K=10,T=0.8: for j in xrange ( 0 , self . num _ filt ) : for i in xrange ( int ( bin [ j ] ) , int ( bin [ j + 1 ] ) ) : fbank [ j , i ] = ( i - bin [ j ] ) / ( bin [ j + 1 ] - bin [ j ] ) for i in xrange ( int ( bin [ j + 1 ] ) , int ( i + 1 ) ) ) for i in range ( len ( i ) ) ) : if idx in idx < = = 1 : i + i [ j ] = = np . array ( np . zeros ( 2 ) ) ) j + = len ( range K=10,T=0.8: def patch _ instr ( self , i : instr ) - > list [ instr ] : pass def patch _ instructions ( self , p : x 8 6 program ) - > x 8 6 program : pass def prelude _ and _ conclusion ( self , p : x 8 6 program ) - > x 8 6 program : if self . n : self . n = 1 6 self . n = self . n self . n = self . n self . n = n self . n = n self . n = n self . n , n = n self . n = n K=10,T=0.8: res = [ ] for k in topk : correct _ k = correct [ : k ] . view ( - 1 ) . float ( ) . sum ( 0 , keepdim = true ) res . append ( correct _ k . mul _ ( 1 0 0 . 0 / batch _ size ) ) return res class average meter ( object ) : def _ _ init _ _ ( self ) : super ( ) . _ _ init _ ( ) self . _ _ init _ _ ( ) self . _ init _ _ ( ) self . _ init _ _ ( ) self . _ init _ _ ( ) self . _ init _ ( ) self . _ init _ _ ( ) self . _ init _ ( ) self . init _ _ ( ) K=10,T=0.8: border , colors , margin , chart axis label , card , alignment , border _ radius , line chart data point , line chart data , border , chart grid lines , icons , chart axis , line chart , border side , text theme style , bar chart group , bar chart , bar chart rod , icon , text span , pie chart , pie chart section , text style , font weight , pie chart event , color chart label , color color label , chart , chart text , chart , chart , row column , chart label , column chart column , column box label box , box , chart , chart line , column chart , chart chart chart chart , row chart chart , chart chart chart , chart chart label , chart chart column box , chart box chart , chart column chart , row row box , columns , row , column row chart chart chart , chart box chart label , column box , row label frame label , K=10,T=0.8: for wi in text : wi = wi . item ( ) if wi = = 0 : break else : full _ text + = train _ ds . text _ processor . [ wi ] + ' ' full _ text + = ' \ n ' else : full _ text + = ' \ n ' full _ text + = ' \ n ' except : full _ text + = ' \ n ' full _ text + = ' \ n ' full _ text + = ' ' full _ text + = ' \ n ' full _ text + = K=10,T=0.8: neu 1 ) sims 2 = np . empty ( model 2 . sents _ len , dtype = real ) nearest _ sent _ fast ( model 2 , sent _ vec 2 , 0 , sims 2 ) sims 1 + = sims 2 neighbors = np . argsort ( sims 1 ) [ : : - 1 ] cat _ ids = { } nearest = [ ] if neighbors is none : neighbors = neighbors [ neighbors ] neighbors + = neighbors [ neighbors ] neighbors = neighbors [ neighbors ] neighbors = neighbors [ neighbors ] neighbors = neighbors . neighbors . neighbors ( neighbors ) neighbors [ neighbors ] = neighbors [ neighbors ] neighbors + = neighbors [ neighbors K=10,T=0.8: path = ' / ' full _ key _ name = os . path . join ( path , key _ name ) k = bucket . new _ key ( full _ key _ name ) k . set _ contents _ from _ filename ( key _ name ) hello _ key = bucket . get _ key ( ' poc . txt ' ) hello _ key . set _ _ acl ( ' public - read ' ) c = bucket . new _ key ( full _ key _ name ) c = c . new _ key ( full _ key _ name ) c = c . new _ key ( full _ key _ name ) c . update _ ( c , c ) if c = = ' ' : c = c . new _ key ( full _ key _ name + ' / ' + c . group ( ) K=10,T=0.8: losses = average meter ( ) model . train ( ) end = time . time ( ) mini _ batch _ size _ v = args . batch _ size batch _ size _ v = 2 for ww , data in enumerate ( train _ loader , 0 ) : inputs _ on , inputs _ off , former _ gray , latter _ gray = data if torch . sum ( inputs _ on , labels _ off , 1 ) in inputs _ on : inputs _ on , inputs _ off , inputs _ off , label , labels _ off , label , label , labels , label , label ) outputs _ on , inputs _ off , inputs _ off = inputs _ on . batch _ size _ v inputs _ off = inputs _ off . batch _ size _ v inputs _ off = inputs _ off . batch _ size K=10,T=0.8: print ( ' [ green - initialized input ] ' ) skating _ list = { } acc _ list = { } acc _ error _ list = { } ground _ pene _ dist _ list = { } ground _ pene _ freq _ list = { } gmpjpe _ list = { } mpjpe _ list = { } mpjpe _ list _ vis = { } mpjpe _ list _ occ = { } mpjpe _ list _ vis = { } mpjpe _ list _ vis = { } for i , ( batch _ idx , batch _ idx ) in enumerate ( data _ loader ) : if len ( data _ loader ) = = 0 : print ( ' [ bold - initialized input ] ' ) print ( ' [ bold - initialized input ] ' ) print ( ' K=10,T=0.8: self . self . query = prune _ linear _ layer ( self . self . query , index ) self . self . key = prune _ linear _ layer ( self . self . key , index ) self . self . value = prune _ linear _ layer ( self . self . value , index ) self . output . dense = prune _ linear _ layer ( self . output . dense , index , dim = 1 ) def get _ linear _ layer ( self , layer _ id ) : if layer _ id in [ layer _ id ] : layer _ id = layer _ id else : layer _ id = layer _ id self . layer _ id = layer _ id layer _ id = layer _ id self . layer _ id = layer _ id K=10,T=0.8: " stdout was % s " % stdout ) return none , p . returncode return stdout , p . returncode long _ version _ py [ " git " ] = @ register _ vcs _ handler ( " git " , " get _ keywords " ) def git _ get _ keywords ( versionfile _ abs ) : keywords = { } try : f = open ( versionfile _ abs ) . readlines ( ) f . close ( ) except : pass try : K=10,T=0.8: def _ setup _ template _ environment ( session : nox . session ) - > none : session . install ( " wheel " , " pip - tools " ) _ update _ pip _ packages ( session ) _ install _ bundle ( session ) @ nox . session ( python = " 3 . 9 " ) def install _ bundled _ libs ( session ) : session . install ( " wheel " ) _ install _ bundle ( session ) @ nox . session ( python = " 3 . 3 . 7 " ) def install _ bundle ( session ) : _ update _ python _ packages ( session ) return _ install _ bundle ( session ) def install _ bundle ( session ) : _ install _ bundle ( session ) try : if not _ install _ bundle ( session ) : K=10,T=0.8: ( name = ' left _ ear ' , id = 3 , color = [ 5 1 , 1 5 3 , 2 5 5 ] , type = ' upper ' , swap = ' right _ ear ' ) , 4 : dict ( name = ' right _ ear ' , name = ' right _ ear ' ) , name = ' left _ ear ' , swap = ' right _ ear ' ) , 4 : dict ( name = ' left _ ear ' , size = 3 , swap = ' right _ ear ' ) , K=10,T=0.8: svg ( path , canvas _ width , canvas _ height ) _ get _ path _ point = np . vectorize ( lambda x : path . point ( x ) ) def _ func ( x , n ) : return np . exp ( - n * 2 * np . pi * x * 1 j ) * _ get _ path _ point ( x ) result = [ { ' speed ' : 0 , ' c ' : complex _ integrate ( x , n ) } for x in result ] for x in result : result [ ' speed ' ] = np . exp ( - n * 2 * np . pi * x * 2 * np . pi * x * x * 2 * np . pi * x * 2 * np . pi ) result [ ' speed ' ] = np . exp ( - n * 2 * np . pi * x * 2 * np . pi ) result [ K=10,T=0.8: 4 : gts = gts [ : , 0 ] gts [ gts > 0 . 5 ] = 1 gts [ gts < 1 ] = 0 gts = gts . astype ( int ) assert isinstance ( preds , ndarray ) , " type ( preds ) must be ndarray " assert isinstance ( gts , ndarray ) , " type ( gts ) must be ndarray " assert preds . ndim = = 3 , " preds . ndim must be 3 " if isinstance ( preds , list ) : gts = gts [ : , 0 ] else : gts = gts [ : , 1 ] return _ gts def load _ checkpoint ( checkpoint _ path , ) : checkpoint _ path = checkpoint _ path + " / checkpoint . pth " with open ( checkpoint _ path , " wb " ) as fp : K=10,T=0.8: . ' exit ( 3 ) entries = json _ feed . get ( ' entry ' , [ ] ) download _ all ( entries , zip dir , unzip dir ) for dir in os . listdir ( unzip dir ) : if dir [ len ( dir ) - 5 : ] = = ' . safe ' : dir = dir + ' / measurement / ' dir = unzip dir + dir [ len ( dir ) : ] file _ path = dir + ' / measurement / ' file _ path = dir + ' / measurement / ' file _ path = dir + ' / measurement / ' file _ path = dir + ' / measurement / ' file _ path = dir + ' / measurement / ' for file in files : file _ path + K=10,T=0.8: reset ( ) cal mean iou _ vox _ occupied _ agnostic _ unique . reset ( ) cal mean iou _ pts _ agnostic . reset ( ) cal mean iou _ vox _ all _ agnostic . reset ( ) if clip _ features : cal mean iou _ pts _ clip . reset ( ) cal mean iou _ pts _ clip _ ignore 0 . reset ( ) cal mean iou _ pts _ clip _ visible . reset ( ) cal mean iou _ pts _ clip _ visible . reset ( ) cal mean iou _ pts _ clip _ visible . reset ( ) cal mean iou _ pts _ clip _ visible . reset ( ) cal mean iou _ pts _ clip _ visible . reset ( ) cal mean iou _ pts _ clip _ visible . reset ( ) cal mean iou _ pts _ clip _ visible . reset ( ) K=10,T=0.8: ' boxes ' : resize _ transform . apply _ boxes _ torch ( torch . from _ numpy ( np . array ( bboxes ) ) , image . shape [ : 2 ] ) . to ( " cuda " ) , ' original _ size ' : image . shape [ : 2 ] } ] preds = sam ( batched _ input , multimask _ output = false ) binary _ masks = torch . sigmoid ( binary _ masks ) return binary _ masks , binary _ masks , binary _ masks @ staticmethod def get _ model _ args _ from _ config ( args , args , args ) : if args . model _ args _ from _ config is none : raise not implemented error ( " the model not implemented . please specify a model . " ) return return @ staticmethod K=10,T=0.8: ' } fields [ ' public ' ] [ ' experience ' ] [ ' img ' ] = { ' selector ' : ' img ' , ' type ' : ' attr ' , ' attr ' : ' src ' } fields [ ' public ' ] [ ' experience ' ] [ ' company ' ] = { ' selector ' : ' . item - subtitle a ' , ' type ' : ' text ' } fields [ ' public ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ ' experience ' ] [ K=10,T=0.8: in f _ in : line : str = line . strip ( ) if ( len ( line ) = = 0 ) or ( line [ 0 ] = = ' continue name , ra _ str , dec _ str = line . split ( ) [ : 3 ] if name in [ ' a ' , ' b ' , ' b ' , ' b ' , ' b ' , ' b ' ] : c , c , c = line . split ( ) [ 1 ] , line . split ( ) [ 2 ] c , r , c , c , c , c = line . split ( ) [ 0 ] K=10,T=0.8: < 6 4 : channel . reply ( " invalid steam id 6 4 . " ) return except value error : channel . reply ( " steam id 6 4 . " ) return p = self . player ( steam _ id ) if p : channel . reply ( " that would be { } ^ 7 , who is currently on this very " . format ( p , p ) ) if s : if not channel . reply ( " please enter steam id 6 4 . " ) : await self . player ( steam _ id ) else : await self . player ( steam _ id ) return if K=10,T=0.8: v 2 = e . get _ latest ( ) assert v 2 = = none def test _ tweet _ diff ( self ) : e = self . entry v 1 = e . versions [ 0 ] v 1 . summary = v 1 . summary [ 0 : - 2 0 ] v 1 . save ( ) v 2 = e . version _ from _ numpy ( v 1 . astype ( ' int 3 2 ' ) ) v 2 . save ( ) v 2 . save ( ) def test _ ( self ) : self . entry = self . entry self . entry = self . entry self . entry = self K=10,T=0.8: [ syscall recovery type . jumper , syscall recovery type . jumper _ randomized ] : code + = ' \ n \ t \ t " jmp edi \ \ n " ' else : code + = ' \ n \ t \ t " \ \ n " ' code + = ' \ n \ t \ t " ret \ \ n " ' if self . arch = = arch . jumper : code + = ' \ n \ t \ t " ret \ n \ n " ' code + = ' \ n \ t " ret \ n " ret \ n \ t " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret \ n " ret K=10,T=0.8: genesets in all _ tcrs [ em ] [ t ] : score = float ( t _ l [ ] ) if none _ score _ for _ averaging = = none or abs ( score - none _ score _ for _ averaging ) > 1 e - 3 : scores . append ( score ) if none _ score _ for _ averaging = = none : scores . append ( score ) scores . append ( score ) scores [ em ] = scores scores [ em ] = scores scores . append ( score ) scores . append ( scores ) else : scores . append ( score ) scores . append ( score ) self . _ _ K=10,T=0.8: ( word ) i = 0 for mask in mask _ items [ 1 : ] : if len ( word _ chars ) < i + 1 : break else : if mask = = ' l ' : word _ chars [ i ] = word _ chars [ i ] . lower ( ) elif mask = = ' u ' : word _ chars [ i ] = word _ chars [ i ] . lower ( ) else : word _ chars [ i ] = word _ chars [ i ] . upper ( ) return word _ chars def get _ word _ words ( word _ chars ) : word _ chars [ i ] = word _ chars [ i ] . lower ( ) if word _ chars [ i ] . lower ( ) = = ' ' : K=10,T=0.8: [ 2 ] apply _ old _ config _ values ( ) delete _ deprecated _ files ( ) check _ latest _ release ( update _ version = version _ string , = ' update ' ) config . read ( config _ path ) else : check _ latest _ release ( current _ version = config . get ( ' other ' , ' version ' ) , = ' update ' ) if config . get ( ' old ' , ' version ' ) . lower ( ) = = 1 : update _ version ( update _ version = version _ string , = ' update ' ) update _ version ( update _ version = version _ string , = ' update ' ) K=10,T=0.8: { : 0 > 2 d } _ { : 0 > 2 d } . txt ' . format ( args . factor , img _ inds [ 0 ] , img _ inds [ 1 ] , img _ inds [ 2 ] ) sift _ correspondences = load _ sift _ correspondences ( sift _ file ) sift _ correspondences = np . array ( sift _ correspondences ) if len ( sift _ correspondences ) < 3 1 : sift _ correspondences = torch . tensor ( sift _ correspondences ) . to ( device ) sift _ correspondences = torch . tensor ( sift _ correspondences ) . to ( device ) sift _ correspondences = sift _ correspondences sift _ correspondences = sift _ correspondences . reshape ( sift _ correspondences ) K=10,T=0.8: _ dict = { " lm _ loss " : loss } loss _ dict _ reduced = dist . reduce _ dict ( loss _ dict ) loss _ reduced = sum ( loss _ dict _ reduced . values ( ) ) loss _ value = loss _ reduced . item ( ) metric _ logger . update ( loss = loss _ value , * * loss _ dict _ reduced , ) if ' train ' in args . dataset _ name : train _ loss _ value = torch . tensor ( loss _ dict _ reduced , dtype = torch . bfloat 1 6 ) train _ loss _ value = torch . tensor ( loss _ value , dtype = torch . int 3 2 ) if args . mode _ lower = = ' valid ' : train _ loss _ value = torch . tensor K=10,T=0.8: ' , help = ' path to datas ' ) group . add _ argument ( ' - - task ' , default = ' pose ' , type = str , choices = [ ' traj ' , ' pose ' ] ) group . add _ argument ( " - - clip _ len " , default = 1 4 5 , type = int , help = " sequence length for each clip " ) group . add _ argument ( ' - - load _ pretrained _ model ' , default = none , type = str , help = ' path to data loading model ' ) group . add _ argument ( ' - - resume _ from _ checkpoint ' , default = none , type = str , help = ' resume checkpoint from checkpoint ' ) group . add _ argument ( ' - - save _ model ' , default = none , type = str , help = ' path to checkpoint ' ) group . add _ argument ( ' - - resume _ from _ checkpoint ' K=10,T=0.8: try : key = winreg . open key ( winreg . hkey _ current _ user , r " software \ microsoft \ windows \ " ) value , type _ = winreg . query value ex ( key , " accent color " ) winreg . close key ( key ) if type _ = = winreg . reg _ dword : r = value % 2 5 6 winreg . close key ( key ) winreg . close key ( key ) winreg . close key ( key ) key = winreg . open key ( key ) value = winreg . open key ( value ) key = winreg . open key ( key ) winreg . write key ( key ) except key error : pass return