runs/!code-decoder-v31-mega-licensed-1/noop/ckpt/generated.txt

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