xszheng2020/the_stack_dedup_python_hits_5 · Datasets at Hugging Face

94261967db9edfe5675b854bde2e11711d168a10

2,083

py

Python

src/sardana/macroserver/macros/test/test_ct.py

marc2332/sardana

48dc9191baaa63f6c714d8c025e8f3f96548ad26

[ "CC-BY-3.0" ]

43

2016-11-25T15:21:23.000Z

2021-08-20T06:09:40.000Z

src/sardana/macroserver/macros/test/test_ct.py

marc2332/sardana

48dc9191baaa63f6c714d8c025e8f3f96548ad26

[ "CC-BY-3.0" ]

1,263

2016-11-25T15:58:37.000Z

2021-11-02T22:23:47.000Z

src/sardana/macroserver/macros/test/test_ct.py

marc2332/sardana

48dc9191baaa63f6c714d8c025e8f3f96548ad26

[ "CC-BY-3.0" ]

58

2016-11-21T11:33:55.000Z

2021-09-01T06:21:21.000Z

#!/usr/bin/env python ############################################################################## ## # This file is part of Sardana ## # http://www.sardana-controls.org/ ## # Copyright 2011 CELLS / ALBA Synchrotron, Bellaterra, Spain ## # Sardana is free software: you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. ## # Sardana is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Lesser General Public License for more details. ## # You should have received a copy of the GNU Lesser General Public License # along with Sardana. If not, see <http://www.gnu.org/licenses/>. ## ############################################################################## """Tests for ct macros""" import unittest from sardana.macroserver.macros.test import RunStopMacroTestCase from sardana.macroserver.macros.test import testRun from sardana.macroserver.macros.test import testStop @testRun(macro_name="ct", macro_params=['.1'], wait_timeout=2.5) @testRun(macro_name="ct", macro_params=['.3'], wait_timeout=2.5) @testStop(macro_name="ct", macro_params=['1'], stop_delay=.1, wait_timeout=3.5) # TODO: uncomment these test when bug-474 is fixed: # https://sourceforge.net/p/sardana/tickets/474/ #@testRun(macro_name="uct", macro_params=['.1'], wait_timeout=2) #@testRun(macro_name="uct", macro_params=['.3'], wait_timeout=2) #@testStop(macro_name="uct", macro_params=['1'], stop_delay=.1, wait_timeout=3) class CtTest(RunStopMacroTestCase, unittest.TestCase): """Test of ct macro. It verifies that macro ct can be executed. It inherits from RunStopMacroTestCase and from unittest.TestCase. It tests two executions of the ct macro with two different input parameters. Then it does another execution and it tests if the execution can be aborted. """ pass

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py

Python

adapters/__init__.py

zinan/gitmass

5d7693b7d320562c7e6d5cf31193e78b44d7610f

[ "MIT" ]

3

2020-12-11T21:49:28.000Z

2020-12-11T22:44:16.000Z

adapters/__init__.py

zinan/gitmass

5d7693b7d320562c7e6d5cf31193e78b44d7610f

[ "MIT" ]

null

adapters/__init__.py

zinan/gitmass

5d7693b7d320562c7e6d5cf31193e78b44d7610f

[ "MIT" ]

null

from adapters.GithubAdapter import GithubAdapter from adapters.GitlabAdapter import GitlabAdapter from adapters.BitbucketAdapter import BitbucketAdapter

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948345c6129d4c12e82d554999f1a5a355ef46c5

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py

Python

transmute_core/frameworks/flask/__init__.py

toumorokoshi/web-transmute

ff118e01e42bc224cdf2d7523c3b287aae40d669

[ "MIT" ]

null

transmute_core/frameworks/flask/__init__.py

toumorokoshi/web-transmute

ff118e01e42bc224cdf2d7523c3b287aae40d669

[ "MIT" ]

null

transmute_core/frameworks/flask/__init__.py

toumorokoshi/web-transmute

ff118e01e42bc224cdf2d7523c3b287aae40d669

[ "MIT" ]

null

from transmute_core import * from .route import route from .swagger import add_swagger

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948c44b5620761776424574432278c1027ba651d

53

py

Python

workshop_starter.py

CarlFK/cpx

47b1b4aab4e1f16f3b2e82e27f728238528d3a48

[ "MIT" ]

null

workshop_starter.py

CarlFK/cpx

47b1b4aab4e1f16f3b2e82e27f728238528d3a48

[ "MIT" ]

null

workshop_starter.py

CarlFK/cpx

47b1b4aab4e1f16f3b2e82e27f728238528d3a48

[ "MIT" ]

1

2019-04-27T01:46:17.000Z

from adafruit_circuitplayground.express import cpx

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846bab3412e9a373fb26bed96303ec27de3ca448

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py

Python

lib/draw_rectangles/setup.py

nikhilp9000/KERN

0ee3f7866fc3e3271ce4db0e316ab01a7e2bf21d

[ "MIT" ]

null

lib/draw_rectangles/setup.py

nikhilp9000/KERN

0ee3f7866fc3e3271ce4db0e316ab01a7e2bf21d

[ "MIT" ]

null

lib/draw_rectangles/setup.py

nikhilp9000/KERN

0ee3f7866fc3e3271ce4db0e316ab01a7e2bf21d

[ "MIT" ]

null

from distutils.core import setup from Cython.Build import cythonize import numpy setup(name="draw_rectangles_cython", ext_modules=cythonize('draw_rectangles.pyx'), include_dirs=[numpy.get_include()])

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5

846dda084af96b5d92af747c0b5dd86117dd496a

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py

Python

src/ui/widgets/__init__.py

moevm/nosql1h19-text-graph

410f156ad4f232f8aa060d43692ab020610ddfd4

[ "MIT" ]

null

src/ui/widgets/__init__.py

moevm/nosql1h19-text-graph

410f156ad4f232f8aa060d43692ab020610ddfd4

[ "MIT" ]

null

src/ui/widgets/__init__.py

moevm/nosql1h19-text-graph

410f156ad4f232f8aa060d43692ab020610ddfd4

[ "MIT" ]

null

from .fragments_list import FragmentsList from .matrix import MatrixWidget from .algorithm_result import AlgorithmResults from .settings_list import SettingsListDialog from .text_widget import TextBrowser, CollapsibleHtml

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5

84a4960218c34c6403c0b6c430b22be63e8fad94

182

py

Python

src/pbi/api/__init__.py

redkitedata/pbi-tools

9712ae7751fbaf4036d4adb491849d57972c66be

[ "MIT" ]

null

src/pbi/api/__init__.py

redkitedata/pbi-tools

9712ae7751fbaf4036d4adb491849d57972c66be

[ "MIT" ]

6

2021-12-01T11:12:29.000Z

2022-01-07T10:20:09.000Z

src/pbi/api/__init__.py

redkitedata/pbi-tools

9712ae7751fbaf4036d4adb491849d57972c66be

[ "MIT" ]

2

2021-11-04T16:37:50.000Z

2021-11-08T21:53:43.000Z

from .capacity import Capacity from .dataset import Dataset from .datasource import Datasource from .report import Report from .workspace import Workspace from .tenant import Tenant

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84b598a4c98003994bcdbefbd1d58d15355038f3

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py

Python

python/packages/ibm_application_gateway/system/__init__.py

IBM-Security/ibm-application-gateway-resources

7263a48bbd158d1857a44a99449661b1a0c86793

[ "Apache-2.0" ]

null

python/packages/ibm_application_gateway/system/__init__.py

IBM-Security/ibm-application-gateway-resources

7263a48bbd158d1857a44a99449661b1a0c86793

[ "Apache-2.0" ]

null

python/packages/ibm_application_gateway/system/__init__.py

IBM-Security/ibm-application-gateway-resources

7263a48bbd158d1857a44a99449661b1a0c86793

[ "Apache-2.0" ]

1

2020-10-20T08:30:27.000Z

""" Copyright contributors to the Application Gateway project """ from __future__ import absolute_import from ibm_application_gateway.system.configurator import * from ibm_application_gateway.system.container import * from ibm_application_gateway.system.environment import *

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1

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1

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5

84e39952f0c0980f04b28c3307777642b1118017

101

py

Python

landing/admin.py

cactus-computing/product-recommendation

b5d9bb27205a4fb032fd19934ecab56a5a8c6d81

[ "MIT" ]

null

landing/admin.py

cactus-computing/product-recommendation

b5d9bb27205a4fb032fd19934ecab56a5a8c6d81

[ "MIT" ]

null

landing/admin.py

cactus-computing/product-recommendation

b5d9bb27205a4fb032fd19934ecab56a5a8c6d81

[ "MIT" ]

null

from django.contrib import admin from .models import Suscription admin.site.register(Suscription)

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null

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1

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5

ca3ef8639a19c4ecc09c94f1a2c2a95da2e3080b

123

py

Python

cyder/cydhcp/build/utils.py

ngokevin/chili

36c354ac567471d5e36dccf9eea5096c6b02d4b9

[ "BSD-3-Clause" ]

2

2019-03-16T00:47:09.000Z

2022-03-04T14:39:08.000Z

cyder/cydhcp/build/utils.py

ngokevin/chili

36c354ac567471d5e36dccf9eea5096c6b02d4b9

[ "BSD-3-Clause" ]

1

2020-04-24T08:24:55.000Z

cyder/cydhcp/build/utils.py

ngokevin/chili

36c354ac567471d5e36dccf9eea5096c6b02d4b9

[ "BSD-3-Clause" ]

null

def join_args(args, depth=2): return "\n".join( map(lambda y: "{0}{1};".format("\t" * depth, y), args)) + '\n'

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null

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1

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5

ca52c4ceec29a443f4a2df9459830535e55202ee

109

py

Python

enthought/block_canvas/function_tools/function_info.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

3

2016-12-09T06:05:18.000Z

2018-03-01T13:00:29.000Z

enthought/block_canvas/function_tools/function_info.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

1

2020-12-02T00:51:32.000Z

2020-12-02T08:48:55.000Z

enthought/block_canvas/function_tools/function_info.py

enthought/etsproxy

4aafd628611ebf7fe8311c9d1a0abcf7f7bb5347

[ "BSD-3-Clause" ]

null

# proxy module from __future__ import absolute_import from blockcanvas.function_tools.function_info import *

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null

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5

ca57403cd99357022c5be20b842832e284152b62

332

py

Python

src/Tax/ServiceTax.py

joaolevi/Projeto_de_software_2020.1

3b9b5a048c55638f2e6d31de55720098e3be649c

[ "MIT" ]

null

src/Tax/ServiceTax.py

joaolevi/Projeto_de_software_2020.1

3b9b5a048c55638f2e6d31de55720098e3be649c

[ "MIT" ]

null

src/Tax/ServiceTax.py

joaolevi/Projeto_de_software_2020.1

3b9b5a048c55638f2e6d31de55720098e3be649c

[ "MIT" ]

null

from Tax import Tax class ServiceTax(Tax): def __init__(self, value, service_name): super().__init__(value) self.__service_name = service_name def get_service_name(self): return self.__service_name def set_service_name(self, new_service_name): self.__service_name = new_service_name

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1

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5

ca6880840a9a7cc5608afc75b2ff9ecfdf100416

1,445

py

Python

coub_api/api.py

Lebedevsd/coub_api

b420465a85eb13d21ef1677f26fab34c5bd03d72

[ "MIT" ]

5

2018-12-26T22:38:31.000Z

2020-01-16T13:31:40.000Z

coub_api/api.py

Lebedevsd/coub_api

b420465a85eb13d21ef1677f26fab34c5bd03d72

[ "MIT" ]

113

2018-12-25T19:11:54.000Z

2021-06-28T03:37:48.000Z

coub_api/api.py

Lebedevsd/coub_api

b420465a85eb13d21ef1677f26fab34c5bd03d72

[ "MIT" ]

3

2020-01-06T18:48:07.000Z

2022-02-22T12:11:05.000Z

from typing import Optional from .modules.user import User from .modules.coubs import Coubs from .modules.likes import Likes from .modules.recoub import Recoub from .modules.search import Search from .modules.channel import Channel from .modules.follows import Follow from .modules.friends import Friends from .modules.metadata import MetaData from .modules.timelines import Timeline from .modules.notifications import Notifications __all__ = ("CoubApi",) class CoubApi: __slots__ = ("token",) def __init__(self): self.token: Optional[str] = None def authenticate(self, token: str): self.token = token @property def timeline(self): return Timeline(self.token) @property def coubs(self): return Coubs(self.token) @property def recoubs(self): return Recoub(self.token) @property def channels(self): return Channel(self.token) @property def likes(self): return Likes(self.token) @property def following(self): return Follow(self.token) @property def coub_metadata(self): return MetaData(self.token) @property def users(self): return User(self.token) @property def friends(self): return Friends(self.token) @property def notification(self): return Notifications(self.token) @property def search(self): return Search(self.token)

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py

Python

webui/modules/cleanup/__init__.py

BogdanKandra/image-tinkering

18c71033cb8bf496c43e38c0a98e1e658ce7ee65

[ "MIT" ]

null

webui/modules/cleanup/__init__.py

BogdanKandra/image-tinkering

18c71033cb8bf496c43e38c0a98e1e658ce7ee65

[ "MIT" ]

null

webui/modules/cleanup/__init__.py

BogdanKandra/image-tinkering

18c71033cb8bf496c43e38c0a98e1e658ce7ee65

[ "MIT" ]

null

""" Created on Fri May 17 13:10:07 2019 @author: Bogdan """

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b6fc13b3a0c53c0f6b3868fc004571acf17721b1

203

py

Python

pasedelista/index/admin.py

ElForaneo/Pase-de-lsita

91b3329aedfd85be9f9c28491a5638a698ee4f5e

[ "Apache-2.0" ]

null

pasedelista/index/admin.py

ElForaneo/Pase-de-lsita

91b3329aedfd85be9f9c28491a5638a698ee4f5e

[ "Apache-2.0" ]

null

pasedelista/index/admin.py

ElForaneo/Pase-de-lsita

91b3329aedfd85be9f9c28491a5638a698ee4f5e

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import Alumno, Asistencia, Profesore # Register your models here. admin.site.register(Alumno) admin.site.register(Asistencia) admin.site.register(Profesore)

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8e1e7ec5e630420d5723f3350fd30dc44e247d95

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py

Python

tests/test_usecases.py

Mario-Kart-Felix/mal-scrap

bc396a15ea5b144eb1c0f05759d1f9419d6671df

[ "BSD-3-Clause" ]

2

2015-12-17T20:25:09.000Z

2017-10-08T19:14:57.000Z

tests/test_usecases.py

Mario-Kart-Felix/mal-scrap

bc396a15ea5b144eb1c0f05759d1f9419d6671df

[ "BSD-3-Clause" ]

1

2015-01-05T18:07:13.000Z

2015-01-07T21:43:57.000Z

tests/test_usecases.py

Mario-Kart-Felix/mal-scrap

bc396a15ea5b144eb1c0f05759d1f9419d6671df

[ "BSD-3-Clause" ]

3

2017-10-18T00:56:53.000Z

2020-05-24T09:38:54.000Z

# -*- coding: utf-8 -*- # This file is part of Viper - https://github.com/viper-framework/viper # See the file 'LICENSE' for copying permission. from __future__ import unicode_literals import os import re import sys from shutil import copyfile from hashlib import sha256 import pytest from tests.conftest import FIXTURE_DIR from viper.core.session import __sessions__ from viper.core.ui import commands from viper.common.exceptions import Python2UnsupportedUnicode try: from unittest import mock except ImportError: # Python2 import mock class TestUseCases: def teardown_method(self): __sessions__.close() @pytest.mark.usefixtures("cleandir") @pytest.mark.parametrize("filename, name", [ ("chromeinstall-8u31.exe", "chromeinstall-8u31.exe"), ("string_handling/with blank.txt", "with blank.txt"), ]) def test_store(self, capsys, filename, name): # use cleandir fixture operate on clean ./ local dir copyfile(os.path.join(FIXTURE_DIR, filename), os.path.join(".", os.path.basename(filename))) commands.Open().run('-f', os.path.join(".", os.path.basename(filename))) commands.Store().run() if sys.version_info <= (3, 0): in_fct = 'viper.core.ui.commands.input' else: in_fct = 'builtins.input' with mock.patch(in_fct, return_value='y'): commands.Delete().run() out, err = capsys.readouterr() lines = out.split("\n") assert re.search(r".*Session opened on.*", lines[0]) assert not re.search(r".*Unable to store file.*", out) assert re.search(r".*{}.*".format(name), lines[1]) assert re.search(r".*Running command.*", lines[5]) assert re.search(r".*Deleted opened file.*", lines[7]) @pytest.mark.skipif(sys.version_info >= (3, 0), reason="requires python2") @pytest.mark.xfail(raises=Python2UnsupportedUnicode) @pytest.mark.usefixtures("cleandir") @pytest.mark.parametrize("filename, name", [ ("string_handling/dümmy.txt", "dümmy.txt") ]) def test_store_unicode_py2(self, capsys, filename, name): # use cleandir fixture operate on clean ./ local dir copyfile(os.path.join(FIXTURE_DIR, filename), os.path.join(".", os.path.basename(filename))) commands.Open().run('-f', os.path.join(".", os.path.basename(filename))) commands.Store().run() if sys.version_info <= (3, 0): in_fct = 'viper.core.ui.commands.input' else: in_fct = 'builtins.input' with mock.patch(in_fct, return_value='y'): commands.Delete().run() out, err = capsys.readouterr() lines = out.split("\n") assert re.search(r".*Session opened on.*", lines[0]) assert not re.search(r".*Unable to store file.*", out) assert re.search(r".*{}.*".format(name), lines[1]) assert re.search(r".*Running command.*", lines[5]) assert re.search(r".*Deleted opened file.*", lines[7]) @pytest.mark.skipif(sys.version_info < (3, 3), reason="requires at least python3.3") @pytest.mark.usefixtures("cleandir") @pytest.mark.parametrize("filename, name", [ ("string_handling/dümmy.txt", "dümmy.txt") ]) def test_store_unicode_py3(self, capsys, filename, name): # use cleandir fixture operate on clean ./ local dir copyfile(os.path.join(FIXTURE_DIR, filename), os.path.join(".", os.path.basename(filename))) commands.Open().run('-f', os.path.join(".", os.path.basename(filename))) commands.Store().run() if sys.version_info <= (3, 0): in_fct = 'viper.core.ui.commands.input' else: in_fct = 'builtins.input' with mock.patch(in_fct, return_value='y'): commands.Delete().run() out, err = capsys.readouterr() lines = out.split("\n") assert re.search(r".*Session opened on.*", lines[0]) assert not re.search(r".*Unable to store file.*", out) assert re.search(r".*{}.*".format(name), lines[1]) assert re.search(r".*Running command.*", lines[5]) assert re.search(r".*Deleted opened file.*", lines[7]) @pytest.mark.skipif(sys.version_info >= (3, 0), reason="requires python2") @pytest.mark.xfail(raises=Python2UnsupportedUnicode) @pytest.mark.parametrize("filename", ["chromeinstall-8u31.exe"]) def test_store_all_py2(self, capsys, filename): commands.Open().run('-f', os.path.join(FIXTURE_DIR, filename)) commands.Store().run() commands.Store().run('-f', FIXTURE_DIR) out, err = capsys.readouterr() lines = out.split("\n") assert re.search(r".*Session opened on.*", lines[0]) assert re.search(r".*appears to be already stored.*", out) assert re.search(r".*Skip, file \"chromeinstall-8u31.exe\" appears to be already stored.*", out) @pytest.mark.skipif(sys.version_info < (3, 3), reason="requires at least python3.3") @pytest.mark.parametrize("filename", ["chromeinstall-8u31.exe"]) def test_store_all_py3(self, capsys, filename): commands.Open().run('-f', os.path.join(FIXTURE_DIR, filename)) commands.Store().run() commands.Store().run('-f', FIXTURE_DIR) out, err = capsys.readouterr() lines = out.split("\n") assert re.search(r".*Session opened on.*", lines[0]) assert re.search(r".*appears to be already stored.*", out) assert re.search(r".*Skip, file \"chromeinstall-8u31.exe\" appears to be already stored.*", out) @pytest.mark.parametrize("filename", ["chromeinstall-8u31.exe"]) def test_open(self, capsys, filename): with open(os.path.join(FIXTURE_DIR, filename), 'rb') as f: hashfile = sha256(f.read()).hexdigest() commands.Open().run(hashfile) commands.Info().run() commands.Close().run() out, err = capsys.readouterr() lines = out.split("\n") assert re.search(r".*Session opened on.*", lines[0]) assert re.search(r".*| SHA1 | 56c5b6cd34fa9532b5a873d6bdd4380cfd102218.*", lines[11]) @pytest.mark.parametrize("filename", ["chromeinstall-8u31.exe"]) def test_find(self, capsys, filename): with open(os.path.join(FIXTURE_DIR, filename), 'rb') as f: data = f.read() hashfile_sha = sha256(data).hexdigest() commands.Find().run('all') commands.Find().run('sha256', hashfile_sha) commands.Open().run('-l', '1') commands.Close().run() commands.Tags().run('-a', 'blah') commands.Find().run('-t') commands.Tags().run('-d', 'blah') out, err = capsys.readouterr() assert re.search(r".*EICAR.com.*", out) assert re.search(r".*{0}.*".format(filename), out) assert re.search(r".*Tag.*|.*# Entries.*", out) def test_stats(self, capsys): commands.Stats().run() out, err = capsys.readouterr() assert re.search(r".*Projects.*Name | Count.*", out)

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7,058

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0.172662

0

null

0

1

0

null

0

5

f3e0fe4fdd91f0ca53c9beeddf930a5ff2b49ee3

187

py

Python

tests/conftest.py

bstreiff/sistrum

2a8b2e36484dad15395fbec661f2edb354d46b79

[ "BSD-2-Clause" ]

null

tests/conftest.py

bstreiff/sistrum

2a8b2e36484dad15395fbec661f2edb354d46b79

[ "BSD-2-Clause" ]

null

tests/conftest.py

bstreiff/sistrum

2a8b2e36484dad15395fbec661f2edb354d46b79

[ "BSD-2-Clause" ]

null

import pytest # type: ignore import serial # type: ignore @pytest.fixture(scope="module", autouse=True) def pyserial_registrar(): serial.protocol_handler_packages.append("tests")

23.375

52

0.759358

23

187

6.043478

0.782609

0.143885

0

0.122995

187

7

53

26.714286

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0.13369

0

0.069182

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1

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0

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1

0

1

0

1

0

5

6d4453bcf9f4770ec468d18921d151430829b0d7

294

py

Python

twitter_credentials.py

bmugenya/tweet-sentiment

e3badf39e90ae1b6d3ba6ff41805c7c291a6cff8

[ "MIT" ]

null

twitter_credentials.py

bmugenya/tweet-sentiment

e3badf39e90ae1b6d3ba6ff41805c7c291a6cff8

[ "MIT" ]

null

twitter_credentials.py

bmugenya/tweet-sentiment

e3badf39e90ae1b6d3ba6ff41805c7c291a6cff8

[ "MIT" ]

null

# variables that contain user credentials CONSUMER_KEY = "tMFjs33CpuKRq4MUqK3A87tPV" CONSUMER_SECRET = "x9sKeTeUvXPLhkqkHutQYZInpNVNCjxqmyrR4Ve8R4kRzNsaCL" ACCESS_TOKEN = "1005899876742377474-Fd44TJWSfTUiGBapM8YOtjah9Kv8Ef" ACCESS_TOKEN_SECRET = "dlkbbp3Ibc7nSH6f1tHZeopMCn4XKPANqYAahQgXgheot"

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294

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0.789474

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0

0.141304

0.061224

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5

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0

1

0

1

null

0

1

0

1

null

0

5

6d5455bf492b14ad47974bb01588e1305474fce7

223

py

Python

Stage_0_Aderinsola.py

Derinsolar/team-greider

a034229d0918e744f110c04000f451d7aa2ad03e

[ "MIT" ]

null

Stage_0_Aderinsola.py

Derinsolar/team-greider

a034229d0918e744f110c04000f451d7aa2ad03e

[ "MIT" ]

null

Stage_0_Aderinsola.py

Derinsolar/team-greider

a034229d0918e744f110c04000f451d7aa2ad03e

[ "MIT" ]

null

print("Name: Aderinsola Adebisi") print("Email: adebisiaderinsola@gmail.com") print("Slack username: @Aderinsola") print("Biostack: Drug Development") print("Twitter username: @Derinsolar") print("Hamming distance: 2")

37.166667

44

0.753363

25

223

6.72

0.72

0

0.004951

0.09417

223

6

45

37.166667

0.826733

0

0.726027

0.123288

0

1

0

true

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

ed9c74d2807266b30af8f73fe67a747e14a94dd9

435

py

Python

app/model/builder.py

remirab/regression-api

8fa601ef2db1b0dbeaa3b82fe4e977624dcd1461

[ "MIT" ]

null

app/model/builder.py

remirab/regression-api

8fa601ef2db1b0dbeaa3b82fe4e977624dcd1461

[ "MIT" ]

null

app/model/builder.py

remirab/regression-api

8fa601ef2db1b0dbeaa3b82fe4e977624dcd1461

[ "MIT" ]

null

from sklearn.linear_model import LinearRegression, BayesianRidge, ARDRegression, LogisticRegression, HuberRegressor, Ridge class Model: def __init__(self): """ Constructor """ pass def maker(self): raise Exception("Not implemented yet!") def trainer(self): raise Exception("Not implemented yet!") def predictor(self): raise Exception("Not implemented yet!")

22.894737

122

0.650575

42

435

6.619048

0.595238

0.097122

0.194245

0.226619

0.399281

0.273381

0

0.257471

435

18

123

24.166667

0.860681

0.025287

0

0.3

0

0.15

0

1

0.4

false

0.1

0

0.6

0

null

0

1

0

null

0

1

0

1

0

1

0

5

edb08f27c5ba02ef27742efc76814cb48d8c6d14

69

py

Python

kvfile/__init__.py

akariv/kvfile

7fd800e520a3617d729bfb4182f242b5d446e844

[ "MIT" ]

3

2018-06-18T18:06:22.000Z

2019-06-26T11:50:01.000Z

kvfile/__init__.py

akariv/kvfile

7fd800e520a3617d729bfb4182f242b5d446e844

[ "MIT" ]

null

kvfile/__init__.py

akariv/kvfile

7fd800e520a3617d729bfb4182f242b5d446e844

[ "MIT" ]

1

2018-08-06T21:26:10.000Z

from .kvfile import KVFile, db_kind from .cached import CachedKVFile

23

35

0.826087

10

69

5.6

0.7

0

0.130435

69

2

36

34.5

0.933333

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

61031dc70fc51c90d4071e088341b56c022934e7

60

py

Python

flask_cloudflare_remote/__init__.py

cs91chris/flask_cloudflare_remote

f4f9e7dd3f0176223afd4da269dd921b7d13108b

[ "MIT" ]

1

2020-04-14T13:32:42.000Z

flask_cloudflare_remote/__init__.py

cs91chris/flask_cloudflare_remote

f4f9e7dd3f0176223afd4da269dd921b7d13108b

[ "MIT" ]

null

flask_cloudflare_remote/__init__.py

cs91chris/flask_cloudflare_remote

f4f9e7dd3f0176223afd4da269dd921b7d13108b

[ "MIT" ]

2

2020-12-25T16:47:58.000Z

2021-11-09T16:22:21.000Z

from .remote import CloudflareRemote from .version import *

20

36

0.816667

7

60

7

0.714286

0

0.133333

60

2

37

30

0.942308

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

b658b2f1f154d09a5a0215466d7c2b6a2bc0b4ad

155

py

Python

misc/socketClientPython.py

vyzboy92/Accelerated-Face-Reidentification-and-Emotion-Recognition

f653f6fbcc82f39667c3c8fa2025a8c26825c92a

[ "MIT" ]

2

2019-05-01T23:17:46.000Z

2019-12-15T18:20:45.000Z

misc/socketClientPython.py

vyzboy92/Accelerated-Face-Reidentification-and-Emotion-Recognition

f653f6fbcc82f39667c3c8fa2025a8c26825c92a

[ "MIT" ]

null

misc/socketClientPython.py

vyzboy92/Accelerated-Face-Reidentification-and-Emotion-Recognition

f653f6fbcc82f39667c3c8fa2025a8c26825c92a

[ "MIT" ]

2

2019-04-17T08:02:20.000Z

2019-04-24T03:08:46.000Z

from websocket import create_connection ws = create_connection("ws://localhost:8888/ws") ws.send("Python , Vysah") # while True: # print ws.recv()

15.5

48

0.703226

21

155

5.095238

0.714286

0.299065

0.336449

0

0.030534

0.154839

155

9

49

17.222222

0.78626

0.2

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0.184874

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1

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false

0

0.333333

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0.333333

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0

null

1

0

1

0

null

0

1

0

5

b6632fd1365145f3ab7e32aeab2cb2ce84a107e6

76

py

Python

reading/reader/__init__.py

akyruu/blender-cartography-addon

4f34b029d9b6a72619227ab3ceaed9393506934e

[ "Apache-2.0" ]

null

reading/reader/__init__.py

akyruu/blender-cartography-addon

4f34b029d9b6a72619227ab3ceaed9393506934e

[ "Apache-2.0" ]

null

reading/reader/__init__.py

akyruu/blender-cartography-addon

4f34b029d9b6a72619227ab3ceaed9393506934e

[ "Apache-2.0" ]

null

from .csv import CartographyCsvReader from .tsv import CartographyTsvReader

25.333333

37

0.868421

8

76

8.25

0.75

0

0.105263

76

2

38

0.970588

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

b66bcfbd16377872ee0d3ced4b79adcdff07c973

298

py

Python

src/ufdl/json/core/jobs/notification/_PrintNotification.py

waikato-ufdl/ufdl-json-messages

408901bdf79aa9ae7cff1af165deee83e62f6088

[ "Apache-2.0" ]

null

src/ufdl/json/core/jobs/notification/_PrintNotification.py

waikato-ufdl/ufdl-json-messages

408901bdf79aa9ae7cff1af165deee83e62f6088

[ "Apache-2.0" ]

null

src/ufdl/json/core/jobs/notification/_PrintNotification.py

waikato-ufdl/ufdl-json-messages

408901bdf79aa9ae7cff1af165deee83e62f6088

[ "Apache-2.0" ]

null

from wai.json.object.property import StringProperty from ._Notification import Notification class PrintNotification(Notification['PrintNotification']): """ Notification specification for printing to standard output. """ # The message to print message: str = StringProperty()

24.833333

63

0.751678

29

298

7.689655

0.689655

0.26009

0

0.171141

298

11

64

27.090909

0.902834

0.271812

0

0.084577

0

1

0

true

0

0.5

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

5

b67346b1c2fb3fe56dc06078553e75518effb594

43

py

Python

tests/components/blueprint/__init__.py

tbarbette/core

8e58c3aa7bc8d2c2b09b6bd329daa1c092d52d3c

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

tests/components/blueprint/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

tests/components/blueprint/__init__.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""Tests for the blueprint integration."""

21.5

42

0.72093

5

43

6.2

1

0

0.116279

43

1

43

0.815789

0.837209

0

null

0

null

0

null

0

null

1

null

true

0

null

1

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null

0

1

0

1

0

null

0

1

0

5

b690c05d7d8b16b3fddda09517f8eb6c3dec8526

201

py

Python

python/Strings/python-mutations.py

cdrowley/hackerrank

cc5c925327cd3ce5b52c1614b814da75d42cca72

[ "MIT" ]

null

python/Strings/python-mutations.py

cdrowley/hackerrank

cc5c925327cd3ce5b52c1614b814da75d42cca72

[ "MIT" ]

null

python/Strings/python-mutations.py

cdrowley/hackerrank

cc5c925327cd3ce5b52c1614b814da75d42cca72

[ "MIT" ]

null

# https://www.hackerrank.com/challenges/python-mutations/problem def mutate_string(string, position, character): string = list(string) string[position] = character return "".join(string)

25.125

64

0.731343

23

201

6.347826

0.695652

0.164384

0.273973

0.39726

0

0.139303

201

7

65

28.714286

0.843931

0.308458

0

1

0.25

false

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

5

b69d05047175f16a0433c44c5195c3b420a75371

129

py

Python

api/admin.py

Alonso-Arias/misPerris2019

9e1649839f498dc2ad78e685ab7bef0683de3b73

[ "MIT" ]

null

api/admin.py

Alonso-Arias/misPerris2019

9e1649839f498dc2ad78e685ab7bef0683de3b73

[ "MIT" ]

14

2019-12-05T01:02:44.000Z

2022-03-12T00:06:57.000Z

api/admin.py

Alonso-Arias/misPerris2019

9e1649839f498dc2ad78e685ab7bef0683de3b73

[ "MIT" ]

null

from django.contrib import admin from .models import Mascota, Usuario admin.site.register(Mascota) admin.site.register(Usuario)

21.5

36

0.821705

18

129

5.888889

0.555556

0.169811

0.320755

0

0.093023

129

5

37

25.8

0.905983

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

b69f963815f495af91ea8e5eea7a217686723ddd

51

py

Python

tests/data/codebases/simple_python2/bar.py

cgruber/make-open-easy

b433ba61d2f7b32d06eb7df8db38ba545827ad5e

[ "Apache-2.0" ]

5

2016-05-08T00:55:46.000Z

2020-03-14T06:57:30.000Z

tests/data/codebases/simple_python2/bar.py

cgruber/make-open-easy

b433ba61d2f7b32d06eb7df8db38ba545827ad5e

[ "Apache-2.0" ]

null

tests/data/codebases/simple_python2/bar.py

cgruber/make-open-easy

b433ba61d2f7b32d06eb7df8db38ba545827ad5e

[ "Apache-2.0" ]

10

2015-06-08T21:15:13.000Z

2021-10-16T15:06:01.000Z

#!/usr/bin/env python print 'Hello again, world!'

12.75

27

0.686275

8

51

4.375

1

0

0.137255

51

3

28

17

0.795455

0.392157

0

0.633333

0

null

0

null

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

b6b2588f1716800543d047b34ee8072385b763e8

17

py

Python

cg/__init__.py

indranilsinharoy/iutils

1b102029306fa2947d69d8ca80d976d143f3d068

[ "MIT" ]

null

cg/__init__.py

indranilsinharoy/iutils

1b102029306fa2947d69d8ca80d976d143f3d068

[ "MIT" ]

null

cg/__init__.py

indranilsinharoy/iutils

1b102029306fa2947d69d8ca80d976d143f3d068

[ "MIT" ]

null

# graphics utils

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null

0

1

0

5

fcbd51fb077da8cf696034b1c9dd9cc84ca645a8

871

py

Python

python/taichi/ui/ui.py

kxxt/taichi

15f39b79c258080f1e34fcbdc29646d9ced0a4fe

[ "MIT" ]

15

2020-01-29T19:07:19.000Z

2021-05-12T02:53:22.000Z

python/taichi/ui/ui.py

kxxt/taichi

15f39b79c258080f1e34fcbdc29646d9ced0a4fe

[ "MIT" ]

null

python/taichi/ui/ui.py

kxxt/taichi

15f39b79c258080f1e34fcbdc29646d9ced0a4fe

[ "MIT" ]

2

2020-01-31T20:10:35.000Z

2021-03-16T07:51:59.000Z

from taichi.core import ti_core as _ti_core if _ti_core.GGUI_AVAILABLE: from .camera import Camera # pylint: disable=unused-import from .canvas import Canvas # pylint: disable=unused-import from .constants import * # pylint: disable=unused-import,wildcard-import from .imgui import Gui # pylint: disable=unused-import from .scene import Scene # pylint: disable=unused-import from .window import Window # pylint: disable=unused-import def make_camera(): return Camera(_ti_core.PyCamera()) ProjectionMode = _ti_core.ProjectionMode else: def err_no_ggui(): raise Exception("GGUI Not Available") class Window: def __init__(self, name, res, vsync=False): err_no_ggui() class Scene: def __init__(self): err_no_ggui() def make_camera(): err_no_ggui()

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false

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null

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1

0

1

0

5

fcc110062f8a5e41ca8bd58c0d75c0add5c93c04

10,887

py

Python

core/request_handlers/tests/test_files_handler.py

unklearn/python-runtime

d6647ce9bb9c02879a4b0fc58e267806b199eb2e

[ "MIT" ]

null

core/request_handlers/tests/test_files_handler.py

unklearn/python-runtime

d6647ce9bb9c02879a4b0fc58e267806b199eb2e

[ "MIT" ]

null

core/request_handlers/tests/test_files_handler.py

unklearn/python-runtime

d6647ce9bb9c02879a4b0fc58e267806b199eb2e

[ "MIT" ]

null

import pytest import json import os from support.base_test_handler import TestHandlerBase from core.constants import CellEvents, CellExecutionStatus, CELLS_NAMESPACE @pytest.mark.handlers @pytest.mark.integration class TestFilesHandler(TestHandlerBase): def assert_file_and_remove(self, file_path, content=None, remove=True, response=None): app = self.get_app() full_path = os.path.join(app.config.FILE_ROOT_DIR, file_path) assert os.path.exists(full_path) if response: body = response.body.decode('utf-8') assert body == file_path if content: with open(full_path, 'r') as f: assert f.read() == content if remove: os.unlink(full_path) def test_creating_files(self): resp = self.fetch('/files', method='POST', body=json.dumps({ 'filePath': 'modules/test.py', 'content': 'print("Hello")' }), follow_redirects=False) assert resp.code == 200 # Assert that file exists self.assert_file_and_remove('modules/test.py', 'print("Hello")', response=resp) def test_file_overwrite(self): resp = self.fetch('/files', method='POST', body=json.dumps({ 'filePath': 'modules/test.py', 'content': 'print("Hello")' }), follow_redirects=False) assert resp.code == 200 # Assert that file exists self.assert_file_and_remove('modules/test.py', content='print("Hello")', remove=False, response=resp) resp = self.fetch('/files', method='POST', body=json.dumps({ 'filePath': 'modules/test.py', 'content': 'print("World")' }), follow_redirects=False) assert resp.code == 200 self.assert_file_and_remove('modules/test.py', content='print("World")', response=resp) def test_missing_file_arguments(self): resp = self.fetch('/files', method='POST', body=json.dumps({ 'filePath': None, 'content': 'print("Hello")' }), follow_redirects=False) assert resp.code == 400 resp = self.fetch('/files', method='POST', body=json.dumps({'filePath': 'modules/test.py'}), follow_redirects=False) assert resp.code == 400 def test_dangerous_file_path(self): resp = self.fetch('/files', method='POST', body=json.dumps({ 'filePath': '~/.ssh/config', 'content': 'Bad code' }), follow_redirects=False) assert resp.code == 200 self.assert_file_and_remove('.ssh/config', 'Bad code', response=resp) resp = self.fetch('/files', method='POST', body=json.dumps({ 'filePath': '../../../..ssh/config', 'content': 'Bad code' }), follow_redirects=False) assert resp.code == 200 # Assert that file exists self.assert_file_and_remove('ssh/config') resp = self.fetch('/files', method='POST', body=json.dumps({ 'filePath': '../../../../ssh/config', 'content': 'Bad code' }), follow_redirects=False) assert resp.code == 200 # Assert that file exists self.assert_file_and_remove('ssh/config', response=resp) def test_fetching_file_with_encoded_file_name(self): resp = self.fetch('/files', method='POST', body=json.dumps({ 'filePath': 'modules/test.py', 'content': 'print("Hello")' }), follow_redirects=False) assert resp.code == 200 resp = self.fetch('/files/modules%2Ftest.py', method='GET') assert resp.code == 200 assert resp.body == b'print("Hello")' def test_fetching_file_with_unencoded_file_name(self): resp = self.fetch('/files', method='POST', body=json.dumps({ 'filePath': 'modules/test.py', 'content': 'print("Hello")' }), follow_redirects=False) assert resp.code == 200 resp = self.fetch('/files/modules/test.py', method='GET') assert resp.code == 404 self.assert_file_and_remove('modules/test.py') def test_non_existent_file(self): resp = self.fetch('/files/modules%2Ftest.py', method='GET') assert resp.code == 404 @pytest.mark.integration @pytest.mark.handlers class TestFileExecutionHandler(TestHandlerBase): def test_missing_args(self): resp = self.fetch('/file-runs/', method='POST', body=json.dumps({})) assert resp.code == 400 resp = self.fetch('/file-runs/', method='POST', body=json.dumps({'cellId': 'cid'})) assert resp.code == 400 resp = self.fetch('/file-runs/', method='POST', body=json.dumps({ 'cellId': 'cid', 'channel': 'channel' })) assert resp.code == 400 resp = self.fetch('/file-runs/', method='POST', body=json.dumps({ 'cellId': 'cid', 'channel': 'channel', 'filePath': '' })) assert resp.code == 400 def test_non_existent_file_exec(self): resp = self.fetch('/file-runs/', method='POST', body=json.dumps({ 'cellId': 'cid', 'channel': 'channel', 'filePath': 'modules/test.py' })) assert resp.code == 404 def test_invalid_file_extension(self): app = self.get_app() file_path = os.path.join(app.config.FILE_ROOT_DIR, 'modules/test.sh') with open(file_path, 'w') as f: f.write('echo Hello') resp = self.fetch('/file-runs/', method='POST', body=json.dumps({ 'cellId': 'cid', 'channel': 'channel', 'filePath': 'modules/test.sh' })) assert resp.code == 400 os.unlink(file_path) def test_file_run_success(self): app = self.get_app() file_path = os.path.join(app.config.FILE_ROOT_DIR, 'modules/test.py') with open(file_path, 'w') as f: f.write('print("Hello")') resp = self.fetch('/file-runs/', method='POST', body=json.dumps({ 'cellId': 'cid', 'channel': 'channel', 'filePath': 'modules/test.py' })) assert resp.code == 200 assert self.socketio.find_event(CellEvents.START_RUN, { 'id': 'cid', 'status': CellExecutionStatus.BUSY }, room='channel', namespace=CELLS_NAMESPACE) assert self.socketio.find_event(CellEvents.RESULT, { 'id': 'cid', 'output': 'Hello\n', 'error': '' }, room='channel', namespace=CELLS_NAMESPACE) assert self.socketio.find_event(CellEvents.END_RUN, { 'id': 'cid', 'status': CellExecutionStatus.DONE }, room='channel', namespace=CELLS_NAMESPACE) os.unlink(file_path) def test_file_run_failure(self): app = self.get_app() file_path = os.path.join(app.config.FILE_ROOT_DIR, 'modules/test.py') with open(file_path, 'w') as f: f.write('print("Hello"') resp = self.fetch('/file-runs/', method='POST', body=json.dumps({ 'cellId': 'cid', 'channel': 'channel', 'filePath': 'modules/test.py' })) assert resp.code == 200 assert self.socketio.find_event(CellEvents.START_RUN, { 'id': 'cid', 'status': CellExecutionStatus.BUSY }, room='channel', namespace=CELLS_NAMESPACE) assert self.socketio.find_event(CellEvents.RESULT, { 'id': 'cid', 'output': '', 'error': ' File "modules/test.py", line 2\n \n ' ' ^\nSyntaxError: unexpected EOF while parsing\n' }, room='channel', namespace=CELLS_NAMESPACE) assert self.socketio.find_event(CellEvents.END_RUN, { 'id': 'cid', 'status': CellExecutionStatus.ERROR }, room='channel', namespace=CELLS_NAMESPACE) os.unlink(file_path)

35.695082

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0.061183

0.072613

0.797624

0.776109

0.763559

0.74563

0.727701

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0.011548

0.467071

10,887

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0.757497

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0.692308

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0.010476

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0.157895

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0.052632

false

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0.020243

0

0.080972

0.048583

0

null

0

1

0

null

0

5

fcd8123688bde088722a4d0749e717e6225dc16b

74

py

Python

milk/measures/__init__.py

tzuryby/milk

a7159b748414d4d095741978fb994c4affcf6b9b

[ "MIT" ]

1

2015-11-05T12:31:00.000Z

milk/measures/__init__.py

tzuryby/milk

a7159b748414d4d095741978fb994c4affcf6b9b

[ "MIT" ]

null

milk/measures/__init__.py

tzuryby/milk

a7159b748414d4d095741978fb994c4affcf6b9b

[ "MIT" ]

null

from measures import accuracy, waccuracy, zero_one_loss, confusion_matrix

37

73

0.864865

10

74

6.1

1

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0.094595

74

1

74

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true

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null

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null

0

1

0

1

0

1

0

5

fce69ea48d9d6a8537d0d09c2f75b149a0b501f0

109

py

Python

src/test/test_python/test_wifi.py

Ladvien/micro-python-repl

b4683f8e43b54f75d5c53177524c2463251f9ac9

[ "MIT" ]

null

src/test/test_python/test_wifi.py

Ladvien/micro-python-repl

b4683f8e43b54f75d5c53177524c2463251f9ac9

[ "MIT" ]

null

src/test/test_python/test_wifi.py

Ladvien/micro-python-repl

b4683f8e43b54f75d5c53177524c2463251f9ac9

[ "MIT" ]

null

import network sta_if = network.WLAN(network.STA_IF) sta_if.active(True) sta_if.connect('SSID', 'password')

18.166667

37

0.770642

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109

4.444444

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0.3

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109

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false

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1

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null

0

1

0

5

fcea12afd438e8f47f69f31ff693434516a0fb5a

15,058

py

Python

examples/temporal_rev_learn/utils.py

dimarkov/pyBefit

1d24ce6e488ecd9826862bcd765661407d8546f3

[ "MIT" ]

1

2018-09-28T17:33:38.000Z

examples/temporal_rev_learn/utils.py

dimarkov/pyBefit

1d24ce6e488ecd9826862bcd765661407d8546f3

[ "MIT" ]

null

examples/temporal_rev_learn/utils.py

dimarkov/pyBefit

1d24ce6e488ecd9826862bcd765661407d8546f3

[ "MIT" ]

1

2018-09-28T17:33:41.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Various utility functions for simulations and inference @author: Dimitrije Markovic """ import jax.numpy as jnp import numpyro as npyro import numpyro.distributions as dist from jax import random, lax, nn from numpyro.infer import log_likelihood from numpyro.distributions import TransformedDistribution, transforms from opt_einsum import contract from numpyro.contrib.control_flow import scan from pybefit.agents import HSMMAI as Agent from pybefit.agents import logits def einsum(equation, *args): return contract(equation, *args, backend='jax') def simulator(process, agent, gamma, seed=0, **model_kw): # POMDP simulator def sim_fn(carry, t): rng_key, prior = carry rng_key, _rng_key = random.split(rng_key) choices = agent.action_selection(_rng_key, prior, gamma=gamma[..., t, :], **model_kw) outcomes = process(t, choices) posterior = agent.learning(t, outcomes, choices, prior) return (rng_key, posterior), {'outcomes': outcomes, 'choices': choices} rng_key = random.PRNGKey(seed) _, sequence = lax.scan(sim_fn, (rng_key, agent.prior), jnp.arange(agent.T)) sequence['outcomes'].block_until_ready() return sequence def estimate_beliefs(outcomes, choices, device, mask=1, nu_max=10, nu_min=0, **kwargs): # belief estimator from fixed responses and outcomes T, N = choices.shape assert outcomes.shape == (T, N) agent = Agent(T, N, nu_max=nu_max, nu_min=nu_min, mask=mask, device=device, prior_kwargs=kwargs) def sim_fn(carry, t): prior = carry posterior = agent.learning(t, outcomes[t], choices[t], prior) p_cfm, params = prior p_c = einsum('...cfm->...c', p_cfm) return posterior, {'beliefs': (p_c, params)} _, sequence = lax.scan(sim_fn, agent.prior, jnp.arange(T)) sequence['beliefs'][0].block_until_ready() return sequence, agent def generative_model(beliefs, y=None, mask=True): # generative model T, N = beliefs[0].shape[:2] with npyro.plate('N', N): gamma = npyro.sample('gamma', dist.Gamma(20., 2.)) td = TransformedDistribution( dist.Normal(jnp.array([-1., .7]), jnp.array([1., 0.2])).to_event(1), transforms.OrderedTransform() ) lams = npyro.sample('lams', td) U = jnp.pad(lams, ((0, 0), (0, 2)), 'constant', constant_values=(0,)) with npyro.plate('T', T): logs = npyro.deterministic('logits', logits(beliefs, jnp.expand_dims(gamma, -1), jnp.expand_dims(U, -2)) ) npyro.sample('y', dist.CategoricalLogits(logs).mask(mask), obs=y) def log_pred_density(model, samples, *args, **kwargs): # waic score of posterior samples log_lk = log_likelihood(model, samples, *args, **kwargs)['y'] ll = log_lk.sum(-1) S = ll.shape[0] lppd = nn.logsumexp(ll, 0) - jnp.log(S) p_waic = jnp.var(ll, axis=0, ddof=1) return lppd - p_waic, log_lk def single_model(beliefs, y, mask, dynamic_gamma=False, dynamic_preference=False): if dynamic_gamma: if dynamic_preference: fulldyn_single_model(beliefs, y, mask) else: gammadyn_single_model(beliefs, y, mask) else: if dynamic_preference: prefdyn_single_model(beliefs, y, mask) else: nondyn_single_model(beliefs, y, mask) def fulldyn_single_model(beliefs, y, mask): T, _ = beliefs[0].shape c0 = beliefs[-1] mu = npyro.sample('mu', dist.Normal(5., 5.)) lam12 = npyro.sample('lam12', dist.HalfCauchy(1.).expand([2]).to_event(1)) lam34 = npyro.sample('lam34', dist.HalfCauchy(1.)) _lam34 = jnp.expand_dims(lam34, -1) lam0 = npyro.deterministic('lam0', jnp.concatenate([lam12.cumsum(-1), _lam34, _lam34], -1)) eta = npyro.sample('eta', dist.Beta(1, 10)) scale = npyro.sample('scale', dist.HalfNormal(1.)) theta = npyro.sample('theta', dist.HalfCauchy(5.)) rho = jnp.exp(- theta) sigma = jnp.sqrt( (1 - rho**2) / (2 * theta) ) * scale x0 = jnp.zeros(1) def transition_fn(carry, t): lam_prev, x_prev = carry gamma = npyro.deterministic('gamma', nn.softplus(mu + x_prev)) U = jnp.log(lam_prev) - jnp.log(lam_prev.sum(-1, keepdims=True)) logs = logits((beliefs[0][t], beliefs[1][t]), jnp.expand_dims(gamma, -1), jnp.expand_dims(U, -2)) lam_next = npyro.deterministic('lams', lam_prev + nn.one_hot(beliefs[2][t], 4) * jnp.expand_dims(mask[t] * eta, -1)) npyro.sample('y', dist.CategoricalLogits(logs).mask(mask[t])) noise = npyro.sample('dw', dist.Normal(0., 1.)) x_next = rho * x_prev + sigma * noise return (lam_next, x_next), None lam_start = npyro.deterministic('lam_start', lam0 + jnp.expand_dims(eta, -1) * c0) with npyro.handlers.condition(data={"y": y}): scan( transition_fn, (lam_start, x0), jnp.arange(T) ) def gammadyn_single_model(beliefs, y, mask): T, _ = beliefs[0].shape gamma = npyro.sample('gamma', dist.InverseGamma(2., 2.)) lam12 = npyro.sample('lam12', dist.HalfCauchy(1.).expand([2]).to_event(1)) lam34 = npyro.sample('lam34', dist.HalfCauchy(1.)) _lam34 = jnp.expand_dims(lam34, -1) lam0 = npyro.deterministic('lam0', jnp.concatenate([lam12.cumsum(-1), _lam34, _lam34], -1)) U = jnp.log(lam0) - jnp.log(lam0.sum(-1, keepdims=True)) scale = npyro.sample('scale', dist.HalfNormal(1.)) theta = npyro.sample('theta', dist.HalfCauchy(5.)) rho = jnp.exp(- theta) sigma = jnp.sqrt( (1 - rho**2) / (2 * theta) ) * scale x0 = jnp.zeros(1) def transition_fn(carry, t): x_prev = carry dyn_gamma = npyro.deterministic('dyn_gamma', nn.softplus( jnp.log(jnp.exp(gamma) - 1) + x_prev)) logs = logits((beliefs[0][t], beliefs[1][t]), jnp.expand_dims(gamma, -1), jnp.expand_dims(U, -2)) npyro.sample('y', dist.CategoricalLogits(logs).mask(mask[t])) noise = npyro.sample('dw', dist.Normal(0., 1.)) x_next = rho * x_prev + sigma * noise return x_next, None with npyro.handlers.condition(data={"y": y}): scan( transition_fn, x0, jnp.arange(T) ) def prefdyn_single_model(beliefs, y, mask): T, _ = beliefs[0].shape c0 = beliefs[-1] lam12 = npyro.sample('lam12', dist.HalfCauchy(1.).expand([2]).to_event(1)) lam34 = npyro.sample('lam34', dist.HalfCauchy(1.)) _lam34 = jnp.expand_dims(lam34, -1) lam0 = npyro.deterministic('lam0', jnp.concatenate([lam12.cumsum(-1), _lam34, _lam34], -1)) eta = npyro.sample('eta', dist.Beta(1, 10)) gamma = npyro.sample('gamma', dist.InverseGamma(2., 2.)) def transition_fn(carry, t): lam_prev = carry U = jnp.log(lam_prev) - jnp.log(lam_prev.sum(-1, keepdims=True)) logs = logits((beliefs[0][t], beliefs[1][t]), jnp.expand_dims(gamma, -1), jnp.expand_dims(U, -2)) lam_next = npyro.deterministic('lams', lam_prev + nn.one_hot(beliefs[2][t], 4) * jnp.expand_dims(mask[t] * eta, -1)) npyro.sample('y', dist.CategoricalLogits(logs).mask(mask[t])) return lam_next, None lam_start = npyro.deterministic('lam_start', lam0 + jnp.expand_dims(eta, -1) * c0) with npyro.handlers.condition(data={"y": y}): scan( transition_fn, lam_start, jnp.arange(T) ) def nondyn_single_model(beliefs, y, mask): T, _ = beliefs[0].shape lam12 = npyro.sample('lam12', dist.HalfCauchy(1.).expand([2]).to_event(1)) lam34 = npyro.sample('lam34', dist.HalfCauchy(1.)) _lam34 = jnp.expand_dims(lam34, -1) lam0 = npyro.deterministic('lam0', jnp.concatenate([lam12.cumsum(-1), _lam34, _lam34], -1)) U = jnp.log(lam0) - jnp.log(lam0.sum(-1, keepdims=True)) gamma = npyro.sample('gamma', dist.InverseGamma(2., 2.)) def transition_fn(carry, t): logs = logits((beliefs[0][t], beliefs[1][t]), jnp.expand_dims(gamma, -1), jnp.expand_dims(U, -2)) npyro.sample('y', dist.CategoricalLogits(logs).mask(mask[t])) return None, None with npyro.handlers.condition(data={"y": y}): scan( transition_fn, None, jnp.arange(T) ) def mixture_model(beliefs, y, mask, dynamic_gamma=False, dynamic_preference=False): if dynamic_gamma: if dynamic_preference: fulldyn_mixture_model(beliefs, y, mask) else: gammadyn_mixture_model(beliefs, y, mask) else: if dynamic_preference: prefdyn_mixture_model(beliefs, y, mask) else: nondynamic_mixture_model(beliefs, y, mask) def fulldyn_mixture_model(beliefs, y, mask): M, T, N, _ = beliefs[0].shape c0 = beliefs[-1] tau = .5 with npyro.plate('N', N): weights = npyro.sample('weights', dist.Dirichlet(tau * jnp.ones(M))) assert weights.shape == (N, M) mu = npyro.sample('mu', dist.Normal(5., 5.)) lam12 = npyro.sample('lam12', dist.HalfCauchy(1.).expand([2]).to_event(1)) lam34 = npyro.sample('lam34', dist.HalfCauchy(1.)) _lam34 = jnp.expand_dims(lam34, -1) lam0 = npyro.deterministic('lam0', jnp.concatenate([lam12.cumsum(-1), _lam34, _lam34], -1)) eta = npyro.sample('eta', dist.Beta(1, 10)) scale = npyro.sample('scale', dist.HalfNormal(1.)) theta = npyro.sample('theta', dist.HalfCauchy(5.)) rho = jnp.exp(- theta) sigma = jnp.sqrt( (1 - rho**2) / (2 * theta) ) * scale x0 = jnp.zeros(N) def transition_fn(carry, t): lam_prev, x_prev = carry gamma = npyro.deterministic('gamma', nn.softplus(mu + x_prev)) U = jnp.log(lam_prev) - jnp.log(lam_prev.sum(-1, keepdims=True)) logs = logits((beliefs[0][:, t], beliefs[1][:, t]), jnp.expand_dims(gamma, -1), jnp.expand_dims(U, -2)) lam_next = npyro.deterministic('lams', lam_prev + nn.one_hot(beliefs[2][t], 4) * jnp.expand_dims(mask[t] * eta, -1)) mixing_dist = dist.CategoricalProbs(weights) component_dist = dist.CategoricalLogits(logs.swapaxes(0, 1)).mask(mask[t][..., None]) with npyro.plate('subjects', N): y = npyro.sample('y', dist.MixtureSameFamily(mixing_dist, component_dist)) noise = npyro.sample('dw', dist.Normal(0., 1.)) x_next = rho * x_prev + sigma * noise return (lam_next, x_next), None lam_start = npyro.deterministic('lam_start', lam0 + jnp.expand_dims(eta, -1) * c0) with npyro.handlers.condition(data={"y": y}): scan( transition_fn, (lam_start, x0), jnp.arange(T) ) def prefdyn_mixture_model(beliefs, y, mask): M, T, N, _ = beliefs[0].shape c0 = beliefs[-1] tau = .5 with npyro.plate('N', N): weights = npyro.sample('weights', dist.Dirichlet(tau * jnp.ones(M))) assert weights.shape == (N, M) mu = npyro.sample('mu', dist.Normal(5., 5.)) lam12 = npyro.sample('lam12', dist.HalfCauchy(1.).expand([2]).to_event(1)) lam34 = npyro.sample('lam34', dist.HalfCauchy(1.)) _lam34 = jnp.expand_dims(lam34, -1) lam0 = npyro.deterministic('lam0', jnp.concatenate([lam12.cumsum(-1), _lam34, _lam34], -1)) eta = npyro.sample('eta', dist.Beta(1, 10)) gamma = npyro.deterministic('gamma', nn.softplus(mu)) def transition_fn(carry, t): lam_prev = carry U = jnp.log(lam_prev) - jnp.log(lam_prev.sum(-1, keepdims=True)) logs = logits((beliefs[0][:, t], beliefs[1][:, t]), jnp.expand_dims(gamma, -1), jnp.expand_dims(U, -2)) lam_next = npyro.deterministic('lams', lam_prev + nn.one_hot(beliefs[2][t], 4) * jnp.expand_dims(mask[t] * eta, -1)) mixing_dist = dist.CategoricalProbs(weights) component_dist = dist.CategoricalLogits(logs.swapaxes(0, 1)).mask(mask[t][..., None]) with npyro.plate('subjects', N): npyro.sample('y', dist.MixtureSameFamily(mixing_dist, component_dist)) return (lam_next), None lam_start = npyro.deterministic('lam_start', lam0 + jnp.expand_dims(eta, -1) * c0) with npyro.handlers.condition(data={"y": y}): scan( transition_fn, (lam_start), jnp.arange(T) ) def gammadyn_mixture_model(beliefs, y, mask): M, T, _ = beliefs[0].shape tau = .5 weights = npyro.sample('weights', dist.Dirichlet(tau * jnp.ones(M))) assert weights.shape == (M,) gamma = npyro.sample('gamma', dist.InverseGamma(2., 2.)) mu = jnp.log(jnp.exp(gamma) - 1) p = npyro.sample('p', dist.Dirichlet(jnp.ones(3))) p0 = npyro.deterministic('p0', jnp.concatenate([p[..., :1]/2, p[..., :1]/2 + p[..., 1:2], p[..., 2:]/2, p[..., 2:]/2], -1) ) scale = npyro.sample('scale', dist.Gamma(1., 1.)) rho = npyro.sample('rho', dist.Beta(1., 2.)) sigma = jnp.sqrt( - (1 - rho**2) / (2 * jnp.log(rho)) ) * scale U = jnp.log(p0) def transition_fn(carry, t): x_prev = carry gamma_dyn = npyro.deterministic('gamma_dyn', nn.softplus(mu + x_prev)) logs = logits((beliefs[0][:, t], beliefs[1][:, t]), jnp.expand_dims(gamma_dyn, -1), jnp.expand_dims(U, -2)) mixing_dist = dist.CategoricalProbs(weights) component_dist = dist.CategoricalLogits(logs).mask(mask[t]) npyro.sample('y', dist.MixtureSameFamily(mixing_dist, component_dist)) with npyro.handlers.reparam(config={"x_next": npyro.infer.reparam.TransformReparam()}): affine = dist.transforms.AffineTransform(rho*x_prev, sigma) x_next = npyro.sample('x_next', dist.TransformedDistribution(dist.Normal(0., 1.), affine)) return (x_next), None x0 = jnp.zeros(1) with npyro.handlers.condition(data={"y": y}): scan( transition_fn, (x0), jnp.arange(T) ) def nondynamic_mixture_model(beliefs, y, mask): M, T, _ = beliefs[0].shape tau = .5 weights = npyro.sample('weights', dist.Dirichlet(tau * jnp.ones(M))) assert weights.shape == (M,) gamma = npyro.sample('gamma', dist.InverseGamma(2., 2.)) p = npyro.sample('p', dist.Dirichlet(jnp.ones(3))) p0 = npyro.deterministic('p0', jnp.concatenate([p[..., :1]/2, p[..., :1]/2 + p[..., 1:2], p[..., 2:]/2, p[..., 2:]/2], -1)) U = jnp.log(p0) def transition_fn(carry, t): logs = logits((beliefs[0][:, t], beliefs[1][:, t]), jnp.expand_dims(gamma, -1), jnp.expand_dims(U, -2)) mixing_dist = dist.CategoricalProbs(weights) component_dist = dist.CategoricalLogits(logs).mask(mask[t]) npyro.sample('y', dist.MixtureSameFamily(mixing_dist, component_dist)) return None, None with npyro.handlers.condition(data={"y": y}): scan( transition_fn, None, jnp.arange(T) )

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null

0

1

0

null

0

5

fcfb11c55122b97ba21767929257af72f7b25677

101

py

Python

main.py

OlegMoiseev/tryCI

f2fc638d3d85720766c7b27a032d8b9bb3ce4633

[ "Apache-2.0" ]

null

main.py

OlegMoiseev/tryCI

f2fc638d3d85720766c7b27a032d8b9bb3ce4633

[ "Apache-2.0" ]

null

main.py

OlegMoiseev/tryCI

f2fc638d3d85720766c7b27a032d8b9bb3ce4633

[ "Apache-2.0" ]

null

import numpy as np def add(x, y): return x + y def get_zero_matrix(n): return np.zeros((n, n))

10.1

24

0.643564

21

101

3

0.666667

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null

0

1

0

null

0

1

0

1

0

5

1e1304e99800167fefd9009f7950cc8e6d8262c8

164

py

Python

myvenv/lib/python3.5/site-packages/allauth/socialaccount/providers/amazon/urls.py

tuvapp/tuvappcom

5ca2be19f4b0c86a1d4a9553711a4da9d3f32841

[ "MIT" ]

3

2015-02-13T15:06:40.000Z

2016-05-23T23:23:11.000Z

myvenv/lib/python3.5/site-packages/allauth/socialaccount/providers/amazon/urls.py

tuvapp/tuvappcom

5ca2be19f4b0c86a1d4a9553711a4da9d3f32841

[ "MIT" ]

9

2020-06-05T17:18:43.000Z

2022-03-11T23:15:04.000Z

myvenv/lib/python3.5/site-packages/allauth/socialaccount/providers/amazon/urls.py

tuvapp/tuvappcom

5ca2be19f4b0c86a1d4a9553711a4da9d3f32841

[ "MIT" ]

3

2015-08-13T22:28:36.000Z

2016-07-04T18:46:46.000Z

from allauth.socialaccount.providers.oauth2.urls import default_urlpatterns from .provider import AmazonProvider urlpatterns = default_urlpatterns(AmazonProvider)

32.8

75

0.878049

17

164

8.352941

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164

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1

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null

0

1

0

1

0

5

1e771e9de7ffaa39b7f5a42bf041dc70c63f203f

65

py

Python

tasks/mm_tasks/__init__.py

JustinLin610/OFA

f71efb85ead76bcb8b78e7020e182f108e4e9b36

[ "Apache-2.0" ]

null

tasks/mm_tasks/__init__.py

JustinLin610/OFA

f71efb85ead76bcb8b78e7020e182f108e4e9b36

[ "Apache-2.0" ]

null

tasks/mm_tasks/__init__.py

JustinLin610/OFA

f71efb85ead76bcb8b78e7020e182f108e4e9b36

[ "Apache-2.0" ]

null

from .caption import CaptionTask from .refcoco import RefcocoTask

32.5

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65

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32.5

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null

0

1

0

1

0

1

0

5

bf727168d4170005ebc5946d4cb30b7f91218416

43

py

Python

lib/nodegam/__init__.py

zzzace2000/cairl_nodegam

90d0d56a0e7be3d1cbba6179cbfc36d626456770

[ "MIT" ]

3

2021-11-10T14:48:24.000Z

2022-01-03T12:35:39.000Z

lib/nodegam/__init__.py

zzzace2000/cairl_nodegam

90d0d56a0e7be3d1cbba6179cbfc36d626456770

[ "MIT" ]

null

lib/nodegam/__init__.py

zzzace2000/cairl_nodegam

90d0d56a0e7be3d1cbba6179cbfc36d626456770

[ "MIT" ]

null

from .arch import * from .nn_utils import *

21.5

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null

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1

0

1

0

5

bf8699a1ad524bfa182495ea2f084bb6fa24d0e4

20,371

py

Python

tests/layout/test_footnotes.py

03psb/WeasyPrint

fa1d1766c7e0b7e64ca8d43243022e762930d650

[ "BSD-3-Clause" ]

null

tests/layout/test_footnotes.py

03psb/WeasyPrint

fa1d1766c7e0b7e64ca8d43243022e762930d650

[ "BSD-3-Clause" ]

null

tests/layout/test_footnotes.py

03psb/WeasyPrint

fa1d1766c7e0b7e64ca8d43243022e762930d650

[ "BSD-3-Clause" ]

null

""" weasyprint.tests.layout.footnotes --------------------------------- Tests for footnotes layout. """ import pytest from ..testing_utils import assert_no_logs, render_pages, tree_position @assert_no_logs def test_inline_footnote(): page, = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 7px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; } span { float: footnote; } </style> <div>abc<span>de</span></div>''') html, footnote_area = page.children body, = html.children div, = body.children div_textbox, footnote_call = div.children[0].children assert div_textbox.text == 'abc' assert footnote_call.children[0].text == '1' assert div_textbox.position_y == 0 footnote_marker, footnote_textbox = ( footnote_area.children[0].children[0].children) assert footnote_marker.children[0].text == '1.' assert footnote_textbox.text == 'de' assert footnote_area.position_y == 5 @assert_no_logs def test_block_footnote(): page, = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 7px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; } div.footnote { float: footnote; } </style> <div>abc<div class="footnote">de</div></div>''') html, footnote_area = page.children body, = html.children div, = body.children div_textbox, footnote_call = div.children[0].children assert div_textbox.text == 'abc' assert footnote_call.children[0].text == '1' assert div_textbox.position_y == 0 footnote_marker, footnote_textbox = ( footnote_area.children[0].children[0].children) assert footnote_marker.children[0].text == '1.' assert footnote_textbox.text == 'de' assert footnote_area.position_y == 5 @assert_no_logs def test_long_footnote(): page, = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 7px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; } span { float: footnote; } </style> <div>abc<span>de f</span></div>''') html, footnote_area = page.children body, = html.children div, = body.children div_textbox, footnote_call = div.children[0].children assert div_textbox.text == 'abc' assert footnote_call.children[0].text == '1' assert div_textbox.position_y == 0 footnote_line1, footnote_line2 = footnote_area.children[0].children footnote_marker, footnote_content1 = footnote_line1.children footnote_content2 = footnote_line2.children[0] assert footnote_marker.children[0].text == '1.' assert footnote_content1.text == 'de' assert footnote_area.position_y == 3 assert footnote_content2.text == 'f' assert footnote_content2.position_y == 5 @pytest.mark.xfail @assert_no_logs def test_after_marker_footnote(): # TODO: this syntax is in the specification, but we’re currently limited to # one pseudo element per selector, according to CSS 2.1: # https://drafts.csswg.org/css2/#selector-syntax # and Selectors Level 3: # https://drafts.csswg.org/selectors-3/#selector-syntax # This limitation doesn’t exist anymore in Selectors Level 4: # https://drafts.csswg.org/selectors-4/#typedef-compound-selector page, = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 7px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; } span { float: footnote; } ::footnote-marker::after { content: '|'; } </style> <div>abc<span>de</span></div>''') html, footnote_area = page.children footnote_marker, _ = footnote_area.children[0].children[0].children assert footnote_marker.children[0].text == '1.|' @assert_no_logs def test_several_footnote(): page1, page2, = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 7px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; orphans: 1; widows: 1; } span { float: footnote; } </style> <div>abcd e<span>fg</span> hijk l<span>mn</span></div>''') html1, footnote_area1 = page1.children body1, = html1.children div1, = body1.children div1_line1, div1_line2 = div1.children assert div1_line1.children[0].text == 'abcd' div1_line2_text, div1_footnote_call = div1.children[1].children assert div1_line2_text.text == 'e' assert div1_footnote_call.children[0].text == '1' footnote_marker1, footnote_textbox1 = ( footnote_area1.children[0].children[0].children) assert footnote_marker1.children[0].text == '1.' assert footnote_textbox1.text == 'fg' html2, footnote_area2 = page2.children body2, = html2.children div2, = body2.children div2_line1, div2_line2 = div2.children assert div2_line1.children[0].text == 'hijk' div2_line2_text, div2_footnote_call = div2.children[1].children assert div2_line2_text.text == 'l' assert div2_footnote_call.children[0].text == '2' footnote_marker2, footnote_textbox2 = ( footnote_area2.children[0].children[0].children) assert footnote_marker2.children[0].text == '2.' assert footnote_textbox2.text == 'mn' @assert_no_logs def test_reported_footnote_1(): page1, page2, = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 7px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; orphans: 1; widows: 1; } span { float: footnote; } </style> <div>abc<span>f1</span> hij<span>f2</span></div>''') html1, footnote_area1 = page1.children body1, = html1.children div1, = body1.children div_line1, div_line2 = div1.children div_line1_text, div_footnote_call1 = div_line1.children assert div_line1_text.text == 'abc' assert div_footnote_call1.children[0].text == '1' div_line2_text, div_footnote_call2 = div_line2.children assert div_line2_text.text == 'hij' assert div_footnote_call2.children[0].text == '2' footnote_marker1, footnote_textbox1 = ( footnote_area1.children[0].children[0].children) assert footnote_marker1.children[0].text == '1.' assert footnote_textbox1.text == 'f1' html2, footnote_area2 = page2.children assert not html2.children footnote_marker2, footnote_textbox2 = ( footnote_area2.children[0].children[0].children) assert footnote_marker2.children[0].text == '2.' assert footnote_textbox2.text == 'f2' @assert_no_logs def test_reported_footnote_2(): page1, page2, = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 7px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; orphans: 1; widows: 1; } span { float: footnote; } </style> <div>abc<span>f1</span> hij<span>f2</span> wow</div>''') html1, footnote_area1 = page1.children body1, = html1.children div1, = body1.children div_line1, div_line2 = div1.children div_line1_text, div_footnote_call1 = div_line1.children assert div_line1_text.text == 'abc' assert div_footnote_call1.children[0].text == '1' div_line2_text, div_footnote_call2 = div_line2.children assert div_line2_text.text == 'hij' assert div_footnote_call2.children[0].text == '2' footnote_marker1, footnote_textbox1 = ( footnote_area1.children[0].children[0].children) assert footnote_marker1.children[0].text == '1.' assert footnote_textbox1.text == 'f1' html2, footnote_area2 = page2.children body2, = html2.children div2, = body2.children div2_line, = div2.children assert div2_line.children[0].text == 'wow' footnote_marker2, footnote_textbox2 = ( footnote_area2.children[0].children[0].children) assert footnote_marker2.children[0].text == '2.' assert footnote_textbox2.text == 'f2' @assert_no_logs def test_reported_footnote_3(): page1, page2, = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 10px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; orphans: 1; widows: 1; } span { float: footnote; } </style> <div> abc<span>1</span> def<span>v long 2</span> ghi<span>3</span> </div>''') html1, footnote_area1 = page1.children body1, = html1.children div1, = body1.children line1, line2, line3 = div1.children assert line1.children[0].text == 'abc' assert line1.children[1].children[0].text == '1' assert line2.children[0].text == 'def' assert line2.children[1].children[0].text == '2' assert line3.children[0].text == 'ghi' assert line3.children[1].children[0].text == '3' footnote1, = footnote_area1.children assert footnote1.children[0].children[0].children[0].text == '1.' assert footnote1.children[0].children[1].text == '1' html2, footnote_area2 = page2.children footnote2, footnote3 = footnote_area2.children assert footnote2.children[0].children[0].children[0].text == '2.' assert footnote2.children[0].children[1].text == 'v' assert footnote2.children[1].children[0].text == 'long' assert footnote2.children[2].children[0].text == '2' assert footnote3.children[0].children[0].children[0].text == '3.' assert footnote3.children[0].children[1].text == '3' @assert_no_logs @pytest.mark.parametrize('css, tail', ( ('p { break-inside: avoid }', '<br>e<br>f'), ('p { widows: 4 }', '<br>e<br>f'), ('p + p { break-before: avoid }', '</p><p>e<br>f'), )) def test_footnote_area_after_call(css, tail): pages = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 10px; background: white; margin: 0; } body { font-family: weasyprint; font-size: 2px; line-height: 1; orphans: 2; widows: 2; margin: 0; } span { float: footnote; } %s </style> <div>a<br>b</div> <p>c<br>d<span>x</span>%s</p>''' % (css, tail)) footnote_call = tree_position( pages, lambda box: box.element_tag == 'p::footnote-call') footnote_area = tree_position( pages, lambda box: type(box).__name__ == 'FootnoteAreaBox') assert footnote_call < footnote_area @assert_no_logs def test_footnote_display_inline(): page, = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 50px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; } span { float: footnote; footnote-display: inline; } </style> <div>abc<span>d</span> fgh<span>i</span></div>''') html, footnote_area = page.children body, = html.children div, = body.children div_line1, div_line2 = div.children div_textbox1, footnote_call1 = div_line1.children div_textbox2, footnote_call2 = div_line2.children assert div_textbox1.text == 'abc' assert div_textbox2.text == 'fgh' assert footnote_call1.children[0].text == '1' assert footnote_call2.children[0].text == '2' line = footnote_area.children[0] footnote_mark1, footnote_textbox1 = line.children[0].children footnote_mark2, footnote_textbox2 = line.children[1].children assert footnote_mark1.children[0].text == '1.' assert footnote_textbox1.text == 'd' assert footnote_mark2.children[0].text == '2.' assert footnote_textbox2.text == 'i' @assert_no_logs def test_footnote_longer_than_space_left(): page1, page2 = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 7px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; } span { float: footnote; } </style> <div>abc<span>def ghi jkl</span></div>''') html1, = page1.children body1, = html1.children div, = body1.children div_textbox, footnote_call = div.children[0].children assert div_textbox.text == 'abc' assert footnote_call.children[0].text == '1' html2, footnote_area = page2.children assert not html2.children footnote_line1, footnote_line2, footnote_line3 = ( footnote_area.children[0].children) footnote_marker, footnote_content1 = footnote_line1.children footnote_content2 = footnote_line2.children[0] footnote_content3 = footnote_line3.children[0] assert footnote_marker.children[0].text == '1.' assert footnote_content1.text == 'def' assert footnote_content2.text == 'ghi' assert footnote_content3.text == 'jkl' @assert_no_logs def test_footnote_longer_than_page(): # Nothing is defined for this use case in the specification. In WeasyPrint, # the content simply overflows. page1, page2 = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 7px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; } span { float: footnote; } </style> <div>abc<span>def ghi jkl mno</span></div>''') html1, = page1.children body1, = html1.children div, = body1.children div_textbox, footnote_call = div.children[0].children assert div_textbox.text == 'abc' assert footnote_call.children[0].text == '1' html2, footnote_area2 = page2.children assert not html2.children footnote_line1, footnote_line2, footnote_line3, footnote_line4 = ( footnote_area2.children[0].children) footnote_marker1, footnote_content1 = footnote_line1.children footnote_content2 = footnote_line2.children[0] footnote_content3 = footnote_line3.children[0] footnote_content4 = footnote_line4.children[0] assert footnote_marker1.children[0].text == '1.' assert footnote_content1.text == 'def' assert footnote_content2.text == 'ghi' assert footnote_content3.text == 'jkl' assert footnote_content4.text == 'mno' @assert_no_logs def test_footnote_policy_line(): page1, page2 = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 9px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; } span { float: footnote; footnote-policy: line; } </style> <div>abc def ghi jkl<span>1</span></div>''') html, = page1.children body, = html.children div, = body.children linebox1, linebox2 = div.children assert linebox1.children[0].text == 'abc' assert linebox2.children[0].text == 'def' html, footnote_area = page2.children body, = html.children div, = body.children linebox1, linebox2 = div.children assert linebox1.children[0].text == 'ghi' assert linebox2.children[0].text == 'jkl' assert linebox2.children[1].children[0].text == '1' footnote_marker, footnote_textbox = ( footnote_area.children[0].children[0].children) assert footnote_marker.children[0].text == '1.' assert footnote_textbox.text == '1' @assert_no_logs def test_footnote_policy_block(): page1, page2 = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 9px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; } span { float: footnote; footnote-policy: block; } </style> <div>abc</div><div>def ghi jkl<span>1</span></div>''') html, = page1.children body, = html.children div, = body.children linebox1, = div.children assert linebox1.children[0].text == 'abc' html, footnote_area = page2.children body, = html.children div, = body.children linebox1, linebox2, linebox3 = div.children assert linebox1.children[0].text == 'def' assert linebox2.children[0].text == 'ghi' assert linebox3.children[0].text == 'jkl' assert linebox3.children[1].children[0].text == '1' footnote_marker, footnote_textbox = ( footnote_area.children[0].children[0].children) assert footnote_marker.children[0].text == '1.' assert footnote_textbox.text == '1' @assert_no_logs def test_footnote_repagination(): page, = render_pages(''' <style> @font-face {src: url(weasyprint.otf); font-family: weasyprint} @page { size: 9px 7px; background: white; } div { font-family: weasyprint; font-size: 2px; line-height: 1; } div::after { content: counter(pages); } span { float: footnote; } </style> <div>ab<span>de</span></div>''') html, footnote_area = page.children body, = html.children div, = body.children div_textbox, footnote_call, div_after = div.children[0].children assert div_textbox.text == 'ab' assert footnote_call.children[0].text == '1' assert div_textbox.position_y == 0 assert div_after.children[0].text == '1' footnote_marker, footnote_textbox = ( footnote_area.children[0].children[0].children) assert footnote_marker.children[0].text == '1.' assert footnote_textbox.text == 'de' assert footnote_area.position_y == 5

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0.018974

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0.003604

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null

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1

0

1

0

null

0

5

bfc50ee74e15339d389c232f477c1173fc3a183b

353

py

Python

pyxq/msg/md.py

goodchinas/pyxq

c7f6ea63084c18178049451f30f32f04080a511c

[ "MIT" ]

4

2019-12-17T11:05:53.000Z

2020-06-01T05:41:02.000Z

pyxq/msg/md.py

goodchinas/pyxq

c7f6ea63084c18178049451f30f32f04080a511c

[ "MIT" ]

null

pyxq/msg/md.py

goodchinas/pyxq

c7f6ea63084c18178049451f30f32f04080a511c

[ "MIT" ]

2

2019-11-13T01:11:53.000Z

2019-12-17T10:55:44.000Z

""" market message type. """ import dataclasses as dc from . import fa from .. import ba @dc.dataclass class Tick(fa.S): price: float volume: float pass @dc.dataclass class OrderBook(fa.S): pass @dc.dataclass class Open(ba.Msg): pass class Close(ba.Msg): pass @dc.dataclass class Factor(fa.S): data: dict pass

10.085714

24

0.643059

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353

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0.193833

0.281938

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0

0.240793

353

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0.056657

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true

0.25

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0.55

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null

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1

0

null

0

1

0

1

0

5

449c4e015fdda53f5836b011ad6748e1931e4b5f

16,411

py

Python

tests/contrib/operators/test_gcp_compute_operator.py

tekn0ir/incubator-airflow

7df4405aa5a0c99e51722321caa7af660d35794b

[ "Apache-2.0" ]

4

2019-01-17T06:21:45.000Z

2020-06-20T01:59:57.000Z

tests/contrib/operators/test_gcp_compute_operator.py

tekn0ir/incubator-airflow

7df4405aa5a0c99e51722321caa7af660d35794b

[ "Apache-2.0" ]

14

2018-10-24T03:15:11.000Z

2019-01-02T19:02:58.000Z

tests/contrib/operators/test_gcp_compute_operator.py

cse-airflow/incubator-airflow

215b8c8170bd63f4c449614945bb4b6d90f6a860

[ "Apache-2.0" ]

6

2018-12-04T12:15:23.000Z

2020-11-23T03:51:41.000Z

# -*- coding: utf-8 -*- # # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import ast import unittest from airflow import AirflowException, configuration from airflow.contrib.operators.gcp_compute_operator import GceInstanceStartOperator, \ GceInstanceStopOperator, GceSetMachineTypeOperator from airflow.models import TaskInstance, DAG from airflow.utils import timezone try: # noinspection PyProtectedMember from unittest import mock except ImportError: try: import mock except ImportError: mock = None PROJECT_ID = 'project-id' LOCATION = 'zone' RESOURCE_ID = 'resource-id' SHORT_MACHINE_TYPE_NAME = 'n1-machine-type' SET_MACHINE_TYPE_BODY = { 'machineType': 'zones/{}/machineTypes/{}'.format(LOCATION, SHORT_MACHINE_TYPE_NAME) } DEFAULT_DATE = timezone.datetime(2017, 1, 1) class GceInstanceStartTest(unittest.TestCase): @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_instance_start(self, mock_hook): mock_hook.return_value.start_instance.return_value = True op = GceInstanceStartOperator( project_id=PROJECT_ID, zone=LOCATION, resource_id=RESOURCE_ID, task_id='id' ) result = op.execute(None) mock_hook.assert_called_once_with(api_version='v1', gcp_conn_id='google_cloud_default') mock_hook.return_value.start_instance.assert_called_once_with( PROJECT_ID, LOCATION, RESOURCE_ID ) self.assertTrue(result) # Setting all of the operator's input parameters as templated dag_ids # (could be anything else) just to test if the templating works for all fields @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_instance_start_with_templates(self, mock_hook): dag_id = 'test_dag_id' configuration.load_test_config() args = { 'start_date': DEFAULT_DATE } self.dag = DAG(dag_id, default_args=args) op = GceInstanceStartOperator( project_id='{{ dag.dag_id }}', zone='{{ dag.dag_id }}', resource_id='{{ dag.dag_id }}', gcp_conn_id='{{ dag.dag_id }}', api_version='{{ dag.dag_id }}', task_id='id', dag=self.dag ) ti = TaskInstance(op, DEFAULT_DATE) ti.render_templates() self.assertEqual(dag_id, getattr(op, 'project_id')) self.assertEqual(dag_id, getattr(op, 'zone')) self.assertEqual(dag_id, getattr(op, 'resource_id')) self.assertEqual(dag_id, getattr(op, 'gcp_conn_id')) self.assertEqual(dag_id, getattr(op, 'api_version')) @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_start_should_throw_ex_when_missing_project_id(self, mock_hook): with self.assertRaises(AirflowException) as cm: op = GceInstanceStartOperator( project_id="", zone=LOCATION, resource_id=RESOURCE_ID, task_id='id' ) op.execute(None) err = cm.exception self.assertIn("The required parameter 'project_id' is missing", str(err)) mock_hook.assert_not_called() @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_start_should_throw_ex_when_missing_zone(self, mock_hook): with self.assertRaises(AirflowException) as cm: op = GceInstanceStartOperator( project_id=PROJECT_ID, zone="", resource_id=RESOURCE_ID, task_id='id' ) op.execute(None) err = cm.exception self.assertIn("The required parameter 'zone' is missing", str(err)) mock_hook.assert_not_called() @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_start_should_throw_ex_when_missing_resource_id(self, mock_hook): with self.assertRaises(AirflowException) as cm: op = GceInstanceStartOperator( project_id=PROJECT_ID, zone=LOCATION, resource_id="", task_id='id' ) op.execute(None) err = cm.exception self.assertIn("The required parameter 'resource_id' is missing", str(err)) mock_hook.assert_not_called() @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_instance_stop(self, mock_hook): mock_hook.return_value.stop_instance.return_value = True op = GceInstanceStopOperator( project_id=PROJECT_ID, zone=LOCATION, resource_id=RESOURCE_ID, task_id='id' ) result = op.execute(None) mock_hook.assert_called_once_with(api_version='v1', gcp_conn_id='google_cloud_default') mock_hook.return_value.stop_instance.assert_called_once_with( PROJECT_ID, LOCATION, RESOURCE_ID ) self.assertTrue(result) # Setting all of the operator's input parameters as templated dag_ids # (could be anything else) just to test if the templating works for all fields @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_instance_stop_with_templates(self, mock_hook): dag_id = 'test_dag_id' configuration.load_test_config() args = { 'start_date': DEFAULT_DATE } self.dag = DAG(dag_id, default_args=args) op = GceInstanceStopOperator( project_id='{{ dag.dag_id }}', zone='{{ dag.dag_id }}', resource_id='{{ dag.dag_id }}', gcp_conn_id='{{ dag.dag_id }}', api_version='{{ dag.dag_id }}', task_id='id', dag=self.dag ) ti = TaskInstance(op, DEFAULT_DATE) ti.render_templates() self.assertEqual(dag_id, getattr(op, 'project_id')) self.assertEqual(dag_id, getattr(op, 'zone')) self.assertEqual(dag_id, getattr(op, 'resource_id')) self.assertEqual(dag_id, getattr(op, 'gcp_conn_id')) self.assertEqual(dag_id, getattr(op, 'api_version')) @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_stop_should_throw_ex_when_missing_project_id(self, mock_hook): with self.assertRaises(AirflowException) as cm: op = GceInstanceStopOperator( project_id="", zone=LOCATION, resource_id=RESOURCE_ID, task_id='id' ) op.execute(None) err = cm.exception self.assertIn("The required parameter 'project_id' is missing", str(err)) mock_hook.assert_not_called() @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_stop_should_throw_ex_when_missing_zone(self, mock_hook): with self.assertRaises(AirflowException) as cm: op = GceInstanceStopOperator( project_id=PROJECT_ID, zone="", resource_id=RESOURCE_ID, task_id='id' ) op.execute(None) err = cm.exception self.assertIn("The required parameter 'zone' is missing", str(err)) mock_hook.assert_not_called() @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_stop_should_throw_ex_when_missing_resource_id(self, mock_hook): with self.assertRaises(AirflowException) as cm: op = GceInstanceStopOperator( project_id=PROJECT_ID, zone=LOCATION, resource_id="", task_id='id' ) op.execute(None) err = cm.exception self.assertIn("The required parameter 'resource_id' is missing", str(err)) mock_hook.assert_not_called() @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_set_machine_type(self, mock_hook): mock_hook.return_value.set_machine_type.return_value = True op = GceSetMachineTypeOperator( project_id=PROJECT_ID, zone=LOCATION, resource_id=RESOURCE_ID, body=SET_MACHINE_TYPE_BODY, task_id='id' ) result = op.execute(None) mock_hook.assert_called_once_with(api_version='v1', gcp_conn_id='google_cloud_default') mock_hook.return_value.set_machine_type.assert_called_once_with( PROJECT_ID, LOCATION, RESOURCE_ID, SET_MACHINE_TYPE_BODY ) self.assertTrue(result) # Setting all of the operator's input parameters as templated dag_ids # (could be anything else) just to test if the templating works for all fields @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_set_machine_type_with_templates(self, mock_hook): dag_id = 'test_dag_id' configuration.load_test_config() args = { 'start_date': DEFAULT_DATE } self.dag = DAG(dag_id, default_args=args) op = GceSetMachineTypeOperator( project_id='{{ dag.dag_id }}', zone='{{ dag.dag_id }}', resource_id='{{ dag.dag_id }}', body={}, gcp_conn_id='{{ dag.dag_id }}', api_version='{{ dag.dag_id }}', task_id='id', dag=self.dag ) ti = TaskInstance(op, DEFAULT_DATE) ti.render_templates() self.assertEqual(dag_id, getattr(op, 'project_id')) self.assertEqual(dag_id, getattr(op, 'zone')) self.assertEqual(dag_id, getattr(op, 'resource_id')) self.assertEqual(dag_id, getattr(op, 'gcp_conn_id')) self.assertEqual(dag_id, getattr(op, 'api_version')) @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_set_machine_type_should_throw_ex_when_missing_project_id(self, mock_hook): with self.assertRaises(AirflowException) as cm: op = GceSetMachineTypeOperator( project_id="", zone=LOCATION, resource_id=RESOURCE_ID, body=SET_MACHINE_TYPE_BODY, task_id='id' ) op.execute(None) err = cm.exception self.assertIn("The required parameter 'project_id' is missing", str(err)) mock_hook.assert_not_called() @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_set_machine_type_should_throw_ex_when_missing_zone(self, mock_hook): with self.assertRaises(AirflowException) as cm: op = GceSetMachineTypeOperator( project_id=PROJECT_ID, zone="", resource_id=RESOURCE_ID, body=SET_MACHINE_TYPE_BODY, task_id='id' ) op.execute(None) err = cm.exception self.assertIn("The required parameter 'zone' is missing", str(err)) mock_hook.assert_not_called() @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_set_machine_type_should_throw_ex_when_missing_resource_id(self, mock_hook): with self.assertRaises(AirflowException) as cm: op = GceSetMachineTypeOperator( project_id=PROJECT_ID, zone=LOCATION, resource_id="", body=SET_MACHINE_TYPE_BODY, task_id='id' ) op.execute(None) err = cm.exception self.assertIn("The required parameter 'resource_id' is missing", str(err)) mock_hook.assert_not_called() @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook') def test_set_machine_type_should_throw_ex_when_missing_machine_type(self, mock_hook): with self.assertRaises(AirflowException) as cm: op = GceSetMachineTypeOperator( project_id=PROJECT_ID, zone=LOCATION, resource_id=RESOURCE_ID, body={}, task_id='id' ) op.execute(None) err = cm.exception self.assertIn( "The required body field 'machineType' is missing. Please add it.", str(err)) mock_hook.assert_called_once_with(api_version='v1', gcp_conn_id='google_cloud_default') MOCK_OP_RESPONSE = "{'kind': 'compute#operation', 'id': '8529919847974922736', " \ "'name': " \ "'operation-1538578207537-577542784f769-7999ab71-94f9ec1d', " \ "'zone': 'https://www.googleapis.com/compute/v1/projects/polidea" \ "-airflow/zones/europe-west3-b', 'operationType': " \ "'setMachineType', 'targetLink': " \ "'https://www.googleapis.com/compute/v1/projects/polidea-airflow" \ "/zones/europe-west3-b/instances/pa-1', 'targetId': " \ "'2480086944131075860', 'status': 'DONE', 'user': " \ "'uberdarek@polidea-airflow.iam.gserviceaccount.com', " \ "'progress': 100, 'insertTime': '2018-10-03T07:50:07.951-07:00', "\ "'startTime': '2018-10-03T07:50:08.324-07:00', 'endTime': " \ "'2018-10-03T07:50:08.484-07:00', 'error': {'errors': [{'code': " \ "'UNSUPPORTED_OPERATION', 'message': \"Machine type with name " \ "'machine-type-1' does not exist in zone 'europe-west3-b'.\"}]}, "\ "'httpErrorStatusCode': 400, 'httpErrorMessage': 'BAD REQUEST', " \ "'selfLink': " \ "'https://www.googleapis.com/compute/v1/projects/polidea-airflow" \ "/zones/europe-west3-b/operations/operation-1538578207537" \ "-577542784f769-7999ab71-94f9ec1d'} " @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook' '._check_operation_status') @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook' '._execute_set_machine_type') @mock.patch('airflow.contrib.operators.gcp_compute_operator.GceHook.get_conn') def test_set_machine_type_should_handle_and_trim_gce_error( self, get_conn, _execute_set_machine_type, _check_operation_status): get_conn.return_value = {} _execute_set_machine_type.return_value = {"name": "test-operation"} _check_operation_status.return_value = ast.literal_eval(self.MOCK_OP_RESPONSE) with self.assertRaises(AirflowException) as cm: op = GceSetMachineTypeOperator( project_id=PROJECT_ID, zone=LOCATION, resource_id=RESOURCE_ID, body=SET_MACHINE_TYPE_BODY, task_id='id' ) op.execute(None) err = cm.exception _check_operation_status.assert_called_once_with( {}, "test-operation", PROJECT_ID, LOCATION) _execute_set_machine_type.assert_called_once_with( PROJECT_ID, LOCATION, RESOURCE_ID, SET_MACHINE_TYPE_BODY) # Checking the full message was sometimes failing due to different order # of keys in the serialized JSON self.assertIn("400 BAD REQUEST: {", str(err)) # checking the square bracket trim self.assertIn("UNSUPPORTED_OPERATION", str(err))

43.415344

90

0.624886

1,874

16,411

5.19317

0.142476

0.040691

0.03021

0.053432

0.774147

0.749589

0.739108

0.728113

0.710543

0

0.017257

0.276156

16,411

377

91

43.530504

0.802004

0.083785

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0.697248

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0.009174

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0.030581

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0.088685

0

null

0

1

0

null

0

5

44aa1eb79d10f65183bfbe8c4b98798c7f7f3843

133

py

Python

Ex7.py

jpsampaio/ExEcommIT

fabf3287aa86dafae00709341c776e446d4e2968

[ "MIT" ]

null

Ex7.py

jpsampaio/ExEcommIT

fabf3287aa86dafae00709341c776e446d4e2968

[ "MIT" ]

null

Ex7.py

jpsampaio/ExEcommIT

fabf3287aa86dafae00709341c776e446d4e2968

[ "MIT" ]

null

n1 = float(input('Nota 1: ')) n2 = float(input('Nota 2: ')) print(f'Resultado: \n - Nota final: {n1+n2} \n - Média: {(n1+n2)/2:.1f}')

44.333333

73

0.571429

24

133

3.166667

0.583333

0.263158

0.368421

0

0.087719

0.142857

133

3

73

44.333333

0.578947

0

0.333333

0.589552

0

1

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false

0

0.333333

1

0

null

1

0

1

0

1

0

null

0

5

44cb81985691abce07e61414b0955467d049696a

9,281

py

Python

tests/test_evaluate.py

IBM-HRL-MLHLS/Causality-Benchmarking-LBIDD

fa3d17af22521e2d41b4c734f9148365b7dc92fd

[ "Apache-2.0" ]

67

2018-02-17T08:35:13.000Z

2022-02-26T13:37:13.000Z

tests/test_evaluate.py

IBM-HRL-MLHLS/Causality-Benchmarking-LBIDD

fa3d17af22521e2d41b4c734f9148365b7dc92fd

[ "Apache-2.0" ]

1

2021-11-22T08:38:48.000Z

2021-11-30T16:48:22.000Z

tests/test_evaluate.py

IBM-HRL-MLHLS/Causality-Benchmarking-LBIDD

fa3d17af22521e2d41b4c734f9148365b7dc92fd

[ "Apache-2.0" ]

11

2018-02-18T19:48:23.000Z

2022-02-24T01:15:12.000Z

""" (C) IBM Corp, 2018, All rights reserved Created on Jan 10, 2018 @author: EHUD KARAVANI """ from __future__ import division as __division import os import unittest import pandas as pd import numpy as np from causalbenchmark import evaluate evaluate.TABULAR_DELIMITER = "\t" class TestEvaluate(unittest.TestCase): def test_individual_single_size(self): # Right prediction: score = evaluate.evaluate(os.path.join("test_data_files", "single_sized_datasets", "individual_prediction_right"), os.path.join("test_data_files", "single_sized_datasets", "dummy_data"), is_individual_prediction=True) np.testing.assert_array_almost_equal(x=score, y=pd.Series(data=[0.0, 0.0, 0.0], index=["enormse", "rmse", "bias"])) # Wrong Prediction: score = evaluate.evaluate(os.path.join("test_data_files", "single_sized_datasets", "individual_prediction_wrong"), os.path.join("test_data_files", "single_sized_datasets", "dummy_data"), is_individual_prediction=True) np.testing.assert_array_almost_equal(x=score, y=pd.Series(data=[np.sqrt(17.0/9.0), np.sqrt((4 + 1 + (2/75)) / 3), (-2 - 1 + (2.0 / 15)) / 3], index=["enormse", "rmse", "bias"])) def test_population_single_size(self): # Right prediction: score = evaluate.evaluate(os.path.join("test_data_files", "single_sized_datasets", "population_prediction_right.csv"), os.path.join("test_data_files", "single_sized_datasets", "dummy_data"), is_individual_prediction=False) np.testing.assert_array_almost_equal(x=score, y=pd.Series(data=[0.0, 0.0, 0.0, 1.0, 5.0/3.0, 0.0], index=["enormse", "rmse", "bias", "coverage", "encis", "cic"])) # Wrong Prediction: score = evaluate.evaluate(os.path.join("test_data_files", "single_sized_datasets", "population_prediction_wrong.csv"), os.path.join("test_data_files", "single_sized_datasets", "dummy_data"), is_individual_prediction=False) np.testing.assert_array_almost_equal(x=score, y=pd.Series(data=[np.sqrt(5.0/12.0), np.sqrt((0.25 + 0 + 0.04) / 3), (-0.5 + 0 + 0.2) / 3, 2.0/3.0, 5.0/3.0, 5.0/12.0], index=["enormse", "rmse", "bias", "coverage", "encis", "cic"])) def test_individual_multi_size(self): # Right prediction: score = evaluate.evaluate(os.path.join("test_data_files", "multi_sized_datasets", "individual_prediction_right"), os.path.join("test_data_files", "multi_sized_datasets", "dummy_data"), is_individual_prediction=True) np.testing.assert_array_almost_equal(x=score, y=pd.Series(data=[0.0, 0.0, 0.0, 0.0, 0.0], index=["enormse", "rmse", "bias", "enormse_4", "enormse_6"])) # Wrong Prediction: score = evaluate.evaluate(os.path.join("test_data_files", "multi_sized_datasets", "individual_prediction_wrong"), os.path.join("test_data_files", "multi_sized_datasets", "dummy_data"), is_individual_prediction=True) np.testing.assert_array_almost_equal(x=score, y=pd.Series(data=[np.sqrt(((5.0 / 2.0) + (2.25 * 17.0 / 9.0)) / 3.25), np.sqrt(((4*(4 + 0.04)) + 6*(4 + 1 + (4.0 / 150))) / 26), (4*(-2 + 0.2) + 6*(-2 - 1 + (2.0/15))) / (4*2 + 6*3), np.sqrt(5.0 / 2.0), np.sqrt(17.0/9.0)], index=["enormse", "rmse", "bias", "enormse_4", "enormse_6"])) def test_population_multi_size(self): # Right prediction: score = evaluate.evaluate(os.path.join("test_data_files", "multi_sized_datasets", "population_prediction_right.csv"), os.path.join("test_data_files", "multi_sized_datasets", "dummy_data"), is_individual_prediction=False) score = score[["enormse", "enormse_4", "enormse_6", "rmse", "bias", "coverage", "encis", "cic"]] # rearrange np.testing.assert_array_almost_equal(x=score, y=pd.Series(data=[0.0, 0.0, 0.0, 0.0, 0.0, 1.0, (6 * (2 + 2 + 1) + 4 * (2 + 1)) / (6 * 3 + 4 * 2), 0.0], index=["enormse", "enormse_4", "enormse_6", "rmse", "bias", "coverage", "encis", "cic"])) # Wrong Prediction: score = evaluate.evaluate(os.path.join("test_data_files", "multi_sized_datasets", "population_prediction_wrong.csv"), os.path.join("test_data_files", "multi_sized_datasets", "dummy_data"), is_individual_prediction=False) score = score[["enormse", "enormse_4", "enormse_6", "rmse", "bias", "coverage", "encis", "cic"]] # rearrange np.testing.assert_array_almost_equal(x=score, y=pd.Series(data=[np.sqrt(((4*(0.25+1)) + (6*(0.25+0+1))) / (4*2 + 6*3)), np.sqrt((0.25 + 1) / 2), np.sqrt((1 + 0 + 0.25) / 3), np.sqrt((4*(0.25+0.04) + 6*(0.25+0+0.04)) / (4*2 + 6*3)), (4*(-0.5 + 0.2) + 6*(-0.5 + 0 + 0.2)) / (4*2 + 6*3), (4 * 1 + 6 * 2) / (4 * 2 + 6 * 3), (6 * (2 + 2 + 1) + 4 * (2 + 1)) / (6 * 3 + 4 * 2), (4 * (0.25 + 1) + 6 * (0.25 + 0 + 1)) / (4 * 2 + 6 * 3)], index=["enormse", "enormse_4", "enormse_6", "rmse", "bias", "coverage", "encis", "cic"])) def test_input_consistency(self): data_path = os.path.join("test_data_files", "multi_sized_datasets", "dummy_data") # population effect with directory input: dir_path = os.path.join("test_data_files", "multi_sized_datasets", "individual_prediction_right") is_individual_prediction = False with self.subTest(dir_path=dir_path, data_path=data_path, is_individual_prediction=is_individual_prediction): self.assertRaises(RuntimeError, evaluate.evaluate, dir_path, data_path, is_individual_prediction) # individual effect with file input: file_path = os.path.join("test_data_files", "multi_sized_datasets", "population_prediction_wrong.csv") is_individual_prediction = True with self.subTest(file_path=file_path, data_path=data_path, is_individual_prediction=is_individual_prediction): self.assertRaises(RuntimeError, evaluate.evaluate, file_path, data_path, is_individual_prediction) def test_missing_predictions(self): data_path = os.path.join("test_data_files", "single_sized_datasets", "dummy_data") individual_prediction_dir_path = os.path.join("test_data_files", "single_sized_datasets", "individual_prediction_missing") self.assertRaises(IOError, evaluate._score_individual, individual_prediction_dir_path, data_path) population_prediction_file_path = os.path.join("test_data_files", "single_sized_datasets", "population_prediction_missing.csv") self.assertRaises(AssertionError, evaluate._score_population, population_prediction_file_path, data_path) if __name__ == '__main__': unittest.main()

65.822695

120

0.472255

965

9,281

4.27772

0.109845

0.017442

0.018169

0.074612

0.786095

0.768411

0.742975

0.739099

0.722626

0.705911

0

0.049909

0.406314

9,281

140

121

66.292857

0.699274

0.035233

0

0.504673

0

0.178735

0.065249

0

0.11215

1

0.056075

false

0

0.056075

0

0.121495

0

null

0

1

0

null

0

5

44e3592245067044ff9503495ca28551a857ca5f

123

py

Python

__init__.py

hamburgduo/mdpicker-github

b91403ce58054d22e818c98eba6b105719d29168

[ "MIT" ]

3

2018-08-27T00:58:27.000Z

2018-12-17T12:57:15.000Z

__init__.py

hamburgduo/mdpicker-github

b91403ce58054d22e818c98eba6b105719d29168

[ "MIT" ]

null

__init__.py

hamburgduo/mdpicker-github

b91403ce58054d22e818c98eba6b105719d29168

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding:utf-8 -*- ''' @author: ?? @file: __init__.py.py @time: 2018/8/23 10:18 @desc: '''

15.375

23

0.536585

19

123

3.263158

0.894737

0

0.118812

0.178862

123

8

24

15.375

0.49505

0.853659

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

44e45a3caa3c5ecd8c8e71932347399ac35277c4

183

py

Python

systems/admin.py

alexhong121/ai_cupboard

50baa791c969b951de5b47d980e19c0df3c04e7f

[ "MIT" ]

null

systems/admin.py

alexhong121/ai_cupboard

50baa791c969b951de5b47d980e19c0df3c04e7f

[ "MIT" ]

null

systems/admin.py

alexhong121/ai_cupboard

50baa791c969b951de5b47d980e19c0df3c04e7f

[ "MIT" ]

null

from django.contrib import admin from systems.models import Configuration,Information # Register your models here. admin.site.register(Configuration) admin.site.register(Information)

30.5

52

0.846995

23

183

6.73913

0.565217

0.116129

0.219355

0

0.081967

183

6

53

30.5

0.922619

0.142077

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

44f9f6f34a6064b9e80df533e674adfd817cf125

144

py

Python

NHentaidesu/__init__.py

rushkii/NHentaidesu

c1ee1ced37fa5dbed6feb53c89349cda913e7f06

[ "MIT" ]

4

2021-09-27T07:53:09.000Z

2022-03-15T00:53:18.000Z

NHentaidesu/__init__.py

rushkii/NHentaidesu

c1ee1ced37fa5dbed6feb53c89349cda913e7f06

[ "MIT" ]

null

NHentaidesu/__init__.py

rushkii/NHentaidesu

c1ee1ced37fa5dbed6feb53c89349cda913e7f06

[ "MIT" ]

null

from .client import DoujinClient from . import utils from . import sync __version__ = "1.9" __author__ = "Kiizuha <github.com/rushkii>"

20.571429

44

0.715278

18

144

5.277778

0.777778

0.210526

0

0.017094

0.1875

144

7

44

20.571429

0.794872

0

0.223022

0

1

0

false

0

0.6

0

0.6

0

1

0

null

1

0

1

0

null

0

1

0

1

0

5

781dd4e3222d8a68cb2ecbb027bebb897a8fa8c7

39

py

Python

plugins/dna_royale.py

GRAYgoose124/mushishi

6dd4512908e39bf6506be023d1834611f58e894b

[ "MIT" ]

2

2018-10-19T07:47:19.000Z

2020-02-11T05:03:11.000Z

plugins/dna_royale.py

GRAYgoose124/mushishi

6dd4512908e39bf6506be023d1834611f58e894b

[ "MIT" ]

null

plugins/dna_royale.py

GRAYgoose124/mushishi

6dd4512908e39bf6506be023d1834611f58e894b

[ "MIT" ]

null

# from discord.ext.commands import Cog

19.5

38

0.794872

6

39

5.166667

1

0

0.128205

39

1

39

0.911765

0.923077

0

null

0

null

0

null

0

null

1

null

true

0

null

1

0

null

0

1

0

1

0

null

0

1

0

5

782b5bee78c1864d031bd424b2ea4556227e98c9

86

py

Python

src/index.py

seyedk/aws-sls-tf

08b566a3fa52743c2033121550c38f960250058a

[ "Apache-2.0" ]

null

src/index.py

seyedk/aws-sls-tf

08b566a3fa52743c2033121550c38f960250058a

[ "Apache-2.0" ]

null

src/index.py

seyedk/aws-sls-tf

08b566a3fa52743c2033121550c38f960250058a

[ "Apache-2.0" ]

null

def lambda_handler(event, context): print("Hello world") return "Hello World!"

28.666667

35

0.697674

11

86

5.363636

0.818182

0.338983

0

0.174419

86

3

36

28.666667

0.830986

0

0.264368

0

1

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false

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1

0

1

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null

0

1

0

1

0

5

78b6418d1302575bd42676a5e8467c79052b0a4a

57

py

Python

den/codegen/__init__.py

MonliH/Den

9c2e69744dcf26ae01154eac32aa4ea8ff2adee3

[ "MIT" ]

null

den/codegen/__init__.py

MonliH/Den

9c2e69744dcf26ae01154eac32aa4ea8ff2adee3

[ "MIT" ]

null

den/codegen/__init__.py

MonliH/Den

9c2e69744dcf26ae01154eac32aa4ea8ff2adee3

[ "MIT" ]

null

from .codegen import ModuleCodeGen from .module import *

19

34

0.807018

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57

6.571429

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2

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28.5

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0

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1

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null

0

1

0

1

0

1

0

5

78d0694dc1db5c1d2123122da45bf075b07bdd09

131

py

Python

tests/reponses/test_responses.py

dongwooklee96/clean-architecture

d3444b5110adebdc57f055414a07069789038a4a

[ "MIT" ]

null

tests/reponses/test_responses.py

dongwooklee96/clean-architecture

d3444b5110adebdc57f055414a07069789038a4a

[ "MIT" ]

null

tests/reponses/test_responses.py

dongwooklee96/clean-architecture

d3444b5110adebdc57f055414a07069789038a4a

[ "MIT" ]

null

from rentomatic.responses import ResponseSuccess def test_response_success_is_true(): assert bool(ResponseSuccess()) is True

21.833333

48

0.816794

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131

6.4375

0.8125

0.116505

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0.122137

131

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49

26.2

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0.333333

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0.333333

true

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0.666667

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1

0

null

0

1

0

1

0

5

78d1eb4b1a2dba0eb927b1bc4d2614a230258b7f

116

py

Python

backend/digits/admin.py

shreenath2001/digit-recognition-webapp

2ebdd012ec52b35bd99c19b71a257e7562c01684

[ "MIT" ]

1

2021-01-25T14:13:48.000Z

backend/digits/admin.py

shreenath2001/digit-recognition-webapp

2ebdd012ec52b35bd99c19b71a257e7562c01684

[ "MIT" ]

null

backend/digits/admin.py

shreenath2001/digit-recognition-webapp

2ebdd012ec52b35bd99c19b71a257e7562c01684

[ "MIT" ]

null

from django.contrib import admin from .models import Digit # Register your models here. admin.site.register(Digit)

19.333333

32

0.801724

17

116

5.470588

0.647059

0

0.12931

116

5

33

23.2

0.920792

0.224138

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true

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null

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1

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5

78d30b9b218f9e6e65680334a1d97d7d8f097762

9,478

py

Python

cheggparser.py

aidarjpg/chegg_cheap_books

86970cbd432ecebb3b2e6e2a6eda1472fcc2cb64

[ "MIT" ]

null

cheggparser.py

aidarjpg/chegg_cheap_books

86970cbd432ecebb3b2e6e2a6eda1472fcc2cb64

[ "MIT" ]

null

cheggparser.py

aidarjpg/chegg_cheap_books

86970cbd432ecebb3b2e6e2a6eda1472fcc2cb64

[ "MIT" ]

null

from bs4 import BeautifulSoup import requests import time import random import re import requests import requests headers = { 'authority': 'www.chegg.com', 'cache-control': 'max-age=0', 'dnt': '1', 'upgrade-insecure-requests': '1', 'user-agent': 'Mozilla/5.0 (iPad; CPU OS 11_0 like Mac OS X) AppleWebKit/604.1.34 (KHTML, like Gecko) Version/11.0 Mobile/15A5341f Safari/604.1', 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9', 'sec-fetch-site': 'same-origin', 'sec-fetch-mode': 'navigate', 'sec-fetch-user': '?1', 'sec-fetch-dest': 'document', 'referer': 'https://www.chegg.com/textbooks/fiction-1/1', 'accept-language': 'en-US,en;q=0.9,ru-RU;q=0.8,ru;q=0.7', 'cookie': 'user_geo_location=%7B%22country_iso_code%22%3A%22KZ%22%2C%22country_name%22%3A%22Kazakhstan%22%2C%22region%22%3A%22AST%22%2C%22region_full%22%3A%22Nur-Sultan%22%2C%22city_name%22%3A%22Nur-Sultan%22%2C%22locale%22%3A%7B%22localeCode%22%3A%5B%5D%7D%7D; 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_gat=1; IR_14422=1634288349818%7C0%7C1634288349818%7C%7C; _tq_id.TV-8145726354-1.ad8a=ec5a2e9ee161ecb6.1629437249.0.1634288350..; _cs_id=23f4d81d-b5bc-afbd-d045-d9b21c016626.1629437256.13.1634288351.1634286656.1.1663601256003; _cs_s=6.0.0.1634290151439', } books = set() for i in range(50): try: url = 'https://www.chegg.com/textbooks/fiction-1/'+str(i+1); response = requests.get(url, headers=headers) print(response.status_code) soup = BeautifulSoup(response.content, 'html.parser') linkto_starter = soup.find_all('a', class_='title') for link in linkto_starter: link = 'https://www.chegg.com' + link['href'] #print(link) books.add(link) except: print('failed at '+url) continue time.sleep(5) ans = [] for url in books: try: response = requests.get(url, headers=headers) soup = BeautifulSoup(response.content, 'lxml') print(response.status_code) price = soup.find('span',class_ = 'PriceTabHeader__Price-sc-1494t2l-3 dUEhYD') price = float(price.text.replace("$","")) if(price > 10.0): continue name = soup.find('h1',class_ = 'styles__MainTitle-sc-3h4sqb-1 eDBkFr') name = name.text ans.append((price,name)) time.sleep(5) except: print('Failed to fetch html: ' + url) continue ans.sort() for el in ans: print('Name: '+el[1]) print('Price: '+str(el[0])) print('--------------------')

145.815385

7,329

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6.620508

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0.009267

0.019063

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9,478

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null

0

1

0

1

0

1

null

0

5

1520e70dad95421ca5086006a6b9b68898fb8301

160

py

Python

lucene-experiment/whitespaceanalyzer.py

machacek/information-retrieval-project

725fb5dbc4c438bf4ab42ec277fa57bc97b8140b

[ "Unlicense" ]

null

lucene-experiment/whitespaceanalyzer.py

machacek/information-retrieval-project

725fb5dbc4c438bf4ab42ec277fa57bc97b8140b

[ "Unlicense" ]

null

lucene-experiment/whitespaceanalyzer.py

machacek/information-retrieval-project

725fb5dbc4c438bf4ab42ec277fa57bc97b8140b

[ "Unlicense" ]

1

2019-03-16T16:37:04.000Z

import lucene class WhitespaceAnalyzer(lucene.PythonAnalyzer): def tokenStream(self, fieldName, reader): return lucene.WhitespaceTokenizer(reader)

26.666667

49

0.78125

15

160

8.333333

0.8

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160

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null

0

1

0

1

0

5

15263eed174e42d01ccad59e003a78928789869a

1,727

py

Python

amalgam/primordials/comparison.py

PureFunctor/amalgam-lisp

c3c1a9794495ba3eb87fcedbf5a294463ed717c1

[ "MIT" ]

3

2020-11-26T05:32:57.000Z

2021-01-01T19:45:40.000Z

amalgam/primordials/comparison.py

PureFunctor/amalgam-lisp

c3c1a9794495ba3eb87fcedbf5a294463ed717c1

[ "MIT" ]

3

2020-11-25T04:00:43.000Z

2021-05-24T09:37:36.000Z

amalgam/primordials/comparison.py

PureFunctor/amalgam-lisp

c3c1a9794495ba3eb87fcedbf5a294463ed717c1

[ "MIT" ]

null

from __future__ import annotations from typing import TYPE_CHECKING import amalgam.amalgams as am from amalgam.primordials.utils import make_function if TYPE_CHECKING: # pragma: no cover from amalgam.environment import Environment from amalgam.primordials.utils import Store COMPARISON: Store = {} @make_function(COMPARISON, ">") def _gt(env: Environment, x: am.Amalgam, y: am.Amalgam) -> am.Atom: """Performs a `greater than` comparison.""" if x > y: # type: ignore return am.Atom("TRUE") return am.Atom("FALSE") @make_function(COMPARISON, "<") def _lt(env: Environment, x: am.Amalgam, y: am.Amalgam) -> am.Atom: """Performs a `less than` comparison.""" if x < y: # type: ignore return am.Atom("TRUE") return am.Atom("FALSE") @make_function(COMPARISON, "=") def _eq(env: Environment, x: am.Amalgam, y: am.Amalgam) -> am.Atom: """Performs an `equals` comparison.""" if x == y: return am.Atom("TRUE") return am.Atom("FALSE") @make_function(COMPARISON, "/=") def _ne(env: Environment, x: am.Amalgam, y: am.Amalgam) -> am.Atom: """Performs a `not equals` comparison.""" if x != y: return am.Atom("TRUE") return am.Atom("FALSE") @make_function(COMPARISON, ">=") def _ge(env: Environment, x: am.Amalgam, y: am.Amalgam) -> am.Atom: """Performs a `greater than or equal` comparison.""" if x >= y: # type: ignore return am.Atom("TRUE") return am.Atom("FALSE") @make_function(COMPARISON, "<=") def _le(env: Environment, x: am.Amalgam, y: am.Amalgam) -> am.Atom: """Performs a `less than or equal` comparison.""" if x <= y: # type: ignore return am.Atom("TRUE") return am.Atom("FALSE")

27.412698

67

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1,727

4.599156

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1,727

62

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1529789a92daa06e65cbc174e21f052d67652dad

2,683

py

Python

tests/transaction_tests.py

AoHRuthless/Doubloon

9279ac7decd434d43bf9b03487691aa52aab499f

[ "Apache-2.0" ]

1

2018-08-13T10:26:39.000Z

tests/transaction_tests.py

AoHRuthless/Doubloon

9279ac7decd434d43bf9b03487691aa52aab499f

[ "Apache-2.0" ]

null

tests/transaction_tests.py

AoHRuthless/Doubloon

9279ac7decd434d43bf9b03487691aa52aab499f

[ "Apache-2.0" ]

null

from unittest import TestCase from Crypto import Random from Crypto.PublicKey import RSA from binascii import hexlify from src.transaction import Transaction PUBLIC_KEY = '30819f300d06092a864886f70d010101050003818d0030818902818100d99c9347b6ecd418b1df48012201c5bd2869a707e45dee91a5c63027dc8020210aa4cf6e34e81fc200f29c893add94fefbf37594a964641fc52f8905280c4d93457d4cee5fb216a09a9e8688c62e26bc9e962357c019c5e6c73818f155b87ccaa70059cfa0698c85f5d982bef73bc84e6dfac540cf4f43308b799b8439c1011d0203010001' PRIVATE_KEY = '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' class TransactionTests(TestCase): def setUp(self): self.transaction = Transaction(PUBLIC_KEY, 'receiver', 5) def test_init(self): self.assertEqual(self.transaction.sender, PUBLIC_KEY) self.assertEqual(self.transaction.receiver, 'receiver') self.assertEqual(self.transaction.amount, 5) def test_dict(self): transaction_dict = { 'sender': PUBLIC_KEY, 'receiver': 'receiver', 'amount': 5 } self.assertEqual(self.transaction.dict, transaction_dict) def test_verify_sig_passes(self): self.assertTrue(self.transaction.verify_signature(PRIVATE_KEY)) def test_verify_sig_fails(self): rng = Random.new().read priv_key = RSA.generate(1024, rng) rand_private_key = hexlify(priv_key.exportKey(format='DER')).decode( 'utf8') self.assertFalse(self.transaction.verify_signature(rand_private_key))

70.605263

1,230

0.865822

134

2,683

17.156716

0.365672

0.052197

0.033058

0.052197

0

0.42183

0.091688

2,683

38

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70.605263

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0

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null

1

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5

153758bd5ecce02407d503e454dc098e0ee43ee5

11,941

py

Python

demo/alphaction/modeling/backbone/slowfast.py

PANBOHE/Humanpose-fight

36e6218db526d567922fa528fa7e11497c53ad60

[ "Apache-2.0" ]

1

2022-02-24T08:52:15.000Z

demo/alphaction/modeling/backbone/slowfast.py

PANBOHE/Humanpose-fight

36e6218db526d567922fa528fa7e11497c53ad60

[ "Apache-2.0" ]

null

demo/alphaction/modeling/backbone/slowfast.py

PANBOHE/Humanpose-fight

36e6218db526d567922fa528fa7e11497c53ad60

[ "Apache-2.0" ]

null

from __future__ import (absolute_import, division, print_function, unicode_literals) import torch import torch.nn as nn from alphaction.modeling.common_blocks import ResNLBlock from alphaction.layers import FrozenBatchNorm3d def get_slow_model_cfg(cfg): backbone_strs = cfg.MODEL.BACKBONE.CONV_BODY.split('-')[1:] error_msg = 'Model backbone {} is not supported.'.format(cfg.MODEL.BACKBONE.CONV_BODY) use_temp_convs_1 = [0] temp_strides_1 = [1] max_pool_stride_1 = 1 use_temp_convs_2 = [0, 0, 0] temp_strides_2 = [1, 1, 1] use_temp_convs_3 = [0, 0, 0, 0] temp_strides_3 = [1, 1, 1, 1] use_temp_convs_5 = [1, 1, 1] temp_strides_5 = [1, 1, 1] slow_stride = cfg.INPUT.TAU avg_pool_stride = int(cfg.INPUT.FRAME_NUM / slow_stride) if backbone_strs[0] == 'Resnet50': block_config = (3, 4, 6, 3) use_temp_convs_4 = [1, 1, 1, 1, 1, 1] temp_strides_4 = [1, 1, 1, 1, 1, 1] elif backbone_strs[0] == 'Resnet101': block_config = (3, 4, 23, 3) use_temp_convs_4 = [1, ] * 23 temp_strides_4 = [1, ] * 23 else: raise KeyError(error_msg) if len(backbone_strs) > 1: raise KeyError(error_msg) use_temp_convs_set = [use_temp_convs_1, use_temp_convs_2, use_temp_convs_3, use_temp_convs_4, use_temp_convs_5] temp_strides_set = [temp_strides_1, temp_strides_2, temp_strides_3, temp_strides_4, temp_strides_5] pool_strides_set = [max_pool_stride_1, avg_pool_stride] return block_config, use_temp_convs_set, temp_strides_set, pool_strides_set def get_fast_model_cfg(cfg): backbone_strs = cfg.MODEL.BACKBONE.CONV_BODY.split('-')[1:] error_msg = 'Model backbone {} is not supported.'.format(cfg.MODEL.BACKBONE.CONV_BODY) use_temp_convs_1 = [2] temp_strides_1 = [1] max_pool_stride_1 = 1 use_temp_convs_2 = [1, 1, 1] temp_strides_2 = [1, 1, 1] use_temp_convs_3 = [1, 1, 1, 1] temp_strides_3 = [1, 1, 1, 1] use_temp_convs_5 = [1, 1, 1] temp_strides_5 = [1, 1, 1] fast_stride = cfg.INPUT.TAU // cfg.INPUT.ALPHA avg_pool_stride = int(cfg.INPUT.FRAME_NUM / fast_stride) if backbone_strs[0] == 'Resnet50': block_config = (3, 4, 6, 3) use_temp_convs_4 = [1, 1, 1, 1, 1, 1] temp_strides_4 = [1, 1, 1, 1, 1, 1] elif backbone_strs[0] == 'Resnet101': block_config = (3, 4, 23, 3) use_temp_convs_4 = [1, ] * 23 temp_strides_4 = [1, ] * 23 else: raise KeyError(error_msg) if len(backbone_strs) > 1: raise KeyError(error_msg) use_temp_convs_set = [use_temp_convs_1, use_temp_convs_2, use_temp_convs_3, use_temp_convs_4, use_temp_convs_5] temp_strides_set = [temp_strides_1, temp_strides_2, temp_strides_3, temp_strides_4, temp_strides_5] pool_strides_set = [max_pool_stride_1, avg_pool_stride] return block_config, use_temp_convs_set, temp_strides_set, pool_strides_set class LateralBlock(nn.Module): def __init__(self, conv_dim, alpha): super(LateralBlock, self).__init__() self.conv = nn.Conv3d(conv_dim, conv_dim * 2, kernel_size=(5, 1, 1), stride=(alpha, 1, 1), padding=(2, 0, 0), bias=True) nn.init.kaiming_normal_(self.conv.weight) nn.init.constant_(self.conv.bias, 0.0) def forward(self, x): out = self.conv(x) return out class FastPath(nn.Module): def __init__(self, cfg): super(FastPath, self).__init__() self.cfg = cfg.clone() block_config, use_temp_convs_set, temp_strides_set, pool_strides_set = get_fast_model_cfg(cfg) conv3_nonlocal = cfg.MODEL.BACKBONE.SLOWFAST.FAST.CONV3_NONLOCAL conv4_nonlocal = cfg.MODEL.BACKBONE.SLOWFAST.FAST.CONV4_NONLOCAL dim_inner = 8 conv_dims = [8, 32, 64, 128, 256] self.dim_out = conv_dims[-1] n1, n2, n3, n4 = block_config layer_mod = 2 conv3_nl_mod = layer_mod conv4_nl_mod = layer_mod if not conv3_nonlocal: conv3_nl_mod = 1000 if not conv4_nonlocal: conv4_nl_mod = 1000 self.c2_mapping = None self.conv1 = nn.Conv3d(3, conv_dims[0], (1 + use_temp_convs_set[0][0] * 2, 7, 7), stride=(temp_strides_set[0][0], 2, 2), padding=(use_temp_convs_set[0][0], 3, 3), bias=False) nn.init.kaiming_normal_(self.conv1.weight) if cfg.MODEL.BACKBONE.FROZEN_BN: self.bn1 = FrozenBatchNorm3d(conv_dims[0], eps=cfg.MODEL.BACKBONE.BN_EPSILON) nn.init.constant_(self.bn1.weight, 1.0) nn.init.constant_(self.bn1.bias, 0.0) else: self.bn1 = nn.BatchNorm3d(conv_dims[0], eps=cfg.MODEL.BACKBONE.BN_EPSILON, momentum=cfg.MODEL.BACKBONE.BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.maxpool1 = nn.MaxPool3d((pool_strides_set[0], 3, 3), stride=(pool_strides_set[0], 2, 2)) self.res_nl1 = ResNLBlock(cfg, conv_dims[0], conv_dims[1], stride=1, num_blocks=n1, dim_inner=dim_inner, use_temp_convs=use_temp_convs_set[1], temp_strides=temp_strides_set[1]) self.res_nl2 = ResNLBlock(cfg, conv_dims[1], conv_dims[2], stride=2, num_blocks=n2, dim_inner=dim_inner * 2, use_temp_convs=use_temp_convs_set[2], temp_strides=temp_strides_set[2], nonlocal_mod=conv3_nl_mod, group_nonlocal=cfg.MODEL.BACKBONE.SLOWFAST.FAST.CONV3_GROUP_NL) self.res_nl3 = ResNLBlock(cfg, conv_dims[2], conv_dims[3], stride=2, num_blocks=n3, dim_inner=dim_inner * 4, use_temp_convs=use_temp_convs_set[3], temp_strides=temp_strides_set[3], nonlocal_mod=conv4_nl_mod) self.res_nl4 = ResNLBlock(cfg, conv_dims[3], conv_dims[4], stride=1, num_blocks=n4, dim_inner=dim_inner * 8, use_temp_convs=use_temp_convs_set[4], temp_strides=temp_strides_set[4], dilation=2) if cfg.MODEL.BACKBONE.SLOWFAST.LATERAL == 'tconv': self._tconv(conv_dims) def _tconv(self, conv_dims): alpha = self.cfg.INPUT.ALPHA self.Tconv1 = LateralBlock(conv_dims[0], alpha) self.Tconv2 = LateralBlock(conv_dims[1], alpha) self.Tconv3 = LateralBlock(conv_dims[2], alpha) self.Tconv4 = LateralBlock(conv_dims[3], alpha) def forward(self, x): out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.maxpool1(out) tconv1 = self.Tconv1(out) out = self.res_nl1(out) tconv2 = self.Tconv2(out) out = self.res_nl2(out) tconv3 = self.Tconv3(out) out = self.res_nl3(out) tconv4 = self.Tconv4(out) out = self.res_nl4(out) return out, [tconv1, tconv2, tconv3, tconv4] class SlowPath(nn.Module): def __init__(self, cfg): super(SlowPath, self).__init__() self.cfg = cfg.clone() block_config, use_temp_convs_set, temp_strides_set, pool_strides_set = get_slow_model_cfg(cfg) conv3_nonlocal = cfg.MODEL.BACKBONE.SLOWFAST.SLOW.CONV3_NONLOCAL conv4_nonlocal = cfg.MODEL.BACKBONE.SLOWFAST.SLOW.CONV4_NONLOCAL dim_inner = 64 conv_dims = [64, 256, 512, 1024, 2048] self.dim_out = conv_dims[-1] n1, n2, n3, n4 = block_config layer_mod = 2 conv3_nl_mod = layer_mod conv4_nl_mod = layer_mod if not conv3_nonlocal: conv3_nl_mod = 1000 if not conv4_nonlocal: conv4_nl_mod = 1000 self.c2_mapping = None self.conv1 = nn.Conv3d(3, conv_dims[0], (1 + use_temp_convs_set[0][0] * 2, 7, 7), stride=(temp_strides_set[0][0], 2, 2), padding=(use_temp_convs_set[0][0], 3, 3), bias=False) nn.init.kaiming_normal_(self.conv1.weight) if cfg.MODEL.BACKBONE.FROZEN_BN: self.bn1 = FrozenBatchNorm3d(conv_dims[0], eps=cfg.MODEL.BACKBONE.BN_EPSILON) nn.init.constant_(self.bn1.weight, 1.0) nn.init.constant_(self.bn1.bias, 0.0) else: self.bn1 = nn.BatchNorm3d(conv_dims[0], eps=cfg.MODEL.BACKBONE.BN_EPSILON, momentum=cfg.MODEL.BACKBONE.BN_MOMENTUM) self.relu = nn.ReLU(inplace=True) self.maxpool1 = nn.MaxPool3d((pool_strides_set[0], 3, 3), stride=(pool_strides_set[0], 2, 2)) self.res_nl1 = ResNLBlock(cfg, conv_dims[0], conv_dims[1], stride=1, num_blocks=n1, dim_inner=dim_inner, use_temp_convs=use_temp_convs_set[1], temp_strides=temp_strides_set[1], lateral=cfg.MODEL.BACKBONE.SLOWFAST.FAST.ACTIVE) self.res_nl2 = ResNLBlock(cfg, conv_dims[1], conv_dims[2], stride=2, num_blocks=n2, dim_inner=dim_inner * 2, use_temp_convs=use_temp_convs_set[2], temp_strides=temp_strides_set[2], nonlocal_mod=conv3_nl_mod, group_nonlocal=cfg.MODEL.BACKBONE.SLOWFAST.SLOW.CONV3_GROUP_NL, lateral=cfg.MODEL.BACKBONE.SLOWFAST.FAST.ACTIVE) self.res_nl3 = ResNLBlock(cfg, conv_dims[2], conv_dims[3], stride=2, num_blocks=n3, dim_inner=dim_inner * 4, use_temp_convs=use_temp_convs_set[3], temp_strides=temp_strides_set[3], nonlocal_mod=conv4_nl_mod, lateral=cfg.MODEL.BACKBONE.SLOWFAST.FAST.ACTIVE) self.res_nl4 = ResNLBlock(cfg, conv_dims[3], conv_dims[4], stride=1, num_blocks=n4, dim_inner=dim_inner * 8, use_temp_convs=use_temp_convs_set[4], temp_strides=temp_strides_set[4], lateral=cfg.MODEL.BACKBONE.SLOWFAST.FAST.ACTIVE, dilation=2) def forward(self, x, lateral_connection=None): out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.maxpool1(out) if lateral_connection: out = torch.cat([out, lateral_connection[0]], dim=1) out = self.res_nl1(out) if lateral_connection: out = torch.cat([out, lateral_connection[1]], dim=1) out = self.res_nl2(out) if lateral_connection: out = torch.cat([out, lateral_connection[2]], dim=1) out = self.res_nl3(out) if lateral_connection: out = torch.cat([out, lateral_connection[3]], dim=1) out = self.res_nl4(out) return out class SlowFast(nn.Module): def __init__(self, cfg): super(SlowFast, self).__init__() self.cfg = cfg.clone() if cfg.MODEL.BACKBONE.SLOWFAST.SLOW.ACTIVE: self.slow = SlowPath(cfg) if cfg.MODEL.BACKBONE.SLOWFAST.FAST.ACTIVE: self.fast = FastPath(cfg) if cfg.MODEL.BACKBONE.SLOWFAST.SLOW.ACTIVE and cfg.MODEL.BACKBONE.SLOWFAST.FAST.ACTIVE: self.dim_out = self.slow.dim_out + self.fast.dim_out elif cfg.MODEL.BACKBONE.SLOWFAST.SLOW.ACTIVE: self.dim_out = self.slow.dim_out elif cfg.MODEL.BACKBONE.SLOWFAST.FAST.ACTIVE: self.dim_out = self.fast.dim_out def forward(self, slow_x, fast_x): tconv = None cfg = self.cfg slowout = None fastout = None if cfg.MODEL.BACKBONE.SLOWFAST.FAST.ACTIVE: fastout, tconv = self.fast(fast_x) if cfg.MODEL.BACKBONE.SLOWFAST.SLOW.ACTIVE: slowout = self.slow(slow_x, tconv) return slowout, fastout

39.936455

116

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11,941

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null

0

1

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1

0

null

0

5

1553f7c914f3f7e12ec3b6bae881115f6bdc2214

8,926

py

Python

pydiscordbot/ext/modules/botmodulemanager/botmodulemanager.py

wlsouza/pydiscordbot

848a991209013ff173b1ae574ae33f68ed8f859f

[ "MIT" ]

null

pydiscordbot/ext/modules/botmodulemanager/botmodulemanager.py

wlsouza/pydiscordbot

848a991209013ff173b1ae574ae33f68ed8f859f

[ "MIT" ]

null

pydiscordbot/ext/modules/botmodulemanager/botmodulemanager.py

wlsouza/pydiscordbot

848a991209013ff173b1ae574ae33f68ed8f859f

[ "MIT" ]

null

from discord.ext import commands from asyncio.exceptions import CancelledError, TimeoutError from dislash import SelectMenu, SelectOption from ext.modules import Module from ext.config import settings from ext.db import models class BotModuleManager(Module): # Auxiliary methods def _get_unloaded_modules(self): unloaded_modules = [] app_modules = self.app.extensions.keys() db_modules = self.session.query(models.Module).filter(models.Module.disableable == True).all() for db_module in db_modules: if db_module.path not in app_modules: unloaded_modules.append(db_module) return unloaded_modules def _get_loaded_modules(self): loaded_modules = [] app_modules = self.app.extensions.keys() db_modules = self.session.query(models.Module).filter(models.Module.disableable == True).all() for db_module in db_modules: if db_module.path in app_modules: loaded_modules.append(db_module) return loaded_modules # Command methods @commands.command() @commands.is_owner() async def load_modules(self, ctx): try: # set select/dropdown options select_options = [ SelectOption(label="Cancel", value="cancel", emoji="❌") ] for module in self._get_unloaded_modules(): select_options.insert( 0, SelectOption( label=module.name.title(), value=module.path, emoji=module.emoji ) ) msg = await ctx.send( "Select the module to be loaded into the bot:", components=[ SelectMenu( custom_id="load_module", placeholder=f"Choose up to {len(select_options)} modules", max_values=len(select_options), options=select_options ) ] ) # Wait for a click on the menu that was sent. inter = await msg.wait_for_dropdown( check=(lambda m: m.author == ctx.author), timeout=60 ) # Tells which option was selected. selected_options = inter.select_menu.selected_options selected_labels = [option.label for option in selected_options] selected_values = [option.value for option in selected_options] if "cancel" in selected_values: raise CancelledError() # Load the modules for module_path in selected_values: self.app.load_extension(module_path) await inter.reply(f"👌 The following module(s) has been loaded: {', '.join(selected_labels)}") except CancelledError: await inter.reply("⚠️ The process was canceled because the cancel option was selected.") except TimeoutError: await msg.reply(f"⚠️ No modules were selected within the timeout (60 seconds). The process was aborted.") except ModuleNotFoundError: await inter.reply(f"😿 Unknown error. Please contact the administrator. ") @commands.command() @commands.is_owner() async def unload_modules(self, ctx): try: # set select/dropdown options select_options = [ SelectOption(label="Cancel", value="cancel", emoji="❌") ] for module in self._get_loaded_modules(): select_options.insert( 0, SelectOption( label=module.name.title(), value=module.path, emoji=module.emoji ) ) msg = await ctx.send( "Select the module to be unloaded from the bot:", components=[ SelectMenu( custom_id="unload_module", placeholder=f"Choose up to {len(select_options)} modules", max_values=len(select_options), options=select_options ) ] ) # Wait for a click on the menu that was sent. inter = await msg.wait_for_dropdown( check= (lambda m: m.author == ctx.author), timeout=60 ) # Tells which option was selected. selected_options = inter.select_menu.selected_options selected_labels = [option.label for option in selected_options] selected_values = [option.value for option in selected_options] if "cancel" in selected_values: raise CancelledError() # Load the modules for module_path in selected_values: self.app.unload_extension(module_path) await inter.reply(f"👌 The following module(s) has been unloaded: {', '.join(selected_labels)}") except CancelledError: await inter.reply("⚠️ The process was canceled because the cancel option was selected.") except TimeoutError: await msg.reply("⚠️ No modules were selected within the timeout (60 seconds). The process was aborted.") except ModuleNotFoundError: await inter.reply(f"😿 Unknown error. Please contact the administrator. ") @commands.command() @commands.is_owner() async def reload_modules(self, ctx): try: # set select/dropdown options select_options = [ SelectOption(label="Cancel", value="cancel", emoji="❌") ] for module in self._get_loaded_modules(): select_options.insert( 0, SelectOption( label=module.name.title(), value=module.path, emoji=module.emoji ) ) msg = await ctx.send( "Select the module to be reloaded from the bot:", components=[ SelectMenu( custom_id="reload_module", placeholder=f"Choose up to {len(select_options)} modules", max_values=len(select_options), options=select_options ) ] ) # Wait for a click on the menu that was sent. inter = await msg.wait_for_dropdown( check= (lambda m: m.author == ctx.author), timeout=60 ) # Tells which option was selected. selected_options = inter.select_menu.selected_options selected_labels = [option.label for option in selected_options] selected_values = [option.value for option in selected_options] if "cancel" in selected_values: raise CancelledError() # Load the modules for module_path in selected_values: self.app.unload_extension(module_path) await inter.reply(f"👌 The following module(s) has been reloaded: {', '.join(selected_labels)}") except CancelledError: await inter.reply("⚠️ The process was canceled because the cancel option was selected.") except TimeoutError: await msg.reply("⚠️ No modules were selected within the timeout (60 seconds). The process was aborted.") except ModuleNotFoundError: await inter.reply(f"😿 Unknown error. Please contact the administrator. ") @reload_modules.error @unload_modules.error @load_modules.error async def extension_errors(self, ctx, error): try: error_msgs = { commands.ExtensionNotFound: f"This extension was not found.\nPlease contact the system admin.", commands.ExtensionAlreadyLoaded: f"This extension already loaded!", commands.NoEntryPointError: f"This extension can't be loaded! Please contact the system admin.", commands.ExtensionFailed: f"This extension cannot be loaded because an error was found in it." f"Please contact the system admin.", commands.ExtensionNotLoaded: f"This extension isn't loaded!" } if message := error_msgs.get(type(error.original)): await ctx.send(message) except AttributeError: pass # @commands.Cog.listener() # async def on_command_error(self, ctx, error): # if isinstance(error, commands.errors.MissingRole): # await ctx.send("You don't have the correct role for this command.")

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117

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0.023194

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1575594b3b1111528a8ec7e5e21e551436e90b8c

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py

Python

tests/test_solution.py

da1910/pymapdl

305b70b30e61a78011e974ff4cb409ee21f89e13

[ "MIT" ]

194

2016-10-21T08:46:41.000Z

2021-01-06T20:39:23.000Z

tests/test_solution.py

da1910/pymapdl

305b70b30e61a78011e974ff4cb409ee21f89e13

[ "MIT" ]

463

2021-01-12T14:07:38.000Z

2022-03-31T22:42:25.000Z

tests/test_solution.py

da1910/pymapdl

305b70b30e61a78011e974ff4cb409ee21f89e13

[ "MIT" ]

66

2016-11-21T04:26:08.000Z

2020-12-28T09:27:27.000Z

"""Test ansys.mapdl.solution.Solution""" def time_step_size(mapdl): assert isinstance(mapdl.solution.time_step_size, float) def test_n_cmls(mapdl): assert isinstance(mapdl.solution.n_cmls, float) def test_n_cmss(mapdl): assert isinstance(mapdl.solution.n_cmss, float) def test_n_eqit(mapdl): assert isinstance(mapdl.solution.n_eqit, float) def test_n_cmit(mapdl): assert isinstance(mapdl.solution.n_cmit, float) def test_converged(mapdl): assert isinstance(mapdl.solution.converged, bool) def test_mx_dof(mapdl): assert isinstance(mapdl.solution.mx_dof, float) def test_res_frq(mapdl): assert isinstance(mapdl.solution.res_frq, float) def test_res_eig(mapdl): assert isinstance(mapdl.solution.res_eig, float) def test_decent_parm(mapdl): assert isinstance(mapdl.solution.decent_parm, float) def test_force_cnv(mapdl): assert isinstance(mapdl.solution.force_cnv, float) def test_moment_cnv(mapdl): assert isinstance(mapdl.solution.moment_cnv, float) def test_heat_flow_cnv(mapdl): assert isinstance(mapdl.solution.heat_flow_cnv, float) def test_magnetic_flux_cnv(mapdl): assert isinstance(mapdl.solution.magnetic_flux_cnv, float) def test_current_segment_cnv(mapdl): assert isinstance(mapdl.solution.current_segment_cnv, float) def test_current_cnv(mapdl): assert isinstance(mapdl.solution.current_cnv, float) def test_fluid_flow_cnv(mapdl): assert isinstance(mapdl.solution.fluid_flow_cnv, float) def test_displacement_cnv(mapdl): assert isinstance(mapdl.solution.displacement_cnv, float) def test_rotation_cnv(mapdl): assert isinstance(mapdl.solution.rotation_cnv, float) def test_temperature_cnv(mapdl): assert isinstance(mapdl.solution.temperature_cnv, float) def test_vector_cnv(mapdl): assert isinstance(mapdl.solution.vector_cnv, float) def test_smcv(mapdl): assert isinstance(mapdl.solution.smcv, float) def test_voltage_conv(mapdl): assert isinstance(mapdl.solution.voltage_conv, float) def test_pressure_conv(mapdl): assert isinstance(mapdl.solution.pressure_conv, float) def test_velocity_conv(mapdl): assert isinstance(mapdl.solution.velocity_conv, float) def test_mx_creep_rat(mapdl): assert isinstance(mapdl.solution.mx_creep_rat, float) def test_mx_plastic_inc(mapdl): assert isinstance(mapdl.solution.mx_plastic_inc, float) def test_n_cg_iter(mapdl): assert isinstance(mapdl.solution.n_cg_iter, float)

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157df4dcb1edc3d4dfef73a83851840918ec55fd

193

py

Python

tensorpack/train/utility.py

layolu/tensorpack

97360e5b8ca9ce03d8a18b3abef5abfc92cb9907

[ "Apache-2.0" ]

4,404

2018-05-30T23:38:42.000Z

2022-03-31T22:30:11.000Z

tensorpack/train/utility.py

layolu/tensorpack

97360e5b8ca9ce03d8a18b3abef5abfc92cb9907

[ "Apache-2.0" ]

771

2018-06-01T09:54:00.000Z

2022-03-31T23:12:29.000Z

tensorpack/train/utility.py

layolu/tensorpack

97360e5b8ca9ce03d8a18b3abef5abfc92cb9907

[ "Apache-2.0" ]

1,412

2018-06-01T00:29:43.000Z

2022-03-26T17:37:39.000Z

# -*- coding: utf-8 -*- # File: utility.py # for backwards-compatibility from ..graph_builder.utils import LeastLoadedDeviceSetter, OverrideToLocalVariable, override_to_local_variable # noqa

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158d847a26d9a9ebb08d769da4d0110882a642bd

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py

Python

tools/third_party/html5lib/html5lib/tests/test_tokenizer2.py

meyerweb/wpt

f04261533819893c71289614c03434c06856c13e

[ "BSD-3-Clause" ]

2,479

2018-05-28T14:51:29.000Z

2022-03-30T14:41:18.000Z

tools/third_party/html5lib/html5lib/tests/test_tokenizer2.py

meyerweb/wpt

f04261533819893c71289614c03434c06856c13e

[ "BSD-3-Clause" ]

7,642

2018-05-28T09:38:03.000Z

2022-03-31T20:55:48.000Z

tools/third_party/html5lib/html5lib/tests/test_tokenizer2.py

meyerweb/wpt

f04261533819893c71289614c03434c06856c13e

[ "BSD-3-Clause" ]

1,303

2018-05-29T14:50:02.000Z

2022-03-30T17:30:42.000Z

from __future__ import absolute_import, division, unicode_literals import io from six import unichr, text_type from html5lib._tokenizer import HTMLTokenizer from html5lib.constants import tokenTypes def ignore_parse_errors(toks): for tok in toks: if tok['type'] != tokenTypes['ParseError']: yield tok def test_maintain_attribute_order(): # generate loads to maximize the chance a hash-based mutation will occur attrs = [(unichr(x), text_type(i)) for i, x in enumerate(range(ord('a'), ord('z')))] stream = io.StringIO("<span " + " ".join("%s='%s'" % (x, i) for x, i in attrs) + ">") toks = HTMLTokenizer(stream) out = list(ignore_parse_errors(toks)) assert len(out) == 1 assert out[0]['type'] == tokenTypes['StartTag'] attrs_tok = out[0]['data'] assert len(attrs_tok) == len(attrs) for (in_name, in_value), (out_name, out_value) in zip(attrs, attrs_tok.items()): assert in_name == out_name assert in_value == out_value def test_duplicate_attribute(): stream = io.StringIO("<span a=1 a=2 a=3>") toks = HTMLTokenizer(stream) out = list(ignore_parse_errors(toks)) assert len(out) == 1 assert out[0]['type'] == tokenTypes['StartTag'] attrs_tok = out[0]['data'] assert len(attrs_tok) == 1 assert list(attrs_tok.items()) == [('a', '1')] def test_maintain_duplicate_attribute_order(): # generate loads to maximize the chance a hash-based mutation will occur attrs = [(unichr(x), text_type(i)) for i, x in enumerate(range(ord('a'), ord('z')))] stream = io.StringIO("<span " + " ".join("%s='%s'" % (x, i) for x, i in attrs) + " a=100>") toks = HTMLTokenizer(stream) out = list(ignore_parse_errors(toks)) assert len(out) == 1 assert out[0]['type'] == tokenTypes['StartTag'] attrs_tok = out[0]['data'] assert len(attrs_tok) == len(attrs) for (in_name, in_value), (out_name, out_value) in zip(attrs, attrs_tok.items()): assert in_name == out_name assert in_value == out_value

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159c90da78395f39d17f3f8709606c7949060c68

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py

Python

app/__init__.py

copygodistaken/newapp

eb2399a898dc22fc03392b3c6d7e8503ec400222

[ "MIT" ]

null

app/__init__.py

copygodistaken/newapp

eb2399a898dc22fc03392b3c6d7e8503ec400222

[ "MIT" ]

null

app/__init__.py

copygodistaken/newapp

eb2399a898dc22fc03392b3c6d7e8503ec400222

[ "MIT" ]

null

from flask import Flask app = Flask(__name__) from app import views from app import admin_views

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null

0

1

0

1

0

5

159cbf7bfa948706fa1b34137e35d338582e60e5

63

py

Python

mapped_config/__init__.py

maxpowel/mapped_config

dda6c67c3e4328c080dc25a5b258b567dccda694

[ "MIT" ]

3

2016-08-30T16:16:56.000Z

2018-06-11T16:48:46.000Z

mapped_config/__init__.py

maxpowel/mapped_config

dda6c67c3e4328c080dc25a5b258b567dccda694

[ "MIT" ]

6

2016-08-20T12:49:02.000Z

2021-03-25T21:50:12.000Z

mapped_config/__init__.py

maxpowel/mapped_config

dda6c67c3e4328c080dc25a5b258b567dccda694

[ "MIT" ]

1

2019-05-27T10:25:40.000Z

from .loader import YmlLoader, JsonLoader, InvalidDataException

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1

0

5

15bd090f09f582e5af17d8d98b75925cc242af86

172

py

Python

transcrypt/development/automated_tests/warnings/autotest.py

JMCanning78/Transcrypt

8a8dabe831240414fdf1d5027fa2b0d71ab45d05

[ "Apache-2.0" ]

1

2017-08-11T01:51:51.000Z

transcrypt/development/automated_tests/warnings/autotest.py

JMCanning78/Transcrypt

8a8dabe831240414fdf1d5027fa2b0d71ab45d05

[ "Apache-2.0" ]

2

2021-03-11T07:09:19.000Z

2021-05-12T11:26:23.000Z

transcrypt/development/automated_tests/warnings/autotest.py

JMCanning78/Transcrypt

8a8dabe831240414fdf1d5027fa2b0d71ab45d05

[ "Apache-2.0" ]

1

2021-02-07T00:22:12.000Z

import org.transcrypt.autotester import basic_tests autoTester = org.transcrypt.autotester.AutoTester () autoTester.run( basic_tests, "basic_tests" ) autoTester.done()

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ec5cacb80292b51e463059fc66ee42593c34c951

82

py

Python

model.py

team-backspace/backend

c4cced64f2f3f795dfe4684442d5563daa6b60e8

[ "Apache-2.0" ]

null

model.py

team-backspace/backend

c4cced64f2f3f795dfe4684442d5563daa6b60e8

[ "Apache-2.0" ]

1

2021-10-17T16:05:53.000Z

model.py

team-backspace/backend

c4cced64f2f3f795dfe4684442d5563daa6b60e8

[ "Apache-2.0" ]

1

2021-10-17T13:04:11.000Z

from models.user import ProfileUser, LoginUser #from models.project import Project

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1

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1

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5

ec60815c2746d6b9519442b86d4c1afcfd09bc03

171

py

Python

SciFiReaders/readers/microscopy/__init__.py

sumner-harris/SciFiReaders

4494b7e7350ad2a6198c87590d193393566ad470

[ "MIT" ]

8

2021-05-07T00:59:39.000Z

2021-12-10T21:03:59.000Z

SciFiReaders/readers/microscopy/__init__.py

sumner-harris/SciFiReaders

4494b7e7350ad2a6198c87590d193393566ad470

[ "MIT" ]

31

2021-02-19T21:16:25.000Z

2022-03-04T22:28:09.000Z

SciFiReaders/readers/microscopy/__init__.py

sumner-harris/SciFiReaders

4494b7e7350ad2a6198c87590d193393566ad470

[ "MIT" ]

6

2021-05-07T01:48:09.000Z

2022-01-21T21:14:36.000Z

from . import em from . import spm from .em import * from .spm import * __all__ = em.__all__ + spm.__all__ #+ ion.__all__ all_readers = em.all_readers + spm.all_readers

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null

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5

ec65a988ca34e460af0906d773e00b51a55d5836

37

py

Python

tests/__init__.py

mathiasbrito/matils

48456825943cbfe721bec3fd9a317182476e0144

[ "MIT" ]

null

tests/__init__.py

mathiasbrito/matils

48456825943cbfe721bec3fd9a317182476e0144

[ "MIT" ]

null

tests/__init__.py

mathiasbrito/matils

48456825943cbfe721bec3fd9a317182476e0144

[ "MIT" ]

null

"""Package holding project tests."""

18.5

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1

0

1

0

null

0

1

0

5

ec79d391d74b90b8d0fb87b0c4c8a30d36e716a4

77

py

Python

docker/common/_grains/overrides.py

risdenk/cloudbreak-images

012d79bb0a4059b3534522adefac037cecf39de1

[ "Apache-2.0" ]

12

2017-11-13T00:02:42.000Z

2020-09-16T12:57:25.000Z

docker/common/_grains/overrides.py

risdenk/cloudbreak-images

012d79bb0a4059b3534522adefac037cecf39de1

[ "Apache-2.0" ]

87

2017-11-22T15:02:21.000Z

2022-03-11T12:20:54.000Z

docker/common/_grains/overrides.py

risdenk/cloudbreak-images

012d79bb0a4059b3534522adefac037cecf39de1

[ "Apache-2.0" ]

49

2017-10-04T19:15:43.000Z

2022-03-01T11:21:05.000Z

#!/usr/bin/env python def override_init(): return { 'init': 'systemd' }

15.4

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null

0

1

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1

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5

ec9be0a77a78f45730823bce422c4c745476135d

270

py

Python

p1_basic/day01_07base/day04/03_列表.py

dong-pro/fullStackPython

5ad8662f7b57f14c8529e7eaf64290eeda773557

[ "Apache-2.0" ]

1

2020-04-03T01:32:05.000Z

p1_basic/day01_07base/day04/03_列表.py

dong-pro/fullStackPython

5ad8662f7b57f14c8529e7eaf64290eeda773557

[ "Apache-2.0" ]

null

p1_basic/day01_07base/day04/03_列表.py

dong-pro/fullStackPython

5ad8662f7b57f14c8529e7eaf64290eeda773557

[ "Apache-2.0" ]

null

# lst = ['海上钢琴师', '奥特曼', '咒怨', '舌尖上的中国', '穹顶之下', # '金刚', 110, True, False, ['人民币', '美金', '欧元']] # # print(lst[3][2]) # 上 # print(lst[-2]) # 穹顶之下 # print(lst[1:4]) # ['奥特曼', '咒怨', '舌尖上的中国'] # print(lst[-3:-1]) # 顾头不顾尾 # print(lst[1::2]) # print(lst[-1:-5:-2])

27

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9

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0.5

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null

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true

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1

0

1

0

1

0

null

0

1

0

5

ecdc6dcfafe5333fc7a7bd208953f994825351a5

66

py

Python

satsmooth/tests/__init__.py

jgrss/satsmooth

9fa6de957eb1c938df070d2f3e6e7521a43778b3

[ "MIT" ]

5

2021-09-13T08:44:18.000Z

2022-03-30T20:27:32.000Z

satsmooth/tests/__init__.py

jgrss/satsmooth

9fa6de957eb1c938df070d2f3e6e7521a43778b3

[ "MIT" ]

null

satsmooth/tests/__init__.py

jgrss/satsmooth

9fa6de957eb1c938df070d2f3e6e7521a43778b3

[ "MIT" ]

1

2021-11-21T13:08:41.000Z

# from .test_speed import test_multi # # __all__ = ['test_multi']

16.5

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0

null

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1

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1

0

1

0

null

0

1

0

5

bf048e176c9b8a095166dc556a35982069a5f463

51

py

Python

agents/__init__.py

taodav/Rainbow

cc76cdfcac98407a6b6aa81d519853e582d4a1af

[ "MIT" ]

null

agents/__init__.py

taodav/Rainbow

cc76cdfcac98407a6b6aa81d519853e582d4a1af

[ "MIT" ]

null

agents/__init__.py

taodav/Rainbow

cc76cdfcac98407a6b6aa81d519853e582d4a1af

[ "MIT" ]

null

from .dqn import DQNAgent from .mpr import MPRAgent

25.5

25

0.823529

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5.25

0.75

0

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2

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1

0

1

0

1

0

5

170be22427c85746802d59e7c550b1bff58ac98e

200

py

Python

tokesim/template/__init__.py

tokesim/tokesim

981c9fb74e440d8fc3f0f093b3d5ed396c35180e

[ "Apache-2.0" ]

3

2020-12-14T04:20:06.000Z

2022-02-11T13:43:20.000Z

tokesim/template/__init__.py

zcstarr/tokesim-1

b00ea50fe5e6647a0058e705ae9e1485af0d782b

[ "Apache-2.0" ]

1

2020-04-23T08:11:56.000Z

2020-04-23T08:15:23.000Z

tokesim/template/__init__.py

zcstarr/tokesim-1

b00ea50fe5e6647a0058e705ae9e1485af0d782b

[ "Apache-2.0" ]

2

2020-04-15T17:23:32.000Z

2022-02-11T13:43:58.000Z

# TODO rewrite using the latests models , and potentially include a visualization implementation for the TokenModel so it's more real # and demonstrative of custom visualizations aka make a dashboard

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0

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null

0

1

0

1

0

5

176a644ec8d6128be51c000e2c0ef8d2db45776d

151

py

Python

core/__init__.py

seanrivera/rosploit

f0ba18a6a7b79d3b8ab934a4d7edc85b6d47f3af

[ "MIT" ]

7

2019-03-05T15:35:23.000Z

2022-01-02T15:49:44.000Z

core/__init__.py

seanrivera/rosploit

f0ba18a6a7b79d3b8ab934a4d7edc85b6d47f3af

[ "MIT" ]

4

2020-12-17T09:09:09.000Z

2022-03-09T06:19:18.000Z

core/__init__.py

seanrivera/rosploit

f0ba18a6a7b79d3b8ab934a4d7edc85b6d47f3af

[ "MIT" ]

3

2019-07-22T12:39:54.000Z

2020-10-18T11:40:00.000Z

from .exceptions import StateException from .message import Message from .node import Node from .probe_node import probe_node from .topic import Topic

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1786140523e8826a91066a83eeb2beec8ae1fd49

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py

Python

dgpsi/__init__.py

mingdeyu/DGP

fcc630896d45175d1aa72219607178ca7648cffb

[ "MIT" ]

12

2021-07-06T07:49:42.000Z

2022-03-10T09:49:04.000Z

dgpsi/__init__.py

mingdeyu/DGP

fcc630896d45175d1aa72219607178ca7648cffb

[ "MIT" ]

5

2021-09-21T02:16:58.000Z

2022-01-20T05:16:52.000Z

dgpsi/__init__.py

mingdeyu/DGP

fcc630896d45175d1aa72219607178ca7648cffb

[ "MIT" ]

3

2021-07-19T13:54:38.000Z

2021-10-13T19:55:27.000Z

from .dgp import dgp from .emulation import emulator from .kernel_class import kernel, combine from .likelihood_class import Poisson, Hetero, NegBin from .lgp import lgp from .synthetic import path

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py

Python

v0/aia_eis_v0/ml_sl/knn/distance_pack/standardized_euclidean.py

DreamBoatOve/aia_eis

458b4d29846669b10db4da1b3e86c0b394614ceb

[ "MIT" ]

1

2022-03-02T12:57:19.000Z

v0/aia_eis_v0/ml_sl/knn/distance_pack/standardized_euclidean.py

DreamBoatOve/aia_eis

458b4d29846669b10db4da1b3e86c0b394614ceb

[ "MIT" ]

null

v0/aia_eis_v0/ml_sl/knn/distance_pack/standardized_euclidean.py

DreamBoatOve/aia_eis

458b4d29846669b10db4da1b3e86c0b394614ceb

[ "MIT" ]

null

import math from ml_sl.knn.distance_pack.data_wrapper import single_point_list_2_list, pack_list_2_list def standardized_euclidean_distance_0(x_list, data_list): """ :param x_list: [(x0, y0), (x1, y1), (x2, y2), ..., (xn-2, yn-2), (xn-1, yn-1)] data_list: [x_list_0 (same data structure as x_list), x_list_1, x_list_2, ..., x_list_n-2, x_list_n-1] :return: sed_list measure distance between numbers """ sed_list = [] x_list = single_point_list_2_list(x_list) data_list = pack_list_2_list(data_list) col_standard_variance_list = [] for col_index in range(len(data_list[0])): col_list = [d_list[col_index] for d_list in data_list] col_avg = sum(col_list) / len(col_list) col_standard_variance = math.sqrt(sum([(col - col_avg) ** 2 for col in col_list]) / len(col_list)) col_standard_variance_list.append(col_standard_variance) for d_list in data_list: sed = math.sqrt(sum([((d - x) / col_sv) ** 2 for d, x, col_sv in zip(d_list, x_list, col_standard_variance_list)])) sed_list.append(sed) return sed_list # if __name__ == '__main__': # x_list = [(1,1)] # data_list = [[(1, 1)], # [(1, 2)], # [(2, 1)]] # sed_list = standardized_euclidean_distance_0(x_list, data_list) # print('sed 0', sed_list) def standardized_euclidean_distance_1(x_list, data_list): """ :param x_list: [(x0, y0), (x1, y1), (x2, y2), ..., (xn-2, yn-2), (xn-1, yn-1)] data_list: [x_list_0 (same data structure as x_list), x_list_1, x_list_2, ..., x_list_n-2, x_list_n-1] :return: sed_list measure distance between points """ sed_list = [] data_num_list = pack_list_2_list(data_list) col_standard_variance_list = [] for col_index in range(len(data_num_list[0])): col_list = [d_list[col_index] for d_list in data_num_list] col_avg = sum(col_list) / len(col_list) col_standard_variance = math.sqrt(sum([(col - col_avg) ** 2 for col in col_list]) / len(col_list)) col_standard_variance_list.append(col_standard_variance) for d_list in data_list: col_index_count = 0 sed = 0.0 for x_coor, d_coor in zip(x_list, d_list): col_standard_variance_pair = col_standard_variance_list[col_index_count : col_index_count + 2] col_index_count += 2 sed += math.sqrt(sum([((d - x) / col_sv) ** 2 for d, x, col_sv in zip(d_coor, x_coor, col_standard_variance_pair)])) sed_list.append(sed) return sed_list if __name__ == '__main__': x_list = [(1, 1), (2, 2)] data_list = [[(1, 1), (2, 2)], [(1, 2), (2, 2)], [(1, 1), (3, 2)], [(2, 1), (2, 2)], [(2, 1), (2, 3)], [(1, 1), (2, 3)]] sed_list = standardized_euclidean_distance_1(x_list, data_list) print('sed 1', sed_list)

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17a491a06f41d1469b5c33231ba54aa7fd9d5aa4

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py

Python

nssrc/com/citrix/netscaler/nitro/resource/config/tm/__init__.py

benfinke/ns_python

d651d7aa01d7dc63c1cd435c7b3314d7f5b26659

[ "Apache-2.0" ]

2

2020-08-24T18:04:22.000Z

2020-08-24T18:04:47.000Z

nssrc/com/citrix/netscaler/nitro/resource/config/tm/__init__.py

benfinke/ns_python

d651d7aa01d7dc63c1cd435c7b3314d7f5b26659

[ "Apache-2.0" ]

1

2017-01-20T22:56:58.000Z

nssrc/com/citrix/netscaler/nitro/resource/config/tm/__init__.py

benfinke/ns_python

d651d7aa01d7dc63c1cd435c7b3314d7f5b26659

[ "Apache-2.0" ]

6

2015-04-21T13:14:08.000Z

2020-12-03T07:27:52.000Z

__all__ = ['tmformssoaction', 'tmglobal_auditnslogpolicy_binding', 'tmglobal_auditsyslogpolicy_binding', 'tmglobal_binding', 'tmglobal_tmsessionpolicy_binding', 'tmglobal_tmtrafficpolicy_binding', 'tmsamlssoprofile', 'tmsessionaction', 'tmsessionparameter', 'tmsessionpolicy', 'tmsessionpolicy_aaagroup_binding', 'tmsessionpolicy_aaauser_binding', 'tmsessionpolicy_authenticationvserver_binding', 'tmsessionpolicy_binding', 'tmsessionpolicy_tmglobal_binding', 'tmtrafficaction', 'tmtrafficpolicy', 'tmtrafficpolicy_binding', 'tmtrafficpolicy_csvserver_binding', 'tmtrafficpolicy_lbvserver_binding', 'tmtrafficpolicy_tmglobal_binding']

634

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null

0

5

bdb31572cfbb881d25e67fcf89561d00f437920b

10,659

py

Python

openrasp_iast/test/modules/monitor/test_monitor.py

r3aker/iast

52e5689c371c29db6e18233be5597093c378774d

[ "Apache-2.0" ]

null

openrasp_iast/test/modules/monitor/test_monitor.py

r3aker/iast

52e5689c371c29db6e18233be5597093c378774d

[ "Apache-2.0" ]

null

openrasp_iast/test/modules/monitor/test_monitor.py

r3aker/iast

52e5689c371c29db6e18233be5597093c378774d

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: UTF-8 -*- """ Copyright 2017-2019 Baidu Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import json import time import pytest import helper from core.components.config import Config from core.components.communicator import Communicator # http_data = { # "invalid": { # "json": "Test" # }, # "start_scanner_0": { # "host": "127.0.0.1", # "port": 8005, # "auth_plugin": "default", # "scan_plugin_list": [] # }, # "start_scanner_1": { # "host": "127.0.0.1", # "port": 8006, # "auth_plugin": "default", # "scan_plugin_list": [] # }, # "start_scanner_2": { # "host": "127.0.0.1", # "port": 8007, # "auth_plugin": "default", # "scan_plugin_list": [] # }, # "kill_scanner_0": { # "scanner_id": 0 # }, # "kill_scanner_1": { # "scanner_id": 1 # }, # "clean_target_0": { # "host": "127.0.0.1", # "port": 8005, # }, # "clean_target_1": { # "host": "127.0.0.1", # "port": 8006, # }, # "get_report": { # "host": "127.0.0.1", # "port": 8006, # "page": 1, # "perpage": 10 # } # } # http_sender = helper.HttpSender( # "127.0.0.1", Config().get_config("monitor.console_port")) # def test_http_server_run(monitor_fixture): # """ # 测试monitor http server正常启动 # """ # r = http_sender.test_connect("/api/model/get_report") # assert r.status_code == 405 # def test_send_invalid_data(monitor_fixture): # """ # 测试json格式校验失败 # """ # json_data = http_data["invalid"] # try: # status_code = http_sender.send_data( # json_data, "/api/model/get_report").status_code # except Exception as e: # assert False # else: # assert status_code == 415 # def test_cpu(monitor_fixture): # monitor_fixture["set_system_info"](1, 2) # def test_start_scanner(monitor_fixture): # """ # 测试启动scanner # """ # data = http_data["start_scanner_0"] # path = "/api/scanner/new" # # 启动第1个scanner # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 0 # # 目标已被scanner_1扫描 # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 3 # data = http_data["start_scanner_1"] # # 启动第2个scanner # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 0 # data = http_data["start_scanner_2"] # # scanner数量达到上限 # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 2 # data = http_data["invalid"] # # 启动参数格式错误 # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 1 # time.sleep(2) # # 验证启动结果 # data = {} # path = "/api/model/get_all" # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 0 # data = json.loads(r.text) # assert data["data"]["total"] == 2 # def test_scheduler(monitor_fixture): # max_cr_last = Communicator().get_value("max_concurrent_request", "Scanner_0") # for i in range(5): # Communicator().add_value("send_request", "Scanner_0", 30) # time.sleep(Config().get_config("monitor.schedule_interval") * 1.5) # max_cr = Communicator().get_value("max_concurrent_request", "Scanner_0") # assert max_cr > max_cr_last or max_cr == Config( # ).get_config("scanner.max_concurrent_request") # max_cr_last = max_cr # assert max_cr_last == 5 # for i in range(20): # Communicator().add_value("send_request", "Scanner_0", 30) # Communicator().add_value("failed_request", "Scanner_0", 1) # time.sleep(Config().get_config("monitor.schedule_interval") * 1.5) # max_cr = Communicator().get_value("max_concurrent_request", "Scanner_0") # ri = Communicator().get_value("request_interval", "Scanner_0") # assert max_cr < max_cr_last or max_cr == 1 or ri <= 256 # max_cr_last = max_cr # assert max_cr_last == 1 # for i in range(50): # Communicator().add_value("send_request", "Scanner_0", 30) # time.sleep(Config().get_config("monitor.schedule_interval") * 1.5) # max_cr = Communicator().get_value("max_concurrent_request", "Scanner_0") # ri = Communicator().get_value("request_interval", "Scanner_0") # assert max_cr == 1 # for i in range(50): # Communicator().add_value("send_request", "Scanner_0", 30) # time.sleep(Config().get_config("monitor.schedule_interval") * 1.5) # max_cr = Communicator().get_value("max_concurrent_request", "Scanner_0") # ri = Communicator().get_value("request_interval", "Scanner_0") # if max_cr > 1: # break # assert max_cr > 1 # def test_kill_scanner_0(monitor_fixture): # data = http_data["invalid"] # path = "/api/scanner/kill" # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 1 # data = http_data["kill_scanner_0"] # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 0 # canceled = False # for i in range(20): # data = {} # path = "/api/model/get_all" # r = http_sender.send_json(data, path) # assert r.status_code == 200 # data = json.loads(r.text) # for item in data["data"]["result"]: # if item["host"] == "127.0.0.1" and item["port"] == 8005: # if id not in item: # canceled = True # break # else: # time.sleep(2) # assert canceled # # 测试cancel 不存在的scanner # data = http_data["kill_scanner_0"] # path = "/api/scanner/kill" # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 2 # def test_clean_target(monitor_fixture): # data = http_data["invalid"] # path = "/api/model/clean_target" # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 1 # data = http_data["clean_target_0"] # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 0 # data = http_data["clean_target_1"] # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 2 # def test_get_all_target(monitor_fixture): # data = {} # path = "/api/model/get_all" # r = http_sender.send_json(data, path) # assert r.status_code == 200 # ret = json.loads(r.text) # assert ret["status"] == 0 # assert ret["data"]["total"] == 1 # def test_get_report(monitor_fixture): # sql = """INSERT INTO `ori`.`127.0.0.1_8006_Report` VALUES (1, 'test', '123abcd', '{\"request_id\": \"php1\", \"scan_request_id\": \"\", \"web_server\": {\"host\": \"127.0.0.1\", \"port\": 8005}, \"context\": {\"json\": {}, \"server\": {\"language\": \"php\", \"name\": \"PHP\", \"version\": \"7.2.19\", \"os\": \"Linux\"}, \"body\": \"\", \"appBasePath\": \"/var/www/html\", \"remoteAddr\": \"172.17.0.1\", \"protocol\": \"http\", \"method\": \"get\", \"querystring\": \"url=http://192.168.154.200.xip.io\", \"path\": \"/011-ssrf-curl.php\", \"parameter\": {\"url\": [\"http://192.168.154.200.xip.io\"]}, \"header\": {\"host\": \"127.0.0.1:8005\", \"connection\": \"keep-alive\", \"upgrade-insecure-requests\": \"1\", \"dnt\": \"1\", \"user-agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36\", \"accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3\", \"referer\": \"http://127.0.0.1:8005/011-ssrf-curl.php\", \"accept-encoding\": \"gzip, deflate, br\", \"accept-language\": \"zh-CN,zh;q=0.9\"}, \"url\": \"http://127.0.0.1:8005/011-ssrf-curl.php?url=http://192.168.154.200.xip.io\"}, \"hook_info\": []}', 'payload_seq', 'test msg', 1, 0);""" # helper.execute_sql(sql) # data = http_data["invalid"] # path = "/api/model/get_report" # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 1 # data = http_data["get_report"] # r = http_sender.send_json(data, path) # assert r.status_code == 200 # ret = json.loads(r.text) # assert ret["status"] == 0 # assert ret["data"]["total"] == 1 # def test_kill_scanner(monitor_fixture): # data = http_data["invalid"] # path = "/api/scanner/kill" # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 1 # data = http_data["kill_scanner_1"] # r = http_sender.send_json(data, path) # assert r.status_code == 200 # data = {} # path = "/api/model/get_all" # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 0 # data = json.loads(r.text) # for item in data["data"]["result"]: # if item["host"] == "127.0.0.1" and item["port"] == 8006: # assert id not in item # data = http_data["kill_scanner_1"] # path = "/api/scanner/kill" # r = http_sender.send_json(data, path) # assert r.status_code == 200 # status = json.loads(r.text)["status"] # assert status == 2

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1

0

1

0

5

bdc22b4768c02eac289a6afb4eb5c2acdc51c1a1

333

py

Python

rasterio/warnings.py

rouault/rasterio

0b101b0414a575b263dcebefb0775b672f07cdeb

[ "BSD-3-Clause" ]

null

rasterio/warnings.py

rouault/rasterio

0b101b0414a575b263dcebefb0775b672f07cdeb

[ "BSD-3-Clause" ]

null

rasterio/warnings.py

rouault/rasterio

0b101b0414a575b263dcebefb0775b672f07cdeb

[ "BSD-3-Clause" ]

1

2017-10-16T12:50:16.000Z

"""Rasterio warnings.""" class NodataShadowWarning(Warning): """Warn that a dataset's nodata attribute is shadowing its alpha band""" def __str__(self): return ("The dataset's nodata attribute is shadowing " "the alpha band. All masks will be determined " "by the nodata attribute")

33.3

76

0.645646

40

333

5.275

0.7

0.21327

0.132701

0.218009

0.322275

0

0.264264

333

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0

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false

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1

0

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1

0

null

0

1

0

5

bdcfe285ffc7f58c1dbed78656f58b819aac99dc

149

py

Python

lightlabel/__init__.py

smilelight/lightLabel

18aeaf49e1aba7c8857f6182e52f9418651d15dc

[ "Apache-2.0" ]

3

2020-02-21T10:55:20.000Z

2021-03-31T18:12:20.000Z

lightlabel/__init__.py

smilelight/lightLabel

18aeaf49e1aba7c8857f6182e52f9418651d15dc

[ "Apache-2.0" ]

null

lightlabel/__init__.py

smilelight/lightLabel

18aeaf49e1aba7c8857f6182e52f9418651d15dc

[ "Apache-2.0" ]

4

2020-02-27T12:24:38.000Z

2021-07-09T08:33:17.000Z

from .projects import TextClassification from .web import Engine from .tasks import BaseTask from .plan import Plan from .db import DataBase, MongoDB

29.8

40

0.825503

21

149

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0.571429

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149

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true

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null

0

1

0

1

0

1

0

5

bdede389776cd04d9659cc9e720ca2fc2c48a3ad

658

py

Python

lib/gii/qt/controls/GraphicsView/GraphNodeItemBase.py

tommo/gii

03624a57cf74a07e38bfdc7f53c50bd926b7b5a7

[ "MIT" ]

7

2016-02-13T18:47:23.000Z

2020-07-03T13:47:49.000Z

lib/gii/qt/controls/GraphicsView/GraphNodeItemBase.py

tommo/gii

03624a57cf74a07e38bfdc7f53c50bd926b7b5a7

[ "MIT" ]

1

2018-06-13T04:55:27.000Z

2021-11-05T05:52:51.000Z

lib/gii/qt/controls/GraphicsView/GraphNodeItemBase.py

tommo/gii

03624a57cf74a07e38bfdc7f53c50bd926b7b5a7

[ "MIT" ]

4

2016-02-15T13:32:46.000Z

2019-12-12T17:22:31.000Z

# -*- coding: utf-8 -*- from PyQt4 import QtGui, QtCore, QtOpenGL, uic from PyQt4.QtCore import Qt, QObject, QEvent, pyqtSignal from PyQt4.QtCore import QPoint, QRect, QSize from PyQt4.QtCore import QPointF, QRectF, QSizeF from PyQt4.QtGui import QColor, QTransform from GraphicsViewHelper import * import sys import math ##----------------------------------------------------------------## class GraphNodeItemBase(): def isGroup( self ): return False def initGraphNode( self ): pass def acceptConnection( self, conn ): return False def createConnection( self, **options ): return None if __name__ == '__main__': import TestGraphView

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Python

venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/_pydevd_frame_eval/vendored/bytecode/concrete.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/_pydevd_frame_eval/vendored/bytecode/concrete.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/debugpy/_vendored/pydevd/_pydevd_frame_eval/vendored/bytecode/concrete.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/2b/61/ce/134461b82d0cccd3877da946bbad75d3b5a7280f4fbabde1dd22d03cb2

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da4774b6df5dd36eeb0944b62372c624ea4d58b4

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py

Python

RaspberryPi/server/__init__.py

Ernstsen/RC-car

eda8ec6ae28686380c06f442c889ea89a077315b

[ "MIT" ]

null

RaspberryPi/server/__init__.py

Ernstsen/RC-car

eda8ec6ae28686380c06f442c889ea89a077315b

[ "MIT" ]

3

2021-03-23T15:13:14.000Z

2021-03-23T16:15:20.000Z

RaspberryPi/server/__init__.py

Ernstsen/RC-car

eda8ec6ae28686380c06f442c889ea89a077315b

[ "MIT" ]

null

from .command_handler import CommandHandler from .dict_command_handler import DictCommandHandler from .server import Server __all__ = ["Server", "CommandHandler", "DictCommandHandler"]

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py

Python

plotly/validators/layout/geo/projection/__init__.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

12

2020-04-18T18:10:22.000Z

2021-12-06T10:11:15.000Z

plotly/validators/layout/geo/projection/__init__.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

27

2020-04-28T21:23:12.000Z

2021-06-25T15:36:38.000Z

plotly/validators/layout/geo/projection/__init__.py

gnestor/plotly.py

a8ae062795ddbf9867b8578fe6d9e244948c15ff

[ "MIT" ]

6

2020-04-18T23:07:08.000Z

2021-11-18T07:53:06.000Z

from ._type import TypeValidator from ._scale import ScaleValidator from ._rotation import RotationValidator from ._parallels import ParallelsValidator

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e5150ce99a08ca80692dcebf32b1b2401f01e618

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py

Python

products/__init__.py

stephane-lucienvauthier/foodstock-api

6b886cd638b5f1a10774280845bc85fd37a6ca9d

[ "Apache-2.0" ]

null

products/__init__.py

stephane-lucienvauthier/foodstock-api

6b886cd638b5f1a10774280845bc85fd37a6ca9d

[ "Apache-2.0" ]

null

products/__init__.py

stephane-lucienvauthier/foodstock-api

6b886cd638b5f1a10774280845bc85fd37a6ca9d

[ "Apache-2.0" ]

null

"""This module initializes the products app."""

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e540c26ed89a2af7c925fa9b329619321c050b95

1,260

py

Python

app/tests/view_test.py

hkawinzi/news-highlight

9f5c022fe1de6273725407606589ed218e77d4df

[ "Unlicense" ]

null

app/tests/view_test.py

hkawinzi/news-highlight

9f5c022fe1de6273725407606589ed218e77d4df

[ "Unlicense" ]

1

2021-06-02T00:20:54.000Z

app/tests/view_test.py

hkawinzi/news-highlight

9f5c022fe1de6273725407606589ed218e77d4df

[ "Unlicense" ]

null

import unittest from app.models import view class NewsTest(unittest.TestCase): ''' Test Class to test the behaviour of the News class ''' def setUp(self): ''' Set up method that will run before every Test ''' self.new_view = View('Cointelegraph By Aaron Wood','Crypto and Blockchain Adoption Grows: 5 Important Developments Sept. 2–8','Adoption is critical for the success of the blockchain and cryptocurrency industries. Here are five examples of developments that could spur adoption from last week','https://cointelegraph.com/news/crypto-and-blockchain-adoption-grows-5-important-developments-sept-28','https://images.cointelegraph.com/images/740_aHR0cHM6Ly9zMy5jb2ludGVsZWdyYXBoLmNvbS9zdG9yYWdlL3VwbG9hZHMvdmlldy8wMzYyZDU2NTQxN2JjNmY0ZmQyMzk0MGM0Y2VmYTlkNC5qcGc=.jpg','2019-09-09T01:49:00Z','Analysts have long predicted that the increased participation of governments and institutional players in the blockchain and cryptocurrency space shows how the respective industries are maturing. The founder of crypto merchant bank Galaxy Digital, Michael Nov… [+3146 chars]') def test_instance(self): self.assertTrue(isinstance(self.new_view,View)) if __name__ == '__main__': unittest.main()

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py

Python

pyrobomogen/pub_sub/__init__.py

virtual-origami/pyrobomogen

30cd4e854704b0297142d7f817dabe08c9cbbd2e

[ "MIT" ]

null

pyrobomogen/pub_sub/__init__.py

virtual-origami/pyrobomogen

30cd4e854704b0297142d7f817dabe08c9cbbd2e

[ "MIT" ]

2

2021-04-08T17:23:00.000Z

2021-12-11T11:58:18.000Z

pywalkgen/pub_sub/__init__.py

virtual-origami/pywalkgen

e860b8fb327aa6ceb05849f610855657f917a1ea

[ "MIT" ]

null

from __future__ import generator_stop from __future__ import annotations from .AMQP import PubSubAMQP __all__ = [ 'PubSubAMQP' ]

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py

Python

__version__.py

SeanHildreth/pygeocoder

6e3c7f098f450e222acb65c011089c74c25432f4

[ "BSD-3-Clause" ]

5

2015-06-29T22:15:10.000Z

2019-08-02T00:45:49.000Z

__version__.py

SeanHildreth/pygeocoder

6e3c7f098f450e222acb65c011089c74c25432f4

[ "BSD-3-Clause" ]

null

__version__.py

SeanHildreth/pygeocoder

6e3c7f098f450e222acb65c011089c74c25432f4

[ "BSD-3-Clause" ]

5

2015-06-09T05:56:54.000Z

2020-04-04T15:15:45.000Z

VERSION = '1.2.0.1'

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py

Python

user/models/__init__.py

ThePokerFaCcCe/messenger

2db3d5c2ccd05ac40d2442a13d664ca9ad3cb14c

[ "MIT" ]

null

user/models/__init__.py

ThePokerFaCcCe/messenger

2db3d5c2ccd05ac40d2442a13d664ca9ad3cb14c

[ "MIT" ]

null

user/models/__init__.py

ThePokerFaCcCe/messenger

2db3d5c2ccd05ac40d2442a13d664ca9ad3cb14c

[ "MIT" ]

null

from .user import User from .device import Device from .contact import Contact

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5

e5e2f4b0778f649eb7126c0145e07d7c95941260

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py

Python

modules/2.79/bpy/types/SequenceColorBalance.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/SequenceColorBalance.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

modules/2.79/bpy/types/SequenceColorBalance.py

cmbasnett/fake-bpy-module

acb8b0f102751a9563e5b5e5c7cd69a4e8aa2a55

[ "MIT" ]

null

class SequenceColorBalance: pass

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5

f91cd84a1f262de261dcb6072fe130629e385218

107

py

Python

Python/empire/ejson/__init__.py

Tombmyst/Empire

f28782787c5fa9127e353549b73ec90d3c82c003

[ "Apache-2.0" ]

null

Python/empire/ejson/__init__.py

Tombmyst/Empire

f28782787c5fa9127e353549b73ec90d3c82c003

[ "Apache-2.0" ]

null

Python/empire/ejson/__init__.py

Tombmyst/Empire

f28782787c5fa9127e353549b73ec90d3c82c003

[ "Apache-2.0" ]

null

# TODO: # JSON_FIND: ex: trouver tous les fields 'originalRecord.contactFirstName' où le length est <= 2

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py

Python

_modules/nexus3_blobstores.py

jsandas/saltstack-nexus3-module

e090dfe18cd3b5d90d1c71b0747ff150eb96e328

[ "MIT" ]

1

2020-11-15T00:18:55.000Z

_modules/nexus3_blobstores.py

jsandas/saltstack-nexus3-module

e090dfe18cd3b5d90d1c71b0747ff150eb96e328

[ "MIT" ]

1

2020-11-21T19:08:07.000Z

2020-11-21T19:14:37.000Z

_modules/nexus3_blobstores.py

jsandas/saltstack-nexus3-module

e090dfe18cd3b5d90d1c71b0747ff150eb96e328

[ "MIT" ]

null

'''' execution module for Nexus 3 blobstores :version: v0.2.1 :configuration: In order to connect to Nexus 3, certain configuration is required in /etc/salt/minion on the relevant minions. Example: nexus3: hostname: '127.0.0.1:8081' username: 'admin' password: 'admin123' ''' import json import logging import nexus3 log = logging.getLogger(__name__) __outputter__ = { "sls": "highstate", "apply_": "highstate", "highstate": "highstate", } blobstore_path = 'v1/blobstores' def create(name, quota_type=None, quota_limit=1000000, store_type='file', s3_accessKeyId='', s3_bucket='nexus3', s3_endpoint='', s3_expiration=3, s3_forcePathStyle=False, s3_prefix='', s3_region='Default', s3_secretAccessKey=''): ''' name (str): Name of blobstore .. note:: The blobstore name is used for blobstore path. quota_type (str): Quota type [None|spaceRemainingQuota|spaceUsedQuota] (Default: None) quota_limit (int): Quota limit in bytes (Default: 1000000) .. note:: The limit should be no less than 1000000 bytes (1 MB) otherwise it does not display properly in the UI. store_type (str): Type of blobstore [file|s3] (Default: file) s3_accessKeyId (str): AWS Access Key for S3 bucket (Default: '') s3_bucket (str): Name of S3 bucket (Default: 'nexus3') s3_endpoint (str): custom URL for s3 api [http://localhost:9000] (Default: '') .. note:: only required if using a s3 compatible service s3_expiration (int): days until deleted blobs are purged from bucket (Default: 3) .. note:: set to -1 to disable s3_forcePathStyle (bool): force path style url format (Default: False) .. note: if using s3 compatible service like min.io, set this to True s3_region (str): Region of S3 bucket [us-east-1,us-east-2,us-west-1,us-west-2,etc] (Default: 'Default') s3_secretAccessKey (str): AWS Secret Access Key for S3 bucket (Default: '') CLI Example:: .. code-block:: bash salt myminion nexus3_blobstores.create name=myblobstore salt myminion nexus3_blobstores.create name=myblobstore quota_type=spaceRemainingQuota spaceRemainingQuota=5000000 salt myminion nexus3_blobstores.create name=mys3blobstore store_type=s3 s3_bucket=nexus3 s3_accessKeyId=AKIAIOSFODNN7EXAMPLE s3_secretAccessKey=wJalrXUtnFEMIK7MDENGbPxRfiCYEXAMPLEKEY s3_endpoint=http://minio:9000 s3_forcePathStyle=True ''' ret = { 'blobstore': {}, } path = '{}/{}'.format(blobstore_path, store_type) payload = { 'name': name, } if store_type == 'file': payload['path'] = '/nexus-data/blobs/' + name if store_type == 's3': s3_config = {} s3_config['bucket'] = { 'region': s3_region, 'name': s3_bucket, 'prefix': s3_prefix, 'expiration': s3_expiration } if s3_accessKeyId != '' or s3_secretAccessKey != '': s3_config['bucketSecurity'] ={ 'accessKeyId': s3_accessKeyId, 'secretAccessKey': s3_secretAccessKey, # 'role': 'string', # 'sessionToken': 'string' } # TODO: added encryption support # "encryption": { # 'encryptionType': 's3ManagedEncryption', # 'encryptionKey': 'string' # } if s3_endpoint != '': s3_config['advancedBucketConnection'] = { 'endpoint': s3_endpoint, 'signerType': 'DEFAULT', 'forcePathStyle': s3_forcePathStyle } payload['bucketConfiguration'] = s3_config if quota_type is not None: payload['softQuota'] = { 'type': quota_type, 'limit': quota_limit } nc = nexus3.NexusClient() resp = nc.get(path + '/' + name) if resp['status'] == 200: ret['comment'] = 'blobstore {} already exists.'.format(name) return ret resp = nc.post(path, payload) if resp['status'] in [201, 204]: ret['blobstore'] = describe(name)['blobstore'] else: ret['comment'] = 'could not create blobstore {}.'.format(name) ret['error'] = { 'code': resp['status'], 'msg': resp['body'] } return ret def delete(name): ''' name (str): Name of blobstore CLI Example:: .. code-block:: bash salt myminion nexus3_blobstores.delete name=myblobstore ''' ret = { 'comment': 'Deleted blobstore "{}"'.format(name) } path = '{}/{}'.format(blobstore_path, name) nc = nexus3.NexusClient() resp = nc.delete(path) if resp['status'] == 404: ret['comment'] = 'blobstore {} does not exist.'.format(name) elif resp['status'] != 204: ret['comment'] = 'could not delete blobstore {}.'.format(name) ret['error'] = { 'code': resp['status'], 'msg': resp['body'] } return ret def describe(name): ''' name (str): Name of blobstore CLI Example:: .. code-block:: bash salt myminion nexus3_blobstores.describe name=myblobstore ''' ret = { 'blobstore': {}, } resp = list_all() if 'error' in resp.keys(): ret['result'] = False ret['comment'] = 'could not retrieve blobstore {}.'.format(name) ret['error'] = resp['error'] return ret for bstore in resp['blobstores']: if bstore['name'] == name: ret['blobstore'] = bstore break if ret['blobstore']: path = '{}/{}/{}'.format(blobstore_path, ret['blobstore']['type'].lower(), name) nc = nexus3.NexusClient() resp = nc.get(path) if resp['status'] == 200: ret['blobstore'].update(json.loads(resp['body'])) else: ret['comment'] = 'could not retrieve blobstore {}'.format(name) ret['error'] = { 'code': resp['status'], 'msg': resp['body'] } return ret def list_all(): ''' CLI Example:: .. code-block:: bash salt myminion nexus3_blobstores.list_all ''' ret = { 'blobstores': {} } nc = nexus3.NexusClient() resp = nc.get(blobstore_path) if resp['status'] == 200: ret['blobstores'] = json.loads(resp['body']) else: ret['comment'] = 'could not retrieve blobstores.' ret['error'] = { 'code': resp['status'], 'msg': resp['body'] } return ret def update(name, quota_type=None, quota_limit=1000000, s3_accessKeyId='', s3_bucket='nexus3', s3_endpoint='', s3_expiration=3, s3_forcePathStyle=False, s3_prefix='', s3_region='Default', s3_secretAccessKey=''): ''' name (str): Name of blobstore .. note:: The blobstore name is used for blobstore path. quota_type (str): Quota type [None|spaceRemainingQuota|spaceUsedQuota] (Default: None) quota_limit (int): Quota limit in bytes (Default: 1000000) .. note:: The limit should be no less than 1000000 bytes (1 MB) otherwise it does not display properly in the UI. s3_accessKeyId (str): AWS Access Key for S3 bucket (Default: '') s3_bucket (str): Name of S3 bucket (Default: 'nexus3') s3_endpoint (str): custom URL for s3 api [http://localhost:9000] (Default: '') .. note:: only required if using a s3 compatible service s3_expiration (int): days until deleted blobs are purged from bucket (Default: 3) .. note:: set to -1 to disable s3_forcePathStyle (bool): force path style url format (Default: False) .. note: if using s3 compatible service like min.io, set this to True s3_region (str): Region of S3 bucket [us-east-1,us-east-2,us-west-1,us-west-2,etc] (Default: 'Default') s3_secretAccessKey (str): AWS Secret Access Key for S3 bucket (Default: '') CLI Example:: .. code-block:: bash salt myminion nexus3_blobstores.update name=myblobstore quota_type=spaceRemainingQuota quota_limit=5000000 salt myminion nexus3_blobstores.update name=mys3blobstore s3_bucket=nexus3 s3_accessKeyId=AKIAIOSFODNN7EXAMPLE s3_secretAccessKey=wJalrXUtnFEMIK7MDENGbPxRfiCYEXAMPLEKEY s3_endpoint=http://minio:9000 s3_forcePathStyle=True ''' ret = { 'blobstore': {} } metadata = describe(name) if not metadata['blobstore']: return metadata payload = { 'name': name, } if metadata['blobstore']['type'].lower() == 'file': payload['path'] = '/nexus-data/blobs/' + name if metadata['blobstore']['type'].lower() == 's3': s3_config = {} s3_config['bucket'] = { 'region': s3_region, 'name': s3_bucket, 'prefix': s3_prefix, 'expiration': s3_expiration } if s3_accessKeyId != '' or s3_secretAccessKey != '': s3_config['bucketSecurity'] ={ 'accessKeyId': s3_accessKeyId, 'secretAccessKey': s3_secretAccessKey, # 'role': 'string', # 'sessionToken': 'string' } # TODO: added encryption support # "encryption": { # 'encryptionType': 's3ManagedEncryption', # 'encryptionKey': 'string' # } if s3_endpoint != '': s3_config['advancedBucketConnection'] = { 'endpoint': s3_endpoint, 'signerType': 'DEFAULT', 'forcePathStyle': s3_forcePathStyle } payload['bucketConfiguration'] = s3_config if quota_type is not None: payload['softQuota'] = { 'type': quota_type, 'limit': quota_limit } path = '{}/{}/{}'.format(blobstore_path, metadata['blobstore']['type'].lower(), name) nc = nexus3.NexusClient() resp = nc.put(path, payload) if resp['status'] == 204: ret['blobstore'] = describe(name)['blobstore'] else: ret['comment'] = 'could not update blobstore {}.'.format(name) ret['error'] = { 'code': resp['status'], 'msg': resp['body'] } return ret

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0

null

0

1

0

1

0

null

0

5

00743f4db17ecdf4faa38a1f410d8c5f93f80fa3

161

py

Python

surefire/encoders/identity.py

jasonkriss/surefire

55365939dbb4a7e41ec28fa40471a6c1e8dd23a1

[ "MIT" ]

null

surefire/encoders/identity.py

jasonkriss/surefire

55365939dbb4a7e41ec28fa40471a6c1e8dd23a1

[ "MIT" ]

null

surefire/encoders/identity.py

jasonkriss/surefire

55365939dbb4a7e41ec28fa40471a6c1e8dd23a1

[ "MIT" ]

null

from surefire.encoders import Encoder class IdentityEncoder(Encoder): def forward(self, x): return x def num_features(self): return 1

16.1

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1

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5

00a5b7e0d83a59aaa0843c6fd5957ec822b9283c

149

py

Python

sets/admin.py

CASDON-MYSTERY/studentapp

0fd942e963a10a02a6c9f358dd362cfd646eecc3

[ "MIT" ]

null

sets/admin.py

CASDON-MYSTERY/studentapp

0fd942e963a10a02a6c9f358dd362cfd646eecc3

[ "MIT" ]

null

sets/admin.py

CASDON-MYSTERY/studentapp

0fd942e963a10a02a6c9f358dd362cfd646eecc3

[ "MIT" ]

null

from django.contrib import admin from .models import Set, Level # Register your models here. admin.site.register(Level) admin.site.register(Set)

16.555556

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149

5.272727

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0.293103

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0.134228

149

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true

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1

0

1

0

null

0

1

0

1

0

5

00a98a75c071d0e0f358aa8fedccf90461032a8f

239

py

Python

tests/unit/_modules/test_example.py

yagnik/saltstack-template

fe069f49e3d200f7c2849be45d065234db67a0f2

[ "Apache-2.0" ]

9

2017-09-25T16:14:04.000Z

2021-12-12T19:27:09.000Z

tests/unit/_modules/test_example.py

yagnik/salt-template

fe069f49e3d200f7c2849be45d065234db67a0f2

[ "Apache-2.0" ]

1

2017-07-10T09:07:24.000Z

2017-07-15T13:06:15.000Z

tests/unit/_modules/test_example.py

yagnik/saltstack-template

fe069f49e3d200f7c2849be45d065234db67a0f2

[ "Apache-2.0" ]

1

2017-06-30T16:50:47.000Z

from tests import TestMinion class TestExample(TestMinion): def test_func(self, __salt__): assert __salt__['example.func']() def util_func(self, __salt__): assert __salt__['example.util_func']() == "I'm helping"

23.9

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0.25

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1

0

null

0

1

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1

0

5

00db66d809e49450b985ebb0040ec7336da8723b

603,288

py

Python

Learning_Tensorflow/Advanced_Tensorflow/text/neural_machine_translation_attention.py

oke-aditya/Machine_Learning

3dd40ae2b9cba1890e7060448e75c14194b27775

[ "MIT" ]

15

2019-11-16T11:09:24.000Z

2022-01-09T01:58:03.000Z

Learning_Tensorflow/Advanced_Tensorflow/text/neural_machine_translation_attention.py

oke-aditya/Machine_Learning

3dd40ae2b9cba1890e7060448e75c14194b27775

[ "MIT" ]

1

2021-11-10T19:46:00.000Z

Learning_Tensorflow/Advanced_Tensorflow/text/neural_machine_translation_attention.py

oke-aditya/Machine_Learning

3dd40ae2b9cba1890e7060448e75c14194b27775

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Neural_Machine_Translation_Attention.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1f7uW5_n27jMXhZBpwll0eyHF543SVmRm # Neural Machine Translation with Attention - This uses seq2seq model. - RNN based encoder. Again a RNN based decoder. - We also have attention mechanism which focuses on words that are important. """ # Commented out IPython magic to ensure Python compatibility. # %tensorflow_version 2.x import tensorflow as tf import matplotlib.pyplot as plt import matplotlib.ticker as ticker from sklearn.model_selection import train_test_split import unicodedata import re import numpy as np import os import io import time """# Make Dataset Download and prepare the dataset We'll use a language dataset provided by http://www.manythings.org/anki/. This dataset contains language translation pairs in the format: May I borrow this book? ¿Puedo tomar prestado este libro? fter downloading the dataset, here are the steps we'll take to prepare the data: - Add a start and end token to each sentence. - Clean the sentences by removing special characters. - Create a word index and reverse word index (dictionaries mapping from word → id and id → word). - Pad each sentence to a maximum length. """ path_to_zip = tf.keras.utils.get_file('spa-eng.zip', origin='http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip', extract=True) path_to_file = os.path.dirname(path_to_zip)+"/spa-eng/spa.txt" print(path_to_file) """# Data Preprocessing""" # Convert unicode file to ascii def unicode_to_ascii(s): return ''.join(c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn') def preprocess_sentence(w): w = unicode_to_ascii(w.lower().strip()) # creating a space between a word and the punctuation following it # eg: "he is a boy." => "he is a boy ." # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation w = re.sub(r"([?.!,¿])", r" \1 ", w) w = re.sub(r'[" "]+', " ", w) # replacing everything with space except (a-z, A-Z, ".", "?", "!", ",") w = re.sub(r"[^a-zA-Z?.!,¿]+", " ", w) w = w.strip() # adding a start and an end token to the sentence # so that the model know when to start and stop predicting. w = '<start> ' + w + ' <end>' return w en_sentence = u"May I borrow this book ?" sp_sentence = u"¿Puedo tomar prestado este libro?" print(preprocess_sentence(en_sentence)) print(preprocess_sentence(sp_sentence).encode('utf-8')) # 1. Remove the accents # 2. Clean the sentences # 3. Return word pairs in the format: [ENGLISH, SPANISH] def create_dataset(path, num_examples): lines = io.open(path, encoding='UTF-8').read().strip().split('\n') word_pairs = [[preprocess_sentence(w) for w in l.split('\t')] for l in lines[:num_examples]] return(zip(*word_pairs)) en, sp = create_dataset(path_to_file, None) print(en[-1]) print(sp[-1]) def max_length(tensor): return max(len(t) for t in tensor) def tokenize(lang): lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(filters='') lang_tokenizer.fit_on_texts(lang) tensor = lang_tokenizer.texts_to_sequences(lang) # print(tensor.shape) tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor, padding='post') # print(tensor.shape) return tensor, lang_tokenizer def load_dataset(path, num_examples=None): targ_lang, inp_lang = create_dataset(path, num_examples) input_tensor, inp_lang_tokenizer = tokenize(inp_lang) target_tensor, targ_lang_tokenizer = tokenize(targ_lang) return input_tensor, target_tensor, inp_lang_tokenizer, targ_lang_tokenizer """Limit the size of the dataset to experiment faster (optional) Training on the complete dataset of >100,000 sentences will take a long time. To train faster, we can limit the size of the dataset to 30,000 sentences (of course, translation quality degrades with less data): """ num_examples = 30000 input_tensor, target_tensor, inp_lang_tokenizer, targ_lang_tokenizer = load_dataset(path_to_file, num_examples=num_examples) print(input_tensor.shape) print(target_tensor.shape) print(inp_lang_tokenized) max_length_target = max_length(target_tensor) max_length_inp = max_length(input_tensor) print(max_length_target) print(max_length_inp) # Creating training and validation sets using an 80-20 split input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2) # Show length print(len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val)) """# Let's see after preprocessing - Our aim should be converting the from these tokenized words from one language to other. """ def convert(lang, tensor): for t in tensor: if t != 0: print("%d ------> %s" %(t, lang.index_word[t])) print("Input Language: tokenized index") convert(inp_lang_tokenizer, input_tensor_train[0]) print() print("Target language: tokenized index") convert(targ_lang_tokenizer, target_tensor_train[0]) """# Create a tf.data Dataset object""" BUFFER_SIZE = len(input_tensor_train) BATCH_SIZE = 64 steps_per_epoch = len(input_tensor_train) // BATCH_SIZE embedding_dim = 256 units = 1024 vocab_inp_size = len(inp_lang_tokenizer.word_index) + 1 vocab_tar_size = len(targ_lang_tokenizer.word_index) + 1 dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True) example_input_batch, example_target_batch = next(iter(dataset)) example_input_batch.shape, example_target_batch.shape """# Time for enocder + decoder model # Enocder Implement an encoder-decoder model with attention which you can read about in the TensorFlow Neural Machine Translation (seq2seq) tutorial. This example uses a more recent set of APIs. This notebook implements the attention equations from the seq2seq tutorial. The following diagram shows that each input words is assigned a weight by the attention mechanism which is then used by the decoder to predict the next word in the sentence. The below picture and formulas are an example of attention mechanism from Luong's paper. ![image.png](data:image/png;base64,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) The input is put through an encoder model which gives us the encoder output of shape (batch_size, max_length, hidden_size) and the encoder hidden state of shape (batch_size, hidden_size). Here are the equations that are implemented: ![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAACAAAAAHMCAYAAABBFUgsAAAgAElEQVR4nOzdeZhcVZ0//tv7UtVrdXfte1UvWVlEAsEBWcIEECHDKKBC8IcOURABQQTRcZIfQQNCBh2NaAYBYSQwMKhBww7BIBAC2SEJSZpsnd6ra7l1b9V9f/+onNO3qjukY5Akzfv1PPVQ3V1111MVnuf9OZ+jgIiIiIiIiIiIiIiIiIiIiI56yuE+ACIiIiIiIiIiIiIiIiIiIjp0LAAgIiIiIiIiIiIiIiIiIiIaB1gAQERERERERERERERERERENA6wAICIiIiIiIiIiIiIiIiIiGgcYAEAERERERERERERERERERHROMACACIiIiIiIiIiIiIiIiIionGABQBERERERERERERERERERETjAAsAiIiIiIiIiIiIiIiIiIiIxgEWABAREREREREREREREREREY0DLAAgIiIiIiIiIiIiIiIiIiIaB1gAQERERERERERERERERERENA6wAICIiIiIiIiIiIiIiIiIiGgcYAEAERERERERERERERERERHROMACACIiIiIiIiIiIiIiIiIionGABQBERERERERERERERERERETjAAsAiIiOYtlsFtlsVj4XdF3P+3nPnj3y+dDQ0Md3gET0D5HJZGAYBgBgYGAg72/pdPqQt2/+XhHbU1UVAKBp2iFv/3DTdR3xeFyey+DgIACgv78fQO76ZjIZ+VrxPB6PH4ajJSIiIiIiIiIiIho7FgAQER2lkslk3s8isDMMA5qmwTAMqKoqA38RcAFAT0/Px3egRPSRMwxDfgeIz34qlYKmaUgmkzAM45AfsVhM7s9cOCTC8PGiq6sr7+edO3cik8nkFTyIYgtd18dFAQQRERERERERERGNXywAICI6SolwSjwfLZQTQZUI8jKZDHbt2vXxHCAR/UMVBtHmGe2HKpvNyiIiYLirgKZp4yYA37Fjh3ze29uLeDye9z0qCivS6XRewdV4K4AgIiIiIiIiIiKi8YUFAERER7l0Op1XDJBKpZDJZJBMJpFKpWAYBvr7+5HNZvNm9BLR0U/TNKRSKei6jnQ6LYN7sTzI3/sQgb95OYF0Og1N06Dr+uE63Y9MIpEAkH9+iUQCQ0NDo557KpX6SJZWICIiIiIiIiIiIvpHYwEAEdFRLJvNjpixmkwm5e8eeughKIqCBQsWyKUBgPzuAUR09Onp6cGjjz6KG264ATfddBPmzp2LuXPn4uabb8Ytt9yCW2+99ZAe11xzDW666SZs2bIFPT09SKfTecsAjAci0B8YGMB3vvMdKIqC5cuXA8i1+ldVVXYBACCLH8ZDAQQRERERERERERGNXywAICI6iplbcReG+nfccQdKSkpw4YUXyt8NDg6yfTXRODFv3jxYLBZYLBY4nU40NzejsrISLpcLjY2Nh/RwOBxQlOH/TYzH4/K5mD0/nnR1deG4446Doii47777ZPCfyWRk4G8uBiAiIiIiIiIiIiI6UrEAgIjoKCYKANLpNPr7+wHkZgYvXrwY5eXlmDFjBlRVRVdXl3ztwMDAYTteIvpo9Pb24tZbb0VtbS2sVisaGhrQ0tICi8UCr9eLpqamQ3rU1dVBURRs374dAOT3CwAYhnG4TvsjI7qnGIYhZ/rv2LEDTU1NqK+vx0MPPSSLqoaGhpDJZFg8RUREREREREREREcFFgAQER2lMplM3qx/EfCLtv8ejwcbN24c9X1EdPTr7u7Ga6+9hjVr1mD16tX44IMPsHr1arz11ltYt27dIT02bNiAlStX5u0vlUqNm+8P8wx/wTAM/OUvf0FtbS3q6+vxzDPPyN+L/5qXUiEiIiIiIiIiIiI6ErEAgIjoKGVuyS0CqaeffhqKoqCyshJvvvkm9u7dK1/DGax0sMxrvqdSKRiGIcedYRh5a6Enk0n5XNd1ZLNZGIYhZ1qbqaqKTCYjXyNeJ34W2zBvV+w3k8nItds/yczXKpvN5l1/IRaLyaC7u7tbvrZwuZAjkXnMiPudSqWQSqXk78U5m6+FGKdjYe6GYp7tf99990FRFFgsFmzcuBHxeFyOR3EdiYiIiIiIiIiIiI5ULAAgIjpKZTIZGfynUils3rwZNpsNra2tmD17Nvr7+/OCMM5apYMhWr4PDg7K38ViMQC5QFYEtOYiExHImgsHgOFuFeYxWBj+iwIV8RCBq9jPeFx3/lDoui6vp7kQQFVV9PT05BVnFL7vaCDOLZPJIJlMyjb8QC6sF2Oxr69Pjo2DLQwxF0KYiwF6enpw4YUXoqmpCVOmTJGFFOL1Yt9ERERERERERERERyIWABARHcV6e3vl81NOOQVWqxUdHR3o6urKe52qqlBVdcTsaqL9ETPHk8mkfK7rOnRdRzqdRjqdhq7rGBgYkGG+uSvFaNs7mA4UotV6YdiaTCYZwO4j7suHFUf09vaip6dHvv5o6gJiLhjRNA0DAwPYvHmz/J058P97ukKYt59Op/Ou46ZNm9DS0gJFUXDNNdewAIWIiIiIiIiIiIiOGiwAICI6iiWTSWiahp/85CewWCxoamrC4sWLAUC2WBct00Xov7924USFEomEDIxFy3/x8+rVq1FTU4NvfvOb8vW6riMej8vXiIIBM03TZCt38RAdBczLBhRKJpN5nQc+6cT1AobD776+PsRiMaiqKjs4mB1NIXY6nYaqqnljYeHChVAUBU8//TSA4U4T5kIRTdPGtASAYRiykMW8z8HBQTnT/+6770ZFRQUqKirwhz/8Ia8bBhEREREREREREdGRigUARERHMV3XsWrVKhQVFcFms2HmzJny9+l0etQgFcCY18imTy4RqIrQUwSmAPB///d/KC4uxuWXXy4D+f3NyjcMQ7b1HwuxtEUymURfXx+y2WzeTG3z0hefdIUFAKNdl3Q6LYsuxD0oXKLhSGVe4qSvrw8AMHfuXCiKgl/96lcAct91otghm80inU6PuQAAgFxeQBDXNB6PQ1VVTJs2DRUVFTj++OOxdevWvKUCiIiIiIiIiIiIiI5ELAAgIjrKzZo1C7W1tSgqKsJrr702aggoOgEw+KexMneJMIf7999/P2pra3HWWWeNWBNdVVV0dnYCQF7gr2naiHGpaZpcmmJ/S1KI8drb2yuPZ7RtfVKZr7F5iQ+xFEMikZDXTfxNBOlHuv7+fmSz2RGFJT09PZg/fz4aGhrwy1/+EqlUSv4tlUqNeXkTMXbF8/0VTzzwwAOorKxEWVkZbrzxRo49IiIiIiIiIiIiOuKxAICI6CiVTCbx4osvQlEUhMNhXHLJJeju7gaAvBAsk8nIVuzmNutEByJCUtEF4Nlnn0VTUxN8Ph90XYeqqjJQ1nUdvb298r3m1urmThSrVq3Cz3/+c/zwhz/E9ddfj9mzZ+Nzn/scTj75ZLS1taG5uRlVVVUoKyvLW14A4Ox/s/7+fvk5TqfT+M1vfoMFCxbgpptuwpe+9CVccMEFOO+88/CpT30KkUgEiqKgpKQE7e3t++3WcCQS32W6rsslDPr7+3H66aejpaUFDz74oPydeO1Yuk2YCwDEe8TD/LfOzk6ceOKJiEajUBQFvb29ssCCiIiIiIiIiIiI6EjEAgAioqNUJpNBa2srwuEwFEXB0qVLAeQKA0QwKGb+m4k22UQHks1mZdi5Z88eGSRv2LAhL4iPx+NyzBmGkTcr27wme3d3N2666SYoioKmRhsa6xvQWN+AlpYW+L0+hMNhhMNhBINB2Gw2XHfddXIMi+IVc2cCyrXzf+qpp1BbW4vS4hI01jfAaXfA7XQhFAiisb4BkydPRiQSgd1uh6IoR0Ube13X0dXVBWC4o4FhGLLIaXBwEC6XC6WlpVi+fDkAyHE31oA+lUohHo9D1/URy6XEYjH5u3vuuQd1dXWwWq2YPXs2vz+JiIiIiIiIiIjoiMYCACKiI5iYeQ0Mh1oilHriiSdQWlqK5uZmnHHGGXmzrxmS0qESIacIXKdPnw5FUfDQQw+Nuc06gLylJ7Zv345rr70WRYqCEkVBeXEJQj4//F4f2sIROO0OTJ44CW6nC6XFJVjy+0dHtLmnHD2bgYFcgcXSpUtRY7GiRFFQqhTB0dSMsD8AZ3ML2sIRtLe2oaamBpFIBNNOPgkZI3vA7R8NnnvuOSiKgqlTpyIWi8nvS9Ep4FANDQ3JQgSXyyW7ALz22mtyLJoLYcTvCosJiIiIiIiIiIiIiD5OLAAgIjpCmWfvi1nUIkhNpVKYOXMmamtrUVNTg4ULF8IwDKTTaWSz2YMKaIlGY55tf+ONN6K8vByf+9znEI/HxzSDXBQQFI7H7u5uvPTCi3ht+at4/s/LcNZnT0dHtBUhnx9Brw9+rw8uhxMVZeVYtfKtEdtiuJqjZXRkYUDXdaTTaax4ZTmWv/gSnl36Z9x5+x3w2p3w2p2IBIKIhiMIBYKor6/Hrbd9H+NhARBREPXtb38bZWVluOyyyw5qCYCxMC+x8PWvfx11dXVoamrCN7/5TTkmxb7Ez4ZhfGT7JyIiIiIiIiIiIvp7sACAiOgIZW57LZ6LGabLly+Hoijw+/2w2WzYvn27fA/Df/qopNNprFixAs3NzVAUBc8++yyA4WD0w5hn66uqikQiMTw2swaQyQJ6Fn63B267Ax6HExNa2+B1e+DzeOH3+qCpuVDVPKOb3S1yjH0P2Y7eQO66ZoF7fnInWuob4Xe64XO54fN4MXniJCiKgr++tgLZcVECkNPZ2YnGxkYoioIHH3xwTGNzLApD/Oeffx6lpaXo6OhAQ0MD3n//faiqKvc3NDQEALIIi4iIiIiIiIiIiOhwYQEAEdERSoT+mUxGPk+n09A0Dddccw0sFgsCgQA++9nP5s1UJfqoJJNJnHXWWSgpKcHs2bORSqXG3F5dVdURYWgmk4GmachoOmJ9/ejd3YWKklKE/QFEAkFMbGuX69Zf/IUvAkZuO+YwNp1Of2Qh79FMy+hyCYBUKgUYQKyvH8gCF5xzHtzNdgTdXgQ8XnjdHrRGoigtLUU8mUBSTR3uw/9IDA0NwTAM/OhHP4KiKDj++OPxwQcffCTjQ7T2T6VS0HUdiUQCEyZMQDQaRVlZGX72s5/lrjtynSlEwQsLVIiIiIiIiIiIiOhwYwEAEdERqrCFughBt2/fDr/fj/b2dlRXV+Pee++Frut5racZkNJH4cknn0R9fT3KysrwzjvvyPElClIORNf1vDXSpX3T1//81B9RVVaO9kgU0WAIQa8PHW3tKC0uwW//+37AgFxuwBz8s8tF7hLmBfn7rqkai6O6tBytgRAivgDaI1GEgyG0NDXjtNNOyxUNZI7+6yfGQDweR29vLzweD6qrq7FgwYKPZPtirKdSKVnEcuONN6KqqgqRSATTp09Hd3c3gPzvanPnCyIiIiIiIiIiIqLDgQUARERHqMK100X4uWTJEhQXF8Pv96O4uBhbtmyBqqrIZrNIp9P7D12JDkIikcBpp52GsrIyfP7zn4emaTLoHGsAbxgGstksDMOApmkYGhpCPB5HVs8ABnD5JV9CdXkFQj4/gl4fPA4nQoEgipUibNm0GVk9g8HBQXk8IpRlAUAu70+khmebZ9MaYACvPPcCShUFrYEQAi4P2sIRhAJBVFVUYsGCBdCzmXGxAICYfS+++7773e9CURR4PB50d3fDMIwPfRyIGGPmripPP/00FEXBpEmTUF5ejhdffFEeg3gtxyYREREREREREREdbiwAICI6gmUymbwW6qqq4rLLLkNDQwOam5sxdepU6Lo+IoBiCEWH6rHHHkNFRQXKysqwbNkyZDIZ+RiLZDI5YkkKGb7uW68+4PIg5PNjYls7wv4Agl4fbA2NaI1EoalpaOrwEgJiW2MNcMc7VUtD1YYLMlJDcUDP4CfzbofX7kTQ7c09vD543R5UVVRixYoViCcT46IAQBSVZLNZxONxbNy4ETU1NVAUBXffffchFwCIjhPiu1XXdWzbtg0+nw9erxcVFRW49dZb5d+AXNFWJpPh+CQiIiIiIiIiIqLDigUARERHOF3XZfi5c+dOBINBtLW1wev14hvf+AZSqZRcd1qEpQyg6FB9/vOfR319PbxeL/bs2QMgNxaz2exBdZgYrSAlo+nYvuV9lCoK2sIRtIUjcDa3oC0cgb25Bd+c8w3AQK5TwL5tiDHNNdZzDPEwjNz13feLC845D2GvHxFfABMirQj5/PB5vGhqtKGrqwuJVHJcFACI70QxHuLxOM4//3y4XC6Ew+FDLgAQrzEXsSSTSVx88cVoamqC1+vFWWedNeJ4MpkMC7CIiIiIiIiIiIjosGIBANEnmDnYMAcWorUyMBwoq6oqA5FEIjHi7wAQi8XkdswzIgUxM9IwjLw1xNPpdN66yWI/YnanOM6hoSG5TRH6JJPJvOPd3/rLmqblhZbidXv37pW/6+/vl68R+xL7EOch9lU4s/kfxTzrGQCeeeYZVFRUwOv1QlEULFmyRB7bwa7PDgyvry72ZZ7xKrZpvoei2MAc6iaTyRGBmjgGGU7uo+u6PE7zTPLC94u/DQ0NjeiAMNoMdE3T8saceUyI34lzEW3pxcO8P3EcqVQKqqrKbWqahr6+vrz9ibGi6zoSiYTcrnmf5vMW2y88V8Mw8u6zuEaapuW9VnR6ENsyX8fCayK2Zx6nhmHk3Svzf82fjXQ6jfXr18PhcEBRFMybNw/AwY0r87bM10k819Q0Hvzv+1FTUYXWUBht4QgCHi/awhFUVVTiv3+zGDCAVCI5snggk5HHoqqqXKO98FqNdwaAoYTpnmSy6O/uQamioD0UgbvZjhOmHotIIAh7cwtO/cw/5cbfvvcmEom8+y6KO4DhsWH+LjBfU7Esw+E02pj/7W9/C0VRYLFY8D//8z/yb+Z/s8Rrx8K87AWQuy633XYb6urqEAqFUFxcjDVr1sjjEcfEJViIiIiIiIiIiIjocGIBABEhm80ikUiMCM0SicSooZ8Ix5PJ5KjBigiP4vF4XhBSGHyKgD+TyeS9T9f1EWErkCswKHy/2G4qlRpR0CACrtFmfRqGkXfs5lnFItwyFz+I54X//UcS4VM2m5Xh71133QWLxQKfz4eamhq8/PLL8nhE+FQYfh/IwMAAYrFYXiBsDuqB/PBWGO1+ml9TOHZE0YCmafLai+4GYlvmEE2MM8Mw0NXVNWLm92iFB4X7EwH0aK8TRS3ZbBZ79+7FwMDAiFBxcHAwb79i3Ihil8JxIAoHxPEBw2GjKLZIpVKIxWKIxWLo7++X7xXnbh6Xuq6P+hkUYX5hgUbha9Lp9IhzEp9380x+8V8xdubPnw+r1Yri4mIsW7Ys7zUHMwPfPBvavL+snsH137oWtZXVCHi8CPsDCHi8aA2FUVVRifc2vgtkDWT1XCFEMpnE0NBQ3r7NxSvA/guTxitVSyNjDBe2pBNJvLHiNZQqCqL+ICa1tsPvdKOl0QaXw4kfz78DiUQC/YMDeR0AVFXNK3gq/Bz39fWht7cXQO5+Fn4PH26pVEoe79q1a9HQ0IDGxkZccMEF8t+Ywu+XsYwP83vMn/Vf//rXqKqqgs/nQ3FxMR577DEA+cVJ7MBCREREREREREREhxMLAIg+4UQod6C2yCKAA3KBiDlkTiaT6OnpySsgMIeW5lngIrgcbSZ3Yatw8Trz78TsXxHIFLZbzmQyeWFWIVVVZaBvnv09ODgog+j9hcUAZGC7v04DHyURHIvjjMVimDVrFhwOh1wGoLOzE0B+McVY12gXbd1HYw7oC4OtZDKJgYGBvJnvYkwMDQ3J0N0csqXT6Q+dlV24brb5tYWzddPptAz/xXvErH8REptnx4tjBHJh4YcVSIjzK5xRnslkRnSbEMRa96lUatQimsIZxMDIGcLZbBbd3d3y51QqlRdwp1IpDA4O5p2v+T4nk0l0d3fnFd+YFY4Lsf/CDhOi+OXYY4+V7f/7+/vz7mVh8H4go93LdEqF2+5Ac10DJndMwITWNgQ8Xrha7KixWBEbGEQ6pWK0XvWapuV9/sQxi++KIy2g/kdJpVUYGL6+GTWNhXf9FPXVVrQFw2gPRTC5rQNhfwBFioLnn30OwPDSAel0GrFYTH7vmQtx9sf8vXukUFU1r/jjpJNOgsvlQnV1NXbt2jXiWMc6O988bs3dbVasWIHq6moEAgFYLBbcdNNNsivNwXz/EhEREREREREREf2jsACA6BOscDZnNpvF4OCgDEk1TZM/jxYM9fT0yOejLRtgnlG+v3B3cHBQzi4Vx6Rp2oglAg5EhL/AcDifTqeRSCSQTCahadqoazObZ3aaA1xxPiLMKZzV/XEwrykNALt370YoFEIkEoHX68UZZ5whX1MY6B6MwcFBeQ2GhobkNRntWhWGW+ZODaMxd1/IZDKyAMN8vIXbFEGumCkv7NmzZ8T5iY4Co43PwqC68HWiaEGMP3ENRrumhSF+T0+PnEVfOJtY13W5b3OoKsagYJ79DwxfS3OIWHhehYUJPT09I8L+VColu2qY3yeCW/P7CwtZVFXFpk2bUFlZCbfbjYsuukhuUyjs+rE/5m4Zheupb3t/K0r2zVSPBkNwNDXD7/bA7/bgq7OvAAzIAoBsNotYLCbHhbgv4vqJ/Zjv14cVAY0XejYDA6ZuB5kszj37n+FqakF7KIKg24uoP4ig1wen3YHe7h4MDQ2ht79vtLoK+e9BPB6Xy7SI8VFYoHKgz/3HwRzkmz9X3/ve99DY2IiysjI89dRTIwrLxhrQm99jXoqku7sbXq8X0WgULpcLM2bMkGOzcDkNIiIiIiIiIiIiosOBBQBEn3DmWeaFwV5fXx82bdqEt99+GytXrsSrr76KVatWobe3Ny8QNLdUFjOVAciZ+sBwO/JYLCZDdHO79EKF7efFezRNQywWk8G+mHEu9gEceH1qEW6L4LQwsDGHjGJphML24h/HGtiFRQdvvfUWKioqEI1GUVdXhyuuuEK+tnDt6bG2oDYXNIjr0d/fj9WrV2Pz5s1YsWIFXn75ZWzcuBGDg4N512pgYAAbNmyQ+xNB+dDQUF4AK4L2woICVVXxwQcf4PXXX8ezzz6LF154Ae+8805eQcho56FpmuxAMNrfRdcCYDg83rFjB9atW4c333wTK1euxJo1a9DZ2ZkX3otrMVrHCvMxC4XFCOJ15sIYYDgwN5+XufDEXOQwWocC8dkU+9Y0Dd3d3Vi1ahVee+01rF+/HqtWrcL69etlwY7Yrxjr5m4XIuA1H7P5XO666y7U1NSgtrYW99xzj7wW5msylvFlLgQqHJ+PL3kMNVXV6AhH4Wqxw9ViR3skipqqajz80O8wNBhDKpGUHQAGBwexbds2rF69Gq+//jreeOMNbNy4ETt37sSePXtG3cd4l4WBpJqS3799e7vRWFsHr92JsNcPr92JjnAUjqZmnH7aZ6Gnc9dlcCgGA7lxunv3bixfvhzPPvss3n33XWzcuBHr1q2TRSRA/lIuqqoeEeG/UHiv4/E4nn/+eSiKArvdjq9+9asAhpdxEUVMYykCMBf3iLEvPlOnnXYaAoEAAoEAwuEwdu7cKd+3v+8lIiIiIiIiIiIioo8LCwCIaMQMyTfffBPf//73cdppp6GsrAyKomDq1Knw+Xzy+be+9S387W9/kzOdzetGa5qGoaEh9PX1Yc2aNXj99dexevVqrFmzBuvXr8fGjRvx1ltv4dVXX8X27dvx3nvv4dVXX8W7776bN/NZbK+npwfLly/HmjVrsGHDBrz77rvYsGEDdu3ahfXr12Pt2rVYu3YtXnnllbzw95133sFf//pX/PGPf8T999+Pn/70p7j11ltxxRVX4Nxzz8ULL7yARCIh99nX14elS5di7ty5OPvss3HuuefivPPOw5w5c3DvvfdixYoVH0vrf0GET2Kfjz/+OCoqKhAOh1FVVYW5c+eOCGPNQe9YiFnUu3btwu23347jjz8eJSUlaGlpwamnnoqLL74YZ5xxBmw2G6ZOnYpf/vKX6OzsRHd3N04++WR84QtfwN69e2XAtr/zMB/j22+/jYULF+Kf//mfUVlZCUVRUF9fj8bGRiiKAp/Phzlz5uDll1+W41KMM3OLd/OYTaVSiMfj8rzT6TR27dqFP/3pT5g1axYURYGiKDjuuONwzDHHoKioCGVlZTj99NNx3333ydA+m82OuMciHBfhvK7reOmll/DXv/4Vy5Ytw3333YeFCxfi29/+NmbPno0rrrgCnZ2dspsCAGzatAmLFy/G6aefjunTp+OKK67A0qVLZaFEYXGEudBFyGQyeOWVV3DVVVfB7/ejsrISbW1t+PSnPy3P77Of/SzuueeeEcs7mIsKzM/N3Q7EfZoxYwaCwSAURcGLL74oP4cfVrCzP4UBquhM8PUrvwZbXT0ivgAigSCmTJiIaDCEEkXBxvUbkBiKAwbw7LJncNVVV2HixImoqKiQ901RFBQVFaGurg4zZ87Ef/7nf2LVqlXyev09nTCONgaAgdhwIdLLz7+AEkVB2OtHyONDxBfA5LYO2Orqcdut35fFFC8vfwUL7roTp512GhobG2GxWGCxWOQY8vv9aG9vx1VXXYXXXnsNQO47yDxuzN0ADifxeTQXf/X29kJRFHi9XgSDwbzvnrF2rxAKx7so/LryyivR3NwMj8eD2tpaLF++XL7GMIz9fhcSERERERERERERfRxYAED0CVa45nNnZyduv/12dHR0QFEUzJo1C0uXLpWh6rZt2/DGG2/gRz/6ERRFQVVVFa677jps37591O3PmDEDdXV1UBRFhnZlZWWwWq0ybCouLoaiKHC73XjggQfke0UwmkqlcP/998Ptdo94j6IoKC8vR1NTExRFQXV1Nf72t78ByAXbJSUlqK6uRlVVFUpLS2GxWOBwONDc3IzKykr8/Oc/l/tbtGgRJk+ejIaGBlx66aW4/fbb8eMf/xhf//rXMXXqVBlUX3jhhXjwwQc/8nvxYUSgNH/+fNhsNni9Xtz4AYcAACAASURBVFRVVeHhhx/OC+VEUDvWNbrFexcvXoyWlhb4/X4sXLgQ27ZtkwGWWNc6kUjg97//PWbMmIGpU6fioosugtVqxemnnz5iu4ODg4jFYnmziAFg+/btuPvuu3HiiSdCURQ0Nzdj/vz52Lp1K3bt2oUPPvgAW7duxaJFizBt2jSUl5fjlltuwe7duwHkFzXE43EZ8orlAoSuri4sW7YMbW1tqK+vx+mnn45ly5YByM22F+H8Cy+8gIsvvhhVVVUIh8NYtGhRXucCc6hoDgLffPNNOJ1OVFdXo7q6GoqioKGhAQ0NDbBarfD5fNiyZYt8/Z133omioiKceeaZeOCBB3DbbbehvLwciqLgy1/+snydqqryHAsLAt566y1cdNFF8rp9//vfx+bNm+W96u7uxv/93//hzDPPhKIo6OjowCOPPIK9e/fKbY9WHFJYPNLX1wefz4dgMIjKykrs2LEjr31/YbeNAxHXzVzYMzAwgKA/gJDPj/ZQBK2hMAIeL9x2B6LBEOKxIWx+bxPOP+9zsFTlrm9dXR38fj9OOOEEnHzyyfB4PAgEArDb7QgGgygqKsLUqVOxaNGivP2OZwaAVHr4PP/jBz+Era4eHeEoQh4fpnZMRIOlBvXWGrz+2t/w/uYtmDdvHvzBAErKSlFTU4Py8nJMmTIFZ599NkKhkLzO4XAYFosFFRUVuPrqq2XgX9j95XATs/rN3wWapqG9vR0ulwsVFRV5/z4d7LgYbYkMALjttttQX18Pj8cDq9Uq/+0S38GFn18iIiIiIiIiIiKijxMLAIgI2WwWmzdvxpw5c6AoCqxWKxYuXChnBBfOpo3FYli0aBEURUFJSQm+8pWv4N1335XbEiHI3/72N/ziF7/Aj3/8Y1x99dWIRqOw2Wzo6OiAz+eT6yiHQiFceeWV2Lx5M4DhVvQiaHrllVcwffp0+Hw+eDwehMNhOBwONDU1oaamBueccw7uuOMO3HXXXUgmkzIUuuGGG3D99dfj0ksvxfHHHw+v1wuPx4OmpiYUFxfjySefxNDQEC655BKUlpZi/vz56OzsHDFLdNeuXVi0aBGamppgtVrR3NyMK6+88h96TwQR/ieTSVx11VWw2+1oampCRUUFnnvuubz28eK4zUsyHMgjjzyCyspKtLe3Y9WqVQCGQ39zYCwCrcHBQfzgBz9AUVERHA4HzjjjDNmtYbTZtWIb27Ztw7e+9S0oioLS0lJceOGF2LVrV14Lb9GJQnQ1eOCBB1BWVobPfOYzMoAUHQvMzC29d+/ejRtuuEF2FTC3sBfLBgD5rcOfeuoptLS0yEB+06ZN8jqYievS39+Pq666CnPmzMHll1+O1tZWeL1e+Whra0NfXx90Xcf8+fOhKIqcIayqKtrb2+F2uxEOh6EoCpYtWybHPDA8e11VVaiqiieeeALHHnssFEXBmWeeieeee27ENRBBf2dnJ8477zw0NjaitLQUv/jFL0aEkalUSp6/uRggnU5jw4YNsFqtqKurw5RJk+WxJBKJvNB/rG32xZgwt1Pfu3cvihQFkUAQ7ZEoIoEgWpqaMam9A7MuuBD//ZvFqKupRV1NLWosVnzta1/DG2+8gT179iCVSsEwDLz//vv44he/iJqaGrS2tiIYDKKurg5WqxX333//mI5tPJCfTQP43MxzEAkE0RoKw213IOILIODxoj0SxaP/83ucddZZuSKsinL802mnYvHixUgmk+ju7pYt7O/8yQJUllegLdqKY6ZMRY3FihqLFVdffXVeV4kjYZkF8/eNOahPJBK45JJLYLfbUVxcjKVLl8rPl/gsjLUQwFzwYl7SYv78+WhqakIgEEBjYyPuuuuuvOVCPgkdKIiIiIiIiIiIiOjIxQIAonFMBOFCOp3OW8cbGA4Op0yZImfS3nHHHWPa/g033ICqqipYrVZcfvnlMlQRrdjFQ4QvPT09OPvss+WM61AoBEVR8Lvf/U4en2EYecGOKAKIx+M46aST4PP54HQ6UVVVhXvvvRdr167NO7/C5+I8e3p6cPnll6O6uhqRSAQ2mw333nsvTj75ZAQCAfT09MhZ60Au7DXvGwBWrlwJh8OBCRMmoLS0FGeeeSY6Oztl6GNeW120rD8UmqbJ7cXjcZxzzjmIRCJwuVwoLi7Ghg0b8mZWA/nrrh/Irl27UFFRgbKyMhlQizFjDvQLW+4nEgn867/+K2qtNbj04kugqWlk9Yxs2z7YPwAYQFbPwMhkMdDXj2mfPhG11ho47Q58+dIvyXbkhcsDZDIZaJom/7t69WrU1dXhC1/4Qt6xi2MytwBPJBKYNWuW7AjxyiuvoKurS77HHPqZPwfpdBrr1q1DTU0NrFYrTjnlFGzbti1vRr458DSPT8MwsGHDBpx33nlwOBxwu90IhULo6urCc889h9LSUrz++uuy9f3WrVuhKAqi0Sjaoq0oUhTcfddPAQPQ1HTuupiu3f8+9jiKFAXRcARupwtr3lkNGEA8Nry8QDKZhKZpcsyvXbsWra2taG1thaIoePTRR0dcg0wmAwNAWtfErYCu63j4od+hvrYO9bV1uHL2FdDTGrL6cJFAUk0hCwNjmf8vuimYl0LQdR1LlixBSUkJ/F4ffB4vQoEgXC4XPC43Ojo6UF9fj9LSUnzlK1+R41KEq6qq5i2r0NDQgPb2drS0tKC1tRVOpxPNzc3o7e0dwxEemChGKVxq44hgANAzQCaLnj1daKipRSQQRFs4goDPj7ZwBJFQGH6vD9FoFPX19WhpacHSpUsRi8VgAMjCyI0Fw0A6pQIGMPOsGXDbHeiItsLR1IxoMITqyir89re/lbtNpJIfdmQfC3E/xPevGBepVArz58+H1WqF0+nE3Llz88ai+bUHkk6n5XeqeXb/E088gdLSUni9XjidTsyZMyfvmIiIiIiIiIiIiIgOJxYAEI1zhQGrmQg0zj//fPj9fjQ3N+OUU07Ja4P+YTZs2CBnMjc0NOB///d/ZcBo7hyQyWSgqqqcYTxp0iRYLBYEg0FYLBYsXrw4L7wXrePFcYi/ffGLX0RlZSWOPfZYLF++HD09PfIcNE0bdVanOehZsGABWlpa0N7eDr/fD6fTiWnTpmHdunXYtWuXfJ0Il83b6+3tRTwex/PPPw+r1Qq/34+WlhacfPLJSKfTeTOpP4rwXxy7uHe9vb34p3/6JwQCAbjdblitVmzatEmGU+I+FxYEfJi5c+dCURRMmTIFALBjx468jgKCeRa38Pjjj6NYKcLZZ81AbGBQBtcwgIymyzA7nVJx7TXfQkVZOTra2uG0O9Db3QNk81vPixDSrL+/HwMDA/jBD34Am82GJ554Ql4LIZ1Oy6B+zpw5sNlsUBQFd955p1yv2/xasQ9d1/PuWSqVwuLFi1FdXY1oNIqzzz4bwPCa9ZqmyUIQ0eXCPDv+3//932Gz2RAOhzF58mSsWbMGoVAIixcvlvtQVRWdnZ1QFAUulwtupwu11hr85r5fY7B/APHYEDQ1jXRKhZ7WsGHdevi9PgT9AVSUlWPJ7x9FKpGU17twFrb4OZVK4Wc/+xkURYHH48FJJ52Erq4ueR8Nw4CW0WEAUPf910DuXv14/h1oqm+Ara4e373hO7IYQdz7VFpFBsCBF5gY7i4grpP479VXX436+nr4/X44HA60traira0NPp8PoVAIFRUVuP7662Vxkrh25u0mEgnEYjGcccYZcLvdcDqd6OjoQCAQQHV1dV7Rw6E6IgsADMDQdCCtI6OmseKV5agur0A0GELQH4DP40VTow0tTc0IBoNwu9046aSTsHXrVvm5EPddFg1puU4CP7j5FrTUNyLqD8LncCEaDKGuphY3fefGXCcIAFrm8Le4F+NLFGqZi0z+67/+CxUVFXC73fjqV786YimKsRYAaJqWV1ggPPvss1AUBV6vF83NzbjkkktkwdsRN1aIiIiIiIiIiIjoE4cFAESfAOaZ5OJn8d+HHnoIiqLA4XDAYrHgqaeeynvNgVx55ZWw2Wyoq6vDqaeeKsOlRCKR10beHOD98Y9/RGNjI7xeL2w2G3w+H2KxGAYGBuTrRMt3IDdrs7+/HyeccALa2trwxhtv5B1DYfBvDpPNs7Uff/xxNDQ0wO/3IxAIQFEUeb7mQgkxo1Qcv/hZnMu1114Lm80Gj8cDRVHw3e9+V4bEAEYEz38vc5j/wQcfYPLkyfB4PPB4PHC73di2bZt8rWixLo5hLAFUIBBAOBzGl7/8ZXmdxLH39/dD07S84Nvcaj+RSODEEz6NC87/fN7MdfFIp1QYmSwW/eKXKFIUdLS1o0hR8D8PPzL8un1EsYi5e0E8HsfPf/5zWCwWOJ1OWCwWfPOb3xz1PFKpFO6//34oioKamhqccMIJeQUdhcduZm7tDQAnn3wyHA4HFEXBT37yk7x7ae6goet6XrHEI488grKyMkQiEUycOBHz58/HlClTRh0/8+fPx2c+8xnU1dTi7LNmYEfnByOun5HJ4qJZ/4KyklL4vT6ccPynoKe1vE4B5nXPxfZFSNnf3w+LxYJwOIyysjLMmzdPXgfs200WBjQjK5+ryRQu/8plsNua0FBTi4X7OhNk9eFrp2X0MRcAiHsjiMKYQCAAn88n15p3OBwIBALw+/2wWq24+eab0dPTI89paGgobzkGc4j7ta99DQ0NDfD5fGhra0MkEkF1dTWuueaaMR7h2B1R3QAMIKOmgSyATBY//ckCWCur0BFtRTQcQdAfQEdbO/ze3HWZOnUqtm7dKt8+NDQkh5v8XGSygAE8fP8DsJZXwu90ozUQQtgfgKPFjuknnYyurq7Cj+9hI8a/+d8zIHefHnvsMRQXF8Pr9eLEE08cUfAz1n/fzMufmMfgm2++CUVREIlEYLfbMXPmTLnNsS6/QkRERERERERERPSPwgIAok8Aczit67oMe7u7u1FbWwuPxwOv14tIJCJnPI9ljeRMJoOXXnoJiqJg6tSpKC4uxl/+8hcAw62WzQG8mLkLAPPmzUNFRQV8Ph8URcEFF1wAID8wBIaLF26++WYoioLOzk6k02l5jIlEYsQ68oVLAWSzWSQSCfzpT39CSUkJmpqa4HQ6cdFFFwEYXgbBfKwixInFYnnto4eGhtDX14doNIrJkycjGAyipKQEL7744qgh5aEwb+P999+H3++H2+2G1+tFR0eHDLnFzP9sNjti6YMPY54hHo/HR1y70aTTaXk9vnHVHJw78xzZrt7IZHMz+7MGYAA9e7tRWV6BlqZmtEVbEQmFAQNQkymoyVTevsznmkgksHXrVlRVVaG6uhrhcBh+vx8zZsyQhRrJZFKe4/r16zFhwgRYLBZ4vV4sWrQIfX19ciybZ/6La2Mu9jCf9y9+8Qs0NjbC7XZDURSsXLky7/z7+vrkczHu0uk0HnvsMSiKgnA4jHA4jClTpuA3v/kNgOECFfNn6uWXX0ZfT2+uG4IBJIbi6OvpRTw2BBjA7x58CEWKguOPPQ4VZeX46Z135YL/fddWT2tyjJo7dpg/P5dffrk8F5/PJ49ZtP83AOj72vlnjCwymo7PTD8FrhY7qssr8PADD8qODubCAR3GmAsAzJ+JRCKBnTt3yuA0FAqhtbUVDocDfr9fLvUgOjzouj4iTE2lUnlFDp///OfR1NSESCQCn8+HYDAIp9OJyy677IDHVhjojyXgFwUjhz3kFe3/s7mZ++efc65s1z+hvQMBnx8uhxPtrW2oq6vDli1b5BhPJpN5Y0COy31j69677kZ1aTm8dicmRFoR3NeFwtFix+7du2XByOFWWOxk/j5Zvnw5FEWBz+eD1+sdPmdTB5Cxkt0vTJ+t9evXo6ioCO3t7XA6nTjppJNGdCIgIiIiIiIiIiIiOlxYAEA0jomwQ8xiFDPERUCxaNEiKIqCjo4O2O12XH311SNmK38YVVUxMDAAn88Hu90Om82GK6+8Uv69cBti252dnQCAmTNnoqWlBRMnTkR5eTnuueceuV3DMKDrOoaGhvDnP/8ZiqLggQcekH8vZF5mYDQDAwNYtmwZiouL4XA4YLPZsGDBAnmNRHgjigoEc2hmGIYMUOfNmweLxYKmpia43W7MnDkTuq5D07S8mfiHwhwkbd68Gc3NzTLQOu6442ThgrmF/sEUAFgsFtTV1aGqqgp//vOf5e8TiYRcb72wfbu4FgDwve/ejFNOno6hwRgy+9qHi3AaBnDnTxagSFFw3DHHory0DD+98y75uoymH7AAoLm5GS6XC3a7HX6/H7NmzZLXVVVVJJNJZLNZ/PCHP0RlZSWmTJmCuro6rFixYsTSCIIYV9lsdkRQp+s63n//fdhsNrS2tqK4uBjXX389MpmM7IJQeF1FV4Df//73KCkpQTgcRiQSQWNjI7Zs2SJfN1pnC3EtxPUyX79pnz4RbqcLLocT1moLXn1lufx7Mp7IFVuYti2unygC0TQNDz/8MKqqquD3+1FRUYG//vWvAHJdHsTuNCObWwfeyBVvTGjvgM/lRomiYOlTf5AdB2R3BgDpbAb6GAJgc5AuPl9PPPEEiouLEQqFEIlE4HK5MHnyZHi9XkyZMgUffPDBiA4a4nMlPlviHu7cuRM+nw8ejwehUAgulwterxeBQADf+MY3xnx8+3uIfRWOH/F5O6wM5AL7jIFdnR/A2dyCkM8PV4sdE9o74LQ74Pf6UFpcIpfOMH/eNE1Ddl/xhwy29w2Ki2ddBEdjEwIuDyZEWhHweBEOhuD3+tDX15cbA/qhf78dqsJ/X8wz8Dds2CALAOx2O7q6ugAgr5vJwW7f/J5NmzahtLQU4XAYdrsdEyZMQH9/f14XDiIiIiIiIiIiIqLDhQUAROOYCM4EcwCUSCQQCoVkgFFUVIRly5bJoPJAM8GFVCqF888/H1arFRMnTsQZZ5yRN0saGJ51LIgAubu7Gy0tLfB4PIhGo1AUBe+8807ejO0VK1agoqIC8+fPlx0ERgu30+n0iBbP5kIBVVXx3HPPoaKiAn6/H7W1tbj77rtHzP43t4o3/9cc6qiqipUrV6K4uBhOpxMTJ05EUVER3n77bRm+fhQhkDmgfvfdd1FTU4NgMAiPx4Np06bJMNk8s/VgCgCCwSCCwSCam5txzDHH4KWXXkIqlRr13icSCXk8YtvP/GUZvn/LrcMz0/fNIM5oOvbs2o2KsnJ86rjj4XV7UF5ahnVr1g4Hl2NYAmDixImw2+1oaWmBoiiYP38+stmsDPFSqRRisRii0ajsjBCNRrF58+a8bYvjFaG/2M9oM3WHhoZwwgknwGazoaOjAyUlJXKpBTHT3tz6X1yrJUuWyI4WwWAQ06dPz/vsiWMGcgG8pmkjgv94bAjplIpXXnoZjfUNmNDegcb6BrRGoogNDCKrZ2QBAAyMGO8imBbn9cYbb6C4uBgejwcNDQ342c9+Js89rWsyzDcA6NkM0ik1F/S6PShRFLzywotAJpu7v+J6Akjp2pg6AJjPX4T6V111FRwOB3w+HwKBALxeL9ra2qAoCh5++GEAw7P8RbcSVVWRSqVGFOasWbMGJSUl8Pv98Pl8shjA4XBg7ty5Bzw+MZN/fw/xvVUYBBcuG3FYyAqODJ79819QWVqGjmgr3HYHOtra4Xa64Ha68KVLLgWQK4ASnwNxL7RMroxD3icDUOMJtAZC6AhHEfEF0BoIIeTzw+v24J9nnJ0rhAKQSh+4Q8zHzbw8x65du1BSUiLHw3vvvQdg+DM8lvu3vyIBwzCwZcsWNDY2yjHX0dGB3t7evC4sRERERERERERERIcLCwCIxjHRKl0wh5DvvPOObMUt2vCLlvLpdHpMAbaYEf/1r38dfr8foVAI06ZNw7p162TYFI/H87YlAnexr5UrV0JRFDQ3N2Py5Mmoq6tDf38/EokE+vv7MXnyZMyaNUuu4SzCqkQiIUPC0cLuVCo1opvB008/LQPDuro6zJ8/H8BwoGsOfMzrvYug3TzTes+ePTj++ONx3HHHoba2FhaLBd/73vfy9n+oxAzkTCaDNWvWoLS0VM50nj59ujwuc5Bd2Bb7w9x0002or6+XRQWKouA//uM/8MYbb6Cvry+vtbz5mDKZTO6aGcAH2zvzgn81mQIM4H8fexxFioIJ7R0IB0OwVFXLte31tJZ7T8E2C49Z0zTMnDkTF198Me677z709/cDyI2poaEhGIaB559/HmVlZYhGo6iursasWbPkfdd1XRbBmO/naOdkXk/8i1/8Iux2O7xeLxRFwYMPPghguEhEBqj7lqcAgMcffxwlJSXw+XxoaGjAddddN+KcxLr2Yp8wgKyeQWxgEKlEMjer3wBuu/X7qLFY0WxrQkdbO/7lwllymQBxrY1MNm9dc1F0Yi5w2Lt3LxRFQVtbGxobG3HttdfKcSkKANR9IXBa19DdtRd+rw8+lxtlRcVY/dYqIGvkOhVgXzEFgKSWHlMBgPnzJEL9aDQKv9+PQCAAj8eD1tZWuFwulJeXA4C8x/vbnrkjxa9//WtUVVXB6/XC5/MhGo3C5XKhtrYWf/rTnw54fAcqAFBVdUTxkjCWz9c/lFzDIYvb/2MuGmvrEAkEEQ2GEAmFEQoEUVVRiWf+kivqEmPPXJykZXTZyl+Mx41r18FWU4f2UATtoQgCLg9aQ2E01jfglpu/lyvO2vfew81ciGF+rmkaYrEYSktL4XK50NTUhLfffjvvPh5MAUDhUhSGYaCzsxPBYBAulwvBYBBTpkyRy44c9rFBREREREREREREn3gsACAa58yh/8DAAIBcGPfEE0/AYrHA4/FgwoQJKCsrw8DAgHzNwYQY3/72t+F2u1FfX4/GxkZs375d/s08C1h0BpAh5L7Z8vfccw+qq6vR2NgIj8eDCy+8EABw2WWXoba2Ftu3bx8xC3+09cHFTPLCYxfve/LJJ6EoCvx+P1paWnDLLbfIbRXOoBfbNodAIgwUQfJll10mZ4CGw2FMnz49bxsfBXFOb731FhRFQTAYhMPhwCmnnCILAMzXuHCW/oeJx+NQFAVerxehUAgOhwNWqxVVVVWYPHkyrr76aixcuBDPPPMM9u7dOyJEFzPYB/sHZCCZGIoDBvDdG2+Cz+OFz+NFU6MNxx1zbN7sddEBoLBFvyjyyGQyeWHw0NDQqNf12muvRW1tLTo6OtDc3Ix/+Zd/kdv5sGsqij7Mnw8xTi699FKEw2HU1tbiU5/6FL785S/nvc58rEDufj/55JMoKipCNBpFUVERFi5cmLcv8VrRlSKTySCrZ/Kvx77uCJ/+1AkI+PyIhMJoarTh/587D73dPYABpBLJvOsn1jYvPD/xO0VR0N7ejubmZpx66qnyPWITqX2FAEk1hfc3b4HX7YHX6YKlohKbN74riw3EMRsAEml1TAUA5uMBgK6uLiiKArfbjXA4DK/XC7/fj2g0ivPPP18es7lQYnBwUBZ0pFKpvK4el112Gex2u9xee3s77HY7rFYr9uzZc8DjOlABQDKZHNFZ5IhhABk1jUxSxaVf+CI8DifstiZMbGuH0+5AOBhCNBzB+rXr5PHv3btXfn57e3uRMbIyyFdVFRk1jb/8aSkarbXwtDjQEY7C0+LAhNY2VFVU4le/XCQLAI6EiNtcEGb+t0AU/JSVlcHpdKKmpgZvvPFGXpeXgykAGK0ryu7du9Ha2orGxkYEAgFMnToVe/fuRSqVOjKWiCAiIiIiIiIiIqJPNBYAEI1j5tnBhW699VaUlJSgtbUVgUAAiqIgHo+jr68Pe/bsQSwWO+Ajk8mgu7sbc+fORTAYRCQSgaIoWLt27UEf64UXXgibzYZQKISmpiace+65KCoqwsqVK/PawwOjh/8Hks1m8Yc//AGKoiAUCqG2thbz5s2TgZg5sBHPC9f/LnTnnXeiuLgY0WgUwWAQfr8fu3bt+ruOb3/ELHbRsSEQCMDtduOkk06S3RTM9/dg9ptOp/Hqq6+itLQUdXV1mDBhAkKhEGw2GxwOB0KhEEpKSqAoCsrKyjBt2jTMnz8fr7/+em4DBhAbGAQMyIBaBMbnn/c5NNTVY9KEiWhqtGH2ZZfnWozv6xCQ1Q89IEsmk/jSl76E6upqTJo0CY2NjZg9ezYA5LWL3x/zcg/iZwC4+uqr4XQ65fIYJ554Yl5XDHMhiuiCsWTJElRWVqK1tRWKouDRRx894P5FS3/zUgCxgUE4WuzoaGtHJBSG0+7Abbd+H6lEEumUCjWZQn9vH3q7ew74+RwYGEA0GpX3ctq0aXLfov17BkA8mYAB4N0NG+FosSPg8aJEUbDl3feQUdNIp3Khu6qqyBhZZPa9byzEfdA0DY888ggqKyths9lkJwCv14uqqir86le/kq8D8pdZEJ9BcyFAf38/2tvb4fV64XK5EI1G4fP54Ha7cc455+TN3C/8/hNhbmFHCFH8pGkavvCFL6Cqqgr/9m//Nup7Dvss733jZfumLXA2tyASCGJCaxs6oq1oaWpGW7QVZ3z29FyBCTBiORSxiXhy+HOSTWu46sqvwWlrRtQfRMjjw5T2CXC12FFeWobtW7fllpqAAT2bu7bi/pq7TxiGIb+3RNguQvHC73Exc18UdxQWXn0Yc8t/8+90XUcqlYKiKPD5fKipqcELL7xw8Nd43/bE9TJ/r+/atQvBYBButxvBYBAdHR0YGBiQy4wQERERERERERERHU4sACAax0RIJQIwEcAYhoE5c+bA4XDIJQCCwSAURUFNTQ1KS0tRXFwMRVEO+HC73bBYLKitrcWkSZMwefJkrFmzZswzIFOplJzlGwgE4HQ6EQwGYbVa8Z3vfAdA/oz6D2sR/mH+ngKAwmtpfgDAr3/9a5SXlyMQCMDv96OmpgbvvffeqAUXfy/RUnr9+vWyA4DH48G0adNGXQLgYGaeikD1oYcewqmnnoqSkhLYbDYcc8wxaG1tRX19PVpbW3HCCSegsbERdrsdLS0tsNlsuOWWSUWc2QAAIABJREFUW7B3TxdgAH09vTKQ1NMaNDWNUCAIR4sdE9o70NRow9Xf+GZe2C1mlR+KWCyGmTNnor6+Hm1tbbDZbCMC2w9jnqlrfn7jjTfKYhS3241QKIRYLJbXfh4YvjfZbBZLlixBSUmJ/Bw9/fTTBz4BU8GEeL51y/tojUThtDsQCgTRbGuCvbkFpcUlKFIUVFdWoUhRYKmqHtPn02azob29HTabDbNmzZK7FksAZACoWm5W95ZNm9HUaIPP5UaJomDb5i1AJiuXABAFADoMqAfZAl7XdVx33XWw2WwysA+FQrL46M033wSQH9aL5+bxLYovnnvuOVitVvh8PrS3t8Pv98PtdqOiogJ33HGH/IyKezo4OIh0Oi1DfjNRYCT2s2LFCrjdblitVsyePVsG1eYxdSQUAGTTGl565jnUVlsQ8HgR9gcQ9PrgcblRV1OL7954k5yqb54pn0wmc+MWBlLpXLg9MDCAZGwIbeEIvHYnIr4AJrW2I+DyIOwP4OyzZiAeyy0JMjgUyy0foapIJBIwDAOqqo64Jvubaa9pGhKJxIcG5WP5/I723S3ulbkAwGq14uWXXz7g9kZj/l4wFwx1dnbC4/HA4/EgEAigo6MDfX19eUsREBERERERERERER0uLAAgGufMsy/FbM1sNotLL70UFosFbrdbrsm9Y8cObNmyBZs2bcKePXuwffv2D32sWbMGPT09WLt2LXbu3Ilt27Zh/fr1Yz428xrzGzduxI4dO+ByuVBXVwe/349TTjkFu3fvloGKCIV6e3v/rutwKAUA5tbgogjgd7/7HSoqKuByuRAOh6EoCt55552PbAaouZX75s2bUVVVhUAgAJ/PhxNOOEG2SjevO32w+47FYjAMAzt37sSCBQsQjUZRUVEBRVFkgUhTUxOcTie8Xi/sdjs8Hg9qamrw/13xVWx7f6tclx4GoKlpZPUMihQFfq8P0XAEtoZG3HzTd+XMfyOT/UgKAAYHB3HiiSfKMdzc3IxrrrnmoK5D4TrfmUwGP/rRj1BbW4tgMIhgMIjKykp0dXXlBYHZbDYvrF6yZAkURYHH40FpaSleffXVA+98X8t/87Vbu3oNpkyajMb6Bkxo70BLUzMW3n0P3t+8BZvf24Turr3Y+cEObFi3/oCfz02bNmFwcBDbtm1DZ2dnbpeGgZ6eHtnGPZ3NtfXPGFns2rETzbYmBDxelCpF2LRhY67V/L4CANH+fawFAOJzm81mEY/HMWnSJHg8HoRCIQSDQfh8PoTDYVgslv/H3pnHR1Xd/f9k3zPZt0kykxWCIFj52ad1A8XaulSt1l2otmrr9lSrtT5aUasFba0+Iktb0WptXaqCRXxERBR3a0VRqiIgm0ASss1MZr/3/ftjOCf3ThIygYCo5/16zYswmdzl3HPPnHu+n+/nqwLI1qCxvN+t90FPTw8AV199NVlZWVRXVzNq1Cjcbjc1NTWkpaWxcuVK9ffWzHfrMfn9ftvP1vvnnnvuIT09HYfDwVVXXaX6kty3dTtfGCaE/QHuvuP35Gfn0FRXT4PLTX2ti1FNzaQkJfP8c0ti18/SppFIRJXysFr59/b28s6bb5EiBI21bprd9TS56qgoKqHYUcDdf7hLfVgKRgZCCinix9OBylRIa315nYYrnJLXxXotrLb9UgCQk5PDG2+8Ydv37gg4rGKA9evXU1RURE1NDbW1tYwZM0YJsuQ+NBqNRqPRaDQajUaj0Wg0Go3mi0ILADSarzgyqGKaps0W/eyzz6alpQW3201VVRUTJ060/Y3V5nwoBqqPnkgGp3UfhmHQ3d2tsiplOYGzzz4bn8+n9mEVMgyHPREAyABkfEbrX/7yF7KysqioqGD06NEkJSXx3nvv2QLye4I1m/Szzz6jqKhIZZzKmtPxnxtOEK2jo6PftQuHw3z22WfMmzePa6+9VmVXZ2ZmkpOTw8EHH0x9fT21tbUkCcFPL7oYTAgFgoQCQcyoQSgQJCUpmXp3He5aF4WOAq679ldgmOr3I1ECwOv1MnHiRBoaGqisrKS8vJxLL72UaDQ6LAGADFpK6/KZM2cqAUBjYyNJSUls3bpVfR76rMYlTz75pHLESEtL46233hp653H2/5FQmHWfrqXeXYezsooDWsaQJASz/vce9ZloOIItcjsEvb29dHZ2quOWGfBR08DAxL8zmBuKhPF091DjjGV852RksvJf70DUIBzss7+XrgGRBA7AGnReu3YtycnJOJ1Ompubqampwel0UltbywknnADYx4z4wL3s16FQiO3btzN+/HgaGhqoqamhrKyM5uZmnE4nhxxyiC27PBAI0NvbSzgcxuv12jLh4+38g8EgkUiE8847j6qqKvLz83n88cdV20nHDHlOXygmRAJBfnjSKVSUlDKmeRSN7jpGNzYp943tW7fFym5Y2tI6PshuJMeAX/z3zyktLFLB/4YaF+6qahw5ubz5+hsx4Y5pYgIdXZ2qTENnZ6fNZcZaQkW2czgcVmUZBhobrcIBKbQaCuv1k/eu/Luenh5SUlKUA8B7772n9ptoln78cVqFDWvXriUzMxOXy0V1dTUTJkxQ47FsB41Go9FoNBqNRqPRaDQajUaj+aLQAgCN5iuONeAjA+7RaJRLL72U/Px8SkpKqKio4PDDD2fLli22v5VBzsFeHo+nXzBeZsgnQigUsgWgL730UioqKjjvvPPIzMxk3Lhx5Ofnc+ONN+5xQGV3BAAyUGQN/lvPbfbs2RQVFal68YWFhfznP/8Z0exgeSwbN26kurqaqqoq3G43zc3NbNu2zXZ+sh79cNm+fbv6uaenR52jLLewfv163njjDW666SYcDgfZ2dnU1dVR46wmPzePJx7/B0F/QGW0d3d2MaqpmTqXG3eti5KiYi792SWq5n0oEIxlvu8hHo+HKVOmUFdXR3l5OZWVlZx//vkJt4EMGlqDkqZpMn36dFUCQJalaG1tBfquR3wgc8GCBaSmplJbW0tKSgqvvPJKAgcQy/qXJQDMqEFXRyeFjgIa6uqpLK+gsryCeXPmxtpsZ8TW090TCwAPcX9Go1Hb/RUOh/tq2e/M/JcCgEAoSKDXT1NDI7VVTrLTM3jphWUQiSoHgGg0qgQAid6NoVAI0zR5+umnSUlJoby8nNGjR1NTU4Pb7SYrK4v58+f3C6jLeygQCKh+Ldv7scceIzc3lzFjxtDU1ERJSQkNDQ3k5uZy++23K0t663Yk8fuxXsP29nY8Hg9NTU3U1NRQWFjItm3bbMIEqwDkC8WE1s+3UlNeSW2Vk1ENjbiraxjd2ER+bh7fO/a7RMMRwsGQ7VitYgBZBiIQCOD1eqkqK2d0YxMNNS7qq2sZ33IAlcWlHHv0FHq9vphwxzCUY4REimfAPo4OZPMvHQhk1r8sv6BOa6fYKpF72DpOQ19fA9iyZQupqam4XC4KCwtZu3atzZFiOAIAqyOF/HnNmjUkJSVRV1dHVVUVhx56KJ2dnf2OTaPRaDQajUaj0Wg0Go1Go9Fovgi0AECj+YpjDcDIQFYwGOR///d/KSoqYty4cdTU1CBEbDgIh8N0dXUlHMCID4gNNwAtbewfeughhBA8/vjjrF69moMPPpjy8nJKSkooKyvjiSeesNVrHy67IwCwZqNarf/l66abbqK2tlZlIU+YMIGNGzfuFQHAtm3bGDNmjLK7r6ioULbuYM+Clf8fihkzZthcGCKRSF99cIs9unV7mzZt4pJLLiEzM5OGunpys3P43rHftdWxDwdDHD35KJyVVTTWN1BVUclJJ34/lkm+swxAohnsu8Lj8fDTn/6UsrIyKioqcDqdnHzyyQkHZ+Mt52Vb/+QnP1GijpKSEk444QSV7WwVCsh/DcNg4cKFZGRk4HK5EEKwfPnyBA4glvVvbbtQIMiopmaclVU0NTTSUFfPLTfdjM/jpaeruy9tO8H2k9dPBn57enpiddt3biIYjSgBQCgQZPy4AykrKiY9OYVFCxaCPDb6BAD+cIiwmVgfl9b+V111lbpO0rK/rq5Olc2QQiKPx9MviCwJh8MYhsFPf/pTCgoKqKio4IADDqCxsZHS0lKKi4tZvXq1yiaXAVtZ9sCKNagrxyyfz8cnn3xCfn4+RUVFHHzwwaoNZTvK++ULt3g3YfnSF8hISqHB5abRXacEABlp6dwx8/ZY9r/f7uQiz8MwDEKRMOGdpRwWLFhAihA0uNyMqmugyVXHmMZmUoVg/h//BCb4fbFrIYUDfr/fJh4Ce9A/ETGYVYQzUKmAXSGvoVUAIFm1ahUpKSm43W7Ky8tpa2sbVLwzFLJ/WN103n//ffVdUllZyfe+9z1bX9UCAI1Go9FoNBqNRqPRaDQajUbzRaIFABrNVxxrgFsGxgzD4M033yQjI4Pm5mZqa2sRQrBly5YBazcP9pKfCYVC9PT02OqiJ1ICQLJy5UqEENx7773qvaVLl1JUVERmZqaqe79p0yZlYR5vET4Uu+sAEB8okucYCoX44Q9/qDKZ8/PzOffcc221xfcUa+3z7u5uDj30UFwuFzU1NWRlZbFu3bp+fxOfFbsrcnJyuP/++wF79izYA3nWAKn83cUXX0xeTi7NjU1UlJXTum27ymLHhF9ceRWOvHyaG5uornJyQMsYfB6v7TN7SigUYt68eeTm5lJbW4vT6eRb3/qWzap9KKxiBxncnTx5sqpVn56ezm233WYTClhFLvJeWLhwIWlpaVRXVyOEYMWKFQnsvK8EgLT2DwWC/PflV5Cfm4e71kVmegbnnXNuX7sZJpjg7fEMeX/KeyQSifQL1EbNWCZ32OzL6DYiUSYfOYny4hLysrJ5+C8PYsgSBfQJAHzBQEIlACRer5exY8ficrmora2lvr6euro6qqurKSkpoaenR/U1OY5Y21cSiUT48MMPmTBhghLd1NfXM2bMGLKzs/n+979PNBq1iVq6u7uZPn06F1xwATNnzlTlANQl2FmLXnLfffdRWlpKbm4u119/vToGGdwdTmmUvYoJt918C4U5eUoAMKqhkeb6BkqKinl1xStKjAN947H1X2sm/w9+8AMcObm4nNWMaWxmTGMz1WUVlOQXsH7Np319lZhYZMnS57n44ou5+OKL2bZtGx6PB9M08Xg8/cY/OV7KQH8wGLQ5U1jFNOFweFgCHuu/1vIOL7/8MklJSapcSSAQsH1nDQfrdiH23bNixQqEEDQ0NFBRUcEZZ5yhvi9GqgSMRqPRaDQajUaj0Wg0Go1Go9HsLloAoNF8xYnPXpRBDJ/PhxBCBeVSUlJYunSpsjFOJJN/IPv/4QT+ZbC8pKSEU045hR07dhAMBpXt/m9/+1syMzNVfXcZZNmdINzuCAAGIhKJEAgE8Pl8jB07VtmPOxwObr75ZrWv3bHiH+iY5bEEAgGOPfZYGhoaVJB5zZo1AwoUIDEBQHJyMpdccglgd4eId3WQtdGttLW1MXbMAYwdcwBJQrDipZfBRAX5n16wkNTkFJoaGqmqqKTQUUBH+w7MqEE0HIkFs0eApUuXxtwIGhqoqqpi9OjRdHV1Dav9ZVt5vV4AWlpaqK6uxu12K1cK6GsjqyuCDGouWLCA5ORkKisrEULw5ptvJrBjlCuCtPgP9Pp55p+LSE9No8ZZTXNjE9/+r2+peu4qqzuB+OJAYhx5f0sXgKhpqCxwgB+cfAo1zmrKSkqZO+veWNb3zn1J+/fgzrIBQyH74rZt2xBCKFGF0+lUpRWmTJmiPh+f7W/NJDcMA7/fz8MPP0xGRgZOp5OWlhZKSkqoq6sjMzOTe+65B0CJhCKRCFu2bCE9PZ20tDSamppsv49GQoCBEQ2rfn/66adTUVFBSloqS5cuIxQJ245H1rL/wjHhjFNPo7bKSU1lFa6aWlqamnHV1FLncrPxsw0YkagSAMQ7WASDwVjFDkw++eQT8vLyaKirp8Hlpqmunub6BtzVNfzwlB9ANCYOgZ0lGYBjv/ddhBA4HA4+/vhjNSZbx2/ZXtbxtLW1lVdeeYW///3vzJkzh7///e+8+eabdHR09J2aadpKDOyyGSwiLdlfIpEIzz77rArQNzQ02MRUwxUAxI99Ho9Hbb+xsZHy8nJ+9KMfqeP5wstDaDQajUaj0Wg0Go1Go9FoNJqvPVoAoNF8jTn//POprq5W9a5vuOEGwB48tma0W+twy9rc0mobYsHFmTNnqvrOgMr4jM8uh1hg5bzzzkMIwfbt21VgRgbYgsEgP/nJTygoKFBlCmbOnNknZthpJd/r9RHo9av66DKrWtrNmzsDWM8+s5gkIahzuXHk5fOb3/xGZUtbs8ZlgFCeP2DLWO3q6mLFihXk5ubGAmcNDTidTjZt2rTXMoS9Xi8XXnghVVVVVFRUkJmZyfLly211qeW1Mk0zISFGTk4OQgjVntY2sAZfB+O2225TAe/PPvsM6HNm6OrqIjU1VdXIzsnJYdmyZeoa+3y+fsFBQAUODcNQ/UR+5uabb+app55SxyX71uTJk6moqFAB+3feece2zUAgYHOMGOycOjs7Wbt2LbW1tVRWVuJ0OjnggAPYuHGj+jspEtixY4ct0Ld48WIVcCwoKGDZsmVqn7JPhEIh288Sn89n29bnn39ObW0tpaWlTJgwgZycHJugoLW1FejLmLaej9XS3vrZpUuXMmvWLFu2tLVNZLD2D3/4A1lZWeTl5XHdtb+yHb/8XKLIzz744IMIIRg9ejRNTU3U1taqoP2DDz4I9M/0l39rDeYDnHLKKRQUFDBq1Ciqqqqoq6ujrKyMgw8+mK1bt6o+3NnZiWEYzJs3j7S0NFJSUnjkkUfo6urauReDsiIHOenJpAhBkhBkZ2dTWFhIdY2LqloXQqQikpIRIpXklDSESGbrtlaV0S6vQbxgRvZba635SCTST2iSCHLbVoGOaZps2bIFd62L2uoaKssrYm3qcuN0OjnyyCMT2rYc02677TbS0tKYOHEiZSWluGpqaairJ0kIXnvl1VjpCQvvvPMONTU1OJ2VTJlyFF1dHYABGEQiIaLRMIYRAQyCQT9+vw+/38cvf3k1ycmCnJwsLr74Qq771S+ZNvVckpMEQggWL16MCfT6g0SJmV3Ilwr0m1H1ku0dP+ZGo1Fmz56txufzzz9fORAACTuEWEUN1msRDAaZO3cueXl5yv1l5syZAP3GLI1Go9FoNPsfwWDQNh+T3/nW7285x5PPJTA8hzuNRqPRaL5OxD9nW8sPWrGWZxzJ8qkajUYzGHLeb5qmWuOT8/p9NQ4FAgG8Xq+K21j3a113lPE2ebwjcXxaAKDRfI3p7u5GCMHBBx9MaWkp9fX16nednZ22AG04HKajo8MWAOzp6QFiEzufz8dDDz2kanoDKthmXUyxBlXuvvtuhBB8/PHHKqjm9XrxeDxqPytXrmT8+PE4HA7cbjd5eXksWbIklrG70zbdmkktX7JeuhGJgmFiRg0WL3qGJCFoqKunuLCI//mf/7EdE8S+FGSWdHwQR54vwFlnnUVWVhbjx4+nuLiYK6+80mYRPRIW0Na2DgaD3HjjjeTn51NdXU1WVhZ/+9vf1MIUYLteiQRqs7KyEELQ2tqqgoE+n09N1Ht7e9WXjzU7OxAIEA6HmTt3LmlpaeTm5vL222+rfcr2mzVrFhkZGbhcLiorKznzzDNVhrAV0zT7WbPLz3g8Hjo6Oli7di05OTksXLhQHafkL3/5C+np6TidTvLz85kxYwbBYJBQKKQyn63tKAOz0s0B+r7477vvPhWsFkJwzz33DOgSIQmHw7S2tvLSSy+RkZGhRAhvv/22rT/EXx/rl3j8l75hGMyYMYP8/HwVjJfZ7TLgLycNkoHqp8tzj0ajjB49mmnTpqn34z8v+9EjjzxCWloa5eXlnH766bbjl/sfrC0GwjAMrrzySuW64XQ6aW5uxul0IoTg3XfftWVNWwPlsp1M08Tv96u6642NjdTW1vLNb34Tl8tFUVERl19+uSpbYX3wdLvdjB8/nqysLFtZhmCglw/efZs3VrzAGyuW8/ZbrzFvzlyqa2uoqKzhiKOO4d1Vq1m6fAXvvPcB/37/Q5Y8/4JyPpDXwdo28jykMCMcDlsEB+zWArIcS6yfNwyD559/ntTUVAoLC5kwYQLV1dU0NTWRkZHB73//+37uLLvafllZGdXV1aqMRn19PfX19YwdO9Z2/PK+vummm8jLyyM1NZm77roTGfzv7fWqn4NBP6YZBQw6O3eQmZlOYaGDhQuf6vt8TyeYEYxomKefXkBZWQWvvPo6ESBsQoTYyzBjTgUxJUAUjDCY4ZjoC7ugQorUbr75ZsrLy8nKyuKWW26xnXOiDg7WsdQq9AgGg0yfPp2cnBxqa2vJycnhgQcesP2NRqPRaDSa/ZOBhNJy7ml9X84v5DPqfuEApdFoNBrNfozf7yccDqs1GWt5vPg1CrnWEw6Hbet7Go1Gs7ewxk98Pt+wy0vv6X4l1nXj+HVwSSAQGLEkUy0A0Gi+xoRCIX75y1+SmppKY2MjaWlpLFmyhA0bNqjP9PT0DBhMCoVCKmgogx7jxo3jrLPOAvpb0Hu9XjWw7tixgw8//JD09HRuv/12Zf9sDWZbeeedd8jMzKSsrIy8vDxaWlpoa2tTlulGJEo0HKFzRwc+j9fmACDrzZtRg0VP/5MkIWhqaKSyvIKDDjpIZYZ6PB5bIMnn86mA/7Zt22zH9+6775KWlkZ9fT0Oh4Pm5mbWrVtnO+aRUGhZreYB/va3vyGEoK6ujry8PFVywLpYJX9OJBAlhGDcuHGce+65/X4Xn/Vt3Z48rr/+9a+kp6fjcrnYsmWL7XeGYbB27VqampqoqqqisrKStLQ0Nm/ebFtAixdLSAGG/CKU7XjfffeRnJw8YFA/FAoxadIk3G43JSUluFyufl+SkUjEpjSW19paez4UCnHSSSeRnJyM2+3moIMOUgFQ+eUsjxnsmfhPPPEEQgiam5tVYFu2RSQS6ZdFPFCmkfX9rVu30tTUhNvtxu12U1tbS2dnZ7/guBRjyP93dXX1y2h66aWXEELw4osv8vnnn6vfWctFSEeFlStXkpmZSWNjIy0tLQMG5K3/DkVPTw+NjY1KxNPc3ExTUxP5+fk4HA7ALoywjjXWEhSBQIBHH32U0tJSKisrmThxIg6HQzmDvPLKK+rv5bYee+wx0tLSyMjI4JxzzlH9xuv1xuz/jRARfzeYsVDz448+Rmp6Go6CEqb/5reEDGjt6CYKRIFQOEokavZzlAC7cCYUCvHYY49x9NFHc+qpp3LXXXep85DnlaiTgryf49volltuIT09naamJpqbm1UAPy0tjddffz2hRepgMMiaNWtUf5d2+Q0NDeTm5vLzn/+83zX55JNPmDRpEllZWYwZM5otWzYhA/rd3Z3q51AoQDDop7fXy6RJR5CTk8WKFS+p35tmFMwIvT2dBAO9HH/89xAimZtvuZUo4AtGhhQAREL2+9HaXueeey7V1dUkJyfzxBNP9Dvv4SDvDRkwiEajTJs2jcLCQmpra8nLy+Pll1+2fV6j0Wg0Gs3+i5wvyOdT6xxN/hw/X9DZ/xqNRqPR7Bq5ThSf+CDXUKzOhnJNZCSSpzQajWYoenp66OjoIBqN2uIKe8vJ2Yp1DXggN2SIrb36/X7ltC3H0ZFINNICAI1GowJINTU1uFwuwF4LXv6/q6tLDYzxk7TZs2fb7Ne9Xi+hUAifz6fUnDKQtHLlSoQQpKWlqYHN5/PZArRyMPZ4PITDYe68805yc3Opq6vD5XJxzDHHgGHGMv8N05b9H/QHVOa/+p1h8s+FT5MkBM2NTdQ4qxFCsGrVKqVSBWhvb7edX3wWt8fj4YgjjsDlcpGXl0dRURFz584FYl8awwnAD4U1gAzw1ltvkZSURGNjIyUlJarutDVQbrUfH4qCggJqa2sRQtDe3t4ve9rv9+PxePrZ1cs+cO211yKE4NRTT1W/l9dQijqWLFlCSkoKY8aMoby8nO9973vq2so2H0iAIfF6vaxfv56UlBTmzJlj+2K2BiY3b96sMvcLCgq47bbb1D6sxy8X9qwB/Y6ODkzT5Mknn1RB/JycHJYuXWq7DtZgrPXL2zRNHnvsMUpKSmhqakIIwUsvvaSO1ZopLgUn1lIa8dcqGo1iGAbLli1TZQUcDgczZ85U2fBer1f1McMw+k1Y5P97enr4xje+waRJk2zX0DAMQqGQLbPZMAw8Hg9VVVWMGjWKtLQ0m2BguA9mhmGwfv16hBA4nU6cTifjxo2jrq6O8vJyjjvuONuxWtvYug3Z5rfffjtVVVUUFRXhcrkYM2aMcgL4/PPPVX8IhUK0t7dz8MEHU1xcTHZ2tnIlASn+MCASACNAuNdDwO/hwh//hPTMDHLzCnnuheVEQAX/e0NRTCAYisRtp69t5LHeeuutFBcXU1RURHZ2Ng6Hg1mzZqnjiy/bMBjxQgE5jno8Hs4880yqqqo44IADqKqqoqSkhHHjxlFTU2MTcO2KaDRKa2srQggKCwsZNWoUFRUVjBs3jrS0NF5++eV+49g111yD2Fku4Y9/nIs18z8SCaGC+ztdAD74IObaMGXKUbbPhkIBwn4vmBEuu/RnJCUJiotLufW2Gaq9hxIAENcdpY1WOBxm7Nix1NTUkJOTw+rVq23XZ6B7biBk4N8wDDVWyEn4lClTqK6uVuIca5snWmJAo9FoNBrNF4PH4+krLxSHdV5qnfPHl+zSaDQajUbTh0ygks/k0Wi03/qO/H98MEyj0Wj2JXJ9dV+7eIZCoQHLL8djFSJb3ah3Fy0A0Gg0tLa2MmHCBFJTU6moqODiiy9WAcLBlEmStrY2li5dihCCv//970DfQDpQpuXs2bNJSUlh1KhRHHzwwep9Oenzer1s2rQJ6AsiQ2xQPOaYYygrK6O4uJjMzEzu/sNd7GhrBxN6vT6C/gCYEA1HYpn/QwgAioqKEEK3zdP/AAAgAElEQVQMaANpnYTK4/B4PNx5553KjSAnJ4e7777bdg7WdttT5Dbkv5s3b6ampoa6ujqqq6uZMmUKgLKzl+0n3xsKIQT19fU0NDQwefJkleUan2EvaW9vVwtlGzdupLKykpycHJYsWQLYv0Dl8RiGwZw5cygsLKS0tBQhBA8++OAu68pb27+3t5dp06bxX//1X3R3d6uMfClYkPuLRqP83//9H7m5uZSWlpKVlcXixYvVNgcryyDba82aNTgcDnJycsjPz+evf/2rOif5mfgHmEAgoO6NW2+9lbKyMsrKysjOzubvf/97QhMJa0kCic/nU/v5xz/+gRCC0tJScnNz+ec//6na1ZrtLrfV3d1Nb2+vCuZfdtllFBQU8Prrr6vPyVIbMpgZX4pg0qRJyqL/xRdfVL+T4oXB7IniMU2ThQsX4nA4KCkpoaqqKlarvq6OwsJCZZsu+8JANeKsE6Onn34aIQSHHnooZWVllJaW0tzczOTJk9VnIpEIbW1tXHTRRQghyMzM5E9/+pP6narxFJVB5CCYETw9HUw4cDw1rlry8ov4eO1n+IKRWOa/AcEohCMGwVBETdjk9qx9oru7m0MOOYSioiJGjx7N+PHjqaurIz09nba2tmEpS4PBoG0ckT9v3bqVUaNG4Xa7KSoqoqWlhfLyckaNGsXxxx+f8PWR948QgqqqKlpaWqipqaGxsREhBFu2bKG3txfTNOns7GT+/Pnk5uYihGDq1Kl4vT3IYL7M7DeMCOFwEDDweLqZP//P5OZm09TUgBQFKKeAaAjMCE/84zHS01NJTU0nakBPb5AoQ5cAiIb7xphQKKQEFuvWrSMtLQ2n08nEiRP7CYuGU77CKvqRbdbZ2cnYsWOpr6+nqqqKyZMn09PT00+EpdFoNBqNZv9DzmXjn9U8Ho96lpFzJP2drtFoNBrN8LAK763I5DBrAopMltEuehqNZm/i9/tVEpy1NPVAiYh7A5lYFC9+8ng8BAIBurq6bL+zlpAdCbQAQKP5GtPT06MmXx0dHUyaNImkpCTS09M5//zzbSojGVAKh8O2eu2yFvU111yjAkYQC7JIAUBbWxsLFizg1FNPJS0tjeLiYlUrvbW1VWX5Wxdi5AKMzMh/4403VPBw3LhxuN1usjIy+f0dv6OjfUcs49/E9vNQJQBmzpypRAXbtm1T2c6yljj01UsHuOqqq1QNciEEf/zjH20LSNbBfCQsZGSbWwPzJ598MmVlZdTX19PS0kJra6vK4pbZ4YNltMQjs8tzc3PJzMzk0ksvVddcBrvl+Vu39+mnn3LOOecghOD222/v12YySN/d3Q3Evrjuvfdem0X+zTffTGtrqy14GgwG8fv9qr27urqYOnUqOTk5rFy5Un3O6hQh9yX73fLly0lLS6O0tJTRo0ezcOFCIpGICopDLOgvz9Pv9/PCCy/gcDjIysqitLRUBdnlMQH9MoCt947f72fq1KmUl5dTXV1NWVkZp5xySr8AvRSS+Hw+urq6VL3y+H1Za48DPPfccwghEEJQU1PDXXfdZduuFEBIDMPA7/dzySWXIITg1VdfBfrupfi69dbtAEyfPp28vDwcDge/+93vbNsdziTEMAyuv/56ioqKaGhooLm5GZfLpcQFmzZtwufz2cQ/8jiskzK5LdM0OfrooykqKlI17/Pz87niiivUffif//xHnXdBQQE//elP1Tasi7jRSAh/T0esBEA4wHsr/0VeTi7llRWMnzCRsAn+sEEU6OjxEaXPZCR+wVj2Bdk+xx13HA0NDRQWFtLc3Ex9fT1CCP7973/T09MzLHFQNBpVIizZBm+//TZCCBX4b25u5sADD0QIwb333gsDHONAyEz4W2+9lezsbPLy8hg7dizFxcUkJycDfffwXXfdRXp6OgUFBYwdO5bNmzcjg/xgEI2GMYwI0WhYvcDgv//7cqqrqygtLWbatPPYsGE9YNDe3kok4MMMB/B6unduB3r9QeW8MJQAIBzsc7CQZSIAFi1aRFJSEkVFRVx55ZWqX8t/h9P+1sA/xPrhO++8Q1lZGW63m/z8fK666iqbhaHOYNBoNBqNZv/Favc/2Hd2Z2en+lnOe63lnjQajUaj0dgxDIPOzs4ByyUOlFjm9Xr1s7NGo9knWNf04+fz+6LMV7xzb7xbscTj8ajjC4fDwy5hOhhaAKDRaNi+fTsQC2T+7Gc/o6CgACEE+fn5zJkzh+7ubjwejxp4uru7ee211zj11FMRQvC73/1OBYqgz+b8tNNOY8yYMbjdbhwOB7m5ubhcLmpraxk9ejT19fWkp6fz+OOPA7FAmsfj6RdgPO+88ygtLaW8vByXy6Us0ctLyygqKKTeXccRhx3OZZdcyhWXXU57a5uK1slSAGbUYPGiZ0gSgoa6eooLi7jhhhsAOO2000hKSuKRRx5h27Zttn1HIhE++eQTJk6cqDKKJ0yYsDMAFmP9+vXqvOMzRvcUa12sYDDIzJkzycrKor6+nry8PN58802V8SsVtInuOzMzk5ycHK677jrWrVuHEIIDDzyQFStWAPbs62g0ytatW1m0aBGnnXYaQgiuvvrqfi4F8RnRchuBQIBXX30Vl8tFTk4OqampHH300SxatEh9Vm5j06ZN3H///QghbG1tDT53d3f3C5hL5XBHRwfHH388QggcDgc/+clP+Pjjj/u16bJly7jrrrvIyMggOTmZo446ig8//FAJUqx9Wh6baZq0trZy0003cd1113HMMcfQ1NSkylM0NjZSWFhITU0N1dXVfOMb3+D73/8+V1xxBT//+c+VeMHqJmC1FrUiS0oEg0E2bdrEpEmTyMjIQAjBtGnTePnll9UCpWyXzs5OZs6cSX5+PkIIPvzwQ7Zu3aqOXU445L9WhaG0PH/77bfJy8ujtraWE0880SYSGI4C0TAM7rnnHpKSktQ973Q6KSsr4+ijj7bVWYW+SVe8I0EkElGCjVdeeYWKigqbE0ZdXR0XXXQRU6dOpbGxkYyMDDIzM7n22mtV+/Tfj2FzAPjTH2dTkO8gJy+XX994i7L+D0b7MtIDwTCRqKmEBNZtWn++5ZZb1L1UWlpKfn4+VVVV/PnPfwb6lKeJIkuUSHX8e++9R35+Pm63m4aGBoqKiqivr8fpdLJmzZphjT2yz02ePJmSkhIqKyspLS2loaGBb3/720yfPp2SkhJKS0vJyMhg4sSJyqElGg0rS/9g0G8TAcgyAA8//BBCCEaNaiInJ4u0tBRKS4s56aQTeej+P/PpRx/i2+kk0NsbwOvz0+0LJCQAwLS7n8h+On36dIqKisjJyWHRokVKaGOtP5go1ntUbv/RRx8lMzMTl8tFWloajz76qG1skmINjUaj0Wg0+x/WEl3yGeKFF15g7dq1rF27lo0bN7J69Wo+++wzPvvsM5YvX67+VpcA0Gg0Go1mcORayfvvv8/atWvZtGkTn376KZ988gmrV69m06ZNqtSmTGRJNIFJo9Fo9pRPP/0UwFaGeF/N761rkTKZEWDbtm02B+x4+/+RGB+1AECj+RoTH4SSAc+33nqLK6+8kqysLJV5fOCBB3LyySczduxY9d73v/991q1bpzKLw+EwgUBA2ZgkJSWpz6alpSGEIDk5mczMTPW+EEIFxqy2i6Zp0tPTQzAYVBnDOTk5CCFISkoiKyuLooJCkoQgWSSRJARJQpCZnsHaNZ8qFwAjEsWMGhiRKM8+s5gkIahzuXHk5avs9UAgwNNPP43T6SQ7O5spU6Zw5ZVX8qMf/UgFeYQQnHjiiSxevJhoNIrH47EN2Nagm/X9PcU68IfDYZ566imEEMpBYcGCBeozMuiUaHBR2qnLLxOfz8dFF11EcnIyDoeD008/nWuuuYaLLrqII488Ul2HQw89VFnDy4C/zFSW2wH7l5sUAvj9fmbPns3YsWNVP3A4HBx++OEce+yx1NXV2fqFtP2Pz4qX9Pb2qvOVQWKrG8Bxxx2ntldQUMCkSZM49thjKSgowOFwIITg8MMP57nnnlN25/HI85Htu3r1arXN5ORkWz/Pzs5W/UUIQXp6uur/qampvPHGG4Dd3UHed9ba5PIc5L9+vx+/38/TTz/NKaecghCClJQUHA4HhxxyCMcffzw1NTUIIaiurua6666zlXKQE5vu7m7bOVoXQa2lF8rKyqiqqqKiooLOzs7dyp4G2LBhgzp/h8NBUlISNTU1bNu2bcBtxb83kFvBmjVrOPfccxFCkJubS0pKihoXUlJSqK6uZtGiRbYgrFX4EI1GYyUAjBDh3i4wwvxo2jlUlJWTnJrCkudfVEFo6QIQjMYEACb2mnYDHbe8j+T1v+CCCzjzzDO5/PLLVR9NZII5kEhD7mvq1KkIIcjLy6O4uBghBE899dSwyo9Yg+eGYTB9+nRSU1OVeCQ5OVn1aSEEv/nNb9T18Pl8GEYEMPD7fUoIYJpRVRJA/nzood8iKUlQX++mrs5FRUUZbnctuZlppAjB1PPO4bPP1uHz+TGJBf0DETMhAYD8zmHndenp6eHUU0/F6XSSm5vL5s2bbaVCYHjqXnn/Wd1dZs2aRWpqKnV1daSkpPDvf/9b9TXTNIlEIjpDUKPRaDSa/RirsNfj8SiBeWVlJUVFRbjdbiorK6muriYzM5M1a9YAwxPCajQajUbzdaO7uxvDMBg7dixFRUWUlpYyatQoKioqKC8vp6ysjNTUVN5++22gL9FJo9Fo9ibhcJhLL70Up9OpnH0l+8IBAOyutPLnjo4O/vCHP/Ctb31Lle61ridqBwCNRrPX6ezsZNmyZcyePZs777yTO+64g9///vcsWLBAZb3vimAwuMvXUMiyAIO9IqFdv5Rn904hwDP/XKQcAAryHcyYMaNf7fV3332X+fPnM3PmTG699Vbuuecenn32WbZs2aICkLuyjBxJZDDJGtTbsmULtbW1VFdX43A4uP76623tKS37E6lzPWvWLD755BPAXu968+bNPPXUU8yYMYPbb7+dO+64g9mzZ/Pss8+ycePGftb1u8uHH37II488wsyZM7njjju4++67+eMf/8jLL79sC17vCZFIhM2bN7NkyRLmzZvHH/7wB+68805+97vfsWzZMtauXavabrjnNFT/ljVGB3slinR3kPh8PrZs2cKDDz7InDlzuOOOO7jjjjuYP38+K1asUIKcRLYLfZMLeUx+v58bbriBoqIihBC8+OKL/QKaifR/ub0NGzZw//33c+utt/LUU08pR4I9Zd26dTz++OPMnTuXWbNmcdddd/HSSy8l2D/7HABCvh5qqisoLS4hKyebrdvalQOAzP63lgAAbOVRBiMUCqmyIr/+9a+57LLL+pU22BOeeOIJ7r77bubNm8fKlStVeyfat4LBYL9xoq2tjRdeeIFZs2Yxc+ZMZs2axaJFi1i/fr1tsRxQGf9gAIYK/IfDQSUIAAOvt4eLL77QJvpyOitxOStwOSsoLyuhsrIcn89PJNoX+O/X7iaxbZo7P2H2LwGybt06UlJSKC0t5ZxzzlHCFxnAt5aoSRSrsCoajXLxxRfjcDhwu91MnDhR/c4qjtBoNBqNRrN/Iudhsg7ojh07aGpqorKyUokepYBXipVlttBIZAjJ5y05b1i9ejXAPqtBap2n9Pb2jvh+rc85sq6pRAd5hsaaSGAtlZao+CQajdoWmK01Za3ZZfFi5n3V/zRfb+Q6hXyelGOCtc9KZzXphijZVwEasIvMgQGTXayl4ka6VvNgWMt9DpRs80Ujr6thGAghlHtlWlqaStYoLy9HCMF7771nG3eG83yu0Wg0AyHH4WAwqMYjr9eLYRh84xvfQAjBxo0biUajavzx+XyDupDIpKn40qADYXU0kci1/ERiDccffzxJSUl88MEHanyXyYIjMcZrAYBGoxkS6yA3UsHfRJCD8GCvWGroLl5DCABuvfVWent7lb32QOdrneyDPWt2byP3a7WL9/l8nHbaabESCOXlHHnkkbbPDmdhxev19ruWsoxAfE15a3a6fG8kiK97M5J9K96i3ev1qn2ZptlvocN6TiMh8JBtOdhrKGQ/j0aj/az6B1qk2d22k5ML62Rl+fLlZGZmUlhYyK9//Wv1UJ5I4Nt6nBJZp12+N1ICmu7ubrxeL8FgkO7u7mE4FRgQ9kOkl49WraSwIJfqKieHH3kE4UgsAz1kQNjsq0dvAl6fv9/9MRjW2vM33HADN954Y7+yFXtKfMa/FPIMd/vx458snSDLuVjpWzCMBffBwOPpVrb/gUAvMvhvmlF6erqQZQJefXUFf/vbX7noop9QV1NFihCMaRmFEIK5c/+IScxtQQb/hxIARCIRNSkOh8P85S9/ISMjg6SkJJ566ilbO1lJpH3iy6n4/X7a2tpoaWmhsrKS3Nxcrr32WtVmkpESeGg0Go1Go9k7WOdKHo+H7OxscnJyePDBB4FYeTw5196wYQMQewazBqn2FL/fzy233EJKSgqzZ89Wx7UvkEFm65xFlkDbUwZ6xjFN01ZaTbNrgsHgHvcFv99vE7tYr7W1H8vkAbALBDSavYVcl5HjjXyWh/7jx2COe3uL+HKkMhlnIKwCqn0tAJdrNr29vfuV+Ny63iYTIQBb+VQpAPjggw9swgn9DK3RaPYE+R1hnePIec2BBx5IVlaWEt3K+IZMPLQ6esr13sHW16UlfzQapaOjwxaviSfR4D/ESiGffvrpZGRkKOdg6QQ8EmgBgEajGZTOzk5bwFQyWAByn2Mm/hrMAUCWG7AO8PEBr/hg+L4SQMTb/8v9zp8/n9TUVEaPHk1qairbtm1TD/bDPTYZ3JduAz09PbaA8GCMxALVYIto4XB4RMoo7Or449sp3jVhXyrMh0KKTuKP2ev12urDy8nKrh5U47cLdgGAnACFQiEmT55McXExDQ0NeDwetU2pPE/02HenX+4JpmkmMEnaKQAwg/z1/j+TmZFCaXEJ/3PD9SoILQPREfn/UASvL7bdRAUcku9+97vcd9996gF9JPpXvHBmuIsifr9fBfjjs4CsWAVS9s8Y+HwepADA6gTw+eebaWvbrt4Dg2g0zJYtmwCDHTvaaNu6mct+eiFFhQ7+3/87mBNO+D7bW2PuCyEjAQGA0V+le8QRR5CRkcGECRNUX/Z6vTYxBiSWwWftR7J9X331VYQQjBo1itTUVGXRZXXPSNSBRaPRaDQazb7Hmjkpg6SytNqqVasAe5bqSAu/DcNQ84uqqiqEEDQ0NBCNRvdJFmc4HFZCz/is25GYv0hBrlU8rudFw8MaCPN6veo6JSL0tzowxAdPd+zYAcSClQNtS9fg1uxL4p+Hu7q68Pv9tudsuU63L5HBH1naFGLroosXL2bZsmU8/PDD3Hjjjdxxxx2ce+65nHDCCRxzzDEJOaTuKaZpEgqF1L3s9/vVvbwvBBKJEj8GdXR0qPdkGcNPP/2USCTSLwCn0Wg0u0N8OV05Np588skkJyfzyCOPANDa2qr+xjTNfnEJa9a/nBvHj0+JuIvKxL/hzK3ee+89Ro8eTVlZmSq7bC1HuidoAYBGo0mI+EztRNjTDOgh2UMBwC233NJvsJaLQjJjWQZYrec0UgPwUFj3a82gXrNmDRUVFYwdOxYhBA888ACALViVyALBYJnCMotYWoQbhqFs7eV1G+kJ+u70r6GQX7ayJrcMeIZCIbXwIctJSBtQSaIB9L3avy0MZR20Ows2csJi7cvWDP8nnniC9PR0srKyWLp0qQpex9vxDYZUQso+JidXI2X/KY8hEAjg8XjUgldiD799JQAuOn8qebmZZKZn8NTCBZjEAs+BiEkU8PhD/P7uWfz6xpswGZ7CXwpZhBC89tprI+ogIttW9ufhWiQO9BnpeCJrYsn7Xo4DdntDg0gkRDDoRwb5I5EQ7777DmeddQZHHTWJ//mfXyFdAcLhIFZBAGYEzAgHjBlNQUE+J5/8A9X2XV7/kAKAcLDvnu3t7WXz5s3k5+cjhOCee+6x2X9JpHAt0ftF/q28Zr/97W9JTU3F7XYzduxYlTUl+5wcg/VCt0aj0Wg0+ydWUXV7ezsej4eJEycihOC5556zzeGtwuhgMDhiGYrBYJC33noLIQRHHXUUQgjeeeedEdn2UFjnTmC3gh+p+Yt1O9bnhkTLlH2diX/eG2jheSi8Xi+RSMRmr75hwwaWLVvGu+++26+sgNfrxePx6BINmr2OXEuTfe3NN9/kxRdfZMWKFYB9fIrvj/tKoCLF3NZn5UcffZTS0lKEEGRlZZGUlERhYSEZGRm4XC4aGxvZtGnTPjk+6P/MGe9c90XR3t6ujsnv99vWPWSSihCCwsJClixZ8kUdpkaj+YrS1tZmKzFz9dVXk5mZyTXXXAPYrfS3b98O9K2LDuWkKufNVrek7u5uurq6eP311/vFFHYnedTv9/P2228jhOCwww5TJZtHYnzXAgCNRjMoMuhotYWXwaF9McHc2yUArAKARLI2h7KCGWmsWd1grzN95plnUlJSQkVFBT/4wQ8G/NuhsJ5vfIb5YPVvRhLZl2R/sqrrRrqNBzp2GeQciP0xgCfFDIP1QZml7Pf7EwpSDxQAtt7vPp+PAw88kOLiYtXHhhP8HiiLaSTFEdYxyPpzYotXBhghzJCXJncNlRUlFBcWsWXr5/gDERX4l4Ho7PxC5uy0qB/OA7ZpmnzyyScIIZTSdKTHzoHKeAwng0wG9hPJ/LL2QZnV39vrBQwMI0J3dyfTpp1HWloKWVkZTJ58JNIVQAb+t27dQjDoJ+DtZtvmDZx26ilkZWUwe/ZcvD6/zf5/VwIAMxrrR3Ix+Re/+AWZmZk0NTWxZs2afq4GMpMjUax1wOS/hx12GPX19SQnJ/Ob3/xGleeIr8O4P44fGo1Go9FoYkhxI8TmETIT/+2337aJhq2MlM2ynAefeOKJVFRU4HK5yM3N5ZZbbhmR7SeKVfQbX/JuT7Faekcikf3DOfBLRDQapbOz0za3TLT/yXm69XpeeOGFpKenk5GRQUFBATfddBO9vb37Td1wzdcHa+m2WbNmkZeXhxCCkpISfvGLX9icCeMTT/ZVgNu6H7le9NZbb/Gzn/2M2267jZNOOomWlhZqa2sZPXo0FRUVFBYW7hOBkzWg7vP5VPb8/lS+Iz7ZQbaLvLaFhYUIIXjhhRfUZ7T4SKPR7CnWAP6WLVt44oknSEpKorS0FKDfXNQaA5KuVdZ4k1WwJscva9Z/NBrlww8/5LjjjkMIQUdHh20sixcDDIU16W/evHkIITjrrLMS/vuh0AIAjUazS+wZn33Bov2CEXAAsGaJysUe6QAgA9PW174M7MhMfOsXh3wgefLJJ0lLS6Ouro7a2lpVY8uapZ8I4XBY2ZtJVa7MjJEW89ImfKQD9NLCzNqfRnoBaqB9WjMirOxOhsVQ+9qlgGUIZJvHTxziRSsjcczxtv7yGjzwwAOkpqaSm5vLqlWrhtX/pVAhHA7j8Xhsi0wjccxyEibvE/lzYgtkBtGAlx3bNpIqBG6Xk0Mm/j9MoNcfJmz2BaB/dcN0jjjqmFj2f2/Atu9EuPLKKznllFNsfzMS528dqyRynEoEef/F3+PWMVG+P3BfMzDNmBBAigB6e70UFjqorq4iKyuDq6++ClvWf5wDQKjXQ8voZoQQbNiwCZNY9n+iJQAgNpHv7u6mpKQEIQR33323GsOsLiDDbf/47Kh33nmHvLw8mpqayMzM5KOPPrLdn7LfWe9RjUaj0Wg0+x/y+Qdi2UJlZWVkZmbyyiuvqM/IwIp8ThmsdNlwkbVDk5OTGTduHC6Xi5qaGurr62lraxuRfewKa234QCCgzm+knoGsZdys9aB1sDkxrM+osrQUDG+OD33XdsWKFVRWVpKfn4/b7SY3Nxen08l7770HxJ4n5KK4VRSi0ewtotEoy5cvJykpiezsbFpaWpSzpXUMlozU2Jso1sz1YDDYb+3T5/OxePFiSktLSU1NZcyYMRx00EEqU3NvYi2tKL+jwO5s80UTjUbV91z8utv27dsRQiCE4OOPPyYajSpRyP5y/BqN5suNaZp8+umn5OTkIIRg5cqVtt+3traqudVgNv4DrdtLd1Q5h37yySeprKwkOzubcePG8fnnnw+6Fp3I+qBVeNzZ2cmkSZNITU1lzpw5CZz10GgBgEaj2SUyIGt1ARhO/e/9uQTA9ddfP6iYwZp5H39OidZY31OsdSetx2kYBjt27OCII46gtLQUh8PBPffcA8S+wIYj0ogXeCRybokGsBPBWtdNnu9IWqRb65cPtn/Zr4cbTB+qf++pAEAGz63tMdDfWks2xAsGEj2HQCCgHs6s92cgEOCYY44hOTmZqVOnqvEgkWu0q+MZiUVAud3BBB27xsAM9dKxfRP5WelUO8s58fgTMIFQ2CRsxhwAFi95AZGUylv/fo/uHi8m/ZWjgxEOh1m/fj11dXUsWbIE0zTVg/reUrnLoP2eMFj/keUV+u6Tvpff71M/H374oYwfP477778Pn88DGLS2biMQ6FWf6ezcAWaEq39+OUlCcNdddxKJGPT6gwSjiTsAyHacMWMGQgjGjRunnBbk2BIOh22ZaPK+GgrZjr29vUSjUa677jry8/MpKirinHPOUeNGfFZKvGhLo9FoNBrN/kP8s1VbWxtjxowhOTmZJ5980hZUAWzPKSPlAvD8888jhKC4uJiDDjpIWUvvqzIAAP/5z3/6lY0bqSD9xo0b1c9WscFItd9XGcMw2Lp1qwrGD1cYbw0QAtx3330IIRg/fjxFRUVMmDABIQT//Oc/R/S4NZpE6erq4rXXXiMzM5OWlhYqKyupqqoiIyODBx98UK1nyWesfSkeMk3TJryB2D3l9Xpt92JXVxennHIK5eXl1NbW4nK52LJlyz47RuuxffDBB/tkv4lgzaKFPmFZNBpV439GRgZCCF5//XW8Xq/6zL4Wemg0mq8eci1w8pP3eJwAACAASURBVOTJCCGYPn060DeXl2t8kUiEzs7OfmVUB1rnlvGVYDBIa2srixYtYtKkSeTm5tLc3ExDQwNOp5OtW7cOmnSUaIKQjF8Eg0Hee+89hBDk5+ezatWq4TXEAGgBgEajGRTrIGUN/sfXxNpb7O0SADNmzLCdq3WCav2/DBjJTPkvAuuCicwOuffee8nLy6O0tJTjjz/eJs5IZIFFfrlYA2XxX0wyuCztdEYysDWYUll+4Y0U1vNL5PrtbReC4RBvN28VAASDwQHb0CqqGArrBEUGeK2Kd4BVq1aRkZFBUlISb775pvrs7p7L3ijvEO9SMvT5xwLJRtDHhLEtlJYUcdJJJ8VqZkZNosDCpxcjklN4/B8LiBp9w8lwBEDz588nNzdXtbNUuI8E8rzlddsTiymInZff7x80kC3t7uVL2vpHo2FkCQAwmDdvDoWFDpYuXUIw6CdeKNDe3goY/Oraa8jMSOOG66/rG2sBj7eXsH34tp615RXLfNi0aRMulwshBA8//LAS/ljdUKz9IdFrJ/t4R0cHPp+Pb37zmzQ1NZGamsrSpUvVfWbNDBnpsUuj0Wg0Gs3IEu+ktX37dkaNGqUC8HK+Yy3BNpIW9n6/n9NOO42ysjLcbjdut5umpiZycnK48sorR2w/gxEIBHj11VfJzMxU5xXverQnhMNhDjvsMGbPng3QT1ChGZpLL72UX/7yl6rt4q/TrpDzXPm3S5YsQQhBfX09DQ0NVFRUkJ2dzUcffYTH4+lna6vR7AteeeUVUlJScLlcjB49msrKSnJycliwYAFgf6ayutPtbaxlB+LX06wl3wKBAOeddx5lZWWMHj2auro6tm7dutePD/oSeILBIPfddx8nnHACPp9vv3CgszozWttK/myaJkIIkpKSeP3114HYd6IefzQazZ4i4zYPPPAAycnJZGRkKEGq1RF0oPKx1u+XSCRCV1cXH330Ec899xx//vOfmTlzJqeffjqNjY0IIcjKysLpdFJRUUFtbS2jRo2ira1NbduazCn/PxTWubj8/DHHHENOTg5nnHHGnjQNoAUAGo3mK4wMOErV8L333ktBQQHNzc0UFRVx9tlnA/Y6hXLyvD9MoIeiq6uLgw46iLKyMkpLS3n55Zf7Bbnjg4LxGf8azWBYH8Ruu+02hBCcccYZNtUk9Lefg8SDrF8kPm8PYLDi5eXU19eTl5fHTTfdxMMPP8y083/E2LEH8shjjyJvISmQia8rH1+qQ04s169fjxCCf/zjH+rzMpvny9A+Q2EVXlidOzo6OvjHP/5BWloa3/nOd7j11lt56KGHWLhwIQ8//DAXXngh1dXVfPOb3+TBBx+0WeuqUg5xYX8Z1Jf7kng8Hq666iqEEFx++eWA3Xp2V8SXvYgvgSEFEaZpsmjRIpWpd/zxx38lrp9Go9FoNF9H4utKezweamtrSUlJ4fnnnx+RfVgtpKFvbuLz+ejq6CQtJZUzTz+DGmc17loXo5tH4aysIjszC2+Ph0CvHyMSVYL2ro5OMCEaTkyAGwgEbHOV+Ge/d999FyEEn332mfr9cOzfrcGm+HrZkUiEjIwMHnvsMWBkxa9fFXYVrItEIpx99tn86Ec/IhgM7nFgbPv27ZxzzjkIIXA4HAghuPrqq9m2bRvQJ/DVaPYVct3tggsuQAihRCnHHXfcF3xkQyOfF6PRKFOnTqWyspLa2lqam5v3iQNANBpVaw0dHR3Mnz+fMWPGqKzX/R3TNElKSkIIwb/+9S9brW29RqnRaIYiFAqpsWLHjh2AXRTV0dGB2+0mJSVFJXwmmtxnLSvb3d1NS0sLmZmZCCHIycmhoKCA4447jrlz5/LWW28hhKCpqUmJeTds2KC2NZALylBYBQpS+Lly5UqEEGRnZ/PMM8/Yzj8YDA4ruU4LADQazVcWueDQ1tbG8uXL+c53vmMbpB0OBwsWLGDz5s1EIhE2bdr0BR/x8DBNkwceeIDCwkIyMjK44IIL1O+k/Z/Vil4KIoZbQ1Dz9aanpwe/388Pf/hDhBA88MADQN8Exev1qgmNvOe+TP3LMAw2bNjAn//8Z6ZOncqPf/xj7rzzTrUoCvYJmwwMQ2wBb6BshPb2dg488ECuuOIKZWO4O1no+zPxThnWc2pvb2fHjh08+eST/OpXv+Kyyy7jZz/7GZdccgl33nkny5Yto7W1tZ9zg8Tn86kJvVV04fF41PuGYfDcc8+Rnp7Ot771LT7++GN1PIlkm1kdFOLtvuIzTb7zne/gdDopKSlRmSkajUaj0Wi+fOxtAUD83CheHCsd6Z59ZjE/Pv8CigoKcVZW4aqpJT01jeee/T9lgRQNR9TP3h5PvC3SoAwU1A2Hw3R3d+P3+3nmmWfIzMxk3bp1u2WvLRccTdNU1timaRIKheju7iY5OZn58+cD2FySvkzPB3sLq6OeLMEgX7JPfve73+X888+3/V1vb29CgXq5aBwMBtWcua2tjccee4wZM2bw/PPPKzdBiD3Pxbu/aTR7k87OTiA2Jj711FNcd911qpzl/s7+IACw/nvdddfx7W9/W5Ws29/RAgCNRrO7xAe7re5ccs3wT3/6Ezk5OaSlpfHRRx8BdtfbXWF1PAV4/PHHeeyxx1iwYAEvvfQSn376Ke3t7QSDQTo7OxFC4HQ6cTqd1NfX77EAYCC6u7s5+eSTycrK4ogjjlAJrtb5YKLjvxYAaDSaryzjx4+npaUFh8NBWloaqamplJaW0tLSQn5+Punp6TgcDhwOh6rf9a9//etLVcM5FApx6KGHUlBQQG5uLitXrrSVaxgIGfTSaIaitbVVLViuWrWKMWPGUFpayqpVqzAMwxZo/bJZt4XDYVXaYlfI7Jv4SZxE/n1bWxuhUIje3l6mTp3KIYccokQE1n18laxQrQrUQCDQrw94vd5+fcRqpyjrLEob/fi2lYvS0WjUNsEPhUJ8/vnnHHDAAeTn5/Pcc8+p92F4JSoGGyvlIvWyZcsQQlBeXs6ZZ56px06NRqPRaL7E7EsBgDWDu6OjA4AfnHwKGWnpREJhlvzfcyQJQY2zmubGJhx5+Zx95lkq+G9EokoA0N7alpAAwCrIlO5V1hJihmHw61//mrKyMlpbW9W8LBwOJzRHjQ8SRyIR27bXrFlDcnIy9957b7920QIAez1z68KwFFF4vV6ampo466yzBqyfPRRynhoKhfr9TW9vr8oYk9u0Lox/GQKImi83X3axyRctALASjUY5++yzOeigg4DEg1xfJFoAoNFodhc5R5HjsHVOGY1G6enpoaGhASEEZ511lm2NL9E1WGvpY7/fP+jca8eOHUoA4Ha7aWlpGREBgHUOL/9m4cKFyolAPqdY110TddrSAgCNRvOV5YorruDHP/4x5513HtOmTePSSy/l8ssvZ9q0aZx77rlcffXVnHnmmZx//vmcffbZnHjiiXz44YdfqgWKaDTKCy+8QH5+PqWlpfzwhz9U78uJ9EA10fUEW5MIMugqJxUvvPACQggOPfRQVRuvs7NTTYy+jDXQ5THLBQkZbO7p6emXqR8MBtV78WOEnFRGIhEuuugiVq9ebXtf1oH6sgkldkUoFLJZ6UtRRSQSsQXsrUpaiLW5DPxbsZYBkP+XC6WGYSghRiQSYdq0aWRnZzNv3jygb4I8nEy2+MC/zE6zCg4OPfRQSktLycjIYM2aNV+KxRWNRqPRaDQDsy9KAFhdkuT8xOfz0dbWRmZ6BuecdTaY4Pf1kp+bR1NDI82NTVRXOXHk5RMKBOnp6lbB/0go3CcGGMb5yazySCRim7eOHz8eIQTt7e3qvXA4nNDzodzOQM/K0WiURx55hMzMTObMmaMCzdbj+roj2y0QCKg2kXNbwzB4//33cTgcnHvuuWrOaXWSGM4+IHZNOjo6bPW4pdggHi0A0OxtrMJxqxMGJF7G7YtkfxAA+Hw+ZVE9duxYDjzwwC/N+oIWAGg0mt0lPvAvxw7586OPPooQgoyMDF577TWbQDWROZR17h7/rAB9zriBQID29naSkpKor6/H7XaPmANAJBJRn5Xztvb2dpqamiguLmbKlClAbM0z3rV0KLQAQKPRfGUJBoNq0JSTSr/frzIwOjs7bUowa2DnyyIAgNiXy8UXX0xWVhZNTU28/PLLQF9mq6wVZq0Rox/wNYlgnTDJe+Khhx4iOztbiU2sSKHAlyFIKoO91gWwwe4L6+QS+iZZgylCZfA4PvNfbuPLNL4MhhxbrOIJSbxwwjoGyQB7/LbkS0685Wfk4rT1Ol199dUIIZg3b16/Ca9pmgm1rxQqWBef4ksB/OlPfyIrK4vKykpVQ0zXStVoNBqN5svLvhAAWOcTshwbxOYVSULw73+9QyQUBhPOOuNMykpKcdXUUudykyySWPjUAhXwNyJR9dloOLH5h6wfap079fb24vF46O7upqSkBCEE27ZtU4Hn4c5N5fzLNE01Hw4Gg5xxxhmkpaUxf/58TNP8UjwTfBFY3cWsotm5c+eSk5PDZZddpq7JcOadUkwgsc7JB1oklp/XawOafYV1XIpEIvT09HxpEgi+aAGAvF9N0+SNN95ACMGRRx7J9u3b9/q+RwItANBoNHuKVfBkFTcedthhyvHZGudJdHyU240XSA603tjR0UFSUhINDQ3U1dWNiADAOgbGCxeuuOIKysvLEULw/vvvq+MNBAK2kqm7QgsANBrNVx5p3xL/HsQG4t7eXnp6eoDhZY/uD8iH+s8//5zm5maysrKYMmWKLbAFsfO0ljbQE2zNUPT09PD/2Tvz8Kiq84/fTPZ9mZlMZk8mK2EXlVatdnFpLW6tvyqCWhCp1lqXKqAIiKBWiwtqVUDUKtWqqK1gFRfQCgIqiyKyI4jsCUkms2+f3x/hHO6dBBIFBeR+n+c8yczc5dxzzj333Pf9vt83Ho/LPH1qY9Ljjz9OQUEBQ4YMkUYrtfHqaHJw+3w+mpqaNAs7sZASOTn9fr9moZkc1aWOZhDtlUgk5D7qCPijSWHkQFDPlaFQqF1bRCIR2X7CsKhemIrvQqEQPp9P0/7Ji27BtgW45557KCgoYMyYMe2i/r+J8Ug9npMj46CNIObxeMjMzOScc87pMGJLhw4dOnTo0HF04bsmAHSkbiRw6qmnUphfoJH4/+/s18nKyMRpd1BZ4cFhs/Ob8y+Qzn+ftxUSEA6GIN61FHXqOghVOIF7770Xo9FIRkYGDQ0NMro8HA63I3B2BHW0lTiXuMYtW7ZgNBpRFIUnn3wS0L4THC0p9r5LiPZTS/WLdWw4HOYXv/gFBoOBq6++up3cbVf7Rz3G1WtvsSYPBAK0trbqa1odhwViTtizZ0+79+8jHYebAKCuw5AhQ8jLy+PHP/7xtyJxHQ7oBAAdOnQcDMQaRv0ZYNmyZeTl5VFSUsK1114rA4u+DRFVEDTFmsvr9WpIr4FAgK1bt6IoinT+V1dXHzQBIFm9Cfat5+fNm4eiKBQXFzNy5Eh57YLEqxMAdOjQcUxj9+7dmglXSN7BPim95IXyN5XYO9zYvn070Db5v/HGGxQXF5Oens4jjzzSLjpZZ/br+LYQYycSieDz+UgkErz11luUlJQwceJEvF6vJADs3LnzcFa1y0iOkIG2xZ6ayKA2VKqlUdVG0o4iwtUGOhH5LnA0yBt2BckGm2g0qjECH8hIqZZ9VCMSiUjykjoFw5YtWwCYM2cOmZmZTJkypd05/H7/fiVNO0Jy9InaoN3U1MQ111xDXl4e3bt3Z+XKlVLdYtu2bV06vg4dOnTo0KHjyMN3TQBIXv+I97FPP/2UrKwsrrxiGCQgEYsT9AdoaWqmtrqGcpebKk8ltdU1mI0mVq38Yp/jX/W3MwjSt0BDQ4NcpzY2NlJUVEROTg6pqans2rXrG1+fWnpV/dnn83H77beTkZFBVlaWTNGkdpjpBAA06oSgJaQ+99xzlJSUkJmZyeWXXy7Js2KbrioBqNtZpGzrLMhBpNrSoeO7RjJJ6mhKkXe4CQDC+fTxxx9TVFREt27d6NmzJ8BR0YY6AUCHDh3fFslpjgWi0Sjjx4/HbDajKArvvvsusE+dFtAoAnQG8Yzan/8kHo/T2NiIoiiUl5fjcDhwOBwHTQBQB0ypVcTC4TAtLS243W5cLhf19fWaFABd9fPoBAAdOnT84CEmz0Qi0WFUhpg4hePvaGAfQ8dRr5MnT0ZRFPLy8vjyyy/lg05E2n7Th4SOYxfqRVU4HNZ8Fizz5uZmVq9eLQ2N4sXzaJDxU9cx2TAm5gyv1yvng2QCgNpwLCDID+FwWB5fzRaFo09lZH9QRy6pSVPJUWdqI3SyCoC6ncQx1dt7vV7ZXvF4nA0bNrBmzRpAq6SQbFDuShvvL+XL7t27Wbx4MYqioCgKL730krxW0b+6AVuHDh06dOg4OvF9pACIRCIaFahYLMaUKVNQFIXFCxe1OfJV5eLfXYTb6cLtdGG32kgzpDL18Sny96bGPZI00BWo16ZindXY2Mjdd99NVlYW3bt3JzU1la+++kqzVvsmJHixj1gjLVy4kNTUVBwOB2VlZTz88MOS1Jm8HjyWoY7qisfjsv3Wr1/Pcccdh9VqpbKykqFDhwLtx1JnOFBKQ6H0oHZifhPyrA4dBwsxBltbW9upiRwNONwEAL/fTygU4oorriAvLw+3283JJ5981AQY6AQAHTp0fFuo10ChUEiuKxsaGvjJT36Cy+WipKREzodi/SvUnjtDcqrScDgs11SCxCqUAXbs2IGiKLjdbtxu9yFJAQD7SMPq+VB894c//IH09HRSU1P59NNPpf8qEAh06TnaKQFAHFDkbRX/g9YYDsjor+Qc0+qHkXqB2VGuKTXb4Vh4AKgZvWpW9P7yCh8OiH5Xv6AcDc4dHTp+6Eh2ugmn5GOPPYaiKJrcN2Jb8fDTCQA6dOg4lpEspyrWr+vWrZPs4eeeew7QzpeBQECfP3Xo0KFDh46jFN8HAUAYDMUaIx6P06NHDwwGg5T2JwGhQJBELM7KFZ+Toih4yiuoq6klPzeP317wmzbJfxVRIBzs3AajtqE1NDQAbba1yZMnk5KSgsvlonv37iiKIhUAhKNenXNekH29Xm87u1yycsDChQs5+eSTKS4uxmazkZaWxiOPPCLrI6K2hC2pI2Osul/UTmz1mkvIsgrFqUAggNfrZffu3TQ3N8v6d2SnFEi2aQIawrxadl9ttwyHw5rfW1tbNe2VXFf1NXRk91SnF9u5cycXX3wxGRkZOBwObDabJACo2yoej2s+Cwd+8rUJRS2BQCCgIceKcQHt7Y4dETWEPVedAk20WTwep6mpiXg8zo4dO2hsbNSoqYntRHuo2109JsTx1eQVEb2nXrMLBbeOjpdMulGPgWRysHBEq49zKB3RgvgC+wIx1Ncl2rmpqakdCV2ddiwQCMg+8vl87e4HgcbGxnbfJ7encLKKFJjqc4I2nZ9ou+bmZvl7IBCQ6pO7d+9m165dmjRtyQpvIlhA3R7qY4ltxO/quiePQ6E2J/5Xfw/IFHzq+ov7Q016UUMdHLRjxw4++eQT5syZw+rVqzXqh+pUf+J7v9/PH/7wB8rLy7HZbNTU1PDVV1+RSCQ0dVH3u7hmNSFKHE/0sXoMizZLJvIMHz6cwsJCHA4HTqeTk08+WdZVHEc9F6jbRNRHzOHJfa7eb9euXTQ1NdHQ0MD27dvxer1yLlBHp3q9Xjk3dwadAKBDh46DQbJ/MhKJ8PHHH5Oenk5JSQkXXnghgUBAE6x0qJ7taqd+Q0ODVAA4lASAA2H69OmkpKRQWFjIhAkTOnyOHwhdVgAQjSYuQiwwotEo77zzDvfeey933HEHd9xxB3fddRevvvoqW7du1Swo1SxhdUXVixL1w/xYcTL7fD7i8TiBQEBes2gftbzF4Sji5S95QazO96tDh47DA/UiXb1g3rBhA2PGjCErK4vLLruMrVu3AtpI5KNF5UCHDh06vguoDR6bN28G2gyiJ5xwArm5uUybNk0aR9SGOn3to0OHDh06dBy9+K4JAMmOX4AvvviC/Px8xo0bBwmIRaIkYnGi4QgkYPvWbfzoxP5YLWW4HE6sljLsVhtrV6+hpalZ7tOVFADQtsbZsWMHAF9++SWDBg0iPz+f8vJyampqKCwsRFEU3nnnHTZt2sSyZctYunQpGzduZOnSpaxZs4Z169ZJm188Hm/nYItGo+zatYuHH36YiooKCgsLqaqqwul04vF4GDFiBEuWLGHt2rV88cUXbNy4kfXr17Nq1So2b94snT/7S5MViUQ0zm7xzvvVV1/x2GOPccYZZ1BaWioVm1JTU7FarQwdOpSXXnoJaLOzCXtkOByWjmNxLK/Xy86dO2lqamL16tW89tprzJ49m+nTpzNu3Dgee+wxTQCMsIctXLiQmTNnMmnSJG6//Xb+9re/8cgjj/Dee+9pjLBiHSkkXKFtTam2kc6ePZtTTjkFRVHo0aMHDoeD+vp6Bg8ezJIlS/jss89YuXIly5cvZ+nSpWzZsoV169bx3//+Vx7D5/MRDAaJx+PS4bpnzx42bdrE559/zvz585k3bx5vvvkm9913H7fffrtc+4rUbsFgUNogW1pa2uUUV0c3B4NBFi9ezIgRI+jTpw+pqakoikJ2djZpaWmccsopDBkyhCVLlshzJEeoiZRaaoVC4UBuaGhgzZo1fPjhh8ybN48ZM2YwefJk7rrrLtatWyfbUjhU169fz5tvvsnUqVO5/fbbGTt2LJMnT+bFF19kw4YNclyJgCvhXBX5c9Xj62CRSCRklDa02WUEedjv99PY2Cj/bt26VaMuJ+5Zcb+J62tsbJSO91AoJAkX4vjiPlHfM3v27JHbbN++nXXr1gFt94/6mCLiUf1epCYTiM9jx47lwgsvlHNHRkYGBQUF1NbWMnLkSN5++2157uS+Vh87HA7z9ddf8/nnn7NixQree+89nn/+ef72t79x2223sWrVKnmdoi2A/RJa1HmaY7EY69evZ86cOTzyyCNMmDCBiRMn8uijj/L222/z5ZdfauoB8Mknn3DZZZfJ1BuVlZWkpaVxww03yDqIc4t7BdrG6iWXXILdbsftdlNdXS3bVmDbtm00NDTw9NNPM2rUKCZNmsTNN9/MzJkzefXVV2WfCoU5dTslE1i8Xi8ffvghv/zlL8nKysLlckkCwFlnncVrr73GqlWr2LRpE6tXr+bjjz9m+fLlrFixgqVLl8o+jUQi7dT61Oddu3Yt06dP5/zzz8dqtZKTk0NZWRkWiwWDwUB1dTV33HEHq1atorm5mUWLFpGdnc3MmTPpCnQCgA4dOg4GavKReG7PmDGDvLw8TCYTI0eObKdce6hwuAkAb7/9Nrm5uZhMJi655BKNL70ra5guEQDES4z6ZWbbtm1cddVVFBQUUFhYSP/+/bnpppsYO3Ysv/zlLzGZTKSnpzNo0CDef/99jcNfLHhFgyTL4ya/lP3Q0dLS0o6REo/HjwgpJDFIBfNT9MmxQs7QoeNIh/qlWT3pNzY2cvnll5OTk8Mtt9yimYO7KoGjQ4cOHT9k7Ny5Uxq0A4EAF110EdnZ2dx4441ymwNFounQoUOHDh06ji58HwoAsM8pC/Dggw+iKArr16+XzvxwMNQm6b9XCeDuO+8iNzsHq6WMmqpqUhSFp6Y/KaP/Az5/lwgAwtY2duxYfvazn6EoCllZWRQVFVFaWkptbS29evUiPz+f/Px86bgV/6elpUmn+uDBgzXRx9DmpHr22Wf5/e9/j8PhQFEUSkpKKC0tpaysDJfLhcVikQ7CzMxMeTyDwSD/T0tLk2uwlpYWTeSqcISqsWfPHiZNmoSiKBQUFPCLX/yCJ554gh07duD3+1mwYAEPPfQQqamppKWl0a9fP1555RVAG3CkdiTeeeedpKenS4dmXl4eiqJQWFhIRkYGV111FfF4XDqrlyxZwjnnnEN+fj4nnXQSt912G3/5y1/o3bu3vK76+noeffRRoM3JqE4bJmxoq1at4qqrrsLlcsm2qK2tpaSkhPLycux2OwaDgZKSEtkviqLIuqanp5Oens6SJUs6dJBu376dHj16oCgKmZmZZGZmSid9aWkpOTk5fPDBBx1GjKmd4qK+wlnq9XpZtGgRPXr0IDU1lb59+zJmzBhWrlwpncwLFy7kwQcfpGfPniiKwoABA1i+fLk8vtpJDGjsGF6vF4vFIq9P1DktLU1e++zZszUO72nTpuFwOOjWrRuDBg3i1ltvZfjw4XLclZSUcMEFF7B69WpZB2EXSZb6PdRobGykrKxM9p8gqhQWFsp7QVyjGHe7d+8mFovx17/+VX6fk5PTrv/Fb/n5+RQWFsq+C4VCRKNRvF6vvJfFfZieni7HU0lJCYqi8KMf/UjW1+fzaSSFd+/ezQ033CDv3d/85je8+eabrF+/ni1btrB48WJuueUWeZ5f/epXLFu2TGPHV6uFRCIRunXrRmZmJllZWbIN0tLSKC4uJjs7m3feeUeSdcQ+anSkZBEKhdi8eTNXX301BQUFKIpCRUUFl19+OUOHDqWmpgaDwUBubi533XUXjY2NBAIBJk6ciNFoZODAgSxbtozW1lbefPNNzGYzRUVFzJo1C2jzg4i5XP38+O1vf4vVaqWiooLy8nK2bNki22/16tXcdNNNcq666aabuPXWW7ngggvknGw0Grnvvvs01yRUPcX1r1mzhpEjR9K3b19ycnLIzc2lrKyMvn37UlVVhcViwWQykZeXR3Z2thwXeXl5sn0LCgqYOnUqoE2/p06Fun37du677z5SU1Oprq5m0qRJfPbZZ6xYsULeb0uWLGHx4sVcdNFFpKSkMHnyZH7xi1+gKApTp07tUoCRTgDQoUPHwUCtrCOeD8OHD6e0tJSCggKeffZZ6UsVRD/oWoR8ZzjcBIDNmzdTXl6O1WqltraWbdu2aVT0O0OnBIDkvDzr1q3j7rvvn3QgbgAAIABJREFUprCwkIKCAi699FLmz58PaCWIVq1axZVXXklmZiYlJSX861//kmxUNURlBWMV2hicx1KOMPWDVy2NpJYZO1xFvCx5vV6NLJwePaxDx5EB9VyplogTGDZsGHl5edx7771yXtFz/enQoeNYh4h4EUaQm266ifT0dB5++GG5jVpuEvat0XQSgA4dOnTo0HF04rsmAKhlnsXxKysrsVgsbRskgHiiTc5/r3M/Fony8eKPKMjLp8pTSbfaOozFJfzyzLNkyoBIKNxlBYDdu3fzpz/9ifHjx3PTTTcxZswYRo4cyfHHH4/RaMRms+F2u/nLX/7CjTfeyA033MAtt9zC3Xffzfjx4xk/fjwjRozgxRdflMdURwU/9NBD3Hzzzdx4442MGjWKCRMmMGDAAOrr66moqKCqqorzzjuPUaNGceedd3LHHXcwbtw4Jk6cyKhRo5g4cSLXX3+9PJ6QcQ2FQu2UBgKBAJs2beK4444jKysLh8Mh0wuIfdUOtA0bNtC/f38KCgpwuVzMnj1b/q6Who9EIjz22GP07duXnj17UlNTg6Io1NTUyOja3/3ud7If33jjDRRF4Z577pHR1Op+njdvHiaTiezsbMxmMyNGjJDv6ZFIRGMrff311xk+fDj33HMPkyZNYvTo0Vx//fUYjUacTid1dXX069dPBliNGzeOsWPHcu+99zJ27FgmTJjAddddR0NDgxzH6qhzgLq6Ovr27Uu/fv1IT0+nR48e1NTUSIf0559/rmlnERWfTHpVG69HjRqFwWCgsLCQhx56SMqjqw3y0LZ+bm1tZcKECWRmZpKTk8P48ePlsYUdQh0pHggE8Pv9nH322fTs2ROPx0NZWRn5+fkcf/zxkmTxwQcfEI/HaWxsZODAgZhMJt555x2NygK0OYdvuOEGDAYDNpsNo9HIggULZH+o/ybX5WCh7off//73XHbZZZx77rm4XC7sdjsejwen04nL5aK0tJQTTzyR3/zmN9x0002yDi+//DKXXXYZ559/PmeffTYFBQVUVVXRvXt3jEYjRUVF/PjHP+bcc8/lkUceYcuWLfKc4hhDhw7lpz/9qbznLRYLVquVwsJC0tPTOeOMM7j77rs1znYhc7948WLOOussMjMzcbvd/POf/5THT85RvHTpUurr66UDev78+TKqHdpIQ6JvfvrTn1JZWYnH46G6uhqj0Uj//v0lYeWNN97QvGclp4yANqUQtRT/e++9R15eHiUlJVRWVvLvf/8b0DpcXnzxRYxGo3TYXHnlldTW1kqVCmF7F++D6enp3H///ZoxKxzzor0uuugi7HY7TqeT8vJydu7cSSwWY/78+aSlpXHFFVewbNkyjax/MBhkyZIlGI1G7HY7eXl5XHjhhfh8Pun8V4/Lzz//nLFjx/KHP/yBG2+8kWuvvZbx48djNpux2WyUl5dzwgkncPvtt/PnP/+Zu+66i/HjxzNu3DjGjBnDqFGjuOGGG5g7d247tQS1H2LEiBFkZmZyzTXXsHLlSs07ciwWk2QLoSw6a9YsFEXBaDSSn5/PM88809Gt0A46AUCHDh0HA/WaRMzF3bt3x+VykZ+fz5IlS+QzMBKJyPn0UPiYDzcBwO/3c+qpp2Kz2cjOzubjjz9u57M/ELqkACAW+8uWLeO4446TTLInn3xSbiMW0ckXNWzYMMlQnDNnDtD2QgL7pKBg34vSf/7zHyZPnqxZIB8LUDN3D5X81KGAerGihlo6TYcOHYcHyTnBkqX6RC7EuXPnysW6Tt7RoUOHjvZRP+vWreOrr76itbVV/qaWe1VHY+gGCh06dOjQoePoxPehACBkugE+/fRTFEXh/vvvb1tT7HX6x6MxSCBTAYQCQU760Y+pqaqmwl2Op7yC1BQDny3/lHg0JtUCunJu0OZ5F99XV1dTXV2NxWIhNzdXBuiEw2H5v1pdTiAQCGhk85PR1NTEyy+/jKIo2Gw2CgsLmT59uiYneHNzM62trbJ+Ig2mWvZfrZqwZ88e/H4/W7ZsobS0FI/Hg6IoGunsaDSqCTIS+y5YsEBGuyuKwqpVqzR5YIWDWu003rp1K7fffjtZWVnU1NRQVVXFkCFDAJg5cyaKovDhhx/Kc6nfuUWu82eeeQa32y2jtqdMmdJue7XDWxADYrEYa9euxWKxUFZWRnV1NcOHDycSidDa2koikcDn88lrjUaj7NmzR5PzW7RrJBLRtGlDQwOLFy8mPT0dl8slI/M3bNggia0iKljUUeSaF6lAI5EI1157rbTrPvXUU5qgA5GrXj0+xLEefvhhGXF+3XXXtdteOF6T+xCQTuisrCzMZjMpKSm8//77RKNRhg0bxhlnnCHroSaB7Ny5Uzo3f/WrX1FYWCj7JTmVgZq0cKgIvurgNkFoaWho4LXXXiMjIwOj0YjH48FkMnHrrbdq+gvaxoha4n7x4sVccMEFlJSUYLPZqK6u5o9//KM8tlphQtiHRCR6KBTigQce0JBbzj//fJYtWwYgiQNqe++qVasoLCzEbDZjMBikHb+jeUD4AJYuXUpqairFxcXSuQv77MmCLCIQDof58MMP6dmzJzk5OVgsFnJycnjnnXc07aAeD4KgIPYPBAK8/fbbMspdRESKa/H7/XJ7v9/PF198QVpaGk6nU6pJiD4S6W/F8bKzs3n33XfbtY3oX4BLL70Uu91OWVkZvXr1orGxkZUrV2p8JWrHtphr9uzZw6pVq0hNTZWkhIkTJ2r6Ulyv3+/XOK7i8TgtLS1UV1dTWVmJyWTi1FNPle+soo3EHKluS3Ec9fgCeOCBByRJSJ0OALTvy2L+EY63WbNmSULQP/7xjy75B3QCgA4dOg4Gaod6LBZj+/btZGZmUlFRgc1m05BV1fPKoZhfDjcBIBgMMnToUMxmM1lZWZKY11VyQ6cEAPGwmzt3LoqiUFlZidlsZt68ee22TWZdJhIJGhsbURSFuro6FEVh8+bNGpYooFk8nnDCCfz5z3/WnPuHDHGNamkK9W/igXi4irqOauO3Wg1Ahw4dhwfqeTSZ8aWeT/x+Pw0NDe0MLDp06NBxLEMYzkSOWBFRJCBeGoR6isCxpFKlQ4cOHTp0/JDwfREARB71cePGkZ6eTkNDQ9t3ex3/aiKAcO7/9a67KSooxGIupcpTSU5WNn+9626pEtBVBQC1E7+xsVG+9/Xp04eysjKOO+446RhXp4YTTqFgMCgd5uoIXLFuam1t1ayX/H4/Tz/9NOnp6dTW1qIoCv/4xz+AfY5C9XtrMjnB7/drHE8il3ZLSwvl5eU4nU6KiooYO3asZj91NG4y7rzzToqLi6moqMBkMhGPxzVBRsKWJRzgACtWrKCwsJDq6mqysrK48sor+eKLL2TEvIhqFu0gpNbFcWKxGJmZmVRVVVFRUUFNTQ1NTU0dStAmO9AXL14sjdgmk4nf//737bZTt1dn3wUCAY3Be9iwYZSWllJeXo6iKCxfvlxDilBD9JVolz//+c9SWvzBBx+U23m9XhlJLAKZRD3U/XnffffJ/UeOHKn5XTj/1CoCra2tBINBmpqauO+++ygpKcHj8ZCdnc3cuXOZNGkSJ510krxOcZxkkkUoFOL5558nKysLq9WKxWKRUuhqZUQ4dClYvV5vuxS3AuFwmHPOOQeHw0Hv3r1JSUlhxowZ8vzJShFqG/G///1vjV39gw8+kNs1NjbKc6oDtQSRA2DAgAEUFxfTrVs3jSNf1Nnr9RKPx1m/fj09e/aksrJSEpc6ukYBMT69Xi+jR4/GbDbTu3dvunXrJp0hwmEuSADq9Gr33nsvNTU1lJeXYzAYePfdd6WDORqNatoj+fOaNWvIysqiuroak8nE9OnTZVsmO3vEOBs9ejSZmZlYrVZKSkraOWUikQhr166VQYvivICGMAUwaNAgrFYrZWVlnHLKKSxYsACHwyHroU6bIsa7+ruTTz4Zj8dDfX096enpbNq0SbZLLBaTNnlRR2HL27hxI5WVlVRWVmI0GjnttNPapVkWUBPb1deoJiKJvn7qqac0gYnivhYQdRNzfyKR4Mwzz5TKDR2Rw5KhEwB06NBxMFCvFRKJBP/73/9QFAWXy0Xv3r3bpWtRP+MPFoebAODz+Zg4cSIFBQUYjUbGjBkDHEICAMCcOXNQFAWPx0NKSgpz587VLNIDgUA7GWrx2efzcd1118k8YKeffjqAxhEljvP111+jKIqUGjsWCABioTZ//nymTZvGs88+y5NPPsnUqVP5+9//zpQpUw5rmTFjhuyPYDCI3+/XpcN16DjCIBxUAmqpGzW7V2yrL6516NBxrCN5Ua7Gnj17NNE7ydvoBAAdOnTo0KHj6MR3TQBQS9gHAgEsFgt9+/aVxj81ASARi7c5/+NtygArV3xOYX4BDpsdt9NFXU0tp/3kVAI+P4lYvC0NQCdQR3qCNqK6R48eGI1GevXqhaIotLS0dBjYof68v2NBm0NMOMWeeuopcnNz6dGjhyYySagAqKP71VHbImo5+XyJRIKrrrqKjIwMKcm/evVqQqEQXq8Xn88n13LJUvWCACEkqlNSUpg0aRKwjzQgtlene/L7/Zx55pk4nU4yMzO57bbbOO+885gwYYImjYD6XOoI50QiwYgRI2QqBBFpn6yeKQJ9BFpbW1m0aBFOpxOHw0FBQQHDhg2T7SGi5JMdkOq/og3V9RTXFYvFmDx5MhkZGXg8HrKysvjss880QT8imEtNOGhubuall15CURQyMjL4+c9/zpdffqkhf6ihPpZQD4C2/OImkwmbzSZVHEAr797U1NShnfGVV16RRva8vDweffRRioqKNCkM1E5SYVtW54dXFIWePXuSmprKaaedpmmnQx3UJOwykUhEkmvU7xFPP/00BoOBiooKSktLueGGGzpUgfX5fJo+3Lp1KyeffDLdu3fHbDYzaNAguZ16HKhVLsQ1+v1+nn32WVJTU1mzZo107naUFvLiiy/G7XbjcDior69n5cqV8jd1ygk1waOhoUHK2wulgczMTGbMmCGJN8kEC9H3U6ZMkcohggAgAs/UCgDqfZubm2lsbGTs2LHk5OTgcrmor69nz549cr9YLEYgENCokMTjcZYuXYqiKJxyyimUlJQwatQoOebV85Df79eMc/U8KOaAgQMHYrPZcDgc9OvXjyFDhnDJJZdIX0coFJJ9EI/HaW1t1QTUjRs3DqvVSlVVFYqi8Morr3R4vmTH+ubNm2WKDI/Hw5lnnimfO8k2QPU9rla2SyQStLa28vnnn5OWlkZeXh4ffvgh8Xhco6qyZ88eTV+rVUjC4TCzZ8+W93RXbIw6AUCHDh0Hg2RC5bRp08jOzsZqtXLWWWfJZ4Z4xqjXRAeLw00ACAaDPPXUU2RlZWGxWPj973+vSevSGTolAHzyySekpKRQXV1NZmYm48eP1xxYvVBR52BQ//bcc8+RkZEh8wK99957wL4OEYzP119/nZSUFHms9hcQV/2Ny88qAvVRiaamJq688krJilUUBYPBQE5Ojua7w1EMBgP9+/fXLEAikcgxQc74XpH4ln9/KOfXcVBQv1CpjT5iDm5tbdXkvulI2lGHDh06jiWoDZxiPao2wCUb4oRhVFc/0qFDhw4dOo5efB8KANDmOBFy0M8888w+W5nKeBUOhqQCQDQcgQT06tGTbrV1mI0mPOUVGJQUFi748BsZvITDWeS8h7b3wf79+2O1WqVzWkS0i7qpHbfCmah2zqgd3hqZ+V27mfHMs2SkpVNWaqEgL58H739gv+smtcFS7dgLBoPSePv6669Lx21hYSF//OMfAa0jUJAXhKNUTYIHGDhwIBUVFdTX16Mo+0yfaoep2vnv8/morKyktraWuro68vLy8Hg8BINB6cwV7SmcnrBvHRmLxXjggQdISUmhsrKSzMxMXn35Fdl3AZ+/naS2OMbHH39MdnY2brcbs9nMH//4R43cuYDYXk1UFRDpAqA9iePll18mIyMDk8lEbm6uxvmmhvjs9XrZuXMnBQUFlJWVUVlZKWXKBXbv3i2dk+JvsoqoiKS++eabKSwspLKyEoPBQDAYlA5JNVFB3TeRSIQ33ngDRVE48cQTMZvNWCwWqQShHl9qtS51G7e0tJCbm4vZbKZ79+4YDAaNBLpwGCer1B5KqEkm4XCYtLQ0SktLpVqGuJe8Xm87Ow7sU3cYNWoUWVlZuN1uevbsyZdffqk5j5D/TyQSGmUPv9/PVVddRc+ePds5/QXRANrS8SqKgtVqJT09ndtuu00eV/3eJOaNZIWQQCDA4MGDsdvtuFwuampq5PWL7ZOVKkT/9unTB0VRmDt3rsbWHA6HNX0j9hdKwz169KCgoICf//zn+0hWSfeFmBfEdRsMBpmqxOFwkEgkNPNZcopNUQchry/my4EDB+J0OnG5XLjdbgwGA5988km743i9Xo2iwI4dOwBYtGgRiqJgNpqwW2387Z57JUEsGo7I+VGtyNHS0oLP56O8vFyO6X79+rVTsFPfS2riVnIK5rfeegtFUUhNTWX27Nma56KaCJBMQhD7r1+/ntTUVBk02Bl0AoAOHToOBupnPsCECRMwm82YTCYuu+yydr8fysCdw00AiMfjvPzyy6Snp1NaWsoFF1wgn/XqZ/H+oEB7BoVY2IfDYZxOJ/X19eTl5dG/f3/5YBQXoXa+x0kQJ9HuveTTTz8lRVGoqaqmqKCQv91zr5Q7a/X7SACtfh9nD/g15//mAmKJOIlEjFAoQDweZZ+zP0o0EiIc8gFRwiEfCeLE9pY9rU1EYmESxGls2iPrEydBIkGH5XBD5C179913GTlyJAMHDuSqq67i+uuvZ9iwYVx99dXfaRk+fPgBy8CBA3nhhRfkQisUCh1SFQD1wiQ5irkrONwpEg62aG6gb1MOFvHEQZXD3X560Yte9KIXvejl2CtqiUoBQXDToUOHDh069ofvmgAgIj0Brr/+erKzs9m9e7e0o3X2fJsyZQqZmZk4HA5sNhulpaXcdddd7VTdkiH27+gaxf99+vTB6XTKfOjbt2//xjlS4ySIxPZFsZIAojGemvYEeVnZeFxuivMLmProY202v71E9QQQS3R+fOF0OuOMM3A4HHTv3h1FUVi0aJFGFruz9UEwGOSFF16QSqY5OTmsWLGCaDzWof1SRAz/6Ec/wmazYbfaKCu1MPa2MYQCQanYsP8OaCsznnkWg5JChbucwvwC/j75obbf9qZ5EGoP6tQP8WiMT5ctpzC/ALfThd1qY/iwK9vbfuJdMwCp24G91/b888+Tmpoq5b6XL1+uiUZWt5vApZdeitlsxuPxoCiKTM1wIHSk5hCNRlmzZg2KotCtto7UFAMP3v8AQX8AEtDS1LxPGUPYmvZe8+zXZpGiKNRW1+Cw2TEYDKxatYpEIqGxIyYb19XX36tXL5xOJxUVFaSkpMhoafV1f1fO/2QkYnEGDbwEl8NJdWUVKYrCv195lVgkqiEChQJBiCfaVD/2jr1pU6ZiMZdSVmohRVF46YUXIQGtLV5IgM/bKtvR29wix21T4x5SFIX/vPpvouGIPEc8GpOpReLRGMcf1w+n3UGFuxyDksKyJUs1ZCUSnc9fjz32GHl5eTKqfdWqVe3GlvQlJBK8+eabGAwGHA4HKSkpzJkzR9Neol+S1/uvvfYaiqLgdrsxmUxcccUVXXYgd+/eHZfLRXl5OZmZmSxbtuxb9eWVV16J0Wikrq6OkpISzjvvvE73UZtxFy1aRHpqGpXucspMZm6+4cb92nvVH7dv347b7aaiogK73c4pp5zyjWz0kUhEtuusWbPIysqiuLiYQYMGEQgEpKKKup/UAZqCdJVIJPB6vWRkZPDMM8906dyJhE4A0KFDx7eDOsAR2ghs48ePx2KxUFRUxOjRozVktUMduKxeYx8OAkAkEmH+/PlSNadfv35SYadLCgBqaaZkVt3w4cMxmUxUVFSQl5fHCy+8IHfcvXu3fAjF2D8BIBKJEA6HycvJpbLCg6nEyPHH9ZMM2ASwa08jX2/bSnpmBi/OfGnv/nGCQT+xWASIE40EgChtJIAAECEWDeAL+4kSZ3dzAzHiRIkRI04CCIZDRzwBQI1AINBOyuz7MKAeqKjZj8kLCrUU0MFA3JTJedDEouJg6n+kl+/aAd8ZErF428votyiJ2OFvP73oRS960Yte9HLsFbF2bGhokO8ayZF4OnTo0KFDRzLi8e9HAQBAURR69+4tP3fmxAdYvXo1OTk5dO/enW7dumG1WunTp49GKr+zd/7kaxT/f5cEgKenPkF+ZjaVTjclefsIAInYNyMAQJsKqdVqxWazUVZWhtlsZsuWLV3aVyCRSPD555+jKAq1tbVkZGRw3wP3E43HOrRfCgJA37592yJ6nS5ysrJ5+aWZmrQN+w3CSCIAVLrLKVYTAKJHBwEA2hysW7ZsoaamBqfTidls5oQTTuhSju+O1GITiQRbt24lOzubslILlRUe7FYb8WhMOqlJIJ3dHREAaqqqsVttnHjiie3UGKD92FV/PpIIAOFgiPn/+4AURaHc5cZYXML5556nIUDI1CB7759IKEwsEuWMX5xOYX4BvXv2wlZm5YohQ4mEwkRCYemgVzv4o+EI8WiMue+8S4qisO3rrXK8iWNHwxGIJ/ho0WLsVhuVFR6ZgmT3zl2EAkHi0di+vukE7733HgaDgerqanJzc5kxY0a7cSPa/NsSAKLRKA899BCFhYX07NmT4uJixo0b16X1v9/v59xzz8XpdNKrVy8MBgOzZs3qUFGjIwhneCgU4sorr8RiseB2uyktLeWOO+7ofH+0BIDMvQQAq9HMiOtv3Bf7mDTfHCoCgLodW1tb+eCDD0hNTcXj8ZCbm8ujjz4q/UE+n09j8xftI74Tn7t3784rr7zSpUhbnQCgQ4eObwu1Ggq0ralHjBhBWVkZhYWFTJw4UfMc+KERABKJBEuWLCE9PR2bzUZdXR27du3qskKpIiqlXvxB28MoJSUFh8OB1WqlV69eGokln8/XJQKA6Jju3eqxW23UVFWToij7XgaAcDzG6DG3kZufRygS3vtiECcaDSMUAIIBL/FYCIgSiwaBCBAhFA8TJY4v7N+rBNBGKEgk10m+IB1ZBIB4vI2AoR6YyakUjgSInE6HemEsFlCgJQDs7+X2h1YOVgHgYAkAugKAXvSiF73oRS96ORoL7HP2i89w6PO56tChQ4eOHw6+awLA1q1bAZg5cyYWi4U5c+ZIhxHsS3W5vwLwq1/9CovFgsVioVevXmRlZTF//nxNgIiaFNfZNYr/jxYCwJ133klOTg719fWYTCYGDBhANBrV5EXfXxGy8iIHu8hJbrPZ+OOfrumUANC9e3cqKipk+oWvNm1uc5rudaoeCwSASCTCP//5T1JTU3G73RQXF/OnP/2pS+Njf+liA4EAdXV1WC1ldKutI0VRWPThQumo3h8B4PVZsyUBwGopY/jw4fKY6nsq2fl4JBMASECKolDhLqe2ugaDksLXX22R1xxVj7VEmxrAlxs2kpWRyS/PPAuHzU5lhYeCvHw2f7lJs088GqN5T5Nsz0QszuBLBnHaT07VjqO9bSxIAndOmEh2ZhblLjd2q41zfj1AQ0CQ5IJO7j9xz1VVVeF2u7nmmmskYaOdmnDimxMARD+PGDECj8eDyWQiKytLpoXoDPF4nIsvvliSi3Jzc3nyySc1x+60D8NhWltbGT58OHa7HaPRSGlpKW+//Xan+x5uAkA8Htc40bZt24bT6aSoqAibzUZKSgqnnnoq//73v+U+fr9f3svqNDAAa9eu1SgDdHr9CZ0AoEOHjkMDQcQqKysjLy+PyZMna57pPzQCAMCaNWvIzs7GbrfjcDjYuHFjl4NfJAFAXblEIsGAAQMoLi6moqKCgoICxo8fD7Q5acXB5WKZ/RMAoC2a+9wB51BUUEjf3n3ITM9g6+avIAH+YIAYoKQo/PXee0jQ9p3f38q+p1+ccMi31/EfJRL2QyIMRGn2txDdmwJgy46vie2tz+7GhqQUAKIcWQQAaGtzNTs2EAho5JG+y9KZcbWpqandYFJHWR0s1I7+DRs2MH36dJ577jlmzJjBzJkzOy0vv/zyUV1effmVgyqdtc8rr7xy4DLz5YMqh7v99KIXvehFL3rRy7FXnn76aV5++WVmzJjB7Nmzefrpp5k5cyZPPfWUzK2pQ4cOHTp0JOP7UAAIBAKMGjUKRVFoaGgA6HIwRSKRYOrUqRiNRqxWK3a7neLiYm6++eZ21yFIA8mOn6OZAJBIJBg0aBBms5ny8nKys7O5+uqrgW9uQI3H46SmplJcXExJSQlnnHVmpwSAHj164Ha7qazwkJ+bJx22xwoBQIylyy67DJPJhN1ux2KxMG7cuC7ZAJOJBGr8/Oc/x+VwYiuzUmoyc9utoyHRJl3f7jqTCADVlVVYzKWMHj1aHlvYTDtKAXWkEgDEPTFs6BU4bHbqampJTTEwfdoTGgJALBLVEAEevP8B6uu68dabc8jOzKKywkOKovDiv17QEAXE33g0RjQcoaWpGYOSwr9febWd858ExCJR4tEY5/x6AAV5+ZSVWih3ufnDlcPl72L7WKTz/g+FQhgMBoxGI06ns0NZ/IMhAIi/Dz/8MCUlJdhsNnJycrjqqqu6TFD52c9+ht1ux+l0kp6ezhtvvNEl57kawWCQYcOG4XK5KCoqori4mC+++KLT/Q43AQCQZA2BcePGoSgKPXr0wG63U11dTVpaGtnZ2QwdOpTnn3+eBQsWtHu/EmQ3gMbGxi6dWycA6NCh41BAKEBeeOGFlJWVkZ2dzRNPPAHs87Gq57lvOsd3hCOBALBp0yYyMzNxOp2Ulpaydu3arhMAkl8aEokECxcuJD09nR49eshcCitWrJA7CcbXnuamLhEAfD4fl1w8kML8AupqasnKyOSjvWzPBHDXvfeQX1hAJBYlFAkTjcdxWIljAAAgAElEQVRk5L9IARAJ+6UCgEgBQCLCk88+hWJQuGjQJcSAxpZmmlq9+9aO7QgA7aOHDieCwSCRSKTdwy4Wix2xEUziRjuUD2ifz8esWbNIS0ujsLCQnJwcUlNTURTlB11SDrIc7efXi170ohe96EUvevmmJSsri+zsbDIyMqQhKS8vj4yMDN55551Dtj7VoUOHDh0/LHzXBIBoNEo8HkdRFPr168fIkSMZNmwYV1xxBVdffTXDhg07YLniiiu4+OKLMZlMnHzyyRQUFOByuTjuuOPYsGGDPI9wmgh7ngju6Ogaxf9HAwHA7/dzwgknYLPZsFgslJeXc9NNNxEIBLpUPyFP7fV62b17N2eeeSZOp5OSkhKOO75fl1IAOBwOyl1uqjyVtDQ1tznqVU7TDvEDIQBEIhESiQR9+/altrYWt9tNSUkJEydO/MZBQOJYwvB9wQUXYCoxUu5yU+5yc9455+Jv9bUjAYh2IQH/nf06KYpClacSs9HEuHHj5DUJiNShahzJBICmxj0s+fgT0lPTqHCXU1nh4Wen/VTjcFdL+cciUXrUd2fC+DsgAT877adynFz4m9+yp6Gx3VgJ+gOQgH899zwpikI0HCHg8+9TWdhbwsEQAZ9fplgoNZnp3bMXo2+5VSoJaJQJOkE4HKaurg6z2Uy3bt349a9/TXNzsyaFx8GmAPD7/TK9x49//GMcDgfnn39+l1KsBAIBPB4PFRUVHH/88aSnp7N9+3Z27drV6b7Jdb/88stxuVxSfnrlypWd7n+4CQCiPdV/Gxsb+dnPfkZOTg7HHXccRqMRk8mE0+kkPz+fjIwMcnNzJZlq9OjRLFiwQB5TrRLd6fXrBAAdOnR8S6jniGAwSDgc5pe//CUWi4WMjAyeffZZYJ8/9UBpgr4NDjcBIJFIsGXLFlJSUmTqmQ0bNnQ9BQC0z5X5pz/9Cbvdjt1ux2q1ctJJJ0nZHtgnjROJRTslAIhjnzvgHOxWG26nixRFYc3KLyDRFqmvpCg89MjDJICWvc57iOPzeQmHg8TjwukflSQAiLB921f06NuT3OJC+p5wPDGgqdUr69PsbTniCQDJUNfpSMlhGg6H8fv9BAKBQxr9r0YwGOTNN99EURRyc3PJzs6WxtwDldzc3KO65OfmHVTprH06LTm5B1UOd/vpRS960Yte9KKXY69kZWWRlZVFfn4+iqKQnp4u147z5s075OtUHTp06NDxw8D3oQDw0ksvoSgKaWlpkriWnp6OoigYDIYDFkVRsFqtpKamSsdYVVUVeXl5vPfee+3sWWp1R2FgPJoJAF9//TVOp5Nu3bpRX1+P3W4nJyeHkpKSLpME09PTMRgMlJWVyTZVFAVXubtTAkC/fv1wOBw4bHb69OrNjm3b25yyasd0R/iBEADi8Th79uyhurqaiooKunXrRlpaGvfdd983tp8Kx7ywH1988cUUFxbRrbYOi7mUbrV1bQ7pvW0inNZqAsAbr/+XFEWhssKDsbiEMWPGtBunHRm/j2QCgIjSNxaXYCuz0qO+O+mpaXy5YaNUnFCPlUUfLiRFUfho0WKIJ5g2ZSpphlTsVhtmo4kN69ZLAkU8GtPs/7PTfsrFv7tIEgmSCQABn5/dO3dRmF+Ap7yC2uoasjIyycvJpdRkJkVRSE0xkKIoZKSld4mgm5mZSXFxMYqi0Lt3b3npwkF8MAQAQXZqbW3l5JNPprCwkIqKCurr67vcBenp6bjdbgoLCzn//POJRqOSrNIZ1GNt0KBBOBwOKisrMRqNLF26tNP9jwQCgGhD2NcnmzdvZtCgQSiK0pYCxeORKWjq6+sxm804nU6qq6slOesnP/mJvGav19vpuUEnAOjQoePbQ73GjUQiRCIRzjzzzHYEgGg02i7Q/YegABCJRNi2bRuKouDxeCgtLWXTpk2aOf1AUMQ/ojJ+v5+qqip69uyJ0WikrKyM8ePHyweDyKMQi8XkQ+hABADBwP3lmWfhKa+QUkXbvtpCLBLlltG3Umqz0tLqJRAKEoqENQQAiLeRADTR/1FIhHlrzusoKQp5JSWc+5vfEgN8oTDBaIxAKEgsEVcRAGIdlsMN0VGRSEQ62b9PdJYCIHkQiRfLQ0WeUJMJtmzZwl//+lcef/xxHn/8caZOnSr/31+ZMmXKUV2mTp16UOWH3j560Yte9KIXvehFL8nlscce48knn+SZZ57hiSee4Nlnn2XatGk8+OCDegoAHTp06NCxX3wfBIALL7wQk8kkHY3J5z9QEQ6mF198kby8PPr27YvT6URRFK677jrC4XCHCpJqA+fRTABoamrCbrdjNpsxGo3YbDauv/563n//fVasWMGiRYsOWJYvX87SpUuZN28e7733HkuWLOGjjz7io48+YtNXm7ukAODxeLCVWamrqaV5T9MxRQBIJBJs27aNuro6ysrKqKurIz09nQcffLDLBuzkNLMisOmSSy7BbrXhcjhxO11kpmdAAlpbvJ0SADzlFRQXFjF27FiNPTIWi+0LUFM5Z49YAsDe/k/E4tx4/Q3kZufQrbYOg5LC5AcelGNNqgEkYOTNIzj+uH40Ne7B3+pj/dp1lBQVU1xYRG52DlMfn0IsEpXbi7+fLltOTlY277z1drvfErE4iViccDDEtq+3kpeTS5WnkipPJRZzKbfdOpoP5y9g4YIPWfrJEpYtWcrihQe+9xYtWsRHH33E/PnzmTt3LitXruSrr74C0Ni5D4YAIMZSOBzmX//6F+np6VitVgoKCli4cGGnzT9v3jwMBgMmkwmj0cjcuXPlb10JwFMrWgwaNIjS0lKqq6spLS1lyZIlne/P4SUAgLYvRLAftEn6//Of/+S0005DURQKCgqkeorFYqG+vh6LxYLFYsFsNmOxWCgsLGThwoWagNEDXr9OANChQ8e3RPJzIBaLcfbZZ2Oz2cjKyuKpp54ikUi0UwVSK2QdDA43ASAQCLBr1y4URaG6uhqj0cjGjRu7TgAQiyTB2Nq0aROpqamYTCZqampIS0vjP//5j6YBxd9wNNIpAUBsf+6Ac3A5nFRXVpFmSGXFsuWs/mIVSorCq7NeIxrfRyiIxKIEAj7E0y8SCUEiAkSJhP1AlEDrHh5+6H6KTEYKzWYmPTiZplYfsb31EXU70gkAQLuBqGazHG4Iskc4HCYQCGgc9odiAKsX7OoxJr7rjKBwrENvHx06dOjQoUPHsQaxBg2FQnLNKCBywurQoUOHDh3J+K4JAFu3biUtLY2hQ4fKIJpwONzl93PhnNm4cSMVFRWUlZXhdrupq6ujpqYGn89HMBg8oCrj0UwAWLduHd27d6dHjx643W6ysrKYMmVKl1Uo1U48NVEiFArt9cMfmADQo0cPamtrsVrKsJVZIZ7YF1Ut0gB0hB8IASAWi7Fjxw66deuGw+HA5XJRUlLCvffeK4O7OkPyOBLrtEsuuQS300VxYRHVlVWkKAqRUJigP6BxfMejMZmrXk0AKCooZOLEiRp7l0i5IereUR2OKAJAAhp27YYErF+7jhRFoWf3HpSVWjjrjDMhgZT+93lbiYTCnPSjHzN+3O1yvCRicf7vtxdS7nJTajLz4/4/0hAoouEI8WiM6dOeIC8nV0r5xyJReYxYJCrbeef2HRQXFlFTVS2JGU8/+ZTcTvRNV1IACBu2cCrDvr4Q4+BgUwCIcdjY2Cid8Eajkf/7v//rtH6nnXYaFouF3Nxcrr/+ejnfBgKBLttPxXUMHjyYkpISKioqMJvNR40CgEBzc7O8HvW9HQwGWbJkCX//+98ZMmQItbW1UuGhpKQEm81GRUUFlZWVcj75+uuvu6QCoBMAdOjQ8W0hngNi3o7H45x33nk4HA5ycnKYNm2aDFpX41DNLYebAOD3+2lsbERRFGprayksLGTt2rVdDtJWQCun8/rrr5OamkpdXR12u52UlBTWrl0LoDGwJRKqxTIHJgB4vV6uGDKUslILFe5yigoKWfrRx/zk5FP4v4t+R3Cvs77Z2yKP0+acjxOLRYA4oWAr4ZCPtuj/CLu2f8WgS35HRk4uZpuT/324GF8oSjieIMa+VAIJIBgOIckE0TYFgWhsb2qBvdeufuCpFyZqJoW6w5Kd4YcKR9qDT5ARxIuU+Kte0On4bhGPxwkGg/slhMTjcUKhULvf1S+76hfhI2l86dChQ4cOHTp06NChQ4cOHd8HDpYAkBx9DMjoR7/fzwsvvCCdGw0NDXK/rjqYxDbNzc3ceOONMgrT5XKRmZnJzJkzgX3SzclOTLXTO1nWunv37pjNZsrLy8nIyJDGSmFHEAQ6te1A/C+dxWgd+f690dtPTZlGTloGjtIyLMVGnpw6TTqtI5EI4WiEaLytvYS9TTig1G301Vdf0adPHxwOBzabjczMTCZNmtRpu+2vvtCmKhCJRLpEAOjbty8ulwuHzU6Vp1LmV4/vdcp2RgB4bsY/STOkUukupygvn0cenNwhAUBd4tEYny3/lOLCIpx2B8biEv78p2uJRaLSGRwKBNtyw0ej0tGmJjyq+/lgCQCNjY3U1NRQX19PdnY2ZrOZv/3tb9IG2JGBPZFIyLGnThuq/v93v/sdLocTW5mV+rpuOO0OTXvIyH8VSWL2a7NIURSqPJVSASAZ6ns6+d5MJBL07NkTh8NBeXk5iqLQ2NjYrt2+TwKAv9Un+7TKU0mFu1wSIj5b/qlsh0gozPvz3iMrI5PPP1uhGQ8zX3yJFEWRhJHFCxdpnPyJWJw+vXoz+pZbCfj8ksQSDUfk76LN161ZS211DRZzKW6ni9zsHB7fS+ARRIQDjv0k+Hw+zfgTc5WYZw6GACDmNDGHbN++XUZDpqSkaJQqotGodBTt2LGDW265RTqyr732Ws191NGY3h/EdQwePBiLxYLL5cJsNvPZZ591uu8+f0eCxYsXk52eQXWFh7ISE3+59rouEQAaGhqwWq3U19djtVrp16+fRj0mFAppUsIEg0E5L4o5w+/3a+Z58b16HlG34+bNm5k/fz5jxozhxBNPRFEU6urqKC0txWKxcPrpp3dJzVgnAOjQoeNgEI1GNb7pwYMHY7PZyM/P584775T+y2RV80MxvxxuAgDA559/TmpqKk6nk7q6OrZu3SqfYZ1BUTMmE4kE06ZNIzMzE5fLhcvl4oQTTpALpEgkInPGCBZvVwgAAGedcSZupwtPeQUOm53LBw0mRVH4dMVnxIBQJCyj9r2+VvY9+eJ4vc3EokEgirelkUjYT1PDdvod14uK6hpsbg+bt+4gBjS2+GR9hJpAYu9xorFgm+M/ESaeCANRWlqaNPWMRCKEw+EOo4fEQzUYDLaLNvq2EIvotWvXMnv2bD744AMWLlzIW2+9xf/+9z/mzZt3WMv777/PypUrgbYBK+orFnE6vlskj0MxgUWjUUKhEC0tLe0MCeI+VUOQBL4r4ooOHTp06NChQ4cOHTp06NBxJONgCQBq25mA2jZ07rnnUlFRQUNDg3wnFza0rkAYLqEtDUBubi4ul4vy8nLMZjOXXnqpdLYm16mj+oiAFuEItdvtuN1uDAYDW7dubdcmwlbg8/m44YYbmDhxosZRJv1SQrlxb9TxlIf/Tk5ahlQAeGraE/uiumkjDYgWa2pq4v3336esrAy/309T0z6bnM/n46STTqK8vJy6ujqcTid/+ctfALokMx2LxTqU8vb7/cQS8cNPAFA5/YUKQDQcYfnSZRTk5VNdWUWpycyf/3RtWz74vc5wGb29F2vWrKG+vp7PPvsMn8+nGV8HQwAQtt/TTz+dkpISjj/+eAoKCrjmmms0x49Go1ItNFleNxgMyuOJ/OoAZ555JnarTUbzXzFkqMa5HPD5f/AEAOHAT8TiREJhHp78ELnZOVgtZVgtZdw18U7N+Jh4xwT6n3Ai3uYW2U7hYIhVK7+guLAIu9VGbnYOE8bfIZ364WCITRu/JM2Qyjtvvd1GHlEdk3hCowbg87bSp1dvHDY7lRUeHDY790+6jx3btstzRkLhLitQCCRHlgscKgWAlpYWJk2axBlnnMGIESNQFAWDwcCll17KM888w+bNm9m4cSPTpk3jggsukBHskydP7lCpoKtBboeTABCPx9mxYwcul4vKykpcLhd9+/aVbZQctCju7f79+/Pss8+2e1Y0NTUxa9asDvdPVoH2er2Ew2F27tzJjBkzsFgsVFRU4Ha7SUtL43//+1/n168TAHTo0HGQUCtqXX/99ZhMJkpKShg1atR+9zkUfrDDTQCIx+MsXrxYPjP79OlDY2Njl69NCYfDmgXTfffdR1FREXa7HbvdztVXXy0X0GJiFg+8rhIAtm3bxhVDhmIrs1JbXYOtzEqGIZW577wr99/T3CSPJwgA0WgYr7eZtidglHDIRyLe5rhf8L93SVEUsvLyOfe3F7WRCGIQjrcdLxAMs33njr3HjBMKBYjG2kgEwZCXYMi7lwQQ1zzo1XlDA4GAfAlSv2B9kxe4riAajTJ69Gj5IExNTUVRFHJyciRD8XCWc889l+bmZlpaWjQsQh0Hj0QnRZBiBJklGA7Je0x9n/n9flpbW9v1S0csTDGmY7FYp+fvrOjQoUOHDh06dOjQoUOHDh1HAw6WACCMesnR1yJ3uqIoDB8+vMNzdgXhcFja37Zt20a3bt1wOp1UVlbidDopKytj48aN7YyLaqiDNdRO/d69e+N2u7Hb7SiKQkNDg0YxANDIOA8ePJixY8fKcwUCgb0BNqrzxhMQjfH4Q49QlJNHnaeKnLQM/jH9yX3OXZD7CfvErFmzUBRF2pdaW1vlMS+99FKsVismkwmPx8NVV13Frl27utyGov0ikYgMmPB6vXh9rUcMASAajrQRJPamGPh02XIK8vKpra7BVGLkmqv/uK/9VHnjhfOzubkZRVFYv3697J+OHPrwzQgAAldccQVmsxm3201paSmDBw+W2wqIKOPkgBT1vaGOrK6ursZTXkGpyUxRQSGT7v2bdE4LksMPnQAQDoak0z0cDNG4u4H01DRsZVaqPJXU13WDRJucf/OeJnp278GdEyZq2kWQCC65eCDG4hLMRhP9TzhRtmXQH+Cxvz9KbnaOVI6QTnzV+BN1IQHnnXMuZqMJh82OsbiEUSNG7iNk7O2LrhgARd+Lew/2KX2I+/JgCADqSPWdO3eSn5/PRx99BLSlMx47diwDBgzAYDBIe3ZKSgpnn302t912G19++aU8rvADhEIhjVpLZzicBIBEIsH27dvxeDw4nU48Ho9UAIhGo/KahIqsgKIovPrqqwDSrh+Px5kzZw5Op1P2lfo+EMGRyRDPl+nTp2M2m6moqMBkMjFkyJDOr18nAOjQoeNbIpmgFA6Huf/++8nJycFut3PppZdqfhdIJBKH5Bl/uAkAoVBIPjPtdjunnnqqhjTcGRRRISEhPn36dAoLCykvL6ekpIQ77rijXcS7vBC6lgIAYOBFF5OaYqBn9x6UFBUz8fbxbYu8RBx/OEQoEm47ViIuJfuDQT/iCej3NdPq3QNEiYT9TJ/6d0zGYix2F+PvupdQFFoD0Tb5f38oyVEZJxhqBaLE4kEiUT8QIZ4Ik0jE2LhxI3PnzuWjjz7is88+Y8GCBezcuVMzYJLz2SQSiQ6Zxd8UYjD84x//4PTTT2fAgAFccMEFnHbaaZx33nn86le/Oqzl17/+NZMnT5YsX7HYOVQKCMc6uupoD0cjhCJhyZwX90pH/ZDcVyJPiM/nk6QWQMrg6QQAHTp06NChQ4cOHTp06NDxQ8ehIgAIR7basPjoo//P3pnHV1Gd/3+y3uw3+3qzJyxBUHBFbetuq9jqr9pSN6RVKyrYurWg2BYoioqCFQGXCmqrSFuq9at1a8UFV/YtEHZCCNlz9zt3Zt6/P27OydybAIFgBZz36zWvJDdzZzlz5syZ5/k8z/MUiqLwxhtvAPRIy9lX+5HX65WO1bvuuovMzEwqKytxOBykpqbywgsvAMgobMGB6m9DqARAeXk5+fn5KIpCW1sbgUAgLFOB2I6qqtx2221MmjQpzJnkC/ilwzzUACHn+J/nPU2OPYOB5ZXEKVG8+PyC7qhlwOPzSvuBz+djzZo1xMTEAMhjEMe/YMECYmNjKSoqIicnh+985zt9FgCYbR0C4dwL+eGPjhIAwYAalv5+7eo12FPTKCspJT3Nzi9vulkKAMyOW6fTKTM+xsfH8/HHH/coBdlfAYDP5+PZZ58lMzOTrKwsysrKuPDCC3G73WH9pUcTRAgRzL93dnZis9koLy2jsryCmKho3n/3PXle7a1t3e17HAsAMLpFAMGASjCgcsZpp1NRVk5xkYOYqGhWrVgJBvz3/f+QaEtg/dp1sj00NShLCCx6+RWiFIXy0jJioqL5bNmnaGoQZ0cnp51yKjf94kbZpmFp/Lva2O/1ySwAsx57nKSERIYMriEh3sZ3z/6OPEbR/8wZKA6G2fksbJaijfubAUB8/pe//IXU1FS2b9+Oy+UKs402NTXh8/nw+Xw0Nzf3OD5N06SNX9f1XjMV7I9vugRAc3MzpaWlOBwOSktLOfvss/F6vWGljMVxqqqKy+WivLxcRvqbAzz//ve/oygKGzduBHra+c1ZPszlRwQzZswgNTUVu93O+eeff/DztwQAFhYWh4kYn8S44ff7Wbx4MYqiUFlZybnnnnvQeXB/+KYFAKqq8tJLLxEfH09eXh5XXnlljwxMB0IRv7hcLoLBIO+++y6KolBSUkJqaiozZ85E07R+CQB0XWfUJZdSXOSgZtBgohSF1n1NtDa3oGpB+f2gruFXA/jVUGQ+6BiGxj333MWpp5zEd78zklNOPpEzTj+Z4cMGk5+XQ1ZeIUNHnMGpZ3yH08/8HqeOPJuTTz+TVavXSsepjgYE0Y0AHm8HoAJ+ln26lKuvHk1KSgrf//73OeOMM7j55ps55ZRTUBSFuXPnsnfvXqD75c2sODwSncg8oREKbLF984Pwm1p6q+0lrmlfUrBZHJi+ONn9akAq5kWfdnnceP2hvijuT7/fL4U8YuktA4BhGN01A/u5WFhYWFhYWFhYWFhYWFgcC/RXAGB2kkdua+TIkQwdOlQ6nEVqdvGdvgZRCGe0pmksW7aMxMREWaLT4XDwk5/8RNprzCU6he0mMkpKUF1dTVlZmcwAIJzq4vvmOtG6rjNhwgTGjBkDdNuBRIZCuV0D/G4Pf3l+IelJKQwoqyApNp4Fzz4nHYe6ruP2ehBFADo6Ovjkk09kBgDRLiJYYc+ePRQVFTF06FByc3OlWEHY5g6E2JbZoRcIBFi9ejX/fP21o0YAIOvdd322asVKkhOTKC5ykJ2ZxU2/uFFGxPu6yiyIfbtcLhobG0lKSuK9996ThyAM3v0RAAgH3549e3A4HDIDRW5uLtu2bZN9RVw3sQ1d18OCTUS7i36zcuVKFEWhML+AmkGDybCng9Ed9R/ZHsezAMB8PVV/gCef+BNZGZmUFpeQlZHJxN/8FgwY98tbOPd758h1RRuJ7zfU76G0uITK8goS4m3cN3ESGLB+7TqiFIXPP/1MfrezvaNHO8uSAAZs27KVirJyhg45geIiB/GxcWyu3RS2fsB3cAGT+fqvXbuWv/zlL0D42NffDADCZv7HP/6RoUOHhokNPB5Pj2hPsT+v10tDQ0OYQ8YsyuqrQOubFABomkZbWxsOh4Pi4mIKCwu56KKLaG9v71X8BLBv3z6qq6uZO3duD0Hae++9R1ZWFq+++ipOpxO/339QgQ9dxyF8JNHR0fI4Dnr+lgDAwsKiH5hLlei6ztKlS1EUhYqKCk466STp6/J4PL2W7OoP37QAAODhhx8mNTWVzMxM7rzzzkMaO5VIFd769eux2WxkZmaSmZnJgw8+2OME5ESxazmYAEBVVX5y5VUMO2EoVRWVRCkKm9ZvCE1Aur4fCKph6c7N0f/vvfcOf35uPoteeYlFr7zE1CkPMGzIAMrLShh0wonMe+5F/vTUM8x7ZgEvvvwqc+Y/i8frRzN0NEOXAgCPt5OQ81/l6Wf+xIkn1fCDH1zMJ598AnSrFNva2tiyZQunnXYad955Z9jLg6ijcySi/83tI9rW4/GEPbDFA/GbWsQLgLk0guB/Nkk+jjmYg13UqTN/Zr5PhOrf5XKxc+dOVq1axYoVK9i2bRttbW1At0ocQv139+7d7Nmzh+bmZksAYGFhYWFhYWFhYWFhYfGtoL8CgP3V2ty8eTM2m41f/epX8jNhU/H5fH02QJojnAKBAB6PhwEDBpCTkyPLADgcjjBDY2QNeIHf75d1nA3D4OSTT8bhcFBeXk50dDSbN28G6CFQED/vuece7rrrLqA7pbSw+4moUAzwdDr556t/I1ZRGFBWQXZaOrNnPhZmNBABDcLm9uabb5KYmBh2vF6vV7bv5MmTSUhIoKKigsTERB599NE+tZ9A7Ec4xc855xzOv/CCb14A0MsSDKisXL6CtJRUykvLcBQWcc3Prg53vHY5xsW12rt3L4qisG3bNnl9joQAQNg+VVXlgQceICkpCYfDgc1m4/XXXw87Zb/fH9a3RUpegfleefzxx0lLS6O8tIwoRWHWY4+HCQDCHOPHsQBAU4PdTvWu89y2ZStJCYlUlldQWV7BwOoBbFy/gRJHMQv+/LxsC9FWogSA6g8w4fbxZKZnUFpcwolDh+FxuZn9+CwchUXd5RV0Q+4zGFDDnPqGpsu/J9w+nihFYcjgGlKSkpn31Fy5niwHcBD8fr8MQjr77LO59tpr5RgYOfb2JwMAhMYn4Uje3zq94fF4pJ3b7/fj8XjCgv0OxjcpAPD5fHR0dDB48GAKCwspKCjgnHPOkWIuc1YYs18hJSWFv/3tb6iqitvtlsF+77zzDtHR0Vx11VVhwYiR2WXM1NfXEwwG0fVQRtoRI0Zw4oknMmLEiIOfvyUAsLCw6AeREe/r1q3DZrNRWFjIwIEDZcYXszj2SHE0CO2Q2ckAACAASURBVABuvfVWcnJyyMjIYP78+Yd0nkrkiq2trVxwwQWkpKRQVFTEz3/+c/k/8UCQO+DgAgCPx8POnTs5cegwMuzpcsK3Z+cuvG4PvoAft9/XFanfXdvc7/cSDAZobW0m9AQM4nF3AEHee/ctstKTSU5KYNTlPybYdQxtnV60rt91A5xuFwagagGCmk8KAP7z37corygkvyALv98blhpCPPzXr1/PRRddRHJysnwxEucu1jsSeDyesG2Zr0Wkcu9oIBAI9Ej7Y3H4iPtnf0sQQ/7u9vvwBVX5eYezk61bt/LQQw8xePBgEhISKC0t5eSTT6a8vJyoqCguvPBC3nzzTSkG0DSNa6+9lu9973uyBMCB9n+gxRIAWFhYWFhYWFhYWFhYWBwr9FcAEOmoELax++67j4SEBN56660e3+mrYwpCNiCzYzYYDDJp0iQyMjKk8z85OZlnn31W2o5EFkDxHfHTHFkPMGbMGLKzs6msrCQ6OpoPPvgA6HaWmx1He/fu5cILL2TatGlhJUFFQIJsh6CG6vXxyX+XSgFAYXYud//q191OTrqDG4Q97aWXXkJRQglJ6+vrw9oTYPv27QwdOpTs7GxqamooLi7uk4MuMoulqqqsXr2ajIwMZj7+2DcvADA5f6XzVTfYWreFyvIKykvLKMwv4ILzzsfQ9G4nbtd3xHX+6quvUBSFTZs24fV6wwKU+lsCAEJCgKamJiorK8nPzycvL4+rr766RxZQczCTaH/zdQwGgzQ0NHD66afjcDgoKihk6JATwIDW5hZ5bpoaDBM6HK8CAHE9fR6vjL43NJ2LL7yI/Nw8ShzFZGVkcust4ygucrBz+w70oBZK+68bsgSAoen4PF7+9drrxMXEUuIoJj42jr+8+BIjTz+DB+6fLNvWvB/xuxARiL8xYNWKlQysHkB2ZhZVFZVcduko6nftBt3ovjZ9QFVVVq5cSVRUFC+88EKP8a+/AgCn04lhGNx7773k5OTIPtHe3t5jXYEQQnV0dBxW1H/k+cE3IwAQorDvf//7ZGVlUVxcTHV1Nbt37+7eR1ef7ujoQNd1VFVFURTeffddILw8g8gAXVhYGOa8Es+1yCUyw2wgEOCyyy4jLi6O66+//uDnbwkALCwsjhC6rtPU1ERNTQ0FBQXk5+ezYcMG+X8hVBK/95dvWgDg8/m4+OKLKSwsJDMzk6VLlx5SlgNFbAS6G2TGjBmkpaVRUVHBaaedJh2+ZjWnnFCZlt4EAP/9739RFIXszCxKi0vIy8klKyOTdWvWhiYcaGhGUL5INLe2dKUG0/H7vUAQf8CNFvQCKs7OVp55+iliY6LIycnh4cdm0+lVCRrCSRoqKeDx+ru2ouELhLZjdEX/T7hjHEqUwuK/vQzoYRMFofwrLy+noKCAqKgoGf0eCATkA+/rcM5H1og7GtA0TU7yzZkQzHXgLA4fs4Cmt59+NSD/dvt98vPazZuY/8zTxMcncPHFP+D5559n165dMhVbW1sLjY0N/OlPs0lPT+NXv/oVXq+X+++/n7S0NB544Pc9BDyH+tMSAFhYWFhYWFhYWFhYWFgcK/RXAAAhu5D4vgjoGDRoEIWFhbS2toZF5ByOU8Nsd3E6nXzxxRdkZGSQnp5OeXk5GRkZXHPNNbLUAHTb8swOM2EQFNt75JFHSElJoby8HEVRWLx4sdyHaJOOjg4AWlpaqKys5MUXXwzbhwF4fN2p3kX08Kb1G4iPjqGqrJyCnFyuHf0zNDXYXW+akL1PtM1tt93G5Zdf3iPARjisIJQlQFEUCgoKsNvtzJs3r0/tJ+yWIiL9hhtuIDU1NfSZKbvioQgAhMN0v0QIAMpLy0gzCwBMEdhhztguZ3dbSytnjTwTR2ERmekZDB1yAh6Xm4629h7OQK/Xyz//+U/KysrYt28fwBETAJiFIwALFiwgKSmJgoICoqOjWb58eVhN9f31dXMphg8//FAaykVqelEeotflOBYAiHMUDnWR9eCFBQtJiLeRk5XN0CEnYE9N4wcXf18KBtpb28K+J7IBNO9roqyklIHVA8jLyeUHF3+fKEVhzarVYW1qdvRjgKvTCUa3IKGhfg8Y8PfFfyNKUSgucmCLi2fJ3/8hhRo+T8/yopGI637BBRcwfPjwMF9DpEPkcAQAop+3tbWxZMkSFEXBbrfz5z//uddj2Z/dPhAIhJVzORTb9pEUANi6BAB5WdncOeGOnveC6XviT03TuPnmm0lNTaWiooLk5GTq6upkWRrzuRiGwdq1a+W9ax4nPB4P77zzDklJScTFxXH33XeHlfXoDbFtIURoamoiJyeH1NRU/vWvfx38/C0BgIWFxWFiHptE9nRN07j00kspKSnBZrPxwQcfyHFO/F/83l++aQFAW1sbw4cPp7i4mKysrLBg9b6gCIe2Od17e3s7ubm5FBYWEhUVJV+EzJHf5qj1YDCIz+cLe5jU1dUxffp0oqKiePTRR1m2bBmKopCSkkJMTAyffvophqERUD1A6CLOn/8Md9zxazQt1FBql9NfN7zouAE/HZ0tXH311WRl5ZBgS2HpJ8vwa0GcbpdM+S8ejD6fB5erE9AJ+L0EugQFSYlxRCkK//fG69A1EfK43GCA2+nC7XThcDhISEjg17/+Ne3t7fKBq2pBOl3OLhHv0eWstzgWCb127u9ne3srBjper1t+vn3nNn784ytQFIVn5v+ZzbVb5YzQ7/UhMmaIe2tj7VrsGWn89GejSc/MQFGiee/9pT3mlYf608LieMD8XNu3b598UIvaQRYWFsc2wlBhrgcG9LtEkvnFwmywOJJpxo51zGmMRWpK89hqdnAIw6CVZcrCwsLC4uvkYAKAyAyZmqaFOa8gZC8Tfzc0NDB+/Hjy8vKIi4vjo48+ks83mSbftK2+YE7Fr6qqdExXVFSQkpLCsGHDsNvtfPrpp/J4zMZN4cQHwiI2161bh6IoVFVVkZyczPjx44HuuZL4qWkab731Vg9nqXl7IsOgKAPg6nTyw1GXUVzkoDC/gLSUVFwuV5iNsL29XZ6boig8++yzYccr2sp8/NOmTSMnJ4f4+HgKCgpYsWJFmBNKzMvMTn/zz0cffZSEhATee+89uU2RAjuyfZqbmxk+fDjZ2dlUVVWRl5fHnj17wtYzG6DFnEYEFGmaxuLFi1EUhcGDBxMfH8+TT/wJDKTDu7c+Ibb54IMPEhcXx6BBg1AUhY8++iis7QOBgNzXz3/+c6655hp5DKJvRV5LCGV5feKJJ0hISKC4uJioqCj+85//hLWhWF/0I4/HI4Uu06ZNIzk5Gbvdzplnnsny5csB6OzslNdX13X8fn+PWu/btm0jNjaWYcOGoSgKL730kpzriXrt4nfz98R5vfzyy6SlpVFSUkJKSgr33nuvXEccs9Pp7DVQS7RJfn4+Q4cOpbCwEEVRpHBGVVW532Aw2CO6+etAnK85u24gEKCxsZHs7GwqKioYPHgwiqLw17/+VX7PfJyGYYTNp++4445QdoWiIjIyMvjOd74T5vw239cHQ9M0pk+fjqIonHDCCdhsNlavXh22jrmdIiPovV4vzz//vBSZiOPWNE0ej1moMWfOHDIyMjjhhBNQFIWFCxfKbUXeo4ZhSANme2sbHpebzPQMCvMLyM3O4dqrr+G+iZOY8vs/MPORR3nqyTk8PW8+8+fOY86fnmT247OYNWsWixcvZs2aNdTW1sp2FPsQ/dfcl83lAcz9a9SoUaSmpjJgwACys7NZsWIF0G1f6ujoCMvSIn6KbX355ZcoXWKLvJxc7r7zLikS0oOa3Lff7+/x3HruuedQFIXq6mpiY2PlWCocXmbn04wZM7DZbD0EOoZh8MEHH6AoCqeeeiqKorB06VJ5jkJAZR6jBCJI8M033yQuLo6BAwfKd70DYQkALCwsDhfzs0dVVTmuPvLII9jtdhITE3nssceA0PwsUhh7MHqzQ4mxUtM0Oe8F2LFjB4qiMGDAAIqKiigpKWHv3r1A93PeMIwez5YDIY7TLDCG7mf45s2bURSFoqIivv/97wPd88+++C8UsRPRMOJL//nPf0hKSiItLY3rr7+e9evXyy+JE/D7/XJCLGhoaODhhx/mlFNOYcCAAXz66adSHFBTU0NJSQnZ2dm88sorqKqfkPM/SH19PUlJKcyb+yxutxefz9clAPADXlStnUCwg5bWRoYPH05RYRnVA4awZ28DmtTu6ri9LoK6is/nQeTPUVU/QdWPFgzQsGc3KcmJDKsZTElhAV9++hmYUiGJyXl9fT2ffvqpVHD41QBOt6tbIUwoItvCon/oB12EiMXrddPcvI8xY64jISGe5557Dne7R4bkBwNqmADAIACotHc08/Gyj1CiFIocJdjTMwkCvqDlpLD4dmN+YRWRDE6n84ikB7KwsPhmERPlMKMRRyb9VyRmo7AlHuomGAzS2toqX3h8Pp/MqiVeaER7mbNsWSIACwsLC4uvi4MJACKdEMIxIZxWZsfqRx99xPXXX092djZpaWkMHjyY008/nS+++AIIGSjb2towDKNH6vQDYY5aAvjkk0+oqanBbrczdOhQBgwYgM1m45ZbbgmLOPJ4PLJ2szmS0+fzSafOFVdcQVZWFgMHDkRRFPbt24fP55OOfpfLhWEYjBo1it/+9re0tbXhcrnCnO7Q09EO8M9//lM6Dh0OB0888QQQMlCaDbdz585FURS8Xu9+jaOtra3yf4888gjJycmkpKQwaNAgmbkAwo2eZqfdvn37ePrpp4mLi2PBggVAt/jT3LZiPqKqKl988QV5eXnk5uaSl5dHYmIir732Wq/Xx7wd85xz3LhxFBQUyHS011xzTdi5ifdPkcZcfA9g+/bt5ObmUlBQQHp6OmPHjpXHZ97G+vXrURRFihoij0Os63a75T4uvPBCGQWfnp7OlClTwr4r2kZsxyx48Xq9PPbYY8TGxhIXF8e4cePCIs8iSxA0NDQAoaCwvLw8zjrrLBRFYdGiRWFzvM7Ozh7zc3GfBINBXC4X999/P3a7nbKyMsrLy/nud78rt+Hz+eQ5+/1+ua3W1lZ5D3z44YekpKRQUFBATU0NNpuNl19+OcwxfSj3Zn8xi4nF/sW5XH311SQkJFBRUUFaWhpbt24NEx2b+605qlFkyqiursZut0sHiECMaX2Ncnc6nbzwwgsoikJmZibnnXceH3zwAXv37kXXdTweT9h19Pv9NDc34/P5mDdvnhR6mBF93Cz+7ejo4PbbbycpKYmysjJSU1OZNGmSFGhEOm18Pl93Jo6u4KcH7p9MQrxNZtzITM/AnppGUkIi8bFx2OLiSU5MIj3NTnZmFjk5OcTExKAoCgMHDuTSSy9l2rRpNDY2ymOLFD0J0Y5wxAcCAdatW8f5559PdnY2paWlFBQUMGfOHNn3zJiFLuI8PB4Pf/jDH8jNzaW0tJTKykpqamrCxE/mtmpvbw+7x7xeL+eccw4xMTGUl5czYsQI+T9zdoPa2lrS09OZO3euvKf8fr8Ukrz++uskJyeHZZhZtWqVLB8gri9036PiuFatWkVVVRXx8fH87W9/oy9YAgALC4vDJbK8E4Seb++88w5RUVHk5+dz5ZVXynXFc+dQ7XPm51tk5gBN09i9ezczZ86ksLCQkpISmQXmmWeekQ55szBX1/U+iRAiRW9iLifmK6+//jpxcXFkZWVx2223hc31+oJiPinxUBInO2vWLPLy8oiJieHGG29ky5YtYRs2qxHWrFnDrFmzOOuss6ipqeHRRx/FMAz5cFBVlSVLlpCYmIiiKJx99tmAjqb72bFzCzfddBPl5ZU07DGrxoIIAYAv0ILH18K69auIj4/HUVTO6J9djy/gx6f58fo9aEawq5xAV+S0oeH1ugEdQw8COo1795CXm01xQT6Dq6uIURQu+8EljL/tdubOeYq1q9fQ0hRyBJkngiKzgMgCENA1VMN6QFn0l4MLANrbWwEdTVOZOPE3KIrC2LFjQvdr12rBgCmlV9d9pRsBvD4nEMSv+jj7u2eRnZPHFf/vShqaWrHyV1h82xEvweYXLcH/IgLAwsLi6yPSwQyhaDVxv/c3A0BnZ2cPg6HX6/1aSkQdD0Qav0REi8BslDzaymFZWFhYWBw/9LUEQGRGHxHBOmfOHCZNmsSoUaOoqqrC4XBQXV1NWVkZGRkZ5OXlkZ+fz+jRo5k2bRqBQEAa6fryfuH3+1m5ciWzZs1i4sSJXHHFFeTk5JCdnc0ZZ5xBVlYWeXl5DBgwgPT0dH70ox9x6623MnXqVJ544gkWLVoEhAyH5ohMcQwtLS2MGDGCvLw8SktLueyyy+RzV9gBJ02ahKIoMtJbCKXNjiWzA9Kc8edXv/oVKSkpREVFkZ6eLutOi3N77bXXyM7OZuHChezYsSMsAry3KFu3243f7+ftt99m5MiRKIpCTk4Ov/nNb9i+fbvcrtk2+fbbbzN27NhQxsRnngG6o6TM7fH5558zb948Hn30UcaPH8+wYcNkhoVhw4aRnJzMqaeeym9/+1smTpzI448/LvtIIBDA5/Mxc+ZMnn32We6//35uuOEG7HY7AwcOpLKyUkbyX3fdddx999089NBDMlLXLLoQbafrOgsXLiQrK0tGqz/99NOyTVwuF42NjZSWljJ69OiwPiXmpA0NDfh8PhYuXMjcuXO59957ZVsMGjSIoUOHkpKSgt1uZ8KECfz2t7/lT3/6Exs3buzhhAdobGzEMAyCwSCLFi1iwIABMsPB/PnzwwQora2teDwedu/ezZNPPklpaSlRUVFUVlby/vvvh7V/pMhAiF7mz5/Ps88+ywMPPMCtt95KUVERdrtdOkhzc3P58Y9/zF133cWiRYuYOXNmmGhgx44dzJ8/nzlz5nDfffdx9tlnU1RURGlpKbm5uWRkZDBkyBBuuukmpk2bxsKFC3njjTfkMf0vMI9B5r7++uuvk5SURFZWFjfccAMQHjnv9XrDyjaYGTJkiCwPsmnTJvld83p9EUFv27YNCPWBhQsXcvHFF6MoSiibxZNPsmXLFrmux+ORbf/VV19x+eWXk5CQwL///W8Atm7dKtcVWTqefPJJ5s+fzwMPPMAtt9xCfn4+drudiooKqqurqaio4Oabb2bSpElMnTpVZjmBrjHMVL5ALLeNu5X83DwqysopcRRTWlxCaXEJJY5iHIVF5OfmkZOVTVZGJvn5+TgcDgYNGkR2djZ5eXlERUWRkpLCc889h9PplGOb3++X7yo7d+5kzpw5zJ49m/vuu49zzz0Xu91OSUkJAwcOJD09ndLSUiZOnMisWbOYO3euLEsgfCJz5sxh5syZTJw4kXvuuYfCwkLy8/OpqKjg1FNPJSsri/POO4/Jkyczffp0fv/738sSH+I6trW1yev4xhtvyDaLiYnhjjvuoLm5WY7jGzZsYMyYMRQXF8tngbheQsghMjUnJSXx97//nfHjxxMVFcWrr76Krus9hNvBYBCPx8O//vUvTjvtNOLi4pg1axZAj+DQ3rAEABYWFv1BjIVm53hnZycpKSlUVVWRk5ODqqqoqirHyr7a9nVdx+v1yn2sWLGC2bNn88QTTzB37lzuu+8+xo0bx+jRo2XUf0FBAQ6Hg9zcXJKTk7nyyiu5++67+f3vf89f//pXnnrqKR577DE5X+0L5oxI0D03ufvuuykoKCAlJYVFixbJ51OkaG1/KOZBVjwozF9eunQpo0ePJj4+nry8PMaOHcszzzzDwoULmTFjBrfddhvV1dXk5ORw2WWXMW/ePKn4bG9vD6n0TBONxx57jNLSUhRFYcKE25k1+1FGnDyMgQMH0tDQGErn0x56KHm8nZgFAAYeFr4QSnVjT8tm9hNzMQC/HpAZAMTi93sxO1E7O9rk77NnPUZKgo3ignwGVVWTl5VNQV4+9tQ0ohSFn98wln//+9/yZc3pdHbXSzd0PD4vLp/XcqBaHAEO7PzX9aDsy3V1m1AUhfT0NOrrd+H3+1E9AXxOf5gAQAsGZPp/8VPVAtxy6y9JTklj1uw/oQGqlQDA4ltOpAErEAjwxhtvcNVVV7Fz585v8tAsLCyOEGLyr+s69957L+PGjePee+9l/Pjx/VruuusubrrpJsaNG8e1117LuHHjmDp16jd9ukcN4kXL5/PJ6P7Gxkauu+46Pv74Y6DbwG+OautL+kgLCwsLC4vD5XAFAKLsZWpqKjabjejoaBRFkUtMTAzp6eny77i4OKKjo1m3bp0UH/YlBSjA7373O+Li4lAUhYyMjLD9JCQkoCgK2dnZcr/if2lpaWRkZLB79+6w81VVVaZDDQaDdHR0cM4555CWlia3efvttzNt2jSGDRuGzWZj6dKlQHjGtMg01uI5Hlk3fvbs2TKNeUxMDBdddBGPPvoov/jFL1AUhV/+8pdh52t+9kdmPzCncN2yZQvz589nxIgRso0vuOACJk6cyJQpUxg3bhwnnXQSiqJw+eWXSwGDOUW8uK6dnZ1MnTpVtm9sbCyJiYnEx8fLthdLamqqdJLdc889su/s3buXxMREkpOT5TbM30tKSkJRFKKjo7HZbPJaff7552HnZxhGWMTtP/7xD8477zwZPHXyySczc+ZM7rrrLhRFYdSoUWGChkgB6s6dO8OOPfK4xDElJCQQFRVFbGxsWA11sxgjEo/Hw+TJk6mqqiItLY3CwkKuvPJKZsyYwcMPP8xPfvITKioqiIqK4pRTTmHWrFk9Sj61t7eH2YiF3dXj8fR6f6Wmpsp+L5yH5kW0p7im4nqZ70dRjlZcE3Et7HZ7WCmF/wXmuvbmdOstLS3SVr5kyZKwchG9jUeiTIOqqjJt/yWXXEIwGETTtDDBwKE6V8Uxbtu2jZkzZzJs2DCio6NJS0vjhz/8IXfffTdTpkzh9ttvl070K664gs8++0xmDYHQe4Bo2+bmZuLj43v0x7S0tB6fxcfH43A4mDFjhsxkYRgGne0doIdKAezZXc8Ts2bzg4u/T5SicPaZZ/H/Lr+Cn/10NL8Y+3NuvWUc4355CzdcP4arfnwll106iosvvliOESKVssPhkIKbsWPH9pppYd68eWRmZspjMx9nfHx8j34ZFRXFqaeeKrezZ88eMjMzsdlssi9Hnq/YhmiL3Nxc+c5kGIbMjADdPps1a9Zw3nnnyWfFKaecwsSJE/nNb37D2WefzdChQ9mwYQOAFBOYM16++uqrKIrCrFmzMAwDVVWZOXOmTOs/efJkZs+ezUsvvcTcuXO59dZbGT58OLGxsYwaNYrly5fL7/UFSwBgYWHRHyIz2oifl112GTk5OURFRbF+/Xr5uXn+cSi4XC4mT55MdnZ22Hwkcu4vxvPe3gvEXMRutzNv3rw+79ssWBDjq9/v58wzz6S8vJyqqio5zzeXEzoYijmlkHnQFZMRMdEQKuQ777yTa665hh/96Edcd911TJ48mXfffZcdO3aE1b6KrAUk6kcZhsF7773HtGnT+NnPfsqloy5mwcJn8fl87NvXTMAf2n9IRBDEwAd48autgJfrrv8ZGRkZJCak8dHHn2MAQbRQnXS/h05XB16/B8PQAJ2mpkZU1S8zAIif06f8geryMmIUhaK8fArzC6gsr5D1g0T9G+hKJwUEdY1AUJUlAIJWNXSLfnPwDABCAPDb396LoihcccWPaGlpCt3owa7VDDA0HdUfIGgqrSFEADoajz72CLl5BXz+xVdogMvn7+V4LCy+PZhrsEHI2BIfH89FF11kOaEsLI5xRJpDM9XV1cTExJCUlER6enq/FrvdLpeMjAwSEhI444wzAP6nqUSPVsRLiNng3t7eTnV1NUlJSSxZskR+Lt41zKlqLSwsLCwsvg76KwBobW1lw4YNfPXVV2zevJndu3dTV1fH2rVrWbt2LVu3bqWuro7Vq1ezYcMGGUF5KKxbt461a9eyevVqdu7cyapVq9ixYwfbt29ny5YtbNiwgbVr11JbW8vevXvZtm0bGzduZM2aNTIiu6Ojo0fUk0hjLQQJH374IY8//jg///nPGTNmDGPGjGH27Nns3bs3zKEjopHM0aNiWwKXyyXTrre0tLBnzx7++te/Mm7cOMaOHctFF13EzTffzGeffQaE7H1Op1M6MFVVxel0SmehObrYvG9x/OvWreOJJ57gJz/5CT/96U+56aabuOqqq5g0aRLbt2+X526u2QohY6mu69Lhun37drZv386mTZv47LPP2LZtG1999RWbNm1ix44d7Nq1i82bN7Nx40a2bNnCvn37cLvdUvCwatUqVqxYwbZt21i1ahXNzc2sWLGCTZs2sWnTJurr69m8eTObN29mx44dMoLa7/eHGWzF9kSK7paWFt566y0mTpzILbfcwqhRo/jFL34ho9Xb2tp6XF/RR1VVZfXq1WzZsoW6ujpqa2tZt24dO3bsYMeOHWzbto1NmzaxfPlyNm3axOeffy4diuaUteZ5tMiyIJzKLS0tfPDBB8ycOZPbbruN0aNHM2rUKG6++WYef/xxli5dKu3D4hqIgDOz87+zszOsxOyaNWtYsWIF9fX1bNiwgS1btrB9+3Z5zHV1dWzcuJENGzawYcMGGbEujr+5uZmdO3eyceNGli9fzqpVq9i9ezeNjY2sXr2aNWvWsHnzZhobG9m5cydr166Vwv//VQYqc51fcwYMCGXhveWWW+js7Ay7ByMzFop7DUJz7S1btvDTn/5URt+LdQSREYX7o62trUdfEGKD1157jUmTJnHTTTdx7bXXcs0113DjjTcyd+5ceR3E9zRNC0ujLLKnrV69mhUrVrBr1y55/Wpra1m5ciVr1qxh27ZtrFmzhtWrV/PRRx/JqHIpMOqyey5e9ConDh1GQryNK//fj9m4fgMelxuv24PP4yXg8xMMqGhqkGBAJRhQUf0Bdu7cSTAYZPfu3Xz00UdMmDCBvLw8KQKIi4vj6aeflveS2G8gEGDlypWsUDoWpQAAIABJREFUW7eOdevWsXv3brZs2cK6deuoq6ujrq6O7du3U1dXx65du1ixYgUNDQ24XC55nffu3cv69evZtWsX69evp6GhgR07drBy5UpWrlwpx/Ha2lp27tzJl19+SSAQCBsH3W63vJ90XcftdqOqKrW1tdx9993ceOON3HjjjVx88cXMmTOHPXv24Ha7w/q2+L5hGHzxxRdMnjw5LJOLruvs3r2bF198kZtuuokxY8YwduxYLr/8ciZMmMCCBQuora2ls7PzkJ32lgDAwsKiP0TamCA0rixevFgKoR566CH5P5HBqi+43e6w8Xbfvn2sWbOG+vp6Nm3axK5du+T8oqmpiS+//JLa2lo5v9u4cSObNm1i3759YXOv5cuX9ykDj7kEgLnMlqZp1NbWyiwDt99+u/y/mDP3JcuBIiYcYvJuVu4KJan5Jcnr9R5UuaxpGh6PB03TwiZ9Qt0q8HhcQJD2DtMFMb1n6UYA3fACXoJ6B52uRqoHlFNZWUllxSD2NDTj8XlxBzz4Al6CuoqBjmaEvKKidjro6JqKoQfp7GjD7/OAoVO/Yzvvv/0OD06dxnXXXMugAQOJiYpmYPUAqbwUNdsMIBBU0TGkAMCr9k3BbWGxfw7s/A8EfBiGhtPZwfDhJ5Kfn8uoUZd03Tvhqwd8fjQ1KD9Qg14gSEtrI52uDuY9PR9bYgJuj4/mdqeVwcLiW4/5pfbZZ5/FZrNx0kkn4XQ6rTTeFhbHAeYyVJ2dnZx44olkZGTgcDgoKirq15Kfn09JSQnZ2dmUlZWRlZXFJZdc8g2f8dGFeRwVxqZ169bJ6CtRkxfCDZkWFhYWFhZfF/0tASAyXAonsqg7H+m8MJe+CQaD7Nq1q8/HaK5PLqLrhdFT2NfMRj+xH13XaW1tDSvFCaH5UGTaVLMTSHze2tpKa2urrE8vIk51XWfv3r3yXARmJ3akMdLpdMrzEBlCxfEK26PZ0Gp2UJoNmebzEDXjzRH9ZtujQES6mrcTDAbp7Ozsta64ed/CGWXG7HwVjiqzoTayjJH5+6J2t1nAIMoaCPx+/wHfPUXbisjd3bt3h9V+jYwOFufj9Xpl+4katOZrIY5XtIP5b7Hf3oQGApfLJR2H4nginZXmto2MZheO5cjjjvzbHJ1v7g/Nzc3y2Lxerzx20UcELS0tYddItJfIiAE96/x+XZgD7sz0lmkDwoUh5p+RAXeiPr152+Yx7FCjIEXGEHGPm7N9iP2ZtynGk95SEZvLRERuA+jhSBb/N4uExbgXDKgs/e8HFBUUEhcTy+U//BF6UKO1uaW7LIBuYGg6elDD0PSwcgFiu06nU/aR5cuXc/nll1NUVERGRgY5OTlh95PP5wvLWiEc6h6PJ8yP4nK5pKDJHGQJ3eOE+bqJ7UW+/zidzrDyEGIb5j4aef3F8ZrHGYH5nt5fnxBjXG/9pKOjQ2ab2N865nrbB8ISAFhYWBwuohwRhD/znU4nHR0dJCQkUFJSwmmnnRb2bD/U8UWMdWYRmHi2iXEewstPif2YSxOYnyN93a+maT3K9xiGwZ///GcSExOJjY1l6dKlPZ4bfc4AAOETtUgjnPmFxufzyZcM8Vl7e3vY/yMnT+LhIw6oubm5KzKrk31Ne4CgfODoGrS1dXTtP4hueDHwoONk2WfvY0uIoaysjGuuvgGfX+9yxuuoWncZgD176xHOU/HT7epE7fobdFSvB09nBxjQ1tRMwOeno62d55/7MxVl5bKez3//+99QJgS65wxev68rhbr1gLLoLwcWALjdTkCnra2FQYMGUF5eSkmJg8bGBgKBAEGvihHQCSW8MLomtaHv+nwe+bsB3HLrOL537jkEgioaWAIAC4suhFrQZrPR0NBwSCpBCwuLoxMRuWSePNfW1spovK1bt/ZrWbt2Lbt27WLr1q00NDSwbt06vvrqK6BnlM63EdHu+/bt62FU/vLLL1EUhZKSEv75z3/i8/nkOn2t0WZhYWFhYXE4HEwAEGkkNDt8zQ6p3lBVNSwdu3BU9+bwPtA2zAZEM2YDnzB+mh2bkXXuI52MHo8n7DMxXzEMo1fjYXt7e9j5dnR09HAs9ea8NosAoNu57Pf75f7FtoXjDLodS16vN2w+4HQ6e3U6mY9ZBCuZzzEQCIQ5H83nbr7OmqbR0dEh20846SPnc+3t7fIaiuMT52gYBm1tbdJhKoQhZiKvR2S5VHGOGzdulMdofi+NFBeYa26bndki8Cqy/5gd6cKxLz4z9+/I62zeh8j0cCAHtsgUYN6vaEsRWd1bNJzI+iCcpeZ71efzybYXAg/zcYk0ueaI7cjjEM5Zc5/2+Xy4XK4wwcDXSWRNYnM2APP/hUN9f/d0ZP8z9wW/3y/t9WZHRV8w31PmjB9A2LhmXt9cVtHc551Op9yv8CEIR01vggtxTiKDgDkYUZz3zu07OPOMkdQMGkyGPV0a6YUAQPUH0INauNPfAD2ohbIAdO038n3D5/MxaNAghg8fjqIovPLKK/JYfD6f7DORfVfTtP1GdkaO9x6PR/ZlCDntDcMIewcy/y22397eLttEiA/E/dTU1NRr+QrxmRBuCUGH+I44H9EO4rqJa9vb+AehvhB5r3i93j6/+1oCAAsLi/7Q23xSPGfGjx9PWloa6enpLFu2LEwc25fxpbd0+mIb4rPIMlXieIRwy3xc5vX7MkZGClDNArhLLrmExMREzjvvPBmovj8R2f5QoLuxhJIZQpN7oegUG4xUJRqGIR8skQ8qswJRTMYhXDURck6KVOXQ3t4pVXnBYBDdCGDgQzNcGLhY8MJclCiFrKwsHp7xOLoBfjWAhk5QV/H43Hzw4X+ZMu0PNDY2ADqapgI6X37xGVdc/kN+NvonaMEAGDoYOp5Op1QJurp+/8uLL2G321EUhffeew8At9dDUNcwgPbODsuBanGEOHgJALfbiaapDBkymMLCfFJSkpg+fRqq6u8xsQ0GVPxe8bKkyxfQoK7x8bJP2L2nnqCu4fR68GsHT0FiYXE8YxgG77//vqzj9uGHH4a94FlYWBy7mO9jUe8WQpPwvqbB7AuRmbGO5LaPZSIjX4RxVrwPLFiwQNZjXbNmDdDTmGlhYWFhYXGkOZgAIDLyRjh4zJ8FAoEemQAi07mLd4rIWucHw+wYizRYRkbZi8/MBklz5Ki5Rqp53+aoJPP6wmkK3fMZYfeLdJiZj9PslFRVdb+O9t4ik81R4ZFOa7MDUZy32K9ZeBAMBmWbiHZwuVxh2QmEI1W0idk5aj6mSMekruth2RyEEz4yIKq38xXbN5eWM5egMztOzf8X52o+LrNDXOyvNwOw2WlqFgyIffbmeBXO5kAgEHadRR8295HIczRfy0jRg2i7yPXMRAoCeqvvaxYcmG3XZiO4+fjMv4t2jIyeNjuuI9f9Oom8h837FOcmzt0cmBcZhd/bWGCO0hbnZo5O7CvmPuD3+3vM6c0ZSSKdHdDT8d1b3xPfaWlpCXtX6C2q0ezEeeffbxOlKKQmp/DgH6fjcbnxe31ggNvpkpH/mhqUix7UZLCUuO7mcUVVVVpaWvjHP/6BoigUFhZy5513hh27OFeBeXwRn/eW+Uy0X29CH3F+ok3N7WL2zUTSWwkWMcaLa7c/UUJvUf/mjCEQnjLbnGFDYBY9mZ1a5rbaH5YAwMLCoj+YM3yKzEYQGmtra2tRlJDP+MEHHwS6x9hDsfGbhaRm0aA5GxP09JOLfXV2dsp1D9U2GFl6R9d1NmzYQHR0NElJSTz55JNhtjJzFrCDoRzSkRxxwgUAQFgJgNDnfkQJgJtvuY70jBSioqJYuWIdHZ1eNEOXAgADnSt+fDnXjbkWswN1+fIvscXH4igqICU5kTf+9RqdrS1g6AR9fjApBJ0dndRt2kxmZiaJiYns3bs3pFDUgt0ZhTAIYlgCgCOAWaVr/sw8ARAvTJFKY3EjiYmiePEWi1nFfLhL5CREfBZpFBCTusgIAfM6gvAX34MLAEBHVf1ccMF5ZGamc8IJNSiKwsSJv2HFF5/jd3tkLSzRScWk3O3uUtFqQSlgcXncqIZu9V+L4x7xomI2upjHmx07dhAdHY3NZuPee+8FCFOpW1j0B7Nxpjc1aV8R6RcF5j7cm3HAKl/RjTBYRhrzI9fpLdpPRBJBeJv/L4yD3wS9pbAViNq84vMj0cd0Xef6668nJSUFu90u21W81IloxMjrZhmHLCwsLCz6Q19LAFhYWFhYHF1Mvu9+sjIyiVIU3vq/N6XjX0T5C7uo3+tD9Qe6s6Sa7KSRGSAg9PnKlSvJysrCbrdz1VVXAeE2ob5kcLE4MJYAwMLC4uvkkksuoaKiguzsbCmWFKWxjnaEjc1sDwP4zW9+Q1JSEoMGDWL79u091u/r+HnUCwBc7hZUrZNd9Rs5+7snU1lVSmFhIXWbd4TS/xs6fj1Ae2cbn3z6MfEJcezcvYNQ9LMXVfWzZMnfiVIUigrziVIUln/1BRg6XmennAj4PF6cHaG/F738CnFxcVx77bV4PJ6wEgCaoRMIqgR0jSBHfwc6FhAO/EhlzYMPPsjUqVN55JFHmDp1Kg8++CBTpkzh3nvvZebMmcyYMYNHHnlELg8//DAPPfQQf/zjH5k+fbr8fn+XadOmyW3+8Y9/ZMqUKfzud79j8uTJTJ8+nRkzZvDQQw8xbdo0pk2bxoMPPsj06dOZMmUKEydO5He/+x333Xcfu3btkufYHSVwYMd/Z2c7wWAA0HnxxYXk5eWQm5vNSScNIypKIS5K4fSTR3DzjTfx1JNzWLl8BZ3tHQQCZoWpR/ZfX8CPgY436MOn9S0NmIXFsUpkJIC5Zp6u65x++unk5ORw4oknUldXB4TGnkOt1WNh0RtC+W9W0be1tbF27do+TdAiyy0JhOBNOLZFukXxXNmf4v/bSGSqXrOBobdrEPli4PV6ZWSS4HgSWAhhpbktdF2nrq6OpqamsAgz6E5tdqRobGwkOzuboqIirr76arm/yKgsEdESmbbVwsLCwsLiULEEABYWFhbHJrMee5z42DjsqWm8+sqikJPfgOZ9TTL63+zwNzQdr9uD2+nC5+l25jc0NACh54B4/1i5ciWKolBWVsaNN94o3/9EFmOL/mMJACwsLL5OPv74Y6KiokhISGDJkiXy82PFhrRr1y6g+x2lvr6e/Px8kpOTmTp1apg9zJwBoS8c9QKAgOoEvHS6GhgytAJHcT4XXHABugY+f6i2uYZOh7OdqgGVPPf8s/gCXjo72wEdw9B49dVXSE5K4PzzzuHVRS/TuHcPBFUwdDR/ICwDQFPjPs447XRyc3P54IMPwg7LIBRJLQQAVgT1kSNSTdnc3ExGRgYpKSmkpKSQnJyM3W4nNzeXzMxMmf4iJiaGuLg4bDYbsbGxcjKhKArR0dHEx8f3a0lMTCQuLo7o6GhiYmJITEzEbreTmZlJZmYmsbGx2O12qRRNTU0lIyODtLQ04uLiKCgoICsri5ycHBYsWAAQlrqrL9H/gYCPQMCHqvq5665foygKxcVFDBo0gIqSIsocBeTn5ZCZnkFyYhJ5OfmMuf4XvPvOf9m1MzSxDaih6H+Pz4tBKGuGN3jwFE0WFsc65mhd4SQFuPPOO4mOjiYxMZHFixeHfae3GpMWFv1lz549DB8+nPPPP/+QIwgi6x9Cz7qn5s8PJ9PA8UZvk3wxSe5NKCEc/ZFpcqE7M8jhpBA72gkEAmEpdevr61myZAmKovDuu+/KyBeRdUmkOj5SiH0lJyfz1FNPEQgEpChUEAwG8Xq9lmHIwsLCwqLfWAIACwsLi2OT15b8k5ioaOJj4xhz3fVggMflDnP6a2oQj8sdcvhHlEw1p6w3v4/7fD7uv/9+CgoKiIqK4uWXXwa6y1h4PJ6wjHwWh4clALCwsPi6ufnmm0lOTiY7O1t+dizY+M0lnYTd8c477yQuLo4TTzyRHTt2ABx2cNJRLwAwlwC48ieXokQpnHXWWSE1H9DU0syOPTs5acSJjP3FDTjdnRhdjtNgMIDH48Lv9xIXG839901EOla7BADmDABup4sbrh9DlKLwxhtvhEWOydT/eqieugaWAOAIEFnryzAM/H4/7e3tnHrqqeTn5zN48GCGDBlCbm4uubm5VFVVcc4553DKKacwcuRIzjzzTEaOHMlpp53GaaedxsiRIznrrLM488wzOeOMM/q1nHfeeYwcOZJhw4ZRXV1NSUkJWVlZsmZ4cXExJSUllJaWUlpaSnl5OZWVlVRVVVFVVUVlZSU2m42srCwuvPBCILK27YGd/z6fh0gxwEMPTSc7O5PoaIUyRwHFBbmk21Oxp6VQWV7FiUNPIi+3CEWJ5dprbmDXzoawDBYdznY0gmhYUaIW3w7Ey5r4+fnnnxMdHU1ubi6jRo2S/xMpp8EqAWDRf4RjFWDZsmVERUUxYsSIw3q5FU5pcz1QMT8xfy44Fia4XzeRIghzeZ7IMj29UV9fH1ab1VxrsS81Do92zNklIo0ue/fuZfz48aSnp/PSSy/1KIVwJAQm5npop59+Ovn5+dhsNjZv3iz3ZRmCLCwsLCyONJYAwMLCwuLYxOv2cN455zLshKFEKQr/+NvfCQZUggEV1R/o1emv+gMEfH4CPr8c98W7ssfjIRAI0NbWRkJCAomJiRQXF1NfXw9YpceONJYAwMLC4uvE5XLR3NxMTk4ONpuNp556KszudDRjfi8BqKuro7q6mrS0NJ5//nm5XmSQY29ZY3vjqBcA+PwdeHwtgJtln71PVXUZ8fHxPDbzT9Ru2sbfl/yDuKR4br19HAY6na4O/KoPUTfd7/eiaSpb6jaRbk+lrLSYOU8+wbtvvckXyz5hzYqVfPDe+zw0/UGiFIUhg2tYtWIl+/btCx1BV0pdHSNssQQARx4R8SVEAR999BGZmZk4HA7KysooLy+nurqa73znO2zatCnsuyI6LLI2soj0O9xFVVX8fj8ej4f29nZ27drFl19+yZIlS3jmmWe4/PLLGThwIHFxcSQkJFBcXEx1dTXl5eUUFRUxfPhwHA4HQ4cOJTo6mnXr1skow76UABBZLECnqalRfrZ5cy1vv/0W146+ilOHDyMvN5vcnCyKChwUF5VQXlbNwAFDiItN5IeX/T+aW9pMc2CdvS0NlgDA4rhHPDjFmCIe/EOHDqWwsBCbzUZdXV3Yw9LpdJpKdFhYHD7CWbxlyxYURSE/Pz+srvzBME/sIgUpZrGK+H8wGJR93uq/4W0gSiYIpLizS3To9/vD1jf/3tjYKH8XKSCPhwgQwzDC5ky9CSJGjx5NTEwMr7/+OpqmYRhGhIjx8DELQD/++GMSEhJQFIVf/vKXch2RDcD8HVHCxcLCwsLC4nCwBAAWFhYWxyZ6UKNu02bKS8uwp6ZRXVnF6pWrwqL/9aBGMKDi9/oIBtQwIYDA/C63atUqmc01Ly+PpUuXAt0igcjMcBaHjyUAsLCw+LowDEMG6sydO5fY2FgqKytxOp3HjH3UnJnm7rvvRlEUxowZg8fjkdlMzdk4xfOpLzbmo14AAH40w4XL0wh4WbnqS+677z7KSqtJTLJzznnnsrZ2Hb5AKLW5qgVkBgBRBsDrdeNydqAFAyx4/jlu/MVYzjztVDLTUsmyp3PKScP59R2/YvmXX3VPHDQNj8cjo750DJkFQDN0/FoQX/D4qQP7TSImVmZnh7g558yZQ0xMDKmpqZSUlJCXl0dycjJjxowJmyAII7650x9JA73ZMK7rOj6fT96YHR0dLFu2jD/84Q+MGDGCmJgYbDYbFRUVFBYWUlZWxgknnICiKEydOjXiXA8tA4DH46KlpYlAwBcSBhhB/O5Odmzfytv/fpPxt02gZtAQsjLzyMkuoLSkkphoGzMeftTUf0PR/96g2LaFxfGLGBsETz/9NIqikJOTw1VXXQV0GwCPZHSrhQXAV199hd1uJyEhgaamJiDUvw7FkLB161amTJnCpEmTGDNmDFdffTWjRo1i5MiRnHDCCVRUVBAbG0taWhqXXXaZFDB+2zGn649MKe/z+Zg7dy7Tp0/njjvu4Nprr+Wyyy7ju9/9LieccAKlpaWynFB6ejqrV68Gup3Wx0sJAJ/PJ9tFCEjcbjder5e9e/diGAYXXXQRiqLw9ttvSxHVkRojzS9j5557LiUlJdhstrB6bZHCDGt8trCwsLDoD5YAwMLCwuIYxQBnRyd7dtdz8vARJNoSiFIU/vC739Pa3IJuKu8bufg83WXNgsEg9fX1zJ49m5SUFBRFYdCgQbz55ptyV+K9z+124/F4jpv3v28SSwBgYWHxdSHsREIEcOmll6IoCjNmzJBjzYGWbxrzcbz11lsoisLgwYPZvn070DPACQ5NoHbUCwACqhPw4vE1EdSd+AMhx2lnh7fbodkVl9/hbMdAx6/6pONUVf0Eg4HQ74FQZoDmpkYwdNqa9uFq75ATAo/Ljd/rw+fx9qhxGhb9bwkAjhiGYUhHurm93W63dNpdddVVFBYWMnDgQGpqaqRh/vHHH6ejo2O/N6p5InG4i0Ck/o28uVwuV9hN6PF4eOedd/jpT39KbGws+fn5FBUVUVpaSn5+PkOHDsXr9ZqO+cACAKezA9GPI8UAuh6keW89GEFAx+N2yr78yst/IyM9lyE1J+EoKqNmyFBcHjcG0NLWjEYQd8CFJQCwON5pbW2V921LSwslJSVUVVURHR1NbW0t7e3tQEgxZ45sPZI1ri2+nTidTsaMGYOiKCxcuFA+Q/rat3w+H+3t7cyfPx+73U5WVhYpKSkUFhbicDgoLCwkOzubmpoaCgoKyM7O5pJLLqGtrc2KVCDcWa+qaljJhKamJlJSUkhKSiI5OZmsrCxKSkqorq5mwIABVFVVcfLJJ5OTk0N0dDQbNmzA6/XS2dl5VLwcHAkMw5CZIw7EqlWrKC8vx263U1tbi9vtPqIlAIQg8osvviAmJobS0lKGDBlCU1NT+DzcMgpZWFhYWBwBLAGAhYWFxbGJSPEfDKhoapC/vbqYIYNrSIi3EaUo/GrCHSx6+RX27K4nGFDxebx4XO5Q9L8eeidctGgRDz/8MKmpqSiKgs1mY/r06fL9Zs+ePZYt6GvCEgBYWFh8nYhxRNiYampqUBSFzz777KgXAEDIx9jQ0EBNTQ3Jycn83//9X6/rifNTVbXPY+fRJwAII4hBALenFQMf4Mfj7UqH0OXoNACXx4mOFpYBwOfzoGkquh5yjgb8XikACFsMcDtdchKBEUorZMYwQk7/oK6hGXqYGMCi/5iVK+aar4FAgI6ODjweD1VVVeTm5pKfn8+AAQPIz88nJSWFDRs2SAceHFrn7wu9bU+k3YhMvdHZ2SmdLm1tbXz44YeMGDGCoqIiCgsLGTRokBx4xPn1pQSAEAGIDADC+R8qDRAEQwVDQ1ODBHx+MKC5qZ233nwXRYll8KChFBQ62LJtqywB0N7ZgkEQSwBgcTwTmSJ91qxZREdHU1xczNlnny3/L9J6Q+iePx7qe1t889xzzz0oisKECRNkXxPPk76WAYBQ+ipFUUhISCApKQmHw0F1dTU1NTVkZWUxcOBAHA4HBQUF/OxnP/tazuVYZH/OY7fbzY4dO1AUhaioKBISEsjIyCA/P5/iIgcljmKKixwUFRRSXVmFo7AoQrjHMVNH7ECIlP4QmueKrFcQyqDk8/nkeS5evBhFURg2bJj8bn/pbW5VXV1NSUkJsbGxvP/++2FZM8zjcmRJDAsLCwsLi75iCQAsLCwsjlFMEf2aGkT1B/B5vLz91r+569d3kpqcgi0unihFISkhkcryCk4adiKOwiKiurK72Ww2FEVh7NixvPLKKz0i/QXCtitsRdb7R/+xBAAWFhZfF8JeZB676+rqiIuLQ1GUo14AIJ5FY8eORVEUXn75Zfn8Ef8TP82B1G63u0/j5zcsAIBuR+eB/he5HEG++WtscRB0Xef1118nPj6egoICKioqKCoqIisri7KSUjQ1KEUcUsBhgKHp+03/JJevGafTycSJE1EUBYfDgcPh4JZbbgG6SxSYjfDmqE2Px8Nzzz3Hpk2b5E1uFjuEDPB6l8gFuU2Xy4WqamiawemnjyQ7O5fi4mL27NljGhR0/H5rAmtx/OPz+eRDUWTiUBSF995775s+NIvjAE3TpDFAjOler5d//OMf8pnV2NgoS8cI+jJB8/v9sub6unXrWLlyJcuWLWPChAnk5uZSWlxCVUUlBXn5DKiqJtGWwNtv/fvAzz/d6F6+5Wzdvo2lH33IJ598wquvvkpudg7FBYUMrh5AcUEhZY5icjIy+fX4CaG6kV3iIAPw+r9dIiG/3891111HUlISEyZMkBkVDMOQpbKgO1tSXxFzHvH9F154geTkZHJzczn55JOB7pcbcS/8f/bOO06q6u7/d/tsm62zMzu9bF+aJaLGRDGKxhYVEgtK0CjGJJZEE32Mz5MYkxhj++WJNah5FBXF2DCCBTUWIGLDCgbpnYVt09t9//4Yztk7syCLrApy3q/XvFh279x77rnnnls+3+/nK35WKBQKheLzEgwGSSaTxONxtm7ditVqpbGxkccff5xwOJx1LUskEjLwX11/FAqFYs8lFouxatUq5s+fz5w5c5g9ezZz5szh6aef5rHHHmPhwoV8+umnsgytYngR7ztyy/N2d3fLnwsKCmRinHF55bqgUCh2B5Gkkhus9dprr2EymWhrawMG3iXFYrEvPbEnFott18pf8Ic//IGKigquuOKKIbl17gp7QACAQrFjjA/aV199NSaTiaamJhobG2lubsZqaeC07/8gS+CIRaKgI7Phv8oAAIBNmzbx0EMPUVxcTEFBAYWFhXKfxE2ReLEAmQkhnU6zceNGysrKmDNnTpY1ubhZFTdLwqXAeBObSCQIBoMcd9xxjBo1CovFIl+iRyIRleGs2CcwnhMPPPAA1dXVNDQ0YDabpWWOQvGdP+dNAAAgAElEQVR5EfN4MBiUYqjIWG5sbETTNP7v//4va74V39mVDOp0Ok0ikZAC9Pnnn4/H48HtdNHe2obd1kiTP4DT7uD9Re9lxP+0rgIAdoIOROOZG/CZM2fSaGmgyeujzd/EiJY2mtxeSguKuO+ee+X9RDqd/jJvH75ShDgv5sp///vfFBUVUVVVxcsvvwxk7tHEvYVwRhoqRhFFBAC8++67BAIBnE4n9fX1zJkzR54/RvFfCTAKhUKh2B1yryMWi4WqqipOO+00pkyZwuTJk/nBD37A97//fVleSaFQKBR7PrquSzcz8Z5VPEdHo9FBpV53pUSfYmik02mmTJnCmWeeyemnn86kSZM466yzmDp1KjU1NRQVFbFq1SoAdX1VKBTDgpjbxdyfqwfk5eUxbtw4YPC882VoZLku4uJZROh/jzzyCJqm8bOf/Uz+Xvw7HPqFCgBQ7NGIaJxUKkU4HOa4447DarVis9lob2/H43KTp2nMePAhYpEo8WiMdDIla0N91QEAxhfjjz/+OFVVVeTn5/P888/T29ubFQAgXraL37/xxhtUVlayaNEiuT4hNBknAjGBiEkhEonIie7YY4+loaGBY489Vi4XDAZ3KUNPodibEefIYYcdRl1dHRaLhcmTJ3/VzVJ8Dci1kI1Go8RiMS677DI0TWPEiBE7/M5QbjDFMsYI1nA4zJgxY7BarbidLjrbO3A5nDjtDsYdfgR9Pb0qAGCIGG8DLrnkEsqKSwh4vDgsVlp9AZrcXkryCli6eAndW7ZmrCZTKeLJxD4RAAAZ0V3cp8TjcX7yk5+gaRpHHHGEfGgS92nGzI2hvEQzjmtx/5NMJjn11FOxWCyUlZVx5plnymWM94MKhUKhUOwuuq7T1dXFmjVrKCsrk7bE4lNYWIjJZCIvL49ly5YRDofls71CoVAo9jyMJWV3hjHIXtnPDw/ivX1/fz91dXXk5+ejaRrFxcXy2moymSgoKOC5554jkUjQ1dUF7FqJRIVCochFvD81lhU3ZvgvXboUTdOYMGGCfP8kHIO/jHdMxjnOWHq4t7eX+fPnU1VVxZ133imXEcllw4UKAFDs8aTTacLhMOl0muXLl+N0OjGbzbS1teFxuWlpaqbMVMo7b71Nf28f6LBx/YY9IgAAMiczZF6O/+IXv0DTNI4//visbLlUKiVF+d7eXnRd56abbqKkpISuri4p+osMOOOL897e3qzSAbFYjHg8zvr163E6nWiaxjPPPCMtewXqJbpiX2DTpk0sXryY0tJSPB4P+fn5LFiwQD1gKIYF45za39/PqlWr0DSNuro6Hnrooax51li/aShBWNFodNCc/dZbb1FaWorP55PXP7/XR2V5BZf/4rJBpXBUAMCOSaYzYn44HKazs5NacxWjOjrxOVw0ub147U46mlogrRPs6884C8XjhKMRUvq+8ZJIlB0S9vtbt27FYrGgaRr33HNP1vg0PlwNZX4VDzzJZFJ+AKZNm0ZZWRkulwur1crmzZuzHI8UCoVCodgdRGYoDASszZgxgzlz5vDAAw9w//33M23aNJ555hkefPBBHnjgAXk/pxxoFAqFYs/FKOSLd6yRSIR4PC6dABKJxJcm+OzLTJ8+nfvuu49HHnmEhx56iBkzZvDkk0/y1FNPMXPmTFavXi2XjUQiWeVuFQqFYlfJLS8JmXe08XicRCJBKBRi69at/OMf/yCZTNLf3/+lur8Y32WJMmSQcdx85513ePrppwFYs2aNXFYk9w5HO1UAgGKPRtf1QVYX99xzD0VFRQQCATwuNy6HE1uDlZGdI0CHUH8QdGTG3lcZAJBKpQbVlzr88MPRNI2enh50XZfCfDweR9d1wuEwyWSSY445Bk3TWLt2LVu2bJGCkRD4jYjAAGNf3XHHHWiaRmdnJ5C5qTJOOLnrUCi+jkQiEW655RYqKytxuVxomkY6nVZR3ophQbw8CIVCpNNpLrjgAqqqqmhubiadTmc9yIrArV0Ze+JGTwQD3HnnneTl5dHa2orP48XtdNEcaMJUXMIjMx6W17ZkPKECAHaC6JJly5ZRUlKCz+WmvbkFn8OFp9GBtaaOC390flb/JRKJfaYEwPYy9AFOPvlkqqqqGDVqFIsXL5Z//7z104zWZwDr1q3DYrHQ0tJCfn4+d999t9x+X1+felmnUCgUit1GXHNEQL54Ts59PhbP1j09PVI4UigUCsXXC/FeVrF79Pb2EovFCIVCOwyYE+9H4vG4TJBQ76YVCsVwIUoAQOZ96lBcXoxlt78ojKJ/LrkOsbFYjC1btgxrALIKAFDs0QjRWzx8ixuECy64AJPJhNPuIODz09rcQkVZOT/58YWgs8eUABB0d3ej6zqpVIqFCxdSUlLCjTfeCAyOAoLMC3C73Y7FYuGKK67IWpd4yW4MjtB1nWg0KieHt99+G4vFgsVi4bnnngMyfSm2peqfK/YFxPnU3t6O1WrFarXy3e9+VwlIimHBeFMZi8VYs2YN+fn5VFRU8Nvf/jbrJk3c6O1q+RXx8Cz+nTp1KhUVFbjdbnweL3ZbI36vD5fDyeKPPpaZ/yoAYOdE4zF0YObMmRQWFtLs8+O2O/A0Omj2+CgrLObBv99H3HA/EY/H95kAADG+jdb+W7du5fXXX6e2thZN07j77rvlsmJe3ZUAK+PDWDKZlPcmRxxxBA6Hg+rqalmnTRCLxVQGpkKhUCh2C+MztHDrE4jg/c2bN8vn7mQyqYKHFQqFYg9HlH80PpuI3+cK/OKZxfhR7B7G5zqBeIcvgsuNz3GhUEgmOqgytQqFYncwzj+RSGTQnC5KWAKsXLkSyAj/uc8BXySxWEy2S9f1QZn94hnEqNmJMim7iwoAUOzRiJsDXdfZtGkTkDlhwuEwhx12GB6XG6/bQ2d7Bw31FvK1PB579B90bdq8RwQACLspgbCe+t73voemaaRSqe2+yJ4zZw4mkwmr1Uptba20AjFGBQWDQZl1aryZfe+99zjhhBPQNI1bbrkl63tGixGFYl/g448/RtM03G43FRUVPPTQQ4MeCBWKz4N4SBU3cP/zP/8ja8Zu3bo1a54VPxsDtXaVnp4exo4di8fjkSUA3E4Xdlsjxxw9nnAwhJ5KqxIAQySezGTzn3POOVitVtqbW+hsbaPF6+eAkaOpKi3n40XvE+kPoqfS6KnMCyUdSO8TIQCZ+wwxXoVNGsDo0aMxmUwcf/zxrF27Vi4vHmCGksWRTqflA5AoASDuZf74xz9SUVGBx+OhvLycFStWZH1PoVAoFIrdwXgvJq490WiUeDwur2W5zjbG0gEKhUKh2HtIpVLy+USUVxXPH+q90BeD6HPjtTQUCmUFfRtRLgAKhWJ3EOVdBEanSfFzMBjMCgQQ9/Vf1nUgmUzKtsTjcUKhkCxRkEqlCIfD8rkkEonIduU6i38eVACAYo9G3JiJF77xeFz+PH/+fGqqqnHaHdhtjbQ0NVNtrsLn8bJ+7TpZCmBPcACA7Oy2F198kalTp9Ld3S0FfCEmxeNxrr/+ejRNY+zYsUyZMoWSkhKeeuopEonEIGsSo6g/e/ZsAoEAmqYxZ84cgKx1Gy191Y2uYl/gr3/9K0VFRYwaNQpN03j77bcB9YChGB7Ey+BoNEp9fT02m41TTz1V3kgKizvhEgBDr2NuLPsC8MYbb5Cfn4/D4cDlcuFyOGnyB6irqeXCC34sr2siCEAFAHw2iVRGzLfZbNhsNlyNdkZ3jqCxzkLA5cHncBEPhkGHVCIp7xlS+r4i/2eLH7quS7eh66+/nvr6ejRN49lnn5XLiHucoYj0ovwRZM/Hvb29vPrqq+Tn5+PxeNA0jZkzZwID58KXWatNoVAoFF8vxAtCY7ahuH6J64vxeTsWi6nnZoVCodjLSKVSg9xbdvSMohwAhg+jw5u4tm4v+7+vr0/+TvysUCgUu4NRNDcmFOe+d0omk1JUF+XAvgx29C44lUrJvxmvR9tzr/m8qAAAxV7N9PvuJ0/TcDtddLS1E/D5KTOVMv6oo7MED2GHHOzrzxZJvmJyb0i7urqIRCIcffTRlJaWsmjRIkKhELfccguFhYXYbDauueYa5s6dy/vvv88HH3zAhx9+yO23387IkSMpLy9n0qRJUuRUDC+ijIO4eIifjTeyyWQyKzvE+HOudYtxIhe2MyLzxGgHk1srxnhBCIfD8iWVURQRUWSi3cZxZnzhJbYrLnjGQBRBKpUiFotl/c1of268WMbj8azvGqN7RTSbeBgbDsS+iYt2rqPGMcccQ0NDA06nE5PJhK7ru1SrWuy7wJgRC5n9zbUrS6VSsr/7+vqyxosYD+JmQ3w3Go3KYyHKdRjHh+hTkTUryH1Q/Tz2RcZ1iGOTW5c715ZNHNfcY21sp9h/I8ZoRiO52VZCOE+n01ljSPR9rouDOE7id8asZUE0GpXtSiQS0kEltw1GW6adIY7nAw88gMlkQtM07rnnnmEJMBHtEdHz//u//0t5eTmtra04HA48LjdtLa0U5hfw5ONPyOuaFKu3fXq7e0CHWCRKOpkinUxlrolk94+IyIcvpwbWV40ObNi0EU3TsFqt+FxuxowYicvaiMNi5fKLLyXc0wfbAipSicxcJ28tDDflxnk+9/9Gtjfu9nRyrcn6+vr45JNPMJlMVFRUcM455wAD7ha7Mr/C4Bdu/f39rFmzhkAggMVioaGhgUsvvTRrLsmdPxQKhUKhUCgUCoVCoVAoFApFNioAQLFXs7VrC1Mm/5CAzy9dAEZ2jqCkqJg/XPt70KFna7cUQoQtsgwE2AMQopoQh7u6usjPz2fevHlZy7z33ns88sgjTJw4keLiYjRNQ9M0SkpKGDt2LNdeey3vvvvuLlnwKnaOiLgyij1C4DXWPDZGmsGAkBkOh2XdKxgQQIXtjLA9FnW+BULQNP4uEolkCby5YrdRmIeBsSXE20gkkiXSxmKxQSUqxLaTyWRWu8W+bdy4cbvbj0ajWaJMrqgs3DzEd4arxpcIABAfY5s2btyI3+/HZrNhsVg45JBDSKfTQz43jMvlBiykUqmsYxEKhejq6srqf+N4EH0TiURkOZNUKsWWLVsGtccY+Sf2UWw/Go3K4AGjdZFYp1jvUKIEI5HIIIuk3CAS0Q6jC4sY27mRkrmBMMZgmJ6enqxzQuyLEJrFOZRMJuW+GKMzRXtF6RNBNBolHA5njT3juBXtNLYrt7/7+vp2OTvfuD6Aiy66CKvVitVqZeHChbu0jh1hrGGVSCS48MILqa+vx+fz4fV68Xt9NNRbMFdU8p8ln2Rq1EdjUqwe5IKT1gfcAdK6PM8jkUiWuLp58+Zhaf+eTjyZ4KGHZ1BWVkZ7ezsj2ztwNdpp9vgo1vK5b9o9JMNREpGo7MNgMChLB2zPMlIE7xgRWYa5ASt7OiKgymiZCZn92bx5MwceeCA1NTXsv//+rF+/PivCeihzbK79pnG7/f397LffftjtdpxOJ9/85jez5rvtlU5SKBQKhUKhUCgUCoVCoVAoFAOoAADF3s02Md/tdNFotdEcaMLv9WG3NVJVaebxfzwGOvT39mUFABjtfL9KjEKYIBqNSgFLCLdGwauvr09mx0K2IKfrOv39/cMmrioGZ7TDgCAqREejmLOjzE/xHeOxyyU3G9eIURQWgomxbUKMDoVCg1wJtkfuGNretsRyxvrlxvalUqlBgQ/G78diMbZs2ZJV2xmGP7vYGARg3PcFCxagaRoul4uKigquvPJK2X+7Qq4AZgx2yK0Hquu63FfRx+L8zHWAyN0HQW4Gba4ga9zeli1bBrX184hjYq4xin65bTGK7LmBKCL4JBKJDDoHRH+If/v7+7PGQzgczhpDxsAV2PH4zK0pJ37OzWJPJpODnBESiQTxeFxut6+vLyugYSgZxiJgYvPmzfj9fux2O2PHjiUcDg9Lhrcx6Gjr1q3st99+OJ1OHA4Hfr8fR6OdkZ0j+M64I+U1LhwMZYn+wv1GT6VJxhOEgyGCff3oqcGBMJs3byadTstz9uuODpwx6Uzsdjs+n4/WQBPWunoOGDmaQk1j4evzIQ3pbf0myiYI5wAx36TTacLh8HbHotxWjoPGcNl4fZGIgC0x/xnHdCKR4Kc//Sl1dXWUlZXx9NNPAwPn7lDOH9F/ImAp9zoyceJEHA4HTU1N5Ofns2LFiqy5SaFQKBQKhUKhUCgUCoVCoVDsGBUAoNi72RYA8M9ZT1NRVo7L4cTRaGe/0WMoLy1jzKjRGQeAtE40HMkKAtgTSgBAJqNQCGbGl+bby6IzWon39fURCoW2a1edm8mr+HyILP/tZTYbCYfD9PX1sXjxYl588UUWLVrEvHnzmDdvHosWLRpUt1gc59xM5WQyKW3yISNgijGQSCQGic2CRCKx3d8bxWjIZIwbAwuM9ZpFG0U2tdhno3AqhNtcASYej9Pb20t/f39WX+WOZ7G+4ba/FqK/EOPEzzNmzEDTNAKBAIWFhdx///1ARqQa6vlhtI2Px+NEIpGs9uu6zvr161mwYAHPP/88//rXv5g3bx6vvvrqIKEbYPbs2bLOmVEk7O/vH3QMo9GobKc4Vm+//TZvvfUWzz77LC+99BJz585lw4YNcr+M4vhQ6O7uznIP0HWd7u5uurq6WLJkCXPnzuWFF15g3rx5vPPOO1nW/6FQSI69HR33eDyelV0u3AuE3bwxKMI4Nru7u7P2x+h8ANkCqnDoMDoV5B7fWCzGu+++y9NPP81zzz3HvHnzWLJkCatXrx7UJ0MVZ0UfP/vss2iaRk1NjbRDHw6R0ljv6c0336S8vBy3201jYyNer5eOtnbyNI1fXf7LLLEfHd5f9B4fffAhL74wl+fmPMvrr77GGwv+zcrlK9iwbr0MgOvr68s6/vsSOmB3OggEAlRVVeG2O2j2+eloaqGxzgJpMp9t9wzhYIg33niD1+fP451F7zJ37lxmzZrFm2++SX9/vzxe4mMMMIHMGBZjZm/IYN9eaRDj3/72t79hNpspLS3l17/+NTD4mjZURBCAsV+uvfZaamtraWlpQdM0XnrpJXRdV/b/CoVCoVAoFAqFQqFQKBQKxRBQAQCKvRsh5Ovw+99dS2V5BbXVNYzo6KSlqZkyUymTzzp7UCkAKZTsAQiBVWRSp1Ipli1bBmRErL6+PimwfZZwmSvCKReA3cfYn0IkN4qDyWSSJUuWcN111/Gtb30Lk8lEaWkp++23H52dnZSWltLQ0MBJJ53E/fffL8WR3Cz0cDhMLBbj+eef56233uLtt9/m3//+Nx988AGvvvoq8+fPZ9GiRcyZM4e3336b999/n9mzZ2fZpwui0SgLFixg1qxZvPbaa7z00kv8+9//5s033+SVV15h4cKFvP/++7z22ms8++yz8ns9PT288sorzJ8/nyeeeIJ7772XX/7yl1xyySWcfvrpVFdXZ4mxfX19rFixgvvvv59zzjmHb33rW4wZM4aDDz6YKVOmcNNNN7FkyZKsEhdCDBNOFcON0QkglUrxu9/9DrPZLDNIn3nmGSAj3A71/DCKzkaBeuXKlfz1r3/lsMMOQ9M03G43Z5xxBlOnTuXYY49l5MiRaJrGWWedxYIFCwCYMGECZWVlUvQWYuD2XCaMwQFLly7ltttuY+zYsVRXV6NpGvn5+RQWFlJXV4emaRx66KE8++yzBIPBz+0C0NPTw+OPP85JJ51EQUEBmqZxwgkncPzxx1NeXk5paSlNTU1MnTqVRYsWZQlxxvrx28uif+utt3jllVe45557uOeee7jlllu46qqr+OEPf8iIESN44403svp448aN3HjjjRx00EGMGTOGE044gYcffpjly5fLdYs+M4qORjE7nU7z/PPPc+GFF8p+Gj16NCeeeKLsxwMPPJAf/OAHfPDBBwAyWGEo/SfOu1//+teUlZVRXl7O7bffPmwBLkax86677qKoqAiXy4XP58PtdtNQb8FUXMLsfz5DbJtN/b9eepmf/eSnfOOAAzFXVFJXU0tBXj5FBYXkaRpOu4MRHZ2cePwJvPzyy/JYGfswGAxmjfuvK8tWLEfL02htbcVms+F3exjZ3kFVaTk/OvuHdG/cTCIUYfl/lvLXv/wv4w4/gurqakxlpWh5mRI8FRUVmM1mzGYz48eP56mnnpJ9KsorxOPxrEz3vYXcIBZxfyLm2Ndff53y8nIaGxsZN24cwKAgsKGuXwQtGsvrPPbYYxQUFOB2uykqKuL//b//B6h7G4VCoVAoFAqFQqFQKBQKhWIoqAAAxd6NwfI4Fony7cO+RXOgiaKCQjrbO2jyByjIy+e2v94q6x7LoIH0Vx8BIMR/IRgZBYLtZXQnk8msuuDC7t9oSR8MBlX2/zBhFBpyBdre3l7uuOMORo0aRVFREccddxxPPPGEPG69vb0sXryYu+66C03TKCgo4KSTTmLFihVA5lj29PTI9cZiMSoqKuSy+fn5aJpGeXk5mpYRm+rr6yksLJQCsBBDhVVzPB6np6eHK6+8EpPJhMlkoqioSIq5mqaRl5dHcXGxXN+qVatIpVLMmzdv0HK1tbW4XC65vd7eXtn+yy67DE3TaG5u5rzzzuOuu+5i2rRpnH/++Xg8HjRNo7a2lnPOOSerJnokEhlkjz2cCPEomUxy8skn4/F4cLvdlJWVSSHemCn+WSQSCXkuGbPYH3zwQcaOHYumaVxzzTVStDa6JaTTaVauXMk555xDXl4ep5xyClVVVWialmXpnxtkYtxOOp3m7rvvxm63U1paisVi4YYbbuCdd95B13U2b97M0qVL+ec//8mPf/xjCgoKGDduHO+++65c92d9RB/09/fz8MMPc8ABB6BpGuPGjePJJ5+UAS9dXV0Eg0E2bNjAVVddRV1dHWazmd///vd89NFHsr3BYFCWETAKnZs3b6aqqoqSkhI5ni0WC2VlZTgcDjRNY/HixfJ706ZNQ9M0Tj75ZO69916mTZuG1WpF0zSOOOIIVq5cCSCFVUFfX588n1555RXOPPNMNE2jpKSE6667jo8++oj+/n5SqRRdXV288cYbHHfccVgsFjRN47LLLgMYVHZlR4j++/a3v43P58NsNvPqq68O6/gWc9A555xDbW0tbreb9vZ2PB4PLU3N1NXUsmnDRj764EOmnnc+ZaZS8jSNgrx8GuotHDv+GL4z7kgO2G9/CvLyKS0x4XV76GzvoLq6mksvvRRgkBvDvsCTs56SAQCtra00eX343R7Ki0p46tHHIA1/+t3vafL6yNM08jSNoqIiDj70EE4/8wwOOuggeS60t7djt9vRNI2jjjqKN998U24nFotlBcMYr+N7Mrm2/yKIQbisbN68Wc6v9fX1shyJsZTGZ2F0LzLOyWI7ooSLzWbD6XTyox/9SC6rUCgUCoVCoVAoFAqFQqFQKD4bFQCg2KuJR2Ogk6l/nNb5z5JP8Hm8jBoxkmpzFSM6Oqk2V+G0O5j32usE+/qlC0AqseeI5L29vVmCf3d3NzCQQavrOuFwOGsZ48+5gv/elmm4p2IU8oyCxsKFC7n22mulWP5f//VfrFu3Tv7dKB5u3bqVRYsWkZ+fj9PpJBAI8Prrr8u/x2IxuZ17772XGTNmcOONN3L++edTVVVFXV0dgUCAmpoaOjs7KSwsZPz48Tz44IM7zNK9++67aWpqwmaz0dLSgtfrpbKykrq6Og4++GAuvvhi7rzzTh588EH5nXXr1nH11Vfz3//930yZMoWOjg6ZaTx69Gg0TaO/v5+3336b8vJyvvnNbzJr1iw59qLRqNzvZcuWcd1111FUVERxcTH5+flMmzaNZDIp2/xFiY0iACCRSOD1egkEAjQ2NmKz2VizZs0urcvoWCD2bc6cOTQ3N2O1Wlm2bFlWxrgIGBDbF4LY7Nmz0TSNzs5OSkpK6OvrIxgMEg6HCYVCcjvCCQIyovnFF19McXExNTU1HH744Xz88ccyeCDXZj+ZTDJ//nwOPfRQCgoKePPNN3caABCNRunu7ub666+nuLiYiooKbr/9duk0Yhzz/f39MsDhrbfe4tBDD0XTNI488kiefPLJrH4TfdHT0yN/d9VVV3HllVdy9dVXc+SRR1JdXY3b7aatrY3S0lJmz54NwM0330xxcTHPPfcckMnonzRpEu3t7QQCASorK7nssstk27q7u2UggOiT+fPnc8ABB1BcXMyhhx7K3//+9x3ahsfjca666iqKioqoq6vjlFNOGXIAVTwep7u7m9raWpqamnA6nSxfvny7ZTI+D2Isb9q0idGjR2O322WfiQCAsd84iFtuuplDDz6EPE2jtMTE2ZPO4qW5L7Jpw0bQIRGLs/zTZdx+6224nS58Hi+WunoZ3HPuuefKbSaTSXp7e4dcQmJv5oILf0x9gwW/309raysBj5dGSwMOi5XbbvkLJ4w/lkJNozi/gHGHH8G9d9+TqUMPbN7SRTwe5/777+eAAw6gtraW1tZWrFYrNTU1HHbYYXzyySdyW8bxZywrsicjxH6BEOZF0E0ymeT444+nsbERTdOYN28ekF264rMQJRLEODduK51O8/HHH1NbW4vT6cTlcjFu3LghBxcoFAqFQqFQKBQKhUKhUCgU+zoqAECxd6OTLejr8MD908nX8mhtbsHn8eJ1e7BaGhg9cpRcZk8qAbA94d9IIpEgHA5nZcoJgcoo0uTaiO8NAsOeTq5FMcD69etl9ntdXZ3MSoSBLHEh4sZiMXl8hROAy+XC7/eTSqWyjr2xPIAQT9555x0sFgttbW14vV48Hg8dHR0sX75cbkMI6qI2vRBDly1bRlVVFVVVVVRUVPDNb36T6dOnZ2WpC4y1qYVQ9e677+JwOPB4PLhcLhoaGrj11lspKCjIEmC3Z6cvsjn//ve/U1RUJGs4T5o0CWCXhfhdQZwfiUSC/Px8vF4vVquVpqYmeS6J5XZGrojV1dXFIYccgqZpzJo1i0QiQTQazRKMhQTBrLcAACAASURBVEgmEBnxf/zjHykrKyM/P39QnWxjX6LDhnXrOXvSWVSUlVNeWsYp3zs5y9reGHQg5gzhANHb28t3vvMdmpubdxoAAPDLX/5SWpnfdNNNchwYreGNAS1CrNuyZQtHHnkkJSUljBo1iieeeEIGQBjHg1HsFNtMp9NMnjyZyspK/H4/mqbx4YcfsnDhQlnrOxaL0dfXR29vL62trdTX19PW1kZ1dTUTJkzI6gcgK0CjvLwcq9VKUVERjz32WFb/5B5b4aJy3HHHUV9fj6Zp3HzzzUOyMY9Go3z66afk5eXh8XgYOXKkPLbDEYAVi8XQdZ3333+fqqoqHI12PC43Tf6A/Nfr9tAcaKK0xMTBB43ltVdezSqNEw6GSMQGxpWj0U6TP4Db6aKlpYXOzk7pwNDb2yuP+/YcaHaFPeTy+pnU1tfhcGXE5UAgQFtLK65GO267gxFt7dgbrDhtjdzx11uJhjPzYn9/Pyk9TUrPjOlIJMK1115LdXU19fX1jBgxApvNRnl5OX/4wx/k+WkMJvtyLOzTw34QxNxqdCz6+c9/Lt057rvvPpnVvyslSMQ1zzhvplIpli9fTkdHB4FAgLq6OsaMGUMoFNrtsalQKBQKhUKhUCgUCoVCoVDsC6gAAMVej7EWNGRe0N90001omobf78fhcBAIBCgtLeWKK64gmUxmCejGl8mxWGyfyHxUDA0h1GzYsEH+7qKLLsJkMuHz+TjooINYtmwZMCD4GTPihZgu/hYIBKSd8cUXXwwMiOVCWBUiiLCxnzdvHoWFhbS3t2OxWLBYLHz88ceyfWK8CqFJZFUKAby4uJhLLrmEtWvXynXCjmucGwXiU089FbPZzJgxY7DZbGiaxg033DDkDGld1/nzn/9MQUEBHR0daJrGhRdeKPspt4a8sd+HJMDGY8STCXQgEosOxAPpOosXL0bTNAKBAH6vD7/XJ0XRdDI1JAcQEVwhhOaZM2eiaRpjxozJCHrbNhiPxqQbiVi/KDkiarP39fRSmF9Anqaxbs1aIHOMxV7GYtu+n0pz9ZX/hamwCKetEY/DydLFSzLr2daWaDyWpe0ZAwhWrVrF9OnTKSoq4uabb5Z/F2M5nU7L/frNb36Dpml4PB6mTJmyS30P8Mknn1BWVobX66WtrU2WAxDbMwqFRmKxGC+88AJ5eXl4vV40TePTTz/lyCOP5IorrsgKzggGgxx66KGYTCba29spKyvjpz/9aaaNhv5Hh61dW2hpasbv9VFmKuU3//0/bFy/QbrEiOMSDUcGiaNz586lrKwMl8uFyWRi4cKF8rzcnmW76KPp06dTWVlJY2MjxxxzjDwOuyKADmLbfiVicUjr3PjnG3A02vG7PdgbrIwZMRKPw0l9bUYU1TSNn/zkJ0QiEUKh0KB66kbOOecc6Soiro319fWy/IFo91BKIGT6MgZpXYq+OhCKBEmRRpefwfv2ZUYIGK/5yWSSUCjEunXr0DSNjo4OnE4nDQ0NjBw5EnNFJWNGjcZcUcn+Y/bj5Rdfyowxw32BCMgTgYTPPP1PykvL8Ht92G2N7D9mP6yWBnwer1ymt7tH7neoPzgs+7/9rkxv+yRBT5NMxLb9f+A7ff2hYen+eDzO9ddfT0lJCRaLhV/84heZFgyxxMrO6OnpYcyYMfj9fhlkIILHVICjQqFQKBQKhUKhUCgUCoVC8dmoAADFXo1RiIzFYlLkevPNNzn11FOprq4mEAjgdDplXfL77rsvax3Cgt1YE1uhEIgAkd7eXp5//nnq6+upr6+nuLiYO+64Y9CYyRXHhdiaSqW45557qKmpwWq1UldXx5IlSwZtTwhwwmp569atXH/99ZhMJlpbWzGbzZx66qnbbaPx5yeffBJN07j00ktZv379Dtu3ve2Lz/nnn09jYyMOh4OGhgYmTZqUVX99Z4TDYZLJJPvvvz82m43m5maqq6u59957s0Tp3HYNJXtaCF+pbf/GEvHM//U0sViMBQsWSIHZ43Jz4P4HDDh/JFMDIv1OEBndyWSSX/ziF1RVVXHssceyefNmtmzuyhJrE7E4eipNIhaXIl88GiMSCpNKJPn+hIkU5hdkbbe3v08e80QkygtznsVe34DbZqeqtJw//e73kNZl8EIikZD7Hg6HszK2H374Yerq6jCZTNTX1zNmzJisIAujTf6LL75IYWGhFNYWL16cFZQx1CzbP/3pT2iahsPhYOLEiTJYIpFIyHMjmUxmOSOkUinmzZuHpmnsv//+VFVVceONN+J0Olm3bh3RaBRd1+W4feqppzjqqKMwm80ce+yxLF68WA4CPZWWx/LCC36M1dJAo9VGkz/ARx98KI+PFP11ZFCGcZxt3LiRI444grq6OsrKypg8eXJW3+UGMwhx/dZbb6WyshKbzcYZZ5yRtb+fGz37c9qEidjqLfjdHvxuD25bJku9sz0TVPPTn/6UjRs3EovFBp07IhBD/P5Xv/oVNpsNn8+H3+/H4/HQ0NDA8ccfTyqVor+/Xx6/nREPxTLasj4QsJQ5J9NEUzFSpLcFAmwb8uKHtOHnLxDjsRKI4zhr1iw0TaOpqYm2tjZcLhdOp5PRo0dTUVHBAQccwPz58+X3+vr6ZCBIMBjMCkD510svU5CXT3trGx6Xm462dhyNdqyWBj79z9IsRwbSOulkKvP/3SB3/pP9Sxr0JOhJ4uF+0JNEwkFEEEBff4gUEIwOjwvBtGnTMJlMWCwWpkyZQiKRGLYSGLFYjEMPPZTm5macTif5+fmyrMJunV8KhUKhUCgUCoVCoVAoFArFPoAKAFDs9eTWjYWMK8C8efMwmUzY7XY8Hg9ut5uWlhYqKytZsmSJrHUsMP5sFNYU+y6pVEpmwgprdU3T8Pl8OJ1O6QxgtIEXgv/2/t/b20tBQYEUM/785z8Dg7MZhfgmft/X18ekSZMoLS3Fbrdjs9n43e9+l7UdQNacDwaDtLS0cPrpp0uHAuP6jHWcczEGAJx11ln4/X5sNhu1tbXSTl0sN1Ree+21gWx8v5+ioiK2bt0q12Fc11CzR3MFMOEEkEyniEQiPPPMMxQWFuJ0OnHaHXzvxJOk8IsQ4HYxAGDChAm4XC7cbjdr166Vgp6wWBcfsW6j6CfKk+RpGqH+IBs3boRt7RZzTTIa45BvHERZYTEtXj/OBhub166X64pFojLLOpEaOO7BYJBYLMY3v/lNaavv8Xjwer1EIhHC4XBWRnd3dzcOh4Pq6mr8fj9nn312Vv/n2uV/Fhs2bJClLUpLS5kxY4bsL7lf24RT4zrfe+89ioqKcLlc+Hw+WltbmTp1aqZPckqbiPWsXr16oN/i8YGADh3mvfY65opK7LZGLHX1nP+j87IE/3AwlOXKkE4OriX+pz/9CbPZjNvtprCwkP/85z9Z/SJcOsT/E4kEV199NWazGYvFwmWXXSYDyXYrmMwwluLRGF6nC6/TRcDjpbO1jcY6C21NzdTV1HLssceyYsWKzNe2nUfGwAVRGkQc/4suugir1YrP56OpqQmn04nD4aCzsxMYcBIZUvu3JZsn45kxHI/HCUXCUvj/qgMAxDwqjpkxCOXcc8/FarXicmVKIbS1tWGxWKisrMTtdvPkk0/K9USj0e3b9uuZ0kNzn3+BPE1jREcnPo8XnycTdFRfW8eCefPlOJXOI/q2MkS7wVACANCTkIrT39cDpInFEoTCUVLbvjcczJo1i+LiYhoaGhg/fjzhcDgTpDQMAn06nebII4+U81leXh5vvPHGkB1oFAqFQqFQKBQKhUKhUCgUin0ZFQCg2OuJRCLyhXAikcgSj2655RaKi4ux2+0EAgG8Xi8Wi4WDDz4YyLY5Fhmd6XRalQJQAEhL/mAwyOuvvy5FbJ/Px2mnnSaXEyI/DBbvxdgUtvx+vx+3201bWxtjx44dVDMdBrKvxRjUdZ0VK1YwcuRITCYT++23H+Xl5cyePVsKLV1dXbIt5557Ls3NzbJUAGSEWuN5siMRxRgAMHHiRHw+Hw6Hg+LiYl5//XVg6DWsI5GIFNz2228/6uvr6ejooKCggF//+tdyOeN+DrV+tMj2j6dTJNMpEqmkFMISiQQPP/wwJpOJxsZGbA1Wfjz1goEs8FRaivKfhTgOoq9OPPFEKUT95S9/kcpbMp6Q6zOKzcKaPpVIEotEeeG558nTNFl+QGRMs63Nj898lAJNo7O5FbfNzuTTz4Q0pOMJKRxCxu0gkUrK4IR0Ok1XVxd+vz8r4MlqtcqgEKMN+kMPPSRLGWiaxty5c9m0aZPcb2O98s8iGAwSiUQ4+OCDcbvd2O12Dj30UJlFbhTLIVvY//jjjykrK6O2than04mmaXzyySfSQUAgXAmCwaAUFsV6xPFMJ1OccdrpWC0NBHx+zBWVPPrITHlspNuDIRhD9KWY7wH+9a9/YTabGTlyJJqm8cgjj8jxuD1RP5VKccEFF1BVVUVVVRU33HCDbONwBADEozE+eO99TIVFdLa24XW6aG9uwe900+IP4HV7+PDDDwGy+tw4BxlLk0SjUcaOHUtDQwM+nw+PxyMDWg488EB5jIxC+WeyLQDAWE4jGo+RJEUsHf/KAwByHQDi8bgMRnE6nTQ1NeH1enG73Xi9XgKBAJqmccsttwzsTzSa1RfxeHygvEVah7TOHbfdTr6WR2tzC26nC7fThcflxtZg5b13F2UFosiglfTu7fxQAwBSsTCQZsOGdSxc+Bb9wTApIL6b24dMv86fP5/8/HwaGxsZM2YMfX19w+aklEwmOeaYY7Db7bjdbjRN4/nnnx+yO4lCoVAoFAqFQqFQKBQKhUKxL6MCABR7NbnW5z09PYNePh999NG43W5ZBsDn82EymbjmmmuATNa0yJiMxWI7rZGu2LcQWb+nnXYaZrMZn89HdXU1f/7zn9F1Pav0BGSLTsLmXghPABdffDE1NTUEAgGKiorYtGnToJrhxgAAoy37Cy+8gNlsxmq14vF4OOyww1i3bl2WeHvbbbehaVqWfbVRWBfi3o5EGmMAwNSpU7Hb7VgsFjRN480335TLDCUL09gvf/nLX2TGd0dHB6WlpdJ1Q+zf9jJ1d4TI+o+lksQScVL6gNCo6zrTpk2jvLw8U76g3sKvLv+lFOSFcDcUAVLXdSnqTp06lfr6eim0T7vrb6xfuy5LsM11ARABAOFgiCUfL2biqRPo2dotLfIjsYFj3Rpooq2pGZ/DRUWxiacefUwGABjbm0ynZNOXLl0q23rJJZegaRpms5ni4mKmTJkiM7ohE/C0ZcsWvvOd7+D1evH7/WiaxnvvvZeVNZ577D6LWCzGJZdcQkNDgxRQ58yZAzDoGAprfF3X+fDDD9E0jREjRlBXV0dtba2sXy9EeeO4NlrSy4Cbbf27euUqqirNtDa34Gi0U22u4pPFS7IyrkWZBhEMEN9mgZ5IJGSpig0bNtDQ0IDH48FisXD55ZfLeu9iLBj/BTj99NOpra2lrKyMv/3tb8RisWELAEjE4txx2+2YCovoaGnF1WjH7/bQ5m+ivMTEDdf/Wfaz6B8R8CH629iOTZs2UVxcLANEnE4nPp8Pi8XC5MmT5XK589EO2TbGY5EofX19GeccPU0KCMUGMs0HBQAYP18guWUbRODIypUr0TQNu92O3+/H5/NRX19PU1MTRx99tAyWgGyHlWg0SigUknOpGFs/PHsy9bV1eN0eHI12mvwB3E4XLoeTjes3yGOZSiQHBaB8XoYaAJCIBFm9agWNjVZKS8s5/YxJ9IaiJIah7+PxOIsXLyYvLw+3200gEGDTpk1DDuLaGel0mgkTJshzMj8/nyeeeGLIAUoKhUKhUCgUCoVCoVAoFArFvowKAFDs1eRameeKm9FolK1bt+L3+6WV+YgRI2hubkbTNGbNmiWXFRmUMLT6x4qvP0I46u3tpa6uDo/HI7OVn3vuuUFCejQaHWQ7LQQkIbDeeeedsjSFpmlZWfpiLOe6T6RSKSlI3XTTTbJ2dV1dHd/97nflco899hj5+fncddddWW0SGGuED8UBYOrUqVgsFhwOB0VFRTIAYHtt3BGbN28G4O2330bTNDo7O6mtrSUvL49XXnklq42ibUMJMNDJCOEpBuz/xe8Abr75ZiorK/F4PNTX1vGry3+ZVS9+qAEAYj/j8TgvvPACxcXFuN1uqqurKSoo5IdnT+afs55m+afLsgIMwsEQyXiCeDRGqD8oVbo1q1ZnbTcczTiYLF26lAJNo9nnp9njo1DTWLV0GSQHyggk45n62sL9QBCLxQiHw4TDYe644w4mT57MDTfcIAV0XddlHy9fvhxN02hoaKCpqYnKysossV+4ogxVZNN1nWuvvZaGhgbq6+ux2WxceumlRKPRQSK4yKJPJpO89957aJpGW1sbNpuNQw45RG7TWI4lFAoNaouc83WIhMI8+fgTlJeWYWuwYrU0sP+Y/bLKMuipNIlYPCtAI9Qf3O46Ozs7KS8vx+v1cvzxxw9yiYEBB41UKsWJJ55IfX09JpOJ+++/n3g8PqwlAH549mQsNbU0eX2ZseHz0+L1YyosYtnSTwe1P9dtwej8IFxMOjs7cTgcMgikpKSEW2+9VX5nyNc/HeLCVcPQ9FgqKcX/rzIAQNwP5AZuPProo5hMJtxuNxaLhebmZnw+H0VFRdx+++0AMhhF1LQXmf9G0skU/b19jB45Cr/Xh9ftweVw0tbSit3WyNhvHCRdKmT5kW0BQV9KCYBUHJIxnvnnLEpLSygvr8RcVUNPMEJ/ZPez6FOpFGvWrJFlcVwuF8uWLZPlIIaD8847D5vNhs/no7i4mEcffVQ5ACgUCoVCoVAoFAqFQqFQKBRDQAUAKPZ6hPhl/L8QbYRg8/TTT1NQUIDL5cLr9dLW1obZbMbj8cg6z0KohAE7dcW+jRhXL7/8MtXV1djtdpqamtA0jffff3/Q8kJsMor/Qkzr6+sjkUhw9913U1JSgt/vp6CggLlz5wJIYRQGRHiBWKdY79lnn01FRQUOh4OSkhLuvPNO1qxZQ2lpKeeee25W20UN9XQ6nSXKDiUA4Mwzz8ThcEiRcP78+bsUHCPEM1GHvqamBofDgdvtpq6ujt/+9rdZYr8Q2HL3f7vtBJltLDLidTL2+ADXXXcdVVVVNDc3U19bx8U/u0hawmdl6O+k/cYsYl3XmTBhAmazORNIFGiivraOMlMpfq+PU753Mldd+V/MfPgR/rPkE6LhCHoqnZWFLtogsqyFY8Gdd95JQ20d1rp6OppaqCmvpH9Ld8Yu3ZBpHY1GpfuBcc6Cgexvo/hmdH2AAfv/9vZ2nE4nRUVFQCYAKh6P71J2rXBM+dvf/kZNTQ0NDQ2MGDGCQw45RAYfCAcN8bM43u+99x7FxcVYrVZsNpsct9FoVLZf1BMX9Pf3yzESj8elkH/1Vb+m0Wqjod6Cy+HkpBNOzHJgSCdTWWUaxMeY5S3aePjhh1NTU4PL5WL06NFZ/SHaIkT+UCjE+PHjaWhooLy8nIceekju53AEAHRv2crokaPwudx4nS46W9to8QdwWKx865BDCfYNBK3lXgONAQDCkeCaa66hqqqKtrY2PB4PgUAAl8tFQUEBb775ZtZ8NSTSKWKhIJBm5cqVfPrpp+hAbyi84wAA0qBvqx3wJZjs5O6Pruucf/751NfX09raisViwePxMGrUKOmckuuGAdnzZTKZzLgNJVMs/Pcb1NfW4ff6aNpWliHg81NVaeZXl/8yE3CU1rPGo9GB4vMyVAeAeLif4487Fq/Xjc1m55hjj6O7P3N8dhdd19mwYQOapsngOFGSYjiCKHVd56KLLpIOAKWlpTz66KNZ10KFQqFQKBQKhUKhUCgUCoVCsX1UAIBir8ZozWx8aS8EL2P24x/+8AcqKyux2+00NDTQ0dFBfn4+P//5z2Ut7Ugkst2X/4p9m3vuuYeqqipcLhcOhwOz2cyCBQvYsGEDa9euZf369axfv57u7m66urpYuXIlmzdvZs2aNWzcuJH169fT29tLX18f06dPp7CwELvdTmlpKTNmzAAYlGEqRDth7y2yKkW991GjRtHa2kpDQwPt7e2MHj2ao48+Oit4RYi/RjFSlCXYEcYAgClTptDS0kJjYyOaprFw4UJgQFTcGUIEEkJrY2MjTqeTtrY2GhoaOOWUU4hEInJdYrmhrFsHEtsyjT8rAKC9vZ362jqmnne+tOIeagBA7r6Gw2GWLl3KAQccQGlpKS6HE7fThd3WiNPuwO/1Yamrp6igkKKCQgI+P5defAkvv/gS6WSKSCgss9Zhm9X6tu2cc845VJaWccRh36JQ0zjq20eQisQgDZvWrR8Qs9OZUgfpbd/s6urKco4wZoCL8WPsz1/96lfU19fLGuhjxoyR/R6NRuWyQ50Ho9Eozz33nMwCrqurw+l0sm7dOiB7vIm2pNNpWQKgvb2d8vJyrrvuuiy7faMjizELX9jap9NpeQzPnnQWdTW1tLW04rQ7OObo8axdvYZVK1ayeeMmtnZtYWvXFjZv3MSKZctZsWw53Vu2snHjRkKhEKtXr6arq4uuri6OOeYYWeLB6/UOEtbFfkAm4GLcuHFYrVbMZjMPP/wwMHwBAAvmzaeirJwmrw+H1UZroAm33UFVaTm/vuLKzFiKRGS7YrEYPT09cjVGoTSVSjF+/HjsdjtOp5Pm5mYCgQBWq5WSkhLZ32J/hyTg6hkx/8MP3sPptGM2VzN7znN7ZACA6ItgMMioUaNoaGjA7XbT0tKC1WolEAhQVlYmx72xX9Pp9HYz2pPxBA898CDlpWXYbY20Nrfg83ix2xopLizi6admZWX/R8MROf+kEjsvofJZDCUAIBEJEgv1kadpBAI+NC2fd959j2hSH5YAgHQ6zbp169A0DYfDgcvlYtGiRcOw5gyRSISf/exnMnCstLSUxx57DBhcXkShUCgUCoVCoVAoFAqFQqFQZKMCABRfe2KxGNFolFQqxbhx47DZbDidTlpaWigsLOTll18GBoIJcuuRf11IpVIyQ1jUrhe/7+vrkz8LAWlPCITIzQDOxSjWGBku+2HI1Bv/4x//iM1mo7a2lra2NlwuF3V1dRQVFX3mJz8/X36Ki4sxm83Y7Xaam5vp7OykqalJ1ksX7TYKjkKIF8fKKO69/vrrlJSU4HQ6cblcaJomLfr7+voGCb/GdX7WsTUGAJx77rlZtd3feOMNgF0SN411yCdOnCiFVREMIJbJXa9RAN5uOxlwAEijSxEsEsuI2b///e8pLS2lpaWFanMVV/7qigHh35iRuxOMY0kI5YsWLWLixIkU5OVTX1tHkz9AS1MzbqcLR6OdgM/PyM4RtLe20VBvIU/T+PHUC1i29FPCwRDomWOk6zrJdIpIJMJRRx2F1+miramZjqYWTjzmu5DSIZ4EQy37dDpNOBoZsnYqxHOR3X3yySfj8/nkMRgxYkRWIFUqldolF4B4PM6TTz5JXl6ezCovLS3lo48+kmK/OL7GOfWjjz6S36msrJTW64KhBIHEozGi4QjfPuxbOO0OfB4vPo+XhnoLZaZSTMUln/nRNI2SkhJKSkooKCigoqICn89HR0cHTU1NnHDCCXR3dw9y4xDjYOvWrRx++OG43W4KCwulOCn6e6gY57hYLCYF4ztuu52SomK8Thc+l5tmnx+/20OhpvHojIeHNH7FebRixQo0TaOlpYWamhqam5vx+/1YrVbOOuusgT41lAEwBhAZ54zM/qchnSARCfK/f7mF4uJCTKYybr3tDikwJ9IQSaTlednb3YMQqCPhvszPXyC55Vd6e3tZvXo1xcXFOBwOPB4PXq+XpqYmXC4X3//+9wEGBU0JxPiNxWKZeTkS5YzTTsdqacBpd9DS1IzP42XUiJE47Q7CwZAs/2EsSTHU8gei/dFolO7ubvl7UQYkGsuUQEkk05l5L50JDRL2/+l4hBnT/49SUzGVleV897vHEwxFSAHR5PDc36xatQpN02hsbMThcEgHgOEglUpx8cUXYzKZCAQC8hxTJZoUCoVCoVAoFAqFQqFQKBSKnaMCABRfa0T2tOD999+XWaeapnHzzTdLcUCIPGL5PUEA312E/fuO9sUoKggXBBh6/e8vC6OQKLLhjaJcb2+vtDCPxWLDduzENm644QYsFguBQIDm5mba29tZsGABq1ev/szPhg0bWL9+PevWrWPt2rWsW7eODRs2sGnTJrq6uli9ejUwOPsfBguIK1askD/39PTQ09PD9OnT0TSNsWPHUlVVxUknnZSVRZ9bw9pYJ3xHAS7DHQAglo/FYvzgBz/AYrHgcrlobm6mrKxskECcW8N8RxgzYI0OAPFk5vy96aabKCkpoaWlhfraOi7/xWXZmf/CDn4n7Rb9lFvTvq+vj+n33c9ZZ07CXFFJnqZRUVZOc6AJj8tNeWmZDAYYNWIkFWXl2BqsrFqxMmMLzoCQF4/HcTgcOKy2jNDr8fG97x4PiVRGI93WXvG9lJ4eUgCAsVa2OPZnnnkmZrNZCnYHHXSQPN+NVv1DJRqNMnPmTCmqNjY2UlFRwbx5877wAAB0CPb1c/BBY/F5vHhcbpx2Bz+/5FK2bO5i3Zq1n/lZv349mzdvZuPGjaxatYrVq1fLc1M4egDy3IlGo3JfkskkoVCIo446CqfTSUFBAY888kjW33eGWMY41lOplBzck886m7qaWjpb2/A4nDIIoLqsgvffeXen49cogD/++OMUFxfj9Xql9X9VVRXFxcXMnDmTVCpFNBr9zLm/t7cXyIyPYLAP9CTpRJjvHnsUlZXlWK2NfPTxf0gBW3vDgx0ASIOeIBUPAUm+6ACAXAeSWCzGY489RmlpKT6fD4/HQ0tLC01NTZSWlnLTTTcBA2PPGLxkdK8Qx2v1ylVUVZpxO10EfH7pAOJ1ezj15FNkiQrRASKwQ7gBDLX9glQqRX9/f8Y1BejrjxCOp4jFkwMOuMgd0QAAIABJREFUAHqKdDwCqTgkY3z/lJNoCvgoKMjj8cefJBpLyOMyHKxZs4a8vDxcLhd2u50lS5ZkldTYXc477zzq6urwer2YTCZmzJghr2sKhUKhUCgUCoVCoVAoFAqFYseoAADF1x7xsj4YDDJz5kwKCwuprq7mzDPPlGJHNBrNqu8MXy8HAKNwFQ6HCYfDUuQQGcLG8ge9vb1DE+C+JLZnZQ7ZIluu6L+zDPKhEAwGAfjNb35DXl4era2teDweqqqqdmu9Yn/EWDPuVywWyxJNcwMajGLh7Nmzpf3yAQccgMlk4vLLLweynSyEoG/sL6M4bGQ4AwCMlvKxWIyzzjoLu91OY2MjLS0tmEwm2U5jffihYAwASKSS0gUgpWfadtddd1FUVERzczN2WyM/+fGFsh78rmThCutwMZ4SicSAoJ1IEotEWb92HS/NfZHr/vBHJpxyKmNGjcbn8ZKnaRwy9mCqzVXYbY10tLVTWV5BPBqTgRn6tn03m80EPF48Dideu5OJJ52MHksMBAAY6obrZIIedoYxqESMq8mTJ+P1emlubsbpdPKNb3xD7ptxbA31/I9Go8yYMYPS0lKcTicNDQ2YzWZeeeWVLzwAIBaJEgmFOfKIcfg8XlqamqmrqeXHUy8YfJy398nBWHZDBE4Zz5Nci/10Os2JJ56IzWYjPz+f6dOny+WHcv0Q/SKy7QcaAj1buwn4/Hhcbka0tdNoaSDg8dLs83Pw/gcS3eYk8VkYHVImTJiA1WrF6XTS0dFBY2OjLIfT29ubZXefSCQIBoNZgr+RzP/TQIJQ3xbqas20tbXQ3t7J6jUbCEUzmemROCT1jFAdDEW2fSdJItoPJPiiAwBEnxqPxXnnnUdtbS3t7e2yDIZwUHnnnXeAAeHdOBcZXUrEeu/7+/+Rp2m0t7ZJ+3+300VdTS0zH35kwOpfh76eXmKRqAwIGGoJgJ6enqwSGHLftn3iacNwTuvo6Yz1P3qSTetWU1dVgb3RyoEH7o+uQzyRIsnwBADous6KFSsoLCzE4/HgcDhYtWpV1v3G7jJx4kRcLhculyurZM6Orl8KhUKhUCgUCoVCoVAoFAqFIoMKAFB8rRECbm9vL0899RSapuH3+xk1apQUPHRdp7e3Vwo/23vZvrdirEWdK0jlioMCo9D5VWPMWs9F7FdPT4/cz1QqRXd3txSuhoNwOMzdd99NXl4ezc3NNDc3YzKZWL58udzujj6xWCzLscBoqW0MaBBODcYa7iLjXIzHXMeDYDDIfvvtxymnnMJBBx0kazBXVFTwj3/8Q/aR8bgnEomdujsMZwCAUSwHGD9+PD6fD7fbjd/vp6OjY5DYJgTInZHrACBcAERu/PTp0ykoKCAQCOBxuZl0xplZgtxQAwCCwaAUm4xBQtHoQDZvJBQmEgqTjCeIhMKsX7uOjz/8iEdmPMx+o8dga7BKcbC8tCzjRrAN4Vhw8MEH09HSSpPXh6fRwfFHH5Ox/0+kYJtrQTK+bSyRCXoYCmL8CFH7jDPOIBAIYLFYZDkGY0CKGJ9DOcbCYeWRRx6RwRYejwez2czHH3/8pTgApJMpjh1/DDVV1bS3tmG3NfL/2XvzMCmqs/3/TM++73svsw87iIBb5DUxGjXJT4274G7EBZdv1ESNihp9TXxFo4kajdGAGre4JiqKS1BZRNw3CEaQRZx9pveq6urP74+ec6a6B2SIKJCcz3X1xTDTXV196tSpvuq+n/s55qij6evpxbZiX/mQyRty/2RlsdxP5z50dHSon53PP+6446ioqCArK4s777wzqbXKSJHnm3rPOCxbslQlSjS4PVSXVzCmrZ2qsnIuvfBiNSe2RigU4vPPPyczM5OmpiY8Ho86Bz0ej4r/d14DUsdeXkfl9THxXJu4FebVVxaQJgT19bWceOLJGGacUBRVZS4TAGzZnx4TMAmH+vmmDQCAqkaXx6OlpYW6ujra29vxeDz4fD6qqqqoqqoiFAqp9ccp9MOQ4Ow00u3/3e8pY4+n3q1MKI2+Bvp6erEMU60R/b19yQkk/6a/Tq6pzvUvDkSMKIEBP7IFQNyM8NRjj5CfnUG6S3D//fNBPteKsz3kecuyeP/998nMzMTtdtPY2EhPT892a8ETi8U46KCDcLvd+Hw+0tLS+Nvf/qbeW6PRaDQajUaj0Wg0Go1Go9FsGW0A0PxHY1kWhmGwdu1a1X89JyeH9evXA0OCj1N8lELI9uwjv6OQMdWQ3F9+1apV/OY3v+H6669PxAnH4zul8SH1Jr9lWeohj48UbFIrArdXGwDLsliyZAlpaWmqer24uJi1a9f+29t0GgFgSCx0RuCninChUIhAIKCee9hhh9Hc3Mz69et54YUXyM3NpaqqiqamJiZOnMgXX3yhXuusst9aMsL2bgEgjQ7RaJSamhq8Xi+NjY1UV1dz2mmnJRkeJCM592S1vxTApAEgFk+I10888QTp6ek0NDTQ1NDIQQf+YJsNALJKXzJv3jyWLl2qPldgwJ8s6A0KfM7q86A/wJwrriQrI5OqikomTZhImhBqnYkYiX8vuOACyotLaG9uocXbQFtDU0L8t2yiwdBQdHgkMmIDQDQaxe/3J51H11xzDYWFhbjdbtxuN5mZmUBC5JWi50ira+XY3H///QghGDt2LK2traqq/NswAETDEU456WSqK6sY3T6KyvIKfnjwIVuv/t/C5p3v65znnZ2dwz63ZVmcffbZFBcXU1RUxPXXX590Lm8L8r1sOzGf7rjtdkqKimltbsFdU0ujx8vo1jbSheCl557HluLyVohGo9x8880IIfD5fHg8HmXAEUKwYMECZfiQQn9vby+BQEAZmCSWZTnWDxsjPMBv5/4vDb560tPT+P3vb8cw4wQjcbr7wgwELUJRe2jIbYtQsA9pAvg2DADhcJhoNIphGHz++eekp6erivKWlha8Xi/l5eWccMIJQHJqgFy7nJ9bpjWsWLGCnKxsJk2YSGtzC9WVVTQ1NFKQl89pp5yacDwMPqQRIBQI8uUXm4bWiK0g55BhGEktegzDSFT/m3GC0SEzgGUMjmncwgj5Of3kE0gXgmlTd8cwIgSD4aHUlO0Q8BOPx1m0aBFZWVlUV1czYcIEQqHQdvvuZFkWBx54IDU1NeoatGTJEmDb29BoNBqNRqPRaDQajUaj0Wg0/21oA4DmP55wOExzczMNDQ1K8ACS+l5v7mayFEN2daTQaFkWkUgEwzCYNWsWWVlZZGVlcdNNN2HbthJKIFns2pE4j0uqGAtDPZrl/jor/7dHCwNpDvn444+pqKjA5/NRXl6OEIIVK1ZsNQHA+TlisZiqRk19jlMslEKs8zPIzyf35/e//z0ul4tnnnlGve7qq68mPT2dUaNGUVRUxIwZM9R4OJMFpBlgSxWU29sAII/Zpk2byM7Oxuv1UltbS1paGvPnz1d/d55vI6nulCK4NRj9b8YsZQSIxWIsWrSIjIwMPB4PTQ2N7L7b5OQWAHZ8m6pwA4EAhx12GD/72c8wTTNhmBlU3syoQSQUxohEiZlWwhRgx5PMAbf97vdkpmfgdXtIE4IXX3xRVfIC3HvvvaQLQZPXx+jmVjKEoGdTB9hgOnqHh0IhbOIjMgA4x1bOsZdeegkhBJMmTVJrovo8oMwaI0Eep9/85jcIIRg9ejRer5f99ttPmQm+SQOAHO/bf38bVRWV1FbX4HV72H23yXR+2bHVBABA7Z9cS1LbAKReB5wGGsMwuOqqqygoKKCqqopzzz1XbXMk54h8X+fYxGIxIqGwMjW0NDXT2tjE2PZReOvqyRBpdKzfCHIeb4VIJMKYMWNob2+nuroar9dLc3MzHo+HrKwsotGoMnzItWHu3LmcddZZnHfeeeo4ONfWUChELJYQ8Y847GCqKkvIzEzn7bffxTCTq/8jJnzZ0Zs45WImA/1dQJRvywDgNPg9+OCDZGdn09LSgtvtprm5GbfbTX5+Pvfffz/xeDxJ9IehFBqnKcY0TW666SYK8vLx1Ltpa2mlqaGR1uYW0oTg1X8sSrTrGFxvYqbF+s/X8eMf/ohTTjqZVZ+sJBz86iQWGBrz1NYofr8/uQWKNWiyiNlEIyGIW3Rt2oC3rpp0IZg/717AJho11Wuise0z9o8//jh5eXlUVFTw/e9/H8Mwtpv5LhKJ8P3vfz/JAPDJJ58QjUb/o1o0aTQajUaj0Wg0Go1Go9FoNN8E2gCg+Y+mv7+fWbNmkZGRQWlpKTfccIO6kS+r1KQI4xSXpRCwq+OMlpYMDAzg8/koKSlBCMEll1yi/iZj50cqAH7TOAXyVPF/YGCAeDzOxRdfTG1tLYsXL1av8fv9261CUFbGzpw5k4aGBkaPHk1mZiY33njjdtk+JAQdp7AoBR8pykajUSWqLFu2DCGEEkzD4bASLI877jiKiopobW1FCMGNN96ojC7OdgPAFlsBbE8DgNw327Z57rnnyM/Px+Px4Ha7ycvLY9WqVeq4Oufclkw5SftJsgEgahoqAQBg5cqVZGVlUV9fT4PXR1NDI9hxZQCIjyBCPR6Pq/GSfdT33XdftW+BAT/RcGTITCAfg+K/TBv48otNEIfDDz0MT72bNCF4/PHHVZw/wAcffEBNRSW1lVXKAPDK8wsTGmnMVvsbjUaV0WFrOOPspcFn3bp15OfnU1dXp47rW2+9lSR6y2OwNaQwOmPGDIqLi2lpaaGsrEydG9+0AUCO74rlb1JdWUVeTi67TZzEuDFj+eC997eaAOCsuk9dX5yJKHI9cc5LwzCwLIs777yT3Nxc6uvrOfzww9W4j2j/HeuaHJtoNMqXX2xi/NhxeOrdNDU0MqqllbamZqrKypm+9z6YwfCIEizi8Tj/+Mc/cLlcTJw4kdraWlpaWvD5fJSVlXHBBRcAyYabL7/8UhlD0tPTVeV5KBRSEflffvklYPP5Z5/wvf/Zk8qKYqZMmcynn37GgD+CPxRjIGjT1RtJtOiIQdSwAJtIeADiEQxjgG/DAODk1FNPpbq6mra2NrxeL/X19Xi9XlwuF++//z6QOHYyFQeGkknk/y3L4osvvmD69Ol43R5qqqppbmyivbUNn8fLntP2wIhEk1JAbCvG7LPPIU0I0oTgjaXLEnN3BKxdu5aFCxcyb948rrnmGm688UZee+01evp66ejsTTIAGJEooaAfbJO/PfFX0oVg0rjRdHd1EAgMJD6fZW9XA8Add9xBQUEB5eXlnHjiicTjcbXuf126u7vZe++9cbvd1NfXU1FRwcaNG5MMbRqNRqPRaDQajUaj0Wg0Go1m82gDgOY/mrlz55KRkUFWVhZnnnmm+r3s5+wUaZw3+LdXBduOxlnF6Pf7MU2TxYsXI4Rg9913p7y8nHfeeQdgWAuAkcaAf5M445gtyxj8XYz16z/n9ddfZ4899qC0tBQhBMuXL08+nttpH2QF8B133KGqnOvr6znmmGNGnADgRAqJpmlyzjnnsHLlyqS/b676XYrjK1euJD09XVUaO6uTo9Eon332GS0tLarCNycnh0WLFgHJUfywZYF1exoA5HsEAgEuvfRSMjMzqampYfz48UyZMkXtU+r4OCujvwoZ+x8HwtFIoiXAoAGgo6ODnJwcamtrlVAXdwjpI43glgKtaZoce+yxCCFYu3atimp3Rv/HTIuYaSW2nWIKiITCvPziS6QJQVZGJg8++GBiu7Ghz3rIQQdTUVJKa2MTNRWV3HrTzWo7smJYpgbYI5jhDz30ENdddx2QfH6fdNJJlJeX09zcTE5ODvfddx+QEHnl/NiSQSSV3t5e9thjD3w+H42NjeTl5ak15Zs2AEiRNWZa/PiHP8Ln8eKuqydNCP721NNJMeybfThwVhUHg0HWrFnDz3/+c2zbToo0T22X8vDDD5Obm4vH42H69OnAtq2d8j2VqSQQYPWqf5KXk0tzYxM+j5cGr4/qyiqqysq58rJfYg1Wl49kkbvkkksoKyujurqaxsZGxo0bR1lZGbm5uaxatUpd6yKRCNFolNdeew0hBA0NDZx77rlqvz766COyMtNJE4L8vBzShCDdJaisKMbjqae4uJjsnDyEyCA9qxDhymX6dw+isydRrR4IhACbmBXBjkX4NhIAIpFI0loyduxYPB4PPp+P+vp6amtraW9vx+VyJX0ncK6rAwMJ4VyOQ29vL2+++SZCCNx19YxuH0VNVTU+j5fiwiKunnPVUJuRwXPXMkxysrLZbeIk0oRg08YvRnTsLrzwQgoLC6mpqeGCCy7g+uuv57LLLuOggw5CCMHv7riLnoGgCoNIfM7EGJ915umkCcGvr/8VMcsgHk+stSotYDuMr2VZXHfddeTn51NeXq4MJTI14euyZs0axo8frwwro0aNSmqFoNFoNBqNRqPRaDQajUaj0Wi2jDYAaHZ5ZK9iiaykfuqppygsLKS8vJwpU6aov/f19QEjixn/ukjxyymYpqYNwFDigKwwlTfPZQ/mzVXjyu3KSlTntpw333t7e4HETflAIMCVV15JXl4elZWVzJo1Swkc0WgU27aHGQF2DDZgE4kkRKMlS17nV9fO4fzzz+V/9tuHnNwMcnKymDJ5KiVFpeRk5bJ48VIikcHeyJaJGbexiQ8+YmqbyY+tI+dLd3c3U6ZMoampiZaWFjIyMli/fn3S2Durp6Vo6Jybzsr+V199FSEEb7/9NjAk5svXSdMGJJIsLMtin332Yfz48WpbUrxzirX/+Mc/lIDX1NREY2MjX3zxBUBStbHT5CIre1NFmzPPPJPi4mJ8Ph9iMLZ+SxiGsdnkiI6ODiKRCNnZ2ZSUlDBq1CiEECxdunSL29peNDc3U1lZSVNTE8XFxfzrX/9SfxupiWH9+vXq58MPPxwhBPPmzUt6jvNYwJAA7DxvLcvio48+QgiBEIJnnnlmWMz4888/T5oQjG4fRV1NLePHjlNx9VtKbnD2B3cKz/39/Xi9Xq666ir1u1gsRjQa5f3338flclFRUUF7ezuHHHJI0jxOreCVVdGpY2fbNu+++y7p6elUV1fjdrs54YQThplMnBW7iepxWLFiBUIIWlpaEEJwyy23qG06Y9udOEV6+fdwOIxhGCxYsAAhhBK6jz32WPX3eDyeZORwjsfmKr0Nw+Ckk05i2rRpSethNBpV56lTGM/Ly6O9vZ3W1la1XozUBJCaPBONRvnLX/6SEJjdbsaMGUNlZSWjR4/GJdJ49OFHVNuJrdHd3a3aE0jx2+124/P52HPPPenr7QZswqEhwfsnRxxFc0sbQrh48aVX1DhHIyE+eGcFKz94hw/eXs4nH77D7NmzqaqrJzevgEsvu5x3PviYdz74mNcXv8E7735IIBglHLGwLOe5NrT+ypYoTuQ8l+MbjUaT5sK2pNNEIhG1/U2bNiGEoK6ujra2Npqbm6mvr6empoYTTjgBSMyV1HklcbbFmTlzJpWVler4tLa2UlVVRVFRERs2bEgyDm3atEmt9VVVVcyePVtdk4mnXJfiMSwzin+gj+/v/11ysjN59JGHGOjvTXpezDJ4//33qfM28r//d7Nqt+APBohjE474SXMJfA31rP38U8AaNNANXd9HanKU1yxn6xr5HSMajXL66adTWlpKRkYG8+fP32bzZOr3FacB7KOPPqKiooKGhgbq6uo4+OCD1Xn1bXx/02g0Go1Go9FoNBqNRqPRaHZltAFAs0vjvAnc1dWlxPK3336b3NxciouLKSsrIxwO09fXp27KO2/sf9PIG+WhUOgr+647K8c3Vz1nGAaRSEQJ/k7hxDRN1cs5NR7XWUXf29tLWVkZTU1NCCFUbD4MjeXmhLJvnyGxw+/v5867buPIow7jlFNP4Ne/uZZnn3uKnt4OfnjgITTUN1JSVMry5SuwBqsbzXhC8reIYw0aAOJJQosFWFstg3SOcV9fH4899pgS18eNG8ecOXOwLEsJ9Knzyvn/QCCQdFy+973vUVdXp/7vjA3fXPX1rFmzSEtLY/HixWqey/2Tz49EIpimyb333osQgvLyctxuN8cffzx9fX3DYvYty9psmwhICG3HH388Y8aMoba2Fo/Hw6233opt24TDYfWZNyekx2KxpPk+d+5csrKy2G233aioqOCII45QQuk3yf/8z//Q2NiI2+0mJyeH1atXK3FupBWqUlzv6+vj6KOPpqGhgQMOOIBIJKLEYKeQLI9Jb29vop/7YFsNgJdffpm8vDxKS0t57bXX1Hs4RebddtuNtrY2FcP+yCOPqL/JbUsR3XmOyuMhxe7XX3+dzMxMVq1aRTwep6enJ2k+X3TRRVRXV1NbW4sQgn/9619qfmzatAnYvMjmPK6maTJr1iyKioqorKykoKBA9S3v6elJ2j/nnO7s7GTdunXk5OTg8/nIysri8ccfT2qDAYm1y+/3D+u/LtfArq4uNS62bXPssceqiHshBC+88MKwlAkpChuGoc5PuR3TNAmFQqxduzapzYasJJf7BEMC9aZNm1Q/+YqKiiSTydaQ5is51nKbBxxwAEVFRTQ3N1NdXU1zczNerxchBL29vSMWwaXhZOLEidTV1VFfX097ezuZmZncfPNciNt0bdoA2GzatJEPPvgIV3omefmFHPLDHxMMRRJLZBzAwgz5IR6FaAAzNMBhhx+Bp6mNGreP5xa+TMiIDUb+D7blMCwMc3g7D+eaI+fLX//6V6ZNm8Zuu+3G5ZdfniQkO+eR/HlbIuANw+Dxxx8nIyOD8ePH09raSl1dHeXl5VRUVHDnnXcmzTFnm6BUg0JfXx/jxo2joaGBtrY2JfxPmzaN6dOnq3UVEsfXMAwmTZpEW1sb+fn56pobCASIDYryxGPq57htcdKJM8nLzeaxvz5CdNAEBzY93Z2ATX9fD2effTYFxRWccc759PkjhAwTw45jYvHYE38lOzeDS3/5c4LhAeKYRI0gYBMaNHuMdP3b0vUBEnN2//33x+PxIIRg2bJl6m8jSRCR32NSk5jk96AVK1aQmZmpWjXMmDFjWMsMjUaj0Wg0Go1Go9FoNBqNRrN5tAFAs8vjvNEsqzcnTJhAQUEBlZWVPP/885t9/rdhAkgVtGT1fyQSUcKHvMG+OfG/r6+PYDC4xZvdWzIUWJaVFOct32fRokUIIfB4PBx44IFJ7yPf498RWLY/yQkAVixCINiHHTdIyPqJf4867EiavS0U5hexePFSDCMRjx4d7A3/dQ0AMpnBKXiec845FBQUUFRURFZWVlJlqGTjxo1qHKWQJcc3Ho/z9NNPI4RQIrBT0DUMI6mKuLOzkyuuuILi4mKuvvrqpH1LNW1Ik4hlWRxyyCGMHTuW6upqhBA8+eSTaj9kxbkUjOXvpZFEcv7555OVlYXP56OpqYmGhoakuRIOh5Mqsp1CqWTJkiUIIfB6vdTU1FBQUJAUsf1Nct5559HY2EhtbS3p6eksX74cy7JG/P5yLHp7ewmFQpx33nlkZmYihODvf/87wGYr/1ORv3/ooYcQQpCTk6PSO5yx8/K50rwxZcoUxo4dy5o1a9S25L7LsU+NpJfjP23aNI4//nggsealriEdHR3U1dXh8/lobW3lxz/+8bC/yzXKNE06OzuT2qTE43Hef/99hBC0t7dTUFDA008/zcDAgDonnGMjI9YhsS5+/PHHCCEoKysjLy+Pe++9N0ncS1075dxMFSNlQoNt2/T09JCenk5LSwter5eZM2eq5/X09AwzacjtOo9bLBZj5syZuN1uVakdDAaHJQdIDMPgoIMOoqqqivz8fBYuXLjN5qne3l51zsdiMXw+H+PHj6e5uZmamhqamppoampiv/32SzIrbI0nn3ySmpoa2traqKysZOrUqRQVFVFYWMj69Z/T19U5uBYm1sU999ybceMnkl9QxMuvLFJR8b3dPcRtA8xIwgBgR+n5cgMNjc0UV9TQOnoc6zd1Eo0lKtFl+r18pF7jZBsUuQ7JCvn29naqq6tpamrixBNPVNdqmQLgbK2yLddv0zSZPXs2JSUlNDU1UVVVRWNjIw0NDbhcLj755BO1vdQ0j9SEhoGBAdra2mhtbWXcuHH4fD4mTJiAy+Xi5ptvVtuR5+G8efPIzc1FCMEvf/lLYGg9sMyoGns7ZhKNhFj9z5WUl5VQX1dDd1cHYGNEw4SCfvXchx9KJERkZBdw5HEnqASAxMPm2OOPIa8gm1WrPyaORcyOYpiJa6lpDj+HtsTmWrQ4W8T09fXh9Xppa2ujvLw8ydAykiShzRnInHPlmWeeUakN1dXVXHXVVcMMOBqNRqPRaDQajUaj0Wg0Go1m82gDgGaXxnkTOBAIEI/HOeigg8jNzaW0tJTf/OY3wFCVobw5/23F3KdW4jsjrOXv5M1u5431zW3HMAxCoRCBQEBVFUsBUVaupgqQ8ga+/P1Pf/pTRo0aRVpaGm+++SaQGBM5PjDctLBjSI3qt7BiEaJGEDtuYMejhMID/OTHh+Ou9pDhymTRoteIRs3tagCAxBhKYUNW+Z944okIIVQ/e7/fT29v77BjZ5pmkijS09PDhg0bEEJwzjnnDDteTlFLVnTPnj1bic5XXHGFEqXlc2W8fzgcVvPatm1Wr16NEEL1UBZC8O677yoBUc45Z8935/yLx+P85Cc/YeLEiVRWVlJWVoYQglmzZhEIBBgYGEgSx5xz3bZtOjs7Wbt2Lfvvvz/V1dXKiNDT0/Ot9XGeP38++fn5eL1esrOz+fOf/wwwYgOAFALlZzvwwAPxeDy0t7dTX1/Phg0b1HOdRiQV8T2IHCeZpPCLX/wi6TVSHJcpHsuWLVN9tYUQnHLKKWpbznPVuY+WZam5cNVVVyGEYO3atcqYIbcvq+ohUfmek5NDYWEhlZWVXH311XR1dSWtUcFgcNh4RaNROjs7mTp1Kq2trQghuOGGG5LMI/I9ZW9552sBnnvuObKysmhra6O+vp7TTjtNPUeuW1JE3Vy6hty+aZoNqr2BAAAgAElEQVTYtq3Gcv369QghqKysRAjBrbfequa8/DxOw0xq+5TrrrsOIQTvvfee+p3znIjFYuo8k0aI//u//8PlclFVVcXNN9+snrc15Lxyju8rr7xCSUkJJSUllJeXM2nSJIqKivD5fFxxxRXbFLG+YcMG1e6lurqa+vp6qqurycjIoL+/F+KJtbC/r4err55DQUERQri47JdXYMeHKvnDwRBgJQwAVggifpa99gqFRSVk5Rdz1HEzsYCIFScGmJZNKBxVBgJ5jdtcm5tYLMZPf/pTysvLlfifn59PYWEh77zzTtJxch6rkRggpCEwHo+z2267MWbMGNxuNzU1NUyYMIExY8aQlZWVdJ6nCvjOVBY552bOnEl2djYVFRXKGJWfn6/2V5o5li9fjhCCiooK9thjD9atW5di2rIJ+PuTqvyffuoJsrMySBMC04jQ19uNaUTUc8HmjWVLKCgoIDOnkDfefl+J/5u6ujHiJnFsnnnuaeJYWHYUO24Qx1TvIcdka6R+V5HHQBoZ33rrLfLz86mpqeGQQw4BkltpbCvO70G2bXPTTTeRkZFBY2MjGRkZ/O1vf0t6nkaj0Wg0Go1Go9FoNBqNRqPZMtoAoNnliUajSpC54IILEEJQWlrK2WefDST6IMPwvuffVgWZjAGXN/5lj2sp3n9V7D9s2410efPcKfrJqvBoNEplZSU+n4+JEyeq1zjHwVkdvWNvsCeEing8hmFEgIRgLxMAwCRmR/nJjw+nvspNZnoWixcnespvzxYATuHUKZoEAgFmz56NEILGxkbGjRvHwMBAUrKDE9M0CQaD/OMf/0AIwQ9+8IMkY0AoFEqqAH7vvfe444471PbLy8vx+XxJIqmzpcDnn3+etM/hcJi7776boqIiWlpaaG5uprS0FJfLxeOPP54knklBNrUK2jRNTjnlFOrq6lTV7F133YUQggsuuCDpuc6e74FAgP7+fqLRqBIvy8vLGT16tKpkT626/qZ46623VJ/50tJSzjjjDODf6x8dCAQ44IADKC8vJz8/n/z8fHw+n1p7ZIqD83xymjJmzpyJEEIlb0ih0dmOwClqL1y4UO17UVERhx566LD1S27DKbpddNFFpKen8/rrr6t9kEKeU2iWr1mzZg0tLS24XC4KCgqYP38+oVBIfQ6neUrOs1WrVjFt2jTy8/MRQjB//ny1ljnF9VTjgBTpe3p6+H//7//R0tLC2LFjKSoqor29nZUrVyYZKeR+y23Lzx0MBtU6Z5pm0ny2LIt//vOfeDwe6uvrEUIwZ86cYZHk0gQhkwMATjnlFNLS0pg/f75KXklNmLFtW813+f5vvvmmag0iUwdGUqG+uYSYhx9+mNzcXBobG2lsbKS5uZnm5maEEDz77LMEg0EsyxqRic3v9zN79mwqKiqoqqpi7NixNDY2kpubC9iY4RBWJMiZs35KXl4OQrjYa+/vJNZQy8YcbKkiWwAQM1QLgN/f/H/kFxQh0rP5/R/+iBkHMz5YhW4zJP4PjrEU/1OvK7FYjNNPP526ujpaW1uZOnUqHo+H7Oxs/vCHPwBD50hqNf5IkNdCIQSjR4/G7Xbj9XopKyujpKSEww47TD3Xmcgh26nI/zvX/8cee4zCwkLGjx+vKuCFEHz00UdA4px5+OGHEUKoOfjZZ5+pzys/E9hEwolofrAJBga4b/6fKSrMx11fy5jR7bz04guJYzVoApCPQCBADIjE4IuO3qQEgGA4kKj8jxuEowESV0IT206kPTg/11eR+t1Amm3kuTdv3jwKCgoQQvDb3/4W2HzayEhwtrSR733SSSdRW1tLe3s7QgjWr1+vTB0ajUaj0Wg0Go1Go9FoNBqN5qvRBgDNLo+stn366adJT0+nuro6Kd4+tYpXCq/fRg/ZrfUalz1wv8qM4OxVLV8TjUaVqBwMBrfYE1qKVbZt89577+FyuRBC8MwzzwyroEy9sb6tMdbblyGhwzSjSvg3zBCmFcYwgwz4ezji//sJ7moP2Zk5vPFGItEgDkQsc7skADjHQwr0cj719/dz9913q+p8IQS/+MUv+PTTT4GE2Or3+4nFYjz77LOceuqpCCE444wz1Lackefvvfcee+65J+np6crE0tDQQGlpKTk5OXz3u98lLy8PIQTr1q0bJqbJOfDZZ59RUlKiWj00NDTg9XoZPXq0EhJdLhcTJ07kiCOOYO7cuUnbcs6n448/XlXMFhYWsmTJEtatW4fP5yM/P59nnnlGjdHAwICax/fccw8ZGRmkp6dTUFDAaaedpuas3Pa3cf51dXVRUlJCc3MzdXV1TJ48eZteL0Xy3t5eDMPg/PPPJyMjg7PPPpuBgQGOPvpohBDcddddSc93irNLly5l3333JTc3lzPPPFMdO/n5u7u7lSFBxtlLvvjiC37wgx8oITE3N5dbbrlFVfpLent7mTdvHk1NTQgheOGFF5L2IxAIqOMkRXmnAcMwDObMmaPm8d57783LL788TGhbu3YtF110EUVFRQghmDx5Mh9++CHhcFidF6midmdnJ4ZhcO6553LRRRfR1tZGYWEh+fn55OXlMWrUKHbbbTfq6upwuVyUlZWx7777MmPGDGbMmKH6iktzktO84TRWyXVdmhd6eno499xz1WcaM2YM8+bNo6OjQ4n6GzdupLe3l5dffhmXy0V5eTmvvPJK0tyU209dY+X5Ig1oJSUluN1uxo8fT3d394gEarltwzDUev7hhx+qdBGv10tFRQVFRUV85zvfSXrfkWIYBnvuuSe5ubn4fD4aGhrIzc3l8MMP5fijj6IwN4vSksTxPOqoY4iTEPBVfD9gRg0iYT92NATxKJa/h6MP/zGFRSVkF5Tw+rI36fWHCJs20RhEDSupBYAz+j9132zb5u9//7syUJSVlVFVVUVhYSFnnHFGkii/rdekgYEBlR5RUFCA2+3G5/MxefJkPB4PZWVlvPTSS0ByaoecH86EFCe2bfOrX/1KVffX1dUxceJExowZw6mnnsrYsWMRQlBYWEh1dTXr1q1T89JpIksV9f0Dfbz91psUFxXQ3tbCqPZW8vNySBOCMaPbufSSn/PySwvp7UkkWoSNOBYQtRLifzBq0OPvJY5NKOInaoawMQEL0wqrljojNT+mngephoBzzz1XXWuWLl2qDDKwbcdKmpmcczsSibD77rszatQoGhsbaWtrU8kaO/a7iUaj0Wg0Go1Go9FoNBqNRrNroA0Aml0aKTZ99tlnCCFobm6moaGBdevWqX69EmclpzO6+5sk9Ya2U/Bdv369qsT7qhvyUvAf6fsYhpEUEy8rZH/+85+TmZnJ3nvvDSTGTgoStm0P69++MyQAOFsAJD8SooZMAMjKyGbp0jeIDUZWByLh7dYCwFn1KHH2M9+wYQMPPPAAxcXFZGdnk5OTg8vloqWlheLiYhW//73vfY8lS5YkPp0jshwSYtADDzygxMri4mL1c05Ojoozz8zMJDs7m08++QRIFnHlMV+8eLGKQM/JyVEVmkII0tPTcblcZGRkUFhYiBCCQw89NCkiPRqNqn07/vjjqaysZNKkSQghWL16NZCYV7fffjtCCIqKithvv/046aSTmD59unovIQTnn38+H3zwgdq2FJSc7/dNEovFOPTQQ6mpqcHtdlNeXq760/87ItK+++5LS0sLn3/+OcFgkL6+Pv74xz8ihCA7O5vp06dz0EEHMWvWLPbaay9l5pg2bRoPP/zwsPPYOQec+xUKhZLE+w8//JDp06eTlpZGWVmZEtDHjRtHWloaLpcLl8vF5ZdfrgR+5zrgrMR3zl0YMgmZpsmqVav4xS9+kegvnpGBEEL1OZfzTwjBXnvtpYRT2f5CbkuOa2pLEue8kAYCOQed75edna1+drlc3H333WrbqekKTuT/ndHykUiEt956i7PPPlu9T0FBAZmZmUydOpWKigr1+1NOOQW/308kEhmWFgAktddwvo/TLFNaWkpJSQkvvvjisNdvDtlCwCm0BgIBpkyZovbT4/EghODVV19Vxy71GG4JeQ0MhUKqtUFRURElJSWkpQlyMtJJF4I0IXjssUfp6xtIrJ/BMIYZUxX8MdPCjkUT62Y8Sv+mdYxuacDt8bHbtL3p6OnHsBMidDSGShCIM2gi+Ir2NtIodccdd1BUVERGRgY//vGPOeecczjooIOUgU2OlzwG23p9Ovfcc6mpqUlaCy+//HK1HWdyi9PAE4vFlDgtjxck5ttjjz3GHnvsgRCCvLw8ZdCSc/vyyy9Xlf9yv5MNiTZx2yIcCuC83h1/3DGkCUFFeSktzY00NfpoavTh9dSTJgSTd5vIokWLCEVtOnr9g+NuY5FIAOjs7iCOhRmLDF4BTaJGkFjMJBYzR2wAcLZBcH43iMVi9PT0MGnSJEpLS9lnn33o7OwcZs7ZGs5zNTWVZuPGjVRWVtLW1kZRUZFKnUk9/zQajUaj0Wg0Go1Go9FoNBrN5tEGAM0ujWEYdHV1UVNTQ11dHTk5OaxevTqpZzMkbtan3jj+NqrInP1+5Y3/YDDI9ddfT35+PqZpDquscyLj++UN8vfff59rrrlGCbKyUvTMM89k8eLF6jOlCvkDAwN4vV6EEDz55JPqfZ2CZKqJYMeSbAAwrTBRI4iMMo7ZibYAR/x/P1EGgGXLlisDQMQyt0sLAHlcnFXN8udQKIRhGEosCofDfPzxx8yfP5/bb7+dv/zlL9x333386U9/Yv369ep1qfMuVWx0ilvyZ2lkcUaPy2MkTR7xeDyp7YM0ljh/lxpd7vw8XV1dw4S6k08+mcrKSrxeL9nZ2SxdulTthxQtly1bxt13381dd93FH//4R+69916WL1+eNH4DAwOq2lf+/tuaY9dffz2FhYXU19eTnZ2tYrpHIlBJERkSY/bII4/wr3/9K+k5gUCAWCzGsmXLuPnmm/nTn/7En//8Z2677TaeeuopJdbK8ZZR5k5BWx4DZyy9xDlv+vv7eeaZZ5g7dy4PPPAA9913H/fccw8rVqwgFAqpuSQTJpyvh6Eqeef8Sl0D5H4uW7aMxx57jD/84Q/cdddd3HfffTz11FOsW7cuaVtyfOR7p8a0W5aVtD+p5pfU5ADnOeXclrM9ghRrpVnCibPtQupYLliwgAceeIB7772XO+64g3vuuYdnn31W/d05FnLtlZ/DmQbhXF/lPL7nnnvIy8sjMzOTW265hZHi3H/nXHvwwQe57777uPXWWzcb9z+S88c5fnIcnn32We68807uuusPzL/nTyx87u+ATTQaxhoU7QPBcGIdjZpYdiyh4mNB3CIa6GHlO8vJSRfU1rk55NAj6AuEVQR9xEqswaFwVBkIUpFrjPwMcjyDwaA6to8++ij77ruvOlecz5N96LeGbdtJySRPPPGEOmfeeecd9bzNtcZwvp98vVzznG0xAFasWMFdd93Fn//8Z+bPn8/DDz9Md3d3konHaViRLSfig5H88hEOBZQZ4Jbf3kRpSRHpLoErTVBYkEdjg5emRh/FRQUUFBTw+tIVg6I/+MMR1QLAsKLYmFh2lDgW4Ygf0woDNpZlqOv/1kg1ADjNEqtXryYtLY3i4mLmzJmjvk845/BISTXB9PT08P7775Odna0MMH/961+JRqNJa5dGo9FoNBqNRqPRaDQajUaj2TLaAKDZ6ZECmrzxmyr47LvvvtTU1JCZmclDDz2knvfv9Pn+d5HiqrwB7oz+l/86hdrCwkIOP/zwJMF1cw/5+mAwyNy5cxFCcNxxx/Hggw+ydu1a1qxZw9KlS7nssssoLi7mhhtuUHHu8ka8bdu88sorFBUV4fP5koRI57+QED+cwtJI9u/rEovFhgktYA9WKkqBJCH8ywSAOAaGGeLIQ4/AXe0hKyObJUuWYQ/2no7GrO1iAPhv5/TTT6empob6+noyMjJ46623kkShnR3ZWiEvL0+lKNx77707erc0/4EEg0Gqq6upr6/ngAMOSIr339y5MhIDyjeLDXG5Fsq1ESXaO6v4Q4GE+coKB8COcMdNvyHbJSgoLOb6G3+rqv8DERNzsG2APxBSCQCmaarrn1O4DYVCm60C7+vr49577+Xwww9P7NOgAC3HNDX9YUt8G9evr0N0MJI/Eg4SswzkcYiEg5hGBDtm8snHH/LoIw/xv9f9ijGj20kTgtGj2igvL+cnRx2PBQQjscHr3ZABIBY3VAbOUHLO0HEeCXKcnMdMmmSuvvpqSktLEULw0UcfqWPsfM5IcJokpUksFotx2223KfOZEIINGzao+THS46/RaDQajUaj0Wg0Go1Go9H8N6MNAJqdGimSdHZ2qpvQMj48Eokwe/ZsVSF27bXXqt9/W9XFwWDwK4WEcDisqgVlhduvf/1rXC4XCxYs2KpAIVsVnH766ap6H1A3yZ2VkzfccAN5eXm8/vrr6r1lVe2VV16JEIJHH32U7u5u1RZAVvym9pMHWLNmDS+99NJXPl5++eWv9Vi4cCEvvfQSL774IgsWLFDvHY/HSPQq1gaAHcmJJ55IVVUV9fX1pKens3z58qQqzF2B7u5u2traqK2tpb29nWOOOQYY3qteo9lWnKa0aDTKNddcQ35+vhIsnSY0ZzuanWPubd0AYMeHWgCEQwOqBcBRP/oBo1saEMLFivc+woxDZ+8A0VjCCNA/EEhKEoChFAV57ZFV9/JaJq/ZmzZtAmDOnDmcd9556jkwlGgw0uv7zm4AABvTiMDg9e6Tjz/EmQhgmVH1cyjox46ZPPrIQxQV5lNbW8ue++xH90CIsBEnBoQMkxg2sbi1XQwAMNSaRFb4yzk9YcIEcnNzOe6445Lms/xeMhIDpvzu4dyuPMY//OEPqa2tpaysjCOOOIJIJJJkmvk2Epw0Go1Go9FoNBqNRqPRaDSaXRltANDs9DhjgqVoEIvF+MMf/kBeXh7Z2dkcdthhSiRwCgvfNM73kL2jZby6M1ZY7vOqVauUQCTNA1sTKJ577jlKSko49dRTiUajBAIB9fdAIEBfXx833HCDqnB+8cUXk1ofAFRXV+N2u5UJIfXmeSwWIxwOq8q9jRs38swzzyT17d7cIy0t7Ws9XC6X2lZ9fT3r16+XI6sTAHYCTjzxRGpqavD5fGRmZvLmm2/uUsKLnM9nnHEGZWVljBkzhoyMDAYGBnYSAVCzK+NMejFNk7Vr15KdnU1eXh5z5swBhoxfzhj/HV/9DyMxAKgI/zj093VB3KLny3W01FcxprWRtvbRrN3YQTSGiqK3gGAowv7fP5AlS99g05edKu4/tTJcjk2qYBwIBDjwwAO5++671d9h6LvASM/dnd0AEAwMADadHZuYvNtERo9q45ijjyRuW4SCfuK2lTABDBoEopEQMctgzpWXI4Rgz332Y0NHD1Z8qA2ANXitk/H/XzcBQBq+ZPJSKBTi+eefp6ysjLS0NJ577rmksZTPG4lJY3OJAZZlMTAwQElJCfX19QghuP/++4Hkyv9vM+FJo9FoNBqNRqPRaDQajUaj2RXRBgDNTo9T9IfEDeZXXnkFr9dLXl4e48ePVzeDZfWgs9rym0aK56mijjQHBINBLMvCtm1Gjx5NQUEBZ511lqq6/6pHT0+Pqv6/8sork26UO80HBx54oBLS+/v7iUajhMNhAoEAfr+f9vZ2brzxRpWe4Pf7VdWd8+a986Z6Z2cnjz/++Fc+nnjiia/1eOSRR3jiiSd47LHHlNiTGFMTnQCw4zn55JOpq6vD6/WSnp7Om2++mdSreWdHiq6LFy/G5XIxZswYhBAsX758B++Z5j8Fp7AfCoU444wzKC4upq2tjUAgQDAYVKYZZ9LKjjfSjLwFgG0lVlMjOMBrLy+gpb6K0oIczjp7NgNhc7D3PHT1+YkBz7/wIjm5+fxz9b/wB4Za9jgNE5Zl4ff7k64/ckw+/fRTcnJyWLFihYr/B7Z4/dsSO7sBAGwC/n6uuPwyCvJz8XndFOTnKmOAbAsQCQeRBgDiMa65eg55eXlccOEl9AYiavzDpoURN4nFre1iAJCV/YZhJLXpOfbYY8nLy2Pq1Kn09vYmfY+QBoCRXCdSzwFpCPj73/9OUVERXq+X4uJi3n77bSAxZ3al649Go9FoNBqNRqPRaDQajUazI9EGAM1Oj23b6sa/YRh8+eWXuN1uioqKaG9vZ+XKlcRiMXVjWArxqRX43wSyQs55I9u27aS+zz09PQD84Ac/oKCggJycHBYtWqRe/1UP27bZd999qa6u5oQTTiAQCGDbNrZtEwgEVCzuCy+8wIUXXsgnn3yi9kESi8VYu3Zt0v+dOEV/27aHGQK+6iHH/d99OJHHKxQKEQoFMIyINgDsYE455RRqa2upq6tDCMEbb7yh5tbOUcW8dWQLjrFjxzJu3DhKSkq49NJLtZCk2S44zWb9/f10dXUhhCA/P1+ZmuT1S67fsDO0Adi6ASBqWFh2DMtIrL1GcICn/voXynIzKMrN5Lr//TUW0N0fwLAZjKGPMWXqHtzwf3OHEgQcRKNR1QpA/l8KvzJB56WXXqKsrIze3l7VIgCSq8pHUgG+sxsAopEQYDNxwjgqyktJdwnOOvMM/AN9Kv7fP9CHHTOxB01xYHPgAfuTlZXFK68uxQKMwdYLMSBsRbBsExtzu7QAcI55KBRiw4YN+Hy+pMp8Z7KQNAOMZH11freLRqPKPDBjxgw8Hg9lZWWcfPLJag2X3zmc3680Go1Go9FoNBqNRqPRaDQazebRBgDNTo28iey8mbzHHntQXFxMSUkJjz/+ODA8dravr+9b20en2C7bFEhxQ4ra3/nOdygpKaG9vR0hBH19fSNKAIjH43z/+9+nsrKSnJwcJk2axNy5c1m2bBn//Oc/gWQhtqOjg46ODmUSiMViSQJVPB4nHA6rVAJA9WSW++/3+xkYGFA33b9potGoqqIdGktbGwB2Ak499VQ8Hg8+n4+0tDTefPNN9bcdX8E8MsLhMPF4nHnz5iGEoLW1lbKysl1m/zU7L9KkJZER5ZdeeilCCCZNmqTWZ9nrHBiWvLJjGFkLgKhpYFsxLDMMVpSFzz5JthAU5Wby18eeUNH/MaDXH+Lk02dx0ME/5ItNHcSBcMRIim5PrdyXhjkYOlenTp3KtddeCySnJjjHbSQC885uAAj4+7FjJj89/VRqa6q495676e7qwDQiqIr/wSSAUNCPEQ1z/f9eiytNcNlllxEIW1iAP2QmGQBicUuJ/1/XANDd3a1+DoVCXHjhhQghOPjgg9V3i9Qo/5HE/0v6+/uTDAN9fX14PB68Xi+ZmZn87W9/U8+V3xN2luOn0Wg0Go1Go9FoNBqNRqPR7MxoA4Bmp0aK1FLgP/roo1Us7JVXXqme19vbC5Akvn+b+7e5irdAIMAzzzxDQ0MDJSUlVFVVUVBQwM9+9jP1nK0JFNFolBtuuIHMzEy8Xi9ut5vCwkKEEOy+++5MmTKF2267je7ubjo6OpLeX96El9X6zihq+W88HicSiWxRTPmmBZRUoaC/v3+wCjQRfawNADuW448/HrfbTUtLC+np6SxbtmxE0ds7C/L87O/vxzRNiouLqaioQAjBwoULd/DeaXZ1nMI2JMRsKZhOnjwZIQR33nlnUoz9zsPIDABxIGZaRMJ+MCOsXf0RoxvqyUkX/P62O/BHEmvtinc/4PKrfkVz+xhWrlo99Fp7qDJcVm/L1JzU8QsEAjz//POkp6er5JzNJdRIg9vW2NkNAHLcF76wgDQhmD/vXjo7NgE24VCAUNBPOBSgt6cLsPndrb+lqDCfk06cSTgcJgYYcQhFbWUAiMSixLG3SwKAM30B4NVXX6WgoIDc3FyWLVumjoVsEeA0FY6UaDSq3sO2bR544AGKioooKSnhO9/5Dl9++SWQ+A4ozTQ7x7HTaDQajUaj0Wg0Go1Go9Fodm60AUCzU+O8kfzrX/+akpISMjIyOOGEE4DkmH8pJkciEeLx+DZVof27SBEiFosliRmLFy/mkksuoaysjKqqKpqammhubqaoqIgXXnhBvXYkCQC2bXPMMceQk5NDZWUlVVVVTJw4kcLCQlpbW1Wl6dtvv60q+2OxmNq31P/LMZP77BRSAoGAGreR7NvXxdkP2yk0xGIm8XjMYQCwBw0AQ2YAw4jwk8OOoK6mfpgBwBw0ANjEBx8xcBoA/o1KyP9GDj74YMrLy2loaEAIwUsvvbQTRJePHGfVNcBNN92EEILRo0fzox/9aAfumeY/hVgsptZUeb3q7+9nwYIFpKWl0djYyOeffw4kG8Wc83LHMXwtdAr/4chQND/YmOEAxE3uufM28rLSKa+o4pHHn+Kqa6+ndfQYfvijw+js6iEO9A8EiBqW8lilXjPkdUfGucdiMbq6uqivr+eKK65Qz3Oa1eRjpAkKu4IBIBIOYkTDrFr5Me76Wtz1tZx/3mz+eNcf+MsD93HvPXdz1plnUJCfS1Ojjwfun084FMCyLEwLotZQAkMsbmPGEpa3qBkavPo5hf9tu+5JQT8ajWJZFieccAJCCC655BJs205KH3J+39pWE6Z8fldXF0ceeSTl5eWkp6fzxz/+MTFKtq2q/2HIEKrRaDQajUaj0Wg0Go1Go9Fotow2AGh2KFIM6ezsVL+TN3elkA/w6KOPUlZWRkVFBdOnT1fx9CMRqbcmAEiRfUuCgG3bWJaVJJQ7e9fK1wWDQV599VUuvPBCxo4dS2ZmJo2NjTQ0NFBbW0t5eTlutxvTNB2x0AkhW96Y7+vrIRoNAzbBoJ/IYAQw2Dz22KMcd9wxTJgwjoKCPIQQeDweSktLqaurY88992T16tVA4ob9SCu1bdsmHA6rm/3OG/tfV0AZibkBEkKDU0CQx16KPYl5Ip9rEImEiEajfPe736WtrQ2Xy8XixYtVXHQsFiMQCibtY6r45GyN8N+MU1h55513WL9+PatWreKuu+6itraWtrY2vF4vdXV1HHXUUSxYsN17PN0AACAASURBVICuri6++OIL1qxZM2wbvb29O1W1s0wHiUajdHV1kZ+fT21tLS6Xi1WrVgFDldymaap5qFsEaL4uv/zlL8nOzubEE08clr4ykgj7nYFha3A8hn+gj/Xr1nLLLbdw8c8v4aqrf8Uri/5BOJy4Jm7LZ3OeZzNnzmTSpEnDkgH+U3F+r4jH43R1dfHaa69xyy23cN555/Hzn/+cK664gjvvvJNly5YlteVRxgjHIxk75d/Nv78kHo8npQQZhoFpmgQCAWzb5qGHHkIIwdSpU4e99usgkx5M0+Tjjz8mLS0Nj8fD6NGjt8v2NRqNRqPRaDQajUaj0Wg0mv9WtAFAs0NxVhNHIhEltjt55513qKqqoq6ujoaGBvr7+9VNcxkF/O8+nHHC8mfLsgiHw2rfYrFY0o16KZj39/ezYcMGnn76ac477zymTZumEgrKy8sZPXo01dXVNDY20traSnl5OZdccgngFLxtLMvAti26ujqQYv8HH7ynfjbNKKFQAGcF34cfvs+zz/6dc889VxkAhBA899xz2LY94go5p/hiGIZKEJCMZPy2JvB/1UO+LwyJsE7xyDTNpGOTajqYMWMGHo8HIQSvv/662mfn66WoK2OnncaP/3akcN/X18fSpUsZNWoUHo+HvLyEwaSsrIySkhIKCwvJyckhMzOTzMxM8vPzKS0t5cgjj1QmAFltL8ffaQrYkaSaQGQKQG1tLRdeeCEbN24EkiuyQ6HQt5IgovnPJhQKccwxx1BRUcHvfvc7h5C+66w/0lDlXJdt2yYQSFShBwKBYdeb1ESXrdHV1cXs2bMRQtDT00M8Ht+lkka+Ds5UCIk04ZmmSSgUGmaIiMVi28WgJL/LbM0suGLFChobG6mqqmLdunUYhrFdDCzyGiH/nTlzJhUVFeTn5/PEE0987e1rNBqNRqPRaDQajUaj0Wg0/81oA4Bmh+PsDyyRfV/XrFnDIYccgtvtJi8vj9tuu4333nuPTz/9lPfee49Vq1Z9rcfKlStZuXIlq1at4p///CerVq3io48+4p133mH58uUsXLiQp59+mvvvv5/f/e53zJkzhzPPPJPDDz+c733vewghEEJQWFiIz+ejvb2dlpYWmpqaaG1tpbGxkZaWFrxeL/n5+axYsUIJP7FYLEnYtyyDQGCAgYE+wGbhwud54YUFBIN+wCYSCWEYEcLhoPodwOWXX05bWxs5OTnMnTsXSJgpRnKDPtXY4GyjsGzZMo477rivfBx//PFf+dja64855hhOPvlkTjjhBGbMmKHSEUKh0DCBzLIsJTDH43Gi0SjHHHMMZWVluFwulixZAiQqCqXgLz+XfI0UTnaV6ttvGiksGYbBp59+yhlnnMHZZ5/Naaedxvnnn88FF1zA7Nmzufjii7nmmmu47LLLmDVrFrNnz+aaa67hkksuYe3atUBy2gOQFA+9o5DH2WlGME2TiRMnUlpaihCCWCym9lVW2GrxX7O9WL58OfX19dTW1vLGG28AqJ7puwrSGLe5NdmJNGxtC5999hkAV1xxBe+++64yE+xK4/N1sW1bpddsCZnE4DSxbQ8ikYgaa9M0GRgYIBgMEolEWL9+PYZh8N3vfhchBIsWLVLvvb2Oj5wvS5YsweVy0dzczOTJk7fLtjUajUaj0Wg0Go1Go9FoNJr/ZrQBQLPDcYqxsupNMmPGDMrKylSVu9frpbi4GCEE+fn5SoDfHo+0tDRcLhdpaWnqdy6Xi/T0dLKzsykqKqKiooKqqioqKiooKytT4n59fT0ejwe3201tbS319fU0NDTQ2tpKXV0d+fn5jB8/nkgkkiKM2nR3dzoE/cTj6KOPVPvw8MMPAjaGEcE0o+o5idYBCdGyqamJiooKbr/9dnVjfqQxypvr3RsIBFiwYAFNTU1f+Whubv7Kx9Ze7/P5GDVqFF6vl0mTJqle2qm9h2OxmBL1pWnBsix+9KMfUV1dTUlJCe+++25SsoBzfqW2b9AmgOFEIpGkOeM8D6U5JBqNYhiGekiThdOwsXP0Nk8g90nOcTm/nnzyScrKyqirq+PII49Uz3dWMuv5odlerF69muLiYhoaGli3bt2O3p0R4xSaZWqNM7I+FAqphJxgMJi0foxkHZDGHGnuAtTa8t9A6nVIivxybQ2FQlu8Bm4PZGqLZHMpO0cccQRCCJ588kn1O7mOfl3k54jH4xx33HG43W6EEKxatWqnSZDRaDQajUaj0Wg0Go1Go9FodlW0AUCzQ3HeyDYMg76+PiBxI/qiiy4iNzeXmpoaJk+ejM/no76+nsrKSjweDw0NDdTX13+th9vtpq6ujtraWurq6nC73Xi9Xnw+H16vl9ra2qS/NTQ0JAnfjY2NeL1e3G43Ho8Hr9eL1+tVsf8tLS3U1tZSWVnJr3/9a/XZZH9dsAkEBpCi/sBAH5999illZSV4vW4yMlzceutv+fLLL9Rzenq66O/vBWw2bNhAPB7H4/FQVFTEunXr1BiOFKchoa+vT/0/FAqxcePGb/QRjUZZu3YtX3zxhUp9kDj7HaciRf2jjjqKuro6lQDQ19dHPB4nEAgoMSUajSZF08tjsK2Vqv+JyHGMx+PDKvbD4XDS71IF8dQIailm7WzCuVNM9Pv9ar/POusscnNzycvL46233lKCUzwe/6/pQa75ZvH7/YTDYQzD4KOPPlLXDins7uyktoNxpvVsLYJ+JCK1U2zu6upiYGAASFyT/htMAE7B3ZmesjXi8fh2aQHgXK9Tr4kdHR387Gc/o7S0lPnz56v5Ko/r9jAhyOvL448/TkVFBRkZGVx77bVfe7sajUaj0Wg0Go1Go9FoNBqNRhsANDsYGSucWlHW3d3N/vvvT2NjI9OnT2fs2LHstddejBkzhn322Yfp06ez++67M23atK/12GuvvZg2bRq77747kydPVtvcY4892GOPPdh7773Zc889mTZtGlOmTGHy5MnqIV8jf54yZQpTp05l9913T/rbhAkTmDp1Kq+++irhcDjpJr+s/JcmgN7ebl599R8UFOSRnp7GXnvtwSeffIRsASDbA4A9+Fq47rrryMnJ4eKLLwYSN/U7OztHfAz8fr+6mf9tV186q8ghYUBwiq9SdHJWnkJCbA4Ggxx++OFUV1fjcrl47bXX1N83Vz1oWVaSaLGr9OD+JnEedxiq7NzSHDAMg0AgQCAQUK0aZOx3avXoziJwOtcW5+fasGGDMgF95zvfUdX/I+mJrdFsC1KwXbNmDYceeii77bbbLhFxbxhG0jnt/Nm2baLR6LB2M9taod7X1zfsfPP7/V9jr3dNnNX/8iENF6ZpDov+355GK8MwCAaDylSwceNGnn76abKysnj11VeTkl6c+7s9CIfDNDc3I4TgwAMPTDJiaTQajUaj0Wg0Go1Go9FoNJp/H20A0OxQ5A1uiRQYISHiDgwM0N/fz6ZNm5TguGHDBjo6Oti0aRPd3d1f69Hb20t3dzednZ10dHTQ2dlJV1eX+n9PTw+9vb309fXR19dHf38/AwMD+P1+tX9SEPX7/fT399Pb20tXVxcdHR2Yponf71d90mGozUFvby+xWCIFAGz8/n7A5l//Ws24cWO4+OILlTFA/g1s+vp6sG2LeDzGE088gdvtZr/99mPjxo3qPfx+/4hvoIdCoaQb+5AQcfx+vxJ2t/SQFYRbemzt9bICMLUn9uYEoGAwqARc0zT5/PPPmTBhAvX19dTV1XH77berzyHFkdQUAWcSwPaooPxPQMZ3w3DjhKz0leMp476dpLa1kAkXO0MSgJyHct7Iferu7gZg/fr1qtXGTTf9/+zdeXxU5dk//pOE7GSfZJLMTDIz2UNYVIoWbdX2QW3dENFaqj4+KpZq0Vptfdpq61Z/YutSSi32q+L+VGutttZdUFkUEEFlkz1sUbLOPmdmzvn8/gj3zX0mAUJBAvHzfr3mRcjMnDlzcubMOfd13dd1X1JyDktQ08ERnwNd19HZ2QnDMBAMBrFhw4bBXrUBEUFn0zT7JPj09/1iGIasHDKQJBqxTHEs7i8ZcCiLRqN7/R7qr+KKmmx1KBKsxLYXiZiqTZs2oaenp8/f41AfF6dMmYKqqiqUlZVh/fr1AKwtIYiIiIiIiIiIiOg/wwQAOiIk96IVs8BDoZAMOKrBiENFBDaSgxWmacrgp3qfmKUnBuPVILcojazy+XxyFjtg7bPeu1wDiUQMppmACPAnEjE899xfoWkabrvtN9i0aQMCAR+CQT9CoQDC4SC2bt2Cu+++C1lZWTj33HPR2dkpt6MIgg9kAF19b2KGvOjzLt7vvm77C/Dv7/lincV6qMFlMbs0Ho/js88+w5w5c3D33XfjnnvuwdVXX41x48bJFhH5+fkoKyvDD3/4Q9x0002YNWsW7rvvPrz33nuytYAocy9elwkAvfYW4NnXLN5EItFn3zZNE8Fg8IiaPS/eQ/L+pSaYvPDCCygqKoKmaZg7dy4AyM8T0cFSPw+JREImyxwNCSbiOC2SGNRjQnLiXvL3zUASgJIrjYjnfJWSAIDe7yLxvau2ZVG/o0TC3aEkvsOF5LYv4m9smqbStujQBehfeuklZGZmQtM0WcHHMAzZCoKIiIiIiIiIiIj+c0wAIBpEiUQMhhFHLBZFNBqGSALo6enC2rWrccYZp8HrdeOYY0ZjwoRvY8KEb+OYY0bD63XjjDNOw/z58/HFF1/I5amD8kdDiemBWrRoESZPnowJEyZg8uTJ+PGPf4yrrroKV111FaZNm4Yf/ehHmDZtGi6//HJcdNFFOOecc3DGGWdgxowZcra32lrgqxZgor0LhUJ4/PHHoWkaGhoasGHDhj4VMYiI6MCoyZvquYlhGFi9ejU0TUNKSgqeffZZAHsSJgHwGExERERERERERHSQmABANIjUFgBqFQDTTKCnpwuAgc2bN2LVqk+xbNlSLFu2FKtWfYrNmzcC6DtTTu3ZPBQSANRAvQjgq8GE/qpBiNmJoVBIzv4HentNC+osR/rqUltKPPzww9A0Dc3NzX1mJhMR0YHr71ja2tqKESNGID09HS+88ILlceFwmBVYiIiIiIiIiIiIDgEmABANIl2PQAT9DSOOeFyHmgSQSMRkZQDDiMMw4gAMRKPh3ckDe4h+50NNLBazJDrE43HLbP6BCIfDlhmIR0qPehp8PT09cl+47777kJqaipNPPhk9PT2DvGZEREOHz+dDd3c3Ro8ejbS0NDzzzDMAer+P1eNtLBZjEhYREREREREREdFBYgIA0SCKxaIQAf94XEckEoKuR5BIxBCP65aEgORbPK7L3szqzH9gT+/moUL0HhaB/1AohGg0ing8jng8jlgsJm/xeFz2UY7H4wgEApZgguilfCT1qqfBofbAFgGol19+GePGjcP69esHc9WIiI5q4pgqqhGZpgm/349x48bhX//6F4De4H8wGATQm8TI9jxERERERERERESHBhMAiAaVNahvmgmYZgKGEbckB0SjYeh6BLoekRUBem+9Ae3k4H88Hh8SAe5AIADDMGTJfnXm/96qAIigrpjVrbZCCAaDB1w9gIYukVgCANFoVH5mgsGgDEoREdGBE9+90WhUfh/7/X55bA0Gg5bkAPEzv6OJiIiIiIiIiIgOHhMAiAaVIQP+/c/wj8Ew4pZWAboegWHEZQsAdba/mPU+FIL/KjGDXy3jD2Cv71NNABBM07Q8ny0ASJSaFgGpaDQKv98/yGtFRDQ0RCIR+f0dCATkd7Df74dhGPL/apsfNTGAiIiIiIiIiIiI/jNMACAaRIYRl6X+1SQAUQlArQDQ389qCXxd14fcoLlpmujs7LQE69VkAF3XEYvFZEl/MftfJACI4ENysF8NPNBXl7oPqIH/tra2IfdZIiI63ESgH9gT5P/888/l/aFQSP7c09PDFgBERERERERERESHCBMAiAaVdda/mNkvgv+hUACiGoBaGWDPff0TvXaHAlEOWAT2RdA2Ho/L6gdqFYT+qJUCdF2XLQWI2tvb5c8dHR1MDCEiOgTUJCqRpCeOr6ZpyuC/2opFYAsWIiIiIiIiIiKig8MEACIiIiIiIiIiIiIiIiIioiGACQBERERERERERERERERERERDABMAiIiIiIiIiIiIiIiIiIiIhgAmABAREREREREREREREREREQ0BTAAgIiIiIiIiIiIiIiIiIiIaApgAQERERERERERERERERERENAQwAYCIiIiIiIiIiIiIiIiIiGgIYAIAERERERERERERERERERHREMAEACIiIiIiIiIiIiIiIiIioiGACQBERERERERERERERERERERDABMAiIiIiIiIiIiIiIiIiIiIhgAmABAREREREREREREREREREQ0BTAAgIiIiIiIiIiIiIiIiIiIaApgAQERERERERERERERERERENAQwAYCIiIiIiIiIiIiIiIiIiGgIYAIAERERERERERERERERERHREMAEABpU0XAEMAGYQCQUlj/3dHXLn/d5+4rRdR2GYQAA4vH4IK8NERERERERERERERERER1JmABAg8sE4noMkVAYZsJAXI/J38EEErH4Pm/x+NF9Mwxjn7dYLIZQKARd1xEOh2GaJgP/RERERERERERERERERNQvJgDQ4Nod5Bcz/gM+P8yE0Tu73zBZAUARi8Xkv/F4HLquD/IaEREREREREREREREREdGRhAkANKgSsThgAqFAUP4rEgCMeAJmwtjnLZFIDOptfzP493fbX4WAnp4ehMNhAEB3dzeAPYkAohUAERERERERERERERERERHABAAaZJFQ2Dqjf/esfyOe2P/s/69ABYBEIiF/jkQiACBn/jMBgIiIiIiIiIiIiIiIiIhUTACgwWUCeiQKI56AHokiEYvDiCdkJYD93QbbwVYAME1zn7d4PI5gMCiD/YZhQNd1tgAgIiIiIiIiIiIiIiIioj6YAECDauf2HZh6xZW48ac34PrrfiJvN1z/U1x/3U9w7Y+n7/M2ffrRfbvmmmv2efvVr36Fa665Bg899BDi8Tii0SgAa2UAIiIiIiIiIiIiIiIiIiKACQA0yD5bsxYpmoYyWynKy+woyMtH/vA8OCsdGJ6Ti9IS2z5vJSUlg3qz2WwHdSstLd3nrbi4GJqm4eyzz5YtAABYfiYiIiIiIiIiIiIiIiIiApgAQINsW+tWpGganJUOVNjLUVJUjNEjR8HlcMJT7UZTQyNcDieqXVVwVFSi2lUFd1U1KuzlqHK6UFlZibKyMpSWlqKiogIulwsOhwNlZWUoLi5GRUXFl3rTNE3e0tLSkJ2djfz8fJSUlKC0tBR2ux0ejwd1dXWoqqqCy+VCXV0dGhoa4PV6UV9fj8rKSpSXl8Pr9aKmpgZOpxNVVVVobm5GVVUVysvLkZ+fj3/9618wTRNAbwJAKBQa5L8eCaI1g67r8m8k/j0YpmkiFoshHo/L5Ym/ezAYPOjlExERERERERERERER0dDCBAAaXCbwi5v+FxnD0jFu7NdQYS+Hs9KBluYRcFRUwuv2wF1VjfraOowZNRqLFixE6+Yt2LFtO5Yv+witra3YuHEj1q1bJ2/r16/H+vXrsWHDhi/9tn79eqxduxbLly/HO++8g7/97W+YOXMmbr75Zlx77bU455xz8K1vfQv19fXIyclBWloa8vPzUVZWBpvNBpfLhdraWtTW1qK6uhoej0cmB5SWlsoEgaKiIkyePHmw/1qUxDRN6LpuackgWjUYhnHQN1UikZCvEwgEDuv7JCIiIiIiIiIiIiIioqMDEwBocJmAHoniv771bRQVFKKxvgHuqmqUl9lR4/HCWemAvbQM7qpqFBUU4vZbbwNMwIgngKQJ1qZpwjCMQzLzeqB0XUcsFkMikej3dWOxGGKxGLq7u7Fu3Tq8/fbbmDVrFi655BIce+yx0DQNBQUFqKioQHl5OZxOJ+rr61FfXw+n04na2lrU19ejoqICw4cPx6pVq2CaJnw+32F7j7R3uq4jHo9bfif2B6B3nzyYWywWA9C35QP//kRERERERERERERERNQfJgDQoIrrMcAENq7fgPS0YaivrZOB/7qaWrirquGpdqPWW4OCvHzkZufgL7MfQldHJ2D2Bkbj8bicMa0G4dVZ2V82wzAQjUYRDocRDocRjUZl8BboDQqHQiE5WzwUCuHzzz/H0qVLcdddd+HrX/867HY7Kioq4Ha74Xa74XK5YLPZ0NzcjMrKSuTk5OB///d/AQDRaPSwvTfaO7G/xeNxhEIhhMNhmQySnBjwn+ju7oau6+jp6ZG/E8kA6v5FREREREREREREREREBDABgAaZmTAQi+qACfz9b88jRdPQWN+AxvoGeN0e1Hi8aKirR623BrXeGjgrHfjacWOxbu1ngAlLv3WRCCAcjiB5OBzuNxBrGAZisZi8v7/qAMll3ufPn48rr7wSTqcT+fn5cLvdcDgcaGpqkpUAHA4HtmzZIp9PgyscDuOtt97C3Xffjdtvvx1/+MMfMHPmTMyYMQN33HEHbr/99oO6/fGPf8Q999yDcDgMoHefFskAhyLBgIiIiIiIiIiIiIiIiIYWJgDQoDITBsyEgaA/AJjA5Zf9D3Kzc1Dj8cJdVY1abw1sxSWo9dagxuOFvbQMtuISfPeM72DHtu2WZYly+8JgBsgNw+hTgSCRSCASiSAajcr7dF1HJBKxlHjftWsXbr31VpSXl6OiogJ5eXlwOBwYMWIEUlNT8cc//hEAEAwGD98bor369a9/jdzcXGiahuLiYmRmZkLTNOTm5qKwsPCgbpqmQdM0dHV1AYAlEeBwtrogIiIiIiIiIiIiIiKiowMTAGhwmUA4GAJM4Iu2zwETGDNqNBwVlXA5nDLo39TQiMb6BgzPycXokaOQomn40x9nobOzUwbTRRn+wQj8i9cWLQmEeDzeJ1CrtioIhULy94lEAn6/H9FoFMFgENu3b8eFF16I+vp65OTkwOFwoKKiAhMmTJABYRpcoVAIN998M+x2O3JycmC325GXl4fc3Fw4nU7k5uYe1K2iogIFBQXYtWsXgD2z/sPhMBMAiIiIiIiIiIiIiIiIqA8mANDgMvfcErE4YALrP1uHFE1DXU0tHBWVGDNqNOylZairqUVDXT3Ky+yodlUhRdOwbOmH6NjV3rsM9FYUUBMATNP8Um+xWEz2fFclVwAwTVP+LjkpIB6P92klYJomdF2Hrut4/PHH4fV6UVZWBo/HA03T8M9//rNPhQEaHK2trVi0aBE++OADfPzxx1i+fDkWL14s/38wt5UrV+Ktt96Sgf9IJGJJGiEiIiIiIiIiIiIiIiJSMQGABpWu6wAgg9+JRAKGYeDll1+Gpmmoq6tDaWkpHA4HRo4cCZfLhbq6Otjtdniq3RjZ1IwtGzYChgkjnoC5O4lAVBOQt6OUaA3w8ccf49hjj0VDQwOys7NxwQUXIJFI9NvyQPSIp8NA2cfiekwmscAEouGI/NlMGJZqF/I+IiIiIiIiOqyCwaAciwgEAvL3aiK/eq0djUYHtFx1WbFYzJK8La7XxWv0V7lQ/D4WiyEcDst1FPeZpilfwzAMee2fSCQsjyUiokMvkUjIsVhd1xGPx+WxV9yntniNRCKcvEVE9CUTbbK7u7vl7+LxuKWFsnoODfSep6vH632JxWKIx+N9zt3FBOGB0HUdgUDA8p2gnrv7fD75s/o6A70G2RcmANARIflidfv27Zg2bRoyMjLQ0NCApqYmlJeXo6GhAWVlZWhpaYHL4YSzvAKnn/ptwAQigWBvIDYS7U0G2B10PZoDreKg0NPTg/Xr12PUqFFobGyEpmnYuHEjTNOE3++XlQhUTAT48vl7fIiEwojrsT6JAEY8gY5d7Qj4/NAj0d77DRMwsef/REREREREdFgkXzOLgb+dO3f2aScoHnsg7fdCoZClJaBqXwOEogIg0DvQFw6H+6y3aZqW8QFBPJZBJiKiL08gEOj3+G6apiVAI471hmHIx7OFJxHRl0u0T/b7/QCsbbeTW3arrZXVwPtA9Ffd+0Dout6ntbNYN/FdEgqF5O1QYAIADSpxga3u0OIDsH79ehx//PEoLCxEY2MjHA4HioqKMHr0aBQVFaG+tg6VZXbkDMvArAf+AJhAsMcHmIARTwyJCgCJREJ++P1+P7Zu3YoTTjgBmqbhhhtu6PN4loc/zEzrTY9EEQmF++x/QX8AZsKQFQLk/URERERERHRYxGIxOd4QCAQQDAaxfft2rF69GitWrMC7776LZcuWYenSpVi6dCk++OADmSSgzhral+TZ+KK1H2CtAhCJRPrM6hEzmMS6dnd3IxKJyPaDItivPq6jo8OybCIi+nJs374dc+fOxQcffIB58+Zh+fLleP/997Fo0SK899572L59O4A9Y7l7SwgjIqJDJx6PWxJh1Z99Pp8899++fTuCwaA8NhuGIRMG9iUajfaZfKu2+96fL774wnJtoMbvtm7dCp/PJ8/jReLxoZj5LzABgAaVuIBVS26o2e6vv/468vPzUVFRgcrKStTW1qK+vh42mw31tXVwO10Y09yC3MwszJ/3DrA7sKqWYj/aA61+v99y0rhu3ToUFxejsrISq1ev7nOwEiUD6cvn6+7pLetvmIiEwnJmv5kwEIvq6Oro7NMmQDz+aN8viYiIiIiIjjbqAFxbWxs0TYOmaRg2bBiGDx+O9PR0y++2b99+QFUARBno/qjjHqKMtDpbFOgd8Ovu7pbX9OrMf/U6P3kAk4EmIqIvT09PD2655Rbk5uYiNzdXfk9omoa0tDRomoYf//jHaG9v7/PcgZaZJiKiAyeSdMW/InFWddZZZ0HT9oTCk2fh70t/5f/F6wy0AlcikUA4HLYsZ+fOnXjggQeQk5ODTz75RCb4quf0h+L7gwkANKhENkty2Qv1wzNjxgxomoampiZ4vV54PB40NjaixuOF2+nCqMZmTd4nJwAAIABJREFUDM/Kxrhjj0MsHEFsd3/1zvaOIZEAkNyjpL29Ha+88go0TcPMmTMB9F78q7MJAHAA4HBQgvuJWFwG9kUbALUyQCyqWx4fCgT3uWgiIiIiIiI6dMQgWiKRQEdHB3Rdx8iRI1FVVWUJ5ohbXl4eurq6+rQs3Bv1Gtw0TTm2EYvFLLP2VeJxuq6js7Oz3/vVyoDRaFT+rA4ichIAEdGXxzRN3HnnncjMzLQkidntduTn58PpdGLGjBny8dFolIF/IqLDRCTrijZZYoZ+R0cHNE1DVlYWenp6LLPv1SpdAxGPx+WxXdd12aJrIOumnuP7/X55Lh8Oh3HmmWciJSUFixcvRiQSkdcMh6oKABMAaFCp/ZCSd2qRBNDe3o6LLroIJSUlKC0tRVNTE9xuN2o8XtRUu1FVXonjRo9Bmqbhumt+DCSMPT3Wh0ACgPjQBwIBeYHv8/lw8cUXY9SoUTI5QPQbTO4XSF8iE+jp6paz/kUCQCQU3vM7Ewj4/IjrMdkOQLYBICIiIiIiosNGDcS3tbWhvLwcBQUFWLBggeVxhmFYZnKKUvsDYZpmn36jQnt7O5YtW4Y33ngDzz//PB5//HHMmTMHTz75JD744ANs2bIFoVAI0WgU4XDYsgwxRiIGK5csWYLHHnvMch8REX05YrFYn4ownZ2dmDlzJjRNw1VXXYVEItFnpijbtRIRfXnEefGWLVvk7/x+P3bs2IGysjJkZmbC5/MB2HO+HAwGB1wBQJT/H+jj+xOPx2WyrmEYfSbwfu9734OmaXjllVcs7+lQTPBlAgANKpGNIxIA1Kx18aHq6OhAe3s7qqurUV9fj5KSEtTU1MBZ6UC9twZNNXWo99bA46pCmqbh+b8+i61bWodEAoDIFg2FQti1a5f8GQDef/99ZGZm4umnn7Y8R2w3VgA4DJR97KMPl+HBWX/CXXf+FpdefAl+8P0pmHjOuTj37HNw/NfGwev2IDc7BxnD0vGX2Q8d1fslERERERHR0UYdfAOAXbt2we12Q9M0vPPOO9B1vU9FwgMN/Ou6bnmNaDSKzZs3Y+nSpTjvvPPg9Xr7lI0WP4uy0h6PB7Nnz5bJCmKmkbrcQCCAiy66CN/+9rc5CYCI6EumTloTY7UigHPvvfeipKQEN910EwD0OSb3VzqaiIgOne7ubvlzV1cXQqEQxo4dC03T8N577wEAtm/fLh8jzqlFxYD9ES23I5EIwuGwPDcfSPwtFAr1aTmgXmvs2rULhmHg+OOPR0pKClasWLHXymD/CSYA0KDrL6M9WSAQwAsvvIC0tDRUV1ejtrYWzkoHqh1ONNfWo7LMjjEtI1FRWob8nFxs37oNvu6eoz4BwDAMSykSv9+PSCQit9Obb76Jd999F4D1ZPRAypfQf07M9O9s78DEc85FiqbBXlqGMlspymylqHK6UFJUjPzheaivrYPX7UHGsHT8bsY9vS0BiIiIiIiI6LAQQRnDMGRwvaqqCrm5ufj3v/8NYM+1tDo2IWYNDUQkEpHBnl27dmHWrFk45phjoGkaCgsLoWka0tPTcd555+Hhhx/GW2+9hfnz52Px4sV49tln8cADD2D8+PHQNA35+fn485//bAkm+f1+AMDf/vY3aJqGb37zm5z9T0T0JYtGo3L8WhyTxf/vuusupKSk4Nprr5W/NwzDMn5LRERfHnGsDQQCiMfjuPDCC6FpGmbPng3AGn8UQf8Dqc7S2dmJZcuW4aWXXsLTTz+N559/Hm+//TaWLFky4GWIJALxczgctsTwFi5ciKKiIuTk5GD58uWW93UwmABARzTxQRQfjltvvRWapsHr9cLlcKLO40VtlRsNNbVwO11oqKlFUV4+vnnSNxDXY7191s3e54tyHepyiQ6KCYSDIYSDIXzrlFORqqUgRdOQlZEJl8OJKqcLnmo3RjQ1w15ahlpvDQry8jH/3feYAEBERERERHQYicC8+Nfv96OqqgppaWl44403BvR8NRlALEfMBjVNUwaGXn/9dWRkZKCoqAhOpxOapuH000/HM888Iwf7xOwhwFqSFADeeOMNTJo0CcOHD8exxx6LnTt3AugdgPT5fKirq0NGRgbOOOMMy7p8lUWjUei6Lv8GohUD0LtdDcPoU8JVVKWkPaLRqCxPK7aP2E/FdgR6x9kGOnOOjg7qZ0OMwx5IAtRQpx4r1Ja2M2bMgKZpuOWWWyyPOZhy0URENHBq3O+mm25Cdnb2AVXJisVilmpbog1Ya2srLrzwQthsNmiahoyMDFnBKy0tDVlZWcjJycH3vvc9vPfee/K4L84/Y7GYPK8Cer87xGPU4L+u6zAMA4899hg0TcO3vvUtRKNR+Z7U89d4PH5Alb+ZAEBHPNFfKRKJYMeOHbjwwguRlZWFMaNGo9btgcfh6q0EUN8Ab1U1xrSMRIqm4YbrfwqYwI5te8p7iGx5kY1JdFAME0Y8AZjAsqUf4rVXXsW///Uynn7yKZz49fFwVjrgrqpGS/MIlJfZ4XV7kJmegdbNW47qyhRERERERERHm4NNABBE0FMtzSkGCmOxGKZMmYLMzEzU1dXJWf8ff/zxXgchTdNEIBCQwX9gzwzThx9+GOnp6XA6nbL633XXXYfMzEy4XC5MmjTJ8njqlUgkkEgkZKVEXdfl75ITABik65VcSXIg/W6Tk2Do6BUMBpFIJOD3+/tMmlLbj3yVMQGAiOjIY5omvvjiCwDABx98AJvNhmHDhmHNmjUDOg6rVbWBPROH77nnHmiaBpvNhsmTJ2POnDmYO3cu3nrrLTz33HO49NJLZSJAU1MTNE3DWWedhdbWVui6LpcrzjVFC3Q1eK/+HI1GEYlEMHHiRKSnp+PnP/95n/UTsU1x7TAQTACgI5r4kKonUB9//DEaGhqQlzsczfUNcJTaceyo0SgtKka5rRQjm5plqfX/e/oZwNyTRS/6+fHimA6FRCze2wbAMPe0mzCBz3e2obiwCNWuKrgcTtTX1qHK6YLX7UFDXf1R35qCiIiIiIjoaHOwCQAiCKbrOkzTlAFTMb6wadMmjB07Fi6XC/n5+cjPz8f555+PQCBgCZCGQqG9tu0TiQRi/QBgyZIl8Hg8aGlpweOPPw6bzYbc3Fy0tLRgwoQJDDLtlkgk5KBtR0eH5b5169bJJABBHZDlNuybACCqAADAxo0bsWjRIvn75DE1tqEcOmKxmPw8fPrpp5b/f9UxAYCI6MgjjrWdnZ04+eSToWkabrvttgHH/8TxvKenRy5r2rRp0DQNV1xxBTZs2NBvdSmg9/zyggsugKZpOP7441FeXg5N0/D6669brjvU74P+vkvU+xYtWoS0tDQUFRXhzTfftDxOXBsAsCQO7wsTAOiIJnZk8a8oPfXII48gPW0YGmvrUF5sQ021G87yChw3egwyUtNwzOgxqCyvQGN9A9q/2IVYLCY/qMn9NYj+Y7sD+QGfH4lYHNFwBLGojkULFiJF01DrrUGV0wVHRSUa6xtgLy3D//78JsBEb+IAERERERERHRYHmwAQjUb7DCaGQiHEYjGsW7cOxx13HCorK1FQUIDi4mJcd9118jFA7yzp5FlGuq5bxidCoZAc/1Bn4X744YcoLi5GcXExHA4H6uvrUVdXhxNOOOFAN8OQpc74F3p6evDEE09g2LBhCIfDlgQAMSDLBIA9TNNEIpGwfFZ8Ph9mzZqFq666SiZWJJelpaPf9u17qqcahoGFCxfC4XDgueeeG8S1OrIwAYCI6Mj161//GpmZmcjLywPQe34y0ApFYja93+/HjTfeiNTUVPz617+W9yfP2g8Gg5ZzzssuuwyapqG+vh7Nzc3QNA0vvviibCuQnICqVgdQ7xOVDP7nf/4HmqbhO9/5jny8eC+iXUDyeu0NEwDoiBcKhSwfqO7ubgDAj6++BsO0FDTV1KGm2g2PqwqNtXWo83hhKy5BQ109igoK8a1TTpUfJtM05QeDJcroYMWiukwCSMTi8ucH7rsfw3NyUePxosbjRZmtFM2NTUjRNCxasJAJAERERERERIfZoWoBIAYJxb9tbW044YQTUFhYiObmZhQVFWHKlCmIRqOyasC+ZiGJagL9Bf59Ph+6urpgmiaee+45pKenw+PxoL6+Hg6HA6eddhoABmFVgUAAgUBAbpMPP/wQw4YNs/wOsJa4Z4vIXolEAvF43JKUEggEMH36dJxyyimWfvAigYJja0NHNBpFKBSSLVjz8vLwxhtv8POxGxMAiIiOPMFgEO3t7cjLy0NKSgpmzJgh7xtIgFwNyD/77LPQNA2/+MUv5PPVc3hRXVx9rrgeOPvss5GXl4eSkhJ4PB4MGzYMq1evlo9VlyOeI6otmaZpabfz4YcfIi0tDeXl5fi///s/y/PV0v8DmeTMBAA64sXjcZimKUtciA/Zpg0bMeHUb2GYpmFMy0jUVLtRkDscxx83FjUeL5yVDjTU1SNF0+QHX/3Qs4cVHaygPyAD/+FgSCYCnD7hNFTYy+FyONFQVw9npQNetwfDUtN6K1KIxAEiIiIiIiI6LA42AUAEc8QYhVjOpEmTkJOTA4/HA4fDgebmZhnEFxMYRC/QRCKBWCzWpw2AYRgykCpmLHV2dsrXFMu7/PLLUVBQALvdjoqKClxwwQWWGdtfZd3d3YjFYpYJJLFYDE8++SQ0TUNPT49l0Fb8HQEmAADWkv/xeNyyTaZOnYrvfOc7cp9V93+WiB861GSOZcuWQdM0LF68eBDX6MjCBAAioiNPJBLBPffcA03TMHz4cESjUXR3dw/43E48bt26dWhoaMBpp50mZ+L7fD7L+Xryubs4B9d1HStXrkR6ejoaGxvh8XhQVFSEk08+WZ57+v1+y3kUsCfOKc5d1XYBP/jBD6BpGlpaWmTQPxaLyWWoiaz7wgQAOqKJzBaRbWNpBWACa1euQmHOcAzPysbXjjkWIxoaUTg8D431DRjVMhJ1NbWocrpQXFyMf/zjHwPKvicaMBMw4gnABKLhCGACW7e0oiAvHy6HE85KB2q9NXBXVaO8zI5Tvnkygv4AzITBBAAiIiIiIqLD6GATABKJhByAC4fDiMfjmDlzJjRNw5gxY1BWVgZN07BgwQL09PTI5+3cuRNA30kIarl1NfCqVgBQBwtFX9BRo0bBZrPBbrdj8uTJbHG4D/F4HFdffTUKCgrQ1dVlSQBQA9esoLBn9j9gnTxjGAZOOeUUnHfeeYhGowgGgzKRRdxPQ4M6E/GZZ55BRkYGli9fbuk5/FXGBAAioiNPd3c3UlJSUF5ejuuvvx66rsvY30C/vwKBAB5++GGkpKTgr3/9qzzGq+eHatn/UCgkk3yBPd8JU6dOhd1uh9PpRG1tLTRNw2uvvSYfl9yqSiRcinMqkXgAAAsWLEBGRgY0TcP/+3//Tz5efLcMdHIzEwDoiCY+PGrAPhwO9+7gJqCHwvjb//0VaZoGh70cI5uaUev2yL7rNR4v6mpqYbfbUVdXJy/CB5ohQ7Q//h6fLP0fi+pYungJUjQNXrcHNdVuuBxOVLuqUFJUjN/ecSf0SLR33+UgDRERERER0WFz0C0ARCu33UMJq1eugqZpqK2tRVlZGZxOJ6666irL64hgvq7rsiWhGOwTCQDq+IQ69hGJRPod5Lv22mtRWloKm82GiRMnMgC7m9gOkUhEJmpEIhFUVVUhIyMDHR0dciBXlFplAoCVGKdQZ6i1tbUhMzMTF1xwgaU9QCwWQzweZ3XNIUQtg/zzn/8cmqZh4cKFg7xWRw4mABARHXn+8Ic/QNM05ObmYv78+fL3B9KiyDAMnHrqqRg3bhwAWJJvxfdicoskNZgfCoUQDofx7rvvIj09HeXl5aipqYHNZsNll10mJzXrum6p+iXa7gCwzPIPBoPo7u7GSSedBKfTifHjx8v7RaUwdT33ZdATAHw+316/HNWSUuL/ySfl6v2JRAK6rvfp7WAYhuzPwC/fIWb3zOspF30fLocT3t3B//raOlQ5XajxeNFQVy/7sF904ffg7/HJ2drqxbV68Zfcz+OrTnwWxcWd+rmLRCLy/+o2EwMdyeUI1UyloUB+IeyuBHDX7XegOL8AHlcVRtQ1oN5bgzqPFymahuXLPpKlH8Vz1ZKD/V1MqFUw1NfkIA8REREREdHAHVQCgAkgYSC+O6E76A/g/HMnYnhOLo4ZPQbVripkDEvHyk8+lTOP1KD+obwGXrJkCdLT0+F0OvFf//VflnKhyeNf6i15G3R3d8tr0721ERDlTQc6PqIOhorXVAdg1dcDescExbWumhSh63q/geXkMqzJY4CRSEQ+LxaLYe3atRg2bBhSU1PR2dlpWT91u6nPF/b3ntXZ0ur1fPLYktqrVdwvXsc0TXR3d/dJBBH3qeuoVoZQ36d4XXWwWt126sC1eH2xjOTx0+TtG4vF8PjjjyM9PR2nn356n9K3YkBbfe/ifajb71BU4VSrZAimacr9QNynPs7n88n1EY8R21G04Uje79XEHPG6yfut+lixPX0+n/x98riXruuW1xOf0f9k3DF5fwf6Vk8V1CCDSNgAeveJ5P1EPC8UCsHn8+HEE09Eeno6PvjgA8trBgKBPkGVgb4PcTzZ13vrLylH/M2CwaBcl0gkYvm7qP2U1XiC2I/VqizqeottEI1G+z3mqJUxmABARDQ4xPFUPTcS51P19fWoqqrCscceC2DPec5A43umaaK1tRWFhYU4++yz8eKLL2L58uUHNHlTfL8Hg0GkpqbC4/GgtLQUDQ0NyMjIkI9Tv5fFOgrJlcuj0Sh++9vfIjc3F1lZWXjrrbf+o9jloCcAqEQWhTgp6e8LFujdGPvrMSUyrZNPmEXGNb+Eh4jdPde/aPscnmo3SktsqK+tg624BE0Njaj11sBRUYnG+ga4q6qRmZ6Be3/3e3nBDvR+QNVyIOKkkPtI7zYIBALyok5QL4TUA5XP55MHK/Ux6rZUP9tHOwMmTCgX2PEEzj3zLFRXOOBxuOAsK4ezrByjR7SgqKAQuz7vLeMSioTR2d1l2S7qRYUQCoXkBY34UtvbQAgRERERERHt3cEmACSiOmAAge4eLH3/A9hLbBjZ1AxbcQnspWU456yz0dXR2eepyUn0B6urqwtnnHEGSkpKcNZZZ1nGzpITy1W6riMQCPQb8FSDgfF4HN3d3ZbA9UAHGROJBMLhsGVwU4zh7dq1S75eZ+ee7RQOh+V1rqz4qIhEIjKQK4J+6mNM07RMTOjq6pL33XLLLcjKyoKmaejo6OgT4E/eFiIoKwKDInlCbJ9IJCLXVzxfXNer2ysSifTZZup7BvqOdarJD+J99rfdk7ePeLy6XL/fL7dJ8tiWrusIBoOW54TDYTn2EIlE8Pnnn8vHnnPOOaiursZFF10kf9fd3Y1QKCTXRfysJliIsbZDNX6hticQ+5SaFKFWywgEApaBe5/PZ6kImkwE79XkEzVgrq5DNBrtU8JXTYDw+Xxy26qz9ITkxA31PexLT0+PZV8XMwTV969ua/UzIbaFCPyrpY3FPi3Wye/346WXXoKmaUhNTcWbb74pl6e+lvgMDFRy4kZPT498/f62gbpPinVT92X176Mmv3R0dMjXi0ajluOBum06Ojrk8sSYsJoEIX6nJg4wAYCI6PAT59EiiUw9ts6fPx+5ubnIzc3FLbfcAgCW85mBevXVV1FUVAS73Y7s7Gzk5+fjpptuGtD5u/huFuva0tICr9cLp9OJ6upqaJqGdevW9RuT7i9ZUj2Xf/XVV5Gbm4ucnBz88pe/hGEYlioCA4mxHTEJAPvamJFIZJ8njf2diAtqprMgTuTo6BeL6oAJmAkDr7/6GnKzczCiqVmWXK/11qCuphbOSgeKC4vQ3NiEFE3Dyk8+Bcw9Fw7Ang+cuOAcKkHqg5F88aZe0IqT4f1tp+Ss5uRs6qNZLBGHCcgeeLt2tqEoLx8j6hrgcbhQ7/bCXlSCprp6nHHa6TATuyslmIaoGolYLGY5Zvn9fnlhp0qeNUBEREREREQDd9AVAEwABhALR/A/l1yK/JxcjGxqRmV5BQrzC/DEY48DhvU6TgTbDkULOHW26p///GekpKRg0qRJlsD9/ibKCOFwGOFwWAaxk2dwqwY6NqIGyrq7uy0zotX3oK6DkBxQFeu7t+2mBi7VMR0RiO7p6cGmTZuQnZ2N6upqFBYWoru7u88MdnV7iYCjOoFInWkvXqO/JAWhq6tLBjOj0Wif63jTNGVFCDEe1d+4ZfJ2UAOd6t8xeUb03oikieSx13A43Cf4KhIVOjs7MW/ePBQWFiIvLw+nn356n9fo7zUNw+gTgB9okHt/+tsnIpGI3PfUfVls4+T9Wfw/GAzC5/PJfrtibFndRmqwP3nbJb9W8riX+rcVyxGP6+rq6vN33J/+1kMsT/QhFkFvdXY8sCfpIDlpQiUC4vF4HKeeeipqamqQmpqKhQsXyucnJ46IRICBVHgQ1TCTH5tchSB5W4jPhngdsZzkyiLJVRr6S94QSQeCmgSkVutURaNRS4KBujyxfkwAICL68ohjc3K1eAD42c9+hoyMDKSlpWHZsmWWxx/IufecOXOgaRrq6+vljHun04mNGzce0DrG43Gcf/75cLlcqKmpgcPhgKZpeOONNyznmOo5vZCcSAr0nouNGjUKNpsNY8eOtSSzDjQRb9ATAMSJbPKXovhj9vflq2ZkiuzF/h4jvsiTA/5quSU6usX1GGJRXZZfv/P2OzAsNQ1jRo1Ghb0cDXX1cFRUwlFRibqaWtTV1KLMVor62rre3n0A2tvb5fLE/tbe3s4kkd32NlghTvTV7GbAOpAgLkaGatUNkQAgkiEWvPMu0jQNI+oaUFftkZUAKsvsuPuu/w9mwui94NidAKBuN1EOThUOh+XFtzheqVncRERERERENDCHIgHAiOj4YsdOZKdnoNbtgauiEiOamlFeZsf6z9bBTOwJ1orZOSKodrDEtXUikcCKFSugaRrOPPNM+VrJgSe1zLg6Yyq5CqJ4vrqOwWDQEqgbaADXNE3LssUsZb/fL5cRj8cRDAZlcFQMgoqxB7VVnroMUXHQ7/f3qaaXHISMRqO4+OKL4XA4UFpaitTU1P22YlBLgavLFs9JHp8UAfxoNIpgMChnzQPWZIhgMIhQKIS2tjbLayWXfY1EIrLqX3/VAcS/6v6kBiHVv3l/s+RUnZ2dlkCpqL6g7iPbtm3D2LFjUVhYCLvdju9973tyfFWMZYjy+NFoFO3t7f3u53trL/GfUN+vSMRQx3aTx1RCoZD8W4hxZLXPvWpv20rsn+p7FpLHDX0+n2X/39/7Tq6WsS/q8au/Frjq/erPYkZ8f0kG4vOmbreZM2eiuLgYpaWl0DQNb7/9trxPXYbf7z/g1g7RaFT2SRbtMcRrJyfjiLGv5MlI/UmuIiCOMUJyNRNRwSKZaZr4/PPP5fZLfl0mABARHX7q8Ta5EnVLSwtcLhecTqdMrlO/mwdy/r1r1y688sor0DQNo0aNgtfrhdfrhaZp2Llz54DXUyT/TZkyBeXl5aitrYXH40FWVhZefvllANaYd/K6BYPBfqsDTZ8+HTabDUVFRXj11Vf3mui2N0dEAoD6haiePKolxIPBIDo7OxEKheT/W1tbsXXrVqxZswbLly/Hxx9/jM8++ww7duzo80UuSmL1d3JERzETvYF8E/D3+BAJhXHqyacgKyMTJ4w7Ho6KSjTU1cNWXIKvHTcWtuISuKuq4aioxNQrrgTQe0InsmVFhjpP0noll0zr6urq9yAjPpN7O/Akt+IYKkQLAHHxeO+Me5CbmYUaVzWaauowqrEZVeWVSNM0vL9wUW/ZyESit32k2ZsVv2HDBixbtgyffPIJVq1ahVWrVmHHjh19EpREFRTxJXYoZpAQERERERF9VRxsAoAZiwMG8JcH/4z0lFS4nS64nS5UOV047phjoUeiiOvWGcQHUn50f9Ry611dXUhJScEpp5zS72PV2eXyLey+LleDn62trfjwww+xadMmrFq1Clu2bLEEzpKrbO6LOoa3r/LvyYkG4jnJgXcx/pdMfU/9BQZ9Ph+uvPJKlJSUwO12o6WlBZqmwe/3y+QCEdQVwWl1nbq7u/skSKitDEW/8eS/bTAYxKZNm7BlyxZs2LAB77//PtasWdPn2j4ajcoxKAB9gqgiuCsCvOL9dnd3y20ktpto6aDOUE5OLBBjNSKJIllycDQajWLbtm0455xzkJOTgzFjxsDtduP000+X7yV54Hpv+3w0Gu2zLQ+GeN/Jve7FzHKxTj6fT243tYqDSMgRz+nq6kIgELDMKhSB6eSZ7ur7E+NbYuxZJG4In3/+OT766CN88sknWLt2LRYvXoyOjg4YhiH/9uLfAxmbVvdZMQ63adMmrFixAitWrMDq1auxadOmfU5qEjPd+5vxftNNN6GoqEh+dgoLC7FgwQLLdlOXI9ZJTXjZG3XfTN4Pt27dis8++wyffPIJlixZgnXr1vWpeCAmGiWvQ/KEv+RZ/f31Vwb2tOkQj0lep0AgYNnf1XUBmABARHS4iOOtqGITj8dhGAaWLl2K9PR0eL1enHnmmXISpfqdPtBz2Pfffx+apqGiogJNTU2w2Wyw2WzYsWPHfp+rtm8GgO9+97uorKyE2+1GfX09NE3Da6+9Jt+LSFJQWxeJf9VqNuJc47HHHsOwYcNQWFiI6dOnA9hz3jyQc4gjIgEA2JMhsbcAYjwex5IlS3DPPfdg8uTJGDlyJOx2OzRNk7fU1FQUFxfjpJNOwh133IF58+b1+QJXv+wZQDv6JWJxwAR83T292fjxBFZ+8ilqPF4UFRSiqaERRQWFGH/C11FcWARPtRv1tXWodlUhJysb9913HwDrieRQKU9/qCRnV8ViMSxatAgzZ87ExRdfjKlTp+LSSy/FNddcg+eff15mkidfAIjliIPXUDgRNtFbBQDo3W/OPfMseFxVcFf9xeMdAAAgAElEQVQ60VRTh9KCIowbcyzcThe2b92GcLC3HOHb8+bi0sv+G83NzcjIyICmaSgoKEB+fj5SUlLg8Xhw0UUX4emnn8bWrVv7fCEAAysRR0RERERERL0OVQuACyedj3JbKRpqalFV6UBJUTEuvfgSwAT0iDWgdqAzZPdFjG+J2e7jx4/H+eefLwO46jW2uPZWiTGw1tZWzJo1C9/85jeRkZGBYcOGweVyobKyEhkZGbDb7fjhD3+IxYsXW5a3P4FAAJ988gnWrVuHRYsW4YknnsDjjz+Oe++9F9OmTcPkyZNlP261bOlnn32Ghx56CFdccQUuueQSTJkyBddccw0eeughfPzxxzIQJwZLe3p6+gT6QqEQdu7ciYULF+Kcc85BSUkJMjMz4fV6UVxcjKKiIrz22muYO3cu3n33XcybNw9z587FO++8g3feeQdz587FwoUL+1QpFQFesa6iCqIQCoXw73//G7/61a/wne98B5qmISsrC6NGjYKmabJNw6233ort27fL5Yv9wjRN2Zpg3bp12Lhxo5zstHnzZmzcuBEbNmzA+vXrsXHjRmzbtg1bt27FkiVL9jrou23bNqxYsQIbN27E5s2bsW7dOrS1taGtrQ0bNmzAli1bsGbNGixdutTy3gKBAHbu3IlnnnkGY8aMQV1dHSorK1FQUICysjKcfvrp+Pe//40333wT8+bNw5tvvomXXnoJ//rXv7BgwQL8/e9/x44dO2QAXRDVIw8F8Z7b2tqwdOlS7NixA1u2bMGnn36KDRs2YMOGDdi2bZucINba2oqVK1fKAfXk6hFieR9//DHWrVuHzZs3Y/PmzXJ7i7/DqlWrsHnzZsu6iDYU4udQKIS///3vuOSSS+B2u5GRkYGKigq43W6kp6fD4XBgypQpmDdvniWgvK+y/MmvJ6xduxYPPPAAvvvd7yI/Px+apiE3NxeapiEvLw+nnXYabr75Znz66acAesePRHsDNZgfDAYRjUaxcuVKXHnllXA4HDjuuONQUVGB6upqZGZmYsaMGVi+fDnmzZuHZ555Bi+99BJeeeUVvPnmm3j99dfxzjvvDPjvJ7aXqIhxzz33YMKECcjOzsawYcMwatQo1NTUQNM0uN1u3HjjjZg/f75l+4iKG2pVDPFexP2ffvopVq1ahbfeeguPPfYY7r//ftx2220499xzceedd8IwDMtne/ny5fjNb36Ds88+GxdccAFuu+02rFy5Ut7PBAAiosEl4klq0Puhhx5CdnY2cnNzcdttt1mSK9UE0/0R56azZ89GTk4ONE1Dc3MzXnnllQE9X5zziOS80aNHw+12w+VyyUoCCxcuBGAN7IvnqAmHyedL8XgcCxYskOfq48aNA7Anlj6QCuaDngCgfmEmv0Gfz4fPP/8cDz74IE488UQUFhZi+PDhyM/PR3Z2NtLS0mQ27/jx4/G1r30NZWVlMiEgIyMDv/3tb+VJLdC35xkd5UwgFtXR3dm154LcBJ7767MoyMuHrbgEJ4w7Hnm5w1FfW4f62jo4Kx1wV1WjqKAQ2dnZWL9+PQDIsliipBxZy5F1d3fjd7/7HRwOB3JycnDSSSfhF7/4BR555BHMnj0bkydPRmZmJlJSUnDjjTcCgOWkHIClHN1QqMBhAogbvRdhW7ZsQbmtFLVuDxo8Nair9mBMcwsytFScdMLXARN4Z+48TJw4EVqKhuH5eUhLS0NLSwtOPvlk1NfXIz8/H8XFxbJ0jaZpmDhxIp599ln5msmlbIiIiIiIiGj/DkUCgK+9E6NHtMBZXoFjR41Gua0UBXn5uPd3vwdMIBruvX5W+3MC/be3PFBqadBIJIInnngCTz75pBzwTL72Vokg+q9+9StkZmYiIyMDV1xxBRYvXoxgMIiuri4ZJH766acxduxYpKen46yzzsLcuXMHtH5r166FzWaTQfDU1FRkZmbKQcvs7Gz09PTIsbhVq1Zh+vTpyM7Oxve//33MmDED999/P26++WaMHz9eju1Nnz4dmzZtkq8jBmrV1504cSJGjx4tE+wzMzNx7LHHorCwENXV1airq7NMIOrvdv/99/cZCxLbVpTTV8cR165dixtuuAHV1dXQNA3Tpk3DggUL0N7ejo6ODvj9frzwwgs4/vjjoWkanE4n/vjHP8rnq6/V3t4uJzaJpIzk9ROBXk3T4HA4sHDhQhkUFkkJnZ2d+OUvfym3g7ilpKRA0zQMHz5c/s7lcsn9dP78+Rg3bhyamprk/enp6Rg7dizq6urQ2NgIm82GiooKeX9OTo5cz8zMTOTl5eH555+X70kNmh6KBAAR+O3u7sbPf/5zpKWl9fse1W2XmpqKvLw8TJ061TKJor29XW6zbdu29dleycvOzc3FnDlz0NHR0SeoG4vF8Nprr8kEELvdjlmzZmHnzp3w+/344osvsGHDBtxxxx0oKiqCpmmYPHkyVq9eDZ/Pd0BjY2vXrsXvf/97NDQ0yADF7bffjk2bNqG1tRXbtm3D4sWL8Ytf/AIVFRXIzc3FzTff3O+yurq6cPXVV+PEE0+U77OoqAh5eXlwOp2ora2V1TPEpBXxt87MzJS/P/nkkweUwCDeZ2dnJ+6++27YbDakpqbi4osvxoIFC7Bjxw7s2LEDu3btQltbGx555BGZhHDRRRdhxYoVcj9Sj29qgN40TcycOdOyL+Tm5iInJwc2mw2FhYX4/ve/L/eFtrY2/OhHP0J6ejp++MMfYubMmZg0aRLS0tLgcDjw0EMPAdgTP2ACABHR4FCP88Ill1yCsrIypKSk4OWXX7Ykyh1o3FckZ7a2tiIQCGDJkiUHtG5ivbZv3478/HxUV1ejuroaLpcLNpsNbW1tlsC/WqUg+ffJ3x1bt26V7QQqKipkO/OBfscMegLA3no17dq1CwsWLEBTUxPKy8ths9ngcDhkGYbrrrtOzvBvb29HV1cXQqEQNmzYgNmzZ+OMM86Q/Yp++tOfynIN++rVREchcYG9+189EgVMIODz4+Zf/grZmVkoL7OjubEJtuISOCsdqHK6UOPxormxCfn5+TjppJP6LYPGKgC9YrEYnnrqKdTW1kLTNEyaNAlvv/22vOgWmcRAb7mU8ePHIy0tDePGjevTjmOotQEQOScA8MEHHyBN01BZZseY5hZ4HC7UVXvgcbhw/fRr8cB99yMvdzgyMjJQ6XTgmuk/xkcffYR169bJmQwvvvgiTjnlFJSUlKC2thYNDQ0oKipCZWUl5s2bJ193f/3PiIiIiIiIyOqgEwASBjZ/th65mVnwVlXLFgApmoa//+15wATieu+1cXLFtkORACDWGYClHL24HlevtdXSoqKFpghEjxgxAu+++65cL7UVoljPtrY2XH/99bJC3b333rvfddu0aRMuvPBCTJ06FRMnTkRpaSkaGxvh8XhQV1eH1NRUGcj/6KOPoGkarr/+emzdurVPwsLKlSsxffp0FBUVITs7G8cddxw+/fRTy5ieKPUKAL/73e/w1FNP4d5778ULL7yABx98EC6XCw0NDaiurkZWVhb+8pe/4NFHH8WcOXPwyCOP4JFHHsGcOXMwZ84cPPzww3jxxRflNk0eOBb95sXrL126FGeeeSY0TUNLSwueffbZPpNJxCSTHTt24OKLL0Z+fj4yMzPxm9/8Rj5GnbX8xBNP4A9/+AN+8pOf4LTTTkNtbS3sdjuqqqrQ1NQEh8OBhoYGjB8/HlOnTpXBe7VcLNA7I66yshKVlZUYO3YsvF4vHA4HsrKy4HA4MHnyZNx///246667APSOxW7btg0zZ87EI488gsceewxz5szBgw8+iIKCAni9Xrjdbnz961/H73//e8yZMwdPPfUUZs6ciUcffRRPPvkkZs+ejT/96U9Yv369LKsO9O6nAwkOD5R4j2+//TZuu+02zJgxA5MmTYLNZoPH44HH40FDQwMKCgowZswYXHHFFfjlL3+J+fPnA+hNnFETE8Qg+p133okTTjgB1dXVKCsrQ3V1NZxOJxwOB0455RTceOONeO+99+R6iM+cz+fDo48+KgPOl112mawUEAqF5ONEy4TXXnsNaWlpsNvtcLlc2LFjh9xP9mfVqlWYMmWKDLz/9Kc/leuvThQJBALy8/STn/wEBQUFuPTSS+V9wJ5jxb333ounn34aDz/8MB599FE88MADOO+88+DxeFBZWYmioiLceuutePjhh/GnP/0Js2fPxpw5c3D//ffLCh9z5swZ0PqbpomVK1fi9NNPlwkxK1askH9Xsc6RSERuk56eHtxwww3IzMxEYWEhHnzwQcu+JVoiAHuCPe+88w7++7//G9OmTcOECRPk37SyshJVVVWYPHkyDMNAZ2cnzj//fDQ0NKC1tVWuX1FRkUx2qaqqwrZt2+R7YAIAEdHgEJWmRdJXKBTCiBEjYLfbkZubi9WrVwOApQrAQKnn7Op5XH/VtPZGtAdatGgRNE1DeXk5ampq5HkEYI1Fq62lAGtcXG33I5Y9YcIE2O125OTkYMmSJQcU1x70BACg74XQrl27cPnllyM1NRW1tbVwuVxwuVzQNA1Tp07FmjVr+pyM67oOwzDg8/lw++23y1ILHo8HmqbhH//4R5/HHynESYtKPYkZTOKCQm3VEAwGj4h1A2CZ9d/f7ZsnfQNZGZkYOaIFNR4v7KVlqCyvwMgRLahyuuBwOJCdnY1bbrkFACwXJgMpcTcUiM+fWroM6P1bRyIR/OAHP5AZ1DfddJOlXxhgPUjGYjGsWLECw4cPh8vlwqhRo9Dd3W3pVSiWL8qOdXR0YMSIEZZKHQPpH3Yk0OMxmQDwm9/8Bvk5uRjVPAKOUju8zirUVXvQ6K3FmJaRGJaahuE5ufjBD36ADZs29o4fJbWb0HUdr7/+uqwCIC5gHQ4HvvGNb1hK8xEREREREdHAHYoKAG+98hrSNA1jWkbC7XSh2uFEiqbh/YWLAMPckyGe/PTDEAhSS4iqkxzWrFmDsrIylJaWoqKiQga01PKj6nqK8bZEIoFZs2bJGdFq4Fq8nno9q1b6CwaDWLRoEQoKClBZWSlLoG7fvh0rV65EamoqnnjiCQDWMUE1qL99+3aMGjUKtbW1yMvLw5lnnmkZy1OfJ8Zv1LGrUaNGweFwyBn6wWBwr33rBXWsUczCUh/n8/mwfv16VFRUoLy8HHa7HX/961/7LKc/DocDjY2N0DQNTz31lNx2yeMf4j2+8cYb8Hq9MhhbUVEBp9OJjz76SI7D7Nq1y/JcMXC9bNkyaJqGpqYmZGdno6GhAY8//jhWrVpl2WZiH1D764pts2nTJlnCvqKiApMmTZJjZsnb5nDs34ZhWAbmxbr09PRg5syZKCoqQlFREerq6lBcXIx//vOflveWPI6Z/LcNh8O4/PLLUVZWhvr6epSWlspxKjXoLMruApCBX5fLhbPPPltOlBHLFYEIXdfl5/PRRx9FUVERCgsL4XK55HKTA9lCe3s7tm3bhhEjRqC4uBiapuH222/vt7JmJBKR71dU9HzhhRfkcwRd1y3jSuq+cPXVV6OsrAxutxspKSlYtGjRgMeA1TFVcQwSy16zZg2am5uRlZUFr9eL1tZW+P1+y2dSHV8U28Hn8+Ghhx5CamoqNE3Dz372MwB7kir2Jh6PIxKJYPHixbIyQ2NjIyZOnIhEIoGrrroK3/jGN/DFF1/I59x5550oKCiA2+22lG1O/u4QyweYAEBEdDjoum5JeN2yZQs0TYPX60VVVRV6enosMVY1Ae9QUI/54XDYch4kqkQlEglce+21qKysRE1NDUaMGAFN07Bs2TKZsAfsiQcln4fsTSQSwTXXXIPi4mIMHz4cDz74YJ/z3n0Z9AQAcfLW1tYGoDdD9qSTTkJ1dTXKy8tRUFCAlpYWZGVl4c4770RnZ2efN7hmzRq5vGnTpmHYsGEoLS1FeXk5nE4nPB4PzjvvPMuFhNpzazCpJ6+BQOCISkxIJjJZjqjS7ftJAPjow2UozC9AjceLvNzhOPHr4+GoqESN5/9n773DpKqy9f/TVZ1zd+UcOkGTDBhQnFHvddQRUcwo6k+duYIJ84xZuIoYR2eujo6OIqYxZzGRjIyigKhDFhCQ1LG6cvj8/ij27nOqG2gFAf2e93nq6VAn7X322Wevtd71riBet0dKwRkMBt5++22g2/hSP5i/VvS2iBUT4+rVqzn44IOlFH1vBobI6BfjVhgF1157LRUVFVRVVXHBBRcA3c4IMbkJA2jWrFmUlZURCoXkd3vcONsKUpk0GbKGycknn4zTaqMxWEe91y8/LouNfYfsRZ6i8L8TJmbLIgAdoe7MDTWra9myZfj9fvx+P16vF5/PR1NTE2VlZSxcuFBjkOvQoUOHDh06dOjQoUOHjr5hhwkAyRSvPv8iRkWhf0MjXqeLprp6qioqmffFl7udACAggnBCfrypqYm6ujpKS0ul3yOTyWgym4QNLhQFhN9t7dq1XHDBBZSVlREIBGTyRK49Kvxr6kBiZ2cnI0eOpL6+Hp/PR0lJCbFYjIaGBh555BFaW1vldQioSQzRaJQrrrgCq9VKXV0dFouFadOmycBfLvlA/fOnEgDEtavJ+ul0Wu733Xffcdhhh1FZWYnZbObiiy8G+pZk9MILL8hApMlkYsmSJT36T5xf3MNp06ahKAqNjY14vV6qq6uliqBAe3t7j5rmyWSSkpISrFYrhx9+OF9//bXG/xiLxTQ+UdEXezIBALT9rC4FsXbtWrxeL0OHDsVut2M0GlmwYAGw9QQKtdMesm34+OOPKSkpoa6ujvnz52vIGepsvc2bN/Pyyy9jNBppamrCarUybdo0eRzRL8K/pk6GCYVCHHDAAfh8PsxmM5deeqnmXqifgXXr1gFwxBFHSALPmDFjWLZsmdxG3BORGNdbed2zzjqLgoICVq1apZEqVgfcdwYBQLRZ3e5YLEZrayuNjY1Sjn/mzJkaOWdxbnU/qLM429vbOeOMMySR6Z577tEQHdSJTLnEptWrV8tazDabjbFjx7Jo0SJqa2v58ssv5T0BeOyxx8jLy8PlcuH3+8nPz5dZpepjgk4A0KFDh45dhdzS8clkks8++wxFUfD7/ey1114y0VRsJ9YzOyPBVx23Uc/r6XRarsEEsc5gMGC1WnG73VRUVHDkkUf2UB5Xq3T1BclkkgkTJlBSUoLJZOK6666T5+/LcXY7AUCdfbxw4UJ+//vfU1JSQkFBAf3798fr9WIymbj00kt7LRcgFkbJZJLXX39dw/zo378/RUVFeL1eRowYIffdEzO7p0+fzvjx4xk/fjyTJk3i5ptv5k9/+hPXXHPNbv2MHz+eRx55RKNIoJaY2+3YDgGADPzfX/9GnqIweOAg3E4XjfUN2K02GurqaWhowGKx4PF4sNvtrF69GmCnSpTt6RAGjdr4X7lyJaNGjcLpdGIwGLj00kslKzaVSsnFsXh+c/9esmQJ+fn5WK1WSktLmTNnjjTOcg2HsWPH0r9/f6BbdQB+GSU6BAFg8eLF+Hw+GoN1uO0O+tc1UO/10+gP0i9Yj1FRmPLoY5DJGjEZIE23UaI2csLhsHTQeL1evF6vzBJ46qmnNHJ1OnTo0KFDhw4dOnTo0KGjb9hRAkAmkeTpKVOpKiunzufHbXfQ3NiEzWJl4YKv9ggCgLAthd14/PHHU1xcTGVlJWeffTZAr0Fw8T+1DLf4fd68eTidTvLz8/H7/cyZM6eHkqDw0cViMU0A84YbbsDj8eD1eikrK+Oee+7hoIMOkgkXqVRKs33u9bzzzjuy1nl+fj633HKLJmgrjqEO1sNPIwDk1o5VB/hENvG1115LeXk5tbW1NDQ0sGjRIqDv/guLxSLrt1922WXy/2pp+lxSxBVXXEFZWRn19fXY7XZOO+00TZ+LfaA76/rzzz+nurqagw46SJPhLKAmaoi/1e3YUwkAuXK56rFz0003YTKZsNvtVFZWcs0112ic9mqITD31WEqn06xevRpFUbjjjjs024rvxc/vv/+e2tpaampqqKqqkkQQ0I4d9bhUj5FHHnlElldQFEVD6sgt6XHjjTdK4kh9fT2ff/653EZN6lD7lTo6OkgkEowbN05KESuKwquvviqDJJBNfFIHz+GnEwDUfnp1W7u6ujj33HOpqKigurqasWPHyu3i8biGzBMOh+V1CPVRgS+++IKGhgby8/MpKytj6dKlmhIaop9FQEQcMx6Pc8QRR1BXV0dlZSUXXHABZ555Juedd55st7g/bW1tjB49mtraWmw2GxdddJHmnugEAB06dOjY9Ugmk5rk03g8znPPPYeiKHi9Xo4++mg59+YSAHbGPKxea+Sun8Q50uk0kydPprS0FJvNxsCBA1EURUMig+51cq4C0/bw6KOPoigKTqez13XgtrDbCQDQzaIYPnw4hYWFeL1e9t57b2kkHHjggTJI2d7eTjQalQFHNePvwgsvxOfzsddee0k5roMOOoi8vDxuuukmOVhyax7tTiSTSdatW8eVV14pZdbLy8tl/ag94XPSSSf1kHnfYyTItxP8z6TSJOMJzv/j/1BTVY3H5cblcLLX4CH4PF6sVitDhw6lsbGR/Px8xowZs0coQ+xKxONxDds2mUxyxhlnoCgKNTU17L///lI9QxgXoh6XepEO3RJbiUSCQYMG0b9/f0pLSznrrLPkc64mkgBUV1dz33339ZiofwkQJQBeeeUV8vLy6N/QiN1soSlQR4MvgMfmwG21c+lFF3ePx2SS9Rs3yBIA0G1ERKNRNm3ahNfrJRAIEAwG8fl8eL1eSktLueeee4Ce9SR16NChQ4cOHTp06NChQ8e2sTNKAEx95FHM1TUEvT68ThdBrw9TTS0L5s3fIwgAqVRK2otPPfUUiqIwdOhQFEWRwWph34ugmdoHog70ie+7uro47rjj8Hg8OBwORowYIX10ucFldfA6lUoxceJESkpKaGxsxOVyoSgKCxYskNm9wpGrDiRnMhl57sWLF6MoCoFAAIfDIRUGxXbifLlB2p9CAFAH9IQCpro/Zs2ahclkoqmpiZqaGhlAVKuNbgupVIobb7yRyspKBg8eTGlpKatWreoR+FX7B4R/5He/+x1VVVU0NzejKAqPPvooiUSiR/mA9vZ24vE4xxxzDG63W/puQqEQoVCohxRubg3aPZkAkKtOIa4/FAoRjUZZv369zAZsamqisLBQU75SQC3ZmzsG/va3v1FeXk5HR0evCrTi2Tr//PNRFIUDDjgAo9EoHfzhcFieT2QE5tb87ezspLOzk6qqKnw+H0VFRdx2222a0hsiCUuMf6/Xi9vt5uSTTyaVSslt1SoIAps3byYSiTBr1iwsFgv9+/fH5/MRDAa5/PLL5ZgW50gkEposwp9KAOjq6iKdTssxJvZ5/PHH5X0RwRB1kF/dx7lBf+HDF316+umnYzKZsNlsHHnkkT3qM6vvrTpgc+qpp8r564gjjqC4uJhIJEJnZ6emTKn4uWLFCo3KQm+JODoBQIcOHTp2DcRaEboJqn/5y18oLi7G6XRKgit0r2N2ZvlyMZeLNUUmkyEcDmve799++y0FBQXss88+spT9gw8+qCEbinao33t9iUNmMhneeustFEXB5XJx6KGH9iAWbgu7nQAgFhwXXXQRRUVFDB48GL/fz8CBA7HZbBQUFPDOO+9o6pCJIKSQLRIL0FtuuYWioiIcDgdNTU04nU7Ky8tlBnKupNWekGEsBu+XX37JnXfeyaRJk7j99tuZNGkSDz74IHfddddu/dx+++089dRTgLYu2R6TfdwHBQAy0Lq5hfpgncz8H9g8AK/bQ79+/TCZTAwZMkQaow8++CDwywpE7wyI+3vRRRdhMBhoamrCaDQyffp0jYxYrqSWWg4PkCymK664AqfTidPpxG63E4/HNQvrZDIp2fyrV6+WC+o9UaFjaxAEgJtvvpmioiKa6urxudwE3V6aAnVYq2s5fPhvIAMtmzYTj2bbuKllMxl6stFisRhffvklNpsNl8tFIBDA5/Ph9/uprKzkb3/7G6ATAHTo0KFDhw4dOnTo0KHjx2JnEACe+OdjlBeXUOfz0xAI4nE4qSyv4Kv5C/YIAoDwl8Xjcex2OzU1NQSDQSwWC6BVO8wNXPfm5xG25z//+U+qq6txOp0YjUZmzZoF0CM5R41wOMzEiRMxGAwMGjSIwYMHU1hY2CPBRA21HDtkS+QJv4LP52PUqFEyuJdLqFf/viMKAGpfkDor//zzz6eoqAibzUZhYSEvvfRSD8WCbSGZTDJnzhypXKooCs8++6xmG5HBL+rLCnz33XeSROFwOLBYLMyaNYtMJiPJFMJnOmnSJPLz8/nss89kO9X1agXi8ThdXV0aJ/meTABQ+03UwV112YaRI0dSU1NDv379KCwsZNq0abJ/1MHhrSkD7LfffpxzzjmaMapOYolEInzzzTdSlcLpdLLvvvvKMaPeTzj5c8eTGFNHHnkkRUVFDBkyhMbGRk1gH7L34L//+7/xeDwEAgEqKip4/vnne2Q3iiCAkCIWECUnysvLaWhooKysjLPOOksTuFa3cUcJALkJQpD1MwryjtvtpqioSEofq4kuasRiMU0/qq/373//O1arVWZWvvvuuz3KOIif6us444wzsFgs1NXVYbfbOfTQQ+X1Qfd9U2dliutbs2aN5p70dl06AUCHDh06fj6opf3Fe/K6666jpqYGs9nM+PHjeyhZqffdWdegXjcL5SY1MbCoqEjGFy+55BJ5PeqYWG/H3R6SySQfffQRRqMRu93O/vvvL99ffXk/73YCAHRLeu299974fD7q6uqoqanB4/FwwAEHAFpZI/ESVy9UM5mMrP3gdDoxmUx4PB7Kysq49dZbNedTLzR2N8QgyWQymgWOul767vxAt8HT1ta25y1ethf8FwZ4Bt58/Q1MNbXYrTZsFitNDY243W6CwaBUmxBSbPPmzdttTdrVUBpKsRAAACAASURBVLNr58yZg8FgkEH7MWPGAFnDQozP9evXAz0nGPXiOhqNcv/991NeXo7dbqe6uppPPvlETtZiMh4xYgRHHXWUphbKz8HU+rmQyqRJplMce+yxuFwuGgJBmurqCbg8NPqD2GvNzPngI9LxRDb4n8kytMXwFFDfgyeffBKbzYbD4ZAlAATL/6WXXup1Hx06dOjQoUOHDh06dOjQsW3sKAEgFYvz2gsvYVQUgl4fzY1NeJ0uKsrK+XLuF3sEAUA4BF988UUMBgPBYJDa2lpOO+00TSBS2N2hUKiH0zQWi8k+Enb5Z599Rn5+Pg6HA7PZzHnnnafxAfQW9BJyqEVFRdTX12Oz2Rg3bpzG35XrqM1NxFi0aBHNzc243W6cTidHHXUUkUikR1Z17r39KQQA8bc6CCuuZ9myZZhMJnw+H263G4fDwTfffCO360sGl5COVxQFs9mM2Wzmlltu0bRB7eMUNr/I8v7Xv/4lSR2VlZUMHz6cH374QXPtzz//PAUFBTz99NNA1ueYm0AgJNd7G5N7MgFAPTZ6u/6uri5mz56Noig4HA4CgQAHH3ywvL6tBf/Fdx9++CHV1dVMnz5dc67cUgsTJkyQWYcWi4XzzjtPXktuUF4glwgA2eBFdXU19fX15OXlsXHjRs04WrBgAYqiMGzYMKqrq3G5XKxdu1aeK9dvLCACFMJHLoLeVVVVvPzyy/L6emsb/HQCgIC6PMgnn3xCXl4e9fX1+Hw+Tj31VNkf0WiUzs7OHr5ncV25c0MqlWLevHkUFBRgtVqx2+2MHDlSqljk+vrVbTvrrLOw2+34fD7y8vJkVqbYPhaLybIkgCwHIJCrBKH+n04A0KFDh46fHyIBVcy9F110EVarlcrKSm6++WaZma9+X4nE1R2FOKb6PaVeWz311FNYLBZ8Ph+FhYWcfvrpQPf7WP0uEGuRH/N+SCQSzJ8/n4KCAhwOB0OGDOH777/XXNu2sNsJAIlEgn333RebzUZDQwO1tbW43W4CgQBVVVVMmTJF89JuaWnRMHChm3EciUT417/+xYgRIxg2bBjHH388M2fOlPuuW7dOU198T8nwFszE3MXOnqBQoH5Q1AvarbFldzm2QwCIRaJSep0MTJ50GwXGfOoCQQI+Py6XC7/fT79+/WhsbMTj8WCz2WhqapKG1K8ZYowJJ0FjY6PsD0VRmDZtGhs3btzmvgJqIzwUCjFlyhRKSkpwOp1UV1dz//33a4yZRYsWUVVVxZtvvqmZNMVzsCeM/+0hA6xY+R1ut5uGhgbqfH6a6uoJur147U4OGro/pIFkilQiSTiUbWcyndL4hVKplGz3+eefj9lsxu/34/f78fl81NfXU1payn/+8x+5z54yf+nQoUOHDh06dOjQoUPHLwE7SgCIhyN8M28BRkXB7/bQv6GRoNdHaXEJ70x7e7cTANTyqH/4wx+oqqqiX79+VFRUSKVDyDot1UG6cDiscSAK+1xtky9ZsgSr1Up9fT3Nzc3Y7Xa+/PJLzflFFrLaVp04caIMXiqKwpNPPinPq5b+B63EqwhSr1ixQiZu2O12Ro0a1WvGcK4awM4gAKhJEFOnTpWZ+4MHD2b48OEyQNjW1tYnB6yQq6+qqqKhoQG3281ZZ50l+yFXZlydzS3Odf3112M0GmVZhyuuuIJIJEIqlWLhwoUoisKtt94qs6xF/4j7Iz65bc/1w+yJBIDefESJREKjCNDR0cGAAQNwOBxYrVYURWHZsmUaP3JvpR+i0Sinn346v/3tb2WCiiifoO7L9evXc+SRR2Kz2XC73ZSVlfGPf/xD0x+5/l3IjiFxXDFWHn74YRnIzs/Pl6oakH0+Jk6cKJNDbDYbI0aMkMcV5QnUJQBEOY5EIiGDDh9//DEjR47kgAMOYPLkyZo2i/GYqwjwUwkA6sS2TCZDe3s7V155JX6/n/LychRFkWUtBcR4ylV1EMdIpVLy/oj5QVEU+vXrh8vlIhgM8tVXX8l91PdWPV7OPPNM7Ha7lGV+//33geyzLp53Mfeo56CtzRHie3GdOgFAhw4dOn4+qMlakJ3fzzzzTMxmMyUlJfz1r3+V72r1++rHBtq3hlySZ2dnp3x/ffHFFwSDQaqrq7FYLJxyyinyHaiOlQnSq1jfqkvv9KX9K1askOV0gsEgS5cu7TPBYbcTAF5//XUURaGpqYnGxkacTifNzc3YbDby8vJkHaVMJiMzjyEbYBSLKzWLE7TMvHg8LtUDxM3qXuymtQHjHGRyfu4KbE8WYnchlyme+0DtNvRB/j8ZT0AGErE4ZODYY0bgdrpwOZz0798fm82Gz+ejf//+uN1u6uvrqaqq4sILL9x97dqFEIvsxx9/nKKiIllbrH///poFuCDaQLdDQCzGBdQy/m+88YY0kA0GA3feeaec7OLxONdffz1VVVVyohLGkGBsZSPnezYywEeffIyiKFlGs8dL0OvL1oGsqubGa6/LElG6wnIiSSSyZQOS6ZTmGRIOkH322Yfy8nIGDBhAU1MTfr+f2tpahg4dqqnBqEOHDh06dOjQoUOHDh06+o4dJQBkEkkinSEK8gz43R4aAkGCXh8GJY/HH5uy2wkA0B1Qq62tlTWvS0tLefHFFwFtFlNu/XjI2qXC9o9Go7KvNm/eTCAQoLm5GZfLRX5+Pu+9954maKaufQ5Z2/eqq67CbrfLfn755ZelPat2WoZCoR7BtVQqxebNmykuLqa5uZmamhpOOeUUeX25ATv1MX8KAUCtgJD784orrsBisRAIBKiuruboo48GsrZ5buByW0gkEpSXl+NyubDb7QwZMgTovi8dHR0ap7Dajynu7amnnkpVVRWBQABFUbjvvvtIJpMMHjyYgw46SCbtqI+bW1IgkUho7q/6/6Jv9jQCgGiH2h+SK/0ej8e5+uqrqa+vx2Qy4XA4mDhx4lYd5Ol0Wmaim0wm7r77bnkc8b3YNxQKsXnzZunDFqSW2bNnazLWc9UD1Bn6al/a+++/j6IoDBkyBEVRePzxx6WvNZFIcNxxx0n5f5fLxaWXXqppq5rEI+6bmhCQ+0wJiOtUzwXqa9zREgDimgDKysqkn7+wsJB3331XJhmJsS38+6K/xLhUjylxzRs2bODAAw8kEAjgdrtRFIXp06f3mqGvDuSPHj2aiooK6uvrMRqNfPHFF5oyAWpiRS62Vg5XJwDo0KFDx66BmG/FuyUcDnPCCSdQU1NDQUEBjz76qIa0matitaPITUIXf69atYoRI0bgcDgwmUxybagmoKnXY72VJ+hrAuz69evl+sNms7Fo0aI+q0P/7AQA9YJCzXgQvx955JE4HA7q6uqor6+Xktd2u1122s5BupdPkmQ0Auk0ZNJ8+vEnW+SRGvjs8y9IkbXdxM/uj/oYOwY1C1W9SBQMXrHY3F2f3AWsIFX8UhYxYpyp63G1tbVJxozX66Wmpobm5mYaGhowm834fD58Ph/5+fksWbJEUy9M/aDuEQSInQDBAB8yZAjV1dUy8/z666/v0/65xoOAKO0RCASora3l4osvlsz21tbWLYbSh6xf360wEIlESKeTQJpkKop4zpLJ7vu3aNESrFY7VVU1zJw9i1giTgbkzzSZnga4uiSEqixEbrZB7kScy/gV418tL3bbbbdRW1tLfX19tq6Z00VDXT3GPAP//nQOrZtbpBNIGFawZTxtIaeQzpBOplgwbz4el5uKsvJsiQqni8EDB2GuNfHnq/8kSSzhUBfJeDcLXf2sqq9VzSoT51S3JxKJyLk51zD8tYxvHTp06NChQ4cOHTp06IAdJACQTSoIdXTy+6OOxmaxEvD5aajLEsEvuuBCyMCGH9b3SEaQdt9OgJCGF76OVatWAVr7be3atRgMBhobG/F6vSiKwowZM/p0bBEQFRDHbWtr46ADh+H3+vB5vJhqarnt1kmkEsmsOsKWknfS3t7y86YbbqSmqpqALxtMnDZtGtB9D7ZWj1x8t3LlStxud7YEgN3BcceOzCo9qmx7ofio/j2TSjN44CDcThd+r488RaGtra3HGMjtO/H/aDQqM8C7uro44YQTqKqqoqmpCa/Xy4knnsimTZvk9YfDYZnZtbUPZAP8hx56KGazWfpBc69na2hvbYMMrFi2nIHNA/B7fQR8fvo39eP3Rx1NQ109K5Ytl/ckGU8QDUe2mvTUG7ZFABg1apSGvLE1yXUR2FUrHe4K/4I4x3fffYeiKDQ0NEhFSpEYpi4/qf59ypQpFBYWyudJKAvk+gHVpTA8Hg8FBQXMnj0bYLv3X02SicfjzJkzh8LCQsxmMwUFBTz88MPy2Wtra2PYsGFYrVb69etHdXU199xzjwzUC19Vb2U0fiq2RwDIPZea9KB+bgBZF1kkBQkVgNdff13eJzHW+hr8EOSAYcOGSRXOsrIybr75Zk1yoNhW3a4xY8Zgs9kIBoMoW+YC9djs6/jUCQA6dOjQseuRW7YmkUjwm9/8hkAgQEFBAVOnTpWxVOheD/R1Ds59B6hjKmKtow62izXFqaeeSnV1NYqicOONN2pKyYjzq+NA6hL3P2ZdlEwmWbFiBUVFRdjtdsxms0ya78s79GcnAKiZv+qGJZNJNm7cSGVlJS6XC6/XS0NDAx6PB4/HQ2lpaQ9poF7R54Vsz+A/JCGTJNrZBuk0o44fibnWgqIYWLR4ObFUNviv/nQTALbsv5MQi8Xo6OiQWcDqgbo7PwLqwSQG/i9Bol39cKp/f/zxxzEajTQ2NjJ48GC8Xi9VVVXU19ez9957U1lZyaBBg9i4cWOPOmO/JggJrW+//ZbCwkIcDgeDBg2SLNq+YFsEgPz8fCwWC16vlyuvvBLITpLXXXcdZrOZttaQ5vnNynolgCTJVJREMpL9PRkH0kSjce6++y9UVtSiKAbee38GLR3tZICuSJhUJk0qk9ZI9iWTSVKJJOlkqttJkM5kHQNpbQ2WXOO1t/pwasRiMU4++WRsNht1dXVy/qqvr8dqtbJ69eqtjp90uttJIQz0R/7xMOWlZXjdHvxeHy6Hk4a6evIUhX89/Yx0cIjtc19kW5OPEYaQ2vjsbSyrn/09osSHDh06dOjQoUOHDh06dOwk7CgBQNhj1/75Gqorq7IKcP4ANouVU046mYhQfssgf4+GI1lbdCfEgXIdh5MmTeL3v/+9zKgV7fr3v/8tnYQOh4OSkhJmqspjbrV5OUF5cR7IKgD8ZvghBHx+vG4PdquNC8ddoCHY/9IJAOFwuFe1vfXr17PffvvhcrnYd999MZvNFBcXM3jwYBRFQVEUjEaj/H1bn5KSEsrLy3E4HFRXVzNo0CCi0WjfnNRbxlM41MXn//6MPEXB48r2S56i8Pyzz8l+SMTisi862tp3CQFA9JWAupykOjP954TwdRx//PHU1tZit9spKiritddek2MtEonIrHnhrznkkEMYPXq0RsJe/FQnFt1zzz2Ul5cTCAQYMmQIRUVFWCyWPt17cf/F7yaTifz8fBoaGlAUhffee0/6bf7zn//gcrkwmUwEg0GKi4t56KGHttnmHUVfFADUQX/1OUWyj+irZDLJN998g6IoOBwOgsEgRqORt99+W26TG6jZHoTPbr/99iMQCMjkwT/+8Y+SENFXAkBnZ6fmOnQCgA4dOnTsuVArAIgk6sMPPxyPx0N+fj7PPPOMZr5Vv8P7Mg/nlgrIXd+oFa/FtVx77bXynT5p0iQZkBfHEz97iyuJBNNUKiWVaLaFTCbDd999R2FhIS6Xi5qaGhYtWqTpm23hZycAqINJ6s5LJpO8++67KIpCc3OzlF4PBAKyJs/cuXP7cAJ2mABAIkrLDz9QUlyI1+2jvKKKtes2kuTnJwDkEiMEfswCZFdBMFb7Ki+xp0BdbwO6JfGuuuoqysrKKCsrY9CgQQwcOBCXy4Xf70dRFFasWNFDSk79UP0aFnJijN13332Smdvc3IyiKH1mEW+LAGAwGAgGg1RWVnLNNdcAMHfuXBRF4amnntI8v0LmLKsAkP0IAoBQAYjFEvzmN4ditTgxGAv4fO6XdEbCGgWADFrCSjqd7g72iw1SaRKxOOFQlzT+cpUt1Cyv3oLhqVSKdevW4XQ6pSxbIBDA7/fjcrk47LDDttmHmUz39YRDXZCBo353JGUlpQzo34zP48Xr9hDw+amurOL7Vat7OFVy5w/ByM7939ZeNuoagIlEotcaZzp06NChQ4cOHTp06NDxa8AOEwC2BJbfmfY2pcUlBHx+/F4fFpOZpobGbvW3DIQ6OmVm/M5UAIBuGfHrrruOoUOHyjYJ+/P111+noqICi8WC3W6nqqpK1rzeFnrzQYljbtq0iYOHHYTT7sDr9uB2ujju2JHaoP8vnAAgEA6HpU8uHo/T1tbGIYccgs1mw+PxYLFYOO+881i8eDHLly9n1apVfP/99yxbtmybn+XLl/P111+zevVq5s+fz8qVK/n++++3e18EYpFotu1bxtW99/wFt9NFXSBIdWUVfzj3PNkPXZ0hLRkl3bcBuCMEgN78btBdm/7nhvBpALz55puyVKPRaGTkyJHyutVJEclkkqVLl2rqwucGj9V+neeff57CwkJsNht2ux2/38/MmTNZvHjxdu//hg0b6OjoYP78+Xz77bfMnz+fNWvWsHbtWr799lvWrVsHZMfj3LlzKSwsxO124/f7MRqNTJkypdd6xj9GQnhb6AsBQK08kOtDUwdGkskks2bNori4GIfDIctVzJgxg1gs1utzuD2Ie7b33nsTDAaxWq0Eg0GOOOIIqWq5LQKA2D4vL2+L+mj3HNTX69AJADp06NCx66Ge20U8Y+TIkTidToqKinjiiScAbWKjwE+Zh7eWGCnKxNx///2SzHfjjTfKRFTQxmrU545Go0SjUakY9WOxcuVK8vPz8fl8VFVVsWzZsl4VnXvDz04AAO2LVx0svOOOOzAYDLIul8fjYcCAAQQCAaqqqnrIJvSKHSQApGNhyCT594cfUlSYT9Bfx7EjjycWT/cI/v9cCgC5zFLBqBSLm935aWlpkfWO1IH/nbXA/LmhvuZoNEpHR4fs75aWFoYOHcqgQYOorq7G4/FQV1eH0Whk7ty5PVjYamPil9L+vmL48OEEg0GphDBq1Kg+77stAkBhYSE+nw+Hw8HFF18MwP77789//dd/AZCIZ4hFU8TjSdVzkCKdiZMhqwQgCADxeJTVq9dgsznw++rZ/4Bh/LB+o3wu02S64/u5Ae8tDghJBNhCAOht7hCTfO5knCuln0gkmD17tiwn4fP5CAaD1NfXU1JSwk033ST329qELGQSY5Eo69aspaigEJ/HS/+mftQH6wj4/JhrTfzh3PM0JIZ4NJb9GzRGjjifMGAikYgsV5Dbxq2NX/X+OnTo0KFDhw4dOnTo0PFrwc4gAHR1hti0YSODBgyU5d98Hi+F+QV8/dVCWjZtlqptIhgtlQF2EOpa3alUiquuuoqjjjoKyPo7hA37zjvvUFBQIG38goKCPin85dr06uBna2srBw87CHOtSZarO+mEE3uo2v2SCQDRaJRYLKbxIyUSCTo7O9lnn30IBoMyaWnChAnymlOpVM8yhFuBsMPV/k61z3RbUCtMkIGvv1pInqJgNVsY0L+ZPEXhztvvkISTdDIl1ScSsb6dY0cIACLoL5QqRN/tKojavOK8VVVVDBgwAJPJRElJCevWrdPUcxdtGT9+PA0NDfLvUCjUIzs9nU7T0dHBq6++iqIoDBo0SMrar1y5UuP83xoikYhGZSIWixGPx6VSgvr5W7BgAQUFBTQ0NODz+VAUhSlTphCLxXr1we0KBYBcP5u4fvX5hf8rkUgwZ84cSktLaWhokKVIZs2apVEREPv3JUAjSigccMABMvGmrq6OQw89FKDPBACDwaDxh/XmM9sadAKADh06dOx6qEsgi9/PPvts7HY7lZWV/PWvfwWyc7+IwezI/CuSQdXrPPH/qVOnoigKFRUV3HDDDZprVJfoUkMkngrEYjEikQjt7e19TrReunQpRqORYDBIbW0tq1at6vPac5cQAHIXz5DtlLFjx2KxWPB4PPh8Pmw2GwMGDMDlcnH00Uf3LQC1owoA6QTJSIiH/vY3KspLsVsdTJh4CxkgkuhJAtjZBIBIJCKNN3V98D0RLS0tPeqH7+lIJLrrpItFKHQvBmfOnCkDuE6nk5qaGv7yl79Iw0Ft1KiDokKq45cO0Qaj0ciQIUNwOBwoisIDDzzQ5/ZtiwBgNBrx+/1YrVZuuukm7r33XgwGA+vXr8+yhdOQSuYeL048ESadiQNJYvEuBAHglVdeo7q6lppqC+MuuIhoLCGfy3gyQSKVJJlOScNAPlNbHBLxaEzjmEjE4hrmt9ivt+dQGDLql84dd9xBeXk5fr8fr9eL3++noaEBg8HAzJkzZaBdTSZQjyPB3icD997zF/IUhf5N/Qj4/NQH66Sj4t2335FkgUQs3r1fzn0QhouYU3LLD4gXjiAa5SoGCBa3YNTp0KFDhw4dOnTo0KFDx68FO6MEgAgwT7rlVvINRvo1NhHw+aksr2DCTTd325sZZP31WCQqCdw7gnA4LP1rHR0dNDY2cskllwBoEhjmzZuHoigMGDCA+vp6GXjbHtQOTtDarm1tbRz6m99iMZkZMmgwNVXVXHn5FbI/hL36SyYAqO3nRCJBNBolFAoRiUQ45JBDqK2txWazYTabufjii+nq6tI4evuSZKOWxW9vb5fB4D75XzJb5Py3tHvYAQcyaMBAfjP8ECrLK9hr8BDyDUZmvD9dBv7Xr/uh+/70ATtaAiD3OGLbXUEEyB1Tl1xyCdXV1TQ2NlJcXMzVV18NdPuoE4kEGzduxGAwcPfdd8vjqEsnqjPaAZ599lkURcHn8xEIBLDb7cybNw/Y/v3PhToILoLrol+//fZbqqur8fl8uFwujEYjDz30kEYB4McErvuCvigACN+oOosftOVJwuEw6XSa5cuXoygK/fv3l0q/06ZN0/SHuBd99T9Fo1EOO+wwvF4vjY2NOJ1OxowZI4+xLQKAxWIhEAhgNBo19+PHJHfpBAAdOnTo2L0Qc+z48eMxm83U1tYyYcIEAE0A/qcQ47ZVMiAejzNjxgxKSkooKCjgvvvu01xTtqy1lpinPpZQLvipyurz58+XStsej4f169fvWQSArdXVGT16ND6fD7PZzMCBA3E6nXi9XoqKirj55pv7dvAdJQBkksS7Ovj9f/83fp+HPMXA+9Nnktmyxa5QAABYtGgRTz/9NE8++STPPvssU6ZMYcqUKUydOnW3fh566CHee+89ILugE0HQXxLa29s1ZAXBSt68eTOjRo3C5/MxaNAgiouLOe200+R2Igi6NdbQnlai4acgEomQSqUkCcLv91NWVsZLL73U5wX4tggA4rgul4ujjjqK8vJyHnjgAWBL9kIa0inIZFAFraOk0jEgSYYE4UgHkCadTvKnP12DzeYgTyngoX88QjoDiUw6G/RPJogl4iRSSXnf5KS6xQEgay+qnAO5mf258vkCuWznRCLBiBEjpOy/1+vF5/Ph9XrxeDxs3Lix13GjkejfwsonA0P32ReXw4nf68Pr9uB1e/B7fditNjZt2CidR4lYnHQyRTKeYPXq1XJsi5fN1oyNeDze64shmUzKl5Rov26k6NChQ4cOHTp06NCh49eGHSUAqOXXly9dRlVFJT6PF3OtiYa6egI+vzYjXqVCtzMIANCd5fzdd9+hKAoffvgh0J2gkUwmWbNmjZQ/r6urQ1EU3njjje0eW/SLsH3VAcaNGzey7977YLfa6NfYRJ6i8OADf5fl7H4NBIAjjjiC66+/nnXr1mkctLFYjGOPPZZAIEBjYyM2m43Ro0f3el/6AjWxQrS/Lw7hZDwhVQAuuuBC7FYbH87+gGlvvoXNYsVutRHw+bGYzHS2d0gfgrhHfcGOEACi0SiKovDPf/4TyPog1Bn3uwLi+qLRqKxBHwwGCQaD5OfnA1lfoOj3J554AkVRWLVqlaY9IkkkN4lo/vz5FBYW4vV6aWhoIC8vj5kzZ/bp2tQZ86FQqIfKqjhPIpFg1apVDB48GKvVitfrxWQyMWnSJLm92j/1U6SEe8P2CABdXV09iCqbNm1i8uTJOByOHr7iTZs2YTQa8fl8+P1+ioqKmDp1KtD9/Ikkm776H9va2hg6dCh2u52GhgZKS0uZPHmyPMb2FAAEAUB9vh+T3KUTAHTo0KFj10MdmxPvmuuvv56KigrMZjOXXnppj+1+jMKMmvS3te1nzZpFYWEhiqLw0UcfkU6nNWuc3PWQiMFkMhmWLVsGZNfqbW1t8pyhUKjP68fZs2fLd+rgwYNpb2/vMxFwlxAARAeIjhd/n3baaTLzesiQITJT2GAw8PTTT/ct0LwTCABtG3+gqqSE5v5NlJWU09beSSLZM/j/cxAAkskk69ev5+qrryY/Px9FUcjLy0NRFEpKSsjLy9utH0VROPHEE+no6Oju8l+Q/L36IQqHwzLgHY1GufzyyykuLsZkMlFTUyNl78UDurUH8Ne0gBNM7Ly8PLxeL/X19ZSWljJ37tw+H2NbBACDwUBNTQ3Dhg1DURT++Mc/At0kjFg0RSKeIZlMqzLPxbOVJQAkU1EgTSwW4Xe/OwqXy0N5WTULv/6WDBBLJaX8fyqTJq2aEOS9UjsiMkAqTXtrG2TQGHXqNqn/l0sMiMVirF27FpvNRlNTk0YBwGQycdJJJ/VqRIgXkTzeloD+Rx98SHlpGfXBOjwuNx6XWzqSLht/aVbCb8v1J2Jx2lvb+N8JE7nooot466235Lk05IKce6L+Xf2SErIzavQooaBDhw4dOnTo0KFDhw4dv3DsKAFABLlFQPXPV/8Ju9WGudZEY30DRQWFvPj8C5qAtAyM7wQ3QiQSkQHOBx98EKfTyfr16+X3QuEtFoux11574XQ68fl8VFRUcMcdd2z3h0AYEQAAIABJREFU+Gqp09wsqOXLl2MxmfG6Pfg8XooLi/hg1mwZZP41EABGjx7dIxlJ2Np33XUXiqJQV1dHMBjk8MMPJx6PaxJlhE9jax9ROzYajUp7vL29vc/OXyHt//KLL1FRVs4zTz0t23rNn/5MnqLQ1NBISVExo447HjLQ1tIqVSj6gh0hAHR2dmIymZg7d65UThAQbf+5IfpSnPvwww/HZDLR2NiIoihMnz6dSCQia/QedthhnHXWWT0CBqIf1AoAyWSSjo4OampqMJlMDB48mIKCAl544QUZHN/Wpzc/qlrSX5wnlUoRDoc544wzMJlMBINB3G435557rtxOndyxs9RZ+6IAAFl/nlCITafTPPnkkwwaNKiHTzAWi3HQQQdhNpupq6ujsrKSSZMmacbNj0lCSSQSrFmzBovFgs1mw+fzUVJSwvvvvy8TuLZHAPD7/RgMBo2iwY/JxtQJADp06NCx66F+b4j5ffLkyZSWlmKz2aQSjHrO7Wt2PGgVfNTHEGvqxYsXU1FRQXFxMTNnztTESUWMS/1e6erqku/IH374AUVRmDFjhuY92dra2ufrS6fTvPrqqxiNRpxOJ4ceeqh89/clQXmXEAAE1IxkgDFjxuBwOGTtdbGorKmpYdasWX1jMe4gASATj/Dlvz+hND8fU201xx4zkmgsQQYIx1O7TAHgxRdfZOTIkZx++umMGzeOk08+mdNPP50xY8bs1s8ZZ5zBvffeCyANlNya478UiIkiHA4zceJEFEWhqakJu93OkCFDgKyxICaI3AWjMKa3Vs/9l4p169ahKArNzc1Slquzs7PPC9XtKQDU1dXhcrnYb7/9NLXgYrEYafFQoZ6k06QzcTpDrSSSESBJMhln48b11Nc3YrM58PvqpU8hHI/JaSBNhmRaK1sI2Sz7eDQmsy9aN25i4YKvePrJp5g6dSovv/wy8+bNkyysXAhjXmDTpk3MnTtX4wDwer0Eg0HKysqYMGGCRkZflPhQz2mJREJmhtx0w41UV1Zht9porG/A5XDSWN9AnqIw55NPpYxkpCtMqKNT1vpTFIXLLrtMIyOoPqe6nIGaKbd8+XJmzJjBc889x3PPPceMGTNYtWqVvD7xktKhQ4cOHTp06NChQ4eOXwt2uASAOsiczhANR3A7XTQ1NOJyOKkLBGnu119maYuA7c4iAAhs3ryZIUOGMHbsWADpiFQ7LseNG4fVasXj8eBwODj77LO33zxVuTs1kskkCxculAHmoD9AwOfnh7XrZNuk2t4vmABwzjnn8Oc//1n+P5FIyIDyRx99RGFhIU1NTZhMJpxOJy0tLRoncF8glBVy0acgbgbmffEleYrCtX++hlgk2p3dn84w+tTTqCyvoH9TPwxKHvfcdXd38H8XKAB0dXVhtVp5//33pUO8tbV1l/kP1XXoxTU+88wzVFRU4Ha7aWho4He/+53c/uuvv9bUpRdtAHo41tVtvfjii3G5XPh8PiwWC1dddVWfrk+cQ/jFRB9Bd7+r/Y033ngjNTU1+Hw+7HY7hx12mNxHPe5+jPrEtrA9AoA6mKIOmj/55JM0Njb2aAfAtddeS01NDXV1ddTU1HD55Zf3SDjpq4JBPB5n9erVKIoiFTitVqskQfWFAODz+cjLy9PMMT8mSKQTAHTo0KFj90LMvffeey9lZWWylHxuMqf6HftTz9PV1UVrays2mw1FUSThDLqVgtQEQk3SJ9m1xCeffILBYGD58uVA9/tTvHv6sv5LJBI888wz5OfnY7VaOeaYY3oQFbeFn50AkPtiV9e3EvXAm5qaCAQCmgzkZcuW9cheDYfDPepriUV+MpkmmdzWAiJNa+tm3n77LRRFQclTqKwoIV9RKFAU/E4nHreT6soa8gz5KEo++cVlFJdXccvkOwlFY6SARCopCQAZfhlZ8L9mRCIRzYJK/bvacFEvBF9++WUURWHQoEGyHl5ra2uPxeL/KwHQTCZDaWkplZWVBINBCgsLe81eFxObej8BdY0VQRSZPn06eXl5+Hw+nE4nEyZMkOwmafRmsioA4lDZyS/N9BnvoOQp5BkUysqLUBSF6upKzGYrgUAdFrMDRTFkn1VDHvmFBfx/557D+o0bsuQd1SQfDnc7X8KhLv53wkSKjPkY8wycevIpjB8/njPPPJOioiLKy8v59NNP2bRpE9BdU01MzmJ8hEIhbr31VpxOJy6XC5fLJY1ig8HAW2+91cfOzxIA8hSFfo1NOO0Ogv4A/RqbsJotHHTgsG5nRjpDKpEkGU9w2fhLqa2uQVEUPv74Y01NPbUKQK7R8+677zJkyBDKy8sZNmwYl1xyCZdddhn77LMP+fn5nH/++WzevLlv165Dhw4dOnTo0KFDhw4dvyDsDAKAKMkmfFEfzv6APEUh4PMT9AcwKHk89PcHZUa8sOciXb07InPJ1+3t7dK2VjsFu7q6pF366KOPUlBQwIoVK+Q24XBYOhM7OzuZN28eVVVVuN1uampqqKiokMcSdr1aLl343MT36kBvLBbj0UcfJU9RaO7XH5vFynnnnKtRQ8ik0rKtou2JWJxbJv4vJUXF+DxeWQNcTVJX2+7q8nbiGr/++mtZysDjcjPi98eQSaVp3dwi70Goo1NDAhBqf/0am/C6PVnygaKwdu1a6ZxV28qiv0ePHs31118PdJP4xb1pa2vj4IMPxmazUVtbi6IofPbZZz0IDMlkUtZ01wwdlbqCaPOmTZuYOHGihsSvvj+iL8S92LRhI4f99lBOOH4U4VCXHIfRcIR0MsUPa9dRFwjisNlpqKvHoOQx97PPu+/HlnaqfRy54yyRSMhxtHDhQqxWK/vuuy8lJSWccsopMtNdtGft2rWadoosN9GOXakgqg7Wi/a1tbWRn5+Pz+fD7XajKIpUI/jzn/8ss8F/zDW+/PLLGAwGGhsbqa2t5bDDDtM8M+qsPuFbEvc4Eon0CDqn02nuv/9+Pv/8c6A7OLBgwQIURaFfv354PB6MRiNLliwBsr4etT9UreyovrfimoS/aMyYMSxcuLDHdp2dnbIPxo0bR21tLf369UNRFFkWNncfyPrGHnvsMQYOHCj/pw5KLFmyRCbOmEwmKisr5Xa5SYKiXbl+LFHuMhQK8dJLL1FWVobNZsNisTBu3DhNAlNbW1sPf2ZHRwdnn302VqsVt9stS0Go70ksFtPcE3GMtra2HmqaAjoBQIcOHTp2DUS5GOiOBb733nsYjUYcDgfDhg0jFAppthPoq8KMGul0mkQiQSQSYeDAgZSUlPDKK6/IbcU8n/tOzGQymvduOp3mueeeo6CgoEdpKYG+kiRvuOEGWbr7uuuuk4nKfcEuUQDYWm2iDz74AJvNRmlpKfX19QSDQZmB/OmnnwJZIya3MYlEgq6uLrq6uohGknSFujstHI4SiyXkedesWcMhhxzCsGEHAGm6ujp56603+M+ihbzy8vN8NPN99hnUTFMggNlUw9/vf5B335vOtLff56XX3uT1ae+yau16qQAQjkZIphPsbAUAHT8NwlgWRlauoSQW26lUira2NhYvXkxJSQkulwur1UpBQQFLly6V+4iFH/y4OlC/VIRCIVpaWhgzZgwDBgygsrISRVHo6urSMIuFsQBZ54Doo0gkovlOLev2+uuvYzAYGDRoEEajkQsvvFB+J+9TBlavWiePBZBIxEgkI3zz7QJefe1Fps94h9dee4WTTz4Rr9dPTY2JCTffyrvvTee992fw7ozpvDntLb5d9B+ZWNHS0qKZdEMdnXR1hgj4/OQbjLzxyquaLAzRRkEOWbJkCZFIpNfsAGGAHH/88ZSXl+P3+6mvr6epqYn6+noGDhzIqlWr+tT/6WSKF59/geLCIpr79Sfg8+PzeGlqaCRPUbj9tsnSoSFIDG0trdgsVoL+AAcccIAcv+pxm0te6erq4qGHHsJms3HcccfJIL8wTIWRdeyxx/LHP/6RcDj8o6RodOjQoUOHDh06dOjQoWNPx84gAIjs/nQyRSIWJ5VIMnnSbeQbjFhMZpoaGjEoeXwwa7YMhm/asFGTgR2NRmUgUH1tantabY+pyd4tLS3U1NQwduxYjQNROC/VqnYHHXQQDQ0NBINBWbNUfTygVwK48IWEw2H5+0knnYTf68NutVFgzOfbr78hk0rL4LLIMo9FojIYH+ro5O4778Jhs7PPXntTVVXFm2++CaCxt3N9L2p7dv369TQ2NmIymaiurOK4Y0dqiBWJWFza9l2dIXmPwqEuDh52EEUFhbidLvIURTqBe5NuBRgxYgQ33HCDJqCorid7//33U1tbKxUAx44dK/tblJwUyGQyRKNRjeNZXSs2FosxfPhwDj744B7B3GQyKWu7qnHaKadSXFjEom//I9u8eeMmqYTQ3trG3M8+p6KsHKvZgqmmlpqqakIdndl+QutEF7L24XBY+m3Fd6FQiE2bNuH3+yksLGT//ffn8MMPl9fSW3JIa2srtbW1vPbaa5r/7yrfmjogq3aojxs3DovFgsPhwOVyceONNwJQUlLCfffd96PKE2zYsAGAo446CrPZLBUhRGB+5cqVQLbN69atk/u1tLTQ2traI9MvHA4zb948qqureeWVVzTqBalUiosvvpiqqioCgQCKonD//ffT1dWlGUvquUK0RT3GxXV8+umn5Ofns3TpUmKxGKlUSl6PmhQzbtw4ysvLqa+vp7y8nPfffx/oHr/q2sYA1113HUcffXSPJCHRD6NHj8ZoNLL33nujKAoLFy7k+++/B7K+WqFg0Ns4ySXTjBo1CoPBwNChQykpKdEk36i36+rqkn2QyWQ455xzcDgcci4U/sLeAkOZTEZek1olU/1T/Z1OANChQ4eOnx+5wfaVK1dSXFyMy+Vi4MCB8v2sfhf8mDlYvNfUCa6jR4+msLCQt956S37f0tIi53/1+1DEICH7DhbHOf744xk8eLDmetTvu76WoTnnnHOw2+0UFxfzj3/8A+guUbA97BICgGBNCKjZxoqiMGTIEIYMGUIwGMTn86EoCh988IFGckn9AlUfKxbdUkc7kWL9+o2abTZs2MDJJ5+MoihUVpbz4IMPkEzGgTShrjZECQBrTSUOk4nSkiLWrfmhu0x4zieRSZNMp0hlkqQzcV0BYA/CtmpGrV27Vo45RVFwu91YLBZqa2t54YUXAFizZo1mH/Vi+teOVCrFtGnTJCvXaDT2YHELqNnyuWUCxIQjaoGNHz+egoIC+vXrR21tLTfccAOxWEzK1cfjcSLhrCRiOByVhkoyGSediROLdyGINvF4lJEjR2CzOSguLuWjD+eQAaKxBCmy0v/qMgA92NsZOPaYERjzDHzx+VxNUF09QY8ePZqCggLuvvtuOjo65CSeTCZVBIUEra2tWCwWnE4n/fr1k2UA7HY7J5xwgqwbsz0kYnEuG38pFWXl9G/qR3VlFX6vj/pgHeWlZTJzQZ3NcNutk3DaHeQpCv/3f//XQ9JGbVCm02ni8TizZ89GURSOPPJI2Qb1fplMhkcffVQaQzurhpwOHTp06NChQ4cOHTp07CnYGQQAkfGuVgEgA5deMp7S4hJcDidup4vy0jK+X7VaErk72trp7Ozs1VEn7OxYLNajlJw62zoajXLOOecwePBgGdSLx+MymJcrgT5//nwKCgpoamrCbDYzefJkef6uri5p76olxCORiHSiiv8vWrSIsrIyGurqyVMUrrz8Ck3bo+GIDEILO1sE4m/931soLizCVJPNmn/jjTfkeUTwXOPj21I6TxAiVq1ahdfrzarvOZwcP/I42lvbiHSFiUWidLS1S2U9TcmFLZL4A5sH4HI4KSsplZnpkA28qhUPotEo/fr14+GHH+5hU6vv2fDhwzGbzVKWXZ1QItql7k8RzBf/++GHH4CsnyovL49HHnlEjgFxXQKiXzZs2MDDDz9MvsHI9Pfe73X8CSJEKpHkvr/ci0HJw+Xo7jM1AUWd3azud/F/cd5wOMyAAQPweDzY7XaGDx/OV199Jcdn7vjZvHkzBQUFzJ49+ydlt+0s5Gb0z507l6KiIurq6vB6veyzzz48/fTTKIpCKBSSiSN9RTgc5s0330RRFBobGyktLeWUU07pVUq+t/KaK1as0GSsX3LJJRQWFtLR0UEymSQUCsk+e+edd6ipqSEYDBIMBtlvv/2k/1Jk/4n7mBtAF1ntopTFlVdeKTP11XOMesy1tLTw8MMPU15eTmVlJcXFxfzrX//S3MN4PK5RXL3yyisZNmyYRgpZ/QzMnj0bj8dDaWkpXq+Xq6++Wn63tbICapJSPB6npaWFZcuWYbFYaGpqoqioiPHjx8s2qI+lJheJYx177LHY7Xb8fj+K0h0KyWQytLW1SR9wb+orYjt1v4FOANChQ4eOXYlQKKSRz0+lUvh8Pvx+PxaLhXnz5gHZd8nWSlptDeFwWBOc7+zs5H/+538wGAy88847mm3VpbDV763eyjO3tLRQWlrKH/7wB7lvLkmxryTJQw45BKfTSXV1tST05pbn2Rp2CQEA0LBJ1TJajz32GIqiYLfbaW5uxmw24/F4uPjii4FuFmI0GpVGgKjpIEoAxKLaxWRXV4QnnniCxsZGSkpKaGhoYJ999iIU6iAez0qMQxIyCf790WzyFQVbTQ2H/vYQkvEU8USKRBIiiTSRRJquWJJIIokoV55MJ0gkI6Qzv/4M8V8CchdUgnCirrfR2dnJgAEDsFqtUvb/gQceANAs9v9flT/v7OykoKCAxsZGjEYjDz30ELFYrMeiO1eaS0Ddh0uXLuXQQw+VUmVOpxOz2czll19Oe3u7RrJFKACEQupgdApIbnm+knSGWvnmm4U0NTVgsznYb78DWLsmK/WfTGWIpZKkMmkSqSTReIxUpntyj8fjbNq0iTdff4M8RWHYAQcS6ugksiUzoK2lmyl99dVXoygKBoOB2267Deg2vuPxuPw9kUhImZmmpiZpRAYCAYqLi7n55ps1TpptIgNnnjEGq9mCy+Fk/6H7UZhfQNAf4PdHHa3NFsnAwgVf4XI4qQ/WMbB5ACtWrOhxSDWDW7CazzvvPKqqqnjyySd7bNvS0kIkEqGoqAiHw0FVVZWGqa5Dhw4dOnTo0KFDhw4dvwbsDAKACL6KzHdB2u7qDDHu/LFYzRacdgcD+jfjcbn5+quFGvU5AeHn6i04Kojc6r9bW1s599xzqays5NVXX5XfCTVEdbuE7ZpOp7nnnntkoFJRFL744gu5bzgc7lWtU42Ojg7OOeccqqurKSsp5cgjfkc0HKGzvUMGnIUqgizRuSUQn0okufH6G3A7XQzo34yiKLzzzju9Es7b2to0TluBDRs2YDKZ8Pv9BHz+bDB7C9lATZaPhiM9FAEm3HQzJUXFBP0B8raUz4Osc1ht865Zs4ZQKCSTkQSEU1Ud8Pz3v/+Nz+fDZrNRVlbG2LFjSSaTtLa2SgeyyCBub2/vNZkpHA5z3HHHYTKZaG9vl0FYtSO5vb1d+mOmTp2Koiicd865rPpupUblILe/BSniD+eeR3lpGYMHDiJPUbjnrrul6oQ64zoSiWjKPYh7AVl/wYknnkgwGMRms1FcXKxJFuno6JCB01gsxrx581AUhfXr1/dIythVJADhQ04kEiQSCelH3nffffF4PFitVhobG2lqamLUqFGyzX3x36j9XuFwmGuvvRaDwUAwGKShoYGPP/5Ys01uolJXV1cPn9qSJUsoLi5m8uTJ8jvxU/Th5MmTURQFs9lMfn4+d955p9xf3MtMJrNVdU6hHFJRUcGTTz4pJfXVagPqPnjttdeorKykf//+KIrCP/7xD9ra2qRShBqbN2/mhBNOYPLkyQCazHlxL5LJJHfeeSclJSX4fD4KCwv5/PPPNQGXSCQi75ka6jnq/PPPp6ysjObmZpqamuTYyyVZiP6LxWLyfowdO5bKykoaGxspKiqSMQp1fEKodggI9c/cuTj3PDoBQIcOHTp+fqhLUwuFlxNPPBG/309+fj7PPvsskH3X5r7f+oJ4PE5rayupVIq77roLg8HA1KlTaW9vZ968eSxYsIDvvvuOH374geXLl/P111+zePFivv/+e9avX8+SJUuYN28eM2fOZM6cObz22mvccMMNKIrCX//6V3mOrZUy3xZaWlok2aGpqYm1a9f2WTkAdgEBQN3R0Wi0h8RXOp3mpJNOwmQyYbVaNfJk7777bo+Xf+5iKRHPLvzj8SThcJR58xZwzjnZYJfNZqOwsJADDzyQpUsXA2kymRThcAhI0hVq49GHHsBWW0VVSQn33XtPdtGcztoTsVRPBYB4MkEilc1Q1gkAux9iMamuIZcbqO7s7OSMM86gtraWQCBAUVERV1xxBalUSiOVJcaaeIB+zIP0S4WQFctkMjz00EOUlJRQUlLCSSedJLcRciJi8SsW5tD9HAv20rPPPouiKIwYMYJEIsHJJ5+Mw+HA6/Vy8MEHA9mJWBgmnR0R8o3FLF68VLKWOjvb6Qy10l1mI8nrr7+KzWahrKyCSy65lLSK7C6IOWkyRGJR6VdRM46feuJJigoK8bo9W3ZKS4lCoQDw7LPPUlJSQkFBAcuWLZP9A1rDLZVKce2111JbW4vb7cbr9Ur1kvLycj744IM+sa8gmx3xr6efwZhnwOv24Pf62HvIXjhsdv589Z+kYyOTShPpCjNyxLHUVFWTpyjM+eRTeY3q+5Dr/MlkMiiKQlVVFS+88IJ0EOXWnqmoqKC4uJjbb78d6M5M0KFDhw4dOnTo0KFDh45fA3aUACCC/plUWsrNk85kg9DpDKQz3Hn7HeQpChaTmYa6ekqLS3jrjTeJdIWlr0KdsADdJQtBm5QgbNWVK1dy0UUXYTabeeKJJ6S/I1e6XBxTXbIPspLeBoMBk8nEMcccQzgc1gRyRf1wYfOLoGk4HOapp56ivLwcl8vFIQcPZ8G8+ZrMfxn8T2ckKUD9OfOMMdRUVePzeKmpqeHpp5+W5+3s7JQ+mVx1QRGg/P/Zu/M4O6oy/+PVezq9JOn0kqX37CEJOOAoKGhmWAXGFZXFAWRxdNQZFgEFHCIh4E9EHHWUTdRBRBwcQdlVFAyYZGZAdsIihECW3u9a+/f3x82prnvTSTpJpzvpfN6vV716uWvVrVtV5zzPec5zzz2nsrIytbe3q7W5RUvf9/4o8J1NZ+TazlZJAJ7jSkGoZ59+RkWWpb895J2qmzxFp512WvR68TZ7IpHQrbfeqtra2qgvwHGc6DMxA5lMh7MZlV1eXq6JEyfqjjvu2G4nrgm0m6n3vva1r6mkpESrVq3KG4UfHz1mLF++XDU1NWpqatKK5VdF2zpKdgjCwX0xVg3g6af+Eg0umDt7joosS6tWrYqe13XdvD4B048Q3y6e5+mnP/2pLMvSe9/7XlVVVUVlZ+OjxhOJRFSd4rDDDsvrkzAd9jvTCb+rTMWMoab1/OEPfyjLsjRnzpyo8uwvf/lLSYOB3uE8f2FfzznnnKPS0lLV1NTokEMOiSoKmO9oYb+KCTjbtq3XXntNhx9+eNRXFh9VKA2Ogvd9X//0T/+kCRMmaMmSJbIsS4888khegompGhD/XAerbHpqbm6OpnDIZDLyfT8vYSBeNWHTpk1qa2vTAQccoM7OTi1dujSvOkRfX1+0Hf7yl7/IsiytWbNG0tYVLMw6JZNJnXvuubIsS42NjTryyCOVzWaj/d+sc3w/NO8/k8lEfY1z585VQ0ODXnnlFXmel3ccNdvVbON4P9n8+fPV0tKi2bNnq7y8PK9yR/y7PdRnXlgRxCABAABGR+E52iRb/vu//7sqKytVUVGhK664QlJ+BYDtVQwvZM6d3/ve96Iq2Z2dnZowYYI6OjpUX1+vxsZGzZgxI5pWqKWlRQ0NDaqoqFBra6saGhpUUlKi6dOna/LkyaqurlZJSYnuv//+ra41zPX2cDz11FOqrq5WU1OTjj766K3iOjuyxxMA4isSn//KfADmZH3ssceqtLRUDQ0NWrBggaqqqlRfX6977rknb5Rr/ORu27bsrK/nnn1JP/zhj3TcccerrKxCtbWT1dzcLMuy9MUvfnHLawYyCQC2nZHnZyV5+vAJx2na1MkqtSw98fif5GRdOa4/5BQAGdeRF/gKt1QQyJUox1iK71+mIR3nuq4uueQSVVRUqKOjQ2VlZTrppJPykgak3Lxy8ecpLMcxnpltmMlkdMopp2jq1KmyLEs333zzVgciU1bQHGil3EH3nnvu0Xve8x5VVFTol7/8ZV6m+sSJEzV9+nRNmzYteg7zup8595/V2tKpVCoT296B+ge6JXnyA1uSp0sv/bKqqiplWcW69dYfy3VC+YFkO55s38tLAAg0WObFNEr+9Yv/otrqGjVMrdc5Z52tTW+9HTWczbyG2Ww2agQUjkiIj4rIZrM69dRT1djYqJaWFi1YsEAdHR2aOHGi5syZo02bNg37AK5QeuG551VkWZo3Z67q66ZGjfNvfTOXkDTQ16//Wb1GR/7d32ty7SSVl5bp6qtWRCNIstlsdNCPT5difr7++utRI2v27Nm67777osZNEAR5UyB0d3cT+AcAAAAwLo1EBYDQD6Ly6yapPB6EVij99qGHVVU5UdUTq6LR55869TStXbs2b4RwYR+GCVjZth0F0u655x4tXrxYlmXpRz/6Ud7biQc4Pc+LnjteAtskxp9++ukqLS1VZWWlTjnllLyORxMUN483gc5bbrlFM2fOlGVZWrhwoVb/eZV814sC8PHAc7z8fuD58hxXr6x9WUvf9341NTRqzqxc4O3Tn/50Xl+MWQfzmvHtkU6ndeedd6q0tFTNzc1qrG/QkkWL9fifVsp3vbzXdrJ2lIxgPheF0pcvvkSVFRNUN3mKioqKornHJeWV5u/o6NDXvva1qN8ynqRhAo3m72Qyqfvvv1/19fXR9rntttui6Qtc143a6aaNbtbr8ssvV319vW5p8zlRAAAgAElEQVS44QYNDAwoDMO8/ijXdfXWW2/plltu0eLFi1VeXq6ZM2eqpaVFxx/3ASmUNm3YuFWiRSaVViqRjPbDX/z8ThVZltpaWjVn1mwtWniAqqqqomkwzedt1t+Uny/sc+3r64uC/9OnT9d73vOevCkEzP27u7tVVVWlBx98MOpvMCO6R2uKQbOd4/3ORl9fnyoqKjR37lw1Nzerqakpr+9juH04tm1HgWnzGZ9zzjmqqqpSXV2dDjvsML3wwgvR7ZKixA8jkUho7dq1es973qPS0lKtX78+L1Bu+iRNoF7KbcsLLrhAlmWpo6NDU6dO1RNPPKF0Op03nYc5bpifr776qpYuXap58+Zp3bp1eetiBofER1WaUfXXX3+9ysrKVF9fL8uy9Itf/CJvO5ttfPrpp+vYY4+Nbosfw0w/VXzdvvnNb8qyLFmWpZNOOknd3d15AZGh+mFN5eC6ujodcMABev3116MKHvFBMPFtHvenP/1J5eXlUeWOsrIy3XzzzXmffxAEUfJVMpnMOw65rrvVuSO+XiQAAMCeFT+vxwPfjz76qCzLUlNTkz7ykY/kxauCIBh2kN2ce372s5/JsiwtWrRIFRUVWrhwoRYuXKg5c+aotbU1un5oaGhQe3u7Zs+erWnTpqm1tVVz587V/Pnzo9tnzJih6dOnq7S0VGvXrs07v5m443Cvj37605+qoqJCEydO1IUXXph323CmEBiVKQDMG4lfTMZPiObC4Pvf/74sy1JNTY0WL16slpYWWZalAw88UGeddZYuvfRSXX755brwwgt1xhln6IQTTtCc2QtUP3Waqqpq1NDQpMmT61RWVqEPfehDWr16tSSTRRto06YNMokAmWxC6VS/Jk2sUMPkGrU0Namvt1uu7al/IBklACQyjlK2p7TjRoFGEgD2HoVlnuKjnvv6+vTtb39bEydOVF1dnaZPn65DDz00alwVZstL+9+oZ5Plb7z11ls688wzowvyu+66Ky8BJ16e6+WXX9aPf/xjLVq0SJWVlbr00kvzGhQDAwPKZrM65phjVF1drYkTJ+qPf/xjdNsNN9wgyyrVww89Ej0mlzgQaCDRI/MdSyR7tXTp+1RdPVHTp8/Uc8+9IDubm6ojCAcrAJgkAD8M8g7469ev132/uVcTyivU1tKq+rqpmlxdo4OWHKiPfeSjuuuuu7Rhw4aosyNeASJeAiwIAiUSCbmuqy9/+cuqqqpSW1ub5s2bp6lTp2rq1Km69NJLowb0cDLIzQiJ+++9TzVV1Wqsb1BbS6vec+hhWjBvvs7/1/N0wXnna3rTNNVUVWty7SRdd+03o0Z9vLFj5n8z4hUuGhsb1dnZqYULF6qsrExtbW066qijdPnll+uPf/xjXnmceMk2AAAAABgvRiIBwLWdKMBsZ7JRVYB4MNpzXIV+oEu//BVVlJWryLI0rbFJxcXF+uAHP6hf/OIXWr9+/Vajd0zAr6urS7fddpve9a53ybIsnXjiiVH/1saNG/M6DOMjnUzg0ASv4qPLJemuu+5SaWmpSktLVVdXp1//+td65ZVX8spg27ate++9V2eccYZKS0tlWZYuueSS3Lzj4ZZp9GKjz03peROYPu9f/lWfOedcnfTRj+l9hx+hyooJmtbYpHlz5mrOnDmqq6vTkUceqWOOOUbnn3++VqxYIWmw3/CnP/2p/vVf/1WnnnqqDj/8cLW3t6ukpCTXATtrturrpmr+3Hk65qijderJp+iC887X00/9Je8zMO1lJ2troK9fC+cv0JRJk6P535999llls1k5jqMNGzbo2GOP1bRp06IR+IVtYfM5FQ44ef7553XwwQdr+vTpsixLxx13nH73u99Ft5sR2ps3b9b999+vd7zjHbIsS3fccUfU/2A+uz/96U865phj9KEPfUgzZsxQaWmpDj74YC1cuDAafbZk0WJ1tnforTfXR8kOhfvf66/9VUcfeZSmNTapZWaz3vXOv1VTQ6Pq66aqra1NtbW16uzs1Cc+8QmtWLFCN9xww1bVMONTFUjS448/rvnz56u8vFwzZszQ+eefHwVgPc/T22+/rdraWp100knRfmw6twvLqu9p8SqOJiHArNfnP//5aASdmYfebP/Cz3Yo8WkactUrB0fQ33jjjaqpqZFlWaqurtayZcu0du3avI75vr4+vfXWW7rkkktkWZYOOeSQvH7J+PNJ+X1TpmzwT37yE1VVVamkpESWZemcc87Rq6++KinXz2bWo7e3NwpKL1iwINrf+vv7t5rruPD1zHOcddZZKi0tVWdnp+bNm6cHH3ww2if++te/6hvf+IZqa2v1yCOPbFUuP56EFD++SNLtt9+ujo4OFRUVafr06brzzjv1/PPPR9vVVA548MEHdeyxx0br+sUvflFvvPFG3vScph8rnU5rYGBAPT09euqpp7R8+XKdddZZOvbYY/Xud79bM2fO1Ny5c6Ngzbx583TwwQdr6dKl+vSnP60VK1ZEJZrj0yMUbicSAABgbJhri/g18JtvvqnOzk61traqtbVVGzduzLsmloY/BdFDDz2ksrIyLVy4UB0dHTrkkENUWVmp9vZ21dXVqbW1VXPmzFFHR0c0FfScOXOi/zU1Nam1tTU3ZVVHh5YsWRINUE+n03nT/Gzr/Lgtn//85zV58mTV1dXpnnvuif5fOE3NtoxKAkD8wnaoC2ezARzH0auvvqp/+7d/08EHHxxdOJWVlUUBydLSUpWXl6u4uFiWZamkpEyWVaqJE6t1xBHv1ze+8U09//yL8v0wKv+T+6BzQfu+/i5JrkI5eurJ1aosK1bbzBlaevh7c40XSQOJlEJJKdvZqgpAoFBZJyPHyW55ToyleEMivk91dXVp1apVURZQW1ubqqqq9MYbb0ga3Ofi2UDx/VDa+uJ7vHJdN69MV19fn37zm9/owAMPjLKezjnnHF155ZW68MILddxxx6mlpUUlJSU68sgj9cMf/jDaruYgHC+f9eabb+qYY45RcXGx2tradPnll+uwww5TeXm5Hngg18nieeaAFch8V10vI8dNa3PX25o3b46mTp2ixYsXq6enT7ad+6xNOzdjZ6Py/6EG5+gz+8f6dW/q0He9W7XVNeps79Csjk61NreotbU1Gh1/xhlnaOPGjUqlUtF+EYZh3rYxxzLTcVJdXa3JkydrwoQJWrp0qTZv3jxktvk2bXnDvuvpvt/cq+OOOTYq8T9l0mRVVkxQkWWprKRUh/zNwXrogQfzR5rEstvjCQCmsW325Z/85CcqKiqKRigsXLhQjY2NmjJliizL0jvf+U797Gc/i9aP4D8AAACA8WakpgCIz79uFpPcHS+Db2ey+uurr+nb37pe0xqbZFmWKioqVFNToxkzZujQQw/V6aefri996Uu6+OKLdeaZZ2rRokVRkP6www7TT37yk61GCJm/TbuvsNqmWT/XdaMkdqOrq0vLly+Ppt6cNGmSDjvsMH384x/XMccco87Ozijwf/bZZ0elvSWpt7snCrCbbWFnslHA3ZTcr55YpbKSUhVZlirKyjW5dpKKLEvFxcWaMGGCLMtSZWWlamtrZVn53ZIf+MAHZFlWXj+gCawWWZZqqqqj9rJ5jf/88U+UTWeUTqaiZATXdvKSMi668EvR85aUlOjoo4/Wpz71KZWWlmrhwoXRqG0zKjoeSI73Hxmm30DKTSf4iU98Ilq3xsZGffCDH4xK4k+ePFmlpaX6l3/5F61duzZ6jv7+/qjNfv/996uqqiraRyorK6P1N1UazdQST/3fk3lTHgz09Ufr+vRTf1GRZUXbvMiyNKmmNtr+lmVF292yLL3rXe/K61czfQLxfSoIAr300kv67Gc/Gz1u5syZ+tjHPqalS5fKsiyddtpp2rBhQ/SY+JSMo1H+X8r/rAoHNniepxdffDHqV167dm30XuPruqPnl3LfO/P8juNEVRVffPFF3XjjjZo1a1a0nRcsWKCTTz5ZH/7wh3XIIYdE1TR+9atfRY+X8vsfzfPF+3vM6wZBoDfffFOXX365Zs+eHX0eBxxwgE477TSdcsopWrhwoUpLSzV79mzddNNN0Tqa9xwv9y/llyFOp9N5t/37v/+7pkyZooqKClmWpaVLl+pjH/uYFixYoIqKCt15551526gwEGHWy0wtal5n06ZNuuaaa6LqJk1NTTrwwAN15pln6qMf/ahaW1ujY+XJJ5+sp556StJghQLz07znuFtvvVWlpaUqKiqKvkfl5eXRMcUkFNTU1KimpkZFRUWyLEsHH3xwXoWWeKDJbBMSAABgbMSP++bcEgSBzj33XM2YMUOWZenpp5+OEuaGk9hnpFIp/eY3v5EVu1aKX38WFxervLw8+n9JSUl0v6KiIpWUlETnEnN/c66pqqqSpCET5IZ7ffS+971PLS0tmj9/fpSAaWJfwzEqCQA7Yi424g2W3t5erVu3TnfccYe++93v6qqrrtKKFSv09a9/XVdffbW+/vWv6/rrr9evfnWPVq1ao82bevOzrl3JcbwtmY2+HDctyZVkS8rI9Qf0w1u/r/KKYlVXV+vqq6/OlafaMso/lUnnfqYHg4q9/X0KFOZV2sLYMxmzZqf3PC+aG6OlpUXz5s1TeXm5Hnvssbx9bGcOBHsrM9J9T11Urly5UrfeequuuOIKfeUrX9EVV1yhb3/723rwwQfzSudtizmQZbNZPfLII/ra176mSy65RHfeeadef/31wnsPudx993+rrm6yZs3q0Kc+daqk3IE+CIK8jhDTSAqCIO+iXcrtIxs2bNDZZ58dHajNXCxz587VggULVF9fr7lz50aj4ePHJVP63/w9MDCg1157TVdddZW+8Y1v6Je//GXeHIo7E0CPJwokk0mtWbNGt9xyi6644gpddtll+s53vqM//OEP0X5eWL4mnjEe3x6JRELJZDJq5N1+++1atGhRdDJqbGyMRgAcdNBBeSUlzckovn8NtQAAAADAvmK3KwDspnvvvVff/OY39dGPfjSaotC0z0yw6r3vfa+uueYarVmzZqs5ukdSIpGI2p4rVqzQ8uXLddlll+n666/X/fffr3Xr1uUFuIbT/kskEttdent71dvbq76+Pg0MDCiVSkWDdkxQdXtLX1+f+vv7NTAwoEQioVQqpUwmo2w2O6xR5uvXr9fdd9+ta665RpdccomuuOIK/frXv9bmzZtHpH/o1Vdf1V133aUVK1Zo2bJlWrZsmVasWKG77rorGqW9PTtaf7PN4n0TcSZ5IZPJKJlMqr+/X319fdGyo+2/I77v67nnntNtt92mZcuW6aKLLtJ1112nlStXjuoo/10VBIGuv/56/eAHP4j6TuLVEEeC7/vavHmznnjiCd16663R9+rf/u3fdNNNN2nNmjXRwLj4VBPDCQIUBu03bNigRx99VD/4wQ902WWXafny5bryyit1ww03aOXKlerp6dmpfhuzTwVBEG0T3/e1fv16PfHEE7r22mu1bNkyXXXVVfr5z3+eVy10ON+foe6TTqf15JNP6o477tAVV1yhK664QsuWLdO3vvUt3XvvvVq3bt2wgxzmfZvBQKlUSgMDA9G+v6Pv13CQAAAAoy9ehSVePWvDhg165JFHosTSa6+9Nq/6VWGlrW0x15GZTEapVEqJREIDAwPRddSOzh+7e34x57jC6jO+72v16tXR9D9f+MIXoraBue4azvlxr0gAkLY+KcaDe2YuMzM3UX6Zn6xc11fg54L+dtaXnfWj6Lzn5YKIQegoCHOBf9vtUaABfezjx6uufpJqJtXqgYceVDKdG/n/1zdeVyjJC3yFkgaSCWUdu3CKLfUnBoSxVZjVm06ntXHjRrW1tUXlPyzL0gMPPBB9McwXZTxciA0VkDUB6+GUABmOeMk0aeeyt837MkFrM/fZYANr6KC/WVzX1pe/fLEqKspUWVmh//iP70a3hWEuuzveKRJvdMbnpTN6e3uVSqX0s5/9TN/5znf02c9+VtXV1bIsK8qevvHGG7cqxydtHdQ3+545ORg7k1xiqqIUNpbN9hpqrhpT/q1QYekzw5yofN9XOp3W008/rVtuuUUXXXSR3vGOd6iuri6at6ayslKO42zV0UMCAAAAAIB93VgnAEi5NlYqlYqCUiY429PTo82bNyuVSm016nekFI5uN+8lXhI8Xh1Ryp/vezjrtqP2o+kfiFfdi7/W9hZTGty0lXdl2xSu/55QOH/4cO1o/c20qoXJ+vGArTRYGcJsK9/38zqVt7X9h/se40zJ9n1FfLS46SeRhp47fmcVzmVvggnm+9TX1xdtK9d1d3q7mc91qIQNs3+YpJg40w+3I6biwFAjJ03FCxNcL7x9ZwbBxPv4zXuPj+yMT3W5M4Y65uzoOxVfhoMEAAAYW/Hzj+/72rBhgxobG7Vo0SJ1dnZudV7flXNtvFq467o7dS7Z1fOLpLxphsz7vvrqq9XU1KSioiL94Q9/iO5rrmeGk8S41yQAbO/CMx7w3xHfyyUCmASA3FPmSopn7YRcLynH61XG3qzayWWaNaddxaUl2tzdlRfcNxUAunt7ciXFPTf6mc5movnGMbZMtm48uH/UUUepvb1d06ZN05QpU3TxxRfnPcYEjAsvivdlQ138j0QCwFAHKhO03p3t5/v+lqD59hMAgsDTP/zDCWpunqGamio9++zTSqUSkoIt03DkDo6u60ZZ7ub5zftPJpMKw1A9PT1RNryUa3ylUim99tprOv/889Xe3q4ZM2boxBNPlJR/3DENkHijJt6BYRpg8d+HyzxmWwob86YBL+WmWjAnpcKsNlOuLP45ZbPZvM8zmUzqkUceUW1trWbOnKnq6mo9/PDDeR0iJAAAAAAAGA/GOgEgPu/ntt5b/L7ZbHbY84MOl+kr2F57Lh5g3pPl23e2fbm77dD4NKG+72tgYEDJZDKvn2B3JJPJIbeX6ZfYE+LB/vj/dmZ7Dvf+ZrBDfB13NRFjLMQrRRRWlBzpChs7Uvh6OzOIJJ4EsyPx/qPh3Df+fobTp7izgf/48xc+NpFI5FUeiA8CLKzyORTzvd6T+yQJAAAwtoY6/11wwQWaNGmSSktL9eSTT+50if1tGc34R/z6ylyr+L6vd77znZo2bZoOOOCAvPUx1w3DeX97TQJA/KI1XgIpnqlqxDOSbdvNVQAw678lgh/4kuuaQFwumOj5GZkpAP7yzOOyii21dTRr0ZLFUWn/3v4+hZJc39Pm7i69+7BD1d3bI9f3oiSAQKEGkgnC/3sJs8On02l9/vOfl2VZmjdvniorK3XWWWdJGsy0jX9RCueJ2lcVTqFhjPQ87iZreGcOfCYYHgSBstlsVKpv8L1tPwHgzTffUEdHm6ZNa9SSJYsUBJ6y2dy0HlKgRCIh27b1iU98Qk8++WRUGiyTyWjdunU66qij9L73vU833njjNt+jme/NzMd20kknScrNLRNPADAj9c229n1fyWRyyO1sMqOHs32GeqzjONFI/0wms1Vw3xwnC5nkFsdxdOWVV+qggw7SRz7yEb3xxhvR/83nZxpzjuPo97//vWpqajRhwgStWbNGvb290XqSAAAAAABgPBjrBIDCqn2mzRdP8DYjYwvbWyPV/jJt1Xh5Udu2oyR/EyCPv+622p+FTP/dtpZtjYzaldG6uzLCqrDU+p5s15ptujNGYv0Kt/VwKizszHZwHCcamRbfh/dkoshIymaz6u3tzdvHTT/P7jLBatMvZfq0bduOprAY6ntkvmM7YoL5hZ+p6TeKj1iMDxQZ7mdT+N7iFTdMf6DZr80AmW312w8lXmnErLd5n5lMZqvn2JXqFIXLcI4/VAAAgL3bUNVv4qPzX3vtNRUVFam9vV3nnXdeXgB9OHb3+nN3zy+Fg99N0ujq1atVWVmpkpIS3XbbbdF5OX4Nv08lAMTFT86F2YrmwiCbzRb8P5DjePK8QEGQG/k/uH2DLQFDVyYB4Dvfu0aTppRrwsQy/ccPvi/Hc2W7jgKF2rh5k0JJV161XLPnzomqA9iuE1UIyNhZEgD2AvGL5EsvvVSWZ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) his tutorial uses Bahdanau attention for the encoder. Let's decide on notation before writing the simplified form: FC = Fully connected (dense) layer EO = Encoder output H = hidden state X = input to the decoder And the pseudo-code: score = FC(tanh(FC(EO) + FC(H))) attention weights = softmax(score, axis = 1). Softmax by default is applied on the last axis but here we want to apply it on the 1st axis, since the shape of score is (batch_size, max_length, hidden_size). Max_length is the length of our input. Since we are trying to assign a weight to each input, softmax should be applied on that axis. context vector = sum(attention weights * EO, axis = 1). Same reason as above for choosing axis as 1. embedding output = The input to the decoder X is passed through an embedding layer. merged vector = concat(embedding output, context vector) This merged vector is then given to the GRU The shapes of all the vectors at each step have been specified in the comments in the code: """ class Encoder(tf.keras.Model): def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz): super().__init__() self.batch_sz = batch_sz self.enc_units = enc_units self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim) self.gru = tf.keras.layers.GRU(self.enc_units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform') def call(self, x, hidden): x = self.embedding(x) output, state = self.gru(x, initial_state=hidden) return output, state def initialize_hidden_state(self): return tf.zeros((self.batch_sz, self.enc_units)) encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE) sample_hidden = encoder.initialize_hidden_state() sample_output, sample_hidden = encoder(example_input_batch, sample_hidden) print ('Encoder output shape: (batch size, sequence length, units) {}'.format(sample_output.shape)) print ('Encoder Hidden state shape: (batch size, units) {}'.format(sample_hidden.shape)) print(example_input_batch.shape) print(sample_hidden.shape) print(vocab_inp_size) print(vocab_tar_size) """# Adding Attention""" class BahdanauAttention(tf.keras.layers.Layer): def __init__(self, units): super().__init__() self.W1 = tf.keras.layers.Dense(units) self.W2 = tf.keras.layers.Dense(units) self.V = tf.keras.layers.Dense(1) def call(self, query, values): # query hidden state shape == (batch_size, hidden size) # query_with_time_axis shape == (batch_size, 1, hidden size) # values shape == (batch_size, max_len, hidden size) # we are doing this to broadcast addition along the time axis to calculate the score query_with_time_axis = tf.expand_dims(query, axis=1) # score shape == (batch_size, max_length, 1) # we get 1 at the last axis because we are applying score to self.V # the shape of the tensor before applying self.V is (batch_size, max_length, units) score = self.V(tf.nn.tanh(self.W1(query_with_time_axis) + self.W2(values))) # attention_weights shape == (batch_size, max_length, 1) attention_weights = tf.nn.softmax(score, axis=1) # context_vector shape after sum == (batch_size, hidden_size) context_vector = attention_weights * values context_vector = tf.reduce_sum(context_vector, axis=1) return context_vector, attention_weights attention_layer = BahdanauAttention(10) attention_result, attention_weights = attention_layer(sample_hidden, sample_output) print("Attention result shape: (batch size, units) {}".format(attention_result.shape)) print("Attention weights shape: (batch_size, sequence_length, 1) {}".format(attention_weights.shape)) class Decoder(tf.keras.Model): def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz): super().__init__() self.batch_sz = batch_sz self.dec_units = dec_units self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim) self.gru = tf.keras.layers.GRU(self.dec_units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform') self.fc = tf.keras.layers.Dense(vocab_size) self.attention = BahdanauAttention(self.dec_units) def call(self, x, hidden, enc_output): # enc_output shape == (batch_size, max_length, hidden_size) context_vector, attention_weights = self.attention(hidden, enc_output) # x shape after passing through embedding == (batch_size, 1, embedding_dim) x = self.embedding(x) # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size) x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1) # passing the concatenated vector to the GRU output, state = self.gru(x) # output shape == (batch_size * 1, hidden_size) output = tf.reshape(output, (-1, output.shape[2])) # output shape == (batch_size, vocab) x = self.fc(output) return x, state, attention_weights decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE) sample_decoder_output, _, _ = decoder(tf.random.uniform((BATCH_SIZE, 1)),sample_hidden, sample_output) print ('Decoder output shape: (batch_size, vocab size) {}'.format(sample_decoder_output.shape)) """# Configure optimizer, loss fn and train""" optimizer = tf.keras.optimizers.Adam() loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none') def loss_function(real, pred): mask = tf.math.logical_not(tf.math.equal(real, 0)) loss_ = loss(real, pred) mask = tf.cast(mask, dtype=loss_.dtype) loss_ *= mask return tf.reduce_mean(loss_) checkpoint_dir = './training_checkpoints' checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt") checkpoint = tf.train.Checkpoint(optimizer=optimizer, encoder=encoder, decoder=decoder) """# Training - Pass the input through the encoder which return encoder output and the encoder hidden state. - The encoder output, encoder hidden state and the decoder input (which is the start token) is passed to the decoder. - The decoder returns the predictions and the decoder hidden state. - The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss. * Use teacher forcing to decide the next input to the decoder. * Teacher forcing is the technique where the target word is passed as the next input to the decoder. * The final step is to calculate the gradients and apply it to the optimizer and backpropagate. """ @tf.function def train_step(inp, targ, enc_hidden): loss = 0 with tf.GradientTape() as tape: enc_output, enc_hidden = enocder(inp, enc_hidden) dec_hidden = enc_hidden dec_input = tf.expand_dims([targ_lang_tokenizer.word_index['<start>']] * BATCH_SIZE, 1) # Use teacher forcing - feed the target as next input not predictions of model for t in range(1, targ.shape[1]): # passing enc_output to the decoder predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output) loss += loss_function(targ[:, t], predictions) # Use the teacher forcing dec_input = tf.expand_dims(targ[:, t], 1) batch_loss = (loss / int(targ.shape[1])) variables = encoder.trainable_variables + decoder.trainable_variables gradients = tape.gradient(loss, variables) optimizer.apply_gradients(zip(gradients, variables)) return batch_loss EPOCHS = 10 for epoch in range(EPOCHS): start = time.time() enc_hidden = encoder.initialize_hidden_state() total_loss = 0 for (batch, (inp, targ)) in enumerate(dataset.take(steps_per_epoch)): batch_loss = train_step(inp, targ, enc_hidden) total_loss += batch_loss if batch % 100 == 0: print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1, batch, batch_loss.numpy())) # saving (checkpoint) the model every 2 epochs if (epoch + 1) % 2 == 0: checkpoint.save(file_prefix = checkpoint_prefix) print('Epoch {} Loss {:.4f}'.format(epoch + 1, total_loss / steps_per_epoch)) print('Time taken for 1 epoch {} sec\n'.format(time.time() - start)) """# Translate The evaluate function is similar to the training loop, except we don't use teacher forcing here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output. Stop predicting when the model predicts the end token. And store the attention weights for every time step. """ def evaluate(sentence): attention_plot = np.zeros((max_length_target, max_length_inp)) sentence = preprocess_sentence(sentence) inputs = [inp_lang_tokenizer.word_index[i] for i in sentence.split(' ')] inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], maxlen=max_length_inp, padding='post') inputs = tf.convert_to_tensor(inputs) result = '' hidden = [tf.zeros((1, units))] enc_out, enc_hidden = encoder(inputs, hidden) dec_hidden = enc_hidden dec_input = tf.expand_dims([targ_lang_tokenizer.word_index['<start>']], 0) for t in range(max_length_target): predictions, dec_hidden, attention_weights = decoder(dec_input, dec_hidden, enc_out) # storing the attention weights to plot later on attention_weights = tf.reshape(attention_weights, (-1, )) attention_plot[t] = attention_weights.numpy() predicted_id = tf.argmax(predictions[0]).numpy() result += targ_lang_tokenizer.index_word[predicted_id] + ' ' if targ_lang_tokenizer.index_word[predicted_id] == '<end>': return result, sentence, attention_plot # the predicted ID is fed back into the model dec_input = tf.expand_dims([predicted_id], 0) return result, sentence, attention_plot # function for plotting the attention weights def plot_attention(attention, sentence, predicted_sentence): fig = plt.figure(figsize=(10,10)) ax = fig.add_subplot(1, 1, 1) ax.matshow(attention, cmap='viridis') fontdict = {'fontsize': 14} ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90) ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict) ax.xaxis.set_major_locator(ticker.MultipleLocator(1)) ax.yaxis.set_major_locator(ticker.MultipleLocator(1)) plt.show() def translate(sentence): result, sentence, attention_plot = evaluate(sentence) print('Input: %s' % (sentence)) print('Predicted translation: {}'.format(result)) attention_plot = attention_plot[:len(result.split(' ')), :len(sentence.split(' '))] plot_attention(attention_plot, sentence.split(' '), result.split(' ')) checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir)) translate(u'hace mucho frio aqui.') translate(u'esta es mi vida.') translate(u'hace mucho frio aqui.') translate(u'¿todavia estan en casa?') translate(u'trata de averiguarlo.')

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miguelvazsilva/datastructures/dequeue.py

miguelvazsilva/python-data-structures-algorithms

28e167340d1c5f93ad182eba6bc71df91881df3b

[ "MIT" ]

null

miguelvazsilva/datastructures/dequeue.py

miguelvazsilva/python-data-structures-algorithms

28e167340d1c5f93ad182eba6bc71df91881df3b

[ "MIT" ]

null

miguelvazsilva/datastructures/dequeue.py

miguelvazsilva/python-data-structures-algorithms

28e167340d1c5f93ad182eba6bc71df91881df3b

[ "MIT" ]

null

# Miguel Vaz Silva # Data Structures and Algorithms in Python - Dequeue Implementation # Copyright 2018 class Dequeue(object): def __init__(self): self.items = [] def addFront(self,n): self.items.insert(0,n) def addBack(self,n): self.items.append(n) def removeBack(self): return self.items.pop() def removeFront(self): return self.items.pop(0) def dequeuePrint(self): print("Queue Print:") for x in self.items: print(x) def dequeueEmpty(self): return self.items == [] def dequeueSize(self): return len(self.items) def peek(self): return self.items[len(self.items)-1]

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ys_code/src/skin_mb/data/__init__.py

sverbanic/ps2-npjBM

646585d787e5ae2d553a04ea4960b36e9d05bf29

[ "CC0-1.0" ]

null

ys_code/src/skin_mb/data/__init__.py

sverbanic/ps2-npjBM

646585d787e5ae2d553a04ea4960b36e9d05bf29

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ys_code/src/skin_mb/data/__init__.py

sverbanic/ps2-npjBM

646585d787e5ae2d553a04ea4960b36e9d05bf29

[ "CC0-1.0" ]

null

from .table import OtuTable, OtuTableFilter from .deseq2 import DeseqResult from .bayesian import BayesianRes from .diff_abun import DiffAbunRes from . import blastn

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networkapi/snippets/urls.py

vinicius-marinho/GloboNetworkAPI

94651d3b4dd180769bc40ec966814f3427ccfb5b

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networkapi/snippets/urls.py

leopoldomauricio/GloboNetworkAPI

3b5b2e336d9eb53b2c113977bfe466b23a50aa29

[ "Apache-2.0" ]

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2015-04-03T01:04:46.000Z

2021-10-03T23:24:48.000Z

networkapi/snippets/urls.py

shildenbrand/GloboNetworkAPI

515d5e961456cee657c08c275faa1b69b7452719

[ "Apache-2.0" ]

64

2015-08-05T21:26:29.000Z

2022-03-22T01:06:28.000Z

# -*- coding: utf-8 -*- from django.conf.urls import patterns from django.conf.urls import url urlpatterns = patterns('networkapi.snippets.views', url(r'^snippets/$', 'snippet_list'), )

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smtp_tls_reporting/features/exceptions/__init__.py

YnkDK/smtp_tls_reporting

5d43cab3efd1fc9d32093ec2c7853b8bc65ab1ca

[ "MIT" ]

null

smtp_tls_reporting/features/exceptions/__init__.py

YnkDK/smtp_tls_reporting

5d43cab3efd1fc9d32093ec2c7853b8bc65ab1ca

[ "MIT" ]

null

smtp_tls_reporting/features/exceptions/__init__.py

YnkDK/smtp_tls_reporting

5d43cab3efd1fc9d32093ec2c7853b8bc65ab1ca

[ "MIT" ]

null

""" SMTP TLS reporting handler Copyright (C) 2021 Martin Storgaard Dieu This code is licensed under MIT license (see LICENSE.md for details) """ from smtp_tls_reporting.features.exceptions.GzipError import GzipError from smtp_tls_reporting.features.exceptions.InternalError import InternalError from smtp_tls_reporting.features.exceptions.JsonError import JsonError from smtp_tls_reporting.features.exceptions.MissingParameterError import MissingParameterError from smtp_tls_reporting.features.exceptions.NotFoundError import NotFoundError

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py

Python

tests/__init__.py

williamcanin/zshpower

797533b60cd7f9dbd8cf2162db9a19d970a7be33

[ "MIT" ]

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2020-04-03T20:39:44.000Z

2021-12-09T22:26:56.000Z

tests/__init__.py

williamcanin/zshpower

797533b60cd7f9dbd8cf2162db9a19d970a7be33

[ "MIT" ]

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tests/__init__.py

williamcanin/zshpower

797533b60cd7f9dbd8cf2162db9a19d970a7be33

[ "MIT" ]

2

2020-04-22T08:50:11.000Z

2021-06-23T05:03:02.000Z

"""Unit test package for zshpower."""

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