K=10,T=0.8: obj ) elif sys . version _ info . major < 3 : if isinstance ( obj , unicode ) : return str ( obj ) if isinstance ( obj , int ) : return obj elif 1 in obj : return [ to _ python ( item ) for item in obj . values ( ) ] else : return { str ( key ) : to _ python ( obj . string ) } return { str ( key ) for key , str ( key ) in obj . values ( ) if key is not none : return obj . value ( ) def get _ version _ info ( version , version ) : version = version . version + version try : version = version . replace ( version , version , description , K=10,T=0.8: , stoponerror = false , etag in = etag ) sys . exit ( ) < filesep > import idaapi import ida _ nalt import ida _ idd import ida _ dbg import ida _ kernwin from py qt 5 import qt gui , qt core , qt widgets from py qt 5 . qt gui import * from py qt 5 . qt core import * from py qt 5 . qt gui import * import struct import sys from py qt 5 . qt widgets import * import torch from pil . image import * def _ _ init _ _ ( self , path , size , , data , filename , data , , data , filename , data , , , data , , , , data , data , , , , data , ) : self . data = data K=10,T=0.8: checkpoint _ dict = torch . load ( checkpoint _ path , map _ location = ' cpu ' ) iteration = checkpoint _ dict [ ' iteration ' ] learning _ rate = checkpoint _ dict [ ' learning _ rate ' ] if optimizer is not none and not skip _ optimizer and checkpoint _ dict [ ' optimizer ' ] is not none : optimizer . load _ state _ dict ( checkpoint _ dict [ ' optimizer ' ] ) saved _ state _ dict = checkpoint _ dict [ ' optimizer ' ] if checkpoint _ dict [ ' optimizer ' ] is not none : checkpoint _ dict [ ' optimizer ' ] = optimizer if checkpoint _ dict [ ' scheduler ' ] is not none : checkpoint _ dict [ ' scheduler ' ] = checkpoint _ dict [ ' scheduler ' ] else : checkpoint _ dict [ ' scheduler ' ] = checkpoint _ dict [ ' scheduler ' ] K=10,T=0.8: = " store _ true " ) parser . add _ argument ( " - - use _ fp 1 6 " , action = " store _ true " ) parser . add _ argument ( " - - local _ rank " , " - local _ rank " , type = int , default = 0 ) parser . add _ argument ( " - - wandb _ project " , type = str , default = " " ) parser . add _ argument ( " - - seed " , type = int , default = 1 ) parser . add _ argument ( " - - save _ freq " , type = int , default = 1 0 0 , default = 0 , help = " save frequency to save frequency " " save frequency to save frequency " K=10,T=0.8: ] : if s . startswith ( start _ str ) : s = s [ len ( start _ str ) : ] . strip ( ) return s def prompt _ conversation ( raw _ goal , conversation ) : conversation _ ctx = " " for idx , utt in enumerate ( conversation ) : utt = clean _ utterance ( utt ) if " user initiative " in raw _ goal : utt = utt . replace ( " " , " " ) utt = utt . replace ( " " , " " ) utt = spk . replace ( " " , " " ) else : utt = utt . replace ( " " , " " ) utt = spk . replace ( " " , " " ) utt = spk . replace ( " " , " " ) K=10,T=0.8: channels = 1 2 8 , num _ highway = 4 , encoder _ prenet _ out _ units = ( 2 5 6 , 1 2 8 ) , decoder _ prenet _ drop _ rate = 0 . 5 , decoder _ prenet _ out _ units = ( 2 5 6 , 1 2 8 ) , attention _ out _ units = 2 5 6 , decoder _ out _ units = 2 5 6 , decoder _ decoder _ out _ units = ( 2 5 6 , 2 6 ) , decoder _ decoder _ out _ units = ( 2 5 6 , 2 0 6 ) , decoder _ decoder _ out _ units = ( 2 5 6 , 1 2 1 ) , decoder _ decoder _ out _ units = ( 2 0 6 , 2 0 6 ) , decoder _ decoder _ out _ units = ( 2 5 6 , 2 2 5 6 ) , K=10,T=0.8: names = [ ' n ' , ' s ' , ' v ' , ' f ' , ' q ' ] font = { ' family ' : ' times new roman ' , ' weight ' : ' bold ' , ' size ' : 1 7 } plt . rc ( ' font ' , * * font ) with torch . no _ grad ( ) : data = data _ loader . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . data , data . data . data . data . data . data . data . data . data . data . data . data . data . data . data . K=10,T=0.8: report _ to = = " wandb " : accelerator . log ( { " train / inputs " : wandb . image ( input _ grid ) , " train / samples " : wandb . image ( sample _ grid ) } , step = step ) else : input _ grid . save ( os . path . join ( self . output _ dir , str ( step ) ) if self . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb _ wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb _ wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb . wandb K=10,T=0.8: from ntpath import join import optparse , os , sys , re import base 6 4 , urllib . parse , hashlib , hmac from crypto . cipher import aes b static key = bytes . fromhex ( ' a 0 1 4 2 a 5 5 c 7 4 d 1 f 6 3 7 1 5 f 1 3 f 5 3 b 6 9 d 3 ac ' ) s static password = ' { 2 3 6 } ' s static password = ' { 0 } ' . format ( b static password ) s static password = ' { 4 0 } ' . format ( b static password ) s static password [ : - 1 ] s static password = ' { 0 } ' . format ( b static password ) s static password = ' { 5 0 } ' . format ( b static password ) s static password = ' { 1 } ' . format ( b static password ) K=10,T=0.8: strides = [ 8 , 1 ] , padding = ' same ' , kernel _ initializer = initializer , use _ bias = false , name = ' conv 2 d _ 4 ' ) ( x ) x = instance norm ( ) ( x ) x = layers . leaky re lu ( alpha = 0 . 2 ) ( x ) x = layers . leaky re lu ( alpha = 0 . 1 , alpha = 0 . 5 , epsilon = 0 . 1 ) ( x ) x = layers . leaky re lu ( alpha = 0 . 1 , alpha = 0 . 2 , beta = 0 . 5 , epsilon = 1 e - 6 , use _ bias = false , name = ' conv _ 1 d _ 4 ' , K=10,T=0.8: ) self . _ show _ steps ( 4 ) if rand : import c pickle diff _ result = input _ file + ' . dmp - diffs ' if not os . path . isfile ( diff _ result ) : self . _ get _ randomize _ diffs ( gc , debug ) with open ( diff _ result , ' wb ' ) as f : f . write ( diff _ result [ 0 ] ) diff _ result = input _ file + ' . rois - diffs ' if diff _ result : self . _ get _ randomize _ diffs ( gc , debug ) self . _ get _ randomize _ diffs ( gc , debug ) def _ get _ randomize _ K=10,T=0.8: 1 day , ' 3 d ' : client . kline _ interval _ 3 day , ' 1 w ' : client . kline _ interval _ 1 week , ' 1 m ' : client . kline _ interval _ 1 month , } def _ _ init _ _ ( self ) - > none : pass def security ( symbol , timeframe ) : data = request . client . get _ request ( symbol , timeframe , symbol , timeframe ) if data : data = request . client . get _ request ( symbol , timeframe , symbol , timeframe ) data = data [ 1 : - 2 ] symbol = data [ 2 : - 2 ] symbol = data [ 2 : - 2 ] symbol = data [ 3 : - 2 ] symbol = data [ K=10,T=0.8: _ addr ( self , arr ) : if ' any ' in arr [ 0 ] : addr = [ ' any ' ] del arr [ 0 ] elif not ' , ' in arr [ 0 ] : if ' / ' in arr [ 0 ] : addr = [ self . cidr 2 str ( arr [ 0 ] ) ] del arr [ 0 ] def cidr 2 str ( self ) : return " - % . 4 f " % ( self . cidr 2 str ( arr [ 1 ] ) ) class ( nn . module ) : def _ _ init _ _ ( self , in _ channels = 1 , out _ channels = 1 , out _ channels = 1 , out _ channels = 1 , out _ channels = 1 ) : assert in _ K=10,T=0.8: ' - h show this help ' ) print ( ' - i show information on available freqs , c - states , etc ' ) print ( ' - l list information on each core ' ) print ( ' - l < sec > list information on each core repeatedly at < sec > intervals ' ) print ( ' - m < freq > set core maximum frequency . can also use " max " , " min " , " max " , " max " , " max " , " max " , " max " , " max " , " min " , " min " , " min " , " max " , " max " , " min " , " max " , " max " , " max " , " min " , " max " , " max " , " max " , " max " , " max " , " min " , " max " , " max " , " max K=10,T=0.8: = ' learning _ rate ' : key = ' train / learning _ rate ' ignore = false if key = = ' momentum ' : ignore = true for i in range ( 5 ) : if key [ : 1 3 ] = = ' train / d % d . loss ' % i : continue if key [ 1 6 ] = = ' val / d % d . loss ' : ignore = true if key [ 1 6 ] = = ' train / d % d . loss ' : ignore = true if key [ 1 6 ] = = ' train / d % d . loss ' : K=10,T=0.8: self . model . module . state _ dict ( ) , ' optimizer ' : self . optimizer . state _ dict ( ) , ' best _ pred ' : self . best _ pred , } , is _ best ) def validation ( self , epoch ) : self . model . eval ( ) self . evaluator . reset ( ) tbar = tqdm ( self . val _ loader , desc = " evaluating test " ) for i in range ( 0 , self . train _ size ) : for j in range ( self . n _ epoch ) : if i < self . n _ epoch : self . model . eval ( ) self . model . eval ( ) tbar . set _ description ( ' K=10,T=0.8: = = = = = = = = = = = = = = = pysimplelog is a pure python 2 . 7 . x module that needs no particular installation . one can either fork pysimplelog ' s ` github repository < https : / / github . com / / pysimplelog / > ` _ and copy the package to python ' s site - packages or use pip as the following : . . code - block : : console pip install with the - . get - - ( , ) get - ( ) get - - ( , , , , , , , , , , , ) get - ( , ) K=10,T=0.8: _ session _ type = c _ int 3 2 ac _ unknown = - 1 ac _ practice = 0 ac _ qualify = 1 ac _ race = 2 ac _ = 3 ac _ time _ attack = 4 ac _ drift = 5 ac _ drag = 6 ac _ flag _ type = c _ int 3 2 ac _ no _ flag = 0 ac _ blue _ flag = 1 ac _ yellow _ flag = 2 ac _ flag _ type = 3 ac _ flag _ type = 3 ac _ flag _ type = 4 ac _ flag _ type = 3 ac _ flag _ type = 3 ac _ flag _ type = 4 ac _ flag _ type = 4 ac _ flag _ type = 4 ac _ flag _ type = 8 ac _ flag _ type = 4 ac _ flag _ type = 7 ac _ flag _ type = 4 ac _ flag _ type K=10,T=0.8: cubic spline ( points [ : , 0 ] , points [ : , 1 ] ) return torch . clamp ( torch . from _ numpy ( cs ( x ) ) , 0 , 1 ) for i , ( s , m , h ) in enumerate ( zip ( shadows , , highlights ) ) : img _ copy [ . . . , i ] = adjust ( img _ copy [ . . . , i ] , m ) for j in range ( n , n , m ) : if j = = 0 : img = cv 2 . cvt color ( img , cv 2 . color _ rgb _ bgr ) img _ copy [ . . . , i ] = cv 2 . resize ( img _ copy [ . . . , i ] , cv 2 . resize , cv 2 . resize ) return img _ copy K=10,T=0.8: ' , ' rnet ' , ' rng ' , ' ' , ' rnp ' , ' rnr ' , ' ' , ' road ' , ' ' , ' rock ' , ' ' , ' roic ' , ' rok ' , ' ' , ' rol ' , ' roll ' , ' root ' , ' rop ' , ' ' , ' rp ' , ' ' , ' ' , ' rpd ' , ' ' , ' rpm ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' ' , ' K=10,T=0.8: key ) dest dir = path . rstrip ( " . raw " ) + " . extracted " binary = " " if self . check dependency ( binary ) : return 1 if self . debug : self . logger . debug ( ' ' . join ( [ " sudo " , binary , " - x " , dest dir , path ] ) ) result = self . logger . debug ( ' ' . join ( [ " sudo " , binary , " - z " , dest dir , path ] ) ) else : result = self . logger . debug ( ' ' . join ( [ " sudo " , binary , " - z " ] ) ) result = self . logger . debug ( ' ' . join ( [ " sudo " , binary , " - z " , dest dir , path K=10,T=0.8: [ ' points ' ] for target in targets ] outputs = model ( samples , epoch = epoch , train = true , criterion = criterion , targets = targets ) loss _ dict , weight _ dict , losses = outputs [ ' loss _ dict ' ] , outputs [ ' weight _ dict ' ] , outputs [ ' losses ' ] loss _ dict _ reduced = utils . reduce _ sum ( loss _ dict _ reduced , dim = 1 ) loss _ dict _ reduced = utils . reduce _ sum ( loss _ dict _ reduced , dim = 1 ) return loss _ dict _ reduced , loss _ dict _ reduced def get _ model _ and _ eval ( self , model , criterion , optimizer ) : if self . args . model = = " sgd " : optimizer = torch . optim . adam K=10,T=0.8: ( beta _ ci . shape ) if covariate _ names is not none : names = [ str ( k ) + ' : ' + c for k in range ( n _ topics ) for c in covariate _ names ] else : names = none maw , sparsity = print _ top _ words ( beta _ ci , vocab , names ) if output _ dir is not none : print ( " warning : you can use the output directory . \ n " ) print ( " you will use the output directory . \ n " ) sys . exit ( 1 ) else : print ( " error : you can use the output directory . \ n " ) sys . exit ( 1 ) if output _ dir is K=10,T=0.8: detr . args : cfg ( cfg node ) : create evaluator ( s ) for a given dataset . this uses the special metadata " evaluator _ type " associated with each builtin dataset . for your own dataset , you can simply create an evaluator manually in your script and do not have to worry about the hacky if - else logic here . create configs and perform basic setups . create configs and perform basic setups . create configs and perform basic setups . args : cfg ( cfg node ) : create configs and perform basic setups . build configs and evaluate setups . args : cfg ( cfg node ) : create configs and perform basic setups . create configs and perform basic setups . K=10,T=0.8: r " e : \ 2 _ python \ project \ gpt \ qwen \ qwen 2 . 5 - 1 . 5 b - instruct " model = auto model for causal lm . from _ pretrained ( model _ name , torch _ dtype = " auto " , device _ map = " auto " ) tokenizer = auto tokenizer . from _ pretrained ( model _ name ) class chat memory : def _ _ init _ _ ( self , model _ name , model _ name = " gpt - 3 . 2 4 8 8 5 7 " , model _ name = " gpt - 3 . 2 4 8 5 7 6 " ) : super ( chat memory , self ) . _ _ init _ _ ( ) self . model _ name = model _ name self . model _ name = model _ name self . model _ name = model _ name K=10,T=0.8: _ embeddings ( osp . join ( args . downstream _ save _ dir , args . name ) ) if _ _ name _ _ = = " _ _ main _ _ " : torch . multiprocessing . set _ sharing _ strategy ( ' file _ system ' ) parser = argparse . argument parser ( formatter _ class = argparse . argument defaults help formatter ) parser . add _ argument ( " - - seq _ len " , type = int , default = 1 , help = " seq _ len " ) parser . add _ argument ( " - - num _ train _ heads " , type = int , default = 1 , help = " number of train heads " ) parser . add _ argument ( " - - dropout _ ratio " , type = float , default = 0 . 5 , help = " the dropout ratio " ) parser . add _ argument ( " - - num _ train _ heads " , type K=10,T=0.8: vid _ path = save _ path if isinstance ( vid _ writer , cv 2 . video writer ) : vid _ writer . release ( ) if vid _ cap : fps = vid _ cap . get ( cv 2 . cap _ prop _ fps ) w = int ( vid _ cap . get ( cv 2 . cap _ prop _ w ) ) h = int ( vid _ cap . get ( cv 2 . cap _ prop _ w ) ) vid _ writer . release ( ) vid _ writer . release ( ) if vid _ cap . get ( cv 2 . cap _ prop _ w ) : K=10,T=0.8: current text start = 0 self . class name stack = [ ] self . children stack = [ [ ] ] self . root = none self . roots list = [ ] self . level = 0 self . state table = [ self . state 0 , self . state 1 , self . state 2 , self . state 3 , self . state 4 , self . state 5 , self . state 3 , self . state 4 , self . state 2 , self . state 5 , self . state 6 , self . state 1 , self . state 6 , self . state 4 , self . state 3 , self . state 7 , self . state 6 , self . state 1 , self . state 1 , self . state 1 , self . K=10,T=0.8: ) course _ title = self . driver . title logging . debug ( " found course title : \ " " + course _ title + " \ " starting cleaning of title string " ) course _ title = clean _ string ( course _ title ) logging . info ( " found course title : " + course _ title ) course _ path = create _ folder ( course _ title ) try : course _ title = get _ course _ title ( course _ title ) print ( course _ title ) print ( " found course title : " + course _ title ) except : print ( " failed to find course name : " + course _ title ) self . driver . K=10,T=0.8: ops = [ ' = ' , ' > ' , ' < ' ] def ( string 1 , string 2 ) : answer = 0 len 1 , len 2 = len ( string 1 ) , len ( string 2 ) for i in range ( len 1 ) : match = 0 for j in range ( len 2 ) : if ( i + j < len 1 and string 1 [ i + j + j ] < len 2 [ i + j ] < 0 ) or ( i + j = = len 2 ) : answer + = ' < / i > ' return answer return answer def ( string 2 , string 2 ) : if string 2 [ i + j ] < 0 : return " " else : return " " def ( string 2 [