id stringlengths 3 8 | content stringlengths 100 981k |
|---|---|
1791020 | import unittest
import os
from shutil import rmtree
import numpy as np
import torch
import torch.nn as nn
from inferno.trainers.basic import Trainer
from torch.utils.data.dataset import TensorDataset
from torch.utils.data.dataloader import DataLoader
from inferno.trainers.callbacks.logging.tensorboard import Tensorboa... |
1791059 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os, logging
from pprint import pprint
from utils import config as cfg
if cfg.ROOT_DIR.startswith('/home'):
import torch
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # set tensorflow logger to WARNING... |
1791072 | from __future__ import absolute_import
__author__ = 'katharine'
from enum import IntEnum
from .base import PebblePacket
from .base.types import *
__all__ = ["MetaProtocolMessage"]
class MetaProtocolMessage(PebblePacket):
class Meta:
endpoint = 0x00
class Type(IntEnum):
Disallowed = 0xdd
... |
1791101 | import time
import numpy as np
import argparse
import sys
sys.path.append("../../")
import grpc
from grpc_ps import ps_service_pb2_grpc
from grpc_ps.client import ps_client
# algorithm setting
NUM_EPOCHS = 10
NUM_BATCHES = 1
MODEL_NAME = "w.b"
LEARNING_RATE = 0.1
def handler(event, context):
s... |
1791108 | from kqueen.kubeapi import KubernetesAPI
from kubernetes.client.rest import ApiException
from pprint import pprint as print
import pytest
import yaml
import kubernetes
def fake_raise(exc):
def fn(self, *args, **kwargs):
raise exc
return fn
class TestKubeApi:
def test_missing_cluster_param(self... |
1791110 | from metagraph import translator
from metagraph.plugins import has_scipy, has_networkx, has_grblas, has_pandas
import numpy as np
if has_scipy:
import scipy.sparse as ss
from .types import ScipyEdgeMap, ScipyEdgeSet, ScipyGraph
@translator
def edgemap_to_edgeset(x: ScipyEdgeMap, **props) -> ScipyEdgeS... |
1791115 | from docs_snippets_crag.concepts.io_management.output_config import execute_my_job_with_config
def test_execute_job():
execute_my_job_with_config()
|
1791158 | import math, random
import numpy as np
from PuzzleLib.Backend import gpuarray
from PuzzleLib.Backend.Kernels.Costs import ctcLoss, ctcLossTest
from PuzzleLib.Cost.Cost import Cost
class CTC(Cost):
def __init__(self, blank, vocabsize=None, normalized=False):
super().__init__()
self.normalized = normalized
i... |
1791160 | from datetime import datetime
import os
from sqlalchemy import Column, DateTime, String, BigInteger, Integer, ForeignKey
from sqlalchemy.orm import relationship
from sqlalchemy.schema import Table
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.dialects.postgresql import JSON
from sqlalchemy im... |
1791164 | import FWCore.ParameterSet.Config as cms
from RecoParticleFlow.PFTracking.particleFlowDisplacedVertex_cfi import *
|
1791167 | from itertools import chain
from cvxopt import blas, lapack, solvers
from cvxopt import matrix, spmatrix, sin, mul, div, normal, spdiag
solvers.options['show_progress'] = 0
def get_second_derivative_matrix(n):
"""
:param n: The size of the time series
:return: A matrix D such that if x.size == (n,1), D ... |
1791191 | import sys
import numpy as np
sys.path.insert(0, "./")
from bayes_optim.extension import PCABO, RealSpace
np.random.seed(123)
dim = 5
lb, ub = -5, 5
def fitness(x):
x = np.asarray(x)
return np.sum((np.arange(1, dim + 1) * x) ** 2)
space = RealSpace([lb, ub]) * dim
opt = PCABO(
search_space=space,
... |
1791197 | from ggplib import interface
from ggplib.player.proxy import ProxyPlayer
class CppRandomPlayer(ProxyPlayer):
def meta_create_player(self):
return interface.create_random_player(self.sm, self.match.our_role_index)
class CppLegalPlayer(ProxyPlayer):
def meta_create_player(self):
return interfa... |
1791222 | class Alert:
"""Maps a Rule to an Action, and triggers the action if the rule
matches on any stock update"""
def __init__(self, description, rule, action):
self.description = description
self.rule = rule
self.action = action
def connect(self, exchange):
self.exchange = ... |
1791246 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .aspp import ASPP_Module
up_kwargs = {'mode': 'bilinear', 'align_corners': False}
norm_layer = nn.BatchNorm2d
class _ConvBNReLU(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0,
di... |
1791249 | import requests
url = "http://localhost:8080/local"
for i in -4,-2,2,4,6:
data = {"rom":"psos","type":"temp", "device":"1wire", "ip":"", "gpio":"", "i2c":"", "usb":"","name":"psos","value":i}
r = requests.post(url,json=data)
data = {"rom":"press","type":"press","device":"usb", "ip":"", "gpio":"", "i2c":"", "us... |
1791264 | from sys import version_info
from pytest import fixture
from mock import Mock, call
if version_info[0] == 3:
unicode = str
bytes_type = bytes
else:
unicode = lambda k: k.decode('utf8')
bytes_type = str
def mocked_smtp(*args, **kwargs):
smtp = Mock()
smtp.return_value = smtp
smtp(*args, *... |
1791287 | from toee import *
def OnBeginSpellCast( spell ):
print "Read Magic OnBeginSpellCast"
print "spell.target_list=", spell.target_list
print "spell.caster=", spell.caster, " caster.level= ", spell.caster_level
#game.particles( "sp-divination-conjure", spell.caster )
def OnSpellEffect( spell ):
print "Read Magic OnS... |
1791289 | import random
import torch
import torchvision
from pathlib import Path
import utils.logging as logging
import data.utils as utils
from data.build import DATASET_REGISTRY
logger = logging.get_logger(__name__)
@DATASET_REGISTRY.register()
class UCF101(torch.utils.data.Dataset):
"""
UCF101 video loader. Cons... |
1791384 | import sys
def hanoi(n: int, start: int, by: int, end: int) -> None:
if n == 1:
move.append([start, end])
else:
hanoi(n - 1, start, end, by)
move.append([start, end])
hanoi(n - 1, by, start, end)
n = int(sys.stdin.readline())
move = []
hanoi(n, 1, 2, 3)
print(len(move))
print... |
1791407 | import rospy
import datetime
import os
import json
from std_msgs.msg import String
from sensor_msgs.msg import Image
from acrv_apc_2017_perception.msg import autosegmenter_msg
import cv_bridge
import cv2
NUM_IMGS = 7
seen_items = [
"plastic_wine_glass",
"hinged_ruled_index_cards",
"black_fashion_gloves"... |
1791463 | from starkware.starknet.business_logic.state import BlockInfo
from starkware.starknet.public.abi import get_selector_from_name
import logging
from ast import Constant
import pytest
from enum import Enum
import asyncio
from starkware.starknet.testing.starknet import Starknet
from utils import (
Signer, uint, str_to_... |
1791554 | import numpy as np
from .pyramid import Pyramid
from .filters import parse_filter
from .c.wrapper import corrDn, upConv
class WaveletPyramid(Pyramid):
"""Multiscale wavelet pyramid
Parameters
----------
image : `array_like`
1d or 2d image upon which to construct to the pyramid.
height : '... |
1791568 | from shared_config import *
word_size = 7
num_words = 256
words_per_row = 4
local_array_size = 25
output_extended_config = True
output_datasheet_info = True
netlist_only = True
nominal_corner_only = True
|
1791594 | from twitchbot import BaseBot, Mod, Event
def test_base_bot_events_are_set():
for event in Event:
assert event.name in BaseBot.__dict__, f'BaseBot must implement event {event}'
def test_mod_events_are_set():
for event in Event:
assert event.name in Mod.__dict__, f'Mod must implement event {e... |
1791632 | import claripy
from ..shellcode import Shellcode
class X86SetRegister(Shellcode):
os = ["cgc", "unix"]
arches = ["X86"]
name = "setregister"
codes = {
'eax': [b"\xb8", b"\xbb", b"\xff\xe3"],
'ebx': [b"\xbb", b"\xb8", b"\xff\xee"],
'ecx': [b"\xb9", b"\xbb", b"\xff\... |
1791636 | import torch
def adjust_log_weights(log_weights, component_log_probs):
'''
Adjust log_weights for multivariate mixture distributions.
Uses that `sum_m w_m p_m1(x_1) p_m2(x_2|x_1) p_m3(x_3|x_2,x_1) = [sum_m w_m1 p_m1(x_1)] [sum_m w_m2 p_m2(x_2|x_1)] [sum_m w_m3 p_m3(x_3|x_2,x_1)]`.
This computes `[w_m... |
1791650 | from click.testing import CliRunner
from tiletanic import cli
def test_tiletanic():
"""Basic call to root command"""
runner = CliRunner()
result = runner.invoke(cli.cli)
assert result.exit_code == 0
def test_version():
runner = CliRunner()
result = runner.invoke(cli.cli, ['--version'])
as... |
1791653 | from triggerflow.dags import DAG
from triggerflow.dags.operators import (
IBMCloudFunctionsCallAsyncOperator,
IBMCloudFunctionsMapOperator
)
dag = DAG(dag_id='fault-tolerance')
first_task = IBMCloudFunctionsCallAsyncOperator(
task_id='first_task',
function_name='echo',
function_package='triggerflo... |
1791686 | import argparse
import itertools as it
import json
import math
import matplotlib.pyplot as plt
import os
import pathlib
_here = pathlib.Path(__file__).resolve().parent
def main(dataset, models, forward, accepts, rejects):
assert not (accepts and rejects)
if forward:
if accepts:
string = ... |
1791688 | from typing import Callable
import pytest
from mimesis.locales import DEFAULT_LOCALE
from mimesis.schema import Field
_CacheCallable = Callable[[str], Field]
@pytest.fixture(scope='session') # noqa: PT005
def _mimesis_cache() -> _CacheCallable: # noqa: PT005
cached_instances = {}
def factory(locale: str)... |
1791691 | import argparse
import re
import os
import ray
from ray.tune import run_experiments
from ray.tune.registry import register_trainable, register_env, get_trainable_cls
import ray.rllib.contrib.maddpg.maddpg as maddpg
from rllib_multiagent_particle_env import env_creator
from util import parse_args
def setup_ray():
... |
1791729 | import os
import tempfile
import unittest
import logging
from pyidf import ValidationLevel
import pyidf
from pyidf.idf import IDF
from pyidf.surface_construction_elements import MaterialNoMass
log = logging.getLogger(__name__)
class TestMaterialNoMass(unittest.TestCase):
def setUp(self):
self.fd, self.pa... |
1791766 | class Solution(object):
def mySqrt(self, x):
"""
:type x: int
:rtype: int
"""
if x < 2:
return x
left, right = 0, x // 2
while left <= right:
mid = left + (right - left) // 2
num = mid * mid
if num > x:
... |
1791778 | from circuits import Event
class MessageReceivedEvent(Event):
"""event"""
class ContextCreatedEvent(Event):
"""event"""
class NLPConfidenceLowEvent(Event):
"""event"""
class ChatRequestedEvent(Event):
"""event"""
class SkillRequestedEvent(Event):
"""event"""
class EntitiesPreprocessedEvent(Eve... |
1791850 | from django.db import models
from filingcabinet.models import (
AbstractDocument,
AbstractDocumentCollection,
DocumentManager as FCDocumentManager,
DocumentCollectionManager as FCDocumentCollectionManager,
get_page_image_filename,
Page,
)
from froide.helper.auth import (
can_read_object_au... |
1791860 | from substance.monads import *
from substance.logs import *
from substance import (Engine, Command)
from substance.exceptions import (SubstanceError)
class Sshinfo(Command):
def getUsage(self):
return "substance sshinfo [ENGINE NAME]"
def getHelpTitle(self):
return "Obtain the ssh info confi... |
1791878 | import torch
from pytorchltr.datasets.list_sampler import ListSampler
from pytorchltr.datasets.list_sampler import UniformSampler
from pytorchltr.datasets.list_sampler import BalancedRelevanceSampler
from pytest import approx
def rng(seed=1608637542):
gen = torch.Generator()
gen.manual_seed(seed)
return ... |
1791880 | import ocean
idx = ocean.index[[1,2,3],:,...,-2]
idx2 = ocean.gpu[0](idx)
ocean.cpu(idx2, True)
idx3 = idx2.setDevice(ocean.gpu[0])
idx2.setDevice(ocean.gpu[0],True)
|
1791887 | import torch
from omegaconf import DictConfig
from torch import nn
from code2seq.model.modules import PathEncoder
class TypedPathEncoder(PathEncoder):
def __init__(
self,
config: DictConfig,
n_tokens: int,
token_pad_id: int,
n_nodes: int,
node_pad_id: int,
... |
1791890 | from .delete import Delete
from .get_many import GetMany
from .get_single import GetSingle
from .post import Post
from .patch import Patch
from .defs import HttpMethods
__all__ = ['HttpMethods', 'Delete', 'GetMany', 'GetSingle', 'Post', 'Patch']
|
1791910 | import logging
import os
import sys
import cv2
import numpy as np
import torch
import torch.nn as nn
import _init_paths
from config import cfg, update_config
from models.utils import _gather_feat, _transpose_and_gather_feat
from tensorrt_model import TRTModel
from utils.image import get_affine_transform, transform_pr... |
1791924 | def register_routes(api, app, root="app"):
from app.api.model import register_routes as attach_model
from app.api.meta import register_routes as attach_meta
from app.api.pipelines import register_routes as attach_pipelines
from app.api.composer import register_routes as attach_composer
from app.api.... |
1791942 | from .basenotifier import BaseNotifier as Base
from ..config import config
class Pushdeer(Base):
def __init__(self):
self.name = 'Pushdeer'
self.token = config.PUSHDEER_KEY
self.retcode_key = 'code'
self.retcode_value = 0
def send(self, text, status, desp):
url = 'http... |
1791965 | import os, time, sys, datetime
from random import randint
from huepy import *
__version__ = "1.3.6"
def cc_gen(bin):
cc = ""
if len(bin) != 16:
while len(bin) != 16:
bin += 'x'
else:
pass
if len(bin) == 16:
for x in range(15):
if bin[x] in ("0", "... |
1792009 | from imports import *
from rescale_numeric_feature import *
"""
This class calculates feature importance
Input:
"""
class calculate_shap():
def __init__(self):
super(calculate_shap, self).__init__()
self.param = None
def xgboost_shap(self, model, X):
# explain the model's predict... |
1792031 | from ifem import test_solve_system
test_solve_system()
from applications import test_uniform_bar
test_uniform_bar()
|
1792035 | from django.core.management.base import BaseCommand, CommandError
from django.db.models.loading import AppCache
from django.conf import settings
import simpledb
class Command(BaseCommand):
help = ("Sync all of the SimpleDB domains.")
def handle(self, *args, **options):
apps = AppCache()
check... |
1792045 | from django.core.cache import cache
from django.dispatch import Signal
from speedbar.utils import DETAILS_PREFIX, TRACE_PREFIX, loaded_modules
from speedbar.modules.base import RequestTrace
DETAILS_CACHE_TIME = 60 * 30 # 30 minutes
request_trace_complete = Signal(providing_args=['metrics', 'request', 'response'])
... |
1792046 | from __future__ import absolute_import
import click
from .run import run
@click.command()
def shell():
"""Start the Django shell."""
run.main(["python", "manage.py", "shell"])
|
1792104 | from metadata.metadata import AI_MODEL
from mlalgms.hpaprediction import checkHPAAnomaly
class hpametricinfo(object):
def __init__(self, priority, metricType, currentdataframe, algorithm=None, mlmodel=None, hpaproperties=None, modelparameters=None):
self.priority = priority
self.metricType = metricType
self.... |
1792111 | from os.path import join
import pytest
from pyleecan.Functions.load import load
from pyleecan.definitions import DATA_DIR
@pytest.mark.IPMSM
def test_material_dict():
Toyota_Prius = load(join(DATA_DIR, "Machine", "Toyota_Prius.json"))
mat_dict = Toyota_Prius.get_material_dict()
# Check only names
fo... |
1792117 | plot(t, rad2deg(y[:, 3:]))
xlabel('Time [s]')
ylabel('Angular Rate [deg/s]')
legend(["${}$".format(vlatex(s)) for s in speeds]) |
1792156 | class Solution:
def climbStairs(self, n: int) -> int:
if n <= 1:
return n
s1 = 1
s2 = 2
for i in range(2, n):
s = s1 + s2
s1, s2 = s2, s
return s2
|
1792163 | from setuptools import setup
setup(name='amplification',
version='0.1',
install_requires=[
]
)
|
1792182 | import sys
import os, os.path
import shutil
if sys.version_info < (3,):
range = xrange
def CheckParameter():
outputPath = None
searchStartDir = None
isIncludeFolder = None
excludePaths = None
count = len(sys.argv)-1
if count >= 8:
for i in range(1, count):
if sys.argv[i] == "-OutputPath":
ou... |
1792202 | from ceo.tools import ascupy
from ceo.pyramid import Pyramid
import numpy as np
import cupy as cp
from scipy.ndimage import center_of_mass
class PyramidWFS(Pyramid):
def __init__(self, N_SIDE_LENSLET, N_PX_LENSLET, modulation=0.0, N_GS=1, throughput=1.0, separation=None):
Pyramid.__init__(self)
sel... |
1792280 | from .builder import CUDAOpBuilder
from .kernel_builder import KernelBuilder
from .transformer_builder import TransformerBuilder
from .adam_builder import AdamBuilder
# TODO: infer this list instead of hard coded
# List of all available ops
__op_builders__ = [
KernelBuilder(),
TransformerBuilder(),
AdamBui... |
1792302 | from ffi_navigator import langserver
from ffi_navigator.util import join_path, normalize_path
import logging
import os
curr_path = os.path.dirname(os.path.realpath(os.path.expanduser(__file__)))
def run_find_definition(server, path, line, character):
uri = langserver.path2uri(path)
res = server.m_text_docume... |
1792315 | from django.utils.translation import ugettext_lazy as _
from django.apps import AppConfig
class DatabankConfig(AppConfig):
name = _('databank')
|
1792348 | from .. import testing
class CountIfTest(testing.FunctionalTestCase):
filename = "IF.xlsx"
def test_evaluation_ABCDE_1(self):
for col in "ABCDE":
cell = f'Sheet1!{col}1'
excel_value = self.evaluator.get_cell_value(cell)
value = self.evaluator.evaluate(cel... |
1792389 | def gcd(a: int, b: int) -> int:
return a if b == 0 else gcd(b, a % b)
def lcm(a: int, b: int) -> int:
return a * b // gcd(a, b)
|
1792399 | import torch
from torch import nn
from typing import Optional
from typing import NamedTuple
from .discriminators import DiscriminatorOutput
from ....misc.toolkit import get_gradient
class GANTarget(NamedTuple):
is_real: bool
labels: Optional[torch.Tensor] = None
class GradientNormLoss(nn.Module):
def ... |
1792443 | from __future__ import unicode_literals
from django.shortcuts import render
def allowed(request):
return render(request, 'default.html')
|
1792492 | import traceback
import uuid
import humanfriendly
from flask import Flask, request, render_template, abort, send_from_directory
from functools import wraps, update_wrapper
from datetime import datetime
from flask import make_response
from panoptes.database import init_db, db_session
from panoptes.models import Workfl... |
1792546 | from rete.common import BetaNode, Token
class PNode(BetaNode):
kind = 'p'
def __init__(self, children=None, parent=None, items=None, **kwargs):
"""
:type items: list of Token
"""
super(PNode, self).__init__(children=children, parent=parent)
self.items = items if items... |
1792576 | import math
import traceback
from pathlib import Path
from typing import Any, Callable, Iterator, List, NoReturn, Optional, Union
from seutil import IOUtils, LoggingUtils
from tqdm import tqdm
logger = LoggingUtils.get_logger(__name__)
class FilesManager:
"""
Handles the loading/dumping of files in a datase... |
1792608 | import unittest
import os
from wtrie import Trie
from rtrie import value_for_vid, vid_for_value
pwd = os.getcwd()
if os.path.basename(pwd) != 'test':
fixture = os.path.join(pwd, 'test/fixtures/keys')
else:
fixture = os.path.join(pwd, 'fixtures/keys')
class TestStressWTrie(unittest.TestCase):
def test_st... |
1792641 | from microbit import *
from neopixel import NeoPixel
class neo16x16:
def __init__(self, pin):
self.np = NeoPixel(pin, 256)
self.color = (0,0,8)
def clear(self):
self.np.clear()
def set(self, n, color=''):
if color!='':
self.np[n] = color
else:
... |
1792669 | import numpy as np
from sklearn.decomposition import PCA
from statsmodels.tsa.adfvalues import mackinnoncrit
from statsmodels.tsa.adfvalues import mackinnonp
from statsmodels.tsa.stattools import adfuller
from ._utils import rms
def aeg_pca(X0, X1, trend):
__sqrteps = np.sqrt(np.finfo(np.double).eps)
# Comp... |
1792671 | import asyncio
from cleo import Command
from cleo.helpers import option
from netaudio.dante.browser import DanteBrowser
class SubscriptionAddCommand(Command):
name = "add"
description = "Add a subscription"
options = [
option("rx-channel-name", None, "Specify Rx channel by name", flag=False),
... |
1792730 | import os, sys
from AnyQt.QtWidgets import QSizePolicy, QStyle, QMessageBox, QFileDialog
from AnyQt.QtCore import QTimer
from Orange.misc import DistMatrix
from Orange.widgets import widget, gui
from Orange.data import get_sample_datasets_dir
from Orange.widgets.utils.filedialogs import RecentPathsWComboMixin
from Or... |
1792735 | from selenium import webdriver
from time import sleep
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By
from selenium.webdriver.support.wait import WebDriverWait
def Like_by_keyword(driver, keyword, num):
'''... |
1792771 | import cartography.intel.aws.ec2
import cartography.intel.aws.iam
import tests.data.aws.ec2.instances
import tests.data.aws.iam
from cartography.util import run_analysis_job
TEST_ACCOUNT_ID = '000000000000'
TEST_REGION = 'us-east-1'
TEST_UPDATE_TAG = 123456789
def test_load_ec2_instances(neo4j_session, *args):
"... |
1792775 | from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, f1_score
from sklearn import metrics
from sklearn.metrics import precision_recall_curve
from dataset import dataset_names
import numpy as np
import torc... |
1792811 | class H52VTR:
"""Convert HDF5 file to rectilinear VTR file.
Args:
h5file (str): input HDF5 filename
axisnames (str): ('elev', 'lat', 'axial')
dataname (str): 'arfidata'
vtrname (str): 'rectilinear'
"""
def __init__(self, h5file=None, axisnames=('elev', 'lat', 'axial'),
... |
1792829 | import sys
sys.path.insert(1,"../../")
import h2o
from tests import pyunit_utils
import pandas as pd
def pubdev_7119():
# Test 1
pd_df = pd.DataFrame({'col1': [1,2], 'col2': ['foo"foo\nfoo','foo2'], 'col3': [1,2]})
h2o_df = h2o.H2OFrame(pd_df)
pd_df2 = h2o_df.as_data_frame()
print(pd_df)
... |
1792837 | import numpy as np
import matplotlib.image as io
import matplotlib.pyplot as plt
alpha = 30
seedX = 998244353
seedY = 1000000007
''' First alpha = 5
oriFile = "qrcode.png"
waterMarkFile = "testwm.png"
outFile = "watermarked.png"
'''
''' Second alpha = 30
oriFile = "test.png"
waterMarkFile = "website.png"
outFile = "... |
1792854 | import attr
import json
import numpy as np
from text2qdmr.datasets.utils.extract_values import GroundingKey, ValueUnit
from text2qdmr.datasets.qdmr import QDMRStepArg
def to_dict_with_sorted_values(d, key=None):
return {k: sorted(v, key=key) for k, v in d.items()}
def to_dict_with_set_values(d):
result = {}... |
1792862 | import yaml
def read_config(config_path):
with open(config_path, "r") as f_config:
config = yaml.load(f_config)
return config
|
1792865 | load("@io_bazel_rules_docker//container:load.bzl", "container_load")
BUILD_BAZEL = """
java_import(
name = "server",
jars = ["buildfarm-server_deploy.jar"],
visibility = ["//visibility:public"],
)
java_import(
name = "worker",
jars = ["buildfarm-worker_deploy.jar"],
visibility = ["//visibility:... |
1792887 | import os
import sentencepiece as spm
DATAFILE = '../data/pg16457.txt'
MODELDIR = 'models'
spm.SentencePieceTrainer.train(f'''\
--model_type=bpe\
--input={DATAFILE}\
--model_prefix={MODELDIR}/bpe\
--vocab_size=500''')
sp = spm.SentencePieceProcessor()
sp.load(os.path.join(MODELDIR, 'bpe.model'))
inp... |
1792904 | import sys
import os
import torch
from allennlp.data.iterators import BucketIterator
from allennlp.data.iterators import BasicIterator
from allennlp.modules.text_field_embedders import TextFieldEmbedder
import torch.optim as optim
from acsa.acsc_pytorch.my_allennlp_trainer import Trainer
from allennlp.data.vocabulary ... |
1792950 | import requests
topics = {
"topics": [
{
"text": [
"Art_Event",
"Celebrities",
"Entertainment",
"Fashion",
"Food_Drink",
"Games",
"Literature",
"Math",
... |
1792997 | import fasttext
import numpy as np
import joblib
def dump_split(sents_f, embed_model_f, model_f, prefix):
model = joblib.load(model_f)
embed_model = fasttext.load_model(embed_model_f)
sentences = []
embeddings = []
with open(sents_f) as handle:
for new_line in handle:
if len(n... |
1793070 | from io import BytesIO
from buidl.helper import (
encode_varint,
hash256,
int_to_little_endian,
read_varint,
read_varstr,
)
from buidl.siphash import SipHash_2_4
BASIC_FILTER_TYPE = 0
GOLOMB_P = 19
GOLOMB_M = int(round(1.497137 * 2 ** GOLOMB_P))
def _siphash(key, value):
if len(key) != 16:
... |
1793071 | from tvm import relay
import tvm
from collage.pattern_manager.utils import is_function_node
from collage.pattern_manager.cost_func import *
from collage.optimizer.custom_fusion_pass import CustomFusionPass
from workloads.torch_workloads import get_network_from_torch
from workloads.relay_workloads import get_network_fro... |
1793115 | class Calculadora(object):
"""docstring for Calculadora"""
memoria = 10
def suma(self, a, b):
return a + b
def resta(self, a, b):
return a - b
def multiplicacion(self, a, b):
return a * b
def division(self, a, b):
return a / b
@classmethod
def numerosPrimos(cls, limite):
rango = range(2, limite)
... |
1793126 | import logging
from ._aioredis import redis_manager
from ._aiomysql import mysql_manager
logger = logging.getLogger(__name__)
__all__ = ['db_manager', 'get_pool', 'get_manager']
db_manager_map = {
'mysql': mysql_manager,
'redis': redis_manager,
}
class DBManager:
@staticmethod
def get_manager(db... |
1793128 | try:
a = int(input("Escolha entre 1 e 6"))
if a < 1 or a > 6:
print ("o valor deve ser entre 1 e 6")
except ValueError:
print ("Escolha uma opção válida")
#https://pt.stackoverflow.com/q/433462/101
|
1793149 | class Callback(object):
'''Callback base class'''
def __init__(self):
pass
def on_train_begin(self, train_iterator, num_epochs):
pass
def on_epoch_begin(self, train_iterator, num_epochs, epoch):
pass
def on_batch_begin(self, train_iterator, num_epochs, epoch, iteration, b... |
1793150 | import numpy as np
import gym, gym.spaces
import time
import os
from collections import OrderedDict
import yaml
EPS = 1e-5
def parse_config(config):
with open(config, 'r') as f:
config_data = yaml.load(f)
return config_data
class ToyEnv:
def __init__(self,
config_file,
... |
1793173 | import numpy as np
import os, sys
import copy
import torch
import torch.utils.data
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..'))
from common.geometry import Camera
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from mesh_dataset import MeshLoader
class LoaderSingle(... |
1793226 | class Solution:
def balancedStringSplit(self, s: str) -> int:
cnt = tmp = 0
for i in s:
if i == 'R':
tmp += 1
if i == 'L':
tmp -= 1
if not tmp:
cnt += 1
return cnt |
1793261 | from collections import deque
from hypothesis import given
from hypothesis.strategies import (frozensets, integers, lists, one_of, sets,
tuples)
from tests.entities import (DataClassWithDeque, DataClassWithFrozenSet,
DataClassWithList, DataClassWithOption... |
1793275 | import os
import unittest
from spotinst_sdk2 import SpotinstSession
from spotinst_sdk2.models.managed_instance.aws import *
class SimpleNamespace:
def __init__(self, **kwargs):
self.__dict__.update(kwargs)
class AwsManagedInstanceTestCase(unittest.TestCase):
def setUp(self):
self.session =... |
1793304 | import math
from datetime import timedelta
from datetime import datetime
MAX_GEE_PIXELS_DOWNLOAD = 1048576
GEE_ERROR_PLACEHOLDER = "ImageCollection.getRegion: Too many values: "
__all__ = ('tile_coordinates', 'retrieve_max_pixel_count_from_pattern',
'cmp_coords', 'get_date_interval_array', 'make_polygon')
... |
1793340 | import nltk.tokenize.punkt
from os import listdir
from os import path
import re
import io
import shutil
import os
import sys, getopt
# load the sentence tokenizer
ab_tokenizer = nltk.data.load("abkhaz_tokenizer.pickle")
ru_tokenizer = nltk.data.load("russian.pickle")
speech_tokenset = (
"иҳәеит", "рҳәеит", "сҳәеит", ... |
1793347 | import unittest
from setup.settings import *
from numpy.testing import *
import numpy as np
import dolphindb_numpy as dnp
import pandas as pd
import orca
class FunctionMedianTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
# connect to a DolphinDB server
orca.connect(HOST, PORT, "ad... |
1793353 | import pyutilib.component.app
import pyutilib.misc
import os
import sys
currdir = sys.argv[-1] + os.sep
app = pyutilib.component.app.SimpleApplication("foo")
pyutilib.misc.setup_redirect(currdir + "summary.out")
app.config.summarize()
pyutilib.misc.reset_redirect()
|
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