upstream/mercurial-mirror Commit - r46405:84eb4c83

merge with stable

Augie Fackler -

r46405:84eb4c83 default

parent child

.hgsigs

0 +1 0

 fb62a3a673d5322f6274a44ba6456e5e4b3b37 0 iD8DBQBEYmO2ywK+sNU5EO8RAnaYAKCO7x15xUn5mnhqWNXqk/ehlhRt2QCfRDfY0LrUq2q4oK/KypuJYPHgq1A=
 be3001847cb18a23c403439d9e7d0ace30804e9 0 iD8DBQBExUbjywK+sNU5EO8RAhzxAKCtyHAQUzcTSZTqlfJ0by6vhREwWQCghaQFHfkfN0l9/40EowNhuMOKnJk=
 a957364b1b89c150f2d0e60a99befe0ee08bd3 0 iD8DBQBFfL2QywK+sNU5EO8RAjYFAKCoGlaWRTeMsjdmxAjUYx6diZxOBwCfY6IpBYsKvPTwB3oktnPt5Rmrlys=
 c29bfec36d5540fbe1c976810aefecfd1d2 0 iD8DBQBFheweywK+sNU5EO8RAt7VAKCrqJQWT2/uo2RWf0ZI4bLp6v82jACgjrMdsaTbxRsypcmEsdPhlG6/8F4=
              fb4b6d5fe100b0886f8bc3d6731ec0e5ed5c4694 0 iD8DBQBGgHicywK+sNU5EO8RAgNxAJ0VG8ixAaeudx4sZbhngI1syu49HQCeNUJQfWBgA8bkJ2pvsFpNxwYaX3I=
              23889160905a1b09fffe1c07378e9fc1827606eb 0 iD8DBQBHGTzoywK+sNU5EO8RAr/UAJ0Y8s4jQtzgS+G9vM8z6CWBThZ8fwCcCT5XDj2XwxKkz/0s6UELwjsO3LU=
              bae2e9c838e90a393bae3973a7850280413e091a 0 iD8DBQBH6DO5ywK+sNU5EO8RAsfrAJ0e4r9c9GF/MJsM7Xjd3NesLRC3+ACffj6+6HXdZf8cswAoFPO+DY00oD0=
              d5cbbe2c49cee22a9fbeb9ea41daa0ac4e26b846 0 iD8DBQBINdwsywK+sNU5EO8RAjIUAKCPmlFJSpsPAAUKF+iNHAwVnwmzeQCdEXrL27CWclXuUKdbQC8De7LICtE=
              d2375bbee6d47e62ba8e415c86e83a465dc4dce9 0 iD8DBQBIo1wpywK+sNU5EO8RAmRNAJ94x3OFt6blbqu/yBoypm/AJ44fuACfUaldXcV5z9tht97hSp22DVTEPGc=
 a67430f92f15ea5159c26b09ec4839a0c549a26 0 iEYEABECAAYFAkk1hykACgkQywK+sNU5EO85QACeNJNUanjc2tl4wUoPHNuv+lSj0ZMAoIm93wSTc/feyYnO2YCaQ1iyd9Nu
 e510d433969e277b1863c317b674cbee2065 0 iEYEABECAAYFAklNbbAACgkQywK+sNU5EO8o+gCfeb2/lfIJZMvyDA1m+G1CsBAxfFsAoIa6iAMG8SBY7hW1Q85Yf/LXEvaE
 a4eb81fb4f4742451591489e2797dc47903277 0 iEYEABECAAYFAklcAnsACgkQywK+sNU5EO+uXwCbBVHNNsLy1g7BlAyQJwadYVyHOXoAoKvtAVO71+bv7EbVoukwTzT+P4Sx
 efa41037e280d08cfb07c09ad485df30fb0ea8 0 iEYEABECAAYFAkmvJRQACgkQywK+sNU5EO9XZwCeLMgDgPSMWMm6vgjL4lDs2pEc5+0AnRxfiFbpbBfuEFTqKz9nbzeyoBlx
              02981000012e3adf40c4849bd7b3d5618f9ce82d 0 iEYEABECAAYFAknEH3wACgkQywK+sNU5EO+uXwCeI+LbLMmhjU1lKSfU3UWJHjjUC7oAoIZLvYDGOL/tNZFUuatc3RnZ2eje
 d40e7c885fa6e95f89134809b3ec7bdbca34b 0 iEYEABECAAYFAkpL2X4ACgkQywK+sNU5EO9FOwCfXJycjyKJXsvQqKkHrglwOQhEKS4An36GfKzptfN8b1qNc3+ya/5c2WOM
 ef6c14a1e8e83a31226f5881b7fe6095bbfa6f6 0 iEYEABECAAYFAkpopLIACgkQywK+sNU5EO8QSgCfZ0ztsd071rOa2lhmp9Fyue/WoI0AoLTei80/xrhRlB8L/rZEf2KBl8dA
 ec469f9b556f11819937cf68ee53f2be927ebf 0 iEYEABECAAYFAksBuxAACgkQywK+sNU5EO+mBwCfagB+A0txzWZ6dRpug3LEoK7Z1QsAoKpbk8vsLjv6/oRDicSk/qBu33+m
 d7ea6fe3aa4ab9ec274a68846779153789de9 0 iEYEABECAAYFAksVw0kACgkQywK+sNU5EO/oZwCfdfBEkgp38xq6wN2F4nj+SzofrJIAnjmxt04vaJSeOOeHylHvk6lzuQsw
 a0b14a68621f6990c54fdba0083f6f20935bf 0 iEYEABECAAYFAks+jCoACgkQywK+sNU5EO9J8wCeMUGF9E/gS2UBsqIz56WS4HMPRPUAoI5J95mwEIK8Clrl7qFRidNI6APq
 aa619c4c2c09907034d9824ebb1dd0e878206eb 0 iEYEABECAAYFAktm9IsACgkQywK+sNU5EO9XGgCgk4HclRQhexEtooPE5GcUCdB6M8EAn2ptOhMVbIoO+JncA+tNACPFXh0O
              ff2704a8ded37fbebd8b6eb5ec733731d725da8a 0 iEYEABECAAYFAkuRoSQACgkQywK+sNU5EO//3QCeJDc5r2uFyFCtAlpSA27DEE5rrxAAn2FSwTy9fhrB3QAdDQlwkEZcQzDh
 b01dab594167bc0dd33331dbaa6dca3dca1b3aa 0 iEYEABECAAYFAku1IwIACgkQywK+sNU5EO9MjgCdHLVwkTZlNHxhcznZKBL1rjN+J7cAoLLWi9LTL6f/TgBaPSKOy1ublbaW
 f725929f0c48c5fb3b90c071fc3066012456ca 0 iEYEABECAAYFAkvclvsACgkQywK+sNU5EO9FSwCeL9i5x8ALW/LE5+lCX6MFEAe4MhwAn1ev5o6SX6GrNdDfKweiemfO2VBk
              fdcf80f26604f233dc4d8f0a5ef9d7470e317e8a 0 iEYEABECAAYFAkvsKTkACgkQywK+sNU5EO9qEACgiSiRGvTG2vXGJ65tUSOIYihTuFAAnRzRIqEVSw8M8/RGeUXRps0IzaCO
 fe2629c6fd0c74c90bd066e77387c2b02e8437 0 iEYEABECAAYFAkwFLRsACgkQywK+sNU5EO+pJACgp13tPI+pbwKZV+LeMjcQ4H6tCZYAoJebzhd6a8yYx6qiwpJxA9BXZNXy
              f786fc4b8764cd2a5526d259cf2f94d8a66924d9 0 iEYEABECAAYFAkwsyxcACgkQywK+sNU5EO+crACfUpNAF57PmClkSri9nJcBjb2goN4AniPCNaKvnki7TnUsi1u2oxltpKKL
              bf1774d95bde614af3956d92b20e2a0c68c5fec7 0 iEYEABECAAYFAkxVwccACgkQywK+sNU5EO+oFQCeJzwZ+we1fIIyBGCddHceOUAN++cAnjvT6A8ZWW0zV21NXIFF1qQmjxJd
              c00f03a4982e467fb6b6bd45908767db6df4771d 0 iEYEABECAAYFAkxXDqsACgkQywK+sNU5EO/GJACfT9Rz4hZOxPQEs91JwtmfjevO84gAmwSmtfo5mmWSm8gtTUebCcdTv0Kf
              ff5cec76b1c5b6be9c3bb923aae8c3c6d079d6b9 0 iD8DBQBMdo+qywK+sNU5EO8RAqQpAJ975BL2CCAiWMz9SXthNQ9xG181IwCgp4O+KViHPkufZVFn2aTKMNvcr1A=
 d8bff78c96fe7e33237b257558ee97290048a4 0 iD8DBQBMpfvdywK+sNU5EO8RAsxVAJ0UaL1XB51C76JUBhafc9GBefuMxwCdEWkTOzwvE0SarJBe9i008jhbqW4=
 b9e0f96c7bc788e5667c146a58a9440a55 0 iD8DBQBMz0HOywK+sNU5EO8RAlsEAJ0USh6yOG7OrWkADGunVt9QimBQnwCbBqeMnKgSbwEw8jZwE3Iz1mdrYlo=
              4438875ec01bd0fc32be92b0872eb6daeed4d44f 0 iD8DBQBM4WYUywK+sNU5EO8RAhCVAJ0dJswachwFAHALmk1x0RJehxzqPQCbBNskP9n/X689jB+btNTZTyKU/fw=
 aff4f144ad356311318b0011df0bb21f2c97429 0 iD8DBQBM9uxXywK+sNU5EO8RAv+4AKCDj4qKP16GdPaq1tP6BUwpM/M1OACfRyzLPp/qiiN8xJTWoWYSe/XjJug=
              e3bf16703e2601de99e563cdb3a5d50b64e6d320 0 iD8DBQBNH8WqywK+sNU5EO8RAiQTAJ9sBO+TeiGro4si77VVaQaA6jcRUgCfSA28dBbjj0oFoQwvPoZjANiZBH8=
              a6c855c32ea081da3c3b8ff628f1847ff271482f 0 iD8DBQBNSJJ+ywK+sNU5EO8RAoJaAKCweDEF70fu+r1Zn7pYDXdlk5RuSgCeO9gK/eit8Lin/1n3pO7aYguFLok=
 b2155623ee2559caf288fd333f30475966c4525 0 iD8DBQBNSJeBywK+sNU5EO8RAm1KAJ4hW9Cm9nHaaGJguchBaPLlAr+O3wCgqgmMok8bdAS06N6PL60PSTM//Gg=
              2616325766e3504c8ae7c84bd15ee610901fe91d 0 iD8DBQBNbWy9ywK+sNU5EO8RAlWCAJ4mW8HbzjJj9GpK98muX7k+7EvEHwCfaTLbC/DH3QEsZBhEP+M8tzL6RU4=
              aa1f3be38ab127280761889d2dca906ca465b5f4 0 iD8DBQBNeQq7ywK+sNU5EO8RAlEOAJ4tlEDdetE9lKfjGgjbkcR8PrC3egCfXCfF3qNVvU/2YYjpgvRwevjvDy0=
              b032bec2c0a651ca0ddecb65714bfe6770f67d70 0 iD8DBQBNlg5kywK+sNU5EO8RAnGEAJ9gmEx6MfaR4XcG2m/93vwtfyzs3gCgltzx8/YdHPwqDwRX/WbpYgi33is=
 cb1e95676ad089596bd81d0937cad37d6e3b7fb 0 iD8DBQBNvTy4ywK+sNU5EO8RAmp8AJ9QnxK4jTJ7G722MyeBxf0UXEdGwACgtlM7BKtNQfbEH/fOW5y+45W88VI=
 af5d9f6b22387913e1d11350fb8cb7c1487dd 0 iD8DBQBN5q/8ywK+sNU5EO8RArRGAKCNGT94GKIYtSuwZ57z1sQbcw6uLACfffpbMV4NAPMl8womAwg+7ZPKnIU=
              de9eb6b1da4fc522b1cab16d86ca166204c24f25 0 iD8DBQBODhfhywK+sNU5EO8RAr2+AJ4ugbAj8ae8/K0bYZzx3sascIAg1QCeK3b+zbbVVqd3b7CDpwFnaX8kTd4=
 a43e23b8c55b4566b8200bf69fe2158485a2634 0 iD8DBQBONzIMywK+sNU5EO8RAj5SAJ0aPS3+JHnyI6bHB2Fl0LImbDmagwCdGbDLp1S7TFobxXudOH49bX45Iik=
              d629f1e89021103f1753addcef6b310e4435b184 0 iD8DBQBOWAsBywK+sNU5EO8RAht4AJwJl9oNFopuGkj5m8aKuf7bqPkoAQCeNrEm7UhFsZKYT5iUOjnMV7s2LaM=
 a9292e430e35766c552066ed3e87c557b803b 0 iD8DBQBOh3zUywK+sNU5EO8RApFMAKCD3Y/u3avDFndznwqfG5UeTHMlvACfUivPIVQZyDZnhZMq0UhC6zhCEQg=
              384082750f2c51dc917d85a7145748330fa6ef4d 0 iD8DBQBOmd+OywK+sNU5EO8RAgDgAJ9V/X+G7VLwhTpHrZNiOHabzSyzYQCdE2kKfIevJUYB9QLAWCWP6DPwrwI=
 d55b481ddfcc1dacb445179649e24ca861d 0 iD8DBQBOsFhpywK+sNU5EO8RAqM6AKCyfxUae3/zLuiLdQz+JR78690eMACfQ6JTBQib4AbE+rUDdkeFYg9K/+4=
 dbd1cef0c2f9f8bcf4ea303238105f716bda3 0 iD8DBQBO1/fWywK+sNU5EO8RAmoPAKCR5lpv1D6JLURHD8KVLSV4GRVEBgCgnd0Sy78ligNfqAMafmACRDvj7vo=
              6344043924497cd06d781d9014c66802285072e4 0 iD8DBQBPALgmywK+sNU5EO8RAlfhAJ9nYOdWnhfVDHYtDTJAyJtXBAQS9wCgnefoSQt7QABkbGxM+Q85UYEBuD0=
              db33555eafeaf9df1e18950e29439eaa706d399b 0 iD8DBQBPGdzxywK+sNU5EO8RAppkAJ9jOXhUVE/97CPgiMA0pMGiIYnesQCfengAszcBiSiKGugiI8Okc9ghU+Y=
 aa5b51f310fb3befd26bed99c02267f5c12c734 0 iD8DBQBPKZ9bywK+sNU5EO8RAt1TAJ45r1eJ0YqSkInzrrayg4TVCh0SnQCgm0GA/Ua74jnnDwVQ60lAwROuz1Q=
 e2cd303ecf8ca7c7eeebd785c34e5ed6b0f4a4 0 iD8DBQBPT/fvywK+sNU5EO8RAnfYAKCn7d0vwqIb100YfWm1F7nFD5B+FACeM02YHpQLSNsztrBCObtqcnfod7Q=
              b9bd95e61b49c221c4cca24e6da7c946fc02f992 0 iD8DBQBPeLsIywK+sNU5EO8RAvpNAKCtKe2gitz8dYn52IRF0hFOPCR7AQCfRJL/RWCFweu2T1vH/mUOCf8SXXc=
              d9e2f09d5488c395ae9ddbb320ceacd24757e055 0 iD8DBQBPju/dywK+sNU5EO8RArBYAJ9xtifdbk+hCOJO8OZa4JfHX8OYZQCeKPMBaBWiT8N/WHoOm1XU0q+iono=
 b3d087909e3c3ae44761efecdde8f319ef3 0 iD8DBQBPoFhIywK+sNU5EO8RAhzhAKCBj1n2jxPTkZNJJ5pSp3soa+XHIgCgsZZpAQxOpXwCp0eCdNGe0+pmxmg=
 de86462c5a9f42a3ad0f5e90ce5b1d221d25 0 iD8DBQBPovNWywK+sNU5EO8RAhgiAJ980T91FdPTRMmVONDhpkMsZwVIMACgg3bKvoWSeuCW28llUhAJtUjrMv0=
 a358df5bbbe404ca25730c9c459b34263441dc 0 iD8DBQBPyZsWywK+sNU5EO8RAnpLAJ48qrGDJRT+pteS0mSQ11haqHstPwCdG4ccGbk+0JHb7aNy8/NRGAOqn9w=
              b013baa3898e117959984fc64c29d8c784d2f28b 0 iD8DBQBP8QOPywK+sNU5EO8RAqimAKCFRSx0lvG6y8vne2IhNG062Hn0dACeMLI5/zhpWpHBIVeAAquYfx2XFeA=
 f5094bb3f423fc799e471aac2aee81a7ce57a0b 0 iD8DBQBQGiL8ywK+sNU5EO8RAq5oAJ4rMMCPx6O+OuzNXVOexogedWz/QgCeIiIxLd76I4pXO48tdXhr0hQcBuM=
 ae4ddb654eb2d5fd35bff358c738414432 0 iD8DBQBQQkq0ywK+sNU5EO8RArDTAJ9nk5CySnNAjAXYvqvx4uWCw9ThZwCgqmFRehH/l+oTwj3f8nw8u8qTCdc=
              b3f0f9a39c4e1d0250048cd803ab03542d6f140a 0 iD8DBQBQamltywK+sNU5EO8RAlsqAJ4qF/m6aFu4mJCOKTiAP5RvZFK02ACfawYShUZO6OXEFfveU0aAxDR0M1k=
              d118a4f4fd16d9b558ec3f3e87bfee772861d2b7 0 iD8DBQBQgPV5ywK+sNU5EO8RArylAJ0abcx5NlDjyv3ZDWpAfRIHyRsJtQCgn4TMuEayqgxzrvadQZHdTEU2g38=
 ad823b5d58c68903a6153a25e3fb4ed25239d 0 iD8DBQBQkuT9ywK+sNU5EO8RAhB4AKCeerItoK2Jipm2cVf4euGofAa/WACeJj3TVd4pFILpb+ogj7ebweFLJi0=
 c10cf8191469e7c3c8844922e17e71a176cb7cb 0 iD8DBQBQvQWoywK+sNU5EO8RAnq3AJoCn98u4geFx5YaQaeh99gFhCd7bQCgjoBwBSUyOvGd0yBy60E3Vv3VZhM=
              a4765077b65e6ae29ba42bab7834717b5072d5ba 0 iD8DBQBQ486sywK+sNU5EO8RAhmJAJ90aLfLKZhmcZN7kqphigQJxiFOQACeJ5IUZxjGKH4xzi3MrgIcx9n+dB0=
              f5fbe15ca7449f2c9a3cf817c86d0ae68b307214 0 iD8DBQBQ+yuYywK+sNU5EO8RAm9JAJoD/UciWvpGeKBcpGtZJBFJVcL/HACghDXSgQ+xQDjB+6uGrdgAQsRR1Lg=
              a6088c05e43a8aee0472ca3a4f6f8d7dd914ebbf 0 iD8DBQBRDDROywK+sNU5EO8RAh75AJ9uJCGoCWnP0Lv/+XuYs4hvUl+sAgCcD36QgAnuw8IQXrvv684BAXAnHcA=
 d4df752e61fe7ae4f3682e0a0008573b0402 0 iD8DBQBRFYaoywK+sNU5EO8RAuErAJoDyhXn+lptU3+AevVdwAIeNFyR2gCdHzPHyWd+JDeWCUR+pSOBi8O2ppM=
 b7175377babacce80a6c1e12366d8032a6d4340 0 iD8DBQBRMCYgywK+sNU5EO8RAq1/AKCWKlt9ysibyQgYwoxxIOZv5J8rpwCcDSHQaaf1fFZUTnQsOePwcM2Y/Sg=
 c922c1b5145dab8baefefb0437d363b6a6c21c 0 iD8DBQBRWnUnywK+sNU5EO8RAuQRAJwM42cJqJPeqJ0jVNdMqKMDqr4dSACeP0cRVGz1gitMuV0x8f3mrZrqc7I=
 a7bd2dccd44ed571afe7424cd7f95594f27c092 0 iD8DBQBRXfBvywK+sNU5EO8RAn+LAKCsMmflbuXjYRxlzFwId5ptm8TZcwCdGkyLbZcASBOkzQUm/WW1qfknJHU=
 cd385856d98bacb2c3086f8897bc660c2beea 0 iD8DBQBRcM0BywK+sNU5EO8RAjp4AKCJBykQbvXhKuvLSMxKx3a2TBiXcACfbr/kLg5GlZTF/XDPmY+PyHgI/GM=
 f785b38af38d2fca6b8f3db56b8007a84cd73a 0 iD8DBQBRgZwNywK+sNU5EO8RAmO4AJ4u2ILGuimRP6MJgE2t65LZ5dAdkACgiENEstIdrlFC80p+sWKD81kKIYI=
              ddc7a6be20212d18f3e27d9d7e6f079a66d96f21 0 iD8DBQBRkswvywK+sNU5EO8RAiYYAJsHTHyHbJeAgmGvBTmDrfcKu4doUgCeLm7eGBjx7yAPUvEtxef8rAkQmXI=
              cceaf7af4c9e9e6fa2dbfdcfe9856c5da69c4ffd 0 iD8DBQBRqnFLywK+sNU5EO8RAsWNAJ9RR6t+y1DLFc2HeH0eN9VfZAKF9gCeJ8ezvhtKq/LMs0/nvcgKQc/d5jk=
 acc6e37a650f0fae37872e733382ac1c0c 0 iD8DBQBR0guxywK+sNU5EO8RArNkAKCq9pMihVzP8Os5kCmgbWpe5C37wgCgqzuPZTHvAsXF5wTyaSTMVa9Ccq4=
              f0d7721d7322dcfb5af33599c2543f27335334bb 0 iD8DBQBR8taaywK+sNU5EO8RAqeEAJ4idDhhDuEsgsUjeQgWNj498matHACfT67gSF5w0ylsrBx1Hb52HkGXDm0=
              f37b5a17e6a0ee17afde2cdde5393dd74715fb58 0 iD8DBQBR+ymFywK+sNU5EO8RAuSdAJkBMcd9DAZ3rWE9WGKPm2YZ8LBoXACfXn/wbEsVy7ZgJoUwiWmHSnQaWCI=
 a558f81dc73afeab4d7be63617392b130117f 0 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
              e7fa36d2ad3a7944a52dca126458d6f482db3524 0 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
 f2d8f2421314b1ddead8f7d0c91009358994 0 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
              d825e4025e39d1c39db943cdc89818abd0a87c27 0 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
 e04a06467e2969c0cc6501335be0406d46ef0 0 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
              ca387377df7a3a67dbb90b6336b781cdadc3ef41 0 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
              8862469e16f9236208581b20de5f96bd13cc039d 0 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
 cec5134e9c4bceab6a00c60f52a4f80677a78f2 0 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
              b96cb15ec9e04d8ac5ee08b34fcbbe4200588965 0 iQIVAwUAUxJPlyBXgaxoKi1yAQLIRA//Qh9qzoYthPAWAUNbzybWXC/oMBI2X89NQC7l1ivKhv7cn9L79D8SWXM18q7LTwLdlwOkV/a0NTE3tkQTLvxJpfnRLCBbMOcGiIn/PxsAae8IhMAUbR7qz+XOynHOs60ZhK9X8seQHJRf1YtOI9gYTL/WYk8Cnpmc6xZQ90TNhoPPkpdfe8Y236V11SbYtN14fmrPaWQ3GXwyrvQaqM1F7BxSnC/sbm9+/wprsTa8gRQo7YQL/T5jJQgFiatG3yayrDdJtoRq3TZKtsxw8gtQdfVCrrBibbysjM8++dnwA92apHNUY8LzyptPy7rSDXRrIpPUWGGTQTD+6HQwkcLFtIuUpw4I75SV3z2r6LyOLKzDJUIunKOOYFS/rEIQGxZHxZOBAvbI+73mHAn3pJqm+UAA7R1n7tk3JyQncg50qJlm9zIUPGpNFcdEqak5iXzGYx292VlcE+fbJYeIPWggpilaVUgdmXtMCG0O0uX6C8MDmzVDCjd6FzDJ4GTZwgmWJaamvls85CkZgyN/UqlisfFXub0A1h7qAzBSVpP1+Ti+UbBjlrGX8BMRYHRGYIeIq16elcWwSpLgshjDwNn2r2EdwX8xKU5mucgTzSLprbOYGdQaqnvf6e8IX5WMBgwVW9YdY9yJKSLF7kE1AlM9nfVcXwOK4mHoMvnNgiX3zsw=
 f83fc5cfe715d292069ee8417c83804f6c6c1e4 0 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
 f55b251224f16508dd1311452db7780dafe2b 0 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
 ac506c6ababe86985b932f4948837c0891b5 0 iQIVAwUAU2LO/CBXgaxoKi1yAQI/3w/7BT/VRPyxey6tYp7i5cONIlEB3gznebGYwm0SGYNE6lsvS2VLh6ztb+j4eqOadr8Ssna6bslBx+dVsm+VuJ+vrNLMucD5Uc+fhn6dAfVqg+YBzUEaedI5yNsJizcJUDI7hUVsxiPiiYd9hchCWJ+z2tVt2jCyG2lMV2rbW36AM89sgz/wn5/AaAFsgoS6up/uzA3Tmw+qZSO6dZChb4Q8midIUWEbNzVhokgYcw7/HmjmvkvV9RJYiG8aBnMdQmxTE69q2dTjnnDL6wu61WU2FpTN09HRFbemUqzAfoJp8MmXq6jWgfLcm0cI3kRo7ZNpnEkmVKsfKQCXXiaR4alt9IQpQ6Jl7LSYsYI+D4ejpYysIsZyAE8qzltYhBKJWqO27A5V4WdJsoTgA/RwKfPRlci4PY8I4N466S7PBXVz/Cc5EpFkecvrgceTmBafb8JEi+gPiD2Po4vtW3bCeV4xldiEXHeJ77byUz7fZU7jL78SjJVOCCQTJfKZVr36kTz3KlaOz3E700RxzEFDYbK7I41mdANeQBmNNbcvRTy5ma6W6I3McEcAH4wqM5fFQ8YS+QWJxk85Si8KtaDPqoEdC/0dQPavuU/jAVjhV8IbmmkOtO7WvOHQDBtrR15yMxGMnUwMrPHaRNKdHNYRG0LL7lpCtdMi1mzLQgHYY9SRYvI=
 c80ee5b3cb3684fa8edc61501b3506d02eb10 0 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
 d8cd3d0e83c7336c0cb45a9f88638363f993848 0 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
 c36dc6cd61a0e1b563f1d51e55bdf4dacf12162 0 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
 e49892020336491cdc6945885c4de26ffa8b 0 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
 dc91146f35369949ea56b40172308158b59063a 0 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
              f768c888aaa68d12dd7f509dcc7f01c9584357d0 0 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
 f8d16af8cae246fa5a48e723d48d58b015aed94 0 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
              ced632394371a36953ce4d394f86278ae51a2aae 0 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
 c58303fb0ec020907af28b9e486be299ba043 0 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
              902554884335e5ca3661d63be9978eb4aec3f68a 0 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
 dad422ecc5adb63d9fa649eeb8e05a5f9bc4900 0 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
 a3a71d75396f5d4cf6935ae7d9ba5407a547 0 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
              db8e3f7948b1fdeb9ad12d448fc3525759908b9f 0 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
              fbdd5195528fae4f41feebc1838215c110b25d6a 0 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
 b4ed033390bf6e2879c8f5c28c84e1ee3b87231 0 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
 a92bbd02e5e3a625e0820389b47786b02b2cea 0 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
 e2e9a0750f91a6fe0ad88e4de34f8efefdcab08 0 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
              e89f909edffad558b56f4affa8239e4832f88de0 0 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
 cc6036bca532e06681c5a8fa37efaa812de67b5 0 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
              ed18f4acf435a2824c6f49fba40f42b9df5da7ad 0 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
 cd0ddac49c1125b2e013aa2ff18ecbd4dd954 0 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
 a38d44ba093bd1d1ecfd34119e94056030278b 0 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
 aa1c313b05b1a85f8ffa1120d51579ddf6bf24 0 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
 a45e49a6bed023deb229102a8903234d18054d3 0 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
 a466b9f9792e3ad7ae3fc6c43c3ff2e136b718d 0 iQIVAwUAVg1oMSBXgaxoKi1yAQLPag/+Pv0+pR9b9Y5RflEcERUzVu92q+l/JEiP7PHP9pAZuXoQ0ikYBFo1Ygw8tkIG00dgEaLk/2b7E3OxaU9pjU3thoX//XpTcbkJtVhe7Bkjh9/S3dRpm2FWNL9n0qnywebziB45Xs8XzUwBZTYOkVRInYr/NzSo8KNbQH1B4u2g56veb8u/7GtEvBSGnMGVYKhVUZ3jxyDf371QkdafMOJPpogkZcVhXusvMZPDBYtTIzswyxBJ2jxHzjt8+EKs+FI3FxzvQ9Ze3M5Daa7xfiHI3sOgECO8GMVaJi0F49lttKx08KONw8xLlEof+cJ+qxLxQ42X5XOQglJ2/bv5ES5JiZYAti2XSXbZK96p4wexqL4hnaLVU/2iEUfqB9Sj6itEuhGOknPD9fQo1rZXYIS8CT5nGTNG4rEpLFN6VwWn1btIMNkEHw998zU7N3HAOk6adD6zGcntUfMBvQC3V4VK3o7hp8PGeySrWrOLcC/xLKM+XRonz46woJK5D8w8lCVYAxBWEGKAFtj9hv9R8Ye9gCW0Q8BvJ7MwGpn+7fLQ1BVZdV1LZQTSBUr5u8mNeDsRo4H2hITQRhUeElIwlMsUbbN078a4JPOUgPz1+Fi8oHRccBchN6I40QohL934zhcKXQ+NXYN8BgpCicPztSg8O8Y/qvhFP12Zu4tOH8P/dFY=
              b66e3ca0b90c3095ea28dfd39aa24247bebf5c20 0 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
 dd34f2e7272be9e3b2a5a83cd0d20be44293f4 0 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
 aa5083cbebbe7575c88f3402ab377539b484897 0 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
 d437a0f3355834a9485bbbeb30a52a052c98f19 0 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
              ea389970c08449440587712117f178d33bab3f1e 0 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
 bdc8965720ca4061f8f8d806563cfc7cdb62e 0 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
              2408645de650d8a29a6ce9e7dce601d8dd0d1474 0 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
              b698abf971e7377d9b7ec7fc8c52df45255b0329 0 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
              d493d64757eb45ada99fcb3693e479a51b7782da 0 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
              ae279d4a19e9683214cbd1fe8298cf0b50571432 0 iQIVAwUAVvqzViBXgaxoKi1yAQKUCxAAtctMD3ydbe+li3iYjhY5qT0wyHwPr9fcLqsQUJ4ZtD4sK3oxCRZFWFxNBk5bIIyiwusSEJPiPddoQ7NljSZlYDI0HR3R4vns55fmDwPG07Ykf7aSyqr+c2ppCGzn2/2ID476FNtzKqjF+LkVyadgI9vgZk5S4BgdSlfSRBL+1KtB1BlF5etIZnc5U9qs1uqzZJc06xyyF8HlrmMZkAvRUbsx/JzA5LgzZ2WzueaxZgYzYjDk0nPLgyPPBj0DVyWXnW/kdRNmKHNbaZ9aZlWmdPCEoq5iBm71d7Xoa61shmeuVZWvxHNqXdjVMHVeT61cRxjdfxTIkJwvlRGwpy7V17vTgzWFxw6QJpmr7kupRo3idsDydLDPHGUsxP3uMZFsp6+4rEe6qbafjNajkRyiw7kVGCxboOFN0rLVJPZwZGksEIkw58IHcPhZNT1bHHocWOA/uHJTAynfKsAdv/LDdGKcZWUCFOzlokw54xbPvdrBtEOnYNp15OY01IAJd2FCUki5WHvhELUggTjfank1Tc3/Rt1KrGOFhg80CWq6eMiuiWkHGvYq3fjNLbgjl3JJatUFoB+cX1ulDOGsLJEXQ4v5DNHgel0o2H395owNlStksSeW1UBVk0hUK/ADtVUYKAPEIFiboh1iDpEOl40JVnYdsGz3w5FLj2w+16/1vWs=
 eedf2c450aee58b1a90b0e826f47c5da64 0 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
              f85de28eae32e7d3064b1a1321309071bbaaa069 0 iQIVAwUAVyZQaiBXgaxoKi1yAQJhCQ//WrRZ55k3VI/OgY+I/HvgFHOC0sbhe207Kedxvy00a3AtXM6wa5E95GNX04QxUfTWUf5ZHDfEgj0/mQywNrH1oJG47iPZSs+qXNLqtgAaXtrih6r4/ruUwFCRFxqK9mkhjG61SKicw3Q7uGva950g6ZUE5BsZ7XJWgoDcJzWKR+AH992G6H//Fhi4zFQAmB34++sm80wV6wMxVKA/qhQzetooTR2x9qrHpvCKMzKllleJe48yzPLJjQoaaVgXCDav0eIePFNw0WvVSldOEp/ADDdTGa65qsC1rO2BB1Cu5+frJ/vUoo0PwIgqgD6p2i41hfIKvkp6130TxmRVxUx+ma8gBYEpPIabV0flLU72gq8lMlGBBSnQ+fcZsfs/Ug0xRN0tzkEScmZFiDxRGk0y7IalXzv6irwOyC2fZCajXGJDzkROQXWMgy9eKkwuFhZBmPVYtrATSq3jHLVmJg5vfdeiVzA6NKxAgGm2z8AsRrijKK8WRqFYiH6xcWKG5u+FroPQdKa0nGCkPSTH3tvC6fAHTVm7JeXch5QE/LiS9Y575pM2PeIP+k+Fr1ugK0AEvYJAXa5UIIcdszPyI+TwPTtWaQ83X99qGAdmRWLvSYjqevOVr7F/fhO3XKFXRCcHA3EzVYnG7nWiVACYF3H2UgN4PWjStbx/Qhhdi9xAuks=
              a56296f55a5e1038ea5016dace2076b693c28a56 0 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
              aaabed77791a75968a12b8c43ad263631a23ee81 0 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
              a9764ab80e11bcf6a37255db7dd079011f767c6c 0 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
 a5d605b8683a292bb89aea11f37a81b06ac016 0 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
 bb4f9d3a47a6e83c2b414d58811ed38f503c2 0 iQIVAwUAV42tNyBXgaxoKi1yAQI/Iw//V0NtxpVD4sClotAwffBVW42Uv+SG+07CJoOuFYnmHZv/plOzXuuJlmm95L00/qyRCCTUyAGxK/eP5cAKP2V99ln6rNhh8gpgvmZlnYjU3gqFv8tCQ+fkwgRiWmgKjRL6/bK9FY5cO7ATLVu3kCkFd8CEgzlAaUqBfkNFxZxLDLvKqRlhXxVXhKjvkKg5DZ6eJqRQY7w3UqqR+sF1rMLtVyt490Wqv7YQKwcvY7MEKTyH4twGLx/RhBpBi+GccVKvWC011ffjSjxqAfQqrrSVt0Ld1Khj2/p1bDDYpTgtdDgCzclSXWEQpmSdFRBF5wYs/pDMUreI/E6mlWkB4hfZZk1NBRPRWYikXwnhU3ziubCGesZDyBYLrK1vT+tf6giseo22YQmDnOftbS999Pcn04cyCafeFuOjkubYaINB25T20GS5Wb4a0nHPRAOOVxzk/m/arwYgF0ZZZDDvJ48TRMDf3XOc1jc5qZ7AN/OQKbvh2B08vObnnPm3lmBY1qOnhwzJxpNiq+Z/ypokGXQkGBfKUo7rWHJy5iXLb3Biv9AhxY9d5pSTjBmTAYJEic3q03ztzlnfMyi+C13+YxFAbSSNGBP8Hejkkz0NvmB1TBuCKpnZA8spxY5rhZ/zMx+cCw8hQvWHHDUURps7SQvZEfrJSCGJFPDHL3vbfK+LNwI=
 f84e68dbb9bd026f0f3a974ce4bdb93686 0 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
              ccd436f7db6d5d7b9af89715179b911d031d44f1 0 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
 e68974eb5c63ccb03f794d8b57339a80c4 0 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
 c415874f6ac653efc1099dec9c9150e90f 0 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
              eab27446995210c334c3d06f1a659e3b9b5da769 0 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
              b3b1ae98f6a0e14c1e1ba806a6c18e193b6dae5c 0 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
              e69874dc1f4e142746ff3df91e678a09c6fc208c 0 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
              a1dd2c0c479e0550040542e392e87bc91262517e 0 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
              e1526da1e6d84e03146151c9b6e6950fe9a83d7d 0 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
 b624d27e3917d978af56d6ad59331e0464a 0 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
              ed5b25874d998ababb181a939dd37a16ea644435 0 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
 eaf9539499a1b8be259ffe7ada787d07857f80 0 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
 e788321cc4ae9975b7f0c54c849f36d82182b 0 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
              bb96d4a497432722623ae60d9bc734a1e360179e 0 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
              c850f0ed54c1d42f9aa079ad528f8127e5775217 0 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
 c49ed51a698ec016d2b4c6b44ca3c3f73cc788 0 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
 ebaed4e315f63157bd157d6ce553c7ab73 0 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
 af8622863796a0027566f6b646e10d522c4c 0 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
 c91326b23954e6e1c6960d0239511f9530258 0 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
 fee7f7d2da04226914c2258cc2884dc27384fd7 0 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
 f72c7b70acfdaf56ab35360584d7845827 0 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
 f427b57bf9019c6dc3750baa539dc22c1be50f6 0 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
 e2454b60e5936f5e77498cab2648db469504487 0 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
 ccb43d4cf01d013ae05917ec4f305509f851b2d 0 iQJVBAABCAA/FiEEOoFVFj0OIKUw/LeGR6Z/+qNGqs4FAln6Qp8hHGtidWxsb2NrK21lcmN1cmlhbEByaW5nd29ybGQub3JnAAoJEEemf/qjRqrOJ8MP/2ufm/dbrFoE0F8hewhztG1vS4stus13lZ9lmM9kza8OKeOgY/MDH8GaV3O8GnRiCNUFsVD8JEIexE31c84H2Ie7VQO0GQSUHSyMCRrbED6IvfrWp6EZ6RDNPk4LHBfxCuPmuVHGRoGZtsLKJBPIxIHJKWMlEJlj9BZuUxZp/8kurQ6CXwblVbFzXdOaZQlioOBH27Bk3S0+gXfJ+wA2ed5XOQvT9jwjqC8y/1t8obaoPTpzyAvb9NArG+9RT9vfNN42aWISZNwg6RW5oLJISqoGrAes6EoG7dZfOC0UoKMVYXoNvZzJvVlMHyjugIoid+WI+V8y9bPrRTfbPCmocCzEzCOLEHQta8roNijB0bKcq8hmQPHcMyXlj1Srnqlco49jbhftgJoPTwzb10wQyU0VFvaZDPW/EQUT3M/k4j3sVESjANdyG1iu6EDV080LK1LgAdhjpKMBbf6mcgAe06/07XFMbKNrZMEislOcVFp98BSKjdioUNpy91rCeSmkEsASJ3yMArRnSkuVgpyrtJaGWl79VUcmOwKhUOA/8MXMz/Oqu7hvve/sgv71xlnim460nnLw6YHPyeeCsz6KSoUK3knFXAbTk/0jvU1ixUZbI122aMzX04UgPGeTukCOUw49XfaOdN+x0YXlkl4PsrnRQhIoixY2gosPpK4YO73G
              cabc840ffdee8a72f3689fb77dd74d04fdc2bc04 0 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
              a92b9f8e11ba330614cdfd6af0e03b15c1ff3797 0 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
 b6df1b5adbdf647cf5c6675b40575e1b197c60 0 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
              d334afc585e29577f271c5eda03378736a16ca6b 0 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
 aadf7a3264b03c8b09efce715bc41e6ab4a9b 0 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
 bba684efde7f45add05f737952093bb2aa07155 0 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
 de7bd407251af2bc98e5b809c8598ee95830daf 0 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
              ed5448edcbfa747b9154099e18630e49024fd47b 0 iQJEBAABCAAuFiEEK8zhT1xnJaouqK63ucncgkqlvdUFAlrXnuoQHHJhZkBkdXJpbjQyLmNvbQAKCRC5ydyCSqW91fSHEACBVg4FsCE2nN5aEKAQb7l7rG4XTQ9FbvoTYB3tkvmsLQSRfh2GB2ZDBOI7Vswo2UxXupr4qSkUQbeHrwrk9A1s5b/T5e4wSKZuFJOrkwLVZDFfUHumKomqdoVj/D8+LDt7Rz+Wm7OClO/4dTAsl2E4rkl7XPtqjC3jESGad8IBANlPVBhNUMER4eFcPZzq1qi2MrlJKEKpdeZEWJ/ow7gka/aTLqHMfRwhA3kS5X34Yai17kLQZGQdWISWYiM9Zd2b/FSTHZGy8rf9cvjXs3EXfEB5nePveDrFOfmuubVRDplO+/naJjNBqwxeB99jb7Fk3sekPZNW/NqR/w1jvQFA3OP9fS2g1OwfXMWyx6DvBJNfQwppNH3JUvA5PEiorul4GJ2nuubXk+Or1yzoRJtwOGz/GQi2BcsPKaL6niewrInFw18jMVhx/4Jbpu+glaim4EvT/PfJ5KdSwF7pJxsoiqvw7A2C2/DsZRbCeal9GrTulkNf/hgpCJOBK1DqVVq1O5MI/oYQ69HxgMq9Ip1OGJJhse3qjevBJbpNCosCpjb3htlo4go29H8yyGJb09i05WtNW2EQchrTHrlruFr7mKJ5h1mAYket74QQyaGzqwgD5kwSVnIcwHpfb8oiJTwA5R+LtbAQXWC/fFu1g1KEp/4hGOQoRU04+mYuPsrzaA==
 ec874717d8a93b19e0d50628443e0ee5efab3a9 0 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
 cac550aea66d19c601e45efd1b7bd08d7c40 0 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
 c5ced5276d6e7d54f7c3dadf5247b7ee98ec79c 0 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
 b63a6743010dfdbf8a8154186e119949bdaa1cc 0 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
              e90130af47ce8dd53a3109aed9d15876b3e7dee8 0 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
 ac6a72308a215e6086fbced347ec10aa963b0a 0 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
              ede3bf31fe63677fdf5bd8db687977d4e3d792ed 0 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
 cb1a79010ac50c58cd84e6f50c4556bf2a4c 0 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
 ec6f1320df26f3133ec40f3de866ea0695fd7 0 iQJEBAABCAAuFiEEK8zhT1xnJaouqK63ucncgkqlvdUFAlvOG20QHHJhZkBkdXJpbjQyLmNvbQAKCRC5ydyCSqW91eZ+EACb/XfPWaMkwIX54JaFWtL/nVkDcaL8xLVzlI+PxL0ZtHdQTGVQNp5f1BnZU9RKPZ9QOuz+QKNvb4hOOXBwmCi2AAjmTYUqtKThHmOT50ZRICkllY+YlZ3tI6JXRDhh7pSXaus8jBFG/VwuUlVmK5sA2TP+lIJijOgV9rThszfS4Q2I8sBTIaeZS1hyujFxGRO++tjYR+jPuo/98FhqJ5EylVYvKmnflWkOYLFNFqgDI6DQs7Dl+u2nrNAzZJQlgk+1ekd66T3WyK8U3tcFLZGRQ+gpzINH0Syn6USaaE+0nGi4we1hJS8JK0txWyHXJGNZYaWQAC2l1hIBfA38azwVLSe2w9JatXhS3HWByILy8JkEQ2kSo1xTD4mBkszZo/kWZpZRsAWydxCnzhNgKmTJYxASFTTX1mpdX4EzJBOs/++52y1OjVc0Ko0+6vSwxsC6zgIGJx1Os7vVgWHql0XbDmJ1NDdNmz7q5HjFcbNOWScKf6UGcBKV4dpW1w+7CvdoMFHUsVTa2zn6YOki3NEt0GWLXq+0aXbHSw8XETcyunQKjDi9ddKOw0rYGip6EKUKhOILZimQ0lgYRE23RDdT5Tl2D8s66SUuipgP9vGjbMaE/FhO3OAb7406jyCrOVfDis7sK0Hvw074GhIfZUjA4W4Ey2TeExCZHHhBdoPTrg==
              a91a2837150bdcb27ae76b3646e6c93cd6a15904 0 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
 c8c54cf97256f4468da2eb4dbee24f7f3888e71 0 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
 f092b2cd9691e2a55d198f717b231af9be6f9 0 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
 ff5844cad7a27ee3eb5adad89ac8550949 0 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
 b4b4ae0e9a6b8e579f7b0a693b8cf5c3b10 0 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
 ea21df312ec7159c5b3633096b6ecf68750b0dd 0 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
 a8d9ed864754837a185a642170cde24392f9abf 0 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
 e479ef7c9639be0029f00e6a722b96dcc05fee 0 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
              c3484ddbdb9621256d597ed86b90d229c59c2af9 0 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
 ada9b8d51bef24c5cb4cdca4243f0db694ab6e 0 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
              e386b5f4f8360dbb43a576dd9b1368e386fefa5b 0 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
              e91930d712e8507d1bc1b2dffd96c83edc4cbed3 0 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
              a4e32fd539ab41489a51b2aa88bda9a73b839562 0 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
 e52f2b62f4768aa0d988936c929dc7c4a41a0 0 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
 f9561099de77c684c00f76507f11e46ebe8 0 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
              ca3dca416f8d5863ca6f5a4a6a6bb835dcd5feeb 0 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
              a50fecefa691c9b72a99e49aa6fe9dd13943c2bf 0 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
              b4c82b70418022e67cc0e69b1aa3c3aa43aa1d29 0 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
 a0102c05c7852c8215ef6cf21d809927586b69 0 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
              e4344e463c0c888a2f437b78b5982ecdf3f6650a 0 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
 f5410dfc8a64bb587d19637deb95d378fd1eb5c 0 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
 d121acbb82e65fe4dd3c2318a1b61981b958492 0 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
 fca7e8449a847e3cf1054f2c07b51237699fad3 0 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
 ce8e7515036d3431a03aaeb7bc72dd96cb1112 0 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
              cf3e07d7648a4371ce584d15dd692e7a6845792f 0 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
 cbdbdbb05dcd6bb814eb9bbdd982211b28 0 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
 ea9c86fac8974cd74dc12ea681c8986eb6da6c4 0 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
 c5de797e5416f9b588940f4608269b4d50a 0 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
 fc3c5fbc65f6fe85d70ea63923b8767dda4f2e0 0 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
              f62bb5d07848ca598aa860a517394130b61bf2ee 0 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
              07731064ac41dacdf0ec869ebd05c2e848c14fbf 0 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
 e06a7ab9e0d5c65af4e511aee1e0342998799df 0 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
+c17d63fdabd009e70bf994e5efb7db422f4f7f 0 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

.hgtags

0 +1 0

              d40cc5aacc31ed673d9b5b24f98bee78c283062c 0.4f
 c590d34bf61e2ea12c71738e5a746cd74586157 0.4e
 eca4cfa8aad5fce9a04f7d8acadcd0452e2f34e 0.4d
              b4d0c3786ad3e47beacf8412157326a32b6d25a4 0.4c
              f40273b0ad7b3a6d3012fd37736d0611f41ecf54 0.5
 a28dfe59f8fab54a5118c5be4f40da34a53cdb7 0.5b
 e0fdbc57a0be78f0e817fd1d170a3615cd35da 0.6
 ccf3de52989b14c3d84e1097f59e39a992e00bd 0.6b
              eac9c8efcd9bd8244e72fb6821f769f450457a32 0.6c
 c049974485125e1f9357f6bbe9c1b548a64c3 0.7
 a56574f329a368d645853e0f9e09472aee62349 0.8
 a03cff2b0f5d30281e6addefe96b993582f2eac 0.8.1
 fb62a3a673d5322f6274a44ba6456e5e4b3b37 0.9
 be3001847cb18a23c403439d9e7d0ace30804e9 0.9.1
 a957364b1b89c150f2d0e60a99befe0ee08bd3 0.9.2
 c29bfec36d5540fbe1c976810aefecfd1d2 0.9.3
              fb4b6d5fe100b0886f8bc3d6731ec0e5ed5c4694 0.9.4
              23889160905a1b09fffe1c07378e9fc1827606eb 0.9.5
              bae2e9c838e90a393bae3973a7850280413e091a 1.0
              d5cbbe2c49cee22a9fbeb9ea41daa0ac4e26b846 1.0.1
              d2375bbee6d47e62ba8e415c86e83a465dc4dce9 1.0.2
 a67430f92f15ea5159c26b09ec4839a0c549a26 1.1
 e510d433969e277b1863c317b674cbee2065 1.1.1
 a4eb81fb4f4742451591489e2797dc47903277 1.1.2
 efa41037e280d08cfb07c09ad485df30fb0ea8 1.2
              02981000012e3adf40c4849bd7b3d5618f9ce82d 1.2.1
 d40e7c885fa6e95f89134809b3ec7bdbca34b 1.3
 ef6c14a1e8e83a31226f5881b7fe6095bbfa6f6 1.3.1
 ec469f9b556f11819937cf68ee53f2be927ebf 1.4
 d7ea6fe3aa4ab9ec274a68846779153789de9 1.4.1
 a0b14a68621f6990c54fdba0083f6f20935bf 1.4.2
 aa619c4c2c09907034d9824ebb1dd0e878206eb 1.4.3
              ff2704a8ded37fbebd8b6eb5ec733731d725da8a 1.5
 b01dab594167bc0dd33331dbaa6dca3dca1b3aa 1.5.1
 f725929f0c48c5fb3b90c071fc3066012456ca 1.5.2
              fdcf80f26604f233dc4d8f0a5ef9d7470e317e8a 1.5.3
 fe2629c6fd0c74c90bd066e77387c2b02e8437 1.5.4
              f786fc4b8764cd2a5526d259cf2f94d8a66924d9 1.6
              bf1774d95bde614af3956d92b20e2a0c68c5fec7 1.6.1
              c00f03a4982e467fb6b6bd45908767db6df4771d 1.6.2
              ff5cec76b1c5b6be9c3bb923aae8c3c6d079d6b9 1.6.3
 d8bff78c96fe7e33237b257558ee97290048a4 1.6.4
 b9e0f96c7bc788e5667c146a58a9440a55 1.7
              4438875ec01bd0fc32be92b0872eb6daeed4d44f 1.7.1
 aff4f144ad356311318b0011df0bb21f2c97429 1.7.2
              e3bf16703e2601de99e563cdb3a5d50b64e6d320 1.7.3
              a6c855c32ea081da3c3b8ff628f1847ff271482f 1.7.4
 b2155623ee2559caf288fd333f30475966c4525 1.7.5
              2616325766e3504c8ae7c84bd15ee610901fe91d 1.8
              aa1f3be38ab127280761889d2dca906ca465b5f4 1.8.1
              b032bec2c0a651ca0ddecb65714bfe6770f67d70 1.8.2
 cb1e95676ad089596bd81d0937cad37d6e3b7fb 1.8.3
 af5d9f6b22387913e1d11350fb8cb7c1487dd 1.8.4
              de9eb6b1da4fc522b1cab16d86ca166204c24f25 1.9
 a43e23b8c55b4566b8200bf69fe2158485a2634 1.9.1
              d629f1e89021103f1753addcef6b310e4435b184 1.9.2
 a9292e430e35766c552066ed3e87c557b803b 1.9.3
              384082750f2c51dc917d85a7145748330fa6ef4d 2.0-rc
 d55b481ddfcc1dacb445179649e24ca861d 2.0
 dbd1cef0c2f9f8bcf4ea303238105f716bda3 2.0.1
              6344043924497cd06d781d9014c66802285072e4 2.0.2
              db33555eafeaf9df1e18950e29439eaa706d399b 2.1-rc
 aa5b51f310fb3befd26bed99c02267f5c12c734 2.1
 e2cd303ecf8ca7c7eeebd785c34e5ed6b0f4a4 2.1.1
              b9bd95e61b49c221c4cca24e6da7c946fc02f992 2.1.2
              d9e2f09d5488c395ae9ddbb320ceacd24757e055 2.2-rc
 b3d087909e3c3ae44761efecdde8f319ef3 2.2
 de86462c5a9f42a3ad0f5e90ce5b1d221d25 2.2.1
 a358df5bbbe404ca25730c9c459b34263441dc 2.2.2
              b013baa3898e117959984fc64c29d8c784d2f28b 2.2.3
              a06e2681dd1786e2354d84a5fa9c1c88dd4fa3e0 2.3-rc
 f5094bb3f423fc799e471aac2aee81a7ce57a0b 2.3
 ae4ddb654eb2d5fd35bff358c738414432 2.3.1
              b3f0f9a39c4e1d0250048cd803ab03542d6f140a 2.3.2
              d118a4f4fd16d9b558ec3f3e87bfee772861d2b7 2.4-rc
 ad823b5d58c68903a6153a25e3fb4ed25239d 2.4
 c10cf8191469e7c3c8844922e17e71a176cb7cb 2.4.1
              a4765077b65e6ae29ba42bab7834717b5072d5ba 2.4.2
              f5fbe15ca7449f2c9a3cf817c86d0ae68b307214 2.5-rc
              a6088c05e43a8aee0472ca3a4f6f8d7dd914ebbf 2.5
 d4df752e61fe7ae4f3682e0a0008573b0402 2.5.1
 b7175377babacce80a6c1e12366d8032a6d4340 2.5.2
 c922c1b5145dab8baefefb0437d363b6a6c21c 2.5.3
 a7bd2dccd44ed571afe7424cd7f95594f27c092 2.5.4
 cd385856d98bacb2c3086f8897bc660c2beea 2.6-rc
 f785b38af38d2fca6b8f3db56b8007a84cd73a 2.6
              ddc7a6be20212d18f3e27d9d7e6f079a66d96f21 2.6.1
              cceaf7af4c9e9e6fa2dbfdcfe9856c5da69c4ffd 2.6.2
 acc6e37a650f0fae37872e733382ac1c0c 2.6.3
              f0d7721d7322dcfb5af33599c2543f27335334bb 2.7-rc
              f37b5a17e6a0ee17afde2cdde5393dd74715fb58 2.7
 a558f81dc73afeab4d7be63617392b130117f 2.7.1
              e7fa36d2ad3a7944a52dca126458d6f482db3524 2.7.2
 f2d8f2421314b1ddead8f7d0c91009358994 2.8-rc
              d825e4025e39d1c39db943cdc89818abd0a87c27 2.8
 e04a06467e2969c0cc6501335be0406d46ef0 2.8.1
              ca387377df7a3a67dbb90b6336b781cdadc3ef41 2.8.2
              8862469e16f9236208581b20de5f96bd13cc039d 2.9-rc
 cec5134e9c4bceab6a00c60f52a4f80677a78f2 2.9
              b96cb15ec9e04d8ac5ee08b34fcbbe4200588965 2.9.1
 f83fc5cfe715d292069ee8417c83804f6c6c1e4 2.9.2
 f55b251224f16508dd1311452db7780dafe2b 3.0-rc
 ac506c6ababe86985b932f4948837c0891b5 3.0
 c80ee5b3cb3684fa8edc61501b3506d02eb10 3.0.1
 d8cd3d0e83c7336c0cb45a9f88638363f993848 3.0.2
 c36dc6cd61a0e1b563f1d51e55bdf4dacf12162 3.1-rc
 e49892020336491cdc6945885c4de26ffa8b 3.1
 dc91146f35369949ea56b40172308158b59063a 3.1.1
              f768c888aaa68d12dd7f509dcc7f01c9584357d0 3.1.2
 f8d16af8cae246fa5a48e723d48d58b015aed94 3.2-rc
              ced632394371a36953ce4d394f86278ae51a2aae 3.2
 c58303fb0ec020907af28b9e486be299ba043 3.2.1
              902554884335e5ca3661d63be9978eb4aec3f68a 3.2.2
 dad422ecc5adb63d9fa649eeb8e05a5f9bc4900 3.2.3
 a3a71d75396f5d4cf6935ae7d9ba5407a547 3.2.4
              db8e3f7948b1fdeb9ad12d448fc3525759908b9f 3.3-rc
              fbdd5195528fae4f41feebc1838215c110b25d6a 3.3
 b4ed033390bf6e2879c8f5c28c84e1ee3b87231 3.3.1
 a92bbd02e5e3a625e0820389b47786b02b2cea 3.3.2
 e2e9a0750f91a6fe0ad88e4de34f8efefdcab08 3.3.3
              e89f909edffad558b56f4affa8239e4832f88de0 3.4-rc
 cc6036bca532e06681c5a8fa37efaa812de67b5 3.4
              ed18f4acf435a2824c6f49fba40f42b9df5da7ad 3.4.1
 cd0ddac49c1125b2e013aa2ff18ecbd4dd954 3.4.2
 a38d44ba093bd1d1ecfd34119e94056030278b 3.5-rc
 aa1c313b05b1a85f8ffa1120d51579ddf6bf24 3.5
 a45e49a6bed023deb229102a8903234d18054d3 3.5.1
 a466b9f9792e3ad7ae3fc6c43c3ff2e136b718d 3.5.2
              b66e3ca0b90c3095ea28dfd39aa24247bebf5c20 3.6-rc
 dd34f2e7272be9e3b2a5a83cd0d20be44293f4 3.6
 aa5083cbebbe7575c88f3402ab377539b484897 3.6.1
 d437a0f3355834a9485bbbeb30a52a052c98f19 3.6.2
              ea389970c08449440587712117f178d33bab3f1e 3.6.3
 bdc8965720ca4061f8f8d806563cfc7cdb62e 3.7-rc
              2408645de650d8a29a6ce9e7dce601d8dd0d1474 3.7
              b698abf971e7377d9b7ec7fc8c52df45255b0329 3.7.1
              d493d64757eb45ada99fcb3693e479a51b7782da 3.7.2
              ae279d4a19e9683214cbd1fe8298cf0b50571432 3.7.3
 eedf2c450aee58b1a90b0e826f47c5da64 3.8-rc
              f85de28eae32e7d3064b1a1321309071bbaaa069 3.8
              a56296f55a5e1038ea5016dace2076b693c28a56 3.8.1
              aaabed77791a75968a12b8c43ad263631a23ee81 3.8.2
              a9764ab80e11bcf6a37255db7dd079011f767c6c 3.8.3
 a5d605b8683a292bb89aea11f37a81b06ac016 3.8.4
 bb4f9d3a47a6e83c2b414d58811ed38f503c2 3.9-rc
 f84e68dbb9bd026f0f3a974ce4bdb93686 3.9
              ccd436f7db6d5d7b9af89715179b911d031d44f1 3.9.1
 e68974eb5c63ccb03f794d8b57339a80c4 3.9.2
 c415874f6ac653efc1099dec9c9150e90f 4.0-rc
              eab27446995210c334c3d06f1a659e3b9b5da769 4.0
              b3b1ae98f6a0e14c1e1ba806a6c18e193b6dae5c 4.0.1
              e69874dc1f4e142746ff3df91e678a09c6fc208c 4.0.2
              a1dd2c0c479e0550040542e392e87bc91262517e 4.1-rc
              e1526da1e6d84e03146151c9b6e6950fe9a83d7d 4.1
 b624d27e3917d978af56d6ad59331e0464a 4.1.1
              ed5b25874d998ababb181a939dd37a16ea644435 4.1.2
 eaf9539499a1b8be259ffe7ada787d07857f80 4.1.3
 e788321cc4ae9975b7f0c54c849f36d82182b 4.2-rc
              bb96d4a497432722623ae60d9bc734a1e360179e 4.2
              c850f0ed54c1d42f9aa079ad528f8127e5775217 4.2.1
 c49ed51a698ec016d2b4c6b44ca3c3f73cc788 4.2.2
 ebaed4e315f63157bd157d6ce553c7ab73 4.3-rc
 af8622863796a0027566f6b646e10d522c4c 4.3
 c91326b23954e6e1c6960d0239511f9530258 4.2.3
 fee7f7d2da04226914c2258cc2884dc27384fd7 4.3.1
 f72c7b70acfdaf56ab35360584d7845827 4.3.2
 f427b57bf9019c6dc3750baa539dc22c1be50f6 4.3.3
 e2454b60e5936f5e77498cab2648db469504487 4.4-rc
 ccb43d4cf01d013ae05917ec4f305509f851b2d 4.4
              cabc840ffdee8a72f3689fb77dd74d04fdc2bc04 4.4.1
              a92b9f8e11ba330614cdfd6af0e03b15c1ff3797 4.4.2
 b6df1b5adbdf647cf5c6675b40575e1b197c60 4.5-rc
              d334afc585e29577f271c5eda03378736a16ca6b 4.5
 aadf7a3264b03c8b09efce715bc41e6ab4a9b 4.5.1
 bba684efde7f45add05f737952093bb2aa07155 4.5.2
 de7bd407251af2bc98e5b809c8598ee95830daf 4.5.3
              ed5448edcbfa747b9154099e18630e49024fd47b 4.6rc0
 ec874717d8a93b19e0d50628443e0ee5efab3a9 4.6rc1
 cac550aea66d19c601e45efd1b7bd08d7c40 4.6
 c5ced5276d6e7d54f7c3dadf5247b7ee98ec79c 4.6.1
 b63a6743010dfdbf8a8154186e119949bdaa1cc 4.6.2
              e90130af47ce8dd53a3109aed9d15876b3e7dee8 4.7rc0
 ac6a72308a215e6086fbced347ec10aa963b0a 4.7
              ede3bf31fe63677fdf5bd8db687977d4e3d792ed 4.7.1
 cb1a79010ac50c58cd84e6f50c4556bf2a4c 4.7.2
 ec6f1320df26f3133ec40f3de866ea0695fd7 4.8rc0
              a91a2837150bdcb27ae76b3646e6c93cd6a15904 4.8
 c8c54cf97256f4468da2eb4dbee24f7f3888e71 4.8.1
 f092b2cd9691e2a55d198f717b231af9be6f9 4.8.2
 ff5844cad7a27ee3eb5adad89ac8550949 4.9rc0
 b4b4ae0e9a6b8e579f7b0a693b8cf5c3b10 4.9
 ea21df312ec7159c5b3633096b6ecf68750b0dd 4.9.1
 a8d9ed864754837a185a642170cde24392f9abf 5.0rc0
 e479ef7c9639be0029f00e6a722b96dcc05fee 5.0
              c3484ddbdb9621256d597ed86b90d229c59c2af9 5.0.1
 ada9b8d51bef24c5cb4cdca4243f0db694ab6e 5.0.2
              e386b5f4f8360dbb43a576dd9b1368e386fefa5b 5.1rc0
              e91930d712e8507d1bc1b2dffd96c83edc4cbed3 5.1
              a4e32fd539ab41489a51b2aa88bda9a73b839562 5.1.1
 e52f2b62f4768aa0d988936c929dc7c4a41a0 5.1.2
 f9561099de77c684c00f76507f11e46ebe8 5.2rc0
              ca3dca416f8d5863ca6f5a4a6a6bb835dcd5feeb 5.2
              a50fecefa691c9b72a99e49aa6fe9dd13943c2bf 5.2.1
              b4c82b70418022e67cc0e69b1aa3c3aa43aa1d29 5.2.2
 a0102c05c7852c8215ef6cf21d809927586b69 5.3rc0
              e4344e463c0c888a2f437b78b5982ecdf3f6650a 5.3rc1
 f5410dfc8a64bb587d19637deb95d378fd1eb5c 5.3
 d121acbb82e65fe4dd3c2318a1b61981b958492 5.3.1
 fca7e8449a847e3cf1054f2c07b51237699fad3 5.3.2
 ce8e7515036d3431a03aaeb7bc72dd96cb1112 5.4rc0
              cf3e07d7648a4371ce584d15dd692e7a6845792f 5.4
 cbdbdbb05dcd6bb814eb9bbdd982211b28 5.4.1
 ea9c86fac8974cd74dc12ea681c8986eb6da6c4 5.4.2
 c5de797e5416f9b588940f4608269b4d50a 5.5rc0
 fc3c5fbc65f6fe85d70ea63923b8767dda4f2e0 5.5
              f62bb5d07848ca598aa860a517394130b61bf2ee 5.5.1
              07731064ac41dacdf0ec869ebd05c2e848c14fbf 5.5.2
 e06a7ab9e0d5c65af4e511aee1e0342998799df 5.6rc0
+c17d63fdabd009e70bf994e5efb7db422f4f7f 5.6

i18n/hggettext

0 +1 -1

              #!/usr/bin/env python
              #
              # hggettext - carefully extract docstrings for Mercurial
              #
              # Copyright 2009 Matt Mackall <mpm@selenic.com> and others
              #
              # This software may be used and distributed according to the terms of the
              # GNU General Public License version 2 or any later version.
              # The normalize function is taken from pygettext which is distributed
              # with Python under the Python License, which is GPL compatible.
              """Extract docstrings from Mercurial commands.
              Compared to pygettext, this script knows about the cmdtable and table
              dictionaries used by Mercurial, and will only extract docstrings from
              functions mentioned therein.
              Use xgettext like normal to extract strings marked as translatable and
              join the message cataloges to get the final catalog.
              """
              from __future__ import absolute_import, print_function
              import inspect
              import os
              import re
              import sys
              def escape(s):
                  # The order is important, the backslash must be escaped first
                  # since the other replacements introduce new backslashes
                  # themselves.
                  s = s.replace('\\', '\\\\')
                  s = s.replace('\n', '\\n')
                  s = s.replace('\r', '\\r')
                  s = s.replace('\t', '\\t')
                  s = s.replace('"', '\\"')
                  return s
              def normalize(s):
                  # This converts the various Python string types into a format that
                  # is appropriate for .po files, namely much closer to C style.
                  lines = s.split('\n')
                  if len(lines) == 1:
                      s = '"' + escape(s) + '"'
                  else:
                      if not lines[-1]:
                          del lines[-1]
                          lines[-1] = lines[-1] + '\n'
                      lines = map(escape, lines)
                      lineterm = '\\n"\n"'
                      s = '""\n"' + lineterm.join(lines) + '"'
                  return s
              def poentry(path, lineno, s):
                  return (
                      '#: %s:%d\n' % (path, lineno)
                      + 'msgid %s\n' % normalize(s)
                      + 'msgstr ""\n'
                  )
              doctestre = re.compile(r'^ +>>> ', re.MULTILINE)
              def offset(src, doc, name, lineno, default):
                  """Compute offset or issue a warning on stdout."""
                  # remove doctest part, in order to avoid backslash mismatching
                  m = doctestre.search(doc)
                  if m:
                      doc = doc[: m.start()]
                  # Backslashes in doc appear doubled in src.
                  end = src.find(doc.replace('\\', '\\\\'))
                  if end == -1:
                      # This can happen if the docstring contains unnecessary escape
                      # sequences such as \" in a triple-quoted string. The problem
                      # is that \" is turned into " and so doc wont appear in src.
                      sys.stderr.write(
                          "%s:%d:warning:"
                          " unknown docstr offset, assuming %d lines\n"
                          % (name, lineno, default)
                      )
                      return default
                  else:
                      return src.count('\n', 0, end)
              def importpath(path):
                  """Import a path like foo/bar/baz.py and return the baz module."""
                  if path.endswith('.py'):
                      path = path[:-3]
                  if path.endswith('/__init__'):
                      path = path[:-9]
                  path = path.replace('/', '.')
                  mod = __import__(path)
                  for comp in path.split('.')[1:]:
                      mod = getattr(mod, comp)
                  return mod
              def docstrings(path):
                  """Extract docstrings from path.
                  This respects the Mercurial cmdtable/table convention and will
                  only extract docstrings from functions mentioned in these tables.
                  """
                  mod = importpath(path)
                  if not path.startswith('mercurial/') and mod.__doc__:
                      with open(path) as fobj:
                          src = fobj.read()
                      lineno = 1 + offset(src, mod.__doc__, path, 1, 7)
                      print(poentry(path, lineno, mod.__doc__))
                  functions = list(getattr(mod, 'i18nfunctions', []))
                  functions = [(f, True) for f in functions]
                  cmdtable = getattr(mod, 'cmdtable', {})
                  if not cmdtable:
                      # Maybe we are processing mercurial.commands?
                      cmdtable = getattr(mod, 'table', {})
-                 functions.extend((c[0], False) for c in cmdtable.itervalues())
+                 functions.extend((c[0], False) for c in cmdtable.values())
                  for func, rstrip in functions:
                      if func.__doc__:
                          docobj = func  # this might be a proxy to provide formatted doc
                          func = getattr(func, '_origfunc', func)
                          funcmod = inspect.getmodule(func)
                          extra = ''
                          if funcmod.__package__ == funcmod.__name__:
                              extra = '/__init__'
                          actualpath = '%s%s.py' % (funcmod.__name__.replace('.', '/'), extra)
                          src = inspect.getsource(func)
                          lineno = inspect.getsourcelines(func)[1]
                          doc = docobj.__doc__
                          origdoc = getattr(docobj, '_origdoc', '')
                          if rstrip:
                              doc = doc.rstrip()
                              origdoc = origdoc.rstrip()
                          if origdoc:
                              lineno += offset(src, origdoc, actualpath, lineno, 1)
                          else:
                              lineno += offset(src, doc, actualpath, lineno, 1)
                          print(poentry(actualpath, lineno, doc))
              def rawtext(path):
                  with open(path) as f:
                      src = f.read()
                  print(poentry(path, 1, src))
              if __name__ == "__main__":
                  # It is very important that we import the Mercurial modules from
                  # the source tree where hggettext is executed. Otherwise we might
                  # accidentally import and extract strings from a Mercurial
                  # installation mentioned in PYTHONPATH.
                  sys.path.insert(0, os.getcwd())
                  from mercurial import demandimport
                  demandimport.enable()
                  for path in sys.argv[1:]:
                      if path.endswith('.txt'):
                          rawtext(path)
                      else:
                          docstrings(path)

mercurial/metadata.py

0 +1 -1

-             # coding: utf8
+             # coding: utf-8
              # metadata.py -- code related to various metadata computation and access.
              #
              # Copyright 2019 Google, Inc <martinvonz@google.com>
              # Copyright 2020 Pierre-Yves David <pierre-yves.david@octobus.net>
              #
              # This software may be used and distributed according to the terms of the
              # GNU General Public License version 2 or any later version.
              from __future__ import absolute_import, print_function
              import multiprocessing
              import struct
              from . import (
                  error,
                  node,
                  pycompat,
                  util,
              )
              from .revlogutils import (
                  flagutil as sidedataflag,
                  sidedata as sidedatamod,
              )
              class ChangingFiles(object):
                  """A class recording the changes made to files by a changeset
                  Actions performed on files are gathered into 3 sets:
                  - added:   files actively added in the changeset.
                  - merged:  files whose history got merged
                  - removed: files removed in the revision
                  - salvaged: files that might have been deleted by a merge but were not
                  - touched: files affected by the merge
                  and copies information is held by 2 mappings
                  - copied_from_p1: {"<new-name>": "<source-name-in-p1>"} mapping for copies
                  - copied_from_p2: {"<new-name>": "<source-name-in-p2>"} mapping for copies
                  See their inline help for details.
                  """
                  def __init__(
                      self,
                      touched=None,
                      added=None,
                      removed=None,
                      merged=None,
                      salvaged=None,
                      p1_copies=None,
                      p2_copies=None,
                  ):
                      self._added = set(() if added is None else added)
                      self._merged = set(() if merged is None else merged)
                      self._removed = set(() if removed is None else removed)
                      self._touched = set(() if touched is None else touched)
                      self._salvaged = set(() if salvaged is None else salvaged)
                      self._touched.update(self._added)
                      self._touched.update(self._merged)
                      self._touched.update(self._removed)
                      self._p1_copies = dict(() if p1_copies is None else p1_copies)
                      self._p2_copies = dict(() if p2_copies is None else p2_copies)
                  def __eq__(self, other):
                      return (
                          self.added == other.added
                          and self.merged == other.merged
                          and self.removed == other.removed
                          and self.salvaged == other.salvaged
                          and self.touched == other.touched
                          and self.copied_from_p1 == other.copied_from_p1
                          and self.copied_from_p2 == other.copied_from_p2
                      )
                  @property
                  def has_copies_info(self):
                      return bool(
                          self.removed
                          or self.merged
                          or self.salvaged
                          or self.copied_from_p1
                          or self.copied_from_p2
                      )
                  @util.propertycache
                  def added(self):
                      """files actively added in the changeset
                      Any file present in that revision that was absent in all the changeset's
                      parents.
                      In case of merge, this means a file absent in one of the parents but
                      existing in the other will *not* be contained in this set. (They were
                      added by an ancestor)
                      """
                      return frozenset(self._added)
                  def mark_added(self, filename):
                      if 'added' in vars(self):
                          del self.added
                      self._added.add(filename)
                      self.mark_touched(filename)
                  def update_added(self, filenames):
                      for f in filenames:
                          self.mark_added(f)
                  @util.propertycache
                  def merged(self):
                      """files actively merged during a merge
                      Any modified files which had modification on both size that needed merging.
                      In this case a new filenode was created and it has two parents.
                      """
                      return frozenset(self._merged)
                  def mark_merged(self, filename):
                      if 'merged' in vars(self):
                          del self.merged
                      self._merged.add(filename)
                      self.mark_touched(filename)
                  def update_merged(self, filenames):
                      for f in filenames:
                          self.mark_merged(f)
                  @util.propertycache
                  def removed(self):
                      """files actively removed by the changeset
                      In case of merge this will only contain the set of files removing "new"
                      content. For any file absent in the current changeset:
                      a) If the file exists in both parents, it is clearly "actively" removed
                      by this changeset.
                      b) If a file exists in only one parent and in none of the common
                      ancestors, then the file was newly added in one of the merged branches
                      and then got "actively" removed.
                      c) If a file exists in only one parent and at least one of the common
                      ancestors using the same filenode, then the file was unchanged on one
                      side and deleted on the other side. The merge "passively" propagated
                      that deletion, but didn't "actively" remove the file. In this case the
                      file is *not* included in the `removed` set.
                      d) If a file exists in only one parent and at least one of the common
                      ancestors using a different filenode, then the file was changed on one
                      side and removed on the other side. The merge process "actively"
                      decided to drop the new change and delete the file. Unlike in the
                      previous case, (c), the file included in the `removed` set.
                      Summary table for merge:
                      case | exists in parents | exists in gca || removed
                       (a) |       both        |     *         ||   yes
                       (b) |       one         |     none      ||   yes
                       (c) |       one         | same filenode ||   no
                       (d) |       one         |  new filenode ||   yes
                      """
                      return frozenset(self._removed)
                  def mark_removed(self, filename):
                      if 'removed' in vars(self):
                          del self.removed
                      self._removed.add(filename)
                      self.mark_touched(filename)
                  def update_removed(self, filenames):
                      for f in filenames:
                          self.mark_removed(f)
                  @util.propertycache
                  def salvaged(self):
                      """files that might have been deleted by a merge, but still exists.
                      During a merge, the manifest merging might select some files for
                      removal, or for a removed/changed conflict. If at commit time the file
                      still exists, its removal was "reverted" and the file is "salvaged"
                      """
                      return frozenset(self._salvaged)
                  def mark_salvaged(self, filename):
                      if "salvaged" in vars(self):
                          del self.salvaged
                      self._salvaged.add(filename)
                      self.mark_touched(filename)
                  def update_salvaged(self, filenames):
                      for f in filenames:
                          self.mark_salvaged(f)
                  @util.propertycache
                  def touched(self):
                      """files either actively modified, added or removed"""
                      return frozenset(self._touched)
                  def mark_touched(self, filename):
                      if 'touched' in vars(self):
                          del self.touched
                      self._touched.add(filename)
                  def update_touched(self, filenames):
                      for f in filenames:
                          self.mark_touched(f)
                  @util.propertycache
                  def copied_from_p1(self):
                      return self._p1_copies.copy()
                  def mark_copied_from_p1(self, source, dest):
                      if 'copied_from_p1' in vars(self):
                          del self.copied_from_p1
                      self._p1_copies[dest] = source
                  def update_copies_from_p1(self, copies):
                      for dest, source in copies.items():
                          self.mark_copied_from_p1(source, dest)
                  @util.propertycache
                  def copied_from_p2(self):
                      return self._p2_copies.copy()
                  def mark_copied_from_p2(self, source, dest):
                      if 'copied_from_p2' in vars(self):
                          del self.copied_from_p2
                      self._p2_copies[dest] = source
                  def update_copies_from_p2(self, copies):
                      for dest, source in copies.items():
                          self.mark_copied_from_p2(source, dest)
              def compute_all_files_changes(ctx):
                  """compute the files changed by a revision"""
                  p1 = ctx.p1()
                  p2 = ctx.p2()
                  if p1.rev() == node.nullrev and p2.rev() == node.nullrev:
                      return _process_root(ctx)
                  elif p1.rev() != node.nullrev and p2.rev() == node.nullrev:
                      return _process_linear(p1, ctx)
                  elif p1.rev() == node.nullrev and p2.rev() != node.nullrev:
                      # In the wild, one can encounter changeset where p1 is null but p2 is not
                      return _process_linear(p1, ctx, parent=2)
                  elif p1.rev() == p2.rev():
                      # In the wild, one can encounter such "non-merge"
                      return _process_linear(p1, ctx)
                  else:
                      return _process_merge(p1, p2, ctx)
              def _process_root(ctx):
                  """compute the appropriate changed files for a changeset with no parents
                  """
                  # Simple, there was nothing before it, so everything is added.
                  md = ChangingFiles()
                  manifest = ctx.manifest()
                  for filename in manifest:
                      md.mark_added(filename)
                  return md
              def _process_linear(parent_ctx, children_ctx, parent=1):
                  """compute the appropriate changed files for a changeset with a single parent
                  """
                  md = ChangingFiles()
                  parent_manifest = parent_ctx.manifest()
                  children_manifest = children_ctx.manifest()
                  copies_candidate = []
                  for filename, d in parent_manifest.diff(children_manifest).items():
                      if d[1][0] is None:
                          # no filenode for the "new" value, file is absent
                          md.mark_removed(filename)
                      else:
                          copies_candidate.append(filename)
                          if d[0][0] is None:
                              # not filenode for the "old" value file was absent
                              md.mark_added(filename)
                          else:
                              # filenode for both "old" and "new"
                              md.mark_touched(filename)
                  if parent == 1:
                      copied = md.mark_copied_from_p1
                  elif parent == 2:
                      copied = md.mark_copied_from_p2
                  else:
                      assert False, "bad parent value %d" % parent
                  for filename in copies_candidate:
                      copy_info = children_ctx[filename].renamed()
                      if copy_info:
                          source, srcnode = copy_info
                          copied(source, filename)
                  return md
              def _process_merge(p1_ctx, p2_ctx, ctx):
                  """compute the appropriate changed files for a changeset with two parents
                  This is a more advance case. The information we need to record is summarise
                  in the following table:
                  ┌──────────────┬──────────────┬──────────────┬──────────────┬──────────────┐
                  │ diff ╲  diff │       ø      │ (Some, None) │ (None, Some) │ (Some, Some) │
                  │  p2   ╲  p1  │              │              │              │              │
                  ├──────────────┼──────────────┼──────────────┼──────────────┼──────────────┤
                  │              │              │🄱  No Changes │🄳  No Changes │              │
                  │  ø           │🄰  No Changes │      OR      │     OR       │🄵  No Changes │
                  │              │              │🄲  Deleted[1] │🄴  Salvaged[2]│     [3]      │
                  ├──────────────┼──────────────┼──────────────┼──────────────┼──────────────┤
                  │              │🄶  No Changes │              │              │              │
                  │ (Some, None) │      OR      │🄻  Deleted    │       ø      │      ø       │
                  │              │🄷  Deleted[1] │              │              │              │
                  ├──────────────┼──────────────┼──────────────┼──────────────┼──────────────┤
                  │              │🄸  No Changes │              │              │              │
                  │ (None, Some) │     OR       │      ø       │🄼   Added     │🄽   Merged    │
                  │              │🄹  Salvaged[2]│              │   (copied?)  │   (copied?)  │
                  ├──────────────┼──────────────┼──────────────┼──────────────┼──────────────┤
                  │              │              │              │              │              │
                  │ (Some, Some) │🄺  No Changes │      ø       │🄾   Merged    │🄿   Merged    │
                  │              │     [3]      │              │   (copied?)  │   (copied?)  │
                  └──────────────┴──────────────┴──────────────┴──────────────┴──────────────┘
                  Special case [1]:
                    The situation is:
                      - parent-A:     file exists,
                      - parent-B:     no file,
                      - working-copy: no file.
                    Detecting a "deletion" will depend on the presence of actual change on
                    the "parent-A" branch:
                    Subcase 🄱 or 🄶 : if the state of the file in "parent-A" is unchanged
                    compared to the merge ancestors, then parent-A branch left the file
                    untouched while parent-B deleted it. We simply apply the change from
                    "parent-B" branch the file was automatically dropped.
                    The result is:
                        - file is not recorded as touched by the merge.
                    Subcase 🄲 or 🄷 : otherwise, the change from parent-A branch were explicitly dropped and
                    the file was "deleted again". From a user perspective, the message
                    about "locally changed" while "remotely deleted" (or the other way
                    around) was issued and the user chose to deleted the file.
                    The result:
                        - file is recorded as touched by the merge.
                  Special case [2]:
                    The situation is:
                      - parent-A:     no file,
                      - parent-B:     file,
                      - working-copy: file (same content as parent-B).
                    There are three subcases depending on the ancestors contents:
                    - A) the file is missing in all ancestors,
                    - B) at least one ancestor has the file with filenode ≠ from parent-B,
                    - C) all ancestors use the same filenode as parent-B,
                    Subcase (A) is the simpler, nothing happend on parent-A side while
                    parent-B added it.
                      The result:
                          - the file is not marked as touched by the merge.
                    Subcase (B) is the counter part of "Special case [1]", the file was
                      modified on parent-B side, while parent-A side deleted it. However this
                      time, the conflict was solved by keeping the file (and its
                      modification). We consider the file as "salvaged".
                      The result:
                          - the file is marked as "salvaged" by the merge.
                    Subcase (C) is subtle variation of the case above. In this case, the
                      file in unchanged on the parent-B side and actively removed on the
                      parent-A side. So the merge machinery correctly decide it should be
                      removed. However, the file was explicitly restored to its parent-B
                      content before the merge was commited. The file is be marked
                      as salvaged too. From the merge result perspective, this is similar to
                      Subcase (B), however from the merge resolution perspective they differ
                      since in (C), there was some conflict not obvious solution to the
                      merge (That got reversed)
                  Special case [3]:
                    The situation is:
                      - parent-A:     file,
                      - parent-B:     file (different filenode as parent-A),
                      - working-copy: file (same filenode as parent-B).
                    This case is in theory much simple, for this to happens, this mean the
                    filenode in parent-A is purely replacing the one in parent-B (either a
                    descendant, or a full new file history, see changeset). So the merge
                    introduce no changes, and the file is not affected by the merge...
                    However, in the wild it is possible to find commit with the above is not
                    True. For example repository have some commit where the *new* node is an
                    ancestor of the node in parent-A, or where parent-A and parent-B are two
                    branches of the same file history, yet not merge-filenode were created
                    (while the "merge" should have led to a "modification").
                    Detecting such cases (and not recording the file as modified) would be a
                    nice bonus. However do not any of this yet.
                  """
                  md = ChangingFiles()
                  m = ctx.manifest()
                  p1m = p1_ctx.manifest()
                  p2m = p2_ctx.manifest()
                  diff_p1 = p1m.diff(m)
                  diff_p2 = p2m.diff(m)
                  cahs = ctx.repo().changelog.commonancestorsheads(
                      p1_ctx.node(), p2_ctx.node()
                  )
                  if not cahs:
                      cahs = [node.nullrev]
                  mas = [ctx.repo()[r].manifest() for r in cahs]
                  copy_candidates = []
                  # Dealing with case 🄰 happens automatically.  Since there are no entry in
                  # d1 nor d2, we won't iterate on it ever.
                  # Iteration over d1 content will deal with all cases, but the one in the
                  # first column of the table.
                  for filename, d1 in diff_p1.items():
                      d2 = diff_p2.pop(filename, None)
                      if d2 is None:
                          # this deal with the first line of the table.
                          _process_other_unchanged(md, mas, filename, d1)
                      else:
                          if d1[0][0] is None and d2[0][0] is None:
                              # case 🄼 — both deleted the file.
                              md.mark_added(filename)
                              copy_candidates.append(filename)
                          elif d1[1][0] is None and d2[1][0] is None:
                              # case 🄻 — both deleted the file.
                              md.mark_removed(filename)
                          elif d1[1][0] is not None and d2[1][0] is not None:
                              # case 🄽 🄾 🄿
                              md.mark_merged(filename)
                              copy_candidates.append(filename)
                          else:
                              # Impossible case, the post-merge file status cannot be None on
                              # one side and Something on the other side.
                              assert False, "unreachable"
                  # Iteration over remaining d2 content deal with the first column of the
                  # table.
                  for filename, d2 in diff_p2.items():
                      _process_other_unchanged(md, mas, filename, d2)
                  for filename in copy_candidates:
                      copy_info = ctx[filename].renamed()
                      if copy_info:
                          source, srcnode = copy_info
                          if source in p1_ctx and p1_ctx[source].filenode() == srcnode:
                              md.mark_copied_from_p1(source, filename)
                          elif source in p2_ctx and p2_ctx[source].filenode() == srcnode:
                              md.mark_copied_from_p2(source, filename)
                  return md
              def _find(manifest, filename):
                  """return the associate filenode or None"""
                  if filename not in manifest:
                      return None
                  return manifest.find(filename)[0]
              def _process_other_unchanged(md, mas, filename, diff):
                  source_node = diff[0][0]
                  target_node = diff[1][0]
                  if source_node is not None and target_node is None:
                      if any(not _find(ma, filename) == source_node for ma in mas):
                          # case 🄲 of 🄷
                          md.mark_removed(filename)
                      # else, we have case 🄱 or 🄶 : no change need to be recorded
                  elif source_node is None and target_node is not None:
                      if any(_find(ma, filename) is not None for ma in mas):
                          # case 🄴 or 🄹
                          md.mark_salvaged(filename)
                      # else, we have case 🄳 or 🄸 : simple merge without intervention
                  elif source_node is not None and target_node is not None:
                      # case 🄵  or 🄺 : simple merge without intervention
                      #
                      # In buggy case where source_node is not an ancestors of target_node.
                      # There should have a been a new filenode created, recording this as
                      # "modified". We do not deal with them yet.
                      pass
                  else:
                      # An impossible case, the diff algorithm should not return entry if the
                      # file is missing on both side.
                      assert False, "unreachable"
              def _missing_from_all_ancestors(mas, filename):
                  return all(_find(ma, filename) is None for ma in mas)
              def computechangesetfilesadded(ctx):
                  """return the list of files added in a changeset
                  """
                  added = []
                  for f in ctx.files():
                      if not any(f in p for p in ctx.parents()):
                          added.append(f)
                  return added
              def get_removal_filter(ctx, x=None):
                  """return a function to detect files "wrongly" detected as `removed`
                  When a file is removed relative to p1 in a merge, this
                  function determines whether the absence is due to a
                  deletion from a parent, or whether the merge commit
                  itself deletes the file. We decide this by doing a
                  simplified three way merge of the manifest entry for
                  the file. There are two ways we decide the merge
                  itself didn't delete a file:
                  - neither parent (nor the merge) contain the file
                  - exactly one parent contains the file, and that
                    parent has the same filelog entry as the merge
                    ancestor (or all of them if there two). In other
                    words, that parent left the file unchanged while the
                    other one deleted it.
                  One way to think about this is that deleting a file is
                  similar to emptying it, so the list of changed files
                  should be similar either way. The computation
                  described above is not done directly in _filecommit
                  when creating the list of changed files, however
                  it does something very similar by comparing filelog
                  nodes.
                  """
                  if x is not None:
                      p1, p2, m1, m2 = x
                  else:
                      p1 = ctx.p1()
                      p2 = ctx.p2()
                      m1 = p1.manifest()
                      m2 = p2.manifest()
                  @util.cachefunc
                  def mas():
                      p1n = p1.node()
                      p2n = p2.node()
                      cahs = ctx.repo().changelog.commonancestorsheads(p1n, p2n)
                      if not cahs:
                          cahs = [node.nullrev]
                      return [ctx.repo()[r].manifest() for r in cahs]
                  def deletionfromparent(f):
                      if f in m1:
                          return f not in m2 and all(
                              f in ma and ma.find(f) == m1.find(f) for ma in mas()
                          )
                      elif f in m2:
                          return all(f in ma and ma.find(f) == m2.find(f) for ma in mas())
                      else:
                          return True
                  return deletionfromparent
              def computechangesetfilesremoved(ctx):
                  """return the list of files removed in a changeset
                  """
                  removed = []
                  for f in ctx.files():
                      if f not in ctx:
                          removed.append(f)
                  if removed:
                      rf = get_removal_filter(ctx)
                      removed = [r for r in removed if not rf(r)]
                  return removed
              def computechangesetfilesmerged(ctx):
                  """return the list of files merged in a changeset
                  """
                  merged = []
                  if len(ctx.parents()) < 2:
                      return merged
                  for f in ctx.files():
                      if f in ctx:
                          fctx = ctx[f]
                          parents = fctx._filelog.parents(fctx._filenode)
                          if parents[1] != node.nullid:
                              merged.append(f)
                  return merged
              def computechangesetcopies(ctx):
                  """return the copies data for a changeset
                  The copies data are returned as a pair of dictionnary (p1copies, p2copies).
                  Each dictionnary are in the form: `{newname: oldname}`
                  """
                  p1copies = {}
                  p2copies = {}
                  p1 = ctx.p1()
                  p2 = ctx.p2()
                  narrowmatch = ctx._repo.narrowmatch()
                  for dst in ctx.files():
                      if not narrowmatch(dst) or dst not in ctx:
                          continue
                      copied = ctx[dst].renamed()
                      if not copied:
                          continue
                      src, srcnode = copied
                      if src in p1 and p1[src].filenode() == srcnode:
                          p1copies[dst] = src
                      elif src in p2 and p2[src].filenode() == srcnode:
                          p2copies[dst] = src
                  return p1copies, p2copies
              def encodecopies(files, copies):
                  items = []
                  for i, dst in enumerate(files):
                      if dst in copies:
                          items.append(b'%d\0%s' % (i, copies[dst]))
                  if len(items) != len(copies):
                      raise error.ProgrammingError(
                          b'some copy targets missing from file list'
                      )
                  return b"\n".join(items)
              def decodecopies(files, data):
                  try:
                      copies = {}
                      if not data:
                          return copies
                      for l in data.split(b'\n'):
                          strindex, src = l.split(b'\0')
                          i = int(strindex)
                          dst = files[i]
                          copies[dst] = src
                      return copies
                  except (ValueError, IndexError):
                      # Perhaps someone had chosen the same key name (e.g. "p1copies") and
                      # used different syntax for the value.
                      return None
              def encodefileindices(files, subset):
                  subset = set(subset)
                  indices = []
                  for i, f in enumerate(files):
                      if f in subset:
                          indices.append(b'%d' % i)
                  return b'\n'.join(indices)
              def decodefileindices(files, data):
                  try:
                      subset = []
                      if not data:
                          return subset
                      for strindex in data.split(b'\n'):
                          i = int(strindex)
                          if i < 0 or i >= len(files):
                              return None
                          subset.append(files[i])
                      return subset
                  except (ValueError, IndexError):
                      # Perhaps someone had chosen the same key name (e.g. "added") and
                      # used different syntax for the value.
                      return None
              # see mercurial/helptext/internals/revlogs.txt for details about the format
              ACTION_MASK = int("111" "00", 2)
              # note: untouched file used as copy source will as `000` for this mask.
              ADDED_FLAG = int("001" "00", 2)
              MERGED_FLAG = int("010" "00", 2)
              REMOVED_FLAG = int("011" "00", 2)
              SALVAGED_FLAG = int("100" "00", 2)
              TOUCHED_FLAG = int("101" "00", 2)
              COPIED_MASK = int("11", 2)
              COPIED_FROM_P1_FLAG = int("10", 2)
              COPIED_FROM_P2_FLAG = int("11", 2)
              # structure is <flag><filename-end><copy-source>
              INDEX_HEADER = struct.Struct(">L")
              INDEX_ENTRY = struct.Struct(">bLL")
              def encode_files_sidedata(files):
                  all_files = set(files.touched)
                  all_files.update(files.copied_from_p1.values())
                  all_files.update(files.copied_from_p2.values())
                  all_files = sorted(all_files)
                  file_idx = {f: i for (i, f) in enumerate(all_files)}
                  file_idx[None] = 0
                  chunks = [INDEX_HEADER.pack(len(all_files))]
                  filename_length = 0
                  for f in all_files:
                      filename_size = len(f)
                      filename_length += filename_size
                      flag = 0
                      if f in files.added:
                          flag |= ADDED_FLAG
                      elif f in files.merged:
                          flag |= MERGED_FLAG
                      elif f in files.removed:
                          flag |= REMOVED_FLAG
                      elif f in files.salvaged:
                          flag |= SALVAGED_FLAG
                      elif f in files.touched:
                          flag |= TOUCHED_FLAG
                      copy = None
                      if f in files.copied_from_p1:
                          flag |= COPIED_FROM_P1_FLAG
                          copy = files.copied_from_p1.get(f)
                      elif f in files.copied_from_p2:
                          copy = files.copied_from_p2.get(f)
                          flag |= COPIED_FROM_P2_FLAG
                      copy_idx = file_idx[copy]
                      chunks.append(INDEX_ENTRY.pack(flag, filename_length, copy_idx))
                  chunks.extend(all_files)
                  return {sidedatamod.SD_FILES: b''.join(chunks)}
              def decode_files_sidedata(sidedata):
                  md = ChangingFiles()
                  raw = sidedata.get(sidedatamod.SD_FILES)
                  if raw is None:
                      return md
                  copies = []
                  all_files = []
                  assert len(raw) >= INDEX_HEADER.size
                  total_files = INDEX_HEADER.unpack_from(raw, 0)[0]
                  offset = INDEX_HEADER.size
                  file_offset_base = offset + (INDEX_ENTRY.size * total_files)
                  file_offset_last = file_offset_base
                  assert len(raw) >= file_offset_base
                  for idx in range(total_files):
                      flag, file_end, copy_idx = INDEX_ENTRY.unpack_from(raw, offset)
                      file_end += file_offset_base
                      filename = raw[file_offset_last:file_end]
                      filesize = file_end - file_offset_last
                      assert len(filename) == filesize
                      offset += INDEX_ENTRY.size
                      file_offset_last = file_end
                      all_files.append(filename)
                      if flag & ACTION_MASK == ADDED_FLAG:
                          md.mark_added(filename)
                      elif flag & ACTION_MASK == MERGED_FLAG:
                          md.mark_merged(filename)
                      elif flag & ACTION_MASK == REMOVED_FLAG:
                          md.mark_removed(filename)
                      elif flag & ACTION_MASK == SALVAGED_FLAG:
                          md.mark_salvaged(filename)
                      elif flag & ACTION_MASK == TOUCHED_FLAG:
                          md.mark_touched(filename)
                      copied = None
                      if flag & COPIED_MASK == COPIED_FROM_P1_FLAG:
                          copied = md.mark_copied_from_p1
                      elif flag & COPIED_MASK == COPIED_FROM_P2_FLAG:
                          copied = md.mark_copied_from_p2
                      if copied is not None:
                          copies.append((copied, filename, copy_idx))
                  for copied, filename, copy_idx in copies:
                      copied(all_files[copy_idx], filename)
                  return md
              def _getsidedata(srcrepo, rev):
                  ctx = srcrepo[rev]
                  files = compute_all_files_changes(ctx)
                  return encode_files_sidedata(files), files.has_copies_info
              def getsidedataadder(srcrepo, destrepo):
                  use_w = srcrepo.ui.configbool(b'experimental', b'worker.repository-upgrade')
                  if pycompat.iswindows or not use_w:
                      return _get_simple_sidedata_adder(srcrepo, destrepo)
                  else:
                      return _get_worker_sidedata_adder(srcrepo, destrepo)
              def _sidedata_worker(srcrepo, revs_queue, sidedata_queue, tokens):
                  """The function used by worker precomputing sidedata
                  It read an input queue containing revision numbers
                  It write in an output queue containing (rev, <sidedata-map>)
                  The `None` input value is used as a stop signal.
                  The `tokens` semaphore is user to avoid having too many unprocessed
                  entries. The workers needs to acquire one token before fetching a task.
                  They will be released by the consumer of the produced data.
                  """
                  tokens.acquire()
                  rev = revs_queue.get()
                  while rev is not None:
                      data = _getsidedata(srcrepo, rev)
                      sidedata_queue.put((rev, data))
                      tokens.acquire()
                      rev = revs_queue.get()
                  # processing of `None` is completed, release the token.
                  tokens.release()
              BUFF_PER_WORKER = 50
              def _get_worker_sidedata_adder(srcrepo, destrepo):
                  """The parallel version of the sidedata computation
                  This code spawn a pool of worker that precompute a buffer of sidedata
                  before we actually need them"""
                  # avoid circular import copies -> scmutil -> worker -> copies
                  from . import worker
                  nbworkers = worker._numworkers(srcrepo.ui)
                  tokens = multiprocessing.BoundedSemaphore(nbworkers * BUFF_PER_WORKER)
                  revsq = multiprocessing.Queue()
                  sidedataq = multiprocessing.Queue()
                  assert srcrepo.filtername is None
                  # queue all tasks beforehand, revision numbers are small and it make
                  # synchronisation simpler
                  #
                  # Since the computation for each node can be quite expensive, the overhead
                  # of using a single queue is not revelant. In practice, most computation
                  # are fast but some are very expensive and dominate all the other smaller
                  # cost.
                  for r in srcrepo.changelog.revs():
                      revsq.put(r)
                  # queue the "no more tasks" markers
                  for i in range(nbworkers):
                      revsq.put(None)
                  allworkers = []
                  for i in range(nbworkers):
                      args = (srcrepo, revsq, sidedataq, tokens)
                      w = multiprocessing.Process(target=_sidedata_worker, args=args)
                      allworkers.append(w)
                      w.start()
                  # dictionnary to store results for revision higher than we one we are
                  # looking for. For example, if we need the sidedatamap for 42, and 43 is
                  # received, when shelve 43 for later use.
                  staging = {}
                  def sidedata_companion(revlog, rev):
                      data = {}, False
                      if util.safehasattr(revlog, b'filteredrevs'):  # this is a changelog
                          # Is the data previously shelved ?
                          sidedata = staging.pop(rev, None)
                          if sidedata is None:
                              # look at the queued result until we find the one we are lookig
                              # for (shelve the other ones)
                              r, data = sidedataq.get()
                              while r != rev:
                                  staging[r] = data
                                  r, sidedata = sidedataq.get()
                          tokens.release()
                      sidedata, has_copies_info = data
                      new_flag = 0
                      if has_copies_info:
                          new_flag = sidedataflag.REVIDX_HASCOPIESINFO
                      return False, (), sidedata, new_flag, 0
                  return sidedata_companion
              def _get_simple_sidedata_adder(srcrepo, destrepo):
                  """The simple version of the sidedata computation
                  It just compute it in the same thread on request"""
                  def sidedatacompanion(revlog, rev):
                      sidedata, has_copies_info = {}, False
                      if util.safehasattr(revlog, 'filteredrevs'):  # this is a changelog
                          sidedata, has_copies_info = _getsidedata(srcrepo, rev)
                      new_flag = 0
                      if has_copies_info:
                          new_flag = sidedataflag.REVIDX_HASCOPIESINFO
                      return False, (), sidedata, new_flag, 0
                  return sidedatacompanion
              def getsidedataremover(srcrepo, destrepo):
                  def sidedatacompanion(revlog, rev):
                      f = ()
                      if util.safehasattr(revlog, 'filteredrevs'):  # this is a changelog
                          if revlog.flags(rev) & sidedataflag.REVIDX_SIDEDATA:
                              f = (
                                  sidedatamod.SD_P1COPIES,
                                  sidedatamod.SD_P2COPIES,
                                  sidedatamod.SD_FILESADDED,
                                  sidedatamod.SD_FILESREMOVED,
                              )
                      return False, f, {}, 0, sidedataflag.REVIDX_HASCOPIESINFO
                  return sidedatacompanion

relnotes/5.6 relnotes/next

0 copied 0 0

NO CONTENT: file copied from relnotes/next to relnotes/5.6

relnotes/next

0 0 -10

              == New Features ==
-              * `hg mv -A` can now be used with `--at-rev`. It behaves just like
-                `hg cp -A --at-rev`, i.e. it marks the destination as a copy of the
-                source whether or not the source still exists (but the source must
-                exist in the parent revision).
-              * New revset predicate `diffcontains(pattern)` for filtering revisions
-                in the same way as `hg grep --diff pattern`.
               * The memory footprint per changeset and per file during pull/unbundle
                 operations has been significantly reduced.
              == New Experimental Features ==
              == Bug Fixes ==
              == Backwards Compatibility Changes ==
              == Internal API Changes ==
-              * `merge.update()` is now private (renamed to `_update()`). Hopefully
-                the higher-level functions available in the same module cover your
-                use cases.
               * `phases.registernew` now takes a set of revisions instead of a list
                 of nodes. `phases.advanceboundary` takes an optional set of revisions
                 in addition to the list of nodes. The corresponeding members of the
                 `phasecache` class follow this change.
               * The `addgroup` member of `revlog` classes no longer keeps a list of
                 all found nodes. It now returns True iff a node was found in the group.
                 An optional callback for duplicated nodes can be used by callers to keep
                 track of all nodes themselve.
               * The `_chaininfocache` of `revlog` classes has been changed from a dict
                 to a LRU cache.

General Comments 0

Write
Preview

You need to be logged in to leave comments. Login now

No TODOs yet

	Site-wide shortcuts
/	Use quick search box
g h	Goto home page
g g	Goto my private gists page
g G	Goto my public gists page
g 0-9	Goto bookmarked items from 0-9
n r	New repository page
n g	New gist page

	Repositories
g s	Goto summary page
g c	Goto changelog page
g f	Goto files page
g F	Goto files page with file search activated
g p	Goto pull requests page
g o	Goto repository settings
g O	Goto repository access permissions settings
t s	Toggle sidebar on some pages