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

K=10,T=0.8: <data><tab> params [ ' input _ noise ' ] = 0 . 0 <newline> <tab> params [ ' build _ theano _ funcs ' ] = false <newline> <tab> p _ zi _ given _ xi = inf net ( rng = rng , xd = x _ in _ sym , \ <newline> <tab> <tab> <tab> params = params , shared _ param _ dicts = none ) <newline> <tab> p _ zi _ given _ xi . init _ biases ( 0 . 2 ) <newline> <tab> <newline> <tab> <newline> <tab> <newline> <tab> params = { } <newline> </data><tab> <tab> <newline> <tab> <tab> for k in range ( 0 . 0 . 0 0 ) : <newline> <tab> <tab> <tab> print ( ' % s ' % s % d % s % f % s % % ( % s , i % f % s % s % d % d % s . % s % s % ( % % s % m % s % % s % s % % m % % % % s % s % ( % m % s , % s % ( % s ) 
K=10,T=0.8: <data>" 7 " , <newline> <tab> <tab> fullname . replace ( " " , " " ) + " 6 " , <newline> <tab> <tab> fullname . replace ( " " , " " ) + " 4 " , <newline> <tab> <tab> fullname . replace ( " " , " " ) + " 3 " , <newline> <tab> <tab> fullname . replace ( " " , " " ) + " 2 " , <newline> <tab> <tab> fullname . replace ( " " , " " ) + " 1 " , <newline> <tab> <tab> fullname . replace </data>( ' " , " " ) ) ) <newline> <tab> else : <newline> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> for j in range ( 1 ) : <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <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> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> 
K=10,T=0.8: <data>_ _ init _ _ ( " debug " , gdb . command _ data ) <newline> <newline> <tab> def invoke ( self , arg , from _ tty ) : <newline> <tab> <tab> args = parse _ arg ( arg ) <newline> <tab> <tab> if args [ 0 ] = = ' symbolic ' : <newline> <tab> <tab> <tab> symbolic ( ) . debug = true <newline> <tab> <tab> elif args [ 0 ] = = ' gdb ' : <newline> <tab> <tab> <tab> gdb util ( ) . debug = true <newline> <tab> <tab> else : <newline> <tab> </data><tab> <tab> <newline> <tab> <tab> <tab> <tab> assert not os . path . exists ( args . <UNK> ) <newline> <newline> <tab> <tab> <tab> if args . <UNK> : <newline> <tab> <tab> <tab> <tab> assert os . path . exists ( args . <UNK> ) <newline> <tab> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> <tab> <tab> continue <newline> <tab> <tab> <tab> print ( " debug : " , " ) <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> if not args . <UNK> : <newline> <tab> <tab> <tab> <tab> os . system ( args . <UNK> ) 
K=10,T=0.8: <data>) : <newline> <tab> <tab> contacts . append ( [ i . strip ( ) , k . strip ( ) , k . strip ( ) , none ] ) <newline> <newline> <tab> return contacts <newline> <newline> <newline> def fill contacts ( contacts , path , name , id ) : <newline> <tab> for i , x in enumerate ( contacts ) : <newline> <tab> <tab> if x [ 0 ] = = id and x [ 3 ] is none : <newline> <tab> <tab> <tab> contacts [ i ] [ 2 ] = name <newline> <tab> <tab> <tab> </data>if i . startswith ( ' x ' ) : <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> return [ x ] <newline> <newline> def _ create _ labels ( labels , name , name ) : <newline> <tab> <newline> <tab> labels = [ ] <newline> <tab> labels = [ ] <newline> <tab> labels . append ( [ <newline> <tab> <tab> ( name , name , name , name , name , name , name , name , id , id , name , id , id ) , <newline> <tab> <tab> ( name , 
K=10,T=0.8: <data><newline> <tab> <tab> sheet _ md = [ s for s in metadata [ ' sheets ' ] if s [ ' properties ' ] [ ' sheet id ' ] = = self . sheet . id ] [ 0 ] <newline> <tab> <tab> row _ md = sheet _ md [ ' data ' ] [ 0 ] [ ' row metadata ' ] <newline> <tab> <tab> col _ md = sheet _ md [ ' data ' ] [ 0 ] [ ' column metadata ' ] <newline> <tab> <tab> for row in row _ md [ </data>' data ' ] : <newline> <tab> <tab> <tab> col _ md = sheet _ md [ ' data ' ] [ ' row _ md ' ] [ ' label ' ] [ ' label ' ] [ ' label ' ] [ ' label ' ] <newline> <tab> <tab> <tab> col _ md [ ' data ' ] [ ' label ' ] [ ' label ' ] [ ' label ' ] [ ' label ' ] [ ' label ' ] [ ' label ' ] = label [ ' label ' ] <newline> <tab> <tab> 
K=10,T=0.8: <data>_ genes : <newline> <tab> <tab> <tab> print ' { : . 3 f } ' . format ( rep _ dists [ v 1 ] [ v 2 ] ) , <newline> <tab> <tab> print <newline> <newline> < filesep > <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> from sconv . module import spherical conv , sphere mse <newline> from torch import nn <newline> import numpy as np <newline> import torch as th <newline> from torch . autograd import variable <newline> <newline> class final 1 ( nn . module ) : <newline> </data><tab> def _ _ init _ _ ( self , x _ tensor _ size , x _ tensor _ size ) : <newline> <tab> <tab> self . x _ tensor _ size = x _ tensor _ size <newline> <newline> <tab> <tab> self . x _ tensor _ size = x _ tensor _ size <newline> <newline> <tab> <tab> self . x _ tensor _ size = x _ tensor _ size <newline> <tab> <tab> self . x _ tensor _ size = x _ tensor _ size <newline> <tab> <tab> self . x _ tensor _ size = 
K=10,T=0.8: <data>( " start " , self . start ) ) <newline> <tab> <tab> self . app . add _ handler ( command handler ( " help " , self . help ) ) <newline> <tab> <tab> self . app . add _ handler ( command handler ( " quit " , self . quit ) ) <newline> <tab> <tab> self . app . add _ handler ( command handler ( " setting " , self . setting ) ) <newline> <newline> <tab> <tab> <newline> <tab> <tab> self . app . run _ polling ( ) <newline> <tab> <newline> <tab> async </data>def run _ forever ( self ) : <newline> <tab> <tab> <newline> <tab> <tab> self . app . run _ enabled ( ) <newline> <tab> <tab> self . app . run _ enabled ( ) <newline> <tab> <tab> self . app . run _ enabled ( ) <newline> <newline> < filesep > <newline> import argparse <newline> import json <newline> from json import dumps , json <newline> from json import dump , dumps <newline> <newline> from json import dump <newline> import json <newline> <newline> def check _ file ( self ) : <newline> <tab> <newline> <tab> <newline> <tab> return ' ' 
K=10,T=0.8: <data>: , 1 : : 2 , 1 : : 2 ] ) * 0 . 2 5 <newline> <tab> <tab> <tab> quant = np . uint 8 ( np . clip ( np . round ( img ) , 0 , 2 5 5 ) ) <newline> <tab> <tab> <tab> ofs = 0 <newline> <tab> <tab> <tab> while ofs < quant . shape [ 0 ] : <newline> <tab> <tab> <tab> <tab> num = min ( quant . shape [ 0 ] - ofs , self . buffers [ lod ] . shape [ 0 ] - self . </data><UNK> ) <newline> <tab> <tab> <tab> <tab> if ( ( self . <UNK> [ i ] - ofs - quant . shape [ 1 ] ) = = quant . shape [ 1 ] , self . <UNK> [ i ] ) <newline> <newline> <tab> <tab> <tab> <tab> <tab> <tab> if ( self . <UNK> [ i ] - ofs + quant . shape [ 0 ] ) % 2 5 5 5 5 5 ) % 2 5 5 5 5 ) % 2 5 5 5 <newline> <tab> <tab> <tab> <tab> <tab> if ( self . <UNK> [ 
K=10,T=0.8: <data>loader = torch . utils . data . data loader ( <newline> <tab> <tab> <tab> datasets . mnist ( path , train = true , download = true , <newline> <tab> <tab> <tab> <tab> transform = transforms . compose ( [ <newline> <tab> <tab> <tab> <tab> <tab> transforms . to tensor ( ) , <newline> <tab> <tab> <tab> <tab> <tab> transforms . normalize ( ( 0 . 1 3 0 7 , ) , ( 0 . 3 0 8 1 , ) ) <newline> <tab> <tab> <tab> <tab> <tab> ] ) ) , <newline> <tab> <tab> <tab> <tab> batch </data>_ size = 1 , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> transforms . compose ( [ <newline> <tab> <tab> <tab> <tab> <tab> <tab> transforms . to tensor ( ) , <newline> <tab> <tab> <tab> <tab> <tab> <tab> transforms . to tensor ( ) , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> ) , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> 
K=10,T=0.8: <data>: ( 2 , 8 0 , 0 ) , <newline> <tab> " location " : " view 3 d > tools > node osc " , <newline> <tab> " description " : " realtime control of blender using osc data protocol " , <newline> <tab> " wiki _ url " : " https : / / github . com / <UNK> / blender . node osc / wiki " , <newline> <tab> " tracker _ url " : " https : / / github . com / <UNK> / blender . node osc / issues " , <newline> <tab> </data>" wiki _ url " : " https : / / github . com / github - <UNK> / blender . node osc " , <newline> <tab> " wiki _ url " : " https : / / github . com / <UNK> / blender . node osc / wiki / blender . node . <UNK> / blender . node . <UNK> / blender . node . <UNK> / blender . node - <UNK> / blender . node . <UNK> / blender . node . <UNK> / blender . node . <UNK> / blender . node . <UNK> / blender 
K=10,T=0.8: <data>) + ' ce = ' + <UNK> . ljust ( 5 , ' ' ) , fontsize = 1 4 , family = ' monospace ' ) <newline> <tab> tyl = tyl - offset <newline> <tab> plt . text ( txl , tyl , ' ( lmr , era - 2 0 c ) : r = ' + lec . ljust ( 5 , ' ' ) + ' ce = ' + <UNK> . ljust ( 5 , ' ' ) , fontsize = 1 4 , family = ' monospace ' ) <newline> <tab> tyl = </data>( 1 / 4 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 1 0 0 1 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 
K=10,T=0.8: <data>\ x 9 b \ xb 2 \ x 4 c \ x 7 e \ x 3 4 \ x 3 9 \ xd 3 \ x 0 1 \ x 0 0 \ x 0 d \ <newline> \ x 5 c \ xac \ xf 1 \ x 6 3 \ x 0 0 \ xe 2 \ x 3 e \ x 4 0 \ xbd \ x 1 0 \ xbf \ x 3 2 \ x 8 c \ x 7 5 \ x 0 a \ xb 5 \ <newline> \ xf 5 </data>\ x 1 e \ x 0 1 \ x 8 7 \ xe 9 \ x 5 0 \ x 3 7 \ x 0 8 \ x 8 2 \ x 0 8 \ x 2 5 \ x 0 0 \ xf 8 \ x 0 1 \ x 4 7 \ x 3 2 \ x 8 3 \ x 7 2 \ x 5 8 \ x 0 1 \ x 9 6 \ x 0 0 \ x 9 8 \ x 0 4 \ x 4 9 \ x 4 9 \ x 
K=10,T=0.8: <data>split ( axis = 0 , num _ or _ size _ splits = 2 , value = self . d _ all _ pose _ logits ) <newline> <newline> <tab> <tab> self . d _ r _ pose _ <UNK> _ logits = tf . slice ( self . d _ r _ pose _ logits , [ 0 , 0 ] , [ self . batch _ size , - 1 ] ) <newline> <tab> <tab> self . d _ r _ id _ casia _ logits = tf . slice ( self . d _ r _ </data>id _ map , [ 0 , 0 , 0 ] , 0 , [ self . batch _ size , - 1 ] , 0 , [ self . batch _ size , - 1 ] ) <newline> <tab> <tab> self . d _ r _ id _ map = tf . slice ( self . g _ r _ id _ map , [ 0 , 0 , 0 , 0 ] , [ self . batch _ size , - 1 ] ) <newline> <newline> <tab> <tab> return tf . reshape ( [ 1 , self . 
K=10,T=0.8: <data>self . _ _ actual ! = none and self . _ _ actual ! = self . _ _ ultimo ) : <newline> <tab> <tab> <tab> self . _ _ actual = self . _ _ actual . sig <newline> <newline> <tab> def ant ( self ) : <newline> <tab> <tab> if ( self . _ _ actual ! = none and self . _ _ actual ! = self . _ _ primero ) : <newline> <tab> <tab> <tab> self . _ _ actual = self . _ _ actual . ant <newline> <tab> <tab> <tab> <newline> <tab> </data><tab> <tab> self . _ _ actual = self . _ _ actual <newline> <tab> <tab> <tab> self . _ _ actual = self . _ _ actual . ant <newline> <tab> <tab> <tab> self . _ _ actual = self . _ _ actual <newline> <tab> <tab> <tab> self . _ _ actual = self . _ _ actual <newline> <tab> <tab> <tab> self . _ _ actual = self . _ _ actual <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> self . _ _ actual = self . _ _ actual <newline> <tab> <tab> <tab> self 
K=10,T=0.8: <data>: 1 5 0 , " 6 h " : 1 5 0 , " 8 h " : 1 5 0 , <newline> <tab> <tab> " 1 2 h " : 1 5 0 , " 1 6 h " : 1 5 0 , " 1 d " : 1 5 0 , " 2 d " : 1 5 0 , " 3 d " : 1 5 0 , " 4 d " : 1 5 0 , <newline> <tab> <tab> " 7 d " : 1 5 0 , " 1 4 d " : </data>1 6 0 , " 4 d " : 1 5 0 } <newline> <tab> <tab> self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal ( self . assert equal 
K=10,T=0.8: <data>" , " cust 4 7 " , " cust 4 8 " , " cust 4 9 " , " cust 5 " , " cust 5 0 " , " cust 5 1 " , " cust 5 2 " , " cust 5 3 " , " cust 5 4 " , " cust 5 5 " , " cust 5 6 " , " cust 5 7 " , " cust 5 8 " , " cust 5 9 " , " cust 6 " , " cust 6 0 " , " cust 6 1 </data>" , " cust 6 1 " , " cust 6 7 " , " cust 6 1 " , " cust 6 8 " , " cust 6 4 " , " cust 6 0 " , " cust 6 6 " , " cust 6 0 " , " cust 6 4 " , " cust 6 6 " , " cust 6 3 " , " cust 6 0 " , " cust 6 4 " , " cust 6 6 " , " cust 6 5 " , " cust 6 8 " , " cust 
K=10,T=0.8: <data><tab> args = initialize _ config ( ) <newline> <tab> manager = workflow manager ( args ) <newline> <tab> manager . execute _ workflow ( ) <newline> <newline> < filesep > <newline> import logging <newline> import datetime <newline> import time <newline> import os <newline> import sys <newline> <newline> def get _ handlers ( log _ file = true , log _ stdout = true ) : <newline> <tab> handlers = [ ] <newline> <tab> ts = time . time ( ) <newline> <tab> date = datetime . datetime . fromtimestamp ( ts ) . strftime ( ' % m - </data>% d - % h - % m - % s ' ) for ts in ts ] <newline> <tab> logger . info ( f ' * * * * * * * * * * * * * * * * * * ' ) <newline> <tab> logger . info ( f ' * * * * * * * * * * * * * * * * * * * ' ) <newline> <tab> logger . info ( f ' * * * * * * * * * * * * * * * * * 
K=10,T=0.8: <data>manually when restoring from cudnn lstm - trained <newline> <tab> <tab> <tab> checkpoints . <newline> <tab> <tab> state _ is _ tuple : if true , accepted and returned states are 2 - tuples of <newline> <tab> <tab> <tab> the ` c _ state ` and ` m _ state ` . if false , they are concatenated <newline> <tab> <tab> <tab> along the column axis . the latter behavior will soon be deprecated . <newline> <tab> <tab> activation : activation function of the inner states . default : ` tanh ` . <newline> <tab> <tab> reuse : ( optional </data>) : whether to use to use for the same as a batch ( default : 1 0 0 ) . <newline> <tab> <tab> dropout : dropout function that the dropout of the training . <newline> <tab> <tab> activation : activation function of the inner states . default : ` tanh ` . <newline> <tab> <tab> activation : activation function of the inner states . default : " relu " . <newline> <tab> <tab> dropout : dropout function that the dropout of the training . <newline> <newline> <tab> <tab> returns an iterable of a tuple of a tuple of a 
K=10,T=0.8: <data>) } " ) <newline> <tab> <tab> raise <newline> <newline> <tab> yield <newline> <newline> <tab> <newline> <tab> logger . info ( " . . . " ) <newline> <newline> <newline> app = fast api ( lifespan = lifespan ) <newline> <newline> <newline> <newline> <newline> async def full _ model _ search ( prompt : str ) : <newline> <tab> <newline> <tab> local _ result = await local _ search _ engine . asearch ( prompt ) <newline> <tab> global _ result = await global _ search _ engine . asearch ( prompt ) <newline> <tab> drift _ result = await </data>local _ search _ engine . generate ( <newline> <tab> <tab> prompt , <newline> <tab> <tab> max _ tokens = max _ generation _ len , <newline> <tab> <tab> max _ tokens = max _ generation _ len , <newline> <tab> <tab> max _ tokens = max _ generation _ len , <newline> <tab> <tab> max _ tokens = max _ tokens , <newline> <tab> ) - > dict [ str , str ] : <newline> <tab> <newline> <tab> return { " role " : " user " } <newline> <newline> <newline> <newline> async def main ( ) : <newline> 
K=10,T=0.8: <data>( opt . <UNK> , ' depths ' ) , <newline> <tab> <tab> seg root = osp . join ( opt . <UNK> , ' masks ' ) , <newline> <tab> <tab> im height = opt . im height , <newline> <tab> <tab> im width = opt . im width , <newline> <tab> <tab> phase = ' train ' ) <newline> train dataset = data loader _ nyu . concat dataset ( brdf dataset , nyudataset ) <newline> brdf loader = data loader ( train dataset , batch _ size = opt . batch size , num _ workers = </data>opt . num _ workers , pin _ memory = true ) <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> def parse _ args ( ) : <newline> <tab> parser = argparse . argument parser ( ) <newline> <tab> parser . add _ argument ( ' - - opt ' , ' - - batch _ size ' , type = int , default = 6 4 , help = ' number of epochs to use ' ) <newline> <tab> parser . add _ argument ( ' - - batch ' , 
K=10,T=0.8: <data>shm _ usage env variable ) . ' , <newline> <tab> <tab> <tab> <tab> dev _ shm _ size , required _ dev _ shm _ size <newline> <tab> <tab> <tab> ) <newline> <tab> <tab> <tab> sys . exit ( 2 ) <newline> <newline> <tab> logging . info ( ' starting browser to chat ! ! ' ) <newline> <newline> <tab> browser = webdriver . chrome ( executable _ path = ' . / chromedriver ' , options = options ) <newline> <newline> def bbb _ browser ( ) : <newline> <tab> global browser <newline> <newline> <tab> logging . info </data>( ' starting browser from the browser . . ' ) <newline> <tab> browser . set _ option ( ' - - browser ' , ' - - browser ' , ' - - browser ' , ' - - browser ' , ' - - browser ' , ' - - browser ' , ' - - browser ' , ' - - browser ' , ' - - browser ' , ' - - browser ' , <newline> <tab> <tab> <tab> <tab> <tab> <tab> ' - - browser ' , ' - - browser ' , ' - 
K=10,T=0.8: <data><tab> ret . append ( track ) <newline> <newline> <tab> <tab> self . tracks = ret <newline> <tab> <tab> if train _ data : <newline> <tab> <tab> <tab> return ret , train _ set <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> return ret <newline> <newline> < filesep > <newline> import json <newline> import math <newline> import os <newline> import time <newline> from datetime import datetime <newline> from functools import partial <newline> <newline> import einx <newline> import fire <newline> import matplotlib . pyplot as plt <newline> import mlx . core as mx <newline> import mlx . nn as nn </data><newline> import os . path as osp <newline> import torch <newline> import mlx . nn . parallel <newline> import numpy as np <newline> from torch . utils . data import data loader <newline> <newline> <newline> def find _ model _ from _ model _ from _ model _ class _ name ( model _ class _ name ) : <newline> <tab> model _ class _ name = model _ class _ name <newline> <tab> model _ class = model _ class _ name <newline> <tab> model _ class = model _ class <newline> <tab> model _ class = model _ 
K=10,T=0.8: <data>= 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 , <newline> <tab> <tab> <tab> curr _ year _ cool = 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 , <newline> <tab> <tab> <tab> prev _ year _ cool = 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 <newline> <tab> <tab> <tab> ( * _ year </data>_ heat + 1 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 0 / 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 / 1 0 / 0 / 0 
K=10,T=0.8: <data>l = node . get ( " inputs " , { } ) . get ( " text _ l " ) <newline> <tab> <tab> if text _ g and text _ l : <newline> <tab> <tab> <tab> if text _ g = = text _ l : <newline> <tab> <tab> <tab> <tab> return text _ g <newline> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> return f " text _ g : { text _ g } text _ l : { text _ l } " <newline> <tab> <tab> elif text _ g : <newline> <tab> </data><tab> <tab> return text _ g <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> return text _ g <newline> <tab> return f " text _ g : { text _ g } " <newline> <newline> <newline> def main ( ) : <newline> <tab> parser = argparse . argument parser ( description = " py torch slimming cifar training " ) <newline> <tab> parser . add _ argument ( " - - train _ set " , type = str , default = " " , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> help = " path to train 
K=10,T=0.8: <data>, _ = sample _ data ( dump _ paths , max _ norm = max _ norm , para = args . para , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> doc _ sample _ ratio = args . doc _ sample _ ratio , vec _ sample _ ratio = args . vec _ sample _ ratio , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> num _ dummy _ zeros = args . num _ dummy _ zeros , norm _ th = args . norm _ th ) <newline> <tab> <tab> </data><tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data><tab> convo [ " results " ] , <newline> <tab> <tab> <tab> <tab> <tab> 0 , <newline> <tab> <tab> <tab> <tab> <tab> 5 , <newline> <tab> <tab> <tab> <tab> <tab> len ( convo [ " results " ] ) , <newline> <tab> <tab> <tab> <tab> ) <newline> <tab> <tab> <tab> <tab> context . bot . edit _ message _ text ( <newline> <tab> <tab> <tab> <tab> <tab> chat _ id = query . message . chat . id , <newline> <tab> <tab> <tab> <tab> <tab> message _ id = query . message . message _ id , <newline> <tab> <tab> </data><tab> <tab> <tab> <tab> message = " " , <newline> <tab> <tab> <tab> <tab> <tab> ) <newline> <newline> <newline> if _ _ name _ _ = = " _ _ main _ _ " : <newline> <tab> <newline> <tab> parser = argparse . argument parser ( ) <newline> <tab> parser . add _ argument ( " - - dataset " , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> type = str , <newline> <tab> <tab> <tab> <tab> <tab> <tab> default = ' ' , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> help = " dataset id for 
K=10,T=0.8: <data>up direction with the gravity direction . " , <newline> <tab> <tab> <tab> ) <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> @ set _ top _ up _ button . on _ click <newline> <tab> <tab> <tab> def _ ( event : viser . gui event ) - > none : <newline> <tab> <tab> <tab> <tab> assert event . client is not none <newline> <tab> <tab> <tab> <tab> event . client . camera . up _ direction = self . aria _ up _ direction <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> lock </data>. add _ button ( self . on _ click _ button . on _ click _ button . on _ click _ button . on _ click _ button . on _ click _ button . on _ click _ button . on _ click _ button . on _ click _ button . on _ click _ button . on _ click _ button . on _ click _ button . on _ click _ button . on _ click _ button . on _ click _ button . on _ click _ button . on _ click 
K=10,T=0.8: <data>max _ r , stage + 1 ) <newline> <newline> <tab> def pulse ( self , event = [ ] , stage = 0 , height = 6 ) : <newline> <newline> <tab> <tab> <newline> <newline> <tab> <tab> if self . pulse pause and stage = = 0 : return <newline> <newline> <tab> <tab> <newline> <tab> <tab> <newline> <newline> <tab> <tab> if stage = = 0 : <newline> <tab> <tab> <tab> <newline> <newline> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> if time . time ( ) - self . prev pulse time < = 1 : <newline> <tab> <tab> </data><tab> <tab> current = self . prev pulse time <newline> <tab> <tab> <tab> <tab> if current > = 1 : return <newline> <newline> <tab> <tab> <newline> <tab> <tab> if ( current > = 1 ) : return <newline> <newline> <tab> <tab> else : <newline> <tab> <tab> <tab> return <newline> <newline> <tab> <tab> <newline> <tab> <tab> if ( current < = 1 ) or current > = 1 : return <newline> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <newline> <tab> <tab> <tab> if ( current < = 1 ) : <newline> <tab> 
K=10,T=0.8: <data>= model . eval ( ) . cuda ( ) <newline> <newline> <tab> <newline> <tab> print ( " ~ ~ ~ ~ ~ ~ ~ warming up cuda cache ~ ~ ~ ~ ~ ~ ~ " ) <newline> <tab> input _ context = " a cuda cache warm - up is needed to " <newline> <tab> input _ ids = tokenizer . encode ( input _ context , return _ tensors = " pt " ) . cuda ( ) <newline> <tab> output = model . generate ( <newline> <tab> <tab> input _ ids , <newline> <tab> <tab> precision </data>= output , <newline> <tab> <tab> input _ ids = input _ ids , <newline> <tab> <tab> do _ sample _ rate = do _ sample _ rate , <newline> <tab> ) <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> def get _ model _ dir ( model , model , model _ name = ' model . pkl ' , model = ' res _ model ' ) : <newline> <tab> model = res net ( model = model , model _ 
K=10,T=0.8: <data><tab> " <UNK> _ smart " : true , <newline> <tab> <newline> <tab> " speech _ recognition " : false , <newline> <tab> " group _ speech _ recognition " : false , <newline> <tab> " voice _ reply _ voice " : false , <newline> <tab> " always _ reply _ voice " : false , <newline> <tab> " voice _ to _ text " : " openai " , <newline> <tab> " text _ to _ voice " : " openai " , <newline> <tab> " text _ to _ voice _ model " : " tts - </data>tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - tts - voice tts - tts - tts - voice tts - tts - tts - tts - tts -