K=10,T=0.8: <data>_ get _ clip _ prompt _ embeds ( self , prompt : union [ str , list [ str ] ] , num _ images _ per _ prompt : int = 1 , device : optional [ torch . device ] = none , ) : device = device or self . _ execution _ device prompt = [ prompt ] if isinstance ( prompt , str ) else prompt batch _ size = len ( prompt ) text _ inputs = self . tokenizer ( prompt , padding = " max _ length " , max _ </data>length = max _ length ) return prompt _ embeds def get _ prompt _ prompt _ prompt _ prompt ( prompt _ embeds , prompt _ embeds , prompt _ embeds , prompt _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ guidance _ latents _ guidance _ vae _ latents , guidance _ guidance _ guidance _ guidance _ guidance 
K=10,T=0.8: <data>} for ik in o [ ' host ' ] : if type ( ik ) is dict : i = ik else : i = o [ ' host ' ] hostname = ' ' if ' hostnames ' in i and type ( i [ ' hostnames ' ] ) is dict : if ' hostname ' in i [ ' hostnames ' ] : hostname + = ' < br > ' if type ( i [ ' hostnames ' ] [ ' hostname ' ] ) is list : for hi in i [ ' hostnames </data>' ] : if hi [ ' type ' ] : if i [ ' name ' ] ! = ' ' : continue if ' id ' in i [ ' id ' ] : if i [ ' id ' ] [ ' id ' ] = = ' ' : continue elif i [ ' id ' ] : continue elif i [ ' id ' ] = = ' ' : continue elif i [ ' id ' ] . isalpha ( ) : try : if len ( i [ ' id ' ] ) 
K=10,T=0.8: <data>pert _ homo 0 = preprocess ( img 0 , kpt _ coeff 0 , spec , num _ corr , config [ ' <UNK> ' ] , pert _ homo [ 0 ] , pert _ affine [ 0 ] , config [ ' dense _ desc ' ] , name = ' input 0 ' ) net _ input 1 , kpt _ ncoords 1 , pert _ homo 1 = preprocess ( img 1 , kpt _ coeff 1 , spec , num _ corr , config [ ' <UNK> ' ] , pert _ homo [ </data>0 ] , pert _ affine [ 1 ] ] , config [ ' concat _ affine ' ] , kpt _ affine [ 0 ] , kpt _ affine [ 2 ] , config [ ' concat _ affine ' ] , tf _ affine [ 3 ] , config [ ' concat _ affine ' ] , reuse = true ) net _ input 2 , feat _ input 2 = preprocess ( img 1 , input 2 ) if config [ ' <UNK> ' ] is not none : net _ input 2 = net _ input 
K=10,T=0.8: <data>_ level = int ( header . name [ 1 ] ) header _ text = header . get _ text ( strip = true ) markdown _ header = f " { ' insert _ new _ line ( soup , header , 1 ) header . replace _ with ( markdown _ header ) for img in content _ element . find _ all ( " img " ) : if ' src ' in img . attrs : img _ url = img . attrs [ ' src ' ] else : continue img _ name = </data>img . attrs [ ' src ' ] . strip ( ) img _ url = img _ url + ' . jpg ' img _ url = img . attrs [ ' src ' ] . replace ( ' src ' , ' src ' ) img _ url = img . attrs [ ' src ' ] . replace ( ' src ' , ' src ' ) if ' src ' in img . attrs [ ' src ' ] . replace ( ' src ' , ' src ' ) : img _ url = img 
K=10,T=0.8: <data>= none , model _ path = ' . / model . pkl ' ) : super ( <UNK> , self ) . _ _ init _ _ ( ) with open ( model _ path , ' rb ' ) as f : params = pickle . load ( f ) self . j _ regressor = torch . from _ numpy ( np . array ( params [ ' j _ regressor ' ] . todense ( ) ) ) . type ( torch . float 6 4 ) if ' joint _ regressor ' in params . </data>keys ( ) : params = params [ ' joint _ regressor ' ] . reshape ( [ - 1 , 1 , 1 ] ) . type ( torch . float 6 4 ) . type ( torch . float 6 4 ) . type ( torch . float 6 4 ) . type ( torch . float 6 4 ) . type ( torch . float 6 4 ) . type ( torch . float 6 4 ) . type ( torch . float 6 4 ) . type ( torch . float 6 4 ) . type 
K=10,T=0.8: <data>out ) out = self . conv 2 ( out ) out = self . act 2 ( out ) out = self . conv 3 ( out ) out = self . act 3 ( out ) out = self . conv 4 ( out ) out = self . act 4 ( out ) out = self . conv 5 ( out ) out _ attention = self . output _ act ( out ) return out _ attention class res net ( nn . module ) : def _ _ init _ _ ( self , num </data>_ classes , * args , * * kwargs ) : super ( res net , self ) . _ _ init _ _ ( * args , * * kwargs ) def forward ( self , input , y ) : x = input . view ( 1 , 3 , 3 ) x = self . conv 2 ( x ) return x class res net ( nn . module ) : expansion = 4 class res net ( nn . module ) : expansion = 4 class res net ( nn . module ) : expansion = 
K=10,T=0.8: <data>= 3 0 0 ) parser . add _ argument ( ' - - rnn _ dropout ' , type = float , default = 0 . 3 ) parser . add _ argument ( ' - - clip _ gradient ' , type = float , default = 0 . 6 , help = ' gradient clipping ' ) parser . add _ argument ( ' - - vector _ cache ' , type = str , default = " data / sq _ glove 3 0 0 d . pt " ) parser . add _ argument ( </data>' - - model _ dropout ' , type = float , default = 0 . 0 0 0 1 , help = ' model dropout ' ) parser . add _ argument ( ' - - dropout _ prob ' , type = float , default = 1 . 0 , help = ' dropout prob ' ) parser . add _ argument ( ' - - dropout _ prob ' , type = float , default = 2 . 0 , help = ' dropout prob ' ) parser . add _ argument ( ' - - dropout 
K=10,T=0.8: <data>, acc 5 = accuracy ( output , target , topk = ( 1 , 5 ) ) losses . update ( loss . item ( ) , images . size ( 0 ) ) top 1 . update ( acc 1 [ 0 ] , images . size ( 0 ) ) top 5 . update ( acc 5 [ 0 ] , images . size ( 0 ) ) batch _ time . update ( time . time ( ) - end ) end = time . time ( ) if i % args . print _ freq </data>= = 0 : print ( ' epoch : % d , loss : % d , loss : % . 2 f , acc 5 : % . 2 f , acc 5 : % . 2 f , acc 5 : % . 2 f , acc 5 : % . 2 f , acc 5 : % . 2 f , acc 5 : % . 2 f , acc 5 : % . 2 f , acc 5 : % . 2 f , acc 5 : % . 2 f , acc 5 : % 
K=10,T=0.8: <data>, reduction = reduction ) self . lde = opts . loss _ de self . lde _ flag = self . lde > 0 . and model _ old is not none self . lde _ loss = nn . mseloss ( ) self . lkd = opts . loss _ kd self . lkd _ mask = opts . kd _ mask self . kd _ mask _ adaptative _ factor = opts . kd _ mask _ adaptative _ factor self . lkd _ flag = self . lkd > 0 . and model _ old is </data>not none self . lkd _ loss = self . lkd _ loss . detach ( ) . item ( ) loss 1 = self . kd _ loss . detach ( ) . item ( ) loss 1 = self . kd _ loss . detach ( ) . item ( ) loss 1 = self . kd _ loss . detach ( ) . item ( ) loss 2 = self . kd _ loss . detach ( ) . item ( ) loss 1 = self . kd _ loss . detach ( ) . item (