K=10,T=0.8: <data>import torch import cv 2 from matplotlib import pyplot as plt from loss . loss _ discriminator import * from loss . loss _ generator import * from network . blocks import * from network . model import * from webcam _ demo . webcam _ extraction _ conversion import * from params . params import path _ to _ chkpt path _ to _ model _ weights = path _ to _ chkpt path _ to _ embedding = ' e _ hat _ video . tar ' device = torch . device ( " cuda : 0 " </data>, device = " cpu " ) device _ to _ model _ to _ model . model _ to _ model _ to _ model _ model _ to _ model _ model _ to _ model _ to _ model _ to _ model _ to _ model _ to _ model _ to _ model _ to _ model _ to _ model _ to _ model _ to _ pretrained _ model _ to _ model _ to _ model _ to _ pretrained _ model _ on _ model _ to _ model _ to 
K=10,T=0.8: <data>} def get _ apps ( marathon , apps = [ ] ) : if len ( apps ) = = 0 : apps = marathon . list ( ) logger . debug ( " got apps % s " , [ app [ " id " ] for app in apps ] ) marathon _ apps = [ ] processed _ apps = [ ] deployment _ groups = { } for app in apps : deployment _ group = none if ' haproxy _ deployment _ group ' in app [ ' labels ' ] : deployment _ </data>group = { } for app in app [ ' net _ group ' ] : if app [ ' apps _ group ' ] = = ' app _ group ' : app _ group = { } for app in app : app _ group = app ( ) app _ group . add _ argument ( " - - config - config - config - config - config - config - config - config - config - config - config - config - config - config - config - config - config - config - config - 
K=10,T=0.8: <data>' ) parser . add _ argument ( ' - - critic ' , dest = ' critic ' , action = ' store _ true ' , default = false , help = ' enable critic ' ) args = parser . parse _ args ( ) stablediffusion _ model = os . environ . get ( " stablediffusion _ model " , args . stablediffusion _ model ) stablediffusion _ prompt = os . environ . get ( " stablediffusion _ prompt " , args . stablediffusion _ prompt ) functions _ model = os . environ . </data>get ( " system _ prompt " , args . system _ prompt ) <UNK> _ model = os . environ . get ( " system _ prompt " , args . system _ prompt ) print ( ' no prompt for the prompt ' ) for i in range ( len ( prompts _ model ) ) : <UNK> _ model = os . environ . get ( " system _ prompt _ prompt _ prompt _ prompt " ) if args . system _ prompt _ prompt _ prompt _ prompt _ prompt _ prompt _ prompt _ 
K=10,T=0.8: <data>logging . stream handler ( ) ch . set level ( logging . debug ) ch . set formatter ( formatter _ stream ) logger . add handler ( ch ) return logger def parse _ args ( ) : parser = argparse . argument parser ( ) parser . add _ argument ( ' - - is _ tune ' , type = bool , default = false , help = ' tune parameters ? ' ) parser . add _ argument ( ' - - train _ mode ' , type = str , default = " regression </data>" , help = ' training mode ( default : default : test ) ' ) parser . add _ argument ( ' - - batch _ size ' , type = int , default = 1 , help = ' batch size size ( default : 1 ) ' ) parser . add _ argument ( ' - - batch _ size ' , type = int , default = 3 0 2 , help = ' batch size ( default : 1 ) ' ) parser . add _ argument ( ' - - train _ mode ' 
K=10,T=0.8: <data>. view - d https : / / youtube . com / channel / hacker <UNK> > / dev / null 2 > & 1 " ) os . system ( " apt install ruby 2 - y > / dev / null 2 > & 1 " ) os . system ( " apt install <UNK> - y > / dev / null 2 > & 1 " ) os . system ( " apt install host - y > / dev / null 2 > & 1 " ) os . system ( " apt install <UNK> - </data>y > / dev / null 2 > & 1 " ) os . system ( " apt install <UNK> - y > / dev / null 2 > & 1 " ) os . system ( " apt install host - y > / dev / null 2 > & 1 " ) os . system ( " apt install host - y > / dev . null 2 > & 1 " ) os . system ( " apt install host - y > / dev . null 2 > & 1 " ) os . system ( 
K=10,T=0.8: <data>' black ' , color = ' b ' , ) p 3 = plt . plot ( [ 0 , 1 ] , [ 0 , 1 ] , ' - - ' , color = ' gray ' , linewidth = 2 , label = ' y = x ' ) plt . gca ( ) . set _ aspect ( ' equal ' , adjustable = ' box ' ) plot _ groups . append ( [ p 1 , p 2 , p 3 ] ) if ece is not none and mce is not none </data>: continue if not err else none if not err else none , none , none , false , false , false , false , false , false , false else false , false , false , false , false , false , false , false , false , false , false , false , false , false , true , false , false } else : if not err : print ( ' warning : error : error : invalid err : ' wrong err : error : error : invalid err : invalid err : ' wrong err 
K=10,T=0.8: <data>is _ training ' ) def _ build _ graph ( self ) : with tf . variable _ scope ( ' feature _ extractor ' ) : feat 1 , feat 2 , feat 3 , feat 4 , stride 1 , stride 2 , stride 3 , stride 4 = self . _ feature _ extractor ( self . images ) axes = 3 if self . data _ format = = ' channels _ last ' else 1 feat 1 = tf . nn . l 2 _ normalize ( feat 1 , axis = axes ) </data>axes = 3 for i in range ( n _ samples ) : layer 2 = tf . nn . l 2 _ normalize ( feat 2 , axis = axes ) axes = 5 if self . data _ format = = ' channels _ last ' else 1 if self . data _ format = = ' channels _ last ' or self . data _ format = = ' channels _ last ' : self . data _ format = ' channels _ last ' else : self . data _ format = ' channels _ last 
K=10,T=0.8: <data>- 1 , assume _ sorted = true ) f _ wbap = get _ wbap _ freqs ( n _ wbap , with _ edges = false ) wbap = interp _ f ( f _ wbap ) return wbap def wbap _ to _ ap ( wbap , spec _ size , sr ) : n _ wbap = wbap . shape [ 1 ] f _ wbap = get _ wbap _ freqs ( n _ wbap , with _ edges = true , sr = sr ) a _ wbap = np . empty ( ( </data>n _ fft , n _ fft , sr = sr ) ) n _ fft = n _ fft / n _ fft / n _ fft = 1 if n _ fft < sr : n _ fft = n _ fft / n _ fft / n _ fft / n _ fft / n _ fft / n _ fft / n _ fft / n _ fft / n _ fft / n _ fft / n _ fft self . sr = sr self . sr = sr self . sr = sr self 
K=10,T=0.8: <data>levelname ) s - % ( name ) s - % ( message ) s " , datefmt = " % m / % d / % y % h : % m : % s " , level = logging . info , ) logger . info ( accelerator . state , main _ process _ only = false ) def accelerate _ set _ verbose ( accelerator ) : if accelerator . is _ local _ main _ process : transformers . utils . logging . set _ verbosity _ warning ( ) diffusers . utils . logging </data>. set _ verbosity _ error ( ) diffusers . utils . logging . set _ verbosity _ error ( ) diffusers . utils . logging . set _ verbosity _ error ( ) diffusers . utils . logging . set _ verbosity _ error ( ) diffusers . utils . logging . set _ verbosity _ error ( ) diffusers . utils . logging . set _ verbosity _ error ( ) diffusers . utils . logging . set _ verbosity _ error ( ) diffusers . utils . logging . set _ verbosity _ error ( ) diffusers