runs/code-decoder-v30-alltrains-v3/noop/ckpt/generated.txt

K=10,T=0.8: <data>- 8 " ) as f : <newline> <tab> <tab> <tab> if config _ dict is none : <newline> <tab> <tab> <tab> <tab> yaml . dump ( self . config _ dict , f , allow _ <UNK> = true ) <newline> <tab> <tab> <tab> else : <newline> <tab> <tab> <tab> <tab> yaml . dump ( config _ dict , f , allow _ <UNK> = true ) <newline> <newline> <tab> def load _ config ( self ) : <newline> <tab> <tab> config _ path = os . path . join ( self . base _ path , self . </data>= ( ( <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> = ( ( ( <newline> _ <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data><tab> <tab> <tab> <tab> <tab> <tab> <tab> tf . reduce _ sum ( self . placeholders [ ' node _ mask ' ] , axis = 1 , keep _ dims = true ) <newline> <tab> <tab> global _ graph _ repr = tf . expand _ dims ( global _ graph _ repr _ before _ <UNK> , 1 ) <newline> <tab> <tab> global _ graph _ repr = tf . tile ( global _ graph _ repr , [ 1 , v , 1 ] ) <newline> <tab> <tab> <newline> <tab> <tab> distance _ repr = tf . </data><UNK> = ' ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> if ( , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> model _ _ _ size ( ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> 
K=10,T=0.8: <data>optax . apply _ <UNK> ( trainable _ params , <UNK> ) <newline> return loss , new _ params , new _ model _ state , new _ optimizer _ state , mixed <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> import functools <newline> from typing import callable , tuple , dict , any <newline> <newline> from absl import logging <newline> import haiku as hk <newline> import jax <newline> import jax . numpy as jnp <newline> import numpy as np <newline> import tensorflow as tf <newline> <newline> import data as cpdata </data>: <newline> <newline> <newline> <newline> <tab> <tab> <newline> <newline> <newline> <newline> from . append ( ) <newline> <newline> <tab> <tab> <UNK> = " . append ( : <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <tab> <tab> <UNK> _ <UNK> ( ) : <newline> <newline> <newline> <newline> <newline> <newline> <newline> <newline> <tab> " ) <newline> <newline> <newline> <newline> <newline> <newline> <newline> import <UNK> ( <UNK> _ <UNK> ( " : " : <newline> from . <UNK> _ data _ <UNK> _ <UNK> ( <newline> <newline> <newline> <tab> <tab> <tab> <tab> if <UNK> _ <UNK> _ <UNK> _ <UNK> ( 
K=10,T=0.8: <data>_ symbols ' ] <newline> <tab> <tab> batch _ size = tf . shape ( self . placeholders [ ' initial _ node _ <UNK> ' ] ) [ 0 ] <newline> <tab> <tab> <newline> <tab> <tab> filtered _ z _ sampled = self . ops [ " initial _ <UNK> _ for _ decoder " ] <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <newline> <tab> <tab> incre _ adj _ mat = self . placeholders [ ' incre _ adj _ mat ' ] [ : , idx , : , : , : </data><newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> print ( ' ] = ' ] [ ' ] ( self . append ( ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> self ) : <newline> <tab> <tab> 
K=10,T=0.8: <data>= ' ' , loss = ' ' , lr = ' ' ) : <newline> <tab> <tab> super ( ) . _ _ init _ _ ( ) <newline> <tab> <tab> device = torch . device ( " cuda " if torch . cuda . is _ available ( ) else " cpu " ) <newline> <tab> <tab> self . layer = layer [ 0 ] <newline> <tab> <tab> self . res _ seq = list ( layer [ 1 ] ) <newline> <newline> <tab> <tab> for idx , i in enumerate ( self . res _ seq ) </data><newline> <newline> <tab> <tab> <tab> <tab> <tab> <tab> return np . <UNK> ( self . append ( self . nn . <UNK> _ a . reshape ( ) : <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> if _ dim , <newline> <tab> return self , 3 2 , <newline> <tab> <newline> <tab> self . reshape ( x = 1 ) <newline> <tab> <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> 
K=10,T=0.8: <data>) <newline> <tab> print ( " cout _ num : { } " . format ( cout _ num ) ) <newline> <tab> print ( " cout _ num _ del : { } " . format ( cout _ num _ del ) ) <newline> <tab> print ( " cout _ num _ res : { } " . format ( cout _ num _ res ) ) <newline> <tab> print ( " cout _ hand _ del : { } " . format ( cout _ hand _ del ) ) <newline> <tab> print ( " cout _ </data><UNK> " ] ) <newline> <tab> print ( ) <newline> <tab> <tab> <tab> " ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> def _ <UNK> . get _ <UNK> " . <UNK> " : <newline> <tab> <tab> <tab> <tab> <tab> for dataset = true <newline> <tab> <tab> <tab> " , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> if self . append ( " : <newline> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> " 
K=10,T=0.8: <data>from _ pretrained ( model _ args . tokenizer _ name , cache _ dir = model _ args . cache _ dir ) <newline> <tab> elif model _ args . model _ name _ or _ path : <newline> <tab> <tab> tokenizer = auto tokenizer . from _ pretrained ( model _ args . model _ name _ or _ path , cache _ dir = model _ args . cache _ dir ) <newline> <tab> else : <newline> <tab> <tab> raise value error ( <newline> <tab> <tab> <tab> " you are instantiating a new tokenizer from scratch . </data>config . <UNK> , <newline> <tab> <tab> model _ name = <UNK> _ args . <UNK> ( args . model _ args . <UNK> _ loss _ <UNK> , <newline> <tab> model _ <UNK> _ model _ name = config . " <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <newline> print _ path , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> <tab> <tab> <newline> <tab> <tab> <tab> <tab> args . <newline> <tab> <tab> <tab> if args . model _ 
K=10,T=0.8: <data>_ <UNK> ' ] <newline> <tab> <tab> <tab> config [ ' <UNK> ' ] = 1 <newline> <tab> <tab> <tab> config [ ' <UNK> _ r ' ] = none <newline> <tab> <tab> <tab> config [ ' stem _ w ' ] = 1 2 <newline> <tab> <tab> <tab> l = sum ( config [ ' ds ' ] ) <newline> <tab> <tab> <tab> if ' res n ' in self . searchspace : <newline> <tab> <tab> <tab> <tab> config [ ' stem _ type ' ] = ' res _ stem _ in ' <newline> <tab> <tab> <tab> else </data>: <newline> <tab> <tab> <tab> <tab> <tab> self . placeholders [ ' ] , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> self . params [ ' ] = self . ops [ ' node _ params [ ' ] = self . placeholders [ ' ] [ ' ] , self . params [ ' ] = self . placeholders [ ' ] ) , <newline> <tab> <tab> <tab> <tab> <tab> <tab> self . conv _ _ size = self . conv _ dim = self . get _ size = self . get _ 
K=10,T=0.8: <data>incre _ adj _ mat , num _ vertices , <newline> <tab> <tab> <tab> <tab> <tab> distance _ to _ others , overlapped _ edge _ dense , node _ sequence , edge _ type _ masks , edge _ masks , random _ normal _ states ) : <newline> <tab> <tab> if incre _ adj _ mat is none : <newline> <tab> <tab> <tab> incre _ adj _ mat = np . zeros ( ( 1 , 1 , self . num _ edge _ types , 1 , 1 ) ) <newline> <tab> <tab> <tab> distance _ to </data>_ prob _ symbol _ masks , neighbor , edge _ prob , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> self . <UNK> _ labels , edge _ idx ) <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> if not none : + = self . <UNK> ( res _ type _ edge _ masks , <newline> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> <tab> self . conv _ type _ features