| K=10,T=0.8: <data>} ' " ) config . model . inner _ params [ pidx ] = new _ p return model def get _ tokenizer ( config ) : tok _ name = config . model . tokenizer _ name if config . model . tokenizer _ name is not none else config . model . name return getattr ( transformers , config . model . tokenizer _ class ) . from _ pretrained ( tok _ name , cache _ dir = scr ( ) ) if _ _ name _ _ = = ' _ _ main _ _ </data>' : print ( " [ + ] " . . . " ) print ( " [ + ] . " . . " ) print ( " [ + + ] loading { } : { } . . . . . " . format ( config . model . model . model _ scope , cache _ dir = cache _ dir , model _ dir = output _ dir ) ) print ( " [ + ] { } . . . . . " . format ( config . model . model _ scope , | |
| K=10,T=0.8: <data>( input ( f " \ n { fore . white } [ { fore . red } console { fore . white } ] how many people are in this shortcut : " ) ) break except value error : print ( f " \ n { fore . white } [ { fore . red } console { fore . white } ] there was an error with that input , please try again : ) " ) self . print _ console ( " slow pc " , " 1 " ) self . print _ console </data>( " slow pc " , " 1 " ) self . print _ console ( " no pc " , " 1 " ) self . print _ console ( " no pc " , " 1 " ) self . print _ console ( " > " ) def _ get _ output ( self ) : input _ ids = self . input _ ids . get _ output _ ids ( ) . get _ output _ ids ( ) . get _ input _ ids ( ) . get _ output _ ids ( ) | |
| K=10,T=0.8: <data>4 3 afb 5 0 4 5 f 9 1 a 9 0 ed 8 6 c 9 3 0 0 1 5 af 9 7 df 4 6 6 5 9 cd 3 6 5 5 f 4 3 3 7 1 c 0 8 af 5 a 6 5 a 5 e 4 5 d 9 <UNK> 9 6 2 7 5 7 c 7 c 0 c 4 2 6 fab 4 db 5 fe 5 4 cda 5 9 0 2 4 db 9 <UNK> 0 a 8 4 a 2 8 <UNK> 1 6 9 </data>5 7 0 0 0 4 8 5 8 2 3 7 7 5 7 0 8 7 1 7 4 2 9 4 8 3 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | |
| K=10,T=0.8: <data>. add ( [ x , h ] ) h = inputs = keras . input ( shape = input _ shape ) h = tf . pad ( h , [ [ 0 , 0 ] , [ 3 , 3 ] , [ 3 , 3 ] , [ 0 , 0 ] ] , mode = ' reflect ' ) h = keras . layers . conv 2 d ( dim , 7 , padding = ' valid ' , use _ bias = false ) ( h ) h = norm ( ) ( h ) </data>h = norm ( h ) ( w ) w = activation ( ' relu ' ) ( w ) h = flatten ( ) ( h ) w = flatten ( ) ( h ) w = flatten ( ) ( w ) w = flatten ( ) ( w ) w = flatten ( ) ( w ) w = flatten ( ) ( w ) w = flatten ( ) ( w ) w = flatten ( ) ( w ) w = flatten ( ) w = flatten ( ) ( w ) w = flatten | |
| K=10,T=0.8: <data>= name : scene . vseqf . zoom _ presets . move ( index , len ( scene . vseqf . zoom _ presets ) - 1 ) return { ' finished ' } preset = scene . vseqf . zoom _ presets . add ( ) preset . name = name preset . left = left preset . right = right preset . bottom = bottom preset . top = top return { ' finished ' } class vseqfquick <UNK> ( bpy . types . operator ) : bl _ idname = ' vseqf . quickzooms ' bl _ </data>label = ' zoom fade ' bl _ separator = ' bl ' bl _ options = { ' finished ' } category = ' <UNK> ' bl _ options = { ' finished ' } category = ' <UNK> ' bl _ options = { ' finished ' } category = ' <UNK> ' bl _ options = { ' finished ' } category = ' <UNK> ' bl _ options = { ' finished ' } category = ' <UNK> ' bl _ label = ' zoom ' bl _ options = { ' finished ' } category | |
| K=10,T=0.8: <data>doc _ header = " core commands " cmd . prompt = " { } <UNK> { } > " . format ( underline , endc ) cmd . path _ complete cmd . _ _ init _ _ ( self ) self . list _ parameter = [ ' domain ' , ' username ' , ' email ' , ' <UNK> ' , ' proxy ' , ' proxy _ file ' ] self . parameters = { } self . multiple _ proxy _ list = [ ] self . current _ time = datetime . datetime </data>. now ( ) try : self . queue = queue . get ( " queue " ) except urllib . parse . httperror as e : self . queue . put ( e ) else : self . queue . put ( e ) except sqlite . httperror as e : self . queue . put ( e ) def _ _ setitem _ _ _ ( self ) : self . queue . put ( e ) def _ _ getitem _ _ ( self , index ) : index = index + index if index = = | |
| K=10,T=0.8: <data>] : model . init _ prompt _ embeds ( ) if args . fp 1 6 : model = fp 1 6 _ module ( model ) model = ddp ( model ) return model def get _ masks _ and _ position _ ids ( args , tokenizer : enc dec tokenizer , contexts , targets , labels ) : batch _ size , enc _ seq _ length = contexts . size ( ) enc _ attn _ mask = torch . zeros ( batch _ size , 1 , enc _ seq _ length , enc </data>_ attn _ mask ) enc _ attn _ mask = self . decoder ( enc _ attn _ mask ) enc _ attn _ mask = enc _ attn _ mask . view ( batch _ size , 1 ) enc _ attn _ mask = self . enc ( enc _ attn _ mask ) return enc _ attn _ mask class model ( nn . module ) : def _ _ init _ _ ( self , vocab _ size , vocab _ size = none , dropout _ prob = 0 . 0 0 0 0 | |
| K=10,T=0.8: <data>= cmd + " " + tempdir + fn + " " + gpsppp _ erp print ( " rename command : " , cmd ) p = subprocess . popen ( cmd , shell = true ) p . communicate ( ) print ( " rinex = " , rinex ) ( tmp , rinexfile ) = os . path . split ( rinex ) inputfile = rinexfile [ : - 2 ] if inputfile [ - 1 ] = = " . " : inputfile = inputfile [ : - 1 ] if inputfile [ - 1 ] </data>= = " . " : inputfile = <UNK> [ : - 2 ] if outputfile [ - 1 ] = = " . " : outputfile = <UNK> [ 1 2 ] if outputfile [ - 1 ] = = " . " : outputfile = <UNK> [ 2 ] if outputfile [ - 1 ] = = " . " : outputfile = <UNK> [ 2 ] if outputfile [ - 1 ] = = " . " : outputfile = inputfile [ - 1 ] if outputfile [ - 1 ] = = " . " : | |
| K=10,T=0.8: <data>4 . 5 % top - 1 accuracy on image net with k - nn classification . vi t - small / 8 x 8 pre - trained with dino . <UNK> 7 8 . 3 % top - 1 accuracy on image net with k - nn classification . vi t - base / 1 6 x 1 6 pre - trained with dino . <UNK> 7 6 . 1 % top - 1 accuracy on image net with k - nn classification . vi t - base / 8 x 8 pre - trained with dino . <UNK> </data>7 6 . 1 % top - 1 accuracy on image net with k - nn classification . <UNK> 1 % top - 2 accuracy on image net with k - nn classification . <UNK> 1 % top - 1 accuracy on image net with k - nn classification . <UNK> 1 % top - 1 accuracy on image net with k - nn classification . <UNK> 2 % top - 1 accuracy on image net with k - nn classification . <UNK> 1 % top - 2 accuracy on image net with k - nn classification . <UNK> 7 | |