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import os from datetime import datetime from pathlib import Path from typing import List from unittest import TestCase from unittest.mock import Mock, patch from requests.exceptions import HTTPError import gphotos_sync.Utils as Utils import tests.test_setup as ts from gphotos_sync.BadIds import BadIds from gphotos_sy...
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from haystack.nodes.other.docs2answers import Docs2Answers from haystack.nodes.other.join_docs import JoinDocuments from haystack.nodes.other.route_documents import RouteDocuments from haystack.nodes.other.join_answers import JoinAnswers from haystack.nodes.other.join import JoinNode from haystack.nodes.other.document_...
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""" special detectors using rules, for (perhaps auxiliarily) detecting PR, Brady (including SB), LQRSV, RAD, LAD, STach pending arrhythmia classes: LPR, LQT NOTE: ----- 1. ALL signals are assumed to have units in mV 2. almost all the rules can be found in `utils.ecg_arrhythmia_knowledge` 3. "PR" is superior to electr...
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_base_ = ['../_base_/default_runtime.py'] use_adversarial_train = True img_res = 256 # evaluate evaluation = dict(interval=10, metric=['pa-mpjpe', 'mpjpe']) optimizer = dict( backbone=dict(type='Adam', lr=1.0e-4, weight_decay=1.0e-4), head=dict(type='Adam', lr=1.0e-4, weight_decay=1.0e-4), ) optimizer_config...
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# Copyright (c) OpenMMLab. All rights reserved. import mmengine features = mmengine.load('result.pkl') video_list = mmengine.list_from_file('hacs_data.txt') feature_dir = '../../../data/HACS/slowonly_feature' mmengine.mkdir_or_exist(feature_dir) head = ','.join([f'f{i}' for i in range(700)]) + '\n' for feature, vide...
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import asyncio import asyncpg from config import DB_BIND QUERIES = open("migrate.sql", "r").read() def log(connection, message): print(message) async def main(): db = await asyncpg.connect(DB_BIND) db.add_log_listener(log) async with db.transaction(): await db.execute(QUERIES) #...
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# Code based on https://github.com/PetarV-/DGI/blob/master/models/dgi.py import torch torch.manual_seed(0) torch.cuda.manual_seed_all(0) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False import torch.nn as nn from embedder import embedder from layers import GCN, Discriminator import numpy...
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def cells(): ''' # 3/ Problem solutions ''' ''' ''' from sympy import * init_printing() ''' ''' ''' ''' ''' ## P3.1 ''' ''' ''' AUG = Matrix([ [1, 5, 25], [2, 1, 32]]) AUG ''' ''' AUG.rref() ''' ''...
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--- third_party/blink/renderer/build/scripts/run_with_pythonpath.py.orig 2022-02-07 13:39:41 UTC +++ third_party/blink/renderer/build/scripts/run_with_pythonpath.py @@ -22,6 +22,7 @@ def main(): existing_pp = ( os.pathsep + env['PYTHONPATH']) if 'PYTHONPATH' in env else '' env['PYTHONPATH...
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#!/usr/bin/env python ##################################### # Installation module for skipfish ##################################### # AUTHOR OF MODULE NAME AUTHOR="Mauro Risonho de Paula Assumpcao (firebits)" # DESCRIPTION OF THE MODULE DESCRIPTION="This module will install/update skipfish - web application security...
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from typing import Dict import torch from torch import nn from kornia.core.check import KORNIA_CHECK_SHAPE from kornia.utils.helpers import map_location_to_cpu urls: Dict[str, str] = {} urls["lib"] = "https://github.com/yuruntian/SOSNet/raw/master/sosnet-weights/sosnet_32x32_liberty.pth" urls["hp_a"] = "https://gith...
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class Solution: def longestSubsequenceRepeatedK(self, s: str, k: int) -> str: ans = '' count = [0] * 26 possibleChars = [] q = collections.deque(['']) # Store subseqs, length grows by 1 each time for c in s: count[ord(c) - ord('a')] += 1 for c in string.ascii_lowercase: if count...
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from conan import ConanFile from conan.tools.files import get, copy from conan.tools.cmake import CMake, CMakeToolchain, cmake_layout from conan.tools.microsoft import is_msvc from conan.errors import ConanInvalidConfiguration import os required_conan_version = ">=1.47.0" class QuickJSConan(ConanFile): name = "q...
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""" hubspot owners api """ from hubspot3.crm_associations import CRMAssociationsClient from hubspot3.base import BaseClient OWNERS_API_VERSION = "v2" class OwnersClient(BaseClient): """ hubspot3 Owners client :see: https://developers.hubspot.com/docs/methods/owners/owners_overview """ def _get_...
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# -*- coding: utf-8 -*- """ Profile: http://hl7.org/fhir/StructureDefinition/RegulatedAuthorization Release: R4B Version: 4.3.0 Build ID: c475c22 Last updated: 2022-05-28T12:47:40.239+10:00 """ from pydantic.validators import bytes_validator # noqa: F401 from .. import fhirtypes # noqa: F401 from .. import regulated...
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# 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 ...
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#!/usr/bin/python # -*- coding: utf-8 -*- # # (c) 2015, Patrick F. Marques <patrickfmarques@gmail.com> # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = {'metadata_versi...
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#!/usr/bin/env python # # Launch the script with the command: ./compareDQM.py # Set below the two DQM input files (DQMfileOld,DQMfileNew) # # This script compares the plots cointained in two DQM files and save the superimposed plots # from __future__ import print_function DQMfileOld="/afs/cern.ch/user/s/sdonato/AFSwo...
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xterm_colors = [] # This is ripped out of pygments # I leave out the 16 standard colors since people tend to re-define them to their liking. # colors 16..232: the 6x6x6 color cube _valuerange = (0x00, 0x5f, 0x87, 0xaf, 0xd7, 0xff) for i in range(217): r = _valuerange[(i // 36) % 6] g = _valuerange[(i // 6) % 6] b...
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from .c2_complex import generate_figure, \ plot_vector from .c2_fourier import generate_matrix_dft, \ generate_matrix_dft_inv, \ dft, \ idft, \ fft, \ ifft_noscale, \ ifft, \ stft_basic, \ istft_basic, \ stft, istft, \ stft_convention_fmp from .c2_interpolation import compu...
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""" Copyright (c) 2018 Intel Corporation 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 writin...
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# Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from builtins import _test_sink, _test_source def test_from_1_to_0(): x = 0 change_arg0(x, _test_source()) return x def test...
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# Copyright (C) 2018 Simon Biggs # 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,...
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"""All pytest-django fixtures""" import os from contextlib import contextmanager from functools import partial from typing import Any, Generator, Iterable, List, Optional, Tuple, Union import pytest from . import live_server_helper from .django_compat import is_django_unittest from .lazy_django import skip_if_no_djan...
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import logging import os import re import h5py import numpy as np import matplotlib from matplotlib import pyplot from matplotlib.image import NonUniformImage from westpa.tools import WESTMasterCommand, WESTSubcommand from westpa.core import h5io, textio from westpa.fasthist import normhistnd from westpa.core.extloa...
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# Copyright 2020 Planet Labs, Inc. # Copyright 2022 Planet Labs PBC. # # 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 a...
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import torch # each scaler is a function that takes as input X (B x N x Din), adj (B x N x N) and # avg_d (dictionary containing averages over training set) and returns X_scaled (B x N x Din) as output def scale_identity(X, adj, avg_d=None): return X def scale_amplification(X, adj, avg_d=None): # log(D + 1...
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import os import pytest from pyspark import SparkConf, SparkContext from sagemaker_pyspark import (S3DataPath, EndpointCreationPolicy, RandomNamePolicyFactory, SageMakerClients, IAMRole, classpath_jars) from sagemaker_pyspark.algorithms import LinearLearnerMultiClassClassifier from sag...