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

K=10,T=0.8: <data><newline> <tab> <tab> elif is _ torch _ tpu _ available ( ) : <newline> <tab> <tab> <tab> device = xm . xla _ device ( ) <newline> <tab> <tab> <tab> self . _ n _ gpu = 0 <newline> <tab> <tab> elif self . local _ rank = = - 1 : <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> device = torch . device ( " cuda : 0 " if torch . cuda . is _ available </data>( ) ) <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> if args . model else torch . device ! = " : <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> self . _ model _ state _ state = torch . nn . nn . linear ( [ 0 ] [ 1 , 0 ] ) <newline> <newline> <tab> <tab> <tab> <tab> <tab> if self . tokenizer . model _ state [ 0 ] and torch . cuda . cuda ( ) = = = ' float ( ) and hasattr ( " 
K=10,T=0.8: <data><newline> <tab> <tab> subnet , log _ dir = args . log _ dir , data _ path = args . data , dataset = args . dataset , n _ epochs = args . n _ epochs , <newline> <tab> <tab> resolution = resolution , trn _ batch _ size = args . trn _ batch _ size , vld _ batch _ size = args . vld _ batch _ size , <newline> <tab> <tab> num _ workers = args . num _ workers , valid _ size = args . valid _ size , is _ test </data>_ train _ size = args . num _ workers , <newline> <tab> <tab> ) <newline> <tab> if args . dataset : <newline> <tab> <tab> print ( f f " { args . dataset } . " ) <newline> <tab> <tab> print ( f " { args . dataset _ name } " ) <newline> <tab> <tab> print ( f " { args . dataset _ name } . { args . dataset _ name } . { args . dataset _ name } " ) <newline> <newline> < filesep > <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> 
K=10,T=0.8: <data>we can fully make use of negative samples even in distributed training mode . for example , if you use 8 gpus and each gpus has batch size of 3 2 x 2 ( two views of one image ) , you will use total negative samples of 3 2 x 2 x 8 - 2 = 5 1 0 negative samples to train your simclr model . <newline> <newline> <tab> this implementation uses a " model distributed " method rather than " data distributed " method , so you should use this in distributed training mode , but not </data>be distributed . " <newline> <newline> <tab> <tab> the training . <newline> <tab> <tab> the training . <newline> <tab> <tab> the method to use the training . if the training . <newline> <newline> <tab> <tab> the model and the dataset . <newline> <tab> <tab> the dataset . <tab> the dataset . <newline> <tab> <tab> the dataset . <newline> <tab> <newline> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <tab> the validation dataset , <newline> <tab> <tab> returns the dataset of dataset . <newline> <tab> <tab> the dataset <newline> <tab> <tab> : <newline> <tab> <tab> the dataset . <newline> <tab> <tab> this 
K=10,T=0.8: <data><newline> <newline> < filesep > <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> import xml . dom . minidom <newline> from xml . dom . minidom import node <newline> import re <newline> from sys import stderr <newline> import struct <newline> import copy <newline> import sys <newline> <newline> <newline> def increase to valid section size ( size ) : <newline> <tab> block size = 1 6 <newline> <tab> incomplete block bytes = ( size % block size ) <newline> <tab> if incomplete block bytes ! = 0 </data>or block size ! = 0 : <newline> <tab> <tab> <tab> block size = 1 8 9 8 <newline> <tab> <tab> if len ( blocks ) ! = 0 : <newline> <tab> <tab> <tab> block size = 2 5 8 8 <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> block size = 1 7 8 <newline> <tab> <tab> else : <newline> <tab> <tab> block size = 3 2 8 4 <newline> <tab> <tab> block size = 1 8 9 <newline> <tab> <tab> block size = 2 5 8 8 8 <newline> <tab> <tab> block size = 1 2 2 
K=10,T=0.8: <data>] ) <newline> <tab> <tab> item . append ( simi _ array [ i ] [ j ] ) <newline> <tab> <tab> rel _ data . append ( item ) <newline> <tab> <tab> rel _ id + = 1 <newline> <newline> <newline> rel = pd . data frame ( rel _ data , columns = [ ' rel _ id ' , ' type ' , ' origin _ id ' , ' destination _ id ' , ' distance ' , ' connection ' , ' similarity ' ] ) <newline> rel . to _ csv ( dataname + </data>' / rel / rel _ data ' ) <newline> <newline> <newline> <tab> <newline> <tab> if args . debug : <newline> <tab> <tab> print ( " % s " % rel _ data [ i + 1 ] ) <newline> <tab> <tab> print ( " % s " % rel _ data [ i ] [ j ] + ' / rel / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp / tmp 
K=10,T=0.8: <data><newline> <tab> <tab> sentences _ dict = test _ dataloader . dataset . sentences _ dict <newline> <tab> <tab> video _ sentences _ dict = test _ dataloader . dataset . video _ sentences _ dict <newline> <tab> <tab> for idx in range ( len ( sentences _ dict ) ) : <newline> <tab> <tab> <tab> video _ id , _ = sentences _ dict [ idx ] <newline> <tab> <tab> <tab> sentences = video _ sentences _ dict [ video _ id ] <newline> <tab> <tab> <tab> all _ caption _ lists . append ( sentences ) <newline> <tab> </data><tab> <tab> sentences _ dict [ idx ] = len ( sentences _ dict ) <newline> <newline> <newline> <newline> <newline> <tab> <tab> <newline> <tab> <tab> sentences _ dict [ idx ] = [ ] <newline> <tab> <tab> for idx in sentences _ dict [ idx ] : <newline> <tab> <tab> <tab> sentences _ dict [ idx ] = [ ] <newline> <tab> <tab> <tab> sentences _ dict [ idx ] = [ ] <newline> <tab> <tab> <tab> sentences _ dict [ idx ] = [ ] <newline> <tab> <tab> <tab> sentences _ dict [ idx ] . append ( sentences 
K=10,T=0.8: <data>train _ ld ) ) , len ( train _ ld ) - 1 ) <newline> <tab> <tab> print ( " <UNK> , total batches : " , <UNK> , len ( train _ ld ) ) <newline> <newline> <tab> <tab> for i , seed in enumerate ( seeds ) : <newline> <newline> <tab> <tab> <tab> set _ seed ( seed , use _ gpu ) <newline> <tab> <tab> <tab> tbsm , device = get _ tbsm ( args , use _ gpu ) <newline> <newline> <tab> <tab> <tab> g a _ test = iterate _ train _ data ( </data>args , device ) <newline> <tab> <tab> <tab> if args . device is not none : <newline> <tab> <tab> <tab> <tab> print ( " cuda : " , seed , args . seed ) <newline> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> print ( " cuda : " ) <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> if args . use _ gpu : <newline> <tab> <tab> <tab> <tab> print ( " cuda and gpu : " , args . model ) <newline> <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> print ( " seed : " 
K=10,T=0.8: <data>" , errors = " ignore " ) as f : <newline> <tab> <tab> reader = csv . dict reader ( f ) <newline> <tab> <tab> for row in reader : <newline> <tab> <tab> <tab> data _ list . append ( row ) <newline> <newline> <tab> nlp = spacy . load ( model ) <newline> <newline> <tab> with open ( out _ path , " w " , encoding = " utf 8 " , errors = " ignore " ) as f : <newline> <tab> <tab> w = csv . writer ( f ) <newline> <tab> <tab> for row </data>in reader : <newline> <tab> <tab> <tab> <tab> for i in reader : <newline> <tab> <tab> <tab> <tab> <tab> if row [ ' name ' ] . lower ( ) and " a " in row [ ' name ' ] . lower ( ) : <newline> <tab> <tab> <tab> <tab> <tab> row [ ' name ' ] . lower ( ) [ 1 ] [ ' name ' ] . lower ( ) [ 2 ] ) <newline> <newline> <tab> <newline> <tab> <newline> <tab> for i in reader : <newline> <tab> <tab> data _ list . append ( row 
K=10,T=0.8: <data>) <newline> <tab> <tab> match _ filenames = tf . io . matching _ files ( file _ names ) <newline> <tab> else : <newline> <tab> <tab> file _ names = os . path . join ( valid _ path , " validation * " ) <newline> <tab> <tab> match _ filenames = tf . io . matching _ files ( file _ names ) <newline> <tab> dataset = tf . data . tfrecord dataset ( match _ filenames , name = " train _ data " ) <newline> <tab> dataset _ iterator = dataset . as _ numpy _ </data>arrays ( <newline> <tab> <tab> <tab> flags . num _ epochs , flags . num _ epochs ) <newline> <newline> <tab> <tab> tf . summary . histogram ( flags . num _ epochs * batch _ size , flags . num _ epochs <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> flags . num _ epochs * batch _ size , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> flags . num _ epochs ) <newline> <tab> <tab> <tab> <tab> if 
K=10,T=0.8: <data>np . linalg . norm ( z 2 ) <newline> <tab> <tab> dirs = slerp 2 ( normalized _ z 1 , normalized _ z 2 , percentages ) <newline> <tab> <tab> length = np . linspace ( np . log ( np . linalg . norm ( z 1 ) ) , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> np . log ( np . linalg . norm ( z 2 ) ) , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> interpolation _ count ) <newline> <tab> <tab> out = ( dirs * np . exp ( </data>np . prod ( np . prod ( z 2 ) ) , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> np . prod ( np . prod ( z 2 ) ) , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> np . prod ( z 2 ) ) , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> np . prod ( z 2 ) ) <newline> <newline> <newline> @ awsretry . backoff ( ) <newline> def get _ session ( session ) : 
K=10,T=0.8: <data><tab> <tab> <tab> cv . circle ( image , ( landmark _ x , landmark _ y ) , 5 , ( 0 , 2 5 5 , 0 ) , 2 ) <newline> <tab> <tab> if index = = 1 4 : <newline> <tab> <tab> <tab> cv . circle ( image , ( landmark _ x , landmark _ y ) , 5 , ( 0 , 2 5 5 , 0 ) , 2 ) <newline> <tab> <tab> if index = = 1 5 : <newline> <tab> <tab> <tab> cv . circle ( image , ( landmark _ </data>y , landmark _ y ) , 5 , ( 0 , 2 5 5 , 0 ) , 5 ) <newline> <tab> <tab> <tab> cv . circle ( image , ( landmark _ y , landmark _ y ) , 5 , ( 0 , 2 5 5 , 0 ) , 5 ) <newline> <tab> <tab> <tab> cv . circle ( image , ( landmark _ y , landmark _ y , landmark _ y ) , 5 , ( 0 , 2 5 5 , 0 ) , 5 ) <newline> <newline> <tab> <tab> <newline> <tab> <tab> 
K=10,T=0.8: <data>cookies ) <newline> <tab> <tab> self . body = body <newline> <newline> <tab> @ classmethod <newline> <tab> def from _ any ( cls , any ) : <newline> <tab> <tab> if isinstance ( any , int ) : <newline> <tab> <tab> <tab> return cls ( status = any , body = httpstatus ( any ) . phrase . encode ( ) ) <newline> <tab> <tab> elif isinstance ( any , str ) : <newline> <tab> <tab> <tab> return cls ( status = 2 0 0 , body = any . encode ( ) ) <newline> <tab> <tab> elif isinstance ( </data>any , str ) : <newline> <tab> <tab> <tab> return cls ( status = = any , body = body ) . phrase . decode ( ) <newline> <newline> <tab> <tab> elif isinstance ( any , str ) : <newline> <tab> <tab> <tab> return cls ( status = = any , body = body ) . phrase . encode ( ) <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> return cls ( status = = any ) . phrase . encode ( ) ) <newline> <newline> < filesep > <newline> import os <newline> import sys <newline> import json <newline> 
K=10,T=0.8: <data>_ batch _ norm , <newline> <tab> <tab> <tab> ) , <newline> <tab> <tab> <tab> transpose 1 d layer ( <newline> <tab> <tab> <tab> <tab> ( self . dim _ mul * model _ size ) / / 2 , <newline> <tab> <tab> <tab> <tab> ( self . dim _ mul * model _ size ) / / 4 , <newline> <tab> <tab> <tab> <tab> 2 5 , <newline> <tab> <tab> <tab> <tab> stride , <newline> <tab> <tab> <tab> <tab> upsample = upsample , <newline> <tab> <tab> <tab> <tab> use _ batch _ norm = use _ batch _ norm </data>, <newline> <tab> <tab> <tab> <tab> upsample = upsample , <newline> <tab> <tab> <tab> <tab> upsample = upsample , <newline> <tab> <tab> <tab> ) , <newline> <tab> <tab> <tab> transpose 1 d layer ( <newline> <tab> <tab> <tab> <tab> ( self . dim _ mul * model _ size ) / 6 , <newline> <tab> <tab> <tab> <tab> ( self . dim _ mul * model _ size ) / / 2 , <newline> <tab> <tab> <tab> <tab> ( self . dim _ mul * model _ size ) / / 4 , <newline> <tab> <tab> <tab> <tab> upsample = 
K=10,T=0.8: <data>links <newline> <tab> else : <newline> <tab> <tab> background _ data = none <newline> <tab> <tab> background _ links = none <newline> <tab> <tab> <newline> <tab> links = old _ grid . links . clone ( ) <newline> <tab> basis _ dim = ( sh _ data . shape [ 1 ] ) / / 3 <newline> <tab> radius = deepcopy ( old _ grid . radius ) <newline> <tab> center = deepcopy ( old _ grid . center ) <newline> <tab> grid _ new = <UNK> 2 . sparse grid ( <newline> <tab> <tab> 1 , <newline> <tab> <tab> </data>1 , <newline> <tab> <tab> 1 , <newline> <tab> <tab> 2 , <newline> <tab> <tab> 2 , <newline> <tab> <tab> 2 , <newline> <tab> <tab> 3 , <newline> <tab> <tab> 2 , <newline> <tab> <tab> 2 , <newline> <tab> <tab> 3 , <newline> <tab> <tab> 4 , <newline> <tab> <tab> 4 , <newline> <tab> <tab> 1 , <newline> <tab> <tab> 3 , <newline> <tab> <tab> 4 , <newline> <tab> <tab> 2 , <newline> <tab> <tab> 4 , <newline> <tab> <tab> 3 , <newline> <tab> <tab> 2 , <newline> <tab> <tab> 5 , <newline> <tab> <tab> 5 , <newline> <tab> <tab> 
K=10,T=0.8: <data>( " wm _ delete _ window " , close _ gui ) <newline> <newline> <tab> <newline> <tab> <newline> <tab> entry _ max _ play _ time = add _ label _ and _ entry ( root , " ( ) " , autohs _ config . max _ play _ time , update _ max _ play _ time ) <newline> <tab> entry _ max _ win _ count = add _ label _ and _ entry ( root , " " , autohs _ config . max _ win _ count , update _ max _ win _ </data>count , update _ max _ window ) <newline> <tab> entry _ num = add _ label _ and _ entry ( root , " " , autohs _ config . max _ win _ count , update _ max _ win _ count , update _ max _ win _ count , update _ max _ window ) <newline> <tab> entry _ max _ step _ size = add _ label _ and _ entry ( root , " " , autohs _ config . max _ win _ count , update _ max _ window ) <newline> <tab> 
K=10,T=0.8: <data>_ type = ' lstm ' ) <newline> <tab> <tab> <tab> self . cnn _ encoder = cnn encoder ( embed _ dim = self . embed _ dim ) <newline> <newline> <tab> <tab> <tab> self . ca _ net = ca _ net ( c _ dim = self . z _ dim ) <newline> <tab> <tab> <tab> self . generator = generator ( channels = self . g _ dim ) <newline> <newline> <tab> <tab> <tab> self . discriminator = discriminator ( channels = self . d _ dim , embed _ dim = self . embed _ </data>dim ) <newline> <tab> <tab> <tab> self . generator . load _ state _ dict ( torch . load ( self . g _ dim ) ) <newline> <tab> <tab> <tab> self . discriminator . load _ state _ dict ( torch . load ( self . g _ dim ) ) <newline> <tab> <tab> <tab> self . discriminator . load _ state _ dict ( torch . load ( self . g _ dim ) ) <newline> <tab> <tab> <tab> self . discriminator . load _ state _ dict ( torch . load ( self . g _ dim 
K=10,T=0.8: <data>_ process _ group ( <newline> <tab> <tab> backend = " nccl " , <newline> <tab> <tab> init _ method = args . dist _ url , <newline> <tab> <tab> world _ size = args . world _ size , <newline> <tab> <tab> rank = args . rank , <newline> <tab> ) <newline> <newline> <tab> if args . rank = = 0 : <newline> <tab> <tab> args . exp _ dir . mkdir ( parents = true , exist _ ok = true ) <newline> <tab> <tab> stats _ file = open ( args . exp _ dir / " </data>stats . log " ) <newline> <tab> <tab> stats _ file . close ( ) <newline> <tab> <tab> stats _ file . close ( ) <newline> <tab> <tab> stats _ file . close ( ) <newline> <tab> else : <newline> <tab> <tab> stats _ file . close ( ) <newline> <newline> <tab> <newline> <tab> stats _ file . close ( ) <newline> <tab> stats _ file . close ( ) <newline> <newline> < filesep > <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> import os <newline> import time <newline> import torch <newline> import torch . nn as nn <newline> from 
K=10,T=0.8: <data><newline> import argparse <newline> import re <newline> import time <newline> import tensorflow as tf <newline> import tensorflow . contrib . slim as slim <newline> import sys <newline> <newline> from monodepth _ model import * <newline> from monodepth _ dataloader import * <newline> from average _ gradients import * <newline> <newline> parser = argparse . argument parser ( description = ' monodepth tensor flow implementation . ' ) <newline> parser . add _ argument ( ' - - mode ' , <tab> <tab> <tab> <tab> <tab> type = str , help = ' train or test ' , default = ' </data>' ) <newline> parser . add _ argument ( ' - - eval ' , <tab> <tab> <tab> <tab> <tab> type = str , help = ' eval or dev ' , default = ' ' ) <newline> parser . add _ argument ( ' - - batch _ norm ' , <tab> <tab> type = int , help = ' batch norm ( for each batch norm ) ' , default = 1 e - 8 ) <newline> parser . add _ argument ( ' - - eval _ steps ' , <tab> <tab> type = int , help 
K=10,T=0.8: <data>' text / synonym _ openai _ t 0 1 . txt ' ) as infile : <newline> <tab> lines = infile . readlines ( ) <newline> <tab> for ind , line in enumerate ( lines ) : <newline> <tab> <tab> temp _ list = line . rstrip ( ) . lstrip ( ) . split ( ' , ' ) <newline> <tab> <tab> paste _ text _ map 0 . append ( temp _ list ) <newline> <tab> <tab> <newline> <newline> paste _ text _ map 1 = [ ] <newline> <newline> with open ( ' text / sentence </data>_ map 1 . txt ' ) as infile : <newline> <tab> for line in infile : <newline> <tab> <tab> paste _ text _ map 1 = [ ] <newline> <newline> <tab> for j in range ( 5 ) : <newline> <tab> <tab> paste _ text _ map 1 . append ( paste _ text _ map 1 ) <newline> <tab> <tab> paste _ text _ map 1 . append ( paste _ text _ map 1 ) <newline> <newline> <newline> < filesep > <newline> import os <newline> import sys <newline> import random <newline> import argparse <newline> import numpy as 
K=10,T=0.8: <data>_ files and possible _ to _ plot , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> out _ scene _ dir = out _ dir _ images ) <newline> <newline> <tab> <tab> if test _ optim : <newline> <tab> <tab> <tab> save _ all [ ' w _ test _ optim ' ] = results _ dict <newline> <tab> <tab> elif model _ name in [ " joint _ pose _ nerf _ training " , ' nerf _ fixed _ noisy _ poses ' ] : <newline> <tab> <tab> <tab> save _ all [ </data>' w _ test _ optim ' ] = results _ dict <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> save _ all [ ' w _ test _ optim ' ] = results _ dict <newline> <newline> <tab> <tab> if train _ optim : <newline> <tab> <tab> <tab> save _ all [ ' w _ test _ optim ' ] = results _ dict <newline> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> if train _ optim : <newline> <tab> <tab> <tab> print ( " training " 
K=10,T=0.8: <data><tab> <tab> <tab> return <newline> <newline> <tab> <tab> <newline> <tab> <tab> train ( train _ loader , model , criterion , criterion _ ib , optimizer , epoch , args , log _ training , tf _ writer ) <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> acc 1 = validate ( val _ loader , model , criterion , criterion _ ib , epoch , args , log _ testing , tf _ writer ) <newline> <newline> <newline> <tab> <tab> <newline> <tab> <tab> is _ best = acc 1 > best _ acc 1 <newline> <tab> <tab> best </data>_ acc 1 = acc 1 + best _ acc 1 <newline> <tab> <tab> if ( epoch % args . print _ freq = = 0 ) : <newline> <tab> <tab> <tab> print ( f " epoch { epoch } - - epoch { args . print _ training } " ) <newline> <tab> <tab> <tab> if args . save _ training : <newline> <tab> <tab> <tab> <tab> model . save _ training ( ) <newline> <tab> <tab> <tab> <tab> model . load _ validation ( ) <newline> <tab> <tab> <tab> <tab> model . save _ testing ( ) <newline> 
K=10,T=0.8: <data>( ' uv . univ _ select _ border _ edge _ by _ angle ' , icon _ value = icons . border _ by _ angle ) . edge _ dir = ' both ' <newline> <tab> <tab> row . operator ( ' uv . univ _ select _ border _ edge _ by _ angle ' , text = ' ' , icon _ value = icons . horizontal _ a ) . edge _ dir = ' horizontal ' <newline> <tab> <tab> row . operator ( ' uv . univ _ select _ border _ edge </data>_ by _ angle ' , text = ' ' , icon _ value = icons . vertical _ a ) . edge _ dir = ' vertical ' <newline> <tab> <tab> row . operator ( ' uv . univ _ select _ border _ edge _ by _ angle ' , text = ' ' , icon _ value = icons . vertical _ b ) . edge _ dir = ' vertical ' <newline> <tab> <tab> row . operator ( ' uv . univ _ select _ border _ edge _ by _ angle ' , text = 
K=10,T=0.8: <data>, [ hyp [ ' warmup _ bias _ lr ' ] if j = = 2 else 0 . 0 , x [ ' initial _ lr ' ] * lf ( epoch ) ] ) <newline> <tab> <tab> <tab> <tab> <tab> if ' momentum ' in x : <newline> <tab> <tab> <tab> <tab> <tab> <tab> x [ ' momentum ' ] = np . interp ( ni , xi , [ hyp [ ' warmup _ momentum ' ] , hyp [ ' momentum ' ] ] ) <newline> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> if </data>' momentum ' in x : <newline> <tab> <tab> <tab> <tab> if not x [ ' momentum ' ] : <newline> <tab> <tab> <tab> <tab> <tab> x [ ' momentum ' ] = np . interp ( ni , xi , [ hyp [ ' momentum ' ] , hyp [ ' momentum ' ] ] , hyp [ ' momentum ' ] ) <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> optimizer . zero _ grad ( ) <newline> <tab> <tab> <tab> <tab> optimizer . zero _ grad ( ) <newline> <newline> <tab> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data><tab> <tab> <tab> <tab> <tab> <newline> <newline> <tab> <tab> <tab> <tab> if address = = 0 x c 0 5 0 : <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> self . screen . set text ( false ) <newline> <tab> <tab> <tab> <tab> <tab> return 0 <newline> <tab> <tab> <tab> <tab> elif address = = 0 x c 0 5 1 : <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> self . screen . set text ( true ) <newline> <tab> <tab> <tab> </data><tab> <tab> if address = = 0 x c 0 4 1 : <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> self . screen . set text ( true ) <newline> <tab> <tab> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> <tab> <tab> self . screen . set text ( true ) <newline> <tab> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> <tab> self . screen . set text ( true ) <newline> <newline> <tab> def set text ( self ) : <newline> <tab> <tab> self 
K=10,T=0.8: <data><tab> <tab> <tab> <tab> <tab> <tab> latest _ checkpoint _ file , os . r _ ok <newline> <tab> <tab> <tab> <tab> <tab> ) : <newline> <tab> <tab> <tab> <tab> <tab> <tab> print ( <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> f " warning : latest vae checkpoint { latest _ checkpoint _ file } is empty or <UNK> . " <newline> <tab> <tab> <tab> <tab> <tab> <tab> ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> if len ( checkpoint _ files ) > 1 : <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> </data><tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> model = model , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> model = model , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> model = model , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data>_ 2 d _ to _ vector _ 3 d ( region , region . data , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> self . mouse _ position ) <newline> <newline> <tab> <tab> mouse _ coord _ screen _ flat = vector ( ( self . mouse _ position [ 0 ] , self . mouse _ position [ 1 ] , 0 ) ) <newline> <newline> <tab> <tab> depsgraph = context . evaluated _ depsgraph _ get ( ) <newline> <tab> <tab> hover _ object = " " </data><newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> if self . view _ type = = " view " : <newline> <tab> <tab> <tab> if self . view _ type = = " view " : <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> if self . view _ type = = " region " : <newline> <tab> <tab> <tab> <tab> <tab> self . view _ type = = " region " <newline> <tab> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> <tab> self . view _ type = 
K=10,T=0.8: <data>display _ pin _ rs <newline> <tab> pin _ e = pyd piper _ config . display _ pin _ e <newline> <tab> [ pin _ d 4 , pin _ d 5 , pin _ d 6 , pin _ d 7 ] = pyd piper _ config . display _ pins _ data <newline> <tab> rows = pyd piper _ config . display _ height <newline> <tab> cols = pyd piper _ config . display _ width <newline> <tab> i 2 c _ address = pyd piper _ config . display _ i 2 c _ address <newline> </data><newline> <tab> <newline> <tab> if not isinstance ( temp , float ) : <newline> <tab> <tab> temp = temp . strip ( ) <newline> <tab> <tab> temp = temp . strip ( ) <newline> <tab> <tab> temp = temp . replace ( ' ' , ' ' , ' ' ) <newline> <tab> <tab> temp = temp . strip ( ) <newline> <tab> <tab> temp = temp . replace ( ' ' , ' ' ) <newline> <tab> <tab> temp = temp . strip ( ) <newline> <tab> <tab> temp = temp . strip ( ) <newline> <tab> <tab> temp 
K=10,T=0.8: <data>current _ dir = os . path . dirname ( os . path . abspath ( _ _ file _ _ ) ) <newline> pygame _ file = os . path . join ( current _ dir , ' pygame _ current . py ' ) <newline> <newline> def create _ agent ( ) : <newline> <tab> <newline> <tab> model = model factory . create ( <newline> <tab> <tab> model _ platform = model platform type . qwen , <newline> <tab> <tab> model _ type = model type . qwen _ turbo , <newline> <tab> <tab> api _ key = </data>args . api _ key , <newline> <tab> <tab> api _ key = args . api _ key , <newline> <tab> <tab> api _ key = args . api _ key , <newline> <tab> <tab> api _ key = args . api _ key ) <newline> <tab> model . load _ state _ dict ( model ) <newline> <tab> model . save _ state _ dict ( model ) <newline> <tab> model . load _ state _ dict ( model ) <newline> <newline> <tab> <newline> <tab> if args . model _ type = = " text " : <newline> <tab> 
K=10,T=0.8: <data>} x 0 e + { max _ atom * 1 0 } x 1 o + { max _ atom * 1 0 } x 2 e " ) <newline> irreps _ input _ conv _ main _ 2 = irreps _ output _ conv _ main <newline> irreps _ output _ conv _ main _ 2 = o 3 . irreps ( " 5 0 x 0 e " ) <newline> irreps _ query _ main = o 3 . irreps ( " 2 0 x 0 e + 2 0 x 1 o " ) <newline> irreps </data>_ input _ conv _ main _ 2 = o 3 . irreps ( " 2 0 x 0 e " ) <newline> irreps _ output _ conv _ main _ 2 = o 3 . irreps ( " 2 0 x 0 e " ) <newline> irreps _ output _ conv _ main _ 2 = o 3 . irreps ( " 2 0 x 0 e " ) <newline> irreps _ output _ conv _ main _ 2 = o 3 . irreps ( " 2 0 x 0 e " ) <newline> irreps _ output _ conv 
K=10,T=0.8: <data>https : / / www . google . co . jp / " , " " , " " ) ] , <newline> <tab> [ " facebook " , <tab> <tab> keymap . shell execute command ( none , r " https : / / www . facebook . com / " , " " , " " ) ] , <newline> <tab> [ " twitter " , <tab> <tab> keymap . shell execute command ( none , r " https : / / twitter . com / " , " " , " " ) ] , <newline> <tab> </data>[ " twitter " , <tab> keymap . shell execute command ( none , r " https : / / twitter . com / " , " " ) ] , <newline> <tab> [ " twitter " , <tab> keymap . shell execute command ( none , r " https : / / twitter . com / " , " " ) ] , <newline> <tab> [ " twitter " , <tab> keymap . shell execute command ( none , r " https : / / twitter . com / " , " " ) ] , <newline> <tab> [