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

K=10,T=0.8: <data><newline> <tab> <tab> for j in xrange ( 0 , self . num _ filt ) : <newline> <tab> <tab> <tab> for i in xrange ( int ( bin [ j ] ) , int ( bin [ j + 1 ] ) ) : <newline> <tab> <tab> <tab> <tab> fbank [ j , i ] = ( i - bin [ j ] ) / ( bin [ j + 1 ] - bin [ j ] ) <newline> <tab> <tab> <tab> for i in xrange ( int ( bin [ j + 1 ] ) , int ( </data>i + 1 ) ) ) <newline> <tab> <tab> <tab> <tab> <tab> for i in range ( len ( i ) ) ) : <newline> <tab> <tab> <tab> <tab> <tab> if idx in idx < = = 1 : <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> i + i [ j ] = = np . array ( np . zeros ( 2 ) ) ) <newline> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> j + = len ( range 
K=10,T=0.8: <data><tab> <newline> <tab> <newline> <tab> <newline> <newline> <tab> def patch _ instr ( self , i : instr ) - > list [ instr ] : <newline> <tab> <tab> <newline> <tab> <tab> pass <tab> <tab> <newline> <newline> <tab> def patch _ instructions ( self , p : x 8 6 program ) - > x 8 6 program : <newline> <tab> <tab> <newline> <tab> <tab> pass <tab> <tab> <newline> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <newline> <tab> def prelude _ and _ conclusion ( self , p : x 8 6 program ) - > x 8 6 program </data>: <newline> <tab> <tab> <tab> <newline> <newline> <tab> <tab> <newline> <tab> <tab> if self . n : <newline> <tab> <tab> <tab> <tab> self . n = 1 6 <newline> <tab> <tab> <tab> self . n = self . n <newline> <tab> <tab> <tab> self . n = self . n <newline> <tab> <tab> <tab> <newline> <tab> <tab> self . n = n <newline> <tab> <tab> self . n = n <newline> <tab> <tab> <newline> <tab> <tab> <tab> self . n = n <newline> <tab> <tab> self . n , n = n <newline> <tab> <tab> self . n = n 
K=10,T=0.8: <data><newline> <newline> <tab> <tab> res = [ ] <newline> <tab> <tab> for k in topk : <newline> <tab> <tab> <tab> correct _ k = correct [ : k ] . view ( - 1 ) . float ( ) . sum ( 0 , keepdim = true ) <newline> <tab> <tab> <tab> res . append ( correct _ k . mul _ ( 1 0 0 . 0 / batch _ size ) ) <newline> <tab> return res <newline> <newline> class average meter ( object ) : <newline> <tab> <newline> <tab> def _ _ init _ _ ( self ) </data>: <newline> <tab> <tab> super ( ) . _ _ init _ ( ) <newline> <tab> <tab> self . _ _ init _ _ ( ) <newline> <tab> <tab> self . _ init _ _ ( ) <newline> <tab> <tab> self . _ init _ _ ( ) <newline> <tab> <tab> self . _ init _ _ ( ) <newline> <tab> <tab> self . _ init _ ( ) <newline> <tab> <tab> self . _ init _ _ ( ) <newline> <tab> <tab> self . _ init _ ( ) <newline> <tab> <tab> self . init _ _ ( ) 
K=10,T=0.8: <data>border , colors , margin , chart axis label , card , alignment , <newline> <tab> <tab> <tab> <tab> border _ radius , line chart data point , line chart data , border , chart grid lines , icons , <newline> <tab> <tab> <tab> <tab> chart axis , line chart , border side , text theme style , bar chart group , bar chart , bar chart rod , icon , <newline> <tab> <tab> <tab> <tab> text span , pie chart , pie chart section , text style , font weight , pie chart event , <newline> <tab> <tab> <tab> </data><tab> color chart label , color color label , chart , chart text , chart , chart , row column , chart label , column chart column , column box label box , box , chart , chart line , column chart , chart chart chart chart , row chart chart , chart chart chart , chart chart label , chart chart column box , chart box chart , chart column chart , row row box , columns , row , column row chart chart chart , chart box chart label , column box , row label frame label , 
K=10,T=0.8: <data><tab> <tab> <tab> <tab> for wi in text : <newline> <tab> <tab> <tab> <tab> <tab> wi = wi . item ( ) <newline> <tab> <tab> <tab> <tab> <tab> if wi = = 0 : <newline> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> break <newline> <tab> <tab> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> <tab> <tab> full _ text + = train _ ds . text _ processor . <UNK> [ wi ] + ' ' <newline> <tab> <tab> <tab> <tab> full _ text + = ' \ n ' <newline> <tab> </data><tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> full _ text + = ' \ n ' <newline> <tab> <tab> <tab> full _ text + = ' \ n ' <newline> <tab> <tab> except : <newline> <tab> <tab> <tab> <tab> full _ text + = ' \ n ' <newline> <tab> <tab> <tab> full _ text + = ' \ n ' <newline> <tab> <tab> <tab> full _ text + = ' ' <newline> <tab> <tab> full _ text + = ' \ n ' <newline> <tab> <tab> full _ text + = 
K=10,T=0.8: <data>neu 1 ) <newline> <tab> <tab> <tab> <tab> sims 2 = np . empty ( model 2 . sents _ len , dtype = real ) <newline> <tab> <tab> <tab> <tab> nearest _ sent _ fast ( model 2 , sent _ vec 2 , 0 , sims 2 ) <newline> <tab> <tab> <tab> <tab> sims 1 + = sims 2 <newline> <tab> <tab> <tab> <tab> neighbors = np . argsort ( sims 1 ) [ : : - 1 ] <newline> <tab> <tab> <tab> <tab> cat _ ids = { } <newline> <tab> <tab> <tab> <tab> nearest = [ </data>] <newline> <tab> <tab> <tab> <tab> if neighbors is none : <newline> <tab> <tab> <tab> <tab> <tab> <tab> neighbors = neighbors [ neighbors ] <newline> <tab> <tab> <tab> <tab> <tab> neighbors + = neighbors [ neighbors ] <newline> <tab> <tab> <tab> <tab> <tab> neighbors = neighbors [ neighbors ] <newline> <tab> <tab> <tab> <tab> neighbors = neighbors [ neighbors ] <newline> <tab> <tab> <tab> <tab> neighbors = neighbors . neighbors . neighbors ( neighbors ) <newline> <tab> <tab> <tab> <tab> neighbors [ neighbors ] = neighbors [ neighbors ] <newline> <tab> <tab> <tab> <tab> neighbors + = neighbors [ neighbors 
K=10,T=0.8: <data>path = ' / ' <newline> <tab> <tab> full _ key _ name = os . path . join ( path , key _ name ) <newline> <tab> <tab> k = bucket . new _ key ( full _ key _ name ) <newline> <tab> <tab> k . set _ contents _ from _ filename ( key _ name ) <newline> <tab> <tab> hello _ key = bucket . get _ key ( ' poc . txt ' ) <newline> <tab> <tab> hello _ key . set _ <UNK> _ acl ( ' public - read ' ) <newline> <tab> </data><tab> c = bucket . new _ key ( full _ key _ name ) <newline> <tab> <tab> c = c . new _ key ( full _ key _ name ) <newline> <tab> <tab> c = c . new _ key ( full _ key _ name ) <newline> <tab> <tab> c . update _ <UNK> ( c , c ) <newline> <newline> <tab> <tab> if c = = ' ' : <newline> <tab> <tab> <tab> c = c . new _ key ( full _ key _ name + ' / ' + c . group ( ) 
K=10,T=0.8: <data><tab> losses = average meter ( ) <newline> <newline> <tab> <newline> <tab> model . train ( ) <newline> <tab> end = time . time ( ) <newline> <tab> mini _ batch _ size _ v = args . batch _ size <newline> <tab> batch _ size _ v = 2 <newline> <newline> <tab> for ww , data in enumerate ( train _ loader , 0 ) : <newline> <tab> <tab> <newline> <tab> <tab> inputs _ on , inputs _ off , former _ gray , latter _ gray = data <newline> <newline> <tab> <tab> if torch . sum ( inputs </data>_ on , labels _ off , 1 ) in inputs _ on : <newline> <tab> <tab> <tab> inputs _ on , inputs _ off , inputs _ off , label , labels _ off , label , label , labels , label , label ) <newline> <tab> <tab> outputs _ on , inputs _ off , inputs _ off = inputs _ on . batch _ size _ v <newline> <tab> <tab> inputs _ off = inputs _ off . batch _ size _ v <newline> <tab> <tab> inputs _ off = inputs _ off . batch _ size 
K=10,T=0.8: <data><tab> print ( ' [ green - initialized input ] ' ) <newline> <newline> <tab> <newline> <tab> skating _ list = { } <newline> <tab> acc _ list = { } <newline> <tab> acc _ error _ list = { } <newline> <tab> ground _ pene _ dist _ list = { } <newline> <tab> ground _ pene _ freq _ list = { } <newline> <tab> gmpjpe _ list = { } <newline> <tab> mpjpe _ list = { } <newline> <tab> mpjpe _ list _ vis = { } <newline> <tab> mpjpe _ list _ occ = { </data>} <newline> <tab> mpjpe _ list _ vis = { } <newline> <tab> mpjpe _ list _ vis = { } <newline> <tab> for i , ( batch _ idx , batch _ idx ) in enumerate ( data _ loader ) : <newline> <tab> <tab> <tab> if len ( data _ loader ) = = 0 : <newline> <tab> <tab> <tab> <tab> <tab> print ( ' [ bold - initialized input ] ' ) <newline> <tab> <tab> <tab> <tab> <tab> print ( ' [ bold - initialized input ] ' ) <newline> <tab> <tab> <tab> <tab> print ( ' 
K=10,T=0.8: <data><tab> <tab> self . self . query = prune _ linear _ layer ( self . self . query , index ) <newline> <tab> <tab> self . self . key = prune _ linear _ layer ( self . self . key , index ) <newline> <tab> <tab> self . self . value = prune _ linear _ layer ( self . self . value , index ) <newline> <tab> <tab> self . output . dense = prune _ linear _ layer ( self . output . dense , index , dim = 1 ) <newline> <newline> <tab> <tab> <newline> </data><tab> <newline> <tab> def get _ linear _ layer ( self , layer _ id ) : <newline> <tab> <tab> <newline> <tab> <tab> if layer _ id in [ layer _ id ] : <newline> <tab> <tab> <tab> layer _ id = layer _ id <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> layer _ id = layer _ id <newline> <tab> <tab> <tab> self . layer _ id = layer _ id <newline> <tab> <tab> <tab> layer _ id = layer _ id <newline> <tab> <tab> <tab> self . layer _ id = layer _ id <newline> <tab> 
K=10,T=0.8: <data>" stdout was % s " % stdout ) <newline> <tab> <tab> return none , p . returncode <newline> <tab> return stdout , p . returncode <newline> <newline> <newline> long _ version _ py [ <newline> <tab> " git " <newline> ] = <newline> <newline> <newline> @ register _ vcs _ handler ( " git " , " get _ keywords " ) <newline> def git _ get _ keywords ( versionfile _ abs ) : <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> keywords = { } <newline> <tab> try : <newline> <tab> <tab> f </data>= open ( versionfile _ abs ) . readlines ( ) <newline> <tab> <tab> f . close ( ) <newline> <tab> except : <newline> <tab> <tab> pass <newline> <tab> <tab> <newline> <newline> <tab> <newline> <tab> try : <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> 
K=10,T=0.8: <data>def _ setup _ template _ environment ( session : nox . session ) - > none : <newline> <tab> session . install ( " wheel " , " pip - tools " ) <newline> <tab> _ update _ pip _ packages ( session ) <newline> <tab> _ install _ bundle ( session ) <newline> <newline> <newline> @ nox . session ( python = " 3 . 9 " ) <newline> def install _ bundled _ libs ( session ) : <newline> <tab> <newline> <tab> session . install ( " wheel " ) <newline> <tab> _ install _ bundle ( </data>session ) <newline> <newline> <newline> @ nox . session ( python = " 3 . 3 . 7 " ) <newline> def install _ bundle ( session ) : <newline> <tab> <newline> <tab> <newline> <tab> _ update _ python _ packages ( session ) <newline> <tab> return _ install _ bundle ( session ) <newline> <newline> def install _ bundle ( session ) : <newline> <tab> <newline> <tab> <newline> <tab> _ install _ bundle ( session ) <newline> <tab> try : <newline> <tab> <tab> <newline> <tab> <tab> if not _ install _ bundle ( session ) : <newline> <tab> <tab> 
K=10,T=0.8: <data>( <newline> <tab> <tab> <tab> <tab> <tab> name = ' left _ ear ' , <newline> <tab> <tab> <tab> <tab> <tab> id = 3 , <newline> <tab> <tab> <tab> <tab> <tab> color = [ 5 1 , 1 5 3 , 2 5 5 ] , <newline> <tab> <tab> <tab> <tab> <tab> type = ' upper ' , <newline> <tab> <tab> <tab> <tab> <tab> swap = ' right _ ear ' ) , <newline> <tab> <tab> <tab> <tab> 4 : <newline> <tab> <tab> <tab> <tab> dict ( <newline> <tab> <tab> <tab> <tab> <tab> name = ' right _ ear ' </data>, <newline> <tab> <tab> <tab> <tab> <tab> name = ' right _ ear ' ) , <newline> <tab> <tab> <tab> <tab> <tab> name = ' left _ ear ' , <newline> <tab> <tab> <tab> <tab> swap = ' right _ ear ' ) , <newline> <tab> <tab> <tab> <tab> 4 : <newline> <tab> <tab> <tab> <tab> dict ( <newline> <tab> <tab> <tab> <tab> <tab> name = ' left _ ear ' , <newline> <tab> <tab> <tab> <tab> size = 3 , <newline> <tab> <tab> <tab> <tab> swap = ' right _ ear ' ) , <newline> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data>svg ( path , canvas _ width , canvas _ height ) <newline> <tab> _ get _ path _ point = np . vectorize ( lambda x : path . point ( x ) ) <newline> <newline> <tab> def _ func ( x , n ) : <newline> <tab> <tab> return np . exp ( - n * 2 * np . pi * x * 1 j ) * _ get _ path _ point ( x ) <newline> <newline> <tab> result = [ { ' speed ' : 0 , ' c ' : complex _ integrate ( </data>x , n ) } for x in result ] <newline> <tab> for x in result : <newline> <tab> <tab> result [ ' speed ' ] = np . exp ( - n * 2 * np . pi * x * 2 * np . pi * x * x * 2 * np . pi * x * 2 * np . pi ) <newline> <tab> result [ ' speed ' ] = np . exp ( - n * 2 * np . pi * x * 2 * np . pi ) <newline> <tab> result [ 
K=10,T=0.8: <data>4 : <newline> <tab> <tab> gts = gts [ : , 0 ] <newline> <tab> gts [ gts > 0 . 5 ] = 1 <newline> <tab> gts [ gts < 1 ] = 0 <newline> <tab> gts = gts . astype ( int ) <newline> <tab> assert isinstance ( preds , ndarray ) , " type ( preds ) must be ndarray " <newline> <tab> assert isinstance ( gts , ndarray ) , " type ( gts ) must be ndarray " <newline> <tab> assert preds . ndim = = 3 , " preds . ndim must be 3 </data>" <newline> <newline> <tab> if isinstance ( preds , list ) : <newline> <tab> <tab> gts = gts [ : , 0 ] <newline> <tab> else : <newline> <tab> <tab> gts = gts [ : , 1 ] <newline> <tab> return _ gts <newline> <newline> <newline> def load _ checkpoint ( checkpoint _ path , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> ) : <newline> <tab> checkpoint _ path = checkpoint _ path + " / checkpoint . pth " <newline> <tab> with open ( checkpoint _ path , " wb " ) as fp : <newline> 
K=10,T=0.8: <data>. ' <newline> <tab> <tab> exit ( 3 ) <newline> <newline> <tab> entries = json _ feed . get ( ' entry ' , [ ] ) <newline> <tab> download _ all ( entries , zip dir , unzip dir ) <newline> <tab> <newline> <tab> for dir in os . listdir ( unzip dir ) : <newline> <tab> <tab> if dir [ len ( dir ) - 5 : ] = = ' . safe ' : <newline> <tab> <tab> <tab> dir = dir + ' / measurement / ' <newline> <tab> <tab> <tab> dir = unzip dir + dir </data>[ len ( dir ) : ] <newline> <tab> <tab> <tab> file _ path = dir + ' / measurement / ' <newline> <tab> <tab> <tab> file _ path = dir + ' / measurement / ' <newline> <tab> <tab> <tab> file _ path = dir + ' / measurement / ' <newline> <tab> <tab> <tab> file _ path = dir + ' / measurement / ' <newline> <tab> <tab> <tab> file _ path = dir + ' / measurement / ' <newline> <tab> <tab> <tab> for file in files : <newline> <tab> <tab> <tab> <tab> file _ path + 
K=10,T=0.8: <data>reset ( ) <newline> <tab> cal mean iou _ vox _ occupied _ agnostic _ unique . reset ( ) <newline> <tab> cal mean iou _ pts _ agnostic . reset ( ) <newline> <tab> cal mean iou _ vox _ all _ agnostic . reset ( ) <newline> <tab> if clip _ features : <newline> <tab> <tab> cal mean iou _ pts _ clip . reset ( ) <newline> <tab> <tab> cal mean iou _ pts _ clip _ ignore 0 . reset ( ) <newline> <tab> <tab> cal mean iou _ pts _ clip _ visible . reset </data>( ) <newline> <tab> <tab> cal mean iou _ pts _ clip _ visible . reset ( ) <newline> <tab> <tab> cal mean iou _ pts _ clip _ visible . reset ( ) <newline> <tab> <tab> cal mean iou _ pts _ clip _ visible . reset ( ) <newline> <tab> <tab> cal mean iou _ pts _ clip _ visible . reset ( ) <newline> <tab> <tab> cal mean iou _ pts _ clip _ visible . reset ( ) <newline> <newline> <tab> <tab> cal mean iou _ pts _ clip _ visible . reset ( ) <newline> 
K=10,T=0.8: <data><tab> <tab> ' boxes ' : resize _ transform . apply _ boxes _ torch ( torch . from _ numpy ( np . array ( bboxes ) ) , image . shape [ : 2 ] ) . to ( " cuda " ) , <newline> <tab> <tab> <tab> ' original _ size ' : image . shape [ : 2 ] <newline> <newline> <newline> <tab> <tab> } ] <newline> <tab> <tab> <newline> <tab> <tab> preds = sam ( batched _ input , multimask _ output = false ) <newline> <tab> <tab> binary _ masks = torch . sigmoid </data>( binary _ masks ) <newline> <tab> <tab> return binary _ masks , binary _ masks , binary _ masks <newline> <newline> <tab> <newline> <tab> @ staticmethod <newline> <tab> def get _ model _ args _ from _ config ( args , args , args ) : <newline> <tab> <tab> if args . model _ args _ from _ config is none : <newline> <tab> <tab> <tab> raise not implemented error ( " the model not implemented . please specify a model . " ) <newline> <tab> <tab> return <newline> <tab> <tab> return <newline> <newline> <newline> <tab> @ staticmethod <newline> 
K=10,T=0.8: <data>' } <newline> <tab> fields [ ' public ' ] [ ' experience ' ] [ ' img ' ] = { ' selector ' : ' img ' , ' type ' : ' attr ' , ' attr ' : ' src ' } <newline> <tab> fields [ ' public ' ] [ ' experience ' ] [ ' company ' ] = { ' selector ' : ' . item - subtitle a ' , ' type ' : ' text ' } <newline> <tab> fields [ ' public ' ] [ ' experience ' ] [ </data>' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ 
K=10,T=0.8: <data>in f _ in : <newline> <tab> <tab> <tab> <tab> line : str = line . strip ( ) <newline> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> if ( len ( line ) = = 0 ) or ( line [ 0 ] = = ' <newline> <tab> <tab> <tab> <tab> <tab> continue <newline> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> name , ra _ str , dec _ str = line . split ( ) [ : 3 ] <newline> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> if name in </data>[ ' a ' , ' b ' , ' b ' , ' b ' , ' b ' , ' b ' ] : <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> c , c , c = line . split ( ) [ 1 ] , line . split ( ) [ 2 ] <newline> <tab> <tab> <tab> <tab> <tab> c , r , c , c , c , c = line . split ( ) [ 0 ] <newline> 
K=10,T=0.8: <data>< 6 4 : <newline> <tab> <tab> <tab> <tab> channel . reply ( " invalid steam id 6 4 . " ) <newline> <tab> <tab> <tab> <tab> return <newline> <tab> <tab> except value error : <newline> <tab> <tab> <tab> channel . reply ( " <UNK> steam id 6 4 . " ) <newline> <tab> <tab> <tab> return <newline> <newline> <tab> <tab> p = self . player ( steam _ id ) <newline> <tab> <tab> if p : <newline> <tab> <tab> <tab> channel . reply ( " that would be { } ^ 7 , who is currently on this very </data><UNK> " . format ( p , p ) ) <newline> <tab> <tab> <tab> if s : <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> if not channel . reply ( " please enter steam id 6 4 . " ) : <newline> <tab> <tab> <tab> <tab> <tab> await self . player ( steam _ id ) <newline> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> await self . player ( steam _ id ) <newline> <tab> <tab> <tab> <tab> return <newline> <newline> <tab> <tab> if 
K=10,T=0.8: <data><newline> <tab> <tab> v 2 = e . get _ latest ( ) <newline> <tab> <tab> assert v 2 = = none <newline> <newline> <tab> <newline> <tab> <newline> <tab> def test _ tweet _ diff ( self ) : <newline> <tab> <tab> e = self . entry <newline> <tab> <tab> v 1 = e . versions [ 0 ] <newline> <newline> <tab> <tab> <newline> <tab> <tab> v 1 . summary = v 1 . summary [ 0 : - 2 0 ] <newline> <tab> <tab> v 1 . save ( ) <newline> <newline> <tab> <tab> v 2 = e . </data>version _ from _ numpy ( v 1 . astype ( ' int 3 2 ' ) ) <newline> <tab> <tab> v 2 . save ( ) <newline> <tab> <tab> v 2 . save ( ) <newline> <newline> <tab> def test _ <UNK> ( self ) : <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> self . entry = self . entry <newline> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> self . entry = self . entry <newline> <tab> <tab> self . entry = self 
K=10,T=0.8: <data>[ syscall recovery type . jumper , syscall recovery type . jumper _ randomized ] : <newline> <tab> <tab> <tab> <tab> code + = ' \ n \ t \ t " jmp edi \ \ n " ' <newline> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> code + = ' \ n \ t \ t " <UNK> \ \ n " ' <newline> <tab> <tab> <tab> code + = ' \ n \ t \ t " ret \ \ n " ' <newline> <newline> <tab> <tab> if self . arch = = arch . </data>jumper : <newline> <tab> <tab> <tab> code + = ' \ n \ t \ t " ret \ n \ n " ' <newline> <tab> <tab> <tab> code + = ' \ n \ t " ret \ n " ret \ n \ t " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret 
K=10,T=0.8: <data>genesets in all _ tcrs [ em ] [ t ] : <newline> <tab> <tab> <tab> <tab> <tab> <tab> score = float ( t _ l [ <UNK> ] ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> if none _ score _ for _ averaging = = none or abs ( score - none _ score _ for _ averaging ) > 1 e - 3 : <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> scores . append ( score ) <newline> <tab> <tab> <tab> <tab> if none _ score _ for _ averaging = = none : <newline> <tab> </data><tab> <tab> <tab> <tab> <tab> scores . append ( score ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> scores . append ( score ) <newline> <tab> <tab> <tab> <tab> scores [ em ] = scores <newline> <tab> <tab> <tab> <tab> scores [ em ] = scores <newline> <tab> <tab> <tab> <tab> scores . append ( score ) <newline> <tab> <tab> <tab> <tab> scores . append ( scores ) <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> scores . append ( score ) <newline> <tab> <tab> scores . append ( score ) <newline> <newline> <tab> <tab> self . _ _ 
K=10,T=0.8: <data>( word ) <newline> <tab> i = 0 <newline> <tab> for mask in mask _ items [ 1 : ] : <newline> <tab> <tab> if len ( word _ chars ) < i + 1 : <newline> <tab> <tab> <tab> break <newline> <tab> <tab> else : <tab> <newline> <tab> <tab> <tab> if mask = = ' l ' : <newline> <tab> <tab> <tab> <tab> word _ chars [ i ] = word _ chars [ i ] . lower ( ) <newline> <tab> <tab> <tab> elif mask = = ' u ' : <newline> <tab> <tab> <tab> <tab> word _ </data>chars [ i ] = word _ chars [ i ] . lower ( ) <newline> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> word _ chars [ i ] = word _ chars [ i ] . upper ( ) <newline> <tab> return word _ chars <newline> <newline> def get _ word _ words ( word _ chars ) : <newline> <tab> word _ chars [ i ] = word _ chars [ i ] . lower ( ) <newline> <tab> if word _ chars [ i ] . lower ( ) = = ' ' : 
K=10,T=0.8: <data>[ 2 ] <newline> <newline> <tab> <newline> <tab> apply _ old _ config _ values ( ) <newline> <tab> <newline> <tab> <newline> <tab> delete _ deprecated _ files ( ) <newline> <newline> <tab> <newline> <tab> check _ latest _ release ( update _ version = version _ string , <UNK> = ' update ' ) <newline> <newline> <tab> <newline> <tab> config . read ( config _ path ) <newline> else : <newline> <tab> <newline> <tab> check _ latest _ release ( current _ version = config . get ( ' other ' , ' version ' ) , <UNK> = </data>' update ' ) <newline> <newline> <tab> <newline> <tab> <newline> <newline> <tab> if config . get ( ' old ' , ' version ' ) . lower ( ) = = 1 : <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> update _ version ( update _ version = version _ string , <UNK> = ' update ' ) <newline> <tab> <tab> <newline> <tab> <tab> update _ version ( update _ version = version _ string , <UNK> = ' update ' ) <newline> <newline> <newline> <newline> 
K=10,T=0.8: <data>{ : 0 > 2 d } _ { : 0 > 2 d } . txt ' . format ( args . factor , img _ inds [ 0 ] , img _ inds [ 1 ] , img _ inds [ 2 ] ) <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> sift _ correspondences = load _ sift _ correspondences ( sift _ file ) <newline> <tab> <tab> <tab> <tab> sift _ correspondences = np . array ( sift _ correspondences ) <newline> <tab> <tab> <tab> <tab> if len ( sift _ correspondences ) </data>< 3 1 : <newline> <tab> <tab> <tab> <tab> <tab> sift _ correspondences = torch . tensor ( sift _ correspondences ) . to ( device ) <newline> <tab> <tab> <tab> <tab> <tab> sift _ correspondences = torch . tensor ( sift _ correspondences ) . to ( device ) <newline> <tab> <tab> <tab> <tab> <tab> sift _ correspondences = sift _ correspondences <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> sift _ correspondences = sift _ correspondences . reshape ( sift _ correspondences ) <newline> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data>_ dict = { " lm _ loss " : loss } <newline> <tab> <tab> loss _ dict _ reduced = dist . reduce _ dict ( loss _ dict ) <newline> <tab> <tab> loss _ reduced = sum ( loss _ dict _ reduced . values ( ) ) <newline> <tab> <tab> loss _ value = loss _ reduced . item ( ) <newline> <newline> <tab> <tab> metric _ logger . update ( <newline> <tab> <tab> <tab> loss = loss _ value , <newline> <tab> <tab> <tab> * * loss _ dict _ reduced , <newline> <tab> <tab> ) </data><newline> <newline> <tab> <tab> if ' train ' in args . dataset _ name : <newline> <tab> <tab> <tab> train _ loss _ value = torch . tensor ( loss _ dict _ reduced , dtype = torch . bfloat 1 6 ) <newline> <tab> <tab> <tab> train _ loss _ value = torch . tensor ( loss _ value , dtype = torch . int 3 2 ) <newline> <newline> <tab> <tab> <tab> if args . mode _ lower = = ' valid ' : <newline> <tab> <tab> <tab> <tab> train _ loss _ value = torch . tensor 
K=10,T=0.8: <data>' , help = ' path to datas ' ) <newline> <newline> <newline> group . add _ argument ( ' - - task ' , default = ' pose ' , type = str , choices = [ ' traj ' , ' pose ' ] ) <newline> group . add _ argument ( " - - clip _ len " , default = 1 4 5 , type = int , help = " sequence length for each clip " ) <newline> <newline> group . add _ argument ( ' - - load _ pretrained _ model ' , </data>default = none , type = str , help = ' path to data loading model ' ) <newline> group . add _ argument ( ' - - resume _ from _ checkpoint ' , default = none , type = str , help = ' resume checkpoint from checkpoint ' ) <newline> <newline> group . add _ argument ( ' - - save _ model ' , default = none , type = str , help = ' path to checkpoint ' ) <newline> group . add _ argument ( ' - - resume _ from _ checkpoint ' 
K=10,T=0.8: <data><newline> <tab> <tab> try : <newline> <tab> <tab> <tab> key = winreg . open key ( winreg . hkey _ current _ user , r " software \ microsoft \ windows \ <UNK> " ) <newline> <tab> <tab> <tab> value , type _ = winreg . query value ex ( key , " accent color " ) <newline> <tab> <tab> <tab> winreg . close key ( key ) <newline> <tab> <tab> <tab> if type _ = = winreg . reg _ dword : <newline> <tab> <tab> <tab> <tab> r = value % 2 5 6 <newline> <tab> <tab> <tab> <tab> </data>winreg . close key ( key ) <newline> <tab> <tab> <tab> winreg . close key ( key ) <newline> <tab> <tab> <tab> <tab> winreg . close key ( key ) <newline> <tab> <tab> <tab> <tab> key = winreg . open key ( key ) <newline> <tab> <tab> <tab> <tab> value = winreg . open key ( value ) <newline> <tab> <tab> <tab> <tab> key = winreg . open key ( key ) <newline> <tab> <tab> <tab> <tab> winreg . write key ( key ) <newline> <tab> <tab> except key error : <newline> <tab> <tab> <tab> pass <newline> <tab> <tab> return