K=10,T=0.8: : " the input training data file ( a text file ) . " } ) eval _ data _ file : optional [ str ] = field ( default = none , metadata = { " help " : " an optional input evaluation data file to evaluate the perplexity on ( a text file ) . " } , ) line _ by _ line : bool = field ( default = true , metadata = { " _ ) K=10,T=0.8: _ v 3 ( weights = none ) self . features = nn . sequential ( self . inception _ v 3 . conv 2 d _ 1 a _ 3 x 3 , self . inception _ v 3 . conv 2 d _ 2 a _ 3 x 3 , self . inception _ v 3 . conv 2 d _ 2 b _ 3 x 3 , nn . max pool 2 d ( kernel _ size _ . ( ) ) . ( _ _ ' _ ] = ( ' _ ' . , . _ = ) K=10,T=0.8: from typing import optional , callable , any import jax import jax . numpy as jnp _ quantile fn = callable [ [ any , float ] , float ] _ calibrate fn = callable [ [ jnp . ndarray , jnp . ndarray , jnp . ndarray ] , any ] _ predict fn = callable [ [ jnp . ndarray , any , jnp . ndarray ] , jnp . ndarray ] self . = = ( ) return ( ' ) return ( K=10,T=0.8: elif len ( argv ) = = 3 : train _ list = argv [ 1 ] test _ list = argv [ 2 ] elif len ( argv ) > 3 : help ( ) return new _ labels _ path , train _ list , test _ list def help ( ) : print ( ' usage : python data 2 lmdb . py [ path | [ train test ] ] \ n ' ' path points to a directory with ground ( ) : print ( ' , if print ( K=10,T=0.8: ) ) ) results . update ( result ) return results def _ mp _ fn ( index ) : main ( ) if _ _ name _ _ = = " _ _ main _ _ " : main ( ) import os from typing import any , optional import argparse import torch from transformers import auto config , auto tokenizer , training arguments , pre trained model , pre trained tokenizer , set _ = self . ( " ) " , . " , print ( ) print ( " ) : . " : ( " : if ' . _ ( " : if _ dict ( self . 1 ) import K=10,T=0.8: ff ] + " , " " , caption ) caption = re . sub ( r " [ \ u 3 4 0 0 - \ u 4 ] + " , " " , caption ) caption = re . sub ( r " [ \ u 4 0 - \ u 4 ] + " , " " , caption ) caption = re . sub ( r " [ \ u 4 e 0 0 - \ u 9 ] + " , " " , self . args . . " , for ( 2 , _ size ( ( " : K=10,T=0.8: incremental _ adj _ mat , distance _ to _ others , node _ sequence , edge _ type _ masks , edge _ type _ labels , local _ stop , edge _ masks , edge _ labels , overlapped _ edge _ features = \ construct _ incremental _ graph ( dataset , edges _ no _ master , chosen _ bucket _ size , len ( nodes _ no _ master ) , nodes _ no _ master _ dim , K=10,T=0.8: . 7 9 7 8 8 4 5 6 + 0 . 1 0 7 0 3 2 2 2 4 3 * x * x ) ) + 0 . 5 * ( 1 + tanh _ out ) return ff class tttcache : def _ _ init _ _ ( self , model , batch _ size : int ) : config = model . config self . seqlen _ offset = 0 self . mini _ batch _ . output . 2 output . output . 1 , model . path . 2 d . 1 output . path . output ) 2 _ path . 2 4 2 _ . ( 1 , 1 2 _ path . . _ dim , 1 , 2 2 , , output . 2 , _ . K=10,T=0.8: super ( ) . _ _ init _ _ ( ) self . config = config self . layer _ idx = layer _ idx self . norm = ( config . hidden _ size , eps = config . _ norm _ eps ) self . conv = nn . conv 1 d ( config . hidden _ size , config . hidden _ size , bias = true ) output _ _ path _ dim , def output _ dim , output _ output _ _ _ size = batch _ size )