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ٹYئˆXHƒہD‰ëِآu^H‹y@ِاtHƒç‏H‰ئè÷أ€tjI‹؟€fاتHƒہ‹Wپْ€r>‰ض‰ٌ„€ت€ˆءéHےہپ‏ے?ˆت‰خwéë‹Qئ‰PHƒہH‹y@ِاu•ëںˆرˆHےہ1ِH‰آè÷أ„ڈI‹ڈˆfازHƒہ‹qپ‏€r"‰÷‰ْfگ@€خ€@ˆ0ءêHےہپےے?@ˆض‰×wوë@ˆٍˆ‹Qِآu Hےہِآuë ‹qئ@ ‰pHƒہِآt ‹q ئ‰pHƒہِآudH‹y@ِاtHƒç‏H‰ئè÷أ„مI‹ڈگfاعHƒہ‹qپ‏€r@‰÷‰ْ€@€خ€@ˆ0ءêHےہپےے?@ˆض‰×wوë‹Qئ‰PHƒہH‹y@ِاuڈë™@ˆٍˆ‹Qِآu Hےہِآuë[‹qئ@ ‰pHƒہِآtHHcqHپ‏€ئHچ@r,H‰÷H‰ْ€@€خ€@ˆ0HءêHےہHپےے?@ˆضH‰×wمë@ˆٍˆHےہH‹y@ِاtHƒç‏H‰ئè÷أ„´I‹ڈکfاâHƒہ‹qپ‏€r0‰÷‰ْ„@€خ€@ˆ0ءêHےہپےے?@ˆض‰×wوˆِAu ëP@ˆٍˆِAtEHcqHپ‏€ئ@Hچ@r+H‰÷H‰ْfD@€خ€@ˆ0HءêHےہHپےے?@ˆضH‰×wمë@ˆٍˆHےہH‹y@ِاtHƒç‏H‰ئè÷أ„I‹ڈ fاêHƒہ‹qپ‏€r(‰÷‰ْ„@€خ€@ˆ0ءêHےہپےے?@ˆض‰×wوë@ˆٍˆ‹Qِآu Hےہِآu_é،HcyHپے€ئ@Hچ@r4H‰ûH‰قffffff.„@€د€@ˆ8HءîHےہHپûے?@ˆ÷H‰َwمë@ˆ‏@ˆ0HےہD‰ëِآtGHcqHپ‏€ئHچ@r+H‰÷H‰ْfD@€خ€@ˆ0HءêHےہHپےے?@ˆضH‰×wمë@ˆٍˆHےہH‹y@ِاtHƒç‏H‰ئè÷أ„pM‹ڈ¨fاٍHƒہA‹Iپù€r$‰خ‰ٍ€€ة€ˆءêHےہپ‏ے?ˆر‰ضwéëˆتˆE‹AAِہt:A‹q(ئ@Hƒہپ‏€r!‰ٌ‰تگ@€خ€@ˆ0ءêHےہپùے?@ˆض‰رwوë@ˆٍˆHےہIcqH…ِtbM‹Q 1غffff.„A‹TڑئHےہپْ€r-‰×‰ùf„€ت€ˆءéHےہپےے?ˆت‰دwéë 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Hƒؤ[A^A_]أLچ{Hچ=èëخI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèfDUH‰هAWAVATSI‰÷H‰ûHچH‰HاCA‹G‰CاCM‹gAِؤtLچsL‰÷èIƒن‏H‰اL‰وèA‹G ‰C I‹GH‰C[A\A^A_]أI‰اI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰ےèH‰اèff.„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèf„UH‰ه]أf.„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèf„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèL‰÷[A^]éH‰اèf.„UH‰ه‰w]أ€H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹ أ@‹…ہuHچأUH‰هHچ=è]HچأDUH‰هAWAVATSI‰ôI‰‏Mچ~ë A‰FDI‹$I;D$s¶„غ~HےہI‰$ë1غL‰ç‰قè‰أےبƒّ~‡¹‰ط‰ظءéƒùtMƒùtxƒù…ں<…—A€NI‹$I;D$ƒ´¶0@„ِˆھA‰vHےہI‰$éwےےے€<u\A€NI‹$I;L$ƒ’¶„ہˆ‰HےءI‰$é8ےےےگ<u,A€NI‹$I;L$s}¶„ہxxHےءI‰$ë~fff.„…غtwI‹ِآtHƒâ‏L‰ç‰قè„ہ…î‏ےےëbL‰ےèH‰آëك1ِL‰çèA‰FH…ہ‰ب‏ےےë<1ہL‰ç‰ئèH…ہ‰¨‏ےےë%1ہL‰ç‰ئèH…ہxA‰F é•‏ےے°[A\A^A_]أ1ہëَfDUH‰هAWAVSPI‰ِI‰ےA‹_ِأuِأu0ِأuAI‹@ِاuRHƒؤ[A^A_]أA‹w؟L‰ٍèِأtذA‹w ؟L‰ٍèِأt؟A‹w؟L‰ٍèI‹@ِاt®Hƒç‏L‰ِHƒؤ[A^A_]éff.„UH‰هD‹GAِہu#AِہudAِہ…²H‹@ِا…ُH‰ذ]أ‹OئHےآپù€r'‰ب‰ئf.„€ة€ˆ ءîHےآ=ے?@ˆٌ‰ًwéë@ˆخ@ˆ2HےآAِہtœHcw Hپ‏€ئHچRr2H‰ًH‰ءfffff.„@€خ€@ˆ2HءéHےآH=ے?@ˆخH‰بwنë@ˆٌˆ HےآAِہ„NےےےHcOHپù€ئHچRr(H‰خH‰ًfD€ة€ˆ HءèHےآHپ‏ے?ˆءH‰ئwوëˆبˆHےآH‹@ِا„ےےےHƒç‏H‰ض]éfگUH‰هSPH‰ûH‹{@ِاu51ہ‹Kِء„‹ِءu5ِءugِءt|‹K …ةxDƒة½ةچLةIءéےءë8Hƒç‏è‹KِءuبëQ‹S…زx#ƒت½زچTزIءêےآHذِءt²ë¹Hبë'؛Hذِءt™‹Sƒت½زچTزIءêHچDِءu„‰CHƒؤ[]أf.„UH‰هAWAVATSH‰َI‰‏H‹5Hچ1ةH‰كèI‰ؤM…نtSM‹|$AِاtIچFIƒç‏H‹8@ِاtfHƒç‏L‰‏èA‹D$¨t¨u1¨u:¨t A‹L$ A‰N A F[A\A^A_]أH‰كL‰ِ[A\A^A_]éA‹L$A‰N¨tئA‹L$A‰N¨u½ëؤH‰اèH‰اë‘UH‰هAVSI‰ِH‰ûL9َtJِCtH¸H‰CاC اCH‹{@ِاtHƒç‏Hƒ?tèH‰كL‰ِ[A^]é[A^]أffffff.„UH‰هAWAVSPI‰ِH‰ûI9ق„€ِCtH¸H‰CاC اCH‹{@ِاtHƒç‏Hƒ?tèM‹~AِاtHچCIƒç‏H‹8@ِاtMHƒç‏L‰‏èA‹F¨t¨u¨u$¨tA‹N ‰K CHƒؤ[A^A_]أA‹N‰K¨tـA‹N‰K¨uصëعH‰اèH‰اëھ€UH‰ه°]أ„UH‰هAVSH‰ûH9قtd‹C‹N‰K‰F‹C‹N‰K‰F‹C ‹N ‰K ‰F ‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëد„UH‰هAVSH‰û‹C‹N‰K‰F‹C‹N‰K‰F‹C ‹N ‰K ‰F ‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëدffff.„H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹ H‹(أffff.„UH‰ه]أf.„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہuHاCاC Hƒؤ[A^A_]أLچ{Hچ=èëشI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèfff.„UH‰هHاGاG ]أff.„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہuHاCاC Hƒؤ[A^A_]أLچ{Hچ=èëشI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèfff.„UH‰هAWAVATSI‰÷H‰ûHچH‰HاCA‹G‰CاCM‹gAِؤtLچsL‰÷èIƒن‏H‰اL‰وèA‹G ‰C I‹GH‰C[A\A^A_]أI‰اI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰ےèH‰اèff.„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèf„UH‰ه]أf.„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèf„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèL‰÷[A^]éH‰اèf.„UH‰ه‰w]أ€H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹0أ@‹…ہuHچأUH‰هHچ=è]HچأDUH‰هAWAVATSI‰ôI‰‏Mچ~ë A‰FDI‹$I;D$s¶„غ~HےہI‰$ë1غL‰ç‰قè‰أےبƒّ~‡¹‰ط‰ظءéƒùtMƒùtxƒù…ں<…—A€NI‹$I;L$ƒ§¶„ہˆœHےءI‰$é‌fff.„<u\A€NI‹$I;L$ƒ“¶„ہˆٹHےءI‰$é8ےےےگ<u,A€NI‹$I;D$s~¶0@„ِxxA‰v HےہI‰$éےےے@…غtyI‹ِآt2Hƒâ‏L‰ç‰قè„ہ…î‏ےےëUL‰çèِآtHH…ہA•Féز‏ےےL‰ےèH‰آëإ1ہL‰ç‰ئèH…ہ‰§‏ےےë1ِL‰çèA‰F H…ہ‰—‏ےے1ہë°[A\A^A_]أ„UH‰هAWAVSPI‰ِI‰ےA‹_ِأuِأu1ِأuBI‹@ِاuSHƒؤ[A^A_]أA¶w؟L‰ٍèِأtدA‹w ؟L‰ٍèِأt¾A‹w؟L‰ٍèI‹@ِاtHƒç‏L‰ِHƒؤ[A^A_]éf.„UH‰هD‹GAِہuAِہu,AِہunH‹@ِا…¹H‰ذ]أٹGئˆBHƒآAِہtش‹O ئHےآپù€r(‰ب‰ئff.„€ة€ˆ ءîHےآ=ے?@ˆٌ‰ًwéë@ˆخ@ˆ2HےآAِہt’HcOHپù€ئHچRr0H‰خH‰ًfffff.„€ة€ˆ HءèHےآHپ‏ے?ˆءH‰ئwوëˆبˆHےآH‹@ِا„GےےےHƒç‏H‰ض]éfگUH‰هSPH‰ûH‹{@ِاu1ہ‹Sِآuë`Hƒç‏è‹SِآtOHچHِآHDبِآt ‹C…ہxƒب½ہچDہIءèےہë¸HءِآuH‰بë‹C ƒب½ہچDہIءèHچD‰CHƒؤ[]أ€UH‰هAWAVATSH‰َI‰‏H‹5Hچ1ةH‰كèI‰ؤM…نtSM‹|$AِاtIچFIƒç‏H‹8@ِاtfHƒç‏L‰‏èA‹D$¨t¨u1¨u:¨t A‹L$ A‰N A F[A\A^A_]أH‰كL‰ِ[A\A^A_]éAٹL$AˆN¨tئA‹L$A‰N¨u½ëؤH‰اèH‰اë‘UH‰هAVSI‰ِH‰ûL9َtDِCtHاCاC اCH‹{@ِاtHƒç‏Hƒ?tèH‰كL‰ِ[A^]é[A^]أDUH‰هAWAVSPI‰ِH‰ûI9قtzِCtHاCاC اCH‹{@ِاtHƒç‏Hƒ?tèM‹~AِاtHچCIƒç‏H‹8@ِاtMHƒç‏L‰‏èA‹F¨t¨u¨u$¨tA‹N ‰K CHƒؤ[A^A_]أAٹNˆK¨tـA‹N‰K¨uصëعH‰اèH‰اëھگUH‰ه°]أ„UH‰هAVSH‰ûH9قtdٹCٹNˆKˆF‹C‹N‰K‰F‹C ‹N ‰K ‰F ‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëد„UH‰هAVSH‰ûٹCٹNˆKˆF‹C‹N‰K‰F‹C ‹N ‰K ‰F ‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëدffff.„H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹0H‹8أffff.„UH‰ه]أf.„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہuH¸H‰CHƒؤ[A^A_]أLچ{Hچ=èëصI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèffff.„UH‰هH¸H‰G]أfff.„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہuH¸H‰CHƒؤ[A^A_]أLچ{Hچ=èëصI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèffff.„UH‰هAWAVATSI‰ِH‰ûHچH‰HاCA‹F‰CاCM‹fAِؤtLچ{L‰ےèIƒن‏H‰اL‰وèI‹FH‰C[A\A^A_]أI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèfگUH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèf„UH‰ه]أf.„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèf„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèL‰÷[A^]éH‰اèf.„UH‰ه‰w]أ€H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹@أ@‹…ہuHچأUH‰هHچ=è]HچأDUH‰هAWAVATSI‰ôI‰‏Mچ~ë A‰FDI‹$I;D$s¶„غ~HےہI‰$ë1غL‰ç‰قè‰أےبƒّ~wm‰ط‰ظءéƒùt1ƒùu\<uXA€NI‹$I;D$sy¶0@„ِxsA‰vHےہI‰$ë“<u,A€NI‹$I;L$sf¶„ہxaHےءI‰$é`ےےےf„…غt_I‹ِآtHƒâ‏L‰ç‰قè„ہ…>ےےےë;L‰ےèH‰آëك1ِL‰çèA‰FH…ہ‰ےےےë1ہL‰ç‰ئèH…ہ‰ّ‏ےے1ہë°[A\A^A_]أfگUH‰هAWAVSPI‰ِH‰ûD‹{AِاuAِاu+H‹{@ِاu;Hƒؤ[A^A_]أ‹s؟L‰ٍèAِاtص‹s؟L‰ٍèH‹{@ِاtإHƒç‏L‰ِHƒؤ[A^A_]éDUH‰هD‹GAِہuAِہuTH‹@ِا…ںH‰ذ]أ‹OئHےآپù€r!‰ب‰ئ@€ة€ˆ ءîHےآ=ے?@ˆٌ‰ًwéë@ˆخ@ˆ2HےآAِہt¬HcOHپù€ئHچRr0H‰خH‰ًfffff.„€ة€ˆ HءèHےآHپ‏ے?ˆءH‰ئwوëˆبˆHےآH‹@ِا„aےےےHƒç‏H‰ض]éfگUH‰هSPH‰ûH‹{@ِاuA1ہ‹KِءtRِءt‹Sƒت½زچTزIءêHچDِءt3‹K…ةx$ƒة½ةچLةIءéےءëHƒç‏è‹Kِءu¸ë¹Hب‰CHƒؤ[]أ€UH‰هAWAVATSH‰َI‰‏H‹5Hچ1ةH‰كèI‰ؤM…نtXM‹|$AِاtIچFIƒç‏H‹8@ِاtOHƒç‏L‰‏èA‹D$¨t¨t A‹L$A‰N¨t A‹L$A‰NA F[A\A^A_]أH‰كL‰ِ[A\A^A_]éH‰اèH‰اë¨€UH‰هAVSI‰ِH‰ûL9َtCِCtH¸H‰CاCH‹{@ِاtHƒç‏Hƒ?tèH‰كL‰ِ[A^]é[A^]أfDUH‰هAWAVSPI‰ِH‰ûI9قt|ِCtH¸H‰CاCH‹{@ِاtHƒç‏Hƒ?tèM‹~AِاtHچCIƒç‏H‹8@ِاt8Hƒç‏L‰‏èA‹F¨t¨tA‹N‰K¨tA‹N‰K CHƒؤ[A^A_]أH‰اèH‰اë؟€UH‰ه°]أ„UH‰هAVSH‰ûH9قtX‹C‹N‰K‰F‹C‹N‰K‰F‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëد@UH‰هAVSH‰û‹C‹N‰K‰F‹C‹N‰K‰F‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëدf„H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹@H‹Hأffff.„UH‰ه]أf.„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہuئCH¸w¾?¬إ'7H‰CHƒؤ[A^A_]أLچ{Hچ=èëرI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèf„UH‰هئGH¸w¾?¬إ'7H‰G]أ„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہuئCH¸w¾?¬إ'7H‰CHƒؤ[A^A_]أLچ{Hچ=èëرI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèf„UH‰هAWAVATSI‰÷H‰ûHچH‰HاCA‹G‰CاCM‹gAِؤtLچsL‰÷èIƒن‏H‰اL‰وèA‹G ‰C I‹GH‰C[A\A^A_]أI‰اI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰ےèH‰اèff.„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèf„UH‰ه]أf.„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèf„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèL‰÷[A^]éH‰اèf.„UH‰ه‰w]أ€H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹Pأ@‹…ہuHچأUH‰هHچ=è]HچأDUH‰هAWAVAUATSPH‰َI‰‏Mچ~Lچmذë‹‰MذHƒہH‰A‰NfگH‹H;CsD¶ 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ئ"Hےآ‹Oپù€r!‰خ‰ً@€ة€ˆ ءèHےآپ‏ے?ˆء‰ئwéëˆبˆHےآ1ِèH‰آِأ „à‏ےےIcN0Hپù€ئ(HچRr%H‰خH‰ً€ة€ˆ HءèHےآHپ‏ے?ˆءH‰ئwوëˆبˆHےآِأ„ ‏ےےAٹF,ئ0ˆBHƒآI‹~@ِا„’‏ےےHƒç‏H‰ض[A^]é„UH‰هAVSI‰‏I‹~@ِاum1غA‹F¨?„ض¨uq¨…“¨tA‹N(ƒة½ةچLةIءéHچ\HچK¨HDثHچY¨HDظ¨ „”A‹F0…ہˆ€ƒب½ہچDہIءèےہëtHƒç‏èH‰أA‹F¨?uچëaI‹~èHأƒب½ہچDہIءèHچ\A‹F¨„mےےےI‹~ èHأƒب½ہچDہIءèHچ\A‹F¨…GےےےéXےےے¸HأA‰^H‰ط[A^]أfff.„UH‰هAVSH‰َI‰‏H‹5Hچ1ةH‰كèH…ہtL‰÷H‰ئ[A^]éH‰كL‰ِ[A^]é„UH‰هAVSI‰ِH‰ûL9َt4H‰كèH‹5Hچ1ةL‰÷èH…ہtH‰كH‰ئ[A^]é[A^]أL‰÷H‰ق[A^]éfDUH‰هAVSI‰ِH‰ûI9قtH‰كèH‰كL‰ِ[A^]é[A^]أfگUH‰ه°]أ„UH‰هH9‏t]é]أffffff.„UH‰هAVSH‰ûH‹CH‹NH‰KH‰FH‹C H‹N H‰K H‰F ‹C(‹N(‰K(‰F(ٹC,ٹN,ˆK,ˆF,ٹC-ٹN-ˆK-ˆF-‹C0‹N0‰K0‰F0‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëدگH‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹گH‹کأffff.„UH‰ه]أf.„UH‰هAWAVAUATSPI‰ےHچI‰IاG(IاG 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ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëدگH‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹ذH‹طأffff.„UH‰ه]أf.„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہufاCاC_p‰0Hƒؤ[A^A_]أLچ{Hچ=èëضI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèfffff.„UH‰هfاGاG_p‰0]أffff.„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہufاCاC_p‰0Hƒؤ[A^A_]أLچ{Hچ=èëضI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèfffff.„UH‰هAWAVATSI‰ِH‰ûHچH‰HاCA‹F‰CاCM‹fAِؤtLچ{L‰ےèIƒن‏H‰اL‰وèI‹FH‰C[A\A^A_]أI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèfگUH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèf„UH‰ه]أf.„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèf„UH‰هAVSI‰‏HچI‰I‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèL‰÷[A^]éH‰اèf.„UH‰ه‰w]أ€H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹àأ@‹…ہuHچأUH‰هHچ=è]HچأDUH‰هAWAVAUATSPI‰ُI‰‏Mچ~Lچeشë‹‰MشHƒہI‰EA‰NگI‹EI;Es¶„غ~HےہI‰Eë1غ@L‰ï‰قè‰أےبƒّ~‡ة‰ط‰ظءéƒùtMƒù„„ƒù…«<…£A€NI‹MI;Mƒ´¶„ہˆ©HےءI‰Méھf„<ulA€NA‹MI‹E)ءƒùچBےےےL‰ïL‰وè„ہ„·‹MشA‰Né5ےےےD<u,A€NI‹MI;Ms[¶„ہxTHےءI‰MëXffff.„…غtaI‹ِآtLHƒâ‏L‰ï‰قè„ہ…ق‏ےےëRL‰ïèِآtEH…ہA•Féآ‏ےےL‰ïèِآt+H…ہA•Fé¨‏ےےL‰ےèH‰آë«°Hƒؤ[A\A]A^A_]أ1ہëيfDUH‰هAWAVSPI‰ِI‰ےA‹_ِأuِأu1ِأuCI‹@ِاuVHƒؤ[A^A_]أA¶w؟L‰ٍèِأtدA¶w؟L‰ٍèِأt½َAG؟L‰ِèI‹@ِاtھHƒç‏L‰ِHƒؤ[A^A_]é€UH‰ه‹G¨u¨u$¨u1H‹@ِاu>H‰ذ]أٹOئˆJHƒآ¨tـٹOئˆJHƒآ¨tد‹Gئ‰BHƒآH‹@ِاtآHƒç‏H‰ض]é„UH‰هSPH‰ûH‹{@ِاu1ہ‹Kِءuë2Hƒç‏è‹Kِءt!HچPِءHDذHچrِءHDٍHچFِءHDئ‰CHƒؤ[]أDUH‰هAWAVATSH‰َI‰‏H‹5Hچ1ةH‰كèI‰ؤM…نtSM‹|$AِاtIچFIƒç‏H‹8@ِاtfHƒç‏L‰‏èA‹D$¨t¨u1¨u:¨t A‹L$A‰NA F[A\A^A_]أH‰كL‰ِ[A\A^A_]éAٹL$AˆN¨tئAٹL$AˆN¨u½ëؤH‰اèH‰اë‘UH‰هAVSI‰ِH‰ûL9َtBِCt fاCاC_p‰0اCH‹{@ِاtHƒç‏Hƒ?tèH‰كL‰ِ[A^]é[A^]أ€UH‰هAWAVSPI‰ِH‰ûI9قtxِCt fاCاC_p‰0اCH‹{@ِاtHƒç‏Hƒ?tèM‹~AِاtHچCIƒç‏H‹8@ِاtMHƒç‏L‰‏èA‹F¨t¨u¨u$¨tA‹N‰K CHƒؤ[A^A_]أAٹNˆK¨tـAٹNˆK¨uصëعH‰اèH‰اëھUH‰ه°]أ„UH‰هAVSH‰ûH9قtdٹCٹNˆKˆFٹCٹNˆKˆF‹C‹N‰K‰F‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëد„UH‰هAVSH‰ûٹCٹNˆKˆFٹCٹNˆKˆF‹C‹N‰K‰F‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëدffff.„H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹àH‹èأffff.„UH‰هHچH‰]أfff.„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہuHاCHƒؤ[A^A_]أLچ{Hچ=èëغI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèUH‰هHاG]أfگUH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہuHاCHƒؤ[A^A_]أLچ{Hچ=èëغI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèUH‰هAWAVAUATSPI‰÷I‰‎HچI‰EMچuIاEA‹GA‰EAاEI‹_ِأtL‰÷èHƒم‏H‰اH‰قèAِGuE1نë؟0èI‰ؤI‹wL‰çèM‰eHƒؤ[A\A]A^A_]أI‰اL‰çèI‹ِأu ë0I‰اI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰ےèH‰اèگUH‰هAVSI‰‏HچI‰HچI9ئtI‹~H…ےtH‹ےPI‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèfffff.„UH‰هHچH9اtH‹H…ےtH‹]ے`]أfffff.„UH‰هAVSI‰‏HچI‰HچI9ئtI‹~H…ےtH‹ےPI‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèfffff.„UH‰هAVSI‰‏HچI‰HچI9ئtI‹~H…ےtH‹ےPI‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèL‰÷[A^]éH‰اèffffff.„UH‰ه‰w]أ€H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹ًأ@‹…ہuHچأUH‰هHچ=è]HچأDUH‰هAWAVAUATSPI‰ôI‰‏Mچ~„I‹$I;D$»sP¶„غ~IHےہI‰$°„ہt\ƒû uWA€NM‹nM…يu1ےèI‰إM‰nI‹$I;L$sT¶„ہxMHےءI‰$ëPL‰ç‰قè‰أےبƒّ’ہ„ہu¬„…غtqI‹ِآt\Hƒâ‏L‰ç‰قè„ہ…^ےےےëbL‰çè…ہxVL‰ç‰ئèH‰أHءè …ہxAL‰ïL‰وè„ہt2L‰ç‰قè„ہ…ےےےëL‰ےèH‰آë›°Hƒؤ[A\A]A^A_]أ1ہëيfDUH‰هAVSI‰ِH‰ûِCtH‹s؟L‰ٍèH‹{@ِاu[A^]أHƒç‏L‰ِ[A^]éffff.„UH‰هAVSI‰‏AِF„M‹Fئ HےآA‹@=€r^‰ئ‰ٌfگ€ˆءéHےآپ‏ے?ˆب‰خwêˆ Aƒx~DئB HƒآA‹@(=€rC‰ئ‰ٌf.„€ˆءéHےآپ‏ے?ˆب‰خwêëˆءˆ Aƒx¼HےآI‹x@ِاuyéƒˆءˆ HےآM‹H Icp1ےf.„I‹DùH=€r3H‰ءH‰ثffff.„€ˆHءëHےآHپùے?ˆطH‰ظwçëDˆأˆHےآHےاH9÷|±I‹x@ِاtHƒç‏H‰ضèH‰آI‹~@ِاuH‰ذ[A^]أHƒç‏H‰ض[A^]éfDUH‰هAWAVAUATSPI‰‏I‹~@ِاuE1ےAِFuéŒHƒç‏èI‰اAِFtyM‹fI‹|$E1ي@ِاu1غëHƒç‏èH‰أIچ|$èH…ہt$A‰إE…يxD‰éƒة½ةچLةIءéےءë¹HثE‰l$(HأA‰\$‰طƒب½ہچDہIءèIكNچ|8E‰~L‰ّHƒؤ[A\A]A^A_]أ@UH‰هAVSH‰َI‰‏H‹5Hچ1ةH‰كèH…ہtL‰÷H‰ئ[A^]éH‰كL‰ِ[A^]é„UH‰هAVSI‰ِH‰ûL9َt}ِCt+H‹Cا@ا@H‹x@ِاtHƒç‏Hƒ?tèاCH‹{@ِاtHƒç‏Hƒ?tèH‹5Hچ1ةL‰÷èH…ہtH‰كH‰ئ[A^]é[A^]أL‰÷H‰ق[A^]éffff.„UH‰هAVSI‰ِH‰ûI9قt`ِCt+H‹Cا@ا@H‹x@ِاtHƒç‏Hƒ?tèاCH‹{@ِاtHƒç‏Hƒ?tèH‰كL‰ِ[A^]é[A^]أf„UH‰ه°]أ„UH‰هAVSH‰ûH9قtPH‹CH‹NH‰KH‰F‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëدfff.„UH‰هAVSH‰ûH‹CH‹NH‰KH‰F‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëدگH‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹ًH‹ّأffff.„UH‰ه]أf.„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہuBئC@HاC8HاC0HاC(HاC HاCاCDئCHHƒؤ[A^A_]أLچ{Hچ=èë¬I‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اè@UH‰هئG@HاG8HاG0HاG(HاG HاGاGDئGH]أUH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہuBئC@HاC8HاC0HاC(HاC 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ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëدf„H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹PH‹Xأffff.„UH‰هHچH‰]أfff.„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہu!H¸ےےےےH‰C HاCHƒؤ[A^A_]أLچ{Hچ=èëحI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèDUH‰هH¸ےےےےH‰G HاG]أ@UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہu!H¸ےےےےH‰C HاCHƒؤ[A^A_]أLچ{Hچ=èëحI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèDUH‰هAWAVATSI‰÷H‰ûHچH‰LچsHاCA‹G‰CاCM‹gAِؤtL‰÷èIƒن‏H‰اL‰وèAِGuE1نë؟0èI‰ؤI‹wL‰çèL‰cI‹G H‰C [A\A^A_]أI‰اL‰çèI‹ِأu ë0I‰اI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰ےèH‰اè@UH‰هAVSI‰‏HچI‰HچI9ئtI‹~H…ےtH‹ےPI‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèfffff.„UH‰هHچH9اtH‹H…ےtH‹]ے`]أfffff.„UH‰هAVSI‰‏HچI‰HچI9ئtI‹~H…ےtH‹ےPI‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèfffff.„UH‰هAVSI‰‏HچI‰HچI9ئtI‹~H…ےtH‹ےPI‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèL‰÷[A^]éH‰اèffffff.„UH‰ه‰w]أ€H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹`أ@‹…ہuHچأUH‰هHچ=è]HچأDUH‰هAWAVAUATSPI‰ôI‰‏Mچ~ëA‰F$fگI‹$I;D$»s¶„غ~ HےہI‰$ëL‰ç‰قè‰أےبƒّ~‡ة‰ط‰ظءéƒùt]ƒù„„ƒù…«< …£A€NM‹nM…يu1ےèI‰إM‰nI‹$I;L$ƒ£¶„ہˆکHےءI‰$éœگ<u\A€NI‹$I;L$ƒب¶„ہˆ؟HےءI‰$é+ےےےگ<u,A€NI‹$I;L$ƒ¯¶„ہˆ¦HےءI‰$é©گ…غ„©I‹ِآtcHƒâ‏L‰ç‰قè„ہ…ع‏ےےé—L‰çè…ہˆ‡L‰ç‰ئèH‰أHءè …ہxrL‰ïL‰وè„ہtcL‰ç‰قè„ہ…ڈ‏ےےëOL‰ےèH‰آë”1ہL‰ç‰ئèH…ہ‰e‏ےےë+1ہL‰ç‰ئèH…ہxA‰F éO‏ےے°Hƒؤ[A\A]A^A_]أ1ہëيf.„UH‰هAWAVSPI‰ِI‰ےA‹_ِأuِأu0ِأuAI‹@ِاuRHƒؤ[A^A_]أI‹w؟L‰ٍèِأtذA‹w ؟L‰ٍèِأt؟A‹w$؟L‰ٍèI‹@ِاt®Hƒç‏L‰ِHƒؤ[A^A_]éff.„UH‰هAWAVSPI‰‏E‹~Aِا„M‹Fئ HےآA‹pپ‏€rj‰ً‰ءff.„@€خ€@ˆ2ءéHےآ=ے?@ˆخ‰بwçˆ Aƒx~EئB HƒآA‹p(پ‏€rC‰ً‰ءfD@€خ€@ˆ2ءéHےآ=ے?@ˆخ‰بwçë!@ˆٌˆ Aƒx»HےآI‹x@ِاuué@ˆٌˆ HےآM‹H Icp1ےDI‹\ùHپû€r2H‰ظH‰بfff.„€ث€ˆHءèHےآHپùے?ˆأH‰ءwوë@ˆطˆHےآHےاH9÷|±I‹x@ِاtHƒç‏H‰ضèH‰آAِاu"AِاueI‹~@ِا…¯H‰ذHƒؤ[A^A_]أIcN Hپù€ئHچRr(H‰خH‰ًfD€ة€ˆ HءèHےآHپ‏ے?ˆءH‰ئwوëˆبˆHےآAِاt›IcN$Hپù€ئHچRr/H‰خH‰ًffff.„€ة€ˆ HءèHےآHپ‏ے?ˆءH‰ئwوëˆبˆHےآI‹~@ِا„QےےےHƒç‏H‰ضHƒؤ[A^A_]éf„UH‰هAWAVAUATSPI‰‏I‹~@ِاu;1غA‹F¨„¨uB¨…ث¨„÷A‹F$…ہxxƒب½ہچDہIءèےہëlHƒç‏èH‰أA‹F¨u؟éإM‹fI‹|$E1ي@ِاuE1ےëHƒç‏èI‰اIچ|$èH…ہt.A‰إE…يxD‰éƒة½ةچLةIءéےءë¸Hأëm¹IدE‰l$(IاE‰|$D‰ّƒب½ہچDہIءèLûHچ\A‹F¨„5ےےےA‹N …ةxƒة½ةچLةIءéےءHث¨…ےےےë¹Hث¨… ےےےA‰^H‰طHƒؤ[A\A]A^A_]أfDUH‰هAVSH‰َI‰‏H‹5Hچ1ةH‰كèH…ہtL‰÷H‰ئ[A^]éH‰كL‰ِ[A^]é„UH‰هAWAVSPI‰ِH‰ûL9َ„›D‹{Aِاt+H‹Cا@ا@H‹x@ِاtHƒç‏Hƒ?tèAِاtH¸ےےےےH‰C اCH‹{@ِاtHƒç‏Hƒ?tèH‹5Hچ1ةL‰÷èH…ہt H‰كH‰ئHƒؤ[A^A_]éHƒؤ[A^A_]أL‰÷H‰قHƒؤ[A^A_]éfff.„UH‰هAWAVSPI‰ِH‰ûI9قt~D‹{Aِاt+H‹Cا@ا@H‹x@ِاtHƒç‏Hƒ?tèAِاtH¸ےےےےH‰C اCH‹{@ِاtHƒç‏Hƒ?tèH‰كL‰ِHƒؤ[A^A_]éHƒؤ[A^A_]أfگUH‰ه°]أ„UH‰هAVSH‰ûH9قthH‹CH‹NH‰KH‰F‹C ‹N ‰K ‰F ‹C$‹N$‰K$‰F$‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëد@UH‰هAVSH‰ûH‹CH‹NH‰KH‰F‹C ‹N ‰K ‰F ‹C$‹N$‰K$‰F$‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëدf„H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹`H‹hأffff.„UH‰هHچfHnہfpہDَ]أگUH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہu-ئC(HاC HاCH¸H‰C,Hƒؤ[A^A_]أLچ{Hچ=èëءI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèf„UH‰هئG(HاG HاGH¸H‰G,]أ„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہu-ئC(HاC HاCH¸H‰C,Hƒؤ[A^A_]أLچ{Hچ=èëءI‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèf„UH‰هAWAVAUATSPI‰÷I‰‎HچI‰EMچuIاEA‹GA‰EAاEI‹_ِأtL‰÷èHƒم‏H‰اH‰قèA‹G¨uE1نM‰e¨u*E1نë>؟@èI‰ؤI‹wL‰çèA‹GM‰e¨tض؟@èI‰ؤI‹w L‰çèM‰e A‹G0A‰E0I‹G(I‰E(Hƒؤ[A\A]A^A_]أëI‰اL‰çèI‹ِأu ë0I‰اI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰ےèH‰اèfffff.„UH‰هAVSI‰‏HچI‰HچI9ئtI‹~H…ےtH‹ےPI‹~ H…ےtH‹ےPI‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèffffff.„UH‰هSPH‰ûHچH9أt$H‹{H…ےtH‹ےPH‹{ H…ےtH‹Hƒؤ[]ے`Hƒؤ[]أUH‰هAVSI‰‏HچI‰HچI9ئtI‹~H…ےtH‹ےPI‹~ H…ےtH‹ےPI‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèffffff.„UH‰هAVSI‰‏HچI‰HچI9ئtI‹~H…ےtH‹ےPI‹~ H…ےtH‹ےPI‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèL‰÷[A^]éH‰اèUH‰ه‰w]أ€H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹pأ@‹…ہuHچأUH‰هHچ=è]HچأDUH‰هAWAVAUATSPI‰ôI‰‏MچnLچ=aL‰mذëH…ہA•F(I‹$I;D$»s¶„غ~ HےہI‰$ëL‰ç‰قè‰أےبƒّ~‡)‰ظءéےةƒù‡‰طIcڈLùےل<…A€NI‹$I;L$ƒ™¶„ہˆگHےءI‰$é“<…سA€NI‹$I;L$ƒ€¶„ہˆwHےءI‰$éz<… A€NM‹nM…يum1ےèI‰إM‰nI‹$I;L$r_é¤< upA€NI‹$I;L$ƒà¶„ہˆصHےءI‰$éع‏ےے<*uAA€NM‹n M…يu1ےèI‰إM‰n I‹$I;L$sJ¶„ہxCHےءI‰$ëJf.„…غ„سI‹Uِآ„‚Hƒâ‏L‰ç‰قè„ہ…u‏ےےé¼L‰çè…ہˆ¬L‰ç‰ئèH‰أHءè …ہˆ“L‰ïL‰وè„ہ„€L‰ç‰قè„ہL‹mذ…‏ےےëhL‰çèِآ…‏ےےëUL‰ïèH‰آérےےے1ہL‰ç‰ئèH…ہx4A‰F,éك‎ےے1ہL‰ç‰ئèH…ہxA‰F0éإ‎ےے°Hƒؤ[A\A]A^A_]أ1ہëي‏ےے5‏ےےh‏ےےœ‏ےےث‏ےےfff.„UH‰هAWAVSPI‰ِI‰ےA‹_ِأu)ِأu:ِأuKِأu\ِأunI‹@ِاuHƒؤ[A^A_]أA‹w,؟L‰ٍèِأtئA‹w0؟L‰ٍèِأtµI‹w؟L‰ٍèِأt¤A¶w(؟L‰ٍèِأt’I‹w ؟L‰ٍèI‹@ِاtپHƒç‏L‰ِHƒؤ[A^A_]é@UH‰هAVSI‰‏A‹^ِأu:ِأ…|ِأ…اِأ…ِأ…#I‹~@ِا…sH‰ذ[A^]أIcN,Hپù€ئHچRr+H‰خH‰ًf„€ة€ˆ HءèHےآHپ‏ے?ˆءH‰ئwوëˆبˆHےآِأt„IcN0Hپù€ئHچRr0H‰خH‰ًfffff.„€ة€ˆ HءèHےآHپ‏ے?ˆءH‰ئwوëˆبˆHےآِأ„9ےےےI‹~ئHےآ‹Oپù€r(‰خ‰ًff.„€ة€ˆ ءèHےآپ‏ے?ˆء‰ئwéëˆبˆHےآ1ِèH‰آِأ„ë‏ےےAٹF(ئ ˆBHƒآِأ„ف‏ےےI‹~ ئ*Hےآ‹Oپù€r*‰خ‰ًffff.„€ة€ˆ ءèHےآپ‏ے?ˆء‰ئwéëˆبˆHےآ1ِèH‰آI‹~@ِا„چ‏ےےHƒç‏H‰ض[A^]é„UH‰هAVSI‰‏I‹~@ِا…„1غA‹F¨„½¨t"I‹~èHأƒب½ہچDہIءèHچ\A‹F¨t"I‹~ 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„‏ےےë¨؛Hر©@…َ‎ےےé‏ےےf.„UH‰هAVSH‰َI‰‏H‹5Hچ1ةH‰كèH…ہtL‰÷H‰ئ[A^]éH‰كL‰ِ[A^]é„UH‰هAVSI‰ِH‰ûL9َt4H‰كèH‹5Hچ1ةL‰÷èH…ہtH‰كH‰ئ[A^]é[A^]أL‰÷H‰ق[A^]éfDUH‰هAVSI‰ِH‰ûI9قtH‰كèH‰كL‰ِ[A^]é[A^]أfگUH‰ه°]أ„UH‰هH9‏t]é]أffffff.„UH‰هAVSH‰ûH‹C(H‹N(H‰K(H‰F(‹C ‹N ‰K ‰F ‹C$‹N$‰K$‰F$H‹C8H‹N8H‰K8H‰F8‹C0‹N0‰K0‰F0‹C4‹N4‰K4‰F4H‹CHH‹NHH‰KHH‰FH‹C@‹N@‰K@‰F@‹CD‹ND‰KD‰FDH‹CPH‹NPH‰KPH‰FPH‹CXH‹NXH‰KXH‰FXH‹C`H‹N`H‰K`H‰F`H‹ChH‹NhH‰KhH‰FhH‹CpH‹NpH‰KpH‰FpH‹CxH‹NxH‰KxH‰FxH‹ƒ€H‹ژ€H‰‹€H‰†€H‹ƒˆH‹ژˆH‰‹ˆH‰†ˆ‹ƒگ‹ژگ‰‹گ‰†گ‹ƒ”‹ژ”‰‹”‰†”‹ƒک‹ژک‰‹ک‰†ک‹ƒœ‹ژœ‰‹œ‰†œ‹ƒ ‹ژ ‰‹ ‰† ‹ƒ¤‹ژ¤‰‹¤‰†¤‹ƒ¨‹ژ¨‰‹¨‰†¨ٹƒ¬ٹژ¬ˆ‹¬ˆ†¬ٹƒٹژˆ‹ˆ†‹ƒ°‹ژ°‰‹°‰†°‹ƒ´‹ژ´‰‹´‰†´‹ƒ¸‹ژ¸‰‹¸‰†¸‹ƒ¼‹ژ¼‰‹¼‰†¼‹ƒہ‹ژہ‰‹ہ‰†ہ‹ƒؤ‹ژؤ‰‹ؤ‰†ؤٹƒبٹژبˆ‹بˆ†ب‹ƒج‹ژج‰‹ج‰†ج‹ƒذ‹ژذ‰‹ذ‰†ذ‹ƒش‹ژش‰‹ش‰†ش‹ƒط‹ژط‰‹ط‰†ط‹ƒـ‹ژـ‰‹ـ‰†ـ‹ƒà‹ژà‰‹à‰†à‹ƒن‹ژن‰‹ن‰†ن‹ƒè‹ژè‰‹è‰†è‹ƒى‹ژى‰‹ى‰†ى‹ƒً‹ژً‰‹ً‰†ً‹ƒô‹ژô‰‹ô‰†ô‹C‹N‰K‰F‹C‹N‰K‰FH‹CL‹v‰ءD ٌِءt"HƒأAِئtIƒو‏¨t+Hƒà‏H‹I‹H‰I‰[A^]أHƒئH‰÷èI‰ئH‹¨uصH‰كèëدffff.„H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹H‹أffff.„UH‰هHچH‰]أfff.„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہuئC HاCHƒؤ[A^A_]أLچ{Hچ=èë×I‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèffffff.„UH‰هئG HاG]أfffff.„UH‰هAWAVSPH‰ûHچH‰HاCHاC‹…ہuئC HاCHƒؤ[A^A_]أLچ{Hچ=èë×I‰ئI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰÷èH‰اèffffff.„UH‰هAWAVATSI‰÷H‰ûHچH‰LچsHاCA‹G‰CاCM‹gAِؤtL‰÷èIƒن‏H‰اL‰وèAِGuE1نë؟@èI‰ؤI‹wL‰çèL‰cAٹG ˆC [A\A^A_]أI‰اL‰çèI‹ِأu ë0I‰اI‹ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاL‰ےèH‰اèDUH‰هAVSI‰‏HچI‰HچI9ئtI‹~H…ےtH‹ےPI‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèfffff.„UH‰هHچH9اtH‹H…ےtH‹]ے`]أfffff.„UH‰هAVSI‰‏HچI‰HچI9ئtI‹~H…ےtH‹ےPI‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèIاF[A^]أH‰اèfffff.„UH‰هAVSI‰‏HچI‰HچI9ئtI‹~H…ےtH‹ےPI‹^ِأt%Hƒم‏tH‹CH…ہuHƒ;tH‰كèH‰كèL‰÷[A^]éH‰اèffffff.„UH‰ه‰w]أ€H‹Hƒّےt7UH‰هHƒىHچH‰EًHچEًH‰EّHچ=HچHچuّèHƒؤ]H‹ أ@‹…ہuHچأUH‰هHچ=è]HچأDUH‰هAWAVAUATSPI‰ôI‰‏Mچ~ëH…ہA•F fffff.„I‹$I;D$»s¶„غ~ HےہI‰$ëL‰ç‰قè‰أےبƒّ~w}‰ط‰ظءéƒùtAƒùul< uhA€NM‹nM…يu1ےèI‰إM‰nI‹$I;L$si¶„ہxbHےءI‰$ëeگ<u,A€NI‹$I;L$ƒ„¶„ہx}HےءI‰$é?ےےےD…غ„„I‹ِآtoHƒâ‏L‰ç‰قè„ہ…*ےےےëuL‰çè…ہxiL‰ç‰ئèH‰أHءè 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parameter dropout_ratio for layer type lrnUnknown parameter local_size for layer type Unknown parameter alpha for layer type Unknown parameter beta for layer type Unknown parameter k for layer type datahdf5_dataimageswindow_datainfogain_lossUnknown parameter source for layer type Unknown parameter batchsize for layer type Unknown parameter rand_skip for layer type Unknown parameter shuffle for layer type Unknown parameter new_height for layer type Unknown parameter new_width for layer type concatUnknown parameter concat_dim for layer type Unknown parameter det_fg_threshold for layer type Unknown parameter det_bg_threshold for layer type Unknown parameter det_fg_fraction for layer type Unknown parameter det_context_pad for layer type Unknown parameter det_crop_mode for layer type hdf5_outputUnknown parameter hdf5_output_param for layer type accuracybnlleuclidean_lossflattenim2colmultinomial_logistic_lossrelusigmoidsoftmaxsoftmax_losssplittanhUpgradeV0LayerTypeUnknown layer name: UpgradeNetAsNeededAttempting to upgrade input file specified using deprecated V0LayerParameter: Warning: had one or more problems upgrading V0NetParameter to NetParameter (see above); continuing anyway.INFOSuccessfully upgraded file specified using deprecated V0LayerParameterNote that future Caffe releases will not support V0NetParameter; use ./build/tools/upgrade_net_proto_text for prototxt and ./build/tools/upgrade_net_proto_binary for model weights upgrade this and any other net protos to the new format.transformation parameters: data transformation parameters.Note that future Caffe releases will only support transform_param messages for transformation fields.V1LayerParameter: V1LayerParameter (see above); continuing anyway.V1LayerParameterAttempting to upgrade batch norm layers using deprecated params: Successfully upgraded batch norm layers using deprecated params.UpgradeV1NetInput NetParameter to be upgraded already specifies 'layer' fields; these will be ignored for the upgrade.Upgrade of input layer failed.BatchNormUpgradeV1LayerParameterUnknown blob_share_mode: Input NetParameter has V0 layer -- ignoring.AbsValAccuracyArgMaxBNLLConcatContrastiveLossConvolutionDeconvolutionDataDropoutDummyDataEuclideanLossEltwiseExpFlattenHDF5DataHDF5OutputHingeLossIm2colImageDataInfogainLossInnerProductLRNMemoryDataMultinomialLogisticLossMVNPoolingPowerReLUSigmoidSigmoidCrossEntropyLossSilenceSoftmaxSoftmaxWithLossSplitSliceTanHWindowDataThresholdUpgradeV1LayerTypeUnknown V1LayerParameter layer type: ReadProtoFromTextFilefs.is_open()Can't open ""ReadProtoFromBinaryFileReadNetParamsFromTextFileOrDieReadProtoFromTextFile(param_file, param)Failed to parse NetParameter file: ReadNetParamsFromTextBufferOrDieReadProtoFromTextBuffer(data, len, param)Failed to parse NetParameter buffermemory bufferReadNetParamsFromBinaryFileOrDieReadProtoFromBinaryFile(param_file, param)ReadNetParamsFromBinaryBufferOrDieReadProtoFromBinaryBuffer(data, len, 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¤&ں\{'\!ة%َ#¢$g›%ژ‌$¢¶!پ$×y$”›%ô J$خ E$TE›%آص$ïâ$D&6¾&[±&أ&ثT5& ¢&:!'ص–%طr$0m$آ k$دم!O$=$MEw$ؤ ê$(!}5$d"ف$'#“'A#‹'ش#2($-$$ $($R$c$„$•$آ$ح$%پژ%¼%ء%÷%&j&u&’&ڑ&ٌ&ù&:'s'c'k'ش'(ٍ' ((((„(خ((ئ(¹(ے›غ‚زVلa~ L‎qB3",U $go¤"Yئ QوD"م6"£X ›jم§"ةة ءN كل¹ ل/ :\ g‹“¦ ±ج ×ص+ے›)'››†ھïے›}uتت!@ë 8ôت¾!"ك % 0C TLے›][]°eٹïأ÷œ“®›¬£qے›‹پ‚!»& ³/×!ظ' ر0ژ¾ ةـ يهے›¶€€4„س„ ×jNص¸3ے›کڈ§W £صc ²a دU ؛ هa %ùM 6 rY ط kE ~ zّ ے›ذNfAرNظV/?7A?xے›)'ڈڈP‍إے›)'ڈڈP‍إے›ض€€N!…4 }=Kˆ ™‘ے›ض€€N!ژ" †+f‘ ¢ڑے›²پ€©تت!آë ؛ô؛®!¤د œطA!†: ~‰ ”§ ²إ ضخے›)'ھھï¹Iے›¶€€4??‍«قے›ڑ‚€‘DD’´¤'¬Y ¢¼ ´ٹ¼*B´ڑ؛½ 7ƒ a—_ش"Eِ ="/L Wƒڑع÷مےïے›†…€‎IIA ƒ%( ¨ ؟ K $ -An ôw^صïü ظGL yU?”c· 9ہJ E[%ِ€îŒ و•ق، ضھ خ³ئ؟ ¾ب ¶K سq•èC PK ù) 1< SE[f q| ±… ¹ژءڑ ة£ رـ ç÷ + 9 W k c ے›][Eز€،كe‎ْ ّFنQ”ے›¼4d<lhش4ـhے›}uHH!.i &rK½!Lق D1 <O `Xے›€÷n"فگ ص®"؟ذ ·ظyR!7s /|‰!û& َ/Q€!، آ حà ë‏ ': KCے›¢€€uہ—¸ے›ًhصصفٹg «è ؤ à ض ق .ے›أ€Aéé2ٌ9*22.ے› -Cے› -Cے›¶€€4Q'÷)''ے›¶€€4bW7YOWWے›ض€€N[[F‌lے qzb÷ـ+ے›)'°ذے›)'ڈڈP‍إے›)'ژژO‌إے›)'œœِ¤eے›IAکک·§ؤkتndے›IA„„‎“!´¼\ے›¯€'ققy~W/ے› Liٍے›][*E2•اGدZ)C1A9*ے›¢€€<âùے›ض€€N°°پيl:Y\µb2+ے›IA}}Œ?ث*خdے›)'——ز¢Hے›ذNW°-¸U {ے›)'ڈڈP‍إے› (Là?ً?€ےےے€ےےےX¾ض¾ZB؟ض¾b\ہض¾®6ءض¾إءءض¾دآض¾ضvآض¾â½آض¾ٌأض¾#€أض¾>ïأض¾Z1ؤ;ؤض¾h|ؤ…ؤض¾vظؤض¾}.إزإض¾1گئض¾~&اض¾–œاض¾·@بض¾êڈبذبض¾| 1ةض¾ٹ ٹةض¾“ .إتض¾« ‌تض¾و /ث[ثض¾÷ /ث·ثض¾ /ث4ذض¾ ِذض¾ذcv::Mat cv::dnn::experimental_dnn_v3::blobFromImage(cv::InputArray, double, const cv::Size &, const cv::Scalar &, bool, bool)/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/src/dnn.cppcv::Mat cv::dnn::experimental_dnn_v3::blobFromImages(const std::vector &, double, cv::Size, const cv::Scalar &, bool, bool)image0.dims == 2blobFromImagesimage.depth() == 5image.dims == 2 && (nch == 3 || nch == 4)image.size() == image0.size()image.dims == 2 && (nch == 1)int cv::dnn::experimental_dnn_v3::Net::addLayer(const cv::String &, const cv::String &, cv::dnn::experimental_dnn_v3::LayerParams &)Added layer name "" must not contain dot symboladdLayerLayer "" already into netint cv::dnn::experimental_dnn_v3::Net::addLayerToPrev(const cv::String &, const cv::String &, cv::dnn::experimental_dnn_v3::LayerParams &)void cv::dnn::experimental_dnn_v3::Net::connect(int, int, int, int)void cv::dnn::experimental_dnn_v3::Net::connect(cv::String, cv::String)outPin.valid() && inpPin.valid()connectcv::Mat cv::dnn::experimental_dnn_v3::Net::forward(const cv::String &)void cv::dnn::experimental_dnn_v3::Net::forward(cv::OutputArrayOfArrays, const cv::String &)void cv::dnn::experimental_dnn_v3::Net::forward(cv::OutputArrayOfArrays, const std::vector &)void cv::dnn::experimental_dnn_v3::Net::forward(std::vector > &, const std::vector &)void cv::dnn::experimental_dnn_v3::Net::setPreferableBackend(int)backendIdvoid cv::dnn::experimental_dnn_v3::Net::setPreferableTarget(int)targetIdvoid cv::dnn::experimental_dnn_v3::Net::setInputsNames(const std::vector &)void cv::dnn::experimental_dnn_v3::Net::setInput(cv::InputArray, const cv::String &)nameRequested blob "" not foundsetInputnumParam < (int)layerBlobs.size()getParamsetParamdeleteLayerRequested layer "%s" was not initializedgetLayerInputsint64 cv::dnn::experimental_dnn_v3::Net::getFLOPS(const std::vector &) constinShapes.size() == outShapes.size()getFLOPSinShapes.size() == ids.size()layer != impl->layers.end()void cv::dnn::experimental_dnn_v3::Net::getMemoryConsumption(const int, const std::vector &, size_t &, size_t &) constgetMemoryConsumptionvoid cv::dnn::experimental_dnn_v3::Net::getMemoryConsumption(const std::vector &, size_t &, size_t &) constvoid cv::dnn::experimental_dnn_v3::Net::getMemoryConsumption(const std::vector &, std::vector &, std::vector &, std::vector &) constvoid cv::dnn::experimental_dnn_v3::Net::setHalideScheduler(const cv::String &)schedulerHalide pipeline of layers is not defined.initHalidevoid cv::dnn::experimental_dnn_v3::Layer::finalize(const std::vector &, std::vector &)std::vector cv::dnn::experimental_dnn_v3::Layer::finalize(const std::vector &)void cv::dnn::experimental_dnn_v3::Layer::forward_fallback(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays)void cv::dnn::experimental_dnn_v3::Layer::run(const std::vector &, std::vector &, std::vector &)inputs.size()getMemoryShapesstatic void cv::dnn::experimental_dnn_v3::LayerFactory::registerLayer(const cv::String &, cv::dnn::experimental_dnn_v3::LayerFactory::Constuctor)type" already was registeredregisterLayerstatic void cv::dnn::experimental_dnn_v3::LayerFactory::unregisterLayer(const cv::String &)static Ptr cv::dnn::experimental_dnn_v3::LayerFactory::createLayerInstance(const cv::String &, cv::dnn::experimental_dnn_v3::LayerParams &)Constructor of backend wrapper must be implementedBackendWrappertotal() == 0 || data != __nullMat/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/core/include/opencv2/core/mat.inl.hppStep must be a multiple of esz1_input__NetInputLayer__inputs.size() == requiredOutputsallocator::allocate(size_t n) 'n' exceeds maximum supported sizecv::dnn::experimental_dnn_v3::LayerData::LayerData(int, const cv::String &, const cv::String &, cv::dnn::experimental_dnn_v3::LayerParams &)outLayerId < inLayerIdInput #of layer "" already was connectedaddLayerInputvoid cv::dnn::experimental_dnn_v3::Net::Impl::setUpNet(const std::vector &)preferableBackend != DNN_BACKEND_HALIDEsetUpNetvoid cv::dnn::experimental_dnn_v3::Net::Impl::allocateLayers(const std::vector &)!layers[0].outputBlobs.empty()allocateLayerslayers[0].outputBlobs[i].total()void cv::dnn::experimental_dnn_v3::BlobManager::reset()void cv::dnn::experimental_dnn_v3::Net::Impl::allocateLayer(int, const cv::dnn::experimental_dnn_v3::Net::Impl::LayersShapesMap &)from.valid()allocateLayerlayerShapesIt != layersShapes.end()void cv::dnn::experimental_dnn_v3::BlobManager::allocateBlobsForLayer(cv::dnn::experimental_dnn_v3::LayerData &, const cv::dnn::experimental_dnn_v3::(anonymous namespace)::LayerShapes &, std::vector &, bool)ld.requiredOutputs.size() <= outShapes.size()allocateBlobsForLayerld.umat_inputBlobs[0].total() == total(shapes[index])ld.inputBlobs[0]->total() == total(shapes[index])mapIt != reuseMap.end()numReferencesrefIt != refCounter.end()reuseMap.find(user) == reuseMap.end()reusereuseMap.find(host) != reuseMap.end()umat_memHosts.find(lp) == umat_memHosts.end()addHostmemHosts.find(lp) == memHosts.end()haveHalide()wrapUnknown backend identifierwrapMatreleaseReferencerefIt->second > 0void cv::dnn::experimental_dnn_v3::Net::Impl::fuseLayers(const std::vector &)ConvolutionReLUChannelsPReLUPowerEltwisefirstConvLayerData->umat_outputBlobs.size() == 1 && ld.umat_inputBlobs.size() == 1fuseLayersoutput_slice.isContinuous() && output_slice.size == curr_output.sizevoid cv::dnn::experimental_dnn_v3::Net::Impl::computeNetOutputLayers()void cv::dnn::experimental_dnn_v3::Net::Impl::initBackend()preferableTarget == DNN_TARGET_CPU || preferableTarget == DNN_TARGET_OPENCLinitBackend!ldBot.backendNodes[preferableBackend].empty()void cv::dnn::experimental_dnn_v3::Net::Impl::forwardToLayer(cv::dnn::experimental_dnn_v3::LayerData &, bool)void cv::dnn::experimental_dnn_v3::Net::Impl::forwardLayer(cv::dnn::experimental_dnn_v3::LayerData &)forwardLayerRequsted layer "getLayerDatavoid cv::dnn::experimental_dnn_v3::Net::Impl::getBlob(cv::UMat &, const cv::dnn::experimental_dnn_v3::LayerPin &)Requested blob not foundgetBlob" produce only outputs, the # was requstedLayer with requested id=%d not foundcv::Mat cv::dnn::experimental_dnn_v3::Net::Impl::getBlob(const cv::dnn::experimental_dnn_v3::LayerPin &)void cv::dnn::experimental_dnn_v3::Net::Impl::clear()it != layers.end()clearinum == (int)inumresolvePinOutputNamelayerDesc.isInt() || layerDesc.isString()(idx == -1 && size() == 1) || (idx >= 0 && idx < size())get/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/dnn.inl.hppfracpart == 0.0isInt() || isReal()sizeisString()(idx == -1 && ps->size() == 1) || (idx >= 0 && idx < (int)ps->size())Ptr cv::dnn::experimental_dnn_v3::LayerData::getLayerInstance()Can't create layer "" of type "getLayerInstancestart <= (int)shape.size() && end <= (int)shape.size() && start <= 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yqے›œxh„ے›¯€'5‰ڈà~l‡ے›¯€'+ƒـح~شg^جے›)'°ذ€?€?€?Z-q+„ج-N1q+xج-06q+2ج-¤6q+*ج-N2cv3dnn20ConvolutionLayerImplEN2cv3dnn24BaseConvolutionLayerImplEN2cv3dnn19experimental_dnn_v316ConvolutionLayerEN2cv3dnn19experimental_dnn_v320BaseConvolutionLayerEN2cv3dnn19experimental_dnn_v315ActivationLayerEN2cv3dnn19experimental_dnn_v39ReLULayerEN2cv3dnn19experimental_dnn_v318ChannelsPReLULayerEN2cv3dnn20ConvolutionLayerImpl12ParallelConvEN2cv6detail12PtrOwnerImplINS_3dnn7ocl4dnn18OCL4DNNConvSpatialIfEENS_14DefaultDeleterIS5_EEEEN2cv6detail8PtrOwnerEN2cv3dnn19experimental_dnn_v310PowerLayerEN2cv3dnn22DeConvolutionLayerImplEN2cv3dnn22DeConvolutionLayerImpl13MatMulInvokerEN2cv3dnn22DeConvolutionLayerImpl13Col2ImInvokerEN2cv6detail12PtrOwnerImplINS_3dnn20ConvolutionLayerImplENS_14DefaultDeleterIS3_EEEEN2cv6detail12PtrOwnerImplINS_3dnn22DeConvolutionLayerImplENS_14DefaultDeleterIS3_EEEEinputs.size() > 0finalize/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/src/layers/convolution_layer.cppblobs.size() >= 1 && blobs.size() <= 2blobs[0].dims == 4 && blobs[0].size[3] == kernel.width && blobs[0].size[2] == kernel.heightinput.dims == 4 && (input.type() == 5 || input.type() == 6)inputs[i]->type() == input.type()inputs[i]->dims == 4 && inputs[i]->size[1] == input.size[1]inputs[i]->size[2] == input.size[2] && inputs[i]->size[3] == input.size[3]virtual void cv::dnn::ConvolutionLayerImpl::forward(std::vector &, std::vector &, std::vector &)nameinputs.size() == (size_t)1 && inputs[0]->size[1] % blobs[0].size[1] == 0forwardoutputs[0].size[1] % ngroups == 0scale.isContinuous() && shift.isContinuous() && scale.type() == 5 && shift.type() == 5 && scale.total() == (size_t)outCn && shift.total() == (size_t)outCnscale2.isContinuous() && scale2.type() == 5 && scale2.total() == (size_t)outCnshift2.isContinuous() && shift2.type() == 5 && shift2.total() == (size_t)outCnm.isContinuous() && m.type() == 5 && (int)m.total() == outCnallocator::allocate(size_t n) 'n' exceeds maximum supported sizeinput.dims == 4 && output.dims == 4runinput.size[0] == output.size[0]weights.rows == output.size[1]weights.cols == (input.size[1]/ngroups)*kernel.width*kernel.heightinput.type() == output.type()input.type() == weights.type()input.type() == 5input.isContinuous()output.isContinuous()biasvec.size() == (size_t)output.size[1]+2virtual void cv::dnn::ConvolutionLayerImpl::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays)Powerforward_oclactiv_power!reluslope.empty()UMat/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/core/include/opencv2/core/mat.inl.hpptotal() == 0 || data != __nullMatStep must be a multiple of esz1blobs.size() != 0getMemoryShapes!hasBias() || blobs[1].total() == (size_t)blobs[0].size[0]inputs.size() == (size_t)1ngroups > 0 && inpCn % ngroups == 0 && outCn % ngroups == 0inputs.size() == outputs.size()getFLOPSstart <= (int)shape.size() && end <= (int)shape.size() && start <= endtotal/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/shape_utils.hppnum_outputgroupadj_hadj_wl->numOutput % ngroups == 0initConvDeconvLayerFromCaffel->adjustPad.width < l->stride.width && l->adjustPad.height < l->stride.heightRequired argument "" not found into dictionaryget/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/dnn.inl.hpp(idx == -1 && size() == 1) || (idx >= 0 && idx < size())fracpart == 0.0isInt() || isReal()sizevirtual void cv::dnn::DeConvolutionLayerImpl::forward(std::vector &, std::vector &, std::vector &)virtual void cv::dnn::DeConvolutionLayerImpl::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays)!hasBias() || blobs[1].total() == (size_t)numOutputinputs.size() != 0outCn % blobs[0].size[1] == 0inpCn % ngroups == 0 && outCn % ngroups == 0blobs[0].size[0] == inpCneرXAp&رXA¬ ƒaA0 @`p€گ رXAtہآ-رXAü گ?وA€@ @ہ@ذ@ aAXًB A¤DùAجE‘رXAô JuرXA Lr!Ad M°MہMàMcaA„PPƒàT0 رXAà_ رXA,°`3ً`;0a÷رXAX0{ رXA”ذ|ر9رXAہ°¶NرX»®رXA°°¼ہ¼ ذ¼0!½U!A`؟€رXA„àءyرXA`إGAt°ئAرXAہةSaAl`ج pجگج‌رXAp0â§AlàâمبرXA¸ذëپرXA4 `ي„aAگ ًî—A° گً ًنAن گٍ°ٍ%رXàù ًùْOرX`‏®çرXA! 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0.DetectionOutputLayerImpl/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/src/layers/detection_output_layer.cpp layer parameter does not contain parameter.getParameterRequired argument "" not found into dictionaryget/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/dnn.inl.hpp(idx == -1 && size() == 1) || (idx >= 0 && idx < size())fracpart == 0.0isInt() || isReal()sizeisReal() || isInt()code_typecenter_sizeCENTER_SIZECORNERisString()(idx == -1 && ps->size() == 1) || (idx >= 0 && idx < (int)ps->size())virtual void cv::dnn::DetectionOutputLayerImpl::forward(std::vector &, std::vector &, std::vector &)name_bboxesNormalized || inputs.size() >= 4forwardinputs[3]->dims == 4count == numKeptnumLocClasses == 1GetLocPredictionsallocator::allocate(size_t n) 'n' exceeds maximum supported sizeall_loc_preds.size() == numDecodeBBoxesAllCould not find location predictions for label %dprior_bboxes.size() == prior_variances.size()DecodeBBoxesprior_bboxes.size() == bboxes.size()num_bboxes == 0 || prior_variances[0].size() == 4prior_width > 0DecodeBBoxprior_height > 0Unknown type.Could not find confidence predictions for label %dprocessDetections_idx < scores.size()bboxes.size() == scores.size()NMSFast_/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/src/layers/../nms.inl.hppoutputDetections_virtual void cv::dnn::DetectionOutputLayerImpl::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays)forward_oclDecodeBBoxesCORNERDecodeBBoxesCENTER_SIZEinputs.size() >= 3getMemoryShapesinputs[0][0] == inputs[1][0](numPriors * _numLocClasses * 4) == inputs[0][1]int(numPriors * _numClasses) == 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[Func = cv::dnn::ReLUFunctor]src.size == dst.size && src.type() == dst.type() && src.isContinuous() && dst.isContinuous() && src.type() == 5forwardvirtual void cv::dnn::ElementWiseLayer::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) [Func = cv::dnn::ReLUFunctor]src.isContinuous() && dst.isContinuous() && !src.offset && !dst.offsetapplyOCLinitKernel(kernel, src)kernel.run(1, &gSize, &wgSize, false)-DRELU_NO_SLOPEReLUForwardstart <= (int)shape.size() && end <= (int)shape.size() && start <= endtotal/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/shape_utils.hppvirtual void cv::dnn::ElementWiseLayer::forward(std::vector &, std::vector &, std::vector &) [Func = cv::dnn::ReLU6Functor]virtual void cv::dnn::ElementWiseLayer::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) [Func = cv::dnn::ReLU6Functor]virtual void cv::dnn::ElementWiseLayer::forward(std::vector &, std::vector &, std::vector &) [Func = cv::dnn::TanHFunctor]virtual void cv::dnn::ElementWiseLayer::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) [Func = cv::dnn::TanHFunctor]virtual void cv::dnn::ElementWiseLayer::forward(std::vector &, std::vector &, std::vector &) [Func = cv::dnn::SigmoidFunctor]virtual void cv::dnn::ElementWiseLayer::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) [Func = cv::dnn::SigmoidFunctor]virtual void cv::dnn::ElementWiseLayer::forward(std::vector &, std::vector &, std::vector &) [Func = cv::dnn::ELUFunctor]virtual void cv::dnn::ElementWiseLayer::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) [Func = cv::dnn::ELUFunctor]virtual void cv::dnn::ElementWiseLayer::forward(std::vector &, std::vector &, std::vector &) [Func = cv::dnn::AbsValFunctor]virtual void cv::dnn::ElementWiseLayer::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) [Func = cv::dnn::AbsValFunctor]virtual void cv::dnn::ElementWiseLayer::forward(std::vector &, std::vector &, std::vector &) [Func = cv::dnn::BNLLFunctor]virtual void cv::dnn::ElementWiseLayer::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) [Func = cv::dnn::BNLLFunctor]virtual void cv::dnn::ElementWiseLayer::forward(std::vector &, std::vector &, std::vector &) [Func = cv::dnn::PowerFunctor]virtual void cv::dnn::ElementWiseLayer::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) [Func = cv::dnn::PowerFunctor]virtual void cv::dnn::ElementWiseLayer::forward(std::vector &, std::vector &, std::vector &) [Func = cv::dnn::ChannelsPReLUFunctor]scale.isContinuous() && scale.type() == 5apply0 <= cn0 && cn0 < cn1 && cn1 <= (int)scale.total()virtual void cv::dnn::ElementWiseLayer::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays) [Func = cv::dnn::ChannelsPReLUFunctor]pؤُآ¬ٹإُآ،³اُآ¬Wبُآ،ةُآ¬©ةُآ،Wتُآ¬ےتُآ،³ثُآ¬Sجُآ،ےجُآ¬¥حُآ،Wخُآ¬ùخُآ،§دُآ¬Kذُآ،ûذُآ¬زُآ،N2cv3dnn16ElementWiseLayerINS0_11ReLUFunctorEEEN2cv3dnn19experimental_dnn_v39ReLULayerEN2cv3dnn19experimental_dnn_v315ActivationLayerEN2cv3dnn16ElementWiseLayerINS0_11ReLUFunctorEE5PBodyEN2cv6detail12PtrOwnerImplINS_3dnn16ElementWiseLayerINS2_11ReLUFunctorEEENS_14DefaultDeleterIS5_EEEEN2cv6detail8PtrOwnerEN2cv3dnn16ElementWiseLayerINS0_12ReLU6FunctorEEEN2cv3dnn19experimental_dnn_v310ReLU6LayerEN2cv3dnn16ElementWiseLayerINS0_12ReLU6FunctorEE5PBodyEN2cv6detail12PtrOwnerImplINS_3dnn16ElementWiseLayerINS2_12ReLU6FunctorEEENS_14DefaultDeleterIS5_EEEEN2cv3dnn16ElementWiseLayerINS0_11TanHFunctorEEEN2cv3dnn19experimental_dnn_v39TanHLayerEN2cv3dnn16ElementWiseLayerINS0_11TanHFunctorEE5PBodyEN2cv6detail12PtrOwnerImplINS_3dnn16ElementWiseLayerINS2_11TanHFunctorEEENS_14DefaultDeleterIS5_EEEEN2cv3dnn16ElementWiseLayerINS0_14SigmoidFunctorEEEN2cv3dnn19experimental_dnn_v312SigmoidLayerEN2cv3dnn16ElementWiseLayerINS0_14SigmoidFunctorEE5PBodyEN2cv6detail12PtrOwnerImplINS_3dnn16ElementWiseLayerINS2_14SigmoidFunctorEEENS_14DefaultDeleterIS5_EEEEN2cv3dnn16ElementWiseLayerINS0_10ELUFunctorEEEN2cv3dnn19experimental_dnn_v38ELULayerEN2cv3dnn16ElementWiseLayerINS0_10ELUFunctorEE5PBodyEN2cv6detail12PtrOwnerImplINS_3dnn16ElementWiseLayerINS2_10ELUFunctorEEENS_14DefaultDeleterIS5_EEEEN2cv3dnn16ElementWiseLayerINS0_13AbsValFunctorEEEN2cv3dnn19experimental_dnn_v38AbsLayerEN2cv3dnn16ElementWiseLayerINS0_13AbsValFunctorEE5PBodyEN2cv6detail12PtrOwnerImplINS_3dnn16ElementWiseLayerINS2_13AbsValFunctorEEENS_14DefaultDeleterIS5_EEEEN2cv3dnn16ElementWiseLayerINS0_11BNLLFunctorEEEN2cv3dnn19experimental_dnn_v39BNLLLayerEN2cv3dnn16ElementWiseLayerINS0_11BNLLFunctorEE5PBodyEN2cv6detail12PtrOwnerImplINS_3dnn16ElementWiseLayerINS2_11BNLLFunctorEEENS_14DefaultDeleterIS5_EEEEN2cv3dnn16ElementWiseLayerINS0_12PowerFunctorEEEN2cv3dnn19experimental_dnn_v310PowerLayerEN2cv3dnn16ElementWiseLayerINS0_12PowerFunctorEE5PBodyEN2cv6detail12PtrOwnerImplINS_3dnn16ElementWiseLayerINS2_12PowerFunctorEEENS_14DefaultDeleterIS5_EEEEN2cv3dnn16ElementWiseLayerINS0_20ChannelsPReLUFunctorEEEN2cv3dnn19experimental_dnn_v318ChannelsPReLULayerEN2cv3dnn16ElementWiseLayerINS0_20ChannelsPReLUFunctorEE5PBodyEN2cv6detail12PtrOwnerImplINS_3dnn16ElementWiseLayerINS2_20ChannelsPReLUFunctorEEENS_14DefaultDeleterIS5_EEEE'رXAک«0¤رXA(¬àسA 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7ظyR!™s ‘|ƒے!{ sB M` k~ ‰œ ¥ے›¢€€uہ—¸ے›œ“ژژ!F¯>»J!%&2Hz!›ü§•<!à]ضi"‹~¨vسقB"½d µ،°و"— ڈd_ڑ ¨ہ خمô(6IWU,پ•چے›ê€€h::BT–‍Y÷ے*€?€?ةCھA7DyEھA7DoperationprodsummaxUnknown operaticon type "EltwiseLayerImpl/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/src/layers/eltwise_layer.cppcoeffisString()get/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/dnn.inl.hpp(idx == -1 && ps->size() == 1) || (idx >= 0 && idx < (int)ps->size())Required argument "" not found into dictionarysizeallocator::allocate(size_t n) 'n' exceeds maximum supported size(idx == -1 && size() == 1) || (idx >= 0 && idx < size())isReal() || isInt()virtual void cv::dnn::EltwiseLayerImpl::forward(std::vector &, std::vector &, std::vector &)nameoutputs.size() == 1forward1 < dst.dims && dst.dims <= 4rundst.type() == 5dst.isContinuous()coeffs.empty() || coeffs.size() == (size_t)nsrcssrcs[i]->size == dst.size && 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dictionaryget/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/dnn.inl.hpp(idx == -1 && size() == 1) || (idx >= 0 && idx < size())fracpart == 0.0isInt() || isReal()sizevirtual void cv::dnn::FullyConnectedLayerImpl::forward(std::vector &, std::vector &, std::vector &)name0 <= startDim && startDim <= endDimtotal/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/core/include/opencv2/core/mat.inl.hppsrcMat.dims == 2 && srcMat.cols == weights.cols && dstMat.rows == srcMat.rows && dstMat.cols == weights.rows && srcMat.type() == weights.type() && weights.type() == dstMat.type() && srcMat.type() == 5 && (biasMat.empty() || (biasMat.type() == srcMat.type() && biasMat.isContinuous() && (int)biasMat.total() == dstMat.cols))runvirtual void cv::dnn::FullyConnectedLayerImpl::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays)start <= (int)shape.size() && end <= 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cv::dnn::NormalizeBBoxLayerImpl::forward(std::vector &, std::vector &, std::vector &)nameinputs.size() == 1 && outputs.size() == 1forwardinputs[0]->total() == outputs[0].total()scale.total() == channelstotal() == 0 || data != __nullMat/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/core/include/opencv2/core/mat.inl.hppStep must be a multiple of esz1virtual void cv::dnn::NormalizeBBoxLayerImpl::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays)inputs.size() == 1getMemoryShapesN2cv3dnn22NormalizeBBoxLayerImplEN2cv3dnn19experimental_dnn_v318NormalizeBBoxLayerEN2cv6detail12PtrOwnerImplINS_3dnn22NormalizeBBoxLayerImplENS_14DefaultDeleterIS3_EEEEN2cv6detail8PtrOwnerEoaA)p%رXAD) °ذàًyرXAH*گ§Al,@TرXA¸, °µaA-€SaAن-à €رXAè.`#نAh/P%yرXAœ/ذ(à( ً('zPLRx›گ,$0ئےےےےےےo7ïےےےےےےA†C Fƒژڈ4Tpئےےےےےے%3ïےےےےےےA†C MƒŒچژڈzRxگ$Pتےےےےےے A†C $D8تےےےےےےA†C Bƒ$l0تےےےےےےA†C $”تےےےےےےA†C $¼تےےےےےےA†C $نèةےےےےےےA†C 4”ذةےےےےےےy÷îےےےےےےA†C 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2C[l„•إwAŒہûƒ€ £ u.B:ے›IADD…g }ژ ں—ے›؟پ¶ee!V† Nڈ|!8, 05[گ!± ؛c (; FY jbے›جأn"]گ U®"?ذ 7ظyR!™s ‘|ƒے!{ sB M` k~ ‰œ ¥ے›}uHH!.i &rK½!Lق D1 <O `Xے›)'،،ژ°ٌے›€÷n"فگ ص®"؟ذ ·ظyR!7s /|‰!û& َ/Q€!، آ حà ë‏ ': KCے›أ€Aëë÷قصنzے›)'،،ژ°ٌے›)'ëëْ7ے›)'ڈڈP‍إے›ض€€Nhh!K‰C•¹NbZے›ê€€h\&جٍْ wFM؛Jh´MGO؛J`´Mvalueinput_dimsconstantparams.has("paddings")PaddingLayerImpl/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/src/layers/padding_layer.cpp(paddingsParam.size() & 1) == 0paddings[i].first >= 0paddings[i].second >= 0(idx == -1 && size() == 1) || (idx >= 0 && idx < size())get/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/dnn.inl.hppisReal() || isInt()isString()(idx == -1 && ps->size() == 1) || (idx >= 0 && idx < (int)ps->size())allocator::allocate(size_t n) 'n' exceeds maximum supported sizeRequired argument "" not found into dictionarysizefracpart == 0.0isInt() || isReal()virtual void cv::dnn::PaddingLayerImpl::forward(std::vector &, std::vector &, std::vector &)namereflectinputs.size() == 1forwardoutputs.size() == 1inputs[0]->dims == 4outputs[0].dims == 4Only spatial reflection padding is supported.padTop < inpHeightpadBottom < inpHeightpadLeft < inpWidthpadRight < inpWidthUnknown padding type: m.dims > 2getPlane/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/shape_utils.hppvirtual void cv::dnn::PaddingLayerImpl::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays)getMemoryShapesinpShape.size() >= paddings.size()inputDims == -1 || inpShape.size() == inputDims || inpShape.size() > paddings.size()N2cv3dnn16PaddingLayerImplEN2cv3dnn19experimental_dnn_v312PaddingLayerEN2cv6detail12PtrOwnerImplINS_3dnn16PaddingLayerImplENS_14DefaultDeleterIS3_EEEEN2cv6detail8PtrOwnerEoaAˆ<pرXA´<گAرXA€?à Z@b°ہذàًVرXPJرXA,@ §ATCP9گ°rرXA C0$P$µaAdD'hA4E€(،رXA´E0*SaAàEگ-^رXAنFً0،رXA,G 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__nullMat/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/core/include/opencv2/core/mat.inl.hppStep must be a multiple of esz1virtual void cv::dnn::PermuteLayerImpl::forward(std::vector &, std::vector &, std::vector &)nameoutputs[k].total() == inputs[k]->total()forwardinp.dims == numAxes && inp.size == inputs[0]->sizeout.dims == numAxes && out.size == outputs[0].sizeinp.isContinuous() && out.isContinuous()inp.type() == 5 && out.type() == 5out.size[0] == inp.size[order[0]] && out.size[1] == inp.size[order[1]] && out.size[2] == inp.size[order[2]] && out.size[3] == inp.size[order[3]]runvirtual void cv::dnn::PermuteLayerImpl::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays)permutegetMemoryShapes(int)_numAxes == inputs[0].size()inputs[i].size() == 4inputs[i][2] == shapeBefore[2] && inputs[i][3] == shapeBefore[3]total(inputs[i]) == total(shapeAfter)start <= (int)shape.size() && end <= (int)shape.size() && start <= 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HاEکHاEگHاEˆƒ½|ےےے~H‹E¸1ةاˆHےءHc•|ےےےH9ر|êH‹}ہL9ïtèH‹…PےےےH…ہt¹ےےےےًءHƒùuHچ½ےےےèHا…PےےےHا…@ےےےHا…8ےےےHا…0ےےےHا…(ےےےƒ½ےےے~$H‹…Xےےے1ةDاˆHےءHc•ےےےH9ر|êH‹½`ےےےL9çtèH‰كèH‰اèH‰اèگUH‰هAWAVAUATSHپىکI‰خI‰×I‰ôH‰ûèا…xےےےےBHچM€اE´HاE¬HاE¤HاEœHاE”HاEŒHاE„Hا…|ےےےH‰M¸LچmبL‰mہHاEذHاEب‹KH‰چ`ےےے‹KL‰چdےےے‹KP‰چhےےے‹KT‰چlےےے‹KX‰چpےےے‹K\‰چtےےے‹KDَƒ„¶Sa‰D$‰T$‰L$Hچ…pےےےH‰$HچچxےےےLچ…`ےےےLچچhےےےL‰çL‰‏L‰ٍèH‹E°H…ہt¹ےےےےًءHƒùuHچ½xےےےèHاE°HاE HاEکHاEگHاEˆƒ½|ےےے~!H‹E¸1ةDاˆHےءHc•|ےےےH9ر|êH‹}ہL9ïtèHپؤک[A\A]A^A_]أH‰اèH‰أH‹E°H…ہt¹ےےےےًءHƒùuHچ½xےےےèHاE°HاE HاEکHاEگHاEˆƒ½|ےےے~ H‹E¸1ة@اˆHےءHc•|ےےےH9ر|êH‹}ہL9ïtèH‰كèH‰اèfff.„UH‰هAWAVAUATSHپىکَ…\ےےےL‰ثM‰إH‰MذH‰UبH‰µPےےےI‰‏AِF@ulWہ)…`ےےےLچ½`ےےے¾L‰ےèH¹ntinuousH‰HH¹src.isCoH‰fا@()HچّTHچ §Q؟)ےےےA¸ùL‰‏èHچ½`ےےےèH‹Eبِ@@ulWہ)…`ےےےLچ½`ےےے¾L‰ےèH¹ntinuousH‰HH¹dst.isCoH‰fا@()Hچ‚THچ 1Q؟)ےےےA¸ùL‰‏èHچ½`ےےےèA‹‰ءپلےƒùtiWہ)…`ےےےLچ½`ےےے¾L‰ےèH¹e() == 5H‰HH¹src.typeH‰HچTHچ »P؟)ےےےA¸ùL‰‏èHچ½`ےےےèA‹H‹Mب3f©ےttWہ)…`ےےےLچ½`ےےے¾L‰ےèH¹t.type()H‰HH¹() == dsH‰HH¹src.typeH‰Hچ‰SHچ 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E NP‍!ْ؟ ٍô l j‍·!ظط رل›|!¸‌ °¦» ةـ ê‎ # .9 DW b`)ے›}uHH!.i &rK½!Lق D1 <O `Xے›€÷n"فگ ص®"؟ذ ·ظyR!7s /|‰!û& َ/Q€!، آ حà ë‏ ': KCے›¥پ€€œز %غM( ‏1Z‹"è à¶ طë ِ ( 91ے›جأn"]گ U®"?ذ 7ظyR!™s ‘|ƒے!{ sB M` k~ ‰œ ¥ے›IAضض:®(l¦ةk<4ے›م€[ff>é¼hل?ˆظg†پے›IAضض:®(l¦ةk<4ے›„ûzz!گ›ˆ§Iً!ogIf!K‡C“Vé!± ©@V!'wƒCئ!çûَ`S!كt×€§'!’HٹT¦ْ!¶®شز@ت•¦¹تâَ*;N_r€“،´آہ:ے›}uqq!°’¨ڑ7ر!ڈٍ‡’ ³ا؟ے›)' چ¯ٌے›جأ>Yة اF إٌ ´‏¯ظjm|$[ي ٹ?!t`lچŒ‹§w ‚€«ے›][N“VjہaبˆP“X aaNے›ê€€h::BT–‍Y÷ے*ے›IAkk!+Œ #. ?7€?€??؟€?حجج=ےےےےےےےےےےےےےےے€؟€؟€؟€؟€?€?€?€?حٹoˆہ;‹dچoˆ´;‹kernel_sizemaxavestochasticUnknown pooling type "PoolingLayerImpl/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/src/layers/pooling_layer.cppoutput_dimgroup_sizeCannot determine pooling typeceil_modespatial_scaleisString()get/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/dnn.inl.hpp(idx == -1 && ps->size() == 1) || (idx >= 0 && idx < (int)ps->size())(idx == -1 && size() == 1) || (idx >= 0 && idx < size())fracpart == 0.0isInt() || isReal()sizeRequired argument "" not found into dictionaryisReal() || isInt()!inputs.empty()finalizevirtual void cv::dnn::PoolingLayerImpl::forward(std::vector &, std::vector &, std::vector &)nameinputs.size() == 1forwardoutputs.size() == 2outputs.size() == 1inputs.size() == 2Not implementedsrc.isContinuous()rundst.isContinuous()src.type() == 5src.type() == dst.type()src.dims == 4dst.dims == 4((poolingType == ROI || poolingType == PSROI) && dst.size[0] ==rois.size[0] || src.size[0] == dst.size[0])poolingType == PSROI || src.size[1] == dst.size[1](mask.empty() || (mask.type() == src.type() && mask.size == dst.size))roisData[0] < src->size[0]operator()allocator::allocate(size_t n) 'n' exceeds maximum supported sizevirtual void cv::dnn::PoolingLayerImpl::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays)inpMat.offset == 0 && outMat.offset == 0forward_oclinputs.size() != 0getMemoryShapes(out.height 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ےےےےP@H‹…ذ‏ےےH…ہt¹ےےےےًءHƒùuHچ½ک‏ےےèHا…ذ‏ےےHا…ہ‏ےےHا…¸‏ےےHا…°‏ےےHا…¨‏ےےƒ½œ‏ےے~!H‹…ط‏ےے1ةfگاˆHےءHc•œ‏ےےH9ر|êH‹½à‏ےےHچ…è‏ےےH9اtèL‹µ ےےےM…ِ„»H‹‌(ےےےL9َ„œfگLچ{ H‹CطH…ہt¹ےےےےًءHƒùuL‰ےèHاCطHاCبHاCہHاC¸HاC°ƒ{¤~!H‹Cà1ة„اˆHےءHcS¤H9ر|يH‹{èHƒأًH9كtèM9÷L‰û…oےےےH‹½ ےےےëL‰÷L‰µ(ےےےèH‹½ےےےH…ےtH‰½ےےےèH‹H‹H;EذuHپؤH[A\A]A^A_]أèH‰اèI‰ئHچ½@ےےےèéبH‰اèéڈH‰اèH‰اèH‰اèI‰ئH‹…xےےےH…ہt¹ےےےےًءHƒùuHچ½@ےےےèHا…xےےےHا…hےےےHا…`ےےےHا…XےےےHا…Pےےےƒ½Dےےے~H‹E€1ةاˆHےءHc•DےےےH9ر|êH‹}ˆL9ï„ؤèé؛H‰اèéھé¥éأI‰ئH‹…xےےےH…ہt¹ےےےےًءHƒùuHچ½@ےےےèHا…xےےےHا…hےےےHا…`ےےےHا…XےےےHا…Pےےےƒ½Dےےے~H‹E€1ةاˆHےءHc•DےےےH9ر|êH‹}ˆL9ïtèë H‰اèëI‰ئH‹} H…ےtCH‰}¨èH‹…ذ‏ےےH…ہu:ëSI‰ئH‹…ذ‏ےےH…ہu)ëBI‰ئL‹½ ےےےM…ے…¶é]I‰ئH‹…ذ‏ےےH…ہt¹ےےےےًءHƒùuHچ½ک‏ےےèHا…ذ‏ےےHا…ہ‏ےےHا…¸‏ےےHا…°‏ےےHا…¨‏ےےƒ½œ‏ےے~H‹…ط‏ےے1ةاˆHےءHc•œ‏ےےH9ر|êH‹½à‏ےےHچ…è‏ےےH9اtèL‹½ ےےےM…ے„¬H‹‌(ےےےL9û„¼Lچc H‹CطH…ہt¹ےےےےًءHƒùuL‰çèHاCطHاCبHاCہHاC¸HاC°ƒ{¤~H‹Cà1ةاˆHےءHcS¤H9ر|يH‹{èHƒأًH9كtèM9üL‰م…wےےےH‹½ ےےےL‰½(ےےےèH‹½ےےےH…ےtH‰½ےےےèL‰÷èL‰ےëدH‰اèH‰اèH‰اèffff.„UH‰هAWAVAUATSHپىبH‰چp‏ےےH‰•8‏ےےH‰َI‰‎H‹H‹H‰EذHچ5Hچ½(‏ےےèHƒ½(‏ےےtI‹E H…ہHچ5!گHEًHچ=èH‹CH+HƒّtmWہ)…ےےےHچ½ےےے¾èH¹ize() ==H‰HH¹inputs.sH‰fا@ 3Hچ)“Hچ H‘Hچµےےے؟)ےےےA¸¤èHچ½ےےےèH‹…p‏ےےL‹ H‹@L)àH= tWہ)…ےےےHچ½ےےے¾èH¹e() == 3H‰H H¹s.size()H‰HH¹internalH‰Hچ›’Hچ ؛گHچµےےے؟)ےےےA¸¥èHچ½ےےےèH‹…p‏ےےL‹ H‹L‹0H‹HH‰چ ‏ےےL‹xE‹GAƒّIcGIcOH¯بHƒù†®éI‹W@IچHےE‰ءAƒلHƒùs¹1ےM…ةugé}M)ب¹1ےHc؛H¯ظHcL؛Hct؛H¯ٌH¯َHcL؛Hc\؛H¯ظHcD؛H¯أH¯ئHct؛HcL؛H¯خH¯بHƒاI9ّu°M…ةtHچ؛I÷ظ@HcH¯بHƒآIےءuًHƒùwqWہ)…ےےےHچ½ےےے¾èH¹otal() >H‰HH¹imInfo.tH‰ئ@2fا@= Hچ=‘Hچ \ڈHچµےےے؟)ےےےA¸èHچ½ےےےèI‹GَAِG@u I‹O@ƒ9…`Hƒہَ,بَ,H؛H‰•pےےے‰چxےےے‰…|ےےےWہ)…ےےےHا…ےےےHچU€Hچ½ےےےHچµpےےےèH‹…ےےےH‹چےےےH9بtDƒ8xHƒہH9ءuٍH‰بé†H9ب„}HچPH9تttHچqّH)ئ‰÷ءïےاHƒçtH÷كگ‹…غx‰HƒہHƒآHےاuëHƒ‏rAD‹2…ِx‰0Hƒہ‹r…ِx‰0Hƒہ‹r…ِx‰0Hƒہ‹r…ِx‰0HƒہHƒآH9تuؤIچ•گH‰•ّ‏ےےH9ءtH‰…ےےےHچµےےے1زH‹½ّ‏ےےèH‹½ےےےH…ےtH‰½ےےےèWہ)…à‏ےےHا…ً‏ےے؟ہèH‰…à‏ےےHچˆہH‰چً‏ےےاےBHچPا@<Hا@4Hا@,Hا@$Hا@Hا@Hا@Hا@H‰P@HچPPH‰PHHا@XHا@Pا@`ےBHچPhا€œHا€”Hا€ŒHا€„Hا@|Hا@tHا@lHا@dH‰گ 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‏ےےHا…ک‏ےےHا…گ‏ےےHا…ˆ‏ےےƒ½|‏ےے~+H‹…¸‏ےے1ةfff.„اˆHےءHc•|‏ےےH9ر|êH‹½ہ‏ےےL9ïtèH‹E¨H…ہt¹ےےےےًءHƒùuHچ½pےےےèHاE¨HاEکHاEگHاEˆHاE€ƒ½tےےے~*H‹E°1ةfffff.„اˆHےءHc•tےےےH9ر|êH‹}¸L9çtèH‹…8ےےےH…ہt¹ےےےےًءHƒùuHچ½ےےےèHا…8ےےےHا…(ےےےHا… ےےےHا…ےےےHا…ےےےƒ½ےےے~+H‹…@ےےے1ةfff.„اˆHےءHc•ےےےH9ر|êH‹½HےےےHچ…PےےےH9اtèL‹µ@‏ےےM…ِ„»H‹‌H‏ےےL9َ„œfگLچ{ H‹CطH…ہt¹ےےےےًءHƒùuL‰ےèHاCطHاCبHاCہHاC¸HاC°ƒ{¤~!H‹Cà1ة„اˆHےءHcS¤H9ر|يH‹{èHƒأًH9كtèM9÷L‰û…oےےےH‹½@‏ےےëL‰÷L‰µH‏ےےèL‹µà‏ےےM…ِ„ہH‹‌è‏ےےL9َ„،€Lچ{ H‹CطH…ہt¹ےےےےًءHƒùuL‰ےèHاCطHاCبHاCہHاC¸HاC°ƒ{¤~!H‹Cà1ة„اˆHےءHcS¤H9ر|يH‹{èHƒأًH9كtèM9÷L‰û…oےےےH‹½à‏ےےëL‰÷L‰µè‏ےےèƒ½0‏ےےtHچ½(‏ےےèH‹H‹H;EذuEHپؤب[A\A]A^A_]أA‹OچQ1ِƒْBٌ¯خ؛)تI‹OHHcِH¯1HًHcتHچˆé$çےےèH‰اèH‰اèH‰اèéH‰اèI‰ئHچ½ےےےèéسH‰اèéuH‰اèI‰ئHچ½ےےےèé±H‰اèH‰اèI‰ئƒ½0‏ےے…ڑé،I‰ئHچ½ےےےèéxH‰اèH‰اèI‰ئƒ½0‏ےے…aéhI‰ئHچ½ےےےèé?H‰اèI‰ئƒ½0‏ےے…0é7I‰ئH‹…8ےےےH…ہt¹ےےےےًءHƒùuHچ½ےےےèHا…8ےےےWہ)… ےےے)…ےےےƒ½ےےے~H‹…@ےےے1ةاˆHےءHc•ےےےH9ر|êH‹½HےےےHچ…PےےےH9ا„–èéŒH‰اèI‰ئƒ½0‏ےے…„é‹H‰اèH‰اèH‰اèH‰اèH‰اèH‰اèI‰ئH‹…°‏ےےH…ہt¹ےےےےًءHƒùuHچ½x‏ےےèHا…°‏ےےHا… ‏ےےHا…ک‏ےےHا…گ‏ےےHا…ˆ‏ےےƒ½|‏ےے~H‹…¸‏ےے1ةاˆHےءHc•|‏ےےH9ر|êH‹½ہ‏ےےL9ï„lèébH‰اèé²I‰ئH‹…°‏ےےH…ہt¹ےےےےًءHƒùuHچ½x‏ےےèHا…°‏ےےHا… ‏ےےHا…ک‏ےےHا…گ‏ےےHا…ˆ‏ےےƒ½|‏ےے~H‹…¸‏ےے1ةاˆHےءHc•|‏ےےH9ر|êH‹½ہ‏ےےHچ…ب‏ےےH9ا„µèé«H‰اèI‰ئH‹E¨H…ہ…œé²I‰ئH‹…8ےےےH…ہ…éI‰ئH‹E¨H…ہt¹ےےےےًءHƒùuHچ½pےےےèHاE¨HاEکHاEگHاEˆHاE€ƒ½tےےے~H‹E°1ةاˆHےءHc•tےےےH9ر|êH‹}¸HچEہH9ا„+èé!H‰اèéأé¾é¹é´I‰ئH‹…8ےےےH…ہt¹ےےےےًءHƒùuHچ½ےےےèHا…8ےےےHا…(ےےےHا… ےےےHا…ےےےHا…ےےےƒ½ےےے~H‹…@ےےے1ةاˆHےءHc•ےےےH9ر|êH‹½HےےےHچ…PےےےH9ا„eèé[H‰اèëI‰ئL‹½@‏ےےM…ے…NéْI‰ئL‹½à‏ےےM…ے…÷ë+I‰ئƒ½0‏ےےu(ë2ëI‰ئH‹½ےےےH…ےtH‰½ےےےèƒ½0‏ےےtHچ½(‏ےےèL‰÷èH‰اèI‰ئH‹…°‏ےےH…ہt¹ےےےےًءHƒùuHچ½x‏ےےèHا…°‏ےےHا… ‏ےےHا…ک‏ےےHا…گ‏ےےHا…ˆ‏ےےƒ½|‏ےے~H‹…¸‏ےے1ةاˆHےءHc•|‏ےےH9ر|êH‹½ہ‏ےےL9ïtèH‹E¨H…ہt¹ےےےےًءHƒùuHچ½pےےےèHاE¨HاEکHاEگHاEˆHاE€ƒ½tےےے~H‹E°1ةاˆHےءHc•tےےےH9ر|êH‹}¸L9çtèH‹…8ےےےH…ہt¹ےےےےًءHƒùuHچ½ےےےèHا…8ےےےHا…(ےےےHا… ےےےHا…ےےےHا…ےےےƒ½ےےے~H‹…@ےےے1ةاˆHےءHc•ےےےH9ر|êH‹½HےےےHچ…PےےےH9اt"èëH‰اèH‰اèH‰اèI‰ئL‹½@‏ےےM…ے„±H‹‌H‏ےےL9û„’Lچc H‹CطH…ہt¹ےےےےًءHƒùuL‰çèHاCطHاCبHاCہHاC¸HاC°ƒ{¤~H‹Cà1ةاˆHےءHcS¤H9ر|يH‹{èHƒأًH9كtèM9üL‰م…wےےےH‹½@‏ےےëL‰ےL‰½H‏ےےèL‹½à‏ےےM…ے„6‎ےےH‹‌è‏ےےL9û„œLچc H‹CطH…ہt¹ےےےےًءHƒùuL‰çèHاCطHاCبHاCہHاC¸HاC°ƒ{¤~H‹Cà1ةاˆHےءHcS¤H9ر|يH‹{èHƒأًH9كtèM9üL‰م…wےےےH‹½à‏ےےL‰½è‏ےےé…üےےL‰ےëïH‰اèH‰اèH‰اèH‰اèf.„UH‰هAWAVATSHƒىI‰خI‰×I‰ôH‰ûHچ5Hچ}ذèHƒ}ذtH‹C H…ہHچ5²kHEًHچ=èH‰كL‰وL‰ْL‰ٌèƒ}طt Hچ}ذèHƒؤ[A\A^A_]أH‰اèH‰أƒ}طt Hچ}ذèH‰كèH‰اèf„UH‰هAWAVAUATSHپىطM‰اH‰چےےےH‰َH‰½ےےےH‹H‹H‰EذL‹+H‹CL)èHƒّHtoWہ)…@ےےےLچµ@ےےے¾L‰÷èH¹ize() ==H‰HH¹inputs.sH‰fا@ 3HچوnHچ =l؟)ےےےA¸^L‰ِèHچ½@ےےےèL‹+Wہ)…@ےےےHا…Pےےے)…`ےےےHا…pےےے)… ےےےHا…0ےےےHچ½@ےےے¾L‰êèH‹…ےےےH‹xPH‹Hچµ@ےےےHچچ`ےےےLچ… ےےے؛ےگ¨H‹…hےےےH+…`ےےےHƒّtiWہ)E€Hچ}€¾èH¹e() == 1H‰HH¹puts.sizH‰HH¹layerOutH‰HچémHچ @kHچu€؟)ےےےA¸hèHچ}€èH‹… ےےےH;…(ےےےtiWہ)E€Hچ}€¾èH¹.empty()H‰HH¹ernals.eH‰HH¹layerIntH‰HچpmHچ اjHچu€؟)ےےےA¸ièHچ}€èL‹µ`ےےےI‹_I;_L‰½ےےے„€HاCHاCHاI‹~I+>I‰üIءütKH¸ےےےےےےے?I9ؤ‡LèI‰اL‰{L‰;Kچ§H‰CI‹6M‹vI)ِM…ِ~L‰ےL‰ٍèM÷L‰{L‹½ےےےIƒGëL‰ےL‰ِèI‹EI+EHƒّtaWہ)E€Hچ}€¾èH¹ize() ==H‰HH¹scores.sH‰fا@ 4HچZlHچ ±iHچu€؟)ےےےA¸mèHچ}€èWہ)E€HاEگI‹uI‹}H)÷H‰ûHءût\H¸ےےےےےےے?H9أ‡MèI‰ئL‰uˆL‰u€Iچ‍H‰EگI‹uI‹]H)َH…غ~L‰÷H‰عèL‰ًHطH‰EˆI‹uِFuëoE1ِِFtfWہ)E Hچ} ¾èH¹1] & 1) H‰HH¹(scores[H‰ا@== 0HچpkHچ اhHچu ؟)ےےےA¸oèHچ} èL‹u€A‹F‰ءءéءرùA‰NHچ½@ےےےHچU€¾èH‹…ےےےH‹¸€H‹Hچµ@ےےےHچچ`ےےےLچ… ےےے؛ےگ¨H‹…hےےےH+…`ےےےHƒّtiWہ)E Hچ} ¾èH¹e() == 1H‰HH¹puts.sizH‰HH¹layerOutH‰HچکjHچ ïgHچu ؟)ےےےA¸sèHچ} èH‹… ےےےH;…(ےےےtiWہ)E Hچ} ¾èH¹.empty()H‰HH¹ernals.eH‰HH¹layerIntH‰HچjHچ vgHچu ؟)ےےےA¸tèHچ} èL‹µ`ےےےI‹_I;_„€HاCHاCHاI‹~I+>I‰üIءütKH¸ےےےےےےے?I9ؤ‡ èI‰اL‰{L‰;Kچ§H‰CI‹6M‹vI)ِM…ِ~L‰ےL‰ٍèM÷L‰{L‹½ےےےIƒGëL‰ےL‰ِèIƒإHچ½@ےےے¾L‰êèH‹…ےےےH‹xpH‹Hچµ@ےےےHچچ`ےےےLچ… ےےے؛ےگ¨H‹…hےےےH+…`ےےےHƒّtiWہ)E Hچ} ¾èH¹e() == 1H‰HH¹puts.sizH‰HH¹layerOutH‰Hچ¼hHچ fHچu ؟)ےےےA¸zèHچ} èH‹… ےےےH;…(ےےےtiWہ)E Hچ} ¾èH¹.empty()H‰HH¹ernals.eH‰HH¹layerIntH‰HچChHچ ڑeHچu ؟)ےےےA¸{èHچ} èL‹µ`ےےےI‹_I;_„€HاCHاCHاI‹~I+>I‰üIءütKH¸ےےےےےےے?I9ؤ‡6èI‰اL‰{L‰;Kچ§H‰CI‹6M‹vI)ِM…ِ~L‰ےL‰ٍèM÷L‰{H‹…ےےےHƒ@ëL‰ےL‰ِèH‹…ےےے‹€ˆ‰EہH¸ےےےےH‰EؤاEجےےےےWہ)E HاE°HچUذHچ} HچuہèH‹E 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‎ےے…IéPH‰أH‹…ک‏ےےH…ہ…[éqëëëH‰أH‹…(üےےH…ہ…فéَH‰اèH‰اèH‰أH‹E€H…ہLچےےےt¹ےےےےًءHƒùuHچ½HےےےèHاE€Hچ…XےےےHا@Hا@Hا@Hاƒ½Lےےے~H‹Eˆ1ةاˆHےءHc•LےےےH9ر|êH‹}گHچEکH9ا„(èéH‰اèH‰أH‹E€H…ہLچےےےt¹ےےےےًءHƒùuHچ½HےےےèHاE€Hچ…XےےےHا@Hا@Hا@Hاƒ½Lےےے~H‹Eˆ1ةاˆHےءHc•LےےےH9ر|êH‹}گHچEکH9ا„„èézH‰اèécé^éYéTH‰اèéGH‰اèH‰اèH‰اèH‰اèH‰اèH‰اèëëH‰أH‹E€H…ہLچےےےt¹ےےےےًءHƒùuHچ½HےےےèHاE€Hچ…XےےےHا@Hا@Hا@Hاƒ½Lےےے~"H‹Eˆ1ةfDاˆHےءHc•LےےےH9ر|êH‹}گHچEکH9ا„…èé{H‰اèédH‰أH‹E€H…ہLچےےےt¹ےےےےًءHƒùuHچ½HےےےèHاE€Hچ…XےےےHا@Hا@Hا@Hاƒ½Lےےے~#H‹Eˆ1ة€اˆHےءHc•LےےےH9ر|êH‹}گHچEکH9ا„صèéثH‰اèé´é¯éھé¥H‰أLچےےےH‹…گüےےH…ہ…CéYH‰أLچےےےH‹…`‎ےےH…ہ…ؤéعH‰أLچےےےH‹…hûےےH…ہ…Eé[ëëëë@H‰أLچےےےH‹…pْےےH…ہ…¾éشH‰أLچےےےH‹…(‏ےےH…ہ…?éUëH‰أLچےےےH‹…ًüےےH…ہt¹ےےےےًءHƒùuHچ½¸üےےèHا…ًüےےHچ…بüےےHا@Hا@Hا@Hاƒ½¼üےے~)H‹…ّüےے1ةf.„اˆHےءHc•¼üےےH9ر|êH‹½‎ےےHچ…‎ےےH9اtèH‹…گüےےH…ہt¹ےےےےًءHƒùuHچ½XüےےèHا…گüےےHچ…hüےےHا@Hا@Hا@Hاƒ½\üےے~'H‹…کüےے1ة„اˆHےءHc•\üےےH9ر|êH‹½ üےےHچ…¨üےےH9اtèH‹…`‎ےےH…ہt¹ےےےےًءHƒùuHچ½(‎ےےèHا…`‎ےےHچ…8‎ےےHا@Hا@Hا@Hاƒ½,‎ےے~'H‹…h‎ےے1ة„اˆHےءHc•,‎ےےH9ر|êH‹½p‎ےےHچ…x‎ےےH9اtèH‹…hûےےH…ہt¹ےےےےًءHƒùuHچ½0ûےےèHا…hûےےHچ…@ûےےHا@Hا@Hا@Hاƒ½4ûےے~'H‹…pûےے1ة„اˆHےءHc•4ûےےH9ر|êH‹½xûےےHچ…€ûےےH9اtèH‹…pْےےH…ہt¹ےےےےًءHƒùuHچ½8ْےےèHا…pْےےHچ…HْےےHا@Hا@Hا@Hاƒ½<ْےے~'H‹…xْےے1ة„اˆHےءHc•<ْےےH9ر|êH‹½€ْےےHچ…ˆْےےH9اtèH‹…(‏ےےH…ہt¹ےےےےًءHƒùuHچ½ً‎ےےèHا…(‏ےےHا…‏ےےHا…‏ےےHا…‏ےےHا…‏ےےƒ½ô‎ےے~!H‹…0‏ےے1ةfگاˆHےءHc•ô‎ےےH9ر|êH‹½8‏ےےHچ…@‏ےےH9اtèH‹…ûےےH…ہt¹ےےےےًءHƒùuHچ½ذْےےèHا…ûےےHا…ّْےےHا…ًْےےHا…èْےےHا…àْےےƒ½شْےے~!H‹…ûےے1ةfگاˆHےءHc•شْےےH9ر|êH‹½ûےےHچ… ûےےH9اtèH‹…ک‏ےےH…ہt¹ےےےےًءHƒùuHچ½`‏ےےèHا…ک‏ےےHا…ˆ‏ےےHا…€‏ےےHا…x‏ےےHا…p‏ےےƒ½d‏ےے~!H‹… ‏ےے1ةfگاˆHےءHc•d‏ےےH9ر|êH‹½¨‏ےےHچ…°‏ےےH9اtèH‹…(üےےH…ہt¹ےےےےًءHƒùuHچ½ًûےےèHا…(üےےfïہHچ…üےےf@fƒ½ôûےے~)H‹…0üےے1ةf.„اˆHےءHc•ôûےےH9ر|êH‹½8üےےHچ…@üےےH9اtèH‹…بûےےH…ہt¹ےےےےًءHƒùuHچ½گûےےèHا…بûےےfïہHچ… ûےےf@fƒ½”ûےے~)H‹…ذûےے1ةf.„اˆHےءHc•”ûےےH9ر|êH‹½طûےےHچ…àûےےH9اtèH‹…ب‎ےےH…ہt¹ےےےےًءHƒùuHچ½گ‎ےےèHا…ب‎ےےfïہHچ… ‎ےےf@fƒ½”‎ےے~)H‹…ذ‎ےے1ةf.„اˆHےءHc•”‎ےےH9ر|êH‹½ط‎ےےHچ…à‎ےےH9اtèH‹…ّ‏ےےH…ہt¹ےےےےًءHƒùuHچ½ہ‏ےےèHا…ّ‏ےےfïہHچ…ذ‏ےےf@fƒ½ؤ‏ےے~)H‹…ےےے1ةf.„اˆHےءHc•ؤ‏ےےH9ر|êH‹½ےےےL9ïtèƒ½ ‎ےےtHچ½‎ےےèH‰كèH‰اèH‰اèH‰اèH‰اèH‰اèH‰اèH‰اèH‰اèH‰اèH‰اèH‰اèH‰اèH‰اè@UH‰هAWAVATSHƒىI‰خI‰×I‰ôH‰ûHچ5Hچ}ذèHƒ}ذtH‹C H…ہHچ5|ثHEًHچ=èH‰كL‰وL‰ْL‰ٌèƒ}طt Hچ}ذèHƒؤ[A\A^A_]أH‰اèH‰أƒ}طt Hچ}ذèH‰كèH‰اèf„UH‰هAWAVAUATSHپى¨L‰…0ےےےI‰دI‰ôH‰ûH‹H‹H‰EذH‹CH+C€»چt H=@u ëgH= t_Wہ)…pےےےLچµpےےے¾EL‰÷èHچ5چث؛EH‰اèHچƒخHچ ¥ب؟)ےےےA¸±L‰ِèHچ½pےےےèM‹,$I‹D$L)èHƒّL‰½8ےےےtpWہ)…pےےےLچµpےےے¾L‰÷èH¹ize() ==H‰HH¹inputs.sH‰fا@ 1Hچ‏حHچ ب؟)ےےےA¸²L‰ِèHچ½pےےےèM‹,$H‹CH‹H@‹I‰MگH‹€ D‹xWہ)…pےےےHاE€L‹sPH‰‌@ےےےH‹[XL)َI‰ـIءü„ D‰½lےےےL‰àHءè>…ڈ H‰كèI‰اHچ…xےےےH‰EکL‰½xےےےL‰½pےےےKچ§H‰E€H…غژàL‰ےL‰ِH‰عèIكL‰½xےےےWہ)…PےےےHا…`ےےےH…غ„ Hچ½pےےے¾ےےےےLچ¥xےےےèD‹½lےےے;Eگ„؛Wہ)E Hچ} ¾èH¹ _numOutH‰HH¹pe_) == H‰HH¹tTailShaH‰HH¹total(ouH‰HچگجHچ ²ئHچu ؟)ےےےA¸»èHچ} èëALچ¥xےےےëLچ¥xےےےD‹½lےےےWہ)…PےےےHا…`ےےےHچ½pےےےHچUگ¾èI‹EI+EHءّH‹چ@ےےے€¹€„Hƒّr¾L‰ïèD9ّ„“Wہ)E Hچ} ¾-èH¹ _numInpH‰H%H¹2) == _nH‰H H¹l(inp0, H‰HH¹ && totaH‰HH¹e() >= 2H‰HH¹inp0.sizH‰Hچ~ثHچ إHچu ؟)ےےےA¸آèHچ} èI‹M‹‰…Lےےے‹I‰M”Lچ½XےےےH‹چXےےےH;چ`ےےے„‰HƒءH‰چXےےےH‹µXےےےH;µ`ےےے…éHƒّr¾L‰ïèD9ّ„“Wہ)E Hچ} ¾-èH¹ _numInpH‰H%H¹1) == _nH‰H H¹l(inp0, H‰HH¹ && totaH‰HH¹e() >= 2H‰HH¹inp0.sizH‰Hچ|تHچ ‍ؤHچu ؟)ےےےA¸ةèHچ} èا…LےےےI‹E‹‰E”Lچ½XےےےH‹µXےےےH;µ`ےےےu%ë5Hچ½PےےےHچµLےےےèH‹µXےےےH;µ`ےےےt‹E”‰HƒئH‰µXےےےëHچ½PےےےHچu”èH‹µXےےےH‹•pےےےH‹چxےےےHچ½PےےےèH‹…@ےےے€¸پ¾HƒقHچ•PےےےH‹½8ےےےè‹E”‹Mگ‰Eہ‰MؤHاEبےےےےWہ)E HاE°LچuذHچ} HچuہL‰ٍèL‹0ےےےH‹E H‹M¨H9بtƒ8xHƒہH9ءuٍH‰بH9ء…—é–H9ب„„HچPH9تt{HچqّH)ئ‰÷ءïےاHƒçt H÷ك„‹…غx‰HƒہHƒآHےاuëHƒ‏rAD‹2…ِx‰0Hƒہ‹r…ِx‰0Hƒہ‹r…ِx‰0Hƒہ‹r…ِx‰0HƒہHƒآH9تuؤH9ءtH‰E¨HچU ¾L‰ïèH‹} H…ےt H‰}¨è‹E”‹Mگ‰Eہ‰MؤHاEبےےےےWہ)E HاE°Hچ} HچuہL‰ٍèH‹E H‹M¨H9بt'„ƒ8xHƒہH9ءuٍH‰بH9ء…—é–H9ب„„HچPH9تt{HچqّH)ئ‰÷ءïےاHƒçt H÷ك„‹…غx‰HƒہHƒآHےاuëHƒ‏rAD‹2…ِx‰0Hƒہ‹r…ِx‰0Hƒہ‹r…ِx‰0Hƒہ‹r…ِx‰0HƒہHƒآH9تuؤH9ءtH‰E¨I‹EI;Es= 0 H‰H H¹1) || (iH‰HH¹ze() == H‰HH¹-1 && siH‰HH¹(idx == H‰Hچ=©Hچ :©؟)ےےےA¸XL‰‏èHچ}ذè1ہƒûےEأA‹ƒùt…ة…§I‹NH‹ HکH‹ءéI‹NH‹ HکٍءٍEبHچ}ہèWةf.ءٍEبu{bWہ)EذHچ]ذ¾H‰كèH¹t == 0.0H‰HH¹fracpartH‰Hچٹ¨Hچ ‡¨؟)ےےےA¸eH‰قèHچ}ذèٍEبٍH,ہëqƒةƒùtgWہ)EذHچ]ذ¾H‰كèH¹|| isReaH‰HH¹isInt() H‰ئ@)fا@l(Hچ¨Hچ ¨؟)ےےےA¸kH‰قèHچ}ذè1ہHƒؤ([A^A_]أH‰اèH‰أHچ}ذèëvH‰اèH‰اèH‰أHچ}ذèëXH‰اèH‰اèH‰أHچ}ذèë:H‰اèH‰اèH‰أHچ}ذèëH‰اèH‰اèH‰أHچ}ذèH‰كèH‰اèffff.„UH‰هAWAVSHƒى‰َI‰‏ƒûےt-…غˆ؛A‹ƒّt ƒّt…ہu7I‹F‹@9طژڑé8A‹ƒّt ƒّt…ہuRI‹FHƒہƒ8uwéWہ)EذHچi§Hچ ¾¦Hچuذ؟‎ےےےA¸èHچ}ذè¸ےےےے9ط~7éصWہ)EذHچ)§Hچ ~¦Hچuذ؟‎ےےےA¸èHچ}ذèWہ)EذLچ}ذ¾8L‰ےèH¹ size())H‰H0H¹&& idx = 0 H‰H H¹1) || (iH‰HH¹ze() == H‰HH¹-1 && siH‰HH¹(idx == H‰Hچف¥Hچ ع¥؟)ےےےA¸ٹL‰‏èHچ}ذè1ہƒûےEأA‹…ةtƒùu'I‹NH‹ Hکٍءé‰I‹NH‹ HکWہٍH*ءëuƒةWہƒùtjWہ)EذHچ]ذ¾H‰كèH¹ || isInH‰HH¹isReal()H‰ئ@)fا@t(Hچ0¥Hچ -¥؟)ےےےA¸—H‰قèHچ}ذèWہHƒؤ[A^A_]أH‰اèH‰أHچ}ذèëXH‰اèH‰اèH‰أHچ}ذèë:H‰اèH‰اèH‰أHچ}ذèëH‰اèH‰اèH‰أHچ}ذèH‰كèH‰اèff.„UH‰هAWAVAUATSHƒىA‰ُI‰‏1ہAƒ‎ےDDèI‹M‹fL‰مH)ثI)ج„إHءëA9ف~XfïہfEہLچ}ہ¾FL‰ےèHچ5³¥؛FH‰اèHچو¥Hچ ه¥؟)ےےےA¸ L‰‏èHچ}ہè¸A9فچVM‹McصIءىIcجH‰دL)×¸Hƒے‚I‰ùIƒلّIچQّH‰ضHءîچFƒàHƒْsfoB،1ِfoبH…ہ…‰é؛Kچ\گpHچPےH)ٍfo،1ِfoب@َoT³گf8@ذَoD³ f8@ءَoL³°f8@تَoT³ہf8@ذَo\³ذf8@ظَod³àf8@âَoD³ًf8@أَo³f8@جHƒئ 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endtotal/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/shape_utils.hppallocator::allocate(size_t n) 'n' exceeds maximum supported sizevirtual void cv::dnn::LSTMLayerImpl::forward(std::vector &, std::vector &, std::vector &)nameFunction supports only floating point typestanhvirtual void cv::dnn::LSTMLayerImpl::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays)getMemoryShapesinputs.size() == 1total(outTailShape_) == _numOutinp0.size() >= 2 && total(inp0, 2) == _numInpinp0.size() >= 2 && total(inp0, 1) == _numInpsetWeights!allocated || total(outTailShape) == total(outTailShape_)setOutShape!allocatedsetUseTimstampsDimsetProduceCellOutputRNNinput.size() >= 1 && input.size() <= 2inp0.dims >= 2inp0.total(2) == numXinp0.type() == dtypevirtual void cv::dnn::RNNLayerImpl::forward(std::vector &, std::vector &, std::vector &)virtual void cv::dnn::RNNLayerImpl::forward(cv::InputArrayOfArrays, 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إ1@bias_term(idx == -1 && size() == 1) || (idx >= 0 && idx < size())get/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/dnn.inl.hppfracpart == 0.0isInt() || isReal()sizevirtual void cv::dnn::ScaleLayerImpl::forward(std::vector &, std::vector &, std::vector &)/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/src/layers/scale_layer.cppnameinpBlob.size[1] == blobs[0].total()forwardinpBlob.size[1] == blobs[1].total()inpBlob.type() == 5 && outBlob.type() == 5m.dims >= 2slice/Users/build/buildAgent/work/b5d3ba53e49a4b81/build-victor-deps/build-opencv/opencv/modules/dnn/include/opencv2/dnn/shape_utils.hppvirtual void cv::dnn::ScaleLayerImpl::forward(cv::InputArrayOfArrays, cv::OutputArrayOfArrays, cv::OutputArrayOfArrays)blobs.size() == 1 + hasBiasgetMemoryShapesstart <= (int)shape.size() && end <= (int)shape.size() && start <= 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