K=10,T=0.8: self . _ _ dict _ _ [ t ] , maxlen ) < filesep > import sys import os import re import c pickle import gzip import urllib from functools import reduce import numpy as np import snapconf import as sc region args = sc . region args default _ size = 1 def main ( ) : try : raise value error ( " error : " print ( " error : { } " . format ( str ( " [ 0 ] ) ) ) except exception as e : except exception as e : print ( " error : { } " . format ( " ) } " ) print ( " error : K=10,T=0.8: in tests , this bit - shift is omitted . a mixin for mocks that provides the aliasing of ( accent _ ) color - > ( accent _ ) colour like discord . py does . provides common functionality for our custom mock types . the ` _ get _ child _ mock ` method automatically returns an async mock for coroutine methods of the mock object . as discord . py also uses synchronous methods that return coroutine objects , the class attribute ` additional _ spec ` from _ context ` ` ` ` _ ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` * ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` . ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ` ' ` ` ` ` ` ` K=10,T=0.8: { " input " : " could the members of the police perform ? " , " output " : " what can the members of the police do ? " , } , { " input " : " jan was born in what country ? " , " output " : " what is jan personal history ? " , } , ] _ example _ prompt = chat prompt template . create chat prompt ( [ " input " ] [ " input " ] [ " input " ] , ] _ _ example _ prompt = chat completion . create chat prompt ( " input " , " input " : " input " , " input " : " input " , } , ) def get chat prompt ( self ) : if self . chat . chat . chat . chat K=10,T=0.8: nn . init . constant _ ( m . bias , 0 ) elif isinstance ( m , nn . linear ) : nn . init . normal _ ( m . weight , 0 , 0 . 0 1 ) nn . init . constant _ ( m . bias , 0 ) def make _ layers ( cfg : list [ union [ str , int ] ] , batch _ norm : bool = false ) - > list [ str , any ] = batch _ norm : for i , batch in enumerate ( cfg ) : for i , ( i , j , j , j ) in enumerate ( cfg . num _ layers ) : if isinstance ( data , list [ str , any ] , batch _ norm = true ) : nn . init . constant _ K=10,T=0.8: id def resolve _ jump _ target ( self , bytecode , stack _ size ) : push _ bytecode = bytecode . dependencies [ 0 ] jump _ address = unfold _ constant ( push _ bytecode ) if jump _ address not in self . jump _ dests : raise jump address error ( " jump address % d is invalid " % jump _ address ) return self . _ _ resolve _ address ( jump _ address ) else : return self . _ _ get _ jump _ addr ( block _ address ) return self . _ _ get _ jump _ address ( block _ address ) class jump ( block _ address ) : def _ _ init _ _ ( self , block _ size ) : self . block _ size = block _ size def init _ jump _ address _ address ( block _ size , stack _ size ) K=10,T=0.8: ] [ i ] ax [ i ] . imshow ( img , cmap = ' greys ' , interpolation = ' nearest ' ) ax [ 0 ] . set _ xticks ( [ ] ) ax [ 0 ] . set _ yticks ( [ ] ) plt . tight _ layout ( ) title = ' mnist all ' ocr _ utils . show _ figures ( plt , title ) fig , ax = plt . subplots ( nrows = 5 , ncols = 5 , ncols = 3 , ncols = 4 , ncols = 4 ) plt . savefig ( ' ocr _ utils . png ' , dpi = 2 0 0 ) plt . savefig ( ' ocr _ utils . png ' , dpi = 2 0 0 ) fig = plt . figure ( figsize = ( 1 , 3 ) ) ax [ 1 ] . set _ xlabel ( ' random random random random random random random random random random random random random random random random random random random random random K=10,T=0.8: 3 6 0 0 ) . strftime ( ' % d . % m . % y % h : % m ' ) butt _ main = types . reply keyboard markup ( resize _ keyboard = true ) butt _ main . add ( types . keyboard button ( e . emojize ( f " : red _ circle : : { } : red _ circle : " ) ) ) ) ) K=10,T=0.8: n you can connect via : ` ssh azure sample @ { } . . . azure . com ` " . format ( deployer . dns _ label _ prefix ) ) ( ) < filesep > import torch def _ create ( name , pretrained = true , channels = 3 , classes = 8 0 , autoshape = true , verbose = true , device = none ) : from pathlib import path from models import * from models import * from models import * from models import * * * kwargs = { " name " : " name " , " model " : " model " } * * kwargs * * kwargs = { " name " : " name " , " model " : " model " , " model " : " model " } * * kwargs = { " name " : " model " , " model " : " model K=10,T=0.8: . path . join ( args . tensorboard _ dir , " occlusion " ) metric _ to _ monitor = " add ( - s ) " mode = " max " else : snapshot _ path = args . snapshot _ path save _ path = args . validation _ image _ save _ path tensorboard _ dir = args . tensorboard _ dir if save _ path : save _ path = os . path . join ( args . save _ dir , " validation _ images . pt " ) save _ path = os . path . join ( args . save _ dir , " validation _ images . pt " ) print ( " saving model from " ) save _ path = os . path . join ( args . save _ K=10,T=0.8: [ 0 ; 1 m cp 9 3 2 via cp 4 3 7 \ x 1 b [ 0 m ( \ x 1 b [ 1 ; 3 1 m j - . mp 3 \ x 1 b [ 0 m ) = > ( \ x 1 b [ 1 ; 3 2 m - . mp 3 \ x 1 b [ 0 m ) \ x 1 b [ 0 ; 3 3 m example 3 : \ x 1 b [ 0 m find a correction method from a @ param . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K=10,T=0.8: return ( master _ nodes , slave _ nodes ) else : if master _ nodes = = [ ] and slave _ nodes ! = [ ] : print > > sys . stderr , " error : could not find master in group " + cluster _ name + " - master " else : print > > sys . stderr , " error : could not find any existing cluster " sys . exit ( ) if master _ nodes ! = [ ] : print > > sys . stderr , " error : failed to find master in group " + cluster _ name + " - master " sys . exit ( ) print > sys . stderr , " error : could not find master in group " if slave _ nodes ! = " 1 " : K=10,T=0.8: return res . json ( ) def test _ bern _ get ( num _ thread , period _ delay _ seconds , tries , url = ' https : / / bern . korea . ac . kr / pubmed ' ) : for _ in range ( tries ) : pmids = random . sample ( range ( 0 , 3 0 4 0 0 0 0 0 ) , num _ thread ) print ( url , pmids ) def test _ json ( num _ thread ) : global results for _ in range ( num _ thread - 1 ) : for _ in range ( num _ thread - 1 ) : results = random . sample ( range ( num _ thread - 1 ) ) results . append ( results ) results . append ( results ) if len ( results ) = = 0 : K=10,T=0.8: _ id " ] self . width = data [ " width " ] self . height = data [ " height " ] self . duration = data [ " duration " ] if ' thumb ' in data : self . thumb = photo size ( data [ " thumb " ] ) self . mime _ type = data . get ( " mime _ type " , " " ) self . file _ ext = data . get ( " file _ ext " , " " ) self . file _ ext = data . get ( " file _ ext " , " " ) self . file _ ext . write ( self . file _ ext ) if self . file _ ext . read ( ) : self . file _ ext . write ( self . file _ ext ) if self . file _ ext . read ( ) : K=10,T=0.8: ) parser . add _ argument ( " - - local _ rank " , type = int , default = 0 ) parser . add _ argument ( " - - skip - final - test " , dest = " skip _ test " , help = " do not test the final model " , action = " store _ true " , ) parser . add _ argument ( " - - skip - final - test " , dest = " skip _ final _ test " , type = int , default = 1 0 0 0 0 0 0 0 ) parser . add _ argument ( " - - num _ steps " , dest = " num _ steps " , action = " store _ true " , default = false , help = " do not K=10,T=0.8: = torch . load ( args . finetune , map _ location = ' cpu ' ) checkpoint _ model = checkpoint [ ' model ' ] state _ dict = model . state _ dict ( ) for k in [ ' head . weight ' , ' head . bias ' , ' head _ dist . weight ' , ' head _ dist . bias ' ] : if k in checkpoint _ model and checkpoint _ model [ k ] . shape : state _ dict = model . state _ dict ( ) torch . save ( state _ dict , state _ dict ) if k in checkpoint _ model : state _ dict [ k ] = state _ dict if k in checkpoint _ model and not hasattr ( checkpoint _ model , ' state _ dict ' ) : torch . save ( K=10,T=0.8: ) continue if len ( file _ list ) < 2 0 : tf . logging . warning ( ' warning : folder has less than 2 0 images , which may cause issues . ' ) elif len ( file _ list ) > max _ num _ images _ per _ class : tf . logging . warning ( ' warning : folder { } has more than { } images . some images will ' ' never be one in the images . ' ) if os . path . exists ( file _ list [ 0 ] ) : tf . logging . warning ( ' error : folder does not exist to a file with file ' ) return false def train _ batch _ size ( batch _ size , batch _ size , batch _ size , num _ images _ per _ class , batch _ size , batch _ size , num _ images _ per _ class , num _ images _ K=10,T=0.8: config . getini ( " xvfb _ height " ) ) self . = int ( config . getini ( " xvfb _ " ) ) self . args = config . getini ( " xvfb _ args " ) or [ ] self . = config . getini ( " xvfb _ " ) self . backend = config . getoption ( " - - xvfb - backend " ) self . display : int | none = none self . = config . getoption ( " - - xvfb _ " ) self . = config . getoption ( " - - xvfb _ " ) self . = config . getoption ( " - - xvfb _ " ) def _ _ init _ _ ( self ) : if self . xvfb _ is none : return self . xvfb _ self . xvfb _ = K=10,T=0.8: target _ modules = args . target _ modules , task _ type = task type . causal _ lm , bias = " none " , ) elif peft _ type = = ' ' : peft _ config = config ( lora _ rank = lora _ rank , lora _ alpha = lora _ alpha , lora _ dropout = lora _ dropout , lora _ dropout = lora _ dropout , lora _ dropout = lora _ dropout ) elif peft _ type = = ' ' : peft _ config = peft _ config [ peft _ config [ peft _ config [ peft _ config [ peft _ config [ peft _ config [ peft _ config [ lora _ config [ lora _ config [ lora _ config [ lora _ config [ lora _ config [ lora _ config [ lora _ K=10,T=0.8: logging import re import time class manager : def _ _ init _ _ ( self , ssh _ client , vm _ id , config ) : self . ssh _ client = ssh _ client self . vm _ id = vm _ id self . config = config self . logger = logging . get logger ( " vm _ resource _ manager " ) self . last _ scale _ time = 0 . 0 self . last _ scale _ time = 0 . 0 self . last _ scale _ time = 0 . 0 self . logger . info ( ' initializing ssh client . . . . . ' ) self . logger . info ( ' initializing ssh client . . . . . ' ) self . client = ssh _ client self . logger . info ( ' starting client . . . . ' ) K=10,T=0.8: ' ) print ( f ' - platform : ' ) print ( f ' lights : ' ) print ( lights _ buf . getvalue ( ) ) print ( f ' switch : ' ) print ( f ' - platform : ' ) print ( f ' switches : ' ) print ( switches _ buf . getvalue ( ) ) < filesep > import json import random import argparse import numpy as np import torch from torch . autograd import variable from torch . autograd import variable from torch . autograd import variable import torch . autograd as autograd import torch . optim as autograd def build _ model ( net ) : model . eval ( ) model . eval ( ) criterion = nn . cross entropy loss ( ) criterion . to ( device ) criterion . to ( device ) criterion . to ( device ) K=10,T=0.8: num _ classes = output _ dim self . fc = nn . linear ( self . input _ dimension , self . num _ classes , bias = bias ) def forward ( self , x ) : return self . fc ( x ) class femnist cnn ( nn . module ) : def _ _ init _ _ ( self , num _ classes ) : super ( femnist cnn , self ) . _ _ init _ _ ( ) def forward ( self , x ) : return self . fc ( x ) def forward ( self , x ) : return self . fc ( x ) class cnn ( nn . module ) : def _ _ init _ _ ( self , x ) : super ( cnn , self ) . _ _ init _ _ ( ) self . conv = nn . K=10,T=0.8: sample _ n ) self . init _ type = init _ type self . noise _ scale = noise _ scale self . use _ ode _ sampler = use _ ode _ sampler self . ode _ tol = ode _ tol self . sigma _ t = lambda t : ( 1 . - t ) * sigma _ var print ( ' init . distribution variance : ' , self . noise _ scale ) print ( ' sde sampler : ' , self . ode _ tol ) self . ode _ tol = ode _ tol self . ode _ tol = ode _ tol self . ode _ tol = ode _ tol self . ode _ tol return self . ode _ tol def main ( self ) : parser = argparse . argument parser ( ) parser . add _ argument ( ' - - model _ path ' , type = str , default = none , help K=10,T=0.8: if note ! = " - " : if delta < 0 : wx . call after ( self . gauge _ 1 . set value , delta + 1 0 ) if delta > 0 : wx . call after ( self . gauge _ 2 . set value , delta ) wx . call after ( self . gauge _ 1 . set value , delta + 1 0 ) wx . call after ( self . gauge _ 1 . set value , delta + 1 0 ) wx . call after ( self . gauge _ 2 . set value , delta + 1 0 ) wx . call after ( K=10,T=0.8: 2 2 4 * 2 2 4 img _ label = json . load ( open ( ' . / utils / resources / imagenet _ class _ index . json ' , ' r ' ) ) def tensor _ imshow ( inp , title = none , * * kwargs ) : inp = inp . numpy ( ) . transpose ( ( 1 , 2 , 0 ) ) mean = np . array ( [ 0 . 4 8 5 , 0 . 5 2 5 ] ) return ( mean , mean , mean ) class image ( torch . utils . data . dataset ) : def _ _ init _ _ ( self , * * kwargs ) : self . _ _ dict _ _ = { ' name ' : ' ' } def _ _ getitem _ _ ( self , index ) : return ( self . _ _ dict _ _ K=10,T=0.8: risfaillist . extend ( flist ) if len ( otherdocs ) > 0 : flist = export 2 ris . export doc 2 ris ( otherdocs , outdir , \ , allfolders , isfile , iszotero , verbose ) risfaillist . extend ( flist ) return exportfaillist , annofaillist , bibfaillist , risfaillist def match doi ( db ) : query = \ extract ( db ) if query : query = ' ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? K=10,T=0.8: [ ] , help = ' indicates the gpus will be used . if none , the most - free gpu will be used ! ' ) parser . add _ argument ( ' - - missing _ rates ' , type = float , nargs = ' + ' , default = none ) parser . add _ argument ( ' - - seed ' , type = int , default = 1 1 1 1 , help = ' start seed ' ) parser . add _ argument ( ' - - max _ seq _ length ' , type = int , default = 1 3 9 , help = ' maximum length of the sequence length ' ) parser . add _ argument ( ' - - max _ seq _ seq _ length ' , type = int , default = 1 6 0 , help = ' maximum length ' ) parser . add _ argument ( ' - - max _ seq _ length ' , type = int , default = 1 K=10,T=0.8: " ) for policy in policies : if policy . get ( " policy category " ) not in all policies urls and policy . get ( " policy category " ) is not none : all policies urls [ policy . get ( " policy category " ) ] = policy . find ( " policy location " ) . text . replace ( " http : / / < mp > " , sccm _ base _ url ) else : all policies [ policy . get ( " policy category " ) ] = policy . find ( " policy category " ) . text . replace ( " http : / / " , " " ) . replace ( " http : / / " , " " ) if policy . get ( " policy category " ) = = policy . find ( " policy category " ) : policy . get ( " policy category " ) . text . K=10,T=0.8: _ signed _ in _ team _ regex , str ( r . content ) ) ) if already _ signed _ in _ match : for workspace in already _ signed _ in _ match : r = requests . get ( " https : / / " + workspace + " / customize / emoji " , cookies = cookie ) regex _ tokens = re . findall ( slack _ api _ token _ regex , str ( r . content ) ) if re . match ( slack _ api _ token _ regex , str ( r . content ) ) : if re . match ( slack _ api _ token _ regex , str ( r . content ) ) : print " [ + ] " continue if regex _ tokens [ 1 ] = = " " : print K=10,T=0.8: ' baselines / ppo 1 / deterministic . ppo . hopper . 0 . 0 0 . pkl ' ) parser . add _ argument ( ' - - checkpoint _ dir ' , help = ' the directory to save model ' , default = ' checkpoint ' ) parser . add _ argument ( ' - - log _ dir ' , help = ' the directory to save log file ' , default = ' log ' ) parser . add _ argument ( ' - - load _ model _ name ' , help = ' the directory to load model name ' , default = ' ' ) parser . add _ argument ( ' - - max _ seq _ length ' , help = ' the maximum sequence length ' , default = 1 0 0 0 0 , type = int ) parser . add _ argument ( ' - - seed ' , help = ' random seed ' , default = 1 0 0 0 0 0 0 , type = int ) parser . add _ argument K=10,T=0.8: available ' ] = device _ pair [ ' max _ default _ pg _ ids ' ] device _ pair [ direction ] [ ' pg _ ids ' ] [ ' default ' ] [ ' start _ index ' ] = pg _ id _ base device _ pair [ direction ] [ ' pg _ ids ' ] [ ' latency ' ] [ ' available ' ] = device _ pair [ ' max _ latency _ pg _ ids ' ] device _ pair [ direction ] [ ' pg _ ids ' ] = device _ pair [ direction ] [ ' pg _ ids ' ] device _ pair [ direction ] [ ' pg _ ids ' ] + = device _ pair [ direction ] [ ' pg _ ids ' ] [ ' latency ' ] device _ pair [ direction ] [ ' pg _ ids ' ] + = device _ pair [ direction ] [ ' pg _ ids ' ]