No subject

 no semicolon at end of struct or union no suitable `operator delete' for `%T' no symbol table found no type `%D' in `%T' no type named `%#T' in `%#T' no unique final overrider for `%D' in `%T' no vector mode with the size and type specified could be found non thread-local declaration of `%s' follows thread-local declaration non-ANSI-standard escape sequence, `\%c' non-ISO escape sequence `\%c' non-ISO-standard escape sequence, '\%c' non-constant `%E' cannot be used as template argument non-empty initializer for array of empty elements non-floating-point argument to function `%s' non-hex digit '%c' in universal-character-name non-local function `%#D' uses anonymous type non-local function `%#D' uses local type `%T' non-lvalue in %s non-member `%s' cannot be declared `mutable' non-object member `%s' cannot be declared `mutable' non-prototype definition here non-static const member `%#D' in class without a constructor non-static const member `%#D', can't use default assignment operator non-static declaration for `%s' follows static non-static initialization of a flexible array member non-static reference `%#D' in class without a constructor non-static reference member `%#D', can't use default assignment operator non-template type `%T' used as a template non-template used as template non-trivial labeled initializers nonconstant array index in initializer nonnull argument has invalid operand number (arg %lu) nonnull argument references non-pointer operand (arg %lu, operand %lu) nonnull argument with out-of-range operand number (arg %lu, operand %lu) nonnull attribute without arguments on a non-prototype not enough type information not found
 note: null argument where non-null required (arg %lu) null character(s) ignored null character(s) preserved in literal number of arguments doesn't match prototype number of bb notes in insn chain (%d) != n_basic_blocks (%d) object `%E' cannot be used as template argument object `%E' of incomplete type `%T' will not be accessed in %s object missing in `%E' object missing in use of `%E' object missing in use of pointer-to-member construct object of incomplete type `%T' will not be accessed in %s object of type `%T' will not be accessed in %s obsolete use of designated initializer with `:' obsolete use of designated initializer without `=' octal escape sequence out of range offset outside bounds of constant string old raw header file old style placement syntax, use () instead only declarations of constructors can be `explicit' only initialized variables can be placed into program memory area only uninitialized variables can be placed in a .bss section only uninitialized variables can be placed in the .noinit section only weak aliases are supported in this configuration open %s opening dependency file %s opening output file %s operand 1 must be a hard register operand constraint contains incorrectly positioned '+' or '=' operand constraints for `asm' differ in number of alternatives operand is const_double operand is neither a constant nor a condition code, invalid operand code 'c' operand is r0 operand number missing after %%-letter operand number out of range operand number out of range in format operand number specified for format taking           m;

    long tvdiff; struct timeval tvshow, tvnow;
    int  step;

    mmsr = cairo_image_surface_create_from_png("mm.png");
    mmcr = cairo_create(mmsr);
    cairo_set_source_rgba(mmcr, 1, 0.5, 0.5, 0.8);
    cairo_set_line_width(mmcr, 4);
    mmw = cairo_image_surface_get_width(mmsr);
    mmh = cairo_image_surface_get_height(mmsr);
    cairo_move_to(mmcr, 0, 0);
    cairo_line_to(mmcr, mmw, 0);
    cairo_line_to(mmcr, mmw, mmh);
    cairo_line_to(mmcr, 0, mmh);
    cairo_close_path(mmcr);
    cairo_stroke(mmcr);
    cairo_destroy(mmcr);

    if (!XInitThreads()) {
	fprintf(stderr, "%s: XInitThreads failed!\n", argv[0]);
	return -1;
    }

    if ((dpy = XOpenDisplay(NULL)) == NULL) {
	fprintf(stderr, "%s: Can not open display: %s\n",
		argv[0], XDisplayName(NULL));
	return -1;
    }

    scrn = DefaultScreen(dpy);

    XLockDisplay(dpy);

    mask = 0;
    templ.samples = 1;
    mask |= GLITZ_FORMAT_SAMPLES_MASK;
    templ.types.window = 1;
    mask |= GLITZ_FORMAT_WINDOW_MASK;

    dformat = glitz_glx_find_drawable_format(dpy,
					     scrn,
					     mask,
					     &templ,
					     0);
    if (!dformat) {
	fprintf(stderr, "%s: Can not find window format\n", argv[0]);
	return -1;
    }

    vinfo = glitz_glx_get_visual_info_from_format(dpy,
						  DefaultScreen(dpy),
						  dformat);

    if (!vinfo) {
	fprintf(stderr, "%s: No visual info\n", argv[0]);
	return -1;
    }

    xsh.flags = PSize;
    xsh.x = 0;
    xsh.y = 0;
    xsh.width = bgwidth;
    xsh.height = bgheight;

    xswa.colormap = XCreateColormap(dpy, RootWindow(dpy, scrn),
				    vinfo->visual, AllocNone);
    win = XCreateWindow(dpy, RootWindow(dpy, scrn),
			xsh.x, xsh.y, xsh.width, xsh.height,
			0, vinfo->depth, CopyFromParent,
			vinfo->visual, CWColormap, &xswa);
    XSetStandardProperties(dpy, win, argv[0], argv[0], None, argv, argc, &xsh);
    XSetWMHints(dpy, win, &xwmh);
    XSelectInput(dpy, win, StructureNotifyMask);

    drawable =
	glitz_glx_create_drawable_for_window(dpy, scrn,
					     dformat, win,
					     bgwidth, bgheight);

    if (!drawable) {
	fprintf(stderr, "%s: failed to create glitz drawable\n", argv[0]);
	return -1;
    }

    format = glitz_find_standard_format(drawable, GLITZ_STANDARD_ARGB32);
    if (!format) {
	fprintf(stderr, "%s: Could not find ARGB32 surface format\n", argv[0]);
	return -1;
    }
    buffer = GLITZ_DRAWABLE_BUFFER_BACK_COLOR;

    sr = resize_glitz_drawable(drawable, format, buffer, bgwidth, bgheight);
    cr = cairo_create(sr);
    cairo_set_tolerance(cr, 0.5);

    mmsr_glitz = cairo_surface_create_similar(sr, CAIRO_CONTENT_COLOR_ALPHA,
					      mmw, mmh);
    mmcr = cairo_create(mmsr_glitz);
    cairo_set_source_surface(mmcr, mmsr, 0, 0);
    cairo_paint(mmcr);
    cairo_destroy(mmcr);

    XMapWindow(dpy, win);
    XUnlockDisplay(dpy);

    tvshow.tv_sec = 0; tvshow.tv_usec = 0;

    signal(SIGALRM, alarmhandler);
    alarm(5);

    for (;;) {
	gettimeofday(&tvnow, NULL);
	XLockDisplay(dpy);
	if (XPending(dpy)) {
	    XNextEvent(dpy, &event);
	    if (event.type == ConfigureNotify) {
		cairo_surface_destroy(sr);
		cairo_destroy(cr);
		sr = resize_glitz_drawable(drawable,
					   format, buffer,
					   bgwidth, bgheight);
		cr = cairo_create(sr);
		cairo_select_font_face (cr, "sans",
					CAIRO_FONT_SLANT_NORMAL,
					CAIRO_FONT_WEIGHT_NORMAL);
		cairo_set_font_size (cr, 18);
	    }
	    XUnlockDisplay(dpy);
	} else if (tvshow.tv_sec
		   && (tvdiff = timediff(&tvnow, &tvshow)) < FRAMELIMIT) {
	    XUnlockDisplay(dpy);
	    usleep(FRAMELIMIT - tvdiff);
	} else {
	    tvshow = tvnow;
	    step = tvnow.tv_sec % 10 * 100 + tvnow.tv_usec / 10000;

	    cairo_save(cr);
	    cairo_rectangle(cr, 0, 0, bgwidth, bgheight);
	    cairo_set_source_rgb(cr, 0, 0, 1.0);
	    cairo_fill(cr);
	    cairo_restore(cr);

	    cairo_save(cr);
	    cairo_matrix_init_translate(&m,
					bgwidth / 2,
					(step < 500) ?
					step * bgheight / 500 :
					(1000 - step) * bgheight / 500);
	    cairo_matrix_rotate(&m, step * 3.1415926 * 2 / 1000);
	    cairo_matrix_scale(&m, 1.5, -1.5);
	    cairo_set_matrix(cr, &m);
	    cairo_set_source_surface(cr, mmsr_glitz, - mmw / 2, - mmh / 2);
	    /*
	     * why cairo_paint doesn't work?
	     * And the performance is very bad.
	     cairo_set_operator(cr, CAIRO_OPERATOR_SOURCE);
	     cairo_paint(cr);
	    */
	    cairo_set_operator(cr, CAIRO_OPERATOR_SOURCE);
	    cairo_paint_with_alpha(cr, 0.99);
	    cairo_restore(cr);

	    glitz_drawable_swap_buffers(drawable);

	    XSync(dpy, 0);
	    ++frame_cnt;
	    XUnlockDisplay(dpy);
	}
    }

    return 0;
}

--------------040401060700090303040608
Content-Type: image/png;
 name="mm.png"
Content-Transfer-Encoding: base64
Content-Disposition: inline;
 filename="mm.png"
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 fluix del programa %s.
 No es pot obrir el fitxer de codi font %s.
 Crear una aplicació amb interfície gràfica d'usuari (GUI) Crear una aplicació de consola Crear fitxers de dades necessàries per a gcov Creant %s.
 La referència de la declaració DO a l'etiqueta en %0 i la definició de l'etiqueta en %2 estan separades per un bloc sense acabar que comença en %1 La referència de la declaració DO a l'etiqueta en %1 segueix la seva definició en %0 Les referències de la declaració DO a l'etiqueta en %0 i %2 estan separades per un bloc sense acabar que comença en %1 DW_LOC_OP %s no està implementat
 Suport per a depuració d'arguments en el compilador Suport per a depuració d'adreces de memòria en el compilador Suport per a depuració de pila en el compilador Diferir l'extracció d'arguments de funcions de la pila fins més tard Esborrar els intrínsecs MIL-STD 1753 Esborrar els intrínsecs libU77 Esborrar els intrínsecs libU77 amb interfícies errònies Esborrar els intrínsecs de FORTRAN que no és 77 que F90 suporta Esborrar els intrínsecs de FORTRAN que no és 77 que VXT FORTRAN suporta Esborrar els intrínsecs de FORTRAN que no és 77 que f2c suporta Esborrar els intrínsecs de FORTRAN que no és 77 que g77 suporta Esborrar les revisions de punters nuls sense ús Determinar el estàndard de llenguatge Desactivar la informació de la línia de depuració Dwarf 2 a través com de GNU Desactivar els registres FP Desactivar els intrínsecs MIL-STD 1753 Desactivar les instruccions MPY||ADD i MPY||SUB Desactivar la depuració Desactivar els diagnòstics fatals sobre problemes interprocedurals Desactivar les instruccions FP multiply/add i multiply/substract de curt circuit Desactivar adreçament d'index Desactivar els intrínsecs libU77 Desactivar els intrínsecs libU77 amb interfícies errònies Desactivar noves característiques en desenvolupament Desactivar els intrínsecs de FORTRAN que no és 77 que g77 suporta Desactivar els intrínsecs de FORTRAN que no és 77 que F90 suporta Desactivar els intrínsecs de FORTRAN que no és 77 que VXT FORTRAN suporta Desactivar els intrínsecs de FORTRAN que no és 77 que f2c suporta Desactivar els diagnòstics opcionals Desactivar les funcions paral·leles Desactiva l'agregació de subratllats als externs Desactivar l'ús de la instrucció DB Desactivar l'ús de la instrucció RTPB Desactivar l'ús de la instrucció RTPS Desactivar l'ús de les instruccions condicionals move Desactivar l'ús de sdata/scommon/sbss Desactivar els avisos sobre problemes interprocedurals Desactiva totes les característiques lletjes No permetre comptes de iteracions unsigned per a RPTB/DB Descartar funcions virtual sense usar Mostrar estadístiques de tepms de compilació Mostrar les estadístiques acumulades durant la compilació Divisió per 0 (zero) en %0 (IEEE encara no té suport) No generar codi per a un 68851 No alinear el codi a límits de 8 octet No alinear destinació de les operacions de cadenes No assignar el registre BK No permetre que els camps de bits creuin els límits de word No intervenir en immediats de grandàries arbitràries en operacions de bit No assumir GAS No alinear automàticament els objectius de les ramificacions No compilar per a el ABI de v8plus No desactivar registres FP No desactivar adreçament indexat No desactivar registres d'espai No emetre maneres d'adreçament amb assignacions col·laterals No emetre constants enteres complexes a memòria de només lectura No emetre pròleg o epíleg de funcions No emetre seqüències load/store llarges Desactivar la relaxació del enllaçador No generar directives .size No generar codi H8/300H No generar codi H8S No generar codi H8S/2600 No generar només un punt de sortida per a cada funció No generar instruccions char No generar codi per a un Sun FPA No generar codi per a declaracions switch grandes No generar codi per a revisar excepcions d'especificacions No generar cridades indirectes ràpides No generar múltiples instruccions load/store No generar load/store amb instruccions d'actualització No generar informació del tipus de descriptor en temps d'execució No generar sin, cos, sqrt per a FPU No generar instruccions de cadena per a moviment de blocs No generar insns de salt de matriu No convertir a inline totes les operacions de cadenes conegudes No fer inline per omissió a les funcions membre No presentar tipus com en el gcc v1.3 de Intel No carregar el registre PIC en els pròlegs de funció No fer paral·leles les instruccions adjacents. No moure les instruccions al pròleg d'una funció No obeir les semàntiques de control d'accés No optimitzar les instruccions de la cridada de l'extrem en el ensemblador i el enllaçador No passar el text pur de -assert al enllaçador No realitzar optimització de la cridada de l'extrem No permetre accessos sense alinear No proveir una adreça d'inici per omissió 0x100 del programa No posar globals sense iniciar en la secció comuna No reconèixer les paraules claus definides per GNU No reconèixer cap funció interna No reconèixer la paraula clau "asm" No retornar valors de funcions en registres FPU No serialitzar les referències a memòria volàtil amb instruccions MEMW No guardar floats en els registres No donar suport per a funcions internes 3DNow! No donar suport per a funcions internes MMX i SSE No donar suport per a funcions internes MMX No donar suport per a funcions internes MMX, SSE i SSE2 No suprimir els avisos dels encapçalats del sistema No ajustar l'alineació del codi i de dades només de lectura No ajustar l'alineació de la pila No ajustar l'alineació de les dades modificables No usar els registres ABI reservats No usar matemàtica IEEE per a comparances fp No usar instrucció MPYI per a C3x No usar el conjunt d'instruccions POWER No usar el conjunt d'instruccions POWER2 No usar el conjunt d'instruccions PowerPC No usar la convenció d'enllaç Sky No usar fp VAX No usar adreces que reservin registres globals No usar entrades de funció fulles alternades No usar instruccions de camps de bit No usar maneres de adreçament complexos No usar codis de condició per a les instruccions normals No usar el mode d'adreçament direct per a registres soft No usar el model pla de finestra de registre No usar registres fp No usa coma flotant de maquinari No usar fp de maquinari No usar instruccions de fp quad de maquinari No usar instruccions de fp per a multiplicar-acumular No usar convencions de cridada portable No usar instruccions push per a guardar els arguments de sortida No usar red-zone en el codi x86-64 No usar el registre sb No usar registres per a pas de paràmetres No usar els registres r2 i r5 No usa coma flotant de programari No usar tendència de la pila No usar structs en alineació més forta per a còpies double-word No usar l'opció MAC16 de Xtensa No usar l'opció MIN/MAX de Xtensa No usar l'opció MUL16 de Xtensa No usar l'opció MUL32 de Xtensa No usar l'opció NSA de Xtensa No usar l'opció SEXT de Xtensa No usar l'opció de registre booleà de Xtensa No usar l'opció de densitat del codi Xtensa No usar la unitat de coma flotant de Xtensa No usar instruccions de camps de bit No usar la instrucció callt No usar la instrucció divideix No permetre referències a memòria sense alinear No utilitzar el Conjunt d'Instruccions Visuals No avisar sobre la divisió entera per zero en temps de compilació No avisar sobre l'ús de "long long" quan s'usi -pedantic No evitar el error de multiplicació de maquinari Fa el pas complet d'optimització de moviment de registres Fer el pas d'optimització de còpia-propagació de registres Fer el pas d'optimització de renomenació de registres No alinear elements en el codi o les dades No alinear al tipus base del camp de bit No passar sempre els arguments de coma flotant en memòria No anunciar característiques obsoletes del compilador No assumir que els accessos sense alinear són manejats pel sistema No cridar cap funció de neteja de memòria cau No emetre bits de desocupada abans i després de asmsestesos amb volatile No forçar les constants en els registres No generar codi per a una unitat de manipulació de bits No generar codi per a cridades near No generar codi per a salts near No generar instruccions multiply/add de curt circuit No permetre que el assignador de registres usi registres ybase No optimitzar els moviments de blocs No optimitzar les càrregues de les adreces lui/addiu No mostrar la sortida d'estadístiques del compilador No passar els paràmetres en els registres No avisar pedantment sobre els usos d'extensions Microsoft No col·locar les constants de coma flotant en TOC No col·locar les constants símbol+desplaçament en TOC No imprimir impressions addicionals de depuració No produir codi re-ubicable en el moment d'execució No posar les constants sense inicialitzar en ROM No guardar DP entre ISR en el model de memòria small No calendaritzar l'inici i el final del procediment No establir les definicions de Windows No establir la cadena cap a endarrere (més ràpid, però més difícil de depurar No atrapar la divisió entera per zero No atrapar desbordaments en la divisió entera No usar instruccions AltiVec No usar EABI No usar seccions sdata/sbss relatives a GP No usar PIC de Irix No usar instruccions MIPS16 No usar Mnemónicos-P per a ramificacions No usar el grup opcional d'instruccions PowerPC de Propòsit General No usar el grup opcional d'instruccions PowerPC de Gràfiques No usar el conjunt d'instruccions PowerPC-64 No usar ROM enlloc de RAM No usar noms de registre alternats No usar bras No usar el PIC incrustat No usar el fp de maquinari No usar cridades indirectes No usar mips-tfile asm postpass No usar el acumulador de multiplicació No usar únicament una sola FP (32-bit) No usar noms simbòlics de registre No avisar sobre formats de strftime que produeixen dos dígits per a l'any No avisar sobre massa arguments per a les funcions de format No avisar quan tots els ctors/dtors són privats No avisar quan les funcions friend sense patró són declarades dintre d'un patró avisar quan el tipus converteix punters a funcions membre No evitar el bug del primer maquinari 4300 Degradar els errors de concordança a advertiments La matriu faltament dimensionada a (1) és de grandària assumida Buidar la unitat de traducció completa a un fitxer Valors/rangs casi duplicats o traslapats en %0 i %1 A la declaració END en %0 li falta la paraula clau `%A' requerida per a procediment(s) intern(s) o mòdul(s) units per %1 ERROR: nombre de línies fora de rang en la funció %s
 ERROR: massa blocs bàsics en la funció %s
 Emetre reassignació de 16 bits per a las àreas de dades petites Emetre reassignació de 32 bits per a las àreas de dades petites Emetre codi que compleixi amb IEEE, amb excepcions inexactes Emetre codi que compleixi amb IEEE, sense excepcionsinexactes Emetre informació de graf de cridades Emetre codi compatible amb les eines TI Emetre codi per a Itanium (TM) processador de pas B Emetre codi per a l'extensió ISA octet/word Emetre codi per a l'extensió ISA de compte Emetre codi per a l'extensió ISA de move i sqrt de fp Emetre codi per a l'extensió ISA de vídeo en moviment Emetre codi per a usar les extensions de GAS Emetre codi utilitzant directives explícites de reassignació Emetre símbols comuns com símbols febles Emetre informació de referència creuada Emetre informació especial de depuració per a COMMON i EQUIVALENCE (desactivat) Emetre variables static const encara si no s'usen Emetre bits de desocupada abans i després de asms estesos amb volatile Emetre informació de depuració detallada en el codi assemblador Activar la informació de la línia de depuració Dwarf2 a través com de GNU Activar les instruccions MPY||ADD i MPY||SUB Activar la propagació de les constants condicionals SSA Activar les optimitzacions SSA Activar l'eliminació agressiva de codi mort SSA Activar l'instanciació automàtica de patrons Habilitar la depuració per la fi Activar el codi bàsic d'anàlisi de perfil del programa Activar la compatibilitat amb iC960 v2.0 Activar la compatibilitat amb iC960 v3.0 Activar la compatibilitat amb el assemblador ic960 Activar la sortida de depuració Activar la depuració Activar el maneig d'excepcions Activar les instruccions FP multiply/add i multiply/substract de curt circuit Activar les optimitzacions del enllaçador Activar la relaxació del enllaçador Activar la relaxació del enllaçador Activar la relaxació del enllaçador Activar les optimitzacions de forats específiques de la màquina Activar gairebé tots els missatges d'avís Activar noves característiques en desenvolupament Activar les funcions paral·leles Permetre guardar registres al voltant de cridades de funció Activar la calendarització entre blocs bàsics Habilitar la prova de la pila Activar el suport per a objectes enormes Activar l'ús de les instruccions short load Activar l'ús de la instrucció DB Activar l'ús de la instrucció RTPB Activar l'ús de la instrucció RTPS Activar l'ús de les instruccions condicionals move Activar l'ús de sdata/scommon/sbss Permet una optimització de moviment de registres Activa una execució de passada de forats rtl abans de sched2 Activar la predicció de probabilitats de ramificació Fi de la llista de recerca.
 Fi del fitxer font abans que comencés el bloc en %0 Reforçar l'alineació estricta Error a l'escriure el fitxer de descripció DFA %s Error a l'escriure al fitxer de sortida %s.
 Errors en la descripció DFA Seqüència d'escapi en %0 fora de rang per al caràcter S'esperava un operador binari entre les expressions en %0 i en %1 Exportar funcions encara si poden ser inline L'expressió en %0 té el tipus de dada o rang incorrecte per al seu context Els símbols externs tenen un subratllat inicial Coma sobrant en la declaració FORMAT en %0 Vora de caiguda després del salt incondicional %i Noms de camps en %0 per a la definició de l'estructura exterior -- especifiqui'ls en el seu lloc en una declaració RECORD subsecuents Les operacions de coma flotant poden capturar Per a elinux, sol·licitar una grandària de pila especificada per a aquest programa Per a biblioteques intrínsecs: passar els parametres en registres Forçar que la generació de RTL emeti 3 operandes insns vàlids Forçar que totes les computacions invariantes del cicle siguin fora del cicle Forçar les constants dintre de registres per a millorar l'aixecament Forçar que les funcions s'alineïn a un límit de 2 octet Forçar que les funcions s'alineïn a un límit de 4 octet Característica Fortran 90 en %0 sense suport Forma específica de Fortran de -fbounds-check Els inicis de les funcions són alineats a aquesta potència de 2 GCC no implementa encara correctament declaradors de parametres "[*]" GCSE desactivat: %d > 1000 blocs bàsics i %d >= 20 blocs bord/bàsics GCSE desactivat: %d blocs bàsics i %d registres GNU C no dóna suport a -CC sense usar -E Generar codi 32 bit per a i386 Generar codi 64 bit per a x86-64 Generar marcs de pila que compleixin amb APCS Generar codi CA Generar codi CF Generar informació de depuració en el format COFF Generar informació de depuració en el format DWARF-1 Generar informació de depuració en el format DWARF-2 Generar sortida ELF Generar codi H8/300H Generar codi H8S Generar codi H8S/2600 Generar codi JA Generar codi JD Generar codi JF Generar codi KA Generar codi KB Generar codi MC Generar codi PA1.0 Generar codi PA1.1 Generar codi SA Generar codi SB Generar informació de depuració en el format STABS Generar informació de depuració en el format VMS Generar informació de depuració en el format XCOFF Generar una cridada a avortar si una funció noreturnretorna Generar només un punt de sortida per a cada funció Generar codi big endian Generar les cridades insns com cridades indirectes, si és necessari Generar instruccions char Generar codi per al CPU C30 Generar codi per al CPU C31 Generar codi per al CPU C32 Generar codi per al CPU C33 Generar codi per al CPU C40 Generar codi per al CPU C44 Generar codi com de GNU Generar codi per a ld de GNU Generar codi com de Intel Generar codi per a ld de Intel Generar codi per a un 520X Generar codi per a un 68000 Generar codi per a un 68020 Generar codi per a un 68030 Generar codi per a un 68040 Generar codi per a un 68040, sense cap instrucció nova Generar codi per a un 68060 Generar codi per a un 68060, sense cap instrucció nova Generar codi per a un 68302 Generar codi per a un 68332 Generar codi per a un 68851 Generar codi per a un 68881 Generar codi per a una DLL Generar codi per a un Sun FPA GenerOh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--------------040401060700090303040608--