLVuibdu28Pb2VurHyeKeSzl7/yrbI1PVnq/vnpuZmZkqHasOt27dQs+ePSVJ2ubNm+P48eO5Eo+akPM5q9LJJGe7Q50/6OVVlio9/AtiYGCAOXPmoEKFCvjss88AiN+5o0aNQnBwsEqfzTlfr4LamEREeeGwEkQ6asiQIXn2nti5c2exx+pS5Rf1bDkTM8VNcuZszBXlNvCixFShQgWly3/z5o3SPWKV9e64epqg6nur7GuYmZmJoUOHYvbs2cVKFqn79SxMUXoxKcPFxQXXrl3Lc2iXtLQ0XL58GStXrkS/fv1gZ2eHzp074/fff1dpeISyGEtOhw4dQseOHYuVGAZUO29U+RsvLTRxnubsTVWU16Uo3xWqSEpKQrdu3bB8+fIiJ4aBkv9cKaqqVavCyclJ8qhXrx569+6NDRs24N69e5IhBf79999ct4+XFmXhO1zdNPH9+vr162LFlBdl2xlFeS379++PU6dOoXbt2rm2xcXF4eTJk5g/fz7at2+PypUrY8SIEYX25H33XDIwMFA50VnU9qmm2gea4O/vjy5dukh+CGvUqBFOnz6t8c/pbDkToampqUof6+DgIFl++PChWmLKq6xKlSqp/UfiKVOmSMYQf/bsGXbt2qVSGTlfL3Nzc7XERkS6o+x8axGRWu3evTvPSXa2bNlS7LLVcetZccvImZgqDTHlVJxkRVmV32u4evVqHDp0SLLO29sby5cvx/nz5xEcHIz4+HikpaVBEATJo7xydnbGuXPn4Ofnh0mTJuXqJZZNLpfjn3/+waBBg9CqVStERESU61iyRUREYPTo0ZLeMXZ2dpg+fToOHz4Mf39/xMTEICUlBVlZWZJzZvTo0RqLS1cYGxtLlovyeabpz8CZM2dKxkeVyWTo3Lkz1q5di0uXLiE8PByJiYlIT0+XnB9hYWEajUtbXF1d8ddff0mSBr/99ht+//13LUaVt7LwHa5uqsaXc/+8jtfE35imv3c7d+6Mx48f4/Dhwxg6dGi+w58kJSVhz549aN68OT788EOlkom6cB6p6tGjR+jUqZMk6e3p6Ym///5brRO7FSZnElqVYRFydnZRZWipwuQclqR+/fpqKfddMpks192cp06dUqmMnK9XSSX1iaj84LASRDro4cOHktmn37Vlyxa0adMGY8aMKXL5RbkFN+cxxZ2IQh3jj6k7ppxy3pbm5uaGJ0+eqLUOdUtISEDlypWV3l+Z1zA9PR3fffedYlkmk2Hbtm1KJfASEhKUjqWs8vb2VtwqHh0djatXr+LSpUs4f/48bt26JblQv3r1Kjp06IBbt25p5KKuNMWydOlSycVf7969sXfvXqV6y+jCeaNpOf+WVb2NNysrS6PDNTx//hybNm1SLJuYmODPP/+UTJKZn/J8ftStWxeLFi3CjBkzFOumT5+OHj16lKrhJcrCd7i6qfocc/bez+v52draSpaXLFmi8YnF1MHAwAD9+/dH//79AQDBwcHw8/PD5cuXcebMGYSGhkr237ZtG+Li4nD48OFcZVlbW+P58+cAxFvtU1NTVTrXy9p5pIonT56gY8eOePXqlWKdu7s7/vnnn1znjqbVqFFDsvz8+XOlx21u166dZFkul+Ps2bPo3bt3sWLKzMzE+fPnJetUmXBbFXXq1JEsBwcHq3T8u8PUAblfTyKiwrDnMJGOSUpKwqBBgyS/MPfp00dy+9unn35arNu0AwMDVdo/MzNTMkGKvr5+scctzXnLl6oxxcbGSsbUtLKyKnSsQlUZGRlJLjLCwsJK/Rhhqr6OQUFBkuW8bsW7ePGi5OJr5MiRSvfszNkYLu/s7OzQp08fLFu2DDdu3EBkZCTmzJkj6cUZGhqKFStWlPtY/vzzT8X/LSwssGvXLqVvo9S180YTco4xfP/+fZWODwgI0Ojn3V9//SUZDuKrr75SKjEMlP/zY8qUKZLb9iMjI7F+/XotRpRbWfgOVzdNfL/m7HWrah2lRe3atTFy5Ehs2rQJISEhuHPnDoYNGybZ58iRI/jnn39yHVvccynnj/Zlcdz5vAQHB6NDhw548eKFYp2rqyvOnTunlckqnZ2dJctPnz5V+lgnJ6dcyVV13Al5/PhxvHz5UrIu+wcLdcv5+ZSWlqbS8VFRUZLld4cQIiJSBpPDRDpm3LhxionfAHF26CNHjmDevHmKdampqRg0aFCRe0/dunVLpck07t27h7dv3yqWvby8ct2yrKqmTZtKlvMaQqMgOfdv1qxZseLJz7u3wmVkZODChQsaqUddrl69qtL+165dkyzn9TrmvMDt1auX0uWr+r6Wt9tBHRwcsHjxYuzcuVOy/siRI6U+luK8FykpKZIEXrt27ZS+hfLt27e4c+dOkesuivJ23gHiREXv/qj477//Qi6XK318zt5Y6sbPlfwZGhrim2++kaxbunSpxsesV0VZ+Q5XJ018v7Zs2VJyfp45c6ZcDMfUsGFD7NmzB59++qlkfV7fN7p4LhUmLCwMHTp0kHyPZg8fVZzJoIujQYMGkuV3r1WUMW3aNMny8ePH8ejRoyLHIwhCrh+3O3furPKEhsrKmQxXNUH/+PFjyXLDhg2LGxIR6Rgmh4l0yLp163DgwAHFsoODA/bs2QM9PT3Mnz9f0qsqKCgIY8eOLVI9b968UWmsrD179kiW27RpU6R6Cyrjr7/+UinZnTPBpY6Y8tK1a1fJ8q+//qqRetRl7969Su/7+vVr/P3334plPT09tGzZMtd+OW+NVWXioe3btyu9L5B7nFRVe2aUVoMGDZK8btocM1XZWIrzXhTnnNmzZ0+Jj/ddHs87S0tLSZLk+fPnOH78uFLHCoKg8c+6op4jcrlc5YmAyuL7O3ToUElPu9evX2PdunVajEiqrHyHq9Pff/+t0gRyyrSdKleujEaNGimWo6KicPLkyaIHWcqMGzdOspzX903O12X37t1Kly+Xy3O1e8rCuVSQ//77Dx06dEBkZKRinaOjI86dO4fq1atrLa6cSfd79+6pdPzo0aMlQ2FkZmZi/PjxRf4xZMuWLbh06ZJk3bsdadTt3fYygHzndshPzjs+mzdvXuyYiEi3MDlMpCOuXr2KL774QrFsaGiI/fv3K26P09PTw+7duyUNw8OHD2PVqlVFqm/hwoVKzfD+9OlT/PLLL5J16pgsysnJCW3btlUsp6SkSMa1LcitW7ckE/To6enhgw8+KHZMeRk5cqRkPNaDBw/i7NmzGqlLHW7duiW5nb8gixYtkiThevToked4xTnH71O2t8iff/4pmWxKGTnHvs0eh7Csk8lkkl6cxe15XxKxFOe9KOo5Ex8fj2+//VbpetSlvJ53EyZMkCzPmDFDqUmENm7cqPKFv6qKeo6sX79e5R9XyuL7q6enh7lz50rWrVy5UqPjQKuirHyHq1N6ejoWL16s1L5HjhzB7du3Fcu2trbo2bNnnvtOnjxZsvzFF18gKSmp6IGWIgYG0ulz8vq+GTBgACpUqKBYvnz5stIJ8nXr1uXqXVuWk8PPnj1Dx44dJUO5OTg44Ny5c7mGCippzs7OknFyb9y4oVJi19TUNNc1y5UrV3L1KFaGn58fpk+fLlk3btw4yWeSOvn7++fq9d63b1+lj8/IyJDcEeXp6ZlrXhMiosIwOUykA2JiYjB48GDJ+I7Lli1D69atJfvZ2triwIEDknGvZs6cicuXL6tc561btzBz5swC90lNTcXw4cMlFymtW7dGkyZNVK4vL+9OuAMAq1atwrFjxwo85uXLlxg+fLjk9uj+/fvnGgtNXaysrPDll19K1g0aNChXb4XCyOVyHD58WDLbtKZMmDABISEhBe7zxx9/YMOGDZJ1n332WZ775ryVcP369YUmmG7evFmknu0eHh6S5Zw9NUqDCxcuqPwDwV9//SWZECzn8yyNsZiYmMDJyUmxfOPGjVy9PfNjamoq6VVz586dQt/LlJQUDB06VHJRXFKK81xLs6FDh0rGrg0ODkbPnj0L7P24e/fuIl2sqyrn58qKFSsKHfbi5MmThX5v5aUsfK7kZdiwYZJbpN+8eYPVq1drMSKpsvAdrm4//vhjoT/ABgcH5xpO4ZNPPsn3h7hRo0bB3d1dsfz48WP0799f5UkkX716leeEb+qye/fuXLfGF+a3336TLOf1fWNpaZmrh/G4ceMKbcdcvnw51w8o06dPl/z4WZZER0ejY8eOkonO7O3tcf78+VLz99G9e3fF/2NiYvDgwQOVjh85ciRGjhwpWbdu3TpMmjQJqampSpVx+vRpdO/eXXJtUrduXaxZs6bA48LDw7Ft2zaVhlcCgJCQEPTp00dyXO3atXPdWViQGzduSNrN+f1QRERUIIGItMbR0VEAoHjs3btXCAsLK9Lj5cuXedYhl8uFLl26SOoZOHBggXGtWbNGsn+1atXyLV8QBOH8+fOS/a2trRX/Hz58uPD06dNcx1y/fl1o0qSJ5DgjIyPh/v37BcY2evRoyTFhYWEF7j9w4MBcdXzzzTdCYmKiZL+srCzhxIkTQq1atST729jY5Bn/u959Hx0dHQvcNy8ZGRm53iN9fX3h008/FQICAvI9Lj09Xbh8+bLw1VdfKWIo7PVQ1XvvvZfne1u1alVh//79glwul+yflJQkfPvtt4KRkZHkuOHDh+dbR2ZmZq6/hZYtWwqPHj3KtW9iYqKwdOlSwdzcXAAgVK5cWXLce++9V+DzCQsLE2QymeR8mDt3ruDr6ysEBgZK/qYiIyNzHb9t2zZJfdu2bVPqdcymTKyrV68WAAgeHh7CggULhNu3bwuZmZl57vv27Vvh559/FipUqCApe9OmTSrFlR9NxzJ27FjJvi1atBD27dsn3L9/XwgNDZW8Hzn/ZufOnSs51tLSUti1a1euczIrK0s4c+aM4OXlpdg353lT0N9Ncd9zdTxXQcj9t1iQkjhPs126dEnQ09OTHFOpUiVhzpw5wvnz54WAgADh9u3bwm+//SZ07txZsU+bNm0EBweHYn12FiQ2NjbXudi7d2/hv//+y7VvTEyMMHPmTMHAwCDP82P06NEF1nXx4sVc5+LSpUsFPz8/ISgoSPLePn/+XG3PMefn5vnz51Uu47fffssVe2xsrNpiFATxc1eV1/NdZeE7vKgWLFiQ5/erkZGRsHjxYiEpKUmyf2ZmprB//36hSpUqkuNq164tJCcnF1jXgwcPBEtLS8lx1atXFzZu3CgkJCTke9zr16+Fffv2CUOHDhWMjY0L/Dwo7udO3759BZlMJvj4+Ajr168v8HM5OjpamDFjhuT7XE9PT3j48GGe+8fHx+c6N6pWrSrs3bs313daamqqsG7dOsHCwkKyf6tWrfL9/hOE4p3ngqDaZ7yq3rx5I/kOBCCYm5sLJ06cKNI1h6acPHlSEuPKlStVLiMxMVFo0aKFpBwAgrOzs/Dbb78J8fHxuY7JysoS/Pz8hGHDhuU6ztXVVXj27Fmh9d65c0cAINSqVUuYO3eucOfOnULPl3nz5glmZmaS+vT09IQzZ86o9Jy/+eYbSRl+fn4qHU9EJAiCwOQwkRblvLArzqNv37551jF//nzJfm5ubnk2jHJ6//33Jcd16NAhV9IlW87k8KxZs4SGDRtKGjotWrQQBg8eLPTr10+oU6dOns9BmYSWqsnh2NhYoX79+rnqMjExEXx8fIShQ4cKPXv2FKpXr57nPidPniw0JnVcWMbFxQmtWrXK83VxcHAQunbtKgwbNkwYMmSI0L17d6F+/fqCoaFhrn01nRzet2+fZLlKlSpCjx49hGHDhgkdOnTI1cgFINSrV094/fp1gfXs378/13EymUxo0KCBMHjwYGHIkCFC27ZtBWNjY8V2AwODXBcShSWyBCF3siG/R17vZUkmh999mJmZCc2bNxd69+4tfPDBB8KQIUOE1q1b5/l6v/fee/n+rapK07HcuXNHkZAr7JHztX7z5o1QrVq1XPtVrVpV6NWrlzBixAihe/fugr29vWT7iBEjVPocUVdyuDjPVRBKb3JYEARh165dgr6+vlLPDYBgb28vREVFST47nZycVIpRGUuXLs1Vt76+vtC8eXNh6NChwqBBg4QWLVpIYrewsBCOHDkiOUaZJE/Tpk2Veu7KvJ7KUkdyODMzU3B1dZWU8/XXX6stRkEoXtKsrHyHF0XO5PDPP/8sVKxYUfJZ26FDB2HYsGFCjx49ciWFATGZf/36daXqO3PmjKT8d/8mGjZsKPTp00f44IMPhAEDBggdOnTI8zXVdHI4Z32VKlUS2rVrJ/Tv318RW8OGDfP8vJk7d26B5d+4cUOwsrLKdVzlypWFbt26CcOGDRM6d+6c60el7PMiIiKiwPJLc3I4Zzu9uA9NyczMlHxnt2rVqkjlxMfHCz169MgzdmNjY6FZs2ZCnz59hCFDhgg+Pj55tiUACD169BBevXqlVJ3ZyeF3H6ampkKzZs2Enj17CsOHDxcGDx4sdOnSRahRo0ae9enp6Qm//fabys/33c/IOnXqqHw8EZEgMDlMpFWaTg6fOnVK0qPLzMxM8Pf3Vyq2hIQEwc3NTVLHnDlz8tw3Z6NzwYIFwrNnz/K8oMvrYWhoKPz0009KxaVqclgQxEZi165dVXo9q1atKly+fFmpmNR1YZmeni58/vnnKiVZ3n2YmZkp1btBFXldrKxbty5XT8H8Ho0bNxaio6OVquvbb7+V9AIq6GFqaiocPHhQEATVE1mvX78WWrZsWWgdpSk5rOyjX79+uXqbFUdJxLJ161bBxMSk0PLyeq1v374t2NnZKR3TiBEjhPT0dK0kh4v7XEtzclgQxO8bJyenQp+bj4+P4nPq3feufv36KsWorPHjxyt9flSqVEm4ePFikZI8ISEhgru7e6F1lLbksCAIwvbt2yXlWFhYKJ0QUUZxk2Zl5TtcVTmTw+fPnxd8fX3zTGDm9ahcubLKPQSDgoKU/iEjv8/1/GgiOazMw8DAQPjmm2+UquPhw4eCi4uLSuU3b95cqR7/TA6rx8yZMxX1yGSyPO/iUkZWVpawbt26PH8QKexRs2ZNYevWrUJWVpbS9eWVHFbl4eHhIVy8eFHl5xkQECAp54cfflC5DCIiQRCEsjloEhEVKjIyEiNGjJBMCvfTTz+hfv36Sh1foUIF/P777zAzM1OsW7JkidKz0dvb2+Pq1auYP38+HBwc8txHX18f3bp1w927d3NNbKROlpaWOHXqFI4ePYqWLVsWOF5c9erV8c033yAoKAitWrXSWEx5MTQ0xMqVKxEYGIiJEyeiSpUqhR5TqVIlDBgwANu3b8fLly9hb2+v8TinTJmCf//9F126dIG+vn6e+1SrVg3Lly/HtWvX8pyELi9z587FiRMn0Lhx43z3sbS0xOjRo3H//n0MGjSoSPHb2NjA19cXf/zxB0aNGgUvLy/Y2NhIxtrWprFjx2L37t0YMWKEZHKW/BgYGKBbt244ceIEjhw5AnNz8zIVy9ixYxEQEIBFixahU6dOqF69OszNzSGTyQo9tlGjRrh79y5GjRqV7/snk8nQpk0bHDx4ELt27dLq+1yc51rade3aFY8ePcLWrVvRq1cvODo6wsTEBNbW1vD09MTo0aNx9uxZnDt3TvE59e64yxUrVtRIXL/88gt27dpV4MzvlStXxuTJk/Hw4UO0a9euSPU4Ozvj7t272LVrFwYPHgwPDw9UrFgx14RZpdEHH3wgGTs6KSkJy5Yt02JEUmXlO1wd2rRpA39/f4wZMwYWFhZ57mNhYYFPPvkEAQEB8Pb2Vqn82rVr4/r16zh27Bg6deqk1OSlHh4emDJlCnx9fTU65vDatWuxbt069OjRI9eEknnJbg/4+/tj/vz5StVRt25dPHz4EGvWrJGc83lp0KABdu7cCT8/P1StWlWp8qn4Jk+erPieFgQBW7duLVI5MpkMU6ZMQUREBJYtWwYvLy+lj/31118xduxYlb6b3d3dsX//fnz44YdwcXFR6hgjIyN069YNe/bswd27d4v0/fPrr78q/m9ubo7x48erXAYREQDIBEGFaUCJiPJw4cIFtG/fXrG8YMECLFy4ULGclZUFPz8/hIaG4tmzZzAxMYGDgwPatWsHOzu7Eo83JiYGly9fxvPnzxEbGwtzc3NUrlwZXl5e8PT0LPF4CvL48WP4+/vj9evXiIuLg4GBASwtLVGjRg24u7vD2dlZo4klHx8fXLx4UbGc8yvj+fPnuHbtGqKiopCYmAg7Ozu4u7vD29u7WJO2BAYG4urVq3j58iUEQUCVKlVQvXp1tG7dGiYmJkUutyx6/vw5Hj9+jPDwcMTGxiI1NRWmpqawsrJCnTp10KBBA1haWupcLDnFxcXh0qVLCA0NRVJSEmxsbFC1alU0bdoU1atX10pMlL+QkBBJcmbQoEE4ePCgxuoTBAH379/HzZs38erVK+jr66Nq1apwdHREy5Yty0QSl0Rl6Tu8IAsXLsQ333yjWD5//jx8fHwUy8nJybh06RIiIyPx6tUrWFtbw9HRET4+PjA1NVVLDG/fvsW1a9cQERGB169fIzk5Gebm5rC2tkbt2rXh4eGBSpUqqaUuVQiCgODgYAQFBeG///5DfHw8MjIyYGFhgUqVKqFevXrw9PSEkZFRseoJCQnBzZs3ER0djcTERFhZWaFKlSpo2bIlqlWrpqZnQ6oaNWoUdu7cCQCoUaMGwsLC8u2MoIro6Gjcvn0bYWFhiIuLQ3p6OrKysrBx40bExMQo9qtQoQLOnTuHpk2bFrmu2NhYPHr0CBEREXj16hWSk5Ohp6cHS0tLWFtbo27duqhXr16xfrBOS0uDg4ODYhLYKVOmYN26dUUuj4h0G5PDRFRshSWHqewqLDlMRFQU27Ztw4cffqhY/v777zFr1iwtRkRUsgpLDhPpqsDAQNSrVw+ZmZkAgL1792Lo0KEaq+/+/fvo0KGDJEFsa2uLf//9Fx4eHhqrt7h++eUXfPLJJwAAU1NTBAUF5Xu3JhFRYTisBBERERGVqE2bNkmWW7ZsqaVIiIioNHFzc8v146EmOyfUr18fZ8+eha2trWJdTEwMOnfujPDwcI3VWxyZmZlYunSpYnnKlClMDBNRsTA5TEREREQlZuPGjbh+/bpi2cnJCW3bttViREREVJp88803imGq/P39NTreNQB4eXnh7NmzkmFUoqKi0KlTJ7x48UKjdRfFjh07EBoaCkAcN3/27NlajoiIyjomh4mIiIioyCZNmoQDBw5ALpcXuF9WVhbWrFmDqVOnStZPnTq1WGOUExFR+VK1alUsXrxYsTxr1ixkZGRotM68EsQhISHo0qUL3rx5o9G6VZGcnCyZhHH58uWwsrLSXkBEVC5w9g0iIiIiKrKHDx9i48aNqFq1KgYOHAhvb2/UqVMHVlZWSEtLw4sXL+Dn54ddu3YhMDBQcmyrVq1yJYuJiIg+/fRTJCcn4+3btwCAsLAwuLm5abTOBg0aSMYeLo1CQkIwfvx4AICFhQVGjx6t5YiIqDxgcpiIiIiIiu3FixfYsGEDNmzYoNT+DRo0wMGDB9UyCz0REZUv+vr6nKg0D15eXvDy8tJ2GERUzvAePiIiIiIqsncn8VGGsbExJk2ahMuXL6NatWoaioqIiIiIiJTBnsNEREREVGSHDh2Cv78//vnnH1y7dg2BgYF4+vQpEhMTkZmZCSsrK9jY2MDLywvvvfceBg0aBHt7e22HTUREREREAGSCIAjaDoKIqKg2b96M8ePHw9zcHElJSYXuf+LECVy/fh0LFy7USDw+Pj4AgAsXLmikfCIiIiKi8o5tfCKiksPkMBGVWVFRUahXrx7Mzc0RHx+vVMNx8uTJ2LBhAzT10ffo0SMAQN26dTVSPhERERFRecY2PhFRyeKwEkRUZk2YMAHt2rWDjY0NDh06pPbyBUHA27dvYWpqqvQxbDASERERERUd2/hERCWLE9IRUZm0a9cuXLx4ERs3blT6mDFjxmDDhg0AAJlMpniEh4cr1k2ePBmbNm2Ch4cHjI2NsWPHDgDAN998gxYtWsDGxgaWlpZo3LgxtmzZkqt3go+Pj+K2s2w//fQTGjRoAAsLC1SoUAHu7u74+uuvi/7kiYiIiIjKIbbxiYhKHnsOE1GZEx0djWnTpuGHH35A9erVlT5u3rx5SE5OxqFDh+Dn56dY/+7ESH/88Qd8fX0xf/58VK1aFXZ2dgCA8PBwfPLJJ6hZsyYA4OrVq5gyZQqioqIwf/78fOvct28fPv30U0yZMgUrVqyAnp4egoODFbemERERERER2/hERNrC5DARlTmffvop6tSpg4kTJ6p0nIuLC6pUqQIA8Pb2znOfpKQk3L9/H9bW1pL127ZtU/w/KysLPj4+EAQBa9euxbx58yCTyfIs7/Lly7CyssK6desU6zp27KhS3ERERERE5R3b+ERE2sFhJYioTPn9999x7Ngx/Prrr/k21oqjQ4cOuRqNAHDu3Dl06tQJFStWhL6+PgwNDTF//ny8fv0a0dHR+ZbXvHlzxMXFYdiwYfjzzz8RExOj9piJiIiIiMoytvGJiLSHyWEiKjOSkpIwadIkTJkyBdWqVUNcXBzi4uKQnp4OAIiLi0NycnKx6nj39rNs169fR5cuXQAAv/76Ky5fvowbN25gzpw5AIDU1NR8yxs5ciS2bt2KiIgIDBw4EHZ2dmjRogXOnDlTrDiJiIiIiMoDtvGJiLSLyWEiKjNiYmLw8uVLrFy5EtbW1orH3r17kZycDGtra4wYMaJYdeTVU2Hfvn0wNDTEX3/9hcGDB6NVq1Zo2rSp0mWOHTsWV65cQXx8PI4fPw5BENCrVy9EREQUK1YiIiIiorKObXwiIu3imMNEVGZUrVoV58+fz7X+hx9+wMWLF3Hy5EnY2toWWIaxsTEAsSeAqampUvXKZDIYGBhAX19fsS41NRU7d+5UIXrA3Nwc3bt3R3p6Ovr164eHDx/C0dFRpTKIiIiIiMoTtvGJiLSLyWEiKjNMTEzg4+OTa/327duhr6+f57ac6tevDwBYunQpunfvDn19fXh5ecHIyCjfY3r27IlVq1Zh+PDh+Pjjj/H69WusWLFC0QgtyPjx42FqaorWrVvD3t4eL168wPfff4+KFSuiWbNmhR5PRERERFSesY1PRKRdTA4TkU4ZPnw4Ll++jI0bN2LRokUQBAFhYWFwcnLK95gOHTpg69atWLp0KXr37g0HBweMHz8ednZ2GDduXK79371trW3btti+fTsOHDiAN2/ewNbWFm3atMFvv/2GypUra+IpEhERERHpFLbxiYiKTiYIgqDtIIiIyotGjRrBxcUFhw4d0nYoRERERESkBmzjE1F5xp7DRERqEBgYCF9fX9y/fx8ffPCBtsMhIiIiIqJiYhufiHQBew4TEanB2LFjcezYMfTp0wcbNmxQeiIMIiIiIiIqndjGJyJdwOQwERERERERERERkQ7S03YARERERERERERERFTymBwmIiIiIiIiIiIi0kFMDhMRERERERERERHpICaHiYiIiIiIiIiIiHQQk8NEREREREREREREOojJYSIiIiIiIiIiIiIdxOQwERERERERERERkQ5icpiIiIiIiIiIiIhIBzE5TERERERERERERKSDmBwmIiIiIiIiIiIi0kFMDhMRERERERERERHpICaHiYiIiIiIiIiIiHQQk8NEREREREREREREOojJYSIiIiIiIiIiIiIdxOQwERERERERERERkQ5icpiIiIiIiIiIiIhIBzE5TERERERERERERKSDmBwmIiIiIiIiIiIi0kFMDhMRERERERERERHpICaHiYiIiIiIiIiIiHQQk8NEREREREREREREOshA2wEQEWnb27dAaCgQEgLExQEZGYAgAEZGQIUKgIsL4OwMmJtrO1IiIiIiIlJGcvL/2viJiUB6OiCTAYaGgJXV/9r4JibajpSISLuYHCYinSMIQEAAcOYMcOcOEBQkNh5TUwG9HPdTZGWJDUZzc7Hx2LAh0LEj0KCB2LgkIiIiIiLtEwTg3j3g7Fng7l0xMZycLHYEyauNb2oqtvFdXYFGjYDOnQF3d7bxiUj3yARBELQdBBFRSXj7FrhwATh8GLh1C0hKEnsHm5uLDxOT3A1HQRCPS04WH2lpgJmZmBzu319MFFtYaOXpEBERERHpvKQkMSF8+DDg7w+kpADGxtI2fs6Eb1aWtI2fni626Zs0AQYMAHx82KOYiHQHk8NEVO4JgpgUXr0aCA4W19naApaWqvcMEASxAfrqlfj/GjWAqVOBHj3Yy4CIiIiIqKQIAnDiBLBuHRAZKbbFK1cWk7xFaeMnJAAxMeJy7drA558D773HNj4RlX9MDhNRuRYbC6xfDxw5IvYIcHAQexKoQ0YG8OyZ2Jjs3h2YMQOoUkU9ZRMRERERUd5evABWrQJOnhSTt9WqiWMJq0NamtjGNzQU7xScPBmwsVFP2UREpRGTw0RUbl29CixeLI4pXLkyYG2tmXoSEsQGqqMjMHOmONQEERERERGp39mzwNKlQEQEULWqeDegJrx5A0RHA25uwLx5QIsWmqmHiEjbmBwmonLpzBlgwQIgLk5M2hpoePpNuRz47z9xYotZs4CBAzVbHxERERGRrjl0SEwMp6YCNWsC+vqarS8zU0xCW1kBixYBnTpptj4iIm1gcpiIyp0zZ4C5c//XaMweJ+zu3TF4+nSHUmXo6ZnCwMAcxsZVYG5eBxUrNkHVqn1RoUK9fI8RBCAqSpzUbvZsYNAgdTwbIiIiIiI6eBD4/nuxze3gULJt/IgIcVLqxYuBzp3V8WyIiEoPJoeJqFzx8xPH/k1KkiaGAdUajvmxte2MevXWokIFj3z3efpU7Km8eDHQtWuxqiMiIiIi0nmnTolDO2RmAtWrS7eVRBtfEMS7BC0sxLGOvb2LVR0RUamip+0AiIjU5fVrMSEbF5c7MawuMTFncOVKK7x5cz3ffRwcgLdvgR9+EGdOJiIiIiKioomMFNvVb9+K7WxNKKyNL5OJ1xdxccC334rXHURE5QWTw0RULggCsHYtEBwsjjGsicRwtoyMONy9OxJyeVqe27Mbj8+eAcuXi+MRExERERGRauRyYNky4PlzzXX+yKZMG9/RUbzeWLdOvP4gIioPNDxFExFRyTh3Djh6FKhcuWiTz7m4fAVDQ2sAQFZWOlJTIxAdfRxpaS/z3D85ORDR0Sdgb98/z+16ekC1amJcf/zBCeqIiIiIiFR15Ahw/rzYrtYrQtc2dbfxDQzE640//wTatQM6dlQ9JiKi0obJYSIq85KTxbG/MjMBa+uileHoOBFmZk6SdZmZybhxow9evz6X5zExMWfybTgC4phksbHAhg1i47Fy5aLFRkRERESka169EtvRBgZiu7ooNNHGt7YWh5fIHnvY3LxosRERlRYcVoKIyrxz54CwMPWPQWZgYA539yX5bn/7NqrQMhwcxOElTp1SZ2REREREROXbqVPicBLVqqm3XHW18cPCxOsQIqKyjslhIirTBAH4/Xfx/0ZG6i/f0rJBvtuyst4Wery+vhjX77+LPZuJiIiIiKhgGRli+9nISGxPq1tx2/hGRuIYxIcPc+xhIir7mBwmojLt/n3A31+TQzbk39ozNq6qVAl2dkBICHDlirpiIiIiIiIqv/z8xPaznZ2maih+G9/WFrh3T7weISIqy5gcJqIy7fRpcczhChU0U358/J18t1lbt1KqDFNTsffDiRPqioqIiIiIqPw6cUK8687UVDPlq6ONX6GCeB1y+rS6oiIi0g4mh4moTLt1CzAxEW/rUrfMzCQEBHyd5zZDQytUqzZE6bIsLIDbt3nbGRERERFRQbKyxHazpiZ6U1cbXyYTr0Nu31ZndEREJc9A2wEQERVVYiIQEaGehmNExE8wNLQGAGRlpSM1NQLR0ceRlvYyj71l8PT8CYaGVkqXb24OvHkDREUB1asXP14iIiIiovIoKkpsN5eVNn54uHhdoqk7GYmINI3JYSIqs0JCgJQU9Yw3HBKyTKn9DA2tUb/+z6hW7X2VyjczA169AoKCmBwmIiIiIspPcLA4XIOtbfHLKqk2fkgI0LBhEQIkIioFOKwEEZVZwcFAWpp4O5em6ekZw83tG7RvH6JyoxEADAzEISVCQjQQHBERERFRORESIrabDUqgK1tx2/gmJuL1SHCwBoIjIiohTA4TUZkVFwfo6WlmvOGcsrLSEBGxEa9enSxWOW/eqCkgIiIiIqJyqCTby8Vt48tk4vVIXJx64yIiKklMDhNRmZWWVtL1vcSdOyMQErKiSMcLApCRoeagiIiIiIjKkfT0kp3EubhtfECMmYiorOKYw0REAGrV+geGhtUhlychPT0EcXE7kJh4Is99Hz+eCSur5qhUqZ3K9WRlFTdSIiIiIqLyS52J4ZJq45dkMpuISN2YHCaiMsvQUH0NsbCwMAAp/79kCmACAGMAR/LYOwv370/Ae+/dh0ymr1I9xsbFCpOIiIiIqFwzMlJfWSXRxhcE9cZMRFTSOKwEEZVZFSqI/2rul/pRAGrmuSUp6TEiI7epVJpMBlhYqCEsIiIiIqJyqkIFTc8por42fvZ1CNv4RFSWMTlMRGWWs7M4i7HmxvHVh9h4zFtQ0HfIylKucrlc/NfZWQ1hERERERGVU7Vqif9qbjg29bXx09PF6xG28YmoLGNymIjKLBcXwMwMSE7WZC3NAeTd2ktNDcfTp78pVUpqqhhr7dpqDI2IiIiIqJypXVtsN6ekFL5v0amnjZ+SApibs41PRGUbk8NEVGZVqgTY22s6OQwAQ/LdEhy8BFlZmYWWkJwMWFoCjo7qjIuIiIiIqHxxdBTbzWWljW9vD9jYqDMuIqKSxeQwEZVZMhnQqJHYK1ezvAHkndVNSQlFVNSuQktISgI8PcXbzoiIiIiIKG+GhmK7OSlJ0zUVv42fmgo0bKjpMZKJiDSLyWEiKtM6dhQbkJq97UyGgnoWBAV9B0GQ57s9PV38t1s3NYdFRERERFQOde0q/pvdjtaM4rXxU1IAIyOgUycNhEZEVIKYHCaiMs3bG3BzA6KjNV1TKwA18tySkhKMqKg9+R756hVQvTrQvr2GQiMiIiIiKkfatxfbz69eabqmorfxo6PF65AWLTQUGhFRCWFymIjKNAMDYMAAICMDkOf/w74a6AF4P9+tQUGL8+xZkJUljkXWr584sQYRERERERXM3Bzo21dsR2dlabKmorXx5XLx+mPAAA4bR0Rln0wQBEHbQRARFUdMDDBwIJCQIPYwUEVmZgZOnTql9P7dunWDgYGh0vu/fAno6QH79gFOTqrFRkRERESkq8LDgaFDxeRwlSqqHavpNv7Tp0DFisDvv4uTZBMRlWXsOUxEZZ6tLTBhApCWVhKzGivv7VsxYT1qFBPDRERERESqcHIS29EJCWK7urRIThbHQp4wgYlhIiofmBwmonLh/feB994DoqI0feuZcgQBiIwEGjcGPvxQ29EQEREREZU9H34otqcjI8X2tbZlZYnXG+3aAYMGaTsaIiL1YHKYiMoFAwPgq6/EW86iorQdjTicRMWKwKxZgImJtqMhIiIiIip7TEzE9nTFimL7WtuiooCqVYGZMznWMBGVH0wOE1G5UasWMH26+H9tNh5fvxZvffv4Y8DLS3txEBERERGVdV5eYrs6NVVsZ2tL9vXFtGkcMo6Iyhcmh4moXOnXD5g6VWw8aiNB/Po1EB8PjBnD4SSIiIiIiNThww+BsWPFdrY2EsQvX4rXF1OnitcbRETlCW+EIKJyRSYTG45ZWcD69eJMwg4O4npNEoT/NRo//FDsUaDpOomIiIiIdIFMJravAWDbNiAjQxxOriTa+FFR4r/TponXGWzjE1F5w57DRFTuyGTAuHHA118DpqZAcLCYtNWUtDQgJOR/jdbPPwf09TVXHxERERGRrtHXF9vZ2Z0wgoPFdrimpKaKdZiaAnPmiB1AmBgmovKIyWEiKpdkMmDwYLFnQcuW4i/+z56JPYrVJbu38H//AQ0bAr/8Anz0ERuNRERERESaIJOJ7e1ffgEaNBDb4dHRYrtcXbKyxOuGqCjxOmLbNuD999nGJ6Lyi8lhIirX6tQBfv4Z+Oorcbbj4GBxqIn09KKXmZEhNhiDgsRG4uTJwJYtYoKYiIiIiIg0q2FDMWk7aZK4HBQkts8zMopeZnq6eJ0QHCxeN8ycKV5H1KmjlpCJiEotmSCo8zc2IqLS67//gOPHgSNHxIYfAJiZZeLWrX8BpADI7+NQBsAUgBnq1GkOPT19VK0K9OkD9OwJuLqWSPhERERERJRDUBDw11/AsWPAixfiOnPzTNy8eRFAKgpu45sBMEedOk2hr68PBwegf3+xjV+zZomET0SkdUwOE5HOSU0Fzp8XE8V372biyhV/iMlfQGwkZo89oYf/NSbfAkjCqFEt0L+/ETp2BCpUKOHAiYiIiIgoT4mJwD//ACdOAA8eZOLq1fsATP5/a35t/FQASfjooxbo398Y7duLYwwTEekSJoeJSKfFxCSgcuVmAFwA1AZgCbEHASA2FhMBhAIIAhCG+PhoWFpaaiVWIiIiIiIqXHR0AqpUaQHAFYAzgAr4X2eQFAAJAIIBhEBs48ewjU9EOstA2wEQEWmTkREABP7/g4iIiIiIyjoTEwAI+P8HEREVhBPSEREREREREREREekgJoeJiIiIiIiIiIiIdBCTw0REREREREREREQ6iMlhIiIiIiIiIiIiIh3E5DARERERERERERGRDmJymIiIiIiIiIiIiEgHMTlMREREREREREREpIOYHCYiIiIiIiIiIiLSQUwOExEREREREREREekgJoeJiIiIiIiIiIiIdBCTw0REREREREREREQ6iMlhIiIiIiIiIiIiIh3E5DARERERERERERGRDmJymIiIiIiIiIiIiEgHMTlMREREREREREREpIOYHCYiIiIiIiIiIiLSQUwOExEREREREREREekgJoeJiIiIiIiIiIiIdBCTw0REREREREREREQ6iMlhIiIiIiIiIiIiIh3E5DARERERERERERGRDmJymIiIiIiIiIiIiEgHMTlMREREREREREREpIOYHCYiIiIiIiIiIiLSQUwOExEREREREREREekgJoeJiIiIiIiIiIiIdBCTw0REREREREREREQ6iMlhIiIiIiIiIiIiIh3E5DARERERERERERGRDmJymIiIiIiIiIiIiEgHMTlMREREREREREREpIOYHCYiIiIiIiIiIiLSQUwOExEREREREREREekgJoeJiIiIiIiIiIiIdBCTw0REREREREREREQ6iMlhIiIiIiIiIiIiIh3E5DARERERERERERGRDmJymIiIiIiIiIiIiEgHMTlMREREREREREREpIOYHCYiIiIiIiIiIiLSQUwOExEREREREREREekgJoeJiIiIiIiIiIiIdBCTw0REREREREREREQ6iMlhIiIiIiIiIiIiIh3E5DARERERERERERGRDmJymIiIiIiIiIiIiEgHMTlMREREREREREREpIOYHCYiIiIiIiIiIiLSQUwOExEREREREREREekgJoeJiIiIiIiIiIiIdBCTw0REREREREREREQ6iMlhIiIiIiIiIiIiIh3E5DARERERERERERGRDmJymIiIiIiIiIiIiEgHMTlMREREREREREREpIOYHCYiIiIiIiIiIiLSQUwOExEREREREREREekgJoeJiIiIiIiIiIiIdBCTw0REREREREREREQ6iMlhIiIiIiIiIiIiIh3E5DARERERERERERGRDmJymIiIiIiIiIiIiEgHMTlMREREREREREREpIOYHCYiIiIiIiIiIiLSQUwOExEREREREREREekgJoeJiIiIiIiIiIiIdBCTw0REREREREREREQ6iMlhIiIiIiIiIiIiIh3E5DARERERERERERGRDmJymIiIiIiIiIiIiEgHMTlMREREREREREREpIOYHCYiIiIiIiIiIiLSQUwOExEREREREREREekgJoeJiIiIiIiIiIiIdBCTw0REREREREREREQ6iMlhIiIiIiIiIiIiIh3E5DARERERERERERGRDmJymIiIiIiIiIiIiEgHMTlMREREREREREREpIOYHCYiIiIiIiIiIiLSQQbaDoCIqHSQAXAAUBGA0f8vpwFIAvAUgFx7oRERERERURHoA6gOwAKAMQABQDqAeABR/79MRKTbmBwmIp2Ung5cuwbcuWMIYCUATwDWAAzxv5sqsgBkAogD8AjAY1y8qI9OnQBTUy0ETURERERE+UpNBfz8gLt3DQGsBVAXgBXE1Me7bfwMAG8APADwGFeu6KNDB8DISAtBExFpmUwQBP5URkQ64/lz4ORJ4MgRICwMyMiQIzDwCYDk/39kQGwwAmLvYSMAZgDMAejB3d0NNWvqo18/oEcPwNFRG8+CiIiIiIiyRUQAJ06IbfyoKCAzU47AwACI7fsUiL2Fs1MfehA7hJj//0MGDw83uLjoo39/oHt3wN5eK0+DiEgrmBwmIp3w4gXw00/A338Db94AxsaAnR1gYJCBU6dOKV1Ohw7dEBtriJQUwMoKeO89YNIkJomJiIiIiEpaRASwYQNw8SIQHy/e3WdnB+jpqdbG9/ER2/hpaYC1NdClC/Dpp0CVKhoMnoiolOCwEkRUrgmC2Itg7Vqx8WhjA7i4AHr/f1dZZqZq5RkZAdWrA1lZQFwc8OefwPXrYuNx4EBAX1/tT4GIiIiIiN4hlwO//w5s3CjeGWhrW7w2vokJULOm2MaPjQX27ROHp/jsM/FuQZlM/c+BiKi0YHKYiMqt6GhgxQpxGAmZTGwwqit5q6cnJpqtrMRb1775RuyxMGsWUKOGeuogIiIiIiKpyEjghx+ACxfEjhu1a/8vKVxcenpiotnaGnj6VGzbX7wIfPGF2COZiKg8UtNHKBFR6RIeDkycKPbstbERh33QRK9ePT0xGWxnB/zzDzBhAvD4sfrrISIiIiLSdY8fi+3tf/4R29/Vq6svMfwufX3x+sHGRryemDhRvAuRiKg8Ys9hIip3QkOBqVOBoCCgVi3A0FBcf/fuGDx9ukPJUowAmECc3dgBgAsA73z3trAQeyaHhABTpgCrVwP16xfnWRARERERUbb794Hp08Ueve/eEajJNr6lpTiO8YMH4jwj69YBzs7FeRZERKUPew4TUbkSFQV8/jkQHCw23LITw6pLB5AA4D8AfgB2AZiMGzd6IDEx767B+vpiQzUqSrz1LCioqHUTEREREVG2oCCxfZ0zMaw61dv4hobidUVQEDBjhtjWJyIqT5gcJqJyIz0dmD9fvN3MyQkw0MC9Ea9f/4MrV1rhzZvreW7X0xN7K0dEAHPmAMnJ6o+BiIiIiEhXJCeL7eqICDFJq4lhJApr4xsYiG38R4+ABQuAjAz1x0BEpC1MDhNRubF3rzircPXqxekxXLiMjDjcvTsScnlantv19cXZjv39gc2bNRcHEREREVF59+uvYrtaU3OIZCusjW9oKF5nXLkiXncQEZUXHHOYiMqFoCDgl1/EMcHMzIpSwgAAFv///0wA0QBuAojLc+/k5EBER5+AvX3/PLcbGwMVKwK7dgFt2gBNmhQlJiIiIiIi3XXzJrB7N2BlBRgZFaUE9bbxzczE642ffwZatgRcXYsSExFR6cKew0RU5snlwA8/AK9fA/b2RS2lO4BB//8YCmAqgF8AeOV7REzMmQJLrFwZSEoCli4F3r4talxERERERLrn7Vtg2TKxPW1rW9RS1N/Gr1oViIkR2/hyeVHjIiIqPZgcJqIy7/p1sVeBvT0gk6mzZBMAI/Pd+vZtwbNRyGRAjRri7MYXLqgzLiIiIiKi8u38ebEdXaNG6Wrj6+kB1aoBN26IDyKiso7JYSIq844eBdLSAAuLwvdVXa18t2RlFd4d2NhY/PfwYUAQ1BUTEREREVH5JQjAkSPi/7Pb0+pVvDa+hYV4/fHnn+qMiYhIO5gcJqIyLSpK7FVgba2pGvLP6BobV1WqBFtb4NYtICBAXTEREREREZVfjx+L7eeiDydRmOK38a2txeuQqII7GhMRlXpMDhNRmXbyJBAXB9jYaKqG0Hy3WFu3UqoES0txrLTjx9UVExERERFR+XX8uNh+trTUVA3Fb+Pb2IjXIadOqSkkIiItYXKYiMq0a9cAAwNx7C/1SwWwM88thoZWqFZtiFKlyGTizMaXL6sxNCIiIiKickgQxHazmZm6xxrOpp42vp6eeB1y7ZoaQyMi0gIDbQdARFRU6enAkyeAubk6SjsJIHvQ4kwA0QBuAojLY18ZPD1/gqGhldKlm5sDz58DsbGa7OVMRERERFS2xcaK7eay0sZ/8kS8LjEyKmaoRERawuQwEZVZYWHi7WYVK6qjtMNK7WVoaI369X9GtWrvq1S6uTnw7BkQFAS0aFGU+IiIiIiIyr/gYCAlBahWTR2labaNb2YGxMeL1yV16hQlPiIi7eOwEkRUZgUHA6mpYqNM8wwBDEe7dgEqNxoBwNAQyMwUYyYiIiIiorwFB4vtZkPDkqiteG18MzPxeiQkRP2RERGVFCaHiajMevlS/Fcz4w3nlAHgBF69KtqME9njpb14ob6IiIiIiIjKm+z2smbGG86peG18PT0xTrbxiagsY3KYiMqstLSSajRmi4O//2iEhKwo0tGCII5HRkREREREeUtPF9vNJad4bXyAbXwiKts45jARlVlyufrKsrb+HW/eGAFIBhAJ4CgA3zz3ffx4JqysmqNSpXYq16POmImIiIiIypvMTPWVVbHiIcTHGwFIgSbb+OqMmYiopLHnMBGVWeoch+zNm1gAAgAzAHUAfAmgfz57Z+H+/QkQBNUzvSUzdhoRERERUdlkZKS+suLj3/z//zTbxldnzEREJY3JYSIqs0xMNH3L2SgANfPckpT0GJGR21QqTSYTYyYiIiIiorwZG2t66Dj1tvEFQYyZiKisYnKYiMqsmjXFhqOyt3HJCxjToWpVe1haWuZYqw9T0wn5HvPw4QIkJcUrVbcgiI+aebdDiYiIiIgIgKPj/9rOysgs4GLAwaE6LC0r5lhbcBv/0aOFSElJVLJu8XqEbXwiKsuYHCaiMqt2bcDcHEhJKXi/1NRUPHhwH8+fP893n7p160FPTz/XemPjtrC0bJTnMXL5M1y4MA93795FcnJSgTG8fSv2Gq5du+BYiYiIiIh0mYuL2G5++7bg/ZKTk3D37l28eJF/G79OHXfo6eVOexTUxs/MjML583Px4MF9pKamFhhDSop4PcI2PhGVZZyQjojKLAcHwNoaiI0FcnX6BZCcnIzg4GA8ffoUgpAFcUxh1bm6zsOtWwPy2XoQT592wNOnT1GtWjW4urqiQoUKecQiNhxdXIoUAhERERGRTnBxAczMxPazqWnu7YmJCQgKCsazZ89Q1PY9UHAbXxAOIDy8PSIi/kP16tXh6lobZmbmufZLTgZsbMTrEiKisorJYSIqs/T0AE9P4ORJ6fqkpEQEBQUjKioK7zYYZTJZkcYorlq1HypUqI/ExPt5bH0B4AKAjnj2LArPnkWhalV7uLq6omLF/93ClpQENGokNnSJiIiIiChv2R0q7t4FbG3/tz4+Ph5BQYF48eKFZH+ZTE9jbXxB6IjIyP8QGRkJBwcHuLq6wsLCQrFXcjLw3nvidQkRUVnFjzAiKtPatRPH+crIABISEnDr1i1cuHABUVFPkZ0YNjAwQO3arrC3r1akOmQyGVxd5+a73dDwDxgY/G9IihcvnsPX919cv34db968gVwuxtehQ5GqJyIiIiLSKR07iu1nuRx48yYW169fg6/vv5LEsKGhEdzdPWBvb1+kOgpv4x+Bvn72zHgCoqKe4sKF87h16xYSEhKQkSFeh7RtW6TqiYhKDfYcJqIyrWNHwNw8AVevvkR8fIBkm6GhIZydneHkVAuGhoa4e7fov4fZ2w+ChYUHkpIe59qWkREBL69oZGa2Q0hICNLSxAHSoqNfIjr6JSwtXVCjhgO6dbMEoNGpl4mIiIiIyryuXQUsX56Ay5ejkJAQItlmbGwCFxcXODrWhL6+gQbb+P+hfv1XSEtrjbCwUGRkZAAAnj9/hufPn6FiRQ+4utqhU6c8xrcjIipD2HOYiMqsS5cu4f33u+HmzXmIj89SrDcyMoaHhwc6duwEV1c3GBoaFrsumUwPtWvPyXd7SMj3qFXLER07doCnZ32YmPxvgLSEBD08fLga77/fDqdPn4ZQlPveiIiIiIjKOUEQcPr0aQwe3A6PHq1FQsL/UhYmJqbw9KyPjh07wNnZGfr6xe/rVlgbPzT0B7i6uqBjx07w8PCAkZGxYlt8vBw3b87FoEHdcPny5WLHQkSkLTKBWQoiKkMEQcC5c+fw7bff4uLFi/+/tjaA3TAy0oOrayXUrFkT+vr6BRWTp0uXLiEu7o1knZWVNdq0aaNyWVlZWXj69CkCA5/j7VsjAB8DuAkAaNasGebOnYvevXtDJmNPYiIiIiLSbYIg4NixY1i8eDFu3Ljx/2ubAvgFJibpqFPHHg4O1aFXhMF91dnGl8vl+O+//xAU9Brp6VkARgAIBgD4+Phg7ty56NChA9v4RFSmsOcwEZUJgiDg+PHjaNWqFTp16vROYhhwcspEnz6Ao2NjVK9eq0iJYXXT09ODg0NN1KjRHN27G8PdPVmx7caNG+jbty8aNmyIgwcPQi6XazFSIiIiIiLtkMvlOHDgABo2bIi+ffu+kxgG3N2T0b27MWrUaI5q1WoWKTGsbvr6+qhevRYcHRujTx/xOiTbhQsX0KlTJ7Ru3RonTpzg3YJEVGZo/9OViKgAWVlZOHz4MJo0aYJevXrh6tWrim1ubm7Yvn07AgMDsWNHc7i76yEyUovB5vD0KVCzpgxbttTFw4cPcPDgQTRo0ECx3d/fH4MHD4anpyd27dqFzMzMAkojIiIiIiofMjMzsXPnTnh6emLIkCHw9/dXbGvQoAEOHjyIhw8fYMuWuqhRQ4anT7UYbA6RkYC7ux527GiOwMBAbN++HW5ubortfn5+6NmzJ5o2bYojR44gKyurgNKIiLSPyWEiKpXkcjn27t0LLy8vDBw4EHfu3FFs8/T0xN69e/Ho0SOMHj0ahoaGsLICvvgCMDYGYmO1F3e2hARAEIDPPgPs7cWexIMGDcKdO3dw9OhRNGvWTLFvQEAARo4ciTp16mDz5s1IT0/XYuRERERERJqRnp6OzZs3o06dOhg1ahQCAv43oXTz5s1x7Ngx3LlzB4MGDYKenh7s7cX2tCCI7Wtti40Vrze+/BKwshInwB49ejQePXqEvXv3ol69eop9b9++jQEDBsDLywt79+7l3YJEVGoxOUxEpUpGRga2b98ODw8PDB8+HA8fPlRsa9y4MY4cOYJ79+5h6NChuYaPaNsWeP994PVrICmppCP/n9RU4MULoFs3oFcv6TaZTIbevXvj2rVrOH36tGSss9DQUIwfPx61a9fGhg0b8Pbt2xKOnIiIiIhI/VJTU7F+/XrUrl0b48ePR2hoqGJb27Zt8ffff+Pq1avo1atXrvF6e/cW29UvXojtbG1JShKvM95/H8g5XLG+vj6GDh0Kf39/HD58GI0bN1Zse/jwIYYPHw4PDw9s374dGRkZJRw5EVHBmBwmolIhLS0NmzZtgqurK8aOHYugoCDFtpYtW+LEiRO4efMm+vXrl+94YzIZ8PnnQM+ewPPnQHJynrtpVGqqeKvZe+8B8+eLMeVFJpOhS5cu8PX1xcWLF9GpUyfFtsjISEyePBm1atXCqlWrkKyNJ0JEREREVEzJyclYuXIlnJ2dMWXKFES+MwZc586dcfHiRfz777/o3LlzvpO4yWRiu/q998R2tjYSxMnJ4vVFz57i9UZ+bXw9PT30798fN2/exPHjx+Ht7a3YFhQUhLFjx8LNzQ0///wz0tLSSih6IqKCMTlMRFqVkpKCtWvXwtnZGRMnTkRERIRiW/v27XH27FlcvnwZ3bt3V2rWX2NjYNEioGtX4Nmzkr39LDlZbLC2aQP88ANQoYJyx7Vr1w5nzpyBn58fer3T1fjFixeYMWMGnJycsGTJEiSUhnvpiIiIiIgKER8fjyVLlsDR0RFffPEFXrx4odjWq1cv+Pn54e+//0a7du2UKq9CBbF93bq12N4uyb4TCQnidUXXruJ1hrFx4cfIZDL06NEDV65cwdmzZ+Hj46PYFh4ejgkTJsDFxQXr1q1DSkqK5oInIlICk8NEpBWJiYlYtmwZatWqhWnTpuHZs2eKbd26dcOlS5dw7tw5dOjQQamk8LvMzYGlS4F+/YCYGLEBqcl5IARBbDA+fw507gysXg3Y2Khejre3N44dO4bbt29j4MCBivUxMTGYM2cOHB0dsWDBAsSWhkGViYiIiIhyiI2NxYIFC+Dk5IQ5c+bg9evXim0DBw7E7du3cezYMUmPWmXZ2ABr1ojt7efPxfa3IKgx+ByyssTriJgY8bpi6VLxOkMVMpkMHTp0wPnz5+Hr64tu3boptkVFReGzzz5DrVq1sGzZMiQmJqr3CRARKUkmCJr8OCUikoqLi8O6deuwZs0avHnzRrKtb9++mDt3Lpo2baqWuuRy4PffgY0bxQZktWqAhUX++1+6dAlxcdKYrKysJeMC55SaCjx9KjZWP/4YGD4cMDJSS/h4+PAhlixZgn379klmObawsMCkSZPw+eefw87OTj2VEREREREVUXR0NFauXImNGzci6Z3JP/T09DBs2DDMnj1bMllbcaSnA3v2AL/8Io4BXKMGYGqa//5FaeMnJYnJZ3t7YNIkYMAAIMd0J0V248YNfPfdd/jzzz8l621sbDBt2jRMmTIFVlZW6qmMiEgJTA4TUYmIiYnB6tWrsX79esnwCDKZDIMHD8bXX38NLy8vjdQdEQEsWwZcvCj2LqhcWUwS5+yQrErDMTkZiI4WexS0aAHMmgXUqaOR8BEUFITvv/8eO3fuRGZmpmK9qakpPvnkE3zxxRdwcHDQTOVERERERPmIiorC8uXL8csvvyD1ncGADQwMMGrUKMyaNQuurq4aqfvJE3GoiWvXAD09wM4u7569yrbxBUFMCr96JV4n+PgAX34JODpqJHz4+/vju+++w8GDB/FuWsbS0hJTpkzBtGnTYGtrq5nKiYjeweQwEWnU8+fPsXLlSvz000+S8bT09fUxYsQIzJ49G+7u7hqPQy4HTpwADh4E7t8Xe/xaWgIVK4rjhslkBTccBUHspZCQAMTFice4u4uzFffpo77ewgUJDw/HsmXLsGXLFqSnpyvWGxkZ4cMPP8TMmTPh5OSk+UCIiIiISKeFh4dj6dKl2Lp1a6526bhx4zBz5kw4aiqr+o70dODoUbGNHxAApKUBVlZiO9/ISLk2floaEB8vPszMAC8vYNAgoEcP9fUWLkhAQAC+//577N69G3K5XLHezMwMEydOxBdffIGqVatqPhAi0llMDhORRvz3339Yvnw5fv31V8lMvIaGhhgzZgxmzZoFZ2fnEo9LEIB794Bjx4DTp8Vkb0aGuP6//wKRmhoHIHsIBxnMzKxQs6bYJdjAQOxx3LEj0Ls30LRpyTQYc4qKisKKFSvw888/5+qhMXLkSMyePVtjPTSIiIiISHcFBgbi+++/x65du3Ld0TZhwgR88cUXqFatWonHJZcDN2+KieJz58QewNnh/fffE6SkxAHITn3owdTUCjVrukEmAwwNxWRy165iG79Bg9x3GJaEkJAQLF26FNu3b0dGRoZivbGxMcaPH4+vvvoKNWrUKPnAiKjcY3KYiNQqJCQEP/zwA3bs2CFp1JiYmGD8+PH48ssvS02jJj4eCA4GgoLEx5o1ZxEbKwDInoI4HdbWepgypT3c3IDatQFX16JNNqcJ0dHRWLVqFTZs2JBrbLchQ4Zgzpw5ahvbjYiIiIh014MHD7BkyRLs378/11wYkydPxvTp00vNXBixsWLbPjgYCAwEfvzxPN68yQKQfatfGmxsZJg2rSNcXcX2fe3a4h2FpUFBnWxGjx6N2bNna6WTDRGVX0wOE5FaPH78GN9//z327NkjuR3K3NwcEydOxIwZM0r97VAtW7bE1atXJeu8vb3h5+enpYiUExsbi3Xr1mHt2rWIi4uTbBswYADmzJmDxo0bayc4IiIiIiqzbt++jcWLF+PIkSOS9VZWVvjss88wdepU2JSWnhP5KKtt/IKG5xs+fDi+/vrrEhmej4jKPz1tB0BEZZu/vz8GDx6MevXqYefOnYrEsKWlJebMmYPw8HAsX7681CeGyzIbGxssXLgQ4eHhWLJkiWTiisOHD6NJkybo1atXrkYxEREREVFe/Pz80LNnTzRp0kSSGLa1tcWSJUsQERGBhQsXlvrEcFlmb2+PFStWICIiAnPmzIGlpSUAQC6XY+fOnahbty6GDBkCf39/LUdKRGUdk8NEVCQ3btxA37590aBBA8kMuzY2Nli0aBEiIiKwePFizrBbgipWrIjZs2cjPDwcK1eulCTkjx8/jpYtW6JTp064ePEieNMIEREREb1LEARcuHABnTp1QqtWrXDixAnFNnt7e6xatQrh4eGYPXu2IlFJmmdra4vFixcjIiICixYtUiTkBUHAgQMH0KBBA/Tt2xc3btzQcqREVFYxOUxEKrl06RK6deuG5s2b4+jRo4r1dnZ2WLZsGcLDwzFv3jxYWVlpL0gdZ25ujs8//xxhYWFYv369ZIzns2fPwsfHB+3atcPp06eZJCYiIiLScYIg4PTp02jXrh3at2+Ps2fPKrbVqFEDGzZsQGhoKKZPnw5zc3MtRqrbrKysMG/ePISHh2Pp0qWSMZ6PHj2K5s2bo1u3brh8+bIWoySisojJYSIqlCAIiqRi27Ztcfr0acU2BwcHrF27FmFhYfjyyy9RoUIFLUZK7zIxMcGkSZMQHByMzZs3SyauyE7yt2jRAkePHmWSmIiIiEjHCIKAo0ePokWLFujWrRsuXbqk2Obi4oLNmzcjODgYn376KUxMTLQYKb2rQoUK+OqrrxAWFoa1a9fCwcFBse306dNo06aNIsnPNj4RKYPJYSLKlyAIOH78OFq1aqUYjiCbk5MTNm3ahJCQEEydOhVmZmZajJQKYmRkhHHjxuHJkyfYuXOnZOKK7OFBGjZsiIMHD0omEyQiIiKi8kcul+PAgQNo2LBhruEIPDw8sGvXLgQEBGDcuHEwMjLSYqRUEDMzM0ydOhUhISHYtGkTnJycFNuyhwdp3bo1Tpw4wSQxERWIyWEiyiUrKyvficzc3Nywfft2BAYG4pNPPoGxsbEWIyVVGBgY4IMPPsCDBw9w4MABeHl5KbZlTyzo6emJXbt2ITMzU4uREhEREZG6ZWZmYufOnfD09Mw1kVn2PCIPHjzAiBEjYGBgoMVISRXGxsb45JNPEBgYiG3btsHV1VWxLXtiwaZNm+LIkSPIysrSYqREVFoxOUxECnK5HHv37oWXlxcGDhyIO3fuKLbVq1cPe/fuxaNHjzB69GgYGhpqMVIqDn19fbz//vu4e/cujh49imbNmim2BQQEYOTIkahTpw42b96M9PR0LUZKRERERMWVnp6OzZs3o06dOhg1ahQCAgIU25o1a4ajR4/izp07GDRoEPT0mCIoqwwNDTFmzBg8fvwYe/bsQb169RTbbt++jQEDBsDLywt79+7l3YJEJMFPfiJCRkYGtm/fDg8PDwwfPhwPHz5UbGvcuDEOHz4Mf39/DB06FPr6+lqMlNRJJpOhd+/euHbtmmJ8smyhoaEYP348ateujQ0bNuDt27dajJSIiIiIVJWamor169ejdu3aGD9+PEJDQxXb2rRpg9OnT+PatWvo3bs3ZDKZFiMlddLX18ewYcPg7++Pw4cPo1GjRoptDx8+xPDhw+Hh4YHt27cjIyNDi5ESUWnB5DCRDktLS8OmTZvg6uqKsWPHIigoSLHN29sbx48fx82bN9G/f3/2IijHZDIZunTpAl9fX8X4ZNkiIyMxefJk1KpVC6tWrUJycrIWIyUiIiKiwiQnJ2PlypVwdnbGlClTEBkZqdjWuXNnXLx4Eb6+vujSpQuTwuWYnp4e+vfvj1u3buH48ePw9vZWbAsKCsLYsWPh5uaGn3/+GWlpaVqMlIi0jdkeIh2UkpKCtWvXwtnZGRMnTkRERIRim4+PD86ePYsrV66gR48ebDDqmPfeew9nzpyBn58fevXqpVj/4sULzJgxA05OTliyZAkSEhK0GCURERER5RQfH48lS5bA0dERX3zxBV68eKHY1qtXL/j5+eHvv/9Gu3bttBgllTSZTIYePXrgypUr+Oeff+Dj46PYFh4ejgkTJsDFxQXr1q1DSkqK9gIlIq1hcphIhyQmJmLZsmWoVasWpk2bhmfPnim2devWDb6+vjh//jw6dOjApLCO8/b2xrFjx3D79m0MHDhQsT4mJgZz5syBo6MjFixYgNjYWC1GSURERESxsbFYsGABnJycMGfOHLx+/VqxbeDAgbh9+zaOHTsm6TlKukcmk6Fjx444f/48fH190bVrV8W2qKgofPbZZ6hVqxaWLVuGxMRELUZKRCWNyWEiHRAXF4dFixbB0dERM2fORHR0tGJb3759cf36dZw8eVIy5iwRADRq1AiHDh3CgwcPMHz4cMXwIu+eU7NmzZKcU0RERESkedHR0Zg5cyYcHR2xaNEixMXFARCHExgxYgQePHiAQ4cOScacJQLEMadPnTqF69evo2/fvor12eeUk5MTvv32W8U5RUTlG5PDROVYzl6eb968ASD+ajx48GDcu3cPf/zxB5o1a6blSKm0q1evHnbv3o2AgACMHTsWBgYGAICkpCQsXboUTk5OmD59OqKiorQcKREREVH5FhUVhWnTpsHJyQnLli1DUlISAMDAwAAffvghAgICsGvXLtSrV0/LkVJp16xZM/zxxx+4e/cuBg8erLh7NDY2FvPnz4ejoyPmzp2LmJgYLUdKRJrE5DBROfT8+XN88cUXcHR0lIwPq6+vj1GjRuHRo0fYv38/vLy8tBwplTWurq7YunUrgoKCMHHiRBgZGQEQZ8Nes2aNYhzr8PBw7QZKREREVM6Eh4dj4sSJcHZ2xtq1a5GamgoAMDIywsSJExEcHIwtW7bA1dVVy5FSWdOgQQPs378fDx8+xMiRI6Gvrw8ASEhIwHfffZfnONZEVH4wOUxUjkRGRmLKlCmoVasWVq5cqZhQwNDQEOPHj8eTJ0+wY8cOuLu7azlSKuucnJywceNGhIaGYtq0aTA1NQUApKenY9OmTXB1dcWHH36IoKAgLUdKREREVLYFBQVh7NixcHV1xaZNm5Ceng4AMDU1xfTp0xEWFoaNGzfC0dFRy5FSWefh4YHffvsNT548wUcffQRDQ0MA4oTmK1euhJOTE6ZMmYLIyEgtR0pE6sTkMFE5EBoaivHjx8PFxQXr169HWloaAMDY2BiTJ09GcHAwfvnlF7i4uGg5UipvHBwcsHr1aoSHh2PmzJmwsLAAAGRmZmLbtm1wd3fHiBEj8PDhQy1HSkRERFS2PHz4EMOHD4e7uzu2b9+OzMxMAICFhQVmzZqF8PBwrFq1CtWqVdNypFTeuLi44Ndff0VwcDAmT54MY2NjAEBaWhrWr18PFxcXfPzxxwgNDdVypESkDkwOE5VhAQEBGDVqFNzc3LB582ZkZGQAAMzNzfHFF18gLCwMP/74I2rWrKnlSKm8s7Ozww8//ICIiAgsWLAAVlZWAICsrCzs2bMHnp6eGDhwIO7cuaPdQImIiIhKudu3b2PgwIHw9PTE3r17kZWVBQCwsrLCggULEBERge+//x52dnZajpTKu5o1a+LHH39EWFgYZsyYATMzMwBARkYGfv31V7i5uWH06NEICAjQcqREVBxMDhOVQf7+/hgyZAjq1q2LnTt3Qi6XAwAsLS0xZ84chIeHY/ny5bC3t9dypKRrbGxssHDhQoSHh2PJkiWwtbVVbDt8+DAaN26MXr164erVq1qMkoiIiKj0uXr1Knr16oUmTZrg8OHDivW2trZYsmQJIiIisHDhQtjY2GgxStJF9vb2WLFiBSIiIjBnzhxYWloCAORyOX777TfUrVsXQ4YMgb+/v5YjJaKiYHKYqAy5ceMG+vbtiwYNGuDAgQMQBAGAmJBbtGgRIiIisHjxYklCjkgbKlasiNmzZyM8PBwrV65E1apVFduOHz+Oli1bonPnzrh48aIWoyQiIiLSLkEQcPHiRXTq1AktW7bE8ePHFdvs7e2xatUqhIeHY/bs2YqEHJG22NraYvHixYiIiMCiRYsUP1QIgoADBw6gQYMG6NevH27evKnlSIlIFUwOE5UBly9fRrdu3dC8eXMcPXpUsd7Ozg7Lli1DeHg45s2bp7iVn6i0MDc3x+eff46wsDCsX78eNWrUUGz7559/4OPjg3bt2uHvv/9W/NhBREREVN4JgoDTp0+jXbt28PHxwdmzZxXbatSogQ0bNiA0NBTTp0+Hubm5FiMlys3Kygrz5s1DeHg4li5dKhni5M8//0SzZs3QvXt3XL58WYtREpGymBwmKqUEQcDZs2fRvn17tGnTBqdPn1Zsc3BwwNq1axEWFoYvv/wSFSpU0GKkRIUzMTHBpEmTEBwcjM2bN8PZ2VmxzdfXF127doW3tzeOHTvGJDERERGVW4Ig4OjRo2jRogW6deuGS5cuKba5uLhg8+bNCA4OxqeffgoTExMtRkpUuAoVKuCrr75CWFgY1q5dCwcHB8W2U6dOoU2bNmjfvj3OnTvHNj5RKcbkMFEpIwgCTpw4gdatW6NTp064cOGCYpuTkxM2bdqEkJAQTJ06VTEhAFFZYWRkhHHjxuHJkyfYuXMn3N3dFduuX7+OPn36oFGjRjh48KBi8hUiIiKisk4ul+PgwYNo2LAh+vbtixs3bii2eXh4YNeuXQgICMC4ceNgZGSkxUiJVGdmZoapU6ciJCQEmzZtgpOTk2LbhQsX0LFjR7Ru3RonTpxgkpioFGJymKiUyMrKwpEjR9C0aVP07NkTfn5+im1ubm7Yvn07AgMD8cknn8DY2FiLkRIVn4GBAT744AM8ePAABw4cgJeXl2LbvXv3MHjwYHh6emLXrl3IzMzUYqRERERERZeZmYldu3bB09MTgwcPlkzY1aBBAxw8eBAPHjzAiBEjYGBgoMVIiYrP2NgYn3zyCQIDA7Ft2za4uroqtvn5+aFnz55o2rQpjhw5wo4gRKUIk8NEWiaXy7F37154eXlhwIABuH37tmJbvXr1sHfvXjx69AijR4+GoaGhFiMlUj99fX28//77uHv3Lo4ePYpmzZoptj1+/BgjR46Eu7s7tmzZgvT0dC1GSkRERKS89PR0bN68GXXq1MHIkSMREBCg2NasWTMcPXoUd+7cwaBBg6Cnx8tyKl8MDQ0xZswYPH78GHv27EG9evUU227fvo0BAwagQYMG2LdvH+RyuRYjJSKAyWEircnIyMD27dvh4eGB4cOH4+HDh4ptjRs3xuHDh+Hv74+hQ4dCX19fi5ESaZ5MJkPv3r1x7do1nD59Gm3atFFsCwkJwUcffYTatWtjw4YNePv2rRYjJSIiIsrf27dvsWHDBtSuXRvjx49HaGioYlv2PCLXrl1D7969IZPJtBgpkebp6+tj2LBh8Pf3x+HDh9GoUSPFtgcPHmDYsGGoW7cuduzYgYyMDC1GSqTbmBwmKmFpaWn4+eef4ebmhrFjxyIoKEixzdvbG8ePH8fNmzfRv39/9iIgnSOTydClSxf4+vriwoUL6NSpk2JbZGQkJk+ejFq1amHVqlVITk7WYqRERERE/5OcnIxVq1ahVq1amDx5MiIjIxXbOnXqhIsXL8LX1xddunRhUph0jp6eHvr3749bt27h+PHj8Pb2VmwLDAzEmDFj4Obmhp9//hlpaWlajJRINzHzRFRCUlJSsG7dOri4uGDChAkIDw9XbPPx8cHZs2dx5coV9OjRgw1GIgDvvfcezpw5gytXrqBnz56K9S9evMCMGTPg5OSE77//HgkJCVqMkoiIiHRZQkIClixZAicnJ8yYMQMvXrxQbOvVqxf8/Pxw5swZtGvXTotREpUOMpkMPXr0wJUrV/DPP//Ax8dHsS08PBwTJkyAi4sL1q1bh5SUFO0FSqRjmBwm0rDExEQsW7YMtWrVwmeffYaoqCjFtm7dusHX1xfnz59Hhw4dmBQmykPLli3x119/KcYnyxYTE4Ovv/4ajo6OWLhwIWJjY7UYJREREemS2NhYLFiwAI6OjpgzZw5iYmIU2wYOHIjbt2/j2LFjkh6SRCSSyWTo2LEjzp8/D19fX3Tt2lWxLSoqCp999hlq1aqF5cuXIzExUYuREukGJoeJNCQuLg7ffvstnJycMHPmTERHRyu29e3bF9evX8fJkyclY6sSUf4aNWqE33//HQ8ePMDw4cMVw67ExcXhm2++gaOjI2bNmiX5WyMiIiJSp+joaMyaNQuOjo5YtGgR4uLiAIi3zQ8fPhwPHjzAoUOHJGOrElH+2rRpg1OnTuH69evo27evYn10dDS++uorODk54dtvv1X8rRGR+jE5TKRmMTExmDt3LhwdHTF//nxFb0aZTIbBgwfj3r17+OOPP9CsWTMtR0pUNtWrVw+7d+/G48ePMXbsWBgYGAAAkpKSsHTpUjg5OWH69OmSXvpERERExREVFYXp06fDyckJS5cuRVJSEgDAwMAAY8eORUBAAHbv3o169eppOVKisqlZs2b4448/cPfuXQwePFhxV21sbCzmz58PR0dHzJ07V9JLn4jUg8lhIjV5/vw5vvjiCzg6OuK7775TjIOqr6+PUaNG4dGjR9i/fz+8vLy0HClR+eDm5oatW7ciKCgIEyZMgJGREQAgNTUVa9asgbOzMz799FPJ+N5EREREqggPD8fEiRPh7OyMNWvWIDU1FQBgZGSEiRMnIigoCFu3boWrq6uWIyUqHxo0aID9+/fj4cOHGDlyJPT19QGI43t/9913cHJywpdffikZ35uIiofJYaJiioyMxJQpU1CrVi2sXLlSMXC+oaEhxo8fjydPnmDHjh1wd3fXcqRE5ZOTkxN++uknhIaGYtq0aTA1NQUApKen46effoKrqys+/PBDBAUFaTlSIiIiKiuCgoIwduxYuLq6YtOmTUhPTwcAmJqaYtq0aQgNDcXGjRvh5OSk3UCJyikPDw/89ttvePLkCT766CMYGhoCAJKTk7FixQo4OTlhypQpiIyM1HKkRGUfk8NERRQaGorx48fDxcUF69evR1paGgDA2NgYkydPRnBwMH755Re4uLhoOVIi3eDg4IDVq1cjLCwMM2fOhIWFBQAgMzMT27Ztg7u7O0aMGIGHDx9qOVIiIiIqrR4+fIjhw4fD3d0d27dvR2ZmJgDAwsICM2fORHh4OFavXg0HBwctR0qkG1xcXPDrr78iODgYkydPhrGxMQAgLS0N69evh4uLCz7++GOEhoZqOVKisovJYSIVBQQEYNSoUXBzc8PmzZuRkZEBADAzM8OMGTMQFhaGH3/8ETVr1tRypES6qUqVKvjhhx8QHh6O+fPno2LFigCArKws7NmzB56enhg4cCDu3Lmj5UiJiIiotLh9+zYGDhwIT09P7N27F1lZWQAAKysrLFiwABEREfjhhx9gZ2en5UiJdFPNmjXx448/IiwsDDNmzICZmRkAICMjA7/++ivc3NwwevRoBAQEaDlSorKHyWEiJfn7+2PIkCGoW7cudu7cCblcDgCwtLTEnDlzEBERgRUrVsDe3l7LkRIRAFSqVAnffPMNIiIisGTJEtja2iq2HT58GI0bN0avXr1w9epVLUZJRERE2nT16lX06tULTZo0weHDhxXrbW1tsWTJEoSHh2PhwoWwsbHRYpRElM3e3h4rVqxAREQEvv76a1SoUAEAIJfL8dtvv6Fu3boYMmQI/P39tRwpUdnB5DBRIW7cuIG+ffuiQYMGOHDgAARBAADY2Nhg0aJFiIiIwOLFiyWJJyIqPSpWrIjZs2cjPDwcK1euRNWqVRXbjh8/jpYtW6Jz5864ePGiFqMkIiKikiIIAi5evIhOnTqhZcuWOH78uGKbvb09Vq1ahfDwcMyePVtxBxIRlS62trb47rvvEBERgUWLFsHa2hqA+Pd94MABNGjQAP369cPNmze1HClR6cfkMFE+Ll++jG7duqF58+Y4evSoYr2dnR2WLl2K8PBwzJs3D1ZWVtoLkoiUZm5ujs8//xxhYWFYv349atSoodj2zz//wMfHB+3atcPff/+t+BGIiIiIyg9BEHD69Gm0a9cOPj4+OHv2rGJbjRo1sGHDBoSGhmL69OkwNzfXYqREpCxra2vMmzcPERERWLp0qWTolz///BPNmjVD9+7dcfnyZS1GSVS6MTlM9A5BEHD27Fm0b98ebdq0wenTpxXbHBwcsHbtWoSFheGrr75S3L5CRGWLiYkJJk2ahODgYGzevBnOzs6Kbb6+vujatSu8vb1x7NgxJomJiIjKAUEQcPToUbRo0QLdunXDpUuXFNtcXFywefNmBAcH49NPP4WJiYkWIyWioqpQoQK++uorhIWFYc2aNahWrZpi26lTp9CmTRu0b98e586dYxufKAcmh4kgNhhPnDiB1q1bo1OnTrhw4YJim5OTEzZt2oSQkBBMnTpVMfA9EZVtRkZGGDduHJ48eYKdO3fC3d1dse369evo06cPGjVqhIMHDyompSEiIqKyQy6X4+DBg2jYsCH69u2LGzduKLZ5eHhg165dCAgIwLhx42BkZKTFSIlIXczMzPDZZ58hNDQUP/30ExwdHRXbLly4gI4dO6J169Y4ceIEk8RE/4/JYdJpWVlZOHLkCJo2bYqePXvCz89Psc3V1RXbtm1DYGAgPvnkExgbG2sxUiLSFAMDA3zwwQd48OABDhw4AC8vL8W2e/fuYfDgwfD09MSuXbuQmZmpxUiJiIhIGZmZmdi1axc8PT0xePBgycRUDRo0wMGDB/HgwQOMGDECBgYGWoyUiDTF2NgYEyZMQFBQELZt2wZXV1fFNj8/P/Ts2RNNmzbFkSNH2BGEdB6Tw6ST5HI59u7dCy8vLwwYMAC3b99WbKtXrx727NmDx48fY8yYMTA0NNRipERUUvT19fH+++/j7t27OHr0KJo1a6bY9vjxY4wcORLu7u7YsmUL0tPTtRgpERER5SU9PR2bN29GnTp1MHLkSAQEBCi2NWvWDEePHsWdO3cwaNAg6OnxUphIFxgaGmLMmDF4/Pgx9uzZg3r16im23b59GwMGDECDBg2wb98+yOVyLUZKpD38RiSdkpGRge3bt8PDwwPDhw/Hw4cPFdsaNWqEw4cPw9/fH8OGDfs/9u49Puf6/+P449r5YGZzJjab81klhzCHIcdK6RuhRKX4pVJ0UomSqJRKUqnIV0URcprTiBQ5E2PM+Tiz8/G6fn98vru0bLax+ezanvfbza32eV/X9XmtXO/P+/P6vN+vN87OziZGKiJmsVgs9OrViy1btrBixQratGljbzt8+DBDhw6lZs2afPLJJyQnJ5sYqYiIiAAkJyfzySefULNmTR577DEiIyPtbZn7iGzZsoVevXphsVhMjFREzOLs7Ey/fv3YtWsXCxYsoFmzZva2PXv20K9fP+rXr88333xDWlqaiZGK3HxKDkuJkJKSwowZM6hduzaDBw8mIiLC3tayZUuWLl3Ktm3buPfeezWLQEQAI0ncpUsXNmzYwLp16wgNDbW3HT9+nBEjRlCjRg3ef/99EhISTIxURESkZEpISOD999+nRo0ajBgxguPHj9vbQkNDWb9+PRs2bKBLly5KCosIAE5OTvTp04dt27axdOlSWrZsaW87ePAgjzzyCLVr12bGjBmkpKSYGKnIzaMsmBRriYmJfPTRRwQHBzNs2DCOHj1qb2vfvj1hYWFs2rSJ7t27a8AoIjkKCQlh1apVbNq0iR49etiPnzlzhlGjRhEYGMjEiROJjY01MUoREZGSITY2lrfffpvAwEBGjRrFmTNn7G09e/Zk8+bNrFq1inbt2pkYpYgUZRaLhe7du7Np0ybCwsIICQmxtx09epRhw4YRHBzMRx99RGJioomRihQ+JYelWIqLi+Pdd9+lRo0ajBw5kpMnT9rbunbtyoYNG1i7di2dOnVSUlhE8qxVq1YsWbLEXp8s04ULF3j55ZcJCAjgjTfeIDo62sQoRUREiqfo6Ghef/11AgICeOWVV7hw4YK97b777uOvv/5i8eLFWWYCiohci8VioVOnTqxbt47w8HC6du1qbzt58iQjR46kRo0aTJ48mbi4OBMjFSk8Sg5LsRITE8P48eMJDAxkzJgxnDt3zt52991388cff7B8+fIsNURFRPKrWbNmLFiwgD179tC/f397OZqYmBjGjRtHQEAAL774YpY+SERERK7PuXPnePHFFwkICODNN98kJiYGMJaH9+/fnz179jB//vwsNURFRPKrbdu2LF++nC1bttC7d2/78XPnzjF69GgCAwMZP368vQ8SKS6UHJZi4cKFC7z66qsEBATw2muv2WftWSwWHnjgAXbs2MHChQtp3ry5yZGKSHHSoEEDvvvuO/bv38/gwYNxcXEBID4+nkmTJhEYGMizzz6bZfWCiIiI5M3Jkyd59tlnCQwMZNKkScTHxwPg4uLC4MGD+fvvv/nuu+9o0KCByZGKSHFyxx13sGjRInbs2EHfvn3tq42jo6N57bXXCAgI4NVXX82yekHEkSk5LA7tzJkzPP/88wQEBPDWW2/Z6306OzszcOBA9u7dy/fff0+TJk1MjlREirPatWvz1VdfERERwbBhw3BzcwMgKSmJqVOnEhQUxFNPPUVUVJTJkYqIiBR9R48e5cknnyQoKIipU6eSlJQEgJubG08++SQRERF89dVX1KpVy+RIRaQ4a9KkCT/88AN79+5l4MCBODs7A0bd87feeovAwEBeeOGFLHXPRRyRksPikI4fP87//d//ERgYyHvvvWcvEO/q6srQoUM5cOAA3377LfXq1TM5UhEpSQIDA5k+fTqRkZE888wzeHp6ApCamsr06dOpWbMmjz76KBERESZHKiIiUvRERETw6KOPUqtWLT777DNSU1MB8PT05JlnniEyMpJPP/2UwMBAcwMVkRKlXr16fPvttxw4cIChQ4fi6uoKQEJCAlOmTKFGjRo8/fTTHD9+3ORIRa6PksPiUCIjI3n88ccJDg7m448/JiUlBQB3d3dGjBjBoUOHmDlzJsHBwSZHKiIlWdWqVfnggw84cuQIY8aMoVSpUgCkp6cza9Ys6taty0MPPcTevXtNjlRERMR8e/fu5aGHHqJu3brMmjWL9PR0AEqVKsWYMWM4evQoH3zwAVWrVjU5UhEpyYKDg5k5cyaHDh1ixIgRuLu7A5CcnMy0adMIDg7m8ccfJzIy0uRIRfJHyWFxCH///TcPP/wwtWvXZubMmaSlpQHg5eXFqFGjOHLkCNOmTaN69eomRyoickXFihV55513OHr0KK+99hq+vr4AWK1W5s6dS8OGDbn//vvZvn27yZGKiIjcfNu3b+e+++6jYcOGzJ07F6vVCkCZMmV4/fXXiYqK4p133qFChQomRyoickX16tWZNm0aR44cYdSoUXh5eQGQlpbGzJkzqV27Ng8//DB///23yZGK5I2Sw1Kk7dq1i//85z/Ur1+fb7/9loyMDABKly7NK6+8QlRUFFOmTKFy5comRyoikrOyZcsybtw4oqKieOuttyhbtqy9bcGCBdx666306tWLLVu2mBiliIjIzfH777/Ts2dPbr31Vn766Sf78XLlyvH2229z9OhR3njjDfz9/U2MUkTk2ipXrsyUKVOIiori5ZdfxsfHB4CMjAy+/fZb6tevz4MPPsju3btNjlTk2pQcliJp69at3HPPPfYC8DabDQB/f3/efPNNoqKimDBhAuXKlTM5UhGRvPP19eXll18mKiqK9957j0qVKtnblixZQsuWLencuTPh4eEmRikiIlI41q9fT+fOnWnVqhVLly61H69UqRLvvfceR48e5aWXXrKvtBERcQTlypXjrbfeIioqijfffBM/Pz8AbDYb33//PY0bN+aee+5h69atJkcqkj0lh6VI+e233+jWrRvNmzdn0aJF9uMVKlRg0qRJHD16lLFjx1KmTBnzghQRuUHe3t4899xzREZG8vHHH1OtWjV7W1hYGCEhIbRr146VK1faH46JiIg4IpvNxsqVK2nXrh3t27cnLCzM3latWjU+/vhjjhw5wnPPPYe3t7eJkYqI3Bg/Pz/Gjh1LVFQUkyZNylISZ9GiRTRv3pxu3bqxadMmE6MUuZqSw2I6m83GmjVr6NChA23atGH58uX2tqpVq/Lhhx9y5MgRRo8ebV+mISJSHHh6ejJ8+HD7ZppBQUH2tg0bNtC1a1datmzJ4sWLlSQWERGHYrPZWLx4MS1btqRr165s2LDB3hYUFMQXX3zBoUOHGD58OB4eHiZGKiJSsHx8fBg9ejRHjhxh6tSpVKlSxd62fPly7rzzTjp27MiaNWs0xpciQclhMY3NZuPXX3/lzjvvpFOnTqxbt87eFhgYyGeffcbhw4d5+umn7QXeRUSKIzc3N4YOHcqBAweYPXs2devWtbf98ccf9O7dm2bNmjF//nz7Zj0iIiJFkdVq5ccff6RZs2b07t2bP/74w95Wt25dZs+ezYEDBxgyZAhubm4mRioiUri8vLwYOXIkkZGRTJ8+nYCAAHvb2rVr6dSpE23atGHZsmVKEouplByWm85qtfLzzz9z++2306NHDzZv3mxvq1WrFrNmzeLgwYM88cQTuLu7mxipiMjN5eLiwoABA9izZw8//PADjRs3trft3LmTvn370rBhQ+bMmUN6erqJkYqIiGSVnp7OnDlzaNiwIQ888AA7d+60tzVu3JgffviBPXv2MGDAAFxcXEyMVETk5nJ3d2fYsGFEREQwa9YsatWqZW/btGkT3bt3p3nz5ixcuFATQcQUSg7LTZORkcG8efNo0qQJffr04a+//rK3NWjQgLlz57J//34eeeQRXF1dTYxURMRczs7O9O3bl+3bt7No0SJuv/12e9v+/fsZOHAgdevW5csvvyQ1NdXESEVEpKRLTU3lyy+/pG7dugwcOJD9+/fb25o3b84vv/zCjh076Nu3L87OziZGKiJiLldXVx555BH279/P3LlzadCggb1t27Zt3HvvvTRp0oR58+aRkZFhYqRS0ig5LIUuLS2Nb775hvr169OvXz/27Nljb2vWrBk//fQTu3btol+/fhowioj8g5OTk31JbmZ9skyHDx9m6NCh1KpVi08//ZTk5GQTIxURkZImOTmZTz75hJo1azJ06FAOHz5sb2vTpg0rVqxgy5Yt9OrVC4vFYmKkIiJFi7OzM/369WPXrl0sWLCAZs2a2dv27NlDv379qF+/Pt988w1paWkmRiolhZLDUmhSUlKYMWMGtWvX5pFHHuHgwYP2tpYtW7J06VL70zEnJ/1VFBHJicVisW/ms27dOjp16mRvO3bsGMOHDycoKIgPPviAhIQEEyMVEZHiLiEhgffff58aNWowYsQIjh8/bm8LDQ1l3bp1bNiwgS5duigpLCJyDU5OTvTp04dt27axdOlSWrZsaW87ePAgjzzyCHXq1OHzzz8nJSXFxEiluFNGTgpcYmIiH330EcHBwQwbNoyjR4/a29q3b09YWJi9ro4GjCIieWexWAgJCbH3oz169LC3nT59mueee47AwEAmTpxIbGysiZGKiEhxExsby8SJEwkMDGTUqFGcOXPG3tazZ082b97MqlWrCAkJMTFKERHHY7FY6N69O5s2bSIsLCxLP3rkyBGeeOIJatasybRp00hKSjIxUimulByWAhMXF8fkyZOpUaMGI0eO5OTJk/a2zBlvmTtyKiksInJjWrVqxZIlS/jrr7/o06eP/fiFCxd4+eWXCQgI4I033iA6OtrEKEVExNFFR0fzxhtvEBAQwMsvv8yFCxfsbffddx9//fUXixcvzjLjTURE8s9isdCpUyfWrVtHeHg4Xbt2tbedOHGCp59+mho1ajB58mTi4+NNjFSKGyWH5YbFxMQwfvx4AgMDGT16NOfOnbO33X333fZamW3atDExShGR4qlZs2YsWLCAPXv20L9/f3uZnpiYGMaNG0dgYCAvvfRSlr5ZREQkN+fOnePFF18kICCAcePGERMTAxjLoPv378+ePXuYP39+llqZIiJSMNq2bcvy5cvZsmULvXv3th8/e/Yso0ePJiAggAkTJtj7ZpEboeSwXLcLFy7w6quvEhAQwGuvvWafnWaxWHjggQfYsWMHCxcupHnz5iZHKiJS/DVo0IDvvvuO/fv3M3jwYFxcXABjVcc777xDYGAgzz77LKdOnTI5UhERKcpOnjzJs88+S2BgIJMmTbLPTnNxcWHw4MH8/ffffPfddzRo0MDkSEVEir877riDRYsWsWPHDvr27WtfhR0dHc3YsWMJCAhg7NixWVZ1iOSXksOSb2fOnOH5558nICCAt956y17X0tnZmYEDB7J3716+//57mjRpYnKkIiIlT+3atfnqq6+IiIhg2LBhuLm5AZCUlMTUqVOpUaMGTz31FFFRUSZHKiIiRcnRo0d58sknCQoKYurUqfa6lm5ubjz55JNERETw1VdfUatWLZMjFREpeZo0acIPP/zA3r17GThwIM7OzoBRD37ChAkEBgbywgsvZKkHL5JXSg5Lnh0/fpz/+7//IzAwkPfee4/ExEQAXF1dGTp0KAcOHODbb7+lXr16JkcqIiKBgYFMnz6dyMhInnnmGTw9PQFITU1l+vTp1KxZk0cffZSIiAiTIxURETNFRETw6KOPUqtWLT777DNSU1MB8PT05JlnniEyMpJPP/2UwMBAcwMVERHq1avHt99+y4EDBxg6dCiurq4AJCQkMGXKFGrUqMHTTz/N8ePHTY5UHImSw5KryMhIHn/8cYKDg/n4449JSUkBwN3dnREjRnDo0CFmzpxJcHCwyZGKiMi/Va1alQ8++IAjR44wZswYSpUqBUB6ejqzZs2ibt26PPTQQ+zdu9fkSEVE5Gbau3cvDz30EHXr1mXWrFmkp6cDUKpUKcaMGcPRo0f54IMPqFq1qsmRiojIvwUHBzNz5kwOHTrE8OHDcXd3ByA5OZlp06YRHBzM448/TmRkpMmRiiNQclhy9Pfff/Pwww9Tu3ZtZs6cSVpaGgBeXl6MGjWKI0eOMG3aNKpXr25ypCIikpuKFSvyzjvvcPToUV577TV8fX0BsFqtzJ07l4YNG3L//fezfft2kyMVEZHCtH37du677z4aNmzI3LlzsVqtAJQpU4bXX3+dqKgo3nnnHSpUqGBypCIikpvq1avz8ccfc+TIEZ577jm8vLwASEtLY+bMmdSuXZuHH36Yv//+2+RIpShTcliusmvXLv7zn/9Qv359vv32WzIyMgDw8fHh5ZdfJioqiilTplC5cmWTIxURkfwqW7Ys48aNIyoqirfeeouyZcva2xYsWMCtt95Kr1692LJli4lRiohIQfv999/p2bMnt956Kz/99JP9eLly5Xj77bc5evQob7zxBv7+/iZGKSIi16Ny5cq89957HD16lJdffhkfHx8AMjIy+Pbbb6lfvz4PPvggu3fvNjlSKYqUHBa7rVu3cs8999gLndtsNgD8/Px488037YmEcuXKmRypiIjcKF9fX/sDv/fee49KlSrZ25YsWULLli3p3Lkz4eHhJkYpIiI3av369XTu3JlWrVqxdOlS+/FKlSrZEwkvvfSSfUWJiIg4rvLly/PWW28RFRXFuHHj8PPzA8Bms/H999/TuHFj7rnnHrZu3WpypFKUKDks/Pbbb3Tr1o3mzZuzaNEi+/EKFSowadIkoqKiGDt2rL1TERGR4sPb25vnnnuOyMhIPv74Y6pVq2ZvCwsLIyQkhHbt2rFy5Ur7Q0MRESnabDYbK1eupF27drRv356wsDB7W7Vq1bIsQfb29jYxUhERKQx+fn689tpr9lJB5cuXt7ctWrSI5s2b061bNzZt2mRilFJUKDlcQtlsNtasWUOHDh1o06YNy5cvt7dVqVKFqVOncuTIEUaPHm1fjiAiIsWXp6cnw4cPt28yGhQUZG/bsGEDXbt2pWXLlixevFhJYhGRIspms7F48WJatmxJ165d2bBhg70tKCiIL774wr55kYeHh4mRiojIzeDj42PfZHTq1KlUqVLF3rZ8+XLuvPNOOnbsyJo1azTGL8GUHC5hbDYbv/76K3feeSedOnVi3bp19raAgACmT59OZGQkI0eOtBcyFxGRksPNzY2hQ4dy4MABZs+eTd26de1tf/zxB71796ZZs2bMnz/fvomRiIiYy2q18uOPP9KsWTN69+7NH3/8YW+rW7cus2fP5sCBAwwZMgQ3NzcTIxURETN4eXkxcuRIDh8+zPTp0wkICLC3rV27lk6dOtGmTRuWLVumJHEJpORwCWG1Wvn555+5/fbb6dGjB5s3b7a31apVi1mzZhEREcGwYcNwd3c3MVIRESkKXFxcGDBgAHv27OGHH36gcePG9radO3fSt29fGjZsyJw5c0hPTzcxUhGRkis9PZ05c+bQsGFDHnjgAXbu3Glva9y4MT/88AN79uxhwIABuLi4mBipiIgUBR4eHgwbNoyIiAi++uoratasaW/btGkT3bt3p3nz5ixcuFATQUoQJYeLuYyMDObNm0eTJk3o06cPf/31l72tQYMGzJ07l/379/PII4/g6upqYqQiIlIUOTs707dvX7Zv386iRYu4/fbb7W379+9n4MCB1K1bly+//JLU1FQTIxURKTlSU1P58ssvqVu3LgMHDmT//v32tubNm/PLL7+wY8cO+vbti7Ozs4mRiohIUeTq6srgwYPZv38/3333HfXr17e3bdu2jXvvvZcmTZowb948MjIyTIxUbgYlh4uptLQ0vvnmG+rXr0+/fv3Ys2ePva1Zs2YsWLCAXbt20a9fPw0YRUQkV05OTvalypn1yTIdPnyYoUOHUqtWLT799FOSk5NNjFREpPhKTk7mk08+oWbNmgwdOpTDhw/b29q0acOKFSvYsmULvXr1wmKxmBipiIg4AhcXF/r378/u3btZsGABzZo1s7ft2bOHfv36Ub9+fb755hvS0tJMjFQKk5LDxUxKSgozZsygdu3aPPLIIxw8eNDe1rJlS5YuXcq2bdvo06cPTk763y8iIvljsVjsmxytW7eOTp062duOHTvG8OHDCQoK4oMPPiAhIcHESEVEio+EhATef/99atSowYgRIzh+/Li9LTQ0lHXr1rFhwwa6dOmipLCIiOSbk5MTffr0Ydu2bSxZsoQWLVrY2w4ePMgjjzxCnTp1+Pzzz0lJSTExUikMyg4WE4mJiXz00UcEBwczbNgwjh49am8LCQkhLCzMXj9GA0YREblRFosly/WlR48e9rbTp0/z3HPPERgYyMSJE4mNjTUxUhERxxUbG8vEiRMJDAxk1KhRnDlzxt7Wo0cPNm3axKpVqwgJCTExShERKS4sFot9n6p/X1+OHDnCE088Qc2aNZk2bRpJSUkmRioFSclhBxcXF8fkyZOpUaMGI0eO5OTJk/a2rl27Eh4ebp/ZpaSwiIgUhlatWrFkyRL7ypRMFy5c4OWXXyYgIIA33niD6OhoE6MUEXEc0dHRvPHGGwQEBPDyyy9z4cIFe1vmPiJLliyhVatWJkYpIiLFlcVisa9MCQ8Pp0uXLva2EydO8PTTT1OjRg0mT55MfHy8iZFKQVBy2EHFxMQwfvx4AgMDGT16NOfOnbO39e7dmy1btrB8+XLatm1rYpQiIlKS3HrrrSxYsIDdu3fTr18/e/mimJgYxo0bR2BgIC+99FKWa5aIiFxx7tw5XnzxRQICAhg3bhwxMTGAsdy3f//+7Nmz56qakCIiIoWpbdu29pr2vXv3th8/e/Yso0ePJiAggAkTJtivWeJ4Snxy2GYz/jiKCxcu8OqrrxIQEMBrr71mn4VlsVjo27cvO3bsYNGiRdxxxx0mRyoiIiVVw4YNmTt3Lvv37+eRRx6xb3waFxfHO++8Q2BgIM8++yynTp0yOdK8c7TxgkhJ52jf2ZMnT/Lss88SGBjIpEmT7LOwXFxcsuwm36BBA5MjFRGRkuqOO+5g0aJFbN++nb59+9pXp0dHRzN27FgCAgIYO3ZsltUuRZ2jjRcKi8VmKxn/GS5fhoMH4dAh45+7d0N0NKSmGu1ubuDnB40aQa1aULOm8U9/f3PjznTmzBnee+89pk+fnmWDH2dnZ/r3789LL71EvXr1TIxQxPFcvgwREUa/EBEBU6euxnje4va/V6Th5+fE//1fe2rXLnr9goijOHr0KJMmTeKrr74iNfPCC7i5uTFkyBDGjBlDQECAiRFeERtr9AeZf3btMsYLmftuuLpCmTLGeOGf/ULZsqaGLVJiXbx45VqeOcaPiYHMDdXd3Y3rduPGxnc180/p0qaGbRcVFcWkSZP48ssvr+ofH330UcaMGUNgYKB5AYo4oOjorP3CtGnruHTJCrj+7xWp+PvDM890ynLv7+trYtAiDmjfvn1MnDiRuXPnYrVa7ce9vb158sknGTVqFJUqVTIxwiv+2S9kjvFjYrLmBP39s47xa9cuOf1CsU4O22zw11+weDGsWmXc8KWnG8c9PIz/+f9b8YrVagwik5ONn11cwMcHOnWCXr3g9tuvvPZmOn78OJMnT2bmzJkkZwYHuLq68vDDD/Piiy8SHBx88wMTcVA2G+zYYfQLK1ca/ULmBeH48YMkJl4CMi9sTnh5laF69TqA0S+UKgUdO0Lv3ka/8L8JkSKSBydOnGDKlCnMmDEjyzXNxcWFQYMG8dJLL1GzZs2bHpfNBjt3wpIlsGKF8eAoNRUsFiOxlNN4wWYzksWlSkFIiNEvtGihfkGksGVkwJYt8MsvsH49xMcb30uLxRjju7pm/c6mphoPeGw24/vs6wtduxpj/MaNjffdbBEREUycOJHZs2eTnp5uP+7p6ckTTzzB888/T9WqVW9+YCIOKiMD/vzT6BfWrjX6hcyv1rFjB0hMjCHrGN+PatVqA0a/ULo0dOli9AtNm5rTL4g4qkOHDjFp0iS++eYb0jKfzgIeHh489thjvPDCC1SrVu2mx2W1Gv3C4sWwZg3ExV3pF3IaLyQnG99/F5cr/ULPnnDrrcW7XyiWyeH0dFi6FH78EfbsMf7n+voaf9zccv8farMZfyliY40nCR4eULcu9O1r3Pi5ul77/QUhMjKSd955h6+//jrLl8vd3d3+5apevXrhByJSTGRkwK+/Gv3C7t2QlGR09r6+RvLHYoGNGzcSE3Mpy/vKlPGjTZs22GzGjWdsLFy6ZLynbl24/364+26jbxGRvDl79izvv/8+n376aZYNLJycnOjXrx8vv/wy9evXL/Q4MjJg+XKjX9i5ExITr4wXMvuFa/l3v+DmlrVfcHcv9F9BpERJSYGFC2HBAvj7b2O87udnXM9dXfP2nU1JMR4AXb4MXl7QpAk88ICRLL4ZD3b27t3L22+/zbx587LMsipVqhTDhw/n2WefpWLFioUfiEgxkZoKixbB/PlGv5CScnW/kNsYP7NfiI0FT09j5mDfvtC9ux74iuTHsWPHePfdd/niiy9IyVx2hzG5cfDgwYwZM4agoKBCjyMtzXhQ9OOPsH+/8R0vU8boF/KTE8wcL3h4GP3C/fdDjx5G4ri4KXbJ4SNH4N13ITzc+LlCBfD2vrEMf3w8nD9v/AVp2RJefNFYdlIY/v77byZOnMh3331HRkaG/biXl5d9Wn7lypUL5+QixVRUlNEvrFtn/Fy+vDHT79/9wrUGjv+WkADnzhlPGFu0MPqFOnUK6RcQKaYuXrzIRx99xIcffsjly5ftxy0WC3369OHVV1+ladOmhXLu48dh8mRjFoHVavQLPj43Nl74Z7/QvLnRL6jik0jB2L8f3nnHmAHk5HRljH+9bDZjBtH588bndewIL7wAhTWxafv27bz11lssWLAgy3FfX19GjhzJ008/TVnVpxHJlwMHjH5hy5Zr9wt5HePbbFfu/QHat4fRo6GIVL4ScRinT59mypQpfPbZZyQmJtqPOzs7M2DAAF566SXqFNLNc0SE0S/8/rsxrs+8979eNtuVMb7FAm3bGv1CjRoFF3NRUGySw+npxlOB6dPh7FmoWvXGBozZSUyEEyegXDl44gno16/gZhHv3r2bt956ix9++IF//i/x8fHh//7v/3j22WcpV65cwZxMpITIyDBmF33yCZw5A1WqXPvCkJ/kcKakJCPJVLYsPP449O+vWcQi+XX58mU++eQT3n//fS5evJilrWfPnrz66qu0aNGiQM6VkQE//wwffwynT+feL1yPpCRjvODnB0OHwoABmkUscr1SUmD2bPjyS2OG/i23GDP7ClJ8PJw6ZfQHw4fDvfcW3GzBLVu2MGHCBJYsWZLleNmyZXnuuecYPnw4viWloKFIAUlNhe++g5kzjbrj1apdu1+4njF+Zr9QuTI89RTcd59mEYvk1/nz55k6dSrTpk0jLi7OftxisfDAAw/wyiuv0KhRowI5V1oazJ0Ln39u9AtVqxorhApSQgKcPAkVKxr9wv33F59ZxMUiOZyYCK+/biwZd3MzBnaFVR/YajWSTElJ0LkzTJhgzDS6Xlu3bmXChAksWrQoy3E/Pz+effZZRowYgZ+f3w1GLVLyJCXBuHFGfaG89gvXM3AE42ni6dNGX9SpE7z9dtHZ6EbEkcTHx/P5558zefJkzpw5k6Wtc+fOvPrqq7Rr1+66Pz85GcaPN5afurgYg8bCGi/YbMZ4ISHBmHn0zjslZ0MLkYISEwMvvWSs/PH2hkqVCq/en9VqPNTJyDDKwowdaywjvV7h4eGMHz+esLCwLMcrVarECy+8wBNPPIF3Qc9kESkBYmONfmHNGiPxU7ly7v3C9Y7xrVYjEZSWZtQifuONG+sXREqqS5cuMW3aNKZOncqlS1m/i/fccw+vvvoqt91223V/flwcvPqqsdeYp6cxXijMnOCpU8ZDqu7djZxDQSehzeDwyeG4OGPZ5urVxl+AG0nU5kd8vJEMatfOWJaa3xu+TZs2MX78eJYvX57leIUKFRg1ahRPPvkkPjfrlxEpZhIS4OWXjY2lKlbMe6L2egeO/zzvyZNw550wZYqx26mI5F9SUhJfffUVkyZN4vjx41na2rZty9ixYwkNDcWSjyxRYiK88gosW5a/fuFGZa46atUK3nvPWGUgIrm7eBFGjYLNm43ZwjfrxuvyZWPpaLdu8NZb+TuvzWZj1apVTJgwgQ0bNmRpq1atGmPGjGHIkCF4KLskcl2io+H55+G33/K3UvhGx/ixscbq5K5djUkgeq4jcn3i4uL49NNPee+99zifWb/lf7p168arr75K69at8/WZMTFGmYf16wtnRWBO4uKMiSChoTBx4s3LRRYWh04OJyfDmDHGZjL/HDTu3DmU48e/zPY9deu+Q82aY/L0+X/80Ytz55Zk29aw4cdUrDic48eNGmVTpuR+kbDZbKxdu5YJEyawdu3aLG1VqlRh9OjRPPbYY3gVh8cOIiZJSTEeGC1bZlwc/vm93LHjEU6c+Cbb93XseIS//jpxzYHjpk3tiY5en+37e/Y0utLMMhMhIUYiyNEvEiJmSk1N5dtvv2XixIlERkZmaWvRogWvvvoqPXr0yDVJnJpqJIYXLzZmGGU3aLxW/5AfFSveTfPmC7McS06GY8egTRv44AOtLBDJzeXL8MwzsGkTVK+ev5l6eblW5yZzEkivXkaCOLdyUTabjSVLljBhwgT++OOPLG1BQUG8/PLLDBw4EDfVnRK5bnFx8Nxzxt5C/+4XbsYYPyHBmC3Yo4eRIFa5KJHrl5iYyMyZM3n33Xc5depUlrYOHTowduxY2rdvn+sYPyHBeJC8dm3W8jK59QleXoG5xpjXfuHkSbjrLpg0ybFXFhTSROvCZ7MZ//FXrLi6lkiDBh/g5ZV9deiDB18jNnZXrp9/7NgXOSaGy5fvQkDAU3h6GknpNWuM8hI5pdltNhvLli2jTZs2dOrUKUtiOCAggOnTpxMZGcnIkSOVGBa5ATYbvP++kRiuXNmcp/qensaFaf16ePPNnPsFEcmdm5sbQ4cO5cCBA8yePZu6deva27Zs2UKvXr1o1qwZ8+fPx2q15vg5U6caieFKlW7ebIJ/8vAw+oWNG40yWNcIVaTEs1qNpdvXkxguKKVKGf3FkiVG/5ETq9XK/PnzadasGb17986SGK5bty6zZ8/mwIEDDBkyRIlhkRtgsxnj6vBw43pqRr/g7W3cXyxdatxvaIwvcv28vLwYOXIkhw8fZvr06QT8Y9fHtWvX0rFjR9q0acOyZcvIaT6rzWbk4daty73ueGHx9jYmpK1YYeQnHblfcNjk8Lp1xoYy5cpdnQBycfGhSZNvyO7Xs1pT2b59IFZrao6fnZh4lH37nsu2zdXVj8aNv7I/wfDyMpanLl1qJKSynsvKwoULad68Od27d2fTpk32tlq1ajFr1iwiIiIYNmwY7nr0KHLDNmwwNqYsW9acBFCmzDpHy5cbN5YicmNcXFwYMGAAe/bs4YcffqBx48b2tp07d9K3b18aNmzId999R3p6epb3btoE339vbA5n5kx+T0/jpjIszKh5LCLZW7TIqBlYubK5M3B8fKBMGaP/+McQHoD09HTmzJlDw4YN6du3Lzt37rS3NW7cmB9++IE9e/YwYMAAXIrLTjUiJlq82BhXV6pkTgIoU6lSxn3Gjz8aD3xF5MZ4eHgwbNgwIiIi+Oqrr6hZs6a9bdOmTXTv3p3mzZuzcOHCqyaCLFtm5OEqVDC/XyhXDhYuNPKUjsohk8PR0UYZh7Q042YvO2XLtiUoaFS2bXFxuzhw4LVs22w2Kzt2PEx6ely27Q0bfoKnZ9Usx3x9jScEU6catYgyMjKYN28eTZo04d5772Xbtm321zZo0IC5c+eyf/9+HnnkEVxdXXP/hUUkVzExRr+QklI0av2WLm1sjvHhh8bSVBG5cc7OzvTt25ft27ezaNEibr/9dnvb/v37GTBgAPXq1eOrr74iNTWV2Fh4912j3EtRqPXr42NsjjFtmrEETUSyOnECPvoInJ2LRlmmsmWN/mPyZKPmaGpqKl9++SV169Zl4MCB7N+/3/7a5s2b88svv7Bjxw769u2Ls7OziZGLFB+nThnjaYulaJRl8vc37jcmTzbuP0Tkxrm6ujJ48GD279/Pd999R/369e1t27Zt495776Vp06Z8//33ZGRkcPaskX+z2YrGhs9+fkYZu/feM/KVjsjhHmXbbMag8dAhCAq69mvr1BnP+fPLiYvbfVXb4cOTqVixF/7+d2Y5Hhn5AdHR4dl+XuXKD1C1ar9s26pVg0OHbDz66B4iIu4nIuJglvZmzZrx6quvcs899+BUWNsmipRgn3wCBw5Ajewrypjillvg8GHjIjF5cuHtsC5S0jg5OdG7d2969erFypUrGT9+PL/99hsAhw4dYsiQIYwbN45mzeaxb18LatRwuqHvX3DwaFxdc3ga/S/e3rWv2V61qtEvTJ5sLEvVkEDEYLUa34tTp+AfE4dMZbEYY/y9e60MGrSF7dsf5NixY1le06ZNG8aOHUvnzp3ztUmmiOTOZjPG0SdOQHCw2dFcUa2acd/xySfGngYiUjBcXFzo378/Dz74IAsXLmTChAls374dgN27d/Pggw9Su3YdgoPnc/RoA2rWLDrX3erVISLCyFe+/rrj3fs7XHL4wAFjmXa5cpDbKi1nZ3eaNZvNxo13ZFNGwsqOHYNo124nLi7G+vO4uH0cOPBqtp/l7l6ZRo2mZ9tmtWZw4sQJjh07S0SEC3ClbnDLli0ZO3Ys3bp104BRpJBERBjLOMqWhaI0Gd/Z2Vj+FhYGW7dC8+ZmRyRSvFgsFrp27UqXLl0IDw9n/PjxrF69GoBjx5w4dswJV9ftuLr6ERBQHWfn6xv2BAQ8maeNK/Iis19Yuxa2bIFWrQrkY0Uc3pYtxnLMypWLzkOTjIx0Tpw4xtGjl4iIcOKfiy5DQ0N59dVXCQkJMS9AkWJu61ZYvdq4bhalyfiursZ9x8KF8MADUKuW2RGJFC9OTk706dOHe++9l19//ZXx48ezZcsWAA4e9OTgwVTc3f/Ew6Mi1ardgpOT+R2Ei4uRp1yyBB58EP6xVYpDKCJDr7xbutTYqTSnchL/Vrp0E2rXfiPbtsTESPbtM0pPWK1p7NgxEKs1OdvXNmnyJW5uWdeqZ2RkcOTIEdasWcPu3btISTmLkRjuSUhICGFhYfY6KUoMixSepUuN5Z5FoZzEv/n4QHKyag+LFCaLxZLlutujRw+gO1CGtLRT7Nu3l9WrV3Po0CHS09PMDpfSpY0lqb/8YnYkIkXHL78Y34uiUE4iPT2NQ4ciCAtbzb59e0lLOw34At3p0aMHmzZtYtWqVUoMixSyxYuNcXRRKCfxb/7+xv3Hr7+aHYlI8WWxWOjRowebN2/+x3W3F+BFSspZdu/exZo1azhy5AgZGRlmh4ufn5GvXLrU7Ejyz6FmDl++bFwgfHzyN0U7OHg0Z88u4dKlTVe1HTv2OZUq3cOlS5u4fPmvbN8fEDCMChW62X9OT08nKuoohw9HkpqakuW1pUtD9erPM3/+eMqVy3uMInJ94uKMG8pSpYrm0g2LxdjQZtUqeOopYwNLESk8rVq14vvvl9CxYywHDpzn8mXjeGpqKn//vZ9Dhw4RFFSDGjVq4OrqZlqcfn7G7OGTJ41SEyIl2cmTxvchr5M/CktaWipHjhwhMvLIvx4k2fD1daNu3Yl8/33pqzbDFpGCd+aMsfquTBmzI8mexWLcf/zyCwwZYu5m2CLFncViITQ0lCZNQunYMZZjx04SG2u0JScns3fvHiIiIggODiYgIMDEOI185eLF8NhjRfPBVk4caubwqlVw7hyUL5+/91kszjRt+i3OztmP5HbsGMShQ+9k2+blFUy9elMASEtL4+DBg6xeHcb+/fuzJIYrVqxImzZtuPPOuqSl+bJsWf5iFJHrs3q1MXjMb79wM/n7G4Xp1S+I3Bxr1kBcXGlatAgmJCSEKlWqAsbTo/R041oeFraa/fv3k5KScu0PKyT+/nDpkvoFETC+BzEx5q0ASklJYf/+/YSFrebgwYP/SAxbqFKlKiEh7WnRIojY2NKsWWNOjCIlzfLlxvi5KK4MzFS+vLHxdFiY2ZGIlAzLl0NaWmnuvLMebdq0oeI/Zl6lpqawf/8+Vq8OIzYzc2yC8uXh7FlYudK0EK6LQyWHw/+3T1xutYaz4+0dTP3672Xblpp6AZst/arjmUllq9WVv//+m9Wrwzh48ABpaVdmElSuXIV27drRvPkdlCnjh7OzEd/atfmPUUTyLzzc2KyiKNUa/jdnZyM+3VCK3BwbNxr9gpsb+PiU5tZbb6VDh/bccks1MpPEGRnpHD58iNWrV7N3796bvhTNyQnc3XVDKQLG98DN7ebXGk5OTmLvXqPszOHDh8jIMO4HLBYL1apVo0OH9tx66634+Pjg5mb0Kxs33twYRUqq1auN8XNRqjX8b66uRr+wYYPZkYiUDGvWGPk2Z2coU8aP5s3voF27dlSuXMX+mrS0NGJjL5sWo4uLMYM4M3/pKBymrER6Ouzdyw0t4woIeIIzZxZx/nzepukEBDzHmTNliIoK+9dNo4WqVatSq1ZNSpW6ujBaqVJw8KBRH8nD4/rjFZFrs1phz54b6xdullKl4PBhSEwEL6/cXy8i18dmg927r/6eeXuXomnTptSuXZvDhw9x7NhxbDYrVmsGR45EAqdz/Mw1a2rk+fw9e9ry/NpSpSAqyiiPUxTqrIqYITbW+B7czCXZSUmJHDp0mGPHjmGzWe3HLRYnqlevRnBwTbyyuVh7eRn9i81WNEtZiRQXCQnGuNkRSjV4exv9gtVadDbTFCmOkpONjej/3S+ULu3LbbfdRlxcbQ4dOsTJkyev+Tn5GddfL29vI3+ZkVG0H3D9k8N0X8ePG8vNbjQJ1KTJl7i6ls31dW5udYmKak1k5GF7YthicaJatep06NCBZs2aZZsYBmPgmJAAR47cWKwicm0nTxrLsh0hOeztfWWgKyKF5/RpuHAh537By8uLRo0a06lTR2rUqIGT/U4u70ndguLlZTwwOnTopp9apMg4dMj4HtyMa3lCQjw7d+5gzZo1REUdtSeGnZycqVEjiE6dOtKoUeNsE8NgfGcvXDDKWYlI4cmcUOEoY/xLl4z7EhEpPJGRxv10ThOtfHx8aNasGR06dMDLy9zOw9vb2DPt+HFTw8gXh0kOZw4cb3TGnYdHZerXn5zr61JTn8BmM1L8Tk5OBAYG0rFjR5o0aYJ3LlcpT09IStLNnkhhO3To2heIosTDw3jaqX5BpHDltV/w8PCkQYOGdOoUSnBwMBbLzR8SeXhASor6BSnZDh0yvgfu7oV/rrVr13H8+HFsNuNhkLOzC8HBNenUqRMNGjTAw8Pzmu/PfNCr76xI4Tp82OgXHGEVbubEMPULIoXr0CEjz+Z57Us13t7e+JtcrNwRJ4A4TFmJY8eMfxbElOwzZ37Ow6t+x8mpNoGBAQQFBeORjyuTk5Ox1CwzZhEpHMeOGUs7r6cO+c1msahfELkZjh83lnbmtQ65u7s79erVJympMqdOFW5s/6Z+QcT4zmZ+FwqfkRR2cXGlRo0a1KhRAzc3tzy/O7O+qL6zIoUrKsr4pyOUb3FxUb8gcjNkjhccoXyLs7Pj9QsOkFIxJCcXzMXh2LEvOHt2cR5eOZ/mzUdQvnyD6z5XcvJ1v1VE8qCg+oWbxWZTvyBS2JKSrq9fcLrmSLMPUIoKFSoW+EwEq1X9gpRsSUnG9+BmcHV1IygoiMDAQFxvYCdbfWdFCldKijFudhQWixGziBSepKSC6Rcslvux2bIuMaxcuQq+vr5Zjh079jmJiddfK9ZicazxgsMkh9PSbvwvQmLiEfbtey6Pr85gz54htGu3HWfn/K9Zt9kgNTXfbxORfEhLy9/rc1s2bsumk/nnRjXX+OQ8x5DfmEUkfwrnO9YNqESDBu3x9i7Y3XF0QyklXWrqzXvQGxraCWfnG7/90RhfpHDl9zuW2xjfms0TqOzG/dl8cp5jUL8gUrgKaozftu1bnDkDR45EkpaWhpOTM40adcLNLWt9q3Pnlt9QctjRcoIOkxy+0XISNpuVHTseJj09Ls/vSUg4yL59o2nU6ON8n89icYyl7iKOLL/9grNz9ptIAqSmJmLJ5u40I8NKcnIyHh4eWK3ZZ3BcXX2zPZ59DHl+qYhch8K49tauXYf09LIFnhjOlI9V7SLFTuaS7JuhIBLDkPeyNSJyffJ7Lb/WGD8xMZb09PSrjttsVlJSUnB3dy+QMb7u/UUKV0F9x1xc3KhdO5CgoBocPRqFzWa9KjFcUBypX3CAah2GG92kIjLyPaKjN2TbVqVKP3x8GmXbFhX1KefPr7quc+pmT6Rwubvn74bS1dUvx7bt2zcQE3MJMGoRZl4g4uPj2Lx5E8nJSaSnX8735/6TxaJ+QaSwubkVfKLplluqUb9+/YL90H9QoklKMnd3xyoRBfrOihQ2N7f89QvXGov/9Vc4iYkJALi7e9hLysTGxrJpU8GM8TNjFpHCk99+ITcuLq7UrFmTWrVqF9yH/oOj3fs7THK4YkXjn9dzwxcbu4cDB8Zm2+bhUY1GjabTrNkcnJyy+z9nY+fOR0lLi8nz+Ww240+lSvmPVUTyLrNfyGutQi+vwBzbEhIOAuDm5kbr1q1p06YNnv/bCjUhIYHffttAYmJktu/19Mz5czNl9l3qF0QKV2a/kJFhbhx5ZbGoX5CSrVIlx0kOZ2RojC9yM2R+x/J673+tMX5qqrEs3Nu7FG3btuHOO9vYN5tPSIhn48YbG+NbreoXRG6GihWv5NqKOqvV+ONI/YLDJIdr1jRmFiQl5e99VmsaO3YMzGGpiIWmTWfh6upL6dKNqV37zWw/Izn5BLt3D8/zOVNSjCcENWvmL1YRyZ+aNcHTExIT8/Z6f/8212jdjru7B61bt6Z06dJ4eXnRuvWdeHl5A5CUtCXHJWf+/nfmeu60NGNZifoFkcJVsyZ4eeV/vGCG1FSj1Iz6BSnJgoON74Ej1OVLTDT6F31nRQpXzZrGuDmvNUZzG+OXLl2a1q1b4+HhSalSpbKM8ZOTb2yMn5ho3I8EB+ctVhG5PjVrGnk2R9irIznZ6BccabzgMMnh4GAoVQoSEvL3voMHXyc2dke2bYGBIyhXrtM/zvEC/v5ts33tqVNzOXXqxzydMzERvL0d6y+CiCOqUcPoF/KaHPb2romXV04jt400aVKGUqWu1Czz9PSkdevWeHl5AP/N8XPLl++a67kTEox+oVatvMUqItcnIABKl87/eMEMmeMF9QtSktWqZSRc83otN1NiotG/BASYHYlI8Zb5oDev1/LcxviNGvni/o86lcYkkNZ4e3tyo2P8xETw8THuS0Sk8NSsaYybHWG8kHnvHxRkdiR55zDlkT08jMHj1q1Qvnze3nPp0mYOH3432zZv79rUqzcpyzGLxYmmTb8lPLxxthvX7d79JP7+bfDwqHzN8yYkGLH65r1+vYhcBzc3qFMHNm/O+3uCgp5lz54R2bSksWNHT+rUmUClSnfj7l6BjIwk4uO34O4+nsTE/dl+nq9v81xmKxgSEiAwEPz98x6riOSfqyvUqwfr1xfcZ0ZFTc9X3cEaNZ7F2Tn3zRISEqBq1byPa0SKowoVjO/A6dNQpozZ0VxbQgLccYdjbTAj4oj8/aFyZYiKAr88Xn6vNcb/88+u2Y7x3dzGk5Bw42P8Jk0cq7aoiCMqUwZuuQUiIor+PXVCAtx+u5HHdBQONbRp2dJIAlmt4JTLnOf09AS2bx+EzXZ10UGLxZmmTb/F2dnzqjYvr0Dq15/Krl1DrmpLS7vIrl1DueOOpTme12YzlrK2bp377yMiN65FC9iwIW/9AoC//3+wWCZjs0Vd1Zaaeo7dux9n9+7HsVics+0/snKmXr3sH0D9k81mPOG8M/eVaSJSAO64A9asMeqDOjvf+Ofl9KA5JwEBw3JNDttsxsCxdWvHqbcqUhgsFuP6+M03xveiqH4fMjKMP82bmx2JSPGX2S/s35/3fsHZuStQDTh+VVthjfGtVqP0RYsWuccnIjeudWvYtatojxcy+4VWrcyOJH8cpqwEQLduxmzcmJjcX7t///MkJh7Ktq1mzZfw88u5B69e/VEqVrw727Zz534lKurzHN97+bKxrKRHj9xjFJEb162b8RQxOjr318bHx7Nly3ZstpcAn2u+NvdBIzg5DcHFpWmur4uNNcpfqF8QuTm6djVmFOSlXzBLXJyx3Ez9ggj07Gl8H+KuXrhXYCyWG3tSFB1t9Ctdc19lLiIFoEcPY/wcG5v7a6Oioti5cz/wCgUxxofBpKXVyfVV0dHGzOa77srDR4rIDeve3ci3Xb5sdiQ5u3TJyFt262Z2JPnjUMnh6tWhXbvcb/bOnVtOVNRn2baVLt2MWrVey/VcjRvPxM2tQrZt+/aNIiEh+x1NL140nhyqfqDIzVG1KnToYHTC1xIXF8umTZtISUkGquDt/Tm+vndc51m9gdFYrT35/ffNXLp07U7pwgW47TaoW/c6Tyci+VK5MnTqlLeHyWa5cMFYhtqokdmRiJivUSNo3Nj4XtyojIzsi5S6ul7/GlSbzRhnhIY61s7jIo6sXj1j/Jxbv3D48GF27971v5+qULXqD5Qpc31TeS0WY4wPvdm2bRvHj189C/mfLl0y7kOqVr2u04lIPtWubawQvHjR7EhydukShIRAtWpmR5I/DpUcBrj7bqPOV05FqFNTo9m58+qSEABOTu40azYbJyfXXM/j7l6eJk2+yLYtIyOeHTsGYbNZsxxPTjamtt9zT9Gd4i5SHPXuDe7uEB+ffXts7GU2bdpMaqqxtWnp0qW5884+tGmzmdtv/5ly5UJxcsq9UJiXV03q1n2bjh0P4+9vrC5IT0/n99+3cDGHK1Tmbqp9+qhfELmZevc26nzl1C+YKTXVSDapXxAxWCxw333G9yI19fo/x2az5bhy0N09+0kfeREfb+w63qvXdX+EiOSTxQL33mv8e+Z4OisbBw4cYP/+ffYjwcHBNGvWlTvv3HRdY/zQ0CPccks/++fv3LmDo0ePZPue+Hjj/uPu7Bcci0ghyOwXLBYj/1bUJCQY+crevc2OJP8sNpvNZnYQ+ZGeDkOHwu+/G7sVFpWbKpsNDh2CW2+FWbOMC4WI3BwZGfD447Bxo9Ev/LP2cExMDFu2/E5aWhoAvr5laNmyBa6ubv/6jBRiY/8iIeEwaWmXSE+PxcnJA1fXMri7V6ZMmduz3FhmZGTw559/cOF/0xmcnJy5447mlCt3ZWepzH6hcWP49lvHKkgv4uisVnjySVi37up+wUw2Gxw+DPXrw+zZxm7sImJM/Bg40KgxGhR0fWP8s2eX8Oef2Wdwq1V7lCZNvsz3Z1qtxrW8Qwf49NOi05eIlATJyTBoEOzeDcHB/+wXbOzdu48jR66s5q1Tpy61atUEsnYe+R3j22w29u7dmyUpXLduXWrWvLI0OLNfaNsWZswomP0NRCRvUlLgkUdgx45/9wvmstmMzfJat4aZMx1v81qHSw6DMWgcMsSYWVC5stnRGM6eNf45c6axTFREbq6ICOMikZwMVaoYxy5dimbLli2kp6cD4Ofnxx13tMDVNffVA3lhtWawdetWzp07B4CTkxO3396cChWMAea5c8bg8bPPjGVxInJzRUbCww8bs3tuucXsaAwXLhibVHz6qbEsTkSu2LIFhg8HV1coVy5/742P/5vff+9CcnL2y8BvvXUeVar8J98xnThh1Df8+msjaS0iN9e2bTBsmJGALV/eSN7u2rWL48eP2V/ToEFDatSoUYBntfH33wc4dCjCfiQ4uCb16tUFLJw6ZawmmDVL5SRFzLBjhzE5DKBiRVNDsTt1ypgk+tVXjllO0iGffderZ8weTkiApCSzozGeXMTFGU81lRgWMUetWsYFIinJmH108eJFfv/9d3ti2N+/LC1atCywxDAYs4Vvv/12KlY0ChBarVb+/PNPzp49Q0qKUSh/wAAlhkXMEhRk3FCmphpjBrOlphp1yPr1U2JYJDstWhjfj0uXci8vkZGRwqFD73DgwOts3dqH9esb55gY9vKqQaVK9+U7noQEY5w/bJgSwyJmue02eOghYx+BpCQrf/311z8SwxaaNGlSwIlh43Pr1q1LvXr17EcOHz7E7t17SEiwkZRk3HcoMSxijqZNjfxbbGxOZWdurswcxNChjpkYBgedOQzGgPHxx2HTJmMquVlTtjMyjOWht90GX35pPEEUEXOkpRk3cCtWJHDq1G/YbMaVoly58jRv3hznQlrzZbVa2b59O6dPn/rfEWeqVGnDnXeWZtYsYwd2ETFHejo89RSsXw81ahgzEs2QkWHMZG7QAL75xtiBXUSuFhdnrATau9dIyOZ06U5Li2HFCr88fKITzZv/QsWKPfIVR1oaHDlilJP4+GPHWx4qUpwkJMCgQemsWHGahIRdgBWLxYlmzZpRJXPJYCE5evQoe/bs/t9PLvj4NOX++ysyY4aTaWMKETESskOGwF9/XXu8UNjS042c4J13GmVm3HIvc14kOeTMYTD+g48fb2TljxwxbrputswbvZo1YcIEJYZFzObqCiEhqzl9ejU2W3XAmQoVKnDHHYWXGAajnMSttzajatVbMOqc1eDUqU20aPGLEsMiJnNxgXHjoGFDY7zwv8UEN5XVapy7enV4+20lhkWuxcfH+J5Ur258b6zW3N+TE4vFhYYNP853Yjg93Th3o0bw+utKDIuYzWqN48SJR0hI2AcEYbE407x580JPDAMEBgbStGkzwAWoQVzc75w58wRWaxGYrihSgnl6Gnm4oCAjL2dWTvDIESMv+eabjpsYBgdODgNUqwbvv2/MHD582HjCf7Okpxt/AatXhylTtNRMpChYtGgRQ4d2w2p9BoigVKkmNGlyO05Ohf8Y0WJxolGjpvj63gacAF7ghRfuYdasWYV+bhG5tsqV4b33oE4d49qd23L1gpS5wqhKFZg82YhBRK6tTh14913je3P48PXd8Pn63k7r1hsJDHwyX+9LTTX6iTp1jH6jqOxvIlJSRUdHExoayh9/fAc8j5PTGW65pT1ly1bI9b0FpWLFW6hSpQ0Wy2HgOZYt+4K7776bxMTEmxaDiFwtKMjIx1Wvbly7b+YkkLQ0Y4wSHGzkJatVu3nnLgwOnRwGY9butGnGrt9Hjhg1RwpbfPyVGcPTphk1kEXEXD/++CP3338/aWlpwBF69FhBaGhljh51von9goXWrSvx4IObgT3YbDYeffRRZsyYUfgBiMg1BQbCJ59A48Zw9KhRE7ywxccbg8bAQPjwQ+1LIJIfTZsa35vAQDh0KOe64RaLM66ufnh51aB8+W7UqjWWNm220rbtn/j5tcjXOS9fNvqHJk2M/iIg4EZ/CxG5EWfOnKF9+/b88ccfAPj5nWTu3LI0auTF4cPGdbawxcYaeYZWrXz45hsfPD2NnehXrFhB165duXwzBhQikqP69Y28XM2aRp7uZvULkZHGuT/+2Di3o3PYmsP/duaM8cRgxQqwWIxdyQt6FbnVauxYnJEBHTvCmDFQtWrBnkNE8u+7775j0KBBWP+39nTAgAHMmjWL6GgXpkyBZcsKt184edJ4ctihg9Ev3HKLjeeee46pU6faX/fhhx/y9NNPF+zJRSTfLlwwxgu//go2m/GUvzD6hVOnjBmI7drBiy8qySRyvaKi4J13IDzcWK5ZpQo4FfD0lowMOH7c+Nzu3WHUKChXrmDPISL5c+zYMUJDQ4mIiACgYsWKrFq1ikaNGnH8uNEvrFtnlJWrWrVw+oUTJ4x/v+sueP55qFABNm7cSI8ePYj93+yTW2+9lRUrVlBOnYaIqU6ehEmTYM0aY2x/yy2F1y/YbNC1q9EvVKpUsOcwS7FJDoPxP2jxYvjoI2OA5+9v/LnRvxBWq7Fr8sWLxoB0xAi45x7zCl6LyBVfffUVQ4cOJbMre/TRR/n888/tNYZtNiMJ9OGHxg1mYfQLlSvD8OHQp8+VfsFms/HSSy8xadIk+3veffddXnjhhRs7sYjcMJvNeJj8wQfGLEE/P6NfuNHrutVq7KZ+4YIxUHzySbj/ftUrFblR6ekwfz58+imcPWskbsuUufFreUYGREcb1/PAQHj2WeNmz2IpiKhF5HodPHiQ0NBQjh8/DkD16tUJCwujVq1a9tdkZMCCBUa/cPo0lC1rXM8LYowfHW38CQiAZ56Bbt2y9gt//fUXXbt25cKFCwDUr1+fVatW3ZQayCKSs4wM+PlnYzZvYfUL1arByJHQs2fxGi8Uq+RwptOnjYvEqlXGTZqHB5Qvb/wzP1JS4Px5SEw0BqAdOhgJIEevJSJSXHz22Wc8+eSVWoJPPvkkH3/8MU7Z9P5nzsD06bBypXET6O5uPP3Pb7+Qmgrnzl3pF9q3h6eeyn5WoM1mY9y4cYwbN85+7M0332Ts2LH5O6mIFIpz54x+YcUKY7Dn7m6MF/K7wWxmv5CUBKVLQ0iIMV4IDCyUsEVKrCNHjDH++vVGCQgvL+Nant8NYJKSjDF+SorxYKhrV+NhToWbV8JURHKwa9cuOnfuzLlz5wCoXbs2YWFhVMvhJjwqyugX1q0z7v2vt19ITjau5Zn9QufOxhi/YsXsX79//35CQ0M5deoUAEFBQYSFhVGjRo38nVhECtzx40Z5qLVrr/QL5csbY/38SE42xgvJyca9f2a/UBz3IyiWyeFMp04Zy8l//tmYGZSRYWT2vbzA29tYgpKZQ7JajWXhCQlG0sdmM9qqV4d77zWeFiopLFJ0fPjhhzzzzDP2n5955hnef/99LLk8vjt9Omu/kJ6ee7+QmGj0DZn9QrVqxuqB7t2NPiI3b7/9Nq+88or951deeYXx48fnGquI3Bxnz8Ly5cYMpCNHrvQLnp5Gv+DmlrVfSE83+oR/9gtVq14ZLygpLFK4jh41VgUtXGgsI7Vaje+st7fxx8Ul63c2NdX4viYlGd9ZFxdjE5s+fYzl4jklf0Tk5tqyZQt33XUXMTExADRp0oQVK1ZQMQ9f0mPHrvQLx49n7Re8vHK/93dxMa7fmf1CXpI/kZGRhIaGcuTIEQCqVKlCWFgY9bQpkUiRcPz4lXv/Y8eu9At5zQk6Oxv9QuYYvzgvDijWyeFMKSmwZQv8/Tfs3w979hhPD9LSjL8AYPyFcHExngY0bGhsMle7NrRqlf+ZhSJSuN59913GjBlj/3nMmDFMnDgxX8nW1NSr+4VLl7LvF3x9oUEDo+B8nTrQsmX+Zxa+//77jBo1yv7z888/z7vvvqsEsUgRkpoKf/xh9At//w27duXeL/xzvODlZW78IiVNYiJs3gwHD8K+fcafy5eNBzj//M66uhrLShs3hrp1jT933JH/mYUiUnjWrl1Lr169SPjf7pMtWrRg2bJl+Pn55etzkpLg99/hwAGjT9i799r9QsOGV8b4LVrkv184efIknTt3Zv/+/QCUK1eOlStX0qxZs/x9kIgUmuTkK+OFf+YEs+sX/pkTrFvXuPcvCeOFEpEc/rfMDaQuXzZu+Gw243926dLGzB/VEhYpusaPH89rr71m//n111/n9ddfv+Eka+YGUpcvGwmizH7Bx6fgNrL75JNPGDFihP3n//u//+PDDz9UglikiLLZjH4hJiZrv1CqlNEvqJawSNGSnm5sFBMfb3xnLRbjO1umjDHbR5dbkaJp6dKl3H///SQnJwPQoUMHFi1ahI+Pzw1/duYGUnFxWfsFX9+C2+Dy/PnzdO3ale3btwPg6+vL0qVLufPOO2/8w0WkwGVkGDnB2Ngr/YKrq9EvFMYGl46gRCaHRcTx2Gw2xo4dy1tvvWU/9vbbb/PSSy+ZGFX+zZw5kyeeeMK+gd7jjz/O9OnTs62TLCIiIiJSnH3//fcMGDCA9PR0AHr27MmPP/6Ih4Mt3718+TI9evTgt99+A8DLy4uFCxfSuXNnkyMTEcmdshEiUuTZbDZGjx6dJTH83nvvOVxiGOCxxx5j1qxZ9mTw559/zpAhQ8jIyDA5MhERERGRm+fLL7+kX79+9sTwgw8+yE8//eRwiWEwZguvWLGCLl26AJCYmEjPnj1ZuHChuYGJiOSBksMiUqTZbDZGjhzJlClT7Mc+/vhjnnvuOROjujEPP/ww3333Hc7/q1Xx9ddfM2jQIPvAWERERESkOJs6dSpDhw61r6YbOnQoc+bMwdXV1eTIrp+3tze//PIL9957LwCpqancf//9zJkzx+TIRESuTclhESmyrFYrw4YNY9q0aQBYLBY+//xzhg8fbnJkN+7BBx/khx9+sA+A586dy4MPPkhqaqrJkYmIiIiIFA6bzcabb77Js88+az/23HPP8fnnn9snTjgyd3d3fvjhBwYOHAhARkYGgwYN4rPPPjM5MhGRnKnmsIgUSRkZGQwZMoRvvvkGACcnJ2bNmsWgQYNMjqxgLVmyhPvuu8+eFO7Vqxc//vgj7u7uJkcmIiIiIlJwbDYbL7zwAu+995792Lhx4xg7dmyx26DZarUyYsQIpk+fbj/27rvv8sILL5gYlYhI9pQcFpEiJz09nUGDBvHf//4XAGdnZ+bMmcODDz5ocmSFY8WKFdxzzz32HZrvuusufvrpJzw9PU2OTERERETkxmVkZPDUU0/x+eef24+9//77WWYQFzc2m42XXnqJSZMm2Y+9+uqrvPnmm8UuGS4ijk3JYREpUlJTU+nfvz8LFiwAwNXVlXnz5tGnTx+TIytca9asoVevXiQmJgLQsWNHfvnlF7y9vU2OTERERETk+qWlpfHwww/bJ35YLBZmzJjBY489ZnJkN8fEiRN5+eWX7T8//fTTfPDBB/YNqkVEzKbksIgUGSkpKfTt25fFixcD4ObmxoIFC+jZs6fJkd0cGzdupHv37sTFxQHQtm1bli5dio+Pj8mRiYiIiIjkX3JyMg888IB9fO/i4sLs2bOL7YrAnHz88cf83//9n/3nwYMHM3PmzGJRZ1lEHJ+SwyJSJCQlJXHvvfeyYsUKADw8PFi0aBFdunQxObKba8uWLXTt2pXLly8D0LJlS5YtW0aZMmXMDUxEREREJB/i4+O5++67WbNmDWBs1jZ//vwSM/Hj37755hseffRRrFYrAH379mXOnDm4ubmZHJmIlHRKDouI6RISEujdu7d94Ojl5cWSJUvo0KGDyZGZY9u2bXTp0oXo6GgAbrvtNlauXIm/v7/JkYmIiIiI5O7SpUt0796d33//HQBvb28WL15cYsf3mebPn0///v1JS0sDoFu3bsyfPx8vLy+TIxORkkzJYRExVVxcHD169GDDhg0A+Pj48Ouvv9KmTRuTIzPXrl27CA0N5fz58wA0btyYsLAwypcvb3JkIiIiIiI5O3fuHF26dGHnzp0AlClThmXLltGyZUuTIysali1bRp8+feybUbdr147FixdTunRpkyMTkZJKyWERMU1MTAzdunWzzyjw9fVlxYoVtGjRwuTIioZ9+/bRqVMnzpw5A0D9+vVZvXo1lSpVMjkyEREREZGrHT9+nM6dO3PgwAEAKlSowKpVq2jcuLHJkRUt4eHh9OzZ077XSPPmzVm2bBlly5Y1OTIRKYmUHBYRU0RHR9OlSxe2bdsGgL+/P6tWreLWW281ObKi5eDBg3Ts2JGTJ08CULt2bdasWUPVqlVNjkxERERE5IpDhw4RGhpKVFQUALfccgthYWHUqVPH5MiKpq1bt9K1a1d7KbmGDRuycuVKKleubHJkIlLSKDksIjfd+fPnCQ0NZdeuXQCUL1+esLAwzSjIQWRkJB07drQPtIOCglizZg0BAQEmRyYiIiIiAnv27KFz5872FW81a9YkLCxM49Vc6L+biBQFTmYHICIly+nTp2nfvr09MVypUiXWrVunxPA1BAUFER4eTnBwMGAki9u1a8fhw4dNjkxERERESro///yTkJAQe4KzYcOGbNiwQQnOPPj3f6tDhw7Rpk0be1kOEZGbQclhEblpTpw4Qfv27dm3bx9gLDULDw+nfv36JkdW9FWvXp3169fbl+UdO3aMkJAQDRxFRERExDTh4eF06tTJXhqhefPmrF+/Xntk5EPNmjXZuHGjfZx/4sQJ2rZta9/QT0SksCk5LCI3RVRUFCEhIRw8eBCAgIAAwsPDqVWrlsmROY6qVauyfv16GjZsCMDJkycJCQlh7969JkcmIiIiIiXNsmXL6Nq1q31TtZCQEFavXo2/v7/JkTmezEkzTZo0AYwyfO3bt2fz5s0mRyYiJYGSwyJS6A4fPky7du2IjIwEIDg4mPDwcGrUqGFyZI6nYsWKrF27lqZNmwJw9uxZ2rdvr5kFIiIiInLTzJ8/n7vvvpvk5GQAunXrxrJly/Dx8TE5MsdVoUIF1q5dS6tWrQCIiYmhc+fOrFmzxuTIRKS4U3JYRArVgQMHaNeuHceOHQOgTp06hIeHU716dZMjc1zlypVj9erVNG/eHIALFy7QoUMHtm7danJkIiIiIlLcff311/znP/8hLS0NgL59+7Jw4UI8PT1Njszx+fn5sXLlSjp16gRAQkIC3bt3Z/HixSZHJiLFmZLDIlJo9uzZQ0hICKdOnQKMDRfWr19PlSpVTI7M8fn7+7Nq1Spat24NwKVLl+jUqZOWnomIiIhIoZk2bRqDBw/GarUCMHjwYP773//i5uZmcmTFR6lSpViyZAm9e/cGICUlhT59+jBv3jyTIxOR4krJYREpFDt27KBDhw6cPXsWgKZNm7J27VoqVqxocmTFh6+vL8uXLyckJASA2NhYunTpQnh4uMmRiYiIiEhxYrPZePvtt3n66aftx55++mm++OILnJ2dTYysePLw8GD+/Pn0798fgPT0dPr378/MmTNNjkxEiiMlh0WkwG3dupWOHTty4cIFwNi1ePXq1ZQrV87kyIofHx8ffv31V0JDQwGIj4/nrrvuYvXq1SZHJiIiIiLFgc1m48UXX+SVV16xHxs7dixTp07FyUkphcLi6urKt99+y+OPPw4Y/x8ef/xx3n//fZMjE5HiRj25iBSozZs306lTJy5dugRA69atWbVqlXYtLkReXl4sXryY7t27A5CUlESPHj1YtmyZyZGJiIiIiCOzWq089dRTvPvuu/Zj7777Lm+++SYWi8XEyEoGZ2dnPvvsM0aNGmU/NmrUKN544w1sNpuJkYlIcWKxqUcRkQISHh5O9+7dSUhIACAkJITFixdr1+KbJCUlhf/85z8sWrQIADc3N3788Ud7vTIRERERkbxKT09n8ODBzJkzBwCLxcL06dN54oknTI6s5LHZbEyYMIHXXnvNfuzZZ5/lvffeU5JeRG6YksMiUiDCwsLo3bs3SUlJAISGhrJo0SK8vLxMjqxkSUtL46GHHuLHH38EwMXFhf/+97/cf//9JkcmIiIiIo4iJSWFfv368fPPPwPGDNZvvvmGhx56yOTISrapU6fy7LPP2n8eOnQon332meo+i8gNUXJYRG7YsmXLuPfee0lJSQGge/fuLFiwAA8PD5MjK5n+PcvDycmJ2bNn2ze0EBERERHJSUJCAvfeey+rVq0CjNVoP/zwA3fffbfJkQnAl19+yWOPPWYvK/Gf//yH2bNn4+rqanJkIuKolBwWkRuyaNEiHnjgAVJTUwG4++67+f7773F3dzc5spItIyODxx9/nK+++gowlgF++eWXDB482OTIRERERKSoiomJoWfPnvz222+AsbfFokWL7JsfS9Hw/fffM2DAANLT0wHo2bMnP/zwA56eniZHJiKOSMlhEbluP/74I/3797cPSvr27ct3332np9ZFhNVqZcSIEUyfPt1+7LPPPlOdOBERERG5yvnz5+natSvbt28HwNfXl19//ZXWrVubHJlkZ+nSpdx333321ZsdOnRg0aJF2u9FRPLNyewARMQxfffddzz44IP2xPCAAQOYO3euEsNFiJOTE5988gkjR460Hxs2bBgfffSRiVGJiIiISFFz8uRJ2rVrZ08MlytXjrVr1yoxXIT16NGDZcuWUapUKQDWrl1L586diY6ONjkyEXE0Sg6LSL7NmjWLgQMHYrVaAXj00Uf5+uuvcXFxMTky+TeLxcIHH3zAmDFj7MdGjhzJ5MmTTYxKRERERIqKyMhI2rZty99//w1A1apV2bBhA82aNTM5MslNhw4dCAsLw8/PD4AtW7bQoUMHzp49a3JkIuJIlBwWkXyZMWMGjz76qH0DhCeffJKZM2dqh9wizGKxMHHiRF577TX7sdGjRzNhwgQToxIRERERs+3bt482bdpw5MgRAIKCgtiwYQN169Y1OTLJqxYtWrBu3ToqVqwIwK5du2jbti3Hjh0zOTIRcRRKDotInn300UcMGzbM/vPIkSP55JNPcHJSV1LUWSwWxo0bx1tvvWU/NnbsWMaOHYtKz4uIiIiUPH/99RchISGcPn0agPr167NhwwZq1KhhcmSSX40bNyY8PJxq1aoBEBERQdu2bYmIiDA5MhFxBMroiEieTJ48OUvt2jFjxvDBBx9gsVhMjEry6+WXX2bKlCn2nydMmMCYMWOUIBYREREpQTZu3EiHDh24cOECALfddhvr16+nSpUqJkcm16t27dps3LiRWrVqAXDs2DHatm3L7t27TY5MRIo6JYdFJFfjx49n9OjR9p9fe+01Jk6cqMSwgxo1ahTTpk2z/zx58mSeeeYZJYhFRERESoCVK1fSpUsXYmNjAWjTpg2rV6+mXLlyJkcmN6p69eqEh4fTqFEjAM6ePUtISAh//PGHyZGJSFGm5LCI5Mhms/Hqq69mqVX71ltvMW7cOCWGHdyIESOYMWOG/f/jRx99xJNPPmnfZFBEREREip+ff/6ZXr16kZSUBEDXrl1ZsWIFvr6+JkcmBaVSpUqsW7eOO+64A4BLly7RqVMn1q1bZ25gIlJkKTksItmy2WyMHj06S43aKVOm8PLLL5sYlRSkxx9/nFmzZtlrRs+YMYMhQ4aQkZFhcmQiIiIiUtBmz55N3759SU1NBeC+++5j0aJFeHl5mRyZFDR/f3/CwsJo3749APHx8XTr1o1ff/3V3MBEpEhSclhErmKz2Rg5cmSW2rTTpk1j1KhRJkYlheHhhx9mzpw5ODs7A/D1118zaNAg0tPTTY5MRERERArKp59+yqBBg+yTAAYNGsS8efNwd3c3OTIpLD4+Pvz666/06NEDgOTkZO6++25+/PFHkyMTkaJGyWERycJqtTJs2DB7TVqLxcKMGTMYMWKEyZFJYenXrx/ff/89Li4uAMydO5d+/fqRlpZmcmQiIiIicqMmTZrE8OHD7T8PHz6cWbNm2cd+Unx5enry008/8cADDwCQnp7Ogw8+yFdffWVyZCJSlCg5LCJ2GRkZDBkyhM8//xwAJycnZs2axeOPP25yZFLY7rvvPn766Sfc3NwAmD9/Pvfffz8pKSkmRyYiIiIi18Nms/HKK6/w4osv2o+99NJLTJs2zV5WTIo/Nzc35s6dy5AhQwBjMtCQIUP48MMPTY5MRIoKi03b04sIxlPkhx9+mLlz5wLg7OzM7Nmz6devn8mRyc20YsUK7rnnHpKTkwG46667+Omnn/D09DQ5MhERERHJK6vVysiRI/n444/txyZOnJglUSwli81m47nnnmPq1Kn2Y+PHj+eVV17RZuMiJZySwyJCamoq/fv3Z8GCBQC4uLgwb9487rvvPpMjEzOsWbOGXr16kZiYCECnTp1YtGgR3t7eJkcmIiIiIrlJT09n6NChfPPNN/Zjn3zyCU899ZSJUUlRYLPZeOONN3jzzTftx55//nneffddJYhFSjAlh0VKuJSUFPr27cvixYsBY9nR/Pnz6dWrl8mRiZk2bNhA9+7diY+PB6Bt27YsXboUHx8fkyMTERERkZykpKTw0EMP2Sd9ZJaJGzRokMmRSVEyZcoUXnjhBfvPTzzxBJ988ol9k2oRKVmUHBYpwZKSkujTpw/Lly8HwMPDg4ULF9K1a1eTI5Oi4Pfff+euu+7i8uXLALRs2ZJly5ZRpkwZcwMTERERkaskJiZy33332cf2rq6uzJs3jz59+pgcmRRFn3/+OcOGDSMzJdS/f3++/vprXF1dTY5MRG42JYdFSqiEhAR69+7NmjVrAPDy8mLx4sV07NjR5MikKNm2bRtdunQhOjoagNtuu42VK1fi7+9vcmQiIiIikik2NpaePXuyYcMGADw9Pfn555816UOu6b///S8DBw4kIyMDgLvvvpt58+bh4eFhcmQicjMpOSxSAsXFxdGjRw/74NHHx4dff/2VNm3amByZFEW7du0iNDSU8+fPA9C4cWPCwsIoX768yZGJiIiIyMWLF7nrrrvYunUrAKVLl2bJkiW0bdvW5MjEEfzyyy888MADpKSkABAaGsrChQu134hICaLksEgJExMTQ7du3fj9998B8PX1ZcWKFbRo0cLkyKQo27dvH506deLMmTMA1K9fn9WrV1OpUiWTIxMREREpuU6fPk3nzp3Zu3cvAGXLlmXFihXcdtttJkcmjmT16tX07t3bviF169atWbp0qcrJiZQQSg6LlCDR0dF06dKFbdu2AeDv78/KlSs1eJQ8OXjwIB07duTkyZMA1K5dmzVr1lC1alWTIxMREREpeY4ePUpoaCiHDx8GoHLlyoSFhVG/fn2TIxNHtGnTJrp3727fb6Rp06asWLGCChUqmByZiBQ2JYdFSojz588TGhrKrl27AChfvjxhYWE0btzY5MjEkURGRtKxY0eioqIACAoKYs2aNQQEBJgcmYiIiEjJ8ffffxMaGmp/aB8YGMjq1asJCgoyOTJxZDt27KBLly72cnJ16tQhLCyMW265xeTIRKQwOZkdgIgUvtOnT9O+fXt7YrhSpUqsW7dOiWHJt6CgIMLDwwkODgaMZHG7du3sM1ZEREREpHBt376ddu3a2RPDdevWZePGjUoMyw1r2rQp4eHh9mTwgQMHaNOmjcb6IsWcksMixdyJEydo3749+/btA6Bq1aqsX79ey83kulWvXp3169dTp04dAI4dO0ZISAgHDhwwOTIRERGR4m3z5s106NDBPrMzM5mnMl9SUOrWrcuGDRvsk0GioqJo27atva61iBQ/Sg6LFGNRUVGEhIRw8OBBAAICAggPD6d27domRyaOLvMhQ4MGDQA4efIkISEhGjSKiIiIFJLVq1fTuXNne03Y1q1bs3btWsqXL29yZFLcBAYGsmHDBvtY//Tp04SEhNj3rhGR4kXJYZFi6vDhw7Rr147IyEgAgoODCQ8P13IzKTAVK1Zk3bp1NG3aFICzZ8/Svn17du7caW5gIiIiIsXML7/8Qvfu3UlISAAgNDSUlStXUqZMGXMDk2KrcuXKrF+/nttvvx2Aixcv0qFDBzZs2GByZCJS0JQcFimGDhw4QLt27Th27BhgbCSwfv16qlevbnJkUtyUK1eO1atX2weNFy5coEOHDmzdutXkyERERESKh7lz59KnTx9SU1MBuPvuu1m8eDHe3t4mRybFXdmyZVm9ejVt27YFIC4ujq5du7J8+XKTIxORgqTksEgxs3fvXkJCQjh16hQADRo0YP369apDJoXG39+fsLAwWrVqBcClS5fo1KkTmzdvNjkyEREREcf2+eefM2DAADIyMgB46KGH+PHHH/Hw8DA5MikpSpcuzfLly7nrrrsASEpKonfv3ixYsMDkyESkoCg5LFKM7Ny5k/bt23P27FnA2KBi3bp1VKxY0eTIpLjz9fVlxYoVtGvXDoDY2Fi6dOlCeHi4yZGJiIiIOKYpU6bwxBNPYLPZABg2bBjffvstrq6uJkcmJY2XlxeLFi3ivvvuAyAtLY0HHniAb775xuTIRKQgKDksUkxs3bqVDh06cOHCBQBuv/12Vq9eTbly5UyOTEoKHx8fli1bRmhoKADx8fF069aN1atXmxyZiIiIiOOw2Wy89tprvPDCC/ZjL7zwAp9++ilOTrqFF3O4ubkxb948HnnkEQCsViuPPPIIn3zyibmBicgN05VFpBjYvHkznTp14tKlSwC0atWKsLAw/P39TY5MShovLy8WL15Mt27dAEhMTKRnz56qSyYiIiKSBzabjeeee47x48fbj02YMIFJkyZhsVhMjEwEXFxc+PLLL/m///s/+7ERI0YwceJEE6MSkRul5LCIgwsPD6dLly7ExsYC0K5dO1asWIGvr6/JkUlJ5eHhwc8//8zdd98NQHJyMnfffTe//PKLyZGJiIiIFF0ZGRk89thjTJ061X7sww8/5JVXXlFiWIoMJycn+9/LTC+//DIvvfSSvQSKiDgWi03fXhGHtXr1anr16kVSUhIAoaGhLFq0CC8vL5MjEzFqkWVumgLGTIP//ve/3H///SZHJiIiIlK0pKamMnDgQH744QfASMB98cUXDB482OTIRHI2adIkXnzxRfvPTz31FNOmTVP5ExEHo+SwiINatmwZ9957LykpKQB069aNn376STsXS5GSnp7OI488wnfffQeAs7Mz3377Lf379zc5MhEREZGiISkpifvvv59ff/0VAFdXV7777jv69u1rcmQiufv0008ZPny4/eeBAwfy1Vdf4eLiYmJUIpIfSg6LOKBffvmFvn37kpqaCsDdd9/N999/j7u7u8mRiVwtc4nkrFmzALBYLHz11Vf2zSxERERESqq4uDh69erF+vXrAaM8108//WTfv0HEEcyePZvBgweTkZEBwL333st///tf3Z+KOAglh0UczPz58+nXrx/p6ekA9O3bl++++w5XV1eTIxPJmdVqZfjw4Xz22Wf2YzNmzODxxx83MSoRERER80RHR9OtWzf++OMPAHx8fFi8eDEhISEmRyaSfz///DMPPvigfQJTly5d+Pnnn1XyUMQBqBCMiAOZO3cu//nPf+yJ4Yceeoi5c+cqMSxFnpOTE59++ikjR460H3viiSeYNm2aiVGJiIiImOPMmTO0b9/enhj28/Nj9erVSgyLw7r33ntZvHgxnp6eAKxcuZKuXbty+fJlkyMTkdwoOSziIGbNmsWAAQOwWq0ADB48mG+++Ua1nMRhWCwWPvjgA0aPHm0/9vTTTzNlyhQToxIRERG5uY4dO0a7du3YvXs3ABUrVmT9+vU0b97c5MhEbkyXLl1YuXIlpUuXBmDjxo107NiRCxcumByZiFyLksMiDmDGjBk8+uijZFaBGTZsGF988QXOzs4mRyaSPxaLhXfeeYfXXnvNfuyFF15gwoQJJkYlIiIicnMcPHiQNm3aEBERAUD16tXZsGEDjRo1MjkykYLRpk0b1q5dS9myZQH466+/CAkJ4dSpUyZHJiI5UXJYpIj76KOPGDZsmP3nkSNH8umnn+LkpK+vOCaLxcK4ceOyJITHjh3L2LFjURl8ERERKa527dpF27ZtOX78OAC1a9dm48aN1KpVy+TIRArWrbfeSnh4OFWqVAFg3759tG3bliNHjpgcmYhkR9klkSJs8uTJWWq0jh49mg8++ACLxWJiVCIF45VXXslSUmLChAmMGTNGCWIREREpdrZs2UJISAjnzp0DoEmTJoSHh1OtWjWTIxMpHPXr12fDhg3UqFEDgMjISNq0acP+/ftNjkxE/k3JYZEiasKECVlqs7722mu88847SgxLsTJq1Kgsm9JNnjyZZ555RgliERERKTbWrl1Lp06diImJAaBly5asXbuWihUrmhuYSCELCgpiw4YN1KtXD4BTp07Rrl07/vrrL5MjE5F/UnJYpIix2Wz2JfaZJkyYwLhx45QYlmJpxIgRzJgxw/73+6OPPuLJJ5+0b74oIiIi4qiWLl1Kt27dSEhIAKBjx46sWrUKPz8/kyMTuTmqVq3K+vXradasGQAXLlygQ4cO/PbbbyZHJiKZlBwWKUJsNhtjxozJUot1ypQpvPLKKyZGJVL4Hn/8cWbNmmWvpT1jxgyGDh1KRkaGyZGJiIiIXJ/vv/+ee+65h5SUFAB69uzJ0qVLKVWqlMmRidxc5cuXZ+3atdx5550AxMbG0qVLF1atWmVyZCICSg6LFBk2m42RI0cyefJk+7Fp06YxatQoE6MSuXkefvhh5syZg7OzMwCzZs1i0KBBpKenmxyZiIiISP58+eWX9OvXzz6OefDBB/npp5/w8PAwOTIRc/j6+rJixQo6d+4MQGJiIj179mThwoXmBiYiSg6LFAVWq5Vhw4bZa69aLBZmzJjBiBEjTI5M5Obq168f33//PS4uLgDMnTuXfv36kZaWZnJkIiIiInkzdepUhg4dat9DYejQocyZMwdXV1eTIxMxl7e3N4sXL+aee+4BIDU1lfvvv585c+aYG5hICafksIjJMjIyGDJkCJ9//jkATk5OzJo1i8cff9zkyETMcd999/HTTz/h5uYGwPz587n//vvtSzJFREREiiKbzcabb77Js88+az/23HPP8fnnn9tXRomUdO7u7vz4448MGDAAMO6HBw0axGeffWZyZCIll5LDIiZKT09n0KBBfP311wA4OzszZ84cHn74YXMDEzFZr169+OWXX+xLL3/55RfuuecekpKSTI5MRERE5Go2m40XXniB119/3X5s3LhxTJkyRZtKi/yLi4sL33zzDU8++SRgfH+efPJJ3n33XZMjEymZLLbMtS4iclOlpqbSv39/FixYABgXyHnz5nHfffeZHJlI0bFmzRp69epFYmIiAJ06dWLRokV4e3ubHJmIiIiIISMjgyeffJKZM2faj73//vtZZhCLyNVsNhsvvfQSkyZNsh975ZVXGD9+vB6qiNxESg6LmCAlJYW+ffuyePFiANzc3Jg/fz69evUyOTKRomfDhg10796d+Ph4ANq2bcvSpUvx8fExOTIREREp6dLS0nj44Yf573//C1zZO+Sxxx4zOTIRxzFx4kRefvll+89PP/00H3zwAU5OWuwucjMoOSxykyUlJdGnTx+WL18OgIeHBz///DN33XWXyZGJFF2///47d911F5cvXwagZcuWLFu2jDJlypgbmIiIiJRYycnJPPDAA/YJHy4uLsyePZsHH3zQ5MhEHM+0adN4+umn7T8PHjyYmTNnql63yE2g5LDITZSQkEDv3r1Zs2YNAF5eXixevJiOHTuaHJlI0bdt2za6dOlCdHQ0ALfddhsrV67E39/f5MhERESkpImPj+fuu++2j+vd3d2ZP38+PXv2NDkyEcf19ddfM2TIEKxWKwB9+/Zlzpw59o2qRaRwKDkscpPExcXRo0cPNmzYAECpUqX49ddfadu2rcmRiTiOnTt30rlzZ86fPw9AkyZNWLVqFeXLlzc5MhERESkpLl26RPfu3fn9998B8Pb2ZvHixXTo0MHkyEQc3/z58+nfvz9paWkAdOvWjfnz5+Pl5WVyZCLFl5LDIjdBTEwM3bp1sw8gfX19Wb58OS1btjQ5MhHHs2/fPjp16sSZM2cAqF+/PqtXr6ZSpUomRyYiIiLF3blz5+jSpQs7d+4EoEyZMixbtkzjepECtGzZMvr06UNycjIA7dq1Y/HixZQuXdrkyESKJyWHRQpZdHQ0Xbp0Ydu2bQD4+fmxatUqbrvtNpMjE3FcBw8epGPHjpw8eRKA2rVrs2bNGqpWrWpyZCIiIlJcHT9+nNDQUA4ePAhAhQoVWLVqFY0bNzY5MpHiJzw8nJ49exIXFwdA8+bNWbZsGWXLljU5MpHiR8lhKZFSU+HIETh0CA4fhkuXICUFbDbw8ABfXwgKgpo1jX96eFzfec6fP09oaCi7du0CoFy5coSFhdGkSZMC/G1ESqbIyEg6duxIVFQUAEFBQaxZs4aAgIDr+rzMfuHwYaNviImB5OQr/ULp0lf6heDg6+8XREREpGBYrXDyJEREGNfuc+cgKQnS08HT0/gTEAC1ahnXb1/f6z/XoUOHCA0NtY87qlWrRlhYGLVr1y6g30ZE/m3r1q107drVvudIw4YNWblyJZUrV76uz7PZIDra6C8OHYLjx40+IzkZ3NyM8X3lylfG+1WqgMVSkL+RSNGk5LCUGImJsHYtLF0Ke/ZAfLxxIQBjYOnkZPz7P78Rnp7g7Q316kH37hAaCj4+eTvfmTNn6NSpE/v27QOgUqVKrF69mvr16xfgbyVSskVFRdGpUycOHz4MQPXq1VmzZg3BwcF5en9SktEv/Por7NqVt37BwwNKlYK6dY1+oXPnvPcLIiIicmOsVvjzT1iyBLZsMRI9iYnGtTqna7ebmzGmr1YNunSBbt0gP4uN9uzZQ+fOne0lrWrWrElYWNh1P5AWkbwriO/fkSPGeD8sDE6fhoQE4yFSZj+RmQDO7EOcnMDLC8qWhTZtoGdPaNJEiWIpvpQclmIvKspICC9cCCdOGMd8fIwBoqfnlQHkv1mtRpIoMRFiY41jlSpB797GxaFmzZzPefLkSTp27Ghfcla1alXWrFmjmQUiheDkyZN06tSJAwcOAMb3bfXq1dSpUyfH9xw/btxU/vxz1n7By8v4c61+ITnZGFDGxRk/V6oEvXoZf2rVKujfTkRERAAuX4YVK+Cnn2D/fmPVX6lSxpjeywtcXLJ/n81mvDYx0XgInJICZcpAhw7GuL5Fi5yv+wB//vknd911V5aZi6tWrdJeByI30b9n7t9yyy2EhYVdc7yfng4bN8KiRfDbb0Yf4ul5pd9wdc052ZuWZvQZCQnGH09PIzl8773GhDFv78L4LUXMo+SwFFupqTB7Nnz5JVy8aHTg5csbMweuR1oanD9vDCrLlIGBA2HIEONC8U9RUVF07NiRyMhIAAICAlizZg1BQUE39guJSI7OnDlDaGgoe/fuBaBixYqsXr2aBg0aZHldairMnQszZ8KFC8bNZIUKBdcvPPSQ0S9owCgiIlIwbDZYswbefx8iI8HZ2bh2X++11mo1ZhtfumSsBurYEZ5/3lg+/m/Z1Txdvnw5/v7+N/Abicj1+HfN7/Lly7Nq1apsSzZGRsKkSbBpk5Ek9vc3xurXehCUE5vNGOufP2/8e926MGaM8WBJpLhQcliKpf37jYvBli3GwLFixeu7EGTHZjPqmcXFQdOmxoWhaVOj7fDhw3Ts2JFjx44BN14DVUTy7vz583Tu3Nm+e/i/a3wfPAjvvAO//2481KlUqWD7hfPnjRkJTZrAiy9Cs2YF89kiIiIlVXQ0fPihMfMvPR1uucWY7VdQYmPhzBnjc59+2lgFlDk2WLZsGX369CE5ORmAkJAQFi9ejI9qSYmY5ty5c3Tp0sU+3i9Tpgy//vorrVq1Aox+4vvv4bPPjHv2qlULdtJGaqqxAtHLC/r1g8cfV3k5KR6UHJZixWYzLgYffWQMJm+55eqZvQUlJQWOHTM2qRo2DFq3PkBoaCdOnjwJQO3atVmzZg1V81PQTERuSHR0NF27dmXr1q0A+Pn5sWLFSqKibuf9929Ov3D8uDFIHDoUHntMtclERESux/bt8OqrxqZR5cuDn1/hnCcjwygxZbPBXXfB66/DsmU/8tBDD5GWlgZA9+7dmT9/Pp6FNYAQkTy7dOkSPXr0YPPmzQB4e3uzaNEimjbtxKuvQni4sSqwSpWCmwjyTzabsTL50iVo0AAmTgRVjxRHp+SwFBs2m7FU/NNPjYvAzdhZ1GaDs2chJiaZM2feIj7+bcBKgwYNCAsLUy0yERNcvnyZbt262QeMHh5PUa3au3h7e1O16s3pF86dM+qUPfIIPPNMznUQRURE5GqbNsFLLxnX08DAm3MdzZxFXLnyAcLDW2OzGTWG+/bty5w5c3C73hpUIlLg4uPjueeee1i9ejUAbm7VadXqN06fvqXAZwvnJDXV2N+oenWYMgUaNy78c4oUFiWHpViw2eCLL4wZw5k1RAF27hzK8eNfZvueunXfoWbNMXn6/D/+6MW5c0uybatZczJHjrQjI8MbmEnjxusIC1tF+fLlr+dXEZECEBcXR48ePdmwIRh4BienNFq2DMTfv2yO70lNvcTFi2u4eHEdMTF/kpp6ntTUi2RkJOLq6oebW1k8PG7B378tZcuG4OfXGiennO9Wo6MhJgYGD4ZRozSDWEREJC+2bDGum5cuGYnh/Mz8u3z5Ly5eDCc6egMJCYdIS4smNfUikIGLS2nc3StSqlR9fH1vo3z5rpQu3QTLPy7QBw8e4+DBJCAceI7Bg/syc+ZMnJ2dC/i3FJEblZycTMWKFYnN3D3+HypW7EXz5r9c8/0REW9x4MCr2bbdfvsiKlXqfc33r15dg6Sko1cdHz58OB9//PE13ytS1Cg5LMXC/PkwYYKxfKRixSvH09PjCA9vQmLikave4+TkRps2f1K69LUf8R079gW7dj2WbVuZMh2Jj3+e9PR0oCweHhWYPLkSI0aUupFfR0QKwLx5yTz22Ani46OBszg5OXPHHc0pVy7rg5ukpOMcPjyF48e/ICMjMc+f7+VVg6CgF6hWbTDOzh7ZviY62qhDPHKkUWJCREREcrZ/Pzz1lDFjuEaNvD1YtVrTOXVqHocPTyYuble+zuflVYPq1R+nRo1niIw8zoEDfwMeQDWaNDnLH3+0xc2tENali0iBePLJp/nss2lXHXd19aNLl4tZHv7825Ytd3H+/Ips24KCnqd+/ck5vjcp6QSrV1fLtm3evHn85z//ySVykaJFVzpxeJGRxkYVFkvWxDCAi4sPTZp8Q3Z/1a3WVLZvH4jVmprjZycmHmXfvueybXNxKUNc3CP/SwyDn5+VunUDmT27FHv2XPevIyIF4OhRmD7dg8DAIMqXN56BWq0Z/PHHH5w7d87+uhMn5rBuXV2OHv0oX4lhgMTEI+zZ8xQbNtxGfPyBbF/j72/UN545E/63b4aIiIhkIzkZ3n4bTp82ZgznJTEcH3+QjRtvZ8eOgflODINxLf/775fYu3fD/xLDAMkEBLiRmtqOH3/U7bJIUZaW1j6HDEcpyQAAHZBJREFU45eIi8v5ptxmy+DSpc05tkdHb7jmeaOjw3Nsa9WqzTXfK1IU6WonDi09Hd55B86fN2oMZ6ds2bYEBY3Kti0ubhcHDryWbZvNZmXHjodJT4/Ltj0j43EyMsoA4O9flhYtWlK1qguXLxsx/W9jYxG5ydLT4d13jbqB1as70bz57VSsaNT/tlqt/Pnnn5w9e4Z9+55nx46B+U4K/1t8/D42bmzO+fOrsm2vWBHi4mDSJEhKuqFTiYiIFFvffgtbt0K1ankrJXHmzCI2bLiN2Ngbf/p67FiU/d/r1atHo0ZBeHhY+OwzOHz4hj9eRArB9u2wbVvbHNv//vtHIPuF8rGxO0lPv7ocRabLl/+65j3CxYvZJ4ddXYPYskUb0ovjUXJYHNr338Nvv+W+E2mdOuPx8WmUbdvhw5OJjv7tquORkR9c44lgW2w244lguXLlaNHiDlxcXLBYjAHttm3GAFdEbr4FC2D9+iv9gpOTM7fddhuVKxtPkGw2K3/++RqRke/l+BkWiwvlynUmKGgUtWu/QUDAMLy9a+X4+vT0OLZt65vtDOLMfuGvv+DL7Eugi4iIlGh79hjXyFKlwCP7Sk1ZREdv5K+//kNGRnyOr3Fy8qR8+W7UqDGS2rXfJCjoeapWHYCXV3CO72nUqDHBwTUBqFzZKG8xaZLx4FlEio6EhMwJWeXx9q6b7WvOnVvL3r37yC5BfPHitWcG22xpXLr0e47tOeUJSpVqyyefGBvViTgS7Z8uDuv8efj8c6POcG67kTo7u9Os2Ww2brwjmzISVnbsGES7djtxcTFqBcfF7cuxOD34A8MAKF++ArfffnuWTSo8PIyB7axZ0LUrBARc3+8nIvkXHQ2ffWb0C6X+UfrbycmJW29txo4dFk6eXA3knKUtV64zjRvPxMvr6i/v6dM/s3v3MFJTz13Vlp5+ma1b76Vdu11XbVTn7g6lS8Ps2dC9OwQFXfevKCIiUqzYbEaJuMuXoWbN3F+flHSCP/+8B6s1Jdt2JycPatZ8kaCg53Fxyf4mIT4+gj/+GE9i4s9APGChWbNmVK16yz8+B6pWhU2b4Ndfofe196YSkZto/nzYtcuYgJGY2I6EhL+zedU+jhyJJD09ncaNG2epP3ytshCZLl4Mp1y5jlcdT029QHx8dueDW25py+nTxv3IxIl5/nVETKeZw+Kwli+Hs2eNp/p5Ubp0E2rXfiPbtsTESPbtM0pPWK1p7NgxEKs1p7oQ/wf4ULFiJZo3vz3b3YsrVDB2WP7117zFJiIFY+VKo5xEpUpXt1ksTjRt2gx39/mANdv3ly/flTvuWJptYhigcuV7adkyDBcX32zb4+P3c/Lk7GzbKlSAmBhYujQvv4mIiEjJsHevsbqmYsW81Rk+ePB10tIuZtvm4lKaFi1WUrv26zkmhjMy0tmz5yKJiX2BGcBdNG7cNEtiOJO3t5G8XrDA+KeImC81FX76yZgM4u4O/v7tcnhlNHCK48ePsX37X1itV8b/0dEbs7zSy6sG/06P5VR32Jh1nH2HULZsW8qWhTVr4MSJPP5CIkWAksPikNLTjUGamxtkk5vNUXDwaPz8WmfbduzY55w7t4yIiDe5fPmvHD7hLsBYnn7bbbfh5JT9yZ2cjE2ofv5ZNUZFbpbMfsHFxfiTnZiY30lJ+SPbNheX0jRp8jVOTq7XPE/p0o2oW/etHNsjIt7Cls0dpMVi3GQuWmQshRMRERFYvBji48HHJ/fXxscf5MSJb3Jsb9x4JmXL5lyDNC0tjd9/38KFC+cBcHIqQ8uW31K9+m05vqd8eWOGojaWFSkafvvNqAVevrzxc9myOSWHAfYDcOrUKbZu3UpGRgbx8QeuWgVYvvxdlC7dJMuxmJjfsVrTrvrEnJLGbm4VKFWqNn5+xkqIZcvy/juJmE3JYXFImzYZF4QKFfL3PovFmaZNv8XZOfuZBDt2DOLQoXdyeHcl4FGqVq3Krbc2wymXnTIqVICTJ2Ht2vzFKCLX548/4MCBa/cLp0/Pz7HN17cvHh7ZTDnORvXqT+DmVj7btsTEw8TF7c62rXx5OHXKmE0gIiJS0kVHGwkUX9+8zRo+dWoeNltGtm1ly3akSpUHcnxvamoKmzdv5tKlaABcXFxp1aol5cplfz3PVKoUJCbCkiW5xycihW/RIsjIMCZjAXh6VsPTM/tVf+XKnbHft587d5Y//viD8+fXXfU6f/+2VyWZMzISs500llNJCn9/48HUPyeKaZN6cRRKDotDCguDtLQrF4T88PYOpn797DeiSk29gM2W3Y4TTsCzVKtWi6ZNm2Gx5P7VcXMzlp+tWJH/GEUk/1avNpaZeXnl/JoLF3LOyl68WI/DedyS3MnJhcqV77vGebJ/KuTmZvxTMwlERERgwwZjH5GyZfP2+vPnl+fYFhg4PMe25OQkfvttE7GxlwFwdXWjVatW+Pn553pOi8VIXq9YoZU/ImY7fRo2bwb/f311cyotkZj4J3fc0QJnZ2NZ4cWLFzh4cOFVr/P3b5vtZ/x7lnB6ehyxsTuyPVdmchiMCSHHjhmTV0QcgZLD4nBsNtix4/oSw5kCAp6gfPlu+XhHHwIC7qJx4yZZCtnnxtsbdu/WDsciN8P27dfuFzIykoiNzWlNqAtQg/379xERcTBP5ytT5o4c22JituTYVqoU7NtnJLJFRERKsoMHjbF9TuWg/slqTSUmJqdMi4WyZTtk25KYmMBvv20iISEeAA8PD+688058fbPfPyA7pUtDXJyxclFEzBMRYXwXS5fOevyfidl/SkyMpFSpVFq2bImrq1E6Li0t62xgL68aeHreku1n/HuWcHT0pmusXrjyfnd3Y3ZzRESuv5JIkaDksDic8+eNjei8s68MkWdNmnyJq2tepinUIDDwZRo1apSvxDAYMV6+bDw1FJHCEx1tlGu41qzh1NTz5LR5hKtrNcAYMB44cIADB/7O8bWZvL3rXONc53Js8/aG2FiIirrmx4uIiBR7u3ZdWVWTm5SU8zkmZTw9A3Bz87vqeFxcHL/9tomkpEQAvLy8aN36TkqVKpWvOD08jOXhhw7l620iUsAiIrJ/oHStusMXL4bj5+dHq1atcXWNBbKO0zOTwu7u5SlVql6Wtujo37LsJZJTSQkXF5+rahZbLPD337n9RiJFg5LD4nAOHTKWdN1octjDozL160/O9XW33DKZhg2bAvlLDIORqEpM1CwDkcJ2+HDu/UJqavY7mwN4eZWjXr369p8jIiLYv38/10oQu7rmPOMoNfXCNc5lbFSpG0wRESnJEhIgMjLvY3rjIW/23NyunvBx+fJlNm3aREqKUfSzVCkfWre+E69rPUnOgcVi/NEsQBFz5ZRsLVWqDu7uFbNty0zoli5dmlq1rl665+3d3P7v/y4tkZYWTVzc3qs+69/8/FpjsWTdrN7T03gAls0+1SJFjpLD4nCOHDGWaOR1lsG1nDnzc66v8fTcwfUkhgGc/3d9UHJYpHBFRhrlW67VL2RkJOXY5uzsTnBwMA0bNrQfO3z4MHv27CWnBLGTk3uOn5eennNRwsy9LCMjc45VRESkuDt61EgQ5zVXa7XmfB3/9zU5OjqazZs3kZZmJIJ8fX1p3bo1Hh4e1xsurq6wd2/urxORwrN3b859hp9fm2yP/7NucELCn1e1R0Z6EhsbC2RfniLz/RkZKcTEXP1+431Xz1z29jZWN54+nX28IkWJksPicOLi8rabcW6OHfuCs2cX5/q6Q4cmcunSjVWSj4+/obeLSC4y+4Vr9Q1ubjlvOpOebnxJAwNr0KhRY/vxo0ePsHv3brJLEKenx+b4ea6upXNsA2MGgfoFEREpyeLjjQe7/ysDmqtrlYNLT79s//cLF87z+++/k/6/TT/8/Pxp2bIVbjc4s8TVFWJibugjROQGWK1Gabac+oycSkvExe0lNTUauHqDOfAlLa0Cmzdv4vLlGMqWDbnq/ZnviYnZgtWaku05sksqu7oafZzG/OIIlBwWh5OaeuNLMxITj7Bv33N5eq3NlsGOHQPJyEi8rnPZbNp4SqSwpaXl/ppr3VSmpJy1/3tAQABNmjQlc8WAl5c32a0eSE4+k+PnubmVu2YsNptRu1BERKSkSk01kj1OebwjdXMrn2NbUtJxbDYrAO7uHjj/b/le+fLls2xEdSOcnHTtFjFTWpoxhs5pMkh2s3cNNqKjN5KamrVEBICrq1En2NnZBTc3dzw9b8HTMzDLazKTw1cnlg1OTu6UKdM8m+NGH6dcgDgCJYfF4dzorGGbzcqOHQ+Tnh6X5/ckJBxk377R133OgpjpLCI3xs2tLC4u2dcJTkk5naUmcbVq1WjWrBl16tQlODg42/fExe3K8Vw+Po2uGYvFcqXsjIiISEmUueInr5M+3Nz8cHOrkG1benqsPenj4+NDy5YtuOWWajRv3tyeKL5RNlveE9kiUvByu6cuXbpxjmP96OhwoqM38u/VgMHBvalSpQqtWrXC09MTuHoGcnLyCRITj+RYb7hMmeY4O19dsiYzka1cgDgCXd7E4bi731gHGxn5Xo5P/apU6ZdjUicq6lPOn1+V7/M5ORkxi0jhyctKUYvF6Zo7GV+4sDrLz1WrVqVWrVo5vv7cueU5tpUp0yLXeAqibrqIiIijcnMzxsn5WRFYvnyXHNtOnfqv/d99fcvQtGlTnJwK7kmszaYxvYiZXF2v/UDJYnHC3//ObNsuXgzPNgdQrlx7br31Nrz/sTNmdjOQL1xYy6VLm7P97OxKSsCVB0oa84sjUHJYHE6ZMkZHez2lJWJj93DgwNhs2zw8qtGo0XSaNZuDk1N2PbiNnTsfJS0tJl/ntNmMmEWk8Pj5Gcu2cusXypULzbHt+PGv83y++PiIHGcPODl5UqFCt2u+32JRvyAiIiVbmTJGsicl+xKe2apQoUeObVFRM0hNvXDjgeUgJQUqZD9xWURuAosFypa9dp+RU2mJ2NjtnD+fdWKHi4sPvr5N8/QZR49Oy3HlcU7J4ZQUo4/TmF8cgZLD4nCCg/M/kASwWtPYsWNgDkXkLTRtOgtXV19Kl25M7dpvZvsZyckn2L17eJ7PmZ5uPC3MYVW6iBSQmjWN2Ty51QKsWnUALi4+2badP7+M8+dX5noum83G3r0jyW6TOuMcD+HiUirH9/9vfxz1CyIiUqIFBoKPDyQk5P09lSvfh5dXULZtaWnR7NgxGJstI19xJCYeyVO5uYwMaNAgXx8tIgWsceNrj/dzWiVos6UTF7cnyzE/v9ZYLFevLihVqhbu7pWzHIuN3ZHDGZ3w82udbUtiIlSsCOWuvRWJSJGg5LA4nJo1wds7fwNJgIMHX8+xUw8MHEG5cp3sPwcHv5DjE8BTp+Zy6tSPeTpnQgJ4eRkxi0jhCQ7OW7/g5uZPYOD/5dj+118PEhOzLcd2my2DvXuf5vz5Zdm2Ozm5U6vWK9eMITHRiPUaFStERESKPTc3qFPHuC7mlZOTK3XqjM+x/dy5JWzdel+eVvqlpcVw8OA41q1rkGXfgexYjb3udO0WMVmdOtdeRezrezvOzl55+qyc7vdza/un0qUb4+qafZ3jpCRo0kQ1h8UxKDksDsfXF6pVy99A8tKlzRw+/G62bd7etalXb1KWYxaLE02bfpvjDMPdu58kOfl0rudNSDCWvlSpkvdYRST/vL2hRo289Qs1a75IqVL1s21LS7vEb7+1Yu/eZ4iJ+ZP09ARsNhspKec4efK/bNzYgqNHP87xs+vWnYiXV+A1z5+QYJTBuOWW3GMVEREpzho2NGbk5keVKv2oUqVfju1nzy5izZpgDhx4nZiYP0lLiwUgIyOJxMQjnDw5j507hxAWVpWDB9/Aak36//buPTaqMo3j+G96hekFKMjFcrMWRMSWQrgsiAqiFS8QQGTdNSSyRBMj4g0FFXTVPzbeImqirms2rjErbgJhdV0FCRoNJoJB2HBRLrXlJpSWlnaGYabt2T8eG4R2OmPpdKZzvp+EFOacOefwR99553mf93ki3tPvl7p3J+EDiLfCQttFHAq1fjwlJT2q3h9S2wHgtvqURHsNSRo+PKrLAHGXFu8HANpj7Fhp+/azHUDb0tDg07ZtC1rdYubxpGr06H8oNbV7i2Ne71CNHPmKduz4U4tjoVCVduxYpPHj/9PmvX0+qbSU1UKgM4wZI23dGnlcSEvL0bhx/9bXX49XKFTd4rjjhFRWtkplZat+eSVFUlPE++fn/1GXXPJAxPN8PmnqVDqeAwBw+eX2mR0MRt+0yePxqLj47woEDoet/x8KVWvv3me0d+8zv7wn9TeXm/i12lopL88WogHEz/DhUm6uVFMTvgZ4Xt7Vqqra1OZ1UlIy1LPn+LDHw9Uubnle68Fhn8/GtBEjoroMEHd8NUWXVFpq5Rrq6yOfu3v3I/L797V6rLBwuXr1Cr+yOHjwQvXrN6vVY8ePf6Ly8r+Gfa/fb6uaN90U+RkBXLjSUssgrotcNlBZWZdq0qSvlJ0dzYwtcmB40KBFGj36XXkirASdPi2lpko3h++nAwCAa0yebDtpjh//be9LTc3UhAn/1aBBC6M6/0ICw45jgZ6ZM6MPYAOIjbw8afp0Cw6HKy3Ru3fkkhA9eoxTamq3sMdzckYpPT0viudp/V6VlVYCY8yYiJcAEgLBYXRJxcVWjL6ysu3zjh//VOXlb7Z6LDe3RMOGrYx4r6Kit5WR0fqy5K5dD8vnOxDm3rayOXFixFsA6ABXXCGNHh15XGiWkzNSV121RYMH36OUlPZ928vM7KeSkvdVXPx2qw0tznf8uNVHnjy5XbcDACCpeL3S7NmWVNEUeS32HKmpXhUXv6OSkn/K621/l9f09J6t7iJsVlNjmYos7AKJYeZMK/MSrtdIr16/k8eT3uY1IpWD8Hg8ysu7qs1zvN5CdevWv8XrDQ1W9mLOHEsKAboCgsPokjweG2wdJ3y9oWCwWtu3tywJIVnTqJKS95SS0vaHhiRlZl6k4uK/tXqssbFe33+/QI7TdN7rZz8Q0ijeAnQKj0eaO9f+HgxG9560tGwVFb2padN+UmHhcuXkjJIUXR2Y3NxiTZ26X/n5f4jq/MZGe645c2xXAQAAkGbMsJ4iNTXte39+/u81deqPGjPmX+rb9xalp/eM+J7UVK/69r1JRUXvaPr0w8rM7Bf23KoqadIkW9wFEH9jxlhSSLgdB6mpXvXoMbbNa0TTcC5SaYlwGcpVVVby4oYbIt4CSBgexwmXjA8ktvp66fbbpYqKxKv/VVEh9ekjrV5tPwF0Dr9fmj9fOnBAKiho3zWCwZOqrd2iM2eOKRisVkNDrQ4deld+f8tdAsOGrdRll/05qusePCj17Cl9+GH4GmkAALjRsmXS2rUWgL3QTDvHaVJd3f/k8+1TMFitUKhajtOotLQcZWb2V3b2ZcrOHhHVrqGaGitX9eqr0tXRlSAF0AnWrZMef9zm1NnZ8X6as0IhqaxMWrRIWro03k8DRI/gMLq0DRts0M3JsaBLIqirs9XCZ56xbXIAOtemTdKDD9pEsVevjrmm31+uzZunKBA42OLYiBF/UWHhY22+v77eshueflqaN69jngkAgGRx+LC0YIF04oQ0eHC8n8Y0NNhi87x50rPP0mAaSCQNDdL990sbN0qFhYnT6PnAAUtce++9jvseAnSGBPkVAtpn+nTp1lst6NLQEO+nsW3jR49K110nzWq9jx2AGLv2Wvv9q6zsuHHB6x2iiRM3KjOzZV2xPXuWqazstbDvbWqSjhyRpk61khIAAOBc+fnSfffZZ2Y0jWU7w8GDtgvpgQcIDAOJJi1NevRRacAAm2cngupqa1r5yCMEhtH1EBxGl+bxSEuW2MStvDx8x9LO4Dg2iczPt2zmRFm9BNymeVwoLJR++qnjxoXs7GGaOHGjMjJa1orZuXOJKipa1iZvHhcGDLAJLE0pAABo3axZlvhx9Gj4niKdpbragk8PPUSJOCBRDR0q3Xuv9fQ4dSq+z3LmjO18mD1buuaa+D4L0B6Er9Dl9ekjrVhhZSUqKuIXID582LqmLl9uAWIA8ZOXJ61caT87cuEoJ2ekJkzYoPT089MBHO3YcY8OHXr/nFePHLEMgmXLEmebLAAAiSglxRZSR460mp3x2hVYWyudPCndcYcFqwEkrjlzbGHp2DEr4xYPwaB93xg/3kpdsNMAXRE1h5E01q+XnnxSCgQsCNNZg7LjSIcOWUbgE0+wbRxIJBs3WrMKv18aMqRzx4XDh+1+y5ZZ80wAABBZWZmVmNi/32p3pqV13r1raiz7b948W2TuzHsDaJ9AwObbn34q9e9v/Yg6SzBoOxWLiqTXXrP7A10RwWEklfXrreFTTY0FgmI9oWtstGxlr9cyhmlABySejRvtC151tY0L6emxvV/zuNC9u/TYY9Jtt8X2fgAAJJv9+6WHH5Z275YGDrS5diw5jvUw8fmk+fMt0JSREdt7Aug4fr/01FPSxx/bjuLevWOfFFJXZ2Vwxo6VXnrJysgBXRXBYSSdb76RnntO2rdPuuii2BWDr6217StDhtgEctq02NwHwIX79lvrNP7jj1aKJi8vNvc5dcomiUOG2NbY66+PzX0AAEh2hw5Z0sfmzVK3bhZ4iUVPj0DA+gPk5kp33SXdfTc9AoCuKBiUXnlFWr1aOn1aGjQoNos8TU02PjU2WiP6FSuoTY6uj+AwklJ1tbRqlbRunTW0GDiw4z4YgkGrI+rxSDNmWFZD374dc20AsXPypPT669KaNfZ7nJ8vZWZ2zLVDIRsXHEe68UZrYEP2AAAAFyYUkj74QHrrLamyUrr4Yikrq2MyApua7Jp1dVJJie32KS6+8OsCiK/Nm6UXXpB27bJEsd69O2ZhyXEsEeTYMfsesXix1TumET2SAcFhJC3Hkb74Qnr5ZduaJtmKXm7ub59QOo5NHCsr7d+DBklLllhwmILzQNfhONJXX0kvvmjjguNc2LhQX2/jguPYuLB4sXTzzYwLAAB0pL17peefl7ZssUzfnj1tF1B7MnwDASshceaMBY7uvFNauNDKQQFIDqdOSW+8YUkhNTVWmqZv3/YljDU0SFVVtnM4K0uaMkVautQS0IBkQXAYSS8QsCDxmjXSd99ZMCcjwwZ2r9cmguev9jmObUXx+6322Jkzdn5RkTR3rpWQyMqKy38HQAcIBKQvv7RxYevW6MeFQMDGhOZxweu1cWHOHNtWlp0dn/8PAADJrqnJ5vIffSR9/rntFExJsc/u5s/v84PFjmO7hZrn9H6/9SQpKLBeITNmsNMHSGbl5dInn0hr11opiKYmGyu8Xhs3WgsWNzScO2Y0J5PMmCHdcot05ZUkgiD5EByGaziOtGePtGGDtG2bZSD4fBYEbi0IlJlpHxiXXiqNHm2Bn6IiPgiAZOI40g8/nDsu1NdbEPj83/VfjwsFBbb1dPp0+8m4AABA5/n5Z+mzzyxYvHOnZfT5/S3PcxwLBmdlWUmKoiLL+psyhYZzgJucPi1t2mQlJ3bssNIQPp/VDW5tHp+VZTsLRo2Sxo2TSktj17MESAQEh+FagYBUVmaN62prLavAcWyimJtrQeGCgth3RwaQONoaF3Jyzo4L7BwAACAxNDZaQ7l9+yzgEwpZ5l9GhjWyGzpUKiwksAPAOI504oSNGRUVNv8PhaT0dPszYICNGfn51BOGexAcBgAAAAAAAAAXYh0EAAAAAAAAAFyI4DAAAAAAAAAAuBDBYQAAAAAAAABwIYLDAAAAAAAAAOBCBIcBAAAAAAAAwIUIDgMAAAAAAACACxEcBgAAAAAAAAAXIjgMAAAAAAAAAC5EcBgAAAAAAAAAXIjgMAAAAAAAAAC4EMFhAAAAAAAAAHAhgsMAAAAAAAAA4EIEhwEAAAAAAADAhQgOAwAAAAAAAIALERwGAAAAAAAAABciOAwAAAAAAAAALkRwGAAAAAAAAABciOAwAAAAAAAAALgQwWEAAAAAAAAAcCGCwwAAAAAAAADgQgSHAQAAAAAAAMCFCA4DAAAAAAAAgAsRHAYAAAAAAAAAFyI4DAAAAAAAAAAuRHAYAAAAAAAAAFyI4DAAAAAAAAAAuND/Afc55nyv/KyuAAAAAElFTkSuQmCC", + "image/png": "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", "text/plain": [ "
" ] @@ -637,48 +464,6 @@ } ], "source": [ - "\n", - "def plot_reeb_explore(df, person, salient_node, q=\"\", save=True, use_turns=True):\n", - " fig, axs = plt.subplots(1, 2, figsize=(18, 6))\n", - " # print(df[df[\"Subject\"]==person])\n", - " c = 0\n", - " for i, person_info in df[df[\"Subject\"]==person].iterrows():\n", - " ax = axs[c]\n", - " c += 1\n", - " \n", - " path = person_info[\"e_paths\"] if use_turns else person_info[\"paths\"]\n", - " \n", - " G, num_trajs = do_reeb_for_explore_node(path, salient_node)\n", - " pos = graphviz_layout(G, prog=\"dot\")\n", - " \n", - " edges = G.edges()\n", - " # colors = [G[u][v]['color'] for u,v in edges]\n", - " weights = [G[u][v]['weight'] * 2 for u,v in edges]\n", - " \n", - " nodes = G.nodes()\n", - " # for n in nodes:\n", - " # print(n, G.nodes[n])\n", - " n_labels = {n: n[:-2] for n in G}\n", - " n_colors = [\"blue\" for n in nodes]\n", - " # print(n_colors)\n", - " \n", - " nx.draw_networkx_nodes(G, pos, node_color=n_colors, node_size=1000, alpha=0.7, ax=ax)\n", - " nx.draw_networkx_edges(G, pos, edgelist=edges, width=weights, ax=ax)\n", - " nx.draw_networkx_labels(G, pos, labels=n_labels, font_size=26, font_weight=\"bold\", ax=ax)\n", - " \n", - " ax.axis(\"off\")\n", - " ax.set_title(f\"{num_trajs} trajs\")\n", - " \n", - " fig.suptitle(f\"Explore paths starting at {salient_node} for person {person} (Q{q})\", fontsize=30)\n", - " \n", - " if save:\n", - " os.makedirs(f'reebs/explore/starting_{salient_node}/', exist_ok=True) \n", - " fig.savefig(f'reebs/explore/starting_{salient_node}/subj_{person}-{\"YES\" if use_turns else \"NO\"}TURN.png')\n", - " plt.close()\n", - " else:\n", - " plt.show()\n", - "\n", - "\n", "explore_df = df[~df[\"eprocs\"].str.contains(\"Test\")]\n", "\n", "salient_node = \"R\"\n", @@ -701,24 +486,69 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "f13c8237", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A\n", + "B\n", + "C\n", + "D\n", + "E\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 14\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(qs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 13\u001b[0m q \u001b[38;5;241m=\u001b[39m qs\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m---> 14\u001b[0m \u001b[43mplot_reeb_explore\u001b[49m\u001b[43m(\u001b[49m\u001b[43mexplore_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msalient_node\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msave\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muse_turns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m count \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# if count == 20: break\u001b[39;00m\n", + "Cell \u001b[0;32mIn[7], line 12\u001b[0m, in \u001b[0;36mplot_reeb_explore\u001b[0;34m(df, person, salient_node, q, save, use_turns)\u001b[0m\n\u001b[1;32m 9\u001b[0m path \u001b[38;5;241m=\u001b[39m person_info[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124me_paths\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m use_turns \u001b[38;5;28;01melse\u001b[39;00m person_info[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpaths\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 11\u001b[0m G, num_trajs \u001b[38;5;241m=\u001b[39m do_reeb_for_explore_node(path, salient_node)\n\u001b[0;32m---> 12\u001b[0m pos \u001b[38;5;241m=\u001b[39m \u001b[43mgraphviz_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mG\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdot\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 14\u001b[0m edges \u001b[38;5;241m=\u001b[39m G\u001b[38;5;241m.\u001b[39medges()\n\u001b[1;32m 15\u001b[0m \u001b[38;5;66;03m# colors = [G[u][v]['color'] for u,v in edges]\u001b[39;00m\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/networkx/drawing/nx_pydot.py:322\u001b[0m, in \u001b[0;36mgraphviz_layout\u001b[0;34m(G, prog, root)\u001b[0m\n\u001b[1;32m 292\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgraphviz_layout\u001b[39m(G, prog\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mneato\u001b[39m\u001b[38;5;124m\"\u001b[39m, root\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 293\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Create node positions using Pydot and Graphviz.\u001b[39;00m\n\u001b[1;32m 294\u001b[0m \n\u001b[1;32m 295\u001b[0m \u001b[38;5;124;03m Returns a dictionary of positions keyed by node.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[38;5;124;03m This is a wrapper for pydot_layout.\u001b[39;00m\n\u001b[1;32m 321\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 322\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpydot_layout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mG\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mG\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mroot\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/networkx/drawing/nx_pydot.py:371\u001b[0m, in \u001b[0;36mpydot_layout\u001b[0;34m(G, prog, root)\u001b[0m\n\u001b[1;32m 367\u001b[0m P\u001b[38;5;241m.\u001b[39mset(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mroot\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mstr\u001b[39m(root))\n\u001b[1;32m 369\u001b[0m \u001b[38;5;66;03m# List of low-level bytes comprising a string in the dot language converted\u001b[39;00m\n\u001b[1;32m 370\u001b[0m \u001b[38;5;66;03m# from the passed graph with the passed external GraphViz command.\u001b[39;00m\n\u001b[0;32m--> 371\u001b[0m D_bytes \u001b[38;5;241m=\u001b[39m \u001b[43mP\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_dot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprog\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 373\u001b[0m \u001b[38;5;66;03m# Unique string decoded from these bytes with the preferred locale encoding\u001b[39;00m\n\u001b[1;32m 374\u001b[0m D \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(D_bytes, encoding\u001b[38;5;241m=\u001b[39mgetpreferredencoding())\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/pydot.py:1733\u001b[0m, in \u001b[0;36mDot.__init__..new_method\u001b[0;34m(f, prog, encoding)\u001b[0m\n\u001b[1;32m 1729\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnew_method\u001b[39m(\n\u001b[1;32m 1730\u001b[0m f\u001b[38;5;241m=\u001b[39mfrmt, prog\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprog,\n\u001b[1;32m 1731\u001b[0m encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 1732\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Refer to docstring of method `create`.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1733\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1734\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprog\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprog\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/pydot.py:1923\u001b[0m, in \u001b[0;36mDot.create\u001b[0;34m(self, prog, format, encoding)\u001b[0m\n\u001b[1;32m 1920\u001b[0m arguments \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m-T\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mformat\u001b[39m), ] \u001b[38;5;241m+\u001b[39m args \u001b[38;5;241m+\u001b[39m [tmp_name]\n\u001b[1;32m 1922\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1923\u001b[0m stdout_data, stderr_data, process \u001b[38;5;241m=\u001b[39m \u001b[43mcall_graphviz\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1924\u001b[0m \u001b[43m \u001b[49m\u001b[43mprogram\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprog\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1925\u001b[0m \u001b[43m \u001b[49m\u001b[43marguments\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43marguments\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1926\u001b[0m \u001b[43m \u001b[49m\u001b[43mworking_dir\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtmp_dir\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1927\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1928\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 1929\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39merrno \u001b[38;5;241m==\u001b[39m errno\u001b[38;5;241m.\u001b[39mENOENT:\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/pydot.py:141\u001b[0m, in \u001b[0;36mcall_graphviz\u001b[0;34m(program, arguments, working_dir, **kwargs)\u001b[0m\n\u001b[1;32m 130\u001b[0m program_with_args \u001b[38;5;241m=\u001b[39m [program, ] \u001b[38;5;241m+\u001b[39m arguments\n\u001b[1;32m 132\u001b[0m process \u001b[38;5;241m=\u001b[39m subprocess\u001b[38;5;241m.\u001b[39mPopen(\n\u001b[1;32m 133\u001b[0m program_with_args,\n\u001b[1;32m 134\u001b[0m env\u001b[38;5;241m=\u001b[39menv,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m 140\u001b[0m )\n\u001b[0;32m--> 141\u001b[0m stdout_data, stderr_data \u001b[38;5;241m=\u001b[39m \u001b[43mprocess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcommunicate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m stdout_data, stderr_data, process\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/subprocess.py:1209\u001b[0m, in \u001b[0;36mPopen.communicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m 1206\u001b[0m endtime \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1208\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1209\u001b[0m stdout, stderr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_communicate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mendtime\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1210\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m 1211\u001b[0m \u001b[38;5;66;03m# https://bugs.python.org/issue25942\u001b[39;00m\n\u001b[1;32m 1212\u001b[0m \u001b[38;5;66;03m# See the detailed comment in .wait().\u001b[39;00m\n\u001b[1;32m 1213\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/subprocess.py:2115\u001b[0m, in \u001b[0;36mPopen._communicate\u001b[0;34m(self, input, endtime, orig_timeout)\u001b[0m\n\u001b[1;32m 2108\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_timeout(endtime, orig_timeout,\n\u001b[1;32m 2109\u001b[0m stdout, stderr,\n\u001b[1;32m 2110\u001b[0m skip_check_and_raise\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 2111\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m( \u001b[38;5;66;03m# Impossible :)\u001b[39;00m\n\u001b[1;32m 2112\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_check_timeout(..., skip_check_and_raise=True) \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 2113\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfailed to raise TimeoutExpired.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 2115\u001b[0m ready \u001b[38;5;241m=\u001b[39m \u001b[43mselector\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2116\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_timeout(endtime, orig_timeout, stdout, stderr)\n\u001b[1;32m 2118\u001b[0m \u001b[38;5;66;03m# XXX Rewrite these to use non-blocking I/O on the file\u001b[39;00m\n\u001b[1;32m 2119\u001b[0m \u001b[38;5;66;03m# objects; they are no longer using C stdio!\u001b[39;00m\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/selectors.py:415\u001b[0m, in \u001b[0;36m_PollLikeSelector.select\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 413\u001b[0m ready \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 414\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 415\u001b[0m fd_event_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_selector\u001b[38;5;241m.\u001b[39mpoll(timeout)\n\u001b[1;32m 416\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mInterruptedError\u001b[39;00m:\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ready\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABZIAAAIOCAYAAAAiF/jdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAADFsUlEQVR4nOzdd3gUZdvG4Ws3vRMSWuhVSkiQXgUUUVQUu2LFAuiLvCI2xP6qWJDPjqgg9t5QAQWRDlIDCb2HACEJkEZ6dr8/RoJIdtklW1J+53Hsgcw8M3NHJhO45pl7TFar1SoAAAAAAAAAAGwwe7sAAAAAAAAAAEDlRpAMAAAAAAAAALCLIBkAAAAAAAAAYBdBMgAAAAAAAADALoJkAAAAAAAAAIBdBMkAAAAAAAAAALsIkgEAAAAAAAAAdhEkAwAAAAAAAADsIkgGAAAAAAAAANhFkAygSrn99ttlMplsflauXGl3+9mzZ+vpp592W30DBgzQgAED3LZ/AAAAAAAAbzBZrVart4sAAEft2rVL6enppy0fOnSoAgICtG/fPvn4+NjcfsyYMXr77bflrkvf5s2bJUnt27d3y/4BAAAAAAC8wdfbBQCAM1q2bKmWLVuesmzRokXKyMjQ448/bjdEdpbValVBQYGCgoIc3oYAGQAAAAAAVEe0tgBQ5U2fPl0mk0l33HGH3XG333673n77bUk6pR3G3r17y5aNGTNG7777rtq1a6eAgAB99NFHkqRnnnlGPXr0UO3atRUeHq7OnTtr+vTpp81sLq+1xdSpUxUfH6/Q0FCFhYWpbdu2euyxx1zzxQMAAAAAAHgAM5IBVGlZWVn69ttvdcEFF6h58+Z2xz7xxBM6fvy4vv32W61YsaJseYMGDcr++8cff9SSJUv05JNPqn79+qpbt64kae/evRo1apSaNGkiSVq5cqXuu+8+HThwQE8++aTNY3755Ze69957dd9992ny5Mkym83auXNnWQsMAAAAAACAqoAgGUCV9sUXXyg/P1933nnnGce2bNlS9erVkyT17Nmz3DG5ublKTExUZGTkKcs//PDDsv+2WCwaMGCArFarXn/9dT3xxBMymUzl7m/ZsmWqVauW3njjjbJlF1xwwRlrBQAAAAAAqExobQGgSps+fbqioqJ05ZVXumR/559//mkhsiQtWLBAgwYNUkREhHx8fOTn56cnn3xSR44cUVpams39de/eXZmZmbrxxhv1008/KSMjwyV1AgAAAAAAeBJBMoAqa+PGjVqzZo1uvvlmBQQEuGSf/2xzccKqVas0ePBgSdL777+vZcuWafXq1Zo4caIkKT8/3+b+brnlFs2YMUP79u3T1Vdfrbp166pHjx6aN2+eS+oFAAAAAADwBIJkAFXW9OnTJUl33XWXy/ZZXouKL7/8Un5+fvrll1903XXXqXfv3uratavD+xwxYoSWL1+urKws/frrr7Jarbrsssu0b98+l9UNAAAAAADgTvRIBlAlFRYW6tNPP1X37t0VGxvr8HYnZi7n5+crKCjIoW1MJpN8fX3l4+NTtiw/P1+ffPKJUzWHhIRoyJAhKioq0rBhw7Rp0yY1bdrUqX0AAAAAAAB4A0EygCrpxx9/1NGjR52ejdyxY0dJ0ksvvaQhQ4bIx8dHcXFx8vf3t7nNpZdeqilTpmj48OEaOXKkjhw5osmTJzvUTuPuu+9WUFCQ+vTpowYNGig1NVWTJk1SRESEunXr5lTtAAAAAAAA3kKQDKBKmj59ukJCQnTDDTc4td3w4cO1bNkyvfPOO3r22WdltVq1Z88eNWvWzOY2559/vmbMmKGXXnpJQ4cOVcOGDXX33Xerbt26uvPOO08b/8/2GP369dPMmTP19ddf69ixY4qOjlbfvn318ccfq06dOk7VDgAAAAAA4C0mq9Vq9XYRAFBdnHvuuWrZsqW+/fZbb5cCAAAAAADgMsxIBgAX2L59u5YsWaLExETdfPPN3i4HAAAAAADApZiRDAAuMGLECP3888+6/PLL9fbbbzv8Ij8AAAAAAICqgCAZAAAAAAAAAGCX2dsFAAAAADXd4sWLNXToUMXExMhkMunHH3884zaLFi1Sly5dFBgYqBYtWujdd991f6EAAACosQiSAQAAAC87fvy44uPj9dZbbzk0fs+ePbrkkkvUr18/rV+/Xo899pjGjh2r7777zs2VAgAAoKaitQUAAABQiZhMJv3www8aNmyYzTGPPPKIZs2apS1btpQtGz16tDZs2KAVK1Z4oEoAAADUNL7eLgAAAACAc1asWKHBgwefsuyiiy7S9OnTVVxcLD8/v9O2KSwsVGFhYdnvLRaLjh49qqioKJlMJrfXDAAAAM+wWq3KyclRTEyMzGbXNaQgSAYAAACqmNTUVNWrV++UZfXq1VNJSYkyMjLUoEGD07aZNGmSnnnmGU+VCAAAAC/bv3+/GjVq5LL9ESQDAAAAVdC/ZxGf6Fhna3bxhAkT9MADD5T9PisrS02aNNH+/fsVHh7uvkIBAADgUdnZ2WrcuLHCwsJcul+CZAAAAKCKqV+/vlJTU09ZlpaWJl9fX0VFRZW7TUBAgAICAk5bHh4eTpAMAABQDbm6fZnrmmQAAAAA8IhevXpp3rx5pyz7/fff1bVr13L7IwMAAAAVRZAMAAAAeFlubq4SEhKUkJAgSdqzZ48SEhKUnJwsyWhLceutt5aNHz16tPbt26cHHnhAW7Zs0YwZMzR9+nQ9+OCD3igfAAAANQCtLQAAAAAvW7NmjQYOHFj2+xO9jG+77TbNnDlThw4dKguVJal58+aaPXu2xo0bp7ffflsxMTF64403dPXVV3u8dgAAANQMJuuJt3IAAAAAqDGys7MVERGhrKwseiQDAABUI+76ex6tLQAAAAAAAAAAdhEkAwAAAAAAAADsIkgGAAAAAAAAANhFkAwAAAAAAAAAsIsgGQAAAAAAAABgF0EyAAAAAAAAAMAugmQAAAAAAAAAgF0EyQAAAAAAAAAAuwiSAQAAAAAAAAB2ESQDAAAAAAAAAOzy9XYBAFCd5OZKBw5IhYVScbHk5ycFBEgxMVJYmLerAwAAAAAAODsEyQBQAfv2SWvWSDt2SBs3Snv3Svn5ksVifMxm4xMYKDVrJsXFSa1bS126SM2be7t6AAAAAAAAxxAkA4CTSkqk5culn36SliyRsrMlk0ny95eCg6XatU8GyCcC5fx8afNmaf16Y2xYmNSnj3TFFcavfn7e/qoAAAAAAABsI0gGAAdZrdIvv0jTp0u7dhmBcu3aUsuWRmhsT3CwFBVl/LfFImVmSnPnSvPmSa1aSbffLl1++Zn3AwAAAAAA4A0EyQDggEOHpMmTpd9/NwLl+vWloKCz25fZbATQtWsbM5V37ZKeeEJauFB66CGpYUOXlg4AAAAAAFBhzH0DADusVunnn6WbbzZmI9eubfQ2PtsQ+d+CgozeyVFR0pw50i23SD/8YBwXAAAAAACgsmBGMgDYYLFI77wjvf++Eey2bCn5+LjnWGFhRvuLAwekp56SkpOl++6j1QUAAAAAAKgcCJIBoBxWq/R//yfNmGGEvHXqnD4mIeF2paR8VOFj1at3hbp1+1E+PlKTJlJGhvTee0bbi4cfJkwGAAAAAADeR5AMAP9itUpTp0offihFRJx8SZ6nREcbM58/+cRofTF2rGQyebYGAAAAAACAf2KeGwD8y/z5xozg0FDPh8gnREZK4eHGjOg5c7xTAwAAAAAAwAkEyQDwDxkZ0pQpUklJ+e0sPCk62ujT/Npr0uHD3q0FAAAAAADUbLS2AIC/Wa1GaLt7t9SiRcX21bLlw/Lzi3RobEhIG5vrGjeWdu0ywu0XX6TFBQAAAAAA8A6CZAD424IF0s8/S3XrSr4VvDo2bXqPgoObVbgmHx+pXj2jvcXAgdLFF1d4lwAAAAAAAE6jtQUAyJiN/MknUnGxVKuWt6s5VUSE0Wrj00+NOgEAAAAAADyNIBkAJCUmShs3er8vsi316klJSdK6dd6uBAAAAAAA1EQEyQAgo6VFXp4UFubtSsoXEiIVFEizZnm7EgAAAAAAUBMRJAOo8Y4elebOlcLDK+/L7Ewmo+XGvHlSWpq3qwEAAAAAADUNL9sDUOMlJRlhcuPGrtvnggXNHR572WWONT6OjJT27jXacFxwwVkWBgAAAAAAcBaYkQygxtu5U7JYJD8/b1din+/ft/527fJuHQAAAAAAoOYhSAZQ423d6u0KHGcySVu2eLsKAAAAAABQ0xAkA6jRrFZp40YpONjblTgmONhobVFa6u1KAAAAAABATUKPZAA1Wna2lJUlBQW5dr8tWz4sP79I1+5URp05OVJmphQV5fLdAwAAAAAAlIsgGUCNVlxszO51dX/kpk3vUXBwM9fuVJLZbPRzLipy+a4BAAAAAABsIkgGUKOVlBi/mkzObZefn6/s7Cyb6/fs2SU/v0KH99e4cRMFOTAt2mQy2nGcqBsAAAAAAMATCJIB1Gh+fkY4a7E4t11BQb6ys7Ntrt+zZ4+kPIf3V6dOHYeCZKvVmJXs7+/wrgEAAAAAACqMl+0BqNECA41gtqq8vK601Kg3MNDblQAAAAAAgJqEIBlAjRYSIsXESPn53q7EMXl5Ut26Uni4tysBAAAAAAA1CUEygBovLk4qKPB2FY7JzzfqdbanMwAAAAAAQEXQIxlAjdemjdF72Gp1ZUA7R1Kow6Mtlq4Ojz3nnLMoBwAAAAAAoAIIkgHUeK1aGS+vKyx0Ze/h750abbE8fcYxhYWSj4/UsuVZlgQAAAAAAHCWaG0BoMbr2FFq1EjKyPB2JfZlZBj9nDt18nYlAAAAAACgpiFIBlDjBQZKw4YZ/YctFm9XUz6LRTp+3KgzONjb1QAAAAAAgJqGIBkAJF1yiRQRIR075u1KypeZadR3ySXergQAAAAAANRE9EgGABmtLQYMkH78Uapd29GX7t3/96fifH0jbK6zWo22FpdcIjVr5pLDAQAAAAAAOIUZyQDwtxEjpKgoKTXV25Wc6vBhqVYt6c47vV0JAAAAAACoqQiSAeBvbdtKd91l9CLOz/d2NYaCAik31wiRY2O9XQ0AAAAAAKipCJIB4B9uvlnq0UNKSTFaSniT1Srt3y916ybdeqt3awEAAAAAADUbQTIA/IO/v/Too1LdutLevd4Lk61Wad8+KTpaevhhKTDQO3UAAAAAAABIBMkAcJq2baXnnpPCwqTkZM+HySdmIgcHS88+S0sLAAAAAADgfQTJAFCO884zQtyQEGNmssXimeNaLMbxAgOlp56Szj/fM8cFAAAAAACwhyAZAGy48ELp5ZeN9hI7dxov4XOnvDzjOJGR0osvSpdc4t7jAQAAAAAAOIogGQDs6NdPmjFD6t9fOnxYOnDAHbOTTUpP99ehQyePN3Cgq48BAAAAAABw9ny9XQAAVHYtWkjvvCN99ZX07rvGrGEfHz9JPpJKK7BnH0lRkmopIMCihx6SbrhB8vNzSdkAAAAAAAAuw4xkAHCAr690003SRx9Jt98u+ftbJTWX1ExSqBy/nJr+Ht/07+2LJH2sJ57YpVtuIUQGAAAAAACVEzOSAcAJLVpIEyZInTtv08UXvyrpakktJNWXZJVkkZQvY6ayVUZw7CMp6O9fJSlP0g5J30v6TdIRNWp0mUe/DgAAAAAAAGcQJAPAWQgPL5H0laRvZMwsbiWppaT2klpLCpQxS9kiqUDSWklJknZK2iVpjyrWFgMAAAAAAMBzCJIBoEIsMoLhXeWsOxEkAwAAAAAAVG30SAYAtyFEBgAAAAAA1QNBMgAAAAAAAADALoJkAAAAAAAAAIBdBMkAAAAAAAAAALsIkgEAAAAAAAAAdhEkAwAAAAAAAADsIkgGAAAAAAAAANhFkAwAAAAAAAAAsIsgGQAAAAAAAABgF0EyAAAAAAAAAMAugmQAAAAAAAAAgF0EyQAAAAAAAAAAuwiSAQAAAAAAAAB2ESQDAAAAAAAAAOwiSAYAAAAAAAAA2EWQDAAAAAAAAACwiyAZAAAAAAAAAGAXQTIAAAAAAAAAwC6CZAAAAAAAAACAXQTJAAAAAAAAAAC7CJIBAAAAAAAAAHYRJAMAAAAAAAAA7CJIBgAAAAAAAADYRZAMAAAAAAAAALCLIBkAAACoBN555x01b95cgYGB6tKli5YsWWJ3/Geffab4+HgFBwerQYMGGjFihI4cOeKhagEAAFDTECQDAAAAXvbVV1/p/vvv18SJE7V+/Xr169dPQ4YMUXJycrnjly5dqltvvVV33nmnNm3apG+++UarV6/WXXfd5eHKAQAAUFMQJAMAAABeNmXKFN15552666671K5dO7322mtq3Lixpk6dWu74lStXqlmzZho7dqyaN2+uvn37atSoUVqzZo2HKwcAAEBNQZAMAAAAeFFRUZHWrl2rwYMHn7J88ODBWr58ebnb9O7dWykpKZo9e7asVqsOHz6sb7/9VpdeeqknSgYAAEANRJAMAAAAeFFGRoZKS0tVr169U5bXq1dPqamp5W7Tu3dvffbZZ7r++uvl7++v+vXrq1atWnrzzTdtHqewsFDZ2dmnfAAAAABHESQDAAAAlYDJZDrl91ar9bRlJ2zevFljx47Vk08+qbVr12ru3Lnas2ePRo8ebXP/kyZNUkRERNmncePGLq0fAAAA1RtBMgAAAOBF0dHR8vHxOW32cVpa2mmzlE+YNGmS+vTpo4ceekhxcXG66KKL9M4772jGjBk6dOhQudtMmDBBWVlZZZ/9+/e7/GsBAABA9UWQDAAAAHiRv7+/unTponnz5p2yfN68eerdu3e52+Tl5clsPvWv8j4+PpKMmczlCQgIUHh4+CkfAAAAwFEEyQAAAICXPfDAA/rggw80Y8YMbdmyRePGjVNycnJZq4oJEybo1ltvLRs/dOhQff/995o6dap2796tZcuWaezYserevbtiYmK89WUAAACgGvP1dgEAAABATXf99dfryJEjevbZZ3Xo0CHFxsZq9uzZatq0qSTp0KFDSk5OLht/++23KycnR2+99ZbGjx+vWrVq6fzzz9dLL73krS8BAAAA1ZzJauvZNwCATStWrLD5uPHZWL58uXr16uWy/QEAcCbZ2dmKiIhQVlYWbS4AAACqEXf9PY/WFgAAAAAAAAAAuwiSAQAAAAAAAAB2ESQDAAAAAAAAAOwiSAYAAAAAAAAA2EWQDAAAAAAAAACwiyAZAAAAAAAAAGAXQTIAAAAAAAAAwC6CZAAAAAAAAACAXQTJAAAAAAAAAAC7CJIBAAAAAAAAAHYRJAMAAAAAAAAA7CJIBgAAAAAAAADYRZAMAAAAAAAAALCLIBkAAAAAAAAAYJevtwsAgKrPT1IzSa3+/jSTFPz38mJJ+ZL2SNopadff/13shToBAAAAAADODkEyAJwFi0WSuksaKqmvpHBJQSfWSiqVZJVkkuSjkw+A5EvKkbRM0s+SVv09HgAAAAAAoPIiSAYAJ+TkSHPmSO+911rSe5L8JWVLypR0UEZ4bItJxkzlEElXSrpU0jZJ3+v4cR93lg0AAAAAAFAhBMkA4KAVK6SXX5a2bpWKioIkZUjKc2IPVknH//6kyQiV20l6Ss88U0+BgVLfvi4vGwAAAAAAoMJ42R4AnEFOjhEgjxkjbdsmNWkiNWhQKOdC5PLkSdoraa9SUgL13/9KkyZJWVkVLhkAAAAAAMClCJIBwI6dO6Xbb5dmzJD8/aUWLYxfXatEMTEFCgiQZs40jrdtm6uPAQAAAAAAcPYIkgHAhk2bjFnImzZJTZtK0dGSyeSeY5lMUlSU1Ly5tGWLdN990saN7jkWAAAAAACAs+iRDADl2L5dGjdOSk42ZiH7lPsuvNckLXDB0XpImi1J8vMzjrdnjzR+vPTGG1K7di44BAAAAAAAQAUwIxkA/iU1VXrwwTOFyO7j42Mcd/9+o44DBzx7fAAAAAAAgH8jSAaAf7BapSlTjB7FzZt7PkQ+wWw2wuSdO6XJkyWLxTt1AAAAAAAASATJAHCK2bOlOXOk+vUlXy83//HxkRo0kObPl37+2bu1AAAAAACAmo0eyQDwt9RU6bXXjBffhYdXdG9XSQp1cGyMzTVhYdLRo0av5K5dpYYNK1oXAAAAAACA8wiSAeBvH31k9EVu2dIVexsiqZ4rdqRGjaRdu6QPP5Qef9wluwQAAAAAAHAKrS0AQNKxY9Kvvxozkb3VF9kWHx8pIsJouZGR4e1qAAAAAABATUSQDACSfv9dSkuToqO9XUn5oqOlI0ek337zdiUAAAAAAKAmIkgGUOOVlkrffy/5+Xn/BXu2+PgY9X33nVRS4u1qAAAAAABATUOQDKDG27xZ2rat8s5GPqFuXaNX8saN3q4EAAAAAADUNJV07h0AeM6OHVJhoRQS4sq93u3E2FkOjQoKkoqKpJ07pc6dz64qAAAAAACAs8GMZAA13o4dktUqmUzersQ+k8moc8cOb1cCAAAAAABqGoJkADVeYqLk7+/tKhwTGCht2ODtKgAAAAAAQE1DkAygRisokPbscXVbC/cJDpb275dyc71dCQAAAAAAqEnokQygRsvLk0pK3DEj+SpJoa7eqXx9jfA7L08Kdf3uAQAAAAAAykWQDKBGKyoy+g6bXf58xhBJ9Vy9U5nNksVi1A0AAAAAAOApBMkAarQTAbLV6vy2Pj6+Ki11bpumTZspIOD06c+BgUEObX/ipYCuD74BAAAAAABsI0gGUKP5+5+c5esYq5KT92vTpk0qLS1x+ng+Pma1bt1GJpPJ6W2lk0FyVXk5IAAAAAAAqB4IkgHUaKGhxgvscnLOPLagoEAbN25UWtrhsz7e7t27dexYpjp16qSQs3jDX0GBFBQkhYefdQkAAAAAAABO4+FoADWar6/UoYPx8jp7Dh48qEWLFlYoRD7h2LGjWrx4kfbt2yvJuZ4aeXlS27bMSAYAAAAAAJ5FkAygxmvf3nZri6KiIq1bt1br1q1VcXFxhY8VGBgoSSotLVViYqL++usvFRTkO7x9aakRfAMAAAAAAHgSrS0A1HitWhm/lpZKPj4nl6elHdaGDRtUWFh4FnudIyn0tKX16jVTZmamsrIyJUnp6dKCBT7q2HGiGjduIcl27+QTYfeJegEAAAAAADyFIBlAjde+vdFzOCtLql1bKikp0aZNm7R/f3IF9vp9uUv37Tt9mcUibdhwoQ4fPqa4uI7y9w8od9usLCkszKgXAAAAAADAk2htAaDGa9hQOu886cgR6ciRDC1atKiCIfLZSU09pIULFyk1NbXc9UeOSL16Sc2aebYuAAAAAAAAgmQAkHTRRYXKyDioFSsSlJ9/hjfvuYGvr/H2vKKiQq1Zs1oJCQmn9GTOzzfabgwb5vHSAAAAAAAACJIBYPXq1RozpouOHFkpqa5Xaujbt4/q1atX9vuUlP1atGiRMjLSJRm9lFu0kPr08Up5AAAAAACghqNHMoAaq6ioSM8995xeeOEFlZaWSvpS0lOSgiTl293Wz89PHTt+rJiYhmXLcnJytGjRQrvb9e8/QGFhYeWu69atnvbvT9GmTUkqKSlRQUG+Vq5cqUaNWiskpI2uv94sf3+nvkQAAAAAAACXIEgGUCMlJSXp1ltv1fr16/+x9AdJgyT1l7RDkrXcbevUqav4+HgFBga6uCqTGjdurOjoaCUkJOjIkQxJUkqKFBb2gxo3biipp4uPCQAAAAAAcGa0tgBQo5SWluqVV15Rly5d/hUiS1KJpJclpUtqcNq2Pj6+iouLV48e3d0QIp8UFBSknj17KjY2ViZTQ0lHlZPzmAYO7KMJEyaosLDQbccGAAAAAAAoD0EygBpj165dGjBggB5++GEVFRXZGiVpmqRASSdbUNSuHaX+/furSZMmkkxur9VkMikqqrlatOigFi0WSNoui8WiF198Ud27d9eGDRvcXgMAAAAAAMAJBMkAqj2r1aqpU6cqLi5OS5cudWCLLyXNklRfJlOo2rfvoF69eik4ONjNlZ6UlyelpkpXXhmgxMRH9cILL8jPz0+StHHjRnXr1k0vvPCCSkpKPFYTAAAAAACouQiSAVRrKSkpuvjii3XvvfcqLy/Pwa1KJD2runXXq2nT3qpTp4VMJvfPQj4hN9foi3zBBdIzz0jBwb6aMGGCVq9erbi4OElScXGxJk6cqH79+mn79u0eqw0AAAAAANRMBMkAqiWr1apPP/1UsbGx+v33353a1tfXV88++5i2bbtG11wToMOHpfR0yVr+u/dcxmqVMjKkQ4ekSy6RXn5Z+uck6Pj4eK1atUoTJkyQ2WxcvleuXKlOnTrpzTfflMVicW+BAAAAAACgxiJIBlDtpKen65prrtEtt9yirKwsp7bt0KGD/vrrLz3xxBOqVctPzz8vjRwplZRIu3ZJNlsrV1BRkbR7t/HrXXdJkyZJISGnjwsICNALL7ygpUuXqnXr1pKk/Px8jR07VhdeeKH27dvnngIBAAAAAECNRpAMoFr56aefFBsbq++//96p7Uwmkx566CGtWbNGnTt3Llvu7y+NGydNnSrFxkr79kmHD0ulpa6p12KR0tKkvXultm2ld96RHnxQCgy0v12vXr20fv16jRkzpmzZggUL1LFjR3344Yeyunv6NAAAAAAAqFEIkgFUC5mZmbrttts0bNgwpaWlObVtixYttHjxYr388ssKtJHgdusmffihNGqU5OtrzB5OTpby88+u3oICY/tduySz2Zj1/NFHUo8eju8jJCREb775pubPn6/GjRtLknJycnTHHXfoiiuuUGpq6tkVBwAAAAAA8C8mK9PWAFRx8+fP14gRI5SSkuL0tqNHj9Yrr7yi0NBQh7c5fFiaO1f67jsjUC4pMWYum0x52rhxuaTy0uVASSFq06azTKZA+fhILVpIV14pDRkiNWjgdOmnyMrK0v3336+ZM2eWLYuKitK7776ra665pmI7BwBUS9nZ2YqIiFBWVpbCw8O9XQ4AAABcxF1/zyNIBlBlHT9+XI888ojefvttp7dt2LChpk+frosuuuisj19UJC1bJq1eLSUmSklJeUpI2C4p4O8RVkmmvz8Fko7r0ks7qF+/WuraVerXzwigXWnWrFm6++67T5mVPXz4cL355puqXbu2aw8GAKjSCJIBAACqJ4JkAPiH5cuX67bbbtPOnTud3vbmm2/WG2+8ocjISJfWtHr1ZnXvfr2kRpL8JflJKpFUJClF0i4lJa1Shw4dXHrcf0tPT9c999yj7777rmxZTEyMPvjgAw0ZMsStxwYAVB0EyQAAANWTu/6eR49kAFVKYWGhJkyYoH79+jkdIkdHR+u7777TJ5984vIQWZKCg62SkiTNlTRL0neSfpI0R1KipDyXH7M8derU0TfffKNPP/1UtWrVkiQdPHhQl1xyiUaNGqXc3FyP1AEAAAAAAKoPgmQAVUZCQoK6deumF198URaLxaltr7jiCiUlJemqq65yU3WVi8lk0k033aSkpKRT2ne89957iouL05IlS7xYHQAAAAAAqGoIkgFUeiUlJXr++efVvXt3JSYmOrVteHi4Zs6cqR9++EH16tVzU4WVV8OGDTVnzhy9++67CgkJkSTt2bNH/fv314MPPqiCggIvVwgAAAAAAKoCgmQAldq2bdvUp08fPf744youLnZq2wsuuECJiYm67bbbZDKZ3FRh5WcymTRq1Cht2LBBffv2lSRZrVa9+uqr6ty5s9asWePlCgEAAAAAQGVHkAygUrJYLHrjjTfUqVMnrVq1yqltg4KC9NZbb+n3339XkyZN3FRh1dOyZUstXLhQr7zyivz9/SVJW7ZsUc+ePfX00087HdQDAAAAAICagyAZQKWzb98+DRo0SP/973+dbr3Qq1cvbdiwQf/5z39kNnOJ+zcfHx89+OCDWrdunTp37ixJKi0t1TPPPKNevXpp8+bNXq4QAAAAAABURqQsACoNq9WqGTNmqGPHjvrzzz+d2tbPz0+TJk3SkiVL1Lp1azdVWH106NBBK1eu1FNPPSUfHx9J0tq1a9W5c2e9+uqrKi0t9XKFAAAAAACgMiFIBlAppKam6vLLL9edd96pnJwcp7aNj4/XmjVr9Oijj5aFojgzPz8/Pf3001q5cqXatWsnSSosLNSDDz6ogQMHavfu3V6uEAAAAAAAVBYEyQC87uuvv1aHDh30yy+/OLWd2WzWxIkTtWrVKsXFxbmpuuqva9euWrt2rR544IGylxIuWbJEcXFxmjZtmqxWq5crBAAAAAAA3kaQDMBrjh49qhtvvFHXX3+9jh496tS2bdq00fLly/Xcc8+VvTgOZy8oKEivvvqq/vzzTzVr1kySdPz4cY0ePVqXXHKJDhw44N0CAQAAAACAVxEkA/CKOXPmKDY2Vl9++aXT244dO1br169Xjx493FBZzda/f39t3LhRI0eOLFs2d+5cdezYUZ9//jmzkwEAAAAAqKEIkgF4VE5OjkaOHKlLLrlEhw4dcmrbJk2a6I8//tDrr7+u4OBgN1WIsLAwTZs2TbNnz1aDBg0kSceOHdNNN92k6667ThkZGV6uEAAAAAAAeBpBMgCPWbx4seLj4/X+++87ve2IESO0ceNGnX/++W6oDOUZMmSIkpKSdOONN5Yt+/bbbxUbG6uff/7Zi5UBAAAAAABPI0gG4HYFBQUaP368BgwYoD179ji1bb169TRr1izNmDFDERERbqoQttSuXVuff/65vv76a0VFRUmSDh8+rMsvv1x33HGHsrOzvVwhAAAAAADwBIJkAG61Zs0ade7cWVOmTHG6v+61116rpKQkDR061E3VwVEn/iwuu+yysmUffvihOnbsqAULFnixMgAAAAAA4AkEyQDcori4WE899ZR69uypLVu2OLVtZGSkPv/8c3311VeKjo52U4VwVv369TVr1ixNnz5dYWFhkqTk5GRdcMEFGjt2rPLy8rxcIQAAAAAAcBeCZAAut2nTJvXs2VPPPvusSktLndr24osvLuvLazKZ3FQhzpbJZNIdd9yhxMREDRgwoGz5m2++qXPPPVcrV670XnEAAAAAAMBtCJIBuExpaakmT56sLl26aN26dU5tGxISomnTpmn27NmKiYlxU4VwlaZNm+qPP/7Q66+/rsDAQEnS9u3b1adPH02cOFFFRUVerhAAAAAAALgSQTIAl9i9e7cGDBighx56SIWFhU5te95552njxo0aOXIks5CrELPZrLFjxyohIUHdu3eXJFksFr3wwgvq3r27Nm7c6OUKAQAAAACAqxAkA6gQq9WqadOmKS4uTkuXLnVq24CAAL366qv6888/1aJFCzdVCHc755xztGzZMj333HPy8/OTJG3YsEFdu3bViy++qJKSEi9XCAAAAAAAKoogGcBZO3DggIYMGaLRo0fr+PHjTm17ov3FAw88ILOZS1FV5+vrq4kTJ2rVqlXq2LGjJOOFixMmTFC/fv20Y8cOL1cIAAAAAAAqgvQGgNOsVqs+++wzxcbG6rfffnNqW19fXz399NNasWKF2rdv76YK4S2dOnXS6tWr9eijj5bdIFi5cqXi4+P11ltvyWKxeLlCAAAAAABwNgiSATglPT1d1157rW6++WZlZmY6tW379u21cuVKPfXUU2UtEFD9BAQEaNKkSVqyZIlatWolScrPz9d9992nwYMHa//+/V6uEAAqp3feeUfNmzdXYGCgunTpoiVLltgdX1hYqIkTJ6pp06YKCAhQy5YtNWPGDA9VCwAAgJqGIBmAw2bNmqXY2Fh99913Tm1nMpn04IMPau3aterSpYubqkNl07t3byUkJOg///lP2bI//vhDsbGx+uijj2S1Wr1YHQBULl999ZXuv/9+TZw4UevXr1e/fv00ZMgQJScn29zmuuuu0x9//KHp06dr27Zt+uKLL9S2bVsPVg0AAICaxGTlX/IAziArK0v333+/Zs6c6fS2LVq00MyZM9WvXz/XF1bJbNq0SbGxsXbHJCUlqUOHDh6qqPKYP3++RowYoZSUlLJlV1xxhaZNm6Z69ep5sTIAqBx69Oihzp07a+rUqWXL2rVrp2HDhmnSpEmnjZ87d65uuOEG7d69W7Vr1z6rY2ZnZysiIkJZWVkKDw8/69oBAABQubjr73nMSAZg1x9//KGOHTueVYg8atQobdiwoUaEyLBv0KBBSkxM1K233lq27KeffjqrGe4AUN0UFRVp7dq1Gjx48CnLBw8erOXLl5e7zaxZs9S1a1e9/PLLatiwodq0aaMHH3xQ+fn5No9TWFio7OzsUz4AAACAowiSAZQrLy9P9913nwYNGuR0T9uYmBjNmTNH7777rkJDQ91UIaqaWrVq6aOPPtIPP/ygOnXqSJIyMjJ0zTXX6Oabb9axY8e8XCEAeEdGRoZKS0tPe0KjXr16Sk1NLXeb3bt3a+nSpUpKStIPP/yg1157Td9+++0p7YT+bdKkSYqIiCj7NG7c2KVfBwAAAKo3gmQAp1mxYoU6deqkt956y+ltb7rpJiUlJeniiy92Q2WoDoYNG6akpCRdeeWVZcs+++wzdezYUb/99psXKwMA7zKZTKf83mq1nrbsBIvFIpPJpM8++0zdu3fXJZdcoilTpmjmzJk2ZyVPmDBBWVlZZR9efgoAAABnECQDKFNYWKjHHntMffv21Y4dO5zaNioqSt98840+/fRTRUZGuqlCVBd169bVd999p08++UQRERGSpAMHDujiiy/WPffco9zcXC9XCACeEx0dLR8fn9NmH6elpdnsI9+gQQM1bNiw7BoqGT2VrVbrKf3o/ykgIEDh4eGnfAAAAABHESQDkCRt2LBB3bt316RJk2SxWJza9vLLL9emTZt0zTXXuKk6VEcmk0k333yzkpKSTukL+u677yo+Pl5Lly71YnUA4Dn+/v7q0qWL5s2bd8ryefPmqXfv3uVu06dPHx08ePCUG2/bt2+X2WxWo0aN3FovAAAAaiaCZKCGKykp0QsvvKBu3bpp48aNTm0bHh6uDz/8UD/++KPNGVPAmTRq1Ehz587VO++8o+DgYElG78/zzjtPDz30kAoKCrxcIQC43wMPPKAPPvhAM2bM0JYtWzRu3DglJydr9OjRkoy2FP98Yenw4cMVFRWlESNGaPPmzVq8eLEeeugh3XHHHQoKCvLWlwEAAIBqjCAZqMG2b9+ufv36aeLEiSouLnZq2/PPP1+JiYm6/fbbbfZvBBxlMpl0zz33aMOGDerTp48kozfo5MmT1bVrV61bt87LFQKAe11//fV67bXX9Oyzz6pTp05avHixZs+eraZNm0qSDh06pOTk5LLxoaGhmjdvnjIzM9W1a1fddNNNGjp0qN544w1vfQkAAACo5kxWq9Xq7SIAeJbFYtHbb7+tRx55xOYLeWwJCgrSyy+/rHvvvVdmM/ei/mnTpk2KjY21OyYpKUkdOnTwUEVVU2lpqaZMmaLHH39cRUVFkiRfX1898cQTmjBhgvz8/LxcIQBUD9nZ2YqIiFBWVhb9kgEAAKoRd/09jxQIqGGSk5N14YUXauzYsU6HyD179lRCQoLGjBlDiAy38fHx0UMPPaS1a9fq3HPPlWS0YHnqqafUu3dvbdmyxcsVAgAAAABQ85AEATWE1WrVzJkz1bFjRy1YsMCpbf38/PTCCy9oyZIlatOmjZsqBE4VGxurlStX6oknnpCPj48kac2aNTr33HM1ZcoUp18KCQAAAAAAzh5BMlADpKamatiwYRoxYoSys7Od2jYuLk6rV6/WhAkT5Ovr66YKgfL5+/vr2Wef1fLly9W2bVtJUmFhocaPH6+BAwdqz549Xq4QAAAAAICagSAZqOa+/fZbxcbGatasWU5tZzabNWHCBK1atUrx8fFuqg5wTPfu3bVu3Trdf//9ZcsWL16suLg4vf/++6LdPwAAAAAA7kWQDFRTx44d00033aRrr71WR44ccWrb1q1ba9myZXrhhRcUEBDgpgoB5wQFBen//u//9Oeff6pZs2aSpNzcXI0cOVKXXXaZDh486N0CAQAAAACoxgiSgWpo7ty5io2N1eeff+70tvfdd58SEhLUs2dPN1QGVNyAAQO0ceNG3XXXXWXLZs+erdjYWH355ZderAwAAAAAgOqLIBmoRnJzczV69GgNGTLE6dmZjRs31vz58/XGG28oODjYTRUCrhEWFqb3339fv/zyi+rXry/JmIV/44036vrrr1dGRoaXKwQAAAAAoHohSAaqiSVLliguLk7Tpk1zetvbb79diYmJuuCCC9xQGeA+l156qZKSknT99deXLfv6668VGxurX375xYuVAQAAAABQvRAkA1VcQUGBHnzwQfXv31979uxxatu6devqp59+0ocffqiIiAg3VQi4V1RUlL788kt9+eWXql27tiTp8OHDGjp0qO68805lZ2d7uUIAAAAAAKo+gmSgClu7dq26dOmiV199VVar1altr7nmGm3atEmXX365m6oDPOv6669XUlKSLr300rJlM2bMUFxcnP78808vVgYAAAAAQNVHkAxUQcXFxXrmmWfUs2dPbd682alta9Wqpc8++0xff/21oqOj3VQh4B0NGjTQzz//rA8++EChoaGSpH379un888/X/fffr/z8fC9XCAAAAABA1USQDFQxmzdvVq9evfT000+rpKTEqW0vuugiJSUlafjw4TKZTG6qEPAuk8mkO++8U4mJierfv3/Z8tdff13nnnuu/vrrLy9WBwAAAABA1USQDFQRpaWlevXVV9W5c2etXbvWqW1DQkL07rvvas6cOWrYsKGbKgQql2bNmmnBggX6v//7PwUGBkqStm3bpt69e+vxxx9XUVGRlysEAAAAAKDqIEgGqoDdu3dr4MCBevDBB1VYWOjUtn379tWGDRs0atQoZiGjxjGbzbr//vu1fv16devWTZJksVj0/PPPq0ePHkpMTPRyhQAAAAAAVA0EyUAlZrVa9d577ykuLk5LlixxatuAgABNnjxZCxcuVMuWLd1UIVA1tG3bVsuXL9f//vc/+fr6SpISEhLUtWtXvfTSSyotLfVyhQAAAAAAVG4EyUAldfDgQV166aUaNWqUjh8/7tS2J9pfjB8/Xj4+Pm6qEKhafH199fjjj2vVqlWKjY2VJBUVFenRRx/Veeedp507d3q5QgAAAAAAKi9fbxcAVGZWq3T0qLR3r5SXJxUVSWazFBAgRUdLzZpJ/v6uPqZVX3zxhcaMGaNjx445ta2Pj48ef/xxTZw4UX5+fq4tDGdktUpSw78/AZL8JBVLKpJ04O8PvO3cc8/VmjVr9OSTT+qVV16R1WrV8uXLFR8fr1deeUX33HOPR9rAFBVJ+/ZJ6enGf5eWGteT4GCpaVMpKkqiGw0AAAAAoLIwWa1G9AHAsH+/tGSJtG2btHGjdOiQESKXlJwICk+GyWFh0jnnSLGxxqdPH2P52crIyNA999yjb7/91ult27Vrp48//lhdu3Y9+wLgFKtV2rBBWrNG2rpVWr48VytWbJYUJMlHkkmSVZJFUr6ko7r11i7q2zdaXbtKnToRFHrbsmXLdNttt2nXrl1lyy688EJNnz5djRs3dumxioqkZcukxERp0ybjnMnNlQoKJIvFGGMySb6+Rphcr54UH29cY/r2NcJlAHCl7OxsRUREKCsrS+Hh4d4uBwAAAC7irr/nESQDMkLilSulWbOkRYukrCwj0AkMlEJCjFDH1/dk6GexGOHP8eNGyFxaKvn4GEHPlVdKQ4ZIDRs6V8PPP/+su+++W4cPH3ZqO5PJpHHjxum5555TUFCQcwfFWcnNlf74Q/rhByNIzs83bi6YTPnavHmVjNC4VEaIbJIRKgdJClGLFrHy8wtUUJDUsaNxvgwaZNyUgHfk5ubqkUce0TvvvFO2LCIiQm+88YZuueWWCs9OPnRImjPHOF/27DGuNyfC4pAQ4zpj/rvRlNVqrM/LM64v+fnGdScsTOrfX7r8cqlXL2N7AKgogmQAAIDqiSAZcAOr1QiO337bmIFcXCxFRhofs5MdxAsKjEfUCwqkWrWMMPk//zFaYNiTnZ2t+++/Xx9++KHT9Tdv3lwzZ87Ueeed5/S2cF5xsfTll9JHH0kHDxoBX506Umio8d85OTlatGih3X307z9AoaFhys01zherVWrQQLrlFmn4cNe3SoHjfv/9d91xxx06cOBkC5Jhw4Zp2rRpqlu3rtP7O3pUmjpV+uUX6dgx42mFunWN4NgZFouUmSkdOSL5+UmtWxvXlvPPZ0Y7gIohSAYAAKieCJIBFzt6VHrrLWOWYFGRVL++MUOwoiwWY99Hj0qtWknjxkkXXFB+4LNgwQKNGDFCycnJTh9n5MiRmjx5ssKYyuoRO3ZIL74orVhxMhD8d+jraJD8zz+z4mIpLc24AdGjh/Too0YrA3jHsWPH9N///leffPJJ2bI6depo2rRpuvLKKx3ah9UqLVwoTZ4s7dxp3JiKinL+5lR58vONGc5+ftIVV0hjxxr7BoCzQZAMAABQPbnr73ku+GctUPUsWSLdeqv02WfGo+UtWrgmRJaMsCg62tjnvn3Sgw9KTz5pzCg8IS8vT2PHjtUFF1zgdIjcoEEDzZ49W9OmTSNE9oDSUunjj6Xbbzf62zZoIDVq5LqZw35+RhuUmBhp+XJpxAjpww+N9gbwvMjISH388cf67rvvVKdOHUlSenq6rrrqKt16663K/Oc3cjmysqSnnzZuIO3da1wH6tRxTYgsSUFBxj5DQ43Z8bfeaoTWAAAAAAC4G0EyapwffpDGjzd6lTZvbswWdAdfX2P/YWHS118bMwfT0qSVK1fq3HPP1Ztvvun0Pm+88UYlJSVpyJAhbqgY/1ZcbMxCfuklY8Zwy5auu+Hwb0FBRsuCoiJjJusLLxj/De+46qqrlJSUpGHDhpUt++STT9SxY0fNmzev3G0yMqT77zcC3tBQI/B1Vy/jyEhj/3v3Sg89JH3zjXuOAwAAAADACbS2QI3y9ddGKGixGLNA/91uIiHhdqWkfFTh49Srd4W6dfux7PeFhdLevRb5+iZp69bBslqde6FeVFSUpk6dqmuvvbbCtcExJSXSM89I335rtA44ccPhzOeIr4yX6/lJCv37Ey2pnqSW6tHjLtWp087usTMzjVBy2DDp2WeNWcvwDqvVqk8//VT33XefsrKyypbfc889euWVVxQSEiLJ+PMaO1Zau1Zq0sTxPsj2zqfzz9+j4OBmZ6jP6NdttRqB8vDhjh0XACRaWwAAAFRXtLYAKmj2bOnll43ApVEjz76kqrAwW6mpK7RlS4Cs1pcl1XJ426FDhyopKYkQ2YMsFumVV4wQuU4dZ2etl0gqlJQrKVXSTkkrJf0kaYr++quDli3ro4MHv5HVail3D7VqGT2Yf/zRuPFRWlqRrwYVYTKZdMsttygxMVGDBg0qWz516lTFx8dr2bJlysoyQty1a6VmzZx/mV7F6jNuipnN0quvSj/95LljAwAAAABqFoJk1Ag7dkjPP2+0CoiJ8dxxrVaLdu7cqSVLlign56ikvZJ6SHrojNuGhYVpxowZ+umnn1S/fn03V4p/+ukn6fPPjZnIERGu3rtVx44t17p112nZst7Kzd1a7qjwcKPX9pdfSt9/7+oa4KzGjRvrt99+09tvv63gv/ub7Nq1S3379tMll8zTsmUWNWniut7ZzoqJMW44vPSStGWLd2oAAAAAAFRvBMmo9oqKjD63GRlS48aem4l8/Hiuli1brq1bt/xj5mmRpMOSLpU02Oa2AwcOVGJiokaMGCGTJ6dOQwcOSG++aczwdFf/7BMyM//S0qU9lJ4+v9z1tWoZPXbfftt4cSO8y2w2695771VCQoJ69+7999KBWr48WgcOrFdhYZbd7d2tUSPp6FEjTKa/NgAAAADA1dz0GiCg8vjiC2nFCiNkMVfg1knLlg/Lz+/MyaLValVeXi0tWrRYFkt5PQmyJEVKul/SOkkZZWsCAwP10ksvacyYMTJXpFiclRMtLQ4cMF6s56zGje/X/v1HJVklHZfxZ71dUsrfy05XUpKtNWuGqXfvJYqIOPe09Q0bSjt3GnW9/rrk4+N8XXCt1q1ba/HixXrmmbf03HNtZLX6KS9vj5YuPaQ2bdqoVatWMpk8//1rMhnXub/+kj79VLrjDo+XAAAAAACoxgiSUa3t3i29954UFCT9/TT6WWva9J4zvvgqPz9fGzYkKCMjQ5K9xrb7JbWUdJ+kpyRJPXr00EcffaRzzjmnYoXirP36q/THH1KDBmcX2MbE3Kn9+/eUs+aQpO8kzVN5gXJp6XGtXj1U552XIH//6FPWmc1G24I//5RmzZKuvNL5uuB6Pj4+8vP7r5o0KdTRo6uVk2PcRNq2bZtSUw/r3HPPVWhoqMfrCgqSQkKk99+X+vWTWrf2eAkAAAAAgGqKKY+o1n780Whp4f4Ww1bt379fixYt/DtEPpNSGTORL5Kvbws9//zzWrp0KSGyF1ksRl9ki0UKC3P13htIGiPpMZlMAeWOKCg4oJ07J5W7LjTUmG36+edSSYmra8PZOHDAuPHQoEGA+vXrrVatWksy2tBkZWVq8eJF2r17t6zW8meiu1P9+kaLix9+8PihAQAAAADVGEEyqq3sbOnnn41Q0J1dIgoLC7V69Rpt2JCgEqdSvmPy96+rRx5ZpMcee0y+vjwg4E1r1xovKatb151H6aHWrV+1uXbv3ndUUHCo3HV160rbtkmrVrmrNjhjzhzp2DGpdm2jd3Lbtm3Vp08fhYQYs5AtFos2b96klStXKC8vz6O1mUzGyxp//dWoEQAAAAAAVyBIRrU1f750+LAUHX3msWfr0KFDWrhwoQ4fTnVyS5Natmypdu2aas2aRsrPd0t5cMKsWVJBgTH7150aNLhVERGdy11nsRQoNbX8aaQhIVJxsVEnvKuw0JjtGxR06k2qyMhInXfeeWrevHnZsiNHjmjRokVKTk6WrT7Z7hAdLaWlSfPmeeyQAAAAAIBqjiAZ1ZLVKn3/vTEzz8/P9fsvLi7S+vXrtHbtGhUXFzm1bXBwiPr06a127dqpXj2zkpOlxYtdXyMcl5Zm9EaOPPO7FF2iadN7ba5LT//N5rrISKNX8sGD7qgKjlq6VNq3T6pT5/R1Pj4+6tAhVj179lJgYJAkqbS0RBs3btCqVatVUFDgkRp9fY0+3999Z1wPAQAAAACoKJ6lR7V05Ii0Y4drg8EFC5qfeVCZ8qeNNmvWTO3atZOPj/Gt5+9vhDyJidJFF7mgSJyVpCQpM1Nq2tQzx4uKGmBz3bFjK2yuq1VL2rPHqDcmxvV1wTGJiUav6sBA22Oio6M1YEB/bdq0Sfv375ckpaUd1qJFC9WxY0fFxDR0e52Rkcb5kppqvEASAAAAAICKYEYyqqWdO6W8PKMdQGUQGBiknj17Kja2Y1mIfIKfn7Rxo5cKgyTjfLFajVmcnhAS0lImU/lT5YuKjshqtZS7zsfH+HXnTndVBkckJjr2pIOvr5/i4zupW7du8vc3XrJYXFysdevWad26tSoqcu5pBmeFhEjHj0u7drn1MAAAAACAGoIgGdXSzp3GjEF3tLVwVqNGjdW/f39FR5fzHLyMsGfXLiP4hnds2WK0QfEkf//aNtZYVFR0xOZ2ZrO0ebN7asKZFRZK27dLwcGOb1OvXn0NGDBADRqcnEZ+8OBBLVq0UAUF7muQ7ucnWSzceAAAAAAAuAZBMqql7duNGaaeDgf/yd8/QF27dlOnTp3kZyfRDg42Zg3u3u3B4lDGYpE2bXIuGHQFW7OOJclk58QNCTGC5JISd1SFM9mzx/h+dfZpB39/f3Xp0kWdO3cuux4UFhYqIyPDDVWeats2tx8CAAAAAFAD0CMZ1dL+/Ub/YVeKihqpI0cce1FW/foNFBfXsexxdnsCA41ZjmlpFa0QZyMnR8rOtt/v1h2Ki4/ZWGOWn5+t2cpSQIARZGZmStHRbikNdqSnSwUFZ3++xMQ0VO3aUdqwYYPS093/Te/vLyUnu/0wAAAAAIAagCAZ1VJBgdECwJWCg69RUZG/cnKybY7x9fVTx46xatiwoSTHpkObTMbsaTe3S4UNRUXGrGRXny+2WZWbu11Wa/lTiv39o2Uy2S7GbDbqLS52V32wp6jI+H6tyPkSGBioHj26Kzk5WYmJZlmtrqvv38xm40YVAAAAAAAVRZCMaqm01PVtLfbv3yepns31derUUXx8vAIDg5za74k6S0srUBzO2on/7644X0JCQnXZZUPLfr948WJlZ2edMmbr1m2qXfsvm/uIjOxp9xgnbjzQ2sI7SkvlouDXpCZNmiotrZ5SU12xPxtHMXHTAQAAAADgGgTJqJYCAoxZm55gNvuoQ4f2atq0qRydhfxPJ0IpV7figGP8/IywzdXny6FDB08LkSXp8OFUpaVNsbldnTqD7e73xGzYgDN3TYEbnDhfXNWD3dfXvT+GrVbPt20BAAAAAFRPBMmolsLCPDPDNzKytjp16qQQZ9+89Q+lpZKPjxTk3ERmuEhQkPH/37UzfK3asWOHjXWzZbXuKXeN2Ryo+vWvtLvnkhKjXsJB7wgOlnx9jT+HqnDzp6RECg31dhUAAAAAgOrAY11BAU9q08b9QXJQUJB69+5doRBZMl6cFhwsNWvmmrrgnOBgqXFjKS/PlXs1qVev3jKbff61fJmkD2xu1bTpPQoMjLG757w8qUEDKTy84lXCeU2bGjcfXHu+uE9xsdSunberAAAAAABUBwTJqJZatTJ+daZdQV5entLS0hweHxAQKJMLnm0/flyKjJQaNqzwrnCW4uNd/7JDPz8/+fv7/f27Q5LekPSSpPKnPvv41FXz5g+dcb8FBVJcnKuqhLPq1ZPq1DG+byu7E+03TlwPAQAAAACoCFpboFpq1cqYNZifL515wrBVycnJ2rRps0pLCz1R3iny840g09UvB4TjTgRtFe17u2/fVPn5RUqyqrg4S8XF6yVtkbT/DFsGqrR0glau3KJOnQJUu3btcked6Kfdps3Z14iKMZmMIH/3bm9Xcmb5+UYvbYJkAAAAAIArECSjWmre3OiTnJtrP0guKCjQxo0bnJqJ7EpWqzFr+pxzvHJ4/K11a6PfbX6+0eribO3a9fJZbBUs6VFJLZWXd1zLly9Ty5Ytdc4555zWGqOgwHjZG8Ggd7Vta3zvuuqFe+6Sm2v0R27RwtuVAAAAAACqA1pboFoKCJAuuEDKzj45i/PfDh48oEWLFp4SIgcHV6zfsbOysoygp18/jx4W/xIba/SoTk/39JHPkTRZUqdTlu7atUtLlixRVlbWKcszMox+zvHxHisQ5ejb17hRlZnp7Ursy8qSBg6s2M0RAAAAAABOIEhGtTV0qNHeIjf31OVFRUVau3at1q1bp+LiYklSQECAunXrbrOlgLscOSJ17UqrAm/z85Ouusrok+zulzRKUmRkbzVu/JZMpsmSGpU7JicnR0uXLtGOHdtltVpksRgzkq+6yrhRAu9p2VLq2dP4/q2scnOlwEDp8su9XQkAAAAAoLqgtQWqrU6dpI4dpbVrjdmDknT48GFt2LBBRUUneyHHxMQoNraj/P39deiQ5+orLDQei7/yysr9eHxNMWSING2adPSo8TK1s2Uy+chk8pOPT6BKSgJktYbKbK6rpk17KiKis6KizlNQUBNJUqtWuUpISNCxY8fK3ZfVatW2bduUmnpYzZp1Va1aQRoy5Oxrg+sMGyb9+acR7gcGerua06WnG9e/Ll28XQkAAAAAoLowWa22HvwHqr4ff5QmTpTq1i3Wrl2btH//yZee+fn5qWPHjoqJaXjG/SxdulSZmaeGfbVqRapv375nXdu+fUabgu++q5xBVE301FPSl18aPYjNLnheY/78eSooKFBgYKAGDbqw3DFWq1W7du3Stm3bZLVabOzJJKmNBg06pt9+6yazK4pDhRQVSddcY7x0r3lzb1dzqoIC6cAB6dlnjRoBwJbs7GxFREQoKytL4eHh3i4HAAAALuKuv+eRRqBaGzJEatgwVUuXJp8SItetW0/9+w9wKER2hxO9m0ePJkSuTEaMkOrXlw4e9NwxTSaTWrVqpX79+tm5uMdIOqz584frggsu0N69ez1XIMrl7y/dc49xw+Ffray9ymqV9u83nsi49FJvVwMAAAAAqE4IklFt5efn65FH7te8eReopCRNUn35+PgqLi5e3bt3U6CXEtzSUik1VbroIumyy7xSAmxo1ky6916j7ci/e2u7W3h4uPr27afWrVvLmIF8QrCkAEnvStqthQsXKi4uTtOnTxcPlHjXxRcbN6sOH/ZMb21HpKVJERHSo48aPeIBAAAAAHAVgmRUS6tWrdK5556r119/XdJmSTMUHFxHPXoMUJMmTXRqUOdZKSlGS4vx4+mNXBldfbU0YIAxK9liq9OEm5jNZp1zTlv16dNHISGhMs7ThpKWSvqqbFxOTo7uuusuDR06VIc82dgbpzCZjO/jpk2l5GRvV2O0tMjOlm67TYqL83Y1AAAAAIDqhiAZ1UpRUZGeeOIJ9e7dW9u2bZMkBQYG6pVX4nTddU2Vnh6koiLv1ZeaKvn6Sg88IDVo4L06YJuvr/Tww0bYv2eP58NkSYqMjFS/fuepdu0ukg5IelnS6VNef/31V8XGxuqrr746bR08o25d6cEHpYAAefRlnf9WVGSE2d27S3fc4b06AAAAAADVF0Eyqo3ExET16NFDzz33nEr/fs68W7duWr9+vR588D69/LJJ554r7d0rr4TJaWlGy4SxY6VLLvH88eG4Zs2kl16S6tQxzhdPh8kWi5Sc7KP27RvonXci1LSp7b4JR48e1Q033KAbbrhBR44c8WCVOGHwYGncOKm42Ghz4WnFxcZ5GhcnvfIKfdcBAAAAAO5BkIwqr7S0VC+99JK6du2qhIQESZKvr6/+97//afny5Wrbtq0kIxScMsV4CdXevVJ+vmfqs1qNNgmFhdKYMcZj56j8unSRXnzROG927/ZcD9zSUmMmdHS0NGmSdM89XbVx40bdcYZppl999ZViY2M1e/ZszxSKU9x0k3T//cZNqgMHjO97TygoMM6XuDjj+lavnmeOCwAAAACoeQiSUaXt3LlT5513nh599FEV/T3NuEOHDvrrr7/0+OOPy9fX95TxDRtKb70l9e5thD2pqe4NfAoLpZ07JR8fo13C3XfTF7kq6dVLeu01qUkTadcuKSvLvcfLzjaO06iREQr27WssDw8P1/Tp0zVr1izVs5MUpqam6tJLL9Xdd9+tnJwc9xaLU5hM0u23S489Jvn5Gd/3BQXuO57Vasx+TkmRevQwrmuNG7vveAAAAAAAECSjSrJarXrnnXcUHx+v5cuXS5JMJpMefvhhrV27Vp07d7a5bZ060tSpxuxBs9k9gY/VarSySE6W4uOl996Thg8nRK6KOnWSPv5YGjZMysyU9u1z/ezk0lJjv0ePSpdfLn30kTEj+t+GDh2qTZs26brrrrO7vw8++EBxcXFauHChawuFXSaTdN110vvvS507S/v3G2Gvq29WFRYaNxwk4ymHadOYiQwAAAAAcD+CZFQ5+/fv10UXXaT//Oc/ysvLkyS1bNlSS5Ys0UsvvaSAgIAz7iMwUBo1SvrgAyOwO3DAeDw8N7ditZWWGgHyjh1GePSf/0gffmiEkai66tUz2ly8+KJUv74R4qWkGIFeRRQVGfvZtct4adsLL0gvv2z/RYxRUVH66quv9MUXXygyMtLmuL1792rgwIEaN26c8j3VxwWSjDYTM2YY/dBNJuN6cPhwxW9AHD9uXKdSUoxryvvvG9eYoCCXlA0AAAAAgF0mq9VTnRyBirFarfrkk080duxYZf2jx8C9996rl156SaGhoWe134IC6ZdfpO++k7ZsMX4fESGFhRmBs8kkLV26VJmZx07ZrlatSPXt21clJVJennTsmBEMRkVJF10kXXWVFBtboS8ZlVBamvTDD8YnOdlYFhkphYYaLQ3+Oet8/vx5KigoUGBgoAYNulBWq/FitOPHjdnHktHG4sorjRnP9gLk8hw8eFB33XWX5syZY3dc27Zt9fHHH6tbt27OHQAVtnmz9P330pw50pEjxjlSu7YUHCz9q/POaaxW43qUm2vMhg8IkNq2la6+WrrsMmMfAFAR2dnZioiIUFZWlsLDw71dDgAAAFzEXX/PI0hGlZCWlqZRo0bpxx9/LFvWsGFDzZgxQ4MHD3bJMSwWae1aadYs6c8/jfDmxIzTfft2Kj8/W5JFkkmSWUFBtdS0aXOZTFJIiNF/+eqrjRC5bl2XlIRKLD9fWrTICAk3bjTC4ZISI/wLCDD6YiclbVBxcYl8fQPVsmUHSUaQGBwsdexo3GwYMKBigaDVatUHH3ygBx54QLl2ptT7+Pjoscce0+OPPy5/f/+zPyDOSkaGNHeucb7s32+cL1arceMhIMBosyMZy0pKjJtSJ9aFhBjnyeWXS127GucWALgCQTIAAED1RJCMGuuHH37QqFGjlJ6eXrbslltu0euvv2730f6KOH7caDewc6fxWPrkyb8oI6NAUqCkUkkFql/fomefvVGtWkmtWhlBsplmMTWO1WrMND1xvmzfbvyany/Nnj1fhYXZCgoy68EHh6lNG5WdL1FRru2ZvWfPHt1+++1avHix3XHnnnuuPv74Y8UyXd4rLBajlc7OncY5s3WrESwXFBitLwICjNnt7dpJrVsb50rLlsYyAHA1gmQAAIDqiSAZNU5mZqbGjh2rTz75pGxZnTp19O677+qqq67yaC29evXSypUrT1nWs2dPrVixwqN1oGpp1KiRDhw4oIYNGyolJcXtx7NYLHr99dc1YcIEFdpp4Ozv76///e9/Gj9+vHyY3goANRZBMgAAQPXkrr/nMX8SldK8efPUsWPHU0LkYcOGKSkpyeMhMlBVmM1mjRs3TuvXr1fXrl1tjisqKtIjjzyi/v37a9euXR6sEAAAAAAAVFUEyahUjh8/rnvvvVeDBw8um8EZHh6ujz76SN9//73q0nwYOKN27dpp+fLleuaZZ+Rr541uy5YtU1xcnKZOnSoeTgEAAAAAAPYQJKPSWLZsmeLj4zV16tSyZYMGDVJSUpJuvfVWmVzZUBao5vz8/PTkk09q5cqVat++vc1xeXl5uvfee3XxxRd7pP0GAAAAAAComgiS4XWFhYV65JFH1K9fv7LH7IODg/X222/rt99+U+PGjb1cIVB1denSRWvXrtVDDz1k92bM77//rtjYWH366afMTgYAAAAAAKchSIZXnejl+vLLL5eFV71791ZCQoLuvfdemc2cokBFBQYG6uWXX9bixYvVokULm+OysrJ0yy236JprrlF6eroHKwQAAAAAAJUdKR28oqSkRM8995y6d++upKQkSZK/v79efPFFLV68WK1bt/ZyhUD107dvX23YsEGjR4+2O+77779Xhw4d9OOPP3qmMAAAAAAAUOkRJMPjtm7dqt69e+uJJ55QSUmJJCk+Pl5r1qzRI488Ih8fHy9XCFRfoaGhmjp1qubOnauYmBib49LT03XllVfqtttuU2ZmpucKBAAAAAAAlRJBMjzGYrHotdde07nnnqvVq1dLksxmsyZOnKhVq1apY8eOXq4QqDkuuugiJSUl6aabbrI77uOPP1bHjh01f/58D1UGAAAAAAAqI4JkeMTevXt1wQUXaNy4cSooKJAknXPOOVq+fLmee+45+fv7e7lCoOaJjIzUp59+qm+//VbR0dE2x6WkpOjCCy/UmDFjdPz4cQ9WCAAAAAAAKguCZLiV1WrV9OnTFRcXp4ULF5Yt/+9//6t169apR48e3isOgCTp6quvVlJSkq644gq7495++2116tRJy5cv91BlAAAAAACgsiBIhtscOnRIQ4cO1V133aWcnBxJUpMmTbRgwQK99tprCg4O9nKFAE6oV6+efvjhB82cOVPh4eE2x+3cuVP9+vXThAkTVFhY6MEKAQAAAACANxEkwy2++uorxcbG6tdffy1bdscddygxMVEDBw70YmUAbDGZTLrtttuUmJioCy64wOY4i8WiF198Ud26dVNCQoLnCgQAAAAAAF5DkAyXOnLkiG644QbdcMMNOnr0qCRjpuOsWbM0ffp0uzMdAVQOTZo00e+//64333xTQUFBNsclJiaqe/fuev7551VSUuLBCgEAAAAAgKcRJMNlZs+erdjYWH311Vdly6677jpt2rRJQ4cO9WJlAJxlNps1ZswYJSQkqGfPnjbHFRcX6/HHH1efPn20bds2D1YIAAAAAAA8iSAZFZaTk6O7775bl156qVJTUyVJkZGR+uKLL/TVV18pKirKyxUCOFtt2rTR0qVLNWnSJPn5+dkct2rVKnXq1ElvvPGGLBaLBysEAAAAAACeQJCMClm4cKHi4uL0wQcflC0bMmSIkpKSdMMNN3ixMgCu4uPjo0cffVRr1qxRXFyczXEFBQX673//q0GDBmnfvn0erBAAAAAAALgbQTLOSn5+vsaNG6eBAwdq7969kqTQ0FC99957+vXXXxUTE+PdAgG4XFxcnFavXq3HHntMZrPtHx9//vmnOnbsqBkzZshqtXqwQgAAAAAA4C4EyXDa6tWr1blzZ7322mtly8477zxt3LhRd999t0wmk/eKA+BW/v7+ev7557Vs2TK1adPG5ricnBzdeeeduvzyy8ta3gAAAAAAgKqLIBkOKyoq0pNPPqlevXpp69atkqSAgABNmTJFf/75p5o3b+7lCgF4Ss+ePbV+/XqNHTvW7rhffvlFsbGx+uabbzxUGQBUXe+8846aN2+uwMBAdenSRUuWLHFou2XLlsnX11edOnVyb4EAAACo0QiS4ZCkpCT17NlT//vf/1RaWipJ6tq1q9avX69x48bZfcwdQPUUHBys119/XX/88YeaNGlic9yRI0d03XXXafjw4Tp69KgHKwSAquOrr77S/fffr4kTJ2r9+vXq16+fhgwZouTkZLvbZWVl6dZbb9UFF1zgoUoBAABQU5H+wa7S0lK9/PLL6tKli9avXy9J8vX11TPPPKPly5erXbt2Xq4QgLedf/752rhxo0aMGGF33BdffKHY2FjNmTPHQ5UBQNUxZcoU3XnnnbrrrrvUrl07vfbaa2rcuLGmTp1qd7tRo0Zp+PDh6tWrl4cqBQAAQE1FkAybdu3apf79++uRRx5RUVGRJKl9+/ZauXKlnnzySfn5+Xm5QgCVRUREhGbMmKGffvpJ9erVsznu0KFDuuSSSzRy5Ejl5OR4sEIAqLyKioq0du1aDR48+JTlgwcP1vLly21u9+GHH2rXrl166qmnHDpOYWGhsrOzT/kAAAAAjiJIxmmsVqumTp2quLg4LVu2TJJkMpn00EMPae3aterSpYuXKwRQWV1++eVKSkrSNddcY3fc+++/r/j4eC1evNhDlQFA5ZWRkaHS0tLTbsTVq1fP5gtLd+zYoUcffVSfffaZfH19HTrOpEmTFBERUfZp3LhxhWsHAABAzUGQjFOkpKTo4osv1r333qu8vDxJUosWLbR48WK9/PLLCgwM9HKFACq76Ohoff311/rss89Uq1Ytm+P27NmjAQMGaPz48SooKPBcgQBQSZlMplN+b7VaT1smGa3Hhg8frmeeeUZt2rRxeP8TJkxQVlZW2Wf//v0VrhkAAAA1B0EyJBn/UPn0008VGxur33//vWz56NGjtWHDBvXt29eL1QGoakwmk4YPH66kpCRdfPHFNsdZrVZNmTJFnTt31po1azxYIQBUHtHR0fLx8Tlt9nFaWlq57YJycnK0Zs0ajRkzRr6+vvL19dWzzz6rDRs2yNfXVwsWLCj3OAEBAQoPDz/lAwAAADiKIBlKT0/XNddco1tuuUVZWVmSpJiYGM2dO1dTp05VaGiolysEUFU1bNhQs2fP1rRp0xQSEmJz3JYtW9SzZ0899dRTKi4u9mCFAOB9/v7+6tKli+bNm3fK8nnz5ql3796njQ8PD1diYqISEhLKPqNHj9Y555yjhIQE9ejRw1OlAwAAoAYhSK7hfvzxR3Xo0EHff/992bKbbrpJSUlJuuiii7xYGYDqwmQyaeTIkdq4caP69etnc1xpaameffZZ9ezZU5s2bfJghQDgfQ888IA++OADzZgxQ1u2bNG4ceOUnJys0aNHSzLaUtx6662SJLPZrNjY2FM+devWVWBgoGJjY+3euAMAAADOFkFyDZWZmanbbrtNV155pdLT0yUZj1V+++23+vTTTxUZGenlCgFUNy1atNCff/6pyZMnKyAgwOa4devWqUuXLpo8ebJKS0s9WCEAeM/111+v1157Tc8++6w6deqkxYsXa/bs2WratKkk6dChQ0pOTvZylQAAAKjJTFar1ertIuBZ8+fP14gRI5SSklK27IorrtC0adPK7cMHqVevXlq5cuUpy3r27KkVK1Z4qSJUBY0aNdKBAwfUsGHDU77fIG3evFm33HKL1q1bZ3dc37599dFHH6lFixYeqgwAao7s7GxFREQoKyuLfskAAADViLv+nseM5Brk+PHjGjNmjC688MKyUCs8PFwzZ87UDz/8QIgMwGPat2+vlStX6qmnnpKPj4/NcUuXLlVcXJymTZsm7nsCAAAAAOA9BMk1xPLly9WpUye9/fbbZcsuuOACJSYm6rbbbpPJZPJidQBqIj8/Pz399NP666+/1L59e5vjjh8/rtGjR2vIkCE6cOCABysEAAAAAAAnECRXc4WFhZowYYL69eunnTt3SpKCgoL05ptv6vfff1eTJk28XCGAmq5Lly5au3atxo8fb/em1m+//abY2Fh99tlnzE4GAAAAAMDDCJKrsYSEBHXr1k0vvviiLBaLJKOvb0JCgsaMGSOzmT9+AJVDYGCgJk+erIULF6p58+Y2x2VmZurmm2/WtddeW/aiUAAAAAAA4H4kidVQSUmJnn/+eXXv3l2JiYmSjEfIJ02apKVLl6pNmzZerhAAynfeeedpw4YNGjlypN1x3333nWJjYzVr1iwPVQYAAAAAQM1GkFzNbNu2TX369NHjjz+u4uJiSVJcXJzWrFmjRx991O5LrQCgMggLC9O0adM0e/ZsNWjQwOa4tLQ0XXHFFRoxYoSysrI8WCEAAAAAADUPQXI1YbFY9MYbb6hTp05atWqVJMlsNuuxxx7T6tWrFRcX5+UKAcA5Q4YMUVJSkoYPH2533MyZM9WxY0f98ccfHqoMAAAAAICahyC5Gti3b58GDRqk//73vyooKJAktWnTRsuWLdPzzz8vf39/L1cIAGendu3a+uyzz/TNN98oKirK5rj9+/dr0KBBuu+++5SXl+fBCgEAAAAAqBkIkqswq9WqGTNmqGPHjvrzzz/Llo8dO1br169Xz549vVgdALjONddco6SkJA0dOtTuuLfeekudOnXSypUrPVQZAAAAAAA1A0FyFZWamqrLL79cd955p3JyciRJTZo00R9//KHXX39dwcHBXq4QAFyrfv36+umnnzRjxgyFhYXZHLdjxw716dNHjz32mAoLCz1YIQAAAAAA1RdBchX0zTffKDY2Vr/88kvZshEjRmjjxo06//zzvVgZALiXyWTSiBEjlJiYqIEDB9ocZ7FYNGnSJHXv3l0bNmzwYIUAAAAAAFRPBMlVyNGjRzV8+HBdd911OnLkiCSpXr16ZTP0IiIivFwhAHhG06ZNNX/+fL3xxhsKCgqyOW7jxo3q1q2bXnjhBZWUlHiwQgAAAAAAqheC5Cpizpw5io2N1RdffFG27ETP0Msvv9yLlQGAd5jNZt13331KSEhQjx49bI4rLi7WxIkT1a9fP23fvt2DFQIAAAAAUH0QJFdyOTk5GjlypC655BIdOnRIklSrVi199tln+vrrrxUdHe3lCgHAu9q0aaOlS5fq+eefl5+fn81xK1euVKdOnfTmm2/KYrF4sEIAAAAAAKo+guRKbPHixYqPj9f7779ftuziiy9WUlKShg8fLpPJ5MXqAKDy8PX11WOPPabVq1erY8eONsfl5+dr7NixuvDCC5WcnOzBCgEAAAAAqNoIkiuhgoICjR8/XgMGDNCePXskSSEhIZo2bZpmz56thg0berlCAKic4uPjtXr1ak2YMEFms+0fcQsWLFDHjh01c+ZMWa1WD1YIAAAAAEDVRJBcyaxZs0adO3fWlClTysKNfv36aePGjRo5ciSzkAHgDAICAvTCCy9o6dKlat26tc1x2dnZGjFihIYNG6bU1FQPVggAAAAAQNVDkFxJFBcX66mnnlLPnj21ZcsWSUYYMnnyZP35559q0aKFlysEgKqlV69eWr9+vcaMGWN33KxZsxQbG6tvv/3WQ5UBAAAAAFD1ECRXAps2bVLPnj317LPPqrS0VJLUuXNnrVu3TuPHj5ePj4+XKwSAqikkJERvvvmm5s2bp8aNG9scd+TIEV177bW66aabdOzYMQ9WCAAAAABA1UCQ7EWlpaWaPHmyunTponXr1kmSfHx89NRTT2nlypVq3769lysEgOph0KBBSkxM1G233WZ33Oeff67Y2FjNnTvXQ5UBAAAAAFA1+Hq7AE8pKZH27ZN27JB27ZI2b5YyMqSCAslqlQIDpagoqX17qVUr49OsmeTn5556du/erdtuu01Lly4tW9a+fXt9/PHH6tKli3sOCofl5Rnnyc6dxmfXromSCiT5SyqVVKB9+yz64IOT50vDhhItrGumo0dPnivbtxvXmYICKSPjQ0k5OnpUevppqXXrk+dL7dqcL54WERGhmTNn6sorr9TIkSOVlpZW7riDBw9qyJAhGjVqlCZPnqzQ0FCX1WC1SgcPnjxftm6V9u+XCgul0lLJ318KC5PatTPOl5YtjU9IiMtKAAAAAADgrJis1fx19bt3S3PmSD/9JB05YgSEkmQ2G/9gN/89J9tikYqLjX/IS1JQkBH0XHaZdOmlxj/oXcFqteq9997T+PHjdfz4cUmSyWTSAw88oOeee06BgYGuORCcZrFI69ZJP/8s/fGHlJtrhIEmk7Rv307l52dLskgySTIrKKiWmjZtLpPJCHkaN5auukq6+GIpOtrLXwzcrqBAWrRI+uEHKSFBOn7cuGElGdcWHx8pKWmjiotL5OsboFatOkiSfH2N8yUuTrrySmnAAON6A89KT0/X6NGj9f3339sd17x5c3300Ufq169fhY535Ig0d65xvuzbZ5wvJ376BgQYP4tMJuM6VFpq/Dw6cZMzNFQaOFAaOlTq2vXkzy0AqKjs7GxFREQoKytL4eHh3i4HAAAALuKuv+dVyyDZapWWLDH+wb5smZSVZQQ3ERFGYHOmlsMWixE4Z2cbYWJ4uNSjh3T11Uboc7azCA8cOKA777xTv/32W9my5s2ba+bMmTrvvPPObqeosIIC6ddfpe+/lzZtMn4fEWHMCgwMNP68ly5dqszMU/um1qoVqb59+6qkxDhfjh2TioqMme0XX2yEyh06eOmLgtukpxvXlh9+kJKTjetNZKRxjfH3P/X6MH/+PBUUFCgwMFCDBl0oq9UICHNzjfNFMm5AXHml8alXzztfU01ltVr1+eefa8yYMcrMzLQ5zmQyafz48frf//7n9M2+rVul774zQuT0dOMplxPni+8ZngmyWo3rUU6O8XMsMNCYqXzVVUaozH1HABVFkAwAAFA9ESQ7KC1N+r//M2YhFxYas4ojI88+/LVapcxMYzaZn580eLA0frzUoIEz+yg/rBg5cqQmT56ssLCwsysOFZaUJL30krR2rXGO1K1rBDz/Pl/sBcn/VFpqnCuZmUYYfeut0p13MuO0OrBajTDw//7PCJCDgqQ6dYzZpLb8O0j+t6Ii45qVlyc1aSL997/GExC0vPCslJQU3XXXXafc5CuPM+2HCgqkDz+UZs48eT2Ijj7zjUx7cnONMNpikTp1kh591JjZDgBniyAZAACgenLX3/OqzQOyJ0KeW24xZpaGh7umD6nJZATRrVoZv/78s3GMn38++ViyPenp6br22mt18803l4XIDRo00OzZszVt2jRCZC8pKJDee88IeVevNvobt2hhPEJekfPFx8cIo0+0Qnn7bemOO4zWB6i60tKM0O6RR6RDh4xzpVEj+yGyI/z9jf20bCkdPixNmCA9/LCUmuqauuGYRo0aac6cOZo6dapC7DQj3rx5s3r27KlnnnlGxcXFNsclJhrXltdfN35OtG5tzDavSIgsGden5s2N69W6ddLdd0vvvGNczwAAAAAAcLdqESQXF0uTJhkhz8GDRsgTEeH644SHG4FPWpr02GPSU08Zs55tmTVrlmJjY/Xdd9+VLRs+fLiSkpI0ZMgQ1xcIh6SnS/fcY8wstViMmwSufkTcZDKCo8aNpfXrpVGjpC+/dOzmAyqXDRuk226TfvxRqlXLeAlnRQPBf/PxkZo2NW58/fSTcby1a117DNhnMpk0evRobdiw4bQnDf6ppKRETz/9tHr16qXNmzefss5qlb791gh41641vv/r1XP9DPPAQOO6ZbVKb7xhXF9svDcQAAAAAACXqfJBcmGh9OST0iefGD1tmzU7c9/JijgR+ERGSt98YwTKJ17gd0JWVpZGjBihK664Qml//+s+KipK33zzjT777DPVrl3bfQXCroMHpTFjpOXLjVl99eu7t41AQIAR+JSUSC++KE2fTphclfz1l9FuYu9e4yaSO25Q/dOJJyn275ceeMDo8Q7PatmypRYuXKhXXnlF/v7+NsetXbtWnTt31quvvqrS0lJZrdJHH0nPP2/8XHLHDap/+ufNqpUrpf/8R0pJcd/xAAAAAACo0j2SS0qkZ54xZoDVrWuEMCckJNyulJSPHNqP2RwkX98QBQTUU0jIOYqI6KL69a9QWJj9N6Xl5hqPuQ8daoQH/v7SH3/8oREjRmj//v1l44YOHar33ntP9evXP6uvE66RkWGEyOvXGzcc7GREp1m4cLpyc1dI2izpkKRcSTkym63y9Q1XQEA9hYa2V0REF9Wpc5HCw+Nl+ldCffiw8Qj6+PHGjFNUbuvXS+PGGTM9mzeXzA7cdktPn6e//hpsd0zfvmtUq5b9HrsWixFeR0VJr74qdevmROFwmU2bNunWW2/VunXr7I4777zzNGzYN/roo7ry8zNuUDkjK2udjhxZrKNHl+j48Z0qLj6qoqIjkkodur4UFRnnS3y89NZbxs9DAHAEPZIBAACqJ162V47XXpOmTTs9RJacC5JtiY6+UB06vK6wsHY2x+TkGP1Mb7yxSOnp4/X222+VrQsLC9Prr7+u22+//bRQEZ5VWGi0s1i2zPEQ2WIp0cGDX2rXrleUk7PRqeMFBzdXkyYj1bz5OPn4nGykm5pqvJDv+ecluptUXvv2SXfdJR044HiILEnr1t2kgwc/tzumWbMxio1984z7slql3buNF3u+/77RsgeeV1xcrOeee07PP/+8SktLbYwaJLP5ZTVq1EhxcdGSzny9d/X15USY3LOn8XPRnbOhAVQfBMkAAADVEy/b+5dVq6RPPzUeNXfX33szMuZp+fLeOnZslc0xYWGSv3+2Xnhhh95+e0PZ8oEDByoxMVEjRowgRK4EPvnEePy7cWPHQuTc3O1aurSrEhJucTrkkaS8vD3aunWCCgsPnbK8fn1jJv2UKcZsdlQ+JSXSyy9LycnOhcjFxdlKTf3hjOMOHPhCFkvRGceZTMbxU1KMtiglJY7VAdfy8/PTM888oxUrVqht27bljKgj6UFZLL5KTl6pv/5apYIzvP3OHdcXf3+pSROjHcuHHzq9SwAAAAAAzqhKBsk5OdJLLxm9iaOj3Xus4uJMJSTcotLS09+qZ7GUauvWLUpMXKyiIkl6UIGB9fXGG29o/vz5atq0qXuLg0M2bzZ6EwcHS0FBZx6fmvqTlizpouzsDWcefBYaNTJCyilT6JdcGX3/vbRwoRQT43iILEkHD34liyX/jOOKi4/o8OGfHdqn2Wz08l62TPr6a8drget169ZN69at07hx4/51c/B+Sc0kGe2M0tPTtGjRQh04cEDS6d/g7ry+BAYaN1ZnzpQSE12+ewAAAABADefG19K5z3vvSZs2GS+9O5vJvi1bPiw/v0hJksVSpPz8fUpL+1WFhYfLHX/8+Halpc1WgwZXli3Lzs7S+vUJysnJ/ntJsgID4zVqVKLGjIl26wvc4LjCQuOmw7FjxsuvzuTo0aVat+56WSyn3zg4yV9SR/n7t1CzZh1VUpKtwsJUHTu2Qnl5u854DB8fY2by3LlS//7SZZc5/OXAzfbtk955R/Lzk0JDnds2JWWmw2P375+pBg2udmhsSIgx23TqVKNtAS0uvCcoKEhTpkzRFVdcodtvv117954j6VJJqZJOtr0oLi7W+vXrlJp6SB07xpW9tM+R64vZHKSoqAEKDW0jP78op68vdetKO3eefLknLS4AAAAAAK5S5YLkPXuMmXm1ajn3srR/atr0HgUHNztlWUnJca1efbmOHFlQ7jYZGfPUoMGVslot2rlzl7Zv3y6r1SJJMpnMatOmlWrXbqylS83askVq3/7saoNrzZ0rrVljzAI+U7ifn5+i1auH2Ql5/CVdLelKSYEKDo5UmzZ9Txlx/PhuJSdPU3Ly+youPmbzWOHhRrg9dao0aBBhT2Xx4YdGyxFHbjr8U27uDh07ttzh8enpc1VYeFgBAfUcGh8TI+3YYQSDzz/vXG1wvf79+2vNmo3q3Hm3kpPNkrLLHXfo0CEdOXJU8fFxCg8vsXt9MZsD1arVo2rR4kH5+oaUO8aR64vJZLTwWb9e+uUX6ZprzupLBAAAAADgNFWutcXs2VJmputbWvj6hqht2xdsri8oOKDjx3O1bNlybdu2tSxEDgsLV79+fdW6dWvVrm1WTo7066+urQ1nx2o12hRIjrW02L79KRUXH7GxNkTSM5JulGQ79Q0JaaF27V7SwIE71aTJKJlMPjbH1q9vvBxr8eIz1wb3S0uTfv/duEnlTEsLyfZsZJOpoYweuqeyWkuUkvKpw/s3m6WoKOmPP+itXVls2BCmsLB4depUXwEBtq8JRUWFWr16tVauHGPz+uLrG64ePX5XmzZP2QyRJcevL4GBxjnz/feSxeLc1wUAAAAAgC1VKkg+flz68UfjUW93tI4ID4+3uS4395gWLVqszMwTs8BMatWqlfr166vw8AhjicmYafrrr1JWluvrg3M2bJA2bpTqnJ7jnSY3d7tSUj6yuT44+GFJHRw+tr9/bcXFvaugoMY2xwQEGGH3Dz/QK7kymDtXOnrUCGydYbValJLySbnrzOaBkvqWu86ZVhiSVLu2cRNtzhzn6oN7/PSTVFoqNWpURwMG9FdMTIyd0Qd0/Ljtvthxce8rKqqfw8d25PpSp47RH37tWod3CwAAAACAXVUqSF6wQDpwwLFg8OzYTvOOH/eVxWL0wAwJCVGfPr3Vtm07mc2nzgiLjpYOHzZmNsK7fv5Zys93rNftwYNfymotLXddVNT58vc/38XVndi3tHq1tH27W3YPBxUXG7M3/f2NHtbOyMj4QwUF+8td5+MzQNJ55a7LyUlSZqbjKZ/ZbNx8+P57/f1yT3jLrl3S8uUnbzr4+fmrc+cu6ty5i/z8/MrZYomk8qcGR0Wdr5iY61xeY0iIcf372bH3OgIAAAAAcEZVKkheuNCYuXm2vZHPJCtrvZ217SRJzZo103nnnafIyNrljvL1NQKf+fPdUCAcVlRk/BmEhzs2ez09fa7Ndc2a/ceFlZ0qIkLKzZWWLnXbIeCATZuM/utnc5Nq//6Z5S4PD+8ks7mJpJYymRqVO8bZWcl16kjJycZse3jP0qVSTo7RBuWfYmJi1L//ANWt++/e1+ts7std1xeTybi+LFgg5eW55RAAAAAAgBqmygTJFouUmGjMsnKHkpJcbd36mI21IQoIuFA9e/ZUbGxH+fjYf0dhSIi0dSuzBr1pzx4j6HFkNrLFUqTMzFU21poUFTXQpbWdsneT8dm61W2HgAN27JAKCx3rpf1PxcXZSk39odx1MTE3lP232dy/3DEHDnwhi8XxC0VgoHFd2bHDuTrhWtu2nfze/bfAwEB1795N8fHxf/+sKJZk65EDk44ebSiLmxoZh4UZN6p273bL7gEAAAAANYz9RLQSOXBAOnbMNUHyvn1T5ecXKckIEfPz9ykt7VcVFh4uZ7RJtWs/rm7dLrXxyPLpQkKMHsl790pt2lS8Xjhvxw7jse4GDc48trAw3WZbi6CgpvL3j3RxdacKDjZmmFqt7un9jTPbscN2MGjPwYNfyWLJL2eNSTExN2jvXiNA9PEZqNLSz04bVVx8RIcP/6wGDa526HgnaqQVivdYrcb3q/2bDiY1btxEUVHRWrdunjIzbQXFdbRnT5qOHFmiTp3OVXh4uEtrDQoybpDs3CnFxrp01wAAAACAGqjKBMk7dxov24uOrvi+du162cGRoWrRYorat7/bqf0HB0upqUbNBMnesXOn8avZgTn3RUXpNtf5+zv55rWzEBJi3CQ5cEBqVH4HBLjZxo1n1zLHVmuKyMheCg5uqhMzUc3mJgoLi1NOzsbTxu7fP9PhIFkyZiVvPH038JDDh6X0dMduagYHBys2tomd1jVGcJydna0lS5bonHPOUcuWLWQyueZhoRM3Rk5cDwEAAAAAqIgq09pi715jJpivR6JvP4WGjtTAgTucDpGlk+Hlvn0uLgsO277d8ZemlT+j1GA2B7ioIttCQowepnv3uv1QKEdentF3ODjYue1yc3fo2LHl5a6LibnxtGUNG56+TDL6c5f/NET5goOlQ4eM1i3wvH37jKcdHD1frNYCO2t9/zHOoq1bt2jTpk0VK/DfR/CldQ4AAAAAwDWqTJCcl+fJx/6LVVz8kzIzF1RoL/m280m4WU6O40Gyn5/tWcclJVkuqsg2Hx+ptJTzxVvy843//87epLI1G9lk8lFMzLWnLf9nz+R/slpLlJLyqcPH5Xzxrrw8qaTE8fPF3vVFOvUteGazjxo2dO1jCb6+3HQAAAAAALhGlQmSi4uNGcmeUlh4WOvX36Rduyaf1fZWq9GbEt5RWOhYWwtJ8vevY3Ndfv5+Wa0WSe47+U7cIOHljN5x4tri6PkiGbNHU1I+KXddVNRABQTUO215cHAz1arVs9xtbIXS5TGbjZePcn3xjhPni6M3Nu1dX6QMSSf7J4eHh2vNmjXKycmuUI3/ZDJJBfYmRQMAAAAA4KAq0yPZx8d1M5LPP3+PgoObqbg4W7m5W5SU9Jyysn4pd+yWLY+oVq3uioo6z+njeKYNB8rj4+P4jQd//0j5+9dVUVHaaetKSrKVk7NJ0dF1lJmZWbbc19dPXbp0dkmtJ+p0dAY1XOvE/3dnblRlZPyhgoL95a4zmXy1c+eLkqTw8CMKDbXIbDZr58618vEpvx9CTk6SMjPXqlatLmc89okQk+uLdzj7s8je9cWYkbxfUlNJUmbmMUnSsmXL1b17d9WuXbvC9VqtkoPviQUAAAAAwK4qMyP5bF6EdSZ+fuGKjOyhfv1+VosWD9kYZVFi4mhZraVO7dtk4h/v3hQQYMzadFSdOoNtrtu5c7p27txxyrKSkmKtW7fu79nKFXMiGHTHOY4z8/c/OcvXUfv3z7S5Lj19rrZunaCtWycoLW2yMjKmKC1tsrZunaAjR2y3y3F0VrLFYtTL9cU7/PyM71dXXV8aNtx12sv1SkqKtXLlCh0+7HjvbFusVuN6CAAAAABARVWZILlOHeMfxO5qb9G27QsKC+tQ7rrc3C3av/9Dh/d1osY69p5ohls1aWI8gu6ounUvtbnu4MEZkoxHzdu0aaOgIGNW6bFjx7Rt2/aKlCnJeOw8IIDzxVvCwoyPo4//FxdnKzX1B5fXceDAF7JYztzfpLDQeNFbrVouLwEOqFPH+H51pl2EvevL4cMfyWrNlCRFRUWrzt8XAovFotWrV2v//vJnvjuqsFBq5Nq2ywAAAACAGqrKBMmtWjn/j3dnmM2+att2ks31O3Y8L4vFsWSyqMh47LxVK1dVB2e1aWP86uiNhwYNrlZwcAsba3Mkva6oqNpq3bqNOnfuLNPfz7bv3LlDGRkZ5W6Vl7dHJSVnfstVXp4UEiK1bOlYrXAts1mKjTX+HBxx8OBXslhc/6a74uIjOnz45zOOO35cat+e1hbe0qKFFBpq/Dk4yt71paTkmKTXJZWqSZPG6tatu2JiGv691qoNGxK0a9fO07Zz9PoiSW3bOl4rAAAAAAC2VJkguWVLYxaeM/94d1a9ekMVHn5uuevy8/cqJeVjh/ZzIhhs3dqV1cEZrVoZQZujs5LNZj+dc87/7IxYLbP5RZWUZCkyMlLnnHMymVm/fp0K//Hms+LiTG3f/owWLuygoqIjZzz28eMnz294R7t2jt90cObFeM6y1zLjBItF6lD+wxPwgIAA40aVozceJMeuL9JLiooKlNls1rnnnqvmzZuXrd2yZYs2b94sq9Xq1PWluNi4UcLPIgAAAACAK1SZOW0hIcZMsI0bpeho9x2ndesntHbtVeWu27nzBTVqdJvMZvv/244fl5o1k1zwniScpVatTt54cLT3cEzMjTp8+BcdPPhFuevT03/RggUt1azZGNWte6lq1w7W0aN5KizM1tq1s9W0aYEyMubr4MEvVVrqeMpUXCzFxTk8HG7QqpXR97akxP5M39zcHTp2bHm561q3fkrnnPO0Q8fbvPlh7d79ymnL09PnqrDwsAIC6pW7XWnpyXrhPR07SsuWObfNma4v0kotWnSOmjUbo3r1LlObNm0UEBCgrVs3SDqm3bsXKzV1hwoL5zl8fTl+nKcdAAAAAACuU2WCZEnq3l1avfrky8ncoX79YQoL66icnMTT1uXl7daBA5+qcePbbW5vtRoz1Xr0cE99cExUlDELLyFBiox0bBuTyaT4+A+Vn5+iY8eWlDumuPiodux4Vjt2PPv3ErMki44elY4edb7OoiLjXI6NdX5buE5srBQRIWVm2r9RZW82ckzMtQ4fLybm2nKDZKu1RCkpn6ply/HlbpeZKYWHMyPZ2zp2NG44FBY6/iK7E9eXgoIDOnp0cblj/n19MZl8JJ180aszs6Al43xp316qX9+57QAAAAAAKE+VaW0hSUOGGCFKVpb7jmEymdS69eM21+/Y8bys1lKb63NyjBlgl9p+txI8wGSSrrzSmMHpzEv3fHz8FRAwSdIgB7ewnE15ZdLSjBdh9e9fod2ggurWlQYNko4dsz3GarUoJeWTcteFhra3+bLO8tSq1U1BQc3KXWcvrD56VBowQGrY0OYQeEDfvlLTpsb3rzN8fALUo8ccNW58h0Pj7f2sOZOSEuNz1VXuu/EKAAAAAKhZqlSQ3Lq1MdPXxrvNXKZBg2sUGtqu3HV5eTt14MDnNrdNT5c6dWKGaWVw4YXGTDxnzpf9+1OUmnpU0liZzY8oKMjWC/jOzM+vlnx8gmyut1ik/Hwj8A6yPQweMnSoMbs0N7f89RkZf6igYH+56xo0cHw28sltril3eU5OkjIz1562PC/PaNNyxRVOHwouFhBgfN8WFBjfx87w8QlWfPx0NWr0mqSKTBUOVVGR7bUZGVKdOtLgwRU4BAAAAAAA/1ClgmTJ+Me7j4/xD3h3MZnMatVqos31O3Y8V+5MsRP/qGcGWOUQHi5ddpkxS9yRF6nl5uYqMfFkS5NOne7T+efvUOfO36hu3cvk51fLgaMGKCCgj+LiPtCgQQds9rqVjNmvERHSJZc4sFu4Xdeuxkv30tPLX5+S8pHNbWNirnP6ePbC5/JmJR8+bLzkrXt3pw8FNxgyRKpV6+xa2khSdva5kt6V9Ihq177YoeuL2Rwss7m7pPskfag1a3YoNzfntHFWq5SdbTwZ42hrHwAAAAAAzsRktToSsVUeRUXSLbdIGzacfEFWZWC1Srt3G0HPZ58Z7S3gfbt3SzffbPQyjYmxPc5iKdXSpUuVnZ0tSWrcuIni4+NPGWO1WpSTk6jjx3eqqOioiouPymotla9vmEymWtqyJUelpfUl+Sk+vpMaN25s83glJUZt110n/e9/rvhK4Qo//yw99pjRYzsszNvVnJSba7RR+N//jBtVqByeeUb6/HPjRbD2XtL4bzk5OVq0aKEkKSIiQv36nWf3+hIQUF+hoecoNLStCgpKtHLlSuXlHZck+fn5qXv3Hor8R2J86JDk5yd99JHxMwkAbMnOzlZERISysrIUHh7u7XIAAADgIu76e16VetmeZDza/cgj0qhRxszBunW9XZHh6FGjPcHDDxMiVyYtWhjnyksvGa0BgoPLH7d585ayEDk0NFSx5fQmMZnMCg+PV3h4/GnrJMnf/4DWrVsnSUpMTFRkZKRCQ0PLHZuSIjVvLt1331l8UXCbSy+V5s+X5s41vo/NleCZDYtFOnjQ6OFMW4vKZcwYadUqad8+4/vZUSkpKWX/3aiRccPpTNeXE4KD/dWnTx/99ddfys7OUnFxsVasWKGuXbuqbt26ys83rnUPPECIDAAAAABwrUoQkzivc2djlmlWljHT1NuKiowg+dprpd69vV0N/u3GG6WePaUDB8pvcXH4cKr27t0jSTKbzercuYt8fHycPk5MTEM1btxEkjHDed26tbJYTm+BkpVlBJT33195boTAYDZLDz1kvMzuH1mfVx04IDVoYNykOovTEm4UFWUEtr6+9l/U+E9Wq1UHDhgnl8lkUoy9RyVsCAgIUO/evRQdHS3JuN6sXr1KKSkpSkmRunUzntwBAAAAAMCVqmSQLEl33aX/b+++46Oo8z+OvzadJCQEQgktdFBQMIAIyKEICCqIogKKKD/F48QGpyfqySnnnRVFUUQ9G0qzgYgoYKOrlIAiKJ3QQkgCaaTuzu+Pr1lEQkjZzSSb9/PxyEN2dnbms85kNvv5fubzpWNHSEgAZ9knti83l8tUo7VrB3feaV8ccmZBQTBxokn67N9/ajI5JyebTZs2uR+fe277cpX8d+jQ3l2FnJ6eztat2055PjfX9Lq94gq4/PIy70a8qHFjUynucpU8Oegtx4+bNih33gmxsfbGIkXr0wcGDzZ3yJSkd39KSjI5v69Yr149goODy7TfgIBALrzwQmJiTCLasiw2bTpKTs4hJk40EwKKiIiIiIiIeFKVTSSHhcHkydC0KezZY5I+Fc3lgr17Te/dJ54wk7tJ5dSmDTz0kKkcPHzYLLMsi40b48nPzwegQYMGNGtWvmydv38AcXGd8fu9J8LevXs4ciQRMJXr+/aZydIeeKDy9PeW0119talkT0kxk5bZIT0dkpNNH+2hQ+2JQc7O4YC//x26dzcDm4WTrp7JH9taNGrUuFz79vPzJy4ujmbNmgExgIt9++7k3XcnUsWmPxAREREREZEqoMomkgHatoVnnzW3fe/aVbGVyS6XSWBHR5v+u0W01JVK5qqrTH9tyzJtC7Zv305qagoAISE1fp9cr/zZ3YiICM49t7378aZNm0hPz2bvXlNF/9xzULt2uXcjXlTY4uK668wkd8ePV+z+09JM5frVV5tqerW0qNwiI81nUVyc+Vw4U2Wy01nA4d9HsgICAmnQoL4H9u4gKqoDDRo0Bp4HPuXpp5/m9ttvp6CgwAPbFxERERERETGqdCIZTGLuxRfNbd+7dplJhrwtOxt27jQJ7OefN/0opWq44Qb45z8hOzudHTsKAH/AQVxcHIGBQR7bT7NmsTRo0ACA/PxQ1q07SteuLqZNg/qeyB2J1wUGwqRJcNNNpsXFoUNF99j2JMsyFfMpKTB8uLnrIshzp6V4Ud268NJLpk9+QkLRbVEOH07E+fuIZ8OGDfHzK98IQUGBSVz7+Tl46aUGTJ/eC8fvtzq89dZbDB06lOzs7HLtQ0RERERERKRQlU8kA5x3Hrz6Klx8sUnCeCvhU5jkOXgQunWD6dOhc2fP70e8q3fvFFJSRgN7gJY0bXo+tT1eIuygffuOBAS0ASLIzp5J48bPKolcxQQGmpYoDzxges7u3GkGkryhcIAqMNBM4PbPfyqJXNXUrWsGNocNg8xMk+T9Y1HwH9taNG5cvrYWx4/D7t1mEPXZZ81kr3/721g+/PBDgn4/cRYuXEj//v05ZnezbxEREREREfEJDsuHGinm5cGcOfD666air14907e4vL1oLQsyMsyt5lFRcNttcPPNmsyoKrIsi6uvvprPPvsMiCI29gVq1RpFfr6DBg0gNLT8+3C5IDXV/ERFHWPduhG4XEvw8/Pjm2++oXfv3uXfiVS4336Dp56C77+HkBBTWR4YWP7t5ueb9hnZ2WaA6sEH4Zxzyr9dsY9lwTffmDtWdu40nxthYdl8881XANSoEcpll/WhLK10srPNgGZgIAwaBPfea1os/dG3337L1VdfTUZGBgAdOnTgyy+/pFGjRuV9ayLiY9LT04mMjCQtLa1ckw2LiIiISOXirb/zfCqRXGj7dvMFft06UxUWEWG+aAcElG47BQUmIZ2WZib3i4szlYLnnuuduMX7pk2bxj333ANAdHQ0mzZtZvv2hrz8sjlvCgpM/+JatUyf3NLIzTUJwZwc8/oBA+Cuu2DGjCd49NFHAWjUqBGbN2+mTp06nn1jUiEKB6tmzjTJPIfDVKGGh5duwMqyICvLnC8ul2mTM3Kk+VEVsu9ISTF3rnz+OezencLRo/uBJNq0iaVNm7Yl3o7LZSqQU1PN51irVnDnndC375nPu/j4eAYMGEBSUhIAsbGxLFmyhLZtS75fEfF9SiSLiIiI+CYlkkvJsuDXX+Gzz2DxYpOwsSyTpAkNNYnhoKCTX8ItyySJTpwwCZ7c3JNJooEDzURtHTqUv7pZ7LNp0ya6detGXl4eAIsWLeLKK68ETAL5++/h009hxQozeOBwmMrTsDBzzgQEmOSyZZnETm6uOVeyssxEj35+5jbza68150xh8Z/T6aRv37589913AAwePJgFCxa4e5lK1ZORYSpOP/4Yfv7ZVIn6+Z28ttSoYR77+ZlzxeUy62RlmWuMZZk7Gjp0gKFDTUKwZk2735V4y8GDFhde+C8OHboQaEaLFq2pUSPYfW0JCTHnisNhzhWn8+S1pXDivpo1TfumIUOge/eSVcPv3LmTyy+/nN27dwNm8Gzx4sV0VWN/EfmdEskiIiIivkmJ5HI4fhyWL4dt20zSZ88ek8zJyzvZS9nhMF/MQ0OhWTM4/3xo1w569zYVqlK1ZWZm0rlzZ7Zv3w7A+PHjef7554tcd98+WLXKtDLYvBkSE8354nSaJI/DYZI+wcGmErVtW5MQ7NABevY0SaE/O3ToEB07diQ5ORmAl156ibvvvttr71cqhmXBpk2wfr25vmzZYiZZy84+mUAuTCiHhJgWBx06mNYVnTubuxw0nuD74uPjiYuLA4Jo3/4O7rhjGlu2mGtMRoYZlHK5zPnk5wf+/uazqEED81nUpg306mU+m0orMTGRAQMGsHnzZgDCwsKYP38+/fr18+h7FJGqSYlkEREREd+kRLIHZWXBrl2Qnn4ymRwcbCq+WrRQZaAvGj16NO+88w4AcXFxrFmzhuASNLm2LHN7+t69Jwcf/P1NNXt0NDRvXvJWBJ9//jlXXXUVAEFBQfzwww906tSpbG9IKiWXy0zGefCgSQ7m55sBqqAgU6HeuHHpW6ZI1TdhwgReeOEFAGbMmMFf//pXwFxP9u6Fo0fNv51Oc67UqGGuLXXqeGagIS0tjauvvprly5cDEBgYyMyZMxk+fHj5Ny4iVZoSySIiIiK+SYlkkTKaNWsWI0eOBCA8PJyNGzfSunVrW2IZP348U6dOBaBNmzZs2LCB8PBwW2IREe8rKCigUaNGJCUlERQURGJiIlFRURUeR05ODjfeeCPz588HwOFw8OKLL+rOCJFqTolkEREREd/krb/zVBsnPm3nzp2MHTvW/fjVV1+1LYkM8NRTT/1+izts375dSRwRH7d06VL3hHeDBg2yJYkMEBISwocffsiYMWMAsCyLe+65h0cffRSNJ4uIiIiIiEhJKJEsPisvL4/hw4eTmZkJwC233OKuTLZLcHAwc+fOdVchv/POO8yaNcvWmETEe2bOnOn+96hRo2yMBPz9/Xnttdd49NFH3cueeOIJxo4di9PptDEyERERERERqQqUSBaf9dBDD7FhwwbAtJF4+eWXbY7IaN26Na+++qr78dixY9m5c6eNEYmIN6SlpfHpp58CEB0dzYABA2yOyLS0mDx5MtOmTcPxewPm119/neuvv56cnByboxMREREREZHKTIlk8UlffPEFzz//PGAmtvtjFXBlMHLkSHd1YmZmJiNGjCAvL8/mqETEkz766CN3cnb48OEElXRmzgpw1113MWfOHAIDAwGYP38+AwYMIC0tzebIREREREREpLJSIll8zqFDh065hfzZZ5/lggsusDGior3yyivufs3r16/n4YcftjkiEfGkytTWoijDhg3j888/JywsDIDly5fTu3dvDh8+bHNkIiIiIiIiUhkpkSw+xel0cvPNN5OcnAyYya0q64R24eHhzJ07112lOGXKFL744guboxIRT9i7dy8rVqwAoF27dnTp0sXmiIrWr18/vvvuO6KjowHYvHkzPXv2VLsdEREREREROY0SyeJTnn76ab755hsAGjZsyFtvveXuA1oZxcXF8cwzz7gfjxo1ikOHDtkYkYh4wvvvv+/+980331ypr0NdunRh9erVxMbGArBnzx569uzJxo0bbY5MREREREREKhMlksVnrFmzhkmTJgHg5+fH7Nmz3VV2ldk999zDVVddBUBycjI333wzTqfT5qhEpKwsyzqlrcXIkSNtjKZk2rRpw5o1a+jQoQMASUlJXHLJJe6BORERERERERElksUnHDt2jBEjRrgTsP/85z/p3bu3zVGVjMPh4O2336Zhw4YAfPPNNzz99NM2RyUiZfXjjz+yY8cOAC699FKaNm1qc0Ql07BhQ1asWMHFF18MQEZGBgMHDuSjjz6yOTIRERERERGpDJRIlirPsizGjBlDQkICAL169eLRRx+1OarSiY6OZtasWe7b3ydNmsSaNWtsjkpEyqKyT7JXnKioKJYuXcqgQYMAyMvL44YbbmDGjBk2RyYiIiIiIiJ2UyJZqrzXXnuNjz/+GDBJkFmzZhEQEGBzVKV3ySWXuBPgTqeTESNGcOzYMZujEpHSyMvLY+7cuQDUqFGDoUOH2hxR6dWoUYNPPvmE0aNHA2aw7m9/+xuPP/44lmXZHJ2IiIiIiIjYRYlkqdK2bNnC+PHj3Y/ffvttmjRpYmNE5fPoo4/Sq1cvABISEhgzZowSNyJVyOLFi0lNTQXgmmuuoWbNmjZHVDYBAQG8+eabPPjgg+5ljz32GHfddZd6uIuIiIiIiFRTSiRLlXXixAmGDRtGTk4OAHfddRdXX321zVGVT0BAALNmzSIqKgqAjz/+mNdff93mqESkpKpyW4s/czgcPPXUUzz//PPuZdOnT2fEiBHk5ubaGJmIiIiIiIjYwWGp3FGqqDvuuIM33ngDgI4dO/L9998TEhJic1SesWDBAq655hoAQkJCWLduHR06dLA5KhEpTkpKCjExMeTn59OgQQP2799fJdvsFOX9999n9OjRFBQUAHDZZZcxf/78KltxLSJGeno6kZGRpKWlERERYXc4IiIiIuIh3vo7TxXJUiV98MEH7iRyaGgoc+fO9ZkkMsCQIUMYN24cADk5OQwbNowTJ07YHJWIFOeDDz4gPz8fgJtuuslnksgAI0eO5LPPPiM0NBSAr7/+mksuuYSkpCSbIxPxLdOnT6d58+aEhITQuXNnVq5cecZ1P/nkE/r160fdunWJiIige/fuLFmypAKjFREREZHqRolkqXL27NnDmDFj3I9ffvll2rVrZ2NE3vHcc89x/vnnA7B161buu+8+ewMSkWL5UluLogwYMIBvvvmG2rVrA7Bx40Z69uzJnj17bI5MxDfMmzeP++67j0ceeYT4+Hh69erFwIEDSUhIKHL9FStW0K9fPxYvXsyGDRu49NJLGTRoEPHx8RUcuYiIiIhUF2ptIVVKfn4+vXr14ocffgBgxIgRzJo1C4fDYXNk3vHrr7/SuXNndzXyBx98wPXXX29zVCLyZ9u3b6dt27YAnH/++WzevNnmiLxn27ZtXH755ezfvx+ABg0a8OWXX9KxY0ebIxOp2rp160ZcXByvvvqqe9k555zDkCFDePLJJ0u0jfbt2zNs2DAmTZpUovXV2kJERETEN6m1hQgwadIkdxK5RYsWzJgxw2eTyADt2rVj2rRp7sdjxoxR9Z9IJfTee++5/+2L1ch/dM4557B69WrOOeccABITE/nLX/7CihUrbI5MpOrKy8tjw4YN9O/f/5Tl/fv3Z82aNSXahsvlIiMjw33XgIiIiIiIpymRLFXGsmXLeOqppwAICAhg7ty51aJ6ZvTo0YwYMQKAtLQ0RowY4e7DKiL2c7lcvP/++wD4+flx44032hyR9zVp0oSVK1dy0UUXAWa0u3///ixYsMDewESqqOTkZJxOJ/Xr1z9lef369UlMTCzRNqZMmUJWVhY33HDDGdfJzc0lPT39lB8RERERkZJSIlmqhCNHjnDzzTe7Hz/11FN07drVxogqjsPhYMaMGbRo0QKAH374ocS3rIqI961atYq9e/cCpnowJibG3oAqSJ06dfjqq68YOHAgYBJUQ4cO5X//+5/NkYlUXX++y8qyrBLdeTVnzhwee+wx5s2bR7169c643pNPPklkZKT7p0mTJuWOWURERESqDyWSpdJzuVyMGjWKI0eOAGbCp/Hjx9scVcWKiIhg7ty5BAQEACaRvmzZMpujEhE4dZK9Pw54VQdhYWF8+umn7vftcrkYM2YM//3vf9EUDCIlFx0djb+//2nVx0lJSadVKf/ZvHnzuO222/jggw/o27dvses+9NBDpKWluX8Ke52LiIiIiJSEEslS6U2ZMoWlS5cCZlKnd999Fz+/6nfqdu3a9ZTJdm6++WZ3cl1E7JGdnc2HH34IQHh4OEOGDLE3IBsEBgbyzjvv8Pe//9297JFHHuG+++7D5XLZGJlI1REUFETnzp1PGyRetmwZPXr0OOPr5syZw6233srs2bO58sorz7qf4OBgIiIiTvkRERERESmp6peNkyrlxx9/5OGHHwbM7Z7vvfdesbds+roJEyZw+eWXA6bdxy233KJEjYiNFi5c6O4xev311xMaGmpzRPbw8/Pjueee45lnnnEve+mllxg5ciR5eXk2RiZSdUyYMIH//e9/vPXWW2zbto3x48eTkJDA2LFjAVNN/MfJPOfMmcOoUaOYMmUKF110EYmJiSQmJpKWlmbXWxARERERH6dEslRaaWlpDB8+nIKCAgAmTpx41ls2fZ2fnx8zZ86kQYMGACxZsoQpU6bYHJVI9VWd21oU5YEHHuDtt9/G398fMImuQYMGkZmZaXNkIpXfsGHDmDp1KpMnT6ZTp06sWLGCxYsXExsbC8Dhw4dJSEhwr//aa69RUFDAuHHjiImJcf/ce++9dr0FEREREfFxDktNDKUSsiyLG2+8kblz5wLQvXt3li9fTmBgoM2RVQ5fffUV/fv3x7IsAgICWL16NRdeeKHdYYlUK0eOHKFRo0Y4nU6aNGnC3r17q2XbnaJ89tln3HDDDeTk5ABw4YUX8vnnnxMdHW1zZCLyR+np6URGRpKWlqY2FyIiIiI+xFt/5+kbr1RKb7/9tjuJHBkZyezZs5VE/oO+ffvy4IMPAlBQUMCIESN0K6tIBZszZw5OpxMw1chKIp80aNAgvvrqK2rVqgWYNkUXX3wx+/btszcwERERERERKTN965VKZ9u2bdx1113ux//73/9o1qyZfQFVUpMnT+aiiy4CYPfu3YwdOxbdYCBScdTWong9e/Zk5cqVNGzYEIDffvuNnj17smXLFpsjExERERERkbJQIlkqlezsbIYPH052djYAf/3rX7nuuutsjqpyCgwMZM6cOURGRgIwd+5c3n77bZujEqketmzZQnx8PABdu3alXbt2NkdUOXXo0IE1a9bQtm1bAA4ePEivXr1YvXq1zZGJiIiIiIhIaSmRLJXK/fffz08//QRA+/bteeGFF2yOqHJr1qwZb7zxhvvxXXfdxbZt22yMSKR6eO+999z/HjVqlI2RVH6xsbGsWrWKrl27AnD8+HH69u3LokWLbI5MRERERERESkOJZKk05s+fz/Tp0wGoUaMG8+bNo0aNGjZHVfldf/313HHHHcDJiu7CCa5ExPOcTifvv/8+AAEBAQwfPtzmiCq/6OhovvnmG/r37w9ATk4OQ4YM4Z133rE3MBERERERESkxJZKlUkhISOC2225zP546dSrt27e3MaKq5YUXXnD///rpp5+4//77bY5IxHd9++23HDp0CIArrriC6OhomyOqGsLDw/nss88YMWIEYBLyo0eP5tlnn7U5MhERERERESkJJZLFdgUFBdx4440cO3YMMBW2Y8aMsTmqqiU0NJS5c+cSEhICwCuvvML8+fNtjkrEN/1xkj21tSidoKAg3n//fe655x73sn/84x/cf//9uFwuGyMTERERERGRs1EiWWw3efJk98RLsbGxvP766zgcDpujqno6dOjA1KlT3Y9vu+02EhIS7AtIxAdlZmby8ccfA1CrVi2uuuoqmyOqevz8/Jg6dSr/+c9/3MumTJnCrbfeSn5+vo2RiYiIiIiISHGUSBZbffvttzzxxBMA+Pv7M2fOHGrVqmVvUFXYHXfcwdChQwE4duwYN954IwUFBTZHJeI7PvnkE06cOAHAsGHDCA4OtjmiqsnhcPDwww/zxhtv4Odn/hR57733GDJkCFlZWTZHJyIiIiIiIkVRIllsk5yczMiRI7EsC4B///vfdO/e3eaoqjaHw8Ebb7xBbGwsAKtXr2by5Mk2RyXiO9577z33v9XWovxuv/12Pv74Y3dCfvHixfTt25fU1FSbIxMREREREZE/c1iFWTyRCmRZFoMGDeLzzz8HoG/fvixZssRdmSbls3btWnr16oXT6cThcPD1119z6aWX2h2WSJV24MABmjZtimVZtGzZkh07dqgNj4csX76cwYMHk56eDsC5557LkiVLaNy4sc2Rifi29PR0IiMjSUtLIyIiwu5wRERERMRDvPV3nrJ2YosXX3zRnUSuW7cuM2fOVBLZg7p3786///1vwCTtR44cSXJyss1RiVRts2bNct9BcfPNNyuJ7EG9e/dmxYoVNGjQAICtW7fSo0cPtm3bZnNkIiIiIiIiUkiZO6lwGzdu5B//+If78cyZM4mJibExIt/04IMPctlllwFw6NAhRo8ejW5AECkby7KYOXOm+/HNN99sYzS+qWPHjqxevZpWrVoBsH//fi6++GJ++OEHmyMTERERERERUCJZKlhGRgbDhw8nPz8fgPvvv58BAwbYHJVv8vPz47333qNu3boALFq0iJdeesnmqESqpvj4eLZu3QrAxRdfTIsWLWyOyDe1aNGCVatWccEFFwCQmppKnz59+OKLL2yOTERERERERJRIlgo1btw4duzYAUDXrl35z3/+Y3NEvi0mJoZ3333X/fiBBx5g48aNNkYkUjX9sRpZk+x5V/369fnuu+/o06cPACdOnGDw4MHMmjXL5shERERERESqNyWSpcLMnDmT9957D4CaNWsyZ84cgoKCbI7K9w0cOJC///3vAOTn5zN8+HAyMjJsjkqk6sjPz2fOnDkABAcHc/3119scke+LiIhg8eLF7v/XBQUFjBw5khdeeMHmyERERERERKovJZKlQmzfvp0777zT/fi1116jZcuWNkZUvfz3v/+lS5cuAOzYsYNx48bZHJFI1bF06VKSkpIAGDx4MLVq1bI3oGoiODiYOXPmnPLZMWHCBCZOnKh+7yIiIiIiIjZQIlm8Ljc3l+HDh5OVlQXA//3f/zFixAibo6pegoKCmDt3LjVr1gTgvffec1eHi0jx1NbCPv7+/rz88ss89thj7mVPP/00t99+OwUFBfYFJiIiIiIiUg05LJX1iJeNHz+eqVOnAtCuXTvWr19PWFiYvUFVU7Nnz+amm24CICwsjI0bN9KmTRuboxKpvI4fP06DBg3Izc2lbt26HDx4kMDAQLvDqpZmzJjBnXfe6a5GHjx4MHPnzqVGjRo2RyZSdaWnpxMZGUlaWhoRERF2hyMiIiIiHuKtv/NUkSxetWjRIncSOTg4mLlz5yqJbKMbb7yR0aNHA5CVlcXw4cPJzc21OSqRyuujjz5y/46MGDFCSWQbjR07lg8++MDdW3/hwoX079+fY8eO2RyZiIiIiIhI9aBEsnjNwYMHufXWW92Pp0yZQseOHe0LSACYNm0abdu2BSA+Pp6JEyfaHJFI5aW2FpXLddddxxdffOFu07Nq1Sr+8pe/cOjQIZsjExERERER8X1KJItXOJ1ObrrpJlJSUgAYMmTIKRMmiX3CwsKYO3euu6pv6tSpLFq0yOaoRCqfPXv2sHLlSgDOOecc4uLibI5IAPr06cN3331HvXr1ANiyZQs9evRg+/btNkcmIiIiIiLi25RIFq/473//y/LlywFo0qQJb775Jg6Hw+aopFCnTp2YMmWK+/Gtt97KwYMHbYxIpPJ5//333f8eNWqUrmGVSFxcHKtXr6Z58+YA7Nu3j549e7J+/XqbIxMREREREfFdmmxPAHC54MAB2LULMjMhPx8cDggKgqgoaNkS6tUzy85m5cqVXHLJJbhcLvz8/Pjuu+/o1auX99+ElIplWVxzzTV8+umnAFxyySV89dVX+Pv7n/W1eXmwZw8kJEB2tjlfAgIgMBAaNIBWrUBz9khllJYGO3fCkSPmPHY6zXlbowbExkKzZua6Z1kWbdq0YefOnTgcDvbt20eTJk3sDl/+5PDhwwwcOJDNmzcDEB4ezieffEK/fv3KvW3LgsRE87mYlmbOF8sy50fNmtCiBTRqBH4akpcqTJPtiYiIiPgmb/2dp0RyNbZzJ3z3HfzyC2zZAseOwYkTJqlcmDC2LJMgDAuDunXh/POhQwfo29cklv8sNTWVTp06sX//fgAef/xxJk2aVHFvSkolJSWFTp06ceDAAQAmT57Mo48+etp6LhesWwdr15rz5bffzIDDiRPm+T+eL8HBEBoKTZtCx47QqRNccolZJlLRMjPh229h82bzc+AAZGWZpOAfz1sw52jNmtC2LYSH7+WFF64DNnLZZX346quvbHsPUry0tDQGDx7MihUrAAgMDGTmzJkMHz681Ns6fBi+/tpc5zZvhuRkc744naeeL35+5nMxKsp8JrZvD336wO8F0iJVhhLJIiIiIr5JiWTxiLw8WLkSFiyA77+HjAzzhTg01HwpDg01ieM/v+bECfNlOjvbfImuXRv69YPBgyEuznzBtiyLa6+9lgULFgDQu3dvvv766xJVuIp9/lxBvnz5ci6++GIAjh+HpUvhk09g2zbIzTXVm4XnS40ap1bjWZZZJyvL/OTmmucbN4YhQ+CKK0zFp4i37doFixbBwoUmOehyQUjIyetccPCpd1i4XOY6V3itO3gwkePHE4EtjBvXiP/+91JV2VdiOTk5jBgxwv3543A4ePHFF7n77rvP+lqXC9avh88+g6++Mtc9MOdJ4bUuMPDU1xQUnDxXCgdgIyKgZ09zrevR4/TXiFRGSiSLiIiI+CYlkqVcLMtUH7/4oqlEdjqhTh2oVatk7Sr+yOmE1FTzZTskBC64AP7xD/j22+mMGzcOgDp16rB582YaNWrk6bciXvD444/z2GOPAaan9Y8/bmLJktq8/bZpAeDvbyrQw8JKv+28PDh61CRcIiNhwAC45x6IjvbsexABc75OnWoSgunpEB5uzrXf55YsEZfLydKlyygoCMLhaEDr1m1p1Mif226DYcNOH2yTyqGgoIA777yTN954w73sn//8J5MnTz5jf+uffoLnnjP/zckxFca1a5e+XYXLZT4TU1PN+dG6NUyYAL+PyYlUWkoki4iIiPgmJZKlzFJT4aWXTBVyfj40bGgSwOVlWea28cRECArKYtu2+ykoeAvI47PPPuOqq64q/06kQjidTi677LLfJ0hsQ9OmrxAaehk1ajioX98ziTOXy7RPSUkxvUUnTDAtUjR/mXiCZcEXX5gk8r59JnkcFVW28+vw4cNs2GAmbWvUqDEdOlzg7qncsydMnGjOYal8LMti0qRJPPHEE+5ld9xxB9OnTz/l7picHHjrLXj3XdP/OCbGDDp4Qna2qYIPDobrroNx48wgmkhlpESyiIiIiG9SIlnKZPVqePJJ2LHD9DiOivL8PgoKCvj2223k5voD67jllv28884/Pb8j8aqEhAOce+5/ycq6CahDmzZhtGnj+cnFCgpg/36TnB40CO6/X0kWKZ/UVHj6aZNIBtNKpTwdddatW8eRI4kAdOt2EXXr1gUKW16Y6vxx40x1sgZCKqdp06Zx7733UvgnzjXXXMPs2bMJCQnh119h8mTYuNG0oyjpRLKllZpqBs7atoWHH4Zu3Ty/D5HyUiJZRERExDd56+88zTXuwxYtMlWfu3eb6jlvJJEBfvllC7m5e4GDBAf35sCBh/npJ+/sS7yjoADmzGlMnTpPAcHADnbu/In09DSP7ysgwExIVbMmfPAB3HefmdBKpCyOHIF77zV3XERFQWxs+ZLIeXl5JCUlARAcHEL0H3qwhIVBq1Ymofyf/5hWQU5nOd+AeMXdd9/N7NmzCfy9UfH8+fMZMGAAy5dnMG6cSSI3aQL163tvMKB2bdMTfscOGD8eli3zzn5EREREREQqiiqSfdTChabiKj/ffFn+8xflTZtu5cCBd8u9n8jIy0lLM32R/f396dnzLyQlhRMTAy+8AB07lnsX4mVOJzz1FMyaZRJxhw5tYe/ePQCEhYXzl7/0wt/fO01hc3IgIQG6dDHtV+rU8cpuxEclJcHdd8OmTSaBHBxslp/t+uZwBOLnF4ifXzCBgVEEBkYREtKEsLAW5OU15sCBSKAuLVu25Jxzzi1yG8nJpgfzrbeaqnpVJldOy5Yt45prriErKwvoTFjYDBo27Ejr1oGl7oNcVpZlrnMhIeZzecCAitmvSEmoIllERETEN3nr7zxNGeSDvv4annjCVJkWlUT2pLS0kxWr5513HhER4YSHmyroBx6Al1+GNm28t38pH8syCdzZs09Ovlir1jmkpqaQnp5OVlYmW7b8QsciRgQyM3eQmrqSY8dWk57+E/n5qeTnp1JQkIGfXw0CAsIICqpPWFgrwsPbEhXVg6iongQFnSyNDwkxCcD1682EjVOnmkplkbM5ftwkcDdtMlWfpZlMz7LycTrzcTpPkJ9/DIC0tA1/WMMBtCUoaCKW1Q6H4/SMY3S0mZDt3XdNpfLf/qZkcmXUr18/vv32W/r3H8/x44+TlRXKgQMrady4G2ElmD3UE9c5hwOaNjUtfR5/3JwvvXp5812LiIiIiIh4hyqSfcz+/XDLLXD0qEnQnSmx4amKZOgGPEKjRo254IIL3EudTti1Czp3NhMaeWJyP/G8r74yybgaNUxirFBmZiYrV67A+ft9+3FxcTRs2AjLcnHkyCL27HmBlJTvyrBHP+rU6U3DhiOIjR3jXpqdDQcOmHP3oYfK957E91kWPPYYzJ17aiVyIc9d36BWrW506vQO4eHtinz+6FEzCd+LLyo5WFllZcHQoRl8/fURCgp+BSyCgoLp1q0bkUU0aPfWdc6yYM8eaNQIZs6EBg3K/JZEPEYVySIiIiK+ST2S5axcLnjuOTMZVNOmFVcdFxoaxnnnnXfKMn9/E8PGjSaRLJVPcjI8/7ypXP9jEhkgPDycDh1OHtPNm38iNfVX1q7tzfr1V5cxuQLgIiXlW37++Q5crgL30ho1TD/Rjz4yE0SKFOfbb+HTT80Eon9OInva8eM/sGpVN44e/arI5+vWhdxcePZZUyUtlc/rr8PevTXp0aMRNWuGA5CXl8uaNWtI/lOD9uzsBK9d5xwOM/Cxb5+59moYX0REREREqhq1tvAhCxaYthYxMZSr92PLlv8gMPDMM/NlZWWyf//+3x81pnPnOAICTj+VQkIgIsLc+n3xxXD++WWPSTzLskwF5a5dZiLGojRp0pjk5KMcPHgQp/NH1q4dhmVleC2m2rVNS5Rnn4UOHaCIQkERUlNhyhQzAFKWCURPXt8s8vOPk5ubxPHjP5CZaSpVi1JQkM769UPo0WMlkZEXnPZ8kybw22/wyivw8MNqcVGZrFtnWvfUqgURETXo0aMHP/64jmPHUnE6C/jhhx+Ii7uAmJiGJCUtIT5+hLvdiTf4+5tK5C+/hN694corvbYrERERERERj1Mi2UckJZl+xP7+5e8xGxv7N0JDmxX5XG5uDsuXrwByATj33PZERtY647bq1YOdO+Hpp01CuYh8s9hg5Ur47DNzfM58TBycd975JCevJTf3SSwrt5gtOqhZswO1anUjOLgufn41yM9PJTf3CGlpG8nK2s6ZknTuLfzeR3TbNnjzTZgwoYxvTnzajBnmmnKmAZCzOdP17eDB9cTHPwIso6hz1enMYt26QfzlL5sICjq1hD8w0FT1f/wx9O0L3bqVLTbxrLw8MzCVmQktW5plgYFBXHTRRWzYsIGkpCNYlosNGzbQsuUP7N07EqfzRDFbLP91DswA67FjZjCve3cziCYiIiIiIlIVKK3nIz7/HA4dKntypSQsyyI+Pp68PJNQrFevPi1aNC/2NQ6H6Qf588/www/Qs6f34pOS++ADczt+kybFr1dQcBSn818UDhyczkHTpmNo1ephQkNjz7idvLxjHDmygEOH5nL0aNGJOjAJuYgIU11/661KsMipkpJg0SJTXerpQank5ADgLqArDsdzRQ6c5OQcZOfOJzn33CmnPVe7tklwf/SREsmVxcqVsHWr+Qz6Y5W4v78/Xbt2YfPmnzhwYD+Qyq5d9wBnSiJ79joHJqa9e01l8o03luHNiYiIiIiI2EA9kn1AXh588onpFerv77397Nq1y91PMjg4hE6dOgJnv4c7NNTEuHCh92KTktuxwyT1/9wXuSjbt/+LgoKUIp/z86tBly7zOf/814pNrgAEBUXRpMlounVbQu/eW2jY8EYcZ7j/PzraTGC2dOnZ45Pq5YsvTGuLOnU8u12n08mhQ4cBCAjoSfv208647t6908nJOVzkc7Vrw4oVpgeu2G/BAjPxa40apz/ncPjRqVNHWrZsCcwG0ovchreucwEB5ueTT0ybFhERERERkapAiWQfsGqVmQm+Xj3v7ePYsWP8+uuv7scXXHABQUEln+Wqdm347jtwt1YW2yxaBOnpZ+9BnJm5g/37zzxTYocOL9OgwdWl3n/NmucSFzcLh6PoUY8/JliczlJvXnxUfr45J4KCPD9gduRIIk6nyebFxMTQrNkYIiPjilzX5cohMXF+kc/VqmUm3Fu82LPxSent2AHff3+2QQcHTZoEAl+fcY3zznvFK9c5MJ/Z27ebgT0REREREZGqQIlkH7BokaloCgnxzvbz8/PZuHEjhbfptm7dmuiSlLP+QVQUpKXBkiVeCFBK7MQJc76Eh599QrBDh+ZiWWfK5J5LXt5fPB5fobp1zeRlGzZ4bRdSxfzwg5mM0RsDZvv3H3D/u3Fj0+8lNvbOM65/9GjRFzI/P3MHxoIF5i4Msc/SpWbArFat4tc7dGguUPR1LiqqJ02ajPZ4bIUK79ZZtMhruxAREREREfEo9Uiu4vLzIT7e9JX1lG++Kb7v8Y4d5gfgqqvOPrEQmASLvz9s2lTO4KRcdu6ElJSStbU4evTLYp69il9//ZU6deoQFRXlsfgKhYXB4cOmv+mFF3p881IF/fKLGTArqk1BeeTm5nD06FEAatSoQe3fG3PXqXPJGV9z7NjaMz5Xq5ZpzbJnD7Rt68lIpTTWrzfV62cbMCvuOpeT04/c3FyCg0t+901phYebATOXy3xOioiIiIiIVGb62lLF7dtnqq7CwuyO5OzCwkxiUP0g7bNrl5lk72zV6y5XHsePn+l+awfQEbDYuHEj+fn5Ho7ypN9+89qmpYrZuvXsScGyOHjwEIV3WzRq1Njd0zYsrCUOR2CRr8nLS8GyXEU+FxoK2dnmd03skZNjBjvP9rl4tutcdnZrVq9ezYkTZ5qEr/zCwuDYMTh40Gu7EBERERER8RhVJFdxO3eadgUNGtgdydmFhpr2FgkJ0KKF3dFUT9u3g2WdPSGXm3v0jG0tatRoSkhILMeOpZKdfYKffvqJzp3jKJx4MTFxIZmZW0sUT6tWE8/4XGgobN5csnjFtzmdsGWLdwbMDhw42bi9cePGpzwXFFSb3NwjRbzKRV5eCsHBdU97prCqtPCuDal4u3dDVpZpqVSc4q5zUA+oyYkTWaxevYpu3S4i4g+3/njyOnf0qPksb9KkRJsTERERERGxjRLJVVxh1VtVuCU2NBSOHDFfmJVItsfPP5vbvc8mL+/oGZ8LCqpDXNwFLF++goKCfA4fPkRCQjRNm8YCcOjQbA4dmleieM6WYElOhsREiIkp0ebER+3fbwahPJ1ITk9PJz09HYBataIIDw8/5fkzVR0D7srlovj7m8S32GPXLlOVfLY2KMVd52rWjMGywsnMzCQ3N5c1a9bQtWtX6vw+e5+nrnMBAWawbOdOuPTSEm1ORERERETENkokV3HJyeZLqCe1bPkPUlJyOH78OABBQcE0a9YMv3Jmq/39TWVpSooHgpQySUyEkrT7dLmyz/icn18INWqE0rFjRzZsWA/Ali2/EBVVm5o1a3oqVIKDISMDUlOVSK7uUlJMS5bfc3gec/DgHyfZa3za8/n5x87wSj8CA2ufcbvBweZ3TeyRkmI+a872kVXcdS4gIJSuXXvy448/cvz4MQoK8vnhh++Ji+tMAw/fAmRZ5jonIiIiIiJS2SmRXMWdOOH5auRGjW4nMXE/kIWfnz89evQiPNxzCcK8PI9tSkrBskwyriTnS2DgmTN2BQVpAMTExBAb24x9+/bicjk5ejTJo4lkPz8zAZXOF8nP906Lk5QUk71zOPxo2LDhKc9lZm7Hsopu6B4UFI3DceZfJD8/87sm9ijpNeNs17mgoCC6d7+I9es3cPRoEi6Xi4MHD3glkZyT49FNioiIiIiIeIUSyVWc68x3XpdZQEAgvXr9hS1bfqZ27ToeTSKDd2KWkilpMi4o6PTer4Wysw9gWRYOh4Nzzz2XzMxMWrRoQf369T0YqYnTsnS+iDkHvJFI7tmzJ6mpKaSnZxD0p54vKSnfnvF1UVEXFbtdh8P0dRZ7lPT/fUmuc/7+AXTt2pXNmzeTnZ3NBRdc4KEoT3I4dJ0TEREREZGqQYnkKq5GDc+3tgAICAigU6cLAM9vvCQ9esXzHA4IDCxZtV5QUBRBQfXIy0s67bmCgjQyM7dSs2Z7/P396d79Igon2gOIi5tLXNxc9+PffnuMHTseL3W8Lpep7NT5IoGBJyvUPcnhcFCnTjR16kSf9tzevdPP+Lq6dfsXu12Xq2QtZMQ7Svr/vqTXOT8/Pzp16oTL5cTPzx/w3HWuUEhImV8qIiIiIiJSYarAFG1SnPBw7ySST/JcCaBlmZ/QUI9tUkopIgIKir5b/zTFJctOnWTKw2WivysoMBNR6XyRsDDTY72k52557d07nYyMn4p8zs8vhAYNrin29QUF4MEuL1JKoaEnP2/OpqTXOYfDgb+/d8beHY6zTwwoIiIiIiJSGSiRXMU1a3ayBUBll5NjqkubN7c7kuqrfXvIPvP8UqeoV+/KMz63b9+r5OWdaSIyz8jKMgMlTZp4dTdSBcTGmmTyiRPe39ehQx+xdev4YmL5GyEhDc/4PJhrXfv2no5MSqp5czMIlZ9/9nXtvs4Vtm3R56KIiIiIiFQFSiRXca1bm9u+q8LETidOmGRQy5Z2R1J9tW1b8nVjYoYSGtqiyOfy8pLZvPlWLMt7jT1PnDDxqrWFFF43srK8t4/MzB1s3nwbGzdej8tVdP+X4OCGtGr1ULHbKezl3KaNN6KUkmjVypwzJTlf7L7O5eSYauRWrby2CxEREREREY9Rj+Qq7o9fmCt7j8WsLFOlp1u+7dOqlek1m59vBiCK4+cXSNu2/yY+/qYinz9yZCHr1l1Np07vEhRU2+OxOp3QoYPHNytVVIcOsG5d+bezb9+rBAZGARb5+Wnk5h7h+PEfyMzcVuzr/P3DuPDCzwgOPvMEbWB6kAcEKDFop9q1oUED2LcPoqKKX9fu61xWlgZYRURERESk6lAiuYqrWdO0t9iyBerUsTua4uXmwvnn2x1F9da6tWkXkZFhki1n07DhCI4cWcShQ3OKfD4paRHffNOcpk3voF69K4iIOJ/AwCgsy0V+/jHS0jaSmrqy1HEWFJiqztatS/1S8VGFFb5Op+mXXFa7dj1T6tcEBETQufPHREbGnXXdjAzzO6ZEsn0cDujUCX79tWTr23WdA8jMhAsvrPwDwSIiIiIiIqBEsk8YOBDi48ufYPGmrCwIDoZLLrE7kuqtXj246CJYurRkiWSHw0HHjm+Tk3OQ1NQVRa5TUJDO7t3PsXv3c78v8QOs33/KJjnZxNqjR5k3IT7m4ovNOZuSYs6NilKr1kV06vQO4eEl6wtz/DgMHgy1ank1LDmLSy6Bjz82PeHPNpGdXde5ggLzuX355WXehIiIiIiISIVSj2QfMGCAqUZOSbE7kjM7ehTatYOuXe2ORIYMMQMOJZ10z98/mG7dvqBJk/8r4R5clCe5YlmQng5XXgmRkWXejPiY6GhzrUtLq5jJRaOiehAX9wE9e64ucRL5xAnTMubqq70cnJxVz56mXURSUsnWr+jrHJwcMOvfv1ybERERERERqTBKJPuAevVMRVNFJVhKq6DA/AwdWnkrpquTnj2hRQuT3C8pf/9QOnZ8k7i4uYSFlb/fRM2a53Puuc8X+Vx6umkNcNVV5d6N+JhBg0x1aWZm+bbjcPjj5xdCYGAtatRoRmRkHPXrX03z5uPp1Ok9LrtsHz17rqZhw+txOEr+MZmUZCaI7NatfPFJ+QUGwrXXmp7VTmfJXlOR17k/DpidrY+ziIiIiIhIZeGwrMqYepTS2rgRbr8dQkMr35fSAwfMbd6ffFL5Yquu3nkHnn4amjaFoKDSvdayXCQmLuDAgXdITV1Ffv6xs77G3z+UqKgeREdfRt26A4iM7HSGbcPOndC7N8yYYXqdihSyLBg9Gr7/3vQgrkznR26uudZNmgTDh9sdjYBJ7F97rbn7omHD0r3Wm9c5MHcQ5eXBu++aSWhF7JKenk5kZCRpaWlERETYHY6IiIiIeIi3/s5Tj2QfccEFpoJz3jwzAV9AJTmyWVkmwTJmjJLIlcl118Hnn5tJGlu2LF1CzuHwIybmWmJirsWyLDIytpCVtYP8/FTy8lKxrAICAsIJCKhJSEgjwsLaUqNGUxwl2MmRI6adxV13Va4koVQODgfcfTf88otpC1C3rt0RGZYFCQkQF6e2FpVJvXpw223wzDMl65X8R968zuXnm0TyLbcoiSwiIiIiIlWLKpJ9SEoKjBoF+/ZB8+Z2RwMul6kuvfRSePnlypPcFmPdOvjb38wt4NHRdkcDOTmwfz/ce6+JS+RMpk41FetNm5pJPO125Ij57xtvQMeO9sYip8rLg7/+FdaurRxV7JYFe/aYWGbOVB94sZ8qkkVERER8k7f+zlOPZB9Spw5MmGAStsfOfheu1x06BPXrwz/+oSRyZdS1K9x4ozlXcnPtjcXlMknkzp1N6wKR4owZA+efb6qA7R4KzcmBjAxTXaokcuUTFAQTJ0Lt2nD4sN3RQGqqGfx44AElkUVEREREpOpRItnH9Olj2hYkJ5vkhl2Skkxy8O67zcRuUjndcYe5HX/fPnO7tR0sC/buNYMODz8MISH2xCFVR1gYPPSQqaTft8++ZHJenklmd+sG//d/9sQgZ9e2Ldx5pzleycn2xZGebgbuRoyAiy+2Lw4REREREZGyUiLZxzgcpgJ40CBITLQnmXz0KJw4YdoTXHddxe9fSi48HKZMMX069+yp+GRyYRI5MhL+8x/1C5WSu+ACmDzZTDBqR2VyXp45dzt1gmefLV3/Xal4N91kJqRNTzdtoCpaerppgXLttXDffRW/fxEREREREU9QItkHBQXB44+byfcSE+H48YrZr2WZW4dPnDA9Kf/6V/v7UcrZxcSYnrPnnWeSyTk5FbNfp9Psr1Ytk0RWhZ6UVp8+JpkcFmbOJaezYvabnW3216kTvPBC5Zn0T86scKLG224zA6xHjlTc4ENqqrlL59pr4dFHTV96ERERERGRqkiJZB8VGmqSc8OHm0qovXu9m2TJzYVdu8DPz/R+HDdOSeSqpGlTMyHiRRfBgQNmAMKbSZb0dHO+NGoEzz1nJmQUKYvLL4dnnoEGDcw55c27MAoHyw4ehF69zO9Mw4be2594lp8fjB9vJvS0LHO+5OV5b38FBWbAISvLTIT7+ONq3SMiIiIiIlWbw7LsnqpIvMmyYMkSUzVX2IfWkxP8WJaptEpPN712H3xQE05VZTk5MHMmvPkmpKVBkyaeTXw4nSZRbVkwYICZHDImxnPbl+rr4EHTYuLrr03CsFEj8Pf33Pazs825W7u2aZEwcqS5+0OqpvXr4emn4eefzV0R0dGeHfw8dsy0eWrVylzn+vTR4KpUTt6azVtERERE7OWtv/OUSK4mkpJMMvmLL0z1cFSU+fErY016QYGZtCg93XwJHzXKTDalPqG+YcsWk2TZuNE8jo6GmjXLngjJyTHnYG6uSU7fey9ceaUSK+JZLhd8+ilMmwaHDplBkHr1IDi4bNuzLFPhXDhBW9euMHEitGvnuZjFPllZ8PrrMGeOGTiLjIQ6dSAgoGzbc7lMG4tjx8xn4aBB5lpXp45n4xbxJCWSRURERHyTEslSbpYFa9eaRMt335kvziEhJkEYFlZ830bLMrcAZ2WZ1zmdJkFz5ZUweLASK74oJweWLoVPPoGffjLHvmZNM0FfaGjx1Z6WZSo4T5wwPbr9/KB5c9Mj9KqrzLkj4i2HD8OiRTB/PuzbZ87HyEhznQsJKX4Aw+k0521mpkkih4ebif2uuQb69VMVsi/asgUWLjQDrcnJJpFceL4EBhZ/vuTnm2tjeroZKKtVCy67zHwuXnihBsuk8lMiWURERMQ3KZEsHrV/v/nS/NlnZtKhrCxTTQXmS3Thl1+XyyRWLMt8oQ4LMwnBwqRKVJR970EqhmWZRPKiRaZtQFqaSbSBOU/8/U2i2LLMueJymX+HhJxMwg0ZYnrKKgknFSknB1asMINn8fHmvM3NNc/5+Zlz1+E49ToHZqAkKspc4668Etq3V0KwOkhJMYNnCxaYAYisLHP3jWWZz8XCO3gsyywHsywszLToGTwYBg5U32ypWpRIFhEREfFNSiSLV1iWmVht1y7YuRO2bze35WZnmyRL4a3hbdqYXo8tW5oeoVI9FRRAQoI5X3bsMP/NzDTJucBAc77ExppzpVUraNHCJOVE7JaVZc7XwnN3/35znSsoMK0vatY017fWrc2527hx2VscSNWXmmrOk507zX+PHjUDEy6XaVsRFXXyc7FVK/M5qcEGqYqUSBYRERHxTUoki4iIiIiIxyiRLCIiIuKbvPV3XhmnWhMRERERERERERGR6kKJZBEREREREREREREplhLJIiIiIiIiIiIiIlIsJZJFREREREREREREpFhKJIuIiIiIiIiIiIhIsZRIFhEREREREREREZFiKZEsIiIiIiIiIiIiIsVSIllEREREREREREREiqVEsoiIiIiIiIiIiIgUS4lkERERERERERERESmWEskiIiIiIiIiIiIiUiwlkkVERERERERERESkWEoki4iIiIhUAtOnT6d58+aEhITQuXNnVq5cWez6y5cvp3PnzoSEhNCiRQtmzJhRQZGKiIiISHWkRLKIiIiIiM3mzZvHfffdxyOPPEJ8fDy9evVi4MCBJCQkFLn+nj17uOKKK+jVqxfx8fE8/PDD3HPPPXz88ccVHLmIiIiIVBcOy7Isu4MQEREREanOunXrRlxcHK+++qp72TnnnMOQIUN48sknT1v/wQcfZOHChWzbts29bOzYsWzevJm1a9eWaJ/p6elERkaSlpZGRERE+d+EiIiIiFQK3vo7L8BjWxIRERERkVLLy8tjw4YNTJw48ZTl/fv3Z82aNUW+Zu3atfTv3/+UZZdffjlvvvkm+fn5BAYGnvaa3NxccnNz3Y/T0tIA80VDRERERHxH4d93nq4fViJZRERERMRGycnJOJ1O6tevf8ry+vXrk5iYWORrEhMTi1y/oKCA5ORkYmJiTnvNk08+yeOPP37a8iZNmpQjehERERGprFJSUoiMjPTY9pRIFhERERGpBBwOxymPLcs6bdnZ1i9qeaGHHnqICRMmuB8fP36c2NhYEhISPPoFQyq/9PR0mjRpwv79+9XWpJrRsa+edNyrLx376istLY2mTZtSu3Ztj25XiWQRERERERtFR0fj7+9/WvVxUlLSaVXHhRo0aFDk+gEBAdSpU6fI1wQHBxMcHHza8sjISH25rKYiIiJ07KspHfvqSce9+tKxr778/Pw8uz2Pbk1EREREREolKCiIzp07s2zZslOWL1u2jB49ehT5mu7du5+2/tKlS+nSpUuR/ZFFRERERMpLiWQREREREZtNmDCB//3vf7z11lts27aN8ePHk5CQwNixYwHTlmLUqFHu9ceOHcu+ffuYMGEC27Zt46233uLNN9/k/vvvt+stiIiIiIiPU2sLERERERGbDRs2jJSUFCZPnszhw4fp0KEDixcvJjY2FoDDhw+TkJDgXr958+YsXryY8ePH88orr9CwYUNeeuklhg4dWuJ9BgcH869//avIdhfi23Tsqy8d++pJx7360rGvvrx17B1W4awcIiIiIiIiIiIiIiJFUGsLERERERERERERESmWEskiIiIiIiIiIiIiUiwlkkVERERERERERESkWEoki4iIiIiIiIiIiEixlEgWEREREfFR06dPp3nz5oSEhNC5c2dWrlxZ7PrLly+nc+fOhISE0KJFC2bMmFFBkYqnlebYf/LJJ/Tr14+6desSERFB9+7dWbJkSQVGK55S2t/5QqtXryYgIIBOnTp5N0DxmtIe+9zcXB555BFiY2MJDg6mZcuWvPXWWxUUrXhSaY/9rFmz6NixI6GhocTExDB69GhSUlIqKFrxhBUrVjBo0CAaNmyIw+FgwYIFZ32Np/7GUyJZRERERMQHzZs3j/vuu49HHnmE+Ph4evXqxcCBA0lISChy/T179nDFFVfQq1cv4uPjefjhh7nnnnv4+OOPKzhyKa/SHvsVK1bQr18/Fi9ezIYNG7j00ksZNGgQ8fHxFRy5lEdpj3uhtLQ0Ro0axWWXXVZBkYqnleXY33DDDXz99de8+eab/Pbbb8yZM4d27dpVYNTiCaU99qtWrWLUqFHcdttt/PLLL3z44YesW7eO22+/vYIjl/LIysqiY8eOvPzyyyVa35N/4zksy7JK/SoREREREanUunXrRlxcHK+++qp72TnnnMOQIUN48sknT1v/wQcfZOHChWzbts29bOzYsWzevJm1a9dWSMziGaU99kVp3749w4YNY9KkSd4KUzysrMd9+PDhtG7dGn9/fxYsWMCmTZsqIFrxpNIe+y+//JLhw4eze/duateuXZGhioeV9tg/99xzvPrqq+zatcu9bNq0aTzzzDPs37+/QmIWz3I4HMyfP58hQ4accR1P/o2nimQRERERER+Tl5fHhg0b6N+//ynL+/fvz5o1a4p8zdq1a09b//LLL2f9+vXk5+d7LVbxrLIc+z9zuVxkZGQowVSFlPW4v/322+zatYt//etf3g5RvKQsx37hwoV06dKFZ555hkaNGtGmTRvuv/9+srOzKyJk8ZCyHPsePXpw4MABFi9ejGVZHDlyhI8++ogrr7yyIkIWm3jyb7wATwYmIiIiIiL2S05Oxul0Ur9+/VOW169fn8TExCJfk5iYWOT6BQUFJCcnExMT47V4xXPKcuz/bMqUKWRlZXHDDTd4I0TxgrIc9x07djBx4kRWrlxJQIBSA1VVWY797t27WbVqFSEhIcyfP5/k5GTuvPNOUlNT1Se5CinLse/RowezZs1i2LBh5OTkUFBQwODBg5k2bVpFhCw28eTfeKpIFhERERHxUQ6H45THlmWdtuxs6xe1XCq/0h77QnPmzOGxxx5j3rx51KtXz1vhiZeU9Lg7nU5uvPFGHn/8cdq0aVNR4YkXleZ33uVy4XA4mDVrFhdeeCFXXHEFzz//PO+8846qkqug0hz7rVu3cs899zBp0iQ2bNjAl19+yZ49exg7dmxFhCo28tTfeBp2FBERERHxMdHR0fj7+59WkZSUlHRaRUqhBg0aFLl+QEAAderU8Vqs4lllOfaF5s2bx2233caHH35I3759vRmmeFhpj3tGRgbr168nPj6eu+66CzDJRcuyCAgIYOnSpfTp06dCYpfyKcvvfExMDI0aNSIyMtK97JxzzsGyLA4cOEDr1q29GrN4RlmO/ZNPPknPnj154IEHADj//PMJCwujV69ePPHEE7r7yEd58m88VSSLiIiIiPiYoKAgOnfuzLJly05ZvmzZMnr06FHka7p3737a+kuXLqVLly4EBgZ6LVbxrLIcezCVyLfeeiuzZ89Wr8wqqLTHPSIigp9//plNmza5f8aOHUvbtm3ZtGkT3bp1q6jQpZzK8jvfs2dPDh06RGZmpnvZ9u3b8fPzo3Hjxl6NVzynLMf+xIkT+Pmdmgr09/cHTlaoiu/x6N94loiIiIiI+Jy5c+dagYGB1ptvvmlt3brVuu+++6ywsDBr7969lmVZ1sSJE62bb77Zvf7u3but0NBQa/z48dbWrVutN9980woMDLQ++ugju96ClFFpj/3s2bOtgIAA65VXXrEOHz7s/jl+/Lhdb0HKoLTH/c/+9a9/WR07dqygaMWTSnvsMzIyrMaNG1vXXXed9csvv1jLly+3Wrdubd1+++12vQUpo9Ie+7ffftsKCAiwpk+fbu3atctatWqV1aVLF+vCCy+06y1IGWRkZFjx8fFWfHy8BVjPP/+8FR8fb+3bt8+yLO/+jafWFiIiIiIiPmjYsGGkpKQwefJkDh8+TIcOHVi8eDGxsbEAHD58mISEBPf6zZs3Z/HixYwfP55XXnmFhg0b8tJLLzF06FC73oKUUWmP/WuvvUZBQQHjxo1j3Lhx7uW33HIL77zzTkWHL2VU2uMuvqO0xz48PJxly5Zx991306VLF+rUqcMNN9zAE088YddbkDIq7bG/9dZbycjI4OWXX+bvf/87tWrVok+fPjz99NN2vQUpg/Xr13PppZe6H0+YMAE4+bntzb/xHJal2nUREREREREREREROTP1SBYRERERERERERGRYimRLCIiIiIiIiIiIiLFUiJZRERERERERERERIqlRLKIiIiIiIiIiIiIFEuJZBEREREREREREREplhLJIiIiIiIiIiIiIlIsJZJFREREREREREREpFhKJIuIiIiIiIiIiIhIsZRIFhEREREREREREZFiKZEsIiIiIiIiIiIiIsVSIllEREREREREREREiqVEsoiIiIiIiIiIiIgU6/8BzZrL+cUiVGAAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "### RUN THIS TO GENERATE ALL EXPLORE REEBS FROM EACH NODE FOR EACH PERSON ###\n", "# only do first 20 for tractability and space\n", "explore_df = df[~df[\"eprocs\"].str.contains(\"Test\")]\n", "for j, salient_node in enumerate(\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"):\n", - "\n", + " print(salient_node)\n", " count = 0\n", " for subj in explore_df[\"Subject\"].unique():\n", + " if os.path.exists('figures/explore/starting_{salient_node}/subj_{subj}-NOTURN.png'):\n", + " continue\n", + " # print(subj,)\n", " qs = test_df[test_df[\"Subject\"] == subj][\"quartile\"]\n", " if len(qs) > 0:\n", " q = qs.iloc[0]\n", " plot_reeb_explore(explore_df, subj, salient_node, q=q, save=True, use_turns=False)\n", " count += 1\n", - " if count == 20: break" + " # if count == 20: break" ] }, { diff --git a/mazedyn/reeb_utils.py b/mazedyn/reeb_utils.py new file mode 100644 index 0000000..c87e2c6 --- /dev/null +++ b/mazedyn/reeb_utils.py @@ -0,0 +1,310 @@ +"""Defines the the functions for generating Reeb trees.""" + +import matplotlib.pyplot as plt +import networkx as nx +from networkx.drawing.nx_pydot import graphviz_layout +import os +import pandas as pd +import pydot + + +def all_traj_same(trajs, which_trajs, max_traj_len): + """Determines whether any trajectory differs from the others. + + Helper function for generating Reeb tree. + + Parameters + ---------- + trajs : list, + List of all trajectories over which to compute the Reeb tree. + which_trajs : list, + Indices of the trajectories to consider. + max_traj_len : integer + Length of the longest path, for convenience. + + Returns + ------- + boolean + True if all trajectories are the same length, otherwise False. + """ + if len(set([len(trajs[t]) for t in which_trajs])) != 1: return False + + for i in range(max_traj_len): + if len(set([trajs[t][i] for t in which_trajs])) != 1: return False + return True + +def generate_reeb_tree(start_i, start_node, trajs, which_trajs, G, visited, SUCCESS="", DEBUG=False): + """Generates Reeb tree. + + TODO talk about what this means + + Parameters + ---------- + start_i : TODO, + TODO + start_node : TODO, + TODO + trajs : list, + Shape = [n_trajs, various] + List of all trajectories over which to compute the Reeb tree. + G : nx.Graph, + Reeb tree to mutate in place as graph is construted. + visited : TODO, + TODO + DEBUG : boolean, + If True print more things. + + Returns + ------- + None + """ + if DEBUG: print(f"DO REEB, {start_i}, {start_node}, {which_trajs}") + times_before = visited.get(start_node, 0) + visited[start_node] = times_before + to_add = start_node + "_" + str(times_before) + + max_traj_len = max([len(trajs[t]) for t in which_trajs]) + + ## BASE CASE 1: SINGLETON ## + ## BASE CASE 2: ALL THE SAME ## + if len(which_trajs) == 1 or all_traj_same(trajs, which_trajs, max_traj_len): + end_node = trajs[which_trajs[0]][-1] + times_before = visited.get(end_node, -1) + 1 + visited[end_node] = times_before + + end_node += "_" + str(times_before) + if DEBUG: print(f"BASE CASE: {which_trajs}, last: {end_node}") + + color = "green" if SUCCESS in end_node else "yellow" + G.add_node(end_node, color=color, end=True) + G.add_edge(to_add, end_node, weight=len(which_trajs)) + + return + + + ## RECURSIVE STEP ## + if DEBUG: print(f"FOR LOOP, {start_i}, {which_trajs}") + for step in range(start_i, max_traj_len): + + new_gn = {} + for i in which_trajs: + traj = trajs[i] + + ## STOPPING CONDITION FOR THIS ONE GUY ## + if step >= len(traj): + new_gn["DONE"] = new_gn.get("DONE", []).copy() + [i] + else: + new_node = traj[step] + new_gn[new_node] = new_gn.get(new_node, []).copy() + [i] + + if DEBUG: print("NEW_GN", new_gn) + # decide whether we need a reeb node + + # people went different ways!! + if len(new_gn.keys()) > 1: + times_before = visited.get(start_node, 0) + sn = start_node + "_" + str(times_before) + + # the thing happened at the node before this! all the paths in this fn are at the same point there so it doesnt mattter + old_one = trajs[which_trajs[0]][step-1] + times_before = visited.get(old_one, -1) + 1 + visited[old_one] = times_before + + on = old_one + "_" + str(times_before) + G.add_node(on, color="blue", end=False) + if DEBUG: print(f"ADDING NEW BLUE NODE {on}") + + G.add_edge(sn, on, weight=len(which_trajs)) + if DEBUG: print(f"ADDING NEW EDGE from {sn} to {on}, weight is {len(which_trajs)}") + + + for k, v in new_gn.items(): + if DEBUG: print("RECURSE") + if DEBUG and k == "DONE": + print("*@#)$*$#()*(#@($") + print(new_gn) + generate_reeb_tree(step+1, old_one, trajs, v, G, visited, SUCCESS=SUCCESS) + + return + +def collapse_traj(traj): + """ + Remove turns so that A A B B G G G becomes A B G. + + Parameters + ---------- + traj: list, + Trajectory through maze. + + Returns + ------- + new_traj: list, + Same trajectory through maze, no turns. + """ + new_traj = [traj[0]] + for t in traj: + if t != new_traj[-1]: + new_traj.append(t) + return new_traj + + +###################################### +## Specific generation functions ## +## for test and explore. ## +###################################### +def do_reeb_for_start_end(df, start_node, end_node, Q, use_turns=True): + this_df = df[(df["StartAt"] == start_node) & (df["EndAt"] == end_node) & (df["quartile"] == Q)] + + trajs = [] + count = 0 + for _, row in this_df.iterrows(): + path = row["e_paths"] if use_turns else row["paths"] + if path[-2:] == "NA": path = path[:-2] # this happens if the test ends unsuccessfully + + states = path.split() + if not use_turns: states = collapse_traj(states) + trajs.append(states) + count += 1 + # if count == 15: break + + visited = {} + + G = nx.Graph() + G.add_node(start_node + "_0", color="red", end=False) + + # print(start_node) + # for t in trajs: + # print(t) + generate_reeb_tree(0, start_node, trajs, [i for i in range(len(trajs))], G, visited, SUCCESS=end_node[0], DEBUG=False) + return G + +def do_reeb_for_explore_node(path, salient_node, use_turns=False): + + trajs = [] + if path[-2:] == "NA": path = path[:-2] # this happens if the test ends unsuccessfully + + states = path.split() + if not use_turns: states = collapse_traj(states) + for i, s in enumerate(states): + if salient_node in s: + trajs.append(states[i: i+10]) + + G = nx.Graph() + G.add_node(salient_node + "_0", color="red", end=False) + + if len(trajs) == 0: return G, 0 + + # for t in trajs: + # print(t) + visited = {} + generate_reeb_tree(0, salient_node, trajs, [i for i in range(len(trajs))], G, visited, SUCCESS="IMPOSSIBLE") + return G, len(trajs) + +###################################### +## Plotting functions ## +## for test and explore. ## +###################################### +def plot_treeb(G): + fig = plt.figure(figsize=(20,10)) + this_ax = plt.gca() + + pos = graphviz_layout(G, prog="dot") + + edges = G.edges() + # colors = [G[u][v]['color'] for u,v in edges] + weights = [G[u][v]['weight'] * .8 for u,v in edges] + + nodes = G.nodes() + # for n in nodes: + # print(n, G.nodes[n]) + n_labels = {n: n[:-2] for n in G} + n_colors = [G.nodes[n].get("color", "blue") for n in nodes] + # print(n_colors) + # print ((q-1) // 2, (q-1) % 2) + nx.draw_networkx_nodes(G, pos, node_color=n_colors, node_size=1000, alpha=0.7, ax=this_ax) + nx.draw_networkx_edges(G, pos, edgelist=edges, width=weights, ax=this_ax) + nx.draw_networkx_labels(G, pos, labels=n_labels, font_size=26, font_weight="bold", ax=this_ax) + + this_ax.axis("off") + +def plot_quartile_reebs_for_start_end(test_df, start_node, end_node, save=True, use_turns=True): + fig, axs = plt.subplots(2, 2, figsize=(40,20)) + for q in (1, 2, 3, 4): + this_ax = axs[(q-1) // 2][(q-1) % 2] + + G = do_reeb_for_start_end(test_df, start_node, end_node, q, use_turns=use_turns) + # print(G.nodes) + # print(G.edges) + # pos = nx.spectral_layout(G) + # pos = nx.shell_layout(G) + # pos = nx.spring_layout(G) + # pos = nx.planar_layout(G) + pos = graphviz_layout(G, prog="dot") + + edges = G.edges() + # colors = [G[u][v]['color'] for u,v in edges] + weights = [G[u][v]['weight'] * .8 for u,v in edges] + + nodes = G.nodes() + # for n in nodes: + # print(n, G.nodes[n]) + n_labels = {n: n[:-2] for n in G} + n_colors = [G.nodes[n].get("color", "blue") for n in nodes] + # print(n_colors) + # print ((q-1) // 2, (q-1) % 2) + nx.draw_networkx_nodes(G, pos, node_color=n_colors, node_size=1000, alpha=0.7, ax=this_ax) + nx.draw_networkx_edges(G, pos, edgelist=edges, width=weights, ax=this_ax) + nx.draw_networkx_labels(G, pos, labels=n_labels, font_size=26, font_weight="bold", ax=this_ax) + + + this_ax.axis("off") + this_ax.set_title(f"Q{q}", fontsize=30) + + fig.suptitle(f"{start_node} to {end_node}", fontsize=50) + + if save: + fig.savefig(f'figures/testpath-reeb-by-quartile/{start_node+end_node}_{"YES" if use_turns else "NO"}TURN.png') + plt.close() + else: + plt.show() + + +def plot_reeb_explore(df, person, salient_node, q="", save=True, use_turns=True): + fig, axs = plt.subplots(1, 2, figsize=(18, 6)) + # print(df[df["Subject"]==person]) + c = 0 + for i, person_info in df[df["Subject"]==person].iterrows(): + ax = axs[c] + c += 1 + + path = person_info["e_paths"] if use_turns else person_info["paths"] + + G, num_trajs = do_reeb_for_explore_node(path, salient_node, use_turns=use_turns) + pos = graphviz_layout(G, prog="dot") + + edges = G.edges() + # colors = [G[u][v]['color'] for u,v in edges] + weights = [G[u][v]['weight'] * 2 for u,v in edges] + + nodes = G.nodes() + # for n in nodes: + # print(n, G.nodes[n]) + n_labels = {n: n[:-2] for n in G} + n_colors = ["blue" for n in nodes] + # print(n_colors) + + nx.draw_networkx_nodes(G, pos, node_color=n_colors, node_size=1000, alpha=0.7, ax=ax) + nx.draw_networkx_edges(G, pos, edgelist=edges, width=weights, ax=ax) + nx.draw_networkx_labels(G, pos, labels=n_labels, font_size=26, font_weight="bold", ax=ax) + + ax.axis("off") + ax.set_title(f"{num_trajs} trajs") + + fig.suptitle(f"Explore paths starting at {salient_node} for person {person} (Q{q})", fontsize=30) + + if save: + os.makedirs(f'figures/explore/starting_{salient_node}/', exist_ok=True) + fig.savefig(f'figures/explore/starting_{salient_node}/subj_{person}-{"YES" if use_turns else "NO"}TURN.png') + plt.close() + else: + plt.show() \ No newline at end of file diff --git a/mazedyn/treeb_embeddings.ipynb b/mazedyn/treeb_embeddings.ipynb new file mode 100644 index 0000000..14a8a83 --- /dev/null +++ b/mazedyn/treeb_embeddings.ipynb @@ -0,0 +1,441 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Graph embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "from networkx.drawing.nx_pydot import graphviz_layout\n", + "import os\n", + "import pandas as pd\n", + "import pydot\n", + "\n", + "from reeb_utils import do_reeb_for_start_end, do_reeb_for_explore_node, plot_treeb\n", + "\n", + "pd.options.mode.chained_assignment = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
datestimesXSubjecteprocsTask_typeProcedureSampleobjlistPairList...Press.S.LPress.S.M.Lfmv_durationpath_dist_traveuc_dist_travpath_efficienciespath_efficiencies_acc_onlysubj_mean_accpath_mean_accquartile
22017-02-0715:29:2332Test_Trials_B_1-002-1B1TrialProc1.020.08.0...SM1618.031.2510.8541472.551020NaN0.9166670.6865673
32017-02-0715:29:2342Test_Trials_B_1-002-1B1TrialProc2.061.07.0...SS1051.023.0011.4509821.000000NaN0.9166670.6714293
42017-02-0715:29:2352Test_Trials_B_1-002-1B1TrialProc3.067.01.0...LM1051.022.502.2638461.0000001.0000000.9166670.5571433
52017-02-0715:29:2362Test_Trials_B_1-002-1B1TrialRevProc4.021.02.0...LL1051.031.509.0138781.2352941.2352940.9166670.6376813
62017-02-0715:29:2372Test_Trials_B_1-002-1B1TrialRevProc5.058.03.0...LL1051.023.0014.7054411.0000001.0000000.9166670.7164183
\n", + "

5 rows Ɨ 58 columns

\n", + "
" + ], + "text/plain": [ + " dates times X Subject eprocs Task_type \\\n", + "2 2017-02-07 15:29:23 3 2 Test_Trials_B_1-002-1 B1 \n", + "3 2017-02-07 15:29:23 4 2 Test_Trials_B_1-002-1 B1 \n", + "4 2017-02-07 15:29:23 5 2 Test_Trials_B_1-002-1 B1 \n", + "5 2017-02-07 15:29:23 6 2 Test_Trials_B_1-002-1 B1 \n", + "6 2017-02-07 15:29:23 7 2 Test_Trials_B_1-002-1 B1 \n", + "\n", + " Procedure Sample objlist PairList ... Press.S.L Press.S.M.L \\\n", + "2 TrialProc 1.0 20.0 8.0 ... S M \n", + "3 TrialProc 2.0 61.0 7.0 ... S S \n", + "4 TrialProc 3.0 67.0 1.0 ... L M \n", + "5 TrialRevProc 4.0 21.0 2.0 ... L L \n", + "6 TrialRevProc 5.0 58.0 3.0 ... L L \n", + "\n", + " fmv_duration path_dist_trav euc_dist_trav path_efficiencies \\\n", + "2 1618.0 31.25 10.854147 2.551020 \n", + "3 1051.0 23.00 11.450982 1.000000 \n", + "4 1051.0 22.50 2.263846 1.000000 \n", + "5 1051.0 31.50 9.013878 1.235294 \n", + "6 1051.0 23.00 14.705441 1.000000 \n", + "\n", + " path_efficiencies_acc_only subj_mean_acc path_mean_acc quartile \n", + "2 NaN 0.916667 0.686567 3 \n", + "3 NaN 0.916667 0.671429 3 \n", + "4 1.000000 0.916667 0.557143 3 \n", + "5 1.235294 0.916667 0.637681 3 \n", + "6 1.000000 0.916667 0.716418 3 \n", + "\n", + "[5 rows x 58 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# load data into dataframe\n", + "df = pd.read_csv(\"data/MLINDIV_train_full.csv\")\n", + "\n", + "test_df = df[df[\"eprocs\"].str.contains(\"Test\")]\n", + "\n", + "test_df[\"subj_mean_acc\"] = test_df.groupby(\"Subject\")[\"accuracy\"].transform(\"mean\")\n", + "test_df[\"path_mean_acc\"] = test_df.groupby([\"StartAt\", \"EndAt\"])[\"accuracy\"].transform(\"mean\")\n", + "\n", + "test_df['quartile'] = (\n", + " pd.qcut(test_df['subj_mean_acc'], 4, labels=[1, 2, 3, 4])\n", + ")\n", + "\n", + "test_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Let's make a graph to use!" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "G = do_reeb_for_start_end(test_df, \"A\", \"W\", 1, use_turns=True)\n", + "plot_treeb(G)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# JK let's make ten of them!\n", + "treebs = []\n", + "count = 0\n", + "for start_node, end_node in test_df.groupby([\"StartAt\", \"EndAt\"]).groups.keys():\n", + " G = do_reeb_for_start_end(test_df, start_node, end_node, 1, use_turns=True)\n", + " treebs.append(G)\n", + " count += 1\n", + " if count == 10: break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Graph embeddings\n", + "\n", + "https://karateclub.readthedocs.io/en/latest/modules/root.html#whole-graph-embedding" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### WaveletCharacteristic\n", + "class WaveletCharacteristic(order: int = 5, eval_points: int = 25, theta_max: float = 2.5, tau: float = 1.0, pooling: str = 'mean')\n", + "An implementation of ā€œWaveCharacteristicā€ from the CIKM ā€˜21 paper ā€œGraph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterizationā€. The procedure uses characteristic functions of node features with wavelet function weights to describe node neighborhoods. These node level features are pooled by mean pooling to create graph level statistics.\n", + "\n", + "Parameters:\t\n", + "order (int) – Adjacency matrix powers. Default is 5.\n", + "eval_points (int) – Number of characteristic function evaluations. Default is 5.\n", + "theta_max (float) – Largest characteristic function time value. Default is 2.5.\n", + "tau (float) – Wave function heat - time diffusion. Default is 1.0.\n", + "pooling (str) – Pooling function appliead to the characteristic functions. Default is ā€œmeanā€." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### FeatherGraph\n", + "class FeatherGraph(order: int = 5, eval_points: int = 25, theta_max: float = 2.5, seed: int = 42, pooling: str = 'mean')[source]\n", + "An implementation of ā€œFEATHER-Gā€ from the CIKM ā€˜20 paper ā€œCharacteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Modelsā€. The procedure uses characteristic functions of node features with random walk weights to describe node neighborhoods. These node level features are pooled by mean pooling to create graph level statistics." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'numpy.strings'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkarateclub\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FeatherGraph\n\u001b[1;32m 3\u001b[0m model \u001b[38;5;241m=\u001b[39m FeatherGraph()\n\u001b[1;32m 4\u001b[0m model\u001b[38;5;241m.\u001b[39mfit(graphs)\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/karateclub/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkarateclub\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcommunity_detection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkarateclub\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnode_embedding\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkarateclub\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgraph_embedding\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/karateclub/community_detection/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moverlapping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnon_overlapping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m \n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/karateclub/community_detection/overlapping/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdanmf\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DANMF\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmnmf\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MNMF\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mego_splitter\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m EgoNetSplitter\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/karateclub/community_detection/overlapping/danmf.py:4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnetworkx\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnx\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m List, Dict\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdecomposition\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m NMF\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkarateclub\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mestimator\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Estimator\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mDANMF\u001b[39;00m(Estimator):\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/sklearn/__init__.py:87\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;66;03m# We are not importing the rest of scikit-learn during the build\u001b[39;00m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;66;03m# process, as it may not be compiled yet\u001b[39;00m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[38;5;66;03m# later is linked to the OpenMP runtime to make it possible to introspect\u001b[39;00m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;66;03m# it and importing it first would fail if the OpenMP dll cannot be found.\u001b[39;00m\n\u001b[1;32m 83\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 84\u001b[0m __check_build, \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[1;32m 85\u001b[0m _distributor_init, \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[1;32m 86\u001b[0m )\n\u001b[0;32m---> 87\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m clone\n\u001b[1;32m 88\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_show_versions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m show_versions\n\u001b[1;32m 90\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 91\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcalibration\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 92\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcluster\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshow_versions\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 134\u001b[0m ]\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/sklearn/base.py:19\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_config\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config_context, get_config\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m InconsistentVersionWarning\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _IS_32BIT\n\u001b[1;32m 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_estimator_html_repr\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _HTMLDocumentationLinkMixin, estimator_html_repr\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_metadata_requests\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _MetadataRequester, _routing_enabled\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/sklearn/utils/__init__.py:16\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mitertools\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compress, islice\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msparse\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m issparse\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_config\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DataConversionWarning\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/scipy/sparse/__init__.py:294\u001b[0m\n\u001b[1;32m 288\u001b[0m \u001b[38;5;66;03m# Original code by Travis Oliphant.\u001b[39;00m\n\u001b[1;32m 289\u001b[0m \u001b[38;5;66;03m# Modified and extended by Ed Schofield, Robert Cimrman,\u001b[39;00m\n\u001b[1;32m 290\u001b[0m \u001b[38;5;66;03m# Nathan Bell, and Jake Vanderplas.\u001b[39;00m\n\u001b[1;32m 292\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01m_warnings\u001b[39;00m\n\u001b[0;32m--> 294\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_base\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_csr\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 296\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_csc\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/scipy/sparse/_base.py:5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m warn\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_lib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_util\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m VisibleDeprecationWarning\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_sputils\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (asmatrix, check_reshape_kwargs, check_shape,\n\u001b[1;32m 8\u001b[0m get_sum_dtype, isdense, isscalarlike,\n\u001b[1;32m 9\u001b[0m matrix, validateaxis,)\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_matrix\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m spmatrix\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/scipy/_lib/_util.py:18\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 11\u001b[0m Optional,\n\u001b[1;32m 12\u001b[0m Union,\n\u001b[1;32m 13\u001b[0m TYPE_CHECKING,\n\u001b[1;32m 14\u001b[0m TypeVar,\n\u001b[1;32m 15\u001b[0m )\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_lib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_array_api\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m array_namespace\n\u001b[1;32m 21\u001b[0m AxisError: \u001b[38;5;28mtype\u001b[39m[\u001b[38;5;167;01mException\u001b[39;00m]\n\u001b[1;32m 22\u001b[0m ComplexWarning: \u001b[38;5;28mtype\u001b[39m[\u001b[38;5;167;01mWarning\u001b[39;00m]\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/scipy/_lib/_array_api.py:17\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_lib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m array_api_compat\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_lib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray_api_compat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 18\u001b[0m is_array_api_obj,\n\u001b[1;32m 19\u001b[0m size,\n\u001b[1;32m 20\u001b[0m numpy \u001b[38;5;28;01mas\u001b[39;00m np_compat,\n\u001b[1;32m 21\u001b[0m )\n\u001b[1;32m 23\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124marray_namespace\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_asarray\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 26\u001b[0m \u001b[38;5;66;03m# To enable array API and strict array-like input validation\u001b[39;00m\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/scipy/_lib/array_api_compat/numpy/__init__.py:1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# from numpy import * doesn't overwrite these builtin names\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;28mabs\u001b[39m, \u001b[38;5;28mmax\u001b[39m, \u001b[38;5;28mmin\u001b[39m, \u001b[38;5;28mround\u001b[39m\n", + "File \u001b[0;32m/opt/miniconda3/envs/mazedyn/lib/python3.11/site-packages/numpy/__init__.py:376\u001b[0m, in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m 0\u001b[0m \n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numpy.strings'" + ] + } + ], + "source": [ + "from karateclub import FeatherGraph\n", + "\n", + "model = FeatherGraph()\n", + "model.fit(graphs)\n", + "X = model.get_embedding()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### IGE\n", + "class IGE(feature_embedding_dimensions: List[int] = [3, 5], spectral_embedding_dimensions: List[int] = [10, 20], histogram_bins: List[int] = [10, 20], seed: int = 42)[source]\n", + "An implementation of ā€œInvariant Graph Embeddingā€ from the ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Data paper ā€œInvariant Embedding for Graph Classificationā€. The procedure computes a mixture of spectral and node embedding based features. Specifically, it uses scattering, eigenvalues and pooled node feature embeddings to create graph descriptors." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Graph2Vec\n", + "\n", + "class Graph2Vec(wl_iterations: int = 2, attributed: bool = False, dimensions: int = 128, workers: int = 4, down_sampling: float = 0.0001, epochs: int = 10, learning_rate: float = 0.025, min_count: int = 5, seed: int = 42, erase_base_features: bool = False)[source]\n", + "An implementation of ā€œGraph2Vecā€ from the MLGWorkshop ā€˜17 paper ā€œGraph2Vec: Learning Distributed Representations of Graphsā€. The procedure creates Weisfeiler-Lehman tree features for nodes in graphs. Using these features a document (graph) - feature co-occurrence matrix is decomposed in order to generate representations for the graphs.\n", + "\n", + "The procedure assumes that nodes have no string feature present and the WL-hashing defaults to the degree centrality. However, if a node feature with the key ā€œfeatureā€ is supported for the nodes the feature extraction happens based on the values of this key." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mazedyn", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..706be92 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,5759 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "aioboto3" +version = "11.0.1" +description = "Async boto3 wrapper" +optional = false +python-versions = ">=3.7,<4.0" +files = [ + {file = "aioboto3-11.0.1-py3-none-any.whl", hash = "sha256:88d1df978e3b937c57ae638a40097e10f8ef0be64712ece7f79841388629e71e"}, + {file = "aioboto3-11.0.1.tar.gz", hash = "sha256:f415922e1515dbe06ffa0df7cb4d4309ddd0f880f33c8c4eb1a9daf7c9e3a440"}, +] + +[package.dependencies] +aiobotocore = {version = "2.4.2", extras = ["boto3"]} + +[package.extras] +chalice = ["chalice (>=1.24.0)"] +s3cse = ["cryptography (>=2.3.1)"] + +[[package]] +name = "aiobotocore" +version = "2.4.2" +description = "Async client for aws services using botocore and aiohttp" +optional = false +python-versions = ">=3.7" +files = [ + {file = "aiobotocore-2.4.2-py3-none-any.whl", hash = "sha256:4acd1ebe2e44be4b100aa553910bda899f6dc090b3da2bc1cf3d5de2146ed208"}, + {file = "aiobotocore-2.4.2.tar.gz", hash = "sha256:0603b74a582dffa7511ce7548d07dc9b10ec87bc5fb657eb0b34f9bd490958bf"}, +] + +[package.dependencies] +aiohttp = ">=3.3.1" +aioitertools = ">=0.5.1" +boto3 = {version = ">=1.24.59,<1.24.60", optional = true, markers = "extra == \"boto3\""} +botocore = ">=1.27.59,<1.27.60" +wrapt = ">=1.10.10" + +[package.extras] +awscli = ["awscli (>=1.25.60,<1.25.61)"] +boto3 = ["boto3 (>=1.24.59,<1.24.60)"] + +[[package]] +name = "aiohttp" +version = "3.9.5" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:fcde4c397f673fdec23e6b05ebf8d4751314fa7c24f93334bf1f1364c1c69ac7"}, + {file = "aiohttp-3.9.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5d6b3f1fabe465e819aed2c421a6743d8debbde79b6a8600739300630a01bf2c"}, + {file = "aiohttp-3.9.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6ae79c1bc12c34082d92bf9422764f799aee4746fd7a392db46b7fd357d4a17a"}, + {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4d3ebb9e1316ec74277d19c5f482f98cc65a73ccd5430540d6d11682cd857430"}, + {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:84dabd95154f43a2ea80deffec9cb44d2e301e38a0c9d331cc4aa0166fe28ae3"}, + {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c8a02fbeca6f63cb1f0475c799679057fc9268b77075ab7cf3f1c600e81dd46b"}, + {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c26959ca7b75ff768e2776d8055bf9582a6267e24556bb7f7bd29e677932be72"}, + {file = "aiohttp-3.9.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:714d4e5231fed4ba2762ed489b4aec07b2b9953cf4ee31e9871caac895a839c0"}, + {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:e7a6a8354f1b62e15d48e04350f13e726fa08b62c3d7b8401c0a1314f02e3558"}, + {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:c413016880e03e69d166efb5a1a95d40f83d5a3a648d16486592c49ffb76d0db"}, + {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:ff84aeb864e0fac81f676be9f4685f0527b660f1efdc40dcede3c251ef1e867f"}, + {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:ad7f2919d7dac062f24d6f5fe95d401597fbb015a25771f85e692d043c9d7832"}, + {file = "aiohttp-3.9.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:702e2c7c187c1a498a4e2b03155d52658fdd6fda882d3d7fbb891a5cf108bb10"}, + {file = "aiohttp-3.9.5-cp310-cp310-win32.whl", hash = "sha256:67c3119f5ddc7261d47163ed86d760ddf0e625cd6246b4ed852e82159617b5fb"}, + {file = "aiohttp-3.9.5-cp310-cp310-win_amd64.whl", hash = "sha256:471f0ef53ccedec9995287f02caf0c068732f026455f07db3f01a46e49d76bbb"}, + {file = "aiohttp-3.9.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:e0ae53e33ee7476dd3d1132f932eeb39bf6125083820049d06edcdca4381f342"}, + {file = "aiohttp-3.9.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c088c4d70d21f8ca5c0b8b5403fe84a7bc8e024161febdd4ef04575ef35d474d"}, + {file = "aiohttp-3.9.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:639d0042b7670222f33b0028de6b4e2fad6451462ce7df2af8aee37dcac55424"}, + {file = "aiohttp-3.9.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f26383adb94da5e7fb388d441bf09c61e5e35f455a3217bfd790c6b6bc64b2ee"}, + {file = "aiohttp-3.9.5-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:66331d00fb28dc90aa606d9a54304af76b335ae204d1836f65797d6fe27f1ca2"}, + {file = "aiohttp-3.9.5-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4ff550491f5492ab5ed3533e76b8567f4b37bd2995e780a1f46bca2024223233"}, + {file = "aiohttp-3.9.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f22eb3a6c1080d862befa0a89c380b4dafce29dc6cd56083f630073d102eb595"}, + {file = "aiohttp-3.9.5-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a81b1143d42b66ffc40a441379387076243ef7b51019204fd3ec36b9f69e77d6"}, + {file = "aiohttp-3.9.5-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f64fd07515dad67f24b6ea4a66ae2876c01031de91c93075b8093f07c0a2d93d"}, + {file = "aiohttp-3.9.5-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:93e22add827447d2e26d67c9ac0161756007f152fdc5210277d00a85f6c92323"}, + {file = "aiohttp-3.9.5-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:55b39c8684a46e56ef8c8d24faf02de4a2b2ac60d26cee93bc595651ff545de9"}, + {file = "aiohttp-3.9.5-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:4715a9b778f4293b9f8ae7a0a7cef9829f02ff8d6277a39d7f40565c737d3771"}, + {file = "aiohttp-3.9.5-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:afc52b8d969eff14e069a710057d15ab9ac17cd4b6753042c407dcea0e40bf75"}, + {file = "aiohttp-3.9.5-cp311-cp311-win32.whl", hash = "sha256:b3df71da99c98534be076196791adca8819761f0bf6e08e07fd7da25127150d6"}, + {file = "aiohttp-3.9.5-cp311-cp311-win_amd64.whl", hash = "sha256:88e311d98cc0bf45b62fc46c66753a83445f5ab20038bcc1b8a1cc05666f428a"}, + {file = "aiohttp-3.9.5-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:c7a4b7a6cf5b6eb11e109a9755fd4fda7d57395f8c575e166d363b9fc3ec4678"}, + {file = "aiohttp-3.9.5-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:0a158704edf0abcac8ac371fbb54044f3270bdbc93e254a82b6c82be1ef08f3c"}, + {file = "aiohttp-3.9.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d153f652a687a8e95ad367a86a61e8d53d528b0530ef382ec5aaf533140ed00f"}, + {file = "aiohttp-3.9.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:82a6a97d9771cb48ae16979c3a3a9a18b600a8505b1115cfe354dfb2054468b4"}, + {file = "aiohttp-3.9.5-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:60cdbd56f4cad9f69c35eaac0fbbdf1f77b0ff9456cebd4902f3dd1cf096464c"}, + {file = "aiohttp-3.9.5-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8676e8fd73141ded15ea586de0b7cda1542960a7b9ad89b2b06428e97125d4fa"}, + {file = "aiohttp-3.9.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da00da442a0e31f1c69d26d224e1efd3a1ca5bcbf210978a2ca7426dfcae9f58"}, + {file = "aiohttp-3.9.5-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:18f634d540dd099c262e9f887c8bbacc959847cfe5da7a0e2e1cf3f14dbf2daf"}, + {file = "aiohttp-3.9.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:320e8618eda64e19d11bdb3bd04ccc0a816c17eaecb7e4945d01deee2a22f95f"}, + {file = "aiohttp-3.9.5-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:2faa61a904b83142747fc6a6d7ad8fccff898c849123030f8e75d5d967fd4a81"}, + {file = "aiohttp-3.9.5-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:8c64a6dc3fe5db7b1b4d2b5cb84c4f677768bdc340611eca673afb7cf416ef5a"}, + {file = "aiohttp-3.9.5-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:393c7aba2b55559ef7ab791c94b44f7482a07bf7640d17b341b79081f5e5cd1a"}, + {file = "aiohttp-3.9.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:c671dc117c2c21a1ca10c116cfcd6e3e44da7fcde37bf83b2be485ab377b25da"}, + {file = "aiohttp-3.9.5-cp312-cp312-win32.whl", hash = "sha256:5a7ee16aab26e76add4afc45e8f8206c95d1d75540f1039b84a03c3b3800dd59"}, + {file = "aiohttp-3.9.5-cp312-cp312-win_amd64.whl", hash = "sha256:5ca51eadbd67045396bc92a4345d1790b7301c14d1848feaac1d6a6c9289e888"}, + {file = "aiohttp-3.9.5-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:694d828b5c41255e54bc2dddb51a9f5150b4eefa9886e38b52605a05d96566e8"}, + {file = "aiohttp-3.9.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:0605cc2c0088fcaae79f01c913a38611ad09ba68ff482402d3410bf59039bfb8"}, + {file = "aiohttp-3.9.5-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4558e5012ee03d2638c681e156461d37b7a113fe13970d438d95d10173d25f78"}, + {file = "aiohttp-3.9.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9dbc053ac75ccc63dc3a3cc547b98c7258ec35a215a92bd9f983e0aac95d3d5b"}, + {file = "aiohttp-3.9.5-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4109adee842b90671f1b689901b948f347325045c15f46b39797ae1bf17019de"}, + {file = "aiohttp-3.9.5-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a6ea1a5b409a85477fd8e5ee6ad8f0e40bf2844c270955e09360418cfd09abac"}, + {file = "aiohttp-3.9.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f3c2890ca8c59ee683fd09adf32321a40fe1cf164e3387799efb2acebf090c11"}, + {file = "aiohttp-3.9.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3916c8692dbd9d55c523374a3b8213e628424d19116ac4308e434dbf6d95bbdd"}, + {file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:8d1964eb7617907c792ca00b341b5ec3e01ae8c280825deadbbd678447b127e1"}, + {file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:d5ab8e1f6bee051a4bf6195e38a5c13e5e161cb7bad83d8854524798bd9fcd6e"}, + {file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:52c27110f3862a1afbcb2af4281fc9fdc40327fa286c4625dfee247c3ba90156"}, + {file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:7f64cbd44443e80094309875d4f9c71d0401e966d191c3d469cde4642bc2e031"}, + {file = "aiohttp-3.9.5-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8b4f72fbb66279624bfe83fd5eb6aea0022dad8eec62b71e7bf63ee1caadeafe"}, + {file = "aiohttp-3.9.5-cp38-cp38-win32.whl", hash = "sha256:6380c039ec52866c06d69b5c7aad5478b24ed11696f0e72f6b807cfb261453da"}, + {file = "aiohttp-3.9.5-cp38-cp38-win_amd64.whl", hash = "sha256:da22dab31d7180f8c3ac7c7635f3bcd53808f374f6aa333fe0b0b9e14b01f91a"}, + {file = "aiohttp-3.9.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:1732102949ff6087589408d76cd6dea656b93c896b011ecafff418c9661dc4ed"}, + {file = "aiohttp-3.9.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c6021d296318cb6f9414b48e6a439a7f5d1f665464da507e8ff640848ee2a58a"}, + {file = "aiohttp-3.9.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:239f975589a944eeb1bad26b8b140a59a3a320067fb3cd10b75c3092405a1372"}, + {file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b7b30258348082826d274504fbc7c849959f1989d86c29bc355107accec6cfb"}, + {file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:cd2adf5c87ff6d8b277814a28a535b59e20bfea40a101db6b3bdca7e9926bc24"}, + {file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e9a3d838441bebcf5cf442700e3963f58b5c33f015341f9ea86dcd7d503c07e2"}, + {file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e3a1ae66e3d0c17cf65c08968a5ee3180c5a95920ec2731f53343fac9bad106"}, + {file = "aiohttp-3.9.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9c69e77370cce2d6df5d12b4e12bdcca60c47ba13d1cbbc8645dd005a20b738b"}, + {file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:0cbf56238f4bbf49dab8c2dc2e6b1b68502b1e88d335bea59b3f5b9f4c001475"}, + {file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:d1469f228cd9ffddd396d9948b8c9cd8022b6d1bf1e40c6f25b0fb90b4f893ed"}, + {file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:45731330e754f5811c314901cebdf19dd776a44b31927fa4b4dbecab9e457b0c"}, + {file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:3fcb4046d2904378e3aeea1df51f697b0467f2aac55d232c87ba162709478c46"}, + {file = "aiohttp-3.9.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8cf142aa6c1a751fcb364158fd710b8a9be874b81889c2bd13aa8893197455e2"}, + {file = "aiohttp-3.9.5-cp39-cp39-win32.whl", hash = "sha256:7b179eea70833c8dee51ec42f3b4097bd6370892fa93f510f76762105568cf09"}, + {file = "aiohttp-3.9.5-cp39-cp39-win_amd64.whl", hash = "sha256:38d80498e2e169bc61418ff36170e0aad0cd268da8b38a17c4cf29d254a8b3f1"}, + {file = "aiohttp-3.9.5.tar.gz", hash = "sha256:edea7d15772ceeb29db4aff55e482d4bcfb6ae160ce144f2682de02f6d693551"}, +] + +[package.dependencies] +aiosignal = ">=1.1.2" +attrs = ">=17.3.0" +frozenlist = ">=1.1.1" +multidict = ">=4.5,<7.0" +yarl = ">=1.0,<2.0" + +[package.extras] +speedups = ["Brotli", "aiodns", "brotlicffi"] + +[[package]] +name = "aioitertools" +version = "0.11.0" +description = "itertools and builtins for AsyncIO and mixed iterables" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aioitertools-0.11.0-py3-none-any.whl", hash = "sha256:04b95e3dab25b449def24d7df809411c10e62aab0cbe31a50ca4e68748c43394"}, + {file = "aioitertools-0.11.0.tar.gz", hash = "sha256:42c68b8dd3a69c2bf7f2233bf7df4bb58b557bca5252ac02ed5187bbc67d6831"}, +] + +[[package]] +name = "aiosignal" +version = "1.3.1" +description = "aiosignal: a list of registered asynchronous callbacks" +optional = false +python-versions = ">=3.7" +files = [ + {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"}, + {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"}, +] + +[package.dependencies] +frozenlist = ">=1.1.0" + +[[package]] +name = "anyio" +version = "4.4.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.8" +files = [ + {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, + {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, +] + +[package.dependencies] +idna = ">=2.8" +sniffio = ">=1.1" + +[package.extras] +doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] +trio = ["trio (>=0.23)"] + +[[package]] +name = "appdirs" +version = "1.4.4" +description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." +optional = false +python-versions = "*" +files = [ + {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"}, + {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"}, +] + +[[package]] +name = "appnope" +version = "0.1.4" +description = "Disable App Nap on macOS >= 10.9" +optional = false +python-versions = ">=3.6" +files = [ + {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"}, + {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"}, +] + +[[package]] +name = "argon2-cffi" +version = "23.1.0" +description = "Argon2 for Python" +optional = false +python-versions = ">=3.7" +files = [ + {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"}, + {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"}, +] + +[package.dependencies] +argon2-cffi-bindings = "*" + +[package.extras] +dev = ["argon2-cffi[tests,typing]", "tox (>4)"] +docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"] +tests = ["hypothesis", "pytest"] +typing = ["mypy"] + +[[package]] +name = "argon2-cffi-bindings" +version = "21.2.0" +description = "Low-level CFFI bindings for Argon2" +optional = false +python-versions = ">=3.6" +files = [ + {file = "argon2-cffi-bindings-21.2.0.tar.gz", hash = "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3"}, + {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367"}, + {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9524464572e12979364b7d600abf96181d3541da11e23ddf565a32e70bd4dc0d"}, + {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b746dba803a79238e925d9046a63aa26bf86ab2a2fe74ce6b009a1c3f5c8f2ae"}, + {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:58ed19212051f49a523abb1dbe954337dc82d947fb6e5a0da60f7c8471a8476c"}, + {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:bd46088725ef7f58b5a1ef7ca06647ebaf0eb4baff7d1d0d177c6cc8744abd86"}, + {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_i686.whl", hash = "sha256:8cd69c07dd875537a824deec19f978e0f2078fdda07fd5c42ac29668dda5f40f"}, + {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:f1152ac548bd5b8bcecfb0b0371f082037e47128653df2e8ba6e914d384f3c3e"}, + {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-win32.whl", hash = "sha256:603ca0aba86b1349b147cab91ae970c63118a0f30444d4bc80355937c950c082"}, + {file = "argon2_cffi_bindings-21.2.0-cp36-abi3-win_amd64.whl", hash = "sha256:b2ef1c30440dbbcba7a5dc3e319408b59676e2e039e2ae11a8775ecf482b192f"}, + {file = "argon2_cffi_bindings-21.2.0-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:e415e3f62c8d124ee16018e491a009937f8cf7ebf5eb430ffc5de21b900dad93"}, + {file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:3e385d1c39c520c08b53d63300c3ecc28622f076f4c2b0e6d7e796e9f6502194"}, + {file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2c3e3cc67fdb7d82c4718f19b4e7a87123caf8a93fde7e23cf66ac0337d3cb3f"}, + {file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6a22ad9800121b71099d0fb0a65323810a15f2e292f2ba450810a7316e128ee5"}, + {file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f9f8b450ed0547e3d473fdc8612083fd08dd2120d6ac8f73828df9b7d45bb351"}, + {file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:93f9bf70084f97245ba10ee36575f0c3f1e7d7724d67d8e5b08e61787c320ed7"}, + {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:3b9ef65804859d335dc6b31582cad2c5166f0c3e7975f324d9ffaa34ee7e6583"}, + {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d4966ef5848d820776f5f562a7d45fdd70c2f330c961d0d745b784034bd9f48d"}, + {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:20ef543a89dee4db46a1a6e206cd015360e5a75822f76df533845c3cbaf72670"}, + {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ed2937d286e2ad0cc79a7087d3c272832865f779430e0cc2b4f3718d3159b0cb"}, + {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5e00316dabdaea0b2dd82d141cc66889ced0cdcbfa599e8b471cf22c620c329a"}, +] + +[package.dependencies] +cffi = ">=1.0.1" + +[package.extras] +dev = ["cogapp", "pre-commit", "pytest", "wheel"] +tests = ["pytest"] + +[[package]] +name = "arrow" +version = "1.3.0" +description = "Better dates & times for Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"}, + {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"}, +] + +[package.dependencies] +python-dateutil = ">=2.7.0" +types-python-dateutil = ">=2.8.10" + +[package.extras] +doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints", "sphinx_rtd_theme (>=1.3.0)"] +test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (==3.*)"] + +[[package]] +name = "asciitree" +version = "0.3.3" +description = "Draws ASCII trees." +optional = false +python-versions = "*" +files = [ + {file = "asciitree-0.3.3.tar.gz", hash = "sha256:4aa4b9b649f85e3fcb343363d97564aa1fb62e249677f2e18a96765145cc0f6e"}, +] + +[[package]] +name = "asttokens" +version = "2.4.1" +description = "Annotate AST trees with source code positions" +optional = false +python-versions = "*" +files = [ + {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"}, + {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"}, +] + +[package.dependencies] +six = ">=1.12.0" + +[package.extras] +astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"] +test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"] + +[[package]] +name = "async-lru" +version = "2.0.4" +description = "Simple LRU cache for asyncio" +optional = false +python-versions = ">=3.8" +files = [ + {file = "async-lru-2.0.4.tar.gz", hash = "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627"}, + {file = "async_lru-2.0.4-py3-none-any.whl", hash = "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"}, +] + +[[package]] +name = "attrs" +version = "23.2.0" +description = "Classes Without Boilerplate" +optional = false +python-versions = ">=3.7" +files = [ + {file = "attrs-23.2.0-py3-none-any.whl", hash = "sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1"}, + {file = "attrs-23.2.0.tar.gz", hash = "sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30"}, +] + +[package.extras] +cov = ["attrs[tests]", "coverage[toml] (>=5.3)"] +dev = ["attrs[tests]", "pre-commit"] +docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"] +tests = ["attrs[tests-no-zope]", "zope-interface"] +tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"] +tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"] + +[[package]] +name = "autoflake8" +version = "0.4.1" +description = "Tool to automatically fix some issues reported by flake8 (forked from autoflake)." +optional = false +python-versions = ">=3.7,<4.0" +files = [ + {file = "autoflake8-0.4.1-py3-none-any.whl", hash = "sha256:fdf663b627993ac38e5b55b7d742c388fb2a4f34798a052f43eecc5e8d629e9d"}, + {file = "autoflake8-0.4.1.tar.gz", hash = "sha256:c17da499bd2b71ba02fb11fe53ff1ad83d7dae6efb0f115fd1344f467797c679"}, +] + +[package.dependencies] +pyflakes = ">=2.3.0" + +[[package]] +name = "babel" +version = "2.15.0" +description = "Internationalization utilities" +optional = false +python-versions = ">=3.8" +files = [ + {file = "Babel-2.15.0-py3-none-any.whl", hash = "sha256:08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb"}, + {file = "babel-2.15.0.tar.gz", hash = "sha256:8daf0e265d05768bc6c7a314cf1321e9a123afc328cc635c18622a2f30a04413"}, +] + +[package.extras] +dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"] + +[[package]] +name = "beautifulsoup4" +version = "4.12.3" +description = "Screen-scraping library" +optional = false +python-versions = ">=3.6.0" +files = [ + {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"}, + {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"}, +] + +[package.dependencies] +soupsieve = ">1.2" + +[package.extras] +cchardet = ["cchardet"] +chardet = ["chardet"] +charset-normalizer = ["charset-normalizer"] +html5lib = ["html5lib"] +lxml = ["lxml"] + +[[package]] +name = "black" +version = "23.12.1" +description = "The uncompromising code formatter." +optional = false +python-versions = ">=3.8" +files = [ + {file = "black-23.12.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e0aaf6041986767a5e0ce663c7a2f0e9eaf21e6ff87a5f95cbf3675bfd4c41d2"}, + {file = "black-23.12.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c88b3711d12905b74206227109272673edce0cb29f27e1385f33b0163c414bba"}, + {file = "black-23.12.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a920b569dc6b3472513ba6ddea21f440d4b4c699494d2e972a1753cdc25df7b0"}, + {file = "black-23.12.1-cp310-cp310-win_amd64.whl", hash = "sha256:3fa4be75ef2a6b96ea8d92b1587dd8cb3a35c7e3d51f0738ced0781c3aa3a5a3"}, + {file = "black-23.12.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:8d4df77958a622f9b5a4c96edb4b8c0034f8434032ab11077ec6c56ae9f384ba"}, + {file = "black-23.12.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:602cfb1196dc692424c70b6507593a2b29aac0547c1be9a1d1365f0d964c353b"}, + {file = "black-23.12.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9c4352800f14be5b4864016882cdba10755bd50805c95f728011bcb47a4afd59"}, + {file = "black-23.12.1-cp311-cp311-win_amd64.whl", hash = "sha256:0808494f2b2df923ffc5723ed3c7b096bd76341f6213989759287611e9837d50"}, + {file = "black-23.12.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:25e57fd232a6d6ff3f4478a6fd0580838e47c93c83eaf1ccc92d4faf27112c4e"}, + {file = "black-23.12.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2d9e13db441c509a3763a7a3d9a49ccc1b4e974a47be4e08ade2a228876500ec"}, + {file = "black-23.12.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d1bd9c210f8b109b1762ec9fd36592fdd528485aadb3f5849b2740ef17e674e"}, + {file = "black-23.12.1-cp312-cp312-win_amd64.whl", hash = "sha256:ae76c22bde5cbb6bfd211ec343ded2163bba7883c7bc77f6b756a1049436fbb9"}, + {file = "black-23.12.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1fa88a0f74e50e4487477bc0bb900c6781dbddfdfa32691e780bf854c3b4a47f"}, + {file = "black-23.12.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:a4d6a9668e45ad99d2f8ec70d5c8c04ef4f32f648ef39048d010b0689832ec6d"}, + {file = "black-23.12.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b18fb2ae6c4bb63eebe5be6bd869ba2f14fd0259bda7d18a46b764d8fb86298a"}, + {file = "black-23.12.1-cp38-cp38-win_amd64.whl", hash = "sha256:c04b6d9d20e9c13f43eee8ea87d44156b8505ca8a3c878773f68b4e4812a421e"}, + {file = "black-23.12.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3e1b38b3135fd4c025c28c55ddfc236b05af657828a8a6abe5deec419a0b7055"}, + {file = "black-23.12.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4f0031eaa7b921db76decd73636ef3a12c942ed367d8c3841a0739412b260a54"}, + {file = "black-23.12.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:97e56155c6b737854e60a9ab1c598ff2533d57e7506d97af5481141671abf3ea"}, + {file = "black-23.12.1-cp39-cp39-win_amd64.whl", hash = "sha256:dd15245c8b68fe2b6bd0f32c1556509d11bb33aec9b5d0866dd8e2ed3dba09c2"}, + {file = "black-23.12.1-py3-none-any.whl", hash = "sha256:78baad24af0f033958cad29731e27363183e140962595def56423e626f4bee3e"}, + {file = "black-23.12.1.tar.gz", hash = "sha256:4ce3ef14ebe8d9509188014d96af1c456a910d5b5cbf434a09fef7e024b3d0d5"}, +] + +[package.dependencies] +click = ">=8.0.0" +mypy-extensions = ">=0.4.3" +packaging = ">=22.0" +pathspec = ">=0.9.0" +platformdirs = ">=2" + +[package.extras] +colorama = ["colorama (>=0.4.3)"] +d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"] +jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"] +uvloop = ["uvloop (>=0.15.2)"] + +[[package]] +name = "bleach" +version = "6.1.0" +description = "An easy safelist-based HTML-sanitizing tool." +optional = false +python-versions = ">=3.8" +files = [ + {file = "bleach-6.1.0-py3-none-any.whl", hash = "sha256:3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6"}, + {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"}, +] + +[package.dependencies] +six = ">=1.9.0" +webencodings = "*" + +[package.extras] +css = ["tinycss2 (>=1.1.0,<1.3)"] + +[[package]] +name = "bokeh" +version = "3.5.0" +description = "Interactive plots and applications in the browser from Python" +optional = false +python-versions = ">=3.10" +files = [ + {file = "bokeh-3.5.0-py3-none-any.whl", hash = "sha256:1a1c7d35aa9aba1ae86916e92c1d9f19d706ba1c4929e0ff8647902b32e25689"}, + {file = "bokeh-3.5.0.tar.gz", hash = "sha256:65e89addbe900c37af25a2052ae174b6d3ba1ef08c91fd5ab6dfd712e184c399"}, +] + +[package.dependencies] +contourpy = ">=1.2" +Jinja2 = ">=2.9" +numpy = ">=1.16" +packaging = ">=16.8" +pandas = ">=1.2" +pillow = ">=7.1.0" +PyYAML = ">=3.10" +tornado = ">=6.2" +xyzservices = ">=2021.09.1" + +[[package]] +name = "boto3" +version = "1.24.59" +description = "The AWS SDK for Python" +optional = false +python-versions = ">= 3.7" +files = [ + {file = "boto3-1.24.59-py3-none-any.whl", hash = "sha256:34ab44146a2c4e7f4e72737f4b27e6eb5e0a7855c2f4599e3d9199b6a0a2d575"}, + {file = "boto3-1.24.59.tar.gz", hash = "sha256:a50b4323f9579cfe22fcf5531fbd40b567d4d74c1adce06aeb5c95fce2a6fb40"}, +] + +[package.dependencies] +botocore = ">=1.27.59,<1.28.0" +jmespath = ">=0.7.1,<2.0.0" +s3transfer = ">=0.6.0,<0.7.0" + +[package.extras] +crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] + +[[package]] +name = "botocore" +version = "1.27.59" +description = "Low-level, data-driven core of boto 3." +optional = false +python-versions = ">= 3.7" +files = [ + {file = "botocore-1.27.59-py3-none-any.whl", hash = "sha256:69d756791fc024bda54f6c53f71ae34e695ee41bbbc1743d9179c4837a4929da"}, + {file = "botocore-1.27.59.tar.gz", hash = "sha256:eda4aed6ee719a745d1288eaf1beb12f6f6448ad1fa12f159405db14ba9c92cf"}, +] + +[package.dependencies] +jmespath = ">=0.7.1,<2.0.0" +python-dateutil = ">=2.1,<3.0.0" +urllib3 = ">=1.25.4,<1.27" + +[package.extras] +crt = ["awscrt (==0.14.0)"] + +[[package]] +name = "captum" +version = "0.7.0" +description = "Model interpretability for PyTorch" +optional = false +python-versions = ">=3.6" +files = [ + {file = "captum-0.7.0-py3-none-any.whl", hash = "sha256:2cbec9aa4b6ec325c2fdf369c1fdabb011017122e2314e2af009496d53a0757c"}, + {file = "captum-0.7.0.tar.gz", hash = "sha256:7ca87d0dc67b3b7589a730b970b9536172a5468e0e31bf8657fdd73abc568a33"}, +] + +[package.dependencies] +matplotlib = "*" +numpy = "*" +torch = ">=1.6" +tqdm = "*" + +[package.extras] +dev = ["annoy", "black (==22.3.0)", "flake8", "flask", "flask-compress", "ipython", "ipywidgets", "jupyter", "mypy (>=0.760)", "parameterized", "pytest", "pytest-cov", "scikit-learn", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-katex", "ufmt", "usort (==1.0.2)"] +insights = ["flask", "flask-compress", "ipython", "ipywidgets", "jupyter"] +test = ["parameterized", "pytest", "pytest-cov"] +tutorials = ["flask", "flask-compress", "ipython", "ipywidgets", "jupyter", "torchtext", "torchvision"] + +[[package]] +name = "cdsapi" +version = "0.6.1" +description = "Climate Data Store API" +optional = false +python-versions = "*" +files = [ + {file = "cdsapi-0.6.1.tar.gz", hash = "sha256:7d40c58e3fd3e75a8acdcdc81eab4ef9b6f763b2902ba01d7d1738f3652a5a30"}, +] + +[package.dependencies] +requests = ">=2.5.0" +tqdm = "*" + +[[package]] +name = "certifi" +version = "2024.7.4" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"}, + {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"}, +] + +[[package]] +name = "cffi" +version = "1.16.0" +description = "Foreign Function Interface for Python calling C code." +optional = false +python-versions = ">=3.8" +files = [ + {file = "cffi-1.16.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6b3d6606d369fc1da4fd8c357d026317fbb9c9b75d36dc16e90e84c26854b088"}, + {file = "cffi-1.16.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ac0f5edd2360eea2f1daa9e26a41db02dd4b0451b48f7c318e217ee092a213e9"}, + {file = "cffi-1.16.0-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7e61e3e4fa664a8588aa25c883eab612a188c725755afff6289454d6362b9673"}, + {file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a72e8961a86d19bdb45851d8f1f08b041ea37d2bd8d4fd19903bc3083d80c896"}, + {file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5b50bf3f55561dac5438f8e70bfcdfd74543fd60df5fa5f62d94e5867deca684"}, + {file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7651c50c8c5ef7bdb41108b7b8c5a83013bfaa8a935590c5d74627c047a583c7"}, + {file = "cffi-1.16.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4108df7fe9b707191e55f33efbcb2d81928e10cea45527879a4749cbe472614"}, + {file = "cffi-1.16.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:32c68ef735dbe5857c810328cb2481e24722a59a2003018885514d4c09af9743"}, + {file = "cffi-1.16.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:673739cb539f8cdaa07d92d02efa93c9ccf87e345b9a0b556e3ecc666718468d"}, + {file = "cffi-1.16.0-cp310-cp310-win32.whl", hash = "sha256:9f90389693731ff1f659e55c7d1640e2ec43ff725cc61b04b2f9c6d8d017df6a"}, + {file = "cffi-1.16.0-cp310-cp310-win_amd64.whl", hash = "sha256:e6024675e67af929088fda399b2094574609396b1decb609c55fa58b028a32a1"}, + {file = "cffi-1.16.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b84834d0cf97e7d27dd5b7f3aca7b6e9263c56308ab9dc8aae9784abb774d404"}, + {file = "cffi-1.16.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:1b8ebc27c014c59692bb2664c7d13ce7a6e9a629be20e54e7271fa696ff2b417"}, + {file = "cffi-1.16.0-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ee07e47c12890ef248766a6e55bd38ebfb2bb8edd4142d56db91b21ea68b7627"}, + {file = "cffi-1.16.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d8a9d3ebe49f084ad71f9269834ceccbf398253c9fac910c4fd7053ff1386936"}, + {file = "cffi-1.16.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e70f54f1796669ef691ca07d046cd81a29cb4deb1e5f942003f401c0c4a2695d"}, + {file = "cffi-1.16.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5bf44d66cdf9e893637896c7faa22298baebcd18d1ddb6d2626a6e39793a1d56"}, + {file = "cffi-1.16.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7b78010e7b97fef4bee1e896df8a4bbb6712b7f05b7ef630f9d1da00f6444d2e"}, + {file = "cffi-1.16.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:c6a164aa47843fb1b01e941d385aab7215563bb8816d80ff3a363a9f8448a8dc"}, + {file = "cffi-1.16.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e09f3ff613345df5e8c3667da1d918f9149bd623cd9070c983c013792a9a62eb"}, + {file = "cffi-1.16.0-cp311-cp311-win32.whl", hash = "sha256:2c56b361916f390cd758a57f2e16233eb4f64bcbeee88a4881ea90fca14dc6ab"}, + {file = "cffi-1.16.0-cp311-cp311-win_amd64.whl", hash = "sha256:db8e577c19c0fda0beb7e0d4e09e0ba74b1e4c092e0e40bfa12fe05b6f6d75ba"}, + {file = "cffi-1.16.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:fa3a0128b152627161ce47201262d3140edb5a5c3da88d73a1b790a959126956"}, + {file = "cffi-1.16.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:68e7c44931cc171c54ccb702482e9fc723192e88d25a0e133edd7aff8fcd1f6e"}, + {file = "cffi-1.16.0-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:abd808f9c129ba2beda4cfc53bde801e5bcf9d6e0f22f095e45327c038bfe68e"}, + {file = "cffi-1.16.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:88e2b3c14bdb32e440be531ade29d3c50a1a59cd4e51b1dd8b0865c54ea5d2e2"}, + {file = "cffi-1.16.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fcc8eb6d5902bb1cf6dc4f187ee3ea80a1eba0a89aba40a5cb20a5087d961357"}, + {file = "cffi-1.16.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b7be2d771cdba2942e13215c4e340bfd76398e9227ad10402a8767ab1865d2e6"}, + {file = "cffi-1.16.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e715596e683d2ce000574bae5d07bd522c781a822866c20495e52520564f0969"}, + {file = "cffi-1.16.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:2d92b25dbf6cae33f65005baf472d2c245c050b1ce709cc4588cdcdd5495b520"}, + {file = "cffi-1.16.0-cp312-cp312-win32.whl", hash = "sha256:b2ca4e77f9f47c55c194982e10f058db063937845bb2b7a86c84a6cfe0aefa8b"}, + {file = "cffi-1.16.0-cp312-cp312-win_amd64.whl", hash = "sha256:68678abf380b42ce21a5f2abde8efee05c114c2fdb2e9eef2efdb0257fba1235"}, + {file = "cffi-1.16.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:0c9ef6ff37e974b73c25eecc13952c55bceed9112be2d9d938ded8e856138bcc"}, + {file = "cffi-1.16.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a09582f178759ee8128d9270cd1344154fd473bb77d94ce0aeb2a93ebf0feaf0"}, + {file = "cffi-1.16.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e760191dd42581e023a68b758769e2da259b5d52e3103c6060ddc02c9edb8d7b"}, + {file = "cffi-1.16.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:80876338e19c951fdfed6198e70bc88f1c9758b94578d5a7c4c91a87af3cf31c"}, + {file = "cffi-1.16.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a6a14b17d7e17fa0d207ac08642c8820f84f25ce17a442fd15e27ea18d67c59b"}, + {file = "cffi-1.16.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6602bc8dc6f3a9e02b6c22c4fc1e47aa50f8f8e6d3f78a5e16ac33ef5fefa324"}, + {file = "cffi-1.16.0-cp38-cp38-win32.whl", hash = "sha256:131fd094d1065b19540c3d72594260f118b231090295d8c34e19a7bbcf2e860a"}, + {file = "cffi-1.16.0-cp38-cp38-win_amd64.whl", hash = "sha256:31d13b0f99e0836b7ff893d37af07366ebc90b678b6664c955b54561fc36ef36"}, + {file = "cffi-1.16.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:582215a0e9adbe0e379761260553ba11c58943e4bbe9c36430c4ca6ac74b15ed"}, + {file = "cffi-1.16.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b29ebffcf550f9da55bec9e02ad430c992a87e5f512cd63388abb76f1036d8d2"}, + {file = "cffi-1.16.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dc9b18bf40cc75f66f40a7379f6a9513244fe33c0e8aa72e2d56b0196a7ef872"}, + {file = "cffi-1.16.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9cb4a35b3642fc5c005a6755a5d17c6c8b6bcb6981baf81cea8bfbc8903e8ba8"}, + {file = "cffi-1.16.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b86851a328eedc692acf81fb05444bdf1891747c25af7529e39ddafaf68a4f3f"}, + {file = "cffi-1.16.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c0f31130ebc2d37cdd8e44605fb5fa7ad59049298b3f745c74fa74c62fbfcfc4"}, + {file = "cffi-1.16.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8f8e709127c6c77446a8c0a8c8bf3c8ee706a06cd44b1e827c3e6a2ee6b8c098"}, + {file = "cffi-1.16.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:748dcd1e3d3d7cd5443ef03ce8685043294ad6bd7c02a38d1bd367cfd968e000"}, + {file = "cffi-1.16.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8895613bcc094d4a1b2dbe179d88d7fb4a15cee43c052e8885783fac397d91fe"}, + {file = "cffi-1.16.0-cp39-cp39-win32.whl", hash = "sha256:ed86a35631f7bfbb28e108dd96773b9d5a6ce4811cf6ea468bb6a359b256b1e4"}, + {file = "cffi-1.16.0-cp39-cp39-win_amd64.whl", hash = "sha256:3686dffb02459559c74dd3d81748269ffb0eb027c39a6fc99502de37d501faa8"}, + {file = "cffi-1.16.0.tar.gz", hash = "sha256:bcb3ef43e58665bbda2fb198698fcae6776483e0c4a631aa5647806c25e02cc0"}, +] + +[package.dependencies] +pycparser = "*" + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." +optional = false +python-versions = ">=3.7.0" +files = [ + {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-win32.whl", hash = "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73"}, + {file = "charset_normalizer-3.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-win32.whl", hash = "sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab"}, + {file = "charset_normalizer-3.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8f4a014bc36d3c57402e2977dada34f9c12300af536839dc38c0beab8878f38a"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a10af20b82360ab00827f916a6058451b723b4e65030c5a18577c8b2de5b3389"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8d756e44e94489e49571086ef83b2bb8ce311e730092d2c34ca8f7d925cb20aa"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:8bdb58ff7ba23002a4c5808d608e4e6c687175724f54a5dade5fa8c67b604e4d"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:6b3251890fff30ee142c44144871185dbe13b11bab478a88887a639655be1068"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:b4a23f61ce87adf89be746c8a8974fe1c823c891d8f86eb218bb957c924bb143"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:efcb3f6676480691518c177e3b465bcddf57cea040302f9f4e6e191af91174d4"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-win32.whl", hash = "sha256:d965bba47ddeec8cd560687584e88cf699fd28f192ceb452d1d7ee807c5597b7"}, + {file = "charset_normalizer-3.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:96b02a3dc4381e5494fad39be677abcb5e6634bf7b4fa83a6dd3112607547001"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:95f2a5796329323b8f0512e09dbb7a1860c46a39da62ecb2324f116fa8fdc85c"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c002b4ffc0be611f0d9da932eb0f704fe2602a9a949d1f738e4c34c75b0863d5"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a981a536974bbc7a512cf44ed14938cf01030a99e9b3a06dd59578882f06f985"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:c083af607d2515612056a31f0a8d9e0fcb5876b7bfc0abad3ecd275bc4ebc2d5"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:87d1351268731db79e0f8e745d92493ee2841c974128ef629dc518b937d9194c"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:bd8f7df7d12c2db9fab40bdd87a7c09b1530128315d047a086fa3ae3435cb3a8"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:c180f51afb394e165eafe4ac2936a14bee3eb10debc9d9e4db8958fe36afe711"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-win32.whl", hash = "sha256:db364eca23f876da6f9e16c9da0df51aa4f104a972735574842618b8c6d999d4"}, + {file = "charset_normalizer-3.3.2-cp37-cp37m-win_amd64.whl", hash = "sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:6463effa3186ea09411d50efc7d85360b38d5f09b870c48e4600f63af490e56a"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:6c4caeef8fa63d06bd437cd4bdcf3ffefe6738fb1b25951440d80dc7df8c03ac"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ae5f4161f18c61806f411a13b0310bea87f987c7d2ecdbdaad0e94eb2e404238"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b2b0a0c0517616b6869869f8c581d4eb2dd83a4d79e0ebcb7d373ef9956aeb0a"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:eb00ed941194665c332bf8e078baf037d6c35d7c4f3102ea2d4f16ca94a26dc8"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:a50aebfa173e157099939b17f18600f72f84eed3049e743b68ad15bd69b6bf99"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:923c0c831b7cfcb071580d3f46c4baf50f174be571576556269530f4bbd79d04"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-win32.whl", hash = "sha256:6ef1d82a3af9d3eecdba2321dc1b3c238245d890843e040e41e470ffa64c3e25"}, + {file = "charset_normalizer-3.3.2-cp38-cp38-win_amd64.whl", hash = "sha256:eb8821e09e916165e160797a6c17edda0679379a4be5c716c260e836e122f54b"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:c235ebd9baae02f1b77bcea61bce332cb4331dc3617d254df3323aa01ab47bd4"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5b4c145409bef602a690e7cfad0a15a55c13320ff7a3ad7ca59c13bb8ba4d45d"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:68d1f8a9e9e37c1223b656399be5d6b448dea850bed7d0f87a8311f1ff3dabb0"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e27ad930a842b4c5eb8ac0016b0a54f5aebbe679340c26101df33424142c143c"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:d0eccceffcb53201b5bfebb52600a5fb483a20b61da9dbc885f8b103cbe7598c"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:9f96df6923e21816da7e0ad3fd47dd8f94b2a5ce594e00677c0013018b813458"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-win32.whl", hash = "sha256:aed38f6e4fb3f5d6bf81bfa990a07806be9d83cf7bacef998ab1a9bd660a581f"}, + {file = "charset_normalizer-3.3.2-cp39-cp39-win_amd64.whl", hash = "sha256:b01b88d45a6fcb69667cd6d2f7a9aeb4bf53760d7fc536bf679ec94fe9f3ff3d"}, + {file = "charset_normalizer-3.3.2-py3-none-any.whl", hash = "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc"}, +] + +[[package]] +name = "click" +version = "8.1.7" +description = "Composable command line interface toolkit" +optional = false +python-versions = ">=3.7" +files = [ + {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"}, + {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"}, +] + +[package.dependencies] +colorama = {version = "*", markers = "platform_system == \"Windows\""} + +[[package]] +name = "colorama" +version = "0.4.6" +description = "Cross-platform colored terminal text." +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" +files = [ + {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, + {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, +] + +[[package]] +name = "comm" +version = "0.2.2" +description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc." +optional = false +python-versions = ">=3.8" +files = [ + {file = "comm-0.2.2-py3-none-any.whl", hash = "sha256:e6fb86cb70ff661ee8c9c14e7d36d6de3b4066f1441be4063df9c5009f0a64d3"}, + {file = "comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e"}, +] + +[package.dependencies] +traitlets = ">=4" + +[package.extras] +test = ["pytest"] + +[[package]] +name = "contourpy" +version = "1.2.1" +description = "Python library for calculating contours of 2D quadrilateral grids" +optional = false +python-versions = ">=3.9" +files = [ + {file = "contourpy-1.2.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bd7c23df857d488f418439686d3b10ae2fbf9bc256cd045b37a8c16575ea1040"}, + {file = "contourpy-1.2.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5b9eb0ca724a241683c9685a484da9d35c872fd42756574a7cfbf58af26677fd"}, + {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4c75507d0a55378240f781599c30e7776674dbaf883a46d1c90f37e563453480"}, + {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:11959f0ce4a6f7b76ec578576a0b61a28bdc0696194b6347ba3f1c53827178b9"}, + {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:eb3315a8a236ee19b6df481fc5f997436e8ade24a9f03dfdc6bd490fea20c6da"}, + {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39f3ecaf76cd98e802f094e0d4fbc6dc9c45a8d0c4d185f0f6c2234e14e5f75b"}, + {file = "contourpy-1.2.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:94b34f32646ca0414237168d68a9157cb3889f06b096612afdd296003fdd32fd"}, + {file = "contourpy-1.2.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:457499c79fa84593f22454bbd27670227874cd2ff5d6c84e60575c8b50a69619"}, + {file = "contourpy-1.2.1-cp310-cp310-win32.whl", hash = "sha256:ac58bdee53cbeba2ecad824fa8159493f0bf3b8ea4e93feb06c9a465d6c87da8"}, + {file = "contourpy-1.2.1-cp310-cp310-win_amd64.whl", hash = "sha256:9cffe0f850e89d7c0012a1fb8730f75edd4320a0a731ed0c183904fe6ecfc3a9"}, + {file = "contourpy-1.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6022cecf8f44e36af10bd9118ca71f371078b4c168b6e0fab43d4a889985dbb5"}, + {file = "contourpy-1.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:ef5adb9a3b1d0c645ff694f9bca7702ec2c70f4d734f9922ea34de02294fdf72"}, + {file = "contourpy-1.2.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6150ffa5c767bc6332df27157d95442c379b7dce3a38dff89c0f39b63275696f"}, + {file = "contourpy-1.2.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4c863140fafc615c14a4bf4efd0f4425c02230eb8ef02784c9a156461e62c965"}, + {file = "contourpy-1.2.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:00e5388f71c1a0610e6fe56b5c44ab7ba14165cdd6d695429c5cd94021e390b2"}, + {file = "contourpy-1.2.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d4492d82b3bc7fbb7e3610747b159869468079fe149ec5c4d771fa1f614a14df"}, + {file = "contourpy-1.2.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:49e70d111fee47284d9dd867c9bb9a7058a3c617274900780c43e38d90fe1205"}, + {file = "contourpy-1.2.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:b59c0ffceff8d4d3996a45f2bb6f4c207f94684a96bf3d9728dbb77428dd8cb8"}, + {file = "contourpy-1.2.1-cp311-cp311-win32.whl", hash = "sha256:7b4182299f251060996af5249c286bae9361fa8c6a9cda5efc29fe8bfd6062ec"}, + {file = "contourpy-1.2.1-cp311-cp311-win_amd64.whl", hash = "sha256:2855c8b0b55958265e8b5888d6a615ba02883b225f2227461aa9127c578a4922"}, + {file = "contourpy-1.2.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:62828cada4a2b850dbef89c81f5a33741898b305db244904de418cc957ff05dc"}, + {file = "contourpy-1.2.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:309be79c0a354afff9ff7da4aaed7c3257e77edf6c1b448a779329431ee79d7e"}, + {file = "contourpy-1.2.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2e785e0f2ef0d567099b9ff92cbfb958d71c2d5b9259981cd9bee81bd194c9a4"}, + {file = "contourpy-1.2.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1cac0a8f71a041aa587410424ad46dfa6a11f6149ceb219ce7dd48f6b02b87a7"}, + {file = "contourpy-1.2.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:af3f4485884750dddd9c25cb7e3915d83c2db92488b38ccb77dd594eac84c4a0"}, + {file = "contourpy-1.2.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ce6889abac9a42afd07a562c2d6d4b2b7134f83f18571d859b25624a331c90b"}, + {file = "contourpy-1.2.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:a1eea9aecf761c661d096d39ed9026574de8adb2ae1c5bd7b33558af884fb2ce"}, + {file = "contourpy-1.2.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:187fa1d4c6acc06adb0fae5544c59898ad781409e61a926ac7e84b8f276dcef4"}, + {file = "contourpy-1.2.1-cp312-cp312-win32.whl", hash = "sha256:c2528d60e398c7c4c799d56f907664673a807635b857df18f7ae64d3e6ce2d9f"}, + {file = "contourpy-1.2.1-cp312-cp312-win_amd64.whl", hash = "sha256:1a07fc092a4088ee952ddae19a2b2a85757b923217b7eed584fdf25f53a6e7ce"}, + {file = "contourpy-1.2.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:bb6834cbd983b19f06908b45bfc2dad6ac9479ae04abe923a275b5f48f1a186b"}, + {file = "contourpy-1.2.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:1d59e739ab0e3520e62a26c60707cc3ab0365d2f8fecea74bfe4de72dc56388f"}, + {file = "contourpy-1.2.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bd3db01f59fdcbce5b22afad19e390260d6d0222f35a1023d9adc5690a889364"}, + {file = "contourpy-1.2.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a12a813949e5066148712a0626895c26b2578874e4cc63160bb007e6df3436fe"}, + {file = "contourpy-1.2.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fe0ccca550bb8e5abc22f530ec0466136379c01321fd94f30a22231e8a48d985"}, + {file = "contourpy-1.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e1d59258c3c67c865435d8fbeb35f8c59b8bef3d6f46c1f29f6123556af28445"}, + {file = "contourpy-1.2.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:f32c38afb74bd98ce26de7cc74a67b40afb7b05aae7b42924ea990d51e4dac02"}, + {file = "contourpy-1.2.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:d31a63bc6e6d87f77d71e1abbd7387ab817a66733734883d1fc0021ed9bfa083"}, + {file = "contourpy-1.2.1-cp39-cp39-win32.whl", hash = "sha256:ddcb8581510311e13421b1f544403c16e901c4e8f09083c881fab2be80ee31ba"}, + {file = "contourpy-1.2.1-cp39-cp39-win_amd64.whl", hash = "sha256:10a37ae557aabf2509c79715cd20b62e4c7c28b8cd62dd7d99e5ed3ce28c3fd9"}, + {file = "contourpy-1.2.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:a31f94983fecbac95e58388210427d68cd30fe8a36927980fab9c20062645609"}, + {file = "contourpy-1.2.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ef2b055471c0eb466033760a521efb9d8a32b99ab907fc8358481a1dd29e3bd3"}, + {file = "contourpy-1.2.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:b33d2bc4f69caedcd0a275329eb2198f560b325605810895627be5d4b876bf7f"}, + {file = "contourpy-1.2.1.tar.gz", hash = "sha256:4d8908b3bee1c889e547867ca4cdc54e5ab6be6d3e078556814a22457f49423c"}, +] + +[package.dependencies] +numpy = ">=1.20" + +[package.extras] +bokeh = ["bokeh", "selenium"] +docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"] +mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.8.0)", "types-Pillow"] +test = ["Pillow", "contourpy[test-no-images]", "matplotlib"] +test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"] + +[[package]] +name = "coverage" +version = "7.6.0" +description = "Code coverage measurement for Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "coverage-7.6.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:dff044f661f59dace805eedb4a7404c573b6ff0cdba4a524141bc63d7be5c7fd"}, + {file = "coverage-7.6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a8659fd33ee9e6ca03950cfdcdf271d645cf681609153f218826dd9805ab585c"}, + {file = "coverage-7.6.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7792f0ab20df8071d669d929c75c97fecfa6bcab82c10ee4adb91c7a54055463"}, + {file = "coverage-7.6.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d4b3cd1ca7cd73d229487fa5caca9e4bc1f0bca96526b922d61053ea751fe791"}, + {file = "coverage-7.6.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e7e128f85c0b419907d1f38e616c4f1e9f1d1b37a7949f44df9a73d5da5cd53c"}, + {file = "coverage-7.6.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:a94925102c89247530ae1dab7dc02c690942566f22e189cbd53579b0693c0783"}, + {file = "coverage-7.6.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:dcd070b5b585b50e6617e8972f3fbbee786afca71b1936ac06257f7e178f00f6"}, + {file = "coverage-7.6.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:d50a252b23b9b4dfeefc1f663c568a221092cbaded20a05a11665d0dbec9b8fb"}, + {file = "coverage-7.6.0-cp310-cp310-win32.whl", hash = "sha256:0e7b27d04131c46e6894f23a4ae186a6a2207209a05df5b6ad4caee6d54a222c"}, + {file = "coverage-7.6.0-cp310-cp310-win_amd64.whl", hash = "sha256:54dece71673b3187c86226c3ca793c5f891f9fc3d8aa183f2e3653da18566169"}, + {file = "coverage-7.6.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c7b525ab52ce18c57ae232ba6f7010297a87ced82a2383b1afd238849c1ff933"}, + {file = "coverage-7.6.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:4bea27c4269234e06f621f3fac3925f56ff34bc14521484b8f66a580aacc2e7d"}, + {file = "coverage-7.6.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ed8d1d1821ba5fc88d4a4f45387b65de52382fa3ef1f0115a4f7a20cdfab0e94"}, + {file = "coverage-7.6.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:01c322ef2bbe15057bc4bf132b525b7e3f7206f071799eb8aa6ad1940bcf5fb1"}, + {file = "coverage-7.6.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:03cafe82c1b32b770a29fd6de923625ccac3185a54a5e66606da26d105f37dac"}, + {file = "coverage-7.6.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:0d1b923fc4a40c5832be4f35a5dab0e5ff89cddf83bb4174499e02ea089daf57"}, + {file = "coverage-7.6.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:4b03741e70fb811d1a9a1d75355cf391f274ed85847f4b78e35459899f57af4d"}, + {file = "coverage-7.6.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a73d18625f6a8a1cbb11eadc1d03929f9510f4131879288e3f7922097a429f63"}, + {file = "coverage-7.6.0-cp311-cp311-win32.whl", hash = "sha256:65fa405b837060db569a61ec368b74688f429b32fa47a8929a7a2f9b47183713"}, + {file = "coverage-7.6.0-cp311-cp311-win_amd64.whl", hash = "sha256:6379688fb4cfa921ae349c76eb1a9ab26b65f32b03d46bb0eed841fd4cb6afb1"}, + {file = "coverage-7.6.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f7db0b6ae1f96ae41afe626095149ecd1b212b424626175a6633c2999eaad45b"}, + {file = "coverage-7.6.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:bbdf9a72403110a3bdae77948b8011f644571311c2fb35ee15f0f10a8fc082e8"}, + {file = "coverage-7.6.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9cc44bf0315268e253bf563f3560e6c004efe38f76db03a1558274a6e04bf5d5"}, + {file = "coverage-7.6.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:da8549d17489cd52f85a9829d0e1d91059359b3c54a26f28bec2c5d369524807"}, + {file = "coverage-7.6.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0086cd4fc71b7d485ac93ca4239c8f75732c2ae3ba83f6be1c9be59d9e2c6382"}, + {file = "coverage-7.6.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1fad32ee9b27350687035cb5fdf9145bc9cf0a094a9577d43e909948ebcfa27b"}, + {file = "coverage-7.6.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:044a0985a4f25b335882b0966625270a8d9db3d3409ddc49a4eb00b0ef5e8cee"}, + {file = "coverage-7.6.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:76d5f82213aa78098b9b964ea89de4617e70e0d43e97900c2778a50856dac605"}, + {file = "coverage-7.6.0-cp312-cp312-win32.whl", hash = "sha256:3c59105f8d58ce500f348c5b56163a4113a440dad6daa2294b5052a10db866da"}, + {file = "coverage-7.6.0-cp312-cp312-win_amd64.whl", hash = "sha256:ca5d79cfdae420a1d52bf177de4bc2289c321d6c961ae321503b2ca59c17ae67"}, + {file = "coverage-7.6.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:d39bd10f0ae453554798b125d2f39884290c480f56e8a02ba7a6ed552005243b"}, + {file = "coverage-7.6.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:beb08e8508e53a568811016e59f3234d29c2583f6b6e28572f0954a6b4f7e03d"}, + {file = "coverage-7.6.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b2e16f4cd2bc4d88ba30ca2d3bbf2f21f00f382cf4e1ce3b1ddc96c634bc48ca"}, + {file = "coverage-7.6.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6616d1c9bf1e3faea78711ee42a8b972367d82ceae233ec0ac61cc7fec09fa6b"}, + {file = "coverage-7.6.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad4567d6c334c46046d1c4c20024de2a1c3abc626817ae21ae3da600f5779b44"}, + {file = "coverage-7.6.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:d17c6a415d68cfe1091d3296ba5749d3d8696e42c37fca5d4860c5bf7b729f03"}, + {file = "coverage-7.6.0-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:9146579352d7b5f6412735d0f203bbd8d00113a680b66565e205bc605ef81bc6"}, + {file = "coverage-7.6.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:cdab02a0a941af190df8782aafc591ef3ad08824f97850b015c8c6a8b3877b0b"}, + {file = "coverage-7.6.0-cp38-cp38-win32.whl", hash = "sha256:df423f351b162a702c053d5dddc0fc0ef9a9e27ea3f449781ace5f906b664428"}, + {file = "coverage-7.6.0-cp38-cp38-win_amd64.whl", hash = "sha256:f2501d60d7497fd55e391f423f965bbe9e650e9ffc3c627d5f0ac516026000b8"}, + {file = "coverage-7.6.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7221f9ac9dad9492cecab6f676b3eaf9185141539d5c9689d13fd6b0d7de840c"}, + {file = "coverage-7.6.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ddaaa91bfc4477d2871442bbf30a125e8fe6b05da8a0015507bfbf4718228ab2"}, + {file = "coverage-7.6.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c4cbe651f3904e28f3a55d6f371203049034b4ddbce65a54527a3f189ca3b390"}, + {file = "coverage-7.6.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:831b476d79408ab6ccfadaaf199906c833f02fdb32c9ab907b1d4aa0713cfa3b"}, + {file = "coverage-7.6.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:46c3d091059ad0b9c59d1034de74a7f36dcfa7f6d3bde782c49deb42438f2450"}, + {file = "coverage-7.6.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:4d5fae0a22dc86259dee66f2cc6c1d3e490c4a1214d7daa2a93d07491c5c04b6"}, + {file = "coverage-7.6.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:07ed352205574aad067482e53dd606926afebcb5590653121063fbf4e2175166"}, + {file = "coverage-7.6.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:49c76cdfa13015c4560702574bad67f0e15ca5a2872c6a125f6327ead2b731dd"}, + {file = "coverage-7.6.0-cp39-cp39-win32.whl", hash = "sha256:482855914928c8175735a2a59c8dc5806cf7d8f032e4820d52e845d1f731dca2"}, + {file = "coverage-7.6.0-cp39-cp39-win_amd64.whl", hash = "sha256:543ef9179bc55edfd895154a51792b01c017c87af0ebaae092720152e19e42ca"}, + {file = "coverage-7.6.0-pp38.pp39.pp310-none-any.whl", hash = "sha256:6fe885135c8a479d3e37a7aae61cbd3a0fb2deccb4dda3c25f92a49189f766d6"}, + {file = "coverage-7.6.0.tar.gz", hash = "sha256:289cc803fa1dc901f84701ac10c9ee873619320f2f9aff38794db4a4a0268d51"}, +] + +[package.extras] +toml = ["tomli"] + +[[package]] +name = "cycler" +version = "0.12.1" +description = "Composable style cycles" +optional = false +python-versions = ">=3.8" +files = [ + {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"}, + {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"}, +] + +[package.extras] +docs = ["ipython", "matplotlib", "numpydoc", "sphinx"] +tests = ["pytest", "pytest-cov", "pytest-xdist"] + +[[package]] +name = "debugpy" +version = "1.8.2" +description = "An implementation of the Debug Adapter Protocol for Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "debugpy-1.8.2-cp310-cp310-macosx_11_0_x86_64.whl", hash = "sha256:7ee2e1afbf44b138c005e4380097d92532e1001580853a7cb40ed84e0ef1c3d2"}, + {file = "debugpy-1.8.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3f8c3f7c53130a070f0fc845a0f2cee8ed88d220d6b04595897b66605df1edd6"}, + {file = "debugpy-1.8.2-cp310-cp310-win32.whl", hash = "sha256:f179af1e1bd4c88b0b9f0fa153569b24f6b6f3de33f94703336363ae62f4bf47"}, + {file = "debugpy-1.8.2-cp310-cp310-win_amd64.whl", hash = "sha256:0600faef1d0b8d0e85c816b8bb0cb90ed94fc611f308d5fde28cb8b3d2ff0fe3"}, + {file = "debugpy-1.8.2-cp311-cp311-macosx_11_0_universal2.whl", hash = "sha256:8a13417ccd5978a642e91fb79b871baded925d4fadd4dfafec1928196292aa0a"}, + {file = "debugpy-1.8.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:acdf39855f65c48ac9667b2801234fc64d46778021efac2de7e50907ab90c634"}, + {file = "debugpy-1.8.2-cp311-cp311-win32.whl", hash = "sha256:2cbd4d9a2fc5e7f583ff9bf11f3b7d78dfda8401e8bb6856ad1ed190be4281ad"}, + {file = "debugpy-1.8.2-cp311-cp311-win_amd64.whl", hash = "sha256:d3408fddd76414034c02880e891ea434e9a9cf3a69842098ef92f6e809d09afa"}, + {file = "debugpy-1.8.2-cp312-cp312-macosx_11_0_universal2.whl", hash = "sha256:5d3ccd39e4021f2eb86b8d748a96c766058b39443c1f18b2dc52c10ac2757835"}, + {file = "debugpy-1.8.2-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:62658aefe289598680193ff655ff3940e2a601765259b123dc7f89c0239b8cd3"}, + {file = "debugpy-1.8.2-cp312-cp312-win32.whl", hash = "sha256:bd11fe35d6fd3431f1546d94121322c0ac572e1bfb1f6be0e9b8655fb4ea941e"}, + {file = "debugpy-1.8.2-cp312-cp312-win_amd64.whl", hash = "sha256:15bc2f4b0f5e99bf86c162c91a74c0631dbd9cef3c6a1d1329c946586255e859"}, + {file = "debugpy-1.8.2-cp38-cp38-macosx_11_0_x86_64.whl", hash = "sha256:5a019d4574afedc6ead1daa22736c530712465c0c4cd44f820d803d937531b2d"}, + {file = "debugpy-1.8.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:40f062d6877d2e45b112c0bbade9a17aac507445fd638922b1a5434df34aed02"}, + {file = "debugpy-1.8.2-cp38-cp38-win32.whl", hash = "sha256:c78ba1680f1015c0ca7115671fe347b28b446081dada3fedf54138f44e4ba031"}, + {file = "debugpy-1.8.2-cp38-cp38-win_amd64.whl", hash = "sha256:cf327316ae0c0e7dd81eb92d24ba8b5e88bb4d1b585b5c0d32929274a66a5210"}, + {file = "debugpy-1.8.2-cp39-cp39-macosx_11_0_x86_64.whl", hash = "sha256:1523bc551e28e15147815d1397afc150ac99dbd3a8e64641d53425dba57b0ff9"}, + {file = "debugpy-1.8.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e24ccb0cd6f8bfaec68d577cb49e9c680621c336f347479b3fce060ba7c09ec1"}, + {file = "debugpy-1.8.2-cp39-cp39-win32.whl", hash = "sha256:7f8d57a98c5a486c5c7824bc0b9f2f11189d08d73635c326abef268f83950326"}, + {file = "debugpy-1.8.2-cp39-cp39-win_amd64.whl", hash = "sha256:16c8dcab02617b75697a0a925a62943e26a0330da076e2a10437edd9f0bf3755"}, + {file = "debugpy-1.8.2-py2.py3-none-any.whl", hash = "sha256:16e16df3a98a35c63c3ab1e4d19be4cbc7fdda92d9ddc059294f18910928e0ca"}, + {file = "debugpy-1.8.2.zip", hash = "sha256:95378ed08ed2089221896b9b3a8d021e642c24edc8fef20e5d4342ca8be65c00"}, +] + +[[package]] +name = "decorator" +version = "5.1.1" +description = "Decorators for Humans" +optional = false +python-versions = ">=3.5" +files = [ + {file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"}, + {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"}, +] + +[[package]] +name = "defusedxml" +version = "0.7.1" +description = "XML bomb protection for Python stdlib modules" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +files = [ + {file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"}, + {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"}, +] + +[[package]] +name = "docker-pycreds" +version = "0.4.0" +description = "Python bindings for the docker credentials store API" +optional = false +python-versions = "*" +files = [ + {file = "docker-pycreds-0.4.0.tar.gz", hash = "sha256:6ce3270bcaf404cc4c3e27e4b6c70d3521deae82fb508767870fdbf772d584d4"}, + {file = "docker_pycreds-0.4.0-py2.py3-none-any.whl", hash = "sha256:7266112468627868005106ec19cd0d722702d2b7d5912a28e19b826c3d37af49"}, +] + +[package.dependencies] +six = ">=1.4.0" + +[[package]] +name = "einops" +version = "0.7.0" +description = "A new flavour of deep learning operations" +optional = false +python-versions = ">=3.8" +files = [ + {file = "einops-0.7.0-py3-none-any.whl", hash = "sha256:0f3096f26b914f465f6ff3c66f5478f9a5e380bb367ffc6493a68143fbbf1fd1"}, + {file = "einops-0.7.0.tar.gz", hash = "sha256:b2b04ad6081a3b227080c9bf5e3ace7160357ff03043cd66cc5b2319eb7031d1"}, +] + +[[package]] +name = "et-xmlfile" +version = "1.1.0" +description = "An implementation of lxml.xmlfile for the standard library" +optional = false +python-versions = ">=3.6" +files = [ + {file = "et_xmlfile-1.1.0-py3-none-any.whl", hash = "sha256:a2ba85d1d6a74ef63837eed693bcb89c3f752169b0e3e7ae5b16ca5e1b3deada"}, + {file = "et_xmlfile-1.1.0.tar.gz", hash = "sha256:8eb9e2bc2f8c97e37a2dc85a09ecdcdec9d8a396530a6d5a33b30b9a92da0c5c"}, +] + +[[package]] +name = "executing" +version = "2.0.1" +description = "Get the currently executing AST node of a frame, and other information" +optional = false +python-versions = ">=3.5" +files = [ + {file = "executing-2.0.1-py2.py3-none-any.whl", hash = "sha256:eac49ca94516ccc753f9fb5ce82603156e590b27525a8bc32cce8ae302eb61bc"}, + {file = "executing-2.0.1.tar.gz", hash = "sha256:35afe2ce3affba8ee97f2d69927fa823b08b472b7b994e36a52a964b93d16147"}, +] + +[package.extras] +tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"] + +[[package]] +name = "fasteners" +version = "0.19" +description = "A python package that provides useful locks" +optional = false +python-versions = ">=3.6" +files = [ + {file = "fasteners-0.19-py3-none-any.whl", hash = "sha256:758819cb5d94cdedf4e836988b74de396ceacb8e2794d21f82d131fd9ee77237"}, + {file = "fasteners-0.19.tar.gz", hash = "sha256:b4f37c3ac52d8a445af3a66bce57b33b5e90b97c696b7b984f530cf8f0ded09c"}, +] + +[[package]] +name = "fastjsonschema" +version = "2.20.0" +description = "Fastest Python implementation of JSON schema" +optional = false +python-versions = "*" +files = [ + {file = "fastjsonschema-2.20.0-py3-none-any.whl", hash = "sha256:5875f0b0fa7a0043a91e93a9b8f793bcbbba9691e7fd83dca95c28ba26d21f0a"}, + {file = "fastjsonschema-2.20.0.tar.gz", hash = "sha256:3d48fc5300ee96f5d116f10fe6f28d938e6008f59a6a025c2649475b87f76a23"}, +] + +[package.extras] +devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"] + +[[package]] +name = "filelock" +version = "3.15.4" +description = "A platform independent file lock." +optional = false +python-versions = ">=3.8" +files = [ + {file = "filelock-3.15.4-py3-none-any.whl", hash = "sha256:6ca1fffae96225dab4c6eaf1c4f4f28cd2568d3ec2a44e15a08520504de468e7"}, + {file = "filelock-3.15.4.tar.gz", hash = "sha256:2207938cbc1844345cb01a5a95524dae30f0ce089eba5b00378295a17e3e90cb"}, +] + +[package.extras] +docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"] +testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-asyncio (>=0.21)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)", "virtualenv (>=20.26.2)"] +typing = ["typing-extensions (>=4.8)"] + +[[package]] +name = "flake8" +version = "6.1.0" +description = "the modular source code checker: pep8 pyflakes and co" +optional = false +python-versions = ">=3.8.1" +files = [ + {file = "flake8-6.1.0-py2.py3-none-any.whl", hash = "sha256:ffdfce58ea94c6580c77888a86506937f9a1a227dfcd15f245d694ae20a6b6e5"}, + {file = "flake8-6.1.0.tar.gz", hash = "sha256:d5b3857f07c030bdb5bf41c7f53799571d75c4491748a3adcd47de929e34cd23"}, +] + +[package.dependencies] +mccabe = ">=0.7.0,<0.8.0" +pycodestyle = ">=2.11.0,<2.12.0" +pyflakes = ">=3.1.0,<3.2.0" + +[[package]] +name = "fonttools" +version = "4.53.1" +description = "Tools to manipulate font files" +optional = false +python-versions = ">=3.8" +files = [ + {file = "fonttools-4.53.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0679a30b59d74b6242909945429dbddb08496935b82f91ea9bf6ad240ec23397"}, + {file = "fonttools-4.53.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e8bf06b94694251861ba7fdeea15c8ec0967f84c3d4143ae9daf42bbc7717fe3"}, + {file = "fonttools-4.53.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b96cd370a61f4d083c9c0053bf634279b094308d52fdc2dd9a22d8372fdd590d"}, + {file = "fonttools-4.53.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a1c7c5aa18dd3b17995898b4a9b5929d69ef6ae2af5b96d585ff4005033d82f0"}, + {file = "fonttools-4.53.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:e013aae589c1c12505da64a7d8d023e584987e51e62006e1bb30d72f26522c41"}, + {file = "fonttools-4.53.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:9efd176f874cb6402e607e4cc9b4a9cd584d82fc34a4b0c811970b32ba62501f"}, + {file = "fonttools-4.53.1-cp310-cp310-win32.whl", hash = "sha256:c8696544c964500aa9439efb6761947393b70b17ef4e82d73277413f291260a4"}, + {file = "fonttools-4.53.1-cp310-cp310-win_amd64.whl", hash = "sha256:8959a59de5af6d2bec27489e98ef25a397cfa1774b375d5787509c06659b3671"}, + {file = "fonttools-4.53.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:da33440b1413bad53a8674393c5d29ce64d8c1a15ef8a77c642ffd900d07bfe1"}, + {file = "fonttools-4.53.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:5ff7e5e9bad94e3a70c5cd2fa27f20b9bb9385e10cddab567b85ce5d306ea923"}, + {file = "fonttools-4.53.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c6e7170d675d12eac12ad1a981d90f118c06cf680b42a2d74c6c931e54b50719"}, + {file = "fonttools-4.53.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bee32ea8765e859670c4447b0817514ca79054463b6b79784b08a8df3a4d78e3"}, + {file = "fonttools-4.53.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:6e08f572625a1ee682115223eabebc4c6a2035a6917eac6f60350aba297ccadb"}, + {file = "fonttools-4.53.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:b21952c092ffd827504de7e66b62aba26fdb5f9d1e435c52477e6486e9d128b2"}, + {file = "fonttools-4.53.1-cp311-cp311-win32.whl", hash = "sha256:9dfdae43b7996af46ff9da520998a32b105c7f098aeea06b2226b30e74fbba88"}, + {file = "fonttools-4.53.1-cp311-cp311-win_amd64.whl", hash = "sha256:d4d0096cb1ac7a77b3b41cd78c9b6bc4a400550e21dc7a92f2b5ab53ed74eb02"}, + {file = "fonttools-4.53.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:d92d3c2a1b39631a6131c2fa25b5406855f97969b068e7e08413325bc0afba58"}, + {file = "fonttools-4.53.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:3b3c8ebafbee8d9002bd8f1195d09ed2bd9ff134ddec37ee8f6a6375e6a4f0e8"}, + {file = "fonttools-4.53.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:32f029c095ad66c425b0ee85553d0dc326d45d7059dbc227330fc29b43e8ba60"}, + {file = "fonttools-4.53.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:10f5e6c3510b79ea27bb1ebfcc67048cde9ec67afa87c7dd7efa5c700491ac7f"}, + {file = "fonttools-4.53.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:f677ce218976496a587ab17140da141557beb91d2a5c1a14212c994093f2eae2"}, + {file = "fonttools-4.53.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:9e6ceba2a01b448e36754983d376064730690401da1dd104ddb543519470a15f"}, + {file = "fonttools-4.53.1-cp312-cp312-win32.whl", hash = "sha256:791b31ebbc05197d7aa096bbc7bd76d591f05905d2fd908bf103af4488e60670"}, + {file = "fonttools-4.53.1-cp312-cp312-win_amd64.whl", hash = "sha256:6ed170b5e17da0264b9f6fae86073be3db15fa1bd74061c8331022bca6d09bab"}, + {file = "fonttools-4.53.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:c818c058404eb2bba05e728d38049438afd649e3c409796723dfc17cd3f08749"}, + {file = "fonttools-4.53.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:651390c3b26b0c7d1f4407cad281ee7a5a85a31a110cbac5269de72a51551ba2"}, + {file = "fonttools-4.53.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e54f1bba2f655924c1138bbc7fa91abd61f45c68bd65ab5ed985942712864bbb"}, + {file = "fonttools-4.53.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9cd19cf4fe0595ebdd1d4915882b9440c3a6d30b008f3cc7587c1da7b95be5f"}, + {file = "fonttools-4.53.1-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:2af40ae9cdcb204fc1d8f26b190aa16534fcd4f0df756268df674a270eab575d"}, + {file = "fonttools-4.53.1-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:35250099b0cfb32d799fb5d6c651220a642fe2e3c7d2560490e6f1d3f9ae9169"}, + {file = "fonttools-4.53.1-cp38-cp38-win32.whl", hash = "sha256:f08df60fbd8d289152079a65da4e66a447efc1d5d5a4d3f299cdd39e3b2e4a7d"}, + {file = "fonttools-4.53.1-cp38-cp38-win_amd64.whl", hash = "sha256:7b6b35e52ddc8fb0db562133894e6ef5b4e54e1283dff606fda3eed938c36fc8"}, + {file = "fonttools-4.53.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:75a157d8d26c06e64ace9df037ee93a4938a4606a38cb7ffaf6635e60e253b7a"}, + {file = "fonttools-4.53.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4824c198f714ab5559c5be10fd1adf876712aa7989882a4ec887bf1ef3e00e31"}, + {file = "fonttools-4.53.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:becc5d7cb89c7b7afa8321b6bb3dbee0eec2b57855c90b3e9bf5fb816671fa7c"}, + {file = "fonttools-4.53.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:84ec3fb43befb54be490147b4a922b5314e16372a643004f182babee9f9c3407"}, + {file = "fonttools-4.53.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:73379d3ffdeecb376640cd8ed03e9d2d0e568c9d1a4e9b16504a834ebadc2dfb"}, + {file = "fonttools-4.53.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:02569e9a810f9d11f4ae82c391ebc6fb5730d95a0657d24d754ed7763fb2d122"}, + {file = "fonttools-4.53.1-cp39-cp39-win32.whl", hash = "sha256:aae7bd54187e8bf7fd69f8ab87b2885253d3575163ad4d669a262fe97f0136cb"}, + {file = "fonttools-4.53.1-cp39-cp39-win_amd64.whl", hash = "sha256:e5b708073ea3d684235648786f5f6153a48dc8762cdfe5563c57e80787c29fbb"}, + {file = "fonttools-4.53.1-py3-none-any.whl", hash = "sha256:f1f8758a2ad110bd6432203a344269f445a2907dc24ef6bccfd0ac4e14e0d71d"}, + {file = "fonttools-4.53.1.tar.gz", hash = "sha256:e128778a8e9bc11159ce5447f76766cefbd876f44bd79aff030287254e4752c4"}, +] + +[package.extras] +all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "pycairo", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"] +graphite = ["lz4 (>=1.7.4.2)"] +interpolatable = ["munkres", "pycairo", "scipy"] +lxml = ["lxml (>=4.0)"] +pathops = ["skia-pathops (>=0.5.0)"] +plot = ["matplotlib"] +repacker = ["uharfbuzz (>=0.23.0)"] +symfont = ["sympy"] +type1 = ["xattr"] +ufo = ["fs (>=2.2.0,<3)"] +unicode = ["unicodedata2 (>=15.1.0)"] +woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] + +[[package]] +name = "fqdn" +version = "1.5.1" +description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers" +optional = false +python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4" +files = [ + {file = "fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014"}, + {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"}, +] + +[[package]] +name = "frozenlist" +version = "1.4.1" +description = "A list-like structure which implements collections.abc.MutableSequence" +optional = false +python-versions = ">=3.8" +files = [ + {file = "frozenlist-1.4.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:f9aa1878d1083b276b0196f2dfbe00c9b7e752475ed3b682025ff20c1c1f51ac"}, + {file = "frozenlist-1.4.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:29acab3f66f0f24674b7dc4736477bcd4bc3ad4b896f5f45379a67bce8b96868"}, + {file = "frozenlist-1.4.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:74fb4bee6880b529a0c6560885fce4dc95936920f9f20f53d99a213f7bf66776"}, + {file = "frozenlist-1.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:590344787a90ae57d62511dd7c736ed56b428f04cd8c161fcc5e7232c130c69a"}, + {file = "frozenlist-1.4.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:068b63f23b17df8569b7fdca5517edef76171cf3897eb68beb01341131fbd2ad"}, + {file = "frozenlist-1.4.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5c849d495bf5154cd8da18a9eb15db127d4dba2968d88831aff6f0331ea9bd4c"}, + {file = "frozenlist-1.4.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9750cc7fe1ae3b1611bb8cfc3f9ec11d532244235d75901fb6b8e42ce9229dfe"}, + {file = "frozenlist-1.4.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a9b2de4cf0cdd5bd2dee4c4f63a653c61d2408055ab77b151c1957f221cabf2a"}, + {file = "frozenlist-1.4.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:0633c8d5337cb5c77acbccc6357ac49a1770b8c487e5b3505c57b949b4b82e98"}, + {file = "frozenlist-1.4.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:27657df69e8801be6c3638054e202a135c7f299267f1a55ed3a598934f6c0d75"}, + {file = "frozenlist-1.4.1-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:f9a3ea26252bd92f570600098783d1371354d89d5f6b7dfd87359d669f2109b5"}, + {file = "frozenlist-1.4.1-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:4f57dab5fe3407b6c0c1cc907ac98e8a189f9e418f3b6e54d65a718aaafe3950"}, + {file = "frozenlist-1.4.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:e02a0e11cf6597299b9f3bbd3f93d79217cb90cfd1411aec33848b13f5c656cc"}, + {file = "frozenlist-1.4.1-cp310-cp310-win32.whl", hash = "sha256:a828c57f00f729620a442881cc60e57cfcec6842ba38e1b19fd3e47ac0ff8dc1"}, + {file = "frozenlist-1.4.1-cp310-cp310-win_amd64.whl", hash = "sha256:f56e2333dda1fe0f909e7cc59f021eba0d2307bc6f012a1ccf2beca6ba362439"}, + {file = "frozenlist-1.4.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:a0cb6f11204443f27a1628b0e460f37fb30f624be6051d490fa7d7e26d4af3d0"}, + {file = "frozenlist-1.4.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b46c8ae3a8f1f41a0d2ef350c0b6e65822d80772fe46b653ab6b6274f61d4a49"}, + {file = "frozenlist-1.4.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:fde5bd59ab5357e3853313127f4d3565fc7dad314a74d7b5d43c22c6a5ed2ced"}, + {file = "frozenlist-1.4.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:722e1124aec435320ae01ee3ac7bec11a5d47f25d0ed6328f2273d287bc3abb0"}, + {file = "frozenlist-1.4.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2471c201b70d58a0f0c1f91261542a03d9a5e088ed3dc6c160d614c01649c106"}, + {file = "frozenlist-1.4.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c757a9dd70d72b076d6f68efdbb9bc943665ae954dad2801b874c8c69e185068"}, + {file = "frozenlist-1.4.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f146e0911cb2f1da549fc58fc7bcd2b836a44b79ef871980d605ec392ff6b0d2"}, + {file = "frozenlist-1.4.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4f9c515e7914626b2a2e1e311794b4c35720a0be87af52b79ff8e1429fc25f19"}, + {file = "frozenlist-1.4.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:c302220494f5c1ebeb0912ea782bcd5e2f8308037b3c7553fad0e48ebad6ad82"}, + {file = "frozenlist-1.4.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:442acde1e068288a4ba7acfe05f5f343e19fac87bfc96d89eb886b0363e977ec"}, + {file = "frozenlist-1.4.1-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:1b280e6507ea8a4fa0c0a7150b4e526a8d113989e28eaaef946cc77ffd7efc0a"}, + {file = "frozenlist-1.4.1-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:fe1a06da377e3a1062ae5fe0926e12b84eceb8a50b350ddca72dc85015873f74"}, + {file = "frozenlist-1.4.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:db9e724bebd621d9beca794f2a4ff1d26eed5965b004a97f1f1685a173b869c2"}, + {file = "frozenlist-1.4.1-cp311-cp311-win32.whl", hash = "sha256:e774d53b1a477a67838a904131c4b0eef6b3d8a651f8b138b04f748fccfefe17"}, + {file = "frozenlist-1.4.1-cp311-cp311-win_amd64.whl", hash = "sha256:fb3c2db03683b5767dedb5769b8a40ebb47d6f7f45b1b3e3b4b51ec8ad9d9825"}, + {file = "frozenlist-1.4.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:1979bc0aeb89b33b588c51c54ab0161791149f2461ea7c7c946d95d5f93b56ae"}, + {file = "frozenlist-1.4.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:cc7b01b3754ea68a62bd77ce6020afaffb44a590c2289089289363472d13aedb"}, + {file = "frozenlist-1.4.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c9c92be9fd329ac801cc420e08452b70e7aeab94ea4233a4804f0915c14eba9b"}, + {file = "frozenlist-1.4.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5c3894db91f5a489fc8fa6a9991820f368f0b3cbdb9cd8849547ccfab3392d86"}, + {file = "frozenlist-1.4.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ba60bb19387e13597fb059f32cd4d59445d7b18b69a745b8f8e5db0346f33480"}, + {file = "frozenlist-1.4.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8aefbba5f69d42246543407ed2461db31006b0f76c4e32dfd6f42215a2c41d09"}, + {file = "frozenlist-1.4.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:780d3a35680ced9ce682fbcf4cb9c2bad3136eeff760ab33707b71db84664e3a"}, + {file = "frozenlist-1.4.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9acbb16f06fe7f52f441bb6f413ebae6c37baa6ef9edd49cdd567216da8600cd"}, + {file = "frozenlist-1.4.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:23b701e65c7b36e4bf15546a89279bd4d8675faabc287d06bbcfac7d3c33e1e6"}, + {file = "frozenlist-1.4.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:3e0153a805a98f5ada7e09826255ba99fb4f7524bb81bf6b47fb702666484ae1"}, + {file = "frozenlist-1.4.1-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:dd9b1baec094d91bf36ec729445f7769d0d0cf6b64d04d86e45baf89e2b9059b"}, + {file = "frozenlist-1.4.1-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:1a4471094e146b6790f61b98616ab8e44f72661879cc63fa1049d13ef711e71e"}, + {file = "frozenlist-1.4.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:5667ed53d68d91920defdf4035d1cdaa3c3121dc0b113255124bcfada1cfa1b8"}, + {file = "frozenlist-1.4.1-cp312-cp312-win32.whl", hash = "sha256:beee944ae828747fd7cb216a70f120767fc9f4f00bacae8543c14a6831673f89"}, + {file = "frozenlist-1.4.1-cp312-cp312-win_amd64.whl", hash = "sha256:64536573d0a2cb6e625cf309984e2d873979709f2cf22839bf2d61790b448ad5"}, + {file = "frozenlist-1.4.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:20b51fa3f588ff2fe658663db52a41a4f7aa6c04f6201449c6c7c476bd255c0d"}, + {file = "frozenlist-1.4.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:410478a0c562d1a5bcc2f7ea448359fcb050ed48b3c6f6f4f18c313a9bdb1826"}, + {file = "frozenlist-1.4.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:c6321c9efe29975232da3bd0af0ad216800a47e93d763ce64f291917a381b8eb"}, + {file = "frozenlist-1.4.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:48f6a4533887e189dae092f1cf981f2e3885175f7a0f33c91fb5b7b682b6bab6"}, + {file = "frozenlist-1.4.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6eb73fa5426ea69ee0e012fb59cdc76a15b1283d6e32e4f8dc4482ec67d1194d"}, + {file = "frozenlist-1.4.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fbeb989b5cc29e8daf7f976b421c220f1b8c731cbf22b9130d8815418ea45887"}, + {file = "frozenlist-1.4.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:32453c1de775c889eb4e22f1197fe3bdfe457d16476ea407472b9442e6295f7a"}, + {file = "frozenlist-1.4.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:693945278a31f2086d9bf3df0fe8254bbeaef1fe71e1351c3bd730aa7d31c41b"}, + {file = "frozenlist-1.4.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:1d0ce09d36d53bbbe566fe296965b23b961764c0bcf3ce2fa45f463745c04701"}, + {file = "frozenlist-1.4.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:3a670dc61eb0d0eb7080890c13de3066790f9049b47b0de04007090807c776b0"}, + {file = "frozenlist-1.4.1-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:dca69045298ce5c11fd539682cff879cc1e664c245d1c64da929813e54241d11"}, + {file = "frozenlist-1.4.1-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:a06339f38e9ed3a64e4c4e43aec7f59084033647f908e4259d279a52d3757d09"}, + {file = "frozenlist-1.4.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:b7f2f9f912dca3934c1baec2e4585a674ef16fe00218d833856408c48d5beee7"}, + {file = "frozenlist-1.4.1-cp38-cp38-win32.whl", hash = "sha256:e7004be74cbb7d9f34553a5ce5fb08be14fb33bc86f332fb71cbe5216362a497"}, + {file = "frozenlist-1.4.1-cp38-cp38-win_amd64.whl", hash = "sha256:5a7d70357e7cee13f470c7883a063aae5fe209a493c57d86eb7f5a6f910fae09"}, + {file = "frozenlist-1.4.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:bfa4a17e17ce9abf47a74ae02f32d014c5e9404b6d9ac7f729e01562bbee601e"}, + {file = "frozenlist-1.4.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b7e3ed87d4138356775346e6845cccbe66cd9e207f3cd11d2f0b9fd13681359d"}, + {file = "frozenlist-1.4.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c99169d4ff810155ca50b4da3b075cbde79752443117d89429595c2e8e37fed8"}, + {file = "frozenlist-1.4.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:edb678da49d9f72c9f6c609fbe41a5dfb9a9282f9e6a2253d5a91e0fc382d7c0"}, + {file = "frozenlist-1.4.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6db4667b187a6742b33afbbaf05a7bc551ffcf1ced0000a571aedbb4aa42fc7b"}, + {file = "frozenlist-1.4.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:55fdc093b5a3cb41d420884cdaf37a1e74c3c37a31f46e66286d9145d2063bd0"}, + {file = "frozenlist-1.4.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:82e8211d69a4f4bc360ea22cd6555f8e61a1bd211d1d5d39d3d228b48c83a897"}, + {file = "frozenlist-1.4.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:89aa2c2eeb20957be2d950b85974b30a01a762f3308cd02bb15e1ad632e22dc7"}, + {file = "frozenlist-1.4.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:9d3e0c25a2350080e9319724dede4f31f43a6c9779be48021a7f4ebde8b2d742"}, + {file = "frozenlist-1.4.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:7268252af60904bf52c26173cbadc3a071cece75f873705419c8681f24d3edea"}, + {file = "frozenlist-1.4.1-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:0c250a29735d4f15321007fb02865f0e6b6a41a6b88f1f523ca1596ab5f50bd5"}, + {file = "frozenlist-1.4.1-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:96ec70beabbd3b10e8bfe52616a13561e58fe84c0101dd031dc78f250d5128b9"}, + {file = "frozenlist-1.4.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:23b2d7679b73fe0e5a4560b672a39f98dfc6f60df63823b0a9970525325b95f6"}, + {file = "frozenlist-1.4.1-cp39-cp39-win32.whl", hash = "sha256:a7496bfe1da7fb1a4e1cc23bb67c58fab69311cc7d32b5a99c2007b4b2a0e932"}, + {file = "frozenlist-1.4.1-cp39-cp39-win_amd64.whl", hash = "sha256:e6a20a581f9ce92d389a8c7d7c3dd47c81fd5d6e655c8dddf341e14aa48659d0"}, + {file = "frozenlist-1.4.1-py3-none-any.whl", hash = "sha256:04ced3e6a46b4cfffe20f9ae482818e34eba9b5fb0ce4056e4cc9b6e212d09b7"}, + {file = "frozenlist-1.4.1.tar.gz", hash = "sha256:c037a86e8513059a2613aaba4d817bb90b9d9b6b69aace3ce9c877e8c8ed402b"}, +] + +[[package]] +name = "fsspec" +version = "2022.11.0" +description = "File-system specification" +optional = false +python-versions = ">=3.7" +files = [ + {file = "fsspec-2022.11.0-py3-none-any.whl", hash = "sha256:d6e462003e3dcdcb8c7aa84c73a228f8227e72453cd22570e2363e8844edfe7b"}, + {file = "fsspec-2022.11.0.tar.gz", hash = "sha256:259d5fd5c8e756ff2ea72f42e7613c32667dc2049a4ac3d84364a7ca034acb8b"}, +] + +[package.dependencies] +aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""} +requests = {version = "*", optional = true, markers = "extra == \"http\""} +s3fs = {version = "*", optional = true, markers = "extra == \"s3\""} + +[package.extras] +abfs = ["adlfs"] +adl = ["adlfs"] +arrow = ["pyarrow (>=1)"] +dask = ["dask", "distributed"] +dropbox = ["dropbox", "dropboxdrivefs", "requests"] +entrypoints = ["importlib-metadata"] +fuse = ["fusepy"] +gcs = ["gcsfs"] +git = ["pygit2"] +github = ["requests"] +gs = ["gcsfs"] +gui = ["panel"] +hdfs = ["pyarrow (>=1)"] +http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"] +libarchive = ["libarchive-c"] +oci = ["ocifs"] +s3 = ["s3fs"] +sftp = ["paramiko"] +smb = ["smbprotocol"] +ssh = ["paramiko"] +tqdm = ["tqdm"] + +[[package]] +name = "gensim" +version = "4.3.2" +description = "Python framework for fast Vector Space Modelling" +optional = false +python-versions = ">=3.8" +files = [ + {file = "gensim-4.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:31b3cb313939b6940ee21660177f6405e71b920da462dbf065b2458a24ab33e1"}, + {file = "gensim-4.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:67c41b15e19e4950f57124f633c45839b5c84268ffa58079c5b0c0f04d2a9cb9"}, + {file = "gensim-4.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a9bf1a8ee2e8214499c517008a0fd175ce5c649954a88569358cfae6bfca42dc"}, + {file = "gensim-4.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5e34ee6f8a318fbf0b65e6d39a985ecf9e9051febfd1221ae6255fff1972c547"}, + {file = "gensim-4.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:c46b7395dc57c83329932f3febed9660891fdcc75327d56f55000e3e08898983"}, + {file = "gensim-4.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a919493339cfad39d5e76768c1bc546cd507f715c5fca93165cc174a97657457"}, + {file = "gensim-4.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8dcd1419266bd563c371d25530f4dce3505fe78059b2c0c08724e4f9e5479b38"}, + {file = "gensim-4.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e3e8035ac3f54dca3a8ca56bec526ddfe5b23006e0134b7375ca5f5dbfaef70a"}, + {file = "gensim-4.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8c3b537c1fd4699c8e6d59c3ffa2fdd9918cd4e5555bf5ee7c1fbedd89b2d643"}, + {file = "gensim-4.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:5a52001226f9e89f7833503f99c9b4fd028fdf837002f24cdc1bc3cf901a4003"}, + {file = "gensim-4.3.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:e8d62604efb8281a25254e5a6c14227034c267ed56635e590c9cae2635196dca"}, + {file = "gensim-4.3.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:bf7a9dc37c2ca465c7834863a7b264369c1373bb474135df225cee654b8adfab"}, + {file = "gensim-4.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6a33ff0d4cf3e50e7ddd7353fb38ed2d4af2e48a6ef58d622809862c30c8b8a2"}, + {file = "gensim-4.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:99876be00b73c7cef01f427d241b07eb1c1b298fb411580cc1067d22c43a13be"}, + {file = "gensim-4.3.2-cp38-cp38-win_amd64.whl", hash = "sha256:f785b3caf376a1f2989e0f3c890642e5b1566393fd3831dab03fc6670d672814"}, + {file = "gensim-4.3.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c86915cf0e0b86658a40a070bd7e04db0814065963657e92910303070275865d"}, + {file = "gensim-4.3.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:548c7bf983e619d6b8d78b6a5321dcbcba5b39f68779a0d36e38a5a971416276"}, + {file = "gensim-4.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:226690ea081b92a2289661a25e8a89069ae09b1ed4137b67a0d6ec211e0371d3"}, + {file = "gensim-4.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4715eafcd309c2f7e030829eddba72fe47bbe9bb466811fce3158127d29c8979"}, + {file = "gensim-4.3.2-cp39-cp39-win_amd64.whl", hash = "sha256:b3f26299ac241ff54329a54c37c22eac1bf4c4a337068adf2637259ee0d8484a"}, + {file = "gensim-4.3.2.tar.gz", hash = "sha256:99ac6af6ffd40682e70155ed9f92ecbf4384d59fb50af120d343ea5ee1b308ab"}, +] + +[package.dependencies] +numpy = ">=1.18.5" +scipy = ">=1.7.0" +smart-open = ">=1.8.1" + +[package.extras] +distributed = ["Pyro4 (>=4.27)"] +docs = ["POT", "Pyro4", "Pyro4 (>=4.27)", "annoy", "matplotlib", "memory-profiler", "mock", "nltk", "pandas", "pytest", "pytest-cov", "scikit-learn", "sphinx (==5.1.1)", "sphinx-gallery (==0.11.1)", "sphinxcontrib-napoleon (==0.7)", "sphinxcontrib.programoutput (==0.17)", "statsmodels", "testfixtures", "visdom (>=0.1.8,!=0.1.8.7)"] +test = ["POT", "mock", "pytest", "pytest-cov", "testfixtures", "visdom (>=0.1.8,!=0.1.8.7)"] +test-win = ["POT", "mock", "pytest", "pytest-cov", "testfixtures"] + +[[package]] +name = "gitdb" +version = "4.0.11" +description = "Git Object Database" +optional = false +python-versions = ">=3.7" +files = [ + {file = "gitdb-4.0.11-py3-none-any.whl", hash = "sha256:81a3407ddd2ee8df444cbacea00e2d038e40150acfa3001696fe0dcf1d3adfa4"}, + {file = "gitdb-4.0.11.tar.gz", hash = "sha256:bf5421126136d6d0af55bc1e7c1af1c397a34f5b7bd79e776cd3e89785c2b04b"}, +] + +[package.dependencies] +smmap = ">=3.0.1,<6" + +[[package]] +name = "gitpython" +version = "3.1.43" +description = "GitPython is a Python library used to interact with Git repositories" +optional = false +python-versions = ">=3.7" +files = [ + {file = "GitPython-3.1.43-py3-none-any.whl", hash = "sha256:eec7ec56b92aad751f9912a73404bc02ba212a23adb2c7098ee668417051a1ff"}, + {file = "GitPython-3.1.43.tar.gz", hash = "sha256:35f314a9f878467f5453cc1fee295c3e18e52f1b99f10f6cf5b1682e968a9e7c"}, +] + +[package.dependencies] +gitdb = ">=4.0.1,<5" + +[package.extras] +doc = ["sphinx (==4.3.2)", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "sphinxcontrib-applehelp (>=1.0.2,<=1.0.4)", "sphinxcontrib-devhelp (==1.0.2)", "sphinxcontrib-htmlhelp (>=2.0.0,<=2.0.1)", "sphinxcontrib-qthelp (==1.0.3)", "sphinxcontrib-serializinghtml (==1.1.5)"] +test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"] + +[[package]] +name = "gudhi" +version = "3.10.1" +description = "The Gudhi library is an open source library for Computational Topology and Topological Data Analysis (TDA)." +optional = false +python-versions = ">=3.5.0" +files = [ + {file = "gudhi-3.10.1-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:dd4ce17dafb7c54ecf08d69c30f54cb6b8e3d8629a0fc298a3c22684171dadaf"}, + {file = "gudhi-3.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4ba089193a5b2acec7aca95922ee6bcdbae8410694fa1fd6869eba6ef6711120"}, + {file = "gudhi-3.10.1-cp310-cp310-win_amd64.whl", hash = "sha256:740fae0165ebd208bdf40228e9403ce10e30ca2dd524913ed8bb8adaa467d705"}, + {file = "gudhi-3.10.1-cp311-cp311-macosx_12_0_universal2.whl", hash = "sha256:b5bd69a2e8ebde85896d3a3f8e0f0703596249fa5e2a26fad60fa71877bae355"}, + {file = "gudhi-3.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6a986379dfdc1bb2e5a2948c2b9a1c6b47f079a91f240611e2f0ae89aad1f1c0"}, + {file = "gudhi-3.10.1-cp311-cp311-win_amd64.whl", hash = "sha256:2344640e8854effec29b2aecaba36b9f0e8aad4f2fc230727ccc4a932db28cc5"}, + {file = "gudhi-3.10.1-cp312-cp312-macosx_12_0_universal2.whl", hash = "sha256:30623f9f749775b0c6d67091c4ee8e8cbffd48144639b1bcbd2c6d721fcb497a"}, + {file = "gudhi-3.10.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e1f808404baf91b4adbab28359d59286af0fc54a992556c126d157c03f7bbfaa"}, + {file = "gudhi-3.10.1-cp312-cp312-win_amd64.whl", hash = "sha256:8b273a8a6de83926dea6f1e088f364534063868451fd2484d90b85f5cf3f5309"}, + {file = "gudhi-3.10.1-cp38-cp38-macosx_12_0_universal2.whl", hash = "sha256:3608b7c9624fbb7cfecf74b094d37242a2b379fdc838b90a528676e02e3c79e1"}, + {file = "gudhi-3.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8603159166e9f7af1f706d0aaecd3626c0ab64b6953ed85948004d4ec10000ca"}, + {file = "gudhi-3.10.1-cp38-cp38-win_amd64.whl", hash = "sha256:93dd9a15314d2e26ef28c44833ba385301bd26a255a5a9a5811bad8a604ca4b4"}, + {file = "gudhi-3.10.1-cp39-cp39-macosx_12_0_universal2.whl", hash = "sha256:c4be031d7059b4b3b0a20a05c1ca8e5b0b912b3d7cd92b8108bfe7718a475726"}, + {file = "gudhi-3.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:97a4f6c27987d1b297be7b795f6790bd512a8a244622a7100f1b9672b8645542"}, + {file = "gudhi-3.10.1-cp39-cp39-win_amd64.whl", hash = "sha256:0b7efdc5b3bbfea02d32a800a1ec14b345090c90a129d88905c6e28a479d3989"}, +] + +[package.dependencies] +numpy = ">=1.15.0" + +[[package]] +name = "h11" +version = "0.14.0" +description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1" +optional = false +python-versions = ">=3.7" +files = [ + {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"}, + {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"}, +] + +[[package]] +name = "httpcore" +version = "1.0.5" +description = "A minimal low-level HTTP client." +optional = false +python-versions = ">=3.8" +files = [ + {file = "httpcore-1.0.5-py3-none-any.whl", hash = "sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5"}, + {file = "httpcore-1.0.5.tar.gz", hash = "sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61"}, +] + +[package.dependencies] +certifi = "*" +h11 = ">=0.13,<0.15" + +[package.extras] +asyncio = ["anyio (>=4.0,<5.0)"] +http2 = ["h2 (>=3,<5)"] +socks = ["socksio (==1.*)"] +trio = ["trio (>=0.22.0,<0.26.0)"] + +[[package]] +name = "httpx" +version = "0.27.0" +description = "The next generation HTTP client." +optional = false +python-versions = ">=3.8" +files = [ + {file = "httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5"}, + {file = "httpx-0.27.0.tar.gz", hash = "sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5"}, +] + +[package.dependencies] +anyio = "*" +certifi = "*" +httpcore = "==1.*" +idna = "*" +sniffio = "*" + +[package.extras] +brotli = ["brotli", "brotlicffi"] +cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"] +http2 = ["h2 (>=3,<5)"] +socks = ["socksio (==1.*)"] + +[[package]] +name = "huggingface-hub" +version = "0.17.3" +description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" +optional = false +python-versions = ">=3.8.0" +files = [ + {file = "huggingface_hub-0.17.3-py3-none-any.whl", hash = "sha256:545eb3665f6ac587add946e73984148f2ea5c7877eac2e845549730570c1933a"}, + {file = "huggingface_hub-0.17.3.tar.gz", hash = "sha256:40439632b211311f788964602bf8b0d9d6b7a2314fba4e8d67b2ce3ecea0e3fd"}, +] + +[package.dependencies] +filelock = "*" +fsspec = "*" +packaging = ">=20.9" +pyyaml = ">=5.1" +requests = "*" +tqdm = ">=4.42.1" +typing-extensions = ">=3.7.4.3" + +[package.extras] +all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"] +cli = ["InquirerPy (==0.3.4)"] +dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)"] +docs = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "black (==23.7)", "gradio", "hf-doc-builder", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.0.241)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "urllib3 (<2.0)", "watchdog"] +fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"] +inference = ["aiohttp", "pydantic (<2.0)"] +quality = ["black (==23.7)", "mypy (==1.5.1)", "ruff (>=0.0.241)"] +tensorflow = ["graphviz", "pydot", "tensorflow"] +testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (<2.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"] +torch = ["torch"] +typing = ["pydantic (<2.0)", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3"] + +[[package]] +name = "idna" +version = "3.7" +description = "Internationalized Domain Names in Applications (IDNA)" +optional = false +python-versions = ">=3.5" +files = [ + {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"}, + {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"}, +] + +[[package]] +name = "iniconfig" +version = "2.0.0" +description = "brain-dead simple config-ini parsing" +optional = false +python-versions = ">=3.7" +files = [ + {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"}, + {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"}, +] + +[[package]] +name = "intel-openmp" +version = "2021.4.0" +description = "Intel OpenMP* Runtime Library" +optional = false +python-versions = "*" +files = [ + {file = "intel_openmp-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:41c01e266a7fdb631a7609191709322da2bbf24b252ba763f125dd651bcc7675"}, + {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:3b921236a38384e2016f0f3d65af6732cf2c12918087128a9163225451e776f2"}, + {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:e2240ab8d01472fed04f3544a878cda5da16c26232b7ea1b59132dbfb48b186e"}, + {file = "intel_openmp-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:6e863d8fd3d7e8ef389d52cf97a50fe2afe1a19247e8c0d168ce021546f96fc9"}, + {file = "intel_openmp-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:eef4c8bcc8acefd7f5cd3b9384dbf73d59e2c99fc56545712ded913f43c4a94f"}, +] + +[[package]] +name = "ipykernel" +version = "6.29.5" +description = "IPython Kernel for Jupyter" +optional = false +python-versions = ">=3.8" +files = [ + {file = "ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5"}, + {file = "ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215"}, +] + +[package.dependencies] +appnope = {version = "*", markers = "platform_system == \"Darwin\""} +comm = ">=0.1.1" +debugpy = ">=1.6.5" +ipython = ">=7.23.1" +jupyter-client = ">=6.1.12" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" +matplotlib-inline = ">=0.1" +nest-asyncio = "*" +packaging = "*" +psutil = "*" +pyzmq = ">=24" +tornado = ">=6.1" +traitlets = ">=5.4.0" + +[package.extras] +cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"] +docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"] +pyqt5 = ["pyqt5"] +pyside6 = ["pyside6"] +test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"] + +[[package]] +name = "ipython" +version = "8.26.0" +description = "IPython: Productive Interactive Computing" +optional = false +python-versions = ">=3.10" +files = [ + {file = "ipython-8.26.0-py3-none-any.whl", hash = "sha256:e6b347c27bdf9c32ee9d31ae85defc525755a1869f14057e900675b9e8d6e6ff"}, + {file = "ipython-8.26.0.tar.gz", hash = "sha256:1cec0fbba8404af13facebe83d04436a7434c7400e59f47acf467c64abd0956c"}, +] + +[package.dependencies] +colorama = {version = "*", markers = "sys_platform == \"win32\""} +decorator = "*" +jedi = ">=0.16" +matplotlib-inline = "*" +pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""} +prompt-toolkit = ">=3.0.41,<3.1.0" +pygments = ">=2.4.0" +stack-data = "*" +traitlets = ">=5.13.0" +typing-extensions = {version = ">=4.6", markers = "python_version < \"3.12\""} + +[package.extras] +all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"] +black = ["black"] +doc = ["docrepr", "exceptiongroup", "intersphinx-registry", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "tomli", "typing-extensions"] +kernel = ["ipykernel"] +matplotlib = ["matplotlib"] +nbconvert = ["nbconvert"] +nbformat = ["nbformat"] +notebook = ["ipywidgets", "notebook"] +parallel = ["ipyparallel"] +qtconsole = ["qtconsole"] +test = ["packaging", "pickleshare", "pytest", "pytest-asyncio (<0.22)", "testpath"] +test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"] + +[[package]] +name = "ipywidgets" +version = "8.1.3" +description = "Jupyter interactive widgets" +optional = false +python-versions = ">=3.7" +files = [ + {file = "ipywidgets-8.1.3-py3-none-any.whl", hash = "sha256:efafd18f7a142248f7cb0ba890a68b96abd4d6e88ddbda483c9130d12667eaf2"}, + {file = "ipywidgets-8.1.3.tar.gz", hash = "sha256:f5f9eeaae082b1823ce9eac2575272952f40d748893972956dc09700a6392d9c"}, +] + +[package.dependencies] +comm = ">=0.1.3" +ipython = ">=6.1.0" +jupyterlab-widgets = ">=3.0.11,<3.1.0" +traitlets = ">=4.3.1" +widgetsnbextension = ">=4.0.11,<4.1.0" + +[package.extras] +test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"] + +[[package]] +name = "isoduration" +version = "20.11.0" +description = "Operations with ISO 8601 durations" +optional = false +python-versions = ">=3.7" +files = [ + {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"}, + {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"}, +] + +[package.dependencies] +arrow = ">=0.15.0" + +[[package]] +name = "isort" +version = "5.13.2" +description = "A Python utility / library to sort Python imports." +optional = false +python-versions = ">=3.8.0" +files = [ + {file = "isort-5.13.2-py3-none-any.whl", hash = "sha256:8ca5e72a8d85860d5a3fa69b8745237f2939afe12dbf656afbcb47fe72d947a6"}, + {file = "isort-5.13.2.tar.gz", hash = "sha256:48fdfcb9face5d58a4f6dde2e72a1fb8dcaf8ab26f95ab49fab84c2ddefb0109"}, +] + +[package.extras] +colors = ["colorama (>=0.4.6)"] + +[[package]] +name = "jedi" +version = "0.19.1" +description = "An autocompletion tool for Python that can be used for text editors." +optional = false +python-versions = ">=3.6" +files = [ + {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"}, + {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"}, +] + +[package.dependencies] +parso = ">=0.8.3,<0.9.0" + +[package.extras] +docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"] +qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"] +testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"] + +[[package]] +name = "jinja2" +version = "3.1.4" +description = "A very fast and expressive template engine." +optional = false +python-versions = ">=3.7" +files = [ + {file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"}, + {file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"}, +] + +[package.dependencies] +MarkupSafe = ">=2.0" + +[package.extras] +i18n = ["Babel (>=2.7)"] + +[[package]] +name = "jmespath" +version = "1.0.1" +description = "JSON Matching Expressions" +optional = false +python-versions = ">=3.7" +files = [ + {file = "jmespath-1.0.1-py3-none-any.whl", hash = "sha256:02e2e4cc71b5bcab88332eebf907519190dd9e6e82107fa7f83b1003a6252980"}, + {file = "jmespath-1.0.1.tar.gz", hash = "sha256:90261b206d6defd58fdd5e85f478bf633a2901798906be2ad389150c5c60edbe"}, +] + +[[package]] +name = "joblib" +version = "1.4.2" +description = "Lightweight pipelining with Python functions" +optional = false +python-versions = ">=3.8" +files = [ + {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"}, + {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"}, +] + +[[package]] +name = "json5" +version = "0.9.25" +description = "A Python implementation of the JSON5 data format." +optional = false +python-versions = ">=3.8" +files = [ + {file = "json5-0.9.25-py3-none-any.whl", hash = "sha256:34ed7d834b1341a86987ed52f3f76cd8ee184394906b6e22a1e0deb9ab294e8f"}, + {file = "json5-0.9.25.tar.gz", hash = "sha256:548e41b9be043f9426776f05df8635a00fe06104ea51ed24b67f908856e151ae"}, +] + +[[package]] +name = "jsonpointer" +version = "3.0.0" +description = "Identify specific nodes in a JSON document (RFC 6901)" +optional = false +python-versions = ">=3.7" +files = [ + {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"}, + {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"}, +] + +[[package]] +name = "jsonschema" +version = "4.23.0" +description = "An implementation of JSON Schema validation for Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"}, + {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"}, +] + +[package.dependencies] +attrs = ">=22.2.0" +fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} +idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} +isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} +jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""} +jsonschema-specifications = ">=2023.03.6" +referencing = ">=0.28.4" +rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} +rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""} +rpds-py = ">=0.7.1" +uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} +webcolors = {version = ">=24.6.0", optional = true, markers = "extra == \"format-nongpl\""} + +[package.extras] +format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"] +format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"] + +[[package]] +name = "jsonschema-specifications" +version = "2023.12.1" +description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry" +optional = false +python-versions = ">=3.8" +files = [ + {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"}, + {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"}, +] + +[package.dependencies] +referencing = ">=0.31.0" + +[[package]] +name = "jupyter" +version = "1.0.0" +description = "Jupyter metapackage. Install all the Jupyter components in one go." +optional = false +python-versions = "*" +files = [ + {file = "jupyter-1.0.0-py2.py3-none-any.whl", hash = "sha256:5b290f93b98ffbc21c0c7e749f054b3267782166d72fa5e3ed1ed4eaf34a2b78"}, + {file = "jupyter-1.0.0.tar.gz", hash = "sha256:d9dc4b3318f310e34c82951ea5d6683f67bed7def4b259fafbfe4f1beb1d8e5f"}, + {file = "jupyter-1.0.0.zip", hash = "sha256:3e1f86076bbb7c8c207829390305a2b1fe836d471ed54be66a3b8c41e7f46cc7"}, +] + +[package.dependencies] +ipykernel = "*" +ipywidgets = "*" +jupyter-console = "*" +nbconvert = "*" +notebook = "*" +qtconsole = "*" + +[[package]] +name = "jupyter-client" +version = "8.6.2" +description = "Jupyter protocol implementation and client libraries" +optional = false +python-versions = ">=3.8" +files = [ + {file = "jupyter_client-8.6.2-py3-none-any.whl", hash = "sha256:50cbc5c66fd1b8f65ecb66bc490ab73217993632809b6e505687de18e9dea39f"}, + {file = "jupyter_client-8.6.2.tar.gz", hash = "sha256:2bda14d55ee5ba58552a8c53ae43d215ad9868853489213f37da060ced54d8df"}, +] + +[package.dependencies] +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" +python-dateutil = ">=2.8.2" +pyzmq = ">=23.0" +tornado = ">=6.2" +traitlets = ">=5.3" + +[package.extras] +docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] +test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] + +[[package]] +name = "jupyter-console" +version = "6.6.3" +description = "Jupyter terminal console" +optional = false +python-versions = ">=3.7" +files = [ + {file = "jupyter_console-6.6.3-py3-none-any.whl", hash = "sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485"}, + {file = "jupyter_console-6.6.3.tar.gz", hash = "sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539"}, +] + +[package.dependencies] +ipykernel = ">=6.14" +ipython = "*" +jupyter-client = ">=7.0.0" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" +prompt-toolkit = ">=3.0.30" +pygments = "*" +pyzmq = ">=17" +traitlets = ">=5.4" + +[package.extras] +test = ["flaky", "pexpect", "pytest"] + +[[package]] +name = "jupyter-core" +version = "5.7.2" +description = "Jupyter core package. A base package on which Jupyter projects rely." +optional = false +python-versions = ">=3.8" +files = [ + {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"}, + {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"}, +] + +[package.dependencies] +platformdirs = ">=2.5" +pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""} +traitlets = ">=5.3" + +[package.extras] +docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"] +test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"] + +[[package]] +name = "jupyter-events" +version = "0.10.0" +description = "Jupyter Event System library" +optional = false +python-versions = ">=3.8" +files = [ + {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"}, + {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"}, +] + +[package.dependencies] +jsonschema = {version = ">=4.18.0", extras = ["format-nongpl"]} +python-json-logger = ">=2.0.4" +pyyaml = ">=5.3" +referencing = "*" +rfc3339-validator = "*" +rfc3986-validator = ">=0.1.1" +traitlets = ">=5.3" + +[package.extras] +cli = ["click", "rich"] +docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"] +test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"] + +[[package]] +name = "jupyter-lsp" +version = "2.2.5" +description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server" +optional = false +python-versions = ">=3.8" +files = [ + {file = "jupyter-lsp-2.2.5.tar.gz", hash = "sha256:793147a05ad446f809fd53ef1cd19a9f5256fd0a2d6b7ce943a982cb4f545001"}, + {file = "jupyter_lsp-2.2.5-py3-none-any.whl", hash = "sha256:45fbddbd505f3fbfb0b6cb2f1bc5e15e83ab7c79cd6e89416b248cb3c00c11da"}, +] + +[package.dependencies] +jupyter-server = ">=1.1.2" + +[[package]] +name = "jupyter-server" +version = "2.14.2" +description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." +optional = false +python-versions = ">=3.8" +files = [ + {file = "jupyter_server-2.14.2-py3-none-any.whl", hash = "sha256:47ff506127c2f7851a17bf4713434208fc490955d0e8632e95014a9a9afbeefd"}, + {file = "jupyter_server-2.14.2.tar.gz", hash = "sha256:66095021aa9638ced276c248b1d81862e4c50f292d575920bbe960de1c56b12b"}, +] + +[package.dependencies] +anyio = ">=3.1.0" +argon2-cffi = ">=21.1" +jinja2 = ">=3.0.3" +jupyter-client = ">=7.4.4" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" +jupyter-events = ">=0.9.0" +jupyter-server-terminals = ">=0.4.4" +nbconvert = ">=6.4.4" +nbformat = ">=5.3.0" +overrides = ">=5.0" +packaging = ">=22.0" +prometheus-client = ">=0.9" +pywinpty = {version = ">=2.0.1", markers = "os_name == \"nt\""} +pyzmq = ">=24" +send2trash = ">=1.8.2" +terminado = ">=0.8.3" +tornado = ">=6.2.0" +traitlets = ">=5.6.0" +websocket-client = ">=1.7" + +[package.extras] +docs = ["ipykernel", "jinja2", "jupyter-client", "myst-parser", "nbformat", "prometheus-client", "pydata-sphinx-theme", "send2trash", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-openapi (>=0.8.0)", "sphinxcontrib-spelling", "sphinxemoji", "tornado", "typing-extensions"] +test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0,<9)", "pytest-console-scripts", "pytest-jupyter[server] (>=0.7)", "pytest-timeout", "requests"] + +[[package]] +name = "jupyter-server-proxy" +version = "4.3.0" +description = "A Jupyter server extension to run additional processes and proxy to them that comes bundled JupyterLab extension to launch pre-defined processes." +optional = false +python-versions = ">=3.8" +files = [ + {file = "jupyter_server_proxy-4.3.0-py3-none-any.whl", hash = "sha256:0e664cf46ff8acd4c66b947ef33eb6e8a1a7bc3896ba47517ab8f24da5d198d7"}, + {file = "jupyter_server_proxy-4.3.0.tar.gz", hash = "sha256:d14db5044dfc2e672f80b75b34df2c3439efd6fc90a7999aa37b0d592075ce70"}, +] + +[package.dependencies] +aiohttp = "*" +jupyter-server = ">=1.24.0" +simpervisor = ">=1.0.0" +tornado = ">=6.1.0" +traitlets = ">=5.1.0" + +[package.extras] +acceptance = ["pytest", "pytest-asyncio", "pytest-cov", "pytest-html", "robotframework-jupyterlibrary (>=0.4.2)"] +classic = ["jupyter-server (<2)", "jupyterlab (>=3.0.0,<4.0.0a0)", "notebook (<7.0.0a0)"] +lab = ["jupyter-server (>=2)", "jupyterlab (>=4.0.5,<5.0.0a0)", "nbclassic", "notebook (>=7)"] +test = ["pytest", "pytest-asyncio", "pytest-cov", "pytest-html"] + +[[package]] +name = "jupyter-server-terminals" +version = "0.5.3" +description = "A Jupyter Server Extension Providing Terminals." +optional = false +python-versions = ">=3.8" +files = [ + {file = "jupyter_server_terminals-0.5.3-py3-none-any.whl", hash = "sha256:41ee0d7dc0ebf2809c668e0fc726dfaf258fcd3e769568996ca731b6194ae9aa"}, + {file = "jupyter_server_terminals-0.5.3.tar.gz", hash = "sha256:5ae0295167220e9ace0edcfdb212afd2b01ee8d179fe6f23c899590e9b8a5269"}, +] + +[package.dependencies] +pywinpty = {version = ">=2.0.3", markers = "os_name == \"nt\""} +terminado = ">=0.8.3" + +[package.extras] +docs = ["jinja2", "jupyter-server", "mistune (<4.0)", "myst-parser", "nbformat", "packaging", "pydata-sphinx-theme", "sphinxcontrib-github-alt", "sphinxcontrib-openapi", "sphinxcontrib-spelling", "sphinxemoji", "tornado"] +test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>=0.5.3)", "pytest-timeout"] + +[[package]] +name = "jupyterlab" +version = "4.2.4" +description = "JupyterLab computational environment" +optional = false +python-versions = ">=3.8" +files = [ + {file = "jupyterlab-4.2.4-py3-none-any.whl", hash = "sha256:807a7ec73637744f879e112060d4b9d9ebe028033b7a429b2d1f4fc523d00245"}, + {file = "jupyterlab-4.2.4.tar.gz", hash = "sha256:343a979fb9582fd08c8511823e320703281cd072a0049bcdafdc7afeda7f2537"}, +] + +[package.dependencies] +async-lru = ">=1.0.0" +httpx = ">=0.25.0" +ipykernel = ">=6.5.0" +jinja2 = ">=3.0.3" +jupyter-core = "*" +jupyter-lsp = ">=2.0.0" +jupyter-server = ">=2.4.0,<3" +jupyterlab-server = ">=2.27.1,<3" +notebook-shim = ">=0.2" +packaging = "*" +setuptools = ">=40.1.0" +tornado = ">=6.2.0" +traitlets = "*" + +[package.extras] +dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.3.5)"] +docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"] +docs-screenshots = ["altair (==5.3.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.2)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.1.post2)", "matplotlib (==3.8.3)", "nbconvert (>=7.0.0)", "pandas (==2.2.1)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"] +test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"] +upgrade-extension = ["copier (>=9,<10)", "jinja2-time (<0.3)", "pydantic (<3.0)", "pyyaml-include (<3.0)", "tomli-w (<2.0)"] + +[[package]] +name = "jupyterlab-pygments" +version = "0.3.0" +description = "Pygments theme using JupyterLab CSS variables" +optional = false +python-versions = ">=3.8" +files = [ + {file = "jupyterlab_pygments-0.3.0-py3-none-any.whl", hash = "sha256:841a89020971da1d8693f1a99997aefc5dc424bb1b251fd6322462a1b8842780"}, + {file = "jupyterlab_pygments-0.3.0.tar.gz", hash = "sha256:721aca4d9029252b11cfa9d185e5b5af4d54772bb8072f9b7036f4170054d35d"}, +] + +[[package]] +name = "jupyterlab-server" +version = "2.27.3" +description = "A set of server components for JupyterLab and JupyterLab like applications." +optional = false +python-versions = ">=3.8" +files = [ + {file = "jupyterlab_server-2.27.3-py3-none-any.whl", hash = "sha256:e697488f66c3db49df675158a77b3b017520d772c6e1548c7d9bcc5df7944ee4"}, + {file = "jupyterlab_server-2.27.3.tar.gz", hash = "sha256:eb36caca59e74471988f0ae25c77945610b887f777255aa21f8065def9e51ed4"}, +] + +[package.dependencies] +babel = ">=2.10" +jinja2 = ">=3.0.3" +json5 = ">=0.9.0" +jsonschema = ">=4.18.0" +jupyter-server = ">=1.21,<3" +packaging = ">=21.3" +requests = ">=2.31" + +[package.extras] +docs = ["autodoc-traits", "jinja2 (<3.2.0)", "mistune (<4)", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-copybutton", "sphinxcontrib-openapi (>0.8)"] +openapi = ["openapi-core (>=0.18.0,<0.19.0)", "ruamel-yaml"] +test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-validator (>=0.6.0,<0.8.0)", "pytest (>=7.0,<8)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter[server] (>=0.6.2)", "pytest-timeout", "requests-mock", "ruamel-yaml", "sphinxcontrib-spelling", "strict-rfc3339", "werkzeug"] + +[[package]] +name = "jupyterlab-widgets" +version = "3.0.11" +description = "Jupyter interactive widgets for JupyterLab" +optional = false +python-versions = ">=3.7" +files = [ + {file = "jupyterlab_widgets-3.0.11-py3-none-any.whl", hash = "sha256:78287fd86d20744ace330a61625024cf5521e1c012a352ddc0a3cdc2348becd0"}, + {file = "jupyterlab_widgets-3.0.11.tar.gz", hash = "sha256:dd5ac679593c969af29c9bed054c24f26842baa51352114736756bc035deee27"}, +] + +[[package]] +name = "karateclub" +version = "1.3.4" +description = "A general purpose library for community detection, network embedding, and graph mining research." +optional = false +python-versions = "*" +files = [] +develop = false + +[package.dependencies] +decorator = "==5.1.*" +gensim = ">=4.0.0" +networkx = "==2.8.*" +numpy = ">=1.22.0" +pandas = ">=1.2.0" +pygsp = "*" +python-Levenshtein = "*" +python-louvain = "*" +scikit-learn = "*" +scipy = "*" +six = "*" +tqdm = "*" + +[package.source] +type = "git" +url = "https://github.com/benedekrozemberczki/karateclub.git" +reference = "d35e05526455599688f1c4dd92e397cf92316ae4" +resolved_reference = "d35e05526455599688f1c4dd92e397cf92316ae4" + +[[package]] +name = "kiwisolver" +version = "1.4.5" +description = "A fast implementation of the Cassowary constraint solver" +optional = false +python-versions = ">=3.7" +files = [ + {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:05703cf211d585109fcd72207a31bb170a0f22144d68298dc5e61b3c946518af"}, + {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:146d14bebb7f1dc4d5fbf74f8a6cb15ac42baadee8912eb84ac0b3b2a3dc6ac3"}, + {file = "kiwisolver-1.4.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6ef7afcd2d281494c0a9101d5c571970708ad911d028137cd558f02b851c08b4"}, + {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:9eaa8b117dc8337728e834b9c6e2611f10c79e38f65157c4c38e9400286f5cb1"}, + {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:ec20916e7b4cbfb1f12380e46486ec4bcbaa91a9c448b97023fde0d5bbf9e4ff"}, + {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:39b42c68602539407884cf70d6a480a469b93b81b7701378ba5e2328660c847a"}, + {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aa12042de0171fad672b6c59df69106d20d5596e4f87b5e8f76df757a7c399aa"}, + {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2a40773c71d7ccdd3798f6489aaac9eee213d566850a9533f8d26332d626b82c"}, + {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:19df6e621f6d8b4b9c4d45f40a66839294ff2bb235e64d2178f7522d9170ac5b"}, + {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:83d78376d0d4fd884e2c114d0621624b73d2aba4e2788182d286309ebdeed770"}, + {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:e391b1f0a8a5a10ab3b9bb6afcfd74f2175f24f8975fb87ecae700d1503cdee0"}, + {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:852542f9481f4a62dbb5dd99e8ab7aedfeb8fb6342349a181d4036877410f525"}, + {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:59edc41b24031bc25108e210c0def6f6c2191210492a972d585a06ff246bb79b"}, + {file = "kiwisolver-1.4.5-cp310-cp310-win32.whl", hash = "sha256:a6aa6315319a052b4ee378aa171959c898a6183f15c1e541821c5c59beaa0238"}, + {file = "kiwisolver-1.4.5-cp310-cp310-win_amd64.whl", hash = "sha256:d0ef46024e6a3d79c01ff13801cb19d0cad7fd859b15037aec74315540acc276"}, + {file = "kiwisolver-1.4.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:11863aa14a51fd6ec28688d76f1735f8f69ab1fabf388851a595d0721af042f5"}, + {file = "kiwisolver-1.4.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:8ab3919a9997ab7ef2fbbed0cc99bb28d3c13e6d4b1ad36e97e482558a91be90"}, + {file = "kiwisolver-1.4.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:fcc700eadbbccbf6bc1bcb9dbe0786b4b1cb91ca0dcda336eef5c2beed37b797"}, + {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dfdd7c0b105af050eb3d64997809dc21da247cf44e63dc73ff0fd20b96be55a9"}, + {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76c6a5964640638cdeaa0c359382e5703e9293030fe730018ca06bc2010c4437"}, + {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bbea0db94288e29afcc4c28afbf3a7ccaf2d7e027489c449cf7e8f83c6346eb9"}, + {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ceec1a6bc6cab1d6ff5d06592a91a692f90ec7505d6463a88a52cc0eb58545da"}, + {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:040c1aebeda72197ef477a906782b5ab0d387642e93bda547336b8957c61022e"}, + {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f91de7223d4c7b793867797bacd1ee53bfe7359bd70d27b7b58a04efbb9436c8"}, + {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:faae4860798c31530dd184046a900e652c95513796ef51a12bc086710c2eec4d"}, + {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:b0157420efcb803e71d1b28e2c287518b8808b7cf1ab8af36718fd0a2c453eb0"}, + {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:06f54715b7737c2fecdbf140d1afb11a33d59508a47bf11bb38ecf21dc9ab79f"}, + {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:fdb7adb641a0d13bdcd4ef48e062363d8a9ad4a182ac7647ec88f695e719ae9f"}, + {file = "kiwisolver-1.4.5-cp311-cp311-win32.whl", hash = "sha256:bb86433b1cfe686da83ce32a9d3a8dd308e85c76b60896d58f082136f10bffac"}, + {file = "kiwisolver-1.4.5-cp311-cp311-win_amd64.whl", hash = "sha256:6c08e1312a9cf1074d17b17728d3dfce2a5125b2d791527f33ffbe805200a355"}, + {file = "kiwisolver-1.4.5-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:32d5cf40c4f7c7b3ca500f8985eb3fb3a7dfc023215e876f207956b5ea26632a"}, + {file = "kiwisolver-1.4.5-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f846c260f483d1fd217fe5ed7c173fb109efa6b1fc8381c8b7552c5781756192"}, + {file = "kiwisolver-1.4.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:5ff5cf3571589b6d13bfbfd6bcd7a3f659e42f96b5fd1c4830c4cf21d4f5ef45"}, + {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7269d9e5f1084a653d575c7ec012ff57f0c042258bf5db0954bf551c158466e7"}, + {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:da802a19d6e15dffe4b0c24b38b3af68e6c1a68e6e1d8f30148c83864f3881db"}, + {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3aba7311af82e335dd1e36ffff68aaca609ca6290c2cb6d821a39aa075d8e3ff"}, + {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:763773d53f07244148ccac5b084da5adb90bfaee39c197554f01b286cf869228"}, + {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2270953c0d8cdab5d422bee7d2007f043473f9d2999631c86a223c9db56cbd16"}, + {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:d099e745a512f7e3bbe7249ca835f4d357c586d78d79ae8f1dcd4d8adeb9bda9"}, + {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:74db36e14a7d1ce0986fa104f7d5637aea5c82ca6326ed0ec5694280942d1162"}, + {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:7e5bab140c309cb3a6ce373a9e71eb7e4873c70c2dda01df6820474f9889d6d4"}, + {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:0f114aa76dc1b8f636d077979c0ac22e7cd8f3493abbab152f20eb8d3cda71f3"}, + {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:88a2df29d4724b9237fc0c6eaf2a1adae0cdc0b3e9f4d8e7dc54b16812d2d81a"}, + {file = "kiwisolver-1.4.5-cp312-cp312-win32.whl", hash = "sha256:72d40b33e834371fd330fb1472ca19d9b8327acb79a5821d4008391db8e29f20"}, + {file = "kiwisolver-1.4.5-cp312-cp312-win_amd64.whl", hash = "sha256:2c5674c4e74d939b9d91dda0fae10597ac7521768fec9e399c70a1f27e2ea2d9"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:3a2b053a0ab7a3960c98725cfb0bf5b48ba82f64ec95fe06f1d06c99b552e130"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3cd32d6c13807e5c66a7cbb79f90b553642f296ae4518a60d8d76243b0ad2898"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:59ec7b7c7e1a61061850d53aaf8e93db63dce0c936db1fda2658b70e4a1be709"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:da4cfb373035def307905d05041c1d06d8936452fe89d464743ae7fb8371078b"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2400873bccc260b6ae184b2b8a4fec0e4082d30648eadb7c3d9a13405d861e89"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:1b04139c4236a0f3aff534479b58f6f849a8b351e1314826c2d230849ed48985"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:4e66e81a5779b65ac21764c295087de82235597a2293d18d943f8e9e32746265"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:7931d8f1f67c4be9ba1dd9c451fb0eeca1a25b89e4d3f89e828fe12a519b782a"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:b3f7e75f3015df442238cca659f8baa5f42ce2a8582727981cbfa15fee0ee205"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:bbf1d63eef84b2e8c89011b7f2235b1e0bf7dacc11cac9431fc6468e99ac77fb"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:4c380469bd3f970ef677bf2bcba2b6b0b4d5c75e7a020fb863ef75084efad66f"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-win32.whl", hash = "sha256:9408acf3270c4b6baad483865191e3e582b638b1654a007c62e3efe96f09a9a3"}, + {file = "kiwisolver-1.4.5-cp37-cp37m-win_amd64.whl", hash = "sha256:5b94529f9b2591b7af5f3e0e730a4e0a41ea174af35a4fd067775f9bdfeee01a"}, + {file = "kiwisolver-1.4.5-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:11c7de8f692fc99816e8ac50d1d1aef4f75126eefc33ac79aac02c099fd3db71"}, + {file = "kiwisolver-1.4.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:53abb58632235cd154176ced1ae8f0d29a6657aa1aa9decf50b899b755bc2b93"}, + {file = "kiwisolver-1.4.5-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:88b9f257ca61b838b6f8094a62418421f87ac2a1069f7e896c36a7d86b5d4c29"}, + {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3195782b26fc03aa9c6913d5bad5aeb864bdc372924c093b0f1cebad603dd712"}, + {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fc579bf0f502e54926519451b920e875f433aceb4624a3646b3252b5caa9e0b6"}, + {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5a580c91d686376f0f7c295357595c5a026e6cbc3d77b7c36e290201e7c11ecb"}, + {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:cfe6ab8da05c01ba6fbea630377b5da2cd9bcbc6338510116b01c1bc939a2c18"}, + {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:d2e5a98f0ec99beb3c10e13b387f8db39106d53993f498b295f0c914328b1333"}, + {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:a51a263952b1429e429ff236d2f5a21c5125437861baeed77f5e1cc2d2c7c6da"}, + {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:3edd2fa14e68c9be82c5b16689e8d63d89fe927e56debd6e1dbce7a26a17f81b"}, + {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:74d1b44c6cfc897df648cc9fdaa09bc3e7679926e6f96df05775d4fb3946571c"}, + {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:76d9289ed3f7501012e05abb8358bbb129149dbd173f1f57a1bf1c22d19ab7cc"}, + {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:92dea1ffe3714fa8eb6a314d2b3c773208d865a0e0d35e713ec54eea08a66250"}, + {file = "kiwisolver-1.4.5-cp38-cp38-win32.whl", hash = "sha256:5c90ae8c8d32e472be041e76f9d2f2dbff4d0b0be8bd4041770eddb18cf49a4e"}, + {file = "kiwisolver-1.4.5-cp38-cp38-win_amd64.whl", hash = "sha256:c7940c1dc63eb37a67721b10d703247552416f719c4188c54e04334321351ced"}, + {file = "kiwisolver-1.4.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:9407b6a5f0d675e8a827ad8742e1d6b49d9c1a1da5d952a67d50ef5f4170b18d"}, + {file = "kiwisolver-1.4.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:15568384086b6df3c65353820a4473575dbad192e35010f622c6ce3eebd57af9"}, + {file = "kiwisolver-1.4.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0dc9db8e79f0036e8173c466d21ef18e1befc02de8bf8aa8dc0813a6dc8a7046"}, + {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:cdc8a402aaee9a798b50d8b827d7ecf75edc5fb35ea0f91f213ff927c15f4ff0"}, + {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:6c3bd3cde54cafb87d74d8db50b909705c62b17c2099b8f2e25b461882e544ff"}, + {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:955e8513d07a283056b1396e9a57ceddbd272d9252c14f154d450d227606eb54"}, + {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:346f5343b9e3f00b8db8ba359350eb124b98c99efd0b408728ac6ebf38173958"}, + {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b9098e0049e88c6a24ff64545cdfc50807818ba6c1b739cae221bbbcbc58aad3"}, + {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:00bd361b903dc4bbf4eb165f24d1acbee754fce22ded24c3d56eec268658a5cf"}, + {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:7b8b454bac16428b22560d0a1cf0a09875339cab69df61d7805bf48919415901"}, + {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:f1d072c2eb0ad60d4c183f3fb44ac6f73fb7a8f16a2694a91f988275cbf352f9"}, + {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:31a82d498054cac9f6d0b53d02bb85811185bcb477d4b60144f915f3b3126342"}, + {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:6512cb89e334e4700febbffaaa52761b65b4f5a3cf33f960213d5656cea36a77"}, + {file = "kiwisolver-1.4.5-cp39-cp39-win32.whl", hash = "sha256:9db8ea4c388fdb0f780fe91346fd438657ea602d58348753d9fb265ce1bca67f"}, + {file = "kiwisolver-1.4.5-cp39-cp39-win_amd64.whl", hash = "sha256:59415f46a37f7f2efeec758353dd2eae1b07640d8ca0f0c42548ec4125492635"}, + {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:5c7b3b3a728dc6faf3fc372ef24f21d1e3cee2ac3e9596691d746e5a536de920"}, + {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:620ced262a86244e2be10a676b646f29c34537d0d9cc8eb26c08f53d98013390"}, + {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:378a214a1e3bbf5ac4a8708304318b4f890da88c9e6a07699c4ae7174c09a68d"}, + {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aaf7be1207676ac608a50cd08f102f6742dbfc70e8d60c4db1c6897f62f71523"}, + {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:ba55dce0a9b8ff59495ddd050a0225d58bd0983d09f87cfe2b6aec4f2c1234e4"}, + {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:fd32ea360bcbb92d28933fc05ed09bffcb1704ba3fc7942e81db0fd4f81a7892"}, + {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:5e7139af55d1688f8b960ee9ad5adafc4ac17c1c473fe07133ac092310d76544"}, + {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:dced8146011d2bc2e883f9bd68618b8247387f4bbec46d7392b3c3b032640126"}, + {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c9bf3325c47b11b2e51bca0824ea217c7cd84491d8ac4eefd1e409705ef092bd"}, + {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5794cf59533bc3f1b1c821f7206a3617999db9fbefc345360aafe2e067514929"}, + {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e368f200bbc2e4f905b8e71eb38b3c04333bddaa6a2464a6355487b02bb7fb09"}, + {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e5d706eba36b4c4d5bc6c6377bb6568098765e990cfc21ee16d13963fab7b3e7"}, + {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:85267bd1aa8880a9c88a8cb71e18d3d64d2751a790e6ca6c27b8ccc724bcd5ad"}, + {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:210ef2c3a1f03272649aff1ef992df2e724748918c4bc2d5a90352849eb40bea"}, + {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:11d011a7574eb3b82bcc9c1a1d35c1d7075677fdd15de527d91b46bd35e935ee"}, + {file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"}, +] + +[[package]] +name = "levenshtein" +version = "0.25.1" +description = "Python extension for computing string edit distances and similarities." +optional = false +python-versions = ">=3.8" +files = [ + {file = "Levenshtein-0.25.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:eb4d1ec9f2dcbde1757c4b7fb65b8682bc2de45b9552e201988f287548b7abdf"}, + {file = "Levenshtein-0.25.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b4d9fa3affef48a7e727cdbd0d9502cd060da86f34d8b3627edd769d347570e2"}, + {file = "Levenshtein-0.25.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c1b6cd186e58196ff8b402565317e9346b408d0c04fa0ed12ce4868c0fcb6d03"}, + {file = "Levenshtein-0.25.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:82637ef5428384dd1812849dd7328992819bf0c4a20bff0a3b3ee806821af7ed"}, + {file = "Levenshtein-0.25.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e73656da6cc3e32a6e4bcd48562fcb64599ef124997f2c91f5320d7f1532c069"}, + {file = "Levenshtein-0.25.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5abff796f92cdfba69b9cbf6527afae918d0e95cbfac000bd84017f74e0bd427"}, + {file = "Levenshtein-0.25.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:38827d82f2ca9cb755da6f03e686866f2f411280db005f4304272378412b4cba"}, + {file = "Levenshtein-0.25.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2b989df1e3231261a87d68dfa001a2070771e178b09650f9cf99a20e3d3abc28"}, + {file = "Levenshtein-0.25.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:2011d3b3897d438a2f88ef7aed7747f28739cae8538ec7c18c33dd989930c7a0"}, + {file = "Levenshtein-0.25.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:6c375b33ec7acc1c6855e8ee8c7c8ac6262576ffed484ff5c556695527f49686"}, + {file = "Levenshtein-0.25.1-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:ce0cb9dd012ef1bf4d5b9d40603e7709b6581aec5acd32fcea9b371b294ca7aa"}, + {file = "Levenshtein-0.25.1-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:9da9ecb81bae67d784defed7274f894011259b038ec31f2339c4958157970115"}, + {file = "Levenshtein-0.25.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:3bd7be5dbe5f4a1b691f381e39512927b39d1e195bd0ad61f9bf217a25bf36c9"}, + {file = "Levenshtein-0.25.1-cp310-cp310-win32.whl", hash = "sha256:f6abb9ced98261de67eb495b95e1d2325fa42b0344ed5763f7c0f36ee2e2bdba"}, + {file = "Levenshtein-0.25.1-cp310-cp310-win_amd64.whl", hash = "sha256:97581af3e0a6d359af85c6cf06e51f77f4d635f7109ff7f8ed7fd634d8d8c923"}, + {file = "Levenshtein-0.25.1-cp310-cp310-win_arm64.whl", hash = "sha256:9ba008f490788c6d8d5a10735fcf83559965be97e4ef0812db388a84b1cc736a"}, + {file = "Levenshtein-0.25.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:f57d9cf06dac55c2d2f01f0d06e32acc074ab9a902921dc8fddccfb385053ad5"}, + {file = "Levenshtein-0.25.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:22b60c6d791f4ca67a3686b557ddb2a48de203dae5214f220f9dddaab17f44bb"}, + {file = "Levenshtein-0.25.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d0444ee62eccf1e6cedc7c5bc01a9face6ff70cc8afa3f3ca9340e4e16f601a4"}, + {file = "Levenshtein-0.25.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7e8758be8221a274c83924bae8dd8f42041792565a3c3bdd3c10e3f9b4a5f94e"}, + {file = "Levenshtein-0.25.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:147221cfb1d03ed81d22fdd2a4c7fc2112062941b689e027a30d2b75bbced4a3"}, + {file = "Levenshtein-0.25.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a454d5bc4f4a289f5471418788517cc122fcc00d5a8aba78c54d7984840655a2"}, + {file = "Levenshtein-0.25.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5c25f3778bbac78286bef2df0ca80f50517b42b951af0a5ddaec514412f79fac"}, + {file = "Levenshtein-0.25.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:181486cf465aff934694cc9a19f3898a1d28025a9a5f80fc1608217e7cd1c799"}, + {file = "Levenshtein-0.25.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:b8db9f672a5d150706648b37b044dba61f36ab7216c6a121cebbb2899d7dfaa3"}, + {file = "Levenshtein-0.25.1-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:f2a69fe5ddea586d439f9a50d0c51952982f6c0db0e3573b167aa17e6d1dfc48"}, + {file = "Levenshtein-0.25.1-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:3b684675a3bd35efa6997856e73f36c8a41ef62519e0267dcbeefd15e26cae71"}, + {file = "Levenshtein-0.25.1-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:cc707ef7edb71f6bf8339198b929ead87c022c78040e41668a4db68360129cef"}, + {file = "Levenshtein-0.25.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:41512c436b8c691326e2d07786d906cba0e92b5e3f455bf338befb302a0ca76d"}, + {file = "Levenshtein-0.25.1-cp311-cp311-win32.whl", hash = "sha256:2a3830175c01ade832ba0736091283f14a6506a06ffe8c846f66d9fbca91562f"}, + {file = "Levenshtein-0.25.1-cp311-cp311-win_amd64.whl", hash = "sha256:9e0af4e6e023e0c8f79af1d1ca5f289094eb91201f08ad90f426d71e4ae84052"}, + {file = "Levenshtein-0.25.1-cp311-cp311-win_arm64.whl", hash = "sha256:38e5d9a1d737d7b49fa17d6a4c03a0359288154bf46dc93b29403a9dd0cd1a7d"}, + {file = "Levenshtein-0.25.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:4a40fa16ecd0bf9e557db67131aabeea957f82fe3e8df342aa413994c710c34e"}, + {file = "Levenshtein-0.25.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:4f7d2045d5927cffa65a0ac671c263edbfb17d880fdce2d358cd0bda9bcf2b6d"}, + {file = "Levenshtein-0.25.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40f96590539f9815be70e330b4d2efcce0219db31db5a22fffe99565192f5662"}, + {file = "Levenshtein-0.25.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2d78512dd25b572046ff86d8903bec283c373063349f8243430866b6a9946425"}, + {file = "Levenshtein-0.25.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c161f24a1b216e8555c874c7dd70c1a0d98f783f252a16c9face920a8b8a6f3e"}, + {file = "Levenshtein-0.25.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:06ebbfd010a00490795f478d18d7fa2ffc79c9c03fc03b678081f31764d16bab"}, + {file = "Levenshtein-0.25.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eaa9ec0a4489ebfb25a9ec2cba064ed68d0d2485b8bc8b7203f84a7874755e0f"}, + {file = "Levenshtein-0.25.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:26408938a6db7b252824a701545d50dc9cdd7a3e4c7ee70834cca17953b76ad8"}, + {file = "Levenshtein-0.25.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:330ec2faff957281f4e6a1a8c88286d1453e1d73ee273ea0f937e0c9281c2156"}, + {file = "Levenshtein-0.25.1-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:9115d1b08626dfdea6f3955cb49ba5a578f7223205f80ead0038d6fc0442ce13"}, + {file = "Levenshtein-0.25.1-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:bbd602edab758e93a5c67bf0d8322f374a47765f1cdb6babaf593a64dc9633ad"}, + {file = "Levenshtein-0.25.1-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:b930b4df32cd3aabbed0e9f0c4fdd1ea4090a5c022ba9f1ae4ab70ccf1cf897a"}, + {file = "Levenshtein-0.25.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:dd66fb51f88a3f73a802e1ff19a14978ddc9fbcb7ce3a667ca34f95ef54e0e44"}, + {file = "Levenshtein-0.25.1-cp312-cp312-win32.whl", hash = "sha256:386de94bd1937a16ae3c8f8b7dd2eff1b733994ecf56ce4d05dfdd0e776d0261"}, + {file = "Levenshtein-0.25.1-cp312-cp312-win_amd64.whl", hash = "sha256:9ee1902153d47886c9787598a4a5c324ce7fde44d44daa34fcf3652ac0de21bc"}, + {file = "Levenshtein-0.25.1-cp312-cp312-win_arm64.whl", hash = "sha256:b56a7e7676093c3aee50402226f4079b15bd21b5b8f1820f9d6d63fe99dc4927"}, + {file = "Levenshtein-0.25.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:6b5dfdf6a0e2f35fd155d4c26b03398499c24aba7bc5db40245789c46ad35c04"}, + {file = "Levenshtein-0.25.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:355ff797f704459ddd8b95354d699d0d0642348636c92d5e67b49be4b0e6112b"}, + {file = "Levenshtein-0.25.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:933b827a3b721210fff522f3dca9572f9f374a0e88fa3a6c7ee3164406ae7794"}, + {file = "Levenshtein-0.25.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:be1da669a240f272d904ab452ad0a1603452e190f4e03e886e6b3a9904152b89"}, + {file = "Levenshtein-0.25.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:265cbd78962503a26f2bea096258a3b70b279bb1a74a525c671d3ee43a190f9c"}, + {file = "Levenshtein-0.25.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:63cc4d53a35e673b12b721a58b197b4a65734688fb72aa1987ce63ed612dca96"}, + {file = "Levenshtein-0.25.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:75fee0c471b8799c70dad9d0d5b70f1f820249257f9617601c71b6c1b37bee92"}, + {file = "Levenshtein-0.25.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:045d6b0db124fbd37379b2b91f6d0786c2d9220e7a848e2dd31b99509a321240"}, + {file = "Levenshtein-0.25.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:db7a2e9c51ac9cc2fd5679484f1eac6e0ab2085cb181240445f7fbf10df73230"}, + {file = "Levenshtein-0.25.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:c379c588aa0d93d4607db7eb225fd683263d49669b1bbe49e28c978aa6a4305d"}, + {file = "Levenshtein-0.25.1-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:966dd00424df7f69b78da02a29b530fbb6c1728e9002a2925ed7edf26b231924"}, + {file = "Levenshtein-0.25.1-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:09daa6b068709cc1e68b670a706d928ed8f0b179a26161dd04b3911d9f757525"}, + {file = "Levenshtein-0.25.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:d6bed0792635081accf70a7e11cfece986f744fddf46ce26808cd8bfc067e430"}, + {file = "Levenshtein-0.25.1-cp38-cp38-win32.whl", hash = "sha256:28e7b7faf5a745a690d1b1706ab82a76bbe9fa6b729d826f0cfdd24fd7c19740"}, + {file = "Levenshtein-0.25.1-cp38-cp38-win_amd64.whl", hash = "sha256:8ca0cc9b9e07316b5904f158d5cfa340d55b4a3566ac98eaac9f087c6efb9a1a"}, + {file = "Levenshtein-0.25.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:45682cdb3ac4a5465c01b2dce483bdaa1d5dcd1a1359fab37d26165b027d3de2"}, + {file = "Levenshtein-0.25.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:f8dc3e63c4cd746ec162a4cd744c6dde857e84aaf8c397daa46359c3d54e6219"}, + {file = "Levenshtein-0.25.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:01ad1eb09933a499a49923e74e05b1428ca4ef37fed32965fef23f1334a11563"}, + {file = "Levenshtein-0.25.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cbb4e8c4b8b7bbe0e1aa64710b806b6c3f31d93cb14969ae2c0eff0f3a592db8"}, + {file = "Levenshtein-0.25.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b48d1fe224b365975002e3e2ea947cbb91d2936a16297859b71c4abe8a39932c"}, + {file = "Levenshtein-0.25.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a164df16d876aab0a400f72aeac870ea97947ea44777c89330e9a16c7dc5cc0e"}, + {file = "Levenshtein-0.25.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:995d3bcedcf64be6ceca423f6cfe29184a36d7c4cbac199fdc9a0a5ec7196cf5"}, + {file = "Levenshtein-0.25.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bdaf62d637bef6711d6f3457e2684faab53b2db2ed53c05bc0dc856464c74742"}, + {file = "Levenshtein-0.25.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:af9de3b5f8f5f3530cfd97daab9ab480d1b121ef34d8c0aa5bab0c645eae219e"}, + {file = "Levenshtein-0.25.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:78fba73c352383b356a30c4674e39f086ffef7122fa625e7550b98be2392d387"}, + {file = "Levenshtein-0.25.1-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:9e0df0dcea3943321398f72e330c089b5d5447318310db6f17f5421642f3ade6"}, + {file = "Levenshtein-0.25.1-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:387f768bb201b9bc45f0f49557e2fb9a3774d9d087457bab972162dcd4fd352b"}, + {file = "Levenshtein-0.25.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5dcf931b64311039b43495715e9b795fbd97ab44ba3dd6bf24360b15e4e87649"}, + {file = "Levenshtein-0.25.1-cp39-cp39-win32.whl", hash = "sha256:2449f8668c0bd62a2b305a5e797348984c06ac20903b38b3bab74e55671ddd51"}, + {file = "Levenshtein-0.25.1-cp39-cp39-win_amd64.whl", hash = "sha256:28803fd6ec7b58065621f5ec0d24e44e2a7dc4842b64dcab690cb0a7ea545210"}, + {file = "Levenshtein-0.25.1-cp39-cp39-win_arm64.whl", hash = "sha256:0b074d452dff8ee86b5bdb6031aa32bb2ed3c8469a56718af5e010b9bb5124dc"}, + {file = "Levenshtein-0.25.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e9e060ef3925a68aeb12276f0e524fb1264592803d562ec0306c7c3f5c68eae0"}, + {file = "Levenshtein-0.25.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5f84b84049318d44722db307c448f9dcb8d27c73525a378e901189a94889ba61"}, + {file = "Levenshtein-0.25.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:07e23fdf330cb185a0c7913ca5bd73a189dfd1742eae3a82e31ed8688b191800"}, + {file = "Levenshtein-0.25.1-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d06958e4a81ea0f0b2b7768a2ad05bcd50a9ad04c4d521dd37d5730ff12decdc"}, + {file = "Levenshtein-0.25.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:2ea7c34ec22b2fce21299b0caa6dde6bdebafcc2970e265853c9cfea8d1186da"}, + {file = "Levenshtein-0.25.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:fddc0ccbdd94f57aa32e2eb3ac8310d08df2e175943dc20b3e1fc7a115850af4"}, + {file = "Levenshtein-0.25.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7d52249cb3448bfe661d3d7db3a6673e835c7f37b30b0aeac499a1601bae873d"}, + {file = "Levenshtein-0.25.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e8dd4c201b15f8c1e612f9074335392c8208ac147acbce09aff04e3974bf9b16"}, + {file = "Levenshtein-0.25.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:23a4d95ce9d44161c7aa87ab76ad6056bc1093c461c60c097054a46dc957991f"}, + {file = "Levenshtein-0.25.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:65eea8a9c33037b23069dca4b3bc310e3c28ca53f60ec0c958d15c0952ba39fa"}, + {file = "Levenshtein-0.25.1.tar.gz", hash = "sha256:2df14471c778c75ffbd59cb64bbecfd4b0ef320ef9f80e4804764be7d5678980"}, +] + +[package.dependencies] +rapidfuzz = ">=3.8.0,<4.0.0" + +[[package]] +name = "lightning-utilities" +version = "0.11.6" +description = "Lightning toolbox for across the our ecosystem." +optional = false +python-versions = ">=3.8" +files = [ + {file = "lightning_utilities-0.11.6-py3-none-any.whl", hash = "sha256:ecd9953c316cbaf56ad820fbe7bd062187b9973c4a23d47b076cd59dc080a310"}, + {file = "lightning_utilities-0.11.6.tar.gz", hash = "sha256:79fc27ef8ec8b8d55a537920f2c7610270c0c9e037fa6efc78f1aa34ec8cdf04"}, +] + +[package.dependencies] +packaging = ">=17.1" +setuptools = "*" +typing-extensions = "*" + +[package.extras] +cli = ["fire"] +docs = ["requests (>=2.0.0)"] +typing = ["mypy (>=1.0.0)", "types-setuptools"] + +[[package]] +name = "littleutils" +version = "0.2.4" +description = "Small personal collection of python utility functions" +optional = false +python-versions = ">=3.8" +files = [ + {file = "littleutils-0.2.4-py3-none-any.whl", hash = "sha256:d10d5fe2e107c49fe2fc2904a08d6e5a302b41f8405921835ffcc323782d5dbc"}, + {file = "littleutils-0.2.4.tar.gz", hash = "sha256:c7835b01020ced42e291118b7d78fb16bc2d9a1b4f3f42f3cb3787bb4da53d19"}, +] + +[[package]] +name = "markdown-it-py" +version = "3.0.0" +description = "Python port of markdown-it. Markdown parsing, done right!" +optional = false +python-versions = ">=3.8" +files = [ + {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"}, + {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"}, +] + +[package.dependencies] +mdurl = ">=0.1,<1.0" + +[package.extras] +benchmarking = ["psutil", "pytest", "pytest-benchmark"] +code-style = ["pre-commit (>=3.0,<4.0)"] +compare = ["commonmark (>=0.9,<1.0)", "markdown (>=3.4,<4.0)", "mistletoe (>=1.0,<2.0)", "mistune (>=2.0,<3.0)", "panflute (>=2.3,<3.0)"] +linkify = ["linkify-it-py (>=1,<3)"] +plugins = ["mdit-py-plugins"] +profiling = ["gprof2dot"] +rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"] +testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] + +[[package]] +name = "markupsafe" +version = "2.1.5" +description = "Safely add untrusted strings to HTML/XML markup." +optional = false +python-versions = ">=3.7" +files = [ + {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a17a92de5231666cfbe003f0e4b9b3a7ae3afb1ec2845aadc2bacc93ff85febc"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:72b6be590cc35924b02c78ef34b467da4ba07e4e0f0454a2c5907f473fc50ce5"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e61659ba32cf2cf1481e575d0462554625196a1f2fc06a1c777d3f48e8865d46"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2174c595a0d73a3080ca3257b40096db99799265e1c27cc5a610743acd86d62f"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ae2ad8ae6ebee9d2d94b17fb62763125f3f374c25618198f40cbb8b525411900"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:075202fa5b72c86ad32dc7d0b56024ebdbcf2048c0ba09f1cde31bfdd57bcfff"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:598e3276b64aff0e7b3451b72e94fa3c238d452e7ddcd893c3ab324717456bad"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fce659a462a1be54d2ffcacea5e3ba2d74daa74f30f5f143fe0c58636e355fdd"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-win32.whl", hash = "sha256:d9fad5155d72433c921b782e58892377c44bd6252b5af2f67f16b194987338a4"}, + {file = "MarkupSafe-2.1.5-cp310-cp310-win_amd64.whl", hash = "sha256:bf50cd79a75d181c9181df03572cdce0fbb75cc353bc350712073108cba98de5"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:629ddd2ca402ae6dbedfceeba9c46d5f7b2a61d9749597d4307f943ef198fc1f"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5b7b716f97b52c5a14bffdf688f971b2d5ef4029127f1ad7a513973cfd818df2"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6ec585f69cec0aa07d945b20805be741395e28ac1627333b1c5b0105962ffced"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b91c037585eba9095565a3556f611e3cbfaa42ca1e865f7b8015fe5c7336d5a5"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7502934a33b54030eaf1194c21c692a534196063db72176b0c4028e140f8f32c"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:0e397ac966fdf721b2c528cf028494e86172b4feba51d65f81ffd65c63798f3f"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:c061bb86a71b42465156a3ee7bd58c8c2ceacdbeb95d05a99893e08b8467359a"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:3a57fdd7ce31c7ff06cdfbf31dafa96cc533c21e443d57f5b1ecc6cdc668ec7f"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-win32.whl", hash = "sha256:397081c1a0bfb5124355710fe79478cdbeb39626492b15d399526ae53422b906"}, + {file = "MarkupSafe-2.1.5-cp311-cp311-win_amd64.whl", hash = "sha256:2b7c57a4dfc4f16f7142221afe5ba4e093e09e728ca65c51f5620c9aaeb9a617"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:8dec4936e9c3100156f8a2dc89c4b88d5c435175ff03413b443469c7c8c5f4d1"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:3c6b973f22eb18a789b1460b4b91bf04ae3f0c4234a0a6aa6b0a92f6f7b951d4"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ac07bad82163452a6884fe8fa0963fb98c2346ba78d779ec06bd7a6262132aee"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f5dfb42c4604dddc8e4305050aa6deb084540643ed5804d7455b5df8fe16f5e5"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ea3d8a3d18833cf4304cd2fc9cbb1efe188ca9b5efef2bdac7adc20594a0e46b"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:d050b3361367a06d752db6ead6e7edeb0009be66bc3bae0ee9d97fb326badc2a"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:bec0a414d016ac1a18862a519e54b2fd0fc8bbfd6890376898a6c0891dd82e9f"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:58c98fee265677f63a4385256a6d7683ab1832f3ddd1e66fe948d5880c21a169"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-win32.whl", hash = "sha256:8590b4ae07a35970728874632fed7bd57b26b0102df2d2b233b6d9d82f6c62ad"}, + {file = "MarkupSafe-2.1.5-cp312-cp312-win_amd64.whl", hash = "sha256:823b65d8706e32ad2df51ed89496147a42a2a6e01c13cfb6ffb8b1e92bc910bb"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:c8b29db45f8fe46ad280a7294f5c3ec36dbac9491f2d1c17345be8e69cc5928f"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ec6a563cff360b50eed26f13adc43e61bc0c04d94b8be985e6fb24b81f6dcfdf"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a549b9c31bec33820e885335b451286e2969a2d9e24879f83fe904a5ce59d70a"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4f11aa001c540f62c6166c7726f71f7573b52c68c31f014c25cc7901deea0b52"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:7b2e5a267c855eea6b4283940daa6e88a285f5f2a67f2220203786dfa59b37e9"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:2d2d793e36e230fd32babe143b04cec8a8b3eb8a3122d2aceb4a371e6b09b8df"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:ce409136744f6521e39fd8e2a24c53fa18ad67aa5bc7c2cf83645cce5b5c4e50"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-win32.whl", hash = "sha256:4096e9de5c6fdf43fb4f04c26fb114f61ef0bf2e5604b6ee3019d51b69e8c371"}, + {file = "MarkupSafe-2.1.5-cp37-cp37m-win_amd64.whl", hash = "sha256:4275d846e41ecefa46e2015117a9f491e57a71ddd59bbead77e904dc02b1bed2"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:656f7526c69fac7f600bd1f400991cc282b417d17539a1b228617081106feb4a"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:97cafb1f3cbcd3fd2b6fbfb99ae11cdb14deea0736fc2b0952ee177f2b813a46"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1f3fbcb7ef1f16e48246f704ab79d79da8a46891e2da03f8783a5b6fa41a9532"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fa9db3f79de01457b03d4f01b34cf91bc0048eb2c3846ff26f66687c2f6d16ab"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ffee1f21e5ef0d712f9033568f8344d5da8cc2869dbd08d87c84656e6a2d2f68"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5dedb4db619ba5a2787a94d877bc8ffc0566f92a01c0ef214865e54ecc9ee5e0"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:30b600cf0a7ac9234b2638fbc0fb6158ba5bdcdf46aeb631ead21248b9affbc4"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8dd717634f5a044f860435c1d8c16a270ddf0ef8588d4887037c5028b859b0c3"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-win32.whl", hash = "sha256:daa4ee5a243f0f20d528d939d06670a298dd39b1ad5f8a72a4275124a7819eff"}, + {file = "MarkupSafe-2.1.5-cp38-cp38-win_amd64.whl", hash = "sha256:619bc166c4f2de5caa5a633b8b7326fbe98e0ccbfacabd87268a2b15ff73a029"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:7a68b554d356a91cce1236aa7682dc01df0edba8d043fd1ce607c49dd3c1edcf"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:db0b55e0f3cc0be60c1f19efdde9a637c32740486004f20d1cff53c3c0ece4d2"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3e53af139f8579a6d5f7b76549125f0d94d7e630761a2111bc431fd820e163b8"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:17b950fccb810b3293638215058e432159d2b71005c74371d784862b7e4683f3"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4c31f53cdae6ecfa91a77820e8b151dba54ab528ba65dfd235c80b086d68a465"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:bff1b4290a66b490a2f4719358c0cdcd9bafb6b8f061e45c7a2460866bf50c2e"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:bc1667f8b83f48511b94671e0e441401371dfd0f0a795c7daa4a3cd1dde55bea"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5049256f536511ee3f7e1b3f87d1d1209d327e818e6ae1365e8653d7e3abb6a6"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-win32.whl", hash = "sha256:00e046b6dd71aa03a41079792f8473dc494d564611a8f89bbbd7cb93295ebdcf"}, + {file = "MarkupSafe-2.1.5-cp39-cp39-win_amd64.whl", hash = "sha256:fa173ec60341d6bb97a89f5ea19c85c5643c1e7dedebc22f5181eb73573142c5"}, + {file = "MarkupSafe-2.1.5.tar.gz", hash = "sha256:d283d37a890ba4c1ae73ffadf8046435c76e7bc2247bbb63c00bd1a709c6544b"}, +] + +[[package]] +name = "matplotlib" +version = "3.9.1" +description = "Python plotting package" +optional = false +python-versions = ">=3.9" +files = [ + {file = "matplotlib-3.9.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:7ccd6270066feb9a9d8e0705aa027f1ff39f354c72a87efe8fa07632f30fc6bb"}, + {file = "matplotlib-3.9.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:591d3a88903a30a6d23b040c1e44d1afdd0d778758d07110eb7596f811f31842"}, + {file = "matplotlib-3.9.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dd2a59ff4b83d33bca3b5ec58203cc65985367812cb8c257f3e101632be86d92"}, + {file = "matplotlib-3.9.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0fc001516ffcf1a221beb51198b194d9230199d6842c540108e4ce109ac05cc0"}, + {file = "matplotlib-3.9.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:83c6a792f1465d174c86d06f3ae85a8fe36e6f5964633ae8106312ec0921fdf5"}, + {file = "matplotlib-3.9.1-cp310-cp310-win_amd64.whl", hash = "sha256:421851f4f57350bcf0811edd754a708d2275533e84f52f6760b740766c6747a7"}, + {file = "matplotlib-3.9.1-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:b3fce58971b465e01b5c538f9d44915640c20ec5ff31346e963c9e1cd66fa812"}, + {file = "matplotlib-3.9.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a973c53ad0668c53e0ed76b27d2eeeae8799836fd0d0caaa4ecc66bf4e6676c0"}, + {file = "matplotlib-3.9.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:82cd5acf8f3ef43f7532c2f230249720f5dc5dd40ecafaf1c60ac8200d46d7eb"}, + {file = "matplotlib-3.9.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ab38a4f3772523179b2f772103d8030215b318fef6360cb40558f585bf3d017f"}, + {file = "matplotlib-3.9.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:2315837485ca6188a4b632c5199900e28d33b481eb083663f6a44cfc8987ded3"}, + {file = "matplotlib-3.9.1-cp311-cp311-win_amd64.whl", hash = "sha256:a0c977c5c382f6696caf0bd277ef4f936da7e2aa202ff66cad5f0ac1428ee15b"}, + {file = "matplotlib-3.9.1-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:565d572efea2b94f264dd86ef27919515aa6d629252a169b42ce5f570db7f37b"}, + {file = "matplotlib-3.9.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6d397fd8ccc64af2ec0af1f0efc3bacd745ebfb9d507f3f552e8adb689ed730a"}, + {file = "matplotlib-3.9.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:26040c8f5121cd1ad712abffcd4b5222a8aec3a0fe40bc8542c94331deb8780d"}, + {file = "matplotlib-3.9.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d12cb1837cffaac087ad6b44399d5e22b78c729de3cdae4629e252067b705e2b"}, + {file = "matplotlib-3.9.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:0e835c6988edc3d2d08794f73c323cc62483e13df0194719ecb0723b564e0b5c"}, + {file = "matplotlib-3.9.1-cp312-cp312-win_amd64.whl", hash = "sha256:44a21d922f78ce40435cb35b43dd7d573cf2a30138d5c4b709d19f00e3907fd7"}, + {file = "matplotlib-3.9.1-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:0c584210c755ae921283d21d01f03a49ef46d1afa184134dd0f95b0202ee6f03"}, + {file = "matplotlib-3.9.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:11fed08f34fa682c2b792942f8902e7aefeed400da71f9e5816bea40a7ce28fe"}, + {file = "matplotlib-3.9.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0000354e32efcfd86bda75729716b92f5c2edd5b947200be9881f0a671565c33"}, + {file = "matplotlib-3.9.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4db17fea0ae3aceb8e9ac69c7e3051bae0b3d083bfec932240f9bf5d0197a049"}, + {file = "matplotlib-3.9.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:208cbce658b72bf6a8e675058fbbf59f67814057ae78165d8a2f87c45b48d0ff"}, + {file = "matplotlib-3.9.1-cp39-cp39-win_amd64.whl", hash = "sha256:dc23f48ab630474264276be156d0d7710ac6c5a09648ccdf49fef9200d8cbe80"}, + {file = "matplotlib-3.9.1-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:3fda72d4d472e2ccd1be0e9ccb6bf0d2eaf635e7f8f51d737ed7e465ac020cb3"}, + {file = "matplotlib-3.9.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:84b3ba8429935a444f1fdc80ed930babbe06725bcf09fbeb5c8757a2cd74af04"}, + {file = "matplotlib-3.9.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b918770bf3e07845408716e5bbda17eadfc3fcbd9307dc67f37d6cf834bb3d98"}, + {file = "matplotlib-3.9.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:f1f2e5d29e9435c97ad4c36fb6668e89aee13d48c75893e25cef064675038ac9"}, + {file = "matplotlib-3.9.1.tar.gz", hash = "sha256:de06b19b8db95dd33d0dc17c926c7c9ebed9f572074b6fac4f65068a6814d010"}, +] + +[package.dependencies] +contourpy = ">=1.0.1" +cycler = ">=0.10" +fonttools = ">=4.22.0" +kiwisolver = ">=1.3.1" +numpy = ">=1.23" +packaging = ">=20.0" +pillow = ">=8" +pyparsing = ">=2.3.1" +python-dateutil = ">=2.7" + +[package.extras] +dev = ["meson-python (>=0.13.1)", "numpy (>=1.25)", "pybind11 (>=2.6)", "setuptools (>=64)", "setuptools_scm (>=7)"] + +[[package]] +name = "matplotlib-inline" +version = "0.1.7" +description = "Inline Matplotlib backend for Jupyter" +optional = false +python-versions = ">=3.8" +files = [ + {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = "sha256:df192d39a4ff8f21b1895d72e6a13f5fcc5099f00fa84384e0ea28c2cc0653ca"}, + {file = "matplotlib_inline-0.1.7.tar.gz", hash = "sha256:8423b23ec666be3d16e16b60bdd8ac4e86e840ebd1dd11a30b9f117f2fa0ab90"}, +] + +[package.dependencies] +traitlets = "*" + +[[package]] +name = "mccabe" +version = "0.7.0" +description = "McCabe checker, plugin for flake8" +optional = false +python-versions = ">=3.6" +files = [ + {file = "mccabe-0.7.0-py2.py3-none-any.whl", hash = "sha256:6c2d30ab6be0e4a46919781807b4f0d834ebdd6c6e3dca0bda5a15f863427b6e"}, + {file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"}, +] + +[[package]] +name = "mdurl" +version = "0.1.2" +description = "Markdown URL utilities" +optional = false +python-versions = ">=3.7" +files = [ + {file = "mdurl-0.1.2-py3-none-any.whl", hash = "sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8"}, + {file = "mdurl-0.1.2.tar.gz", hash = "sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba"}, +] + +[[package]] +name = "mistune" +version = "3.0.2" +description = "A sane and fast Markdown parser with useful plugins and renderers" +optional = false +python-versions = ">=3.7" +files = [ + {file = "mistune-3.0.2-py3-none-any.whl", hash = "sha256:71481854c30fdbc938963d3605b72501f5c10a9320ecd412c121c163a1c7d205"}, + {file = "mistune-3.0.2.tar.gz", hash = "sha256:fc7f93ded930c92394ef2cb6f04a8aabab4117a91449e72dcc8dfa646a508be8"}, +] + +[[package]] +name = "mkl" +version = "2021.4.0" +description = "IntelĀ® oneAPI Math Kernel Library" +optional = false +python-versions = "*" +files = [ + {file = "mkl-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:67460f5cd7e30e405b54d70d1ed3ca78118370b65f7327d495e9c8847705e2fb"}, + {file = "mkl-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:636d07d90e68ccc9630c654d47ce9fdeb036bb46e2b193b3a9ac8cfea683cce5"}, + {file = "mkl-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:398dbf2b0d12acaf54117a5210e8f191827f373d362d796091d161f610c1ebfb"}, + {file = "mkl-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:439c640b269a5668134e3dcbcea4350459c4a8bc46469669b2d67e07e3d330e8"}, + {file = "mkl-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:ceef3cafce4c009dd25f65d7ad0d833a0fbadc3d8903991ec92351fe5de1e718"}, +] + +[package.dependencies] +intel-openmp = "==2021.*" +tbb = "==2021.*" + +[[package]] +name = "mpmath" +version = "1.3.0" +description = "Python library for arbitrary-precision floating-point arithmetic" +optional = false +python-versions = "*" +files = [ + {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"}, + {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"}, +] + +[package.extras] +develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"] +docs = ["sphinx"] +gmpy = ["gmpy2 (>=2.1.0a4)"] +tests = ["pytest (>=4.6)"] + +[[package]] +name = "msgpack" +version = "1.0.8" +description = "MessagePack serializer" +optional = false +python-versions = ">=3.8" +files = [ + {file = "msgpack-1.0.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:505fe3d03856ac7d215dbe005414bc28505d26f0c128906037e66d98c4e95868"}, + {file = "msgpack-1.0.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e6b7842518a63a9f17107eb176320960ec095a8ee3b4420b5f688e24bf50c53c"}, + {file = "msgpack-1.0.8-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:376081f471a2ef24828b83a641a02c575d6103a3ad7fd7dade5486cad10ea659"}, + {file = "msgpack-1.0.8-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5e390971d082dba073c05dbd56322427d3280b7cc8b53484c9377adfbae67dc2"}, + {file = "msgpack-1.0.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:00e073efcba9ea99db5acef3959efa45b52bc67b61b00823d2a1a6944bf45982"}, + {file = "msgpack-1.0.8-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:82d92c773fbc6942a7a8b520d22c11cfc8fd83bba86116bfcf962c2f5c2ecdaa"}, + {file = "msgpack-1.0.8-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:9ee32dcb8e531adae1f1ca568822e9b3a738369b3b686d1477cbc643c4a9c128"}, + {file = "msgpack-1.0.8-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e3aa7e51d738e0ec0afbed661261513b38b3014754c9459508399baf14ae0c9d"}, + {file = "msgpack-1.0.8-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:69284049d07fce531c17404fcba2bb1df472bc2dcdac642ae71a2d079d950653"}, + {file = "msgpack-1.0.8-cp310-cp310-win32.whl", hash = "sha256:13577ec9e247f8741c84d06b9ece5f654920d8365a4b636ce0e44f15e07ec693"}, + {file = "msgpack-1.0.8-cp310-cp310-win_amd64.whl", hash = "sha256:e532dbd6ddfe13946de050d7474e3f5fb6ec774fbb1a188aaf469b08cf04189a"}, + {file = "msgpack-1.0.8-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:9517004e21664f2b5a5fd6333b0731b9cf0817403a941b393d89a2f1dc2bd836"}, + {file = "msgpack-1.0.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d16a786905034e7e34098634b184a7d81f91d4c3d246edc6bd7aefb2fd8ea6ad"}, + {file = "msgpack-1.0.8-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e2872993e209f7ed04d963e4b4fbae72d034844ec66bc4ca403329db2074377b"}, + {file = "msgpack-1.0.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5c330eace3dd100bdb54b5653b966de7f51c26ec4a7d4e87132d9b4f738220ba"}, + {file = "msgpack-1.0.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:83b5c044f3eff2a6534768ccfd50425939e7a8b5cf9a7261c385de1e20dcfc85"}, + {file = "msgpack-1.0.8-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1876b0b653a808fcd50123b953af170c535027bf1d053b59790eebb0aeb38950"}, + {file = "msgpack-1.0.8-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:dfe1f0f0ed5785c187144c46a292b8c34c1295c01da12e10ccddfc16def4448a"}, + {file = "msgpack-1.0.8-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:3528807cbbb7f315bb81959d5961855e7ba52aa60a3097151cb21956fbc7502b"}, + {file = "msgpack-1.0.8-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e2f879ab92ce502a1e65fce390eab619774dda6a6ff719718069ac94084098ce"}, + {file = "msgpack-1.0.8-cp311-cp311-win32.whl", hash = "sha256:26ee97a8261e6e35885c2ecd2fd4a6d38252246f94a2aec23665a4e66d066305"}, + {file = "msgpack-1.0.8-cp311-cp311-win_amd64.whl", hash = "sha256:eadb9f826c138e6cf3c49d6f8de88225a3c0ab181a9b4ba792e006e5292d150e"}, + {file = "msgpack-1.0.8-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:114be227f5213ef8b215c22dde19532f5da9652e56e8ce969bf0a26d7c419fee"}, + {file = "msgpack-1.0.8-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:d661dc4785affa9d0edfdd1e59ec056a58b3dbb9f196fa43587f3ddac654ac7b"}, + {file = "msgpack-1.0.8-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:d56fd9f1f1cdc8227d7b7918f55091349741904d9520c65f0139a9755952c9e8"}, + {file = "msgpack-1.0.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0726c282d188e204281ebd8de31724b7d749adebc086873a59efb8cf7ae27df3"}, + {file = "msgpack-1.0.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8db8e423192303ed77cff4dce3a4b88dbfaf43979d280181558af5e2c3c71afc"}, + {file = "msgpack-1.0.8-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:99881222f4a8c2f641f25703963a5cefb076adffd959e0558dc9f803a52d6a58"}, + {file = "msgpack-1.0.8-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:b5505774ea2a73a86ea176e8a9a4a7c8bf5d521050f0f6f8426afe798689243f"}, + {file = "msgpack-1.0.8-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:ef254a06bcea461e65ff0373d8a0dd1ed3aa004af48839f002a0c994a6f72d04"}, + {file = "msgpack-1.0.8-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:e1dd7839443592d00e96db831eddb4111a2a81a46b028f0facd60a09ebbdd543"}, + {file = "msgpack-1.0.8-cp312-cp312-win32.whl", hash = "sha256:64d0fcd436c5683fdd7c907eeae5e2cbb5eb872fafbc03a43609d7941840995c"}, + {file = "msgpack-1.0.8-cp312-cp312-win_amd64.whl", hash = "sha256:74398a4cf19de42e1498368c36eed45d9528f5fd0155241e82c4082b7e16cffd"}, + {file = "msgpack-1.0.8-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:0ceea77719d45c839fd73abcb190b8390412a890df2f83fb8cf49b2a4b5c2f40"}, + {file = "msgpack-1.0.8-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1ab0bbcd4d1f7b6991ee7c753655b481c50084294218de69365f8f1970d4c151"}, + {file = "msgpack-1.0.8-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:1cce488457370ffd1f953846f82323cb6b2ad2190987cd4d70b2713e17268d24"}, + {file = "msgpack-1.0.8-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3923a1778f7e5ef31865893fdca12a8d7dc03a44b33e2a5f3295416314c09f5d"}, + {file = "msgpack-1.0.8-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a22e47578b30a3e199ab067a4d43d790249b3c0587d9a771921f86250c8435db"}, + {file = "msgpack-1.0.8-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bd739c9251d01e0279ce729e37b39d49a08c0420d3fee7f2a4968c0576678f77"}, + {file = "msgpack-1.0.8-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:d3420522057ebab1728b21ad473aa950026d07cb09da41103f8e597dfbfaeb13"}, + {file = "msgpack-1.0.8-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:5845fdf5e5d5b78a49b826fcdc0eb2e2aa7191980e3d2cfd2a30303a74f212e2"}, + {file = "msgpack-1.0.8-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:6a0e76621f6e1f908ae52860bdcb58e1ca85231a9b0545e64509c931dd34275a"}, + {file = "msgpack-1.0.8-cp38-cp38-win32.whl", hash = "sha256:374a8e88ddab84b9ada695d255679fb99c53513c0a51778796fcf0944d6c789c"}, + {file = "msgpack-1.0.8-cp38-cp38-win_amd64.whl", hash = "sha256:f3709997b228685fe53e8c433e2df9f0cdb5f4542bd5114ed17ac3c0129b0480"}, + {file = "msgpack-1.0.8-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:f51bab98d52739c50c56658cc303f190785f9a2cd97b823357e7aeae54c8f68a"}, + {file = "msgpack-1.0.8-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:73ee792784d48aa338bba28063e19a27e8d989344f34aad14ea6e1b9bd83f596"}, + {file = "msgpack-1.0.8-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f9904e24646570539a8950400602d66d2b2c492b9010ea7e965025cb71d0c86d"}, + {file = "msgpack-1.0.8-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e75753aeda0ddc4c28dce4c32ba2f6ec30b1b02f6c0b14e547841ba5b24f753f"}, + {file = "msgpack-1.0.8-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5dbf059fb4b7c240c873c1245ee112505be27497e90f7c6591261c7d3c3a8228"}, + {file = "msgpack-1.0.8-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4916727e31c28be8beaf11cf117d6f6f188dcc36daae4e851fee88646f5b6b18"}, + {file = "msgpack-1.0.8-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:7938111ed1358f536daf311be244f34df7bf3cdedb3ed883787aca97778b28d8"}, + {file = "msgpack-1.0.8-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:493c5c5e44b06d6c9268ce21b302c9ca055c1fd3484c25ba41d34476c76ee746"}, + {file = "msgpack-1.0.8-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5fbb160554e319f7b22ecf530a80a3ff496d38e8e07ae763b9e82fadfe96f273"}, + {file = "msgpack-1.0.8-cp39-cp39-win32.whl", hash = "sha256:f9af38a89b6a5c04b7d18c492c8ccf2aee7048aff1ce8437c4683bb5a1df893d"}, + {file = "msgpack-1.0.8-cp39-cp39-win_amd64.whl", hash = "sha256:ed59dd52075f8fc91da6053b12e8c89e37aa043f8986efd89e61fae69dc1b011"}, + {file = "msgpack-1.0.8.tar.gz", hash = "sha256:95c02b0e27e706e48d0e5426d1710ca78e0f0628d6e89d5b5a5b91a5f12274f3"}, +] + +[[package]] +name = "multidict" +version = "6.0.5" +description = "multidict implementation" +optional = false +python-versions = ">=3.7" +files = [ + {file = "multidict-6.0.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:228b644ae063c10e7f324ab1ab6b548bdf6f8b47f3ec234fef1093bc2735e5f9"}, + {file = "multidict-6.0.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:896ebdcf62683551312c30e20614305f53125750803b614e9e6ce74a96232604"}, + {file = "multidict-6.0.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:411bf8515f3be9813d06004cac41ccf7d1cd46dfe233705933dd163b60e37600"}, + {file = "multidict-6.0.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1d147090048129ce3c453f0292e7697d333db95e52616b3793922945804a433c"}, + {file = "multidict-6.0.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:215ed703caf15f578dca76ee6f6b21b7603791ae090fbf1ef9d865571039ade5"}, + {file = "multidict-6.0.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7c6390cf87ff6234643428991b7359b5f59cc15155695deb4eda5c777d2b880f"}, + {file = "multidict-6.0.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:21fd81c4ebdb4f214161be351eb5bcf385426bf023041da2fd9e60681f3cebae"}, + {file = "multidict-6.0.5-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3cc2ad10255f903656017363cd59436f2111443a76f996584d1077e43ee51182"}, + {file = "multidict-6.0.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:6939c95381e003f54cd4c5516740faba40cf5ad3eeff460c3ad1d3e0ea2549bf"}, + {file = "multidict-6.0.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:220dd781e3f7af2c2c1053da9fa96d9cf3072ca58f057f4c5adaaa1cab8fc442"}, + {file = "multidict-6.0.5-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:766c8f7511df26d9f11cd3a8be623e59cca73d44643abab3f8c8c07620524e4a"}, + {file = "multidict-6.0.5-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:fe5d7785250541f7f5019ab9cba2c71169dc7d74d0f45253f8313f436458a4ef"}, + {file = "multidict-6.0.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:c1c1496e73051918fcd4f58ff2e0f2f3066d1c76a0c6aeffd9b45d53243702cc"}, + {file = "multidict-6.0.5-cp310-cp310-win32.whl", hash = "sha256:7afcdd1fc07befad18ec4523a782cde4e93e0a2bf71239894b8d61ee578c1319"}, + {file = "multidict-6.0.5-cp310-cp310-win_amd64.whl", hash = "sha256:99f60d34c048c5c2fabc766108c103612344c46e35d4ed9ae0673d33c8fb26e8"}, + {file = "multidict-6.0.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:f285e862d2f153a70586579c15c44656f888806ed0e5b56b64489afe4a2dbfba"}, + {file = "multidict-6.0.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:53689bb4e102200a4fafa9de9c7c3c212ab40a7ab2c8e474491914d2305f187e"}, + {file = "multidict-6.0.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:612d1156111ae11d14afaf3a0669ebf6c170dbb735e510a7438ffe2369a847fd"}, + {file = "multidict-6.0.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7be7047bd08accdb7487737631d25735c9a04327911de89ff1b26b81745bd4e3"}, + {file = "multidict-6.0.5-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de170c7b4fe6859beb8926e84f7d7d6c693dfe8e27372ce3b76f01c46e489fcf"}, + {file = "multidict-6.0.5-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:04bde7a7b3de05732a4eb39c94574db1ec99abb56162d6c520ad26f83267de29"}, + {file = "multidict-6.0.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:85f67aed7bb647f93e7520633d8f51d3cbc6ab96957c71272b286b2f30dc70ed"}, + {file = "multidict-6.0.5-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:425bf820055005bfc8aa9a0b99ccb52cc2f4070153e34b701acc98d201693733"}, + {file = "multidict-6.0.5-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:d3eb1ceec286eba8220c26f3b0096cf189aea7057b6e7b7a2e60ed36b373b77f"}, + {file = "multidict-6.0.5-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:7901c05ead4b3fb75113fb1dd33eb1253c6d3ee37ce93305acd9d38e0b5f21a4"}, + {file = "multidict-6.0.5-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:e0e79d91e71b9867c73323a3444724d496c037e578a0e1755ae159ba14f4f3d1"}, + {file = "multidict-6.0.5-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:29bfeb0dff5cb5fdab2023a7a9947b3b4af63e9c47cae2a10ad58394b517fddc"}, + {file = "multidict-6.0.5-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e030047e85cbcedbfc073f71836d62dd5dadfbe7531cae27789ff66bc551bd5e"}, + {file = "multidict-6.0.5-cp311-cp311-win32.whl", hash = "sha256:2f4848aa3baa109e6ab81fe2006c77ed4d3cd1e0ac2c1fbddb7b1277c168788c"}, + {file = "multidict-6.0.5-cp311-cp311-win_amd64.whl", hash = "sha256:2faa5ae9376faba05f630d7e5e6be05be22913782b927b19d12b8145968a85ea"}, + {file = "multidict-6.0.5-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:51d035609b86722963404f711db441cf7134f1889107fb171a970c9701f92e1e"}, + {file = "multidict-6.0.5-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:cbebcd5bcaf1eaf302617c114aa67569dd3f090dd0ce8ba9e35e9985b41ac35b"}, + {file = "multidict-6.0.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2ffc42c922dbfddb4a4c3b438eb056828719f07608af27d163191cb3e3aa6cc5"}, + {file = "multidict-6.0.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ceb3b7e6a0135e092de86110c5a74e46bda4bd4fbfeeb3a3bcec79c0f861e450"}, + {file = "multidict-6.0.5-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:79660376075cfd4b2c80f295528aa6beb2058fd289f4c9252f986751a4cd0496"}, + {file = "multidict-6.0.5-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e4428b29611e989719874670fd152b6625500ad6c686d464e99f5aaeeaca175a"}, + {file = "multidict-6.0.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d84a5c3a5f7ce6db1f999fb9438f686bc2e09d38143f2d93d8406ed2dd6b9226"}, + {file = "multidict-6.0.5-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:76c0de87358b192de7ea9649beb392f107dcad9ad27276324c24c91774ca5271"}, + {file = "multidict-6.0.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:79a6d2ba910adb2cbafc95dad936f8b9386e77c84c35bc0add315b856d7c3abb"}, + {file = "multidict-6.0.5-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:92d16a3e275e38293623ebf639c471d3e03bb20b8ebb845237e0d3664914caef"}, + {file = "multidict-6.0.5-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:fb616be3538599e797a2017cccca78e354c767165e8858ab5116813146041a24"}, + {file = "multidict-6.0.5-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:14c2976aa9038c2629efa2c148022ed5eb4cb939e15ec7aace7ca932f48f9ba6"}, + {file = "multidict-6.0.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:435a0984199d81ca178b9ae2c26ec3d49692d20ee29bc4c11a2a8d4514c67eda"}, + {file = "multidict-6.0.5-cp312-cp312-win32.whl", hash = "sha256:9fe7b0653ba3d9d65cbe7698cca585bf0f8c83dbbcc710db9c90f478e175f2d5"}, + {file = "multidict-6.0.5-cp312-cp312-win_amd64.whl", hash = "sha256:01265f5e40f5a17f8241d52656ed27192be03bfa8764d88e8220141d1e4b3556"}, + {file = "multidict-6.0.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:19fe01cea168585ba0f678cad6f58133db2aa14eccaf22f88e4a6dccadfad8b3"}, + {file = "multidict-6.0.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6bf7a982604375a8d49b6cc1b781c1747f243d91b81035a9b43a2126c04766f5"}, + {file = "multidict-6.0.5-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:107c0cdefe028703fb5dafe640a409cb146d44a6ae201e55b35a4af8e95457dd"}, + {file = "multidict-6.0.5-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:403c0911cd5d5791605808b942c88a8155c2592e05332d2bf78f18697a5fa15e"}, + {file = "multidict-6.0.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aeaf541ddbad8311a87dd695ed9642401131ea39ad7bc8cf3ef3967fd093b626"}, + {file = "multidict-6.0.5-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e4972624066095e52b569e02b5ca97dbd7a7ddd4294bf4e7247d52635630dd83"}, + {file = "multidict-6.0.5-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:d946b0a9eb8aaa590df1fe082cee553ceab173e6cb5b03239716338629c50c7a"}, + {file = "multidict-6.0.5-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:b55358304d7a73d7bdf5de62494aaf70bd33015831ffd98bc498b433dfe5b10c"}, + {file = "multidict-6.0.5-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:a3145cb08d8625b2d3fee1b2d596a8766352979c9bffe5d7833e0503d0f0b5e5"}, + {file = "multidict-6.0.5-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:d65f25da8e248202bd47445cec78e0025c0fe7582b23ec69c3b27a640dd7a8e3"}, + {file = "multidict-6.0.5-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:c9bf56195c6bbd293340ea82eafd0071cb3d450c703d2c93afb89f93b8386ccc"}, + {file = "multidict-6.0.5-cp37-cp37m-win32.whl", hash = "sha256:69db76c09796b313331bb7048229e3bee7928eb62bab5e071e9f7fcc4879caee"}, + {file = "multidict-6.0.5-cp37-cp37m-win_amd64.whl", hash = "sha256:fce28b3c8a81b6b36dfac9feb1de115bab619b3c13905b419ec71d03a3fc1423"}, + {file = "multidict-6.0.5-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:76f067f5121dcecf0d63a67f29080b26c43c71a98b10c701b0677e4a065fbd54"}, + {file = "multidict-6.0.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:b82cc8ace10ab5bd93235dfaab2021c70637005e1ac787031f4d1da63d493c1d"}, + {file = "multidict-6.0.5-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:5cb241881eefd96b46f89b1a056187ea8e9ba14ab88ba632e68d7a2ecb7aadf7"}, + {file = "multidict-6.0.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e8e94e6912639a02ce173341ff62cc1201232ab86b8a8fcc05572741a5dc7d93"}, + {file = "multidict-6.0.5-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:09a892e4a9fb47331da06948690ae38eaa2426de97b4ccbfafbdcbe5c8f37ff8"}, + {file = "multidict-6.0.5-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:55205d03e8a598cfc688c71ca8ea5f66447164efff8869517f175ea632c7cb7b"}, + {file = "multidict-6.0.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:37b15024f864916b4951adb95d3a80c9431299080341ab9544ed148091b53f50"}, + {file = "multidict-6.0.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f2a1dee728b52b33eebff5072817176c172050d44d67befd681609b4746e1c2e"}, + {file = "multidict-6.0.5-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:edd08e6f2f1a390bf137080507e44ccc086353c8e98c657e666c017718561b89"}, + {file = "multidict-6.0.5-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:60d698e8179a42ec85172d12f50b1668254628425a6bd611aba022257cac1386"}, + {file = "multidict-6.0.5-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:3d25f19500588cbc47dc19081d78131c32637c25804df8414463ec908631e453"}, + {file = "multidict-6.0.5-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:4cc0ef8b962ac7a5e62b9e826bd0cd5040e7d401bc45a6835910ed699037a461"}, + {file = "multidict-6.0.5-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:eca2e9d0cc5a889850e9bbd68e98314ada174ff6ccd1129500103df7a94a7a44"}, + {file = "multidict-6.0.5-cp38-cp38-win32.whl", hash = "sha256:4a6a4f196f08c58c59e0b8ef8ec441d12aee4125a7d4f4fef000ccb22f8d7241"}, + {file = "multidict-6.0.5-cp38-cp38-win_amd64.whl", hash = "sha256:0275e35209c27a3f7951e1ce7aaf93ce0d163b28948444bec61dd7badc6d3f8c"}, + {file = "multidict-6.0.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:e7be68734bd8c9a513f2b0cfd508802d6609da068f40dc57d4e3494cefc92929"}, + {file = "multidict-6.0.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:1d9ea7a7e779d7a3561aade7d596649fbecfa5c08a7674b11b423783217933f9"}, + {file = "multidict-6.0.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ea1456df2a27c73ce51120fa2f519f1bea2f4a03a917f4a43c8707cf4cbbae1a"}, + {file = "multidict-6.0.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cf590b134eb70629e350691ecca88eac3e3b8b3c86992042fb82e3cb1830d5e1"}, + {file = "multidict-6.0.5-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5c0631926c4f58e9a5ccce555ad7747d9a9f8b10619621f22f9635f069f6233e"}, + {file = "multidict-6.0.5-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:dce1c6912ab9ff5f179eaf6efe7365c1f425ed690b03341911bf4939ef2f3046"}, + {file = "multidict-6.0.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c0868d64af83169e4d4152ec612637a543f7a336e4a307b119e98042e852ad9c"}, + {file = "multidict-6.0.5-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:141b43360bfd3bdd75f15ed811850763555a251e38b2405967f8e25fb43f7d40"}, + {file = "multidict-6.0.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:7df704ca8cf4a073334e0427ae2345323613e4df18cc224f647f251e5e75a527"}, + {file = "multidict-6.0.5-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:6214c5a5571802c33f80e6c84713b2c79e024995b9c5897f794b43e714daeec9"}, + {file = "multidict-6.0.5-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:cd6c8fca38178e12c00418de737aef1261576bd1b6e8c6134d3e729a4e858b38"}, + {file = "multidict-6.0.5-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:e02021f87a5b6932fa6ce916ca004c4d441509d33bbdbeca70d05dff5e9d2479"}, + {file = "multidict-6.0.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ebd8d160f91a764652d3e51ce0d2956b38efe37c9231cd82cfc0bed2e40b581c"}, + {file = "multidict-6.0.5-cp39-cp39-win32.whl", hash = "sha256:04da1bb8c8dbadf2a18a452639771951c662c5ad03aefe4884775454be322c9b"}, + {file = "multidict-6.0.5-cp39-cp39-win_amd64.whl", hash = "sha256:d6f6d4f185481c9669b9447bf9d9cf3b95a0e9df9d169bbc17e363b7d5487755"}, + {file = "multidict-6.0.5-py3-none-any.whl", hash = "sha256:0d63c74e3d7ab26de115c49bffc92cc77ed23395303d496eae515d4204a625e7"}, + {file = "multidict-6.0.5.tar.gz", hash = "sha256:f7e301075edaf50500f0b341543c41194d8df3ae5caf4702f2095f3ca73dd8da"}, +] + +[[package]] +name = "mypy" +version = "1.11.0" +description = "Optional static typing for Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "mypy-1.11.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a3824187c99b893f90c845bab405a585d1ced4ff55421fdf5c84cb7710995229"}, + {file = "mypy-1.11.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:96f8dbc2c85046c81bcddc246232d500ad729cb720da4e20fce3b542cab91287"}, + {file = "mypy-1.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1a5d8d8dd8613a3e2be3eae829ee891b6b2de6302f24766ff06cb2875f5be9c6"}, + {file = "mypy-1.11.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:72596a79bbfb195fd41405cffa18210af3811beb91ff946dbcb7368240eed6be"}, + {file = "mypy-1.11.0-cp310-cp310-win_amd64.whl", hash = "sha256:35ce88b8ed3a759634cb4eb646d002c4cef0a38f20565ee82b5023558eb90c00"}, + {file = "mypy-1.11.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:98790025861cb2c3db8c2f5ad10fc8c336ed2a55f4daf1b8b3f877826b6ff2eb"}, + {file = "mypy-1.11.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:25bcfa75b9b5a5f8d67147a54ea97ed63a653995a82798221cca2a315c0238c1"}, + {file = "mypy-1.11.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0bea2a0e71c2a375c9fa0ede3d98324214d67b3cbbfcbd55ac8f750f85a414e3"}, + {file = "mypy-1.11.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:d2b3d36baac48e40e3064d2901f2fbd2a2d6880ec6ce6358825c85031d7c0d4d"}, + {file = "mypy-1.11.0-cp311-cp311-win_amd64.whl", hash = "sha256:d8e2e43977f0e09f149ea69fd0556623919f816764e26d74da0c8a7b48f3e18a"}, + {file = "mypy-1.11.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:1d44c1e44a8be986b54b09f15f2c1a66368eb43861b4e82573026e04c48a9e20"}, + {file = "mypy-1.11.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:cea3d0fb69637944dd321f41bc896e11d0fb0b0aa531d887a6da70f6e7473aba"}, + {file = "mypy-1.11.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a83ec98ae12d51c252be61521aa5731f5512231d0b738b4cb2498344f0b840cd"}, + {file = "mypy-1.11.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:c7b73a856522417beb78e0fb6d33ef89474e7a622db2653bc1285af36e2e3e3d"}, + {file = "mypy-1.11.0-cp312-cp312-win_amd64.whl", hash = "sha256:f2268d9fcd9686b61ab64f077be7ffbc6fbcdfb4103e5dd0cc5eaab53a8886c2"}, + {file = "mypy-1.11.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:940bfff7283c267ae6522ef926a7887305945f716a7704d3344d6d07f02df850"}, + {file = "mypy-1.11.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:14f9294528b5f5cf96c721f231c9f5b2733164e02c1c018ed1a0eff8a18005ac"}, + {file = "mypy-1.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d7b54c27783991399046837df5c7c9d325d921394757d09dbcbf96aee4649fe9"}, + {file = "mypy-1.11.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:65f190a6349dec29c8d1a1cd4aa71284177aee5949e0502e6379b42873eddbe7"}, + {file = "mypy-1.11.0-cp38-cp38-win_amd64.whl", hash = "sha256:dbe286303241fea8c2ea5466f6e0e6a046a135a7e7609167b07fd4e7baf151bf"}, + {file = "mypy-1.11.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:104e9c1620c2675420abd1f6c44bab7dd33cc85aea751c985006e83dcd001095"}, + {file = "mypy-1.11.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f006e955718ecd8d159cee9932b64fba8f86ee6f7728ca3ac66c3a54b0062abe"}, + {file = "mypy-1.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:becc9111ca572b04e7e77131bc708480cc88a911adf3d0239f974c034b78085c"}, + {file = "mypy-1.11.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:6801319fe76c3f3a3833f2b5af7bd2c17bb93c00026a2a1b924e6762f5b19e13"}, + {file = "mypy-1.11.0-cp39-cp39-win_amd64.whl", hash = "sha256:c1a184c64521dc549324ec6ef7cbaa6b351912be9cb5edb803c2808a0d7e85ac"}, + {file = "mypy-1.11.0-py3-none-any.whl", hash = "sha256:56913ec8c7638b0091ef4da6fcc9136896914a9d60d54670a75880c3e5b99ace"}, + {file = "mypy-1.11.0.tar.gz", hash = "sha256:93743608c7348772fdc717af4aeee1997293a1ad04bc0ea6efa15bf65385c538"}, +] + +[package.dependencies] +mypy-extensions = ">=1.0.0" +typing-extensions = ">=4.6.0" + +[package.extras] +dmypy = ["psutil (>=4.0)"] +install-types = ["pip"] +mypyc = ["setuptools (>=50)"] +reports = ["lxml"] + +[[package]] +name = "mypy-extensions" +version = "1.0.0" +description = "Type system extensions for programs checked with the mypy type checker." +optional = false +python-versions = ">=3.5" +files = [ + {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"}, + {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"}, +] + +[[package]] +name = "nbclient" +version = "0.10.0" +description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor." +optional = false +python-versions = ">=3.8.0" +files = [ + {file = "nbclient-0.10.0-py3-none-any.whl", hash = "sha256:f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f"}, + {file = "nbclient-0.10.0.tar.gz", hash = "sha256:4b3f1b7dba531e498449c4db4f53da339c91d449dc11e9af3a43b4eb5c5abb09"}, +] + +[package.dependencies] +jupyter-client = ">=6.1.12" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" +nbformat = ">=5.1" +traitlets = ">=5.4" + +[package.extras] +dev = ["pre-commit"] +docs = ["autodoc-traits", "mock", "moto", "myst-parser", "nbclient[test]", "sphinx (>=1.7)", "sphinx-book-theme", "sphinxcontrib-spelling"] +test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=7.0.0)", "pytest (>=7.0,<8)", "pytest-asyncio", "pytest-cov (>=4.0)", "testpath", "xmltodict"] + +[[package]] +name = "nbconvert" +version = "7.16.4" +description = "Converting Jupyter Notebooks (.ipynb files) to other formats. Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script. nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)." +optional = false +python-versions = ">=3.8" +files = [ + {file = "nbconvert-7.16.4-py3-none-any.whl", hash = "sha256:05873c620fe520b6322bf8a5ad562692343fe3452abda5765c7a34b7d1aa3eb3"}, + {file = "nbconvert-7.16.4.tar.gz", hash = "sha256:86ca91ba266b0a448dc96fa6c5b9d98affabde2867b363258703536807f9f7f4"}, +] + +[package.dependencies] +beautifulsoup4 = "*" +bleach = "!=5.0.0" +defusedxml = "*" +jinja2 = ">=3.0" +jupyter-core = ">=4.7" +jupyterlab-pygments = "*" +markupsafe = ">=2.0" +mistune = ">=2.0.3,<4" +nbclient = ">=0.5.0" +nbformat = ">=5.7" +packaging = "*" +pandocfilters = ">=1.4.1" +pygments = ">=2.4.1" +tinycss2 = "*" +traitlets = ">=5.1" + +[package.extras] +all = ["flaky", "ipykernel", "ipython", "ipywidgets (>=7.5)", "myst-parser", "nbsphinx (>=0.2.12)", "playwright", "pydata-sphinx-theme", "pyqtwebengine (>=5.15)", "pytest (>=7)", "sphinx (==5.0.2)", "sphinxcontrib-spelling", "tornado (>=6.1)"] +docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sphinx-theme", "sphinx (==5.0.2)", "sphinxcontrib-spelling"] +qtpdf = ["pyqtwebengine (>=5.15)"] +qtpng = ["pyqtwebengine (>=5.15)"] +serve = ["tornado (>=6.1)"] +test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest (>=7)"] +webpdf = ["playwright"] + +[[package]] +name = "nbformat" +version = "5.10.4" +description = "The Jupyter Notebook format" +optional = false +python-versions = ">=3.8" +files = [ + {file = "nbformat-5.10.4-py3-none-any.whl", hash = "sha256:3b48d6c8fbca4b299bf3982ea7db1af21580e4fec269ad087b9e81588891200b"}, + {file = "nbformat-5.10.4.tar.gz", hash = "sha256:322168b14f937a5d11362988ecac2a4952d3d8e3a2cbeb2319584631226d5b3a"}, +] + +[package.dependencies] +fastjsonschema = ">=2.15" +jsonschema = ">=2.6" +jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" +traitlets = ">=5.1" + +[package.extras] +docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] +test = ["pep440", "pre-commit", "pytest", "testpath"] + +[[package]] +name = "nest-asyncio" +version = "1.6.0" +description = "Patch asyncio to allow nested event loops" +optional = false +python-versions = ">=3.5" +files = [ + {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"}, + {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"}, +] + +[[package]] +name = "networkx" +version = "2.8.8" +description = "Python package for creating and manipulating graphs and networks" +optional = false +python-versions = ">=3.8" +files = [ + {file = "networkx-2.8.8-py3-none-any.whl", hash = "sha256:e435dfa75b1d7195c7b8378c3859f0445cd88c6b0375c181ed66823a9ceb7524"}, + {file = "networkx-2.8.8.tar.gz", hash = "sha256:230d388117af870fce5647a3c52401fcf753e94720e6ea6b4197a5355648885e"}, +] + +[package.extras] +default = ["matplotlib (>=3.4)", "numpy (>=1.19)", "pandas (>=1.3)", "scipy (>=1.8)"] +developer = ["mypy (>=0.982)", "pre-commit (>=2.20)"] +doc = ["nb2plots (>=0.6)", "numpydoc (>=1.5)", "pillow (>=9.2)", "pydata-sphinx-theme (>=0.11)", "sphinx (>=5.2)", "sphinx-gallery (>=0.11)", "texext (>=0.6.6)"] +extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.9)", "sympy (>=1.10)"] +test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"] + +[[package]] +name = "notebook" +version = "7.2.1" +description = "Jupyter Notebook - A web-based notebook environment for interactive computing" +optional = false +python-versions = ">=3.8" +files = [ + {file = "notebook-7.2.1-py3-none-any.whl", hash = "sha256:f45489a3995746f2195a137e0773e2130960b51c9ac3ce257dbc2705aab3a6ca"}, + {file = "notebook-7.2.1.tar.gz", hash = "sha256:4287b6da59740b32173d01d641f763d292f49c30e7a51b89c46ba8473126341e"}, +] + +[package.dependencies] +jupyter-server = ">=2.4.0,<3" +jupyterlab = ">=4.2.0,<4.3" +jupyterlab-server = ">=2.27.1,<3" +notebook-shim = ">=0.2,<0.3" +tornado = ">=6.2.0" + +[package.extras] +dev = ["hatch", "pre-commit"] +docs = ["myst-parser", "nbsphinx", "pydata-sphinx-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] +test = ["importlib-resources (>=5.0)", "ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[test] (>=2.27.1,<3)", "nbval", "pytest (>=7.0)", "pytest-console-scripts", "pytest-timeout", "pytest-tornasync", "requests"] + +[[package]] +name = "notebook-shim" +version = "0.2.4" +description = "A shim layer for notebook traits and config" +optional = false +python-versions = ">=3.7" +files = [ + {file = "notebook_shim-0.2.4-py3-none-any.whl", hash = "sha256:411a5be4e9dc882a074ccbcae671eda64cceb068767e9a3419096986560e1cef"}, + {file = "notebook_shim-0.2.4.tar.gz", hash = "sha256:b4b2cfa1b65d98307ca24361f5b30fe785b53c3fd07b7a47e89acb5e6ac638cb"}, +] + +[package.dependencies] +jupyter-server = ">=1.8,<3" + +[package.extras] +test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync"] + +[[package]] +name = "numcodecs" +version = "0.13.0" +description = "A Python package providing buffer compression and transformation codecs for use in data storage and communication applications." +optional = false +python-versions = ">=3.10" +files = [ + {file = "numcodecs-0.13.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:56e49f68ce6aeba29f144992524c8897d94f846d02bbcc820dd29d7c5c2a073e"}, + {file = "numcodecs-0.13.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:17bc4b568214582f4c623700592f633f3afd920848630049c584fa1e535253ad"}, + {file = "numcodecs-0.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eed420a9c62d0a569aa94a387f93045f068ad3e7bbd787c6ce70bc5fefbaa7d9"}, + {file = "numcodecs-0.13.0-cp310-cp310-win_amd64.whl", hash = "sha256:e7d3b9693df52eeaf978d2a56971d01cf9b4e284ae769ec764807f2087cce51d"}, + {file = "numcodecs-0.13.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f208a1b8b5e66c767ed043812ca74d9045e09b7b2e085d064a585c30b9efc8e7"}, + {file = "numcodecs-0.13.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a68368d3ce625ec76fcacd84785f6110d30a232909d5c6093a7aa25628880477"}, + {file = "numcodecs-0.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f5904216811f2e9d312c23ffaad3b3d4c7442a3583d3a8bf81ca8319e9f5deb5"}, + {file = "numcodecs-0.13.0-cp311-cp311-win_amd64.whl", hash = "sha256:208cab0f4d9cf4409e9c4a4c935e165833786614822c81dee9d865af372da9df"}, + {file = "numcodecs-0.13.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f3cf462d2357998d7f6baaa0427657b0eeda3eb79fba2b146d2d04542912a513"}, + {file = "numcodecs-0.13.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ac4dd5556fb126271e93bd1a02266e21b01d3617db448d70d00eec8e034506b4"}, + {file = "numcodecs-0.13.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:820be89729583c91601a6b35c052008cdd2665b25bfedb91b367cc155fb34ba0"}, + {file = "numcodecs-0.13.0-cp312-cp312-win_amd64.whl", hash = "sha256:d67a859dd8a7f026829e91cb1799c26720cc9d29ee4ae0060cc7a581670abc06"}, + {file = "numcodecs-0.13.0.tar.gz", hash = "sha256:ba4fac7036ea5a078c7afe1d4dffeb9685080d42f19c9c16b12dad866703aa2e"}, +] + +[package.dependencies] +numpy = ">=1.7" + +[package.extras] +docs = ["mock", "numpydoc", "pydata-sphinx-theme", "sphinx (<7.0.0)", "sphinx-issues"] +msgpack = ["msgpack"] +pcodec = ["pcodec (>=0.2.0)"] +test = ["coverage", "pytest", "pytest-cov"] +test-extras = ["importlib-metadata"] +zfpy = ["numpy (<2.0.0)", "zfpy (>=1.0.0)"] + +[[package]] +name = "numpy" +version = "1.26.4" +description = "Fundamental package for array computing in Python" +optional = false +python-versions = ">=3.9" +files = [ + {file = "numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0"}, + {file = "numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a"}, + {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4"}, + {file = "numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"}, + {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a"}, + {file = "numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2"}, + {file = "numpy-1.26.4-cp310-cp310-win32.whl", hash = "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07"}, + {file = "numpy-1.26.4-cp310-cp310-win_amd64.whl", hash = "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5"}, + {file = "numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71"}, + {file = "numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef"}, + {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7ab55401287bfec946ced39700c053796e7cc0e3acbef09993a9ad2adba6ca6e"}, + {file = "numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:666dbfb6ec68962c033a450943ded891bed2d54e6755e35e5835d63f4f6931d5"}, + {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:96ff0b2ad353d8f990b63294c8986f1ec3cb19d749234014f4e7eb0112ceba5a"}, + {file = "numpy-1.26.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:60dedbb91afcbfdc9bc0b1f3f402804070deed7392c23eb7a7f07fa857868e8a"}, + {file = "numpy-1.26.4-cp311-cp311-win32.whl", hash = "sha256:1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20"}, + {file = "numpy-1.26.4-cp311-cp311-win_amd64.whl", hash = "sha256:cd25bcecc4974d09257ffcd1f098ee778f7834c3ad767fe5db785be9a4aa9cb2"}, + {file = "numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218"}, + {file = "numpy-1.26.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b"}, + {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9fad7dcb1aac3c7f0584a5a8133e3a43eeb2fe127f47e3632d43d677c66c102b"}, + {file = "numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:675d61ffbfa78604709862923189bad94014bef562cc35cf61d3a07bba02a7ed"}, + {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:ab47dbe5cc8210f55aa58e4805fe224dac469cde56b9f731a4c098b91917159a"}, + {file = "numpy-1.26.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:1dda2e7b4ec9dd512f84935c5f126c8bd8b9f2fc001e9f54af255e8c5f16b0e0"}, + {file = "numpy-1.26.4-cp312-cp312-win32.whl", hash = "sha256:50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110"}, + {file = "numpy-1.26.4-cp312-cp312-win_amd64.whl", hash = "sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818"}, + {file = "numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c"}, + {file = "numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be"}, + {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764"}, + {file = "numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3"}, + {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd"}, + {file = "numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c"}, + {file = "numpy-1.26.4-cp39-cp39-win32.whl", hash = "sha256:a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6"}, + {file = "numpy-1.26.4-cp39-cp39-win_amd64.whl", hash = "sha256:3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea"}, + {file = "numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30"}, + {file = "numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c"}, + {file = "numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0"}, + {file = "numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010"}, +] + +[[package]] +name = "nvidia-cublas-cu12" +version = "12.1.3.1" +description = "CUBLAS native runtime libraries" +optional = false +python-versions = ">=3" +files = [ + {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:ee53ccca76a6fc08fb9701aa95b6ceb242cdaab118c3bb152af4e579af792728"}, + {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-win_amd64.whl", hash = "sha256:2b964d60e8cf11b5e1073d179d85fa340c120e99b3067558f3cf98dd69d02906"}, +] + +[[package]] +name = "nvidia-cuda-cupti-cu12" +version = "12.1.105" +description = "CUDA profiling tools runtime libs." +optional = false +python-versions = ">=3" +files = [ + {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:e54fde3983165c624cb79254ae9818a456eb6e87a7fd4d56a2352c24ee542d7e"}, + {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:bea8236d13a0ac7190bd2919c3e8e6ce1e402104276e6f9694479e48bb0eb2a4"}, +] + +[[package]] +name = "nvidia-cuda-nvrtc-cu12" +version = "12.1.105" +description = "NVRTC native runtime libraries" +optional = false +python-versions = ">=3" +files = [ + {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2"}, + {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:0a98a522d9ff138b96c010a65e145dc1b4850e9ecb75a0172371793752fd46ed"}, +] + +[[package]] +name = "nvidia-cuda-runtime-cu12" +version = "12.1.105" +description = "CUDA Runtime native Libraries" +optional = false +python-versions = ">=3" +files = [ + {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"}, + {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:dfb46ef84d73fababab44cf03e3b83f80700d27ca300e537f85f636fac474344"}, +] + +[[package]] +name = "nvidia-cudnn-cu12" +version = "8.9.2.26" +description = "cuDNN runtime libraries" +optional = false +python-versions = ">=3" +files = [ + {file = "nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl", hash = "sha256:5ccb288774fdfb07a7e7025ffec286971c06d8d7b4fb162525334616d7629ff9"}, +] + +[package.dependencies] +nvidia-cublas-cu12 = "*" + +[[package]] +name = "nvidia-cufft-cu12" +version = "11.0.2.54" +description = "CUFFT native runtime libraries" +optional = false +python-versions = ">=3" +files = [ + {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"}, + {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-win_amd64.whl", hash = "sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253"}, +] + +[[package]] +name = "nvidia-curand-cu12" +version = "10.3.2.106" +description = "CURAND native runtime libraries" +optional = false +python-versions = ">=3" +files = [ + {file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"}, + {file = "nvidia_curand_cu12-10.3.2.106-py3-none-win_amd64.whl", hash = "sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a"}, +] + +[[package]] +name = "nvidia-cusolver-cu12" +version = "11.4.5.107" +description = "CUDA solver native runtime libraries" +optional = false +python-versions = ">=3" +files = [ + {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"}, + {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"}, +] + +[package.dependencies] +nvidia-cublas-cu12 = "*" +nvidia-cusparse-cu12 = "*" +nvidia-nvjitlink-cu12 = "*" + +[[package]] +name = "nvidia-cusparse-cu12" +version = "12.1.0.106" +description = "CUSPARSE native runtime libraries" +optional = false +python-versions = ">=3" +files = [ + {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"}, + {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"}, +] + +[package.dependencies] +nvidia-nvjitlink-cu12 = "*" + +[[package]] +name = "nvidia-nccl-cu12" +version = "2.20.5" +description = "NVIDIA Collective Communication Library (NCCL) Runtime" +optional = false +python-versions = ">=3" +files = [ + {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"}, + {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56"}, +] + +[[package]] +name = "nvidia-nvjitlink-cu12" +version = "12.5.82" +description = "Nvidia JIT LTO Library" +optional = false +python-versions = ">=3" +files = [ + {file = "nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_aarch64.whl", hash = "sha256:98103729cc5226e13ca319a10bbf9433bbbd44ef64fe72f45f067cacc14b8d27"}, + {file = "nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl", hash = "sha256:f9b37bc5c8cf7509665cb6ada5aaa0ce65618f2332b7d3e78e9790511f111212"}, + {file = "nvidia_nvjitlink_cu12-12.5.82-py3-none-win_amd64.whl", hash = "sha256:e782564d705ff0bf61ac3e1bf730166da66dd2fe9012f111ede5fc49b64ae697"}, +] + +[[package]] +name = "nvidia-nvtx-cu12" +version = "12.1.105" +description = "NVIDIA Tools Extension" +optional = false +python-versions = ">=3" +files = [ + {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"}, + {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"}, +] + +[[package]] +name = "ogb" +version = "1.3.6" +description = "Open Graph Benchmark" +optional = false +python-versions = "*" +files = [ + {file = "ogb-1.3.6-py3-none-any.whl", hash = "sha256:29ab84078c66a7846bf137c9be9545978616a053e734c032a2ff731d026bb5e9"}, + {file = "ogb-1.3.6.tar.gz", hash = "sha256:ce90418a0e3206483187aa7b7ecac1a2c5d85b3b99aceedb807138ee43115914"}, +] + +[package.dependencies] +numpy = ">=1.16.0" +outdated = ">=0.2.0" +pandas = ">=0.24.0" +scikit-learn = ">=0.20.0" +six = ">=1.12.0" +torch = ">=1.6.0" +tqdm = ">=4.29.0" +urllib3 = ">=1.24.0" + +[[package]] +name = "opencv-python" +version = "4.10.0.84" +description = "Wrapper package for OpenCV python bindings." +optional = false +python-versions = ">=3.6" +files = [ + {file = "opencv-python-4.10.0.84.tar.gz", hash = "sha256:72d234e4582e9658ffea8e9cae5b63d488ad06994ef12d81dc303b17472f3526"}, + {file = "opencv_python-4.10.0.84-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:fc182f8f4cda51b45f01c64e4cbedfc2f00aff799debebc305d8d0210c43f251"}, + {file = "opencv_python-4.10.0.84-cp37-abi3-macosx_12_0_x86_64.whl", hash = "sha256:71e575744f1d23f79741450254660442785f45a0797212852ee5199ef12eed98"}, + {file = "opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:09a332b50488e2dda866a6c5573ee192fe3583239fb26ff2f7f9ceb0bc119ea6"}, + {file = "opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9ace140fc6d647fbe1c692bcb2abce768973491222c067c131d80957c595b71f"}, + {file = "opencv_python-4.10.0.84-cp37-abi3-win32.whl", hash = "sha256:2db02bb7e50b703f0a2d50c50ced72e95c574e1e5a0bb35a8a86d0b35c98c236"}, + {file = "opencv_python-4.10.0.84-cp37-abi3-win_amd64.whl", hash = "sha256:32dbbd94c26f611dc5cc6979e6b7aa1f55a64d6b463cc1dcd3c95505a63e48fe"}, +] + +[package.dependencies] +numpy = [ + {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, + {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""}, +] + +[[package]] +name = "openpyxl" +version = "3.1.5" +description = "A Python library to read/write Excel 2010 xlsx/xlsm files" +optional = false +python-versions = ">=3.8" +files = [ + {file = "openpyxl-3.1.5-py2.py3-none-any.whl", hash = "sha256:5282c12b107bffeef825f4617dc029afaf41d0ea60823bbb665ef3079dc79de2"}, + {file = "openpyxl-3.1.5.tar.gz", hash = "sha256:cf0e3cf56142039133628b5acffe8ef0c12bc902d2aadd3e0fe5878dc08d1050"}, +] + +[package.dependencies] +et-xmlfile = "*" + +[[package]] +name = "outdated" +version = "0.2.2" +description = "Check if a version of a PyPI package is outdated" +optional = false +python-versions = "*" +files = [ + {file = "outdated-0.2.2-py2.py3-none-any.whl", hash = "sha256:3e9c2ee6d17e86ae8cc7bb71d70c4172690121cda367155a30994742172678c8"}, + {file = "outdated-0.2.2.tar.gz", hash = "sha256:4b7fdec88e36711120d096d485fc4d5035e4e5ffbd907cf3a6ce2af43058b970"}, +] + +[package.dependencies] +littleutils = "*" +requests = "*" +setuptools = ">=44" + +[[package]] +name = "overrides" +version = "7.7.0" +description = "A decorator to automatically detect mismatch when overriding a method." +optional = false +python-versions = ">=3.6" +files = [ + {file = "overrides-7.7.0-py3-none-any.whl", hash = "sha256:c7ed9d062f78b8e4c1a7b70bd8796b35ead4d9f510227ef9c5dc7626c60d7e49"}, + {file = "overrides-7.7.0.tar.gz", hash = "sha256:55158fa3d93b98cc75299b1e67078ad9003ca27945c76162c1c0766d6f91820a"}, +] + +[[package]] +name = "packaging" +version = "24.1" +description = "Core utilities for Python packages" +optional = false +python-versions = ">=3.8" +files = [ + {file = "packaging-24.1-py3-none-any.whl", hash = "sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124"}, + {file = "packaging-24.1.tar.gz", hash = "sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002"}, +] + +[[package]] +name = "pandas" +version = "2.2.2" +description = "Powerful data structures for data analysis, time series, and statistics" +optional = false +python-versions = ">=3.9" +files = [ + {file = "pandas-2.2.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:90c6fca2acf139569e74e8781709dccb6fe25940488755716d1d354d6bc58bce"}, + {file = "pandas-2.2.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c7adfc142dac335d8c1e0dcbd37eb8617eac386596eb9e1a1b77791cf2498238"}, + {file = "pandas-2.2.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4abfe0be0d7221be4f12552995e58723c7422c80a659da13ca382697de830c08"}, + {file = "pandas-2.2.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8635c16bf3d99040fdf3ca3db669a7250ddf49c55dc4aa8fe0ae0fa8d6dcc1f0"}, + {file = "pandas-2.2.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:40ae1dffb3967a52203105a077415a86044a2bea011b5f321c6aa64b379a3f51"}, + {file = "pandas-2.2.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:8e5a0b00e1e56a842f922e7fae8ae4077aee4af0acb5ae3622bd4b4c30aedf99"}, + {file = "pandas-2.2.2-cp310-cp310-win_amd64.whl", hash = "sha256:ddf818e4e6c7c6f4f7c8a12709696d193976b591cc7dc50588d3d1a6b5dc8772"}, + {file = "pandas-2.2.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:696039430f7a562b74fa45f540aca068ea85fa34c244d0deee539cb6d70aa288"}, + {file = "pandas-2.2.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8e90497254aacacbc4ea6ae5e7a8cd75629d6ad2b30025a4a8b09aa4faf55151"}, + {file = "pandas-2.2.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:58b84b91b0b9f4bafac2a0ac55002280c094dfc6402402332c0913a59654ab2b"}, + {file = "pandas-2.2.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d2123dc9ad6a814bcdea0f099885276b31b24f7edf40f6cdbc0912672e22eee"}, + {file = "pandas-2.2.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:2925720037f06e89af896c70bca73459d7e6a4be96f9de79e2d440bd499fe0db"}, + {file = "pandas-2.2.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:0cace394b6ea70c01ca1595f839cf193df35d1575986e484ad35c4aeae7266c1"}, + {file = "pandas-2.2.2-cp311-cp311-win_amd64.whl", hash = "sha256:873d13d177501a28b2756375d59816c365e42ed8417b41665f346289adc68d24"}, + {file = "pandas-2.2.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:9dfde2a0ddef507a631dc9dc4af6a9489d5e2e740e226ad426a05cabfbd7c8ef"}, + {file = "pandas-2.2.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:e9b79011ff7a0f4b1d6da6a61aa1aa604fb312d6647de5bad20013682d1429ce"}, + {file = "pandas-2.2.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1cb51fe389360f3b5a4d57dbd2848a5f033350336ca3b340d1c53a1fad33bcad"}, + {file = "pandas-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eee3a87076c0756de40b05c5e9a6069c035ba43e8dd71c379e68cab2c20f16ad"}, + {file = "pandas-2.2.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:3e374f59e440d4ab45ca2fffde54b81ac3834cf5ae2cdfa69c90bc03bde04d76"}, + {file = "pandas-2.2.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:43498c0bdb43d55cb162cdc8c06fac328ccb5d2eabe3cadeb3529ae6f0517c32"}, + {file = "pandas-2.2.2-cp312-cp312-win_amd64.whl", hash = "sha256:d187d355ecec3629624fccb01d104da7d7f391db0311145817525281e2804d23"}, + {file = "pandas-2.2.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0ca6377b8fca51815f382bd0b697a0814c8bda55115678cbc94c30aacbb6eff2"}, + {file = "pandas-2.2.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9057e6aa78a584bc93a13f0a9bf7e753a5e9770a30b4d758b8d5f2a62a9433cd"}, + {file = "pandas-2.2.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:001910ad31abc7bf06f49dcc903755d2f7f3a9186c0c040b827e522e9cef0863"}, + {file = "pandas-2.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:66b479b0bd07204e37583c191535505410daa8df638fd8e75ae1b383851fe921"}, + {file = "pandas-2.2.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:a77e9d1c386196879aa5eb712e77461aaee433e54c68cf253053a73b7e49c33a"}, + {file = "pandas-2.2.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:92fd6b027924a7e178ac202cfbe25e53368db90d56872d20ffae94b96c7acc57"}, + {file = "pandas-2.2.2-cp39-cp39-win_amd64.whl", hash = "sha256:640cef9aa381b60e296db324337a554aeeb883ead99dc8f6c18e81a93942f5f4"}, + {file = "pandas-2.2.2.tar.gz", hash = "sha256:9e79019aba43cb4fda9e4d983f8e88ca0373adbb697ae9c6c43093218de28b54"}, +] + +[package.dependencies] +numpy = [ + {version = ">=1.26.0", markers = "python_version >= \"3.12\""}, + {version = ">=1.23.2", markers = "python_version == \"3.11\""}, +] +python-dateutil = ">=2.8.2" +pytz = ">=2020.1" +tzdata = ">=2022.7" + +[package.extras] +all = ["PyQt5 (>=5.15.9)", "SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)", "beautifulsoup4 (>=4.11.2)", "bottleneck (>=1.3.6)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=2022.12.0)", "fsspec (>=2022.11.0)", "gcsfs (>=2022.11.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.9.2)", "matplotlib (>=3.6.3)", "numba (>=0.56.4)", "numexpr (>=2.8.4)", "odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "pandas-gbq (>=0.19.0)", "psycopg2 (>=2.9.6)", "pyarrow (>=10.0.1)", "pymysql (>=1.0.2)", "pyreadstat (>=1.2.0)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "qtpy (>=2.3.0)", "s3fs (>=2022.11.0)", "scipy (>=1.10.0)", "tables (>=3.8.0)", "tabulate (>=0.9.0)", "xarray (>=2022.12.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)", "zstandard (>=0.19.0)"] +aws = ["s3fs (>=2022.11.0)"] +clipboard = ["PyQt5 (>=5.15.9)", "qtpy (>=2.3.0)"] +compression = ["zstandard (>=0.19.0)"] +computation = ["scipy (>=1.10.0)", "xarray (>=2022.12.0)"] +consortium-standard = ["dataframe-api-compat (>=0.1.7)"] +excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.1.0)", "python-calamine (>=0.1.7)", "pyxlsb (>=1.0.10)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.5)"] +feather = ["pyarrow (>=10.0.1)"] +fss = ["fsspec (>=2022.11.0)"] +gcp = ["gcsfs (>=2022.11.0)", "pandas-gbq (>=0.19.0)"] +hdf5 = ["tables (>=3.8.0)"] +html = ["beautifulsoup4 (>=4.11.2)", "html5lib (>=1.1)", "lxml (>=4.9.2)"] +mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"] +output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.9.0)"] +parquet = ["pyarrow (>=10.0.1)"] +performance = ["bottleneck (>=1.3.6)", "numba (>=0.56.4)", "numexpr (>=2.8.4)"] +plot = ["matplotlib (>=3.6.3)"] +postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"] +pyarrow = ["pyarrow (>=10.0.1)"] +spss = ["pyreadstat (>=1.2.0)"] +sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"] +test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"] +xml = ["lxml (>=4.9.2)"] + +[[package]] +name = "pandocfilters" +version = "1.5.1" +description = "Utilities for writing pandoc filters in python" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +files = [ + {file = "pandocfilters-1.5.1-py2.py3-none-any.whl", hash = "sha256:93be382804a9cdb0a7267585f157e5d1731bbe5545a85b268d6f5fe6232de2bc"}, + {file = "pandocfilters-1.5.1.tar.gz", hash = "sha256:002b4a555ee4ebc03f8b66307e287fa492e4a77b4ea14d3f934328297bb4939e"}, +] + +[[package]] +name = "parso" +version = "0.8.4" +description = "A Python Parser" +optional = false +python-versions = ">=3.6" +files = [ + {file = "parso-0.8.4-py2.py3-none-any.whl", hash = "sha256:a418670a20291dacd2dddc80c377c5c3791378ee1e8d12bffc35420643d43f18"}, + {file = "parso-0.8.4.tar.gz", hash = "sha256:eb3a7b58240fb99099a345571deecc0f9540ea5f4dd2fe14c2a99d6b281ab92d"}, +] + +[package.extras] +qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"] +testing = ["docopt", "pytest"] + +[[package]] +name = "pathspec" +version = "0.12.1" +description = "Utility library for gitignore style pattern matching of file paths." +optional = false +python-versions = ">=3.8" +files = [ + {file = "pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08"}, + {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"}, +] + +[[package]] +name = "pathtools" +version = "0.1.2" +description = "File system general utilities" +optional = false +python-versions = "*" +files = [ + {file = "pathtools-0.1.2.tar.gz", hash = "sha256:7c35c5421a39bb82e58018febd90e3b6e5db34c5443aaaf742b3f33d4655f1c0"}, +] + +[[package]] +name = "pexpect" +version = "4.9.0" +description = "Pexpect allows easy control of interactive console applications." +optional = false +python-versions = "*" +files = [ + {file = "pexpect-4.9.0-py2.py3-none-any.whl", hash = "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523"}, + {file = "pexpect-4.9.0.tar.gz", hash = "sha256:ee7d41123f3c9911050ea2c2dac107568dc43b2d3b0c7557a33212c398ead30f"}, +] + +[package.dependencies] +ptyprocess = ">=0.5" + +[[package]] +name = "pillow" +version = "10.4.0" +description = "Python Imaging Library (Fork)" +optional = false +python-versions = ">=3.8" +files = [ + {file = "pillow-10.4.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:4d9667937cfa347525b319ae34375c37b9ee6b525440f3ef48542fcf66f2731e"}, + {file = "pillow-10.4.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:543f3dc61c18dafb755773efc89aae60d06b6596a63914107f75459cf984164d"}, + {file = "pillow-10.4.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7928ecbf1ece13956b95d9cbcfc77137652b02763ba384d9ab508099a2eca856"}, + {file = "pillow-10.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4d49b85c4348ea0b31ea63bc75a9f3857869174e2bf17e7aba02945cd218e6f"}, + {file = "pillow-10.4.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:6c762a5b0997f5659a5ef2266abc1d8851ad7749ad9a6a5506eb23d314e4f46b"}, + {file = "pillow-10.4.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:a985e028fc183bf12a77a8bbf36318db4238a3ded7fa9df1b9a133f1cb79f8fc"}, + {file = "pillow-10.4.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:812f7342b0eee081eaec84d91423d1b4650bb9828eb53d8511bcef8ce5aecf1e"}, + {file = "pillow-10.4.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:ac1452d2fbe4978c2eec89fb5a23b8387aba707ac72810d9490118817d9c0b46"}, + {file = "pillow-10.4.0-cp310-cp310-win32.whl", hash = "sha256:bcd5e41a859bf2e84fdc42f4edb7d9aba0a13d29a2abadccafad99de3feff984"}, + {file = "pillow-10.4.0-cp310-cp310-win_amd64.whl", hash = "sha256:ecd85a8d3e79cd7158dec1c9e5808e821feea088e2f69a974db5edf84dc53141"}, + {file = "pillow-10.4.0-cp310-cp310-win_arm64.whl", hash = "sha256:ff337c552345e95702c5fde3158acb0625111017d0e5f24bf3acdb9cc16b90d1"}, + {file = "pillow-10.4.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:0a9ec697746f268507404647e531e92889890a087e03681a3606d9b920fbee3c"}, + {file = "pillow-10.4.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:dfe91cb65544a1321e631e696759491ae04a2ea11d36715eca01ce07284738be"}, + {file = "pillow-10.4.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5dc6761a6efc781e6a1544206f22c80c3af4c8cf461206d46a1e6006e4429ff3"}, + {file = "pillow-10.4.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5e84b6cc6a4a3d76c153a6b19270b3526a5a8ed6b09501d3af891daa2a9de7d6"}, + {file = "pillow-10.4.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:bbc527b519bd3aa9d7f429d152fea69f9ad37c95f0b02aebddff592688998abe"}, + {file = "pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:76a911dfe51a36041f2e756b00f96ed84677cdeb75d25c767f296c1c1eda1319"}, + {file = "pillow-10.4.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:59291fb29317122398786c2d44427bbd1a6d7ff54017075b22be9d21aa59bd8d"}, + {file = "pillow-10.4.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:416d3a5d0e8cfe4f27f574362435bc9bae57f679a7158e0096ad2beb427b8696"}, + {file = "pillow-10.4.0-cp311-cp311-win32.whl", hash = "sha256:7086cc1d5eebb91ad24ded9f58bec6c688e9f0ed7eb3dbbf1e4800280a896496"}, + {file = "pillow-10.4.0-cp311-cp311-win_amd64.whl", hash = "sha256:cbed61494057c0f83b83eb3a310f0bf774b09513307c434d4366ed64f4128a91"}, + {file = "pillow-10.4.0-cp311-cp311-win_arm64.whl", hash = "sha256:f5f0c3e969c8f12dd2bb7e0b15d5c468b51e5017e01e2e867335c81903046a22"}, + {file = "pillow-10.4.0-cp312-cp312-macosx_10_10_x86_64.whl", hash = "sha256:673655af3eadf4df6b5457033f086e90299fdd7a47983a13827acf7459c15d94"}, + {file = "pillow-10.4.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:866b6942a92f56300012f5fbac71f2d610312ee65e22f1aa2609e491284e5597"}, + {file = "pillow-10.4.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:29dbdc4207642ea6aad70fbde1a9338753d33fb23ed6956e706936706f52dd80"}, + {file = "pillow-10.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bf2342ac639c4cf38799a44950bbc2dfcb685f052b9e262f446482afaf4bffca"}, + {file = "pillow-10.4.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:f5b92f4d70791b4a67157321c4e8225d60b119c5cc9aee8ecf153aace4aad4ef"}, + {file = "pillow-10.4.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:86dcb5a1eb778d8b25659d5e4341269e8590ad6b4e8b44d9f4b07f8d136c414a"}, + {file = "pillow-10.4.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:780c072c2e11c9b2c7ca37f9a2ee8ba66f44367ac3e5c7832afcfe5104fd6d1b"}, + {file = "pillow-10.4.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:37fb69d905be665f68f28a8bba3c6d3223c8efe1edf14cc4cfa06c241f8c81d9"}, + {file = "pillow-10.4.0-cp312-cp312-win32.whl", hash = "sha256:7dfecdbad5c301d7b5bde160150b4db4c659cee2b69589705b6f8a0c509d9f42"}, + {file = "pillow-10.4.0-cp312-cp312-win_amd64.whl", hash = "sha256:1d846aea995ad352d4bdcc847535bd56e0fd88d36829d2c90be880ef1ee4668a"}, + {file = "pillow-10.4.0-cp312-cp312-win_arm64.whl", hash = "sha256:e553cad5179a66ba15bb18b353a19020e73a7921296a7979c4a2b7f6a5cd57f9"}, + {file = "pillow-10.4.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8bc1a764ed8c957a2e9cacf97c8b2b053b70307cf2996aafd70e91a082e70df3"}, + {file = "pillow-10.4.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:6209bb41dc692ddfee4942517c19ee81b86c864b626dbfca272ec0f7cff5d9fb"}, + {file = "pillow-10.4.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bee197b30783295d2eb680b311af15a20a8b24024a19c3a26431ff83eb8d1f70"}, + {file = "pillow-10.4.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1ef61f5dd14c300786318482456481463b9d6b91ebe5ef12f405afbba77ed0be"}, + {file = "pillow-10.4.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:297e388da6e248c98bc4a02e018966af0c5f92dfacf5a5ca22fa01cb3179bca0"}, + {file = "pillow-10.4.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:e4db64794ccdf6cb83a59d73405f63adbe2a1887012e308828596100a0b2f6cc"}, + {file = "pillow-10.4.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:bd2880a07482090a3bcb01f4265f1936a903d70bc740bfcb1fd4e8a2ffe5cf5a"}, + {file = "pillow-10.4.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:4b35b21b819ac1dbd1233317adeecd63495f6babf21b7b2512d244ff6c6ce309"}, + {file = "pillow-10.4.0-cp313-cp313-win32.whl", hash = "sha256:551d3fd6e9dc15e4c1eb6fc4ba2b39c0c7933fa113b220057a34f4bb3268a060"}, + {file = "pillow-10.4.0-cp313-cp313-win_amd64.whl", hash = "sha256:030abdbe43ee02e0de642aee345efa443740aa4d828bfe8e2eb11922ea6a21ea"}, + {file = "pillow-10.4.0-cp313-cp313-win_arm64.whl", hash = "sha256:5b001114dd152cfd6b23befeb28d7aee43553e2402c9f159807bf55f33af8a8d"}, + {file = "pillow-10.4.0-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:8d4d5063501b6dd4024b8ac2f04962d661222d120381272deea52e3fc52d3736"}, + {file = "pillow-10.4.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7c1ee6f42250df403c5f103cbd2768a28fe1a0ea1f0f03fe151c8741e1469c8b"}, + {file = "pillow-10.4.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b15e02e9bb4c21e39876698abf233c8c579127986f8207200bc8a8f6bb27acf2"}, + {file = "pillow-10.4.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a8d4bade9952ea9a77d0c3e49cbd8b2890a399422258a77f357b9cc9be8d680"}, + {file = "pillow-10.4.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:43efea75eb06b95d1631cb784aa40156177bf9dd5b4b03ff38979e048258bc6b"}, + {file = "pillow-10.4.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:950be4d8ba92aca4b2bb0741285a46bfae3ca699ef913ec8416c1b78eadd64cd"}, + {file = "pillow-10.4.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:d7480af14364494365e89d6fddc510a13e5a2c3584cb19ef65415ca57252fb84"}, + {file = "pillow-10.4.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:73664fe514b34c8f02452ffb73b7a92c6774e39a647087f83d67f010eb9a0cf0"}, + {file = "pillow-10.4.0-cp38-cp38-win32.whl", hash = "sha256:e88d5e6ad0d026fba7bdab8c3f225a69f063f116462c49892b0149e21b6c0a0e"}, + {file = "pillow-10.4.0-cp38-cp38-win_amd64.whl", hash = "sha256:5161eef006d335e46895297f642341111945e2c1c899eb406882a6c61a4357ab"}, + {file = "pillow-10.4.0-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:0ae24a547e8b711ccaaf99c9ae3cd975470e1a30caa80a6aaee9a2f19c05701d"}, + {file = "pillow-10.4.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:298478fe4f77a4408895605f3482b6cc6222c018b2ce565c2b6b9c354ac3229b"}, + {file = "pillow-10.4.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:134ace6dc392116566980ee7436477d844520a26a4b1bd4053f6f47d096997fd"}, + {file = "pillow-10.4.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:930044bb7679ab003b14023138b50181899da3f25de50e9dbee23b61b4de2126"}, + {file = "pillow-10.4.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:c76e5786951e72ed3686e122d14c5d7012f16c8303a674d18cdcd6d89557fc5b"}, + {file = "pillow-10.4.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:b2724fdb354a868ddf9a880cb84d102da914e99119211ef7ecbdc613b8c96b3c"}, + {file = "pillow-10.4.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:dbc6ae66518ab3c5847659e9988c3b60dc94ffb48ef9168656e0019a93dbf8a1"}, + {file = "pillow-10.4.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:06b2f7898047ae93fad74467ec3d28fe84f7831370e3c258afa533f81ef7f3df"}, + {file = "pillow-10.4.0-cp39-cp39-win32.whl", hash = "sha256:7970285ab628a3779aecc35823296a7869f889b8329c16ad5a71e4901a3dc4ef"}, + {file = "pillow-10.4.0-cp39-cp39-win_amd64.whl", hash = "sha256:961a7293b2457b405967af9c77dcaa43cc1a8cd50d23c532e62d48ab6cdd56f5"}, + {file = "pillow-10.4.0-cp39-cp39-win_arm64.whl", hash = "sha256:32cda9e3d601a52baccb2856b8ea1fc213c90b340c542dcef77140dfa3278a9e"}, + {file = "pillow-10.4.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:5b4815f2e65b30f5fbae9dfffa8636d992d49705723fe86a3661806e069352d4"}, + {file = "pillow-10.4.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:8f0aef4ef59694b12cadee839e2ba6afeab89c0f39a3adc02ed51d109117b8da"}, + {file = "pillow-10.4.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9f4727572e2918acaa9077c919cbbeb73bd2b3ebcfe033b72f858fc9fbef0026"}, + {file = "pillow-10.4.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ff25afb18123cea58a591ea0244b92eb1e61a1fd497bf6d6384f09bc3262ec3e"}, + {file = "pillow-10.4.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:dc3e2db6ba09ffd7d02ae9141cfa0ae23393ee7687248d46a7507b75d610f4f5"}, + {file = "pillow-10.4.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:02a2be69f9c9b8c1e97cf2713e789d4e398c751ecfd9967c18d0ce304efbf885"}, + {file = "pillow-10.4.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:0755ffd4a0c6f267cccbae2e9903d95477ca2f77c4fcf3a3a09570001856c8a5"}, + {file = "pillow-10.4.0-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:a02364621fe369e06200d4a16558e056fe2805d3468350df3aef21e00d26214b"}, + {file = "pillow-10.4.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:1b5dea9831a90e9d0721ec417a80d4cbd7022093ac38a568db2dd78363b00908"}, + {file = "pillow-10.4.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9b885f89040bb8c4a1573566bbb2f44f5c505ef6e74cec7ab9068c900047f04b"}, + {file = "pillow-10.4.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:87dd88ded2e6d74d31e1e0a99a726a6765cda32d00ba72dc37f0651f306daaa8"}, + {file = "pillow-10.4.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:2db98790afc70118bd0255c2eeb465e9767ecf1f3c25f9a1abb8ffc8cfd1fe0a"}, + {file = "pillow-10.4.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:f7baece4ce06bade126fb84b8af1c33439a76d8a6fd818970215e0560ca28c27"}, + {file = "pillow-10.4.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:cfdd747216947628af7b259d274771d84db2268ca062dd5faf373639d00113a3"}, + {file = "pillow-10.4.0.tar.gz", hash = "sha256:166c1cd4d24309b30d61f79f4a9114b7b2313d7450912277855ff5dfd7cd4a06"}, +] + +[package.extras] +docs = ["furo", "olefile", "sphinx (>=7.3)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"] +fpx = ["olefile"] +mic = ["olefile"] +tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"] +typing = ["typing-extensions"] +xmp = ["defusedxml"] + +[[package]] +name = "platformdirs" +version = "4.2.2" +description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`." +optional = false +python-versions = ">=3.8" +files = [ + {file = "platformdirs-4.2.2-py3-none-any.whl", hash = "sha256:2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee"}, + {file = "platformdirs-4.2.2.tar.gz", hash = "sha256:38b7b51f512eed9e84a22788b4bce1de17c0adb134d6becb09836e37d8654cd3"}, +] + +[package.extras] +docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"] +test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"] +type = ["mypy (>=1.8)"] + +[[package]] +name = "pluggy" +version = "1.5.0" +description = "plugin and hook calling mechanisms for python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"}, + {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"}, +] + +[package.extras] +dev = ["pre-commit", "tox"] +testing = ["pytest", "pytest-benchmark"] + +[[package]] +name = "prometheus-client" +version = "0.20.0" +description = "Python client for the Prometheus monitoring system." +optional = false +python-versions = ">=3.8" +files = [ + {file = "prometheus_client-0.20.0-py3-none-any.whl", hash = "sha256:cde524a85bce83ca359cc837f28b8c0db5cac7aa653a588fd7e84ba061c329e7"}, + {file = "prometheus_client-0.20.0.tar.gz", hash = "sha256:287629d00b147a32dcb2be0b9df905da599b2d82f80377083ec8463309a4bb89"}, +] + +[package.extras] +twisted = ["twisted"] + +[[package]] +name = "prompt-toolkit" +version = "3.0.47" +description = "Library for building powerful interactive command lines in Python" +optional = false +python-versions = ">=3.7.0" +files = [ + {file = "prompt_toolkit-3.0.47-py3-none-any.whl", hash = "sha256:0d7bfa67001d5e39d02c224b663abc33687405033a8c422d0d675a5a13361d10"}, + {file = "prompt_toolkit-3.0.47.tar.gz", hash = "sha256:1e1b29cb58080b1e69f207c893a1a7bf16d127a5c30c9d17a25a5d77792e5360"}, +] + +[package.dependencies] +wcwidth = "*" + +[[package]] +name = "protobuf" +version = "4.25.3" +description = "" +optional = false +python-versions = ">=3.8" +files = [ + {file = "protobuf-4.25.3-cp310-abi3-win32.whl", hash = "sha256:d4198877797a83cbfe9bffa3803602bbe1625dc30d8a097365dbc762e5790faa"}, + {file = "protobuf-4.25.3-cp310-abi3-win_amd64.whl", hash = "sha256:209ba4cc916bab46f64e56b85b090607a676f66b473e6b762e6f1d9d591eb2e8"}, + {file = "protobuf-4.25.3-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:f1279ab38ecbfae7e456a108c5c0681e4956d5b1090027c1de0f934dfdb4b35c"}, + {file = "protobuf-4.25.3-cp37-abi3-manylinux2014_aarch64.whl", hash = "sha256:e7cb0ae90dd83727f0c0718634ed56837bfeeee29a5f82a7514c03ee1364c019"}, + {file = "protobuf-4.25.3-cp37-abi3-manylinux2014_x86_64.whl", hash = "sha256:7c8daa26095f82482307bc717364e7c13f4f1c99659be82890dcfc215194554d"}, + {file = "protobuf-4.25.3-cp38-cp38-win32.whl", hash = "sha256:f4f118245c4a087776e0a8408be33cf09f6c547442c00395fbfb116fac2f8ac2"}, + {file = "protobuf-4.25.3-cp38-cp38-win_amd64.whl", hash = "sha256:c053062984e61144385022e53678fbded7aea14ebb3e0305ae3592fb219ccfa4"}, + {file = "protobuf-4.25.3-cp39-cp39-win32.whl", hash = "sha256:19b270aeaa0099f16d3ca02628546b8baefe2955bbe23224aaf856134eccf1e4"}, + {file = "protobuf-4.25.3-cp39-cp39-win_amd64.whl", hash = "sha256:e3c97a1555fd6388f857770ff8b9703083de6bf1f9274a002a332d65fbb56c8c"}, + {file = "protobuf-4.25.3-py3-none-any.whl", hash = "sha256:f0700d54bcf45424477e46a9f0944155b46fb0639d69728739c0e47bab83f2b9"}, + {file = "protobuf-4.25.3.tar.gz", hash = "sha256:25b5d0b42fd000320bd7830b349e3b696435f3b329810427a6bcce6a5492cc5c"}, +] + +[[package]] +name = "psutil" +version = "6.0.0" +description = "Cross-platform lib for process and system monitoring in Python." +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" +files = [ + {file = "psutil-6.0.0-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:a021da3e881cd935e64a3d0a20983bda0bb4cf80e4f74fa9bfcb1bc5785360c6"}, + {file = "psutil-6.0.0-cp27-cp27m-manylinux2010_i686.whl", hash = "sha256:1287c2b95f1c0a364d23bc6f2ea2365a8d4d9b726a3be7294296ff7ba97c17f0"}, + {file = "psutil-6.0.0-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:a9a3dbfb4de4f18174528d87cc352d1f788b7496991cca33c6996f40c9e3c92c"}, + {file = "psutil-6.0.0-cp27-cp27mu-manylinux2010_i686.whl", hash = "sha256:6ec7588fb3ddaec7344a825afe298db83fe01bfaaab39155fa84cf1c0d6b13c3"}, + {file = "psutil-6.0.0-cp27-cp27mu-manylinux2010_x86_64.whl", hash = "sha256:1e7c870afcb7d91fdea2b37c24aeb08f98b6d67257a5cb0a8bc3ac68d0f1a68c"}, + {file = "psutil-6.0.0-cp27-none-win32.whl", hash = "sha256:02b69001f44cc73c1c5279d02b30a817e339ceb258ad75997325e0e6169d8b35"}, + {file = "psutil-6.0.0-cp27-none-win_amd64.whl", hash = "sha256:21f1fb635deccd510f69f485b87433460a603919b45e2a324ad65b0cc74f8fb1"}, + {file = "psutil-6.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:c588a7e9b1173b6e866756dde596fd4cad94f9399daf99ad8c3258b3cb2b47a0"}, + {file = "psutil-6.0.0-cp36-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6ed2440ada7ef7d0d608f20ad89a04ec47d2d3ab7190896cd62ca5fc4fe08bf0"}, + {file = "psutil-6.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5fd9a97c8e94059b0ef54a7d4baf13b405011176c3b6ff257c247cae0d560ecd"}, + {file = "psutil-6.0.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2e8d0054fc88153ca0544f5c4d554d42e33df2e009c4ff42284ac9ebdef4132"}, + {file = "psutil-6.0.0-cp36-cp36m-win32.whl", hash = "sha256:fc8c9510cde0146432bbdb433322861ee8c3efbf8589865c8bf8d21cb30c4d14"}, + {file = "psutil-6.0.0-cp36-cp36m-win_amd64.whl", hash = "sha256:34859b8d8f423b86e4385ff3665d3f4d94be3cdf48221fbe476e883514fdb71c"}, + {file = "psutil-6.0.0-cp37-abi3-win32.whl", hash = "sha256:a495580d6bae27291324fe60cea0b5a7c23fa36a7cd35035a16d93bdcf076b9d"}, + {file = "psutil-6.0.0-cp37-abi3-win_amd64.whl", hash = "sha256:33ea5e1c975250a720b3a6609c490db40dae5d83a4eb315170c4fe0d8b1f34b3"}, + {file = "psutil-6.0.0-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:ffe7fc9b6b36beadc8c322f84e1caff51e8703b88eee1da46d1e3a6ae11b4fd0"}, + {file = "psutil-6.0.0.tar.gz", hash = "sha256:8faae4f310b6d969fa26ca0545338b21f73c6b15db7c4a8d934a5482faa818f2"}, +] + +[package.extras] +test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"] + +[[package]] +name = "ptyprocess" +version = "0.7.0" +description = "Run a subprocess in a pseudo terminal" +optional = false +python-versions = "*" +files = [ + {file = "ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35"}, + {file = "ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"}, +] + +[[package]] +name = "pure-eval" +version = "0.2.3" +description = "Safely evaluate AST nodes without side effects" +optional = false +python-versions = "*" +files = [ + {file = "pure_eval-0.2.3-py3-none-any.whl", hash = "sha256:1db8e35b67b3d218d818ae653e27f06c3aa420901fa7b081ca98cbedc874e0d0"}, + {file = "pure_eval-0.2.3.tar.gz", hash = "sha256:5f4e983f40564c576c7c8635ae88db5956bb2229d7e9237d03b3c0b0190eaf42"}, +] + +[package.extras] +tests = ["pytest"] + +[[package]] +name = "pyarrow" +version = "17.0.0" +description = "Python library for Apache Arrow" +optional = false +python-versions = ">=3.8" +files = [ + {file = "pyarrow-17.0.0-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:a5c8b238d47e48812ee577ee20c9a2779e6a5904f1708ae240f53ecbee7c9f07"}, + {file = "pyarrow-17.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:db023dc4c6cae1015de9e198d41250688383c3f9af8f565370ab2b4cb5f62655"}, + {file = "pyarrow-17.0.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:da1e060b3876faa11cee287839f9cc7cdc00649f475714b8680a05fd9071d545"}, + {file = "pyarrow-17.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:75c06d4624c0ad6674364bb46ef38c3132768139ddec1c56582dbac54f2663e2"}, + {file = "pyarrow-17.0.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:fa3c246cc58cb5a4a5cb407a18f193354ea47dd0648194e6265bd24177982fe8"}, + {file = "pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:f7ae2de664e0b158d1607699a16a488de3d008ba99b3a7aa5de1cbc13574d047"}, + {file = "pyarrow-17.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:5984f416552eea15fd9cee03da53542bf4cddaef5afecefb9aa8d1010c335087"}, + {file = "pyarrow-17.0.0-cp311-cp311-macosx_10_15_x86_64.whl", hash = "sha256:1c8856e2ef09eb87ecf937104aacfa0708f22dfeb039c363ec99735190ffb977"}, + {file = "pyarrow-17.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2e19f569567efcbbd42084e87f948778eb371d308e137a0f97afe19bb860ccb3"}, + {file = "pyarrow-17.0.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6b244dc8e08a23b3e352899a006a26ae7b4d0da7bb636872fa8f5884e70acf15"}, + {file = "pyarrow-17.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0b72e87fe3e1db343995562f7fff8aee354b55ee83d13afba65400c178ab2597"}, + {file = "pyarrow-17.0.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:dc5c31c37409dfbc5d014047817cb4ccd8c1ea25d19576acf1a001fe07f5b420"}, + {file = "pyarrow-17.0.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:e3343cb1e88bc2ea605986d4b94948716edc7a8d14afd4e2c097232f729758b4"}, + {file = "pyarrow-17.0.0-cp311-cp311-win_amd64.whl", hash = "sha256:a27532c38f3de9eb3e90ecab63dfda948a8ca859a66e3a47f5f42d1e403c4d03"}, + {file = "pyarrow-17.0.0-cp312-cp312-macosx_10_15_x86_64.whl", hash = "sha256:9b8a823cea605221e61f34859dcc03207e52e409ccf6354634143e23af7c8d22"}, + {file = "pyarrow-17.0.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:f1e70de6cb5790a50b01d2b686d54aaf73da01266850b05e3af2a1bc89e16053"}, + {file = "pyarrow-17.0.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0071ce35788c6f9077ff9ecba4858108eebe2ea5a3f7cf2cf55ebc1dbc6ee24a"}, + {file = "pyarrow-17.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:757074882f844411fcca735e39aae74248a1531367a7c80799b4266390ae51cc"}, + {file = "pyarrow-17.0.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:9ba11c4f16976e89146781a83833df7f82077cdab7dc6232c897789343f7891a"}, + {file = "pyarrow-17.0.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:b0c6ac301093b42d34410b187bba560b17c0330f64907bfa4f7f7f2444b0cf9b"}, + {file = "pyarrow-17.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:392bc9feabc647338e6c89267635e111d71edad5fcffba204425a7c8d13610d7"}, + {file = "pyarrow-17.0.0-cp38-cp38-macosx_10_15_x86_64.whl", hash = "sha256:af5ff82a04b2171415f1410cff7ebb79861afc5dae50be73ce06d6e870615204"}, + {file = "pyarrow-17.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:edca18eaca89cd6382dfbcff3dd2d87633433043650c07375d095cd3517561d8"}, + {file = "pyarrow-17.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7c7916bff914ac5d4a8fe25b7a25e432ff921e72f6f2b7547d1e325c1ad9d155"}, + {file = "pyarrow-17.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f553ca691b9e94b202ff741bdd40f6ccb70cdd5fbf65c187af132f1317de6145"}, + {file = "pyarrow-17.0.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:0cdb0e627c86c373205a2f94a510ac4376fdc523f8bb36beab2e7f204416163c"}, + {file = "pyarrow-17.0.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:d7d192305d9d8bc9082d10f361fc70a73590a4c65cf31c3e6926cd72b76bc35c"}, + {file = "pyarrow-17.0.0-cp38-cp38-win_amd64.whl", hash = "sha256:02dae06ce212d8b3244dd3e7d12d9c4d3046945a5933d28026598e9dbbda1fca"}, + {file = "pyarrow-17.0.0-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:13d7a460b412f31e4c0efa1148e1d29bdf18ad1411eb6757d38f8fbdcc8645fb"}, + {file = "pyarrow-17.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9b564a51fbccfab5a04a80453e5ac6c9954a9c5ef2890d1bcf63741909c3f8df"}, + {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:32503827abbc5aadedfa235f5ece8c4f8f8b0a3cf01066bc8d29de7539532687"}, + {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a155acc7f154b9ffcc85497509bcd0d43efb80d6f733b0dc3bb14e281f131c8b"}, + {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:dec8d129254d0188a49f8a1fc99e0560dc1b85f60af729f47de4046015f9b0a5"}, + {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:a48ddf5c3c6a6c505904545c25a4ae13646ae1f8ba703c4df4a1bfe4f4006bda"}, + {file = "pyarrow-17.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:42bf93249a083aca230ba7e2786c5f673507fa97bbd9725a1e2754715151a204"}, + {file = "pyarrow-17.0.0.tar.gz", hash = "sha256:4beca9521ed2c0921c1023e68d097d0299b62c362639ea315572a58f3f50fd28"}, +] + +[package.dependencies] +numpy = ">=1.16.6" + +[package.extras] +test = ["cffi", "hypothesis", "pandas", "pytest", "pytz"] + +[[package]] +name = "pycodestyle" +version = "2.11.1" +description = "Python style guide checker" +optional = false +python-versions = ">=3.8" +files = [ + {file = "pycodestyle-2.11.1-py2.py3-none-any.whl", hash = "sha256:44fe31000b2d866f2e41841b18528a505fbd7fef9017b04eff4e2648a0fadc67"}, + {file = "pycodestyle-2.11.1.tar.gz", hash = "sha256:41ba0e7afc9752dfb53ced5489e89f8186be00e599e712660695b7a75ff2663f"}, +] + +[[package]] +name = "pycparser" +version = "2.22" +description = "C parser in Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "pycparser-2.22-py3-none-any.whl", hash = "sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc"}, + {file = "pycparser-2.22.tar.gz", hash = "sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6"}, +] + +[[package]] +name = "pydantic" +version = "1.10.17" +description = "Data validation and settings management using python type hints" +optional = false +python-versions = ">=3.7" +files = [ + {file = "pydantic-1.10.17-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0fa51175313cc30097660b10eec8ca55ed08bfa07acbfe02f7a42f6c242e9a4b"}, + {file = "pydantic-1.10.17-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c7e8988bb16988890c985bd2093df9dd731bfb9d5e0860db054c23034fab8f7a"}, + {file = "pydantic-1.10.17-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:371dcf1831f87c9e217e2b6a0c66842879a14873114ebb9d0861ab22e3b5bb1e"}, + {file = "pydantic-1.10.17-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4866a1579c0c3ca2c40575398a24d805d4db6cb353ee74df75ddeee3c657f9a7"}, + {file = "pydantic-1.10.17-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:543da3c6914795b37785703ffc74ba4d660418620cc273490d42c53949eeeca6"}, + {file = "pydantic-1.10.17-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:7623b59876f49e61c2e283551cc3647616d2fbdc0b4d36d3d638aae8547ea681"}, + {file = "pydantic-1.10.17-cp310-cp310-win_amd64.whl", hash = "sha256:409b2b36d7d7d19cd8310b97a4ce6b1755ef8bd45b9a2ec5ec2b124db0a0d8f3"}, + {file = "pydantic-1.10.17-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:fa43f362b46741df8f201bf3e7dff3569fa92069bcc7b4a740dea3602e27ab7a"}, + {file = "pydantic-1.10.17-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2a72d2a5ff86a3075ed81ca031eac86923d44bc5d42e719d585a8eb547bf0c9b"}, + {file = "pydantic-1.10.17-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b4ad32aed3bf5eea5ca5decc3d1bbc3d0ec5d4fbcd72a03cdad849458decbc63"}, + {file = "pydantic-1.10.17-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:aeb4e741782e236ee7dc1fb11ad94dc56aabaf02d21df0e79e0c21fe07c95741"}, + {file = "pydantic-1.10.17-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:d2f89a719411cb234105735a520b7c077158a81e0fe1cb05a79c01fc5eb59d3c"}, + {file = "pydantic-1.10.17-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:db3b48d9283d80a314f7a682f7acae8422386de659fffaba454b77a083c3937d"}, + {file = "pydantic-1.10.17-cp311-cp311-win_amd64.whl", hash = "sha256:9c803a5113cfab7bbb912f75faa4fc1e4acff43e452c82560349fff64f852e1b"}, + {file = "pydantic-1.10.17-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:820ae12a390c9cbb26bb44913c87fa2ff431a029a785642c1ff11fed0a095fcb"}, + {file = "pydantic-1.10.17-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c1e51d1af306641b7d1574d6d3307eaa10a4991542ca324f0feb134fee259815"}, + {file = "pydantic-1.10.17-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e53fb834aae96e7b0dadd6e92c66e7dd9cdf08965340ed04c16813102a47fab"}, + {file = "pydantic-1.10.17-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0e2495309b1266e81d259a570dd199916ff34f7f51f1b549a0d37a6d9b17b4dc"}, + {file = "pydantic-1.10.17-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:098ad8de840c92ea586bf8efd9e2e90c6339d33ab5c1cfbb85be66e4ecf8213f"}, + {file = "pydantic-1.10.17-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:525bbef620dac93c430d5d6bdbc91bdb5521698d434adf4434a7ef6ffd5c4b7f"}, + {file = "pydantic-1.10.17-cp312-cp312-win_amd64.whl", hash = "sha256:6654028d1144df451e1da69a670083c27117d493f16cf83da81e1e50edce72ad"}, + {file = "pydantic-1.10.17-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:c87cedb4680d1614f1d59d13fea353faf3afd41ba5c906a266f3f2e8c245d655"}, + {file = "pydantic-1.10.17-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:11289fa895bcbc8f18704efa1d8020bb9a86314da435348f59745473eb042e6b"}, + {file = "pydantic-1.10.17-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:94833612d6fd18b57c359a127cbfd932d9150c1b72fea7c86ab58c2a77edd7c7"}, + {file = "pydantic-1.10.17-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:d4ecb515fa7cb0e46e163ecd9d52f9147ba57bc3633dca0e586cdb7a232db9e3"}, + {file = "pydantic-1.10.17-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:7017971ffa7fd7808146880aa41b266e06c1e6e12261768a28b8b41ba55c8076"}, + {file = "pydantic-1.10.17-cp37-cp37m-win_amd64.whl", hash = "sha256:e840e6b2026920fc3f250ea8ebfdedf6ea7a25b77bf04c6576178e681942ae0f"}, + {file = "pydantic-1.10.17-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:bfbb18b616abc4df70591b8c1ff1b3eabd234ddcddb86b7cac82657ab9017e33"}, + {file = "pydantic-1.10.17-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:ebb249096d873593e014535ab07145498957091aa6ae92759a32d40cb9998e2e"}, + {file = "pydantic-1.10.17-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d8c209af63ccd7b22fba94b9024e8b7fd07feffee0001efae50dd99316b27768"}, + {file = "pydantic-1.10.17-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d4b40c9e13a0b61583e5599e7950490c700297b4a375b55b2b592774332798b7"}, + {file = "pydantic-1.10.17-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:c31d281c7485223caf6474fc2b7cf21456289dbaa31401844069b77160cab9c7"}, + {file = "pydantic-1.10.17-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:ae5184e99a060a5c80010a2d53c99aee76a3b0ad683d493e5f0620b5d86eeb75"}, + {file = "pydantic-1.10.17-cp38-cp38-win_amd64.whl", hash = "sha256:ad1e33dc6b9787a6f0f3fd132859aa75626528b49cc1f9e429cdacb2608ad5f0"}, + {file = "pydantic-1.10.17-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7e17c0ee7192e54a10943f245dc79e36d9fe282418ea05b886e1c666063a7b54"}, + {file = "pydantic-1.10.17-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:cafb9c938f61d1b182dfc7d44a7021326547b7b9cf695db5b68ec7b590214773"}, + {file = "pydantic-1.10.17-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95ef534e3c22e5abbdbdd6f66b6ea9dac3ca3e34c5c632894f8625d13d084cbe"}, + {file = "pydantic-1.10.17-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:62d96b8799ae3d782df7ec9615cb59fc32c32e1ed6afa1b231b0595f6516e8ab"}, + {file = "pydantic-1.10.17-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:ab2f976336808fd5d539fdc26eb51f9aafc1f4b638e212ef6b6f05e753c8011d"}, + {file = "pydantic-1.10.17-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:b8ad363330557beac73159acfbeed220d5f1bfcd6b930302a987a375e02f74fd"}, + {file = "pydantic-1.10.17-cp39-cp39-win_amd64.whl", hash = "sha256:48db882e48575ce4b39659558b2f9f37c25b8d348e37a2b4e32971dd5a7d6227"}, + {file = "pydantic-1.10.17-py3-none-any.whl", hash = "sha256:e41b5b973e5c64f674b3b4720286ded184dcc26a691dd55f34391c62c6934688"}, + {file = "pydantic-1.10.17.tar.gz", hash = "sha256:f434160fb14b353caf634149baaf847206406471ba70e64657c1e8330277a991"}, +] + +[package.dependencies] +typing-extensions = ">=4.2.0" + +[package.extras] +dotenv = ["python-dotenv (>=0.10.4)"] +email = ["email-validator (>=1.0.3)"] + +[[package]] +name = "pydot" +version = "3.0.1" +description = "Python interface to Graphviz's Dot" +optional = false +python-versions = ">=3.8" +files = [ + {file = "pydot-3.0.1-py3-none-any.whl", hash = "sha256:43f1e878dc1ff7c1c2e3470a6999d4e9e97771c5c862440c2f0af0ba844c231f"}, + {file = "pydot-3.0.1.tar.gz", hash = "sha256:e18cf7f287c497d77b536a3d20a46284568fea390776dface6eabbdf1b1b5efc"}, +] + +[package.dependencies] +pyparsing = ">=3.0.9" + +[package.extras] +dev = ["chardet", "parameterized", "ruff"] +release = ["zest.releaser[recommended]"] +tests = ["chardet", "parameterized", "ruff", "tox", "unittest-parallel"] + +[[package]] +name = "pyflakes" +version = "3.1.0" +description = "passive checker of Python programs" +optional = false +python-versions = ">=3.8" +files = [ + {file = "pyflakes-3.1.0-py2.py3-none-any.whl", hash = "sha256:4132f6d49cb4dae6819e5379898f2b8cce3c5f23994194c24b77d5da2e36f774"}, + {file = "pyflakes-3.1.0.tar.gz", hash = "sha256:a0aae034c444db0071aa077972ba4768d40c830d9539fd45bf4cd3f8f6992efc"}, +] + +[[package]] +name = "pygments" +version = "2.18.0" +description = "Pygments is a syntax highlighting package written in Python." +optional = false +python-versions = ">=3.8" +files = [ + {file = "pygments-2.18.0-py3-none-any.whl", hash = "sha256:b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a"}, + {file = "pygments-2.18.0.tar.gz", hash = "sha256:786ff802f32e91311bff3889f6e9a86e81505fe99f2735bb6d60ae0c5004f199"}, +] + +[package.extras] +windows-terminal = ["colorama (>=0.4.6)"] + +[[package]] +name = "pygsp" +version = "0.5.1" +description = "Graph Signal Processing in Python" +optional = false +python-versions = "*" +files = [ + {file = "PyGSP-0.5.1-py2.py3-none-any.whl", hash = "sha256:884765260256f143a92053c420797053fda0f4eba1573471526fb4e62a4c4cde"}, + {file = "PyGSP-0.5.1.tar.gz", hash = "sha256:4874ad88793d622d4f578b40c6617a99b1f02bc6c6c4077f0e48cd71c7275800"}, +] + +[package.dependencies] +numpy = "*" +scipy = "*" + +[package.extras] +alldeps = ["PyOpenGL", "PyQt5", "PySide", "matplotlib", "pyflann", "pyflann3", "pyqtgraph", "pyunlocbox", "scikit-image"] +doc = ["numpydoc", "sphinx", "sphinx-rtd-theme", "sphinxcontrib-bibtex"] +pkg = ["twine", "wheel"] +test = ["coverage", "coveralls", "flake8"] + +[[package]] +name = "pyparsing" +version = "3.1.2" +description = "pyparsing module - Classes and methods to define and execute parsing grammars" +optional = false +python-versions = ">=3.6.8" +files = [ + {file = "pyparsing-3.1.2-py3-none-any.whl", hash = "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"}, + {file = "pyparsing-3.1.2.tar.gz", hash = "sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad"}, +] + +[package.extras] +diagrams = ["jinja2", "railroad-diagrams"] + +[[package]] +name = "pytest" +version = "7.4.4" +description = "pytest: simple powerful testing with Python" +optional = false +python-versions = ">=3.7" +files = [ + {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"}, + {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"}, +] + +[package.dependencies] +colorama = {version = "*", markers = "sys_platform == \"win32\""} +iniconfig = "*" +packaging = "*" +pluggy = ">=0.12,<2.0" + +[package.extras] +testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"] + +[[package]] +name = "pytest-cov" +version = "4.1.0" +description = "Pytest plugin for measuring coverage." +optional = false +python-versions = ">=3.7" +files = [ + {file = "pytest-cov-4.1.0.tar.gz", hash = "sha256:3904b13dfbfec47f003b8e77fd5b589cd11904a21ddf1ab38a64f204d6a10ef6"}, + {file = "pytest_cov-4.1.0-py3-none-any.whl", hash = "sha256:6ba70b9e97e69fcc3fb45bfeab2d0a138fb65c4d0d6a41ef33983ad114be8c3a"}, +] + +[package.dependencies] +coverage = {version = ">=5.2.1", extras = ["toml"]} +pytest = ">=4.6" + +[package.extras] +testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtualenv"] + +[[package]] +name = "python-dateutil" +version = "2.9.0.post0" +description = "Extensions to the standard Python datetime module" +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" +files = [ + {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"}, + {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"}, +] + +[package.dependencies] +six = ">=1.5" + +[[package]] +name = "python-json-logger" +version = "2.0.7" +description = "A python library adding a json log formatter" +optional = false +python-versions = ">=3.6" +files = [ + {file = "python-json-logger-2.0.7.tar.gz", hash = "sha256:23e7ec02d34237c5aa1e29a070193a4ea87583bb4e7f8fd06d3de8264c4b2e1c"}, + {file = "python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd"}, +] + +[[package]] +name = "python-levenshtein" +version = "0.25.1" +description = "Python extension for computing string edit distances and similarities." +optional = false +python-versions = ">=3.8" +files = [ + {file = "python-Levenshtein-0.25.1.tar.gz", hash = "sha256:b21e7efe83c8e8dc8260f2143b2393c6c77cb2956f0c53de6c4731c4d8006acc"}, + {file = "python_Levenshtein-0.25.1-py3-none-any.whl", hash = "sha256:654446d1ea4acbcc573d44c43775854834a7547e4cb2f79f638f738134d72037"}, +] + +[package.dependencies] +Levenshtein = "0.25.1" + +[[package]] +name = "python-louvain" +version = "0.16" +description = "Louvain algorithm for community detection" +optional = false +python-versions = "*" +files = [ + {file = "python-louvain-0.16.tar.gz", hash = "sha256:b7ba2df5002fd28d3ee789a49532baad11fe648e4f2117cf0798e7520a1da56b"}, +] + +[package.dependencies] +networkx = "*" +numpy = "*" + +[[package]] +name = "pytorch-lightning" +version = "2.3.3" +description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate." +optional = false +python-versions = ">=3.8" +files = [ + {file = "pytorch-lightning-2.3.3.tar.gz", hash = "sha256:5f974015425af6873b5689246c5495ca12686b446751479273c154b73aeea843"}, + {file = "pytorch_lightning-2.3.3-py3-none-any.whl", hash = "sha256:4365e3f2874e223e63cb42628d24c88c2bdc8d1794453cac38c0619b31115fba"}, +] + +[package.dependencies] +fsspec = {version = ">=2022.5.0", extras = ["http"]} +lightning-utilities = ">=0.10.0" +numpy = ">=1.17.2" +packaging = ">=20.0" +PyYAML = ">=5.4" +torch = ">=2.0.0" +torchmetrics = ">=0.7.0" +tqdm = ">=4.57.0" +typing-extensions = ">=4.4.0" + +[package.extras] +all = ["bitsandbytes (>=0.42.0)", "deepspeed (>=0.8.2,<=0.9.3)", "hydra-core (>=1.2.0)", "ipython[all] (<8.15.0)", "jsonargparse[signatures] (>=4.27.7)", "lightning-utilities (>=0.8.0)", "matplotlib (>3.1)", "omegaconf (>=2.2.3)", "requests (<2.32.0)", "rich (>=12.3.0)", "tensorboardX (>=2.2)", "torchmetrics (>=0.10.0)", "torchvision (>=0.15.0)"] +deepspeed = ["deepspeed (>=0.8.2,<=0.9.3)"] +dev = ["bitsandbytes (>=0.42.0)", "cloudpickle (>=1.3)", "coverage (==7.3.1)", "deepspeed (>=0.8.2,<=0.9.3)", "fastapi", "hydra-core (>=1.2.0)", "ipython[all] (<8.15.0)", "jsonargparse[signatures] (>=4.27.7)", "lightning-utilities (>=0.8.0)", "matplotlib (>3.1)", "omegaconf (>=2.2.3)", "onnx (>=0.14.0)", "onnxruntime (>=0.15.0)", "pandas (>1.0)", "psutil (<5.9.6)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "requests (<2.32.0)", "rich (>=12.3.0)", "scikit-learn (>0.22.1)", "tensorboard (>=2.9.1)", "tensorboardX (>=2.2)", "torchmetrics (>=0.10.0)", "torchvision (>=0.15.0)", "uvicorn"] +examples = ["ipython[all] (<8.15.0)", "lightning-utilities (>=0.8.0)", "requests (<2.32.0)", "torchmetrics (>=0.10.0)", "torchvision (>=0.15.0)"] +extra = ["bitsandbytes (>=0.42.0)", "hydra-core (>=1.2.0)", "jsonargparse[signatures] (>=4.27.7)", "matplotlib (>3.1)", "omegaconf (>=2.2.3)", "rich (>=12.3.0)", "tensorboardX (>=2.2)"] +strategies = ["deepspeed (>=0.8.2,<=0.9.3)"] +test = ["cloudpickle (>=1.3)", "coverage (==7.3.1)", "fastapi", "onnx (>=0.14.0)", "onnxruntime (>=0.15.0)", "pandas (>1.0)", "psutil (<5.9.6)", "pytest (==7.4.0)", "pytest-cov (==4.1.0)", "pytest-random-order (==1.1.0)", "pytest-rerunfailures (==12.0)", "pytest-timeout (==2.1.0)", "scikit-learn (>0.22.1)", "tensorboard (>=2.9.1)", "uvicorn"] + +[[package]] +name = "pytz" +version = "2024.1" +description = "World timezone definitions, modern and historical" +optional = false +python-versions = "*" +files = [ + {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"}, + {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"}, +] + +[[package]] +name = "pywin32" +version = "306" +description = "Python for Window Extensions" +optional = false +python-versions = "*" +files = [ + {file = "pywin32-306-cp310-cp310-win32.whl", hash = "sha256:06d3420a5155ba65f0b72f2699b5bacf3109f36acbe8923765c22938a69dfc8d"}, + {file = "pywin32-306-cp310-cp310-win_amd64.whl", hash = "sha256:84f4471dbca1887ea3803d8848a1616429ac94a4a8d05f4bc9c5dcfd42ca99c8"}, + {file = "pywin32-306-cp311-cp311-win32.whl", hash = "sha256:e65028133d15b64d2ed8f06dd9fbc268352478d4f9289e69c190ecd6818b6407"}, + {file = "pywin32-306-cp311-cp311-win_amd64.whl", hash = "sha256:a7639f51c184c0272e93f244eb24dafca9b1855707d94c192d4a0b4c01e1100e"}, + {file = "pywin32-306-cp311-cp311-win_arm64.whl", hash = "sha256:70dba0c913d19f942a2db25217d9a1b726c278f483a919f1abfed79c9cf64d3a"}, + {file = "pywin32-306-cp312-cp312-win32.whl", hash = "sha256:383229d515657f4e3ed1343da8be101000562bf514591ff383ae940cad65458b"}, + {file = "pywin32-306-cp312-cp312-win_amd64.whl", hash = "sha256:37257794c1ad39ee9be652da0462dc2e394c8159dfd913a8a4e8eb6fd346da0e"}, + {file = "pywin32-306-cp312-cp312-win_arm64.whl", hash = "sha256:5821ec52f6d321aa59e2db7e0a35b997de60c201943557d108af9d4ae1ec7040"}, + {file = "pywin32-306-cp37-cp37m-win32.whl", hash = "sha256:1c73ea9a0d2283d889001998059f5eaaba3b6238f767c9cf2833b13e6a685f65"}, + {file = "pywin32-306-cp37-cp37m-win_amd64.whl", hash = "sha256:72c5f621542d7bdd4fdb716227be0dd3f8565c11b280be6315b06ace35487d36"}, + {file = "pywin32-306-cp38-cp38-win32.whl", hash = "sha256:e4c092e2589b5cf0d365849e73e02c391c1349958c5ac3e9d5ccb9a28e017b3a"}, + {file = "pywin32-306-cp38-cp38-win_amd64.whl", hash = "sha256:e8ac1ae3601bee6ca9f7cb4b5363bf1c0badb935ef243c4733ff9a393b1690c0"}, + {file = "pywin32-306-cp39-cp39-win32.whl", hash = "sha256:e25fd5b485b55ac9c057f67d94bc203f3f6595078d1fb3b458c9c28b7153a802"}, + {file = "pywin32-306-cp39-cp39-win_amd64.whl", hash = "sha256:39b61c15272833b5c329a2989999dcae836b1eed650252ab1b7bfbe1d59f30f4"}, +] + +[[package]] +name = "pywinpty" +version = "2.0.13" +description = "Pseudo terminal support for Windows from Python." +optional = false +python-versions = ">=3.8" +files = [ + {file = "pywinpty-2.0.13-cp310-none-win_amd64.whl", hash = "sha256:697bff211fb5a6508fee2dc6ff174ce03f34a9a233df9d8b5fe9c8ce4d5eaf56"}, + {file = "pywinpty-2.0.13-cp311-none-win_amd64.whl", hash = "sha256:b96fb14698db1284db84ca38c79f15b4cfdc3172065b5137383910567591fa99"}, + {file = "pywinpty-2.0.13-cp312-none-win_amd64.whl", hash = "sha256:2fd876b82ca750bb1333236ce98488c1be96b08f4f7647cfdf4129dfad83c2d4"}, + {file = "pywinpty-2.0.13-cp38-none-win_amd64.whl", hash = "sha256:61d420c2116c0212808d31625611b51caf621fe67f8a6377e2e8b617ea1c1f7d"}, + {file = "pywinpty-2.0.13-cp39-none-win_amd64.whl", hash = "sha256:71cb613a9ee24174730ac7ae439fd179ca34ccb8c5349e8d7b72ab5dea2c6f4b"}, + {file = "pywinpty-2.0.13.tar.gz", hash = "sha256:c34e32351a3313ddd0d7da23d27f835c860d32fe4ac814d372a3ea9594f41dde"}, +] + +[[package]] +name = "pyyaml" +version = "6.0.1" +description = "YAML parser and emitter for Python" +optional = false +python-versions = ">=3.6" +files = [ + {file = "PyYAML-6.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d858aa552c999bc8a8d57426ed01e40bef403cd8ccdd0fc5f6f04a00414cac2a"}, + {file = "PyYAML-6.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fd66fc5d0da6d9815ba2cebeb4205f95818ff4b79c3ebe268e75d961704af52f"}, + {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69b023b2b4daa7548bcfbd4aa3da05b3a74b772db9e23b982788168117739938"}, + {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:81e0b275a9ecc9c0c0c07b4b90ba548307583c125f54d5b6946cfee6360c733d"}, + {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba336e390cd8e4d1739f42dfe9bb83a3cc2e80f567d8805e11b46f4a943f5515"}, + {file = "PyYAML-6.0.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:326c013efe8048858a6d312ddd31d56e468118ad4cdeda36c719bf5bb6192290"}, + {file = "PyYAML-6.0.1-cp310-cp310-win32.whl", hash = "sha256:bd4af7373a854424dabd882decdc5579653d7868b8fb26dc7d0e99f823aa5924"}, + {file = "PyYAML-6.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d"}, + {file = "PyYAML-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6965a7bc3cf88e5a1c3bd2e0b5c22f8d677dc88a455344035f03399034eb3007"}, + {file = "PyYAML-6.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f003ed9ad21d6a4713f0a9b5a7a0a79e08dd0f221aff4525a2be4c346ee60aab"}, + {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42f8152b8dbc4fe7d96729ec2b99c7097d656dc1213a3229ca5383f973a5ed6d"}, + {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc"}, + {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"}, + {file = "PyYAML-6.0.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e7d73685e87afe9f3b36c799222440d6cf362062f78be1013661b00c5c6f678b"}, + {file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"}, + {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"}, + {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"}, + {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"}, + {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"}, + {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"}, + {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"}, + {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"}, + {file = "PyYAML-6.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:0d3304d8c0adc42be59c5f8a4d9e3d7379e6955ad754aa9d6ab7a398b59dd1df"}, + {file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"}, + {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"}, + {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"}, + {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:afd7e57eddb1a54f0f1a974bc4391af8bcce0b444685d936840f125cf046d5bd"}, + {file = "PyYAML-6.0.1-cp36-cp36m-win32.whl", hash = "sha256:fca0e3a251908a499833aa292323f32437106001d436eca0e6e7833256674585"}, + {file = "PyYAML-6.0.1-cp36-cp36m-win_amd64.whl", hash = "sha256:f22ac1c3cac4dbc50079e965eba2c1058622631e526bd9afd45fedd49ba781fa"}, + {file = "PyYAML-6.0.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:b1275ad35a5d18c62a7220633c913e1b42d44b46ee12554e5fd39c70a243d6a3"}, + {file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:18aeb1bf9a78867dc38b259769503436b7c72f7a1f1f4c93ff9a17de54319b27"}, + {file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:596106435fa6ad000c2991a98fa58eeb8656ef2325d7e158344fb33864ed87e3"}, + {file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:baa90d3f661d43131ca170712d903e6295d1f7a0f595074f151c0aed377c9b9c"}, + {file = "PyYAML-6.0.1-cp37-cp37m-win32.whl", hash = "sha256:9046c58c4395dff28dd494285c82ba00b546adfc7ef001486fbf0324bc174fba"}, + {file = "PyYAML-6.0.1-cp37-cp37m-win_amd64.whl", hash = "sha256:4fb147e7a67ef577a588a0e2c17b6db51dda102c71de36f8549b6816a96e1867"}, + {file = "PyYAML-6.0.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1d4c7e777c441b20e32f52bd377e0c409713e8bb1386e1099c2415f26e479595"}, + {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"}, + {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"}, + {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"}, + {file = "PyYAML-6.0.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:49a183be227561de579b4a36efbb21b3eab9651dd81b1858589f796549873dd6"}, + {file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"}, + {file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"}, + {file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"}, + {file = "PyYAML-6.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c8098ddcc2a85b61647b2590f825f3db38891662cfc2fc776415143f599bb859"}, + {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"}, + {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"}, + {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"}, + {file = "PyYAML-6.0.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:04ac92ad1925b2cff1db0cfebffb6ffc43457495c9b3c39d3fcae417d7125dc5"}, + {file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"}, + {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"}, + {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"}, +] + +[[package]] +name = "pyzmq" +version = "26.0.3" +description = "Python bindings for 0MQ" +optional = false +python-versions = ">=3.7" +files = [ + {file = "pyzmq-26.0.3-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:44dd6fc3034f1eaa72ece33588867df9e006a7303725a12d64c3dff92330f625"}, + {file = "pyzmq-26.0.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:acb704195a71ac5ea5ecf2811c9ee19ecdc62b91878528302dd0be1b9451cc90"}, + {file = "pyzmq-26.0.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5dbb9c997932473a27afa93954bb77a9f9b786b4ccf718d903f35da3232317de"}, + {file = "pyzmq-26.0.3-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6bcb34f869d431799c3ee7d516554797f7760cb2198ecaa89c3f176f72d062be"}, + {file = "pyzmq-26.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:38ece17ec5f20d7d9b442e5174ae9f020365d01ba7c112205a4d59cf19dc38ee"}, + {file = "pyzmq-26.0.3-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:ba6e5e6588e49139a0979d03a7deb9c734bde647b9a8808f26acf9c547cab1bf"}, + {file = "pyzmq-26.0.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:3bf8b000a4e2967e6dfdd8656cd0757d18c7e5ce3d16339e550bd462f4857e59"}, + {file = "pyzmq-26.0.3-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:2136f64fbb86451dbbf70223635a468272dd20075f988a102bf8a3f194a411dc"}, + {file = "pyzmq-26.0.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:e8918973fbd34e7814f59143c5f600ecd38b8038161239fd1a3d33d5817a38b8"}, + {file = "pyzmq-26.0.3-cp310-cp310-win32.whl", hash = "sha256:0aaf982e68a7ac284377d051c742610220fd06d330dcd4c4dbb4cdd77c22a537"}, + {file = "pyzmq-26.0.3-cp310-cp310-win_amd64.whl", hash = "sha256:f1a9b7d00fdf60b4039f4455afd031fe85ee8305b019334b72dcf73c567edc47"}, + {file = "pyzmq-26.0.3-cp310-cp310-win_arm64.whl", hash = "sha256:80b12f25d805a919d53efc0a5ad7c0c0326f13b4eae981a5d7b7cc343318ebb7"}, + {file = "pyzmq-26.0.3-cp311-cp311-macosx_10_15_universal2.whl", hash = "sha256:a72a84570f84c374b4c287183debc776dc319d3e8ce6b6a0041ce2e400de3f32"}, + {file = "pyzmq-26.0.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:7ca684ee649b55fd8f378127ac8462fb6c85f251c2fb027eb3c887e8ee347bcd"}, + {file = "pyzmq-26.0.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e222562dc0f38571c8b1ffdae9d7adb866363134299264a1958d077800b193b7"}, + {file = "pyzmq-26.0.3-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f17cde1db0754c35a91ac00b22b25c11da6eec5746431d6e5092f0cd31a3fea9"}, + {file = "pyzmq-26.0.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b7c0c0b3244bb2275abe255d4a30c050d541c6cb18b870975553f1fb6f37527"}, + {file = "pyzmq-26.0.3-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:ac97a21de3712afe6a6c071abfad40a6224fd14fa6ff0ff8d0c6e6cd4e2f807a"}, + {file = "pyzmq-26.0.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:88b88282e55fa39dd556d7fc04160bcf39dea015f78e0cecec8ff4f06c1fc2b5"}, + {file = "pyzmq-26.0.3-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:72b67f966b57dbd18dcc7efbc1c7fc9f5f983e572db1877081f075004614fcdd"}, + {file = "pyzmq-26.0.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:f4b6cecbbf3b7380f3b61de3a7b93cb721125dc125c854c14ddc91225ba52f83"}, + {file = "pyzmq-26.0.3-cp311-cp311-win32.whl", hash = "sha256:eed56b6a39216d31ff8cd2f1d048b5bf1700e4b32a01b14379c3b6dde9ce3aa3"}, + {file = "pyzmq-26.0.3-cp311-cp311-win_amd64.whl", hash = "sha256:3191d312c73e3cfd0f0afdf51df8405aafeb0bad71e7ed8f68b24b63c4f36500"}, + {file = "pyzmq-26.0.3-cp311-cp311-win_arm64.whl", hash = "sha256:b6907da3017ef55139cf0e417c5123a84c7332520e73a6902ff1f79046cd3b94"}, + {file = "pyzmq-26.0.3-cp312-cp312-macosx_10_15_universal2.whl", hash = "sha256:068ca17214038ae986d68f4a7021f97e187ed278ab6dccb79f837d765a54d753"}, + {file = "pyzmq-26.0.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:7821d44fe07335bea256b9f1f41474a642ca55fa671dfd9f00af8d68a920c2d4"}, + {file = "pyzmq-26.0.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:eeb438a26d87c123bb318e5f2b3d86a36060b01f22fbdffd8cf247d52f7c9a2b"}, + {file = "pyzmq-26.0.3-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:69ea9d6d9baa25a4dc9cef5e2b77b8537827b122214f210dd925132e34ae9b12"}, + {file = "pyzmq-26.0.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7daa3e1369355766dea11f1d8ef829905c3b9da886ea3152788dc25ee6079e02"}, + {file = "pyzmq-26.0.3-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:6ca7a9a06b52d0e38ccf6bca1aeff7be178917893f3883f37b75589d42c4ac20"}, + {file = "pyzmq-26.0.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:1b7d0e124948daa4d9686d421ef5087c0516bc6179fdcf8828b8444f8e461a77"}, + {file = "pyzmq-26.0.3-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:e746524418b70f38550f2190eeee834db8850088c834d4c8406fbb9bc1ae10b2"}, + {file = "pyzmq-26.0.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:6b3146f9ae6af82c47a5282ac8803523d381b3b21caeae0327ed2f7ecb718798"}, + {file = "pyzmq-26.0.3-cp312-cp312-win32.whl", hash = "sha256:2b291d1230845871c00c8462c50565a9cd6026fe1228e77ca934470bb7d70ea0"}, + {file = "pyzmq-26.0.3-cp312-cp312-win_amd64.whl", hash = "sha256:926838a535c2c1ea21c903f909a9a54e675c2126728c21381a94ddf37c3cbddf"}, + {file = "pyzmq-26.0.3-cp312-cp312-win_arm64.whl", hash = "sha256:5bf6c237f8c681dfb91b17f8435b2735951f0d1fad10cc5dfd96db110243370b"}, + {file = "pyzmq-26.0.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:0c0991f5a96a8e620f7691e61178cd8f457b49e17b7d9cfa2067e2a0a89fc1d5"}, + {file = "pyzmq-26.0.3-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:dbf012d8fcb9f2cf0643b65df3b355fdd74fc0035d70bb5c845e9e30a3a4654b"}, + {file = "pyzmq-26.0.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:01fbfbeb8249a68d257f601deb50c70c929dc2dfe683b754659569e502fbd3aa"}, + {file = "pyzmq-26.0.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1c8eb19abe87029c18f226d42b8a2c9efdd139d08f8bf6e085dd9075446db450"}, + {file = "pyzmq-26.0.3-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:5344b896e79800af86ad643408ca9aa303a017f6ebff8cee5a3163c1e9aec987"}, + {file = "pyzmq-26.0.3-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:204e0f176fd1d067671157d049466869b3ae1fc51e354708b0dc41cf94e23a3a"}, + {file = "pyzmq-26.0.3-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:a42db008d58530efa3b881eeee4991146de0b790e095f7ae43ba5cc612decbc5"}, + {file = "pyzmq-26.0.3-cp37-cp37m-win32.whl", hash = "sha256:8d7a498671ca87e32b54cb47c82a92b40130a26c5197d392720a1bce1b3c77cf"}, + {file = "pyzmq-26.0.3-cp37-cp37m-win_amd64.whl", hash = "sha256:3b4032a96410bdc760061b14ed6a33613ffb7f702181ba999df5d16fb96ba16a"}, + {file = "pyzmq-26.0.3-cp38-cp38-macosx_10_15_universal2.whl", hash = "sha256:2cc4e280098c1b192c42a849de8de2c8e0f3a84086a76ec5b07bfee29bda7d18"}, + {file = "pyzmq-26.0.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5bde86a2ed3ce587fa2b207424ce15b9a83a9fa14422dcc1c5356a13aed3df9d"}, + {file = "pyzmq-26.0.3-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:34106f68e20e6ff253c9f596ea50397dbd8699828d55e8fa18bd4323d8d966e6"}, + {file = "pyzmq-26.0.3-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:ebbbd0e728af5db9b04e56389e2299a57ea8b9dd15c9759153ee2455b32be6ad"}, + {file = "pyzmq-26.0.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f6b1d1c631e5940cac5a0b22c5379c86e8df6a4ec277c7a856b714021ab6cfad"}, + {file = "pyzmq-26.0.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:e891ce81edd463b3b4c3b885c5603c00141151dd9c6936d98a680c8c72fe5c67"}, + {file = "pyzmq-26.0.3-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:9b273ecfbc590a1b98f014ae41e5cf723932f3b53ba9367cfb676f838038b32c"}, + {file = "pyzmq-26.0.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:b32bff85fb02a75ea0b68f21e2412255b5731f3f389ed9aecc13a6752f58ac97"}, + {file = "pyzmq-26.0.3-cp38-cp38-win32.whl", hash = "sha256:f6c21c00478a7bea93caaaef9e7629145d4153b15a8653e8bb4609d4bc70dbfc"}, + {file = "pyzmq-26.0.3-cp38-cp38-win_amd64.whl", hash = "sha256:3401613148d93ef0fd9aabdbddb212de3db7a4475367f49f590c837355343972"}, + {file = "pyzmq-26.0.3-cp39-cp39-macosx_10_15_universal2.whl", hash = "sha256:2ed8357f4c6e0daa4f3baf31832df8a33334e0fe5b020a61bc8b345a3db7a606"}, + {file = "pyzmq-26.0.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:c1c8f2a2ca45292084c75bb6d3a25545cff0ed931ed228d3a1810ae3758f975f"}, + {file = "pyzmq-26.0.3-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:b63731993cdddcc8e087c64e9cf003f909262b359110070183d7f3025d1c56b5"}, + {file = "pyzmq-26.0.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:b3cd31f859b662ac5d7f4226ec7d8bd60384fa037fc02aee6ff0b53ba29a3ba8"}, + {file = "pyzmq-26.0.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:115f8359402fa527cf47708d6f8a0f8234f0e9ca0cab7c18c9c189c194dbf620"}, + {file = "pyzmq-26.0.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:715bdf952b9533ba13dfcf1f431a8f49e63cecc31d91d007bc1deb914f47d0e4"}, + {file = "pyzmq-26.0.3-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:e1258c639e00bf5e8a522fec6c3eaa3e30cf1c23a2f21a586be7e04d50c9acab"}, + {file = "pyzmq-26.0.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:15c59e780be8f30a60816a9adab900c12a58d79c1ac742b4a8df044ab2a6d920"}, + {file = "pyzmq-26.0.3-cp39-cp39-win32.whl", hash = "sha256:d0cdde3c78d8ab5b46595054e5def32a755fc028685add5ddc7403e9f6de9879"}, + {file = "pyzmq-26.0.3-cp39-cp39-win_amd64.whl", hash = "sha256:ce828058d482ef860746bf532822842e0ff484e27f540ef5c813d516dd8896d2"}, + {file = "pyzmq-26.0.3-cp39-cp39-win_arm64.whl", hash = "sha256:788f15721c64109cf720791714dc14afd0f449d63f3a5487724f024345067381"}, + {file = "pyzmq-26.0.3-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:2c18645ef6294d99b256806e34653e86236eb266278c8ec8112622b61db255de"}, + {file = "pyzmq-26.0.3-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7e6bc96ebe49604df3ec2c6389cc3876cabe475e6bfc84ced1bf4e630662cb35"}, + {file = "pyzmq-26.0.3-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:971e8990c5cc4ddcff26e149398fc7b0f6a042306e82500f5e8db3b10ce69f84"}, + {file = "pyzmq-26.0.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d8416c23161abd94cc7da80c734ad7c9f5dbebdadfdaa77dad78244457448223"}, + {file = "pyzmq-26.0.3-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:082a2988364b60bb5de809373098361cf1dbb239623e39e46cb18bc035ed9c0c"}, + {file = "pyzmq-26.0.3-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:d57dfbf9737763b3a60d26e6800e02e04284926329aee8fb01049635e957fe81"}, + {file = "pyzmq-26.0.3-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:77a85dca4c2430ac04dc2a2185c2deb3858a34fe7f403d0a946fa56970cf60a1"}, + {file = "pyzmq-26.0.3-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:4c82a6d952a1d555bf4be42b6532927d2a5686dd3c3e280e5f63225ab47ac1f5"}, + {file = "pyzmq-26.0.3-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4496b1282c70c442809fc1b151977c3d967bfb33e4e17cedbf226d97de18f709"}, + {file = "pyzmq-26.0.3-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:e4946d6bdb7ba972dfda282f9127e5756d4f299028b1566d1245fa0d438847e6"}, + {file = "pyzmq-26.0.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:03c0ae165e700364b266876d712acb1ac02693acd920afa67da2ebb91a0b3c09"}, + {file = "pyzmq-26.0.3-pp38-pypy38_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:3e3070e680f79887d60feeda051a58d0ac36622e1759f305a41059eff62c6da7"}, + {file = "pyzmq-26.0.3-pp38-pypy38_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:6ca08b840fe95d1c2bd9ab92dac5685f949fc6f9ae820ec16193e5ddf603c3b2"}, + {file = "pyzmq-26.0.3-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e76654e9dbfb835b3518f9938e565c7806976c07b37c33526b574cc1a1050480"}, + {file = "pyzmq-26.0.3-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:871587bdadd1075b112e697173e946a07d722459d20716ceb3d1bd6c64bd08ce"}, + {file = "pyzmq-26.0.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:d0a2d1bd63a4ad79483049b26514e70fa618ce6115220da9efdff63688808b17"}, + {file = "pyzmq-26.0.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0270b49b6847f0d106d64b5086e9ad5dc8a902413b5dbbb15d12b60f9c1747a4"}, + {file = "pyzmq-26.0.3-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:703c60b9910488d3d0954ca585c34f541e506a091a41930e663a098d3b794c67"}, + {file = "pyzmq-26.0.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:74423631b6be371edfbf7eabb02ab995c2563fee60a80a30829176842e71722a"}, + {file = "pyzmq-26.0.3-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:4adfbb5451196842a88fda3612e2c0414134874bffb1c2ce83ab4242ec9e027d"}, + {file = "pyzmq-26.0.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:3516119f4f9b8671083a70b6afaa0a070f5683e431ab3dc26e9215620d7ca1ad"}, + {file = "pyzmq-26.0.3.tar.gz", hash = "sha256:dba7d9f2e047dfa2bca3b01f4f84aa5246725203d6284e3790f2ca15fba6b40a"}, +] + +[package.dependencies] +cffi = {version = "*", markers = "implementation_name == \"pypy\""} + +[[package]] +name = "qtconsole" +version = "5.5.2" +description = "Jupyter Qt console" +optional = false +python-versions = ">=3.8" +files = [ + {file = "qtconsole-5.5.2-py3-none-any.whl", hash = "sha256:42d745f3d05d36240244a04e1e1ec2a86d5d9b6edb16dbdef582ccb629e87e0b"}, + {file = "qtconsole-5.5.2.tar.gz", hash = "sha256:6b5fb11274b297463706af84dcbbd5c92273b1f619e6d25d08874b0a88516989"}, +] + +[package.dependencies] +ipykernel = ">=4.1" +jupyter-client = ">=4.1" +jupyter-core = "*" +packaging = "*" +pygments = "*" +pyzmq = ">=17.1" +qtpy = ">=2.4.0" +traitlets = "<5.2.1 || >5.2.1,<5.2.2 || >5.2.2" + +[package.extras] +doc = ["Sphinx (>=1.3)"] +test = ["flaky", "pytest", "pytest-qt"] + +[[package]] +name = "qtpy" +version = "2.4.1" +description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)." +optional = false +python-versions = ">=3.7" +files = [ + {file = "QtPy-2.4.1-py3-none-any.whl", hash = "sha256:1c1d8c4fa2c884ae742b069151b0abe15b3f70491f3972698c683b8e38de839b"}, + {file = "QtPy-2.4.1.tar.gz", hash = "sha256:a5a15ffd519550a1361bdc56ffc07fda56a6af7292f17c7b395d4083af632987"}, +] + +[package.dependencies] +packaging = "*" + +[package.extras] +test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"] + +[[package]] +name = "rapidfuzz" +version = "3.9.4" +description = "rapid fuzzy string matching" +optional = false +python-versions = ">=3.8" +files = [ + {file = "rapidfuzz-3.9.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c9b9793c19bdf38656c8eaefbcf4549d798572dadd70581379e666035c9df781"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:015b5080b999404fe06ec2cb4f40b0be62f0710c926ab41e82dfbc28e80675b4"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:acc5ceca9c1e1663f3e6c23fb89a311f69b7615a40ddd7645e3435bf3082688a"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1424e238bc3f20e1759db1e0afb48a988a9ece183724bef91ea2a291c0b92a95"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ed01378f605aa1f449bee82cd9c83772883120d6483e90aa6c5a4ce95dc5c3aa"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:eb26d412271e5a76cdee1c2d6bf9881310665d3fe43b882d0ed24edfcb891a84"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8f37e9e1f17be193c41a31c864ad4cd3ebd2b40780db11cd5c04abf2bcf4201b"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:d070ec5cf96b927c4dc5133c598c7ff6db3b833b363b2919b13417f1002560bc"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:10e61bb7bc807968cef09a0e32ce253711a2d450a4dce7841d21d45330ffdb24"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:31a2fc60bb2c7face4140010a7aeeafed18b4f9cdfa495cc644a68a8c60d1ff7"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:fbebf1791a71a2e89f5c12b78abddc018354d5859e305ec3372fdae14f80a826"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:aee9fc9e3bb488d040afc590c0a7904597bf4ccd50d1491c3f4a5e7e67e6cd2c"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-win32.whl", hash = "sha256:005a02688a51c7d2451a2d41c79d737aa326ff54167211b78a383fc2aace2c2c"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-win_amd64.whl", hash = "sha256:3a2e75e41ee3274754d3b2163cc6c82cd95b892a85ab031f57112e09da36455f"}, + {file = "rapidfuzz-3.9.4-cp310-cp310-win_arm64.whl", hash = "sha256:2c99d355f37f2b289e978e761f2f8efeedc2b14f4751d9ff7ee344a9a5ca98d9"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:07141aa6099e39d48637ce72a25b893fc1e433c50b3e837c75d8edf99e0c63e1"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:db1664eaff5d7d0f2542dd9c25d272478deaf2c8412e4ad93770e2e2d828e175"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bc01a223f6605737bec3202e94dcb1a449b6c76d46082cfc4aa980f2a60fd40e"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1869c42e73e2a8910b479be204fa736418741b63ea2325f9cc583c30f2ded41a"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:62ea7007941fb2795fff305ac858f3521ec694c829d5126e8f52a3e92ae75526"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:698e992436bf7f0afc750690c301215a36ff952a6dcd62882ec13b9a1ebf7a39"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b76f611935f15a209d3730c360c56b6df8911a9e81e6a38022efbfb96e433bab"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:129627d730db2e11f76169344a032f4e3883d34f20829419916df31d6d1338b1"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:90a82143c14e9a14b723a118c9ef8d1bbc0c5a16b1ac622a1e6c916caff44dd8"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:ded58612fe3b0e0d06e935eaeaf5a9fd27da8ba9ed3e2596307f40351923bf72"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:f16f5d1c4f02fab18366f2d703391fcdbd87c944ea10736ca1dc3d70d8bd2d8b"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:26aa7eece23e0df55fb75fbc2a8fb678322e07c77d1fd0e9540496e6e2b5f03e"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-win32.whl", hash = "sha256:f187a9c3b940ce1ee324710626daf72c05599946bd6748abe9e289f1daa9a077"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-win_amd64.whl", hash = "sha256:d8e9130fe5d7c9182990b366ad78fd632f744097e753e08ace573877d67c32f8"}, + {file = "rapidfuzz-3.9.4-cp311-cp311-win_arm64.whl", hash = "sha256:40419e98b10cd6a00ce26e4837a67362f658fc3cd7a71bd8bd25c99f7ee8fea5"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:b5d5072b548db1b313a07d62d88fe0b037bd2783c16607c647e01b070f6cf9e5"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:cf5bcf22e1f0fd273354462631d443ef78d677f7d2fc292de2aec72ae1473e66"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0c8fc973adde8ed52810f590410e03fb6f0b541bbaeb04c38d77e63442b2df4c"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f2464bb120f135293e9a712e342c43695d3d83168907df05f8c4ead1612310c7"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8d9d58689aca22057cf1a5851677b8a3ccc9b535ca008c7ed06dc6e1899f7844"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:167e745f98baa0f3034c13583e6302fb69249a01239f1483d68c27abb841e0a1"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:db0bf0663b4b6da1507869722420ea9356b6195aa907228d6201303e69837af9"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:cd6ac61b74fdb9e23f04d5f068e6cf554f47e77228ca28aa2347a6ca8903972f"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:60ff67c690acecf381759c16cb06c878328fe2361ddf77b25d0e434ea48a29da"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:cb934363380c60f3a57d14af94325125cd8cded9822611a9f78220444034e36e"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:fe833493fb5cc5682c823ea3e2f7066b07612ee8f61ecdf03e1268f262106cdd"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2797fb847d89e04040d281cb1902cbeffbc4b5131a5c53fc0db490fd76b2a547"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-win32.whl", hash = "sha256:52e3d89377744dae68ed7c84ad0ddd3f5e891c82d48d26423b9e066fc835cc7c"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-win_amd64.whl", hash = "sha256:c76da20481c906e08400ee9be230f9e611d5931a33707d9df40337c2655c84b5"}, + {file = "rapidfuzz-3.9.4-cp312-cp312-win_arm64.whl", hash = "sha256:f2d2846f3980445864c7e8b8818a29707fcaff2f0261159ef6b7bd27ba139296"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:355fc4a268ffa07bab88d9adee173783ec8d20136059e028d2a9135c623c44e6"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4d81a78f90269190b568a8353d4ea86015289c36d7e525cd4d43176c88eff429"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9e618625ffc4660b26dc8e56225f8b966d5842fa190e70c60db6cd393e25b86e"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b712336ad6f2bacdbc9f1452556e8942269ef71f60a9e6883ef1726b52d9228a"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:84fc1ee19fdad05770c897e793836c002344524301501d71ef2e832847425707"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1950f8597890c0c707cb7e0416c62a1cf03dcdb0384bc0b2dbda7e05efe738ec"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4a6c35f272ec9c430568dc8c1c30cb873f6bc96be2c79795e0bce6db4e0e101d"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:1df0f9e9239132a231c86ae4f545ec2b55409fa44470692fcfb36b1bd00157ad"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:d2c51955329bfccf99ae26f63d5928bf5be9fcfcd9f458f6847fd4b7e2b8986c"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:3c522f462d9fc504f2ea8d82e44aa580e60566acc754422c829ad75c752fbf8d"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:d8a52fc50ded60d81117d7647f262c529659fb21d23e14ebfd0b35efa4f1b83d"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:04dbdfb0f0bfd3f99cf1e9e24fadc6ded2736d7933f32f1151b0f2abb38f9a25"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-win32.whl", hash = "sha256:4968c8bd1df84b42f382549e6226710ad3476f976389839168db3e68fd373298"}, + {file = "rapidfuzz-3.9.4-cp38-cp38-win_amd64.whl", hash = "sha256:3fe4545f89f8d6c27b6bbbabfe40839624873c08bd6700f63ac36970a179f8f5"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9f256c8fb8f3125574c8c0c919ab0a1f75d7cba4d053dda2e762dcc36357969d"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f5fdc09cf6e9d8eac3ce48a4615b3a3ee332ea84ac9657dbbefef913b13e632f"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d395d46b80063d3b5d13c0af43d2c2cedf3ab48c6a0c2aeec715aa5455b0c632"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7fa714fb96ce9e70c37e64c83b62fe8307030081a0bfae74a76fac7ba0f91715"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1bc1a0f29f9119be7a8d3c720f1d2068317ae532e39e4f7f948607c3a6de8396"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6022674aa1747d6300f699cd7c54d7dae89bfe1f84556de699c4ac5df0838082"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dcb72e5f9762fd469701a7e12e94b924af9004954f8c739f925cb19c00862e38"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:ad04ae301129f0eb5b350a333accd375ce155a0c1cec85ab0ec01f770214e2e4"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:f46a22506f17c0433e349f2d1dc11907c393d9b3601b91d4e334fa9a439a6a4d"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:01b42a8728c36011718da409aa86b84984396bf0ca3bfb6e62624f2014f6022c"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:e590d5d5443cf56f83a51d3c4867bd1f6be8ef8cfcc44279522bcef3845b2a51"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:4c72078b5fdce34ba5753f9299ae304e282420e6455e043ad08e4488ca13a2b0"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-win32.whl", hash = "sha256:f75639277304e9b75e6a7b3c07042d2264e16740a11e449645689ed28e9c2124"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-win_amd64.whl", hash = "sha256:e81e27e8c32a1e1278a4bb1ce31401bfaa8c2cc697a053b985a6f8d013df83ec"}, + {file = "rapidfuzz-3.9.4-cp39-cp39-win_arm64.whl", hash = "sha256:15bc397ee9a3ed1210b629b9f5f1da809244adc51ce620c504138c6e7095b7bd"}, + {file = "rapidfuzz-3.9.4-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:20488ade4e1ddba3cfad04f400da7a9c1b91eff5b7bd3d1c50b385d78b587f4f"}, + {file = "rapidfuzz-3.9.4-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:e61b03509b1a6eb31bc5582694f6df837d340535da7eba7bedb8ae42a2fcd0b9"}, + {file = "rapidfuzz-3.9.4-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:098d231d4e51644d421a641f4a5f2f151f856f53c252b03516e01389b2bfef99"}, + {file = "rapidfuzz-3.9.4-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:17ab8b7d10fde8dd763ad428aa961c0f30a1b44426e675186af8903b5d134fb0"}, + {file = "rapidfuzz-3.9.4-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e272df61bee0a056a3daf99f9b1bd82cf73ace7d668894788139c868fdf37d6f"}, + {file = "rapidfuzz-3.9.4-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:d6481e099ff8c4edda85b8b9b5174c200540fd23c8f38120016c765a86fa01f5"}, + {file = "rapidfuzz-3.9.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:ad61676e9bdae677d577fe80ec1c2cea1d150c86be647e652551dcfe505b1113"}, + {file = "rapidfuzz-3.9.4-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:af65020c0dd48d0d8ae405e7e69b9d8ae306eb9b6249ca8bf511a13f465fad85"}, + {file = "rapidfuzz-3.9.4-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4d38b4e026fcd580e0bda6c0ae941e0e9a52c6bc66cdce0b8b0da61e1959f5f8"}, + {file = "rapidfuzz-3.9.4-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f74ed072c2b9dc6743fb19994319d443a4330b0e64aeba0aa9105406c7c5b9c2"}, + {file = "rapidfuzz-3.9.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aee5f6b8321f90615c184bd8a4c676e9becda69b8e4e451a90923db719d6857c"}, + {file = "rapidfuzz-3.9.4-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:3a555e3c841d6efa350f862204bb0a3fea0c006b8acc9b152b374fa36518a1c6"}, + {file = "rapidfuzz-3.9.4-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:0772150d37bf018110351c01d032bf9ab25127b966a29830faa8ad69b7e2f651"}, + {file = "rapidfuzz-3.9.4-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:addcdd3c3deef1bd54075bd7aba0a6ea9f1d01764a08620074b7a7b1e5447cb9"}, + {file = "rapidfuzz-3.9.4-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3fe86b82b776554add8f900b6af202b74eb5efe8f25acdb8680a5c977608727f"}, + {file = "rapidfuzz-3.9.4-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b0fc91ac59f4414d8542454dfd6287a154b8e6f1256718c898f695bdbb993467"}, + {file = "rapidfuzz-3.9.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a944e546a296a5fdcaabb537b01459f1b14d66f74e584cb2a91448bffadc3c1"}, + {file = "rapidfuzz-3.9.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:4fb96ba96d58c668a17a06b5b5e8340fedc26188e87b0d229d38104556f30cd8"}, + {file = "rapidfuzz-3.9.4.tar.gz", hash = "sha256:366bf8947b84e37f2f4cf31aaf5f37c39f620d8c0eddb8b633e6ba0129ca4a0a"}, +] + +[package.extras] +full = ["numpy"] + +[[package]] +name = "ray" +version = "2.32.0" +description = "Ray provides a simple, universal API for building distributed applications." +optional = false +python-versions = ">=3.8" +files = [ + {file = "ray-2.32.0-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:16088e43c4d3c86343b0d8249a4fc2d47d2fabee733046eb5c6edf2d967a5b15"}, + {file = "ray-2.32.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2a4bf45892f22a19ef0fda0faa039f013490d94603210cb56c395ce25607f27d"}, + {file = "ray-2.32.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:101197fe65f9e4fe7f2bd467deb5332b7c69f9a2e8e050386ae371c8639e96af"}, + {file = "ray-2.32.0-cp310-cp310-manylinux2014_x86_64.whl", hash = "sha256:6254368685b9323424389e0bdbf1aca269725f7ae43438dd0bcda1674470cc6c"}, + {file = "ray-2.32.0-cp310-cp310-win_amd64.whl", hash = "sha256:0c4e41a41563e7d1e3d34523966e6344d826d365c6fe29efe8aacd2e75476719"}, + {file = "ray-2.32.0-cp311-cp311-macosx_10_15_x86_64.whl", hash = "sha256:dd087543b02086175b6f61ea340a6c3684f3c53191f5274f7dd30692bd8f3ea9"}, + {file = "ray-2.32.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:983bc002b07549775a514de09b768f6f515248537b9a8404126df776f78ed861"}, + {file = "ray-2.32.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:9d5c65dce5bd61e82d83fe0312e3c27337abe913ffa8767fddc48a122d166096"}, + {file = "ray-2.32.0-cp311-cp311-manylinux2014_x86_64.whl", hash = "sha256:5616f053cb1271c6eec9b1823c678417e957bfd7c61ad56cd7e7b84b9bce5112"}, + {file = "ray-2.32.0-cp311-cp311-win_amd64.whl", hash = "sha256:2f72254c3e3808fb87d0c307d5bb23920ecd8e347eb42e2a84232b9bd4cd207e"}, + {file = "ray-2.32.0-cp312-cp312-macosx_10_15_x86_64.whl", hash = "sha256:352a7e491dbc3c3967141dbab5eb87de5f4c0a75bdc4575f5f207829565c2f2e"}, + {file = "ray-2.32.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8e5542f0cc311c05dadbaa5363e154639a2ff14cf69f7b96a30794fbe5830d0a"}, + {file = "ray-2.32.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:b32e4588acea7ef01eed85028655b36c53e778525b7e9c46b96b1bddccc9ef3b"}, + {file = "ray-2.32.0-cp312-cp312-manylinux2014_x86_64.whl", hash = "sha256:118ba0dfd1394571bc5bcc7b6904da8dc6c59afabc159897c5d892295de1b411"}, + {file = "ray-2.32.0-cp312-cp312-win_amd64.whl", hash = "sha256:19f6810bb61b49173564e0594411f753595d99c9c868e6c15d7c090fb2eef253"}, + {file = "ray-2.32.0-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:1a294b03fe795bfe05338260c86cc687daa6da2bc6b478b0c3944484a23470b1"}, + {file = "ray-2.32.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9f8556559b23774f9231a90423c179150ab9e75929d3c69f7ce0bf2e9be233e1"}, + {file = "ray-2.32.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:4e9d32a193b4b4bb8a9d8a147aab770119b4d4473d266f71224d2daa5ebe3d0f"}, + {file = "ray-2.32.0-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:5b334e0d80de19e4aab2555f07635f1f7f7718911f30a9632507497b9a686211"}, + {file = "ray-2.32.0-cp39-cp39-win_amd64.whl", hash = "sha256:f7a3e211eaf9a57e2a27c3df0e568486c42bd6903ca3fcff5789694b791e90b7"}, +] + +[package.dependencies] +aiosignal = "*" +click = ">=7.0" +filelock = "*" +frozenlist = "*" +fsspec = {version = "*", optional = true, markers = "extra == \"tune\""} +jsonschema = "*" +msgpack = ">=1.0.0,<2.0.0" +packaging = "*" +pandas = {version = "*", optional = true, markers = "extra == \"tune\""} +protobuf = ">=3.15.3,<3.19.5 || >3.19.5" +pyarrow = {version = ">=6.0.1", optional = true, markers = "extra == \"tune\""} +pyyaml = "*" +requests = "*" +tensorboardX = {version = ">=1.9", optional = true, markers = "extra == \"tune\""} + +[package.extras] +air = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "fastapi", "fsspec", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "memray", "numpy (>=1.20)", "opencensus", "pandas", "pandas (>=1.3)", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2.0.dev0 || >=2.5.dev0,<3)", "requests", "smart-open", "starlette", "tensorboardX (>=1.9)", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"] +all = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "dm-tree", "fastapi", "fsspec", "grpcio (!=1.56.0)", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "gymnasium (==0.28.1)", "lz4", "memray", "numpy (>=1.20)", "opencensus", "opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk", "pandas", "pandas (>=1.3)", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2.0.dev0 || >=2.5.dev0,<3)", "pyyaml", "ray-cpp (==2.32.0)", "requests", "rich", "scikit-image", "scipy", "smart-open", "starlette", "tensorboardX (>=1.9)", "typer", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"] +client = ["grpcio (!=1.56.0)"] +cpp = ["ray-cpp (==2.32.0)"] +data = ["fsspec", "numpy (>=1.20)", "pandas (>=1.3)", "pyarrow (>=6.0.1)"] +default = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "memray", "opencensus", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pydantic (<2.0.dev0 || >=2.5.dev0,<3)", "requests", "smart-open", "virtualenv (>=20.0.24,!=20.21.1)"] +observability = ["opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk"] +rllib = ["dm-tree", "fsspec", "gymnasium (==0.28.1)", "lz4", "pandas", "pyarrow (>=6.0.1)", "pyyaml", "requests", "rich", "scikit-image", "scipy", "tensorboardX (>=1.9)", "typer"] +serve = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "fastapi", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "memray", "opencensus", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pydantic (<2.0.dev0 || >=2.5.dev0,<3)", "requests", "smart-open", "starlette", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"] +serve-grpc = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "fastapi", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "memray", "opencensus", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pydantic (<2.0.dev0 || >=2.5.dev0,<3)", "requests", "smart-open", "starlette", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"] +train = ["fsspec", "pandas", "pyarrow (>=6.0.1)", "requests", "tensorboardX (>=1.9)"] +tune = ["fsspec", "pandas", "pyarrow (>=6.0.1)", "requests", "tensorboardX (>=1.9)"] + +[[package]] +name = "referencing" +version = "0.35.1" +description = "JSON Referencing + Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "referencing-0.35.1-py3-none-any.whl", hash = "sha256:eda6d3234d62814d1c64e305c1331c9a3a6132da475ab6382eaa997b21ee75de"}, + {file = "referencing-0.35.1.tar.gz", hash = "sha256:25b42124a6c8b632a425174f24087783efb348a6f1e0008e63cd4466fedf703c"}, +] + +[package.dependencies] +attrs = ">=22.2.0" +rpds-py = ">=0.7.0" + +[[package]] +name = "requests" +version = "2.32.3" +description = "Python HTTP for Humans." +optional = false +python-versions = ">=3.8" +files = [ + {file = "requests-2.32.3-py3-none-any.whl", hash = "sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"}, + {file = "requests-2.32.3.tar.gz", hash = "sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760"}, +] + +[package.dependencies] +certifi = ">=2017.4.17" +charset-normalizer = ">=2,<4" +idna = ">=2.5,<4" +urllib3 = ">=1.21.1,<3" + +[package.extras] +socks = ["PySocks (>=1.5.6,!=1.5.7)"] +use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"] + +[[package]] +name = "rfc3339-validator" +version = "0.1.4" +description = "A pure python RFC3339 validator" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +files = [ + {file = "rfc3339_validator-0.1.4-py2.py3-none-any.whl", hash = "sha256:24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa"}, + {file = "rfc3339_validator-0.1.4.tar.gz", hash = "sha256:138a2abdf93304ad60530167e51d2dfb9549521a836871b88d7f4695d0022f6b"}, +] + +[package.dependencies] +six = "*" + +[[package]] +name = "rfc3986-validator" +version = "0.1.1" +description = "Pure python rfc3986 validator" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +files = [ + {file = "rfc3986_validator-0.1.1-py2.py3-none-any.whl", hash = "sha256:2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9"}, + {file = "rfc3986_validator-0.1.1.tar.gz", hash = "sha256:3d44bde7921b3b9ec3ae4e3adca370438eccebc676456449b145d533b240d055"}, +] + +[[package]] +name = "rich" +version = "13.7.1" +description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal" +optional = false +python-versions = ">=3.7.0" +files = [ + {file = "rich-13.7.1-py3-none-any.whl", hash = "sha256:4edbae314f59eb482f54e9e30bf00d33350aaa94f4bfcd4e9e3110e64d0d7222"}, + {file = "rich-13.7.1.tar.gz", hash = "sha256:9be308cb1fe2f1f57d67ce99e95af38a1e2bc71ad9813b0e247cf7ffbcc3a432"}, +] + +[package.dependencies] +markdown-it-py = ">=2.2.0" +pygments = ">=2.13.0,<3.0.0" + +[package.extras] +jupyter = ["ipywidgets (>=7.5.1,<9)"] + +[[package]] +name = "rpds-py" +version = "0.19.0" +description = "Python bindings to Rust's persistent data structures (rpds)" +optional = false +python-versions = ">=3.8" +files = [ + {file = "rpds_py-0.19.0-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:fb37bd599f031f1a6fb9e58ec62864ccf3ad549cf14bac527dbfa97123edcca4"}, + {file = "rpds_py-0.19.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3384d278df99ec2c6acf701d067147320b864ef6727405d6470838476e44d9e8"}, + {file = "rpds_py-0.19.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e54548e0be3ac117595408fd4ca0ac9278fde89829b0b518be92863b17ff67a2"}, + {file = "rpds_py-0.19.0-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:8eb488ef928cdbc05a27245e52de73c0d7c72a34240ef4d9893fdf65a8c1a955"}, + {file = "rpds_py-0.19.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a5da93debdfe27b2bfc69eefb592e1831d957b9535e0943a0ee8b97996de21b5"}, + {file = "rpds_py-0.19.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:79e205c70afddd41f6ee79a8656aec738492a550247a7af697d5bd1aee14f766"}, + {file = "rpds_py-0.19.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:959179efb3e4a27610e8d54d667c02a9feaa86bbabaf63efa7faa4dfa780d4f1"}, + {file = "rpds_py-0.19.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a6e605bb9edcf010f54f8b6a590dd23a4b40a8cb141255eec2a03db249bc915b"}, + {file = "rpds_py-0.19.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:9133d75dc119a61d1a0ded38fb9ba40a00ef41697cc07adb6ae098c875195a3f"}, + {file = "rpds_py-0.19.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:dd36b712d35e757e28bf2f40a71e8f8a2d43c8b026d881aa0c617b450d6865c9"}, + {file = "rpds_py-0.19.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:354f3a91718489912f2e0fc331c24eaaf6a4565c080e00fbedb6015857c00582"}, + {file = "rpds_py-0.19.0-cp310-none-win32.whl", hash = "sha256:ebcbf356bf5c51afc3290e491d3722b26aaf5b6af3c1c7f6a1b757828a46e336"}, + {file = "rpds_py-0.19.0-cp310-none-win_amd64.whl", hash = "sha256:75a6076289b2df6c8ecb9d13ff79ae0cad1d5fb40af377a5021016d58cd691ec"}, + {file = "rpds_py-0.19.0-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:6d45080095e585f8c5097897313def60caa2046da202cdb17a01f147fb263b81"}, + {file = "rpds_py-0.19.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c5c9581019c96f865483d031691a5ff1cc455feb4d84fc6920a5ffc48a794d8a"}, + {file = "rpds_py-0.19.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1540d807364c84516417115c38f0119dfec5ea5c0dd9a25332dea60b1d26fc4d"}, + {file = "rpds_py-0.19.0-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:9e65489222b410f79711dc3d2d5003d2757e30874096b2008d50329ea4d0f88c"}, + {file = "rpds_py-0.19.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9da6f400eeb8c36f72ef6646ea530d6d175a4f77ff2ed8dfd6352842274c1d8b"}, + {file = "rpds_py-0.19.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:37f46bb11858717e0efa7893c0f7055c43b44c103e40e69442db5061cb26ed34"}, + {file = "rpds_py-0.19.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:071d4adc734de562bd11d43bd134330fb6249769b2f66b9310dab7460f4bf714"}, + {file = "rpds_py-0.19.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:9625367c8955e4319049113ea4f8fee0c6c1145192d57946c6ffcd8fe8bf48dd"}, + {file = "rpds_py-0.19.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:e19509145275d46bc4d1e16af0b57a12d227c8253655a46bbd5ec317e941279d"}, + {file = "rpds_py-0.19.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:4d438e4c020d8c39961deaf58f6913b1bf8832d9b6f62ec35bd93e97807e9cbc"}, + {file = "rpds_py-0.19.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:90bf55d9d139e5d127193170f38c584ed3c79e16638890d2e36f23aa1630b952"}, + {file = "rpds_py-0.19.0-cp311-none-win32.whl", hash = "sha256:8d6ad132b1bc13d05ffe5b85e7a01a3998bf3a6302ba594b28d61b8c2cf13aaf"}, + {file = "rpds_py-0.19.0-cp311-none-win_amd64.whl", hash = "sha256:7ec72df7354e6b7f6eb2a17fa6901350018c3a9ad78e48d7b2b54d0412539a67"}, + {file = "rpds_py-0.19.0-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:5095a7c838a8647c32aa37c3a460d2c48debff7fc26e1136aee60100a8cd8f68"}, + {file = "rpds_py-0.19.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:6f2f78ef14077e08856e788fa482107aa602636c16c25bdf59c22ea525a785e9"}, + {file = "rpds_py-0.19.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b7cc6cb44f8636fbf4a934ca72f3e786ba3c9f9ba4f4d74611e7da80684e48d2"}, + {file = "rpds_py-0.19.0-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:cf902878b4af334a09de7a45badbff0389e7cf8dc2e4dcf5f07125d0b7c2656d"}, + {file = "rpds_py-0.19.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:688aa6b8aa724db1596514751ffb767766e02e5c4a87486ab36b8e1ebc1aedac"}, + {file = "rpds_py-0.19.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:57dbc9167d48e355e2569346b5aa4077f29bf86389c924df25c0a8b9124461fb"}, + {file = "rpds_py-0.19.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3b4cf5a9497874822341c2ebe0d5850fed392034caadc0bad134ab6822c0925b"}, + {file = "rpds_py-0.19.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:8a790d235b9d39c70a466200d506bb33a98e2ee374a9b4eec7a8ac64c2c261fa"}, + {file = "rpds_py-0.19.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1d16089dfa58719c98a1c06f2daceba6d8e3fb9b5d7931af4a990a3c486241cb"}, + {file = "rpds_py-0.19.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:bc9128e74fe94650367fe23f37074f121b9f796cabbd2f928f13e9661837296d"}, + {file = "rpds_py-0.19.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c8f77e661ffd96ff104bebf7d0f3255b02aa5d5b28326f5408d6284c4a8b3248"}, + {file = "rpds_py-0.19.0-cp312-none-win32.whl", hash = "sha256:5f83689a38e76969327e9b682be5521d87a0c9e5a2e187d2bc6be4765f0d4600"}, + {file = "rpds_py-0.19.0-cp312-none-win_amd64.whl", hash = "sha256:06925c50f86da0596b9c3c64c3837b2481337b83ef3519e5db2701df695453a4"}, + {file = "rpds_py-0.19.0-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:52e466bea6f8f3a44b1234570244b1cff45150f59a4acae3fcc5fd700c2993ca"}, + {file = "rpds_py-0.19.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:e21cc693045fda7f745c790cb687958161ce172ffe3c5719ca1764e752237d16"}, + {file = "rpds_py-0.19.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6b31f059878eb1f5da8b2fd82480cc18bed8dcd7fb8fe68370e2e6285fa86da6"}, + {file = "rpds_py-0.19.0-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:1dd46f309e953927dd018567d6a9e2fb84783963650171f6c5fe7e5c41fd5666"}, + {file = "rpds_py-0.19.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:34a01a4490e170376cd79258b7f755fa13b1a6c3667e872c8e35051ae857a92b"}, + {file = "rpds_py-0.19.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bcf426a8c38eb57f7bf28932e68425ba86def6e756a5b8cb4731d8e62e4e0223"}, + {file = "rpds_py-0.19.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f68eea5df6347d3f1378ce992d86b2af16ad7ff4dcb4a19ccdc23dea901b87fb"}, + {file = "rpds_py-0.19.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:dab8d921b55a28287733263c0e4c7db11b3ee22aee158a4de09f13c93283c62d"}, + {file = "rpds_py-0.19.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:6fe87efd7f47266dfc42fe76dae89060038f1d9cb911f89ae7e5084148d1cc08"}, + {file = "rpds_py-0.19.0-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:535d4b52524a961d220875688159277f0e9eeeda0ac45e766092bfb54437543f"}, + {file = "rpds_py-0.19.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:8b1a94b8afc154fbe36978a511a1f155f9bd97664e4f1f7a374d72e180ceb0ae"}, + {file = "rpds_py-0.19.0-cp38-none-win32.whl", hash = "sha256:7c98298a15d6b90c8f6e3caa6457f4f022423caa5fa1a1ca7a5e9e512bdb77a4"}, + {file = "rpds_py-0.19.0-cp38-none-win_amd64.whl", hash = "sha256:b0da31853ab6e58a11db3205729133ce0df26e6804e93079dee095be3d681dc1"}, + {file = "rpds_py-0.19.0-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:5039e3cef7b3e7a060de468a4a60a60a1f31786da94c6cb054e7a3c75906111c"}, + {file = "rpds_py-0.19.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ab1932ca6cb8c7499a4d87cb21ccc0d3326f172cfb6a64021a889b591bb3045c"}, + {file = "rpds_py-0.19.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f2afd2164a1e85226fcb6a1da77a5c8896c18bfe08e82e8ceced5181c42d2179"}, + {file = "rpds_py-0.19.0-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:b1c30841f5040de47a0046c243fc1b44ddc87d1b12435a43b8edff7e7cb1e0d0"}, + {file = "rpds_py-0.19.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f757f359f30ec7dcebca662a6bd46d1098f8b9fb1fcd661a9e13f2e8ce343ba1"}, + {file = "rpds_py-0.19.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:15e65395a59d2e0e96caf8ee5389ffb4604e980479c32742936ddd7ade914b22"}, + {file = "rpds_py-0.19.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb0f6eb3a320f24b94d177e62f4074ff438f2ad9d27e75a46221904ef21a7b05"}, + {file = "rpds_py-0.19.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:b228e693a2559888790936e20f5f88b6e9f8162c681830eda303bad7517b4d5a"}, + {file = "rpds_py-0.19.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:2575efaa5d949c9f4e2cdbe7d805d02122c16065bfb8d95c129372d65a291a0b"}, + {file = "rpds_py-0.19.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:5c872814b77a4e84afa293a1bee08c14daed1068b2bb1cc312edbf020bbbca2b"}, + {file = "rpds_py-0.19.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:850720e1b383df199b8433a20e02b25b72f0fded28bc03c5bd79e2ce7ef050be"}, + {file = "rpds_py-0.19.0-cp39-none-win32.whl", hash = "sha256:ce84a7efa5af9f54c0aa7692c45861c1667080814286cacb9958c07fc50294fb"}, + {file = "rpds_py-0.19.0-cp39-none-win_amd64.whl", hash = "sha256:1c26da90b8d06227d7769f34915913911222d24ce08c0ab2d60b354e2d9c7aff"}, + {file = "rpds_py-0.19.0-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:75969cf900d7be665ccb1622a9aba225cf386bbc9c3bcfeeab9f62b5048f4a07"}, + {file = "rpds_py-0.19.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:8445f23f13339da640d1be8e44e5baf4af97e396882ebbf1692aecd67f67c479"}, + {file = "rpds_py-0.19.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5a7c1062ef8aea3eda149f08120f10795835fc1c8bc6ad948fb9652a113ca55"}, + {file = "rpds_py-0.19.0-pp310-pypy310_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:462b0c18fbb48fdbf980914a02ee38c423a25fcc4cf40f66bacc95a2d2d73bc8"}, + {file = "rpds_py-0.19.0-pp310-pypy310_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3208f9aea18991ac7f2b39721e947bbd752a1abbe79ad90d9b6a84a74d44409b"}, + {file = "rpds_py-0.19.0-pp310-pypy310_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c3444fe52b82f122d8a99bf66777aed6b858d392b12f4c317da19f8234db4533"}, + {file = "rpds_py-0.19.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:88cb4bac7185a9f0168d38c01d7a00addece9822a52870eee26b8d5b61409213"}, + {file = "rpds_py-0.19.0-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:6b130bd4163c93798a6b9bb96be64a7c43e1cec81126ffa7ffaa106e1fc5cef5"}, + {file = "rpds_py-0.19.0-pp310-pypy310_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:a707b158b4410aefb6b054715545bbb21aaa5d5d0080217290131c49c2124a6e"}, + {file = "rpds_py-0.19.0-pp310-pypy310_pp73-musllinux_1_2_i686.whl", hash = "sha256:dc9ac4659456bde7c567107556ab065801622396b435a3ff213daef27b495388"}, + {file = "rpds_py-0.19.0-pp310-pypy310_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:81ea573aa46d3b6b3d890cd3c0ad82105985e6058a4baed03cf92518081eec8c"}, + {file = "rpds_py-0.19.0-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:3f148c3f47f7f29a79c38cc5d020edcb5ca780020fab94dbc21f9af95c463581"}, + {file = "rpds_py-0.19.0-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:b0906357f90784a66e89ae3eadc2654f36c580a7d65cf63e6a616e4aec3a81be"}, + {file = "rpds_py-0.19.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f629ecc2db6a4736b5ba95a8347b0089240d69ad14ac364f557d52ad68cf94b0"}, + {file = "rpds_py-0.19.0-pp38-pypy38_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c6feacd1d178c30e5bc37184526e56740342fd2aa6371a28367bad7908d454fc"}, + {file = "rpds_py-0.19.0-pp38-pypy38_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ae8b6068ee374fdfab63689be0963333aa83b0815ead5d8648389a8ded593378"}, + {file = "rpds_py-0.19.0-pp38-pypy38_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:78d57546bad81e0da13263e4c9ce30e96dcbe720dbff5ada08d2600a3502e526"}, + {file = "rpds_py-0.19.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a8b6683a37338818646af718c9ca2a07f89787551057fae57c4ec0446dc6224b"}, + {file = "rpds_py-0.19.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:e8481b946792415adc07410420d6fc65a352b45d347b78fec45d8f8f0d7496f0"}, + {file = "rpds_py-0.19.0-pp38-pypy38_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:bec35eb20792ea64c3c57891bc3ca0bedb2884fbac2c8249d9b731447ecde4fa"}, + {file = "rpds_py-0.19.0-pp38-pypy38_pp73-musllinux_1_2_i686.whl", hash = "sha256:aa5476c3e3a402c37779e95f7b4048db2cb5b0ed0b9d006983965e93f40fe05a"}, + {file = "rpds_py-0.19.0-pp38-pypy38_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:19d02c45f2507b489fd4df7b827940f1420480b3e2e471e952af4d44a1ea8e34"}, + {file = "rpds_py-0.19.0-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:a3e2fd14c5d49ee1da322672375963f19f32b3d5953f0615b175ff7b9d38daed"}, + {file = "rpds_py-0.19.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:93a91c2640645303e874eada51f4f33351b84b351a689d470f8108d0e0694210"}, + {file = "rpds_py-0.19.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e5b9fc03bf76a94065299d4a2ecd8dfbae4ae8e2e8098bbfa6ab6413ca267709"}, + {file = "rpds_py-0.19.0-pp39-pypy39_pp73-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:5a4b07cdf3f84310c08c1de2c12ddadbb7a77568bcb16e95489f9c81074322ed"}, + {file = "rpds_py-0.19.0-pp39-pypy39_pp73-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ba0ed0dc6763d8bd6e5de5cf0d746d28e706a10b615ea382ac0ab17bb7388633"}, + {file = "rpds_py-0.19.0-pp39-pypy39_pp73-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:474bc83233abdcf2124ed3f66230a1c8435896046caa4b0b5ab6013c640803cc"}, + {file = "rpds_py-0.19.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:329c719d31362355a96b435f4653e3b4b061fcc9eba9f91dd40804ca637d914e"}, + {file = "rpds_py-0.19.0-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:ef9101f3f7b59043a34f1dccbb385ca760467590951952d6701df0da9893ca0c"}, + {file = "rpds_py-0.19.0-pp39-pypy39_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:0121803b0f424ee2109d6e1f27db45b166ebaa4b32ff47d6aa225642636cd834"}, + {file = "rpds_py-0.19.0-pp39-pypy39_pp73-musllinux_1_2_i686.whl", hash = "sha256:8344127403dea42f5970adccf6c5957a71a47f522171fafaf4c6ddb41b61703a"}, + {file = "rpds_py-0.19.0-pp39-pypy39_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:443cec402ddd650bb2b885113e1dcedb22b1175c6be223b14246a714b61cd521"}, + {file = "rpds_py-0.19.0.tar.gz", hash = "sha256:4fdc9afadbeb393b4bbbad75481e0ea78e4469f2e1d713a90811700830b553a9"}, +] + +[[package]] +name = "s3fs" +version = "2022.11.0" +description = "Convenient Filesystem interface over S3" +optional = false +python-versions = ">= 3.7" +files = [ + {file = "s3fs-2022.11.0-py3-none-any.whl", hash = "sha256:42d57a3ceedb478b18ee53e34bbe3305a3f07f6381ca1ab76135efe076c6a07d"}, + {file = "s3fs-2022.11.0.tar.gz", hash = "sha256:10c5ac283a4f5b67ffad6d1f25ff7ee026142750c5c5dc868746cd904f617c33"}, +] + +[package.dependencies] +aiobotocore = ">=2.4.0,<2.5.0" +aiohttp = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1" +fsspec = "2022.11.0" + +[package.extras] +awscli = ["aiobotocore[awscli] (>=2.4.0,<2.5.0)"] +boto3 = ["aiobotocore[boto3] (>=2.4.0,<2.5.0)"] + +[[package]] +name = "s3transfer" +version = "0.6.2" +description = "An Amazon S3 Transfer Manager" +optional = false +python-versions = ">= 3.7" +files = [ + {file = "s3transfer-0.6.2-py3-none-any.whl", hash = "sha256:b014be3a8a2aab98cfe1abc7229cc5a9a0cf05eb9c1f2b86b230fd8df3f78084"}, + {file = "s3transfer-0.6.2.tar.gz", hash = "sha256:cab66d3380cca3e70939ef2255d01cd8aece6a4907a9528740f668c4b0611861"}, +] + +[package.dependencies] +botocore = ">=1.12.36,<2.0a.0" + +[package.extras] +crt = ["botocore[crt] (>=1.20.29,<2.0a.0)"] + +[[package]] +name = "safetensors" +version = "0.4.3" +description = "" +optional = false +python-versions = ">=3.7" +files = [ + {file = "safetensors-0.4.3-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:dcf5705cab159ce0130cd56057f5f3425023c407e170bca60b4868048bae64fd"}, + {file = "safetensors-0.4.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:bb4f8c5d0358a31e9a08daeebb68f5e161cdd4018855426d3f0c23bb51087055"}, + {file = "safetensors-0.4.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:70a5319ef409e7f88686a46607cbc3c428271069d8b770076feaf913664a07ac"}, + {file = "safetensors-0.4.3-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fb9c65bd82f9ef3ce4970dc19ee86be5f6f93d032159acf35e663c6bea02b237"}, + {file = "safetensors-0.4.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:edb5698a7bc282089f64c96c477846950358a46ede85a1c040e0230344fdde10"}, + {file = "safetensors-0.4.3-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:efcc860be094b8d19ac61b452ec635c7acb9afa77beb218b1d7784c6d41fe8ad"}, + {file = "safetensors-0.4.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d88b33980222085dd6001ae2cad87c6068e0991d4f5ccf44975d216db3b57376"}, + {file = "safetensors-0.4.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:5fc6775529fb9f0ce2266edd3e5d3f10aab068e49f765e11f6f2a63b5367021d"}, + {file = "safetensors-0.4.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:9c6ad011c1b4e3acff058d6b090f1da8e55a332fbf84695cf3100c649cc452d1"}, + {file = "safetensors-0.4.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:8c496c5401c1b9c46d41a7688e8ff5b0310a3b9bae31ce0f0ae870e1ea2b8caf"}, + {file = "safetensors-0.4.3-cp310-none-win32.whl", hash = "sha256:38e2a8666178224a51cca61d3cb4c88704f696eac8f72a49a598a93bbd8a4af9"}, + {file = "safetensors-0.4.3-cp310-none-win_amd64.whl", hash = "sha256:393e6e391467d1b2b829c77e47d726f3b9b93630e6a045b1d1fca67dc78bf632"}, + {file = "safetensors-0.4.3-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:22f3b5d65e440cec0de8edaa672efa888030802e11c09b3d6203bff60ebff05a"}, + {file = "safetensors-0.4.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7c4fa560ebd4522adddb71dcd25d09bf211b5634003f015a4b815b7647d62ebe"}, + {file = "safetensors-0.4.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e9afd5358719f1b2cf425fad638fc3c887997d6782da317096877e5b15b2ce93"}, + {file = "safetensors-0.4.3-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:d8c5093206ef4b198600ae484230402af6713dab1bd5b8e231905d754022bec7"}, + {file = "safetensors-0.4.3-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e0b2104df1579d6ba9052c0ae0e3137c9698b2d85b0645507e6fd1813b70931a"}, + {file = "safetensors-0.4.3-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8cf18888606dad030455d18f6c381720e57fc6a4170ee1966adb7ebc98d4d6a3"}, + {file = "safetensors-0.4.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0bf4f9d6323d9f86eef5567eabd88f070691cf031d4c0df27a40d3b4aaee755b"}, + {file = "safetensors-0.4.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:585c9ae13a205807b63bef8a37994f30c917ff800ab8a1ca9c9b5d73024f97ee"}, + {file = "safetensors-0.4.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:faefeb3b81bdfb4e5a55b9bbdf3d8d8753f65506e1d67d03f5c851a6c87150e9"}, + {file = "safetensors-0.4.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:befdf0167ad626f22f6aac6163477fcefa342224a22f11fdd05abb3995c1783c"}, + {file = "safetensors-0.4.3-cp311-none-win32.whl", hash = "sha256:a7cef55929dcbef24af3eb40bedec35d82c3c2fa46338bb13ecf3c5720af8a61"}, + {file = "safetensors-0.4.3-cp311-none-win_amd64.whl", hash = "sha256:840b7ac0eff5633e1d053cc9db12fdf56b566e9403b4950b2dc85393d9b88d67"}, + {file = "safetensors-0.4.3-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:22d21760dc6ebae42e9c058d75aa9907d9f35e38f896e3c69ba0e7b213033856"}, + {file = "safetensors-0.4.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8d22c1a10dff3f64d0d68abb8298a3fd88ccff79f408a3e15b3e7f637ef5c980"}, + {file = "safetensors-0.4.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b1648568667f820b8c48317c7006221dc40aced1869908c187f493838a1362bc"}, + {file = "safetensors-0.4.3-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:446e9fe52c051aeab12aac63d1017e0f68a02a92a027b901c4f8e931b24e5397"}, + {file = "safetensors-0.4.3-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fef5d70683643618244a4f5221053567ca3e77c2531e42ad48ae05fae909f542"}, + {file = "safetensors-0.4.3-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2a1f4430cc0c9d6afa01214a4b3919d0a029637df8e09675ceef1ca3f0dfa0df"}, + {file = "safetensors-0.4.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2d603846a8585b9432a0fd415db1d4c57c0f860eb4aea21f92559ff9902bae4d"}, + {file = "safetensors-0.4.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a844cdb5d7cbc22f5f16c7e2a0271170750763c4db08381b7f696dbd2c78a361"}, + {file = "safetensors-0.4.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:88887f69f7a00cf02b954cdc3034ffb383b2303bc0ab481d4716e2da51ddc10e"}, + {file = "safetensors-0.4.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:ee463219d9ec6c2be1d331ab13a8e0cd50d2f32240a81d498266d77d07b7e71e"}, + {file = "safetensors-0.4.3-cp312-none-win32.whl", hash = "sha256:d0dd4a1db09db2dba0f94d15addc7e7cd3a7b0d393aa4c7518c39ae7374623c3"}, + {file = "safetensors-0.4.3-cp312-none-win_amd64.whl", hash = "sha256:d14d30c25897b2bf19b6fb5ff7e26cc40006ad53fd4a88244fdf26517d852dd7"}, + {file = "safetensors-0.4.3-cp37-cp37m-macosx_10_12_x86_64.whl", hash = "sha256:d1456f814655b224d4bf6e7915c51ce74e389b413be791203092b7ff78c936dd"}, + {file = "safetensors-0.4.3-cp37-cp37m-macosx_11_0_arm64.whl", hash = "sha256:455d538aa1aae4a8b279344a08136d3f16334247907b18a5c3c7fa88ef0d3c46"}, + {file = "safetensors-0.4.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cf476bca34e1340ee3294ef13e2c625833f83d096cfdf69a5342475602004f95"}, + {file = "safetensors-0.4.3-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:02ef3a24face643456020536591fbd3c717c5abaa2737ec428ccbbc86dffa7a4"}, + {file = "safetensors-0.4.3-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:7de32d0d34b6623bb56ca278f90db081f85fb9c5d327e3c18fd23ac64f465768"}, + {file = "safetensors-0.4.3-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2a0deb16a1d3ea90c244ceb42d2c6c276059616be21a19ac7101aa97da448faf"}, + {file = "safetensors-0.4.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c59d51f182c729f47e841510b70b967b0752039f79f1de23bcdd86462a9b09ee"}, + {file = "safetensors-0.4.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1f598b713cc1a4eb31d3b3203557ac308acf21c8f41104cdd74bf640c6e538e3"}, + {file = "safetensors-0.4.3-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:5757e4688f20df083e233b47de43845d1adb7e17b6cf7da5f8444416fc53828d"}, + {file = "safetensors-0.4.3-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:fe746d03ed8d193674a26105e4f0fe6c726f5bb602ffc695b409eaf02f04763d"}, + {file = "safetensors-0.4.3-cp37-none-win32.whl", hash = "sha256:0d5ffc6a80f715c30af253e0e288ad1cd97a3d0086c9c87995e5093ebc075e50"}, + {file = "safetensors-0.4.3-cp37-none-win_amd64.whl", hash = "sha256:a11c374eb63a9c16c5ed146457241182f310902bd2a9c18255781bb832b6748b"}, + {file = "safetensors-0.4.3-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:b1e31be7945f66be23f4ec1682bb47faa3df34cb89fc68527de6554d3c4258a4"}, + {file = "safetensors-0.4.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:03a4447c784917c9bf01d8f2ac5080bc15c41692202cd5f406afba16629e84d6"}, + {file = "safetensors-0.4.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d244bcafeb1bc06d47cfee71727e775bca88a8efda77a13e7306aae3813fa7e4"}, + {file = "safetensors-0.4.3-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:53c4879b9c6bd7cd25d114ee0ef95420e2812e676314300624594940a8d6a91f"}, + {file = "safetensors-0.4.3-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:74707624b81f1b7f2b93f5619d4a9f00934d5948005a03f2c1845ffbfff42212"}, + {file = "safetensors-0.4.3-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0d52c958dc210265157573f81d34adf54e255bc2b59ded6218500c9b15a750eb"}, + {file = "safetensors-0.4.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6f9568f380f513a60139971169c4a358b8731509cc19112369902eddb33faa4d"}, + {file = "safetensors-0.4.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:0d9cd8e1560dfc514b6d7859247dc6a86ad2f83151a62c577428d5102d872721"}, + {file = "safetensors-0.4.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:89f9f17b0dacb913ed87d57afbc8aad85ea42c1085bd5de2f20d83d13e9fc4b2"}, + {file = "safetensors-0.4.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:1139eb436fd201c133d03c81209d39ac57e129f5e74e34bb9ab60f8d9b726270"}, + {file = "safetensors-0.4.3-cp38-none-win32.whl", hash = "sha256:d9c289f140a9ae4853fc2236a2ffc9a9f2d5eae0cb673167e0f1b8c18c0961ac"}, + {file = "safetensors-0.4.3-cp38-none-win_amd64.whl", hash = "sha256:622afd28968ef3e9786562d352659a37de4481a4070f4ebac883f98c5836563e"}, + {file = "safetensors-0.4.3-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:8651c7299cbd8b4161a36cd6a322fa07d39cd23535b144d02f1c1972d0c62f3c"}, + {file = "safetensors-0.4.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:e375d975159ac534c7161269de24ddcd490df2157b55c1a6eeace6cbb56903f0"}, + {file = "safetensors-0.4.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:084fc436e317f83f7071fc6a62ca1c513b2103db325cd09952914b50f51cf78f"}, + {file = "safetensors-0.4.3-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:41a727a7f5e6ad9f1db6951adee21bbdadc632363d79dc434876369a17de6ad6"}, + {file = "safetensors-0.4.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e7dbbde64b6c534548696808a0e01276d28ea5773bc9a2dfb97a88cd3dffe3df"}, + {file = "safetensors-0.4.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:bbae3b4b9d997971431c346edbfe6e41e98424a097860ee872721e176040a893"}, + {file = "safetensors-0.4.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:01e4b22e3284cd866edeabe4f4d896229495da457229408d2e1e4810c5187121"}, + {file = "safetensors-0.4.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:0dd37306546b58d3043eb044c8103a02792cc024b51d1dd16bd3dd1f334cb3ed"}, + {file = "safetensors-0.4.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:d8815b5e1dac85fc534a97fd339e12404db557878c090f90442247e87c8aeaea"}, + {file = "safetensors-0.4.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:e011cc162503c19f4b1fd63dfcddf73739c7a243a17dac09b78e57a00983ab35"}, + {file = "safetensors-0.4.3-cp39-none-win32.whl", hash = "sha256:01feb3089e5932d7e662eda77c3ecc389f97c0883c4a12b5cfdc32b589a811c3"}, + {file = "safetensors-0.4.3-cp39-none-win_amd64.whl", hash = "sha256:3f9cdca09052f585e62328c1c2923c70f46814715c795be65f0b93f57ec98a02"}, + {file = "safetensors-0.4.3-pp310-pypy310_pp73-macosx_10_12_x86_64.whl", hash = "sha256:1b89381517891a7bb7d1405d828b2bf5d75528299f8231e9346b8eba092227f9"}, + {file = "safetensors-0.4.3-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:cd6fff9e56df398abc5866b19a32124815b656613c1c5ec0f9350906fd798aac"}, + {file = "safetensors-0.4.3-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:840caf38d86aa7014fe37ade5d0d84e23dcfbc798b8078015831996ecbc206a3"}, + {file = "safetensors-0.4.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f9650713b2cfa9537a2baf7dd9fee458b24a0aaaa6cafcea8bdd5fb2b8efdc34"}, + {file = "safetensors-0.4.3-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:e4119532cd10dba04b423e0f86aecb96cfa5a602238c0aa012f70c3a40c44b50"}, + {file = "safetensors-0.4.3-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:e066e8861eef6387b7c772344d1fe1f9a72800e04ee9a54239d460c400c72aab"}, + {file = "safetensors-0.4.3-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:90964917f5b0fa0fa07e9a051fbef100250c04d150b7026ccbf87a34a54012e0"}, + {file = "safetensors-0.4.3-pp37-pypy37_pp73-macosx_10_12_x86_64.whl", hash = "sha256:c41e1893d1206aa7054029681778d9a58b3529d4c807002c156d58426c225173"}, + {file = "safetensors-0.4.3-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ae7613a119a71a497d012ccc83775c308b9c1dab454806291427f84397d852fd"}, + {file = "safetensors-0.4.3-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4f9bac020faba7f5dc481e881b14b6425265feabb5bfc552551d21189c0eddc3"}, + {file = "safetensors-0.4.3-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:420a98f593ff9930f5822560d14c395ccbc57342ddff3b463bc0b3d6b1951550"}, + {file = "safetensors-0.4.3-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:f5e6883af9a68c0028f70a4c19d5a6ab6238a379be36ad300a22318316c00cb0"}, + {file = "safetensors-0.4.3-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:cdd0a3b5da66e7f377474599814dbf5cbf135ff059cc73694de129b58a5e8a2c"}, + {file = "safetensors-0.4.3-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:9bfb92f82574d9e58401d79c70c716985dc049b635fef6eecbb024c79b2c46ad"}, + {file = "safetensors-0.4.3-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:3615a96dd2dcc30eb66d82bc76cda2565f4f7bfa89fcb0e31ba3cea8a1a9ecbb"}, + {file = "safetensors-0.4.3-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:868ad1b6fc41209ab6bd12f63923e8baeb1a086814cb2e81a65ed3d497e0cf8f"}, + {file = "safetensors-0.4.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b7ffba80aa49bd09195145a7fd233a7781173b422eeb995096f2b30591639517"}, + {file = "safetensors-0.4.3-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c0acbe31340ab150423347e5b9cc595867d814244ac14218932a5cf1dd38eb39"}, + {file = "safetensors-0.4.3-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:19bbdf95de2cf64f25cd614c5236c8b06eb2cfa47cbf64311f4b5d80224623a3"}, + {file = "safetensors-0.4.3-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:b852e47eb08475c2c1bd8131207b405793bfc20d6f45aff893d3baaad449ed14"}, + {file = "safetensors-0.4.3-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:5d07cbca5b99babb692d76d8151bec46f461f8ad8daafbfd96b2fca40cadae65"}, + {file = "safetensors-0.4.3-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:1ab6527a20586d94291c96e00a668fa03f86189b8a9defa2cdd34a1a01acc7d5"}, + {file = "safetensors-0.4.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:02318f01e332cc23ffb4f6716e05a492c5f18b1d13e343c49265149396284a44"}, + {file = "safetensors-0.4.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ec4b52ce9a396260eb9731eb6aea41a7320de22ed73a1042c2230af0212758ce"}, + {file = "safetensors-0.4.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:018b691383026a2436a22b648873ed11444a364324e7088b99cd2503dd828400"}, + {file = "safetensors-0.4.3-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:309b10dbcab63269ecbf0e2ca10ce59223bb756ca5d431ce9c9eeabd446569da"}, + {file = "safetensors-0.4.3-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:b277482120df46e27a58082df06a15aebda4481e30a1c21eefd0921ae7e03f65"}, + {file = "safetensors-0.4.3.tar.gz", hash = "sha256:2f85fc50c4e07a21e95c24e07460fe6f7e2859d0ce88092838352b798ce711c2"}, +] + +[package.extras] +all = ["safetensors[jax]", "safetensors[numpy]", "safetensors[paddlepaddle]", "safetensors[pinned-tf]", "safetensors[quality]", "safetensors[testing]", "safetensors[torch]"] +dev = ["safetensors[all]"] +jax = ["flax (>=0.6.3)", "jax (>=0.3.25)", "jaxlib (>=0.3.25)", "safetensors[numpy]"] +mlx = ["mlx (>=0.0.9)"] +numpy = ["numpy (>=1.21.6)"] +paddlepaddle = ["paddlepaddle (>=2.4.1)", "safetensors[numpy]"] +pinned-tf = ["safetensors[numpy]", "tensorflow (==2.11.0)"] +quality = ["black (==22.3)", "click (==8.0.4)", "flake8 (>=3.8.3)", "isort (>=5.5.4)"] +tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"] +testing = ["h5py (>=3.7.0)", "huggingface-hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools-rust (>=1.5.2)"] +torch = ["safetensors[numpy]", "torch (>=1.10)"] + +[[package]] +name = "scikit-learn" +version = "1.5.1" +description = "A set of python modules for machine learning and data mining" +optional = false +python-versions = ">=3.9" +files = [ + {file = "scikit_learn-1.5.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:781586c414f8cc58e71da4f3d7af311e0505a683e112f2f62919e3019abd3745"}, + {file = "scikit_learn-1.5.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:f5b213bc29cc30a89a3130393b0e39c847a15d769d6e59539cd86b75d276b1a7"}, + {file = "scikit_learn-1.5.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1ff4ba34c2abff5ec59c803ed1d97d61b036f659a17f55be102679e88f926fac"}, + {file = "scikit_learn-1.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:161808750c267b77b4a9603cf9c93579c7a74ba8486b1336034c2f1579546d21"}, + {file = "scikit_learn-1.5.1-cp310-cp310-win_amd64.whl", hash = "sha256:10e49170691514a94bb2e03787aa921b82dbc507a4ea1f20fd95557862c98dc1"}, + {file = "scikit_learn-1.5.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:154297ee43c0b83af12464adeab378dee2d0a700ccd03979e2b821e7dd7cc1c2"}, + {file = "scikit_learn-1.5.1-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:b5e865e9bd59396220de49cb4a57b17016256637c61b4c5cc81aaf16bc123bbe"}, + {file = "scikit_learn-1.5.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:909144d50f367a513cee6090873ae582dba019cb3fca063b38054fa42704c3a4"}, + {file = "scikit_learn-1.5.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:689b6f74b2c880276e365fe84fe4f1befd6a774f016339c65655eaff12e10cbf"}, + {file = "scikit_learn-1.5.1-cp311-cp311-win_amd64.whl", hash = "sha256:9a07f90846313a7639af6a019d849ff72baadfa4c74c778821ae0fad07b7275b"}, + {file = "scikit_learn-1.5.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:5944ce1faada31c55fb2ba20a5346b88e36811aab504ccafb9f0339e9f780395"}, + {file = "scikit_learn-1.5.1-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:0828673c5b520e879f2af6a9e99eee0eefea69a2188be1ca68a6121b809055c1"}, + {file = "scikit_learn-1.5.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:508907e5f81390e16d754e8815f7497e52139162fd69c4fdbd2dfa5d6cc88915"}, + {file = "scikit_learn-1.5.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:97625f217c5c0c5d0505fa2af28ae424bd37949bb2f16ace3ff5f2f81fb4498b"}, + {file = "scikit_learn-1.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:da3f404e9e284d2b0a157e1b56b6566a34eb2798205cba35a211df3296ab7a74"}, + {file = "scikit_learn-1.5.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:88e0672c7ac21eb149d409c74cc29f1d611d5158175846e7a9c2427bd12b3956"}, + {file = "scikit_learn-1.5.1-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:7b073a27797a283187a4ef4ee149959defc350b46cbf63a84d8514fe16b69855"}, + {file = "scikit_learn-1.5.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b59e3e62d2be870e5c74af4e793293753565c7383ae82943b83383fdcf5cc5c1"}, + {file = "scikit_learn-1.5.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1bd8d3a19d4bd6dc5a7d4f358c8c3a60934dc058f363c34c0ac1e9e12a31421d"}, + {file = "scikit_learn-1.5.1-cp39-cp39-win_amd64.whl", hash = "sha256:5f57428de0c900a98389c4a433d4a3cf89de979b3aa24d1c1d251802aa15e44d"}, + {file = "scikit_learn-1.5.1.tar.gz", hash = "sha256:0ea5d40c0e3951df445721927448755d3fe1d80833b0b7308ebff5d2a45e6414"}, +] + +[package.dependencies] +joblib = ">=1.2.0" +numpy = ">=1.19.5" +scipy = ">=1.6.0" +threadpoolctl = ">=3.1.0" + +[package.extras] +benchmark = ["matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "pandas (>=1.1.5)"] +build = ["cython (>=3.0.10)", "meson-python (>=0.16.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)"] +docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.3.4)", "memory_profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "polars (>=0.20.23)", "pooch (>=1.6.0)", "pydata-sphinx-theme (>=0.15.3)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)", "sphinx (>=7.3.7)", "sphinx-copybutton (>=0.5.2)", "sphinx-design (>=0.5.0)", "sphinx-gallery (>=0.16.0)", "sphinx-prompt (>=1.4.0)", "sphinx-remove-toctrees (>=1.0.0.post1)", "sphinxcontrib-sass (>=0.3.4)", "sphinxext-opengraph (>=0.9.1)"] +examples = ["matplotlib (>=3.3.4)", "pandas (>=1.1.5)", "plotly (>=5.14.0)", "pooch (>=1.6.0)", "scikit-image (>=0.17.2)", "seaborn (>=0.9.0)"] +install = ["joblib (>=1.2.0)", "numpy (>=1.19.5)", "scipy (>=1.6.0)", "threadpoolctl (>=3.1.0)"] +maintenance = ["conda-lock (==2.5.6)"] +tests = ["black (>=24.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.9)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "polars (>=0.20.23)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pyarrow (>=12.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.2.1)", "scikit-image (>=0.17.2)"] + +[[package]] +name = "scipy" +version = "1.12.0" +description = "Fundamental algorithms for scientific computing in Python" +optional = false +python-versions = ">=3.9" +files = [ + {file = "scipy-1.12.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:78e4402e140879387187f7f25d91cc592b3501a2e51dfb320f48dfb73565f10b"}, + {file = "scipy-1.12.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:f5f00ebaf8de24d14b8449981a2842d404152774c1a1d880c901bf454cb8e2a1"}, + {file = "scipy-1.12.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e53958531a7c695ff66c2e7bb7b79560ffdc562e2051644c5576c39ff8efb563"}, + {file = "scipy-1.12.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5e32847e08da8d895ce09d108a494d9eb78974cf6de23063f93306a3e419960c"}, + {file = "scipy-1.12.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:4c1020cad92772bf44b8e4cdabc1df5d87376cb219742549ef69fc9fd86282dd"}, + {file = "scipy-1.12.0-cp310-cp310-win_amd64.whl", hash = "sha256:75ea2a144096b5e39402e2ff53a36fecfd3b960d786b7efd3c180e29c39e53f2"}, + {file = "scipy-1.12.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:408c68423f9de16cb9e602528be4ce0d6312b05001f3de61fe9ec8b1263cad08"}, + {file = "scipy-1.12.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:5adfad5dbf0163397beb4aca679187d24aec085343755fcdbdeb32b3679f254c"}, + {file = "scipy-1.12.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c3003652496f6e7c387b1cf63f4bb720951cfa18907e998ea551e6de51a04467"}, + {file = "scipy-1.12.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8b8066bce124ee5531d12a74b617d9ac0ea59245246410e19bca549656d9a40a"}, + {file = "scipy-1.12.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:8bee4993817e204d761dba10dbab0774ba5a8612e57e81319ea04d84945375ba"}, + {file = "scipy-1.12.0-cp311-cp311-win_amd64.whl", hash = "sha256:a24024d45ce9a675c1fb8494e8e5244efea1c7a09c60beb1eeb80373d0fecc70"}, + {file = "scipy-1.12.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:e7e76cc48638228212c747ada851ef355c2bb5e7f939e10952bc504c11f4e372"}, + {file = "scipy-1.12.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:f7ce148dffcd64ade37b2df9315541f9adad6efcaa86866ee7dd5db0c8f041c3"}, + {file = "scipy-1.12.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9c39f92041f490422924dfdb782527a4abddf4707616e07b021de33467f917bc"}, + {file = "scipy-1.12.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a7ebda398f86e56178c2fa94cad15bf457a218a54a35c2a7b4490b9f9cb2676c"}, + {file = "scipy-1.12.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:95e5c750d55cf518c398a8240571b0e0782c2d5a703250872f36eaf737751338"}, + {file = "scipy-1.12.0-cp312-cp312-win_amd64.whl", hash = "sha256:e646d8571804a304e1da01040d21577685ce8e2db08ac58e543eaca063453e1c"}, + {file = "scipy-1.12.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:913d6e7956c3a671de3b05ccb66b11bc293f56bfdef040583a7221d9e22a2e35"}, + {file = "scipy-1.12.0-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:bba1b0c7256ad75401c73e4b3cf09d1f176e9bd4248f0d3112170fb2ec4db067"}, + {file = "scipy-1.12.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:730badef9b827b368f351eacae2e82da414e13cf8bd5051b4bdfd720271a5371"}, + {file = "scipy-1.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6546dc2c11a9df6926afcbdd8a3edec28566e4e785b915e849348c6dd9f3f490"}, + {file = "scipy-1.12.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:196ebad3a4882081f62a5bf4aeb7326aa34b110e533aab23e4374fcccb0890dc"}, + {file = "scipy-1.12.0-cp39-cp39-win_amd64.whl", hash = "sha256:b360f1b6b2f742781299514e99ff560d1fe9bd1bff2712894b52abe528d1fd1e"}, + {file = "scipy-1.12.0.tar.gz", hash = "sha256:4bf5abab8a36d20193c698b0f1fc282c1d083c94723902c447e5d2f1780936a3"}, +] + +[package.dependencies] +numpy = ">=1.22.4,<1.29.0" + +[package.extras] +dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"] +doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"] +test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] + +[[package]] +name = "send2trash" +version = "1.8.3" +description = "Send file to trash natively under Mac OS X, Windows and Linux" +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" +files = [ + {file = "Send2Trash-1.8.3-py3-none-any.whl", hash = "sha256:0c31227e0bd08961c7665474a3d1ef7193929fedda4233843689baa056be46c9"}, + {file = "Send2Trash-1.8.3.tar.gz", hash = "sha256:b18e7a3966d99871aefeb00cfbcfdced55ce4871194810fc71f4aa484b953abf"}, +] + +[package.extras] +nativelib = ["pyobjc-framework-Cocoa", "pywin32"] +objc = ["pyobjc-framework-Cocoa"] +win32 = ["pywin32"] + +[[package]] +name = "sentry-sdk" +version = "2.10.0" +description = "Python client for Sentry (https://sentry.io)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "sentry_sdk-2.10.0-py2.py3-none-any.whl", hash = "sha256:87b3d413c87d8e7f816cc9334bff255a83d8b577db2b22042651c30c19c09190"}, + {file = "sentry_sdk-2.10.0.tar.gz", hash = "sha256:545fcc6e36c335faa6d6cda84669b6e17025f31efbf3b2211ec14efe008b75d1"}, +] + +[package.dependencies] +certifi = "*" +urllib3 = ">=1.26.11" + +[package.extras] +aiohttp = ["aiohttp (>=3.5)"] +anthropic = ["anthropic (>=0.16)"] +arq = ["arq (>=0.23)"] +asyncpg = ["asyncpg (>=0.23)"] +beam = ["apache-beam (>=2.12)"] +bottle = ["bottle (>=0.12.13)"] +celery = ["celery (>=3)"] +celery-redbeat = ["celery-redbeat (>=2)"] +chalice = ["chalice (>=1.16.0)"] +clickhouse-driver = ["clickhouse-driver (>=0.2.0)"] +django = ["django (>=1.8)"] +falcon = ["falcon (>=1.4)"] +fastapi = ["fastapi (>=0.79.0)"] +flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"] +grpcio = ["grpcio (>=1.21.1)", "protobuf (>=3.8.0)"] +httpx = ["httpx (>=0.16.0)"] +huey = ["huey (>=2)"] +huggingface-hub = ["huggingface-hub (>=0.22)"] +langchain = ["langchain (>=0.0.210)"] +loguru = ["loguru (>=0.5)"] +openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"] +opentelemetry = ["opentelemetry-distro (>=0.35b0)"] +opentelemetry-experimental = ["opentelemetry-instrumentation-aio-pika (==0.46b0)", "opentelemetry-instrumentation-aiohttp-client (==0.46b0)", "opentelemetry-instrumentation-aiopg (==0.46b0)", "opentelemetry-instrumentation-asgi (==0.46b0)", "opentelemetry-instrumentation-asyncio (==0.46b0)", "opentelemetry-instrumentation-asyncpg (==0.46b0)", "opentelemetry-instrumentation-aws-lambda (==0.46b0)", "opentelemetry-instrumentation-boto (==0.46b0)", "opentelemetry-instrumentation-boto3sqs (==0.46b0)", "opentelemetry-instrumentation-botocore (==0.46b0)", "opentelemetry-instrumentation-cassandra (==0.46b0)", "opentelemetry-instrumentation-celery (==0.46b0)", "opentelemetry-instrumentation-confluent-kafka (==0.46b0)", "opentelemetry-instrumentation-dbapi (==0.46b0)", "opentelemetry-instrumentation-django (==0.46b0)", "opentelemetry-instrumentation-elasticsearch (==0.46b0)", "opentelemetry-instrumentation-falcon (==0.46b0)", "opentelemetry-instrumentation-fastapi (==0.46b0)", "opentelemetry-instrumentation-flask (==0.46b0)", "opentelemetry-instrumentation-grpc (==0.46b0)", "opentelemetry-instrumentation-httpx (==0.46b0)", "opentelemetry-instrumentation-jinja2 (==0.46b0)", "opentelemetry-instrumentation-kafka-python (==0.46b0)", "opentelemetry-instrumentation-logging (==0.46b0)", "opentelemetry-instrumentation-mysql (==0.46b0)", "opentelemetry-instrumentation-mysqlclient (==0.46b0)", "opentelemetry-instrumentation-pika (==0.46b0)", "opentelemetry-instrumentation-psycopg (==0.46b0)", "opentelemetry-instrumentation-psycopg2 (==0.46b0)", "opentelemetry-instrumentation-pymemcache (==0.46b0)", "opentelemetry-instrumentation-pymongo (==0.46b0)", "opentelemetry-instrumentation-pymysql (==0.46b0)", "opentelemetry-instrumentation-pyramid (==0.46b0)", "opentelemetry-instrumentation-redis (==0.46b0)", "opentelemetry-instrumentation-remoulade (==0.46b0)", "opentelemetry-instrumentation-requests (==0.46b0)", "opentelemetry-instrumentation-sklearn (==0.46b0)", "opentelemetry-instrumentation-sqlalchemy (==0.46b0)", "opentelemetry-instrumentation-sqlite3 (==0.46b0)", "opentelemetry-instrumentation-starlette (==0.46b0)", "opentelemetry-instrumentation-system-metrics (==0.46b0)", "opentelemetry-instrumentation-threading (==0.46b0)", "opentelemetry-instrumentation-tornado (==0.46b0)", "opentelemetry-instrumentation-tortoiseorm (==0.46b0)", "opentelemetry-instrumentation-urllib (==0.46b0)", "opentelemetry-instrumentation-urllib3 (==0.46b0)", "opentelemetry-instrumentation-wsgi (==0.46b0)"] +pure-eval = ["asttokens", "executing", "pure-eval"] +pymongo = ["pymongo (>=3.1)"] +pyspark = ["pyspark (>=2.4.4)"] +quart = ["blinker (>=1.1)", "quart (>=0.16.1)"] +rq = ["rq (>=0.6)"] +sanic = ["sanic (>=0.8)"] +sqlalchemy = ["sqlalchemy (>=1.2)"] +starlette = ["starlette (>=0.19.1)"] +starlite = ["starlite (>=1.48)"] +tornado = ["tornado (>=6)"] + +[[package]] +name = "setproctitle" +version = "1.3.3" +description = "A Python module to customize the process title" +optional = false +python-versions = ">=3.7" +files = [ + {file = "setproctitle-1.3.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:897a73208da48db41e687225f355ce993167079eda1260ba5e13c4e53be7f754"}, + {file = "setproctitle-1.3.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8c331e91a14ba4076f88c29c777ad6b58639530ed5b24b5564b5ed2fd7a95452"}, + {file = "setproctitle-1.3.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bbbd6c7de0771c84b4aa30e70b409565eb1fc13627a723ca6be774ed6b9d9fa3"}, + {file = "setproctitle-1.3.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c05ac48ef16ee013b8a326c63e4610e2430dbec037ec5c5b58fcced550382b74"}, + {file = "setproctitle-1.3.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1342f4fdb37f89d3e3c1c0a59d6ddbedbde838fff5c51178a7982993d238fe4f"}, + {file = "setproctitle-1.3.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc74e84fdfa96821580fb5e9c0b0777c1c4779434ce16d3d62a9c4d8c710df39"}, + {file = "setproctitle-1.3.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:9617b676b95adb412bb69645d5b077d664b6882bb0d37bfdafbbb1b999568d85"}, + {file = "setproctitle-1.3.3-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:6a249415f5bb88b5e9e8c4db47f609e0bf0e20a75e8d744ea787f3092ba1f2d0"}, + {file = "setproctitle-1.3.3-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:38da436a0aaace9add67b999eb6abe4b84397edf4a78ec28f264e5b4c9d53cd5"}, + {file = "setproctitle-1.3.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:da0d57edd4c95bf221b2ebbaa061e65b1788f1544977288bdf95831b6e44e44d"}, + {file = "setproctitle-1.3.3-cp310-cp310-win32.whl", hash = "sha256:a1fcac43918b836ace25f69b1dca8c9395253ad8152b625064415b1d2f9be4fb"}, + {file = "setproctitle-1.3.3-cp310-cp310-win_amd64.whl", hash = "sha256:200620c3b15388d7f3f97e0ae26599c0c378fdf07ae9ac5a13616e933cbd2086"}, + {file = "setproctitle-1.3.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:334f7ed39895d692f753a443102dd5fed180c571eb6a48b2a5b7f5b3564908c8"}, + {file = "setproctitle-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:950f6476d56ff7817a8fed4ab207727fc5260af83481b2a4b125f32844df513a"}, + {file = "setproctitle-1.3.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:195c961f54a09eb2acabbfc90c413955cf16c6e2f8caa2adbf2237d1019c7dd8"}, + {file = "setproctitle-1.3.3-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f05e66746bf9fe6a3397ec246fe481096664a9c97eb3fea6004735a4daf867fd"}, + {file = "setproctitle-1.3.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b5901a31012a40ec913265b64e48c2a4059278d9f4e6be628441482dd13fb8b5"}, + {file = "setproctitle-1.3.3-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:64286f8a995f2cd934082b398fc63fca7d5ffe31f0e27e75b3ca6b4efda4e353"}, + {file = "setproctitle-1.3.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:184239903bbc6b813b1a8fc86394dc6ca7d20e2ebe6f69f716bec301e4b0199d"}, + {file = "setproctitle-1.3.3-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:664698ae0013f986118064b6676d7dcd28fefd0d7d5a5ae9497cbc10cba48fa5"}, + {file = "setproctitle-1.3.3-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:e5119a211c2e98ff18b9908ba62a3bd0e3fabb02a29277a7232a6fb4b2560aa0"}, + {file = "setproctitle-1.3.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:417de6b2e214e837827067048f61841f5d7fc27926f2e43954567094051aff18"}, + {file = "setproctitle-1.3.3-cp311-cp311-win32.whl", hash = "sha256:6a143b31d758296dc2f440175f6c8e0b5301ced3b0f477b84ca43cdcf7f2f476"}, + {file = "setproctitle-1.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:a680d62c399fa4b44899094027ec9a1bdaf6f31c650e44183b50d4c4d0ccc085"}, + {file = "setproctitle-1.3.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:d4460795a8a7a391e3567b902ec5bdf6c60a47d791c3b1d27080fc203d11c9dc"}, + {file = "setproctitle-1.3.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:bdfd7254745bb737ca1384dee57e6523651892f0ea2a7344490e9caefcc35e64"}, + {file = "setproctitle-1.3.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:477d3da48e216d7fc04bddab67b0dcde633e19f484a146fd2a34bb0e9dbb4a1e"}, + {file = "setproctitle-1.3.3-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ab2900d111e93aff5df9fddc64cf51ca4ef2c9f98702ce26524f1acc5a786ae7"}, + {file = "setproctitle-1.3.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:088b9efc62d5aa5d6edf6cba1cf0c81f4488b5ce1c0342a8b67ae39d64001120"}, + {file = "setproctitle-1.3.3-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a6d50252377db62d6a0bb82cc898089916457f2db2041e1d03ce7fadd4a07381"}, + {file = "setproctitle-1.3.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:87e668f9561fd3a457ba189edfc9e37709261287b52293c115ae3487a24b92f6"}, + {file = "setproctitle-1.3.3-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:287490eb90e7a0ddd22e74c89a92cc922389daa95babc833c08cf80c84c4df0a"}, + {file = "setproctitle-1.3.3-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:4fe1c49486109f72d502f8be569972e27f385fe632bd8895f4730df3c87d5ac8"}, + {file = "setproctitle-1.3.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:4a6ba2494a6449b1f477bd3e67935c2b7b0274f2f6dcd0f7c6aceae10c6c6ba3"}, + {file = "setproctitle-1.3.3-cp312-cp312-win32.whl", hash = "sha256:2df2b67e4b1d7498632e18c56722851ba4db5d6a0c91aaf0fd395111e51cdcf4"}, + {file = "setproctitle-1.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:f38d48abc121263f3b62943f84cbaede05749047e428409c2c199664feb6abc7"}, + {file = "setproctitle-1.3.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:816330675e3504ae4d9a2185c46b573105d2310c20b19ea2b4596a9460a4f674"}, + {file = "setproctitle-1.3.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:68f960bc22d8d8e4ac886d1e2e21ccbd283adcf3c43136161c1ba0fa509088e0"}, + {file = "setproctitle-1.3.3-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:00e6e7adff74796ef12753ff399491b8827f84f6c77659d71bd0b35870a17d8f"}, + {file = "setproctitle-1.3.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:53bc0d2358507596c22b02db079618451f3bd720755d88e3cccd840bafb4c41c"}, + {file = "setproctitle-1.3.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad6d20f9541f5f6ac63df553b6d7a04f313947f550eab6a61aa758b45f0d5657"}, + {file = "setproctitle-1.3.3-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:c1c84beab776b0becaa368254801e57692ed749d935469ac10e2b9b825dbdd8e"}, + {file = "setproctitle-1.3.3-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:507e8dc2891021350eaea40a44ddd887c9f006e6b599af8d64a505c0f718f170"}, + {file = "setproctitle-1.3.3-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:b1067647ac7aba0b44b591936118a22847bda3c507b0a42d74272256a7a798e9"}, + {file = "setproctitle-1.3.3-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:2e71f6365744bf53714e8bd2522b3c9c1d83f52ffa6324bd7cbb4da707312cd8"}, + {file = "setproctitle-1.3.3-cp37-cp37m-win32.whl", hash = "sha256:7f1d36a1e15a46e8ede4e953abb104fdbc0845a266ec0e99cc0492a4364f8c44"}, + {file = "setproctitle-1.3.3-cp37-cp37m-win_amd64.whl", hash = "sha256:c9a402881ec269d0cc9c354b149fc29f9ec1a1939a777f1c858cdb09c7a261df"}, + {file = "setproctitle-1.3.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:ff814dea1e5c492a4980e3e7d094286077054e7ea116cbeda138819db194b2cd"}, + {file = "setproctitle-1.3.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:accb66d7b3ccb00d5cd11d8c6e07055a4568a24c95cf86109894dcc0c134cc89"}, + {file = "setproctitle-1.3.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:554eae5a5b28f02705b83a230e9d163d645c9a08914c0ad921df363a07cf39b1"}, + {file = "setproctitle-1.3.3-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a911b26264dbe9e8066c7531c0591cfab27b464459c74385b276fe487ca91c12"}, + {file = "setproctitle-1.3.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2982efe7640c4835f7355fdb4da313ad37fb3b40f5c69069912f8048f77b28c8"}, + {file = "setproctitle-1.3.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:df3f4274b80709d8bcab2f9a862973d453b308b97a0b423a501bcd93582852e3"}, + {file = "setproctitle-1.3.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:af2c67ae4c795d1674a8d3ac1988676fa306bcfa1e23fddb5e0bd5f5635309ca"}, + {file = "setproctitle-1.3.3-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:af4061f67fd7ec01624c5e3c21f6b7af2ef0e6bab7fbb43f209e6506c9ce0092"}, + {file = "setproctitle-1.3.3-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:37a62cbe16d4c6294e84670b59cf7adcc73faafe6af07f8cb9adaf1f0e775b19"}, + {file = "setproctitle-1.3.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:a83ca086fbb017f0d87f240a8f9bbcf0809f3b754ee01cec928fff926542c450"}, + {file = "setproctitle-1.3.3-cp38-cp38-win32.whl", hash = "sha256:059f4ce86f8cc92e5860abfc43a1dceb21137b26a02373618d88f6b4b86ba9b2"}, + {file = "setproctitle-1.3.3-cp38-cp38-win_amd64.whl", hash = "sha256:ab92e51cd4a218208efee4c6d37db7368fdf182f6e7ff148fb295ecddf264287"}, + {file = "setproctitle-1.3.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:c7951820b77abe03d88b114b998867c0f99da03859e5ab2623d94690848d3e45"}, + {file = "setproctitle-1.3.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5bc94cf128676e8fac6503b37763adb378e2b6be1249d207630f83fc325d9b11"}, + {file = "setproctitle-1.3.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1f5d9027eeda64d353cf21a3ceb74bb1760bd534526c9214e19f052424b37e42"}, + {file = "setproctitle-1.3.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2e4a8104db15d3462e29d9946f26bed817a5b1d7a47eabca2d9dc2b995991503"}, + {file = "setproctitle-1.3.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c32c41ace41f344d317399efff4cffb133e709cec2ef09c99e7a13e9f3b9483c"}, + {file = "setproctitle-1.3.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cbf16381c7bf7f963b58fb4daaa65684e10966ee14d26f5cc90f07049bfd8c1e"}, + {file = "setproctitle-1.3.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:e18b7bd0898398cc97ce2dfc83bb192a13a087ef6b2d5a8a36460311cb09e775"}, + {file = "setproctitle-1.3.3-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:69d565d20efe527bd8a9b92e7f299ae5e73b6c0470f3719bd66f3cd821e0d5bd"}, + {file = "setproctitle-1.3.3-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:ddedd300cd690a3b06e7eac90ed4452348b1348635777ce23d460d913b5b63c3"}, + {file = "setproctitle-1.3.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:415bfcfd01d1fbf5cbd75004599ef167a533395955305f42220a585f64036081"}, + {file = "setproctitle-1.3.3-cp39-cp39-win32.whl", hash = "sha256:21112fcd2195d48f25760f0eafa7a76510871bbb3b750219310cf88b04456ae3"}, + {file = "setproctitle-1.3.3-cp39-cp39-win_amd64.whl", hash = "sha256:5a740f05d0968a5a17da3d676ce6afefebeeeb5ce137510901bf6306ba8ee002"}, + {file = "setproctitle-1.3.3-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:6b9e62ddb3db4b5205c0321dd69a406d8af9ee1693529d144e86bd43bcb4b6c0"}, + {file = "setproctitle-1.3.3-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9e3b99b338598de0bd6b2643bf8c343cf5ff70db3627af3ca427a5e1a1a90dd9"}, + {file = "setproctitle-1.3.3-pp310-pypy310_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:38ae9a02766dad331deb06855fb7a6ca15daea333b3967e214de12cfae8f0ef5"}, + {file = "setproctitle-1.3.3-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:200ede6fd11233085ba9b764eb055a2a191fb4ffb950c68675ac53c874c22e20"}, + {file = "setproctitle-1.3.3-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:0d3a953c50776751e80fe755a380a64cb14d61e8762bd43041ab3f8cc436092f"}, + {file = "setproctitle-1.3.3-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e5e08e232b78ba3ac6bc0d23ce9e2bee8fad2be391b7e2da834fc9a45129eb87"}, + {file = "setproctitle-1.3.3-pp37-pypy37_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f1da82c3e11284da4fcbf54957dafbf0655d2389cd3d54e4eaba636faf6d117a"}, + {file = "setproctitle-1.3.3-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:aeaa71fb9568ebe9b911ddb490c644fbd2006e8c940f21cb9a1e9425bd709574"}, + {file = "setproctitle-1.3.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:59335d000c6250c35989394661eb6287187854e94ac79ea22315469ee4f4c244"}, + {file = "setproctitle-1.3.3-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:c3ba57029c9c50ecaf0c92bb127224cc2ea9fda057b5d99d3f348c9ec2855ad3"}, + {file = "setproctitle-1.3.3-pp38-pypy38_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d876d355c53d975c2ef9c4f2487c8f83dad6aeaaee1b6571453cb0ee992f55f6"}, + {file = "setproctitle-1.3.3-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:224602f0939e6fb9d5dd881be1229d485f3257b540f8a900d4271a2c2aa4e5f4"}, + {file = "setproctitle-1.3.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:d7f27e0268af2d7503386e0e6be87fb9b6657afd96f5726b733837121146750d"}, + {file = "setproctitle-1.3.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f5e7266498cd31a4572378c61920af9f6b4676a73c299fce8ba93afd694f8ae7"}, + {file = "setproctitle-1.3.3-pp39-pypy39_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:33c5609ad51cd99d388e55651b19148ea99727516132fb44680e1f28dd0d1de9"}, + {file = "setproctitle-1.3.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:eae8988e78192fd1a3245a6f4f382390b61bce6cfcc93f3809726e4c885fa68d"}, + {file = "setproctitle-1.3.3.tar.gz", hash = "sha256:c913e151e7ea01567837ff037a23ca8740192880198b7fbb90b16d181607caae"}, +] + +[package.extras] +test = ["pytest"] + +[[package]] +name = "setuptools" +version = "71.1.0" +description = "Easily download, build, install, upgrade, and uninstall Python packages" +optional = false +python-versions = ">=3.8" +files = [ + {file = "setuptools-71.1.0-py3-none-any.whl", hash = "sha256:33874fdc59b3188304b2e7c80d9029097ea31627180896fb549c578ceb8a0855"}, + {file = "setuptools-71.1.0.tar.gz", hash = "sha256:032d42ee9fb536e33087fb66cac5f840eb9391ed05637b3f2a76a7c8fb477936"}, +] + +[package.extras] +core = ["importlib-metadata (>=6)", "importlib-resources (>=5.10.2)", "jaraco.text (>=3.7)", "more-itertools (>=8.8)", "ordered-set (>=3.1.1)", "packaging (>=24)", "platformdirs (>=2.6.2)", "tomli (>=2.0.1)", "wheel (>=0.43.0)"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"] +test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.11.*)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (<0.4)", "pytest-ruff (>=0.2.1)", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] + +[[package]] +name = "simpervisor" +version = "1.0.0" +description = "Simple async process supervisor" +optional = false +python-versions = ">=3.8" +files = [ + {file = "simpervisor-1.0.0-py3-none-any.whl", hash = "sha256:3e313318264559beea3f475ead202bc1cd58a2f1288363abb5657d306c5b8388"}, + {file = "simpervisor-1.0.0.tar.gz", hash = "sha256:7eb87ca86d5e276976f5bb0290975a05d452c6a7b7f58062daea7d8369c823c1"}, +] + +[package.extras] +test = ["aiohttp", "psutil", "pytest", "pytest-asyncio", "pytest-cov"] + +[[package]] +name = "six" +version = "1.16.0" +description = "Python 2 and 3 compatibility utilities" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" +files = [ + {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"}, + {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"}, +] + +[[package]] +name = "smart-open" +version = "7.0.4" +description = "Utils for streaming large files (S3, HDFS, GCS, Azure Blob Storage, gzip, bz2...)" +optional = false +python-versions = "<4.0,>=3.7" +files = [ + {file = "smart_open-7.0.4-py3-none-any.whl", hash = "sha256:4e98489932b3372595cddc075e6033194775165702887216b65eba760dfd8d47"}, + {file = "smart_open-7.0.4.tar.gz", hash = "sha256:62b65852bdd1d1d516839fcb1f6bc50cd0f16e05b4ec44b52f43d38bcb838524"}, +] + +[package.dependencies] +wrapt = "*" + +[package.extras] +all = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "paramiko", "requests", "zstandard"] +azure = ["azure-common", "azure-core", "azure-storage-blob"] +gcs = ["google-cloud-storage (>=2.6.0)"] +http = ["requests"] +s3 = ["boto3"] +ssh = ["paramiko"] +test = ["azure-common", "azure-core", "azure-storage-blob", "boto3", "google-cloud-storage (>=2.6.0)", "moto[server]", "paramiko", "pytest", "pytest-rerunfailures", "requests", "responses", "zstandard"] +webhdfs = ["requests"] +zst = ["zstandard"] + +[[package]] +name = "smmap" +version = "5.0.1" +description = "A pure Python implementation of a sliding window memory map manager" +optional = false +python-versions = ">=3.7" +files = [ + {file = "smmap-5.0.1-py3-none-any.whl", hash = "sha256:e6d8668fa5f93e706934a62d7b4db19c8d9eb8cf2adbb75ef1b675aa332b69da"}, + {file = "smmap-5.0.1.tar.gz", hash = "sha256:dceeb6c0028fdb6734471eb07c0cd2aae706ccaecab45965ee83f11c8d3b1f62"}, +] + +[[package]] +name = "sniffio" +version = "1.3.1" +description = "Sniff out which async library your code is running under" +optional = false +python-versions = ">=3.7" +files = [ + {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"}, + {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"}, +] + +[[package]] +name = "soupsieve" +version = "2.5" +description = "A modern CSS selector implementation for Beautiful Soup." +optional = false +python-versions = ">=3.8" +files = [ + {file = "soupsieve-2.5-py3-none-any.whl", hash = "sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7"}, + {file = "soupsieve-2.5.tar.gz", hash = "sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690"}, +] + +[[package]] +name = "stack-data" +version = "0.6.3" +description = "Extract data from python stack frames and tracebacks for informative displays" +optional = false +python-versions = "*" +files = [ + {file = "stack_data-0.6.3-py3-none-any.whl", hash = "sha256:d5558e0c25a4cb0853cddad3d77da9891a08cb85dd9f9f91b9f8cd66e511e695"}, + {file = "stack_data-0.6.3.tar.gz", hash = "sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9"}, +] + +[package.dependencies] +asttokens = ">=2.1.0" +executing = ">=1.2.0" +pure-eval = "*" + +[package.extras] +tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"] + +[[package]] +name = "sympy" +version = "1.13.1" +description = "Computer algebra system (CAS) in Python" +optional = false +python-versions = ">=3.8" +files = [ + {file = "sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8"}, + {file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"}, +] + +[package.dependencies] +mpmath = ">=1.1.0,<1.4" + +[package.extras] +dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"] + +[[package]] +name = "tbb" +version = "2021.13.0" +description = "IntelĀ® oneAPI Threading Building Blocks (oneTBB)" +optional = false +python-versions = "*" +files = [ + {file = "tbb-2021.13.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:a2567725329639519d46d92a2634cf61e76601dac2f777a05686fea546c4fe4f"}, + {file = "tbb-2021.13.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:aaf667e92849adb012b8874d6393282afc318aca4407fc62f912ee30a22da46a"}, + {file = "tbb-2021.13.0-py3-none-win32.whl", hash = "sha256:6669d26703e9943f6164c6407bd4a237a45007e79b8d3832fe6999576eaaa9ef"}, + {file = "tbb-2021.13.0-py3-none-win_amd64.whl", hash = "sha256:3528a53e4bbe64b07a6112b4c5a00ff3c61924ee46c9c68e004a1ac7ad1f09c3"}, +] + +[[package]] +name = "tensorboardx" +version = "2.6.2.2" +description = "TensorBoardX lets you watch Tensors Flow without Tensorflow" +optional = false +python-versions = "*" +files = [ + {file = "tensorboardX-2.6.2.2-py2.py3-none-any.whl", hash = "sha256:160025acbf759ede23fd3526ae9d9bfbfd8b68eb16c38a010ebe326dc6395db8"}, + {file = "tensorboardX-2.6.2.2.tar.gz", hash = "sha256:c6476d7cd0d529b0b72f4acadb1269f9ed8b22f441e87a84f2a3b940bb87b666"}, +] + +[package.dependencies] +numpy = "*" +packaging = "*" +protobuf = ">=3.20" + +[[package]] +name = "terminado" +version = "0.18.1" +description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library." +optional = false +python-versions = ">=3.8" +files = [ + {file = "terminado-0.18.1-py3-none-any.whl", hash = "sha256:a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0"}, + {file = "terminado-0.18.1.tar.gz", hash = "sha256:de09f2c4b85de4765f7714688fff57d3e75bad1f909b589fde880460c753fd2e"}, +] + +[package.dependencies] +ptyprocess = {version = "*", markers = "os_name != \"nt\""} +pywinpty = {version = ">=1.1.0", markers = "os_name == \"nt\""} +tornado = ">=6.1.0" + +[package.extras] +docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"] +test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"] +typing = ["mypy (>=1.6,<2.0)", "traitlets (>=5.11.1)"] + +[[package]] +name = "threadpoolctl" +version = "3.5.0" +description = "threadpoolctl" +optional = false +python-versions = ">=3.8" +files = [ + {file = "threadpoolctl-3.5.0-py3-none-any.whl", hash = "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467"}, + {file = "threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107"}, +] + +[[package]] +name = "timm" +version = "0.9.16" +description = "PyTorch Image Models" +optional = false +python-versions = ">=3.8" +files = [ + {file = "timm-0.9.16-py3-none-any.whl", hash = "sha256:bf5704014476ab011589d3c14172ee4c901fd18f9110a928019cac5be2945914"}, + {file = "timm-0.9.16.tar.gz", hash = "sha256:891e54f375d55adf31a71ab0c117761f0e472f9f3971858ecdd1e7376b7071e6"}, +] + +[package.dependencies] +huggingface_hub = "*" +pyyaml = "*" +safetensors = "*" +torch = "*" +torchvision = "*" + +[[package]] +name = "tinycss2" +version = "1.3.0" +description = "A tiny CSS parser" +optional = false +python-versions = ">=3.8" +files = [ + {file = "tinycss2-1.3.0-py3-none-any.whl", hash = "sha256:54a8dbdffb334d536851be0226030e9505965bb2f30f21a4a82c55fb2a80fae7"}, + {file = "tinycss2-1.3.0.tar.gz", hash = "sha256:152f9acabd296a8375fbca5b84c961ff95971fcfc32e79550c8df8e29118c54d"}, +] + +[package.dependencies] +webencodings = ">=0.4" + +[package.extras] +doc = ["sphinx", "sphinx_rtd_theme"] +test = ["pytest", "ruff"] + +[[package]] +name = "torch" +version = "2.3.1" +description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration" +optional = false +python-versions = ">=3.8.0" +files = [ + {file = "torch-2.3.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:605a25b23944be5ab7c3467e843580e1d888b8066e5aaf17ff7bf9cc30001cc3"}, + {file = "torch-2.3.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:f2357eb0965583a0954d6f9ad005bba0091f956aef879822274b1bcdb11bd308"}, + {file = "torch-2.3.1-cp310-cp310-win_amd64.whl", hash = "sha256:32b05fe0d1ada7f69c9f86c14ff69b0ef1957a5a54199bacba63d22d8fab720b"}, + {file = "torch-2.3.1-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:7c09a94362778428484bcf995f6004b04952106aee0ef45ff0b4bab484f5498d"}, + {file = "torch-2.3.1-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:b2ec81b61bb094ea4a9dee1cd3f7b76a44555375719ad29f05c0ca8ef596ad39"}, + {file = "torch-2.3.1-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:490cc3d917d1fe0bd027057dfe9941dc1d6d8e3cae76140f5dd9a7e5bc7130ab"}, + {file = "torch-2.3.1-cp311-cp311-win_amd64.whl", hash = "sha256:5802530783bd465fe66c2df99123c9a54be06da118fbd785a25ab0a88123758a"}, + {file = "torch-2.3.1-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:a7dd4ed388ad1f3d502bf09453d5fe596c7b121de7e0cfaca1e2017782e9bbac"}, + {file = "torch-2.3.1-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:a486c0b1976a118805fc7c9641d02df7afbb0c21e6b555d3bb985c9f9601b61a"}, + {file = "torch-2.3.1-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:224259821fe3e4c6f7edf1528e4fe4ac779c77addaa74215eb0b63a5c474d66c"}, + {file = "torch-2.3.1-cp312-cp312-win_amd64.whl", hash = "sha256:e5fdccbf6f1334b2203a61a0e03821d5845f1421defe311dabeae2fc8fbeac2d"}, + {file = "torch-2.3.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:3c333dc2ebc189561514eda06e81df22bf8fb64e2384746b2cb9f04f96d1d4c8"}, + {file = "torch-2.3.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:07e9ba746832b8d069cacb45f312cadd8ad02b81ea527ec9766c0e7404bb3feb"}, + {file = "torch-2.3.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:462d1c07dbf6bb5d9d2f3316fee73a24f3d12cd8dacf681ad46ef6418f7f6626"}, + {file = "torch-2.3.1-cp38-cp38-win_amd64.whl", hash = "sha256:ff60bf7ce3de1d43ad3f6969983f321a31f0a45df3690921720bcad6a8596cc4"}, + {file = "torch-2.3.1-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:bee0bd33dc58aa8fc8a7527876e9b9a0e812ad08122054a5bff2ce5abf005b10"}, + {file = "torch-2.3.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:aaa872abde9a3d4f91580f6396d54888620f4a0b92e3976a6034759df4b961ad"}, + {file = "torch-2.3.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:3d7a7f7ef21a7520510553dc3938b0c57c116a7daee20736a9e25cbc0e832bdc"}, + {file = "torch-2.3.1-cp39-cp39-win_amd64.whl", hash = "sha256:4777f6cefa0c2b5fa87223c213e7b6f417cf254a45e5829be4ccd1b2a4ee1011"}, + {file = "torch-2.3.1-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:2bb5af780c55be68fe100feb0528d2edebace1d55cb2e351de735809ba7391eb"}, +] + +[package.dependencies] +filelock = "*" +fsspec = "*" +jinja2 = "*" +mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""} +networkx = "*" +nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} +nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} +nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} +nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} +nvidia-cudnn-cu12 = {version = "8.9.2.26", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} +nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} +nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} +nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} +nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} +nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} +nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""} +sympy = "*" +triton = {version = "2.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""} +typing-extensions = ">=4.8.0" + +[package.extras] +opt-einsum = ["opt-einsum (>=3.3)"] +optree = ["optree (>=0.9.1)"] + +[[package]] +name = "torch-geometric" +version = "2.5.3" +description = "Graph Neural Network Library for PyTorch" +optional = false +python-versions = ">=3.8" +files = [ + {file = "torch_geometric-2.5.3-py3-none-any.whl", hash = "sha256:8277abfc12600b0e8047e0c3ea2d55cc43f08c1448e73e924de827c15d0b5f85"}, + {file = "torch_geometric-2.5.3.tar.gz", hash = "sha256:ad0761650c8fa56cdc46ee61c564fd4995f07f079965fe732b3a76d109fd3edc"}, +] + +[package.dependencies] +aiohttp = "*" +fsspec = "*" +jinja2 = "*" +numpy = "*" +psutil = ">=5.8.0" +pyparsing = "*" +requests = "*" +scikit-learn = "*" +scipy = "*" +tqdm = "*" + +[package.extras] +benchmark = ["matplotlib", "networkx", "pandas", "protobuf (<4.21)", "wandb"] +dev = ["pre-commit", "torch_geometric[test]"] +full = ["ase", "captum (<0.7.0)", "graphviz", "h5py", "matplotlib", "networkx", "numba", "opt_einsum", "pandas", "pgmpy", "pynndescent", "pytorch-memlab", "rdflib", "rdkit", "scikit-image", "statsmodels", "sympy", "tabulate", "torch_geometric[graphgym,modelhub]", "torchmetrics", "trimesh"] +graphgym = ["hydra-core", "protobuf (<4.21)", "pytorch-lightning", "yacs"] +modelhub = ["huggingface_hub"] +test = ["onnx", "onnxruntime", "pytest", "pytest-cov"] + +[[package]] +name = "torchmetrics" +version = "1.4.0.post0" +description = "PyTorch native Metrics" +optional = false +python-versions = ">=3.8" +files = [ + {file = "torchmetrics-1.4.0.post0-py3-none-any.whl", hash = "sha256:ab234216598e3fbd8d62ee4541a0e74e7e8fc935d099683af5b8da50f745b3c8"}, + {file = "torchmetrics-1.4.0.post0.tar.gz", hash = "sha256:ab9bcfe80e65dbabbddb6cecd9be21f1f1d5207bb74051ef95260740f2762358"}, +] + +[package.dependencies] +lightning-utilities = ">=0.8.0" +numpy = ">1.20.0" +packaging = ">17.1" +torch = ">=1.10.0" + +[package.extras] +all = ["SciencePlots (>=2.0.0)", "ipadic (>=1.0.0)", "matplotlib (>=3.3.0)", "mecab-python3 (>=1.0.6)", "mypy (==1.9.0)", "nltk (>=3.6)", "piq (<=0.8.0)", "pretty-errors (>=1.2.0)", "pycocotools (>2.0.0)", "pystoi (>=0.3.0)", "regex (>=2021.9.24)", "scipy (>1.0.0)", "sentencepiece (>=0.2.0)", "torch (==2.3.0)", "torch-fidelity (<=0.4.0)", "torchaudio (>=0.10.0)", "torchvision (>=0.8)", "tqdm (>=4.41.0)", "transformers (>4.4.0)", "transformers (>=4.10.0)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"] +audio = ["pystoi (>=0.3.0)", "torchaudio (>=0.10.0)"] +debug = ["pretty-errors (>=1.2.0)"] +detection = ["pycocotools (>2.0.0)", "torchvision (>=0.8)"] +dev = ["SciencePlots (>=2.0.0)", "bert-score (==0.3.13)", "dython (<=0.7.5)", "fairlearn", "fast-bss-eval (>=0.1.0)", "faster-coco-eval (>=1.3.3)", "huggingface-hub (<0.23)", "ipadic (>=1.0.0)", "jiwer (>=2.3.0)", "kornia (>=0.6.7)", "lpips (<=0.1.4)", "matplotlib (>=3.3.0)", "mecab-ko (>=1.0.0)", "mecab-ko-dic (>=1.0.0)", "mecab-python3 (>=1.0.6)", "mir-eval (>=0.6)", "monai (==1.3.0)", "mypy (==1.9.0)", "netcal (>1.0.0)", "nltk (>=3.6)", "numpy (<1.27.0)", "pandas (>1.0.0)", "pandas (>=1.4.0)", "piq (<=0.8.0)", "pretty-errors (>=1.2.0)", "pycocotools (>2.0.0)", "pystoi (>=0.3.0)", "pytorch-msssim (==1.0.0)", "regex (>=2021.9.24)", "rouge-score (>0.1.0)", "sacrebleu (>=2.3.0)", "scikit-image (>=0.19.0)", "scipy (>1.0.0)", "sentencepiece (>=0.2.0)", "sewar (>=0.4.4)", "statsmodels (>0.13.5)", "torch (==2.3.0)", "torch-complex (<=0.4.3)", "torch-fidelity (<=0.4.0)", "torchaudio (>=0.10.0)", "torchvision (>=0.8)", "tqdm (>=4.41.0)", "transformers (>4.4.0)", "transformers (>=4.10.0)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"] +image = ["scipy (>1.0.0)", "torch-fidelity (<=0.4.0)", "torchvision (>=0.8)"] +multimodal = ["piq (<=0.8.0)", "transformers (>=4.10.0)"] +text = ["ipadic (>=1.0.0)", "mecab-python3 (>=1.0.6)", "nltk (>=3.6)", "regex (>=2021.9.24)", "sentencepiece (>=0.2.0)", "tqdm (>=4.41.0)", "transformers (>4.4.0)"] +typing = ["mypy (==1.9.0)", "torch (==2.3.0)", "types-PyYAML", "types-emoji", "types-protobuf", "types-requests", "types-setuptools", "types-six", "types-tabulate"] +visual = ["SciencePlots (>=2.0.0)", "matplotlib (>=3.3.0)"] + +[[package]] +name = "torchvision" +version = "0.18.1" +description = "image and video datasets and models for torch deep learning" +optional = false +python-versions = ">=3.8" +files = [ + {file = "torchvision-0.18.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3e694e54b0548dad99c12af6bf0c8e4f3350137d391dcd19af22a1c5f89322b3"}, + {file = "torchvision-0.18.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:0b3bda0aa5b416eeb547143b8eeaf17720bdba9cf516dc991aacb81811aa96a5"}, + {file = "torchvision-0.18.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:573ff523c739405edb085f65cb592f482d28a30e29b0be4c4ba08040b3ae785f"}, + {file = "torchvision-0.18.1-cp310-cp310-win_amd64.whl", hash = "sha256:ef7bbbc60b38e831a75e547c66ca1784f2ac27100f9e4ddbe9614cef6cbcd942"}, + {file = "torchvision-0.18.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:80b5d794dd0fdba787adc22f1a367a5ead452327686473cb260dd94364bc56a6"}, + {file = "torchvision-0.18.1-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:9077cf590cdb3a5e8fdf5cdb71797f8c67713f974cf0228ecb17fcd670ab42f9"}, + {file = "torchvision-0.18.1-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:ceb993a882f1ae7ae373ed39c28d7e3e802205b0e59a7ed84ef4028f0bba8d7f"}, + {file = "torchvision-0.18.1-cp311-cp311-win_amd64.whl", hash = "sha256:52f7436140045dc2239cdc502aa76b2bd8bd676d64244ff154d304aa69852046"}, + {file = "torchvision-0.18.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2be6f0bf7c455c89a51a1dbb6f668d36c6edc479f49ac912d745d10df5715657"}, + {file = "torchvision-0.18.1-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:f118d887bfde3a948a41d56587525401e5cac1b7db2eaca203324d6ed2b1caca"}, + {file = "torchvision-0.18.1-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:13d24d904f65e62d66a1e0c41faec630bc193867b8a4a01166769e8a8e8df8e9"}, + {file = "torchvision-0.18.1-cp312-cp312-win_amd64.whl", hash = "sha256:ed6340b69a63a625e512a66127210d412551d9c5f2ad2978130c6a45bf56cd4a"}, + {file = "torchvision-0.18.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b1c3864fa9378c88bce8ad0ef3599f4f25397897ce612e1c245c74b97092f35e"}, + {file = "torchvision-0.18.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:02085a2ffc7461f5c0edb07d6f3455ee1806561f37736b903da820067eea58c7"}, + {file = "torchvision-0.18.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:9726c316a2501df8503e5a5dc46a631afd4c515a958972e5b7f7b9c87d2125c0"}, + {file = "torchvision-0.18.1-cp38-cp38-win_amd64.whl", hash = "sha256:64a2662dbf30db9055d8b201d6e56f312a504e5ccd9d144c57c41622d3c524cb"}, + {file = "torchvision-0.18.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:975b8594c0f5288875408acbb74946eea786c5b008d129c0d045d0ead23742bc"}, + {file = "torchvision-0.18.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:da83c8bbd34d8bee48bfa1d1b40e0844bc3cba10ed825a5a8cbe3ce7b62264cd"}, + {file = "torchvision-0.18.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:54bfcd352abb396d5c9c237d200167c178bd136051b138e1e8ef46ce367c2773"}, + {file = "torchvision-0.18.1-cp39-cp39-win_amd64.whl", hash = "sha256:5c8366a1aeee49e9ea9e64b30d199debdf06b1bd7610a76165eb5d7869c3bde5"}, +] + +[package.dependencies] +numpy = "*" +pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0" +torch = "2.3.1" + +[package.extras] +scipy = ["scipy"] + +[[package]] +name = "tornado" +version = "6.4.1" +description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed." +optional = false +python-versions = ">=3.8" +files = [ + {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:163b0aafc8e23d8cdc3c9dfb24c5368af84a81e3364745ccb4427669bf84aec8"}, + {file = "tornado-6.4.1-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:6d5ce3437e18a2b66fbadb183c1d3364fb03f2be71299e7d10dbeeb69f4b2a14"}, + {file = "tornado-6.4.1-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2e20b9113cd7293f164dc46fffb13535266e713cdb87bd2d15ddb336e96cfc4"}, + {file = "tornado-6.4.1-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8ae50a504a740365267b2a8d1a90c9fbc86b780a39170feca9bcc1787ff80842"}, + {file = "tornado-6.4.1-cp38-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:613bf4ddf5c7a95509218b149b555621497a6cc0d46ac341b30bd9ec19eac7f3"}, + {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:25486eb223babe3eed4b8aecbac33b37e3dd6d776bc730ca14e1bf93888b979f"}, + {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:454db8a7ecfcf2ff6042dde58404164d969b6f5d58b926da15e6b23817950fc4"}, + {file = "tornado-6.4.1-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a02a08cc7a9314b006f653ce40483b9b3c12cda222d6a46d4ac63bb6c9057698"}, + {file = "tornado-6.4.1-cp38-abi3-win32.whl", hash = "sha256:d9a566c40b89757c9aa8e6f032bcdb8ca8795d7c1a9762910c722b1635c9de4d"}, + {file = "tornado-6.4.1-cp38-abi3-win_amd64.whl", hash = "sha256:b24b8982ed444378d7f21d563f4180a2de31ced9d8d84443907a0a64da2072e7"}, + {file = "tornado-6.4.1.tar.gz", hash = "sha256:92d3ab53183d8c50f8204a51e6f91d18a15d5ef261e84d452800d4ff6fc504e9"}, +] + +[[package]] +name = "tqdm" +version = "4.66.4" +description = "Fast, Extensible Progress Meter" +optional = false +python-versions = ">=3.7" +files = [ + {file = "tqdm-4.66.4-py3-none-any.whl", hash = "sha256:b75ca56b413b030bc3f00af51fd2c1a1a5eac6a0c1cca83cbb37a5c52abce644"}, + {file = "tqdm-4.66.4.tar.gz", hash = "sha256:e4d936c9de8727928f3be6079590e97d9abfe8d39a590be678eb5919ffc186bb"}, +] + +[package.dependencies] +colorama = {version = "*", markers = "platform_system == \"Windows\""} + +[package.extras] +dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"] +notebook = ["ipywidgets (>=6)"] +slack = ["slack-sdk"] +telegram = ["requests"] + +[[package]] +name = "traitlets" +version = "5.14.3" +description = "Traitlets Python configuration system" +optional = false +python-versions = ">=3.8" +files = [ + {file = "traitlets-5.14.3-py3-none-any.whl", hash = "sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f"}, + {file = "traitlets-5.14.3.tar.gz", hash = "sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7"}, +] + +[package.extras] +docs = ["myst-parser", "pydata-sphinx-theme", "sphinx"] +test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,<8.2)", "pytest-mock", "pytest-mypy-testing"] + +[[package]] +name = "triton" +version = "2.3.1" +description = "A language and compiler for custom Deep Learning operations" +optional = false +python-versions = "*" +files = [ + {file = "triton-2.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c84595cbe5e546b1b290d2a58b1494df5a2ef066dd890655e5b8a8a92205c33"}, + {file = "triton-2.3.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c9d64ae33bcb3a7a18081e3a746e8cf87ca8623ca13d2c362413ce7a486f893e"}, + {file = "triton-2.3.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eaf80e8761a9e3498aa92e7bf83a085b31959c61f5e8ac14eedd018df6fccd10"}, + {file = "triton-2.3.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b13bf35a2b659af7159bf78e92798dc62d877aa991de723937329e2d382f1991"}, + {file = "triton-2.3.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:63381e35ded3304704ea867ffde3b7cfc42c16a55b3062d41e017ef510433d66"}, + {file = "triton-2.3.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d968264523c7a07911c8fb51b4e0d1b920204dae71491b1fe7b01b62a31e124"}, +] + +[package.dependencies] +filelock = "*" + +[package.extras] +build = ["cmake (>=3.20)", "lit"] +tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)", "torch"] +tutorials = ["matplotlib", "pandas", "tabulate", "torch"] + +[[package]] +name = "types-python-dateutil" +version = "2.9.0.20240316" +description = "Typing stubs for python-dateutil" +optional = false +python-versions = ">=3.8" +files = [ + {file = "types-python-dateutil-2.9.0.20240316.tar.gz", hash = "sha256:5d2f2e240b86905e40944dd787db6da9263f0deabef1076ddaed797351ec0202"}, + {file = "types_python_dateutil-2.9.0.20240316-py3-none-any.whl", hash = "sha256:6b8cb66d960771ce5ff974e9dd45e38facb81718cc1e208b10b1baccbfdbee3b"}, +] + +[[package]] +name = "typing-extensions" +version = "4.12.2" +description = "Backported and Experimental Type Hints for Python 3.8+" +optional = false +python-versions = ">=3.8" +files = [ + {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"}, + {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"}, +] + +[[package]] +name = "tzdata" +version = "2024.1" +description = "Provider of IANA time zone data" +optional = false +python-versions = ">=2" +files = [ + {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"}, + {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"}, +] + +[[package]] +name = "uri-template" +version = "1.3.0" +description = "RFC 6570 URI Template Processor" +optional = false +python-versions = ">=3.7" +files = [ + {file = "uri-template-1.3.0.tar.gz", hash = "sha256:0e00f8eb65e18c7de20d595a14336e9f337ead580c70934141624b6d1ffdacc7"}, + {file = "uri_template-1.3.0-py3-none-any.whl", hash = "sha256:a44a133ea12d44a0c0f06d7d42a52d71282e77e2f937d8abd5655b8d56fc1363"}, +] + +[package.extras] +dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"] + +[[package]] +name = "urllib3" +version = "1.26.19" +description = "HTTP library with thread-safe connection pooling, file post, and more." +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" +files = [ + {file = "urllib3-1.26.19-py2.py3-none-any.whl", hash = "sha256:37a0344459b199fce0e80b0d3569837ec6b6937435c5244e7fd73fa6006830f3"}, + {file = "urllib3-1.26.19.tar.gz", hash = "sha256:3e3d753a8618b86d7de333b4223005f68720bcd6a7d2bcb9fbd2229ec7c1e429"}, +] + +[package.extras] +brotli = ["brotli (==1.0.9)", "brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"] +secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"] +socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"] + +[[package]] +name = "wandb" +version = "0.15.12" +description = "A CLI and library for interacting with the Weights & Biases API." +optional = false +python-versions = ">=3.6" +files = [ + {file = "wandb-0.15.12-py3-none-any.whl", hash = "sha256:75c57b5bb8ddae21d45a02f644628585bdd112fea686de3177099a0996f1c41c"}, + {file = "wandb-0.15.12.tar.gz", hash = "sha256:c344d92fb8044b072a6138afd9adc5d3801ad050cf11378fe2af2fe899dcca84"}, +] + +[package.dependencies] +appdirs = ">=1.4.3" +Click = ">=7.1,<8.0.0 || >8.0.0" +docker-pycreds = ">=0.4.0" +GitPython = ">=1.0.0,<3.1.29 || >3.1.29" +pathtools = "*" +protobuf = {version = ">=3.19.0,<4.21.0 || >4.21.0,<5", markers = "python_version > \"3.9\" or sys_platform != \"linux\""} +psutil = ">=5.0.0" +PyYAML = "*" +requests = ">=2.0.0,<3" +sentry-sdk = ">=1.0.0" +setproctitle = "*" +setuptools = "*" + +[package.extras] +async = ["httpx (>=0.22.0)"] +aws = ["boto3"] +azure = ["azure-identity", "azure-storage-blob"] +gcp = ["google-cloud-storage"] +kubeflow = ["google-cloud-storage", "kubernetes", "minio", "sh"] +launch = ["PyYAML (>=6.0.0)", "awscli", "azure-containerregistry", "azure-identity", "azure-storage-blob", "boto3", "botocore", "chardet", "google-auth", "google-cloud-artifact-registry", "google-cloud-compute", "google-cloud-storage", "iso8601", "kubernetes", "nbconvert", "nbformat", "optuna", "typing-extensions"] +media = ["bokeh", "moviepy", "numpy", "pillow", "plotly", "rdkit-pypi", "soundfile"] +models = ["cloudpickle"] +nexus = ["wandb-core (>=0.16.0b1)"] +perf = ["orjson"] +sweeps = ["sweeps (>=0.2.0)"] + +[[package]] +name = "wcwidth" +version = "0.2.13" +description = "Measures the displayed width of unicode strings in a terminal" +optional = false +python-versions = "*" +files = [ + {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"}, + {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"}, +] + +[[package]] +name = "webcolors" +version = "24.6.0" +description = "A library for working with the color formats defined by HTML and CSS." +optional = false +python-versions = ">=3.8" +files = [ + {file = "webcolors-24.6.0-py3-none-any.whl", hash = "sha256:8cf5bc7e28defd1d48b9e83d5fc30741328305a8195c29a8e668fa45586568a1"}, + {file = "webcolors-24.6.0.tar.gz", hash = "sha256:1d160d1de46b3e81e58d0a280d0c78b467dc80f47294b91b1ad8029d2cedb55b"}, +] + +[package.extras] +docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"] +tests = ["coverage[toml]"] + +[[package]] +name = "webencodings" +version = "0.5.1" +description = "Character encoding aliases for legacy web content" +optional = false +python-versions = "*" +files = [ + {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"}, + {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"}, +] + +[[package]] +name = "websocket-client" +version = "1.8.0" +description = "WebSocket client for Python with low level API options" +optional = false +python-versions = ">=3.8" +files = [ + {file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"}, + {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"}, +] + +[package.extras] +docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"] +optional = ["python-socks", "wsaccel"] +test = ["websockets"] + +[[package]] +name = "widgetsnbextension" +version = "4.0.11" +description = "Jupyter interactive widgets for Jupyter Notebook" +optional = false +python-versions = ">=3.7" +files = [ + {file = "widgetsnbextension-4.0.11-py3-none-any.whl", hash = "sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36"}, + {file = "widgetsnbextension-4.0.11.tar.gz", hash = "sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474"}, +] + +[[package]] +name = "wrapt" +version = "1.16.0" +description = "Module for decorators, wrappers and monkey patching." +optional = false +python-versions = ">=3.6" +files = [ + {file = "wrapt-1.16.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ffa565331890b90056c01db69c0fe634a776f8019c143a5ae265f9c6bc4bd6d4"}, + {file = "wrapt-1.16.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e4fdb9275308292e880dcbeb12546df7f3e0f96c6b41197e0cf37d2826359020"}, + {file = "wrapt-1.16.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb2dee3874a500de01c93d5c71415fcaef1d858370d405824783e7a8ef5db440"}, + {file = "wrapt-1.16.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2a88e6010048489cda82b1326889ec075a8c856c2e6a256072b28eaee3ccf487"}, + {file = "wrapt-1.16.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ac83a914ebaf589b69f7d0a1277602ff494e21f4c2f743313414378f8f50a4cf"}, + {file = "wrapt-1.16.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:73aa7d98215d39b8455f103de64391cb79dfcad601701a3aa0dddacf74911d72"}, + {file = "wrapt-1.16.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:807cc8543a477ab7422f1120a217054f958a66ef7314f76dd9e77d3f02cdccd0"}, + {file = "wrapt-1.16.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:bf5703fdeb350e36885f2875d853ce13172ae281c56e509f4e6eca049bdfb136"}, + {file = "wrapt-1.16.0-cp310-cp310-win32.whl", hash = "sha256:f6b2d0c6703c988d334f297aa5df18c45e97b0af3679bb75059e0e0bd8b1069d"}, + {file = "wrapt-1.16.0-cp310-cp310-win_amd64.whl", hash = "sha256:decbfa2f618fa8ed81c95ee18a387ff973143c656ef800c9f24fb7e9c16054e2"}, + {file = "wrapt-1.16.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:1a5db485fe2de4403f13fafdc231b0dbae5eca4359232d2efc79025527375b09"}, + {file = "wrapt-1.16.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:75ea7d0ee2a15733684badb16de6794894ed9c55aa5e9903260922f0482e687d"}, + {file = "wrapt-1.16.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a452f9ca3e3267cd4d0fcf2edd0d035b1934ac2bd7e0e57ac91ad6b95c0c6389"}, + {file = "wrapt-1.16.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:43aa59eadec7890d9958748db829df269f0368521ba6dc68cc172d5d03ed8060"}, + {file = "wrapt-1.16.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:72554a23c78a8e7aa02abbd699d129eead8b147a23c56e08d08dfc29cfdddca1"}, + {file = "wrapt-1.16.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:d2efee35b4b0a347e0d99d28e884dfd82797852d62fcd7ebdeee26f3ceb72cf3"}, + {file = "wrapt-1.16.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:6dcfcffe73710be01d90cae08c3e548d90932d37b39ef83969ae135d36ef3956"}, + {file = "wrapt-1.16.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:eb6e651000a19c96f452c85132811d25e9264d836951022d6e81df2fff38337d"}, + {file = "wrapt-1.16.0-cp311-cp311-win32.whl", hash = "sha256:66027d667efe95cc4fa945af59f92c5a02c6f5bb6012bff9e60542c74c75c362"}, + {file = "wrapt-1.16.0-cp311-cp311-win_amd64.whl", hash = "sha256:aefbc4cb0a54f91af643660a0a150ce2c090d3652cf4052a5397fb2de549cd89"}, + {file = "wrapt-1.16.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:5eb404d89131ec9b4f748fa5cfb5346802e5ee8836f57d516576e61f304f3b7b"}, + {file = "wrapt-1.16.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:9090c9e676d5236a6948330e83cb89969f433b1943a558968f659ead07cb3b36"}, + {file = "wrapt-1.16.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:94265b00870aa407bd0cbcfd536f17ecde43b94fb8d228560a1e9d3041462d73"}, + {file = "wrapt-1.16.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f2058f813d4f2b5e3a9eb2eb3faf8f1d99b81c3e51aeda4b168406443e8ba809"}, + {file = "wrapt-1.16.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:98b5e1f498a8ca1858a1cdbffb023bfd954da4e3fa2c0cb5853d40014557248b"}, + {file = "wrapt-1.16.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:14d7dc606219cdd7405133c713f2c218d4252f2a469003f8c46bb92d5d095d81"}, + {file = "wrapt-1.16.0-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:49aac49dc4782cb04f58986e81ea0b4768e4ff197b57324dcbd7699c5dfb40b9"}, + {file = "wrapt-1.16.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:418abb18146475c310d7a6dc71143d6f7adec5b004ac9ce08dc7a34e2babdc5c"}, + {file = "wrapt-1.16.0-cp312-cp312-win32.whl", hash = "sha256:685f568fa5e627e93f3b52fda002c7ed2fa1800b50ce51f6ed1d572d8ab3e7fc"}, + {file = "wrapt-1.16.0-cp312-cp312-win_amd64.whl", hash = "sha256:dcdba5c86e368442528f7060039eda390cc4091bfd1dca41e8046af7c910dda8"}, + {file = "wrapt-1.16.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:d462f28826f4657968ae51d2181a074dfe03c200d6131690b7d65d55b0f360f8"}, + {file = "wrapt-1.16.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a33a747400b94b6d6b8a165e4480264a64a78c8a4c734b62136062e9a248dd39"}, + {file = "wrapt-1.16.0-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b3646eefa23daeba62643a58aac816945cadc0afaf21800a1421eeba5f6cfb9c"}, + {file = "wrapt-1.16.0-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ebf019be5c09d400cf7b024aa52b1f3aeebeff51550d007e92c3c1c4afc2a40"}, + {file = "wrapt-1.16.0-cp36-cp36m-musllinux_1_1_aarch64.whl", hash = "sha256:0d2691979e93d06a95a26257adb7bfd0c93818e89b1406f5a28f36e0d8c1e1fc"}, + {file = "wrapt-1.16.0-cp36-cp36m-musllinux_1_1_i686.whl", hash = "sha256:1acd723ee2a8826f3d53910255643e33673e1d11db84ce5880675954183ec47e"}, + {file = "wrapt-1.16.0-cp36-cp36m-musllinux_1_1_x86_64.whl", hash = "sha256:bc57efac2da352a51cc4658878a68d2b1b67dbe9d33c36cb826ca449d80a8465"}, + {file = "wrapt-1.16.0-cp36-cp36m-win32.whl", hash = "sha256:da4813f751142436b075ed7aa012a8778aa43a99f7b36afe9b742d3ed8bdc95e"}, + {file = "wrapt-1.16.0-cp36-cp36m-win_amd64.whl", hash = "sha256:6f6eac2360f2d543cc875a0e5efd413b6cbd483cb3ad7ebf888884a6e0d2e966"}, + {file = "wrapt-1.16.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:a0ea261ce52b5952bf669684a251a66df239ec6d441ccb59ec7afa882265d593"}, + {file = "wrapt-1.16.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7bd2d7ff69a2cac767fbf7a2b206add2e9a210e57947dd7ce03e25d03d2de292"}, + {file = "wrapt-1.16.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9159485323798c8dc530a224bd3ffcf76659319ccc7bbd52e01e73bd0241a0c5"}, + {file = "wrapt-1.16.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a86373cf37cd7764f2201b76496aba58a52e76dedfaa698ef9e9688bfd9e41cf"}, + {file = "wrapt-1.16.0-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:73870c364c11f03ed072dda68ff7aea6d2a3a5c3fe250d917a429c7432e15228"}, + {file = "wrapt-1.16.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:b935ae30c6e7400022b50f8d359c03ed233d45b725cfdd299462f41ee5ffba6f"}, + {file = "wrapt-1.16.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:db98ad84a55eb09b3c32a96c576476777e87c520a34e2519d3e59c44710c002c"}, + {file = "wrapt-1.16.0-cp37-cp37m-win32.whl", hash = "sha256:9153ed35fc5e4fa3b2fe97bddaa7cbec0ed22412b85bcdaf54aeba92ea37428c"}, + {file = "wrapt-1.16.0-cp37-cp37m-win_amd64.whl", hash = "sha256:66dfbaa7cfa3eb707bbfcd46dab2bc6207b005cbc9caa2199bcbc81d95071a00"}, + {file = "wrapt-1.16.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1dd50a2696ff89f57bd8847647a1c363b687d3d796dc30d4dd4a9d1689a706f0"}, + {file = "wrapt-1.16.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:44a2754372e32ab315734c6c73b24351d06e77ffff6ae27d2ecf14cf3d229202"}, + {file = "wrapt-1.16.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8e9723528b9f787dc59168369e42ae1c3b0d3fadb2f1a71de14531d321ee05b0"}, + {file = "wrapt-1.16.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dbed418ba5c3dce92619656802cc5355cb679e58d0d89b50f116e4a9d5a9603e"}, + {file = "wrapt-1.16.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:941988b89b4fd6b41c3f0bfb20e92bd23746579736b7343283297c4c8cbae68f"}, + {file = "wrapt-1.16.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:6a42cd0cfa8ffc1915aef79cb4284f6383d8a3e9dcca70c445dcfdd639d51267"}, + {file = "wrapt-1.16.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:1ca9b6085e4f866bd584fb135a041bfc32cab916e69f714a7d1d397f8c4891ca"}, + {file = "wrapt-1.16.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:d5e49454f19ef621089e204f862388d29e6e8d8b162efce05208913dde5b9ad6"}, + {file = "wrapt-1.16.0-cp38-cp38-win32.whl", hash = "sha256:c31f72b1b6624c9d863fc095da460802f43a7c6868c5dda140f51da24fd47d7b"}, + {file = "wrapt-1.16.0-cp38-cp38-win_amd64.whl", hash = "sha256:490b0ee15c1a55be9c1bd8609b8cecd60e325f0575fc98f50058eae366e01f41"}, + {file = "wrapt-1.16.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9b201ae332c3637a42f02d1045e1d0cccfdc41f1f2f801dafbaa7e9b4797bfc2"}, + {file = "wrapt-1.16.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:2076fad65c6736184e77d7d4729b63a6d1ae0b70da4868adeec40989858eb3fb"}, + {file = "wrapt-1.16.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c5cd603b575ebceca7da5a3a251e69561bec509e0b46e4993e1cac402b7247b8"}, + {file = "wrapt-1.16.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b47cfad9e9bbbed2339081f4e346c93ecd7ab504299403320bf85f7f85c7d46c"}, + {file = "wrapt-1.16.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f8212564d49c50eb4565e502814f694e240c55551a5f1bc841d4fcaabb0a9b8a"}, + {file = "wrapt-1.16.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:5f15814a33e42b04e3de432e573aa557f9f0f56458745c2074952f564c50e664"}, + {file = "wrapt-1.16.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:db2e408d983b0e61e238cf579c09ef7020560441906ca990fe8412153e3b291f"}, + {file = "wrapt-1.16.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:edfad1d29c73f9b863ebe7082ae9321374ccb10879eeabc84ba3b69f2579d537"}, + {file = "wrapt-1.16.0-cp39-cp39-win32.whl", hash = "sha256:ed867c42c268f876097248e05b6117a65bcd1e63b779e916fe2e33cd6fd0d3c3"}, + {file = "wrapt-1.16.0-cp39-cp39-win_amd64.whl", hash = "sha256:eb1b046be06b0fce7249f1d025cd359b4b80fc1c3e24ad9eca33e0dcdb2e4a35"}, + {file = "wrapt-1.16.0-py3-none-any.whl", hash = "sha256:6906c4100a8fcbf2fa735f6059214bb13b97f75b1a61777fcf6432121ef12ef1"}, + {file = "wrapt-1.16.0.tar.gz", hash = "sha256:5f370f952971e7d17c7d1ead40e49f32345a7f7a5373571ef44d800d06b1899d"}, +] + +[[package]] +name = "xyzservices" +version = "2024.6.0" +description = "Source of XYZ tiles providers" +optional = false +python-versions = ">=3.8" +files = [ + {file = "xyzservices-2024.6.0-py3-none-any.whl", hash = "sha256:fecb2508f0f2b71c819aecf5df2c03cef001c56a4b49302e640f3b34710d25e4"}, + {file = "xyzservices-2024.6.0.tar.gz", hash = "sha256:58c1bdab4257d2551b9ef91cd48571f77b7c4d2bc45bf5e3c05ac97b3a4d7282"}, +] + +[[package]] +name = "yarl" +version = "1.9.4" +description = "Yet another URL library" +optional = false +python-versions = ">=3.7" +files = [ + {file = "yarl-1.9.4-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a8c1df72eb746f4136fe9a2e72b0c9dc1da1cbd23b5372f94b5820ff8ae30e0e"}, + {file = "yarl-1.9.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a3a6ed1d525bfb91b3fc9b690c5a21bb52de28c018530ad85093cc488bee2dd2"}, + {file = "yarl-1.9.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:c38c9ddb6103ceae4e4498f9c08fac9b590c5c71b0370f98714768e22ac6fa66"}, + {file = "yarl-1.9.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d9e09c9d74f4566e905a0b8fa668c58109f7624db96a2171f21747abc7524234"}, + {file = "yarl-1.9.4-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b8477c1ee4bd47c57d49621a062121c3023609f7a13b8a46953eb6c9716ca392"}, + {file = "yarl-1.9.4-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d5ff2c858f5f6a42c2a8e751100f237c5e869cbde669a724f2062d4c4ef93551"}, + {file = "yarl-1.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:357495293086c5b6d34ca9616a43d329317feab7917518bc97a08f9e55648455"}, + {file = "yarl-1.9.4-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:54525ae423d7b7a8ee81ba189f131054defdb122cde31ff17477951464c1691c"}, + {file = "yarl-1.9.4-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:801e9264d19643548651b9db361ce3287176671fb0117f96b5ac0ee1c3530d53"}, + {file = "yarl-1.9.4-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e516dc8baf7b380e6c1c26792610230f37147bb754d6426462ab115a02944385"}, + {file = "yarl-1.9.4-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:7d5aaac37d19b2904bb9dfe12cdb08c8443e7ba7d2852894ad448d4b8f442863"}, + {file = "yarl-1.9.4-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:54beabb809ffcacbd9d28ac57b0db46e42a6e341a030293fb3185c409e626b8b"}, + {file = "yarl-1.9.4-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:bac8d525a8dbc2a1507ec731d2867025d11ceadcb4dd421423a5d42c56818541"}, + {file = "yarl-1.9.4-cp310-cp310-win32.whl", hash = "sha256:7855426dfbddac81896b6e533ebefc0af2f132d4a47340cee6d22cac7190022d"}, + {file = "yarl-1.9.4-cp310-cp310-win_amd64.whl", hash = "sha256:848cd2a1df56ddbffeb375535fb62c9d1645dde33ca4d51341378b3f5954429b"}, + {file = "yarl-1.9.4-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:35a2b9396879ce32754bd457d31a51ff0a9d426fd9e0e3c33394bf4b9036b099"}, + {file = "yarl-1.9.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:4c7d56b293cc071e82532f70adcbd8b61909eec973ae9d2d1f9b233f3d943f2c"}, + {file = "yarl-1.9.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d8a1c6c0be645c745a081c192e747c5de06e944a0d21245f4cf7c05e457c36e0"}, + {file = "yarl-1.9.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4b3c1ffe10069f655ea2d731808e76e0f452fc6c749bea04781daf18e6039525"}, + {file = "yarl-1.9.4-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:549d19c84c55d11687ddbd47eeb348a89df9cb30e1993f1b128f4685cd0ebbf8"}, + {file = "yarl-1.9.4-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a7409f968456111140c1c95301cadf071bd30a81cbd7ab829169fb9e3d72eae9"}, + {file = "yarl-1.9.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e23a6d84d9d1738dbc6e38167776107e63307dfc8ad108e580548d1f2c587f42"}, + {file = "yarl-1.9.4-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d8b889777de69897406c9fb0b76cdf2fd0f31267861ae7501d93003d55f54fbe"}, + {file = "yarl-1.9.4-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:03caa9507d3d3c83bca08650678e25364e1843b484f19986a527630ca376ecce"}, + {file = "yarl-1.9.4-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:4e9035df8d0880b2f1c7f5031f33f69e071dfe72ee9310cfc76f7b605958ceb9"}, + {file = "yarl-1.9.4-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:c0ec0ed476f77db9fb29bca17f0a8fcc7bc97ad4c6c1d8959c507decb22e8572"}, + {file = "yarl-1.9.4-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:ee04010f26d5102399bd17f8df8bc38dc7ccd7701dc77f4a68c5b8d733406958"}, + {file = "yarl-1.9.4-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:49a180c2e0743d5d6e0b4d1a9e5f633c62eca3f8a86ba5dd3c471060e352ca98"}, + {file = "yarl-1.9.4-cp311-cp311-win32.whl", hash = "sha256:81eb57278deb6098a5b62e88ad8281b2ba09f2f1147c4767522353eaa6260b31"}, + {file = "yarl-1.9.4-cp311-cp311-win_amd64.whl", hash = "sha256:d1d2532b340b692880261c15aee4dc94dd22ca5d61b9db9a8a361953d36410b1"}, + {file = "yarl-1.9.4-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:0d2454f0aef65ea81037759be5ca9947539667eecebca092733b2eb43c965a81"}, + {file = "yarl-1.9.4-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:44d8ffbb9c06e5a7f529f38f53eda23e50d1ed33c6c869e01481d3fafa6b8142"}, + {file = "yarl-1.9.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:aaaea1e536f98754a6e5c56091baa1b6ce2f2700cc4a00b0d49eca8dea471074"}, + {file = "yarl-1.9.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3777ce5536d17989c91696db1d459574e9a9bd37660ea7ee4d3344579bb6f129"}, + {file = "yarl-1.9.4-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9fc5fc1eeb029757349ad26bbc5880557389a03fa6ada41703db5e068881e5f2"}, + {file = "yarl-1.9.4-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ea65804b5dc88dacd4a40279af0cdadcfe74b3e5b4c897aa0d81cf86927fee78"}, + {file = "yarl-1.9.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aa102d6d280a5455ad6a0f9e6d769989638718e938a6a0a2ff3f4a7ff8c62cc4"}, + {file = "yarl-1.9.4-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:09efe4615ada057ba2d30df871d2f668af661e971dfeedf0c159927d48bbeff0"}, + {file = "yarl-1.9.4-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:008d3e808d03ef28542372d01057fd09168419cdc8f848efe2804f894ae03e51"}, + {file = "yarl-1.9.4-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:6f5cb257bc2ec58f437da2b37a8cd48f666db96d47b8a3115c29f316313654ff"}, + {file = "yarl-1.9.4-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:992f18e0ea248ee03b5a6e8b3b4738850ae7dbb172cc41c966462801cbf62cf7"}, + {file = "yarl-1.9.4-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:0e9d124c191d5b881060a9e5060627694c3bdd1fe24c5eecc8d5d7d0eb6faabc"}, + {file = "yarl-1.9.4-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:3986b6f41ad22988e53d5778f91855dc0399b043fc8946d4f2e68af22ee9ff10"}, + {file = "yarl-1.9.4-cp312-cp312-win32.whl", hash = "sha256:4b21516d181cd77ebd06ce160ef8cc2a5e9ad35fb1c5930882baff5ac865eee7"}, + {file = "yarl-1.9.4-cp312-cp312-win_amd64.whl", hash = "sha256:a9bd00dc3bc395a662900f33f74feb3e757429e545d831eef5bb280252631984"}, + {file = "yarl-1.9.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:63b20738b5aac74e239622d2fe30df4fca4942a86e31bf47a81a0e94c14df94f"}, + {file = "yarl-1.9.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d7d7f7de27b8944f1fee2c26a88b4dabc2409d2fea7a9ed3df79b67277644e17"}, + {file = "yarl-1.9.4-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c74018551e31269d56fab81a728f683667e7c28c04e807ba08f8c9e3bba32f14"}, + {file = "yarl-1.9.4-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ca06675212f94e7a610e85ca36948bb8fc023e458dd6c63ef71abfd482481aa5"}, + {file = "yarl-1.9.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5aef935237d60a51a62b86249839b51345f47564208c6ee615ed2a40878dccdd"}, + {file = "yarl-1.9.4-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2b134fd795e2322b7684155b7855cc99409d10b2e408056db2b93b51a52accc7"}, + {file = "yarl-1.9.4-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:d25039a474c4c72a5ad4b52495056f843a7ff07b632c1b92ea9043a3d9950f6e"}, + {file = "yarl-1.9.4-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:f7d6b36dd2e029b6bcb8a13cf19664c7b8e19ab3a58e0fefbb5b8461447ed5ec"}, + {file = "yarl-1.9.4-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:957b4774373cf6f709359e5c8c4a0af9f6d7875db657adb0feaf8d6cb3c3964c"}, + {file = "yarl-1.9.4-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:d7eeb6d22331e2fd42fce928a81c697c9ee2d51400bd1a28803965883e13cead"}, + {file = "yarl-1.9.4-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:6a962e04b8f91f8c4e5917e518d17958e3bdee71fd1d8b88cdce74dd0ebbf434"}, + {file = "yarl-1.9.4-cp37-cp37m-win32.whl", hash = "sha256:f3bc6af6e2b8f92eced34ef6a96ffb248e863af20ef4fde9448cc8c9b858b749"}, + {file = "yarl-1.9.4-cp37-cp37m-win_amd64.whl", hash = "sha256:ad4d7a90a92e528aadf4965d685c17dacff3df282db1121136c382dc0b6014d2"}, + {file = "yarl-1.9.4-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:ec61d826d80fc293ed46c9dd26995921e3a82146feacd952ef0757236fc137be"}, + {file = "yarl-1.9.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:8be9e837ea9113676e5754b43b940b50cce76d9ed7d2461df1af39a8ee674d9f"}, + {file = "yarl-1.9.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:bef596fdaa8f26e3d66af846bbe77057237cb6e8efff8cd7cc8dff9a62278bbf"}, + {file = "yarl-1.9.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2d47552b6e52c3319fede1b60b3de120fe83bde9b7bddad11a69fb0af7db32f1"}, + {file = "yarl-1.9.4-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:84fc30f71689d7fc9168b92788abc977dc8cefa806909565fc2951d02f6b7d57"}, + {file = "yarl-1.9.4-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4aa9741085f635934f3a2583e16fcf62ba835719a8b2b28fb2917bb0537c1dfa"}, + {file = "yarl-1.9.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:206a55215e6d05dbc6c98ce598a59e6fbd0c493e2de4ea6cc2f4934d5a18d130"}, + {file = "yarl-1.9.4-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:07574b007ee20e5c375a8fe4a0789fad26db905f9813be0f9fef5a68080de559"}, + {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5a2e2433eb9344a163aced6a5f6c9222c0786e5a9e9cac2c89f0b28433f56e23"}, + {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:6ad6d10ed9b67a382b45f29ea028f92d25bc0bc1daf6c5b801b90b5aa70fb9ec"}, + {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:6fe79f998a4052d79e1c30eeb7d6c1c1056ad33300f682465e1b4e9b5a188b78"}, + {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:a825ec844298c791fd28ed14ed1bffc56a98d15b8c58a20e0e08c1f5f2bea1be"}, + {file = "yarl-1.9.4-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:8619d6915b3b0b34420cf9b2bb6d81ef59d984cb0fde7544e9ece32b4b3043c3"}, + {file = "yarl-1.9.4-cp38-cp38-win32.whl", hash = "sha256:686a0c2f85f83463272ddffd4deb5e591c98aac1897d65e92319f729c320eece"}, + {file = "yarl-1.9.4-cp38-cp38-win_amd64.whl", hash = "sha256:a00862fb23195b6b8322f7d781b0dc1d82cb3bcac346d1e38689370cc1cc398b"}, + {file = "yarl-1.9.4-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:604f31d97fa493083ea21bd9b92c419012531c4e17ea6da0f65cacdcf5d0bd27"}, + {file = "yarl-1.9.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:8a854227cf581330ffa2c4824d96e52ee621dd571078a252c25e3a3b3d94a1b1"}, + {file = "yarl-1.9.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:ba6f52cbc7809cd8d74604cce9c14868306ae4aa0282016b641c661f981a6e91"}, + {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a6327976c7c2f4ee6816eff196e25385ccc02cb81427952414a64811037bbc8b"}, + {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8397a3817d7dcdd14bb266283cd1d6fc7264a48c186b986f32e86d86d35fbac5"}, + {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e0381b4ce23ff92f8170080c97678040fc5b08da85e9e292292aba67fdac6c34"}, + {file = "yarl-1.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:23d32a2594cb5d565d358a92e151315d1b2268bc10f4610d098f96b147370136"}, + {file = "yarl-1.9.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ddb2a5c08a4eaaba605340fdee8fc08e406c56617566d9643ad8bf6852778fc7"}, + {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:26a1dc6285e03f3cc9e839a2da83bcbf31dcb0d004c72d0730e755b33466c30e"}, + {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:18580f672e44ce1238b82f7fb87d727c4a131f3a9d33a5e0e82b793362bf18b4"}, + {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:29e0f83f37610f173eb7e7b5562dd71467993495e568e708d99e9d1944f561ec"}, + {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:1f23e4fe1e8794f74b6027d7cf19dc25f8b63af1483d91d595d4a07eca1fb26c"}, + {file = "yarl-1.9.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:db8e58b9d79200c76956cefd14d5c90af54416ff5353c5bfd7cbe58818e26ef0"}, + {file = "yarl-1.9.4-cp39-cp39-win32.whl", hash = "sha256:c7224cab95645c7ab53791022ae77a4509472613e839dab722a72abe5a684575"}, + {file = "yarl-1.9.4-cp39-cp39-win_amd64.whl", hash = "sha256:824d6c50492add5da9374875ce72db7a0733b29c2394890aef23d533106e2b15"}, + {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"}, + {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"}, +] + +[package.dependencies] +idna = ">=2.0" +multidict = ">=4.0" + +[[package]] +name = "zarr" +version = "2.18.2" +description = "An implementation of chunked, compressed, N-dimensional arrays for Python" +optional = false +python-versions = ">=3.9" +files = [ + {file = "zarr-2.18.2-py3-none-any.whl", hash = "sha256:a638754902f97efa99b406083fdc807a0e2ccf12a949117389d2a4ba9b05df38"}, + {file = "zarr-2.18.2.tar.gz", hash = "sha256:9bb393b8a0a38fb121dbb913b047d75db28de9890f6d644a217a73cf4ae74f47"}, +] + +[package.dependencies] +asciitree = "*" +fasteners = {version = "*", markers = "sys_platform != \"emscripten\""} +numcodecs = ">=0.10.0" +numpy = ">=1.23" + +[package.extras] +docs = ["numcodecs[msgpack]", "numpydoc", "pydata-sphinx-theme", "sphinx", "sphinx-automodapi", "sphinx-copybutton", "sphinx-design", "sphinx-issues"] +jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"] + +[metadata] +lock-version = "2.0" +python-versions = "^3.11" +content-hash = "571cd4c17812c154a3debc418fb0804fa6fb576b4b116c038b2a182d63904116" diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..3f8ad7e --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,90 @@ +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry] +name = "mazedyn" +version = "0.0.1" +description = "" +authors = ["Geometric Intellince Lab"] +readme = "README.md" +repository = "https://github.com/geometry-intellince/mazedyn" + +[tool.poetry.dependencies] +python = "^3.11" +click = "<9.0" +aioboto3 = ">=10.0.0" +aiobotocore = {extras = ["boto3"], version = "^2.4.0"} +boto3 = ">=1.24.0" +timm = "^0.9.12" +einops = "^0.7.0" +fsspec = {extras = ["s3"], version = "^2022.11.0"} +joblib = "^1.2.0" +matplotlib = "^3.7.1" +tqdm = "^4.65.0" +rich = "^13.3.4" +zarr = "^2.14.2" +pydantic = "^1.10.7" +s3fs = ">=2021.08.0" +bokeh = "^3.1.0" +pytorch-lightning = "^2.1.2" +wandb = "^0.15.3" +pyyaml = "^6.0" +cdsapi = "^0.6.1" +mypy = "^1.4.1" +torch-geometric = "^2.4.0" +six = "^1.16.0" +ogb = "^1.3.6" +scikit-learn = "^1.3.2" +gudhi = "^3.9.0" +opencv-python = "^4.0" +openpyxl = "^3.1.5" +captum = "^0.7.0" +ray = {extras = ["tune"], version = "^2.24.0"} +pydot = "*" +karateclub = {git = "https://github.com/benedekrozemberczki/karateclub.git", rev = "d35e05526455599688f1c4dd92e397cf92316ae4"} +scipy = "1.12" + +[tool.poetry.group.dev.dependencies] +black = "^23.3.0" +isort = "^5.12.0" +pytest = "^7.3.1" +pytest-cov = "^4.0.0" +flake8 = "^6.0.0" +autoflake8 = "^0.4.0" +ipython = "^8.12.0" +jupyter = "^1.0.0" +jupyterlab = "^4.0" +jupyter-server-proxy = "^4.0.0" + +[tool.black] +line-length = 100 +target-version = ['py39'] +skip-string-normalization = true +include = '\.pyi?$' + +[tool.coverage] +run.omit = ["*_test.py"] + +[tool.bandit] +skips = ['B101'] +assert_used.skips = ['test_*.py', '*_test.py'] + +[tool.isort] +py_version = 39 +profile = "black" # be nice with black +line_length = 100 +skip = ['.gitignore', '.dockerignore'] + +[tool.pytest.ini_options] +python_files = "*_test.py" +filterwarnings = [ + "ignore::DeprecationWarning:pkg_resources.*:", +] + +[tool.mypy] +python_version = "3.9" +show_error_codes = true +show_column_numbers = true +show_error_context = true +ignore_missing_imports = true # prefer explicit ignores \ No newline at end of file