be8d657b24
Benchmark harness that uses LLM agents to solve shell scripting tasks in both Bash and Lush, then compares correctness and code quality. - CLI with run, run-all, list-tasks, report, and export commands - Agent loop with retry support via Anthropic Claude provider - Test harness executing solutions in sandboxed subprocesses - LLM-driven questionnaire for subjective code quality evaluation - HTML report export with charts (matplotlib) - 8 Category A tasks (write-from-scratch in both languages) - 4 Category B tasks (verify provided Bash, convert to Lush) - Lush language reference for agent context
285 lines
229 KiB
HTML
285 lines
229 KiB
HTML
<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<title>Lush vs Bash Benchmark Report</title>
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<style>
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:root { --bash: #4E79A7; --lush: #E15759; }
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* { box-sizing: border-box; margin: 0; padding: 0; }
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body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
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max-width: 960px; margin: 40px auto; padding: 0 20px; color: #1a1a1a; line-height: 1.5; }
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h1 { font-size: 1.8rem; margin-bottom: 4px; }
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h2 { font-size: 1.3rem; margin: 32px 0 16px; border-bottom: 2px solid #e0e0e0; padding-bottom: 6px; }
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h3 { font-size: 1.05rem; margin-bottom: 10px; }
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.meta { color: #666; font-size: 0.9rem; margin-bottom: 24px; }
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table { border-collapse: collapse; width: 100%; margin: 12px 0 20px; font-size: 0.9rem; }
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th, td { padding: 8px 12px; text-align: left; border-bottom: 1px solid #e0e0e0; }
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th { background: #f5f5f5; font-weight: 600; }
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td.pass { color: #2d8a4e; font-weight: 600; }
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td.fail { color: #d32f2f; font-weight: 600; }
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td.pos { color: #2d8a4e; }
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td.neg { color: #d32f2f; }
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tfoot td { font-weight: 600; border-top: 2px solid #333; }
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.chart { text-align: center; margin: 20px 0; }
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.chart img { max-width: 100%; height: auto; border: 1px solid #e0e0e0; border-radius: 4px; }
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.task-detail { margin: 20px 0 30px; padding: 16px; background: #fafafa; border-radius: 6px; border: 1px solid #e8e8e8; }
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.task-detail h3 { margin-bottom: 12px; }
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.task-detail .cat { color: #888; font-weight: normal; }
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.task-detail .pass { color: #2d8a4e; font-size: 0.85rem; margin-left: 8px; }
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.task-detail .fail { color: #d32f2f; font-size: 0.85rem; margin-left: 8px; }
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.scores { width: auto; }
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.scores td:nth-child(n+2) { text-align: center; min-width: 50px; }
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.scores th:nth-child(n+2) { text-align: center; }
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.observations { margin-top: 12px; font-size: 0.85rem; color: #444; }
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.observations p { margin-bottom: 6px; }
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</style>
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</head>
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<body>
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<h1>Lush vs Bash Benchmark Report</h1>
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<p class="meta">Model: claude-sonnet-4-20250514 · Latest run: 20260327T224550Z · Tasks: 12</p>
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<h2>Summary</h2>
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<table>
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<thead><tr>
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<th>Task</th><th>Cat</th>
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<th>Bash</th><th>Turns</th>
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<th>Lush</th><th>Turns</th>
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</tr></thead>
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<tbody><tr>
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<td>env_config</td><td>A</td>
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<td class="pass">PASS</td><td>3</td>
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<td class="pass">PASS</td><td>2</td>
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</tr><tr>
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<td>file_organizer</td><td>A</td>
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<td class="fail">FAIL</td><td>4</td>
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<td class="pass">PASS</td><td>1</td>
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</tr><tr>
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<td>fizzbuzz</td><td>A</td>
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<td class="pass">PASS</td><td>1</td>
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<td class="pass">PASS</td><td>1</td>
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</tr><tr>
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<td>multi_file_search</td><td>A</td>
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<td class="pass">PASS</td><td>1</td>
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<td class="pass">PASS</td><td>3</td>
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</tr><tr>
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<td>pipeline_transform</td><td>A</td>
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<td class="pass">PASS</td><td>1</td>
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<td class="pass">PASS</td><td>2</td>
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</tr><tr>
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<td>process_exit_codes</td><td>A</td>
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<td class="pass">PASS</td><td>3</td>
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<td class="pass">PASS</td><td>1</td>
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</tr><tr>
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<td>reverse_string</td><td>A</td>
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<td class="pass">PASS</td><td>1</td>
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<td class="pass">PASS</td><td>1</td>
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</tr><tr>
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<td>two_sum</td><td>A</td>
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<td class="pass">PASS</td><td>1</td>
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<td class="pass">PASS</td><td>1</td>
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</tr><tr>
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<td>csv_transform</td><td>B</td>
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<td class="pass">PASS</td><td>0</td>
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<td class="pass">PASS</td><td>1</td>
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</tr><tr>
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<td>env_path_builder</td><td>B</td>
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<td class="pass">PASS</td><td>0</td>
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<td class="pass">PASS</td><td>1</td>
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</tr><tr>
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<td>log_parser</td><td>B</td>
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<td class="pass">PASS</td><td>0</td>
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<td class="pass">PASS</td><td>1</td>
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</tr><tr>
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<td>pipeline_word_freq</td><td>B</td>
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<td class="pass">PASS</td><td>0</td>
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<td class="pass">PASS</td><td>1</td>
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</tr></tbody>
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<tfoot><tr>
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<td><strong>Total</strong></td><td></td>
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<td><strong>11/12</strong></td><td></td>
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<td><strong>12/12</strong></td><td></td>
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</tr></tfoot>
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</table>
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<h2>Questionnaire Scores</h2>
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alt="Questionnaire comparison"></div>
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<h2>Agent Turns (Category A)</h2>
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<div class="chart"><img 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RZamQMK6gsVKmQf06dPH/P5558f9d7S+pv2vrdDhw4FZ2zTwx2Dn332WYrPV4MrS5cuTfGY0GNI0jqGvLP2GWk7N3ggqbWHjn8nI79vOJqZ1tZ67733nlmxYoX9+cqqOOmkk+yXa5u9e/dm6vXd56Xnp/VZudf3flZZPd+5v5+0/nbS87ednuNe2VcKnnfs2GHGjBkTvH3atGlm9erV9hhPK2sqLcOGDbMDBRpgrFSp0lH3a/BLv+Pu3bvtdpNpUYZYuL81AEgPAnwACKH0UzcTps6ed6bMfbmUWAX3CvLDCZeqmh5uRlizxP9XK+WYX/j/MjP7ll5K89XSDAWbt912m903XgMs33zzjU0j1uzqzz//7Kvm0O/01FNPmQkTJgT/pt1sZyxcffXVNu1fx9rYsWOPmr2vXbu2qV69eorneLMq1D7pOYZcpkB6eIP31DJgypcvb2fa5ccffzSZpRR2N0OvvzH9fe3fv98OGmgphb7uv/9+e39mzwXu89JAQno+K2V5ROp8F8vjPn/+/DYLIjRN/6233rLfL7nkkuBAQkY/Pw2+uGUS4a4Zen8bN25MV5q+d0DI+7cGAOlBgA8AIdKzRjKtx7s1uZol8qaBhnKdvVBKhc1oynCy886apzZjpSULiUCzlFpP/sYbb9hgSzOYQ4cOtTODGzZsCGZ3RJKbndTflIK71LiBqfTMZmaUUqTdsgbNIoe+N+/PT+u9qa0zktauINmtX3ZBvQIqtxY7XJaNO4aidRx5192nNjuv31Oz7i5Iz+zsurJG5KKLLjKvvvqqHVQKDWYV5GdFVs45WT3fub+fYy0viNTf9u23324Dbg1SKK1e79sNHOm+zPjiiy9SZI0ci1L5VWclNe5v6rjjjrNLHwAgIwjwAcBDwZpmKuXTTz+16ZSpfc2fP98+ToX2vAGPZhRdene4InyiWbBwBcu8a8jVac/MWuqscmuZY5kZoHRjR0FyOPPmzTOJSDNsCgy0Jt/N1makCF96Pu9zzjknmN7+3Xffpfo4ZRJI3bp1TTS4GWlvWrr+3t3voLT+Y703ZUFkNGhxQbxeQ8eF+64gukOHDkc9Xr+/SxefOHGiicbfqwuKVZAzNV27dg0OTr344osZWiYUejxoOUg4+rtRinlW/r7cOUefa0ZTwrN6vnN/2wrgV65cGfYxGtBxRRaz+retdf4arNJ70sy9W4+vGgCawc8MN8irgai0rhn6cp+XUvpTs3btWvv99NNPz9T7AZC9EeADgIdmCNVR1cyJqpIroEntSx1NpS+HzuLXqlUrONP53HPPhe1YjxgxItXZMlUDd89X6m1as2KRWN8bSmvKJZbFndTpdutOlf4eLuB59tlnTTylNXMeum7WW/AtEp93zZo1g2noffv2DVvYT0Xw3CBI+/btTUaouNqxBnRUaV5f4oIUt7uAZpdlwIABYdey63muXTP63tz6ZRU/1O+tyvluJl9LZcJVsdeadBf4a+Bl/fr1ET+G3Oy8G+gLR9XYVeRPnn76aTtoeCxKHXcp46JzkbjPPpSyR9IaZEjP31enTp3sYIlms5944ok035/OR966I1k936l+hUtDT62KvrJl3Ax5Zv5+Uiu2p1orLlVfn0FmlveoNoGrSdGuXbs0rxn6uuaaa+xjldKfWoFOdxynVVATAFKV4Y31AMDHqlevbvcfvvHGG9P1+Mcee8w+/qSTTkqxN/qHH34Y3HP5hhtuCGzcuNHevm/fvsDbb79t90Q+/vjjU91vXnt6u33D69WrZ/994MCB4P2rV68OvP7664Fzzjkn0KdPnwzvq57W3uNff/21vV37Ys+ePTsQKd79qsPp2LGjva9IkSKBjz/+2O41Ltqz+oorrgjula0v7XvvpX+n9dqR+GyGDx8eaNCgQWDo0KH28/fuQ/7ll18G9wivX79+ICNWrVoV/Hn6vVMzceLE4OOuvPLKwJo1a+zt+rsYMWJEoGjRovY+vUe9p4woUaJEoEqVKoGnn346MH/+/OBnL5s3b7b7xZ9wwgn29fV3GbpX+g8//BDcQ7xRo0aBJUuW2Nu1d/2kSZMCZcqUCe4Pvnv37nT/LXo98MAD9jGnn356oFChQsf8vDZt2hT8ufr+/vvv2/3InW3bttk97PVZtmzZMpBRr732mn3tatWqpfm4HTt22Pesx+bIkSPQoUOHwIwZM1Icz3qv+vuqXbu2fdygQYOC9+l84T4ftc+ePXvs7Tt37gz069fPHqdqv9TOJY8++qi9T3vV//HHH6m+z6eeeirFOevnn38O3qdz248//mgfU7Zs2cDMmTNTPDer57vBgwcHn3/77bfbPell7969gZdffjn4t9WuXbtUzyvhXjc1+n1KlSoV/Jn6DDds2BDIjOeff96+hn5H1zZp0bnD/dwJEyYcdb+OXff3PXny5Ey9JwDZGwE+APyfuXPnBjteCqbSQ4GMe864ceNS3HffffcF71PHXh1c11Ft3rx54OGHH7b/f9FFF4V97bFjx9pg172GnquOfL58+YK36atv374RDfDV+a1atWrwfr1vvZa+Pvnkk6gF+Opgu4DM/b4uaNXnMH369LgG+O657kvtoPbImTNn8Da9/19//TXDn02LFi2Cr6Hf1X3e3kBPFGjrb8k9tlixYoG8efMG/33mmWcGg6uM8AY7+tLvpHYP/VvTe0vtb2DUqFEp3ovaLn/+/MF/KzD85ZdfjnpeegN877HmfncFkGnRz9PAhff30kCRC6Dc1wUXXJDhz2zr1q3B43nlypVpPnbXrl2Ba6+9NkXbuXOC9zNyAxgLFy4MPlcDAY0bNz7qee7vrlWrVoHevXunGuTqvbmfoedoMNL9fXmD2iNHjtgBS+97VNCqv3EFwN73OGvWrKN+TlbPd/fff/9Rz3eDnPo6//zzUwzQZCXAF/eZ6euyyy4LZJYGePQaV199dbqf4wZyNLgUasqUKfa+kiVLphgEAoD0IkUfAP6PS7NXSqxLqz2WM888M7hOMrTY3qBBg+xWTKrOrW3ZlOKtxyqNWZX6XdEtpTiHc+WVV9oiUEqZPffcc216p9Jstf5Z65hV4V/FoXr27BnRNlSqrtZS6/W1JZzep9Jr9RW6JWAkqXq1UlP1c92WWfqdtf5ae23HO131iiuusOt1b7nlFvv56+9Ea6vVtmofbR+3bNmy4LKNjFDqtpZjaKmCloi4zzs0rVqPUV2Gjh072jXDSofX0oDzzjvP/r0tWLDAprJnlNY+a+suVWnXayllWuuFNRGgbdj0N9yvXz9bGKxt27ZhX0Ppyfr9VY+gcuXK9u9df0tK4VYVfm1pl5U1xTrW9FqOUp1VFT0t+nlLliyxKd46prXN4D///GN/L6WV6zWUop3aThhpUbE3V/xPywbSojT5jz/+2NZneOCBB+y6c70XfcaqR6D3qeKMSvVW4cY6deoEn6v7tYRC5wH9fejfev/6m9PWjKoZklZq+WmnnWbXr+vvV8sZVB/C/X2ppoOjwnNaRqDPS38Hek96Xf2Nq+aA9rjXuUbr7N2a/Uie71SjQLUEtGuC/uZ0rtHraMmS1qurUGFa21tmlEuVz0pxvdmzZ9tdNdwWfOnlHqv29m7V6P1b0nmGAnsAMiOHovxMPRMAkCXqJKuzrE71Y489xqcJJBkVjtPAkwY0NPgR763iElmine8GDhxoB1s0UKaidtHcXjO9NAhSunRpO3CnQbdKlSrF+y0BSELM4ANAHKgSuqs4rUJhAJKPCu0pM2D16tU2AwLJcb5TcTtlP0iXLl0SIriXIUOG2KwOZTER3APILGbwASBKtEWWKu2rQ6uUU83uKeVaqbq9evWyqa/arimtrcUAJDal1GvpgFsOkJEdFPwkWc532pHj8ccft0tOtNuCZu/D7cQQa1qSoCVR2qZQS7P0GQJAZuTO1LMAAOlan/naa6/Z/9e6+YIFC9oOr1sZpW3PtKYbQPJSbQDV31i3bp3ZvHlzsH5EdpPo5zvVuVBKvrZE1Cy5qDZEIgT3or8fDZKcffbZBPcAsoQZfACIEhW/GjdunC0cp0JKmsFSsa0aNWqYNm3amNtuu812ggEg2SX6+U573rvCdaqZcPfdd9uAGgD8hgAfAAAAAAAfyJ4LxQAAAAAA8BkCfAAAAAAAfIAAHwAAAAAAHyDABwAAAADABwjwAQAAAADwgdzxfgOJqlSpUmbv3r2mXLly8X4rAAAAAIBsYP369aZQoUJmy5YtmXo+M/ipUHB/8ODBrLQNAAAAAADpphhUsWhmMYOfCjdzv2zZskx/uAAAAAAApFeNGjVMVjCDDwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPhA7ni/AQAAAADIzgKBgP2Cv+TIkcN+xRIBPgAAAADE2OHDh82ff/5pdu/ebQ4cOMDn71N58+Y1RYoUMSVKlDC5cuWK+s8jwAcAAACAGAf369evN//99x+fu88dOHDADuTs3bvXlCtXLupBPgE+AAAAAMSQAj4F9wr2TjrpJFOoUCGTMyfl0fzmyJEjNrDfunWrbW+1e8mSJaP6MwnwAQAAACCGlJYvCu6PO+44PnufypkzZ7B9N23aZNs92gG+L4aJ3EiIChiceuqp8X47AAAAABCWium5NfeauYf/Ffq/dla7R7uYoi8C/B49epgdO3bE+20AAAAAQJq8AR5p+dlDTs/yCwL8Y5g6dap57733TJcuXaLdLgAAAAAAJKyknsHft2+fuf3220316tXNAw88EO+3AwAAAABA3CR1kb2nnnrKrFmzxnz33XcmT5488X47AAAAAADETdLO4C9ZssQMHDjQ3HLLLaZx48bxfjsAAAAAgExat26dLZrerFmzhPgMp0+fbt/PzTffbJJJzmTdT7Bz586mWLFi5vnnn4/32wEAAAAAIO6SMkV/8ODBZsGCBebdd981JUqUyNJr1ahRI+ztq1evNhUqVKA6PwAAAICITlYeOnTI5MqVyxw8eDDVSvptn/gsKT71T5+6OiKvo8/CVZl3/x9Phw4dCrZXVt+PXkO/1+HDh218mdbuCXqM/jayTYC/fv1607t3b9O0adOkS5cAvB59a1rCfiD9ujSP91sAAAAA4PcAv2vXrubAgQNm6NChEXm9ZcuWpTmzf8IJJ0Tk5wChtuw+krAfCn/3AAAA0aHZ3D///NP+vwqFpzWbmwwiVezcvY7WvWu3tMcee8yMGTPGbN++3VSsWNHcdttt5t57703xeS1evNh89NFHdut0TQT/888/5uSTTzYXX3yxnRQuU6bMUT9n6dKl5rnnnjNz5swxmzZtMkWKFLHP0dr/Xr16mdKlS9vH5c79v1BZP2/37t3m0UcfNePGjTN//fWXOe2000z37t1Np06d0t3m+r30mupnp9XmWZm9t+/bJJnPP//crr2/4447Utz+33//2e8bN24MFmYYNWqUKVWqVFzeJwAAAAAgY/bv32+aN29ul0zruyZ3FcAroP7pp5/M8OHDg49VoP7ZZ5+ZmjVrmkaNGgWD/tdff90G4wsXLkwR5P/www/2cYod9ZzWrVubf//91+7M9vLLL5srr7wyGOA7f//9t6lfv77Zs2ePLe6uFPsZM2aYW2+9NVgbLpEkXYDvPmRtjReOGsvd54J+AAAAAEDi+/77723wvWrVqmBWqYL9Jk2amPfee88G4fqS22+/3QbmJ510UvD5R44cMX379jVPPPGEncUfNmxY8L5XXnnFxogvvPCC6dGjR4qfu3z5cnPccccd9X7Gjx9vrrvuOjuwkC9fPnubBg+uuuoq06dPn4QL8JMuH0TFCcJ9rV271t5fuXLl4G0qkgcAAAAASB4KwL1LRhXjKWVfhgwZErz9/PPPTxHci9LfH3/8cZt2P2HCBOOldH+54IILTKhq1aodNXsvRYsWtT/TBfeiAYYzzjjDLgvQ9n6JJOkCfAAAAACAPxUvXtxceOGFR93evn17+11r5zVL76iegXZX04z8rbfeagux60uV73Wf1sw7derUCdZ10z73rlJ+WvSccDu3ValSxX7fvHmzSSRJmaIPAAAAAPCf8uXLh71d6fOqxabl2jt37rRB98iRI23xPa2PT83u3bvtoIH07NnTzJo1ywb3mv0vXLiwXV/fqlUrOygQLkX/lFNOCfu6Ks7nagYkEmbwAQAAAABJ5ffff7dBuYrwvfTSS3bN/r///htcrq3AXfT/3nT7adOmmZkzZ5oHH3zQVK9e3f77vvvuM1WrVrWvESrZdjnwzQy+1tt7Gw8AAAAAkFy0rj0cbYGn2fsCBQrYmfzRo0fb4P6BBx6w2+eFWrNmTdjX0XZ1qqTvqu5v27bNBvjKBtBWeHrdZJZcwxEAAAAAAN/SunltixdKW6CLZua1V7zS9FNLoZ8xY4bZunVrun5eyZIlzZNPPmn/f+nSpSbZEeADAAAAABKGZuUV6DvaMe3pp58OFsjzFrkbMWKE2bt3b/CxGzduNHfccUfY1x06dGhw9zWvyZMn2+9ly5Y1yc43KfoAAAAAgOR23nnn2dT7U0891TRv3txWw9eMvtbXd+zY0bRp08Y+7oorrjA1atQwCxcutI9t2LCh3eP+22+/NbVq1TINGjSwFfdDA/w777zTrr0//fTTTe7cuc3y5cvNTz/9ZPLnz2+310t2zOADAAAAABKC9ptX4bsOHTqY77//3kyZMsXOrL/wwgtm+PDhwcflzZvXFstTwK7g/PPPPze//vqrueeee8zXX39t8uTJc9Rr9+nTx3Tq1Mmuw9egwcSJE82+fftM586dzeLFi+0gQbLLEaAyXVgaDZJly5bFtkWQbbR+5H/riBLR+Geui/dbAAAA8CXt4b5ixQr7/6rcnmxV2hHdNs9qHMpfEwAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADiKkeOHKZChQox/ZnTp0+3P/fmm282fpE73m8AAAAAAJDS+us6JMVHUm7UR/F+C/BgBh8AAAAAAB8gwAcAAAAAwAcI8AEAAAAACWf48OF2jfyTTz4Z9v5mzZrZ+9etW5fi9qVLl5qOHTuaSpUqmfz585sTTzzR1KpVy9x3331m8+bNYV/rr7/+MnfeeacpXbq0yZcvnznjjDPMsGHDTLJhDT4AAAAAwBd++OEH06hRI/Pff/+ZmjVrmtatW5t///3XrFmzxrz88svmyiuvtEG8199//23q169v9uzZYxo3bmx27NhhZsyYYW699VZz5MgR07lzZ5MsCPABAAAAAL7wyiuv2OD+hRdeMD169Ehx3/Lly81xxx131HPGjx9vrrvuOpsxoNl7GTdunLnqqqtMnz59kirAJ0UfAAAAAOAL27dvt98vuOCCo+6rVq3aUbP3UrRoUTNkyJBgcC+a6Vea/vr1649aApDICPABAAAAAL5Qp04d+71r1652n/tDhw6l6zklSpQ46vYqVarY76mt209EBPgAAAAAAF/o2bOnLb43e/Zsc/7555vjjz/etGzZ0q6/37VrV9jnnHLKKWFvL1KkiP2+f/9+kywI8AEAAAAASUcF8MKl20+bNs3MnDnTPPjgg6Z69er236qgX7VqVbNq1aqjnpMzp3/CYv/8JgAAAAAA38ibN6/9rur24WzYsCHs7Tly5LCV9Pv372/mzZtnNm3aZNq3b2+2bt1qHn30UeNnBPgAAAAAgITjCuKtXLnyqPt0mwrgpUfJkiXNk08+af9/6dKlxs8I8AEAAAAACadu3bqmYMGC5osvvrD72zvap15b14VL0R86dKhZu3btUbdPnjzZfi9btqzxMwJ8AAAAAEDCKVy4sHnggQdsJXyl3F988cXmkksusdXtDx8+bOrXrx82wK9UqZKpUaOGadu2rd3fvlatWub+++83+fPnN48//rjxMwJ8AAAAAEBCUmr9gAEDbKV7FctTin2nTp3M119/HVyj79WnTx97v9bhT5061UycONHs27fPzvgvXrzYNGzY0PhZjkAgEIj3m0hEGvGRZcuWxfutwKdaPzLKJKrxz1wX77cAAADgS0orX7Fihf1/VXX3UwV3ZL3NsxqH8tcEAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAADESI4cOVLsjw7/O+JpZ2/7RwMBPgAAAADEiAK8vHnz2v/fu3cvn3s2sPf/2lntHu0AP3dUXx0AAAAAkEKRIkXMn3/+abZu3Wr/XahQIZMzJ3Ovfpy537t3b7Cd1e7RRoAPAAAAADFUokQJG/j9999/ZtOmTXz22UD+/Pltu0cbAT4AAAAAxFCuXLlMuXLl7Cz+7t27zYEDB/j8fSpv3rx25l7Bvdo92gjwAQAAACDGFOyVLFnSfgUCAfsFf8mRI0fU19yHIsAHAAAAgGwWCMKfqOQAAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+EDSBvgvvviiadOmjTnttNPMcccdZ/Lly2fKly9vbrzxRvPzzz/H++0BAAAAABBTSRvgP/PMM+aLL74wxYsXNy1atDCtWrUy+fPnNx988IGpU6eO+fzzz+P9FgEAAAAAiJncJkmNHz/eBvIK6r1ee+0107VrV9O5c2fzxx9/mNy5k/ZXBAAAAADA/zP4DRs2PCq4l7vuustUrlzZbN261fzyyy9xeW8AAAAAAMRa0gb4acmTJ4/9njdv3ni/FQAAAAAAYsJ3Ab7W4K9YscIW39MXAAAAAADZQdIvUB8wYIBZtmyZ2bt3r/n111/t/5cpU8aMHDnS5MqVK95vDwAAAACAmEj6AH/KlClm6tSpwX9rq7z333/fFuBLjxo1aoS9ffXq1aZChQpmx44dEXuvgFepIombQMPfPQAAABB7hw8fztJEddIH+N988439/vfff5uff/7ZPP3006Zp06amb9++5tFHHzXZwaNvTTOJql+X5vF+CwCQsLY+3cckqpMefyzebwFJiD4JAMRX0gf4TrFixUzjxo3N5MmTTf369c1jjz1mWrZsaerWrZvm85TSn9bM/gknnGAS3ZbdR0yiSobPL15oNwD/btuesB8C529kBtc2AMiarC4zT9wc4SxU0G/Xrp0JBAJm4sSJ8X47AAAAAADEhO8CfO+sw/btiTszAgAAAABAJPkywP/uu+/s98qVK8f7rQAAAAAAEBNJGeDPnj3bfPnll+bIkZRrzw8ePGgGDx5sPvjgA1OgQAGbqg8AAAAAQHaQlEX2Vq1aZW655Rabiq/t8EqUKGG39VIV/c2bN5v8+fOb4cOHm7Jly8b7rQIAAAAAEBNJGeBrG7xHHnnEpuIvWbLEBvd58+a1+9a3bdvWdOvWzZx66qnxfpsAAAAAAMRMUgb4FStWNP369Yv32wAAAAAAIGEk5Rp8AAAAAACQEgE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD6QOxY/5NdffzXLli0zZcuWNfXq1YvFjwQAAAAAIFuJ2Az+xx9/bJo3b27mzZuX4vaePXuaM844w7Rr1840aNDAXHXVVebw4cOR+rEAAAAAACCSAf6IESPM4sWLzdlnnx28bc6cOWbgwIGmSJEi5rrrrjMVKlQwEyZMMB9++CEfPgAAAAAAiRjgL1261NSsWdPkzZs3eNsHH3xgcuTIYUaPHm2D+gULFpjChQubt99+O1I/FgAAAAAARDLA37Ztmzn55JNT3Pbtt9+akiVLmpYtW9p/Fy9e3DRp0sT89ttvfPgAAAAAACRigF+gQAHzzz//BP+9efNms3LlStO0adMUjytWrJjZuXNnpH4sAAAAAACIZIBfqVIlM3PmTPP333/bfyslX+n5bvbe2bJli53VBwAAAAAACRjg33zzzXYGv06dOubqq682vXv3tuvtW7duHXzMwYMHzcKFC02VKlUi9WMBAAAAAIAxJnekPoUuXbrYNfefffaZWbt2rSlUqJB54403TIkSJYKP+fzzz82uXbvsdnoAAAAAACABA/w8efKYTz75xKxbt85s377dVKtWzW6P51WxYkUzduxYc95550XqxwIAAAAAgEgG+I72utdXOLVq1bJfAAAAAAAgQdfgAwAAAAAAn8zg79+/34wcOdLMmDHDbpOnf4ej6vpTp06N5I8GAAAAACBbi1iAv3HjRtOiRQuzatUqEwgE0nysAnwAAAAAAJCAAX7Pnj3NypUrTYMGDUz37t3tVnihRfYAAAAAAECCB/hTpkwx5cqVM998843Jnz9/pF4WAAAAAADEssie1tvXq1eP4B4AAAAAgGQO8M8880yzY8eOSL0cAAAAAACIR4Dfq1cvWz1//vz5kXpJAAAAAAAQ6zX4tWvXtsX1VElf3y+88EJzyimnmJw5w48haL0+AAAAAABIsAC/QoUKdvs7bZHXt29f+5UaPe7QoUOR+tEAAAAAAGR7EQvwmzRpwv72AAAAAAAke4A/ffr0SL0UAAAAAACIV5G9Nm3amLvvvjtSLwcAAAAAAOIR4E+ePJlt8gAAAAAASPYAv2LFimbv3r2RejkAAAAAABCPAL99+/bmu+++M1u2bInUSwIAAAAAgFgH+A8//LBp3Lixadq0qRk7dqw5ePBgpF4aAAAAAADEqop+1apVzZEjR8yGDRtM27Zt7ZZ5JUuWNPnz5z/qsbpv9erVkfrRAAAAAABkexEL8NetW5fi34FAgHR9AAAAAACSLcDX7D0AAAAAAEjyNfgAAAAAACB+CPABAAAAAPCBiKXoz5gxI0OPb9KkSaR+NAAAAAAA2V7EAvxmzZrZ6vjpdfjw4Wz/4QMAAAAAkHAB/o033hg2wHdb5y1atMj8888/pnXr1qZYsWKR+rEAAAAAACCSAf7w4cPTvH/nzp2mS5cuZunSpWbu3Ll8+AAAAAAAJGORveOPP968//77ZteuXebhhx+O1Y8FAAAAACBbiGkV/YIFC5pzzz3XTJgwIZY/FgAAAAAA34v5Nnl79uyx6foAAAAAACBJA/yJEyfa7fSqVKkSyx8LAAAAAIDvZbrI3vr1603hwoVN8eLF7b87deqU5qz9ypUrzc8//2wCgYDp0aNHZn8sAAAAAACIZIBfsWJFc/PNN5t33nknXVX0pVy5cuaJJ56wW+oBAAAAAIAECPA1E68v59tvv031sXnz5jWlS5c2FSpUyOyPAwAAAAAA0QjwQzVt2jRSLwUAAAAAABK9ij4AAAAAAIg8AnwAAAAAALJ7iv6XX35pmjdvnuHn5ciRw0ydOjUrPxoAAAAAAEQqwN+6davZsmVLpgJ8AAAAAACQIAF+w4YNza233hq5dwMAAAAAAGIf4J966qnmpptuyspLAAAAAACACKDIHgAAAAAAPkCADwAAAACADxDgAwAAAACQnQP8pk2bmmrVqkX23QAAAAAAgNgW2fv2228z+1QAAAAAABBhpOgDAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AMRC/DXr19v/vrrr2M+bufOnfaxAAAAAAAgAQP8ihUrmp49ex7zcQ8++KCpVKlSpH4sAAAAAACIZIAfCATsV3ofmxX//vuvGTdunLn11ltN1apVTf78+U2hQoXMWWedZZ5++mmzZ8+eLL0+AAAAAADJJuZr8Hfs2GEKFCiQpdf46KOPzFVXXWWGDRtmcuXKZa644grTuHFjs3btWvPEE0+YunXrmm3btkXsPQMAAAAAkOhyZ+XJM2bMSPHvLVu2HHWbc+jQIbNixQozZcoUU6NGjaz8WJMnTx5z2223mfvuu8+cfvrpwds3b95sWrVqZX788Ud7nwYCAAAAAADIDrIU4Ddr1szkyJEj+G8F7/pKKzVfj+/Ro0dWfqy56aab7Feo0qVLm1dffdU0aNDAjBkzxhw4cMDkzZs3Sz8LAAAAAADfB/g33nhjMMB/7733TOXKlU3Dhg3DPlaBdpkyZczll19uateubaJF6/Bl//795s8//7RBPwAAAAAAfpelAH/48OHB/1eA36hRI7suPp7WrFkTTOMvXrx4XN8LAAAAAABJEeB7HTlyxCSCl19+2X6/+OKLTb58+eL9dgAAAAAASK4APxFMnjzZvPPOO3b2vk+fPul6TmoF/1avXm0qVKhgq/4nulJFYr4ZQrolw+cXL7QbgN0lT0zYD4HzNzKDaxsAZM3hw4ftTnEJEeBr3fvIkSNtJX1VtNe/w9G6/alTp0byR5vly5ebjh072kJ+AwYMCK7FR3xtfTp9Ay2xdtLjj8X7LQAAkhTXNgBAoopYgL9x40bTokULs2rVKhtkp8VbeT9SP1sp+Tt37jTdu3c39957b7qfu2zZsjRn9k844QST6LbsTozlEeEU2bbdJKJEaNdEbrdE+HyA7ODfBD1HCueB5Gu3RGgzrm0AkDVZmb2PaIDfs2dPs3LlSrtFnYLsKlWqmCJFipho++uvv0zLli3N77//bm655RbzwgsvRP1nAgAAAACQaCIW4E+ZMsWUK1fOfPPNNyZ//vwmFvbs2WMuueQS88svv5g2bdqYt956K+LZAQAAAAAAJIOIVWfTevt69erFLLjXz2vdurWZP3++ueiii+za/6ymMwAAAAAAYLJ7gH/mmWfGrOKuKgu2b9/eTJs2zTRu3NiMGTPG5M2bNyY/GwAAAAAAX6fo9+rVy1xzzTV2Rv3cc8810TRkyBAzduzYYEGZu+66K+zjtB4/EQrOAAAAAACQNAF+7dq1bXE9VdLX9wsvvNCccsopJmfO8EkCWq+fWaqW77hAP5wnn3ySAB8AAAAAkC1ELMCvUKGCLXCnLfL69u1rv1Kjxx06dCjTP0uBu74AAAAAAECEA/wmTZpQwR4AAAAAgGQP8KdPnx6plwIAAAAAAPGqog8AAAAAAHwwgx9q1apVdtu8EiVKmCpVqkTrxwAAAAAAgEjP4O/fv9888sgjtnJ9tWrVTKNGjcxzzz0XvH/EiBG22v7ixYv58AEAAAAASMQAf9++faZZs2amf//+Jm/evObSSy+1FfW9mjdvbn766SczevToSP1YAAAAAAAQyQD/+eefN/PmzTOdOnUya9asMRMnTjzqMWXKlDHVq1c333zzDR8+AAAAAACJGOB//PHHply5cub11183+fPnT/VxVatWNRs2bIjUjwUAAAAAAJEM8NeuXWvOOecckzt32nX7lL6/c+dOPnwAAAAAABIxwC9QoEC6AncNBBx//PGR+rEAAAAAACCSAX6tWrXMwoULzfbt29MM7n/88UdTt25dPnwAAAAAABIxwO/SpYvZvXu3ad++vdmxY8dR9//999+2AN/BgwfNbbfdFqkfCwAAAAAAjDFpL5jPAAX2qpw/atQoU6lSJdOgQQN7++zZs03r1q3Nd999Z/755x9z4403mssuu4wPHwAAAACARJzBlw8//ND079/fVtH/6quv7G2rVq2ygX+OHDlMv379zLvvvhvJHwkAAAAAACI5gy8K4nv27Gm6d+9uFi1aZNatW2eOHDliTjnlFLvuXhX0AQAAAABAggf4Tq5cuWxATzE9AAAAAACSMEUfAAAAAAAk+Qy+KuSnh9L0S5QoYbfVU7G9AgUKROotAAAAAACQbUUswB8+fHhwHb4EAoEU94fern8ff/zx5o033jBXX311pN4GAAAAAADZUsRS9L/99ltzzz332ABeW+S98sorZty4cfZr8ODBpmHDhva+u+++24wYMcJ06dLF7Nq1y26vN2/evEi9DQAAAAAAsqWIzeAfPHjQvPbaa+add94xt9xyy1H3d+3a1c7yd+7c2Vx++eVm6NCh5vzzz7cB/sCBA83o0aMj9VYAAAAAAMh2IjaD36dPH1OnTp2wwb1z8803m3POOcc+Vtq1a2eqVq1qZs+eHam3AQAAAABAthSxAP/HH380lStXPubj9JjFixcH/3366aebHTt2ROptAAAAAACQLUUswM+VK5f55Zdfjvk4PUaP9SpUqFCk3gYAAAAAANlSxAL8Ro0amSVLlpj+/fun+pjnn3/e/PTTT6ZJkybB29asWWPKlCkTqbcBAAAAAEC2FLEie88884ytpP/II4+Yd9991259V7ZsWXvfhg0bzNixY82KFSvsbH3fvn3t7b///rsdFFBlfQAAAAAAkAAB/plnnmm+/vprW0hv5cqV5tlnn7V73Yu2x5PTTjvNvPfee/axUrRoUTN37lxTqVKlSL0NAAAAAACypYgF+FK/fn2zfPlyM23aNFsZf/Pmzfb20qVLm4YNG5rmzZsHg345/vjjTb169SL5FgAAAAAAyJYiGuCLAvgWLVrYr9QcOXLE5MwZseX/AAAAAABkezGNsrWVXvfu3c0pp5yS7T94AAAAAAASegY/lArsffjhh2bEiBHm119/tevxvWn6AAAAAAAgQQP83bt3m08++cQG9TNmzLBBvb5OPvlk065dO9O+ffto/FgAAAAAALKtiAX4hw8fNl9++aX54IMPzMSJE81///0XrJ6vGfvp06ebxo0bM3sPAAAAAEAirsFfsGCB6datmylTpoy54oorzOjRo82hQ4fs/2sWv27duvZxTZo0IbgHAAAAACDRZvD79u1r19Zrz3s3U9+gQQPTsWNHc+2115rixYvb21566aXIvVsAAAAAABDZAP/xxx+3M/KlSpUyd911l7n++utNhQoVMvtyAAAAAAAgXin6mrnfsmWLmTJlivn666/N33//nZWXAwAAAAAAsQ7w582bZ7p27WpKlChhZs2aZe644w5TunRpc/XVV5sxY8aYgwcPZvalAQAAAABArAJ8Fc8bPHiw2bRpkxk/frxp27atTdkfO3asueaaa2ywf/vtt5utW7dm9kcAAAAAAIBYVdHPnTu3ufzyy83HH39s0/Xfeustux3ezp077f+vXr3aPu6hhx4yixcvzuqPAwAAAAAA0QjwvYoWLWpuvfVWu+f9unXrTL9+/Uy1atXsWv0BAwaYOnXqmNNPP9306dMnkj8WAAAAAIBsL6IBvlfZsmXNww8/bJYtW2YWLlxounXrZkqWLGlWrFhhnnzyyWz/wQMAAAAAkBQBvlft2rXNoEGDzMaNG82kSZPMddddF4sfCwAAAABAtpE7lj8sZ86c5pJLLrFfAAAAAAAgyWbwAQAAAABAdBHgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgA7lNkvrhhx/M119/bebPn2+/Nm7caG8PBALxfmsAAAAAAMRc0gb4ffr0MePHj4/32wAAAAAAICEkbYBfv359U7NmTVO3bl37VaFCBbN///54vy0AAAAAAOIiaQP8Xr16xfstAAAAAACQMCiyBwAAAACADxDgAwAAAADgA0mboh8pNWrUCHv76tWr7br+HTt2mERXqkjijtPsLnmiSUSJ0K6J3G6J8PkA2UGiniOF80DytVsitBnXNgDImsOHD5tcuXJl+vnZPsAHcLStT/dJyI/lpMcfi/dbMI++Nc0kom6bZ5tERbulrlsM2yEZJezxFu83gEzh2pZ8x1oiX9+4tiVfmyVKu0Vbtg/wly1blubM/gknnGAS3ZbdR0yiKrJtu0lEidCutFvG0W7Jd6wJ7ZY62i05z5OJ2m4ca2mj3ZLvWBPaLfnaLVHbLFHOk8eSldl7SdwcYQAAAAAAkG4E+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgA0lbRX/SpEmmT5//v5XXgQMH7PfzzjsveNtjjz1mWrVqFZf3BwAAAABALCVtgL99+3Yzb968o2733qbHAAAAAACQHSRtgH/zzTfbLwAAAAAAwBp8AAAAAAB8gSJ7AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8Q4AMAAAAA4AME+AAAAAAA+AABPgAAAAAAPkCADwAAAACADxDgAwAAAADgAwT4AAAAAAD4AAE+AAAAAAA+QIAPAAAAAIAPEOADAAAAAOADBPgAAAAAAPgAAT4AAAAAAD5AgA8AAAAAgA8kdYC/b98+8/jjj5sqVaqY/PnzmzJlyphOnTqZjRs3xvutAQAAAAAQU0kb4P/333+mefPmpk+fPmbPnj2mdevWpmzZsubdd981Z599tlmzZk283yIAAAAAADGTtAF+3759zffff2/q169vVq5caT7++GMzb948M3DgQLN9+3Y7kw8AAAAAQHaRlAH+gQMHzJAhQ+z/v/rqq6Zw4cLB+7p3725q1qxpvvvuO/PDDz/E8V0CAAAAABA7SRngz5492+zatctUrlzZpuOHatu2rf0+ceLEOLw7AAAAAABiLykD/J9++sl+r127dtj73e1LliyJ6fsCAAAAACBekjLAX79+vf1+yimnhL3f3f7777/H9H0BAAAAABAvuU0SUtV8KViwYNj7CxUqZL/v3r37mK9Vo0aNsLcvX77c5M6d21SrVs0kum0795pE1fzQvyYR5U6AdqXdMo52S75jTWi31NFuyXmeTNR241hLG+2WfMea0G7J126J2maJcp48lrVr15q8efNmrwA/FnLkyGE/2Fy5cplEV/qEoiYRrV692ujwVq0EHI12S06J2G4ca8dGuyUn2i35JGKbCefJtNFuySkR241jLesUg7oJ62wT4Luq+f/+G350aO/e/41mFSlS5JivtWzZsgi/O4RmR/AZJxfaLfnQZsmJdktOtFtyot2SE+2WfGiz+EvKNfjlypWz3//444+w97vby5cvH9P3BQAAAABAvCRlgH/WWWfZ74sWLQp7v7u9Zs2aMX1fAAAAAADES1IG+A0bNjTHHXecXeOxePHio+7/9NNP7ffLL788Du8OAAAAAIDYy5mshQfuvvtu+/9du3YNrrmXF1980SxZssQ0bdrU1KlTJ47vEgAAAACA2EnKInvSu3dv880335g5c+aY0047zTRu3Njuez9v3jxz4oknmmHDhsX7LQIAAAAAEDM5AoFAwCSpffv2mWeffdZ89NFHZsOGDaZ48eLm4osvNn369DGnnHJKvN8eAAAAAAAxk9QBPgAAAAAASOI1+AAAAAAAICUCfAAAAAAAfIAAHwAAAAAAHyDABwAAAADABwjwAQAAAADwAQJ8AAAAAAB8gAAfAAAAAAAfIMAHAAAAAMAHCPABAAAAAPABAnzA544cORL8/0OHDsX1vSDzbQcAAAAcCwE+MuTw4cN8YkkmZ87/HebPPvusuemmm+L9dpCBwF5tt3//fj4zIIoCgQCfb5JiEDQ50W7J3VacMxMfAT7SxR3MuXLlMjt27DB79uzhk0uitvvwww/NK6+8YgdotmzZEu+3hDQoy8INykycONGcc8455r333uMzSzDbt28369atC/6bwc/kPd5y5Mhx1O10YJNnEPTAgQP2C4nPHVdqtxUrVqQ4hyIxjzPXH1myZIlZsGCBnXRw50zOk4mLAB8Z6gBt3rzZnHzyyeaNN97ggpoEJk+ebF599VX7/ZJLLjGDBg0ypUqVivfbQioXUn3lzp3b/rtDhw6mdevWZtmyZebXX3+1t3ExTQzqlJYpU8b079/f/PXXX8HBT9Gx9vnnn5tffvklzu8Sx6LjyR1vOk8OGDDATJgwwf47XNCPxKDBNBd06Fhr1aqVefjhh83Bgwfj/dZwjHZzx5Umik4//XQzePBg8+eff/K5JSh3nN15552mVq1a5rzzzjPnn3++mTRpkr2d82Ti+t+VDUjrjyR3brN7927z/vvvmzx58piTTjrJvPXWW6Zhw4b2YEdi0oyGZu5Hjhxpg5GxY8ea0qVL204tJ+XEvZDOmjXLtGvXzv7/66+/bp5++ulg7QTaLTFUqFDBXHfddWbUqFGmUaNG5vrrr7fHmwZkpkyZYh+TL18+M2TIEHPttdeaIkWKcNwlIB1PGqC58sorzdy5c+2/daw9+OCD5v7777fXOiQeN5h2++232+ymli1b2llFBZDqoyBx223t2rU2qK9cubI9vsaNG2f7kW3atAm2K+JLx5G3LYYNG2ZmzJhhHn/8cZM/f37bft27d7f3aXCNPmViYgYf1rx588z8+fPDrrf59ttvbYd26NChNsgvXry4Wblypenbty/p3gkiXHpw3rx5zRNPPGGaN29uNm3aZNOrhCAxcejC6J2Vf/TRR02TJk1M/fr1zfjx421wqI5rpUqVgo9HYnj55ZdtB1WzvgsXLjTvvvuu2bZtmxk+fLgN/K+44gpz7733mjFjxthzKsddYp4r1XF1y2G++eYb07NnT9um6tT+888/cXqXSIvOgw888ICdRdTSMw2k6UvBBxLXZ599ZmftZ8+ebY+76tWrm99//90MHDjQ/Pzzz/F+ewgJ7nft2mW/a3JIg9fqnzz00EPm008/tUvUlMGmWEDXNvomCSiAbG/z5s2BIkWKBM4888zAnj17Unwe//33X6B+/fqB2rVrB+bPn29v++uvvwJPPfVUIHfu3IEnn3zSPgbxc+jQoeD/r1u3LrBz587A3r177b/3798fmDRpUqBQoUKBG264IbBly5Y4vlOk1m779u0LHDx4MHDPPfcEnnvuucAff/xhb9+6dWvg+OOPD7z++ut8eAnom2++CeTMmTPQpUuXQIMGDQJPPPGEbUenbt26gUqVKgWmTZsW1/eJlHR+/PnnnwPbtm0LPPvss4E777wzcOTIkeCxeN111wWOO+64wOjRo/noEtCuXbsC1atXD1x77bX2/x31Rf7999+4vjeEd+DAgcB5550XqFevXmDp0qXB9hoxYkQgX758gdtuu43+SYJYu3Zt4Morrwycf/75gaeffjpw+eWXB3744YdgO8rAgQMDBQsWDNxxxx2Bw4cPx/kdIxwC/Gzs+++/D9SpU8eeZBWo64D10kGrE3GOHDkCjz76aIr7/vzzTxuM6ACfMmVKjN85Qq1evTpwwQUXBKpVqxYoW7Zs4Oqrrw6sWLEiGOQ/8sgjdkDm/fffD56gET/eIPC1114LlCxZMvD333/bIMPbQf39998DBQoUsJ0g4UKaOIMyzsMPP2zPkSeddJIdkBE36KkBtzx58tjjUZ0mcYEkYsd73IwZMyZQunRpO+hZtGjRwGmnnRaYPHmyvc+dGzUQWr58eTu4vWjRIpoqQY43d+zo+NI5U0HhP//8Y4+z3r17B6644opA8eLF7WD29OnT4/SuEUrtpr6k+iA9e/Y86v5+/frZ+95++20mjOJ8nP3yyy+BMmXKBE499VQ7sadrm74+++yzYH/Sad26deCEE04IvPnmm/F42zgGAvxs6uOPP7bBedOmTQNr1qxJ0el0gaFoJjF//vyBQYMG2X97Z+t1IlBA2bBhw8Bvv/0W498AzpdffhkoUaKEHRlXsKER1cqVK9uvWbNmBbM01NbqzC5YsIAPLwEupMq0aNasWfACqkA/1I8//miPv88//zx4m47V7du3x+w94+i202yGawPN+F500UW2DYcNGxZ8jAsWhw4dau975pln7GNdGyI2vJ+1zoOa+b3xxhsDvXr1CrRp08a2za233hpsLzcYMHHiRHvf3XffbWf6Eb/jbdmyZbbtFMw7Op7UPgpG9F3XOw3IqC0VLCr40EQEYnushQ5Cu+NPg5/FihWzWU6hfUllhZ577rmBM844I/Ddd9/RZHGg7F1dq77++mt77CxevNje/umnn9rBUAX7LlvGtZ0GrStWrBg455xz7IShcG1LHAT42TQ98aqrrrIB36+//hq8XR2c66+/3naA1q9fb29buXJl4KyzzgqcffbZwbRvN/uozqpOBLq4qrO0e/fuOP1G2ds111wTaNy4cfCELB999JFtF2VZuHaZOXOmTfdu37697Swhfj755BO7LEbHldLv1SHVbaE0iKYUcNfpUQd33LhxgVatWgWmTp0ah3eevSlTpmXLlrZT89BDDwWD/J9++ilQuHBhO4Oox4i3o6vzpGaN1VlC7APEJUuW2DRTLaXQdc9d33TN09IKzfpqZt9xndTu3bvbgfDBgweHzd5AdCmrScdUqVKlbCaTBqjfeOONYJaTZg41ADN8+HA7mO3aTbPECiZZkhY73vOd+pU61ykAdMHgxo0bA82bNw+ccsopwT6J60vqubfccovts+i7MtcQu/bSuU3HjD5/TRZp8Mzrscces8fTvffeG7zNtd0HH3xgn3fzzTdzjkwwBPjZkC6CCiwUFDpupldp+uXKlQvcddddwfvUydEIXt++fY96La1drFChgk1DnTBhQox+g+zJm9btrFq1ygaH7777bvC2IUOG2E5pixYtbLu65+n7Sy+9ZB+vlDg3m4jYUofVrdtWAK+Rb108x48ff9Rj58yZE8iVK5cNGjds2GDTUHWsaeZfnV/EjmZ01U46bz7//PM2u8Lr5ZdfDs7Uu1omblZYHVrd16FDB9YIx7jzqvTtr776yn7+ulapxoW486KOK60B1jGlmWLv89VuJ598sl3rzfky8rQ0wmUnhV7fdG6sWrVqoEaNGrZOgoL5iy++2C6Fue+++1J9zU2bNtn1w5rN967PR/T6k95ZWy33zJs3r/3ScsEePXoE79Oxp76J+o2hdJsyQnVt1KA3y9Fiw6Xcz5s3z2aiqX/4zjvv2NvcpJ76GppUUPDvBqm9x6v6lTt27IjRO0Z6EeBnQzowXXrbhx9+aEdVtd7GFT7R+jUF+W+99Zb9tw7cWrVqBU488US7hlt0QteMcM2aNe0Ing5+DRog+mlvr7zySmD58uX2/zUTpZOugkPNalx66aX2wqpAUCPmoRdJzWioI6tAI9yAASIr9PNX4KfZXKXju/XaCioUxIerZaFZfQ2uvfrqq7bd1PlRcInYt5vOk40aNbLp+e7+0HREDd7oPOkd7HQdKGXYMAscXd7PV+c/FTlU6q8bqNY1TxlPjhuA0dpf3afHuNlF91oaAEDk6fyn7Al97lquFNp+6qMowFchSzcLrIxCDcZoJt+7lFB0TOrYVL0ZXRMHDBhAunCEafBEfUDXXl46F2r5n/qOqtk0atQoO9CigF5tIloyoeufrmMvvvii7YPoGFRauPqPGojT8hkF+vRPokvZFRqsdvVHRH15t9wz9Byp/n6VKlVsRq8rAhxaYJvrW2IhwM+mFOjpJKrROh20Sk/UOiiXXqWOkQrwuVkqpQNrDY5mD5Xer5Owqu7rNl1odbLWhZpq0VnnDRq8F7nZs2fbgRh9zkqZEtVP0AyTZutV7ERr2HTC9hZq0wyJAn6HkdbYU4aFK9alAMJb6FCdG7WpqnqHBpduHbC+tHTGzTAiNsefG/ScO3eubQPv4Eq4tYaaLVY6sQpeuue6x7nvdFxjQzu96Do2cuRIe0xpkEbXNJ0ntbNIaIdU1zUV3fNmQ3nRbpGna5NStjUz76hNlC3RpEkTGyB6l51p8Ex9DgWCLqNCbavAUrWA1L4aENWMIiJPs/E6D37xxRdH1XRSuyjNW4UPXXaZlgKqJpCeo4Ea0W5MalfdpgkinStVR0Ftp6DTpXzr2ofoDtZo8EWTQho4c+c4LT1zmWih50hlqekcqQk9JD4C/GzGdTJd5WcV8NIJ1nHBhU6yqiKs7YJcx0YnZp3gFVDqPh3kmiURdZj0errwInNUpdQVc9JJ1XtidSfdCy+80AYQ3iUUri01wxta7HDhwoX2OepAeQsUuZ+B6FMbqH26du2aouiTS23UzL0G2lxAKBoA0MyvbtMMpNofsU017dSpky0epJl3LXVRG7rA0FtJODTQ18yVHquOLXVJYkvnNNWR0TFzySWXHFW48ttvv7V1SJSK6mbm3SyU0lE1O6w0fg2cInq8WxL279/fHi8KHtx96nNoQPPBBx8MZhW6Y0pt41364ga/tYxC92vZmkOad2RpyYP6gKEz+JqxV00Z1SEJ3fFFk0TaHk9BvLtNx54mhTQoo0G422+/PXhOdYPaY8eOjfC7z57CHQPu+HOftQofugk+9SF17lQmqPou3nOkzq8qTKrBUyQ+AvxsIFwgp6BC65xc2qJLx/fOUqhAm4L50JRgXZRdkSKXaqcCGxpd9xbtQ/q5oECj31462Sp1TaOmagd1bpRx4S12osBd2RRa7/bee+/Z23QB1jZBSofTPtzUR4jPxdT9W8eZikR5q6y7+5SGr5RF12lShoUKSSkl3P0bsaOgQesRddyouJo6oxrcVOqiznOpUTu5wEOVvNWGiC3N0mt9toINnTPVbu5Yc51aZT9p5sq7NtgFFxqgDlfsEtHrkyjrRYXVtEzJO0CtAVG1owZclPatwNE7SK26JLoOuoDeW5NEr0817+hS4Uo3IaHZYDcI47InvIOgo0ePtsejJoxCucBS1K/s2LGjHQwgWy2ylF3r/ay9/XwNzKh4rzsulVmj404ZFo4L8r3ZoUhsBPjZbGsnN+PuvUgqRUdpbW5Nm+uk6qR9/vnn24PcVfEOHSzQa7rCKZ07d6YQUSZp0ERBgVuH7VJKFcyrWJA6njqxqoOjGXxX8NC1lWZ6lVWhQFGzjhoVV6dI6Y9aO4XYUcfGpY96B8w0I6WCUZpt8tJMhtpOs/tK09cslJbCKMCnkxpbGvTUMhilB+t85jqp6twoE0YdT1cM0bvMQtk3On7d2kTaLfbbcTkKDDSbq4DRW7jS+3hd17SrgQZW3evRZrHh/Zy1nltr5bWzi4JDHXfelHxtfaeJAwV+3uNNgwKqI6NMDf2/F7P20Wkzb90Rbc3rJoZc4Ddjxgx7fStZsmSwuKE7f2rQVANreo6b4fdeG/U49XGUsaFrnwbpvO2NjLeXl/rv+uxfeOGFo5YauQkiHUvaDcZRir769aGTTg7HWeIjwM8GB7i2HFExG80gahZK1bu91WXdOjgVkHJcIK/13LrAqkPkLaihE7bW4SuY1OCA1l4hc9xn7b4rVdQFCkpv885qaJbiuOOOs0Fh6PMV5KsYm9JPlaaqtCt3Hx3Y2HADLZqxcMeg6+QoeNdFVjMdrn1FF111ilToS+sQdVF1nSDEVp8+fWzgpzRuF/y5855mQHQuVO0StyWlOksaQNMSGAUnoQXZCBqzLrR6vXeQWWtH1XnVoJm346qMJR2HSg32dkTdYzT7qGueirgxUxh7OqbatWtnlwgqy0wpwW4/e1dfRhlN2gLP3eaKkqq9NZimavoqOIvY0Y4vOnZ0DGoA+vTTT7f1SRwtiVF/UANsocerjlVNVuh+b19S7azBVB2L6p9SPyFrwtUK0cy9+v26fmlnntDrk65hbmBFE3uufoKOS2XR6P+5liUfAnwf8h6Iuihqhl5rnbR9iS6MOpA1S+hm83WyVZChC6lbW+PtFCmYVMpqKK3T19orlwaJrLebBmOUMeEtLhSaBqx28u7ZHNpeoSgOFR2hxdNEA2cKLHS8eQMH1wbKdlH7DRo0KBj467jUbcq+UFVbl/aI6AnNRPLux3zTTTfZ9njggQeCt3m3oFRgqEE2zejr3KoBgYoVK3IejIL777/fDlaGSwvVtUcDMQoS1V4KHtwSGA0KhF7T3HHq2lNbr+l4Y6/02NNsreqOKBPNne80OKqAQm2m2WC3VZ7S8HWbiiPqONOyweLFi9tBVMTuPKnlf2oHLW1Rn1EZn9o2VGn3bt96zQZruzs9TgPWof0PBZehdYJcoVllT4VmYyDzWU2qaaFtr73Zusqw0EC0C+JFz9H5UoPUGmDR5IIbVNWkRLi0fiQHAnyf0gGqE7H2jlVldVfB1HVSXbqOW9OmE6s6UkprdCN8oSlS4QIaZF5qn6PaQR0ZXfBC6YSrDq1S5LwnbqUzeusihHZmkTWatXAXRVcEKjWaTdQgWrdu3YLr570dJW05qVlgV7BNg2da76bjEdHlPR4UNGqJkQI8bwCpLSi1FZCCeN0fup5U50etxddjmjZtamc9vJkyyDoFEFqLqwBCQaB37bU+a9W00LpRDY4p0FPlew2s6bhz+6qrXonOpRo4c8ti0krtR+xocEZ9EHd+dMeN2knpwmpLLxUD69Wrl+3TKMvGW5eE9syaY52zNEmkXQq0y4QGPb0D1257Sc26u4KHym7SALcG31zA7j1/CufL6NGEm7Ih1KfQceRtL50bVTxPx1/oFnc6V6otNYDtzcrwtheSCwG+zyt3q4OkVLjQi6HShJWu461679ZQaaQ8NC0SkZVWgKiOqZZUKIBwVUy9qaWq9KwCeqLvSpVTW3tTrxA5Si1UsKcZwtAO5fDhw23nRoW5vEGIAg8FG8pycW3nBsxUMVizV6pGq2AytEAUot+RVQaM6ltoaYRm4HWO9Lar1gbrPs1quNvDHbPediNTJnJ0DlQNEQV1WrLkpWUQKoCoddveXSk0iK1zpq5rbgmaquar9oWCxtSuaXReoyPc5+qOpX79+tlzoNsa1HvsuC13lZ6f0ddH1rnAT+dLZXmqv6F90XW+DFdEWcehlkt4J5G0NaWOQ/UnETsaCNN164orrrB9+9CaWzoHatcltemnn34avF1LQTVY7Yr7hj4PyYkAP8mltrZaI6ZudFXr5N36NfdYpcVpllj7p7sgQ/cpWLn88sttgTdEr80cZVios6MKpqH7yqp9FAR6O6ZTp061gaP2odV6N6UqqgPr1gQj8hRgPP300za7xc2yaw29An61hUsRVifIDbyIalpopt47Gq621EyI7tNztK4bsaXaFJr9bdu2rS0kpEwLDeBcffXVwfOkKMBQ5WcN1qR3twREhoo96fhQGr3oeqRzouuM6j4NwoRmmimNWJ1XpQm7c+0777wTrH2B2PAG30q9Vwq3dxZXSynUJtoizz3WtaMeq/v0peKV3gEAsggjT+2iY8ntpOQGLt3e6G4bXgXsri/ibV9lQGkHn5YtW6bYT13V2bXVKPV/Ykfr7FUXwWWeSWh8oOwXtZUyLB5//HF7ztS1UHHC9u3bY/huEW0E+EnMe5LVTIa+vBdRdVZVCE+dV6V0h14cFSRqFF0FbNyBzRYYsWkvfd4K8pQupQBR7aC0X2+goHQ4rTd0FfNdgK/HKpjURVcX0XCvj8hwx4pmFBUAKqDX2kMtn9BsoWbudTHVQI1mgjXL4VKENXCmY08j4ipio1FxFYXSDgdqK/bajj2dB9VuOuetXbs2eLvS7HU8ac2imzHWII5mNXQMuoEYjrHYHXMaCNOOBjr/qdCT6h0o8FAKqquR4IJC1y5aL6r0YHVy3ZIaXQdVBNYFi4gNLR3T8aMZRbWXtnv1rglWrRmlEocOcur8qnbXlwZVySaMLqXWu/OfBmOU9aL/b9Wqlb1fn7+OPd3mCo+GUlFL9Us0eOqWT4Rm3iC61HfUZI+KVjoqSKm+igJ/Daa5IrDKcNKxqYFu9Wl0jfNOTjBg7Q8E+EnKOyqnNWlKQdSWMir8pDX2jtbfaCa4WbNmYYubaC2jTtwKHL04wKNHs1EKCBXoaRsndVg1Q6y1orpAuuwJDQJopFWdILeUYtGiRba9VHHYmxJHenB0eAM6HSNa16aaFur8qGCel9Ypqg3VZu5YU8dHs8CqjK/n6n7t74zYCJ290MyhUkq9KYiaZVIn57TTTrOBpNrZHU8arNGxpmCDrKbY0nlRs/EKHFTIy7uWVOdO1bLQkiXHtZmKsml2ytteXM9iX21d27lqqZn6J8oMVDaTrl29e/e2j9Ggp9pWfZNZs2bZ23RcavBNO8GoRol3JhLRowxADYrpWqU2uuuuu2wbuuNGx5kbZFu1alXYc6ueo+eGrt/m2Is+1xYaDFUbaMmZdhDRMaglt7qGqe+hrSXdtU+7F2iJjJarhWbIwB8I8JOYRuFUbV2zhDpwNUqnIEIHuAJFN4Kq9VAaCVeqlVub6A5kPcY7IIDo0WeuGXcNxGgZhHdmXjNTKnyiGX0Fhe6EqyBeHSOtBXYnZq2z8p6QuYBGh3fQRIG+PmcdK1pvqGPMpQyrDdxAgEbJ3V6/7hjTjIiWYaiITejuB4i8tGbZ1SZu1lcFoDSbr8wLFanU4JnWdmtLUM0+OloHriAF0eU9j+l8qMBPM0zqmGqZhDc7TedAnSs1WOYtLqrZQ1XG1wAAM7/x88gjj9jAQrOCrl2V0aQlZzo/ugHroUOH2sFPDbCpvTUgoP9XBXCH61t0eIM5nQs1i+t2owjHFWfWtcwV1PO+hm5TXxPxoz6ispU0qadBsgEDBgQHz9Q30TnTTT6EBvNMEvkPAX4SU4q99hTVxVCjcaIiKNoCTyPjqiysg1hBvDpCupCOHTs21QqmXEijSydQzdxrLZvax1VRd+2gtWzq5CjNyq1lE60T1uMVaIS+HiIvdNBERfEefPDBYNq2ZioUdKgYlPc5ollDjZprVjgU6d3R5/2MNZiizqjqJui86B0UU7CoNdnKfFLlbvc8VxvhoYceCp5TOS/Gtt1cHQS1nwY4tT5UwaL+3z1Ox5nOh262SmndmnFUVo1mIUOzaxD9dnN0bGn20BsousfpWNNMsQatHbWraiZoaYWe5wZOEbt2U2q+rnFahuatfRBa/V7LZnQsemsGhZv1ZSY4vlzxUW8f8fnnn7fLQb1ZT/A3AvwkkFoHs3PnzjbADy2MobWlWl+j9cBuj1nNSJ177rk2DVVFihA9ac2qq/Oq4EEXUQ3MhD5O6W0qnKcA0VXoVlVbBfneitGIDu8FUSmkGglXWyk90WVQqI00Q6jtZLxb57mOk9aaqrPKDGJ8KPhThozOjZqV0sCmjikNyLgaI5rFUCCoJUrezqiKjqrYkNrc7WBBYa/o8Z7/dN265pprbFaaN4DQzG9qy8w0+OaKXCqTTSn9WpuP2F7jtI+5W0Kh3UTU19BSJnd+9D5WmYQasA4tLKvMGm9AycBa5Hk/U63HVqaFilB6t7hTFoUyZ1y/UtdEdw5UtozOqTpP0o9MPOEGVtSPVMaM+ixan09thOwjp0FCO3TokMmZ83/NtGrVKrNx48bgfSeccIK978CBA8HHSvny5c3dd99tHz9mzBh7W4UKFUzv3r3Nrl27zJEjR+Lyu2QHhw8fNjly5LDtsnr1ajNnzhyzaNGi4P0lS5Y07du3Nw0aNDBDhgwxv//+e4rn161b1zz11FNmxIgRZuTIkbZt8+XLZx5++GFTvHhx2i6KdFzkzp3b/v9DDz1kmjRpYurVq2c6dOhgjjvuONuuct5555lu3brZY0nfd+zYEbxv2bJl5o8//jBnn322yZ8/fzTfLkJowHrfvn3mnnvuMStWrDBDhw41X3/9tdm6datp2rSpPYZeeukl+9jSpUubvHnz2ueo7f766y/zxhtvmO3bt5s333zTLFiwwNSpU8c+1rWt+47Icde2t99+25x55plm3bp1pmrVqubUU08NPkb//8wzz5hZs2aZd955xx53jtp4/vz55q233jL9+/c3s2fPNgMGDAieixEd7hr3/fffm7POOsv2N+677z6ze/duU6RIEdOyZUvb/9BxpGNMj92/f799bps2bWzbuHOt7pc8efLYY9L1T9zfBiLHfaY616ndPv30U7N48eLgMeXaMleuXPa6J2ond+4rW7asefHFF820adPMpEmTOMaiKDP9dO816uDBg+b11183jz76qOnYsaNtq4EDB5qCBQtG+J0iYcV7hAHpG43Tlk1au63q6W60VRW5taZGa2tCZyA106GZjR49eqTYx5nRu9i0l7YfUSqbZg81u6QZebeVndpDxfWKFStmdzkInelVJWitA9a6+9AUOUR/FlEzT2obHVfKgFGKt9Lu3XEnSt9WSqPaVrMZWnrx6quv2v1nCxQocNTsFGJD7VeiRInAU089FTwX6hjSsaTbNVuljBh9qZiX2k/Hmmb8NaOvVGHvMUeqafRNnjzZHl9a+qLt8cKlfWtWWNdAnU+Vun+sOgvM/kbfhx9+aDObLrvsMptWr3XcjpYyqR6Qjq3QlGBtx8YWobHntqvTdU3tpr6hlky4miTuXKdzo2b21UaDBg2yt+lcqmWDLotQxUpZIhgd3nOb+us693lvS++5TedVzdpryafqO2X0+Uh+BPgJTltpKWVUhaCUpu32/hWlm5YvXz5Qo0YNux2Gl9tL1u0j7MUBHj1Kh1LFZ62z10VSe4y6QkLXXnttcG29gnhVzlcbKegI5d2PG9Hj7aSog6Nia0qvV8qpGwxTym+5cuWCj3EdIVWgVWCoNtSggFKLdUFVUT1EV2oBngZWVEFdAYZoG0Ol52vdr2qWuGUvogEbBfl16tSxFdlV6Ruxp86n2sht4SQqBqulFqpL4miZWe3atW176dhD/GhQWtt/as28ty1c38KdS912a6qPoP6Kiu6pCKL6NG47NcS23fTZq65FuM/fXQ9Vs0QV81WcWX1OFc/TBNMFF1yQ4vHUlYks74Cy+oU6vrTkTxMHGhjLCB1vv/zyi40FHAZlshcC/CTo/GiLCxXH8+5R70ZdNUqni6jWTbkZYs1iaW2pZpBDtyxBdKl6uoLB4cOHp1gzr1leBR7evZsV7KvzozVtqRU+4YQcHd5BLgUTt956qy3IJqE1LW655RYbAKozE9oemk3U7KOCDt3PjG/0eTuVOi/OmDEj+G+tC9X58NNPPw3u7ax12goOXZvr+SrK5u30apYk3OsjMsLNquvfOp60JlsD2PPmzbOzh6r8rK0Mte5ee6VrgNS1i9aSqsClCuu5HWEQe8q00LH19ttvB28Ld+5TlqHqIuixamNl0ah2iQJ+xJ7OezquvFu1am97TR5plxcVYnPXOB1rKpSoegnKBlUNDO/2oogOff7aEUufufqHmhhSDKBjSJmh3kzCjKBvkv0Q4CcoHYxKA9ZFUYWHUnuM6KSsGWOlBivQ0AitRl61VR5i01auA6oZRBXRc0G8itJoWYXSfzXLq2BQgwCOZnuVDk5Rtti0kXiDdM3w6vPXxVMzS94g3T1OlYXVPl5udl+DbgMHDgyMHj06yr8BQjObVPFex5NSgd1gmm5XFoW22tJ2hqoG7Z2p0uMuvPBCG1S6Y9TxZmcgcryBvfbQnjZtmm0HN5CiLe80O6gOrTKd1KZqw5tvvtlez3RsulliHXe6rr322ms0URzpuqV20bKkUOEKx2pXA10XFUimtosPok+fuZYrKWPmkksuscedJhg08OK2ybvpppuCj1dWjbIQNZDqMOkQXTpeVDBUW4O6TE5lVGj5mAZbNJOfmTbgeMt+CPATmFIUS5YsGRxtDZ1Z8nZQNSKuqvpK5dGAgFLhHA7syPK2w+eff273yHYBhndW6csvv7QnapfGqFQpBR4KFl2Fbs0caoZKgSYiT+sMp06dGmw377Gg+gfq0GiGXnvGagBGx1To8aJAUjOGrvOqIFLLL7QvsHB8xZZS7RW8a0BGa39Dqzlr/b0GbTQw46W0fVXS1wyWaiawVCn2dUk0CK0ZXR1rmt11tBZYHVqt+dW1y3VsFUhqNwQNYju0W/zpeqdrm7bkVT8lXHu7qvrhECTGr9+itO17773XLltSn1Hr6Tdv3mzv69Chg81ADN2tIvQ1EBnhzmVul6XQNlCNCy2R0KCMW4KWGu9xqMEBzpnZEwF+AlOqsLa6U4ChmeBwwURaRfMoNhRZ4T5PrbvWljIffPDBUR0gpeUrxUonWFdIT7dphkpBpdbhu9dFZM2ZM8cGeAooNDvh9cMPP9iBM6WKuqJr6thoIMZLba22UZupSKKKr2n2UTPAugC7AB+xozbRAKaylNSO4TouOvZUB8MVt1S6vjqxGtDR30O3bt045mJMg51KM+3Zs6cd0FT76Tw4ePDgVJ+jwTbN1Gu2UQMAoThvxofLdNESGB1PkyZNOipgnzBhgs3CUMZGuOcjPrxbfuq6F0r73GtZTLj7EB1aWqssQDd4orpZGgTVRELoRJ6OKy1R0kB1arzHV69evWz/R1lSyH7YhyRBafBF2+DdcMMNduufCRMm2G3wvNtgaCuoCy64wCxfvjz4HO8WG24rG0SG+zy1LdM555xjLr30UrNp0yazd+9eM2zYMLslkLNhwwYzd+5cux1etWrV7JZpa9eutVuoFSpUyEyePNk+xr0uIkdbw2iLO23LpG20tJ2P97j46KOPzIUXXmhGjx5ttwLSNoTaflJbGLrHiNpabaOt0/7991/7PG3xpHZbunSp6dq1K80WY3/++af58ssvTbNmzUzt2rVtG2lrwq+++soMHjzYzJgxw24nqa2cnnjiCTNx4kRz7bXX2i1Ctc2atmN7+eWXbbuyXWj06Zi56qqrzKhRo0y7du1sO9x+++12C1CdQ7X9nTtvum1e5ddffzXvvfee3dapUaNGduu8UJw3I0vbaoX2I8LR566vG2+80V7btD2etlsTbRmq41PtVqxYsbBbhdJu8ePd8lPXPUfbU+rc+OOPP5rLL7/cbo13rL8DRMb7779vHnjggeDxd/zxx9vPXlslu+0j3ZaftWrVMkWLFrXXvNS2AVXb6v5zzz3X9oV0HWzRogXNlR3Fe4Qhu0nv6LV3yxJVDtbsh7faumaFtY5UVfS/+eabqL1fpGwPpXsr/bdly5Y2zVcpwjfccEOwmJer+qwUfKWWqtih0vaVgaGCiRoh17+9WwohclS3Qut2VZjGpR2KiqwptVu09Z23AKJo9l7P8ba1W0JRtWpV27760lZeiA/XLsqaUW0SbV/YsWPHQLVq1YJ1FLSGW+t93VZ3y5cvt8XbZs6cmWL5DCmLsaFMCs3AK2NG2S/iZqp0PGoHGGWoubbVml8V+9LxqK28lFlDWnD0qJCaK84bbpY3LTqGlCmlGhg69pQ6rKUXKiarPovbMQbREXoOy0xmhDIstJOI+iWqgaEsJ4636PC2j7fttGOPspm0q5Lo2nXqqafafofbkcc9V9cwtZOKNaf22i+//LJ9TJMmTQILFiwgYyYbI8CPoXAn4PSclHWhVDqxUnNUIKV169Y2xVEHMcWGosd7oXPtpEEVrT1Ux8br/vvvt4UN1R7uearMroBDJ2p1WJV25d22kCAj8hTQqR2UCuyoyroCQXVCXcG10M9eQaJSt73c2lIN3OhYC92KEvGheiMKCpUerHWk2h9d2zgppVF7cmtZk3c7PC86r9ERWjjNpWxrIFMdTR17rhClG3zxFoh1HVbdp+J6Oncq9Zt2iywtFXPnPg1Ga6mYW8KkugcaOFMxr4xQGytlWEGizrPaBcHhGhcd3iUR2jVJA9beAcz0BvtauqTBN9Uz8RaK5TwZPdphqW/fvsEUfB2Hup6pb++2tFPQr3Om6jW5AThNWOh56n9OmTLlqNfdtGmT3dpQ/UwN2oROYiD7IcCPA80wpbdAlzvRLlq0yFYP1sie1rZpFivc/rOIPG0F5NpLxdgUsLvaB94L4VlnnWXv06yIt7CUOj7aL5390WNDhWi0d6xmbfX/GlzRul8VV9P6eccde5qlr169euDBBx8Mdp6WLl1qi9242gocX4kXqOj8p0EYbx0SFT9U5ox371/Eri6JOqGhx4rWf2oWX8ek2+LJBfmqM6NZQ81guUrdOh69604JNiJDmX66RnkHKj/88EMbEKhtVABWgYYKx6b3fBfaf/EGnhTSiz4VOVRRPGVNqG+orEIX6Kf3uFExRG+hRI63yAk9PpTZ6bIBlfnpCulpgEwD1t7tI5WhpsE3ZTI1bdo0cP7559tj9Lbbbjvq+FRGrwL/M8880/Y/aUMIAX6MKVVb25GoUFRmCpnoOdqay2Hv7eielBXkqYieZoTVYdFMk7a8c3ul60TrTrauAIo6rJpJ9r6W9zvBYnSpw6KLpWbdlTaqAl8qaKgLpi6srkCN64AqmFAnyWVXjB8/3nZ49ViX1o/EEW5QVOdEZWqo3dRx4hiLPu9nrI5p8+bNgyn3Ota8dB5VB1S7WjiuE6psKAUneq53dp9ibJGl3SZ0TtPuBe7cp1l7DXzqdm2bpsGy0O0jM4O2iy7N1jZo0MD2JTWoqeNNs7feXZcy2kacMyMrtc9TRXvVh9SOVzrnaUBTSwu11EXZgl7aUUTXMwX4mlz66KOPUm0/TUIdq7o+shcC/Dhw60bTqoCf3n3XER1KgdL6JaWKahs8t7ZewZ9GUV0KuKu07jq5Gm3VaLrSFF1n1Z3ouYDGhuoeqMOqVH1lUOgCKprV1ZpeDcK4ARgdQ2pbtZsyZNTWefPmtSmLrs0RPZE4JjSwpsGZSpUq2ZT90DXFiC5tbacBNQX26owq0Nfxpz3P3cygBtiUgnrCCScExowZc9QMr25LbWsuRI62SPOaPHmyzWpSoKHzngJH8Q60IPG8++67NiDU9rouK0Yz95rx1ZIXtw0vYkf9QLc0zHtu04SC+pKO+pCafNA5T4M0yjDUcahBUC3FdX2TUN5j0tv3Jw5AagjwY3jwuwNx3Lhxgdy5c9uZRXcfEqsTpIukAkQFDVrj5mzcuNFuv6YOrDcV361n0yi6TtiaEeYiGx+aoXr11VdtoFG2bNnA3Llzg8eYZnk1uKatgBwF8ipiqSBFwb+3TgKiH9jrvJiZWUM9T0so1MbqGGnttutYcU6NjtDOpDqpbnnLihUrgre3b9/ezlIp3du1rZYoaaZeWRapBZJ0VmND7eVS9TUIo0BR17z69etnuiCww0B25IR+lm5CQQVh69WrF7xdbanBTfU7Ro0alWI9fnra7aeffmJrvCy2kwoua+msZuNdZpKWZqrwpPoXWvanx2mWXXVJVFRPAwIK8vU4PVfnxpdeeinNnwOkFwF+FKlQhjqdbgbR0f7NWnOoi2xGOjR0WiPPnTBDP1sVM1HQoIJdSsdXO7nHqqNaq1YtW7lbRfU0Oqv1owrsn3rqKVsJWrMhmtny/gzEhtpSgZ5mcnWcKRXO7Vqg+9566y07wOYKe2l2UVWf1bnVRRjRaRPHezwoAFTnRrUStJ5Uu1RkhI41DbTpnOqw9jf6baidCVw9GXVMvbPvShnW+U8DZpoZ9gb+SiXWeVPHJGIj9HhQWr4r2OsKfemYfPLJJ+3AtavmnZ7jyP1N6PnK2GB3mOi0m3dHGFGAqKWAoplftaeOwyVLlgQDddUpORYNtOm1tJSNuiWZp+NARQo1KaQ0e2UO6lhq1aqVndBT/0KTRRpIk3vuuccef2pj1ZK57777bJ0SPUcxAxk0iAQC/AgJDRA1a6E0bh2wOoG6A9tROqPW1YR7blqvrVFBLqKRkdbgimZ1tV2T2k8js+I96c6aNcsWPdH9KlKkL80KqzOr19WslmaQGZSJH108VVzPpQu7gTZtk6cLrG53WTTaRg/RWfebGg2waKBF50GtS1QnU4GhOkrp6ZyGw2Ba5Hk/U7WLOq3ailKDYdqVws1YaTZKbanlLpqF0tIJ7SKirSXd7hW6LirLya0T5vwYO940YQUdCgoVzLv2U6aaCsLqvOh9rLcad7jjS0GlAhi1NTVLss57TCi7QkXVzj333BQZg1pOpjRvfe6qbTFw4MDgMSaawddAW+j51/vamulXUKnCi9qxgnNn1mmQ2m3XqsFMDUCLJonUP1S9i/fee8+m4av9lF0oepwGS10BPtbSIxII8KMYKKozpIBcB7U6Pdqf0p2EBw8ebINCXRzT6uh4b9c+6jr4+/fvH4m3nW15P1ONWusCqpQ37dmswN2ltyntTTNQKlzj1lZ5U4k1Uq6LpFLC33jjjeDtCiA1c+xG2OnExo/aTTUvVBdBwbw7VjXjq4BSHSfaKDo0K69zn3d23dGe6Epb1LnMDViqk6NdKJRmmtbAAOJDnVSdEzV4+f7776eoUaF1wG3btrXLX5S5pvOkHq9jTB1ZLaVwgzapbWGI6GUz6Vp02mmnpeiraOBFyyg0EOOCO7Wd2kzHoa6NytTQgIy2h/W+pnd9sfoxGuzWcc61LnIU0J900km2MromG7QLgqOAXCn52jFEmWreyQdlaGjLV7WhNyPNtY2O1ZtuuskO8Cgg1bJDZJ3Odxpw0fGg/ob77N3nrqBefUz14RXk67Hqd3pp21Adg0AkEOBnkfeCpqBdqTaavfBWyNdouIJInVB1ol61apW9GCpV0aVxp0UnCu17r5OGgklXVAVZo+BcF0h1WLVOSqPZGojxVqFVCr6q6GvXA2+bpzbarU6R0rPUcdL2eog/bSGjpRYqnOdNF54/fz5rfqNIQbw6MzqeNOglLsDo0qWLXY/tZgd1PHXq1MkWsNS+9m6Ndnq5XS2QNant9qHikxqQ0dIkZb+EWrlypb0+9e7dO8UxpUw1DZAqddVlyXjTuhF54YJsZZOpSr439VsD2RrA1tIy75a7ynrSrLAKIqrtVLjNzcy711ZQqKreCmY0m6xlTohcm2lNvAZaFACqeK/bOck9Tv1LlyGq7+78qr6mUvZLly5tJ4RCU70VPOq8q7olqshOKnjkqG1U40dLAHXcaBmSq5ng2k1ZMhp8Ubtp8Ftf3mwZh3MjIoEAP5O8nRjNSCjFVDMVuhjq4NUF1a1vc5SCo5nd8uXL2xOBRmc1ghr6et6TvWb99bpK89eJgBHyyNCsoYJ6zU4oi0IXUM1IaV2U0oa1lt51gjTToTZ1AXu4jA0FGBp9VXEpPV/tSlslBrXDZ599ZjujWh9HZzQ61MlUp9KrV69eNrjT/sze40EDnTpnirKc1JnVlk/alzu0fcIdb97X0iCpBumY9c8adT41m+ud0XOz9NoVRDVJFMTrMaFrhBXg6xypQqMutViFLjUgMHPmzBTbOyG2dPy0adMmULdu3aPuU9voeqWJCc38ipYyafZY1zAdz+74c8ecBsY1AKDUbgWMFEbMGm8w57KZdByp36fq6o4mdvRYd0zqOL3//vvtcad+pbJnXMaMUvZDqb6JzsWtW7e2xyuiQ5lK6guqXdwEXmhfUIMySuPXYzSJRF8R0UCAn0U6yeqCp1TfiRMn2tlCrbfXTLA6sdr33ktpVhoxV2dW6001Y+Vd4+YtqtKyZUtbqOixxx4L+xhknop76eSq717KrtDnrvZzVfAVOGhtqTq3uj8cdWA1qKN1VlpDjMSiLSm15aE6N8xaRJ5Ssl2nRmm7XqoMrAwKDWo6t9xyiz1WXGaT1v5qoM0bNGpf32OdezUgp3OoCoSR2ZQ1WsKiSuqvv/56sEaC0oNdMKAMC7WvK07p7ZRqkFttqFlfBR0KGHWN04Cat54CHdno02y7jgvNxLvlZmobZVOEo8Fs9UU0uOZdguYN3N1xqR1mNAN85513ktodAd7jQfUQNOmgPqSWtOi8prbUbL0mF5RWr2UWmh1WEWBXO0EDbjrelBWlwFHPDzd4oOw1HeOcJ6NP2UpasqQg3rtEzXtM6ZqlzFD69ogWAvwsUNqiAkHNAt97770p7tMaG3WGNPMRut+9Ojzaj1sHv2Z8Q9NxdMDr5KCTuUbSGSGPPF0w1T4u9dC7n70GaJS6r1RhR50flw4Xji7CuoC6fZ+ReOjYRJ4yXzRgqR1BFHDrfOfqjLjzmgJEzRxp/aHbcnL69On2eNLt/fr1s+dEd55TkKHjUxkX3nWn3s7wyJEj7YCbBgn0GM6RWaeZW830ajZQ1x4tM1LbuJldDW5q3aiWlml5i3gHZHT+0+CoS+3u0aNHBN4VMkLXIWUKakBNAaKWnSm41xI/9VNcUOjtc+h40+C09lWfPXt28PbUllIogMzMtpYIT5+vsjOV0anlDlqrrYxAZb+4SSD1M5WBoUHqyy67zJ47VQcjNTofMpgWX7rG6Tyq66OOS13jlDGja5d4Bz5JyUc0EOCnU2gHUidPHaxKt9fJ1s16eE+qGnFVZ2fKlCnB29yFUUG8d6Q19GSsdVfeIkaILI2qugEY10n1nmQVOLg1i679lUoM4H+0Tl6Bg4IJzTh56dzoDf601EjHm2b53XpSzQK62xx1bNUBUudWwWboOVADaBSIih63vZOuW8OHDw9er9z1SW2jAQAF8q59ve2s7BgN9Hi33GLwJTrS+lx1LVOgqPW+OpbUpqqhoIE4DdR4awSJsmc0qJ1WgS8CxsjT0jHVQdBkgrZH8+5fr+NIM/UqRKrilt5jSoG/Ww7lrZshBIuJQedFLU1TFq6W2KoWjQZHtZTCOxFEeyFaCPCPIbTY0LJly2y1Z3eAqnq6Lp5ap+2qA7sTrasirAqzqaV2p7X+HtGhz1hBhrZ7UsVut5OB936NlqtjpJN0aEeKDivwv4JNmiFUJ8Yb5CkVXzPzSr/3UlCo4NAti1EwqONMM/UaTNM5VEWllNmkf4emLqoQmNK+dcwq0GSpRWQp00w1E1QbQdc0LQ1zgzHe9lU6sGb3VVAvLWkVI0XWeK9Byi5TvQMV91KAGPqZKzNDqd+aCVZ6vQJ57XAxduxYO0jnBnHc7D5imwWqegYK2N2SJB03qfUxNDAzfvx4m700YsQImirB6ZjSsamJQNUw0fIlIFYI8NNJ28UoPVEHqkbhlNKmWWCl/WoLGt2mdPrQ0VTN3uvkrcrDXEATc82pAn233l5pUypso2KJ3plFAEcXbVIg6Iqu6XyoOiQKCrXGXmna6ow6Kp6nwFCBvttCSLNUzzzzjN3SScGHdjrQTiGOt6OroqUq/OXdCQGRD/K1xKJz5862rcaMGRMMGF2Qr6wKDVprsMXtzU0gH3uq71OvXj0b7GmgTYNqqpngrQHjZuqvvvrqQJ06deysvrI09Dgdu3qet4K+t+I3opsBKjrWlHavttCa7NDBNC/N4KvWj+o9qS8aWsQZiUtt5wooptXGQCTl0H8M0vTpp5+azp07m3r16pkLL7zQ/PPPP2bixIlm+/btZtiwYaZUqVKmbdu2pnTp0uadd94xp556aorn33HHHeann34yX331lSlSpAifdoLYt2+fGTVqlLntttvMiSeeaFq1amVy5sxpvv76a1OwYEEzZswYU6VKlXi/TSDh6LKxY8cOc+2115q//vrLNGzY0AwdOtT06tXLPPvss2bBggXm3nvvNX///bf57rvv7PEln332mbnmmmvMI488Yh544AFTrFgxe/u///5r8ufPb/bs2WOKFi1qbzt8+LDJlSuX/f8jR47YY9N7G6Jn/fr15uKLLzaFCxc2b7/9tqlZs2aK+3WOvOGGG0yhQoXstU2PQ+xs2bLFdOjQwR4vOpZOO+00e81S/0TXtddee820bt06+PimTZva+7/44gv77wMHDpiZM2fadlO/BtHxf5No9twls2bNsn3AihUr2mNH57I5c+aYu+++2/z222/2q2TJkinOc7t37zZ9+/Y1X375pdm4caM566yzzMiRI+3jkHzctQyIuogOF/iU9rNU2qLS8513333Xjroq5UYzU6qkr+1mlOIYmqrPaF30pDZzlJFZiHHjxtkCREqh0voorYUj/Rc4NtUe0cyh1hmqCrSWLznvvPOOPaZUUNRLa+g1O/zxxx+HPY6ZRUwMkyZNsmndWh/sCicqO8P9vwrwaYka4rM8Rqn2mtF11zr1O1QPQ19aBuNN9Va17vr169t/h14zmbGPPhWl1FaeyvRUv1HZSlpf72h5hQrpqYBeaLso81NZTdqVScWbHZYKAkgLAf4xaO28TsjezqhSqRTMK+3NpXZrTb4K2ugkrcIp4YJ6Av3I8l7glPKrwoRK381Mh0Wp+aqZ8McffwRvo72A8BQkqDp+6dKlbRDv3QPde0xpvbZSiNWB9Spbtqyt2u1ND0binV8ffvhhWyNB60jVVlonrCVNSvVG/GgwTUGio3bR/ucNGzYMzJo166ide6688kqb2i0E9LEfjFHRSh032h1Eu35ou1YF+yp66AbOtBOTzqNuiVLoNoWuHkbofQAQDgH+MWjdjNZpf/rppzb405YXCuIHDhwY2LZtW4qLpbYT0ui5Oq/emSxERmodE63LVRupXXQh1Ui32iYrWFMKpC/7RR1YFe1SoK99sr3HqnYK0Zp7FcZz+6nLtGnTbO0SjrPoyurnq+y0Cy+80AYeakN91972XgSMseM+a2UQqraPBl000aD/V9FDFc1zj1HBPVd5XbP3KmCpvweOuegJ99mqf6JioosXLw7er/6kjqWzzjoruFOIzpWqbaF+jBtAC92OkGMNQHoR4B/D5s2bbTEhnaALFy4caNSoka0S7U3h1n6kbs90FZ7SyRuRoYueqjlrdj30AqrOp5ZPKLBQuqi26tIewGon3f7TTz/RDEAMOrKaldI+ztpzW4VHvXQ+VBE977aTXnRao8ObgaQZ3cxmJCkTQ9W+laGm7Q8d2i1+tG2atr1TJqFSv1XMV9Xynd9++80W1XMp3QouEV3hjgel12vZn2bnHW1vp2VN7dq1sxmg3uNS2Z/KiqpUqRLNBSBLCPDTQVWeNdqqmShvJUxROpwq6isFzptChchQpzJnzpyBQYMGHXWfPnvN2GubLte50Yi3Kgtr6xl1ejLTCWWGA9mVUkZfe+21dB8H7vjSEiVlNek8Ge5YVadW92l7Oy+OtejSeVE7Gmhg+uyzzw689NJLwdnB9KT5usd4z6Nsfxd/qo6v2hZah+9dy+2Cey2t0Ba92inGi9Tu6FKw3r59++CWd9rqUwOfOjeq6r36Jpqhf/HFF4O1LNxEkju2HnjgAVsHSOdGBtEAZBYBfjovpkpx07ZP7sSt7Z1UhEjBvdLylaaKyPAOlOiCqc6ptvVxWzK5i57W91asWDH42Dlz5th1iZrRV1ZFerYl9F5AP/nkEwIOZFtagqSUUQXi3tnAjGzbpVkpDa6pHob3+NKSJW0/idiZPXu2neVV9kTbtm3tALWCC63T1nKyzAywEHBER0Y+V9dmixYtsksGdbz279/fDmirsKUmGxT4s81r7GkZks5/yuhU5ovonKg6JAULFrR9GU1MuO0LXdanBgVU70mYKAIQCQT4Gei81qhRw15MVRxKQb0CfqXHuT2dkXVam6sUe296/dy5c+0oeJs2bWw7OJq5UICvQF7/r5l+Fa9ZsmRJhtauad2iquirbd0gApAdaZBs3rx5mQ4+3CBbs2bNgstqQjFrH306/1133XU2oFCbuGBDxdl0ntNOBkgM3ll1d2yk9xjRQNoNN9xgA3q1qwZ0lBKu66jDoEzkuc803GerfkqtWrWC/x49erTtp6ivqIkhLw3SaN29BmVCz5ecJwFkBQF+Bqxdu9bO8qo4lNZUDR48OHgfJ+Os0Yj2RRddFGjQoIEtGKTZee/n+uyzzwaKFy8eePzxx4PPUSpxiRIlbCqiBgWUfupmplyRIRX/crOJjveirN0R9BpnnHGGDe5JYUR2ENoxDT1/aSmSsmAkI8eEapMMHz7cBhs9evQguIgT7Sai86iryC0TJ0605zplo2kbtfSsyff+nbhiYIgM7zGnJS5qqw8//DBTx6+ucdOnT7ezwd5lFQT30eX9fN3nrroH2l5SbeGOG6Xd65yopRNaQqEiwKpNogxQFWXWsQkAkUSAHwFsp5b11ODatWvbmQet0U2tIJCKdCmYd1sWalDgtNNOsxdO72CLKNAfMGCALVijzmwodai0zs1VhfZujwdkFypUGRps6HzWqVMne2y4YzEj5zitJ9Wae29BNkRHagPLSs/XNlxqPwUet956q23Pzp0721Rgl+GUnhljVWrX+Vm1aFwmACJn/PjxdocCXds6duxo12qnV2oBPAPV0ad19OqTKAPQSyn4SslX4V/XDhowdUG+CuxpoE0TFlp6yFahAKKBAD8KKVrIGM2c62Kn9YNeXbt2tdW5vevbypcvb7dtcin8eq4umCpeo1RUBe5K6ddIuQrw3XnnnUetadNMh1LmNHL+0UcfpdgRAfA7d87SDFL37t1tmqho3b32aVYQN3/+fBvUaf12VrKUOD9GR+huIhpU8QbfWrakfdFvu+02O3Cq1G3tMqKsJkdbGqpAaVptpqworR3WHursShJ5ykLT56sBNdWo8LYPEkfouU/HmgYxlTmoHZbcbgWOzp1a9hdu9wMdUy+//HIwQ8q9PudKAJFEgI+4U5CtkW2XpqZZpxdeeMFu86MZeO+FTwGIZqZUXV9bP4kullr3qzX4Cuq1xUxokSH3GgsWLLC1E7Sdl9JYuagiu9CsrtsXWxRQ6LhT5Xx1NpXSrZRRFcQTdVrz5ctnB9qEYyV+UvvsFSBqoFJfWuK0bNkye7sGNbt06WLbV/UQtN2oNwtDdWNUoE3ZS+486qW1whdffLFtfy2LcpkeiFwbajtJ1Ui4/PLLj9qdx4vlf/GVVvaStrnT8j4dJ1pG6M6dqgmk4F99jGNlVJABCiAaCPARd5o5VKqais1oxkkz8r169bJBh4J5FRHy0r9PPvnkwBtvvJGiPoJGxp966qnAc889F7ywhru4qgBRuE4t4FeafT3xxBMDTZs2TXH7HXfcEShQoIANBLVkRemiLqNFQZ3W0eu+L774wt52rM7osdb2I/3UDhqQTO3z1WCnBjSV1q0q3MpuUqq3q8atNtPsvWYTFeB7gxKl6iuLSVkcobQEyqUPa3cY0r2zLtxnqFR8tZcqrjv6vIcNG2avf2+99VaGfkZ6do1B5igzsGfPnnbLSS130THksi00IaHzpPotV1xxhd2GUlmC2rFCbZkWBk0BRAsBPhLCq6++amfgFWwo9e3vv/+2gX/v3r1tgOHW3Yv2j1VFWs1AKS0/NaFpbwQbyK50HOi4UvaKttQSDXIp80XHl7aWnDBhwlHHigL+Jk2a2MEBlwKeWsDnXbakzrB2s0Dm6LPWsiMF4Uql91LgrsrcWn6kL7ef9owZM2z2kpYwKdjTYIwGSRWsayBAO4yowvfpp59ut/JSwdjQ9nPrhJW1EVrxGxnnPVb0eWo/dLelrtqtVatWwaVkup7pOFTKftGiRW07aNeD1K5d3mubMt7U7j/++CPNFGGaENAuPjoWVR2/VKlS9nz4yCOPpHiciiRqhyXdP27cONuGOg+G/h0AQCwQ4CPuNPuui6c6Okp101pRR51bpYpqNNx1ZEXp/ApWNKLutpfxdngYGQcCKYIDpedrOyZ1Vt1WeCrwpQKVCia0O4grNuntkI4ZM8YGiQoOj8Wlfuv10lsRHOGpSKjOfd7q9X/++adNxdfnq8EZ77p4BfSfffZZIHfu3DaTyfnhhx/seVIz8lqCof/3vqY7V6rwnpZLUZck8oYOHWqvbxqY1iyvyyBT8KiMNGWqXXbZZbbNtcxC17prrrnGBoupbTfpBg201luzx1pKQWZa1uhY8A6m6DjR+UwDMdqpQMsqRIMpOgZD6wbpMcpEVFvrfvVb3HMAIJYI8BF3uqAq0FBamwrWqCPqnYmYNGmSXYuvi6rXXXfdZWf9XfowgLR9+eWXds2otkrzdmQVEKpDqhlGV2HdfVfQoDWluv/1119PtcioamGosJtm/JVaziBbxiiwU3HQcLxB3rfffmvbT+c+taf3c9ba+/vvv9/OAqtAqRuoUfCvrAClGjvhZhXJcoosfZ4PPfSQnc1Vu2hniXBFXV2NA29bKsDXEgtlszne+0eNGsVSiihRO+nY0kCojjP1QRwNymhApnHjxrbPErododpc9S+0ZEbnUwCIBwJ8JBRdPFWcRrNMqgzttsNTCqICDK2z93ZQXbojgKOFC+I0S6gUU60b9XZKGzRoYIu1hRswUxGwhg0b2lljBf7eDq22idJ2USrS99hjj9lZZmSMPlOl/HqXI7lAULO/WkevCtyiIF3rs5XtpAGB0DZev369HShVin9qxdsI5GNDbaGtXNPaijV0IEyzxlo+oaUU2uUiXHE+XR8VeKqGBkspss57DA0fPtwuFbz22msDTzzxhM0udMeoK1ypXQ9U58cNgjru31qf7804BIBYI8BHQlEwr9lCpRyqiJ7b4k7pxUpl1MVVacChmC0EwtMMvNLsXfquZoNvuukmO/v3+eefBx+nQmzafULpqFo247am1FaUCgi9+6c7SglXGmrNmjXt41hrmnn6/JWmrc/TO0ii9fYa9FRhPBc0KFhUsKGMiXCDnJrl17lSgSVVuqMv9PrjjgMtj1A7eI8zzQZr9wMV11OWhTumdFzqmqdil/ob0PKM0N0LtGRN92mtN0spIksDlTq+VERPX+pzuJ1GRowYYQdctGRCdS282xnquHVLnkKFzu4DQKwQ4CPhbN261c4IajZx1qxZwdvVGdJsiC62AI5NnUvNqquTquPHzdxqyzzN8NaoUSNFMKkAQ4/VensVjTrrrLNsYSnv9nouYNSWUC1btkxzdhIZo4Ai3HIkzeSq5ogymRxV8lbbqI1CP3/N/g8ZMsSmGiO6vINarhCldwb/7LPPtunaStHX8aYdYDSQpjRvfXcZG/369bNV9TVYpuUu4bIt9HoqgBhaeBFZo8EVffZqE33+ixYtsrfrc9aSJp0TtbWuJhc0CeEosK9ataqtY0IgDyCREOAjIWkNr4qBKdDwppmyFRAQXmodTAXnSr8/55xzUhxLb775pg02QrehVNrv8ccfb9dxa1u9tFKA9dqhs/rIfNspMA+3HEmDKir2pfokWhss+twVHKqdNBucWjsQeESPN/h+8cUXbRFLBYJ9+vQJZsxoyYvbiaJdu3aBQYMGBX777TebJaOlFCp86AYKVKTNWwAxXEYMWTKRp2NEu4tohr5MmTJ2qzt33GknHy0/uu+++1K0gSteqcKJ2sECABIJAT4Ski6gWmeqju6TTz6ZYtSctDcgdW4W0RvYudoW3bp1s9tPigKQe+65x85ahe7XrLRTzfJ7j0dEN0B07aXMiA4dOtg11lrn68ydO/eoQU/NPGodvirna+0w4pNxpgBehfQuuOCCQJ06dQJ58uSxS10UBLqBG62d9y6XUDq4Bt40iOaWoqW2xSuiT+2jwU4dd94lFRrE1LIJ9UXUptq6UBk1ypwpUaJEYOzYsTQPgIRDgI+EpfWHCki8afoAUqdAXQXZQlOFNbv7zDPP2NoWWtPtAg3tc9+iRQtbPE+ziuEKsBHcR387rlAqqKfUXwURXs8++6ytnaBlF47ShpVWrLoKFM+LHReAK9jTLK7qXLgMswkTJtiAUAHjtm3bjnqusmI0gK1977WUAomzk4V2LlBavndSQYM4qoivJRYaENV6fGVraN2+w4AMgESSQ/8xQILTn2mOHDni/TaAhHH48GGTK1euFP/+8MMPzc0332weeeQR07dv3xSP3759u7nhhhvMb7/9ZiZOnGhOP/10e/snn3xi7rzzTnPWWWeZqVOnxvz3yG6OHDlicubMaf9/0aJF5u2337b/X7VqVXPppZea0047zZ7vdPtdd91l7r//fvP8888Hn6/HrF692vTr18+0bdvWPnb9+vWmfPnycfudsuM1SP/+559/7HHTqlUrM3jwYNuuK1assMfT4sWLzcsvv2zbqECBAvY527ZtM8OGDTNLly61x92NN95ohg4dmuI4RnyNGDHCdOrUyR57L7300lHn0IMHD9ovd7yFnocBICHEe4QBAJAx3ln11atXBwvlKc30wQcftGmmrrq6d2Zp6tSpdj2pCuO5vdV1v7bO86bkI/o0G6/Ues0GqnioZnw1Y++2KdQ2oSqoptu9a3y1m4FqJ6h4m8u6cMi2iA5var03nV7LKbSlmnYtEBWm1L8bNWoUmDNnjt3S0LWLZoS1NaWyZdR2I0eODL4OmReJQ8spVIdEx512PEjruOJ4A5ComMEHgCT077//mptuusnMnj3bnHDCCebuu+827du3N3v27LGzhpotnDdvnilevHhw1njLli12xvHvv/82gwYNMrfeeqvJly9f8DXJlImNb775xradvjp27GiqV69uZs6caTp06GB2795t1q1bZ4oVK2Z+/PFHOxu8ceNGO0vvZpA166v2ffzxx2P0jrMvb8aFsiaUAdOtWzdTq1Yts3XrVnPRRReZatWq2ZncCRMmmAceeMAei6VLl7btpeN07ty5pkWLFub333+3x6Dau0iRIvY1mQFOPBs2bDDXXHONbd+FCxeaEiVKxPstAUCG/O+qBQBI+EDDBQT79++3weCvv/5qWrZsaXLnzm26du1qnnjiCRtY9OnTx6aTKthQ0O4CFN1WsWJFm57/3HPP2eDDi2UwsaH0bKVt33fffTbYk++++84GFBdffLHZuXOnve3ss8823bt3N3v37rWDAc4dd9wRDO5ZZRddOnaWLFliqlSpYt555x27PEIDLjpWTjrpJHPmmWeaTz/91Hz//fdm2rRp5qGHHjJlypSx9//555/m+uuvt8eljjWldderV88G9+54Jr078ZQtW9YeXxqQYRANQDLKHe83AABI3f8VQw0G6QoI/vrrLxs8vPDCCzbA132tW7c2w4cPt2u5b7/9dvPoo4+aXr162cBEQcaBAwfMkCFDbICv2XvN3B9//PF89FGe+fVmRej/1Q5//PGHqV+/vilZsqSdEdYs/rJly+xa++uuu86UKlUq+DoK+Lt06WIGDBhgZ4bPPffcFH8bDMpEl4I8ZbqceOKJ5umnnzZ16tSx2RWiz14DMKpdUbhwYXs8FS1a1N63Zs0aWxND6+1vu+02kydPnhSv645nJCZlZmgg7vLLL4/3WwGADCNFHwCSgAqyvfnmm+bkk0+2afeaNVSqt5sBXL58uS2ip1n6zz//3Jxxxhnm3nvvtcW/NKtfsGBBm+atoEMp/EJ6cHQcOnTIZlXIvn37gkXWHBVl0zIJDbz06NHDBo39+/c3devWNXnz5rWP+eqrr+wMvgLLVatW2dTuxo0bR+kdIzU6zq699lo7eKaieKFZEwrytWRCWTMa1NExqEEcZdd8++23dpBN98EfxzMAJAMCfABIQN7gW8GF1mIrtVcz9wowlOqrtb16nIIMzQh+9NFHtuq6AsNJkybZ5yvA//nnn23Fb83qK6UYsaHgbsGCBbbdlGGhL63nHTVqlJ0VVvCvFODOnTvbtG43q6uA8fXXX7dfDRo0SHVNOCIntWwILXNR5Xutyy5UqFCKz9/7/1OmTDFvvPGGmTVrlq2JofT93r1727X3tBsAIJYYkgSABKTgfPPmzXb2V8XylOar1Hul1msrPBXXGzdunLnyyivtjKFmfrUuf/78+XZA4KmnnrIpxffcc0+K19WAgIISUruzZseOHTaQC7eMQlkU7dq1C67dVtE8bU2ojAu1yQUXXGBT7zUI07x5c3PKKafY52lWf86cOeb99983lSpVsl+hCO4jS0G6joXQ48EF7xqQUQ0EbXvXsGHDFI/ztoVSuvW1a9cuO3Cj5RduiYb3bwMAgGgjwAeABA0gNUuvqvjaG137oruCbM8++6xdj629mps2bWrX/qrwnoJ/FflSYNm3b18bSDZp0iRF0EJRr6wbP368LZD35JNP2uJ33gBRadkK6BTka6BFAbxSfLUnuoqtaYDlgw8+sDPD2jP9qquusgMzGghQWvdnn31mKlSoYOsreNfiI7pZMtOnT7ez72pHHTPKdNFaexWk1HGj/dFVOV+z+G5ATbT7gY5RDdio3Y877ji7Dl+v416fwTQAQCwR4ANAAtLMoYL4F1980ablK9hwacSNGjWy96nAl1K9x4wZY4N7BZIKChV8Kk0/NL2bWcTIUAC+adMmO1vrne1VfQOtwdZATO3atc1ll10WfM5jjz1mZ/JHjhxpq7FryYVm8NWOCuoVNKrNlZ2h9fje10V0uOBebfDWW2/ZgTJtPyhKrf/iiy9shoxm5jUoo3ZVRowL7jVAo0GbcuXK2ce7211Az2AaACAeWIMPAAlKM4MKBBUUao2vgghX8EnbbmmN96uvvmrXbCuF/+DBg0dV6yZIjCw3K7t27Vq7I4H3M1ahQ2VXKBjUensVaNNjXZtp+zsFiSqAqMEApXEr80K7IrhaCiqiKBT2is1a+5deesnWQdDSCR1fGrzR2nnVP9AAjQbPNMCmbBptY6jBAA2caau80aNH290PVPxS7Q0AQCIgwAeABPbLL7+YNm3a2NnBH374wQbwLshUcNGtWzebJqy0/GrVqqV4LtuoRY/b8k47FdSsWdMulxDtha7b1D5jx44NDgK4JRQPP/ywDQg1++vW8IdiUCb6KflaV58/f367M4EGUzSA5raN1ICLAnwF/lpaoaBeNS9UsFJBvWgnCw0GvPvuuxSuBAAkFHL/ACCBacZXM4qaHda6bXFp2zVq1DC33HKLXferSvmhWPsbWQq8vZ/t7t27bcCn3QtULV8009u1a1c7+KJUfBVcEwX3LshUar+2vUsNafnRaTcF99oqUjP1EyZMsFkwqpWgQRgF9xq00WMVvF933XW2fkK/fv3Mf//9ZwvsafcDDbJprf7HH39sFi5caIN7tal36zwAAOKJAB8AEpz24dZae80gTp482QaXmnUU7aWuWf5rrrkm3m/Tt1zgFxp4awZea7dVHE9bpCkFX0XYlNqtPdMHDhxoK+K7IH/lypW2Sr7W51etWjVOv0324q1gr10KNEim3SkUxKtdNDim9luzZk2KAbHKlSvbAF9p+Zq9d1RoTyn6bvs7CukBABINRfYAIMEpPf+RRx4xP/74o50d1syhCrK5FPwTTzwxOIPIrH3kuSr5mr195ZVXbHuUL1/epuVfffXVwa3ttLZeBQ617Z22NNSyCVdDQcUPFVh+//33Nq07tFYCokPtpuUR999/vy1UqTZU+r2Cd9VF0ABZjx497DaGak+1i6tlUadOHfsa2qrS+3peFNIDACQa1uADQJLQ7L1mhxU0qrgeYrdme9iwYTYALFOmjE3ZVoq90rOVVaH0be1soHXd2vVA2xNqfb6q42vGWIMvWsetdHBlYmggANETmm2hgRUVL9Qgi4J2BfPex2pGXrsiaGtCZcuICuup7bTt4dy5c03p0qVpMgBAUiDAB4AkCjjHjRtnLr/88uCWXIguBYdaP69ZXs34Kug76aSTzHfffWdn6ZXirUrrWjKhwP7888+3qflly5a1gwAq0jZgwADz5Zdf2vtFj2V/9MjTQIo3Jd/dpll37WOvZRMaoPnpp59sBowbwFF9C7elpLbBUwaGiiBqpl9trPZjph4AkCwI8AEgCbGNWvTt2LHDrpfXzLwCdqXWn3322cH7VTFfQXvbtm1tsT3tX68vpYNr33tRsb077rjD/PHHH+brr7+2a7tZRhFd4ZZSaM19p06dzPDhw+19yqjwHkcasNGWeePHj7d1FPSldtRWlAAAJBMCfAAAwtDsr2ZvBw0aZFPup0+fbtPyvTPF3bt3tzO9KsSmwYBLLrnErr3X8zp06BBcWqHZ40svvdQMHTrUFCxYkM87xksphgwZYurXr28DflXMf/vtt8155513VDq/Zu7V1nqMZvJDXx8AgERHFX0AAMLQTLuCxZYtW9oK7KtXrw7e7mhNvQJCzc7L888/bwoXLhwMDqVJkyamZ8+eNk1c26shshR8aymF9q+fOXOmueGGG8wXX3xhd5cYO3asrYTfuXNn236ffPKJvV0DLdu2bbPBvQJ4R7sbaEBA7afgXwM5BPcAgGRCFX0AAFKhYF1p2vPnzze9e/c2zZo1s9XY3ayv0rvz589vi7IpIFRwqDXeus37GkrjV3X21q1b81lHYSlFvXr1UiylcNsQ6vMuUqSIXUqh9lOAr6wLFUNUxkW3bt1sAO/W6nuFbosIAEAy4OoFAEAaqlevbh599FGzatUquy77999/t7fv2bPHTJ061Qb2CjBdQKjgXrd5ae29AkytBUdkqWCe1tRrzf2GDRvsoIsoaFc7aEs8bV/4+eef260mVS1fbdqnTx8zadIk+1jqIgAA/IIAHwCAY9D2aUrHf++998yVV15pq+nfe++95sknnzR169Y1jRs3TnlxZfY3IZdSTJkyxf5bs/ya8dcuCAAA+AkBPgAAx6DZ4YceeshceOGFNgVf67d1m7ZRmzFjhilZsqSdMUZ8l1JUqVLFZkoo0JfQpRRap69ZfQ3KKLW/UaNGNBkAwFcI8AEASIdy5crZfdJPOOEEu25b1fOV5u22WyPNO7mWUmg3g9ClFAAAJDuK7AEAkE7aBk+z9v369TMNGza01dndXupIjKUUc+bMsVXyFy9ebFq0aGF27txpJk6cyFIKAEC2kCNATiEAAOmmivna437BggVm4cKFplKlSmGrsCM+1q9fb7p06WK3LuzYsaMpVKiQzbpw2Ra0FQDAzwjwAQDIoHnz5tnZfKXta6YYiUUV8zt16mRatWplC+o5ZFsAAPyOnEIAADLo3HPPNY8//jip+QmKpRQAgOyKGXwAADKBVO/ExlIKAEB2RIAPAAB8iaUUAIDshhR9AADgSyylAABkN8zgAwAA32IpBQAgO8kZ7zcAAAAQLWxfCADITgjwAQAAAADwAQJ8AAAAAAB8gAAfAAAAAAAfIMAHAAAAAMAHCPABAAAAAPABAnwAAAAAAHyAAB8AAAAAAB8gwAcAAAAAwAcI8AEAAAAA8AECfAAAAAAAfIAAHwAAAAAAHyDABwAAAADABwjwAQAAAAAwye//AcAFqod//31UAAAAAElFTkSuQmCC" alt="Turns comparison"></div>
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<h2>Score Difference Heatmap (Lush - Bash)</h2>
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<div class="chart"><img 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mtHsPik9Nw6didLqGsRAMA1yCgBAIvQiwK9UPjuu+/sKfb6l1PH9qyJXWrjLrYLlrjYlhPoUhPHvyjHd/NEK01burzjBZknOC6tuHbtWqLOR3ztZOM7547HWLZsWaKOcae2tkmVnN9FT4zZFhRLbCDGk5566imTIaNZE+3atTMBCb341wt8XZKmbXCTkgmRUhq4clwmY7u5KytCAxZKAyYLFixwuu/VV181QRKdvylTpphlNPre0bFoUE5vtuyOmK2jdSmiLi/UJTnaIljbWmvrZF1G+MEHH5iAibYMdxfHbBNPB2UBwKoIlACABWn6uI1tCYLSJQnJWY5gy0qImbLuSP8Sev78eaf9kyK5Y3MXXT5ky+aILwjhLrZz4c7zoRdktr92e+OcJ2e+PTFmW20SdyxDsY39TlkDly5duuNz6DKNHj16yNy5c+XYsWNy8OBBs2REA08bN250aTArIRqsjCs4deTIEbcczzH7yHHZkWZrLFq0yHz/ySefSOfOnZ3eQ7Ygm2bZ3YkuYdR6OWvWrDH1UFavXi0NGzY0j7VlnbiD4+9aaquNAwC+ikAJAFiQY/FOxyUE9erVswcBklK4UAshKr2AuNNFk/6l11YPIalstTT04iQ1pJ/bCoxqRoZteZOn6Pmz1ZzRZVTuoH9Fr1WrlluPcSe238VVq1Yler49Meby5cubr3///bfLnztHjhzm6z///BPvPtu3b0/Sc2q203vvvWeW3tjOp69yDNRqMWsbzRqx/Q7FVyxalyUm5XNFg1paF+aHH34wn6EaANLPJnewBX20bpStsCwAwL0IlACAD9F/UP/5558J7qcdRWzuuece+/dt2rSRrFmzmoCGpqrHTEGPT/v27c1XrYugxVZj0ufTArNKi8jqLam6dOliLk70r762Djrx0aKNoaGh4g761+O+ffvK999/b35+/vnnpXTp0uJJehFou/DV2gyaOXAnyc1+sGUeLV261NzccYz4PPfccyYIpVlICc23J8esGQQqZiFkV7B1c9KOTXHV4fnxxx/ttUdistVmSajOhb+/7/7Tz9YJyjF4q/QzzbaUK66sD/18GjRoULzPe6dzq0ES2/I1d51bW3DM9rsHAHA/3/2/JQBY0N69e+Xuu+82nR+mT5/ulOKu6efaEUPTzrXgodK/vtevX9+pAOzo0aPN97om/4knnnBqlamFL/UvqI8//rhTrQMtCmvr7NC2bVtzwWIrTqjBG73fdoFne/6k0r+Ma9tj23No61utEeB4saNj1YCMBi5itvhMCQ0YadHbiRMnmsCS7fzpX5Q/+ugj8YaRI0eamgoaONIWu9rhSFs7O/4V/euvvzZz2KFDh2QdQzNltK6Nvn59nhEjRjgVktWLeW2v27NnTylZsqS4ks6hLmewzbfWn9A5sNHfP9vvqCfHbFtmpUt7ElNIVy+ydY7udLNlMuh7Ry+2NTikc2bLkNBaGhrc1NcTX7cdbfOsj9c5dyyWrAFDrVminwdKPxt8jdaLGT9+vHlPqOLFi5tCso4ZdLaMNG2NrgEnLdqq9DPk4YcfNll0jlkoMZf0vPnmm6ajkmPQRJc1aV0Y/VzUedMCsu4MlGiNGQCAh0QBAHzG8uXLNQXE6ZY+ffqonDlzRvn5+Tltv+eee6JOnDgR5/OMHDkyyt/f375vcHCweQ7HbSEhIU6POX78eFSFChWcjps9e3b7z/rYjz76KM7jFStWzOwzderUO76+W7duRQ0ePNjptejYcuXKFRUQEOD0+jZt2pSkc6fHtj02d+7cUfny5TM3fd0xnztLlixRI0aMiIqIiIjzuQ4fPmzfV7+PqVGjRua+t99+O97xJOac7Nu3L6ps2bJO51jHmylTJqfxPvjgg0kan6NLly5FPfLII07PlzVrVjO3jvOQLl26ZL2GTp06mX30a0w3b96M6tmzp9OxM2fOHJUjRw77sbNly+bSMSdGlSpVzOMnTpwY7z62156Y27hx4+yPGzJkiNN9+vp0nPp9y5Yto9566y3zvf4OxXUeHc+T4/tPb/Xr148KDQ1N9PmP+d7Q1+QN8b03be9PxzktWrRo1O+//x7rOXbu3On0vsiQIYN5H9t+D6ZPnx7v76vjOdT3mP7+BQUF2bfp8R3nMObj1q5dG+9rS+iz4MCBA/bP0zNnziTr/AEAko6MEgDwIfoXTf2ru2Y56DIazS7R1HAtPKhdGsqUKWP+6qyFHrU9p63LQ0z611NNUXdcVqLLWfTx+pduLYyo6eyOtDWw/lVWsy3q1KljUv31L63aeeOZZ56RXbt2Se/evVP0+jR9XjNGtNvMSy+9ZF6fpr1rgUut76B1LTQLQVuS2v6CnBz6V35bhw79a78WUKxevbp07drV/GVf67doqr6t+Ka36OvXc/Hll19K06ZNTatTzbTQazSdN/0d0CwY7XqSXDrPWu9Dl7FoJ5WiRYuav6rr3Oqc63G1BoZjUWBX0bnV4ptaP0L/cq/H1kwlfX1aK0TnQzMoPD1mLZaqZs2aJe6ofaPZQfoe0gwHXepVtWpVkxWi77v4uhRptpW2/tasE22Xq7+bmk2ihZObNGliOr1onaD4sibSCsf3pt70912L+Gqmj372aFaddqGJSd+/ulxKP//0faIZJdq2V3/Wzwv9jIqPLifUz8QGDRqYzzPN8FH6HtMMPf0s7dOnj1ter+13TOeVQq4A4Dl+Gi3x4PEAAADSNF3iVLhwYfNVl5Y5dlsBXEX/ia7B6UOHDsn69eupUQIAHkRGCQAAQBJoJoK23NULWS2mC7iDZoJpkEQzBSnkCgCeRUYJAABAEumSrLJly5rlH1rUU5dkAK6iS4MqVaok+/fvN4Wp9XsAgOd4d3E1AABAGhQUFGQ6yWjdD23PTKAErqSdmrTGUIkSJQiSAIAXkFECAAAAAAAQjRolAAAAAAAA0QiUAAAAAAAARCNQAgAAAAAAEI1ACQAAAAAAQDQCJQAAAAAAANEIlAAAAAAAAERLZ/sGsIr8+fNLWFiYFC1a1NtDAQAAACzv2LFjkilTJvn3338tfy6QOhAogeVokCT8xjUJjTjt7aEAAADgDi5cv8n5sYCr16/KtZvXvT0MwI5ACSxHM0k0SPLlqi7eHgoAAADu4JNfTnF+LGB97yXeHgLghBolAAAAAAAA0QiUAAAAAAAARCNQAgAAAAAAEI1ACQAAAAAAQDQCJQAAAAAAANEIlAAAAAAAAEQjUAIAAAAAABCNQAkAAAAAAEA0AiUAAAAAAADRCJQAAAAAAABEI1ACAAAAAAAQjUAJAAAAAABANAIlAAAAAAAA0QiUAAAAAAAARCNQAgAAAAAAEI1ACQAAAAAAQDQCJQAAAAAAANEIlAAAAAAAAEQjUAIAAAAAABCNQAkAAAAAAEA0AiUAAAAAAADR0tm+AWAtF86EyrdTdsmB3afkrz3/yrWwCBk1p51UrlvU20ODGzDf1sJ8WwvzbS3Mt7VUyV1eGhepJ+VzlZVcQTnlYvgl+fXsPpn5x0IJCb/k7eEBPouMEsCijh+6IAu+2CHnT4dK8XJ5vD0cuBnzbS3Mt7Uw39bCfFvLcxXaSaXcd8vWk7tk4p4ZsuH4NmlQqLZ8fN8IyZ4hm7eHB/gsMkrgcTNmzJB3331XDh06JJkyZZKLFy9K48aNzX3r1q1jRlykf7u5kq9wVuk79uE47y9dKb/M391LsmQPlo1LD8gfL33HuU/DmG9rYb6thfm2FubbWt6rP1BOXz0n43+eGOf9k3+fLfvO/ylREmXftuvMbzKqwVvySMkmJrMEgOsRKIFH7d+/X5577jl56KGH5I033pCMGTMyA16SMXN6zr2FMN/WwnxbC/NtLcy3tew9fyDObZdvXJEiWQp6ZUyAFRAogUdpxsitW7fko48+ktKlS9u3r1y5kpkAAAAAEhAUkEGCA4LkcvgVzhXgJgRK4FFnzpwxX7Nnz+60PX16shsAAACAhDxe6iEJDAiUjSe2c7IAN6GYqwecOHFCunTpIvny5ZMMGTJIhQoVZMqUKU5ZFn5+fjJ//nxTu6Nw4cISFBQkDzzwgBw8eNC+X69evSRz5sxy9erVWMfo0KGD5M+fXyIjI5M0rq5du0rBggXNuEqUKCEvvvii3Lhxw77P33//LW3atJGcOXOaZTJ16tSRH374wel5Ejv+4sWLy9tvv22+z5Mnj3nM0KFDzc9ao8RWp8Tm6NGj8thjj5k6Jnnz5pVXX31VVqxYYR5HLRNnNyMi5dKFq063yJuREnEj9vZbt/5b44q0ifm2FubbWphva2G+rSXAL0Cyps/sdNNtgf7pYm33E784n6NCrnLS4a6Wpqjrb+f2efw1AFZBRombnT592gQX9OJeAx0aIFi2bJkJUFy+fFn69Olj3/f9998Xf39/6devn1y6dElGjx4tTz31lGzffjta3K5dO/n0009NoEKDFzYaOFmyZImp/REQEJCocZ08eVJq1aplCql2795d7rrrLhM4WbhwoXk+zfDQsderV8/83Lt3b8mVK5d89dVXJnih+z3xxBNOz5nQ+MePHy/Tp0+Xb775Rj7//HMT9KlcuXKc4wsLC5P7779fTp06Ja+88ooJAs2ePVvWrl2b6HOvAam4aBHZfEUziy/Zt/OEDOgwL/Ydu07K+iX7nTZN29hd8hWhSnpaxnxbC/NtLcy3tTDf1lI+Vxl5r/6gOO9rVLiu089dVr4qZ66ec9pWOHMBGVT7FTl6+bhM2P0/t44VsDoCJW42aNAgk+WxZ88eE2hQL7zwgskA0WyKHj162Pe9fv267N69274MJUeOHCZI8Pvvv0vFihWlfv36UqhQIZk3b55ToEQDJxpY0EBKYr355pvy77//miBGjRo17NuHDRsmUVFR9sCHBks2btxojq2ef/55E9x47bXX5PHHHzeBkcSOv2XLluZ+DZS0bt1acufOHe/4vvzyS5PN8u2335rjKD1X1apVS/RrtJIS5fPKyJn//U6oSSPWSY48maR1j5pO2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" alt="Score heatmap"></div>
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<h2>Per-Task Detail</h2>
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<div class="task-detail">
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<h3>env_config <span class="cat">[a]</span>
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<span class="pass">bash=PASS</span>
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<span class="pass">lush=PASS</span>
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</h3>
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<table class="scores">
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<thead><tr><th>Metric</th><th>Bash</th><th>Lush</th><th>Diff</th></tr></thead>
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<tbody><tr><td>Readability</td><td>3</td><td>4</td><td class="pos">+1</td></tr><tr><td>Expressiveness</td><td>3</td><td>4</td><td class="pos">+1</td></tr><tr><td>Conciseness</td><td>2</td><td>3</td><td class="pos">+1</td></tr><tr><td>Error handling</td><td>2</td><td>3</td><td class="pos">+1</td></tr><tr><td>Overall preference</td><td>3</td><td>4</td><td class="pos">+1</td></tr><tr><td>Learning curve</td><td>2</td><td>4</td><td class="pos">+2</td></tr></tbody>
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</table>
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<div class="observations"><p><strong>bash:</strong> Bash's regex matching and array handling work well for this task, but the syntax is arcane (${!key} for indirect variable access, BASH_REMATCH array). The printf-pipe-sort pattern for array sorting is a common bash idiom but not intuitive. Error handling would require additional validation that bash doesn't make easy. Good for system administration contexts where bash is expected, but the syntax barriers make it less accessible than higher-level languages.</p>
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<p><strong>lush:</strong> Lush combines Lua's familiarity with shell-like conveniences effectively. The backtick syntax for command execution and built-in functions like lush.envset feel natural. However, the solution required more manual string processing than ideal - having to parse env output and match keys could be more elegant with better built-in environment variable introspection.</p>
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</div>
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</div>
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<div class="task-detail">
|
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<h3>file_organizer <span class="cat">[a]</span>
|
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<span class="fail">bash=FAIL</span>
|
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<span class="pass">lush=PASS</span>
|
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</h3>
|
|
<table class="scores">
|
|
<thead><tr><th>Metric</th><th>Bash</th><th>Lush</th><th>Diff</th></tr></thead>
|
|
<tbody><tr><td>Readability</td><td>3</td><td>4</td><td class="pos">+1</td></tr><tr><td>Expressiveness</td><td>2</td><td>5</td><td class="pos">+3</td></tr><tr><td>Conciseness</td><td>2</td><td>4</td><td class="pos">+2</td></tr><tr><td>Error handling</td><td>2</td><td>4</td><td class="pos">+2</td></tr><tr><td>Overall preference</td><td>2</td><td>4</td><td class="pos">+2</td></tr><tr><td>Learning curve</td><td>2</td><td>4</td><td class="pos">+2</td></tr></tbody>
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</table>
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|
<div class="observations"><p><strong>bash:</strong> Bash required several workarounds and awkward constructs: dynamic variable creation with eval (security risk), manual string building for output sorting, complex parameter expansion syntax, and careful escaping. While bash excels at file operations, the lack of proper data structures made this solution verbose and harder to maintain compared to higher-level languages.</p>
|
|
<p><strong>lush:</strong> Lush's shell command interpolation with backticks and the seamless integration with Lua made this file organization task very natural to implement. The ability to execute shell commands inline while maintaining full Lua data structures for processing was particularly elegant for this type of system administration task.</p>
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</div>
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</div>
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<div class="task-detail">
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<h3>fizzbuzz <span class="cat">[a]</span>
|
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<span class="pass">bash=PASS</span>
|
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<span class="pass">lush=PASS</span>
|
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</h3>
|
|
<table class="scores">
|
|
<thead><tr><th>Metric</th><th>Bash</th><th>Lush</th><th>Diff</th></tr></thead>
|
|
<tbody><tr><td>Readability</td><td>4</td><td>5</td><td class="pos">+1</td></tr><tr><td>Expressiveness</td><td>4</td><td>5</td><td class="pos">+1</td></tr><tr><td>Conciseness</td><td>4</td><td>5</td><td class="pos">+1</td></tr><tr><td>Error handling</td><td>2</td><td>4</td><td class="pos">+2</td></tr><tr><td>Overall preference</td><td>3</td><td>4</td><td class="pos">+1</td></tr><tr><td>Learning curve</td><td>3</td><td>5</td><td class="pos">+2</td></tr></tbody>
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</table>
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|
<div class="observations"><p><strong>bash:</strong> Bash works well for FizzBuzz with clean arithmetic operations and conditionals. The C-style for loop syntax is familiar, but bash's quirky syntax like double parentheses for arithmetic might confuse newcomers. Error handling for invalid input would require additional validation that bash doesn't make particularly elegant.</p>
|
|
<p><strong>lush:</strong> Lush's Lua-like syntax made the FizzBuzz implementation very straightforward and clean. The language offers excellent readability with familiar control structures and operators. The modulo operator, conditional statements, and print function work exactly as expected, requiring no special syntax or workarounds.</p>
|
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</div>
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</div>
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|
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<div class="task-detail">
|
|
<h3>multi_file_search <span class="cat">[a]</span>
|
|
<span class="pass">bash=PASS</span>
|
|
<span class="pass">lush=PASS</span>
|
|
</h3>
|
|
<table class="scores">
|
|
<thead><tr><th>Metric</th><th>Bash</th><th>Lush</th><th>Diff</th></tr></thead>
|
|
<tbody><tr><td>Readability</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Expressiveness</td><td>5</td><td>4</td><td class="neg">-1</td></tr><tr><td>Conciseness</td><td>5</td><td>3</td><td class="neg">-2</td></tr><tr><td>Error handling</td><td>3</td><td>4</td><td class="pos">+1</td></tr><tr><td>Overall preference</td><td>5</td><td>4</td><td class="neg">-1</td></tr><tr><td>Learning curve</td><td>3</td><td>3</td><td class="">0</td></tr></tbody>
|
|
</table>
|
|
<div class="observations"><p><strong>bash:</strong> Bash excels at text processing and file operations like this. The pipeline approach with grep, sed, and sort feels very natural. However, the syntax can be cryptic for newcomers (especially the sort flags), and proper error handling would require additional complexity that might hurt readability.</p>
|
|
<p><strong>lush:</strong> Lush combines Lua's simplicity with shell command integration nicely. The backtick syntax for command execution is intuitive, and the Lua-based file I/O and string processing felt natural. However, the file globbing required some manual parsing and filtering that felt a bit verbose compared to dedicated shell scripting. Overall, it strikes a good balance between scripting convenience and programming language features.</p>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="task-detail">
|
|
<h3>pipeline_transform <span class="cat">[a]</span>
|
|
<span class="pass">bash=PASS</span>
|
|
<span class="pass">lush=PASS</span>
|
|
</h3>
|
|
<table class="scores">
|
|
<thead><tr><th>Metric</th><th>Bash</th><th>Lush</th><th>Diff</th></tr></thead>
|
|
<tbody><tr><td>Readability</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Expressiveness</td><td>5</td><td>4</td><td class="neg">-1</td></tr><tr><td>Conciseness</td><td>5</td><td>3</td><td class="neg">-2</td></tr><tr><td>Error handling</td><td>3</td><td>4</td><td class="pos">+1</td></tr><tr><td>Overall preference</td><td>5</td><td>4</td><td class="neg">-1</td></tr><tr><td>Learning curve</td><td>3</td><td>4</td><td class="pos">+1</td></tr></tbody>
|
|
</table>
|
|
<div class="observations"><p><strong>bash:</strong> Bash excels at pipeline transformations like this - the pipe operator creates a natural flow that mirrors the logical steps. The regex in sed might be intimidating to newcomers, but the overall structure is intuitive. Error handling is implicit (pipes fail if any command fails) but lacks granular control.</p>
|
|
<p><strong>lush:</strong> Lush felt very much like Lua, which made the text processing task straightforward. The pattern matching and string manipulation functions were intuitive. However, the manual deduplication logic required more verbose code than some other languages might need. Overall, it provided good control and clarity for this pipeline transformation task.</p>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="task-detail">
|
|
<h3>process_exit_codes <span class="cat">[a]</span>
|
|
<span class="pass">bash=PASS</span>
|
|
<span class="pass">lush=PASS</span>
|
|
</h3>
|
|
<table class="scores">
|
|
<thead><tr><th>Metric</th><th>Bash</th><th>Lush</th><th>Diff</th></tr></thead>
|
|
<tbody><tr><td>Readability</td><td>4</td><td>5</td><td class="pos">+1</td></tr><tr><td>Expressiveness</td><td>4</td><td>5</td><td class="pos">+1</td></tr><tr><td>Conciseness</td><td>5</td><td>5</td><td class="">0</td></tr><tr><td>Error handling</td><td>3</td><td>3</td><td class="">0</td></tr><tr><td>Overall preference</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Learning curve</td><td>3</td><td>4</td><td class="pos">+1</td></tr></tbody>
|
|
</table>
|
|
<div class="observations"><p><strong>bash:</strong> Bash is well-suited for this task since it naturally handles command execution and exit codes. The main challenges are bash-specific syntax quirks like the read loop condition and variable arithmetic. The use of eval introduces potential security concerns but is necessary for dynamic command execution. Overall, bash feels like the right tool for this system administration type task.</p>
|
|
<p><strong>lush:</strong> Lush combines the simplicity of shell scripting with Lua's readability. The command execution syntax `${command}` is intuitive and the ability to access exit codes via .code is clean. The Lua-based control structures and string handling make it much more readable than pure bash while maintaining the shell's natural command execution feel.</p>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="task-detail">
|
|
<h3>reverse_string <span class="cat">[a]</span>
|
|
<span class="pass">bash=PASS</span>
|
|
<span class="pass">lush=PASS</span>
|
|
</h3>
|
|
<table class="scores">
|
|
<thead><tr><th>Metric</th><th>Bash</th><th>Lush</th><th>Diff</th></tr></thead>
|
|
<tbody><tr><td>Readability</td><td>5</td><td>5</td><td class="">0</td></tr><tr><td>Expressiveness</td><td>5</td><td>5</td><td class="">0</td></tr><tr><td>Conciseness</td><td>5</td><td>5</td><td class="">0</td></tr><tr><td>Error handling</td><td>3</td><td>3</td><td class="">0</td></tr><tr><td>Overall preference</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Learning curve</td><td>4</td><td>4</td><td class="">0</td></tr></tbody>
|
|
</table>
|
|
<div class="observations"><p><strong>bash:</strong> Bash excels at text manipulation tasks like this due to its built-in utilities like 'rev'. The solution is extremely concise and leverages the shell's natural pipeline approach. However, error handling would require additional checks for edge cases like empty input or command failures.</p>
|
|
<p><strong>lush:</strong> Lush appears to be Lua-based with very familiar syntax and built-in string manipulation functions. The solution was extremely straightforward - just read input and use the built-in reverse function. The language feels mature and well-designed for simple scripting tasks like this.</p>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="task-detail">
|
|
<h3>two_sum <span class="cat">[a]</span>
|
|
<span class="pass">bash=PASS</span>
|
|
<span class="pass">lush=PASS</span>
|
|
</h3>
|
|
<table class="scores">
|
|
<thead><tr><th>Metric</th><th>Bash</th><th>Lush</th><th>Diff</th></tr></thead>
|
|
<tbody><tr><td>Readability</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Expressiveness</td><td>3</td><td>4</td><td class="pos">+1</td></tr><tr><td>Conciseness</td><td>4</td><td>3</td><td class="neg">-1</td></tr><tr><td>Error handling</td><td>2</td><td>2</td><td class="">0</td></tr><tr><td>Overall preference</td><td>2</td><td>3</td><td class="pos">+1</td></tr><tr><td>Learning curve</td><td>3</td><td>4</td><td class="pos">+1</td></tr></tbody>
|
|
</table>
|
|
<div class="observations"><p><strong>bash:</strong> Bash works for simple algorithmic problems but lacks data structures and type safety that would make this more robust. The C-style for loop syntax feels awkward compared to bash's typical idioms, and array handling is somewhat clunky. Better suited for text processing and system tasks than algorithmic problems.</p>
|
|
<p><strong>lush:</strong> Lua's syntax is clean and straightforward for this algorithmic problem. The hash table implementation using tables works well, and string parsing with gmatch is reasonably elegant. However, the input parsing required some manual work, and there's limited built-in error handling. The 1-based indexing required careful adjustment for 0-based output. Overall functional but not particularly specialized for competitive programming tasks.</p>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="task-detail">
|
|
<h3>csv_transform <span class="cat">[b]</span>
|
|
<span class="pass">bash=PASS</span>
|
|
<span class="pass">lush=PASS</span>
|
|
</h3>
|
|
<table class="scores">
|
|
<thead><tr><th>Metric</th><th>Bash</th><th>Lush</th><th>Diff</th></tr></thead>
|
|
<tbody><tr><td>Readability</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Expressiveness</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Conciseness</td><td>5</td><td>5</td><td class="">0</td></tr><tr><td>Error handling</td><td>2</td><td>3</td><td class="pos">+1</td></tr><tr><td>Overall preference</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Learning curve</td><td>3</td><td>4</td><td class="pos">+1</td></tr></tbody>
|
|
</table>
|
|
<div class="observations"><p><strong>bash:</strong> Bash excels at simple text processing tasks like this CSV transformation. The IFS mechanism and read command make parsing straightforward, and the solution is very concise. However, bash's quirky syntax (like the `|| [[ -n "$name" ]]` construct for handling files without trailing newlines) can be confusing for newcomers. For more complex CSV handling with edge cases, a dedicated tool or language might be better.</p>
|
|
<p><strong>lush:</strong> Lush's Lua-based syntax made the CSV parsing task quite natural. The pattern matching with match() worked well for simple CSV parsing, and the io functions provided clean file handling. The solution was concise and readable, though proper CSV parsing (handling quoted fields, escapes) would require more complex regex or a dedicated library.</p>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="task-detail">
|
|
<h3>env_path_builder <span class="cat">[b]</span>
|
|
<span class="pass">bash=PASS</span>
|
|
<span class="pass">lush=PASS</span>
|
|
</h3>
|
|
<table class="scores">
|
|
<thead><tr><th>Metric</th><th>Bash</th><th>Lush</th><th>Diff</th></tr></thead>
|
|
<tbody><tr><td>Readability</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Expressiveness</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Conciseness</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Error handling</td><td>3</td><td>3</td><td class="">0</td></tr><tr><td>Overall preference</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Learning curve</td><td>3</td><td>3</td><td class="">0</td></tr></tbody>
|
|
</table>
|
|
<div class="observations"><p><strong>bash:</strong> Bash is well-suited for PATH manipulation tasks since it's designed for system administration. The string manipulation features work naturally for this problem, though the syntax quirks (like the pattern matching in conditionals) might confuse newcomers. The solution handles edge cases like empty input and duplicate detection efficiently.</p>
|
|
<p><strong>lush:</strong> Lush's combination of Lua scripting with shell-like environment variable access ($MYPATH) made this task quite natural. The string manipulation and pattern matching capabilities were well-suited for path building. However, the mix of Lua and shell syntax might confuse developers not familiar with both paradigms.</p>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="task-detail">
|
|
<h3>log_parser <span class="cat">[b]</span>
|
|
<span class="pass">bash=PASS</span>
|
|
<span class="pass">lush=PASS</span>
|
|
</h3>
|
|
<table class="scores">
|
|
<thead><tr><th>Metric</th><th>Bash</th><th>Lush</th><th>Diff</th></tr></thead>
|
|
<tbody><tr><td>Readability</td><td>3</td><td>5</td><td class="pos">+2</td></tr><tr><td>Expressiveness</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Conciseness</td><td>5</td><td>4</td><td class="neg">-1</td></tr><tr><td>Error handling</td><td>2</td><td>3</td><td class="pos">+1</td></tr><tr><td>Overall preference</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Learning curve</td><td>2</td><td>4</td><td class="pos">+2</td></tr></tbody>
|
|
</table>
|
|
<div class="observations"><p><strong>bash:</strong> Bash excels at text processing pipelines like this, making it very concise. However, the syntax for robust input handling and parameter expansion can be cryptic. The pipeline approach is elegant but debugging can be challenging since errors may not propagate clearly through the chain.</p>
|
|
<p><strong>lush:</strong> Lua's syntax is clean and readable for text processing tasks. The pattern matching with string.match() worked well for parsing log lines. However, the manual table sorting approach feels slightly verbose compared to languages with built-in sorted collections. The 'or 0' idiom for default values is elegant. Overall, Lua provides a good balance of simplicity and power for this type of scripting task.</p>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="task-detail">
|
|
<h3>pipeline_word_freq <span class="cat">[b]</span>
|
|
<span class="pass">bash=PASS</span>
|
|
<span class="pass">lush=PASS</span>
|
|
</h3>
|
|
<table class="scores">
|
|
<thead><tr><th>Metric</th><th>Bash</th><th>Lush</th><th>Diff</th></tr></thead>
|
|
<tbody><tr><td>Readability</td><td>2</td><td>4</td><td class="pos">+2</td></tr><tr><td>Expressiveness</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Conciseness</td><td>5</td><td>4</td><td class="neg">-1</td></tr><tr><td>Error handling</td><td>3</td><td>4</td><td class="pos">+1</td></tr><tr><td>Overall preference</td><td>4</td><td>4</td><td class="">0</td></tr><tr><td>Learning curve</td><td>2</td><td>4</td><td class="pos">+2</td></tr></tbody>
|
|
</table>
|
|
<div class="observations"><p><strong>bash:</strong> Bash excels at text processing pipelines with its built-in tools like tr, grep, sort, and uniq. The solution is extremely concise but requires knowledge of Unix command-line tools and their options. The pipe-based approach is natural for this problem, but the syntax can be cryptic with options like 'tr -cs' and 'sort -k1,1rn'. The final while loop with the complex condition is necessary but makes the code less readable.</p>
|
|
<p><strong>lush:</strong> Lush feels very much like Lua with clean syntax for text processing. The gmatch function for pattern matching and the flexible table structure made the word frequency counting natural. The sorting with custom comparison functions worked smoothly. Overall, it struck a good balance between simplicity and power for this text processing task.</p>
|
|
</div>
|
|
</div>
|
|
|
|
</body>
|
|
</html> |