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.gitattributes

@@ -727,3 +727,4 @@ mplug_owl2/lib/python3.10/lib-dynload/_hashlib.cpython-310-x86_64-linux-gnu.so f
 mplug_owl2/lib/itcl4.2.4/libitcl4.2.4.so filter=lfs diff=lfs merge=lfs -text
 mplug_owl2/lib/sqlite3.44.2/libsqlite3.44.2.so filter=lfs diff=lfs merge=lfs -text
 mplug_owl2/lib/ossl-modules/legacy.so filter=lfs diff=lfs merge=lfs -text
+mplug_owl2/lib/tk8.6/demos/images/teapot.ppm filter=lfs diff=lfs merge=lfs -text

mplug_owl2/lib/pkgconfig/libcrypto.pc

@@ -0,0 +1,13 @@
+prefix=/root/envs/mplug_owl2
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+enginesdir=${libdir}/engines-3
+modulesdir=${libdir}/ossl-modules
+
+Name: OpenSSL-libcrypto
+Description: OpenSSL cryptography library
+Version: 3.0.16
+Libs: -L${libdir} -lcrypto
+Libs.private: -ldl -pthread 
+Cflags: -I${includedir}

mplug_owl2/lib/pkgconfig/libffi.pc

@@ -0,0 +1,11 @@
+prefix=/root/envs/mplug_owl2
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+toolexeclibdir=${libdir}
+includedir=/root/envs/mplug_owl2/include
+
+Name: libffi
+Description: Library supporting Foreign Function Interfaces
+Version: 3.4.4
+Libs: -L${toolexeclibdir} -lffi
+Cflags: -I${includedir}

mplug_owl2/lib/pkgconfig/liblzma.pc

@@ -0,0 +1,16 @@
+# SPDX-License-Identifier: 0BSD
+# Author: Lasse Collin
+
+prefix=/root/envs/mplug_owl2
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: liblzma
+Description: General purpose data compression library
+URL: https://tukaani.org/xz/
+Version: 5.6.4
+Cflags: -I${includedir}
+Cflags.private: -DLZMA_API_STATIC
+Libs: -L${libdir} -llzma
+Libs.private: -pthread 

mplug_owl2/lib/pkgconfig/libssl.pc

@@ -0,0 +1,11 @@
+prefix=/root/envs/mplug_owl2
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: OpenSSL-libssl
+Description: Secure Sockets Layer and cryptography libraries
+Version: 3.0.16
+Requires.private: libcrypto
+Libs: -L${libdir} -lssl
+Cflags: -I${includedir}

mplug_owl2/lib/pkgconfig/menuw.pc

@@ -0,0 +1,19 @@
+# pkg-config file generated by gen-pkgconfig
+# vile:makemode
+
+prefix=/root/envs/mplug_owl2
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include/ncursesw
+abi_version=6
+major_version=6
+version=6.4.20221231
+
+Name: menuw
+Description: ncurses 6.4 add-on library
+Version: ${version}
+URL: https://invisible-island.net/ncurses
+Requires.private: ncursesw
+Libs:  -L/root/envs/mplug_owl2/lib -Wl,-O2 -Wl,--sort-common -Wl,--disable-new-dtags -Wl,--gc-sections -Wl,-rpath,/root/envs/mplug_owl2/lib -Wl,-rpath-link,/root/envs/mplug_owl2/lib -lmenuw
+Libs.private:  
+Cflags:  -D_GNU_SOURCE -DNCURSES_WIDECHAR -I${includedir} -I/root/envs/mplug_owl2/include

mplug_owl2/lib/pkgconfig/python-3.10-embed.pc

@@ -0,0 +1,13 @@
+# See: man pkg-config
+prefix=/root/envs/mplug_owl2
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: Python
+Description: Embed Python into an application
+Requires:
+Version: 3.10
+Libs.private: -lcrypt -lpthread -ldl  -lutil -lm
+Libs: -L${libdir} -lpython3.10
+Cflags: -I${includedir}/python3.10

mplug_owl2/lib/pkgconfig/python3.pc

@@ -0,0 +1,13 @@
+# See: man pkg-config
+prefix=/root/envs/mplug_owl2
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: Python
+Description: Build a C extension for Python
+Requires:
+Version: 3.10
+Libs.private: -lcrypt -lpthread -ldl  -lutil -lm
+Libs:
+Cflags: -I${includedir}/python3.10

mplug_owl2/lib/pkgconfig/readline.pc

@@ -0,0 +1,12 @@
+prefix=/root/envs/mplug_owl2
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: Readline
+Description: Gnu Readline library for command line editing
+URL: http://tiswww.cwru.edu/php/chet/readline/rltop.html
+Version: 8.2
+Requires.private: tinfo
+Libs: -L${libdir} -lreadline
+Cflags: -I${includedir}

mplug_owl2/lib/pkgconfig/sqlite3.pc

@@ -0,0 +1,13 @@
+# Package Information for pkg-config
+
+prefix=/root/envs/mplug_owl2
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+
+Name: SQLite
+Description: SQL database engine
+Version: 3.45.3
+Libs: -L${libdir} -lsqlite3
+Libs.private: -lz -lm -ldl -lpthread 
+Cflags: -I${includedir}

mplug_owl2/lib/pkgconfig/tk.pc

@@ -0,0 +1,15 @@
+# tk pkg-config source file
+
+prefix=/root/envs/mplug_owl2
+exec_prefix=/root/envs/mplug_owl2
+libdir=/root/envs/mplug_owl2/lib
+includedir=${prefix}/include
+
+Name: The Tk Toolkit
+Description: Tk is a cross-platform graphical user interface toolkit, the standard GUI not only for Tcl, but for many other dynamic languages as well.
+URL: https://www.tcl-lang.org/
+Version: 8.6.14
+Requires: tcl >= 8.6
+Libs: -L${libdir} -ltk8.6 -ltkstub8.6
+Libs.private:  -lX11
+Cflags: -I${includedir}

mplug_owl2/lib/pkgconfig/uuid.pc

@@ -0,0 +1,11 @@
+prefix=/root/envs/mplug_owl2
+exec_prefix=/root/envs/mplug_owl2
+libdir=/root/envs/mplug_owl2/lib
+includedir=/root/envs/mplug_owl2/include
+
+Name: uuid
+Description: Universally unique id library
+Version: 2.32.1
+Requires:
+Cflags: -I${includedir}/uuid
+Libs: -L${libdir} -luuid

mplug_owl2/lib/pkgconfig/zlib.pc

@@ -0,0 +1,13 @@
+prefix=/root/envs/mplug_owl2
+exec_prefix=/root/envs/mplug_owl2
+libdir=/root/envs/mplug_owl2/lib
+sharedlibdir=/root/envs/mplug_owl2/lib
+includedir=/root/envs/mplug_owl2/include
+
+Name: zlib
+Description: zlib compression library
+Version: 1.2.13
+
+Requires:
+Libs: -L${libdir} -L${sharedlibdir} -lz
+Cflags: -I${includedir}

mplug_owl2/lib/tk8.6/images/README

@@ -0,0 +1,7 @@
+README - images directory
+
+This directory includes images for the Tcl Logo and the Tcl Powered
+Logo.  Please feel free to use the Tcl Powered Logo on any of your
+products that employ the use of Tcl or Tk.  The Tcl logo may also be
+used to promote Tcl in your product documentation, web site or other
+places you so desire.

mplug_owl2/lib/tk8.6/images/logo.eps

@@ -0,0 +1,2091 @@
+%!PS-Adobe-3.0 EPSF-3.0
+%%Creator: Adobe Illustrator(TM) 5.5
+%%For: (Bud Northern) (Mark Anderson Design)
+%%Title: (TCL/TK LOGO.ILLUS)
+%%CreationDate: (8/1/96) (4:58 PM)
+%%BoundingBox: 251 331 371 512
+%%HiResBoundingBox: 251.3386 331.5616 370.5213 511.775
+%%DocumentProcessColors: Cyan Magenta Yellow
+%%DocumentSuppliedResources: procset Adobe_level2_AI5 1.0 0
+%%+ procset Adobe_IllustratorA_AI5 1.0 0
+%AI5_FileFormat 1.2
+%AI3_ColorUsage: Color
+%%DocumentCustomColors: (TCL RED)
+%%CMYKCustomColor: 0 0.45 1 0 (Orange)
+%%+ 0 0.25 1 0 (Orange Yellow)
+%%+ 0 0.79 0.91 0 (TCL RED)
+%AI3_TemplateBox: 306 396 306 396
+%AI3_TileBox: 12 12 600 780
+%AI3_DocumentPreview: Macintosh_ColorPic
+%AI5_ArtSize: 612 792
+%AI5_RulerUnits: 0
+%AI5_ArtFlags: 1 0 0 1 0 0 1 1 0
+%AI5_TargetResolution: 800
+%AI5_NumLayers: 1
+%AI5_OpenToView: 90 576 2 938 673 18 1 1 2 40
+%AI5_OpenViewLayers: 7
+%%EndComments
+%%BeginProlog
+%%BeginResource: procset Adobe_level2_AI5 1.0 0
+%%Title: (Adobe Illustrator (R) Version 5.0 Level 2 Emulation)
+%%Version: 1.0 
+%%CreationDate: (04/10/93) ()
+%%Copyright: ((C) 1987-1993 Adobe Systems Incorporated All Rights Reserved)
+userdict /Adobe_level2_AI5 21 dict dup begin
+	put
+	/packedarray where not
+	{
+		userdict begin
+		/packedarray
+		{
+			array astore readonly
+		} bind def
+		/setpacking /pop load def
+		/currentpacking false def
+	 end
+		0
+	} if
+	pop
+	userdict /defaultpacking currentpacking put true setpacking
+	/initialize
+	{
+		Adobe_level2_AI5 begin
+	} bind def
+	/terminate
+	{
+		currentdict Adobe_level2_AI5 eq
+		{
+		 end
+		} if
+	} bind def
+	mark
+	/setcustomcolor where not
+	{
+		/findcmykcustomcolor
+		{
+			5 packedarray
+		} bind def
+		/setcustomcolor
+		{
+			exch aload pop pop
+			4
+			{
+				4 index mul 4 1 roll
+			} repeat
+			5 -1 roll pop
+			setcmykcolor
+		}
+		def
+	} if
+	
+	/gt38? mark {version cvx exec} stopped {cleartomark true} {38 gt exch pop} ifelse def
+	userdict /deviceDPI 72 0 matrix defaultmatrix dtransform dup mul exch dup mul add sqrt put
+	userdict /level2?
+	systemdict /languagelevel known dup
+	{
+		pop systemdict /languagelevel get 2 ge
+	} if
+	put
+	level2? not
+	{
+		/setcmykcolor where not
+		{
+			/setcmykcolor
+			{
+				exch .11 mul add exch .59 mul add exch .3 mul add
+				1 exch sub setgray
+			} def
+		} if
+		/currentcmykcolor where not
+		{
+			/currentcmykcolor
+			{
+				0 0 0 1 currentgray sub
+			} def
+		} if
+		/setoverprint where not
+		{
+			/setoverprint /pop load def
+		} if
+		/selectfont where not
+		{
+			/selectfont
+			{
+				exch findfont exch
+				dup type /arraytype eq
+				{
+					makefont
+				}
+				{
+					scalefont
+				} ifelse
+				setfont
+			} bind def
+		} if
+		/cshow where not
+		{
+			/cshow
+			{
+				[
+				0 0 5 -1 roll aload pop
+				] cvx bind forall
+			} bind def
+		} if
+	} if
+	cleartomark
+	/anyColor?
+	{
+		add add add 0 ne
+	} bind def
+	/testColor
+	{
+		gsave
+		setcmykcolor currentcmykcolor
+		grestore
+	} bind def
+	/testCMYKColorThrough
+	{
+		testColor anyColor?
+	} bind def
+	userdict /composite?
+	level2?
+	{
+		gsave 1 1 1 1 setcmykcolor currentcmykcolor grestore
+		add add add 4 eq
+	}
+	{
+		1 0 0 0 testCMYKColorThrough
+		0 1 0 0 testCMYKColorThrough
+		0 0 1 0 testCMYKColorThrough
+		0 0 0 1 testCMYKColorThrough
+		and and and
+	} ifelse
+	put
+	composite? not
+	{
+		userdict begin
+		gsave
+		/cyan? 1 0 0 0 testCMYKColorThrough def
+		/magenta? 0 1 0 0 testCMYKColorThrough def
+		/yellow? 0 0 1 0 testCMYKColorThrough def
+		/black? 0 0 0 1 testCMYKColorThrough def
+		grestore
+		/isCMYKSep? cyan? magenta? yellow? black? or or or def
+		/customColor? isCMYKSep? not def
+	 end
+	} if
+ end defaultpacking setpacking
+%%EndResource
+%%BeginResource: procset Adobe_IllustratorA_AI5 1.1 0
+%%Title: (Adobe Illustrator (R) Version 5.0 Abbreviated Prolog)
+%%Version: 1.1 
+%%CreationDate: (3/7/1994) ()
+%%Copyright: ((C) 1987-1994 Adobe Systems Incorporated All Rights Reserved)
+currentpacking true setpacking
+userdict /Adobe_IllustratorA_AI5_vars 70 dict dup begin
+put
+/_lp /none def
+/_pf
+{
+} def
+/_ps
+{
+} def
+/_psf
+{
+} def
+/_pss
+{
+} def
+/_pjsf
+{
+} def
+/_pjss
+{
+} def
+/_pola 0 def
+/_doClip 0 def
+/cf currentflat def
+/_tm matrix def
+/_renderStart
+[
+/e0 /r0 /a0 /o0 /e1 /r1 /a1 /i0
+] def
+/_renderEnd
+[
+null null null null /i1 /i1 /i1 /i1
+] def
+/_render -1 def
+/_rise 0 def
+/_ax 0 def
+/_ay 0 def
+/_cx 0 def
+/_cy 0 def
+/_leading
+[
+0 0
+] def
+/_ctm matrix def
+/_mtx matrix def
+/_sp 16#020 def
+/_hyphen (-) def
+/_fScl 0 def
+/_cnt 0 def
+/_hs 1 def
+/_nativeEncoding 0 def
+/_useNativeEncoding 0 def
+/_tempEncode 0 def
+/_pntr 0 def
+/_tDict 2 dict def
+/_wv 0 def
+/Tx
+{
+} def
+/Tj
+{
+} def
+/CRender
+{
+} def
+/_AI3_savepage
+{
+} def
+/_gf null def
+/_cf 4 array def
+/_if null def
+/_of false def
+/_fc
+{
+} def
+/_gs null def
+/_cs 4 array def
+/_is null def
+/_os false def
+/_sc
+{
+} def
+/discardSave null def
+/buffer 256 string def
+/beginString null def
+/endString null def
+/endStringLength null def
+/layerCnt 1 def
+/layerCount 1 def
+/perCent (%) 0 get def
+/perCentSeen? false def
+/newBuff null def
+/newBuffButFirst null def
+/newBuffLast null def
+/clipForward? false def
+end
+userdict /Adobe_IllustratorA_AI5 74 dict dup begin
+put
+/initialize
+{
+	Adobe_IllustratorA_AI5 dup begin
+	Adobe_IllustratorA_AI5_vars begin
+	discardDict
+	{
+		bind pop pop
+	} forall
+	dup /nc get begin
+	{
+		dup xcheck 1 index type /operatortype ne and
+		{
+			bind
+		} if
+		pop pop
+	} forall
+ end
+	newpath
+} def
+/terminate
+{
+ end
+ end
+} def
+/_
+null def
+/ddef
+{
+	Adobe_IllustratorA_AI5_vars 3 1 roll put
+} def
+/xput
+{
+	dup load dup length exch maxlength eq
+	{
+		dup dup load dup
+		length 2 mul dict copy def
+	} if
+	load begin
+	def
+ end
+} def
+/npop
+{
+	{
+		pop
+	} repeat
+} def
+/sw
+{
+	dup length exch stringwidth
+	exch 5 -1 roll 3 index mul add
+	4 1 roll 3 1 roll mul add
+} def
+/swj
+{
+	dup 4 1 roll
+	dup length exch stringwidth
+	exch 5 -1 roll 3 index mul add
+	4 1 roll 3 1 roll mul add
+	6 2 roll /_cnt 0 ddef
+	{
+		1 index eq
+		{
+			/_cnt _cnt 1 add ddef
+		} if
+	} forall
+	pop
+	exch _cnt mul exch _cnt mul 2 index add 4 1 roll 2 index add 4 1 roll pop pop
+} def
+/ss
+{
+	4 1 roll
+	{
+		2 npop
+		(0) exch 2 copy 0 exch put pop
+		gsave
+		false charpath currentpoint
+		4 index setmatrix
+		stroke
+		grestore
+		moveto
+		2 copy rmoveto
+	} exch cshow
+	3 npop
+} def
+/jss
+{
+	4 1 roll
+	{
+		2 npop
+		(0) exch 2 copy 0 exch put
+		gsave
+		_sp eq
+		{
+			exch 6 index 6 index 6 index 5 -1 roll widthshow
+			currentpoint
+		}
+		{
+			false charpath currentpoint
+			4 index setmatrix stroke
+		} ifelse
+		grestore
+		moveto
+		2 copy rmoveto
+	} exch cshow
+	6 npop
+} def
+/sp
+{
+	{
+		2 npop (0) exch
+		2 copy 0 exch put pop
+		false charpath
+		2 copy rmoveto
+	} exch cshow
+	2 npop
+} def
+/jsp
+{
+	{
+		2 npop
+		(0) exch 2 copy 0 exch put
+		_sp eq
+		{
+			exch 5 index 5 index 5 index 5 -1 roll widthshow
+		}
+		{
+			false charpath
+		} ifelse
+		2 copy rmoveto
+	} exch cshow
+	5 npop
+} def
+/pl
+{
+	transform
+	0.25 sub round 0.25 add exch
+	0.25 sub round 0.25 add exch
+	itransform
+} def
+/setstrokeadjust where
+{
+	pop true setstrokeadjust
+	/c
+	{
+		curveto
+	} def
+	/C
+	/c load def
+	/v
+	{
+		currentpoint 6 2 roll curveto
+	} def
+	/V
+	/v load def
+	/y
+	{
+		2 copy curveto
+	} def
+	/Y
+	/y load def
+	/l
+	{
+		lineto
+	} def
+	/L
+	/l load def
+	/m
+	{
+		moveto
+	} def
+}
+{
+	/c
+	{
+		pl curveto
+	} def
+	/C
+	/c load def
+	/v
+	{
+		currentpoint 6 2 roll pl curveto
+	} def
+	/V
+	/v load def
+	/y
+	{
+		pl 2 copy curveto
+	} def
+	/Y
+	/y load def
+	/l
+	{
+		pl lineto
+	} def
+	/L
+	/l load def
+	/m
+	{
+		pl moveto
+	} def
+} ifelse
+/d
+{
+	setdash
+} def
+/cf
+{
+} def
+/i
+{
+	dup 0 eq
+	{
+		pop cf
+	} if
+	setflat
+} def
+/j
+{
+	setlinejoin
+} def
+/J
+{
+	setlinecap
+} def
+/M
+{
+	setmiterlimit
+} def
+/w
+{
+	setlinewidth
+} def
+/H
+{
+} def
+/h
+{
+	closepath
+} def
+/N
+{
+	_pola 0 eq
+	{
+		_doClip 1 eq
+		{
+			clip /_doClip 0 ddef
+		} if
+		newpath
+	}
+	{
+		/CRender
+		{
+			N
+		} ddef
+	} ifelse
+} def
+/n
+{
+	N
+} def
+/F
+{
+	_pola 0 eq
+	{
+		_doClip 1 eq
+		{
+			gsave _pf grestore clip newpath /_lp /none ddef _fc
+			/_doClip 0 ddef
+		}
+		{
+			_pf
+		} ifelse
+	}
+	{
+		/CRender
+		{
+			F
+		} ddef
+	} ifelse
+} def
+/f
+{
+	closepath
+	F
+} def
+/S
+{
+	_pola 0 eq
+	{
+		_doClip 1 eq
+		{
+			gsave _ps grestore clip newpath /_lp /none ddef _sc
+			/_doClip 0 ddef
+		}
+		{
+			_ps
+		} ifelse
+	}
+	{
+		/CRender
+		{
+			S
+		} ddef
+	} ifelse
+} def
+/s
+{
+	closepath
+	S
+} def
+/B
+{
+	_pola 0 eq
+	{
+		_doClip 1 eq
+		gsave F grestore
+		{
+			gsave S grestore clip newpath /_lp /none ddef _sc
+			/_doClip 0 ddef
+		}
+		{
+			S
+		} ifelse
+	}
+	{
+		/CRender
+		{
+			B
+		} ddef
+	} ifelse
+} def
+/b
+{
+	closepath
+	B
+} def
+/W
+{
+	/_doClip 1 ddef
+} def
+/*
+{
+	count 0 ne
+	{
+		dup type /stringtype eq
+		{
+			pop
+		} if
+	} if
+	newpath
+} def
+/u
+{
+} def
+/U
+{
+} def
+/q
+{
+	_pola 0 eq
+	{
+		gsave
+	} if
+} def
+/Q
+{
+	_pola 0 eq
+	{
+		grestore
+	} if
+} def
+/*u
+{
+	_pola 1 add /_pola exch ddef
+} def
+/*U
+{
+	_pola 1 sub /_pola exch ddef
+	_pola 0 eq
+	{
+		CRender
+	} if
+} def
+/D
+{
+	pop
+} def
+/*w
+{
+} def
+/*W
+{
+} def
+/`
+{
+	/_i save ddef
+	clipForward?
+	{
+		nulldevice
+	} if
+	6 1 roll 4 npop
+	concat pop
+	userdict begin
+	/showpage
+	{
+	} def
+	0 setgray
+	0 setlinecap
+	1 setlinewidth
+	0 setlinejoin
+	10 setmiterlimit
+	[] 0 setdash
+	/setstrokeadjust where {pop false setstrokeadjust} if
+	newpath
+	0 setgray
+	false setoverprint
+} def
+/~
+{
+ end
+	_i restore
+} def
+/O
+{
+	0 ne
+	/_of exch ddef
+	/_lp /none ddef
+} def
+/R
+{
+	0 ne
+	/_os exch ddef
+	/_lp /none ddef
+} def
+/g
+{
+	/_gf exch ddef
+	/_fc
+	{
+		_lp /fill ne
+		{
+			_of setoverprint
+			_gf setgray
+			/_lp /fill ddef
+		} if
+	} ddef
+	/_pf
+	{
+		_fc
+		fill
+	} ddef
+	/_psf
+	{
+		_fc
+		ashow
+	} ddef
+	/_pjsf
+	{
+		_fc
+		awidthshow
+	} ddef
+	/_lp /none ddef
+} def
+/G
+{
+	/_gs exch ddef
+	/_sc
+	{
+		_lp /stroke ne
+		{
+			_os setoverprint
+			_gs setgray
+			/_lp /stroke ddef
+		} if
+	} ddef
+	/_ps
+	{
+		_sc
+		stroke
+	} ddef
+	/_pss
+	{
+		_sc
+		ss
+	} ddef
+	/_pjss
+	{
+		_sc
+		jss
+	} ddef
+	/_lp /none ddef
+} def
+/k
+{
+	_cf astore pop
+	/_fc
+	{
+		_lp /fill ne
+		{
+			_of setoverprint
+			_cf aload pop setcmykcolor
+			/_lp /fill ddef
+		} if
+	} ddef
+	/_pf
+	{
+		_fc
+		fill
+	} ddef
+	/_psf
+	{
+		_fc
+		ashow
+	} ddef
+	/_pjsf
+	{
+		_fc
+		awidthshow
+	} ddef
+	/_lp /none ddef
+} def
+/K
+{
+	_cs astore pop
+	/_sc
+	{
+		_lp /stroke ne
+		{
+			_os setoverprint
+			_cs aload pop setcmykcolor
+			/_lp /stroke ddef
+		} if
+	} ddef
+	/_ps
+	{
+		_sc
+		stroke
+	} ddef
+	/_pss
+	{
+		_sc
+		ss
+	} ddef
+	/_pjss
+	{
+		_sc
+		jss
+	} ddef
+	/_lp /none ddef
+} def
+/x
+{
+	/_gf exch ddef
+	findcmykcustomcolor
+	/_if exch ddef
+	/_fc
+	{
+		_lp /fill ne
+		{
+			_of setoverprint
+			_if _gf 1 exch sub setcustomcolor
+			/_lp /fill ddef
+		} if
+	} ddef
+	/_pf
+	{
+		_fc
+		fill
+	} ddef
+	/_psf
+	{
+		_fc
+		ashow
+	} ddef
+	/_pjsf
+	{
+		_fc
+		awidthshow
+	} ddef
+	/_lp /none ddef
+} def
+/X
+{
+	/_gs exch ddef
+	findcmykcustomcolor
+	/_is exch ddef
+	/_sc
+	{
+		_lp /stroke ne
+		{
+			_os setoverprint
+			_is _gs 1 exch sub setcustomcolor
+			/_lp /stroke ddef
+		} if
+	} ddef
+	/_ps
+	{
+		_sc
+		stroke
+	} ddef
+	/_pss
+	{
+		_sc
+		ss
+	} ddef
+	/_pjss
+	{
+		_sc
+		jss
+	} ddef
+	/_lp /none ddef
+} def
+/A
+{
+	pop
+} def
+/annotatepage
+{
+userdict /annotatepage 2 copy known {get exec} {pop pop} ifelse
+} def
+/discard
+{
+	save /discardSave exch store
+	discardDict begin
+	/endString exch store
+	gt38?
+	{
+		2 add
+	} if
+	load
+	stopped
+	pop
+ end
+	discardSave restore
+} bind def
+userdict /discardDict 7 dict dup begin
+put
+/pre38Initialize
+{
+	/endStringLength endString length store
+	/newBuff buffer 0 endStringLength getinterval store
+	/newBuffButFirst newBuff 1 endStringLength 1 sub getinterval store
+	/newBuffLast newBuff endStringLength 1 sub 1 getinterval store
+} def
+/shiftBuffer
+{
+	newBuff 0 newBuffButFirst putinterval
+	newBuffLast 0
+	currentfile read not
+	{
+	stop
+	} if
+	put
+} def
+0
+{
+	pre38Initialize
+	mark
+	currentfile newBuff readstring exch pop
+	{
+		{
+			newBuff endString eq
+			{
+				cleartomark stop
+			} if
+			shiftBuffer
+		} loop
+	}
+	{
+	stop
+	} ifelse
+} def
+1
+{
+	pre38Initialize
+	/beginString exch store
+	mark
+	currentfile newBuff readstring exch pop
+	{
+		{
+			newBuff beginString eq
+			{
+				/layerCount dup load 1 add store
+			}
+			{
+				newBuff endString eq
+				{
+					/layerCount dup load 1 sub store
+					layerCount 0 eq
+					{
+						cleartomark stop
+					} if
+				} if
+			} ifelse
+			shiftBuffer
+		} loop
+	}
+	{
+	stop
+	} ifelse
+} def
+2
+{
+	mark
+	{
+		currentfile buffer readline not
+		{
+		stop
+		} if
+		endString eq
+		{
+			cleartomark stop
+		} if
+	} loop
+} def
+3
+{
+	/beginString exch store
+	/layerCnt 1 store
+	mark
+	{
+		currentfile buffer readline not
+		{
+		stop
+		} if
+		dup beginString eq
+		{
+			pop /layerCnt dup load 1 add store
+		}
+		{
+			endString eq
+			{
+				layerCnt 1 eq
+				{
+					cleartomark stop
+				}
+				{
+					/layerCnt dup load 1 sub store
+				} ifelse
+			} if
+		} ifelse
+	} loop
+} def
+end
+userdict /clipRenderOff 15 dict dup begin
+put
+{
+	/n /N /s /S /f /F /b /B
+}
+{
+	{
+		_doClip 1 eq
+		{
+			/_doClip 0 ddef clip
+		} if
+		newpath
+	} def
+} forall
+/Tr /pop load def
+/Bb {} def
+/BB /pop load def
+/Bg {12 npop} def
+/Bm {6 npop} def
+/Bc /Bm load def
+/Bh {4 npop} def
+end
+/Lb
+{
+	4 npop
+	6 1 roll
+	pop
+	4 1 roll
+	pop pop pop
+	0 eq
+	{
+		0 eq
+		{
+			(%AI5_BeginLayer) 1 (%AI5_EndLayer--) discard
+		}
+		{
+			/clipForward? true def
+			
+			/Tx /pop load def
+			/Tj /pop load def
+			currentdict end clipRenderOff begin begin
+		} ifelse
+	}
+	{
+		0 eq
+		{
+			save /discardSave exch store
+		} if
+	} ifelse
+} bind def
+/LB
+{
+	discardSave dup null ne
+	{
+		restore
+	}
+	{
+		pop
+		clipForward?
+		{
+			currentdict
+		 end
+		 end
+		 begin
+			
+			/clipForward? false ddef
+		} if
+	} ifelse
+} bind def
+/Pb
+{
+	pop pop
+	0 (%AI5_EndPalette) discard
+} bind def
+/Np
+{
+	0 (%AI5_End_NonPrinting--) discard
+} bind def
+/Ln /pop load def
+/Ap
+/pop load def
+/Ar
+{
+	72 exch div
+	0 dtransform dup mul exch dup mul add sqrt
+	dup 1 lt
+	{
+		pop 1
+	} if
+	setflat
+} def
+/Mb
+{
+	q
+} def
+/Md
+{
+} def
+/MB
+{
+	Q
+} def
+/nc 3 dict def
+nc begin
+/setgray
+{
+	pop
+} bind def
+/setcmykcolor
+{
+	4 npop
+} bind def
+/setcustomcolor
+{
+	2 npop
+} bind def
+currentdict readonly pop
+end
+currentdict readonly pop
+end
+setpacking
+%%EndResource
+%%EndProlog
+%%BeginSetup
+Adobe_level2_AI5 /initialize get exec
+Adobe_IllustratorA_AI5 /initialize get exec
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+8.1999 58.6 m
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+) &
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+	} if
+	
+	/gt38? mark {version cvx exec} stopped {cleartomark true} {38 gt exch pop} ifelse def
+	userdict /deviceDPI 72 0 matrix defaultmatrix dtransform dup mul exch dup mul add sqrt put
+	userdict /level2?
+	systemdict /languagelevel known dup
+	{
+		pop systemdict /languagelevel get 2 ge
+	} if
+	put
+	level2? not
+	{
+		/setcmykcolor where not
+		{
+			/setcmykcolor
+			{
+				exch .11 mul add exch .59 mul add exch .3 mul add
+				1 exch sub setgray
+			} def
+		} if
+		/currentcmykcolor where not
+		{
+			/currentcmykcolor
+			{
+				0 0 0 1 currentgray sub
+			} def
+		} if
+		/setoverprint where not
+		{
+			/setoverprint /pop load def
+		} if
+		/selectfont where not
+		{
+			/selectfont
+			{
+				exch findfont exch
+				dup type /arraytype eq
+				{
+					makefont
+				}
+				{
+					scalefont
+				} ifelse
+				setfont
+			} bind def
+		} if
+		/cshow where not
+		{
+			/cshow
+			{
+				[
+				0 0 5 -1 roll aload pop
+				] cvx bind forall
+			} bind def
+		} if
+	} if
+	cleartomark
+	/anyColor?
+	{
+		add add add 0 ne
+	} bind def
+	/testColor
+	{
+		gsave
+		setcmykcolor currentcmykcolor
+		grestore
+	} bind def
+	/testCMYKColorThrough
+	{
+		testColor anyColor?
+	} bind def
+	userdict /composite?
+	level2?
+	{
+		gsave 1 1 1 1 setcmykcolor currentcmykcolor grestore
+		add add add 4 eq
+	}
+	{
+		1 0 0 0 testCMYKColorThrough
+		0 1 0 0 testCMYKColorThrough
+		0 0 1 0 testCMYKColorThrough
+		0 0 0 1 testCMYKColorThrough
+		and and and
+	} ifelse
+	put
+	composite? not
+	{
+		userdict begin
+		gsave
+		/cyan? 1 0 0 0 testCMYKColorThrough def
+		/magenta? 0 1 0 0 testCMYKColorThrough def
+		/yellow? 0 0 1 0 testCMYKColorThrough def
+		/black? 0 0 0 1 testCMYKColorThrough def
+		grestore
+		/isCMYKSep? cyan? magenta? yellow? black? or or or def
+		/customColor? isCMYKSep? not def
+	 end
+	} if
+ end defaultpacking setpacking
+%%EndResource
+%%BeginResource: procset Adobe_IllustratorA_AI5 1.1 0
+%%Title: (Adobe Illustrator (R) Version 5.0 Abbreviated Prolog)
+%%Version: 1.1 
+%%CreationDate: (3/7/1994) ()
+%%Copyright: ((C) 1987-1994 Adobe Systems Incorporated All Rights Reserved)
+currentpacking true setpacking
+userdict /Adobe_IllustratorA_AI5_vars 70 dict dup begin
+put
+/_lp /none def
+/_pf
+{
+} def
+/_ps
+{
+} def
+/_psf
+{
+} def
+/_pss
+{
+} def
+/_pjsf
+{
+} def
+/_pjss
+{
+} def
+/_pola 0 def
+/_doClip 0 def
+/cf currentflat def
+/_tm matrix def
+/_renderStart
+[
+/e0 /r0 /a0 /o0 /e1 /r1 /a1 /i0
+] def
+/_renderEnd
+[
+null null null null /i1 /i1 /i1 /i1
+] def
+/_render -1 def
+/_rise 0 def
+/_ax 0 def
+/_ay 0 def
+/_cx 0 def
+/_cy 0 def
+/_leading
+[
+0 0
+] def
+/_ctm matrix def
+/_mtx matrix def
+/_sp 16#020 def
+/_hyphen (-) def
+/_fScl 0 def
+/_cnt 0 def
+/_hs 1 def
+/_nativeEncoding 0 def
+/_useNativeEncoding 0 def
+/_tempEncode 0 def
+/_pntr 0 def
+/_tDict 2 dict def
+/_wv 0 def
+/Tx
+{
+} def
+/Tj
+{
+} def
+/CRender
+{
+} def
+/_AI3_savepage
+{
+} def
+/_gf null def
+/_cf 4 array def
+/_if null def
+/_of false def
+/_fc
+{
+} def
+/_gs null def
+/_cs 4 array def
+/_is null def
+/_os false def
+/_sc
+{
+} def
+/discardSave null def
+/buffer 256 string def
+/beginString null def
+/endString null def
+/endStringLength null def
+/layerCnt 1 def
+/layerCount 1 def
+/perCent (%) 0 get def
+/perCentSeen? false def
+/newBuff null def
+/newBuffButFirst null def
+/newBuffLast null def
+/clipForward? false def
+end
+userdict /Adobe_IllustratorA_AI5 74 dict dup begin
+put
+/initialize
+{
+	Adobe_IllustratorA_AI5 dup begin
+	Adobe_IllustratorA_AI5_vars begin
+	discardDict
+	{
+		bind pop pop
+	} forall
+	dup /nc get begin
+	{
+		dup xcheck 1 index type /operatortype ne and
+		{
+			bind
+		} if
+		pop pop
+	} forall
+ end
+	newpath
+} def
+/terminate
+{
+ end
+ end
+} def
+/_
+null def
+/ddef
+{
+	Adobe_IllustratorA_AI5_vars 3 1 roll put
+} def
+/xput
+{
+	dup load dup length exch maxlength eq
+	{
+		dup dup load dup
+		length 2 mul dict copy def
+	} if
+	load begin
+	def
+ end
+} def
+/npop
+{
+	{
+		pop
+	} repeat
+} def
+/sw
+{
+	dup length exch stringwidth
+	exch 5 -1 roll 3 index mul add
+	4 1 roll 3 1 roll mul add
+} def
+/swj
+{
+	dup 4 1 roll
+	dup length exch stringwidth
+	exch 5 -1 roll 3 index mul add
+	4 1 roll 3 1 roll mul add
+	6 2 roll /_cnt 0 ddef
+	{
+		1 index eq
+		{
+			/_cnt _cnt 1 add ddef
+		} if
+	} forall
+	pop
+	exch _cnt mul exch _cnt mul 2 index add 4 1 roll 2 index add 4 1 roll pop pop
+} def
+/ss
+{
+	4 1 roll
+	{
+		2 npop
+		(0) exch 2 copy 0 exch put pop
+		gsave
+		false charpath currentpoint
+		4 index setmatrix
+		stroke
+		grestore
+		moveto
+		2 copy rmoveto
+	} exch cshow
+	3 npop
+} def
+/jss
+{
+	4 1 roll
+	{
+		2 npop
+		(0) exch 2 copy 0 exch put
+		gsave
+		_sp eq
+		{
+			exch 6 index 6 index 6 index 5 -1 roll widthshow
+			currentpoint
+		}
+		{
+			false charpath currentpoint
+			4 index setmatrix stroke
+		} ifelse
+		grestore
+		moveto
+		2 copy rmoveto
+	} exch cshow
+	6 npop
+} def
+/sp
+{
+	{
+		2 npop (0) exch
+		2 copy 0 exch put pop
+		false charpath
+		2 copy rmoveto
+	} exch cshow
+	2 npop
+} def
+/jsp
+{
+	{
+		2 npop
+		(0) exch 2 copy 0 exch put
+		_sp eq
+		{
+			exch 5 index 5 index 5 index 5 -1 roll widthshow
+		}
+		{
+			false charpath
+		} ifelse
+		2 copy rmoveto
+	} exch cshow
+	5 npop
+} def
+/pl
+{
+	transform
+	0.25 sub round 0.25 add exch
+	0.25 sub round 0.25 add exch
+	itransform
+} def
+/setstrokeadjust where
+{
+	pop true setstrokeadjust
+	/c
+	{
+		curveto
+	} def
+	/C
+	/c load def
+	/v
+	{
+		currentpoint 6 2 roll curveto
+	} def
+	/V
+	/v load def
+	/y
+	{
+		2 copy curveto
+	} def
+	/Y
+	/y load def
+	/l
+	{
+		lineto
+	} def
+	/L
+	/l load def
+	/m
+	{
+		moveto
+	} def
+}
+{
+	/c
+	{
+		pl curveto
+	} def
+	/C
+	/c load def
+	/v
+	{
+		currentpoint 6 2 roll pl curveto
+	} def
+	/V
+	/v load def
+	/y
+	{
+		pl 2 copy curveto
+	} def
+	/Y
+	/y load def
+	/l
+	{
+		pl lineto
+	} def
+	/L
+	/l load def
+	/m
+	{
+		pl moveto
+	} def
+} ifelse
+/d
+{
+	setdash
+} def
+/cf
+{
+} def
+/i
+{
+	dup 0 eq
+	{
+		pop cf
+	} if
+	setflat
+} def
+/j
+{
+	setlinejoin
+} def
+/J
+{
+	setlinecap
+} def
+/M
+{
+	setmiterlimit
+} def
+/w
+{
+	setlinewidth
+} def
+/H
+{
+} def
+/h
+{
+	closepath
+} def
+/N
+{
+	_pola 0 eq
+	{
+		_doClip 1 eq
+		{
+			clip /_doClip 0 ddef
+		} if
+		newpath
+	}
+	{
+		/CRender
+		{
+			N
+		} ddef
+	} ifelse
+} def
+/n
+{
+	N
+} def
+/F
+{
+	_pola 0 eq
+	{
+		_doClip 1 eq
+		{
+			gsave _pf grestore clip newpath /_lp /none ddef _fc
+			/_doClip 0 ddef
+		}
+		{
+			_pf
+		} ifelse
+	}
+	{
+		/CRender
+		{
+			F
+		} ddef
+	} ifelse
+} def
+/f
+{
+	closepath
+	F
+} def
+/S
+{
+	_pola 0 eq
+	{
+		_doClip 1 eq
+		{
+			gsave _ps grestore clip newpath /_lp /none ddef _sc
+			/_doClip 0 ddef
+		}
+		{
+			_ps
+		} ifelse
+	}
+	{
+		/CRender
+		{
+			S
+		} ddef
+	} ifelse
+} def
+/s
+{
+	closepath
+	S
+} def
+/B
+{
+	_pola 0 eq
+	{
+		_doClip 1 eq
+		gsave F grestore
+		{
+			gsave S grestore clip newpath /_lp /none ddef _sc
+			/_doClip 0 ddef
+		}
+		{
+			S
+		} ifelse
+	}
+	{
+		/CRender
+		{
+			B
+		} ddef
+	} ifelse
+} def
+/b
+{
+	closepath
+	B
+} def
+/W
+{
+	/_doClip 1 ddef
+} def
+/*
+{
+	count 0 ne
+	{
+		dup type /stringtype eq
+		{
+			pop
+		} if
+	} if
+	newpath
+} def
+/u
+{
+} def
+/U
+{
+} def
+/q
+{
+	_pola 0 eq
+	{
+		gsave
+	} if
+} def
+/Q
+{
+	_pola 0 eq
+	{
+		grestore
+	} if
+} def
+/*u
+{
+	_pola 1 add /_pola exch ddef
+} def
+/*U
+{
+	_pola 1 sub /_pola exch ddef
+	_pola 0 eq
+	{
+		CRender
+	} if
+} def
+/D
+{
+	pop
+} def
+/*w
+{
+} def
+/*W
+{
+} def
+/`
+{
+	/_i save ddef
+	clipForward?
+	{
+		nulldevice
+	} if
+	6 1 roll 4 npop
+	concat pop
+	userdict begin
+	/showpage
+	{
+	} def
+	0 setgray
+	0 setlinecap
+	1 setlinewidth
+	0 setlinejoin
+	10 setmiterlimit
+	[] 0 setdash
+	/setstrokeadjust where {pop false setstrokeadjust} if
+	newpath
+	0 setgray
+	false setoverprint
+} def
+/~
+{
+ end
+	_i restore
+} def
+/O
+{
+	0 ne
+	/_of exch ddef
+	/_lp /none ddef
+} def
+/R
+{
+	0 ne
+	/_os exch ddef
+	/_lp /none ddef
+} def
+/g
+{
+	/_gf exch ddef
+	/_fc
+	{
+		_lp /fill ne
+		{
+			_of setoverprint
+			_gf setgray
+			/_lp /fill ddef
+		} if
+	} ddef
+	/_pf
+	{
+		_fc
+		fill
+	} ddef
+	/_psf
+	{
+		_fc
+		ashow
+	} ddef
+	/_pjsf
+	{
+		_fc
+		awidthshow
+	} ddef
+	/_lp /none ddef
+} def
+/G
+{
+	/_gs exch ddef
+	/_sc
+	{
+		_lp /stroke ne
+		{
+			_os setoverprint
+			_gs setgray
+			/_lp /stroke ddef
+		} if
+	} ddef
+	/_ps
+	{
+		_sc
+		stroke
+	} ddef
+	/_pss
+	{
+		_sc
+		ss
+	} ddef
+	/_pjss
+	{
+		_sc
+		jss
+	} ddef
+	/_lp /none ddef
+} def
+/k
+{
+	_cf astore pop
+	/_fc
+	{
+		_lp /fill ne
+		{
+			_of setoverprint
+			_cf aload pop setcmykcolor
+			/_lp /fill ddef
+		} if
+	} ddef
+	/_pf
+	{
+		_fc
+		fill
+	} ddef
+	/_psf
+	{
+		_fc
+		ashow
+	} ddef
+	/_pjsf
+	{
+		_fc
+		awidthshow
+	} ddef
+	/_lp /none ddef
+} def
+/K
+{
+	_cs astore pop
+	/_sc
+	{
+		_lp /stroke ne
+		{
+			_os setoverprint
+			_cs aload pop setcmykcolor
+			/_lp /stroke ddef
+		} if
+	} ddef
+	/_ps
+	{
+		_sc
+		stroke
+	} ddef
+	/_pss
+	{
+		_sc
+		ss
+	} ddef
+	/_pjss
+	{
+		_sc
+		jss
+	} ddef
+	/_lp /none ddef
+} def
+/x
+{
+	/_gf exch ddef
+	findcmykcustomcolor
+	/_if exch ddef
+	/_fc
+	{
+		_lp /fill ne
+		{
+			_of setoverprint
+			_if _gf 1 exch sub setcustomcolor
+			/_lp /fill ddef
+		} if
+	} ddef
+	/_pf
+	{
+		_fc
+		fill
+	} ddef
+	/_psf
+	{
+		_fc
+		ashow
+	} ddef
+	/_pjsf
+	{
+		_fc
+		awidthshow
+	} ddef
+	/_lp /none ddef
+} def
+/X
+{
+	/_gs exch ddef
+	findcmykcustomcolor
+	/_is exch ddef
+	/_sc
+	{
+		_lp /stroke ne
+		{
+			_os setoverprint
+			_is _gs 1 exch sub setcustomcolor
+			/_lp /stroke ddef
+		} if
+	} ddef
+	/_ps
+	{
+		_sc
+		stroke
+	} ddef
+	/_pss
+	{
+		_sc
+		ss
+	} ddef
+	/_pjss
+	{
+		_sc
+		jss
+	} ddef
+	/_lp /none ddef
+} def
+/A
+{
+	pop
+} def
+/annotatepage
+{
+userdict /annotatepage 2 copy known {get exec} {pop pop} ifelse
+} def
+/discard
+{
+	save /discardSave exch store
+	discardDict begin
+	/endString exch store
+	gt38?
+	{
+		2 add
+	} if
+	load
+	stopped
+	pop
+ end
+	discardSave restore
+} bind def
+userdict /discardDict 7 dict dup begin
+put
+/pre38Initialize
+{
+	/endStringLength endString length store
+	/newBuff buffer 0 endStringLength getinterval store
+	/newBuffButFirst newBuff 1 endStringLength 1 sub getinterval store
+	/newBuffLast newBuff endStringLength 1 sub 1 getinterval store
+} def
+/shiftBuffer
+{
+	newBuff 0 newBuffButFirst putinterval
+	newBuffLast 0
+	currentfile read not
+	{
+	stop
+	} if
+	put
+} def
+0
+{
+	pre38Initialize
+	mark
+	currentfile newBuff readstring exch pop
+	{
+		{
+			newBuff endString eq
+			{
+				cleartomark stop
+			} if
+			shiftBuffer
+		} loop
+	}
+	{
+	stop
+	} ifelse
+} def
+1
+{
+	pre38Initialize
+	/beginString exch store
+	mark
+	currentfile newBuff readstring exch pop
+	{
+		{
+			newBuff beginString eq
+			{
+				/layerCount dup load 1 add store
+			}
+			{
+				newBuff endString eq
+				{
+					/layerCount dup load 1 sub store
+					layerCount 0 eq
+					{
+						cleartomark stop
+					} if
+				} if
+			} ifelse
+			shiftBuffer
+		} loop
+	}
+	{
+	stop
+	} ifelse
+} def
+2
+{
+	mark
+	{
+		currentfile buffer readline not
+		{
+		stop
+		} if
+		endString eq
+		{
+			cleartomark stop
+		} if
+	} loop
+} def
+3
+{
+	/beginString exch store
+	/layerCnt 1 store
+	mark
+	{
+		currentfile buffer readline not
+		{
+		stop
+		} if
+		dup beginString eq
+		{
+			pop /layerCnt dup load 1 add store
+		}
+		{
+			endString eq
+			{
+				layerCnt 1 eq
+				{
+					cleartomark stop
+				}
+				{
+					/layerCnt dup load 1 sub store
+				} ifelse
+			} if
+		} ifelse
+	} loop
+} def
+end
+userdict /clipRenderOff 15 dict dup begin
+put
+{
+	/n /N /s /S /f /F /b /B
+}
+{
+	{
+		_doClip 1 eq
+		{
+			/_doClip 0 ddef clip
+		} if
+		newpath
+	} def
+} forall
+/Tr /pop load def
+/Bb {} def
+/BB /pop load def
+/Bg {12 npop} def
+/Bm {6 npop} def
+/Bc /Bm load def
+/Bh {4 npop} def
+end
+/Lb
+{
+	4 npop
+	6 1 roll
+	pop
+	4 1 roll
+	pop pop pop
+	0 eq
+	{
+		0 eq
+		{
+			(%AI5_BeginLayer) 1 (%AI5_EndLayer--) discard
+		}
+		{
+			/clipForward? true def
+			
+			/Tx /pop load def
+			/Tj /pop load def
+			currentdict end clipRenderOff begin begin
+		} ifelse
+	}
+	{
+		0 eq
+		{
+			save /discardSave exch store
+		} if
+	} ifelse
+} bind def
+/LB
+{
+	discardSave dup null ne
+	{
+		restore
+	}
+	{
+		pop
+		clipForward?
+		{
+			currentdict
+		 end
+		 end
+		 begin
+			
+			/clipForward? false ddef
+		} if
+	} ifelse
+} bind def
+/Pb
+{
+	pop pop
+	0 (%AI5_EndPalette) discard
+} bind def
+/Np
+{
+	0 (%AI5_End_NonPrinting--) discard
+} bind def
+/Ln /pop load def
+/Ap
+/pop load def
+/Ar
+{
+	72 exch div
+	0 dtransform dup mul exch dup mul add sqrt
+	dup 1 lt
+	{
+		pop 1
+	} if
+	setflat
+} def
+/Mb
+{
+	q
+} def
+/Md
+{
+} def
+/MB
+{
+	Q
+} def
+/nc 3 dict def
+nc begin
+/setgray
+{
+	pop
+} bind def
+/setcmykcolor
+{
+	4 npop
+} bind def
+/setcustomcolor
+{
+	2 npop
+} bind def
+currentdict readonly pop
+end
+currentdict readonly pop
+end
+setpacking
+%%EndResource
+%%EndProlog
+%%BeginSetup
+Adobe_level2_AI5 /initialize get exec
+Adobe_IllustratorA_AI5 /initialize get exec
+%AI5_Begin_NonPrinting
+Np
+%AI3_BeginPattern: (Yellow Stripe)
+(Yellow Stripe) 8.4499 4.6 80.4499 76.6 [
+%AI3_Tile
+(0 O 0 R 0 0.4 1 0 k 0 0.4 1 0 K) @
+(
+800 Ar
+0 J 0 j 3.6 w 4 M []0 d
+%AI3_Note:
+0 D
+8.1999 8.1999 m
+80.6999 8.1999 L
+S
+8.1999 22.6 m
+80.6999 22.6 L
+S
+8.1999 37.0001 m
+80.6999 37.0001 L
+S
+8.1999 51.3999 m
+80.6999 51.3999 L
+S
+8.1999 65.8 m
+80.6999 65.8 L
+S
+8.1999 15.3999 m
+80.6999 15.3999 L
+S
+8.1999 29.8 m
+80.6999 29.8 L
+S
+8.1999 44.1999 m
+80.6999 44.1999 L
+S
+8.1999 58.6 m
+80.6999 58.6 L
+S
+8.1999 73.0001 m
+80.6999 73.0001 L
+S
+) &
+] E
+%AI3_EndPattern
+%AI5_End_NonPrinting--
+%AI5_Begin_NonPrinting
+Np
+3 Bn
+%AI5_BeginGradient: (Black & White)
+(Black & White) 0 2 Bd
+[
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+303.7595 318.7835 L
+306.2734 318.7835 L
+306.2734 317.4593 L
+302.2387 317.4593 L
+302.2387 325.2598 L
+306.2734 325.2598 L
+306.2734 323.9356 L
+303.7595 323.9356 l
+f
+*U
+*u
+319.8602 317.4593 m
+318.0187 317.4593 L
+316.1255 320.6043 L
+316.1048 320.6043 L
+316.1048 317.4593 L
+314.5841 317.4593 L
+314.5841 325.2598 L
+316.6428 325.2598 L
+318.1843 325.2598 319.2499 324.577 319.2499 322.9114 c
+319.2499 321.9182 318.7015 320.925 317.6567 320.7492 C
+319.8602 317.4593 l
+f
+1 D
+316.1048 321.6699 m
+316.3014 321.6699 L
+317.1394 321.6699 317.7291 321.9182 317.7291 322.87 c
+317.7291 323.8321 317.1187 324.0183 316.3117 324.0183 c
+316.1048 324.0183 L
+316.1048 321.6699 l
+f
+*U
+*u
+0 D
+329.1754 323.9356 m
+329.1754 322.2182 L
+331.5962 322.2182 L
+331.5962 320.894 L
+329.1754 320.894 L
+329.1754 318.7835 L
+331.6894 318.7835 L
+331.6894 317.4593 L
+327.6546 317.4593 L
+327.6546 325.2598 L
+331.6894 325.2598 L
+331.6894 323.9356 L
+329.1754 323.9356 l
+f
+*U
+*u
+340 325.2598 m
+342.1725 325.2598 L
+344.4279 325.2598 345.9383 323.5735 345.9383 321.3492 c
+345.9383 319.156 344.3865 317.4593 342.1622 317.4593 c
+340 317.4593 L
+340 325.2598 l
+f
+1 D
+341.5208 318.7835 m
+341.7691 318.7835 L
+343.6416 318.7835 344.3658 319.8181 344.3658 321.3596 c
+344.3658 323.0562 343.4968 323.9356 341.7691 323.9356 c
+341.5208 323.9356 L
+341.5208 318.7835 l
+f
+*U
+LB
+%AI5_EndLayer--
+%%PageTrailer
+gsave annotatepage grestore showpage
+%%Trailer
+Adobe_IllustratorA_AI5 /terminate get exec
+Adobe_level2_AI5 /terminate get exec
+%%EOF

mplug_owl2/lib/tk8.6/msgs/cs.msg

@@ -0,0 +1,77 @@
+namespace eval ::tk {
+    ::msgcat::mcset cs "&Abort" "&P\u0159eru\u0161it"
+    ::msgcat::mcset cs "&About..." "&O programu..."
+    ::msgcat::mcset cs "All Files" "V\u0161echny soubory"
+    ::msgcat::mcset cs "Application Error" "Chyba programu"
+    ::msgcat::mcset cs "Bold Italic"
+    ::msgcat::mcset cs "&Blue" "&Modr\341"
+    ::msgcat::mcset cs "Cancel" "Zru\u0161it"
+    ::msgcat::mcset cs "&Cancel" "&Zru\u0161it"
+    ::msgcat::mcset cs "Cannot change to the directory \"%1\$s\".\nPermission denied." "Nemohu zm\u011bnit atku\341ln\355 adres\341\u0159 na \"%1\$s\".\nP\u0159\355stup odm\355tnut."
+    ::msgcat::mcset cs "Choose Directory" "V\375b\u011br adres\341\u0159e"
+    ::msgcat::mcset cs "Cl&ear" "Sma&zat"
+    ::msgcat::mcset cs "&Clear Console" "&Smazat konzolu"
+    ::msgcat::mcset cs "Color" "Barva"
+    ::msgcat::mcset cs "Console" "Konzole"
+    ::msgcat::mcset cs "&Copy" "&Kop\355rovat"
+    ::msgcat::mcset cs "Cu&t" "V&y\u0159\355znout"
+    ::msgcat::mcset cs "&Delete" "&Smazat"
+    ::msgcat::mcset cs "Details >>" "Detaily >>"
+    ::msgcat::mcset cs "Directory \"%1\$s\" does not exist." "Adres\341\u0159 \"%1\$s\" neexistuje."
+    ::msgcat::mcset cs "&Directory:" "&Adres\341\u0159:"
+    ::msgcat::mcset cs "&Edit" "&\332pravy"
+    ::msgcat::mcset cs "Error: %1\$s" "Chyba: %1\$s"
+    ::msgcat::mcset cs "E&xit" "&Konec"
+    ::msgcat::mcset cs "&File" "&Soubor"
+    ::msgcat::mcset cs "File \"%1\$s\" already exists.\nDo you want to overwrite it?" "Soubor \"%1\$s\" ji\u017e existuje.\nChcete jej p\u0159epsat?"
+    ::msgcat::mcset cs "File \"%1\$s\" already exists.\n\n" "Soubor \"%1\$s\" ji\u017e existuje.\n\n"
+    ::msgcat::mcset cs "File \"%1\$s\" does not exist." "Soubor \"%1\$s\" neexistuje."
+    ::msgcat::mcset cs "File &name:" "&Jm\351no souboru:"
+    ::msgcat::mcset cs "File &names:" "&Jm\351na soubor\u016f:"
+    ::msgcat::mcset cs "Files of &type:" "&Typy soubor\u016f:"
+    ::msgcat::mcset cs "Fi&les:" "Sou&bory:"
+    ::msgcat::mcset cs "&Filter" "&Filtr"
+    ::msgcat::mcset cs "Fil&ter:" "Fil&tr:"
+    ::msgcat::mcset cs "Font st&yle:"
+    ::msgcat::mcset cs "&Green" "Ze&len\341"
+    ::msgcat::mcset cs "&Help" "&N\341pov\u011bda"
+    ::msgcat::mcset cs "Hi" "Ahoj"
+    ::msgcat::mcset cs "&Hide Console" "&Schovat Konzolu"
+    ::msgcat::mcset cs "&Ignore" "&Ignorovat"
+    ::msgcat::mcset cs "Invalid file name \"%1\$s\"." "\u0160patn\351 jm\351no souboru \"%1\$s\"."
+    ::msgcat::mcset cs "Log Files" "Log soubory"
+    ::msgcat::mcset cs "&No" "&Ne"
+    ::msgcat::mcset cs "&OK"
+    ::msgcat::mcset cs "OK"
+    ::msgcat::mcset cs "Ok"
+    ::msgcat::mcset cs "Open" "Otev\u0159\355t"
+    ::msgcat::mcset cs "&Open" "&Otev\u0159\355t"
+    ::msgcat::mcset cs "Open Multiple Files" "Otev\u0159\355t v\355ce soubor\u016f"
+    ::msgcat::mcset cs "P&aste" "&Vlo\u017eit"
+    ::msgcat::mcset cs "&Quit" "&Ukon\u010dit"
+    ::msgcat::mcset cs "&Red" "\u010ce&rven\341"
+    ::msgcat::mcset cs "Replace existing file?" "Nahradit st\341vaj\355c\355 soubor?"
+    ::msgcat::mcset cs "&Retry" "Z&novu"
+    ::msgcat::mcset cs "&Save" "&Ulo\u017eit"
+    ::msgcat::mcset cs "Save As" "Ulo\u017eit jako"
+    ::msgcat::mcset cs "Save To Log" "Ulo\u017eit do logu"
+    ::msgcat::mcset cs "Select Log File" "Vybrat log soubor"
+    ::msgcat::mcset cs "Select a file to source" "Vybrat soubor k nahr\341n\355"
+    ::msgcat::mcset cs "&Selection:" "&V\375b\u011br:"
+    ::msgcat::mcset cs "Skip Messages" "P\u0159esko\u010dit zpr\341vy"
+    ::msgcat::mcset cs "&Source..." "&Zdroj..."
+    ::msgcat::mcset cs "Tcl Scripts" "Tcl skripty"
+    ::msgcat::mcset cs "Tcl for Windows" "Tcl pro Windows"
+    ::msgcat::mcset cs "Text Files" "Textov\351 soubory"
+    ::msgcat::mcset cs "abort" "p\u0159eru\u0161it"
+    ::msgcat::mcset cs "blue" "modr\341"
+    ::msgcat::mcset cs "cancel" "zru\u0161it"
+    ::msgcat::mcset cs "extension" "p\u0159\355pona"
+    ::msgcat::mcset cs "extensions" "p\u0159\355pony"
+    ::msgcat::mcset cs "green" "zelen\341"
+    ::msgcat::mcset cs "ignore" "ignorovat"
+    ::msgcat::mcset cs "ok"
+    ::msgcat::mcset cs "red" "\u010derven\341"
+    ::msgcat::mcset cs "retry" "znovu"
+    ::msgcat::mcset cs "yes" "ano"
+}

mplug_owl2/lib/tk8.6/msgs/de.msg

@@ -0,0 +1,91 @@
+namespace eval ::tk {
+    ::msgcat::mcset de "&Abort" "&Abbruch"
+    ::msgcat::mcset de "&About..." "&\u00dcber..."
+    ::msgcat::mcset de "All Files" "Alle Dateien"
+    ::msgcat::mcset de "Application Error" "Applikationsfehler"
+    ::msgcat::mcset de "&Apply" "&Anwenden"
+    ::msgcat::mcset de "Bold" "Fett"
+    ::msgcat::mcset de "Bold Italic" "Fett kursiv"
+    ::msgcat::mcset de "&Blue" "&Blau"
+    ::msgcat::mcset de "Cancel" "Abbruch"
+    ::msgcat::mcset de "&Cancel" "&Abbruch"
+    ::msgcat::mcset de "Cannot change to the directory \"%1\$s\".\nPermission denied." "Kann nicht in das Verzeichnis \"%1\$s\" wechseln.\nKeine Rechte vorhanden."
+    ::msgcat::mcset de "Choose Directory" "W\u00e4hle Verzeichnis"
+    ::msgcat::mcset de "Cl&ear" "&R\u00fccksetzen"
+    ::msgcat::mcset de "&Clear Console" "&Konsole l\u00f6schen"
+    ::msgcat::mcset de "Color" "Farbe"
+    ::msgcat::mcset de "Console" "Konsole"
+    ::msgcat::mcset de "&Copy" "&Kopieren"
+    ::msgcat::mcset de "Cu&t" "Aus&schneiden"
+    ::msgcat::mcset de "&Delete" "&L\u00f6schen"
+    ::msgcat::mcset de "Details >>"
+    ::msgcat::mcset de "Directory \"%1\$s\" does not exist." "Das Verzeichnis \"%1\$s\" existiert nicht."
+    ::msgcat::mcset de "&Directory:" "&Verzeichnis:"
+    ::msgcat::mcset de "&Edit" "&Bearbeiten"
+    ::msgcat::mcset de "Effects" "Effekte"
+    ::msgcat::mcset de "Error: %1\$s" "Fehler: %1\$s"
+    ::msgcat::mcset de "E&xit" "&Ende"
+    ::msgcat::mcset de "&File" "&Datei"
+    ::msgcat::mcset de "File \"%1\$s\" already exists.\nDo you want to overwrite it?" "Die Datei \"%1\$s\" ist bereits vorhanden.\nWollen sie diese Datei \u00fcberschreiben ?"
+    ::msgcat::mcset de "File \"%1\$s\" already exists.\n\n" "Die Datei \"%1\$s\" ist bereits vorhanden.\n\n"
+    ::msgcat::mcset de "File \"%1\$s\" does not exist." "Die Datei \"%1\$s\" existiert nicht."
+    ::msgcat::mcset de "File &name:" "Datei&name:"
+    ::msgcat::mcset de "File &names:" "Datei&namen:"
+    ::msgcat::mcset de "Files of &type:" "Dateien des &Typs:"
+    ::msgcat::mcset de "Fi&les:" "Dat&eien:"
+    ::msgcat::mcset de "&Filter"
+    ::msgcat::mcset de "Fil&ter:"
+    ::msgcat::mcset de "Font" "Schriftart"
+    ::msgcat::mcset de "&Font:" "Schriftart:"
+    ::msgcat::mcset de "Font st&yle:" "Schriftschnitt:"
+    ::msgcat::mcset de "&Green" "&Gr\u00fcn"
+    ::msgcat::mcset de "&Help" "&Hilfe"
+    ::msgcat::mcset de "Hi" "Hallo"
+    ::msgcat::mcset de "&Hide Console" "&Konsole unsichtbar machen"
+    ::msgcat::mcset de "&Ignore" "&Ignorieren"
+    ::msgcat::mcset de "Invalid file name \"%1\$s\"." "Ung\u00fcltiger Dateiname \"%1\$s\"."
+    ::msgcat::mcset de "Italic" "Kursiv"
+    ::msgcat::mcset de "Log Files" "Protokolldatei"
+    ::msgcat::mcset de "&No" "&Nein"
+    ::msgcat::mcset de "&OK"
+    ::msgcat::mcset de "OK"
+    ::msgcat::mcset de "Ok"
+    ::msgcat::mcset de "Open" "\u00d6ffnen"
+    ::msgcat::mcset de "&Open" "\u00d6&ffnen"
+    ::msgcat::mcset de "Open Multiple Files" "Mehrere Dateien \u00F6ffnen"
+    ::msgcat::mcset de "P&aste" "E&inf\u00fcgen"
+    ::msgcat::mcset de "&Quit" "&Beenden"
+    ::msgcat::mcset de "&Red" "&Rot"
+    ::msgcat::mcset de "Regular" "Standard"
+    ::msgcat::mcset de "Replace existing file?" "Existierende Datei ersetzen?"
+    ::msgcat::mcset de "&Retry" "&Wiederholen"
+    ::msgcat::mcset de "Sample" "Beispiel"
+    ::msgcat::mcset de "&Save" "&Speichern"
+    ::msgcat::mcset de "Save As" "Speichern unter"
+    ::msgcat::mcset de "Save To Log" "In Protokoll speichern"
+    ::msgcat::mcset de "Select Log File" "Protokolldatei ausw\u00e4hlen"
+    ::msgcat::mcset de "Select a file to source" "Auszuf\u00fchrende Datei ausw\u00e4hlen"
+    ::msgcat::mcset de "&Selection:" "Auswah&l:"
+    ::msgcat::mcset de "&Size:" "Schriftgrad:"
+    ::msgcat::mcset de "Show &Hidden Directories" "Zeige versteckte Dateien"
+    ::msgcat::mcset de "Show &Hidden Files and Directories" "Zeige versteckte Dateien und Verzeichnisse"
+    ::msgcat::mcset de "Skip Messages" "Weitere Nachrichten \u00fcberspringen"
+    ::msgcat::mcset de "&Source..." "&Ausf\u00fchren..."
+    ::msgcat::mcset de "Stri&keout" "&Durchgestrichen"
+    ::msgcat::mcset de "Tcl Scripts" "Tcl-Skripte"
+    ::msgcat::mcset de "Tcl for Windows" "Tcl f\u00fcr Windows"
+    ::msgcat::mcset de "Text Files" "Textdateien"
+    ::msgcat::mcset de "&Underline" "&Unterstrichen"
+    ::msgcat::mcset de "&Yes" "&Ja"
+    ::msgcat::mcset de "abort" "abbrechen"
+    ::msgcat::mcset de "blue" "blau"
+    ::msgcat::mcset de "cancel" "abbrechen"
+    ::msgcat::mcset de "extension" "Erweiterung"
+    ::msgcat::mcset de "extensions" "Erweiterungen"
+    ::msgcat::mcset de "green" "gr\u00fcn"
+    ::msgcat::mcset de "ignore" "ignorieren"
+    ::msgcat::mcset de "ok"
+    ::msgcat::mcset de "red" "rot"
+    ::msgcat::mcset de "retry" "wiederholen"
+    ::msgcat::mcset de "yes" "ja"
+}

mplug_owl2/lib/tk8.6/msgs/el.msg

@@ -0,0 +1,86 @@
+## Messages for the Greek (Hellenic - "el") language.
+## Please report any changes/suggestions to:
+##    petasis@iit.demokritos.gr
+
+namespace eval ::tk {
+    ::msgcat::mcset el "&Abort"              "\u03a4\u03b5\u03c1\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03cc\u03c2"
+    ::msgcat::mcset el "About..."           "\u03a3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ac..."
+    ::msgcat::mcset el "All Files"          "\u038c\u03bb\u03b1 \u03c4\u03b1 \u0391\u03c1\u03c7\u03b5\u03af\u03b1"
+    ::msgcat::mcset el "Application Error"  "\u039b\u03ac\u03b8\u03bf\u03c2 \u0395\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae\u03c2"
+    ::msgcat::mcset el "&Blue"               "\u039c\u03c0\u03bb\u03b5"
+    ::msgcat::mcset el "&Cancel"             "\u0391\u03ba\u03cd\u03c1\u03c9\u03c3\u03b7"
+    ::msgcat::mcset el \
+"Cannot change to the directory \"%1\$s\".\nPermission denied." \
+"\u0394\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03c5\u03bd\u03b1\u03c4\u03ae \u03b7 \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03ba\u03b1\u03c4\u03b1\u03bb\u03cc\u03b3\u03bf\u03c5 \u03c3\u03b5 \"%1\$s\".\n\u0397 \u03c0\u03c1\u03cc\u03c3\u03b2\u03b1\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9."
+    ::msgcat::mcset el "Choose Directory"   "\u0395\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u039a\u03b1\u03c4\u03b1\u03bb\u03cc\u03b3\u03bf\u03c5"
+    ::msgcat::mcset el "Clear"              "\u039a\u03b1\u03b8\u03b1\u03c1\u03b9\u03c3\u03bc\u03cc\u03c2"
+    ::msgcat::mcset el "Color"              "\u03a7\u03c1\u03ce\u03bc\u03b1"
+    ::msgcat::mcset el "Console"            "\u039a\u03bf\u03bd\u03c3\u03cc\u03bb\u03b1"
+    ::msgcat::mcset el "Copy"               "\u0391\u03bd\u03c4\u03b9\u03b3\u03c1\u03b1\u03c6\u03ae"
+    ::msgcat::mcset el "Cut"                "\u0391\u03c0\u03bf\u03ba\u03bf\u03c0\u03ae"
+    ::msgcat::mcset el "Delete"             "\u0394\u03b9\u03b1\u03b3\u03c1\u03b1\u03c6\u03ae"
+    ::msgcat::mcset el "Details >>"         "\u039b\u03b5\u03c0\u03c4\u03bf\u03bc\u03ad\u03c1\u03b5\u03b9\u03b5\u03c2 >>"
+    ::msgcat::mcset el "Directory \"%1\$s\" does not exist." \
+                                        "\u039f \u03ba\u03b1\u03c4\u03ac\u03bb\u03bf\u03b3\u03bf\u03c2 \"%1\$s\" \u03b4\u03b5\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9."
+    ::msgcat::mcset el "&Directory:"         "&\u039a\u03b1\u03c4\u03ac\u03bb\u03bf\u03b3\u03bf\u03c2:"
+    ::msgcat::mcset el "Error: %1\$s"       "\u039b\u03ac\u03b8\u03bf\u03c2: %1\$s"
+    ::msgcat::mcset el "Exit"               "\u0388\u03be\u03bf\u03b4\u03bf\u03c2"
+    ::msgcat::mcset el \
+               "File \"%1\$s\" already exists.\nDo you want to overwrite it?" \
+               "\u03a4\u03bf \u03b1\u03c1\u03c7\u03b5\u03af\u03bf \"%1\$s\" \u03ae\u03b4\u03b7 \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9.\n\u0398\u03ad\u03bb\u03b5\u03c4\u03b5 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03ba\u03b1\u03bb\u03c5\u03c6\u03b8\u03b5\u03af;"
+    ::msgcat::mcset el "File \"%1\$s\" already exists.\n\n" \
+                   "\u03a4\u03bf \u03b1\u03c1\u03c7\u03b5\u03af\u03bf \"%1\$s\" \u03ae\u03b4\u03b7 \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9.\n\n"
+    ::msgcat::mcset el "File \"%1\$s\" does not exist." \
+                   "\u03a4\u03bf \u03b1\u03c1\u03c7\u03b5\u03af\u03bf \"%1\$s\" \u03b4\u03b5\u03bd \u03c5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9."
+    ::msgcat::mcset el "File &name:"         "\u038c&\u03bd\u03bf\u03bc\u03b1 \u03b1\u03c1\u03c7\u03b5\u03af\u03bf\u03c5:"
+    ::msgcat::mcset el "File &names:"        "\u038c&\u03bd\u03bf\u03bc\u03b1 \u03b1\u03c1\u03c7\u03b5\u03af\u03c9\u03bd:"
+    ::msgcat::mcset el "Files of &type:"     "\u0391\u03c1\u03c7\u03b5\u03af\u03b1 \u03c4\u03bf\u03c5 &\u03c4\u03cd\u03c0\u03bf\u03c5:"
+    ::msgcat::mcset el "Fi&les:"             "\u0391\u03c1\u03c7\u03b5\u03af\u03b1:"
+    ::msgcat::mcset el "&Filter"             "\u03a6\u03af\u03bb\u03c4\u03c1\u03bf"
+    ::msgcat::mcset el "Fil&ter:"            "\u03a6\u03af\u03bb\u03c4\u03c1\u03bf:"
+    ::msgcat::mcset el "&Green"              "\u03a0\u03c1\u03ac\u03c3\u03b9\u03bd\u03bf"
+    ::msgcat::mcset el "Hi"                 "\u0393\u03b5\u03b9\u03b1"
+    ::msgcat::mcset el "Hide Console"       "\u0391\u03c0\u03cc\u03ba\u03c1\u03c5\u03c8\u03b7 \u03ba\u03bf\u03bd\u03c3\u03cc\u03bb\u03b1\u03c2"
+    ::msgcat::mcset el "&Ignore"             "\u0391\u03b3\u03bd\u03cc\u03b7\u03c3\u03b7"
+    ::msgcat::mcset el "Invalid file name \"%1\$s\"." \
+                   "\u0386\u03ba\u03c5\u03c1\u03bf \u03cc\u03bd\u03bf\u03bc\u03b1 \u03b1\u03c1\u03c7\u03b5\u03af\u03bf\u03c5 \"%1\$s\"."
+    ::msgcat::mcset el "Log Files"          "\u0391\u03c1\u03c7\u03b5\u03af\u03b1 \u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03b1\u03c6\u03ae\u03c2"
+    ::msgcat::mcset el "&No"                 "\u038c\u03c7\u03b9"
+    ::msgcat::mcset el "&OK"                 "\u0395\u03bd\u03c4\u03ac\u03be\u03b5\u03b9"
+    ::msgcat::mcset el "OK"                 "\u0395\u03bd\u03c4\u03ac\u03be\u03b5\u03b9"
+    ::msgcat::mcset el "Ok"                 "\u0395\u03bd\u03c4\u03ac\u03be\u03b5\u03b9"
+    ::msgcat::mcset el "Open"               "\u0386\u03bd\u03bf\u03b9\u03b3\u03bc\u03b1"
+    ::msgcat::mcset el "&Open"               "\u0386\u03bd\u03bf\u03b9\u03b3\u03bc\u03b1"
+    ::msgcat::mcset el "Open Multiple Files" \
+                                        "\u0386\u03bd\u03bf\u03b9\u03b3\u03bc\u03b1 \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03ce\u03bd \u03b1\u03c1\u03c7\u03b5\u03af\u03c9\u03bd"
+    ::msgcat::mcset el "P&aste"              "\u0395\u03c0\u03b9\u03ba\u03cc\u03bb\u03bb\u03b7\u03c3\u03b7"
+    ::msgcat::mcset el "Quit"               "\u0388\u03be\u03bf\u03b4\u03bf\u03c2"
+    ::msgcat::mcset el "&Red"                "\u039a\u03cc\u03ba\u03ba\u03b9\u03bd\u03bf"
+    ::msgcat::mcset el "Replace existing file?" \
+                                        "\u0395\u03c0\u03b9\u03ba\u03ac\u03bb\u03c5\u03c8\u03b7 \u03c5\u03c0\u03ac\u03c1\u03c7\u03bf\u03bd\u03c4\u03bf\u03c2 \u03b1\u03c1\u03c7\u03b5\u03af\u03bf\u03c5;"
+    ::msgcat::mcset el "&Retry"              "\u03a0\u03c1\u03bf\u03c3\u03c0\u03ac\u03b8\u03b7\u03c3\u03b5 \u03be\u03b1\u03bd\u03ac"
+    ::msgcat::mcset el "&Save"               "\u0391\u03c0\u03bf\u03b8\u03ae\u03ba\u03b5\u03c5\u03c3\u03b7"
+    ::msgcat::mcset el "Save As"            "\u0391\u03c0\u03bf\u03b8\u03ae\u03ba\u03b5\u03c5\u03c3\u03b7 \u03c3\u03b1\u03bd"
+    ::msgcat::mcset el "Save To Log"        "\u0391\u03c0\u03bf\u03b8\u03ae\u03ba\u03b5\u03c5\u03c3\u03b7 \u03c3\u03c4\u03bf \u03b1\u03c1\u03c7\u03b5\u03af\u03bf \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03b1\u03c6\u03ae\u03c2"
+    ::msgcat::mcset el "Select Log File"    "\u0395\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b1\u03c1\u03c7\u03b5\u03af\u03bf\u03c5 \u03ba\u03b1\u03c4\u03b1\u03b3\u03c1\u03b1\u03c6\u03ae\u03c2"
+    ::msgcat::mcset el "Select a file to source" \
+                                        "\u0395\u03c0\u03b9\u03bb\u03ad\u03be\u03c4\u03b5 \u03b1\u03c1\u03c7\u03b5\u03af\u03bf \u03b3\u03b9\u03b1 \u03b5\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7"
+    ::msgcat::mcset el "&Selection:"         "\u0395\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae:"
+    ::msgcat::mcset el "Skip Messages"      "\u0391\u03c0\u03bf\u03c6\u03c5\u03b3\u03ae\u03bc\u03b7\u03bd\u03c5\u03bc\u03ac\u03c4\u03c9\u03bd"
+    ::msgcat::mcset el "&Source..."          "\u0395\u03ba\u03c4\u03ad\u03bb\u03b5\u03c3\u03b7..."
+    ::msgcat::mcset el "Tcl Scripts"        "Tcl Scripts"
+    ::msgcat::mcset el "Tcl for Windows"    "Tcl \u03b3\u03b9\u03b1 Windows"
+    ::msgcat::mcset el "Text Files"         "\u0391\u03c1\u03c7\u03b5\u03af\u03b1 \u039a\u03b5\u03b9\u03bc\u03ad\u03bd\u03bf\u03c5"
+    ::msgcat::mcset el "&Yes"                "\u039d\u03b1\u03b9"
+    ::msgcat::mcset el "abort"              "\u03c4\u03b5\u03c1\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03cc\u03c2"
+    ::msgcat::mcset el "blue"               "\u03bc\u03c0\u03bb\u03b5"
+    ::msgcat::mcset el "cancel"             "\u03b1\u03ba\u03cd\u03c1\u03c9\u03c3\u03b7"
+    ::msgcat::mcset el "extension"          "\u03b5\u03c0\u03ad\u03ba\u03c4\u03b1\u03c3\u03b7"
+    ::msgcat::mcset el "extensions"         "\u03b5\u03c0\u03b5\u03ba\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2"
+    ::msgcat::mcset el "green"              "\u03c0\u03c1\u03ac\u03c3\u03b9\u03bd\u03bf"
+    ::msgcat::mcset el "ignore"             "\u03b1\u03b3\u03bd\u03cc\u03b7\u03c3\u03b7"
+    ::msgcat::mcset el "ok"                 "\u03b5\u03bd\u03c4\u03ac\u03be\u03b5\u03b9"
+    ::msgcat::mcset el "red"                "\u03ba\u03cc\u03ba\u03ba\u03b9\u03bd\u03bf"
+    ::msgcat::mcset el "retry"              "\u03c0\u03c1\u03bf\u03c3\u03c0\u03ac\u03b8\u03b7\u03c3\u03b5 \u03be\u03b1\u03bd\u03ac"
+    ::msgcat::mcset el "yes"                "\u03bd\u03b1\u03b9"
+}

mplug_owl2/lib/tk8.6/msgs/en_gb.msg

@@ -0,0 +1,3 @@
+namespace eval ::tk {
+    ::msgcat::mcset en_gb Color Colour
+}

mplug_owl2/lib/tk8.6/msgs/it.msg

@@ -0,0 +1,73 @@
+namespace eval ::tk {
+    ::msgcat::mcset it "&Abort" "&Interrompi"
+    ::msgcat::mcset it "&About..." "Informazioni..."
+    ::msgcat::mcset it "All Files" "Tutti i file"
+    ::msgcat::mcset it "Application Error" "Errore dell' applicazione"
+    ::msgcat::mcset it "&Blue" "&Blu"
+    ::msgcat::mcset it "Cancel" "Annulla"
+    ::msgcat::mcset it "&Cancel" "&Annulla"
+    ::msgcat::mcset it "Cannot change to the directory \"%1\$s\".\nPermission denied." "Impossibile accedere alla directory \"%1\$s\".\nPermesso negato."
+    ::msgcat::mcset it "Choose Directory" "Scegli una directory"
+    ::msgcat::mcset it "Cl&ear" "Azzera"
+    ::msgcat::mcset it "&Clear Console" "Azzera Console"
+    ::msgcat::mcset it "Color" "Colore"
+    ::msgcat::mcset it "Console"
+    ::msgcat::mcset it "&Copy" "Copia"
+    ::msgcat::mcset it "Cu&t" "Taglia"
+    ::msgcat::mcset it "Delete" "Cancella"
+    ::msgcat::mcset it "Details >>" "Dettagli >>"
+    ::msgcat::mcset it "Directory \"%1\$s\" does not exist." "La directory \"%1\$s\" non esiste."
+    ::msgcat::mcset it "&Directory:"
+    ::msgcat::mcset it "Error: %1\$s" "Errore: %1\$s"
+    ::msgcat::mcset it "E&xit" "Esci"
+    ::msgcat::mcset it "File \"%1\$s\" already exists.\nDo you want to overwrite it?" "Il file \"%1\$s\" esiste gi\u00e0.\nVuoi sovrascriverlo?"
+    ::msgcat::mcset it "File \"%1\$s\" already exists.\n\n" "Il file \"%1\$s\" esiste gi\u00e0.\n\n"
+    ::msgcat::mcset it "File \"%1\$s\" does not exist." "Il file \"%1\$s\" non esiste."
+    ::msgcat::mcset it "File &name:" "&Nome del file:"
+    ::msgcat::mcset it "File &names:" "&Nomi dei file:"
+    ::msgcat::mcset it "Files of &type:" "File di &tipo:"
+    ::msgcat::mcset it "Fi&les:" "Fi&le:"
+    ::msgcat::mcset it "&Filter" "&Filtro"
+    ::msgcat::mcset it "Fil&ter:" "Fil&tro:"
+    ::msgcat::mcset it "&Green" "&Verde"
+    ::msgcat::mcset it "Hi" "Salve"
+    ::msgcat::mcset it "&Hide Console" "Nascondi la console"
+    ::msgcat::mcset it "&Ignore" "&Ignora"
+    ::msgcat::mcset it "Invalid file name \"%1\$s\"." "Nome di file non valido \"%1\$s\"."
+    ::msgcat::mcset it "Log Files" "File di log"
+    ::msgcat::mcset it "&No"
+    ::msgcat::mcset it "&OK"
+    ::msgcat::mcset it "OK"
+    ::msgcat::mcset it "Ok"
+    ::msgcat::mcset it "Open" "Apri"
+    ::msgcat::mcset it "&Open" "A&pri"
+    ::msgcat::mcset it "Open Multiple Files" "Apri file multipli"
+    ::msgcat::mcset it "P&aste" "Incolla"
+    ::msgcat::mcset it "&Quit" "Esci"
+    ::msgcat::mcset it "&Red" "&Rosso"
+    ::msgcat::mcset it "Replace existing file?" "Sostituisci il file esistente?"
+    ::msgcat::mcset it "&Retry" "&Riprova"
+    ::msgcat::mcset it "&Save" "&Salva"
+    ::msgcat::mcset it "Save As" "Salva come"
+    ::msgcat::mcset it "Save To Log" "Salva il log"
+    ::msgcat::mcset it "Select Log File" "Scegli un file di log"
+    ::msgcat::mcset it "Select a file to source" "Scegli un file da eseguire"
+    ::msgcat::mcset it "&Selection:" "&Selezione:"
+    ::msgcat::mcset it "Skip Messages" "Salta i messaggi"
+    ::msgcat::mcset it "Source..." "Esegui..."
+    ::msgcat::mcset it "Tcl Scripts" "Script Tcl"
+    ::msgcat::mcset it "Tcl for Windows" "Tcl per Windows"
+    ::msgcat::mcset it "Text Files" "File di testo"
+    ::msgcat::mcset it "&Yes" "&S\u00ec"
+    ::msgcat::mcset it "abort" "interrompi"
+    ::msgcat::mcset it "blue" "blu"
+    ::msgcat::mcset it "cancel" "annulla"
+    ::msgcat::mcset it "extension" "estensione"
+    ::msgcat::mcset it "extensions" "estensioni"
+    ::msgcat::mcset it "green" "verde"
+    ::msgcat::mcset it "ignore" "ignora"
+    ::msgcat::mcset it "ok"
+    ::msgcat::mcset it "red" "rosso"
+    ::msgcat::mcset it "retry" "riprova"
+    ::msgcat::mcset it "yes" "s\u00ec"
+}

mplug_owl2/lib/tk8.6/msgs/nl.msg

@@ -0,0 +1,91 @@
+namespace eval ::tk {
+    ::msgcat::mcset nl "&Abort" "&Afbreken"
+    ::msgcat::mcset nl "&About..." "Over..."
+    ::msgcat::mcset nl "All Files" "Alle Bestanden"
+    ::msgcat::mcset nl "Application Error" "Toepassingsfout"
+    ::msgcat::mcset nl "&Apply" "Toepassen"
+    ::msgcat::mcset nl "Bold" "Vet"
+    ::msgcat::mcset nl "Bold Italic" "Vet Cursief"
+    ::msgcat::mcset nl "&Blue" "&Blauw"
+    ::msgcat::mcset nl "Cancel" "Annuleren"
+    ::msgcat::mcset nl "&Cancel" "&Annuleren"
+    ::msgcat::mcset nl "Cannot change to the directory \"%1\$s\".\nPermission denied." "Kan niet naar map \"%1\$s\" gaan.\nU heeft hiervoor geen toestemming."
+    ::msgcat::mcset nl "Choose Directory" "Kies map"
+    ::msgcat::mcset nl "Cl&ear" "Wissen"
+    ::msgcat::mcset nl "&Clear Console" "&Wis Console"
+    ::msgcat::mcset nl "Color" "Kleur"
+    ::msgcat::mcset nl "Console"
+    ::msgcat::mcset nl "&Copy" "Kopi\u00ebren"
+    ::msgcat::mcset nl "Cu&t" "Knippen"
+    ::msgcat::mcset nl "&Delete" "Wissen"
+    ::msgcat::mcset nl "Details >>"
+    ::msgcat::mcset nl "Directory \"%1\$s\" does not exist." "Map \"%1\$s\" bestaat niet."
+    ::msgcat::mcset nl "&Directory:" "&Map:"
+    ::msgcat::mcset nl "&Edit" "Bewerken"
+    ::msgcat::mcset nl "Effects" "Effecten"
+    ::msgcat::mcset nl "Error: %1\$s" "Fout: %1\$s"
+    ::msgcat::mcset nl "E&xit" "Be\u00ebindigen"
+    ::msgcat::mcset nl "&File" "Bestand"
+    ::msgcat::mcset nl "File \"%1\$s\" already exists.\nDo you want to overwrite it?" "Bestand \"%1\$s\" bestaat al.\nWilt u het overschrijven?"
+    ::msgcat::mcset nl "File \"%1\$s\" already exists.\n\n" "Bestand \"%1\$s\" bestaat al.\n\n"
+    ::msgcat::mcset nl "File \"%1\$s\" does not exist." "Bestand \"%1\$s\" bestaat niet."
+    ::msgcat::mcset nl "File &name:" "Bestands&naam:"
+    ::msgcat::mcset nl "File &names:" "Bestands&namen:"
+    ::msgcat::mcset nl "Files of &type:" "Bestanden van het &type:"
+    ::msgcat::mcset nl "Fi&les:" "&Bestanden:"
+    ::msgcat::mcset nl "&Filter"
+    ::msgcat::mcset nl "Fil&ter:"
+    ::msgcat::mcset nl "Font"
+    ::msgcat::mcset nl "&Font:"
+    ::msgcat::mcset nl "Font st&yle:" "Font stijl:"
+    ::msgcat::mcset nl "&Green" "&Groen"
+    ::msgcat::mcset nl "&Help"
+    ::msgcat::mcset nl "Hi" "H\u00e9"
+    ::msgcat::mcset nl "&Hide Console" "Verberg Console"
+    ::msgcat::mcset nl "&Ignore" "&Negeren"
+    ::msgcat::mcset nl "Invalid file name \"%1\$s\"." "Ongeldige bestandsnaam \"%1\$s\"."
+    ::msgcat::mcset nl "Italic" "Cursief"
+    ::msgcat::mcset nl "Log Files" "Log Bestanden"
+    ::msgcat::mcset nl "&No" "&Nee"
+    ::msgcat::mcset nl "&OK"
+    ::msgcat::mcset nl "OK"
+    ::msgcat::mcset nl "Ok"
+    ::msgcat::mcset nl "Open" "Openen"
+    ::msgcat::mcset nl "&Open" "&Openen"
+    ::msgcat::mcset nl "Open Multiple Files" "Open meerdere bestanden"
+    ::msgcat::mcset nl "P&aste" "Pl&akken"
+    ::msgcat::mcset nl "&Quit" "Stoppen"
+    ::msgcat::mcset nl "&Red" "&Rood"
+    ::msgcat::mcset nl "Regular" "Standaard"
+    ::msgcat::mcset nl "Replace existing file?" "Vervang bestaand bestand?"
+    ::msgcat::mcset nl "&Retry" "&Herhalen"
+    ::msgcat::mcset nl "Sample"
+    ::msgcat::mcset nl "&Save" "Op&slaan"
+    ::msgcat::mcset nl "Save As" "Opslaan als"
+    ::msgcat::mcset nl "Save To Log" "Opslaan naar Log"
+    ::msgcat::mcset nl "Select Log File" "Selecteer Log bestand"
+    ::msgcat::mcset nl "Select a file to source" "Selecteer bronbestand"
+    ::msgcat::mcset nl "&Selection:" "&Selectie:"
+    ::msgcat::mcset nl "&Size:" "Grootte"
+    ::msgcat::mcset nl "Show &Hidden Directories" "Laat verborgen mappen zien"
+    ::msgcat::mcset nl "Show &Hidden Files and Directories" "Laat verborgen bestanden mappen zien"
+    ::msgcat::mcset nl "Skip Messages" "Berichten overslaan"
+    ::msgcat::mcset nl "&Source..." "Bron..."
+    ::msgcat::mcset nl "Stri&keout"
+    ::msgcat::mcset nl "Tcl Scripts"
+    ::msgcat::mcset nl "Tcl for Windows" "Tcl voor Windows"
+    ::msgcat::mcset nl "Text Files" "Tekstbestanden"
+    ::msgcat::mcset nl "&Underline" "Onderstreept"
+    ::msgcat::mcset nl "&Yes" "&Ja"
+    ::msgcat::mcset nl "abort" "afbreken"
+    ::msgcat::mcset nl "blue" "blauw"
+    ::msgcat::mcset nl "cancel" "annuleren"
+    ::msgcat::mcset nl "extension"
+    ::msgcat::mcset nl "extensions"
+    ::msgcat::mcset nl "green" "groen"
+    ::msgcat::mcset nl "ignore" "negeren"
+    ::msgcat::mcset nl "ok"
+    ::msgcat::mcset nl "red" "rood"
+    ::msgcat::mcset nl "retry" "opnieuw"
+    ::msgcat::mcset nl "yes" "ja"
+}

openflamingo/lib/python3.10/site-packages/transformers/models/herbert/__init__.py

@@ -0,0 +1,45 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING
+
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
+
+
+_import_structure = {"tokenization_herbert": ["HerbertTokenizer"]}
+
+try:
+    if not is_tokenizers_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["tokenization_herbert_fast"] = ["HerbertTokenizerFast"]
+
+
+if TYPE_CHECKING:
+    from .tokenization_herbert import HerbertTokenizer
+
+    try:
+        if not is_tokenizers_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .tokenization_herbert_fast import HerbertTokenizerFast
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

openflamingo/lib/python3.10/site-packages/transformers/models/herbert/tokenization_herbert.py

@@ -0,0 +1,659 @@
+# coding=utf-8
+# Copyright 2020 The Google AI Language Team Authors, Allegro.pl, Facebook Inc. and the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import json
+import os
+import re
+import unicodedata
+from typing import List, Optional, Tuple
+
+from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {
+    "vocab_file": "vocab.json",
+    "merges_file": "merges.txt",
+}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
+    },
+    "merges_file": {
+        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
+    },
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"allegro/herbert-base-cased": 514}
+PRETRAINED_INIT_CONFIGURATION = {}
+
+
+# Copied from transformers.models.xlm.tokenization_xlm.get_pairs
+def get_pairs(word):
+    """
+    Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length
+    strings)
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+# Copied from transformers.models.xlm.tokenization_xlm.replace_unicode_punct
+def replace_unicode_punct(text):
+    """
+    Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl
+    """
+    text = text.replace("，", ",")
+    text = re.sub(r"。\s*", ". ", text)
+    text = text.replace("、", ",")
+    text = text.replace("”", '"')
+    text = text.replace("“", '"')
+    text = text.replace("∶", ":")
+    text = text.replace("：", ":")
+    text = text.replace("？", "?")
+    text = text.replace("《", '"')
+    text = text.replace("》", '"')
+    text = text.replace("）", ")")
+    text = text.replace("！", "!")
+    text = text.replace("（", "(")
+    text = text.replace("；", ";")
+    text = text.replace("１", "1")
+    text = text.replace("」", '"')
+    text = text.replace("「", '"')
+    text = text.replace("０", "0")
+    text = text.replace("３", "3")
+    text = text.replace("２", "2")
+    text = text.replace("５", "5")
+    text = text.replace("６", "6")
+    text = text.replace("９", "9")
+    text = text.replace("７", "7")
+    text = text.replace("８", "8")
+    text = text.replace("４", "4")
+    text = re.sub(r"．\s*", ". ", text)
+    text = text.replace("～", "~")
+    text = text.replace("’", "'")
+    text = text.replace("…", "...")
+    text = text.replace("━", "-")
+    text = text.replace("〈", "<")
+    text = text.replace("〉", ">")
+    text = text.replace("【", "[")
+    text = text.replace("】", "]")
+    text = text.replace("％", "%")
+    return text
+
+
+# Copied from transformers.models.xlm.tokenization_xlm.remove_non_printing_char
+def remove_non_printing_char(text):
+    """
+    Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl
+    """
+    output = []
+    for char in text:
+        cat = unicodedata.category(char)
+        if cat.startswith("C"):
+            continue
+        output.append(char)
+    return "".join(output)
+
+
+# Copied from transformers.models.bert.tokenization_bert.whitespace_tokenize
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a piece of text."""
+    text = text.strip()
+    if not text:
+        return []
+    tokens = text.split()
+    return tokens
+
+
+# Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
+class BasicTokenizer(object):
+    """
+    Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
+
+    Args:
+        do_lower_case (`bool`, *optional*, defaults to `True`):
+            Whether or not to lowercase the input when tokenizing.
+        never_split (`Iterable`, *optional*):
+            Collection of tokens which will never be split during tokenization. Only has an effect when
+            `do_basic_tokenize=True`
+        tokenize_chinese_chars (`bool`, *optional*, defaults to `True`):
+            Whether or not to tokenize Chinese characters.
+
+            This should likely be deactivated for Japanese (see this
+            [issue](https://github.com/huggingface/transformers/issues/328)).
+        strip_accents (`bool`, *optional*):
+            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+            value for `lowercase` (as in the original BERT).
+        do_split_on_punc (`bool`, *optional*, defaults to `True`):
+            In some instances we want to skip the basic punctuation splitting so that later tokenization can capture
+            the full context of the words, such as contractions.
+    """
+
+    def __init__(
+        self,
+        do_lower_case=True,
+        never_split=None,
+        tokenize_chinese_chars=True,
+        strip_accents=None,
+        do_split_on_punc=True,
+    ):
+        if never_split is None:
+            never_split = []
+        self.do_lower_case = do_lower_case
+        self.never_split = set(never_split)
+        self.tokenize_chinese_chars = tokenize_chinese_chars
+        self.strip_accents = strip_accents
+        self.do_split_on_punc = do_split_on_punc
+
+    def tokenize(self, text, never_split=None):
+        """
+        Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer.
+
+        Args:
+            never_split (`List[str]`, *optional*)
+                Kept for backward compatibility purposes. Now implemented directly at the base class level (see
+                [`PreTrainedTokenizer.tokenize`]) List of token not to split.
+        """
+        # union() returns a new set by concatenating the two sets.
+        never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
+        text = self._clean_text(text)
+
+        # This was added on November 1st, 2018 for the multilingual and Chinese
+        # models. This is also applied to the English models now, but it doesn't
+        # matter since the English models were not trained on any Chinese data
+        # and generally don't have any Chinese data in them (there are Chinese
+        # characters in the vocabulary because Wikipedia does have some Chinese
+        # words in the English Wikipedia.).
+        if self.tokenize_chinese_chars:
+            text = self._tokenize_chinese_chars(text)
+        # prevents treating the same character with different unicode codepoints as different characters
+        unicode_normalized_text = unicodedata.normalize("NFC", text)
+        orig_tokens = whitespace_tokenize(unicode_normalized_text)
+        split_tokens = []
+        for token in orig_tokens:
+            if token not in never_split:
+                if self.do_lower_case:
+                    token = token.lower()
+                    if self.strip_accents is not False:
+                        token = self._run_strip_accents(token)
+                elif self.strip_accents:
+                    token = self._run_strip_accents(token)
+            split_tokens.extend(self._run_split_on_punc(token, never_split))
+
+        output_tokens = whitespace_tokenize(" ".join(split_tokens))
+        return output_tokens
+
+    def _run_strip_accents(self, text):
+        """Strips accents from a piece of text."""
+        text = unicodedata.normalize("NFD", text)
+        output = []
+        for char in text:
+            cat = unicodedata.category(char)
+            if cat == "Mn":
+                continue
+            output.append(char)
+        return "".join(output)
+
+    def _run_split_on_punc(self, text, never_split=None):
+        """Splits punctuation on a piece of text."""
+        if not self.do_split_on_punc or (never_split is not None and text in never_split):
+            return [text]
+        chars = list(text)
+        i = 0
+        start_new_word = True
+        output = []
+        while i < len(chars):
+            char = chars[i]
+            if _is_punctuation(char):
+                output.append([char])
+                start_new_word = True
+            else:
+                if start_new_word:
+                    output.append([])
+                start_new_word = False
+                output[-1].append(char)
+            i += 1
+
+        return ["".join(x) for x in output]
+
+    def _tokenize_chinese_chars(self, text):
+        """Adds whitespace around any CJK character."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if self._is_chinese_char(cp):
+                output.append(" ")
+                output.append(char)
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+    def _is_chinese_char(self, cp):
+        """Checks whether CP is the codepoint of a CJK character."""
+        # This defines a "chinese character" as anything in the CJK Unicode block:
+        #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+        #
+        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+        # despite its name. The modern Korean Hangul alphabet is a different block,
+        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+        # space-separated words, so they are not treated specially and handled
+        # like the all of the other languages.
+        if (
+            (cp >= 0x4E00 and cp <= 0x9FFF)
+            or (cp >= 0x3400 and cp <= 0x4DBF)  #
+            or (cp >= 0x20000 and cp <= 0x2A6DF)  #
+            or (cp >= 0x2A700 and cp <= 0x2B73F)  #
+            or (cp >= 0x2B740 and cp <= 0x2B81F)  #
+            or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
+            or (cp >= 0xF900 and cp <= 0xFAFF)
+            or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
+        ):  #
+            return True
+
+        return False
+
+    def _clean_text(self, text):
+        """Performs invalid character removal and whitespace cleanup on text."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if cp == 0 or cp == 0xFFFD or _is_control(char):
+                continue
+            if _is_whitespace(char):
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+
+class HerbertTokenizer(PreTrainedTokenizer):
+    """
+    Construct a BPE tokenizer for HerBERT.
+
+    Peculiarities:
+
+    - uses BERT's pre-tokenizer: BaseTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of a
+      punctuation character will be treated separately.
+
+    - Such pretokenized input is BPE subtokenized
+
+    This tokenizer inherits from [`XLMTokenizer`] which contains most of the methods. Users should refer to the
+    superclass for more information regarding methods.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+
+    def __init__(
+        self,
+        vocab_file,
+        merges_file,
+        tokenizer_file=None,
+        cls_token="<s>",
+        unk_token="<unk>",
+        pad_token="<pad>",
+        mask_token="<mask>",
+        sep_token="</s>",
+        bos_token="<s>",
+        do_lowercase_and_remove_accent=False,
+        additional_special_tokens=[
+            "<special0>",
+            "<special1>",
+            "<special2>",
+            "<special3>",
+            "<special4>",
+            "<special5>",
+            "<special6>",
+            "<special7>",
+            "<special8>",
+            "<special9>",
+        ],
+        lang2id=None,
+        id2lang=None,
+        **kwargs,
+    ):
+        super().__init__(
+            unk_token=unk_token,
+            bos_token=bos_token,
+            sep_token=sep_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            mask_token=mask_token,
+            additional_special_tokens=additional_special_tokens,
+            lang2id=lang2id,
+            id2lang=id2lang,
+            do_lowercase_and_remove_accent=do_lowercase_and_remove_accent,
+            tokenizer_file=None,
+            **kwargs,
+        )
+
+        try:
+            import sacremoses
+        except ImportError:
+            raise ImportError(
+                "You need to install sacremoses to use HerbertTokenizer. "
+                "See https://pypi.org/project/sacremoses/ for installation."
+            )
+
+        self.sm = sacremoses
+
+        # cache of sm.MosesPunctNormalizer instance
+        self.cache_moses_punct_normalizer = {}
+        # cache of sm.MosesTokenizer instance
+        self.cache_moses_tokenizer = {}
+        self.lang_with_custom_tokenizer = {"zh", "th", "ja"}
+        # True for current supported model (v1.2.0), False for XLM-17 & 100
+        self.do_lowercase_and_remove_accent = do_lowercase_and_remove_accent
+        self.lang2id = lang2id
+        self.id2lang = id2lang
+        if lang2id is not None and id2lang is not None:
+            assert len(lang2id) == len(id2lang)
+
+        self.ja_word_tokenizer = None
+        self.zh_word_tokenizer = None
+
+        with open(vocab_file, encoding="utf-8") as vocab_handle:
+            self.encoder = json.load(vocab_handle)
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        with open(merges_file, encoding="utf-8") as merges_handle:
+            merges = merges_handle.read().split("\n")[:-1]
+        merges = [tuple(merge.split()[:2]) for merge in merges]
+        self.bpe_ranks = dict(zip(merges, range(len(merges))))
+        self.cache = {}
+
+        self.bert_pre_tokenizer = BasicTokenizer(
+            do_lower_case=False,
+            never_split=self.all_special_tokens,
+            tokenize_chinese_chars=False,
+            strip_accents=False,
+        )
+
+    @property
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.do_lower_case
+    def do_lower_case(self):
+        return self.do_lowercase_and_remove_accent
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_punct_norm
+    def moses_punct_norm(self, text, lang):
+        if lang not in self.cache_moses_punct_normalizer:
+            punct_normalizer = self.sm.MosesPunctNormalizer(lang=lang)
+            self.cache_moses_punct_normalizer[lang] = punct_normalizer
+        else:
+            punct_normalizer = self.cache_moses_punct_normalizer[lang]
+        return punct_normalizer.normalize(text)
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_tokenize
+    def moses_tokenize(self, text, lang):
+        if lang not in self.cache_moses_tokenizer:
+            moses_tokenizer = self.sm.MosesTokenizer(lang=lang)
+            self.cache_moses_tokenizer[lang] = moses_tokenizer
+        else:
+            moses_tokenizer = self.cache_moses_tokenizer[lang]
+        return moses_tokenizer.tokenize(text, return_str=False, escape=False)
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.moses_pipeline
+    def moses_pipeline(self, text, lang):
+        text = replace_unicode_punct(text)
+        text = self.moses_punct_norm(text, lang)
+        text = remove_non_printing_char(text)
+        return text
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.ja_tokenize
+    def ja_tokenize(self, text):
+        if self.ja_word_tokenizer is None:
+            try:
+                import Mykytea
+
+                self.ja_word_tokenizer = Mykytea.Mykytea(
+                    f"-model {os.path.expanduser('~')}/local/share/kytea/model.bin"
+                )
+            except (AttributeError, ImportError):
+                logger.error(
+                    "Make sure you install KyTea (https://github.com/neubig/kytea) and it's python wrapper"
+                    " (https://github.com/chezou/Mykytea-python) with the following steps"
+                )
+                logger.error("1. git clone git@github.com:neubig/kytea.git && cd kytea")
+                logger.error("2. autoreconf -i")
+                logger.error("3. ./configure --prefix=$HOME/local")
+                logger.error("4. make && make install")
+                logger.error("5. pip install kytea")
+                raise
+        return list(self.ja_word_tokenizer.getWS(text))
+
+    @property
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.vocab_size
+    def vocab_size(self):
+        return len(self.encoder)
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.get_vocab
+    def get_vocab(self):
+        return dict(self.encoder, **self.added_tokens_encoder)
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.bpe
+    def bpe(self, token):
+        word = tuple(token[:-1]) + (token[-1] + "</w>",)
+        if token in self.cache:
+            return self.cache[token]
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token + "</w>"
+
+        while True:
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                except ValueError:
+                    new_word.extend(word[i:])
+                    break
+                else:
+                    new_word.extend(word[i:j])
+                    i = j
+
+                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+                    new_word.append(first + second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = " ".join(word)
+        if word == "\n  </w>":
+            word = "\n</w>"
+        self.cache[token] = word
+        return word
+
+    def _tokenize(self, text):
+        pre_tokens = self.bert_pre_tokenizer.tokenize(text)
+
+        split_tokens = []
+        for token in pre_tokens:
+            if token:
+                split_tokens.extend(list(self.bpe(token).split(" ")))
+
+        return split_tokens
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer._convert_token_to_id
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.encoder.get(token, self.encoder.get(self.unk_token))
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer._convert_id_to_token
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.decoder.get(index, self.unk_token)
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.convert_tokens_to_string
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        out_string = "".join(tokens).replace("</w>", " ").strip()
+        return out_string
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.build_inputs_with_special_tokens
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. An XLM sequence has the following format:
+
+        - single sequence: `<s> X </s>`
+        - pair of sequences: `<s> A </s> B </s>`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+
+        """
+        bos = [self.bos_token_id]
+        sep = [self.sep_token_id]
+
+        if token_ids_1 is None:
+            return bos + token_ids_0 + sep
+        return bos + token_ids_0 + sep + token_ids_1 + sep
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.get_special_tokens_mask
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is not None:
+            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1]
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.create_token_type_ids_from_sequences
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLM sequence
+        pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.save_vocabulary
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+        merge_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
+        )
+
+        with open(vocab_file, "w", encoding="utf-8") as f:
+            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
+
+        index = 0
+        with open(merge_file, "w", encoding="utf-8") as writer:
+            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning(
+                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
+                        " Please check that the tokenizer is not corrupted!"
+                    )
+                    index = token_index
+                writer.write(" ".join(bpe_tokens) + "\n")
+                index += 1
+
+        return vocab_file, merge_file
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.__getstate__
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sm"] = None
+        return state
+
+    # Copied from transformers.models.xlm.tokenization_xlm.XLMTokenizer.__setstate__
+    def __setstate__(self, d):
+        self.__dict__ = d
+
+        try:
+            import sacremoses
+        except ImportError:
+            raise ImportError(
+                "You need to install sacremoses to use XLMTokenizer. "
+                "See https://pypi.org/project/sacremoses/ for installation."
+            )
+
+        self.sm = sacremoses

openflamingo/lib/python3.10/site-packages/transformers/models/herbert/tokenization_herbert_fast.py

@@ -0,0 +1,173 @@
+# coding=utf-8
+# Copyright 2020 The Google AI Language Team Authors, Allegro.pl, Facebook Inc. and the HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import List, Optional, Tuple
+
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import logging
+from .tokenization_herbert import HerbertTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
+    },
+    "merges_file": {
+        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
+    },
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"allegro/herbert-base-cased": 514}
+PRETRAINED_INIT_CONFIGURATION = {}
+
+
+class HerbertTokenizerFast(PreTrainedTokenizerFast):
+    """
+    Construct a "Fast" BPE tokenizer for HerBERT (backed by HuggingFace's *tokenizers* library).
+
+    Peculiarities:
+
+    - uses BERT's pre-tokenizer: BertPreTokenizer splits tokens on spaces, and also on punctuation. Each occurrence of
+      a punctuation character will be treated separately.
+
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the methods. Users should refer to the
+    superclass for more information regarding methods.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        merges_file (`str`):
+            Path to the merges file.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    slow_tokenizer_class = HerbertTokenizer
+
+    def __init__(
+        self,
+        vocab_file=None,
+        merges_file=None,
+        tokenizer_file=None,
+        cls_token="<s>",
+        unk_token="<unk>",
+        pad_token="<pad>",
+        mask_token="<mask>",
+        sep_token="</s>",
+        **kwargs,
+    ):
+        super().__init__(
+            vocab_file,
+            merges_file,
+            tokenizer_file=tokenizer_file,
+            cls_token=cls_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            mask_token=mask_token,
+            sep_token=sep_token,
+            **kwargs,
+        )
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. An HerBERT, like BERT sequence has the following format:
+
+        - single sequence: `<s> X </s>`
+        - pair of sequences: `<s> A </s> B </s>`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+
+        cls = [self.cls_token_id]
+        sep = [self.sep_token_id]
+        if token_ids_1 is None:
+            return cls + token_ids_0 + sep
+
+        return cls + token_ids_0 + sep + token_ids_1 + sep
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. HerBERT, like
+        BERT sequence pair mask has the following format:
+
+        ```
+        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+        | first sequence    | second sequence |
+        ```
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+        return tuple(files)

openflamingo/lib/python3.10/site-packages/transformers/models/instructblip/processing_instructblip.py

@@ -0,0 +1,172 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for InstructBLIP. Largely copy of Blip2Processor with addition of a tokenizer for the Q-Former.
+"""
+
+import os
+from typing import List, Optional, Union
+
+from ...image_processing_utils import BatchFeature
+from ...image_utils import ImageInput
+from ...processing_utils import ProcessorMixin
+from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
+from ...utils import TensorType
+from ..auto import AutoTokenizer
+
+
+class InstructBlipProcessor(ProcessorMixin):
+    r"""
+    Constructs an InstructBLIP processor which wraps a BLIP image processor and a LLaMa/T5 tokenizer into a single
+    processor.
+
+    [`InstructBlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`AutoTokenizer`]. See the
+    docstring of [`~BlipProcessor.__call__`] and [`~BlipProcessor.decode`] for more information.
+
+    Args:
+        image_processor (`BlipImageProcessor`):
+            An instance of [`BlipImageProcessor`]. The image processor is a required input.
+        tokenizer (`AutoTokenizer`):
+            An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input.
+        qformer_tokenizer (`AutoTokenizer`):
+            An instance of ['PreTrainedTokenizer`]. The Q-Former tokenizer is a required input.
+    """
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "BlipImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(self, image_processor, tokenizer, qformer_tokenizer):
+        super().__init__(image_processor, tokenizer)
+
+        # add QFormer tokenizer
+        self.qformer_tokenizer = qformer_tokenizer
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        add_special_tokens: bool = True,
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Union[bool, str, TruncationStrategy] = None,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        pad_to_multiple_of: Optional[int] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        return_token_type_ids: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        This method uses [`BlipImageProcessor.__call__`] method to prepare image(s) for the model, and
+        [`BertTokenizerFast.__call__`] to prepare text for the model.
+
+        Please refer to the docstring of the above two methods for more information.
+        """
+        if images is None and text is None:
+            raise ValueError("You have to specify at least images or text.")
+
+        encoding = BatchFeature()
+
+        if text is not None:
+            text_encoding = self.tokenizer(
+                text=text,
+                add_special_tokens=add_special_tokens,
+                padding=padding,
+                truncation=truncation,
+                max_length=max_length,
+                stride=stride,
+                pad_to_multiple_of=pad_to_multiple_of,
+                return_attention_mask=return_attention_mask,
+                return_overflowing_tokens=return_overflowing_tokens,
+                return_special_tokens_mask=return_special_tokens_mask,
+                return_offsets_mapping=return_offsets_mapping,
+                return_token_type_ids=return_token_type_ids,
+                return_length=return_length,
+                verbose=verbose,
+                return_tensors=return_tensors,
+                **kwargs,
+            )
+            encoding.update(text_encoding)
+            qformer_text_encoding = self.qformer_tokenizer(
+                text=text,
+                add_special_tokens=add_special_tokens,
+                padding=padding,
+                truncation=truncation,
+                max_length=max_length,
+                stride=stride,
+                pad_to_multiple_of=pad_to_multiple_of,
+                return_attention_mask=return_attention_mask,
+                return_overflowing_tokens=return_overflowing_tokens,
+                return_special_tokens_mask=return_special_tokens_mask,
+                return_offsets_mapping=return_offsets_mapping,
+                return_token_type_ids=return_token_type_ids,
+                return_length=return_length,
+                verbose=verbose,
+                return_tensors=return_tensors,
+                **kwargs,
+            )
+            encoding["qformer_input_ids"] = qformer_text_encoding.pop("input_ids")
+            encoding["qformer_attention_mask"] = qformer_text_encoding.pop("attention_mask")
+
+        if images is not None:
+            image_encoding = self.image_processor(images, return_tensors=return_tensors)
+            encoding.update(image_encoding)
+
+        return encoding
+
+    # Copied from transformers.models.blip.processing_blip.BlipProcessor.batch_decode with BertTokenizerFast->PreTrainedTokenizer
+    def batch_decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
+        refer to the docstring of this method for more information.
+        """
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    # Copied from transformers.models.blip.processing_blip.BlipProcessor.decode with BertTokenizerFast->PreTrainedTokenizer
+    def decode(self, *args, **kwargs):
+        """
+        This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer
+        to the docstring of this method for more information.
+        """
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+    # overwrite to save the Q-Former tokenizer in a separate folder
+    def save_pretrained(self, save_directory, **kwargs):
+        if os.path.isfile(save_directory):
+            raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file")
+        os.makedirs(save_directory, exist_ok=True)
+        qformer_tokenizer_path = os.path.join(save_directory, "qformer_tokenizer")
+        self.qformer_tokenizer.save_pretrained(qformer_tokenizer_path)
+        return super().save_pretrained(save_directory, **kwargs)
+
+    # overwrite to load the Q-Former tokenizer from a separate folder
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
+        qformer_tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="qformer_tokenizer")
+        args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
+        args.append(qformer_tokenizer)
+        return cls(*args)

openflamingo/lib/python3.10/site-packages/transformers/models/jukebox/__init__.py

@@ -0,0 +1,70 @@
+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING
+
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
+
+
+_import_structure = {
+    "configuration_jukebox": [
+        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "JukeboxConfig",
+        "JukeboxPriorConfig",
+        "JukeboxVQVAEConfig",
+    ],
+    "tokenization_jukebox": ["JukeboxTokenizer"],
+}
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_jukebox"] = [
+        "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "JukeboxModel",
+        "JukeboxPreTrainedModel",
+        "JukeboxVQVAE",
+        "JukeboxPrior",
+    ]
+
+if TYPE_CHECKING:
+    from .configuration_jukebox import (
+        JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        JukeboxConfig,
+        JukeboxPriorConfig,
+        JukeboxVQVAEConfig,
+    )
+    from .tokenization_jukebox import JukeboxTokenizer
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_jukebox import (
+            JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
+            JukeboxModel,
+            JukeboxPreTrainedModel,
+            JukeboxPrior,
+            JukeboxVQVAE,
+        )
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

openflamingo/lib/python3.10/site-packages/transformers/models/jukebox/configuration_jukebox.py

@@ -0,0 +1,614 @@
+# coding=utf-8
+# Copyright 2022 The OpenAI Team Authors and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Jukebox configuration"""
+
+import os
+from typing import List, Union
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "openai/jukebox-5b-lyrics": "https://huggingface.co/openai/jukebox-5b-lyrics/blob/main/config.json",
+    "openai/jukebox-1b-lyrics": "https://huggingface.co/openai/jukebox-1b-lyrics/blob/main/config.json",
+}
+
+_LARGE_ATTENTION = [
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "cross_attention",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "cross_attention",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "cross_attention",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "cross_attention",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "cross_attention",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "cross_attention",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "block_attn",
+    "transpose_block_attn",
+    "prev_block_attn",
+    "cross_attention",
+]
+_RawColumnPreviousRowAttention = ["block_attn", "transpose_block_attn", "prev_block_attn"]
+_FullDenseAttention = ["dense_attention"]
+_PrimePrimeDenseAttention = ["prime_attn", "prime_attn", "dense_attn"]
+
+
+def full_dense_attention(layer):
+    return _FullDenseAttention[0]
+
+
+def raw_column_previous_row_attention(layer):
+    return _RawColumnPreviousRowAttention[layer % 3]
+
+
+def large_separated_enc_dec_w_lyrics(layer):
+    return _LARGE_ATTENTION[layer % 79]
+
+
+def enc_dec_with_lyrics(layer):
+    if layer % 16 == 15:
+        return _PrimePrimeDenseAttention[layer % 3]
+    return _RawColumnPreviousRowAttention[layer % 3]
+
+
+ATTENTION_PATTERNS = {
+    "full_dense_attention": full_dense_attention,
+    "raw_column_previous_row_attention": raw_column_previous_row_attention,  # Alternate row, column and previous row attn
+    "large_separated_enc_dec_w_lyrics": large_separated_enc_dec_w_lyrics,  # Used by large separated_enc_dec model with lyrics
+    "enc_dec_with_lyrics": enc_dec_with_lyrics,  # Used by encoder_decoder model with lyrics
+}
+
+
+class JukeboxPriorConfig(PretrainedConfig):
+    """
+        This is the configuration class to store the configuration of a [`JukeboxPrior`]. It is used to instantiate a
+        `JukeboxPrior` according to the specified arguments, defining the model architecture. Instantiating a
+        configuration with the defaults will yield a similar configuration to that of the top level prior from the
+        [openai/jukebox-1b-lyrics](https://huggingface.co/openai/jukebox
+    -1b-lyrics) architecture.
+
+        Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+        documentation from [`PretrainedConfig`] for more information.
+
+
+
+    Args:
+        act_fn (`str`, *optional*, defaults to `"quick_gelu"`):
+            Activation function.
+        alignment_head (`int`, *optional*, defaults to 2):
+            Head that is responsible of the alignment between lyrics and music. Only used to compute the lyric to audio
+            alignment
+        alignment_layer (`int`, *optional*, defaults to 68):
+            Index of the layer that is responsible of the alignment between lyrics and music. Only used to compute the
+            lyric to audio alignment
+        attention_multiplier (`float`, *optional*, defaults to 0.25):
+            Multiplier coefficient used to define the hidden dimension of the attention layers. 0.25 means that
+            0.25*width of the model will be used.
+        attention_pattern (`str`, *optional*, defaults to `"enc_dec_with_lyrics"`):
+            Which attention pattern to use for the decoder/
+        attn_dropout (`int`, *optional*, defaults to 0):
+            Dropout probability for the post-attention layer dropout in the decoder.
+        attn_res_scale (`bool`, *optional*, defaults to `False`):
+            Whether or not to scale the residuals in the attention conditioner block.
+        blocks (`int`, *optional*, defaults to 64):
+            Number of blocks used in the `block_attn`. A sequence of length seq_len is factored as `[blocks, seq_len //
+            blocks]` in the `JukeboxAttention` layer.
+        conv_res_scale (`int`, *optional*):
+            Whether or not to scale the residuals in the conditioner block. Since the top level prior does not have a
+            conditioner, the default value is to None and should not be modified.
+        num_layers (`int`, *optional*, defaults to 72):
+            Number of layers of the transformer architecture.
+        emb_dropout (`int`, *optional*, defaults to 0):
+            Embedding dropout used in the lyric decoder.
+        encoder_config (`JukeboxPriorConfig`, *optional*) :
+            Configuration of the encoder which models the prior on the lyrics.
+        encoder_loss_fraction (`float`, *optional*, defaults to 0.4):
+            Multiplication factor used in front of the lyric encoder loss.
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Hidden dimension of the attention layers.
+        init_scale (`float`, *optional*, defaults to 0.2):
+            Initialization scales for the prior modules.
+        is_encoder_decoder (`bool`, *optional*, defaults to `True`):
+            Whether or not the prior is an encoder-decoder model. In case it is not, and `nb_relevant_lyric_tokens` is
+            greater than 0, the `encoder` args should be specified for the lyric encoding.
+        mask (`bool`, *optional*, defaults to `False`):
+            Whether or not to mask the previous positions in the attention.
+        max_duration (`int`, *optional*, defaults to 600):
+            Maximum supported duration of the generated song in seconds.
+        max_nb_genres (`int`, *optional*, defaults to 1):
+            Maximum number of genres that can be used to condition the model.
+        merged_decoder (`bool`, *optional*, defaults to `True`):
+            Whether or not the decoder and the encoder inputs are merged. This is used for the separated
+            encoder-decoder architecture
+        metadata_conditioning (`bool`, *optional*, defaults to `True)`:
+            Whether or not to condition on the artist and genre metadata.
+        metadata_dims (`List[int]`, *optional*, defaults to `[604, 7898]`):
+            Number of genres and the number of artists that were used to train the embedding layers of the prior
+            models.
+        min_duration (`int`, *optional*, defaults to 0):
+            Minimum duration of the generated audio on which the model was trained.
+        mlp_multiplier (`float`, *optional*, defaults to 1.0):
+            Multiplier coefficient used to define the hidden dimension of the MLP layers. 0.25 means that 0.25*width of
+            the model will be used.
+        music_vocab_size (`int`, *optional*, defaults to 2048):
+            Number of different music tokens. Should be similar to the `JukeboxVQVAEConfig.nb_discrete_codes`.
+        n_ctx (`int`, *optional*, defaults to 6144):
+            Number of context tokens for each prior. The context tokens are the music tokens that are attended to when
+            generating music tokens.
+        n_heads (`int`, *optional*, defaults to 2):
+                Number of attention heads.
+        nb_relevant_lyric_tokens (`int`, *optional*, defaults to 384):
+            Number of lyric tokens that are used when sampling a single window of length `n_ctx`
+        res_conv_depth (`int`, *optional*, defaults to 3):
+            Depth of the `JukeboxDecoderConvBock` used to upsample the previously sampled audio in the
+            `JukeboxMusicTokenConditioner`.
+        res_conv_width (`int`, *optional*, defaults to 128):
+            Width of the `JukeboxDecoderConvBock` used to upsample the previously sampled audio in the
+            `JukeboxMusicTokenConditioner`.
+        res_convolution_multiplier (`int`, *optional*, defaults to 1):
+            Multiplier used to scale the `hidden_dim` of the `JukeboxResConv1DBlock`.
+        res_dilation_cycle (`int`, *optional*):
+            Dilation cycle used to define the `JukeboxMusicTokenConditioner`. Usually similar to the ones used in the
+            corresponding level of the VQVAE. The first prior does not use it as it is not conditioned on upper level
+            tokens.
+        res_dilation_growth_rate (`int`, *optional*, defaults to 1):
+            Dilation grow rate used between each convolutionnal block of the `JukeboxMusicTokenConditioner`
+        res_downs_t (`List[int]`, *optional*, defaults to `[3, 2, 2]`):
+            Downsampling rates used in the audio conditioning network
+        res_strides_t (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
+            Striding used in the audio conditioning network
+        resid_dropout (`int`, *optional*, defaults to 0):
+            Residual dropout used in the attention pattern.
+        sampling_rate (`int`, *optional*, defaults to 44100):
+            Sampling rate used for training.
+        spread (`int`, *optional*):
+            Spread used in the `summary_spread_attention` pattern
+        timing_dims (`int`, *optional*, defaults to 64):
+            Dimension of the timing embedding.
+        zero_out (`bool`, *optional*, defaults to `False`):
+            Whether or not to zero out convolution weights when initializing.
+    """
+
+    model_type = "jukebox_prior"
+    attribute_map = {
+        "max_position_embeddings": "n_positions",
+        "num_attention_heads": "n_head",
+    }
+
+    def __init__(
+        self,
+        act_fn="quick_gelu",
+        level=0,
+        alignment_head=2,
+        alignment_layer=68,
+        attention_multiplier=0.25,
+        attention_pattern="enc_dec_with_lyrics",
+        attn_dropout=0,
+        attn_res_scale=False,
+        blocks=64,
+        conv_res_scale=None,
+        num_layers=72,
+        emb_dropout=0,
+        encoder_config=None,
+        encoder_loss_fraction=0.4,
+        hidden_size=2048,
+        init_scale=0.2,
+        is_encoder_decoder=True,
+        lyric_vocab_size=80,
+        mask=False,
+        max_duration=600,
+        max_nb_genres=1,
+        merged_decoder=True,
+        metadata_conditioning=True,
+        metadata_dims=[604, 7898],
+        min_duration=0,
+        mlp_multiplier=1.0,
+        music_vocab_size=2048,
+        n_ctx=6144,
+        n_heads=2,
+        nb_relevant_lyric_tokens=384,
+        res_conv_depth=3,
+        res_conv_width=128,
+        res_convolution_multiplier=1,
+        res_dilation_cycle=None,
+        res_dilation_growth_rate=1,
+        res_downs_t=[3, 2, 2],
+        res_strides_t=[2, 2, 2],
+        resid_dropout=0,
+        sampling_rate=44100,
+        spread=None,
+        timing_dims=64,
+        zero_out=False,
+        **kwargs,
+    ):
+        self.act_fn = act_fn
+        self.alignment_head = alignment_head
+        self.alignment_layer = alignment_layer
+        self.attention_multiplier = attention_multiplier
+        self.attention_pattern = attention_pattern
+        self.attn_dropout = attn_dropout
+        self.attn_res_scale = attn_res_scale
+        self.blocks = blocks
+        self.conv_res_scale = conv_res_scale
+        self.num_layers = num_layers
+        self.emb_dropout = emb_dropout
+        self.music_vocab_size = music_vocab_size
+        if encoder_config is not None:
+            self.encoder_config = JukeboxPriorConfig(**encoder_config)
+        else:
+            self.encoder_config = None
+        self.encoder_loss_fraction = encoder_loss_fraction
+        self.init_scale = init_scale
+        self.is_encoder_decoder = is_encoder_decoder
+        self.lyric_vocab_size = lyric_vocab_size
+        self.level = level
+        self.mask = mask
+        self.max_duration = max_duration
+        self.max_nb_genres = max_nb_genres
+        self.merged_decoder = merged_decoder
+        self.metadata_conditioning = metadata_conditioning
+        self.metadata_dims = metadata_dims
+        self.min_duration = min_duration
+        self.mlp_multiplier = mlp_multiplier
+        self.n_ctx = n_ctx
+        self.n_heads = n_heads
+        self.nb_relevant_lyric_tokens = nb_relevant_lyric_tokens
+        self.res_conv_depth = res_conv_depth
+        self.res_conv_width = res_conv_width
+        self.res_convolution_multiplier = res_convolution_multiplier
+        self.res_dilation_cycle = res_dilation_cycle
+        self.res_dilation_growth_rate = res_dilation_growth_rate
+        self.res_downs_t = res_downs_t
+        self.res_strides_t = res_strides_t
+        self.resid_dropout = resid_dropout
+        self.sampling_rate = sampling_rate
+        self.spread = spread
+        self.timing_dims = timing_dims
+        self.hidden_size = hidden_size
+        self.zero_out = zero_out
+
+    @classmethod
+    def from_pretrained(
+        cls, pretrained_model_name_or_path: Union[str, os.PathLike], level=0, **kwargs
+    ) -> "PretrainedConfig":
+        cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        # get the prior config dict if we are loading from JukeboxConfig
+        if config_dict.get("model_type") == "jukebox":
+            config_dict = config_dict[f"prior_{level}"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class JukeboxVQVAEConfig(PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a [`JukeboxVQVAE`]. It is used to instantiate a
+    `JukeboxVQVAE` according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the VQVAE from
+    [openai/jukebox-1b-lyrics](https://huggingface.co/openai/jukebox-1b-lyrics) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        act_fn (`str`, *optional*, defaults to `"relu"`):
+            Activation function of the model.
+        nb_discrete_codes (`int`, *optional*, defaults to 2048):
+            Number of codes of the VQVAE.
+        commit (`float`, *optional*, defaults to 0.02):
+            Commit loss multiplier.
+        conv_input_shape (`int`, *optional*, defaults to 1):
+            Number of audio channels.
+        conv_res_scale (`bool`, *optional*, defaults to `False`):
+            Whether or not to scale the residuals of the `JukeboxResConv1DBlock`.
+        embed_dim (`int`, *optional*, defaults to 64):
+            Embedding dimension of the codebook vectors.
+        hop_fraction (`List[int]`, *optional*, defaults to `[0.125, 0.5, 0.5]`):
+            Fraction of non-intersecting window used when continuing the sampling process.
+        levels (`int`, *optional*, defaults to 3):
+            Number of hierarchical levels that used in the VQVAE.
+        lmu (`float`, *optional*, defaults to 0.99):
+            Used in the codebook update, exponential moving average coefficient. For more detail refer to Appendix A.1
+            of the original [VQVAE paper](https://arxiv.org/pdf/1711.00937v2.pdf)
+        multipliers (`List[int]`, *optional*, defaults to `[2, 1, 1]`):
+            Depth and width multipliers used for each level. Used on the `res_conv_width` and `res_conv_depth`
+        res_conv_depth (`int`, *optional*, defaults to 4):
+            Depth of the encoder and decoder block. If no `multipliers` are used, this is the same for each level.
+        res_conv_width (`int`, *optional*, defaults to 32):
+            Width of the encoder and decoder block. If no `multipliers` are used, this is the same for each level.
+        res_convolution_multiplier (`int`, *optional*, defaults to 1):
+            Scaling factor of the hidden dimension used in the `JukeboxResConv1DBlock`.
+        res_dilation_cycle (`int`, *optional*):
+            Dilation cycle value used in the `JukeboxResnet`. If an int is used, each new Conv1 block will have a depth
+            reduced by a power of `res_dilation_cycle`.
+        res_dilation_growth_rate (`int`, *optional*, defaults to 3):
+            Resnet dilation growth rate used in the VQVAE (dilation_growth_rate ** depth)
+        res_downs_t (`List[int]`, *optional*, defaults to `[3, 2, 2]`):
+            Downsampling rate for each level of the hierarchical VQ-VAE.
+        res_strides_t (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
+            Stride used for each level of the hierarchical VQ-VAE.
+        sample_length (`int`, *optional*, defaults to 1058304):
+            Provides the max input shape of the VQVAE. Is used to compute the input shape of each level.
+        init_scale (`float`, *optional*, defaults to 0.2):
+            Initialization scale.
+        zero_out (`bool`, *optional*, defaults to `False`):
+            Whether or not to zero out convolution weights when initializing.
+    """
+
+    model_type = "jukebox_vqvae"
+
+    def __init__(
+        self,
+        act_fn="relu",
+        nb_discrete_codes=2048,
+        commit=0.02,
+        conv_input_shape=1,
+        conv_res_scale=False,
+        embed_dim=64,
+        hop_fraction=[0.125, 0.5, 0.5],
+        levels=3,
+        lmu=0.99,
+        multipliers=[2, 1, 1],
+        res_conv_depth=4,
+        res_conv_width=32,
+        res_convolution_multiplier=1,
+        res_dilation_cycle=None,
+        res_dilation_growth_rate=3,
+        res_downs_t=[3, 2, 2],
+        res_strides_t=[2, 2, 2],
+        sample_length=1058304,
+        init_scale=0.2,
+        zero_out=False,
+        **kwargs,
+    ):
+        self.hop_fraction = hop_fraction
+        self.conv_input_shape = conv_input_shape
+        self.sample_length = sample_length
+
+        # VQVAE parameters (all used)
+        self.levels = levels
+        self.embed_dim = embed_dim
+        self.nb_discrete_codes = nb_discrete_codes
+        self.res_conv_width = res_conv_width
+        self.res_conv_depth = res_conv_depth
+        self.res_convolution_multiplier = res_convolution_multiplier
+        self.res_dilation_growth_rate = res_dilation_growth_rate
+        self.res_dilation_cycle = res_dilation_cycle
+        self.multipliers = multipliers
+        self.res_downs_t = res_downs_t
+        self.res_strides_t = res_strides_t
+        self.lmu = lmu
+        self.commit = commit
+        self.conv_res_scale = conv_res_scale
+        self.act_fn = act_fn
+        self.init_scale = init_scale
+        self.zero_out = zero_out
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        cls._set_token_in_kwargs(kwargs)
+
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        # get the text config dict if we are loading from CLIPConfig
+        if config_dict.get("model_type") == "jukebox":
+            config_dict = config_dict["vqvae_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class JukeboxConfig(PretrainedConfig):
+    """
+    This is the configuration class to store the configuration of a [`JukeboxModel`].
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information. Instantiating a configuration with the defaults will
+    yield a similar configuration to that of
+    [openai/jukebox-1b-lyrics](https://huggingface.co/openai/jukebox-1b-lyrics) architecture.
+
+
+    The downsampling and stride are used to determine downsampling of the input sequence. For example, downsampling =
+    (5,3), and strides = (2, 2) will downsample the audio by 2^5 = 32 to get the first level of codes, and 2**8 = 256
+    to get the second level codes. This is mostly true for training the top level prior and the upsamplers.
+
+    Args:
+        vqvae_config (`JukeboxVQVAEConfig`, *optional*):
+            Configuration for the `JukeboxVQVAE` model.
+        prior_config_list (`List[JukeboxPriorConfig]`, *optional*):
+            List of the configs for each of the `JukeboxPrior` of the model. The original architecture uses 3 priors.
+        nb_priors (`int`, *optional*, defaults to 3):
+            Number of prior models that will sequentially sample tokens. Each prior is conditional auto regressive
+            (decoder) model, apart from the top prior, which can include a lyric encoder. The available models were
+            trained using a top prior and 2 upsampler priors.
+        sampling_rate (`int`, *optional*, defaults to 44100):
+            Sampling rate of the raw audio.
+        timing_dims (`int`, *optional*, defaults to 64):
+            Dimensions of the JukeboxRangeEmbedding layer which is equivalent to traditional positional embedding
+            layer. The timing embedding layer converts the absolute and relative position in the currently sampled
+            audio to a tensor of length `timing_dims` that will be added to the music tokens.
+        min_duration (`int`, *optional*, defaults to 0):
+            Minimum duration of the audios to generate
+        max_duration (`float`, *optional*, defaults to 600.0):
+            Maximum duration of the audios to generate
+        max_nb_genres (`int`, *optional*, defaults to 5):
+            Maximum number of genres that can be used to condition a single sample.
+        metadata_conditioning (`bool`, *optional*, defaults to `True`):
+            Whether or not to use metadata conditioning, corresponding to the artist, the genre and the min/maximum
+            duration.
+
+    Example:
+
+    ```python
+    >>> from transformers import JukeboxModel, JukeboxConfig
+
+    >>> # Initializing a Jukebox configuration
+    >>> configuration = JukeboxConfig()
+
+    >>> # Initializing a model from the configuration
+    >>> model = JukeboxModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
+    """
+
+    model_type = "jukebox"
+
+    def __init__(
+        self,
+        vqvae_config=None,
+        prior_config_list=None,
+        nb_priors=3,
+        sampling_rate=44100,
+        timing_dims=64,
+        min_duration=0,
+        max_duration=600.0,
+        max_nb_genres=5,
+        metadata_conditioning=True,
+        **kwargs,
+    ):
+        if vqvae_config is None:
+            vqvae_config = {}
+            logger.info("vqvae_config is None. initializing the JukeboxVQVAE with default values.")
+
+        self.vqvae_config = JukeboxVQVAEConfig(**vqvae_config)
+        if prior_config_list is not None:
+            self.prior_configs = [JukeboxPriorConfig(**prior_config) for prior_config in prior_config_list]
+        else:
+            self.prior_configs = []
+            for prior_idx in range(nb_priors):
+                prior_config = kwargs.pop(f"prior_{prior_idx}", None)
+                if prior_config is None:
+                    prior_config = {}
+                    logger.info(
+                        f"prior_{prior_idx}'s  config is None. Initializing the JukeboxPriorConfig list with default"
+                        " values."
+                    )
+                self.prior_configs.append(JukeboxPriorConfig(**prior_config))
+
+        self.hop_fraction = self.vqvae_config.hop_fraction
+
+        self.nb_priors = nb_priors
+
+        # Metadata conditioning
+        self.max_nb_genres = max_nb_genres
+        self.sampling_rate = sampling_rate
+        self.timing_dims = timing_dims
+        self.min_duration = min_duration
+        self.max_duration = max_duration
+        self.metadata_conditioning = metadata_conditioning
+
+        super().__init__(**kwargs)
+
+    @classmethod
+    def from_configs(cls, prior_configs: List[JukeboxPriorConfig], vqvae_config: JukeboxVQVAEConfig, **kwargs):
+        r"""
+        Instantiate a [`JukeboxConfig`] (or a derived class) from clip text model configuration and clip vision model
+        configuration.
+
+        Returns:
+            [`JukeboxConfig`]: An instance of a configuration object
+        """
+        prior_config_list = [config.to_dict() for config in prior_configs]
+        return cls(prior_config_list=prior_config_list, vqvae_config_dict=vqvae_config.to_dict(), **kwargs)
+
+    def to_dict(self):
+        # Override the default to_dict to apply to_dict to the list of prior configs.
+        result = super().to_dict()
+        result["prior_config_list"] = [config.to_dict() for config in result.pop("prior_configs")]
+        return result

openflamingo/lib/python3.10/site-packages/transformers/models/jukebox/convert_jukebox.py

@@ -0,0 +1,279 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Convert Jukebox checkpoints"""
+
+import argparse
+import json
+import os
+from pathlib import Path
+
+import requests
+import torch
+
+from transformers import JukeboxConfig, JukeboxModel
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger(__name__)
+
+
+PREFIX = "https://openaipublic.azureedge.net/jukebox/models/"
+MODEL_MAPPING = {
+    "jukebox-1b-lyrics": [
+        "5b/vqvae.pth.tar",
+        "5b/prior_level_0.pth.tar",
+        "5b/prior_level_1.pth.tar",
+        "1b_lyrics/prior_level_2.pth.tar",
+    ],
+    "jukebox-5b-lyrics": [
+        "5b/vqvae.pth.tar",
+        "5b/prior_level_0.pth.tar",
+        "5b/prior_level_1.pth.tar",
+        "5b_lyrics/prior_level_2.pth.tar",
+    ],
+}
+
+
+def replace_key(key):
+    if key.endswith(".model.1.bias") and len(key.split(".")) > 10:
+        key = key.replace(".model.1.bias", ".conv1d_1.bias")
+    elif key.endswith(".model.1.weight") and len(key.split(".")) > 10:
+        key = key.replace(".model.1.weight", ".conv1d_1.weight")
+    elif key.endswith(".model.3.bias") and len(key.split(".")) > 10:
+        key = key.replace(".model.3.bias", ".conv1d_2.bias")
+    elif key.endswith(".model.3.weight") and len(key.split(".")) > 10:
+        key = key.replace(".model.3.weight", ".conv1d_2.weight")
+
+    if "conditioner_blocks.0." in key:
+        key = key.replace("conditioner_blocks.0", "conditioner_blocks")
+
+    if "prime_prior" in key:
+        key = key.replace("prime_prior", "encoder")
+
+    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
+        key = key.replace(".emb.", ".")
+
+    if key.endswith("k"):  # replace vqvae.X.k with vqvae.X.codebook
+        return key.replace(".k", ".codebook")
+    if "y_emb." in key:
+        return key.replace("y_emb.", "metadata_embedding.")
+
+    if "x_emb.emb." in key:
+        key = key.replace("0.x_emb.emb", "embed_tokens")
+
+    if "prime_state_ln" in key:
+        return key.replace("prime_state_ln", "encoder.final_layer_norm")
+    if ".ln" in key:
+        return key.replace(".ln", ".layer_norm")
+    if "_ln" in key:
+        return key.replace("_ln", "_layer_norm")
+
+    if "prime_state_proj" in key:
+        return key.replace("prime_state_proj", "encoder.proj_in")
+    if "prime_x_out" in key:
+        return key.replace("prime_x_out", "encoder.lm_head")
+    if "prior.x_out" in key:
+        return key.replace("x_out", "fc_proj_out")
+    if "x_emb" in key:
+        return key.replace("x_emb", "embed_tokens")
+
+    return key
+
+
+def fix_jukebox_keys(state_dict, model_state_dict, key_prefix, mapping):
+    new_dict = {}
+    import re
+
+    re_encoder_block_conv_in = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)")
+    re_encoder_block_resnet = re.compile(
+        r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"
+    )
+    re_encoder_block_proj_out = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)")
+
+    re_decoder_block_conv_out = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)")
+    re_decoder_block_resnet = re.compile(
+        r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"
+    )
+    re_decoder_block_proj_in = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)")
+
+    re_prior_cond_conv_out = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)")
+    re_prior_cond_resnet = re.compile(
+        r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"
+    )
+    re_prior_cond_proj_in = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)")
+
+    for original_key, value in state_dict.items():
+        # rename vqvae.encoder keys
+        if re_encoder_block_conv_in.fullmatch(original_key):
+            regex_match = re_encoder_block_conv_in.match(original_key)
+            groups = regex_match.groups()
+            block_index = int(groups[2]) * 2 + int(groups[3])
+            re_new_key = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
+            key = re_encoder_block_conv_in.sub(re_new_key, original_key)
+
+        elif re_encoder_block_resnet.fullmatch(original_key):
+            regex_match = re_encoder_block_resnet.match(original_key)
+            groups = regex_match.groups()
+            block_index = int(groups[2]) * 2 + int(groups[3])
+            conv_index = {"1": 1, "3": 2}[groups[-2]]
+            prefix = f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
+            resnet_block = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
+            re_new_key = prefix + resnet_block
+            key = re_encoder_block_resnet.sub(re_new_key, original_key)
+
+        elif re_encoder_block_proj_out.fullmatch(original_key):
+            regex_match = re_encoder_block_proj_out.match(original_key)
+            groups = regex_match.groups()
+            re_new_key = f"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
+            key = re_encoder_block_proj_out.sub(re_new_key, original_key)
+
+        # rename vqvae.decoder keys
+        elif re_decoder_block_conv_out.fullmatch(original_key):
+            regex_match = re_decoder_block_conv_out.match(original_key)
+            groups = regex_match.groups()
+            block_index = int(groups[2]) * 2 + int(groups[3]) - 2
+            re_new_key = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
+            key = re_decoder_block_conv_out.sub(re_new_key, original_key)
+
+        elif re_decoder_block_resnet.fullmatch(original_key):
+            regex_match = re_decoder_block_resnet.match(original_key)
+            groups = regex_match.groups()
+            block_index = int(groups[2]) * 2 + int(groups[3]) - 2
+            conv_index = {"1": 1, "3": 2}[groups[-2]]
+            prefix = f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
+            resnet_block = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
+            re_new_key = prefix + resnet_block
+            key = re_decoder_block_resnet.sub(re_new_key, original_key)
+
+        elif re_decoder_block_proj_in.fullmatch(original_key):
+            regex_match = re_decoder_block_proj_in.match(original_key)
+            groups = regex_match.groups()
+            re_new_key = f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
+            key = re_decoder_block_proj_in.sub(re_new_key, original_key)
+
+        # rename prior cond.model to upsampler.upsample_block and resnet
+        elif re_prior_cond_conv_out.fullmatch(original_key):
+            regex_match = re_prior_cond_conv_out.match(original_key)
+            groups = regex_match.groups()
+            block_index = int(groups[1]) * 2 + int(groups[2]) - 2
+            re_new_key = f"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
+            key = re_prior_cond_conv_out.sub(re_new_key, original_key)
+
+        elif re_prior_cond_resnet.fullmatch(original_key):
+            regex_match = re_prior_cond_resnet.match(original_key)
+            groups = regex_match.groups()
+            block_index = int(groups[1]) * 2 + int(groups[2]) - 2
+            conv_index = {"1": 1, "3": 2}[groups[-2]]
+            prefix = f"conditioner_blocks.upsampler.upsample_block.{block_index}."
+            resnet_block = f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
+            re_new_key = prefix + resnet_block
+            key = re_prior_cond_resnet.sub(re_new_key, original_key)
+
+        elif re_prior_cond_proj_in.fullmatch(original_key):
+            regex_match = re_prior_cond_proj_in.match(original_key)
+            groups = regex_match.groups()
+            re_new_key = f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
+            key = re_prior_cond_proj_in.sub(re_new_key, original_key)
+
+        # keep original key
+        else:
+            key = original_key
+
+        key = replace_key(key)
+
+        if f"{key_prefix}.{key}" not in model_state_dict or key is None:
+            print(f"failed converting {original_key} to {key}, does not match")
+
+        # handle missmatched shape
+        elif value.shape != model_state_dict[f"{key_prefix}.{key}"].shape:
+            val = model_state_dict[f"{key_prefix}.{key}"]
+            print(f"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match")
+            key = original_key
+
+        mapping[key] = original_key
+        new_dict[key] = value
+
+    return new_dict
+
+
+@torch.no_grad()
+def convert_openai_checkpoint(model_name=None, pytorch_dump_folder_path=None):
+    """
+    Copy/paste/tweak model's weights to our Jukebox structure.
+    """
+    for file in MODEL_MAPPING[model_name]:
+        if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/')[-1]}"):
+            r = requests.get(f"{PREFIX}{file}", allow_redirects=True)
+            os.makedirs(f"{pytorch_dump_folder_path}/", exist_ok=True)
+            open(f"{pytorch_dump_folder_path}/{file.split('/')[-1]}", "wb").write(r.content)
+
+    model_to_convert = MODEL_MAPPING[model_name.split("/")[-1]]
+
+    config = JukeboxConfig.from_pretrained(model_name)
+    model = JukeboxModel(config)
+
+    weight_dict = []
+    mapping = {}
+    for i, dict_name in enumerate(model_to_convert):
+        old_dic = torch.load(f"{pytorch_dump_folder_path}/{dict_name.split('/')[-1]}")["model"]
+
+        new_dic = {}
+        for k in old_dic.keys():
+            if k.endswith(".b"):
+                new_dic[k.replace("b", "bias")] = old_dic[k]
+            elif k.endswith(".w"):
+                new_dic[k.replace("w", "weight")] = old_dic[k]
+            elif "level_2" not in dict_name and "cond.model." in k:
+                new_dic[k.replace(".blocks.", ".model.")] = old_dic[k]
+            else:
+                new_dic[k] = old_dic[k]
+
+        key_prefix = "vqvae" if i == 0 else f"priors.{3 - i}"
+        new_dic = fix_jukebox_keys(new_dic, model.state_dict(), key_prefix, mapping)
+        weight_dict.append(new_dic)
+
+    vqvae_state_dict = weight_dict.pop(0)
+    model.vqvae.load_state_dict(vqvae_state_dict)
+    for i in range(len(weight_dict)):
+        model.priors[i].load_state_dict(weight_dict[2 - i])
+
+    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
+    with open(f"{pytorch_dump_folder_path}/mapping.json", "w") as txtfile:
+        json.dump(mapping, txtfile)
+
+    print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
+    model.save_pretrained(pytorch_dump_folder_path)
+
+    return weight_dict
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Required parameters
+    parser.add_argument(
+        "--model_name",
+        default="jukebox-5b-lyrics",
+        type=str,
+        help="Name of the model you'd like to convert.",
+    )
+    parser.add_argument(
+        "--pytorch_dump_folder_path",
+        default="jukebox-5b-lyrics-converted",
+        type=str,
+        help="Path to the output PyTorch model directory.",
+    )
+    args = parser.parse_args()
+    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)

openflamingo/lib/python3.10/site-packages/transformers/models/jukebox/tokenization_jukebox.py

@@ -0,0 +1,418 @@
+# coding=utf-8
+# Copyright 2022 The Open AI Team Authors and The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for OpenAI Jukebox."""
+
+
+import json
+import os
+import re
+import unicodedata
+from json.encoder import INFINITY
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import regex
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...tokenization_utils_base import BatchEncoding
+from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
+from ...utils.generic import _is_jax, _is_numpy
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {
+    "artists_file": "artists.json",
+    "lyrics_file": "lyrics.json",
+    "genres_file": "genres.json",
+}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "artists_file": {
+        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json",
+    },
+    "genres_file": {
+        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json",
+    },
+    "lyrics_file": {
+        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json",
+    },
+}
+
+PRETRAINED_LYRIC_TOKENS_SIZES = {
+    "jukebox": 512,
+}
+
+
+class JukeboxTokenizer(PreTrainedTokenizer):
+    """
+    Constructs a Jukebox tokenizer. Jukebox can be conditioned on 3 different inputs :
+        - Artists, unique ids are associated to each artist from the provided dictionary.
+        - Genres, unique ids are associated to each genre from the provided dictionary.
+        - Lyrics, character based tokenization. Must be initialized with the list of characters that are inside the
+        vocabulary.
+
+    This tokenizer does not require training. It should be able to process a different number of inputs:
+    as the conditioning of the model can be done on the three different queries. If None is provided, defaults values will be used.:
+
+    Depending on the number of genres on which the model should be conditioned (`n_genres`).
+    ```python
+    >>> from transformers import JukeboxTokenizer
+
+    >>> tokenizer = JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics")
+    >>> tokenizer("Alan Jackson", "Country Rock", "old town road")["input_ids"]
+    [tensor([[   0,    0,    0, 6785,  546,   41,   38,   30,   76,   46,   41,   49,
+               40,   76,   44,   41,   27,   30]]), tensor([[  0,   0,   0, 145,   0]]), tensor([[  0,   0,   0, 145,   0]])]
+    ```
+
+    You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you
+    call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
+
+    <Tip>
+
+    If nothing is provided, the genres and the artist will either be selected randomly or set to None
+
+    </Tip>
+
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to:
+    this superclass for more information regarding those methods.
+
+    However the code does not allow that and only supports composing from various genres.
+
+    Args:
+        artists_file (`str`):
+            Path to the vocabulary file which contains a mapping between artists and ids. The default file supports
+            both "v2" and "v3"
+        genres_file (`str`):
+            Path to the vocabulary file which contain a mapping between genres and ids.
+        lyrics_file (`str`):
+            Path to the vocabulary file which contains the accepted characters for the lyrics tokenization.
+        version (`List[str]`, `optional`, default to `["v3", "v2", "v2"]`) :
+            List of the tokenizer versions. The `5b-lyrics`'s top level prior model was trained using `v3` instead of
+            `v2`.
+        n_genres (`int`, `optional`, defaults to 1):
+            Maximum number of genres to use for composition.
+        max_n_lyric_tokens (`int`, `optional`, defaults to 512):
+            Maximum number of lyric tokens to keep.
+        unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_lyric_input_size = PRETRAINED_LYRIC_TOKENS_SIZES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        artists_file,
+        genres_file,
+        lyrics_file,
+        version=["v3", "v2", "v2"],
+        max_n_lyric_tokens=512,
+        n_genres=5,
+        unk_token="<|endoftext|>",
+        **kwargs,
+    ):
+        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+        super().__init__(
+            unk_token=unk_token,
+            n_genres=n_genres,
+            version=version,
+            max_n_lyric_tokens=max_n_lyric_tokens,
+            **kwargs,
+        )
+        self.version = version
+        self.max_n_lyric_tokens = max_n_lyric_tokens
+        self.n_genres = n_genres
+
+        with open(artists_file, encoding="utf-8") as vocab_handle:
+            self.artists_encoder = json.load(vocab_handle)
+
+        with open(genres_file, encoding="utf-8") as vocab_handle:
+            self.genres_encoder = json.load(vocab_handle)
+
+        with open(lyrics_file, encoding="utf-8") as vocab_handle:
+            self.lyrics_encoder = json.load(vocab_handle)
+
+        oov = r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"
+        # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
+        if len(self.lyrics_encoder) == 79:
+            oov = oov.replace(r"\-'", r"\-+'")
+
+        self.out_of_vocab = regex.compile(oov)
+        self.artists_decoder = {v: k for k, v in self.artists_encoder.items()}
+        self.genres_decoder = {v: k for k, v in self.genres_encoder.items()}
+        self.lyrics_decoder = {v: k for k, v in self.lyrics_encoder.items()}
+
+    @property
+    def vocab_size(self):
+        return len(self.artists_encoder) + len(self.genres_encoder) + len(self.lyrics_encoder)
+
+    def get_vocab(self):
+        return dict(self.artists_encoder, self.genres_encoder, self.lyrics_encoder)
+
+    def _convert_token_to_id(self, list_artists, list_genres, list_lyrics):
+        """Converts the artist, genre and lyrics tokens to their index using the vocabulary.
+        The total_length, offset and duration have to be provided in order to select relevant lyrics and add padding to
+        the lyrics token sequence.
+        """
+        artists_id = [self.artists_encoder.get(artist, 0) for artist in list_artists]
+        for genres in range(len(list_genres)):
+            list_genres[genres] = [self.genres_encoder.get(genre, 0) for genre in list_genres[genres]]
+            list_genres[genres] = list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]))
+
+        lyric_ids = [[self.lyrics_encoder.get(character, 0) for character in list_lyrics[0]], [], []]
+        return artists_id, list_genres, lyric_ids
+
+    def _tokenize(self, lyrics):
+        """
+        Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
+        vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
+
+        Do NOT take care of added tokens. Only the lyrics are split into character for the character-based vocabulary.
+        """
+        # only lyrics are not tokenized, but character based is easily handled
+        return list(lyrics)
+
+    def tokenize(self, artist, genre, lyrics, **kwargs):
+        """
+        Converts three strings in a 3 sequence of tokens using the tokenizer
+        """
+        artist, genre, lyrics = self.prepare_for_tokenization(artist, genre, lyrics)
+        lyrics = self._tokenize(lyrics)
+        return artist, genre, lyrics
+
+    def prepare_for_tokenization(
+        self, artists: str, genres: str, lyrics: str, is_split_into_words: bool = False
+    ) -> Tuple[str, str, str, Dict[str, Any]]:
+        """
+        Performs any necessary transformations before tokenization.
+
+        Args:
+            artist (`str`):
+                The artist name to prepare. This will mostly lower the string
+            genres (`str`):
+                The genre name to prepare. This will mostly lower the string.
+            lyrics (`str`):
+                The lyrics to prepare.
+            is_split_into_words (`bool`, *optional*, defaults to `False`):
+                Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the
+                tokenizer assumes the input is already split into words (for instance, by splitting it on whitespace)
+                which it will tokenize. This is useful for NER or token classification.
+        """
+        for idx in range(len(self.version)):
+            if self.version[idx] == "v3":
+                artists[idx] = artists[idx].lower()
+                genres[idx] = [genres[idx].lower()]
+            else:
+                artists[idx] = self._normalize(artists[idx]) + ".v2"
+                genres[idx] = [
+                    self._normalize(genre) + ".v2" for genre in genres[idx].split("_")
+                ]  # split is for the full dictionary with combined genres
+
+        if self.version[0] == "v2":
+            self.out_of_vocab = regex.compile(r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+")
+            vocab = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"
+            self.vocab = {vocab[index]: index + 1 for index in range(len(vocab))}
+            self.vocab["<unk>"] = 0
+            self.n_vocab = len(vocab) + 1
+            self.lyrics_encoder = self.vocab
+            self.lyrics_decoder = {v: k for k, v in self.vocab.items()}
+            self.lyrics_decoder[0] = ""
+        else:
+            self.out_of_vocab = regex.compile(r"[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+")
+
+        lyrics = self._run_strip_accents(lyrics)
+        lyrics = lyrics.replace("\\", "\n")
+        lyrics = self.out_of_vocab.sub("", lyrics), [], []
+        return artists, genres, lyrics
+
+    def _run_strip_accents(self, text):
+        """Strips accents from a piece of text."""
+        text = unicodedata.normalize("NFD", text)
+        output = []
+        for char in text:
+            cat = unicodedata.category(char)
+            if cat == "Mn":
+                continue
+            output.append(char)
+        return "".join(output)
+
+    def _normalize(self, text: str) -> str:
+        """
+        Normalizes the input text. This process is for the genres and the artist
+
+        Args:
+            text (`str`):
+                Artist or Genre string to normalize
+        """
+
+        accepted = (
+            [chr(i) for i in range(ord("a"), ord("z") + 1)]
+            + [chr(i) for i in range(ord("A"), ord("Z") + 1)]
+            + [chr(i) for i in range(ord("0"), ord("9") + 1)]
+            + ["."]
+        )
+        accepted = frozenset(accepted)
+        pattern = re.compile(r"_+")
+        text = "".join([c if c in accepted else "_" for c in text.lower()])
+        text = pattern.sub("_", text).strip("_")
+        return text
+
+    def convert_lyric_tokens_to_string(self, lyrics: List[str]) -> str:
+        return " ".join(lyrics)
+
+    def convert_to_tensors(
+        self, inputs, tensor_type: Optional[Union[str, TensorType]] = None, prepend_batch_axis: bool = False
+    ):
+        """
+        Convert the inner content to tensors.
+
+        Args:
+            tensor_type (`str` or [`~utils.TensorType`], *optional*):
+                The type of tensors to use. If `str`, should be one of the values of the enum [`~utils.TensorType`]. If
+                unset, no modification is done.
+            prepend_batch_axis (`int`, *optional*, defaults to `False`):
+                Whether or not to add the batch dimension during the conversion.
+        """
+        # Convert to TensorType
+        if not isinstance(tensor_type, TensorType):
+            tensor_type = TensorType(tensor_type)
+
+        # Get a function reference for the correct framework
+        if tensor_type == TensorType.TENSORFLOW:
+            if not is_tf_available():
+                raise ImportError(
+                    "Unable to convert output to TensorFlow tensors format, TensorFlow is not installed."
+                )
+            import tensorflow as tf
+
+            as_tensor = tf.constant
+            is_tensor = tf.is_tensor
+        elif tensor_type == TensorType.PYTORCH:
+            if not is_torch_available():
+                raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed.")
+            import torch
+
+            as_tensor = torch.tensor
+            is_tensor = torch.is_tensor
+        elif tensor_type == TensorType.JAX:
+            if not is_flax_available():
+                raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed.")
+            import jax.numpy as jnp  # noqa: F811
+
+            as_tensor = jnp.array
+            is_tensor = _is_jax
+        else:
+            as_tensor = np.asarray
+            is_tensor = _is_numpy
+
+        # Do the tensor conversion in batch
+
+        try:
+            if prepend_batch_axis:
+                inputs = [inputs]
+
+            if not is_tensor(inputs):
+                inputs = as_tensor(inputs)
+        except:  # noqa E722
+            raise ValueError(
+                "Unable to create tensor, you should probably activate truncation and/or padding "
+                "with 'padding=True' 'truncation=True' to have batched tensors with the same length."
+            )
+
+        return inputs
+
+    def __call__(self, artist, genres, lyrics="", return_tensors="pt") -> BatchEncoding:
+        """Convert the raw string to a list of token ids
+
+        Args:
+            artist (`str`):
+                Name of the artist.
+            genres (`str`):
+                List of genres that will be mixed to condition the audio
+            lyrics (`str`, *optional*, defaults to `""`):
+                Lyrics used to condition the generation
+        """
+        input_ids = [0, 0, 0]
+        artist = [artist] * len(self.version)
+        genres = [genres] * len(self.version)
+
+        artists_tokens, genres_tokens, lyrics_tokens = self.tokenize(artist, genres, lyrics)
+        artists_id, genres_ids, full_tokens = self._convert_token_to_id(artists_tokens, genres_tokens, lyrics_tokens)
+
+        attention_masks = [-INFINITY] * len(full_tokens[-1])
+        input_ids = [
+            self.convert_to_tensors(
+                [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]], tensor_type=return_tensors
+            )
+            for i in range(len(self.version))
+        ]
+        return BatchEncoding({"input_ids": input_ids, "attention_masks": attention_masks})
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Saves the tokenizer's vocabulary dictionary to the provided save_directory.
+
+        Args:
+            save_directory (`str`):
+                A path to the directory where to saved. It will be created if it doesn't exist.
+
+            filename_prefix (`Optional[str]`, *optional*):
+                A prefix to add to the names of the files saved by the tokenizer.
+
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+
+        artists_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["artists_file"]
+        )
+        with open(artists_file, "w", encoding="utf-8") as f:
+            f.write(json.dumps(self.artists_encoder, ensure_ascii=False))
+
+        genres_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["genres_file"]
+        )
+        with open(genres_file, "w", encoding="utf-8") as f:
+            f.write(json.dumps(self.genres_encoder, ensure_ascii=False))
+
+        lyrics_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["lyrics_file"]
+        )
+        with open(lyrics_file, "w", encoding="utf-8") as f:
+            f.write(json.dumps(self.lyrics_encoder, ensure_ascii=False))
+
+        return (artists_file, genres_file, lyrics_file)
+
+    def _convert_id_to_token(self, artists_index, genres_index, lyric_index):
+        """
+        Converts an index (integer) in a token (str) using the vocab.
+
+        Args:
+            artists_index (`int`):
+                Index of the artist in its corresponding dictionary.
+            genres_index (`Union[List[int], int]`):
+               Index of the genre in its corresponding dictionary.
+            lyric_index (`List[int]`):
+                List of character indices, which each correspond to a character.
+        """
+        artist = self.artists_decoder.get(artists_index)
+        genres = [self.genres_decoder.get(genre) for genre in genres_index]
+        lyrics = [self.lyrics_decoder.get(character) for character in lyric_index]
+        return artist, genres, lyrics

openflamingo/lib/python3.10/site-packages/transformers/models/layoutxlm/tokenization_layoutxlm.py

@@ -0,0 +1,1176 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
+""" Tokenization classes for LayoutXLM model."""
+
+
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import sentencepiece as spm
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...tokenization_utils_base import (
+    BatchEncoding,
+    EncodedInput,
+    PreTokenizedInput,
+    TextInput,
+    TextInputPair,
+    TruncationStrategy,
+)
+from ...utils import PaddingStrategy, TensorType, add_end_docstrings, logging
+from ..xlm_roberta.tokenization_xlm_roberta import (
+    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES,
+    PRETRAINED_VOCAB_FILES_MAP,
+    SPIECE_UNDERLINE,
+    VOCAB_FILES_NAMES,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+LAYOUTXLM_ENCODE_KWARGS_DOCSTRING = r"""
+            add_special_tokens (`bool`, *optional*, defaults to `True`):
+                Whether or not to encode the sequences with the special tokens relative to their model.
+            padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
+                Activates and controls padding. Accepts the following values:
+
+                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+                  sequence if provided).
+                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+                  acceptable input length for the model if that argument is not provided.
+                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+                  lengths).
+            truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
+                Activates and controls truncation. Accepts the following values:
+
+                - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or
+                  to the maximum acceptable input length for the model if that argument is not provided. This will
+                  truncate token by token, removing a token from the longest sequence in the pair if a pair of
+                  sequences (or a batch of pairs) is provided.
+                - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+                  maximum acceptable input length for the model if that argument is not provided. This will only
+                  truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+                - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+                  maximum acceptable input length for the model if that argument is not provided. This will only
+                  truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+                - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
+                  greater than the model maximum admissible input size).
+            max_length (`int`, *optional*):
+                Controls the maximum length to use by one of the truncation/padding parameters.
+
+                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
+                is required by one of the truncation/padding parameters. If the model has no specific maximum input
+                length (like XLNet) truncation/padding to a maximum length will be deactivated.
+            stride (`int`, *optional*, defaults to 0):
+                If set to a number along with `max_length`, the overflowing tokens returned when
+                `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
+                returned to provide some overlap between truncated and overflowing sequences. The value of this
+                argument defines the number of overlapping tokens.
+            pad_to_multiple_of (`int`, *optional*):
+                If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
+                the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
+            return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
+                If set, will return tensors instead of list of python integers. Acceptable values are:
+
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return Numpy `np.ndarray` objects.
+            return_token_type_ids (`bool`, *optional*):
+                Whether to return token type IDs. If left to the default, will return the token type IDs according to
+                the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+                [What are token type IDs?](../glossary#token-type-ids)
+            return_attention_mask (`bool`, *optional*):
+                Whether to return the attention mask. If left to the default, will return the attention mask according
+                to the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+                [What are attention masks?](../glossary#attention-mask)
+            return_overflowing_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not to return overflowing token sequences. If a pair of sequences of input ids (or a batch
+                of pairs) is provided with `truncation_strategy = longest_first` or `True`, an error is raised instead
+                of returning overflowing tokens.
+            return_special_tokens_mask (`bool`, *optional*, defaults to `False`):
+                Whether or not to return special tokens mask information.
+            return_offsets_mapping (`bool`, *optional*, defaults to `False`):
+                Whether or not to return `(char_start, char_end)` for each token.
+
+                This is only available on fast tokenizers inheriting from [`PreTrainedTokenizerFast`], if using
+                Python's tokenizer, this method will raise `NotImplementedError`.
+            return_length  (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the lengths of the encoded inputs.
+            verbose (`bool`, *optional*, defaults to `True`):
+                Whether or not to print more information and warnings.
+            **kwargs: passed to the `self.tokenize()` method
+
+        Return:
+            [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model.
+
+              [What are input IDs?](../glossary#input-ids)
+
+            - **bbox** -- List of bounding boxes to be fed to a model.
+
+            - **token_type_ids** -- List of token type ids to be fed to a model (when `return_token_type_ids=True` or
+              if *"token_type_ids"* is in `self.model_input_names`).
+
+              [What are token type IDs?](../glossary#token-type-ids)
+
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names`).
+
+              [What are attention masks?](../glossary#attention-mask)
+
+            - **labels** -- List of labels to be fed to a model. (when `word_labels` is specified).
+            - **overflowing_tokens** -- List of overflowing tokens sequences (when a `max_length` is specified and
+              `return_overflowing_tokens=True`).
+            - **num_truncated_tokens** -- Number of tokens truncated (when a `max_length` is specified and
+              `return_overflowing_tokens=True`).
+            - **special_tokens_mask** -- List of 0s and 1s, with 1 specifying added special tokens and 0 specifying
+              regular sequence tokens (when `add_special_tokens=True` and `return_special_tokens_mask=True`).
+            - **length** -- The length of the inputs (when `return_length=True`).
+"""
+
+
+class LayoutXLMTokenizer(PreTrainedTokenizer):
+    """
+    Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
+    [SentencePiece](https://github.com/google/sentencepiece).
+
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        bos_token (`str`, *optional*, defaults to `"<s>"`):
+            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
+
+            <Tip>
+
+            When building a sequence using special tokens, this is not the token that is used for the beginning of
+            sequence. The token used is the `cls_token`.
+
+            </Tip>
+
+        eos_token (`str`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+
+            <Tip>
+
+            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
+            The token used is the `sep_token`.
+
+            </Tip>
+
+        sep_token (`str`, *optional*, defaults to `"</s>"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        cls_token (`str`, *optional*, defaults to `"<s>"`):
+            The classifier token which is used when doing sequence classification (classification of the whole sequence
+            instead of per-token classification). It is the first token of the sequence when built with special tokens.
+        unk_token (`str`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        pad_token (`str`, *optional*, defaults to `"<pad>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        mask_token (`str`, *optional*, defaults to `"<mask>"`):
+            The token used for masking values. This is the token used when training this model with masked language
+            modeling. This is the token which the model will try to predict.
+        cls_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+            The bounding box to use for the special [CLS] token.
+        sep_token_box (`List[int]`, *optional*, defaults to `[1000, 1000, 1000, 1000]`):
+            The bounding box to use for the special [SEP] token.
+        pad_token_box (`List[int]`, *optional*, defaults to `[0, 0, 0, 0]`):
+            The bounding box to use for the special [PAD] token.
+        pad_token_label (`int`, *optional*, defaults to -100):
+            The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
+            CrossEntropyLoss.
+        only_label_first_subword (`bool`, *optional*, defaults to `True`):
+            Whether or not to only label the first subword, in case word labels are provided.
+        additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
+            Additional special tokens used by the tokenizer.
+        sp_model_kwargs (`dict`, *optional*):
+            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+            to set:
+
+            - `enable_sampling`: Enable subword regularization.
+            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+
+              - `nbest_size = {0,1}`: No sampling is performed.
+              - `nbest_size > 1`: samples from the nbest_size results.
+              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+                using forward-filtering-and-backward-sampling algorithm.
+
+            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+              BPE-dropout.
+
+    Attributes:
+        sp_model (`SentencePieceProcessor`):
+            The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        bos_token="<s>",
+        eos_token="</s>",
+        sep_token="</s>",
+        cls_token="<s>",
+        unk_token="<unk>",
+        pad_token="<pad>",
+        mask_token="<mask>",
+        cls_token_box=[0, 0, 0, 0],
+        sep_token_box=[1000, 1000, 1000, 1000],
+        pad_token_box=[0, 0, 0, 0],
+        pad_token_label=-100,
+        only_label_first_subword=True,
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        **kwargs,
+    ) -> None:
+        # Mask token behave like a normal word, i.e. include the space before it
+        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
+
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            sep_token=sep_token,
+            cls_token=cls_token,
+            pad_token=pad_token,
+            mask_token=mask_token,
+            cls_token_box=cls_token_box,
+            sep_token_box=sep_token_box,
+            pad_token_box=pad_token_box,
+            pad_token_label=pad_token_label,
+            only_label_first_subword=only_label_first_subword,
+            sp_model_kwargs=self.sp_model_kwargs,
+            **kwargs,
+        )
+
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(str(vocab_file))
+        self.vocab_file = vocab_file
+
+        # Original fairseq vocab and spm vocab must be "aligned":
+        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
+        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
+        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
+        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'
+
+        # Mimic fairseq token-to-id alignment for the first 4 token
+        self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
+
+        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
+        self.fairseq_offset = 1
+
+        self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + self.fairseq_offset
+        self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
+
+        # additional properties
+        self.cls_token_box = cls_token_box
+        self.sep_token_box = sep_token_box
+        self.pad_token_box = pad_token_box
+        self.pad_token_label = pad_token_label
+        self.only_label_first_subword = only_label_first_subword
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+
+        # for backward compatibility
+        if not hasattr(self, "sp_model_kwargs"):
+            self.sp_model_kwargs = {}
+
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. An XLM-RoBERTa sequence has the following format:
+
+        - single sequence: `<s> X </s>`
+        - pair of sequences: `<s> A </s></s> B </s>`
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+
+        if token_ids_1 is None:
+            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+        cls = [self.cls_token_id]
+        sep = [self.sep_token_id]
+        return cls + token_ids_0 + sep + sep + token_ids_1 + sep
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does
+        not make use of token type ids, therefore a list of zeros is returned.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of zeros.
+
+        """
+
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
+
+    @property
+    def vocab_size(self):
+        return len(self.sp_model) + self.fairseq_offset + 1  # Add the <mask> token
+
+    def get_vocab(self):
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text: str) -> List[str]:
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        if token in self.fairseq_tokens_to_ids:
+            return self.fairseq_tokens_to_ids[token]
+        spm_id = self.sp_model.PieceToId(token)
+
+        # Need to return unknown token if the SP model returned 0
+        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        if index in self.fairseq_ids_to_tokens:
+            return self.fairseq_ids_to_tokens[index]
+        return self.sp_model.IdToPiece(index - self.fairseq_offset)
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (strings for sub-words) in a single string."""
+        out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
+        return out_string
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    @add_end_docstrings(LAYOUTXLM_ENCODE_KWARGS_DOCSTRING)
+    def __call__(
+        self,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
+        text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
+        boxes: Union[List[List[int]], List[List[List[int]]]] = None,
+        word_labels: Optional[Union[List[int], List[List[int]]]] = None,
+        add_special_tokens: bool = True,
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Union[bool, str, TruncationStrategy] = None,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        pad_to_multiple_of: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+        **kwargs,
+    ) -> BatchEncoding:
+        """
+        Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
+        sequences with word-level normalized bounding boxes and optional labels.
+
+        Args:
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
+                (words of a single example or questions of a batch of examples) or a list of list of strings (batch of
+                words).
+            text_pair (`List[str]`, `List[List[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
+                (pretokenized string).
+            boxes (`List[List[int]]`, `List[List[List[int]]]`):
+                Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
+            word_labels (`List[int]`, `List[List[int]]`, *optional*):
+                Word-level integer labels (for token classification tasks such as FUNSD, CORD).
+        """
+
+        # Input type checking for clearer error
+        def _is_valid_text_input(t):
+            if isinstance(t, str):
+                # Strings are fine
+                return True
+            elif isinstance(t, (list, tuple)):
+                # List are fine as long as they are...
+                if len(t) == 0:
+                    # ... empty
+                    return True
+                elif isinstance(t[0], str):
+                    # ... list of strings
+                    return True
+                elif isinstance(t[0], (list, tuple)):
+                    # ... list with an empty list or with a list of strings
+                    return len(t[0]) == 0 or isinstance(t[0][0], str)
+                else:
+                    return False
+            else:
+                return False
+
+        if text_pair is not None:
+            # in case text + text_pair are provided, text = questions, text_pair = words
+            if not _is_valid_text_input(text):
+                raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
+            if not isinstance(text_pair, (list, tuple)):
+                raise ValueError(
+                    "words must of type `List[str]` (single pretokenized example), "
+                    "or `List[List[str]]` (batch of pretokenized examples)."
+                )
+        else:
+            # in case only text is provided => must be words
+            if not isinstance(text, (list, tuple)):
+                raise ValueError(
+                    "Words must of type `List[str]` (single pretokenized example), "
+                    "or `List[List[str]]` (batch of pretokenized examples)."
+                )
+
+        if text_pair is not None:
+            is_batched = isinstance(text, (list, tuple))
+        else:
+            is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))
+
+        words = text if text_pair is None else text_pair
+        if boxes is None:
+            raise ValueError("You must provide corresponding bounding boxes")
+        if is_batched:
+            if len(words) != len(boxes):
+                raise ValueError("You must provide words and boxes for an equal amount of examples")
+            for words_example, boxes_example in zip(words, boxes):
+                if len(words_example) != len(boxes_example):
+                    raise ValueError("You must provide as many words as there are bounding boxes")
+        else:
+            if len(words) != len(boxes):
+                raise ValueError("You must provide as many words as there are bounding boxes")
+
+        if is_batched:
+            if text_pair is not None and len(text) != len(text_pair):
+                raise ValueError(
+                    f"batch length of `text`: {len(text)} does not match batch length of `text_pair`:"
+                    f" {len(text_pair)}."
+                )
+            batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
+            is_pair = bool(text_pair is not None)
+            return self.batch_encode_plus(
+                batch_text_or_text_pairs=batch_text_or_text_pairs,
+                is_pair=is_pair,
+                boxes=boxes,
+                word_labels=word_labels,
+                add_special_tokens=add_special_tokens,
+                padding=padding,
+                truncation=truncation,
+                max_length=max_length,
+                stride=stride,
+                pad_to_multiple_of=pad_to_multiple_of,
+                return_tensors=return_tensors,
+                return_token_type_ids=return_token_type_ids,
+                return_attention_mask=return_attention_mask,
+                return_overflowing_tokens=return_overflowing_tokens,
+                return_special_tokens_mask=return_special_tokens_mask,
+                return_offsets_mapping=return_offsets_mapping,
+                return_length=return_length,
+                verbose=verbose,
+                **kwargs,
+            )
+        else:
+            return self.encode_plus(
+                text=text,
+                text_pair=text_pair,
+                boxes=boxes,
+                word_labels=word_labels,
+                add_special_tokens=add_special_tokens,
+                padding=padding,
+                truncation=truncation,
+                max_length=max_length,
+                stride=stride,
+                pad_to_multiple_of=pad_to_multiple_of,
+                return_tensors=return_tensors,
+                return_token_type_ids=return_token_type_ids,
+                return_attention_mask=return_attention_mask,
+                return_overflowing_tokens=return_overflowing_tokens,
+                return_special_tokens_mask=return_special_tokens_mask,
+                return_offsets_mapping=return_offsets_mapping,
+                return_length=return_length,
+                verbose=verbose,
+                **kwargs,
+            )
+
+    def _batch_encode_plus(
+        self,
+        batch_text_or_text_pairs: Union[
+            List[TextInput],
+            List[TextInputPair],
+            List[PreTokenizedInput],
+        ],
+        is_pair: bool = None,
+        boxes: Optional[List[List[List[int]]]] = None,
+        word_labels: Optional[List[List[int]]] = None,
+        add_special_tokens: bool = True,
+        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        pad_to_multiple_of: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+        **kwargs,
+    ) -> BatchEncoding:
+        if return_offsets_mapping:
+            raise NotImplementedError(
+                "return_offset_mapping is not available when using Python tokenizers. "
+                "To use this feature, change your tokenizer to one deriving from "
+                "transformers.PreTrainedTokenizerFast."
+            )
+
+        batch_outputs = self._batch_prepare_for_model(
+            batch_text_or_text_pairs=batch_text_or_text_pairs,
+            is_pair=is_pair,
+            boxes=boxes,
+            word_labels=word_labels,
+            add_special_tokens=add_special_tokens,
+            padding_strategy=padding_strategy,
+            truncation_strategy=truncation_strategy,
+            max_length=max_length,
+            stride=stride,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_attention_mask=return_attention_mask,
+            return_token_type_ids=return_token_type_ids,
+            return_overflowing_tokens=return_overflowing_tokens,
+            return_special_tokens_mask=return_special_tokens_mask,
+            return_length=return_length,
+            return_tensors=return_tensors,
+            verbose=verbose,
+        )
+
+        return BatchEncoding(batch_outputs)
+
+    @add_end_docstrings(LAYOUTXLM_ENCODE_KWARGS_DOCSTRING)
+    def _batch_prepare_for_model(
+        self,
+        batch_text_or_text_pairs,
+        is_pair: bool = None,
+        boxes: Optional[List[List[int]]] = None,
+        word_labels: Optional[List[List[int]]] = None,
+        add_special_tokens: bool = True,
+        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        pad_to_multiple_of: Optional[int] = None,
+        return_tensors: Optional[str] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+    ) -> BatchEncoding:
+        """
+        Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
+        adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
+        manages a moving window (with user defined stride) for overflowing tokens
+
+        Args:
+            batch_ids_pairs: list of tokenized input ids or input ids pairs
+        """
+
+        batch_outputs = {}
+        for idx, example in enumerate(zip(batch_text_or_text_pairs, boxes)):
+            batch_text_or_text_pair, boxes_example = example
+            outputs = self.prepare_for_model(
+                batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair,
+                batch_text_or_text_pair[1] if is_pair else None,
+                boxes_example,
+                word_labels=word_labels[idx] if word_labels is not None else None,
+                add_special_tokens=add_special_tokens,
+                padding=PaddingStrategy.DO_NOT_PAD.value,  # we pad in batch afterward
+                truncation=truncation_strategy.value,
+                max_length=max_length,
+                stride=stride,
+                pad_to_multiple_of=None,  # we pad in batch afterward
+                return_attention_mask=False,  # we pad in batch afterward
+                return_token_type_ids=return_token_type_ids,
+                return_overflowing_tokens=return_overflowing_tokens,
+                return_special_tokens_mask=return_special_tokens_mask,
+                return_length=return_length,
+                return_tensors=None,  # We convert the whole batch to tensors at the end
+                prepend_batch_axis=False,
+                verbose=verbose,
+            )
+
+            for key, value in outputs.items():
+                if key not in batch_outputs:
+                    batch_outputs[key] = []
+                batch_outputs[key].append(value)
+
+        batch_outputs = self.pad(
+            batch_outputs,
+            padding=padding_strategy.value,
+            max_length=max_length,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_attention_mask=return_attention_mask,
+        )
+
+        batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)
+
+        return batch_outputs
+
+    def _encode_plus(
+        self,
+        text: Union[TextInput, PreTokenizedInput],
+        text_pair: Optional[PreTokenizedInput] = None,
+        boxes: Optional[List[List[int]]] = None,
+        word_labels: Optional[List[int]] = None,
+        add_special_tokens: bool = True,
+        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        pad_to_multiple_of: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+        **kwargs,
+    ) -> BatchEncoding:
+        if return_offsets_mapping:
+            raise NotImplementedError(
+                "return_offset_mapping is not available when using Python tokenizers. "
+                "To use this feature, change your tokenizer to one deriving from "
+                "transformers.PreTrainedTokenizerFast. "
+                "More information on available tokenizers at "
+                "https://github.com/huggingface/transformers/pull/2674"
+            )
+
+        return self.prepare_for_model(
+            text=text,
+            text_pair=text_pair,
+            boxes=boxes,
+            word_labels=word_labels,
+            add_special_tokens=add_special_tokens,
+            padding=padding_strategy.value,
+            truncation=truncation_strategy.value,
+            max_length=max_length,
+            stride=stride,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_tensors=return_tensors,
+            prepend_batch_axis=True,
+            return_attention_mask=return_attention_mask,
+            return_token_type_ids=return_token_type_ids,
+            return_overflowing_tokens=return_overflowing_tokens,
+            return_special_tokens_mask=return_special_tokens_mask,
+            return_length=return_length,
+            verbose=verbose,
+        )
+
+    @add_end_docstrings(LAYOUTXLM_ENCODE_KWARGS_DOCSTRING)
+    def prepare_for_model(
+        self,
+        text: Union[TextInput, PreTokenizedInput],
+        text_pair: Optional[PreTokenizedInput] = None,
+        boxes: Optional[List[List[int]]] = None,
+        word_labels: Optional[List[int]] = None,
+        add_special_tokens: bool = True,
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Union[bool, str, TruncationStrategy] = None,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        pad_to_multiple_of: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+        prepend_batch_axis: bool = False,
+        **kwargs,
+    ) -> BatchEncoding:
+        """
+        Prepares a sequence or a pair of sequences so that it can be used by the model. It adds special tokens,
+        truncates sequences if overflowing while taking into account the special tokens and manages a moving window
+        (with user defined stride) for overflowing tokens.
+
+        Word-level `boxes` are turned into token-level `bbox`. If provided, word-level `word_labels` are turned into
+        token-level `labels`. The word label is used for the first token of the word, while remaining tokens are
+        labeled with -100, such that they will be ignored by the loss function.
+
+        Args:
+            text (`str`, `List[str]`, `List[List[str]]`):
+                The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
+            text_pair (`List[str]` or `List[int]`, *optional*):
+                Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
+                list of list of strings (words of a batch of examples).
+        """
+
+        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+        padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
+            padding=padding,
+            truncation=truncation,
+            max_length=max_length,
+            pad_to_multiple_of=pad_to_multiple_of,
+            verbose=verbose,
+            **kwargs,
+        )
+
+        tokens = []
+        pair_tokens = []
+        token_boxes = []
+        pair_token_boxes = []
+        labels = []
+
+        if text_pair is None:
+            if word_labels is None:
+                # CASE 1: document image classification (training + inference) + CASE 2: token classification (inference)
+                for word, box in zip(text, boxes):
+                    if len(word) < 1:  # skip empty words
+                        continue
+                    word_tokens = self.tokenize(word)
+                    tokens.extend(word_tokens)
+                    token_boxes.extend([box] * len(word_tokens))
+            else:
+                # CASE 2: token classification (training)
+                for word, box, label in zip(text, boxes, word_labels):
+                    if len(word) < 1:  # skip empty words
+                        continue
+                    word_tokens = self.tokenize(word)
+                    tokens.extend(word_tokens)
+                    token_boxes.extend([box] * len(word_tokens))
+                    if self.only_label_first_subword:
+                        # Use the real label id for the first token of the word, and padding ids for the remaining tokens
+                        labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1))
+                    else:
+                        labels.extend([label] * len(word_tokens))
+        else:
+            # CASE 3: document visual question answering (inference)
+            # text = question
+            # text_pair = words
+            tokens = self.tokenize(text)
+            token_boxes = [self.pad_token_box for _ in range(len(tokens))] + [self.sep_token_box]
+
+            for word, box in zip(text_pair, boxes):
+                if len(word) < 1:  # skip empty words
+                    continue
+                word_tokens = self.tokenize(word)
+                pair_tokens.extend(word_tokens)
+                pair_token_boxes.extend([box] * len(word_tokens))
+
+        # Create ids + pair_ids
+        ids = self.convert_tokens_to_ids(tokens)
+        pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None
+
+        # Compute the total size of the returned encodings
+        pair = bool(pair_ids is not None)
+        len_ids = len(ids)
+        len_pair_ids = len(pair_ids) if pair else 0
+        total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
+
+        # Truncation: Handle max sequence length
+        overflowing_tokens = []
+        overflowing_token_boxes = []
+        overflowing_labels = []
+        if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
+            (
+                ids,
+                token_boxes,
+                pair_ids,
+                pair_token_boxes,
+                labels,
+                overflowing_tokens,
+                overflowing_token_boxes,
+                overflowing_labels,
+            ) = self.truncate_sequences(
+                ids,
+                token_boxes,
+                pair_ids=pair_ids,
+                pair_token_boxes=pair_token_boxes,
+                labels=labels,
+                num_tokens_to_remove=total_len - max_length,
+                truncation_strategy=truncation_strategy,
+                stride=stride,
+            )
+
+        if return_token_type_ids and not add_special_tokens:
+            raise ValueError(
+                "Asking to return token_type_ids while setting add_special_tokens to False "
+                "results in an undefined behavior. Please set add_special_tokens to True or "
+                "set return_token_type_ids to None."
+            )
+
+        # Load from model defaults
+        if return_token_type_ids is None:
+            return_token_type_ids = "token_type_ids" in self.model_input_names
+        if return_attention_mask is None:
+            return_attention_mask = "attention_mask" in self.model_input_names
+
+        encoded_inputs = {}
+
+        if return_overflowing_tokens:
+            encoded_inputs["overflowing_tokens"] = overflowing_tokens
+            encoded_inputs["overflowing_token_boxes"] = overflowing_token_boxes
+            encoded_inputs["overflowing_labels"] = overflowing_labels
+            encoded_inputs["num_truncated_tokens"] = total_len - max_length
+
+        # Add special tokens
+        if add_special_tokens:
+            sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
+            token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
+            token_boxes = [self.cls_token_box] + token_boxes + [self.sep_token_box]
+            if pair_token_boxes:
+                pair_token_boxes = pair_token_boxes + [self.sep_token_box]
+            if labels:
+                labels = [self.pad_token_label] + labels + [self.pad_token_label]
+        else:
+            sequence = ids + pair_ids if pair else ids
+            token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
+
+        # Build output dictionary
+        encoded_inputs["input_ids"] = sequence
+        encoded_inputs["bbox"] = token_boxes + pair_token_boxes
+        if return_token_type_ids:
+            encoded_inputs["token_type_ids"] = token_type_ids
+        if return_special_tokens_mask:
+            if add_special_tokens:
+                encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids)
+            else:
+                encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
+
+        if labels:
+            encoded_inputs["labels"] = labels
+
+        # Check lengths
+        self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose)
+
+        # Padding
+        if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
+            encoded_inputs = self.pad(
+                encoded_inputs,
+                max_length=max_length,
+                padding=padding_strategy.value,
+                pad_to_multiple_of=pad_to_multiple_of,
+                return_attention_mask=return_attention_mask,
+            )
+
+        if return_length:
+            encoded_inputs["length"] = len(encoded_inputs["input_ids"])
+
+        batch_outputs = BatchEncoding(
+            encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis
+        )
+
+        return batch_outputs
+
+    def truncate_sequences(
+        self,
+        ids: List[int],
+        token_boxes: List[List[int]],
+        pair_ids: Optional[List[int]] = None,
+        pair_token_boxes: Optional[List[List[int]]] = None,
+        labels: Optional[List[int]] = None,
+        num_tokens_to_remove: int = 0,
+        truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
+        stride: int = 0,
+    ) -> Tuple[List[int], List[int], List[int]]:
+        """
+        Truncates a sequence pair in-place following the strategy.
+
+        Args:
+            ids (`List[int]`):
+                Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
+                `convert_tokens_to_ids` methods.
+            token_boxes (`List[List[int]]`):
+                Bounding boxes of the first sequence.
+            pair_ids (`List[int]`, *optional*):
+                Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
+                and `convert_tokens_to_ids` methods.
+            pair_token_boxes (`List[List[int]]`, *optional*):
+                Bounding boxes of the second sequence.
+            labels (`List[int]`, *optional*):
+                Labels of the first sequence (for token classification tasks).
+            num_tokens_to_remove (`int`, *optional*, defaults to 0):
+                Number of tokens to remove using the truncation strategy.
+            truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
+                The strategy to follow for truncation. Can be:
+
+                - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+                  maximum acceptable input length for the model if that argument is not provided. This will truncate
+                  token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
+                  batch of pairs) is provided.
+                - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+                  maximum acceptable input length for the model if that argument is not provided. This will only
+                  truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+                - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+                  maximum acceptable input length for the model if that argument is not provided. This will only
+                  truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+                - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
+                  than the model maximum admissible input size).
+            stride (`int`, *optional*, defaults to 0):
+                If set to a positive number, the overflowing tokens returned will contain some tokens from the main
+                sequence returned. The value of this argument defines the number of additional tokens.
+
+        Returns:
+            `Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
+            overflowing tokens.
+        """
+        if num_tokens_to_remove <= 0:
+            return ids, token_boxes, pair_ids, pair_token_boxes, labels, [], [], []
+
+        if not isinstance(truncation_strategy, TruncationStrategy):
+            truncation_strategy = TruncationStrategy(truncation_strategy)
+
+        overflowing_tokens = []
+        overflowing_token_boxes = []
+        overflowing_labels = []
+        if truncation_strategy == TruncationStrategy.LONGEST_FIRST:
+            for _ in range(num_tokens_to_remove):
+                if pair_ids is None or len(ids) > len(pair_ids):
+                    if not overflowing_tokens:
+                        window_len = min(len(ids), stride + 1)
+                    else:
+                        window_len = 1
+                    overflowing_tokens.extend(ids[-window_len:])
+                    overflowing_token_boxes.extend(token_boxes[-window_len:])
+                    overflowing_labels.extend(labels[-window_len:])
+                    ids = ids[:-1]
+                    token_boxes = token_boxes[:-1]
+                    labels = labels[:-1]
+                else:
+                    if not overflowing_tokens:
+                        window_len = min(len(pair_ids), stride + 1)
+                    else:
+                        window_len = 1
+                    overflowing_tokens.extend(pair_ids[-window_len:])
+                    overflowing_token_boxes.extend(pair_token_boxes[-window_len:])
+                    pair_ids = pair_ids[:-1]
+                    pair_token_boxes = pair_token_boxes[:-1]
+        elif truncation_strategy == TruncationStrategy.ONLY_FIRST:
+            if len(ids) > num_tokens_to_remove:
+                window_len = min(len(ids), stride + num_tokens_to_remove)
+                overflowing_tokens = ids[-window_len:]
+                overflowing_token_boxes = token_boxes[-window_len:]
+                overflowing_labels = labels[-window_len:]
+                ids = ids[:-num_tokens_to_remove]
+                token_boxes = token_boxes[:-num_tokens_to_remove]
+                labels = labels[:-num_tokens_to_remove]
+            else:
+                logger.error(
+                    f"We need to remove {num_tokens_to_remove} to truncate the input "
+                    f"but the first sequence has a length {len(ids)}. "
+                    f"Please select another truncation strategy than {truncation_strategy}, "
+                    "for instance 'longest_first' or 'only_second'."
+                )
+        elif truncation_strategy == TruncationStrategy.ONLY_SECOND and pair_ids is not None:
+            if len(pair_ids) > num_tokens_to_remove:
+                window_len = min(len(pair_ids), stride + num_tokens_to_remove)
+                overflowing_tokens = pair_ids[-window_len:]
+                overflowing_token_boxes = pair_token_boxes[-window_len:]
+                pair_ids = pair_ids[:-num_tokens_to_remove]
+                pair_token_boxes = pair_token_boxes[:-num_tokens_to_remove]
+            else:
+                logger.error(
+                    f"We need to remove {num_tokens_to_remove} to truncate the input "
+                    f"but the second sequence has a length {len(pair_ids)}. "
+                    f"Please select another truncation strategy than {truncation_strategy}, "
+                    "for instance 'longest_first' or 'only_first'."
+                )
+
+        return (
+            ids,
+            token_boxes,
+            pair_ids,
+            pair_token_boxes,
+            labels,
+            overflowing_tokens,
+            overflowing_token_boxes,
+            overflowing_labels,
+        )
+
+    def _pad(
+        self,
+        encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
+        max_length: Optional[int] = None,
+        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+        pad_to_multiple_of: Optional[int] = None,
+        return_attention_mask: Optional[bool] = None,
+    ) -> dict:
+        """
+        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+        Args:
+            encoded_inputs:
+                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
+            max_length: maximum length of the returned list and optionally padding length (see below).
+                Will truncate by taking into account the special tokens.
+            padding_strategy: PaddingStrategy to use for padding.
+
+                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+                - PaddingStrategy.DO_NOT_PAD: Do not pad
+                The tokenizer padding sides are defined in self.padding_side:
+
+                    - 'left': pads on the left of the sequences
+                    - 'right': pads on the right of the sequences
+            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+                `>= 7.5` (Volta).
+            return_attention_mask:
+                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+        """
+        # Load from model defaults
+        if return_attention_mask is None:
+            return_attention_mask = "attention_mask" in self.model_input_names
+
+        required_input = encoded_inputs[self.model_input_names[0]]
+
+        if padding_strategy == PaddingStrategy.LONGEST:
+            max_length = len(required_input)
+
+        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+        # Initialize attention mask if not present.
+        if return_attention_mask and "attention_mask" not in encoded_inputs:
+            encoded_inputs["attention_mask"] = [1] * len(required_input)
+
+        if needs_to_be_padded:
+            difference = max_length - len(required_input)
+            if self.padding_side == "right":
+                if return_attention_mask:
+                    encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference
+                if "token_type_ids" in encoded_inputs:
+                    encoded_inputs["token_type_ids"] = (
+                        encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
+                    )
+                if "bbox" in encoded_inputs:
+                    encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
+                if "labels" in encoded_inputs:
+                    encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * difference
+                if "special_tokens_mask" in encoded_inputs:
+                    encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
+                encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
+            elif self.padding_side == "left":
+                if return_attention_mask:
+                    encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+                if "token_type_ids" in encoded_inputs:
+                    encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
+                        "token_type_ids"
+                    ]
+                if "bbox" in encoded_inputs:
+                    encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
+                if "labels" in encoded_inputs:
+                    encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
+                if "special_tokens_mask" in encoded_inputs:
+                    encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
+                encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+            else:
+                raise ValueError("Invalid padding strategy:" + str(self.padding_side))
+
+        return encoded_inputs