{"id":347306,"date":"2023-03-26T21:00:53","date_gmt":"2023-03-26T21:00:53","guid":{"rendered":"http:\/\/savepearlharbor.com\/?p=347306"},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-29T21:00:00","slug":"","status":"publish","type":"post","link":"https:\/\/savepearlharbor.com\/?p=347306","title":{"rendered":"\u041b\u043e\u043c\u0430\u0435\u043c \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u0443\u044e \u043a\u0430\u043f\u0447\u0443 \u043d\u0430 \u043f\u0440\u0438\u043c\u0435\u0440\u0435 VK \u0438\u043b\u0438 \u0431\u0440\u0443\u0442\u0444\u043e\u0440\u0441\u0438\u043d\u0433 \u0434\u043e \u0441\u0438\u0445 \u043f\u043e\u0440 \u0430\u043a\u0442\u0443\u0430\u043b\u0435\u043d<\/span>"},"content":{"rendered":"
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\u0412 \u0434\u0430\u043d\u043d\u043e\u0439 \u0441\u0442\u0430\u0442\u044c\u0435-\u043c\u0430\u043d\u0443\u0430\u043b\u0435 \u044f \u043f\u043e\u043a\u0430\u0436\u0443, \u043a\u0430\u043a \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0441\u043e\u0431\u0441\u0442\u0432\u0435\u043d\u043d\u0443\u044e \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u044c \u043f\u043e \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u043d\u0438\u044e \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u044b\u0445 \u043a\u0430\u043f\u0447, \u0438\u043c\u0435\u044f \u043f\u043e\u0434 \u0440\u0443\u043a\u043e\u0439 \u0434\u043e\u043c\u0430\u0448\u043d\u0438\u0439 \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440<\/strong>, \u0431\u0430\u0437\u043e\u0432\u044b\u0435 \u0437\u043d\u0430\u043d\u0438\u044f \u0432 python<\/strong> \u0438 \u043d\u0435<\/s>\u043c\u043d\u043e\u0436\u043a\u043e<\/strong> \u043f\u0440\u0438\u043c\u0435\u0440\u043e\u0432 \u043a\u0430\u043f\u0447.<\/p>\n

\"\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442<\/p>\n
\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0440\u0430\u0431\u043e\u0442\u044b \u043e\u0431\u0443\u0447\u0435\u043d\u043d\u043e\u0439 \u0441\u0435\u0442\u0438<\/figcaption><\/div>\n<\/figure>\n

\u0414\u0438\u0441\u043a\u043b\u0435\u0439\u043c\u0435\u0440:<\/h2>\n
\n

\u0410\u0432\u0442\u043e\u0440 \u0434\u0430\u043d\u043d\u043e\u0439 \u0441\u0442\u0430\u0442\u044c\u0438 \u043d\u0438 \u043a\u043e\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u043d\u0435 \u043f\u0440\u0438\u0437\u044b\u0432\u0430\u0435\u0442 \u0432\u0430\u0441 \u0441\u043e\u0432\u0435\u0440\u0448\u0430\u0442\u044c \u043a\u0430\u043a\u0438\u0435-\u043b\u0438\u0431\u043e \u043d\u0435\u0437\u0430\u043a\u043e\u043d\u043d\u044b\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u044b \u0438\u0437 \u0434\u0430\u043d\u043d\u043e\u0439 \u0441\u0442\u0430\u0442\u044c\u0438 (\u0434\u0430 \u0438 \u0441 \u0434\u0440\u0443\u0433\u0438\u043c\u0438 \u0442\u043e\u0436\u0435 \u043d\u0435 \u0441\u0442\u043e\u0438\u0442) \u0438 \u043d\u0435 \u043d\u0435\u0441\u0435\u0442 \u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0441\u0442\u0438 \u0437\u0430 \u0432\u0440\u0435\u0434, \u043f\u0440\u0438\u0447\u0435\u043d\u0435\u043d\u043d\u044b\u0439 \u0435\u044e.<\/p>\n

\u0426\u0435\u043b\u044c \u0434\u0430\u043d\u043d\u043e\u0439 \u0441\u0442\u0430\u0442\u044c\u0438 \u2014 \u043d\u0430\u0433\u043b\u044f\u0434\u043d\u043e \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u044c \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0451\u043d\u043d\u0443\u044e \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u0443 \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u044b\u0445 \u043a\u0430\u043f\u0447.<\/p>\n<\/blockquote>\n

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\u0411\u044b\u043b\u043e \u0432\u0440\u0435\u043c\u044f, \u043a\u043e\u0433\u0434\u0430 \u044f \u043f\u0438\u0441\u0430\u043b \u043e\u0434\u0438\u043d \u0441\u043a\u0440\u0438\u043f\u0442 \u043f\u043e \u0441\u043f\u0430\u043c\u0443<\/s> (\u0430\u043b\u044c\u0442\u0435\u0440\u043d\u0430\u0442\u0438\u0432\u043d\u043e\u043c\u0443 \u043c\u0435\u0442\u043e\u0434\u0443 \u043f\u0440\u043e\u0434\u0432\u0438\u0436\u0435\u043d\u0438\u044f \u2014 \u0440\u0430\u0441\u0441\u044b\u043b\u043a\u0435<\/em>) \u0432\u043a. \u0421\u043a\u0440\u0438\u043f\u0442 \u0434\u043e\u043b\u0436\u0435\u043d \u0431\u044b\u043b \u0443\u043c\u0435\u0442\u044c \u043e\u0431\u0445\u043e\u0434\u0438\u0442\u044c \u043a\u0430\u043f\u0447\u0443 \u0447\u0435\u0440\u0435\u0437 \u0441\u0442\u043e\u0440\u043e\u043d\u0438\u0438\u0435 \u0441\u0435\u0440\u0432\u0438\u0441\u044b (\u0440\u0435\u0448\u0430\u044e\u0442 1000<\/strong> \u043a\u0430\u043f\u0447 \u0437\u0430 10<\/strong>—20<\/strong> \u0440\u0443\u0431). \u0418 \u044f \u043f\u043e\u0434\u0443\u043c\u0430\u043b, \u043f\u043e\u0447\u0435\u043c\u0443 \u0431\u044b \u043d\u0435 \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u0442\u044c \u0443\u0436\u0435 \u0440\u0435\u0448\u0435\u043d\u043d\u044b\u0435 \u043a\u0430\u043f\u0447\u0438 \u0443 \u0441\u0435\u0431\u044f \u043d\u0430 \u0441\u0435\u0440\u0432\u0435\u0440\u0435? \u0422\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u044f \u0431\u0435\u0441\u043f\u043b\u0430\u0442\u043d\u043e \u043d\u0430\u043a\u043e\u043f\u0438\u043b 150\u043a<\/strong> \u043f\u0440\u0438\u043c\u0435\u0440\u043e\u0432 \u0440\u0435\u0448\u0435\u043d\u043d\u044b\u0445 \u043a\u0430\u043f\u0447 (\u043f\u0440\u0430\u0432\u0434\u0430 5-10% \u0438\u0437 \u043d\u0438\u0445 \u0431\u044b\u043b\u0438 \u0440\u0435\u0448\u0435\u043d\u044b \u043d\u0435\u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e, \u043d\u043e \u0431\u041e\u043b\u044c\u0448\u0430\u044f \u0447\u0430\u0441\u0442\u044c \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430 \u0432\u0441\u0451-\u0442\u0430\u043a\u0438 \u0432\u0430\u043b\u0438\u0434\u043d\u0430).<\/p>\n

\u0421\u0440\u0430\u0437\u0443 \u0441\u043a\u0430\u0436\u0443, \u0447\u0442\u043e \u044f \u2014 \u0447\u0435\u043b\u043e\u0432\u0435\u043a \u043d\u0435 \u0441\u0438\u043b\u044c\u043d\u043e \u0441\u043c\u044b\u0441\u043b\u044f\u0449\u0438\u0439 \u0432 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u043f\u043e\u0434\u043f\u0440\u0430\u0432\u044c\u0442\u0435 \u043c\u0435\u043d\u044f \u0432 \u043a\u043e\u043c\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u044f\u0445, \u0435\u0441\u043b\u0438 \u0447\u0442\u043e)<\/p>\n

\u0412\u044b\u0431\u0438\u0440\u0430\u0435\u043c \u043d\u0435\u0439\u0440\u043e\u0441\u0435\u0442\u044c<\/h2>\n

\u041d\u0435\u0441\u043c\u043e\u0442\u0440\u044f \u043d\u0430 \u0442\u043e, \u0447\u0442\u043e \u043d\u0430 \u0434\u0432\u043e\u0440\u0435 2023<\/strong> \u0433\u043e\u0434, \u043d\u0438\u0447\u0435\u0433\u043e \u0431\u043e\u043b\u0435\u0435-\u043c\u0435\u043d\u0435\u0435 \u0430\u0434\u0435\u043a\u0432\u0430\u0442\u043d\u043e \u0440\u0430\u0431\u043e\u0442\u0430\u044e\u0449\u0435\u0433\u043e \u0441\u0445\u043e\u0434\u0443 \u044f \u043d\u0430\u0439\u0442\u0438 \u043d\u0435 \u0441\u043c\u043e\u0433. \u0411\u043e\u043b\u044c\u0448\u0438\u043d\u0441\u0442\u0432\u043e \u043f\u0440\u0438\u043c\u0435\u0440\u043e\u0432 \u043f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u043b\u0438 \u043f\u0435\u0440\u0435\u0434 \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0435\u043c \u0443\u0431\u0440\u0430\u0442\u044c \u0448\u0443\u043c\u044b<\/strong>, \u0440\u0430\u0437\u0434\u0435\u043b\u0438\u0442\u044c \u043a\u0430\u043f\u0447\u0443 \u041f\u041e\u0411\u0423\u041a\u0412\u0415\u041d\u041d\u041e<\/strong> \u0438 \u043f\u0435\u0440\u0435\u0432\u0435\u0441\u0442\u0438 \u0435\u0451 \u0432 \u0447\u0435\u0440\u043d\u043e-\u0431\u0435\u043b\u044b\u0439<\/strong> \u0444\u043e\u0440\u043c\u0430\u0442 (\u0442\u0438\u043f\u043e \u0434\u043b\u044f \u043e\u0431\u043b\u0435\u0433\u0447\u0435\u043d\u0438\u044f \u0440\u0430\u0431\u043e\u0442\u044b).<\/p>\n

\u041d\u043e \u0432 \u043c\u043e\u0451\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u0431\u044b\u043b\u0438 \u043d\u0435\u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b:<\/p>\n

    \n
  1. \n

    \u041a\u0430\u043a \u0443\u0431\u0440\u0430\u0442\u044c \u0448\u0443\u043c\u044b, \u0435\u0441\u043b\u0438 \u043f\u0440\u0438 \u043f\u0435\u0440\u0435\u0432\u043e\u0434\u0435 \u0432 black and white \u043b\u0438\u043d\u0438\u0438 \u0441\u043b\u0438\u0432\u0430\u044e\u0442\u0441\u044f \u0441 \u0431\u0443\u043a\u0432\u0430\u043c<\/p>\n<\/li>\n

  2. \n

    \u041a\u0430\u043a \u0440\u0430\u0437\u0434\u0435\u043b\u0438\u0442\u044c \u043a\u0430\u043f\u0447\u0443 \u043f\u043e\u0431\u0443\u043a\u0432\u0435\u043d\u043d\u043e, \u0435\u0441\u043b\u0438 \u0437\u0430\u0440\u0430\u043d\u0435\u0435 \u043d\u0435\u0438\u0437\u0432\u0435\u0441\u0442\u043d\u043e, \u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0431\u0443\u043a\u0432 \u043e\u043d\u0430 \u0432 \u0441\u0435\u0431\u0435 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442<\/p>\n<\/li>\n

  3. \n

    \u0412 \u0442\u0435\u043e\u0440\u0438\u0438 \u043c\u043e\u0436\u043d\u043e \u0437\u0430\u043c\u043e\u0440\u043e\u0447\u0438\u0442\u044c\u0441\u044f \u0441 1 \u0438 2 \u043f\u0443\u043d\u043a\u0442\u043e\u043c, \u043d\u043e \u0442\u0435\u0440\u044f\u0442\u044c \u043d\u0435\u0434\u0435\u043b\u044e \u0441\u0432\u043e\u0431\u043e\u0434\u043d\u043e\u0433\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u0440\u0430\u0434\u0438 \u0442\u0430\u043a\u043e\u0433\u043e \u0437\u0430\u043d\u044f\u0442\u0438\u044f \u044f \u0431\u044b\u043b \u043d\u0435 \u0433\u043e\u0442\u043e\u0432, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u043f\u043e\u0448\u0451\u043b \u0438\u0441\u043a\u0430\u0442\u044c \u0434\u0440\u0443\u0433\u0438\u0435 \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u044b.<\/p>\n<\/li>\n<\/ol>\n

    \"RGB<\/p>\n
    RGB \u0432\u0430\u0440\u0438\u0430\u043d\u0442 \u2014 \u0443 \u0431\u0443\u043a\u0432 \u0438 \u043b\u0438\u043d\u0438\u0439 \u0440\u0430\u0437\u043d\u044b\u0439 \u043e\u0442\u0442\u0435\u043d\u043e\u043a<\/figcaption><\/div>\n<\/figure>\n
    \"BLACK<\/p>\n
    BLACK and WHITE \u2014 \u0431\u0443\u043a\u0432\u044b \u0441\u043b\u0438\u0432\u0430\u044e\u0442\u0441\u044f \u0441 \u043b\u0438\u043d\u0438\u044f\u043c\u0438<\/figcaption><\/div>\n<\/figure>\n

    \u0422\u0430\u043a \u044f \u043d\u0430\u0448\u0451\u043b Tensorflow keras OCR model <\/a>\u2014 CNN + RNN \u043c\u043e\u0434\u0435\u043b\u044c, \u0440\u0435\u0430\u043b\u0438\u0437\u0443\u044e\u0449\u0443\u044e \u0444\u0443\u043a\u043d\u0446\u0438\u044e \u043e\u0448\u0438\u0431\u043a\u0438 \u0447\u0435\u0440\u0435\u0437 CTC loss. \u0412 \u043d\u0435\u0439 \u043d\u0435\u043e\u0431\u044f\u0437\u0430\u0442\u0435\u043b\u044c\u043d\u043e \u0432\u043e\u043e\u0431\u0449\u0435 \u0447\u0442\u043e-\u0442\u043e \u0434\u0435\u043b\u0430\u0442\u044c \u0441 \u043a\u0430\u0440\u0442\u0438\u043d\u043a\u0430\u043c\u0438 \u2014 \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e \u043c\u0438\u043d\u0438\u043c\u0430\u043b\u044c\u043d\u043e \u0438\u0445 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c ( transpose(perm=[1, 0, 2])<\/code> ) \u0438 \u0432\u0441\u0451. \u0414\u0430\u043b\u044c\u0448\u0435 \u043c\u043e\u0434\u0435\u043b\u044c \u0440\u0430\u0437\u0431\u0435\u0440\u0451\u0442\u0441\u044f \u0441\u0430\u043c\u0430.<\/p>\n

    \u0414\u043b\u044f \u0432\u043e\u0440\u0447\u0443\u043d\u043e\u0432-\u043f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u043e\u043d\u0430\u043b\u043e\u0432, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0441\u043a\u0430\u0436\u0443\u0442, \u0447\u0442\u043e \u0431\u0435\u0437 \u0433\u0440\u0430\u043c\u043e\u0442\u043d\u043e\u0439 \u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u0434\u0430\u043d\u043d\u044b\u0445 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u0441\u044f \u0445\u0443\u0436\u0435, \u043e\u0442\u0432\u0435\u0447\u0430\u044e: \u0434\u0430, \u043e\u043d \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u0441\u044f \u0445\u0443\u0436\u0435. \u041d\u043e \u043e\u043d \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u0441\u044f. \u0418 \u0437\u0430\u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442. \u0410 \u044d\u0442\u043e \u0433\u043b\u0430\u0432\u043d\u043e\u0435. \u0412 \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u043d\u0438\u0438 \u043a\u0430\u043f\u0447 \u043d\u0435 \u043d\u0443\u0436\u043d\u0430 100% \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c, \u0442\u0430\u043a \u0447\u0442\u043e \u0437\u0430\u0447\u0435\u043c \u0441\u0438\u043b\u044c\u043d\u043e \u043c\u043e\u0440\u043e\u0447\u0438\u0442\u044c\u0441\u044f \u0441 preprocessing, \u0435\u0441\u043b\u0438 \u043c\u043e\u0436\u043d\u043e \u043d\u0435 \u043f\u0430\u0440\u0438\u0442\u044c\u0441\u044f?)<\/span><\/p>\n

    \u041f\u043e\u0434\u0433\u043e\u0442\u0430\u0432\u043b\u0438\u0432\u0430\u0435\u043c \u0434\u0430\u043d\u043d\u044b\u0435<\/h2>\n
    \n\u0423\u0431\u0438\u0440\u0430\u0435\u043c \u043e\u0448\u0438\u0431\u043a\u0438 \u0438\u0437 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0432\u0445\u043e\u0434\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445<\/summary>\n
    \n

    \u0414\u043b\u044f \u043d\u0430\u0447\u0430\u043b\u0430 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u043f\u043e\u043d\u044f\u0442\u044c, \u043a\u0430\u043a\u0438\u0435 \u0432\u043e\u043e\u0431\u0449\u0435 \u0441\u0438\u043c\u0432\u043e\u043b\u044b \u0432\u0441\u0442\u0440\u0435\u0447\u0430\u044e\u0442\u0441\u044f \u0443 \u043d\u0430\u0441 \u0432 \u043a\u0430\u043f\u0447\u0435. \u0414\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u044f \u043d\u0430\u043f\u0438\u0441\u0430\u043b \u043f\u0440\u043e\u0441\u0442\u0435\u043d\u044c\u043a\u0438\u0439 \u0441\u043a\u0440\u0438\u043f\u0442<\/p>\n

    \n\u0421\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0430 \u0447\u0430\u0441\u0442\u043e\u0442\u044b \u0432\u0445\u043e\u0436\u0434\u0435\u043d\u0438\u044f \u0431\u0443\u043a\u0432 \u0432 \u043a\u0430\u043f\u0447\u0443<\/summary>\n
    \n
    import random import string from pathlib import Path  data_dir_test = Path(\"..\/images\/train\")  symbols = {i: 0 for i in string.ascii_lowercase+string.digits} lengths = [0]*100 for i in data_dir_test.glob(\"**\/*\"):     name = i.name.split('-')[0].split('.')[0]     for i in name:         symbols[i] += 1     lengths[len(name)] += 1 ans = [] #print char -> count for i, item in sorted(symbols.items(), key=lambda e:-e[1]):     print(item, i)     if item > 0: ans.append(i) #print length -> count for i in filter(length, lambda e: e != 0):    print(ans) <\/code><\/pre>\n
    Output:<\/p>\n
    67063 z 66807 s 66024 h 64136 q 61743 d 60674 v 25280 2 25208 7 25185 8 25107 x 25001 y 24993 5 24956 e 24784 a 24599 u 24492 4 24185 k 24094 n 22953 m 22318 c 18990 p 30 b 27 f 19 g 18 i 11 j 9 l 6 o 2 r 1 t 0 w 0 0 0 1 0 3 0 6 0 9 _________ LEN[3]=23 LEN[4]=61393 LEN[5]=62670 LEN[6]=14902 LEN[7]=14318 LEN[8]=4  ['z', 's', 'h', 'q', 'd', 'v', '2', '7', '8', 'x', 'y', '5', 'e', 'a', 'u', '4', 'k', 'n', 'm', 'c', 'p']<\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
    \u042d\u0442\u0438 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f \u0441\u0440\u0430\u0437\u0443 \u043f\u043e\u043c\u043e\u0433\u0430\u044e\u0442 «\u043f\u043e\u0447\u0438\u0441\u0442\u0438\u0442\u044c» \u043f\u0440\u0438\u043c\u0435\u0440\u044b: \u0432\u0438\u0434\u043d\u043e, \u0447\u0442\u043e \u0431\u0443\u043a\u0432\u044b b, f, g, i, j, l, o, r, t<\/strong>  \u0437\u0430\u0432\u0435\u0434\u043e\u043c\u043e \u043e\u0448\u0438\u0431\u043e\u0447\u043d\u044b\u0435 \u0438 \u0447\u0442\u043e \u043a\u0430\u043f\u0447, \u0441\u043e\u0441\u0442\u043e\u044f\u0449\u0438\u0445 \u0438\u0437 3 \u0438 8 \u0441\u0438\u043c\u0432\u043e\u043b\u043e\u0432, \u043d\u0435 \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0443\u0435\u0442.<\/p>\n
    \u041e\u0442\u043b\u0438\u0447\u043d\u043e, \u0441 \u0441\u0438\u043c\u0432\u043e\u043b\u0430\u043c\u0438 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u043b\u0438\u0441\u044c<\/p>\n
    max_length = 7 characters = ['z', 's', 'h', 'q', 'd', 'v', '2', '7', '8', 'x',                'y', '5', 'e', 'a', 'u', '4', 'k', 'n', 'm', 'c', 'p']<\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
    \n\u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430<\/summary>\n
    \n
    \u041f\u0435\u0440\u0435\u0434 \u0434\u0430\u043b\u044c\u043d\u0435\u0439\u0448\u0438\u043c\u0438 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f\u043c\u0438 \u0440\u0430\u0437\u043e\u0431\u044c\u0435\u043c \u0434\u0430\u0442\u0430\u0441\u0435\u0442 \u0441 \u043a\u0430\u0440\u0442\u0438\u043d\u043a\u0430\u043c\u0438 \u043d\u0430 2 \u043f\u0430\u043f\u043a\u0438: train<\/strong> \u0438 test<\/strong>. \u041f\u0430\u043f\u043a\u0430 test<\/strong> \u0431\u0443\u0434\u0435\u0442 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u0434\u043b\u044f \u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0433\u043e\u0442\u043e\u0432\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438, train<\/strong> \u2014 \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f (\u0432 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0434\u0430\u0442\u0430\u0441\u0435\u0442 \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u0440\u0430\u0437\u0431\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u043d\u0430 train<\/strong> \u0438 validate<\/strong>). \u042f \u0440\u0430\u0437\u0431\u0438\u043b \u0432 \u043f\u0440\u043e\u043f\u043e\u0440\u0446\u0438\u044f\u0445 1\/15 ( \u0443 \u043c\u0435\u043d\u044f \u043f\u0440\u0438\u043c\u0435\u0440\u043e\u0432 \u043c\u043d\u043e\u0433\u043e, \u043f\u043e\u044d\u0442\u043e\u043c\u0443 10\u043a \u0434\u043b\u044f  \u0442\u0435\u0441\u0442\u0430 \u0431\u0443\u0434\u0435\u0442 \u0434\u043e\u0441\u0442\u0430\u0442\u043e\u0447\u043d\u043e; \u0435\u0441\u043b\u0438 \u0443 \u0412\u0430\u0441 \u0434\u0430\u0442\u0430\u0441\u0435\u0442 \u043c\u0435\u043d\u044c\u0448\u0435, \u0447\u0435\u043c 20\u043a, \u0442\u043e \u0441\u0442\u043e\u0438\u0442  \u0440\u0430\u0437\u0431\u0438\u0432\u0430\u0442\u044c \u0445\u043e\u0442\u044f \u0431\u044b 2\/8 )<\/p>\n
    \u0422\u0435\u043f\u0435\u0440\u044c \u043d\u0430\u0434\u043e \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0443 \u043a\u0430\u0440\u0442\u0438\u043d\u043a\u0438. \u0412 \u043c\u043e\u0434\u0435\u043b\u0438 \u0431\u0443\u0434\u0435\u0442 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c\u0441\u044f RGB float32<\/strong> input:<\/p>\n
    img_width = 130 # \u0448\u0438\u0440\u0438\u043d\u0430 input'a img_height = 50 # \u0432\u044b\u0441\u043e\u0442\u0430 input'a def encode_img(img):     # Preprocessing     #    We will use 3 channels input (in original code were 1 channel)     if img_type == '*.jpeg':         img = tf.io.decode_jpeg(img, channels=3, dct_method='INTEGER_ACCURATE')     #        img = tf.image.rgb_to_grayscale(img)     else:         img = tf.io.decode_png(img, channels=3)      # Convert to float32 in [0, 1] range     img = tf.image.convert_image_dtype(img, tf.float32)     # Resize to the desired size     img = tf.image.resize(img, [img_height, img_width])     # Transpose the image because we want the time     # dimension to correspond to the width of the image.     img = tf.transpose(img, perm=[1, 0, 2])     return img   def encode_single_sample(img_path, label):     # Read image     img = tf.io.read_file(img_path)     # Preprocessing     img = encode_img(img)     # Map the characters in label to numbers     label = char_to_num(tf.strings.unicode_split(label, input_encoding=\"UTF-8\"))     # Return a dict as our model is expecting two inputs     return {\"image\": img, \"label\": label} <\/code><\/pre>\n
    \u0418 \u0437\u0430\u043f\u0438\u0445\u0430\u0442\u044c \u044d\u0442\u043e \u0432\u0441\u0451 \u0432 2 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430: \u043e\u0434\u0438\u043d \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f, \u0434\u0440\u0443\u0433\u043e\u0439 \u2014 \u0434\u043b\u044f \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0438 \u0440\u0430\u0431\u043e\u0442\u043e\u0441\u043f\u043e\u0441\u043e\u0431\u043d\u043e\u0441\u0442\u0438 \u043c\u043e\u0434\u0435\u043b\u0438 (\u0432 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f)<\/p>\n
    def split_data(images: 'list', labels: 'list', train_size=0.9, shuffle=True):     size = len(images)     # 1. Make an indices array and shuffle it, if required     indices = np.arange(size)     if shuffle:         np.random.shuffle(indices)     # 2. Get the size of training samples     train_samples = int(size * train_size)     # 3. Split data into training and validation sets     x_train, y_train = images[indices[:train_samples]], labels[indices[:train_samples]]     x_valid, y_valid = images[indices[train_samples:]], labels[indices[train_samples:]]     return x_train, x_valid, y_train, y_valid  x_train, x_valid, y_train, y_valid = split_data(np.array(images), np.array(labels)) train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)) train_dataset = (     train_dataset.map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE)         .batch(batch_size)         .prefetch(buffer_size=tf.data.AUTOTUNE) )  validation_dataset = tf.data.Dataset.from_tensor_slices((x_valid, y_valid)) validation_dataset = (     validation_dataset.map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE)         .batch(batch_size)         .prefetch(buffer_size=tf.data.AUTOTUNE) ) <\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
    \u0421\u043e\u0437\u0434\u0430\u0451\u043c \u0438 \u043e\u0431\u0443\u0447\u0430\u0435\u043c \u043c\u043e\u0434\u0435\u043b\u044c<\/h2>\n
    \u041f\u0440\u0438\u0448\u043b\u043e \u0432\u0440\u0435\u043c\u044f \u0434\u043b\u044f \u0441\u0430\u043c\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438. \u041e\u043d\u0430 \u0431\u0443\u0434\u0435\u0442 \u0441\u043e\u0441\u0442\u043e\u044f\u0442\u044c \u0438\u0437 2\u0445 \u0431\u043b\u043e\u043a\u043e\u0432 Conv2D<\/em>, RNN<\/em>, (output<\/em>), CTC loss Layer<\/em> (\u0434\u043b\u044f \u043e\u0448\u0438\u0431\u043a\u0438)<\/p>\n
    \n\u0413\u0435\u043d\u0435\u0440\u0438\u0440\u0443\u0435\u043c \u043c\u043e\u0434\u0435\u043b\u044c (\u043c\u043d\u043e\u0433\u0430 \u0441\u043b\u043e\u0436\u043d\u044b\u0445 \u0431\u0443\u043a\u0444)<\/summary>\n
    \n
    # Loss function class CTCLayer(layers.Layer):     def __init__(self, name=None):         super().__init__(name=name)         self.loss_fn = keras.backend.ctc_batch_cost      def call(self, y_true, y_pred, **kwargs):         # Compute the training-time loss value and add it         # to the layer using `self.add_loss()`.         batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")         input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")         label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")          input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")         label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")          loss = self.loss_fn(y_true, y_pred, input_length, label_length)         self.add_loss(loss)          # At test time, just return the computed predictions         return y_pred def build_model():     # Inputs to the model     input_img = layers.Input(         # Lets use RGB input instead of black and white         shape=(img_width, img_height, 3), name=\"image\", dtype=\"float32\"     )     labels = layers.Input(name=\"label\", shape=(None,), dtype=\"float32\")      # First conv block     x = layers.Conv2D(         32,         (3, 3),         activation=\"relu\",         kernel_initializer=\"he_normal\",         padding=\"same\",         name=\"Conv1\",     )(input_img)     x = layers.MaxPooling2D((2, 2), name=\"pool1\")(x)      # Second conv block     x = layers.Conv2D(         64,         (3, 3),         activation=\"relu\",         kernel_initializer=\"he_normal\",         padding=\"same\",         name=\"Conv2\",     )(x)     x = layers.MaxPooling2D((2, 2), name=\"pool2\")(x)      # We have used two max pool with pool size and strides 2.     # Hence, downsampled feature maps are 4x smaller. The number of     # filters in the last layer is 64. Reshape accordingly before     # passing the output to the RNN part of the model     new_shape = ((img_width \/\/ 4), (img_height \/\/ 4) * 64)     x = layers.Reshape(target_shape=new_shape, name=\"reshape\")(x)     x = layers.Dense(64, activation=\"relu\", name=\"dense1\")(x)     x = layers.Dropout(0.2)(x)      # RNNs     x = layers.Bidirectional(layers.LSTM(128, return_sequences=True, dropout=0.25))(x)     x = layers.Bidirectional(layers.LSTM(64, return_sequences=True, dropout=0.25))(x)      # Output layer     x = layers.Dense(         len(char_to_num.get_vocabulary()) + 1, activation=\"softmax\", name=\"dense2\"     )(x)      # Add CTC layer for calculating CTC loss at each step     output = CTCLayer(name=\"ctc_loss\")(labels, x)      # Define the model     model = keras.models.Model(         inputs=[input_img, labels], outputs=output, name=\"ocr_model_v1\"     )     # Optimizer     opt = keras.optimizers.Adam()     # Compile the model and return     model.compile(optimizer=opt)     return model model = build_model()<\/code><\/pre>\n<\/p>\n
    \u0414\u043e\u0431\u0430\u0432\u043b\u044f\u0435\u043c \u0437\u0430\u0449\u0438\u0442\u0443 \u043e\u0442 \u043f\u0435\u0440\u0435\u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f<\/p>\n
    early_stopping = keras.callbacks.EarlyStopping(     monitor=\"val_loss\", patience=early_stopping_patience, restore_best_weights=True )<\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
    ….. \u0417\u0430\u043f\u0443\u0441\u043a\u0430\u0435\u043c! …..<\/h4>\n
    history = model.fit(     train_dataset,     validation_data=validation_dataset,     epochs=100,     callbacks=[early_stopping], )<\/code><\/pre>\n
    \"\u041f\u0440\u043e\u0446\u0435\u0441\u0441<\/p>\n
    \u041f\u0440\u043e\u0446\u0435\u0441\u0441 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0438<\/figcaption><\/div>\n<\/figure>\n
    \u041e\u0431\u0443\u0447\u0430\u043b\u0430\u0441\u044c \u043c\u043e\u0434\u0435\u043b\u044c \u0443 \u043c\u0435\u043d\u044f \u043d\u0430 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u043e\u0440\u0435 (AMD RYZEN 5 3600) ~ 10<\/strong> \u0447\u0430\u0441\u043e\u0432, \u043d\u0430 \u0432\u0438\u0434\u0435\u043e\u043a\u0430\u0440\u0442\u0435 (GTX 1070 TI) ~ 3<\/strong> \u0447\u0430\u0441\u0430.<\/p>\n
    \u0415\u0441\u043b\u0438 \u0432\u0434\u0440\u0443\u0433 \u0437\u0430\u0445\u043e\u0442\u0438\u0442\u0435 \u043e\u0431\u0443\u0447\u0430\u0442\u044c \u043d\u0430 \u0432\u0438\u0434\u0435\u043e\u043a\u0430\u0440\u0442\u0435, \u0442\u043e \u0431\u0443\u0434\u044c\u0442\u0435 \u0433\u043e\u0442\u043e\u0432\u044b \u043f\u043e\u0442\u0440\u0430\u0442\u0438\u0442\u044c 2-3 \u0447\u0430\u0441\u0430 \u043d\u0430 \u0443\u0441\u0442\u0430\u0432\u043a\u0443 CUDA<\/strong>, tensorflow-GPU<\/strong>, \u0438 \u043a\u0443\u0447\u0443 \u0434\u0440\u0443\u0433\u043e\u0433\u043e \u0433\u0435\u043c\u043e\u0440\u043e\u044f (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, conda<\/a>, \u2014 \u0431\u0435\u0437 \u043d\u0435\u0451 \u043d\u0430 windows \u0441 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435\u043c \u0432\u0441\u0451 \u043e\u0447\u0435\u043d\u044c \u0433\u0440\u0443\u0441\u0442\u043d\u043e)<\/p>\n
    \u0422\u0435\u0441\u0442\u0438\u0440\u0443\u0435\u043c<\/h2>\n
    \u0414\u043b\u044f \u0442\u0435\u0441\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u043d\u0443\u0436\u043d\u043e \u043f\u0440\u043e\u0433\u043d\u0430\u0442\u044c \u043d\u0430\u0448\u0443 \u043c\u043e\u0434\u0435\u043b\u044c \u0447\u0435\u0440\u0435\u0437 \u0434\u0430\u0442\u0430\u0441\u0435\u0442, \u0437\u0430\u0440\u0430\u043d\u0435\u0435 \u043e\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u043d\u044b\u0439 \u0432 \u043f\u0430\u043f\u043e\u0447\u043a\u0435 test<\/strong>. <\/p>\n
    \n\u041a\u043e\u0434 \u0442\u0435\u0441\u0442\u043e\u0432<\/summary>\n
    \n
    \u041f\u043e\u0434\u0433\u0440\u0443\u0436\u0430\u0435\u043c \u043d\u0430\u0448\u0443 \u043c\u043e\u0434\u0435\u043b\u044c<\/p>\n
    model: Functional = keras.models.load_model(MODEL_FNAME)<\/code><\/pre>\n
    \u0417\u0430\u0433\u0440\u0443\u0436\u0430\u0435\u043c \u0442\u0435\u0441\u0442\u043e\u0432\u044b\u0439 \u0434\u0430\u0442\u0430\u0441\u0435\u0442<\/p>\n
    images = list(map(str, [i for i in data_dir_test.glob(img_type)])) images = sorted(images, key=lambda *h: random.random()) labels = [     img.split(os.path.sep)[-1].split('-')[0].split('.jpeg')[0].ljust(max_length)[:max_length]     for img in images ]  test_dataset = tf.data.Dataset.from_tensor_slices((numpy.array(images), numpy.array(labels))) test_dataset = (     test_dataset.map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE)     .batch(batch_size)     .prefetch(buffer_size=tf.data.AUTOTUNE) )<\/code><\/pre>\n
    \u0412\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f (\u043f\u043e \u0436\u0435\u043b\u0430\u043d\u0438\u044e):<\/p>\n
    def visualize():     _, ax = plt.subplots(4, 4, figsize=(15, 5))     for i in range(len(pred_texts)):         img = (image[i, :, :, :] * 255).numpy().transpose([1, 0, 2]).astype(np.uint8)         title = f\" {pred_texts[i][0]}{pred_texts[i][1]:.3%} {'ok' if success else 'wrong'}\"         ax[i \/\/ 4, i % 4].imshow(img)         ax[i \/\/ 4, i % 4].set_title(title)         ax[i \/\/ 4, i % 4].axis(\"off\")     plt.show()<\/code><\/pre>\n
    \u0418, \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0435\u043d\u043d\u043e, \u0442\u0435\u0441\u0442\u044b:<\/p>\n
    good = 0 total = 0  for i in test_dataset.take(1000):     label, image = i['label'], i['image']     preds = model.call(image)     pred_texts = decode_batch_predictions(preds)[0]     orig_texts = [tf.strings.reduce_join(num_to_char(l)).numpy().decode(\"utf-8\").replace('[UNK]', '') for l in label]     if need_visualization and total == 0:         visualize()     for i in range(len(pred_texts)):         success = pred_texts[i] == orig_texts[i]         if success:             good += 1         total += 1     print(f\"Progress {total\/len(images):.2%}[{total}\/{len(images)}]...\", end='\\r') print(f\"Success: {(good \/ total):.2%}\\n\")<\/code><\/pre>\n
    \"\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442<\/p>\n
    \u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0442\u0435\u0441\u0442\u043e\u0432<\/figcaption><\/div>\n<\/figure>\n<\/div>\n<\/details>\n
    \u0423 \u043c\u043e\u0434\u0435\u043b\u044c \u0432\u044b\u0434\u0430\u0432\u0430\u043b\u0430 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u044b\u0439 \u043e\u0442\u0432\u0435\u0442 \u0441 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c\u044e 94.05<\/strong>%.<\/p>\n
    \u0423\u0440\u0430?<\/h2>\n
    \u0412 \u0446\u0435\u043b\u043e\u043c, \u043c\u043e\u0436\u043d\u043e \u0441\u043a\u0430\u0437\u0430\u0442\u044c, \u0447\u0442\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u0433\u043e\u0442\u043e\u0432\u0430. \u041d\u041e<\/strong>! \u0422\u0443\u0442, \u043a\u0430\u043a \u0432\u0441\u0435\u0433\u0434\u0430, \u043d\u0435 \u0431\u0435\u0437 \u0441\u044e\u0440\u043f\u0440\u0438\u0437\u043e\u0432. \u0410 \u0441\u044e\u0440\u043f\u0440\u0438\u0437\u044b \u0432 \u0434\u0430\u043d\u043d\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435: 2\u0413\u0411<\/strong> \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 Tensorflow, 10-15 \u0441\u0435\u043a\u0443\u043d\u0434 \u043d\u0430 \u0435\u0451 import<\/strong>, \u0434\u043e\u043b\u0433\u043e\u0435 \u0432\u0440\u0435\u043c\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u0438 \u0442.\u043f.<\/p>\n
    \u0412 \u043e\u0431\u0449\u0435\u043c, \u043f\u043e\u0441\u043b\u0435 \u0434\u043b\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0445 \u043f\u043e\u0438\u0441\u043a\u043e\u0432, \u0431\u044b\u043b\u043e \u0440\u0435\u0448\u0435\u043d\u043e \u043f\u0435\u0440\u0435\u0445\u043e\u0434\u0438\u0442\u044c \u043d\u0430 \u0444\u043e\u0440\u043c\u0430\u0442 onnx<\/a>. \u0410 \u0447\u0442\u043e, \u0441\u0442\u0438\u043b\u044c\u043d\u043e, \u043c\u043e\u0434\u043d\u043e, \u043c\u043e\u043b\u043e\u0434\u0451\u0436\u043d\u043e, \u043f\u043e\u0434\u0443\u043c\u0430\u043b \u044f. \u041d\u043e, \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e, \u0441 1-\u0433\u043e \u0440\u0430\u0437\u0430 \u043d\u0438\u0447\u0435\u0433\u043e \u043d\u0435 \u0437\u0430\u0440\u0430\u0431\u043e\u0442\u0430\u043b\u043e<\/p>\n
    \n\u041a\u043e\u043d\u0432\u0435\u0440\u0442\u0438\u0440\u0443\u0435\u043c \u043c\u043e\u0434\u0435\u043b\u044c \u0432 ONNX<\/summary>\n
    \n
    \u0421\u043d\u0430\u0447\u0430\u043b\u0430 \u044f \u043f\u043e\u043f\u044b\u0442\u0430\u043b\u0441\u044f \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c keras2onnx<\/a>, \u043d\u043e \u043e\u043d \u0443 \u043c\u0435\u043d\u044f \u043d\u0435 \u0437\u0430\u0440\u0430\u0431\u043e\u0442\u0430\u043b. \u041f\u043e\u0433\u0443\u0433\u043b\u0438\u0432 \u043e\u0448\u0438\u0431\u043a\u0443, \u044f \u043f\u0440\u0438\u0448\u0451\u043b \u043a \u0432\u044b\u0432\u043e\u0434\u0443, \u0447\u0442\u043e \u0432\u043e \u0432\u0441\u0451\u043c \u0432\u0438\u043d\u043e\u0432\u0430\u0442\u0430 windows (\u043e \u0432\u0435\u043b\u0438\u043a\u0438\u0435 \u043b\u0438\u043d\u0443\u043a\u0441\u0430<\/em>), \u043f\u0435\u0440\u0435\u0448\u0435\u043b \u043d\u0430 \u0432\u0438\u0440\u0442\u0443\u0430\u043b\u043a\u0443, \u043f\u043e\u043f\u0440\u043e\u0431\u043e\u0432\u0430\u043b \u043d\u0430 \u043d\u0435\u0439 \u0438…. \u041e\u043f\u044f\u0442\u044c \u043d\u0438\u0447\u0435\u0433\u043e \u043d\u0435 \u0437\u0430\u0440\u0430\u0431\u043e\u0442\u0430\u043b\u043e (\u043f\u043e\u0441\u043b\u0435 4\u0445 \u0447\u0430\u0441\u043e\u0432 \u043f\u043e\u043f\u044b\u0442\u043e\u043a \u0437\u0430\u0441\u0442\u0430\u0432\u0438\u0442\u044c \u0447\u0442\u043e-\u043b\u0438\u0431\u043e \u0434\u0432\u0438\u0433\u0430\u0442\u044c\u0441\u044f).<\/p>\n
    \u0414\u0430\u043b\u0435\u0435 \u043d\u0430 \u043e\u0447\u0435\u0440\u0435\u0434\u044c \u043f\u0440\u0438\u0448\u0451\u043b tf2onnx<\/a>. \u041d\u043e \u0437\u0430\u0440\u0430\u0431\u043e\u0442\u0430\u043b \u043e\u043d \u0443 \u043c\u0435\u043d\u044f \u0442\u043e\u043b\u044c\u043a\u043e \u0441 3-\u0433\u043e \u0440\u0430\u0437\u0430: \u0432 \u043f\u0435\u0440\u0432\u044b\u0439 \u0440\u0430\u0437, \u043f\u043e\u0441\u043b\u0435 \u0443\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0438, \u043e\u043a\u0430\u0437\u0430\u043b\u043e\u0441\u044c, \u0447\u0442\u043e python3.10<\/strong> \u043d\u0435 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u0435\u0442\u0441\u044f, \u0437\u0430\u0442\u0435\u043c, \u043d\u0430 2\u0439 \u0440\u0430\u0437 \u2014 \u0447\u0442\u043e \u044f \u043e\u0431\u0443\u0447\u0430\u043b \u043c\u043e\u0434\u0435\u043b\u044c \u043d\u0430 \u0441\u043b\u0438\u0448\u043a\u043e\u043c \u043d\u043e\u0432\u043e\u0439 \u0432\u0435\u0440\u0441\u0438\u0438 tensorflow<\/em>, \u043a\u043e\u0442\u043e\u0440\u0443\u044e tf2onnx<\/em> \u043f\u043e\u043a\u0430 \u043d\u0435 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u0435\u0442 (\u0432\u043e\u0442 \u0437\u0430\u0440\u0430\u0437\u0430). <\/p>\n
    \u0412 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0435 \u0443 \u043c\u0435\u043d\u044f-\u0442\u0430\u043a\u0438 \u043f\u043e\u043b\u0443\u0447\u0438\u043b\u043e\u0441\u044c \u0432\u0441\u0451 \u043f\u0440\u043e\u0432\u0435\u0440\u043d\u0443\u0442\u044c \u0441\u043e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c\u0438 \u0432\u0435\u0440\u0441\u0438\u044f\u043c\u0438:<\/p>\n
    python3.9 tensorflow==2.9.1 tf2onnx==1.11.1 onnxruntime==1.9.0 onnxconverter-common==1.10.0<\/code><\/pre>\n
    \u041d\u0443 \u0438 \u0441\u0430\u043c \u0441\u043a\u0440\u0438\u043f\u0442 \u043a\u043e\u043d\u0432\u0435\u0440\u0442\u0430\u0446\u0438\u0438:<\/p>\n
    import pathlib, tf2onnx.convert, sys from tools import MODEL_FNAME  OUTPUT_ONNX = \"..\/out.model.onnx\"  dir = pathlib.Path(__file__).parent sys.argv.append(\"--saved-model\") sys.argv.append(str(dir \/ MODEL_FNAME)) sys.argv.append(\"--output\") sys.argv.append(str(dir \/ OUTPUT_ONNX)) tf2onnx.convert.main()<\/code><\/pre>\n
    UPDATE<\/strong>: \u0432\u0440\u043e\u0434\u0435 \u043a\u0430\u043a \u0441\u0435\u0439\u0447\u0430\u0441 \u0443\u0436\u0435 \u0435\u0441\u0442\u044c \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0430 python3.10, \u043d\u043e \u043f\u0440\u043e\u0432\u0435\u0440\u044f\u0442\u044c \u044f \u044d\u0442\u043e \u043d\u0435 \u0445\u043e\u0447\u0443)<\/p>\n<\/div>\n<\/details>\n
    \n\u041f\u0435\u0440\u0435\u043f\u0438\u0441\u044b\u0432\u0430\u0435\u043c \u043a\u043e\u0434 \u0441 tensorflow \u043d\u0430 numpy<\/summary>\n
    \n
    \u0422\u0435\u043f\u0435\u0440\u044c, \u043f\u043e\u0441\u043b\u0435 \u043a\u043e\u043d\u0432\u0435\u0440\u0442\u0430\u0446\u0438\u0438 \u0432 ONNX, \u0432\u043e\u0437\u043d\u0438\u043a\u043b\u0430 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e\u0441\u0442\u044c \u043f\u0435\u0440\u0435\u043f\u0438\u0441\u0430\u0442\u044c \u043f\u0440\u0435\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0438\u043d\u0433 \u043a\u0430\u0440\u0442\u0438\u043d\u043e\u043a \u0441 tensorflow<\/strong> \u043d\u0430 \u0432\u0435\u043b\u0438\u043a\u0438\u0439<\/s> numpy<\/strong> (\u0432\u0435\u0441\u0438\u0442 \u043c\u0435\u043d\u044c\u0448\u0435, \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u0448\u0443\u0441\u0442\u0440\u0435\u0435).<\/p>\n
    \u0412 \u0441\u043b\u0443\u0447\u0430\u0435 \u043f\u0440\u0435\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0438\u043d\u0433\u0430 \u044d\u0442\u043e \u043d\u0435 \u0441\u043e\u0441\u0442\u0430\u0432\u0438\u043b\u043e \u043e\u0441\u043e\u0431\u043e\u0433\u043e \u0442\u0440\u0443\u0434\u0430:<\/p>\n
    def decode_img(data_bytes: bytes):   # same actions, as for tensorflow   img = cv2.imdecode(np.asarray(bytearray(data_bytes), dtype=np.uint8), -1)   img: \"np.ndarray\" = img.astype(np.float32) \/ 255.   if img.shape != (img_height, img_width, 3):       cv2.resize(img, (img_width, img_height))   img = img.transpose([1, 0, 2])   #  Creating tensor ( adding 4d dimension )   img = np.array([img])   return img<\/code><\/pre>\n
    \n\u041d\u0435\u043c\u043d\u043e\u0433\u043e \u043e INTEGER_ACCURATE<\/summary>\n
    \n
    \u0422\u0435\u0441\u0442\u0438\u0440\u0443\u0435\u043c \u0438… \u041f\u0440\u043e\u0446\u0435\u043d\u0442 \u0432\u0435\u0440\u043d\u044b\u0445 \u043e\u0442\u0432\u0435\u0442\u043e\u0432 \u0441\u043d\u0438\u0436\u0430\u0435\u0442\u0441\u044f \u043d\u0430 10%. \u042d\u0442\u043e \u0441\u0442\u0430\u043b\u043e \u043e\u0447\u0435\u043d\u044c \u043d\u0435\u043f\u0440\u0438\u044f\u0442\u043d\u043e, \u0438, \u043a\u0430\u043a \u043e\u043a\u0430\u0437\u0430\u043b\u043e\u0441\u044c, \u0432\u0441\u0451 \u0434\u0435\u043b\u043e \u0432 \u043c\u0435\u0442\u043e\u0434\u0435 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0431\u043e\u043a\u0438 \u043a\u0430\u0440\u0442\u0438\u043d\u043a\u0438.<\/p>\n
    \u0421\u0443\u0449\u0435\u0441\u0442\u0432\u0443\u0435\u0442 \u043c\u043d\u043e\u0433\u043e \u043c\u0435\u0442\u043e\u0434\u043e\u0432 \u0434\u0435\u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f JPEG<\/strong> \u043a\u0430\u0440\u0442\u0438\u043d\u043e\u043a, \u0438, \u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e, tensorflow<\/strong> \u0438 numpy<\/strong> \u043f\u043e \u0443\u043c\u043e\u043b\u0447\u0430\u043d\u0438\u044e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0442 \u0440\u0430\u0437\u043d\u044b\u0435. \u0427\u0442\u043e\u0431\u044b \u043e\u043d\u0438 \u0431\u044b\u043b\u0438 \u043d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 c\u0445\u043e\u0436\u0438, \u0441\u0442\u043e\u0438\u0442 \u0443\u043a\u0430\u0437\u0430\u0442\u044c \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440 dct_method='INTEGER_ACCURATE'<\/code>. <\/p>\n
    img = tf.io.decode_jpeg(img, channels=3, dct_method='INTEGER_ACCURATE')<\/code><\/p>\n
    \u041d\u0435\u043c\u043d\u043e\u0433\u043e \u043e\u0444\u0438\u0446\u0438\u0430\u043b\u044c\u043d\u043e\u0439 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u0438:<\/p>\n
    \n
    dct_method — String specifying a hint about the algorithm used for decompression.  Defaults to «» which maps to a system-specific default. Currently valid values are [\"INTEGER_FAST\", \"INTEGER_ACCURATE\"]<\/code>.  The hint may be ignored (e.g., the internal jpeg library changes to a version that does not have that specific option.) <\/p>\n<\/blockquote>\n
    \u041d\u0435 \u043f\u043e\u0432\u0442\u043e\u0440\u044f\u0439\u0442\u0435 \u043c\u043e\u0438\u0445 \u043e\u0448\u0438\u0431\u043e\u043a \ud83d\ude42<\/p>\n<\/div>\n<\/details>\n
    \u0410 \u0432\u043e\u0442 \u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u0434\u0435\u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430 \u043f\u0440\u0438\u0448\u043b\u043e\u0441\u044c \u0432\u043d\u0438\u043a\u043d\u0443\u0442\u044c \u0432 Beam Search Decoding for ctc alorithm<\/a>. \u0415\u0441\u043b\u0438 \u0432\u043a\u0440\u0430\u0442\u0446\u0435, \u0442\u043e \u043d\u0430 \u0432\u044b\u0445\u043e\u0434 \u043c\u044b \u043f\u043e\u043b\u0443\u0447\u0430\u0435\u043c 2-\u043c\u0435\u0440\u043d\u044b\u0439 \u043c\u0430\u0441\u0441\u0438\u0432 \u0434\u043b\u0438\u043d\u043e\u0439 \u0432 32 \u044d\u043b\u0435\u043c\u0435\u043d\u0442\u0430, \u0433\u0434\u0435 \u043a\u0430\u0436\u0434\u044b\u0439 \u044d\u043b\u0435\u043c\u0435\u043d\u0442 \u2014 \u043c\u0430\u0441\u0441\u0438\u0432 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u0435\u0439 \u043d\u0430 \u044d\u0442\u043e\u043c \u043c\u0435\u0441\u0442\u0435 \u043a\u0430\u0436\u0434\u043e\u0439 \u0431\u0443\u043a\u0432\u044b.<\/p>\n
    \u041a\u043e\u0434 \u0434\u0435\u043a\u043e\u0434\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0430:<\/p>\n
    def get_result(pred):     \"\"\"CTC decoder of the output tensor     https:\/\/distill.pub\/2017\/ctc\/     https:\/\/en.wikipedia.org\/wiki\/Connectionist_temporal_classification     :return string, float     \"\"\"     accuracy = 1     last = None     ans = []     # pred - 3d tensor, we need 2d array - first element     for item in pred[0]:         # get index of element with max accuracy         char_ind = item.argmax()         # ignore duplicates and special characters         if char_ind != last and char_ind != 0 and char_ind != len(characters)+1:             # this element is a character - append it to answer             ans.append(characters[char_ind - 1])             # Get accuracy for current character and              # multiply global accuracy by it             accuracy *= item[char_ind]         last = char_ind      answ = \"\".join(ans)[:max_length]     return answ, accuracy <\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
    \n\u0422\u0435\u0441\u0442\u0438\u0440\u0443\u0435\u043c ONNX \u043c\u043e\u0434\u0435\u043b\u044c<\/summary>\n
    \n
    \u041d\u0430\u0441\u0442\u0430\u043b\u043e \u0432\u0440\u0435\u043c\u044f \u0444\u0438\u043d\u0430\u043b\u044c\u043d\u043e\u0439 \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0438: \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u0443\u0434\u043e\u0441\u0442\u043e\u0432\u0435\u0440\u0438\u0442\u044c\u0441\u044f, \u0447\u0442\u043e \u043c\u044b \u0432\u0441\u0451 \u0441\u0434\u0435\u043b\u0430\u043b\u0438 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e. \u0422\u043e \u0435\u0441\u0442\u044c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u0440\u0430\u0431\u043e\u0442\u044b ONNX<\/strong> \u043c\u043e\u0434\u0435\u043b\u0438 \u043d\u0435 \u0434\u043e\u043b\u0436\u0435\u043d \u0441\u0438\u043b\u044c\u043d\u043e \u043e\u0442\u043b\u0438\u0447\u0430\u0442\u044c\u0441\u044f \u043e\u0442 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b\u044c\u043d\u043e\u0439. \u0422\u0430\u043a\u0436\u0435 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u043f\u0440\u043e\u0433\u043d\u0430\u0442\u044c \u0442\u0435\u0441\u0442\u044b \u043d\u0430 «\u0440\u0435\u0430\u043b\u044c\u043d\u044b\u0445» \u043a\u0430\u043f\u0447\u0430\u0445 (\u0433\u0435\u043d\u0435\u0440\u0438\u0440\u0443\u0435\u043c\u044b\u0445 \u0441\u0435\u0440\u0432\u0435\u0440\u043e\u043c) \u0438 \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0435\u0442\u044c, \u043d\u0430\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u043d\u0430\u0448\u0430 \u043c\u043e\u0434\u0435\u043b\u044c \u0441 \u043d\u0438\u043c\u0438 \u0441\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442\u0441\u044f (\u0430 \u0442\u043e \u0432\u0434\u0440\u0443\u0433 \u0432\u0441\u0451 \u0431\u044b\u043b\u043e \u0437\u0440\u044f?)<\/p>\n
    \u0417\u0430\u0433\u0440\u0443\u0436\u0430\u0435\u043c \u043c\u043e\u0434\u0435\u043b\u044c:<\/p>\n
    data_dir_test = data_dir_test.glob(img_type) sess = onnxruntime.InferenceSession(r\"..\/out.model.onnx\") name = sess.get_inputs()[0].name<\/code><\/pre>\n
    \u041f\u0435\u0440\u0435\u0434\u0435\u043b\u044b\u0432\u0430\u0435\u043c \u0444\u0443\u043d\u043a\u0446\u0438\u044e \u0434\u043b\u044f \u0440\u0435\u0448\u0435\u043d\u0438\u044f \u043a\u0430\u043f\u0447\u0438 \u0438\u0437 \u0444\u0430\u0439\u043b\u0430<\/p>\n
    def solve(file_name: str):     with open(file_name, 'rb') as F:         data_bytes = F.read()     img = decode_img(data_bytes)     pred_onx = sess.run(None, {name: img})[0]     ans = get_result(pred_onx)     return ans<\/code><\/pre>\n
    \u0410 \u0434\u0430\u043b\u044c\u0448\u0435 \u0435\u0441\u0442\u044c 2 \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u0430. <\/p>\n
      \n
    1. \n
      \u041f\u0440\u043e\u0433\u043d\u0430\u0442\u044c \u0442\u0435\u0441\u0442\u044b \u043d\u0430 \u0440\u0435\u0430\u043b\u044c\u043d\u044b\u0445 \u043a\u0430\u043f\u0447\u0430\u0445 \u0438 \u0432\u044b\u0432\u0435\u0441\u0442\u0438 \u0438\u0445 \u0447\u0435\u0440\u0435\u0437 matplotlib.pyplot<\/code>\u0438 \u0432\u0440\u0443\u0447\u043d\u0443\u044e \u043f\u0440\u043e\u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b:<\/p>\n
      min_accur = 0.3 time_now = time.time() _, ax = plt.subplots(4, 4, figsize=(10, 5)) for i in range(16):     file = 'image.jpeg'     perc_ = 0     # \u043f\u043e\u043a\u0430 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c \u043a\u043e\u0440\u0440\u0435\u043a\u0442\u043d\u043e\u0433\u043e \u0440\u0435\u0448\u0435\u043d\u0438\u044f, \u0432\u044b\u0434\u0430\u0432\u0430\u0435\u043c\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c\u044e, \u043c\u0435\u043d\u044c\u0448\u0435,     # \u0447\u0435\u043c \u043c\u0438\u043d\u0438\u043c\u0443\u043c - \u0440\u0435\u0448\u0430\u0435\u043c     while perc_ < min_accur:         # \u043d\u0430 \u0432\u0441\u044f\u043a\u0438\u0439 \u0441\u043b\u0443\u0447\u0430\u0439 \u0441\u043a\u0430\u0436\u0443, \u0447\u0442\u043e \u0435\u0441\u043b\u0438 \u0431\u0440\u0430\u0442\u044c \u0440\u0430\u043d\u0434\u043e\u043c\u043d\u044b\u0435 sid,          # \u0442\u043e \u043c\u043e\u0436\u0435\u0442 \u0432\u044b\u043b\u0435\u0437\u0442\u0438 \u0440\u0443\u0441\u0441\u043a\u0430\u044f \u043a\u0430\u043f\u0447\u0430, \u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440 \u043a\u0430\u043a \u0442\u0443\u0442(\u043d\u0435 \u043f\u0443\u0433\u0430\u0439\u0442\u0435\u0441\u044c):          # https:\/\/api.vk.com\/captcha.php?sid=984831         image = requests.get('https:\/\/api.vk.com\/captcha.php?sid=98483')         with open(file, 'wb') as f:             f.write(image.content)         solution, perc_ = solve(file)     img = mpimg.imread(file)      title = f\"{perc_: .2%} {solution}\"     ax[i >> 2, i % 4].imshow(img)     ax[i >> 2, i % 4].set_title(title)     ax[i >> 2, i % 4].axis(\"off\") plt.show() print(\"ARGV:\", (time.time() - time_now) \/ 10, \"sec.\") <\/code><\/pre>\n<\/li>\n
    2. \n
      \u041f\u0440\u043e\u0433\u043d\u0430\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u044c \u0437\u0430\u043d\u043e\u0432\u043e \u043d\u0430 \u0442\u0435\u0441\u0442\u043e\u0432\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u0438 \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0435\u0442\u044c, \u043d\u0435 «\u0438\u0441\u043f\u043e\u0440\u0442\u0438\u043b\u0430\u0441\u044c» \u043b\u0438 \u043e\u043d\u0430 \u0432 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0435 \u043f\u0435\u0440\u0435\u0432\u043e\u0434\u0430 \u0432 \u0434\u0440\u0443\u0433\u043e\u0439 \u0444\u043e\u0440\u043c\u0430\u0442, \u0430 \u0442\u0430\u043a\u0436\u0435 \u043f\u0440\u043e\u0430\u043d\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043e\u0448\u0438\u0431\u043a\u0438, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043e\u043d\u0430 \u0434\u0435\u043b\u0430\u0435\u0442.<\/p>\n
      \u0424\u0443\u043d\u043a\u0446\u0438\u044f \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u043e\u0448\u0438\u0431\u043e\u043a:<\/p>\n
      def mistakes_analyzer(errors: \"list[tuple[str, str]]\"):     \"\"\"Analyze mistakes made by model.     :param errors - list of (model_ans, correct_ans)     :returns (loose_symbol, error_symbols, undefined)             loose_symbol - {symbol: loos_count}             error_symbols - {correct_symbol: {model_answer: count}} (count=int)             undefined - list of other mistakes list[tuple[str, str]]     \"\"\"     loose_symbol = {}     error_symbols = {}     undefined = []     for ans, correct_ans in errors:         if len(ans) != len(correct_ans):             # if we skip a character             if len(correct_ans) == len(ans)+1:                 loosed_sym = dif(correct_ans, ans)                 if loosed_sym not in loose_symbol:                     loose_symbol[loosed_sym] = 0                 loose_symbol[loosed_sym] += 1             else:                 undefined.append((ans, correct_ans))             continue         for sym_correct, sym_fail in zip(correct_ans, ans):             if sym_correct != sym_fail:                 # if we recognize it incorrectly                 if sym_correct not in error_symbols: error_symbols[sym_correct] = {}                 if sym_fail not in error_symbols[sym_correct]: error_symbols[sym_correct][sym_fail] = 0                 error_symbols[sym_correct][sym_fail] += 1     return loose_symbol, error_symbols, undefined <\/code><\/pre>\n
      \u041d\u0443 \u0438 \u043f\u0440\u043e\u0445\u043e\u0434 \u043f\u043e \u0442\u0435\u0441\u0442\u043e\u0432\u044b\u043c \u0434\u0430\u043d\u043d\u044b\u043c:<\/p>\n
      mistakes = [] total = 0 correct = 0 for file in data_dir_test:     ans = file.name.split(\".\")[0].split('-')[0]     soved_ans, _ = solve(str(file))     if soved_ans == ans:         correct += 1     else:         mistakes.append((ans, soved_ans))     total += 1     if total % 100 == 0:         print(f\"Success: {correct \/ total:.2%}\", end='\\r') print(\"Parsing data mistakes: \") anylize = mistakes_analyzer(mistakes) print(\"\\n\\nLoose symbols:\") for i, v in sorted(anylize[0].items(), key=lambda n: -n[1]):     if v == 1: continue  # 1 is not an error - it's a mistake     print(i, '=', v) print(\"\\n\\nWrong symbols:\") for symbol, arr in sorted(anylize[1].items(), key=lambda n: -sum(n[1].values())):     print(symbol, '=', sum(arr.values()), \":\")     for symbol_next, v in sorted(arr.items(), key=lambda n: -n[1]):         if v == 1: continue         print(f'\\t{symbol} - {symbol_next} = {v}') from fuzzywuzzy import fuzz for i in anylize[2]:     # if the difference between answer and item is too big -      # it's wierd. We should pay attention on it          if fuzz.ratio(*i) < 0.4:         print(\"Probably problem in data: \")         print(*i) <\/code><\/pre>\n
      \u0412 \u043c\u043e\u0451\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u044f \u043f\u043e\u043b\u0443\u0447\u0438\u043b \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e \u0442\u0430\u043a\u0443\u044e \u043a\u0430\u0440\u0442\u0438\u043d\u0443:<\/p>\n
      Loose symbols: z = 6 s = 4 n = 4 7 = 3 4 = 2 v = 2 c = 2  Wrong symbols: e = 97 : e - c = 59 e - 8 = 14 e - a = 9 e - p = 4 e - q = 3 e - 2 = 2 e - s = 2 c = 97 : c - e = 61 c - q = 18 c - p = 6 c - d = 3 c - u = 3 c - a = 2 n = 77 : n - h = 41 n - m = 13 n - p = 12 n - u = 4 n - 4 = 2 s = 74 : s - 8 = 16 s - 5 = 14 s - e = 9 s - q = 8 s - c = 7 s - x = 4 s - 2 = 3 s - p = 3 s - a = 3 s - z = 2 s - n = 2 z = 73 : z - 2 = 28 z - 7 = 18 z - s = 5 z - v = 4 z - h = 3 z - x = 3 z - q = 3 z - 5 = 2 z - a = 2 a = 71 : a - d = 26 a - e = 9 a - 4 = 7 a - q = 6 a - n = 5 a - u = 5 a - z = 3 a - 2 = 2 a - 8 = 2 a - c = 2 v = 71 : v - y = 49 v - x = 13 v - d = 2 v - s = 2 q = 47 : q - d = 26 q - e = 4 q - 9 = 2 q - c = 2 q - g = 2 q - 4 = 2 q - z = 2 q - u = 2 u = 45 : u - d = 17 u - n = 7 u - q = 5 u - h = 3 u - v = 3 u - m = 2 u - x = 2 u - 4 = 2 h = 38 : h - n = 26 h - b = 5 h - k = 3 h - u = 2 d = 30 : d - c = 11 d - q = 8 d - a = 3 d - u = 2 d - s = 2 y = 26 : y - v = 20 y - x = 4 y - 7 = 2 x = 24 : x - k = 9 x - v = 5 x - y = 4 x - 7 = 2 5 = 22 : 5 - p = 5 5 - s = 4 5 - h = 3 5 - a = 2 5 - 2 = 2 5 - 7 = 2 2 = 21 : 2 - 8 = 7 2 - z = 5 2 - a = 3 2 - p = 3 7 = 20 : 7 - z = 10 7 - s = 2 p = 16 : p - n = 8 p - h = 2 p - u = 2 p - m = 2 4 = 13 : 4 - 7 = 3 4 - q = 3 4 - h = 2 4 - a = 2 k = 12 : k - x = 5 k - h = 4 8 = 5 : m = 5 : m - n = 3 <\/code><\/pre>\n
      \u0418\u0437 \u044d\u0442\u0438\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432\u0438\u0434\u043d\u043e, \u0447\u0442\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0447\u0430\u0441\u0442\u043e \u043f\u0443\u0442\u0430\u0435\u0442 «e<\/strong>» \u0441 «c<\/strong>«, «h<\/strong>» \u0441 «n<\/strong>«, «z<\/strong>» c «2<\/strong>«, «v<\/strong>» c «y<\/strong>«. \u042d\u0442\u043e \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u0430\u044f \u0441\u0438\u0442\u0443\u0430\u0446\u0438\u044f \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439, \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u044e\u0449\u0438\u0445 \u0442\u0435\u043a\u0441\u0442, \u2014 \u044d\u0442\u0438 \u0441\u0438\u043c\u0432\u043e\u043b\u044b \u0438 \u043b\u044e\u0434\u0438 \u0447\u0430\u0441\u0442\u043e \u043d\u0435 \u043c\u043e\u0433\u0443\u0442 \u0434\u043e \u043a\u043e\u043d\u0446\u0430 \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0442\u044c (\u0430 \u0443 \u043d\u0430\u0441, \u0438\u0437\u0432\u0438\u043d\u0438\u0442\u0435, \u043d\u0435\u0447\u0435\u0442\u0430\u0435\u043c\u0430\u044f \u043a\u0430\u043f\u0447\u0430).<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<\/details>\n
      \u0422\u0430\u043a\u0436\u0435 \u0441\u0442\u043e\u0438\u0442 \u0443\u043f\u043e\u043c\u044f\u043d\u0443\u0442\u044c \u043e\u0434\u0438\u043d \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u0443\u044e \u0444\u0438\u0447\u0443, \u043a\u043e\u0442\u043e\u0440\u043e\u0439 \u043c\u043e\u0436\u043d\u043e \u0432\u043e\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u043f\u0440\u0438 \u0440\u0435\u0448\u0435\u043d\u0438\u0438 \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u044b\u0445 \u043a\u0430\u043f\u0447: \u0435\u0441\u043b\u0438 \u043c\u043e\u0434\u0435\u043b\u044c \u0432\u044b\u0434\u0430\u0435\u0442 \u043f\u043b\u043e\u0445\u0443\u044e \u0443\u0432\u0435\u0440\u0435\u043d\u043d\u043e\u0441\u0442\u044c \u0432 \u043e\u0442\u0432\u0435\u0442\u0435 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043d\u0438\u0436\u0435 \u0447\u0435\u043c 0.2<\/strong>), \u0442\u043e \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0447\u0430\u0441\u0442\u043e \u043c\u043e\u0436\u043d\u043e «\u0437\u0430\u0431\u0438\u0442\u044c<\/em>» \u043d\u0430 \u0440\u0435\u0448\u0435\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u043e\u0439 \u043a\u0430\u043f\u0447\u0438 \u0438 \u043f\u0440\u043e\u0441\u0442\u043e \u0437\u0430\u043f\u0440\u043e\u0441\u0438\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0443\u044e (\u0432 \u0441\u043b\u0443\u0447\u0430\u0435 \u0412\u041a<\/em> \u0438 \u0437\u0430\u043f\u0440\u0430\u0448\u0438\u0432\u0430\u0442\u044c \u043d\u0435 \u043d\u0430\u0434\u043e: \u043f\u0440\u043e\u0441\u0442\u043e \u0437\u0430\u043d\u043e\u0432\u043e \u0441\u043a\u0430\u0447\u0438\u0432\u0430\u0435\u043c \u043a\u0430\u0440\u0442\u0438\u043d\u043a\u0443 \u043f\u043e \u0442\u043e\u0439 \u0436\u0435 \u0441\u0441\u044b\u043b\u043a\u0435, \u0438 \u0441\u0435\u0440\u0432\u0435\u0440 \u0434\u043b\u044f \u043d\u0430\u0441 \u043b\u044e\u0431\u0435\u0437\u043d\u043e \u0441\u0433\u0435\u043d\u0435\u0440\u0438\u0440\u043e\u0432\u0443\u0435\u0442 \u043d\u043e\u0432\u0443\u044e «\u0437\u0430\u0434\u0430\u0447\u043a\u0443»).
      \u0422\u043e \u0435\u0441\u0442\u044c «\u0443\u043c\u043d\u0430\u044f» \u0444\u0443\u043d\u043a\u0446\u0438\u044f \u0431\u0443\u0434\u0435\u0442 \u0440\u0435\u0448\u0430\u0442\u044c \u043a\u0430\u043f\u0447\u0443 \u0434\u043e \u0442\u0435\u0445 \u043f\u043e\u0440 (\u0435\u0441\u0442\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e, \u043c\u043e\u0436\u043d\u043e \u0437\u0430\u0434\u0430\u0442\u044c \u043c\u0430\u043a\u0441. \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u0440\u0430\u0437), \u043f\u043e\u043a\u0430 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c \u0443\u0441\u043f\u0435\u0448\u043d\u043e\u0433\u043e \u043e\u0442\u0432\u0435\u0442\u0430 \u043c\u0435\u043d\u044c\u0448\u0435, \u0447\u0435\u043c \u043d\u0443\u0436\u043d\u0430\u044f \u043d\u0430\u043c.<\/p>\n
      \u0422\u0435\u043f\u0435\u0440\u044c \u0442\u043e \u0432\u0441\u0451?<\/h2>\n
      \u041f\u043e\u0447\u0442\u0438. \u041e\u0441\u0442\u0430\u043b\u043e\u0441\u044c \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u044c \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0443-\u043e\u0431\u0451\u0440\u0442\u0443 \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0438\u0432\u0448\u0435\u0439\u0441\u044f \u043c\u043e\u0434\u0435\u043b\u0438. <\/p>\n
      \n\u041c\u043d\u043e\u0433\u043e code<\/summary>\n
      \n
      import concurrent.futures import os.path import time import aiohttp, asyncio  import onnxruntime as onr import numpy as np import requests import cv2 import vk_api, threading from requests.exceptions import ProxyError  characters = ['z', 's', 'h', 'q', 'd', 'v', '2', '7', '8', 'x', 'y', '5', 'e', 'a', 'u', '4', 'k', 'n', 'm', 'c', 'p'] img_width = 130 img_height = 50 lock = threading.Lock() logging_lock: \"threading.Lock|None\" = None  # you can use some existing lock for logging max_length = 7 class VkCaptchaSolver:     \"\"\"     Vk captcha handling     Fast examples: 1) vk_api.VkApi >>> from vk_captcha import vk_api_handler >>> vk = vk_api_handler.VkApiCaptcha(\"88005553535\", \"efwoewkofokw\")  # this login will create captcha >>> vk_api_handler.Solver.logging = True  # enable logging >>> vk.auth() # getting Password Api error 2) solving captcha from url >>> from vk_captcha import VkCaptchaSolver >>> import random >>> solver = VkCaptchaSolver(logging=True) >>> captcha_response, accuracy = solver.solve(url=f\"https:\/\/api.vk.com\/captcha.php?sid={random.randint(0,10000000)}\", minimum_accuracy=0.15) >>> async def async_way(): ... await solver.solve_async(url=f\"https:\/\/api.vk.com\/captcha.php?sid={random.randint(0,10000000)}\") 3) if you have image in bytes: >>> solver.solve(bytes_data=requests.get(f\"https:\/\/api.vk.com\/captcha.php?sid={random.randint(0,10000000)}\").content)     \"\"\"     TOTAL_COUNT = 0     FAIL_COUNT = 1     TOTAL_TIME = 0     def __init__(self, logging=False, model_fname=os.path.dirname(__file__) + \"\/model.onnx\"):         self.logging = logging         self.Model = onr.InferenceSession(model_fname)         self.ModelName = self.Model.get_inputs()[0].name     def solve(self, url=None, bytes_data=None, minimum_accuracy=0, repeat_count=10, session=None) -> 'str,float':         \"\"\"Solves VK captcha         :param bytes_data: Raw image data         :type bytes_data: bytes         :param url: url of the captcha ( or pass bytes_data )         :type url: str         :param minimum_accuracy: Minimum accuracy of recognition.                                  If accuracy < minimum_accuracy then download captcha again                                  and solve it again. (Do it for repeat_count times)                                  Works only with url passed                                  Range = [0,1]         :type minimum_accuracy: float         :param repeat_count: Repeat solving count ( look at minimum_accuracy )                                 Range = [1,999]         :type repeat_count: int         :param session: requests.Session object or None         :return Tuple[answer:str, accuracy:float ( Range=[0,1]) ]         \"\"\"         if url is not None: url = url.replace('&resized=1', '').replace(\"?resized=1&\", '?')         if self.logging:             with logging_lock:                 print(f\"Solving captcha {url}\")         if repeat_count < 1: raise ValueError(f\"Parameter repeat_count = {repeat_count} < 1\")         for i in range(repeat_count):             if url is not None:                 for _ in range(4):                     try:                         bytes_data = (session or requests).get(url, headers={\"Content-language\": \"en\"}).content                         if bytes_data is None:                             raise ProxyError(\"Can not download data, probably proxy error\")                         break                     except:                         if _ == 3: raise                         time.sleep(0.5)             answer, accuracy = self._solve_task(bytes_data)             if accuracy >= minimum_accuracy or url is None:                 break             if self.logging:                 with logging_lock:                     print(f\"Solved accuracy(={accuracy:.4}) < miniumum(={minimum_accuracy:.4}). Trying again.\")         with lock: VkCaptchaSolver.TOTAL_COUNT += 1         return answer, accuracy      @property     def argv_solve_time(self):         \"\"\"Argv solve time in seconds per one captcha.         Start returning value after first solve ( solve_async) call\"\"\"         with lock:             return VkCaptchaSolver.TOTAL_TIME \/ (VkCaptchaSolver.TOTAL_COUNT or 1)  # zero division error capturing     @property     def _async_runner(self):         if not hasattr(VkCaptchaSolver, \"_runner\"):             VkCaptchaSolver._runner = concurrent.futures.ThreadPoolExecutor(                 max_workers=5             )         return VkCaptchaSolver._runner     async def solve_async(self, url=None, bytes_data=None, minimum_accuracy=0, repeat_count=10, session=None) -> 'str,float':         \"\"\"Solves VK captcha async         :param bytes_data: Raw image data         :type bytes_data: byte         :param url: url of the captcha ( or pass bytes_data )         :type url: str         :param minimum_accuracy: Minimum accuracy of recognition.                                  If accuracy < minimum_accuracy then download captcha again                                  and solve it again. (Do it for repeat_count times)                                  Works only with url passed                                  Range = [0,1]         :type minimum_accuracy: float         :param repeat_count: Repeat solving count ( look at minimum_accuracy )                                 Range = [1,999]         :param session: aiohttp.ClientSession session to download captcha         :type session: aiohttp.ClientSession         :type repeat_count: int         :return answer:str, accuracy:float (Range=[0,1])         \"\"\"         if self.logging: print(f\"Solving captcha {url}\")         if repeat_count < 1: raise ValueError(f\"Parameter repeat_count = {repeat_count} < 1\")         for i in range(repeat_count):             if url is not None:                 for _ in range(4):                     try:                         if session is None:                             async with aiohttp.ClientSession(headers={\"Content-language\": \"en\"}) as session_m, \\                                     session_m.get(url) as resp:                                 bytes_data = await resp.content.read()                         else:                             async with session.get(url) as resp:                                 bytes_data = await resp.content.read()                         if bytes_data is None: raise ProxyError(\"Can not download captcha - probably proxy error\")                         break                     except Exception:                         if _ == 3: raise                         await asyncio.sleep(0.5)             if self.logging: t = time.time()             #  running in background async             res = asyncio.get_event_loop().run_in_executor(self._async_runner, self._solve_task, bytes_data)             completed, _ = await asyncio.wait((res,))             #  getting result             answer, accuracy = next(iter(completed)).result()             if accuracy >= minimum_accuracy or url is None:                 break             print(f\"Solved accuracy(={accuracy:.4}) < miniumum(={minimum_accuracy:.4}). Trying again.\")         with lock: VkCaptchaSolver.TOTAL_COUNT += 1         return answer, accuracy     def _solve_task(self, data_bytes: bytes):         t = time.time()          img = cv2.imdecode(np.asarray(bytearray(data_bytes), dtype=np.uint8), -1)         img: \"np.ndarray\" = img.astype(np.float32) \/ 255.         if img.shape != (img_height, img_width, 3):             cv2.resize(img, (img_width, img_height))         img = img.transpose([1, 0, 2])         #  Creating tensor ( adding 4d dimension )         img = np.array([img])         # !!!HERE MAGIC COMES!!!!         result_tensor = self.Model.run(None, {self.ModelName: img})[0]         # decoding output         answer, accuracy = self.get_result(result_tensor)          delta = time.time() - t         with lock:             VkCaptchaSolver.TOTAL_TIME += delta         if self.logging:             with logging_lock:                 print(f\"Solved captcha = {answer} ({accuracy:.2%} {time.time() - t:.3}sec.)\")          return answer, accuracy      async def vk_wave_captcha_handler(self, error: dict, api_ctx: 'APIOptionsRequestContext'):         method = error[\"error\"][\"request_params\"][0][\"value\"]         request_params = {}         for param in error[\"error\"][\"request_params\"]:             if param[\"key\"] in (\"oauth\", \"v\", \"method\"):                 continue             request_params[param[\"key\"]] = param[\"value\"]          key = await self.solve_async(error[\"error\"][\"captcha_img\"], minimum_accuracy=0.33)          request_params.update({\"captcha_sid\": error[\"error\"][\"captcha_sid\"], \"captcha_key\": key})         return await api_ctx.api_request(method, params=request_params)     def vk_wave_attach_to_api_session(self, api_session):         d = api_session.default_api_options.error_dispatcher         d.add_handler(14, self.vk_wave_captcha_handler)     @staticmethod     def get_result(pred):         \"\"\"CTC decoder of the output tensor         https:\/\/distill.pub\/2017\/ctc\/         https:\/\/en.wikipedia.org\/wiki\/Connectionist_temporal_classification         :return string, float         \"\"\"         accuracy = 1         last = None         ans = []         # pred - 3d tensor, we need 2d array - first element         for item in pred[0]:             # get index of element with max accuracy             char_ind = item.argmax()             # ignore duplicates and special characters             if char_ind != last and char_ind != 0 and char_ind != len(characters) + 1:                 # this element is a character - append it to answer                 ans.append(characters[char_ind - 1])                 # Get accuracy for current character and                 # multiply global accuracy by it                 accuracy *= item[char_ind]             last = char_ind          answ = \"\".join(ans)[:max_length]         return answ, accuracy      def vk_api_captcha_handler(self, captcha, minimum_accuracy=0.3, repeat_count=10):         \"\"\"vk_api.VkApi captcha handler function\"\"\"         key, _ = self.solve(captcha.get_url(), minimum_accuracy=minimum_accuracy, repeat_count=repeat_count)         try:             ans = captcha.try_again(key)             return ans         except vk_api.ApiError as e:             if e.code == vk_api.vk_api.CAPTCHA_ERROR_CODE:                 with lock: VkCaptchaSolver.FAIL_COUNT += 1             raise <\/code><\/pre>\n<\/p>\n<\/div>\n<\/details>\n
      \n\u0423\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0430 \u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435<\/summary>\n
      \n
      \u0423\u0441\u0442\u0430\u043d\u043e\u0432\u043a\u0430 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438<\/a>:<\/p>\n
      pip install vk_captcha or pip install https:\/\/github.com\/imartemy1524\/vk_captcha\/raw\/main\/dist\/vk_captcha-2.0.tar.gz or pip install git+https:\/\/github.com\/imartemy1524\/vk_captcha <\/code><\/pre>\n
      \u041d\u0443 \u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435:<\/p>\n
        \n
      1. \n
        \u0421 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 vk_api<\/p>\n<\/li>\n<\/ol>\n
        from vk_captcha import vk_api_handler vk = vk_api_handler.VkApiCaptcha(\"88005553535\", \"efwoewkofokw\")  # this login will create captcha vk_api_handler.Solver.logging = True  # enable logging vk.auth() # get captcha error and automatically solve it <\/code><\/pre>\n
          \n
        1. \n
          «\u0420\u0443\u0447\u043a\u0430\u043c\u0438»<\/p>\n<\/li>\n<\/ol>\n
          from vk_captcha import VkCaptchaSolver import random, requests  session = requests.Session()   session.headers['User-Agent'] = 'Mozilla\/5.0 (Windows NT 10.0; rv:91.0) Gecko\/20100101 Firefox\/91.0'  solver = VkCaptchaSolver(logging=True)  # use logging=False on deploy sid = random.randint(122112, 10102012012012) easy_captcha = False url = f\"https:\/\/api.vk.com\/captcha.php?sid={sid}&s={int(easy_captcha)}\"  answer, accuracy = solver.solve(     url=url,     minimum_accuracy=0.33,  # keep solving captcha while accuracy < 0.33     repeat_count=14,  # if we solved captcha with less than minimum_accuracy, then retry repeat_count times     session=session  # optional parameter. Useful if we want to use proxy or specific headers ) # or #answer, accuracy = solver.solve(bytes_data=session.get(url)) print(f\"I solved captcha = {answer} with accuracy {accuracy:.4}\") <\/code><\/pre>\n
            \n
          1. \n
            \u041d\u0443 \u0438 \u043a\u0443\u0434\u0430 \u0432 \u0442\u0435\u043a\u0443\u0449\u0438\u0445 \u0440\u0435\u0430\u043b\u0438\u044f\u0445 \u0431\u0435\u0437 \u0432\u0441\u0435\u043c\u0438\u043b\u044e\u0431\u0438\u043c\u043e\u0433\u043e<\/em> async<\/strong><\/p>\n
            from vk_captcha import VkCaptchaSolver import random, asyncio solver = VkCaptchaSolver(logging=False)  # use logging=False on deploy async def captcha_solver():     sid = random.randint(122112, 10102012012012)     easy_captcha = False     url = f\"https:\/\/api.vk.com\/captcha.php?sid={sid}&s={int(easy_captcha)}\"     answer, accuracy = await solver.solve_async(url=url, minimum_accuracy=0.4, repeat_count=10)     print(f\"Solved captcha = {answer} with accuracy {accuracy:.4}\") asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) asyncio.run(captcha_solver()) <\/code><\/pre>\n<\/li>\n<\/ol>\n<\/div>\n<\/details>\n
            \u041d\u0443 \u0438 \u0437\u0430\u0447\u0435\u043c \u0432\u0435\u0441\u044c \u044d\u0442\u043e\u0442 \u0441\u044b\u0440-\u0431\u043e\u0440?<\/h2>\n
            \u0415\u0441\u043b\u0438, \u043f\u0440\u043e\u0447\u0438\u0442\u0430\u0432 \u044d\u0442\u0443 \u0441\u0442\u0430\u0442\u044c\u044e, \u0432\u044b \u0437\u0430\u0434\u0430\u043b\u0438\u0441\u044c \u0432\u043e\u043f\u0440\u043e\u0441\u0430\u043c\u0438 \u043f\u043e \u0442\u0438\u043f\u0443: \u00ab\u0420\u0430\u0434\u0438 \u0447\u0435\u0433\u043e \u044d\u0442\u043e \u0432\u0441\u0451?\u00bb, \u00ab\u0420\u0430\u0434\u0438 20 \u0440\u0443\u0431\u043b\u0435\u0439 \u0437\u0430 1000 \u043a\u0430\u043f\u0447?\u00bb, \u0442\u043e \u0445\u043e\u0447\u0443 \u0432\u0430\u0441 «\u043e\u0431\u0435\u0441\u043f\u043e\u043a\u043e\u0438\u0442\u044c<\/em>«, \u0432\u0441\u0451 \u044d\u0442\u043e \u0431\u044b\u043b\u043e \u043d\u0435 \u0437\u0440\u044f. \u041c\u044b \u0431\u0443\u0434\u0435\u043c \u0432\u0437\u043b\u0430\u043c\u044b\u0432\u0430\u0442\u044c<\/s> \u0432\u043a.<\/p>\n
            \n
            \u0414\u0438\u0441\u043a\u043b\u0435\u0439\u043c\u0435\u0440 \u21162:<\/p>\n
            \u0412\u0441\u0435 \u043f\u043e\u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f \u044f\u0432\u043b\u044f\u044e\u0442\u0441\u044f \u0447\u0438\u0441\u0442\u043e \u0442\u0435\u043e\u0440\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u043c\u0438<\/strong>. \u0410\u0432\u0442\u043e\u0440 \u0441\u0442\u0430\u0442\u044c\u0438 \u043d\u0438\u043a\u043e\u0433\u043e \u043d\u0435 \u0432\u0437\u043b\u0430\u043c\u044b\u0432\u0430\u043b<\/strong> \u0438 \u043d\u0435 \u0441\u043e\u0432\u0435\u0440\u0448\u0430\u043b<\/strong> \u043d\u0438\u043a\u0430\u043a\u0438\u0445 \u0434\u0440\u0443\u0433\u0438\u0445 \u043f\u0440\u043e\u0442\u0438\u0432\u043e\u0437\u0430\u043a\u043e\u043d\u043d\u044b\u0445 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439<\/strong>; \u043e\u043d \u0432\u0441\u0435\u0433\u043e \u043b\u0438\u0448\u044c «\u043f\u0440\u0438\u0434\u0443\u043c\u0430\u043b» \u0442\u0435\u043e\u0440\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0443\u044e \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u044c \u043f\u043e \u043e\u0431\u0445\u043e\u0434\u0443 \u0437\u0430\u0449\u0438\u0442\u043d\u043e\u0433\u043e \u0444\u0443\u043d\u043a\u0446\u0438\u043e\u043d\u0430\u043b\u0430 VK. <\/p>\n
            \u0410\u0432\u0442\u043e\u0440 \u0442\u0430\u043a\u0436\u0435 \u043d\u0435 \u0441\u043e\u0432\u0435\u0442\u0443\u0435\u0442 (\u0438 \u043d\u0438 \u0432 \u043a\u043e\u0435\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u043d\u0435 \u043f\u043e\u0431\u0443\u0436\u0434\u0430\u0435\u0442) \u0432\u0430\u043c \u0441\u043e\u0432\u0435\u0440\u0448\u0430\u0442\u044c \u043d\u0438\u043a\u0430\u043a\u0438\u0445 \u043f\u0440\u043e\u0442\u0438\u0432\u043e\u0437\u0430\u043a\u043e\u043d\u043d\u044b\u0445 \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u0439 \u0441 \u043a\u043e\u0434\u043e\u043c \u043d\u0438\u0436\u0435: \u043f\u043e\u043c\u043d\u0438\u0442\u0435 \u043f\u0440\u043e \u0443\u0433\u043e\u043b\u043e\u0432\u043d\u044b\u0439 \u043a\u043e\u0434\u0435\u043a\u0441<\/strong>.<\/p>\n<\/blockquote>\n
            \u041d\u0435\u043c\u043d\u043e\u0433\u043e \u043e \u0431\u0440\u0443\u0442\u0444\u043e\u0440\u0441\u0435 \u0432 \u0440\u0435\u0430\u043b\u0438\u044f\u0445 2023 \u0433\u043e\u0434\u0430<\/h3>\n
            \u041d\u0430\u0432\u0435\u0440\u043d\u043e\u0435 \u043c\u043d\u043e\u0433\u0438\u0445 \u0448\u043a\u043e\u043b\u044c\u043d\u0438\u043a\u043e\u0432, \u0440\u0435\u0448\u0438\u0432\u0448\u0438\u0445 \u0432\u0437\u043b\u043e\u043c\u0430\u0442\u044c \u0432\u043a, \u043f\u043e\u0441\u0435\u0449\u0430\u043b\u0430 \u043c\u044b\u0441\u044c: «\u0430 \u0434\u0430\u0432\u0430\u0439\u0442\u0435 \u043f\u0440\u043e\u0441\u0442\u043e \u043f\u0435\u0440\u0435\u0431\u0435\u0440\u0435\u043c \u043f\u0430\u0440\u043e\u043b\u0438, \u0438 \u0432\u0437\u043b\u043e\u043c\u0430\u0435\u043c \u043e\u0434\u043d\u043e\u043a\u043b\u0430\u0441\u0441\u043d\u0438[\u0446\u0443]?». \u041d\u043e \u0432\u0441\u044f \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u044f \u0443\u043f\u0438\u0440\u0430\u043b\u0430\u0441\u044c \u0432 \u043d\u0435\u0451 \u2014 \u043a\u0430\u043f\u0447\u0443<\/em>, \u0432\u044b\u0441\u043a\u0430\u043a\u0438\u0432\u0430\u044e\u0449\u0443\u044e \u043f\u043e\u0441\u043b\u0435 3-5<\/strong> \u0437\u0430\u043f\u0440\u043e\u0441\u043e\u0432, \u043a\u043e\u0442\u043e\u0440\u0443\u044e VK<\/strong> \u0434\u043e\u0431\u0440\u043e\u0441\u043e\u0432\u0435\u0441\u0442\u043d\u043e \u043d\u0430\u0447\u0438\u043d\u0430\u043b \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0442\u044c \u043c\u043e\u043b\u043e\u0434\u044b\u043c «\u0437\u043b\u043e\u0443\u043c\u044b\u0448\u043b\u0435\u043d\u043d\u0438\u043a\u0430\u043c<\/em>«. \u041d\u043e \u0442\u0435\u043f\u0435\u0440\u044c \u043e\u0431\u043e\u0439\u0442\u0438 1<\/strong> \u044d\u043a\u0437\u0435\u043c\u043f\u043b\u044f\u0440 \u044d\u0442\u043e\u0439 \u0437\u0430\u0449\u0438\u0442\u044b<\/em> \u0441\u0442\u043e\u0438\u0442 \u043d\u0430\u043c \u0432\u0441\u0435\u0433\u043e 2\u043c\u0441<\/strong>, \u0442\u0430\u043a \u0447\u0442\u043e \u043f\u043e\u0447\u0435\u043c\u0443 \u0431\u044b \u043d\u0435 \u043f\u0440\u043e\u0432\u0435\u0441\u0442\u0438 «\u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u0435<\/em>» \u043e \u0442\u043e\u043c, \u043d\u0430\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0440\u0435\u0430\u043b\u044c\u043d\u043e \u0432\u0437\u043b\u0430\u043c\u044b\u0432\u0430\u0442\u044c \u0441\u0442\u0440\u0430\u043d\u0438\u0447\u043a\u0438 \u0431\u0440\u0443\u0442\u0444\u043e\u0440\u0441\u043e\u043c \u0432 21 \u0432\u0435\u043a\u0435.<\/p>\n
            \u041a\u0441\u0442\u0430\u0442\u0438, VK<\/em> \u0434\u043e \u043d\u0435\u0434\u0430\u0432\u043d\u0435\u0433\u043e \u0432\u0440\u0435\u043c\u0435\u043d\u0438 \u0434\u0430\u0436\u0435 \u043d\u0435 \u0447\u0435\u043a\u0430\u043b \u0442\u0430\u0439\u043c\u0438\u043d\u0433\u0438 (\u0434\u0430 \u0438 \u0432\u0440\u043e\u0434\u0435 \u0441\u0435\u0439\u0447\u0430\u0441 \u043d\u0435 \u0432\u0441\u0435\u0433\u0434\u0430 \u0438\u0445 \u043f\u0440\u043e\u0432\u0435\u0440\u044f\u0435\u0442): \u043c\u043e\u0436\u043d\u043e \u0431\u044b\u043b\u043e \u0440\u0435\u0448\u0438\u0442\u044c \u043a\u0430\u043f\u0447\u0443 \u0437\u0430 0.1<\/strong> \u0441\u0435\u043a\u0443\u043d\u0434\u0443, \u0438, \u0435\u0441\u043b\u0438 \u043e\u043d\u0430 \u0431\u044b\u043b\u0430 \u0440\u0435\u0448\u0435\u043d\u0430 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e, \u043d\u0430\u043c \u0447\u0435\u0441\u0442\u043d\u043e \u0434\u0430\u0432\u0430\u043b\u0438 \u043e\u0448\u0438\u0431\u043a\u0443, \u0447\u0442\u043e \u043f\u0430\u0440\u043e\u043b\u044c \u043d\u0435\u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u044b\u0439 (\u0438\u043b\u0438 \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u044b\u0439).<\/p>\n
            \u0418\u0442\u0430\u043a, \u043f\u0435\u0440\u0432\u0430\u044f \u0438\u0434\u0435\u044f, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043f\u0440\u0438\u0448\u043b\u0430 \u043c\u043d\u0435 \u043d\u0430 \u0443\u043c: \u00ab\u0434\u0430\u0432\u0430\u0439\u0442\u0435, \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438, \u0432\u043e\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c\u0441\u044f \u041e\u0424\u0418\u0426\u0418\u0410\u041b\u042c\u041d\u042b\u041c API VK<\/a>\u00bb. C\u043c\u044b\u0441\u043b \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e \u0442\u0430\u043a\u043e\u0439: \u0438\u0449\u0435\u043c \u0430\u043a\u043a\u0430\u0443\u043d\u0442\u044b \u0441 \u043f\u0443\u0431\u043b\u0438\u0447\u043d\u043e \u0443\u043a\u0430\u0437\u0430\u043d\u043d\u044b\u043c \u043d\u043e\u043c\u0435\u0440\u043e\u043c \u0442\u0435\u043b\u0435\u0444\u043e\u043d\u0430 \u0438 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c \u0435\u0433\u043e \u043a\u0430\u043a \u043b\u043e\u0433\u0438\u043d. \u0414\u0430\u043b\u0435\u0435 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u0434\u0440\u0443\u0433\u0438\u0445 \u043f\u0443\u0431\u043b\u0438\u0447\u043d\u043e-\u043b\u0438\u0447\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 (\u0438\u043c\u0435\u043d\u0438<\/em>, \u0444\u0430\u043c\u0438\u043b\u0438\u0438<\/em>, \u0434\u0430\u0442\u044b \u0440\u043e\u0436\u0434\u0435\u043d\u0438\u044f<\/em>, \u0442\u0435\u0445 \u0436\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0438\u0445 \u0440\u043e\u0434\u0441\u0442\u0432\u0435\u043d\u043d\u0438\u043a\u043e\u0432<\/em>\/2\u0445 \u043f\u043e\u043b\u043e\u0432\u0438\u043d\u043e\u043a<\/em>) \u0431\u0443\u0434\u0435\u043c \u0433\u0435\u043d\u0435\u0440\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0431\u0430\u0437\u0443 \u043f\u0430\u0440\u043e\u043b\u0435\u0439<\/u> \u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0433\u043e<\/strong> \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u043e\u0433\u043e \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044f<\/p>\n
            \n\u0421\u043a\u0440\u0438\u043f\u0442 \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0438 \u043f\u0430\u0440\u043e\u043b\u0435\u0439 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u043b\u0438\u0447\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044f<\/summary>\n
            \n
             # Date: 02\/23\/2019 # Original passgen author: Mohamed # Some changed by: UnderMind0x41 # Description: A social engineering password generator # This script required python version >= 3.0 import argparse import sys   # 3.5k most popular passwords # TOP_PASSWORDS = {'legend', 'bandit', 'deedee', 'FREEDOM', 'ZAQXSW123', 'games12345', 'read10', 'Chevy1', 'madonna', 'nikkoz', 'sfactory', 'nikita', 'snowball', 'aqwert', 'macintos', 'WOODWORK', 'member_name', 'yoyoyo', 'cloclo', 'intern', 'saskia', 'wisdom', 'praise', 'happiness', 'animoto', 'vfhbyf', 'SKIPPER', '123321', 'jesuschrist', '\u043b\u044e\u0431\u043e\u0432\u044c', 'designer', 'racoon', 'vcxzfdsa', 'beetle', 'cccccc', 'asembler', 'qwerty33', 'newbie', 'rosebud', 'harold', 'Snowbal', 'camille', 'regusers', 'iloveaol', 'testtest', 'hollister', '87654321qwerty', 'DEPECHE', 'usrname', 'Jimbob', 'ACIDBURN', 'Tardis', 'sysadmin', 'hilary', 'qwerty4321', 'charles', 'canibal', 'reckah123', 'grover', 'Bowling', 'shorty', 'Cowboy', 'travel', 'Aa123456.', 'matthew1', 'loverboy', 'ncc1701d', 'knight', 'steaven', 'marines', 'irarref', 'upiter', 'christmas', 'liliana', 'imagine', 'rocky1', 'Tiffany', 'unstable', 'GIVEALL', 'logitech', 'liverpool1', 'romuluzz', 'user_n', 'Fishing', 'popeye', 'gateway', 'spammer', 'brooke', 'cecilia', 'victor', 'molson', 'userpasswd', 'megabit', 'goblue', 'impala', 'stargate', 'thunder', 'Tootsie', '987456', 'gustavo', 'MYICQUIN', 'hammer', 'sergei', 'myname', 'popcorn', 'benoit', 'aaaaaaaa', 'Chiefs', '66666666', 'FERRARI', 'sprite', 'stanley', '147258', 'MIR@nda', 'login_name', 'brewster', 'mystic', 'bright', 'dustin', 'thumbs', '143143', 'tatiana', 'CREATION', 'salvador', 'Sanders', 'artist', 'killem0l', 'tristan', '682995', 'winner', 'qweasdzxc', '\u0418\u0422\u042a\u0412\u042f\u041b', 'fireball', 'ilovehim', 'CHELSIA', 'trouble', 'GOBLIN', 'Justin', 'passwords', 'amelia', 'phoenix1', 'kelsey', 'zxcasd', 'linkinpark', 'penguin', 'administrator123', 'kermit', 'zxcdsaqwe11', 'mysql.user', 'hotchick', 'asdf', 'Spirit', 'esther', 'sarita', 'vputin', 'loveme2', 'August', 'Dustin', 'guinness', 'NOTEBENE', 'whatever', 'loser1', 'number', 'Soccer', 'charlie1', 'naruto', 'starter', 'tigers', '54321', 'emerpus', 'symbol', 'mywapis', 'Johnny', '12345qwe', 'administrat0r', 'Babies', 'icqpass', 'sailor', 'uoyevoli', '4444444', 'smashing', 'monster', 'pelusa', 'maria', 'vanilla', 'qwerty007', 'Basket', 'conrad', 'm@ster', 'Elwood', 'cxzasd', 'anyway', 'jackie', 'soccer1', 'p_word', 'blessed', 'drakula', 'converge_pass_salt', 'qazxswedcvfr', 'shanti', 'administrator', 'qcinigol', 'baltika9', 'indiana', 'emmanuel', 'qwerty2012', 'stinky', 'MIRANDA', 'Vampire', 'toyota', 'oldman', 'skaradub', 'red123', 'adminusername', 'scooby', 'niudekcah', 'gregory', 'nadine', '!QWERTY', 'beagle', 'cowboy', 'pogiako', 'daniel1', 'my_password', 'norton', 'oxford', 'powerman', 'ledzep', 'torres', 'broken', 'Shooter', 'electric', 'win2000', 'kittens', 'estrellita', 'schleker', '\u042a\u0422\u0418\u0416\u0421\u0419', 'college', 'orbita', 'monkeys', 'connie', 'patito', 'Lexus21', 'mitchell', 'princess', 'sysadmins', 'user_list', 'cyclone', 'sheena', 'sysusers', 'security', 'buttons', 'aimuin', 'frederic', '1qwerty1', 'dolphins', 'stupid', 'daniel', 'lockout', 'CAT&DOG', 'UNKNOVVN', 'babaytugur', 'guanli', 'Robert', 'ROBINGUD', 'testing', 'rafael', 'zxcvfdsa', 'admin_userinfo', 'alyssa', 'fantom', '1a2b3c', 'booboo', 'barbara', 'Smokey', 'QIP123', 'julian', 'clientname', 'moomoo', 'qazwsx', 'bigfoot', 'maganda', 'phoenix', 'tweety1', 'Grandma', 'screen', 'fantasy', 'guadalupe', 'spanky', 'potaptik', 'brithney', 'undead', 'angelita', 'barney', 'ramazik', 'molly1', 'administrator_name', 'Frosty', 'cantik', 'COVID19', 'Bigfoot', 'quake3', 'tarzan', 'gorgeous', 'wilbur', 'qwe456', 'yourmom', 'zxcdsa44', 'Shotgun', 'morena', 'bonnie', 'corazon', 'friendster', 'Arctic', 'genezis', 'hemmelig', 'ytinirt', 'explorer', 'gretchen', 'pma_table_info', 'monday', 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'\u0418\u0422\u042a\u0412\u0428\u0416', 'denise', 'charlotte', 'tamara', 'svetik', 'CYBORG', 'madrid', 'newuser', 'PRINCESS', 'Chicago', 'adminpass', 'mem_pass', 'P@SVVORD', '21qwerty', 'customer', 'playgirl', 'WIZARD13', 'aqswdefr', 'number1', 'Honda1', 'bond007', 'newsid', 'AAAAAA', 'gasman', 'viper1', '59trick', '888888', 'wolverin', 'mahalko', 'CHANEL', 'qazqaz', 'content', 'shadow', 'PUSSYCAT', 'esmeralda', '123789', 'welcome', 'strawber', 'doogie', 'dragon1', 'Speedy', 'Melissa', 'conner', 'buster', 'nicole1', 'Aikman', 'BON_JOVI', 'UNIVERSE', 'topher', 'STATIC', 'tequieromucho', 'jenjen', 'user_pword', 'bettyboop', 'micheal', 'mookie', 'perfect', 'alisha', 'valhalla', 'taylor', 'ecuador', 'ACHTUNG', '168168', 'emerald', 'password2', 'deadhead', 'julie1', 'shogun', 'ENTRANCE', 'Sparky', 'marissa', 'francis', 'mylife', 'porsche9', 'terminal', 'sexylady', 'caesar', 'naughty', 'Lakota', 'ashlee', '020202', 'Pacers', 'CyberL0rd', 'lalala', 'emailaddress', 'talisman', 'windows2003', 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'123456qwerty', 'ctrlalt', 'cougar', 'maxime', '9876543210', 'condom', 'jeremy', 'Dakota', 'micros', 'cowboys', '!pwd@ti', 'jackson', 'METAFORA', 'Blazer', 'zxcdsa33', 'cookie1', 'holland', 'daytek', 'Little', 'tigger', 'catwoman', 'timothy', 'Knights', 'johnny', 'Author', 'tb_user', 'epidemic1', 'usrpass', 'inuyasha', 'user_ip', 'lastname', 'camilo', 'bebita', 'love12', '00000000', 'chacha', 'diablo', 'Bookit', 'MODERN', 'debbuger', 'usr_nusr', 'control', 'laptop', 'abcdef', '123abc', 'daniela', 'pantera', 'blowfish', 'celeste', 'pangit', 'pierce', 'elijah', 'hector', 'maribel', 'Malibu', 'memberlist', 'victory', 'august', 'silence', 'techno', 'rockon', 'cristo', 'thomas1', 'Kinder', 'spacer', 'abcd1234', 'jessica1', 'leningrad', 'temppass', 'adrian', 'phpmyadmin.pma_table_info', 'wp_users', 'controle', 'navalny', 'aaaaaa', 'GLOBUS', '0123qwerty', 'adminlogin', 'Science', 'Vikings', 'animals', 'nubobap', 'psalms', 'Rodman', 'stephen', 'loveyou', 'richard', 'epidemic', 'chichi', 'sweetpea', 'victoria', 'qazwsxed', 'adress', 'SCORPION', 'online', 'qwerty21', 'DROWPASS', 'master1', 'cookie', '000000', 'glitter', 'killer', 'chimaira', 'Fluffy', 'GARBAGE', 'myspace1', 'vampire', 'wkarlo', 'Heather', 'pallmall', 'tb_login', '1234qwer', 'macafee', 'snoopdog', 'carter', 'dayana', 'Raistlin', 'covid2020', 'poopoo', 'corwin', 'dogbert', 'Shorty', 'Champs', 'test1234', 'KITANA', 'hal9000', 'klepan', 'blasphem', 'chrissy', 'Kristy', 'politics', 'katherin', 'mypassword', 'remal321', 'tr1n1ty', '\u0418\u0416\u0421\u0419\u0415\u041c\u0422\u0428\u0411\u042e', 'Broncos', '161616', 'dolphin', 'bird33', 'micron', 'health', 'eduardo', '2cute4u', 'family', 'COLUMBUS', 'connor', 'indian', 'CTRALTDL', 'sweamer', 'qwerty0123456789', 'thx1138', 'vision', 'leslie', '88888888', 'Braves', 'harvey', 'ICECUBE', 'Farmer', 'rossia', 'pass_w', 'maverick', 'market', 'martini', 'nursing', 'brutus', 'mariana', 'speedo', 'cristina', 'godzilla', 'septembe', 'Surfer', 'hotgirl', 'qwerty123456', 'bigman', 'bigdog', 'rtyuiop', 'Blowme', 'butthead', 'alfred', 'pearljam', 'Friends', '1q2w3e4r5', 'troppus', 'system32', 'michele', '080808', 'Hornets', 'nemezis', 'Hanson', 'pokemon', 'people', 'temporal', 'tester', 'september', 'Number1', 'reggie', 'cc_number', 'dubsmash', 'superman', 'leonardo', 'kittykat', 'hctib123', 'passvord', 'trident', 'laskovaja', 'mailman', 'outbrake', 'santiago', 'mariposa', 'martin', '4815162342', 'u_name', 'zaqxswcde', 'my_email', 'mememe', 'insane', 'flymode', 'xar_pass', 'avrets', 'orgazm', 'catalina', 'junior', 'Digger', 'userpw', 'status', 'b00ster', 'anthony', 'forever', 'hernandez', 'trebor', 'mycomputer', 'wonder', 'Rhonda', 'lavender', 'qwert123', 'qazxsw', 'marina', 'bianca', 'elephant', 'Griffey', 'number9', 'jaguar', 'CIVILIAN', 'dinozavr', 'cuteako', 'sagitario', 'nellie', 'apollo13', 'slideshow', '112233', '242424', 'iloveu', 'arizona', 'VOODOO', 'iloveu2', 'Nicole', 'maracle', 'idontknow', 'gatito', 'sparrow', 'rodrigo', 'samson', 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'adminpsw', 'user_username', 'ststic', '7758521', 'db_hostname', 'user_pwrd', 'mem_pwd', 'shleker', 'Topgun', 'systime', 'cuteko', 'willow', 'KILLEMOL', '1234qwerty1234', 'dragon', 'genesis', 'nguyen', 'crystal', 'pedofil', 'ballet', 'pass1234', 'sakura', 'challeng', 'domino', 'SOLDIER', 'murphy', 'laikos', 'spongebob', 'stefan', 'weapon', 'Spunky', 'ETERNITY', 'marshall', 'LUCKY123', 'chicken', 'teacher', 'VURDALAK', 'jason1', 'yomama', '\u0439\u0446\u0443\u043a\u0435\u043d', 'theatre', 'lourdes', 'Russel', '][aker13', 'heather', 'manage', 'soleil', 'brittany', 'energy', '444444', 'qwerty12345', 'donald', 'Maddog', '1234567qwert', 'Skinny', 'orchid', 'Button', 'star69', 'freddy', 'hannah', 'volleyball', 'impotent', 'abc123', 'monopoly', '22222222', 'magnum', '741852', 'fi$her', 'NATURE', 'sammie', '784512', 'telecom', 'marisa', 'alexande', 'claire', 'lizzie', 'gandalf', 'MYZTIC', '576823', 'a1b2c3d4', 'akrmaz', 'sweetness', 'musica', '@#$%^&', 'alfredo', 'ramona', 'EXORCIST', 'Monica', 'session', 'Cleaner', 'intrepid', 'Windows', 'jackass', 'ACT_INFO', 'lovely1', 'p_assword', 'personal', 'adminroot', 'asdfghj', '2222222', 'qazedc', 'lauren', 'MUDVIN', 'president', 'Footbal', '123qwe', 'xxxxxx', 'andrew', 'jamaica', 'computer', 'setting', '12345', 'aylmer', 'admin_pwd', 'lorraine', 'wright', 'spiderman', 'lupita', 'georgia', 'Groovy', 'isabelle', 'ramirez', 'memlogin', 'iverson', 'johanna', 'beautifu', 'sexybitch', 'dratsab', 'webusers', 'sunny1', 'jasmine', 'topcat', 'workgroup', 'teamomucho', 'Farming', 'million', '12345678qwerty12345678', 'copper', 'millie', 'anonymous', 'zephyr', 'somsoc', 'zenith', 'Challenge', 'christia', 'format', 'Killme', 'nascar', 'qwerty1234', '8888888', '123qwerty123', 'Cooper', 'sb_admin_name', 'renegade', 'chrisbrown', 'michael1', '01234567qwerty', 'phantom', 'susana', 'qw1234er', 'covid19', 'wolfMan', 'blink182', 'qwerty012345678', 'Alicia', 'Senior', 'AndrewK', '895623', '171717', 'scruffy', 'trustno1', 'account', 'Froggy', '1234', 'usr_pass', 'trixie', 'hershey', 'amigos', 'bluesky', 'kissmyass', 'edonkey', 'malcolm', 'darwin', 'user_id', 'sparkle', 'eastwest', 'psycho', '!@#$%^&', 'Jimmy2503', 'site_logins', 'butterfly1', 'stealth', 'astrix', 'Uragan-75', 'access', 'samsung', 'michael', 'APACHE', 'shvine', 'yellow1', 'membername', 'RUNDLL32', '1q2w3e*', 'project', 'potter', 'anyone', 'id_member', '123456789', 'justme', 'qwert1', 'qwerty012345678910', 'cms_admins', 'Squirt', 'bhebhe', 'TEACHERS', 'sweet1', 'COVID19_Access', 'mifesto', 'axszdvfc', 'Subjects', 'reg_users', 'letmein', 'doctor', 'zxcdsa11', 'AOL123', 'zaqxsw', 'janelle', 'outlook', 'kleenex', 'adminpassword', 'Hotrod', 'julius', 'amfibia', 'christopher', 'adminupass', 'sharona', '\u0418\u0416\u0421\u0419\u0415\u041c', 'godisgood', 'marcela', 'sammy1', '789321', 'barclay', 'skyline', 'adminpwd', 'diamonds', '12345qwerty12345', 'inside', 'sunshine', 'amores', 'hybrid', 'direct1', 'enrique', 'europe', 'claude', 'Skidoo', 'phillip', 'darkangel', 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'cxzcxz', 'avalon', 'newyork', 'larry1', 'seven7', 'joyjoy', '10pace', 'majordom', 'serenity', '159159', 'babygurl1', 'freak1', 'Biology', 'Drizzt', 'classroo', 'superstar', 'linkin', 'pentium', 'quebec', 'austin', 'daddysgirl', 'CLEVERMAN', '!@#$%^&*(', 'Animal', 'habibufc', 'danica', 'saturday', 'gabrielle', 'Sidney', 'joanna', 'JEREMY', 'FORMATC', 'lecasolo', 'Tweety', 'nimrod', 'zaxscd', 'houston', 'Jessie', 'absolut', 'southside', 'cancer', 'pebbles', 'marine', 'asdfjkl', 'helena', 'puppies', 'sexy12', 'archie', '77777777', 'Browns', 'BigBird', 'shutup', 'cemetery', 'kelvin', 'cocacola', '44444444', 'cwsiadmin', 'majestic', 'user_pass', 'utopia', 'anamaria', 'Packers', 'user_passw', 'tequila', 'redrum', 'tucker', 'judith', 'taurus', 'g0dm0de', 'punkin', 'login_pass', 'purple', 'nopassword', 'killer66', 'iloveyou', '333333', 'cheerleader', 'lucky1', 'snuffy', 'camaro', 'inlove', 'zaraza', 'Phillip', 'Voyager', 'bugsbunn', 'qazzaq', 'baseball1', 'solemn', 'jordan1', 'zeppelin', 'celticfc', '\u043d\u0430\u0442\u0430\u0448\u0430', 'greenday', 'shadow1', 'taylor1', 'xavier', 'ronnie', 'Hockey', '1234567', 'per$on@l', 'Reading', 'viarif', 'manutd', 'sassy1', 'converse', '99999999', 'please', 'batista', 'alberto', 'jesucristo', 'cms_admin', 'Rebels', 'hottie', 'krystal', 'newcastle', 'oliver', 'ariane', 'pookie1', 'reg_user', '112112', '\u0418\u0422\u042a\u0412\u0428\u0416\u0421\u0411\u042f', 'Chrissy', 'tbladmins', 'scream', 'wednesda', 'loveme1', 'Ginger', 'alex78', '12348765', 'motorola', 'marcel', 'zxcdsa12345', 'Lennon', 'reckah321', 'sentenced', '123456a', 'article', 'qwerty123456789', 'playboy1', 'flores', 'login_passwd', 'angelina', 'loveme', 'MUSTANG', 'deffer', 'velvet', 'jayjay', 'xar_name', 'fabiola', 'baran123', '232323', 'detroit', 'brandon1', '147852369', 'jordan23', 'Beavis', 'hottie1', 'baseball', '1314520', 'support3', 'Beaner', '888999', 'nnallex', 'eagle1', 'gfhjkm', 'laddie', 'thumper', 'Purple', 'desiree', 'superman1', 'tarantul', 'labtec', 'Dwight', 'happy1', 'kissme', 'babycakes', 'taytay', 'COLORADO', 'birthday', '54321qwerty', 'SATANIC', 'qwerty7654321', 'qweqwe', 'bethany', '224466', 'iloveyou!', 'eagles', 'Frankie', 'kostikd', 'love123', 'Hotdog', 'dioretsa', '1111111111', 'Passwor', 'dollar', 'birdie', 'shelley', 'ashley', 'f00tball', 'abigail', 'benfica', 'gocougs', 'brenda', 'marley', 'stella', 'qwerty012', 'corrado', 'iloveyou2', 'william1', 'sunshine1', 'LocalAdministrator', '\u041e\u042e\u041f\u041d\u041a\u042d', 'qwe12345', 'admin123456', 'biteme', 'CHANNEL', 'ranger', 'h2opolo', 'hannah1', '1q2w3e4r5t6y', 'oligarch', 'Snoopy', 'Volley', 'Buttons', 'isabella', '7758258', 'qwertyasdfg', 'amelie', 'cheese', 'jonathan', 'redface', 'diosesamor', 'tekiero', 'krovatka', 'rabbit', 'admin_password', 'huiznaet', 'brooklyn', 'rochelle', 'depurple', 'pass1word', 'qwertyui', '968574', 'Weezer', 'dennis', 'disney', 'amistad', 'adminadmin', 'dianita', 'Tractor', 'mississippi', 'andres', 'GoddoG', 'qwert123456789', 'user_info', 'loulou', '456321', 'beatriz', 'sonof\u0410\u0425\u0412', 'promethe', '1qwerty', 'fozzie', 'puppy123', 'mishka', '%username%1', 'user_passwd', 'Wheels', 'saturn', 'CatdoG', 'gilles', 'gofish', 'bluebird', 'user_name', 'maryjane', 'smiles', 'babydoll', 'mortal', 'pandora', 'pirate', 'ytrewq321', 'republic', 'user_pw', 'screen10', 'juventus', 'Nirvana', '!@#$%^&*', 'FLIGHT', 'qwerty', 'qwerty01234', 'snoopy', 'deliver', 'medical', '1qazxsw2', 'Coronavirus2020', 'esteban', 'Sendit', '159357', 'kenneth', 'megadeth', 'ph4ntom', 'Password1', 'sabrina', 'clubconfig', 'sergio', 'hawaii', 'valerie', 'valentina', 'qwerty0', 'sysuser', 'fapfap', 'herbert', 'SADAMAZA', 'qweasd', '9379992', 'qwerty44', 'ufolog', 'cjkysirj', 'propu$k', 'lovelife', 'SCHWARZ', '1qaz2wsx3edc', 'damian', 'adminemail', 'casino', 'Puckett', 'vfa1993', 'qwertyy', 'sunbird', 'hotdog', 'enigma', 'xcountry', 'tequiero', 'eeyore', 'worked', 'purple1', 'attila', 'eXtreme', 'analsex', 'angel123', 'qazxswedc', 'niger13', 'marcos', 'father', 'chinita', 'miamor', 'skater', 'Trucks', 'P@$VVORD', 'ase4ka', 'tigger1', 'informix', 'catfish', 'jordan', '\u043f\u0440\u0438\u0432\u0435\u0442', 'tintin', '1qaz2wsx', '0987654321', 'dedicated', 'carole', 'amorcito', 'trinity', 'gerrard', 'Travis', 'beauty', 'Library', 'Carlos', 'kitten', '0000000', 'dr0wp@$$', 'MORBID', 'thebest', 'christy', 'hermes', 'chelsea', '%null%', 'logical', 'sunday', 'slipknot1', 'buttercup', 'backup', 'roberto', 'ncc1701', 'handsome', '794613', 'pickle', 'joseluis', 'shakira', 'charlene', '111222', 'ILOVEYOU', 'bailey', 'QWERTY!', 'qwerty12345678910', 'sidorov', 'pandemic1', 'Carrie', 'nemesis', 'sanjose1', 'darren', 'fuckyou2', 'LUNATICK', 'kingfish', 'Bananas', 'config', 'chubby', 'martin1', 'yonghu', 'nopassw', 'cascade', 'chiquita', 'sexyme', 'dragonfl', 'Passwort', 'brittney', 'sunflower', 'a12345', 'joseph', 'Eagles', '123qwerty', 'ZXCZXC', 'rsetprofy', 'everyday', 'tuesday', 'terminator', 'Polaris', 'january', 'nicolas', 'iloveme1', 'mirage', 'test123', 'cutegirl', 'belamor', 'robbie', 'paganini', 'latina', 'ewqewq', 'fountain', 'Converse', 'quake4', 'adminservers', 'cristian', 'melissa', 'miranda', 'easter', 'q3rulez', 'P@ssw0rd', 'sierra', 'concept', 'ronald', 'babyboy', 'arthur', 'qwerty00', 'Larson', 'SADIST', 'Trixie', 'shannon', 'THUNDER', '7654321qwerty', 'Silver', 'lipgloss', 'foobar', 'thething', 'dianne', 'kaitlyn', 'qqqqqq', 'frances', 'niners', 'Flyers', 'friend', '012345678', 'pretty', 'chris1', 'janice', 'zinch', 'babyblue', 'default', 'cannabis', 'dominique', 'Looney', 'SCOOTER', 'Raider', 'qsawefdr', 'ph@nt0m', 'Dolboeb', '666888', 'bernie', 'margarita', 'sexymama', 'pretty1', 'mauricio', 'dallas', '123456', 'bubbles', 'covid192020', 'mem_login', 'MERCEDES', 'notused', 'sterva', 'ferrari', 'VUDUMEN', 'blossom', 'france', 'bradley', 'windows', 'seomas', 'fuck_off', 'Jaeger', 'mem_password', 'celtic', 'courtney', 'curtis', 'services', 'philip', '\u0418\u0416\u0421\u0422\u0428\u0411', 'edcxsw', 'qwaszx', 'Viking', 'db_password', 'George', 'myhome', 'werter', 'rahasia', 'destiny', 'userip', 'db_username', '123456qwert', 'success', 'Charmed', 'kitty1', 'last_login', 'Hamster', 'Helpme', 'Hawaii', 'californ', 'superadm', 'webadmins', 'rascal', 'nothing', 'Leslie', 'Covid-19', 'zenitsk', '321654987', 'beatles', 'mickey', 'hollywood', 'bananas', 'sesskey', 'dinamo', 'swit2002', 'webuser', 'pe_aduser', 'western', 'rebelde', 'covidien', 'ilikeaol', 'jennifer', 'router', 'november', 'charity', 'mercedes', 'formula2', 'userPassword', 'paloma', 'qwerty123', '012345678qwerty', '%username%1234', 'Marvin', 'sporting', 'harrison', 'babylove', 'babies', 'kayleigh', 'miriam', 'rosita', 'domain', 'running', 'compton', 'raymond', 'SVCHOST', 'aurora', 'ronaldinho', 'village', 'madalina', 'company', 'pistaec', 'asdfasdf', 'maldita', '0123456qwerty', 'bubba1', 'excalibu', 'qwerty1981', 'robinzon', 'internet', 'export', 'gangsta', '55555555', 'cynthia', 'fireman', 'userid', 'candyman', 'klas1999', 'caramelo', 'michalex', 'stalone', 'sunflowe', 'babyko', 'angelo', 'BlackRaven', 'SPIDERMAN', 'Tinman', 'ph4nt0m', 'butterfly', 'tbl_users', 'cassie', 'login_admin', 'loveya', 'sports', 'admin_id', 'website', '123123', 'a1b2c3', '\u0419\u041d\u041f\u041d\u041c\u042e', '111111', 'karina', 'michelle1', 'hello', 'mickeymouse', 'Guitar', 'COLUMB', 'Stocker', 'qwerty2011', 'scorpion', '1q2w3e4r5t6', 'NUMLOCK', '11223344', 'reznor', 'vietnam', 'sanchez', 'orlando', 'catdog', 'graphic', '789654', 'junjun', '7654321', 'qwe123456', 'sexsex', 'dilbert', 'mercury', 'admin_pass', 'andrea', 'cannon', 'kroywen', 'connect', '\u0418\u0422\u042a\u0416\u0428\u0412\u0421\u0411\u042f', 'pineapple', 'cannibal', 'gothic', '!@#$%^&*()_+|', 'password1', 'christ', 'sexybabe', 'steaua', 'stormy', 'symantec', 'admsuper', 'christine', 'Kristi', 'lucky7', 'rockme', '123456789qwerty', 'asshole', 'camping', 'megaultra', 'eunice', 'wesley', 'Blaster', 'qscgyj', 'tennis', 'admin_userid', '1q2w3e4', 'nugget', 'tokiohotel', 'sampson', 'ricardo', 'bulldogs', 'italia', 'admin!', 'dbadmins', 'qwerasdf', 'admin_psw', 'Contact', 'DROWSSAP', 'dorothy', 'sundance', 'apple1', '777777', 'strelez', 'camila', 'asdasd', 'lektor', 'tattoo', 'ground', 'carolina', 'hockey', 'administrators', 'COSMOS', 'notabene', 'orange', 'cannonda', 'WIZARD', 'christin', 'justin1', 'martha', 'toronto', 'Miller', 'secrets', '@admin@', '][akep13', 'jeffrey', 'service', 'lionking', 'skypeout', 'Tennis', 'warrior', 'turtle', 'Strider', 'raiders', 'vermont', 'hop242', 'Hendrix', 'Whales', 'fuckyou1', 'jeromy', 'Goldie', 'narciss', 'guanliyuan', 'mortimer', 'prettygirl', 'hiphop', 'samuel', '666666', 'Jordan', 'peanut', '123qwert', 'gabriel', 'Flower', 'tb_admin', 'jerome', 'wolfgang', 'x_admin', 'powers', 'qwer1234', 'user_usrnm', 'lunita', 'nodnol', 'valentine', '010101', 'maurice', '12345qwert', 'zsxdcfv', 'Bastard', 'Christ', '\u042f\u041d\u041a\u041c\u0428\u042c\u0419\u041d', 'jenifer', '11111', 'Pandemic', 'mustang', 'Marino', 'mb_users', 'n@poleon', 'ssssss', 'rangers', '212121', 'Teresa', 'marlon', 'breanna', 'michelle', 'minidixx', 'SIERRA', 'stephani', 'speedy', 'campbell', 'angelica', 'silver', 'damien', 'kaylee', 'moises', 'e_mail', 'person@l', 'portland', 'soulmate', 'DINOZAVT', 'joanne', '718293', 'newpass', 'lkjhgfdsa', 'download', 'passwrd', 'kolobok', 'Awesome', '654321', 'ssw0rd', 'webmaste', 'sophie', 'twinkle', 'Woodland', 'angeles', 'zasranez', 'adminpaw', 'fletch', 'player', 'user_uname', 'whitney', 'fugazi', 'Shadows', 'sylvia', 'q1w2e3r4', 'zxcdsa', 'pinkie', 'papito', 'clientpassword', 'medvedev', 'soledad', 'snapple', 'Trumpet', 'zacefron', 'qwert1234567', 'liverpool', 'seaman', 'Sweets', 'matrix', 'beckham', 'singer', 'hardcore', 'calvin', 'justin', 'arsenal', 'madison', 'adm1nistrator', 'spooky', 'mikael', 'w1w2w3w4', 'chelsea1', 'miranda2', 'Webmaster', 'qwerty01234567', 'kristina', 'member', 'reality', 'scarface', 'general', 'sagevsal', 'FLATRON', 'suzanne', 'Trinity', 'NITROGEN', 'mybaby', 'iloveu1', 'qwert1234', 'Denise', 's@d@m@z@', 'lucky13', 'angelito', 'adminuserid', 'david1', 'mozart', 'love13', 'nirvana', 'lonely', '951753', 'test', 'lorena', 'castle', 'photos', 'omarion', 'admin_login', 'lacrosse', 'yt1n1rt', 'mybb_users', 'Smiley', 'darkness', 'ASECHKA', 'elaine', 'MAMONT', 'MADONNA', '10tons', 'exploit', 'mem_passwd', 'jocelyn', 'cheyenne', '01234567', 'P@$$WORD', '1qaz@WSX', 'piglet', 'newton', 'qwerty987654321', 'adminuser', 'milagros', 'olivia', '55555', 'P@NTER@', 'colombia', 'preciosa', 'admin_passwd', 'Godmode', '31415926', 'escape', 'bamboo', 'hornet', 'clancy', 'calgary', 'Redskin', 'gangster', 'rovercar', '012qwerty', 'ne1469', 'qciretsam', 'Christop', 'Russell', 'm@$ter', 'california', 'Mittens', 'Sunshin', 'telephone', 'charlott', 'pandemic', 'Lindsay', 'afonja', 'Jessica', 'lostmind', 'gandako', 'ran-dom', '1p2o3i', 'morris', 'gemini', 'qwedsazxc', 'amormio', 'condor', 'LINUXOID', 'tblConfigs', 'piolin', 'script', 'parrot', 'Roping', 'qwerty1234567', 'knicks', 'siemens', 'atlanta', 'Defense', 'master', 'ihateu', 'formula1', 'horizon', '7777777', 'miller', 'cheer1', 'coolgirl', 'peterpan', 'adm1n1strator', 'adminmail', 'per$0n@l', 'stever', '123654', 'qazwsxedc', 'Reggie', 'tb_members', 'Covid19', 'zxcvbnm', 'tacobell', 'temp_pass', 'blonde', '\u043f\u0430\u0440\u043e\u043b\u044c', 'manuel', 'sandra', 'GLOBAL', '12345678qwerty', 'Pebbles', '12345678910qwerty', 'customers_password', 'reddog', 'chocolat', 'mahalkita', '123456qwe', '\u0418\u0416\u0421\u0419\u0422\u0428\u0411\u042e', 'asdffdsa', 'eXtremal', 'asdeasd', 'paulina', 'coyote', 'cassandra', 'Celtics', 'iv_sen', 'falloutboy', 'derrick', 'Shithead', 'stimpy', 'maincomputer', 'denmark', 'angela', 'Animals', 'robinhoo', 'genius', 'Ay8IPUtv5e', 'Puppies', 'pumpkin', 'Golden', 'valentin', 'brianna', 'gloria', 'sacret', 'dancer1', 'asdzxc', '30media', '8675309', 'Barney', 'mother', 'qwerty99', '960628', 'unknown', 'ilovegod', 'lincoln', 'MOBMAN', 'jellybean', 'ladybug', '113355', 'sab@k@', 'giovanni', 'beyonce', 'honeyko', 'Whateve', 'veronica', 'molesto', 'moocow', 'qwerty54321', 'yasmin', 'burewar', 'babygirl', '456789', 'database', 'melody', 'steven', 'giggles', '987654321', 'tyler1', '1234567qwerty1234567', 'temp_password', 'diamond', 'combrik', 'brown1', 'Gordon', '123456qwerty123456', 'per$0nal', 'p@ssw0rd', 'casper', 'mamita', 'westlife', 'beautiful', 'tblUser', 'sailing', '12qwerty', 'princess1', 'chester1', 'JUVENTUS', 'laurie', 'simone', 'rambler', 'starwars', 'alpine', '3904iurf', 'olololo', '555555', 'P@SSWORD', 'genetic', 'blondie', 'sayang', 'NITEBIRD', 'drowpa$$', 'skittles', 'accounts', 'Barbie', 'wedding', 'animal', '123654789', 'yankees', 'liverpoo', 'Harvey', 'timber', 'belinda', 'chance', 'howard', 'user123', 'brandi', '@admin', 'qwerty12345678', 'beanie', 'isaiah', 'alexandra', 'roxanne', 'MISTIC', 'secret', 'wrangler', 'semantec', 'bitches', 'Patches', 'qwe321', 's@pretne', 'eugene', 'tanner', 'russia2018', 'winnie', 'walker', 'microsoft', 'willie', 'wwwyaru', '11111111', 'covid2019', 'time2be', 'propusk', 'safety', 'wheeling', 'Center', 'AGAINST', 'Abcdefg', 'camera', 'COMMAND', 'dougie', 'elizabet', 'snuggles', '561989', 'mariel', '123456QQAqqa_', 'goober', 'kopernik', 'PLATON', 'office', 'nofear', 'feedback', 'crazy1', 'gwapako', '332211', 'cooladmin', 'bestfriends', 'iloveyou1', '12345678', 'jeanette', 'beaver', 'thunderb', 'f@ntom@s', 'alejandro', 'C0L0RaD0', 'goforit', 'OCCUPIED', 'gabber', 'hearts', '19weed', 'william', 'BLACKMAN', 'fatefull', 'Florida', 'QwerUQwerU', 'myspace', 'special', '999999', 'spider', 'pauline', 'Hershey', 'digital1', 'nopass', 'FRIDAY13', 'aliens', 'rebmem', 'unlimit', 'tweety', 'Sampler', 'baby123', 'BvtTest123', 'fuck-you', 'horses', 'Denver', 'lizard', 'alpha1', 'danger', 'scarlet', 'someone', 'reguser', 'robert', 'server', 'Slayer', 'castrat', 'moonson', 'alexandru', 'modified', 'wowecarts@123', 'janine', 'future', 'Shirley', 'silvia', 'sherry', 'passw0rd', 'Garrett', 'UNREAL', 'zxcvbn', 'qwertyu', 'midori', 'adriana', 'Golfer', 'rayray', 'qwerty0123456', 'Swimmer', 'javier', 'juancarlos', 'raquel', 'lindsay', 'maymay', 'ARTEFACT', 'dexter', 'arnold', 'random', 'Yellow', 'sebastian', 'simpleplan', 'america', 'mozilla', 'mystica', 'segment', 'tb_administrator', 'shelly', 'Goalie', 'sweets', 'qwerty321', ')4ever', 'muffin', 'marius', 'ArticleID', 'sweet16', 'Gopher', 'SPARTAK', 'esrevinu', '%username%12', 'formula3', 'ohmygod', 'ashleigh', 'carmen', 'VACATION', 'melanie', 'matthew', 'nichole', 'chicago', 'hamster', 'CLEVER', 'austin1', 'cactus', 'sartir', 'network', 'mahalkoh', 'flower', 'iverson3', 'monkey1', '181818', 'frankie', 'rodriguez', 'admin123', 'chapman', 'ironman', 'pikachu', 'sadistic', 'jeremiah', '321654', 'wildcats', 'Racing', '4897798', 'pictures', 'scotland', '012345', 'director', 'trilogy', 'chanel', 'SUNRISE', 'pamela', 'tinker', 'grinch', 'reynolds', 'unknovn', 'montreal', 'qwerty88', 'brownie', 'Kombat', 'preston', 'james1', 'stacey', 'Gymnast', '00090009', 'donkey', 'Cassie', 'n@p0le0n', 'bridge', '321adc', 'raptor', 'SHAKUR', 'METALIKA', 'memberid', '789456', 'Amanda', 'front242', 'einstein', 'galileo', 'Masters', 'jazmin', 'justice', 'agresive', 'harley', 'mpassword', 'tfarcraw', 'zaxscdvfbg', '121212', 'Paladin', 'admin_user', 'qwerty1990', 'Picard', 'williams', 'nathaniel', 'jensen', 'steelers', 'froggy', '100500', 'manager', 'tb_username', 'vb_user', 'forineli', 'ph@ntom', 'warren', 'callum', 'mempassword', 'shalom', 'brooklin', 'savage', 'mouse1', 'lawrence', 'kristen', 'nicholas', 'warner', '@dministrator', '1q2w3e4r5t', 'CAUTION', 'evilmind', 'sex+love', 'homebrew', 'SPIDER', 'support', 'matter', 'nelson', '\u043b\u044e\u0431\u043b\u044e', 'element', 'heaven', 'lolita', 'poiuyt', 'lilmama', '_other_', 'Michell', 'qwertyqwerty', 'diamond1', 'coltrane', 'nenita', 'peewee', 'protel', 'rainbow', 'angel1', '][aker', 'warriors', 'uoykcuf', 'flym0de', 'mike123', '4321qwerty', 'gabriell', 'JACKSON', 'katherine', 'warcraft', 'autumn', 'husband', 'diesel', 'Cancer', 'bullet', 'little', 'Shelly', 'dragons', 'QWERTY', 'zaxscdvf', 'fuck-off', 'rockstar', 'Apples', '}{aker', 'SATAN666', 'Tanker', 'badger', 'Covid2020', 'Casper', 'franklin', 'sheila', 'golden', 'bloods', 'tbl_user', 'kathleen', 'Spiritwear_2004', 'administrator1', 'Hawkeye', 'loving', '12121212', 'galaxy', 'blinked', 'padonok', 'fuckoff', 'dookie', 'february', 'snickers', 'ronaldo', 'EdomdoG', 'Elaine', 'sb_pwd', 'Wicked', '456123', 'md5hash', 'rocket', '12344321', 'Letter', 'VUDUMAN', 'byteme', 'GOODWISH', 'solnyshko', 'evelyn', 'pookie', 'canela', 'k.,jdm', 'revoemag', 'fernando', 'MATRIX', 'servpass', 'maddie', 'kingdom', 'evolxes', 'marilyn', 'twilight', 'mission', '1234567890', 'qwert123456', '24crow', 'sexygirl', '123123123', 'lakers', 'sparky', '1234rewq', 'Alexxxx', 'Chipper', 'avatar', 'qqqqqqqq', 'qwert12345', 'theking', 'usrnam', 'cyfqgth', 'admin_username', 'peanut1', 'bluddy', 'united', '87654321', 'bitch123', 'antonio', 'fluffy', 'westeast', 'poiuytr', 'Pyramid', 'PRODIGY!', 'Scooby', 'support2', 'drpepper', 'python', 'painter', 'property', '010203', 'forest', 'passer', 'q1w2e3', 'validpas', 'cupcake', 'mendoza', 'monica', 'pink123', 'qwerfv', 'patricia', 'admin1', 'simpsons', 'bubble', 'allyson', 'Huskers', 'hjccnz', 'lollypop', 'chocolate', 'TOMORROW', 'george', 'cookies', 'Nathan', 'poison', 'ireland', 'barcelona', 'kucing', 'zasranec', 'savannah', 'tyrone', 'carebear', 'memory', 'SURPRISE', 'sniper', 'falcon', 'MEVERICK', 'FORMATC:', 'sasa321', 'soto4ka', '321321', '\u041e\u041f\u0425\u0411\u0415\u0420', 'butcher', 'SHIFTALT', '123456789qwerty123456789', 'manman', 'express', 'alexandr', 'q1w2e3r', 'kristine', 'q1w2Q!W@', 'adminserver', 'Alyssa', 'ilovejesus', 'userpass', 'qwerty01', 'q1w2e3r4t5y', 'zaq1xsw2', 'Dreams', 'u_pass', 'sanyok', 'sapretne', 'sydney', 'Badboy', 'francisco', 'crazyman', 'datacenter', 'cricket', 'pickles', 'precious', 'travis', 'Compaq', 'apples', 'fashion', 'rambo1', 'celeron', 'p@$$vord', 'klaster', 'october', 'volcom', 'familia', 'smiths', 'Administrateur', '\u042e\u041c\u0414\u041f\u0415\u0418', 'Margulya', 'apollo', 'Studly', 'Spanish', 'E-mail', 'memphis', 'arturo', 'Petunia', 'OVERCLOK', '\u0430\u043d\u0434\u0440\u0435\u0439', 'chivas', 'skiing', 'garden', 'Spanky', 'cuddles', 'bitch@', 'scorpio', 'mirror', 'qwedsa', 'trisha', 'public', 'settings', 'cunning', 'poohbear', 'student', 'kingkong', 'January', 'roxana', 'septic', 'armando', 'Theman', 'JAYSON', 'guitar', 'mypass', 'q1w2e3r4t5', 'ONLINE', 'Raiders', 'hctib321', 'Tigers', 'kelly1', 'valeria', 'knopka', 'Wrestle', 'canada', '852456', 'Cracker', 'target', 'bestfriend', 'WINDBURN', 'tomcat', 'cruise', 'giants', 'login_pwd', 'f1f2f3f4', 'Buffalo', '\u0418\u0416\u0421\u0419\u0415\u041c\u0426\u042c\u042b\u0413', 'Reefer', 'violet', 'mudofil', 'teresa', 'alexander', 'pa$$vord', '789456123', 'hellokitty', 'cuties', 'coronavirus19', 'margaret', 'Google', 'rotfront', 'stuart', 'mountain', 'louise', 'MICHELIN', 'candy1', 'ornery', 'Superuser', 'montana', 'december', 'estrella', 'theboss', 'moroni', 'dreamer', 'qip12345', 'dekart', 'evol+xes', 'NORRIS', 'pierre', 'skippy', 'soccer', 'per$onal', 'bulldog', 'invizibl', 'realmadrid', 'garfield', 'Internet', 'leelee', 'gretzky', 'fdsarewq', 'dancing', 'Basebal', 'snayiclfv', 'School', 'Jester', 'ELECTRO', '5201314', 'fantomas', 'midnight', 'nicarao', 'Royals', 'Volleyb', 'zzzzzz', 'fresita', 'rockyou', 'Krystal', 'BILLGATE', 'trevor', 'celine', 'canced', '012345qwerty', 'sylvie', 'hotpink', 'Aggies', '][akep', 'freedom', 'Buddha', 'babyface', 'dodger', 'nummer', 'spring', 'MYICQ\u2567', '252525', 'rekax321', 'parker', 'Except', 'changeme', 'winter', 'dundee', 'AdminUID', 'Panther', 'password', 'TIMEOUT', 'brigada', 'Lakers', 'nazareth', 'sweetheart', '753951', 'icecream', 'temppasword', 'yolanda', 'lacoste', 'flower1', 'boogie', 'entropy', 'always', 'basketba', '\/\/RaZOR', 'Golfing', 'scotch', 'hunter1', 'jesus1', '357951', 'onelove', 'ytinrete', 'spongebob1', 'go2fuck', 'sukinsin', 'login_pw', 'eskander', 'crawford', 'adminname', 'procesor', 'westside', 'leanne', 'hailey', 'qwerty1', '21122112', 'kennedy', 'rangers1', 'pollito', 'alicia', 'lovergirl', 'tazmania', 'dfcvxv', 'MIRABLIS', 'Maveric', 'Starwars', 'abcdefg', '1212121212', 'albert', 'qwertyuiop', 'jenny1', 'xswqaz', 'madison1', 'fabian', 'theresa', 'ib6ub9', 'KASPER', 'covid1984', 'HOTMAIL', 'cherry', 'shorty1', 'aretnap', 'Ripper', 'cxzdsaewq', '0123456789qwerty', 'audrey', '12345qwerty', 'password123', 'Bubbles', 'Chucky', 'MASACRE', 'MEXICO', '101010', 'zachary', 'maxwell', 'watson', 'skeeter', 'basketball', 'liberty', 'user_admin', 'Hunting', 'ivanov', 'smiley', 'user_email', 'gilbert', '0123456', 'london', '6PISTOLS', 'webmasters', 'burger', 'ashton', '147258369', 'bermud', 'Doobie', 'ZONE51', 'manchester', '369852147', 'hunter', '\u0429\u041e\u0425\u0414\u0415\u041b\u0425\u042a', 'p@svord', 'Webster', '@system', 'regina', 'fuckme', 'Userlogin', 'rooney', 'llawerif', 'football1', 'Password', 'Angela1', 'Avital', 'lovebug', 'zxcdsa22', 'OUTPOST', '%username%', 'Junebug', 'banana', 'monique', '%username%123', 'client', 'Lindsey', 'portugal', 'username', 'qwerty22', 'ferret', 'iceman', 'lomakin', 'a_admin', 'natasha', 'dreams', 'friendship', 'panget', 'p@$$w0rd', '147852', 'grateful', 'deutsch', 'elizabeth', 'SITELOGIN', 'hotmail', 'asdfjkl;', 'chairman', '!@#$%^', 'bigmac', 'Flipper', 'contenu', 'flowers', 'cc_owner', '0000000000', 'oranges', 'money1', 'livetest', 'slayer', 'fernanda', 'khekkly', 'virginia', 'f1refox', 'patches', 'yvonne', 'usernames', 'amsterdam', '321qwerty', 'digital', 'jasmin', 'qwerty66', 'Basketb', 'Iceman', '!@#$%^&*()', 'pidaras', 'Skater', 'bubbles1', 'delfin', '159753', 'kittycat', 'bootsie', 'jupiter', 'NTLOADER', 'qwe123', 'natalia', 'olivier', 'Dallas', 'WebAdmin', 'Rabbit', 'Dolphin', '124578', 'my_username', 'battle', 'nooone', 'iguana', 'simple', 'ornament', 'garnet', 'un1verse', 'stephanie', 'zapata', '012345678910qwerty', 'Zombie', 'clientusername', 'church', 'beware', 'tagged', 'Kittens', 'lovers', 'scoobydoo', 'retsam', 'a123456', 'hayley', 'mantra', 'history', 'anthony1', 'planet', 'johncena', 'samantha', '141414', 'lucero', 'awesome', '12345678910', 'captain', 'bobcat', 'allison', 'merlin', 'herman', 'MUSTDIE', 'badgirl', '_nick_', 'babygirl1', 'dollars', 'pimpin', 'fiction', 'user_password', 'shirley', 'alejandra', 'pass123', 'm_admin', '12qwaszx', 'andrew1', 'q1w2e3r4t', 'nechto', 'smokey', '1q2w3e', 'thuglife', 'qwerty77', '1sanjose', 'ihateyou', 'netware', 'benson', 'bertha', 'catarina', 'caroline', 'Snicker', 'Sonics', 'h_admin', 'cameron', 'pussycat', 'sdfcvxv', 'qwert12', 'prince', 'Sarah1', 'chemistry', 'Blondie', 'Scarlett', 'Sandman', 'wombat', 'Blackie', 'swimming', 'shelby', 'macmac', 'active', 'myserver', 'shumaher', 'Peaches', 'lokita', 'DEICIDE', 'Country', 'morgan', 'research', 'edward', 'ffffff', '12345a', '202020', 'peaches', '\u0418\u0422\u042a\u0416\u0428\u0412', 'admin_name', 'island', 'harrypotter', 'leonard', 'clients', 'tables', 'usuario', '1qw23er45ty67u', 'zaphod', 'poohbear1', 'gerardo', 'nicole', 'windsurf', 'wilson', 'bambam', 'baller', 'carlitos', 'seattle', 'gabriela', '123456789qwert', 'vincent', 'bernard', 'garcia', 'STALIN', '142536', 'qwerty654321', 'compaq', 'goldfish', 'lovely', 'German', 'Brazil', 'rocker', 'DYNAMIC', 'asdfghjk', 'qwerty0123', 'boston', 'police', 'delete', 'orbital', 'bigboy', 'design', 'vanessa', 'fletcher', 'moksana', 'Gambit', 'VERITAS', 'AUTOMOTO', '122333', 'cutie1', 'ZAGADKA', 'johnson', 'thursday', 'Russia', 'mylove', 'stingray', 'rekax123', 'wicked', 'passion', 'cardinal', 'console', 'catherine', 'mailer', 'titanic', 'admpro', 'BIGMUZZY', 'Porsche', 'qwerty012345', 'brandon', 'joshua', 'gibson', 'playboy', 'logins', 'p@ntera', 'danielle', 'customers', 'charmed', 'hootie', 'windowsxp', 'America', '098765', 'MIR@ND@', 'butter', 'garlic', 'campanita', 'clas1999', 'MITNYK', 'andrei', 'shamber', 'alaska', 'seagal', 'Scotty', 'picasso', '1234560', 'bridges', 'richie', 'R.GILL', 'qwerty55', 'Trouble', 'butler', 'qwerty12', 'BONJOVI', 'Sammie', 'marcus', 'mexico', 'cutiepie', 'marian', 'amanda1', 'bigdaddy', 'nissan', 'SUBSEVEN', 'mayday', 'buddy1', 'myusername', 'sitelogins', 'hello1', 'NTOSKRNL', 'PRODIGY', 'remote', 'gooood', 'qwerty666', 'obiwan', 'reverse', 'bismillah', 'hello123', 'zsxdcfvg'} # 100 most popular passwords TOP_PASSWORDS = {'12345678', 'qwerty123', '12345', 'qwerty1', '123456', '123321', 'qwerty', 'asdasd123', '123456789', 'asdasd', 'qqqqqq'}  max_count_default = 1000000 max_count_main_default = 2   def prompt(txt):     return str(input(txt)) def fullname(fname, lname):     return ['{}{}'.format(a, b) for a in cases(fname) for b in cases(lname)] + [\"{}_{}\".format(a, b) for a in cases(fname) for b in cases(lname)] def cases(word):     return [word.lower(), word.title()]   class PassGen:      def __init__(self, max_count=max_count_default, max_count_main=max_count_main_default, silent=False):         self.pet = None         self.child = None         self.spouse = None         self.target = None         self.passwords = set()         self.passwords_not_sorted = list()         self.silent = silent         self.max_count = max_count         self.max_count_main = max_count_main      @staticmethod     def question(target):         answers = {}          answers['firstname'] = prompt('Enter {}\\'s first name: '.format(target))         answers['lastname'] = prompt('Enter {}\\'s last name: '.format(target))         answers['nickname'] = prompt('Enter {}\\'s nick name: '.format(target))          while True:             bday = prompt('Enter {}\\'s birthday (dd.mm.yyyy): '.format(target))              if not len(bday.strip()):                 break              if len(bday.split('.')) != 3:                 print('Invalid birthday format\\n')                 continue              for _ in bday.split('.'):                 if not _.isdigit():                     print('Birthday only requires numbers\\n')                     continue              dd, mm, yyyy = bday.split('.')              if int(mm) > 12 or int(mm) < 1 or int(dd) > 31 or int(dd) < 1 or len(yyyy) != 4:                 print('Invalid birthday\\n')                 continue              bday = {'day': dd, 'month': mm, 'year': int(yyyy)}             break          answers['birthday'] = bday         return answers      def _add(self, password):         if password not in self.passwords:             self.passwords.add(password)             self.passwords_not_sorted.append(password)     def format_names(self):         for _ in range(self.max_count_main):             if not self.silent:                 print(f'Generated: {len(self.passwords)}', end='\\r')              iters = 0             for data in [self.target, self.spouse, self.child, self.pet]:                 if data['birthday']:                     for i in (                             \"{}{}{}\".format(data['birthday']['day'], data['birthday']['month'], data['birthday']['year']),                             \"{}{}{}\".format(data['birthday']['day'], data['birthday']['month'], str(data['birthday']['year'])[2:]),                             \"{}{}{}{}\".format(data['birthday']['day'], data['birthday']['month'], data['birthday']['day'], data['birthday']['month']),                     ):                         self._add(i)                 for n in ['firstname', 'lastname', 'nickname']:                      fullname_list = []                     name = data[n].strip()                      if not len(name):                         continue                      if not iters:                         fullname_list = fullname(data['firstname'], data['lastname'])                         iters += 1                      for word in cases(name) + fullname_list:                          for i in ('{}{}'.format(word, _),                                   '{}{}'.format(_, word),                                   '{}{}!'.format(word, _),                                   '{}{}.'.format(word, _),                                   ):                             if i not in self.passwords: self._add(i)                          bday = data['birthday']                          if bday:                             for i in (                                 '{}{}'.format(word, bday['year']),                                 '{}{}!'.format(word, bday['year']),                                 '{}{}'.format(bday['year'], word),                                 '{}{}'.format(word, str(bday['year'])[2:]),                                 '{}{}{}{}'.format(word, bday['year'], bday['month'], bday['day']),                                 '{}{}{}{}'.format(word, str(bday['year'])[2:], bday['month'], bday['day']),                                 '{}{}{}{}'.format(word, bday['day'], bday['month'], str(bday['year'])[2:]),                                 '{}{}{}{}'.format(word, bday['day'], bday['month'], bday['year']),                                 '{}{}{}'.format(word, bday['day'], bday['month']),                                 '{}{}{}{}'.format(bday['day'], bday['month'], bday['year'], word),                             ):                                 self._add(i)      def generator(self, ignore_additional=True, write=True):         if not self.silent:             print('Generating main passwords... \\nIt\\'s may take a while.')         self.format_names()         if not self.silent:             print(\"...generated {} main passwords\".format(len(self.passwords_not_sorted)), end='\\r')         for i in TOP_PASSWORDS:             self._add(i)         if not self.silent:             print(\"...generated {} popular passwords\".format(len(self.passwords_not_sorted)), end='\\r')         output_file = '{}.txt'.format(self.target['firstname'].lower()                              if self.target['firstname'] else 'pass.txt')         if write:             with open(output_file, 'wt', encoding='utf-8') as f:                 for pwd in self.passwords_not_sorted:                     if not self.silent:                         print('Writing ...')                     f.write('{}\\n'.format(pwd))              if not ignore_additional:                 if not self.silent:                     print(\"Generating additionals combinations...\")                 with open(output_file, 'at', encoding='utf-8') as f:                     i = 0                     while i < self.max_count:                         if not self.silent:                             print('Writing additional combinations ... {}\/{}'.format(i*3, self.max_count*3),end='\\r')                         f.write('{}{}\\n'.format(self.target['firstname'], i))                         f.write('{}{}\\n'.format(self.target['lastname'], i))                         f.write('{}{}\\n'.format(self.target['nickname'], i))                         i += 1                 if not self.silent:                     print(\"...generated {} additional combinations\".format(self.max_count*3))          if not self.silent:             print('Passwords Generated in file: {}'.format(output_file))   def parse_cmd_args(argv):     parser = argparse.ArgumentParser(description='Run password generator')     parser.add_argument('--ignore-additional', dest='ignore_additional', default=False,                         help='ignore additions combinations')     parser.add_argument('--max-count', dest='max_count', default=max_count_default,                         help='maximum count for additional combinations')     parser.add_argument('--max-count-main', dest='max_count_main', default=max_count_main_default,                         help='maximum count for main combinations')     parser.add_argument('--silent', dest='silent', action='store_true',                         help='no print process (faster)')     parser.set_defaults(ignore_additional=False)     parser.set_defaults(silent=False)     args = parser.parse_args(argv[1:])     return args   if __name__ == '__main__':     args = parse_cmd_args(sys.argv)     p = PassGen(         max_count=int(args.max_count),         max_count_main=int(args.max_count_main),         silent=args.silent     )     p.target = p.question('target')     p.spouse = p.question('spouse')     p.child = p.question('child')     p.pet = p.question('pet')      p.generator(ignore_additional=args.ignore_additional) <\/code><\/pre>\n
            \u0417\u0430 \u043e\u0441\u043d\u043e\u0432\u0443 \u0432\u0437\u044f\u0442 \u044d\u0442\u043e\u0442 \u0441\u043a\u0440\u0438\u043f\u0442<\/a><\/p>\n<\/div>\n<\/details>\n
            \u0412\u043e\u0442 \u043f\u0440\u0438\u043c\u0435\u0440 \u0442\u043e\u0433\u043e, \u0447\u0442\u043e \u0433\u0435\u043d\u0435\u0440\u0438\u0440\u0443\u0435\u0442 \u0441\u043a\u0440\u0438\u043f\u0442 (\u0432\u0441\u0435 \u0441\u043e\u0432\u043f\u0430\u0434\u0435\u043d\u0438\u044f \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b<\/u>, \u044d\u0442\u043e \u043f\u0440\u043e\u0441\u0442\u043e \u043f\u0440\u0438\u043c\u0435\u0440<\/strong>)<\/p>\n
            \n\u0412\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0435 \u043f\u0430\u0440\u043e\u043b\u0438 \u041c\u0430\u0440\u0438\u0438 \u041a\u0443\u0437\u043d\u0435\u0446\u043e\u0432\u043e\u0439 01.02.2000\u0433 <\/summary>\n
            \n
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            \u0422\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c \u0431\u044b\u043b\u0438 \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u044b \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0441\u043a\u0440\u0438\u043f\u0442\u043e\u0432 (\u0432\u0441\u0435 \u0441\u0441\u044b\u043b\u043a\u0438 \u0432\u043d\u0438\u0437\u0443 \u0441\u0442\u0430\u0442\u044c\u0438 \u2014 \u043e\u043d\u0430 \u0438 \u0442\u0430\u043a \u0443\u0436\u0435 \u043e\u0447\u0435\u043d\u044c \u0437\u0430\u0441\u043e\u0440\u0435\u043d\u0430), \u0440\u0435\u0430\u043b\u0438\u0437\u0443\u0435\u0449\u0438\u0445 \u044d\u0442\u0443 \u0438\u0434\u0435\u044e, \u0438 \u0432\u043e\u0442 \u0438\u0445 \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u044b\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b:<\/p>\n