From 36ae18e88054846e980b9616604b1b8e59aa735a Mon Sep 17 00:00:00 2001 From: Lars Bogner Date: Wed, 22 Jul 2026 14:19:30 +0200 Subject: [PATCH] Add plotstyle: reusable KIT-branded matplotlib styling toolkit Introduces a new plotstyle package providing consistent, presentation/thesis-ready matplotlib figures: a KIT corporate-design color palette (categorical, sequential, diverging, status), a theme applied via use() (KIT-black bottom spine only, left/bottom ticks, horizontal gridlines, left-aligned titles, LaTeX text in Latin Modern Sans), new_figure() with size presets and figure-level title/params subtitles, a same-size colorbar() helper, style_legend() and panel_label() building blocks, and savefig(). Ships as an optional "plotting" extra so core gallery installs stay lightweight, with a full test suite and a runnable example notebook. Co-Authored-By: Claude Sonnet 5 --- .gitignore | 1 + examples/plotstyle_showcase.ipynb | 536 ++++++++ plotstyle/__init__.py | 30 + plotstyle/annotations.py | 97 ++ plotstyle/assets/plotstyle.mplstyle | 80 ++ plotstyle/colors.py | 102 ++ plotstyle/figures.py | 127 ++ plotstyle/style.py | 64 + pyproject.toml | 7 +- tests/test_plotstyle.py | 310 +++++ uv.lock | 1815 ++++++++++++++++++++++++++- 11 files changed, 3164 insertions(+), 5 deletions(-) create mode 100644 examples/plotstyle_showcase.ipynb create mode 100644 plotstyle/__init__.py create mode 100644 plotstyle/annotations.py create mode 100644 plotstyle/assets/plotstyle.mplstyle create mode 100644 plotstyle/colors.py create mode 100644 plotstyle/figures.py create mode 100644 plotstyle/style.py create mode 100644 tests/test_plotstyle.py diff --git a/.gitignore b/.gitignore index f97be12..dd01b73 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ .vscode *.sif *.ipynb +!examples/*.ipynb backups .pytest_cache .venv diff --git a/examples/plotstyle_showcase.ipynb b/examples/plotstyle_showcase.ipynb new file mode 100644 index 0000000..2ebdeff --- /dev/null +++ b/examples/plotstyle_showcase.ipynb @@ -0,0 +1,536 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4e9fb3ed", + "metadata": {}, + "source": [ + "# `plotstyle` showcase\n", + "\n", + "A tour of the reusable matplotlib building blocks in `plotstyle` — a KIT\n", + "(Karlsruhe Institute of Technology) corporate-design color palette plus a\n", + "handful of small helpers (`new_figure`, `colorbar`, `savefig`,\n", + "`style_legend`, `panel_label`) so every figure produced for a thesis chapter\n", + "or a talk slide looks consistent.\n", + "\n", + "Install the extra once: `pip install -e \".[plotting]\"` (or `uv sync --extra plotting`).\n", + "This notebook requires a working local LaTeX (`latex` + `dvipng`) install —\n", + "`plotstyle` always renders text through real TeX, no mathtext fallback.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9c81491e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LaTeX text rendering enabled: True\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import plotstyle as ps\n", + "\n", + "ps.use()\n", + "print(\"LaTeX text rendering enabled:\", plt.rcParams[\"text.usetex\"])\n" + ] + }, + { + "cell_type": "markdown", + "id": "9f004413", + "metadata": {}, + "source": [ + "`use()` also sets up the plot chrome applied by every figure below: only the\n", + "bottom spine is kept (left/top/right removed), tick marks stay on the left\n", + "and bottom axes, the x-axis gets shorter minor ticks between the major ones\n", + "(the y-axis doesn't — its major gridlines already mark position), gridlines\n", + "are light-grey and horizontal only, major tick labels are darker than minor\n", + "ones, and titles (figure and axes alike) are left-aligned rather than\n", + "centered — all visible in the line plot in section 2. All text, including\n", + "tick numerals, renders through real LaTeX in Latin Modern Sans (via the\n", + "`sfmath` package and `fontenc`'s T1 encoding, so math-mode numbers stay sans\n", + "instead of falling back to a serif math font, and plain characters like \"|\"\n", + "render correctly).\n", + "\n", + "**Titling convention used throughout this notebook:** prefer\n", + "`new_figure(title=..., params=...)` (section 3) for a single axes; reserve\n", + "plain `ax.set_title()` for per-panel titles in multi-axes figures (section 8)\n", + "where there's no single title that could cover every panel.\n" + ] + }, + { + "cell_type": "markdown", + "id": "b20c3f51", + "metadata": {}, + "source": [ + "## 1. The categorical palette\n", + "\n", + "Nine fixed-order, colorblind-safe-where-possible hues via\n", + "`plotstyle.colors.CATEGORICAL` / `get_color(i)` — KIT green, orange, KIT\n", + "blue, brown, cyan, purple, pea green, red, and yellow (in that order, chosen\n", + "to keep adjacent hues distinguishable). The order never changes; a 10th\n", + "series should fold into \"Other\" or move to a facet rather than cycling back\n", + "to slot 0. KIT yellow reads poorly on a white surface — pair it with a\n", + "direct label rather than relying on the fill alone.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5146dcca", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['#009682', '#DF9B1B', '#4664AA', '#A78230', '#23A1E0', '#A3107C', '#8CB63C', '#A22223', '#FCE500']\n" + ] + }, + { + "data": { + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = ps.new_figure(\"thesis-wide\", title=\"Categorical palette (fixed order)\")\n", + "ax.grid(False)\n", + "for i, hex_color in enumerate(ps.colors.CATEGORICAL):\n", + " ax.bar(i, 1, color=hex_color, width=0.8)\n", + "ax.set_xticks(range(len(ps.colors.CATEGORICAL)))\n", + "ax.set_xticklabels([f\"slot {i}\" for i in range(len(ps.colors.CATEGORICAL))])\n", + "ax.set_yticks([])\n", + "ax.set_ylim(0, 1.1)\n", + "ax.spines[\"left\"].set_visible(False)\n", + "plt.show()\n", + "\n", + "print(list(ps.colors.CATEGORICAL))\n" + ] + }, + { + "cell_type": "markdown", + "id": "f524b9a2", + "metadata": {}, + "source": [ + "## 2. Line plots: color, and optionally linestyle\n", + "\n", + "By default `axes.prop_cycle` only varies color — every line solid — so the\n", + "brand colors read cleanly without extra visual noise.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a4edc295", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABxEAAAS0CAYAAACvyPY3AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjEsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvctoD+AAAAAlwSFlzAAAuIwAALiMBeKU/dgABAABJREFUeJzs3XdYFNf7NvB7Wbp0EVCxIXbsvfduYo+aGGuMxhI1icYaSzRqvibR2Es09sTee68oGFTECoJSpHfp7Hn/+MG8rOwsSxPU+3Nde7k7p8wzM8uA++w5RyGEECAiIiIiIiIiIiIiIiIiyqBX1AEQERERERERERERERERUfHCJCIRERERERERERERERERqWESkYiIiIiIiIiIiIiIiIjUMIlIRERERERERERERERERGqYRCQiIiIiIiIiIiIiIiIiNUwiEhEREREREREREREREZEaJhGJiIiIiIiIiIiIiIiISA2TiERERERERERERERERESkhklEIiIiIiIiIiIiIiIiIlLDJCIRERERERERERERERERqWESkYiIiIiIiIiIiIiIiIjUMIlIRERERERERERERERERGr0izqAj52/vz9OnDiBa9euITAwEGFhYQgLC0NqaipKlCiBSpUqwdnZGe3atcMXX3wBfX1eMiIiIiIiIiIiIiIiIipcCiGEKMgOz549ix9//BEvX75EampqnvowMjKCpaUlLC0tUbZsWTRu3BhNmjRBo0aNYGtrW5DhFpmgoCBMnz4d//77L9LS0nRqc/DgQfTt27fQYytONm3ahOXLlyM4OBgqlQr6+vqoUKECFixYgN69exd1ePSBS0pKwpgxY3D58mVER0cDAMzMzNCgQQOsW7cO5cuXL+oQ6QP05MkTjBs3Dl5eXkhKSgIA2NnZoXv37li5ciWUSmVRh0jF1KtXrzBmzBjcu3cPCQkJAICSJUuiQ4cOWLNmDUxMTIo6RCIiIiIiIiIieo8UeBLRyckJvr6+BdmlmmbNmmHEiBH48ssvYWpqWmj7KUy3bt1C3759ERISkqt2f//9N4YPH15ocRU30dHRsLOz05iMLlmyJMLDw4skLvp4bNmyBaNHj9ZYNn78eKxZs0Zj2Z07dzB58mQ8f/4cycnJAAB9fX2UK1cOc+bMwWeffVaocdP77bPPPsO+ffs0lh09ehSffPLJO4+J3g/jx4/HunXrNJZt2bIFI0eO1Fj277//4ueff0ZAQADS09MBAMbGxqhatSpWrFiBxo0bF2rcRERERERERERUPBXomojp6enw8/MryC6zcXV1xbhx41C5cmWsXbsWKpWqUPdX0F6/fo3evXvnOoH4MfL395cdzRoREYHY2Nh3HhN9XHx8fGTLtH1Z4vvvv4erqysiIiIQHx+P+Ph4REdHw9PTE1999VWeR2nTxyGv7zuivLx30tLSMGbMGHh5eSEmJka6Z4WHh+PmzZuYOnVqIUZMRERERERERETFWYEmEdPS0lDAAxtlBQcHY8KECejcuTNev379TvZZECZMmICwsDCNZb1795amIAsICMCpU6cwcOBAqVyhULzDSIteSkqK1nImYqiwaXsPanv/afsgPy4uTvYeQIR8vO+I8vLeCQ0NRVxcnGy7Fy9eFEhsRERERERERET0/tF/lzszNjZGo0aNNJYJIZCcnIz4+HgEBgZq/UArq4sXL6JRo0a4cuUKnJ2dCzjighUaGopjx45pLGvRogUOHDggrXVVtmxZlC1bFt26dcP27dsxdepUVK5c+R1HTER5kVOih4kgIioueL8iIiIiIiIiIiI57zSJ6OjoiGvXrulUNygoCCdPnsSePXtw+fJlrdOWBgUFoWPHjrh27RrKly9fgBEXrP379yMtLU1jWf/+/aUE4tuGDRuGL7/88qMbiUj0vrKystK6Zqe1tfU7jYeISI6lpaXWct6viIiIiIiIiIg+XgU6nWlBKlOmDL766itcuHABr169Qq9evbTWf/XqFcaOHfvO4suLp0+fypbZ29trbcsEYu4lJCTg888/h6OjI8zNzWFubg5nZ2d8//3372zaXfo4jRw5UrZswIABsLCweKfxfKxOnDiBunXrwtraGubm5ihVqhRatGiBO3fuFHVoRMWGlZUV+vbtK1s+evTodxoPEREREREREREVH+90JGJelS1bFseOHcOff/6J7777Dunp6RrrnT59GkeOHEHv3r3feYy6CAoKki3T138vLsV7Zfv27dizZ4/atvj4ePz+++/49NNP0bZt2yKLjT5ss2bNQp8+ffD8+XMkJSUBGT/j5cqVQ+PGjYs6vI/G2LFjERgYKL2Oj49HeHg4vv/+e51HxRN9DA4cOAA3Nzf4+/tLMyYYGRmhatWqqFmzZlGHR0REREREREREReS9ylx9++23iI2Nxdy5c2XrTJkyBV26dIGJick7jU0XoaGhRR3CR+XFixeyZX5+fkwiUqGqWbMmP3wvQsnJyWoJxKz8/PzeeTxExZlCoUCTJk3QpEmTog6FiIiIiIiIiIiKkWI7namcWbNmoVOnTrLlfn5+OHjw4DuNSVfa1nWkgpeamipbJrc2JRF9GLT9jPPnn4iIiIiIiIiIiChn710SUU9PD/Pnz9daZ+/eve8sHiIiIiIiIiIiIiIiIqIPzXuXRASAli1bom7durLlZ86cQWxs7DuNiYiIiIiIiIiIiIiIiOhD8V6tiZhV3759cf/+fY1lycnJOHv2LAYMGJDrfkNCQnD69GlcvXoVL1++xKtXrxAREQEbGxvY2trC2dkZHTt2RJcuXVCmTJkCOJL8CwgIwLVr13Dz5k28fPkSERERCA8PR0REBNLS0lCiRAnY2tqiRo0aaNiwIfr164fKlSsXddhUyIQQ2L59O65du4aYmBgAgI2NDdq3b4/Bgwdnq+/j44N///0X58+fh7+/PyIiImBiYgJbW1vUq1cP3bp1Q//+/WFoaJjnmLy8vHDlyhVcvXoVr169QmRkJCIiIqBUKmFjYwMbGxtUqVIFbdu2Rdu2bVGpUqV8nYP3jRAC27Ztw7Vr16QvQpQsWRIdOnTAZ599lq3+8+fPsXfvXly4cEG6ZqampihZsiTq1auHHj16oF+/fjAwMMhzTMnJybhy5QrOnDmD58+f49WrVwgICICpqSlsbW1RunRptGnTBh07dkSjRo3ytA9PT0/s27cP7u7ueP78OSIjI5GamgoTExOUKFECZcuWRf369VGvXj20bduW969cSkpKwuXLl3H27Fl4e3vj5cuXCAwMRIkSJVCyZEmUKVMGbdu2RadOnVC/fv2iDjcbf39/XLp0Ca6urrh//z6ioqIQFRWFxMRElChRAmZmZqhcuTJq1aqFvn37okWLFkUdsk6ioqJw8eJFuLq6wt3dHeHh4YiKikJ8fLz03i9fvjxq1KiB7t27o1u3btDXL15/tiUkJGDNmjXw9PREYmIiFAoFHBwcMGjQILRs2TJbfVdXVxw8eBDXrl3D69evERMTAwsLCzg4OKBhw4YYOHAg2rdvn6dYAgICcODAAdy+fRuPHj1CaGgoEhISYGRkBHt7e9jZ2cHOzg4tWrRAjx494OTkpNbey8sLu3fvxq1bt+Dj44P4+HgcOHAA7dq1y/P5ISIiIiIiIiJ674kClJSUJADIPpydnQtsX6dOndK6r3nz5uWqvwsXLojWrVsLhUKhtd/Mh76+vvjqq6/Eq1evtPb7+PFj0aNHD+Hs7CyUSqVsf0ZGRsLMzEyYmZkJa2tr0blzZ/HixQvZfiMjI8XatWtFy5YtdYr37UeHDh3Ew4cPdTo3ly5dEg0bNhTW1tZSjE5OTmLSpEkiPT1dpz7mzp0rKleuLMzNzYWZmZmwsrISdevWFUeOHJFt4+7urvUYwsPD1eqnpKSIqVOnirJlywozMzNhYGCg0/nOfFhaWormzZuL//77T+uxLF++XFhaWmZrb21tLXbt2qXT+XgXDh48KHv8J06ckOpFR0eL0aNHa31/Zj6cnJyEm5tbruJQqVTi4MGDokGDBrl+n3bt2lVcvXq1EM6Obn744QfZ2Dp16iTbLjExUQwdOlQ4OjpK7w8HBwfRo0cP8fLlS9l2e/fuld3fmTNnpHpRUVFi5MiROl0zZ2dncffu3Vwfu7+/v/j6669FiRIldL5eTZs2FefOndN5H8+fPxedO3fO9fuiSZMmYufOndnuP3fu3BHNmjUTlpaWWuNWKBTZfn7NzMyEo6Oj+OGHH0RaWprU5y+//CIqV64szMzMhIWFhahatar46quvREhISK7O582bN0WbNm2Eg4OD9H745JNPRGxsbLa6Li4usrEvX75c5336+fmJUaNGCRMTE53PbcuWLcWlS5dydWyF5ebNm6J///46vc/ffs8vXLhQ+Pv7Z+vzwoULonbt2tI9vHz58qJr167C3d09V7GFhoaKQYMGiYoVK0r3/0aNGuX4+0NkvO/Hjh0rTE1Nc3VcdnZ2YsqUKeLBgweyfbdr1062/YwZM2TbvXz5UnTp0kXY2dlJPw8VKlQQI0eOFAkJCbLtpk+frnFfhoaG4vXr12rH3LFjR52Os23btiIoKCjH85jJ19dXfPrppzr//Zb5+PTTT4W7u7s4cOCAaN++vcY6s2fP1jkOIiIiIiIiIqIP0XubRHz9+rXWfQ0cOFCnfkJDQ0WXLl1y/SF25sPKykrrB659+/bNc9+jRo3S2OfZs2dz9cG+3MPAwEBs3749x3NUq1Yt2T5OnTqVY/u7d+/Ktre1tZVtl9sk4smTJ/N9TpCRYNXmyy+/lG07c+bMHM/Hu/Ljjz/KxrlgwQIhMhJFTk5OuX7P+/j46BRDeHi47IezuXmMHj1aJCcnF/IZyy6vScS//vpLtt348eNl233//fey7RYvXixExof9FStWzNX5s7GxEb6+vjods0qlEgsXLhRGRkZ5vl6zZs0SKpVK637u3bsnLC0t8/W++PPPP9X6bNGiRYHcAy5evCj1WbZsWY11mjdvrpZs1CYiIkJYWFho7CdrQj9TfpOIKpVKzJkzRxgaGub5HGTeI4pCamqqmDVrVq6TQm8/jI2NxT///KPW9+jRozXWNTc3F35+fjrH2KlTJ439TJo0SWu7LVu25Dp5+PZDoVCIn376SWP/eU0ifvPNN7LttmzZItuubdu2su0yv/hw+fJlYW5unqtjrFu3rtbkZabDhw/n+3xqe3Tr1i3HGIiIiIiIiIiIPmTv5ZqIyJjiT5tHjx7l2MejR4/QtGlTnD17Ns9xREdHo2vXrrh69arG8tTU1Dz3Ldf2woULePPmjWy7ChUqoFmzZujZsydq1qwJpVIp2/+oUaNw+vRprXE8f/5ctuzFixda2yJjmkw54eHhBbZ+ZUpKSoH0k9M1E0Lkqexd0/YeSUxMRFRUFLp166bTNcwqOjoaM2bMyLGer68vmjZtikuXLuWqf03++usvdOjQQesxFSfa3vO+vr6yZTlds8jISHTt2hV+fn65iicyMhKzZs3KsV5SUhIGDx6Mn376CcnJybnaR1a//PILvvvuO9nyhIQE9OjRQ5pm921KpRLGxsY57kehUKi9Lox7QI8ePTTWuXXrFv766y+d+tuzZ4/G+5xCodC6vm9eJCQkoF+/fli0aFG+zse8efMwc+bMAo1NFyqVCv369cMvv/yS7/tpUlISbt26pbZN7nrGxcVh6tSpOvXr7e2N8+fPayzTNh3s7NmzMWrUKCQkJOi0HzlCCFy5ciVffbytsO5Znp6e6N27N+Li4nIVz/3797Fu3Tqtda5fv47Bgwfn+3wCQIcOHbBo0SJMnToVXbt2haOjI5DDsRMRERERERERfQze2ySigYGB1g+Zg4ODtbaPiIhA165dZT8g6ty5M44fP46goCAkJibCx8cHK1euRKlSpbLVTUlJwfDhwzV+SLxgwQK0atUK9vb2Oh0XAOjr66NZs2ayH/rb2dll21a1alWsWLECfn5+8PPzw61bt3D8+HF4eXnB29sbtWrV0thXWloavv32W6hUKtl4tH0QrUuSNKcPsvOTaM2qW7duGDlyJMzNzfPUXk9PDy4uLli6dGmBxFPcjRgxAl5eXgCA0aNH4/Tp0wgLC8Pr16+xf/9+je/1TIcOHUJUVJRseWpqKgYNGiT7wbSzszNWr16NR48eIS4uDhEREfDw8MC8efNga2ursc2NGzcwYcKEXB9nUcjvz4wmQgh8+eWXePLkCRQKBcaMGaN2zfbu3St77gBg//79OSbsx48fj71792osq1ChAlatWoXnz58jMTERwcHBOHr0KFq1aqWx/sqVK3HmzBmNZX///TeCgoKybZ85cyZ8fHyQmpqKxMREREdH4+HDh1ixYgWaN2+erf7b97X//e9/qFGjBvT08varzcLCAl9//bXammzfffddtmRlpmXLliE9PT3Hfrdv365xe5s2bVC2bNk8xSrnq6++wuHDhzWWOTk5Ye3atfD29kZiYiJev36NQ4cOoVmzZhrr//rrrwWerMrJrFmzcOzYMdlyBwcHTJkyBatXr8auXbuwZs0a/PTTT2jfvr3G9Vpr1qyp9rpPnz5wdnbW2Pfhw4d1+gLSjh07NG43MjJCv379NJb9888/+OWXX2T7NDMzw1dffYU//vgDu3fvxvr167Fo0SJ8+umnGn+nvX1c+VUY96yEhAR8+umniImJgbGxMebNmyet9+rr64vffvtN63qt2pL0QgiMGTMGSUlJGsu//PJLPHjwAG/evEFCQgK8vLwwduxY2f7u37+PyZMn4/fff8fp06fh7++P0NBQnDp1KpdHTURERERERET0gSnIYY3vcjpTIYSws7OT3ZeRkZHWtr169ZJtO3fuXNl2L1++lN3vokWLtO6zVatWsvucM2eOzse9a9cuqZ2pqan43//+l+NUj15eXlqvzaFDh2Tbamu3YsWKHOPduXOn1j7enpY0U26nM31bt27dZNtu3rw5x7jlDB06NE/Txb1rEydOlI2zfv36AoAoVaqU2lp7WWV9n2l6nD17Vnbfc+bMkW3Xvn17ERcXJ9vWz89PODs7y7bft29fgZwfXeR1OtO8ths3blyO18ze3l6cP39eY/tt27ZpvWZZp+l8m7af02bNmomIiAiN7dLS0sSAAQM0tnNxcdHYpnHjxtnqjh49Wja2TMePH1ebyjUsLEy2rrb7h4ODQ477yurTTz+V7WvPnj1a2z579ky27fr16zW2yet0pps3b5Zt17p1axEVFaWxXUpKivjkk080tmvUqFGuzlV+5HTPnzBhgkhJSZFt//r1azF27Fi1aVBv3ryZrd7atWtl9zF8+PAc46xcubLGtn369NFYPyoqSnY6WwCiS5custdGCCHi4+PFggUL1Na2XLt2rca6eZ3ONK/tGjVqlOM9q27duuLx48ca20+aNEm2vUKhENHR0RrbHT9+XLbd0KFDZeMdP368bLt169bJtiMiIiIiIiIi+li9tyMRkTFqRE5ycrLst+cvX76M48ePayzr1q0bFi5cKNtv+fLlsWTJEo1lGzZs0GlUSn6VLl0aAGBra4sLFy7ghx9+0DgCI6uaNWuiYcOGsuVyI4bow+Th4QEAWL9+Pbp06aKxjty0f5nkRhnGx8fjzz//1FhWokQJ7Ny5E2ZmZrL9VqhQAVu2bJEt1zaa50OWec02btyIjh07aqyT12uWlpYmO0Wtubk59u3bBxsbG43lSqUSGzZsgJGRUbayhw8f4tq1a9m2e3t7Z9smN6Ixq549e+Lu3bvo3LkzatasqXXkZUGaNm2abNnvv/+ute2uXbs0btfX18eAAQPyHVum5ORkzJ49W2OZlZUV9u7dCysrK43lBgYG2LRpE/T19bOVubu7w83NrcDi1EbudysAfPLJJ1i9erXWkWsODg5Yv349jh07BmtrayiVStSuXTtbvREjRsiOtN6zZ4/WmQxcXV1lf46GDBmicfvq1atlRwE7Ozvj6NGjstcGGffNn376Ca6urtIoynr16snWLy48PDxgbGyMAwcOoHr16hrraLtnCSFkZ4vYs2ePbLvhw4fLlg0bNky27MSJE7JlREREREREREQfq/c6iajpQ+us5Kbn0vah7/fff5/jfvv06aPxw1Z/f3/cvXs3x/b51bBhQwwbNgw3b96UnYZOE7kp3ADkeo21j5mlpWWeyoqbTz/9VHbqPWQkHrR9YC+3nt22bdtkPzDv1asXypQpk2NsrVu3Ro0aNTSWeXh4aExMfQz69euHTz/9VLa8ZMmSWqfylLtme/fuRUBAgMayzz77TFofTI6NjY1sYvPQoUNqr+Pi4jROhZs5tW5ObGxscPr06Xdyr83UqlUrjdOpAoCbmxtu3Lgh2/aff/7RuL1Lly45ru2bG7t27UJISIjGsi+++AIODg5a29vb26NNmzYay96+hoXB399f637mzZunc189e/bEnTt3sGvXLo1fWDAxMcHEiRM1tk1JScGaNWtk+5a7nmZmZujVq1e27SqVCqtWrZLtb8aMGTn+LZOpTp06uHPnDtatW4emTZvq1Kao/fTTT6hcubJsuabp2bOSu2dpW6vQxcVFtkxuancAePDggdZYiIiIiIiIiIg+Ru91EjEuLk62TKlUokSJEtm2R0dHy37bvEyZMujQoUOO+7WxsZH9sPVdjNiwsLDAtm3bUKVKlVy107bGHZOIulu6dClu3bqF69evqz1cXV0xderUog5PZ999912OdUxNTWXL5Nai0pYIyM0H39oS5EeOHNG5nw9JTtdMoVDAxMREtlzumu3cuVO2zZdffqlTbH369NG4Xdd74rp167SOLspKT09P65q4hSEvoxHv37+Pp0+faiyTG7WWV8XhGubHpUuXZNfmtbCwQP369XPVn7OzMwYNGiRbPmHCBNn727p165CYmJhtu0qlwr59+zS26d27t8b+Hj58iNDQUNk42rVrJ1umibW1NcaNG5fndT/fJQMDA9lkbSZtv2Og5Z718uVL2Taa/vbTZX85raVNRERERERERPQxKv6fQmkh9w11aJnq9ObNm7IfVPbs2VPnD+Y0TZEGAJ6enjq1LwraPnSPj49/p7G8z8zMzNCsWTO0bNlS7dG0aVOdR5QUNaVSKTuyKiuFQpGrflUqldaEQ26SiNrqurq65iquD4GhoaFO5y8v1+zmzZsayywsLGS/MPE2Xe+J5ubmGqdufPPmDT7//HNUrlwZ33//Pfbv3681UfCu9e7dG1WrVtVYdvjwYY1fxDhw4IDG+sbGxujdu3eBxZaamorbt29rLCtVqpTOP3dF+Xvt6tWrsmUtW7Ys8KRZyZIlMWrUKI1lERERGpOyrq6uCAoK0thGLims7bhKly6tdZTe+65evXowNzfXWie396tMkZGRBd6nQqHA/y0DTUREREREREREmbLPyfmeEEJoHYkoN62ktmnnnJycdN6/3NRwmqbpKyqpqakIDQ1FTEwM4uLiEBgYWNQhUTFha2ub4zqaefHkyRPZqUyRsd6hrrTVvXv3LtLS0jROK/yhKlWqVKEcr5eXl+wXMipVqqTzB/Jy98TY2Fikp6dDqVRK27p37y476vDFixdqI/sqVKiAdu3aoX379hg4cGCOI5cKi56eHr7//nuMHTs2W5lKpcL69euxdOlSte2HDx/W2FevXr1yTK7kxr1795CQkKCxrCB+r2lL2BQUbdPTapueMj++++47rFu3TuNaxmvWrMGYMWPUtsldTxsbG9m1ZYviuIoLXaauzitra2uNo0UBIDExUXbdXbmfEwCoWLFinhOQRERERERE9GESQsDT0xPnz5/Hy5cvERYWBhMTE1hZWaFmzZpo3Lgx6tSpU9RhEhWq9/YT+Li4ONkRhdDy4ZW/v79sm4oVK+q8f3t7e43biyqJGBAQgBs3buD27dtwd3eHr68vgoKCtJ4j+njJfcCaXzklqnMzUlNb3aSkJERGRua4ntaHpLCuWWHfE4UQiImJgY2NjbRtypQp2L9/P1JTU3Ps9+XLl9i2bRu2bduG6dOn4+eff8bXX3+tc1wFadiwYZg7d67G6Sk3b96M+fPnSyO+fX19ZUfwFfRUpoV9DVNTU5GQkFCoCdyIiAjZMm3TU+ZHpUqV0L9/f+zduzdb2f3793H9+nW0atVK2iY3jXL//v1l148tiuMqLgrrngUA1apVkx0V6u3tLTt9+7Nnz2T7rFSpUoHFR0RERERERO+/Xbt2YdGiRXjy5InWes7OzhgxYgQmTZokOzsi0fvsvU0i5jTNXYMGDTRu15bkGzlyZLaRB3LS0tI0bn+X32KPi4vDv//+i507d+Lq1auchot0Vljv0+joaK3luVnHLqeEY1RU1EeVRCysa6btnnjixIkCGTH3duxNmjTBihUrMGHChFz1ExoairFjx+LKlSvYsmXLO58+2NjYGN9++y3mzJmTrSwiIgL//PMPRowYAWgZtWZhYYEePXoUaFzaruGBAwd0voZF+TtE2zEUZvJy2rRpGpOIALB69Wopifj48WPZBJS2pHBRHVdxUJh/D7Vp0waXLl3SWLZv3z7Z6brlrjUAdO3atcDiIyIiIiIiovdXTEwMhg0bhqNHj+pU39vbG3PmzMHvv/8Od3f3Yvsl1fDwcJw5c0Z6Xa9ePdSqVatIY6L3w3ubRLx165bW8oYNG2rcri3JkZSUlO+4qlWrlu8+dHHw4EFMmjRJ9pv41tbWaN26Ndq0aYOmTZvC0dERy5cvx5o1a95JfPRxyimJmHVKy5zkNHVncZo6+H2m7ZqlpaXle73UUqVKwdraOtv28ePHw9PTE+vXr891n7t370ZycjL279+fr9jy4ptvvsGSJUvw5s2bbGXbtm2Tkohyo9b69u2bq2S6Lgr7GpYrV65QE17p6elaYyzMZHGjRo3Qvn17jQmpw4cPIyYmBpaWlrLXs3Tp0mjbtq1s/9quzfuyhm5xNHz4cCxatEjjVLR//PEHzM3NMXXqVGn91ZiYGKxcuRK//vqrxv6srKzw1VdfFXrcREREREREVLwlJSWhV69euH79urStUaNGGDFiBGrUqIEqVaogLi4Or1+/xp07d3D48GHcuXMHyPgMoDgPePDx8cHQoUOl10uXLmUSkXTy3iYR5b6BnqlRo0Yat2ubPszExCRXSY6s9PX1UadOHcyYMSNP7XUlhMDw4cOxY8cOjeXly5fHggULMHTo0GxJGBMTk0KNjSinn5+UlBSd34fJycn52hfpRts90cDAIM+JDoVCAQcHh2zrBGa1bt06uLi4YPr06VrXKtPkwIED2Lp1K0aOHJmn+PLKxsYGo0ePxp9//pmt7MqVK/D394eFhYXaH5tZFfRUpijka1i6dGksX748H9HlTKlUwsDAQHZ6W7mR/wVl2rRpGv+myExUjx49GidOnNDYdtCgQdDT05PtW9v9rrCP60NWqVIlDBs2DFu3btVYvnDhQixatEj6AkNUVJTW6d2XLVtWqNOvEhERERER0fthzpw50mc6BgYG2LhxI4YPH55ttp2aNWuiY8eOmDlzJq5evYrvv/8e8fHxH/zSJfRxei+TiCEhITh48KBsebVq1eDi4qKxrGTJkrLtDhw4gO7duxdIjIVl8eLFsgnEFi1a4MiRI7C1tX3ncREhYwSsNjklBnNTN+sae5R32u6JQ4YMwbZt2wp1/xMmTMCQIUOwadMmHDp0CG5ubjqv5bpo0SKMGDHinU4jDQDfffcd1q5dmy0JJITA7t27UbNmTY0jpEqVKoWOHTsWeDzaruHIkSOxYcOGAt9nQbO2tta41iRyed/Ii+7du6N27doa17DcuXMnhgwZgtu3b2tsm1NSWNs9sbCP60O3atUq3Lt3Dx4eHhrLVSqV1jUpMy1evLjI1lklIiIiIiKi4iMgIEDtS+N//PGHNOOUNm3atMGtW7dw7ty5Qo6QqGjIf32+GFuxYgVSUlJky7VNSaUtwRYYGJjv2ArT7du38dNPP2ksMzc3x969e4tlApFrNX48ckoi5ma0WU51mUQsGMXhnmhjY4Mff/wRrq6uiIyMxPnz57F06VL0799f63V+8eIFbt68+U5izKpChQoYOHCgxrJz587h2rVrGssGDhyY4zS9eVEcrmF+abt3xMbGFvr+f/jhB43bb9y4gYsXL2ocJenk5IQmTZpo7beoj+tDVqJECfTq1SvP7WvWrImLFy9i1qxZBRoXERERERERvZ/27t0r/f+/UqVKmDBhgs5t9fX1i/3gpCpVqmD//v3So0+fPkUdEr0n3ruRiBcuXMD//vc/2XIDAwMMGzZMtrx+/fqyZf7+/vmOrzBt375dNiH35ZdfomzZslrbF1UyLywsrEj2S++ek5OT1vKHDx+iYsWKOvWlaVRQJisrK2mtK8ofFxcX6Ovra5xasSjuiZaWlujYsaM0Yi8lJQX//PMPxo8fr3EdwkePHqFly5bvPM5p06Zhz5492bbfvn1bdmTk4MGDCyWWunXrQk9PT+MIzuL+ey1TrVq18PTpU41l9+/fL/T9DxkyBLNnz0ZAQIDa9tTUVGzZskVjG12up7a1Bd7FcX3IQkND8fvvv0NPTw+XLl3CpUuXcPv2bfj4+CAwMFDtfqFQKGBjY4Pq1aujUaNGGDBgAFq2bPnORzETERERERFR8ZX1/+ktWrQo0lgKg42NDfr371/UYdB76L0aiejh4YHBgwdrnCYu03fffad1AVNtU8mdOnUq3zEWpkePHsmW1a1bN8f2ukzrVRi8vb2LZL/a5GcaOZVKhYCAALx8+VLtERQUVKAxvo/Kly+PChUqyJZnLjSsCzc3N9my1q1b88PfAmJubi47murZs2fw8fEp8H3ev38fly9f1qmuoaEhhg0bhn/++UdjeV6SZAUxjWT9+vXRqVOnbNvj4+M1HpujoyNatWqV7/1qYmNjg3r16mks8/T0zJYYK460nRtt94KCYmBggClTpmgsO3bsmMbtuqxvqe24Xr9+/d6MFC2O9u3bhzdv3kClUqFixYqYN28eTp48iadPnyI+Ph6pqal48+YN3rx5g7S0NISHh+P69etYsWIFWrVqxd8hREREREREpCY6Olp6rm0WRKKPzXuRRExPT8fixYvRtGlThIeHy9arXr065s+fr7WvcuXKoWbNmhrL3Nzc8Pz58zzF6Ovri4ULF+apra603bx0+TDsyZMnedqvnl7+3iZFNdrCwMBAtizrL4Xcmj59OsqVK4eKFSuqPcqWLYsVK1bkud8PRZs2bWTLbty4oVMf6enpsmuQ5bQPyr1u3brJlmkabaeL1NRULF26FL6+vtnK5s6diw4dOmDt2rU699e1a1eYmZll2y43IlXbz39MTEyBjMyeNm2axu2aRnUOHjy4UJMWctdQCCGbgM1JcnIyFi9e/E5GM2r7mQ4PD9f6JRpNEhMT8fnnn+fqnvz111/D0tIy23ZN19PFxUV27eWsmjZtCiMjI9nyK1eu6BwfMq7nnDlzuIYfoJas37t3b7ZyfX19mJqawtTUNN9/xxAREREREdGHr0yZMtLzCxcuaJwRi+hjVGw/VRFC4MaNG5g6dSoqVaqEOXPmaFyTKJOhoSG2bt0KY2PjHPuePn26bNnq1atzHeuOHTtQr149zJs3r1BHFZQrV062zNXVVWvbhw8f5lhHjrZpI3P6Voarq6vOiaOCVqJECdmyV69eyZZdvHhR47SAmUJCQvJU9rHQtiZp5nRzOdm6dSuCg4M1lhkbG2Po0KH5ipHUffPNNxoTdACwZcuWXP/R9PjxYzRr1gwzZ87EwYMHs5Xfu3cPQghMmDABy5cv16nPxMREjetkyt0Xtf38q1Qq2Xv1y5cvdf4ySZcuXXQaBQ4dR63lx6RJk2BiYqKxbPPmzUhKSspVf56enmjSpAnmzJmDo0ePFlCU8ho2bIjGjRvLlv/888869/XkyRO0atUKe/bswZkzZ3RuZ25ujnHjxulUV9fraWpqqnWK9SVLlmhMUmoSFhaGfv36YfHixbKjIz8mWX9HzJkzBytWrOA6k0RERERERJRnXbp0kZ5HRkZi6tSpRRoPUXHxTpOIwcHBGDJkiMbH4MGD0atXLzRu3Bjly5eHsbExWrVqhRUrVuQ4CsLMzAwnT55Es2bNdIpj6NChqFKlisayVatWYceOHTr18/z5cwwYMADDhg2TPrh68eKFTm3zokOHDrJlO3bskB2pERERka8PsB0cHGTLtI3oS0tLw+zZs/O83/zStkbk6dOnsyUKQ0JC0K9fP3Ts2BFnz559BxF+mNq0aSM7b7gQAgMGDICXl5ds+7Nnz2Ly5Mmy5aNHj9b6nqTcs7W1xaRJkzSW+fr6Yvjw4ToloeLj4/G///0PDRo0wH///QdouCdGRUWp3dOnTZuGtm3bSvXlbNy4UWNyX27NudKlS2sd+XfixIls2/bv34+GDRvm6n4pNxoxq6pVq6JBgwY695kXDg4Osgmwp0+fYvTo0TpNxREbG4slS5agcePGePDgAVDIv9ey0nYu//nnH/z8889aR5C+evUKM2fORN26daX30+vXr3MVw7fffgtDQ8Mc6+Vmfcvvv/9ediTcw4cPMXToUK3XJjIyEitXrkT16tVx+PBhIGM9QG1Tu38Msk6dnZycjKlTp8LS0hKmpqYwNzeXHhYWFihVqhTKli0LJycnVK9eHXXq1EHXrl3x9ddf448//sD9+/eLbN1oIiIiIiIiKh4+/fRTVK9eXXq9adMmdOjQIc+z+2mTnp6OCxcuYPbs2Rg+fDjGjBmDFStW6PQZTGxsLJYtW4Zly5Zly2OoVCqcO3cOv/32G7755ptsg7IGDRqEgQMHYuDAgbIDON4WEhKC9evX49tvv8UXX3yB7777DkePHtX5C+uxsbE4ffo0Zs+ejaFDh2Lo0KGYNm0afv/9d5w5c0bnL1dTERIFKCkpSQB4p49SpUoJNze3XMd69+5dYWpqKtvvkCFDxKVLl0RqaqpaO29vb7Fjxw7RvXt3oVAosrX7999/ZffZqlUr2f3NmTMnx5hjY2NF2bJltZ6LgwcPioSEBCGEEL6+vuL3338XDg4OAoAwNjaWbevo6Ci737Fjx8q2a9CggcY2ERERomPHjgKAqFSpktZrGB4errEPd3f3PLXLtHXrVq3tv/jiC3Hu3DmxZ88eMW7cOGFubi6Vfffdd7L9Dh06VLbPGTNmaI3pXZo4caJsnM7Ozjr1YWVlJdvH/PnzZdvdvXtXmJiYyLY1MDAQQ4YMEVu2bBHnzp0Tp0+fFuvXrxfdunXT+HOV+ShfvrwICQkpwLOk3Q8//CAbS6dOnQq83bhx42TbVa9eXaeYzczMZPtYtGiRbLvExETRvHlz2ba1atUS27ZtE1FRUWrtIiIixKlTp8SkSZOEhYVFtnZ9+/ZVq3/58mWN/SsUCtG4cWPx008/iX379onLly+Lixcviu3bt4uBAwdqbNO0aVOt56Jy5cqyx2NtbS127NghTp8+LVauXCmaNGmiFktERIRO5zs1NVWUL19e671m3rx5OvWVycXFRbav5cuXy7Z78+aNaNiwoWzbunXrih07dojo6Gi1duHh4eLkyZNi/PjxGt8/Q4YMyVX8eaVSqcQnn3yi9VzWrVtX/PHHH+L48ePi3LlzYvPmzWLu3LmiY8eOGu8dZcuWzXUcI0eO1BpDTu87TebMmaO1T0dHR/Hzzz+LgwcPSu/7n3/+WfTv318YGRlpbBMWFpZtP+3atZPdh7bfT3lt16hRI9l2Q4cOzfG8PHnyROt5OX36tGzbZcuWaW2b20eZMmXEypUrRWJiYo5xExERERER0YfJ1dU122foSqVStGnTRixYsEBcunRJJCUl5Wsfhw8flv28XE9PT4wfP17ExsbKtvfz85PqN2vWTNq+efNm4ezsrNbf27Hq6+tLZd7e3lrjjIqKEhMmTBBKpVJjrBUrVhRHjx6VbZ+WliZmz56t9TNiAKJkyZJi7NixIjQ0NFfnkd6dAk0ipqamFugHOtoehoaGYvLkyRo/RNPVoUOHhIGBgdb9KBQKYWlpKezs7GQ/yMv62LFjh+z+2rZtK9tO1w+ZDxw4kGMM+vr6wtbWVm2bo6OjWLx4sWwbbUnEa9euad1fhw4dxPr168WhQ4fEnj17xIwZM6Rkp4WFhTh06JDW9nLJwHv37mlt9/YH4W97/fq10NPTy9P7q3HjxrL9vi9JxClTpsjGqWtCqmTJkrJ9aEtICSHEP//8ozUhmNuHqamp8PDwKKCzo5sZM2bIxtOtWzfZdj/++GOe2mlL/Lq4uOgUs6WlpWwfS5cu1do2JCQk2x8bmh4mJibC3t5eLfEu9+jZs6faPjZv3lxg74mDBw9qPZ5Jkyblue8jR47odL6FEOKPP/7Q2tfjx4917ksIIerVqyfb14oVK7S2DQwMFBUrVtTp50nXa9i/f/9cxZ8f0dHRolq1agX2HrG0tMx1DF5eXlrvXTldA03S09NFjx49Cuy4AAhfX99s++nUqZNsfW1fVsr80k9u2zVr1ky23YgRI3I8L8+fP9d6jOfPn5dt+/LlS7X//BTUo3r16iIwMDDH2ImIiIiIiOjDdPjwYa2DcYyNjUWHDh3E6tWrcz3YYdasWdn6K1myZLbBTm3atJH9kuvbScSEhAQxaNAgjbHmNYno6+srqlatqtaXUqkUDg4Oap+Z6OnpiT179mjsY9iwYdk+o2nQoIGoX7++KFu2bLbPXphELL4KdDpTfX19lC5duiC7zMbBwQFjxozB06dPsWLFCtja2ua5rz59+uDGjRtwcnKSrSOEQExMDEJDQ5GcnCxbT6lUYuTIkejXr59sHW3Ta2ZduFWbfv36YcSIEVrrpKWlITw8XHpduXJlXLx4Ec7OzrJtrK2tZctatWqldU2nixcvYty4cejbty+GDBmCpUuXIjAwEEZGRjh48CBq1qwp21ahUMDS0lJjWdmyZWWngLOwsJBtl8nBwSFXU85lMjAwwKBBg3LdDhnHU1xoW0Mz6zRw2jg6Oua5j0GDBuH48eP5+hnNVKNGDbi6uqJevXr57is3tJ3D8uXL56lMW59Ffc3s7Ozg7u6OAQMGaK2XmJiIkJAQxMXFaa3XsGHDbGvZ9ezZE23atNHaThfTp09H3759tdaZMGECDAwMct13zZo1czX96FdffSW7dmy9evXUpsLQRV7fd8j4XXL37l307t1ba72EhASdrmGTJk0wf/78HCIuOJaWlrhx4wa6deuW775MTEzw008/5bpdzZo10bNnT41lenp6GDhwYK771NPTw6FDhzBhwoRct9XU15gxY1CxYsVsZe/bPcve3l7rz6i2PsqXLy87DXN+PHnyBG3btkVMTEyB901ERERERETFX+/evXHx4kVUq1ZNY3lSUhIuXryIiRMnokKFCpg7d65OU3suW7YMv/zyCwDA0NAQP/74IwICAhAeHo74+Hh4enpKn0lfvXpVdtmarFQqFYYOHYp///0XyFiyaMGCBTh58iRCQkJgZGSUy6MHYmJi0LlzZzx79gzI+Fz2xIkTiI+Px+vXr/HmzRv8/fffqFChAlQqFYYNG5ZtmaI7d+5g+/btQEbeYf/+/YiKisLdu3fx33//ISAgANHR0Th69CiGDBkCJycnlCpVKtex0jtS0FnJPXv2SNNn5vWhUCiElZWVcHZ2Fs2aNRO9e/cWv/76q7h3755QqVQFHbKIiYkRS5YsEY6OjrmOtVSpUmLmzJni5cuXOe7n/v37ombNmmpZdoVCIVq0aJHjqLq3bdiwIcehwKampmL27NnS8OeQkBDZUUqzZs3Sur/U1FTxzTff6Dyyr0KFCuLGjRtCZIzAqFWrlsZ6PXr00LrfRYsWZZsi0dbWVvz11186naeQkJBs35qQe9jY2IgJEyaIZ8+eae1z9uzZsn2sXbtWp7jehYiICNGuXTu1b5goFArh7Owsbt68qVMfp06dEhUqVFB7zxoaGoqePXtK0+bmJCgoSHz77bc6jXh6+1G2bFnx888/izdv3uTzbORNTEyM6NSpk9qIZT09PVG3bl3x5MkTre06dOigdu51aRcWFiZat26d7ZpVrVpV3L59W6eYjx8/LsqXL5/tmn3yySe5mm7h0KFDok2bNrm+Zvr6+mLAgAHi0qVLWvs/ffq0+OSTT2SnRJB7mJubi40bN+p8HKtWrdK572bNmom//vor2zTWupAbtbps2bJc93Xnzh1RtWrVbNewT58+IiUlRed+9u3bJ1q0aJHra2hgYCAGDx4srl27luvYC0p6errYtGmTqF69ep7uG/PnzxdBQUF53r/ctLvt27fP97GdOHFCtGzZMtfHZWlpKSZOnCgePXok2/ejR49E7dq11X5XGxgYiM6dO2udCsXLy0u4uLjkup2Hh4eoUaOG2ntVX19ftGjRQufz/9tvvwkbGxu1Yy1RooSYNm2abJvY2Fgxfvz4HGeTyM/jl19+0Sl+IiIiIiIi+jClpqaKXbt2iUGDBmmd+QsZIwe1fb5/79496fM+IyMjcfXqVdm6mTOcKRQKjcu4ZR2JmPX/41988UWOszbqMhJx+PDhUp1OnTrJfhYVFBQkypUrJwCIdu3aqZUtWbJE6uP777/XGpMQQsTHx+dYh4qOQgghCiM5mZycnOdFMY2MjKCvr1/gMeUkLS0N58+fx40bN3Dz5k14e3sjKipKGqmhVCrh6OgIZ2dnNG/eHJ06dUKLFi1yPdIlLS1NGtVoYmIiO9ouJ48fP8aqVatw+vRpBAQEIC0tDdbW1mjUqBE6d+6MIUOGZBv9GBQUhAsXLiA0NBTIONfVqlVD586dddqnl5cX9uzZA1dXVwQHByM4OBiRkZEQQsDa2hqtW7dGjx49MHz4cBgbG0vt4uPjceLECQQFBUGlUkFfXx/lypXDJ598otP5S0xMhEqlglKpVOtXF1FRUfj1119x9epVeHp6Ii4uDoaGhnB0dES1atXQvHlztGvXDs2bN9f5fZeUlIT09HS1bfr6+nn6dkdhU6lUSExMBDK+5ZKXkVkpKSnSQrympqZ5GnEZFxeHY8eOwdXVFXfu3MGrV68QHR0txWZmZgYrKytUqVIFTZs2RatWrdC1a9ciuRe8TQiBhIQEAICxsTGUSmWhtisu1wwAHj58iAsXLuDmzZu4d+8eIiIiEB0dLb3/S5YsCWdnZ9SpUwcdO3ZEx44dczX6NDw8HBcvXsTly5dx//59PHv2DBEREcj81WRgYIDSpUujQYMG6Nq1Kz7//HNYWFjk6hjOnTuH9evX48GDB/Dx8ZHuV5UrV0adOnXQtm1bdOzYUeto8ZxMmTIFK1euVNumUCjg6+ur8yjSt6WmpiIlJQXI5zW8f/8+Ll68iJs3b+L+/fuIiIhATEyMdA1tbW3h7OyMunXrStfQxsYmT/sqaEIIXLx4EVeuXMGNGzfw7NkzREVF4c2bN0DG7+XSpUvD2dkZLVu2RNu2bdGhQwedf9bknD59Gt27d8+2fePGjRgzZky++s7033//4dy5c7hx4wY8PT0RFRWF2NhYCCGgUChgZ2eHihUronnz5mjdujW6desGU1NTnfrO+ndGbt47BdEur3/XZP5eVSgUWo8zJSUFbdq0we3bt3O9j9yws7NDUFBQvt9LRERERERE9P5LTU3FzZs3cf36dVy/fh1Xr16VPvPL1LRpU1y6dAkmJibZ2vfp0wdHjhwBAGzYsAFff/217L6EEGjQoAHu3buHkSNHYsuWLWrlL1++zDY70YgRI7B169Ycj8PAwEDK2Xh7e6Ny5cpq5U+fPkXNmjWhUqlQrlw5eHp6ap0N8Pz581JewcvLS5qR8Ntvv8WqVasAAEuWLMGMGTNyjI2Kr0JLIn5I0tLSkJ6eXiwTRJmEEEhPTy+y5CsykmjFnRACSUlJGm/mVDRSU1Ohp6fHD2rfI0IIJCcnw8jIqFCm8M1Mourp6cHY2LhA95GSkgI9Pb0CvV8JIVC2bFm8fv1abXvLli1x/fr1AttPQcq8Fxb0+X1X0tLSkJaWVmjvwZEjR+Lvv/9W22ZgYIDg4OBCTbCqVCrpZyuvXzD60C1atAhz587Ntv3bb7/FpEmTYGdnp/H3iRACqampSEhIwJs3b/D8+XO4urri77//RkBAgMZ9vXz5MscphImIiIiIiOjjk5ycjI0bN+Knn35CdHS0tP2HH37A//73P7W6kZGRsLOzQ3p6OhwcHPDq1ascBwwsX74c06ZNQ5kyZRAYGKhW9nYSUZdkX6ackohz587FokWLAB2TfyqVCo6Ojnj9+jV+//13TJ06FQCwZs0aTJw4EchYsuzQoUNo3LhxjvFR8cQkIhERUT5cvXoVbdu2zbZ99erVBbIGHr1bSUlJKFu2LCIjI9W29+rVC8eOHSuyuOj/tG/fHpcvX1bbVrFiRfj6+uapv9jYWHTv3h03b97MVnblypUCWcOViIiIiIiIPkz+/v5o1qwZgoKCgIwkXWBgoNr6fvv378fAgQOBjIRf06ZNc+w3PDxc+r9vTEyM2qxcbycR169fj7Fjx+oUb05JxGbNmkkz/3Ts2BHW1tY59nnz5k0EBQVhzJgx2LhxI5Dxf+1q1aohODhYqtewYUN06NAB9evXR926dVG9enV+gfo9UfyHjhERERVjq1evzrbNwMBA+gOR3i+7d+/OlkAEgC+++KJI4iF1np6e2bZVrVo1z/1ZWFhg9OjRGpOImt4HRERERERERJnKlSuHgwcPolmzZkDGjGv79+/HN998I9Xx8/OTnvv7+8Pf3z9X+3j9+rXWpX26dOmSp9g1yRrrhQsXctU26wxdFhYWuHTpEjp16iSNpLx79y7u3r0r1SlZsiQ+++wzzJw5E+XKlSuQ+KlwMIlIRESUR1euXMG+ffuybR88eDDs7OyKJCbKu5iYGMyePTvbdkdHR/Tr169IYiJ1tra2iIiIUNv28OFDpKWl5XmaYrn/wNWoUSNP/REREREREdHHo2nTpmjYsKGUIHv69KlaeVxcnPS8RYsW0rqBuipRooTWckdHx1z1p01mrEqlEiNGjMjVEjJvH1f16tXh7u6OFStWYMOGDWrTvgJAREQE1q1bh127dmHbtm3o06dPAR0FFTQmEYmIiGRs2rQJmzZtwpw5c/Dpp5+qlV28eFHjaENjY2PMnz//HUZJunr69Ck++eQTjB07FuPHj1dbHzcwMBB9+vRRm2oj08KFC2FoaPiOoyVNunbtmu0/ZEFBQRg/fjxWr16d6+t06tQp/Prrr9m2m5iYwNnZOd/xEhERERER0YevUqVKUhIxa9IQABwcHKTnjRs3xooVKwpsv/r6+jmur5gbDg4OePHiBdLT0zF//vx8JygdHBywdOlS/Pzzz/Dw8ICHhwdcXV1x9uxZaQrY2NhYDB48GO7u7nBxcSmgI6GCxElniYiIZDx//hxubm7o3bs3rK2t0b59e3z++eeoXr06OnXqpHG6w4ULF8LJyalI4iXtAgMD8fz5c/zwww+wtrZGw4YN8cUXX6BZs2ZwcnKCu7t7tjadOnXCiBEjiiReym7ChAkwMjLKtn3Tpk1wcXHBkiVLcOXKFY3JYGT85+S///7Dxo0bUa9ePfTo0QMJCQnZ6n333XdQKpWFcgxERERERET0Ycm69EaFChXUyurUqSM9d3V1fadx5VZhxWpgYIAmTZpg7Nix2Lp1KwIDA3Hnzh1p9GJycjIWL15cYPujgqUQQoiiDoKIiKg4Wr58OaZNm6Zz/X79+mH//v25mu6B3p2HDx+idu3aOtevUKEC3Nzc1BZEp6K3detWjBo1Ksd6pqamsLCwgL6+PlQqFRISErJNn6JJzZo18d9//2lMVhIREREREdGH6fLly2jevHmu/y9479491K9fX3rt5uaGRo0aSa9VKhXKlCmDkJAQKJVK3L59Gw0bNsxznC9fvkTFihWBjJGIqampOrc1MDBAWloaAMDb2xuVK1dWK9++fTuGDx8OAOjcuTPOnj2b5zh14ebmhiZNmgAAypcvj5cvXxbq/ihvOBKRiIhIRunSpXWu279/f/zzzz9MIBZjubmezs7OuHr1KhOIxdDIkSOxceNGmJuba62XkJCA4OBgBAQEICgoSKcE4ieffILr168zgUhERERERPSRmTJlClxcXHDmzBmd28TGxuKzzz6TXjdr1kwtgQgAenp60hfU09PTMWLECKSkpOjc/7s0aNAgaSTluXPnsGnTJp3bZp3GVaVS6dSmRo0a0nOOdSu+mEQkIiKSUb58+RzrmJmZYdWqVdi3b1+BzkNPBa9kyZIwNTXNsd5XX30Fd3d3na4/FY0xY8bAy8sLX375ZY6LzOuidevW2Lt3L44cOQJra+sCiZGIiIiIiIjeD4mJifDy8oK3tze6deuGpk2bYs2aNQgPD5dtc+DAAdSpUwfPnz8HABgZGWHNmjUa606cOFGaKvThw4fo2rUr/Pz8ZPuOi4vDvHnz0KpVq3wfW24YGRnhzz//lL4g/+233+L333/XmhR0c3NDq1atsHv3bmnbunXr8MUXX8DDw0Pr/lauXCk9zxyRSMUPpzMlIiKSkZCQgBkzZuDChQvw8/NDQkIC9PT04ODggMqVK2PgwIH4/PPPUbJkyaIOlXS0bt06bNu2Dc+ePUNUVBQAwNraGhUqVEDXrl0xYsQIVK9evajDpFxISEjAyZMn4erqigcPHsDb2xsREREav7FpbGwMW1tbVKpUCVWqVEHLli3Rtm3bbFO4EBERERER0ccjJCQE7du3x+PHj9W2KxQKVK5cGXXr1oWDgwP09PTw9OlTPHz4EEFBQVI9fX197NmzBwMGDJDdh5+fH1q2bCm1MzMzw/Dhw9GoUSPUrFkTCQkJePz4MS5evIgTJ04gMTERpqamePPmjVo/hTmdaaYlS5Zg1qxZ0usmTZrg008/RYMGDWBra4uAgAB4eHjg8OHD0nqQP/30ExYsWAAAGDVqFLZu3QoAqFq1Kjp16oTq1avDwcEBJiYm8PX1xaFDh3Dp0iUgI3l57do1NG7cWOdjoXeHSUQiIiIdpaSkQKFQcMThByI9PR1paWmcuvIDlZ6ejtTUVKSnp0OhUMDQ0BD6+vpFHRYREREREREVQ+np6dixYwcWLlwIX19fnduVLVsWu3fvRps2bXKsGxgYiH79+uHOnTs51i1VqhRmzJiBqVOnqi2d8y6SiACwZcsWTJw4EYmJiTn226lTJyxfvhx169YFAHz//fdYsWKFztOa/vXXXxg1apTOx0HvFpOIRERERERERERERET00RNC4Nq1a9i9ezcuXLgAHx8fjev11a1bF8OHD8e4ceNgYmKic/8qlQo7d+7EmjVr4Obmlq3vqlWrYtiwYZg8eTLMzMyytX9XSURkJD0XL16Mffv2ZZva1cTEBG3btsWPP/6Idu3aZWv74sULbN26FceOHYOnp2e2hKJCoUCzZs2wYMECdO7cWedjoHePSUQiIiIiIiIiIiIiIqK3xMTE4OnTp4iKikJycjJKlSoFJycn2Nvb57vv4OBg+Pj4IDIyEjY2NqhUqRLKlCmjtU1CQgJOnToFZCTi+vXrp/P+Dh48KCUtu3XrhhIlSujULj09HY8ePUJQUBCEEHBwcECNGjV0ntkpOjoaL168QHh4ON68eQMbGxvUqlULtra2OsdORYdJRCIiIiIiIiIiIiIiIiJSo1fUARARERERERERERERERFR8cIkIhERERERERERERERERGpYRKRiIiIiIiIiIiIiIiIiNQwiUhEREREREREREREREREaphEJCIiIiIiIiIiIiIiIiI1TCISERERERERERERERERkRomEYmIiIiIiIiIiIiIiIhIjX5RB1AcJScnw93dHQBgbGyMhg0bFnVI+RIbGwtPT08AgKWlJVxcXIo6pI/egwcPEBcXBwCoW7cuzMzMijok0sLLywvR0dEAABcXF1haWhZ1SEREREREREREREREhUohhBBFHURx8/LlS1SsWBEA4OzsjOfPnxd1SPly48YNtGrVCgDQrl07XLp0KVudyMhIPH78WHrNREnhat68OVxdXQEA7u7u732iuiDMnTsX+/fvz1cfhw4dQvXq1Qsspkzdu3fH6dOnAQDnz59Hx44dC3wfRERERERERERERETFCUciEgDg2rVr6NOnj/SaiRJ614KDg/HkyZN89ZGcnFxg8RARERERERERERERfcyYRCSiYkehUKBnz54QQkClUkGlUqk9z/rIqkSJEkUWMxERERERERERERHRh4RJRPqohYeH4+nTpwAAJycnlC5duqhDIgBKpRLHjh0r6jCIiIiIiIiIiIiIiD5aTCISAKBhw4bYuXOn9LpWrVpFGs+7cuHCBQwePBgAsHr1akyYMKGoQyIiIiIiIiIiIiIiIipyTCISAMDR0RFffPFFUYdBRERERERERERERERExYBeUQdARERERERERERERERERMULk4hEREREREREREREREREpKbQpjMVQiAkJARBQUEAAGtra1SsWBEKhUKtXmhoKJ4/fw4AsLCwQO3atXXq/969e3jz5g0AoFGjRjAyMpKtm5aWhqCgIAQHB8PIyAh2dnYoXbp0Po5Oszdv3uDly5dITk5GpUqVYGVlVeD7KCzp6elwdXUFAOjr66Np06Ya62WecwAoUaKEWllYWBhevXoFKysrVKhQAfr6uX97xcbGws/PDwDg5OQEMzMzndumpaXhyZMniI+Ph42NDezs7DReg+fPnyM0NBQA8OTJE2m7j48Pbty4oVbXzMwMdevWxa1bt6BSqQAADRo0gImJSY7xBAcHw8fHB8iYLrZChQo6H4sm6enp8Pf3R1hYGOzt7VG+fPlctQ8KCkJAQAAMDAxgb2+PUqVKwcDAQLb+mzdvcO/ePel1y5Yt8xV/cZCSkgI/Pz8IIVClShXo6RXO9ygyz7WhoSHs7OxyPNdvS0pKwsuXLxEfH48KFSrA1ta2UOIkIiIiIiIiIiIiIpIlCtjr16/F1KlTRdmyZQUAtYe5ubkYPHiwuH79ulT/5cuXQqlUCgCiRIkSIjo6Osd9hIaGCkNDQwFAVKpUSahUKo31/vvvP/HFF18Ic3PzbLGULl1aTJkyRfj4+GRr5+fnJ9VzdnbOMZ5bt26Jnj17CgMDA6mdUqkUbdu2FdeuXcuxfWG7fv26FFe7du001omOjpbq2NrayvZlZWUlAAgrKyshhBBxcXFi8eLFokyZMmrn18bGRsycOVMkJSXpFOOpU6dEhw4dhJ6entSHoaGh6NGjh7h//36Obbt16ybMzMyyXWcHBwfx5ZdfivPnz0vvkxEjRmSrJ/do1KiREEKITz75RNq2evVqnY5p8ODBUpvTp0+rlTVr1kwqc3d319pPeHi4mDx5sihVqpRabJUqVRIrVqwQqampsm1DQkLE1KlTRZUqVbIdm4GBgWjZsqX4448/RHh4eLa29+7dU6uflpam03Hn1VdffSXtS19fv8D69fDwECNGjBCVKlWS7jWZ95t27doJNze3HPvo1q2b1O78+fMa6wQHB4vJkycLZ2fnbOfa0NBQtGrVSqxcuVJERETI7sfLy0t89tlnwsTERGqrUChEo0aNxPHjx/N1HoiIiIiIiIiIiIiIcqNAk4g7d+7UmLDT9Jg+fbrUbsCAAdL23377Lcf9LFu2TKq/dOnSbOUqlUpMmzZNLSEl9zAwMBAHDhxQa5+bJOKcOXOEQqHQuo///e9/uTqPBa2wkoje3t6iRo0aWo99wIABWmNLS0tTSx5peiiVSrFz506N7efMmaPT+01fX19KaOYliXj+/HlpW7Vq1WQT15nCwsK0Jrp1TSJeuXJFlCxZUmuMrVu3FgkJCdnaPn78WDg4OOh0nMuWLcvW/kNIIvbu3TvHY1cqlWLhwoVa+8kpiejl5SXs7e11Otdy97hVq1apfRFB02Py5MkFcl6IiIiIiIiIiIiIiHJSYNOZbtq0CWPHjoUQAgBQp04dDBw4EE5OTlAoFAgLC4O7uzsOHz6MuLg4KJVKqe3kyZOxf/9+AMCaNWswZcoU2WkGhRDYtGkTAMDAwAAjR47MVmfkyJHYtm2b9Lpr167o1q0bypQpg5SUFAQEBOD69es4c+YMUlNTYWxsnKdjnj9/PhYtWiTFMm7cOLRr1w6WlpZwdXXFmjVr8Pr1a0ybNg3lypXDoEGD8rSf4ig5ORmdO3eGn58fJk2ahE8//RRKpRKenp5Yvnw5/P39AQD79++Hp6en7DS1Y8eOxV9//QVkTI86ceJEtGrVCoaGhrh8+TLWr1+PqKgojBw5Eo6Ojmjbtq3U9tatW9L5t7KywoIFC9C2bVuYmJggLCwMT548wblz53D48GE4OztLU96uXLkSS5cuBQCsXbsWCxcuBAD88ssvGDVqlFp8mVOyduzYES4uLnj48CGePn2Ks2fPomvXrrLn5++//0ZKSgoA4Ouvv842ja8uPDw80K1bNyQmJgIAOnXqhM8++wzVqlWDr68vNm7ciJs3b+LatWv44osvcPDgQbX2o0ePRnBwMABgwIABGDt2LCpWrIi4uDgEBgbi5s2b2LdvH7y9vVGnTh2NMeRlStqCIITINrWsNqVLl4aTk1O27b6+vgCAGjVqoG/fvqhevTqqVasGlUqFEydOYMmSJUhPT8dPP/2Ehg0bokePHnmKd9SoUQgJCQEAfPbZZ/j6669RoUIFxMbGIigoCDdu3MC+ffvg4+Oj8Vxv3rwZkyZNAgAoFAqMGDECXbp0gYODAzw8PLBq1Sr4+vpi5cqVKF++PL777rs8xUlEREREREREREREpLOCyER6eHhIo64yR8ukp6drrBsXFyfmzZsnjh07pra9QYMGUvujR4/K7ivriLBBgwZlK9+wYYNUbmhoKLZt2ybb16tXr8TQoUNFUFCQ2nZdRiI+ffpUOmZ7e3vh4eGRrU5ERISoVauWNBpN27SThakwRiJmPpYvX56tzqtXr0S5cuWkOj/88IPGvq5evSrVqVq1qvD29s5Wx8/PTzg6OgoAolmzZmplY8eOldofPnxYNuaQkBCxb98+jWXLly+X+shpmtKNGzdKdXv06CFbT6VSSdOHGhgYiODg4Gx1chqJqFKp1Or88ccfGvc1ZcoUqU7WaYKfP38ubW/VqpXWWPft2ydCQ0O1Hvu7kNOIVG2PiRMnauxz5MiR4uDBg7L73Lp1q9SHo6OjSE5O1lhP20jEx48f5/jzJYQQ6enp4t9//802dWxoaKj0c1WiRAlx6dKlbG0TEhJE69atBQBhaWmp07TPRERERERERERERET5oXm4Xy5Nnz5dGnU1YcIErFixQnYkoZmZGebPn49evXqpbZ88ebL0fO3atbL72rhxo/R87NixamVv3rzB7Nmzpdfbtm3DsGHDZPsqV64cduzYgdKlS2s9Pk2+/fZbpKSkQE9PD3v27EG9evWy1bGxscGpU6dgbGwMX19fHDt2LNf7Kc7q1q2L77//Ptv2cuXKqY0Q9fb2zlYnPT0dEydOBACYmJjgwIEDqFy5crZ6FSpUwJEjR6BQKODq6go3NzeN/TZr1kw2Tjs7OwwYMCCXR5fd0KFDUbJkSQDA6dOn8eLFC431Ll++jOfPnwMA+vbtC3t7+1zva9u2bXB1dQUAjBs3DlOmTNFY7/fff0enTp0AAH/++ae0Xddzo1AoMGDAAJQqVSrXMb4PtmzZgr59+8qWDxs2TBrBGBAQAC8vr1zvQ9dzraenh88++0x6D2WaOXMmoqOjAQAbNmxAu3btsrU1MTHBsWPHYGdnh5iYGGzfvj3XcRIRERERERERERER5Ua+k4iZU0YCgJGREWbNmpWnfgYPHiwlW86cOaMxQRMZGYkjR44AAKpWrYr27durle/Zswfh4eEAgJo1axba9KH//fcfzpw5AwDo3r17tjiyKleuHAYPHgwAuHjxYqHEU1SGDh0qW1arVi3peeY0j1mdOHECDx48ADISOS4uLrJ9NWjQAB07dgTeOocWFhbS8z179uThCHLHxMQEY8aMAQCoVCps2LBBY70tW7ZIz8eNG5enff3yyy9Axs9U5pStmigUCvzwww8AgEuXLknTCWc9NydPnkRMTEye4igqSqUS7u7uOj+mTZuWp/3o6empfQHAw8Mj131kPdcnTpxAbGyszm2Dg4Ol90vdunXxxRdfyNa1tLTE119/DXyA9xIiIiIiIiIiIiIiKn7yveDZ6dOnpeddu3ZFmTJl8tSPoaEhxo0bhwULFkAIgc2bN0uJlEy7d+9GcnIyoGEU4tuxjBgxIk/r0Oni7Nmz0nMLCwusWbNGa/3MmPOSoCjO6tatK1uWuf4gAKSmpmYrz3oO9fT0cjyHBgYGwFvnsFevXjh06BAAYOrUqTh+/Dh69eqFDh06oGbNmoWynt+ECROwfPlypKWl4e+//8aiRYuk2AAgNjZWWpuwatWqGkeV5cTPz08ayVihQgX8888/WusnJSUBAMLCwhAYGAhHR0c0bNgQDg4OCA4OxqNHj+Di4oJ+/fqhW7duaNKkSbbRcMWNQqFAw4YN38m+so5EzlxDMTeaNGmCUqVKISwsDJ6entnOtY2NjWzb8+fPS4nfMmXK5PhzEBERAXyA9xIiIiIiIiIiIiIiKn7ynWV5+vSp9LxKlSr56uubb77BkiVLkJKSgq1bt2LhwoVqiaC///4byEhQDR8+vFBj0SYzwYOMEXC6joILCgoqtJiKgoODQ57bZj2H69at07ld1nM4fPhw7Nq1SxqVdeHCBVy4cAEAYGxsjCZNmuDrr7/GkCFDZKfXzS1HR0f069cPe/fuRWhoKI4cOaI2VerevXuRkJAAZCS685LIznpunj17Jk37qougoCA4OjrCyMgI69atw4ABA5Ceno6AgAD8+eef0pSnTk5O6NmzJ3788UeULVs21zF+CGJiYhASEqJxpGxuGBsbY926dfjss8+gUqng7++PlStXYuXKlQCAypUro1evXpg+fXq2L1lkvdanTp3CqVOndNrnh3YvISIiIiIiIiIiIqLiJ99JxKxT92kbcaMLe3t7DB48GNu3b0dwcDCOHz+OPn36AAA8PT1x9+5dAMDAgQM1jqQqyFi0iYqKkp43adIEhoaGOrWztrYutJiKgq7HrUnmOVQoFGjRooXOybaqVatKz5VKJU6ePIl58+Zh9erVePPmjVSWlJSEq1ev4urVq1iyZAn279+P6tWr5znerKZMmYK9e/cCADZv3qyWRNy6dSugJdGti6zvLycnp1yN7s06KrJPnz64ePEiJk6cCE9PT7V6L168wKpVq7Bp0yb89NNPmDlzZp5ifV/cvXsXx44dg6urKx4/foyQkBBphHBB6N+/Py5cuICJEydmW1fRx8cHK1euxIYNG7Bw4UK1qVezXut69erBzMxM532mp6dDqVQW0BEQERERERERERERFZyEhAS1AUR169ZFp06dijSmd+X+/fvYs2cPfHx8EBMTg3LlyqFDhw7o27cvTE1Nizq8XMl3ErFUqVLSc29v7/x2h8mTJ2P79u1ARkImM4n477//SnXk1pkrVaoUXr16JcXSpk2bfMejSdYE5ooVK9C8efNC2c+HLPMcCiGwZ88elCtXLk/9GBkZYenSpZg5cyYuXboEV1dX3Lt3D9evX5eSil5eXujYsSM8PT0LJLncvHlzNG7cGG5ubjh37hwCAgLg6OgIf39/3Lx5E9CS6NZF1na9evWSRrTlRZs2bfDgwQP8999/uHLlCu7du4dbt25JI+CSkpIwa9YsmJub52rE4/vi9OnT+PHHH6X1NzMZGBigQoUKsLW1RWBgIIKDg/O9r3bt2uHhw4e4e/eu2rnOvC8mJSVh+vTpMDc3l+5hWa/1rFmzMHDgwHzHQURERERERERERFTU4uLi8MMPP0ivx40b98EnEV+/fo2hQ4dKsydmtWXLFlhaWmLVqlX48ssviyS+vMj3HI8NGjSQnr/9QX1e+2vVqhWQMb1f5hpgmevM1apVCy1btnwnscipXbu29PzWrVuFtp8PWUGfQ0tLS/Tp0wdLly7F6dOnERkZibNnz8LZ2RnImP5x27Zt+d5PpsmTJwMAVCoVdu/eDQDS+oyQWbNTV7Vq1ZJGZhbU+6tBgwaYOnUqtm3bhmfPnuHFixeYNGmSVL5kyZIC2U9xsnr1avTo0QMPHjyAubk5Pv/8c+zYsQMBAQFISkqCn58f3N3d1UaSFoSGDRviu+++w/bt2/H8+XP4+PhgwoQJUnnWc817CREREREREREREdH77969e2jYsKFaArFy5cpqM9DFxMRg2LBh79WAnnwnEXv27AkjIyMgY8rAc+fO5TuozARNamoqjh49iqdPn+Lx48eAllGIyJhSMNPWrVsRGhqa71g06d27t7TG3tvTaJJu+vbtKz1fvnw50tPTC7R/Q0NDdO7cWVpHEwBu3LihtU3mWoa6+Oyzz1C6dGkAwIEDB4AsScRatWpJifC8cHBwkEa3uru7S+s8FqRKlSrhzz//RLNmzYCMJKufn1+B76eoREdH44cffoAQAra2tvD09MSuXbswdOhQlC1bVm2NzNTU1EKNxcnJCatXr0ajRo0AAK9evUJAQAAAoEuXLtLw9W3bthXIiEgiIiIiIiIiIiIiencSExMxaNAgvH79GshYuurx48fw9vaGh4cHQkNDsWDBAmnw0Jo1a7Bp06Yijlo3+U4ilixZEt988430esqUKQgPD9faJiYmBseOHZMt79u3L8qXLw8AOHLkCM6cOQMAMDU1xdChQ2Xbde3aFY0bNwYy1kecNGlSjgmCFy9e5JhceluFChWkOHx9fTF58mQIIbS2SUlJkRKh9H9Tgnbo0AEA4ObmhgULFuTY5s2bN2pT5np6eiIuLk5rm6xrBGpaP87CwkJ67uvrq3P8BgYG0vvezc0N3t7euHbtGpDPUYiZ5syZA2RM9zp69GiEhYXl2ObevXvS86SkJPz33385tsnp/LyvAgMDpTUPa9SogQoVKsjWfXu9yNxKTExUO/dyNJ1rMzMzTJkyBQAQGRmJr776Ksd7lkqlynfMRERERERERERERIXJxsYG169flx5Zpzb90MyePRvPnj0DMmaqu3LlCqpXry6Vm5iY4KefflJLHE6aNElKOhZn+U4iAsDChQtRrVo1AMCjR49Qr149nDx5UvoQHxkftHt6emLmzJkoX748fv75Z9n+lEqlNP3f1atXcffuXQDA4MGDYWVlpTWWv/76CyVKlAAA7N27F23atMG9e/egUqmkOtHR0bhx4waGDh2KqlWrSiPJcuPXX3+V1vH766+/0L59e2mduaxCQkKwatUqVKtWDStWrMj1fj5k69atk67nzz//jL59+yIoKChbvZcvX2Lp0qVwcnLCnj17pO2LFi1CuXLlMGPGDDx+/Dhb8sXf3x8//vij9Lpp06bZ+nZ0dJSe79+/H4GBgUBGoubcuXPo2bOnbIJ47NixMDIyghACa9asQXp6OkxMTApkPuPu3btj2LBh0vG7uLjg4MGD2WJJSUnBsWPH0L17d7Rt21ba7unpiYYNG6JTp044fvw4IiMj1dqpVCps2LBBSnyWLVsWZcuWVasTHh6OlStXSo+cEuXFibOzM0xMTICMEahr167NNtr19evXGD9+vLSOZV7du3cP9evXR+fOnXHy5EmN53rt2rXSdKXly5eHg4ODVD5r1izUqVMHAHDixAk0adJEYwI4KioKW7ZsQZ06dTBjxox8xUxERERERERERERUmAwMDNCyZUvpUbly5aIOqVBERERg7dq1AACFQoF169apDV7KavTo0ejatSsAIDk5WWpXnOkXRCfm5uY4ceIEunXrBm9vbwQGBqJnz54wMDBA9erVERMTA39/f7UkREhIiNY+x4wZgwULFiAqKgonT54EdBzhVbt2bRw4cAADBgxAfHw8XF1dUb9+fZiZmaFKlSoICAjINqorp1g0sbe3x8mTJ9G9e3cEBATgypUrqFq1KsqVK4e6detCCIHnz5/D29tbSmDmZT8fsqpVq+Lo0aPo3bs3oqKicPjwYRw+fBhOTk5wcXFBSkoKnj59qjZCMOs5vHfvHmJiYrBs2TIsW7YM+vr6qFSpEsqXL4+wsDA8evQIaWlpQMYUo6NGjcoWQ+vWrWFoaIiUlBSEhYWhatWqcHZ2hre3tzS9qaenp5TkycrOzg5DhgzB33//jZ07dwI6Jrp1tXHjRsTExODIkSMIDQ1F//79YW5ujgYNGsDGxgaBgYF4+PCh2jSsSUlJMDY2lkbGXbhwQZoO1dbWFlWqVIGBgQG8vLyk9Ub19PSwdOlStSk+kTGaL3OUHABMnDjxvRmtaGRkhPHjx+O3336DSqXChAkTsHDhQtStWxcmJiYICAiAh4eH2pcL8irzXJ8/fx7nz58H3jrXDx8+lBKLSqUSy5Ytk4atA0CJEiWkRPCjR4+kubPt7e1Rv359GBgYwNfXF0+ePJHez8bGxvmOm4iIiIiIiIiIiIjyZ8eOHdKAuvbt20uzZcqZPn26NPvm+vXrMX/+/OL9ubsoQOHh4WLkyJFCT09PAND4MDAwEMOGDRP379/Psb+xY8dK7erVq5erWB4+fCjatWsnGwcAYW1tLWbOnCmCgoLU2vr5+Ul1nJ2dte7n9evXYsiQIUKhUMjux8jISIwePVr4+/vn6hgKyvXr16VY2rVrp7FOdHS0VMfW1la2LysrK6nekydPZOsdPnxYqteoUSOt8Xl7e4sePXpovVYlSpQQU6dOFeHh4VK7vXv3isaNG2ttB0A0a9ZMrd3b5s6dq7GdUqkUXbt2FQ8ePJBt6+Hhodbm9u3bWo81U7NmzaQ27u7usvVUKpVYvny52nmXO8bz589L7fz8/MS4ceOEqamp1nYmJiZi9+7dGvd97949tbppaWk6HVteffXVV9K+9PX1891fbGys6Ny5s+yxm5qaitmzZ4uZM2dK2+bMmaOxr27dukl1sp5nIYR48eKF+Prrr3M816ampuLff/+VjTcmJkZ88803Ql9fX7YPpVIpBg4cqPVnj4iIiIiIiIiIiIjejU6dOkmf3/7yyy851k9MTBRKpVKn/EBxoBCFMEfhixcvcODAAXh4eCAkJAQmJiZwcHBAkyZN0LNnz2zTJsp59OgRatWqBWRkZPOy1tydO3dw5MgRPH36FJGRkbCwsED58uXRsmVL9OzZE2ZmZtnaBAcHY+DAgUDGNI///PNPjvt5/vw5Dh48iHv37iE0NBT29vaoXLkyqlevjp49exbY6LS88PT0xPjx4wEA9evXx59//pmtTnx8PLp37w4AsLS0xPHjxzX21b17d8THxwMAdu3aJa1d+bZr165h1qxZAIBq1aph8+bNOcZ5//59HD58GF5eXoiMjESZMmWkUYk9evSAqampxnYvXrzAuXPn8OLFC7x69QqhoaEoWbIknJyc0L9//xwz/yqVClu2bJFGvDo6OqJatWro37+/2rSTctq2bYurV6+iXr168PDwyLE+MkbVPnr0CACwefNmaTpgOZnriF66dAmBgYEwMjKCk5MTnJyc0LlzZ7X5lbNKTEzEhQsXcP/+fbx69QqvXr2Cnp4eHBwc0LZtW/Tt2xfm5uYa23p7e2PkyJHS68uXLxfqNyKWLVsmve+USiUuX76c7z7T0tKwe/duXLt2Dc+fP4e1tTUqV66MNm3aoGPHjihRogS8vLyk0YQuLi6oW7dutn5++OEH3L59GwDwxx9/oFGjRtnqJCYm4vz583jw4AFevnyJV69eQalUwsHBAe3bt0efPn003m/e5u/vj0OHDuH27dsICQmR3stVqlRBr169YGdnl+/zQkREREREREREFJ2chMA3sUUdRrFRtoQFrIw4AxjpTggBKysrxMb+38/R8ePH0bNnzxzb1axZE48fPwYA/Pbbb/juu+8KPda8KpQkYkHx8PBAgwYNYGZmhqCgINlkB1FRatq0Ke7cuZPnRDcRERERERERERHRu3LjtT/GXzuJBxGhRR1KsaIAULukHda16YEWDuWKOhwAQGRkJC5fvowrV67g6dOnSE5Ohp2dnTQAp0+fPihXLudYk5OTcerUKZw5cwYvX74EMpYb69WrFzp16pRj+6lTpwIZS9stXLhQ2p6WloYjR47g7t27eP78OX777Te1gUeDBw9GYGAgoGVwxtv8/Pywd+9e3Lt3DyEhIbCxsUHLli0xYMAAODo65tg+JCQEly5dwtWrV+Ht7Y20tDTY29ujTJkyqFKlCvr27Qt7e/sc+9FFYGCgWkyvXr3S6XoMHjwY//77LwDgm2++KdZrIxbrJOK4ceOwYcMGjB07FuvXry/qcIiyuXfvnrTmJhPdREREREREREREVJyd83+Bnif3IFWlKupQii0DPT2c6DEEncs5FWkc//vf/7Bw4UJpZkBNFAoFWrRogf/9739o3ry5xjoHDx7ElClT4O/vr7G8bdu22Lx5M5ydnbXuBwAcHBzw+vVrAMCGDRuwaNEiBAQESPWePHmiNutelSpV4O3tDQA4f/48OnbsKLuP6OhozJw5Exs3boRKw/vTxMQEs2bNwuzZs6V4slKpVJgzZw5+//13aY1CTZRKJdq3b4+VK1eiZs2asvV04eXlBRcXFym+hIQEndrNnz8fCxYsADISinv27MlXHIVJv6gDkBMfH4/du3cDGVM/fkhcXV2Rnp5eqPuwsbFBjRo1CnUfBGzcuBEA8MUXXzCBSERERERERERERMWWEAKz71xiAjEHqSoV5ty5hE6OlTQmq96FxYsXY86cOdLrMmXKwMXFBQYGBoiMjISPjw9CQ0MhhMCNGzek6TTfljVZBQB6enpSsvDFixdIS0vDlStX0KFDB9y4cUOnUXQpKSkYPnx4tmXg9PX187wcVkBAALp16wYvLy9pm7m5OZydnREQEICwsDAkJiZi7ty5CA8Px4oVK7L1MXnyZKxevVp6XaFCBdSoUQN6enqIiIjA8+fPERkZifT0dJw/fx6pqal5ijWrmJgY6bmlpaXO7bIu3RYdHZ3vOApTsU0i/v3334iLi0OLFi1Qv379og6nQHXv3r3Q3xh9+vTBoUOHCnUfH7vIyEjs2rULAKQ1J4mIiIiIiIiIiIiKo5iUZLiFBhV1GO+FO6FBiElJLpI1EiMiIvDLL78AGUm/rVu3YtiwYdnqPXv2DIcOHcLatWs15lDWrVsnJRCNjIwwbdo0TJ8+XRoMExsbixUrVmDJkiXw9/dHnz594ObmBj09PdnYhBAYPXq0lEC0s7PDzJkz0bx5c9SpUwcmJia5Pt6kpCT06tVLSiDWqlULK1euRIcOHaQkrpubG6ZPn47Lly9j5cqVqFevHkaMGCH14ePjg3Xr1gEZCbrdu3ejd+/e2fbl6emJAwcO4O+//873KES8lQA0MjLSuV3WulFRUfmOozDJvxveoZSUFLXXERERWLRoEQBg+vTpRRQV0f+Xnp6ebQj1vHnzEBsbi27duqFOnTpFFhsRERERERERERERfRju3bsnTYvZoUMHjQlEZKxp+OOPP8LX1xd2dnZqZb6+vtI6hvr6+jh+/Dh+/vlntdn0LCws8NNPP2H79u1QKBT477//sH37dq2xhYSEYOfOnQCAL7/8Eo8fP8aUKVPQtGnTPCUQkfE5+/379wEAzZo1g7u7Ozp27Kg2CrRx48Y4e/Ys2rdvDwCYPXu22tShWWd/7Nevn8YEIgDUrl0b8+fPx4sXL2BgYJCneLOSGwGak6wjNrOOZiyOisVIxEWLFuHkyZNo0aIFbG1tsXfvXoSEhKBNmzayF/t9ZmdnB2Pjwv0Gg5WVVaH2/7EJCQlBvXr10KVLFzg5OeHVq1fYtm0b9PX18euvvxZ1eERERERERERERERaWRoaobFdGY5G1EETuzKwNNR9ZFlBCgsLk57rkujSNHLwl19+kdYFXLRoETp16iTbfuDAgTh06BD27NmDzZs3q43wk9OnT58cE466CAsLk6YgtbCwwNGjR2VzJwYGBti1axfKlSuHoKAgnDx5EgMGDJD6yVovJ9pGW+ZG1sSpEELndlnr5nUK2HelWCQRS5Uqhbt37+Lu3bvSNnt7+wJ5ExZHT58+LeoQKJdKlSqFsLAwafrSTP/73/9Qu3btIouLiIiIiIiIiIiISBcKhQKLm7RHz5N7uC6iFgZ6eljctH2RrYfo4uIiPT937hxWrFiB8ePHw9DQUKf2qamp2Lt3LwCgRIkS+Oabb3Js8/nnn2PPnj1wdXVFbGwsLCwsZOva2tpiw4YNOsWSk0OHDkkjCocPH45SpUpprV+6dGm0a9cOFy5cwJkzZ6QkYtZz9s8//6BNmzb48ssvCz1Bl/U8paWl6dwu6+ychT3gLL+KRRKxTZs2GDVqFEJDQxEfHw8XFxdMnz5dp0U8id4FAwMD/Pjjj/Dx8UFkZCTMzMwwZswY9OrVq6hDIyIiIiIiIiIiItJJ53JOuNx7GL65ehKeEaHQfezUh08BoHZJO6xr0wMtHIouN+Hi4oJ+/frh4MGDSEtLw9SpUzF//nx06NABDRo0QL169dCkSZNsU5hmcnd3l6bZVCqV6NmzZ477zExqpaenw8fHR+Mai5kmTpwou+/cunDhgvT8/PnzaN26dY5t/Pz8gIw1ITN17NgRLVu2xI0bN5CYmIiRI0di2rRpauescePGsLGxKZC4M2VNImadXjUnmaNEAcDMzKxAYypoxSKJWLduXfz1119FHQaRVkuXLi3qEIiIiIiIiIiIiIjypYVDOdz/bCyik5MQ+CZva7p9iMqWsICVUfEYFbZr1y4MHDgQx48fBzLWzTt06BAOHToEZIwqbd68OebMmYPu3burtfX395eex8bG4vr167nad0hIiNbyDh065Ko/bbLG+vjx41y1zRqnQqHAyZMn0aNHD9y4cQMAEB4ejr1790qjMvX09NChQwf89NNPOiUrdZE1iRgXF4f09HSdRj/6+vpKz8uXL18gsRSWYpFEJCIiIiIiIiIiIiKid8fKyLjYJM1InbGxMQ4fPoz9+/fjjz/+wJ07d9TW0RNC4ObNm+jRowfGjx+PNWvWSGVZR8Q1bdoUnTt3ztW+K1asqLW8TJkyuepPm6yxTpo0CZaWljq3tba2VnttYWGBixcvYufOnVi5ciUePHigVq5SqXD+/HlcuHAB8+bNw7x58/Idf7ly5aCvr4+0tDSkp6fj1atXqFSpUo7t7t+/Lz3P6XwXNSYRiYiIiIiIiIiIiIiIihGlUolBgwZh0KBBiIiIwH///Qd3d3ecO3cON27ckKYgXbt2LWrUqIGJEycCGesGZipZsiR+/vnnAo2rINfwK126tJRQ++STT3Kd8HyboaEhRo0ahVGjRiEkJAR3796Fm5sbzp49i9u3byM9PR1CCMyfPx+1atWS1lTMK2NjY7i4uODevXsAgFu3buWYRFSpVPDy8pJeN2/ePF8xFDa9og6AiIiIiIiIiIiIiIiINCtZsiQ6d+6MmTNn4uLFi4iIiMDw4cOl8sWLF0vP69evDz29/0v9uLq6Fkm8umrYsKH0vKBjtbe3R48ePTBv3jzcuHEDISEh+OSTT6TyRYsWFch+OnbsKD3ft29fjvUfPHggjcDU19dHy5YtCySOwsIkIhERERERERERERER0XvCzMwMGzduhJWVFQAgODgYAQEBAAA7Ozu0atUKABAZGYnt27cXaaza9O/fX3q+efNmxMXFFdq+SpYsib///hv6+v83QefDhw+RnJyc734HDx4sPT9+/DiCg4O11t+5c6f0vFevXmrrKhZHTCISEREREREREREREREVA5nr6+XE0NAQDg4O0msDAwPp+ezZs6XnkydPRlBQkE77DgwMzHW8+VG/fn10794dAPDq1St8//33OrUTQqgdU3JystqakXJsbGxgbm4uvc5MKOZHo0aN0LRpUyDj2mlba9HPzw/r16+XXo8dOzbf+y9sTCISEREREREREREREREVA3fu3EHt2rWxdetWadpLTXbt2oUnT54AACpXrgx7e3uprEuXLtIIuejoaLRo0QIXL16U7Ss8PByzZs1CtWrVoFKpCvR4crJixQppNN6mTZswbNgwREdHy9a/fv06WrRogd9++03aduLECTRu3Bj//vuvtFakJr///juioqKAjLUIlUplgRzDunXrpL42btyINWvWZKsTGhqKrl274s2bNwCAnj17olu3bgWy/8KkELqkZ4mIiIiIiIiIiIiIiKhQrVmzBhMnTgQAGBsbo3Xr1qhTpw5Kly4NKysrhISE4OLFi7hw4YLUZu/evRg4cKBaPwkJCejSpQtu3LgBAFAoFOjevTuaNm2K2rVrIy0tDU+fPsW1a9dw8eJFpKWlARlTo2ZNSGa2zRQQEICyZcvqdCxVqlSBt7c3AOD8+fNq6wdmdebMGfTt2xeJiYkAgNKlS6Nnz55o2LAhHB0dERISggcPHuDYsWPw9fUFMqYR3bNnDwBg7ty50hqHJUqUQNu2bVG7dm04ODjA3NwcAQEBOHPmDG7dugVkjEA8d+4c2rVrp9Nx6GLGjBlYtmyZ9Lpz587o3bs3HBwc4OHhgbVr10oJTHt7e7i7u8PR0bHA9l9YmEQkIiIiIiIiIiIiIiIqBg4dOoRvvvkGISEhOtWfNWsWFi9erLEsOTkZ48ePx9atW3Oc7rN06dKYPXs2xo4dm22az8JOIgKAm5sbBg0aJCUJtenevTt++eUX1KtXDwDw119/4YcfftA6gjGrP//8E5MmTdKpbm4sXboUs2bN0nqua9SogZMnT6JixYoFvv/CwOlMiYiIiIiIiIiIiIiIioG+ffvi5cuX0ujCt0cFImMkXevWrXHs2DHZBCIAGBkZ4a+//oKrqysGDx6sth4gACiVSjRu3BjLly+Hj48PJkyYUCDrBOZF48aN8eTJE6xcuRKNGjXKVm5jY4MBAwbgxo0bOHnypJRABIDRo0cjMDAQ27ZtQ+/evWFjY5OtvaGhIbp06YLLly8XSgIRGaMRr1+/js8++0xtjUoAcHZ2xp9//gl3d/f3JoEIjkQkIiIiIiIiIiIiIiIqvsLCwhAWFoaYmBiYmZnByckJJUqUyHU/qampCAwMRFhYGCwtLVG2bFmd+smcEhUZyT5DQ0Od9nf37l0kJSUBAFxcXGBpaalzrNHR0QgMDERSUhLs7e1RpkwZ6OnpNi5OCIHQ0FCEhYUhLi4OlpaWqFSpEkxMTHTef34lJyfj1atXePPmDZycnKR1H983TCISERERERERERERERERkRpOZ0pEREREREREREREREREaphEJCIiIiIiIiIiIiIiIiI1TCISERERERERERERERERkRomEYmIiIiIiIiIiIiIiIhIDZOIRERERERERERERERERKSGSUQiIiIiIiIiIiIiIiIiUsMkIhERERERERERERERERGpYRKRiIiIiIiIiIiIiIiIiNQwiUhEREREREREREREREREaphEJCIiIiIiIiIiIiIiIiI1TCISERERERERERERERERkRomEYmIiIiIiIiIiIiIiIhIDZOIRERERERERERERERERKSGSUQiIiIiIiIiIiIiIiIiUsMkIhERERERERERERERERGpYRKRiIiIiIiIiIiIiIiIiNQwiUhEREREREREREREREREaphEJCIiIiIiIiIiIiIiIiI1TCISERERERERERERERERkRomEYmIiIiIiIiIiIiIiIhIDZOIRERERERERERERERERKSGSUQiIiIiIiIiIiIiIiIiUsMkIhERERERERERERERERGpYRKRiIiIiIiIiIiIiIiIiNQwiUhEREREREREREREREREaphEJCIiIiIiIiIiIiIiogITFxeHadOmSY8DBw4UdUhFwsPDA82bN8elS5eKOpQ80S/qAIiIiIiIiIiIiIiIiOjDkZCQgOXLl0uvx40bh/79+xdpTO9SUFAQNm3ahF9++QUpKSlISEgo6pDyhElEIiIiIiIiIiIiIiIiojwIDAzEf//9h7CwMDx58gTu7u64cuUKVCpVUYeWb0wiEhEREREREREREREREeXB8ePHMW7cuKIOo1AwiUhEREREREREREREREQFxtbWFt7e3tJrCwuLIo3nXVAoFKhQoQKaNWuGfv36YciQIUhPTy/qsPJFIYQQRR0EERERERERERERERER0fsmLi4OKSkpsLKyglKplLYbGBggLS0NyBit2LNnzyKMMm84EpGIiIiIiIiIiIiIiIgoD8zNzYs6hEKjV9QBEBEREREREREREREREVHxwpGIREREREREREREREQfmfTkGKQnBBd1GMWG0tQBSiPLog6DqFhhEpGIiIiIiIiIiIiI6CORFOyGiJuzkBr5uKhDKWYUMLCpjpItl8DYvlFRBwMAePHiBU6fPo0rV67g6dOnSE5Ohp2dHcqUKQMnJyf07dsXjRrlHGtkZCT27t2LM2fO4OXLlwCAqlWrolevXhg8eDD09bWnioYMGQIAsLS0xPr166XtsbGx2LFjB+7evYvnz59j+/btqFSpklTesWNHvHr1CgCwbds2tGjRIsdY3dzcsGfPHty7dw8hISGwsbFBy5Yt8fnnn6NOnTo5tn/8+DHOnDmDq1evwtvbG2lpabC3t0eZMmVQpUoVDBw4ELVq1cqxH/o/CiGEKOogiIiIiIiIiIiIiIiocCUGXEXI2eGAKrWoQym+9Axg32UbTBzbFFkIKpUK06ZNw59//om0tDStdStXroyNGzeiQ4cOGsvXrl2L2bNnIzo6WmN5rVq1sH37djRo0EB2HwqFAgDg4OCA169fIz09HYsWLcJvv/2GuLg4qd6TJ09QrVo16XWVKlXg7e0NADh//jw6duwou4+goCBMmDABhw8f1liup6eHb775BitWrNCY9ExJScG4cePw999/I6e0V61atbBjxw7Ur19fa738MjAwkK7f8ePH0bNnz0LdX2HgSEQiIiIiIiIiIiIiog+cEAJRd5cxgZgTVSqi7v4K47KtpeTZuzZ9+nT8/vvv0uv69eujdu3aMDAwQGRkJHx8fPDw4UOoVCr4+PhoTJoJITBu3Dhs3LhR2mZlZSWN5vPy8kJERAS8vLzQpUsXXL16FTVr1swxtvj4ePTu3RsXL15U216yZEkYGRnl6XifPHmCLl26wN/fH8hIWlauXBlVqlRBQEAAHj9+jLS0NKxZswbR0dHYsWNHtmszatQo7Nq1C8hIODZu3Bg1atSAnp4eIiIi8OzZMzx58gRCCHh5ecHQ0DBPsX5smEQkIiIiIiIiIiIiIvrAqVJikRJ2v6jDeC+khN2DKiW2SNZIDA4OxqpVq4CMkWzHjx9Hly5dstWLiorC0aNHsWrVKo2jCH/55RcpgWhlZYVFixZh3LhxUCqVAIDU1FRs3boV06dPR0REBPr27YuHDx/CwMBANrb09HQMGjRISiA6Ozvj559/RvPmzVGhQoU8HW9MTAx69eolJRDbtm2L1atXw8XFRarz4sUL/Pjjj9i/fz927dqFxo0bY/LkyVL5o0ePsHv3biBjytVTp06hefPm2fYVGhqKgwcP4q+//kL16tXzFO/HRq+oAyAiIiIiIiIiIiIiIiLg/v37SElJAQB06dJFYwIRAKytrTF8+HC4u7vD2tparezRo0eYN28eAMDU1BRnz57FhAkTpAQiMhKUX3/9Nfbv3w99fX08e/YMGzZs0BpbWFgYTp48CQCYNm0aHjx4gMGDB+c5gQgAM2bMgI+PDwCgW7duuHDhgloCEQCcnJzw77//ol+/fgCAhQsXIiYmRip3c3OTRmMOGDBAYwIRAOzs7DBu3Di4ubmpnQuSxyQiEREREREREREREdEHTs/QAoal6hZ1GO8Fw1L1oGdoUST7zrp2YWJiYp76WLRoEdLT0wEAy5cvR+PGjWXrdurUCcOHDwcAbNu2Taf+R44ciV9//RUmJiZ5ii9TQEAAtmzZAmRMh7p3717Z5J6enh42bdokTel6/PhxqawgzhlpxiQiEREREREREREREdEHTqFQwLrhj4Ce/HSVBEDPANaNfiyy9RAbNGgg7fvSpUuYMWMGIiIidG6flJSEI0eOAFlGK+Zk8ODBAIC7d+8iMjJSa91y5crhjz/+0DkebQ4dOiSNuvzqq69gbm6utb6NjY00MvPcuXPS9oYNG0rP9+7di6VLlyI2NrZAYvzYcU1EIiIiIiIiIiIiIqKPgIljGzj03IeIGzORGvkEgCjqkIoRBQxsqqNkyyUwtm9UZFFUqVIFo0ePxubNmyGEwLJly/Dbb7+hcePGaNCgAerVq4emTZuidu3aGtu7ubkhISEByEgo1q2b8+jTzFGLQgj4+vrCxsZGtu6ECRNgaVkwa0VevnxZer59+3YcOHAgxzaZCdXMKVABoFWrVujVqxeOHz+OtLQ0zJw5E/Pnz0fTpk2lc9asWTNUq1atQOL+mDCJSERERERERERERET0kTC2b4Sy/c4hPTkG6QnBRR1OsaE0dYDSqGCSY/m1fv16pKenY+vWrQCAtLQ03Lp1C7du3ZLqVK5cGbNnz8bIkSPV2gYGBkrPExMT4e3tnat9h4aGai1v06ZNrvrTJmusr1+/zlXbt+Pcv38/PvvsMxw9ehQAkJycjKtXr+Lq1atSHRcXF8yfPx/9+/fPd+wfCyYRiYiIiIiIiIiIiIg+Mkojy2KTNCN1SqUSW7ZswfDhw/HHH3/g3Llz0ujCTD4+Phg1ahROnDiBf//9V1pLMHN6UABo164dBgwYkKt9V69eXWu5g4NDrvrTJmusCxYsQMmSJXVua2GhvmalkZERDh8+jFOnTmHlypW4cuUKkpOT1eo8fPgQAwYMwLhx47Bu3boCOIIPH5OIRERERERERERERERExUzbtm3Rtm1bpKWl4fHjx3B3d8e5c+dw7tw5hIeHAwAOHDiApUuXYvbs2QCAMmXKSO319fUxYcKEAo3J0NCwwPoqU6YMPDw8gIx1DXv27Jmv/hQKBXr06IEePXogOTkZXl5ecHNzw9mzZ3HhwgXExMQAGSM9GzVqhNGjRxfIcXzI9Io6ACIiIiIiIiIiIiIiItJMX18ftWvXxsiRI7F7924EBwfjhx9+kMp/++03CPF/61s2bNgQ+vr/N37szp07UKlURRZ3Tpo1ayY9d3V1LdC+jYyM0KBBA4wdOxYHDhxASEgIRowYIZUvX768QPf3oWISkYiIiIiIiIiIiIiI6D2hVCqxdOlSlCpVCgAQFRUFf39/AIC1tTU6duwIAIiNjcXq1auLNFZt+vfvD4VCAQDYuHGjNLqyMBgZGeHPP/+EsbExAODp06dISkoqtP19KJhEJCIiIiIiIiIiIiIiKgaSkpLw5s2bHOsplUq1dQFNTU2l5/PmzZOez5w5E8+ePdNp3w8fPsx1vPlRo0YNDBw4EAAQGhqK8ePH69QuJSVF7Zji4+PV1leUU6JECWk6Vn19/QKdmvVDxSQiERERERERERERERFRMXDnzh1UqVIFS5YsQWBgoMY6Qgj89ttv8PHxAQDUqlULtra2Unnz5s2ltRATEhLQvHlz7NmzR3afT58+xahRo1C/fn2kp6cX+DFp89tvv8He3h4AsG/fPnTt2hUBAQEa66akpGD//v1wcXHBunXrpO0nT55EtWrVsHLlSoSFhWlsm5aWhlmzZiE2NhYA0KZNG+jpMUWWE/2iDoCIiIiIiIiIiIiIiIgADw8PvH79GrNmzcKsWbNQu3Zt1KlTB6VLl4aVlRVCQkJw6dIladSgUqnEihUrsvWzYsUK+Pv74+jRo4iMjMTnn3+O5cuXo2nTpqhduzbS0tLw9OlTXLt2DQ8ePJDahYSEoEyZMu/seB0dHXHkyBF0794dUVFROHv2LGrWrIm2bduiYcOGcHR0REhICB48eIAzZ84gJiYGANQSrB4eHvDz88OUKVMwdepU1K9fH7Vr14aDgwPMzc0REBCACxcu4Pnz5wAAExMT/PrrrwV2DImJibhz50627ZnrVAKAp6cnzMzM1MorVaqE8uXLF1gchUEhsh4FERERERERERERERERFYnz589j0qRJePLkSY51FQoFfv/9d0yZMkVjuUqlwty5c7F8+fIcp/usWbMmFi5ciH79+knrFGbdT6aAgACULVtWp2OpUqUKvL29pePKXKtRk2fPnuGLL76Au7u71j6VSiWGDRuGefPmoUKFCgCA/fv3Y9q0afDz88sxJqVSie3bt+Pzzz/X6Rh08fTpU1SvXj3X7RYtWoTZs2cXWByFgWM1iYiIiIiIiIiIiIiIioFOnTrh8ePHuHr1KiZNmoQ6depAX199UkkrKysMHDgQt2/flk0gAoCenh4WL16MR48eYcqUKahYsaJauaWlJT755BPs3LkTnp6e6N+/f7YE4rtStWpV3LlzB//++y969eoFExMTqUyhUKBatWqYOHEiHj16hC1btkgJRAAYMGAAfHx8cObMGYwdOxbVq1fPNlVpqVKlMGzYMDx48KBAE4gfOo5EJCIiInrPCCEQnaxCaGI6UlUCFoZ6sDJSwsxAAb0i+mOfiIiIiIjyRwiBmBQVQhPSkaoCLAwVsDDUg5mBHpR6/Duf6GMmhEBUVBRiYmJgZmaGUqVK5bmvxMREhIeHw9LSEhYWFjq1yVx7EQAqVqwIpVKpU7tXr14hNTUVAFCmTBm1xKAuIiIikJSUBDs7OxgYGOSqrUqlQmRkJOLi4mBpaQkbG5tctc+N1NRUvHr1KtftbGxsYG1tXSgxFRQmEYmIiIiKqZjkdNwMTsL9sBQEvklDaEI6QhPTEZKQhhRV9vp6CsDCQA+WRnqwNtJDVStDNLY3QmN7Y5QtoSyybxMSEREREdH/9yZVBdfgJNwPT0bQm3QEJ6Qh+E06ghPSkZSu+aNaM4P/SyhaGylRzdoADe2M0MjOGE4W+vw7n4iICg2TiERERETFRJpK4H54Mq4FJeFaUCI8I1KgKqC/1EqbKqWEYnMHY1S0yN03+IiIiIiIKG+EEHganYqrgYm4GpSIu6HJSNXwpcC8sDbSQyM7IzS0M0aL0saoYW3ApCIRERUYJhGJiIiIipAQAh5hydjxJA6XAhMRn/pu/jRrWMoIX1QzR9cKpjBS8kMGIiIiIqKC9iQqBbuexuGifyJCEtPfyT5rWBtgSFVzfOpUAmYGejq0ICIiksckIhEREVERSFMJnHmVgC2PYnE/PKXI4rAx1sNAZzMMqWoORzN9HVoQEREREZEcIQSuv07CZq9Y3HidVGRxlNBX4FOnEhhS1Rw1bQyLLA4iInq/MYlIRERE9A7Fpaiw1zse2x7HIujNu/k2si4UANqWNcHXLhZoYm9c1OEQEREREb1XUtIFjvu9wV9esXganVrU4aipa2uIcS6W6FTOhFOdEhFRrjCJSERERPQOJKcLbPKKwSavWLx5R1OW5lW3CqaY2dAaZTkykYiIiIhIq3SVwD/P47H2Qcw7m7I0r5o7GGNOY2tUs+bIRCIi0g2TiERERESF7ObrRMy7HQnf2LSiDkVnRkoFxtSywFgXC5jocy0VIiIiIqK3eUWkYK5rBB5EFN3yBLmlpwAGVzHDlHpWsDFWFnU4RERUzDGJSERERFRIwhLTscQ9Ekd9E4o6lDwrbarEjIbW6FHRlFMfEREREREBeJOqwop70dj2JA6q9/STVQtDPUyqY4mh1c1hoMe/84mISDMmEYmIiIgKWOaURsv/i0JcAUxdqlQAdWyNUNlSH/Ym+rA3VcLOVAl7EyVM9PUQm6pCbLIK0SnpiElWISIpHffDU+ARloyEtIL5U6+JvRF+bWkLR05xSkREREQfsXOvErDwTiReJ+R/6lIFgFolDeFkYQAHUyUcSihR2vT//t431ddDXKoKsSkqxKX8378RSel4EJGC/0KTEZOiKpDjcbY0wB+tbVHDhlOcEhFRdkwiEhERERWgoPg0TL4WDo+w5Hz1U85MH63LGKNVGRM0dzCGuWHupxRNVQk8jkzBnZBkuIUk4WZwEhLzkVS0MNTDshYl0bm8aZ77ICIiIiJ6H0Unp2P2rUiceZW/WUZKGuuhdRkTtCljgpZljFEyD1OKqoSAd0wq7oYmwy0kGZcDExGbj6SioR4wt4kNBlcx4+wjRESkhklEIiIiogJyIygRU66FIyo5b/+BL6GvwMAqZvi8qjkqWegX+H/g41JUOOQTj13P4uETk5rnfkbWMMe0BtYwVPIDBiIiIiL68D2OTMH4y2Hwj8/bGucGekAfJzN8XtUMtUoaQq+A/85PSlPh5MsE7H4ah3vheV+fsUcFUyxuXjJPX2AkIqIPE5OIRERERPkkhMCGh7H4/V50ntZEKW2qxPAaFhhUxeyd/IddCIHbIcnY/TQOZ18lIC+DE+vaGuLPNqVQltObEhEREdEH7MiLeMy+FYmk9Nz/0WxlqIfPq5nhy+oWKGWS+xGHefEoMgX/PIvDkRdv8CYPf+iXN9fHn21s4VLSqFDiIyKi9wuTiERERET5EJeiwo83w3H2VWKu21a1MsD42pboWsEUBnpFM6ov6E0alv8XhaO+uZ+WycJQD7+2LIlO5Ti9KRERERF9WFJVAsvuRuHvx3G5blumhBJjalmif+USMDUomlF9UUnp+PN+DHY/i0Nu858GesDMhtb4sro5pzclIvrIMYlIRERElEfPo/9vWiPf2NxNa2Sqr8C3da0wvIZ5kSUP33Y3NAkL70TBKzL30x9NrGOJyXUt+QEDEREREX0QwhLT8e2VMLiF5m6dc30FMKqmBSbVtYSJfvGYEvRZdAoWu0XhxuukXLftX7kEFjcvCf1i8n8WIiJ695hEJCIiIsqDK4GJmHQlDAm5nCKoczkTzG1sgzLFcBrQdJXAPu94/OYRnet1HYdUNcP8JjZQ8gMGIqL/x959h0dRdm0Av7em994gkAIBQgm9d2lSpCoqiooNCxbs7RVEUNAPBRvYC0UEpJfQO4QSCBASElp679lsm+8PcAUhM5uQbDbJ/bsuLpPdM7MnkMTZOc9zDhER1WNxuRV4amc2MssNVTouyssGs7q5o4WbutZyqy5BELAjpRwfx+TjSnHVFkD2D7TDF308raYoSkRElsUiIhEREVEVbb5Sipf35UBXhTpbgIMC73Vxx8B60PqzSGvEnJg8rLpYWqXjhja1x4JenrBRsJBIRERERPXP8SwNHt+RhRKd+bdLndVyvBbliolhjpBbeWeOCoOARacL8M2ZIlTlhnBHLxt8N8ALLjaWmetIRETWg0VEIiIioipYnVSCNw7mwliFK6jRzR0wq6t7nc1Dqa41SSV470geyquw27Knny0W9/OCYz37WomIiIiocTuUXo4nd2VX+dp3QS9PeNrVr+LavrRyvLI/B3ka81dFhruq8MMgb/jaW19HFSIiqj0sIhIRERGZ6bf4YnxwNM/seKUMeKuzGx5u4VRv5wVeLNDh+b3ZSCzQmX1MWw81lg70hrtt/bqZQkRERESN066UMkzfnQ1tFTqNPNXGGS+3d6237fyzyvR4eX8ODmeYP/cxwEGBnwb7oJmzqlZzIyIi68EiIhEREZEZvosrxCcnCsyO97ZTYFFfT0R529ZqXpZQrjfif0er1t60ubMSPw3yscrZj0RERERE/6jqqAIHpQzzenpgaFOH2k6t1hmMAhadLsSi04Vmtzd1s5Hjh4HeiPS0qeXsiIjIGrCISERERCRCEAQsjL3+xtpcnb1t8EVfL3jVs7ZGUtYkleDtQ7lmr9AOcFBg+VBf+DmwkEhERERE1mdNUgler8KogubOSnzVzxuhrg1rJ96h9HI8tycHhWZe6Dur5fhjiA9auqlrPTciIqpbLCISERERifjkeD6+O1tkdvyUlk54s5MbVPW0rZGUIxkaPLkrC6U68y4hQ11UWDbEB25sbUpEREREVmRFYjHeOZRn9g68QUF2+LSnJ5zUDXP2d0KBFlOjs5BZZjAr3tNWjuVDfRHM1qZERA0ai4hERERElfjhXBHmxOSbHf9Sexc8G+lSb+cfmutsrhaP7chErsa8lcrtPNX4ZbAPHFQN84YLEREREdUv265en4Fo7k3RCaGOmN3Nvd7OPzRXWokej0ZnIrlIb1Z8oOP1ziO+9uw8QkTUULGISERERHQHGy6VYsa+HLPj3+7khqmtnGs1J2tyuUiHR6MzkVJi3krlnn62+G6AN2wUDfvGCxERERFZt5hMDaZszzS7Rf8jLZ3wdmc3yBv4QsF/5GkMeGJHFk7nas2KZ+cRIqKGjUVEIiIiov84mF6Ox3dkQWfGjQUZgNnd3TEpzMkSqVmVzDI9pkZnIaFAZ1b80Kb2WNjbs8Gv4CYiIiIi65RYoMX9WzLNnv33dBtnvNLBtcF3GvmvUp0Rz+7OxoF0jVnx7DxCRNRw8Tc7ERER0U3O5WnxzO5sswqIChnwWW/PRllABAAfeyWWDfFBRy8bs+K3XCnDe0fywDVsRERERGRpGWV6PBadZXYB8eX2rng1yq3RFRABwEElx5IB3rg32N6s+NgcLZ7dnY0KA6/ziYgaGhYRiYiIiG64VqzD4zuyUKqTfvOrkgOL+nphZDMHi+RmrVxsFPhxkDfae6rNil+RWIL5JwtqPS8iIiIion8Ua414YkcW0svMa8X/dic3PNvWpdbzsmZqhQzze3niniZ2ZsUfSNfg5X05MHLBIBFRg8J2pkREREQAcjUGTNqcgcvFeslYhQz4pr8X+geatzK3MSioMOCBrZlINLO16Zzu7pjYSHdwEhEREZHlVBgEPL4jE4czKsyKf7+LOx5uyevUf1QYBEzbkYWDGea1Nn020hkvd3Cr9byIyPoVFhbiueeeM30+YMAATJ06tU5zsoS///4by5cvx8mTJ5GdnQ1nZ2cEBgZi0KBBmDRpElq2bFnXKVYJi4hERETU6FUYBDy4NQOncrRmxc/r4YFxoY61nld9k1mmx6QtGUgpkV7hrZIDfwzxRQczW6ESEREREVWVIAh4aV8ONlwuMyv+ubYumNHetdbzqm9KdEY8sj0TsWa+X/qyryeGNW3cHVuICMjMzISvr6/p86effhpff/11neZUm1JSUvDMM89gw4YNlcYoFApMnToVn332GZyc6seCFbYzJSIiokZv1tE8swuIL3dwZQGxEj72Svw0yAcettKXmDoj8NzubGSVSe/8JCIiIiKqjiVni8wuII4PccCL7Rp3C9PKOKrkWDrAG6EuKrPiXzuQi/h8895fERE1BNeuXUOHDh1MBURXV1f06NED/fv3R9OmTU1xBoMBS5cuRe/evZGZmVmHGZuPRUQiIiJq1P5MLMHyxBKzYh9q4YRn2jjXek71WbCzCj8O8oGjSiYZm1luwPQ9OagwsDEGEREREdWsQ+nlZs/i7htgi1ndPSCTSV/DNlZutgr8NNgbgY4KydhyvYBndmUjX2PeDEoiovpMr9fj/vvvR05ODmQyGebOnYuMjAwcOHAAO3fuxOXLl3Hy5El07NjRdExsbCwmTpwIvd76F1aznSkRERE1WnG5FZi4OQNao3TskCb2+KKPJxRy3lgwx7FMDR6NzjKrQHh/mCNmd/ewSF5ERERE1PCll+oxemM68jTSF/ptPdT47R4f2Ku418Icl4t0uH9LBnLM+Lvt4WuLHwZ5Q8n3UESNkiAIKCv7dze4UqmEjU3DG2ny+eef4+WXXwYALFu2DPfff/8d47RaLZ555hn88MMPpse+/vprPP300xbLtTpYRCQiIqJGKV9jwJiN6UgtlV4d29nHBj8N8oGNgm9+qyL6Whme2ZUNcy42Z3VzxwPh9WMeABERERFZrwqDgMlbM8ya39fUSYmVQ33hYSe9u47+FZdbgUlbMs1aMDg1wglvd3a3SF5ERJYmCAJatGiBxMREjB07Fn/99ZdovEajQUREBC5fvgwAaNq0qelja8UlNkRERNToGIwCXtqXY1YBMdRFhW/6ebGAWA2Dguwxo715c2U+PJqH41maWs+JiIiIiBq2OTF5ZhUQ3W3l+GGQNwuI1dDGwwZzuptXGPzxfDHWJJk3PoKIqL45fPgwEhMTAQCzZs2SjLe1tcXs2bNNn1+5cgWxsbG1muPdUtZ1AkRERESWtjC2APvTpQtWTioZvunvBRcb3liormciXXA2T4ttV8tF43RGYPrubKy91w++9rxEJSIiIqKqW5NUgt8vSBeslDLg635eaOqkskheDdHo5o44l6fD9+eKJGPfPpSLcFc1WnuoLZIbma+kTIucfPH3ao2Jp5sdHO35fUrm69SpE7p16wadTodWrVqZdUyfPn1u+fzYsWNo165dLWV493iHhoiIiBqV6Gtl+OqM9BtdAFjQ2xPBzryxcDfkMhk+6emJ5MIMXCzUicbmaIyYuT8XPw/2hlzGnZ9EREREZL7zeVq8ezjPrNg3O7mho7dtrefU0M2MckV8vhYHJBZoao3Ay/uzsWa4H2dPWomzF3Pw5e8ncCmlsK5TsSoyGRAc4ILnH4xC61DPuk6H6gGVSoW//voLGzZsMPuYoKAgODs7o6jo+r2pzMzMWszw7vG3NhERETUa14p1eHV/jlmxz7V1wYBA+1rPqTFwVMnxTX8vOKmkC4OHMjT4/qx5RV4iIiIiIgAo1hoxfU82NGbM6BsZbI8pLTmLuyYo5TIs7OOJIEfpfSpJhXrMicm3SF4k7vi5TLw2fzcLiHcgCMCllEK8Nn83jp+zjsLO0aNH8d5776Fv377w9fWFm5sbWrRogf79++Pxxx/Hhg0boNVKt3C+dOkSPvjgA3Tv3h3+/v7w9/dHv379MH/+fJSUSO/g7t+/P/r3749x48bd8nhKSgreffddDB8+HGFhYabWnv9o164dnJyc4OTkhN27d0u+jiAIWL9+PSZPnoxWrVrBw8MDYWFhePTRR7Fjxw7J4wFgz549eOONN9CjRw94e3vD3d0dERERGDhwIJ5++mns2LEDBoP0eBtz+fv748knnzQ73mAw3PJv5ulp3QVrmSAI0v93JSIiIqrnDEYBD27LRExWhWRsH39bLBngDYWcu+Fq0p7UcjyxIwtSF58qOfDnMF+08bCxUGZEREREVJ+9uj8Ha5NLJePCXVVYNcyXu+FqWEK+FuM3Z6BML32b+at+XrinCRdr1hVBEPDCnB1IuMyCrpQWzdyx8M0BkNVRl5yKigo89thj+OOPPyRj3dzc8Ntvv2H48OG3PWc0GvHBBx/gk08+QUXFne+HBAQE4JdffsGAAQMqfY1//h58fX2Rnp4OjUaDl156CUuXLoVerzfFxcfHo0WLFqbPw8LCcPHiRQBAdHQ0Bg4cWOlrXLhwAVOnTsWhQ4cqjRk3bhx++eUX2Nvf/nukuLgYkyZNwubNmys9/h8+Pj74+++/0bVrV8nYmpaQkHDL39HevXvRu3dvi+dhLrYzJSIiokZhydkiswqIgY4KfNbbkwXEWtA3wA6vdHDF/JMFonE6I/DyvhysHcF2R0REREQkbvOVUrMKiI4qGRb38+L1ZS0Id1Pj056emL4nWzL2rYO5aOup5hz0OlJarmMB0UwXLuWhtFxXZzMSp0+fbiogKhQK3HPPPYiMjIRKpUJeXh6SkpJw6NAhFBcXIz8/H46OjredQ6fTYdKkSVizZo3psbCwMHTo0AEAEBsbiwsXLiA1NRWjRo1CdHQ0unXrJplbTk4OBg8ejFOnTpkek8vlaNmyJRwcHKr19R49ehTDhg1DXt71ttR2dnbo2LEjwsLCkJKSgsOHD6O4uBh//fUXSktLsW7dOqhUt46feeCBB0wFRBsbGwwdOhQRERGQy+XIzc1FQkICDh8+jPLycmRmZsLNza1aud6t6Oho08eenp6IioqqkzzMxd/WRERE1OCdzdViYax44QoAbBQyLO7rDVcbhUXyaoyeauOMs3labL5SJhqXXKTHx8fzMaubh8VyIyIiIqL6JatMj3cPmTcH8ZOenmjGeee1ZkhTezwT6YyvJebPF2g5B51IyrVr1/Djjz8CAGxtbXHgwIE7Fpr0ej127NiBL7/80lQYvNnrr79uKiAGBARg4cKFt7UjXbduHZ588klkZmZiwoQJSEhIgJ2dXaW5abVajB492lRA7NKlC+bNm4cuXbrccXegOTIzMzFmzBhTAXH8+PH4/PPPERgYaIrJy8vDe++9h8WLF2PLli2YN28e3nnnHdPzJ06cwMaNGwEAXl5e2L9/P8LDw297rYqKCmzevBnff/89wsLCqpXv3dBoNJgzZ47p81dffbXahVdL4dIbIiIiatA0eiNe2Z8DnVE69sOu7mjtUTerDBsLmUyGOd09EOgoXahdllCC7VfFi41ERERE1DgJgoA3DuaiQCt9of9UG2e20LSAF9u5or2n9PupQxkaLOUc9DrhYKdCeHDd7L6qb1o0c4eDXd0sPDh9+jSMxuu/24YNG1bpTjWlUokhQ4Zgw4YNcHK6ddZrTEwMPv/8c+BGu9MdO3bcVkAEgFGjRmH9+vWwt7dHSkoKFi5cKJpbXl4eDh48CJlMhgULFuDQoUPo169ftQuIAPDKK68gPT0dADB58mT8+eeftxQQAcDd3R2LFi3CtGnTAADz5s1Ddva/u59Pnjxp+nj8+PF3LCDixg7FMWPGYP369XXSqnbOnDlITU0FAHTt2hUvvPCCxXOoKhYRiYiIqEGbf7IAFwt1knETQh0xLvT29h9U85zUcizo5QlzOsa+dSgXmWV66UAiIiIialT+SCjB3jSNZFxXHxu81N7VIjk1dkq5DJ/19oSDSvpC/7OTBYjLlR43QTVLJpPh0fsioVRwF6gYpUKGR8e0qbN5iOXl5aaPMzIyqnWO2bNnmz7+4osvbpnB91+dO3fGY489BgBmzWAEgBkzZuDll1+GXH53JaakpCQsW7YMAODv748ffvhBNH7BggWws7NDSUkJ1q9fb3q8Jv7OatvatWtN/y5hYWHYsGGD6K5Pa8EiIhERETVYB9LK8dP5Ysm4Jk5KvNOZqzEtqaO3LZ6NdJGMy68w4rUDuTAKgkXyIiIiIiLrd6lIh49jpOe6OavlmN/LE0rOO7eYJk4qfNDFXTJOLwAv7ctBmTktY6hGdWzlg09e7YdmgS5gR9lbyWRAs0AXfPJqP0S18qmzPDp37gyF4nr3nkOHDuHRRx9FQkKC2ceXlpZiy5YtAAAfHx9MnDhR8phJkyYBAM6cOXPLDr87adGiBT766COz8xGzZs0a067Lp556CjY2NqLxTk5OGD58OABgx44dpsdvnuW4Zs0azJgxA1euXKmRHGvCoUOH8PDDD0MQBHh7e2PLli3w9PSs67TMIhME3pEhIiKihqewwoDh69ORWWYQjZPLgOVDfRHlJX6hSjVPbxRw/5YMnMrRSsa+3ckNU1s5WyQvIiIiIrJeOqOASZszcDpX+hry896eGNnMumdNNUSCIODlfTlYf1l6NMGDLRzxv66cg15XSsq0yMkvNyOycfB0s4OjvXWMOHnjjTcwb968Wx5r1qwZoqKi0L59e3Tt2hW9e/eGra3tbcfu2rULAwYMAAAoFAqzdrsJgoDS0lIAwJEjR9ClS5dbnr95V+bnn3+OGTNmmPV1hIWF4eLFiwCA6OhoDBw48JbnR48ejXXr1gE35j8qlUrJc2q1Wmi1WnTv3h0HDx40Pf7oo4/i559/viU2PDzc9HfWrVs39OjRAyqVZdvUHjt2DIMGDUJRURH8/f2xefNmtG3b1qI53A3pfxEiIiKieuj9I3mSBUQAeDbShQXEOvJPu6OR69NRqhdf17bgZAEGBtmhiVPdzKQgIiIiIuvw9ZlCswqI9wbbs4BYR2QyGT7s5oET2RVILRV/T/b7hRKMDHZAJ5/bCyFU+xzt1VZTNKNbzZ07F3K5HHPnzsU/+8AuXbqES5cu4a+//gJuzAl89dVXMXPmzFuKb//MFwQAg8GAkpKSKr12Tk6O6PM9evSo4ldTuZtz1WikW1Tf7L95/vDDD1Cr1ViyZInpsYSEBCQkJGD58uUAAD8/P7z99tt49tlnLdKudt++fRg5ciSKiorQtm1bbNiwAUFBQbX+ujWJOxGJiIiowdl0uRQv7BW/6AWASA81Vg7zhYrtjerUmqQSzDyQKxnX088WPw3yrrO5FERERERUt07nVGDC5gwYJO5m+tgrsHGkH1xtFJZKje7geJYGD2zNhFHi3yvERYl19/rDhnP6iG4THx+P//u//8OmTZtw7dq1O8b06dMH27ZtM7UC/e233/Dwww8DAIYNG4bHH3+8Sq/Zo0cP+Pn53fLYze/DL126hODgYLPOJbUTsVOnTjh+/DgAYNGiRfD19TU7T3t7ewwbNuy2x0+cOIGFCxdi27Ztlc5HvO+++7Bq1aq7nukoZsOGDZg4cSLKy8sxbNgwrFixAk5OTrX2erWFOxGJiIioQSnSGjHrmPR8FFuFDAt6ebKAaAXGNHfAntRybJBod3QgXYO1yaW4L8TRYrkRERERkXXQGQW8dShXsoAIAPN6eLCAaAX+mYO+6HShaFxSoR5fnS7ESx1cLZYbUX3RsmVLfPPNNwCA7OxsxMTEYPv27diyZQvOnz8PANi7dy/ef/99zJ07FwDg7+9vOr6srAzjxo2r0Zxqsh2ov7+/qYgYEBCAMWPG3PU5o6KiTG1N09LScOzYMWzbtg1btmxBcnIycGNu4hdffGF2W9aq+vXXX/HYY49Br9fjiSeewNdff21Wq1ZrVHtlViIiIqI68OmJfGSXS7cxfb2jG5q7sDWmNfin3ZG/g/SNno+O5SPXjH9fIiIiImpYfjpXhPh8nWTclJZO6OUvPf+LLOO5ti7oYMb4iG/jChGfL92mlqgx8/LywrBhw/DZZ5/h3Llz+OSTT0zPLV68GEajEQDQpUsX067EmJgY6HTSvzvrSu/evU0f3zzfsKb4+/tj9OjRWLx4MRITE/Hqq6+anvvyyy9r/PUA4OOPP8YjjzwCo9GITz75BEuWLKm3BUSwiEhEREQNSUymBssSpHv99/G3xUMtuJvNmjir5ZjXw0MyrkBrxKxjeRbJiYiIiIisw7ViHRbGiu9mw422mDOjuJvNmijlMnzS0wNqibvQegF462AuDFK9T4nI5NVXX0VAQAAAoKSkBFevXgUAODo6Yvjw4QCA0tJSfPzxx3Wap5hx48ZBobi+oPjbb7+ttGVrTZDL5fjwww/h4HB9Xm5ycjLKy8tr7Pw6nQ6PPfYY3nrrLTg6OmLt2rWYOXNmjZ2/rrCISERERA2C1iDgncPSxSVXtRxze3hwrp4V6u5nh/GhDpJxGy6XYWeKeOtTIiIiImoYBEHA+0fyoJHoY6qUAfN7esJOydud1qaZswrPt5Mu7p7O1eLn+GKL5ERkzUpLS5GZmWlW7D8FOABwcXExffzBBx+Y5v3Nnj0bMTExZp1v165dVc73bjRv3hyPPvooAKCoqAiPP/64WTsni4qKTG1QASAvLw8FBQWSx928I9DGxga2trbVzv1mBQUFGDp0KH788UeEhITg0KFDGDlyZI2cu67x/6pERETUICw5W4iLhdIXmm93doO3ff1tI9HQvdnRDZ620peo7x3OQ7HWaJGciIiIiKjubLpchr1pGsm4pyNdEOkp3TaT6sYTrZ0R4SY9TuLzUwW4Vmy9rReJLOHw4cMIDQ3Fiy++iBMnTkAQbl9EodVq8frrr5t2H3bq1Alubm6m59u2bYt33nkHuLFDrnfv3vj8889NLU9vZjQasWfPHgwbNgwDBw6EXq+v1a/vv+bNm4dmzZoBALZv345u3bohLi7ujrG5ublYtGgRwsLCTHMPAWDz5s0ICQnBm2++ibNnz97x2JKSEjz99NMoLS0FAAwePLhGFpgnJyeje/fu2LlzJ4YOHYpjx46hdevWd31eayET7vQdSERERFSPXCrSYcS6NEjVlHr42uLnwd7chWjlNl8pxfN7ciTjHmrhhA+6ulskJyIiIiKyvCKtEfesTUWORvxCv5mzEhtG+sNGwet8a3YmpwLjNmdAqmNpTz9b/DSI79uo8Zo/f/4tbTC9vb0RGRkJPz8/uLq6IjMzEwcOHEBaWhpwY0fdvn370Llz51vOIwgCnnzySSxdutT0WGBgIDp37ozIyEjo9XpcuHABBw4cQEZGhikmNTUV/v7+t5zr5p/HlJQUUxtVKWFhYbh48SIAIDo6GgMHDrxj3Llz5zBkyBCkpKQAN3YMtm/fHh07dkRgYCAyMzNx+vRpHDx40FTknDBhAlauXAncaO26YMEC0/kCAgLQpk0b+Pr6wsnJCSkpKdi/fz9ycq7fa3BxccGxY8cQFhZm1tdRGb1eD39/f2RnZ5u+3pt3hxqNRlRUVMBgMFR6jhdeeMGq255yGT4RERHVa4Ig4N3DuZIFRBuFDB92c+cb0XpgaBN7DA6yw/Zr4rMJfr9QjJHN7NHRu2bajxARERGRdfn0RL5kAREAPuzmwQJiPRDpaYPHIpyx9FyRaNyBdA1WJ5ViXCjn2FPj1KtXL/Tr1w+7d+8GAGRlZWHHjh13jFWr1ViyZMltBUTcKPwtWbIEbdu2xbvvvovCwkKkpKQgJSUFa9asuS2+b9+++Oijj24rIFpCq1atcPToUTzxxBPYtGkT9Ho9YmJi7tiG1cHB4bbC25AhQ7B3714cO3YMuFEITU1NveNr2dvbY9myZXddQAQAg8FgKiACQGJiYpXPUVQk/juxrrGdKREREdVra5JLcTijQjJuelsXBDtLt8+huieTyfB+V3c4qsRvBAkA3jmcB73UUmYiIiIiqneOZ2mwLKFEMm5ciAO6+3JRWX3xYnsXNHGS3tcy93g+iji+gBqpbt26YdeuXbhw4QI++ugj3HPPPfD394darQZu7NJr2bIlnn/+eZw/fx4PP/yw6Pmef/55XLx4EXPnzkW3bt1MsxPt7OzQokULTJs2DdHR0di9ezd69uxpka/xTvz8/LBx40bs2bMHTzzxBIKDg6FSqSCTyeDh4YG+ffti9uzZSE5Oxpw5c25p3zp48GAcPXoUp06dwvvvv49+/frB19fXNANRrVYjMjISr732Gi5evIhhw4bV2ddZ37CdKREREdVbuRoDhv6dhvwK8TeXYa4q/D3CD2quTq5XliUU493DeZJx73V2w5QIZ4vkRERERES1T2sQMHpjOhILxGfjudnIsW20P9xsFaJxZF0OpZfj4e1ZknGPRjjhnc4cX0B0s/LyctjY2EAuv7v9YTqdDiqV+Qut/5kjiBs7+czt8lRWVmaa6Whra3tLq09zCIIAg8FgKgZWlSAI0Gg0sLW1rbXOVDf/3VSHSqUyFYitEYuIREREVG+9eTAXf16UXp28cqgPotjyst4xCgIe3JaJY5niO02d1XJsH+MPD948IiIiImoQvjlTiPknCyTjPu3pgftC2PKyPjLnvZxCBqwf6YdwV+u9uU5E1NCxnSkRERHVS3G5FVhlRgFxcrgjC4j1lFwmw0fdPKCWuGIt0hrxf6ekbzIRERERkfXLLNPjqzOFknE9fG0xprmDRXKimvdGR1d42YkvAjQIwKyj+eAeGCKiusMiIhEREdU7giBg9rF8SL2V9LZTYGaUm0QUWbPmLipMb+siGbc8oQRnc7UWyYmIiIiIas+CkwUo04tf6avlwIfd3GutNR3VPhcbBd7pJP1e7VCGBluvllkkJyIiuh2LiERERFTvbLpShpgs8RaXAPBuFzc4SW1jI6v3RGsXBDmKzz8QAHx4NI+rlImIiIjqsdM5FVidJD1banpbFwQ7mz/Li6zT8GB7dPGxkYybE5OPcr3RIjkREdGteFeNiIiI6hWN3oh5x/Ml4/oF2GFoE3uL5ES1y0Yhw9udpVcpH8+uwPpLXKVMREREVB/9021ESoiLCtNaS3eqIOsnk8nwXhd3yCU2lKaVGrDkbJGl0iIiopuwiEhERET1ytJzRUgrNYjGKGXAW53c2N6oARkYaIfe/tKzLeedyEepjquUiYiIiOqbjZfLcCJbutvIe53doFbwOr+haOmmxuRwJ8m4b+OKkFKit0hORET0LxYRiYiIqN7IKNPj2zjpFahTIpzQ3IXtjRoSmUyGdzq7QylxvyizzIBv4gotlRYRERER1YByM7uNDAy0Q09/O4vkRJYzo70LXCXGUFQYBHwcI/09QkRENYtFRCIiIqo3Pj1RgHK9+Mw7Nxs5nmvrarGcyHJCXFSYEiG9Snnp2SJcKdZZJCciIiIiuntLzxYhvUy824hKDrzZSbrFPdU/rjYKvNxB+j3c1qtlOJhebpGciIjoOhYRiYiIqF44mV2Bv5NLJeNe7uAKZ4lVrFR/Pd/WFR624v++OiMwx4x5OkRERERU99JL9fjOjHl3U1o6I9iZ3UYaqklhjohwk/73/b9T7DpCRGRJvMNGREREVs8oCJh9LE8yrqWbChNDHS2SE9UNJ7UcM6OkV6DvSCnH0UyNRXIiIiIiouozp9uIu60cz7V1sVhOZHkKuQzvd3GXjDuRXYGsMs5GJCKyFBYRiYiIyOr9nVyK2BytZNzbnd2hkEsMzaN6b2yIA9p6qCXjPjmeD0EQvyFFRERERHXnZHYF1l0yo9tIe1c4sdtIg9fJxxajmtlLxuVVGC2SDxERsYhIREREVq7CIOCzkwWScUOa2KO7r61FcqK6JZfJ8K4Zq5RP5WgRfY0zU4iIiIiskSAImBMj3YK+pZsKE9htpNF4raMb7JWVLwyVAWjiqLRoTkREjRmLiERERGTVfr9QjPQyg2iMSg683tHVYjlR3evgZYP7mjtIxs0/mQ+9kbsRiYiIiKzNjpRynMyukIxjt5HGxddeKTq+4PFWzrBX8ZY2EZGl8DcuERERWa1irRFfnymUjHuslTOaOKkskhNZj5c6uMJGIX5DKalQj7+SSiyWExERERFJMxjN6zZyTxM7dhtphB5u6YTn27rgv7XjUc3sMaM9Z2MSEVkS934TERGR1fr+XBHyJeZdeNkp8Ewk30g2Rv4OSjzS0gnfnS0SjfviVCFGNXOAnZLr54iIiIiswbpLpUgo0InGqOTAGx0r35FGDduL7V1xf7gjoq+VQyUHItzUiPS0qeu0iIgaHd5JISIiIquUW27Aj+fEi0MA8GI7FziynU2j9VQbZzirxf/9M8sN+Pl8scVyIiIiIqLKaQ0CFsZK70J8pCW7jTR2PvZKPNjCCRPDnFhAJCKqI7zjRkRERFbp67hClOrFZ9k1dVJiXKijxXIi6+Nio8AzbZwl476NK0S+Rny2JhERERHVvuWJxUgpEb8uc1TJ8JQZ13hERERUu1hEJCIiIquTUqLH7xekd4693N4Vqv8OyqBG5+GWTvC1V4jGFOsEfBMnvbOViIiIiGpPqc6Ir05Lzzx/srUL3GzFr++IiIio9rGISERERFbni9gC6MRHIaKVuxrDgu0tlRJZMVulHDPau0rG/RJfhNQSvUVyIiIiIqLb/Xy+GDka8Qt9D1s5HolwslhOREREVDkWEYmIiMiqJORrsSapVDLu1Q6ukMu4C5Guu6+5A8JcxWfm6Iwwa/4OEREREdW8ggoDlpyV3oU4va0LHDjznIiIyCoo6zoBsj5nzpzB3r17TZ9HRUWhe/fudZoTERE1Hp+dKoD4JESgq48NevvbWigjqg8Uchle7eCKp3Zli8atSSrFk61dECpRcCQiIiKimvVdXBGKdeJX+oGOCtwfxl2IRERE1oJFRLqFVqvFvHnzkJqaanpMrVaziEhERBZxIrsC0dfKJeNejXKDjLsQ6T8GBNqhk7cNYrIqKo0RACw+XYDP+3hZNDciIiKixiyjTI+f46Vnnr/YzhVqBa/ziYiIrAV7A9AtVqxYgdTUVDg4ONR1KkRE1AgtOJEvGTM4yA4dvGwskg/VLzKZDK9FSc9G3HC5DBcLdBbJiYiIiIiAxacLUWEQ34UY5qrCqGa8H0VERGRNWEQkk4yMDPz+++8AgHbt2tV1OkRE1MgcydDgSGblO8gAQC4DXuogXSSixivK2xaDguxEY/7ZjUhEREREtS+lRI8/E0sk417p4AqFnLsQiYiIrAmLiGSyePFiVFRUICQkBGFhYXWdDhERNTKLTxdKxoxp7oBwV7VF8qH666X25u1GTCzQWiQfIiIiosbs27hC6CWGnnfwssHAQPGFYERERGR5LCISAODo0aPYv38/AGDs2LGcM0VERBZ1PEuDgxka0RiVHHihHXchkrQWbmoMbWovGiOYWbgmIiIioupLK9Vj1UXpXYivdnDlvSgiImqQDAZDXadwV1hEJGi1WnzxxRcAAG9vb9xzzz11nRIRETUyX8ZKF3PuD3NCoKPSIvlQ/fd8WxfJmI3cjUhERERUq76LK4LOKB7Ty88WXX1tLZUSERFZSH5+PgYPHmz6s3DhwrpOySKOHTuGJ598EpGRkXB0dISdnR3atGmDCRMm4Pfff4fRKPE/RivDO3GEFStWIDU1FQDwwAMPQKnktwUREVnOyewK7E+X3oX4ZBtni+VE9d8/uxG3XCmrNOaf3Yj/18fLorkRERERNQYZZXqsSCyWjHvRjFb0RERU/2i1WkRHR5s+Dw0NrdN8atvVq1fxyCOPYPfu3bc9d/bsWZw9exarVq3CvHnzsHDhQvTv379O8qwqVosauYyMDPz+++8AAD8/P9x77711nRIRETUyi04XSMZMDHOEn0PNXbYIghHa8kLotOVQqmyhVNtDobRhC6UG5oW2LqJFRNzYjTi9rRZhnLVJREREVKOWnjVvF2IHL5sae81/rvP1Og0UShte5xMRkUVER0djwoQJKCi4fo8rICAAo0ePRmhoKGxtbZGQkIA//vgDWVlZOHPmDIYPH47o6Gj07NmzrlOXxCJiI7d48WJUVFQAAKZOncpdiEREZFGncyqwJ1V6F+JTraVbU/6XpjQPOSmxyEs9i9KiDGhKc1FRmgdNaR4qSvMhCLf2pJfJFFCq7aC0cYBKbQ8HV3+4+rSAm08LuPq2gJN7EGQydoKvT8Ld1BjW1B6buRuRiIiIyKKyyw1YliA9C/H5dlW/zteWF12/zk87h9KidGhKcq//Kc2BpjQfglH/nyNkUKrtoVTb3bjOD4CrTxhcvcPh6hMGJ89mUChUVc6DiIjEyWQyODo6mj63sam5RSPW5MyZMxg7diyKi4vh6uqKr776ChMnToRCobglbs6cObj//vuxbt06aDQajB49GrGxsQgICKiz3M3BilEjdvToUezfvx8AEB4ejkGDBtV1SkRE1MgsOi09C3FciCP8JWYhCoIRBZmJyEmJRW7KaeSknEZJ/rUq5SIIBugqSqCrKEE5gKKcS0i/eMD0vFJlBxefMLj7tYJ/WB/4BHeCnDcbrN7zbV1Ei4jgbkQiIiKiGrf0bBEqDIJoTHdfW3T0Fp+FKAgCSvJTkHPtFHJSYpFz7RQKs5OqmI0AvbYUem0pNACK864iI/mQ6Vm5XAlnr+Zw928N/7De8G3eHUoVZzQSEd0tb29vFBdLt7Wu737++WcUFxcjODgY0dHRCAkJuWOcnZ0dli1bBm9vb5SWliI3NxdLlizBBx98YPGcq4JFxEZKq9Xiiy++MH0+ffp0i7V2KCsrg0YjvuvkbqnVatja8oKPiMianc3TYmdKuWiMUgY8EeEAvf6/q4mvK8xOwtW4Tbh6dgvKi7NqKdPr9Lpy5KacRm7KaSQeWw6VrTP8w3ojoMUA+DTrCoWyYa6oq++aO8kxtIkdtlyt/HtNAPBlbAE+6+lu0dyIiIiIGqJcjQF/XJC+afxMa8dKr/NLC9Jw9exmXInbhJK8q7WQ5b+MRj0KMhNQkJmA5JNroFDZwTekBwLC+8EvpBdUto5mnIWoZrFbHFH9MXv2bOzcuRMLFy6stID4D3t7e3Tq1Al79uwBAMTGxlooy+rjb6NGauXKlUhNTQUA9OnTB23btrXYa589exYxMTG1+hqtWrVCmzZtavU1iIjo7nxxUryACADDA5VwMpSi8KYNixWluchI3IX0hO0ozqnqKuSao9MU4cqZjbhyZiMUKjt4Nu0G/5b3wCOoE2euWJkpwcAWiXtPm6+UY0rTfDRzYstaIiIiorvxbXwFyiV2IXZwl6OFrQaFhf8uMtdrS5GZtBfpF7YjP+20BTK9M4OuHKnxO5AavwMyuRLugVEIiBgK72Y9IZMrzDgD0d3z8PCo6xSIyEy2trbYt28fHBwczIovKioyfezm5laLmdUMFhEboYyMDPz2228AAJVKhaeeeqquUyIiokYmociAPZkG0RiFDHg09N/2kgXpcbh0Yhlyrh4DBKMFsjSfQVeOzIu7kHlxFxw9miO4wyT4hPSFXMFLLWsQ4qTAQD8ldqTfeaU7buxG/DVZi/fasZMBERERUXUVaAX8eVknGfd42L/X+cW5ybhyciUyk/bCaNDWcoZVIxj1yL16FLlXj8LO2RdN2o1HQMshUKjs6jo1ohqh1RSjvKh2u/rUJ3bO3lDbOtV1GlQPmVtAzM7OxtmzZ02ft2/fvhazqhm8s9UIffXVV6ioqAAAjBs3Dv7+/nWdEhERNTI/JErfHBgaoESAvRz56WeQfOwX5KWctEhud6skNxlx0R/j4uHv0aTdOAS0Gg4lbzLUucfDVKJFRADYmqrHU+FG+NhxNyIRERFRdSy/pEW5+FpBtHWTo5OHAiW5l5AU8yuykvZaKr27Ul6UgQv7FiH56M8IbDMKQZFjYGNv/TtIiO4k+9opHN80BwVZiXWdipWRwdU7FJ2GvwXPIOsr7hiNRmi12rse42UwGCCTySCX1/x7X0EQbunOVFZWBkG4vjvd1tYWCkXVdnTrdDqoVKpq52MwGKDX62FjYx0jaN58801otdfvibm5ueHRRx+t65QksYjYyBw9ehT79u0DAHh6emLKlCkWz6F169aSvYHvFmciEhFZr+RCHXZnlIjGyGXAg76ZOLXxa2Rfqd0W2LVFU5KFhANf49Lx3xHacQLCuz4MlY15K9Oo5kW5AEOb5InORtQLwF+pMrzZ0cWiuRERERE1BEVaI1ZeLpWMe7yZBvG7v0DK+R03+kHUL7qKYlw6/juunPoTwW1HolXvJ2HrwNnaVH9kJB/G3mXPw2gUX2TZOAkoyErEzl+moc8DX8K3ebe6Tgjr16/H+vXrsWfPHiQmJkIQBDg7O8Pf3x/NmzfHfffdh7Fjx8LdXfz3UExMDJYuXYqtW7ciJSUFgiAgNDQU9957L2bOnAkfHx/R4yMiIoAbNYV/6gsAcOrUKSxevBjHjx9HYmIiYmJi0KJFC9Pz7dq1w8WLFwEA0dHRGDhwoOjraLVa/Pzzz1i2bBlOnTqF/Px8ODo6omfPnnj44YcxefJk0REyRqMRf/75JzZv3oy9e/fi8uXLEAQBrq6u8Pf3R1hYGCZMmIBRo0bByclyu06NRiPeeOMNfP/996bHPvnkEzg7O1ssh+qSCf+UganB02q1ePzxx5GSkgIAePvttzFo0KA7xv7888/46aefAACTJ0/GtGnTLJorERE1XG8ezMWfF8WLiF1xBiOvvGuxnCzBzskL7Qe9hCath3JmYh2Jz9fi3vXpojH2Shn2jA2Amy3n3RARERFVxTdnCjH/ZIFoTHN5JqZeehqyelg8rIzKxhGR/Z5BaKeJkMu5X4OsmyAI2P7Dw8hLO2tGdOPm7t8Ggx/7pc7evxcVFWHs2LHYsWOHZKxKpcLy5csxduzY256rqKjA888/j6VLl6KyUpCzszO+//57jB8/vtLX+OfvwdfXF+np6cjPz8djjz2GtWvX3hIXHx9/SxExLCzM7CLikSNHMGXKFCQkJFQa06tXL/z99993LJpmZmZixIgROH78eKXH/8PW1hbbtm1D7969JWOrymg0Ijs7G7m5ucjJycHRo0fxww8/4Pz586aY9957D//73/9q/LVrA//P1oisXLnSVEBs2bIlOnfujMLCwjvGajSaWz6+OU6tVsPOjm3ZiIio6jLL9FibLF5AlAlGdEv7usZeUy5XwtW3JZzcm8DWwQ22Dh6wcfSArb0b1HbOMOgqoNOWQl9RBr2uDLqKMpQVpiM/Ix4FmQnQ6yrfuVYV5cXZOLTmLVw8vgodh74BV5+wGjkvma+lmxp9A2yxJ1VTaUyZXsBvF4rxfDtXi+ZGREREVJ9VGAT8HF8sGdcz47saKyDKZHK4eIddv8539ICtoydsHdxh5+gJta0T9PoKGLTl0GnLYdCVQ6ctvX6dn5mAgswE6DTS+ZpDV1GCE1s/RdKJNYga+jp8gjvVyHmJaoOuooQFRDPlpcVBV1FSZzMSH3/8cVMB0d7eHpMmTUJkZCRUKhXy8vKQlJSEffv24dKlS9DpdPDz87vtHGVlZRg2bBj27v23bXSvXr0QFRUF3NhFuHfvXhQVFWHy5MlwdHTE0KFDJXO7evUq+vXrh0uXLpkec3V1RVRUFFxcqtfZZ/v27RgzZgzKysqAGzse+/Xrh7CwMKSkpGDHjh1IS0vD/v37MWLECERHR982h3DChAmmAqKrqysmTZqEiIgIyOVy5ObmIiEhAXv27EFaWho0Gg0CAwOrlauU8+fPo02bNnd8zsvLC19//TXGjRtXK69dG1hEbEROnTpl+jg+Ph5jxowx67jVq1dj9erVps8HDx6Mt956q1ZyJCKihu3n88XQGcVj2pQdgJc+rdqvYWPvBs+g9vAMbAvPwHZw84uAUlW9FteCYERJ3jXkZyYgL+0sUhP2oDj3crVzA4DsqyewdckDCOs8EW36PsOh7Rb2VBsX0SIiAPwSX4zHWznDXsXZiERERETmWJtcgmyJYYj+FUkIL5feHVIZpdoBHoGR8ApsD8+gdvAIiKz2uABBEFBWlImCzATkpZ9DWsJe5GecN+PIyhVmX8SuX6ehSat70H7wy7B3Fm8NSERUmeTkZKxatQoA4OjoiNOnT6NZs2Z3jI2NjcWXX36Jdu3a3fbcs88+ayogtmjRAt9//z169ux5S8yRI0cwefJkJCcn4+GHH0ZSUpJoi02NRoMRI0aYCogjRozA/Pnz0bJly2p/vZcvX8akSZNMBcTp06fjo48+uqUgqdFo8Omnn+K9997D4cOH8eGHH2LevHmm5w8dOmRqsxoQEIDjx49X2qL18OHD+P777yv9O61NAwcOvOO/lTVjEZGIiIgsolhrxB8J0qt9exeuloz5L5WNIwIjBiI48l54N42CTFYzxR+ZTA4nj6Zw8miKJq0Go93AF1GUnYxr8dFIOb+j2kPoBcGAhKPLcOXsVnQa9haCIsRnAlDN6extgw5eNjiZXVFpTH6FEX9eLMEjEdY/m4CIiIiorhkFAUvPFknG9S1ahao2BZQr1Aho0Q/N2t4L3+bdIFeoqp3nzWQyGRxcfOHg4ouA8D6I7Ps0SgrSkBq/E9fidyLn2qlqz2u8em4bUhP3ov3AGQjtNJGjDMiqqGwc4e7fmrsRzeDu3wYqG8c6ee24uDjTx8OHDxctdrVr1w5Lly697fHdu3fj559/BgD4+/sjOjr6jjvvunbtio0bN6J79+7IycnBp59+ilmzZlX6egUFBSgoKIBKpcJPP/2EyZMnV+MrvNWMGTOQn58PAHjhhRewcOHC22JsbW3x7rvvoqysDHPnzsXChQvxwgsvICAgAABw5swZU+y4ceNEZzx269YN3brV3rxLb29vzJ49G7ixaCUnJwdnz57Frl27sHz5cqxcuRLTp0/HggULoFLVzP/XahNnIjYie/fuRVZWllmxa9euRWpqKgAgKioK3bt3Nz3XpEkTdOnSpdbyJCKihum7uEJ8ckJ8Rkpo+Uk8mmVeT3iZXAm/kB4IjhwB//A+1d5teDeK867iUux6XIxZCa1G+sZJZUKixqHDPa/WydfQGEVfK8PTu7JFY/wdFNhxXwBUct70ISIiIhKz/WoZntktfm3loUvFi2nPQw6JtiQ3eDXpgOC29yIoYnCddO7QlOTi0un1SDj6B8qLxb82MQHhfdFl5PuwsXer0fyI7kZG8mHsXfY8jEZ9XaditeRyJfo88CV8m9deoUnMmjVrTPMN27dvjxMnTlR5QcI999yD7du3m84n1ZVw5syZmD9/PsLDw3HhwoXbnv/v63/44Yd49913JfOQmol49uxZU+vP5s2bIyEhAQqFotLzaTQaeHt7o7i4GF999RWeeeYZAMDXX3+NZ599FgAwZMgQbNmyRTI3S0tOTsaDDz6Iw4cPAwB69+6NDRs2iO78tAYsItIdvfTSS6b2p5MnT8a0adPqOiUiIqrHKgwC+q9ORZZEi6Opme8hRHNaNEYuV6J5h/sQ0XMqHFxu7/lfF3TaMiSfXIsLR35FWWFGtc7h4hWC7mPnwtU7tMbzo1sZBQHD16XjYqFONO7Tnh64L6RuVp4SERER1QeCIGDC5gycytGKxo3O/QqdS7ZJnE2GJq2HoHXvJ+DiFVKjeVaXwaDD1bjNiD/0KwqzL1brHHZOXug25iP4BHeu8fyIqivn2inEbJqDgqyL1d512zDJ4Oodik7D34JnUPs6yyI9PR3BwcHQaq//bh06dCjefPNN9OrVC3K5dOelwsJCeHp6Qq/Xw9/fH+fOnYNSKd6UMiYmBv369QMApKWl3TZj8eYiYocOHXD06FHJc8KMIuKcOXPw9ttvAzcKky+//LLkOadOnYo///wTEydOxIoVK4AbxcjIyEj8U+6aNGkSXn75ZXTu3NmqdoQXFRWhffv2pnawjz/++B13kloTFhHpjlhEJCKimvRnYgnePJQrGuNfcRHPZLxaaYsjuUJ1vXjYYyocXHxrJc+7ZTTocPXcdsQf/KlarU4VSht0uOcVhESNt6qL3IZoTVIJZh4Q/54Mc1Vh40g/yPlvQURERHRHxzI1eGBrpmiMg6EAr6ZMgwqVLeCSoUmrwWjd50mrKR7+lyAIyEg6iPMHf0LWlZhqnEGGVj0fQ5u+T9VYS1aimqDVFKO8yLzOdY2BnbN3nex+vpP58+dj5syZtzzm6OiIdu3aoX379ujatSuGDh0KLy+v246Njo7G4MGDq/3aBw4cQI8ePW557OZ7FDfvAJQiVUS89957sXHjxmrl2blzZxw9etT0+auvvooFCxbcEuPi4oIOHTqgffv26NatG4YOHXrLrMW6sGjRIjz//POmz3ft2mUq4FojzkQkIiKiWmUUBCw5WygZ17tozR0LiDK5EiFRY9Gq52Owd668p701kCtUCI4cjqZthuLS6Q2Ijf4/VJTlm328QV+BmE1zkJF8BF3ufQ9qO+tuaVGf3dvMAZ+dLEB6WeW7YxMLdNiVUo6BQfYWzY2IiIiovvjOjFmI3Ys2VFpADIq4Xjy09m4cMpkMfqE94RfaE6kJe3Fy23yU5F+rwhkEnDvwPTIvH0X3+z6Go1tALWZLZD61rZPVFM3oVq+++irUajVee+01VFRUAABKSkpw4MABHDhwAIsXL4aNjQ2eeuopzJkzBw4ODqZj09PT7+q1CwrER9HU5Kizu8n1v3nOnz8f9vb2+Pjjj6HXX2/XW1hYiN27d2P37t3AjULsjBkz8O6770KtVt9l9tUzZcqUW4qImzZtYhGRiIiIGq/tV4qRXCQ+a8FNl4HWZYdue9wjsC06j3jX6m8q/JdMJkfzdqMQ2KI/4vZ8jcRjKyAI5s1/AYCU+B3Iz4hH3we+hLNn5QPUqfpUchkeb+2M2cfEi7zfxBVhQKAdd4YSERER/UdCfgV2pZSLxqiN5ehasvm2x509mqHTve/Au0lULWZYOwLC+8C3eVfEH/oV5/Z/D4NeY/axualnsO37h9B74mfwatKhVvMkovrvhRdewKRJk/D1119j06ZNiI2NNbU4BYCKigp88cUX2LVrF/bv32+arXfzTMH77rsPr7zySpVeNyIiQvT5O+1+rK6bc122bBmCgoLMPtbGxua2xz788EM88cQTWLx4MbZt24a4uDhTQRE3CrGzZ8/G3r17sX379jopJDo7O8PHxweZmdd38h87dsziOVQFi4hERERUa8qLc7Bgz3lA1kQ0rlfRWsjxb5FNqXZAu4EvILTjeMhk0v3+rZXa1glRQ15D8/ZjcHzLXGRfPWn2saUFqdj+4yPoNeEz+AR3qtU8G6uJoY5YFFuIAm3lBd6T2RWIyapAZx9bi+ZGREREZM102jLM2bQXgPiN5k4l22BnLDV9LpcrEdHzMbTq9TgUyrrZAVITFEobtO79BILb3otT0Z/j2jmpeY//0pYXYNdvT6HLyP8hOHJYreZJRPWfj48PPvjgA3zwwQfQ6/U4c+YMtm/fji1btmDXrl0AgDNnzuCNN97AV199BQAIDAw0HZ+ZmYmePXvWaE43F/7uVmBgoKmIplAoaiTXJk2aYN68eZg3bx4qKipw6tQpbNu2DZs2bcLhw4cBAHv37sXcuXPx3nvv3fXrVYdO9+8O/aIi6V39dan+3pUjIiIiq1acdw1LfpuNZIkCooOhAFGlO02fB7YYgOHPrEZYp4n1uoB4M1efcAyY8j26jvoQSrWDGUdcp9MUY8/vz+BS7Lpaza+xslfJMSVCunXPj+et+4KeiIiIyJI0pXlY/eurOKQX7xYiF/ToUbTe9LlHYFsMmbYckf2eqdcFxJs5uPii57h56Dv5K9g6epp9nNGgw+G1byFu77cQBKFWcySihkOpVKJDhw547bXXsHPnTvz888+m53788UfTjrsuXbqY2pueOHECZWVldZazlP79+5s+3r9/f42f38bGBl27dsW7776LQ4cOYd68eabnlixZUiOvkZCQUKX4zMxM5OXlmT4PCbHOecD/aBh35qjGjRkzBtOnT8f06dPRrVu3uk6HiIjqmby0s4j+8RFsk0mvIOtWvBEqQQu1rTN6jp+PXhMXwN7Z2yJ5WpJMJkOzdiMxZNoyuPu3Nvs4o1GPI+vex5ndX/MGQy14uIUT7JTirUq3Xy3H1eI7z/EhIiIiakxK8lOx46ep2FwWDoNMJRrbtnQfXA05UChtEDX0dQx69Ee4eFv3jdLq8gvpjqFProB/WO8qHRe35xsc+ftdGPRaM6KJiG41ZcoUBAcHAwA0Gg2uXr0KALC1tcWYMWNMj7/zzjt1mqeYsWPHmlqKfvfdd7hw4UKtvt6MGTPg5HR9MXFKSspdF1hTU1PRt29fXLx40exjVq5cecvno0aNuqscahuLiHRHffv2xfjx4zF+/HhERkbWdTpERFSPpCcdxM5fpiFFa494e/Fh22pjOboWb4a7f2sMmbYMQREDLZZnXXFyD8LAR39Ey+6PVum4s/u+w+G1b/MGQw1zs1VgUpijaIwA4Jf4YovlRERERGSN8jMuIPqnR5Gdn4NjTkMk43sVrYWjWxAGTf0Z4Z3vbzBdRipj6+CO3pMWImrITMgV4gXWm10+sxG7f38GFWUFtZofEdUfhYWFZu1uMxgMqKioAG4sXPb0/HdH9AcffACV6vrvooULF2LbNum2y0ajEcuXL7+r3KsqICAAzz77LHCj4Pnoo4+ipKRE8rjU1FTs2bPH9Hl6ejquXbsmeZxerze1EnVwcIC9vf1d5R8XF4eMjAz069cPGzZskIzPzs7GBx98YPq8efPmmDBhwl3lUNsa9v+9iYiIyKIund6AvctfhF5XjkPO90rGdyrZjnZRIzDwkR/g4OpvkRytgUKhQvtBL15ve+TgYfZxV+I2Y/fvT0OrYUGrJj0a4Qy5+GZE/JlYgmKR2YlEREREDVnm5Rjs/OUJaEpycNxxECrk4jddw8ti0LFZM9zzxO9w821hsTzrmkwmQ3iXyRj82K9w9mhm9nHZV08g+sdHUVaUWav5EVH9cPjwYbRu3RqTJk3Cpk2b7rhbrrCwEI8//jjS09MBAL1794azs7Pp+dDQUMyfPx+4URwcNmwYXn/9dVPR8WalpaVYsWIFOnfujMmTJ0Ortezi5VmzZpk2Mh0+fBht27a9pUB4swsXLuCdd95BixYt8Oeff5oe37ZtG8LCwjB16lTs2LHjjl9nZmYmJk+eDI1GAwAYMWLEXec+aNAgjBkzBqmpqRg5ciTGjBmDrVu3mlrL3uzQoUPo3LmzqZWpUqnEd999Bxsbm7vOozbJBPbFIiIiorskCALiD/2C2B3/BwAokzvi04Cl0MltKz1GLhjwW2QSukQNtmCm1kdTkotDa99C5qWjZh/j7t8G/R78Cmpb6Xl+ZJ7n9mRjyxXxNiZvdnLD462cRWOIiIiIGppr57bj0Nq3YTToYIQcnwV8jQKlj+gxcwJOYsKA0ZDJJFZqNWB6bTmObfoIV85sNPsYB7dADHj4Ozi4+NVqbkRk3T7++GO89dZbps9tbGwQGhoKPz8/uLq6IjMzE8ePHzcVF52dnXHkyBG0bNnytnO98847+Oijj0yf29nZoX379oiMjIRer8eFCxdw4sQJlJeXAwAUCgVSUlLg6+t7y3lu/n2ekpKCgIAAs76WsLAwU6vP6OhoDBx45w5UV69exbBhw3Du3DnTY4GBgejYsSMCAwORmZmJ06dP37JD88EHH8Rvv/0GAHjxxRfxxRdfmJ6zt7dHWFgYfH194eTkhJSUFJw8edJUXPT19UVMTIzZX4cYrVaL6dOnY+nSpabHPDw80L59ewQFBaGiogLHjx9HYmKiaUyNo6Mjvv32W0yePPmuX7+2sYhIREREd0UQBJzZtRjnDnxvemyf833Y6vaI6HHD/I34cpD5q3MbMqNBh5jNHyP55Bqzj3H3b41+D37NQmINOZGlwcQt4iu/AxwU2HFfAJRS2xaJiIiIGoikk6txbMPsGw3egXN2XfGH95uix7Ry0uHvMSGNuoD4D0EQcHbfEsTt+drsYxxc/dH/4SVwbESdWojoVufOncOsWbOwevVqyV2BLi4u+P3330V31f355594+eWXkZKSUmmMTCbDxIkT8eGHHyI8PPyOz/+jNoqIAFBUVISXXnoJP/30E4zGyjsB+fj44K233sJTTz1l2sV37NgxzJo1C5s2bYLBYBDNydvbG2vXrkX37t3N+hrMtXXrVrz11ls4ceKEaNyQIUPw7bffomnTpjX6+rWFRUQiIiK6K3F7vkXc3m9Mnxsgx2cB36JQ6SV63NoRvmjjYd0tGyxJEATEH/wJsTu/MCP6One/VtcLiXbcHXe3BEHA+M0ZiM0Rf4P2ZV9PDGvqYLG8iIiIiOrKpdh1OLLuA1MBEQCW+szGZds2osct7OOJEcG8XrrZlbjNOLLufRgNOrPi7V18MeDhJXB0C6z13IjIeuXk5GDdunXYs2cP4uLikJ2djcLCQjg6OiI8PByDBw/GU089BQ8P6TEp5eXlWLlyJdauXYvz588jOzsbLi4uaNKkCfr06YOJEyeiTZvKf787Of27gPnChQvw9zdvoUO7du2QnJwMAFi/fj369esnecz58+fxxx9/IDo6GikpKdBoNPDx8UGbNm0wePBgPPDAA5XOMkxLSzP9ncXHxyM7OxvFxcVwcXFBy5YtMWzYMDzxxBO3fD01SRAEbN26FVu2bMG+ffuQlpYGvV4PX19fdOrUCePGjcO990qP/7EmLCISERFRtZ3b/z1O71p0y2Nx9j2w3Os10eM6e9tg2VBf0ZjG6uq57Ti89h0YDebNIHDzjUC/h76GjZ1LrefW0G24VIoZ+3JEY6K8bLByGL93iYiIqGG7ErcZh9e+A0H4dydImqoZvvL/XPQ4X3sFdo0NgIqdG26Tfe0U9q14CdryArPi7Z190f/hb+Hk3qTWcyMiIqqMvK4TICIiovop/tAvtxUQAeCg00jJYx+JYAvOyjRpNRgDpnwHG3tXs+LzM85j169PoaLMvJsRVLkhTe3ha68QjTmRXYHYnNsHtBMRERE1FNfOR+Pw2ndvKSACwCFn6ev8h1s6sYBYCa+g9hj82M9wcjevfV1ZUQZ2/jINRblXaj03IiKiyrCISERERFWWcHQZTkXfvgo5RR2Kq7YRoscGOCgwKOjObSfoOs/Adhj82K9mrzouyLyAXb+xkHi3VHIZprSULnD/eK7IIvkQERERWVpqwh4cXP0mBOHWeVIlchecdugteqytQoZJYY61nGH95uTeBIMe+xmege3Mii8vzsLOX55Acd7VWs+NiIjoTlhEJCIioiq5eHwVTmz95I7PHXKS7uv+cEtnKLk6WZKjWyAGTFkCJ49gs+ILMhOwd/kL0OvKaz23hmxSmCPslOLfn5uvlCG9VG+xnIiIiIgsIT3pIA6smgnBePt1zlGnITDIVKLH3xfiAFcb8a4OBNjYuaDv5MXwDOpgVrymJAe7/5gOTUluredGRET0XywiEhERkdkuxa5DzKaP7vhckcINZxx6iR5vr5RhIlcnm83OyRsDHv4Ozh7NzIrPTT2Dg3+9AeMdbvyQeVxsFBgXIv49ahCAX+OLLZYTERERUW3LvHwM+1e+DKNBd9tzeihx1GmY5DnM6ehA16lsHNB38iJ4Ne1oVnxpfgr2LHseuorSWs+NiIjoZiwiEhERkVnSkw7h6PoPK33+qOMwGGVK0XOMDXGEs5qXH1Vh5+SFAVOWwNmzuVnxaYl7EbPpYwiCUOu5NVSPmjGzc0ViCcp0Rsk4IiIiImtXkJmIfStegkF/57nPZxx6oUThJnqOnn62CHNV11KGDZNKbY++938J7+DOZsXnZ5zH/lWvwnCHQi8REVFt4V08IiIiklSQmXC9tdF/ZqP8QydT46jTEMnzPGJGcYZuZ+vogQFTlsDFK8Ss+OSTq3F273e1nldDFeysQv9AO9GYQq0Rq5O4EpyIiIjqt/LiLOxZ/jz02jtf1wgADjmNlDzP1AjnWsiu4VOq7dDn/oXwadbVrPjM5MM4uu4DCAIXsxERkWWwiEhERESiyoqysGf5C5XeWACAWIc+KFO4iJ6nX4AdmjmLz1Ghytk6uKP/w9/BxTvMrPi4vd/g4om/aj2vhuoxMwrev10o5o5PIiIiqrd02jLsXf4iyosyK425YhOBNBvxhWzNnJXoE2BbCxk2DkqVHXpP+j/4hvQwK/5K3Caciv6/Ws+LiIgILCISERGRGF1FKfYuf0H0xsL11cn3Sp7LnBaRJM7WwR0DHv7W7NamxzfNQeqF3bWeV0PUzdcWLd3Ei94XC3U4knnntl9ERERE1sxoNODQ6jeRnxEvGmfOLsQpLZ0hl8lqMLvGR6myRe+Jn8E7uItZ8RcO/4r4Q7/Uel5EREQsIhIREdEdGY16HFz9OgoyL4jGXbJpg0x1sGhMqIsKPf24Orkm2Ni7oe/kxbBz8paMFQQjDq5+AzkpsRbJrSGRyWRmteX67UKxRfIhIiIiqimCIODktk+RlrhXNC5f4YVz9uJtNp1UMowNcajhDBsnhdIGvScugKtPuFnxp6I/x9WzW2s9LyIiatxYRCQiIqLbCIKA45vnIv3iAcnYI07DJGMejXCCjKuTa4yDiy/6Tl4Mla307k6DvgL7V76CsqIsi+TWkNzbzAFuNuKXy9uvliGjTG+xnIiIiIjuVsLRP5B4bIVk3DGnIRBkCtGYiWFOcFDx9mJNUdk4ou8Di+Dg6m9W/JF1HyA/XXw3KRER0d3g/+WJiIjoNvGHfkaSGfP0ihRuiHfoLhrjqpZjdHOuTq5prt6h6D3xc8gVaslYTWku9v/5Cgx6tt6sChuFDBNCHUVjDAKwPKHEYjkRERER3Y2U+J04uW2BZJweSpx0GS4aI5cBD7fkyIKaZufkhb6Tv4KNvatkrEGvwb4/X4KmNM8iuRERUePDIiIRERHdIv3iAcTu+MKs2ISm02CQuJyYGOYIOyUvOWqDd9OO6H7fRwCkd3nmpcUhZtMcCIJgkdwaigfCHSX/dlcklkBr4N8rERERWbeCzEQcWvv2janm4i77j0Mx7EVjBgXZIdBRWYMZ0j+cPZqiz/1fQKGSHglRVpiBA3+9BqNBZ5HciIioceEdPSIiIjIpyU/FoTVvmXVjwdmvNQ6qe4nGyAA82IKrk2tTUMQgRA19zazYS7HrkHhsea3n1JAEOanQL9BONCa73IDtV8sslhMRERFRVWk1xdi/6hUYdBrJWDtnH5z2fVAy7mFe59cqj4BI9Bz3CWQSLWUBIPvKcZzcLr3DlIiIqKpYRCQiIiIAgF6nwYFVr0KrKZKMtXfxg6H3fGSVG0Xj+gfaIYCrk2tdeOf7EdFjqlmxJ7ctQOblmFrPqSF5yIwbZL9dKLZILkRERERVJQhGHF77DkryrknGKtUO8B+xCCdyxa/zQ1yU6OYrvUuO7o5/WG90HvmeWbGJx1Yg+dTaWs+JiIgaFxYRiYiICIIg4Pjmj5GfES8Zq7JxRN/7v8SfV6RbaHIXouW0HfA8giIGS8YJggEH/5qJ0oI0i+TVEPT2t0VTJ/Fi+LGsClzI11osJyIiIiJznTvwI9IS90rGyWQK9Bz/KdbnuEnGTg53gkwm/X6A7l7zdqPMXjAYs2kOclJO13pORETUeLCISEREREg6sQqXYtdJxsnkSvSasAA5Nk1wMEO8FVKQoxK9/bk62VJkMhm6jvofXLzDJGMrygqw/89XoNeVWyS3+k4uk2EydyMSERFRPZSedAhndi02K7bTiLfgGNQVa5NLRePslDLcF+JYQxmSOSL7T4dfaE/JOKNBh/1/voLy4iyL5EVERA0fi4hERESNXG7qGZzY8olZsR2Hvg6fZl2wLEG6WDI53BFyrk62KKXaDr0nfga1nYtkbH5GPI5tmA1BkJ5/ScD4EAfYKsS/n/9OLkWxVrz1FxEREZGllBakmT3vvGX3KQjpMBZ/J5eiTC8eP6qZA5zVvKVoSXK5At3v+xhO7k0kYzUlOTiw6jUYjXqL5EZERA0b/49PRETUiGlK87D/z1fNeoPZrN1ohESNQ5nOiL8ulojGquXAuFCuTq4Ljm6B6DF2LmQy6cu8K3GbzNqBSoCLjQIjmzmIxpTpBaxOEv/ZICIiIrIEg74CB1bNhLa8QDLWO7gz2g54HoIg4HczFgtyZEHdUNs6odfEz6FUi1+TAkBOSizidn9jkbyIiKhhYxGRiIiokTIaDTi4+g2zWt24+Uag47A3IJPJsP5yKYp14quTRwQ7wN1WUYPZUlX4Nu+G9oNeMiv2+Ja5KMq5VOs5NQQPmXHD7PcLxdzdSURERHXu+JZPkJd+TjLOzskbPcbOhVyuxLGsCiQW6ETj23uq0cpdXYOZUlW4eDVH9zGzzYo9d+AHZCQfrvWciIioYWMRkYiIqJE6t/97ZF0+JhmntnNBzwnzoVTZQhAE/BbP1cn1QXjXB9E0coRknEGnwcG/XodBX2GRvOqz1h5qdPCyEY1JLtLjYLr4vFAiIiKi2nT5zGYkn1wtGSeXK9Fz/KewdXAHAPxhxnxnXufXvYAW/dCm7zNmRAo4/Pe70JTkWiArIiJqqFhEJCIiaoRyUmJxdu93ZkTK0P2+j+Ho6g8AOJWjxfl88dXJrd3VaOfJ1cl1TSaTofOId+DmGyEZW5CViFPbP7dIXvXdQy2k2/T+kcCWpkRERFQ3SvJTcXzzHLNiOwyZCc/AtgCA7HIDtl4tE413s5FjeLB0K02qfa17P4HAlgMk4zQlOTj897sQBM7tJiKi6mERkYiIqJHRVZTg0Jq3IQgGydjIfs/CL6S76XPzVic7QiaT3XWedPeUKlv0mrgAajtXydjEmBVIid9pkbzqs6FNHeBuK34JveNaGbLLpX++iIiIiGqS0ajH4bVvQ1chvaApuO29CO04wfT5n4kl0EnUmcaHOsJGwet8ayCTydF11Idw8giWjM1IPoT4Q79YJC8iImp4WEQkIiJqZGI2z0VpQapknH9YH7Tq9Zjp8zyNARsvl4oe46SSYWQzrk62Jg4ufug2+kOzYo+s/wClBWm1nlN9ZqOQYVKY+G5EvQD8dZG7EYmIiMiyzu37HjkpsZJxrj7h6DT8LdPCP4NRwLIE8cWCMgD3h0t3ZCDLUdk4XJ9nqZDuAnN612LkpJy2SF5ERNSwsIhIRETUiFw+swlXzmyUjHN0C0K3MbMhk/17qbA6qRRaidXJ40IdYafk5YW18Q/rjfCuD0rG6TTFOLTmLRiNeovkVV89EOYEqTX4Ky+WwCgIFsqIiIiIGruca6dwdp/0uAKVrRN6jp8PpcrO9Niu1HKkl4l3UegTYIumTqoayZVqjptvC3QY/LJknGDU49CaN6HVSHeWISIiuhnv8hERETUSJfkpiNkkPR9FJlei+9iPobZ1Mj0mCAJWJkq/4Zwc7iQZQ3Wj3YAXzJqPmJMSi7jd31gkp/rK31GJvgF2ojFXi/U4nKGxWE5ERETUeGk1xTi09m2z5t51ufd9OLkH3fLYCjPmOfM633qFdpqIwBbS8xFLC9JwbMOHELjQjYiIqoBFRCIiokbAaNTj0Nq3odeKtyMFgLb9noWHf+tbHovJqkBykfjutB6+tmjuwtXJ1kqhVKPH2I+hVNtLxp478ANyrp2ySF71lVRLUwBYbsYNOSIiIqK7dXzzXLNa0jfvcB+CIgbe8lh6qR570spFj/N3UKCfxAIqqjsymQxdRr4Pexdfydhr56NxJW6LRfIiIqJ/5efnIykpyfSntFT6/py1YBGRiIioETi7dwlyzZiB4R3cGS17PHLb4ysSpYshD7TgjBRr5+TRFJ2Gv21GpIAj696HXid+Q6kx6xdoB287hWjM9mtlyC0Xbw1GREREdDcun9mEK3GbJOOc3Jsi6p6Ztz2+6mIJjBIb0+4Pd4JCLtXMneqS2s4ZPe77GDKZ+PUpAJzYMhflxVkWyYuIGrfc3FxERkaa/syaNauuU6oTer0effv2RWhoqOnPli31Z0EHi4hEREQNXE5KLM7tXyoZp7Z1RrfRs26ZgwgAhRUGbL5SJnqsu60cAwOld7hR3QuOHI5m7UZJxhXnXcXpnV9aJKf6SCWXYXyog2iMzgisTuJuRCIiIqodJQVpZo0rkMuV6D52DpTqW3cTGowC/rwofq2ikAHjQ8Svecg6eAa1R2S/ZyTjtJoiHN0wi21NiajW6fV6xMXFmf6kpUnvmm+IFi1ahDNnzkAur5/luPqZNREREZlFr9PgyLoPzJqP0vne92Dv7HPb439fKkWFQfwN5rgQR6gVXJ1cX0QNfR1OHsGScQlHlyHzcoxFcqqPJoQ6Quq7fkViCW/QEBERUY0TBAFH1//PrHEFkf2nw92v1W2PH0jXIK1UvGvCgEA7eNsr7ypXspyInlPh06yrZFz6xf24FPu3RXIiImrMMjIy8P777wMA2rRpU9fpVAuLiERERA1Y3J5vUJx7WTKueYext81HwY2bEyvNaGU60Yz5cGQ9VGp79Bg7F3KF9AzLo+vfh66i/vTqt6QgJxV6+duKxlwu1uNIZoXFciIiIqLGIenkamRdPioZ5x3cBS27T7njc+aMLJgU5lSt/KhuyGRydBszGzb2rpKxJ7bON2uWJhFRdSkUiltaeHp5edV1ShY3c+ZMFBUVoVu3bmjSpEldp1MtLCISERE1ULmpZ3Dh8K+ScU4ewYi659U7Pnc6V4v4fJ3o8V18bNDMWboYRdbFzbcF2vR9WjKutCANp6I/t0hO9dH9ZtxYW5FQbJFciIiIqHEoLUzHqe3S12dqO5c7jisAgJxyA3ZcEx9Z4GevQG+JBVNkfewcPdFp2FuScXptKY6s/59ZXWuIiKrD09MTiYmJpj8ffvhhXadkUfv27cNvv/0GAHj6aen7L9aKRUQiIqIGyKDXmtXGVC5Xosd9H982H+UfKxLMWZ3MXYj1VcvuU+ARECkZl3TiL6QnHbJITvXNgCA7eNqKX1JvuVqGPI14qzAiIiIicwiCgJiNs81qY9rl3vdg7+x9x+dWJ5VAL9FxfXyoIxRyjiyoj4JaDUaT1kMk47IuH8XFmD8tkhMRUWNiMBgwffp0AEBQUBAmT55c1ylVG4uIREREDdDZvd+hKCdZMq5136fh5tfyjs+V6IzYcFn85oSLWo6hTR2qnSfVLblcia6jPoRCaSMZe3TD/6DVcEfdf6nkMowLFS+k64zAmmS2hCUiIqK7d+n0eqQnHZSMa9Z+NAJbDrjjc+aMLJDdKCJS/dVx6BuwdfSUjDu14/9QnHfVIjkRETUWixYtwpkzZwAAr732GlSq+tvBi5ORiYiIGpi89HM4f/AnyTg3vwhE9Hik0uc3Xi5FmcTy5DHNHWCj4Ork+szZMxht+z+Hk9sXiMaVF2Xi5Lb56DrqfxbLrb6YGOaIb+OKRGNWJhbjsQgnyGT8eSEiIqLqKS/Owslt8yXj7Jy80WHwK5U+fzSzApeL9aLn6O1viwBH3jasz2zsXdF5xLvYt+JF0TiDToMj697HwEe+v2PrW2rYirRGZJSJ/z5oTHztlXBW8+eA7k5mZibee+89AECTJk0wbdq0uk7prvBqgIiIqAExGHQ32piKt06Uy5XoOvJ/kMsrvxSQWp2MG8UTqv/Cu05GyoWdyL56UjTuUuw6NGk9BH4hPSyWW33Q1EmFnn62OJCuqTQmqVCPmKwKdPbhXCEiIiKqOkEQcGzjR9CZ0Rmi84h3oLatfG7zCjOu8yeZMfeZrF9AeB80azcal2L/Fo3LuXYKF4+vQliniRbLjerW8SwN3j+Sh/h8XV2nYlVkAFq4qfBhV3dEeVvXezej0YisrCxUVFTAy8sL9vb21TqPIAjIzs4GbswslMtrrmhaUlICGxubW3bdXb16FTrd9e8zf39/2NndeZzOnVRUVCArKwtubm5wdKz6/SeDwYDMzEzo9Xp4e3vD1tYy/6YzZ85EUdH1hcYffPABbGykuz9ZM5bViYiIGpBz+5aiMCtRMq5V72lw9Qmr9Pn4fC1ic7Si52jnqUYLN3W18iTrIpPJ0WXk/6BQSV9Qx2yaA72u3CJ51SfmzAZdbsYNOyIiIqI7uRK3BWmJeyXjgiNHwD+sd6XPF1QYsOWKeJt1T1s5BgSZf5OXrFuHe16BvbOvZNzpnV+ivDjLIjlR3dqfVo6HtmWygHgHAoD4fB0e3JaJ/Wl1/75Xp9NhyZIlGDVqFDw8PODn54fg4GA4ODjAzc0NHTt2xOzZs5GYKH0faNOmTRgzZgwcHBzg4+MDHx8feHp64qGHHkJCQoLk8W5ubnBzc0OLFi1MjwmCgK1bt2LkyJHw9/eHk5MTkpNvHa0zcOBAhIaGIjQ0FAcPSrfjLiwsxKxZs9CqVSvY2dmhSZMmcHJyQvv27bFgwQJTQbIy5eXlWLhwIYYOHQpXV1cEBASgadOmsLOzg4eHB7p164YFCxbg2rVrkrlUx/79+/Hrr78CANq0aYMpU6bUyutYEouIREREDUR+xgWcO/CDZJyrTwu06jlVNGZFgjmrk7kLsSFxcg9C+0EvScaVFqTi7N4lFsmpPhkUZA93W/FL661XylCsNVosJyIiImoYNCW5OLF1nmScraMnOgyZKRrzd3IppC5HxoY4QiVnC/aGQm3rhC4j35eM01WU4MTWTy2SE9UdQRDw2ckC6Pi2RJTOCHx+qgCCID7ipTZlZmaiS5cuePLJJ7F+/XoUFBTc8nxBQQFOnDiBd999F+Hh4fjjjz/ueJ7CwkKMGjUKI0aMwN9//43y8n+Lo/n5+fj9998RGRmJb775RjSfgoICFBQUmHbYpaSkoGfPnhg6dCg2bNiA9PT0u/6aN2/ejBYtWuC9997D+fPnb/n7j42Nxauvvop27dohNTX1jscnJSWhTZs2mDFjBrZu3YqSklvvbeXl5eHIkSN49dVX0bRpU+zcufOuc76ZwWDA9OnTTZ8vWLAACoWiRl+jLrCdKRERUQNgNBpwbONsCEbxWQYyuRJdR/0PckXlA501eiP+viS+OtlBKcOIYIdq50vWKbTjeKTE70DmpaOicfGHf0XTNsNEd7M2NmqFDONDHPHd2cpnI2oMAjZeLsX94WwPRkREROY7sW0+tOWFknGdhr0FGzuXSp8XBMGsVqYcWdDw+DbvhtCOE3Dx+J+icdfORyM1YS8CwvtYLDeyrGKdgNO54l2H6LrYHC2KdQKc1XWzqGLKlCk4deoUAMDLywtPPPEEIiMjoVKpkJeXh6SkJOzbtw9Hjx6FwWBAeHj4becoKChA3759cfr0aQCASqXC6NGjERUVBQA4deoUVq9eDa1Wi2effRbOzs6YPHmyZG7nzp1D3759kZOTAwCQy+UIDw9HVFQU3N3dq/X1rly5Eg8++CD0+uv3tUJCQjBs2DCEhYUhJSUFmzdvRlxcHM6fP4/Bgwdj//79t7yWIAgYP368aSdkUFAQHnvsMUREREAulyM3NxcJCQnYs2cPTp48CUEQ7vh3djcWLVpk+rsePnw47rnnnho9f11hEZGIiKgBSD65GnlpcZJxrXo+BjffFqIxW66WoUhiefLIZg5wULGhQUMjk8nR5d73sfnbCdBryyqNE4x6HNs0G4Me/REyGb8P/jExTLyICACrLpawiEhERERmy0g+jKtnt0jGNWk9BIEt+4vGxOZokVAg3gauq48Ngp0rX3BI9Ve7QTOQdnEfygozROOOb/kY3sGdoFJXb94aEd29+Ph4bNu2DbjRRvT8+fPw8PC4Y2xqaiq+/vprREZG3vbcI488YipqdenSBT///DNatmx5S0xSUhLGjRuH2NhYPPPMMxg8eDC8vLwqza2srAwjRowwFRAff/xxfPLJJ9UuHgLA2bNnMXXqVFMBcdasWXjttdegVv87QmfevHlYunQpnn76aZw/fx5vv/02vv76a9Pze/bsMRVdmzdvjtjY2ErnKCYnJ+PHH39EYGBgtXP+r8zMTLz//vVd30qlEvPnz6+xc9c13vUhIiKq5zQluYjd+aVknIt3GFr1fkIybtVFtjJtzBxc/RHZb7pkXG7KaSSd+MsiOdUXwc4qdPYWH5h+KkeLixI374iIiIgAwKCvwPHNH0vG2di7IWrIa5Jxf5p1nc/FTg2VSm2PTsPekowrK8xA3O6vLJITWZ6TSoa2HmozIqmdpxpOqrrZhXju3DnTx0OHDq20gAgAAQEBmD17Nmxsbn0vumHDBqxbtw4AEBYWhk2bNt1WQMSNHX8bNmyAj48PioqK8PHH4v/fKSoqwuXLl2FnZ4cdO3Zg6dKld1VABIAXX3wRZWXXFzLPmjUL77zzzi0FRACQyWSYNm0a5s273t57yZIlt8xfvPnvbPTo0ZUWEHGjyDhr1qy7yvm/Zs6cicLC610DnnrqKURERNTo+esSi4hERET13Mnoz6HTFIvGyGQKdB31ARQibUwBIKVEj8MZFaIxEW4qtOGbjgYtrPMkuPu1koyL3fEFyouzLZJTfTEuVLrA/leS9A08IiIiovMHf0Jx3lXJuI5D34Ctg/gN3HK9ERsui48scFXLMaQpd581ZP5hvREUMVgyLuHoMuSln5OMo/pHJpPh5Q6uYGMhcSo58HJ7V8hkdVNEVKn+vXdz8uRJaLVVb0H7T7ENN9psihUiAwMDMW3aNADA6tWrzT7/gAEDqpzXfx0/fhw7duwAALRq1QrvvPOOaPyLL74Id3d3GAwGU5EU//k7i4mJgdFoucGf+/fvx6+//goA8PDwwIcffmix17YE/rogIiKqxzIvH8OVMxsl41p0e8isotBqM4obE8Oc6uxCmixDLleg84h3JVuV6ipKcGJbw2nRUROGNbWHvVL852NNUgl0RkE0hoiIiBq34ryrOLf/B8m4gBb9EdRKuii07WoZSnXi1x9jQhxgo+B1fkMXNWQmVDbiC98EwYhjG2bDaNRbLC+ynF7+dvj9Hh+0dFOBP/G3kgFo6abC7/f4oKe/XZ3l0b17dzg4OAA3Wpv26tULK1euNO10k5KTk4ODBw8CAPz9/dGsWTMkJSWJ/uncuTMA4MqVK7h27Zro+Xv27Innnnvurr9OALcUAseMGSOZ59WrV9Gnz/W5rfv27TMd279/fygUCtPjgwYNwrp161BSUruLeA0GA6ZP/7eb05w5c+56Z6a14UxEIiKiesqg1yJm0xzJOHsXX7Tp85RknFEQJIuIajkwqhlXJzcGbn4tEd51Mi4c/k007tq5bUhrey/8w3pbLDdr5qCSY3hTe6xKqnylf47GiL2p5RgYxJ8lIiIiup0gCDi+eS6MBvGdJ0qVHaKGvGbWAr+/LorvQgSACWZ0VKD6z87JC+0GviD5XjI/4zwSjy5Hi24PWSw3spwob1tsGOmPIq0RGWUsFv/D114JZ3Xd77vy9PTEl19+iSeeeAJGoxHHjh3DpEmTIJPJ0Lx5c7Rv3x5du3bFqFGj0KJFi9uOP378uGknXlpaGsLDw6v0+leuXEFQUFClzz/22GM1trj86NGjpo/nzJmDOXOk73P948qVK6aPQ0ND8dFHH+GNN94AAOzatQu7du2CXC5HeHg42rdvj27dumHMmDFo2rRpjeQOAIsXLzbNnezUqROeeEJ6jFB9wyIiERFRPRV/6BcU516WjOs45HUo1dIr6I5mViClxCAaM7iJPVxsFFXKk+qvNn2fwbXz0SgrzBCNi9n8MYY37WTW91ljMC7UUbSIiBuzR1lEJCIioju5dm47MpIPSca16fs0HFx8JeNSS/Q4lKERP5eHGi3cOLKgsQiJGofLpzciJyVWNO7M7q8QGDHIrO8zqp+c1XI4q/mzb42mTp0KW1tbPPfcc8jLywNuLDL5Z0feX3/9hddffx0TJkzAokWL4OXlZTo2I0P8PbwUqR2PUVFRd3X+m91Nrv/N8/XXX4ejoyPefPNNFBdfH/tjNBoRHx+P+Ph4LF++HK+88gqmTp2Kzz77DE5Odz8H+L8zJB988MFKY0+cOGH6+PPPP8eqVatMn0+ZMgXDhg2763xqA4uIRERE9VBJfgrO7V8qGRcQ3g8BLfqZdc6/Lkq3eBgbwtXJjYlKbY+OQ9/EvhUvisaVFabj3IEf0Lb/dNG4xqKTtw2aOilxpbjyFb27UsqRW26Ahx2L8kRERPSv6+3iP5WMc/UOQ3iXB8w655rkEkg1Uh8b4mBmhtQQyGRydBrxDrYueQCCSMtSva4cp6I/R89x8yqNIaLa88ADD2D06NH45ZdfsGnTJhw/fhxpaWmm5wVBwMqVK3HkyBEcPXoU3t7eAABbW9tbzjFr1qwqva6vr/jCAbH5ilV1c67btm1D8+bNzT5Wqby9vDV9+nQ89NBD+OGHH7Bt2zYcP34c2dnZpucNBgOWLl2Ko0eP4sCBA3B0vLv7XBrNv4t0YmJiEBMTY9ZxBw4cuOXzbt26sYhIRERENUMQBBzfMhcGfYVonEJli6ihr5l1zhKdEVuulonG+Ngp0MvPVjSGGp6A8D4IihiEa+ejRePiD/2MZu1Gwcm98pYnjYVMJsO4UEd8drKg0hi9AKy9VIrHWzlbNDciIiKybmd2fw1NSY5kXKfhb0GuUEnGCYIg2cpUJQdGBrOI2Ni4eociovsjOHfge9G4a+e2IbPjBPgEd7JYbkT0L3t7ezz99NN4+umnAQDp6enYvn07tmzZgpUrV8JgMODKlSt45ZVX8OuvvwLALa1IL168iJCQkBrNSS6vuZavQUFBOHz4MAAgOzsbgwdLz/mV4uLigpdeegkvvfQScKPt6bZt27Bp0yasXbsWAHD69Gn873//w6efSi/cEdOsWTPTrkcpSUlJEITry3p8fX1vKWC6uLjcVR61qe4b/BIREVGVpMTvQPrFA5Jxbfo8DQcXP7POuflyGcr14uuTx4Q4QCHn2PXGKGrITKhsxFfnGQ06nNq+wGI5Wbv7mjtA6sdl1cUS0xsIIiIiorz080g8tlwyrnmH++AZ1N6scx7LqsC1EvF5ZwOD7OFmy+4IjVGr3k/A0YxFgCe2fgKjyI5FIrIcPz8/TJkyBX/88QfWrVtnenzFihXQaq/P0u3cubOpKHXq1CkUFFS+wLWuDRo0yPTx7t27a+U1mjZtimnTpmHNmjX4/vt/F0788ssvd33uEydOIDEx0aw/Dg7/LthZtGjRLc89+uijd51LbWERkYiIqB7R6zQ4aUahxsUrFC26Tjb7vH8lSbcyHR/KVqaNlZ2TN9oOeF4yLjVhj1kF7sbAz0EpuXM3sUCHM7lai+VERERE1uufbiOCYBSNs7F3RbsBL5h93tVmjCwYz5EFjZZSZYtOw9+WjCvMSsTF46sk44jIsoYPH47Q0FAAgE6nQ0pKCgBApVJh0qRJpsdffvnlOs1TzNixY03FtR9//BHHjx+v1dd75JFH4OrqCgDIyspCWZl4Vy5iEZGIiKheiT/0C8oKpYdOm9veCAAuF+kQkyXeGjXKywbNnM07HzVMIVHj4O7XSjLuxNZPYTDoLJKTtTOn8L7KjBt7RERE1PBdiduC3JTTknHtBs6Ajb2rWecs1Rmx6Yr4zVFvOwV6+XNkQWPm26wrglrdIxl3ZvdX0JTmWSQnosYuJycHBw8elIyrqKgw7TJUKBTw8fExPffuu+/Czs4OuFGc++233yTPp9Fo8H//9393lXtVeXp6moqcer0ejzzyCDIzMyWPi4uLw4YNG0yfX758GbGxsZLHFRcXo7T0eptvFxcX2Nvb31X+jQGLiERERPVEWVEmzh/8UTKuefsx8GrSwezzmrMLcWwIZ6Q0dnK5AlFDX5eMK867gsSjyyySk7UbGGQPV7X45fb6S6XQ6MV3HBAREVHDpteWI3bnQsk4z6AOaNZupNnn3Xq1DGVSIwuaO0DJkQWNXvtBL0GhFC8m6zTFOLP7K4vlRNSYHT58GD179kTfvn3xww8/IDU19baYa9euYdy4ccjJuT5H95577rmlXWZgYCCWLFli+vzhhx/GI488gqysrNvOlZaWhoULFyIiIgIvvfQSNBpNrX1td/LOO++gZ8+eAICzZ8+iTZs2WLFiBfT6W9soG41G7N27F0888QTat2+PjRs3mp6Ljo5Ghw4dMGzYMPz+++93/DovXLiAUaNGQae7vvB57Nixtf61NQTKuk6AiIiIzBO74wsYdOIXcmo7V7Qb+KLZ5zQYBaxJKhWNsVXIMCKYRUQCPAPbIrjtSFw+vV40Lm7vt2jaZhjsnLwslps1slHIMKq5A36Jr3zIerFOwPZr5RjZjD9jREREjdX5gz+hvEh814VMrkSn4W9BJjN/P4A5HQ/GspUpAXBw8UWrXo9JFgmTTqxGSNRYszqUEFH1nTx5EgCwd+9e7N27FwDg4eEBPz8/uLq6IjMzExcvXoQgXF8o4u3tjW+//fa28zz44IPIz8/HjBkzYDAY8Msvv+CXX35Bs2bNEBkZCb1ejwsXLiA5Odl0LgcHBxQWFsLW1nK71NVqNdauXYvRo0fj4MGDyMnJwf333w9bW1u0a9cOgYGByMzMRFxc3C3zHW8uMp48eRKCIGDLli3YsmULZDIZvL294evrCycnJ6SkpODy5cum+ObNm2P+/PkW+xrrM+5EJCIiqgdyrp3ClbhNknFtBzxndnsjADiYoUFGmUE0ZkhTezhJ7KaixqPdwBegVIsXvPTaMsTu/MJiOVmzcWbcmGNLUyIiosartDAd8Yd+lowL7/IAXL1DzT7v1WIdjmaKjyxo56lGqCtHFtB1LbtPgYNrgESUgONb5pmKDURUO6ZOnYoZM2bAzc3N9Fhubi7i4uKwf/9+JCYmmn4OAwMDsXr1agQFBd3xXM899xyio6MRGRlpeuzSpUtYt24dNm3ahKSkJAiCABsbG7z44otITk6+pS2qpXh6emLnzp146623TG1YNRoNjhw5gr/++gv79+83FRBbtmyJFStW4LvvvjMdP2PGDEybNg2OjtffgwuCgMzMTMTGxmL//v23FBDDwsLw999/w93d3eJfZ33EnYhERERWThCMOLH1U8k4V59wNG8/pkrn/suM4sU4tjKlm9g5eqJNnydxKvpz0bjLpzcgtON4eAa2s1hu1qi1hxoRbiqcz698TuTBdA0yyvTwteelORERUWMTu2MhDHrxYp+Ngzta955WpfOulug2AjMXO1HjoVDaoMM9r2D/ypdF43JTTuPymY1o1vZei+VG1NgEBgbi888/x9y5c7Fjxw7s2bMHcXFxyM7ORmFhIRwdHREeHo7Bgwdj8uTJkrsG+/Xrh1OnTmH79u1Yu3Ytzp8/j+zsbLi4uKBJkybo06cPxowZA39//0rPERr670IWhUJh9tfSpEkT08f/FAcrY2Njg48++ggvvPACVq1ahejoaKSkpECj0cDHxwdt2rTB4MGDMXTo0NtyCAsLw3fffYfPP/8c27dvx549exAfH4/s7GwUFxfDxcUFLVu2xLBhwzBhwgQolZZ//928eXOUlV2fVXxz61lrJxO4dISIiMiqXYpdhyPr3peM6//wEvgEdzL7vEVaI7qtvAatyDi2AAcFdo0NgFzGOSn0L4NBhy3fTkRx7mXRODe/CNzz+G9VarvVEP18vgizjuWLxrwW5Yon27hYLCciIiKqe9lXT2LHz49JxnW+912EdDB/bpNRENB/dSpSSyvvOKKWA4cnBsGZHUfoJoIgYM+y55CRdFA0ztbREyOeXQOVDQvRREQNHa8UiIiIrJiuohSxO7+UjAuKGFSlAiIAbLhUKlpABID7QhxZQKTbKBQqRA2ZKRmXn34el2LXWSQnazaymQNUElfda5JL2RaKiIioEREEI05sM6fbSAs0aze6Suc+klkhWkAEgHua2LOASLeRyWSIumcmZHLxHTqakhycP/CTxfIiIqK6w6sFIiIiK3Zu//fQlOSIxsgVarQb9FKVz70mWbrF0Vi2MqVK+IX0QEB4P8m407sWQ68tt0hO1srdVoF+AeJtWxILdDiXV3nLUyIiImpYLsWuR376ecm4qCEzIZeb3zYOAFabM7IglDvI6M6cPYPRoutkybgLR35DaWGGRXIiIqK6wyIiERGRlSrOu4YLR36TjGvZfQocXSvvW38nl4t0OJktPnuli48NmjipqnRealw63PMK5Aq1aIymJAfxh3+1WE7Wakxz6Rt1a5Klb/gRERFR/aerKMXpXYsk44IiBsO7accqnbtMZ8TWq2WiMb72CvTwFZ+fRY1b697TYOvoKRpj0FfgzO7FFsuJiIjqBouIREREVip2x//BaBDfmWTr6ImInlOrfO6/zdiFOC6k8qKHIAjQVOih1RnYgrERc3QLRMvuUyTj4g/+hHKJHbUNXb9AO7hItAxbf6kUeiN/noiIiBo687uNzKjyubdfK0OZXvx64r4QByjkdx5Z8M91vk5v5HV+I6aycUS7AS9Ixl0+vRH56fEWyYmIiOqGeINrIiIiqhM5104hJX6nZFy7gS9Cpbav0rkFQcDfl8SLiDZywD47Fz+euYSs3DIUl2pNf0rKtCgu08F4o9ghkwFqlQIqpRxqlQI2agVcnWzg6+kIPy8H+Hld/6+/tyPcXWwh44zFBqVVz8dwKXYdyouzKo3R68oRt+cbdB7xjkVzsyY2ChlGBNvjj4TKdxvmaozYn6ZBv0Dx1qdERERUf5UWpuPCkd8l46rTbQQA1pqxWDCovAS/rktDZm4ZiksrUFSiRVGpFsUlFbdc5wOAWiWHSqmAWnX9Wt/FyQZ+Xo7w9XSAr6fD9et9T0d4udtBoeBehYYkuO0IJMasRF5anEiUgJPRn6P/Q9/wfR4RUQMlE7isiIiIyKoIgoAdPz+GnGunROM8AiIxaOpPkMmq9mb9RJYGE7dkisY4ZuXA50Jylc5rDjsbJVo2d0ebMC+0CfNEy2busLXhmqb6Ljl2HY6ue180RiaTY+hTK+HiFVNUAZIAAQAASURBVGKxvKzNiewKTNwsPjdmRLA9FvbxslhOREREZFmH/34Pl0+vF42xc/LC8GfXVnmxYFaZHr3+SoVYYwObohIExp6r0nnNoVbJ0aKZO1qHeqJNqCciQjzgaC/e9p6sX9bVE9j58+OScX3u/wL+Yb0tkhMREVkW79oRERFZmdSE3ZIFRACIGjKzygVEAFh2vlAyxikrt8rnNUd5hR4nz2fh5Pnru9aUChnCgt3RJtQTXSL90DrMs9LWSmS9giNHIOHwbyjISqw0RhCMiN3xBfrcv9CiuVmTDp5qNHVS4kqxvtKY6GvlKNYa4STR+pSIiIjqn4LMBFw+vUEyru2AF6pcQASAVReKRAuIAOCUVTst5rU6I84k5OBMwvXzy2RAswAXtA7zRNdIP7SP8IFKyeub+sa7SRQCWvRH6oVdonGnov8PviHdIZfzVjMRUUPD3+xERERWxGjU4/SOLyXjmrYZDo+ASLPPm5FdiuhDl7H7eCr2NgkBVJVfAii0WtjlSxcaa4LeIOB8Ui7OJ+Xiz60X4O5iiz6dAtGnUxAimntAzoJivSCXK9B+8MvY/fszonFpiXuRefkYfII7Wyw3ayKTyXBfiAP+71TlP18VBgGbr5RiYpiTRXMjIiKi2he78wsA4lU+d/82CI4cbvY58wo12HH4Cg6cSMF2Fz/A0aHyYKMRjjl5VUm52gQBSE4pRHJKIdbvSoKjvQq9ogLRp1Mg2rXwhpIFxXqj3cAXkZa4D4Kx8oVwRTnJSD71N0Kjxlk0NyIiqn1sZ0pERGRFLp74CzEbZ4vGyBUqjHh2LRwkZqRoKvTYdzwF2w5exukL2QCAEg83ZLYKEz3OJSUDnpeuViP7muXlboe+nYLQv2sThDZxq+t0yAy7/5iOjKSDojFuvhG454nfqrWLtiG4VqxD/zVpojFdfGzwxxBfi+VEREREtS/z8jHs+vVJybiBj/wAryYdRGP0eiOOnknH1gOXcfRMOoxGARX2dkjpKL7I0D43H37nKu8cYSnOjmr0igrEgK5N0CbMk7P06oHjW+Yi8dgK0RhbBw+MeG5dtXbREhGR9eJORCIiIiuh15Yjbs83knFhne8XLSCeT8rF5n3J2BuTgvKKW1eLlnh7SJ6/tlocVVV2XjlWbUvAqm0JaBPmibGDwtGtvT/bnVqx9gNfxJakQ6Ir7PMzzuPKmc0IbjvCorlZiyAnFTp72+BYVkWlMUczK5BSokegIy/ViYiIGgJBMCI2+v8k4wLC+4oWEK+mF2HL/kvYcegKCopvvZYw7zq/dkYWVFVRiRab9iZj095kNA90wX2DwtGvSxDUKkVdp0aVaN37SVw+vRG6ipJKYzSluYg/+DMi+4l3JyEiovqFdyaIiIisxIUjv0FTIl7AU9k6oVWv2wfbC4KAE+ezsHzjeZxOyL7jsQalAqXuruLnLy2DurSsipnXvrjEHMQl5sDPywFjBobhnp7BsLdV1XVa9B+uPuFo1n4ULp36WzTu9O5FCGo1CAqljcVysyZjQhxEi4gA8HdyCaa3Ff95JSIiovrh2rntyEs/Jxojk8nRdsDzd3zuwqU8LNt0HodO3bmbgQCgWKKIKNfrYZ+XX4WsLSM5pRALfjqG7/86jZH9QjCiXwjcnG3rOi36D1sHd0T0nIrTO8VHb8Qf+gUhUeNg7+xtsdyIiKh2Nc4+UkRERFZGU5qH8wd/koxr1fMx2Ni5mD43GgUcOJGKFz7agbc+31tpAREASjzdAbn4//qdsnJhzfv80rNL8fXyU3jotY1Y8mcs8os0dZ0S/Udk32ehUIrf+CkrzEDC0WUWy8naDGvqALXEVfja5FJw6gAREVH9ZzDoELtrkWRcs/aj4eIVcstjZxKy8dbne/HCnB2VFhABoNzFCQYb8cVZDjl5kBut99qioLgCv64/h4df34gFPx1DWlblO96oboR3mQx7Z/GW+wa9BnF7v7VYTkREVPu4E5GIiMgKnN33HfRa8R2A9s6+COt8PwDAYBSw5+hVLNsUj6vpRWa9Rom3p3iAINzWytTBToXgAGd4uzvAyUF9yx8HOxUEQYBWZ0CFzgCtzgit1oDSch0yckqRnl2CjJxS5BXWfKGvtFyHVdsSsHFPMsbfE45x97SAnS0va6yBvbM3WnZ/GGf3LRGNO3/gR4REjYPa1sliuVkLZ7Ucg4LsselK5T/zl4r0iM3Ror1X49ytSURE1FAkHV+F0vwU0RiF0haRfZ8GbnQYOX42E8s2nUdconljBiSv8+/QytRGrUBwgAu83e3h5KCGs6Mazg42cHZUw9FebbrO1+mNpv+WluuQmVOK9JxSZGSXIju/DDW95kmnN2LbgcvYefgKRvQNweQREXDlzkSroFTZIrL/dBz5+13RuEun/kZE9ylw8mhqsdyIiKj2yAQucSYiIqpTxXlXsenrcRCMetG4rqP+h2btRuFMQja+WXEKF68WmP0aOlsbXO3cTjTGuaQE99uUoFmgK4L9nREc4AJPNzvIZHe3N1FToUd6dimSrhUgLjEbcYk5uJZRfFfn/C9XJxs8NLIVhvVuDqWSjRbqmq6iFBsWj0JFaZ5oXOve0xDZ71mL5WVNdqaU4cmdle8cBoAHWzjif12l5xsRERGRddJVlGDDopGoKBO/bm/V83G0HfAcLqcW4uvlp3AqPsvs1zDK5bjctQMEZeXzBNVaLe7XZaN5oCuaB7ogOMAFvp4OkN/lrHGtzoCs3DIkpxQgLjEHZy/mIPlaAWpyw6OdjRLjh7TAuMHhXDRoBQTBiG1LH0R+RrxoXJPWQ9Fj7McWy4uIiGoPi4hERER17MBfr+PauW2iMS7eYYga+z1+WHMWu49dq/Jr5DXxR37TQNGYuT3cMT7UMrvCCoo0iLuYgzMJOTh0KhWZuTUzhzHA2xFTx0aiV1TAXRc/6e5cPL4KMZs+Eo1Rquxw7/MbYOvgbrG8rIXOKKDnqhTkaYyVxriq5Tg4IRBqBb+XiYiI6qPTuxbj3P6lojFqO1f0fewvLN9yBRv2JMFYxQpcsZc7slqGisY81doZMzu6Vem81VVarsP5pFycSczGwZOpuJpeM4sHuWjQemReOopdvz0lGTdk2nK4+bawSE5ERFR7WEQkIiKqQ/kZF7B1yf2ScYZmM7HikC0qtIYqv4YA4GqnttDbVd4GyFYhw+GJgXBUWf4NuSAIiE/Ow56Ya9gbcw25BXff/rRNmCdmTOmEIN/G1yrTWhiNemz5ZiKKci+JxrXo+iA63POqxfKyJrOP5eGn8+I31r4b4IUBgfYWy4mIiIhqhqYkF+sX3QuDTvzaVt18KpbFNEVRibZar5PeOhxl7q6iMVtG+SPUVVWt898NQRBwJa3o+nX+sWtIybz7OYfBAc6Y8XAnRISwW0Nd2vPHdKQnHRSN8Q/rgz73L7RYTkREVDtYRCQiIqpDe5e/iLTEvaIxOfrmWJ82CUD1diNpnByQ2r61aMzIYHt83serWuevSUajgLMXc7DzyFXsOHylWkXTf6iUcky+txUmDmnB1cp15Nq57Tjw12uiMXKFGiOm/w0HF1+L5WUt4nIrMGZjhmiMtfxsEhERUdWc2DYfCUd+F40pF9yx8to0GFF5K1IxepUSV7p2AEQ6cLTxUGPtCL9qnb8mCYKA5JRC7Dx8BVv2X0JJma7a55LJgFH9Q/HofW1gb2v54igBeennsW3pZMm4QY/+CM+g9hbJiYiIagfvqBEREdWRnJTTkgVEADiY3bdaBUSVUo6R/ULQcUQHydgxIY5VPn9tkMtliAz3wosPd8Rv80Zg6n1t4OFa+Q5KMTq9ET+vjcNzs6Nx4ZL4bD6qHYERA+HmGyEaYzRocXbfEovlZE1au6sR4iJ+4ys6pRyluspbnhIREZH1KSvKxMWYPyXjjuT2rlYBUS4D+ndpgsH3dxMtIALA6GYOVT5/bZDJZAgJcsW0Ce3w+yf34rnJHRDoU733IIIA/L3zIp58fyuOnE6v8VxJmrtfBIIiBkvGxe5aBO5fISKq37gTkYiIqI7s+u1pZF46IhqTXBqBPbn3Vem8djZK3NsvBGMHh8PJyQY9/kxBfkXlRQhPWzn2jw+EUm6dc9d0eiP2xVzDX9sTcPFqQbXOIZcBYwaG4ZExbWBro6zxHKly6RcPYM+y50RjZDIFhj/zF5w8mlosL2ux+HQBPj9VKBqzoJcHRje3jkI/ERERSYvZ9BEuHl8lGpNT4Yv1mVOrtFhQqZBhcI9gTBzaEv7ejrhvYzrO5FbeBlUhAw6MD4SnXfV2OtY2o1FAzNkMrN6egJPns6p9nn6dg/D0/e3h5ly9xYdUPUU5l7D5m/EQBPEFb/0e/Bq+zbtZLC8iIqpZ3IlIRERUBzIvx0gWEI2CDCcL+5h9TicHNR4e1Rq/zBuBJ8a3hbuLLQ6ma0QLiABwbzMHqy0g4saOygHdmmLRO4MwZ0ZvhASJz3y5E6MArI5OxNP/24Z47kq0KN+QHvBqIr4bVhAMOLPnG4vlZE3uNWN3wPpLZRbJhYiIiO5eSX4qkk+ulYw7XtjP7AKiWiXHmIGh+HHOcMyY0gn+3o64XKQTLSACQC9/W6stIOJGF5IukX6Y+3JffPHWQLRtUb0W7ruPXcNT72/F0TPclWhJzp7NENx2pGTc6Z1fcjciEVE9xiIiERGRhQmCgDO7v5KMu1gaiSK9h2ScWiXHg/e2wq9zR+Chka3g7KA2PbfuUqnk8WOaW0eLIykymQwdW/ti0TuD8PrjXeDjYV/lc6Rnl+LleTuxatsFGI18I2sJMpkMkf3FdyICwNWzW5CfccEiOVmTpk4qtPdUi8bsSytHrqb680GJiIjIcs7uWwKjUS8ak6EJQpqmmeS55DJgZL8Q/DJ3BJ65vwO83f+9/l1v1nV+/elk0KKZOz55pS9mv9gbzQJdqnx8YYkW736xH9/9GQudnq3gLaVNnychV4i3589LP4fUC7sslhMREdUsFhGJiIgsLCP5EHKunRSNMQhyxBb2kjxXn06BWDprKKaMbg0721vbdJbrjYi+Kr6DqbmzEq3dxQsY1kYul2FAt6ZYOmsonprYDk4OVcvfYBCw5M/TeH/RARQWV9RanvQv7yZR8AvtKRlnTnG9IRolsRvRIACbL3M3IhERkbUryr2Cy6fXS8adKJSeed62hRcWvzcYzz0YdVubTkEQJIuI9koZBgbamZm5dZDJZOjcxhdfvTsYr1Vz0eBf2xLw8tydSMsqqZUc6VYOrv4I7TheMu70rsUwGrkojoioPmIRkYiIyIIEQcCZXYsl4xJK2qPEUHnbzpAgV8yf2Q9vP9UdPh53LkDsTClHqV58t929zRwgk1lvK1MxapUCYweH46c5wzB6QCiq+mUcPZOOZz/cjjMJ2bWVIt0kst90yZi0xL3ISYm1SD7WZHiwAxQS37/m7DYgIiKiuhW35xvJ+XCp5c2QWdGk0ud9POzxztPd8ckrfdE88M7vB87l6ZBcJL7bcVCQPexV9fO2n1wuw8AbiwanjG4NlbJqX0fClXxMn7Udu49dq7Uc6V+tej4OhUp8HmVRTjKunNlksZyIiKjm1M+rCSIionoqNWE38tLPicbojUrEFt1515aDnQovPBSFL98ZhMhw8Zkh5hQdRpoxj83aOdqr8ewDHfDZ6wPQxM+pSsfmFPw/e/cdHkd19QH4N7N91XvvsmTJvffeO9gYMJie0En4khAIgZBACCGhh2p6b7Zxwb33Lsm2LNuymtV71/bd+f4wzdiaO5J2R9rVeZ+HJ1h7Zvbg2NLsPfeeY8SfX9iNz9bnUHtTFwuMSENM2gxm3Kldve80YrBOgbER4gsvJ2rMKG0VXywkhBBCSPdprLqA4jNbmHEZ7cw8Vyl53LIgHe8+PRsThkWLbvST8pzP6nTgDtQqBW6en443/zYD/ZKDO3StwWTDcysO4+VPjsNipRNwrqT1DkLqyJuZcVJa/RJCCOl5qIhICCGEyEQQHDi9+y1m3LnWoTDaryyGDUsPwzt/n4l5k5Kg4MWPLTWZ7dhTZhSNGRCkRoKv+PwKd5KeFIQ3npyBWxakQ8k61vULDgH4dN0ZPP3WQRhN9KHWlQZMvg8cJ/74WV10FDXF4u1+PZGUhb7v6TQiIYQQ0mNl73kbgPimtGJjMmotUVd8PTnWH68/MR3LF/aDRq0QvYdDEPB9kfgzQYCGx7hI8Q1K7iQ2whcvPDIZv1s+FHqdUsIVP9u8rxCP/Hc36ptMLsuPAH3H3AqVVnxDZ2tDCZ1GJIQQN0RFREIIIUQmBac2o6n6gmiM1aHC6eYxl31Nq1Hgd8uH4tmHJyAkUNpckC3FBljFOyl5xCnEX1OrFFi+sB/eeHIG0hIDO3Ttoaxy/N/zO1FVR4UaV/ENTkD8wAXMuOy978iST08yPUYPDaP4vY6KiIQQQkiPVFt2BqXndzLjMhsvP4WoUHC4ZUE6Xv3LNMRH+Ul6r+PVZlQaxE/WzYnTQ8XYdOhueJ7DvElJePcfszF2cGSHrj1XWI+Hnt2OvOIGl+XX26l1vkgbezsz7sy+9+g0IiGEuBkqIhJCCCEyaGoxYt/6V5hxOS0jYHL8XNwbmBKCd56ahXmTkjo0u3B9oUH0dQ7AvHhpBUl3FB/lhxcfnYrbrumPjqyfFJY24XfP7kBOfp0r0+vV+k+8GzwvvoO8qvAIakqyZMupJ/BR85gWrRONyW204nyDRbacCCGEEMJmsdqx7rN/M+MKDX1Rbw3/6dexEb549S/TsHxhPyg7MPOvt4wsaE9wgA5/u38s/nDbcOapzV+qbTDiD8/vwv6MUpfm15uljFgGrVeQaAydRiSEEPdDRURCCCHExSpqWvGfF16FVqgRjTM7NMhuHgUAUCo43HP9IDz/x0kID+nYIkCVwYbDleLtekaFaxCm71grIHej4DncNC8N//nTZAT7ixdnfqmxxYw/v7Ab2w9ddGl+vZWXfyQShy5mxvXG04hSFvzoNCIhhBDSczS3WfDPFz+F3pItGicIQGbjhJ9+fd3MFLzx5HT0iQvo0PtZ7AI2FYlvFgzXKzAsVNOh+7objuMwa3wCXn9iOuKjfCVfZ7bY8cxbh/DF9zkQBJqH7mxKtQ59x97GjKPTiIQQ4l6oiEgIIYS40LnCejz83HbECNuZsWeaR8Ei6BAcoMMLj0zB4hkp4DvRhmhjkYExjcWzdyf/2oCUELz11AyMGhgh+RqrzYH/fnAUH6w+DYeDFhicLW3sHezTiAWHUdvLTiNOjNLBVy3+eL6+sA0OWvQihBBCul1FTSv+79874dO8gRmbb+iPJlsIvHQq/OPBcfjt0kFQq6SfovvRgQojGi3iMwsWJHiB70AHE3cWG+GL1x6fjnmTEjt03cdrz+Df7x6B2SLeFpZ0XPKw66DxEh8rQacRCSHEvVARkRBCCHGRAxll+PMLu+FvPwl/lXh7TJNdhzMtIzA0PezSPL8k8TYwYlgtjlQ8MDvWc1uZXo2vtwb/eHAc7rlhEJSMuXO/9PWmc/j3e0dgtTEGTJIO8fILR+KQa5lx2XtXyJJPT6FRcJgTJ/53s7zNjoxqs2w5EUIIIeRK5wrr8fvndsJYl4sYXZ5orEPgkNU0HonRfnj9iekYPahj8/x+qbe3Mr0ajVqB3y0fhr/eMxpeOpXk63YfK8FfX92LNoPVpfn1NkqVTtpsxP10GpEQQtwFFREJIYQQF1i9PRfPvH0QZosNg30PMONPN4/G9fMG45+/nwB/n863HypqtuJUnfjMtImROvhpOr7z2d1xHIfF01Pw0qNTO9TedM+xEvz9jQMwmelDrjOljWOfRqwsONTrTiNSS1NCCCGkZzuYdWmjYFOLGYP82M/5eW0DMXrEELz82FREhnp3+n0NVge2lxhFY5L8lEgLkF5I8yQTh8fgzSdndKi96encWjzywm40NIuPgiAdI+k0Yn0JLp7eJFtOhBBCOo+KiIQQQogTCYKAD1afxjtfn4QgAHG68whQs2Yh6nDd8gdx26L+UHSifekvfV/ELi4s7GW7k38tNSEQr/11GlLipc+gOZ5dib+8vBctbeIFWiKdl18EEodcw4zrbacRR4ZpEKYXL/JvumiAldrsEkIIIbLbuLcAT795EGaLHQGqKsTpc0XjHQKHIVPuxh/vGAGtpmvzyHeWGmGwif/8X5DgBa6XtDK9mvAQL7z82FSMHiR9jEF+SSP+8PwuVNbSJi1nUap0SBsjYTbi/nfpNCIhhLgBKiISQgghTuJwCHj76yx8vencD18RMMhvP/O61NG3YMzQjs3xuBpBELCuQPzDr17JYWqM9FN4nirI/9LcyckjYiRfk5Nfh0de2I36Jtqp7Cxp4+6Udhqx9KRsOXU3nuOwIF680N9gduBgBf05JIQQQuT03fYLePXTE/hxNLGUU4hBSTOxYPZYpxT2pLQync94hugN9FoV/nb/OCydlSr5mvLqVvzh+Z0oKmtyaW69SfKwpXQakRBCPAQVEQkhhBAnsDsEvPrZCazZ8fNMlBjdBQSpq0WvU2l8MWzyLU7JIafeioJm8Z2c02P00Cnpxz9+mJ/y2G9H4dZF/SRfU1jahD88vxOVNbRT2Rm8/CKQMHgRM663nUaU0tJ0g4RTx4QQQghxjq83ncPbX//cYt1fVYME/TnRa8DxGDv7Pqe8f6PZjr3l4q1MBwapEe/bO1uZ/pqC5/Cb6wbiT3eMgEriZ5+6RhP+9N/dOFcgPsueSKNU02lEQgjxFLSKSAghhHSR3e7ACx8cxeZ9hb/4qoDBvlJOId4Mlabzs1F+Scru5N7eyvTXOI7DzfPT8dd7RkOjljYnsqKmDf9HO5WdJl3KacT8g6gtPSVbTt0tPVCFJD/x35OtxQaY7dTSlBBCCHElQRDw8dpsfLD69GVfHyhh5nlc/znwCYpzSh6bLxpgdYjHSNmE1NvMGBuP5/84SfLM+ZY2Cx59aQ8ycqpcnltvkDxsKTR68RESdBqREEJ6PioiEkIIIV1gtTnwrxWHsfNI8WVfj9bmI1hTKXqtSuONlJHLnJKHQxCYJ5MCNDzGRWqd8n6eZuLwGLzwyGT4easlxdc3mfDnF3ZTIdEJvPwjJZ1GPLP3HVny6Qk4jsPcOPGFwFargL1l4icSCCGEENJ5giDgvZWn8MX3Zy/7up+yFon6HMbVHPqNv8tpubDmnnMA5sXrnfZ+nqRfcjBefXwaokKlbdw0me146vX9yDor3lGGsCnVOqSNvZ0Zl7P/fTgcdllyIoQQ0nFURCSEEEI6yWK14+k3D2J/RtmvXpE2CzFl5DKotT5OySWrxowKg/gHrzlxeqj4rs9j8VQp8YF44c9TEBIobWZkU6sFj764B8UVzS7PzdOljbsTHOM0YkX+QdRXnBWN8STzJMw02kgtTQkhhBCXcDgEvPFFJlZuzb3itYF+B8EacRibPgO+wQlOyaXaYMORSrNozOhwLUL14s9SvVl4sBdefHQKkmL8JcVbrA787fX9OHmeColdJeU0Ykv9RZSe2ylbToQQQjqGioiEEEJIJ1isdvzjzYM4erriitcitYUI1ZSLXq9U65Ey6man5bOhyMCMmU8tjphiI3zx0p+nIjpcWnG3scWMR1/cg9LKFpfn5sm8/SORMGghM+7sgQ9kyacnSPZXoW+A+FyjHaVGGG2M3maEEEII6RBBEPC/zzOwfnf+Fa/5KOuRqD/DvEf6hN86LZ/NFw1gNTBfkECnEFkCfLX4758mo3+fYEnxZosdT762H6dza1yemyfryGlEQaBW/YQQ0hNREZEQQgjpILvdgX+/dwTHs6/WrlTAYAmnEPuMuBEanZ9z8nEI2HRRvIgYpldgeKi0WSC9XWiQHi/+eQr6xInvmP1RfZMJf35xN8qqW12emydLH38X8zRiydkdaK4tFI3xJHMZpxENNgG7SqmlKSGEEOIsgiBgxbensHFvwVVfH+h7CDwnXuiI7jsN/qHJTstpI+M5X8UDs2KpiCiFl16Ffz08EaMGRkiKN1vseOK1fTiTV+vy3DyZlNOIjVXnUZHH/hxNCCFEflREJIQQQjrA4RDw4kfHcOCKFqaXhGmKEaYpFb2HUqVD39HLnZbT8Wozqo3sVqY8q+8S+Ym/jwbP/3ESBqaGSIqvazTh0Rd3o7KG2kt2lrd/JOIHzmNECTh78COZMup+UmYbSTmFTAghhBBpPlufg9XbrmxhCgDeikYke51m3qOfE08hVrTZcLxavJXp+Egd/DQKp72np9OoFfjbfWMxbXSspHiT2Y4nXt2Hs/l1Ls/NUynVOqRK6MJDpxEJIaRnoiIiIYQQItGPrY12HC5uN2ag7yHmfZKHX8/cidkRGyTMRZMyX41czkunwrO/n4DRgyIlxdfUG/HnF3ejqo4KiZ2VNvYOAOLF7qLTG9HWKN4u2FPE+agwIEgtGrO7zIgWC7U0JYQQQrpq5dbz+Gx9Truv9/c9Ap4T/5kblTIJAeGpTsuJ1W0EEjcdkcsplTz+dMdILJoq7cSowWTD46/uxbnCepfn5qmSh18PlcZbNKa29CRqijNky4kQQog0VEQkhBBCJGC1NgKAQFUlonXtvw4ACqUWfUff4rS8bA4Bm4vFFxeivBQYHCxeiCBXp1Yp8OS9YzBmsLRCYlWdAY++uAf1TSaX5+aJfIPiEJM+QzRGcNhw7vAnsuXU3VgbAMx2ATtK6TQiIYQQ0hUb9uTj3W9Ptfu6lm9DH6+TzPs48xQiJGwWVPPAtGgqInYGz3O478bB0guJRhv++speXCxvdnlunkit9UGf4Tcw43J60Qx0QghxF1REJIQQQiQQa230IymnEJOGLYHWO8hpeR2pNKHeJL4jem68FzhqZdppSiWPx+8ejZEDpM1Oqahpw5Ov7UOb0ery3DxR+rg7mTEFmWtgau0dLaXmUktTQgghxKV2HL6I/30ufvop3ecYlLxNNCYieTwCI/s5La/SVhtO1lpEYyZF6eCjpqW9zuK4S4XE+ZOTJMW3Gqx4/JW9qK6nZ6/OSBl1ExRKrWhMZf5B1Fe0fyKYEEKI/OhJgxBCCGFgtTYCAF9lPeL050RjeF7p1FOIkFg8oBZHXadWKfDkfWMwrF+YpPi84kY8/dZBWKzisyrJlQLCUxGRPF40xm4z4/yRz2XLqTtFeikxLEQjGrO/3IgmM/1ZI4QQQjrqYGYZXvjwGMTGsKk4M/r6nGDeq9/4u5ya20YaWSALjuPwwLIhmDsxUVJ8bYMRf31lH5rbxAu85Epar0AkDV3MjMvZ/74s+RBCCJGGioiEEEKIiF1HikVbG/2ov+9h8Jz4EPj4gfOh95VWhJLC6hCwldHKNNZHiX6B1MrUGdQqBZ66fxyGpIVKis86W40XPjwGh0P8zwW5UrqERbgLx7+BxdQiSz7djbURwOoAthYbZcuHEEII8QRn8mrxrxWHmc9qqd6Z0PBm0ZiQ2KEIjhns1Pw2MuYhahUcpkTrnPqevRXPc3jo5qGYPT5BUnxxRTOe+t9+mMzip1PJlVJH3wKeV4rGlJ7biaaafNlyIoQQIo6KiIQQQkg7Tp2vwYsfHWPG6RQtSPY6zYji0Hfs7U7LDQAOVpjQaBFvZTovXk+tTJ1Io1bg7w+Mw6DUEEnxe46V4J1vsiCIbW8nVwiJGYyQ2KGiMTZLGy4c+1q2nLrTnHgv8Iy/xt9LOK1ACCGEkEtKKlvw1OsHYLWJP0srYEM/3yPM+6WNu8OJ2QFFzVZk14mfdJsSrYOXipb1nIXnOfz+lmGYMTZeUnxOfh3+teIw7HbxP0Pkcl5+4YgfOJ8Zd/bgR7LkQwghhI2eNgghhJCruFjejH+8yV5YAICB/seh4MRbCcakTYNvUJwTMwQ2UIujbqHVKPH0Q+PRv0+wpPg1O/LwzebzLs/L00g5jZh79HPYLJ5/Ai9Ep8DIMPGWpocrTagzUktTQgghhKWh2YQnXt2HFgntKJO9T0OvEH/m9g9LQUTSOCdmyD6FCIlzk0nH8DyH/7ttOKaOipUUf+RUBV759ARtGOygtLG3g+PEl6Qvnt6E1sZy2XIihBDSPioiEkIIIb9S12jEE6/uQ6vByozVK03o55fFjHP27mSzXcA2RivTJD8lUv1VTn1fcolWo8TTD45HUoy/pPgPVp/G1gNFLs/Lk4QnjkFAeJpojNnQiPzM72TLqTvNZ2wIsAvAZsb3BEIIIaS3M5lt+Nv/9qOylr0Zj4MD4yIzmXFpY+9weucP1jxEvZLD5ChqZeoKCp7DH28fgZEDwiXFbz1QhA+/y3Z5Xp7EJygOMekzRGMEwY5zdBqREEJ6BCoiEkIIIb9gMFnx5Gv7UV3PXozneQ6/nVwNwSZ+EioscTQCI9KdmCWwv9yIFqv4jte5cV7UytSFvPQq/PP3ExAeLO2058ufHEdGTpXL8/IUHMchffydzLhzhz+B3c4u+Lu7mbF6KBh/naWcTiaEEEJ6K7tDwHPvHkFuUYOk+DumGCCYKkVjvANiEJM+3UkZXpLfZMW5BvFnm2nROuiUtKTnKkolj7/eMwZpiYGS4r/edA6b9xW6PC9Pkj6O/ZxfkLUWprZ6WfIhhBDSPnriIIQQQn5gsznwz7cPIb+kUVL8wzcPgLn0e2aclA9IHbWhiF3kpFamrhfop8W/Hp4APx/xVpMA4HAIePadQyipbJElN08Q3XcqfIMSRGOMzVUozt4sW07dJVCrwNgIrWjMsSozqgw22XIihBBC3IUgCHjry0wcPimtPeKyOX3h18p+vug75lbwvNIJGf6MdQoRAObSc77L/TjCICbcR1L8a5+fwMnz1S7Py1P4h6Ugss9E0RiH3YILx76SLSdCCCFXR0VEQggh5IeFhf99kYETZ6SdFLtlYT8kaE/AbBAvOAZG9kdo3HAnZXmJyebAjhLxImKqvwrJ1MpUFlFhPvjn78ZDq1EwY1sNVjz1v/1oljCDhwAcxyNt3O3MuHOHP+0Vs2hYLU0FAFskzFAihBBCepuVW3Oxfne+pNjpY+Iwc2ATGqtyReO03sFIGLTASRn+jLVZ0FvFYSK1MpWFr7cG/3p4IoL92b/fdruAZ946hLLqVlly8wRSuo5cOP5Nr5iBTgghPRkVEQkhhBAA3+24ILkFzezxCVg2JxnnDn3KjE0fd6fTW4ruKTOhzSZeMKFTiPJKiQ/E3+4bCwWr3ySAsupW/POtg7DZHLLk5u7i+s+B3i9CNKap+gIq8w/KllN3mRGrh4rx9L6JioiEEELIZQ5lleO9lackxQ5JC8XDtw7H2QMfMmNTR90MhZLdjaIjchssyGsSb2U6I0YPjYRnTuIcoUF6PPvwBHjr2Rs0W9os+Nv/9qOFNgxKEhw9iLnh1mJsQkHWGtlyIoQQciUqIhJCCOn1TpypxLvfnJQUO6xfGB66eShKcrbB0Cw+I8U3OAFRqZOclOXPpMw9mxuvd/r7EnHD+oXjT3eMlBR78nwNXv8io1ecnusqXqFC39HLmXFnD30iSz7dyVfNY0Kk+E7449VmVFNLU0IIIQQAUFTWhOffOyIpNiHKD0/eOxZNladRU5whGqvSeCN52HVOyvJnUkYW0HO+/OKj/PD0Q+OhZu3mAlBa2YJ/vnOINgxK1HfMrcyY80c+g8NBz7eEENJdqIhICCGkVyurasG/VhyGQ0ItJznWH0/cOwYKBYdzh9kFi7Sxt4PjnPuj1mhzYFeZeDuXfoFqxPtSK9PuMHVULH67dKCk2E37CvHdjgsuz8kTJAy+Bmqtr2hMddFR1FfkyJZTd5kTJ75wKADYXEynEQkhhJDmNgv+/sYBGM3s4kOwvw7P/G48vPQqnJOwManP8Bug0ng7KdNLBEHAxovimwX91DzGRVAr0+7QLzkYf7l7NKQ0mck6W403v8qkDYMSRCSPh19IsmhMW2M5SnK2y5YTIYSQy1ERkRBCSK/VZrDiqdcPoNUg3jIIAEID9Xj6ofHQa1WoKjzCnJGi9w1HXP85Tsz2kj1lRhiZrUxpd3J3WjIjBXMmJEiKffebkzh6usLlObk7lVqP5OHXM+POHfxYlny60/QYdkvTzdTSlBBCSC9ntzvwr3cOoaKG3cFDr1Ximd+PR0igHi31xSg9t0s0XqHUIGXUTU7M9pLzjVYUNosXPGfE6qCmVqbdZuzgKNy1RNqGwQ17CrB2Z57Lc3J3HMeh75hbmHHnDn1CRVlCCOkmVEQkhBDSK9kdAv793hGUVLYwY731Kjz78AQE+V/a9XvuMHsWYuroW8ArnH8aUMq8s7k0D7FbcRyHB28aikGpIcxYhwA8t+IwLpY3y5KbO0sZcSN4hVo0puTsdrQ2lMmWU3fwUfMYz2hpeqzKjBqjXbacCCGEkJ5mxbenkHm2mhmnUHB44r6xSIz2BwDkHvnih3P97UscfA20XoFOy/VHmyS0MqW5593vupkpmDkuXlLsO19nISOnyuU5ubvY/nOg8wkVjWmoPIuqoqOy5UQIIeRnVEQkhBDSK3303WlJJ8B4nsMT945BbMSlVoqNVbmozD8oeo1a64vEIdc4LdcfmWwO7CoVb2U6MEiNaG+l09+bdIxSyeOJ+8YiMpTd5spgsuHpNw+gzcg+Edubab2DkDBogWiMIDhw/gi7yO/u5kpoabqFTiMSQgjppbYcKMQaiS3jf3fzMAxLDwMAmA2NKMhaKxrPcTxSR7NPTXWUIAjYxGhlGqDhMSZc6/T3Jh3DcRx+t3wYBqQEM2MdAvDcu4dRVcc+EdubKRQqpI66mRknpdUwIYQQ56MiIiGEkF5n55FifLP5vKTYe68fhCFpYT/9WsopxORhS6FSO7+l6N5yEwyMVqazGcUFIh9fLzWefmg8vPXsE6mlVa148cNj1KKH4dKinXgLr4KstTAbGmXLqTtMk9DSlLUQSQghhHiinPw6/O+zDEmx10xLxuxftKDPO/Et7DaT6DXRfafBOyCqy3n+2oVGKwpYrUxj9FDy1Mq0J1ApeTx571hEhLBPhja3WvDMW4dgsVKXCDFJQxcz54xW5h9kjhUhhBDifFREJIQQ0qvkFzfi5Y+PSYqdPSEBC6f+POTd0FyFi9mbRa/hFSr0GXFDl/O8mk1F7KIAFRF7lphwH/z1njHgJSz4HMgsk1zc7q18g+IQnTpFNMZuNeHC8W9ky6k7+Kp5jI9gtDStNqOWWpoSQgjpReoajXjmrYOw2hzM2MFpobh76aCffm23mZF77CvmdVJmt3WGlJEF9Jzfs/j5aPD0Q+PhpWNvGLxwsQFvfJEpS17uSqXxRtLQ65hxZ+k0IiGEyI6KiIQQQnqNVoMFz7x9EBYre2GhX3IQHrxpKDju5+JP7tGvIDjEdwjHDZgLnQ97Fl5Hme0CdjJamfYLVCPWx/lzGEnXDE0Pw/3LhkiK/ei708g8S3NTxPQdeysz5sKxr2Czip8kcHdz4sUXEh0CsKWYWpoSQgjpHWw2B5595zDqm9g//yNCvPDXu0dDofh5Sazo9AaY2+pFrwuJHYKgqAFOyffXWEVEPzWPMRHUyrSniY3wxV/vGS1pw+Dm/YXYuLdAlrzcVeqoZeB58dEcxWe2oK2JPZaEEEKI81ARkRBCSK8gCAJe/PAYKmrYp/lCAnV48r6xUCl//jFpNbciP2Ml89q+o1yzO3lvmRFtjFamc2h3co+1YHISFk5JZsY5BOC5FYdRXUfFn/YERw9CcMxg0RizoQFFp9bLllN3mE4tTQkhhJCffLQmG2fyaplxOo0Sf39gHHy9NT99TRAcOH/4M+a1fUezNzJ1xoVGC/KaxGdjz4jRQUWtTHukYf3Cce/1gyREAm9+mYlzheLF6t5M5xOKuAFzRWMEhw25Rz6XLSdCCCFURCSEENJLrNqWi4NZ5cw4jVqBvz8wDgG+l+/0LchcA6u5VfTaiOTx8AtN6nKuV7NZwokianHUs91z/SD07xPMjGtqteCZtw7S3BQRfcewF/HOHf4UDofn/h76qnmMY7Q0PVplRh21NCWEEOLhDmaW4dst7JbwHAc8+ptRiI/yu+zrFXkH0FxbKHqtT2AcIlMmdjnXq9ksoZXpnHj27D3SfRZOTca00XHMOKvNgWfeOojGZs/umNEVUor1BSfXQRDY3YUIIYQ4BxURCSGEeLzTuTV4f9VpSbF/vH0EkmMDLvuaw27F+aPs3Y5SChudYbYL2FEivriQFqBCvC+1Mu3JlEoef71nDAL92K2oci824I0vaW5Ke6JSJsEnKF40prW+BOUX9sqWU3dgnT6mlqaEEEI8XXl1K174UNq881sX9ceYwZFXfP2chBlrqWNuAce5ZgmNVUT0VfMYE06tTHsyjuPwu+VDkRjtx4ytbTDiXysOw26nItjV+IUmIbLPBNEYq6kFLfUlsuVECCG9HRURCSGEeLSGZhP+teIwHA7xVqAAcMOcvpg0IuaKr5ec3Q5DU6XotQHhaQiNG96lXNuzv9yIViurlSntTnYHgX5aPHHvGCgUEuam7CvElgPiu+J7K47j0XcMu3XweQ9vdTQ9RiehpSkVEQkhhHgms8WOf759CG1G8VagADBhWDSWze17xdfry8+g+uJx0Ws1+gDED5jXpVzbU9BkxflG8fynx+iglvDsSLqXVqPE3+4fC289e2PnyfM1+GTdGVnyckeu2pxLCCGkc6iISAghxGPZHQKeW3EY9U3sdjGDUkNw26J+V3xdEAScO/Qp8/q+Y24Bx7nmw720FkfUytRd9EsOxj3Xi8/0+9EbX2SiuKLZ5Tm5o/gB86D1ChKNqbl4Ag0V52TLSW5+GgXGRoifTDhSZaKWpoQQQjzSW19lIr+kkRkXHe6DP9w+/KrP6ucOs5/z+wy/AUqVa04CSnnOp5EF7iMixBuP/WYUpHws/HrTOWTkVMmRltsJiR2G4Oj250zqfELgExgra06EENKbURGREEKIx/p07RmcPF/DjAv00+Ivd4+GQnHlj8Xqi8fRUHlW9Hq9Xzhi0qZ3Kdf2mO0CtjNamab6q5BArUzdysIpSZg2mv3B12yx49l3DsFsoSLQrymUGvQZcSMz7vyRz2TJp7uwTiE7BGAr43sIIYQQ4m62HSzCpn3sjg0atQJP3jsGeu2Vz8ptjeUoydkuer1CqUHy8KVdylXMpottoq97qzjmDGTSs4wYEIFbFl65OfXXBAH4z/tHJG147W04jsOoRU9DpfG+yms8Rsz/m8s28BJCCLkSFREJIYR4pOPZlfhyo3jxDwB4nsPjd49GgO/VdxfnSmiHmDryZvAK1xTxDlWY0MJqZUqnEN3Opbkpw5AgYW5KUVkz3v46S5a83E3ysCVQKDWiMcVntsDYwt5M4K6mx+igZKyhbCqiIiIhhBDPUVTWhP99niEp9nfLhyE+6urPW7nHvoIgiG/UShi0AFqvwE7lyVLUbMXZBvFWptNi9NBQK1O3s2xuGkYPimDGNTSb8Z/3j0gavdHb+ATGYuqt7102HzEwsj8m3PAKIpPHd2tuhBDS21ARkRBCiMdpaDbhvx8clRR715IBGJASctXXWuqLUZa7V/R6lcYbiUOu7VSeUrB2JwPA7Fiah+iOtBol/naftLkpG/cWYM+xElnycicafQDiB84XjXE4bLhw/BvZcpKbv4SWpoerTKgz0WlWQggh7s9sseNfKw5L6tIwd2Iipo+Ju+prVnMbCjK/Y9yBQ8qo5Z3MlE3K3OI51MrULfE8h0fuHInI0CtP0v1a5tlqfL3Jc9vvd0VAeCom3vgarv/rcSz9y2HMuPOTy4qKhBBC5EFFREIIIR7F4RDw3w+OorHFzIwdNyQKS2aktPv6hWNfARDfFZo0dAlUGtcU8Sx2AdtKjKIxffxVSPanVqbuKjLUG4/cOVJS7CufHkdFTavLc3I3KSNvYsbknVgJm9VzW0VJaWm6rZhOIxJCCHF/7357EhfL2fOik2P9cd+N7c+gLjy1Hlaz+HNVVMok+AZdvQjpDKx5iF4qDhMiqZWpu/LWq/HEvWOgUrKXXj9Zm43sC7Wy5OWOeF4BhVJDLUwJIaSbUBGREEKIR/lu+wWcOMMeUB8R4oU/3j6i3Q8iFlMLCrLWit6D4xSSZrJ11qFKE5otDtGY2bG0O9ndjR4UicUixewfGYw2/Oudw7DaxP9M9DZ+IYmISBorGmMxNqLo9AbZcpLbjFh2S9OtVEQkhBDi5g5mlWH97nxmnJdOhSfuHQO1SnHV1wXBgdyjXzDvkzrmlk7lKUVxixVn6i2iMdOiddTK1M0lxfjjnhvaL2b/yCEAz717GM2t7I2whBBCiNyoiEgIIcRjXLjYgA9Wn2LGqVU8nrxvLLxE2kgWZq2FzSK+6B6TNg1efuGdylUKKa1MaR6iZ7hz8QCkxAcw43IvNuCD1adlycmdSGk1lnvkcwiCZ86b8dcoMDpcvKWplE0JhBBCSE9V22DESx8dlxT7yJ0jERHSfhvJ8gv70Vov3iY+ICINITFDOpynVKxTiAAwm9FpgLiH+ZMSMX5oFDOutsGIFz485rHPq4QQQtwXFREJIYR4BKPJhudWHIbNzv7Qdd+NQ5AU49/u6w6HHbnHvmTeJ2XUzR3OUyqrQ8D2YvFWpkl+SvTxo1amnkCl5PH43aOh1ymZsau35eLIqQpZ8nIX4Ymj4ReSJBrTXFuIyvyDsuUkt9mMmUlWB7CjhE4jEkIIcT/2H8YVtLSJn9wDgOtmpmDM4EjRmNyjnzPvkzryZpe2TmTNQ9QrOUyMFN8gRNwDx3H4v9uGIyyIvfnzyKkKrN2ZJ0tehBBCiFRURCSEEOIR3vwqE2XV7HlxE4ZFY86EBNGY8tw9aGssF40JihqA4OiBHc5TqqOVJjQyTg3NifOiuRAeJCLEGw/fMlxS7EsfHUNjs+fO+OsojuOQMoo9G/Hckc9kyac7zIjRg2d8O9hCLU0JIYS4oZVbziPrXDUzLiUuALdfO0A0prHqAqoKj4rGaL2DEdNvZofzlKq01YbTdeIF0SnROmglzNIj7sFbr8bj94yBQkJ72vdWnkJRWZMseRFCCCFS0BMJIYQQt7f7aDG2HihixoUG6vH7W4YxC2/nj7B3J6eMZBcsukLKYj/r5BFxP5NGxGDuxERmXGOLGS9/coLaHf1CXP+50OjbP2EMAFUFh9FY7Zm7u4N0CgwP1YjG7Cs3oc1KLU0JIYS4j3OF9fh4bTYzTqdR4rG7R0PFKLxJmYWYPGwpFArXdfuQMqeYnvM9T9+EQNy5WLzIDQBWmwPPv3cEFqtdlrwIIYQQFioiEkIIcWuVtW149bMTzDieAx79zSj4eKlF4+orzqKmOEM0RucTipi0aR3OVSqHIGBbiXgr0zgfJVL9qZWpJ7r3hsGIj/Jlxh0+WY7N+wtlyckdKFVaJA9byozLlbBJwF3NjhVfcDTbBewuE//eQgghhPQUbUYr/v3uYdgljCt44KYhiAptfw4iAJja6lF0eqNoDK9QI3nYdR3OtSNYRUStgsOkSJ1LcyDdY/H0FIwcEM6MKyhtwidrz8iSEyGEEMJCRURCCCFu68f5KAajjRl70/x09O8TzIyTsju5z4gbwLtwd3JmjRk1RvGdp7Ni9dTK1ENp1Ao8fvcYaNQKZuzbX2dJauPbWyQPv575d7Po9EaY2uply0lOMxhFRADYwpjBRAghhPQUb32VhYqaNmbc5JExmD4mjhmXn7EKDrt4G9G4AXOg9QrsUJ4dUWO040S1WTRmUpQOehUt13kinufwpztGIsifPe9y5dbzOHW+Rpa8CCGEEDH0VEIIIcRtfbc9F9kXaplx/ZKDcdO8NGacsaUGxdmbRWMUSi2ShizuUJ4dRa1MSVykL+6/cQgzzmS247/vH4HdTi0qAUDnHYzYfrNFYxx2C/Izv5MtJzlFeCkxKFj8tPXuMiPMEk50EEIIId3pYFYZth1kjysIC9LjdzezxxXY7VZcOP4N836pLh5ZsK3YANZP4VkSNgUR9+Xno8GjvxkF1n5QQQD++8FRtBmscqVGCCGEXBUVEQkhhLilorImfPQdez6Kt16Fx34zCgoF+0de3olv4XCIn2qMHziPOXetKwRBYJ4UitArMCBIvFBA3N+s8fEYOziSGXe2oB5fbTonS07uIHXUzcyYvOPfwGH3zAUZVktTg03AvnJqaUoIIaTnamwx49VPJIwr4Dn85bej4aVndwgpydkGU6v45sPQ+JHwD0vpUK4dxdosqOKBKdHUytTTDUoNxZIZ7D9r1fUGvPGl+KgNQgghxNWoiEjQ2tqKY8eOYcuWLdi5cyeysrJgNNLiEiGk57LaHPjvB0dhtbFPXz1863CEBrF389ptZuSd+JYZlzJymeQ8O+NMvQVlbYxWpnHUyrQ34DgOv791OAJ8NczYz9bn4HyhZ7bo7KiA8FSExo8UjTG2VKP0/G7ZcpLTLAmnlKmlKSGEkJ5KEAS89ukJNLaIt/wEgFsW9kNaUpCke0qZiZw6yrWnEBvNdhyuNInGjIvQwUdNS3W9wW3X9EdCtB8zbsfhYuw+ViJLToQQQsjVKLs7AdJ96uvr8frrr2PPnj1wOC5fiFer1Rg5ciRuv/12JCUldVuOhBByNV9syEFecSMzbvb4BEwYFi3pnhezN8FsEL9neNJY+IW49nuilFamM6nFUa/h76PBH24fgSdf2y8a53AI+M/7R/DGkzOg1dDjXerIZaguOioak3v0S8Smz5AtJ7nE+qiQFqDC2Yb2T1ruKDXCYhegVtBmBEIIIT3LjsPFOJBZxowbmBKCG+b0lXTP2tKTqK/IEY3xDohBZJ8JkvPsjJ2lRrA6is+MpVOIvYVapcBjvxmFB/+5nbk59n+fnUC/pCCEBNLnQEIIIfKj7U291J49e3D77bdj165dVxQQAcBisWD//v24++678fHHH3dLjoQQcjXnCuvx1UZ268bwYC/cc8MgSfcUBAG5R79ixrl6RgoAbGUUEYO0PIaFsE+mEc8xckAE5k9mF69Lq1rx3spTsuTU00X0mQAvf/FWsLUlmWio8Mw2sKyZqc0WB/MkBCGEECK36noD3vwykxmn1ynxyJ0joeClbYbJPfIFMyZl5DJwnGuXyDYzOgHwHDA9hopEvUl8lB/uXDyAGddqsOKlj49DEGiuNSGEEPlREbEXOn/+PJ599lm0tLRAq9Vi0aJFePLJJ/H888/jkUcewdSpU8Hzl/5oOBwOfPTRR3j77be7O21CCIHJbMN/PzgKh0P8wxPHAX+6YwT0WvZ8FPywO7mx6rxojG9QAsKTxnQo347Ka7Qiv0l8JuOMGL3kBRPiOX573UBEh3kz49bvzkfW2WpZcurJeF6BPsNvYMblHmNvHnBHs2K9mDFSTj0TQgghcnE4BLz00TG0Gdkzi++7YYikcQUAYGiuRum5naIxKo03EgYtlJxrZ7RaHdjPmEk8MkyDQK3CpXmQnueaaX0wOC2UGZeRU4VN+wplyYkQQgj5JSoi9jIGgwFPP/00rFYr/P398dJLL+Hhhx/G1KlTMXLkSMydOxdPPvkkXnnlFYSG/vwQ8/XXX+PQoUPdmjshhHz43WmUVrYw45bMSMGAlBDJ971w9EtmTJ+RN7p8d/KW4jZmDLUy7Z20GiX+fNcoKCS0n3zp42MwmsSL0b1B4uBroFBpRWMutTFukC0nuST7q5DkJ97WdluJAXbGhgxCCCFELt/vyUemhI1QYwZHYsbYOMn3zTvxLQRBfN544uBFUGnYG3C6Yk+ZERbGOHcpm4CI5+F5Dn+6fQS89ewNsO9+exLVdbQRjBBCiLyoiNjLbNq0CeXl5eB5Hv/5z3+QlpZ21bgBAwbgrbfegpfXzw+xK1asoNYJhJBuk3W2Gmt25DHj4iJ9cds1/SXf19hSjRIpu5MHLpB8z87aUiy+O9lXzWN0uHhRhHiu1IRA3Dw/nRlXVWfA+6uorala54v4AfNEYxx2C/IzVsuWk5xmMTYc1JscOF5tli0fQgghpD1lVS2SWrL7eavx8C3DwHHSunLYbRbkZ7J+znPoM+JGiZl23hZGK1MAmEHzEHutkEA9Hlo+jBlnMNnw8ifU1pQQQoi8qIjYy6xfvx4AMHXqVPTp00c0NjAwELfddttPvy4qKkJ2drbLcySEkF8zmmx46eNjzDiFgsOf7xoJtUp6G6C8E6sgOMRPbSUOXgSl2rUf6otbrMipt4jGTI3WQS3hJBrxXDfO6Yu0pCBm3Prd+cg6R21NpSwK5p1YCQfje4A7opamhBBC3IHDIeDFj47BbBE/LQgAv79lOPx9pW+oK8nZBnNbvWhMZJ8J8A6IlnzPzjDbBewuE98sODhYjXC9eBcB4tkmj4jB1FGxzDhqa0oIIURuVETsRYxGI5qbmwEAN94obafdokWLoFD8vBh//Phxl+VHCCHt+fC706iS0LZl+YJ+SI4NkHxfu92K/IxVjCgOycOvl3zPztrKOIUICSeLiOdTKHg8cudIaNTsQvnLHx/v9W1N/UOTERo/QjTG0FyJsvO7ZctJLumBKsR4iy9Gbik2wEE72QkhhHSj9bvycCavjhk3bXQcxg2N6tC9Lxz/mhkjxynE/eVGGGziP29nx9FzPgHuXzYEQf7sQjm1NSWEECInKiL2IjqdDk888QRiYmKQlJQk6Rq1Wo3o6J935VVUVLgwQ0IIuVL2hVqs3cluY9o3IRA3zE7t0L1LcrbD1Ca+aBGRPA4+gewdoV3FOhGkV3KYEEmtTAkQFeqNO64dwIyrrG3D+6uprWnKiGXMmFwJc1HdDcdxzI0HVQY7TtaKn4AmhBBCXKWypg3vrz7NjAsO0OH+ZYM7dO+68jOoKxO/t09QPMITR3Xovp0h5eT/DNosSAD4eKnx+1uGM+OorSkhhBA5Ua+EXmbo0KF47rnnOnRNWFgYLl68CAAwmUwuyowQQq5kttjxsoQ2phq1An+6cyQUio7tjblw/CtmTIrE3cmCIMBhrIWtrQy21p//sRsq4bC0QrC2wWFthWAzXPpfa9ulC3kl6rhQZOIz0fuP862Bo+ICrL6JUPrEgOPpR3hvtmhqMvZnlCL7Qq1o3Ppd+ZgwLBqDUkNly62niUyZCL1fBAxN7W+EqinOQEPleQSEd2wjQk83M06P93KaRWO2FRswJEQjW06EEEIIfnh2fvnT45LamP7x9hHw1qs7dP8Lx9jP+X2GXw+OY39+EAQBDkvTpWf71lLYWkovPesbKiFYWuD48Tn/F/8LXHrOt3NqbMMXAHzavX+KrhUh9TthsSVC5RsHTtGx/1biWUYNjMCMMXHYduiiaNyPbU3nTkyULTdCCCG9E61A9kJRUR1rAVJeXv7Tv//yVCIhhLjap+vOoLSqlRl3x7UDEBPe/gfzq6mvyEFdqfgpLZ/AWIQnjbni63ZTAyz1ObDUnbn0T30OrE0FgN3coRwu3cyMffxg5k/kkdWvoXrLjku/4NVQ+SVA5ZcElX8yVP7JUAcNgMo/SdJCCHF/PM/hD7ePwH3/2MpcfHvpo+N4+6mZ0Gl752MfzyvQZ/gNOLnjFdG4C8e+wsgFT8mWlxwGB6sRplOgytj+n5GtxQY8MtQfHEfzVgkhhMhn8/5CZJ1lz29eMCUJQ9PDOnRvU1s9is9sEY1RqvVIGLTgiq87rG2w1OXAUn8GltrsH57z838uDHaEHcjkRqBFJf45ZWzLl6je9tGlX3AKKH1iLz3j+yVees4PHgh1QCptIuxF7rlhMDLOVqGuUXwj/7vfnsTwfuEIDaKTrIQQQlyHnkCIKJPJdFkRMSEhocv3NBgMLj/RqFarodVS2z9C3FluUQNWbT3PjEtPCsS8ifGw2To2++38EXb7wsSh18Fms8HacB7mykMwVx6GtfY07Abntnbez08WfV0lWDDKcejnLzgssDach7Xh8t8fTu0HTcgQqEOHXfoneBB4lZdTcyU9R1igFrctSseKb8VbdVXWtuG9VSdx3w2DZMutp4kbMB/Ze96C3dZ+ob8oexP6TXoAGr2/rLm52vQYLT7PbX/hs6jFhnN1JvTxV8maFyGEkN6rttGIFd+cZMaFBelx28L0Dj/nXzixCg67VTQmrv88gNfAWH8B5oqDl57z67Jhay4C4LwWkfv4ScyY8cKen38h2GFrLoStuRC/nJjOKfVQBw+COnQo1CFDoQ4ZAoU20Gl5kp5Fp+Hx4E1D8I83D4nGXWpregxPPziWNoR5IKWSlu0JIT0DfTcior7//ns4HA4AQEBAACZOnNjle545cwbHjx93QnbtS09PR//+/V36HoQQ17HZHHjpo2NwMD6/q5Q87rymD1paxNv1/ZrF2IiSHPHdybxCCV3tVpR/+TwES2OH7t8RTfDFSU58xssw4Sj0YM9SESxNMJXthqls9w9f4cEHpEEZNgHKsPHgA/rRSUUPM3FIMPYc88P5oibRuA17CjA8LQApcX6y5dbThKdMQ1nOxnZfd9jMOHf0G8QPuUHWvFxtbIAdnzNivs9rxJ19qHUaIYQQ1xMEAa98fAoGE7sweOc1KbCY22DpQLMPh8OOvOPfMuP8rLko/2Y0BGOV9Jt3kB08DvDiaygxwkXECYXMewk2ww+bGn8uKvE+SVCEX3rOVwQNBsfThiBPkhqrw8Sh4dibUSkal5FTjY17cjF+SLhsuRF5BAUFdXcKhBACAKCVRNIuo9GIL7744qdf33LLLdBoaGYOIcT11uwqQkkVu2XQkukJiAzp+Em7srObmLuTQzQtQNUulxYQAeAIPw4OTnxPzwTHHtHX2+eAo+EMLOfehmHPcrRtmgbjiSdhLd0CwdKxwivpmXiew93XpUGtEn+kEwTg3VXnYLU5ZMutp4kZcA0zpvTMegiCZ/0eDQ5UwJexpri7smMnPAghhJDOOniyCpnn6phxk4dHYECfjp+0qyk8AHNbjWiMn9oCde12lxYQAeAs1w8NnHgRYLxjDzp7fszRkg/rhY9g3P8btG6YDOORP8F6cQ0cJvGZ2cR9LJ/fBwG+7I1en6y/gOZWiyw5EUII6X2oiEja9cILL6ChoQEAMHv2bFx77bXdnRIhpBcormjF2l3iQ+QBICHKB/MmxHT4/g6HHaXZ65hxYXojM8YZDvATRF/nBRtGO/Y75b0Ecz1sxetgOvZntG6cAuPhh2Et2wahM7McSY8REazHDbOSmHFl1W1Yv5v9d8tT+QQlIiBSvKWrsbkSdcWu7ZYgNyXPYUKY+EaF880OVBg8q3hKCCGk52lqteDjdReYcQG+atw8L7lT71Fyei0zJsxLrud8dien8Z3eLPgrtlbYyrfBlPEU2jZNh2H/3bBeXAvByp4vT3oub70Kv1nclxnXarDisw3sv1uEEEJIZ1A7U3JVn332GXbu3AkAGDlyJP70pz857d79+vVDUhJ7sbMraCYiIe7J7hDwwVuZsDP6mCp4Dn+8fQQCAzvemrE461uYWsV3J/uqLdCr7B2+d0eZoMFxbpRozCAhC35wwalBwQZbxS7YKnaBU/lAFz8X+sRroAkfRS1P3dDSOb44cbYOOfn1onFrdl/EtLGJiI3wlS23niR11E04/J34DKaKcxuRNHCGbDnJYW6iGhtKxf9sHG1S4dYIb9lyIoQQ0vu8/e1RtBrEu4EAwEM3D0VkeHCH719fcgwN5eI/59UKOwI0rj+xJQA4yNgsGCpUIkU455J3t9ccgb3mCHDyWehipkOfeA20UZPAKah9ubuZPMoPGecaseNwsWjc/swqzByfhGHpYbLlRgghpHegIiK5wpo1a/D+++8DAPr27Yu///3vUCgUTru/Xq+HXq932v0IIZ5jw44LyL3YwIxbNi8NfeKlzwcQ7GYYiragJfcrnDt1EoD4h+dwmXYnn+BGwMyJb3gY69jn8jwEawsMF76G4cLXUHhFwif1Rnj3XQ6lPtTl702cQwngj7ePxL3/2CrastRmc+D1L07ihUcmg+c72zzLfcWmTcHJ7SEwtrS/kaAy/wBMLVXwDoiSNTdXmhTtBZ2yAUZb+xs0tpeZcGd/f1nzIoQQ0nscO12BvSfKmHFTRsZi3FDp3UYEhx3Gsr1oPf8lsk/uByA+giVcbwQnwyNQMeJRxon/d4xz7O10K1PJ7GYYizbAWLQBvMYf3n2uh0/6bVD5xrn6nYkT3XvjEJw4U4XGFvEOMm98kYUV/5gFrYaWewkhhDgP/VQhl1m3bh1ee+014IcTiE899RR0Ol13p0UI6QVq6g34aE02My4+yhc3zk2TdE9bSymaz3yA1gvfwGFuhNGmQLNFfLaKs3Ync0o9lN7RUHpHgtcFg1d5gVd5g1N5g1fqwam9cSI/CRA/FInJflVQmMNgN9YBguvnltnbytGY8RIas/4Hr4R58Ol3BzQhQ8HJsdpCuiQ63Ac3z09n/j06k1eLTfsKMG+Sa7sC9ES8QoWkoUuQvedtkSgB+RkrMWja72XMzLW0Sh4TI3XYUmxoN+Z4tRl1JjuCtM7bOEYIIYQAgMlsw/8+z2DG+flocN+NgyXd026qR8vZT9By/kvYW8tgc3CoNYpvMuQgIERvkpx3u/dRaKDwjoLSOxoKbRB4tQ+4H571Lz3ve2FNeTRQIn6fST7lUNiiYDfWAjKMF3CYG9GcvQLN2e9CFzMVPum3Qxc9ibqQuAFfLzXuXzYE/1pxWDSuqs6AT9adwd1LxVv4E0IIIR1BRUTyky+//BIrVqwAACxcuBC/+93vnHoCkRBCxLz1VRYMJvEiGc8Bf7htBFRK8Q+65trTaD79DtoK1gPCz21JqwzsNsdhHdqdzEHplwB1YDrUQf2g8u8DpU8MlF6R4DX+ooU3m0PAvsxSAO2fGusXqMaQ+V8CP+yytrWWwNqYD2tjHqxNeZf+veE8HJYmqQlL57CiLX8N2vLXQB08CL797oBX4gJwCvHd3aR7LZ2Vit3HilFUJt4C971VpzB6UCSC/HvfRqGkIdfizL73IDja/36Tn7kG/SfdC4XSc/68z4gVLyI6BGBniRFL+1BLU0IIIc712focVNW1/zPoR/cvGwI/H/GfvdamQjRnv4fW3K8h2H8uCNYaNXAI4g/xwToTVLz42IRfUnhHQx3UD5qgflD5p0D5Q+GQ1wUzN9jtzasA0P7GRD81j9mLX4KS5yAIDthby2Ft+vE5vwDWpnxY6s/BYaqVnK90AowlO2As2QGlbwJ802+Dd8oN4NU+Lngv4iwTh0djx+EIHDlVIRr33bZcTB4Rg5R48c2zhBBCiFRURCQQBAFvv/02vvnmG3Ach3vuuQc33HBDd6dFCOlFDmaV4UAmu73RkpmpSE24+ochQRBgLN2F5tPvwFR+4IrX7QJQwygichAQKrI7mdcGQxsxBtqIUVAHDYA6sC94lRcz76vJqDGjwdx+ARE/LPr/lBuvgMo3HirfeCB22k9fFwQHrE0FMFcdh7n6BMxVJ2BtzO1UTu2x1J5E7Z6H0XDsOfgNvA/efW8Gr+x9xSd3oFTy+L9bh+Phf++EILJGZjDa8MaXmfjbfWPlTK9H0PmEIjp1CkrObms3xmJsRHHONiQMnC9rbq40JUoHJQeIdDTF1hIDFREJIYQ4VUFpI1ZtYz+bjhkciUnDo9t93VydgaZTb8NQtOmHiYM/EwSgysB+Ng33av85n1P7XnrODx8FdVB/qIPSodB0rs13RZsNp+rEO5tMjdZB+UNreY7jofSJhtInGrroST/FCIIAW2sJzFUnLj3nV2fAUpfj1O4ktuZC1B/+OxozXoZPvzvg2+8uKLQBTrs/cR6O4/DgzUNx6vwWGM3t/xlwCMArn5zAa49Pg5Kx+ZYQQgiRgoqIvZzNZsMLL7yALVu2QKPR4LHHHsPkyZO7Oy1CSC9iMFnxxheZzLiwID2WL0i/4uuCIMBQtAmNGS/B2nCu3evrjFrYBfEPUUE682W7k3mNH7QRYy/9EzkWKv8Up7X13CZyIuhHM2LY82M5jofaPxlq/2T4pN4IALCbG2GuPAJDyU4Yi3fAbqh0Ss52Q9WlRYaTb8BvwD3wSbul00VU4jp9E4OwcEoy1u7ME407kFGGg5llGDvEc2b/SdVnxPWiRUQAyDv+jUcVEf00CowK1+JARfsLqAfKjWi1OuCtogUnQgghXWd3CHj1kxNwOMRP/+m1Sjx409Xb5xvLD6Ax4yWYK4+0e32LRQWjTXx5y1tlhZfq58ILp9RBGz4K2shx0EaMhTqoPzjeOZ2YdpSw56vPiJXynM9B5RMLlU8svJOvBQA4bEaYK4/BWLoThpKdsDUVOCVnh6UJTZmvoDn7Pfim3wbf/ndDoZM+g57IIzRQjzuu7Y83v8oSjcsvacTq7bm4fnZf2XIjhBDiuaiI2Iu1trbiqaeeQkZGBoKCgvDMM88gLU3anDFCCHGWT9aeQW0D+4P2Q8uHXTYg/seTh43H/wtL3WnRawUBqGqT1spUoQ+DPn429PFzoQ0fBY53/o9KQRCwnbG4EOOtRIq/qlP3V2j8oY+bBX3cLAiCAGv9WRhKdsBYshPmquNX7N7uKIexBg1H/4mmU2/Ct//d8E2/jdof9TC3X9sfB7PKUFMv/ufs9S8yMDgtFHpt5/6suauQ2GHwDU5Ec237C291ZadRX3EWgRGe82w0M1YvWkS0OIC9ZUbMjafNAYQQQrpu4558nCusZ8bdsXgAggMuP0lors5Aw/H/wFS+n3m91JEFvMYPutiZ8IqfC23UBPBK9nWdsb1EfLOgRsFhfETn3ptX6qCLnghd9EQEjv47rE2FMJZcKiiaKg4CDmsns75EsLai6eQbaD7zAXz63gLfgfdAqQ/r0j2Jc82fkoydR4qZf7c+W5+DicNiEB5Cz3WEEEK6hrYZ91IVFRV48MEHkZGRgdTUVLz11ltUQCSEyO7CxQas3XGBGTd5RAxG9A//6dfG8oOo/H4xqrfcyiwgAkCbVYk2m3iRxMfHD8mLVyJ62TEEjX0WushxLikgAsD5RitKWsXbEM2I1Tnl1CPHcVAHpcN/8EOIWPAdopcdRcDIJ6EO6tfleztM9Wg8/m+Ufj0WzdnvQ7CLt20i8tFrVXjwpqHMuLpGEz5de0aWnHoSjuPQZ/j1zLi849/Iko9cpsewW71JOSVNCCGEsNQ1GvHBd+zn9NSEQMyblPTTry11Oajaegcq1i2UVEC02DnUm8TnKKpUKqQtfAcxN2chZNLL0MfNcFkBsdniwOHK9jfsAMC4CC30Tjr1r/JLgG//uxA+53PE3JSBoHH/hiZsZJfvK9iMaM5egbJvxqPhxH/hsLQ6JV/SdQqew8O3DodCIf5Z0Wyx482vMiGIzTgghBBCJKAiYi+Uk5ODBx54ABcvXsT06dPx2muvISQkpLvTIoT0Mna7A698chyM7kbw1qtwzw2DAQDm2mxUblyGqo3Xw1x1TPJ7SZmR0nfi76ALHw6Oc/2PRme1Mu0MpVcE/Abeg8hrtyBy8Xb4DXoACq/ILt3TYW5A/eGnULZqKtoKN9AH1R5i9KBITBSZLfSjNTvzkF/SKEtOPUn8wHlQqsX/nl3M3gyLsVm2nFwtTK/E4GC1aMyuMiMsdvo7TAghpGve+ioLBqP4pjme5/D7W4ZBwXOwNl9Ezc4HUP7dTBiLxVuO/1KNUQcB4sWUpOE3wTt+Ojje9Z0X9pQZRecPw4XP+QptAHzSliNiwWpE3XAI/sMfg8o/pUv3FGxGNGW+itJvJ6D57KcQHM6bx0g6LyHaDzdIaFV65FQFDmWVy5ITIYQQz0XtTHuZPXv24F//+hcsFgtGjBiBqVOn4sSJEz+9brPZYLW23/4iMDAQgwcPlilbQognW7szD3nF7MLFXUsGwk/dhtr9T6H13BcdbsVpc3CoNTJ2J2u8Edd/Tofu2xXbGK1MAzQ8hoaI5+wM6sC+UAf+Bf7DH4WxdBdaznwIY+nuTt/P1lyEmh33QBM6HAGjn4Q2dJhT8yUdd9+NQ5CRU4VWQ/s/2x0OAf/7PAMv/XkKeN45Mz/dgUrjjfgB85B34tt2Y+w2EwpPrUfqqJtlzc2VZsbqkVXb/qnhVquAQ5UmTIpib74ghBBCrubIqQrsO1HKjFsyIwUJ4So0HHseTaffARwd62ohCNJamSYNXdKh+3YFa7MgzwFTJXQG6CqVTwz8Bz8Iv0EPwFx1HC05H6GtcAMgdK4I6DDWoP7AX9CS/T4CRj4OXewMp82KJ52zbF4a9p4oRWlli2jcm19lYmh62GWjQQghhJCOoJ8gvczHH38Mi+XSg/mxY8dw7Jj0kzwAMHLkSCoiEkK6rLbBiE8ktFBMTwrEmICdKP32JQiWzp0GqjFombuT4wfOh1Itz4J5WasNOfXiCyTTonVQyFjM4Tge+php0MdMg7WpAM05H6E19xsI1s61LTJXH0flukXQJyxA4KgnofTu2klH0nmBflr89rpBePmT46JxZ/PrsOVAIeZMSJQtt54gedhS0SIifmhpmjJymSynlOUwM1aP/2SIb+DYVmygIiIhhJBOMZlteOOLDGZcaJAe16RfQNm3d8BuqOrUezWa1bDYFaIxEUlj4RMY06n7d5TZLmBPmfhmwWEhGgRpxXN2Jo7joA0fAW34CAS0PYmWc5+j9dznsBurO3U/a1MeqrfdCW3EGASOeQbqQPZpOOIaapUCv18+DI+8IL4JtKbeiM/W5+A31w2ULTdCCCGexTNWQwghhLiVFd+ehNEsvgtWyQPLwt5A05G/d7qAKPAaVNtCmXHJw67r1P07Y3sJu5Xp9FjXtDiSQuWXiKAxTyNm2XEEjv0nlN6dX3QxFK5H2crJaDr1DgRH+yfhiGvNHBePfslBzLj3V51GU4tZlpx6Cv+wPgiJHSIa01JfjMqCI7Ll5Grxvir08Rdv57a9xAA7q9c0IYQQchVfbTyHqjr28+7NyRvRevD3nS4gguNRK8Qyw5KHLe3c/TvhcKUJbYxept35nK/0CkfAsD8i+sbDCJ7yOlSBaZ2+l6niEMq/m436I/+Ew0rzlLvLwNQQTB8Tx4xbvT0XRWVNsuRECCHE89BJxF5m8uTJGDBgQKevj4tjP5wQQoiYzLNV2HOshBk3I3wrQu1HO/UevNoPPv3ugFE/AMaVj4nGhsYNh19IUqfepzO2M1qZ6pQcxkew2zK5Gq/2hm/67fDpezNa81ajKet/sDUXdfg+gs2AhqPPoDVvJYLG/RvaMGpxKjee5/DQzcNw/zPb4BApDLW0WfD+qlP4w+0jZM2vu/UZfgNqijNFY/IzViIiaYxsObnazBg9LjS2v5BUa3Igq9aMYaHd/72IEEKI+yitbMHKreeZccMCM9EXazv1HpxCA+/UG8HHLEDdR/eLxur9whHRZ0Kn3qczJG0WlKGVKQunUMM76Rp4JS6EsXgbGjNfhaX2VMdvJNjQfPpttBWsQ9DYZ6CPm+WKdAnDb68biMMny0XHF9jtl8YXvPDIZGpDSwghpMOoiNjL3Hrrrd2dAiGkF7PaHHj9C/HFegAI1dRgbtT2Dt+fU3nBt/9v4Nv/big0fsj+5g/Ma5KHX9/h9+msRrMdR6tMojETIrXQKntOowCOV8En5QZ4Jy9BW/5aNGX9D9amvA7fx1p/FpXrF8E79SYEjPgLFNoAl+RLri4h2g/XTu+DVVtzReO2HCjCrPEJ6JccLFtu3S2q71RovYJgaqtrN6bs/B4YW6qh82GfbHYHM2J1eOO0+G70bcVGKiISQgiRTBAEvPllJqw2h2icVmHEDXFrOv4GvAo+qTfBb/BDUHqFI2v7q8xZ6UlDloDn5Wkd6hAE5mbBFH8V4nzEuwHIieN46ONmQRc7E8bSXWjKfBXm6hMdvo+9rRzV2+6CLnYGAsc8DZWPPO1jySX+vlrcuXgAXvtMvI1w9oVabD90ETPGxsuWGyGEEM/Qc1YpCSGEeLzV23KZg98BYFnCSqh56e0vOYUGvgPuQfT1BxEw7BEoNH4wNFehPHeP6HVa72BEp06W/D5dtavUCDujQ+CMmO5rcSSG45Xw7rMEkUt2IHjKG1D6du5keuv5L1C2cjLa8tc5PUci7pYF/RAcwN79/r/PMmBjLAB6EoVChcQh14rGCIIdBVmdOzHRE/ULVCPKS3xRdXuJAYJALU0JIYRIcyCzDCdy2K1Jr43ZAD91B0YVcDy8+1yPqKV7ETTuWSi9wmG3mVF4UrwQyfNKJA65Rvr7dNHJWgtqjHbRmB77nM9x0MdMRfiCNQib8yXUQZ3rXmUs3obylVPQlP0eBKH3PEv2BHMmJCI1IZAZ9+63J9HcZpElJ0IIIZ6DioiEEEJkUV1nwOff5zDjhgVmId1P/LTUTzglfNJuQ9T1BxA46kkodD/PfcvPWMX88Jo05FrwCvl2A7N2Jys4YHJU97c4EsPxCngnLULUkl0IHP138Br/Dt/DYapDza77Ub3jXtiN7Z/+Is6l0ypx342DmXGFZU1Ys/OCLDn1FElDF4PjxB+L8zNWw+EQXxx0FxzHYTpjIbOoxYb8JvHZtYQQQggAmMw2vP11FjMu1qsEE0MPSr6vPmE+IpfsRPCkly473VaSsx1mQ6PotdF9p0LnLV9nhW3F7FamM2J7+HM+x0EXNQER12xA8OTXoPCO6vA9BLsJDYf/jsoNS2FtvuiSPMmVeJ7D75YPBc/oVNrUasGHq0/LlRYhhBAPQUVEQgghsnj7myyYLeIL8BrejKWx0tob6aInI3LJ9p92JP+Sw25FfuZ3otdzHI/EIYslvZczmGwO7C0XLyKOCNMgQCtPy6Wu4hRq+Pb/DaKu3w/fAfcAvLrD9zAUfo+yVdPQVrTZJTmSK40bEoUR/cOZcZ+uO4PaBvE/r57Eyy8CEcnjRWMMzZWoyDsgW06uJmUm045S9oIoIYQQ8sWGs6ipF39u4ODATfErwXPsU+7q4EEIX7gWodPehto/+YrX8058y7xH8vClzBhnYs1DDNcr0C+w48/L3YHjeHgnL0bUdXsQMOJxcGrfDt/DXHkE5atnoDnnYzqVKJPk2AAsnHrl35df27i3AOcL62XJiRBCiGegIiIhhBCXO5ZdiQMZZcy4eVFbEKARn9Ol9I1H6MyPEDrr06suKgBA+YV9MLXWit4nMmUSvPzYxRRnOVBhgtEmvmjSU1sciVFo/BE46klELd0NfeLCDl/vMNWiZvtvULP7d7CbxXeUk67jOA4P3DQEapX4I6DJbMd7K0/JlldPkDzsOmZMfsYqWXKRw/AwLXzV4n8OWKenCSGEkOKKZqzaep4ZNz70MBK8i0VjeG0wgia+iIhF66ENHXbVmMbqPNSWnhS9j29wIkJir369K+Q3WVHQLH56f0aMHhzHOCbWw/BKLfwG3Y/o6/fDJ/0OgNG14dcEmwH1B/+Kqk03wdbK/ixIuu7WRf0R6Meeaf3Gl5lwOKhtPSGEEGmoiEgIIcSlLFY73vwykxkXoavE9PD2ZxhyKi8EjHgcUUt2QB87XfRDuJSF/uRh8u5O3sbYnQyA2V6wJ1P5xCJ06psIm/MllL4JHb6+LW81yldNg7FUfI4l6bqIEG/cODeNGbfraDFO59bIklNPEJ40FnrGxoLyC/vQ1lguW06upOI5ZvvkrBozahnznQghhPRegiDgjS8zYWMM/fZStuGa6A3tB3DKH+ab74VPyg2iLcbzM1Yy80oevlTWgh3rFCLcoJWpGIU2EEFjn0HEoo1Qhwzp8PWm8v0oWzUNrRfY/9+RrvHSqXDP9YOYcecL67HtUJEsORFCCHF/VEQkhBDiUiu3nEd5dSszbln8Sij4q7e60ScsQNTSvfAbdD84hUb0Pq2N5ajIPyQa4xUQjfDEUcycnMXuELCzVPxET3qgGlHeStlychVd1ARELt4G/6F/BBj/X/2a3VCFqs03o/7osxAcVpflSICls1IRFerNjHvzy0zYe8kuZZ5XIInZ4lhgtkp2J6yWpgK1NCWEECJi7/FSZJ2tZsZdG/M9vFVX/3mijRiDyCXbETjqSfCMtpk2qxFFp0SKkQAUKi3iB8xj5uRMrHmIvmoeI8LYp8N6Ok1wf0QsXIugcf8Gr/br0LWCtRW1ex5Gze7fwWFhfzYknTdpRAyGpIUy4z5YdRptBvrMRQghhI2KiIQQQlymqq4NX248y4wbGXQCqb75V3xdoQ9D6IwPEDrtLSj1YZLesyBj9Q9L3+1LHrJYdIezs2XWmFFvEp8FMkPCfDJ3wSu18B/6f4havB3aqIkdvr751FuoWL8Y1hbxllek89QqBR68eSgzrqC0CRv3XPl301MlDr4GHCc+l7Qgaw0cds9YcJkQqQOjsy21NCWEEHJVRpMNK77JYsbFe13EuJAjV3ydU/kgaPzzCJv7dbsjCn6tOGcbrGbxAlRcv9lQa30k3c8Zqg02ZNVaRGMmR+mg4t2rlWl7OI6HT9pyRC3dA68+7Fbwv9aWtxrla2bDXHvaJfmRS+MLHrxpKJQK8T9zjS1mfLr+jGx5EUIIcV9URCSEEOIyK744BItVvHim5U24LnbdFV/36XsLoq7bBX3cTMnv57BbUXByrWgMzyuRMHiR5Hs6wzYJi/Du3Mq0PSq/BITN/hzBU14Hr/Hv0LWWmkyUfzcbbYXfuyy/3m5oehjGD41ixn20JhtNLWZZcupuOp8QRKVOEo0xtdaiLHevbDm5ko+ax+hw8ZMRBytMMDC+jxNCCOl9vlx3ArWNJtEYDg4si18Fnrt8g58+bhairtsFn743d2hjX/4JdjvMJAkzjp1pB6PbCNy8lWl7FLpghEx6BWFzv4bSO6ZD19qai1CxbiGast+DIPSOjhdyiw73weIZKcy4tTvzUFTWJEtOhBBC3BcVEQkhhLjEkV1rsf9UAzNuQfQm+Kmbf/q10i8R4fNXIWj8c8yWRr9WlrsHptZa0ZiovlOh9Qrs0H27QhAE5jzEaG8F+gaoZMtJThzHwTvpGkQu2QldrPSCMAAIlmbU7LgXdfv/AoeNTkO5wt3XD4JGLX7yrtVgxUdrsmXLqbtJmZcqZe6qu2BtYDDbBRyoEF8kJoQQ0rsU5RzAqh0FzLiJoQcR713y0695XQhCpr2NkOnvQeklPof41xqrclFXJn56LSC8LwIj0jt0365itTJV85dO/nsqXeQ4RC7eBu++yzt2ocOKhsN/R/XWO2A31bsqvV7tpnnpCPIX3yzmcAh488tMKuYSQggR5RbDl6xWKyoqKlBaWorq6mq0tbXBYDD89E9bWxusViv0ej10Oh30ev1P/x4YGIioqChERUUhICCgu/9TCCHE4wl2C2oOPYMVa4IAiJ9yitKVY0r4/p9+7dv/N/Af/ih4Zec+aOdJWNhPHrqkU/furAuNVhS32ERjpsfowXGe0eKoPUp9KEJnvI+2CytRd/gpCJZmCVdd0nLuU5iqjyN0+rtQ+ca7NM/eJizIC9fP7otP14m3Mtq0rwBzJyaiT5znP0uFJYyEd0AMWhtK2o2pLDiElvoS+AR2bOd9TzQtRoenruwyd5ntJQbMiPW809KEEEI6RhAENJ96C299XgmbY4BorLeyFYtiNv30a33iQgSNfRYKbeeeJaQ85ycNXSLrM3WLxYFDleIbbcZF6ODN6h3u5ni1N4LH/xte8XNQu+9PsLdVSL7WWLId5d/NRuj0FdCEDHZpnr2NTqvEb68bhH+/J/6gd/J8DfadKMXE4e7/XEsIIcQ1emwRsbi4GDk5OcjJyUFhYSEcDnYbpdZW8d74/v7+6Nu3L9LS0tCvXz/odJ67G4wQQrqDraUU1TvvxbZsPUoN7NM8N8avgoJzQKEPQ/DEl6GL7vj8vB+11JegquCwaIxPYCxC40d0+j06g3UKEQBmeGAr06vhOA7eKUuhjRqH2r2PwFS2R/K11vqzqFgzD8FTXoc+ZopL8+xtls5KxdYDhaiqa//PqiAAb36ZiZceneLxBW+O45E0dDFO7nhVNC4/YzUGT/+9bHm5SrheiQFBapyua3+e085SI+wOAQoPmedECCGk4+zmRtTu+QMyThchq+E+Zvy1Md/DS2m4NPtw7D/hlby4088QNosRF09vFI1RqvWI6z+nU/fvrL3lRrA6fk/3wFam7dFFT0LUkh2oP/wPtOZ+Lfk6e1s5KtYvRtC4Z+GTusylOfY2k0fGYMPefJzOFe/Ws+Lbkxg5IAJaTY9dJiaEENKNetR2KIPBgF27duGZZ57B888/j/Xr1yM/P19SAVGKxsZGHD58GB9++CEef/xxfPLJJ8jLy3PKvQkhpLczlu5F+ZpZaKjIxdrSucz4EUEZSPEtgD5hHiIXb+9SAREACjJXM2Pk3p0MCfMQAzQ8hoVqZMunJ1B6RSJs9mcIHPccOIX0/3aHpQnVW25FY+ZrEASa0eYsGrUC997A3vmdk1+HHYeLZcmpuyUMWgieF19EKTy5FnZb+4U3dzItRnyBs8HsQGZN75iLSQgh5EqWuhxUrJmL1qLt+Pritcz4OK9ijA05Ck3YSEQu3grvPl17Br94ZjOsZvFN43H950Cl8er0e3TGdkYrUw7AtOjesVnwR7zaF8ETX0TojA86NhPdYUHdvkdQu/8xCHZ65nAWjuNw341DwNoHVlNvxNebzsmVFiGEEDfTI7aYmEwmbN++HTt27IDF0v5iDM/z8PX1/allqU6n++nflUolzGYzLBYLzGYzzGYzGhsbUVtbC5vtyjZyFosFR44cwZEjR5CUlIRFixYhKSnJxf+lhBDieQRBQHP2e2g4+gwgOLCudDHabOIf4FW8BUsSdyB40ivwSu56Yc9ut6Lg5DrRGF6hQsKgBV16n44qb7MhW+R0DwBMjdZB2QtP93AcB9+0W6ANG4GanffD2pgr8UoBjSf+A0vtSQRPegW82sfFmfYOYwZHYmh6GDJyqkTj3l91CmOHREKv9cwZnj/SegUiOm06is9sbjfGbGhA6bmdiOs/W9bcXGF6jB6vZDWJxmwvMWJ4mPhcHUIIIZ6nrXADavc8DMFmxJ7qCagwsmcZ3hC/DoEj/gy/gfeD48VnL0shZRZx0tDFXX6fjrDYBewqE98sODREg2Bd1//73ZE+biYiF29Fza6HYK5k9E3/hdZzn8Fan4OQae9A6RXh0hx7i6QYf8ybnIT1u/JF477dch4zx8UjIsRbttwIIYS4h24vIh48eBDr1q1DS0vLFa9FRkYiJSUFcXFxiIyMRFhYGFSqji1aORwONDQ0oKamBsXFxcjNzUV+fv5lxcr8/Hy89NJLGDBgAJYtWwY/Pz+n/LcRQoinE+xm1O3/C1ovfAMAKDNEYG/VWOZ185Oz0f/Gb6HyjXNKHmXndsHcVi8aE913GjR6eee5bZfQynR6L2ll2h51YF9EXLMB9Yf+htbzX0q+znBxCyrWLkDIjPeg9k92aY69waVdyoNx7z+2wm4X2o2rbzLhq43ncOdi8TlIniB52BLRIiIA5Geu9ogiYqq/CtHeCpS22tuN2V5iwKPD/D2+nS0hhJBLBMGBxoyX0ZT5MgCgxeqF9aXsn3mjw89i/M2vOm2+XUPFOdSXi89uDohIQ2BEulPeT6ojVSa0Wtt/ZgKA6YyT/p5O6RWJ8LnfoCnrVTRmvgJI7CRirs5A+XdzEDrtbWgjRrs8z97g1kX9sedYCZpb29/garU58N7KU3jyPvbneUIIIb1LtxURjUYjPv/8c2RmZl729ZiYGIwbNw6DBw+Gj0/XTxfwPI+goCAEBQWhb9++mDlzJux2OwoKCnDs2DFkZGTAaLy0e+z06dMoLCzE8uXLMWCA5y+OEUJIV9gMVajZ/luYqzOAH2amfX3xWjggvts2xNuM2x98Eiqd84pneRJ2JycPW+K095Nqe7H47mStgsP4SDrZwyt1CJ7wX2gjx6Nu/6MQrOLtqn5kbcpDxdr5CJn6Fs1JdILYCF9cM60PVm0VPxW6elsu5k5IRHiIvC3D5BYSOwy+QQloritsN6a66Bha6i7CJ8g5GyK6C8dxmB6jx0dnr9zU96OiFhvym2xI9vfsU6iEEEIAh7UNtbt/D8PFnzfTrCudA4Nd/Pldo7ThgQfvgSYkzGm55GdKeM4fep3T3k+qbYxWpgAwI7Z3bxYEAI5XwH/oH6CNGIOaXQ/BbqiUdJ3DVIvKjTciaPy/4ZN6o8vz9HS+Xmrcce0AvPrpCdG4/RllOHW+BgNTQ2TLjRBCSM/XLTMRq6qq8Nxzz/1UQOR5HmPGjMGjjz6Kxx57DBMmTHBKAbE9CoUCffr0wU033YTnnnsOt956K8LCLj3ktra24u2338a6deJt8QghpDcz12ShYs28nwqIAJDZMBDnm/swr71n+WRonVhAbKm7iOqio6IxvkEJCIkd5rT3lKLJbMfRKpNozIRILXTKHjWeuFt5Jy1C5LWboQ4eJPkawdqK6q23oTnnI5fm1lvcPD8dgX7ihW2rzYH3Vp2SLafuwnEckiRsPsjPXCNLPq42TcKp6B2l7AVTQggh7s3aUoKKdYsuKyCWtEViX/UY5rXLFgxGaJjzCohWiwFFpzeJxijVXoiVuSuAQxCwgzH3PNlPhXhf2njzI23EGEQu3gpdzDTpFwk21O37E+qP/JPmoTvBrPEJ6BPH7szzzjdZsDvET9kSQgjpXWRfuayoqMArr7yCuro6AEB6ejoef/xxLF++HLGxsXKnA5VKhVGjRuGJJ57AbbfdBn//S4Oft2zZgpUrV8qeDyGE9HRtBetR8f2Sy3aRWh1KrCxeyLx2UGoIxg+Ncmo++ZmrmTFJQxfL3oJvd5kRNsZnL9qdfCWVbzzC56+Cd0oHdhwLDtQffAJ1B5+E4LhyDjKRzkunktSqdN+JUpzOrZElp+4UP3A+eIVaNKbw1DrY7VbZcnKV4aEa+KrFPxpsZyyYEkIIcW+myiOoWDsP1oZzP33tx24jAmP5KCLEC0tmpDg1n+LszbBZ2kRj4gfMgUot7zN1dp0FVcb2W4ADwIzY3t3K9GoU2kCEzvwQ/kP/0KHrmk+/jZrtv4XDSpuZukLBc3hg2RBmXF5xI7YdKJIlJ0IIIe5B1iJiQ0MDXn31VTQ3N8Pb2xv33HMPHnjgAUREdP+wZJ7nMXLkSDzxxBMYN24cAGDXrl1Yu3Ztd6dGCCE9giAIaDr5Bmp23gfYzZe9tqNyEurMQaLX8xxw341DnFrMs9utKDwpfnKcV6gRP2iB095Tqm2MxXaeA6ZE0eLC1fBKLYIm/BdB4/4N8NJ3cLfkfIjqrXfAYWm/JSNhmzY6Dn0TAplxb3/t+buUNTo/xKRNF40xt9WjPHePbDm5iornMJnxPSmrxoxaxqIpIYQQ99SavxaVG5fBYbp8znhmw0BcaGHPn777+kFQq8THGnRUvoSRBUnd0cpUwtzzGb187nl7OI6H/9A/IHTmR+DUvpKvM1zcgsrvF8PWVuHS/DxdWlIQpo5iH+D4aM1ptBndf5McIYQQ55C1iFhbW4uWlhYkJyfjL3/5CwYOHCjn20ui0+lw00034Y477oBKpUJhYftzcAghpLcQHDbUH3gcDceeu+K1Zqs3NpWJL7IDwLzJSUiI9nNqXuXnd8NsaBSNiUmfAY3Oue/LYrYL2FsmXkQcHqpBgNa5Cy2ehOM4+KQtR/i8lVDopbfFMpbuQsX6a2BrKXVpfp6M5znce8NgZlxecSO2H/T8XcpJQ69lxuRnsE9Eu4PpMeJFRIFamhJCiMf5caNg7a4HAIflstesDgVWFbM34w1ND8OYQZFOzauh8jzqK3JEYwIj+yMgPNWp7yvFNsbc8zC9Av2DxDsZ9Hb62OmIXPQ9VAHS//+z1GWjYu18mGs9v62+K92xeAA0avHPoQ3NZny96ZxoDCGEkN5D9namQ4YMwe9+97uf2ob2VMOHD8fDDz8MnY5OiRBCejeHtQ3V2+5Ey7lPr/r62pK5MDnEZ6j5eKlx66L+Ts+tIIs9iyx5KHummbMdrDDCwOhlSruTpdGGDUPENZugCRsp+Rprw3mUr1tACwxdkJYUhCkj2buUP/zuNAwmz96lHBI7DD6BcaIxlQWH0dpYLltOrjIxSgcV49MBtTQlhBDPIbZREAB2VU1ArTlY9B4/bj5y9uiAgqzvmDFJQxc79T2lKGy2Iq9J/NlneowOvMyjFNyRyi8REQvXQZ8gvWuM3VCFyu+XwFC8w6W5ebLQQD2WzmIXb1dvy0VljXg7YUIIIb2DrEXEqKgo3HnnnVAo3OPkRXx8PG6++ebuToMQQrqNzVCFyu+vg7Fk51VfLzVE4EDNKOZ9blvUD75ezt2N29ZUgYr8Q6IxvsEJCI5hn6hyNtbuZEg48UN+ptSHInze1/BJu03yNQ5jDSo3LIWxdK9Lc/Nkdy6Rtkv5q42evUuZ4zgJi5QCCjLZi509nbeKx+hw8U0hBytMMFgdsuVECCHENS5tFLyr3Y2CLVYvbCibybzPwinJiIuU3pZSCpvVhKLTG0VjlGovxPWb7dT3lWJ7MftE/nTaLCgZr/JCyNQ3ETDiLwCkFV4Fm/HSJtfcr12en6daOisVwQHin0etNgfeXXlStpwIIYT0XLIWEfV6PXhe9sOPXeLt7d3dKRBCSLewNOSiYt1CWOpOX/V1QQBWXlwEgfGjJC7SF3MnJjo9v8KsdT8012tf4uBrnL4rmsXuEJjt/tICVIjxkT7rjwAcr0LQuGcROOZpgJP2LCFY21C19Ta05rl/cac7hAbqcd1M2qUMAPED54PnlaIxhSfXwuGwyZaTq7AWPs12AQcqTLLlQwghxPlshuofNgq2f5prfelsmOziRQZfbzWWL0h3en6l53bCahKfcR3XfzaUavk35bHmnnurOIwKE9+QQy7HcRz8Bj2AkOkrwCkl/n8q2FG3949ozHodguDZM7pdQatR4q7FA5hx+zPKcOp8jSw5EUII6bncq6JHCCFEFqbqE6j8/lrYW8vajcluTMfZZnaB4e6lg6BQOPfHjcNhR8FJ8VamPK9E/MD5Tn1fKbJqzagziZ/Sod3Jnefb706EzvwQnEriJh+HFbW7H0LT6RWuTs0jXT87FcH+7F3K763y7NaxWq9ARKVOEY0xttSg4sJ+2XJylWkSTklvL6G5iIQQ4q6szUWoXH9NuxsFAaDcEIZ91WOY91q+oB98nNxtBBJHFiQNYc8sdrZaox2ZNWbRmClROqgV1Mq0M7zi5yB8/uoOzUNvPP5v1B96EoLD7tLcPNHkkbHomxDIjHvnmyzYHVSoJYSQ3oyKiIQQQi5jLN2Dqo03wmFuajfG7uDxbfFC5r2G9w/H8P7hTs4QqCo8AkNTpWhMVOpkaL3YH4qcbYeEeWEzYqmI2BX6mGmIWPAdFN5Rkq9pOPI06o88A0GgNowdodUocecS9i7lfSdKcTrXs3cpS5m7lO8BLU3D9UoMCBJfEN5ZaqTFJEIIcUOWuhxUrl8MW0uxaNyq4oVwQLyleUy4D+a5oNtIS30xqouOicb4h6UgIML5JyBZdpQaGH1QgOn0nN8lmuABiFj0PdRB7OfPH7XkfISaXffDYaNOCR3x4zxTlrziRmw/WCRLToQQQnomtygiXrx4EStWrMDmzZtRU+PZC1SEENKd2grWo2rr7RBs4oWwvdVjUWUS3yHK8xzuXjrIyRleUpDJ3p2c2A27kwFgR6n4712UlwJpAdTKtKvUgWmIWLge6pAhkq9pPv0Oavc8DMFucWlunmbKyFikStilvOLbk3B4cGEpLGEkvPzFC9cVefthaK6SLSdXYc1sbTA7mCcxCCGE9CymymOo2HAd7MZq0bgzjanIbmIX6H67dBCUSucvKRVkrWXGJA65VvaRBQCwk7FZUMUDEyNp7nlXKb0iED5/FfRxsyRfYyjcgKrNy+GwNLs0N0+TlhSEqaNimXEfr82Gyez+bfsJIYR0jlsUES0WC06ePIn169fj3Llz3Z0OIYR4pOazn6Jm5/2Awyoa12bTYX3ZbOb95k5MRFykrxMzvMTUVo+y87tEY/S+4QhLGOX092YparYiv0n8929ajL5bFj08kVIfivB530CfIL1tbVvealRvv5t2KneA1F3KuUUN2Hu8RJacugPH8czNCYLgkNSCraebJqHl8nYJp64JIYT0DIaSnajatAwCo8BiF3isLF7EvN+QtFCMHOD8biMOhw2FJ9eJxvAKNeL7z3X6e7OYbA7mTOAx4Vr4qN1ima3H41V6hEx/F74D7pF8jbnyMCo33gi7qd6luXmaOxYPgEYtfvK4rtGEVdtyZcuJEEJIz0JPN4QQ0ssJgoDGzNdQf+AvALNBD7CxbAbabF6iMXqdErcs7OfELH9WdHoDHA7xXZAJgxeB58U/CLnCTsYpRACYFk27k52JV+oQMvVN+PS7U/I1xpLtqN56GxzWNpfm5knSk4IweWQMM+6D1adhsXruTJrEQQvBceLfWwqy1sDh5nN5Uv1ViPYW/+/cXmKAIHjuyVNCCPEUrflrUL31Tgh29gaqA9WjUG6MEI3hOeCe6we7ZFNcRd4BmFprRWNi0qZBrXP+RkWWg5UmmOziP/ekbMIh0nEcj8BRTyJg1N8kX2OpPYXKDUthM7h/Zwi5hAbqsXRWKjPum83nUN9EGzEJIaQ36jVFxGPHjmHt2rX4/vvvuzsVQgjpMQRBQMORZ9B44j+S4qtNwdhVNYkZd9O8dPj7aJyQ4eUEQUABc+YYh8RB7HmNrrCz1CD6ureKw4gwrWz59BYcxyNw9D/gP+IxydeYyg+gatNNsIvM/iSXu2vJQKhV4o+OVXUGrN2ZJ1tOctP5hCAyZaJojKGpEpUFh2TLyRU4jsN0xkJoUYsN+U3U1ooQQnqy5rOfonbXQ4DA/n5ttGmwrnwBM27W+AQkRPs5KcPLsZ/zu29kAauVKQBMpc2CLuE34G4ET3kd4KWNhLA2nEfl94thayl1eW6eYumsVAT5i39ONZnt+HTdGdlyIoQQ0nP0miJiUFAQtm/fjk2bNiEvz3MXtwghRCpBcKD+4BNozl4h+Zr1DXfDLoj/6AgP9sKiqclOyPBKdaUn0VxbKP7+SWPg5R/pkvcX02S241iV+IywCZE6qBXUytQVOI6D/6AHETTxJYBxUuxH5uoTqNp4A+zGOpfn5wlCA/VYPCOFGfflhrNobvXceXlJEhYvpSyC9nRSTlPsYGycIIQQ0n2aTr8rudMIAOxouQ0tFvEimE6jxG3X9HdShpczttSg/MJ+0RjvgBiExg1zyfuLcQgCs+NIeqAaEV5K2XLqbbyTrkHYrE/AqcQ74vzI1nwRFd9fC2tTgctz8wRaiX+3N+8rQFEZbcIkhJDepkcVEUtLS/H5559j3759KC4uhs3mvN3NiYmJGDJkCADgwIEDTrsvIYS4I0FwoG7/o2g5+7Hka6pCH8LR4hBm3G+uGwi1yjWtRPMlzBpLGnyNS96bZW+5CYwOR5gWQ7uTXc0n5XqEzvwAnFLa77WlLhuVG66Dra3S5bl5gutn94Uf45Rxm9GKz78/K1tOcgtPGgu9r/gcqLLcvTC1ufc8nuGhGvgy5jrRXERCCOmZGk++joYj/5AcbwpZhC2F7FEEN87tiwBf13TVKDy1HoIg3g48YfAicJz8y1hn6iyoNornRqcQXU8XNQHh81aB17E/kwKAva0CFd8vgaUux+W5eYLpY+KZp4wdAvD+qlOy5UQIIaRn6FFFRKPRiIMHD+Krr77C888/jz/+8Y94/vnnsXXrVqfcf+TIkQCArKwsWK1Wp9yTEELcjeCwo3bvH9B6/kvJ1/gN/TO+PjuCGde/TzDGD43qYoZXZzW3ovjMFtEYjd4fkamTXfL+LDtLxE/k8BwwKZIWF+Sgj5mG8Llfg9cESIq3Nl5A5fdLYG0pcXlu7s5Lp5I073T97jyUVbXIkpPceF6BRMZmBcFhQ9HpDbLl5AoqnsPkKPHvWVk1ZtQyFlUJIYTIqzHjZTQe+7fkeO+UG7Ch/lZYrA7RuNBAPa6dzu5I0BmXRhaIbxbkOB6Jg9jtVl1hh4S559Nps6AsNMH9EbFgDZS+8ZLiHcYaVG5cCnNNlstzc3cKnsPdSwcx446erkRGDs2cJISQ3qRHFRF/zWazobi4GDk5P+8aWr16NV555RV89913yMjIQF2d9BZkUVGXFrYtFgsKCqilASGk9xEcVtTu/h3aLqyUeAWHwHHP4Sy3BGfy2N9v7146CBznmnadxWe2wm4VH+QeP3A+FAppszKcyeoQsLtMfHFhWKgGAVrXnNAkV9KEDkX4/NVQ6MMkxdtaLqJyw1IqJEowZ3wCYsJ9RGPsdgEfrD4tW05ySxi8CID497qCzDUQBGkt5Hoq1oKoQC1NCSGkxxAEAQ3H/4PGjBclX+M74B40JTyBbYeKmbF3Lh4Ajdo1z7LVF0+gtUH8GSwieTx0PqEueX+WHYzNgmE6BfoFqmXLp7dT+cYhfP5qqAJSJcU7zE2o3HQTFRIlGJoehhH9xTtuAMC7356E3eHez7mEEEKk61FFxNDQUMycOROpqanQ6a6+aGGxWHDhwgVs374d77//Pv72t7/hsccew1tvvYWNGzfizJkzaG1tveq1NTU1P/07FREJIb2NYLegZuf9aCtYK+0CXoWQKW9A3+dmSS1LJo+MQWpCYNcTbUe+hBljrNNBrnK82owWq/iHqGnU4kh26oA+CJ+/GkrvGEnx9tZSKiRKoFTyuGvJQGbc/owynMmrlSUnuXn5hSM8aYxoTHNtAerLs2XLyRUmRumgYnxaoJamhBDS/QRBQMPRf6Ip6zXJ1wSM+AsCRz2JD1Zng7XnJSU+AJNHSnue6gwps4SlzCR2hfI2G842iHeymhKtc9lGSnJ1Sn0owuethDqYfXIOAARL86VCYnWmy3Nzd7+5biB4xh/ngtIm7Dx8Ua6UCCGEdLMeNfXZz88PixYtAn54CK6pqcHFixeRnZ2N48ePt3tdS0sLsrOzkZ3980JNcHAw4uLiEBUVhYCAALS1tWH37t0/vf7LgiIhhHg6wW5B9Y57YSyW1h6aU2gQMv096GOmYP3ufJRWXX1zxo9USh53XDPASdleqbHqAnMxPjh6EPxCklyWgxhWK1MAmBqtlyUXcjmVbxzCF6xC5cZlsDXlM+N/LCSGz/sWKh/XLZa5u9GDIjAwJQSncsWfp9799iRefmyqRy6sJQ5ahMr8g6IxBVlrERTluu+Nruat4jEmXIu95e2fAj9YYYLB6oCeVW0khBDiEoIgoP7wU2g584HEKzgEjX8OPn2XIyOnCsez2XOhf3ud67qNWIzNKD23QzRG6x2MiD7jXfL+LLsktDKluefdQ6ENQPjcr1C19XaYK48w4wVLMyo334zw2Z9DEzpElhzdUXyUH2aNT8CmfYWicR99l40Jw6Kh1fSopWVCCCEu0GM/7XMch9DQUIwYMQLjx//8sDh+/HgsWLAAgwYNgr+/f7vX19bW4sSJE1i3bh0+/vhjrFy5ErW1P++Gb++0IiGEeBrBYUXNzvulFxCVOoTO+gT6mCkwmKz4bN0Z5jULpiQjPMTLCdleXUGW+IwUAEjspt3JgiAw56Qk+CqR6Cd/m1VyidIrEhHzV0EVmCYpnk4ksnEch98uZZ9GPFtQj73HS2XJSW5RqZOh1vmJxlzM3gybxb1P6k2LEd8AYbYLOFAh3mqaEEKIawiCgIYj/5BeQOR4BE96BT59l8PhECR1Gxk9KAIDU0O6nmw7LmZvgt1mFo1JGLgAPN89hQpWK1OtgsOYcK1s+ZDL8WofhM3+DNroSZLi6USiNLcs7AetRrx9cW2jEau3X5AtJ0IIId2nxxYR2xMdHY3Zs2fj7rvvxrPPPot//etfuPfeezFnzhykp6fD29tb0n1iYuh0ASHE8wkOG2p2PQTDxc2S4jmVN8Jmfw5d5DgAwLdbzqOxRfxDvbdehWXzpBVnOsNuM6Po9AbRGKVaj5j0GS7LQUx+kw3FLTbRGDqF2P0UumCEz/0G6pDBkuKpkMiWEh+IqaNimXEffncaNptDlpzkpFCqET9grmiMzdKGkrPbZcvJFaScrtgu4TQ2IYQQ5xIEAQ3H/oXm7PekXcApEDL5dXj3WQIA2HW0GHnFjaKX8BwktTDvLEEQkJ+5mhmXOHiRy3IQ02Z14FCl+EaZcRFaaJVut7TmUXilDmEzPoA+brakeMHaQoVEhiB/HZbO6suM+3bLOTQx1gsIIYS4P7d/0vHz88OAAQMwf/58PPDAA3j++efxj3/8A3fddRemT5+OPn36XFZY5HkekyZNwowZ3bPYTAghchEcdtTueRiGwu8lxXNqX4TP+QLa8JEAgLpGI1ZtzWVed+PcNPh6qbucb3tKz+2CxdgkGhPbbzZU6u4p1O0sldLKlFoc9QQKbQDC53wFTfhoSfGXConXwdpS7PLc3NXt1/aHirFwVlHThk37PHMWtZQ5rFJOUvdk4XolBgSJf4/fWWqE3cEYqEUIIcSpGk+8gOZTb0kL5lUImfoWvJIWAgAsVjs++o49t3f2hETERvh2NdV2NVSeRWOV+OeN0Ljh8AmKc1kOYvaXm2Bl7IOiVqY9A6fQIGTa2/BKktadhgqJbNfNTEGgn/gpW4PRhi83npUtJ0IIId3DLRpXBwcHY/z48SguLoZCIX6c/sf44OBgDB069KevGY1G2Gw2eHl5gefdvnZKCCGiBIcdtfv+iLZ8aYvXvMYfYXO+gCb4553Gn6w7A7PFLnpdWJAei6YmdzlfMQVZ3zFjkrqplSkA7CgRb1Xop+YxLFQjWz5EHK/2RtisT1C99TaYKg4x4+2tZajacAPCF6yC0itSlhzdSViQF66d3gffbD4vGvfZ+hxMHxMPndYtHj0l8w9LQWBEOuorctqNqSnOQEvdxW5bAHWG6TE6nK6ztPt6g9mBzBozhodROzdCCJFDY8bLaMp6VVowr0bo9BXQx07/6Utrd+ahup7RplOjwPIF6V1NVVRBpoSRBRI27LiKlM2CU6jjSI/B8UoET3oF4Di05bFPuArWFlRtXo6wed9AE9RPlhzdiVajxO3X9MdLHx8XjVu/Kw/XTO3j0vEmhBBCupdbVNMCAgKwbNkyPProoxg7dmyn7qHT6eDj40MFREKIxxMEB+r2/xltF1ZKiue1gQif+81lBcSisiZs3S8+SB0Abr92ANQq9uaOzmptKEVV4VHRGL/QPgiM7J4PfXUmOzJrxdu3TIrSQclzsuVE2HiVHqEzP4Y2YoykeFtrCSo33gi7ocblubmjG+ekwddb/KRaY4sZq7aKFxrdVYKEFmsFJ9fJkourTGfMRQSA7YwNFYQQQpyjMet1NGa8KCmWU2gQNvODywqIza1mfLmBfXLoupmpCPJ33Sk7m9WIi9mbRGNUGm9Ep01zWQ5i7A4Bu8rEf7YNClYjROe6z0Kk4zhegeCJL8MreYmkeIelCVWblsHSwO7A0xtNHxuP+Cjx08g2u4CP17JPNhNCCHFfVFEjhBAPIggC6g48jtbcryXF89oghM/7Fuqgy3cZf/hdNlid6ZJj/TF5hGvnyxZkrWXGJA6+BhzXPUW6PWVG5u/TNGpl2iN1uJDYVIDKTctgNzW4PDd34yVxLurKrbloaBafK+SO4vrPgUIpftq48OQ6OBzis1N7shR/FaK9xRdJpZzWIIQQ0jVNp95G4/F/S4rlFFqEzvwYuujJl339y43n0Ga0il4b4KvBdTNTu5QrS0nOdljNraIx8QPmQanqnlPuJ2vNqDeJ9zKlkQU906VC4kvSC4mmelRtWgZrE3sTbW+j4DncuZg9F3XnkWLkFdPnJEII8VRURCSEEA8hCAIajv4Trec+kxTPawIQPvcrqAMuXyA4k1eLwyfLmdf/dukg8C48Yedw2FDIOL3DK1SIHzDXZTmwsFqZKjlgYhQtLvRUHS0kWhvOoWrzcjgszS7Pzd3Mn5SEsCDx02pGsw1ffO95M1PUWh9E9xU/JWFqrUVlPrt9bk/FcRzzNGJBsw1FzeKL0oQQQjqv+eynaDj6T2nBCg1CZ3wAXdT4y75cXWfA+l15zMtvWdjP5S3IpYwsSOzOkQWl7BP20ySc1Cfd46dCYp/rJMXbDVWo3HQjbK1lLs/N3YwcEI4BKcHMuA9WnZYlH0IIIfKjIiIhhHiIpqzX0Hz6HUmxvMYPYXO/gjrw8tNDgiDg/VWnmNePHBCOwX1DO52rFJX5h2BsqRaNie47FRq9v0vzaI/ZLmB/ufjiwsgwLXzU9KO2J+toIdFSexJVW26Dw0qnrn5JrVLg9mv6M+M27M1HWbX4qQN3lDiEPa9JytynnkzKqeqdEhZcCSGEdFxr3neoP/C4tGBejdDp70IXPfGKlz5ZdwZWm/jpuuhwH8wen9DZVCVprruImuJM0ZiA8DQEhLv2NKQY1s+0SC8FUv1VsuVDOo7jFQie8KL0QmJrGSo33gCbocrlubkTjuNwl4TTiCdyqpCRQ793hBDiiWhlkxBCPEBz9vtoPPFfSbGc2hdhc7686vD4I6cqcCavTvR6ngPuWsL+ENFV+Zk9e3fykUoT2mzivUynxtApRHfQ0UKiueoYqrfdBYfN81pzdsXkkbFIjPYTjbHbBXy8xvNmpoTGDYOXf5RoTNmFvTC11cuWk7MND9PCWyV++pxamhJCiPMZLm5D7Z6HATB66AMAp0TotHegj5l6xUtFZU3YcaiIeYu7lgyAQuHapSIpG2ukbNBxleIWKy40ip+unxqt77aRCkS6nwqJElub2pqLULVxGexG8c/EvU1aUhDGDxV/1gWA91edgoM174MQQojboSIiIYS4udbcb1F/+ClJsZzKB+GzP4cm+MoioN0h4IPV7BYk08bEIz5KvFDQVcbWWpTn7hWN8fKPQlj8CJfmIUbKiRuak+I+LhUSP4ImdJikeFP5PtTsvA+Cg9o3/ojnOUkbDPYcK0FukfsW066G43gkDl4kGiM4bCg69b1sOTmbiucwMVL8e9rxKjOaLeInXAghhEhnLD+I6p33AoKdHcwpEDL1TejjZlz1ZSkzz/slB2HMoMhOZiuNw25F4SnxkQUKpRZx/ee4NA8xUp7zp9FmQbdxqbXpi9AnzJcUb23M/WGEQYvLc3Mnt187gDnOJK+4EXuPl8iWEyGEEHlQEZEQQtxYW+FG1O77o6RYTuWFsNmfQRM65Kqv7zx8ERfLxWe9qZQ8bl145QlGZys69T0ExmJJ4uBF4Lju+TEmCALzxE0ffxVifajFkTvhVV4InfUJ1EEDJMUbi7ehdt+fIQhUNPnRsH5hGJzGbnX8/qrTEATP2qUcP3ABAPGFlYKstW793806XW0TgH1l1NKUEEKcwVyTheptdwB2MzuY4xEy5XV4JVx9VrjUmed3Lh7o8tN15Rf2wcw4mR+TPh1qrY9L8xCzkzH33EvJYWSYVrZ8SNdxvBIhk1+DLma6pHhL3WlUb7uTOo/8Qky4D+ZMYLc6/mhNNrNtMiGEEPci6+prcXEx8vLYQ7x7CpvNhl27dnV3GoQQclXG0j2o2fUAIKGAwSm0CJv1CbRhVz9lZbHa8cnaM8z7LJiSjNAgfafylUoQBBQwWplyHI+EQQtdmoeYcw1WlLeJFzmlzA8jPY9C44ewOZ9D5Z8iKb7twrdoOPqsWxeGnOnSzBR2ETbrXDWOn/GsmSlefuGISBJvidtcW4C6MvaJ755qUqQOjA3o2EFzEQkhpMssDedRtXk5BGubhGgOwZNegVfigqu+emnmOftnz+hBEejfJ7gT2XZMQZaEVqaDu6+VaYvFgaNV4oWj8ZE6aBTUytTdcAo1Qqa9DW3kBEnxpopDqN31AASHzeW5uYub56dDo1aIxlTUtGHjnnzZciKEEOJ6shYRzWYz3nzzTZw+3fMXT4xGI95++22cPHmyu1MhhJArmKpPoHr7bwAprRR5FUJnvAdt+Kh2Q77fnY/qevGTdXqdEsvm9u1Muh1SU5yBlvpi0ZiI5HHQ+4a5PJf2SJn7NTXGtcVW4joKbSDC5n4JpW+8pPjm0++g+dSbLs/LXaTEB2LSiBhm3Effnfa4mSlSFj0LstbKkosrBGgVGBqiEY3ZW2aEzcP+fyWEEDlZW4pRtekmOMyNkuKDxj8H7+TF7b5+aeZ5reg9OA6441ppnRi6wtBchYq8A6IxPoGxCIkd6vJc2rO3zAjG2HNMp1ambotXahE6431owkZKijdc3IK6/Y/RhsEfBPnrsGQme7PlFxvOwmSm4ishhHgKWYuIPM/DbDbjnXfewcaNG+Fw9Mzj7UVFRXjuuedw9uxZKBTiO2wIIURuloYLqN5yGwSbhNMeHI+QKf+DLnpyuyFtBiu+2HCWeauls/rC11t88dgZWKcQASBx8LUuz0PMDkaLo0Atj0FBatnyIc6n1IchfO7XUHhHSYpvOPYcWs594fK83MXt1/SHgrFDP6+4EQcyy2TLSQ6RKZOg1vmLxhSf2QKbxX1P67FmvTZaHMiskdB6jxBCyBXsxjpUbboZdoO00/oBI/8Kn77L27+fQ8CH37E3cU8fHefymecAUHhyHbMNfOKQa13eUlUM60Q9zwGToqiI6M54lR5hsz6GOmSQpPjW3K/QcOw5l+flLq6bmQo/b/HPuo0tZqzZcUG2nAghhLiWrEXEpKQkTJs2DYIgYMOGDfjPf/6DkpKeM3DXZDJh7dq1eOmll1BXVwc/Pz9cf/313Z0WIYT8xNZWgarNN0vfmTzhv/BiDJD/dst5tLRZRGMC/bRYPL1Ph3LtDIupBSVnd4jGaL2CENlnvMtzaU+1wYZTdeK/X1OjdFCwev6RHk/pHYXwOV9BoZd26rXuwGNoK9zo8rzcQWSoN+ZNTGLGfbwmG3Z7z9xU1hkKpRrxA+eJxtgsbSg5u022nJxtioRWzbuopSkhhHSYw9qGqq23wtZcKCneb9BD8Bt4n2jMzsMXUVTGnnl+yyLXzzwXBAfzND7HKxE/UPyziyvZHAL2MGb7DgnWIFBLm83dHa/2Qdisz6AKTJMU33zqTTSdetvlebkDL50KN81PZ8ZJWWcghBDiHmQtIgLA4sWLMWvWLABASUkJnn/+eXz44YeorKyUO5Wf2O127Nu3D3//+9+xdetW2O12+Pv74+GHH0ZYWPe1yyOEkF+ymxtRtXk57G3lkuIDR/8dPik3iMbUN5mwensu8143z0+HVqOUnGtnFedshZ0xvD5+4ALwCpXLc2nPLsbCAqiVqUdR+SUgbM4X4DUSducLDtTsehDGcvE2Xb3FzfPToGN83yipbMGOw+Lti91N4qBFzBgp86B6qmQ/FWK8xf9/pbmIhBDSMYLdguodd8NSI22cik/abfAf/mfRGIvVjk/XSZh5PjkJYUFeknPtrOqLJ9DWKN6BILLPBOi8XT+XsT0nqs1osohvbppGrUw9hkIbgLDZn0PpGycpvuHoP9GS+7XL83IH8yYlITxY/PtGq8GKlVvPy5YTIYQQ15G9iAgACxcuxLJly6BSqSAIAo4fP45//vOfeOutt5CRkQGrVcKMLyeorq7GmjVr8Ne//hVfffUVWlpaAADJycl45JFHEBoaKksehBDC4rAZUb3tLlgbpD2E+w/9A3z7/4YZ98WGHJgtdtGYqFBvzB6fIDnXrig8uY4ZkziEPXPMlVitTFU8MC5CK1s+xPXUAakInfkJOKWERSOHBdXb7oKlLkeO1Ho0f18trp3BPsH82fozsFjFvw+5E/+wPgiMFD/RUVOciea6i7Ll5EwcxzFbmuY3WXGxRZ7neUIIcXeC4EDtvj/BVLpHUrxX8mIEjn2G2fJzw54CVNWxZ57fOFfaSayuKpQwEzhpSPeOLNgpYRPM1GjaLOhJlPpQhM3+AgqdtPW/un1/hqFEvHNOb6BS8li+gH0a8bvtF9DQLL5JmBBCSM/XLUVEABg/fjweffRRREVdmjUkCAKys7Px/vvv4/HHH8dHH32EgwcPoqamxmnvabfbkZ+fj02bNuHFF1/E008/jW3btv1UPOR5HnPnzsXvf/97+PuLz7MhhBC5CA47anc9CHPlEUnxvv1/A78h/8eMq6xpw6a9Bcy4267pD6XS9T8ummsLUVd6SjQmJHYIfIOk7RR1BaPNgYMV4h+CxkZo4aXqth+vxEW0YcMQMm0FwLFP5ArWVlRtuQW2Vs+a99cZ181Ihbde/ORwVZ0Bm/ZJa93mLhIHs08jSllM7alYRURQS1NCCJGs4eizaMtbLSlWFzcLwRNfAseJP2saTTZ8uVHCzPOZqfDzcf3Mc6u5lTmyQOcTgvCkMS7PRcyOEvGia6yPEkl+ru/OQuSl8o1D2OzPwKl92cGCHTU77oVZ4qlhTzZ1dBxiI3xEY8wWO76S8L2IEEJIz9atTz8RERH485//jH379mHz5s1obW0FABgMBhw7dgzHjh0DAPj5+SE8PBwhISEIDQ1FcHAw9Ho9NBoNNBoN1Go1eJ6HyWSC0WiE0WiEwWCA0WhEa2srKisrUVFRgaqqqnZPOfbv3x+LFi1CZGSkrL8HhBAiRhAE1B18HIaLWyTFeyUvQcCovzF3JgPAp+vPwGYXRGP6xAVgwrBoyfl2hZRTiAkS2gS60sEKE0yM3zPaney59DFTEDzpZdTufogZazdUoWrzcoQv+A4KTe/dmOSlV+GGOX3x/qrTonFfbsjBrHHxsrRNlkNsv9nI3Poi7DZzuzGFp9ZjwJT7wfPu9988IkwLLxWHNmv73w93lhhxe5qExThCCOnFmk6vQPPpdyTFasJHI2TKG+Ak/Nz4bscFNLW0/zMIAAJ8NVg8I0Vyrl1RnLNN2siCbvyZWNBkRVGLTTRmWrRO0ucs4n7UQekIm/kRqjYtg2AX/7sj2Iyo2nIbIhauhUpiK1RPpOA53HZNfzzz1iHRuA17CrB4RoosbZMJIYS4RrevWiiVSkyZMgVjxozBrl27cODAATQ0NFwW09TUhKamJpw/79xe2jzPo1+/fpg2bRr69GG32yKEELk1Zb2G1nOfS4rVRU9G8MQXmDuTAaCorAk7DrNb6d25eAB43vUflB0OO4pObxCNUai0iEmb7vJcxEhpcTRFwgkd4r68k6+Fw9yA+kN/Y8ZaGy+gettdCJv9OXhl721xu3BKMr7bfgH1Te0vHjY0m7F2Zx5umNNX1txcRa31QXTadFwU+b5maq1FRd5BRKVMlDU3Z1ArOEyI1GHzxfZPbBytMqHF4oCPmk5mE0LI1bTmr0HDkaclxaoC+iJ0xvuSniea2yxYuYW9diLXzHNI3iy4UJZc2rOzVPwUImjuucfTho9EyLQVqN52JyCIt9p3mGpRtXk5IhasgUIXJFuOPc24IVFIiQtA7sWGdmOsNgc+X5+DP9w+QtbcCCGEOE+P+VSv1WoxZ84cPP3007j//vsxbNgw6PXOf0DjOA4xMTGYP38+nn76adx7771UQCSE9EitF1ai8cR/JcWqQwYhZNo74HjxtoE/+mTdGQjiB+owuG8ohqaHSbpfV1UWHIKxRbx9dUzaDKg03bd70SEIzPZ86YFqRHp1+/4c4mK+/e6E35CHJcWaK4+gds/DEASHy/PqqbQaJW6ax5639M3mc2g1WGTJSQ6Jg9nzW925pek0xoYJmwDsK6eWpoQQcjWmisOo3fMHSbEK72iEzf4MCo2fpPhvN59Dm1F8Lm1EiBdmT0iUdL+uaqm7iNqSLNGY4OhB3TqyABLmnvuoOAwPdX3rV9K99LHTEDzxRUmxtuZCVG29HQ5b733e4TgOty8ewIzbdrAIJZUtsuRECCHE+XrcSuePpwP79esHh8OB4uJinD9/HiUlJSgvL0dNTQ0cDukLcTqdDlFRUYiKikJ8fDz69u0LX19qrUQI6dmM5QdQu+8RSbFKv0SEzfwEvEpage18YT0OZLBntd1xbX9J93OGwqyevzs5u86CaqP4jlQpc8KIZ/Af+kc4TPVoOfsJM9ZQ+D0ajkQgcPRTsuTWE82ekIhvt5xHVV37u/xbDVas3JKL22X83uNKoXHD4B0Qg9aGknZjyi7sham1Dlpv99vBPjFKBw6A2H6UnaVGzI2n1lWEEPJLlsY8VG+/C3CwN87wmgCEz/4cSq9wSfeuazRi7c48ZtytC/tBJcPMcwAoPLmeGZMwuHuf8xtMdpyoEW9hOTlKB5UMHVpI9/Pucx3spgY0HPkHM9ZSk4manQ8gdPoKSa2GPdHQtFAMTA3BqfPtbwp2CMAna7Px13u6d+4pIYSQzunRP+F4nkd8fDzi4+N/+prdbofBYPhp5uGP/26326HT6aDX63/6x8vLCyqVtFM5hBDSU1gazqN6+28Bh/gOYgBQ6MMQNvvzDrVQ+WhNNjNmzOBI9E2UZ1HbbGxCWe5u0Rgv/yiExg2VJZ/2SGllOj2Gioi9BcdxCBzzDOyGKkkzS5uz34XCKxJ+A34rS349jUrJ45aF/fDCh8dE477bkYtrpiXD39f9279yHIeEQQtxevcb7cYIDhuKTm9A3zG3ypqbMwRpFRgSokGGyKLrnjIj7A4BClp0JYQQAIDdUIPqzbfAYW5ixnJKHcJmfQyVf5Lk+3+58SzMFvFNb/FRvpg0MlbyPbvC4bCj8JR4EVGh1CI2faYs+bRnT7kRDkaXFmpl2rv4Dfgt7G3laM5+lxlrLN6K+oNPInDcv3rlzEyO43D7Nf3xh+d3icbtPV6KG+Y0IDk2QLbcCCGEOEePaWcqlUKhgI+PD8LCwhAfH4/09HQMHz4co0aNwsCBA5GcnIzIyEj4+/tTAZEQ4nZsbZWo2nwLBEszM5ZT+SBs1qdQ+cRIvn/WuWpk5FSJ35fD/7N31+FRndkfwL93PBN3JQqEKA5BY7iXUrSlulCo7m673a51K/urbHfbbUuBuuFW3EMS3CUhRCDumXgyGb+/PyiUtuTeO5PJnTvJ+3mePhXOvXOKzNx53/ecg8fm8FcJVJp9ACYj84Zp2MCZnGY9dqejZcxzUnwdxIjxkPGWD2F7lEgMr+RPIPcZyim+8ezraC9inv3Zk6UkhCDY35kxRqM1YuP+XN5y6m5c3rsKr/wImq2/tECxVV83ak24omKu7CAIgugtTHo1ag4/BkNb5xXqd1FieKeuhdyH+yG66rp27M8sZI17bE4cb4c7aorOoaO1ljEmKCoVUrkTL/l0hq2VqZgCxgfY/wEnwjzuI/8OZdhMTrGtud+jJWttt+ckVDF9vTAy3p817lsOB5oJgiAI4bG7TUSCIIieyqRvR+2hx2Bsr2QPFsngM/FLyDyjOd+fpmlOVYgpI4MRGsht5oo1FF5lnwkWGs/ty1t3qWwz4EYj80ZncpBDrzx52tuJJA7wmfQ1JC5hnOJV6c9DW3u52/MSIrGIwtLZ7AcU9qTfQi1D21N7onTxhV8Ec9umFlUR6iuyeMvJmri0cGZbmCUIgugNaJMRdceega7uKqd4z3H/hrJPilmv8cPu6zAYmQ+lDAjzQMJA9oV+ayni8JwfbuORBTojjUyWGb7DfeVwlYt5y4kQBooSwSvxA8j9EjjFN577F9qL9nV7XkL1KIeDyOeyqpFdoOIlH4IgCMJ6yCYiQRCEANAmA+qOPg1dPbeTeV6J/4VDwGizXuPstSrcuFXPGCMWU3h4ZoxZ9+2KppoCNFbdYIzxCR0BJ7cA3nK6Hy6tTFNJK9NeS6zwgO+UHyBSeLHG0kYtag4/AUNrOS+5Cc3YIYHoF8LcwkhvMGHd3hzecupu4YPmsMYUXvmRl1ysrZ+bFEFOzIuqxyrIJiJBEL0bTdNoOPMaOkoPc4p3G/oSnPvPN+s1SipbcPRMCWvc4w/E8XboTdfRgvJc5vaGSld/+IQO4yWfzpyv0aBdz7z5mhJEWpn2ViKJAj4Tv4TUPZJDNN2rDwxG9HFD0nD2Lklf78iy2y4cBEEQvRXZRCQIgrAxmqbRcPof6Chn/pJ9h9vwP8Mpgn1R+l4mE7cqxCljwxDgw187oaJru1hjbH06GQCOljNXRSnEFEb5kRZHvZnUJQS+U74DJWFfZDJ11KHm0KMwcWhb3NPcmZnC5tDJYpRXt/KSU3cL6J8IudKNMab0+iEY9Pa32UZRFJIDmX/PFzTpUdbKPuOXIAiip2rJ/gKtOd9winXqvwCug14w+zW+3ZnNOtNvUJQPBkX5mH1vS5XmHITJqGOMCYsXwMgCDocFuVTeEz2XWO4K38nfQ6z0Y42ljRrUHHoc+lYObYt7oEdmxUDE0i45u0CFi9eZR6wQBEEQwtLjNxFNJhNUKhVycnKQnp6OzZs348cf7fO0N0EQPVPr9a/QeuM7TrFOAx6Ga/wzZr9GxvkyFJU3M8bIpCIsmcG9PWpXmYx6FGcxt3uRyBwRFGVeKydra9ObcKZawxgzxl8BhaTHf6QSLORe8fBOXQNQ7O2u9I15qD36NGhT79tcGRrji7j+zFWbJhON73dd5y2n7iQWSxEaN50xxqBrZ63WEKoUDlXYXKq5CYIgeiJ1yWE0nn2DU6wiMBGeY98xu1Iwr6gBJy9VsMY9zuPMcwAovMJ+WDBsoG1HFtA0jTSWw4IRrlKEukh5y4kQJolTAHynfA9KyjzfGwBMGhVqD/bOA4NBfs6YNCaUNY5UIxIEQdgXia0TsJaWlhbU1tb+4q+amhqoVCoYDIZfxPbr189meRIEQdxLXXoUDWdf5xTr0CcFnqPfMnthwWg04Yfd7Ivxs1P6wdONv1O2VTdPQtvewBgTHDMJEqltT/6erNRAb2KOIa1MiTuUfVLgOfYd1B9/mTVWU5GJ+pN/hefYd3vVPM3b1Yhx+ON7zJtm6efLMH/qAET0Ya7iswdhg+Yg7+w6xpiiq7sQGjeNt5ysZYSvAo4SCu2GzheC0so78GiUC695EQRB2JquPgd1x54BwL5QLvWIgk/qGlAi8zervt3J3m1k9KAADAj3NPvelmquu4WGSua8vEOGwsk9iLec7qegSY/yNiNjTCqpQiR+IvOIgs/EL1CzfwlAGxhj9U35qD36NHwnf2vRn2t7tmRGNI6eLoHe0PmX6JulTThxqQLjhtr2PYAgCILgxq42ETs6OlBXV3d3g/DeDUONhrlKhCAIQmh0DTdQd2wlQLPsUAGQecbCO2U1KJH5b9tpZ0tRXtPGGKNUSDB/Cpc5D9ZB0zRyL+1gjfu4TYHiHd9AZzJCazRCazRAazRCZzLCSNMQUxREd/4CBbFIBAlFwVEqg1IihaNECkepFI4SGZykMngqHOClUN79u5eDEl4KB/gpnSAX3//nlu10MgAkkzkpxD2cIxfB0FKC5qufsMa25a2H1DUMrvEreMlNKGL7eWFEnB/OZVUzxn37YzbeeG4sb3l1FzefvvDwj0ZDVeezHmuKzqG9uQqOrv685tZVcjGFsQEOOFja+XvluRoNWnUmOMtIxTZBEL2DQV2DmkOPgTawP0eKlX7wnfwtRDL2Cqdfu36TvS0gRQGP8liFyPU5fwftgzd+/Pb2M77J8NOz/u3nfRNN//yMT1GgcPvvUpHonmd8GZQSCRylMjhJbj/n//yX8vazvsIB/kpnKKX338Q5QlqZEmZyCBjz04HBl1hje+uBQR8PJWYkRWDHkQLGuG9/zMbowYEQs7Q/JQiCIGxPcJuIer0e9fX1v9kkrK2tRUtL72sFQBBEz2RQ195eWNC3s8aKnQLhM+kbiKSO5r+OwYR1ezpftL7jwUmRcHGSm33/zmgMBhS3NqG4tQlFLU0o+unvJW3NqFa3ob1Nhf+pjjN+CFWKnfBDgwag+Jkn4ePgiD5OLghydEYfJ1cEOTkjxMkNR8qYZ0QO9JLB24G9fSXRu7gN+xP0LcVQF+1hjW089y9IXMLgGDqFl9yE4tE5saybiGevVeH6TRVi+jK3P7UHYQNnMW4iAjSKr+1FzLineMzKOlKCmDcR9SbgZFUHpoSY/zlGEARhb0yGDtQefgLG9krWWErqBN/J30HiGGDRa323k73bSMrIYIQGulp0//vRG40oa29BYUvj7ef8n571S1qbUaVuQ217C96v2w13hnt0UBJ80S6FVl1qtbyYeMgdEOT00zO+ozOCnFwQ5uyGPUXMVfLuchEGe1vvOxLRMzhHLvzpwODHrLFteeshdesL17hlvOQmFAunDsD+44XQaDuv9C2rbkXamRJMHM3e/pQgCIKwLZtsIppMJjQ2Nv5mk7C2thb19fVW74vt4OAAHx8f+Pj4wNfXF2FhYVa9P0EQhDluLyw8CWMb++wSSur808IC+xD3+zl8uhhVdcwblS5OMsydaFmb50ZtB240qpDToMKNJhVyGuqQ06hCaRvz/MWpHSWQsLR2ypSH3D46zZPajnbUdrTjYl3V3f8mF3kiyIF5lllyIDmdTPwWRYnglfgBatoroa29xBqvSn8e0pk/QubJ31xSW+sb7I5xQ4Nw/GI5Y9w3O7Lx3kuJdn+COzhmMi4f/g9Mxs7nYBZf243osU/a3f9rUqADKJaGfWnlZBORIIiej6ZNUGW8CF3dVfZgSgyf1DUWf/ZfzavFldxaxhixmMLDM2Msun+bXofcRhVyGm8/399oVCGnUYXClkaYGNZsBuuq4E5rGe99VhYILcXfclSDtgMN2g5cq//550sEOUKV8xk/c5MCHUiVFHFfbsNehr61BOpC9tmfjefegtQ1AsrgVF5yEwI3FwXmTuiP9XtvMMZ9v+s6Eof3gUxKDuUSBEEImU02EW/duoUPP/ywW+4dHh6O8PBw+Pr63t00dHY2vy0IQRBEd6BpGqrMP0JXd5k9+M7CgrtlbUZ1eiPW72F+aAeA+VMGQKlgntNA0zQq2ltxqa4Kl1TVd/9e0d5qfmI0jSRNCWOICUCmPNj8e1uZUsw+o+HNS1uxrViGeE9fDPT0xXAff0S7e0MsIm37ejuRxAE+E79C1a5ZMLQyn7SnDWrUHHoMAbP3Qqz05i1HW1s6OwYnL5XDxLD7dC2/Dpdu1GJotC+fqVmdXOmGwP6JKLtxpNOY1oZS1JdfhVefQbzm1lWeDmIM9JLhikrXacyx8g4YTTRZjCUIokdruvg+1EV7OcV6jH4LDkFJFr0OTdOcqhAnjwlDgA9zVw0AqOtox6W6alxSVf3092oUtjRalFsiy3M+AKQrQiy6tzUpxQGsh3a+yTuEk7VaxHn6YKCnL0b4BCDe0xcyMdnw6O0oSgSv8f9BTVsFtLUXmYNpE+qOPQP/WTst/m5vjx6c1B+7jt1Em7rzA3Q19WocOFGEWcl9ec2NIAiCMI/g2pl2RiKRwM3NDR4eHnB3d//F35uamvDDDz8AACZMmICBAwfaOl2CIIj7arr0X06nFQHAY9SbcAhKtPi1Dp4oQm0D8xwWdxc5ZiZF/Oa/t+l1OFdTgZPVZThVXY6LdVWo07DPdOEi1NiEYCNze+osqQ8axbav8FOKAxl/3GBSo1ZThdpKIL3y5wUTR4kUw3wCMOLuX4EIdrZeGynCfogdvOAz6VtU7Z4NWsf8+97YXonaI0/Bd9omiCQK3nK0pWB/F0wYHYpDJ4sZ477ZkYUhUT52V6H3a2EDZzFuIgJA4dXddreJCAApfZSMm4iNWhOu1uswhLSFIwiih2or2IbmKx9xinWJfQouUY9Y/FqXbtQiu0DFGCOViLBoWtRv/rvWaMDFuiqcrCrDqZrbz/llbdYZHeNs0mKorooxplrkiDyJp1VeryuUEubDgjRtQoOuFKdr9Dhd83PXBJlIjMFefhh+z7N+fzdPu39GIcz3ywODzJvntL4NtYceh/+s3RA72P73Px+clDLMnzIAX23PYoxbvycHk0aHQiG3myVqgiCIXkcw79COjo53NwbvbA7eu1Ho4uLS6UNZQQHzsF6CIAghaC/cg+bLH3CKdY55Ai7RSy1+LZ3eiA37OFQhTh0AhVyCanUb0iuKcbK6HCery3C1voaxTVFXsFUhAkC6wvZzEcSUAgox8xw2tfH+bRjbDXpkVJYg456NxWAnV4wPCEaifwjGBwSjn6sHWWzoJWTu/eAz4XPU7F8C0AbGWG3tRdSf+BO8Ev/Xa35/PDwzGmlnSmAwdv6ek1/ciNNXKjF6MPPGvtD5RYyCwtETmvb6TmPKcg5hyOSXIJHa/iCFOVKCHPDfy02MMWllarKJSBBEj6SpvQjV8Zc5xTr0SYX7iL9b/Fo0TeP7ndmscVPGhcHHU4lGbQcyK0txsroMJ6vLcKG2CjpT53PKumK0tpx9ZIGC35EF90dBKWaeQ6kx1cKE31ZQ6UxGnK2twNnan0dT+Dg4Yrx/MMYHBGO8fzBiPXxIV5JeQuzgCZ/J36J612yYdMwjPQytpag9ugx+UzeAEst4y9GWZqf0xY9HC9DQrOk0prFFi51pN7Fg6gBecyMIgiC4E8QmYnR0NObNmwdfX/tuU0UQBNEZrSobqowXOcU69EmBx8jXuvR6ezMKUd/U+YM6ADg6SZAmvYW/bzyKnEbmk8zWIqGNGK1lnn/WRklxUebPSz5MuLQybTeyz7W8o7StGT/kZ+GH/NsnMf2UThjvH4zUoDBM7hOOEGe3LuVLCJtDwBh4jn0H9cdfYo1tv7kdUvf+cBv4LC+52ZqvpyOmJ0ZgZ9pNxrhvfsxGwsAAiOy4HaZIJEFI3DTknfm+0xi9tg3luekIjZvKa25dFekmRYCjGJXtnS9MHyvvwEtD3HnNiyAIorsZ2itRd/h3gKnzauw7pB5R8E5eBUpkeTvM89nVuFHYwBgjFlMo8q3H8K1f4JKqutsOB/5aooa5s4BQRhYoRN4QU8yHWtoNzN9Z7lXb0Y6thTewtfD2IU43mQJj/ftgQlAYJvUJxwA3r15zOKw3krn1hfeEz7gdGKw+i/qTr8Jz3Pu94veEQi7BoulRWLWeeZzLloN5mJEUAUcH5jErBEEQhG3Y5GhUeHg4HnroITg6OgIAcnJy8NZbb2H9+vVoamI+wUwQBGFvjOo61B5+ArSReVMPAKTukV1eWNBoDdi0n70K8YJ7OVblXOBtAxEAhuiq4UwzL7CclgdBT9l+zghbK1OaNqLDyNyuiUm1ug2bb+VgecZehP7wMSLXr8LzJw5gb0kB2vTsi1CE/XGOXAiXuOWcYpvOvwt1ycFuz0koFk6LglzG/Oe+pLIFmRe5L+gJVVj8TNaYomvc2l4LCUVRSA5irp7Ma9Kjoo15cY0gCMKemAwdqD38FIwdtayxIgdv+E76BiIZ+4zCznCdhVjoWY//5p/Bhboq3jYQgw1NCDMyV2Jdl/qgXqzkJR8mXA4LdtZxhIsmnQZ7Sgrw4slDiN64BiE/fISnju3G5pvXUW+lERGEsDgEjIHn6Lc4xbblb0JL9ufdnpNQTB0XDl9P5j/3re061gOFBEEQhO3YpBJRLBYjKSkJw4cPx759+5CZmQmTyYSTJ0/i3LlzSE5OxsSJE6FU2v7hkiAIoitooxa1R34HY3sla+zthYVvIZI5d+k1d6ffQmOLljGmQ6ZHuSfzl/zukMRyOhkAKn2HINE1GG4yOVxlCjhJZZCLxT/9JYFMdPufxZQIJtAw0b/8S2s0oN2gR7tej3aDDu16PdQGPZp0GtRrOqDSqNGoZdvQFUEpZq6G7DDWgIb1FsPzmxuQn9WAj7POQyoSITEgBLNC+2NWaH9SpdiDuA//C/RNN9FRdpQlkkbdsefgP/NHyDyjecrOdjxcFZiT2g+b9ucyxq3bfR3jhgZBbMfViG6+/eDuH4XGqs4Pe9QUnoW6pQZKF/vq0pESpMS6vDbGmKPlaiwd4MJbTgRBEN2FpmnUH38JOtU11lhKLIfvxK8gcepaW+7TVytRUNLIGGOkTCjy7bxtdndJ1JSyxhR6xWG8VzBcZXK4ye8850tuP+eLxJD99LwvpijQAEw0DZqmYQINo4mG3mREu+H2s/29z/rNOi3qNWqoNB1o0Hawbpw6Sph/HfSmVuhp68yJBICythZ8mXsFX+ZeAQVglF/Q3ed8UqXYczhHPQxdYx5ac75mjW08+yakbn2h7JPCS262JJWI8MisGLz/9XnGuG2H8jA7uS8claQakSAIQmgomubpWBqD6upqbNu2DTk5OXf/m6OjIyZNmoTExERIpcwfIAUFBfjwww8BAMuWLcPAgQO7PWeCIAg2dxYW2vI3sQeL5fCbvhkKn6Fdes2bqgb8/vVj0GlMjHHXg6tQ4cXPJqJEJEKwkyuiFRIszvkMFMOcFFfvCExZvqXbv0gbTCY0am9vKFar21He1oKythaUt7egvK0FRS0SdOiGM95DpT2HZgPzhoe1DPLyxayQ/pgdFonBXn5kocHOmXStqNo9B/rGPNZYiXMw/GfvhVjR81tAtrbr8Oir+9De8dv5Q/f681MjkTzS9q3QuiL//EZcOvAuY0x88rOIHvskbzlZg9ZIY/imMqgNnb/PjwtQ4OsJ9rU5ShAEcT9NVz5B04V3OMV6p6yGYzh7JToTlVqNZ946jKY65o4VRT71KAiq69JrcUUBCHJyQYSjMx7P+xIyQ+dVdlK5E2b//lC3z/w10TSatBqoNGrUqNtR1taM8vbWu8/5xS06tGjGMd6jSX8D9TrmDQ9r6evqcXdDcYxfH0jILEW7RpsMqDn4KDQVGayxIpkr/GfvhtQ1nJfcbMloorH8tYMoq25ljHtkVgwentnzD1ASBEHYG0FsIt5x/fp1bN++HdXV1Xf/m5ubG6ZPn46EhASIOnmYIpuIBEEIUXP2F2g8809OsV5JH8Gp71yLXqeopRHbC3OxrTAX1Vc70K/SmzFeLdPhZEwhaCvvQwU6OiPa3RtR7l6IdvdCpJsnwlzcEOjoAolIhBunvsHVo/9jvMegCb/HgFFLrZuYBf51vgFf32D+glOi3g4DzVxx0x2CnVwxPyIa8/tGY5i3P9lQtFP6lhJU7ZoJk4Z5phEAKALHw3fyd6BEghhl3a3W7clhbdMW5OeMz16fbNfViFp1E3Z+MBEmU+fVzM4eIZi2cofd/RlfcawWh8s6Ov1xqQg4v6APnKRkkZQgCPulLjmE2sNPAgyH4+5wG/IHuA35g0WvU6Nuw49FedhWmIvsbBXiCgMY4w0iI47HFkIv6Xw+rSW8FUpEe3gj2t0LUT8954e7uCPYyRUysRjlucdwYgvz/2PEkAcxfPrfrJqXJX7IbcU/zzE/f1VqjqDDyN5Jxtq8FEo8GD4A8yOiMT4ghGwo2imjthlVu2bB0HyLNVbq1h/+s3Z1qc2xvUg/X4a3PzvDGOPoIMV370yDk1LGW14EQRAEO0GtRsXExGDAgAE4fvw49u3bh/b2djQ1NWHdunU4evQoZs2aRTYICYKwCx3lGWg8+wanWJf4lWZvIFa2t2LTzetYX5CNC3W35/JJjCKMq4lgvbbQX9WlDUSZSIx4Tx8M8fbHYC8/DPL0RZS7F1zlik6voWkaRVeYZ3xRlBghcdMsT8yK0is6XwAHgHAXCbZOW4Rr9TXIqq/D1foaXFJVoV7DfJ01lLY14/2rp/H+1dMIdXbD/IgoLOgbQyoU7YzUJQQ+Ez5H9b6FgIm58k5TkYnG8+/AY6TtF96625zUfth+OB9t6s5/TsqrW5F+rhSpCSG85mZNcqUbAvqPR3luWqcxrQ0lqK+4Bq8g+3r2TQlSMm4i6k3AyUoNJoeQsQUEQdgnXUMu6tKf47SBqAydBtfBL5p1/wZNB7YV3sD6gmxkVJbcfhUaGF0ZxnptqU9jlzYQRRSFaHcvDPHyxxBvPwzy8kO0uxe8HRwZryu6upP13mEDZ1mclzWxPec7iCmcemAm8prrkFVfi2v1NbioqkZlO/MBQ2tQadRYm3MJa3MuwcfB8e6G4jj/YIjJhqLdEMtd4TvpG1TtmgGTlrn7j74pH6qMF+E94TNQVM/+NR4/NAjrA1xQUtl5q+D2Dj22Hy7A0tkxvOZGEARBMBPUJiLumZc4YsQI7Nu3DxkZGTCZTKiursZnn32GsLAwzJ49G/369bN1qgRBEPelbylGXdpKgGZuKQoADn0mwH3YK5zu26jtwPbCXKwvyMaxiuLfLFsE17pDahQz3qNdrkWVh3nzPaLdvTDarw9G+QZhqLcfoty9IRMzv86vNVRmo6W+iDHGv+8YODh5mXXf7lDcokdRC/Osw+QgJQZ5uWOQl9/d/0bTNIpam3CupgLnaitxrrYSF+uqoDFab27ib3JtbcJ7V07jvSun0dfVA4/0j8PD/eMQ7tLzW1/2BAq/kfAc8zbqj7/EGtuStQYyrzg4RczmJTdbcXSQYt6kSHzzYzZj3A+7c5A0vA/EYvtdbAkbOItxExEAiq7utrtNxKQg9jZ1x8rVZBORIAi7ZNQ0ovbwk6D17ayxUo9oeCV+yGljoMOgx+7ifKwvyMa+0pvQm375PcKv0QVOGjnjPfQiI4p92Dsc3CvcxR2j/YKQ4BuIoV7+iPf0hZJlnMyvadrqUVlwgjHG2TMUnoFxZt23O3QYTDhdzTwbfUyAAoO8fTDI2wcL+v68kVHR1oLzdZU4V3P7Of98XSVadMxz6LuitqMdq69fxOrrF+GvdMLifrFYGhmPeE/SEtweSF3D4J36GWr2LwFo5u+D6pIDaL7yMdwGv8BbfrYgElF4ZFYM3lpzmjFux5F8zJnQDy6OpBqRIAhCKAS3iXiHUqnEvHnzMHbsWGzfvh3Xr99ubVVUVIQPP/wQMTExmD17NgIDuzaYnCAIwppMejVqDz8Fk4593qDUrT+8kz8GJep8Q85gMmF/6U18nXsFe0tuQme6/8liiUGEkBoP1te85V/PWIUoF4uR4BuEsX59MNovCKP8guAu7/rcksIr9nM6+Vg5ezXh/RbJKYpCuIs7wl3csbBfLABAbzTisqoamVWlyKgswfGqUjR302LDzeYGvHY+A6+dz8BYvz5YGhmPhyKi4cZQIUrYnnPkQugb89GS/RlrbH3mHyF16wu5Z88+mTs7tS+2H8lHS1vnM58qa9uQdrYUE0eH8pqbNflHjIbc0QPa9s4XfEuvH8TgSS9BIrWfP8feDmIM9JLhqqrzX79jFR0w0TREpHqaIAg7QpuMqDv2LAytJayxIoUnfCd+BZG08wMTNE3jeFUpvs69im2FN9Cqv//7JkUDEVWerK9Z4tsAg6TzQ4xiisJQb3+M9Q/GaN8gjPYLgr+jM+t92RRn7wNNM1c/hg+cJYiOGWeqNdAamStIkwLv/90n0MkFgU4umBM2APhp9mJ2Qy0yK0uRWVWCzMpS1HSwby5bokrdhv9cPYP/XD2DgZ6+eKR/HBb3i7XKrx/RfRwCxsBz9FuoP/ln1timi+9D5hkDZfAEXnKzlTGDAxEW5Iqi8s7XS9QaA7YfysdjD8TymhtBEATROUHNRGSSk5ODbdu2/WJeIkVRGD58OPr3748ffvgBIDMRCYKwIZqmoTr2LNoL2TfMRHJX+M/eC6nL/RfAcxtV+Dr3Cr7Lz0K1mn3uXt8KL4TXMFfxtSm0OBVVBNzz/V1EURjuHYCUwFCkBoVhtF8QHCTmnT5mY9BrsPODidBrO///kCvdMOvFQxCLrfvalnj0cA1OVnV+QtlJSuH8gj6QWjCPzWgyIauhFhmVJThSXoRjFcVoNzC3suwKuViM2aGReDJqECYEhZMFe4GiTQbUHFwKTUUma6zEqQ/85+yDWNGzq0037c/FV9uzGGP8vR3xxRtTIJHYbzXi5cP/Qd6ZHxhjRj3wNkJip/CWkzV8cq0JH15hPkyzdaofBnkzV9UQBEEIScO5f6Hl2mr2QJEUftM2QeE34r4/XN7Wgm/zruLr3Ku41dLIeruAehfEljDPQtSLjTgeewsG8S83EeM8fJAaFIbUwFCMDwiBi8y677s0TePA2vlorrvZaQxFiTDrhf1wcPax6mtb4h9n6rE+n/m71Yl5gfBTmn/enqZpFDQ33H3OP1JehAZt9406EFEUJgWF48moQZgVGml2pxiCP6oTr6Atdx1rHCV1RsDsPZC6sY8osWcnLpXjzdXM1YgOcgm+fXsaXJ3JsyJBEIQQCLYS8deio6MRGRmJEydOYO/evWhvbwdN0zh37hzOnTtn6/QIgiDQkvUZpw1EUGJ4p372mw3Edr0OG29ex1e5V3Cqupzz60oNYgTXcalCVAEUEOTogukhfTE1uC+SAkIYZxlaQ0XeMcYNRAAIiZ0miA3ENr0J52qYWxyNC3CwaAMRAMQiEQZ53Z4v80L8SOiMRpyqLsOhskIcLLuFS6pqDnfhTms0YvOtHGy+lYNQZzc8GTUIj0cORKCTi1Vfh+gaSiSBd/IqVO2cDkNrKWOsoa0MdWkr4DvlB1Aiu3mMM9us5L7YdigPzQzViFV17Th6pgSTx7LPiBKqsPiZrJuIRdd2290mYkqQknUTMa1cTTYRCYKwG+23dnHbQATgOeb/frOBqDMasbM4D1/duIJD5YUwcTzLTdFAeBV7u/9i3wYYxCZ4KZSYGhyBqcF9kRIYCl+lE6fXsVRjVQ7jBiIA+EWMEsQGIk3TrB1HotylFm0g4qdD7v3dPNHfzRO/ix4Co8mES6pqHCy7hUNlhThdUw6DiX3cBVcmmsaBsls4UHYLPg6OeDQyHk9FDUZ/N/aqVYJfnqPegL4hD9raC4xxtL4VtYefhP/s3RDJem6V6ehBgYjo44ZbZU2dxnRoDdh2OB9PzLV9G2SCIAjCjioR76VWq7Fv3z5kZmbCaPxl24w5c+Zg4sSJNsuNIIjeqaPyJGr2L+I0B9Fj9FtwiX7s7r9fb6jFmuuX8F3+NYvmavQr90ZYLfOXRZOzESlLgjAjtB9iPXx4bSd0bN0K1BSeYYyZ/LuNcPeL5C2nzhwqVWNleh1jzHtjPDE3onsWZGrV7dhXehO7ivNwsKwQ6m6oUhRRFKYH98XvoodgWnBfiEX2W8XV0+jqc1C1ezZoA/updZfYZfBI+AcvednKloN5+GLrNcYYX08lvnprql1XIx78fBEaq3M7/XGKEmHm8/uhdLH9AixXNE1j3LYKVKs7b283wF2KPTOZK2sIgiCEwJzPZ+eYJ+E56vW7/17S2oTPci7hyxtXLGp1GahyRUypP2OMSWrC8MVemNU3EsN9Anh9tru4/x0UXNjEGDP6wfcQHG37NZq8Rh2m765ijFkZ54I/DO6ebg8tOi0Olt3CruJ87C0pQKOW+eCipcb7B+N30YMxLzwaCknPPXBmbwzqGlT9OA1GdQ1rrEPwJPhM/ILTPFV7depKBV5fdYoxRiEX49u3p8ONVCMSBEHYnF1uIt5RU1OD7du3Izs7+xf/vX///pg7dy769Oljs9wIgug9DK3lqNw5DSZN53Ot7nDqvxCe4/4NncmI7YW5WHP9IjKrmCuPmMj0YozLjoCYZv6C8dozozF6EP8zZNubq7H7o2kAOv+ocfONxJRlG3nNqzOvnqrHlpudV01SAM48FARPh+5vF9Rh0COtohi7ivOxqzifU1tbc4U4u2JlzDA8GTUInorOZ/YQ/Gkv3I26tBWcYr2SPoJT37ndnpOtaLQGPPrqPjS1Mh+ueOGRoZg2Ppy3vKwt/9x6XDr4b8aY+JTnED3mCd5ysgYuLeMyHwxEgCNZ4CQIQriMmkZU/TgNhrYy1lhFwFj4TvkBJohwsOwWVl+/iH2lNzlXHf4aZaIwNiccDjrmbh1PzYvHQ5P5P4xnNGix84NJ0GlaOo2RKVww+/eHIJbYfhNgTVYz3r/ceeUTAGye6ochPFTJG0wmnKwuw86iPOwszkchh7a25vJSKPG7qMFYETsUfZxcrX5/wnya2ouo3vMQYOq808YdbkP+ALchf+AlL1ugaRrPvnUEN0uZ/0w+NDkST82L5y0vgiAI4v7s+liLr68vVqxYgWeffRb+/j+fzsvPz8e7776L77//Hk1NzB9IBEEQXWEydKD2yO84bSDKvAdBN+jPeO18BoK//wiLj+zo0gYiAITVeLJuIPYLcceogbap9ijJ2su4gQgA4YNm8ZYPExNNI72C+YR5vJeMlw1EAHCQSDE9pB/WJk5HxdIXcXzOo3g2djj8rNiWqqS1Ga+cOYqg7/6HJ47twuU65tPZRPdzDJ8Jl/iVnGLrj78MrYp5bqA9U8glWDB1AGvchr03oDdYrz0Y30Jip0LE0pq26Opu2Nu5v5Qg9oMJGSzvuQRBELZEmwyoS1vJaQNR4tQHsrEf4L/XzqPf+lWYvm8j9pQUWLyBCABB9a6sG4juLnLMTLLN/LTK/EzGDUT89BknhA1EAKzP+e5yEQZ6ynjJRSISITEgBP8dMwk3Fz+DS/Oewp8Hj0a4i/WqIFUaNd6+fBKhP3yMBw9swbGKYrt7luhpFD5D4TnmX5ximy79F+qSg92ek61QFIVHZsWwxu06dhONLd1TtUsQBEFwZ9ebiHdERUXhL3/5CxYsWABHR0fgp1MtZ86cweuvv469e/dCqzW/RSBBEAQTmqZRf/Iv0NWzL+Kb5B74P8WDCN2wFm9ePI5aC9oZ/ZpcJ0FQnRtr3NLZMby2L72DpmkUZ+1ljBGJJAiJncpbTkxyGnSo6+i89R4AJAU68JbPvUQUhbH+wfh43BSUP/IC0mcvxYqYofBxcLTK/TVGA77OvYohW7/AmB1fY/PN61ad2UKYx33YK1AEJbLG0UYtao88BWNHPS952cL0xAh4uDLPba1tUOPgiSLecrI2udIdAf3GM8a01hejoTKbMUZoEvzkUIiZP3vYZlMRBEHYUuP5t6GpPM4aR4sV+MrzCQRv+hYvnT6CotauH2QWmSiEVbPPtps/dQAUcttUdBdd28MaEyaQw4JNWiMu1TGvCSUGOkBs4dzzrqAoCoO9/fF2QipuLn4GF+Y9hVcGj0aoM/v3PC5MNI3tRblI2fU9YjetwdrrF9HRDSMTCG6cIxfBOepRTrF16S9A11jQ7TnZysh4f/QPZd441+qM2Hwgj7ecCIIgiPvrEZuIACASiTB+/Hj885//REpKCsTi25UiOp0O+/btw+uvv47Tp0/DRBZFCYKwktYb36K9YAtrnBEiLG4fg1WF5dBb4T2IApAUEIInxENZqxCjwj0wPNavy69picaqHLSomBf2A/qNh1zZPXNHzMVlMTs5yDabiPcS/3Ry+dPx01Cx9EXsn74Ii/rGQCG2zgLSqepyLDi8Hf3Wr8KHV8+i1YI5nUTXUCIxvJM+gcQlhDXW2FaBurQVoE09czFILhNzq0bcdwM6PfMhACELGziTNabo6i5ecrEWhUSEUX7MG8CnqzXQGklVBEEQwtNeuActWWs5xb6gHYPXblaj3UobM0O9/fGs80go9MxViJ5uCkwfb5sqRE17A6puMc8zc/XuC3e/KN5yYnK8UgMTy8dNso0OC96LoigM9fbHOwmpKFzyLE7MeQzLoofATcb8ecpVTqMKT2fuQ/D3H+G1c+moVXf9YCthPo+E1yD3HcEaR+vbUHvkSZh0zBW/9oqiKCzlUI24J/0m6pvIwTOCIAhb6jGbiHcolUo8+OCD+Otf/4q4uLi7/725uRk//PAD3nnnHeTn59s0R4Ig7J+m+iwaTv+TU+xbxgScpbveTjTCxR3/GpGMkkeex8axD6Ikm/3LxNLZsTapQgTH08mhA2fwkgsXbC2OfBzEiPbgp8URVxKRCFOC+2L9xLmoeewP+DJpJhID2DeeuChubcLvTx1Cn+//hz+dPoLytp755VWoxAp3+Ez4EpSEvSWkpuoUGs6+xUtetjBtfDg83ZgXz1SNHThgx9WI/n3HsB6oKMk+AKPBvjb12Q5edBhonKkmLaoIghAWXWMBVMf/yCl2rXEgdtN9u/yafkonvDJ4NG4sXIETMx9DzVX2BfOF06Igl/HTZv/XSq8fAG0yMMaEDZxps+8hv3asXM3442IKGCeATcR7URSFMf59sDZxOqoe/T22THoQs0L7QyLq+jKeSqPGGxePI/iH/+F36Xtwo7HOKjkT3FBiGXxS10KsZD/sa2guRN2x50DTPbMgYlisH6LCPRhjdHoTqUYkCIKwsR63iXiHr68vnn76aTz77LMICPh58b6iogL79u2zaW4EQdg3Q3sVao8+DdDMX5wBYIepH74xxVr8WkqJFI9FDkTm7EdRsPgZ/GXoWPRxcsWGvTdgYKneiO3nhcFRPha/dlcYjXqUXj/AGCNzcIN/37G85cSkvsOIayrmAfeJgQ4QCWQh5H5cZHI8ETUI6bOXomjJc/jH0HEIdHTu8n2bdVr8+8pphK37GEuP/oicBrLIwBeZxwB4Jf6XU2zr9S/RVrC123OyBZlUjIXT2CsZNtpxNaJILEVI3DTGGL22DRV56bzlZA1cWkCnk5amBEEIiEnfjrqjy0Dr2Su0Mk1BeM/EXk3UGYlIhLlhA7Bn2kKUPfIC3klIxQB3L+xOv4XGFuZDI94eDpgyNszi1+4qtsOCFCUSzMgCo4lGZgXzgZUhPnK4yIS7PKaQSDAvIho7py5A5dIX8f6oCYh0Y293y0ZrNOKLG5cRvXENZu7biFPV7PM/CesQK73hM/ELQMw+M7Sj7CiaLv6Hl7z4xnU24t6MW1A1kmdGgiAIWxHuU5KVREVF4dVXX8WCBQvg5ORk63QIgrBztFGLuqPLYepg30i5TnviL8ZxPzUgNc8wb398njgD1Y/+Hl+nzMK4gOC7J3mrVe04dKqY9R6P2rAKsfrmSWjVzPNgQmInQyxmbtPEl4zKDrA11BNCK1OuQl3c8PqIJBQ//Dx2T12AWaH9Ie7i7wWDyYTv87MQs2kNHjiwGedqKqyWL9E5x7AZcB34HKdY1YlXoK272u052cKUsWHwcmf+M1jfpMH+zELecrK2sPie19I0wEmCSDfm9/ljFWrQNGlpShCE7dE0DVXmS9A3sc8gK6Fd8LwxFSYLllT6u3rgP6MnouKRF7FtykOYHtLvbnWZRmvAloPsFTeLp0dDJrVNFWJz7S00Vt1gjPELT4CDszdvOTG5otKiScdcxSWEVqZceTs44o+DRuHGwhXInP0oHukfZ5WxBntKCjBmxzdI/PFbHCy9RT6beSD3HgTPMW9zim2+8j+0F+/v9pxsYUi0L2L6Mm+K6w0mbNqfy1tOBEEQxC/1+E1E3DMv8bXXXvvFvESCIAhz1Z/+B7S1l1jjGmk5lhsmQwPum2QOEgmeGDAI5x98EufnPYWnogfDWfbbk4mb9ufCyFKFOCjKB/GRtvvizqmVKYcFc76ksVTCSEXAaH/rzCLhk0QkwozQ/tg5dQFKH3kBb45IQh8nly7f98eiPIzc/hUm7PoBR8uLyCJDN3Mb+hIcgpLZA41a1B5dDqOmkY+0eCWTirGISzXi/lxodfZZjejuFwk330jGmOrCM+horeUtJ2tIYjmAUd5mxM3mnjnTkyAI+9KS/QXURbtZ49S0BE8bJqEZ3J8NxRSFB8MH4MjMh5G7aCX+MDABPkrH38TtTr+F5lbmKkQ/L0dMGh3K+bWtrTiLy3O+cEYW2Mvcc3NRFIVxAcH4LnUOKh99ER+NnYwod68u3zezqhRT9q7H0K1fYMutHBhNPbONplA4958P5+jHOcWqMv4AfbP9HpjrDNdqxP3HC1HbwNyamCAIgugevWIT8Y478xKfeuopW6dCEIQdas1dj7bcdaxxRprCc8YJqAC3VpID3DzxvzGTUbH0RXyZPBPDfDqfn1hT345DJ9nnfnEZUN5dtB3NqCzIZIxx8QyDh380bzkx0ZtonKhkXlwY4auAk9S+PzIDHJ3xt6HjULjkOWybPA/JAV1ffDpaUYQJu3/AqO1f40DpTbKZ2E0okRheyZ9A4sL+a2ZsK4cq/fkeOTdl8tgw+Hgwz4hsaNZgb+Yt3nKytrCBsxh/nKZNKM6yr7b8XKo7SEtTgiBsTVN9Fo3nuM0X/rMxEbng1koywNEZ/xw2HiUPP4+tkx9CalBYp51CuFYhLpkRDYnENs+lJpOR9XNIInNEYGQSbzmxYZt7HuQkRl9XYXRHsZS73AHPxY3A9QVP48jMhzEnLLLLYxguq6ox/9A2xGxag3X5WWQzsRt5JPwDcr8E1jha34raI8tgMvS856ZBA3wQ1595E5xUIxIEQdiOfa+IWsjBwf5OmREEYVva2ktQnforp9h/m0bgJB3EGjc1uC8OzliMnIUr8Hz8CLjL2d+bNu7LZZ2FODTGFzF9u34K1VJlOYdgMjJXlYTGT7dZq9Vfu1CrRZue+eeUy1wveyERiTA3PAppsx9B9oLlWBEzFI6Sri2cnK2twNS9G5Cw/SvsLyGbid1BLHeFz8QvQUl/W7Xwax3lx9B85WNe8uKTVCLCouns1Yib9+dCo2WfWStEIbFTQImYW5IVXd1lV3/GBnnL4coyZ+oYywIvQRBEdzKoa1B9ZDlAs1eyf22MxW66L2tcgm8gNk6ci+Ilz+G14YkI5NAJYk8GexVigI8TUhOCWe/VXWqLz7NWxPeJngCJVBjPzpXtBuQ2Mn8vSQpUCuZ7SVdRFIXUoDDsmDIfRUuew6uDx8BLwXwAi01eUz0ePvojYjatwQ/512Agm4lWR4mk8EldA7Fj54eJ79A35qL+5Kt29SzIBddqxAPHC1FTzz6zliAIgrCuXrmJSBAEYY7c6nzk7n0ElIm93dpeUzjWmgZ2+uNKiRQrYobixsIV2Dd9ESb1ieD8pbW2Xs2pCvHhmbarQgSnVqYUQuKm8ZQNOy4VMPbY4oiLGA8ffDp+GsqXvoh/j5qAIMeutTo9V1uJafvIZmJ3kblHwivxQ06xTRffR0c5c0WwPZo0OhS+nsyLYY0tWuzJsM9qRIWjBwL6jmWMaVEVoaHyOm85dZVERCExkLnl38VaLVpY5lURBEF0h4rWBpzesRDQqFhjL5h88bap82ohMUVhYd8YnH7gcZye+wQW9I2BlOMoFY3WgC0HuMxCjIJYbLtlnGIOIwvCBNTKNIPDIZWe+pwf7OyK/0tIQekjz+OzxOkY4MaterYzeU31eOToTsRsXI3v88hmorWJHbzgM/ELUOLfjjP5tfaCrWjLY++QZG8GRvpgIMtIFoORxsZ9pBqRIAiCb2QTkSAIohM3mxvw6JHtOL9rKVyNzazxubQH/mRMAvDbTUF/pRPeSUhB2SMv4NPx0zDAgnkVmw5wqEKM9kV0RNe+IHZFa30J6suvMcb4hA6Ho6s/bzmxSa9gnqsQ5iJBqIt9tzhi4yZX4KVBo1C45FmsS52DwV5+XbrfvZuJh8sKyWaiFTmGToXroBc4RNKoS38WhrZKHrLij0QiwpIZ7K2QNx/Is9tqRLaWpvipGtGeJAUyb/waaeA4S1tpgiAIa6pVt+MPJw/hs42PoE9HAWu8inbAs8aJ0OO3m4KuMjleGTwaxQ8/jw0T5yLBj70jya/tyyxEE4cqxJSRtqtC1OvUKMs9yhijdPWHd/AQ3nJiwzb3XCGmMNKXfdPGnjlIpPhd9BBcX7gCe6ctREpg10Ya5Dc3YGnaTkRvXI2NBdkwked8q5F7xcNz7LucYutP/QPauqvdnhPfuFQjHjxZhOo6Uo1IEATBJ7KJSBAE8SslrU146thuDNjwKdxvfYtxonLWa1poGZ42TIIav9xsGuDmiS+TZqLo4efwyuAx8FBYdtK1tkGNA8fZh6gvmWnbOYPFWXtZY8Lip/OSCxelrXrcambeaOhJrUzZSMViLO4fh4vznkLarEcwPaRfl+53rrYSk/asQ8qu73GqusxqefZ2bkP/CEVQImucSdOAurSnQRt1vOTFl9SEEPh7M7d1bW7VYtexm7zlZE3+/cZCrnRjjCm5fgBGA/Nis5CMD1RAxFJ0f4zMRSQIggf1GjX+fOYowtZ9jPys9XicYl+EN9AUnjVOQA1++dkT6OiM90dNQOkjL+CdhFQEcWhZej8arQGbD7BX1ti6CrE8Nw1GvYYxJjRuOihKGMtMGoMJp6uY8x3lr4DCRvMl+SaiKEwL6Yejsx7BpXlP4eH+cRB3oY1rQXMDFh3ZgcFbPsPu4nxyaNBKnPrNg3PUUvZAkw61R5fDqGnkIy3exPX3xuAoH8YYo5HGhn03eMuJIAiCIJuIBEEQd9Wq2/HCiYPot34Vvsy9gjEoxfOii6zXmWjgRWMKSuB697+N8g3Cj1Pm4/rCFXgiahDkYuYZV2w272evQhwSbdtZiDRtYt1EFEsVCBqQyltObHpzK1MmFEUhOTAUe6YtxLX5y7C4XyxEXVhkSK8swZgd32DGvo24oqq2aq69EUWJ4J30Mae5KdraS2g4+yYvefFFIhFh8XT2AxNbDuahQ2N/1YhisRQhscwtn/WaVlQWHOctp65yk4sx2Iu50iOjogNGE1mAJAiie7TpdXjr4nGEr/sE714+BX9DLd4Vp3O69j3TSJylf/7MHeDmia+SZ6JwyXP446BRcJF1rZJt3/FCNLYwHwzx93a0aRUiOLYyDY0TzmHBszVaaFi+PyX3osOC9xrs7Y/vU+fg5uJn8UzsMCi68F31Wn0tZu3fhFHbv0ZaOfvoDYKdR8JrkHl3PiLlDmNbOVQZL4Cme1ZrWS7ViBnny2A09qz/b4IgCCEjm4gEQfR6rTot/nk+AxHrP8FHWeegN5ngjzZ8IE5jrZwAgA9Nw3CMDgEATO4TgczZj+LU3McxOyyySxsvd9Q1qHHgBJdZiLatQqwrvYz2JubWiUEDUiGVM1cQ8YmtxZGjlMIwH+ZZXj1dnKcv1k14APmLVmJZ9BDIRNxm+9zP3pICDN7yORYe3oZbzQ1WzbO3ESs84JO6FhCxt9ptzfkabbd28pIXX1ITghHg48QY09Kmw+50+6xGDIufyRrDZTFXSJJYDmQ0ak24Vt+zqmYJgrA9ndGIVdnn0XfdJ/j7uXS06LRwhA5rJIfgSLEfNNlvCsPnpngAwHCfgLuHBB8fMAgyjvMOmWh1Rk6zEBfZuApR3VKDmqJzjDGegXFw8epaq0xr4lLhzvbZ1NOFurjhk3FTUfzwc3h18JgubYifra1A6u4fkLrre1ysq7Jqnr0NJZbDJ2UtRHLmzhQA0FGWhuYrH/OSF19i+nphaIwvY0yH1oCiCvaRMwRBEIR1kE1EgiB6La3RgI+unUPEuk/w+oVMtOlvL15KYcQn4sPwoJjb3wDAEVMIPjENwcyQfjg79wkcmLEY4wKse0p484E86A3Mp+wGRfnYtAoRHBe0w+Jn8JILF+16E87WMP8aj/V3gEzc9Y3gniDC1QNrE6ej6OHn8MeBCVBKLJ8TuelmDqI2rsbvTx5CvYZ5JiXRObnPYHgk/JNTbP3xl6FrZJ/5ZC/EYm6zEbceyrfL2YhufpFw82FuJ1x58yQ07fazGc+lqptLdThBEAQXJprG+vwsRG1cjWePH0BNx535WTTeFWegL9XEeo9btBv+ZEzCGL9gHJi+GGfnPmG1Q4J37D9eiIZm5udRf29HpI4MsdprWqIkax8A5qq+UAE959M0zTr3fIC7FAGOXesW01P4Kp3wfwkpKH34efzfyGR4WjiCAwDSKooxbOsXeOTojyhtJZs8lpI4B8Er6WMA7O83TRffR0d5Ji958WUph2pEd5fefdiXIAiCT2QTkSCIXsdE09hQkI0BG1bjhZMHUferTYw/i85giKiW9T6ltDMy+yzHpYeWYde0hRjhG2j1XFWNHdjPYRbiwxwW07uTQa9Bac5hxhgHZ2/4hA7nLSc2p6o00LN0QOmNrUzZBDg64/3RE1G05Dn8adAoizcT9SYTPrx2Fn3XrcL7V05DY7C/jR4hcI5aCseIOaxxtEGNuqPLYNK3s8bai+QRfRDk58wY09yqxZ6MW7zlZC0URbEuxtImA0qvH+Qtp66KdJPCX8lctXOsgmwiEgTRdUfKCzF06+dYcvRHFLb8cl7YE6IsTBexP1uraQm+9ngMu2Y9ieNzHsXk4AhQVtw8BACd3ohN+9lnIS6aFgWJDef20TSNIpbDgiKRBMHRk3jLic3NZj3K24yMMb1p7jlXrnIFXh0yFsUPP493ElLgpVBafK8f8rPQf8Mq/PnMUTRr2Q/nEr+l7JMM18EvcoikUZf+LAxtzF2B7MmAcE8kDOx8dENIgAs83cifYYIgCL6QTUSCIHqVzMoSJGz/CouP7EBx629PIE+jbuEJcTbrfQwQwztlNb6buhSDvPy6KVtg84Fc9irEAT6I6+/dbTlwUZGXDoOOeXMiJHY6RF1ohWlt6RwWqxPJ4kKnfJSOeHfUBBQ//BxeGTwajhZuJjbpNHj59BFEbVyNjQXZoGkyE80cFEXBc+x7kLpHssbqmwpQf/xPPebnWCwWYcn0KNa4rQfz7LIaMSR2KiiK+VHdnlqaUhTFejAjp0GHGrX9/VoRBCEM2fW1mLZ3AybuXocrqprf/PgIqhKvis5wulfHkL/jhwdfQnJgqNU3D+/Yn8lehejn5YjUBNtWITZW30CLinnjNaDfeMiV7K0X+cKllSk5LNg5J6kMrwweg6KHn8O7CakWbyZqjUa8e/kUItZ/go+zzkFnZN7YJX7LbfCLUAQlssaZNA2oS3satLHntIb/42PD4O/921EocpkYr/4uwSY5EQRB9FZkE5EgiF4hv6keDxzYjMSd3+F87f1P6IWjCe+KMzjdz3fMvxAVkWTlLH+pvqkD+zI5VCHaeBYiABRnsS9kh8ZP5yUXLm63OGJeXIjzlMHbQTibnkLl7eCIdxJSUfTTZqKllYnFrU1YdGQHRm7/CpmVJVbPsycTSZXwSf0MlJR93mh74U605nzDS158SBwRjCBf5tmIjS1a7ONQ0S00Ds7e8AtnXiBpqMpBc539VFpymT3F5YAHQRDEvarVbViWvgcDt3yG/aX3n4XrBTU+Fh+FhGI/SOMc8ySGDn2yGzL9mU5vxKYD7FWIC6cNsGkVIjgeWBFSK1NwqGx3k4kwyMvy+X+9hZNUhj8NHo2ih5/DewmpFrc5rdd04PkTBxGzaQ22F97oMQfa+ECJxPBO+hhix86r8u7Q1l5Cw7m3eMmLDy5Ocnzw5xQsmDoAQb5O6OPnjAmjQrD6tUkIC3K1dXoEQRC9CtlEJAiiR6vXqPHc8QOI2bQGPxbldRqngB6rJIfhROlZ7+nYdy6cBiyxcqa/xaUKMT7S2+ZViB1tKlTfOs0Y4+4XBTefvrzlxOZGox41atLiyJrubCbeWvIsno8bAZmFVafnayuRuPM7PHBgM/IaVVbPs6eSukXAa9x/OMU2nH0DmtqL3Z4TH8QiCgunsVcjbjmQB63O/k6/c1mULb62l5dcrGGUnwJyljmzXKpHCIIgAECt1+PNC5nou+4TfH7jMkydbEyIYML/xEfhQ7HPYZb7DofHyL91Q7a/dOBEEeqbmKsQfT2VmDAqtNtzYWIy6lGSvZ8xRubgCv9+Y3nLiU2z1ohLtVrGmPGBCohFZO45V05SGV4ePBqFS57D68MT4SKzbAP2ZnMDHjy4FeN+/BZnqsutnmdPJVZ4wCd1LSBiP6zZev0rtN3ayUtefHB3UeCJuXH48q2p+OLNKXj5iREI9GE+QEgQBEFYH9lEJAiiRzKYTPgk6zz6rV+FT7LPw2Bi2oyj8Zb4BAZQDaz3lbpHwnPMO93W1ugOe6pCLMneD5pm3uwU3OnkcvZFJNLiyDJ+Sif8b+xkFCx+Bk8OGASRhX9WfizKQ8ymNXj2+H7UdfScOX7dyTF8Blxin2IPNOlRd/RpGDvq+Uir26WMDL5vq6N7NTRrcMAOqxEDI5MgkTH/vxVn74PJZB8bpA4SERL8mBceT1VpoDWSCgWCIDpH0zQ237yOARs/xT/OZ6DdwHwI8HnRRYwWsc8KEzl4wztlNSgOC/VdodMbsWkflyrEKEhtXIVYdesUtOrfjoC4V0jMFIjF3ftzZo4TlRqwfYwkB1k+6683c5HJ8Y9h41G45Fn8adAoOEgkFt3nZHUZRu34GgsObfvN3FLi/uQ+g+GR8E9OsfXHX4ausaDbcyIIgiB6D7KJSBBEj5NWXoTBWz7DcycOoJHDEPcFVC4eFOWzxlESJbxT10Ik7f4vnVsO5kGnZ6lC7O+NgZE+3Z4LG7YqGIoSIyR2Cm/5cMFW6eKlECHWU8ZbPj1RsLMrvkieiZwFT2NBX8s2u400jVXZFxCx7hO8f+U0maPCgfuIv0LuM4w1zthehbr050DbyeYTE7FYhEUcZiNuOpALnd6+/n8lUgf0iZ7AGNPRUoPakgu85dRVSYHMn6FqA41zNeyf3QRB9E5XVdVI3vU9FhzejrK2Ftb4sVQ5nhNdYr8xJYZPyqeQOHbfrPM7Dp4ogqqJ+VnUx0OJiaNtW4WIHtrKVEQB4wIUvOXTE3kqlHh31ATcWvwsnokdBonIsqXFzbdyMGDDp3jl9BG06pirRwnAOWopHCPmsMbRBjXqji6DSU8OYhIEQRDWQTYRCYLoMYpbmjDv4Bak7v4B2Q11nK6Jhgqvi09yivUc92/I3Lq/JWd9Uwf2ZrDPuFoigCrEppp8NNV03iYWAPz7jobC0YO3nNjUa4y4qmIeOJ8U6GBxBR3xS5HuXtg48UGce/BJjPcPtugerXodXj59BPGb1+JQmf3Mf7MFSiSFd+pqiBSerLGaikw0Xf6Al7y6W+rIEPh5MVfs1TdpcPBEEW85WUsYp5am7Iu8QsGlyjudtDQlCOJX6jVqrMzchyFbv0AGx9nJPmjHB+Kj4NK10n34q1D4j+p6oix0eiM27mevQlw03fZViLqOFlTkM8+Ld/YMhUdADG85sTGaaGSwbCIO8ZbDTU7mnluDv6MzPhk3FTkLnsbcsAEW3UNvMuG9K6cRueFTrMvPIvMSGVAUBc+x70HqHskaq28qQP3xP5GfT4IgCMIqyCYiQRB2T63X4x/n0hG1cTW2FbJ/Kb/DGVp8KjkMOcVemeIc/RicImZ3MVNutnKoQozt54WBkbadhQgAxVnss7hC44R1OjmzogNsX6WSSCtTqxvuE4D02Uuxc8p8RLqxb3DdT15TPSbvWY+5BzajuIW5tVZvJnH0h3fyKoBif8xrvvw/dFQc5yWv7iSRiLBwGvvi1cb99leN6B08BEpXf8aY8htHodext2kWgiAnCfq5Mbe9O1bRQRa9CIIAfjWiYPX1i53OPfw1MUz4SHwUXhR7ZbMydCpc4pZbIVt2B08WQ9VoH1WIpTmHYDIyt4oNjZ/R7WMezHG1XodGLfP3KDL33Pr6uXli25SHcGLOYxjpE2jRParUbXj46I8Y/+O3uKKqtnqOPYVIqoRP6megpMyH5wCgvXAn2vLW8ZIXQRAE0bORTUSCIOwWTdPYWJCNyA2f4s2Lx6ExGsy5Gu+L0xFCsbdBknkPgsfIv3cpV64amjXYy2EW4iMzY2z+hd1kMqA4ax9jjFTuhID+43nLiYt0ltPJUhEwxp8sLnQHiqIwKywSWfOXY9W4qfBWWNYaeEdRHqI2rsbr5zPQwTIHqbdyCBwLt6Evc4ikUXfsORjUNTxk1b0mjAqFryfz7ylVYwcOnyrmLSdroCgRQuOmM8YY9B0oz03jLaeuSmZZwC1tNaCoxZzPdIIgeiJzRxTc6/eiCxgpqmKNk7iEwGv8f3h5rtbpjdi0/wZr3IKpA2xehQiurUzjpvGSC1fpZO65TY3x74PTcx/H5kkPItzF3aJ7nKguw9CtX2Bl5j40aEhngvuRukXAa9x/OMXWn34Nuvqcbs+JIAiC6Nls/2TaRWq1GuXl5bhx4wYKC9kX3k0m5lNpBEHYhyuqaiTu/A6LjuxAeTv7RuCv/U50DZNE7AvJIrkbfFLWgBLLLczUPFsO5kGrY66SienrhYEDbF+FWFN0Dpo2FWNMn+hJkEiFM3NEb6JxvJJ5EWqYjwLOMrv/eBQ0qViMlbHDcHPJs/jLkDFQiCVm30NjNOCfFzIRvXENdhTmkqql+3Ad+Awc+jDP0wMAk0YF1bFn7X4+olQiwoKpHKoR9+VCb7Cv50Eu86bsqaUpl2pvtplWBEH0XJaMKLjXeKoUz4gvsweKZPBOWQORzMWyRM10+FQx6hpY5nK7O2DSGNtXIbY2lEJVfpUxxid0OBxZKuX5xjb33F8pRn+WaniiayiKwkMR0chZ+DQ+GD0J7nLzvwuaaBqrr19E/w2rsPb6RRjJOt5vOIbPgEvsU+yBRi1qjy6HSdfGR1oEQRBED2WXq6SlpaXYsGEDXn/9dbz88st4++238cknn2DXrl33jb906RL++te/4vnnn8eXX37Je74EQVhPvUaNpzP2YujWL3C8qtSieyzzBl6VnOcU65X0ESTOQRa9jrkaWzScZiE+PDPa5lWI4LhgzWWWF58u1WrRomNpcUROJ/PGRSbHv0amIH/xSiztHw9LflcXtzZh7sEtmLJ3PXIbmTe1exuKEsEr6UNInPqwxmqqTveI+YiTxoTB24P5z3BtgxpHTttXNaKLZwg8A+MYY2qKzkHdYh8VpUO85XBhOaxxjEM1CUEQPYtar8drFowouFeKuwO+cjjFKdYj4Z+QezG/t1qL3mDCxn3s/08Lp0VBJrX9vL7iaxxGFgjsOb9abcCNRuYOFclBDoL4HtUbyMUSvDhwJG4teRYvDRwFmcj839f1mg48nbkPI7Z9iVPVZd2Spz1zH/FXyH2Hs8YZWopQf/IVcuiSIAiCsJhdbSI2Nzfjo48+wrvvvosTJ06gtraW03WDBg2Cp6cnjEYjrly5gpwcUspPEPbGRNP4OvcKIjd8irU5lzjPQ7lXmLMbdqVOwt/0u0DR7BU3roNegLJPioUZm28rpypETwyO8uEtp87otW0ozz3GGOPoFgivPoN4y4kLtlam4NBmj7C+Pk6u+DZ1Ni7OewopgZadvj9UVoi4zWvx8qkjaNVprZ6jvRLL3eA94TOAQzV1T5iPeLsaMYo1bsPeGzD0uGpEGiUsLaaFQiKiMC6AuTLhQo0WrSyHPgiC6Dn2lhQgZtMavGH2iILbvBRKfD5uMr5XnoBY38warwyfBeeoRyzM1nyHTxWjtoH5cISXuwMmC6AKkaZp1rnnYokCfQak8pYTF+ksVYggrUxtwl3ugH+PnoDcRSuwqG+MRfe4pKrGmB3fYOnRH1HV3mr1HO0VJZLCO+VTiBTs8+bbb5H5iARBEITl7GYTsby8HO+99x7y8vLu/reAgAAkJycjICCA8VqRSIQlS5bcPXF26NChbs+XIAjrya6vReKP3+KJY7tRb8FcBKVEin+NSMb1BU9jRNGnMLazz0dRBIyB25A/WJix+ZpaNNidzl6FuEQAsxABoOzGURgNzG1BQ+NnCCLXe7G1OApxliDMxfzWmoR1DPb2x5GZD2PvtIWIdGP/MvxrBpMJ7189jcgNn2J9fhY5bfsTuVccPEa+xiGyZ8xHnDwmFF5uzIuENfVqHD1bwltO1hAcPQkiEfP7U9G1PXbz+55tIddAAyeqSEtTgujpytta8OCBLZixbyOKW5vMvl5MUXghbgTyF6/EXPVh6GovsF4jcQmD19h3eXtOvV2FyG0WohCqEFVlV9DeVMEYEzQgBVK5I285ccF2WFAuppDgJ5wxC71NmIs71k+ci7Nzn0CCb6BF9/g+PwuRGz7FB1fPwEBanAIAJI7+8E76CODQ04XMRyQIgiAsZRebiFqtFmvWrEFT0+0vFYGBgfjDH/6Av/71r5g3bx48PDxY7+Hr64vo6GgAQEFBAWpq7HuBjCB6g3a9Dq+cPoLBWz/HCQvblyzuF4u8RSvxl6FjobvxFTrKjrJeI1b6wjvpE1AWtFyx1NZD+axViFERnhgigCpEcGxlGho3nZdcuCpvM+BmM2lxJHQURWFaSD9cm78c7yWkwkkqM/seVeo2LDn6IybtWYebzQ3dkqe9cY56BMqwmaxxt+cjPmfX8xFlUjHmc5iNuGHvDRiN9rMAJVe6IaDfeMaYFlUhGqvZF6qFYHyAA+tyF5eqEoIg7JPBZMJ/r55B1MbV2F5kWevSCUFhuDZ/OT4cOxnympNoubaa9RpKLIdP6lqIZM4WvaYljpwuRk09cxWip5sCU8aG8ZYTkyIuz/kCa2WqNdI4VcV8wHGUnwIOErtYAuvRRvgG4uQDj+Ob5FnwdTB/I7pVr8MfTh3G8G1f4HxtZbfkaG8cghLhOug59kAyH5EgCIKwkF08QR06dAiNjY0AgH79+uHll19GRESE2fcZNmzY3X++t6KRIAjh2VWUh+iNa/DeldMWnTIc4uWHE3Mew7oJDyDIyQWa2otoPP82+4WUGN4pn0Ks9LYscQs0t2qxO/0ma9zDM4QxC7G9qRK1JcynvL36DIKzB/scNj6xVSECQBJpZSoYMrEYLw8ejbxFK7GkX6xF9zhSXoTYTWvw1sXj0FrQGq0noSgKXuPeg8QlhDVWU3UKTZc/5CWv7jJ1XBg8XJmrDarq2pF21rLZurbCZdGWyyEPIfBQiDHIm7nNbnpFh0XtywmCELYz1eUYtvUL/PHUYbTpdWZfH+bshh1THsKhGUsQ7eENQ2s5VBkvcLrWY9QbkHlGW5C1ZQwcZyEumCqMWYgGvQZlOcydmxROXvANG8FbTlycrdZAbWD+vCDP+cIhoig8OmAg8hatxB8GJkAiMn9p8oqqBiO3fYlnj+9Hs5Z5A7k3cBvyB8j9RrLGkfmIBEEQhCXsYhPx7NmzwE9tSRcvXgypVGrRfcLDw+/+c0mJfbWwIojeorS1GXP2b8LsA5tR2sY+z+TXvBVKfJ44A+cefBJj/G9vYhm1TahLewag2TcR3Ie9AgWHh29r2n4kHxotSxViuAeGxvjylhOTYg4zt4R2OhkA0iuYT4ArJRSG+5IWR0IT4OiMHyY8gONzHsVAT/P/DGiNRvz9XDoGbf4MGZW9+7NfJHOGd8paQMRe3dl8+UN0VJzgJa/uIJOKMX9Kz6tG9O83FjIHV8aYkuwDMBmZq66Fgm0Gbb3GhOx68zcYCIIQpkZtB57O2IvRO77G1XrzOwPdGVGQs3AF5oQNAEVRoI061KatgEnL/r3BMeIBOEUutjB7y6SdLUW1qp0xxtNNganjhFGFWJmfCb2WuUopNG4aRDx2bOGC09xzMg9RcFzlCvxn9ERcfWgZUgPN/zNAA1iVfQFRG1djy62cXr0xRokk8E7+BCIFe6e22/MR1/OSF0EQBNEzCH4TUaVS3a1CjImJgY+P5a383N3d7/5zWxsp3ycIIdEbjXj/ymlEbVyNncX5Zl8vEYnwYvxI5C9+Bk9FD4b4p9OMNE1DlflHGNvKWe/hEJQMl/inLcrfUq3tOuxKY69CFMosRJqmWatcRGIZgqMm8pYTF2q9CadZWhyN8VdALrb9zzFxf2P9g3Fx3lNYNW4q3OXmb/bmNtUjaed3eOLYLqg6mDeUezK5Vyw8EjjOR0x/DgZ1LQ9ZdY9p48Ph7sJc6VZR24aM85a1y7YFsViKkJgpjDFadSOqbp3mLaeuSOKwoJtGWpoShN2jaRrf511D5PpPsTbnEixZ5r93RIFC8vN82Mbzb0NXd5n1eqlrX3iOfYfX52mjieY0C3H+FGHMQgSA4iwuIwuEdViQpmnWjiP93aQIdCJzz4Uq2sMbh2cuwdZJ8xDsxHxY6n6q1G2Yf2jb7dmqLebPVu0pfp6PyK7+9D/IfESCIAiCM8FvIra0tNz9565sIAKAwdC7W5kRhFCdrCrDkK2f4+XTR6A2mF89MalPOK7NX4YPxkyC2682F1qvf4mOkoOs9xAr/eCV9D9QFL9vizuPFkCtYX5vigzzwDCBVCE2VGajtYG5miuwfyJkDi685cTF6WoNdCzFRlwWswnbEotEWBk7DPmLnsHy6CGs89Tu5+vcqxiw8VN8m3u1155Wdo5aCmUY+wKgqaMOqmPP2u18RLlMjIcms1cjrtt7A0aT/fxe6EktTaPcpfBVMi+cc6kuIQhCuHIbVUjZ9T2Wpu1Encb8QzyDvfxwfM6jd0cU3Ku9+ABasj9nvQclVsA7dQ1EUvPnr3VFxvkyVNQyH172cFVg6rhwxhi+aNrqUXXzFGOMm28k3Hz78ZYTF4UtBpS1MX+fIq1MhY+iKDwYEYUbC1fgH0PHQS42f2N9X+lNRG9ajXcvn4TeaJ/Pr13lEJQE14Ec5yOmPU3mIxIEQRCcCH4TUalU3v1nlUrVpXtVVFTc/ed7qxIJgrCNJq0Gy9L3YOyP3yC7oc7s6wMcnbF10jwcmL4YUe6/nWGorbuKhnP/Yr8RJYZ3yiqIObT+sKb2Dj12HC1gjVsikFmIAFDEYWFamK1MyTzEnsTLQYk1idNxft5TGOUbZPb19ZoOPHZsF1J2fY/cxq49W9ij3jQfcXpiOFydmasRy6tbcfyC/VQjegTEwNkzlDGmIj8Duo4WxhghoCiKtaVpdr0OtWpyEJAg7I3GYMA/zqUjfvNapFvQTtxFJseqcVNx/sEnMdY/+Dc/rm8tQ33mHzndy2PMvyDzYD9UYk0mE40Ne9mrEB+aEgm5TBhViCXXD4CmmTdewgT4nM9l7jlpZWo/lFIpXh+RhBsLV2BOWKTZ13cYDPjzmTQM2fo5TlXbz/OdNbkN/SO3+YjNhWQ+IkEQBMGJ4DcRPT09IfmpXcmtW7eg01k+F+X06Z9bO907H5EgCP7tKMxF9MbV+PwGe/uhXxNRFF6MH4kbC1fgwYio+26wmXQtqEtbCZjYKxvdhr7E+xxEANh97Cba1Mz59Q12w4g4P95yYmI06FB6nbmqU650h3/EKN5y4oJLi6MYDxl8laTFkb0Z6u2PEw88hm9TZsHHwfzqgvTKEgzc/BleO5cOTS/rViCSucA7ZU2Pn4+okEvw0GT2Bah1e27AZCfViBRFsR7WMBl1KL1xmLecuoJLFXhGBXM7aoIghOVEVSkGb/kMb148Dr3J/Lmzd1qXrowddndEwb1okx51aSth0nGYg9jvITj3X2B2Dl118nIFSquYD3O4OcsxTSBViOBQxU5RYgTHMrfUtgW2ueeuMhEGezMfKCKEJ8zFHTumzMeB6YvR19X8w77ZDXUYs+MbLM/Yi0Zt7+pqQOYjEgRBENYm+E1EqVSK+Ph44Kc5hps2bbLoPgUFBXc3Ee+9J0EQ/Kpqb8WDB7Zg7sEtqFKb3zpjhE8ALjz4JD4YMwkusvt/GaRpGqrjL8PQyn7qWRGYCNeBz5idR1dptAZsP8w++3HxdOFUIVbdPAFdB/NiTUjsVIjEUt5y4iKvSY9qNfOpanI62X6JKApLIwcid9EKLIseYvb1OpMRb1w8jvjNa5FpQaWEPZN7xcEj4R8cIu17PuKMxAi4OjFvlpZWteD4RfbZuUIRGjeNNcZeWpqO9lNAxvKNhLQ0JQj70KLT4pnM/Rj347fIbao3+/p+rh44PHMJ1k14AH5Kp07jGi+8x20Oolt/eI7m0JXEymiaxvo97LPGHpzUHwq5MA6xNdUUoLE6lzHGL2IUHJy8eMuJi1adCRdqtIwx4wIUkIiE8X2KMN/k4AhkzV+Ovw8dB+l9DhWw+SznEqI2rMbWW71r/h+Zj0gQBEFYk+A3EQFgwoQJEP30sHDmzBls27YNej33uWnXr1/HmjVr7pboT5o0CQqFgvU6giCsh6ZpfJFzGVEbV2N7EfMX1Ptxlcmxevw0nHrgcQz29meMbb3xHdRFe1nvKVb6wtsGcxABYE/GLTS3MVdWhwa6YNSgAN5yYmOvrUy5tDgirUztn7vcAWsTp+PEnMcQ6/Hb9sZsCpobkLjzO6zM3IcWHfNiVE/iHPUolGHTWePseT6ig0KCByexVyOu35tjN9WIjq7+8AkdzhijKruC1gbht/FSSkUY6cf8XH6iqgM6o3382hBEb7WnOB8xG9fg0+sXzL5WJhLjn8PG49r85ZgQxFyZpy47hpZrq1nvSUkcfpqDqGSNtbYzV6tQWM588M7ZUYYZSRG85cSmOIv9u5MQW5ker+yAgeXjgcw9t38KiQRvjEjCtfnLkRjA3o7/12o62vHQoW2Ye2AzqtpbuyVHIbo9H/FZ9kAyH5EgCIJgYRebiCEhIUhNTb3772lpaXj33Xdx+fJlxs3EyspKrFq1Cp9++ik0mtttkAIDAzFx4kRe8iYI4rabzQ1I3fUDfpexB80WLM4/3D8OeYtW4umYofdtaXQvrSobDWdeZ78pJYJX0scQO/B/mlarM2LrwTzWuEXToyESyKlZrboRVQXHGWNcvSPg7sfvvBku2DYRPRQixHuxt3Qk7MMY/z64NO93eCchBQ4S80/3r75+ETEb12BvCfu80p7g9nzEf0Pi3LPnI85MjoCzI/Of8+KKFpy8XMEYIyRcDm1wWRQWAra5iO16GhdqSUtTghCiWnU7Fh3ejpn7N6G83fxZrBOCwpC9YDleG54IBcvntkFdA1XGi5zu6znmbcjc+5udT1dxrUKcO6EflAphdO8wmYysnxdSuRMC+ifylhNXbJXqIgoYH0A2EXuKAe5eODbrEXydPAueCvN/XXcU5SF64xp8deNKr5kD6Db0Jch9R7DG3Z6P+Ode8/NCEARBmMcuNhEBYNasWUhISLj771VVVfjiiy/wl7/8BcXFxQCA5uZmHDlyBFu2bMG7776L//u//0NOzs8P8F5eXnjmmWcglQrjYZ0gejqDyYT3Lp9C3Ka1OFZZbPb1/V09cHTmw/g+dQ58GVoa3WHStaIu7WnAxD471W3w7+EQMNrsnKzhwPFCNLYwb6YG+Tlj3NAg3nJiU3r9EEwm5plxofEzBNN69Y5GjRFXVMw/10mBDhAJLG+ia6RiMV4ZPAbXFzyNKX3MP+Vf3t6CGfs2YsmRHajraO+WHIVEJHOBd+pq7vMRK0/ykpc1KRVSzJ3Ivpi8Ye8Nu1k86TMgFWIJcwVf8bU9dvH/w6VKJI1DVTlBEPyhaRrf5V1F1MbV2HjzutnX+ymdsGHCAzg0Ywn6uXmyv57JCFX68zBp2NukOvVfCKd+88zOyRouXK9BfkkjY4yjgxSzU/rxlhObmqJz0LSpGGP6RE+ERCqsbk4mmkYGyybiIC85PBRi3nIiuh9FUXhswEDkLlyJxyIHmn19k06DJ9N3Y9KedShsYf6z2hNQIgm8U7jOR/wRbQWbecmLIAiCsC92s4koEomwZMkSzJkzB5J7Tiiq1Wq0td0uua+trcWOHTuQnp6O0tLSXyyaxMXF4eWXX4arq6tN8ieI3uZyXRVGbPsSr5w5Co2RefPp1+RiMd4ckYRrC5YjJSiM0zU0TaP+xJ9haGHfrFQEjIHroOfNysladHojNh/gUIU4LQpigVQhglM1C4WQWPYZXXzLrOwAW3dC0sq05wpzcce+6YuwaeJcxtlKnVlfkI3ojWuwoSDbLjZiukLuFc95PqIq/XkYO8yfdWVrs1P6wknJfJDsVlkTzl6r4i2nrpDKHRE0IIUxpr2pAqqyK7zlZKlgZykiXJl/bdLJJiJBCEZxSxOm7F2PR9N2oUFr3p9NCsAzscNwY+EKLOwXy/kAWvPVVdBwOMQidesPj9FvmpWTtXCtQpyd2heOLJ9HfOIyQ1eIIwuy6nWo15gYY0gr057Ly0GJr1Nm4disRxDJ4SDCrx0pL0LcprX48OpZGE3Mv4/sncQxAF6J/+MU23Dqb9A19o6OLARBEAR3drOJiJ82EidOnIi//OUvGD16NKeKwpCQECxbtgxPP/00nJzMX0AkCMI8HQY9Xjl9BMO3fYnLqmqzr08NDEP2gqfxt6HjIBdzb0XYlrce7YU7WeNEDt7wSvoYlMg2J1IPnyqGqol5scXf2xEnnhYIAAEAAElEQVTJI/rwlhObFlUx6iuyGGN8w0dC6eLDW05csS06SyhgHGlx1KNRFIX5fWOQu3AFVsYMg7lb8yqNGouP7MCs/ZtQ3mZ+mzZ7wnU+olFdA1Xm7+1uY9XRQYoHJrBXI67bk2M3/29cFnW5zLMVAraWpsWtBhS3cJ+JThCE9RlNJnx49SxiNq3BobJCs68f5OWLsw8+iU/GTYWbnHtVm6b6HJouvc8aR4nl8E75FCKJbZ7trubWIecW8yEbB7mE02cRX/TadpTnpjHGOLoFwrvPIN5y4orL3HO2zxbC/iUFhuLq/GV4Y3gi5GLzvuOrDXr8/tQhjNnxDa431HZbjkKg7JPMaT4ibehA3bGVMBnI4S2CIAjiZ+YPCxIAX19fLFmyBHPnzsXNmzdx8+ZNNDU1ob29HTKZDE5OTggMDERUVBR8fIS3qE0QPdXxylI8mb4bBc0NZl/rLlfgv6Mn4dHIeLNbYuoabqDhNJcKGgreSR9BorTN+4LBYMKm/bmscQunRkEsFs4ZDy4ztcLihHc62WCikVnJPENrqI8czjLh/FwT3cdVrsCq8VOxNDIeyzP24mp9jVnX7ykpQOamNfj3qAl4Kmpwj2yBe2c+YqUqG4bWEsbYjrI0tGR/Ade43/GWnzXMSe2LbYfzoO7ovEI+v7gRF67XYHisH6+5WcI3bAQUTl6MbejKcg5hyOSXBdeG7teSghzwRQ7zRv2x8g48Hi2c6h2C6E1yGurwxLHdOFtr/uxYhViCfw4fjz/EJ0Bq5iK/UdOIumPPADR7pZDHqDcg87DdfO51e9mrEGcmR8CFZUYvn8pyj8JoYH5eDo2bDooS3vMy2zxEP6UYA9zJZ0ZvIBdL8Pdh47GwbwxWHt+PI+VFZl1/trYCg7d8jr8NHYc/Dx4DmZnvU/bCbehL0FSfg7bmHGOcvuEGGs++Bc8x/+ItN4IgCELYhPckaAYHBwfExcXhgQcewOOPP45nn30Wy5Ytw+LFi5GYmEg2EAmCJ+16HV44cRCJO7+1aAPxoYgo5CxcgccGDDR7A9GkV6MubSVoI/PcOwBwHfQ8HALHmZ2ftRw9W4KaejVjjI+HEqmjQnjLiQ1Nm1CcxVzFIpE6sLbUs4UrdVo065gXnJJJi6NeZ6RvIC7MewrvJqRCYUa1MwC06LRYnrEXqbu+x00L3uvsgTnzERvP/x+0dVd5yctanJQyzOEwh8peqhFFIjFC45irR/XaNlTmZ/KWk6WG+sjhLGV+BmBbMCYIwvoMJhPevnQCg7d8btEGYmJACK7NX4ZXBo8xewORpmmoMv8IYzt7m2ll2Ew4RS42Oz9ryS5Q4VpeHWOMXCbGgxzm8/KJWytT9i4FfKtVG5Bdr2OMSQp0ENy8dqJ79XPzxKEZS/Btyix4yM37nqc3mfDa+QwM3fo5ztWY/15nD+7OR5S7s8a23vgW7UX7eMmLIAiCED673kQkCML2MipLEL/5M3yUdQ7mLrcGODrjxynzsXnSPIvmlQFAw5nXoG9i79kv9xsJtyG/t+g1rMFoNGHjPvYqxPlTB0AqEc5bc23JJaibmdvSBkWlQiIT3mZcGocWR0mBSl5yIYRFIhLhT4NH49r8ZRjvH2z29emVJYjbtBYfXD3TI2eoyL3i4THir+yBJv3tdke6Vj7SspoHJvSDg5x5A/nGrXpcybWPtlZcWpqyHQYRAqmIwliW9tLnajRo0/e8P3MEIVTZ9bUYtf0r/OXsMehMRrOudZHJsTZxOtJmPYJ+FswrA4DW61+ho/QQa5zEORhe49616YYRl1mI08eHw81FOFXh7c1VqC2+wBjjFTQQzh7mPyt1Ny6HSshhwd6JoigsjRyInIVPY35EtNnXZzfUYdSOr/HK6SPQGDrvXGGvbs9H/JBTbP3xl2FoLe/2nAiCIAjhE85KNUEQdqVNr8Nzxw8gaed3KGxpNPv6p6OHImfB05gdFmlxDu23dqEtbwNrnEjhAe/kT0CJbNfBOeN8GSpr2xhjPN0UmDwmlLecuOB2Oll4rUzBYXGhj5MEEa522dWbsJJ+bp44Nnsp1oyfBmepea3FNEYD/nDqMJJ2ftcjqxKdY56AQ/BE1jhDSwnqT/7ZLqr27nBxkmNmcgRr3Po9N3jJp6vcfPrC3Y+5fV/VzVPQtDHP6RICtgVfvQk4ydKmmiCIrtMbjfjXxeMYsvVzXKhjrwL8tdmh/ZGz4Gksix5icftvrSoLDec4tNKjJPBOXgWRzMWi17GG3KIGXMxhbpMulYgwb7Ll33u6Q0nWPoDlGKi9PufLRMAoP+Fs2BL881U6YdOkB7FjykPwN/PAsomm8d6V0xiy9XOcr63sthxtRRmcCpdY9pEEJl0z6o49A9pEZlITBEH0dmQTkSAIsx2rKEb8prX4JPu82df2d/VAxuylWJ04Da5yy7/Y6VtKoDrxCqdYr8T/QeLob/FrdZXJRGPDPvbF6IcmD4BMKpz5CwZ9B8puHGaMcXDxhU/IMN5y4qqyzYD8JuYvO8lBpMURAYgoCstjhiJn4QpMD2Fvc/lrJ6rLEL95LT66dg4mO9pIY0NRFLzG/xdiJftcwPZbO9FWsJmXvKzlwYn9IZcxv99ey69DVj5zazqhYFvkpWkjSq4f4C0fSyUGOIDtXflYBXNbcIIguuZafQ1Gbv8KfzuXDr2Z1fa+Do7YMulB7JgyH4FOlm/qmXRtqEtbAZiY21UCgPvwVyH3GWzxa1kDlyrEKePC4OkmnMo4mqZRfI157rlILEVw9CTecuJKa6RZD5Qk+CmglJLlLgKYEzYAOQtX4HdR5r9P3GhUIWH7V/jLmTRojT2rKtF9+J8h84pnjdPWXkTTxf/wkhNBEAQhXDZ5qjIYDGhqarLpX3o9OUlDEOZq1WmxMnMfUnZ9j6LWJrOulYhE+MuQMbg6fznGB3Rt5h9t0t8+Eadnb6HnEr8Syj7JXXq9rjpxqRylVcy5ujnLMXVcGG85cVGemw6DjnmxNjR2GkQi4Wx83nGMQ4ujpEDhLOQQthfk5ILdUxdg/YQH4KUwr81th8GAF04eRLKFldlCJVa4wzv5E4Bif1xsOPU36BrZW0sLhZuLAtMT2asR13FYGBaCkJgpoCjm92IuleW25ukgRrwXc1VwRoWmR23YE4RQ6I1GvHEhE8O2foHLKuZW9vfzxIBByFm4AvMiort0SIumadSffBWGlmLWWIegZLjEsVfTdKebpY04e425WlMipjB/CnPFON8aqnLQUl/EGBPQbzxkDrar8OzMhRoN2g3MnwNJpJUpcQ83uQKfJc3A0ZkPI9yFfR7gvUw0jbcvn8TQLV/gQg+qSqTEcngnrwIldWSNbb66Ch3lwp+vTRAEQXQfm/RxKyoqwocfcuvB3V1WrFiB2NhYm+ZAEPbkaHkRnkzfjZLWZrOvHertjy+TZmCgF3tFCxeNF96Dru4Ka5zcZwjch71slde0lMlEY/1e9irEeZMjoWCZ0cU3LjO0QuOn85KLudJZ5iE6SCiMJC2OiF+hKAqL+sViQlAYXjx5COsLss26PrOqFHGb1uK9UalYETPM4hZuQqLwT4Db4N+j6RLzCWTa0IG6YyvhP2sXRBL7WLibN6k/dh+7Cb2h80qbyzdqkXOrHtERls3z4ovCyRP+fUejsuB4pzGN1bloqimAm6/5Fbd8Sg5ywFVV59VHdR1G5DToEOsp5zUvgujJrqiq8fixXbiiYm7JeT/hLu74LHE6UoOscxiurWAL2m/tYI0TK33hlfghKA4HXboTl+f8iaND4eMhrDncxdd2s8aECbSVKZfDgsnksCBxHylBYciavxz/OJ+OD66dNetQ0vXGOiRs/wqvDhmDvw8dD5lYeAdpzSV1DYPnmHegSn+OJZJGXcYLCHzgEMRKb56yIwiCIISE9HcgCIJRi06LpzP2YsLuH8zeQJSLxXg3IRVn5j5htQ3EjvIMtFxbzRpHyVzglbwKlEhqlde11JlrlSgqZ/55c3GSYQaHihg+dbTWoqbwDGOMh380XL2FlTcAaAwmnK5mbnE02k8Budj+N3iI7uHt4Ih1Ex7A7qkLEOjobNa1aoMezx4/gNRd36Ooh1Qlug56HnK/BNY4fcMNNJ59i5ecrMHTzQHTxoezxnFpUycEoXHsi73FWcyt64SAS5X4MZaDIgRBcKMzGvHauXQM3/al2RuIFIDfx49E1vzlVttA1DXdRMOpv3J6da+kjyF2sO0Bj+KKZpy8VMEYIxJRWDBVWFWIRqMeJdkHGWPkSjf49R3DW07mYPsMiHCVoo+zbb8DEsKllErx/uiJOP3A44j1MG9DzEjTeOviCQzb+gUuWTAvVoic+j4Ap/4LWONMHXWoy3gRNG1em2uCIAiiZyCbiARBdOpIeSHiNq3F2pxLZl+b4BuIKw8tw58Gj4ZEZJ23GoO6FnXpL3CK9Rr7HqTOfazyupaiaRrr97CfTn5gQn84KIRVhViSfYD1CwLbDC5bOVOthcbIfKo0mbQ4IjiYEdofOQtX4OnooWZfm15ZgrhNa7E6+4Ldt16kRGJ4J38MkZy9/VPrjW/RXrSPl7ys4aHJkZBKmD+jzmdXI7+4gbecLBXQfzykcifGmJLsfTCZjLzlZIkYDxl8HJhP96dzqEIhCILZFVU1hm/7Am9cPA6DmbMP+7t64MQDj+G/YyZBKbXOZo3J0IG6tBWgDex/vl0HvwCHgNFWed2u2MChCjF1ZDD8vZnfm/lWdfMEdB3MoymCY6ZALBbeRlxRix4lrcyz6UgVIsHFCN9AXJz3O7wxPBFSM9crshpqMXL7V3jtXDp0RmE/V3HhMepNSFzZDwdrKjLQcm0NLzkRBEEQwmKTVevAwEA89xxbufwvNTc34/vvvwdN0wgPD8e4ceM4XWc0GnH06FFUVd0+JTRt2jR4e3sjKCjIotwJojdo1+vwypmjWJV9wexrFWIJ/jUyGS/EjYDYSpuHAEDTJqgyXoRJo2KNdRqwBI7htt/gupBdjYIS5kokJ6UUs5P78pYTFzRNo+gqc4sjSiRBcMxk3nIyR1o58xxHkDkphBlcZHKsTpyGBX2j8cSx3WbNg2036LHy+H5sK8zFl8kzEOLs1q25dieJoz+8Ej9A7aHHWGPrj78MuVc8JM7Cf9by9lBi4uhQ7MssZIxbv/cG/vmMMCsy7pBIFegTPQmFl7d3GtPRWofa4vPwC2evLLUViqKQGOiALTfbOo25ptJB1WGEF8tmI0EQv2UwmfDO5ZN4/UKm2ZuHIorCH+IT8MaIRDhIrLvB1Hj2Tegb2Dfl5H4j4Tb4Rau+tiXKqluRcaGMMUZEAQunRfGWE1dcZuQKtpUph0p0cliQ4EomFuPvw8ZjTlgkHkvbhUtmzIM1mEx44+Jx7CzOx7cps6zWeckWRFIlfFJWo3LXTMCoZYxtvPAe5P4jofAx/5AlQRAEYb9ssomoVCoxYIB5LT1WrVoFmqYRFRWFFStWQGxG//Hhw4fju+++w8WLF3HlyhW8/PLLkMlkFmROED3fyaoyPJq2E7csaMM3xq8Pvkqeif5u1m8t1HJtNTQV7MO8pe6R8Ej4p9Vf31w0TWMdh9PJs1P6wVEprFO+TTX5aK67yRgT0HcMFI4evOXEFU3TrBUqUe5S+CmFVflJCF9SYCiuLViOV04fxafXzTtgcbSiCHGb1uI/oyfiqajBoOx0VqIyeAJcYp9CS/YXjHEmXTPqjj0Dvxlbbd5SmosFUwfg4MkiGBkqmE9fqcStsiZE9BH2RnBY/AzGTUQAKLq2R9CbiPhpAZhpE5EGkFHRgQf7Cqu6hyCELrdRhaVpO3G+ttLsawe4eeLr5FlI8LP+AZH2or1ovfEda5xI7g7v5I9BiWz/HLdx3w2wNRoYP7wPgvzMa4ve3bQdzajMZ/5O5eIVBnf/aN5yMgfb3HNnKYUhPmRmLmGeOE9fnJn7BN69cgpvXMiE3owDFlfrazBs25f4+9BxeHXwGEjtdFaizDMaHiP/wd5SmjZAlfYs/B84ALHcla/0CIIgCBuzi3am+fn5yMnJgVQqxaOPPmrWBiIASCQSPProowgJCUFlZSX27bOfNlsEwReNwYBXTh/BuB+/MXsD0UEiwYdjJiFj9tJu2UDU1F5E44V/s8ZRYjm8Uz6FSGL706dXcmtx41Y9Y4yDXII5E/rxlhNXxdeYqxAh4FamBU16VLYzt5Qhp5MJSzlJZVg1fiqOznwYIc7mfWlu1euwLGMvpuxdj7I28+bLCon78Fch84xjjdPWXkTTxf/yklNX+Xk5YkJCCGscl7Z1tubVZxAc3QIZY8pzj0KvbectJ0uM9ldAyvIt5RhpaUoQnJloGh9ePYvBWz43ewNRRFF4ZfBoXH5oWbdsIOpby6A6/jKnWK/EDyBxDLB6DuaqqmtD2tlS1rhFAqxCLL1+ECYTczvQ0PgZgjzw1KY34Xwt89zzcQEOkIqElzshfFKxGH8bOg4X5j2FQV6+Zl1rMJnw2vkMjNz+FbLqzZsvKyTOUUuhDJnCGmdoK0P9iVdA2/nIBoIgCII7u9hETE9PBwDExsbC2dmyk3xisRhTp04FAGRmZqK9XdiLJwTBp4t1VRi29Qu8d+U0zH0MHOcfjGvzl+OF+JFWbV96h1HbDFXaswDN/GUXADwSXofMPdLqOViCyyzEWSl94eIorKpok8mA4uz9jDFShTMC+o3nLSdzcFlUTgpS8pIL0XOlBIUha/5yLI8eYva1h8oKEbtpLb68cdkuv3jfPqyxCpTUkTW2+eon6Kg4zkteXbVwWhRELIuOxy+Wo7hC2BvAFEUhNG46Y4xRr0F57lHecrKEk1SEEb4KxpgTlR3Qm+zvzxBB8K2opREpu77H708dgsbI/jx9r2h3L5x+4HG8k5AKhcT61X+0SQ/VsWdA61pYY11in4IyeILVc7DExv25MLG8/4wdEojQQOFV6bC3MqUQEjuNp2zMc7JSAz1LgRg5LEh0VbynL87NfRL/HDYeEjPXNy6rqjF06xf4lwWzZoWAoih4jn8fYifmA2kAoC7ag7a8dbzkRRAEQdie4DcRTSYT8vLyAABeXl5duld4eDgAQKvVIjs72yr5EYQ90xuNeP18BhK2f4XrjXVmXauUSPHx2ClIn70UfV27p60lTdOoP/EKDG3M80YAQBk2HU4DlnRLHubKyq/DtXzmn0+5TIy5E/vzlhNX1bdOQ9vewBgTEjMZYomwNj/vYGtx5C4XYaCnMHMn7IuzTI41idNxaMYS9HFyMevaFp0WT6XvwbS9G1Dexr5wKjRS13B4jnmbQyQNVfoLMKrN+3yxhQAfJySPCGaN27BP+NWIofHMm4gAUHRtLy+5dAXbQnCbnsaFWua5PQTRm9E0jc9zLiF+82fIqCwx61oxReHVwWNwcd7vMMKXfTHZUo0X/g1t7SXWOJlXPNyHv9pteZijtl6NI6eKWeMWTxdeO9CW+hLUV2QxxviGDYejqzBnux2rYJ57TgEYH0g2EYmuk4rFeG14Is4/+CTiPX3MulZvMuFv59KRsP0rZNfXdluO3UUsd4N30icAxd4BruH0a9A15PKSF0EQBGFbgt9ErKurg0Zzu2VFfT1za0A29574v3mTed4XQfR01xtqkbD9K/zzQqbZp+SSAkKQtWA5no0bDlE3trppy1sPdRHbaVlA7BQEz7HvCabtznoOLe+mJ0bAzVl48zrYTycLt5Vpk9aIS3XMC8rjAx0gJi2OCCua2Ccc2QuexlNRg82+9kDZLcRuWoNvc6/aXVWiU9+5cOo3nzXO2FGLuswXQdPCP429cNoAsH2MZJ4vQ1l1K18pWcTZIxheQQMZY2qLz6O9uYq3nCyRxGEhmO3gCEH0VpXtrZi+byOWZexFm15n1rUx7t44M/cJ/F9CSrdUH97RUZ6BlmufssZRUid4J68CJRbGc/PmA7kwMMzQBYCEgf6ICBbeDF1Oz/lxwnzON9E063t+vJcMngr7nEdHCNMgLz+cf/Ap/H3oOIjNXGu4WFeFoVu/wDuXTtpdVaLCbzjchr7EGkcbtahLWwmTgTyPEQRB9HSC30S8t+1oaSn73AEmJSU/n8Bsamrq0r16GoPBgKqqKuTk5KC6uhomO3vIIbgzmkz49+VTGLLlC1xSVZt1rYNEgo/HTsHRWY8g3MW923IEAF1jHhpOv8YeSInhnbxKMEO9cwvrcSmHeQ6CVCLCvEnCq0LUaVpRnpfOGOPk0QeegfG85WSO45UasKzpIIWcTia6gYtMjs+TZmD/9EUIdDSv7XqzTovHju3CAwc2o0bd1m05dgeP0W9C4hrBGqcpz0BL1lpecuqKYH8XJA7rwxhjooGNdlGNyLYITKMkS9gzwkNdpAhzYd7ASGepSiGI3oamaWwoyEbspjXYX2reoVnRnerDh57CMJ/unTtoUNeiLv0FTrGeY9+B1DWsW/Phqr6pAwdOFLHGCbEKkaZNKM5irkIXSxUIikrlLSdzXK/XQaVhXqNIIa1MiW4gE4vxxogknH3wScR6eJt1rc5kxKtn0zDux2+Q39S1ogi+ucavhCJgLGucvimf27oNQRAEYdcEv4no5OR0959VKhVu3LB84ebSpZ9bpSgUzHNWeovy8nJ88MEHmDNnDhYvXoxnnnkGixYtwkMPPYTVq1eTzdYe5mZzAxJ3foc/nTkKnclo1rWj/YJw9aFl3V59CAAmQwfq0laCNmpYY92GvgyF79Buzccc6zjMQpw6LhyebsL7klt24zBMRubT6qFxMwRT8flrx1hOJ4spYBzZRCS60ZTgvshe8DQeH8BcBXY/O4vzEbNpDbbeyumW3LqDSOoI75RPAQ7VIY3n3+XUss7WFk2PYo1JO1uKylphb/gGR0+CSCxljCm+tlfwFbBs1Yi3mg0oadXzlg9BCFldRzvmH9qGxUd2oFHL/gx9r/6uHjj1wOP4v4QUyMXdV32InzazVOnPw6RRscY69V8Ap4g53ZqPObYczIPewLyRNTTGF5Fh3TPqoSvqSi9BzVKB3mdAKqQyYc4O5zT3PFCYuRM9w1Bvf1yY9xT+OmSs2VWJZ2oqMHDzZ/jftbMwCfzZ6w5KJIZX0kcQKdjHSrXlrUf7rV285EUQBEHYhuA3ET09PX+xkbhu3Tq0tprfRiorKwtnz569+++Bgd0328FeZGRkYNmyZdi1a9cvKj4BoKGhAZs3b8YTTzyBc+fO2SxHwjpMNI1Psy9g4ObPcLKafb7gvWQiMd5LSEXm7EfRz82z23K8V+OZN6BvzGONUwSMg+vAlbzkxEVBSSPOZTF/OZeIKTw0JZK3nMzBrcUR+6wtWzCaaByvZF5cGOIjh4tM8B97hJ1zkyvwVfIs7J22EAFmViXWazrw0KFtWHx4Oxo09tEWSO4ZA4+Rf2cPpA2oS3sGRm0zH2lZLDTQFWOHMD8jmkw0Nu0X9vwXmYMLAvsnMsa01BehoUrYm9ZcqkpIS1OCAHYV5SF201psLTT/wO0LcSNw+aFlGNmNsw/v1Xx1FTSVJ1jjpG794DHqTV5y4qKpRYN9mYWscUKsQgSAIjseWQAO7/W+DmJEezAfniGIrpKLJXhrZDJOz30C0e7sm2v30hgNePHkIaTs+h5FLY3dlqM1SZQ+8E76kFOs6sQr0LeYN3+XIAiCsB+CX00Vi8UYO/bnEvrGxkb8+9//Nqu16YkTJ/Dll1/ePW0tFouRkJDQLfnai4yMDLz++uvo6Lj9MB4bG4sXXngBb775JlasWIGQkBDgp5/vv/3tb8jJEfYiE9G5srZmTN6zDs8c3w+1wbzT+oO9/HBx3lN4efBoiEX8vF20F+1Fa+73rHEihSe8kj4ERQnnbWwDh1mIk8aEwcdDeKdk2xorUFd6mTHGO3gInNyFeQDjqkqLRi3zyfBkUoVI8GhaSD9kL1iOpf3Nb/+74eZ1xG5ag30lBd2Sm7U5Rz0KZchk1jhDWxnqT7wi+Oo3LgvAh08Xo6a+nTXOlrgsBhdf281LLpYa6qOAo5T5tD9bFTpB9GTNWg0eT9uF2Qc2o7bDvPekEGdXpM16BB+OnQyllJ/NF03NBTRdfJ81jhLL4Z3yKURS4Twzbz2UD62OuZNLfKQ3YvuZt7HAB4Neg7KcI4wxDs4+8AkdzltO5lB1GHGtnrlbSmKgg2C7pRA9z3CfAFyc9zv8efBos7s0ZVSWIH7zZ/gs55Lgn4kBwCEoCS7xK1jjaH0r6o49A5qlsxFBEARhn4Sz+s5g8uTJ8Pf3v/vv9fX1eO+99/D555/j0qVLv6lMpGkaVVVVyMjIwL/+9S9s2LABev3PmyczZ86Em5vwBp3zpbGxER988MHdB5a5c+fio48+wpw5czB27FjMnz8fa9euRWLi7RPser0er732GlpaWmycOWEOmqbxTe5VxG5aiyPl7LM77iWmKLw2bDzOzn0CsZ4+3Zbjr+lby6A6/jKnWK/EDyFR+nZ7TlwVlTfj5OUKxhiRiMKCKQN4y8kcbDNSIPDTyZxaHJE5KQTP3OUO+DZ1NnZNXQA/pROHK35WpW7D9H0b8bv0PWjRabstR2ugKAqe496H2JF9hpa6aA/a8tbzkpelIoLdkDCQ+f/FaKSx+QB7xbwt+UeMhlzJ/Lxbkn0QRqNw24HKxBTG+jO/d5+t0aBdT2Z5E73P0fIixG1ei2/yrpp97ZMDBuHa/OVIDgztltzux6htQt2xZwCafaSCR8LrkHmwt5fmS0ubFrvT2WdMLhFoFWJFXjoMOuZN5tC4aRCJxLzlZI4MDs/5yeQ5n+CZQiLB2wmpOPXA4xhgZsemNr0OyzP2YureDahoE/46m/uwP0HmPZg1Tld3BY0X3uMlJ4IgCIJf3TvwwEpkMhlWrFiBVatWoaamBvhpg+TKlSu4cuUKAEAikcDR0RFGoxFqtRom0/0XExITEzFhwgRe8xeazz77DM3Nt9uJjR49Gs8999xvYuRyOf7+97+jrKwMhYWFUKlU+PHHH7F06VIbZEyYq1rdhuUZe7GrON/sa6PdvfBd6hwM9fbnEG09tEkP1bFnQevYH6Jd4pZD2SeZl7y42rCPvQoxNSEEft6OvORjDpqmUZzF3OJILJGjT5Rw3zvZWhwFOorRz5W0OCJsY2Zof1z364MXTh7ED/lZZl37xY3LOFxeiK+TZ/G62GsuscId3smrUL13HusCccPpf0DuOwwyd2G2dgaAJTOicOZqJWPMwRNFWDQtCl7uwly4FImlCImdivxzGzqN0XU0oargBIIGCOsz9V7JQQ44WKru9Mf1JuBUlQYTg4VTsUQQ3aldr8MrZ45iVfYFs6/1Uzrhi6QZmB7Sr1ty6wxN06g//icY25gP3AGAMmwGnAYs4SUvrnYcLYBGy/zZFh3hiYEDvHnLyRxsz/n4ae65UKWxPOdLRcBofwVv+RDEvUb6BuLyQ8vwj/Pp+M/VM2bNPDxYdguxm9bio7GT8XD/OMFW01IiKbxTVqFy+2TQeubxUi1Za6AIGCO49RqCIAiia+yiEhE/zUZ86aWXMHLkyPt+sBoMBjQ3N6Otre2+G4hOTk5YsmQJ5s+fL9gPZj60trYiLS3t7r8vX76801ixWIyVK3+eN7dz585fVHQSwrTlVg5iN60xewORAvDSwFG4OO93vG8gAkDTxf9CW3uRNU7mPRDuw17hJSeuSqtakHmBedakiAIWTRNmFWJ9xTW0NTDnHxiZBJnCvPlufKlsN+BGI/N7U3KQsle/9xO256FwwPepc7BjykPwcTDvMEFJazNSdn2PF08chFrAn8MKv+FwG/JH1jjaqEVd2kqYDMJtQ9k/1APDYv0YY/QGE7YcFPZsRE4tTTksLttSIodW1OkcqlQIoic4VV2GQVs+t2gDcVHfGGQvWM77BiIAtOb+AHXxPtY4iVMfeI59V1DPbO1qPXYeZa9CXDwjWlB539HRpkL1rdOMMe7+UXD1ieAtJ3PojDROVDG/x4/0VcBRajdLW0QPpJBI8N6oCTgx5zH0c/Uw69omnQZL03Zi7sEtqFG3dVuOXSV1DobXuH9zilVlvAiDuqbbcyIIgiD4Y1dPWkqlEkuXLsXLL7+MhIQEyGQy1mt8fX0xe/ZsvPbaaxg9ejQveQpZeno6dLrbPcojIyMRHBzMGD906NC7rV8bGhpw+TLzzDTCduo1aiw6vB3zD21Dvca8xbQIF3dkznkU/x49AQoJ/wXKHRUn0Hz1E9Y4SuoE7+RVoMTsf/b5tHFfLtgOHCYOD0agrzA34YqvcTidLOBWpqTFEWFP5oQNQPaC5Xgw3PxDBf/LOofBWz7DmerybsnNGlwHPgNFwBjWOH1jHhrO/JOXnCy1ZAZ7W7p9mYVoaNbwko8l3P2i4OIVzhhTmZ8JbUczbzmZy9tBjDhP5s/99IoOu5grRBCW0hoNeOX0EYz78VvcbG4w61pPhQM2T3oQ6yfOhaeC/4pdXcMNNHJ5v6ck8E5ZBbHclY+0ONuZVoD2DuYDPP1D3DEsRjhjFu5Vkr0fNM3c8jlMwM/5F2s1aNczv7+T53xCKEb5BeHKQ8vwfNwIs6/9sSgPsZvWYuutnG7JzRocw7lVips09VClv8D63kMQBEHYD7toZ/prISEheOSRR7Bo0SKUlZWhtLQUra2tUKvVkEgkUCqV8PHxQUhICDw9zetN3tNlZf3cRi0yklsbsYiICFy8eLtC7Nq1axgxwvwHIqJ77S0pwFPpe1Btwcm1lTHD8N6oVDhKbbMxZ+xQQZX+PAD2xT/Pse9A6iKsdn6VtW04dq6UMYaigEXThTPX5V5Ggw4l1w8yxigcPeEXnsBbTuY6xtLiSCGmMNJXzls+BMHG28ERWybNw4aCbDxz/ACadNw3ofKbGzDmx2/wyqDReG34eMjFwnqUo0RieCX+D5U7JsOkqWeMbctdB4eAcXAMF+biZXSEJwZF+eDKjdpOY3R6E7YeysOyhwbymhtXFEUhLH4GrqZ91GmMyWRA6fWD6DdsPq+5mSMp0AFZ9bpOf7xGbcSNRj2iPYR1yIggrOFyXRWWpu1EdkOd2dfOCOmHz5NmmD2X11pMhg7Upa0EbWSf7es+7E+Q+wzhJS+u1Bo9th8pYI0TahUiOBwWpCgxgmOm8JaPucjcc8LeKKVS/G/sZMwJi8Tjx3ahpJX7QS2VRo2HDm3Dor65+GTcVHgohPd72yPhn9DWXIC+kXk2uKbyBJqvfgq3Qc/ylhtBEATRfeyqEvHXJBIJwsLCkJiYiBkzZmD+/PmYO3cupkyZgiFDhpANxPvIy/v5gz48nPlk+h0RET+3NsnJEe6pqN6oVafFk8d2Y8a+jWZvIAY5uuDQjCVYNX6qzTYQadoEVcaLMHZ0vkB7h1P/BXCKmMNLXubYtD8XJhPzBujYIUEICXDhLSdzVBZkQq9hnmsQEjsFIpGwNiru0BppnK5m3oAZ5a+AQmLXH3dED0RRFBb3j8P1hU9janBfs6410TTevnwSI7Z9iauq6m7L0VISRz94JX7AKVZ14k/QtzIfxLClhzlUI+5Jv4WmVvYFclsJiZv2U9PyznGpSLclLlUmbLNxCcLeGEwmvHkhEyO2f2X2BqKLTI6vk2dh19QFNttABICG069B38S+CacITIRL/NO85GSOPem30Nre+QEGAAgPckXCQP5HQXDRWJ2HphrmERf+fcdA4Whe+0U+sb23h7tIEOJM5p4TwpMcGIpr85fjd1GDzb52w83riNm0BntL2N8/+SaSOMA75VNQYvY5pE0X/w1NjfnttwmCIAjhIauqvUxDw8/tbyzZRGxuFm67q96mXa/DlL3r8VXuFbOvfSxyILIWLMfEPtx+D3SXlqzP0VGezhonde0Lj1Fv8pKTOWrq23H4dDFrnFCrEMG5lelMXnKxxJlqDToMLC2OOMzTIghbCXB0xt5pC/F54gw4mXmg41p9LYZv+xL/ungchvvMg7YlZZ8UuMR1Pnf5DlrXgrq0Z0CbhDnrMa6/N+L6ezHGaHVGbD9s3hxiPildfOEbxtxFor4iC631JbzlZK5YTxm8FMxfW7hUqxCEvbjRWIfRO77GP85nmP3+nhIYiqz5y/HYgIE2rY5rv7ULbXnrWeNEDt7wTvwQFCWspQmN1oBth9jf2wVdhZi1lzVGyCMLSlr1KGwxMMaQKkRCyFxkcnyWNAP7pi1CgKN5o02q1W2YsW8jnjy2Gy06YR1Wk7lHwmPU6+yBtBF1x56BUdvER1oEQRBENxJmaQnRLWiahlqtvvvvXl7Mi2J3eHj8fDKxtZW5YokLtVoNjaZ75wfJZDIoFOwno+zZq2eO4pSZc7F8HRyxetxUzAzpBwAwGJi/lHUnneoqGs+/zR4oksE98SOYKBlMNsz3fjbuuwGjkXkDa2S8H0L8nWz6c90ZrboRlTdPMMa4eveFk2e4IPMHgLSydtaYsX5SweZPEHc81j8OiX598FTmXmRWca/M05tM+Nu5dPxYlIevk2ZigJtwujA4D/ojOqrOQK+6yhinq7uMhvPvwXXoK7zlZo4FUyKRla9ijNmVdhMPpEbA2VGY7TSDY6ahpugsY0zh1d2IGS+8SqA7xgUosKNQ3emPX6nToq5NC3eFmNe8CMKaTDSN/2Wdx98vpENrNJp1rYNYgrdHJmNF9FCIKMqmzz6G1jKoTnB7T/cY+x/QMnfBPavtSb/JWmUe7O+MkXG+gssdP7WqLsnaxxgjlTvBN3y0IPMHgKOl7M/54/3kgs2fIO6YGBiKKw8+iRdPHcb6m9fNuvar3Cs4Ul6Iz8dPR0qgcEa7KCLmw6E8Ex3FzIcVjG0VUGW+DI+kTwV74ELIJBKybE8QhDCQd6NeRK1Ww3TPSVaum2z3fmi1tZk/c+/Xrl+/jgsXurelQXR0NGJjY7v1NWyJpmlsvXXDrGtmBUXg30PHw1PuYPOKUlrfhvZjzwA0+xc+edwf0SEORIfAqmAbmrU4dJK9CnHm+CCb/3x3pvTaTtAm5gUqn74paGlp4S0nc9A0jbSyzheUAaCvswiOhnYI9JeAIH7BAxS2jp2Ozwuu4c2sM9CYsYB8oa4Kw7d/ib/FJWBZv3iIBPIlXTbk/6BPWwAYmJ8fWrNWw+AcD4nvaN5y4yrMX4Z+wS4oKO38vbBDa8Cmfdfx0CTbVvh3xsl/CMQSBYyGzg9xFV3bi8D4BYKrBrpjuJsJOxh+nAZw8FYjpgaRtnaEfSpua8Zz54/hdF2l2dcO9/TFJyNSEeHshlYbP7fRJj3UmStB69kPn8r6PQ6t00BoBfagptMbseUg87wvAJiZ2AetrcJ8TlaVnoOmnXk2sU9EItraNQC694CvpY6UMFeYO0qAvvIONDcLM3+CuJcIwEdDEjHZpw/+eDEDKi33DgqlbS2YvG8Dnuwbi3/Ej4KjRBjPOuLYV0HVXgatZv7c6ijZD9XVLyALE+78baEiY7oIghAKu9hE1Gg0qKqqsuo9fX19oVQqrXpPofv1BqBIxG2RSCr9+QGlo6MDBoOBnIaxMRNNo57jQ6ebTI73hozHA336CuLkF03T0Fx5C3Q7exWlxD8Z0rAFvORlrj2ZJTCwVCEO7O+B8CBhzkIEgKq8w8wBlAh+/VP4SsdsRW00qjqYfw3G+JCKFMK+iCgKy/sPRIpfMJ49l4aLDTWcr9UYjfjblZPYV1GEj4enIMTJ9u8/IscgKAb/DZrzf2aN1Vz8K5QpWyBScOuUwBeKovBAahje+5q5ovLgqXJMHx8MpUJ4z0gSqQN8wseiKv9IpzGa1mo0VWXDPSCe19y4GuklhpgCmD56T9YZySYiYXdomsZ3hTn4x9WTaDezokomEuHPMSPwTOQgiDl+t+tuupxVMDVmscaJ3OMgi36Gl5zMlXGhCk2tzLMQ/bwcMCrel7eczMX6nA8gIHIiL7lYQm2gcbmB+TDVSC8xpCLbf78lCHNMDwrHSC9/vHQxA3sqCs269sub2UirLsUnI1Ix0sv2s1gpqTMchr8LdebjrAfEtdf+DbHHIIhd+/OWH0EQBGE9wlvluI+ysjJ8+OGHVr3nihUrenSl2v1Y2ubj1xtPer2ebCLamFgkQriTG3JbGhjjJvqH4INhSfBzcOQtNzaG0p0wlO9njaMc/KAY8rogNj5/rblVh6Nn2U+JP5Aaxks+lmhrKEFLHfOcF8+gIVA4Cmsx/14na9nf08b4kPcqwj71c3HH3pQH8HHuZbyXcx56M2ZinaqrxPhDG/HGwDFYGm77WU3SoKkw1p6FvoSpjgygtQ3QXPwrHEavFlw13O1DIc4oLO+8skatMeDgyTLBvvf7R05k3EQEgMq8w4LdRHSSUhjkIcbF+s4Xlc/UGmAw0ZCQRWXCTlSp2/DihXQcrebexvqOODcvrBqRimgBtbE21JyCruBr9kCJExyGvwNKJLxNf4PBhF3p7DNiZyeFQiTQ9xqDrh21hScZYxxc/OHqF8NbTuY6rzJCz/LoQ57zCXvlpXDA16MnY1tpAV65dBzNeu4zD4vaWjAjbQeeiRyEP8eOgEJs2z8HYo94yKKfhe46y5qtSQfN+VegTFoHStK7CjoIgiB6AvLU1Ys4Ov5yI8nIsU3ar+Nksq7N+4mJiUFERESX7sGmN8xE/PuwcViStvO+P+YsleE/oybgsf7xNl88vpe+6SZqr73DHkiJ4JX0EeTewXykZbZtR7OhNzB/qx0Y6Y3h8cLMHwBKL2eyxkQMng1XV1de8rHE2YY6xh93lVEYE+IOsUAXeAiCi3+OSsED/WPwePoeZDXUcr6u3WDAHy9m4FBNGdaOn4ZAR+duzZON89h/obYpC4bmm4xxxtozEJdtgnOc8GbzLZkRgzfXnGGMOXCqHPOnxUCpEN7CuItzIm6k+/w/e/cdHkd19QH4N9t31cuqd1myZBX33lTcu2mm9w5JCAkp5EsglZCQBEgChhACBGxMs417kyz3XmRZxUXF6lr1tn3n+4Me2JlZaXe0I533eXgw5szucdFq5t57zoGxx/nfo5arBzB16dOQK73zHio/TobTbc5bB/bYgCqrFpPC1KLmRYirWJbF+qsX8YPDe9Bpca0Vo5xh8LNxM/D0+JlQyb2n44LdaEDzmV8Kig2e+UfoorxzA2vXoWq0dXEv6IeH6LAkJxUKuXcdePlC1fkiOOzclZSJ2csRGBgoWk6uOlnewRuzMDkIAVrv+RogxFX3BU7G4uQ0PHxwB3bUXhV8HQvgHxXnUNBch3/nLMMk/dBWJbKTvo/WjtMwNxzkjHP0VIItfwmBMwWsCRFCCPEqtIk4gvzvJqLQykSr1frlj2UyGeSDfFjV6XQjrpWsJ9w6OhuXuzvwhzOHYfnaXLuFsclYO2cJEvy966HQYTOh/cD3wNr427AGTngSPtHeNxcLALp6zNh2gL/tyG3Lx3htxa7DYce1izs5YxQqHeLG5Hvtr6Hb4sAZA/fiyNxoLdQq71vIJ8RVE8OjceqG+/GbUwfw3NnDcLDcbXy/blddJcZ99Ab+MXsRbk3JHLqDJQp/hOW/iobNywA79+Js15kXoI2eDk3YRNHSE2LmhBgkxQSgss757K6ePit2HKzBmsVpouYmjAIJWUtQduQtpxE2Sx+arh5CfOYiUTMTKj/OF386yz1/7GCTBdOivKcDAyH/y2DswyMHtuPjynKXr00LDMHbeSsxJTzaI7kNFMs60Hrox3CYWnljfUffCv+UVaLk5Sq73YEPd3N36gCAm5ekQ6Me3MFaT7p2cTtvTNK45V57n8+yLA40cN8rZIeoEOFHB0aI9MUFBGHb0lvwZvk5/PDwbvRYuZ9xv660sxWzNr+NpyfMwv9NnD2kB0vCcl5G/cYFcBi5D/r2XX4f2pjZ8E1eKVpuhBBCBs877xr/R0xMDJ544gmXr+vu7kZdXR0uXryI+vp6AMDNN9+M1NRUrz515ylKpRJKpfLLTcH+/n5B1319lmJQUJDH8iOue2byXNw5OhuHGmvRZTFjXkwi0oK8s/1kx4nfwtpexhuniZyOgLHfEyWngdi49zJMZu4q3oxRochO1YuWk6taak7B2M09Zy02fT4USq1oObnqYIORcy4WAOTG0GEFMnyo5HL8bmouliek4s59m3Cpi7ud9dd1Wky4fd8mfFJZjrVzl0A/RC2uVcHpCJ76DNqPPM0dyNrQWvA4IlfvhFztPdXQDMPg1mVj8Lu1RznjPt5dgZV5o6BRe99tdkLWMs5NRACovrDVazcRk/wViPVVoLbX+UG4/XVGPDWB7leJd9pcVYEHi7ahxdjn0nUMgCeyp+L3U3OhVXjfAanu4rUw1RfxxikDUxE8/dei5DQQBcevodHA/WcTGqTFvOnxouXkqt7OBhhqTnPGhMaOh29QjGg5uaq03YpmI/fzVk6M9z6nEOIqhmFwX/p4zItJxD0FW1DYUC34WjvL4renD2JLzSW8k7cSWSFDM6tVrtNDP/clNO+8lTe27dBPodaPg9Lfez9LCSGEfJN39t/4H1qtFikpKS7/M3HiRKxcuRI//elPsWjRZ4she/bsQUBAANTqkXlq7eutCdva2gRdU1VV9eWPw8O9d3j8SJXoH4Q7Rmfj8azJXruB2Fe9Ez2lb/PGydRBCM15GYzMO9vS9PZbsLnwMm/cbcvSvaqN7P+qLt7KG5OQvUyUXAaqsI67olXGALOjvLMdHyGDMTU8GmdvfBBPZE91+dpPqsqR8f5abKpyvfrFXfzS74AuYTFvnK23Fm2HfgrWhapLMcwcH434KH/OmK5eC7YV8VesD4WAsGQERaZzxjRdPQpjL3810VBgGAa5PAvHFZ1WNHBsMhIyFDrNJty1bzNW7fzA5Q3EBL9AFK68E3+ducArNxDNLWfQcepPvHGMXA193j8hU3jn5o/dweL97fwHHm9alAaV0jufVQCg5sI23phEb7/Pr+c/7JxHm4hkGIr3C8TeFbfj77MWQetipfC51mZM/OgN/PHMYdhcmKXuTtqYOQgY+xhvHGvthaHwUbA8bZcJIYR4D0lsIg6WXC7H8uXLMX78eLS1tWHnTu42fsPZ12cRXrt2TdA1lZVfLYTFxXnvjDfinWy99Wg7+CNBsaFz/waFz9D28+eyad8V9Bu5FyZHJwZjwhjv3Wy3WvpRV7aPM0YXEIGw+Ami5eQqu4NFUT33JuIEvRqBau9d4CFkMHRKJf42cwEKV9yBBD/XOisYTP1YvfND3LlvEzrM/O2l3Y1hGITM/jPkvvyt+PqrtqK3Yp0oeQklkzG4dSn3JhwAfLS7AmaLsNnTYuNbPGZZB2pKdoiWj6tyovkXjvfzfI8gREw7aq4gY8NavHOp2OVrHxwzAcU3PYi5Ud5ZreGwdMNQ8BjA8m/cB017Fqpg/s/PoXLwVC3qmns5Y4IDNFg0K1G0nFzFsiyqi7k3EWVyFWLHzBctp4HgOyyo18oxJth728kSMhgyhsHjWZNx/sYHMT3ctYphq8OBnx8vwKyNb6GiY2gOhAVO/DHUYfxrCRbDeUEHUAghhHiHEbGJ+IUVK1YAAI4dOwa73TsXdjwtPf2rB7fjx48Luubq1a8GPI8bN84jeZHhiXXYYCh8HA6z8/lRX/DPvB+6uHmi5DUQfUYrNu7ln5Fy61LvrkKsKy+Azcr9YJ6QtQwM473fHorbLOgwc5+uFLLITIjU5UQnoPimB/FA+niXr/3vpQvI2vAadl27KiDaveTqQOhz/wkw/Bv97Ud/BUtHhSh5CTV7UixiIvw4Y9q7TNh50DurEeMyFoGRcZ9uF1KxPlSmRmigVXB/ny2kTUTiBbotZtxfuAVLtq9HQ1+PS9dG6nyxfckteG3uUvipvLODDsuyaD34U9h6a3ljdQlL4Jd2uyh5DYTDwWLdNv4qxBsWjIZa5b2H1NobStDTXsMZEz06ByoN9/ewodRmsqO4lW/uuQYyL37eIsQdUgJDcHDVXfjTtHyoXOzUdLylHuM+/BdeKj7u0ix1d2BkSoTm/hOMirtzBwB0X1iL/tpCUfIihBAyON67SuwBYWFh8PHxQU9PD8rLh66V11CaPXv2lz8+deoU2tu5ZyrV1tZ+OU9SJpNh0qRJHs+RDB+dZ/4Kc/NJ3jhVSCaCJv9clJwGauv+q+jtt3LGjIoLxNRs762khNBWpllLRclloPbznE4GwNvujpDhwk+lxus5y7B9yS2I1Pm6dG19Xw8WbVuHh4u2odcqbjshTfgkBE58ijeOtZthKHgUDpv3bArJZQxuWcJfTfPBzgpYrN53aE3jE4zI5BmcMZ3Nl9DZzH9wZiio5QxmRHC3qz7aaILJNjStvAgBgL11lcjcsBb/Lj/n8rW3pWSiZM3DWBw/yiO5uUtvxXr0V23hjZP7RiNk9p+8+pDdkXP1qGno5owJ8FVh6dwk0XIaiCoB9/ne3sr0QL0RfFseudE095yMDHKZDE+Nn4EzN96PCaERLl1rstvwxOHdyPv0v6jq7vBYjt9F6ReL0FnCqgxbi56Arb/Z4zkRQggZnBG1iQgAoaGfzYyrqeE+oTdcJSUlITHxsxYsdrsdW7ZwP/ht3Ljxyx9PmzYNISEhHs+RDA/G+oPoOvd33jhGoYM+7xUwcu88ZQ0AJrMNH+/mr4S5xcurEPu7W9BcdYIzJiQ6C/6hCaLlNBB8FSaROjlSA71vZhAhnrQ4fhRK1jyM21IyXb72tdIzyN7wGg40iHtvFDD2UWiiZvHGWTsq0H7sWVFyEip3Siwi9T6cMa2dRuw+XC1aTq4QsohcLWCu1lDhOyhisrM43mwWLR9CvtBrteDRA9sxf8t7qO3l3pT6X6EaHT5ccD3enbcawRrvPgxl6biE9qO/4g9k5NDn/hNytWutt8XEsizWC6hCvH7BaGjUrs0oE5PdZsG1i7s4Y9Q+wYhImiZaTgPB18pUKQNm0txzMsJkBIfh2HX34teT50Ihc20Zt6ihBtkfvI7XS8+IOmvcJ2kZfAVUoDtMbWjd/wOwLB3+IoQQbzbiNhGNxs9uShsbG4c6lSHz4IMPfvnj9957D5cvX/7OuBMnTny5ycgwDNasWSNajkTa7P0GtO7/AcB7jhQImfkHKAO8+1TvtqJKdPVyV+kkRPtjxjj+GV9DqaZkO++fSYKXn05u6rehtJ37zyI3RuvVm7mEeEqwRot3563GhwuuR6jGtVP6VT2dyNn8Dn50ZA+MNu6qa3dhGBlCc16CTMN/QKm3/D30VXpPi025XIabF/NXI27YUQ6rF1bERaXOgZKnnV31he1wOPjnnA2FOQJaVvMtRBPibkUNNcje8BpevXja5WtXJqSiZM1DuCF5jEdycyeHzQhDwaNg7Sbe2MCJP4Ym3Ls72RwvbsSVa52cMb46JZbnJouW00A0XjkEi5F7hER85mLI5N570M7qYHGwgfuze3KYBr7KEbeMRQiUcjl+NWkOjl93LzKC9C5d22u14KGibVi8bT3qXDzgMhjB056BMmg0b5yp4RC6zr8iSk6EEEIGZkTdfZlMJnR0fFbG39/fP9TpDJlp06Z92dbUarXipz/9KYqKir78/3a7HTt37sQzzzwDm+2zxaPly5cjOzt7yHIm0sGyDhiKnoDd2MIb65NyA3xTbhAlr4EyW+z4SEgV4pJ0yGTeu3HFsiyqznNXHstkCsSNWSBaTgNBrUwJ4XdD8hiUrHkIKxNSXbqOBfDX88cw8aM3cLKlwWP5fZ1CF47QuS8Kim099BNYe655PCeh8qfHIzyEe7O2pb0f+456X/cLuULN+3lv6m3lrV4fKlE+CqQFcS+E76/vF/XEPRm5+q1W/ODQLuRsfgdVPdybUf/LX6XG23krsHHRTQh3sSX1UOk4/ltYO/hHg2iiZiIg+1FRchoolhU2C3H1vFToNN67+QahrUyzvPuw4OkWM3qs3J/bOXSfT0a4CfpInL7xfvxs/AyXZ4Puqr2KzA1r8d+KYlHukWQK7eddp/irhztP/xmm5lMez4kQQsjAjJhNRKPRiA8++ABW62en6wMCAoY6pSH15JNPIj39sxP0HR0dePbZZ7Fs2TI8+OCDuP766/H888/DZPrsdOmUKVPw8MMPD3HGRCq6i9fCVF/EG6cISELIjN+LktNg7DxYifYu7pPWMRF+mD0pVrScBqKjqRzdrZWcMVEpc6DWeW+7KQDYz9PKVC1nMI1nVhYhI0G4zhcbF92Et/NWIEDlWrvoso5WTP/kTfzqxH5Y7J6f6aeLzYV/Fv99BmvphqHgMbAOcSol+SgVMty0KI037v0dZbDbva8aUUjluZA5ukMlh6casa7Xjitd3vF3hQxfhxtrMfbD1/HyBdc33BfEJqFkzUO4c/RYyXRQ6Kvajp6yd3jjZJpghM59CYxMLkpeA3X6YjMqqto5Y3RaBVble/d8SnN/BxovH+SMCdAnIzCCvyJoKAk5LJhHm4iEQC1X4Llp+Ti06m6kBAS7dG2XxYw7Czbjul0form/12M5fkEVNBrB03/NH8jaYSh8DHaza4dxCCGEiMN7m/p/TU1NDd5+++0BX2+1WtHd3f1lVR0AREZGuik7aQoMDMSLL76IF198Ebt27YLD4UBfX983WptqNBqsWrUK999/P+Ry734AJN7B1HIaHacEDNCWq6HPexUyJfc8qaFmsdrxwU7+KsSbF6dB7sVViBC4EOztrUzNdhZHGrk3dKdHaKBVjJjzMYRwYhgGd44ei9zoBNxXuBV76rgPEnydnWXx29MHsaXmEt7JW4mskHCP5ho06ScwNR2FxXCeM85iOIuOU39G8JSnPZqPUAtmJmD9tjK0djpf+Gw09KHwRC3mTY8XNTc+oTFj4RMUg76OOqcxdeWFsJp7oVR7X4VUbowWa0u4W3LtrzMiJVAlWk5k5DDarPjlif346/ljApr3f5OvUoW/zJiPB9LHS2bzEABsPXVoO/iUoNjQuX+DwifC4zkNBsuyeG9rKW/cytwU+Oq8+3Pk2sXdvO2nE7KXef3fN7655wl+CiT4e3dFKCFimh4Rg3M3PoifHy9w+TDLpqoKHGqsxatzFnu8lbbv6FthrD+E/iruzkj23nq0HfwJ9Pmvef3nFSGEjDSS2ES0WCxobm522+tptVqMHz/eba8nVSqVCj/5yU9wzz33YPfu3aipqUFXVxfCwsIQHx+P+fPnj/iKTSKc3dwFQ8FjAMs/Pyl46i+hDskQJa/B2H24mnNhGAAi9T7ImxonWk4D4bBbUVOygzNGpQ1AZMos0XIaiBPNJvTbeFocCZiTRchIE+sbgF3LbsVrpWfw4yN70OfCzMNzrc2Y+NEb+M3kHDw1bjrkMs9s0jNyFfS5r6Bh4yKw1h7O2O7iV6CNmgltzFyP5OIKlVKOmxaNxivvn+OMW7+tFLlT47zqwAnDMEjMWoaSA2udxthtJtSW7UPSuJWi5ibEuFA1AlUydFqcV3kW1hvxQCbdyxL3Ot5cj7sLNqO8s83la3OjEvBm7nIk+Ht354f/xTpsMBQ+DoeFe+YeAPhnPghdbL4oeQ3G+XIDSq9y/xlq1HKsnpciWk4Dxd/KlEF85hKRshmY2h4rrvJUj1MrU0K+TadU4qVZC7EqcTTuKfwUNT38n9NfaDX148bdH+OWUeX4x+zFCNZ45muMYRiEzn4eDYZzsPXWcsb2V29HT/m78E+/wyO5EEIIGZgRV67h5+eHu+++G6GhoUOditfQ6/W47bbb8PTTT+P555/Hj370I9xwww20gUgEY1kWbQefgr3XeTXDF3Txi+CXfpcoeQ2G1ebAhh38815uXpwOudy7P0obrx6Fub+DMyY+YxHkcu8+2UvzEAkZOIZh8HDGRJy/6UHMinCt/bLV4cDPjxdg1qa3UN7R6rEclf7xCJ31vKBYw/4fwNbPP3tXDItmJyHIn7tlbF1zLw6e4l40GQoJ2Ut5Y7y1palcxmBONHf76tMtZnRzbDIS4gqz3YafH9uHGRv/4/IGok6hxN9nLcLeFbdLbgMRADrP/BXmFv5ZVarQbARN/pkoOQ2WkCrE5TmjEODnWktwsXW3VqG9oYQzJjxpKnT+YaLlNBB8VYgAkEuHBQlxKjc6ARdueggPpLteMLH+ykVkbFiLbTWXBUQPjEzlj9C8fwAMfy1L+7FnYWnj/4wmhBAiHklUIvr7+2PatGkDvl6hUMDf3x/h4eEYO3YslErvXignRGp6yt9Ff/V23ji5bzRC5rwgidYU+47WoKW9nzMmPESHfC9rT/ddhLUy5V9IHkosy/IuLqQGKhHtK4lva4QMmeSAYOxfeSdeLD6OX5wohNmFmYfHmusx7sPX8evJc/GjsdOh8EBVok/yChgbDqK3Yj1nnMPUitaiJxC+6F0wzNAe5FCr5LhxYRpe/5C7Feu6bWWYMykWMi+qRvQNikFo7Hi01p51GtNScwp9nQ3wCYwSNTchcqJ1+LTK+fdqOwscbDBiaYJ3t08n3u+0oRF37duMix0Gl6+dFRGL/+StwCgX51Z5C2PDYXSd+ztvHKP0gT73n2Dk3t36EwAuXDKg+BL3n6VaJccNC1JFy2mgqou38cYkZnn3yAIIOCzoo2AwOZzmnhPCxU+lxus5y7A6MQ33F21FQx93d4+va+rvxbLt7+PetHH464z5CFC7/+tNEzYRQZN+go6Tf+AOtJthKHwMkSu3QabUuT0PQgghrpPEamt4eDjuuINK2QnxRpa2UrQfe5Y/kJFDn/tPyNXef/rabnfg/R1lvHE3LUqD0svn71mM3ai/VMQZ4xccj+CoTNFyGoiqbhuu9XC3yqVWpoQII5fJ8KNx07E4bhTuLNiM04ZGwdea7Xb87FgBPrpahv/krkBmiPsrG4Kn/wbm5lOwdnKfhjbVH0B38asIGPuY23Nw1dK5SdiwowxdvRanMTUN3Th8th6zJ8aImhufxOxlnJuIAFB9YTsyZt8vWk5CzYnWQMYADo5O1/vraBORDJzFbsfvTx/E788cgp11bfqhRq7AH6bm4vtZUzzWCtrT7MY2tO7/PiBg8mPIzD9CGZAoSl6DJaQKcemcJAT6e/emFcs6UH2BexNRodQiJi1PtJwGot/qwLEm7rnnM6M0UMm95xAOId5scfwolKx5CN8/tAvvXrrg0rVvlp/DnrpK/Cd3BfJj3P+Z7p/9MIwNh2Gq516jsHZeRvuxZxA6+89uz4EQQojrpPk0QwjxCg5rPwwFjwJ2M29s4MSnoAmfJEpeg1Vw/BoaDX2cMaGBWiyYmSBaTgN1rWwPHHbni9oAkJC9zOurQwuolSkhbjcmWI+jq+/BryfPdbmq8JShERM++hd+e+oArC5UMwohU2ihz3sFkPO3kOs49WeYWk679f0HQqNW4PoFo3nj1m0tBeviRoSnxY6ZDxlP5VB18VavyxsAAtVyjA/l/ntS1GCEwwtzJ97vZEsDJn70L/zm9EGXNxCnhUfj3I0P4Idjp0l2A5FlHWg98EPY+5t5Y31TboLvqNWi5DVYpVfbcLaMux22UiHDDQv5P9OHWkvNafR3N3HGxKTPg0Ll3ffIR5pM4Os8nRtN1UiEuCJIrcV/81fhk4U3Qq9x7euntrcb87a8i8cP7kCflXstwVUMI4N+7ouQafW8sb0V69F7dbNb358QQsjASPOJhhDiFdqP/hLWriu8cZqo2QgY+6goOQ2W3cFi/TYBVYiL06BSykXJaTAEtTLNWiJKLoOxv567tWyASobxeu+eWUOIN1LK5fjVpDk4ft29yAjif5j/OqvDgV+dLMKUT/6Nsy5UMwqhCk5H8DQBVe6sDa0Fj8Nu7nLr+w/E8txk+Plwb8ZV1nXh2Hn3/l4Nlkrjh+jROZwxPe01vDO3hkoOzwGSdpMDxa3uXQAjw5vRZsVTR/Zi2idvoqTdtfalKpkcz0/Lx6FVd2N0UKjHchRDd8m/Yawt4I1TBCQjeMZvRcnJHdYJqEJcMicJIYHevfEGgff5idnSb2UKAZ/1hJDvtjopDRdvfhjXJ6W5fO0/S05h7Aevo6ihxq05yXV66Oe+JCi27dBPYe127/sTQghxHW0iEkIGpPfKJ+i9tIE3TqYJRWjOS0M+s0qoopO1qG/p5YwJDtBg0Szvb9fU016L1tpznDFh8ZO8cs7V1/VYHDjVzF3tOjtKA4UXzRkjRGom6CNx+sb78dPxMyBzsTL5XGszpnzyJn55ohBmO3fbYVf4pd0OXQL/IQdbby3aDv10yCvldBolVs9L4Y3zxmpEIYvMVQIWq4eCkCr0/TwzdQn5woGGGmR/8DpeOH/U5QrWSfpInL3xAfxk/AzJVh9+wdxazD+zCgBkKujzXoFMKY2WwRVV7ThZwl25p1TIcKMEqhBtFiNqy/Zyxuj8IxCW4N2dYFiW5f2MzgxRQa/1/sObhHgrvdYHHy64AevmrUaQi7MOr3Z3IGfzO3i4aBu6zNxth12hjZkjaCQBa+2FofAxsDzdlQghhHiWtJ9uXGCxWFBeXo69e7lvtAkh/KxdVWg7/HNBsfqcl6DQuX9mlic4HCzWb+M/nXzjwtFQq7z/QZZvRgo+b2Xq7Q41GmHjWcej08mEDJ5arsAfp+Xj4Kq7MCog2KVrbQ4Hfnf6ECZ++AZOtjS4JR+GYRAy+0+Q+0bzxvZXbUVvxXtued/BWJk3Cj5aJWfMpZoOnOJZxBZbRNI0qH24/8yvXdwFu90qWk5CjQ5UIkLH/T25UECVCxnZeixmPHZgB+ZufgdXutpdulYpk+F3U3Jw9Lp7MSbYtYpub+Sw9MBQ8Ajg4P96D576f1CHZIiSlzusE3Cfv2BmAvTB3t86s66iEDYLd6eO+KwlXn+Qs7zDiqZ+7rboNPeckMFjGAa3pGSiZM3DWBI3yuXrXys9g4wNa7GthntmuSsCJ/4Y6rAJvHEWwzl0nPqT296XEEKI67z7jvJz165dw3PPPYfnnnsOZ86cGdBrbNu2DX//+9+xceNGlJbyPzwQQr4bazfDUPAoWCv3zEAACBj7GLQxc0XJyx0OnanDtcYezphAPzWWzEkSLaeBYlmWt8WRXKFBbHq+aDkNFF+LIxkDzImixQVC3GVGRCzO3fgAvp81Ba7W917sMGDaJ2/ip0f3wmgb/IaTXB0Ife4/AYb/4Eb70Wdg6agY9HsOhq9OhZX5/Asz720r86pqRJlcifiMRZwxFmMXGi8fEi0noRiG4a1GvNhuQUu/+6pkyfCyu/YqMje8hlcunnL52nGh4Th1w/34xcTZLs+W9UYsy6Lt8NOwCWgdp41bAL8x94iSlztcudbB205aLmewZrHrLf+GgqDDgllLRcllMAoFVIrT3HNC3CfKxw9bl9yMN3KWwU/J3Yb/f9X39WDZ9vdx296NMBj514P4MDIlQnP/AUblzxvbfWEt+msLB/2ehBBCBkYSTzpmsxl1dXWoq6tDX9/AvlGtXLkS0dGfnWQvKipyc4aEjBwdJ5+Dpe0Cb5w6bCICJ/5YlJzcweFgsU7ALMTrF6RCo1aIktNgtNaeQ19nPWdMTFoulGpf0XIaCIeAFkfjQtUI1nh/ZSghUuKjVOGlWQtxYNVdSHGxKtHBsvjTuaMY/+G/cKjx2qBz0YRPQuDEp3jjvjjk4rANbdXZ6nmp0PJ8nyi72oZz5S2i5SSEkMp0IfO3hoKQKhVqaUr+V4fZiHsKPsXCretwrde1uaoKmQzPTJqD49fdh+yQcI/lKLbeSxvQd3Ujb5zcJxKhc/4CxsX210NJyH3+/OkJCA/x/tasxp4WNFce44wJjspAgN77Dz7yHRYM0ciQFeLaRgchhBvDMLgvfTwurHkIedEJLl+/7nIJxry/Fusvlwz6UJzSLw6hs4RVGbYWPQFbf/Og3o8QQsjASGIT0R1kMhlyc3MBAKWlpQPejCRkJOuv2YPukjd442SqgM9OlMm4W7p5k2PnG1BVx72A5O+rwvIc11t/DAUhs6uk0Mr0QpsFbSYHZwy1MiXEc2ZFxuH8TQ/ix2OnuzwrsaKzDbM3ve2WGSoBYx+FJmo2b5y1owLtx54d1HsNlr+PCivyBFQjbvWuzhhBEWkI0CdzxjRcPgBzf6doOQk1PUIDFc9TTVG9++b4EOnbWFmOMe+vxVsV512+NjNYj+PX3YtnJ8+FSj58DjFZOirQfuT/+AMZGfQ5f4dcEyRGWm5RVdeFw2e4D9fJZNKpQqwp2QmW5b4/lsJ9frvJjrMG7rnnOdFal+8/CCHCxPsFYs/y2/H3WYugVbh2ULrV1I9b927Eih0bUNfbPag8fJKWwTftdt44h6kNrft/wPv5RwghxP1GzCYiACQnf7Yw4nA4UF5ePtTpECIptr4GtB74oaDYkNl/htIv1uM5uQvLsoIWc1fPS4VW4/1ViDarCbWluzljNL6hCE+cIlpOAyVkjlUuzUkhxKO0CiX+PGMejqy+B+lBoS5f/1rpGaS//yo+qeSvAnGGYWQIzXkRMk0Ib2xv+Xvoqxzairnr5qfyzs69cKkVxRUG0XLiwzAM76Kzw2HDNZ7vL0NBp5RhWoSGM+ZQoxEWu/e0kCVDo7m/F2t2f4zrdn2Ipv5el65VymR4dtIcnL7hAUzQR3osx6HgsPbDsO8RsHb+zfbA8U9AEzlNlLzcZf12/u8/+VPjEBXm3R068PlzS9X5LZwxjEyB+IyFouU0UAcajOD7VKbDgoR4loxh8HjWZJy/8UHMiIhx+fqtNZcx5v1X8drF03AMoioxeNozUAaN5o0zNRxC1/lXBvw+hBBCBmZEbSJarV/N5qmtrR3SXAiREtZhg6Hwe3CY+asP/NLvgk/iElHycpcTF5pw5Rr3r81Xp8TKXGlUITZcOgCrmXthLCFrCWQy798Q5Ws/F6GTIy1IOhWvhEjZ1PBonL3xATw9YSbkLlYFNPb34vpdH2H1zg9QP8DTygpdOELnvigotvXQT2DtGXwr1YEK9FNjWQ53VR8AvLfNu6oR4zOXADyTML22pSnPQnOflcWpFqpGHKkcLIs3Ss8ibf2r+OCq6193k/SROH3D/XhmmFUffqH96C9h7bzEG6eOmIaAcT8QJSd3udbYjQOnuJ/9ZQxw85J00XIajM7mS+gyXOGMiRo1C2qd91eK8rUyVTDArEjaRCREDCmBITiw8i68MH2ey1WJPVYLHj6wHfmf/hdXutoH9P4yhRb6vFfAyLkPhQFA5+k/w9R8ekDvQwghZGBGzCai3W7Hvn37vvxvg8F7Tn4T4u06z74Ic9Nx3jhlcDqCpv5SlJzcRXAVYn4KfHTS2KyqviCglWmW97c4aum3oaTNwhmTE62V1DweQqROLVfg91PzcOL6+5AdEuby9ZuqKjBmw1q8WnJqQKeVdbG58M96mDeOtXTDUPAoWDv3Z4gn3bBgNFRK7lvtc2UtKL3aJlpOfHT+YQhPmsoZ01Z/Ad2t1aLlJJSQqnQh1e1k+CnrMCBn8zt4oGgrOi2ubSRr5Ar8aVo+jl53L7KG0ezDr+u9/BF6L23gjZOpA6HPfRmMTFqbqOu3lYHv283cyXGIifATK6VBqS7mrkKERFqZ2hwsDjRwfz1OClfDj69XNSHEbeQyGX40bjqKb3oIOVHxLl+/v6EGWRtewx/PHIbVbnf5elXQaARP/zV/IGtH56nnXX59QgghA+dVZShtbW04cuTIt36+vf2rkyzFxcXo7BQ+i8Vut6O7uxuXLl1CR0eH23IlZKQwNhxG19mXeOMYhRb6vFchU/CfHPMmpy82o6KK+7ScTqPAyvwU0XIaDFNfOxqvfPtz9OsCw0cjMNz7fz1C5lflUosjQobEBH0kTl5/P547cwi/O3MINofw2STdFjMePbgD710uwetzl2JMsN6l9w6a9BOYmo7BYjjHGWcxnEPHyecQPO0Zl17fXYIDNFgyJwmb9nFXjKzbWorf/YB/3qNYErOWobnyGGdM9YVtyM59TLSchIj1UyI5QImrXVanMfvrjfjFZFHTIkPIZLPhuTOH8NzZw7C68Bn1hdmRcXgjZxlSA/nbKEuVpfMK2g4/LSg2dO5LUPhEeTwnd6pv7sH+E9xV6QwD3LJUGlWIDocN1SU7OGNUGn9EpXjP9xRnzhrM6LbwzD2P1omWDyHkK6MCglGw4g68UXYWPz66F90W7tmlX2ey2/Dz4wV47/IFvDZ3KWZEuDbmxnf0rTDWH0J/FfeBCVPjEVh7rkHpF+fS6xNCCBkYr9pEbG9vx86dOzljSktLUVo6+NZPY8aMGfRrEDLc2Y1taN3/fYB3WgUQPOP3UAVKo93nF4RWIa7MS4Gfj0qUnAarpmQnWJb71F+iBE4nQ0ArU5UMmM4zA4sQ4jkquRzPTJ6L1UlpuLdwC04bGl26/nBTLcZ9+DqenjALP58wE2q5sNtSRq6CPvefaNi4CKy1hzO2u+Rf0EROgy5+aGZD3bgwDduKKmG1OV8oPVnShIqqdoxODBY1N2di0vKg2K6Fzer8M7j6wlZk5TwChvGuCpHcaC3nJmJVtw3V3VYk+EujswAZuP311XioaBsuDaCtmo9CiT9Nn4eHMyZCNoy7HThsRhj2PQzW1s8b65/1EHRx+aLk5U7rt5fDwfMYM2tCDOKj/MVKaVCarh6FuY/773RcxkLIFd7/3CJo7jkdFiRkyDAMgwfGTMCSuFF45MB2bKm57NL1Je0GzNz4Fh4cMwF/nJaHILWwr2eGYRA6+3k0GM7B1svdinooO44QQshI411P/iJgGAbLli3DzJkzhzoVQrwayzrQWvQE7P3NvLE+o66Db8qNouTlTufLDbxt5DRqOa6b7/1Ve1/ga3HEMHLEZS4SLZ+BMttZHGrgXlyYFqGBjqdVICHE87JDwnHsunvx3NQ8aARuBH7B6nDg16cOYPyH/8LhRuHzqpX+8QidJayNUWvRk7D2DM0s7NAgLRbOSuSNW7etTJR8hFCotIhJn8cZ09/VhJaaM6LlJNRcAQvOfAdUiLS1m4y4r3ALcj/974A2EBfEJuHizQ/j0cxJw3oDEQDajz0La0c5b5xKPx5Bk38mSk7u1Gjoxb5jNbxxty6TRhUiBM6kTcheKkoug8X3WRznp0CSv1edeSdkRIr29cfmxWvw/vzroNe4Xh38eukZpK1/FesuXQArcJSBTOWP0Lx/AIzzzwBGroFc61o3E0IIIQPnVXdlfn5+mDz52z2Genp6UF7+2QNOREQEgoNdO6mtUqmg0+kQExODjIwMhIaGui1nQoar7uJXYawr5I1T+CciZMYfJDmX7r1t/FWIy3NGwd9XLUo+g9XZfBkdTdyLQRHJ06H19f7PwFPNJvTZuB8ycuh0MiFeQyGT4WcTZuL6pDQ8VLQdhQ2uzcwr62jFrE2fnVb+w9RchAhYpPBJXgFjw0H0VqznjHNYumAoeBSRyz4GIxe/OuOmRaOx42Al7Hbnn2nHzjfg6rVOJMcFipqbM4nZy3gPpVQXb0V4wiTRchJiUpgavkoGvVbnv9eFdUbcnS6NqiMiHMuyWHe5BD88vBsGE39l3f8KVGnw15nzcffosZK8p3VV79XN6C1/jzdOpgqAPu8VMDLpVe9u2FEOB08Z4szx0UiK8Y7PXT4WUw/qKvZzxvgGxyIkOlu0nAaqvteGS53Oq8bxeWX5SPhaJEQKGIbBmlEZyI9OxBOHd+G9yyUuXd9i7MNt+zbhrYpivDpnMZID+Nd0NWETETT55+g48dvv/P/+mfdBrg5wKQ9CCCED51WbiBEREbj77ru/9fOXL1/+chMxJycHs2d7f49/QqTM1HQCHaf+xB8oU0Gf9wpkKl8x0nKrC5cMKK4wcMaoVXLcsCBVtJwGq/oC/+lkqbQyLRRQKZIbTZuIhHiblMAQ7FtxO96qOI8fHdmDDjP/bNOve730DD6uLMOfps3D3WljeSuBgqf/BuaWM7B2VHDGWQxn0XHyjwie9iuX8nGH8BAfzJ+egJ2Hqjjj1m0rxS8fmSFaXlzCEiZB5x+B/u4mpzG1ZXswcfFPoVB6z2exUsZgVpQWO2ucbyKdaDahz+qAD1WyDxuV3R145MB27K6tHND1qxJH45XZixHp4+f23LyRtasSbYd+Iig2ZM5foPRzbZ6VN2hu68OeI/yHWaQyCxEAakv3wMHTui8ha5kkNt6EtDKlw4KEeJ9QrQ7vzluNW1Iy8XDRdtT1dbt0/Z66SmRueA2/nDgbPx43HSq5nDM+IPshMDIF2o//GmC/Gg3gN+YuBIz/4YB/HYQQQlxHT8+EkG+wG9tgKHgE4JmrBwDBU34BdWiWKHm5m5BZiEvnJiPQXxoz9xwOG6ovbOeMUap9EZU6V7ScBmM/z+JCcoASsX7SOxVPyEjAMAzuSRuHspsfwc2jMly+vs1kxH37t2D2prdQ3MbdUlum0EKf9yoYBf9iY3fJ6+iv2eVyPu6wZnEaZDLuhd1DZ+pRXd8lWk5cGEaG+MzFnDE2Sz/qeapihgLfAROrAzjS6NrmNvFOZrsNvz99EJkb1g5oAzHaxw8bF92IjYtuGjEbiA6bCYaCR8Ba+3hj/TLug0+C97fA/y4f7KyAjaP6GwCmZkciJT5ItJwGq0pIK9Os4dHKVKdgMCVcGs9ghIxES+NTcPHmh/FIxkSXrzXZbfjFiUKM//B1HGy4xhvvn3kfYm89g9CcvyN07kuIur4QITN+D5mCPiMIIURMtIlICPkSyzpgKPq+oDmI2rgF8Mu4V5S83K30ahvOlrVwxigVMklVITZXHoept5UzJnbMfCiU3n+zXd1tRXWPjTMmj04nE+L1wnW+WD//OmxdcjNifV1vH3mkqQ4TPvwXnjy8G90Ws9M4VVAqQmY+J+g1h2o+YlSYL/KmxvHGrfei2YhC5moJmc8ltrkCqtSFVMEQ77bz2hVkbngN/3diP4w27nuG/8UA+F7WZJTe/AhWJaZ5LEdv1HHit7C0XeSNU4WORfCUX4iSk7u1dhixi6fyGwBuXTZGlHzcoae9Fq21Zzlj9HET4BsULVpOA2W0OXC0ifsgx4xIDdRy76+oJGQk81ep8cqcJTiw8i6kB7k+LqW0oxVzNr+N+wu3oI2nDblcGwrfUavhm3I9VEEpg8iaEELIQEliEzE0NBQrV67EypUrkZCQMNTpEDJsdZ37B0x1Rbxxcp9IhM75iyTa5XyXdQKqEJfMSUJIoHQ2qoScTk7MXi5KLoMlqMURtTIlRDKWxqfg4pqH8f2sKXD1u4adZfG34uNIW/8KNly5CJb97soS35Qb4Ju6hvf1vpiPyPK0hPOEm5ekg6cYEUWnanGt0bXWUJ4SoE9GcCT3IntT5TEYe7hbg4stVCtHdgj37MuieqPTv0vEu1V3d2L1zg+weNt6XOlqd/n6sSHhOHbdvXh51iL4q6Qx89pd+qq2oqf0bd44Run32RzEIZgh6w4f7iqH1ebgjJmYEY60RP6ZXN6i+sI23pjEsdK4zz/WZIKZp0qU7vMJkY7ZUXE4d+OD+N2UHKh52pN+l3+Xn0Pqulew9uJp2B3cn92EEEKGjiQ2EYOCgrBgwQIsWLAAsbHSm8lAiBQYG46g88wL/IGMAvq8VyHXSKf9z9ddqm7HyRLnM57weRXijQtHi5bTYFnNvaivKOSM8QmKQWjsONFyGgy+TUQ/JYMJYSNr4Y8QqfNTqfHSrIU4et29yAoOc/n6xv5e3LznEyzY+h4qOr676jp4xu+gDOL/7P5iPqLYYiP8MGcy930sywLvby8XLSc+CTxzdFnWgZqSHaLlIxTfLK1mox2l7VbR8iGDZ7LZ8LvTBzFmw6vYVMU9A/W7aBUKPD8tHyevvw9Twr2/WsvdrN01aD3wlKDY0Dl/htI/3uM5eUJ7lwnbD/C3tr1NQlWILOvgrfqWKzSITZ8nWk6DQfMQCRl+VHI5fjFxNkrWPIx5MYkuX99uNuKRA9sx+eN/40iT+B1DCCGE8JPEJiIhxLPs/Qa0Fj7+jWHVzgRN/hk04ZNEycsT3tvK3ypuwcwE6IN1ouTjDtdK98Buc97qDwASs5ZJonK01+rAyRbuFkezo7RQ8pXzEEK80tTwaJy+4X78fkrugE4r762rQtYHr+H/jhei3/rNTSApzEe8ZUk6b0zhiWtoaOkVJR8+cRkLwcgUnDHe2NKUby4iAOyv526dRbzHjporyNywFr8cQOtSAFgYm4yLax7GT8bPgHIAnztSx9otn1VgW3t4Y/3S74JPIvfhAW/20e4KWKzczzPj0sOQMcr11ntDxVB7Dn2d9ZwxMWm5UKp9RctpoFiW5d1EHBOsQoSO+/sOIcQ7jQoIxu5lt+Hd/FUI0/q4fP3Z1ibM3PgW7tq3GU393nEvTAgh5DO0iUjICMc67DDs/z7sRu4ZgQCgjZsP/6yHRMnLE65e68Sx8w2cMXI5gzWLpTUfR8gCrpDZVt7gcIMJPGs/yKXTyYRImlIux9MTZ+HCTQ9hfkySy9dbHQ78/swhZGxYi01V5d9oS+nqfERbT53L7z8YCdEBmD0xhjPG4WCxYYd3VCNqfIIRNWomZ0xny2V0NLleGeZJGSEqhGq4H3NoLqL3q+7uxKodG7Bk+3pc7e5w+fowrQ/WzVuNHUtvQaK/NDtouEPHyT/A0nqeN04VkoGgqb8UJSdP6OwxY+v+q7xxty2VThUiAFSf38Ibw1c17i0udVrR2G/njBFyCIQQ4r0YhsFtqVkou/kRPJA+fkCv8c6lYqSu+yf+dv4YrHbuzwxCCCHiGNJNxP7+fhw8ePDLfy5cuPCdcVarFW1tbW77p7vbO2bNEOINus69DFPDQd44uW8MQuf8VRLVbM6s28Y/C3H+9ASEh7h+am6o9HbUw3DtDGdMaOx4+AZxL1p7i8I67soQBsAcWlwgZFhICQzBrmW34oMF1yPKx8/l66t7OrF654eYv+U9XGhr/vLnXZmP2FLwiOjzEW9Zyl+NuOdoNZpa+0TJh4+QxenqC95VjShjGN6ZWudbLWgz0cKUNzLZbPjtqQNIf/9VbK6+NKDXuD99PMpufgS3pGRK+t51sPqqd6K75A3eOEbpA33eq5ApNKLk5Qmf7LkEs4X7azorNRTZo/Wi5TRYNqsJ10r3cMZofEMRnjhVtJwGg1qZEjJyBGu0eD1nGQ6uugsZQa5/7vZYLXjyyB6M+/B17Kur8kiOhBBChBvSPhGdnZ14//33v/zvlJQUZGVlfSuuuroaL774otveNyUlBU888YTbXo8QqTI2HEbnmb/yB8qUCMt7RbJzEAGgur4Lh85wtwKSySRYhShg4TZxrDROJztYFvvruRcXxoaqEKIZea3ICBmuGIbBjcljsCg2Gc+eOoCXio/D/rXKQiH21Vdh3If/woPpE/CbKXOh1/ogeMbvYDacg7WDu0LOYjiLjlPPI1jE6pvk2EBMHxeFo+ecV8bb7Sw+2FmO798+UbS8nIlKmQOlxg9Wk/NWiDUXdmBs/g8g42l9KqacGC0+uup8I5YFcKDeiNXJ3t8CcKRgWRZbqi/hySN7BlR5CACZwXr8c/ZizImS5kw/d7J2V6O16IeCYkNmPQ9lgOuV4d6iu9eMTwuu8MZJaRYiANRXFMJm4T5QkpC1FDKZNO6N+TYRg9QyZIeoRMuHEOJ5syLjcObGB/CX80fxm1MHYbK71pa8tKMV87a8ixuT0/HC9PmI8wvwWK6EEEKco3amhIxQtv4WGAof/3wZjVvQlF9AHTZBlLw8Zf02/lmI+VPjEBUmncVElmVRXbyNM0auUCM2fb5oOQ1GSZsFrSbuXqZ5MdKZVUkIEc5PpcZfZszH2RsfwMyIWJevd7As1paeRsrnrY9sjEr4fMQLr6G/ZvcAMx8YIQvZuw9Xw9A+9HP75AoV4jMWcsaY+trQVHlMtJyEmBmphZLnSYfv4AoRz7nWJszb8i5W7vxgQBuIfkoV/jZjAc7c8ABtIAJw2Iww7HtI0BxE39G3wjd5lSh5ecrGvZdhNHMvTKcnh2BcWphoObmDsJEF0jgs2GGy42wr9wz3nGgt5DT3nJBhRyWX4+cTZqFkzUNYGJs8oNf48GoZ0t5/Bb8/fRCmAcxHJoQQMji0iUjICMQ67GgtfBwOo4E3Vhe/CP4Z94mSl6dca+xG0alazhgZA9y8hL/FnDdprTuP3g7uX1f06ByoNK63CRwK1OKIEJIVEo4Dq+7Cf3JXIFTj+qGBLosZTx7Zg6wNa7Gnm0HwjD8Iuq71wA9FnY+YEh+EyZkRnDFWmwMf7vKOWYOCWpoKWOwWk59Khklh3K0ZDzaYYHO4VvlK3Kuxrwf3FW7BhA//hYL66gG9xu2pWai45VE8MXYqlHJpVGR5WvvRX8HSdpE3ThmUhuDpvxYlJ0/p7bdgU8Fl3rjblo2RVGtbY08L7+GMoIh0BIaNEi2nwTjQYATfxy3d5xMyvCUHBGPH0lvw8cIbEOfrekWh0WbD/53Yj0Xb1qHHwn0ogRBCiHsNac8hPz8/LFq06Mv/DgkJ+c64mJgYt7Yf1Wrp5pSMbJ1n/wpT4xHeOIVvLELm/EVSD9zf5f3tZeDrjjd3chxiIqSx2faFqvNbeGOkcjoZAAp5KkLCdXKkBylFy4cQMjRkDIO708ZiRUIqfnG8EK+VnhZQM/9Nl7rasWz7+1gYm4x/xK+EomYzZ7zD/Nl8xMhlH4ORi9NK7dZlY3CypIkzZsfBSqxZnIaQwKG9dw2JzoZvcCx6250fXKmv2A+LqcerDq7kxGhxtMnk9P93Wxw4YzBjSrh058BJVb/Vir+cP4rnzx5Bn806oNfICg7DP2YvosrD/9F76UP0VqznjWMUWujz10ImoGLbm23adwX9Ru6qlNSEIEzKCBctJ3eoKdkJluXu0CGVkQUQcFhQwQCzo6T9d5EQwo9hGFyXlI5FsaPw/NnDeP7cEZjtrs2oLmqowYNF27B+/nUey5MQQsg3Dfkm4vLly3njtFotUlJSRMmJkOHOWFeErrMv8wfKlNDnvwq5Wto95+tbelF4/BpnDMMAtyyVVhWizWpCbSl3+z2NbygikqaJltNgtPTbUNJm4YzJjdZKfkObECJcsEaLV+cuwT1pY/HIge0408q94fZddtVexTgmFPt1UQi1OJ9BiM/nI7Yf/w1CZvxuEFkLNyY5BOPTw3C2rMV5TlYHPtp9CQ/dNFaUnJxhGAYJWUtRUrTWaYzdZkZt2V4kj18tam5ccqO1eO4Ud2vM/XVG2kQUkYNlse7yBfz8WCHq+roH9Br+KjV+M3kuHsucDIWMGut8naW9DG2Hfy4oNmTmH6EKlEYVmzN9Ris27r3EGye1KkSWZXkPCzIyBeIyFnHGeAubg8WBBucHOgBgUrga/ir6eiZkpNAplfj1lBzclTYWPzy8G59W83+Wf93HlWVoM/UjZACdSwghhLiO7tIIGUFsfU0w7P++oDmIwVN/CbV+nCh5edL728t4W+fMmhCD+Ch/sVJyi4ZLRbCaezlj4jMXQyYb0rMiggmZS5VLLY4IGZGmhEfjxPX34R+zFyFQ5fpmTx+rwC19s2AUcHaup/Qt9F7dNMBMXSdkNuK2oqvo7OZefBVDQtZS3pjqC9xzesWW6K9AnB/3nzvNRRTPwYZrmPrxv3HHvs0D3kC84/PWpT/InkobiP/DYelBy94Hwdr5Py98026Hb8r1ouTlSVsKr6C3n7uSNTk2EFOzI0XLyR06myrQZbjCGRM1aiY0PsGi5TQYZwxmdFu4qypzomkjgJCRKMk/CJsXr8H2JbcgJUD4Z5rV4UBDH//cX0IIIe5BT16EjBCswwZD4WNwmNp4Y3WJS+E35h5R8vKkJkMf9h6t4Y27dZm0qhABoErA7KnEbP5Kb2/B1+JIJQOmR1ClCCEjlVwmw2OZk3H51sfwaMYkyFysKLmCIPyfbbag2LaDT8HS4dpp6IHKStUjO1XPGWO22PHxHnHy4eIbFAN93HjOGEPNafR2cld8iolhGORGcx9AudRpRUMvdytEMjhXu9pxw64PMWfz2zhlaBzQa2QFh+HAyrvwTv4qROh83Z6j1LEsi9aDT8HWXcUbqwrJQvC0Z0XJy5OMJpugz0apVSECQFXx8BpZsF/A3PM8OixIyIi2OH4ULqx5CM9NzYNOIWyESbiW7gcIIUQstIlIyAjRcfKPMDcd541T+McjdPafJfew/V3e31EGB08Z4szx0UiKCRQtJ3cw9rai6epRzpjA8NEIDJdGG2izncXhRu5T89MjNdAp6VsWISNdqFaHf85ZjPM3Poj5MUkuXfsJm4oPHKN541ibEYa9D8Jh4a72dhch1YhbCq+iu9csSj5chCxa13hZNWKOgIVpvpm8ZGBajf148vBujHl/LT6uLB/QawSo1Hhx5gKcufEBzI6Kc3uOw0XPxTfRX8V/wEymCoB+3muQKaR/MGtr0VV093K3wk+I9sf0cVGi5eQODrsVNSU7OGNUGn9EpcwRLafBKuDZRIzzUyDRXxrdUwghnqOWK/CzCTNRfssjWDOK+/54zagxCNP5iJYbIYSMdLQiS8gI0Fe1Hd0XnM8x+pJMBX3eWshU0mrt+V2aDH3YfaSaN05qsxABoKZkB1iWe/h44ljpVCGeaDah38a92ZtLLY4IIV+TGRKGXctuxaeL17jU+ugZ+0yUs/zx1q4raD30E7Asf/vvwRqbpseY5BDOGKPZho37Lns8Fz6x6fMhk6s4Y6qLt4ry+ybUlHANdArug1FCqmSIcL1WC3576gCS3vs7/lZ8HBYH9z3Ld5EzDB7PnIwrtz5OrUt5mFpOo/34bwXFhs79G5R+0t+MNZlt+HBXBW/crUvHQCaT1sHIxqtHYO7nnuUal7EQcgX3Z7G3qO2x4koXd8vZvBiae04I+UqsbwDen389ClbcgYygb3fsmBAagb/OWDAkuRFCyEhFT2OEDHPWzqtoPfCkoNjgac9CHZrl8ZzEsH5HGex27kXMqdmRSIkPEi0nd6nmaWXKMHLEZywSLZ/BErJ4S/MQCSH/i2EYLE9IRcmah/HC9HnwV6l5rzFBicds89HD8rdJ6q/8FD0X33RTts4xDINbBVQjbtp3Gb393FU3nqbS+CFmdA5nTE/7NbTVXxAtJz5qOYMZkdxVV0ebTDDZuOd1EX4Wux3/uHASye/9A786WYQe68D+vi6NT0HJmofx99mLEKqlQ0Rc7KZ2GPY9ArD8LXn9sx+BLn54LLpu2X8VXT3c1dlxkX6YNTFGtJzche8+HxI7LCik0puv7TQhZGTKjU7A2RsfwLYlN+PBMRPwWOYkvD53KY5ddy+ifPyGOj1CCBlRhqRnRHNzM/bu3TsUbw0ACA8Px7x584bs/QkRi8Paj5Z9D4K18rdk0yWtgF/6HaLk5WlNhj7sEVCFKGTR1tt0NFWgs5l7/kvkqJnQ+HJXtXgLlmV5WxylBioR7Ustjggh300ll+NH46bjjtRs/Orkfvyr7CwcHJVwlQjET+05eEWxh/e124//Fir9OGjCJ7o562+alBGO1IQgXKp2Xn3Sb7Rhc8EVQe1PPSkhexmule7mjKku3orQmGzRcuKTE63F3lrn32tMdhbHmsyCWp+Sb7M7HFh3uQTPnCxCVU/ngF8nKzgMf5kxH/NjXWtVPFKxrAOG/d+DvY9/Dqk6YiqCJv1UlLw8zWiy4cOd/O1xb1mSDrnEqhDNxi7UXyrijPELjkdwVKZoOQ0W39xzHwWDyeHSb69LCPEMpVyOJfEpWBIvjVEthBAyXA3Jqmx3dzeOHDkyFG8NAEhJSaFNRDLssSyLtkNPwdrB3+pH4Z+I0FnPD5s2Muu381chTs6MQFqi8BZ43qL6Av/pZCEzq7xFZbcNtb3cp+dz6HQyIUSAMJ0P1s5dikczJ+GJQ7tR2OD8MMkONglv2LNwv5ynYo61wbDvIUSt3gm5NtT9SX+OYRjctmwMnvnHYc64T/Zcwqr8FPho+SspPSUieTrUPsEw97U7jbl2cRfGL/ix17Tbmyvg+0hhfT9tIrrIwbL44MpFPHvqACo62wb8OuFaH/xuSi7uSRsLObUtFazr3Msw1XFvOAGATKuHPu8VMLLhcSDr08Ir6OKZhRgd5ou5k2NFy8ldakt3w2Hnbv2ZMHa5ZJ7Z+qwOHGvinns+K0oLlVwavx5CCCGEkJGKntIIGaZ6Sv+DvqubeeMYuQZh816HTDU82kE0GnoFzUK8fUWGKPm4k8NhQ82FHZwxSo0folPniJbTYBXW9fPG5NGiLiHEBdkh4di34nZ8svBGJPs7b1n9vGMqTjoieF/P3t+E+n2PgB3AXDdXTM2ORHJsIGdMb78VW/df9WgefGQyBRIyF3PGWEzdaLh8ULSc+ET6KJAexL3xur/O6FWzHL2Zg2Xx8dUyZG94Dbfs3TjgDUSNXIFfTJiFy7c+hvvHjKcNRBcY6w+i8/Rf+AMZGfS5/4BCFy5GWh5nNNnw0W7+A5K3LR8DuVx6f5+qzm/hiWCQkLVEpGwG70ijCVaeTtF0WJAQQgghxPsNyXFEjUaD5OTkoXhrAEBUVNSQvTchYjA1n0L7sd8Iig2Z/SeogtM9npNY1m8rg8PBvQg4JUuaVYhNV4/C1Me9UBc3ZgHkCv65YN6Cr8VRgEqGcXrp/HoIId6BYRisTkrD0vgUvFZ6Gr85dRCtpm8eWrBBjsft87CN+RihDPdnkaPpKD759HHMWfgC9Fofj+V867J0/PbVo5xxH++uwMq8UdCoh66qKCF7OSqOv8cZU128FbHp+aLlxCcnRouyDucVPvV9dlzusiI10DuqJ72Rg2WxpfoSnj1VhHOtzYN6rVtTMvHc1DzE+QW4Lb+RwtbXCEPh4wD4N70DJ/4E2qiZouQlhk8Lr6CbpwoxJtwXORKsQuxuq+GdJxuWMBk+AZGi5TRY+wXMQ6QKcEIIIYQQ7zckqw+xsbF48sknh+KtCRn27P0GGPY9BLDcLSIBwG/MXfAddZ0oeYmhoaUXe47W8Mbdtlx6VYgAUFXM38o0UUKtTLstDpxqMXPGzI3WQCGxeTaEEO+hksvxvawpuGv0WPzp7BH8tfgYjLavvj+2wAffs+fjXfk2yBnuBfmJrVtw57sssjNvwI/HTffIZuKMcdFIiPZHdX2305iuXgu2Fl3FDQtGu/39hQoMT0WAfhS6DFecxjRcOQRzfwfUOufVoGLKjdbi1QvOf1/x+cEW2kT8NrvDgY8ry/C704dwob1lUK81KyIWL8yYj6nh0W7LbyRhHVYYCh6Fw8Rf/amNzUPA2EdFyUsM/SYrPtwloApxmTSrEKsF3ecvFSUXd2BZFvt5Dgtmhaig18pFy4kQQgghhAyM9O6uCSFOsQ4bDIWPwt7PfzpcHTYBwVOfESUvsawTVIUYKckqRIupB/UV+zljfINjERIzVrScButgvRE8oyuRE60TKx1CyDDmr1Ljd1NzcfmWx3B/+njIvjZP6hgbjRcckwW9zh+xG+vO7UL8uy/jiUO7UNvb5dY8ZTIGtywdwxv34c4KmMz8h4U8hWEY3vm7rMOGmou7RMuJz9hQNYLU3I8+fAveI43N4cB/K4qRuWEt1uz5ZFAbiFnBYdi65GYcWHUXbSAOQvvx38HcfJI3Tu4bg9C5L4Fhhs/j/qcFV9DTx1OFGOGHuVPiRMvJXVjWwTv3XK7UICZ9nmg5DVZpuxXNRu424LlUhUgIIYQQIgnD56mCEIKOU8/D1MjdBg0AZJoQ6PPXgpEPn9P29S292HeMvwrx9uX8i7Pe6Frpbjjs3AsnCVnLwDDSqdor5GlxJGOAOdEa0fIhhAx/0b7++FfOMpSseQg3JH3Vyvs1xzjsdcTzXh/AWPCKYg/sNhNeunACye/9A/cVbsGlAc6E+y6zJ8YgJoJ7TnFnjxlbhng2YkLWYt4NCiGVNWKRyxjM4Zm9ddpgRqfZs7MvpcBks+H10jMYvf4V3FmwGeWD+Pud4BeI/+avxNkbH8DS+BRJ3ad4m97LH6Pn4r/5A2VKhOWvhVzjHVXA7tBntOKj3Zd4425bNgZyCXawaKk5jf6uJs6Y2LR8KFXSOVxXWE9zzwkhhBBChgvaRCRkmOir3oHu4lf5AxkZ9HmvQOEzvGaDrt9ayluFODU7EqMlWIUIgQuxfFUh3sTuYFHEs4k4Ua9GoJpaHBFC3C89SI8PF96AUzfcj0WxyWDB4Ef2XNSw/rzXZjGteFZ+GABgdTjwZvk5pK1/BWt2f4yzhsZB5yaXMbh1Kf+s4g93VcBoGrpqRK1fGMITp3LGtDdcRHdrlWg58cnj2UR0sMCBepNo+XibDrMRz505hIR3X8ZDRdtQ2d0x4NcK0/rg77MWoeKWR3F7ajbkMnrsHAxzawnaDv1UUGzwtGeh1o/zeE5i2iygCjEu0g9zJTgLEULv88cuFyUXd+Gbe67XyjEmePgcaCWEEEIIGc7oaY6QYcDaVYnWoh8Kig2c9BNoo2Z6PCcx1bf0Yt/xa7xxUq1C7Gm/htbac5wx+viJ8A2Uzsbw+TYLOswOzpgcOp1MCPGwifpI7Fh2K4pW3onsiFF41DYfZpb/8MItsnJcz3w1m4sF8MHVUkz46A3kf/pfbK+5DAfL06+ZQ87kWESF+XLGdPWY8Wmh85mEYhByeEXIPF+xzI7WQs5TpFRYx189M9zU9nbhR0f2IO6/L+Pp44VoNvYN+LWC1Vr8cVoeKm97HI9nTYZKToeBBstu6oBh7wNg7fwb3D7JK+GXfqcoeYmlz2jFx7sFzkKUYBWizWJEbdlezhitfzjC4ieJltNgtRrtKG7l3vTNidZ+o7U4IYQQQgjxXoqhTmCgenp6cPz4cVRVVaGhoQE9PT0wmUxQqVTw9fWFXq/HqFGjkJmZidhYaZ5IJEQIh7UfLXsfAGvt5Y3Vxi9EQPZjouQlpnUCqhCnjY1EaoJUqxC38cYkZkvtdDL/Ii3NSSGEiGVOVDwOrLoLu2pn4T+H7Hi4fxPvNb+TH0S5LRgXof/GzxfUV6OgvhrpQaF4Mnsabk/Ngkbh2i23XC7DrUvT8cJ/uGeffbirAstzk6HTKF16fXeJGZ0LhUoHm8X5Z3r1hW3IynkUMtnQbyb5q2SYFKbG8Waz05iiBhNsDhYKCW5GuOq0oREvFR/H+isXYXNwH+zhE6jS4EfjpuH7WVPgr1K7LceRjnXYYSh8DLbeWt5YZcAohMz607BrGbt532X09ls5Y+Ii/TB7kjSf+evKCzg/QwEgIWupV3yGClVUbwTfMRq6zyeEEEIIkQ7JbSJWV1fjwIEDOH36NGy2b7dwMpvNMJvNaGtrQ3l5ObZu3YrExEQsX74co0ePHpKcCfEUlmXRdugnsHbwn85V+CdAP/dvw25hob65BwWCZiFmiJKPu7GsA9UXuKs45AoNYtPzRcvJHfbztDiK9pEjJWBoFsUJISMTwzBYFDcKC2/5O87vtCCwfjtnvIax4zXFbqywXYd2fHsxtKyjFQ8UbcUvThTiscxJeHDMBETouKsLvy5vahzWbytDfYvzQ0I9fRZs3ncFtwhof+oJCpUWsenzUHX+U6cxxu5mtFSfRETSNFFzcyY3Rse5idhtceCMwYwp4cNzJq/N4cDmqgq8WHwch5r4N6b4+ClV+GH2VPxw7DQEqofn79lQ6jj1PEz1B3jjGKUP9PNeh0zpI0peYunrt+LjPQJmIS7PkGQVIgBUFW/hjUnIWipKLu7CN/dcKQNmRNLnBSGEEEKIVEimnanVasW6devw5z//GcePH//ODURnqqqq8PLLL2P9+vWw2+0ezZMQMfWUvoW+q/zVEoxcg7D81yFT8c96kpr3tpaBpwgR08ZGISU+SKyU3Mpw7Sz6Ohs4Y2LS8qBUC1+YHmoNfTaUdXCfKM+N0Q27DW9CiDQwDIPs+S9BGcx/+CSa6cU/5HuhgPP7yxZjH545WYS4/76E2/ZuxLGmOrACWp3K5TJBbbg/2l2BPp4qHU8S1NKUY5NRbHkCql/4ZnlJUafZhBfOHcWodf/ADbs/GvQGYqBKg2cmzUH17d/Hr6fk0AaiB/RVbUV38SuCYkPn/A2qoFSP5yS2TQVCqhD9MXtijGg5uVN/dzOaq05wxgRHZSBAnyRaToNlsbM42MD9GTolXANfpWSWogghhBBCRjxJVCI6HA68/vrrKC0t/db/U6vVCA8PR3h4OPR6PRwOBzo7O3H16lUYDIZvxB46dAjd3d144IEHIJPRTSuRNktbKdqP/0ZQbMis56EKkeY8QC61TT0oPM5fhXjHCun+2qsFzJISsoDrTYp4TieDWhwRQoaYTKFF2LzX0bBpMVhLN2fsdFkDfs4ew28d3POGrQ4H1l0uwbrLJZikj8TjmZOxZlQGZ6vTuVPisG5bGWqbepzG9PZbsXHf5SGb+xsWPxG6gEj0dzU6jakrL4DF1AOVxk/U3L5Lor8C8X4K1PQ4P5BYUGfETydK8/DR/zrV0oBXL57G+islMLpwCNOZUI0OT46dikczJiGANg49xtJRgdaiJwXFBox9HD6JSzyek9h6+y34REAV4u3LpTkLEZ+3ewZP40+pjSw41WJCn5X71yTkMAchhBBCCPEekthE3Lt377c2EH18fDBv3jzMnTsXavV3z924fPkyNm7ciJqarzYZiouLsWvXLixevNjjeRPiSZ3n/g44+CsP/NLvgm/K9aLkJLb120p5qxCnj4vCqDhpLgTarEZcK93DGaP10yM8cYpoOblDAU+Fh1bBYFoELUwSQoaW0j8e+rkvomXPvbyx98pLcJENxSessNb5pwyNuLvwU/zo6B7cmZqNB8aMR3qQ/ltxchmD25ePwXP/Os75ep/suYRV+aPgq1MJen93YhgZErOX4+LB153G2G1m1JbuQfKE60TN7bswDIPcGC3eKnO+MXu1y4qaHivi/aTZVrvPasH7Vy7i1YuncdrgfHPXFRE6Xzw1bjoeGjMBPkrx/56NJHZzF1r23A/Wxj8/WhMzF4ETnxIlL7Ft2neFtwoxPkq6VYgsy/IeFpTJFIjLWChaTu4gpJI7hzYRCSGEEEIkxevL8ex2O/bt2/eNn5swYQJ+85vfYMGCBU43EAEgJSUFTz31FO644w4ovnbKe8eOHeju5j5VToi3M17j3lwCAJV+PIKn/UqUfMT2WRXiNd64oarMcIe6sgLYLH2cMfGZSyGTyUXLabBMNgeONpo4Y6ZHaKCWS/NEOSFkeNHFL0DA2O8Jiv2D/CCymRaXXr/NZMTfio9jzPtrMXvjW3in4jyMtm8ums+eFIv4KO525H1GKz7Zc9ml93an4djSlG92r7dhWRZnDI343sGdiH7nRdy/f6tbNhBHBQTjtblLUXXb9/Dk2Gm0gehhLOtA6/4fwNZdxRur8IuDPucfYCR0HyiUK1WIMolWIbY3XER3K/efc1TKHKh1gaLl5A58m4jJAQrJHtAghBBCCBmpvH4TsbW1Fb29vV/+d3p6Ou655x5oNMKqVBiGwbRp03D33Xd/+XN2ux0nTnDPHiDE6/HMi5NpghGW/xoYufONdilbt5W/CnGGhKsQAaCqmH/BNVFirUyPNZlhslOLI0KIdARO/DG0sXm8cWrGjlfluxEK/uqh73KoqRZ3FXyKyLf/hscO7MCJ5nqwLPtlNSKfjXsvobvPMqD3Hiy/4Fjo4yZwxrTWnUd3G38LcjFMCtPAR8l9H8VXNe8tWo39eKn4OMZ9+DomfvQG/lFyEl0W86Bfd3JYFD5acAPKb34ED46ZwNl2l7hP19kXYazdyxvHyDUIm/cG5Brp3udy2bj3MvqM3FWIidEBmDVBmlWIEHiwQmojC6q6rajmaBUNALnROtHyIYQQQggh7uH1m4j9/V8txMhkMtxyyy0Dmmc4fvx4TJjw1eJGXV2d23IkZChoIqY5/5+MDPrcV6DwjRIzJdFca+zG/hNCqhAzRMnHE/o6G9BcdZIzJigyHQFhyaLl5A4FdfyL69TiiBDiTRiZHKE5f4fCP5E3Norpwz/le6CEfcDv12Ux45WLpzD1kzcx5v1X8dyZQ4hP9UNidADndf0mGz7eXTHg9x2sxLEreGOqz28RJRc+KjmD2VHc32tONJvQY3GIlpMrLHY7tlRfwg27PkTUO3/DE4d3o7jNtSpYZxbHjcL+lXfi+HX34vrkdMhpjrxo+mv2oPPMXwXFhsx5YVjOO8fnVYgb9/JXVt8m4SpEu82Mmos7OWNU2kBEpswSLSd3oFamhBBCCCHDk9c/Ffr4+Hz54+TkZISEhAz4tebMmfPlj/v6uFsEEuLtAif+CHBSZRg05ZfQRkvrodMV67aW8VYhzhwfjeQ4abX/+bqq4q0AuH+RSWNXipaPO7Asi/313IsL6UFKROio2oEQ4l3k6gCEzf83GKUPb+wUWRNe0J5xy/uWd7bh6eOFSHzv77gSaeCN37zvCrp6Bl+FNhCx6fMgV3J3Cqm6sBUOx8A3WN2Jr+rd6gAON3pPNaKDZXGgoQYPF21D5Nt/w4odG/BxZTmsjsFvdOoUSjw8ZiJKb34Y25fegrlR8WB4Ol4Q97J2VcKw//uCYv0zH4Bv8iqP5zRUPtkjoAoxJgAzx0eLlpO71Vfsh9XkfC4rACRkLYZcLq22n3xtoP2UDCaGDc8uOYQQQgghw5nXbyKGhoZ+2bo0ODh4UK8VGxv75Y8DA6W7uUAIAKj14xC5fCOUQWlf/pzcJxL6vFcRkPXAkObmSdcau7H/pIAqxBXSPZ3NsiyqirmrNWRyJeIyF4mWkztc6rSioY978TgvhlocEUK8kyooFaFzXxIUu8J2BofG+mNRrHuqxVkA++yV6NZyz5Q1mm34aIiqEZVqH8Smz+OMMXY3o6Wau8peLHOjtODbJhNSVeNJX8w5/MnRvYj/78uYu/kdvFZ6Bu1m9+QV6+uP56flo/aOH+DVuUuQHqR3y+sS1zgsvWjZcz9YK/emEgBoIqcjaMovRMlrKPT0WbBx3/CehQgAlQJamSZm81d3e5MeiwMnmrm/R82O0kIp4T83QgghhJCRyuvLPWQyGVJSUnDhwgX09PA/WHGxWr860RgfH++G7AgZWurQbERfvxd2UwdYax/kvtHD/uT4e1tKwfJUIc6aEI2kGOkeFDBcO4O+Du6Wy9GpOVBruVvbeRtqcUQIkTqfhEWwjH8CXWdf5I2NLn8Jm5Z9hJpZC/FKySn8p+I8ugczq44Brka2Ynwl9wywzQVXcP38VAT6C5sf7k6JY1egungrZ0zV+U8RkcTRkl0kIVo5xoaqcK7V+RzJwnojHCwLmYj3Vg6WxdGmOnxcWYZPqspR09Pl9veYHRmH72VNxurENCioXemQYlkWrQd/BGsn/8aZ3CcK+rxXwci8/hF+wD7Zewn9Ru6ZeokxAZgxTrpViP3dLWiuPMYZExieiqDINM4Yb3Oo0QgbzzNaLt3nE0IIIYRIkiSeQKZPn44LFy7gypUrMBqN0GoHdvNZWVkJAFCpVBg3bpybsyRk6Mg1QYAmaKjT8Liahm4UnarljbttuXSrEAGgSsDMqMSxy0XJxZ34NhGDNTJkh6hEy4cQQgYicMKTsLRdhPHaHu5AhwUtex9A0qodeHHWQvx2Sg7evXQB/yw5hYsd/K1Jv4shoBddOiMC+p3fC5stdvx7SzF+dNuUAb3HYITFT4QuIBL9XY1OY+rKC2Ax9UCl8RM1t++SF6Pj3ERsNzlQ3GrBOL1n2++Z7TYUNdRgU1UFNlZVoKm/1+3v4a9S467R2XhozARkBIe5/fXJwHSd/yf6q7bxB8rVCJv3L8i1oWKkNSS6+yzYJGAW4h3LMyRdhVh9YStYlrsNsZAZs96Gr5UpA2BuNG0iEkIIIYRIkSSOnmZlZSE8PBwWiwU7d3IPIHfGZrPh008/axuyatUq+Pr6ujlLQoinvbeVvwpx9sQYSVchWi39qC3dzRmj8Q1FRPJ00XJyhw6THWdbuStw5kZpIZfwohAhZGRgGBn0OS9BEcDfqtTe3wzD3gfB2i3wU6nxSOYkXFjzEI6svgd3jx4LrcLF83yfVyPy2VFUhYnv/gvPnNiPI021sLlhbp6g9BgZErO5D7nYbWbUlvJswIpESFWMp1qa1vZ24bWLp7FyxwYEv/kCFm5dh1cvnnb7BuKE0Aj8a+4yNNz5BF6etYg2EL1If80edJ56XlBsyMw/QK0f6/GchtInuy+h38RdhZgcG4gZ46NEy8ndWJZF1TnuVqaMTIH4zMWi5eQODgFzz8fp1QjWyEXLiRBCCCGEuI8kKhFlMhnuuOMO/PWvf8W+ffsQGhqK2bNnC76+p6cH7777LpqampCamoo5c+Z4NF9CiPtV1nWi6KSAKsRl0q5CrCvbC5uV+yE8IWsZZBJrZXWgwQgHtTgihAwTMpU/wue/iYbNy3jnmJlbTqHt6K8QOuuPAACGYTA9IgbTI2Lw4swFWHe5BK+XncG51mZB793q38dbjShnZegrc+A3PQfxm9MHEajSID8mAQtik7EwNgnxfp47bJOQvQwXD77OGVN1/lMkT7jOYzkIlRakRIROjqZ+5/N6C+r68cPxg//96rNacKixFvvqq7Dz2lVcaG8Z9Gs6E6jS4NaUTNybPg4T9ZEeex8ycJaOChj2P/75xFNuful3wi91jSh5DZXOHjM27eOvQrx9+RhJj25oqy9GT3sNZ0xUymxofIJFy8kdLrRZ0GbiPqxC9/mEEEIIIdIliVXozZs3w2AwQKfTobe3F++//z6OHDkCpVLJe63RaERLSwtsts9ONXZ1deFvf/sb73UxMTG46aab3JI/IWTw3t50kTdm9sQYJMZIa07g/xLWynSZKLm4E18lh4IBZkfR4gIhRDqUgcnQ57yMlj338Mb2lr8LdWgm/NJu/8bPB6g1eCRzEh7OmIgzrU14p6IY6y6XoNXU7/zFGOBKVCsmXonlfM8YQyCqw9phVtnQaTHh48pyfFxZDgBICQjG3Kh4zI2Kw9yoeMT6uu97p19wLPRxE2C4dsZpTGvdeXS3VsM/NMFt7zsQDMMgL0aLdZecV/+VdVjR2GdDpI9rj01muw3Hm+tRUF+NgvpqHGuug9WDFaEMgPmxSbhn9FisSkyDxtUqVyIau6kDLbvvBWvt441Vh01C8LRnRclrKH24sxxGM38V4vRx0q1CBIBKnipEDNORBQCQS61MCSGEEEIkSxJPl5cvX0ZVVdU3fu7atWsDeq3m5mY0N/Of9JbJJNHplZARobyyDcfON3DGMMxnp5OlrLejHi01pzhjgqMyEaDnb6HnTWwOFgcaTJwxk8LV8FPR5y4hRFp08fMROPHH6Dz9Am9s25FfQhmYAk3E1G/9P4ZhMFEfiYn6SLwwfR52XLuCdy4VY0v1ZVgc366Sa/PrQ6dPPwL7dE7fT87KkNAcjIrYb1e8Xe5qx+WudrxRdhYAkOAXiLlRcZgdGYepYdFIDwqFfBD3woljV3BuIgJAVfEWjM373oDfw11yeTYRAWB/vRG3pHLPcGzp78PR5jocaarFkaY6nDQ0wGx3XuHoLulBobg1JRN3pmYjzk/aB6lGAtZhhWHfQ7D1cFejAYBcFw79vNfAyIf3vOi2TiM+LbzCG3f7CmlXIdqsRt6RBWpdEKJGzRItJ3fh20SM0MmRFsR/AJwQQgghhHgnSWwiEkJGtrc3C6tCTIiW9uJZVTF/FWLSuBWi5OJOZwxmdFv4Whw5XwgnhBBvFjDu+7C0XkB/zS7uQIcVLXsfQOTKrVD6xTkNU8rlWJE4GisSR6PdZMSGKxex4WopDjTUfNX4kAGuRLZi0hXnrwMAsa2BqA7/rBqRS3VPJ6orOvF2RTEAwFepwiR9JKaERWFKWDQmh0Uh1tdf8AJ+bPo8nN75R9itzg+QVBdvRVbOo5DJhnZG1vQIDTRyBia787aSBXXf3ETsNJtwrrUJZ1qbcLa1Ccea63Glq12kjIFYX3/cMioDt6ZkIjskXNIbKyNN+7FnYWo8wh8oU0Kf/xoUunAx0hpS67eVwWLlvk8cFReI6WOlXYVYV14Iq5n7wEJC1hLI5NLabGvqt+Fiu4UzJi9GS59ThBBCCCESJolNxKSkJPj4+Ij6nlFR0n5IIWS4KK4w4Ewpd/WwjAHuWJEhWk6ewLIOVPNsIsrkKsSNWShaTu6yn1ocEUKGMYaRIXTuS2j8dAWsnZc4Yx2mdrTsvgeRyzdDpvLlfe1gjRaPZE7CI5mT0NDXg48ry7DhSikON9Wi3a8fHb79COp1fghDxsqQ1BSCsjhh8xa/0Gu1YH9DDfY3fFUtFaBSIzskHNkhYZ/9OzgMGcF6+KnU37peqfZBbPo8VBdvdfoexp4WNFedQGTydJdyczeNQobpkRrOSpqDDf349clDuNjRhDOGJlzt7hA1RwAI0/rgusQ03JqSiZmRsZDRgrzk9JS9i57StwXFhsx8DprwSR7Paag1tfZhx8FK3ri7VmVKfhOq6ryQVqbSOywo5D4/h+YhEkIIIYRImiQ2Ea+77rqhToEQMgRYlsVbm0p44/KnJyAu0l+UnDylpeY0+jq5W7bGpOVCpZXer7OAZ3Eh3k+BRH9JfDsihJDvJFP5Imz+G2jYvAyspZsz1tpRAUPh4wib/28wLlThRfn44XtZU/C9rCmo6+3Gh1dLsVl1GTjOfV10WyCqItpg4qlG5NNlMeNg4zUcbPzmSIEInS9SA4KREhCM1MAQpAQEIzkgCKHpCzk3EfH5ovpQbyKa7TZkBjtQWOc8xuZg8Kezl9BvrxczNcT5BuC6pDRcl5iGGRExg2oxS4aWqfEo2o78n6BY/8z74Tf6Zo/n5A3e21oKG0cVMACMSQ7B5MwI0XLyhL6uRjRXneCMCYpIR2B4qmg5ucv+eu77fLWcwfQIjWj5EEIIIYQQ96NVW0KI1zp1sRkXr7RyxijkDG5fJu1ZiBjGp5Nre6y40mXljMmlFkeEkGFAGZAEfe7f0bLrbgDci+LG2r3oOPkHBE/95YDeK8bXHz8cOw0/HDsNT7TtQ9kV5600ZSyDxAFUIwrV1N+Lpv5eHPifzUWGZfGyTIdQR7/Ta2vLC3DFUIdQ/1D4KJRQygff2tTBsui1WtBlMaHTbEKXxYxWUz+a+vvQ3N+Lpv4+NBk/y7m2txsNfT2QMVok6G7kfF2dPEaUTcTMYD2Wxafg+qR0TNRH0vfHYcDaVYmWvQ8ALP9GviZ6DoKmCNtslLraph7sPVLNGzccqhA/O1DB/X0hcexy0fJxF7OdxeFG7rnn0yM00CroAAQhhBBCiJTRJiIhxCuxLIu3BVQhLpqdhAi9uO2O3c1q7kNt2V7OGK1fGMITp4qWk7twtYf7ArUyJYQMF7rYfARO/ik6T/6RN7b7wmtQBo2GX+pNg3rP+1Zn48d/3s8ZE/35bESjmvtQhzuxDIMidRyuN5Y7j7Fb8NC7v0KBJhEAoJLJ4aNUwkehgo9SCY1cATnDQMYwkDOyz//NwAEWFrsdFocdZrsdFrsdZocdfVYLui1mnqX6b7OzRpjtbVDLQ5zG+Mhj0MpX9jkAOoUS+dEJWBKfgsVxyYj3C3T7e5ChYzd3onn33XCYO3ljFf6J0Oe9AkY2Mh7R3/30Ihw8X6zj0sMwLi1MrJQ8gmVZVJ3nGVkgUyA+c7FoObnLsSYTjDbuP8RcamVKCCGEECJ5I+MJhRAiOUfONuByDffMIZVShluXpouWk6fUlu2B3cp9ijchexlkLrS98xb7eDYRfRQMJodTiyNCyPARkP0YrB2X0HflE97YtkM/hdI/AZqIKQN+v6xUPcalh+FcWYvTGBkYjG4Kw7l4cdtxHtBwbyICwFxTzZebiBaHHRazHR1m7u+JntBnr+PcRFTIfKCSBcHiGPw8xLEh4ciNTsCi2GTMjYqHRkGPZMMR67DCsO8h2Lr4Z/4xSj+ELfgP5OqRsYlcWdeJ/SdreePuXpkpSj6eZKg9i94O7l9rVOpcqHXS+7MvqHNeaf4FOixICCGEECJ99MRKCPE6dgeLtzfzVyEuyxmFkEDpP5gKamWaLb0WRz0WB040cy8Ez4rSQiWXdosqQgj5OoZhEDLrT7B1V8PccoY72GFFy977EblyK5R+cQN+zztXZHBuIgJAeLsfPlhzA8rtBhQ1XMOR5loYbYObk8inRe6LMkUo0m3OW5On2toRaetBo8LPo7nw6bfXIRhjOWN08pgBbSKmBgQjLzoReTEJyImKh14r7Q4KhB/Lsmg7/AuYGg7zBzMy6PP+CVXgKDFS8wrvbL7IGzM1OxLpyc439qVCyH1+0riVouTiTizLoqCW+7BgaqASUb605EQIIYQQInV0R0cI8TpFJ2tR09DNGaNRy7FmcZpoOXlKT/s1GK6d5YwJicmGf2iCaDm5y6FGI6wO7pg8anFECBmGZAoN9PPeQOPmZbD3NXDGOkztaNl5JyJWbBpwFVLGqFBMzAjH6YvO5x6yLFB6pB3PPDYHvwRgsdtx2tCIA401ONBwDcdb6tFm4m9B7aoiTRzSe7nnG88x12CDYmgrjsyONthYIxSM8+9LPvIYdFovcL6OUibDhNBIzIiI+fyfWET5DO0GKRFfd8m/0FuxTlBs0OSnoYvN83hO3qK8qh1Hz3F/LgLAXcOgCtFmMaK2dA9njMYnBBHJ00XLyV3KO6xo7LdzxtB9PiGEEELI8CDJTUSWZdHf34+enh7YBnCCWq1WQ6/XeyQ3Qsjg2GwO/PdT/tPJq/NTEeinFiUnT+KbkQIASWOldzoZAPbxnE5mAORQiyNCyDCl0IUhbMF/0LRlFVgb9+ehtesKDPseQvjC/4KRqwb0fnetzOTcRASAI+caUF7VjrTEYKjkckyPiMH0iBj8dPxMsCyLyu4OnGhpwPHmepxoacCZ1kaY7dyLxHyOq6JxN85DA+evM9t8DR/oMsAyQ1uZ3m+rh7/SeTWYWhYKOTSw46sq+1EBwZgQGoEJ+gjMCI/FpLBIaBVKkTIm3qi/Zg86jv9WUKxv6hr4Zz3k8Zy8iZCZ53MmxSA5TnrtPf9Xbdle2CzcLT8TspdCJsE5mEJamebF6kTJhRBCCCGEeJak7lbLyspw7NgxnD9/HlardcCvk5KSgieeeMKtuRFC3GPv0Ro0tPRyxvjqlLhhYapoOXkKyzpQXbyVM0au0CB2zHzRcnIXu4NFUT33ovk4vRohWunNeSSEEKHUIRkIzXkZhr0P8MaaGg6j7fDPETL7BTAD2EwbnRiMmeOjcfgs99zD/2y8gOefnPutn2cYBskBwUgOCMYtKZ9VAFntdlR0tqG4rRnF7S0obmtBcVsz6vt6BOdlkilxQh2NOeZrTmNCHCZkWltwQRUu+HU9od9ex7mJyDAM5kVPxaJ4NSboIzAuNAL+KukfaCLuY267CEPhYwBY3lh1xDSEzHxuQF/vUlVcYcCZUu7DDjIGuGNFhmg5eVJVMf9hwcTsFaLk4m4FPHPPgzUyjA0Z2KEYQgghhBDiXSSxiciyLN59910cO3ZsqFMhhHiQxWrHe1tLeeOuXzAavjrpP5Q2V51Af3cTZ0xMWh5UGum1QTtrMKPDzN3LlFocEUJGAp+ExbBO+hk6T/2RN7b30gYo/BMQOO57A3qvO1dm4Mi5erAc+xfnylpwtqwZ49P5N+yUcjkyQ8KQGRKGW7/2822mfpR3tOFSVxsud7bjclf7Zz/uav/OOYtF6njOTUQAmGuuGfJNRJOjESwcYCBzGpPsn44nxlJHE/Jttr4GtOy6C6yNv0JL4Z+AsHn/GnDlsRSxLIu3BFQh5k+LR1ykvyg5eVJvRz1aqk9yxgRHZSAgLFm0nNzFYLTjfKuFMyY3Wgu5bORskBNCCCGEDGeS2EQ8ePAgbSASMgLsOFiFlnbuhZcAPzVW56eIlpMnCWllmjh2uSi5uBvf6WQAyKdNRELICBEw9jFYOy+h78onvLGdp56H0i8ePsmuV6ckRAcgb2o89h2r4Yx7a2MJxqWFDbgCKkSjw8xIHWZGxn7j5x0si+b+XtT19aC2twu1vd2o6+1BbW8nus+cg7/VeQXjZHMDdD4W9Ms8s6milssRofNFhNYXETofROh8Ea71QZxfABL9ApHoH4gYH3/cX9CKw40mp69zqMEIi52FSk6L4+QrDksPmnfdBXs/9+EwAJCpAhC+4G3INUGi5OYtTl1sxsUr3PNR5XIGty8fHlWIfN1GACBxrDSrEAsFtDLNp1amhBBCCCHDhiQ2EQsLC7/x37GxsZg4cSJCQ0OhUqlcXgDx8fFxc4aEkMEymW14f3sZb9zNi9Og1Ujio4uTxdSDuvICzhidfwTCEiaLlpM77ePZRIzxlSMlkGZGEUJGBoZhEDLrT7D11MHcfII33nDgh5D7RkETPsnl97pzRQaKTl6Dze68HLG8qh3Hzjdi+rgol1+fi4xhEOnjh0gfP0wO++ZrX1AZcPHg606vVcGBtYmh6ImbiV6rBX1WC6wOB+ysAw4WcICF3eGA4/M2kWqZAmq5HCq5HCqZHGq5HGq5AgEqNQJUagSqNZ//+LN/+6vUgp4Z8mK0nJuIfTYWJ5tNmBlFB2HIZ1iHFS37HoK1nf8+FowC+vzXoAyUXvXZYLAsK2gW4qJZiYjQS/9ZnWUdvK1MZXIV4jMWiZaTO/EdFlTKgJmRGtHyIYQQQgghnuX1K/GdnZ1oaWn58r/nz5+PVatWDWlOhBD3+7TwCtq7nC/aAUBIoAZL5w6PRZfa0j2w27h/vQnZyyCTSW9mYE2PFVe7uOfW5sXoRtQMIEIIkSk0CJv/Bho/XQFbdzV3sN2Mlj33InL5ZigDEl16nwi9DxbPTsKW/Vc5497adAFTsyMhE6ndXEL2Ms5NRACIaD6DO5b8QJR8nMmJ0eK3Jzs4YwrrjbSJSIDPN8faDv0MpvoDguJDZv4e2uhZHs/L2xw524DLNdxfVyqlDLcuHSNaTp7UUnMGfZ3c82ljRudApZVe21azneU8aAEA0yM08FE6bwtNCCGEEEKkxevv7Lq6ur78cWBgIFaskGbLD0KIc31GKz7YWcEbd+vSMVCrpLep9l2qzn/KGyPZVqa11MqUEEK+i1wTjPCF70CmDuSNdZja0bzrdtiN3O3/vsstS9N5v19W13dj/wnuOYXu5BccC33cBM6YtrpidLfybLB6WLyfEskB3JXy+2qNYLkGT5IRo+vcS+i9tEFQrH/WQ/BLu83jOXkbu4PF25v5qxCX5YxCaNDwuD8Udp8vzXWNo40mGG3cn395MdTKlBBCCCFkOPH6TUS1Wv3lj9PS0iCTeX3KhBAXbdxzCT19Fs6Y8BAdFs5yrRrDW3W3VqO17jxnTGjsePgFx4mWkzvxtTjyVTKYHE4tjgghI5MyIAlh894AZPwtnW3dNWjefRccVv75U18XEqjFitxRvHHvfHoRNpvDpdceDCGL5nwtAMWQx3PQpbbXhspum2j5EO/Ue/kjdJ5+QVCsNm4BgiY/7fGcvFHRyVrUNHRzxmjUcqxZNFq0nDzJau5Dbdkezhitnx7hSdNEy8md9gmYh8j3GUoIIYQQQqTF63fk9Ho9FIrPuq76+0uv3QchhFt3rxkf773EG3fHigwoFV7/kSWIkAXSpHHSPJ3cY3HgZDN3i6PZUVqo5NTKlBAycmkipyF0trDNB4vhPAwFj4B1uLZpddPiNOi03JMLGg192HW4yqXXHYzYMfOhUHIvLlcXb4HDxV+ru+UKWAAvFLCQToYvY8NhtB58SlCsSj8W+rx/gpFgi/rBstkc+O+nF3njVuenItB/eBwwqy3bC7uVZ2RBljRHFrAsy3tYMD1IiShfr5+aQwghhBBCXOD1K/JyuRzR0dEAgN7e3qFOhxDiZh/uqkC/kXuxMDbCD3nT4kXLyZMcDjuqi7dyxsiVGsSmzxctJ3c6UG8ET4cjOp1MCCEAfFOuR+CEJwXFGmv3oe3w0y610PT3UeHGBfyVPe9tLYXZYhf8uoOhVOkQkz6PM8bYY0DT1aOi5OPMBL0a/iruxyS+hXQyfFnaStGy537AwT3/GQAUfnEIn/8WZIqRee+z52g1Glq4n+F9dUrcsDBVtJw8rer8Zt4YqbYyvdhuQXM/9/cLamVKCCGEEDL8eP0mIgAsXLgQAHD+/HkYjfTATshw0d5lwuaCK7xxd6zIgFw2PCrXmquOw9jTwhkTm5YPpdpHtJzcaR/PoqqMAXKiR+ZCGiGE/K+A8T+Ez6jrBMX2VqxD17mXXHr9VfNSEOCn5oxp6zTh00L+78XuImTxvPLcJlFycUYhYzA3irsq6nSLGV1mcTZfifew9lxD887bwVp7eGNl6gCELXwHcp1elNy8jcVqx3tbS3njrl8wGr46lSg5eVpP+zUYrp3ljAmJyYZ/aIJoObmTkMMTebF0n08IIYQQMtxIYhNx7NixSE1NRV9fH15++WV0dXUNdUqEEDfYsKOMt/ohKSYAsyfGiJaTp1Wd/5Q3JnHcSlFycTebg0VRPffiwgS9GkEa6bVvIoQQT2AYBqGz/wxN5AxB8Z2nX0DPpQ2CX1+nUeLmJWm8cRt2lKPPyF9V5Q5h8RPgExjNGVN/6QBMfe2i5ONMLk81jZ0FDjZwtywkw4vd2IbmHbfBbuQ+DAYAkKkQNu/fUAXyzyYdrnYcqIShnfu+MMBPjdX5KaLl5Gl83UYg4SpECNhE1GvlyAoZHhvChBBCCCHkK5LYRASABx54AFFRUbh27Rqef/55bN26Fc3NzUOdFiFkgJrb+rCtqJI37q5VmZANkypEi7EbdeWFnDE+gVEIi58oWk7udMZgRpfFwRlDrUwJIeSbGLkaYfPfgDKIf7MPANoO/gT9tfsEv/6yucnQB3N/9vb0WfDJHv75xO7AMDLeRXTWYRO0GO9Jc6I14Lv92FdLcxFHCoe1D82774StW9gM0dC5f4UmcprH8/JWJrMN67eX8cbdvDgNWs3wmJ/Hsg7euedyhRpxYxaIlpM7NffbUNJm4YzJidZCxgyP5zZCCCGEEPIVydyx63Q6/PCHP8Sf/vQnGAwG7NixAzt27IBarUZAQAB0Oh0YgTessbGxWLNmjcdzJoQ499/NF2G1cW84pSUGY2p2pGg5eVpNyQ447NwP3wnZy8Ewkjnf8Q0FtQJaHNGcFEII+RaZyh/hi95B46crYe9r5A5m7TDsfQjhi9dDEzGZ97VVSjluX5aBv71zijPu492XsCJ3FG/7U3dIHLsCJUVrATif8Vh5bhNGT7tD8P29uwWq5ZioV+Nki9lpTFGDCVYHC+UwOexEvhvrsMKw7yFYDOcFxQdO/hl8k1d5PC9v9sney+jodv61AwAhgRosnZssWk6e1lx1Av1dTZwxMWl5UGn8RMvJnQqFtDKlw4KEEEIIIcOSZDYRr1y5gvfffx8Gg+EbP282m9HSIqClzNcoFJL5ZRMyLFXXd2HvsRreuLtWZQ7Z4qEnCJnxlJi9TJRcPKGgjrsiI85PgeQA+vwlhJDvovCJQviid9G4ZTVYSzdnLGs3oWX33YhY9hFUwem8rz1/Rjw+2FmO+pZepzFGsw0bdpbjwRvHDih/V/gERCAyeToarx5xGtPdWoW2+mKExng+H2dyY7Scm4jdFgdOtZgxPYJ7fiKRLpZl0XrwKRjr9guK9027HQHZj3k8L2/W3WvGh7vKeeNuXToGatXwaXFfeXYjb4yUW5nyzT1XyxnMjKTPQkIIIYSQ4UgS5S5NTU1Yu3YtGht5TmYTQiThPxsvgHVefAAAyB6tx/j0MLFS8rj2xjJ0NHEvqOjjJ8I3SJrzH6u6rajstnHG5MVoh9WmMCGEuJsqaDTC5/8bkPHPlHJYutC883ZYu/kP5cjlMty5KpM37tOCKzC0i9OiM2kcf6VW5Vn+wzeelB/LXz1fQC1Nhy2WZdFx4vfou/yRoHhd/CKEzPj9iL/XeX9HOfqN3PeEEaE+WDgrUbScPM3c34m6Cu6RBTr/CIQl8FePeyOjzYEjjdwzYGdEaKBTSmJ5iRBCCCGEuEgSJSFbtmyB0fjNk28hISHQ6/VQqVQuP6hFRg6f9oiESE3J5VYcO89/IODe1VnDahFGSBWikAVVb7VPQCvTfGpxRAghvDSR06Gf+yIMhY/yxtr7m9G881ZELNsIhY774M2ciTH4IDYQV2s7ncZYbQ6s21aGH9zh+dm8UalzodIGwmJ0ns+10t0Yv/ApKFVD0wo7yV+BBD8Fqnucb4jsqzPi6UnssLpnIZ/pOv9PdF9YKyhWHTEVobn/ACMbPpV1A9HS1o9PC67wxt2+fAyUiuGz4VRTsh0Ou5UzJiF7GWQS/ftxpNEEs537BGheLN3nE0IIIYQMV16/iWgymVBSUvLlf0dEROD2229HYuLwOblIyEjBsize/KSYN27GuCikJ4eIkpMYbFYTai5s54xRqn0Rm54vWk7uxtfK1E/JYFI4tTgihBAhfJJXwNbfhI7jv+GNtXXXoHnn7YhY+iHk6gCncTIZg7tXZ+KXLx/ifL2dh6pww8LRiA7zHVDuQskVKiRkL8Wl4+85jbFZ+lFbunvIDtkwDIP8WB3+Xeq8vey1HhuudFmREshfPUqko7v0bXSe+qOgWGXQaITNfxMyBd3n/HcL/8zzhGh/5E2LFy0nT2NZFlcFVE0njVspSj6eIOSwYC4dFiSEEEIIGba8/vhfR0cHbLbPTv8qFAo88sgjtIFIiEQdL27ExSttnDEyBrh7dZZoOYmhrrwAVrPzOVQAEJ+5GAqlNB++u8x2nOaYGQUAc6K1UMqoSoMQQoQKyHoQ/tmPCIq1tpeiZc+9cNi4F3onZ0YgYxT3IR2Hg8V/N190KdeBEtTS9NxmUXJxJl9AdY2QBXYiHb2XP0b7kV8IipX7RiN80bucG/gjRU1DN/YeqeaNu2d1FuTD6J6wvbEUXS2XOWPCE6dIdmSBg2VRWM/9GZcRrEKEzuvPpxNCCCGEkAHy+k1Es/mrhem0tDSEhoYOaT6EkIGxO1j8Z+MF3rh5MxIQH+UvSk5iqTy7kTcmabx0W5kW1ZvA0+EIeXQ6mRBCXBY0+Wn4pq4RFGtuOg7D3gfB2p0f6mAYBvcIOKiz/+Q1zran7hIYNgrBUdyzGltrz6G7tcrjuTgzQa9GoIr7kWlfHW0iDhf9NbvReuBJQbEydSDCF70HhQ+NygCAtzZegIPnfjBjVAimZg+v3y9B9/kSHllQ0maBwWjnjKH7fEIIIYSQ4c3rNxH1ev2XPw4L4571QgjxXoXHr6G63nk7MABQKmS4Y3mGaDmJoae9Fi01pzhjAsNHIygiXbSc3I2vlamcAeZG0+ICIYS4imEYhMx6Hrr4hYLijXWFMBQ+DtbhfIZfVqoeEzPCOV+HZYE3P+E/+OMOQg7RDGU1okLGIIdngfycwYxWnkV24v2MDYfRUvAIwPL/WTJyDcIWvA1V4ChRcvN2pVfbcORcA2/cfddnD6v5oTaLETUlOzljVBp/xKTliZaTuxUIOCQhpGKbEEIIIYRIl9dvIvr4+MDPz2+o0yCEDILFasc7m0t445bnjkJYiE6UnMRSJWDhM2n8KskuqFgdLIp4WhxNCFMjUC0XLSdCCBlOGJkCobn/hCZyuqD4/uodaC16AqzD+UaIkGrEUyVNOFfW4lKuAxGfsRByJfcsuariLXDYrR7PxRm+BXIWQCFVI0qaueUsWnbfA3BU8n6JUUA/7zVowieKkZrXEzrzfNrYSGSMGl5dhWrL9sBm6eOMic9aArlCLVpO7ravlvuwYLhWjoxgmglLCCGEEDKcef0mIgBMnToVAFBZWTnUqRBCBmBbUSWa27gfQHVaBW5ZkiZaTmJwOGyoPM+9iSiTq5CQuUS0nNztVIsZPVbu3lX51OKIEEIGRabQIGz+m1CFCJsZ3Hd1E9oO/wws+92fzynxQZg9kX8+1xsfF8PB159wkJRqX8Smz+eMMfe1o+HyIY/mwWVWpBZKnqemfTxV+cR7mVsvoHnn7WBtQv4MGehzXoYuNl+EzKThVEkTLlxq5YxhGODuVcNr5jkAXD27iTcmefxqUXLxhIY+G8o6uA9w5MZoJXsYkhBCCCGECCOJTcR58+ZBo9GguroaBQUFQ50OIcQFfUYr1m8r5Y27cWEa/H2le0r3uzReOQJTL/eiSmx6PlRa6c6ALOA5nQwAeTHDq7qUEEKGgkzlh/BF/4XCP1FQfG/FerQfe8bpRuKdKzMg41n3vVzTgQOn6waSrkuSxq3kjak8x79Y7yl+KhmmhnNXSx5qMMFkc4iWE3EPS1spmnfcAoelS1B8yKw/wid5hcfzkgqHgxXU+jh/WjwSYwJEyUks3a3VaK09yxkTHDkGgeGpouXkbkIqrPOolSkhhBBCyLAniU1EPz8/3HvvvZDJZPj444/xwQcfoKtL2IMeIWRofbizAl29Fs6YIH81rpuXIlpOYhGy4Jk0jn8WlLdiWZZ3TkqCnwJJAUrRciKEkOFMrg1FxOL1kOsiBMX3XHwTnaf++J0biXGR/lg4i39D8q2NF2D18OaYPm4C/ILjOGMarxyCscfz7VWdyY/lPhBjsrM40mQSLR8yeJaOCjTtuBkOc6eg+KAp/we/tNs8npeUFBy/hso67udypUKGO1cOr5nnEHqfL2Dmqzfjm3uukTOYEcF9wIIQQgghhEifYqgTEMJgMCAsLAwrVqzA5s2bUVRUhIMHDyI1NRVxcXEICAiATie80sXf3x9pacOrbSIh3qi1w4hP9l7ijbtt2Rho1JL4OBLM2NuKhksHOGN8gmIQljBJtJzcrbLbhpoeG2cMnU4mhBD3UvjFIGLJBjRuvR4OE3e1OwB0nf8nIFMhaOKPvvX/7liRgYLj12C2OJ+f2Gjow/aiq1iZ77nDPgzDIHHcShQX/N1pDMs6UHV+C8bMus9jeXDJj9Hi1ye4YwpqjVR9LxGWzito2n4zHKZ2QfEB476PgOyHPZ6XlFisdry1ib8KcVlOMsJDfETJSSwOuxVVxVs4Y+QKDeIyFomWk7v1Wx042sh9MGJmpAYahSTOpRNCCCGEkEHw+lV7h8OBZ5999jt/vry8HOXl5S6/ZkpKCm0iEiKC/356kXNhEgAi9T5YNDtJtJzEUl28FSzL/WtPGrsSDCPdB28hrUzzaTGVEELcThmYjIgl69G07UZBVVRdZ/8GAN/aSAwJ1OK6ealYv72M8/r3tpZi3owE+Gg9V1memL0cFwpf4fzeWXl+M9Jn3jsk87eifBVID1JyzgcrqDPCwbKQ0Xwwr2btqkTz9jVwGA2C4v3G3IXAiU95PC+p2VxwBYZ27o4UOo0CNy9JFy0nsTRcPgRzH/cGdOyYeVBp/ETLyd0ON5pg4SlCp8OChBBCCCEjg3RXrwkhXq26vgu7D1fxxt21MhPKYXaClWVZVJ7lbnHEMDIkjpX2TB2+VqYBKhkmhg2vOZeEEOItVMHpCF/0HhilsEXqrrN/Q8fpv3zr529cNBoBvirua3st+Gh3xYBzFULrp0fkqJmcMb3ttTBcO+PRPLjwtTRtMdpR0sbdwp0MLWt3DZq2r4G9v1lQvM+o6xE8/bdDsnHtzbp7zVi/jfvwAQBcv2A0Av2G371g5bmNvDFJ41eLkoun7ONpZQoAudG0iUgIIYQQMhIMr5V7QojX+PfHF+D49gimb0iND8LcybFipSQaQ+1Z9LTXcMZEjpoJnX+YaDm5W4fJjtMGM2fM3GgNFDJadCOEEE9R68cifOE7YBTCFnI/20h84RszEn20Sty6bAzvtR/vvoS2Tu7DI4MlZH6YkDlknjJPQNXNvlrP/h6RgbN2V6Np+02w9zUKitclrUDonL9IumuEp6zfXo4+o/OqXAAIDtDg+vmpouUklv7uFjReOcwZ4xccD33seNFycjcHy6KQ57BgdogKYTqvb2xFCCGEEELcwOvv+mQyGX7/+9+79TUVCq//ZRMiaefKW3DiAv8CzX03ZEM2DDeZ+KoQASBpHP9CqTcrajDybhLTXChCCPE8TcRkhM3/D5p33wXYuQ93AEDX2RcBsAic8OMvq6uWzEnCxr2X0dTa5/Q6s8WOd7eU4gd3THRr/l8XNWoWND4hMPW1OY2pLd2LCQt/MiRtAjOCVQjXytFsdN5ydV9dP344PlDUvAg/a+fVzysQmwTF6xKWQJ/zEhgZPTf+ryZDH7YUXuGNu2NFBrSa4ff7V3X+U7Asd5/PpPGrJF29er7VgjYT96+RWpkSQgghhIwckjhWGRgY6NZ/fH19h/qXRMiw5XCw+PdHxbxxU7IiMS5NupV4zlhMPagt3cMZo/YJRlTKbNFy8oQCnkoLBQPMoRZHhBAiCm30LITlvw7IhM0s7Dr7EjrPfFWRqFLKcfeqTN7rdh6qwrXG7kHn64xMrkRC9jLOGLvNhGsXd3ksBy4MwyCfZ+G8vMOKul6baDkRfpaOS2jadqPgDURt3ALoc/8BRuDX00jzn00XYLVxbzDFRvhh4cwE0XISC8s6UHl+M2cMw8h5P8e8XYGAVqZ0WJAQQgghZOSQxCYiIUQ6DpyqxaWaDs4YGQPcd32WaDmJ6drFXbDbTJwxidnLIZNLd2HKYmdR1MC9iTgpXA1/FX2LIYQQseji8qHPexVghFX+dJ19CZ2n/vjlRuLcybFIiQ/ivMbhYPHvjy+4JV9nksat5I0ZypamfHMRAaCgln8BnojD0lb62QaisUVQvDYmF2H5r4KRc88JHakuVbdj/4la3rj7rs+GXD787gNbak6jr6OOMyYqZTa0vqGi5eQJfIcFI3VypAdJ91mGEEIIIYS4Zvjd2RNChozFasd/Npbwxi2YmYiE6ABRchKbkIVNIQuk3uxkswl9Vu5epnQ6mRBCxOeTsAj6/LXCNxLP/xPtR38JlnVAJmMEHfA5dr4B5yuEbcgMhH9oIkJjx3HGtDdcRGfzZY/lwGVahAY6BXebwn08s8SIOMytF9C0/SY4TM7b436dJnoO9PP+BUau9nhuUsSyLP4loNtIZkoopo2NFCUnsVWe3cgbkzR+tSi5eEp9rw0VndzzLnNjtJJu10oIIYQQQlwzIjcR7Xbnc0wIIQO3tegq5zwlAFCr5LhjRYZoOYmps/kS2hsucsaExo6Df2iiaDl5QoGAxdG8GGplSgghQ8EnYRHC5r0meCOxp/QttB34MViHDePTwzFxTDjvNf/6sBgOvsG4gyBkbvBQVSOq5QxmRWk4Y443mdBj4W73SDzLbDiH5u03w2HuFBSviZyBsPn/hkzB/Wc7kp0saUJxhYE37oEbsoflBpPF2I3asn2cMRrfUESOmiFaTp6wT0ArUyEV2YQQQgghZPgYMZuIdrsdJSUlePPNN7F27dqhToeQYae334J1W8t4466bl4rQoOG5wSSoClHip5NZluXdREwOUCDBn1ocEULIUNHFL/xsI1HgTLfeyx/AUPgYWLsF912fDb71/8s1HSg8cc09yX6H2DHzoVBxL1JXX9gOu83isRy45PNU29tY4ABP22/iOcaGw2javgYOS5egeE3ULIQteAsyxfC8P3UHu4PFGwKqEGdPjEFaUogoOYmtumQ7HHbuz5yksSshkwk7wOGt+FqZ6hQMpkXQZjshhBBCyEgy7DcRq6qq8MEHH+Dpp5/Gq6++itOnT8Nq5W7PQQhx3bptZejp436wDvBV4cZFo0XLSUx2mxnVF7ZzxihUPohLny9aTp5wucuK2l4bZwy1MiWEkKGni1+IsHzhG4n9VdvQsvd+JEapMW9aPG/8fzZegNnime4eSpUOcRkLOWMsxk7UXyryyPvzyYnRgq/Oah/PQjzxjL7qnWjedSdYK3dnjC9oY3IQtuA/kCnp3oXLrkNVqGno5oxRyBnce93wnHkOAJVn+Q8LJkp8ZEGv1YHjzdyz3WdGaqCWD79KU0IIIYQQ4tyw3ERsaWnBtm3b8Oyzz+KFF15AUVERent7hzotQoathpZebN7HP5votuUZ8NEOzwq1uvJCWIzcJ97jMxdBoZL2KXe+08mgVqaEEOI1dPELXNpINNYWoGXXnbhjaSLUKjlnrKHdiE0CvvcPlDe3NA3RyDFBzz03b3+9EVYPtnwl39Zz6QMY9j0I2M2C4rVx8z9vYUr3LVz6TVa8vYl/5vmynGREhfmKkpPY2hvL0NlcwRkTljAZfsGxouXkCYcajLDydGKmw4KEEEIIISPPsNlE7O3txYEDB/DCCy/g17/+NbZv3w6DgX9mAyFk8N785AJsdu6FsqgwXyyZkyRaTmIT1MpUwIKot9tTyz0nJVAlw3iehVVCCCHi0cUvQNi8NwC5sM9mU+NR2I/ehdU5Mbyx728vQ2c3d9XKQIVEZ/HOEG66ehR9XY0eeX8++bHcG0/dFgdOtwjbzCKD13XhdbQdeBJghc2i1CUsRlj+a2AEfl2MZBt2lKOzh/vvsk6rwC1Lx4iWk9gqz27kjRkO9/l8FdQMgFw6LEgIIYQQMuJIehPRYrHg9OnTWLt2LZ5++mls2LABVVVV3xnLMAySk5Nxyy234MEHHxQ9V0KGq5LLrTh4uo437t7rsqBUSPojx6nejno0Vx3njAnQj0JwVIZoOXlCS78N51u5W9bOjdFCIaMWR4QQ4k10cfkIX/gOGIWwChJLazFmWX+CQD/u2V79Jhve3VLqpiy/iWEYAYvyLKrOf+qR9+eTH8v/e7mP5+ANGTyWZdFx8nl0HP+N4Gt8klZCn/cKGLnKo7kNB81tffh49yXeuJsWpiHQb3huyNqsRtSU7OCMUap9EZOWJ1pOnmBzsCjkmXs+NlSFUC13lTohhBBCCBl+JDf12+Fw4PLlyzhx4gTOnTsHk4n79HNERAQmT56MyZMnIyRkeA55J2SoOBwsXvvgHG9cxqgQzJoQLUpOQ6Hq/GbemKTxq8Ew0t5cK+BZWACAeTyVGYQQQoaGNmomwpesR8vOO+GwcLffBgBl/xUsj/gU/+1Zwhm37UAlVuSNQlykvxuz/UxC9jKcL/g7WIfzWbyV5zYjY/YDYBhxDyol+SuQ4KdAdY/z3PbVGfH0JFby3/+9Feuwoe3I/6G3/F3B1/iMuh6hc/4CRia5x+Ah8Z9PSmC1cVd36oO1uG5+qmg5ia22bB+sZu7RKAlZS6FQakTLyRNOtZjRaeH+s84TcHiCEEIIIYQMP5J5eqqvr8fJkydx8uRJdHZ2csb6+/tj0qRJmDx5MuLi4kTLkZCRZv+Ja7hU3cEb9+BN44btAprDYUclTxWETK5EQhb3IqwU8LUyVcmA2VG0iUgIId5KEzYR4Us/QPOOW+EwtfHGTw/ah73aiWg0hjuNcThY/PvjYvz68VluzhbQ+AQjOnUO6soLnMb0dzWiqfI4IpOnu/39uTAMg/xYHf5d2u005lqPDVe6rEgJpIo3d3NY+2AoeBTG2n2Cr/FLvwvBM34r+oazVJVXtqHwxDXeuHtXZ/HOUJWykTKyYK+Ayun5dFiQEEIIIWRE8upNxI6ODpw6dQonTpxAQ0ODoGtWrVqF/Px8yGT0cEiIJ5ktdry58QJvXO6UOKQlBouS01BoqjwKY3czZ0z06FyodYGi5eQJvVYHjjRyV37PiNTCV0mfvYQQ4s3UIRmIWPYxmrffDHt/E2esnHHghrhN+HvFQ5xxx8434lxZC8alh7k5288W57k2EQGg8uwnom8i4vO5iFybiPh8xhhtIrqXrb8FLbvugqWN/z70CwHjf4DACT8etofa3I1lWbz2wXneuNGJwciZMnwP7fa01cBQc5ozJigiHUGRaaLl5Aksy/JuIsb7KTAqQClaToQQQgghxHt43Wqv0WjE0aNH8dJLL+GXv/wlNm3a5HQDUaFQIDMzE7m5uV/+XFhYGG0gEiKCT/ZcgqGdu72lSinDvddliZbTUKg8y386OXn8alFy8aQD9UZYuTscUStTQgiRCFXgKEQs/wQKP/7F/4yAcqT7V/DGrf3gHOwO1k0ZfiUieQa0ftybk3UV+2Hq5a+sdLcJejUCVNzPHfsEtAInwlk6LqHx0xUubSAGT3sWQROfog1EFxw8XYfSq/xfUw/dNBayYTwLu/KckJEF0q9CLO+woq7XzhkzP1ZHX0OEEEIIISOUV1Qi2u12lJaW4uTJkyguLobVanUaq1QqkZ6ejvHjxyMrKwtarRaXL19GYWGhqDkTMpK1d5mwYUc5b9x181MRFjJ8Z2eY+trRcKmIM0YXEInwxCmi5eQpe2u5F0EZAPk0J4UQQiRD6ReHiOUb0bzzDljbS53GMQxwfdyn+H3Jj8BynD+squvCrkNVWDInya15ymRyJI1biYsH/+U0hnXYUHX+U6TPvMet781HIWOQE6PF5so+pzHnDGa0Ge0I0Q7fdo9iMTUeRcue+wXN9AQAMHKEzvkLfFNu8HRqw4rFase/P+bfpJ09MQYZo0JFyWkoOBw2VBVv4YyRK9SIz1wsWk6eIqSVKR0WJIQQQggZuYZ0E7GqqgonTpzAmTNn0NvrfFi5SqXCmDFjMH78eGRmZkKjkfbQckKk7p3NJTCabZwxgX5qrFks7dY+fKqLt8Lh4P59SBq7UvKzd6wOFvvruTcRx4aqoKcFUkIIkRSFLhyRyz5Cy577YWo84jQu1qcB0/UnccQwlfP13tpUgrmTYuGjc2/Lu6Rxq3Dx4BsAnFc6Xj27EWkz7hL9e24+zyYiC6Cw3ogbRvmKmtdw8//s3Xd4HOX1NuBntu9q1Xu3JMu9yL3buGNjjMGmY3pLgYSE5JcQAiEkEEi+NEIKAUKvBtzA3Qb33otsSZas3ru27873hxMHB2tmZEujLc99Xb7A0rO7x02anfO+520v/BT1W38I+DpfbHoRrREJM/4GS+acni4t6CzfVIDq+s7/TgOAXqfBfYuDe9pIVcF2ONrrJTNpA2fBYApXraaeskFmsWCMSYMR8UbV6iEiIiIi/9IrTcTq6mr87W9/Q3195xflRqMRQ4YMQV5eHoYMGQKDgWeJEPmDs+XNWLe9WDZ316IhsJiC99wMURRRdPATmZSArLyFKlXUc/bVONDqkp5lOjuDuxCJiAKRxhCBxKvfRt2X34etuPNdNwvT1mB/Qx5cvs5vJLe0OfHu5yfx4I3Du7XGsKgUJPediKrCHZ1m2pvKUFOyD0lZ0o3O7jYlxQy9BpIjvzeV2dhEvEyi6EPz/hfRcuQvih8j6MOROOd1mJLVPycz0DW3OvD+F6dkc4tm5iI5Prj/Thcd+lQ2k50X+KNMK9s9ONnokszMSDNDG8Rja4mIiIhIWq80Edva2i7ZQDSbzRgyZAhGjBiBQYMGQa8P3gYEUSASRRF/++Aw5I48ykqNxNzJWWqV1Stqz+1HW2OpZCYpZwLCIpNVq6mnyI0yBYBZHGVKRBSwBK0R8TNeRuPueLSdeP2SmWhDC+Ymb8aqCunRfSs2FeCaqdlITeze3Tk5I26QbCICwNmDn6reRAw3aDAu0YTtVY5OM9urHHB4fDDpAnsygdp8rjbUffko7KUbFD9GG5aMxLlvwRAzsEdrC1ZvLD8Om116ykak1YBb5gf3tJGOlirZrzfWmHQkZI5SraaeomyUKa/ziYiIiEKZX5yJOHDgQFx11VUYMGAAdDq/KImILmH7wQocPV0nm3vgpuFBv1pVfhcikBMEq5NFUcSGUumbC9kROuREctEHEVEgEwQNYsY/A50lCU37nrtkZk7yFmyvG48mV3Snz+Pxinjl46N45ruTurW+lNwpMFnjJMcLludvhqOjEaawmG59bTkz0y2STUS7R8Suagemp/FGvFLulmLUbrgP7uYzih+jjxmExLlvQhcW+Au4ekNhaRPWKpg2snThYFgtwT0l6Oyh5RBF6Skc548sCPz3O3KLBc06AZOTeZwMERERUSjzi+Ww+fn5WL9+PbZt24ampqbeLoeILsHp8uKfHx+RzY0ZkoRRgxJVqam3ODoaUZ6/WTJjCotFav+rVKupp5xsdKPK5pXMcHUyEVFwEAQBkcO/jbhpfwQ031wcYtC6sTij85Gn/7H7SCUOnqzp1to0Wj2yh18nmfH5PCg5urpbX1eJmWlm2cwmBbv66Tx7xXZUrVzQpQaiKW0akhd8ygbiZRJFEX99/zBEmWkjGcnhmD81W62yeoXP58HZw59JZgSNDll50l+PAkGL04s9NZ0vgACAyckm7qImIiIiCnG9cjUYFxeHoUOHQqvVAv85W6yoCMuWLcOTTz6JF198ERs2bJA8M5GI1PXJ+tOoaZDekabRCHigm89B8kclR1fD53VLZrLyroNGG/i78zYoGnEkf/OUiIgChzV3CZLmvQeNMeobnxsdcwg5VvndSv/46DC8XumdPF2VPeJ62UzRwU8hynVCulmKVYeB0dLf8zeX2+FTua5AI4oiWo+/hpq1t8PnbFH8OGv/W5E45w1oDMF9Rl9P+mp/OU4Uyr/3vn/JcGi1wd1QqirYDnub9OSV1H7TYLbGqVZTT/mywg6vzJclnntORERERL3yDiA6OhoPP/wwnnvuOdx8883Izr54NeO5c+ewfPlyPP300/jNb36DtWvXorq6ujdKJSIAdY02fLAmXza3cHoOMlMiVKmpt4iiqGyUqYIbnYFgk0wTMc6kwfA4o2r1EBGROkzJE5C8cCV0kRdfpwsCcFOm9C4dACipaMUX2+SbjV1hjU5FUvYEyUxb4znUlR7o1tdVYqbMrvxauxfHG1yq1RNofK521G35Dhp3Pw2I0hMQvi5q1I8RO/lFCJfYOUvKOJwevLpMftrI6CFJGDs0SZWaelPRoU9lMzkjF6tSS0/bILNDWiMAV6VysSARERFRqOvVAwitViumTp2KqVOnor6+Hvv27cPevXtRW1t7IVNWVoaysjKsWrUKycnJyMvLw4gRI5CamtqbpROFlNc+OQanS/qGToTVgDuuHaxaTb2l9twBtDWWSmaSsifAGp2mWk09pazNjVNN0jsuZ6RZgv78SyKiUKWPzEbywpWo2/QQHJU7Lny8j7UME+L2Ylf9WMnHv7X8OK4ak47wsO47Py1nxPWoPrtLMlN08FMkZI7uttdUYla6GX85Kr17blOZHcO48OYbXI35qN30EDwtRYofI2iNiJ36e1hzAn+kZG/7eN1p1DVKN5O0WgEP3TQ8KM4AlNLRUoWqwh2SmbCoVCRlj1Otpp7i9IrYViH95z46wYgYk1a1moiIiIjIP/nNLJK4uDjMmzcPTz/9NH784x/jqquuQnh4+EWZqqoqrFmzBs899xyeeeYZrFixAqWlpaqPLCIKJScK67Flr3TTDADuum5It94k9FdFB5fJZnJG3qBKLT1tU7n8+U2zM7g6mYgomGmNUUi8+h1Y+9920ccXpX8Oo8Yp+djWDhfe+uxgt9aT0v8qGMNiJDNlpzbCaWvu1teVMzjGgESz9M32jQpGhIea9oJlqFqxoEsNRG1YMpKu/YwNxG5Q09CBj9bKTxu5bnpfZCQH97QRADh7aDlEUXoMc86I6yEIfnMb5bLtqnKgwyN9H4XnnhMRERER/KmJ+HWZmZm48cYb8etf/xrf/va3MXr0aBgMFzcnamtrsX79erzwwgt45ZVXeq1WomDm84n46/uHZHNZaZGYNzVbNhfoHB2NKM/fLJkxhcUitd801WrqSXI3Oy06AROT2UQkIgp2gkaP2MkvIHrszwGc34kUZWjF1SkbZR+7eus55B/e3m21aLV6ZA9fKJnxed0oObq6215TCUEQMEPmjODTzW6Utknv8A8VPo8D9dt+jPqvvg/R61D8OGPCKCRf9zmMccN6tL5Q8eqyo3C5pZtmkeFG3H7tINVq6i0+nwdnD0uPahY0OmTlBUfzWsmiBp57TkRERETw1ybif2i1WgwePBj33HMPnn/+edx5550YOHAgNJqLy7bb/7tbZsOGDdi9e/dFHyOiy7NhZwkKS+VX8n/rlryQGGlZcnQ1fF7pm39ZeddBow38c3manV7sq5HeYTIlxQyjNvj/3ImI6HyTLHLYQ0i8+h1ojFEAgNnJXyLW0Cj5OJ+owctvbUPTwT9C9Ck/605KtoJzh4sOfar6tJKZafK7djbKnEEWClxNZ1C1ciHaT7/XpcdZ+92MpGs+gs6S0GO1hZKjp+uwdX+5bO7uRUNgtQT/tJGqgu2wt9VJZlL7TYPZGqdaTT3FJ4qyE0f6R+mRER7472mIiIiI6Mr5dRPx60wmE8aNG4fvfve7+NWvfoXFixcjPT39G7ni4mK8/fbb+OlPf4pXX30Vhw8fhtvNFb9EXdVhc+P1T4/J5qaMSsPw/sF/M0cURRQd/EQ2l6PgxmYg2FJuh1fm3itXJxMRhR5z2jQkL/oChtjB0Gs8WJyxUvYxZ9r6YuOGTahZcws8HdVXXEN4TAYS+kifx9haX4y60u4dpSpnQrIJYTrpxTUbQnikqSiKaD3xOqqWz4O78aTyBwoaxIz/BWKn/A6ClmdKdgevT8TfPpCfNtI3IwpzJ2epUlNvK1Rwnd931BJVaulpR+pdqLNLL+qYncFRpkRERER0nq63C7gckZGRmDFjBmbMmIHq6mrs3bsX+/btQ2Pjf1dCu91uHDp0CIcOHYLZbMaIESMwevRo5ObmfmMnIxF909urTqC5TXonmkGvwf1LQmOcVO25A2hrlD4bMil7AqzRaarV1JPkdkpoBWB6GpuIREShSB+egaRrl6Nh+08wUvwE/cILcaatr+Rjlp1biKFRz8P16WzETf0dLJlzr6iGviNvQG3JXslM0cFPkJA56opepyuMWgFTU81Yc67zRuGBWicaHV7EmKTPTww2HlsNGrb+EPbyL7v0OI0pFvHTX4Y5dXKP1RaKvth6FmfLW2RzD98cGtNGOlqqUFW4QzITFp2GxCzpxQuBYkMpR5kSERERkXIB301LSkrCwoUL8ctf/hKPPfYYJk2aBIvl4lVzdrsdO3fuxJ///Gc8+eST2LhR/vwWolBWXN6CFZsLZXNL5vZHUlyYKjX1tqKDy2QzOSNvUKWWnub0ithWKd1EHJNoRJQxtG6AEhHRf2l0ZsRN+yNiJz6LW7JWQAPpXS3N7iisqZgNn7MJtRvuQ92X34PXKT8yvTOp/afDaImSzJSd2ghHh/S41e42O116945PPL/bP5R0lKxF5SezutxANCaORcr1a9lA7GbNbU688Zn8tJGrxqRjaL94VWrqbWcPfQZAegRHzojrIQgBf/sEALCpXLqJmGTRYnBM8I+wJSIiIiJlguMq+N/ntPTt2xe33XYbnn/+eTzwwAPIy8uDTnfxZsuWlhYcP3681+ok8neiKOIv7x2Ezyf9Rjou2oybrx6gWl29ydHRiPL8zZIZU1gsUvtNU62mnrSzyg6bR/rPf5bMTVIiIgp+giAgYvA9GHXjnzE9VX506Ibqq1BjP9+U6Cj8BJWfzIStTPr7a2e0OgOyhl8nmfF53Sg+Ij9utTtdlWaGzETTkBlp6nW2oH7r46jbeD98zqYuPTZi6MNIuuZD6MKSe6y+UPWvT4+h3SZ93IfRoMV9ITJtxOfz4Ozh5ZIZQaND1vCFqtXUk862uFHU4pHMzEq3QBCCfwcqERERESkTNE3Er9PpdMjLy8MDDzyA559/Hrfddhv69u3LC2EiBTbvKcXxgnrZ3H2Lh8FkDMiJyF1WcnQ1fF7pmy1ZeddBo9WrVlNP2lAqv0OCTUQiIvoPU9JYPPTIYwg3So9B94o6fHjueoj/XqfitdWgdt2dqN/6OHyu1i6/rpIJAIUHP4Eo+rr83JcrwqDBuCSTZGZ7pQN2j3o19Ybzuw9noP3MB116nGCIQMLs1xAz7kkImuC4rvInp4oasHZ7sWzupqsHICEmNK71Kgu2wd5WJ5lJ638VzNY41WrqSRsVLGKYzVGmRERERPQ1QdlE/DqLxYJJkybhsccewzPPPIOFCxciMTGxt8si8ksdNjdeXXZUNjckNw7Tx6arUlNvE0UfCg8oGGU64npV6ulpXp8oO+JoYLQeadbQaCATEZEykbHJuP/mibK5Ey0DcbR58EUfaz/zASo+mdXlXYnhMRlIzB4vmeloKkf12T1det4rJbfQxuEVsb3SoVo9avLYalG76SHUbbwfXltNlx5riB2KlEVrrvi8TLo0r+/8tBE5ibEWLJnTT5Wa/IGi6/yRi1WpRQ1y556H6wWMlVkIQUREREShJeibiF8XGxuLuXPn4tZbb+3tUoj80turTqCxRfqmlkYj4Lu3jQyZnb3VZ/egvalMMpOUPQHW6DTVaupJR+qdaHBI746YyV2IRER0CXMmZaF/Voxs7qNz18Plu3iXmbejErXr7kTtxgfh6ahU/Jp9Ry6RzRQpaBJ0p5kKdvEE20hTURTRduYjVH4yHbbiz7v4aAERQx9C8sLl0Edk9lCF9MVXRSgslT+H9OFb8kJm2khbYxmqi3ZKZsKi05CYNVa1mnpSvd2LQ3XSO8anp5mh14TG+zwiIiIiUiakmohE1Lni8has2Fwom7tuRl9kpUWqUpM/KDzwsWxGyTi1QCG3OhkAZrOJSEREl6DRCPjOrSMgt86o3hmLdZUzLvk5W8kXqFg2HS3HXoHokz63CwBS+02FSWbMYMWZr2BrrZV9ru6SEqbD0FiDZGZzmR0emfOnA4Wr6Qxq1tyKhq0/gM/Z0qXHasOSkTj/A8SM+zkErbHHagx1zW1OvLH8uGxu7NAkTBieokpN/qDo4CeymZwR10MQguO2yaZyG+S+6nCxIBERERH9r+C4GiaiKyKK58cb+WRuZkVHGLH02sGSmWBia61B5ZmvJDMmaxxS+01TraaeJrczIiVMi0ExPKOIiIgurX9WDOZOzpLNra2ciVrHpZt/orsDTXt+icrl8+CoOSD5PBqtHjkjpBfziKIXZw9/JltTd5JbcNPs8uFArfSOIH/ndbagYdfTqPx0NhyV27v8eEvWtUi5YQPMKZN6pD76r9c/OYp2m/T53nqdBt+6ZUTITBvxepw4e3iFZEaj0SE7b5FqNfW0jTLnnus1wNQUnodIRERERBdjE5GIsHlPKY4X1MvmHrhxOMIsodNAKjr4KURRerRnzogboNEGx+9JUYsbxa3Suz5mpVtC5uYSERFdnnuuHwqrzPWCR9Tj/ZLFECXWL7kbT6F61XWo+/J78LRXdJrLVrBTqOjQZ/Ap2NnYXWZlBO9IU9HnRVv+u6j4eAraTrwGiN4uPV7QWxE37Y+In/FXaI1RPVYnnXeyqAHrdpTI5m68uj9SEqyq1OQPyk5uhMsuPd41beBMmMLkRzQHgg63DzuqpJuIE5JMCDfwFhERERERXYxXiEQhrsPmxqvLjsrmhuTGYca4DFVq8gc+rxtFhz6VzAiCNshGmcrfzJyl4JwnIiIKbVHhRtx53RDZ3MmWAdjfmCeb6yj8BOUfT0Xj3ufgc7V+4/NhkUlIyZ0i+Rz21hpUFXR9t9zlyo3UIzNc+ly5jWU2iFJdVD/kqN6LqhUL0LD9/+BzNHb58aaUSUi5YT2suUu4KEkFXp+Iv7x7UDaXGGvBzVcPUKUmf6HkyIK+o25UpRY1bKu0wyW9NhKzOMqUiIiIiC6BTUSiEPfG8uNobHFIZjQaAd+5LXTGG+Hf5yc52qV3Z6b0mwpLRKJqNfW0DaXSTcQIgwZjEk2q1UNERIFrwbRsZCs4Q/njc4tg9yg4C8/rROvRv6L8w0loPf4qRK/rok/njFoi+xSFB5fJv043EQRBdqRpebsX+U3SIyb9hbP+GGrWLkX16hvgajjW5cdrDJGInfI7JM77APrw0FmU1ttWbylEUZn0bjsA+NYtI2AySje9g0lzzRnUlx+RzETG5yA+Y6RqNfU0Jeeez+RiQSIiIiK6BDYRiULY6eJGrPqyUDa3cHpfZKeF1ripUFudXGvz4Ei9SzJzVaoZek3oNJKJiOjyabUafPd2+RvwLe5IrCyfr/h5fc4mNO7+BSo+mY72gmUQ/z2iNCl7AsKiUiQfW1W4E+1NnY9F7W5Kdu/7+0hTV1MBajc9hKrl82Av33JZz2HpMx8pSzYjvP8tIbUgrbfVN9nxxvLjsrmxQ5MwfniyKjX5CyXX+Tmjgme3rNsnYku5dBNxeJwBiZbQaSQTERERkXJsIhKFKK/Xhz+9fUDyLCIAiI4w4s6Fg9Uqyy+0NpxDTfFeyYw1Oh1J2eNUq6mnbS63Q26gGkeZEhFRVwzuG4d5U7Jkc1tqJqO0I61Lz+1pPYf6r76PimXT0Hb6fQiiFzkjF8s8SkTRwU+69DpXYkS8ETEm6bdbclMAeou79RzqvnoMlZ/OhK3488t6Dq05AfEzX0HCrFegswTP5IZA8bcPDsHmkD4HVK/T4Fu3hNa0EbezAyXHvpDMaPUm9Bl6jWo19bT9tU60yMwy5ShTIiIiIuoMm4hEIWrFZmXjjR64cTjCLHpVavIXRUpWJ49cDEEIni+hciOO9BpgaiqbiERE1DX3Lh6GSKtBMiNCg/dKb4dP7Hojw9N6Dg3bfoTyj6cg3uyARiO9k+bs4eXwetUZIarVCJiZJn1j/lSTGxXt0o0eNTnrjqB207dQ8fEUdBR8DIgyh6hdiqBB+IClSFmyGWFZyneZUvfZfaQS2w/K77q96eoBSEmwqlKTvyg59jk8LunmfeaQ+TCYwlWrqadtVLBYgYsFiYiIiKgzwXMHnK5YR0cHnE5nb5dBKqhtsOHNFfLjjYb2i8OMcaF1bo3H7UDxkVWSGY3WgOy8harV1NPa3T7sqJJuIk5MNsGq57cMIiLqmogwA+5fMlw2V9yahP2mpwCtgvMRL8HbXoGOg88hxiLdkHPamlB+auNlvcblmJ0hf2N+Yy+PNBVFH2ylm1C1egmqVlwDW/Gqy2seAjAmjUPKorWInfw8tMbQGoXvLxxOD15+75BsLjHWgpvnDVClJn8hiiIKD8ifjZobREcWiKIo+zWmT7gOfSNDa9EoERERESnHofchbvPmzfj8889x4sSJCw1Eq9WKoUOHYurUqZg9eza0Wm1vl0nd7K8fHILD6ZXM6LQCHr1jVEiNNwKAspPr4XK0SmbSB82G0RKtWk09bVulHW6Ze4WzOeKIiIgu0+yJmVi3oxjHC+olcx/si8dV/7cWOPkCbOfWXtZrxRuaUQ/p5lXB/o+QOWTeZT1/V01MMsGiE2DzdD40fH2pDXcNjFClnq/zudrRcXY5Wo+/DnfzmSt6Lm1YCmLGPglL9rUhd+3ob95eeQK1jfKN6UduHwmjIbTe59WXH0FLbYFkJjZ1KKKTg6e5eqrJjYoO6fd9s9It/HdLRERERJ3itpIQVVtbi5/85Cd49tlncfDgwYt2ILa3t2PXrl144YUXcM899+DkyZO9Wit1r52HKrDrcKVs7qZ5A5CRrP4Nrd6mbHXyElVqUYvcKFMAmJHGEUdERHR5BEHAI7ePhFYrfZO6w+7Ga180IGH2q0ha8CkM8SO6/FoRBjdMWundiPVlh9FUfbrLz305TDoNpqRIfw/dX+tEo0P6Jn93EUURjpoDqN/6Q5S9NxIN239yZQ1ErRGRI76P1Bu/QljOQjYiellRaTM+3SjdJAOAaWPSMWZosio1+ZPC/fJHFvQNuut8+Yaykh3TRERERBS62EQMQW1tbXj00UexZ88eAEB8fDxmzJiBhQsXYuLEiQgLC7uQLSsrww9+8APs3r27Fyum7mJzuPHy+/LjjVISrLh1/kBVavInTVX5aKg4JpmJSshFbJr8WLZA4faJ2FIu3UQcHmdAgoUb14mI6PL1SY3E4tn9ZHNf7i3D3mNVMCWNRfLClYif8XfowjMVv44gAIlhDtlcwb4PFD/nlZK7Qe8Vgc0y34uvlNdej5bjr6Ly01moXnUd2s98CNFzBWNUBS2s/W9F2o1bET3qcWh0bEL0Nq9PxJ/eOQCfr/NdrwAQZtbjoZuC51pWKUdHI8pObZDMGEwRSB80R7Wa1LBB5jzEGJMGeXGXN0aaiIiIiEID7wqHoBdeeAE1NTUAgHvvvRe33347NJr/9pPdbjf+9a9/4YMPPoAoinA6nfjVr36Ff/zjH0hNTe3FyulKvbXiBOqb5G9SPXL7SBj0oTXeCAAKDnwkm+k7+sagWmW/v8aBVpf0LFOOMiUiou5w+4JB+GpfGWoapG9q//mdA3jlmbmwmPQIy14AS+YctJ16C82H/gSfs0n2deLNDpS1hcEndv79uuTIcmREOhA94EYY4vMgCD23tnJ6qhk6AZCYaIr1pTYs6Wvt1tf1dFTBVrIGHcWfw1mz77LPObyYgLCc6xA18gfQR2Z3w/NRd/n8qyKcLm6Uzd1zw1DERoVe07f4yEr4vG7JTFbeQuj0JtVq6mkV7R6capL+Nc9Ms0CrCZ73NkRERETU/bgTMcTs3LkTO3bsAAA8+uijWLp06UUNRADQ6/V48MEH8etf//pCs6SjowP/+Mc/eqVm6h4F55qwYpP8eKMZ4zIwclCiKjX5E6e9BeeOrZHM6AwWZA6Zr1pNatigYJTpLDYRiYioG5iMOnzrFvkRpXWNdry5/PiFnwtaAyKG3I+0W3YjeuyT0JjjJR+v04iIM0vvRvSJQPGhZahauRBl741C/Vc/QMfZ1fA6W7rwK1Im0qjFuCTpxsT2Sjva5Q4oliGKIlxNZ9By9O+oWrkQ5e+PQeOup+Cs3tMtDURL5lyk3LAe8dP/wgain2lotuNfn0lP0wCAAVkxuGZq6P3Z+XxeFB5QMMp0ZOiNMp2VHnoNZSIiIiLqGu5EDDErV64EAOTm5mLRokWS2QkTJmDu3LlYu3YtAGD79u2oqKjgbsQA5PH48Ps39kFmuhGsFj0eDMHxRgBQfHgFvB7pG459hs6H3hgmmQkkoihivcyIo6wIHXIi+a2CiIi6x4S8FEzIS5E9n3nF5kJcNSYDA3NiL3xMow9D5LCHET7oLrSffh8tR/4Kr636ko9PtNhRa5O+OV7TYUKSxQ6fvQ7tBR+hveAjQNDCmDga5rTpMCWOhiF+eLeM6pyTYcGOqs6vM1w+YGuFHfP7KL/OEEURnrZSOCp3wFG1A/bKnfDZ66641osJsPS5GpHDvwNjfF43Pzd1B1EU8Zd3D8Jmlz4LVKMR8L07R0ETgrvOqgq3o6NZ+mtOYtY4hMcqH50cCDbINBHNOgGTkoNn5yURERER9QzeGQ4hdrsdBw4cAADcfffdikYy3nfffReaiKIoYvfu3Vi8eHGP10rd66N1p3G2XH5l/X2LhyE6IvTeSIqiDwVKViePulGVetRyrMGFaptXMjMr3RJU41uJiKj3ffe2kTiSXwubo/OmhygCf3hrP17++WzodRdPzdDozIgYfC/CB9yO9oKP0XLkr/C0lV6UCdN7Ea53o82t7/Q1HF4dWlx6RBm/Nu5P9MJZvef87j2cP/vPEDMIxsSRMCaMgjFuOHQRmRA0XXsbNSvdjKf3SGfWl9o6bSKKogivrQauxhNw1Z+Aq/EEnHWH4W2v6FIdSglaI6y5NyFi6APcdejnth0ox06ZpjwALJ7dD9lpUarU5G8K9n0omwm26/xGhxf7apySmSkpJph0HE5FRERERNLYRAwhZrMZS5cuxUcffYSxY8cqekxcXBwSEhJQW1sLACguLu7hKqm7lVa14r3VJ2Vzg/vG4urJWarU5G+qCnego6lcMhOfMRJRif1Uq0kN62R2IQLAbI44IiKibhYXbca9i4fhL+8elMydq2zFx2vzcduCQZf8vKA1InzAHbD2uxX2ss1oPfkGHBVfXfh8Ypgdbc2dNxEBoKbDfHET8X+JXrgajsHVcAxtJ988/zGNHvrwTOiicqCPzIE+Kgc6azq05jhozXHQGKO+ccZiokWHEfFGHKrr/Kb+lgo7bK0V0Ngq4Wkvh6e94vx/20rhajgJn6Ne8tfSHTTGaIQPugsRg+6G1hzX469HV6a13YmX3zskm0uMteCOay/97yjYtdaXoPrsLsmMOSIRqf2nqVaTGjaX2+GVmULDIwuIiIiISAk2EUPM0qVLkZSUBJ1O+R99RkbGhSZic3NzD1ZH3c3rE/H7N/fD7ZE+B0erFfDoHaE53ggACvbLr07OHXOLKrWoRRRFrDsn3URMMGuRF29UrSYiIgod10zNxpY953CisEEy997npzB5VBoykiM6zQgaLSyZs2HJnA1XcyHaTr6J9oKPESO2Q6/xwe3rfKdNk9MAp0cDo64LZwb63HC3FMLdUohLniwsaKAxxUJrioFGbwU0OgiCFhMc03EInZ+t3OEWserj72OcKN3w6AnGhNGwDrgVYVnXQqNnYyFQ/P3DI2huk95tBgDfvX0kTMbQfOtfeOAj2UzfkUug6eLuYn8nt1hQKwAz0rhYkIiIiIjkcXZFiBEEAXPmzOnSY1pbWy/8f1wcVyQHklVbCnGqSPrmHADcNHcA+qRGqlKTv2lrLEVV4Q7JjDk8Hmn9r1KtJjUUtLhR0iZ9ds7sDDM0HGVKREQ9QKMR8P07R39jVOn/cnt8+ONbB+CTO9j53wxRfRE78Vmk37ofcZN+jeS4zpuP5wmokTk7sctEH3z2OribTsNZewDO6j1wVO3EuKY3ZB+6XaPebiiNMRoRQx5EyuLNSF64HOH9bmYDMYDsPVaFTbvPyeamjk7D2KHJqtTkb9wuG4qPrJLMaLR65Iy8QbWa1NDu9mF75SWXOFwwPsmEKKNWtZqIiIiIKHAF13I76nY+nw/nzv33zWlm5pUfNm+z2eBwOK74eaQYDAaYTKF3tt/XVdd34PVPj8nm0pKsuPnqXHg80g2lYHVmr/wuxKy8G+ATBfiC6PdoXUm7bGZmqjFk/14QEVHPS4m34OZ5/fHOqlOSuROF9Vi1pQDXTOvC2XwaEyz9bsfQ5Nkoe/laiGLnOw1r7SakhXegpwcypKICWb5CFGv6dprZqZmM73s10KILOyO7QqOHKXkSLLk3wpw+C4L2/MQBfr8PLDa7G39++4BsLjxMjwdvHBqyf75nD6+C2yl9zZs2YBZ0xoig+j3aXGqDW+ZLyKw0XucTEfm7rkyRIyLqSfxqRJIOHDgAp/P8iBydTocJEyZc8XOeOHEC+/fv74bqOjdo0CAMGTKkR1/Dn4miiD+8eRhOl1cyJwjA/df3h80m31AKRl63HcVHV0pmBI0OcTkz0dLSolpdalhbIj3iKEIP9Dc60NIiPyKLiIjocs0Zl4gv95aivKZDMvf6p8fRP8OC+Jiu7ho0Ij5rEmrPbus04fFp0GA3It7S89/zJotbUYzOm4gtQjSOC8MwXDzcfS+qMUKXOBG6lJnQJU2DYIiAG4C73QGgZxf2Uc94fflp1DVJ7zQDgKULcqERnSF5PSeKIs7s+0A2lzRgftBd539xVvrftQBgbIQ76H7dRETBJjY2trdLICICOM6U5Lz++usX/n/u3LlISkrq1XpIma8OVOF4YZNsbu7ENPTLDM0xpgBQVbAZHpnVyYk5U2C0xKhWkxoqbT6cbpVenjwlUQddiJ6RSURE6tHpNHhg8QDITc92uLx45ZN8xWNNvy596HWymW4fadqJyb6vZDM7umGkqWCIgi51LkxjXoT1mi9hHv9H6DOuhWCQG+9K/u7U2SZs3F0hm8vrH4tJeYmq1OSPmiqPoKOxRDITEd8PEQkDVKtJDU6viB210jsMh0ZrEGfirSAiIiIiUoZXjtSplStXIj8/H/j3GNOHHnqot0siBZpanXhndaFsLj7ahJvmdGEsWJARRRFlx1bI5tKHLFKlHjV9WS0/uuiqJG5UJyIideRmRGLOhDTZ3ImiJmzaI988+V/RKcMRFi09kr/drYc79zHoMhZCCMvo8msolS0WIkmU/jVs10xFl1ulujBok6bCOPRxWGZ8hLD5W2Ae+yL0aXMh6HjOYbBwub3456f5sjmzUYv7ru8PIYTPtlZ0nT90UdD9Hu2t98IuPYyG1/lERERE1CW8eqRLOnLkCF566SUAQFxcHF544QWEh4d3y3MPHjwYOTk53fJcnQnVMxFFUcQf390Nm0O+SfS9paOQmBC6oxHqyg6hveGsZCYqsT8y+k8IupsL2+rrJD9v0QmYnR0Dky64ft1EROS/7r8xDwfzG1DXKD2i8f21RZg0qg+S48O69Py5Y27C4fW/lczUNzVhzII/AwC8jka46g79+8dBuOqOQPRIj1xVQgAwxfcVPtbe1mmmVkhCgdAf/cTTnTyJDvqovtDHDIY+ZhAM8SNgiBsGQcO3dsHu1U+OobpefozpvTcMRXZm6O5CtLVWo654p2TGYI5Ev1ELodUZVatLDTtOyU+juTY3GpFWfr0gIiIiImV45dhLWltbe+wgc4PBAKvVetmPLyoqwlNPPQWPx4OwsDD85je/QWJi970JtVgssFi4IronrNtejL3HqmVzV0/OwpihKarU5K+KDy6TzfQbewv0er0q9ail1ubBoTqXZGZaqhlWU3D9uomIyL9FWHX4/tLR+NmfOj+7EAAcTi/+/M4hvPDDadB0Yex2Tt5CHP/yZXhcnZ8JXHZyPUbM/gFMYTHQWRNgtM4FsuYCAESfF572crhbiuBuLvr3fwvhaTkLr722C79SYJJvq2QTEQC2a6ZhgK4GOmsadNZUaK1pMMQMgCF2CAzR/SBog6vxQfKOnanD8k3y00aG9YvHgqv6dunfR7ApObICoii9HS9nxA0wmrq2GMHfuX0itpRLn4c4MFqPrKjQW2xLRERERJePTcRe8OKLL2LNmjU9+hr33Xcf7rjjji4/rri4GI8//jhaW1sRFRWF5557rsd3DVL3qGnowN8/PCybi4k04YEbh6lSk7+yt9WiLH+zZMZgjkTG4KtVq0ktG8vssiPS5mawyU9EROobPSQJV0/OwtrtxZK5o2fqsOrLIlw3o6/i59YbregzbAEK93/UacbndaPo4CcYPOWBb3xO0Gihj8iEPiITSJ9x8eM8dvgcjfDa6+F1NMBnb4DXUQ+vvQEQPRB9bog+L+DzQBS9GON1I7aqAw3ezhsYe6LvxS8XPaX410fBzeZw43f/2gdR5iLOoNfg+3eOCukGotfjQtHBTyQzgqBB31FLVKtJLftqHGh2SZ97PofX+URERETURWwi9oJZs2YhLCwMXq/MYQWXyWAwYNy4cV1+XH5+Pn7yk5+gpaUFmZmZeP7555GcnNwjNVL38vlE/L9/7VM0xvSRO0bCajGoUpe/KjzwCUSf9O9Vdt510OmDb5Xu+tLOd2AAgF5zficiERFRb3jwxuE4cLJadqzpa58cxZghSUhJUD59I3f0TZJNRAAo2P8RBky8G1qt8h35Gp0ZGmsqdNZUxY+Zs7sB759p7/TzRa1eFLW4kRPJyQAE/PPjo6iulx+ne+d1Q5Ca2D1HUASqspMb4LRJj/RMyZ2KsKjgm8qyTuY6H1wsSERERESXgU3EXjBy5EiMHDmyt8u4yIEDB/Dzn/8cdrsdI0aMwC9/+csrGolK6lq1pRBHTkufcwcA08akY2Ke8htcwcjrcaFQdpSpgL6jblSpIvW0OL3YXS094mhSshnhBo1qNREREX1dmEWPx+4cjSf+KD3W1Ony4vdv7sOLP7xK8a6ryPgcJGaPR83Z3Z1mHO31KD+1EZlD5nW59q6Yk2GRbCLi3wt/vjU0skfrIP+373g1vtgqfY43APTLjMYNs3JVqclfiaKIM/vel83ljrlZlXrU5BNFbCyVXnzRJ1yH3CguTCAiIiKiruGdYsJXX32Fn/70p7Db7Zg9ezZeeOEFNhADSHl1G1779JhsLjrCiO/eNkKVmvxZ6Ym1cHY0SmZScqfAGp2mWk1q2Vxuh0dmDNacDO5CJCKi3jVqcBLmT82WzR07U48Vm+XPiPu6fmNulc2c2SvfhLhS4xJNCNdLNz/lpgdQ8GvrcOEPb+6Tzel1Gjx+71hotaH99r6h4igaK09IZsJj+yAxq+tTe/zdkXoXauzSk47mZFggCKE76paIiIiILk9ov8sgLFu2DL/85S/h8Xhw77334oknnoBez9WJgcLr9eF3/9oLp0t+NO737xyNCKtRlbr8lSiKim4M5o6+SZV61CZ3M1IjADPTOeKIiIh63/1LhiEhRv570uufHkVJRYvi503JnQxrdLpkpqHiGBoqjit+zsth0AqYIfM991iDCxXt8qPqKXi9/N4hNDRLT5EAgLuvH4LMlAhVavJnZ/a8J5vJHXNzUDbSlCw6mJvJ63wiIiIi6jo2EUOU1+vFH/7wB7z88sswGAx4+umnsXTp0t4ui7po2fozOHVWelcdAMyZ1AfjhwffuR9dVVd2CE3V+ZKZ8Ng+SMqZoFpNarG5fdhaKX0TakyCEbEmrWo1ERERdSbMrMdjd42WzbncPrz42l643MrOGhcEDXLH3CKbO7PvA0XPdyXmKFi4o+SMMwpO2w6UY8veUtnckNw4XD+rnyo1+TNbaw3KTm2SzOgMYcgatkC1mtQiiqLs14okixZDYw2q1UREREREwYNNxBBks9nwxBNPYOXKlUhMTMRLL72EadOm9XZZ1EUlFS14a4X8KvmEGAsevjlPlZr8nZLVyf3G3gpBCL4vjVsr7XB6pWeZzs3g6mQiIvIfIwcl4ppp8mNNi8qa8dYK6RGGX5c1/FroDNLf88pOrIO9vV7xc16OKSkmmLTSO6LWnWMTMRQ1tTrw53cOyOZMRi0ev2cMtArPBQ1mBfs/gihKLybIzrsOemPwHdtxutmN0jbpXctzMizQBOEOTCIiIiLqecF3p5wk1dbW4pFHHsHevXsxcuRI/OMf/0Dfvn0vfN7n88HlcnX6w+PhSCV/sWzdaXhkmkIA8IO7RyPMzBG1Hc2VqDi9RTKjN1rRJwhXJ0PhiKNZbCISEZGfuX/JMCTGyn9/Wrb+NI6crlX0nAZTOLKGXyuZ8fk8KDrwieI6L4dFr8G0VOmziA/UOVFj4/V3KBFFEb9/Yx9a212y2QdvHI7k+OBrinWVx21H0UG5f6+Col3IgUjJYoM5vM4nIiIiosuk6+0CSF1PP/00zp49CwA4fPgwFi9efOFzoijC5/NJPn7s2LF44YUXerxOkiaKIrYeKJfNLZzeFyMGJqpSk78r2P8hRFH673f2iOuhl9mZEIicXhGbyu2SmWGxBqSE8VsCERH5F4tJjx/cPQY/+f1XECXWToki8OJre/H3p+cgPEx+ZF/u6FtQsO9DyUzhgY8xcPK90Gp7bjHW1ZkW2TGE60vtWDogvMdqIP+ycnMh9h6rls2NHpKE+VPld+qGgnPH1sBllz4bNSV3CsJjpM9DDVRyX0OijRqMTjCqVg8RERERBRfuRAwxTqfzwv/7fD54vd4LP+QaiOQ/BEGAVmb8VWqCFfctHqpaTf7M47Kj6NBnkhmlZyQFoh1VdnS4pXetcnUyERH5q7wBCVg8W/7Mt/omO/7y3kFFzxkR1wfJORMlM46OBpSdWK+4zstxVaoZepl3ZGvPdfRoDeQ/zpY345/LjsrmrBY9HrtzNASOp4QoijizV8GRBeNuU6UetZ1tceNMs1syMyvdAh1H3hIRERHRZeK2kxATERGBmJiYy3681cpxOf4iKzUSJwobLvk5jQD88J4xMBn5TxwAio+ugtvRJplJ7XcVrFEpqtWkprUKRhzNzWQTkYiI/Nddi4bg4MkanC2X3m305d4yjBuWghnjMmSfs9/Y21BVtFMyc2bve8gcOr/HmjXhBg0mp5ixRWJiwL5aJxocXsSatD1SA/kHp8uL51/ZA7dHfmHnd24dgbho6VG4oaKmZC9a6ookM5HxfZHYZ6xqNalprYIjC+Zk8O8KEREREV0+dhhCzB//+MfeLoG6yf1LhuOx32y+5OfuvG4IBveNU70mfySKPhTs/UA212/crarUozaXV8TGMulRpgOi9ciK4LmZRETkvwx6Lf7v/nH47q82yjZZ/vLuQQzuG4vE2DDJXFLOBITHZKKt8Vynmcaqk2ioOIq4tOGXXbuceZkWySaiTwQ2lNpwSz+ONA1m//z4CEqrWmVzk0emYrqCJnmoOLNHyS7EW4N216bcYkGrXsDEZDYRiYiIiOjycZwpUYAalBOLX39vCuKi/vum0GrR46Gbh+PWawb2am3+pPrsbrQ2FEtmohL7Iz5jlGo1qWlXtQOtLumbrVdzFyIREQWAPqmRuH/JMNlch92NF17dC69X+vufIGiQO1Z+lPlpBU2KKzEzzQydTH9DyVQBCly7Dldi1ZfSu+kAIDbKhO8tHRW0DbGuamssRWXBNsmMwRyFzCHzVatJTefa3DjZ6JLMzEy3wChzDAYRERERkRTuRCQKYKOHJOHN5+ejpqEDre0u5GZGQ6fj2oCvU3pGSrDejFmnZJQpz0MkIqIAsXB6X+w9WoUDJ2skcycK6/H2ypO4+/ohkrmsYdfi6Oa/wOPq/NzB8lMb0d5c2WNjzyONWkxINmFbpaPTzO5qB5qdXkQZOdI02DQ02/H7N/fJ5gQB+PG94xBhNapSVyAo2PcBAOlzv3NG3gCd3qRaTWpScp1/Na/ziYiIiOgKsdtAFOB0Og1SE8MxMCeWDcT/0VpfjKrCHZIZoyUamYPnqlaTmjw+ERvKpG8u5ETqkRtlUK0mIiKiK6HRCPjhPWMQHib/veuDNadkm416Yxiy866TzIiiDwX73u9yrV0ht6DHIwKbZMaTU+Dx+US8+PpetLZL7yYDgJuuHoC8gQmq1BUI3M52nD28UjIjaHTIHX2TajWpTW6HskUnYEpKcDZQiYiIiEg97DgQUdA6s0/+LMS+o5ZAqwvOFd17axxockqPcpvHUaZERBRgYqPM+P6d8mPIRRF48dU9aGiWbr7ljrkFgPREgqKDn8HtbO9yrUrNzrBAIzfStJQjTYPNx+tO4/CpWtlcvz7RuHPhYFVqChRnD6+U3EEMAOkDZ8ISkahaTWqqaPfgaIN083l6mhkmLjIlIiIioivEK0oiCkpOWzOKZVYnazQ69B11o2o1qU3J+Uk8D5GIiALR5JFpmDupj2yuuc2JF17dA6+v85GH4THpSOs/XfJ5PK4OnD20/LJqVSLWpMXYROlFTdsr7WiTOeeYAsexM3V4Y/lx2ZzZqMNPHhjPiSNf4/N5lR1ZMPY2VerpDUoWFfA6n4iIiIi6A9+JEFFQKjywDF5P52cLAUD6oDkwh8erVpOavD4R62RuLvQJ16F/lF61moiIiLrTt24ZgbREq2zuyOk6vP/5KclM//F3yD7P6b3vwefzdKnGrpibESb5ebcP2FLOkabBoKnVgede2Q2fRHP7P75z2wikJsj/PQ8lFfmb0dFcIZmJSRmCuLRhqtWkNrnFgiatgGkpZtXqISIiIqLgxSYiEQUdr8eFgv0fyub6jQve1cn7a51ocEjvVrg60wJBkJmdRkRE5KfMJh2eeGgC9Ap2aL276gSOnO58bGRceh5iUqTHRdpaqlCRv+WyalViToZZZqgqZBcIkf/z+kT85p970NgivdgNAK4am45ZEzJVqSuQ5O9+WzYTzLsQqzo8OFTnlMxMSzXDouftHiIiIiK6cryqJKKgc+74Gjja6yUzcekjECtzszCQKbnJyBFHREQU6HLSo/DwzXmyOZ8I/Oafe9DceunGjSAI6D9Ofjdi/p53LqtOJRItOoyMlx5p+lWFHTY3R5oGsndWncDhfPlzEBNjLXj09lFc8PU/6ssOo6HimGTGHB6P9EGzVKtJbesVXOfz3HMiIiIi6i5sIhJRUBFFEad3y9/gGzB+qSr19AafKGKdzIijNKsWg2MMqtVERETUU66Zlo0po9Jkc40tDrzw2t5Oz0dMHzgTlogkyedoKD+K+vIjl12rHLkFPg6viK8qOdI0UO0/Xi07WhcANBoB/3f/OIRZOHb+f+XvUrYLUasN3t87ufMQDRrgqjSOMiUiIiKi7sEmIhEFleqzu9BSVyiZscakI6XfVNVqUtvhOidq7F7JzNUZYVzZTkREQUEQBDx252gkx0ufKQgAB0/W4M3lxy/5OY1Wj35jb5V9jtO7372sOpWYmyG/e0juLDTyT7WNNrzw2h6I8scg4r7FQzG4b5waZQWUtsZSlJ+WHimsM1iQM3KxajWprc7uxf4a6VGmU1PNsHKUKRERERF1E15ZElFQUXJGSv9xd0Cj0apST29Yo+DmIkeZEhFRMAmz6PHEg+Oh08ovkPlwTT62Hyy/5OeyR1wPnV56B095/ia0N1dedq1SUqw6DI+TnhTwZbkdDg9HmgYSj8eH517ZjdZ2l2x2Yl4KFs/up0pdgebMnvcASHdhs/MWwWAKV60mta0vtcn8DvA6n4iIiIi6F5uIRBQ0mmvOoObsbsmMwRyJrOHXqlaT2kRRlN2hkGzRyt6gJCIiCjT9+sTg/iXDFWV/9/o+lFa1fuPjBlM4skcsknysKPpQsPe9y65TjtxuxA6PiK2Vlz7bkfzTP5cdxamiBtlccnwYfnjPGE6LuASnvQVnj6yQzAiCBv3G3qZaTb1B7jpfrwFmprGJSERERETdh01EIgoa+QrOQuw76kbZHQaB7GiDC1U26VGmczMtvDlFRERBadHMvpiQlyKbszs9eOavO9Fhd3/jc+ebENLfJ4sOLYfb2X5FtXZmroJdRGtKOnrktan7rd9RguWbCmRzep0GP3toAqwWLvS6lMIDy+B1SzfP0wbMhDU6VbWa1Nbg8GJPjfTvweRkM8INvM1DRERERN2HV5dEFBTsbXUoPb5GMqPR6pE75mbVauoNSs5JulrBeUtERESBSBAE/ODuMUiKkz8fsby6Db97fS98vouHA1qj05A2YLrkYz2uDhQd/PSK672UzHA9BsVIN5I2caRpQMg/24A/v3NAUfZbt+QhNzO6x2sKRF6PCwX7PpDNDZiwVJV6esuGUht8MrNMlSxCICIiIiLqCjYRiSgoFOz7ED6fRzKTOWQ+zNY41WpSm5JRpglmLUYmGFWriYiISG0RYQY89a2JMOjl3+rsPFyJD9fkf+Pj/cfLNyNO73kXXu83dzJ2h3kyjQAbR5r6vYZmO5756064FTR7p4/NwPyp2arUFYjOHV8DR3u9ZCYufQRiU4eqVlNvkLvO1wnArPTgnbhCRERERL2DTUQiCngelx2FBz6WzfUff4cq9fSW4w0ulLVLN1LnZFig4ShTIiIKcjkZUfj+naMVZd9ccRz7jldf9LG4tOGISRki+Th7W63sFITLJddEBIAvONLUb7ncXvzyrzvR2CLf6M1IDsf3lo7iqPlOiKKI0wqOLBigoPEfyBodXuyqlv77ND7JhCijVrWaiIiIiCg0sIlIRAGv+MhKuBytkpmknImISuirWk294fMS+VGmHHFEREShYub4TCyaKf+9XxSB51/ZjdKq/15LCIKAAQoWH+Xvegui2P1jRftE6DFYZqTpZo409UuiKOKldw8iv7hRNmsyavGzhybAbNKpUlsgqj67Cy11hZIZa0w6UvpNVa2m3rC+1AavzCjTq3mdT0REREQ9gE1EIgpoPp8H+Xu4OlkURXxxTnpHQpxJg7EcZUpERCHkgSXDMSRXfpR5h92Np17ajpY254WPpQ2cibCoFMnHtdQVoapwR7fU+r+UjDT9qoIjTf3Nys2FWL+jRFH2x/eOQ5/UyB6vKZDl73pLNtN/3B3QaIJ7B94amVGmGgGYzXPPiYiIiKgHsIlIRAGt7ORGdDSVS2aiEnKRmDVOtZp6w5F6Fyo7vJKZqzPDoNVwVBYREYUOnU6Dnz00AbFRJtlsVV0HnvnrTrjc57+fajQ6RWcjntr5shnxEgAAm7lJREFURrfU+r/m9VEw0lRmARGp6/CpWvz9oyOKskuvHYRJI1N7vKZA1lh1EjXFeyQzBnMUsoZfq1pNvaFBwSjTCUkmxJqCu5FKRERERL2DTUQiCliiKCq6cdd//B1Bf86MkpuI8xXcjCQiIgo2MZEmPPnwROi08tcCJwrr8ae3D0AUz88NzB5+HQzmKMnH1JUeRH350W6r9z8yw/UYEis90nRLuR12jjT1C6VVrXj27zvh88nMnAQwaUQqblswSJW6AtmpHfLX+X1HLYFOb1alnt6yvtQGub9WvM4nIiIiop7CJiIRBazqs7vQXHNaMmMOj0fGkHmq1dQbfKKINTLnISaYtRgVz1GmREQUmgblxOJbt4xQlN246xw++CIfAKAzmJE75mbZx+TvfPOKa7wUZSNN7T3y2qRcY4sDT/5pG9ptbtlsn9QIPH7vGGg4HUJSW8M5lOdvksxotHpF/z4D3Rcy1/laAZiTziYiEREREfUMNhGJKGAp2oU47g5otXpV6ukth+tdqLLJjTK1cJQpERGFtGumZWP+1GxF2TeWH8fW/WUAgNzRN0Grkx6HWn56C1rrlZ2D1xVyTUQoaDBQz3I4PXjqpe2oaZD/cwgPM+AX35kEiym4r027Q/7utyGK0rts+wy7Fmar/Jmngaze7sWeGulRphOTTYjmKFMiIiIi6iFsIhJRQGqoOI7akn2SGb0pHDkjb1Ctpt7yRYmCUaYKbkISEREFM0EQ8J1bRyBvYIKi/G9f34v84kaYwmKQnXedTFpE/u63u6XOr8tQMtK0giNNe4vXJ+L5f+5Gwbkm2axGAJ54cDyS462q1BbI7G11KD6yUiYlYOCEO1WqqPesPadglGlmmFrlEBEREVEIYhORiALSqZ3/ks3kjroJemNw36hRMso00azFyASOMiUiItLpNHjyoQlISwqXzbrcPjz90nZU1Lb/+3xl6bdOJUdXwd5e343Vnie3EMjuEfElR5qqThRF/O2DQ9h9pEpR/oGbhmPkoMQerysYnNn7Hnxe6dGw6QNnIjw2U7Waeovcued6DTAnI7jPhCQiIiKi3sUmIhEFnNb6EpTnb5HMaHVG9Bt7q2o19ZaDtU7U2KVHmc7rY4FG4ChTIiIi/Huk5LOPTEaEVXqHHwA0tznxxB+2winEIn3QbMmsz+vGmT3vdWOl53GkqX/6ZMMZrNpSpCh79ZQsXD8zt8drCgYuRxsKDyyTzQ2ceLcq9fSmWpsH+2qckplJyWZEGjnKlIiIiIh6DpuIRBRw8ne9CUB6rk/W8IUwWWNVq6m3fHFO/qbh/D4ccURERPR1KQlWPPWtidBp5RfZVNd34Mk/bUPmiNtks4UHPobb2d5NVZ6XHq7HUJmRpl9W2GFzc6SpWrbuL8M/Pz6qKDtqcCIeuW0kBC7oUqTowDLZf0OJWWMRkzJYtZp6y9pSm8w7HmB+Hx5ZQEREREQ9i01EIgoottZalBxdLZkRBA0GhMAZKV6fiLUyTcRkixZ5cfI7LYiIiELN0H7x+P6doxVlz5a34A/LGhCfOVYy53a2o/DAJ91U4X9xpKn/2H+8Gi+8ukdRNjstEk8+PAE6Hd92K+H1OHF6z7uyuYET71Glnt4mt8NYrwFmpbOJSEREREQ9i+9miCignNn7Lnw+j2QmfdBsWKPTVKuptxyoc6KWo0yJiIgu2+yJfXDL/AGKssfO1ONg4xjZ3Ok978DrkR5B2FXzFEwVWKNgOgFdmeMF9fjl33bC45XbHwbERZvx7KNTYDHpVaktGBQfXQ1HR4NkJjp5IBKzxqlWU2+ptnmwv1b668iUFDMiDLylQ0REREQ9i1ecRBQwXPZWZWekTAj+M1Kg8Pyj+ZkcZUpERCTlruuGYOpoZYuPNp6IgFufLplxtNfj7OEV3VTdeWlWHYbJjDTdUs6Rpj2p4FwTfv7SNjhd0gu4AMBi0uHZRycjLtqsSm3BwOfzIn/nm7K5gRPvCYnRsHLTRsBRpkRERESkEjYRiShgFBz4GB6X9BvqpJyJiE5WtqMgkJ0fZdohmUkJ02I4R5kSERFJ0mgE/OjescgbkKAgLWBH1UjZ1Kmdb8DndXdLff8h1zBweEVsLudI055QWtWKn/1xK2x26WkYAKDVCnjyWxORnRalSm3BovzUJrQ3lUlmrDHpSBswQ7WaepPcYkGDBpiZxiYiEREREfU8NhGJKCB4XHac2fOObG7gxNDYhbi31ol6h/Rug/mZYSGxUpuIiOhKGfRaPP2dicjNjJbNltgGotUt3SCytVSh5PiabqwQuFrBdIHVJdILjKjrqus68JPff4WWdpei/PfuGIVRgxJ7vK5gIooiTu38l2xuwIS7oNFoVampN1V2eHCwTnqU6dRUM8I5ypSIiIiIVMCrTiIKCEUHP4HT1iyZiUkZjITM0arV1JvWKLhJeA1HHBERESlmMenxq0cnIzXBKpkTocGx1gmyz3dq++vw+eRHXyqVZtXJThj4qsKOVhdHmnaXhmY7fvKHr9DQ7FCUX7pwMOZOzurxuoJNVeF2NFXnS2ZM1jhkDVugWk29Scko02sUnJNKRERERNQd2EQkIr/n9ThxahfPSPkPj0/E2lLpmwvpVh2GyJydRERERBeLijDhue9PRUykSTJX2DEUHZ5wyUxb4zmUn9rUrfUtkGkcuH3ABplrBFKmvsmOH//uS1TVKdvdecPsfrh9wcAeryvYiKKIE9v+KZvrP+52aHVGVWrqbZ/LLBY0agVMT+N5m0RERESkDjYRicjvnT28Ao72eslMeGwfpPa/SrWaetPOKgcaZUaZzsu0hERDlYiIqLslxYfhue9PhdWi7zTjgw7H28bJPtfJ7a9CFMVuq21eHwvkvrtzpOmVq2nowOO/3YLymnZF+aunZOHBG4fx2usy1BTvQUPFMcmM3mhFzsjFqtXUm8ra3DhSLz0696pUM6x63sohIiIiInXwypOI/JrX68apHfJnpAyadG9InJEChTcH53OUKRER0WXLSovEM9+dDIPEjfrT7SNg90p/v22uLUBlwdZuqyvJosOYROndWDurHGhwdN8Y1VBTVdeOx19UvgNx2ph0PHrHKDYQL5OSXYi5o2+GwSS98zdYrC6R30k8P5PX+URERESkHjYRicivlRxdDVtrtWQmLCoVmUOuVq2m3uT0ilgvM6YsM1yHwTEcZUpERHQlhuTG4cmHJ0KnvXRzyCvqcaJtrOzznNz+WrfuRpQbaeoVlZ2pRt9UXt2Gx1/8ErWNyn7/xg5Nxo/vHQuthg3Ey1F77gDqSg9KZnR6M/qPv121mnrb6mLp5rWJo0yJiIiISGVsIhKR3/L5PDi543XZ3KBJ90Kj7XzkWDD5qsKOdrf0jchrs8K4Gp6IiKgbjBuWjJ9/q/NGYn7bSDh90jsDGyqOoaZkb7fVdHWmBZ2UcwFHmnbducpW/Oh3X6K+2a4oP6x/PJ58eAJ0Or6lvlxKdiH2HX0jjJZoVerpbWeaXTjd7JbMzEw3w8JRpkRERESkIl59EpHfKj2+Dh1N5ZIZc0Qi+gxboFpNvW2VzOpkKNihQERERMqNH56Cnz08AdpLdO7cogmn2kbLPsfJ7a91Wz0xJi0mJZskM/trnKjq8HTbawa7orJm/Oh3X6KxxaEo369PNJ757iQYDaExSr8n1JcfRU3xHsmMVmdE//FLVaupt8ntQgSv84mIiIioF7CJSER+yefzKrrhNnDi3dDqQmN0Z7vbh83l0qvjB0br0TcqNHZlEhERqWViXip+9uClG4kn28bA7ZP+3ltbsg/15Ue6rZ4FWdKNBBHAFxxpqsihUzV4/Ldb0NLmVJQfmB2D5x+bCouJ11tX4uT2V2Uz2SOuh9kap0o9vU0URdnzECMMGkxN5ShTIiIiIlIXm4hE5JfK8zejtaFYMmOyxiE7b5FqNfW2TWU2OL3So0zlbioSERHR5Zk0MhVPPDj+G41Ep8+C0+0jZB9/Yusr3VbL7HQLDDLv5D5XsKsp1G3cdQ4/+9M22OzKdm0OyY3Dc49NhdUSGgvYekpj1SlUFmyTzGi0egyceLdqNfW2Yw0ulLZJ/z2ck2GGUW6WMRERERFRN2MTkYj8jij6cFLBGSkDxi+FTi89ziuYrC6W31FwDUccERER9ZjJI9Pw0wfGQ6O5+Eb+8bZx8IrSoy2rinaivvxot9QRbtDgqjTpHUlHG1w41yZ9vlqoEkUR731+Cr99fS+8Mgu0/iNvQAJ+/b0p3IHYDU5uk9+FmDV8ISwRiarU4w+UHFlwLa/ziYiIiKgXsIlIRH6n4sxXaK4tkMwYLVHoO+pG1WrqbU0OL7ZVSo8yHRFvRJpVp1pNREREoWjKqDQ88eB46L62I8juDceZ9uGyjz2+9R/dVoeShUOfy4xHDEVerw9/evsA3lx+XPFjRg1OxC8fmQyTkddZV6q5pgDlpzdLZgSNDgMn3ataTb3N6xPxhcy/1TiTBuOTQmfxJBERERH5DzYRiciviKKoaHVy/3F3QGcInTNB1pXa4JFZKH9tH4ta5RAREYW0KaPS8MtHJsNo+O/uw+Ot4+ETpd9eVRft7LazEaenmmHRSY825EjTi9kdHvzi5R1Ys016ZP7XjR+ejF98Z9JFf9Z0+ZSchdhn6DWwRqWoUo8/2FfrRI3dK5mZlxkGrYajTImIiIhIfWwiEpFfqTzzFRqrTkpm9KZw5I65WbWa/MHqEumbgBoBmM8RR0RERKoZNTgJL/5wGsLDzp+P1+6NQmHHUNnHrf/4d/D5lI3QlGLRazBTZqTp6WY3zjS7rvi1gkFFbTse+81m7D1Wrfgxk0em4smHJ8KgZwOxO7TUFqH05AbJjCBoMGjSParV5A9WKxllmsXFgkRERETUO9hEJCK/IYo+HPvqb7K5fmNvg95oVaUmf1Bj82BPtVMyMyHJhDgzb3ARERGpaUB2LH7/f9MRF32+mXekZZLsbkRN+3H87s9vw+7wXPHrL8hSMNJUwZnKwW73kUo88quNKK5oUfyYBVfl4IkHx0Ov41vm7nJ8698BSDfQMwbPRXhspmo19TaXV8Tac9L/RlPDtBgRb1StJiIiIiKir+M7IiLyG+X5m9Fcc0YyozNY0G/srarV5A++KLHJ3G4BruEoUyIiol6RkRyBP/zfDKQnhaPdG4WCjmGyj9HWfobHfrMZ1XVXNm50cooZEQbpt3SrSzogile+8zEQeX0i3lxxHE//ZQc67G7Fj7v3hqH47m0joNXy7XJ3aao+jbJTG2VSAgZNvk+livzDjio7ml0+ycw1fcIgCBxlSkRERES9g++KiMgv+HxeHP/q77K5fmNuhdEcqUpN/kJulKleA8zNYBORiIiotyTEWvD/fjwd/bNicKRlErwyuxFTzSXoqD+O7/xqA7YdKL/s1zVqBczJkB5peq7Ng2MNoTfStLXDhade2o73Vp9S/BidVsD/3T8ON88bwKZNN1NynZ8+cCYi43NUqcdfrFKwU/haBTuOiYiIiIh6CpuIROQXyk5uQEtdkWRGb7Si/4SlqtXkD0rb3DhSL33jb2qKGZFGjjIlIiLqTZHhRrzwg2kYOmQACtqHy+ZHRG5Fu82NX/19F/709gE4nJc33nSBgjORVyo4cy2YFJxrwiO/2oj9x5Wffxhm1uO5x6ZixriMHq0tFDVWnkDFmS9lUgKGTH1YpYr8g93jw8Yy6SZiTqQeA6L1qtVERERERPS/2EQkol7n83lwfOs/ZHP9xt0egrsQuTqZiIgoUJhNOjz97UnIHLlUdjdiiukcEo2lAIAvtp7FI7/eiLPlzV1+zfFJJsSaZEaaFnfA4wv+kaZerw/vrDqJ7z2/CdX1yhun8TFm/P7/pmN4/4QerS9UKTnzPGPwXEQmhNYuxM3ldtg80v8ur82ycFcsEREREfUqNhGJqNedO74WbQ0lkhm9KRz9x92uWk3+YrXMzgGzTsCMNOkxZkRERKQejUbAPbdMhzV9jmx2ROS2C/9fWtWGR3+9CSu3FHbpDEOdRsD8TOkFRfUOH3ZVOxQ/ZyAqq27DYy9swdsrT8DrVf771z8rBn/66Uz0SQ2thWpqqS8/gqrCHZIZQdBgyNSHVKvJX3wuc2QBFO40JiIiIiLqSWwiElGv8nndOLH1FdncgPF3wmAKV6Umf5Hf5MKZZrdkZmaaGRY9v5QTERH5m1k3fA+CRnoMYbLpHBKN5y783O3x4eX3DuHJP29HdZ3ynXQLsxWMND0bnCNNfT4RKzYX4jvPbsDp4sYuPXb+1Gz87kdXITaKC7J6yrEv5XchZg6dj4i4PqrU4y9aXT58WW6XzAyNNaBPBEeZEhEREVHv4p1nIupVxUdXo72pTDJjMEeh39hbVavJXyi52beAo0yJiIj8UlhkEnJGXi+bGxn5FYCLd87tP16NB3+xDh+vOw2v1yf7HHlxBmSE6yQz60ttsHvknyuQVNd34Ik/bsVf3z8Ep8ur+HF6nQY/uHs0vrd0FAx6nivdU2rPHUBN8R7JjCBoMXjKg6rV5C/WnuuAS+af4zXchUhEREREfoBNRCLqNV6vGye2/VM2N3DiXdAbQ+tNtE8UsUJmlGmEQYMpKVw5T0RE5K8GTboXGq30TqIkUznSTEXf+LjT5cWry47iu7/aKLvDThAELJRZWNThEbGpTHrnU6Bwurx4Z+UJPPDUWhw6VdulxybGWvCHn8zA3ElZPVYfAaIo4tiXf5XNZeUtRHhMuio1+ZMVMosFBQDX9LGoVg8RERERUWfYRCSiXlN8eDlsLVWSGWNYDHJH36xaTf5iT40TNTbpFfXzMy0wagXVaiIiIqKusUQkImfEDbK5kVFffmM34n+cLW/B957fhL++fwgdts7HnMs1EQHILlDyd6IoYsfBCjzw1Fq8veokXO6u7awcOSgRL/1sFnIzo3usRjqvpmQv6koPSmY0Gh0GT75ftZr8RWW7B3tqnJKZMYlGJIdJ7y4mIiIiIlIDr0qJqFd43A6c2PaqbG7QxHugM4TebrsVZ9tlM9cpOP+IiIiIetfASfei6NBn8HldnWZiDbXIspxEsW3wJT8visCKzYXYsrcUN88bgIXT+35jDGd2pB5DYw041tD562yrsKPR4UWMKfBGeJZWteJvHxzGwZM1XX6sRiPg9msG4tYFg6DVcAFWT1O6CzF7xPUIi0pRpSZ/sqpEvpm/iNf5REREROQnuBORiHpFwb4PYW+THj9lssYhZ9QS1WryFw6PD2vP2SQzqWFajEowqlYTERERXR5LRAJyR98omxsZuRUCpKcQtLa78M+Pj+Ken63B2m3F3zgvUW43okcEviiRvsbwN1V17fjDW/vx8DPrL6uBmJ4Ujj/9dAbuWDiYDUSVVJ75Cg3lRyUzGq0Bg0JwF6IoilguM8rUoAGuzmQTkYiIiIj8A5uIRKQ6l70Vp3a8LpsbNPk+6PQmVWryJ5vL7Wh3X3qk2X9cmxUGjcAbYURERIFg4KR7oTNIn28WoW9CP+sRRc9X32THH97ajwd/sR7bDpTD5zt/3bAgKwxyfbKVATLStKKmDb/71z7c++TafzdMpa+NLmXRzFy8/PPZ6NcnpkdqpG/y+bw4svkl2VzfUUtgiUhQpSZ/kt/kRkFz52OJAWBGugURBt6qISIiIiL/wHGmRKS6Uzv/BZejVTJjiUhSdIZQMFohszoZHGVKREQUUExhMRgw/k4c3/p3yVxexHYUdgyFV9Qret7y6jb86u+7kJZoxcIZuZg9MRMTkkzYUeXo9DEH65woa3MjPVzZa6ittKoV739+Cl/uLYWv631DAEB8jBmP3z0WeQNDr0nV20qOrkZr/VnJjFZnwsBJ96hWkz9RdJ2v4HxTIiIiIiK1sIlIRKqytdbizN73ZXODpzwArc6gSk3+pMnhxVcVdsnMoBgDcqNC7/eGiIgokPUffwcK9n8Ap62504xF146B1v043jahS89dXtOOv75/CG98dgyp4/sDkG5CrCq24dvDIrv0Gj3J5fZi+8EKrNtejMP50uPu5cye2AcP3zwcVguvldTmcTtw7Ku/yeZyx9wEszVOlZr8idcnYpXMTuAogwbTUkPvPHgiIiIi8l9sIhKRqo5v/Tu8HqdkJiI2C1l5C1WryZ+sOWeDR2bVPXchEhERBR69MQyDJt+PQ+t/J5kbHbcXRfaRsHu6fvaxzeHB6W2nIIwbAVGr7TS3orgd3xoaAaGXR6OfLW/G2m3F2LT7HNpt0iMe5eSkR+Hbt47AkNzQa075i8L9H8HeKn1upd5oxcBJ96pWkz/ZU+NAjV363NN5fSwwaHlkARERERH5DzYRiUg1rfXFKD68QjY3dPp3oNGE5pcnuRFHGgFY0Ef6TCUiIiLyT31HLcHp3e/A1lrdaUbwduCH82qwPH8kjhfUd/k1NF4fwhqb0R4f22mmqMWDE40uDInteqPySnh9IgpKGrH3WDX2HK1EYWnnuzKVslr0uPv6oZg/NRtauQMhqce47K04uf012dzASffAaPafXbBq4ihTIiIiIgpEoXmXnoh6xdHNf4Eo+iQzsalDkTZghmo1+ZPSNjcO1Env0pyQZEKihV+6iYiIApFWZ8SQaQ9j76pfSOaqTy7DL799Jw6cceCNz46jora9S69jrW2QbCICwKv76/DctERYTD17NmJjiwNH8mux73gV9h+vRku7q1ueVxCAeVOycfeiIYgMV7cZSt+k5Mxzc3g8+o29VbWa/Ind48PaUptkJs2qxcgE/l0mIiIiIv/CO9FEpIr68qMoP71ZNjd85qO9Plqrt8idkQKOMiUiIgp4fYZdg/ydb6K1objTjNftwKkdr2LqvJ9iYl4q1u8swTurTqCh2aHoNSxNLdC4PfDpO3+7t6bUjvxHlyMrNRKD+sYhJz0KibEWJMSGISHGAqOh83Gol+JwelDTYMPZsmYUlTXjbFkzzpY3o6lVeoHU5Rg1OBH3XD8UuZnR3f7c1HW21hpFZ54PmfYwdPrQPO9vc5kdHW7pMwsWZoVBE6Lvg4iIiIjIf7GJSEQ9ThRFHNn0J9lcct9JSMgcrUpN/kYURdkRRyatgNnpHGVKREQUyDQaHYZO/zZ2LPuRZK7owCfoN+YWRMRlYf7UbMwcn4mVWwrxwRenZM8PFEQR1roGtKYkdprxGg3oiIzA2fIWnC1v+cbnoyOMSIgNg8Wkg06rgVargV6ngVYrQCMIaO1woaXNef5HuxNOl/RZb91h3LBk3LZgEAZkxfT4a5Fyx7f+Q9mZ58ND88xzAFiuYLHgwiyrKrUQEREREXUFm4hE1OOqCrejrvSgTErAsOmPqFSR/zne4MLZVo9kZla6GeEGjWo1ERERUc9IGzATMcmD0Fh1stOMKHpxeOMfMfWW8wuxjAYtbpzbH/OmZGH5xgKs+rIIzW2dN27kmogA0JYQB0vzpUdQNrU6e2QX4eWYmJeC2xYM4s5DP9RSd1bRmefDZnw3ZM88b3B4sa3CLpkZEmtA36ieHS1MRERERHQ5QvMqnohU4/N5cXTzS7K5zKHzEZ3UX5Wa/NEKjjIlIiIKGYIgYNiMR/Dlu9+SzFUWbEVNyT4k9hlz4WNWiwF3LByMm+YNwLYD5Vi+sQBnzjV947Gm1nboHE54TJ2fsdYRFw1fkQYar/SZ1b1Br9Ng8sg03DSvP7LTonq7HOrE0S3KzjxP7T9dtZr8zRclNnikJ5niuixe5xMRERGRf2ITkYh6VPGRFWiuLZDMaDQ6DJ0mfRMtmHl8oux5iNFGDSanhOYZMkRERMEoKXs8ErPGoqZ4r2Tu8IbfY87970IQLp5GYNBrMXN8JmaMy0D+2UYs31SAbQfL4fWe71YIAKy19WjOSO30uUWtFu2xMYiore+mX9WVy0qNxNVTsjBjXAYirJ03QKn31Z47gIrTW2Rzw2d+L2TPPAeAFWfbJT+vEYAFbCISERERkZ9iE5GIeozb2Y6jW16WzfUdfROs0Z3f4Ap22yrtaHBIr+Be0CcMek3o3nwhIiIKRnmzfoB1/7wVQOfblJqq81FydHWn58kJgoCBObEYmBOLB5vt2Lq/HNsOlONkUT3Caxskm4gA0JYY1+tNRLNRh+njMnD15Cz06xMd0g2nQCGKPhxa//9kcym5U5CQOUqVmvxRSasbh+tdkplJySbEm7Wq1URERERE1BVsIhJRjzm5/XU4OxolMzpDGAZNvk+1mvzRp0Xyo0wXcpQpERFR0IlO6o+s4QtRfET6TLmjW15G+sDZ0BmkpxLERplx/axcXD8rFw3Nduw4VIFnzzrQpDd1+hhHVATcRgP0TulGR3eLjzFjzJBkjBmahJEDE2Ey8q1pICk+uhpN1adkUqF95jkAfKbgOp9HFhARERGRP+M7NSLqEe1NFTi95x3Z3IAJd8IUFqNKTf6oxenFxjKbZCYjXIe8OINqNREREZF6hk3/NkpProPX7eg0Y2+rRf7utzBk6kOKnzc2yoyF0/uiPaUNT+2RXtTVnhCH6LLKLtXdVVqtgME5cRgzNAljhyYjMyWCOw4DlNtlU3TmeZ9h1yAqMVeVmvyRTxTxmcwoU7NOwOx0i2o1ERERERF1FZuIRNQjjmz6E3xet2TGHJGIAROWqlaTP1pdYoNbepIpbsgJ4002IiKiIGUOT8CACXfhxNZ/SObyd76JnBE3wBwe36Xnv6aPBb/a1wiXxPWGLTkeMeWVEDufqtolFrMO2WlRyEk//yM7PQqZKREw6DmyMRic2vEvONqlR+BqdSYMverbqtXkj/ZUO1DZ4ZXMzMmwIEyvkcwQEREREfUmNhGJqNvVlh5E2akNsrnhMx6FTi89livYfVokvToZAK7PtqpSCxEREfWOgRPuQtHBTyQbMx63Hce+/CvGXvt0l5470qjFzHQL1pzrfPKBw2jEs88sQLreh9oGG2oaOlBT34GmNic8Hh883vM/vF4Rbo8PPp8PFrMeUeFGRFqNiAw//yMq3IS4aDMSYy1cABWkOporkb/rLdncgAl3IiwyWZWa/NUnCkaZLs7hKFMiIiIi8m9sIhJRtxJFHw6t/51sLiZlCDKHXK1KTf6qqMWNI/XS5w+NTzIi1cov1URERMFMZzBj2PTvYu+qX0jmzh5egX5jb0VUYr8uPf/1OWGSTUQAWHnOhmfHxyIpLgxA13Y7Uug4P21E+vrVHB6PgRPvVq0mf9Tu9mFdqfS/uWSLFuOTOj+vlIiIiIjIH3BuBhF1q5Kjq9FUdUo2N3Lu4xCE0P4SpGQX4uIc7kIkIiIKBX2GLVDQHBRxaMP/g9jFuaNTUsyINUlfd31eYoPT203zTCko1ZcdRunJ9bK5YdMfgc4Q2tNG1p6zwe6R/vd0fU4YNNyxS0RERER+LrTv4BNRt3K7bDi65S+yuYzBVyMubbgqNfkrr0/E8rPSI44sOgFzMiyq1URERES9R6PRIm/2D2RzNcV7UX5qY5eeW68RsDBLemxiq8uHTWXSO6codImiDweVTBtJHoQ+w65RpSZ/pujIAi4WJCIiIqIAwCYiEXWb/J1vwN5WJ5nR6owYPvNR1WryVzurHaixeSUzV2daEKbnl2kiIqJQkZQ1Dim5U2Rzhzb8Hh6XvUvPraRh8ZnMAicKXSXHvkBj5QnZ3Ii5Pwr5aSOlbW7srXFKZkbGG5EVoVetJiIiIiKiyxXaV/dE1G3amyuRv+st2Vz/8UsRFpmsSk3+7DMFq5Nv4OpkIiKikDN85vchCFrJjK21Gid3vN6l5x0UY8CAaOmmxdYKO+rt0oucKPS4XTYc3fxn2VzGoDmIT89TpSZ/9lmRfDP+hhzpncFERERERP6CTUQi6haH1r0Ir0d6xa3JGoeBk+5RrSZ/1ebyYX2p9O6B1DAtxiYaVauJiIiI/ENkfDb6jloim8vf9SbaGku79NxyuxG9IrCymLsR6WIntr4iO21EozVg+MzvqVaTv/KJ8kcWGLUC5vdhE5GIiIiIAgObiER0xSrObEXFma9kc8NmPAK9gWf8fXGuAw6vKJm5PscKjSCoVhMRERH5jyFXfQsGc5Rkxud14+C630IUpa8pvm5hVhi0MpcXSqYlUOhoqS3C6T3vyuYGTLgTYVEpqtTkz/bVOFHW7pHMzEk3I8LAWzFEREREFBh45UpEV8TjduDguhdlc9FJA5E1bIEqNfk7JSOOrueIIyIiopBlNEdi+IxHZHNVhdtRWbBV8fPGm7WYkmKWzJxqciO/yaX4OSl4iaKI/Wufh+iTbopx2sh/KWnCKzmflIiIiIjIX7CJSERX5NSO19HRXCGbGzH3cQgCv+SUtLqxv1Z67OuYBCMyw6XPLCIiIqLglpV3HWKSB8nmDq77rexI+a9TslBpWSF3IxJw7tgXqDt3QDY3bPp3OW0EgM3tw5pzNslMolmLSckm1WoiIiIiIrpSvKNPRJetreEcTu18QzbXZ+g1SMgYqUpN/k7ujBRwFyIREREB0Gi0GDXvp7K5juYKnNr5puLnnZVuQbheeqbpirMdcMqMXqfg5nK04fDGP8jmYlOHImv4tarU5O/WldrQ4ZH+d3Nddhi0Gh5ZQERERESBg01EIrosoijiwLoX4fO6JXN6oxV5sx5TrS5/5hNF2RFHJq2AeZlsIhIREREQmzoE2XmLZHOndryO9uZKRc9p1Aq4po/0tUaT04fN5dI7qii4Hfvyr3B0NEhmBEGD0fOe4LSRf/tUwZEFN3CUKREREREFGF7tE9FlKc/fhOqinbK5odO/A5M1VpWa/N3uagcqOrySmTkZFoQb+KWZiIiIzhs24xHoTeGSGa/HiUPrfqv4OZf0lW9kfFzAkaahqrHqFAr3fySb6zv6JkQnD1ClJn9X0e7BrmqHZGZ4nAF9o3hkAREREREFFt6pJqIuc7tsOLj+d7K56KQB6DvqRlVqCgRKbsYt5ihTIiIi+hpTWAyGXvVt2VzFmS9Rnr9Z0XMOjzMgV6aZsa3SgcoOj+I6KTiIog8H1jwPUfRJ5kxhsYr+XoYKJeeIchciEREREQUiNhGJqMtObP0n7K01srlR834KjUarSk3+rtnpxbpS6bFgSRYtxieZVKuJiIiIAkPfUUsQlZArm9u/5nm4HG2yOUEQZHcjioDsGHYKPmcPLUdDxTHZXN7sH8Ags0M2VHh9Ij6R+bei1wDX9LGoVhMRERERUXdhE5GIuqS5pgCn97wjm8secT3i0oapUlMgWHm2Ay7pBd1YnGOFViOoVRIREREFCI1Gh1Hzfiqbc7TX4+jmPyt6zkXZYdDLvBtcVtgBnygqLZMCnKOjEUcU/P1JyByNzCHzVKkpEOyocqBS5siCWekWRBm5uJKIiIiIAg+biESkmM/nwd5Vv4Dokx5tZTBHYviMR1Sry9+JooiPFIw4WtyXo0yJiIjo0uIzRiBz6DWyucIDy1BXekg2F2vSYkaa9M6osnYP9sic80bB4+C638Jlb5HMCBodRs37CQSBC9/+Q8l1/k25HGVKRERERIGJTUQiUuzMnvfQWHVSNjd8xqMwWqJVqSkQHG9wIb/JLZmZkGRCRrj02UREREQU2kbMegwGU4Rsbt/qZ+H1uGRzN8qMNAWAjws7FNdHgavizFcoPbFWNtd/3B2IjM9RpaZA0ODwYlOZ9JEFKWFaTErmkQVEREREFJjYRCQiRdoaS3Hsy7/K5mJThyJ7xCJVagoUXJ1MRERE3cFkjUXe7Mdkc60NxTi543XZ3JQUExIt0iMW15Xa0Co3k50CmsvRhv1f/Fo2Z4lIwuCpD6hSU6BYfrYDbpl/Hkv6WqHhzk0iIiIiClBsIhKRLFH0Ye/qX8LrcUrmBEGDUfN+CkHgl5b/sLl9WFUsvYI/0qDBnAzpcWJEREREAJA1/Dok9Bkjmzu1/TW01BVJZrQaATfkSI9Td3pF2WsZCmyHN/4B9rY62dyIOY9Db+A163+IooiPCqQXCwoAluRwsSARERERBS7e6SciWUUHP0XduQOyuf7jlyImeaAqNQWKtaU2tLtFycx12WEwark6mYiIiOQJgoAx838GjdYgmfP5PNi3+lmIovQ2qSWKRprKT1WgwFRdvAdnD30mm0vtNw1pA2aoUlOgOFTnRFGL9JEFk1NMSLHqVKuJiIiIiKi7sYlIRJJsrTU4vPGPsjlrTDqGTHtIlZoCyccyq5Oh8DwiIiIiov8Ij83EkKkPyubqy4+g8MAyyUxmuB7jEo2SmeMNLpxqlD9jkQKLx2XHvtXPyub0RitGz38CAkdyXoRHFhARERFRKGATkYg6JYoi9n/xa3hc8iOsxi54Cjq9WZW6AsXZFjf21UqPgB0aa8DAGOmdBERERET/a8CEOxGVkCubO7LpT2hvqpDMKNmNuIy7EYPO0S9fRkez9N8NAMib/QOYwxNUqSlQtLl8+LzEJpmJNmowM43jX4mIiIgosLGJSESdKj2xFpUF22RzfUctQULmaFVqCiRKbrZxFyIRERFdDo1WjzELnvr3qWud87hs2LPyKcmxpldnWmDVSz/PirMdcHqlR7RT4KgvP4Ize96TzSVmjUV23iJVagokn5d0wO6R/vdwfU4YDDyygIiIiIgCHJuIRHRJjvYGHFz3omzOHJGI4TO/p0pNgcTtE/FJkXQT0aQVcG1WmGo1ERERUXCJTR2CfmNvlc3VlR7E6T3vdvp5s06DhTLXJM0uHzaWSu+8osDg9biwd9UzAKSbYFq9CWOueYpjTC9ByTmhXCxIRERERMGATUQi+gZRFLF39TNw2ppls2Pm/wx6I98g/68vy+1ocHS+4h8A5mVaEG7gl2EiIiK6fEOnfweWyCTZ3NHNf0FLXVGnn1cy0vQDBWc9k/87uuUltNYXy+aGTX8E1uhUVWoKJKebXDhSL31G6Ih4I3KjeGQBEREREQU+3r0mom8oOrhM0RjTzCHzkJI7RZWaAs1HCm6y3ZTL5isRERFdGb3BgtHzfyab83ld2L385/B53Zf8/NBYA/pH6SWfY1e1A8Wtl348BYbq4j04vfsd2Vxs2jDkjrlZlZoCjZJdiDdxFyIRERERBQk2EYnoIq31JTi0/veyOaMlCiPmPK5KTYGm2ubBV5V2yUxWhA6jE4yq1URERETBK6XvZGSPuF4211R9Cie2vXrJzwmCgBsVLHD64Ax3IwYqp70Fe1Y8JZvTaPUYu+BpaDRaVeoKJE6viOVFHZKZMJ2A+X0sqtVERERERNST2EQkogt8Xjd2L/8ZvB6HbHbk3P+DKSxGlboCzUcF7fBJHzGDG/taeb4MERERdZsRs3+IsKgU2dzJ7a+hofLEJT+3KDsMcpPWPy1qh9Mrc6FDfkcURez//Newt9XKZgdPeRCR8dmq1BVo1pfa0OySPrLgmj5hCNPzVgsRERERBQde2RLRBce3voLGqpOyubT+M5AxeK4qNQUaj0/EhzKjTLUCcH0ORxwRERFR99EbwzBu4S8BSC9SEkUvdi9/Eh73NxeNRRm1mN8nTPLxTU4f1p2zXXG9pK6So6tRdmqDbC46aQAGTrxLlZoC0Xtn2mQzPLKAiIiIiIIJm4hEBACoLzuMUztel82ZrHEYs+BJ7qLrxJZyO2psXsnM9DQz4s0cD0VERETdKyFzFPqPv0M219ZQgqOb/3zJz93aT74B8r6CRgr5j/amChxY+4JsTqszYvyiX0OjlT4bM1QVNLuwr8YpmekXpcfwOINqNRERERER9TQ2EYkIbmcHdi1/EqIoPZoHAMYtfAZGS7QqdQUiJauTb+0XrkotREREFHqGTf8OIuLkR1Ge2fs+Kk5/+Y2Pj4w3ol+UdBNpX60TBc2uK6qT1OHzebF7xZPwuKTP8QOAvFmPcYyphPcVnAd6Uy6PLCAiIiKi4MImIhHh4LrfoqO5QjaXO+YWJOdMVKWmQFTa5sa2SunzJNOsWkxJMalWExEREYWW87vJfgVBo5PN7ln5NDqaKy/6mCAIuEXBbkS58e3kH07t+Bfqyw7L5pL7TkLf0TepUlMgsrl9+KxI+u+8SSvg+mzpccBERERERIGGTUSiEFdybA2Kj6yQzUXEZWH4zO+pUlOg+kDB6uRbcsOh4epkIiIi6kExyQMxZOqDsjmXoxU7P/0JfF73RR9flG2FSSt9vfJpUQccHvkpFtR76suP4PjWf8jmjJYojL32F9xBJ2F1SQfa3KJkZkEfCyKNPLKAiIiIiIILm4hEIaylrgj7Pv+lbE6j0WHCoueg03MHXWecXhHLZFYn6zXAjX3lV/YTERERXamBk+5BTMoQ2VxDxTEc3fLyRR+LMGiwoI9F8nGtLh++OGe74jqpZzg6GrHjk/+D6PPIZscseApma5wqdQUqJaNMb+vPIwuIiIiIKPiwiUgUotwuG3Z8/CN43dLjNwFg6PTvIDp5gCp1Bar1pTY0OqRX48/NsCDWzNXJRERE1PM0Gh3GX/cstAoWgeXvehOVBdsu+titChoiShorpD6fz4tdy38Ge2uNbDZ7xA1I6z9dlboC1bF6J441SJ8BOjjGgKGxBtVqIiIiIiJSC5uIRCFIFEXsW/1LtDYUy2bjM0eh//ilqtQVyN470yabubUfVycTERGReiLi+mD0vJ8qyu5e8XN0tFRf+PmwWAMGRuslH3OozonTTdLNFVLfia2voObsbtlceEwGRs55XJWaAtl7Cprlt/azchwsEREREQUlNhGJQlDh/o9QemKdbE5vtGL8wl9Co+HuOSkFzS7sq3FKZnIi9RibaFStJiIiIiIAyBq+EFnDF8rmXPYW7PrspxfORxQEQdECKO5G9C9VhTtwYts/ZXOCoMX4Rb+GzmBWpa5A1eryYXVJh2TGqhdwbVaYajUREREREamJTUSiENNQcRyH1v9OUXbstb9AWFRKj9cU6D5QckYKVycTERFRLxl19U8QEZctm6svO4xjX/71ws+vzQqDRSd9/bL8bDtsbumR7qSOjuZK7Fr+MwCibHboVd9CbKr8mZmhbvnZdtg90r+fi7KtCNPz1goRERERBSde6RKFEKetGTs++RF8Po9stv/4O5A+cKYqdQUym9uHT4ukm4gmrYBF2VydTERERL1DZzBj0uIXodXJn494aucbFyZWhBs0WCizw6rdLWKVzE4t6nlejws7PvkxXPYW2WxK7lQMnHSPKnUFMlEU8d5pZYsFiYiIiIiCFZuIRCFCFH3YveJJ2L521k1n4tKGY/iMR1WpK9B9XmJDm1t6dfKCPhZEGjkSloiIiHpPZEIORs37P0XZPSt/gcaqUwCAWxQ0SN7Jb4Moyu9+o55zaMP/Q2PlCdlcWFQqxl/3LASBtwLk7K91orDFLZkZnWBEv2iDajUREREREamN7xyIQsSRTX9GVeEO2ZzREo2Ji1+ERqtXpa5A9/6ZNtnMrf3lzxMiIiIi6mlZw69Dn6HXyOa8Hge2ffQY7O31GBJrxNBY6SbJqSY3DtZJnw9NPafo0Gco3P+RbE6jNWDSkt/CYI5Qpa5A995pBdf53IVIREREREGOTUSiEFB06DPk73pTQVLAhOufgyUiQYWqAt/xBieONrgkM4NiDBgmc+ONiIiISA2CIGDU/CcQEZslm7W31mD7xz+E1+NStBvxrXz5hgt1v5rivdj/xXOKsiOv/jFikgf2eE3BoMHuxdpSm2Qm2qjB1Zk8soCIiIiIghubiERBrqZkn+IbC0OmPYyk7PE9XlOwUHKz7LZ+VgiCoEo9RERERHL0BgsmLn4BWp1RNttQfhT7v/g1FmRaEGGQfuu47pwNNTb5c7ep+7TWl2D7sschKjjvvM+wBcgZcYMqdQWDjwrb4fZJZ5b0tcKo5XU+EREREQU3NhGJglhrwzns+FjZjYXkvpMweMr9qtQVDBrsXqwq7pDMhOkFXJvF1clERETkX6ISczH22qcVZYuPrETZwfdwY1/p3YgeEXj/THs3VUhynLYmbP3gUbgd8ovaIhNyMXr+E1zYppDHJ+JdBaNMb87lKFMiIiIiCn5sIhIFKaetGVs/eAQuR6ts1hKRhPHX/QqCwC8JSn1YIL86eVF2GML0/D0lIiIi/5M5ZB4GTrpXUfbIxj9ijuk05FpQH5xpg8srdkt91Dmvx4XtH/8Q7U1lslmdIQyTlvwWOr1ZldqCwYYyG6ptXsnM5GQT+kTwDHkiIiIiCn68u00UhLxeN7YvexztjfI3FrR6Eybf9HsYLVGq1BYM3ApXJ9/RP1yVeoiIiIgux7Dp30FK7lTZnCj6UPz545gUJ91YqXf4sPac9DlydGVEUcS+z59FXekhBWkB4697FhGxmSpUFjzeOiV/nX87r/OJiIiIKESwiUgUZERRxP4vfo26cwcUpAVMWPQcYpIHqlBZ8Fh/zoYau/RNtEnJJuRGGVSriYiIiKirBEGDCdf/GpHxObJZj6sDAwr/KJt7K19+CgZdvpM7XkfJ0dWKssNnPoq0AdN7vKZgcqLBhX21TslMmlWLGWnc2UlEREREoYFNRKIgc3TzSyg+vEJRljcWLs9b+fKrk+8cwNXJRERE5P/0Rium3PxHGMyRstmMlu2I99ZIZg7Xu3CsXroJQ5fn7JGVOLblL4qy2XmLMGDCXT1eU7B5W0ET/I7+4dBqeL4kEREREYUGNhGJgsjJ7a/h1M5/Kcpm5V3HGwuX4XiDEwfqpG+MpVt1uCqVq5OJiIgoMFij0zBpyW8hCFrJnAYixraslH2+txWMfaeuKT25AftWPaMom9BnDEbNfwKCwEZXVzQ4vFhZ3CGZMesE3NjXqlpNRERERES9jU1EoiBxZt8HOKpwZXJC5miMnv8z3li4DEp2Id4xgKuTiYiIKLAk9hmDkVf/WDY3on0zDD67ZGZ1cQcaHNKj30m5yoJt2PXZExBFn2w2PLYPJi/5HbRavSq1BZMPC9rhkvktXpQdhkijdLOdiIiIiCiYsIlIFATOHlmJg2tfUJQNj8nApBt5Y+FyNNi9WMXVyURERBSk+o66Ebmjb5bMmEQ7RnRskcy4fMBHBe3dXF1oqinei+0fPw7R55HNGsyRmHrLn2AwR6hSWzBx+0S8q2AH7VIeWUBEREREIYZNRKIAV9aF0UYGUwSm3vJnGBWceUPf9GFBO9wyq5Ovzw5DhIFfWomIiCjwCIKAEXN/hPRBcyRz41q/kH2u9063weMTu7G60FNffgTbPvw+fF6XbFaj0WHyTb9HeEyGKrUFm/WlNtTYpHfPTkwyoV+UQbWaiIiIiIj8Ae90EwWwysLtikcbabQGTL7p/yE8NlOV2oINVycTERFRKNBotBh/3bNIzBrXaSbBU44c+2HJ56myebGxzNYDFYaGpurT+Oq978Ljlh4d+x9jrn0aCRkje7yuYPXWKfnr/DsH8jqfiIiIiEIPm4hEAaq9qRw7Pv4RfApGGwkaHSbf+DskZI5WpbZgtL7Uhhq79OrkSckm5HJ1MhEREQU4rc6AyTf+P8SkDO40M75NfjfiGwoaM/RNTVX5+PLdh+F2KhsJO3Luj5A1bEGP1xWsjjc4caDOKZlJt+owPdWsWk1ERERERP6CTUSiAHV4wx/g9Thkc4KgwYRFv0ZK7hRV6gpWilYncxciERERBQm9MQzTbn0J4bF9Lvn5/vb9iPLUSj7H/lonjtRLN2foYrXnDmDz2w/AaWtWlB86/bvoN/a2Hq8rmL2VL3+df8eAcGg1gir1EBERERH5EzYRiQJUbekBRbkxC36OjMHS59qQNKWrk6/i6mQiIiIKIkZLNK66/a8wRyR+43Ma+DCubY3sc7x+srWHqgs+FWe+wlfvfUfxDsSBE+/B4Mn39XhdwazB7sWq4g7JjFkn4Ma+VtVqIiIiIiLyJ2wiEgUgURThcUq/2QWAEXN+hOy8RarUFMyU3Pzi6mQiIiIKRmGRybjqtpdhMEd+43Oj2jdA75NeaLX2nA0V7fLj90Nd8ZFV2P7RD+H1KNu5mTvmZgyb8UiP1xXs3jvTBrfM8fLXZ4chwsBbJ0REREQUmnglTBSABEFAUs5EyczQq76N/uM42uhKVbZ78HmJTTLD1clEREQUzCLjczDttpehN108ut3ia8fIjk2Sj/WKwBunuBtRyuk972LPyqcgitLnb/9H1vCFGDn3xxAELmC7Eg6PD2+flh9lupRHFhARERFRCGMTkShADZp0D7Q60yU/N2DCXRg0+X7VawpGb+S3witKZ7g6mYiIiIJdbMpgzFj6TxgtURd9fELrKgii9Faujwrb0eaS2e4VgkTRh6Ob/4JD63+n+DHpg+ZgzIKnIAi89rxSy892oNEh/fdyUrIJuVEG1WoiIiIiIvI3fOdBFKDi0vMw7faXEZmQe+FjBnMUxi96DsNnfo8rk7tBq8uHD8/In0lz18AIVeohIiIi6k3RSf0xY+mrMFnjLnwszlOFAfZ9ko/rcIv4sEDZOX+hwu1sx/aPH8fJHa8pfkz6wFkYv+hX0Gi0PVpbKPCJIl5TcGTBXQO5C5GIiIiIQpsgiqLMHhsi8nftTRUQNFpYIhK4Krkb/fN4C1442CyZmZFmxiszElSriYiIiKi3tTWcw+Z3HoK9tQYAUGIchFeTnpN8TJzWjq235MKgZQOstb4Y2z/6IVobihU/JmfkYoya91M2ELvJhlIbvvVlnWQmJ1KHNQtToOHiTCIiIiIKYew2EAUBa3QqwiKT2EDsRi6viDfy5c9IuX8wdyESERFRaAmPzcTMO19DWFQqACDTeRKpzjOSj6n3mvHnj/4Jj8uuUpX+qfz0Fqx/bWmXGoiDJt2H0fN/xgZiN3r1hPwuxHsHRbCBSEREREQhjx0HIqJL+LykAzU2r2RmeJwBYxKMqtVERERE5C+s0amYeddrCI/JhABgUutK2cesau+Lda/ejsaqU6rU6E98Pi+ObnkZ2z/6ATyuDsWPy5v9Awyb8V0eVdCNDtY5caDOKZmJM2mwKNuqWk1ERERERP6KTUQiov8hKjwj5b5BEbyhQ0RERCHLEpGIGXe9ipjkQRhs24lIT61kvsKYi6PtZmx4/U6c3P4afD7pBVvBwtZai60fPIqT219V/BhB0GLswmcwYPzSHq0tFL2mYBfinQMiYNTyOp+IiIiIiE1EIqL/saPKgfwmt2Qm3arDnAyLajURERER+SOzNQ4z7noVfQbNwsTW1bL5HRHXQfR5cHTLX7D5rQfQ3lypSp29QRRFFB36DGv+vhjVRTsVP06jNWDSjb9F9vCFPVpfKCppdWN9qU0yY9YJuLUfdyESEREREYFNRCKib1JyRso9A8Oh03B1MhEREZFOb8bEG36DpcPTYfRJj+o8bR6Del0KAKC+7BDW/uMmFB9ZBVEUVapWHR0tVfjqve9g3+pfwu1sV/w4kzUOM+58BWn9p/dofaHqX6daIfc3bUmOFdEmnj9JRERERAQAghhs79boir355ptYtmzZhZ8vWbIEd911V6/WRKSW/CYXFqyqksxEGjTYujgVYXquwyAiIiL6up+uO4SPa2IkM2Pb1mBh4z8u+lhSzkSMmP0DRMbn9HCFPUsUfSg6+AkOb/wjPC7pHW//Ky49D5MWvwhzeHyP1RfKGh1eTP2kAg5v57dANAKwcVEKMsL1qtZGREREROSveAecLlJRUYF3330X7e3tF364XK7eLotINUp2Id7W38oGIhEREdElPDJpKLSC9DrVg9aZaNNGX/Sx6qKdWPuPm3FgzW/gtDX1cJU9o7b0IDa9eR/2f/FclxuIuWNuxvSlr7CB2IPePd0m2UAEgLkZFjYQiYiIiIi+hnfB6SIvvfQS3G43UlJSersUItVVdXiwulh6BJdeAyztH65aTURERESBJMWqw7zMMMmMRzBgR/g3z/sTRS8K9n+I1X9ZiNO734HXK31Gtb9oqsrHV+9/F5vfvA/1ZYe79FitzohxC5/BqKt/Aq2Wzaue4vD48PbpNtnc/YMjVKmHiIiIiChQ6Hq7APIf27dvx549e6DT6TB69GisXLmyt0siUtW/TrXCIzPgeVG2FQkWfukkIiIi6sx9gyKwukR6J97e8KsxtfUTWHzfPC/Q7WzHoQ3/DwUHPsKACXehz9D50OnNPVjx5WltOIfjX/4VpSfXX9bjLZFJmHzj7xGTPLDba6OLfVrUgUaHTzIzJtGI4XFG1WoiIgpUPp8PbW1taGpqQktLC9xuNzweDzweT9Cdcfy/BEGATqeDTqeDwWBAZGQkoqOjER4eDkEQers8IqIewTvhBABwOp14+eWXAQCTJ09GTIz0OSZEwabB4cX7Z755E+t/3TuIuxCJiIiIpAyNM2JCkgm7qh2dZlwaM3aHz8eMlo86zbQ3lmH/57/C0c0vIWfE9eg7+maERSb1UNXKiKIP1Wf34Oyhz1Cevxmi6L2s58kcMh8j5/4IRktUt9dIF3P7RLxyokU2d/8g7kIkIpLS3NyMyspKNDc3w+u9vO9/gU4URbjdbrjdbtjtdrS0tKC0tBQ6nQ5RUVFITU1FRAS/nxBRcGETkQAA7777LqqrqwEAN998M/bs2dPbJRGp6o1TrbDLbEOcnmpGbpRBtZqIiIiIAtW3hkZINhEBYFf4AkxqXQmjKJ1z2VtwaucbyN/1NtIGzEDumJsQlz4CGo22m6vuXEdLNYqPrMDZwytga6m67OcxWeMwZv7PkNr/qm6tjzq3urgD5e3SN7tzInWYnuZ/u12JiPyB0+lEcXEx6urqersUv+XxeFBfX4/6+nokJiaiT58+MBh4/4iIggObiISKigp88MEHAIDRo0djwIABbCJSSGl1+fB2vvwZKffxjBQiIiIiRSYkmTA8zoAj9a5OM3ZtBPZb52BSm7JjFETRi7JTG1B2agMM5igk952E1NwpSMqZCIOpe6dFiKIPLbVFqD23D5WFO1BdtAvAlY1oyxq+ECNm/xAGM68p1eL1ifjbMfldiPcOioCGY+iIiL6ho6MDx44dg9v9zXOKNRoNrFYrTCYTtFottFpt0I/0FEURXq8XXq8XDocD7e3t8PkuHpddU1OD5uZmDB06FGYzF6gQUeBjE5Hw0ksvXbgYuPvuu3u7HCLVvZ3fina39E2hkfFGjEvkGSlERERESgiCgIeHROJbX0rvWtgesQhj29ZAj2/enJTisjfj3LHPce7Y5xAELeIy8hCfPgIRcdmIjMtCeFym4nMURVGEy96CjpYqNFQcQ23JPtSe2w+nrblLNXXGHJGIMdc8iZS+k7vl+Ui5taU2nG31SGYSzVosyraqVhMRUaC4VANRo9EgNjYW4eHhsFgsQd80lCOKImw2G1pbW9HQ0HDhTEin04ljx46xkUhEQYFNxBC3ffv2C7sOJ06ciMGDB/d2SUSq6nD78K+T8rsQvzMsMuQvjomIiIi6Yma6GblRehQ0d94gbNPF4JB1Osa2r7/s1xFFL+rOHUDduQNf+6iAsKhkhMdkQmewQKPVQaPVQ6M5/1+v1wVbaw1sLdWwtVbD65YeqXo5NFoDckffiMFTH+r2nZIkzyeK+OtRBWchDo6AUcvrfCKir/P5fDh16tRFDcSIiAikpKRAr9f3am3+RBAEhIWFISwsDLGxsaioqEB7ezvw70Zifn4+8vLyeD+JiAIam4ghzOl04uWXXwb+vZLo/vvv7+2SiFT3/pk2NLt8kpnBMQZMTTGpVhMRERFRMNAIAh4eEoEfbm+QzO1LeQBX1Z+BrbGkG19dREdzJTqaK7vxOZURBC2y8hZi8JQHERaZpPrr03mby+04LdHABoAYkwY353IXIhHR/6qoqIDdbr/w86ioKKSlpbEZJsFgMCAzMxNlZWVobW0FALS3t6O6uhrJycm9XR4R0WXT9HYB1HveffddVFdXAwAWLFiArKys3i6JSFUOjw+vKdiF+O2h3IVIREREdDmu6ROGdKv02tUqpx7euW9gxJzHYbREqVZbT8gYNAfzvvUJxi54ig3EXiQq3IV478AIWPS8LUJE9HVutxulpaUXfm4ymdhAVEij0SA9PR0Gg+HCx0pKSuD1enu1LiKiK8GdiCGqoqICH3zwAQAgLCwM99xzj2qvbbPZ4HB0/7igrzMYDDCZuHOMpH14ph11dukLub6ROkxP0cPjkT5LhYiIiIgu7b6BVvxin/T5gq+caMeK+Tehz7DrUHpiLQr2f4DWuiLVarwyApL7TsKgKQ8jOqk/APDasZdtr3LgaINLMhOhF3BLXzP/rIiI/kd9fT18vv9ObEpJSWEDsQs0Gg1SUlJQUnJ+woLH40FjYyOio6O79Dw6HW/bE5F/4FejEPXSSy9dmGt+++23IypKvRW/J06cwP79+3v0NQYNGoQhQ4b06GtQYHP7RLxy3CabW5qlRdu/x1AQERERUdfNiBXxF6OAeqfYaaagxYPVZxoxLUmHmKzpGNvnKjRVHkHp0U9RV7wLQOeP7S3GsHikDrwaKQPmwhxxftdhS4v87jfqeX85In+df1MfHby2NvBPjIjoYnV1dRf+32g0wmQycSddF5nNZuh0ugsLVWpra6HRdG3ne2xsbA9VR0TUNWwihqDt27djz549AIDk5GQsXry4t0siUt2aCg9qHNI3o9IsAmYl88skERER0ZUwagXclq3Hn09J7wx7vdCFqYlaCIIAQRAQk5qHmNQ82FoqUX5iFWqKtsHRVq1a3ZciaLSI7zMRqQPnITZ9FASNtlfroW861ODF4UbpM88tWuCmPgbJDBFRKBJFEe3t7Rd+HhYW1qv1BCpBEGC1WtHcfH4Sw9d/T4mIAg3vjocYp9OJl19++cLPv/3tb180p5soFHh8It4slL6JBQB35Rig03BkBxEREdGVuj5DjzcKXWh1d57Jb/Fha40X05IufptqiUxBv4kPIXfCg+hoKkX9ud2oK9mN5uoTgCjdLOoOenMUYlKGITp1BBKyJ8No6do4MlLX6wqu85f00SPSwOt8IqL/5XA4Ltp1aLVae7WeQPb1JqLL5YLL5eI9WCIKSGwihph3330X1dXnV++OHj0akydPVr2GwYMHIycnp0dfg2cikpRVxTaU2zokM8kWLW4eFAuDljcXiIiIiK5UJIA7B2jwl2NtkrnXijxY0C8Gmk7OXoqKikJq1jAAD8Jlb0H12V2oO7cfLfVn0dZQArdD+vmVMJgjEZ8xEvEZoxGfOQoRcdk8CypAHKl3YW+99G4Pk1bAQ8NjEWniLlIiov/1n6OP/iM8PLzLYzjpvPDw8It+rtPpEBkZ2Wv1EBFdLjYRe8Hrr7+OVatW9dgB7gaDAffccw8WLFhw0ccrKirwwQcfAAD0ej0effTRHnl9ORaLBRaLpVdem8jjE/HycfmbSw8MiYDFqFelJiIiIqJQcPegSPzrVDs6PJ2PlD/d7MGmShfmZcqPT9OFxyJ7+AJkDz//vkcURTg7GtFSX4y2hmK01pfAaWuCz+uBz+uGz/ff/0L0wRQWB0tkIiwRyQiLTIIlMgmWiCQYLdFsGgaol483yGZuzrUi0WpUpR4iokDj8/13h79Go2ED8QpotRcvVhFFETodb8UTUeDhV65ekJaWhv79+/doEzEhIeEbH//Xv/51YUVRRkYGdu3ahV27dl3yOY4fP37h/0+dOoWPPvrows8zMjIwfvz4HqmdqKd9WtSOkjbpf3txJg1u6suRHURERETdKcqoxZ0Dw/G3Y62SuT8dbsGcdAu0XRwrLwgCTNZYmKyxSOwz+gqrpUCzv8aBrZUOyYxeA9w/OEK1moiIAs3X71X+bxOMukYQBGi12gvjYXvqPjARUU9jE7EXzJkzB3PmzFH9df8zhxsAioqK8Le//U3R4w4dOoRDhw5d+Pns2bPZRKSA5PSK+MvRFtncfYMiYNJxtR0RERFRd7tvUATezm9Du7vz3YiFLW58XmLDwmz53YhE+Pfujv93uFk2tzjHiuQw3gYhIurM189D7OkmotPpREFBAQAgOjoaqampPfp6/1FSUoL29nYIgoABAwb06K9To9Fc+D39+u8tEVEg4V1yIgoZHxa0obJD+qIt2qjBrf3DJTNEREREdHmijFrcM1B+J9hLR5vh8XXeaCT6uu1VDuyrcUpmtALw4BDuQiQi8gculwtvv/023n//fWzYsAEREep9fTabzVixYgXee+89LFu27KIRrt3t6+PRRZHXNUQUmLgEL4Q8++yzile9PPHEEzh27BgAYMmSJbjrrrsufI7zuykQ2T0+2dFZAPDQkEhY9VxfQURERNRT7h0Ugbfy29Di6vymXXGrByuLO3BDDkfMkzRRFPH7Q/K7EJf0tSIjnGeeExH1NrfbjXfeeQfFxcUwm8246667EB6u3mLuxMRELF26FK+++iqOHj0KjUaDxYsX8/xHIqJO8KtjCDGbzbBarYp+fH0rv8FguOhzJpOpV38dRJfjnfw21Nmlm+iJZi3u6M8bVUREREQ9KdygwX0KzqV76UgL3NyNSDI2ltlxrMElmdFrgO8Mi1StJiIi6tyKFStQVFQEQRBwyy23ICYmRvUaMjIyMHv2bADA4cOHsWXLFtVrICIKFGwiElHQa3P58I/j8rsQvz0skmchEhEREangzgHhiDZKX3eVtXvwaVG7ajVR4PGJIv6o4CzE2/uHI4VnIRIR/f/27jw8qvLu//hnZjLZ9wVCCIQQdqHs++qKYtFqK09tf9qqWLXW9VErdde2lqqt2rpRK0/VWkurxaUixaqgiCyRHURCWAPZ92UymeX3h3DMsGRmQjKTybxf1+XVczL3Oed7Mp1w5nzOfd9Bt3nzZm3cuFGSNG7cOA0YMCBotUybNk15eXmSpI8//lh79+4NWi0A0JVxtxxAt/d/O2tV3cZwWZKUHW/RZQPohQgAABAI8VazfuJDb8RnttSo2UlvRJzcv/c1ald1S5ttYiJMun44vRABINiqqqr01ltvSZLi4+N1/vnnB7Uek8mkCy+8UGazWS6XS0uWLJHNZgtqTQDQFREiAujWqpud+vMO770Qb/pWsiItJq/tAAAA0DH+35AEpUe3/ZX0cINT/9hNb0ScyOFy6ykfeiH+aEiC0mMsXtsBADrXihUr1NzcLEmaMWNGl5guqWfPnho7dqwkqba2Vp988kmwSwKALocQEUC39qfttapvafvp9f6JEbq4f1zAagIAAIAUE2HW9SO89xB7ZmuNGlraHlUC4efNPfXaV+dos0281aT5PvR4BQB0ruLiYm3ZskU62gtxwoQJwS7JcM4558hi+fphk9WrV6u21vuD6AAQTggRAXRbZU1Ovfxlndd2t4xMVoSZXogAAACBdvmgBPX00kusrMm3kSUQPpqdbv1xS43XdvPPSFRyFL0QASDYPvjgA7ndXz/gPX78eFmt1mCXZIiPj9cZZ5whSWppadGqVauCXRIAdCnMLI6T+vWvfy2n0ylJioyMDHY5QLs8s6VGTY62eyEOSbHqgn6xAasJAAAA34iymHTDiCQ9uK6yzXYvbq/V5YMSlMGwlJD096/qdLjB2WablCizfjyUXogAEGw1NTXatWuXsT5mzJig1nMy48ePN3pKbty4Ueeddx73QwHgKHoi4qRiYmIUHx+v+Ph4/tFESNpT06K/feW9F+Kto5JlNtELEQAAIFguGxivrLi2w8FGh2/z36H7q7W79AcfeiFeNzxJ8VZueQBAsH3xxRdyub4elrxfv35KTU0NdkknyM3NVVpamiTJZrNp27ZtwS4JALoMrqgBdEsL86vkbLsTokamR+rs7JhAlQQAAICTiLKYdNuoZK/tlhTUa3e1PSA1oet6dkuNqprbniOzZ4xF/29wfMBqAgCc2qZNm4zloUOHBrWWUzGZTB61ta4ZAMIdISKAbmfNkSZ9eKjJa7vbRyXLRC9EAACAoLu4f5yGprQ9P5LLLf32C3ojhrMDdS36y5fe58f86beSFB3B7Q4ACLbKykqVl5cb67m5uUGtpy39+/c3lvft2ye7nQeXAECEiAC6G6fLrV9vqPLabkpmtKb0ig5ITQAAAGib2WTS3WNTvLb76FCTPi+2BaQmdD2PfVGtlrY7IapPfIQuG0AvRADoCgoKCozl6Oho9erV67T36XK5VFNTo3379mnXrl06fPiwMVzq6ejXr5/M5q9vlTudThUWFp72PgGgO4gIdgEA0JGWFjZoZ1VLm21MkhaMS6EXIgAAQBcyNStGM7Kitepw2yHhb/Kr9OacTOa1DjP5pTYt29/otd2dY5IVaeH/GwDQFbQO4vr27WuEdP5qbm7WF198oQ0bNqi8vFwOh8Pj9djYWE2ZMkUzZ85s9zGioqKUkZGhkpISSdLevXs1ZMiQdu0LALoTQkQA3UZji0tPbPQ+xNV38+I0NDUyIDUBAADAdz8fm6JPjxyRq425rbdV2PXu3kZd1D8ukKUhiFxu30YbGZMRpQtyYgNSEwDAuyNHjhjLGRkZ7drHf//7X61evVrNzc2nbNPY2KgPPvhAO3fu1Lx585Sent6uY/Xo0cMIEYuLi9u1DwDobhjOFEC38eKOWpU2OdtsExNh0m2jkwNWEwAAAHw3OCVSl+Z5Dwef2FilZmcbSSO6lXf3Nmpzufe5qX7BaCMA0GW0tLSooqLCWE9LS2vXfnbv3m0EiNHR0Ro6dKjOO+88XXLJJbr44os1cuRI429/UVGRXn311RN6KvqqZ8+exnLrABQAwhk9EQF0CyWNDv1pe63XdteekaiesfzpAwAA6KpuHZWsf+9rVJPj1CFhUYNTr3xZq/lnJAW0NgSezeHS4xu990K8KDdWozKiAlITAMC78vJyud3f/Fve3t6BcXFxSkxM1KxZszRmzBhZrVaP1ydMmKARI0botddek8vlUllZmT7++GOdc845fh8rOfmbh84bGhpks9kUHR3drroBoLugJyKAbuH3G6vbvNEkST1jLJo/LDFgNQEAAMB/mbERusaHa7Y/bqlRmZdRKBD6Fu+s0+GGtt/nSLP0v6NTAlYTAMC7xkbPeWxTUtr3d3r8+PG65ZZbNHHixBMCxGOGDh2q0aNHG+urVq064fi+OD4wbGpqakfFANC9ECICCHk7Ku16Y0+D13a3j05WrJU/ewAAAF3d/DMSlRbd9nVbfYtbC/O991BD6Cpvcur5rTVe2109LFG94xltBAC6kuMDuPb26BsyZIhP206cONFYdjqd2rNnj9/HOv447QkiAaC74W46gJDmdrv18LpKeZsRZ2iKVZf4ML8OAAAAgi/eatYtI73PY720sEHrS2wBqQmB98TGajV4GW0kLdqs64YzrC0AdDXHh4iRkZGderzjh0ttPR+jr2JiYjzWbTauMQCAEBFASPtXYYM2lDZ7bfeLcSkyH51oGwAAAF3fvIHxykvy3rvswbWVcri8PVKGUPNFqU3/KKj32u62UclKiOTWBgB0Na0DOLPZrIiIzu0xHhUVpdjYWGO9I4YzpSciABAiAghh1c1OPbrB+xBWZ2XHaHKvGK/tAAAA0HVEmE36xbhUr+12Vbfo1V11AakJgeFwuXX/2kqv7QYmW/W9AfEBqQkA4J/WPRE7uxfiMWbzN7e63W7/HzBiTkQAOBGTBgAIWU9srFZVs6vNNhaT9POx7Zu8GwAAAME1s3eMzu0ToxUH276J9+Smal3YL04ZMZaA1YbO85cv6/RlVYvXdgvGpijCzGgjANAVuVzf3K9pHe51lPLychUVFamurk6NjY1qbGw87eFHLRbP6wiHw3GaVQJA6CNEBBCSNpU16/WvvA9vdMWQBOUlWQNSEwAAADrePeNTterwYTU7T92joL7FrYX5VXp8Wvop2yA0HGlw6OlN1V7bzewdrRm9GW0EALqq1vMLdsTcgk1NTcrPz1dhYaEOHjzYKUONtrR4PsBy/ByJABCOCBEBhJxjwxt5G5iiR4xFt4xMDlBVAAAA6AzZ8RH66YhE/X5TTZvtlhY2aN7AeE3oGd1mO3Rtv1xfpQZH21f6kWbpvvHeh7oFAARP6wDO5XLJbre3a1hTp9Op//73v1qzZo3sdrvHayaTSampqUpMTFRUVJQKCgpOq/dgc3PzKc8BAMIVISKAkPPXXXXaUWn32u6ecSlKiGTqVwAAgFA3/4wkvbmnQfvr2r4x+NDaSr317V4McRmiVhY1afkB7z1LbhiRpH6JjDYCAF3Z8QFcU1OT3yFic3OzXn75Ze3bt8/4WUZGhkaPHq2+ffsqKytLUVFRxmuPPvqo6uu9j1p1KseHlISIAECICCDElDY69Dsfhjea2itac/rFBqQmAAAAdK4oi0n3T0jVNf8tbbPdruoWvfJlna4alhiw2tAxbA6XHlxb6bVdv4QI/WR4UkBqAgC03/EBnM1mU1KSf3+/ly9fbgSIVqtVF110kcaMGdOhdbZ2fIgYHc3oBgBAFx0AIeXRDVVqaGl7eCOrWXpwYqpMJp5ABwAA6C5m9o7RuX289wh4clO1iurbP5QZguO5rbU66MP79tDEVEVZuM4HgK7uZD0R/WG327Vu3Tpj/eKLL+7UAFGSamo8h06nJyIAECICCCGrDzfpnX3ehze6bniSchneCAAAoNu5Z7z3AKnB4dYv1lTI7fY2gza6isKaFi3a3vacl5L07X6xmprFDV0ACAWJiZ6jAjQ0NPi1fXFxsfFvuclk0hlnnNFme5fL5XdQebyKigpj2Wq1Kj4+/rT2BwDdASEigJDQ0OLSPZ9XeG3XJz5C1w9n+CoAAIDuKDs+Qj8d4f1ab/URm5YUtH9OJASOy+3WPZ9XqMXVdrt4q0m/GJcSqLIAAKcpISFBcXFxxnppadtDkh/Pav3m4XC3231CL8HjFRcXy+l0tqPSb5SXlxvLPXr0kNnMrXMA4C8hgJCwML9Kh+q9Xww+ODFV0RH8aQMAAOiu5p+RpL4JEV7bPbqhSkcaGNa0q/vLzjqtL2n22u72UcnqEev9fQcAdB29evUylv0NEVNTU2WxWIz15cuXn3KUgfr6er355punUenXWvdE7Nmz52nvDwC6A+60A+jyPj3cpNe+8v4k+ey+sZrZm+GNAAAAurMoi0kPTkj12q6+xa17P2dY066ssKZFj2+s9tpuWGqkfjA4ISA1AQA6TnZ2trFcXFzs17ZRUVEeQ5ju3LlTzz//vLZt26by8nI1NDTowIEDevvtt/W73/1OR44cOa1anU6nxz5ycnJOa38A0F3wGB+ALq3O7tKCz7wPYxobYdI94xneCAAAIBzM6B2j7+bF6Y09bc+vtLLIpn8VNujSPOY06mqcLrfuWl2uZmfbIa9J0iOTUhVhbnsuTABA1zNw4EB9/PHHkqSysjLV19f7Nc/g9OnTtXPnTrW0tEiSDh06pL/97W8nbZuQkCCn06nGxsZ21VpUVCS73W6sDxgwoF37AYDuhp6IALq0X2+o0pFG78OY3joqWVlxPBcBAAAQLn4xLkU9Yixe2/1yfZVKGhnWtKt5cUetNpXbvba7fFC8RqZHBaQmAEDH6tOnj6Kjo6Wj8xoWFhb6tX1WVpauuOIKj/kRj5ecnKxZs2bp1ltvVVJSUrtrbV1bRkaGkpOT270vAOhOuOMOoMv6+FCT/lHgfRjT8T2i9OOhDG8EAAAQTpKiLHpkUqqu+6iszXa1dpfu/7xSz5+ZIZOJ3mxdwVdVdj25yfswptnxFt01ltFGACBUWSwWDRkyRJs2bZIk7d69W9/61rf82kdeXp5uueUWbdq0SeXl5TKZTIqNjVVGRoZ69+6tXr16Gf++f+973zN6LfrT41GSCgoKjOURI0b4tS0AdGeEiAC6pJpmp+5Z430Y05gIk34zNU1mbggBAACEnbP7xOqi3Di9vbftYU3/e6hJ7+xt1EX94wJWG06uxeXWHasr1OLy3vbRKemKtzKAEgCEsvHjxxsh4rZt2/Ttb39bUVH+9TBPSUnRmWee6bVdZmZmu2qsrq7Wvn37JEkmk0ljxoxp134AoDviahxAl/TI+iqVNHkfxvSuMSnKSTj1sBYAAADo3u4bn6L0aO9fbR9cV6mieoY1DbbnttZoR6X3YUyvHJKgyZnRAakJANB5+vXrp4yMDEmS3W7Xli1bgl3SCTZu3Ci3++s5evPy8pSSQi94ADiGEBFAl7PiQKOWFrb9NLkkTcqM0g8H+zc8BQAAALqXlGiLHpqY5rVdrd2lWz8pV4vLHZC6cKLtFXY9u6XGa7t+CRG6cwxzUQFAdzF9+nRjef369UGt5XhOp1NffPGFsT5r1qyg1gMAXQ0hIoAupajeobs/8z6MaVyESb+Zks4wpgAAANDsnFjNyYn12m5jWbOe8mEuPnS8+haXbvukTA4vGa7ZJP12appiIrhdAQDdxejRo9WzZ09JUlFRkcf8g8G2adMmVVZWSpIGDRqk3NzcYJcEAF0KV+UAugy7062bV5Wpxu59gpQF41KUHc+0rgAAAPjaAxNTlRLl/SvuC9tq9enhpoDUhK+53W7d/3mFCmu9Dyd7zbBEjenBMKYA0J2YzWbNnj3bWH/vvffkcvkwOW4nczqd+vjjj6WT1AgA+BohIoAu4/GNVdpc7n1+lOlZ0fqfgQxjCgAAgG+kRVv04MRUr+3cku74tFzlPsy/jY7x+u56vb230Wu7AUlW3TqKYUwBoDsaPHiwxowZI0kqKSnRhg0bgl2SVq1aZfRCPOuss5SZmRnskgCgyyFEBNAlrDjQqJd21Hltl2A16deT02RiGFMAAAAc58J+cfpuXpzXduU2l/7303K53MyP2Nm2V9j1yLpKr+0sJumxqWmKsnCdDwDd1dy5c5Weni5JWr58uRHgBUNpaanRCzE3N1czZ84MWi0A0JURIgIIuoN1Lfq5D/MgStL9E1LVK45hTAEAAHBy909IVf9E79eLq4/YtGhbbUBqCld1dpduWlUmH2Yr0PXDkzQiPSoQZQEAgiQyMlKXX365YmJiZLPZ9Prrr8vpDPzIAM3Nzfrb3/4mh8OhlJQUzZs3T2Yzt8kB4GT46wggqOxOt25ZVa5aH+4sfDcvTpfkMYwpAAAATi3OatZTMzIU6cO33d9vqtYXZc2BKCvsuN1u3f1ZhQ7UeZ8HcWxGlH42MikgdQEAgiszM1NXXXWVoqOjVVRUpDfffFPuAI4M4HQ69fe//12lpaVKTk7WNddco8TExIAdHwBCDSEigKBamF+lLRXe50EckGTVAxO8z3EDAAAADE2N1C/Geb92dLqlm1eWqbTRe9AF/7z8ZZ2WH/A+D2JKlFlPzkiX1cwwpgAQLnr37q0f//jHGj58uBwOh3bt2hWwY2/ZskVWq1UjRozQ1VdfrZSUlIAdGwBCkckdyEc9AKCVZfsbdNPKcq/tYiJMenNOpgYmRwakLgAAAIQ+t9utn60s9ynIGpkeqb+e11PRETxn2xE2lzfr++8Xq8XLYCMmSS+d00PTs2ICVRoAoA379u3TwYMHJUlRUVEaNGhQsEsKaTt37pTD8fWDSrm5ucrOzg52SQDgN74hAQiKLeXNuvNT3+ZBfHhiKgEiAAAA/GIymfTryanqHWfx2nZzuV0LPqsI6HBq3dXhBodu+KjMa4AoST8dkUSACABdSETEN3MKB2Ouwu6m9e+w9e8WAEIJISKAgCuqd+gnH5bK5vR+k+ayAfHMgwgAAIB2SYqy6PczMmTxYaTMd/Y16tmtNYEoq9uqs7t07X9LVdrk/cbzpMwo3cw8iADQpRAidhyXy+XxcBIhIoBQRYgIIKDq7C7N/7BU5TbvjyYPTrbqgQmMTQ8AAID2G5MRpdtHJ/vU9vebavT+/oZOr6k7anG5dfOqMu2qbvHaNj3arN9Pz5CFeRABoEuxWq3Gstvtlt1uD2o9oay5udljvfXvFgBCCSEigIBpcbn1s5Vl2u3DjYW4CJP+MDODeWkAAABw2q49I1FnZfs2bOYdn1ZoW0WzDy1xjNvt1sPrKvXJYZvXtmaT9PsZGcqI8T7MLAAgsBITEz3W6+rqglZLqGv9uzObzUpISAhqPQDQXtydBxAQbrdbD62t1Ooj3m8sSNIvJ6epfxJPaQEAAOD0mU0m/W56ugYne7++tDnduu6jMpU2OgJSW3fw4vZa/e2rep/a3jIySZMzozu9JgCA/6xWq0fYRYjYfvX13/y7mJSUJLOZ2/AAQhN/vQAExJ931Or13b7dWLh+eKLm5sZ1ek0AAAAIH/FWs144q4dSo71/DS5pdGr+h2WqtXsfgj/cLdvfoIVfVPvU9ry+MbphBPMgAkBXlpLyzbQyDQ0Ncrn4t9BfDodDDQ3fDI/e+ncKAKGGEBFAp/v33gYtzPftxsIFObE+z1kDAAAA+CM7PkLPzcqQ1Ydvwjsq7br6gxLVt3Dz9FS+KGvWHZ9W+NR2ZHqknpiWLrOJeRABoCtrHXi5XC6VlpYGtZ5QdPzvjBARQCgjRATQqd7b16DbPy2X24e2o9Ij9djUNG4sAAAAoNOM7RGtX01O86ntpnK7rv2wVE0OgsTjbSpr1tUflKjZ6f1Kv3ecRS+c2UMxzHcOAF1eQkKCx9yI5eXlamxsDGpNoaS+vl4VFd88YJOSkqKYGN/mZQaArogreACdZtn+Bt32Sbl8uK+g7PivbyxEc2MBAAAAnezSvHj95IxEH1pK60uadf1HZT6FZeFiY1mzfvxBiepbvP9OEqwmvXh2D6XHWAJSGwDg9JhMJuXl5Rnrbrdbe/fuJUj0QX19vfbv32+sH/tdmnhYHkAI4249gE7x/v4G3brKtwAxwWrSi2f1VBo3FgAAABAg/zs6WWdn+9YzYPURm362skx2gkS/AsQIk/TMrAwNTI4MSG0AgI4RHx+v7OxsY93lcqmwsFDFxcVyOp1Bra0rcjqdOnz4sPbu3esxh2ROTg69EAGEPJPb7eZbEIAOtXx/o25ZVSaHD39dIkzSS+f00JReXFQBAAAgsOpbXLr8/WLtrGrxqf3svrF6aka6Iszh2aPgWIDY4EOAKEm/mZKm7w2I7/S6AAAdz+12a8+ePTpy5IjHz00mk+Li4pSQkKCEhARFRkaGXU87t9ut5uZm1dXVqb6+Xg0NDTr+FnufPn2Uk5MTdr8bAN0PISKADvWfA426eaVvAaIk/XpyquYNTOjssgAAAICTKm9y6vLlxdpb6/Cp/fk5sXpiWrqiLOF1U/CLsmZd5UeA+NMRibp9dEqn1wUA6Dxut1v79+/XwYMH22xnsVhksVi6fWDmdrvldDrb7I1pMpmUk5Oj7Ozsbv/7ABAeCBEBdJi3Cuv189UVPgeIt49O1k9HJHV2WQAAAECbjjQ49IPlJTpY71uQODYjSs+fmaGU6PAYjn9diU3Xfljqc4D43bw4/WZKGjdPAaCbaGhoUEFBgWpra4NdSpeWnJysvLw8xcbGBrsUAOgwhIgATpvL7daTm6r17FbfLyZvH5Wsn36LABEAAABdw6F6hy5/v1hHGn2b66lfQoT+fE4P5SRYO722YFqyu04PrK1Ui8uHxpIuzYvTo5PTZAnTIV8BoLtyu91qaGhQVVWVqqqqVFtbe8IQnuHGZDIpKSlJKSkpSklJUWxsLA/QAOh2CBEBnJbGFpfuXF2h5Qcafd7mtlFJuvFbyZ1aFwAAAOCvfbUt+sHyEpU2+RYkpkSZteisHhqdEdXptQWa0+XWb/KrtHhnnc/bfDcvTr8mQASAsOBwOFRXV6eWlhY5HA45HI5uHyqaTCZFREQoIiJCkZGRSkhIkMUSHqMSAAhfhIgA2q240aHrPizT9kq7z9vcOipJPyNABAAAQBe1u9quHywvUVWzb13voiwm/W5aumbndJ+hy+rsLt36SZlWFtl83oYAEQAAAOh+CBEBtMvW8mZd91GZz09pS9ItI5N000gCRAAAAHRtOyvt+n//KVGN3bcg0STpppFJ+umIJEWEeIi2v65FP/mwTHtqWnze5nt5cfoVASIAAADQ7RAiAvCLy+3Wq7vq9Nv8atmcvv/5IEAEAABAKNlW0axr/luqCpuPkwFKGp0RpcenpYXsPIkrDjTqF2sqfO6FqaMB4q+npMnMHFAAAABAt0OICMBnB+tadPdnFVpb0uzXdrePTtZPRyR1Wl0AAABAZzhY16L5fvbKi4sw6b4JqfpuXpxMIRKsVdmcenhdpd7Z5/s855J02YB4/WpyKgEiAAAA0E0RIgLwyuV2629f1WthfpUaHb7/yYiymPTbKWm6MDeuU+sDAAAAOktNs1M3fFymdX4+SDe7b6x+OSlVKdGWTqutIyzf36j711b41ePSJOl/RyfruuGJIROUAgAAAPAfISKANh2qd2jBZxVaU2zza7uMGIuePzNDI9OjOq02AAAAIBCanW4t+KxCb+9t8Gu7HjEW3TkmWRflxnW5+QIrbU49tK5S//az92FshElPTEvXuX1jO602AAAAAF0DISKAkyprcupP22v02q56v+Y+lKRhqZF64cwM9YqL6LT6AAAAgEByu916clONntla4/e2g5KtumNMss7sHRP0nnv1LS69+mWd/ryj1q+5DyUpK86iF87soaGpkZ1WHwAAAICugxARgIfTCQ8l6by+MXp8arpireZOqQ8AAAAIpiW763T/55XyY5R/w7geUbpzTLLG9ojujNLaVGd36ZUv6/TSjlpV2/0LDyVpTEaUnp2VofSYrj08KwAAAICOQ4gIQJJ0pMGhxTtr2x0eStINIxJ126hkmZkXBQAAAN3Y1vJm3f5pufbWOtq1/cze0bpsQLzOzI5VlKVzr52rm5165cs6Ld5Zp9p2hIeSdEn/OP1yclqn1woAAACgayFEBMLY4QaH3t/fqPf3N+qLsuZ27ycrzqJHJ6dpalZMh9YHAAAAdFWNLS79Jr9Kr31V3+59JEaaNScnVt/pH6exPaI6bKjTCptTKw40avmBRq05YmtXr0lJSrCadO/4VF2aFxf0YVgBAAAABB4hIhAm3G63Spuc2lPTom0Vdi0/0KjN5fbT3u/3B8br52NTlBDJ8KUAAAAIPx8eatTdn1Wo0ta+Xn7H9ImP0PSsaA1LjdQZqZEalBLpc8+/KptTe2u/vs7/z8FGrStplus0v+mf2TtGj0xOVWYs85wDAAAA4YoQEQhx+aU2rS1p1sE6hywmyWKWIkwmmU1ShNmk8qPB4Z7aFjW0dNzHvVesRb+ekqbp9D4EAABAmCtvcmrBmgp9dKipw/YZYZLykqwamGxVdIRJkWaTIswmRRy93q+wOVVY69DempZ2zXF4KomRZt03PkXf6U/vQwAAACDcESICIarF5dav11fplV11AT/2ZQPi9Ytx9D4EAAAAjnG73fpnQYOe2Fil8tPslRgsZ2XH6JeTUtWD3ocAAAAACBGB0PXql3V6cF1lQI85JiNKt41O1uTM6IAeFwAAAAgVDS0u/WVnnRZtr1F9B44E0pn6J0bollHJmpMTS+9DAAAAAAZCRCBEnflmkQ7WOwJyrDEZUbplZJKm9IrmpgIAAADgg0qbU89vq9GrX9apA0cb7VADkqz62beSdEFOrCxmrvMBAAAAeCJEBELU8L8ekM3ZuR9fwkMAAADg9Byud+jpzdX6V2GDOvny3WeDkr8OD8/PiZWZ63wAAAAAp0CICISoqf84pJImZ4fvN9IszegdoysGJxAeAgAAAB2kpNGhd/Y2aGlhg76sagn48U2SJvSM0hVDEnReX8JDAAAAAN4RIgIh6rmtNXpiY3WH7CvKYtKs3jE6PydWZ2bHKN5q7pD9AgAAADjRzkq7lhY26J29DSrthAcDj4kwSZMyo3V+TqzO6ROr9BhLpx0LAAAAQPdDiAiEqCaHS3eurtD7+xv93tYkKTs+QiPTI3Vu31jN6h2jOIJDAAAAIKCcLrfWlti0ptimHZV2ba+wq9x2ehMoZsRYNOrodf5Z2TFKjiI4BAAAANA+hIhACHO73Vp+oFGfHbFpf51DLrfkcLvlch39X7dkMX0dGOYlWdU/yaoBSVb1S7QqysLwRQAAAEBXU9ro0I7KFu2otGt3tV21LS45XFKLyy2HS3K43GpxuWU1m5QdH6H+SVblJkaof+LX1/kJkTwcCAAAAKBjECICAAAAAAAAAAAA8MAjigAAAAAAAAAAAAA8ECICAAAAAAAAAAAA8ECICAAAAAAAAAAAAMADISIAAAAAAAAAAAAAD4SIAAAAAAAAAAAAADwQIgIAAAAAAAAAAADwQIgIAAAAAAAAAAAAwAMhIgAAAAAAAAAAAAAPhIgAAAAAAAAAAAAAPBAiAgAAAAAAAAAAAPBAiAgAAAAAAAAAAADAAyEiAAAAAAAAAAAAAA+EiAAAAAAAAAAAAAA8ECICAAAAAAAAAAAA8ECICAAAAAAAAAAAAMADISIAAAAAAAAAAAAAD4SIAAAAAAAAAAAAADwQIgIAAAAAAAAAAADwQIgIAAAAAAAAAAAAwAMhIgAAAAAAAAAAAAAPhIgAAAAAAAAAAAAAPBAiAgAAAAAAAAAAAPBAiAgAAAAAAAAAAADAAyEiAAAAAAAAAAAAAA+EiAAAAAAAAAAAAAA8ECICAAAAAAAAAAAA8ECICAAAAAAAAAAAAMADISIAAAAAAAAAAAAAD4SIAAAAAAAAAAAAADwQIgIAAAAAAAAAAADwQIgIAAAAAAAAAAAAwAMhIgAAAAAAAAAAAAAPhIgAAAAAAAAAAAAAPEQEuwCgozidThUXFwe7DAAAAAAAAADoEJmZmbJYLMEuA0CYIkREt1FcXKzs7OxglwEAAAAAAAAAHeLQoUPq3bt3sMsAEKYYzhQAAAAAAAAAAACAB0JEAAAAAAAAAAAAAB4IEQEAAAAAAAAAAAB4IEQEAAAAAAAAAAAA4MHkdrvdwS4C6AhOp1PFxcVavny5SkpK1LNnT82ePTvYZXW6qqoqvfPOO8b63LlzlZKSEtSaAiHc3udwO1+F4TmH42c53N5jcc7d/pzD8XOsMHuPjwm3cw638+WzHB7vc7idr8LsnPkcd//3+JhwO+dwO18+y1+/z5mZmbJYLMEuC0CYigh2AUBHsVgs6t27t9LS0tTc3Ky0tDT17t072GV1upiYGCUnJxvrWVlZSk1NDWpNgRBu73O4na/C8JzD8bMcbu+xOOduf87h+DlWmL3Hx4TbOYfb+fJZDo/3OdzOV2F2znyOu/97fEy4nXO4nS+f5fB4nwF0bQxnCgAAAAAAAAAAAMADISIAAAAAAAAAAAAAD4SIAAAAAAAAAAAAADwQIgIAAAAAAAAAAADwQIgIAAAAAAAAAAAAwAMhIgAAAAAAAAAAAAAPhIgAAAAAAAAAAAAAPBAiAgAAAAAAAAAAAPAQEewCgI42ePBgZWVlKSEhIdiloBOF2/scbuerMD3ncBOO7zHnjO4oHN/jcDvncDvfcBVu73O4na/C9JzDTTi+x+F2zuF2vuGK9xlAV0KIiG5nyJAhwS4BARBu73O4na/C9JzDTTi+x5wzuqNwfI/D7ZzD7XzDVbi9z+F2vgrTcw434fgeh9s5h9v5hiveZwBdCcOZAgAAAAAAAAAAAPBAiAgAAAAAAAAAAADAAyEiAAAAAAAAAAAAAA+EiAAAAAAAAAAAAAA8ECICAAAAAAAAAAAA8ECICAAAAAAAAAAAAMADISIAAAAAAAAAAAAAD4SIAAAAAAAAAAAAADwQIgIAAAAAAAAAAADwQIgIAAAAAAAAAAAAwENEsAsAcHqio6M1btw4j3UAoYfPMhD6+BwD3QOfZSD08TkGugc+ywAQfCa32+0OdhEAAAAAAAAAAAAAug6GMwUAAAAAAAAAAADggRARAAAAAAAAAAAAgAdCRAAAAAAAAAAAAAAeCBEBAAAAAAAAAAAAeCBEBAAAAAAAAAAAAOCBEBEAAAAAAAAAAACAB0JEAAAAAAAAAAAAAB4IEQEAAAAAAAAAAAB4IEQEAAAAAAAAAAAA4IEQEQAAAAAAAAAAAIAHQkQAAAAAAAAAAAAAHggRAQAAAAAAAAAAAHggRAQAAAAAAAAAAADggRARAAAAAAAAAAAAgIeIYBcAoP1cLpe++uorFRcXq6WlRZmZmRo6dKgiIvhoA6HCbrdr586dKi8vl8PhUFpamvr376/U1NRglwbgNGzbtk2FhYXG+tixY9W7d++g1gTAdy6XS4WFhSotLVVVVZViYmLUt29f5ebmymKxBLs8AF7s27dPhw4dUl1dnRISEpSTk6M+ffoEuywAxzlw4ICKioo0efLk09pPcXGx9u/fr6qqKqWmpmrgwIFKSUnpsDoBIJyRNAAhyOFw6PXXX9c777yj0tJSj9eSk5N1/vnn60c/+pGio6ODViOAttntdv3lL3/Rm2++KZvN5vGa2WzW0KFD9cMf/vC0v0wBCLz6+nrdd999qq6uNn523333ESICIaCmpkZ///vftWLFCpWXl5/wenx8vObNm6crrrgiKPUBODW3263ly5dr8eLFJ3xPlqTs7GzNnz9fM2fODEp9ADy9/fbbeu6553TGGWe0+3vv+vXr9dprr2nTpk0eP7dYLJo4caLmz5+v3NzcDqoYAMKTye12u4NdBADfVVVV6cEHH9SWLVvabNenTx899NBDXCwBXdDu3bv1yCOP6ODBg17bzpw5UwsWLFBUVFRAagNw+p566iktXbpUUVFRam5ulo6GiGeddVawSwPQhg8//FB/+MMfPB4AOJkJEyZo4cKFAasLgHcHDhzQwoULtWPHDq9tR48erbvuukuZmZkBqQ3A1+x2uw4ePKg1a9ZoxYoVOnDggHR0xI7HH3/cr3253W69+uqrWrx4sdq6tR0ZGakbb7xRF1100WnXDwDhip6IQAhxOp1asGCBdu3aJUlKSkrSD3/4Q40cOVIWi0Xbt2/XX//6V5WWlurgwYO655579PzzzysxMTHYpQM4qra2Vvfee6/xdPTMmTM1adIkpaamqra2Vtu3b9eKFSvU0NAgSVq5cqVqamr0+OOPM3waEAJ2796tt99+W2azWYMHD/b60A+AruH111/XCy+8YKxPnz5dF1xwgQYPHqyEhARVV1errKxMmzdvVlNTU1BrBeCpvr5eCxYs0OHDhyVJw4YN0/e//331799faWlpKi4uVmFhof7617+qsLBQGzdu1O23364//vGPTCEABMB1112n6upqlZeXy+Vydcg+lyxZopdeeslYnzNnjs4991ylpqaqqKhIS5cu1bp162S32/Xkk08qLS1NU6dO7ZBjA0C4oSciEEJefvllLV68WJLUs2dPPfbYYyfM61BRUaEFCxZo9+7dkqQpU6boV7/6VVDqBXCiX/ziF1qzZo0iIiL0v//7vzr//PNPaFNcXKxHHnnE40nqK664QldffXWAqwXgD7fbrZtuuknbt2/X1KlTZTKZ9Omnn0r0RAS6tH/96196+umnJUkxMTF64IEHNHHixGCXBcBHCxYs0Oeffy5J+sEPfqBrr732pO1cLpeeeuopvf3229LRh/kefPDBgNYKhKPZs2fLbrcb60lJSYqPj1dRUZHUjp6I+/fv109+8hPZ7XaZzWbddttt+va3v+3Rxu1264UXXtDf//53SVJcXJwWL16sjIyMDjsvAAgX5mAXAMA3lZWVeuWVV4z1e+6556QTw6elpemBBx5QZGSkJOmzzz5TQUFBQGsFcHI7d+7UmjVrJEk33XTTSQNEScrMzNTTTz+tQYMGGT9bsmSJKisrA1YrAP+9//772r59uyTpO9/5TrDLAeCDvXv36rnnnpOOzp+0cOFCAkQghOzYscMIEIcMGaJrrrnmlG3NZrNuvPFG43v0qlWrjN6LADrPJZdcoquuukq33367nn32Wb3xxhuaNm1au/f33HPPGaHkt7/97RMCREkymUy6/vrrNWrUKElSQ0OD3njjjdM4CwAIX4SIQIh4//335XA4JEkTJ07UiBEjTtm2d+/eHhdR//znPwNSI4C2HXvquU+fPrrwwgvbbGuxWHTTTTcZ683NzVqxYkWn1wigferr67Vo0SJJ0uDBgzVu3LhglwTAB4899phaWlqko73+27rGBtD1bN682VieMGGCzOa2b3NFRkYa/0a73W6P7QF0juuvv15XXnml5s6dq6FDh57WNB0lJSVav369JCkiIkI/+tGP2mx/3XXXGcvvvvsuQ5IDQDsQIgIh4r///a+xPHPmTK/tW4/1vnr16g4bdx5A+5WUlEiSLrvsMp++OA0fPlxDhgwx1jds2NCp9QFovz//+c+qrq6WjgYRALq+TZs2aefOnZKkxMREzZs3L9glAfBT60AgLS3Np23S09ON5cbGxk6pC0Dn+Oijj4z7WyNGjPA6r+mQIUOMvw0NDQ3auHFjQOoEgO6EEBEIAY2Njdq3b5+xPnDgQK/b5OXlGcv19fUqLCzstPoA+Obuu+9WamqqXxO65+bmGstHjhzppMoAnI7du3cbPY2HDh3q12ccQPC0Hq1j9uzZiomJCWo9APzXv39/Y3nPnj0+bfPVV18Zy62/NwPo+o5NHSAf743puM/5li1bOqUuAOjOIoJdAADvCgoKjCetLBaL+vbt63WbpKQkpaenq7y8XDr6RWnAgAGdXiuAU+vRo4eefvppr09LttazZ09jubm5uZMqA9BebrdbTz31lPHv9Pz584NdEgAf2O12rVu3zlifPHmysZyfn69PP/1U27dvV1VVlZKTkzVixAh997vfVe/evYNUMYCTmTp1qnr37q2ioiJ99NFHuvzyy5WZmXnK9vv27dPatWulo8OPjxw5MoDVAjhdu3fvNpZbP0TQlry8POPf/NYPEQAAfENPRCAE1NTUGMvZ2dmKjIz0abvWF1S1tbWdUhsA//h787GoqKjd2wLofMuXLzeeiJ40aZLGjBkT7JIA+GD79u3GXIhms1nDhw/Xrl279LOf/Ux33HGHli5dqt27d6u8vFwFBQX617/+pfnz5+vDDz8MdukAWrFarbrrrrsUGxururo63XvvvSooKDhp2x07dui+++6TzWZTUlKS7rjjDplMpoDXDKD9Wt8faz1qT1tat2u9PQDAN/REBEJAfX29sZySkuLzdomJiSfdB4DQ0XooYl+ftAQQGPX19Vq0aJF0dKSAG264IdglAfDRtm3bjOXMzEzl5+froYceks1mU2ZmpgYPHiyr1ap9+/YZgYTNZtOvfvUrxcfHa8KECUGsHkBr3/rWt/TUU0/pgQce0J49e3Tddddp7Nixys3NVXp6usrKylRQUGDMhZaTk6Nf/vKXys7ODnbpAPzgcDhks9mMdV/vjyUlJRnLDQ0NnVIbAHRnhIhACGh9kRMVFeXzdhER33zE6+rqOrwuAJ1rx44dxtwuJpNJc+fODXZJAFp56aWXVFVVJUm66KKLfBpuHEDXcGzIfx29Tr733ntlsVh05513as6cOR5t169fr/vuu0/Nzc1yuVx68skn9corr8hisQShcgAnM2DAAL388sv68MMPtXjxYq1fv17r16/3aNO3b19dffXVmj59usxmBuYCQs3xAWB0dLRP23FvDABOD1dNQAho3YvQny87Vqv1pPsAEBpefPFFY/nss8/2ebgWAJ2voKBAb731liQpPj5eP/rRj4JdEgA/tB7O7NgNxd///vcnBIiSNH78eD344INGaHjkyBF99NFHAawWgC8OHDigzz//XCUlJSd9vaioSGvWrNHhw4cDXhuA03f8fS1fhyNufW+ssbFRTqezw2sDgO6MnohACHC73Sdd9gcXSUBo+ec//2kMuTR48GDdfvvtwS4JwFFut1tPPvmkXC6XJOnKK6/0GCYJQNd3/I3IuXPnatiwYadsP2nSJF144YV6++23JUl/+9vfdM4553R6nQB8s2zZMv3+979XS0uLoqKidMkll2j06NHq0aOHDh8+rA0bNuidd97R8uXLtXLlSt19992aOXNmsMsG4If23g87nsvlYjQBAPADPRGBEBAXF2csH7th6QuHw2EsR0ZGdnhdADrH+vXr9dxzz0mSsrKy9OijjyomJibYZQE4avny5dq+fbt0dF6lSy+9NNglAfDT8UOgzZs3z+s2s2fPNpYLCwtVW1vbKbUB8M+SJUv029/+Vi0tLerbt69efvllXXfddZowYYL69eunKVOm6Oabb9bixYvVs2dP2Ww2Pfjgg3rvvfeCXToAP7S+NyY/HpZvfW/MZDJ59EwEAHhHiAiEgNYXSna73eftCBGB0LN161bdf//9crlcSklJ0cKFC32eMB5A56uvr9eiRYuM9ZtvvpknmYEQFB8fbyzHxsaqV69eXrfp16+fx3pRUVGn1AbAd/v37zemAIiKitIDDzygHj16nLRtdna2Mf+pJD3zzDMqLi4OaL0A2u/4ENHX+2MtLS3GMvfGAMB/hIhACIiNjTWWGxsbfd6u9VwvaWlpHV4XgI61detW3X333bLZbOrTp4/+8Ic/KDs7O9hlAWjlpZdeUlVVlSTpzDPP1JgxY4JdEoB2aB0i+hIg6ug1eev5yQ8dOtQptQHw3Z///GcjIJgxY4b69+/fZvvhw4dr4sSJ0tHv1q+88kpA6gRw+iIjIxUR8c3MXE1NTT5tx70xADg9zIkIhIDWvZDKysp83q6wsNBY7tmzZ4fXBaDjrF27Vg8++KBsNptGjhyphx9+WImJicEuC8BxPvzwQ2N57dq1+s53vnPKtq0f/HniiSf09NNPG+s//vGP29wWQOdqfW3cOhj0JjIyUjabTZJUXV3dKbUB8N22bduM5UGDBvm0zaBBg/TZZ59JkjE8OYDQkJKSYtwXKysrU9++fb1uw70xADg9hIhACBgwYIBMJpPcbreqqqpUVVXldXjDmpoalZeXG+vensgEEDwffPCBFi5cKIfDoXPOOUd33XUX8zQAIcCf0QGOb+vP8OQAOt7QoUON5cOHD/u0jdPpNAJESTzsAwRZZWWlMTqAJOXm5vq0XU5OjrF88OBB2e12hjgEQsTAgQONELGwsFBjx471uk3rENHXvxMAgG8QIgIhID4+Xn379tX+/fslSWvWrNGcOXPa3KagoMBYjouL08CBAzu9TgD+e+ONN/TMM8/I7Xbriiuu0FVXXSWTyRTssgCcwuzZsz1ChLYsW7bMGGJt7Nix6t27t/FaXl5ep9UIwLtBgwYpIiJCDodDDQ0N+uqrr7z2Yjq+52FWVlYnVwmgLTExMR7rrR+ibUvr0X0sFgsP7wEhZNiwYUZP4jVr1uiyyy7zuk3r+2OjR4/u1PoAoDsiRARCxKxZs/SXv/xFkvT+++97DRE//fRTY3nixInG5PEAugaXy6Vnn31Wb7zxhqxWq+68806de+65wS4LgBc33HCDz20//PBDI0ScM2eOzjrrrE6sDIA/IiMjNX78eK1Zs0aStGrVKq8h4saNG43luLg4DRs2rNPrBHBqMTExysrKMnoT792716ft9uzZYyzn5eXxAB8QQmbOnKkXX3xRkrRp0yYdOXKkzbmNd+zYocrKSklSdHQ0ISIAtIPvkz8ACKqzzz7bmK9l69at2rVr1ynbVldX6z//+Y+xfuGFFwakRgC+aWpq0r333qs33nhDqampevLJJwkQAQAIsNbzki5fvrzNXsYul0tLliwx1i+99FIe0gO6gLPPPttY/uCDD1RXV9dm+7KyMqMXkyQe8AFCTHZ2ts444wxJktvt1ptvvtlm+7///e/G8qxZsxQXF9fpNQJAd0OICISIPn366OKLLzbWf/nLX6qhoeGEdna7XQ8//LAx99KIESN40groQsrKynTLLbdozZo1GjJkiJ5//nl6MgAAEATjx4/XkCFDpKPDIP7hD384Zds///nP2r17t3T0Bubll18esDoBnNq8efOUlJQkSaqoqNDvfvc7ORyOk7a12+1auHChETT27NnT4zs2gNDw05/+1OhB/Oabb2r9+vUnbbd06VKtWrVKkmS1Wvm3GwDaieFMgRByzTXXaOXKlaqsrNShQ4d0/fXX62c/+5lGjhypyMhI7dy5U4sWLdKWLVuko8M03XnnnQzPAnQRBQUFWrBggcrLy9WnTx99//vfV0FBgTFHg9PplN1uP+X2ERERmjFjRgArBgCg+zKZTLrnnnt07bXXymaz6b333lNNTY2uuuoq9evXTzo6POJrr72mjz76SJKUmJioRx999IS52AAER3x8vO655x4tWLBATqdTH3/8sfbt26drr71Ww4YNU3JysiorK7Vlyxb96U9/MoY+jYqK0v3336/IyMhgnwIAPw0bNkwXXnih3n33XblcLt1777264oorNGfOHKWmpqqiokL/+Mc/9I9//MPY5kc/+pH69u0b1LoBIFSZ3G63O9hFAPBdQUGB7r33XpWUlBg/M5vNslgsxrxLOjpPy0MPPaSxY8cGqVIAx3v88cf173//u93bx8fH65133unQmgB0nrlz56q+vl6SdN999zFkGtBFffHFF7rvvvuMkTx0tMeCy+WS0+k0fpaSkqLHHntMeXl5QaoUwKmsX79eTzzxhMf3ZB0NC5ubmz1+1qdPH/385z83hkQE0Lmefvpp7dixw+NnpaWlqqqqkiTFxsaqT58+Hq/HxcXpiSeeOOU+bTabfvOb32jlypUeP4+Ojj5hePLvfe97uuGGG4wpggAA/qEnIhBiBgwYoGeffVZPPfWUVq9eLafTKZfLJZfLJR19onrChAm6/vrrjSeoAQAAAJzcmDFj9Mwzz+iZZ57Rhg0bJMnj4TxJmjZtmm6++WZlZGQEqUoAbRk/frwWL16sF198UUuXLjW+H7cOECMiIvQ///M/uvLKK+mBCARQUVGRdu3adcrXGxsbT3j92DDFpxIdHa0HHnhAr7/+upYsWaLq6mrpaLh4TFZWlq688krNnj37tM8BAMIZPRGBEFZWVqY1a9YYT1tmZWVp+PDhysnJCXZpAE5ixYoV2rZtW7u3j4yM1I033tihNQHoPM8884wxRPGcOXM0ePDgYJcEwIt9+/Zpw4YNKikpUUREhHr06KFJkyapV69ewS4NgI9qamq0bt06HTp0SHV1dUpKSlJOTo7GjRun+Pj4YJcHhJ0333xT+/fv92ub6Oho3XDDDT61tdvt+vzzz7V//37V1taqZ8+eysnJ0dixY+l9CAAdgBARAAAAAAAAAAAAgAcexwAAAAAAAAAAAADggRARAAAAAAAAAAAAgAdCRAAAAAAAAAAAAAAeCBEBAAAAAAAAAAAAeCBEBAAAAAAAAAAAAOCBEBEAAAAAAAAAAACAB0JEAAAAAAAAAAAAAB4IEQEAAAAAAAAAAAB4IEQEAAAAAAAAAAAA4IEQEQAAAAAAAAAAAIAHQkQAAAAAAAAAAAAAHggRAQAAAAAAAAAAAHggRAQAAAAAAAAAAADggRARAAAAAAAAAAAAgAdCRAAAAAAAAAAAAAAeCBEBAAAAAAAAAAAAeCBEBAAAAAAAAAAAAOCBEBEAAAAAAAAAAACAB0JEAAAAAAAAAAAAAB4IEQEAAAAAAAAAAAB4IEQEAAAAAAAAAAAA4IEQEQAAAAAAAAAAAIAHQkQAAAAAAAAAAAAAHiKCXQAAAAAAoOOtW7dOTU1Nio6O1oQJE2QymTr8GF999ZWOHDkiSZowYYJiYmI6/BgAAAAAgOCgJyIAAAAAdDNvvPGG/vKXv2jp0qVKT0/vlABRknr16qWVK1dqyZIl+uMf/6impqZOOQ4AAAAAIPBMbrfbHewiAAAAAAROTU2NDh48GJBjWSwWDR06NCDHwtfeeust/ec//5HJZNL8+fM1atSoTj1eZWWlHn30UTU2Nqp///668cYbFR0d3anHBAAAAAB0PkJEAAAAIMxs3LhRL774YkCOFR8fr4ULFwbkWJDWrl2rl19+WZJ03nnn6eKLLw7IcTdv3qxFixZJksaNG6errroqIMcFAAAAAHQehjMFAAAAEFA2m035+fnGf19++WWwS+oWSktLtWTJEklSZmam5syZE7Bjjxw5UlOnTpUkbdiwQWvWrAnYsQEAAAAAnSMi2AUAAAAACKz09HTNnDnzpK+ZzWaZzZ7PGm7btk0lJSXG+qhRo5SWlmasu91uOZ3Ok+4vMjLyhJ/V1NTopZdeMtbz8vI0ZMiQdp0LvuZyufR///d/stlskqTvf//7slqtAa1h7ty5ys/Pl81m05IlSzRw4EClp6cHtAYAAAAAQMchRAQAAADCTJ8+fdSnTx+f21dXV3uEiFOnTtWwYcM6qTq0x/r167V//35J0tChQzVw4MCA15CQkKDzzz9fS5culd1u1zvvvMOwpgAAAAAQwhjOFAAAAABCmMPh0L///W9jPZDDmB5v1qxZSkhIkCTl5+frwIEDQasFAAAAAHB6CBEBAAAABFRMTIxmzpxp/Ddq1KhglxTS1qxZo4qKCklS37591b9//6DVYrVaNWnSJOnoMLfvvfde0GoBAAAAAJwehjMFAAAAEFCJiYmaN29esMvoNlavXm0sT5w4Mai1SNKUKVP0wQcfyO12a/v27aqurlZycnKwywIAAAAA+ImeiAAAAAAQog4ePKiDBw9KkiwWi8aNGxfsktSjRw8NGDBAkuRyubRmzZpglwQAAAAAaAdCRAAAAAAIUevWrTOW+/fvr/j4+KDWc8yIESOM5fXr1we1FgAAAABA+xAiAgAAAECI2rlzp7E8cODAoNbS2qBBg4zlkpISY85GAAAAAEDoYE5EAAAAAF1WfX29qqurJUlWq1U9e/Y0XnO5XKqsrFRxcbFqa2uVkJCgPn36tDn/ns1mU3FxsYqLi2W1WpWamqqcnByZze17vtLhcGjfvn2qrKyUy+VSenq6cnNzZbFY2rU/f1RXV+vIkSPG+umEiC6XS/v371dJSYnsdrusVqvS0tKUnZ2t2NhYv/eXnZ2tuLg4NTQ0SEfDzmnTprW7PgAAAABA4BEiAgAAAAi4NWvWyG63S5L69eunnJyck7bLz8/XkiVLJElZWVm655575HK5tGHDBi1btkylpaUe7U0mk4YOHaof/vCHHmFiWVmZ3n33XeXn58vtdntsk5SUpLlz52ry5Mk+19/c3Kxly5bps88+M4KyY6Kjo3XmmWfqvPPOU2RkpM/79Nfu3buNZZPJpNzcXL/3UVJSon//+9/asWOHmpqaTnjdbDYrNzdX06ZN09ixY30OR00mk/r06aMvv/xSklRQUECICAAAAAAhhhARAAAAQMC9++67Rg/DuXPnnjJEPF5zc7MWL16srVu3nvR1t9utHTt26Le//a3uuOMOpaamasOGDXrllVfkcDhOuk1NTY1effVVNTQ06JxzzvFaw6FDh7Ro0aJTDtFps9m0bNky5efn67bbblNiYqJP5+avoqIiYzk1NVVWq9Wv7bdv365Fixad8veioz0U9+zZoz179ujjjz/WXXfd5fP+e/XqZYSIrWsFAAAAAIQGQkQAAAAAIcHhcOj555/XV199paSkJE2ePFk9e/aUzWZTYWGhNm3apJaWFuloMLhs2TINHDhQL7/8siRp7NixGjx4sMxms8rLy5Wfn6+ysjJj/++++66mTp2qmJiYU9ZQVFSkp59+2uh9aDabNWLECA0YMEBWq1WFhYXasGGDXC6XSktL9cILL+jWW2/1O+DzRetgrkePHn5tW1NToz/96U9GgHis52B2drZaWlpUUVGhyspK1dTUGD034+Li/DpGr169jOXi4mK1tLR0yu8BAAAAANA5CBEBAAAAhITS0lKVlpYqNTVVd9xxh5KSkozXZsyYocOHD+upp55SfX29JOnzzz/X+vXr5Xa79cMf/lBTpkzx2N8FF1ygl19+Wfn5+ZKklpYWbdiwQdOnTz/p8e12u1588UUjQExOTtbVV1+tvLw8o8306dM1Z84cLVq0SIcPH9a+ffv0/vvva+7cuZ3y+zgmIyPDr20//vhjI3BNSEjQ1VdfrUGDBp3Qrq6uTqtXr9Ynn3yi3r17+3WM1sPJulwuVVVV+R12AgAAAACCxxzsAgAAAADAV2azWTfeeKNHgHhMVlaWx3CkLpdLLS0tOvPMM08IECUpIiJC8+bN85jnb+fOnac8dus5GK1Wq26++WaPAPGYjIwM3XjjjYqNjZUkffLJJ8b8jx2p9VyMaWlpfm27f/9+Y/n8888/aYCoowHj+eefr4cfflhnn322X8c4vkdnY2OjX9sDAAAAAIKLEBEAAABAyBg6dKgyMzNP+frIkSNP+NmsWbNO2T4+Pl4DBw401mtra0/azmazadWqVcb6xRdfrJ49e55yv8nJyTr33HOlo2Hftm3bTtm2PVwul2w2m7He1hCsJ1NVVWUsp6SkeG1vsViUkJDg1zGOhajHECICAAAAQGghRAQAAAAQMsaOHdvm68cHYjk5OUpPT29zm9a9Go8NhXq8/Px8I7SLi4s75ZCnrU2ePFkmk0mStHv3bq/t/WGz2Yy5CiUpKirKr+1bB3wdXdsx9EQEAAAAgNBGiAgAAAAgZPTq1avN161Wq1/tj9/G5XKdtM2uXbuM5bFjxyoiwvv08gkJCUZvxYMHD3pt74/jAzl/Q8TWw7B+/PHHWrFiRYcPuUqICAAAAAChzfs3XwAAAADoIk42F2JHtj+VwsJCYzk/P1/bt2/3abtjw6NWVlZ2SB3HHB/IRUZG+rX9jBkztGrVKrW0tMjtdmvp0qVavny5Bg0apJycHPXr10/9+vXzO5w8vqaIiAg5HA7puDkcAQAAAABdHyEiAAAAgJDhb6gVHR3dIcetq6szlhsaGvwOxDq6F17roUzbIz09XfPmzdNrr71m7KupqUmbN2/W5s2bJUkREREaNmyYLrjgAvXt27ddxzGbvxn85lS9PAEAAAAAXRMhIgAAAICQcWyOwUCy2+1GbzpJmjRp0mn10OsIrec01NEA0F9TpkxRUlKS/vrXv6qmpuaE1x0Oh7Zs2aKtW7fqkksu0dlnn+3X/l0ul8cQqcfXDAAAAADo2ggRAQAAAKANVqtVFotFTqdTknTuuecqMzMzqDXFxcV5rLcnRJSkM844Qw899JA2btyobdu2ac+ePaqurvZo43a79eabb8pisWjWrFk+7/v4ORaPnyMRAAAAANC1ESICAAAAQBtMJpPi4+ON3noVFRVBDxGjo6NlMpk8hiJtL6vVqgkTJmjChAmSpKqqKu3atUufffaZ9uzZY7R76623NH78+BMCzFNpbm72WKcnIgAAAACEFrMPbQAAAAAgrOXk5BjLBQUFQa1FR+cabD3fY0fOuZiSkqJJkybp9ttv12WXXWYMIWu327Vhwwaf90OICAAAAAChjRARAAAAALwYOnSosbx27doThuoMhtY9Ak+nJ2JbZs2apdzcXGP98OHDPm9bWVnpsU6ICAAAAAChhRARAAAAALwYO3asoqKiJEk1NTX64IMPgl2SUlJSjOXa2tpOO05eXp6x7HA4fN6urKzMWDaZTEpLS+vw2gAAAAAAnYcQEQAAAAC8iIuL07Rp04z1FStWqLy83KdtDx486NcwoL7q3bu3sVxcXOzXttu2bfO5N2Xr88zIyPD5GKWlpcZyenq6EcICAAAAAEIDISIAAAAA+ODCCy9Uz549paPzAz755JM6cODAKds3NTXp/fff1+OPP67du3d3eD2tQ8SSkhI5nU6ft3377bf1yCOPaO3atWppaTllu61bt2rz5s3Gep8+fXw+RusQsXWtAAAAAIDQEBHsAgAAAAAgFERFRWn+/Pl66qmnVF9fr6qqKj322GMaN26cRo0apbS0NDmdTpWUlGj37t3Kz89Xc3OzJKmurq7D6+nfv7+x7HQ6deTIEWVnZ3vd7liNDodDL7/8sv75z39q2LBhys3NVWpqqiIjI1VdXa2dO3cqPz9fbrdbkpSZmekxN2Rb3G639u3bZ6y3HhIVAAAAABAaCBEBAAAAwEdZWVm67bbb9Oyzz6qiokIul0vr1q3TunXrTrlNSkqKpkyZ0uG1ZGZmKiUlRVVVVZKk3bt3+xQiNjU1KTY21phHsbGxURs2bGhzyNW4uDj9+Mc/ltns22A2R44cUX19vbHua/gIAAAAAOg6CBEBAAAAtGno0KGKj4831lNTU097n5MmTVJTU5MkqW/fvqdsl5WVpZkzZxrrFovF675bt/dl+M1BgwYZ+42NjfXaPjMzU/fee6+WL1+ulStXGudxvPT0dE2bNk0zZszotPkAhw0bptWrV0uSdu3apTPPPNPrNvHx8Xr44Ye1YcMGrVu3ToWFhXI4HKdsP3z4cH33u99Vjx49fK7rq6++MpaTk5PVq1cvn7cFAAAAAHQNJvexsWkAAAAAAH5xOBz68ssvVVxcrJqaGkVGRiolJUW5ubnKysqSyWTq1OPv3LlTf/zjHyVJ0dHRevTRRxUZGenXPmw2mwoLC1VVVaWamhqjp2JKSooGDx6slJQUv+t67rnntG3bNknS2WefrUsvvdTvfQAAAAAAgoueiAAAAADQThERERo+fLiGDx8elOMPGTJEaWlpqqiokM1m08aNGzVx4kS/9hEdHa1hw4Z1WE11dXXasWOHsd4ZQ7kCAAAAADqfbxNaAAAAAAC6HJPJpKlTpxrrx4Y2Dab169fL5XJJkvLy8pSZmRnskgAAAAAA7UCICAAAAAAhbPr06cZcjnv27FFxcXHQanG5XB5B5gUXXBC0WgAAAAAAp4cQEQAAAABCWGxsrGbPnm2sL1u2LGi1fPHFF0aIOWjQIA0dOjRotQAAAAAATg8hIgAAAACEuJkzZyotLU2StGHDBu3duzfgNbhcLr333nuSJLPZrEsuuSTgNQAAAAAAOg4hIgAAAACEOKvVqiuvvFJm89df8d544w253e6A1vDBBx+opKREkjRnzhz17ds3oMcHAAAAAHQsQkQAAAAA6AYGDBhgzEG4d+9erVixImDHLikpMXohDhgwwGN4VQAAAABAaCJEBAAAAIBu4vzzz9fIkSMlSe+8844KCws7/Zg2m02LFi1SS0uL0tPTddVVVxk9IgEAAAAAoYtvdgAAAADQTZjNZl1zzTUaMWKEXC6XFi1aZAwx2hkcDodeeuklFRcXKy0tTbfccouSk5M77XgAAAAAgMAxuQM9UQYAAAAAoFM5HA4tW7ZMTU1NSk5O1rnnniuTydThx9m2bZt27NghSTr77LOVlpbW4ccAAAAAAAQHISIAAAAAAAAAAAAADwxnCgAAAAAAAAAAAMADISIAAAAAAAAAAAAAD4SIAAAAAAAAAAAAADwQIgIAAAAAAAAAAADwQIgIAAAAAAAAAAAAwAMhIgAAAAAAAAAAAAAPhIgAAAAAAAAAAAAAPBAiAgAAAAAAAAAAAPBAiAgAAAAAAAAAAADAAyEiAAAAAAAAAAAAAA+EiAAAAAAAAAAAAAA8ECICAAAAAAAAAAAA8ECICAAAAAAAAAAAAMADISIAAAAAAAAAAAAAD/8fJS6PeZOGkDwAAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = ps.new_figure(\n", + " \"thesis-single\",\n", + " title=\"Default: no linestyle cycling\",\n", + " params={\"cycle_linestyles\": False},\n", + ")\n", + "x = np.linspace(0, 10, 200)\n", + "for i in range(5):\n", + " ax.plot(x, np.sin(x - i * 0.4) * (i + 1), label=f\"series {i}\")\n", + "ax.set_xlabel(\"Time (s)\")\n", + "ax.set_ylabel(r\"Amplitude $A(t)$\")\n", + "ps.style_legend(ax, title=\"Series\")\n", + "ps.panel_label(ax, \"a\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "c8f33d18", + "metadata": {}, + "source": [ + "Pass `cycle_linestyles=True` to `use()` to also vary linestyle — useful when\n", + "a figure might be printed or projected in grayscale, or viewed by someone\n", + "with color-vision deficiency, so series stay distinguishable without color.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "59d68b50", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ps.use(cycle_linestyles=True)\n", + "\n", + "fig, ax = ps.new_figure(\n", + " \"thesis-single\",\n", + " title=\"Opt-in: linestyle cycling\",\n", + " params={\"cycle_linestyles\": True},\n", + ")\n", + "for i in range(5):\n", + " ax.plot(x, np.sin(x - i * 0.4) * (i + 1), label=f\"series {i}\")\n", + "ax.set_xlabel(\"Time (s)\")\n", + "ax.set_ylabel(r\"Amplitude $A(t)$\")\n", + "ps.style_legend(ax, title=\"Series\")\n", + "ps.panel_label(ax, \"a\")\n", + "plt.show()\n", + "\n", + "ps.use() # back to the no-cycle default for the rest of this notebook\n" + ] + }, + { + "cell_type": "markdown", + "id": "22ecdcf9", + "metadata": {}, + "source": [ + "## 3. Figure titles and parameter subtitles\n", + "\n", + "`new_figure(title=..., params=...)` sets a left-aligned, bold figure-level\n", + "title — preferred over an axes title even for a single axes, so single- and\n", + "multi-panel figures stay consistent. `params` is a dict rendered as a\n", + "smaller subtitle line: `key1: value1 | key2: value2 | ...` — handy for\n", + "recording the run parameters that produced a plot, as seen in the two line\n", + "plots above.\n", + "\n", + "This is the recommended way to title a single-axes figure — every example in\n", + "this notebook uses it instead of `ax.set_title()`. The exception is\n", + "multi-axes figures, where each panel needs its own title; section 8 uses\n", + "plain `ax.set_title()` per subplot for exactly that reason.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dc8f77ea", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = ps.new_figure(\n", + " \"thesis-single\",\n", + " title=\"Measured signal\",\n", + " params={\"N\": 512, \"sigma\": 1.2, \"seed\": 42},\n", + ")\n", + "ax.plot(x, np.sin(x))\n", + "ax.set_xlabel(\"Time (s)\")\n", + "ax.set_ylabel(r\"Amplitude $A(t)$\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "a8e9bbe1", + "metadata": {}, + "source": [ + "## 4. Figure size presets\n", + "\n", + "`new_figure(preset=...)` ships named sizes (inches) tuned for thesis print and\n", + "slide decks. Pass an explicit `(w, h)` tuple to bypass the presets entirely.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2edb0c4c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = ps.new_figure((6, 4), title=\"plotstyle.FIGSIZES presets (drawn to scale)\")\n", + "colors_iter = iter(ps.colors.CATEGORICAL)\n", + "y = 0.0\n", + "for name, (w, h) in ps.FIGSIZES.items():\n", + " color = next(colors_iter)\n", + " ax.add_patch(plt.Rectangle((0, y), w, h, fill=False, edgecolor=color, linewidth=2))\n", + " ax.text(w + 0.15, y + h / 2, f\"{name} {w}\\u00d7{h} in\", va=\"center\", fontsize=10, color=color)\n", + " y += h + 0.5\n", + "ax.set_xlim(0, 14)\n", + "ax.set_ylim(0, y)\n", + "ax.set_aspect(\"equal\")\n", + "ax.axis(\"off\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "1d390e31", + "metadata": {}, + "source": [ + "## 5. Sequential colormap — magnitude\n", + "\n", + "One hue, light \\u2192 dark: `colors.sequential_cmap()` is a KIT-blue ramp\n", + "(brand blue at full value, tinting lighter toward zero) for continuous\n", + "magnitude data (heatmaps, density fields).\n", + "\n", + "`ps.colorbar(im, ax)` is used here instead of `fig.colorbar(im, ax=ax)` —\n", + "with `ax.set_aspect(\"equal\")` on a non-square array, the default colorbar\n", + "sizes itself to the axes' nominal box and ends up taller than what's\n", + "actually drawn; `ps.colorbar` appends a same-size axes via\n", + "`make_axes_locatable` so it always matches.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "74a67468", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = ps.new_figure(\"square\", title=\"Sequential colormap (KIT blue)\")\n", + "X, Y = np.meshgrid(np.linspace(-3, 3, 60), np.linspace(-3, 3, 90))\n", + "Z = np.exp(-(X**2 + Y**2))\n", + "im = ax.pcolormesh(X, Y, Z, cmap=ps.colors.sequential_cmap(), shading=\"auto\")\n", + "ps.colorbar(im, ax, label=\"Density\")\n", + "ax.set_aspect(\"equal\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "13388fdd", + "metadata": {}, + "source": [ + "## 6. Diverging colormap — polarity\n", + "\n", + "KIT blue \\u2194 KIT red with a neutral gray midpoint: `colors.diverging_cmap()`\n", + "for signed data such as a correlation matrix, always centered on the data's\n", + "true zero (`vmin`/`vmax` symmetric around it).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bcd8ec15", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rng = np.random.default_rng(0)\n", + "data = rng.normal(size=(6, 200))\n", + "corr = np.corrcoef(data)\n", + "\n", + "fig, ax = ps.new_figure(\"square\", title=\"Diverging colormap (KIT blue / KIT red)\")\n", + "im = ax.imshow(corr, cmap=ps.colors.diverging_cmap(), vmin=-1, vmax=1)\n", + "ps.colorbar(im, ax, label=\"Correlation\")\n", + "ax.set_xticks(range(6))\n", + "ax.set_yticks(range(6))\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "d0fa0677", + "metadata": {}, + "source": [ + "## 7. Status colors\n", + "\n", + "A small, fixed, *reserved* scale (good / warning / serious / critical) —\n", + "never reused for \"series 4\", and always paired with a label rather than\n", + "color alone.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "37d1b539", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = ps.new_figure(\"thesis-single\", title=\"Status colors (reserved, always labeled)\")\n", + "labels = list(ps.colors.STATUS.keys())\n", + "values = [92, 74, 45, 12]\n", + "bars = ax.barh(labels, values, color=[ps.colors.STATUS[k] for k in labels])\n", + "for bar, value in zip(bars, values):\n", + " ax.text(value + 1.5, bar.get_y() + bar.get_height() / 2, str(value), va=\"center\", fontsize=10)\n", + "ax.set_xlim(0, 108)\n", + "ax.set_xlabel(\"Checks passing (score out of 100)\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8a96d616", + "metadata": {}, + "source": [ + "## 8. Multi-panel figures with panel labels\n", + "\n", + "`panel_label()` adds the `(a)`/`(b)`/… labels expected in multi-panel thesis\n", + "and paper figures, combining several of the building blocks above. By\n", + "default it sits in the bottom-right corner, colored to match the axes'\n", + "xlabel, on a light-grey semi-transparent rounded box with a slim solid\n", + "border — pass `box=False` for bare text instead. The figure-level\n", + "`title`/`params` from section 3 still set the figure's overall title — but\n", + "now each panel also gets its own plain `ax.set_title()`, since no single\n", + "title could describe all four at once. This is the one place in this\n", + "notebook where `ax.set_title()` is the right call.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "55084ae6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = ps.new_figure(\n", + " \"thesis-wide\",\n", + " nrows=2,\n", + " ncols=2,\n", + " title=\"A multi-panel figure built from plotstyle building blocks\",\n", + " params={\"seed\": 0},\n", + ")\n", + "\n", + "axes[0, 0].plot(x, np.sin(x), label=\"sin\")\n", + "axes[0, 0].plot(x, np.cos(x), label=\"cos\")\n", + "axes[0, 0].set_title(\"Trigonometric\")\n", + "ps.style_legend(axes[0, 0], title=\"Function\")\n", + "ps.panel_label(axes[0, 0], \"a\")\n", + "\n", + "axes[0, 1].hist(rng.normal(size=1000), bins=30, color=ps.get_color(2))\n", + "axes[0, 1].set_title(\"Histogram\")\n", + "ps.panel_label(axes[0, 1], \"b\")\n", + "\n", + "axes[1, 0].pcolormesh(X, Y, Z, cmap=ps.colors.sequential_cmap(), shading=\"auto\")\n", + "axes[1, 0].set_title(\"Sequential\")\n", + "ps.panel_label(axes[1, 0], \"c\")\n", + "\n", + "axes[1, 1].imshow(corr, cmap=ps.colors.diverging_cmap(), vmin=-1, vmax=1)\n", + "axes[1, 1].set_title(\"Diverging\")\n", + "ps.panel_label(axes[1, 1], \"d\")\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "4dbdac0a", + "metadata": {}, + "source": [ + "## 9. Saving figures\n", + "\n", + "`savefig()` creates parent directories and writes one file per format — PDF\n", + "by default, which slots straight into ETPlot's own gallery pipeline\n", + "(PDF \\u2192 PNG) with no extra conversion step.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "52f994d6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[PosixPath('output/multi_panel_demo.pdf'), PosixPath('output/multi_panel_demo.png')]\n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "\n", + "written = ps.savefig(fig, Path(\"output\") / \"multi_panel_demo\", formats=(\"pdf\", \"png\"))\n", + "print(written)\n" + ] + }, + { + "cell_type": "markdown", + "id": "5a51f67a", + "metadata": {}, + "source": [ + "## Recap\n", + "\n", + "That's the full building-block set: `use()`, `new_figure()` (with `title`,\n", + "`params`, and size presets), `colorbar()`, `colors` (categorical / sequential\n", + "/ diverging / status), `style_legend()`, `panel_label()`, and `savefig()`.\n", + "Call `ps.use()` once at the top of a plot-producing script and every figure\n", + "inherits the same validated, modern look — in a thesis chapter or on a\n", + "slide. For titles specifically: default to `new_figure(title=..., params=...)`;\n", + "only reach for `ax.set_title()` when a figure has multiple axes and each one\n", + "needs its own title.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/plotstyle/__init__.py b/plotstyle/__init__.py new file mode 100644 index 0000000..654044a --- /dev/null +++ b/plotstyle/__init__.py @@ -0,0 +1,30 @@ +"""Reusable matplotlib styling and building blocks for consistent, modern +scientific plots across presentations and thesis figures. + + import plotstyle as ps + + ps.use() + fig, ax = ps.new_figure("thesis-single") + ax.plot(x, y, label="A") + ps.style_legend(ax) + ps.savefig(fig, "plots/my_plot", formats=("pdf", "png")) +""" + +from . import colors +from .annotations import panel_label, style_legend +from .colors import get_color +from .figures import FIGSIZES, colorbar, new_figure, savefig +from .style import reset, use + +__all__ = [ + "colors", + "get_color", + "use", + "reset", + "new_figure", + "colorbar", + "savefig", + "FIGSIZES", + "style_legend", + "panel_label", +] diff --git a/plotstyle/annotations.py b/plotstyle/annotations.py new file mode 100644 index 0000000..3671618 --- /dev/null +++ b/plotstyle/annotations.py @@ -0,0 +1,97 @@ +"""Legend and panel-label helpers for consistent multi-panel figures.""" + +from __future__ import annotations + +import warnings + +from matplotlib.axes import Axes +from matplotlib.colors import to_rgba + +from .colors import INK + +_PANEL_LOCS = { + "upper left": (0.02, 0.98, "left", "top"), + "upper right": (0.98, 0.98, "right", "top"), + "lower left": (0.02, 0.08, "left", "bottom"), + "lower right": (0.98, 0.08, "right", "bottom"), +} + + +def style_legend( + ax: Axes, + loc: str = "outside right upper", + frameon: bool = False, + title: str = None, + **kwargs, +): + """Add a figure-level legend, placed outside the axes by default. + + Builds on `ax`'s handles/labels (or explicit `handles=`/`labels=` kwargs) + but attaches the legend to `ax`'s figure via `fig.legend(...)`, so it sits + outside the plot area rather than overlapping the data. + + A `title` is strongly encouraged: an untitled legend floating outside the + axes loses its visual link to what it's describing, e.g. + `style_legend(ax, title="Series")`. Without one, this emits a warning + rather than failing — the legend still renders. + """ + if title is None: + warnings.warn( + "style_legend() called without a title — an outside legend reads better with one, " + "e.g. style_legend(ax, title='Series').", + stacklevel=2, + ) + + fig = ax.get_figure() + handles = kwargs.pop("handles", None) + labels = kwargs.pop("labels", None) + if handles is None or labels is None: + handles, labels = ax.get_legend_handles_labels() + + legend = fig.legend(handles, labels, loc=loc, frameon=frameon, title=title, **kwargs) + if legend.get_title() is not None: + legend.get_title().set_fontweight("bold") + return legend + + +def panel_label( + ax: Axes, + label: str, + loc: str = "lower right", + fontweight: str = "bold", + box: bool = True, + **kwargs, +): + """Add a panel label like "(a)" for multi-panel thesis/paper figures. + + Defaults to the bottom-right corner, colored to match the axes' xlabel, + on a light-grey semi-transparent rounded box with a slim solid border. + Pass `box=False` for bare text with no box. + """ + try: + x, y, ha, va = _PANEL_LOCS[loc] + except KeyError as exc: + raise ValueError(f"Unknown panel_label loc {loc!r}. Choose from {sorted(_PANEL_LOCS)}.") from exc + + kwargs.setdefault("color", ax.xaxis.label.get_color()) + if box: + kwargs.setdefault( + "bbox", + dict( + boxstyle="round,pad=0.3", + facecolor=to_rgba(INK["gridline"], alpha=0.8), + edgecolor=INK["baseline"], + linewidth=0.8, + ), + ) + + return ax.text( + x, + y, + f"({label})", + transform=ax.transAxes, + ha=ha, + va=va, + fontweight=fontweight, + **kwargs, + ) diff --git a/plotstyle/assets/plotstyle.mplstyle b/plotstyle/assets/plotstyle.mplstyle new file mode 100644 index 0000000..e729542 --- /dev/null +++ b/plotstyle/assets/plotstyle.mplstyle @@ -0,0 +1,80 @@ +# Base rcParams for plotstyle. Loaded via plt.style.use() from style.use(). +# Colors here mirror plotstyle.colors.INK (KIT black-70% gray family on white) — kept in sync by hand. + +figure.facecolor: ffffff +figure.edgecolor: ffffff +figure.constrained_layout.use: True + +axes.facecolor: ffffff +# axes.edgecolor/linewidth style the one visible spine (bottom — the rest are +# off below), so this is really "the bottom spine is black and heavier", not +# a general axes outline color. +axes.edgecolor: 000000 +axes.linewidth: 1.25 +axes.labelcolor: 6a6a6a +axes.titlecolor: 404040 +axes.titleweight: bold +axes.titlelocation: left +axes.grid: True +axes.grid.axis: y +axes.axisbelow: True +axes.spines.top: False +axes.spines.right: False +axes.spines.left: False +axes.spines.bottom: True + +grid.color: ececec +grid.linewidth: 0.8 +grid.alpha: 1.0 + +xtick.color: 969696 +ytick.color: 969696 +# Major tick labels read as the primary ("black") ink; minor tick labels are +# muted grey. rcParams only expose one labelcolor per axis (no major/minor +# split) — the per-major/minor distinction is applied in code by +# plotstyle.figures.new_figure() via ax.tick_params(which=...). These values +# are just the fallback/default for axes that bypass new_figure(). +xtick.labelcolor: 404040 +ytick.labelcolor: 404040 +xtick.direction: out +ytick.direction: out +xtick.bottom: True +xtick.top: False +ytick.left: True +ytick.right: False +# Minor ticks: x-axis only. The y-axis already has horizontal gridlines at +# major ticks, so y minor ticks would just add unlabeled clutter. +xtick.minor.visible: True +ytick.minor.visible: False +xtick.major.size: 6.0 +xtick.minor.size: 3.0 +xtick.major.width: 0.8 +xtick.minor.width: 0.6 +ytick.major.size: 6.0 +ytick.minor.size: 3.0 +ytick.major.width: 0.8 +ytick.minor.width: 0.6 + +lines.linewidth: 2.0 +lines.markersize: 6.0 +lines.solid_capstyle: round + +font.family: sans-serif +font.sans-serif: DejaVu Sans, Arial, Helvetica, sans-serif +font.size: 11 +axes.titlesize: 13 +axes.labelsize: 11 +xtick.labelsize: 10 +ytick.labelsize: 10 +legend.fontsize: 10 + +legend.frameon: False +legend.handlelength: 1.6 +legend.labelspacing: 0.4 +legend.title_fontsize: 10 + +savefig.facecolor: ffffff +savefig.edgecolor: ffffff +savefig.dpi: 300 +savefig.bbox: tight +savefig.pad_inches: 0.05 diff --git a/plotstyle/colors.py b/plotstyle/colors.py new file mode 100644 index 0000000..addf3cb --- /dev/null +++ b/plotstyle/colors.py @@ -0,0 +1,102 @@ +"""KIT (Karlsruhe Institute of Technology) corporate design color palette. + +Hex values for KIT green, KIT blue, black 70%, and the corporate accent +colors are taken verbatim from the KIT corporate design guide +(https://kit-cd.km.kit.edu/english/341.php) — do not hand-edit them without +checking that page. + +The categorical *order* below is not arbitrary: it was chosen by running +every hue through the CVD-safety/contrast checks described in the `dataviz` +skill (fixed hue order, OKLab CVD separation under simulated color-vision +deficiency, a normal-vision separation floor, contrast vs. a white surface) +and keeping the ordering that clears the adjacent-pair checks. KIT yellow +(#FCE500) is the one hue that cannot pass on its own (too light on a white +surface, ~1.3:1 contrast) — that is a property of the hex value itself, not +the ordering, so it is placed last and should always be paired with a +visible direct label rather than relied on as a fill alone. +""" + +from __future__ import annotations + +from matplotlib.colors import LinearSegmentedColormap + +# Fixed-order categorical hues (KIT primary + accent colors). Order is the +# CVD-safety mechanism: never reorder, and never cycle past the last slot +# (fold extra series into "Other"). +CATEGORICAL = [ + "#009682", # 0 KIT green (primary) + "#DF9B1B", # 1 orange + "#4664AA", # 2 KIT blue (primary) + "#A78230", # 3 brown + "#23A1E0", # 4 cyan + "#A3107C", # 5 purple + "#8CB63C", # 6 pea green + "#A22223", # 7 red + "#FCE500", # 8 yellow — low contrast on white; always pair with a direct label +] + +# Single-hue (KIT blue) sequential ramp, steps 100..700. Step 700 is the exact +# brand hex (the high/saturated end); lighter steps are tints blended toward +# white in sRGB — there is no darker-than-brand shade. +SEQUENTIAL_STEPS = [ + "#e3e8f2", # 100 + "#c9d2e6", # 200 + "#afbcda", # 300 + "#95a6ce", # 400 + "#7a90c2", # 500 + "#607ab6", # 600 + "#4664AA", # 700 (KIT blue) +] + +# Diverging KIT blue <-> KIT red, neutral gray midpoint. +DIVERGING = { + "low": "#4664AA", + "mid": "#f7f7f7", + "high": "#A22223", +} + +# Fixed, reserved status scale — deliberately NOT re-themed to KIT colors: +# status is a small fixed scale with reserved meaning that must stay visually +# distinct from the categorical slots so it never impersonates a series. +# Never put these in the categorical cycle; always pair with an icon/label. +STATUS = { + "good": "#0ca30c", + "warning": "#fab219", + "serious": "#ec835a", + "critical": "#d03b3b", +} + +# Chrome / ink roles, derived from KIT's specified "black 70%" (#404040, +# used by KIT for headings and continuous text) on a white surface. +INK = { + "surface": "#ffffff", + "primary": "#404040", # KIT black 70% — headings, titles, continuous text + "secondary": "#6a6a6a", + "muted": "#969696", + "gridline": "#ececec", + "baseline": "#c2c2c2", +} + + +def get_color(index: int) -> str: + """Return the categorical color for series `index` (0-based). + + Raises ValueError past the validated slots instead of silently wrapping + back to slot 0, which would collide two series on the same hue. + """ + if not 0 <= index < len(CATEGORICAL): + raise ValueError( + f"get_color({index}) out of range: only {len(CATEGORICAL)} validated categorical " + "colors exist. Fold extra series into an 'Other' bucket or facet instead of cycling." + ) + return CATEGORICAL[index] + + +def sequential_cmap(name: str = "ps_sequential") -> LinearSegmentedColormap: + """Continuous KIT-blue sequential colormap for magnitude encoding.""" + return LinearSegmentedColormap.from_list(name, SEQUENTIAL_STEPS) + + +def diverging_cmap(name: str = "ps_diverging") -> LinearSegmentedColormap: + """Continuous KIT blue-gray-red diverging colormap for polarity encoding.""" + return LinearSegmentedColormap.from_list(name, [DIVERGING["low"], DIVERGING["mid"], DIVERGING["high"]]) diff --git a/plotstyle/figures.py b/plotstyle/figures.py new file mode 100644 index 0000000..faf75e0 --- /dev/null +++ b/plotstyle/figures.py @@ -0,0 +1,127 @@ +"""Figure creation, colorbar, and saving helpers.""" + +from __future__ import annotations + +from pathlib import Path +from typing import Mapping, Sequence, Union + +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.axes import Axes +from matplotlib.colorbar import Colorbar +from matplotlib.figure import Figure +from mpl_toolkits.axes_grid1 import make_axes_locatable + +from .colors import INK + +FIGSIZES = { + "thesis-single": (6.0, 4.0), + "thesis-wide": (8.0, 4.5), + "slide-16x9": (10.0, 5.625), + "square": (5.0, 5.0), +} + + +def _style_ticks(ax: Axes) -> None: + """Major tick labels read as primary ("black") ink; minor as muted grey. + + rcParams only expose a single labelcolor per axis (no major/minor split), + so this distinction has to be applied per-Axes in code. + """ + ax.tick_params(axis="both", which="major", labelcolor=INK["primary"]) + ax.tick_params(axis="both", which="minor", labelcolor=INK["muted"]) + + +def _format_params(params: Mapping) -> str: + return " | ".join(f"{key}: {value}" for key, value in params.items()) + + +def _set_figure_title(fig: Figure, title: Union[str, None], params: Union[Mapping, None]) -> None: + # A single Text artist gets one color under matplotlib's usetex rendering + # (dvipng rasterizes it as one greyscale mask, tinted uniformly — any + # in-source \color/\textcolor is ignored), so the subtitle is set apart + # from the title by size (\small) only, not color. + lines = [] + if title is not None: + lines.append(r"\textbf{" + title + "}") + if params: + lines.append(r"{\small " + _format_params(params) + "}") + if lines: + fig.suptitle("\n".join(lines), x=0.0, ha="left") + + +def new_figure( + preset: Union[str, tuple] = "thesis-single", + *, + title: str = None, + params: Mapping = None, + **subplots_kwargs, +): + """Create a figure/axes pair sized for a named preset or an explicit (w, h) tuple. + + Presets (inches): thesis-single, thesis-wide, slide-16x9, square. + + `title` sets a left-aligned, bold figure-level title (`fig.suptitle`) — + this is preferred over an axes title even when there's a single axes, so + it stays consistent for single- and multi-panel figures alike. `params` + is an optional dict rendered as a smaller, muted subtitle line below the + title, formatted as "key1: value1 | key2: value2 | ...". + """ + if isinstance(preset, str): + try: + figsize = FIGSIZES[preset] + except KeyError as exc: + raise ValueError( + f"Unknown figure preset {preset!r}. Choose from {sorted(FIGSIZES)} or pass an (w, h) tuple." + ) from exc + else: + figsize = preset + + subplots_kwargs.setdefault("figsize", figsize) + fig, axes = plt.subplots(**subplots_kwargs) + + for ax in [axes] if isinstance(axes, Axes) else np.ravel(axes): + _style_ticks(ax) + + _set_figure_title(fig, title, params) + + return fig, axes + + +def colorbar(mappable, ax: Axes, size: str = "5%", pad: float = 0.05, **kwargs) -> Colorbar: + """Add a colorbar matched to `ax`'s actual on-screen size. + + `fig.colorbar(mappable, ax=ax)` sizes the colorbar to the axes' nominal + bounding box, which is taller than the axes once things like + `ax.set_aspect("equal")` have visually shrunk it (e.g. a non-square + `imshow`). This appends a same-size axes via `make_axes_locatable` + instead, so the colorbar always matches what's actually drawn. + """ + divider = make_axes_locatable(ax) + cax = divider.append_axes("right", size=size, pad=pad) + cb = ax.get_figure().colorbar(mappable, cax=cax, **kwargs) + # Colorbar draws its own border (a dedicated "outline" spine) that isn't + # covered by axes.spines.{left,right,top} — without this it'd pick up + # the bold black bottom-spine color/width from the main theme as a box + # around the whole colorbar, which reads as a stray, heavier-than-intended + # edge rather than the plain baseline it's styled to be elsewhere. + cb.outline.set_visible(False) + return cb + + +def savefig(fig: Figure, path: Union[str, Path], formats: Sequence[str] = ("pdf",), dpi: int = 300) -> list[Path]: + """Save `fig` to `path` once per format, creating parent directories as needed. + + `path` should have no extension — it's appended per format, e.g. + savefig(fig, "plots/my_plot", formats=("pdf", "png")) writes + plots/my_plot.pdf and plots/my_plot.png. + """ + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + + written = [] + for fmt in formats: + out_path = path.with_suffix(f".{fmt}") + fig.savefig(out_path, format=fmt, dpi=dpi) + written.append(out_path) + return written diff --git a/plotstyle/style.py b/plotstyle/style.py new file mode 100644 index 0000000..e73b57e --- /dev/null +++ b/plotstyle/style.py @@ -0,0 +1,64 @@ +"""Apply the plotstyle rcParams theme. + +Text (titles, axis labels, legends, tick labels, annotations — the entirety +of every string) is always rendered through a real LaTeX toolchain, using +Latin Modern Sans as a modern sans-serif LaTeX font (including math mode, via +`sfmath` — otherwise tick numbers fall back to a serif math font even with +`\\familydefault` set to sans). `fontenc`'s T1 encoding is loaded too — without +it, some plain ASCII characters (e.g. "|") render as the wrong glyph under +OT1, LaTeX's default. This requires a working local `latex`/`dvipng` install; +there is no mathtext fallback. + +Note: matplotlib's usetex rendering rasterizes each Text artist as a single +greyscale glyph mask via dvipng and then tints the *whole* thing with that +artist's one `color` — any in-source `\\color`/`\\textcolor` command is +ignored. So two differently-colored spans (e.g. a black title next to a grey +subtitle) can't live in one Text object; `figures.new_figure()`'s title/params +handling only varies font size (`\\small`) between lines, not color, for +exactly this reason. +""" + +from __future__ import annotations + +from cycler import cycler +from importlib import resources + +import matplotlib.pyplot as plt + +from .colors import CATEGORICAL + +_LINESTYLES = ["-", "--", "-.", ":"] + +_LATEX_PREAMBLE = ( + r"\usepackage[T1]{fontenc}\usepackage{amsmath}\usepackage{lmodern}\usepackage{sfmath}" + r"\renewcommand{\familydefault}{\sfdefault}" +) + + +def use(cycle_linestyles: bool = False) -> None: + """Apply the plotstyle theme to matplotlib's global rcParams. + + Call once at the top of a plotting script, before creating any figures. + + By default `axes.prop_cycle` only cycles color (all lines solid) — pass + `cycle_linestyles=True` to also cycle through a repeating linestyle + sequence, so series stay distinguishable even if color is lost + (grayscale printing, projector glare, color-vision deficiency). + """ + style_path = resources.files("plotstyle").joinpath("assets", "plotstyle.mplstyle") + plt.style.use(str(style_path)) + + if cycle_linestyles: + n = len(CATEGORICAL) + linestyles = (_LINESTYLES * (n // len(_LINESTYLES) + 1))[:n] + plt.rcParams["axes.prop_cycle"] = cycler(color=CATEGORICAL) + cycler(linestyle=linestyles) + else: + plt.rcParams["axes.prop_cycle"] = cycler(color=CATEGORICAL) + + plt.rcParams["text.usetex"] = True + plt.rcParams["text.latex.preamble"] = _LATEX_PREAMBLE + + +def reset() -> None: + """Restore matplotlib defaults (useful between tests/notebook cells).""" + plt.rcdefaults() diff --git a/pyproject.toml b/pyproject.toml index a621d53..ccf629f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -50,13 +50,16 @@ dev = [ "pylint>=2.0", "mypy>=0.900", ] +plotting = [ + "matplotlib>=3.7", +] [project.scripts] gallery = "gallery.cli:main" [tool.setuptools] -packages = ["gallery", "gallery.utils", "gallery.config"] -package-data = {gallery = ["templates/*", "assets/css/*", "assets/js/*", "config/*"]} +packages = ["gallery", "gallery.utils", "gallery.config", "plotstyle"] +package-data = {gallery = ["templates/*", "assets/css/*", "assets/js/*", "config/*"], plotstyle = ["assets/*.mplstyle"]} include-package-data = true [tool.black] diff --git a/tests/test_plotstyle.py b/tests/test_plotstyle.py new file mode 100644 index 0000000..c2e7689 --- /dev/null +++ b/tests/test_plotstyle.py @@ -0,0 +1,310 @@ +import matplotlib + +matplotlib.use("Agg") + +import matplotlib.pyplot as plt +import pytest +from matplotlib.colors import Colormap + +import plotstyle as ps +from plotstyle import colors + + +def test_categorical_palette_matches_kit_hex(): + assert colors.CATEGORICAL == [ + "#009682", + "#DF9B1B", + "#4664AA", + "#A78230", + "#23A1E0", + "#A3107C", + "#8CB63C", + "#A22223", + "#FCE500", + ] + + +def test_diverging_palette_matches_kit_hex(): + assert colors.DIVERGING == {"low": "#4664AA", "mid": "#f7f7f7", "high": "#A22223"} + + +def test_status_palette_matches_validated_hex(): + assert colors.STATUS == { + "good": "#0ca30c", + "warning": "#fab219", + "serious": "#ec835a", + "critical": "#d03b3b", + } + + +def test_get_color_returns_categorical_slot(): + assert colors.get_color(0) == colors.CATEGORICAL[0] + assert colors.get_color(len(colors.CATEGORICAL) - 1) == colors.CATEGORICAL[-1] + + +def test_get_color_out_of_range_raises(): + with pytest.raises(ValueError): + colors.get_color(len(colors.CATEGORICAL)) + + +def test_sequential_steps_are_kit_blue_variations(): + assert colors.SEQUENTIAL_STEPS[-1].lower() == "#4664aa" + # Lightest step should be a lighter (higher-lightness) tint than the brand color. + import colorsys + + def lightness(hex_color): + r, g, b = (int(hex_color.lstrip("#")[i : i + 2], 16) / 255 for i in (0, 2, 4)) + return colorsys.rgb_to_hls(r, g, b)[1] + + assert lightness(colors.SEQUENTIAL_STEPS[0]) > lightness(colors.SEQUENTIAL_STEPS[-1]) + + +def test_sequential_cmap_is_usable_colormap(): + cmap = colors.sequential_cmap() + assert isinstance(cmap, Colormap) + assert cmap(0.0) != cmap(1.0) + + +def test_diverging_cmap_is_usable_colormap(): + cmap = colors.diverging_cmap() + assert isinstance(cmap, Colormap) + assert cmap(0.0) != cmap(1.0) + + +def test_use_sets_expected_rcparams(): + ps.use() + try: + assert plt.rcParams["axes.facecolor"] == "#ffffff" + assert plt.rcParams["text.usetex"] is True + assert "lmodern" in plt.rcParams["text.latex.preamble"] + assert "sfmath" in plt.rcParams["text.latex.preamble"] + assert "fontenc" in plt.rcParams["text.latex.preamble"] + assert r"\sfdefault" in plt.rcParams["text.latex.preamble"] + cycle_colors = [entry["color"] for entry in plt.rcParams["axes.prop_cycle"]] + assert cycle_colors == colors.CATEGORICAL + + assert plt.rcParams["axes.spines.top"] is False + assert plt.rcParams["axes.spines.right"] is False + assert plt.rcParams["axes.spines.left"] is False + assert plt.rcParams["axes.spines.bottom"] is True + assert plt.rcParams["xtick.bottom"] is True + assert plt.rcParams["ytick.left"] is True + assert plt.rcParams["axes.edgecolor"] == "#000000" + assert plt.rcParams["axes.linewidth"] > 0.8 + + assert plt.rcParams["axes.grid"] is True + assert plt.rcParams["axes.grid.axis"] == "y" + assert plt.rcParams["axes.titlelocation"] == "left" + + assert plt.rcParams["xtick.minor.visible"] is True + assert plt.rcParams["ytick.minor.visible"] is False + assert plt.rcParams["xtick.major.size"] > plt.rcParams["xtick.minor.size"] + finally: + ps.reset() + + +def test_use_default_does_not_cycle_linestyles(): + ps.use() + try: + cycle_keys = plt.rcParams["axes.prop_cycle"].keys + assert cycle_keys == {"color"} + finally: + ps.reset() + + +def test_use_cycle_linestyles_opt_in(): + ps.use(cycle_linestyles=True) + try: + cycle_keys = plt.rcParams["axes.prop_cycle"].keys + assert cycle_keys == {"color", "linestyle"} + linestyles = {entry["linestyle"] for entry in plt.rcParams["axes.prop_cycle"]} + assert len(linestyles) > 1 + finally: + ps.reset() + + +def test_new_figure_styles_major_and_minor_tick_labels_differently(): + fig, ax = ps.new_figure("square") + try: + assert ax.xaxis.get_tick_params(which="major")["labelcolor"] == colors.INK["primary"] + assert ax.xaxis.get_tick_params(which="minor")["labelcolor"] == colors.INK["muted"] + assert ax.yaxis.get_tick_params(which="major")["labelcolor"] == colors.INK["primary"] + assert ax.yaxis.get_tick_params(which="minor")["labelcolor"] == colors.INK["muted"] + finally: + plt.close(fig) + + +def test_new_figure_preset_sizes(): + fig, ax = ps.new_figure("thesis-single") + try: + assert tuple(fig.get_size_inches()) == ps.FIGSIZES["thesis-single"] + finally: + plt.close(fig) + + +def test_new_figure_explicit_tuple(): + fig, ax = ps.new_figure((3.0, 2.0)) + try: + assert tuple(fig.get_size_inches()) == (3.0, 2.0) + finally: + plt.close(fig) + + +def test_new_figure_unknown_preset_raises(): + with pytest.raises(ValueError): + ps.new_figure("not-a-real-preset") + + +def test_new_figure_title_sets_left_aligned_figure_suptitle(): + fig, ax = ps.new_figure("square", title="My Title") + try: + assert fig._suptitle is not None + assert "My Title" in fig._suptitle.get_text() + assert fig._suptitle.get_ha() == "left" + assert ax.get_title() == "" + finally: + plt.close(fig) + + +def test_new_figure_params_adds_subtitle_line(): + fig, ax = ps.new_figure("square", title="My Title", params={"N": 100, "seed": 42}) + try: + text = fig._suptitle.get_text() + assert "My Title" in text + assert "N: 100" in text + assert "seed: 42" in text + assert "|" in text + finally: + plt.close(fig) + + +def test_new_figure_without_title_or_params_has_no_suptitle(): + fig, ax = ps.new_figure("square") + try: + assert fig._suptitle is None + finally: + plt.close(fig) + + +def test_colorbar_matches_axes_height(): + fig, ax = ps.new_figure("square") + try: + im = ax.imshow([[0, 1], [2, 3]]) + cb = ps.colorbar(im, ax) + fig.canvas.draw() + ax_bbox = ax.get_position() + cax_bbox = cb.ax.get_position() + assert ax_bbox.y0 == pytest.approx(cax_bbox.y0, abs=1e-6) + assert ax_bbox.y1 == pytest.approx(cax_bbox.y1, abs=1e-6) + finally: + plt.close(fig) + + +def test_colorbar_has_no_outline(): + fig, ax = ps.new_figure("square") + try: + im = ax.imshow([[0, 1], [2, 3]]) + cb = ps.colorbar(im, ax) + assert cb.outline.get_visible() is False + finally: + plt.close(fig) + + +def test_savefig_writes_requested_formats(tmp_path): + fig, ax = ps.new_figure("square") + ax.plot([0, 1], [0, 1]) + try: + out_dir = tmp_path / "nested" / "plots" + written = ps.savefig(fig, out_dir / "my_plot", formats=("pdf", "png")) + finally: + plt.close(fig) + + assert [p.name for p in written] == ["my_plot.pdf", "my_plot.png"] + for p in written: + assert p.exists() + assert p.stat().st_size > 0 + + +def test_style_legend_places_legend_on_figure_outside_axes(): + fig, ax = ps.new_figure("square") + try: + ax.plot([0, 1], [0, 1], label="series") + legend = ps.style_legend(ax, title="Series") + assert legend in fig.legends + assert ax.get_legend() is None + assert legend.get_title().get_text() == "Series" + finally: + plt.close(fig) + + +def test_style_legend_without_title_warns(): + fig, ax = ps.new_figure("square") + try: + ax.plot([0, 1], [0, 1], label="series") + with pytest.warns(UserWarning): + ps.style_legend(ax) + finally: + plt.close(fig) + + +def test_panel_label_does_not_raise(): + fig, ax = ps.new_figure("square") + try: + ax.plot([0, 1], [0, 1], label="series") + ps.panel_label(ax, "a") + finally: + plt.close(fig) + + +def test_panel_label_unknown_loc_raises(): + fig, ax = ps.new_figure("square") + try: + with pytest.raises(ValueError): + ps.panel_label(ax, "a", loc="middle") + finally: + plt.close(fig) + + +def test_panel_label_defaults_to_lower_right(): + fig, ax = ps.new_figure("square") + try: + text = ps.panel_label(ax, "a") + assert text.get_ha() == "right" + assert text.get_va() == "bottom" + x, y = text.get_position() + assert x > 0.5 + assert y < 0.5 + finally: + plt.close(fig) + + +def test_panel_label_color_matches_xlabel(): + fig, ax = ps.new_figure("square") + try: + ax.set_xlabel("Time (s)") + text = ps.panel_label(ax, "a") + assert text.get_color() == ax.xaxis.label.get_color() + finally: + plt.close(fig) + + +def test_panel_label_has_rounded_semi_transparent_box_by_default(): + fig, ax = ps.new_figure("square") + try: + text = ps.panel_label(ax, "a") + patch = text.get_bbox_patch() + assert patch is not None + assert "round" in patch.get_boxstyle().__class__.__name__.lower() + assert patch.get_facecolor()[3] < 1.0 + assert patch.get_edgecolor()[3] == 1.0 + finally: + plt.close(fig) + + +def test_panel_label_box_can_be_disabled(): + fig, ax = ps.new_figure("square") + try: + text = ps.panel_label(ax, "a", box=False) + assert text.get_bbox_patch() is None + finally: + plt.close(fig) diff --git a/uv.lock b/uv.lock index 0037677..0a98c97 100644 --- a/uv.lock +++ b/uv.lock @@ -251,6 +251,317 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" }, ] +[[package]] +name = "contourpy" +version = "1.1.1" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.8.1' and python_full_version < '3.9'", + "python_full_version < '3.8.1'", +] +dependencies = [ + { name = "numpy", version = "1.24.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/b1/7d/087ee4295e7580d3f7eb8a8a4e0ec8c7847e60f34135248ccf831cf5bbfc/contourpy-1.1.1.tar.gz", hash = "sha256:96ba37c2e24b7212a77da85004c38e7c4d155d3e72a45eeaf22c1f03f607e8ab", size = 13433167, upload-time = "2023-09-16T10:25:49.501Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/fb/7f/c44a51a83a093bf5c84e07dd1e3cfe9f68c47b6499bd05a9de0c6dbdc2bc/contourpy-1.1.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:46e24f5412c948d81736509377e255f6040e94216bf1a9b5ea1eaa9d29f6ec1b", size = 247207, upload-time = "2023-09-16T10:20:32.848Z" }, + { url = "https://files.pythonhosted.org/packages/a9/65/544d66da0716b20084874297ff7596704e435cf011512f8e576638e83db2/contourpy-1.1.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0e48694d6a9c5a26ee85b10130c77a011a4fedf50a7279fa0bdaf44bafb4299d", size = 232428, upload-time = "2023-09-16T10:20:36.337Z" }, + { url = "https://files.pythonhosted.org/packages/5b/e6/697085cc34a294bd399548fd99562537a75408f113e3a815807e206246f0/contourpy-1.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a66045af6cf00e19d02191ab578a50cb93b2028c3eefed999793698e9ea768ae", size = 285304, upload-time = "2023-09-16T10:20:40.182Z" }, + { url = "https://files.pythonhosted.org/packages/69/4b/52d0d2e85c59f00f6ddbd6fea819f267008c58ee7708da96d112a293e91c/contourpy-1.1.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4ebf42695f75ee1a952f98ce9775c873e4971732a87334b099dde90b6af6a916", size = 322655, upload-time = "2023-09-16T10:20:44.175Z" }, + { url = "https://files.pythonhosted.org/packages/82/fc/3decc656a547a6d5d5b4249f81c72668a1f3259a62b2def2504120d38746/contourpy-1.1.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f6aec19457617ef468ff091669cca01fa7ea557b12b59a7908b9474bb9674cf0", size = 296430, upload-time = "2023-09-16T10:20:47.767Z" }, + { url = "https://files.pythonhosted.org/packages/f1/6b/e4b0f8708f22dd7c321f87eadbb98708975e115ac6582eb46d1f32197ce6/contourpy-1.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:462c59914dc6d81e0b11f37e560b8a7c2dbab6aca4f38be31519d442d6cde1a1", size = 301672, upload-time = "2023-09-16T10:20:51.395Z" }, + { url = "https://files.pythonhosted.org/packages/c3/87/201410522a756e605069078833d806147cad8532fdc164a96689d05c5afc/contourpy-1.1.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:6d0a8efc258659edc5299f9ef32d8d81de8b53b45d67bf4bfa3067f31366764d", size = 820145, upload-time = "2023-09-16T10:20:58.426Z" }, + { url = "https://files.pythonhosted.org/packages/b4/d9/42680a17d43edda04ab2b3f11125cf97b61bce5d3b52721a42960bf748bd/contourpy-1.1.1-cp310-cp310-win32.whl", hash = "sha256:d6ab42f223e58b7dac1bb0af32194a7b9311065583cc75ff59dcf301afd8a431", size = 399542, upload-time = "2023-09-16T10:21:02.719Z" }, + { url = "https://files.pythonhosted.org/packages/55/14/0dc1884e3c04f9b073a47283f5d424926644250891db392a07c56f05e5c5/contourpy-1.1.1-cp310-cp310-win_amd64.whl", hash = "sha256:549174b0713d49871c6dee90a4b499d3f12f5e5f69641cd23c50a4542e2ca1eb", size = 477974, upload-time = "2023-09-16T10:21:07.565Z" }, + { url = "https://files.pythonhosted.org/packages/8b/4f/be28a39cd5e988b8d3c2cc642c2c7ffeeb28fe80a86df71b6d1e473c5038/contourpy-1.1.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:407d864db716a067cc696d61fa1ef6637fedf03606e8417fe2aeed20a061e6b2", size = 248613, upload-time = "2023-09-16T10:21:10.695Z" }, + { url = "https://files.pythonhosted.org/packages/2c/8e/656f8e7cd316aa68d9824744773e90dbd71f847429d10c82001e927480a2/contourpy-1.1.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:dfe80c017973e6a4c367e037cb31601044dd55e6bfacd57370674867d15a899b", size = 233603, upload-time = "2023-09-16T10:21:13.771Z" }, + { url = "https://files.pythonhosted.org/packages/60/2a/4d4bd4541212ab98f3411f21bf58b0b246f333ae996e9f57e1acf12bcc45/contourpy-1.1.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e30aaf2b8a2bac57eb7e1650df1b3a4130e8d0c66fc2f861039d507a11760e1b", size = 287037, upload-time = "2023-09-16T10:21:17.622Z" }, + { url = "https://files.pythonhosted.org/packages/24/67/8abf919443381585a4eee74069e311c736350549dae02d3d014fef93d50a/contourpy-1.1.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3de23ca4f381c3770dee6d10ead6fff524d540c0f662e763ad1530bde5112532", size = 323274, upload-time = "2023-09-16T10:21:21.404Z" }, + { url = "https://files.pythonhosted.org/packages/2a/e5/6da11329dd35a2f2e404a95e5374b5702de6ac52e776e8b87dd6ea4b29d0/contourpy-1.1.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:566f0e41df06dfef2431defcfaa155f0acfa1ca4acbf8fd80895b1e7e2ada40e", size = 297801, upload-time = "2023-09-16T10:21:25.155Z" }, + { url = "https://files.pythonhosted.org/packages/b7/f6/78f60fa0b6ae64971178e2542e8b3ad3ba5f4f379b918ab7b18038a3f897/contourpy-1.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b04c2f0adaf255bf756cf08ebef1be132d3c7a06fe6f9877d55640c5e60c72c5", size = 302821, upload-time = "2023-09-16T10:21:28.663Z" }, + { url = "https://files.pythonhosted.org/packages/da/25/6062395a1c6a06f46a577da821318886b8b939453a098b9cd61671bb497b/contourpy-1.1.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:d0c188ae66b772d9d61d43c6030500344c13e3f73a00d1dc241da896f379bb62", size = 820121, upload-time = "2023-09-16T10:21:36.251Z" }, + { url = "https://files.pythonhosted.org/packages/41/5e/64e78b1e8682cbab10c13fc1a2c070d30acedb805ab2f42afbd3d88f7225/contourpy-1.1.1-cp311-cp311-win32.whl", hash = "sha256:0683e1ae20dc038075d92e0e0148f09ffcefab120e57f6b4c9c0f477ec171f33", size = 401590, upload-time = "2023-09-16T10:21:40.42Z" }, + { url = "https://files.pythonhosted.org/packages/e5/76/94bc17eb868f8c7397f8fdfdeae7661c1b9a35f3a7219da308596e8c252a/contourpy-1.1.1-cp311-cp311-win_amd64.whl", hash = "sha256:8636cd2fc5da0fb102a2504fa2c4bea3cbc149533b345d72cdf0e7a924decc45", size = 480534, upload-time = "2023-09-16T10:21:45.724Z" }, + { url = "https://files.pythonhosted.org/packages/94/0f/07a5e26fec7176658f6aecffc615900ff1d303baa2b67bc37fd98ce67c87/contourpy-1.1.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:560f1d68a33e89c62da5da4077ba98137a5e4d3a271b29f2f195d0fba2adcb6a", size = 249799, upload-time = "2023-09-16T10:21:48.797Z" }, + { url = "https://files.pythonhosted.org/packages/32/0b/d7baca3f60d3b3a77c9ba1307c7792befd3c1c775a26c649dca1bfa9b6ba/contourpy-1.1.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:24216552104ae8f3b34120ef84825400b16eb6133af2e27a190fdc13529f023e", size = 232739, upload-time = "2023-09-16T10:21:51.854Z" }, + { url = "https://files.pythonhosted.org/packages/6d/62/a385b4d4b5718e3a933de5791528f45f1f5b364d3c79172ad0309c832041/contourpy-1.1.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:56de98a2fb23025882a18b60c7f0ea2d2d70bbbcfcf878f9067234b1c4818442", size = 282171, upload-time = "2023-09-16T10:21:55.794Z" }, + { url = "https://files.pythonhosted.org/packages/91/21/8c6819747fea53557f3963ca936035b3e8bed87d591f5278ad62516a059d/contourpy-1.1.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:07d6f11dfaf80a84c97f1a5ba50d129d9303c5b4206f776e94037332e298dda8", size = 321182, upload-time = "2023-09-16T10:21:59.576Z" }, + { url = "https://files.pythonhosted.org/packages/22/29/d75da9002f9df09c755b12cf0357eb91b081c858e604f4e92b4b8bfc3c15/contourpy-1.1.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f1eaac5257a8f8a047248d60e8f9315c6cff58f7803971170d952555ef6344a7", size = 295869, upload-time = "2023-09-16T10:22:03.248Z" }, + { url = "https://files.pythonhosted.org/packages/a7/47/4e7e66159f881c131e3b97d1cc5c0ea72be62bdd292c7f63fd13937d07f4/contourpy-1.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:19557fa407e70f20bfaba7d55b4d97b14f9480856c4fb65812e8a05fe1c6f9bf", size = 298756, upload-time = "2023-09-16T10:22:06.663Z" }, + { url = "https://files.pythonhosted.org/packages/d3/bb/bffc99bc3172942b5eda8027ca0cb80ddd336fcdd634d68adce957d37231/contourpy-1.1.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:081f3c0880712e40effc5f4c3b08feca6d064cb8cfbb372ca548105b86fd6c3d", size = 818441, upload-time = "2023-09-16T10:22:13.805Z" }, + { url = "https://files.pythonhosted.org/packages/da/1b/904baf0aaaf6c6e2247801dcd1ff0d7bf84352839927d356b28ae804cbb0/contourpy-1.1.1-cp312-cp312-win32.whl", hash = "sha256:059c3d2a94b930f4dafe8105bcdc1b21de99b30b51b5bce74c753686de858cb6", size = 410294, upload-time = "2023-09-16T10:22:18.055Z" }, + { url = "https://files.pythonhosted.org/packages/75/d4/c3b7a9a0d1f99b528e5a46266b0b9f13aad5a0dd1156d071418df314c427/contourpy-1.1.1-cp312-cp312-win_amd64.whl", hash = "sha256:f44d78b61740e4e8c71db1cf1fd56d9050a4747681c59ec1094750a658ceb970", size = 486678, upload-time = "2023-09-16T10:22:23.249Z" }, + { url = "https://files.pythonhosted.org/packages/02/7e/ffaba1bf3719088be3ad6983a5e85e1fc9edccd7b406b98e433436ecef74/contourpy-1.1.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:70e5a10f8093d228bb2b552beeb318b8928b8a94763ef03b858ef3612b29395d", size = 247023, upload-time = "2023-09-16T10:22:26.954Z" }, + { url = "https://files.pythonhosted.org/packages/a6/82/29f5ff4ae074c3230e266bc9efef449ebde43721a727b989dd8ef8f97d73/contourpy-1.1.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:8394e652925a18ef0091115e3cc191fef350ab6dc3cc417f06da66bf98071ae9", size = 232380, upload-time = "2023-09-16T10:22:30.423Z" }, + { url = "https://files.pythonhosted.org/packages/9b/cb/08f884c4c2efd433a38876b1b8069bfecef3f2d21ff0ce635d455962f70f/contourpy-1.1.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c5bd5680f844c3ff0008523a71949a3ff5e4953eb7701b28760805bc9bcff217", size = 285830, upload-time = "2023-09-16T10:22:33.787Z" }, + { url = "https://files.pythonhosted.org/packages/8e/57/cd4d4c99d999a25e9d518f628b4793e64b1ecb8ad3147f8469d8d4a80678/contourpy-1.1.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:66544f853bfa85c0d07a68f6c648b2ec81dafd30f272565c37ab47a33b220684", size = 322038, upload-time = "2023-09-16T10:22:37.627Z" }, + { url = "https://files.pythonhosted.org/packages/32/b6/c57ed305a6f86731107fc183e97c7e6a6005d145f5c5228a44718082ad12/contourpy-1.1.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e0c02b75acfea5cab07585d25069207e478d12309557f90a61b5a3b4f77f46ce", size = 295797, upload-time = "2023-09-16T10:22:41.952Z" }, + { url = "https://files.pythonhosted.org/packages/8e/71/7f20855592cc929bc206810432b991ec4c702dc26b0567b132e52c85536f/contourpy-1.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:41339b24471c58dc1499e56783fedc1afa4bb018bcd035cfb0ee2ad2a7501ef8", size = 301124, upload-time = "2023-09-16T10:22:45.993Z" }, + { url = "https://files.pythonhosted.org/packages/86/6d/52c2fc80f433e7cdc8624d82e1422ad83ad461463cf16a1953bbc7d10eb1/contourpy-1.1.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:f29fb0b3f1217dfe9362ec55440d0743fe868497359f2cf93293f4b2701b8251", size = 819787, upload-time = "2023-09-16T10:22:53.511Z" }, + { url = "https://files.pythonhosted.org/packages/d0/b0/f8d4548e89f929d6c5ca329df9afad6190af60079ec77d8c31eb48cf6f82/contourpy-1.1.1-cp38-cp38-win32.whl", hash = "sha256:f9dc7f933975367251c1b34da882c4f0e0b2e24bb35dc906d2f598a40b72bfc7", size = 400031, upload-time = "2023-09-16T10:22:57.78Z" }, + { url = "https://files.pythonhosted.org/packages/96/1b/b05cd42c8d21767a0488b883b38658fb9a45f86c293b7b42521a8113dc5d/contourpy-1.1.1-cp38-cp38-win_amd64.whl", hash = "sha256:498e53573e8b94b1caeb9e62d7c2d053c263ebb6aa259c81050766beb50ff8d9", size = 477949, upload-time = "2023-09-16T10:23:02.587Z" }, + { url = "https://files.pythonhosted.org/packages/16/d9/8a15ff67fc27c65939e454512955e1b240ec75cd201d82e115b3b63ef76d/contourpy-1.1.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:ba42e3810999a0ddd0439e6e5dbf6d034055cdc72b7c5c839f37a7c274cb4eba", size = 247396, upload-time = "2023-09-16T10:23:06.429Z" }, + { url = "https://files.pythonhosted.org/packages/09/fe/086e6847ee53da10ddf0b6c5e5f877ab43e68e355d2f4c85f67561ee8a57/contourpy-1.1.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:6c06e4c6e234fcc65435223c7b2a90f286b7f1b2733058bdf1345d218cc59e34", size = 232598, upload-time = "2023-09-16T10:23:11.009Z" }, + { url = "https://files.pythonhosted.org/packages/a3/9c/662925239e1185c6cf1da8c334e4c61bddcfa8e528f4b51083b613003170/contourpy-1.1.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ca6fab080484e419528e98624fb5c4282148b847e3602dc8dbe0cb0669469887", size = 286436, upload-time = "2023-09-16T10:23:14.624Z" }, + { url = "https://files.pythonhosted.org/packages/d3/7e/417cdf65da7140981079eda6a81ecd593ae0239bf8c738f2e2b3f6df8920/contourpy-1.1.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:93df44ab351119d14cd1e6b52a5063d3336f0754b72736cc63db59307dabb718", size = 322629, upload-time = "2023-09-16T10:23:18.203Z" }, + { url = "https://files.pythonhosted.org/packages/a8/22/ffd88aef74cc045698c5e5c400e8b7cd62311199c109245ac7827290df2c/contourpy-1.1.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:eafbef886566dc1047d7b3d4b14db0d5b7deb99638d8e1be4e23a7c7ac59ff0f", size = 297117, upload-time = "2023-09-16T10:23:21.586Z" }, + { url = "https://files.pythonhosted.org/packages/2b/c0/24c34c41a180f875419b536125799c61e2330b997d77a5a818a3bc3e08cd/contourpy-1.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:efe0fab26d598e1ec07d72cf03eaeeba8e42b4ecf6b9ccb5a356fde60ff08b85", size = 301855, upload-time = "2023-09-16T10:23:25.584Z" }, + { url = "https://files.pythonhosted.org/packages/bf/ec/f9877f6378a580cd683bd76c8a781dcd972e82965e0da951a739d3364677/contourpy-1.1.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:f08e469821a5e4751c97fcd34bcb586bc243c39c2e39321822060ba902eac49e", size = 820597, upload-time = "2023-09-16T10:23:33.133Z" }, + { url = "https://files.pythonhosted.org/packages/e1/3a/c41f4bc7122d3a06388acae1bed6f50a665c1031863ca42bd701094dcb1f/contourpy-1.1.1-cp39-cp39-win32.whl", hash = "sha256:bfc8a5e9238232a45ebc5cb3bfee71f1167064c8d382cadd6076f0d51cff1da0", size = 400031, upload-time = "2023-09-16T10:23:37.546Z" }, + { url = "https://files.pythonhosted.org/packages/87/2b/9b49451f7412cc1a79198e94a771a4e52d65c479aae610b1161c0290ef2c/contourpy-1.1.1-cp39-cp39-win_amd64.whl", hash = "sha256:c84fdf3da00c2827d634de4fcf17e3e067490c4aea82833625c4c8e6cdea0887", size = 435965, upload-time = "2023-09-16T10:23:42.512Z" }, + { url = "https://files.pythonhosted.org/packages/e6/3c/fc36884b6793e2066a6ff25c86e21b8bd62553456b07e964c260bcf22711/contourpy-1.1.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:229a25f68046c5cf8067d6d6351c8b99e40da11b04d8416bf8d2b1d75922521e", size = 246493, upload-time = "2023-09-16T10:23:45.721Z" }, + { url = "https://files.pythonhosted.org/packages/3d/85/f4c5b09ce79828ed4553a8ae2ebdf937794f57b45848b1f5c95d9744ecc2/contourpy-1.1.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a10dab5ea1bd4401c9483450b5b0ba5416be799bbd50fc7a6cc5e2a15e03e8a3", size = 289240, upload-time = "2023-09-16T10:23:49.207Z" }, + { url = "https://files.pythonhosted.org/packages/18/d3/9d7c0a372baf5130c1417a4b8275079d5379c11355436cb9fc78af7d7559/contourpy-1.1.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:4f9147051cb8fdb29a51dc2482d792b3b23e50f8f57e3720ca2e3d438b7adf23", size = 476043, upload-time = "2023-09-16T10:23:54.495Z" }, + { url = "https://files.pythonhosted.org/packages/e7/12/643242c3d9b031ca19f9a440f63e568dd883a04711056ca5d607f9bda888/contourpy-1.1.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:a75cc163a5f4531a256f2c523bd80db509a49fc23721b36dd1ef2f60ff41c3cb", size = 246247, upload-time = "2023-09-16T10:23:58.204Z" }, + { url = "https://files.pythonhosted.org/packages/e1/37/95716fe235bf441422059e4afcd4b9b7c5821851c2aee992a06d1e9f831a/contourpy-1.1.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3b53d5769aa1f2d4ea407c65f2d1d08002952fac1d9e9d307aa2e1023554a163", size = 289029, upload-time = "2023-09-16T10:24:02.085Z" }, + { url = "https://files.pythonhosted.org/packages/e5/fd/14852c4a688031e0d8a20d9a1b60078d45507186ef17042093835be2f01a/contourpy-1.1.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:11b836b7dbfb74e049c302bbf74b4b8f6cb9d0b6ca1bf86cfa8ba144aedadd9c", size = 476043, upload-time = "2023-09-16T10:24:07.292Z" }, +] + +[[package]] +name = "contourpy" +version = "1.3.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.9.*'", +] +dependencies = [ + { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/f5/f6/31a8f28b4a2a4fa0e01085e542f3081ab0588eff8e589d39d775172c9792/contourpy-1.3.0.tar.gz", hash = "sha256:7ffa0db17717a8ffb127efd0c95a4362d996b892c2904db72428d5b52e1938a4", size = 13464370, upload-time = "2024-08-27T21:00:03.328Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6c/e0/be8dcc796cfdd96708933e0e2da99ba4bb8f9b2caa9d560a50f3f09a65f3/contourpy-1.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:880ea32e5c774634f9fcd46504bf9f080a41ad855f4fef54f5380f5133d343c7", size = 265366, upload-time = "2024-08-27T20:50:09.947Z" }, + { url = "https://files.pythonhosted.org/packages/50/d6/c953b400219443535d412fcbbc42e7a5e823291236bc0bb88936e3cc9317/contourpy-1.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:76c905ef940a4474a6289c71d53122a4f77766eef23c03cd57016ce19d0f7b42", size = 249226, upload-time = "2024-08-27T20:50:16.1Z" }, + { url = "https://files.pythonhosted.org/packages/6f/b4/6fffdf213ffccc28483c524b9dad46bb78332851133b36ad354b856ddc7c/contourpy-1.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:92f8557cbb07415a4d6fa191f20fd9d2d9eb9c0b61d1b2f52a8926e43c6e9af7", size = 308460, upload-time = "2024-08-27T20:50:22.536Z" }, + { url = "https://files.pythonhosted.org/packages/cf/6c/118fc917b4050f0afe07179a6dcbe4f3f4ec69b94f36c9e128c4af480fb8/contourpy-1.3.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:36f965570cff02b874773c49bfe85562b47030805d7d8360748f3eca570f4cab", size = 347623, upload-time = "2024-08-27T20:50:28.806Z" }, + { url = "https://files.pythonhosted.org/packages/f9/a4/30ff110a81bfe3abf7b9673284d21ddce8cc1278f6f77393c91199da4c90/contourpy-1.3.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cacd81e2d4b6f89c9f8a5b69b86490152ff39afc58a95af002a398273e5ce589", size = 317761, upload-time = "2024-08-27T20:50:35.126Z" }, + { url = "https://files.pythonhosted.org/packages/99/e6/d11966962b1aa515f5586d3907ad019f4b812c04e4546cc19ebf62b5178e/contourpy-1.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:69375194457ad0fad3a839b9e29aa0b0ed53bb54db1bfb6c3ae43d111c31ce41", size = 322015, upload-time = "2024-08-27T20:50:40.318Z" }, + { url = "https://files.pythonhosted.org/packages/4d/e3/182383743751d22b7b59c3c753277b6aee3637049197624f333dac5b4c80/contourpy-1.3.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:7a52040312b1a858b5e31ef28c2e865376a386c60c0e248370bbea2d3f3b760d", size = 1262672, upload-time = "2024-08-27T20:50:55.643Z" }, + { url = "https://files.pythonhosted.org/packages/78/53/974400c815b2e605f252c8fb9297e2204347d1755a5374354ee77b1ea259/contourpy-1.3.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:3faeb2998e4fcb256542e8a926d08da08977f7f5e62cf733f3c211c2a5586223", size = 1321688, upload-time = "2024-08-27T20:51:11.293Z" }, + { url = "https://files.pythonhosted.org/packages/52/29/99f849faed5593b2926a68a31882af98afbeac39c7fdf7de491d9c85ec6a/contourpy-1.3.0-cp310-cp310-win32.whl", hash = "sha256:36e0cff201bcb17a0a8ecc7f454fe078437fa6bda730e695a92f2d9932bd507f", size = 171145, upload-time = "2024-08-27T20:51:15.2Z" }, + { url = "https://files.pythonhosted.org/packages/a9/97/3f89bba79ff6ff2b07a3cbc40aa693c360d5efa90d66e914f0ff03b95ec7/contourpy-1.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:87ddffef1dbe5e669b5c2440b643d3fdd8622a348fe1983fad7a0f0ccb1cd67b", size = 216019, upload-time = "2024-08-27T20:51:19.365Z" }, + { url = "https://files.pythonhosted.org/packages/b3/1f/9375917786cb39270b0ee6634536c0e22abf225825602688990d8f5c6c19/contourpy-1.3.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0fa4c02abe6c446ba70d96ece336e621efa4aecae43eaa9b030ae5fb92b309ad", size = 266356, upload-time = "2024-08-27T20:51:24.146Z" }, + { url = "https://files.pythonhosted.org/packages/05/46/9256dd162ea52790c127cb58cfc3b9e3413a6e3478917d1f811d420772ec/contourpy-1.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:834e0cfe17ba12f79963861e0f908556b2cedd52e1f75e6578801febcc6a9f49", size = 250915, upload-time = "2024-08-27T20:51:28.683Z" }, + { url = "https://files.pythonhosted.org/packages/e1/5d/3056c167fa4486900dfbd7e26a2fdc2338dc58eee36d490a0ed3ddda5ded/contourpy-1.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dbc4c3217eee163fa3984fd1567632b48d6dfd29216da3ded3d7b844a8014a66", size = 310443, upload-time = "2024-08-27T20:51:33.675Z" }, + { url = "https://files.pythonhosted.org/packages/ca/c2/1a612e475492e07f11c8e267ea5ec1ce0d89971be496c195e27afa97e14a/contourpy-1.3.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4865cd1d419e0c7a7bf6de1777b185eebdc51470800a9f42b9e9decf17762081", size = 348548, upload-time = "2024-08-27T20:51:39.322Z" }, + { url = "https://files.pythonhosted.org/packages/45/cf/2c2fc6bb5874158277b4faf136847f0689e1b1a1f640a36d76d52e78907c/contourpy-1.3.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:303c252947ab4b14c08afeb52375b26781ccd6a5ccd81abcdfc1fafd14cf93c1", size = 319118, upload-time = "2024-08-27T20:51:44.717Z" }, + { url = "https://files.pythonhosted.org/packages/03/33/003065374f38894cdf1040cef474ad0546368eea7e3a51d48b8a423961f8/contourpy-1.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:637f674226be46f6ba372fd29d9523dd977a291f66ab2a74fbeb5530bb3f445d", size = 323162, upload-time = "2024-08-27T20:51:49.683Z" }, + { url = "https://files.pythonhosted.org/packages/42/80/e637326e85e4105a802e42959f56cff2cd39a6b5ef68d5d9aee3ea5f0e4c/contourpy-1.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:76a896b2f195b57db25d6b44e7e03f221d32fe318d03ede41f8b4d9ba1bff53c", size = 1265396, upload-time = "2024-08-27T20:52:04.926Z" }, + { url = "https://files.pythonhosted.org/packages/7c/3b/8cbd6416ca1bbc0202b50f9c13b2e0b922b64be888f9d9ee88e6cfabfb51/contourpy-1.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:e1fd23e9d01591bab45546c089ae89d926917a66dceb3abcf01f6105d927e2cb", size = 1324297, upload-time = "2024-08-27T20:52:21.843Z" }, + { url = "https://files.pythonhosted.org/packages/4d/2c/021a7afaa52fe891f25535506cc861c30c3c4e5a1c1ce94215e04b293e72/contourpy-1.3.0-cp311-cp311-win32.whl", hash = "sha256:d402880b84df3bec6eab53cd0cf802cae6a2ef9537e70cf75e91618a3801c20c", size = 171808, upload-time = "2024-08-27T20:52:25.163Z" }, + { url = "https://files.pythonhosted.org/packages/8d/2f/804f02ff30a7fae21f98198828d0857439ec4c91a96e20cf2d6c49372966/contourpy-1.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:6cb6cc968059db9c62cb35fbf70248f40994dfcd7aa10444bbf8b3faeb7c2d67", size = 217181, upload-time = "2024-08-27T20:52:29.13Z" }, + { url = "https://files.pythonhosted.org/packages/c9/92/8e0bbfe6b70c0e2d3d81272b58c98ac69ff1a4329f18c73bd64824d8b12e/contourpy-1.3.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:570ef7cf892f0afbe5b2ee410c507ce12e15a5fa91017a0009f79f7d93a1268f", size = 267838, upload-time = "2024-08-27T20:52:33.911Z" }, + { url = "https://files.pythonhosted.org/packages/e3/04/33351c5d5108460a8ce6d512307690b023f0cfcad5899499f5c83b9d63b1/contourpy-1.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:da84c537cb8b97d153e9fb208c221c45605f73147bd4cadd23bdae915042aad6", size = 251549, upload-time = "2024-08-27T20:52:39.179Z" }, + { url = "https://files.pythonhosted.org/packages/51/3d/aa0fe6ae67e3ef9f178389e4caaaa68daf2f9024092aa3c6032e3d174670/contourpy-1.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0be4d8425bfa755e0fd76ee1e019636ccc7c29f77a7c86b4328a9eb6a26d0639", size = 303177, upload-time = "2024-08-27T20:52:44.789Z" }, + { url = "https://files.pythonhosted.org/packages/56/c3/c85a7e3e0cab635575d3b657f9535443a6f5d20fac1a1911eaa4bbe1aceb/contourpy-1.3.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9c0da700bf58f6e0b65312d0a5e695179a71d0163957fa381bb3c1f72972537c", size = 341735, upload-time = "2024-08-27T20:52:51.05Z" }, + { url = "https://files.pythonhosted.org/packages/dd/8d/20f7a211a7be966a53f474bc90b1a8202e9844b3f1ef85f3ae45a77151ee/contourpy-1.3.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:eb8b141bb00fa977d9122636b16aa67d37fd40a3d8b52dd837e536d64b9a4d06", size = 314679, upload-time = "2024-08-27T20:52:58.473Z" }, + { url = "https://files.pythonhosted.org/packages/6e/be/524e377567defac0e21a46e2a529652d165fed130a0d8a863219303cee18/contourpy-1.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3634b5385c6716c258d0419c46d05c8aa7dc8cb70326c9a4fb66b69ad2b52e09", size = 320549, upload-time = "2024-08-27T20:53:06.593Z" }, + { url = "https://files.pythonhosted.org/packages/0f/96/fdb2552a172942d888915f3a6663812e9bc3d359d53dafd4289a0fb462f0/contourpy-1.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:0dce35502151b6bd35027ac39ba6e5a44be13a68f55735c3612c568cac3805fd", size = 1263068, upload-time = "2024-08-27T20:53:23.442Z" }, + { url = "https://files.pythonhosted.org/packages/2a/25/632eab595e3140adfa92f1322bf8915f68c932bac468e89eae9974cf1c00/contourpy-1.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:aea348f053c645100612b333adc5983d87be69acdc6d77d3169c090d3b01dc35", size = 1322833, upload-time = "2024-08-27T20:53:39.243Z" }, + { url = "https://files.pythonhosted.org/packages/73/e3/69738782e315a1d26d29d71a550dbbe3eb6c653b028b150f70c1a5f4f229/contourpy-1.3.0-cp312-cp312-win32.whl", hash = "sha256:90f73a5116ad1ba7174341ef3ea5c3150ddf20b024b98fb0c3b29034752c8aeb", size = 172681, upload-time = "2024-08-27T20:53:43.05Z" }, + { url = "https://files.pythonhosted.org/packages/0c/89/9830ba00d88e43d15e53d64931e66b8792b46eb25e2050a88fec4a0df3d5/contourpy-1.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:b11b39aea6be6764f84360fce6c82211a9db32a7c7de8fa6dd5397cf1d079c3b", size = 218283, upload-time = "2024-08-27T20:53:47.232Z" }, + { url = "https://files.pythonhosted.org/packages/53/a1/d20415febfb2267af2d7f06338e82171824d08614084714fb2c1dac9901f/contourpy-1.3.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:3e1c7fa44aaae40a2247e2e8e0627f4bea3dd257014764aa644f319a5f8600e3", size = 267879, upload-time = "2024-08-27T20:53:51.597Z" }, + { url = "https://files.pythonhosted.org/packages/aa/45/5a28a3570ff6218d8bdfc291a272a20d2648104815f01f0177d103d985e1/contourpy-1.3.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:364174c2a76057feef647c802652f00953b575723062560498dc7930fc9b1cb7", size = 251573, upload-time = "2024-08-27T20:53:55.659Z" }, + { url = "https://files.pythonhosted.org/packages/39/1c/d3f51540108e3affa84f095c8b04f0aa833bb797bc8baa218a952a98117d/contourpy-1.3.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:32b238b3b3b649e09ce9aaf51f0c261d38644bdfa35cbaf7b263457850957a84", size = 303184, upload-time = "2024-08-27T20:54:00.225Z" }, + { url = "https://files.pythonhosted.org/packages/00/56/1348a44fb6c3a558c1a3a0cd23d329d604c99d81bf5a4b58c6b71aab328f/contourpy-1.3.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d51fca85f9f7ad0b65b4b9fe800406d0d77017d7270d31ec3fb1cc07358fdea0", size = 340262, upload-time = "2024-08-27T20:54:05.234Z" }, + { url = "https://files.pythonhosted.org/packages/2b/23/00d665ba67e1bb666152131da07e0f24c95c3632d7722caa97fb61470eca/contourpy-1.3.0-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:732896af21716b29ab3e988d4ce14bc5133733b85956316fb0c56355f398099b", size = 313806, upload-time = "2024-08-27T20:54:09.889Z" }, + { url = "https://files.pythonhosted.org/packages/5a/42/3cf40f7040bb8362aea19af9a5fb7b32ce420f645dd1590edcee2c657cd5/contourpy-1.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d73f659398a0904e125280836ae6f88ba9b178b2fed6884f3b1f95b989d2c8da", size = 319710, upload-time = "2024-08-27T20:54:14.536Z" }, + { url = "https://files.pythonhosted.org/packages/05/32/f3bfa3fc083b25e1a7ae09197f897476ee68e7386e10404bdf9aac7391f0/contourpy-1.3.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c6c7c2408b7048082932cf4e641fa3b8ca848259212f51c8c59c45aa7ac18f14", size = 1264107, upload-time = "2024-08-27T20:54:29.735Z" }, + { url = "https://files.pythonhosted.org/packages/1c/1e/1019d34473a736664f2439542b890b2dc4c6245f5c0d8cdfc0ccc2cab80c/contourpy-1.3.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f317576606de89da6b7e0861cf6061f6146ead3528acabff9236458a6ba467f8", size = 1322458, upload-time = "2024-08-27T20:54:45.507Z" }, + { url = "https://files.pythonhosted.org/packages/22/85/4f8bfd83972cf8909a4d36d16b177f7b8bdd942178ea4bf877d4a380a91c/contourpy-1.3.0-cp313-cp313-win32.whl", hash = "sha256:31cd3a85dbdf1fc002280c65caa7e2b5f65e4a973fcdf70dd2fdcb9868069294", size = 172643, upload-time = "2024-08-27T20:55:52.754Z" }, + { url = "https://files.pythonhosted.org/packages/cc/4a/fb3c83c1baba64ba90443626c228ca14f19a87c51975d3b1de308dd2cf08/contourpy-1.3.0-cp313-cp313-win_amd64.whl", hash = "sha256:4553c421929ec95fb07b3aaca0fae668b2eb5a5203d1217ca7c34c063c53d087", size = 218301, upload-time = "2024-08-27T20:55:56.509Z" }, + { url = "https://files.pythonhosted.org/packages/76/65/702f4064f397821fea0cb493f7d3bc95a5d703e20954dce7d6d39bacf378/contourpy-1.3.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:345af746d7766821d05d72cb8f3845dfd08dd137101a2cb9b24de277d716def8", size = 278972, upload-time = "2024-08-27T20:54:50.347Z" }, + { url = "https://files.pythonhosted.org/packages/80/85/21f5bba56dba75c10a45ec00ad3b8190dbac7fd9a8a8c46c6116c933e9cf/contourpy-1.3.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:3bb3808858a9dc68f6f03d319acd5f1b8a337e6cdda197f02f4b8ff67ad2057b", size = 263375, upload-time = "2024-08-27T20:54:54.909Z" }, + { url = "https://files.pythonhosted.org/packages/0a/64/084c86ab71d43149f91ab3a4054ccf18565f0a8af36abfa92b1467813ed6/contourpy-1.3.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:420d39daa61aab1221567b42eecb01112908b2cab7f1b4106a52caaec8d36973", size = 307188, upload-time = "2024-08-27T20:55:00.184Z" }, + { url = "https://files.pythonhosted.org/packages/3d/ff/d61a4c288dc42da0084b8d9dc2aa219a850767165d7d9a9c364ff530b509/contourpy-1.3.0-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4d63ee447261e963af02642ffcb864e5a2ee4cbfd78080657a9880b8b1868e18", size = 345644, upload-time = "2024-08-27T20:55:05.673Z" }, + { url = "https://files.pythonhosted.org/packages/ca/aa/00d2313d35ec03f188e8f0786c2fc61f589306e02fdc158233697546fd58/contourpy-1.3.0-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:167d6c890815e1dac9536dca00828b445d5d0df4d6a8c6adb4a7ec3166812fa8", size = 317141, upload-time = "2024-08-27T20:55:11.047Z" }, + { url = "https://files.pythonhosted.org/packages/8d/6a/b5242c8cb32d87f6abf4f5e3044ca397cb1a76712e3fa2424772e3ff495f/contourpy-1.3.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:710a26b3dc80c0e4febf04555de66f5fd17e9cf7170a7b08000601a10570bda6", size = 323469, upload-time = "2024-08-27T20:55:15.914Z" }, + { url = "https://files.pythonhosted.org/packages/6f/a6/73e929d43028a9079aca4bde107494864d54f0d72d9db508a51ff0878593/contourpy-1.3.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:75ee7cb1a14c617f34a51d11fa7524173e56551646828353c4af859c56b766e2", size = 1260894, upload-time = "2024-08-27T20:55:31.553Z" }, + { url = "https://files.pythonhosted.org/packages/2b/1e/1e726ba66eddf21c940821df8cf1a7d15cb165f0682d62161eaa5e93dae1/contourpy-1.3.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:33c92cdae89ec5135d036e7218e69b0bb2851206077251f04a6c4e0e21f03927", size = 1314829, upload-time = "2024-08-27T20:55:47.837Z" }, + { url = "https://files.pythonhosted.org/packages/b3/e3/b9f72758adb6ef7397327ceb8b9c39c75711affb220e4f53c745ea1d5a9a/contourpy-1.3.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a11077e395f67ffc2c44ec2418cfebed032cd6da3022a94fc227b6faf8e2acb8", size = 265518, upload-time = "2024-08-27T20:56:01.333Z" }, + { url = "https://files.pythonhosted.org/packages/ec/22/19f5b948367ab5260fb41d842c7a78dae645603881ea6bc39738bcfcabf6/contourpy-1.3.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:e8134301d7e204c88ed7ab50028ba06c683000040ede1d617298611f9dc6240c", size = 249350, upload-time = "2024-08-27T20:56:05.432Z" }, + { url = "https://files.pythonhosted.org/packages/26/76/0c7d43263dd00ae21a91a24381b7e813d286a3294d95d179ef3a7b9fb1d7/contourpy-1.3.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e12968fdfd5bb45ffdf6192a590bd8ddd3ba9e58360b29683c6bb71a7b41edca", size = 309167, upload-time = "2024-08-27T20:56:10.034Z" }, + { url = "https://files.pythonhosted.org/packages/96/3b/cadff6773e89f2a5a492c1a8068e21d3fccaf1a1c1df7d65e7c8e3ef60ba/contourpy-1.3.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fd2a0fc506eccaaa7595b7e1418951f213cf8255be2600f1ea1b61e46a60c55f", size = 348279, upload-time = "2024-08-27T20:56:15.41Z" }, + { url = "https://files.pythonhosted.org/packages/e1/86/158cc43aa549d2081a955ab11c6bdccc7a22caacc2af93186d26f5f48746/contourpy-1.3.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4cfb5c62ce023dfc410d6059c936dcf96442ba40814aefbfa575425a3a7f19dc", size = 318519, upload-time = "2024-08-27T20:56:21.813Z" }, + { url = "https://files.pythonhosted.org/packages/05/11/57335544a3027e9b96a05948c32e566328e3a2f84b7b99a325b7a06d2b06/contourpy-1.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:68a32389b06b82c2fdd68276148d7b9275b5f5cf13e5417e4252f6d1a34f72a2", size = 321922, upload-time = "2024-08-27T20:56:26.983Z" }, + { url = "https://files.pythonhosted.org/packages/0b/e3/02114f96543f4a1b694333b92a6dcd4f8eebbefcc3a5f3bbb1316634178f/contourpy-1.3.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:94e848a6b83da10898cbf1311a815f770acc9b6a3f2d646f330d57eb4e87592e", size = 1258017, upload-time = "2024-08-27T20:56:42.246Z" }, + { url = "https://files.pythonhosted.org/packages/f3/3b/bfe4c81c6d5881c1c643dde6620be0b42bf8aab155976dd644595cfab95c/contourpy-1.3.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:d78ab28a03c854a873787a0a42254a0ccb3cb133c672f645c9f9c8f3ae9d0800", size = 1316773, upload-time = "2024-08-27T20:56:58.58Z" }, + { url = "https://files.pythonhosted.org/packages/f1/17/c52d2970784383cafb0bd918b6fb036d98d96bbf0bc1befb5d1e31a07a70/contourpy-1.3.0-cp39-cp39-win32.whl", hash = "sha256:81cb5ed4952aae6014bc9d0421dec7c5835c9c8c31cdf51910b708f548cf58e5", size = 171353, upload-time = "2024-08-27T20:57:02.718Z" }, + { url = "https://files.pythonhosted.org/packages/53/23/db9f69676308e094d3c45f20cc52e12d10d64f027541c995d89c11ad5c75/contourpy-1.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:14e262f67bd7e6eb6880bc564dcda30b15e351a594657e55b7eec94b6ef72843", size = 211817, upload-time = "2024-08-27T20:57:06.328Z" }, + { url = "https://files.pythonhosted.org/packages/d1/09/60e486dc2b64c94ed33e58dcfb6f808192c03dfc5574c016218b9b7680dc/contourpy-1.3.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:fe41b41505a5a33aeaed2a613dccaeaa74e0e3ead6dd6fd3a118fb471644fd6c", size = 261886, upload-time = "2024-08-27T20:57:10.863Z" }, + { url = "https://files.pythonhosted.org/packages/19/20/b57f9f7174fcd439a7789fb47d764974ab646fa34d1790551de386457a8e/contourpy-1.3.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eca7e17a65f72a5133bdbec9ecf22401c62bcf4821361ef7811faee695799779", size = 311008, upload-time = "2024-08-27T20:57:15.588Z" }, + { url = "https://files.pythonhosted.org/packages/74/fc/5040d42623a1845d4f17a418e590fd7a79ae8cb2bad2b2f83de63c3bdca4/contourpy-1.3.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:1ec4dc6bf570f5b22ed0d7efba0dfa9c5b9e0431aeea7581aa217542d9e809a4", size = 215690, upload-time = "2024-08-27T20:57:19.321Z" }, + { url = "https://files.pythonhosted.org/packages/2b/24/dc3dcd77ac7460ab7e9d2b01a618cb31406902e50e605a8d6091f0a8f7cc/contourpy-1.3.0-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:00ccd0dbaad6d804ab259820fa7cb0b8036bda0686ef844d24125d8287178ce0", size = 261894, upload-time = "2024-08-27T20:57:23.873Z" }, + { url = "https://files.pythonhosted.org/packages/b1/db/531642a01cfec39d1682e46b5457b07cf805e3c3c584ec27e2a6223f8f6c/contourpy-1.3.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8ca947601224119117f7c19c9cdf6b3ab54c5726ef1d906aa4a69dfb6dd58102", size = 311099, upload-time = "2024-08-27T20:57:28.58Z" }, + { url = "https://files.pythonhosted.org/packages/38/1e/94bda024d629f254143a134eead69e21c836429a2a6ce82209a00ddcb79a/contourpy-1.3.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:c6ec93afeb848a0845a18989da3beca3eec2c0f852322efe21af1931147d12cb", size = 215838, upload-time = "2024-08-27T20:57:32.913Z" }, +] + +[[package]] +name = "contourpy" +version = "1.3.2" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.10.*'", +] +dependencies = [ + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130, upload-time = "2025-04-15T17:47:53.79Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/12/a3/da4153ec8fe25d263aa48c1a4cbde7f49b59af86f0b6f7862788c60da737/contourpy-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ba38e3f9f330af820c4b27ceb4b9c7feee5fe0493ea53a8720f4792667465934", size = 268551, upload-time = "2025-04-15T17:34:46.581Z" }, + { url = "https://files.pythonhosted.org/packages/2f/6c/330de89ae1087eb622bfca0177d32a7ece50c3ef07b28002de4757d9d875/contourpy-1.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dc41ba0714aa2968d1f8674ec97504a8f7e334f48eeacebcaa6256213acb0989", size = 253399, upload-time = "2025-04-15T17:34:51.427Z" }, + { url = "https://files.pythonhosted.org/packages/c1/bd/20c6726b1b7f81a8bee5271bed5c165f0a8e1f572578a9d27e2ccb763cb2/contourpy-1.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9be002b31c558d1ddf1b9b415b162c603405414bacd6932d031c5b5a8b757f0d", size = 312061, upload-time = "2025-04-15T17:34:55.961Z" }, + { url = "https://files.pythonhosted.org/packages/22/fc/a9665c88f8a2473f823cf1ec601de9e5375050f1958cbb356cdf06ef1ab6/contourpy-1.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8d2e74acbcba3bfdb6d9d8384cdc4f9260cae86ed9beee8bd5f54fee49a430b9", size = 351956, upload-time = "2025-04-15T17:35:00.992Z" }, + { url = "https://files.pythonhosted.org/packages/25/eb/9f0a0238f305ad8fb7ef42481020d6e20cf15e46be99a1fcf939546a177e/contourpy-1.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e259bced5549ac64410162adc973c5e2fb77f04df4a439d00b478e57a0e65512", size = 320872, upload-time = "2025-04-15T17:35:06.177Z" }, + { url = "https://files.pythonhosted.org/packages/32/5c/1ee32d1c7956923202f00cf8d2a14a62ed7517bdc0ee1e55301227fc273c/contourpy-1.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ad687a04bc802cbe8b9c399c07162a3c35e227e2daccf1668eb1f278cb698631", size = 325027, upload-time = "2025-04-15T17:35:11.244Z" }, + { url = "https://files.pythonhosted.org/packages/83/bf/9baed89785ba743ef329c2b07fd0611d12bfecbedbdd3eeecf929d8d3b52/contourpy-1.3.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:cdd22595308f53ef2f891040ab2b93d79192513ffccbd7fe19be7aa773a5e09f", size = 1306641, upload-time = "2025-04-15T17:35:26.701Z" }, + { url = "https://files.pythonhosted.org/packages/d4/cc/74e5e83d1e35de2d28bd97033426b450bc4fd96e092a1f7a63dc7369b55d/contourpy-1.3.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:b4f54d6a2defe9f257327b0f243612dd051cc43825587520b1bf74a31e2f6ef2", size = 1374075, upload-time = "2025-04-15T17:35:43.204Z" }, + { url = "https://files.pythonhosted.org/packages/0c/42/17f3b798fd5e033b46a16f8d9fcb39f1aba051307f5ebf441bad1ecf78f8/contourpy-1.3.2-cp310-cp310-win32.whl", hash = "sha256:f939a054192ddc596e031e50bb13b657ce318cf13d264f095ce9db7dc6ae81c0", size = 177534, upload-time = "2025-04-15T17:35:46.554Z" }, + { url = "https://files.pythonhosted.org/packages/54/ec/5162b8582f2c994721018d0c9ece9dc6ff769d298a8ac6b6a652c307e7df/contourpy-1.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:c440093bbc8fc21c637c03bafcbef95ccd963bc6e0514ad887932c18ca2a759a", size = 221188, upload-time = "2025-04-15T17:35:50.064Z" }, + { url = "https://files.pythonhosted.org/packages/b3/b9/ede788a0b56fc5b071639d06c33cb893f68b1178938f3425debebe2dab78/contourpy-1.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6a37a2fb93d4df3fc4c0e363ea4d16f83195fc09c891bc8ce072b9d084853445", size = 269636, upload-time = "2025-04-15T17:35:54.473Z" }, + { url = "https://files.pythonhosted.org/packages/e6/75/3469f011d64b8bbfa04f709bfc23e1dd71be54d05b1b083be9f5b22750d1/contourpy-1.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b7cd50c38f500bbcc9b6a46643a40e0913673f869315d8e70de0438817cb7773", size = 254636, upload-time = "2025-04-15T17:35:58.283Z" }, + { url = "https://files.pythonhosted.org/packages/8d/2f/95adb8dae08ce0ebca4fd8e7ad653159565d9739128b2d5977806656fcd2/contourpy-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d6658ccc7251a4433eebd89ed2672c2ed96fba367fd25ca9512aa92a4b46c4f1", size = 313053, upload-time = "2025-04-15T17:36:03.235Z" }, + { url = "https://files.pythonhosted.org/packages/c3/a6/8ccf97a50f31adfa36917707fe39c9a0cbc24b3bbb58185577f119736cc9/contourpy-1.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:70771a461aaeb335df14deb6c97439973d253ae70660ca085eec25241137ef43", size = 352985, upload-time = "2025-04-15T17:36:08.275Z" }, + { url = "https://files.pythonhosted.org/packages/1d/b6/7925ab9b77386143f39d9c3243fdd101621b4532eb126743201160ffa7e6/contourpy-1.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65a887a6e8c4cd0897507d814b14c54a8c2e2aa4ac9f7686292f9769fcf9a6ab", size = 323750, upload-time = "2025-04-15T17:36:13.29Z" }, + { url = "https://files.pythonhosted.org/packages/c2/f3/20c5d1ef4f4748e52d60771b8560cf00b69d5c6368b5c2e9311bcfa2a08b/contourpy-1.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3859783aefa2b8355697f16642695a5b9792e7a46ab86da1118a4a23a51a33d7", size = 326246, upload-time = "2025-04-15T17:36:18.329Z" }, + { url = "https://files.pythonhosted.org/packages/8c/e5/9dae809e7e0b2d9d70c52b3d24cba134dd3dad979eb3e5e71f5df22ed1f5/contourpy-1.3.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:eab0f6db315fa4d70f1d8ab514e527f0366ec021ff853d7ed6a2d33605cf4b83", size = 1308728, upload-time = "2025-04-15T17:36:33.878Z" }, + { url = "https://files.pythonhosted.org/packages/e2/4a/0058ba34aeea35c0b442ae61a4f4d4ca84d6df8f91309bc2d43bb8dd248f/contourpy-1.3.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:d91a3ccc7fea94ca0acab82ceb77f396d50a1f67412efe4c526f5d20264e6ecd", size = 1375762, upload-time = "2025-04-15T17:36:51.295Z" }, + { url = "https://files.pythonhosted.org/packages/09/33/7174bdfc8b7767ef2c08ed81244762d93d5c579336fc0b51ca57b33d1b80/contourpy-1.3.2-cp311-cp311-win32.whl", hash = "sha256:1c48188778d4d2f3d48e4643fb15d8608b1d01e4b4d6b0548d9b336c28fc9b6f", size = 178196, upload-time = "2025-04-15T17:36:55.002Z" }, + { url = "https://files.pythonhosted.org/packages/5e/fe/4029038b4e1c4485cef18e480b0e2cd2d755448bb071eb9977caac80b77b/contourpy-1.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:5ebac872ba09cb8f2131c46b8739a7ff71de28a24c869bcad554477eb089a878", size = 222017, upload-time = "2025-04-15T17:36:58.576Z" }, + { url = "https://files.pythonhosted.org/packages/34/f7/44785876384eff370c251d58fd65f6ad7f39adce4a093c934d4a67a7c6b6/contourpy-1.3.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4caf2bcd2969402bf77edc4cb6034c7dd7c0803213b3523f111eb7460a51b8d2", size = 271580, upload-time = "2025-04-15T17:37:03.105Z" }, + { url = "https://files.pythonhosted.org/packages/93/3b/0004767622a9826ea3d95f0e9d98cd8729015768075d61f9fea8eeca42a8/contourpy-1.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:82199cb78276249796419fe36b7386bd8d2cc3f28b3bc19fe2454fe2e26c4c15", size = 255530, upload-time = "2025-04-15T17:37:07.026Z" }, + { url = "https://files.pythonhosted.org/packages/e7/bb/7bd49e1f4fa805772d9fd130e0d375554ebc771ed7172f48dfcd4ca61549/contourpy-1.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:106fab697af11456fcba3e352ad50effe493a90f893fca6c2ca5c033820cea92", size = 307688, upload-time = "2025-04-15T17:37:11.481Z" }, + { url = "https://files.pythonhosted.org/packages/fc/97/e1d5dbbfa170725ef78357a9a0edc996b09ae4af170927ba8ce977e60a5f/contourpy-1.3.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d14f12932a8d620e307f715857107b1d1845cc44fdb5da2bc8e850f5ceba9f87", size = 347331, upload-time = "2025-04-15T17:37:18.212Z" }, + { url = "https://files.pythonhosted.org/packages/6f/66/e69e6e904f5ecf6901be3dd16e7e54d41b6ec6ae3405a535286d4418ffb4/contourpy-1.3.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:532fd26e715560721bb0d5fc7610fce279b3699b018600ab999d1be895b09415", size = 318963, upload-time = "2025-04-15T17:37:22.76Z" }, + { url = "https://files.pythonhosted.org/packages/a8/32/b8a1c8965e4f72482ff2d1ac2cd670ce0b542f203c8e1d34e7c3e6925da7/contourpy-1.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f26b383144cf2d2c29f01a1e8170f50dacf0eac02d64139dcd709a8ac4eb3cfe", size = 323681, upload-time = "2025-04-15T17:37:33.001Z" }, + { url = "https://files.pythonhosted.org/packages/30/c6/12a7e6811d08757c7162a541ca4c5c6a34c0f4e98ef2b338791093518e40/contourpy-1.3.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c49f73e61f1f774650a55d221803b101d966ca0c5a2d6d5e4320ec3997489441", size = 1308674, upload-time = "2025-04-15T17:37:48.64Z" }, + { url = "https://files.pythonhosted.org/packages/2a/8a/bebe5a3f68b484d3a2b8ffaf84704b3e343ef1addea528132ef148e22b3b/contourpy-1.3.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3d80b2c0300583228ac98d0a927a1ba6a2ba6b8a742463c564f1d419ee5b211e", size = 1380480, upload-time = "2025-04-15T17:38:06.7Z" }, + { url = "https://files.pythonhosted.org/packages/34/db/fcd325f19b5978fb509a7d55e06d99f5f856294c1991097534360b307cf1/contourpy-1.3.2-cp312-cp312-win32.whl", hash = "sha256:90df94c89a91b7362e1142cbee7568f86514412ab8a2c0d0fca72d7e91b62912", size = 178489, upload-time = "2025-04-15T17:38:10.338Z" }, + { url = "https://files.pythonhosted.org/packages/01/c8/fadd0b92ffa7b5eb5949bf340a63a4a496a6930a6c37a7ba0f12acb076d6/contourpy-1.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:8c942a01d9163e2e5cfb05cb66110121b8d07ad438a17f9e766317bcb62abf73", size = 223042, upload-time = "2025-04-15T17:38:14.239Z" }, + { url = "https://files.pythonhosted.org/packages/2e/61/5673f7e364b31e4e7ef6f61a4b5121c5f170f941895912f773d95270f3a2/contourpy-1.3.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:de39db2604ae755316cb5967728f4bea92685884b1e767b7c24e983ef5f771cb", size = 271630, upload-time = "2025-04-15T17:38:19.142Z" }, + { url = "https://files.pythonhosted.org/packages/ff/66/a40badddd1223822c95798c55292844b7e871e50f6bfd9f158cb25e0bd39/contourpy-1.3.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3f9e896f447c5c8618f1edb2bafa9a4030f22a575ec418ad70611450720b5b08", size = 255670, upload-time = "2025-04-15T17:38:23.688Z" }, + { url = "https://files.pythonhosted.org/packages/1e/c7/cf9fdee8200805c9bc3b148f49cb9482a4e3ea2719e772602a425c9b09f8/contourpy-1.3.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:71e2bd4a1c4188f5c2b8d274da78faab884b59df20df63c34f74aa1813c4427c", size = 306694, upload-time = "2025-04-15T17:38:28.238Z" }, + { url = "https://files.pythonhosted.org/packages/dd/e7/ccb9bec80e1ba121efbffad7f38021021cda5be87532ec16fd96533bb2e0/contourpy-1.3.2-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:de425af81b6cea33101ae95ece1f696af39446db9682a0b56daaa48cfc29f38f", size = 345986, upload-time = "2025-04-15T17:38:33.502Z" }, + { url = "https://files.pythonhosted.org/packages/dc/49/ca13bb2da90391fa4219fdb23b078d6065ada886658ac7818e5441448b78/contourpy-1.3.2-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:977e98a0e0480d3fe292246417239d2d45435904afd6d7332d8455981c408b85", size = 318060, upload-time = "2025-04-15T17:38:38.672Z" }, + { url = "https://files.pythonhosted.org/packages/c8/65/5245ce8c548a8422236c13ffcdcdada6a2a812c361e9e0c70548bb40b661/contourpy-1.3.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:434f0adf84911c924519d2b08fc10491dd282b20bdd3fa8f60fd816ea0b48841", size = 322747, upload-time = "2025-04-15T17:38:43.712Z" }, + { url = "https://files.pythonhosted.org/packages/72/30/669b8eb48e0a01c660ead3752a25b44fdb2e5ebc13a55782f639170772f9/contourpy-1.3.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:c66c4906cdbc50e9cba65978823e6e00b45682eb09adbb78c9775b74eb222422", size = 1308895, upload-time = "2025-04-15T17:39:00.224Z" }, + { url = "https://files.pythonhosted.org/packages/05/5a/b569f4250decee6e8d54498be7bdf29021a4c256e77fe8138c8319ef8eb3/contourpy-1.3.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:8b7fc0cd78ba2f4695fd0a6ad81a19e7e3ab825c31b577f384aa9d7817dc3bef", size = 1379098, upload-time = "2025-04-15T17:43:29.649Z" }, + { url = "https://files.pythonhosted.org/packages/19/ba/b227c3886d120e60e41b28740ac3617b2f2b971b9f601c835661194579f1/contourpy-1.3.2-cp313-cp313-win32.whl", hash = "sha256:15ce6ab60957ca74cff444fe66d9045c1fd3e92c8936894ebd1f3eef2fff075f", size = 178535, upload-time = "2025-04-15T17:44:44.532Z" }, + { url = "https://files.pythonhosted.org/packages/12/6e/2fed56cd47ca739b43e892707ae9a13790a486a3173be063681ca67d2262/contourpy-1.3.2-cp313-cp313-win_amd64.whl", hash = "sha256:e1578f7eafce927b168752ed7e22646dad6cd9bca673c60bff55889fa236ebf9", size = 223096, upload-time = "2025-04-15T17:44:48.194Z" }, + { url = "https://files.pythonhosted.org/packages/54/4c/e76fe2a03014a7c767d79ea35c86a747e9325537a8b7627e0e5b3ba266b4/contourpy-1.3.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0475b1f6604896bc7c53bb070e355e9321e1bc0d381735421a2d2068ec56531f", size = 285090, upload-time = "2025-04-15T17:43:34.084Z" }, + { url = "https://files.pythonhosted.org/packages/7b/e2/5aba47debd55d668e00baf9651b721e7733975dc9fc27264a62b0dd26eb8/contourpy-1.3.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:c85bb486e9be652314bb5b9e2e3b0d1b2e643d5eec4992c0fbe8ac71775da739", size = 268643, upload-time = "2025-04-15T17:43:38.626Z" }, + { url = "https://files.pythonhosted.org/packages/a1/37/cd45f1f051fe6230f751cc5cdd2728bb3a203f5619510ef11e732109593c/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:745b57db7758f3ffc05a10254edd3182a2a83402a89c00957a8e8a22f5582823", size = 310443, upload-time = "2025-04-15T17:43:44.522Z" }, + { url = "https://files.pythonhosted.org/packages/8b/a2/36ea6140c306c9ff6dd38e3bcec80b3b018474ef4d17eb68ceecd26675f4/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:970e9173dbd7eba9b4e01aab19215a48ee5dd3f43cef736eebde064a171f89a5", size = 349865, upload-time = "2025-04-15T17:43:49.545Z" }, + { url = "https://files.pythonhosted.org/packages/95/b7/2fc76bc539693180488f7b6cc518da7acbbb9e3b931fd9280504128bf956/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c6c4639a9c22230276b7bffb6a850dfc8258a2521305e1faefe804d006b2e532", size = 321162, upload-time = "2025-04-15T17:43:54.203Z" }, + { url = "https://files.pythonhosted.org/packages/f4/10/76d4f778458b0aa83f96e59d65ece72a060bacb20cfbee46cf6cd5ceba41/contourpy-1.3.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc829960f34ba36aad4302e78eabf3ef16a3a100863f0d4eeddf30e8a485a03b", size = 327355, upload-time = "2025-04-15T17:44:01.025Z" }, + { url = "https://files.pythonhosted.org/packages/43/a3/10cf483ea683f9f8ab096c24bad3cce20e0d1dd9a4baa0e2093c1c962d9d/contourpy-1.3.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:d32530b534e986374fc19eaa77fcb87e8a99e5431499949b828312bdcd20ac52", size = 1307935, upload-time = "2025-04-15T17:44:17.322Z" }, + { url = "https://files.pythonhosted.org/packages/78/73/69dd9a024444489e22d86108e7b913f3528f56cfc312b5c5727a44188471/contourpy-1.3.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:e298e7e70cf4eb179cc1077be1c725b5fd131ebc81181bf0c03525c8abc297fd", size = 1372168, upload-time = "2025-04-15T17:44:33.43Z" }, + { url = "https://files.pythonhosted.org/packages/0f/1b/96d586ccf1b1a9d2004dd519b25fbf104a11589abfd05484ff12199cca21/contourpy-1.3.2-cp313-cp313t-win32.whl", hash = "sha256:d0e589ae0d55204991450bb5c23f571c64fe43adaa53f93fc902a84c96f52fe1", size = 189550, upload-time = "2025-04-15T17:44:37.092Z" }, + { url = "https://files.pythonhosted.org/packages/b0/e6/6000d0094e8a5e32ad62591c8609e269febb6e4db83a1c75ff8868b42731/contourpy-1.3.2-cp313-cp313t-win_amd64.whl", hash = "sha256:78e9253c3de756b3f6a5174d024c4835acd59eb3f8e2ca13e775dbffe1558f69", size = 238214, upload-time = "2025-04-15T17:44:40.827Z" }, + { url = "https://files.pythonhosted.org/packages/33/05/b26e3c6ecc05f349ee0013f0bb850a761016d89cec528a98193a48c34033/contourpy-1.3.2-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:fd93cc7f3139b6dd7aab2f26a90dde0aa9fc264dbf70f6740d498a70b860b82c", size = 265681, upload-time = "2025-04-15T17:44:59.314Z" }, + { url = "https://files.pythonhosted.org/packages/2b/25/ac07d6ad12affa7d1ffed11b77417d0a6308170f44ff20fa1d5aa6333f03/contourpy-1.3.2-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:107ba8a6a7eec58bb475329e6d3b95deba9440667c4d62b9b6063942b61d7f16", size = 315101, upload-time = "2025-04-15T17:45:04.165Z" }, + { url = "https://files.pythonhosted.org/packages/8f/4d/5bb3192bbe9d3f27e3061a6a8e7733c9120e203cb8515767d30973f71030/contourpy-1.3.2-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:ded1706ed0c1049224531b81128efbd5084598f18d8a2d9efae833edbd2b40ad", size = 220599, upload-time = "2025-04-15T17:45:08.456Z" }, + { url = "https://files.pythonhosted.org/packages/ff/c0/91f1215d0d9f9f343e4773ba6c9b89e8c0cc7a64a6263f21139da639d848/contourpy-1.3.2-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:5f5964cdad279256c084b69c3f412b7801e15356b16efa9d78aa974041903da0", size = 266807, upload-time = "2025-04-15T17:45:15.535Z" }, + { url = "https://files.pythonhosted.org/packages/d4/79/6be7e90c955c0487e7712660d6cead01fa17bff98e0ea275737cc2bc8e71/contourpy-1.3.2-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:49b65a95d642d4efa8f64ba12558fcb83407e58a2dfba9d796d77b63ccfcaff5", size = 318729, upload-time = "2025-04-15T17:45:20.166Z" }, + { url = "https://files.pythonhosted.org/packages/87/68/7f46fb537958e87427d98a4074bcde4b67a70b04900cfc5ce29bc2f556c1/contourpy-1.3.2-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:8c5acb8dddb0752bf252e01a3035b21443158910ac16a3b0d20e7fed7d534ce5", size = 221791, upload-time = "2025-04-15T17:45:24.794Z" }, +] + +[[package]] +name = "contourpy" +version = "1.3.3" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.15'", + "python_full_version >= '3.12' and python_full_version < '3.15'", + "python_full_version == '3.11.*'", +] +dependencies = [ + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.11.*'" }, + { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/91/2e/c4390a31919d8a78b90e8ecf87cd4b4c4f05a5b48d05ec17db8e5404c6f4/contourpy-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:709a48ef9a690e1343202916450bc48b9e51c049b089c7f79a267b46cffcdaa1", size = 288773, upload-time = "2025-07-26T12:01:02.277Z" }, + { url = "https://files.pythonhosted.org/packages/0d/44/c4b0b6095fef4dc9c420e041799591e3b63e9619e3044f7f4f6c21c0ab24/contourpy-1.3.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:23416f38bfd74d5d28ab8429cc4d63fa67d5068bd711a85edb1c3fb0c3e2f381", size = 270149, upload-time = "2025-07-26T12:01:04.072Z" }, + { url = "https://files.pythonhosted.org/packages/30/2e/dd4ced42fefac8470661d7cb7e264808425e6c5d56d175291e93890cce09/contourpy-1.3.3-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:929ddf8c4c7f348e4c0a5a3a714b5c8542ffaa8c22954862a46ca1813b667ee7", size = 329222, upload-time = "2025-07-26T12:01:05.688Z" }, + { url = "https://files.pythonhosted.org/packages/f2/74/cc6ec2548e3d276c71389ea4802a774b7aa3558223b7bade3f25787fafc2/contourpy-1.3.3-cp311-cp311-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:9e999574eddae35f1312c2b4b717b7885d4edd6cb46700e04f7f02db454e67c1", size = 377234, upload-time = "2025-07-26T12:01:07.054Z" }, + { url = "https://files.pythonhosted.org/packages/03/b3/64ef723029f917410f75c09da54254c5f9ea90ef89b143ccadb09df14c15/contourpy-1.3.3-cp311-cp311-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0bf67e0e3f482cb69779dd3061b534eb35ac9b17f163d851e2a547d56dba0a3a", size = 380555, upload-time = "2025-07-26T12:01:08.801Z" }, + { url = "https://files.pythonhosted.org/packages/5f/4b/6157f24ca425b89fe2eb7e7be642375711ab671135be21e6faa100f7448c/contourpy-1.3.3-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:51e79c1f7470158e838808d4a996fa9bac72c498e93d8ebe5119bc1e6becb0db", size = 355238, upload-time = "2025-07-26T12:01:10.319Z" }, + { url = "https://files.pythonhosted.org/packages/98/56/f914f0dd678480708a04cfd2206e7c382533249bc5001eb9f58aa693e200/contourpy-1.3.3-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:598c3aaece21c503615fd59c92a3598b428b2f01bfb4b8ca9c4edeecc2438620", size = 1326218, upload-time = "2025-07-26T12:01:12.659Z" }, + { url = "https://files.pythonhosted.org/packages/fb/d7/4a972334a0c971acd5172389671113ae82aa7527073980c38d5868ff1161/contourpy-1.3.3-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:322ab1c99b008dad206d406bb61d014cf0174df491ae9d9d0fac6a6fda4f977f", size = 1392867, upload-time = "2025-07-26T12:01:15.533Z" }, + { url = "https://files.pythonhosted.org/packages/75/3e/f2cc6cd56dc8cff46b1a56232eabc6feea52720083ea71ab15523daab796/contourpy-1.3.3-cp311-cp311-win32.whl", hash = "sha256:fd907ae12cd483cd83e414b12941c632a969171bf90fc937d0c9f268a31cafff", size = 183677, upload-time = "2025-07-26T12:01:17.088Z" }, + { url = "https://files.pythonhosted.org/packages/98/4b/9bd370b004b5c9d8045c6c33cf65bae018b27aca550a3f657cdc99acdbd8/contourpy-1.3.3-cp311-cp311-win_amd64.whl", hash = "sha256:3519428f6be58431c56581f1694ba8e50626f2dd550af225f82fb5f5814d2a42", size = 225234, upload-time = "2025-07-26T12:01:18.256Z" }, + { url = "https://files.pythonhosted.org/packages/d9/b6/71771e02c2e004450c12b1120a5f488cad2e4d5b590b1af8bad060360fe4/contourpy-1.3.3-cp311-cp311-win_arm64.whl", hash = "sha256:15ff10bfada4bf92ec8b31c62bf7c1834c244019b4a33095a68000d7075df470", size = 193123, upload-time = "2025-07-26T12:01:19.848Z" }, + { url = "https://files.pythonhosted.org/packages/be/45/adfee365d9ea3d853550b2e735f9d66366701c65db7855cd07621732ccfc/contourpy-1.3.3-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:b08a32ea2f8e42cf1d4be3169a98dd4be32bafe4f22b6c4cb4ba810fa9e5d2cb", size = 293419, upload-time = "2025-07-26T12:01:21.16Z" }, + { url = "https://files.pythonhosted.org/packages/53/3e/405b59cfa13021a56bba395a6b3aca8cec012b45bf177b0eaf7a202cde2c/contourpy-1.3.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:556dba8fb6f5d8742f2923fe9457dbdd51e1049c4a43fd3986a0b14a1d815fc6", size = 273979, upload-time = "2025-07-26T12:01:22.448Z" }, + { url = "https://files.pythonhosted.org/packages/d4/1c/a12359b9b2ca3a845e8f7f9ac08bdf776114eb931392fcad91743e2ea17b/contourpy-1.3.3-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:92d9abc807cf7d0e047b95ca5d957cf4792fcd04e920ca70d48add15c1a90ea7", size = 332653, upload-time = "2025-07-26T12:01:24.155Z" }, + { url = "https://files.pythonhosted.org/packages/63/12/897aeebfb475b7748ea67b61e045accdfcf0d971f8a588b67108ed7f5512/contourpy-1.3.3-cp312-cp312-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b2e8faa0ed68cb29af51edd8e24798bb661eac3bd9f65420c1887b6ca89987c8", size = 379536, upload-time = "2025-07-26T12:01:25.91Z" }, + { url = "https://files.pythonhosted.org/packages/43/8a/a8c584b82deb248930ce069e71576fc09bd7174bbd35183b7943fb1064fd/contourpy-1.3.3-cp312-cp312-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:626d60935cf668e70a5ce6ff184fd713e9683fb458898e4249b63be9e28286ea", size = 384397, upload-time = "2025-07-26T12:01:27.152Z" }, + { url = "https://files.pythonhosted.org/packages/cc/8f/ec6289987824b29529d0dfda0d74a07cec60e54b9c92f3c9da4c0ac732de/contourpy-1.3.3-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4d00e655fcef08aba35ec9610536bfe90267d7ab5ba944f7032549c55a146da1", size = 362601, upload-time = "2025-07-26T12:01:28.808Z" }, + { url = "https://files.pythonhosted.org/packages/05/0a/a3fe3be3ee2dceb3e615ebb4df97ae6f3828aa915d3e10549ce016302bd1/contourpy-1.3.3-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:451e71b5a7d597379ef572de31eeb909a87246974d960049a9848c3bc6c41bf7", size = 1331288, upload-time = "2025-07-26T12:01:31.198Z" }, + { url = "https://files.pythonhosted.org/packages/33/1d/acad9bd4e97f13f3e2b18a3977fe1b4a37ecf3d38d815333980c6c72e963/contourpy-1.3.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:459c1f020cd59fcfe6650180678a9993932d80d44ccde1fa1868977438f0b411", size = 1403386, upload-time = "2025-07-26T12:01:33.947Z" }, + { url = "https://files.pythonhosted.org/packages/cf/8f/5847f44a7fddf859704217a99a23a4f6417b10e5ab1256a179264561540e/contourpy-1.3.3-cp312-cp312-win32.whl", hash = "sha256:023b44101dfe49d7d53932be418477dba359649246075c996866106da069af69", size = 185018, upload-time = "2025-07-26T12:01:35.64Z" }, + { url = "https://files.pythonhosted.org/packages/19/e8/6026ed58a64563186a9ee3f29f41261fd1828f527dd93d33b60feca63352/contourpy-1.3.3-cp312-cp312-win_amd64.whl", hash = "sha256:8153b8bfc11e1e4d75bcb0bff1db232f9e10b274e0929de9d608027e0d34ff8b", size = 226567, upload-time = "2025-07-26T12:01:36.804Z" }, + { url = "https://files.pythonhosted.org/packages/d1/e2/f05240d2c39a1ed228d8328a78b6f44cd695f7ef47beb3e684cf93604f86/contourpy-1.3.3-cp312-cp312-win_arm64.whl", hash = "sha256:07ce5ed73ecdc4a03ffe3e1b3e3c1166db35ae7584be76f65dbbe28a7791b0cc", size = 193655, upload-time = "2025-07-26T12:01:37.999Z" }, + { url = "https://files.pythonhosted.org/packages/68/35/0167aad910bbdb9599272bd96d01a9ec6852f36b9455cf2ca67bd4cc2d23/contourpy-1.3.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5", size = 293257, upload-time = "2025-07-26T12:01:39.367Z" }, + { url = "https://files.pythonhosted.org/packages/96/e4/7adcd9c8362745b2210728f209bfbcf7d91ba868a2c5f40d8b58f54c509b/contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1", size = 274034, upload-time = "2025-07-26T12:01:40.645Z" }, + { url = "https://files.pythonhosted.org/packages/73/23/90e31ceeed1de63058a02cb04b12f2de4b40e3bef5e082a7c18d9c8ae281/contourpy-1.3.3-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286", size = 334672, upload-time = "2025-07-26T12:01:41.942Z" }, + { url = "https://files.pythonhosted.org/packages/ed/93/b43d8acbe67392e659e1d984700e79eb67e2acb2bd7f62012b583a7f1b55/contourpy-1.3.3-cp313-cp313-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5", size = 381234, upload-time = "2025-07-26T12:01:43.499Z" }, + { url = "https://files.pythonhosted.org/packages/46/3b/bec82a3ea06f66711520f75a40c8fc0b113b2a75edb36aa633eb11c4f50f/contourpy-1.3.3-cp313-cp313-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67", size = 385169, upload-time = "2025-07-26T12:01:45.219Z" }, + { url = "https://files.pythonhosted.org/packages/4b/32/e0f13a1c5b0f8572d0ec6ae2f6c677b7991fafd95da523159c19eff0696a/contourpy-1.3.3-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9", size = 362859, upload-time = "2025-07-26T12:01:46.519Z" }, + { url = "https://files.pythonhosted.org/packages/33/71/e2a7945b7de4e58af42d708a219f3b2f4cff7386e6b6ab0a0fa0033c49a9/contourpy-1.3.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659", size = 1332062, upload-time = "2025-07-26T12:01:48.964Z" }, + { url = "https://files.pythonhosted.org/packages/12/fc/4e87ac754220ccc0e807284f88e943d6d43b43843614f0a8afa469801db0/contourpy-1.3.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7", size = 1403932, upload-time = "2025-07-26T12:01:51.979Z" }, + { url = "https://files.pythonhosted.org/packages/a6/2e/adc197a37443f934594112222ac1aa7dc9a98faf9c3842884df9a9d8751d/contourpy-1.3.3-cp313-cp313-win32.whl", hash = "sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d", size = 185024, upload-time = "2025-07-26T12:01:53.245Z" }, + { url = "https://files.pythonhosted.org/packages/18/0b/0098c214843213759692cc638fce7de5c289200a830e5035d1791d7a2338/contourpy-1.3.3-cp313-cp313-win_amd64.whl", hash = "sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263", size = 226578, upload-time = "2025-07-26T12:01:54.422Z" }, + { url = "https://files.pythonhosted.org/packages/8a/9a/2f6024a0c5995243cd63afdeb3651c984f0d2bc727fd98066d40e141ad73/contourpy-1.3.3-cp313-cp313-win_arm64.whl", hash = "sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9", size = 193524, upload-time = "2025-07-26T12:01:55.73Z" }, + { url = "https://files.pythonhosted.org/packages/c0/b3/f8a1a86bd3298513f500e5b1f5fd92b69896449f6cab6a146a5d52715479/contourpy-1.3.3-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d", size = 306730, upload-time = "2025-07-26T12:01:57.051Z" }, + { url = "https://files.pythonhosted.org/packages/3f/11/4780db94ae62fc0c2053909b65dc3246bd7cecfc4f8a20d957ad43aa4ad8/contourpy-1.3.3-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216", size = 287897, upload-time = "2025-07-26T12:01:58.663Z" }, + { url = "https://files.pythonhosted.org/packages/ae/15/e59f5f3ffdd6f3d4daa3e47114c53daabcb18574a26c21f03dc9e4e42ff0/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae", size = 326751, upload-time = "2025-07-26T12:02:00.343Z" }, + { url = "https://files.pythonhosted.org/packages/0f/81/03b45cfad088e4770b1dcf72ea78d3802d04200009fb364d18a493857210/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20", size = 375486, upload-time = "2025-07-26T12:02:02.128Z" }, + { url = "https://files.pythonhosted.org/packages/0c/ba/49923366492ffbdd4486e970d421b289a670ae8cf539c1ea9a09822b371a/contourpy-1.3.3-cp313-cp313t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99", size = 388106, upload-time = "2025-07-26T12:02:03.615Z" }, + { url = "https://files.pythonhosted.org/packages/9f/52/5b00ea89525f8f143651f9f03a0df371d3cbd2fccd21ca9b768c7a6500c2/contourpy-1.3.3-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b", size = 352548, upload-time = "2025-07-26T12:02:05.165Z" }, + { url = "https://files.pythonhosted.org/packages/32/1d/a209ec1a3a3452d490f6b14dd92e72280c99ae3d1e73da74f8277d4ee08f/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a", size = 1322297, upload-time = "2025-07-26T12:02:07.379Z" }, + { url = "https://files.pythonhosted.org/packages/bc/9e/46f0e8ebdd884ca0e8877e46a3f4e633f6c9c8c4f3f6e72be3fe075994aa/contourpy-1.3.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e", size = 1391023, upload-time = "2025-07-26T12:02:10.171Z" }, + { url = "https://files.pythonhosted.org/packages/b9/70/f308384a3ae9cd2209e0849f33c913f658d3326900d0ff5d378d6a1422d2/contourpy-1.3.3-cp313-cp313t-win32.whl", hash = "sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3", size = 196157, upload-time = "2025-07-26T12:02:11.488Z" }, + { url = "https://files.pythonhosted.org/packages/b2/dd/880f890a6663b84d9e34a6f88cded89d78f0091e0045a284427cb6b18521/contourpy-1.3.3-cp313-cp313t-win_amd64.whl", hash = "sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8", size = 240570, upload-time = "2025-07-26T12:02:12.754Z" }, + { url = "https://files.pythonhosted.org/packages/80/99/2adc7d8ffead633234817ef8e9a87115c8a11927a94478f6bb3d3f4d4f7d/contourpy-1.3.3-cp313-cp313t-win_arm64.whl", hash = "sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301", size = 199713, upload-time = "2025-07-26T12:02:14.4Z" }, + { url = "https://files.pythonhosted.org/packages/72/8b/4546f3ab60f78c514ffb7d01a0bd743f90de36f0019d1be84d0a708a580a/contourpy-1.3.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a", size = 292189, upload-time = "2025-07-26T12:02:16.095Z" }, + { url = "https://files.pythonhosted.org/packages/fd/e1/3542a9cb596cadd76fcef413f19c79216e002623158befe6daa03dbfa88c/contourpy-1.3.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77", size = 273251, upload-time = "2025-07-26T12:02:17.524Z" }, + { url = "https://files.pythonhosted.org/packages/b1/71/f93e1e9471d189f79d0ce2497007731c1e6bf9ef6d1d61b911430c3db4e5/contourpy-1.3.3-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5", size = 335810, upload-time = "2025-07-26T12:02:18.9Z" }, + { url = "https://files.pythonhosted.org/packages/91/f9/e35f4c1c93f9275d4e38681a80506b5510e9327350c51f8d4a5a724d178c/contourpy-1.3.3-cp314-cp314-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4", size = 382871, upload-time = "2025-07-26T12:02:20.418Z" }, + { url = "https://files.pythonhosted.org/packages/b5/71/47b512f936f66a0a900d81c396a7e60d73419868fba959c61efed7a8ab46/contourpy-1.3.3-cp314-cp314-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36", size = 386264, upload-time = "2025-07-26T12:02:21.916Z" }, + { url = "https://files.pythonhosted.org/packages/04/5f/9ff93450ba96b09c7c2b3f81c94de31c89f92292f1380261bd7195bea4ea/contourpy-1.3.3-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3", size = 363819, upload-time = "2025-07-26T12:02:23.759Z" }, + { url = "https://files.pythonhosted.org/packages/3e/a6/0b185d4cc480ee494945cde102cb0149ae830b5fa17bf855b95f2e70ad13/contourpy-1.3.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b", size = 1333650, upload-time = "2025-07-26T12:02:26.181Z" }, + { url = "https://files.pythonhosted.org/packages/43/d7/afdc95580ca56f30fbcd3060250f66cedbde69b4547028863abd8aa3b47e/contourpy-1.3.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36", size = 1404833, upload-time = "2025-07-26T12:02:28.782Z" }, + { url = "https://files.pythonhosted.org/packages/e2/e2/366af18a6d386f41132a48f033cbd2102e9b0cf6345d35ff0826cd984566/contourpy-1.3.3-cp314-cp314-win32.whl", hash = "sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d", size = 189692, upload-time = "2025-07-26T12:02:30.128Z" }, + { url = "https://files.pythonhosted.org/packages/7d/c2/57f54b03d0f22d4044b8afb9ca0e184f8b1afd57b4f735c2fa70883dc601/contourpy-1.3.3-cp314-cp314-win_amd64.whl", hash = "sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd", size = 232424, upload-time = "2025-07-26T12:02:31.395Z" }, + { url = "https://files.pythonhosted.org/packages/18/79/a9416650df9b525737ab521aa181ccc42d56016d2123ddcb7b58e926a42c/contourpy-1.3.3-cp314-cp314-win_arm64.whl", hash = "sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339", size = 198300, upload-time = "2025-07-26T12:02:32.956Z" }, + { url = "https://files.pythonhosted.org/packages/1f/42/38c159a7d0f2b7b9c04c64ab317042bb6952b713ba875c1681529a2932fe/contourpy-1.3.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772", size = 306769, upload-time = "2025-07-26T12:02:34.2Z" }, + { url = "https://files.pythonhosted.org/packages/c3/6c/26a8205f24bca10974e77460de68d3d7c63e282e23782f1239f226fcae6f/contourpy-1.3.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77", size = 287892, upload-time = "2025-07-26T12:02:35.807Z" }, + { url = "https://files.pythonhosted.org/packages/66/06/8a475c8ab718ebfd7925661747dbb3c3ee9c82ac834ccb3570be49d129f4/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13", size = 326748, upload-time = "2025-07-26T12:02:37.193Z" }, + { url = "https://files.pythonhosted.org/packages/b4/a3/c5ca9f010a44c223f098fccd8b158bb1cb287378a31ac141f04730dc49be/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe", size = 375554, upload-time = "2025-07-26T12:02:38.894Z" }, + { url = "https://files.pythonhosted.org/packages/80/5b/68bd33ae63fac658a4145088c1e894405e07584a316738710b636c6d0333/contourpy-1.3.3-cp314-cp314t-manylinux_2_26_s390x.manylinux_2_28_s390x.whl", hash = "sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f", size = 388118, upload-time = "2025-07-26T12:02:40.642Z" }, + { url = "https://files.pythonhosted.org/packages/40/52/4c285a6435940ae25d7410a6c36bda5145839bc3f0beb20c707cda18b9d2/contourpy-1.3.3-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0", size = 352555, upload-time = "2025-07-26T12:02:42.25Z" }, + { url = "https://files.pythonhosted.org/packages/24/ee/3e81e1dd174f5c7fefe50e85d0892de05ca4e26ef1c9a59c2a57e43b865a/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4", size = 1322295, upload-time = "2025-07-26T12:02:44.668Z" }, + { url = "https://files.pythonhosted.org/packages/3c/b2/6d913d4d04e14379de429057cd169e5e00f6c2af3bb13e1710bcbdb5da12/contourpy-1.3.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f", size = 1391027, upload-time = "2025-07-26T12:02:47.09Z" }, + { url = "https://files.pythonhosted.org/packages/93/8a/68a4ec5c55a2971213d29a9374913f7e9f18581945a7a31d1a39b5d2dfe5/contourpy-1.3.3-cp314-cp314t-win32.whl", hash = "sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae", size = 202428, upload-time = "2025-07-26T12:02:48.691Z" }, + { url = "https://files.pythonhosted.org/packages/fa/96/fd9f641ffedc4fa3ace923af73b9d07e869496c9cc7a459103e6e978992f/contourpy-1.3.3-cp314-cp314t-win_amd64.whl", hash = "sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc", size = 250331, upload-time = "2025-07-26T12:02:50.137Z" }, + { url = "https://files.pythonhosted.org/packages/ae/8c/469afb6465b853afff216f9528ffda78a915ff880ed58813ba4faf4ba0b6/contourpy-1.3.3-cp314-cp314t-win_arm64.whl", hash = "sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b", size = 203831, upload-time = "2025-07-26T12:02:51.449Z" }, + { url = "https://files.pythonhosted.org/packages/a5/29/8dcfe16f0107943fa92388c23f6e05cff0ba58058c4c95b00280d4c75a14/contourpy-1.3.3-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:cd5dfcaeb10f7b7f9dc8941717c6c2ade08f587be2226222c12b25f0483ed497", size = 278809, upload-time = "2025-07-26T12:02:52.74Z" }, + { url = "https://files.pythonhosted.org/packages/85/a9/8b37ef4f7dafeb335daee3c8254645ef5725be4d9c6aa70b50ec46ef2f7e/contourpy-1.3.3-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:0c1fc238306b35f246d61a1d416a627348b5cf0648648a031e14bb8705fcdfe8", size = 261593, upload-time = "2025-07-26T12:02:54.037Z" }, + { url = "https://files.pythonhosted.org/packages/0a/59/ebfb8c677c75605cc27f7122c90313fd2f375ff3c8d19a1694bda74aaa63/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:70f9aad7de812d6541d29d2bbf8feb22ff7e1c299523db288004e3157ff4674e", size = 302202, upload-time = "2025-07-26T12:02:55.947Z" }, + { url = "https://files.pythonhosted.org/packages/3c/37/21972a15834d90bfbfb009b9d004779bd5a07a0ec0234e5ba8f64d5736f4/contourpy-1.3.3-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5ed3657edf08512fc3fe81b510e35c2012fbd3081d2e26160f27ca28affec989", size = 329207, upload-time = "2025-07-26T12:02:57.468Z" }, + { url = "https://files.pythonhosted.org/packages/0c/58/bd257695f39d05594ca4ad60df5bcb7e32247f9951fd09a9b8edb82d1daa/contourpy-1.3.3-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:3d1a3799d62d45c18bafd41c5fa05120b96a28079f2393af559b843d1a966a77", size = 225315, upload-time = "2025-07-26T12:02:58.801Z" }, +] + +[[package]] +name = "cycler" +version = "0.12.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/a9/95/a3dbbb5028f35eafb79008e7522a75244477d2838f38cbb722248dabc2a8/cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c", size = 7615, upload-time = "2023-10-07T05:32:18.335Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30", size = 8321, upload-time = "2023-10-07T05:32:16.783Z" }, +] + [[package]] name = "dill" version = "0.4.0" @@ -293,9 +604,201 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/8a/0e/97c33bf5009bdbac74fd2beace167cab3f978feb69cc36f1ef79360d6c4e/exceptiongroup-1.3.1-py3-none-any.whl", hash = "sha256:a7a39a3bd276781e98394987d3a5701d0c4edffb633bb7a5144577f82c773598", size = 16740, upload-time = "2025-11-21T23:01:53.443Z" }, ] +[[package]] +name = "fonttools" +version = "4.57.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.8.1' and python_full_version < '3.9'", + "python_full_version < '3.8.1'", +] +sdist = { url = "https://files.pythonhosted.org/packages/03/2d/a9a0b6e3a0cf6bd502e64fc16d894269011930cabfc89aee20d1635b1441/fonttools-4.57.0.tar.gz", hash = "sha256:727ece10e065be2f9dd239d15dd5d60a66e17eac11aea47d447f9f03fdbc42de", size = 3492448, upload-time = "2025-04-03T11:07:13.898Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/db/17/3ddfd1881878b3f856065130bb603f5922e81ae8a4eb53bce0ea78f765a8/fonttools-4.57.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:babe8d1eb059a53e560e7bf29f8e8f4accc8b6cfb9b5fd10e485bde77e71ef41", size = 2756260, upload-time = "2025-04-03T11:05:28.582Z" }, + { url = "https://files.pythonhosted.org/packages/26/2b/6957890c52c030b0bf9e0add53e5badab4682c6ff024fac9a332bb2ae063/fonttools-4.57.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:81aa97669cd726349eb7bd43ca540cf418b279ee3caba5e2e295fb4e8f841c02", size = 2284691, upload-time = "2025-04-03T11:05:31.526Z" }, + { url = "https://files.pythonhosted.org/packages/cc/8e/c043b4081774e5eb06a834cedfdb7d432b4935bc8c4acf27207bdc34dfc4/fonttools-4.57.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f0e9618630edd1910ad4f07f60d77c184b2f572c8ee43305ea3265675cbbfe7e", size = 4566077, upload-time = "2025-04-03T11:05:33.559Z" }, + { url = "https://files.pythonhosted.org/packages/59/bc/e16ae5d9eee6c70830ce11d1e0b23d6018ddfeb28025fda092cae7889c8b/fonttools-4.57.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:34687a5d21f1d688d7d8d416cb4c5b9c87fca8a1797ec0d74b9fdebfa55c09ab", size = 4608729, upload-time = "2025-04-03T11:05:35.49Z" }, + { url = "https://files.pythonhosted.org/packages/25/13/e557bf10bb38e4e4c436d3a9627aadf691bc7392ae460910447fda5fad2b/fonttools-4.57.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:69ab81b66ebaa8d430ba56c7a5f9abe0183afefd3a2d6e483060343398b13fb1", size = 4759646, upload-time = "2025-04-03T11:05:37.963Z" }, + { url = "https://files.pythonhosted.org/packages/bc/c9/5e2952214d4a8e31026bf80beb18187199b7001e60e99a6ce19773249124/fonttools-4.57.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:d639397de852f2ccfb3134b152c741406752640a266d9c1365b0f23d7b88077f", size = 4941652, upload-time = "2025-04-03T11:05:40.089Z" }, + { url = "https://files.pythonhosted.org/packages/df/04/e80242b3d9ec91a1f785d949edc277a13ecfdcfae744de4b170df9ed77d8/fonttools-4.57.0-cp310-cp310-win32.whl", hash = "sha256:cc066cb98b912f525ae901a24cd381a656f024f76203bc85f78fcc9e66ae5aec", size = 2159432, upload-time = "2025-04-03T11:05:41.754Z" }, + { url = "https://files.pythonhosted.org/packages/33/ba/e858cdca275daf16e03c0362aa43734ea71104c3b356b2100b98543dba1b/fonttools-4.57.0-cp310-cp310-win_amd64.whl", hash = "sha256:7a64edd3ff6a7f711a15bd70b4458611fb240176ec11ad8845ccbab4fe6745db", size = 2203869, upload-time = "2025-04-03T11:05:43.712Z" }, + { url = "https://files.pythonhosted.org/packages/81/1f/e67c99aa3c6d3d2f93d956627e62a57ae0d35dc42f26611ea2a91053f6d6/fonttools-4.57.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:3871349303bdec958360eedb619169a779956503ffb4543bb3e6211e09b647c4", size = 2757392, upload-time = "2025-04-03T11:05:45.715Z" }, + { url = "https://files.pythonhosted.org/packages/aa/f1/f75770d0ddc67db504850898d96d75adde238c35313409bfcd8db4e4a5fe/fonttools-4.57.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c59375e85126b15a90fcba3443eaac58f3073ba091f02410eaa286da9ad80ed8", size = 2285609, upload-time = "2025-04-03T11:05:47.977Z" }, + { url = "https://files.pythonhosted.org/packages/f5/d3/bc34e4953cb204bae0c50b527307dce559b810e624a733351a654cfc318e/fonttools-4.57.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:967b65232e104f4b0f6370a62eb33089e00024f2ce143aecbf9755649421c683", size = 4873292, upload-time = "2025-04-03T11:05:49.921Z" }, + { url = "https://files.pythonhosted.org/packages/41/b8/d5933559303a4ab18c799105f4c91ee0318cc95db4a2a09e300116625e7a/fonttools-4.57.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:39acf68abdfc74e19de7485f8f7396fa4d2418efea239b7061d6ed6a2510c746", size = 4902503, upload-time = "2025-04-03T11:05:52.17Z" }, + { url = "https://files.pythonhosted.org/packages/32/13/acb36bfaa316f481153ce78de1fa3926a8bad42162caa3b049e1afe2408b/fonttools-4.57.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:9d077f909f2343daf4495ba22bb0e23b62886e8ec7c109ee8234bdbd678cf344", size = 5077351, upload-time = "2025-04-03T11:05:54.162Z" }, + { url = "https://files.pythonhosted.org/packages/b5/23/6d383a2ca83b7516d73975d8cca9d81a01acdcaa5e4db8579e4f3de78518/fonttools-4.57.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:46370ac47a1e91895d40e9ad48effbe8e9d9db1a4b80888095bc00e7beaa042f", size = 5275067, upload-time = "2025-04-03T11:05:57.375Z" }, + { url = "https://files.pythonhosted.org/packages/bc/ca/31b8919c6da0198d5d522f1d26c980201378c087bdd733a359a1e7485769/fonttools-4.57.0-cp311-cp311-win32.whl", hash = "sha256:ca2aed95855506b7ae94e8f1f6217b7673c929e4f4f1217bcaa236253055cb36", size = 2158263, upload-time = "2025-04-03T11:05:59.567Z" }, + { url = "https://files.pythonhosted.org/packages/13/4c/de2612ea2216eb45cfc8eb91a8501615dd87716feaf5f8fb65cbca576289/fonttools-4.57.0-cp311-cp311-win_amd64.whl", hash = "sha256:17168a4670bbe3775f3f3f72d23ee786bd965395381dfbb70111e25e81505b9d", size = 2204968, upload-time = "2025-04-03T11:06:02.16Z" }, + { url = "https://files.pythonhosted.org/packages/cb/98/d4bc42d43392982eecaaca117d79845734d675219680cd43070bb001bc1f/fonttools-4.57.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:889e45e976c74abc7256d3064aa7c1295aa283c6bb19810b9f8b604dfe5c7f31", size = 2751824, upload-time = "2025-04-03T11:06:03.782Z" }, + { url = "https://files.pythonhosted.org/packages/1a/62/7168030eeca3742fecf45f31e63b5ef48969fa230a672216b805f1d61548/fonttools-4.57.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:0425c2e052a5f1516c94e5855dbda706ae5a768631e9fcc34e57d074d1b65b92", size = 2283072, upload-time = "2025-04-03T11:06:05.533Z" }, + { url = "https://files.pythonhosted.org/packages/5d/82/121a26d9646f0986ddb35fbbaf58ef791c25b59ecb63ffea2aab0099044f/fonttools-4.57.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:44c26a311be2ac130f40a96769264809d3b0cb297518669db437d1cc82974888", size = 4788020, upload-time = "2025-04-03T11:06:07.249Z" }, + { url = "https://files.pythonhosted.org/packages/5b/26/e0f2fb662e022d565bbe280a3cfe6dafdaabf58889ff86fdef2d31ff1dde/fonttools-4.57.0-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:84c41ba992df5b8d680b89fd84c6a1f2aca2b9f1ae8a67400c8930cd4ea115f6", size = 4859096, upload-time = "2025-04-03T11:06:09.469Z" }, + { url = "https://files.pythonhosted.org/packages/9e/44/9075e323347b1891cdece4b3f10a3b84a8f4c42a7684077429d9ce842056/fonttools-4.57.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ea1e9e43ca56b0c12440a7c689b1350066595bebcaa83baad05b8b2675129d98", size = 4964356, upload-time = "2025-04-03T11:06:11.294Z" }, + { url = "https://files.pythonhosted.org/packages/48/28/caa8df32743462fb966be6de6a79d7f30393859636d7732e82efa09fbbb4/fonttools-4.57.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:84fd56c78d431606332a0627c16e2a63d243d0d8b05521257d77c6529abe14d8", size = 5226546, upload-time = "2025-04-03T11:06:13.6Z" }, + { url = "https://files.pythonhosted.org/packages/f6/46/95ab0f0d2e33c5b1a4fc1c0efe5e286ba9359602c0a9907adb1faca44175/fonttools-4.57.0-cp312-cp312-win32.whl", hash = "sha256:f4376819c1c778d59e0a31db5dc6ede854e9edf28bbfa5b756604727f7f800ac", size = 2146776, upload-time = "2025-04-03T11:06:15.643Z" }, + { url = "https://files.pythonhosted.org/packages/06/5d/1be5424bb305880e1113631f49a55ea7c7da3a5fe02608ca7c16a03a21da/fonttools-4.57.0-cp312-cp312-win_amd64.whl", hash = "sha256:57e30241524879ea10cdf79c737037221f77cc126a8cdc8ff2c94d4a522504b9", size = 2193956, upload-time = "2025-04-03T11:06:17.534Z" }, + { url = "https://files.pythonhosted.org/packages/e9/2f/11439f3af51e4bb75ac9598c29f8601aa501902dcedf034bdc41f47dd799/fonttools-4.57.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:408ce299696012d503b714778d89aa476f032414ae57e57b42e4b92363e0b8ef", size = 2739175, upload-time = "2025-04-03T11:06:19.583Z" }, + { url = "https://files.pythonhosted.org/packages/25/52/677b55a4c0972dc3820c8dba20a29c358197a78229daa2ea219fdb19e5d5/fonttools-4.57.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:bbceffc80aa02d9e8b99f2a7491ed8c4a783b2fc4020119dc405ca14fb5c758c", size = 2276583, upload-time = "2025-04-03T11:06:21.753Z" }, + { url = "https://files.pythonhosted.org/packages/64/79/184555f8fa77b827b9460a4acdbbc0b5952bb6915332b84c615c3a236826/fonttools-4.57.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f022601f3ee9e1f6658ed6d184ce27fa5216cee5b82d279e0f0bde5deebece72", size = 4766437, upload-time = "2025-04-03T11:06:23.521Z" }, + { url = "https://files.pythonhosted.org/packages/f8/ad/c25116352f456c0d1287545a7aa24e98987b6d99c5b0456c4bd14321f20f/fonttools-4.57.0-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4dea5893b58d4637ffa925536462ba626f8a1b9ffbe2f5c272cdf2c6ebadb817", size = 4838431, upload-time = "2025-04-03T11:06:25.423Z" }, + { url = "https://files.pythonhosted.org/packages/53/ae/398b2a833897297797a44f519c9af911c2136eb7aa27d3f1352c6d1129fa/fonttools-4.57.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:dff02c5c8423a657c550b48231d0a48d7e2b2e131088e55983cfe74ccc2c7cc9", size = 4951011, upload-time = "2025-04-03T11:06:27.41Z" }, + { url = "https://files.pythonhosted.org/packages/b7/5d/7cb31c4bc9ffb9a2bbe8b08f8f53bad94aeb158efad75da645b40b62cb73/fonttools-4.57.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:767604f244dc17c68d3e2dbf98e038d11a18abc078f2d0f84b6c24571d9c0b13", size = 5205679, upload-time = "2025-04-03T11:06:29.804Z" }, + { url = "https://files.pythonhosted.org/packages/4c/e4/6934513ec2c4d3d69ca1bc3bd34d5c69dafcbf68c15388dd3bb062daf345/fonttools-4.57.0-cp313-cp313-win32.whl", hash = "sha256:8e2e12d0d862f43d51e5afb8b9751c77e6bec7d2dc00aad80641364e9df5b199", size = 2144833, upload-time = "2025-04-03T11:06:31.737Z" }, + { url = "https://files.pythonhosted.org/packages/c4/0d/2177b7fdd23d017bcfb702fd41e47d4573766b9114da2fddbac20dcc4957/fonttools-4.57.0-cp313-cp313-win_amd64.whl", hash = "sha256:f1d6bc9c23356908db712d282acb3eebd4ae5ec6d8b696aa40342b1d84f8e9e3", size = 2190799, upload-time = "2025-04-03T11:06:34.784Z" }, + { url = "https://files.pythonhosted.org/packages/8a/3f/c16dbbec7221783f37dcc2022d5a55f0d704ffc9feef67930f6eb517e8ce/fonttools-4.57.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:9d57b4e23ebbe985125d3f0cabbf286efa191ab60bbadb9326091050d88e8213", size = 2753756, upload-time = "2025-04-03T11:06:36.875Z" }, + { url = "https://files.pythonhosted.org/packages/48/9f/5b4a3d6aed5430b159dd3494bb992d4e45102affa3725f208e4f0aedc6a3/fonttools-4.57.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:579ba873d7f2a96f78b2e11028f7472146ae181cae0e4d814a37a09e93d5c5cc", size = 2283179, upload-time = "2025-04-03T11:06:39.095Z" }, + { url = "https://files.pythonhosted.org/packages/17/b2/4e887b674938b4c3848029a4134ac90dd8653ea80b4f464fa1edeae37f25/fonttools-4.57.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6e3e1ec10c29bae0ea826b61f265ec5c858c5ba2ce2e69a71a62f285cf8e4595", size = 4647139, upload-time = "2025-04-03T11:06:41.315Z" }, + { url = "https://files.pythonhosted.org/packages/a5/0e/b6314a09a4d561aaa7e09de43fa700917be91e701f07df6178865962666c/fonttools-4.57.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a1968f2a2003c97c4ce6308dc2498d5fd4364ad309900930aa5a503c9851aec8", size = 4691211, upload-time = "2025-04-03T11:06:43.566Z" }, + { url = "https://files.pythonhosted.org/packages/bf/1d/b9f4b70d165c25f5c9aee61eb6ae90b0e9b5787b2c0a45e4f3e50a839274/fonttools-4.57.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:aff40f8ac6763d05c2c8f6d240c6dac4bb92640a86d9b0c3f3fff4404f34095c", size = 4873755, upload-time = "2025-04-03T11:06:45.457Z" }, + { url = "https://files.pythonhosted.org/packages/3b/fa/a731c8f42ae2c6761d1c22bd3c90241d5b2b13cabb70598abc74a828b51f/fonttools-4.57.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:d07f1b64008e39fceae7aa99e38df8385d7d24a474a8c9872645c4397b674481", size = 5070072, upload-time = "2025-04-03T11:06:47.853Z" }, + { url = "https://files.pythonhosted.org/packages/1f/1e/6a988230109a2ba472e5de0a4c3936d49718cfc4b700b6bad53eca414bcf/fonttools-4.57.0-cp38-cp38-win32.whl", hash = "sha256:51d8482e96b28fb28aa8e50b5706f3cee06de85cbe2dce80dbd1917ae22ec5a6", size = 1484098, upload-time = "2025-04-03T11:06:50.167Z" }, + { url = "https://files.pythonhosted.org/packages/dc/7a/2b3666e8c13d035adf656a8ae391380656144760353c97f74747c64fd3e5/fonttools-4.57.0-cp38-cp38-win_amd64.whl", hash = "sha256:03290e818782e7edb159474144fca11e36a8ed6663d1fcbd5268eb550594fd8e", size = 1529536, upload-time = "2025-04-03T11:06:52.468Z" }, + { url = "https://files.pythonhosted.org/packages/d2/c7/3bddafbb95447f6fbabdd0b399bf468649321fd4029e356b4f6bd70fbc1b/fonttools-4.57.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:7339e6a3283e4b0ade99cade51e97cde3d54cd6d1c3744459e886b66d630c8b3", size = 2758942, upload-time = "2025-04-03T11:06:54.679Z" }, + { url = "https://files.pythonhosted.org/packages/d4/a2/8dd7771022e365c90e428b1607174c3297d5c0a2cc2cf4cdccb2221945b7/fonttools-4.57.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:05efceb2cb5f6ec92a4180fcb7a64aa8d3385fd49cfbbe459350229d1974f0b1", size = 2285959, upload-time = "2025-04-03T11:06:56.792Z" }, + { url = "https://files.pythonhosted.org/packages/58/5a/2fd29c5e38b14afe1fae7d472373e66688e7c7a98554252f3cf44371e033/fonttools-4.57.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a97bb05eb24637714a04dee85bdf0ad1941df64fe3b802ee4ac1c284a5f97b7c", size = 4571677, upload-time = "2025-04-03T11:06:59.002Z" }, + { url = "https://files.pythonhosted.org/packages/bf/30/b77cf81923f1a67ff35d6765a9db4718c0688eb8466c464c96a23a2e28d4/fonttools-4.57.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:541cb48191a19ceb1a2a4b90c1fcebd22a1ff7491010d3cf840dd3a68aebd654", size = 4616644, upload-time = "2025-04-03T11:07:01.238Z" }, + { url = "https://files.pythonhosted.org/packages/06/33/376605898d8d553134144dff167506a49694cb0e0cf684c14920fbc1e99f/fonttools-4.57.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:cdef9a056c222d0479a1fdb721430f9efd68268014c54e8166133d2643cb05d9", size = 4761314, upload-time = "2025-04-03T11:07:03.162Z" }, + { url = "https://files.pythonhosted.org/packages/48/e4/e0e48f5bae04bc1a1c6b4fcd7d1ca12b29f1fe74221534b7ff83ed0db8fe/fonttools-4.57.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:3cf97236b192a50a4bf200dc5ba405aa78d4f537a2c6e4c624bb60466d5b03bd", size = 4945563, upload-time = "2025-04-03T11:07:05.313Z" }, + { url = "https://files.pythonhosted.org/packages/61/98/2dacfc6d70f2d93bde1bbf814286be343cb17f53057130ad3b843144dd00/fonttools-4.57.0-cp39-cp39-win32.whl", hash = "sha256:e952c684274a7714b3160f57ec1d78309f955c6335c04433f07d36c5eb27b1f9", size = 2159997, upload-time = "2025-04-03T11:07:07.467Z" }, + { url = "https://files.pythonhosted.org/packages/93/fa/e61cc236f40d504532d2becf90c297bfed8e40abc0c8b08375fbb83eff29/fonttools-4.57.0-cp39-cp39-win_amd64.whl", hash = "sha256:a2a722c0e4bfd9966a11ff55c895c817158fcce1b2b6700205a376403b546ad9", size = 2204508, upload-time = "2025-04-03T11:07:09.632Z" }, + { url = "https://files.pythonhosted.org/packages/90/27/45f8957c3132917f91aaa56b700bcfc2396be1253f685bd5c68529b6f610/fonttools-4.57.0-py3-none-any.whl", hash = "sha256:3122c604a675513c68bd24c6a8f9091f1c2376d18e8f5fe5a101746c81b3e98f", size = 1093605, upload-time = "2025-04-03T11:07:11.341Z" }, +] + +[[package]] +name = "fonttools" +version = "4.60.2" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.9.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/3e/c4/db6a7b5eb0656534c3aa2596c2c5e18830d74f1b9aa5aa8a7dff63a0b11d/fonttools-4.60.2.tar.gz", hash = "sha256:d29552e6b155ebfc685b0aecf8d429cb76c14ab734c22ef5d3dea6fdf800c92c", size = 3562254, upload-time = "2025-12-09T13:38:11.835Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ab/de/9e10a99fb3070accb8884886a41a4ce54e49bf2fa4fc63f48a6cf2061713/fonttools-4.60.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:4e36fadcf7e8ca6e34d490eef86ed638d6fd9c55d2f514b05687622cfc4a7050", size = 2850403, upload-time = "2025-12-09T13:35:53.14Z" }, + { url = "https://files.pythonhosted.org/packages/e4/40/d5b369d1073b134f600a94a287e13b5bdea2191ba6347d813fa3da00e94a/fonttools-4.60.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:6e500fc9c04bee749ceabfc20cb4903f6981c2139050d85720ea7ada61b75d5c", size = 2398629, upload-time = "2025-12-09T13:35:56.471Z" }, + { url = "https://files.pythonhosted.org/packages/7c/b5/123819369aaf99d1e4dc49f1de1925d4edc7379114d15a56a7dd2e9d56e6/fonttools-4.60.2-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:22efea5e784e1d1cd8d7b856c198e360a979383ebc6dea4604743b56da1cbc34", size = 4893471, upload-time = "2025-12-09T13:35:58.927Z" }, + { url = "https://files.pythonhosted.org/packages/24/29/f8f8acccb9716b899be4be45e9ce770d6aa76327573863e68448183091b0/fonttools-4.60.2-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:677aa92d84d335e4d301d8ba04afca6f575316bc647b6782cb0921943fcb6343", size = 4854686, upload-time = "2025-12-09T13:36:01.767Z" }, + { url = "https://files.pythonhosted.org/packages/5a/0d/f3f51d7519f44f2dd5c9a60d7cd41185ebcee4348f073e515a3a93af15ff/fonttools-4.60.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:edd49d3defbf35476e78b61ff737ff5efea811acff68d44233a95a5a48252334", size = 4871233, upload-time = "2025-12-09T13:36:06.094Z" }, + { url = "https://files.pythonhosted.org/packages/cc/3f/4d4fd47d3bc40ab4d76718555185f8adffb5602ea572eac4bbf200c47d22/fonttools-4.60.2-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:126839492b69cecc5baf2bddcde60caab2ffafd867bbae2a88463fce6078ca3a", size = 4988936, upload-time = "2025-12-09T13:36:08.42Z" }, + { url = "https://files.pythonhosted.org/packages/01/6f/83bbdefa43f2c3ae206fd8c4b9a481f3c913eef871b1ce9a453069239e39/fonttools-4.60.2-cp310-cp310-win32.whl", hash = "sha256:ffcab6f5537136046ca902ed2491ab081ba271b07591b916289b7c27ff845f96", size = 2278044, upload-time = "2025-12-09T13:36:10.641Z" }, + { url = "https://files.pythonhosted.org/packages/d4/04/7d9a137e919d6c9ef26704b7f7b2580d9cfc5139597588227aacebc0e3b7/fonttools-4.60.2-cp310-cp310-win_amd64.whl", hash = "sha256:9c68b287c7ffcd29dd83b5f961004b2a54a862a88825d52ea219c6220309ba45", size = 2326522, upload-time = "2025-12-09T13:36:12.981Z" }, + { url = "https://files.pythonhosted.org/packages/e0/80/b7693d37c02417e162cc83cdd0b19a4f58be82c638b5d4ce4de2dae050c4/fonttools-4.60.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:a2aed0a7931401b3875265717a24c726f87ecfedbb7b3426c2ca4d2812e281ae", size = 2847809, upload-time = "2025-12-09T13:36:14.884Z" }, + { url = "https://files.pythonhosted.org/packages/f9/9a/9c2c13bf8a6496ac21607d704e74e9cc68ebf23892cf924c9a8b5c7566b9/fonttools-4.60.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:dea6868e9d2b816c9076cfea77754686f3c19149873bdbc5acde437631c15df1", size = 2397302, upload-time = "2025-12-09T13:36:17.151Z" }, + { url = "https://files.pythonhosted.org/packages/56/f6/ce38ff6b2d2d58f6fd981d32f3942365bfa30eadf2b47d93b2d48bf6097f/fonttools-4.60.2-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2fa27f34950aa1fe0f0b1abe25eed04770a3b3b34ad94e5ace82cc341589678a", size = 5054418, upload-time = "2025-12-09T13:36:19.062Z" }, + { url = "https://files.pythonhosted.org/packages/88/06/5353bea128ff39e857c31de3dd605725b4add956badae0b31bc9a50d4c8e/fonttools-4.60.2-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:13a53d479d187b09bfaa4a35ffcbc334fc494ff355f0a587386099cb66674f1e", size = 5031652, upload-time = "2025-12-09T13:36:21.206Z" }, + { url = "https://files.pythonhosted.org/packages/71/05/ebca836437f6ebd57edd6428e7eff584e683ff0556ddb17d62e3b731f46c/fonttools-4.60.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:fac5e921d3bd0ca3bb8517dced2784f0742bc8ca28579a68b139f04ea323a779", size = 5030321, upload-time = "2025-12-09T13:36:23.515Z" }, + { url = "https://files.pythonhosted.org/packages/57/f9/eb9d2a2ce30c99f840c1cc3940729a970923cf39d770caf88909d98d516b/fonttools-4.60.2-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:648f4f9186fd7f1f3cd57dbf00d67a583720d5011feca67a5e88b3a491952cfb", size = 5154255, upload-time = "2025-12-09T13:36:25.879Z" }, + { url = "https://files.pythonhosted.org/packages/08/a2/088b6ceba8272a9abb629d3c08f9c1e35e5ce42db0ccfe0c1f9f03e60d1d/fonttools-4.60.2-cp311-cp311-win32.whl", hash = "sha256:3274e15fad871bead5453d5ce02658f6d0c7bc7e7021e2a5b8b04e2f9e40da1a", size = 2276300, upload-time = "2025-12-09T13:36:27.772Z" }, + { url = "https://files.pythonhosted.org/packages/de/2f/8e4c3d908cc5dade7bb1316ce48589f6a24460c1056fd4b8db51f1fa309a/fonttools-4.60.2-cp311-cp311-win_amd64.whl", hash = "sha256:91d058d5a483a1525b367803abb69de0923fbd45e1f82ebd000f5c8aa65bc78e", size = 2327574, upload-time = "2025-12-09T13:36:30.89Z" }, + { url = "https://files.pythonhosted.org/packages/c0/30/530c9eddcd1c39219dc0aaede2b5a4c8ab80e0bb88d1b3ffc12944c4aac3/fonttools-4.60.2-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:e0164b7609d2b5c5dd4e044b8085b7bd7ca7363ef8c269a4ab5b5d4885a426b2", size = 2847196, upload-time = "2025-12-09T13:36:33.262Z" }, + { url = "https://files.pythonhosted.org/packages/19/2f/4077a482836d5bbe3bc9dac1c004d02ee227cf04ed62b0a2dfc41d4f0dfd/fonttools-4.60.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:1dd3d9574fc595c1e97faccae0f264dc88784ddf7fbf54c939528378bacc0033", size = 2395842, upload-time = "2025-12-09T13:36:35.47Z" }, + { url = "https://files.pythonhosted.org/packages/dd/05/aae5bb99c5398f8ed4a8b784f023fd9dd3568f0bd5d5b21e35b282550f11/fonttools-4.60.2-cp312-cp312-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:98d0719f1b11c2817307d2da2e94296a3b2a3503f8d6252a101dca3ee663b917", size = 4949713, upload-time = "2025-12-09T13:36:37.874Z" }, + { url = "https://files.pythonhosted.org/packages/b4/37/49067349fc78ff0efbf09fadefe80ddf41473ca8f8a25400e3770da38328/fonttools-4.60.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9d3ea26957dd07209f207b4fff64c702efe5496de153a54d3b91007ec28904dd", size = 4999907, upload-time = "2025-12-09T13:36:39.853Z" }, + { url = "https://files.pythonhosted.org/packages/16/31/d0f11c758bd0db36b664c92a0f9dfdcc2d7313749aa7d6629805c6946f21/fonttools-4.60.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1ee301273b0850f3a515299f212898f37421f42ff9adfc341702582ca5073c13", size = 4939717, upload-time = "2025-12-09T13:36:43.075Z" }, + { url = "https://files.pythonhosted.org/packages/d9/bc/1cff0d69522e561bf1b99bee7c3911c08c25e919584827c3454a64651ce9/fonttools-4.60.2-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c6eb4694cc3b9c03b7c01d65a9cf35b577f21aa6abdbeeb08d3114b842a58153", size = 5089205, upload-time = "2025-12-09T13:36:45.468Z" }, + { url = "https://files.pythonhosted.org/packages/05/e6/fb174f0069b7122e19828c551298bfd34fdf9480535d2a6ac2ed37afacd3/fonttools-4.60.2-cp312-cp312-win32.whl", hash = "sha256:57f07b616c69c244cc1a5a51072eeef07dddda5ebef9ca5c6e9cf6d59ae65b70", size = 2264674, upload-time = "2025-12-09T13:36:49.238Z" }, + { url = "https://files.pythonhosted.org/packages/75/57/6552ffd6b582d3e6a9f01780c5275e6dfff1e70ca146101733aa1c12a129/fonttools-4.60.2-cp312-cp312-win_amd64.whl", hash = "sha256:310035802392f1fe5a7cf43d76f6ff4a24c919e4c72c0352e7b8176e2584b8a0", size = 2314701, upload-time = "2025-12-09T13:36:51.09Z" }, + { url = "https://files.pythonhosted.org/packages/2e/e4/8381d0ca6b6c6c484660b03517ec5b5b81feeefca3808726dece36c652a9/fonttools-4.60.2-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:2bb5fd231e56ccd7403212636dcccffc96c5ae0d6f9e4721fa0a32cb2e3ca432", size = 2842063, upload-time = "2025-12-09T13:36:53.468Z" }, + { url = "https://files.pythonhosted.org/packages/b4/2c/4367117ee8ff4f4374787a1222da0bd413d80cf3522111f727a7b8f80d1d/fonttools-4.60.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:536b5fab7b6fec78ccf59b5c59489189d9d0a8b0d3a77ed1858be59afb096696", size = 2393792, upload-time = "2025-12-09T13:36:55.742Z" }, + { url = "https://files.pythonhosted.org/packages/49/b7/a76b6dffa193869e54e32ca2f9abb0d0e66784bc8a24e6f86eb093015481/fonttools-4.60.2-cp313-cp313-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:6b9288fc38252ac86a9570f19313ecbc9ff678982e0f27c757a85f1f284d3400", size = 4924020, upload-time = "2025-12-09T13:36:58.229Z" }, + { url = "https://files.pythonhosted.org/packages/bd/4e/0078200e2259f0061c86a74075f507d64c43dd2ab38971956a5c0012d344/fonttools-4.60.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:93fcb420791d839ef592eada2b69997c445d0ce9c969b5190f2e16828ec10607", size = 4980070, upload-time = "2025-12-09T13:37:00.311Z" }, + { url = "https://files.pythonhosted.org/packages/85/1f/d87c85a11cb84852c975251581862681e4a0c1c3bd456c648792203f311b/fonttools-4.60.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:7916a381b094db4052ac284255186aebf74c5440248b78860cb41e300036f598", size = 4921411, upload-time = "2025-12-09T13:37:02.345Z" }, + { url = "https://files.pythonhosted.org/packages/75/c0/7efad650f5ed8e317c2633133ef3c64917e7adf2e4e2940c798f5d57ec6e/fonttools-4.60.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:58c8c393d5e16b15662cfc2d988491940458aa87894c662154f50c7b49440bef", size = 5063465, upload-time = "2025-12-09T13:37:04.836Z" }, + { url = "https://files.pythonhosted.org/packages/18/a8/750518c4f8cdd79393b386bc81226047ade80239e58c6c9f5dbe1fdd8ea1/fonttools-4.60.2-cp313-cp313-win32.whl", hash = "sha256:19c6e0afd8b02008caa0aa08ab896dfce5d0bcb510c49b2c499541d5cb95a963", size = 2263443, upload-time = "2025-12-09T13:37:06.762Z" }, + { url = "https://files.pythonhosted.org/packages/b8/22/026c60376f165981f80a0e90bd98a79ae3334e9d89a3d046c4d2e265c724/fonttools-4.60.2-cp313-cp313-win_amd64.whl", hash = "sha256:6a500dc59e11b2338c2dba1f8cf11a4ae8be35ec24af8b2628b8759a61457b76", size = 2313800, upload-time = "2025-12-09T13:37:08.713Z" }, + { url = "https://files.pythonhosted.org/packages/7e/ab/7cf1f5204e1366ddf9dc5cdc2789b571feb9eebcee0e3463c3f457df5f52/fonttools-4.60.2-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:9387c532acbe323bbf2a920f132bce3c408a609d5f9dcfc6532fbc7e37f8ccbb", size = 2841690, upload-time = "2025-12-09T13:37:10.696Z" }, + { url = "https://files.pythonhosted.org/packages/00/3c/0bf83c6f863cc8b934952567fa2bf737cfcec8fc4ffb59b3f93820095f89/fonttools-4.60.2-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:e6f1c824185b5b8fb681297f315f26ae55abb0d560c2579242feea8236b1cfef", size = 2392191, upload-time = "2025-12-09T13:37:12.954Z" }, + { url = "https://files.pythonhosted.org/packages/00/f0/40090d148b8907fbea12e9bdf1ff149f30cdf1769e3b2c3e0dbf5106b88d/fonttools-4.60.2-cp314-cp314-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:55a3129d1e4030b1a30260f1b32fe76781b585fb2111d04a988e141c09eb6403", size = 4873503, upload-time = "2025-12-09T13:37:15.142Z" }, + { url = "https://files.pythonhosted.org/packages/dc/e0/d8b13f99e58b8c293781288ba62fe634f1f0697c9c4c0ae104d3215f3a10/fonttools-4.60.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b196e63753abc33b3b97a6fd6de4b7c4fef5552c0a5ba5e562be214d1e9668e0", size = 4968493, upload-time = "2025-12-09T13:37:18.272Z" }, + { url = "https://files.pythonhosted.org/packages/46/c5/960764d12c92bc225f02401d3067048cb7b282293d9e48e39fe2b0ec38a9/fonttools-4.60.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:de76c8d740fb55745f3b154f0470c56db92ae3be27af8ad6c2e88f1458260c9a", size = 4920015, upload-time = "2025-12-09T13:37:20.334Z" }, + { url = "https://files.pythonhosted.org/packages/4b/ab/839d8caf253d1eef3653ef4d34427d0326d17a53efaec9eb04056b670fff/fonttools-4.60.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:6ba6303225c95998c9fda2d410aa792c3d2c1390a09df58d194b03e17583fa25", size = 5031165, upload-time = "2025-12-09T13:37:23.57Z" }, + { url = "https://files.pythonhosted.org/packages/de/bf/3bc862796a6841cbe0725bb5512d272239b809dba631a4b0301df885e62d/fonttools-4.60.2-cp314-cp314-win32.whl", hash = "sha256:0a89728ce10d7c816fedaa5380c06d2793e7a8a634d7ce16810e536c22047384", size = 2267526, upload-time = "2025-12-09T13:37:25.821Z" }, + { url = "https://files.pythonhosted.org/packages/fc/a1/c1909cacf00c76dc37b4743451561fbaaf7db4172c22a6d9394081d114c3/fonttools-4.60.2-cp314-cp314-win_amd64.whl", hash = "sha256:fa8446e6ab8bd778b82cb1077058a2addba86f30de27ab9cc18ed32b34bc8667", size = 2319096, upload-time = "2025-12-09T13:37:28.058Z" }, + { url = "https://files.pythonhosted.org/packages/29/b3/f66e71433f08e3a931b2b31a665aeed17fcc5e6911fc73529c70a232e421/fonttools-4.60.2-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:4063bc81ac5a4137642865cb63dd270e37b3cd1f55a07c0d6e41d072699ccca2", size = 2925167, upload-time = "2025-12-09T13:37:30.348Z" }, + { url = "https://files.pythonhosted.org/packages/2e/13/eeb491ff743594bbd0bee6e49422c03a59fe9c49002d3cc60eeb77414285/fonttools-4.60.2-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:ebfdb66fa69732ed604ab8e2a0431e6deff35e933a11d73418cbc7823d03b8e1", size = 2430923, upload-time = "2025-12-09T13:37:32.817Z" }, + { url = "https://files.pythonhosted.org/packages/b2/e5/db609f785e460796e53c4dbc3874a5f4948477f27beceb5e2d24b2537666/fonttools-4.60.2-cp314-cp314t-manylinux1_x86_64.manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:50b10b3b1a72d1d54c61b0e59239e1a94c0958f4a06a1febf97ce75388dd91a4", size = 4877729, upload-time = "2025-12-09T13:37:35.858Z" }, + { url = "https://files.pythonhosted.org/packages/5f/d6/85e4484dd4bfb03fee7bd370d65888cccbd3dee2681ee48c869dd5ccb23f/fonttools-4.60.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:beae16891a13b4a2ddec9b39b4de76092a3025e4d1c82362e3042b62295d5e4d", size = 5096003, upload-time = "2025-12-09T13:37:37.862Z" }, + { url = "https://files.pythonhosted.org/packages/30/49/1a98e44b71030b83d2046f981373b80571868259d98e6dae7bc20099dac6/fonttools-4.60.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:522f017fdb3766fd5d2d321774ef351cc6ce88ad4e6ac9efe643e4a2b9d528db", size = 4974410, upload-time = "2025-12-09T13:37:40.166Z" }, + { url = "https://files.pythonhosted.org/packages/42/07/d6f775d950ee8a841012472c7303f8819423d8cc3b4530915de7265ebfa2/fonttools-4.60.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:82cceceaf9c09a965a75b84a4b240dd3768e596ffb65ef53852681606fe7c9ba", size = 5002036, upload-time = "2025-12-09T13:37:42.639Z" }, + { url = "https://files.pythonhosted.org/packages/73/f6/ba6458f83ce1a9f8c3b17bd8f7b8a2205a126aac1055796b7e7cfebbd38f/fonttools-4.60.2-cp314-cp314t-win32.whl", hash = "sha256:bbfbc918a75437fe7e6d64d1b1e1f713237df1cf00f3a36dedae910b2ba01cee", size = 2330985, upload-time = "2025-12-09T13:37:45.157Z" }, + { url = "https://files.pythonhosted.org/packages/91/24/fea0ba4d3a32d4ed1103a1098bfd99dc78b5fe3bb97202920744a37b73dc/fonttools-4.60.2-cp314-cp314t-win_amd64.whl", hash = "sha256:0e5cd9b0830f6550d58c84f3ab151a9892b50c4f9d538c5603c0ce6fff2eb3f1", size = 2396226, upload-time = "2025-12-09T13:37:47.355Z" }, + { url = "https://files.pythonhosted.org/packages/55/ae/a6d9446cb258d3fe87e311c2d7bacf8e8da3e5809fbdc3a8306db4f6b14e/fonttools-4.60.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:a3c75b8b42f7f93906bdba9eb1197bb76aecbe9a0a7cf6feec75f7605b5e8008", size = 2857184, upload-time = "2025-12-09T13:37:49.96Z" }, + { url = "https://files.pythonhosted.org/packages/3a/f3/1b41d0b6a8b908aa07f652111155dd653ebbf0b3385e66562556c5206685/fonttools-4.60.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0f86c8c37bc0ec0b9c141d5e90c717ff614e93c187f06d80f18c7057097f71bc", size = 2401877, upload-time = "2025-12-09T13:37:52.307Z" }, + { url = "https://files.pythonhosted.org/packages/71/57/048fd781680c38b05c5463657d0d95d5f2391a51972176e175c01de29d42/fonttools-4.60.2-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fe905403fe59683b0e9a45f234af2866834376b8821f34633b1c76fb731b6311", size = 4878073, upload-time = "2025-12-09T13:37:56.477Z" }, + { url = "https://files.pythonhosted.org/packages/45/bb/363364f052a893cebd3d449588b21244a9d873620fda03ad92702d2e1bc7/fonttools-4.60.2-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:38ce703b60a906e421e12d9e3a7f064883f5e61bb23e8961f4be33cfe578500b", size = 4835385, upload-time = "2025-12-09T13:37:58.882Z" }, + { url = "https://files.pythonhosted.org/packages/1c/38/e392bb930b2436287e6021672345db26441bf1f85f1e98f8b9784334e41d/fonttools-4.60.2-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:9e810c06f3e79185cecf120e58b343ea5a89b54dd695fd644446bcf8c026da5e", size = 4853084, upload-time = "2025-12-09T13:38:01.578Z" }, + { url = "https://files.pythonhosted.org/packages/65/60/0d77faeaecf7a3276a8a6dc49e2274357e6b3ed6a1774e2fdb2a7f142db0/fonttools-4.60.2-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:38faec8cc1d12122599814d15a402183f5123fb7608dac956121e7c6742aebc5", size = 4971144, upload-time = "2025-12-09T13:38:03.748Z" }, + { url = "https://files.pythonhosted.org/packages/ba/c7/6d3ac3afbcd598631bce24c3ecb919e7d0644a82fea8ddc4454312fc0be6/fonttools-4.60.2-cp39-cp39-win32.whl", hash = "sha256:80a45cf7bf659acb7b36578f300231873daba67bd3ca8cce181c73f861f14a37", size = 1499411, upload-time = "2025-12-09T13:38:05.586Z" }, + { url = "https://files.pythonhosted.org/packages/5a/1c/9dedf6420e23f9fa630bb97941839dddd2e1e57d1b2b85a902378dbe0bd2/fonttools-4.60.2-cp39-cp39-win_amd64.whl", hash = "sha256:c355d5972071938e1b1e0f5a1df001f68ecf1a62f34a3407dc8e0beccf052501", size = 1547943, upload-time = "2025-12-09T13:38:07.604Z" }, + { url = "https://files.pythonhosted.org/packages/79/6c/10280af05b44fafd1dff69422805061fa1af29270bc52dce031ac69540bf/fonttools-4.60.2-py3-none-any.whl", hash = "sha256:73cf92eeda67cf6ff10c8af56fc8f4f07c1647d989a979be9e388a49be26552a", size = 1144610, upload-time = "2025-12-09T13:38:09.5Z" }, +] + +[[package]] +name = "fonttools" +version = "4.63.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.15'", + "python_full_version >= '3.12' and python_full_version < '3.15'", + "python_full_version == '3.11.*'", + "python_full_version == '3.10.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/84/69/c97f2c18e0db87d2c7b15da1974dace76ae938f1cfa22e2727a648b7ed43/fonttools-4.63.0.tar.gz", hash = "sha256:caeb583deeb5168e694b65cda8b4ee62abedfa66cf88488734466f2366b9c4e0", size = 3597189, upload-time = "2026-05-14T12:04:30.958Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f2/c9/4141c90a90db20f807c7e10bfd689fe53eb8f7f4caff58ee4d4dfe46919f/fonttools-4.63.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e3297a6a4059b4acc3a1e9a8b04741f240a80044eef08ebd32e8b5bcdddce75b", size = 2884632, upload-time = "2026-05-14T12:02:38.56Z" }, + { url = "https://files.pythonhosted.org/packages/b8/46/ad12b5c10eae602d7ef814b02afa08aacbf89da917fed5b071282b7eadc2/fonttools-4.63.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b1cd75a03ad8cb5bc40c90bfde68c0c47de423aa19e5c0f362b43520645eea94", size = 2429441, upload-time = "2026-05-14T12:02:41.162Z" }, + { url = "https://files.pythonhosted.org/packages/90/8f/bdca24a84c81d56fffed052229cdcff368f6e05882e526f4558891481f65/fonttools-4.63.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c0425b277a59cff3d80ca42162a8de360f318438a2ac83570842a678d826d579", size = 4946346, upload-time = "2026-05-14T12:02:43.41Z" }, + { url = "https://files.pythonhosted.org/packages/04/59/a639c0e136441ee91a65b56fdf89e5d075927e7a09c559d1b0f5276577db/fonttools-4.63.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d7e5c9973aa04c95650c96e5f5ad865fbf42d62079163ecfab1e01cbc2504c22", size = 4903184, upload-time = "2026-05-14T12:02:45.742Z" }, + { url = "https://files.pythonhosted.org/packages/e6/53/91b7e0cb45b536f3da1b29ba8cbab89f27e8b986809e0b1982303a3f4eca/fonttools-4.63.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:cb014d58140a38135f16064c74c652ed57aa0b75cbf8bb59cac821f7edb5334e", size = 4922967, upload-time = "2026-05-14T12:02:48.386Z" }, + { url = "https://files.pythonhosted.org/packages/c7/b7/87439bf44e6b97c5538cd29d0b7e366a5b8ce2cc132a4134fb67fa3f2fa2/fonttools-4.63.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:032038247a96c1690f9f31e377c389383c902531b085aa4e4dabd6f57f870e69", size = 5042799, upload-time = "2026-05-14T12:02:50.424Z" }, + { url = "https://files.pythonhosted.org/packages/ad/7c/8b96c3263b89ef99cded544c0f0636686f85dbd3c211c4dceef0231fca23/fonttools-4.63.0-cp310-cp310-win32.whl", hash = "sha256:a8b33a82979e0a6a34ff435cc81317be1f95ec1ebb7a3a2d1c8a6a54f02ae44e", size = 1519704, upload-time = "2026-05-14T12:02:52.523Z" }, + { url = "https://files.pythonhosted.org/packages/e5/4d/2c2f0069970b6907de8fb5b05c5c0193cc22f717df151d1c7aef1c738f58/fonttools-4.63.0-cp310-cp310-win_amd64.whl", hash = "sha256:0c18358a155d75034911c5ee397a5b44cd19dd325dbb8b35fb60bf421d6a72ac", size = 1568666, upload-time = "2026-05-14T12:02:54.917Z" }, + { url = "https://files.pythonhosted.org/packages/75/2b/a7f1545bdf5da69c4bda0cea2a5781f0ad2a6623e0277267672db43c5fe6/fonttools-4.63.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:2b8ae05d9eacf6081414d759c0a352769ac28ce31280d6bb8e77b03f9e3c449f", size = 2881793, upload-time = "2026-05-14T12:02:56.645Z" }, + { url = "https://files.pythonhosted.org/packages/49/50/965308c703f085f225db2886813b27e015b8b3438c350b22dd65b52c2a2c/fonttools-4.63.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:79cdc9f567aec74a72918fd060283911406750cbc9fd28c1316023deb6ce31a9", size = 2428130, upload-time = "2026-05-14T12:02:58.891Z" }, + { url = "https://files.pythonhosted.org/packages/d8/38/6937fbd7f2dc3a6b48725851bc2c15ec949b9af14d9bbcb5fe83cdf9bdf9/fonttools-4.63.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2c14b4fd138c4bafcca294765c547914e1aa431ae1ca94ab99d8db08c958bd3b", size = 5111952, upload-time = "2026-05-14T12:03:01.263Z" }, + { url = "https://files.pythonhosted.org/packages/0b/43/a81f20050a3115b57d62c8e781446949512eac36690dc384ccea65ff4cc1/fonttools-4.63.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d76ac49f929aecaf82d83250b8347e099d7aecba0f4726c1d9b6df3b8bb5fe18", size = 5082308, upload-time = "2026-05-14T12:03:03.211Z" }, + { url = "https://files.pythonhosted.org/packages/67/00/cdd9d4944ca6ae280d01e69cc37bde3bf663630b837a6fc6d2cd65d80e0e/fonttools-4.63.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:dcf076a4474fe0d7367e5bbf5b052c7284fa1feca729c04176ce513521afd8a0", size = 5087932, upload-time = "2026-05-14T12:03:05.147Z" }, + { url = "https://files.pythonhosted.org/packages/f5/f1/0aa0dbea778c75adbef223c42019fd47d22262b905974d62d829545d485f/fonttools-4.63.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:7dd683fef0663e9f0f45cf541d788d24caa3ec9db50796b588e1757d8b3bc007", size = 5213271, upload-time = "2026-05-14T12:03:07.238Z" }, + { url = "https://files.pythonhosted.org/packages/a8/99/253e4056e1f0e67b9390125a154b73b5eb73ad521bece95c004858fdeec2/fonttools-4.63.0-cp311-cp311-win32.whl", hash = "sha256:afefc1ed0a59785a7fb06ea7e1678e849c193e1e387db783579bc7b3056fcfcb", size = 2304473, upload-time = "2026-05-14T12:03:09.271Z" }, + { url = "https://files.pythonhosted.org/packages/08/60/defa5e69641db890a63be281f41345f4c33b157824eaf0b9fad3e08b0dcb/fonttools-4.63.0-cp311-cp311-win_amd64.whl", hash = "sha256:063e08bd17bd5a90127a14123de0d6a952dbc847695fd98b63c043d58057f90c", size = 2356389, upload-time = "2026-05-14T12:03:11.53Z" }, + { url = "https://files.pythonhosted.org/packages/08/ef/b3c6b9b5be2f82416d73fe2ed2e96e2793cd80e7510bd6a17ca79cdd88ec/fonttools-4.63.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:37dd23e621e3b0aef1baa70a303b80aaf38449632cfc8fd2a55fb285bbccfc02", size = 2881131, upload-time = "2026-05-14T12:03:13.386Z" }, + { url = "https://files.pythonhosted.org/packages/44/a0/c815bea63117fa63e4e1c01f8a1110d2112fa003f838e6467094ec2432ce/fonttools-4.63.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:a9faff9e0c1f76f9fd55899d2ce785832efebab37eb8ae13995853aef178bef0", size = 2426704, upload-time = "2026-05-14T12:03:15.801Z" }, + { url = "https://files.pythonhosted.org/packages/44/04/0b91d8e916e92ad1fac9e4624760baf0fd5ff2ead614c2f68fb21373f03f/fonttools-4.63.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ef3048ef05dbb552b89817713d9cac912e00d0fde4a3105c00d29e52e10c89af", size = 5044298, upload-time = "2026-05-14T12:03:18.085Z" }, + { url = "https://files.pythonhosted.org/packages/77/c7/2342da9830e3e9d4870305ca5d2091d2a83284f2953079b7bdd3b5e029d8/fonttools-4.63.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:58dc6bb86a78d782f00f9190ca02c119cf5bbe2807536e361e18d42019f877d8", size = 4999800, upload-time = "2026-05-14T12:03:20.161Z" }, + { url = "https://files.pythonhosted.org/packages/e6/6d/67fe16c48d7ce050979b33f47e0d28a318f02da030602e944c34f7a16ef3/fonttools-4.63.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ee08ebfa58f6e1aeff5697ab9582105bb620008c1caafb681e4c557e7483027b", size = 4982666, upload-time = "2026-05-14T12:03:22.87Z" }, + { url = "https://files.pythonhosted.org/packages/f2/00/3bbab338c07c71fa56269953845e92c951a61457bbbb0f1022551ea266d9/fonttools-4.63.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:27fdc65af8da6f88b9c6121c47a464cbe359fcfff7ff6fc2d37a1f395d755b78", size = 5133598, upload-time = "2026-05-14T12:03:25.168Z" }, + { url = "https://files.pythonhosted.org/packages/62/f2/aa27c7f98db5b064883dadcc5283947e81e034de42e22a33675878d98b54/fonttools-4.63.0-cp312-cp312-win32.whl", hash = "sha256:af2fd1664d00a397d75f806985ddb36282091c2131a73a6485c23b4a34722263", size = 2292575, upload-time = "2026-05-14T12:03:27.496Z" }, + { url = "https://files.pythonhosted.org/packages/87/36/cccb9bc2a6ab63d1b2980374f0dca72ce95ae267c9b4cfe77455bb70d0d4/fonttools-4.63.0-cp312-cp312-win_amd64.whl", hash = "sha256:59ac449f8cca9b4ffa08d2e7bbadad87ce710d69d1eda5c3c1ce579baa987272", size = 2343211, upload-time = "2026-05-14T12:03:30.057Z" }, + { url = "https://files.pythonhosted.org/packages/0f/8d/d8fec3dcde2963f8c908fb315e5ff2cd0ac34f82394bbbf73a2aa5145ce3/fonttools-4.63.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:cd7e9857e5e63738b9d9fd707bc1f59c8b09e5177726d23664db393c59bb08bd", size = 2876062, upload-time = "2026-05-14T12:03:32.554Z" }, + { url = "https://files.pythonhosted.org/packages/ef/71/d935dc54e4ff121bfdd11e08702db63a7e6f25af21d8a3d7b7212df53641/fonttools-4.63.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c2a2a42198b696a6f48fad91709afb55176e66a5e566131219dba372fb7f8c59", size = 2424594, upload-time = "2026-05-14T12:03:34.86Z" }, + { url = "https://files.pythonhosted.org/packages/8e/40/e76320afa1df918e146155ef239b1719ee266092e96f5423bfd075affba1/fonttools-4.63.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1e874792a8212b44583ea02189d9e693906b2f78b261f372f95d6c563210ac1d", size = 5024840, upload-time = "2026-05-14T12:03:36.745Z" }, + { url = "https://files.pythonhosted.org/packages/ce/36/0b805d8c485f872f65a509cbe3b58a5d0d17bee855333b54a150c79d3061/fonttools-4.63.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:22135da48a348785c5e2d5d2d9d6bec5ed44adacbaeb9db12d9493bf6c6bfa68", size = 4975801, upload-time = "2026-05-14T12:03:38.833Z" }, + { url = "https://files.pythonhosted.org/packages/c8/26/2cee03d0aa083ab022da5c07aff9ed3f689da1defb81ad6917c9627896da/fonttools-4.63.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ccf41f2efdf56994d22d73bef4ced1052161958169428d06ba9724ea9e9a64be", size = 4965009, upload-time = "2026-05-14T12:03:41.494Z" }, + { url = "https://files.pythonhosted.org/packages/7e/48/cc4b66d9058c0d0982c833fad10127c4b0e9324606aafa41382295ca4102/fonttools-4.63.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9ced0bd02ac751dd6319b0da88aaef24414e3b0dbc32bb4f24944821a3741a27", size = 5105892, upload-time = "2026-05-14T12:03:43.525Z" }, + { url = "https://files.pythonhosted.org/packages/d8/1f/a98a30a814b9ddef3a2e706025f90b9e0bc94890e6cb15254bc86547d11a/fonttools-4.63.0-cp313-cp313-win32.whl", hash = "sha256:85be818f5506e8a7753153def2c9550178f0ecae6a47b5e0e8dbb23f7cc90380", size = 2291313, upload-time = "2026-05-14T12:03:45.594Z" }, + { url = "https://files.pythonhosted.org/packages/92/46/5177b01f3b4abfdd4409f31cca4ab279c9343a26efbe9ec78c97fc612e02/fonttools-4.63.0-cp313-cp313-win_amd64.whl", hash = "sha256:ba04cb5891d4c0c21b6da95eda8d7b090021508a294fff33464fc7d241e0856b", size = 2342299, upload-time = "2026-05-14T12:03:47.414Z" }, + { url = "https://files.pythonhosted.org/packages/27/d2/23d25e3f247b328be58d04a4c9f894178a0d1eda7d42867cfb388adaf416/fonttools-4.63.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:fd1e3094f42d806d3d7c79162fc59e5910fcbe3a7360c385b8da969bc4493745", size = 2875338, upload-time = "2026-05-14T12:03:50.052Z" }, + { url = "https://files.pythonhosted.org/packages/cd/58/7dfa0c761cb3b2964e2a84c4dc986c926a87de0cb9fb60d5b28ded3f2914/fonttools-4.63.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:6e528da43bc3791085f8cb6141b1d13e459226790240340fcbb4625649238b03", size = 2422661, upload-time = "2026-05-14T12:03:52.154Z" }, + { url = "https://files.pythonhosted.org/packages/dd/87/64cfa18a7a1621d17b7f4502b2b0ed8a135a90c3db51ea590ee99043e76b/fonttools-4.63.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6b2248c5decb223562f7902ff6325077a073f608ee8e33e88ad88db734eb9f49", size = 5010526, upload-time = "2026-05-14T12:03:54.647Z" }, + { url = "https://files.pythonhosted.org/packages/36/e1/a8933a72c45a87177fbde2696e0d0755c8c9062f8c077a961c6215fa27b1/fonttools-4.63.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:308f957cdeaf8abe4e5f2f124902ef405448af92c90f80e302a3b771c2e6116b", size = 4923946, upload-time = "2026-05-14T12:03:56.984Z" }, + { url = "https://files.pythonhosted.org/packages/27/60/872e6e233b8c5e8b41413796ff18b7fe479661bd40147e071b450dfad7a1/fonttools-4.63.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:bf00f21eb5fb721dbaf73d1e9da6d02a1af7768f2ebcf9798be98beab8ba90f6", size = 4962489, upload-time = "2026-05-14T12:03:59.443Z" }, + { url = "https://files.pythonhosted.org/packages/30/c4/83c24f2ec38b90cfda84bf4b1a1f49df80e84a1db4e7ac6e0d41bf23bc39/fonttools-4.63.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:c1aaa4b9c75798400ac043ce04d74e7830376c85095a5a6ed7cba2f17a266bf4", size = 5071870, upload-time = "2026-05-14T12:04:02.122Z" }, + { url = "https://files.pythonhosted.org/packages/de/40/3ae22b60ff1d41ce0bd044b31238cdc72cef99f28b976f1e128ebd618c9b/fonttools-4.63.0-cp314-cp314-win32.whl", hash = "sha256:22693918177bd9ceabec4736d338045f357769416fc6b0b2508eefef75b08616", size = 2295026, upload-time = "2026-05-14T12:04:04.47Z" }, + { url = "https://files.pythonhosted.org/packages/c3/d4/98078064ccc76b45cb0f6c002452011e93c4bd26f6850344f0951cc1fe89/fonttools-4.63.0-cp314-cp314-win_amd64.whl", hash = "sha256:7d782fac32985914c351556f68ac0855391572bcd87de50e05970d3cd4c96fc5", size = 2347454, upload-time = "2026-05-14T12:04:06.752Z" }, + { url = "https://files.pythonhosted.org/packages/49/4e/652d1580c5f4e39f7d103b0c793e4773129ad633dce4addd0cf4dfebde02/fonttools-4.63.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:6db5140a60a5d731d21ec076745b40a310607731b0a565b50776393188649001", size = 2958152, upload-time = "2026-05-14T12:04:08.706Z" }, + { url = "https://files.pythonhosted.org/packages/0e/55/ad864c9a9b219f552eb46b32cd7906c466e5a578ba0c3abfcc0fe7413eb6/fonttools-4.63.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:7d76edbff9014094dbf03bd2d074709dfa6ec7aba13d838c937a2b33d2d6a86e", size = 2460809, upload-time = "2026-05-14T12:04:10.783Z" }, + { url = "https://files.pythonhosted.org/packages/ea/2b/0aa8db70f18cf52e49b4ed5ecec68547f981160bf5ded3b5aed6faa0a6f9/fonttools-4.63.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0eac00b9118c3c2f87d272e45341871c5b3066baa3c86897fa634a7c3fb59096", size = 5148649, upload-time = "2026-05-14T12:04:12.747Z" }, + { url = "https://files.pythonhosted.org/packages/7f/63/18e4369c25043096f1048e0c9915951adc4f842bd81c6b18155824d6fa99/fonttools-4.63.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:51394295f1a51de8b5f30bdb1e1b9a4231536c7064ef5c6e211eec19fa36036f", size = 4932147, upload-time = "2026-05-14T12:04:14.806Z" }, + { url = "https://files.pythonhosted.org/packages/a1/3f/67f3eac2ffd8a98446c5022f8ed3864eac878a5ff7af8df4c8286dba16cc/fonttools-4.63.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:9e12f105d2b6342c559c298afb674006bb2893afc7102dcf8a1b55b0486b4e40", size = 5027237, upload-time = "2026-05-14T12:04:17.675Z" }, + { url = "https://files.pythonhosted.org/packages/1a/ba/4e6214cb38a7b04779e97bb7636de9a5c7f20af7018d03dee0b64c08510a/fonttools-4.63.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:796f27556dbe094c4824f75ca85267e4df776c79036c8441469a4df37038c196", size = 5053933, upload-time = "2026-05-14T12:04:20.818Z" }, + { url = "https://files.pythonhosted.org/packages/34/3b/214dcc19ee31d3d38fb5ad2755c11ef0514e5dc300bbaf41c0b69f393799/fonttools-4.63.0-cp314-cp314t-win32.whl", hash = "sha256:948428a275741f0b64b113c955425a953314f4b9ab9997f73a72c83e68e569c8", size = 2359326, upload-time = "2026-05-14T12:04:24.22Z" }, + { url = "https://files.pythonhosted.org/packages/dd/1e/3ff1a9b523058c2eeb6a9d50f5574e2a738200d0d94107d5bc4105e8da3f/fonttools-4.63.0-cp314-cp314t-win_amd64.whl", hash = "sha256:6d4741eb179121cab9eea4cb2393d24492373a260d7945006358c08cfbf45419", size = 2425829, upload-time = "2026-05-14T12:04:26.829Z" }, + { url = "https://files.pythonhosted.org/packages/2c/47/c99d5268f354002ce80f8d029cd9d7d872969da1de8b93d32de4dc56d6f4/fonttools-4.63.0-py3-none-any.whl", hash = "sha256:445af2eab030a16b9171ea8bdda7ebf7d96bda2df88ee182a464252f6e05e20d", size = 1164562, upload-time = "2026-05-14T12:04:29.092Z" }, +] + [[package]] name = "gallery" -version = "0.1.2" +version = "0.1.3" source = { editable = "." } dependencies = [ { name = "argcomplete" }, @@ -303,6 +806,9 @@ dependencies = [ { name = "platformdirs", version = "4.3.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, { name = "platformdirs", version = "4.4.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, { name = "platformdirs", version = "4.9.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" }, + { name = "pymupdf", version = "1.24.11", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "pymupdf", version = "1.26.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "pymupdf", version = "1.28.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" }, { name = "pytest", version = "8.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, { name = "pytest", version = "8.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, { name = "pytest", version = "9.0.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" }, @@ -327,34 +833,73 @@ dev = [ { name = "pytest", version = "8.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, { name = "pytest", version = "9.0.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" }, ] +plotting = [ + { name = "matplotlib", version = "3.7.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "matplotlib", version = "3.9.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "matplotlib", version = "3.10.9", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" }, + { name = "matplotlib", version = "3.11.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, +] [package.metadata] requires-dist = [ { name = "argcomplete", specifier = ">=3.0" }, { name = "black", marker = "extra == 'dev'", specifier = ">=22.0" }, { name = "jinja2", specifier = ">=3.0.0" }, + { name = "matplotlib", marker = "extra == 'plotting'", specifier = ">=3.7" }, { name = "mypy", marker = "extra == 'dev'", specifier = ">=0.900" }, { name = "platformdirs", specifier = ">=3.0" }, { name = "pylint", marker = "extra == 'dev'", specifier = ">=2.0" }, + { name = "pymupdf", specifier = ">=1.23" }, { name = "pytest" }, { name = "pytest", marker = "extra == 'dev'", specifier = ">=7.0" }, { name = "pyyaml", specifier = ">=5.0" }, { name = "textual", specifier = ">=0.50" }, ] -provides-extras = ["dev"] +provides-extras = ["dev", "plotting"] [[package]] name = "importlib-metadata" version = "8.7.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "zipp", marker = "python_full_version == '3.9.*'" }, + { name = "zipp", version = "3.23.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/f3/49/3b30cad09e7771a4982d9975a8cbf64f00d4a1ececb53297f1d9a7be1b10/importlib_metadata-8.7.1.tar.gz", hash = "sha256:49fef1ae6440c182052f407c8d34a68f72efc36db9ca90dc0113398f2fdde8bb", size = 57107, upload-time = "2025-12-21T10:00:19.278Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/fa/5e/f8e9a1d23b9c20a551a8a02ea3637b4642e22c2626e3a13a9a29cdea99eb/importlib_metadata-8.7.1-py3-none-any.whl", hash = "sha256:5a1f80bf1daa489495071efbb095d75a634cf28a8bc299581244063b53176151", size = 27865, upload-time = "2025-12-21T10:00:18.329Z" }, ] +[[package]] +name = "importlib-resources" +version = "6.4.5" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.8.1' and python_full_version < '3.9'", + "python_full_version < '3.8.1'", +] +dependencies = [ + { name = "zipp", version = "3.20.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/98/be/f3e8c6081b684f176b761e6a2fef02a0be939740ed6f54109a2951d806f3/importlib_resources-6.4.5.tar.gz", hash = "sha256:980862a1d16c9e147a59603677fa2aa5fd82b87f223b6cb870695bcfce830065", size = 43372, upload-time = "2024-09-09T17:03:14.677Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e1/6a/4604f9ae2fa62ef47b9de2fa5ad599589d28c9fd1d335f32759813dfa91e/importlib_resources-6.4.5-py3-none-any.whl", hash = "sha256:ac29d5f956f01d5e4bb63102a5a19957f1b9175e45649977264a1416783bb717", size = 36115, upload-time = "2024-09-09T17:03:13.39Z" }, +] + +[[package]] +name = "importlib-resources" +version = "6.5.2" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.9.*'", +] +dependencies = [ + { name = "zipp", version = "3.23.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/cf/8c/f834fbf984f691b4f7ff60f50b514cc3de5cc08abfc3295564dd89c5e2e7/importlib_resources-6.5.2.tar.gz", hash = "sha256:185f87adef5bcc288449d98fb4fba07cea78bc036455dd44c5fc4a2fe78fed2c", size = 44693, upload-time = "2025-01-03T18:51:56.698Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/a4/ed/1f1afb2e9e7f38a545d628f864d562a5ae64fe6f7a10e28ffb9b185b4e89/importlib_resources-6.5.2-py3-none-any.whl", hash = "sha256:789cfdc3ed28c78b67a06acb8126751ced69a3d5f79c095a98298cd8a760ccec", size = 37461, upload-time = "2025-01-03T18:51:54.306Z" }, +] + [[package]] name = "iniconfig" version = "2.1.0" @@ -440,6 +985,262 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" }, ] +[[package]] +name = "kiwisolver" +version = "1.4.7" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.9.*'", + "python_full_version >= '3.8.1' and python_full_version < '3.9'", + "python_full_version < '3.8.1'", +] +sdist = { url = "https://files.pythonhosted.org/packages/85/4d/2255e1c76304cbd60b48cee302b66d1dde4468dc5b1160e4b7cb43778f2a/kiwisolver-1.4.7.tar.gz", hash = "sha256:9893ff81bd7107f7b685d3017cc6583daadb4fc26e4a888350df530e41980a60", size = 97286, upload-time = "2024-09-04T09:39:44.302Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/97/14/fc943dd65268a96347472b4fbe5dcc2f6f55034516f80576cd0dd3a8930f/kiwisolver-1.4.7-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:8a9c83f75223d5e48b0bc9cb1bf2776cf01563e00ade8775ffe13b0b6e1af3a6", size = 122440, upload-time = "2024-09-04T09:03:44.9Z" }, + { url = "https://files.pythonhosted.org/packages/1e/46/e68fed66236b69dd02fcdb506218c05ac0e39745d696d22709498896875d/kiwisolver-1.4.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:58370b1ffbd35407444d57057b57da5d6549d2d854fa30249771775c63b5fe17", size = 65758, upload-time = "2024-09-04T09:03:46.582Z" }, + { url = "https://files.pythonhosted.org/packages/ef/fa/65de49c85838681fc9cb05de2a68067a683717321e01ddafb5b8024286f0/kiwisolver-1.4.7-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:aa0abdf853e09aff551db11fce173e2177d00786c688203f52c87ad7fcd91ef9", size = 64311, upload-time = "2024-09-04T09:03:47.973Z" }, + { url = "https://files.pythonhosted.org/packages/42/9c/cc8d90f6ef550f65443bad5872ffa68f3dee36de4974768628bea7c14979/kiwisolver-1.4.7-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:8d53103597a252fb3ab8b5845af04c7a26d5e7ea8122303dd7a021176a87e8b9", size = 1637109, upload-time = "2024-09-04T09:03:49.281Z" }, + { url = "https://files.pythonhosted.org/packages/55/91/0a57ce324caf2ff5403edab71c508dd8f648094b18cfbb4c8cc0fde4a6ac/kiwisolver-1.4.7-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:88f17c5ffa8e9462fb79f62746428dd57b46eb931698e42e990ad63103f35e6c", size = 1617814, upload-time = "2024-09-04T09:03:51.444Z" }, + { url = "https://files.pythonhosted.org/packages/12/5d/c36140313f2510e20207708adf36ae4919416d697ee0236b0ddfb6fd1050/kiwisolver-1.4.7-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:88a9ca9c710d598fd75ee5de59d5bda2684d9db36a9f50b6125eaea3969c2599", size = 1400881, upload-time = "2024-09-04T09:03:53.357Z" }, + { url = "https://files.pythonhosted.org/packages/56/d0/786e524f9ed648324a466ca8df86298780ef2b29c25313d9a4f16992d3cf/kiwisolver-1.4.7-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f4d742cb7af1c28303a51b7a27aaee540e71bb8e24f68c736f6f2ffc82f2bf05", size = 1512972, upload-time = "2024-09-04T09:03:55.082Z" }, + { url = "https://files.pythonhosted.org/packages/67/5a/77851f2f201e6141d63c10a0708e996a1363efaf9e1609ad0441b343763b/kiwisolver-1.4.7-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e28c7fea2196bf4c2f8d46a0415c77a1c480cc0724722f23d7410ffe9842c407", size = 1444787, upload-time = "2024-09-04T09:03:56.588Z" }, + { url = "https://files.pythonhosted.org/packages/06/5f/1f5eaab84355885e224a6fc8d73089e8713dc7e91c121f00b9a1c58a2195/kiwisolver-1.4.7-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:e968b84db54f9d42046cf154e02911e39c0435c9801681e3fc9ce8a3c4130278", size = 2199212, upload-time = "2024-09-04T09:03:58.557Z" }, + { url = "https://files.pythonhosted.org/packages/b5/28/9152a3bfe976a0ae21d445415defc9d1cd8614b2910b7614b30b27a47270/kiwisolver-1.4.7-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:0c18ec74c0472de033e1bebb2911c3c310eef5649133dd0bedf2a169a1b269e5", size = 2346399, upload-time = "2024-09-04T09:04:00.178Z" }, + { url = "https://files.pythonhosted.org/packages/26/f6/453d1904c52ac3b400f4d5e240ac5fec25263716723e44be65f4d7149d13/kiwisolver-1.4.7-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:8f0ea6da6d393d8b2e187e6a5e3fb81f5862010a40c3945e2c6d12ae45cfb2ad", size = 2308688, upload-time = "2024-09-04T09:04:02.216Z" }, + { url = "https://files.pythonhosted.org/packages/5a/9a/d4968499441b9ae187e81745e3277a8b4d7c60840a52dc9d535a7909fac3/kiwisolver-1.4.7-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:f106407dda69ae456dd1227966bf445b157ccc80ba0dff3802bb63f30b74e895", size = 2445493, upload-time = "2024-09-04T09:04:04.571Z" }, + { url = "https://files.pythonhosted.org/packages/07/c9/032267192e7828520dacb64dfdb1d74f292765f179e467c1cba97687f17d/kiwisolver-1.4.7-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:84ec80df401cfee1457063732d90022f93951944b5b58975d34ab56bb150dfb3", size = 2262191, upload-time = "2024-09-04T09:04:05.969Z" }, + { url = "https://files.pythonhosted.org/packages/6c/ad/db0aedb638a58b2951da46ddaeecf204be8b4f5454df020d850c7fa8dca8/kiwisolver-1.4.7-cp310-cp310-win32.whl", hash = "sha256:71bb308552200fb2c195e35ef05de12f0c878c07fc91c270eb3d6e41698c3bcc", size = 46644, upload-time = "2024-09-04T09:04:07.408Z" }, + { url = "https://files.pythonhosted.org/packages/12/ca/d0f7b7ffbb0be1e7c2258b53554efec1fd652921f10d7d85045aff93ab61/kiwisolver-1.4.7-cp310-cp310-win_amd64.whl", hash = "sha256:44756f9fd339de0fb6ee4f8c1696cfd19b2422e0d70b4cefc1cc7f1f64045a8c", size = 55877, upload-time = "2024-09-04T09:04:08.869Z" }, + { url = "https://files.pythonhosted.org/packages/97/6c/cfcc128672f47a3e3c0d918ecb67830600078b025bfc32d858f2e2d5c6a4/kiwisolver-1.4.7-cp310-cp310-win_arm64.whl", hash = "sha256:78a42513018c41c2ffd262eb676442315cbfe3c44eed82385c2ed043bc63210a", size = 48347, upload-time = "2024-09-04T09:04:10.106Z" }, + { url = "https://files.pythonhosted.org/packages/e9/44/77429fa0a58f941d6e1c58da9efe08597d2e86bf2b2cce6626834f49d07b/kiwisolver-1.4.7-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:d2b0e12a42fb4e72d509fc994713d099cbb15ebf1103545e8a45f14da2dfca54", size = 122442, upload-time = "2024-09-04T09:04:11.432Z" }, + { url = "https://files.pythonhosted.org/packages/e5/20/8c75caed8f2462d63c7fd65e16c832b8f76cda331ac9e615e914ee80bac9/kiwisolver-1.4.7-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:2a8781ac3edc42ea4b90bc23e7d37b665d89423818e26eb6df90698aa2287c95", size = 65762, upload-time = "2024-09-04T09:04:12.468Z" }, + { url = "https://files.pythonhosted.org/packages/f4/98/fe010f15dc7230f45bc4cf367b012d651367fd203caaa992fd1f5963560e/kiwisolver-1.4.7-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:46707a10836894b559e04b0fd143e343945c97fd170d69a2d26d640b4e297935", size = 64319, upload-time = "2024-09-04T09:04:13.635Z" }, + { url = "https://files.pythonhosted.org/packages/8b/1b/b5d618f4e58c0675654c1e5051bcf42c776703edb21c02b8c74135541f60/kiwisolver-1.4.7-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ef97b8df011141c9b0f6caf23b29379f87dd13183c978a30a3c546d2c47314cb", size = 1334260, upload-time = "2024-09-04T09:04:14.878Z" }, + { url = "https://files.pythonhosted.org/packages/b8/01/946852b13057a162a8c32c4c8d2e9ed79f0bb5d86569a40c0b5fb103e373/kiwisolver-1.4.7-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3ab58c12a2cd0fc769089e6d38466c46d7f76aced0a1f54c77652446733d2d02", size = 1426589, upload-time = "2024-09-04T09:04:16.514Z" }, + { url = "https://files.pythonhosted.org/packages/70/d1/c9f96df26b459e15cf8a965304e6e6f4eb291e0f7a9460b4ad97b047561e/kiwisolver-1.4.7-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:803b8e1459341c1bb56d1c5c010406d5edec8a0713a0945851290a7930679b51", size = 1541080, upload-time = "2024-09-04T09:04:18.322Z" }, + { url = "https://files.pythonhosted.org/packages/d3/73/2686990eb8b02d05f3de759d6a23a4ee7d491e659007dd4c075fede4b5d0/kiwisolver-1.4.7-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f9a9e8a507420fe35992ee9ecb302dab68550dedc0da9e2880dd88071c5fb052", size = 1470049, upload-time = "2024-09-04T09:04:20.266Z" }, + { url = "https://files.pythonhosted.org/packages/a7/4b/2db7af3ed3af7c35f388d5f53c28e155cd402a55432d800c543dc6deb731/kiwisolver-1.4.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:18077b53dc3bb490e330669a99920c5e6a496889ae8c63b58fbc57c3d7f33a18", size = 1426376, upload-time = "2024-09-04T09:04:22.419Z" }, + { url = "https://files.pythonhosted.org/packages/05/83/2857317d04ea46dc5d115f0df7e676997bbd968ced8e2bd6f7f19cfc8d7f/kiwisolver-1.4.7-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:6af936f79086a89b3680a280c47ea90b4df7047b5bdf3aa5c524bbedddb9e545", size = 2222231, upload-time = "2024-09-04T09:04:24.526Z" }, + { url = "https://files.pythonhosted.org/packages/0d/b5/866f86f5897cd4ab6d25d22e403404766a123f138bd6a02ecb2cdde52c18/kiwisolver-1.4.7-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:3abc5b19d24af4b77d1598a585b8a719beb8569a71568b66f4ebe1fb0449460b", size = 2368634, upload-time = "2024-09-04T09:04:25.899Z" }, + { url = "https://files.pythonhosted.org/packages/c1/ee/73de8385403faba55f782a41260210528fe3273d0cddcf6d51648202d6d0/kiwisolver-1.4.7-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:933d4de052939d90afbe6e9d5273ae05fb836cc86c15b686edd4b3560cc0ee36", size = 2329024, upload-time = "2024-09-04T09:04:28.523Z" }, + { url = "https://files.pythonhosted.org/packages/a1/e7/cd101d8cd2cdfaa42dc06c433df17c8303d31129c9fdd16c0ea37672af91/kiwisolver-1.4.7-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:65e720d2ab2b53f1f72fb5da5fb477455905ce2c88aaa671ff0a447c2c80e8e3", size = 2468484, upload-time = "2024-09-04T09:04:30.547Z" }, + { url = "https://files.pythonhosted.org/packages/e1/72/84f09d45a10bc57a40bb58b81b99d8f22b58b2040c912b7eb97ebf625bf2/kiwisolver-1.4.7-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:3bf1ed55088f214ba6427484c59553123fdd9b218a42bbc8c6496d6754b1e523", size = 2284078, upload-time = "2024-09-04T09:04:33.218Z" }, + { url = "https://files.pythonhosted.org/packages/d2/d4/71828f32b956612dc36efd7be1788980cb1e66bfb3706e6dec9acad9b4f9/kiwisolver-1.4.7-cp311-cp311-win32.whl", hash = "sha256:4c00336b9dd5ad96d0a558fd18a8b6f711b7449acce4c157e7343ba92dd0cf3d", size = 46645, upload-time = "2024-09-04T09:04:34.371Z" }, + { url = "https://files.pythonhosted.org/packages/a1/65/d43e9a20aabcf2e798ad1aff6c143ae3a42cf506754bcb6a7ed8259c8425/kiwisolver-1.4.7-cp311-cp311-win_amd64.whl", hash = "sha256:929e294c1ac1e9f615c62a4e4313ca1823ba37326c164ec720a803287c4c499b", size = 56022, upload-time = "2024-09-04T09:04:35.786Z" }, + { url = "https://files.pythonhosted.org/packages/35/b3/9f75a2e06f1b4ca00b2b192bc2b739334127d27f1d0625627ff8479302ba/kiwisolver-1.4.7-cp311-cp311-win_arm64.whl", hash = "sha256:e33e8fbd440c917106b237ef1a2f1449dfbb9b6f6e1ce17c94cd6a1e0d438376", size = 48536, upload-time = "2024-09-04T09:04:37.525Z" }, + { url = "https://files.pythonhosted.org/packages/97/9c/0a11c714cf8b6ef91001c8212c4ef207f772dd84540104952c45c1f0a249/kiwisolver-1.4.7-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:5360cc32706dab3931f738d3079652d20982511f7c0ac5711483e6eab08efff2", size = 121808, upload-time = "2024-09-04T09:04:38.637Z" }, + { url = "https://files.pythonhosted.org/packages/f2/d8/0fe8c5f5d35878ddd135f44f2af0e4e1d379e1c7b0716f97cdcb88d4fd27/kiwisolver-1.4.7-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:942216596dc64ddb25adb215c3c783215b23626f8d84e8eff8d6d45c3f29f75a", size = 65531, upload-time = "2024-09-04T09:04:39.694Z" }, + { url = "https://files.pythonhosted.org/packages/80/c5/57fa58276dfdfa612241d640a64ca2f76adc6ffcebdbd135b4ef60095098/kiwisolver-1.4.7-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:48b571ecd8bae15702e4f22d3ff6a0f13e54d3d00cd25216d5e7f658242065ee", size = 63894, upload-time = "2024-09-04T09:04:41.6Z" }, + { url = "https://files.pythonhosted.org/packages/8b/e9/26d3edd4c4ad1c5b891d8747a4f81b1b0aba9fb9721de6600a4adc09773b/kiwisolver-1.4.7-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ad42ba922c67c5f219097b28fae965e10045ddf145d2928bfac2eb2e17673640", size = 1369296, upload-time = "2024-09-04T09:04:42.886Z" }, + { url = "https://files.pythonhosted.org/packages/b6/67/3f4850b5e6cffb75ec40577ddf54f7b82b15269cc5097ff2e968ee32ea7d/kiwisolver-1.4.7-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:612a10bdae23404a72941a0fc8fa2660c6ea1217c4ce0dbcab8a8f6543ea9e7f", size = 1461450, upload-time = "2024-09-04T09:04:46.284Z" }, + { url = "https://files.pythonhosted.org/packages/52/be/86cbb9c9a315e98a8dc6b1d23c43cffd91d97d49318854f9c37b0e41cd68/kiwisolver-1.4.7-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9e838bba3a3bac0fe06d849d29772eb1afb9745a59710762e4ba3f4cb8424483", size = 1579168, upload-time = "2024-09-04T09:04:47.91Z" }, + { url = "https://files.pythonhosted.org/packages/0f/00/65061acf64bd5fd34c1f4ae53f20b43b0a017a541f242a60b135b9d1e301/kiwisolver-1.4.7-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:22f499f6157236c19f4bbbd472fa55b063db77a16cd74d49afe28992dff8c258", size = 1507308, upload-time = "2024-09-04T09:04:49.465Z" }, + { url = "https://files.pythonhosted.org/packages/21/e4/c0b6746fd2eb62fe702118b3ca0cb384ce95e1261cfada58ff693aeec08a/kiwisolver-1.4.7-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:693902d433cf585133699972b6d7c42a8b9f8f826ebcaf0132ff55200afc599e", size = 1464186, upload-time = "2024-09-04T09:04:50.949Z" }, + { url = "https://files.pythonhosted.org/packages/0a/0f/529d0a9fffb4d514f2782c829b0b4b371f7f441d61aa55f1de1c614c4ef3/kiwisolver-1.4.7-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:4e77f2126c3e0b0d055f44513ed349038ac180371ed9b52fe96a32aa071a5107", size = 2247877, upload-time = "2024-09-04T09:04:52.388Z" }, + { url = "https://files.pythonhosted.org/packages/d1/e1/66603ad779258843036d45adcbe1af0d1a889a07af4635f8b4ec7dccda35/kiwisolver-1.4.7-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:657a05857bda581c3656bfc3b20e353c232e9193eb167766ad2dc58b56504948", size = 2404204, upload-time = "2024-09-04T09:04:54.385Z" }, + { url = "https://files.pythonhosted.org/packages/8d/61/de5fb1ca7ad1f9ab7970e340a5b833d735df24689047de6ae71ab9d8d0e7/kiwisolver-1.4.7-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:4bfa75a048c056a411f9705856abfc872558e33c055d80af6a380e3658766038", size = 2352461, upload-time = "2024-09-04T09:04:56.307Z" }, + { url = "https://files.pythonhosted.org/packages/ba/d2/0edc00a852e369827f7e05fd008275f550353f1f9bcd55db9363d779fc63/kiwisolver-1.4.7-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:34ea1de54beef1c104422d210c47c7d2a4999bdecf42c7b5718fbe59a4cac383", size = 2501358, upload-time = "2024-09-04T09:04:57.922Z" }, + { url = "https://files.pythonhosted.org/packages/84/15/adc15a483506aec6986c01fb7f237c3aec4d9ed4ac10b756e98a76835933/kiwisolver-1.4.7-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:90da3b5f694b85231cf93586dad5e90e2d71b9428f9aad96952c99055582f520", size = 2314119, upload-time = "2024-09-04T09:04:59.332Z" }, + { url = "https://files.pythonhosted.org/packages/36/08/3a5bb2c53c89660863a5aa1ee236912269f2af8762af04a2e11df851d7b2/kiwisolver-1.4.7-cp312-cp312-win32.whl", hash = "sha256:18e0cca3e008e17fe9b164b55735a325140a5a35faad8de92dd80265cd5eb80b", size = 46367, upload-time = "2024-09-04T09:05:00.804Z" }, + { url = "https://files.pythonhosted.org/packages/19/93/c05f0a6d825c643779fc3c70876bff1ac221f0e31e6f701f0e9578690d70/kiwisolver-1.4.7-cp312-cp312-win_amd64.whl", hash = "sha256:58cb20602b18f86f83a5c87d3ee1c766a79c0d452f8def86d925e6c60fbf7bfb", size = 55884, upload-time = "2024-09-04T09:05:01.924Z" }, + { url = "https://files.pythonhosted.org/packages/d2/f9/3828d8f21b6de4279f0667fb50a9f5215e6fe57d5ec0d61905914f5b6099/kiwisolver-1.4.7-cp312-cp312-win_arm64.whl", hash = "sha256:f5a8b53bdc0b3961f8b6125e198617c40aeed638b387913bf1ce78afb1b0be2a", size = 48528, upload-time = "2024-09-04T09:05:02.983Z" }, + { url = "https://files.pythonhosted.org/packages/c4/06/7da99b04259b0f18b557a4effd1b9c901a747f7fdd84cf834ccf520cb0b2/kiwisolver-1.4.7-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:2e6039dcbe79a8e0f044f1c39db1986a1b8071051efba3ee4d74f5b365f5226e", size = 121913, upload-time = "2024-09-04T09:05:04.072Z" }, + { url = "https://files.pythonhosted.org/packages/97/f5/b8a370d1aa593c17882af0a6f6755aaecd643640c0ed72dcfd2eafc388b9/kiwisolver-1.4.7-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:a1ecf0ac1c518487d9d23b1cd7139a6a65bc460cd101ab01f1be82ecf09794b6", size = 65627, upload-time = "2024-09-04T09:05:05.119Z" }, + { url = "https://files.pythonhosted.org/packages/2a/fc/6c0374f7503522539e2d4d1b497f5ebad3f8ed07ab51aed2af988dd0fb65/kiwisolver-1.4.7-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7ab9ccab2b5bd5702ab0803676a580fffa2aa178c2badc5557a84cc943fcf750", size = 63888, upload-time = "2024-09-04T09:05:06.191Z" }, + { url = "https://files.pythonhosted.org/packages/bf/3e/0b7172793d0f41cae5c923492da89a2ffcd1adf764c16159ca047463ebd3/kiwisolver-1.4.7-cp313-cp313-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f816dd2277f8d63d79f9c8473a79fe54047bc0467754962840782c575522224d", size = 1369145, upload-time = "2024-09-04T09:05:07.919Z" }, + { url = "https://files.pythonhosted.org/packages/77/92/47d050d6f6aced2d634258123f2688fbfef8ded3c5baf2c79d94d91f1f58/kiwisolver-1.4.7-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cf8bcc23ceb5a1b624572a1623b9f79d2c3b337c8c455405ef231933a10da379", size = 1461448, upload-time = "2024-09-04T09:05:10.01Z" }, + { url = "https://files.pythonhosted.org/packages/9c/1b/8f80b18e20b3b294546a1adb41701e79ae21915f4175f311a90d042301cf/kiwisolver-1.4.7-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:dea0bf229319828467d7fca8c7c189780aa9ff679c94539eed7532ebe33ed37c", size = 1578750, upload-time = "2024-09-04T09:05:11.598Z" }, + { url = "https://files.pythonhosted.org/packages/a4/fe/fe8e72f3be0a844f257cadd72689c0848c6d5c51bc1d60429e2d14ad776e/kiwisolver-1.4.7-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7c06a4c7cf15ec739ce0e5971b26c93638730090add60e183530d70848ebdd34", size = 1507175, upload-time = "2024-09-04T09:05:13.22Z" }, + { url = "https://files.pythonhosted.org/packages/39/fa/cdc0b6105d90eadc3bee525fecc9179e2b41e1ce0293caaf49cb631a6aaf/kiwisolver-1.4.7-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:913983ad2deb14e66d83c28b632fd35ba2b825031f2fa4ca29675e665dfecbe1", size = 1463963, upload-time = "2024-09-04T09:05:15.925Z" }, + { url = "https://files.pythonhosted.org/packages/6e/5c/0c03c4e542720c6177d4f408e56d1c8315899db72d46261a4e15b8b33a41/kiwisolver-1.4.7-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:5337ec7809bcd0f424c6b705ecf97941c46279cf5ed92311782c7c9c2026f07f", size = 2248220, upload-time = "2024-09-04T09:05:17.434Z" }, + { url = "https://files.pythonhosted.org/packages/3d/ee/55ef86d5a574f4e767df7da3a3a7ff4954c996e12d4fbe9c408170cd7dcc/kiwisolver-1.4.7-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:4c26ed10c4f6fa6ddb329a5120ba3b6db349ca192ae211e882970bfc9d91420b", size = 2404463, upload-time = "2024-09-04T09:05:18.997Z" }, + { url = "https://files.pythonhosted.org/packages/0f/6d/73ad36170b4bff4825dc588acf4f3e6319cb97cd1fb3eb04d9faa6b6f212/kiwisolver-1.4.7-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:c619b101e6de2222c1fcb0531e1b17bbffbe54294bfba43ea0d411d428618c27", size = 2352842, upload-time = "2024-09-04T09:05:21.299Z" }, + { url = "https://files.pythonhosted.org/packages/0b/16/fa531ff9199d3b6473bb4d0f47416cdb08d556c03b8bc1cccf04e756b56d/kiwisolver-1.4.7-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:073a36c8273647592ea332e816e75ef8da5c303236ec0167196793eb1e34657a", size = 2501635, upload-time = "2024-09-04T09:05:23.588Z" }, + { url = "https://files.pythonhosted.org/packages/78/7e/aa9422e78419db0cbe75fb86d8e72b433818f2e62e2e394992d23d23a583/kiwisolver-1.4.7-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:3ce6b2b0231bda412463e152fc18335ba32faf4e8c23a754ad50ffa70e4091ee", size = 2314556, upload-time = "2024-09-04T09:05:25.907Z" }, + { url = "https://files.pythonhosted.org/packages/a8/b2/15f7f556df0a6e5b3772a1e076a9d9f6c538ce5f05bd590eca8106508e06/kiwisolver-1.4.7-cp313-cp313-win32.whl", hash = "sha256:f4c9aee212bc89d4e13f58be11a56cc8036cabad119259d12ace14b34476fd07", size = 46364, upload-time = "2024-09-04T09:05:27.184Z" }, + { url = "https://files.pythonhosted.org/packages/0b/db/32e897e43a330eee8e4770bfd2737a9584b23e33587a0812b8e20aac38f7/kiwisolver-1.4.7-cp313-cp313-win_amd64.whl", hash = "sha256:8a3ec5aa8e38fc4c8af308917ce12c536f1c88452ce554027e55b22cbbfbff76", size = 55887, upload-time = "2024-09-04T09:05:28.372Z" }, + { url = "https://files.pythonhosted.org/packages/c8/a4/df2bdca5270ca85fd25253049eb6708d4127be2ed0e5c2650217450b59e9/kiwisolver-1.4.7-cp313-cp313-win_arm64.whl", hash = "sha256:76c8094ac20ec259471ac53e774623eb62e6e1f56cd8690c67ce6ce4fcb05650", size = 48530, upload-time = "2024-09-04T09:05:30.225Z" }, + { url = "https://files.pythonhosted.org/packages/57/d6/620247574d9e26fe24384087879e8399e309f0051782f95238090afa6ccc/kiwisolver-1.4.7-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:5d5abf8f8ec1f4e22882273c423e16cae834c36856cac348cfbfa68e01c40f3a", size = 122325, upload-time = "2024-09-04T09:05:31.648Z" }, + { url = "https://files.pythonhosted.org/packages/bd/c6/572ad7d73dbd898cffa9050ffd7ff7e78a055a1d9b7accd6b4d1f50ec858/kiwisolver-1.4.7-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:aeb3531b196ef6f11776c21674dba836aeea9d5bd1cf630f869e3d90b16cfade", size = 65679, upload-time = "2024-09-04T09:05:32.934Z" }, + { url = "https://files.pythonhosted.org/packages/14/a7/bb8ab10e12cc8764f4da0245d72dee4731cc720bdec0f085d5e9c6005b98/kiwisolver-1.4.7-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b7d755065e4e866a8086c9bdada157133ff466476a2ad7861828e17b6026e22c", size = 64267, upload-time = "2024-09-04T09:05:34.11Z" }, + { url = "https://files.pythonhosted.org/packages/54/a4/3b5a2542429e182a4df0528214e76803f79d016110f5e67c414a0357cd7d/kiwisolver-1.4.7-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:08471d4d86cbaec61f86b217dd938a83d85e03785f51121e791a6e6689a3be95", size = 1387236, upload-time = "2024-09-04T09:05:35.97Z" }, + { url = "https://files.pythonhosted.org/packages/a6/d7/bc3005e906c1673953a3e31ee4f828157d5e07a62778d835dd937d624ea0/kiwisolver-1.4.7-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:7bbfcb7165ce3d54a3dfbe731e470f65739c4c1f85bb1018ee912bae139e263b", size = 1500555, upload-time = "2024-09-04T09:05:37.552Z" }, + { url = "https://files.pythonhosted.org/packages/09/a7/87cb30741f13b7af08446795dca6003491755805edc9c321fe996c1320b8/kiwisolver-1.4.7-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5d34eb8494bea691a1a450141ebb5385e4b69d38bb8403b5146ad279f4b30fa3", size = 1431684, upload-time = "2024-09-04T09:05:39.75Z" }, + { url = "https://files.pythonhosted.org/packages/37/a4/1e4e2d8cdaa42c73d523413498445247e615334e39401ae49dae74885429/kiwisolver-1.4.7-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:9242795d174daa40105c1d86aba618e8eab7bf96ba8c3ee614da8302a9f95503", size = 1125811, upload-time = "2024-09-04T09:05:41.31Z" }, + { url = "https://files.pythonhosted.org/packages/76/36/ae40d7a3171e06f55ac77fe5536079e7be1d8be2a8210e08975c7f9b4d54/kiwisolver-1.4.7-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:a0f64a48bb81af7450e641e3fe0b0394d7381e342805479178b3d335d60ca7cf", size = 1179987, upload-time = "2024-09-04T09:05:42.893Z" }, + { url = "https://files.pythonhosted.org/packages/d8/5d/6e4894b9fdf836d8bd095729dff123bbbe6ad0346289287b45c800fae656/kiwisolver-1.4.7-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:8e045731a5416357638d1700927529e2b8ab304811671f665b225f8bf8d8f933", size = 2186817, upload-time = "2024-09-04T09:05:44.474Z" }, + { url = "https://files.pythonhosted.org/packages/f0/2d/603079b2c2fd62890be0b0ebfc8bb6dda8a5253ca0758885596565b0dfc1/kiwisolver-1.4.7-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:4322872d5772cae7369f8351da1edf255a604ea7087fe295411397d0cfd9655e", size = 2332538, upload-time = "2024-09-04T09:05:46.206Z" }, + { url = "https://files.pythonhosted.org/packages/bb/2a/9a28279c865c38a27960db38b07179143aafc94877945c209bfc553d9dd3/kiwisolver-1.4.7-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:e1631290ee9271dffe3062d2634c3ecac02c83890ada077d225e081aca8aab89", size = 2293890, upload-time = "2024-09-04T09:05:47.819Z" }, + { url = "https://files.pythonhosted.org/packages/1a/4d/4da8967f3bf13c764984b8fbae330683ee5fbd555b4a5624ad2b9decc0ab/kiwisolver-1.4.7-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:edcfc407e4eb17e037bca59be0e85a2031a2ac87e4fed26d3e9df88b4165f92d", size = 2434677, upload-time = "2024-09-04T09:05:49.459Z" }, + { url = "https://files.pythonhosted.org/packages/08/e9/a97a2b6b74dd850fa5974309367e025c06093a143befe9b962d0baebb4f0/kiwisolver-1.4.7-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:4d05d81ecb47d11e7f8932bd8b61b720bf0b41199358f3f5e36d38e28f0532c5", size = 2250339, upload-time = "2024-09-04T09:05:51.165Z" }, + { url = "https://files.pythonhosted.org/packages/8a/e7/55507a387ba1766e69f5e13a79e1aefabdafe0532bee5d1972dfc42b3d16/kiwisolver-1.4.7-cp38-cp38-win32.whl", hash = "sha256:b38ac83d5f04b15e515fd86f312479d950d05ce2368d5413d46c088dda7de90a", size = 46932, upload-time = "2024-09-04T09:05:52.49Z" }, + { url = "https://files.pythonhosted.org/packages/52/77/7e04cca2ff1dc6ee6b7654cebe233de72b7a3ec5616501b6f3144fb70740/kiwisolver-1.4.7-cp38-cp38-win_amd64.whl", hash = "sha256:d83db7cde68459fc803052a55ace60bea2bae361fc3b7a6d5da07e11954e4b09", size = 55836, upload-time = "2024-09-04T09:05:54.078Z" }, + { url = "https://files.pythonhosted.org/packages/11/88/37ea0ea64512997b13d69772db8dcdc3bfca5442cda3a5e4bb943652ee3e/kiwisolver-1.4.7-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:3f9362ecfca44c863569d3d3c033dbe8ba452ff8eed6f6b5806382741a1334bd", size = 122449, upload-time = "2024-09-04T09:05:55.311Z" }, + { url = "https://files.pythonhosted.org/packages/4e/45/5a5c46078362cb3882dcacad687c503089263c017ca1241e0483857791eb/kiwisolver-1.4.7-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:e8df2eb9b2bac43ef8b082e06f750350fbbaf2887534a5be97f6cf07b19d9583", size = 65757, upload-time = "2024-09-04T09:05:56.906Z" }, + { url = "https://files.pythonhosted.org/packages/8a/be/a6ae58978772f685d48dd2e84460937761c53c4bbd84e42b0336473d9775/kiwisolver-1.4.7-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:f32d6edbc638cde7652bd690c3e728b25332acbadd7cad670cc4a02558d9c417", size = 64312, upload-time = "2024-09-04T09:05:58.384Z" }, + { url = "https://files.pythonhosted.org/packages/f4/04/18ef6f452d311e1e1eb180c9bf5589187fa1f042db877e6fe443ef10099c/kiwisolver-1.4.7-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:e2e6c39bd7b9372b0be21456caab138e8e69cc0fc1190a9dfa92bd45a1e6e904", size = 1626966, upload-time = "2024-09-04T09:05:59.855Z" }, + { url = "https://files.pythonhosted.org/packages/21/b1/40655f6c3fa11ce740e8a964fa8e4c0479c87d6a7944b95af799c7a55dfe/kiwisolver-1.4.7-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:dda56c24d869b1193fcc763f1284b9126550eaf84b88bbc7256e15028f19188a", size = 1607044, upload-time = "2024-09-04T09:06:02.16Z" }, + { url = "https://files.pythonhosted.org/packages/fd/93/af67dbcfb9b3323bbd2c2db1385a7139d8f77630e4a37bb945b57188eb2d/kiwisolver-1.4.7-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79849239c39b5e1fd906556c474d9b0439ea6792b637511f3fe3a41158d89ca8", size = 1391879, upload-time = "2024-09-04T09:06:03.908Z" }, + { url = "https://files.pythonhosted.org/packages/40/6f/d60770ef98e77b365d96061d090c0cd9e23418121c55fff188fa4bdf0b54/kiwisolver-1.4.7-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5e3bc157fed2a4c02ec468de4ecd12a6e22818d4f09cde2c31ee3226ffbefab2", size = 1504751, upload-time = "2024-09-04T09:06:05.58Z" }, + { url = "https://files.pythonhosted.org/packages/fa/3a/5f38667d313e983c432f3fcd86932177519ed8790c724e07d77d1de0188a/kiwisolver-1.4.7-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3da53da805b71e41053dc670f9a820d1157aae77b6b944e08024d17bcd51ef88", size = 1436990, upload-time = "2024-09-04T09:06:08.126Z" }, + { url = "https://files.pythonhosted.org/packages/cb/3b/1520301a47326e6a6043b502647e42892be33b3f051e9791cc8bb43f1a32/kiwisolver-1.4.7-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:8705f17dfeb43139a692298cb6637ee2e59c0194538153e83e9ee0c75c2eddde", size = 2191122, upload-time = "2024-09-04T09:06:10.345Z" }, + { url = "https://files.pythonhosted.org/packages/cf/c4/eb52da300c166239a2233f1f9c4a1b767dfab98fae27681bfb7ea4873cb6/kiwisolver-1.4.7-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:82a5c2f4b87c26bb1a0ef3d16b5c4753434633b83d365cc0ddf2770c93829e3c", size = 2338126, upload-time = "2024-09-04T09:06:12.321Z" }, + { url = "https://files.pythonhosted.org/packages/1a/cb/42b92fd5eadd708dd9107c089e817945500685f3437ce1fd387efebc6d6e/kiwisolver-1.4.7-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:ce8be0466f4c0d585cdb6c1e2ed07232221df101a4c6f28821d2aa754ca2d9e2", size = 2298313, upload-time = "2024-09-04T09:06:14.562Z" }, + { url = "https://files.pythonhosted.org/packages/4f/eb/be25aa791fe5fc75a8b1e0c965e00f942496bc04635c9aae8035f6b76dcd/kiwisolver-1.4.7-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:409afdfe1e2e90e6ee7fc896f3df9a7fec8e793e58bfa0d052c8a82f99c37abb", size = 2437784, upload-time = "2024-09-04T09:06:16.767Z" }, + { url = "https://files.pythonhosted.org/packages/c5/22/30a66be7f3368d76ff95689e1c2e28d382383952964ab15330a15d8bfd03/kiwisolver-1.4.7-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:5b9c3f4ee0b9a439d2415012bd1b1cc2df59e4d6a9939f4d669241d30b414327", size = 2253988, upload-time = "2024-09-04T09:06:18.705Z" }, + { url = "https://files.pythonhosted.org/packages/35/d3/5f2ecb94b5211c8a04f218a76133cc8d6d153b0f9cd0b45fad79907f0689/kiwisolver-1.4.7-cp39-cp39-win32.whl", hash = "sha256:a79ae34384df2b615eefca647a2873842ac3b596418032bef9a7283675962644", size = 46980, upload-time = "2024-09-04T09:06:20.106Z" }, + { url = "https://files.pythonhosted.org/packages/ef/17/cd10d020578764ea91740204edc6b3236ed8106228a46f568d716b11feb2/kiwisolver-1.4.7-cp39-cp39-win_amd64.whl", hash = "sha256:cf0438b42121a66a3a667de17e779330fc0f20b0d97d59d2f2121e182b0505e4", size = 55847, upload-time = "2024-09-04T09:06:21.407Z" }, + { url = "https://files.pythonhosted.org/packages/91/84/32232502020bd78d1d12be7afde15811c64a95ed1f606c10456db4e4c3ac/kiwisolver-1.4.7-cp39-cp39-win_arm64.whl", hash = "sha256:764202cc7e70f767dab49e8df52c7455e8de0df5d858fa801a11aa0d882ccf3f", size = 48494, upload-time = "2024-09-04T09:06:22.648Z" }, + { url = "https://files.pythonhosted.org/packages/ac/59/741b79775d67ab67ced9bb38552da688c0305c16e7ee24bba7a2be253fb7/kiwisolver-1.4.7-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:94252291e3fe68001b1dd747b4c0b3be12582839b95ad4d1b641924d68fd4643", size = 59491, upload-time = "2024-09-04T09:06:24.188Z" }, + { url = "https://files.pythonhosted.org/packages/58/cc/fb239294c29a5656e99e3527f7369b174dd9cc7c3ef2dea7cb3c54a8737b/kiwisolver-1.4.7-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:5b7dfa3b546da08a9f622bb6becdb14b3e24aaa30adba66749d38f3cc7ea9706", size = 57648, upload-time = "2024-09-04T09:06:25.559Z" }, + { url = "https://files.pythonhosted.org/packages/3b/ef/2f009ac1f7aab9f81efb2d837301d255279d618d27b6015780115ac64bdd/kiwisolver-1.4.7-pp310-pypy310_pp73-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bd3de6481f4ed8b734da5df134cd5a6a64fe32124fe83dde1e5b5f29fe30b1e6", size = 84257, upload-time = "2024-09-04T09:06:27.038Z" }, + { url = "https://files.pythonhosted.org/packages/81/e1/c64f50987f85b68b1c52b464bb5bf73e71570c0f7782d626d1eb283ad620/kiwisolver-1.4.7-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a91b5f9f1205845d488c928e8570dcb62b893372f63b8b6e98b863ebd2368ff2", size = 80906, upload-time = "2024-09-04T09:06:28.48Z" }, + { url = "https://files.pythonhosted.org/packages/fd/71/1687c5c0a0be2cee39a5c9c389e546f9c6e215e46b691d00d9f646892083/kiwisolver-1.4.7-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:40fa14dbd66b8b8f470d5fc79c089a66185619d31645f9b0773b88b19f7223c4", size = 79951, upload-time = "2024-09-04T09:06:29.966Z" }, + { url = "https://files.pythonhosted.org/packages/ea/8b/d7497df4a1cae9367adf21665dd1f896c2a7aeb8769ad77b662c5e2bcce7/kiwisolver-1.4.7-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:eb542fe7933aa09d8d8f9d9097ef37532a7df6497819d16efe4359890a2f417a", size = 55715, upload-time = "2024-09-04T09:06:31.489Z" }, + { url = "https://files.pythonhosted.org/packages/64/f3/2403d90821fffe496df16f6996cb328b90b0d80c06d2938a930a7732b4f1/kiwisolver-1.4.7-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:bfa1acfa0c54932d5607e19a2c24646fb4c1ae2694437789129cf099789a3b00", size = 59662, upload-time = "2024-09-04T09:06:33.551Z" }, + { url = "https://files.pythonhosted.org/packages/fa/7d/8f409736a4a6ac04354fa530ebf46682ddb1539b0bae15f4731ff2c575bc/kiwisolver-1.4.7-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:eee3ea935c3d227d49b4eb85660ff631556841f6e567f0f7bda972df6c2c9935", size = 57753, upload-time = "2024-09-04T09:06:35.095Z" }, + { url = "https://files.pythonhosted.org/packages/4c/a5/3937c9abe8eedb1356071739ad437a0b486cbad27d54f4ec4733d24882ac/kiwisolver-1.4.7-pp38-pypy38_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:f3160309af4396e0ed04db259c3ccbfdc3621b5559b5453075e5de555e1f3a1b", size = 103564, upload-time = "2024-09-04T09:06:36.756Z" }, + { url = "https://files.pythonhosted.org/packages/b2/18/a5ae23888f010b90d5eb8d196fed30e268056b2ded54d25b38a193bb70e9/kiwisolver-1.4.7-pp38-pypy38_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:a17f6a29cf8935e587cc8a4dbfc8368c55edc645283db0ce9801016f83526c2d", size = 95264, upload-time = "2024-09-04T09:06:38.786Z" }, + { url = "https://files.pythonhosted.org/packages/f9/d0/c4240ae86306d4395e9701f1d7e6ddcc6d60c28cb0127139176cfcfc9ebe/kiwisolver-1.4.7-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:10849fb2c1ecbfae45a693c070e0320a91b35dd4bcf58172c023b994283a124d", size = 78197, upload-time = "2024-09-04T09:06:40.453Z" }, + { url = "https://files.pythonhosted.org/packages/62/db/62423f0ab66813376a35c1e7da488ebdb4e808fcb54b7cec33959717bda1/kiwisolver-1.4.7-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:ac542bf38a8a4be2dc6b15248d36315ccc65f0743f7b1a76688ffb6b5129a5c2", size = 56080, upload-time = "2024-09-04T09:06:42.061Z" }, + { url = "https://files.pythonhosted.org/packages/d5/df/ce37d9b26f07ab90880923c94d12a6ff4d27447096b4c849bfc4339ccfdf/kiwisolver-1.4.7-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:8b01aac285f91ca889c800042c35ad3b239e704b150cfd3382adfc9dcc780e39", size = 58666, upload-time = "2024-09-04T09:06:43.756Z" }, + { url = "https://files.pythonhosted.org/packages/b0/d3/e4b04f43bc629ac8e186b77b2b1a251cdfa5b7610fa189dc0db622672ce6/kiwisolver-1.4.7-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:48be928f59a1f5c8207154f935334d374e79f2b5d212826307d072595ad76a2e", size = 57088, upload-time = "2024-09-04T09:06:45.406Z" }, + { url = "https://files.pythonhosted.org/packages/30/1c/752df58e2d339e670a535514d2db4fe8c842ce459776b8080fbe08ebb98e/kiwisolver-1.4.7-pp39-pypy39_pp73-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f37cfe618a117e50d8c240555331160d73d0411422b59b5ee217843d7b693608", size = 84321, upload-time = "2024-09-04T09:06:47.557Z" }, + { url = "https://files.pythonhosted.org/packages/f0/f8/fe6484e847bc6e238ec9f9828089fb2c0bb53f2f5f3a79351fde5b565e4f/kiwisolver-1.4.7-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:599b5c873c63a1f6ed7eead644a8a380cfbdf5db91dcb6f85707aaab213b1674", size = 80776, upload-time = "2024-09-04T09:06:49.235Z" }, + { url = "https://files.pythonhosted.org/packages/9b/57/d7163c0379f250ef763aba85330a19feefb5ce6cb541ade853aaba881524/kiwisolver-1.4.7-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:801fa7802e5cfabe3ab0c81a34c323a319b097dfb5004be950482d882f3d7225", size = 79984, upload-time = "2024-09-04T09:06:51.336Z" }, + { url = "https://files.pythonhosted.org/packages/8c/95/4a103776c265d13b3d2cd24fb0494d4e04ea435a8ef97e1b2c026d43250b/kiwisolver-1.4.7-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:0c6c43471bc764fad4bc99c5c2d6d16a676b1abf844ca7c8702bdae92df01ee0", size = 55811, upload-time = "2024-09-04T09:06:53.078Z" }, +] + +[[package]] +name = "kiwisolver" +version = "1.5.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.15'", + "python_full_version >= '3.12' and python_full_version < '3.15'", + "python_full_version == '3.11.*'", + "python_full_version == '3.10.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/d0/67/9c61eccb13f0bdca9307614e782fec49ffdde0f7a2314935d489fa93cd9c/kiwisolver-1.5.0.tar.gz", hash = "sha256:d4193f3d9dc3f6f79aaed0e5637f45d98850ebf01f7ca20e69457f3e8946b66a", size = 103482, upload-time = "2026-03-09T13:15:53.382Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ac/f8/06549565caa026e540b7e7bab5c5a90eb7ca986015f4c48dace243cd24d9/kiwisolver-1.5.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:32cc0a5365239a6ea0c6ed461e8838d053b57e397443c0ca894dcc8e388d4374", size = 122802, upload-time = "2026-03-09T13:12:37.515Z" }, + { url = "https://files.pythonhosted.org/packages/84/eb/8476a0818850c563ff343ea7c9c05dcdcbd689a38e01aa31657df01f91fa/kiwisolver-1.5.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:cc0b66c1eec9021353a4b4483afb12dfd50e3669ffbb9152d6842eb34c7e29fd", size = 66216, upload-time = "2026-03-09T13:12:38.812Z" }, + { url = "https://files.pythonhosted.org/packages/f3/c4/f9c8a6b4c21aed4198566e45923512986d6cef530e7263b3a5f823546561/kiwisolver-1.5.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:86e0287879f75621ae85197b0877ed2f8b7aa57b511c7331dce2eb6f4de7d476", size = 63917, upload-time = "2026-03-09T13:12:40.053Z" }, + { url = "https://files.pythonhosted.org/packages/f1/0e/ba4ae25d03722f64de8b2c13e80d82ab537a06b30fc7065183c6439357e3/kiwisolver-1.5.0-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:62f59da443c4f4849f73a51a193b1d9d258dcad0c41bc4d1b8fb2bcc04bfeb22", size = 1628776, upload-time = "2026-03-09T13:12:41.976Z" }, + { url = "https://files.pythonhosted.org/packages/8a/e4/3f43a011bc8a0860d1c96f84d32fa87439d3feedf66e672fef03bf5e8bac/kiwisolver-1.5.0-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9190426b7aa26c5229501fa297b8d0653cfd3f5a36f7990c264e157cbf886b3b", size = 1228164, upload-time = "2026-03-09T13:12:44.002Z" }, + { url = "https://files.pythonhosted.org/packages/4b/34/3a901559a1e0c218404f9a61a93be82d45cb8f44453ba43088644980f033/kiwisolver-1.5.0-cp310-cp310-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:c8277104ded0a51e699c8c3aff63ce2c56d4ed5519a5f73e0fd7057f959a2b9e", size = 1246656, upload-time = "2026-03-09T13:12:45.557Z" }, + { url = "https://files.pythonhosted.org/packages/87/9e/f78c466ea20527822b95ad38f141f2de1dcd7f23fb8716b002b0d91bbe59/kiwisolver-1.5.0-cp310-cp310-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:8f9baf6f0a6e7571c45c8863010b45e837c3ee1c2c77fcd6ef423be91b21fedb", size = 1295562, upload-time = "2026-03-09T13:12:47.562Z" }, + { url = "https://files.pythonhosted.org/packages/0a/66/fd0e4a612e3a286c24e6d6f3a5428d11258ed1909bc530ba3b59807fd980/kiwisolver-1.5.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:cff8e5383db4989311f99e814feeb90c4723eb4edca425b9d5d9c3fefcdd9537", size = 2178473, upload-time = "2026-03-09T13:12:50.254Z" }, + { url = "https://files.pythonhosted.org/packages/dc/8e/6cac929e0049539e5ee25c1ee937556f379ba5204840d03008363ced662d/kiwisolver-1.5.0-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:ebae99ed6764f2b5771c522477b311be313e8841d2e0376db2b10922daebbba4", size = 2274035, upload-time = "2026-03-09T13:12:51.785Z" }, + { url = "https://files.pythonhosted.org/packages/ca/d3/9d0c18f1b52ea8074b792452cf17f1f5a56bd0302a85191f405cfbf9da16/kiwisolver-1.5.0-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:d5cd5189fc2b6a538b75ae45433140c4823463918f7b1617c31e68b085c0022c", size = 2443217, upload-time = "2026-03-09T13:12:53.329Z" }, + { url = "https://files.pythonhosted.org/packages/45/2a/6e19368803a038b2a90857bf4ee9e3c7b667216d045866bf22d3439fd75e/kiwisolver-1.5.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:f42c23db5d1521218a3276bb08666dcb662896a0be7347cba864eca45ff64ede", size = 2249196, upload-time = "2026-03-09T13:12:55.057Z" }, + { url = "https://files.pythonhosted.org/packages/75/2b/3f641dfcbe72e222175d626bacf2f72c3b34312afec949dd1c50afa400f5/kiwisolver-1.5.0-cp310-cp310-win_amd64.whl", hash = "sha256:94eff26096eb5395136634622515b234ecb6c9979824c1f5004c6e3c3c85ccd2", size = 73389, upload-time = "2026-03-09T13:12:56.496Z" }, + { url = "https://files.pythonhosted.org/packages/da/88/299b137b9e0025d8982e03d2d52c123b0a2b159e84b0ef1501ef446339cf/kiwisolver-1.5.0-cp310-cp310-win_arm64.whl", hash = "sha256:dd952e03bfbb096cfe2dd35cd9e00f269969b67536cb4370994afc20ff2d0875", size = 64782, upload-time = "2026-03-09T13:12:57.609Z" }, + { url = "https://files.pythonhosted.org/packages/12/dd/a495a9c104be1c476f0386e714252caf2b7eca883915422a64c50b88c6f5/kiwisolver-1.5.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:9eed0f7edbb274413b6ee781cca50541c8c0facd3d6fd289779e494340a2b85c", size = 122798, upload-time = "2026-03-09T13:12:58.963Z" }, + { url = "https://files.pythonhosted.org/packages/11/60/37b4047a2af0cf5ef6d8b4b26e91829ae6fc6a2d1f74524bcb0e7cd28a32/kiwisolver-1.5.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3c4923e404d6bcd91b6779c009542e5647fef32e4a5d75e115e3bbac6f2335eb", size = 66216, upload-time = "2026-03-09T13:13:00.155Z" }, + { url = "https://files.pythonhosted.org/packages/0a/aa/510dc933d87767584abfe03efa445889996c70c2990f6f87c3ebaa0a18c5/kiwisolver-1.5.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:0df54df7e686afa55e6f21fb86195224a6d9beb71d637e8d7920c95cf0f89aac", size = 63911, upload-time = "2026-03-09T13:13:01.671Z" }, + { url = "https://files.pythonhosted.org/packages/80/46/bddc13df6c2a40741e0cc7865bb1c9ed4796b6760bd04ce5fae3928ef917/kiwisolver-1.5.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2517e24d7315eb51c10664cdb865195df38ab74456c677df67bb47f12d088a27", size = 1438209, upload-time = "2026-03-09T13:13:03.385Z" }, + { url = "https://files.pythonhosted.org/packages/fd/d6/76621246f5165e5372f02f5e6f3f48ea336a8f9e96e43997d45b240ed8cd/kiwisolver-1.5.0-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ff710414307fefa903e0d9bdf300972f892c23477829f49504e59834f4195398", size = 1248888, upload-time = "2026-03-09T13:13:05.231Z" }, + { url = "https://files.pythonhosted.org/packages/b2/c1/31559ec6fb39a5b48035ce29bb63ade628f321785f38c384dee3e2c08bc1/kiwisolver-1.5.0-cp311-cp311-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:6176c1811d9d5a04fa391c490cc44f451e240697a16977f11c6f722efb9041db", size = 1266304, upload-time = "2026-03-09T13:13:06.743Z" }, + { url = "https://files.pythonhosted.org/packages/5e/ef/1cb8276f2d29cc6a41e0a042f27946ca347d3a4a75acf85d0a16aa6dcc82/kiwisolver-1.5.0-cp311-cp311-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:50847dca5d197fcbd389c805aa1a1cf32f25d2e7273dc47ab181a517666b68cc", size = 1319650, upload-time = "2026-03-09T13:13:08.607Z" }, + { url = "https://files.pythonhosted.org/packages/4c/e4/5ba3cecd7ce6236ae4a80f67e5d5531287337d0e1f076ca87a5abe4cd5d0/kiwisolver-1.5.0-cp311-cp311-manylinux_2_39_riscv64.whl", hash = "sha256:01808c6d15f4c3e8559595d6d1fe6411c68e4a3822b4b9972b44473b24f4e679", size = 970949, upload-time = "2026-03-09T13:13:10.299Z" }, + { url = "https://files.pythonhosted.org/packages/5a/69/dc61f7ae9a2f071f26004ced87f078235b5507ab6e5acd78f40365655034/kiwisolver-1.5.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:f1f9f4121ec58628c96baa3de1a55a4e3a333c5102c8e94b64e23bf7b2083309", size = 2199125, upload-time = "2026-03-09T13:13:11.841Z" }, + { url = "https://files.pythonhosted.org/packages/e5/7b/abbe0f1b5afa85f8d084b73e90e5f801c0939eba16ac2e49af7c61a6c28d/kiwisolver-1.5.0-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:b7d335370ae48a780c6e6a6bbfa97342f563744c39c35562f3f367665f5c1de2", size = 2293783, upload-time = "2026-03-09T13:13:14.399Z" }, + { url = "https://files.pythonhosted.org/packages/8a/80/5908ae149d96d81580d604c7f8aefd0e98f4fd728cf172f477e9f2a81744/kiwisolver-1.5.0-cp311-cp311-musllinux_1_2_riscv64.whl", hash = "sha256:800ee55980c18545af444d93fdd60c56b580db5cc54867d8cbf8a1dc0829938c", size = 1960726, upload-time = "2026-03-09T13:13:16.047Z" }, + { url = "https://files.pythonhosted.org/packages/84/08/a78cb776f8c085b7143142ce479859cfec086bd09ee638a317040b6ef420/kiwisolver-1.5.0-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:c438f6ca858697c9ab67eb28246c92508af972e114cac34e57a6d4ba17a3ac08", size = 2464738, upload-time = "2026-03-09T13:13:17.897Z" }, + { url = "https://files.pythonhosted.org/packages/b1/e1/65584da5356ed6cb12c63791a10b208860ac40a83de165cb6a6751a686e3/kiwisolver-1.5.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:8c63c91f95173f9c2a67c7c526b2cea976828a0e7fced9cdcead2802dc10f8a4", size = 2270718, upload-time = "2026-03-09T13:13:19.421Z" }, + { url = "https://files.pythonhosted.org/packages/be/6c/28f17390b62b8f2f520e2915095b3c94d88681ecf0041e75389d9667f202/kiwisolver-1.5.0-cp311-cp311-win_amd64.whl", hash = "sha256:beb7f344487cdcb9e1efe4b7a29681b74d34c08f0043a327a74da852a6749e7b", size = 73480, upload-time = "2026-03-09T13:13:20.818Z" }, + { url = "https://files.pythonhosted.org/packages/d8/0e/2ee5debc4f77a625778fec5501ff3e8036fe361b7ee28ae402a485bb9694/kiwisolver-1.5.0-cp311-cp311-win_arm64.whl", hash = "sha256:ad4ae4ffd1ee9cd11357b4c66b612da9888f4f4daf2f36995eda64bd45370cac", size = 64930, upload-time = "2026-03-09T13:13:21.997Z" }, + { url = "https://files.pythonhosted.org/packages/4d/b2/818b74ebea34dabe6d0c51cb1c572e046730e64844da6ed646d5298c40ce/kiwisolver-1.5.0-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:4e9750bc21b886308024f8a54ccb9a2cc38ac9fa813bf4348434e3d54f337ff9", size = 123158, upload-time = "2026-03-09T13:13:23.127Z" }, + { url = "https://files.pythonhosted.org/packages/bf/d9/405320f8077e8e1c5c4bd6adc45e1e6edf6d727b6da7f2e2533cf58bff71/kiwisolver-1.5.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:72ec46b7eba5b395e0a7b63025490d3214c11013f4aacb4f5e8d6c3041829588", size = 66388, upload-time = "2026-03-09T13:13:24.765Z" }, + { url = "https://files.pythonhosted.org/packages/99/9f/795fedf35634f746151ca8839d05681ceb6287fbed6cc1c9bf235f7887c2/kiwisolver-1.5.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ed3a984b31da7481b103f68776f7128a89ef26ed40f4dc41a2223cda7fb24819", size = 64068, upload-time = "2026-03-09T13:13:25.878Z" }, + { url = "https://files.pythonhosted.org/packages/c4/13/680c54afe3e65767bed7ec1a15571e1a2f1257128733851ade24abcefbcc/kiwisolver-1.5.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:bb5136fb5352d3f422df33f0c879a1b0c204004324150cc3b5e3c4f310c9049f", size = 1477934, upload-time = "2026-03-09T13:13:27.166Z" }, + { url = "https://files.pythonhosted.org/packages/c8/2f/cebfcdb60fd6a9b0f6b47a9337198bcbad6fbe15e68189b7011fd914911f/kiwisolver-1.5.0-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b2af221f268f5af85e776a73d62b0845fc8baf8ef0abfae79d29c77d0e776aaf", size = 1278537, upload-time = "2026-03-09T13:13:28.707Z" }, + { url = "https://files.pythonhosted.org/packages/f2/0d/9b782923aada3fafb1d6b84e13121954515c669b18af0c26e7d21f579855/kiwisolver-1.5.0-cp312-cp312-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:b0f172dc8ffaccb8522d7c5d899de00133f2f1ca7b0a49b7da98e901de87bf2d", size = 1296685, upload-time = "2026-03-09T13:13:30.528Z" }, + { url = "https://files.pythonhosted.org/packages/27/70/83241b6634b04fe44e892688d5208332bde130f38e610c0418f9ede47ded/kiwisolver-1.5.0-cp312-cp312-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:6ab8ba9152203feec73758dad83af9a0bbe05001eb4639e547207c40cfb52083", size = 1346024, upload-time = "2026-03-09T13:13:32.818Z" }, + { url = "https://files.pythonhosted.org/packages/e4/db/30ed226fb271ae1a6431fc0fe0edffb2efe23cadb01e798caeb9f2ceae8f/kiwisolver-1.5.0-cp312-cp312-manylinux_2_39_riscv64.whl", hash = "sha256:cdee07c4d7f6d72008d3f73b9bf027f4e11550224c7c50d8df1ae4a37c1402a6", size = 987241, upload-time = "2026-03-09T13:13:34.435Z" }, + { url = "https://files.pythonhosted.org/packages/ec/bd/c314595208e4c9587652d50959ead9e461995389664e490f4dce7ff0f782/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:7c60d3c9b06fb23bd9c6139281ccbdc384297579ae037f08ae90c69f6845c0b1", size = 2227742, upload-time = "2026-03-09T13:13:36.4Z" }, + { url = "https://files.pythonhosted.org/packages/c1/43/0499cec932d935229b5543d073c2b87c9c22846aab48881e9d8d6e742a2d/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:e315e5ec90d88e140f57696ff85b484ff68bb311e36f2c414aa4286293e6dee0", size = 2323966, upload-time = "2026-03-09T13:13:38.204Z" }, + { url = "https://files.pythonhosted.org/packages/3d/6f/79b0d760907965acfd9d61826a3d41f8f093c538f55cd2633d3f0db269f6/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_riscv64.whl", hash = "sha256:1465387ac63576c3e125e5337a6892b9e99e0627d52317f3ca79e6930d889d15", size = 1977417, upload-time = "2026-03-09T13:13:39.966Z" }, + { url = "https://files.pythonhosted.org/packages/ab/31/01d0537c41cb75a551a438c3c7a80d0c60d60b81f694dac83dd436aec0d0/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:530a3fd64c87cffa844d4b6b9768774763d9caa299e9b75d8eca6a4423b31314", size = 2491238, upload-time = "2026-03-09T13:13:41.698Z" }, + { url = "https://files.pythonhosted.org/packages/e4/34/8aefdd0be9cfd00a44509251ba864f5caf2991e36772e61c408007e7f417/kiwisolver-1.5.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:1d9daea4ea6b9be74fe2f01f7fbade8d6ffab263e781274cffca0dba9be9eec9", size = 2294947, upload-time = "2026-03-09T13:13:43.343Z" }, + { url = "https://files.pythonhosted.org/packages/ad/cf/0348374369ca588f8fe9c338fae49fa4e16eeb10ffb3d012f23a54578a9e/kiwisolver-1.5.0-cp312-cp312-win_amd64.whl", hash = "sha256:f18c2d9782259a6dc132fdc7a63c168cbc74b35284b6d75c673958982a378384", size = 73569, upload-time = "2026-03-09T13:13:45.792Z" }, + { url = "https://files.pythonhosted.org/packages/28/26/192b26196e2316e2bd29deef67e37cdf9870d9af8e085e521afff0fed526/kiwisolver-1.5.0-cp312-cp312-win_arm64.whl", hash = "sha256:f7c7553b13f69c1b29a5bde08ddc6d9d0c8bfb84f9ed01c30db25944aeb852a7", size = 64997, upload-time = "2026-03-09T13:13:46.878Z" }, + { url = "https://files.pythonhosted.org/packages/9d/69/024d6711d5ba575aa65d5538042e99964104e97fa153a9f10bc369182bc2/kiwisolver-1.5.0-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:fd40bb9cd0891c4c3cb1ddf83f8bbfa15731a248fdc8162669405451e2724b09", size = 123166, upload-time = "2026-03-09T13:13:48.032Z" }, + { url = "https://files.pythonhosted.org/packages/ce/48/adbb40df306f587054a348831220812b9b1d787aff714cfbc8556e38fccd/kiwisolver-1.5.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:c0e1403fd7c26d77c1f03e096dc58a5c726503fa0db0456678b8668f76f521e3", size = 66395, upload-time = "2026-03-09T13:13:49.365Z" }, + { url = "https://files.pythonhosted.org/packages/a8/3a/d0a972b34e1c63e2409413104216cd1caa02c5a37cb668d1687d466c1c45/kiwisolver-1.5.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:dda366d548e89a90d88a86c692377d18d8bd64b39c1fb2b92cb31370e2896bbd", size = 64065, upload-time = "2026-03-09T13:13:50.562Z" }, + { url = "https://files.pythonhosted.org/packages/2b/0a/7b98e1e119878a27ba8618ca1e18b14f992ff1eda40f47bccccf4de44121/kiwisolver-1.5.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:332b4f0145c30b5f5ad9374881133e5aa64320428a57c2c2b61e9d891a51c2f3", size = 1477903, upload-time = "2026-03-09T13:13:52.084Z" }, + { url = "https://files.pythonhosted.org/packages/18/d8/55638d89ffd27799d5cc3d8aa28e12f4ce7a64d67b285114dbedc8ea4136/kiwisolver-1.5.0-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0c50b89ffd3e1a911c69a1dd3de7173c0cd10b130f56222e57898683841e4f96", size = 1278751, upload-time = "2026-03-09T13:13:54.673Z" }, + { url = "https://files.pythonhosted.org/packages/b8/97/b4c8d0d18421ecceba20ad8701358453b88e32414e6f6950b5a4bad54e65/kiwisolver-1.5.0-cp313-cp313-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:4db576bb8c3ef9365f8b40fe0f671644de6736ae2c27a2c62d7d8a1b4329f099", size = 1296793, upload-time = "2026-03-09T13:13:56.287Z" }, + { url = "https://files.pythonhosted.org/packages/c4/10/f862f94b6389d8957448ec9df59450b81bec4abb318805375c401a1e6892/kiwisolver-1.5.0-cp313-cp313-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:0b85aad90cea8ac6797a53b5d5f2e967334fa4d1149f031c4537569972596cb8", size = 1346041, upload-time = "2026-03-09T13:13:58.269Z" }, + { url = "https://files.pythonhosted.org/packages/a3/6a/f1650af35821eaf09de398ec0bc2aefc8f211f0cda50204c9f1673741ba9/kiwisolver-1.5.0-cp313-cp313-manylinux_2_39_riscv64.whl", hash = "sha256:d36ca54cb4c6c4686f7cbb7b817f66f5911c12ddb519450bbe86707155028f87", size = 987292, upload-time = "2026-03-09T13:13:59.871Z" }, + { url = "https://files.pythonhosted.org/packages/de/19/d7fb82984b9238115fe629c915007be608ebd23dc8629703d917dbfaffd4/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:38f4a703656f493b0ad185211ccfca7f0386120f022066b018eb5296d8613e23", size = 2227865, upload-time = "2026-03-09T13:14:01.401Z" }, + { url = "https://files.pythonhosted.org/packages/7f/b9/46b7f386589fd222dac9e9de9c956ce5bcefe2ee73b4e79891381dda8654/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:3ac2360e93cb41be81121755c6462cff3beaa9967188c866e5fce5cf13170859", size = 2324369, upload-time = "2026-03-09T13:14:02.972Z" }, + { url = "https://files.pythonhosted.org/packages/92/8b/95e237cf3d9c642960153c769ddcbe278f182c8affb20cecc1cc983e7cc5/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_riscv64.whl", hash = "sha256:c95cab08d1965db3d84a121f1c7ce7479bdd4072c9b3dafd8fecce48a2e6b902", size = 1977989, upload-time = "2026-03-09T13:14:04.503Z" }, + { url = "https://files.pythonhosted.org/packages/1b/95/980c9df53501892784997820136c01f62bc1865e31b82b9560f980c0e649/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:fc20894c3d21194d8041a28b65622d5b86db786da6e3cfe73f0c762951a61167", size = 2491645, upload-time = "2026-03-09T13:14:06.106Z" }, + { url = "https://files.pythonhosted.org/packages/cb/32/900647fd0840abebe1561792c6b31e6a7c0e278fc3973d30572a965ca14c/kiwisolver-1.5.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7a32f72973f0f950c1920475d5c5ea3d971b81b6f0ec53b8d0a956cc965f22e0", size = 2295237, upload-time = "2026-03-09T13:14:08.891Z" }, + { url = "https://files.pythonhosted.org/packages/be/8a/be60e3bbcf513cc5a50f4a3e88e1dcecebb79c1ad607a7222877becaa101/kiwisolver-1.5.0-cp313-cp313-win_amd64.whl", hash = "sha256:0bf3acf1419fa93064a4c2189ac0b58e3be7872bf6ee6177b0d4c63dc4cea276", size = 73573, upload-time = "2026-03-09T13:14:12.327Z" }, + { url = "https://files.pythonhosted.org/packages/4d/d2/64be2e429eb4fca7f7e1c52a91b12663aeaf25de3895e5cca0f47ef2a8d0/kiwisolver-1.5.0-cp313-cp313-win_arm64.whl", hash = "sha256:fa8eb9ecdb7efb0b226acec134e0d709e87a909fa4971a54c0c4f6e88635484c", size = 64998, upload-time = "2026-03-09T13:14:13.469Z" }, + { url = "https://files.pythonhosted.org/packages/b0/69/ce68dd0c85755ae2de490bf015b62f2cea5f6b14ff00a463f9d0774449ff/kiwisolver-1.5.0-cp313-cp313t-macosx_10_13_universal2.whl", hash = "sha256:db485b3847d182b908b483b2ed133c66d88d49cacf98fd278fadafe11b4478d1", size = 125700, upload-time = "2026-03-09T13:14:14.636Z" }, + { url = "https://files.pythonhosted.org/packages/74/aa/937aac021cf9d4349990d47eb319309a51355ed1dbdc9c077cdc9224cb11/kiwisolver-1.5.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:be12f931839a3bdfe28b584db0e640a65a8bcbc24560ae3fdb025a449b3d754e", size = 67537, upload-time = "2026-03-09T13:14:15.808Z" }, + { url = "https://files.pythonhosted.org/packages/ee/20/3a87fbece2c40ad0f6f0aefa93542559159c5f99831d596050e8afae7a9f/kiwisolver-1.5.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:16b85d37c2cbb3253226d26e64663f755d88a03439a9c47df6246b35defbdfb7", size = 65514, upload-time = "2026-03-09T13:14:18.035Z" }, + { url = "https://files.pythonhosted.org/packages/f0/7f/f943879cda9007c45e1f7dba216d705c3a18d6b35830e488b6c6a4e7cdf0/kiwisolver-1.5.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4432b835675f0ea7414aab3d37d119f7226d24869b7a829caeab49ebda407b0c", size = 1584848, upload-time = "2026-03-09T13:14:19.745Z" }, + { url = "https://files.pythonhosted.org/packages/37/f8/4d4f85cc1870c127c88d950913370dd76138482161cd07eabbc450deff01/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1b0feb50971481a2cc44d94e88bdb02cdd497618252ae226b8eb1201b957e368", size = 1391542, upload-time = "2026-03-09T13:14:21.54Z" }, + { url = "https://files.pythonhosted.org/packages/04/0b/65dd2916c84d252b244bd405303220f729e7c17c9d7d33dca6feeff9ffc4/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:56fa888f10d0f367155e76ce849fa1166fc9730d13bd2d65a2aa13b6f5424489", size = 1404447, upload-time = "2026-03-09T13:14:23.205Z" }, + { url = "https://files.pythonhosted.org/packages/39/5c/2606a373247babce9b1d056c03a04b65f3cf5290a8eac5d7bdead0a17e21/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:940dda65d5e764406b9fb92761cbf462e4e63f712ab60ed98f70552e496f3bf1", size = 1455918, upload-time = "2026-03-09T13:14:24.74Z" }, + { url = "https://files.pythonhosted.org/packages/d5/d1/c6078b5756670658e9192a2ef11e939c92918833d2745f85cd14a6004bdf/kiwisolver-1.5.0-cp313-cp313t-manylinux_2_39_riscv64.whl", hash = "sha256:89fc958c702ee9a745e4700378f5d23fddbc46ff89e8fdbf5395c24d5c1452a3", size = 1072856, upload-time = "2026-03-09T13:14:26.597Z" }, + { url = "https://files.pythonhosted.org/packages/cb/c8/7def6ddf16eb2b3741d8b172bdaa9af882b03c78e9b0772975408801fa63/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9027d773c4ff81487181a925945743413f6069634d0b122d0b37684ccf4f1e18", size = 2333580, upload-time = "2026-03-09T13:14:28.237Z" }, + { url = "https://files.pythonhosted.org/packages/9e/87/2ac1fce0eb1e616fcd3c35caa23e665e9b1948bb984f4764790924594128/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_ppc64le.whl", hash = "sha256:5b233ea3e165e43e35dba1d2b8ecc21cf070b45b65ae17dd2747d2713d942021", size = 2423018, upload-time = "2026-03-09T13:14:30.018Z" }, + { url = "https://files.pythonhosted.org/packages/67/13/c6700ccc6cc218716bfcda4935e4b2997039869b4ad8a94f364c5a3b8e63/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_riscv64.whl", hash = "sha256:ce9bf03dad3b46408c08649c6fbd6ca28a9fce0eb32fdfffa6775a13103b5310", size = 2062804, upload-time = "2026-03-09T13:14:32.888Z" }, + { url = "https://files.pythonhosted.org/packages/1b/bd/877056304626943ff0f1f44c08f584300c199b887cb3176cd7e34f1515f1/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_s390x.whl", hash = "sha256:fc4d3f1fb9ca0ae9f97b095963bc6326f1dbfd3779d6679a1e016b9baaa153d3", size = 2597482, upload-time = "2026-03-09T13:14:34.971Z" }, + { url = "https://files.pythonhosted.org/packages/75/19/c60626c47bf0f8ac5dcf72c6c98e266d714f2fbbfd50cf6dab5ede3aaa50/kiwisolver-1.5.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:f443b4825c50a51ee68585522ab4a1d1257fac65896f282b4c6763337ac9f5d2", size = 2394328, upload-time = "2026-03-09T13:14:36.816Z" }, + { url = "https://files.pythonhosted.org/packages/47/84/6a6d5e5bb8273756c27b7d810d47f7ef2f1f9b9fd23c9ee9a3f8c75c9cef/kiwisolver-1.5.0-cp313-cp313t-win_arm64.whl", hash = "sha256:893ff3a711d1b515ba9da14ee090519bad4610ed1962fbe298a434e8c5f8db53", size = 68410, upload-time = "2026-03-09T13:14:38.695Z" }, + { url = "https://files.pythonhosted.org/packages/e4/d7/060f45052f2a01ad5762c8fdecd6d7a752b43400dc29ff75cd47225a40fd/kiwisolver-1.5.0-cp314-cp314-macosx_10_15_universal2.whl", hash = "sha256:8df31fe574b8b3993cc61764f40941111b25c2d9fea13d3ce24a49907cd2d615", size = 123231, upload-time = "2026-03-09T13:14:41.323Z" }, + { url = "https://files.pythonhosted.org/packages/c2/a7/78da680eadd06ff35edef6ef68a1ad273bad3e2a0936c9a885103230aece/kiwisolver-1.5.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:1d49a49ac4cbfb7c1375301cd1ec90169dfeae55ff84710d782260ce77a75a02", size = 66489, upload-time = "2026-03-09T13:14:42.534Z" }, + { url = "https://files.pythonhosted.org/packages/49/b2/97980f3ad4fae37dd7fe31626e2bf75fbf8bdf5d303950ec1fab39a12da8/kiwisolver-1.5.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:0cbe94b69b819209a62cb27bdfa5dc2a8977d8de2f89dfd97ba4f53ed3af754e", size = 64063, upload-time = "2026-03-09T13:14:44.759Z" }, + { url = "https://files.pythonhosted.org/packages/e7/f9/b06c934a6aa8bc91f566bd2a214fd04c30506c2d9e2b6b171953216a65b6/kiwisolver-1.5.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:80aa065ffd378ff784822a6d7c3212f2d5f5e9c3589614b5c228b311fd3063ac", size = 1475913, upload-time = "2026-03-09T13:14:46.247Z" }, + { url = "https://files.pythonhosted.org/packages/6b/f0/f768ae564a710135630672981231320bc403cf9152b5596ec5289de0f106/kiwisolver-1.5.0-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4e7f886f47ab881692f278ae901039a234e4025a68e6dfab514263a0b1c4ae05", size = 1282782, upload-time = "2026-03-09T13:14:48.458Z" }, + { url = "https://files.pythonhosted.org/packages/e2/9f/1de7aad00697325f05238a5f2eafbd487fb637cc27a558b5367a5f37fb7f/kiwisolver-1.5.0-cp314-cp314-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:5060731cc3ed12ca3a8b57acd4aeca5bbc2f49216dd0bec1650a1acd89486bcd", size = 1300815, upload-time = "2026-03-09T13:14:50.721Z" }, + { url = "https://files.pythonhosted.org/packages/5a/c2/297f25141d2e468e0ce7f7a7b92e0cf8918143a0cbd3422c1ad627e85a06/kiwisolver-1.5.0-cp314-cp314-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:7a4aa69609f40fce3cbc3f87b2061f042eee32f94b8f11db707b66a26461591a", size = 1347925, upload-time = "2026-03-09T13:14:52.304Z" }, + { url = "https://files.pythonhosted.org/packages/b9/d3/f4c73a02eb41520c47610207b21afa8cdd18fdbf64ffd94674ae21c4812d/kiwisolver-1.5.0-cp314-cp314-manylinux_2_39_riscv64.whl", hash = "sha256:d168fda2dbff7b9b5f38e693182d792a938c31db4dac3a80a4888de603c99554", size = 991322, upload-time = "2026-03-09T13:14:54.637Z" }, + { url = "https://files.pythonhosted.org/packages/7b/46/d3f2efef7732fcda98d22bf4ad5d3d71d545167a852ca710a494f4c15343/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:413b820229730d358efd838ecbab79902fe97094565fdc80ddb6b0a18c18a581", size = 2232857, upload-time = "2026-03-09T13:14:56.471Z" }, + { url = "https://files.pythonhosted.org/packages/3f/ec/2d9756bf2b6d26ae4349b8d3662fb3993f16d80c1f971c179ce862b9dbae/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_ppc64le.whl", hash = "sha256:5124d1ea754509b09e53738ec185584cc609aae4a3b510aaf4ed6aa047ef9303", size = 2329376, upload-time = "2026-03-09T13:14:58.072Z" }, + { url = "https://files.pythonhosted.org/packages/8f/9f/876a0a0f2260f1bde92e002b3019a5fabc35e0939c7d945e0fa66185eb20/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_riscv64.whl", hash = "sha256:e4415a8db000bf49a6dd1c478bf70062eaacff0f462b92b0ba68791a905861f9", size = 1982549, upload-time = "2026-03-09T13:14:59.668Z" }, + { url = "https://files.pythonhosted.org/packages/6c/4f/ba3624dfac23a64d54ac4179832860cb537c1b0af06024936e82ca4154a0/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_s390x.whl", hash = "sha256:d618fd27420381a4f6044faa71f46d8bfd911bd077c555f7138ed88729bfbe79", size = 2494680, upload-time = "2026-03-09T13:15:01.364Z" }, + { url = "https://files.pythonhosted.org/packages/39/b7/97716b190ab98911b20d10bf92eca469121ec483b8ce0edd314f51bc85af/kiwisolver-1.5.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5092eb5b1172947f57d6ea7d89b2f29650414e4293c47707eb499ec07a0ac796", size = 2297905, upload-time = "2026-03-09T13:15:03.925Z" }, + { url = "https://files.pythonhosted.org/packages/a3/36/4e551e8aa55c9188bca9abb5096805edbf7431072b76e2298e34fd3a3008/kiwisolver-1.5.0-cp314-cp314-win_amd64.whl", hash = "sha256:d76e2d8c75051d58177e762164d2e9ab92886534e3a12e795f103524f221dd8e", size = 75086, upload-time = "2026-03-09T13:15:07.775Z" }, + { url = "https://files.pythonhosted.org/packages/70/15/9b90f7df0e31a003c71649cf66ef61c3c1b862f48c81007fa2383c8bd8d7/kiwisolver-1.5.0-cp314-cp314-win_arm64.whl", hash = "sha256:fa6248cd194edff41d7ea9425ced8ca3a6f838bfb295f6f1d6e6bb694a8518df", size = 66577, upload-time = "2026-03-09T13:15:09.139Z" }, + { url = "https://files.pythonhosted.org/packages/17/01/7dc8c5443ff42b38e72731643ed7cf1ed9bf01691ae5cdca98501999ed83/kiwisolver-1.5.0-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:d1ffeb80b5676463d7a7d56acbe8e37a20ce725570e09549fe738e02ca6b7e1e", size = 125794, upload-time = "2026-03-09T13:15:10.525Z" }, + { url = "https://files.pythonhosted.org/packages/46/8a/b4ebe46ebaac6a303417fab10c2e165c557ddaff558f9699d302b256bc53/kiwisolver-1.5.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:bc4d8e252f532ab46a1de9349e2d27b91fce46736a9eedaa37beaca66f574ed4", size = 67646, upload-time = "2026-03-09T13:15:12.016Z" }, + { url = "https://files.pythonhosted.org/packages/60/35/10a844afc5f19d6f567359bf4789e26661755a2f36200d5d1ed8ad0126e5/kiwisolver-1.5.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:6783e069732715ad0c3ce96dbf21dbc2235ab0593f2baf6338101f70371f4028", size = 65511, upload-time = "2026-03-09T13:15:13.311Z" }, + { url = "https://files.pythonhosted.org/packages/f8/8a/685b297052dd041dcebce8e8787b58923b6e78acc6115a0dc9189011c44b/kiwisolver-1.5.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e7c4c09a490dc4d4a7f8cbee56c606a320f9dc28cf92a7157a39d1ce7676a657", size = 1584858, upload-time = "2026-03-09T13:15:15.103Z" }, + { url = "https://files.pythonhosted.org/packages/9e/80/04865e3d4638ac5bddec28908916df4a3075b8c6cc101786a96803188b96/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:2a075bd7bd19c70cf67c8badfa36cf7c5d8de3c9ddb8420c51e10d9c50e94920", size = 1392539, upload-time = "2026-03-09T13:15:16.661Z" }, + { url = "https://files.pythonhosted.org/packages/ba/01/77a19cacc0893fa13fafa46d1bba06fb4dc2360b3292baf4b56d8e067b24/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_24_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:bdd3e53429ff02aa319ba59dfe4ceeec345bf46cf180ec2cf6fd5b942e7975e9", size = 1405310, upload-time = "2026-03-09T13:15:18.229Z" }, + { url = "https://files.pythonhosted.org/packages/53/39/bcaf5d0cca50e604cfa9b4e3ae1d64b50ca1ae5b754122396084599ef903/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_24_s390x.manylinux_2_28_s390x.whl", hash = "sha256:3cdcb35dc9d807259c981a85531048ede628eabcffb3239adf3d17463518992d", size = 1456244, upload-time = "2026-03-09T13:15:20.444Z" }, + { url = "https://files.pythonhosted.org/packages/d0/7a/72c187abc6975f6978c3e39b7cf67aeb8b3c0a8f9790aa7fd412855e9e1f/kiwisolver-1.5.0-cp314-cp314t-manylinux_2_39_riscv64.whl", hash = "sha256:70d593af6a6ca332d1df73d519fddb5148edb15cd90d5f0155e3746a6d4fcc65", size = 1073154, upload-time = "2026-03-09T13:15:22.039Z" }, + { url = "https://files.pythonhosted.org/packages/c7/ca/cf5b25783ebbd59143b4371ed0c8428a278abe68d6d0104b01865b1bbd0f/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:377815a8616074cabbf3f53354e1d040c35815a134e01d7614b7692e4bf8acfa", size = 2334377, upload-time = "2026-03-09T13:15:23.741Z" }, + { url = "https://files.pythonhosted.org/packages/4a/e5/b1f492adc516796e88751282276745340e2a72dcd0d36cf7173e0daf3210/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:0255a027391d52944eae1dbb5d4cc5903f57092f3674e8e544cdd2622826b3f0", size = 2425288, upload-time = "2026-03-09T13:15:25.789Z" }, + { url = "https://files.pythonhosted.org/packages/e6/e5/9b21fbe91a61b8f409d74a26498706e97a48008bfcd1864373d32a6ba31c/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:012b1eb16e28718fa782b5e61dc6f2da1f0792ca73bd05d54de6cb9561665fc9", size = 2063158, upload-time = "2026-03-09T13:15:27.63Z" }, + { url = "https://files.pythonhosted.org/packages/b1/02/83f47986138310f95ea95531f851b2a62227c11cbc3e690ae1374fe49f0f/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:0e3aafb33aed7479377e5e9a82e9d4bf87063741fc99fc7ae48b0f16e32bdd6f", size = 2597260, upload-time = "2026-03-09T13:15:29.421Z" }, + { url = "https://files.pythonhosted.org/packages/07/18/43a5f24608d8c313dd189cf838c8e68d75b115567c6279de7796197cfb6a/kiwisolver-1.5.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:e7a116ae737f0000343218c4edf5bd45893bfeaff0993c0b215d7124c9f77646", size = 2394403, upload-time = "2026-03-09T13:15:31.517Z" }, + { url = "https://files.pythonhosted.org/packages/3b/b5/98222136d839b8afabcaa943b09bd05888c2d36355b7e448550211d1fca4/kiwisolver-1.5.0-cp314-cp314t-win_amd64.whl", hash = "sha256:1dd9b0b119a350976a6d781e7278ec7aca0b201e1a9e2d23d9804afecb6ca681", size = 79687, upload-time = "2026-03-09T13:15:33.204Z" }, + { url = "https://files.pythonhosted.org/packages/99/a2/ca7dc962848040befed12732dff6acae7fb3c4f6fc4272b3f6c9a30b8713/kiwisolver-1.5.0-cp314-cp314t-win_arm64.whl", hash = "sha256:58f812017cd2985c21fbffb4864d59174d4903dd66fa23815e74bbc7a0e2dd57", size = 70032, upload-time = "2026-03-09T13:15:34.411Z" }, + { url = "https://files.pythonhosted.org/packages/1c/fa/2910df836372d8761bb6eff7d8bdcb1613b5c2e03f260efe7abe34d388a7/kiwisolver-1.5.0-graalpy312-graalpy250_312_native-macosx_10_13_x86_64.whl", hash = "sha256:5ae8e62c147495b01a0f4765c878e9bfdf843412446a247e28df59936e99e797", size = 130262, upload-time = "2026-03-09T13:15:35.629Z" }, + { url = "https://files.pythonhosted.org/packages/0f/41/c5f71f9f00aabcc71fee8b7475e3f64747282580c2fe748961ba29b18385/kiwisolver-1.5.0-graalpy312-graalpy250_312_native-macosx_11_0_arm64.whl", hash = "sha256:f6764a4ccab3078db14a632420930f6186058750df066b8ea2a7106df91d3203", size = 138036, upload-time = "2026-03-09T13:15:36.894Z" }, + { url = "https://files.pythonhosted.org/packages/fa/06/7399a607f434119c6e1fdc8ec89a8d51ccccadf3341dee4ead6bd14caaf5/kiwisolver-1.5.0-graalpy312-graalpy250_312_native-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c31c13da98624f957b0fb1b5bae5383b2333c2c3f6793d9825dd5ce79b525cb7", size = 194295, upload-time = "2026-03-09T13:15:38.22Z" }, + { url = "https://files.pythonhosted.org/packages/b5/91/53255615acd2a1eaca307ede3c90eb550bae9c94581f8c00081b6b1c8f44/kiwisolver-1.5.0-graalpy312-graalpy250_312_native-win_amd64.whl", hash = "sha256:1f1489f769582498610e015a8ef2d36f28f505ab3096d0e16b4858a9ec214f57", size = 75987, upload-time = "2026-03-09T13:15:39.65Z" }, + { url = "https://files.pythonhosted.org/packages/17/6f/6fd4f690a40c2582fa34b97d2678f718acf3706b91d270c65ecb455d0a06/kiwisolver-1.5.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:295d9ffe712caa9f8a3081de8d32fc60191b4b51c76f02f951fd8407253528f4", size = 59606, upload-time = "2026-03-09T13:15:40.81Z" }, + { url = "https://files.pythonhosted.org/packages/82/a0/2355d5e3b338f13ce63f361abb181e3b6ea5fffdb73f739b3e80efa76159/kiwisolver-1.5.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:51e8c4084897de9f05898c2c2a39af6318044ae969d46ff7a34ed3f96274adca", size = 57537, upload-time = "2026-03-09T13:15:42.071Z" }, + { url = "https://files.pythonhosted.org/packages/c8/b9/1d50e610ecadebe205b71d6728fd224ce0e0ca6aba7b9cbe1da049203ac5/kiwisolver-1.5.0-pp310-pypy310_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b83af57bdddef03c01a9138034c6ff03181a3028d9a1003b301eb1a55e161a3f", size = 79888, upload-time = "2026-03-09T13:15:43.317Z" }, + { url = "https://files.pythonhosted.org/packages/cd/ee/b85ffcd75afed0357d74f0e6fc02a4507da441165de1ca4760b9f496390d/kiwisolver-1.5.0-pp310-pypy310_pp73-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bf4679a3d71012a7c2bf360e5cd878fbd5e4fcac0896b56393dec239d81529ed", size = 77584, upload-time = "2026-03-09T13:15:44.605Z" }, + { url = "https://files.pythonhosted.org/packages/6b/dd/644d0dde6010a8583b4cd66dd41c5f83f5325464d15c4f490b3340ab73b4/kiwisolver-1.5.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:41024ed50e44ab1a60d3fe0a9d15a4ccc9f5f2b1d814ff283c8d01134d5b81bc", size = 73390, upload-time = "2026-03-09T13:15:45.832Z" }, + { url = "https://files.pythonhosted.org/packages/e9/eb/5fcbbbf9a0e2c3a35effb88831a483345326bbc3a030a3b5b69aee647f84/kiwisolver-1.5.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:ec4c85dc4b687c7f7f15f553ff26a98bfe8c58f5f7f0ac8905f0ba4c7be60232", size = 59532, upload-time = "2026-03-09T13:15:47.047Z" }, + { url = "https://files.pythonhosted.org/packages/c3/9b/e17104555bb4db148fd52327feea1e96be4b88e8e008b029002c281a21ab/kiwisolver-1.5.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:12e91c215a96e39f57989c8912ae761286ac5a9584d04030ceb3368a357f017a", size = 57420, upload-time = "2026-03-09T13:15:48.199Z" }, + { url = "https://files.pythonhosted.org/packages/48/44/2b5b95b7aa39fb2d8d9d956e0f3d5d45aef2ae1d942d4c3ffac2f9cfed1a/kiwisolver-1.5.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:be4a51a55833dc29ab5d7503e7bcb3b3af3402d266018137127450005cdfe737", size = 79892, upload-time = "2026-03-09T13:15:49.694Z" }, + { url = "https://files.pythonhosted.org/packages/52/7d/7157f9bba6b455cfb4632ed411e199fc8b8977642c2b12082e1bd9e6d173/kiwisolver-1.5.0-pp311-pypy311_pp73-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:daae526907e262de627d8f70058a0f64acc9e2641c164c99c8f594b34a799a16", size = 77603, upload-time = "2026-03-09T13:15:50.945Z" }, + { url = "https://files.pythonhosted.org/packages/0a/dd/8050c947d435c8d4bc94e3252f4d8bb8a76cfb424f043a8680be637a57f1/kiwisolver-1.5.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:59cd8683f575d96df5bb48f6add94afc055012c29e28124fcae2b63661b9efb1", size = 73558, upload-time = "2026-03-09T13:15:52.112Z" }, +] + [[package]] name = "librt" version = "0.10.0" @@ -785,6 +1586,285 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/4e/d3/fe08482b5cd995033556d45041a4f4e76e7f0521112a9c9991d40d39825f/markupsafe-3.0.3-cp39-cp39-win_arm64.whl", hash = "sha256:38664109c14ffc9e7437e86b4dceb442b0096dfe3541d7864d9cbe1da4cf36c8", size = 13928, upload-time = "2025-09-27T18:37:39.037Z" }, ] +[[package]] +name = "matplotlib" +version = "3.7.5" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.8.1' and python_full_version < '3.9'", + "python_full_version < '3.8.1'", +] +dependencies = [ + { name = "contourpy", version = "1.1.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "cycler", marker = "python_full_version < '3.9'" }, + { name = "fonttools", version = "4.57.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "importlib-resources", version = "6.4.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "kiwisolver", version = "1.4.7", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "numpy", version = "1.24.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "packaging", marker = "python_full_version < '3.9'" }, + { name = "pillow", version = "10.4.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "pyparsing", version = "3.1.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.9'" }, + { name = "python-dateutil", marker = "python_full_version < '3.9'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/b6/f0/3836719cc3982fbba3b840d18a59db1d0ee9ac7986f24e8c0a092851b67b/matplotlib-3.7.5.tar.gz", hash = "sha256:1e5c971558ebc811aa07f54c7b7c677d78aa518ef4c390e14673a09e0860184a", size = 38098611, upload-time = "2024-02-16T10:50:56.19Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f5/b0/3808e86c41e5d97822d77e89d7f3cb0890725845c050d87ec53732a8b150/matplotlib-3.7.5-cp310-cp310-macosx_10_12_universal2.whl", hash = "sha256:4a87b69cb1cb20943010f63feb0b2901c17a3b435f75349fd9865713bfa63925", size = 8322924, upload-time = "2024-02-16T10:48:06.184Z" }, + { url = "https://files.pythonhosted.org/packages/5b/05/726623be56391ba1740331ad9f1cd30e1adec61c179ddac134957a6dc2e7/matplotlib-3.7.5-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:d3ce45010fefb028359accebb852ca0c21bd77ec0f281952831d235228f15810", size = 7438436, upload-time = "2024-02-16T10:48:10.294Z" }, + { url = "https://files.pythonhosted.org/packages/15/83/89cdef49ef1e320060ec951ba33c132df211561d866c3ed144c81fd110b2/matplotlib-3.7.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fbea1e762b28400393d71be1a02144aa16692a3c4c676ba0178ce83fc2928fdd", size = 7341849, upload-time = "2024-02-16T10:48:13.249Z" }, + { url = "https://files.pythonhosted.org/packages/94/29/39fc4acdc296dd86e09cecb65c14966e1cf18e0f091b9cbd9bd3f0c19ee4/matplotlib-3.7.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ec0e1adc0ad70ba8227e957551e25a9d2995e319c29f94a97575bb90fa1d4469", size = 11354141, upload-time = "2024-02-16T10:48:16.963Z" }, + { url = "https://files.pythonhosted.org/packages/54/36/44c5eeb0d83ae1e3ed34d264d7adee947c4fd56c4a9464ce822de094995a/matplotlib-3.7.5-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6738c89a635ced486c8a20e20111d33f6398a9cbebce1ced59c211e12cd61455", size = 11457668, upload-time = "2024-02-16T10:48:21.339Z" }, + { url = "https://files.pythonhosted.org/packages/b7/e2/f68aeaedf0ef57cbb793637ee82e62e64ea26cee908db0fe4f8e24d502c0/matplotlib-3.7.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1210b7919b4ed94b5573870f316bca26de3e3b07ffdb563e79327dc0e6bba515", size = 11580088, upload-time = "2024-02-16T10:48:25.415Z" }, + { url = "https://files.pythonhosted.org/packages/d9/f7/7c88d34afc38943aa5e4e04d27fc9da5289a48c264c0d794f60c9cda0949/matplotlib-3.7.5-cp310-cp310-win32.whl", hash = "sha256:068ebcc59c072781d9dcdb82f0d3f1458271c2de7ca9c78f5bd672141091e9e1", size = 7339332, upload-time = "2024-02-16T10:48:29.319Z" }, + { url = "https://files.pythonhosted.org/packages/91/99/e5f6f7c9438279581c4a2308d264fe24dc98bb80e3b2719f797227e54ddc/matplotlib-3.7.5-cp310-cp310-win_amd64.whl", hash = "sha256:f098ffbaab9df1e3ef04e5a5586a1e6b1791380698e84938d8640961c79b1fc0", size = 7506405, upload-time = "2024-02-16T10:48:32.499Z" }, + { url = "https://files.pythonhosted.org/packages/5e/c6/45d0485e59d70b7a6a81eade5d0aed548b42cc65658c0ce0f813b9249165/matplotlib-3.7.5-cp311-cp311-macosx_10_12_universal2.whl", hash = "sha256:f65342c147572673f02a4abec2d5a23ad9c3898167df9b47c149f32ce61ca078", size = 8325506, upload-time = "2024-02-16T10:48:36.192Z" }, + { url = "https://files.pythonhosted.org/packages/0e/0a/83bd8589f3597745f624fbcc7da1140088b2f4160ca51c71553c561d0df5/matplotlib-3.7.5-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:4ddf7fc0e0dc553891a117aa083039088d8a07686d4c93fb8a810adca68810af", size = 7439905, upload-time = "2024-02-16T10:48:38.951Z" }, + { url = "https://files.pythonhosted.org/packages/84/c1/a7705b24f8f9b4d7ceea0002c13bae50cf9423f299f56d8c47a5cd2627d2/matplotlib-3.7.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:0ccb830fc29442360d91be48527809f23a5dcaee8da5f4d9b2d5b867c1b087b8", size = 7342895, upload-time = "2024-02-16T10:48:41.61Z" }, + { url = "https://files.pythonhosted.org/packages/94/6e/55d7d8310c96a7459c883aa4be3f5a9338a108278484cbd5c95d480d1cef/matplotlib-3.7.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:efc6bb28178e844d1f408dd4d6341ee8a2e906fc9e0fa3dae497da4e0cab775d", size = 11358830, upload-time = "2024-02-16T10:48:44.984Z" }, + { url = "https://files.pythonhosted.org/packages/55/57/3b36afe104216db1cf2f3889c394b403ea87eda77c4815227c9524462ba8/matplotlib-3.7.5-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3b15c4c2d374f249f324f46e883340d494c01768dd5287f8bc00b65b625ab56c", size = 11462575, upload-time = "2024-02-16T10:48:48.437Z" }, + { url = "https://files.pythonhosted.org/packages/f3/0b/fabcf5f66b12fab5c4110d06a6c0fed875c7e63bc446403f58f9dadc9999/matplotlib-3.7.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3d028555421912307845e59e3de328260b26d055c5dac9b182cc9783854e98fb", size = 11584280, upload-time = "2024-02-16T10:48:53.022Z" }, + { url = "https://files.pythonhosted.org/packages/47/a9/1ad7df27a9da70b62109584632f83fe6ef45774701199c44d5777107c240/matplotlib-3.7.5-cp311-cp311-win32.whl", hash = "sha256:fe184b4625b4052fa88ef350b815559dd90cc6cc8e97b62f966e1ca84074aafa", size = 7340429, upload-time = "2024-02-16T10:48:56.505Z" }, + { url = "https://files.pythonhosted.org/packages/e3/b1/1b6c34b89173d6c206dc5a4028e8518b4dfee3569c13bdc0c88d0486cae7/matplotlib-3.7.5-cp311-cp311-win_amd64.whl", hash = "sha256:084f1f0f2f1010868c6f1f50b4e1c6f2fb201c58475494f1e5b66fed66093647", size = 7507112, upload-time = "2024-02-16T10:48:59.659Z" }, + { url = "https://files.pythonhosted.org/packages/75/dc/4e341a3ef36f3e7321aec0741317f12c7a23264be708a97972bf018c34af/matplotlib-3.7.5-cp312-cp312-macosx_10_12_universal2.whl", hash = "sha256:34bceb9d8ddb142055ff27cd7135f539f2f01be2ce0bafbace4117abe58f8fe4", size = 8323797, upload-time = "2024-02-16T10:49:02.872Z" }, + { url = "https://files.pythonhosted.org/packages/af/83/bbb482d678362ceb68cc59ec4fc705dde636025969361dac77be868541ef/matplotlib-3.7.5-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:c5a2134162273eb8cdfd320ae907bf84d171de948e62180fa372a3ca7cf0f433", size = 7439549, upload-time = "2024-02-16T10:49:05.743Z" }, + { url = "https://files.pythonhosted.org/packages/1a/ee/e49a92d9e369b2b9e4373894171cb4e641771cd7f81bde1d8b6fb8c60842/matplotlib-3.7.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:039ad54683a814002ff37bf7981aa1faa40b91f4ff84149beb53d1eb64617980", size = 7341788, upload-time = "2024-02-16T10:49:09.143Z" }, + { url = "https://files.pythonhosted.org/packages/48/79/89cb2fc5ddcfc3d440a739df04dbe6e4e72b1153d1ebd32b45d42eb71d27/matplotlib-3.7.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4d742ccd1b09e863b4ca58291728db645b51dab343eebb08d5d4b31b308296ce", size = 11356329, upload-time = "2024-02-16T10:49:12.156Z" }, + { url = "https://files.pythonhosted.org/packages/ff/25/84f181cdae5c9eba6fd1c2c35642aec47233425fe3b0d6fccdb323fb36e0/matplotlib-3.7.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:743b1c488ca6a2bc7f56079d282e44d236bf375968bfd1b7ba701fd4d0fa32d6", size = 11577813, upload-time = "2024-02-16T10:49:15.986Z" }, + { url = "https://files.pythonhosted.org/packages/9f/24/b2db065d40e58033b3350222fb8bbb0ffcb834029df9c1f9349dd9c7dd45/matplotlib-3.7.5-cp312-cp312-win_amd64.whl", hash = "sha256:fbf730fca3e1f23713bc1fae0a57db386e39dc81ea57dc305c67f628c1d7a342", size = 7507667, upload-time = "2024-02-16T10:49:19.6Z" }, + { url = "https://files.pythonhosted.org/packages/e3/72/50a38c8fd5dc845b06f8e71c9da802db44b81baabf4af8be78bb8a5622ea/matplotlib-3.7.5-cp38-cp38-macosx_10_12_universal2.whl", hash = "sha256:cfff9b838531698ee40e40ea1a8a9dc2c01edb400b27d38de6ba44c1f9a8e3d2", size = 8322659, upload-time = "2024-02-16T10:49:23.206Z" }, + { url = "https://files.pythonhosted.org/packages/b1/ea/129163dcd21db6da5d559a8160c4a74c1dc5f96ac246a3d4248b43c7648d/matplotlib-3.7.5-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:1dbcca4508bca7847fe2d64a05b237a3dcaec1f959aedb756d5b1c67b770c5ee", size = 7438408, upload-time = "2024-02-16T10:49:27.462Z" }, + { url = "https://files.pythonhosted.org/packages/aa/59/4d13e5b6298b1ca5525eea8c68d3806ae93ab6d0bb17ca9846aa3156b92b/matplotlib-3.7.5-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4cdf4ef46c2a1609a50411b66940b31778db1e4b73d4ecc2eaa40bd588979b13", size = 7341782, upload-time = "2024-02-16T10:49:32.173Z" }, + { url = "https://files.pythonhosted.org/packages/9e/c4/f562df04b08487731743511ff274ae5d31dce2ff3e5621f8b070d20ab54a/matplotlib-3.7.5-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:167200ccfefd1674b60e957186dfd9baf58b324562ad1a28e5d0a6b3bea77905", size = 9196487, upload-time = "2024-02-16T10:49:37.971Z" }, + { url = "https://files.pythonhosted.org/packages/30/33/cc27211d2ffeee4fd7402dca137b6e8a83f6dcae3d4be8d0ad5068555561/matplotlib-3.7.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:53e64522934df6e1818b25fd48cf3b645b11740d78e6ef765fbb5fa5ce080d02", size = 9213051, upload-time = "2024-02-16T10:49:43.916Z" }, + { url = "https://files.pythonhosted.org/packages/9b/9d/8bd37c86b79312c9dbcfa379dec32303f9b38e8456e0829d7e666a0e0a05/matplotlib-3.7.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d3e3bc79b2d7d615067bd010caff9243ead1fc95cf735c16e4b2583173f717eb", size = 11370807, upload-time = "2024-02-16T10:49:47.701Z" }, + { url = "https://files.pythonhosted.org/packages/c0/1e/b24a07a849c8d458f1b3724f49029f0dedf748bdedb4d5f69491314838b6/matplotlib-3.7.5-cp38-cp38-win32.whl", hash = "sha256:6b641b48c6819726ed47c55835cdd330e53747d4efff574109fd79b2d8a13748", size = 7340461, upload-time = "2024-02-16T10:49:51.597Z" }, + { url = "https://files.pythonhosted.org/packages/16/51/58b0b9de42fe1e665736d9286f88b5f1556a0e22bed8a71f468231761083/matplotlib-3.7.5-cp38-cp38-win_amd64.whl", hash = "sha256:f0b60993ed3488b4532ec6b697059897891927cbfc2b8d458a891b60ec03d9d7", size = 7507471, upload-time = "2024-02-16T10:49:54.353Z" }, + { url = "https://files.pythonhosted.org/packages/0d/00/17487e9e8949ca623af87f6c8767408efe7530b7e1f4d6897fa7fa940834/matplotlib-3.7.5-cp39-cp39-macosx_10_12_universal2.whl", hash = "sha256:090964d0afaff9c90e4d8de7836757e72ecfb252fb02884016d809239f715651", size = 8323175, upload-time = "2024-02-16T10:49:57.743Z" }, + { url = "https://files.pythonhosted.org/packages/6a/84/be0acd521fa9d6697657cf35878153f8009a42b4b75237aebc302559a8a9/matplotlib-3.7.5-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:9fc6fcfbc55cd719bc0bfa60bde248eb68cf43876d4c22864603bdd23962ba25", size = 7438737, upload-time = "2024-02-16T10:50:00.683Z" }, + { url = "https://files.pythonhosted.org/packages/17/39/175f36a6d68d0cf47a4fecbae9728048355df23c9feca8688f1476b198e6/matplotlib-3.7.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:5e7cc3078b019bb863752b8b60e8b269423000f1603cb2299608231996bd9d54", size = 7341916, upload-time = "2024-02-16T10:50:05.04Z" }, + { url = "https://files.pythonhosted.org/packages/36/c0/9a1c2a79f85c15d41b60877cbc333694ed80605e5c97a33880c4ecfd5bf1/matplotlib-3.7.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1e4e9a868e8163abaaa8259842d85f949a919e1ead17644fb77a60427c90473c", size = 11352264, upload-time = "2024-02-16T10:50:08.955Z" }, + { url = "https://files.pythonhosted.org/packages/a6/39/b0204e0e7a899b0676733366a55ccafa723799b719bc7f2e85e5ecde26a0/matplotlib-3.7.5-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:fa7ebc995a7d747dacf0a717d0eb3aa0f0c6a0e9ea88b0194d3a3cd241a1500f", size = 11454722, upload-time = "2024-02-16T10:50:13.231Z" }, + { url = "https://files.pythonhosted.org/packages/d8/39/64dd1d36c79e72e614977db338d180cf204cf658927c05a8ef2d47feb4c0/matplotlib-3.7.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3785bfd83b05fc0e0c2ae4c4a90034fe693ef96c679634756c50fe6efcc09856", size = 11576343, upload-time = "2024-02-16T10:50:17.626Z" }, + { url = "https://files.pythonhosted.org/packages/31/b4/e77bc11394d858bdf15e356980fceb4ac9604b0fa8212ef3ca4f1dc166b8/matplotlib-3.7.5-cp39-cp39-win32.whl", hash = "sha256:29b058738c104d0ca8806395f1c9089dfe4d4f0f78ea765c6c704469f3fffc81", size = 7340455, upload-time = "2024-02-16T10:50:21.448Z" }, + { url = "https://files.pythonhosted.org/packages/4a/84/081820c596b9555ecffc6819ee71f847f2fbb0d7c70a42c1eeaa54edf3e0/matplotlib-3.7.5-cp39-cp39-win_amd64.whl", hash = "sha256:fd4028d570fa4b31b7b165d4a685942ae9cdc669f33741e388c01857d9723eab", size = 7507711, upload-time = "2024-02-16T10:50:24.387Z" }, + { url = "https://files.pythonhosted.org/packages/27/6c/1bb10f3d6f337b9faa2e96a251bd87ba5fed85a608df95eb4d69acc109f0/matplotlib-3.7.5-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:2a9a3f4d6a7f88a62a6a18c7e6a84aedcaf4faf0708b4ca46d87b19f1b526f88", size = 7397285, upload-time = "2024-02-16T10:50:27.375Z" }, + { url = "https://files.pythonhosted.org/packages/b2/36/66cfea213e9ba91cda9e257542c249ed235d49021af71c2e8007107d7d4c/matplotlib-3.7.5-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b9b3fd853d4a7f008a938df909b96db0b454225f935d3917520305b90680579c", size = 7552612, upload-time = "2024-02-16T10:50:30.65Z" }, + { url = "https://files.pythonhosted.org/packages/77/df/16655199bf984c37c6a816b854bc032b56aef521aadc04f27928422f3c91/matplotlib-3.7.5-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f0ad550da9f160737d7890217c5eeed4337d07e83ca1b2ca6535078f354e7675", size = 7515564, upload-time = "2024-02-16T10:50:33.589Z" }, + { url = "https://files.pythonhosted.org/packages/5b/c8/3534c3705a677b71abb6be33609ba129fdeae2ea4e76b2fd3ab62c86fab3/matplotlib-3.7.5-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:20da7924a08306a861b3f2d1da0d1aa9a6678e480cf8eacffe18b565af2813e7", size = 7521336, upload-time = "2024-02-16T10:50:36.4Z" }, + { url = "https://files.pythonhosted.org/packages/20/a0/c5c0d410798b387ed3a177a5a7eba21055dd9c41d4b15bd0861241a5a60e/matplotlib-3.7.5-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:b45c9798ea6bb920cb77eb7306409756a7fab9db9b463e462618e0559aecb30e", size = 7397931, upload-time = "2024-02-16T10:50:39.477Z" }, + { url = "https://files.pythonhosted.org/packages/c3/2f/9e9509727d4c7d1b8e2c88e9330a97d54a1dd20bd316a0c8d2f8b38c4513/matplotlib-3.7.5-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a99866267da1e561c7776fe12bf4442174b79aac1a47bd7e627c7e4d077ebd83", size = 7553224, upload-time = "2024-02-16T10:50:42.82Z" }, + { url = "https://files.pythonhosted.org/packages/89/0c/5f3e403dcf5c23799c92b0139dd00e41caf23983e9281f5bfeba3065e7d2/matplotlib-3.7.5-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2b6aa62adb6c268fc87d80f963aca39c64615c31830b02697743c95590ce3fbb", size = 7513250, upload-time = "2024-02-16T10:50:46.504Z" }, + { url = "https://files.pythonhosted.org/packages/87/e0/03eba0a8c3775ef910dbb3a287114a64c47abbcaeab2543c59957f155a86/matplotlib-3.7.5-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:e530ab6a0afd082d2e9c17eb1eb064a63c5b09bb607b2b74fa41adbe3e162286", size = 7521729, upload-time = "2024-02-16T10:50:50.063Z" }, +] + +[[package]] +name = "matplotlib" +version = "3.9.4" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.9.*'", +] +dependencies = [ + { name = "contourpy", version = "1.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "cycler", marker = "python_full_version == '3.9.*'" }, + { name = "fonttools", version = "4.60.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "importlib-resources", version = "6.5.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "kiwisolver", version = "1.4.7", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "packaging", marker = "python_full_version == '3.9.*'" }, + { name = "pillow", version = "11.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "pyparsing", version = "3.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.9.*'" }, + { name = "python-dateutil", marker = "python_full_version == '3.9.*'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/df/17/1747b4154034befd0ed33b52538f5eb7752d05bb51c5e2a31470c3bc7d52/matplotlib-3.9.4.tar.gz", hash = "sha256:1e00e8be7393cbdc6fedfa8a6fba02cf3e83814b285db1c60b906a023ba41bc3", size = 36106529, upload-time = "2024-12-13T05:56:34.184Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/7e/94/27d2e2c30d54b56c7b764acc1874a909e34d1965a427fc7092bb6a588b63/matplotlib-3.9.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:c5fdd7abfb706dfa8d307af64a87f1a862879ec3cd8d0ec8637458f0885b9c50", size = 7885089, upload-time = "2024-12-13T05:54:24.224Z" }, + { url = "https://files.pythonhosted.org/packages/c6/25/828273307e40a68eb8e9df832b6b2aaad075864fdc1de4b1b81e40b09e48/matplotlib-3.9.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d89bc4e85e40a71d1477780366c27fb7c6494d293e1617788986f74e2a03d7ff", size = 7770600, upload-time = "2024-12-13T05:54:27.214Z" }, + { url = "https://files.pythonhosted.org/packages/f2/65/f841a422ec994da5123368d76b126acf4fc02ea7459b6e37c4891b555b83/matplotlib-3.9.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ddf9f3c26aae695c5daafbf6b94e4c1a30d6cd617ba594bbbded3b33a1fcfa26", size = 8200138, upload-time = "2024-12-13T05:54:29.497Z" }, + { url = "https://files.pythonhosted.org/packages/07/06/272aca07a38804d93b6050813de41ca7ab0e29ba7a9dd098e12037c919a9/matplotlib-3.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:18ebcf248030173b59a868fda1fe42397253f6698995b55e81e1f57431d85e50", size = 8312711, upload-time = "2024-12-13T05:54:34.396Z" }, + { url = "https://files.pythonhosted.org/packages/98/37/f13e23b233c526b7e27ad61be0a771894a079e0f7494a10d8d81557e0e9a/matplotlib-3.9.4-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:974896ec43c672ec23f3f8c648981e8bc880ee163146e0312a9b8def2fac66f5", size = 9090622, upload-time = "2024-12-13T05:54:36.808Z" }, + { url = "https://files.pythonhosted.org/packages/4f/8c/b1f5bd2bd70e60f93b1b54c4d5ba7a992312021d0ddddf572f9a1a6d9348/matplotlib-3.9.4-cp310-cp310-win_amd64.whl", hash = "sha256:4598c394ae9711cec135639374e70871fa36b56afae17bdf032a345be552a88d", size = 7828211, upload-time = "2024-12-13T05:54:40.596Z" }, + { url = "https://files.pythonhosted.org/packages/74/4b/65be7959a8fa118a3929b49a842de5b78bb55475236fcf64f3e308ff74a0/matplotlib-3.9.4-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:d4dd29641d9fb8bc4492420c5480398dd40a09afd73aebe4eb9d0071a05fbe0c", size = 7894430, upload-time = "2024-12-13T05:54:44.049Z" }, + { url = "https://files.pythonhosted.org/packages/e9/18/80f70d91896e0a517b4a051c3fd540daa131630fd75e02e250365353b253/matplotlib-3.9.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:30e5b22e8bcfb95442bf7d48b0d7f3bdf4a450cbf68986ea45fca3d11ae9d099", size = 7780045, upload-time = "2024-12-13T05:54:46.414Z" }, + { url = "https://files.pythonhosted.org/packages/a2/73/ccb381026e3238c5c25c3609ba4157b2d1a617ec98d65a8b4ee4e1e74d02/matplotlib-3.9.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2bb0030d1d447fd56dcc23b4c64a26e44e898f0416276cac1ebc25522e0ac249", size = 8209906, upload-time = "2024-12-13T05:54:49.459Z" }, + { url = "https://files.pythonhosted.org/packages/ab/33/1648da77b74741c89f5ea95cbf42a291b4b364f2660b316318811404ed97/matplotlib-3.9.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aca90ed222ac3565d2752b83dbb27627480d27662671e4d39da72e97f657a423", size = 8322873, upload-time = "2024-12-13T05:54:53.066Z" }, + { url = "https://files.pythonhosted.org/packages/57/d3/8447ba78bc6593c9044c372d1609f8ea10fb1e071e7a9e0747bea74fc16c/matplotlib-3.9.4-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a181b2aa2906c608fcae72f977a4a2d76e385578939891b91c2550c39ecf361e", size = 9099566, upload-time = "2024-12-13T05:54:55.522Z" }, + { url = "https://files.pythonhosted.org/packages/23/e1/4f0e237bf349c02ff9d1b6e7109f1a17f745263809b9714a8576dc17752b/matplotlib-3.9.4-cp311-cp311-win_amd64.whl", hash = "sha256:1f6882828231eca17f501c4dcd98a05abb3f03d157fbc0769c6911fe08b6cfd3", size = 7838065, upload-time = "2024-12-13T05:54:58.337Z" }, + { url = "https://files.pythonhosted.org/packages/1a/2b/c918bf6c19d6445d1cefe3d2e42cb740fb997e14ab19d4daeb6a7ab8a157/matplotlib-3.9.4-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:dfc48d67e6661378a21c2983200a654b72b5c5cdbd5d2cf6e5e1ece860f0cc70", size = 7891131, upload-time = "2024-12-13T05:55:02.837Z" }, + { url = "https://files.pythonhosted.org/packages/c1/e5/b4e8fc601ca302afeeabf45f30e706a445c7979a180e3a978b78b2b681a4/matplotlib-3.9.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:47aef0fab8332d02d68e786eba8113ffd6f862182ea2999379dec9e237b7e483", size = 7776365, upload-time = "2024-12-13T05:55:05.158Z" }, + { url = "https://files.pythonhosted.org/packages/99/06/b991886c506506476e5d83625c5970c656a491b9f80161458fed94597808/matplotlib-3.9.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fba1f52c6b7dc764097f52fd9ab627b90db452c9feb653a59945de16752e965f", size = 8200707, upload-time = "2024-12-13T05:55:09.48Z" }, + { url = "https://files.pythonhosted.org/packages/c3/e2/556b627498cb27e61026f2d1ba86a78ad1b836fef0996bef5440e8bc9559/matplotlib-3.9.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:173ac3748acaac21afcc3fa1633924609ba1b87749006bc25051c52c422a5d00", size = 8313761, upload-time = "2024-12-13T05:55:12.95Z" }, + { url = "https://files.pythonhosted.org/packages/58/ff/165af33ec766ff818306ea88e91f9f60d2a6ed543be1eb122a98acbf3b0d/matplotlib-3.9.4-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:320edea0cadc07007765e33f878b13b3738ffa9745c5f707705692df70ffe0e0", size = 9095284, upload-time = "2024-12-13T05:55:16.199Z" }, + { url = "https://files.pythonhosted.org/packages/9f/8b/3d0c7a002db3b1ed702731c2a9a06d78d035f1f2fb0fb936a8e43cc1e9f4/matplotlib-3.9.4-cp312-cp312-win_amd64.whl", hash = "sha256:a4a4cfc82330b27042a7169533da7991e8789d180dd5b3daeaee57d75cd5a03b", size = 7841160, upload-time = "2024-12-13T05:55:19.991Z" }, + { url = "https://files.pythonhosted.org/packages/49/b1/999f89a7556d101b23a2f0b54f1b6e140d73f56804da1398f2f0bc0924bc/matplotlib-3.9.4-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:37eeffeeca3c940985b80f5b9a7b95ea35671e0e7405001f249848d2b62351b6", size = 7891499, upload-time = "2024-12-13T05:55:22.142Z" }, + { url = "https://files.pythonhosted.org/packages/87/7b/06a32b13a684977653396a1bfcd34d4e7539c5d55c8cbfaa8ae04d47e4a9/matplotlib-3.9.4-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:3e7465ac859ee4abcb0d836137cd8414e7bb7ad330d905abced457217d4f0f45", size = 7776802, upload-time = "2024-12-13T05:55:25.947Z" }, + { url = "https://files.pythonhosted.org/packages/65/87/ac498451aff739e515891bbb92e566f3c7ef31891aaa878402a71f9b0910/matplotlib-3.9.4-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f4c12302c34afa0cf061bea23b331e747e5e554b0fa595c96e01c7b75bc3b858", size = 8200802, upload-time = "2024-12-13T05:55:28.461Z" }, + { url = "https://files.pythonhosted.org/packages/f8/6b/9eb761c00e1cb838f6c92e5f25dcda3f56a87a52f6cb8fdfa561e6cf6a13/matplotlib-3.9.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2b8c97917f21b75e72108b97707ba3d48f171541a74aa2a56df7a40626bafc64", size = 8313880, upload-time = "2024-12-13T05:55:30.965Z" }, + { url = "https://files.pythonhosted.org/packages/d7/a2/c8eaa600e2085eec7e38cbbcc58a30fc78f8224939d31d3152bdafc01fd1/matplotlib-3.9.4-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:0229803bd7e19271b03cb09f27db76c918c467aa4ce2ae168171bc67c3f508df", size = 9094637, upload-time = "2024-12-13T05:55:33.701Z" }, + { url = "https://files.pythonhosted.org/packages/71/1f/c6e1daea55b7bfeb3d84c6cb1abc449f6a02b181e7e2a5e4db34c3afb793/matplotlib-3.9.4-cp313-cp313-win_amd64.whl", hash = "sha256:7c0d8ef442ebf56ff5e206f8083d08252ee738e04f3dc88ea882853a05488799", size = 7841311, upload-time = "2024-12-13T05:55:36.737Z" }, + { url = "https://files.pythonhosted.org/packages/c0/3a/2757d3f7d388b14dd48f5a83bea65b6d69f000e86b8f28f74d86e0d375bd/matplotlib-3.9.4-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:a04c3b00066a688834356d196136349cb32f5e1003c55ac419e91585168b88fb", size = 7919989, upload-time = "2024-12-13T05:55:39.024Z" }, + { url = "https://files.pythonhosted.org/packages/24/28/f5077c79a4f521589a37fe1062d6a6ea3534e068213f7357e7cfffc2e17a/matplotlib-3.9.4-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:04c519587f6c210626741a1e9a68eefc05966ede24205db8982841826af5871a", size = 7809417, upload-time = "2024-12-13T05:55:42.412Z" }, + { url = "https://files.pythonhosted.org/packages/36/c8/c523fd2963156692916a8eb7d4069084cf729359f7955cf09075deddfeaf/matplotlib-3.9.4-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:308afbf1a228b8b525fcd5cec17f246bbbb63b175a3ef6eb7b4d33287ca0cf0c", size = 8226258, upload-time = "2024-12-13T05:55:47.259Z" }, + { url = "https://files.pythonhosted.org/packages/f6/88/499bf4b8fa9349b6f5c0cf4cead0ebe5da9d67769129f1b5651e5ac51fbc/matplotlib-3.9.4-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ddb3b02246ddcffd3ce98e88fed5b238bc5faff10dbbaa42090ea13241d15764", size = 8335849, upload-time = "2024-12-13T05:55:49.763Z" }, + { url = "https://files.pythonhosted.org/packages/b8/9f/20a4156b9726188646a030774ee337d5ff695a965be45ce4dbcb9312c170/matplotlib-3.9.4-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8a75287e9cb9eee48cb79ec1d806f75b29c0fde978cb7223a1f4c5848d696041", size = 9102152, upload-time = "2024-12-13T05:55:51.997Z" }, + { url = "https://files.pythonhosted.org/packages/10/11/237f9c3a4e8d810b1759b67ff2da7c32c04f9c80aa475e7beb36ed43a8fb/matplotlib-3.9.4-cp313-cp313t-win_amd64.whl", hash = "sha256:488deb7af140f0ba86da003e66e10d55ff915e152c78b4b66d231638400b1965", size = 7896987, upload-time = "2024-12-13T05:55:55.941Z" }, + { url = "https://files.pythonhosted.org/packages/56/eb/501b465c9fef28f158e414ea3a417913dc2ac748564c7ed41535f23445b4/matplotlib-3.9.4-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:3c3724d89a387ddf78ff88d2a30ca78ac2b4c89cf37f2db4bd453c34799e933c", size = 7885919, upload-time = "2024-12-13T05:55:59.66Z" }, + { url = "https://files.pythonhosted.org/packages/da/36/236fbd868b6c91309a5206bd90c3f881f4f44b2d997cd1d6239ef652f878/matplotlib-3.9.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:d5f0a8430ffe23d7e32cfd86445864ccad141797f7d25b7c41759a5b5d17cfd7", size = 7771486, upload-time = "2024-12-13T05:56:04.264Z" }, + { url = "https://files.pythonhosted.org/packages/e0/4b/105caf2d54d5ed11d9f4335398f5103001a03515f2126c936a752ccf1461/matplotlib-3.9.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6bb0141a21aef3b64b633dc4d16cbd5fc538b727e4958be82a0e1c92a234160e", size = 8201838, upload-time = "2024-12-13T05:56:06.792Z" }, + { url = "https://files.pythonhosted.org/packages/5d/a7/bb01188fb4013d34d274caf44a2f8091255b0497438e8b6c0a7c1710c692/matplotlib-3.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:57aa235109e9eed52e2c2949db17da185383fa71083c00c6c143a60e07e0888c", size = 8314492, upload-time = "2024-12-13T05:56:09.964Z" }, + { url = "https://files.pythonhosted.org/packages/33/19/02e1a37f7141fc605b193e927d0a9cdf9dc124a20b9e68793f4ffea19695/matplotlib-3.9.4-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:b18c600061477ccfdd1e6fd050c33d8be82431700f3452b297a56d9ed7037abb", size = 9092500, upload-time = "2024-12-13T05:56:13.55Z" }, + { url = "https://files.pythonhosted.org/packages/57/68/c2feb4667adbf882ffa4b3e0ac9967f848980d9f8b5bebd86644aa67ce6a/matplotlib-3.9.4-cp39-cp39-win_amd64.whl", hash = "sha256:ef5f2d1b67d2d2145ff75e10f8c008bfbf71d45137c4b648c87193e7dd053eac", size = 7822962, upload-time = "2024-12-13T05:56:16.358Z" }, + { url = "https://files.pythonhosted.org/packages/0c/22/2ef6a364cd3f565442b0b055e0599744f1e4314ec7326cdaaa48a4d864d7/matplotlib-3.9.4-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:44e0ed786d769d85bc787b0606a53f2d8d2d1d3c8a2608237365e9121c1a338c", size = 7877995, upload-time = "2024-12-13T05:56:18.805Z" }, + { url = "https://files.pythonhosted.org/packages/87/b8/2737456e566e9f4d94ae76b8aa0d953d9acb847714f9a7ad80184474f5be/matplotlib-3.9.4-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:09debb9ce941eb23ecdbe7eab972b1c3e0276dcf01688073faff7b0f61d6c6ca", size = 7769300, upload-time = "2024-12-13T05:56:21.315Z" }, + { url = "https://files.pythonhosted.org/packages/b2/1f/e709c6ec7b5321e6568769baa288c7178e60a93a9da9e682b39450da0e29/matplotlib-3.9.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bcc53cf157a657bfd03afab14774d54ba73aa84d42cfe2480c91bd94873952db", size = 8313423, upload-time = "2024-12-13T05:56:26.719Z" }, + { url = "https://files.pythonhosted.org/packages/5e/b6/5a1f868782cd13f053a679984e222007ecff654a9bfbac6b27a65f4eeb05/matplotlib-3.9.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:ad45da51be7ad02387801fd154ef74d942f49fe3fcd26a64c94842ba7ec0d865", size = 7854624, upload-time = "2024-12-13T05:56:29.359Z" }, +] + +[[package]] +name = "matplotlib" +version = "3.10.9" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.10.*'", +] +dependencies = [ + { name = "contourpy", version = "1.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" }, + { name = "cycler", marker = "python_full_version == '3.10.*'" }, + { name = "fonttools", version = "4.63.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" }, + { name = "kiwisolver", version = "1.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" }, + { name = "packaging", marker = "python_full_version == '3.10.*'" }, + { name = "pillow", version = "12.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" }, + { name = "pyparsing", version = "3.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" }, + { name = "python-dateutil", marker = "python_full_version == '3.10.*'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/63/1b/4be5be87d43d327a0cf4de1a56e86f7f84c89312452406cf122efe2839e6/matplotlib-3.10.9.tar.gz", hash = "sha256:fd66508e8c6877d98e586654b608a0456db8d7e8a546eb1e2600efd957302358", size = 34811233, upload-time = "2026-04-24T00:14:13.539Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/18/6f/340b04986e67aac6f66c5145ce68bf72c64bed30f92c8913499a6e6b8f99/matplotlib-3.10.9-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:77210dce9cb8153dffc967efaae990543392563d5a376d4dd8539bebcb0ed217", size = 8296625, upload-time = "2026-04-24T00:11:43.376Z" }, + { url = "https://files.pythonhosted.org/packages/bb/2f/127081eb83162053ebb9678ceac64220b93a663e0167432566e9c7c82aab/matplotlib-3.10.9-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1e7698ac9868428e84d2c967424803b2472ff7167d9d6590d4204ed775343c3b", size = 8188790, upload-time = "2026-04-24T00:11:46.556Z" }, + { url = "https://files.pythonhosted.org/packages/fc/b7/d8bcec2626c35f96972bff656299fef4578113ea6193c8fdad324710410c/matplotlib-3.10.9-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1aa972116abb4c9d201bf245620b433726cb6856f3bef6a78f776a00f5c92d37", size = 8769389, upload-time = "2026-04-24T00:11:48.959Z" }, + { url = "https://files.pythonhosted.org/packages/12/49/b78e214a527ea732033b7f4d37f7afb504d74ba9d134bd47938230dfb8b1/matplotlib-3.10.9-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ae2f11957b27ce53497dd4d7b235c4d4f1faf383dfb39d0c5beb833bff883294", size = 9589657, upload-time = "2026-04-24T00:11:51.915Z" }, + { url = "https://files.pythonhosted.org/packages/5f/15/5246f7b43beae19c74dfee651d58d6cc8112e06f77adb4e88cc04f2e3a23/matplotlib-3.10.9-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:b049278ddce116aaa1c1377ebf58adea909132dfce0281cf7e3a1ea9fc2e2c65", size = 9651983, upload-time = "2026-04-24T00:11:54.766Z" }, + { url = "https://files.pythonhosted.org/packages/75/77/5acecfe672ba0fa1b8c0454f69ce155d1e6fc5852fa7206bf9afaf767121/matplotlib-3.10.9-cp310-cp310-win_amd64.whl", hash = "sha256:82834c3c292d24d3a8aae77cd2d20019de69d692a34a970e4fdb8d33e2ea3dda", size = 8199701, upload-time = "2026-04-24T00:11:58.389Z" }, + { url = "https://files.pythonhosted.org/packages/4c/8c/290f021104741fea63769c31494f5324c0cd249bf536a65a4350767b1f22/matplotlib-3.10.9-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:68cfdcede415f7c8f5577b03303dd94526cdb6d11036cecdc205e08733b2d2bb", size = 8306860, upload-time = "2026-04-24T00:12:01.207Z" }, + { url = "https://files.pythonhosted.org/packages/51/18/325cd32ece1120d1da51cc4e4294c6580190699490183fc2fe8cb6d61ec5/matplotlib-3.10.9-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:dfca0129678bd56379db26c52b5d77ed7de314c047492fbdc763aa7501710cfb", size = 8199254, upload-time = "2026-04-24T00:12:04.239Z" }, + { url = "https://files.pythonhosted.org/packages/79/db/e28c1b83e3680740aa78925f5fb2ae4d16207207419ad75ea9fe604f8676/matplotlib-3.10.9-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8e436d155fa8a3399dc62683f8f5d0e2e50d25d0144a73edd73f82eec8f4abfb", size = 8777092, upload-time = "2026-04-24T00:12:06.793Z" }, + { url = "https://files.pythonhosted.org/packages/55/fa/3ce7adfe9ba101748f465211660d9c6374c876b671bdb8c2bb6d347e8b94/matplotlib-3.10.9-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:56fc0bd271b00025c6edfdc7c2dcd247372c8e1544971d62e1dc7c17367e8bf9", size = 9595691, upload-time = "2026-04-24T00:12:09.706Z" }, + { url = "https://files.pythonhosted.org/packages/36/c4/6960a76686ed668f2c60f84e9799ba4c0d56abdb36b1577b60c1d061d1ec/matplotlib-3.10.9-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:a5a6104ed666402ba5106d7f36e0e0cdca4e8d7fa4d39708ca88019e2835a2eb", size = 9659771, upload-time = "2026-04-24T00:12:12.766Z" }, + { url = "https://files.pythonhosted.org/packages/7e/0d/271aace3342157c64700c9ff4c59c7b392f3dbab393692e8db6fbe7ab96c/matplotlib-3.10.9-cp311-cp311-win_amd64.whl", hash = "sha256:d730e984eddf56974c3e72b6129c7ca462ac38dc624338f4b0b23eb23ecba00f", size = 8205112, upload-time = "2026-04-24T00:12:15.773Z" }, + { url = "https://files.pythonhosted.org/packages/e2/ee/cb57ad4754f3e7b9174ce6ce66d9205fb827067e48a9f58ac09d7e7d6b77/matplotlib-3.10.9-cp311-cp311-win_arm64.whl", hash = "sha256:51bf0ddbdc598e060d46c16b5590708f81a1624cefbaaf62f6a81bf9285b8c80", size = 8132310, upload-time = "2026-04-24T00:12:18.645Z" }, + { url = "https://files.pythonhosted.org/packages/35/c6/5581e26c72233ebb2a2a6fed2d24fb7c66b4700120b813f51b0555acf0b6/matplotlib-3.10.9-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:f0c3c28d9fbcc1fe7a03be236d73430cf6409c41fb2383a7ac52fe932b072cb1", size = 8319908, upload-time = "2026-04-24T00:12:21.323Z" }, + { url = "https://files.pythonhosted.org/packages/b7/18/4880dd762e40cd360c1bf06e890c5a97b997e91cb324602b1a19950ad5ce/matplotlib-3.10.9-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:41cb28c2bd769aa3e98322c6ab09854cbcc52ab69d2759d681bba3e327b2b320", size = 8216016, upload-time = "2026-04-24T00:12:23.4Z" }, + { url = "https://files.pythonhosted.org/packages/32/91/d024616abdba99e83120e07a20658976f6a343646710760c4a51df126029/matplotlib-3.10.9-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ae20801130378b82d647ff5047c07316295b68dc054ca6b3c13519d0ea624285", size = 8789336, upload-time = "2026-04-24T00:12:26.096Z" }, + { url = "https://files.pythonhosted.org/packages/5c/04/030a2f61ef2158f5e4c259487a92ac877732499fb33d871585d89e03c42d/matplotlib-3.10.9-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6c63ebcd8b4b169eb2f5c200552ae6b8be8999a005b6b507ed76fb8d7d674fe2", size = 9604602, upload-time = "2026-04-24T00:12:29.052Z" }, + { url = "https://files.pythonhosted.org/packages/fc/c2/541e4d09d87bb6b5830fc28b4c887a9a8cf4e1c6cee698a8c05552ae2003/matplotlib-3.10.9-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:d75d11c949914165976c621b2324f9ef162af7ebf4b057ddf95dd1dba7e5edcf", size = 9670966, upload-time = "2026-04-24T00:12:32.131Z" }, + { url = "https://files.pythonhosted.org/packages/04/a1/4571fc46e7702de8d0c2dc54ad1b2f8e29328dea3ee90831181f7353d93c/matplotlib-3.10.9-cp312-cp312-win_amd64.whl", hash = "sha256:d091f9d758b34aaaaa6331d13574bf01891d903b3dec59bfff458ef7551de5d6", size = 8217462, upload-time = "2026-04-24T00:12:35.226Z" }, + { url = "https://files.pythonhosted.org/packages/4b/d0/2269edb12aa30c13c8bcc9382892e39943ce1d28aab4ec296e0381798e81/matplotlib-3.10.9-cp312-cp312-win_arm64.whl", hash = "sha256:10cc5ce06d10231c36f40e875f3c7e8050362a4ee8f0ee5d29a6b3277d57bb42", size = 8136688, upload-time = "2026-04-24T00:12:37.442Z" }, + { url = "https://files.pythonhosted.org/packages/aa/d3/8d4f6afbecb49fc04e060a57c0fce39ea51cc163a6bd87303ccd698e4fa6/matplotlib-3.10.9-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:b580440f1ff81a0e34122051a3dfabb7e4b7f9e380629929bde0eff9af72165f", size = 8320331, upload-time = "2026-04-24T00:12:39.688Z" }, + { url = "https://files.pythonhosted.org/packages/63/d9/9e14bc7564bf92d5ffa801ae5fac819ce74b925dfb55e3ebde61a3bbad3e/matplotlib-3.10.9-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:b1b745c489cd1a77a0dc1120a05dc87af9798faebc913601feb8c73d89bf2d1e", size = 8216461, upload-time = "2026-04-24T00:12:42.494Z" }, + { url = "https://files.pythonhosted.org/packages/8a/17/4402d0d14ccf1dfc70932600b68097fbbf9c898a4871d2cbbe79c7801a32/matplotlib-3.10.9-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:8f3bcac1ca5ed000a6f4337d47ba67dfddf37ed6a46c15fd7f014997f7bf865f", size = 8790091, upload-time = "2026-04-24T00:12:44.789Z" }, + { url = "https://files.pythonhosted.org/packages/3e/0b/322aeec06dd9b91411f92028b37d447342770a24392aa4813e317064dad5/matplotlib-3.10.9-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7a8d66a55def891c33147ba3ba9bfcabf0b526a43764c818acbb4525e5ed0838", size = 9605027, upload-time = "2026-04-24T00:12:47.583Z" }, + { url = "https://files.pythonhosted.org/packages/74/88/5f13482f55e7b00bcfc09838b093c2456e1379978d2a146844aae05350ad/matplotlib-3.10.9-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:d843374407c4017a6403b59c6c81606773d136f3259d5b6da3131bc814542cc2", size = 9671269, upload-time = "2026-04-24T00:12:50.878Z" }, + { url = "https://files.pythonhosted.org/packages/c5/e0/0840fd2f93da988ec660b8ad1984abe9f25d2aed22a5e394ff1c68c88307/matplotlib-3.10.9-cp313-cp313-win_amd64.whl", hash = "sha256:f4399f64b3e94cd500195490972ae1ee81170df1636fa15364d157d5bdd7b921", size = 8217588, upload-time = "2026-04-24T00:12:53.784Z" }, + { url = "https://files.pythonhosted.org/packages/47/b9/d706d06dd605c49b9f83a2aed8c13e3e5db70697d7a80b7e3d7915de6b17/matplotlib-3.10.9-cp313-cp313-win_arm64.whl", hash = "sha256:ba7b3b8ef09eab7df0e86e9ae086faa433efbfbdb46afcb3aa16aabf779469a8", size = 8136913, upload-time = "2026-04-24T00:12:56.501Z" }, + { url = "https://files.pythonhosted.org/packages/9b/45/6e32d96978264c8ca8c4b1010adb955a1a49cfaf314e212bbc8908f04a61/matplotlib-3.10.9-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:09218df8a93712bd6ea133e83a153c755448cf7868316c531cffcc43f69d1cc9", size = 8368019, upload-time = "2026-04-24T00:12:58.896Z" }, + { url = "https://files.pythonhosted.org/packages/86/0a/c8e3d3bba245f0f7fc424937f8ff7ef77291a36af3edb97ccd78aa93d84f/matplotlib-3.10.9-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:82368699727bfb7b0182e1aa13082e3c08e092fa1a25d3e1fd92405bff96f6d4", size = 8264645, upload-time = "2026-04-24T00:13:01.406Z" }, + { url = "https://files.pythonhosted.org/packages/3d/aa/5bf5a14fe4fed73a4209a155606f8096ff797aad89c6c35179026571133e/matplotlib-3.10.9-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3225f4e1edcb8c86c884ddf79ebe20ecd0a67d30188f279897554ccd8fded4dc", size = 8802194, upload-time = "2026-04-24T00:13:03.702Z" }, + { url = "https://files.pythonhosted.org/packages/dd/5e/b4be852d6bba6fd15893fadf91ff26ae49cb91aac789e95dde9d342e664f/matplotlib-3.10.9-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:de2445a0c6690d21b7eb6ce071cebad6d40a2e9bdf10d039074a96ba19797b99", size = 9622684, upload-time = "2026-04-24T00:13:06.647Z" }, + { url = "https://files.pythonhosted.org/packages/4c/3d/ed428c971139112ef730f62770654d609467346d09d4b62617e1afd68a5a/matplotlib-3.10.9-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:b2b9516251cb89ff618d757daec0e2ed1bf21248013844a853d87ef85ab3081d", size = 9680790, upload-time = "2026-04-24T00:13:10.009Z" }, + { url = "https://files.pythonhosted.org/packages/e7/09/052e884aaf2b985c63cb79f715f1d5b6a3eaa7de78f6a52b9dbc077d5b53/matplotlib-3.10.9-cp313-cp313t-win_amd64.whl", hash = "sha256:e9fae004b941b23ff2edcf1567a857ed77bafc8086ffa258190462328434faf8", size = 8287571, upload-time = "2026-04-24T00:13:13.087Z" }, + { url = "https://files.pythonhosted.org/packages/f4/38/ae27288e788c35a4250491422f3db7750366fc8c97d6f36fbdecfc1f5518/matplotlib-3.10.9-cp313-cp313t-win_arm64.whl", hash = "sha256:6b63d9c7c769b88ab81e10dc86e4e0607cf56817b9f9e6cf24b2a5f1693b8e38", size = 8188292, upload-time = "2026-04-24T00:13:15.546Z" }, + { url = "https://files.pythonhosted.org/packages/d6/e6/3bd8afd04949f02eabc1c17115ea5255e19cacd4d06fc5abdde4eeb0052c/matplotlib-3.10.9-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:172db52c9e683f5d12eaf57f0f54834190e12581fe1cc2a19595a8f5acb4e77d", size = 8321276, upload-time = "2026-04-24T00:13:18.318Z" }, + { url = "https://files.pythonhosted.org/packages/41/86/86231232fff41c9f8e4a1a7d7a597d349a02527109c3af7d618366122139/matplotlib-3.10.9-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:97e35e8d39ccc85859095e01a53847432ba9a53ddf7986f7a54a11b73d0e143f", size = 8218218, upload-time = "2026-04-24T00:13:20.974Z" }, + { url = "https://files.pythonhosted.org/packages/85/8f/becc9722cafc64f5d2eb0b7c1bf5f585271c618a45dbd8fabeb021f898b6/matplotlib-3.10.9-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:aba1615dabe83188e19d4f75a253c6a08423e04c1425e64039f800050a69de6b", size = 9608145, upload-time = "2026-04-24T00:13:23.228Z" }, + { url = "https://files.pythonhosted.org/packages/32/5d/f7e914f7d9325abff4057cee62c0fa70263683189f774473cbfb534cd13b/matplotlib-3.10.9-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:34cf8167e023ad956c15f36302911d5406bd99a9862c1a8499ea6f7c0e015dc2", size = 9885085, upload-time = "2026-04-24T00:13:25.849Z" }, + { url = "https://files.pythonhosted.org/packages/a5/fd/fa69f2221534e80cc5772ac2b7d222011a2acafc2ec7216d5dd174c864ae/matplotlib-3.10.9-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:59476c6d29d612b8e9bb6ce8c5b631be6ba8f9e3a2421f22a02b192c7dd28716", size = 9672358, upload-time = "2026-04-24T00:13:28.906Z" }, + { url = "https://files.pythonhosted.org/packages/ab/1a/5a4f747a8b271cbb024946d2dd3c913ab5032ba430626f8c3528ada96b4b/matplotlib-3.10.9-cp314-cp314-win_amd64.whl", hash = "sha256:336b9acc64d309063126edcdaca00db9373af3c476bb94388fe9c5a53ad13e6f", size = 8349970, upload-time = "2026-04-24T00:13:31.904Z" }, + { url = "https://files.pythonhosted.org/packages/64/dc/95d60ecaefe30680a154b52ea96ab4b0dab547f1fd6aa12f5fb655e89cae/matplotlib-3.10.9-cp314-cp314-win_arm64.whl", hash = "sha256:2dc9477819ffd78ad12a20df1d9d6a6bd4fec6aaa9072681465fddca052f1456", size = 8272785, upload-time = "2026-04-24T00:13:34.511Z" }, + { url = "https://files.pythonhosted.org/packages/70/a0/005d68bc8b8418300ce6591f18586910a8526806e2ab663933d9f20a41e9/matplotlib-3.10.9-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:da4e09638420548f31c354032a6250e473c68e5a4e96899b4844cf39ddea23fe", size = 8367999, upload-time = "2026-04-24T00:13:36.962Z" }, + { url = "https://files.pythonhosted.org/packages/22/05/1236cc9290be70b2498af20ca348add76e3fffe7f67b477db5133a84f3ea/matplotlib-3.10.9-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:345f6f68ecc8da0ca56fad2ea08fde1a115eda530079eca185d50a7bc3e146c6", size = 8264543, upload-time = "2026-04-24T00:13:39.851Z" }, + { url = "https://files.pythonhosted.org/packages/cd/c2/071f5a5ff6c5bd63aaaf2f45c811d9bf2ced94bde188d9e1a519e21d0cba/matplotlib-3.10.9-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4edcfbd8565339aa62f1cd4012f7180926fdbe71850f7b0d3c379c175cd6b66c", size = 9622800, upload-time = "2026-04-24T00:13:42.296Z" }, + { url = "https://files.pythonhosted.org/packages/95/57/da7d1f10a85624b9e7db68e069dd94e58dc41dbf9463c5921632ecbe3661/matplotlib-3.10.9-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6be157fe17fc37cb95ac1d7374cf717ce9259616edec911a78d9d26dae8522d4", size = 9888561, upload-time = "2026-04-24T00:13:45.026Z" }, + { url = "https://files.pythonhosted.org/packages/67/b2/ef8d6bb59b0edb6c16c968b70f548aa13b54348972def5aa6ac85df67145/matplotlib-3.10.9-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:4e42042d54db34fda4e95a7bd3e5789c2a995d2dad3eb8850232ee534092fbbf", size = 9680884, upload-time = "2026-04-24T00:13:48.066Z" }, + { url = "https://files.pythonhosted.org/packages/61/1c/d21bfeb9931881ebe96bcfcff27c7ae4b160ae0ec291a714c42641a56d75/matplotlib-3.10.9-cp314-cp314t-win_amd64.whl", hash = "sha256:c27df8b3848f32a83d1767566595e43cfaa4460380974da06f4279a7ec143c39", size = 8432333, upload-time = "2026-04-24T00:13:51.008Z" }, + { url = "https://files.pythonhosted.org/packages/78/23/92493c3e6e1b635ccfff146f7b99e674808787915420373ac399283764c2/matplotlib-3.10.9-cp314-cp314t-win_arm64.whl", hash = "sha256:a49f1eadc84ca85fd72fa4e89e70e61bf86452df6f971af04b12c60761a0772c", size = 8324785, upload-time = "2026-04-24T00:13:53.633Z" }, + { url = "https://files.pythonhosted.org/packages/2c/2b/0e92ad0ac446633f928a1563db4aa8add407e1924faf0ded5b95b35afb27/matplotlib-3.10.9-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:1872fb212a05b729e649754a72d5da61d03e0554d76e80303b6f83d1d2c0552b", size = 8293058, upload-time = "2026-04-24T00:13:56.339Z" }, + { url = "https://files.pythonhosted.org/packages/4b/23/74682fd369f5299ceda438fea2a0662e6383b85c9383fb9cdfcf04713e07/matplotlib-3.10.9-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:985f2238880e2e69093f588f5fe2e46771747febf0649f3cf7f7b7480875317f", size = 8186627, upload-time = "2026-04-24T00:13:58.623Z" }, + { url = "https://files.pythonhosted.org/packages/ca/e8/368aab88f3c4cd8992800f31abfe0670c3e47540ba20a97e9fdbcde594b3/matplotlib-3.10.9-pp310-pypy310_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6640f75af2c6148293caa0a2b39dd806a492dd66c8a8b04035813e33d0fd2585", size = 8764117, upload-time = "2026-04-24T00:14:01.684Z" }, + { url = "https://files.pythonhosted.org/packages/63/e2/9f66ca6a651a52abfe0d4964ce01439ed34f3f1e119de10ff3a07f403043/matplotlib-3.10.9-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:42fb814efabe95c06c1994d8ab5a8385f43a249e23badd3ba931d4308e5bca20", size = 8304420, upload-time = "2026-04-24T00:14:04.57Z" }, + { url = "https://files.pythonhosted.org/packages/e8/e8/467c03568218792906aa87b5e7bb379b605e056ed0c74fe00c051786d925/matplotlib-3.10.9-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:f76e640a5268850bfda54b5131b1b1941cc685e42c5fa98ed9f2d64038308cba", size = 8197981, upload-time = "2026-04-24T00:14:07.233Z" }, + { url = "https://files.pythonhosted.org/packages/6f/87/afead29192170917537934c6aff4b008c805fff7b1ccea0c79120d96beda/matplotlib-3.10.9-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:3fc0364dfbe1d07f6d15c5ebd0c5bf89e126916e5a8667dd4a7a6e84c36653d4", size = 8774002, upload-time = "2026-04-24T00:14:09.816Z" }, +] + +[[package]] +name = "matplotlib" +version = "3.11.1" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.15'", + "python_full_version >= '3.12' and python_full_version < '3.15'", + "python_full_version == '3.11.*'", +] +dependencies = [ + { name = "contourpy", version = "1.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "cycler", marker = "python_full_version >= '3.11'" }, + { name = "fonttools", version = "4.63.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "kiwisolver", version = "1.5.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "numpy", version = "2.4.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.11.*'" }, + { name = "numpy", version = "2.5.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" }, + { name = "packaging", marker = "python_full_version >= '3.11'" }, + { name = "pillow", version = "12.3.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "pyparsing", version = "3.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "python-dateutil", marker = "python_full_version >= '3.11'" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/49/64/f9a391af28f518b11ad45a8a712353c94a0aefce09d3703200e5c54b610a/matplotlib-3.11.1.tar.gz", hash = "sha256:69647db5746941c793d6e445a4cd349323ffb87d9cc958c2ad84a659b4832d30", size = 32612045, upload-time = "2026-07-18T03:39:46.63Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6e/d0/791aa183dd88491555cf7d4be0b52b0bcf6c3c2a2c22c815a2e819bf53e2/matplotlib-3.11.1-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:b7cf158e7add54a8d51ac9b5a84abd6d4e13ed4951b4f25f1c5139f41c2addb2", size = 9440302, upload-time = "2026-07-18T03:38:03.844Z" }, + { url = "https://files.pythonhosted.org/packages/35/74/82bbdf683a301f4478384c8aaba6903631a2ca18294b2d7655c9a542bffb/matplotlib-3.11.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d2ace7273b9a5061a3b420918a16fae1f2dc5dfee1abcc13aba71b5d94b1820c", size = 9268549, upload-time = "2026-07-18T03:38:06.144Z" }, + { url = "https://files.pythonhosted.org/packages/f0/f0/9b4298911303f74e6d83e64a81d996c0616405ec95046fac7f17e4258b9e/matplotlib-3.11.1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:aee55e9041211bf84302ab55ec3965df18dd90ae19f8b58332a7feaf208bfe83", size = 10024922, upload-time = "2026-07-18T03:38:08.236Z" }, + { url = "https://files.pythonhosted.org/packages/84/6f/0bc3c3d05b021db44c14bc379a7c0df7d57302aa15380c16fd4e63fd6a9b/matplotlib-3.11.1-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:96f4bdeea33a8d15a071dbfe6d119451b1d719c733ac666d65357082901a9099", size = 10832170, upload-time = "2026-07-18T03:38:10.276Z" }, + { url = "https://files.pythonhosted.org/packages/db/4d/e375f39acdb2af5a9342730618608e39790ec842e6f1b392863028781459/matplotlib-3.11.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:b4c78ceb2f11bcac7389d305cda17aeb1f4586a857854ab5780bd3dd8dbfc407", size = 10916701, upload-time = "2026-07-18T03:38:12.512Z" }, + { url = "https://files.pythonhosted.org/packages/bc/be/fa26ed085b41298f64a8f9b7592c671bbf1acc8b0df124c1c5de96b859f8/matplotlib-3.11.1-cp311-cp311-win_amd64.whl", hash = "sha256:7f33a781e12b1e53b278deb2f5373c2e55ec4f10727be3440c0cfb5cda9f944f", size = 9315331, upload-time = "2026-07-18T03:38:14.949Z" }, + { url = "https://files.pythonhosted.org/packages/b6/f3/eb5bdf3b6e191b200db298b08bbc1638b7f3c82cdc8680f9d88bf72559ae/matplotlib-3.11.1-cp311-cp311-win_arm64.whl", hash = "sha256:67e4c3cd578c65ebd81bdc09a1b6592ceafee6dfafe116dc85dfcb647b5bbb18", size = 9003475, upload-time = "2026-07-18T03:38:17.205Z" }, + { url = "https://files.pythonhosted.org/packages/f2/6c/7ef7ebcb2bd9739b2b66b18b076e077f44bb46fdbe28ca0506edb3c62c79/matplotlib-3.11.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:e15ef41507f3d525f46154ac9e3ae785dacde9f20e593a25de8986267892ef74", size = 9453849, upload-time = "2026-07-18T03:38:19.593Z" }, + { url = "https://files.pythonhosted.org/packages/eb/f8/6d0c312c8d9738e7d9677f09fe5c986b3239e651a7b73a2deb38b65e4a71/matplotlib-3.11.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:21a67b961a6d597bca54fae826cd20695ba4a6e4d05424a08da6e13e3176fd6b", size = 9283113, upload-time = "2026-07-18T03:38:21.95Z" }, + { url = "https://files.pythonhosted.org/packages/c9/cf/b4ad2cc81b6672ea29ea04e64e350a9f9b493b0908ccd884c67eeff8f7b2/matplotlib-3.11.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ba8f811b8ddfac493734d6af0b2dff96919d0c28ca0d641858dab4262777c6ea", size = 10035615, upload-time = "2026-07-18T03:38:24.315Z" }, + { url = "https://files.pythonhosted.org/packages/88/90/4e10e033d9b66589d8ed98b84c95cdbb57033d57c1f41339d7393dbd2f2e/matplotlib-3.11.1-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c52f7ad20ef476806ed212380b1d54d20310c8b86bdc2c9a68b51f0024a44472", size = 10842559, upload-time = "2026-07-18T03:38:26.285Z" }, + { url = "https://files.pythonhosted.org/packages/88/eb/799612d0f8cd3e816a10fec59329fca52cd2353264df80378dfc541ae855/matplotlib-3.11.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:8b14eb22961fe865efb0e4ff167e333e428908b00115a8d800ccb65ee108e481", size = 10927532, upload-time = "2026-07-18T03:38:28.532Z" }, + { url = "https://files.pythonhosted.org/packages/88/89/56649bbaa2fd12e20f3be03dbcc135b0c8676d88bac17977599e3eb442a0/matplotlib-3.11.1-cp312-cp312-win_amd64.whl", hash = "sha256:88a2a27dd9691ae448dfae4b26f59036be90c3c28757edd3553a29559d00859f", size = 9333886, upload-time = "2026-07-18T03:38:30.477Z" }, + { url = "https://files.pythonhosted.org/packages/c1/11/4d124efbbad677b7b7552f6f85a3bd432d4232f95400cea98fcd2ae36ef3/matplotlib-3.11.1-cp312-cp312-win_arm64.whl", hash = "sha256:480194afceca4df2f137c2721227d3cba67121fbf4397b69cee7f83714b0a58a", size = 9007545, upload-time = "2026-07-18T03:38:32.833Z" }, + { url = "https://files.pythonhosted.org/packages/04/6c/4798363b7fb5644e309fe1fac30216e9146c9f70859d80d588c18caf5317/matplotlib-3.11.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:6771b0cd7838c6a857a7209814158c0ad09bfef878db3033dd82d70ad101f191", size = 9454341, upload-time = "2026-07-18T03:38:35.001Z" }, + { url = "https://files.pythonhosted.org/packages/59/98/6acadbe7f98df19d274bc107ac58bb439fa75df82c33dc110d71a4a8501f/matplotlib-3.11.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2abdee5ffa2fe11b2d19f7a5c63b785fb7c28cc46c7bc1814156341d9d1a33e1", size = 9283627, upload-time = "2026-07-18T03:38:37.061Z" }, + { url = "https://files.pythonhosted.org/packages/24/ea/65cec46fe241390ccea1b1754207ee28eb71c5ab866bd5f22fe47e538fa4/matplotlib-3.11.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b0a19dcf73406d3746d25a5ed42d713604c9a3e024d129b102852b0d941cb9f3", size = 10035860, upload-time = "2026-07-18T03:38:39.663Z" }, + { url = "https://files.pythonhosted.org/packages/c7/10/63fdccccbabe002fb0960876baabc5e3f24d9c1bb4cfb25651457f74b3a0/matplotlib-3.11.1-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7389b77ed2ab0552f46d9a90b81b7b8e6dfcdc42adc36c37a0865799843e0e3e", size = 10843594, upload-time = "2026-07-18T03:38:42.144Z" }, + { url = "https://files.pythonhosted.org/packages/98/51/a1155945bff7b91381875022ac1522c5dfdac0d006be8e7df389b3134eae/matplotlib-3.11.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:c90be0b73568da4f662afac580956a76e308437e641b4a45aa08925eeb67d95f", size = 10927962, upload-time = "2026-07-18T03:38:44.302Z" }, + { url = "https://files.pythonhosted.org/packages/0d/3a/3d5e1f42dc761bf53401a62a83ff93389b37de9d2c093b2a3aa49ac34f1b/matplotlib-3.11.1-cp313-cp313-win_amd64.whl", hash = "sha256:68408341f2312836fbbdf6b3c78047f65b2d8752f5fd221c3e72d348f5b34f8b", size = 9334074, upload-time = "2026-07-18T03:38:46.616Z" }, + { url = "https://files.pythonhosted.org/packages/e2/db/3f5ea5a5b64060ef5e1ff60a19170423e41ce21b8497a6fe15a36e0b43e3/matplotlib-3.11.1-cp313-cp313-win_arm64.whl", hash = "sha256:0c1f44890d435c1b4ef52f701ad5828cb450ea97bcc83918fda6be74965d6cd2", size = 9007662, upload-time = "2026-07-18T03:38:49.112Z" }, + { url = "https://files.pythonhosted.org/packages/98/6e/c7ae5e0531425b69c0826b00ebbc264c85cab853f1cd6e096c9983c2cdc1/matplotlib-3.11.1-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:5e510088c27a89d53580a752f959146893563e63c330e161d159b0fee652af6f", size = 9503790, upload-time = "2026-07-18T03:38:51.527Z" }, + { url = "https://files.pythonhosted.org/packages/92/79/15be162e0a2ed546939674e2e97d0e33ec2447d86d4d4e611fa295bb178c/matplotlib-3.11.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:1524e2bdd48a93557aa47ddcfe9c225dfdd57d5a01a5c49128c20f0632980ee1", size = 9336148, upload-time = "2026-07-18T03:38:53.564Z" }, + { url = "https://files.pythonhosted.org/packages/6a/7f/36ffe144fc4aacfe0e3ed2318f72b6755d1e73b041d619b4d393e60f5a66/matplotlib-3.11.1-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:11664c551345553db92e61cae6cf1376f138f8c47cafdf13b64b18f3e3e9e464", size = 10049244, upload-time = "2026-07-18T03:38:55.911Z" }, + { url = "https://files.pythonhosted.org/packages/ab/5f/55812d68c0a840d3a463638f48c00ab1fe338518ec49a640cb6473b444af/matplotlib-3.11.1-cp313-cp313t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5e1f8922ba31959cf6a9dfb51be64b7f7bc582801a3957dc0c2f3afcd3537adf", size = 10860798, upload-time = "2026-07-18T03:38:58.282Z" }, + { url = "https://files.pythonhosted.org/packages/7a/64/cca444b4eb5e6c768c44fc5e1f0b5211f20ca2b282778051996e996a2bdf/matplotlib-3.11.1-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:83235693abde86e5e0129998f80ee39fc7f58e6d56a88fafb28a9278833e9d5f", size = 10943282, upload-time = "2026-07-18T03:39:00.465Z" }, + { url = "https://files.pythonhosted.org/packages/e5/0f/a49c329d394f2e9ef38506982107e8b04ecf94dd41a9d8423ff82cc737c7/matplotlib-3.11.1-cp313-cp313t-win_amd64.whl", hash = "sha256:9a076f4fc5cdc43fdf510f5981418d25c2db4973418d9f22d8bb3dc8045ada78", size = 9383532, upload-time = "2026-07-18T03:39:02.468Z" }, + { url = "https://files.pythonhosted.org/packages/e4/50/103e86afb806d8f64d04ede14e4cfc09dbfc25f512421ff85fdd6ebd59cf/matplotlib-3.11.1-cp313-cp313t-win_arm64.whl", hash = "sha256:216fbb93a74add02ddb4cb38ef5348f59ac00b3e84567eaf16598772d40e150a", size = 9059665, upload-time = "2026-07-18T03:39:04.607Z" }, + { url = "https://files.pythonhosted.org/packages/35/04/3079499fa8cb661ea66d13d6439d5a3ae6710a7afd5c7f72e08914f275f8/matplotlib-3.11.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:30c492d4ba9448595b6fd8708c6725963f8148e25c0d8842948da5b05f0ee8d3", size = 9456022, upload-time = "2026-07-18T03:39:07.041Z" }, + { url = "https://files.pythonhosted.org/packages/53/a2/69acfe84ec1f32930e801a5782a07fc5c79c8c6599a507b806d859d5da8e/matplotlib-3.11.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:ac104be2768ffdd8655db9e71b768cbb45f2b9aa7b450cf1595e8f65d3822319", size = 9285475, upload-time = "2026-07-18T03:39:09.562Z" }, + { url = "https://files.pythonhosted.org/packages/d3/b3/31b15a2ca56d4ddd6aaa1c884c2f51cf9a61cfaf5ca6f6fbd6343d38e6df/matplotlib-3.11.1-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6be943cb68bc6660ead58c55b3aa6366cba2ef7feb06460fbcce32360376f19f", size = 10847102, upload-time = "2026-07-18T03:39:11.532Z" }, + { url = "https://files.pythonhosted.org/packages/64/0d/a17e966e620545c1548125af0b29ac812dd17b197a18a7462ac12fa859ee/matplotlib-3.11.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:5af0dcda57d471440a7b5b623e70e0a61003518443d9098f211a96ecfbbc25be", size = 11131087, upload-time = "2026-07-18T03:39:13.764Z" }, + { url = "https://files.pythonhosted.org/packages/97/c5/5e100efdd67abb7de20befaa333612ef9bfc63417fb71398f904f25d083c/matplotlib-3.11.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:3d3fd84082b1afbd9398466c81309e20045be20d48fe0fb18c43504d164cbbb2", size = 10929036, upload-time = "2026-07-18T03:39:16.888Z" }, + { url = "https://files.pythonhosted.org/packages/ce/04/d719a0a36930ecc8dfc801ff340f9dcfc4223f8ca5d39d06b4020032fff8/matplotlib-3.11.1-cp314-cp314-win_amd64.whl", hash = "sha256:9601a1e90be21e4884c53b4f3dc3ee0544654946f9975258d691f1c2e2f119c6", size = 9489571, upload-time = "2026-07-18T03:39:19.449Z" }, + { url = "https://files.pythonhosted.org/packages/48/65/facabdc2f1f6caba7e856db64dfedddca25f7608df07d96a1c8fd114fd3b/matplotlib-3.11.1-cp314-cp314-win_arm64.whl", hash = "sha256:ae30c6109848ac0f9fa36c5d6270938487614c47ba31860bd5361266dabc5685", size = 9164486, upload-time = "2026-07-18T03:39:21.424Z" }, + { url = "https://files.pythonhosted.org/packages/88/dd/18da6cd01cf96354534f98c468a25380c68ce582a2c9dd0cae12b04af4f2/matplotlib-3.11.1-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:dadfe80797174e2984aae3be0b77594a3c72d2c0a40fbd4a0de48d2728caf3ae", size = 9504876, upload-time = "2026-07-18T03:39:23.633Z" }, + { url = "https://files.pythonhosted.org/packages/79/b0/f0b63555a18b79d038c81fd6126f35fc4dfce0eaff48d96103348c7cf935/matplotlib-3.11.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:89b193b255f4f6f7948dbcee3691f4f341ab05d9a8874a67b45ddb4182922eda", size = 9336120, upload-time = "2026-07-18T03:39:25.797Z" }, + { url = "https://files.pythonhosted.org/packages/c6/dd/f210ec7c4a6f198d5567237048a93d0811fb5a1f1691f13320e592f95b41/matplotlib-3.11.1-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:191163532cdefcb1571ca38a6d7e6474baccde64495783e6ba47aa07ec4b9bbb", size = 10858033, upload-time = "2026-07-18T03:39:27.999Z" }, + { url = "https://files.pythonhosted.org/packages/ec/d2/d6d5324507c5fbb316db48e258c09c2807f3de03d9af47017e120070926f/matplotlib-3.11.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9fdf1c818ab05d0e74002091ddaf414478a3a449ec9d51c8976d45be7e3a01e2", size = 11141827, upload-time = "2026-07-18T03:39:30.092Z" }, + { url = "https://files.pythonhosted.org/packages/0f/68/3c22e9320bdce2c4d2f1320643ef706db7a24cb7420eea28b97a2d67f5a8/matplotlib-3.11.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:b937b9dba5f5f6c1e31c47abe2186c865c0914fd18f2ce0dfc39c9adcef5951d", size = 10943061, upload-time = "2026-07-18T03:39:32.356Z" }, + { url = "https://files.pythonhosted.org/packages/f6/4a/907ed190ee81a9df581e0ed5456134fc0f7cb55ffcfda2f9e54ca900761c/matplotlib-3.11.1-cp314-cp314t-win_amd64.whl", hash = "sha256:f2912f647f3fbe1ccf085f91e213936f9101bead81a5e670565b1f1b3712f4fb", size = 9540074, upload-time = "2026-07-18T03:39:34.789Z" }, + { url = "https://files.pythonhosted.org/packages/23/d4/97c19b77e0a6e3b48581185bb65088f431cd20186076cc0f650a1757ea46/matplotlib-3.11.1-cp314-cp314t-win_arm64.whl", hash = "sha256:54d47b8ae8b579633a3902ca5b4ad6c1e132a5626d64447b2e22a66394e79987", size = 9213472, upload-time = "2026-07-18T03:39:37.141Z" }, + { url = "https://files.pythonhosted.org/packages/ee/38/ceb1d637c4db6d06141f3739e93af3321e7caaabe69b57ae48ffe3ee95b1/matplotlib-3.11.1-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:427258425f9a3fc4ed79a91f9e9b9aaf5a82cb6571e85dc14063cc6fbb993741", size = 9438045, upload-time = "2026-07-18T03:39:39.491Z" }, + { url = "https://files.pythonhosted.org/packages/89/25/72ad8b58602d3a6ef1dfc4b65ecd01634ab65a2bdf494c9fe0e966dbf081/matplotlib-3.11.1-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:1ac697e591c11b6ad04679a73c2d2f9980fe9d9f0311fb414a2e329706343dfb", size = 9266127, upload-time = "2026-07-18T03:39:41.597Z" }, + { url = "https://files.pythonhosted.org/packages/8a/6d/69552382fcc8e93d1f2763ef2665980a900a48b7f3a4c57ed290726d1cbc/matplotlib-3.11.1-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e4b9ac2f1f607ecda2af90a5232beee2af7582fce1cc30c4b6a1b012dc21ee99", size = 10019439, upload-time = "2026-07-18T03:39:43.78Z" }, +] + [[package]] name = "mccabe" version = "0.7.0" @@ -1020,6 +2100,302 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/79/7b/2c79738432f5c924bef5071f933bcc9efd0473bac3b4aa584a6f7c1c8df8/mypy_extensions-1.1.0-py3-none-any.whl", hash = "sha256:1be4cccdb0f2482337c4743e60421de3a356cd97508abadd57d47403e94f5505", size = 4963, upload-time = "2025-04-22T14:54:22.983Z" }, ] +[[package]] +name = "numpy" +version = "1.24.4" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.8.1' and python_full_version < '3.9'", + "python_full_version < '3.8.1'", +] +sdist = { url = "https://files.pythonhosted.org/packages/a4/9b/027bec52c633f6556dba6b722d9a0befb40498b9ceddd29cbe67a45a127c/numpy-1.24.4.tar.gz", hash = "sha256:80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463", size = 10911229, upload-time = "2023-06-26T13:39:33.218Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/6b/80/6cdfb3e275d95155a34659163b83c09e3a3ff9f1456880bec6cc63d71083/numpy-1.24.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c0bfb52d2169d58c1cdb8cc1f16989101639b34c7d3ce60ed70b19c63eba0b64", size = 19789140, upload-time = "2023-06-26T13:22:33.184Z" }, + { url = "https://files.pythonhosted.org/packages/64/5f/3f01d753e2175cfade1013eea08db99ba1ee4bdb147ebcf3623b75d12aa7/numpy-1.24.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ed094d4f0c177b1b8e7aa9cba7d6ceed51c0e569a5318ac0ca9a090680a6a1b1", size = 13854297, upload-time = "2023-06-26T13:22:59.541Z" }, + { url = "https://files.pythonhosted.org/packages/5a/b3/2f9c21d799fa07053ffa151faccdceeb69beec5a010576b8991f614021f7/numpy-1.24.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79fc682a374c4a8ed08b331bef9c5f582585d1048fa6d80bc6c35bc384eee9b4", size = 13995611, upload-time = "2023-06-26T13:23:22.167Z" }, + { url = "https://files.pythonhosted.org/packages/10/be/ae5bf4737cb79ba437879915791f6f26d92583c738d7d960ad94e5c36adf/numpy-1.24.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7ffe43c74893dbf38c2b0a1f5428760a1a9c98285553c89e12d70a96a7f3a4d6", size = 17282357, upload-time = "2023-06-26T13:23:51.446Z" }, + { url = "https://files.pythonhosted.org/packages/c0/64/908c1087be6285f40e4b3e79454552a701664a079321cff519d8c7051d06/numpy-1.24.4-cp310-cp310-win32.whl", hash = "sha256:4c21decb6ea94057331e111a5bed9a79d335658c27ce2adb580fb4d54f2ad9bc", size = 12429222, upload-time = "2023-06-26T13:24:13.849Z" }, + { url = "https://files.pythonhosted.org/packages/22/55/3d5a7c1142e0d9329ad27cece17933b0e2ab4e54ddc5c1861fbfeb3f7693/numpy-1.24.4-cp310-cp310-win_amd64.whl", hash = "sha256:b4bea75e47d9586d31e892a7401f76e909712a0fd510f58f5337bea9572c571e", size = 14841514, upload-time = "2023-06-26T13:24:38.129Z" }, + { url = "https://files.pythonhosted.org/packages/a9/cc/5ed2280a27e5dab12994c884f1f4d8c3bd4d885d02ae9e52a9d213a6a5e2/numpy-1.24.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f136bab9c2cfd8da131132c2cf6cc27331dd6fae65f95f69dcd4ae3c3639c810", size = 19775508, upload-time = "2023-06-26T13:25:08.882Z" }, + { url = "https://files.pythonhosted.org/packages/c0/bc/77635c657a3668cf652806210b8662e1aff84b818a55ba88257abf6637a8/numpy-1.24.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e2926dac25b313635e4d6cf4dc4e51c8c0ebfed60b801c799ffc4c32bf3d1254", size = 13840033, upload-time = "2023-06-26T13:25:33.417Z" }, + { url = "https://files.pythonhosted.org/packages/a7/4c/96cdaa34f54c05e97c1c50f39f98d608f96f0677a6589e64e53104e22904/numpy-1.24.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:222e40d0e2548690405b0b3c7b21d1169117391c2e82c378467ef9ab4c8f0da7", size = 13991951, upload-time = "2023-06-26T13:25:55.725Z" }, + { url = "https://files.pythonhosted.org/packages/22/97/dfb1a31bb46686f09e68ea6ac5c63fdee0d22d7b23b8f3f7ea07712869ef/numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7215847ce88a85ce39baf9e89070cb860c98fdddacbaa6c0da3ffb31b3350bd5", size = 17278923, upload-time = "2023-06-26T13:26:25.658Z" }, + { url = "https://files.pythonhosted.org/packages/35/e2/76a11e54139654a324d107da1d98f99e7aa2a7ef97cfd7c631fba7dbde71/numpy-1.24.4-cp311-cp311-win32.whl", hash = "sha256:4979217d7de511a8d57f4b4b5b2b965f707768440c17cb70fbf254c4b225238d", size = 12422446, upload-time = "2023-06-26T13:26:49.302Z" }, + { url = "https://files.pythonhosted.org/packages/d8/ec/ebef2f7d7c28503f958f0f8b992e7ce606fb74f9e891199329d5f5f87404/numpy-1.24.4-cp311-cp311-win_amd64.whl", hash = "sha256:b7b1fc9864d7d39e28f41d089bfd6353cb5f27ecd9905348c24187a768c79694", size = 14834466, upload-time = "2023-06-26T13:27:16.029Z" }, + { url = "https://files.pythonhosted.org/packages/11/10/943cfb579f1a02909ff96464c69893b1d25be3731b5d3652c2e0cf1281ea/numpy-1.24.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1452241c290f3e2a312c137a9999cdbf63f78864d63c79039bda65ee86943f61", size = 19780722, upload-time = "2023-06-26T13:27:49.573Z" }, + { url = "https://files.pythonhosted.org/packages/a7/ae/f53b7b265fdc701e663fbb322a8e9d4b14d9cb7b2385f45ddfabfc4327e4/numpy-1.24.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:04640dab83f7c6c85abf9cd729c5b65f1ebd0ccf9de90b270cd61935eef0197f", size = 13843102, upload-time = "2023-06-26T13:28:12.288Z" }, + { url = "https://files.pythonhosted.org/packages/25/6f/2586a50ad72e8dbb1d8381f837008a0321a3516dfd7cb57fc8cf7e4bb06b/numpy-1.24.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5425b114831d1e77e4b5d812b69d11d962e104095a5b9c3b641a218abcc050e", size = 14039616, upload-time = "2023-06-26T13:28:35.659Z" }, + { url = "https://files.pythonhosted.org/packages/98/5d/5738903efe0ecb73e51eb44feafba32bdba2081263d40c5043568ff60faf/numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd80e219fd4c71fc3699fc1dadac5dcf4fd882bfc6f7ec53d30fa197b8ee22dc", size = 17316263, upload-time = "2023-06-26T13:29:09.272Z" }, + { url = "https://files.pythonhosted.org/packages/d1/57/8d328f0b91c733aa9aa7ee540dbc49b58796c862b4fbcb1146c701e888da/numpy-1.24.4-cp38-cp38-win32.whl", hash = "sha256:4602244f345453db537be5314d3983dbf5834a9701b7723ec28923e2889e0bb2", size = 12455660, upload-time = "2023-06-26T13:29:33.434Z" }, + { url = "https://files.pythonhosted.org/packages/69/65/0d47953afa0ad569d12de5f65d964321c208492064c38fe3b0b9744f8d44/numpy-1.24.4-cp38-cp38-win_amd64.whl", hash = "sha256:692f2e0f55794943c5bfff12b3f56f99af76f902fc47487bdfe97856de51a706", size = 14868112, upload-time = "2023-06-26T13:29:58.385Z" }, + { url = "https://files.pythonhosted.org/packages/9a/cd/d5b0402b801c8a8b56b04c1e85c6165efab298d2f0ab741c2406516ede3a/numpy-1.24.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2541312fbf09977f3b3ad449c4e5f4bb55d0dbf79226d7724211acc905049400", size = 19816549, upload-time = "2023-06-26T13:30:36.976Z" }, + { url = "https://files.pythonhosted.org/packages/14/27/638aaa446f39113a3ed38b37a66243e21b38110d021bfcb940c383e120f2/numpy-1.24.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9667575fb6d13c95f1b36aca12c5ee3356bf001b714fc354eb5465ce1609e62f", size = 13879950, upload-time = "2023-06-26T13:31:01.787Z" }, + { url = "https://files.pythonhosted.org/packages/8f/27/91894916e50627476cff1a4e4363ab6179d01077d71b9afed41d9e1f18bf/numpy-1.24.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f3a86ed21e4f87050382c7bc96571755193c4c1392490744ac73d660e8f564a9", size = 14030228, upload-time = "2023-06-26T13:31:26.696Z" }, + { url = "https://files.pythonhosted.org/packages/7a/7c/d7b2a0417af6428440c0ad7cb9799073e507b1a465f827d058b826236964/numpy-1.24.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d11efb4dbecbdf22508d55e48d9c8384db795e1b7b51ea735289ff96613ff74d", size = 17311170, upload-time = "2023-06-26T13:31:56.615Z" }, + { url = "https://files.pythonhosted.org/packages/18/9d/e02ace5d7dfccee796c37b995c63322674daf88ae2f4a4724c5dd0afcc91/numpy-1.24.4-cp39-cp39-win32.whl", hash = "sha256:6620c0acd41dbcb368610bb2f4d83145674040025e5536954782467100aa8835", size = 12454918, upload-time = "2023-06-26T13:32:16.8Z" }, + { url = "https://files.pythonhosted.org/packages/63/38/6cc19d6b8bfa1d1a459daf2b3fe325453153ca7019976274b6f33d8b5663/numpy-1.24.4-cp39-cp39-win_amd64.whl", hash = "sha256:befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8", size = 14867441, upload-time = "2023-06-26T13:32:40.521Z" }, + { url = "https://files.pythonhosted.org/packages/a4/fd/8dff40e25e937c94257455c237b9b6bf5a30d42dd1cc11555533be099492/numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef", size = 19156590, upload-time = "2023-06-26T13:33:10.36Z" }, + { url = "https://files.pythonhosted.org/packages/42/e7/4bf953c6e05df90c6d351af69966384fed8e988d0e8c54dad7103b59f3ba/numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95f7ac6540e95bc440ad77f56e520da5bf877f87dca58bd095288dce8940532a", size = 16705744, upload-time = "2023-06-26T13:33:36.703Z" }, + { url = "https://files.pythonhosted.org/packages/fc/dd/9106005eb477d022b60b3817ed5937a43dad8fd1f20b0610ea8a32fcb407/numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:e98f220aa76ca2a977fe435f5b04d7b3470c0a2e6312907b37ba6068f26787f2", size = 14734290, upload-time = "2023-06-26T13:34:05.409Z" }, +] + +[[package]] +name = "numpy" +version = "2.0.2" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.9.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/a9/75/10dd1f8116a8b796cb2c737b674e02d02e80454bda953fa7e65d8c12b016/numpy-2.0.2.tar.gz", hash = "sha256:883c987dee1880e2a864ab0dc9892292582510604156762362d9326444636e78", size = 18902015, upload-time = "2024-08-26T20:19:40.945Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/21/91/3495b3237510f79f5d81f2508f9f13fea78ebfdf07538fc7444badda173d/numpy-2.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:51129a29dbe56f9ca83438b706e2e69a39892b5eda6cedcb6b0c9fdc9b0d3ece", size = 21165245, upload-time = "2024-08-26T20:04:14.625Z" }, + { url = "https://files.pythonhosted.org/packages/05/33/26178c7d437a87082d11019292dce6d3fe6f0e9026b7b2309cbf3e489b1d/numpy-2.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f15975dfec0cf2239224d80e32c3170b1d168335eaedee69da84fbe9f1f9cd04", size = 13738540, upload-time = "2024-08-26T20:04:36.784Z" }, + { url = "https://files.pythonhosted.org/packages/ec/31/cc46e13bf07644efc7a4bf68df2df5fb2a1a88d0cd0da9ddc84dc0033e51/numpy-2.0.2-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:8c5713284ce4e282544c68d1c3b2c7161d38c256d2eefc93c1d683cf47683e66", size = 5300623, upload-time = "2024-08-26T20:04:46.491Z" }, + { url = "https://files.pythonhosted.org/packages/6e/16/7bfcebf27bb4f9d7ec67332ffebee4d1bf085c84246552d52dbb548600e7/numpy-2.0.2-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:becfae3ddd30736fe1889a37f1f580e245ba79a5855bff5f2a29cb3ccc22dd7b", size = 6901774, upload-time = "2024-08-26T20:04:58.173Z" }, + { url = "https://files.pythonhosted.org/packages/f9/a3/561c531c0e8bf082c5bef509d00d56f82e0ea7e1e3e3a7fc8fa78742a6e5/numpy-2.0.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2da5960c3cf0df7eafefd806d4e612c5e19358de82cb3c343631188991566ccd", size = 13907081, upload-time = "2024-08-26T20:05:19.098Z" }, + { url = "https://files.pythonhosted.org/packages/fa/66/f7177ab331876200ac7563a580140643d1179c8b4b6a6b0fc9838de2a9b8/numpy-2.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:496f71341824ed9f3d2fd36cf3ac57ae2e0165c143b55c3a035ee219413f3318", size = 19523451, upload-time = "2024-08-26T20:05:47.479Z" }, + { url = "https://files.pythonhosted.org/packages/25/7f/0b209498009ad6453e4efc2c65bcdf0ae08a182b2b7877d7ab38a92dc542/numpy-2.0.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a61ec659f68ae254e4d237816e33171497e978140353c0c2038d46e63282d0c8", size = 19927572, upload-time = "2024-08-26T20:06:17.137Z" }, + { url = "https://files.pythonhosted.org/packages/3e/df/2619393b1e1b565cd2d4c4403bdd979621e2c4dea1f8532754b2598ed63b/numpy-2.0.2-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:d731a1c6116ba289c1e9ee714b08a8ff882944d4ad631fd411106a30f083c326", size = 14400722, upload-time = "2024-08-26T20:06:39.16Z" }, + { url = "https://files.pythonhosted.org/packages/22/ad/77e921b9f256d5da36424ffb711ae79ca3f451ff8489eeca544d0701d74a/numpy-2.0.2-cp310-cp310-win32.whl", hash = "sha256:984d96121c9f9616cd33fbd0618b7f08e0cfc9600a7ee1d6fd9b239186d19d97", size = 6472170, upload-time = "2024-08-26T20:06:50.361Z" }, + { url = "https://files.pythonhosted.org/packages/10/05/3442317535028bc29cf0c0dd4c191a4481e8376e9f0db6bcf29703cadae6/numpy-2.0.2-cp310-cp310-win_amd64.whl", hash = "sha256:c7b0be4ef08607dd04da4092faee0b86607f111d5ae68036f16cc787e250a131", size = 15905558, upload-time = "2024-08-26T20:07:13.881Z" }, + { url = "https://files.pythonhosted.org/packages/8b/cf/034500fb83041aa0286e0fb16e7c76e5c8b67c0711bb6e9e9737a717d5fe/numpy-2.0.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:49ca4decb342d66018b01932139c0961a8f9ddc7589611158cb3c27cbcf76448", size = 21169137, upload-time = "2024-08-26T20:07:45.345Z" }, + { url = "https://files.pythonhosted.org/packages/4a/d9/32de45561811a4b87fbdee23b5797394e3d1504b4a7cf40c10199848893e/numpy-2.0.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:11a76c372d1d37437857280aa142086476136a8c0f373b2e648ab2c8f18fb195", size = 13703552, upload-time = "2024-08-26T20:08:06.666Z" }, + { url = "https://files.pythonhosted.org/packages/c1/ca/2f384720020c7b244d22508cb7ab23d95f179fcfff33c31a6eeba8d6c512/numpy-2.0.2-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:807ec44583fd708a21d4a11d94aedf2f4f3c3719035c76a2bbe1fe8e217bdc57", size = 5298957, upload-time = "2024-08-26T20:08:15.83Z" }, + { url = "https://files.pythonhosted.org/packages/0e/78/a3e4f9fb6aa4e6fdca0c5428e8ba039408514388cf62d89651aade838269/numpy-2.0.2-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:8cafab480740e22f8d833acefed5cc87ce276f4ece12fdaa2e8903db2f82897a", size = 6905573, upload-time = "2024-08-26T20:08:27.185Z" }, + { url = "https://files.pythonhosted.org/packages/a0/72/cfc3a1beb2caf4efc9d0b38a15fe34025230da27e1c08cc2eb9bfb1c7231/numpy-2.0.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a15f476a45e6e5a3a79d8a14e62161d27ad897381fecfa4a09ed5322f2085669", size = 13914330, upload-time = "2024-08-26T20:08:48.058Z" }, + { url = "https://files.pythonhosted.org/packages/ba/a8/c17acf65a931ce551fee11b72e8de63bf7e8a6f0e21add4c937c83563538/numpy-2.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13e689d772146140a252c3a28501da66dfecd77490b498b168b501835041f951", size = 19534895, upload-time = "2024-08-26T20:09:16.536Z" }, + { url = "https://files.pythonhosted.org/packages/ba/86/8767f3d54f6ae0165749f84648da9dcc8cd78ab65d415494962c86fac80f/numpy-2.0.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:9ea91dfb7c3d1c56a0e55657c0afb38cf1eeae4544c208dc465c3c9f3a7c09f9", size = 19937253, upload-time = "2024-08-26T20:09:46.263Z" }, + { url = "https://files.pythonhosted.org/packages/df/87/f76450e6e1c14e5bb1eae6836478b1028e096fd02e85c1c37674606ab752/numpy-2.0.2-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c1c9307701fec8f3f7a1e6711f9089c06e6284b3afbbcd259f7791282d660a15", size = 14414074, upload-time = "2024-08-26T20:10:08.483Z" }, + { url = "https://files.pythonhosted.org/packages/5c/ca/0f0f328e1e59f73754f06e1adfb909de43726d4f24c6a3f8805f34f2b0fa/numpy-2.0.2-cp311-cp311-win32.whl", hash = "sha256:a392a68bd329eafac5817e5aefeb39038c48b671afd242710b451e76090e81f4", size = 6470640, upload-time = "2024-08-26T20:10:19.732Z" }, + { url = "https://files.pythonhosted.org/packages/eb/57/3a3f14d3a759dcf9bf6e9eda905794726b758819df4663f217d658a58695/numpy-2.0.2-cp311-cp311-win_amd64.whl", hash = "sha256:286cd40ce2b7d652a6f22efdfc6d1edf879440e53e76a75955bc0c826c7e64dc", size = 15910230, upload-time = "2024-08-26T20:10:43.413Z" }, + { url = "https://files.pythonhosted.org/packages/45/40/2e117be60ec50d98fa08c2f8c48e09b3edea93cfcabd5a9ff6925d54b1c2/numpy-2.0.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:df55d490dea7934f330006d0f81e8551ba6010a5bf035a249ef61a94f21c500b", size = 20895803, upload-time = "2024-08-26T20:11:13.916Z" }, + { url = "https://files.pythonhosted.org/packages/46/92/1b8b8dee833f53cef3e0a3f69b2374467789e0bb7399689582314df02651/numpy-2.0.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:8df823f570d9adf0978347d1f926b2a867d5608f434a7cff7f7908c6570dcf5e", size = 13471835, upload-time = "2024-08-26T20:11:34.779Z" }, + { url = "https://files.pythonhosted.org/packages/7f/19/e2793bde475f1edaea6945be141aef6c8b4c669b90c90a300a8954d08f0a/numpy-2.0.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:9a92ae5c14811e390f3767053ff54eaee3bf84576d99a2456391401323f4ec2c", size = 5038499, upload-time = "2024-08-26T20:11:43.902Z" }, + { url = "https://files.pythonhosted.org/packages/e3/ff/ddf6dac2ff0dd50a7327bcdba45cb0264d0e96bb44d33324853f781a8f3c/numpy-2.0.2-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:a842d573724391493a97a62ebbb8e731f8a5dcc5d285dfc99141ca15a3302d0c", size = 6633497, upload-time = "2024-08-26T20:11:55.09Z" }, + { url = "https://files.pythonhosted.org/packages/72/21/67f36eac8e2d2cd652a2e69595a54128297cdcb1ff3931cfc87838874bd4/numpy-2.0.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c05e238064fc0610c840d1cf6a13bf63d7e391717d247f1bf0318172e759e692", size = 13621158, upload-time = "2024-08-26T20:12:14.95Z" }, + { url = "https://files.pythonhosted.org/packages/39/68/e9f1126d757653496dbc096cb429014347a36b228f5a991dae2c6b6cfd40/numpy-2.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0123ffdaa88fa4ab64835dcbde75dcdf89c453c922f18dced6e27c90d1d0ec5a", size = 19236173, upload-time = "2024-08-26T20:12:44.049Z" }, + { url = "https://files.pythonhosted.org/packages/d1/e9/1f5333281e4ebf483ba1c888b1d61ba7e78d7e910fdd8e6499667041cc35/numpy-2.0.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:96a55f64139912d61de9137f11bf39a55ec8faec288c75a54f93dfd39f7eb40c", size = 19634174, upload-time = "2024-08-26T20:13:13.634Z" }, + { url = "https://files.pythonhosted.org/packages/71/af/a469674070c8d8408384e3012e064299f7a2de540738a8e414dcfd639996/numpy-2.0.2-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ec9852fb39354b5a45a80bdab5ac02dd02b15f44b3804e9f00c556bf24b4bded", size = 14099701, upload-time = "2024-08-26T20:13:34.851Z" }, + { url = "https://files.pythonhosted.org/packages/d0/3d/08ea9f239d0e0e939b6ca52ad403c84a2bce1bde301a8eb4888c1c1543f1/numpy-2.0.2-cp312-cp312-win32.whl", hash = "sha256:671bec6496f83202ed2d3c8fdc486a8fc86942f2e69ff0e986140339a63bcbe5", size = 6174313, upload-time = "2024-08-26T20:13:45.653Z" }, + { url = "https://files.pythonhosted.org/packages/b2/b5/4ac39baebf1fdb2e72585c8352c56d063b6126be9fc95bd2bb5ef5770c20/numpy-2.0.2-cp312-cp312-win_amd64.whl", hash = "sha256:cfd41e13fdc257aa5778496b8caa5e856dc4896d4ccf01841daee1d96465467a", size = 15606179, upload-time = "2024-08-26T20:14:08.786Z" }, + { url = "https://files.pythonhosted.org/packages/43/c1/41c8f6df3162b0c6ffd4437d729115704bd43363de0090c7f913cfbc2d89/numpy-2.0.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9059e10581ce4093f735ed23f3b9d283b9d517ff46009ddd485f1747eb22653c", size = 21169942, upload-time = "2024-08-26T20:14:40.108Z" }, + { url = "https://files.pythonhosted.org/packages/39/bc/fd298f308dcd232b56a4031fd6ddf11c43f9917fbc937e53762f7b5a3bb1/numpy-2.0.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:423e89b23490805d2a5a96fe40ec507407b8ee786d66f7328be214f9679df6dd", size = 13711512, upload-time = "2024-08-26T20:15:00.985Z" }, + { url = "https://files.pythonhosted.org/packages/96/ff/06d1aa3eeb1c614eda245c1ba4fb88c483bee6520d361641331872ac4b82/numpy-2.0.2-cp39-cp39-macosx_14_0_arm64.whl", hash = "sha256:2b2955fa6f11907cf7a70dab0d0755159bca87755e831e47932367fc8f2f2d0b", size = 5306976, upload-time = "2024-08-26T20:15:10.876Z" }, + { url = "https://files.pythonhosted.org/packages/2d/98/121996dcfb10a6087a05e54453e28e58694a7db62c5a5a29cee14c6e047b/numpy-2.0.2-cp39-cp39-macosx_14_0_x86_64.whl", hash = "sha256:97032a27bd9d8988b9a97a8c4d2c9f2c15a81f61e2f21404d7e8ef00cb5be729", size = 6906494, upload-time = "2024-08-26T20:15:22.055Z" }, + { url = "https://files.pythonhosted.org/packages/15/31/9dffc70da6b9bbf7968f6551967fc21156207366272c2a40b4ed6008dc9b/numpy-2.0.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1e795a8be3ddbac43274f18588329c72939870a16cae810c2b73461c40718ab1", size = 13912596, upload-time = "2024-08-26T20:15:42.452Z" }, + { url = "https://files.pythonhosted.org/packages/b9/14/78635daab4b07c0930c919d451b8bf8c164774e6a3413aed04a6d95758ce/numpy-2.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f26b258c385842546006213344c50655ff1555a9338e2e5e02a0756dc3e803dd", size = 19526099, upload-time = "2024-08-26T20:16:11.048Z" }, + { url = "https://files.pythonhosted.org/packages/26/4c/0eeca4614003077f68bfe7aac8b7496f04221865b3a5e7cb230c9d055afd/numpy-2.0.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5fec9451a7789926bcf7c2b8d187292c9f93ea30284802a0ab3f5be8ab36865d", size = 19932823, upload-time = "2024-08-26T20:16:40.171Z" }, + { url = "https://files.pythonhosted.org/packages/f1/46/ea25b98b13dccaebddf1a803f8c748680d972e00507cd9bc6dcdb5aa2ac1/numpy-2.0.2-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:9189427407d88ff25ecf8f12469d4d39d35bee1db5d39fc5c168c6f088a6956d", size = 14404424, upload-time = "2024-08-26T20:17:02.604Z" }, + { url = "https://files.pythonhosted.org/packages/c8/a6/177dd88d95ecf07e722d21008b1b40e681a929eb9e329684d449c36586b2/numpy-2.0.2-cp39-cp39-win32.whl", hash = "sha256:905d16e0c60200656500c95b6b8dca5d109e23cb24abc701d41c02d74c6b3afa", size = 6476809, upload-time = "2024-08-26T20:17:13.553Z" }, + { url = "https://files.pythonhosted.org/packages/ea/2b/7fc9f4e7ae5b507c1a3a21f0f15ed03e794c1242ea8a242ac158beb56034/numpy-2.0.2-cp39-cp39-win_amd64.whl", hash = "sha256:a3f4ab0caa7f053f6797fcd4e1e25caee367db3112ef2b6ef82d749530768c73", size = 15911314, upload-time = "2024-08-26T20:17:36.72Z" }, + { url = "https://files.pythonhosted.org/packages/8f/3b/df5a870ac6a3be3a86856ce195ef42eec7ae50d2a202be1f5a4b3b340e14/numpy-2.0.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:7f0a0c6f12e07fa94133c8a67404322845220c06a9e80e85999afe727f7438b8", size = 21025288, upload-time = "2024-08-26T20:18:07.732Z" }, + { url = "https://files.pythonhosted.org/packages/2c/97/51af92f18d6f6f2d9ad8b482a99fb74e142d71372da5d834b3a2747a446e/numpy-2.0.2-pp39-pypy39_pp73-macosx_14_0_x86_64.whl", hash = "sha256:312950fdd060354350ed123c0e25a71327d3711584beaef30cdaa93320c392d4", size = 6762793, upload-time = "2024-08-26T20:18:19.125Z" }, + { url = "https://files.pythonhosted.org/packages/12/46/de1fbd0c1b5ccaa7f9a005b66761533e2f6a3e560096682683a223631fe9/numpy-2.0.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:26df23238872200f63518dd2aa984cfca675d82469535dc7162dc2ee52d9dd5c", size = 19334885, upload-time = "2024-08-26T20:18:47.237Z" }, + { url = "https://files.pythonhosted.org/packages/cc/dc/d330a6faefd92b446ec0f0dfea4c3207bb1fef3c4771d19cf4543efd2c78/numpy-2.0.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:a46288ec55ebbd58947d31d72be2c63cbf839f0a63b49cb755022310792a3385", size = 15828784, upload-time = "2024-08-26T20:19:11.19Z" }, +] + +[[package]] +name = "numpy" +version = "2.2.6" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.10.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/76/21/7d2a95e4bba9dc13d043ee156a356c0a8f0c6309dff6b21b4d71a073b8a8/numpy-2.2.6.tar.gz", hash = "sha256:e29554e2bef54a90aa5cc07da6ce955accb83f21ab5de01a62c8478897b264fd", size = 20276440, upload-time = "2025-05-17T22:38:04.611Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/9a/3e/ed6db5be21ce87955c0cbd3009f2803f59fa08df21b5df06862e2d8e2bdd/numpy-2.2.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b412caa66f72040e6d268491a59f2c43bf03eb6c96dd8f0307829feb7fa2b6fb", size = 21165245, upload-time = "2025-05-17T21:27:58.555Z" }, + { url = "https://files.pythonhosted.org/packages/22/c2/4b9221495b2a132cc9d2eb862e21d42a009f5a60e45fc44b00118c174bff/numpy-2.2.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8e41fd67c52b86603a91c1a505ebaef50b3314de0213461c7a6e99c9a3beff90", size = 14360048, upload-time = "2025-05-17T21:28:21.406Z" }, + { url = "https://files.pythonhosted.org/packages/fd/77/dc2fcfc66943c6410e2bf598062f5959372735ffda175b39906d54f02349/numpy-2.2.6-cp310-cp310-macosx_14_0_arm64.whl", hash = "sha256:37e990a01ae6ec7fe7fa1c26c55ecb672dd98b19c3d0e1d1f326fa13cb38d163", size = 5340542, upload-time = "2025-05-17T21:28:30.931Z" }, + { url = "https://files.pythonhosted.org/packages/7a/4f/1cb5fdc353a5f5cc7feb692db9b8ec2c3d6405453f982435efc52561df58/numpy-2.2.6-cp310-cp310-macosx_14_0_x86_64.whl", hash = "sha256:5a6429d4be8ca66d889b7cf70f536a397dc45ba6faeb5f8c5427935d9592e9cf", size = 6878301, upload-time = "2025-05-17T21:28:41.613Z" }, + { url = "https://files.pythonhosted.org/packages/eb/17/96a3acd228cec142fcb8723bd3cc39c2a474f7dcf0a5d16731980bcafa95/numpy-2.2.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:efd28d4e9cd7d7a8d39074a4d44c63eda73401580c5c76acda2ce969e0a38e83", size = 14297320, upload-time = "2025-05-17T21:29:02.78Z" }, + { url = "https://files.pythonhosted.org/packages/b4/63/3de6a34ad7ad6646ac7d2f55ebc6ad439dbbf9c4370017c50cf403fb19b5/numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fc7b73d02efb0e18c000e9ad8b83480dfcd5dfd11065997ed4c6747470ae8915", size = 16801050, upload-time = "2025-05-17T21:29:27.675Z" }, + { url = "https://files.pythonhosted.org/packages/07/b6/89d837eddef52b3d0cec5c6ba0456c1bf1b9ef6a6672fc2b7873c3ec4e2e/numpy-2.2.6-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:74d4531beb257d2c3f4b261bfb0fc09e0f9ebb8842d82a7b4209415896adc680", size = 15807034, upload-time = "2025-05-17T21:29:51.102Z" }, + { url = "https://files.pythonhosted.org/packages/01/c8/dc6ae86e3c61cfec1f178e5c9f7858584049b6093f843bca541f94120920/numpy-2.2.6-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:8fc377d995680230e83241d8a96def29f204b5782f371c532579b4f20607a289", size = 18614185, upload-time = "2025-05-17T21:30:18.703Z" }, + { url = "https://files.pythonhosted.org/packages/5b/c5/0064b1b7e7c89137b471ccec1fd2282fceaae0ab3a9550f2568782d80357/numpy-2.2.6-cp310-cp310-win32.whl", hash = "sha256:b093dd74e50a8cba3e873868d9e93a85b78e0daf2e98c6797566ad8044e8363d", size = 6527149, upload-time = "2025-05-17T21:30:29.788Z" }, + { url = "https://files.pythonhosted.org/packages/a3/dd/4b822569d6b96c39d1215dbae0582fd99954dcbcf0c1a13c61783feaca3f/numpy-2.2.6-cp310-cp310-win_amd64.whl", hash = "sha256:f0fd6321b839904e15c46e0d257fdd101dd7f530fe03fd6359c1ea63738703f3", size = 12904620, upload-time = "2025-05-17T21:30:48.994Z" }, + { url = "https://files.pythonhosted.org/packages/da/a8/4f83e2aa666a9fbf56d6118faaaf5f1974d456b1823fda0a176eff722839/numpy-2.2.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f9f1adb22318e121c5c69a09142811a201ef17ab257a1e66ca3025065b7f53ae", size = 21176963, upload-time = "2025-05-17T21:31:19.36Z" }, + { url = "https://files.pythonhosted.org/packages/b3/2b/64e1affc7972decb74c9e29e5649fac940514910960ba25cd9af4488b66c/numpy-2.2.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c820a93b0255bc360f53eca31a0e676fd1101f673dda8da93454a12e23fc5f7a", size = 14406743, upload-time = "2025-05-17T21:31:41.087Z" }, + { url = "https://files.pythonhosted.org/packages/4a/9f/0121e375000b5e50ffdd8b25bf78d8e1a5aa4cca3f185d41265198c7b834/numpy-2.2.6-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:3d70692235e759f260c3d837193090014aebdf026dfd167834bcba43e30c2a42", size = 5352616, upload-time = "2025-05-17T21:31:50.072Z" }, + { url = "https://files.pythonhosted.org/packages/31/0d/b48c405c91693635fbe2dcd7bc84a33a602add5f63286e024d3b6741411c/numpy-2.2.6-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:481b49095335f8eed42e39e8041327c05b0f6f4780488f61286ed3c01368d491", size = 6889579, upload-time = "2025-05-17T21:32:01.712Z" }, + { url = "https://files.pythonhosted.org/packages/52/b8/7f0554d49b565d0171eab6e99001846882000883998e7b7d9f0d98b1f934/numpy-2.2.6-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b64d8d4d17135e00c8e346e0a738deb17e754230d7e0810ac5012750bbd85a5a", size = 14312005, upload-time = "2025-05-17T21:32:23.332Z" }, + { url = "https://files.pythonhosted.org/packages/b3/dd/2238b898e51bd6d389b7389ffb20d7f4c10066d80351187ec8e303a5a475/numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba10f8411898fc418a521833e014a77d3ca01c15b0c6cdcce6a0d2897e6dbbdf", size = 16821570, upload-time = "2025-05-17T21:32:47.991Z" }, + { url = "https://files.pythonhosted.org/packages/83/6c/44d0325722cf644f191042bf47eedad61c1e6df2432ed65cbe28509d404e/numpy-2.2.6-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:bd48227a919f1bafbdda0583705e547892342c26fb127219d60a5c36882609d1", size = 15818548, upload-time = "2025-05-17T21:33:11.728Z" }, + { url = "https://files.pythonhosted.org/packages/ae/9d/81e8216030ce66be25279098789b665d49ff19eef08bfa8cb96d4957f422/numpy-2.2.6-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:9551a499bf125c1d4f9e250377c1ee2eddd02e01eac6644c080162c0c51778ab", size = 18620521, upload-time = "2025-05-17T21:33:39.139Z" }, + { url = "https://files.pythonhosted.org/packages/6a/fd/e19617b9530b031db51b0926eed5345ce8ddc669bb3bc0044b23e275ebe8/numpy-2.2.6-cp311-cp311-win32.whl", hash = "sha256:0678000bb9ac1475cd454c6b8c799206af8107e310843532b04d49649c717a47", size = 6525866, upload-time = "2025-05-17T21:33:50.273Z" }, + { url = "https://files.pythonhosted.org/packages/31/0a/f354fb7176b81747d870f7991dc763e157a934c717b67b58456bc63da3df/numpy-2.2.6-cp311-cp311-win_amd64.whl", hash = "sha256:e8213002e427c69c45a52bbd94163084025f533a55a59d6f9c5b820774ef3303", size = 12907455, upload-time = "2025-05-17T21:34:09.135Z" }, + { url = "https://files.pythonhosted.org/packages/82/5d/c00588b6cf18e1da539b45d3598d3557084990dcc4331960c15ee776ee41/numpy-2.2.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:41c5a21f4a04fa86436124d388f6ed60a9343a6f767fced1a8a71c3fbca038ff", size = 20875348, upload-time = "2025-05-17T21:34:39.648Z" }, + { url = "https://files.pythonhosted.org/packages/66/ee/560deadcdde6c2f90200450d5938f63a34b37e27ebff162810f716f6a230/numpy-2.2.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:de749064336d37e340f640b05f24e9e3dd678c57318c7289d222a8a2f543e90c", size = 14119362, upload-time = "2025-05-17T21:35:01.241Z" }, + { url = "https://files.pythonhosted.org/packages/3c/65/4baa99f1c53b30adf0acd9a5519078871ddde8d2339dc5a7fde80d9d87da/numpy-2.2.6-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:894b3a42502226a1cac872f840030665f33326fc3dac8e57c607905773cdcde3", size = 5084103, upload-time = "2025-05-17T21:35:10.622Z" }, + { url = "https://files.pythonhosted.org/packages/cc/89/e5a34c071a0570cc40c9a54eb472d113eea6d002e9ae12bb3a8407fb912e/numpy-2.2.6-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:71594f7c51a18e728451bb50cc60a3ce4e6538822731b2933209a1f3614e9282", size = 6625382, upload-time = "2025-05-17T21:35:21.414Z" }, + { url = "https://files.pythonhosted.org/packages/f8/35/8c80729f1ff76b3921d5c9487c7ac3de9b2a103b1cd05e905b3090513510/numpy-2.2.6-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f2618db89be1b4e05f7a1a847a9c1c0abd63e63a1607d892dd54668dd92faf87", size = 14018462, upload-time = "2025-05-17T21:35:42.174Z" }, + { url = "https://files.pythonhosted.org/packages/8c/3d/1e1db36cfd41f895d266b103df00ca5b3cbe965184df824dec5c08c6b803/numpy-2.2.6-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd83c01228a688733f1ded5201c678f0c53ecc1006ffbc404db9f7a899ac6249", size = 16527618, upload-time = "2025-05-17T21:36:06.711Z" }, + { url = "https://files.pythonhosted.org/packages/61/c6/03ed30992602c85aa3cd95b9070a514f8b3c33e31124694438d88809ae36/numpy-2.2.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:37c0ca431f82cd5fa716eca9506aefcabc247fb27ba69c5062a6d3ade8cf8f49", size = 15505511, upload-time = "2025-05-17T21:36:29.965Z" }, + { url = "https://files.pythonhosted.org/packages/b7/25/5761d832a81df431e260719ec45de696414266613c9ee268394dd5ad8236/numpy-2.2.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:fe27749d33bb772c80dcd84ae7e8df2adc920ae8297400dabec45f0dedb3f6de", size = 18313783, upload-time = "2025-05-17T21:36:56.883Z" }, + { url = "https://files.pythonhosted.org/packages/57/0a/72d5a3527c5ebffcd47bde9162c39fae1f90138c961e5296491ce778e682/numpy-2.2.6-cp312-cp312-win32.whl", hash = "sha256:4eeaae00d789f66c7a25ac5f34b71a7035bb474e679f410e5e1a94deb24cf2d4", size = 6246506, upload-time = "2025-05-17T21:37:07.368Z" }, + { url = "https://files.pythonhosted.org/packages/36/fa/8c9210162ca1b88529ab76b41ba02d433fd54fecaf6feb70ef9f124683f1/numpy-2.2.6-cp312-cp312-win_amd64.whl", hash = "sha256:c1f9540be57940698ed329904db803cf7a402f3fc200bfe599334c9bd84a40b2", size = 12614190, upload-time = "2025-05-17T21:37:26.213Z" }, + { url = "https://files.pythonhosted.org/packages/f9/5c/6657823f4f594f72b5471f1db1ab12e26e890bb2e41897522d134d2a3e81/numpy-2.2.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0811bb762109d9708cca4d0b13c4f67146e3c3b7cf8d34018c722adb2d957c84", size = 20867828, upload-time = "2025-05-17T21:37:56.699Z" }, + { url = "https://files.pythonhosted.org/packages/dc/9e/14520dc3dadf3c803473bd07e9b2bd1b69bc583cb2497b47000fed2fa92f/numpy-2.2.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:287cc3162b6f01463ccd86be154f284d0893d2b3ed7292439ea97eafa8170e0b", size = 14143006, upload-time = "2025-05-17T21:38:18.291Z" }, + { url = "https://files.pythonhosted.org/packages/4f/06/7e96c57d90bebdce9918412087fc22ca9851cceaf5567a45c1f404480e9e/numpy-2.2.6-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:f1372f041402e37e5e633e586f62aa53de2eac8d98cbfb822806ce4bbefcb74d", size = 5076765, upload-time = "2025-05-17T21:38:27.319Z" }, + { url = "https://files.pythonhosted.org/packages/73/ed/63d920c23b4289fdac96ddbdd6132e9427790977d5457cd132f18e76eae0/numpy-2.2.6-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:55a4d33fa519660d69614a9fad433be87e5252f4b03850642f88993f7b2ca566", size = 6617736, upload-time = "2025-05-17T21:38:38.141Z" }, + { url = "https://files.pythonhosted.org/packages/85/c5/e19c8f99d83fd377ec8c7e0cf627a8049746da54afc24ef0a0cb73d5dfb5/numpy-2.2.6-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f92729c95468a2f4f15e9bb94c432a9229d0d50de67304399627a943201baa2f", size = 14010719, upload-time = "2025-05-17T21:38:58.433Z" }, + { url = "https://files.pythonhosted.org/packages/19/49/4df9123aafa7b539317bf6d342cb6d227e49f7a35b99c287a6109b13dd93/numpy-2.2.6-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1bc23a79bfabc5d056d106f9befb8d50c31ced2fbc70eedb8155aec74a45798f", size = 16526072, upload-time = "2025-05-17T21:39:22.638Z" }, + { url = "https://files.pythonhosted.org/packages/b2/6c/04b5f47f4f32f7c2b0e7260442a8cbcf8168b0e1a41ff1495da42f42a14f/numpy-2.2.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:e3143e4451880bed956e706a3220b4e5cf6172ef05fcc397f6f36a550b1dd868", size = 15503213, upload-time = "2025-05-17T21:39:45.865Z" }, + { url = "https://files.pythonhosted.org/packages/17/0a/5cd92e352c1307640d5b6fec1b2ffb06cd0dabe7d7b8227f97933d378422/numpy-2.2.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:b4f13750ce79751586ae2eb824ba7e1e8dba64784086c98cdbbcc6a42112ce0d", size = 18316632, upload-time = "2025-05-17T21:40:13.331Z" }, + { url = "https://files.pythonhosted.org/packages/f0/3b/5cba2b1d88760ef86596ad0f3d484b1cbff7c115ae2429678465057c5155/numpy-2.2.6-cp313-cp313-win32.whl", hash = "sha256:5beb72339d9d4fa36522fc63802f469b13cdbe4fdab4a288f0c441b74272ebfd", size = 6244532, upload-time = "2025-05-17T21:43:46.099Z" }, + { url = "https://files.pythonhosted.org/packages/cb/3b/d58c12eafcb298d4e6d0d40216866ab15f59e55d148a5658bb3132311fcf/numpy-2.2.6-cp313-cp313-win_amd64.whl", hash = "sha256:b0544343a702fa80c95ad5d3d608ea3599dd54d4632df855e4c8d24eb6ecfa1c", size = 12610885, upload-time = "2025-05-17T21:44:05.145Z" }, + { url = "https://files.pythonhosted.org/packages/6b/9e/4bf918b818e516322db999ac25d00c75788ddfd2d2ade4fa66f1f38097e1/numpy-2.2.6-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:0bca768cd85ae743b2affdc762d617eddf3bcf8724435498a1e80132d04879e6", size = 20963467, upload-time = "2025-05-17T21:40:44Z" }, + { url = "https://files.pythonhosted.org/packages/61/66/d2de6b291507517ff2e438e13ff7b1e2cdbdb7cb40b3ed475377aece69f9/numpy-2.2.6-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:fc0c5673685c508a142ca65209b4e79ed6740a4ed6b2267dbba90f34b0b3cfda", size = 14225144, upload-time = "2025-05-17T21:41:05.695Z" }, + { url = "https://files.pythonhosted.org/packages/e4/25/480387655407ead912e28ba3a820bc69af9adf13bcbe40b299d454ec011f/numpy-2.2.6-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:5bd4fc3ac8926b3819797a7c0e2631eb889b4118a9898c84f585a54d475b7e40", size = 5200217, upload-time = "2025-05-17T21:41:15.903Z" }, + { url = "https://files.pythonhosted.org/packages/aa/4a/6e313b5108f53dcbf3aca0c0f3e9c92f4c10ce57a0a721851f9785872895/numpy-2.2.6-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:fee4236c876c4e8369388054d02d0e9bb84821feb1a64dd59e137e6511a551f8", size = 6712014, upload-time = "2025-05-17T21:41:27.321Z" }, + { url = "https://files.pythonhosted.org/packages/b7/30/172c2d5c4be71fdf476e9de553443cf8e25feddbe185e0bd88b096915bcc/numpy-2.2.6-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e1dda9c7e08dc141e0247a5b8f49cf05984955246a327d4c48bda16821947b2f", size = 14077935, upload-time = "2025-05-17T21:41:49.738Z" }, + { url = "https://files.pythonhosted.org/packages/12/fb/9e743f8d4e4d3c710902cf87af3512082ae3d43b945d5d16563f26ec251d/numpy-2.2.6-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f447e6acb680fd307f40d3da4852208af94afdfab89cf850986c3ca00562f4fa", size = 16600122, upload-time = "2025-05-17T21:42:14.046Z" }, + { url = "https://files.pythonhosted.org/packages/12/75/ee20da0e58d3a66f204f38916757e01e33a9737d0b22373b3eb5a27358f9/numpy-2.2.6-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:389d771b1623ec92636b0786bc4ae56abafad4a4c513d36a55dce14bd9ce8571", size = 15586143, upload-time = "2025-05-17T21:42:37.464Z" }, + { url = "https://files.pythonhosted.org/packages/76/95/bef5b37f29fc5e739947e9ce5179ad402875633308504a52d188302319c8/numpy-2.2.6-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:8e9ace4a37db23421249ed236fdcdd457d671e25146786dfc96835cd951aa7c1", size = 18385260, upload-time = "2025-05-17T21:43:05.189Z" }, + { url = "https://files.pythonhosted.org/packages/09/04/f2f83279d287407cf36a7a8053a5abe7be3622a4363337338f2585e4afda/numpy-2.2.6-cp313-cp313t-win32.whl", hash = "sha256:038613e9fb8c72b0a41f025a7e4c3f0b7a1b5d768ece4796b674c8f3fe13efff", size = 6377225, upload-time = "2025-05-17T21:43:16.254Z" }, + { url = "https://files.pythonhosted.org/packages/67/0e/35082d13c09c02c011cf21570543d202ad929d961c02a147493cb0c2bdf5/numpy-2.2.6-cp313-cp313t-win_amd64.whl", hash = "sha256:6031dd6dfecc0cf9f668681a37648373bddd6421fff6c66ec1624eed0180ee06", size = 12771374, upload-time = "2025-05-17T21:43:35.479Z" }, + { url = "https://files.pythonhosted.org/packages/9e/3b/d94a75f4dbf1ef5d321523ecac21ef23a3cd2ac8b78ae2aac40873590229/numpy-2.2.6-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:0b605b275d7bd0c640cad4e5d30fa701a8d59302e127e5f79138ad62762c3e3d", size = 21040391, upload-time = "2025-05-17T21:44:35.948Z" }, + { url = "https://files.pythonhosted.org/packages/17/f4/09b2fa1b58f0fb4f7c7963a1649c64c4d315752240377ed74d9cd878f7b5/numpy-2.2.6-pp310-pypy310_pp73-macosx_14_0_x86_64.whl", hash = "sha256:7befc596a7dc9da8a337f79802ee8adb30a552a94f792b9c9d18c840055907db", size = 6786754, upload-time = "2025-05-17T21:44:47.446Z" }, + { url = "https://files.pythonhosted.org/packages/af/30/feba75f143bdc868a1cc3f44ccfa6c4b9ec522b36458e738cd00f67b573f/numpy-2.2.6-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ce47521a4754c8f4593837384bd3424880629f718d87c5d44f8ed763edd63543", size = 16643476, upload-time = "2025-05-17T21:45:11.871Z" }, + { url = "https://files.pythonhosted.org/packages/37/48/ac2a9584402fb6c0cd5b5d1a91dcf176b15760130dd386bbafdbfe3640bf/numpy-2.2.6-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:d042d24c90c41b54fd506da306759e06e568864df8ec17ccc17e9e884634fd00", size = 12812666, upload-time = "2025-05-17T21:45:31.426Z" }, +] + +[[package]] +name = "numpy" +version = "2.4.6" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.11.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/d0/ad/fed0499ce6a338d2a03ebae59cd15093910c8875328855781952abf6c2fe/numpy-2.4.6.tar.gz", hash = "sha256:f3a3570c4a2a16746ac2c31a7c7c7b0c186b95ce902e33db6f28094ed7387dda", size = 20735807, upload-time = "2026-05-18T23:37:14.07Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b3/49/ec46835a70be8fa6446c495126ac84fdb28cb2558e1620ffb87a10c8b64c/numpy-2.4.6-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0280e0356c0829a18d9de1cb7eee50ec22ca639878d7240307ca0943d73cd2c4", size = 16969194, upload-time = "2026-05-18T23:33:13.503Z" }, + { url = "https://files.pythonhosted.org/packages/0e/0d/f5957185c0ee2f3e12f78715aa9e3b353fd83633316c8532b38faa37e3f6/numpy-2.4.6-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:110f8b71aacb688ec69062bb7f6938a0f8acb01b7c1c4beb453c65b6d234584d", size = 14964111, upload-time = "2026-05-18T23:33:17.795Z" }, + { url = "https://files.pythonhosted.org/packages/ad/40/40a40ee0ddf7ceb782c49af278894b686e586d65d8c1889c8b5da01a3d7d/numpy-2.4.6-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:4cfe66903cc32a9921a6733d96b19bb6abf310397581bbad89c228f5abaf0ee8", size = 5469159, upload-time = "2026-05-18T23:33:20.654Z" }, + { url = "https://files.pythonhosted.org/packages/63/13/f9a8046535cb21deae82f8d03de9617e08882d274fad2539630761888228/numpy-2.4.6-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:8155154c7c691289fe18f510b5d4657c68c67989f293f0535a91360392ff6538", size = 6798936, upload-time = "2026-05-18T23:33:22.987Z" }, + { url = "https://files.pythonhosted.org/packages/33/a8/6fa8c1a345a8c85dbb21932c447bee07c30a2c2a3f31e369c0a84b300147/numpy-2.4.6-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0ab0a9c4ffb1a6d95ef519fe4247dba8eb6b18ad93999f76b7f657039acabd47", size = 15966692, upload-time = "2026-05-18T23:33:26.62Z" }, + { url = "https://files.pythonhosted.org/packages/02/03/74fe2a4cb3817d94d86402f2506554130a2f01414e299b5a843e5a8a957f/numpy-2.4.6-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:89cd468399cfd2504718f0ba50e410dca55a170b61a02ad92bb18c8a65186e93", size = 16918164, upload-time = "2026-05-18T23:33:29.955Z" }, + { url = "https://files.pythonhosted.org/packages/c5/80/3615be3313f7e7696609bc194b9f0101da809df79e859bdb84e0cd043f46/numpy-2.4.6-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:c2d37ab77531417474168eb79d6d80b14f821a966818505d03013d0833edb7a8", size = 17322877, upload-time = "2026-05-18T23:33:34.724Z" }, + { url = "https://files.pythonhosted.org/packages/ca/ac/a691e0fe2675e370d0e08ff905adc49a1c8830e8cae03efe4477e92cd55d/numpy-2.4.6-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:f407cb6b8e9d6d8c626bc73c945db1706035af8fd632295547bf1c9e46d092d6", size = 18651487, upload-time = "2026-05-18T23:33:38.217Z" }, + { url = "https://files.pythonhosted.org/packages/15/a7/9bc1cd626d7bf6869bfedf27b91b6ab5dd607758bf8e959d6fa80c6a59cb/numpy-2.4.6-cp311-cp311-win32.whl", hash = "sha256:ddea102b48f9e339f3948bf22040944184627a30fdf7f858667673b9c5f033c8", size = 6233945, upload-time = "2026-05-18T23:33:41.331Z" }, + { url = "https://files.pythonhosted.org/packages/c5/31/7fc6239c12bce7e931463251cca4426c465e1876ba3cc785402ef4dd8f4e/numpy-2.4.6-cp311-cp311-win_amd64.whl", hash = "sha256:1e254a00cdf42b1e4d5b3d68d33af63268d41340d8885df2ab6470f2e1500147", size = 12608406, upload-time = "2026-05-18T23:33:44.131Z" }, + { url = "https://files.pythonhosted.org/packages/27/83/140f85a466595a16382996a1bf06b2b54bcd597488921b0c9daaeeda72af/numpy-2.4.6-cp311-cp311-win_arm64.whl", hash = "sha256:ed9749eef4cbd126da3dc1d6bcb3a57f5eb7ac6a6484146bdbf743f552dfc577", size = 10479528, upload-time = "2026-05-18T23:33:50.725Z" }, + { url = "https://files.pythonhosted.org/packages/95/2a/3d7b5ac8aac24feaf9ad7ed58f45b0bbc06d37e4338ae84c9f2298b570f9/numpy-2.4.6-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:001fbb8e08d942dd57599e781f2472269ee7f2755fae407b4f67b2f0b17da3f1", size = 16689119, upload-time = "2026-05-18T23:33:54.065Z" }, + { url = "https://files.pythonhosted.org/packages/ea/12/92c4c131527599e8288d6918e888d88726f84d805d784b771f32408aeaef/numpy-2.4.6-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ebfb099f8dcf083deef3ac1ca4c1503f387cf76296fcb3816b66f5ecb5f54fdb", size = 14699246, upload-time = "2026-05-18T23:33:57.621Z" }, + { url = "https://files.pythonhosted.org/packages/ad/fe/c0a6b7b2ca128a8fb228575147073b660656734b8ebe4d76c8fd748dcc79/numpy-2.4.6-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:3213d622a0283a39a93d188f3cf72b26862df52fbb4ca3697f51705016523d41", size = 5204410, upload-time = "2026-05-18T23:34:00.302Z" }, + { url = "https://files.pythonhosted.org/packages/f3/d4/9770d14ba719432bb90a421bfd443872ed0f70f7264b64bec12ea363d5fd/numpy-2.4.6-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:357cc07a6d7b0b182ff02249616a03742827ebb1277546b5c7cd7f7620a45698", size = 6551240, upload-time = "2026-05-18T23:34:02.852Z" }, + { url = "https://files.pythonhosted.org/packages/c9/c6/50a46a6205feba2343f1d6d17438107c5dc491ed1c736e6ea68689fd906b/numpy-2.4.6-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5f9fb9157b4ce2971008323afe46053787b526ef624fea915b261468a8421a0f", size = 15671012, upload-time = "2026-05-18T23:34:05.485Z" }, + { url = "https://files.pythonhosted.org/packages/99/60/14115e6364fa676c5397c2ad3004e527e9aa487abf5d0706ec81bbd08529/numpy-2.4.6-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:90f9849678c75fe7afa2d348ac842c168b0a4d3d61919687216dfc547976d853", size = 16645538, upload-time = "2026-05-18T23:34:09.265Z" }, + { url = "https://files.pythonhosted.org/packages/ae/c5/693cbe59e57db94d2231fa519ca3978dc9e19da5a8f088588f5c6e947ff2/numpy-2.4.6-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c1a2af6c6ef86344a6b0db6b97834208bf598db514f2b155042439b62605601a", size = 17020706, upload-time = "2026-05-18T23:34:13.053Z" }, + { url = "https://files.pythonhosted.org/packages/ef/fc/85b7c4eff9b4966ade25c2273cf7e7012e92366c032058653934b37de044/numpy-2.4.6-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e5805d5a22fd19c8ccff10a9561f9df94436b0545619ea579db2d3c35294bce2", size = 18368541, upload-time = "2026-05-18T23:34:17.024Z" }, + { url = "https://files.pythonhosted.org/packages/f6/81/e1b27545deedce7f4a0b348618c6b62d74e36a4dc9ccd42f3eb2f85eee32/numpy-2.4.6-cp312-cp312-win32.whl", hash = "sha256:e3eeb0aabd6bd5ce64faae67e9935203a6991b4bc2a485a767fbafb2c5125f45", size = 5962825, upload-time = "2026-05-18T23:34:20.3Z" }, + { url = "https://files.pythonhosted.org/packages/ab/ca/feab00bd44aa5fe1ad2c18f08b4d3bb92e26484b0b1d1443897809ed528c/numpy-2.4.6-cp312-cp312-win_amd64.whl", hash = "sha256:d8e8286dd7cea7895157318d1b91cdacac64c479f3cbc8dce548331728484751", size = 12321687, upload-time = "2026-05-18T23:34:23.095Z" }, + { url = "https://files.pythonhosted.org/packages/63/cf/5a6d34850a39d1093558564f77ee8e8e0bee5061151b8f05a55711001ec7/numpy-2.4.6-cp312-cp312-win_arm64.whl", hash = "sha256:4081eb135ac24158bd51cdfbef16f1c64df7063b1143f24731387137c092bec8", size = 10221482, upload-time = "2026-05-18T23:34:25.876Z" }, + { url = "https://files.pythonhosted.org/packages/fb/82/bdab26d7438c6791ca31b7c024ca37c1eab8b726ba236129005cd4a06e45/numpy-2.4.6-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:511dbaf848decaaaf4b4ca48032619fb3138710c4bf7da7617765edad1ef96b0", size = 16684648, upload-time = "2026-05-18T23:34:29.41Z" }, + { url = "https://files.pythonhosted.org/packages/1b/30/a80189bcc7f5e4258b3fbc3968d909d1756f54d023299ecc39ad6fdb9ef8/numpy-2.4.6-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:bf162abab1c1a736333192707cef898e735a5ca00f38f27eeedf44b39d9e85eb", size = 14693902, upload-time = "2026-05-18T23:34:33.013Z" }, + { url = "https://files.pythonhosted.org/packages/97/12/70b5d0d7c15e1ebb8a6a84a8caa1d19e181d84fb58bb6d70aca29099dec1/numpy-2.4.6-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:043191bfa8eab18c776647b62723ac9dddece59743b13f49b2016094129c2b3f", size = 5198992, upload-time = "2026-05-18T23:34:36.132Z" }, + { url = "https://files.pythonhosted.org/packages/ba/8c/ebd2a8f8a83541f8d38cc5667e8c2b69cecfd30da6e45693e8158857d44b/numpy-2.4.6-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:6180d8b35af935aed8ece3a85e0a43f87393ae0ac87c8d2c8bd2c993f7270ef3", size = 6546944, upload-time = "2026-05-18T23:34:38.484Z" }, + { url = "https://files.pythonhosted.org/packages/bb/c5/7b863a97a91671a0338f4253bd3b5a3d3852f0692dae91711c9f4a10e787/numpy-2.4.6-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:72fbe16c6fac95aedf5937fa873445cec2110be35d8a4e9433d7501fd98dae6b", size = 15669392, upload-time = "2026-05-18T23:34:41.257Z" }, + { url = "https://files.pythonhosted.org/packages/a5/9d/3584b9984ca4c047aea75214ce1a4c4c73d849bd71b604264b7f5653f8a8/numpy-2.4.6-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a7830bab239b79cda9c08c2da014761cafb48da6150e1da17ac06283f43b6089", size = 16633220, upload-time = "2026-05-18T23:34:45.075Z" }, + { url = "https://files.pythonhosted.org/packages/05/ae/7c67fba23bd98caec7c99261f3a16072ade14813486b0282cb29846de832/numpy-2.4.6-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:ef4aea96ce4d3b074422cb4f2f64e216bf9e213004bb58ecfdf50ea02ea8eb9a", size = 17020800, upload-time = "2026-05-18T23:34:49.065Z" }, + { url = "https://files.pythonhosted.org/packages/d9/5d/3b6725cb31d983c5e66916f5d36f6d7e5521129e4c4404d64f918292a5b6/numpy-2.4.6-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:dfa20cc6ca228e6b155b11da03825975ce66aea520985dbbddf0f2a5a495c605", size = 18357600, upload-time = "2026-05-18T23:34:52.709Z" }, + { url = "https://files.pythonhosted.org/packages/f7/da/2ccc6c2fe8898dee01d90c75c5f5f914a23daf99e3e0f59516a08760c8b5/numpy-2.4.6-cp313-cp313-win32.whl", hash = "sha256:56b39e5e0622a09a25bf5baf62f4bcf0cb8a41ae6e2819cf49bbc5a74c083f91", size = 5961134, upload-time = "2026-05-18T23:34:55.618Z" }, + { url = "https://files.pythonhosted.org/packages/b5/cd/9cc4dc876fb065d5c220aae4d5e14826b2715331bb7618ce1fb07a679d99/numpy-2.4.6-cp313-cp313-win_amd64.whl", hash = "sha256:c4fc99836233ea196540b17ab0983aff60ed07941751930f5f4d05bc3b3b7359", size = 12318598, upload-time = "2026-05-18T23:34:58.928Z" }, + { url = "https://files.pythonhosted.org/packages/39/1e/c0bcba1f8694116485fe28fd1be698c278fcda4141c5b0e53a2aed8b12a8/numpy-2.4.6-cp313-cp313-win_arm64.whl", hash = "sha256:a7c711e21628b52034bb5ab8d1bce291f752fcc5e92accc615778acee1ff4778", size = 10222272, upload-time = "2026-05-18T23:35:02.167Z" }, + { url = "https://files.pythonhosted.org/packages/63/6d/cc5619247c8f4204e507f5883528372e4ac4bb189e579fb859a12e480b1f/numpy-2.4.6-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:112b06a867b235ef466ed3508ddf0238050df9c727cafb5301ac385b899189a1", size = 14821197, upload-time = "2026-05-18T23:35:05.468Z" }, + { url = "https://files.pythonhosted.org/packages/00/58/f1c39161c87d9e9bed660f1ed4bafc0e403d5ec9650b6dd77aead07d489b/numpy-2.4.6-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:eaf7fa2de5c0be8ae6ff8e9bea2ccd725e980541244521d8d4b5f3354a27babe", size = 5326287, upload-time = "2026-05-18T23:35:08.693Z" }, + { url = "https://files.pythonhosted.org/packages/af/57/3917ab0fd97f271a8694513581b8a36c655f111c446852c302f04ccdb6fc/numpy-2.4.6-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:7265a2f3d436e54ef9f2b52b5c937e6be778781bd97a590319d7348f1c1ca997", size = 6646763, upload-time = "2026-05-18T23:35:11.459Z" }, + { url = "https://files.pythonhosted.org/packages/eb/0f/037e64c494b67581ae18193d770adef354c41f3f2c8ebf865602d949bf8f/numpy-2.4.6-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f74a575920ab21fe304421a3fc28793d82e299cae9eccb37084e9fc7f3617c20", size = 15728070, upload-time = "2026-05-18T23:35:14.79Z" }, + { url = "https://files.pythonhosted.org/packages/21/a6/5d2bae9c9542eb4df16dc9c46dc79c186e9bad53805dfa5399a6023c6db0/numpy-2.4.6-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ede83e07a75dd06bc501566c1eca2afc0d61677c1472ac9ad93fdee6e638a48d", size = 16681752, upload-time = "2026-05-18T23:35:18.836Z" }, + { url = "https://files.pythonhosted.org/packages/92/14/23d1dfb410ae362cd59ce53e936b1513d545eb40db3949ced632e19a459e/numpy-2.4.6-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:68bb27509ac1b9a3443094260f6326150663b06abe40b73a2f81160623da5b67", size = 17086024, upload-time = "2026-05-18T23:35:22.52Z" }, + { url = "https://files.pythonhosted.org/packages/4b/6e/23595a2c642cdf3bc567877064bdd7f91c8b0038a4453cf2daf7248eafe9/numpy-2.4.6-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:a0df0043bdb289bde1f62da130d20df23d58b45429f752bc7a8fc5325a225ecd", size = 18403398, upload-time = "2026-05-18T23:35:26.398Z" }, + { url = "https://files.pythonhosted.org/packages/8a/90/0ac3bc947217e66dec77e7cbc6a1979d1af70b6461b82f620d3bccd5e4c8/numpy-2.4.6-cp313-cp313t-win32.whl", hash = "sha256:29a287e0cf63ff528da061de6b9f64a4618da591ca1046aafc54062e40ca7eab", size = 6084971, upload-time = "2026-05-18T23:35:29.387Z" }, + { url = "https://files.pythonhosted.org/packages/77/71/5673e351671a1d2bd6063b91b44f70c0affea7d1516fa7a6572941ba4aa1/numpy-2.4.6-cp313-cp313t-win_amd64.whl", hash = "sha256:25c692919ac5a01f170a3bfcd62d745b24fd095c353d50812637d6fcab442e75", size = 12458532, upload-time = "2026-05-18T23:35:32.175Z" }, + { url = "https://files.pythonhosted.org/packages/3f/88/19d3503c5046e688f049274b27a3ef3d771152fa80d3ba3d01a3dff61abe/numpy-2.4.6-cp313-cp313t-win_arm64.whl", hash = "sha256:1e978ec1e8bd0e0e4de6bb75de9d30cbb74db6b6a2bb727618613703ca0167dd", size = 10291881, upload-time = "2026-05-18T23:35:35.465Z" }, + { url = "https://files.pythonhosted.org/packages/f8/91/3ab2044d05fd16d343c5ac2e69b127f1b2854040dd20b193257c78028bd3/numpy-2.4.6-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:06ca2f61ec4385a07a6977c55ba998a4466c123642b4a32694d3128fce18c079", size = 16683458, upload-time = "2026-05-18T23:35:38.353Z" }, + { url = "https://files.pythonhosted.org/packages/8e/62/764ce66fa4147ae6d73071a3abf804ffe606f174618697c571acdf26a7c9/numpy-2.4.6-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:38efbc8de75c7a0fc1ac190162d892787f3f47b57cc291231aafee36b80982b7", size = 14704559, upload-time = "2026-05-18T23:35:42.14Z" }, + { url = "https://files.pythonhosted.org/packages/60/61/23f27c172f022e04025b7dc2367f4d63c1a398120607ec896228649a6f48/numpy-2.4.6-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:d581b735e177fdcdce6fed8e7e8880a3fb6ee4e3653a3ac6af01c6f4c03effc5", size = 5209716, upload-time = "2026-05-18T23:35:45.377Z" }, + { url = "https://files.pythonhosted.org/packages/03/71/21cf70dc6ea3e3acb95fc53a265b2fc248b981f0194ceb5b475271b8809d/numpy-2.4.6-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:0a041d3d761dc3c35cc56ce0351506a02bcbc25f7b169f652435141a17db9096", size = 6543947, upload-time = "2026-05-18T23:35:47.926Z" }, + { url = "https://files.pythonhosted.org/packages/d5/91/64288395ee1799bd2e0b04a305dce9666da90c961e1f3fe982a05ee1c036/numpy-2.4.6-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:40fdc1ae7125e518ea98e53e69a4ebc27e1fd50510c47b7ea130cf21e5e1d42b", size = 15685197, upload-time = "2026-05-18T23:35:50.863Z" }, + { url = "https://files.pythonhosted.org/packages/f3/eb/ebffaa97dc55502df69584a8f0dcf07f69a3e0b3e2323670a2722db9aa39/numpy-2.4.6-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a2c306dea656c12c68f51f4cea133cbe78ca7435eb28c735eac1d3ebe73be6e8", size = 16638245, upload-time = "2026-05-18T23:35:54.752Z" }, + { url = "https://files.pythonhosted.org/packages/b8/0b/54f9da33128d7e350fab89c7455902eeae70349ee52bddb448dc4a576f45/numpy-2.4.6-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:33111801a01c12a8a1e3721f0a9232f8cfc8ae2c6b7098167e6f623c6073f402", size = 17036587, upload-time = "2026-05-18T23:35:58.355Z" }, + { url = "https://files.pythonhosted.org/packages/b6/f0/fdebc1052db1cc37c64beb22072d67cd6d1c71adca1299f53dec2b5e20d3/numpy-2.4.6-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:ae506e6902902557576a26ff33eda8695e7ecb3cb36c3b573a0765dee114ebdb", size = 18363226, upload-time = "2026-05-18T23:36:02.845Z" }, + { url = "https://files.pythonhosted.org/packages/aa/b4/298628d98c72b57e57f7165ae6a481a1deaf6f3c28262a6e4c739c275930/numpy-2.4.6-cp314-cp314-win32.whl", hash = "sha256:aaf159caa35993cb1f56fb9b8e4610d35758e7ca005412eb1daa856a78c9c4b1", size = 6010196, upload-time = "2026-05-18T23:36:05.92Z" }, + { url = "https://files.pythonhosted.org/packages/df/ac/46de6dda46478f7942f839e094970be2d4a861e005c4b3bf07c92e291a09/numpy-2.4.6-cp314-cp314-win_amd64.whl", hash = "sha256:b507f5c4c1d508876d1819b6bf9a49d365b96320b5d4993426b33a23ca4b8261", size = 12450334, upload-time = "2026-05-18T23:36:09.107Z" }, + { url = "https://files.pythonhosted.org/packages/78/92/b8b798ac784102c0da830d2257d59358e3d3d90d1e2b3f2575dad976c5cf/numpy-2.4.6-cp314-cp314-win_arm64.whl", hash = "sha256:6f41ae150c4e32db4f3310cdaf64b1593a03dbabe29eec77fc9b50fe64061df6", size = 10495678, upload-time = "2026-05-18T23:36:12.766Z" }, + { url = "https://files.pythonhosted.org/packages/30/34/ec28d1aa8115971537c01469ab2011ee96827930f0a124de1000cc2a7ed7/numpy-2.4.6-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:ece3d2cfe132e7d51f44a832b303895e6f2d499c5e74dfbdb06ee246147a304a", size = 14823672, upload-time = "2026-05-18T23:36:16.473Z" }, + { url = "https://files.pythonhosted.org/packages/16/bd/f6d1fede4e54e8042a7ff97bb495510f3c220f94bcd9e8b228e87c92cc0d/numpy-2.4.6-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:e3e5193ef5a3dc73bceee50f7fdc2c90dbb76c42df8d8fae3d1067a583df579e", size = 5328731, upload-time = "2026-05-18T23:36:19.767Z" }, + { url = "https://files.pythonhosted.org/packages/f4/f0/e105b9e2fd728a9910103884decd6951d9dd73896b914a98d9a231de02ee/numpy-2.4.6-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:17f9ade344e7d9b464a084d69bcf18fc691cb1db67c62ed80820bf4926d78f0e", size = 6649805, upload-time = "2026-05-18T23:36:22.266Z" }, + { url = "https://files.pythonhosted.org/packages/82/dd/1206a7ca6ab15e3f02069707ca96222e202af681bb73756da7527f3cb837/numpy-2.4.6-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9cd5ffd25db4e7ba6a375693b3fc0fc1791ec636c17db3720da19bde7180ec43", size = 15730496, upload-time = "2026-05-18T23:36:25.713Z" }, + { url = "https://files.pythonhosted.org/packages/51/e7/38d3ea825dcab85a591734decb2f6c67caa7c8367d374df1a1c3842f9b07/numpy-2.4.6-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:7d92c3819208a60205a12a245c91ad70cb0a85336659b19b834205573ac8456e", size = 16679616, upload-time = "2026-05-18T23:36:29.652Z" }, + { url = "https://files.pythonhosted.org/packages/93/b7/caabfdf53edf663e0b4eb74d7d405d83baef09eb5e83bcd32d601d72b93e/numpy-2.4.6-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:e85b752a1e912b70eaad4fafbd4d1238007ab221de2009b9a2f5ae7461239895", size = 17085145, upload-time = "2026-05-18T23:36:33.449Z" }, + { url = "https://files.pythonhosted.org/packages/f9/45/68d7c33a6bcf3e5aa3bdbd57a367e6f615286dfd6482f97e8ffeb734306e/numpy-2.4.6-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:29cb7f67d10b479ff07c17d33e39f78c07f71c40ef30d63c153d340e96cd3fb4", size = 18403813, upload-time = "2026-05-18T23:36:37.369Z" }, + { url = "https://files.pythonhosted.org/packages/9c/50/0753655aa844c99cd9e018aacf76f130f1bd81d881bb74bc0aef5d73a8ba/numpy-2.4.6-cp314-cp314t-win32.whl", hash = "sha256:260a5d70215b61ab4fadf5c7baacd64821842975eea312125ed3c39a6391b063", size = 6156982, upload-time = "2026-05-18T23:36:40.817Z" }, + { url = "https://files.pythonhosted.org/packages/b2/d4/7c67becf668f973cb490cec3e98dfd799d866f9c989a54d355672cfa0db6/numpy-2.4.6-cp314-cp314t-win_amd64.whl", hash = "sha256:81a1cca95ed5bb92aa8b10dd2cdc9a0d3853a50fad926c28b5d7e8ea54389627", size = 12638908, upload-time = "2026-05-18T23:36:43.996Z" }, + { url = "https://files.pythonhosted.org/packages/43/bb/e1c71a4295b1b1d1393d50dbb4f2a36283c6859d9d3892e84f00ec5a91d5/numpy-2.4.6-cp314-cp314t-win_arm64.whl", hash = "sha256:0c9136e14ed34a9e343a31c533d78a9813a69a3148332bce5e9821cb2f996e66", size = 10565867, upload-time = "2026-05-18T23:36:47.114Z" }, + { url = "https://files.pythonhosted.org/packages/de/12/b422cc84439adc0d00de605bf4a308890ae5c26f2c71fbd73e5d08fbb0dd/numpy-2.4.6-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:55cced7c52e981362f708ad635198e97a752dfba412cc03c23bbf3bd8d5cd662", size = 16847511, upload-time = "2026-05-18T23:36:50.673Z" }, + { url = "https://files.pythonhosted.org/packages/44/53/f481bef68011740f8849418d82db07230e825013f31f4eef5ba5b805316a/numpy-2.4.6-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:d6da64deb6b8ed903e7560180a92f2d804ee1ba5eeb849ac2748b8c1aba1f6d7", size = 14889064, upload-time = "2026-05-18T23:36:53.879Z" }, + { url = "https://files.pythonhosted.org/packages/7f/57/42ed575c10ced8af951d426bc4e1f8aff16fd851db33f067036215a7f860/numpy-2.4.6-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:68a5124b13fa6cc2086764a20005d30bc0548146f7f5322f02fce212ca14317f", size = 5394157, upload-time = "2026-05-18T23:36:57.194Z" }, + { url = "https://files.pythonhosted.org/packages/6a/ef/f66cc724fcc36c1e364c67f51ae9146090b8b584f27d58b97fdae3edd737/numpy-2.4.6-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:948424b06129ce883307e8cff868c31396d8dc7630a59c61d70d98dbe70f222c", size = 6708728, upload-time = "2026-05-18T23:36:59.575Z" }, + { url = "https://files.pythonhosted.org/packages/1a/9c/c531f2293b91265d8b48e9b329f54fdd7ffae73cb4134ea10cca4237e9cc/numpy-2.4.6-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5dbbdb29840ca3d91ee0fece42fc29278886d908280bfec0a5846c6f901a3eb0", size = 15798374, upload-time = "2026-05-18T23:37:02.674Z" }, + { url = "https://files.pythonhosted.org/packages/1a/b0/413077f6b1153ed3cba361401c6783bbad6114804a000cc22eb71c13e190/numpy-2.4.6-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:8ad03c0965fb3c692200e74d458ca28c1dbb4ce96f9a479a8aa041ad5fabca02", size = 16747286, upload-time = "2026-05-18T23:37:06.327Z" }, + { url = "https://files.pythonhosted.org/packages/15/ce/e5ec180bc41812edcd8daeb8639d205622c0e8c02259d8ab25a0201b3c2a/numpy-2.4.6-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:2803abfebfc990042cd494d8ce2d5f82e9d847af6d35ec486923aa19dbad5e73", size = 12504263, upload-time = "2026-05-18T23:37:09.715Z" }, +] + +[[package]] +name = "numpy" +version = "2.5.1" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.15'", + "python_full_version >= '3.12' and python_full_version < '3.15'", +] +sdist = { url = "https://files.pythonhosted.org/packages/22/fd/89965aa4ac08c74998539fcbf24fa3540f3e15237fbeb6bcf9c908f4aade/numpy-2.5.1.tar.gz", hash = "sha256:a48a113e6afea91f5608793bafa7ef2ad481fefbda87ec5069f483de61cb9fa3", size = 20755553, upload-time = "2026-07-04T17:08:00.933Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/62/7b/14687aa674250e5e546f616f486b0d56d3631cd5b2415739141ce40bdcea/numpy-2.5.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:2c889b56fe48b1018f764b0eec8df59ab654e9148aa91faa12596043500de277", size = 16801574, upload-time = "2026-07-04T17:06:12.423Z" }, + { url = "https://files.pythonhosted.org/packages/e1/19/cc5bb2a3f2913d27d6dbb2c78d25921fabaedc6741d4a5a615a11f3c5bf3/numpy-2.5.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ab451b59c5643c570974c43aef780703ef1d3b4965d2be07afd530615a9358d1", size = 11772250, upload-time = "2026-07-04T17:06:15.726Z" }, + { url = "https://files.pythonhosted.org/packages/42/77/fdf34a71dd30f54979b18603bee915e0aaf825b07afe79acd60b04b691e2/numpy-2.5.1-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:78798bd5b9ad744056af8efa90e3b9ddaa53272a0848a483084a1cc0a13b2dc0", size = 5331516, upload-time = "2026-07-04T17:06:17.913Z" }, + { url = "https://files.pythonhosted.org/packages/ce/e2/eb7efa015b4cce41e2517bf182a7fce0d7d5b9d9ed76a29bfa0f4fe4505c/numpy-2.5.1-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:2ae0ca40bcb22d6ba59c1dfd5446f49940b0f2d821fde133f10dda11f816b84e", size = 6664863, upload-time = "2026-07-04T17:06:20.02Z" }, + { url = "https://files.pythonhosted.org/packages/a9/4b/a2b32dd94ee9ffbeecb28152240042a3949db33b1c834d44090b80e1b3b8/numpy-2.5.1-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:61ac47e772e6b8ea489e1d2f441a34c5c3ac17327e7ce294cbdf535795ad4e75", size = 15167977, upload-time = "2026-07-04T17:06:21.621Z" }, + { url = "https://files.pythonhosted.org/packages/b8/a9/6e73d68500f80773f65f0654ea932019d6694329a0eb0ed0533de38df376/numpy-2.5.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:59fda5e192b570217ec2580c96f00e9a7e12ef6866a900eb089b62c1a32545ca", size = 16672469, upload-time = "2026-07-04T17:06:24.064Z" }, + { url = "https://files.pythonhosted.org/packages/24/7d/ad3e59015135f5261c95fd4cafeff159c955febd83a99a1d9250c4233815/numpy-2.5.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:f7119ebff1a9829e9f431a4f9d28e703023bb6b9fe7c8f724467dbfc27c94ab3", size = 16527531, upload-time = "2026-07-04T17:06:26.69Z" }, + { url = "https://files.pythonhosted.org/packages/83/d0/a39b2fbcde9cb17a1dac678f254b33a6336298af9df338824c685425d5e8/numpy-2.5.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:e824c2acf8862052246be5a44c15da1777940c60d010dd2aab897824d9c430f9", size = 18431940, upload-time = "2026-07-04T17:06:29.521Z" }, + { url = "https://files.pythonhosted.org/packages/04/12/cff070947791c1ed425ff76413189adbdc2fbe215eba7ce7fa454a03c7f8/numpy-2.5.1-cp312-cp312-win32.whl", hash = "sha256:08d60c810432eb83360958dea0999ac4cfb94531ea8efcbf0b7f277c2068aeb2", size = 6066764, upload-time = "2026-07-04T17:06:32.571Z" }, + { url = "https://files.pythonhosted.org/packages/65/66/53f31807a48a750f9d748da273bc3fcedd12b27ff1f3e373bfec55ef2dc0/numpy-2.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:f7d60026c0bdb1380e83bfa7a0419c4577ee4b9a08880afcb6dadeb74c649fa2", size = 12430966, upload-time = "2026-07-04T17:06:34.926Z" }, + { url = "https://files.pythonhosted.org/packages/2b/2a/d1a88066b1c14186f5d3c0d18c94f17b064511982bab0578d49ee9d43c29/numpy-2.5.1-cp312-cp312-win_arm64.whl", hash = "sha256:17a25e09640602e10bc8de0e6fa2b3fd68eedd84ba6d7842dc8f32f9ab87bd0b", size = 10350488, upload-time = "2026-07-04T17:06:37.785Z" }, + { url = "https://files.pythonhosted.org/packages/eb/07/ec2a3f0c91761581d4b7104a740791800025983f9a4dc4e73f91a99aeac4/numpy-2.5.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0bfebd8695f9863592fe744be833a258120b14a9f39da255e8aa8fade2c0ddd1", size = 16796419, upload-time = "2026-07-04T17:06:40.37Z" }, + { url = "https://files.pythonhosted.org/packages/ab/ab/ddb499fc4f8780354395face5b65c7fd107bcd6e1d667a5f07d046956f6f/numpy-2.5.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:30b44a6b53a7ae63c54c089a8726e5563ed302716c5b7ccc85afade40b0e7ff6", size = 11765832, upload-time = "2026-07-04T17:06:42.768Z" }, + { url = "https://files.pythonhosted.org/packages/88/b3/3c28c558a09fc72100c646dac6d2fce8e834c471b0edca01a29996706117/numpy-2.5.1-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:6165343f81b56ef8f514f396989e529b61d9dc709b99421b07e9f3e698e2287d", size = 5325143, upload-time = "2026-07-04T17:06:45.466Z" }, + { url = "https://files.pythonhosted.org/packages/5e/0e/ce19b985bb15c596f4f05954e76cccc77c845083b3b8f938a6c68e523128/numpy-2.5.1-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:4939237038ada79308dda3204ac6462df056b5672b2e25db1149cf873668b3e1", size = 6659749, upload-time = "2026-07-04T17:06:47.288Z" }, + { url = "https://files.pythonhosted.org/packages/2e/20/1ee6614d64332a1bba6411f38e68cb79eec1b2459e20a623777c5c5492a2/numpy-2.5.1-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1c6759f538fb912fc46de0a6b1758ccf7b57bc7c7ebebc23974fdac3de8db0cd", size = 15164716, upload-time = "2026-07-04T17:06:49.494Z" }, + { url = "https://files.pythonhosted.org/packages/ed/a7/2bcd3fdbb87804755c35b729bf8709d62025c5f4cfd7d5b2415997097515/numpy-2.5.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9726558e8db4a5bf7929a70ae50f63abda4daf0efe810e3bfbab95976f75fc1a", size = 16661440, upload-time = "2026-07-04T17:06:52.061Z" }, + { url = "https://files.pythonhosted.org/packages/fc/d7/a41e3310c886fe457d36e670bbf24fae411aca8a7b6ad92a32afd924077c/numpy-2.5.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:3935f3b419b244a02732676fa5317a9193cc596a4c0646db07e5b421229ac9f7", size = 16526305, upload-time = "2026-07-04T17:06:54.605Z" }, + { url = "https://files.pythonhosted.org/packages/53/75/4333a9a707c1edd3a4e1a0c58eca52c0f31e55089fa80db02b5565b24df7/numpy-2.5.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:dc932a65ded7ce9013d120845a2514dcccb1a67bfc8deb8d37633762951904a6", size = 18423008, upload-time = "2026-07-04T17:06:57.54Z" }, + { url = "https://files.pythonhosted.org/packages/ee/90/e314a32b1c11a2ffe818ddad3a57b50b4b6e1b6c487192eb50cdef0415d0/numpy-2.5.1-cp313-cp313-win32.whl", hash = "sha256:4b4ff1608417eb7a59da7b967bbb798cacfe071d2caf526a24281cd562072ed9", size = 6063885, upload-time = "2026-07-04T17:07:00.14Z" }, + { url = "https://files.pythonhosted.org/packages/10/70/800b3fca480af32df9e8ea9f3d4a0c8feb4b32d7f195d174eabbda4829ad/numpy-2.5.1-cp313-cp313-win_amd64.whl", hash = "sha256:6c3fe51bc6a16453d452997053454f309e8e0ed7b42d6b361ce4ac8c32913d74", size = 12425674, upload-time = "2026-07-04T17:07:02.387Z" }, + { url = "https://files.pythonhosted.org/packages/8b/0b/196350c122f50f6ca56846f2d71efd5e0d24b7b2e07355e019b2e2c7a11e/numpy-2.5.1-cp313-cp313-win_arm64.whl", hash = "sha256:f7feb014281029e628ba2d5a007407443b06e418b6fe451d1e2adcbc8eba0107", size = 10350256, upload-time = "2026-07-04T17:07:04.878Z" }, + { url = "https://files.pythonhosted.org/packages/db/f4/731b6085a83faf6ca843394cbd5e217280c214399f7e8b21b9f552af0ae2/numpy-2.5.1-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:7c786fe9a5bbe360022e584c5a34cf6b54265c71bd7ec8ac3d8fec38968071f8", size = 16795063, upload-time = "2026-07-04T17:07:07.374Z" }, + { url = "https://files.pythonhosted.org/packages/bf/64/0e215f2048dd11a55bb989ed41b3585ef57452404e638d703a211a3e4157/numpy-2.5.1-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:32985c896d897419ef8da6917872d80b78ad0ea26d85b23245c7366ffde76d75", size = 11776652, upload-time = "2026-07-04T17:07:09.907Z" }, + { url = "https://files.pythonhosted.org/packages/b5/59/2b844c7a6e9deff69b404a66221e1542937734f65d5e6e39411876053862/numpy-2.5.1-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:efd736408cc97c79b9e6917338dfc8f06013b2274f992e96b1d9a81a71e2a2c2", size = 5335944, upload-time = "2026-07-04T17:07:12.227Z" }, + { url = "https://files.pythonhosted.org/packages/86/51/9bf7cb2cabcebc9e017e4ec7e6322b378317a542c08b4cb68479c1efc716/numpy-2.5.1-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:ab84dc6b074fa881cae55bea94cc4f68e285181ba7f32497bf7dee6b1496165b", size = 6656266, upload-time = "2026-07-04T17:07:14.368Z" }, + { url = "https://files.pythonhosted.org/packages/83/3e/fb7615b211b82a32f44d5180a6d421b61f84d4fadd578b48ba4ac34e189f/numpy-2.5.1-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:caf3e317d33d60c37986b452613f4ab51246d0691350c03d0cb4a898627f4a95", size = 15179720, upload-time = "2026-07-04T17:07:16.272Z" }, + { url = "https://files.pythonhosted.org/packages/41/5f/0f992cb24560673496c5d68de61913b57166ce530ffda07c1f280e0cc464/numpy-2.5.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:54ad769f17bc2d833b620851989f62054fb9ab93c969d9e1dc3c8e3d56beea21", size = 16664835, upload-time = "2026-07-04T17:07:19.021Z" }, + { url = "https://files.pythonhosted.org/packages/a2/2f/97d6475ee91afe2587797d09446f9d3e475ad4cb681662d824809327b75a/numpy-2.5.1-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:c12afb53450fa976d4c681c50a7423729a4c51c0465ed9f32b8a9cabbc472373", size = 16539135, upload-time = "2026-07-04T17:07:22.015Z" }, + { url = "https://files.pythonhosted.org/packages/c4/5b/4db81e4ba0be7e2776b1de68c82aa862c7f8ec27e1b4927d4ae075e20678/numpy-2.5.1-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:e8c11c405efc5ff6816d5983c96cdfa215bab3428961243af3ff59b228490438", size = 18426684, upload-time = "2026-07-04T17:07:24.941Z" }, + { url = "https://files.pythonhosted.org/packages/1f/64/c0ba2d90724d450279a7df8f32057241070250a26a7e2b5337d77347f481/numpy-2.5.1-cp314-cp314-win32.whl", hash = "sha256:f2479a47f8d5932d1718168a681ad6e536a9df484c83cfcf9de365e164537ace", size = 6116103, upload-time = "2026-07-04T17:07:27.622Z" }, + { url = "https://files.pythonhosted.org/packages/c1/1a/837f9ed7405adcd7a40538792eb169eddd8fa5630c16a1ef49dae71a30f4/numpy-2.5.1-cp314-cp314-win_amd64.whl", hash = "sha256:24d0eb82c0541d3415a33425db64ae439dffccd7b4dbcb30e7c35120205c506a", size = 12562177, upload-time = "2026-07-04T17:07:29.887Z" }, + { url = "https://files.pythonhosted.org/packages/22/ed/49707938b6dd0a78a9178dd93227dc89e4c11af47f5c798d70366e8d0483/numpy-2.5.1-cp314-cp314-win_arm64.whl", hash = "sha256:5a4c988b38d261deeeaad9954e3deb091ad905c94e8bb6708654ef1d97f286b0", size = 10627739, upload-time = "2026-07-04T17:07:32.568Z" }, + { url = "https://files.pythonhosted.org/packages/a6/c7/bb4b882cfe7f299cbc8b66e42e7dd78cf9d14e40f9469fc5e3db7e15b3bd/numpy-2.5.1-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:a33276be12fa045805f477f22482088b66bb758ffbe89a9d21457de863a32e22", size = 11894709, upload-time = "2026-07-04T17:07:34.941Z" }, + { url = "https://files.pythonhosted.org/packages/40/3f/5af7f4a7f6224aef48017aa82bb6174c7a659d724be0c75017b7e64a55b4/numpy-2.5.1-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:f089d7b00756190aacf1f5d34bdf38c3c430ac82b4f868f8cede73380460fce7", size = 5453810, upload-time = "2026-07-04T17:07:37.495Z" }, + { url = "https://files.pythonhosted.org/packages/20/c9/3474309bc94d634d3f9c3eddf03250ecb8c22cd948ef16fef69a77cc5d7b/numpy-2.5.1-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:09e9bfd8d2cf479c7d174804fb3811c53a8e9f20a37444008606b57d6b7a826d", size = 6761189, upload-time = "2026-07-04T17:07:39.563Z" }, + { url = "https://files.pythonhosted.org/packages/90/8a/558ae39fdd55d7e7f7fef9a84a6e964ac6b23edbd2a07e52bb084500507d/numpy-2.5.1-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e68d8dd1e7eba712948f2053a29ec86917bc70ba1358df869d9f06649ef9cf09", size = 15225039, upload-time = "2026-07-04T17:07:41.682Z" }, + { url = "https://files.pythonhosted.org/packages/63/27/ca7392b2d030277bdf0273e7d23255b3ee57d57a7c170a6f4fb3981e1e5d/numpy-2.5.1-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:99d5095fa265a0c4152e7bb12759e14381ef5496152f1ce58f44bdf55c44beb4", size = 16701306, upload-time = "2026-07-04T17:07:44.611Z" }, + { url = "https://files.pythonhosted.org/packages/02/42/03d53ae7996c44d4374a8262e9dc41671fd56cbb98f7d47ef85cf5da4c6b/numpy-2.5.1-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:ab87a91b3cc3382b8956095bd8f95e00cf679bb81554339be1a2ba404a1473c1", size = 16589955, upload-time = "2026-07-04T17:07:47.694Z" }, + { url = "https://files.pythonhosted.org/packages/7b/15/6c1784ae469640e65db111e9a34b3d0f14d91e8a38b9ce34810ced370dbb/numpy-2.5.1-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:224ca51130ef7da85bea2191625181cb4f337f9cb64b471f10c1a12aa8b60077", size = 18464252, upload-time = "2026-07-04T17:07:50.684Z" }, + { url = "https://files.pythonhosted.org/packages/94/a8/f98e50356cf167df656c526c2dfeec2d7dde182f2a3da4b458a5938e2776/numpy-2.5.1-cp314-cp314t-win32.whl", hash = "sha256:6eab239876581b2b3c5a242281b6007bbdbcd1c7085d7709bb57c5929b11e6bf", size = 6263298, upload-time = "2026-07-04T17:07:53.445Z" }, + { url = "https://files.pythonhosted.org/packages/72/ac/96ae880cdecad0b3275d9359fcec72667b49a4863c9f12942e43679dda02/numpy-2.5.1-cp314-cp314t-win_amd64.whl", hash = "sha256:83ce9c80d5b521b0d77ddcbe5447c218d247929b6cc056ca5351342accfff0af", size = 12748623, upload-time = "2026-07-04T17:07:55.384Z" }, + { url = "https://files.pythonhosted.org/packages/a1/5a/4d2b1601df3602dba7a14f3348ba9bfe94a18adb428e693df6154c293831/numpy-2.5.1-cp314-cp314t-win_arm64.whl", hash = "sha256:5a6db61f9aaa57e369905c67d852045d3c4f7126405b29d09b19dec118e9c9cb", size = 10697674, upload-time = "2026-07-04T17:07:58.506Z" }, +] + [[package]] name = "packaging" version = "26.2" @@ -1058,6 +2434,313 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/f1/d9/7fb5aa316bc299258e68c73ba3bddbc499654a07f151cba08f6153988714/pathspec-1.1.1-py3-none-any.whl", hash = "sha256:a00ce642f577bf7f473932318056212bc4f8bfdf53128c78bbd5af0b9b20b189", size = 57328, upload-time = "2026-04-27T01:46:07.06Z" }, ] +[[package]] +name = "pillow" +version = "10.4.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.8.1' and python_full_version < '3.9'", + "python_full_version < '3.8.1'", +] +sdist = { url = "https://files.pythonhosted.org/packages/cd/74/ad3d526f3bf7b6d3f408b73fde271ec69dfac8b81341a318ce825f2b3812/pillow-10.4.0.tar.gz", hash = "sha256:166c1cd4d24309b30d61f79f4a9114b7b2313d7450912277855ff5dfd7cd4a06", size = 46555059, upload-time = "2024-07-01T09:48:43.583Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/0e/69/a31cccd538ca0b5272be2a38347f8839b97a14be104ea08b0db92f749c74/pillow-10.4.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:4d9667937cfa347525b319ae34375c37b9ee6b525440f3ef48542fcf66f2731e", size = 3509271, upload-time = "2024-07-01T09:45:22.07Z" }, + { url = "https://files.pythonhosted.org/packages/9a/9e/4143b907be8ea0bce215f2ae4f7480027473f8b61fcedfda9d851082a5d2/pillow-10.4.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:543f3dc61c18dafb755773efc89aae60d06b6596a63914107f75459cf984164d", size = 3375658, upload-time = "2024-07-01T09:45:25.292Z" }, + { url = "https://files.pythonhosted.org/packages/8a/25/1fc45761955f9359b1169aa75e241551e74ac01a09f487adaaf4c3472d11/pillow-10.4.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7928ecbf1ece13956b95d9cbcfc77137652b02763ba384d9ab508099a2eca856", size = 4332075, upload-time = "2024-07-01T09:45:27.94Z" }, + { url = "https://files.pythonhosted.org/packages/5e/dd/425b95d0151e1d6c951f45051112394f130df3da67363b6bc75dc4c27aba/pillow-10.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4d49b85c4348ea0b31ea63bc75a9f3857869174e2bf17e7aba02945cd218e6f", size = 4444808, upload-time = "2024-07-01T09:45:30.305Z" }, + { url = "https://files.pythonhosted.org/packages/b1/84/9a15cc5726cbbfe7f9f90bfb11f5d028586595907cd093815ca6644932e3/pillow-10.4.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:6c762a5b0997f5659a5ef2266abc1d8851ad7749ad9a6a5506eb23d314e4f46b", size = 4356290, upload-time = "2024-07-01T09:45:32.868Z" }, + { url = "https://files.pythonhosted.org/packages/b5/5b/6651c288b08df3b8c1e2f8c1152201e0b25d240e22ddade0f1e242fc9fa0/pillow-10.4.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:a985e028fc183bf12a77a8bbf36318db4238a3ded7fa9df1b9a133f1cb79f8fc", size = 4525163, upload-time = "2024-07-01T09:45:35.279Z" }, + { url = "https://files.pythonhosted.org/packages/07/8b/34854bf11a83c248505c8cb0fcf8d3d0b459a2246c8809b967963b6b12ae/pillow-10.4.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:812f7342b0eee081eaec84d91423d1b4650bb9828eb53d8511bcef8ce5aecf1e", size = 4463100, upload-time = "2024-07-01T09:45:37.74Z" }, + { url = "https://files.pythonhosted.org/packages/78/63/0632aee4e82476d9cbe5200c0cdf9ba41ee04ed77887432845264d81116d/pillow-10.4.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:ac1452d2fbe4978c2eec89fb5a23b8387aba707ac72810d9490118817d9c0b46", size = 4592880, upload-time = "2024-07-01T09:45:39.89Z" }, + { url = "https://files.pythonhosted.org/packages/df/56/b8663d7520671b4398b9d97e1ed9f583d4afcbefbda3c6188325e8c297bd/pillow-10.4.0-cp310-cp310-win32.whl", hash = "sha256:bcd5e41a859bf2e84fdc42f4edb7d9aba0a13d29a2abadccafad99de3feff984", size = 2235218, upload-time = "2024-07-01T09:45:42.771Z" }, + { url = "https://files.pythonhosted.org/packages/f4/72/0203e94a91ddb4a9d5238434ae6c1ca10e610e8487036132ea9bf806ca2a/pillow-10.4.0-cp310-cp310-win_amd64.whl", hash = "sha256:ecd85a8d3e79cd7158dec1c9e5808e821feea088e2f69a974db5edf84dc53141", size = 2554487, upload-time = "2024-07-01T09:45:45.176Z" }, + { url = "https://files.pythonhosted.org/packages/bd/52/7e7e93d7a6e4290543f17dc6f7d3af4bd0b3dd9926e2e8a35ac2282bc5f4/pillow-10.4.0-cp310-cp310-win_arm64.whl", hash = "sha256:ff337c552345e95702c5fde3158acb0625111017d0e5f24bf3acdb9cc16b90d1", size = 2243219, upload-time = "2024-07-01T09:45:47.274Z" }, + { url = "https://files.pythonhosted.org/packages/a7/62/c9449f9c3043c37f73e7487ec4ef0c03eb9c9afc91a92b977a67b3c0bbc5/pillow-10.4.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:0a9ec697746f268507404647e531e92889890a087e03681a3606d9b920fbee3c", size = 3509265, upload-time = "2024-07-01T09:45:49.812Z" }, + { url = "https://files.pythonhosted.org/packages/f4/5f/491dafc7bbf5a3cc1845dc0430872e8096eb9e2b6f8161509d124594ec2d/pillow-10.4.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:dfe91cb65544a1321e631e696759491ae04a2ea11d36715eca01ce07284738be", size = 3375655, upload-time = "2024-07-01T09:45:52.462Z" }, + { url = "https://files.pythonhosted.org/packages/73/d5/c4011a76f4207a3c151134cd22a1415741e42fa5ddecec7c0182887deb3d/pillow-10.4.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5dc6761a6efc781e6a1544206f22c80c3af4c8cf461206d46a1e6006e4429ff3", size = 4340304, upload-time = "2024-07-01T09:45:55.006Z" }, + { url = "https://files.pythonhosted.org/packages/ac/10/c67e20445a707f7a610699bba4fe050583b688d8cd2d202572b257f46600/pillow-10.4.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5e84b6cc6a4a3d76c153a6b19270b3526a5a8ed6b09501d3af891daa2a9de7d6", size = 4452804, upload-time = "2024-07-01T09:45:58.437Z" }, + { url = "https://files.pythonhosted.org/packages/a9/83/6523837906d1da2b269dee787e31df3b0acb12e3d08f024965a3e7f64665/pillow-10.4.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:bbc527b519bd3aa9d7f429d152fea69f9ad37c95f0b02aebddff592688998abe", size = 4365126, upload-time = "2024-07-01T09:46:00.713Z" }, + { url = "https://files.pythonhosted.org/packages/ba/e5/8c68ff608a4203085158cff5cc2a3c534ec384536d9438c405ed6370d080/pillow-10.4.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:76a911dfe51a36041f2e756b00f96ed84677cdeb75d25c767f296c1c1eda1319", size = 4533541, upload-time = "2024-07-01T09:46:03.235Z" }, + { url = "https://files.pythonhosted.org/packages/f4/7c/01b8dbdca5bc6785573f4cee96e2358b0918b7b2c7b60d8b6f3abf87a070/pillow-10.4.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:59291fb29317122398786c2d44427bbd1a6d7ff54017075b22be9d21aa59bd8d", size = 4471616, upload-time = "2024-07-01T09:46:05.356Z" }, + { url = "https://files.pythonhosted.org/packages/c8/57/2899b82394a35a0fbfd352e290945440e3b3785655a03365c0ca8279f351/pillow-10.4.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:416d3a5d0e8cfe4f27f574362435bc9bae57f679a7158e0096ad2beb427b8696", size = 4600802, upload-time = "2024-07-01T09:46:08.145Z" }, + { url = "https://files.pythonhosted.org/packages/4d/d7/a44f193d4c26e58ee5d2d9db3d4854b2cfb5b5e08d360a5e03fe987c0086/pillow-10.4.0-cp311-cp311-win32.whl", hash = "sha256:7086cc1d5eebb91ad24ded9f58bec6c688e9f0ed7eb3dbbf1e4800280a896496", size = 2235213, upload-time = "2024-07-01T09:46:10.211Z" }, + { url = "https://files.pythonhosted.org/packages/c1/d0/5866318eec2b801cdb8c82abf190c8343d8a1cd8bf5a0c17444a6f268291/pillow-10.4.0-cp311-cp311-win_amd64.whl", hash = "sha256:cbed61494057c0f83b83eb3a310f0bf774b09513307c434d4366ed64f4128a91", size = 2554498, upload-time = "2024-07-01T09:46:12.685Z" }, + { url = "https://files.pythonhosted.org/packages/d4/c8/310ac16ac2b97e902d9eb438688de0d961660a87703ad1561fd3dfbd2aa0/pillow-10.4.0-cp311-cp311-win_arm64.whl", hash = "sha256:f5f0c3e969c8f12dd2bb7e0b15d5c468b51e5017e01e2e867335c81903046a22", size = 2243219, upload-time = "2024-07-01T09:46:14.83Z" }, + { url = "https://files.pythonhosted.org/packages/05/cb/0353013dc30c02a8be34eb91d25e4e4cf594b59e5a55ea1128fde1e5f8ea/pillow-10.4.0-cp312-cp312-macosx_10_10_x86_64.whl", hash = "sha256:673655af3eadf4df6b5457033f086e90299fdd7a47983a13827acf7459c15d94", size = 3509350, upload-time = "2024-07-01T09:46:17.177Z" }, + { url = "https://files.pythonhosted.org/packages/e7/cf/5c558a0f247e0bf9cec92bff9b46ae6474dd736f6d906315e60e4075f737/pillow-10.4.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:866b6942a92f56300012f5fbac71f2d610312ee65e22f1aa2609e491284e5597", size = 3374980, upload-time = "2024-07-01T09:46:19.169Z" }, + { url = "https://files.pythonhosted.org/packages/84/48/6e394b86369a4eb68b8a1382c78dc092245af517385c086c5094e3b34428/pillow-10.4.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:29dbdc4207642ea6aad70fbde1a9338753d33fb23ed6956e706936706f52dd80", size = 4343799, upload-time = "2024-07-01T09:46:21.883Z" }, + { url = "https://files.pythonhosted.org/packages/3b/f3/a8c6c11fa84b59b9df0cd5694492da8c039a24cd159f0f6918690105c3be/pillow-10.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bf2342ac639c4cf38799a44950bbc2dfcb685f052b9e262f446482afaf4bffca", size = 4459973, upload-time = "2024-07-01T09:46:24.321Z" }, + { url = "https://files.pythonhosted.org/packages/7d/1b/c14b4197b80150fb64453585247e6fb2e1d93761fa0fa9cf63b102fde822/pillow-10.4.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:f5b92f4d70791b4a67157321c4e8225d60b119c5cc9aee8ecf153aace4aad4ef", size = 4370054, upload-time = "2024-07-01T09:46:26.825Z" }, + { url = "https://files.pythonhosted.org/packages/55/77/40daddf677897a923d5d33329acd52a2144d54a9644f2a5422c028c6bf2d/pillow-10.4.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:86dcb5a1eb778d8b25659d5e4341269e8590ad6b4e8b44d9f4b07f8d136c414a", size = 4539484, upload-time = "2024-07-01T09:46:29.355Z" }, + { url = "https://files.pythonhosted.org/packages/40/54/90de3e4256b1207300fb2b1d7168dd912a2fb4b2401e439ba23c2b2cabde/pillow-10.4.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:780c072c2e11c9b2c7ca37f9a2ee8ba66f44367ac3e5c7832afcfe5104fd6d1b", size = 4477375, upload-time = "2024-07-01T09:46:31.756Z" }, + { url = "https://files.pythonhosted.org/packages/13/24/1bfba52f44193860918ff7c93d03d95e3f8748ca1de3ceaf11157a14cf16/pillow-10.4.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:37fb69d905be665f68f28a8bba3c6d3223c8efe1edf14cc4cfa06c241f8c81d9", size = 4608773, upload-time = "2024-07-01T09:46:33.73Z" }, + { url = "https://files.pythonhosted.org/packages/55/04/5e6de6e6120451ec0c24516c41dbaf80cce1b6451f96561235ef2429da2e/pillow-10.4.0-cp312-cp312-win32.whl", hash = "sha256:7dfecdbad5c301d7b5bde160150b4db4c659cee2b69589705b6f8a0c509d9f42", size = 2235690, upload-time = "2024-07-01T09:46:36.587Z" }, + { url = "https://files.pythonhosted.org/packages/74/0a/d4ce3c44bca8635bd29a2eab5aa181b654a734a29b263ca8efe013beea98/pillow-10.4.0-cp312-cp312-win_amd64.whl", hash = "sha256:1d846aea995ad352d4bdcc847535bd56e0fd88d36829d2c90be880ef1ee4668a", size = 2554951, upload-time = "2024-07-01T09:46:38.777Z" }, + { url = "https://files.pythonhosted.org/packages/b5/ca/184349ee40f2e92439be9b3502ae6cfc43ac4b50bc4fc6b3de7957563894/pillow-10.4.0-cp312-cp312-win_arm64.whl", hash = "sha256:e553cad5179a66ba15bb18b353a19020e73a7921296a7979c4a2b7f6a5cd57f9", size = 2243427, upload-time = "2024-07-01T09:46:43.15Z" }, + { url = "https://files.pythonhosted.org/packages/c3/00/706cebe7c2c12a6318aabe5d354836f54adff7156fd9e1bd6c89f4ba0e98/pillow-10.4.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8bc1a764ed8c957a2e9cacf97c8b2b053b70307cf2996aafd70e91a082e70df3", size = 3525685, upload-time = "2024-07-01T09:46:45.194Z" }, + { url = "https://files.pythonhosted.org/packages/cf/76/f658cbfa49405e5ecbfb9ba42d07074ad9792031267e782d409fd8fe7c69/pillow-10.4.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:6209bb41dc692ddfee4942517c19ee81b86c864b626dbfca272ec0f7cff5d9fb", size = 3374883, upload-time = "2024-07-01T09:46:47.331Z" }, + { url = "https://files.pythonhosted.org/packages/46/2b/99c28c4379a85e65378211971c0b430d9c7234b1ec4d59b2668f6299e011/pillow-10.4.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bee197b30783295d2eb680b311af15a20a8b24024a19c3a26431ff83eb8d1f70", size = 4339837, upload-time = "2024-07-01T09:46:49.647Z" }, + { url = "https://files.pythonhosted.org/packages/f1/74/b1ec314f624c0c43711fdf0d8076f82d9d802afd58f1d62c2a86878e8615/pillow-10.4.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1ef61f5dd14c300786318482456481463b9d6b91ebe5ef12f405afbba77ed0be", size = 4455562, upload-time = "2024-07-01T09:46:51.811Z" }, + { url = "https://files.pythonhosted.org/packages/4a/2a/4b04157cb7b9c74372fa867096a1607e6fedad93a44deeff553ccd307868/pillow-10.4.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:297e388da6e248c98bc4a02e018966af0c5f92dfacf5a5ca22fa01cb3179bca0", size = 4366761, upload-time = "2024-07-01T09:46:53.961Z" }, + { url = "https://files.pythonhosted.org/packages/ac/7b/8f1d815c1a6a268fe90481232c98dd0e5fa8c75e341a75f060037bd5ceae/pillow-10.4.0-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:e4db64794ccdf6cb83a59d73405f63adbe2a1887012e308828596100a0b2f6cc", size = 4536767, upload-time = "2024-07-01T09:46:56.664Z" }, + { url = "https://files.pythonhosted.org/packages/e5/77/05fa64d1f45d12c22c314e7b97398ffb28ef2813a485465017b7978b3ce7/pillow-10.4.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:bd2880a07482090a3bcb01f4265f1936a903d70bc740bfcb1fd4e8a2ffe5cf5a", size = 4477989, upload-time = "2024-07-01T09:46:58.977Z" }, + { url = "https://files.pythonhosted.org/packages/12/63/b0397cfc2caae05c3fb2f4ed1b4fc4fc878f0243510a7a6034ca59726494/pillow-10.4.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:4b35b21b819ac1dbd1233317adeecd63495f6babf21b7b2512d244ff6c6ce309", size = 4610255, upload-time = "2024-07-01T09:47:01.189Z" }, + { url = "https://files.pythonhosted.org/packages/7b/f9/cfaa5082ca9bc4a6de66ffe1c12c2d90bf09c309a5f52b27759a596900e7/pillow-10.4.0-cp313-cp313-win32.whl", hash = "sha256:551d3fd6e9dc15e4c1eb6fc4ba2b39c0c7933fa113b220057a34f4bb3268a060", size = 2235603, upload-time = "2024-07-01T09:47:03.918Z" }, + { url = "https://files.pythonhosted.org/packages/01/6a/30ff0eef6e0c0e71e55ded56a38d4859bf9d3634a94a88743897b5f96936/pillow-10.4.0-cp313-cp313-win_amd64.whl", hash = "sha256:030abdbe43ee02e0de642aee345efa443740aa4d828bfe8e2eb11922ea6a21ea", size = 2554972, upload-time = "2024-07-01T09:47:06.152Z" }, + { url = "https://files.pythonhosted.org/packages/48/2c/2e0a52890f269435eee38b21c8218e102c621fe8d8df8b9dd06fabf879ba/pillow-10.4.0-cp313-cp313-win_arm64.whl", hash = "sha256:5b001114dd152cfd6b23befeb28d7aee43553e2402c9f159807bf55f33af8a8d", size = 2243375, upload-time = "2024-07-01T09:47:09.065Z" }, + { url = "https://files.pythonhosted.org/packages/56/70/f40009702a477ce87d8d9faaa4de51d6562b3445d7a314accd06e4ffb01d/pillow-10.4.0-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:8d4d5063501b6dd4024b8ac2f04962d661222d120381272deea52e3fc52d3736", size = 3509213, upload-time = "2024-07-01T09:47:11.662Z" }, + { url = "https://files.pythonhosted.org/packages/10/43/105823d233c5e5d31cea13428f4474ded9d961652307800979a59d6a4276/pillow-10.4.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7c1ee6f42250df403c5f103cbd2768a28fe1a0ea1f0f03fe151c8741e1469c8b", size = 3375883, upload-time = "2024-07-01T09:47:14.453Z" }, + { url = "https://files.pythonhosted.org/packages/3c/ad/7850c10bac468a20c918f6a5dbba9ecd106ea1cdc5db3c35e33a60570408/pillow-10.4.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b15e02e9bb4c21e39876698abf233c8c579127986f8207200bc8a8f6bb27acf2", size = 4330810, upload-time = "2024-07-01T09:47:16.695Z" }, + { url = "https://files.pythonhosted.org/packages/84/4c/69bbed9e436ac22f9ed193a2b64f64d68fcfbc9f4106249dc7ed4889907b/pillow-10.4.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a8d4bade9952ea9a77d0c3e49cbd8b2890a399422258a77f357b9cc9be8d680", size = 4444341, upload-time = "2024-07-01T09:47:19.334Z" }, + { url = "https://files.pythonhosted.org/packages/8f/4f/c183c63828a3f37bf09644ce94cbf72d4929b033b109160a5379c2885932/pillow-10.4.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:43efea75eb06b95d1631cb784aa40156177bf9dd5b4b03ff38979e048258bc6b", size = 4356005, upload-time = "2024-07-01T09:47:21.805Z" }, + { url = "https://files.pythonhosted.org/packages/fb/ad/435fe29865f98a8fbdc64add8875a6e4f8c97749a93577a8919ec6f32c64/pillow-10.4.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:950be4d8ba92aca4b2bb0741285a46bfae3ca699ef913ec8416c1b78eadd64cd", size = 4525201, upload-time = "2024-07-01T09:47:24.457Z" }, + { url = "https://files.pythonhosted.org/packages/80/74/be8bf8acdfd70e91f905a12ae13cfb2e17c0f1da745c40141e26d0971ff5/pillow-10.4.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:d7480af14364494365e89d6fddc510a13e5a2c3584cb19ef65415ca57252fb84", size = 4460635, upload-time = "2024-07-01T09:47:26.841Z" }, + { url = "https://files.pythonhosted.org/packages/e4/90/763616e66dc9ad59c9b7fb58f863755e7934ef122e52349f62c7742b82d3/pillow-10.4.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:73664fe514b34c8f02452ffb73b7a92c6774e39a647087f83d67f010eb9a0cf0", size = 4590283, upload-time = "2024-07-01T09:47:29.247Z" }, + { url = "https://files.pythonhosted.org/packages/69/66/03002cb5b2c27bb519cba63b9f9aa3709c6f7a5d3b285406c01f03fb77e5/pillow-10.4.0-cp38-cp38-win32.whl", hash = "sha256:e88d5e6ad0d026fba7bdab8c3f225a69f063f116462c49892b0149e21b6c0a0e", size = 2235185, upload-time = "2024-07-01T09:47:32.205Z" }, + { url = "https://files.pythonhosted.org/packages/f2/75/3cb820b2812405fc7feb3d0deb701ef0c3de93dc02597115e00704591bc9/pillow-10.4.0-cp38-cp38-win_amd64.whl", hash = "sha256:5161eef006d335e46895297f642341111945e2c1c899eb406882a6c61a4357ab", size = 2554594, upload-time = "2024-07-01T09:47:34.285Z" }, + { url = "https://files.pythonhosted.org/packages/31/85/955fa5400fa8039921f630372cfe5056eed6e1b8e0430ee4507d7de48832/pillow-10.4.0-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:0ae24a547e8b711ccaaf99c9ae3cd975470e1a30caa80a6aaee9a2f19c05701d", size = 3509283, upload-time = "2024-07-01T09:47:36.394Z" }, + { url = "https://files.pythonhosted.org/packages/23/9c/343827267eb28d41cd82b4180d33b10d868af9077abcec0af9793aa77d2d/pillow-10.4.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:298478fe4f77a4408895605f3482b6cc6222c018b2ce565c2b6b9c354ac3229b", size = 3375691, upload-time = "2024-07-01T09:47:38.853Z" }, + { url = "https://files.pythonhosted.org/packages/60/a3/7ebbeabcd341eab722896d1a5b59a3df98c4b4d26cf4b0385f8aa94296f7/pillow-10.4.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:134ace6dc392116566980ee7436477d844520a26a4b1bd4053f6f47d096997fd", size = 4328295, upload-time = "2024-07-01T09:47:41.765Z" }, + { url = "https://files.pythonhosted.org/packages/32/3f/c02268d0c6fb6b3958bdda673c17b315c821d97df29ae6969f20fb49388a/pillow-10.4.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:930044bb7679ab003b14023138b50181899da3f25de50e9dbee23b61b4de2126", size = 4440810, upload-time = "2024-07-01T09:47:44.27Z" }, + { url = "https://files.pythonhosted.org/packages/67/5d/1c93c8cc35f2fdd3d6cc7e4ad72d203902859a2867de6ad957d9b708eb8d/pillow-10.4.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:c76e5786951e72ed3686e122d14c5d7012f16c8303a674d18cdcd6d89557fc5b", size = 4352283, upload-time = "2024-07-01T09:47:46.673Z" }, + { url = "https://files.pythonhosted.org/packages/bc/a8/8655557c9c7202b8abbd001f61ff36711cefaf750debcaa1c24d154ef602/pillow-10.4.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:b2724fdb354a868ddf9a880cb84d102da914e99119211ef7ecbdc613b8c96b3c", size = 4521800, upload-time = "2024-07-01T09:47:48.813Z" }, + { url = "https://files.pythonhosted.org/packages/58/78/6f95797af64d137124f68af1bdaa13b5332da282b86031f6fa70cf368261/pillow-10.4.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:dbc6ae66518ab3c5847659e9988c3b60dc94ffb48ef9168656e0019a93dbf8a1", size = 4459177, upload-time = "2024-07-01T09:47:52.104Z" }, + { url = "https://files.pythonhosted.org/packages/8a/6d/2b3ce34f1c4266d79a78c9a51d1289a33c3c02833fe294ef0dcbb9cba4ed/pillow-10.4.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:06b2f7898047ae93fad74467ec3d28fe84f7831370e3c258afa533f81ef7f3df", size = 4589079, upload-time = "2024-07-01T09:47:54.999Z" }, + { url = "https://files.pythonhosted.org/packages/e3/e0/456258c74da1ff5bf8ef1eab06a95ca994d8b9ed44c01d45c3f8cbd1db7e/pillow-10.4.0-cp39-cp39-win32.whl", hash = "sha256:7970285ab628a3779aecc35823296a7869f889b8329c16ad5a71e4901a3dc4ef", size = 2235247, upload-time = "2024-07-01T09:47:57.666Z" }, + { url = "https://files.pythonhosted.org/packages/37/f8/bef952bdb32aa53741f58bf21798642209e994edc3f6598f337f23d5400a/pillow-10.4.0-cp39-cp39-win_amd64.whl", hash = "sha256:961a7293b2457b405967af9c77dcaa43cc1a8cd50d23c532e62d48ab6cdd56f5", size = 2554479, upload-time = "2024-07-01T09:47:59.881Z" }, + { url = "https://files.pythonhosted.org/packages/bb/8e/805201619cad6651eef5fc1fdef913804baf00053461522fabbc5588ea12/pillow-10.4.0-cp39-cp39-win_arm64.whl", hash = "sha256:32cda9e3d601a52baccb2856b8ea1fc213c90b340c542dcef77140dfa3278a9e", size = 2243226, upload-time = "2024-07-01T09:48:02.508Z" }, + { url = "https://files.pythonhosted.org/packages/38/30/095d4f55f3a053392f75e2eae45eba3228452783bab3d9a920b951ac495c/pillow-10.4.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:5b4815f2e65b30f5fbae9dfffa8636d992d49705723fe86a3661806e069352d4", size = 3493889, upload-time = "2024-07-01T09:48:04.815Z" }, + { url = "https://files.pythonhosted.org/packages/f3/e8/4ff79788803a5fcd5dc35efdc9386af153569853767bff74540725b45863/pillow-10.4.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:8f0aef4ef59694b12cadee839e2ba6afeab89c0f39a3adc02ed51d109117b8da", size = 3346160, upload-time = "2024-07-01T09:48:07.206Z" }, + { url = "https://files.pythonhosted.org/packages/d7/ac/4184edd511b14f760c73f5bb8a5d6fd85c591c8aff7c2229677a355c4179/pillow-10.4.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9f4727572e2918acaa9077c919cbbeb73bd2b3ebcfe033b72f858fc9fbef0026", size = 3435020, upload-time = "2024-07-01T09:48:09.66Z" }, + { url = "https://files.pythonhosted.org/packages/da/21/1749cd09160149c0a246a81d646e05f35041619ce76f6493d6a96e8d1103/pillow-10.4.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ff25afb18123cea58a591ea0244b92eb1e61a1fd497bf6d6384f09bc3262ec3e", size = 3490539, upload-time = "2024-07-01T09:48:12.529Z" }, + { url = "https://files.pythonhosted.org/packages/b6/f5/f71fe1888b96083b3f6dfa0709101f61fc9e972c0c8d04e9d93ccef2a045/pillow-10.4.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:dc3e2db6ba09ffd7d02ae9141cfa0ae23393ee7687248d46a7507b75d610f4f5", size = 3476125, upload-time = "2024-07-01T09:48:14.891Z" }, + { url = "https://files.pythonhosted.org/packages/96/b9/c0362c54290a31866c3526848583a2f45a535aa9d725fd31e25d318c805f/pillow-10.4.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:02a2be69f9c9b8c1e97cf2713e789d4e398c751ecfd9967c18d0ce304efbf885", size = 3579373, upload-time = "2024-07-01T09:48:17.601Z" }, + { url = "https://files.pythonhosted.org/packages/52/3b/ce7a01026a7cf46e5452afa86f97a5e88ca97f562cafa76570178ab56d8d/pillow-10.4.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:0755ffd4a0c6f267cccbae2e9903d95477ca2f77c4fcf3a3a09570001856c8a5", size = 2554661, upload-time = "2024-07-01T09:48:20.293Z" }, + { url = "https://files.pythonhosted.org/packages/e1/1f/5a9fcd6ced51633c22481417e11b1b47d723f64fb536dfd67c015eb7f0ab/pillow-10.4.0-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:a02364621fe369e06200d4a16558e056fe2805d3468350df3aef21e00d26214b", size = 3493850, upload-time = "2024-07-01T09:48:23.03Z" }, + { url = "https://files.pythonhosted.org/packages/cb/e6/3ea4755ed5320cb62aa6be2f6de47b058c6550f752dd050e86f694c59798/pillow-10.4.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:1b5dea9831a90e9d0721ec417a80d4cbd7022093ac38a568db2dd78363b00908", size = 3346118, upload-time = "2024-07-01T09:48:25.256Z" }, + { url = "https://files.pythonhosted.org/packages/0a/22/492f9f61e4648422b6ca39268ec8139277a5b34648d28f400faac14e0f48/pillow-10.4.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9b885f89040bb8c4a1573566bbb2f44f5c505ef6e74cec7ab9068c900047f04b", size = 3434958, upload-time = "2024-07-01T09:48:28.078Z" }, + { url = "https://files.pythonhosted.org/packages/f9/19/559a48ad4045704bb0547965b9a9345f5cd461347d977a56d178db28819e/pillow-10.4.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:87dd88ded2e6d74d31e1e0a99a726a6765cda32d00ba72dc37f0651f306daaa8", size = 3490340, upload-time = "2024-07-01T09:48:30.734Z" }, + { url = "https://files.pythonhosted.org/packages/d9/de/cebaca6fb79905b3a1aa0281d238769df3fb2ede34fd7c0caa286575915a/pillow-10.4.0-pp39-pypy39_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:2db98790afc70118bd0255c2eeb465e9767ecf1f3c25f9a1abb8ffc8cfd1fe0a", size = 3476048, upload-time = "2024-07-01T09:48:33.292Z" }, + { url = "https://files.pythonhosted.org/packages/71/f0/86d5b2f04693b0116a01d75302b0a307800a90d6c351a8aa4f8ae76cd499/pillow-10.4.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:f7baece4ce06bade126fb84b8af1c33439a76d8a6fd818970215e0560ca28c27", size = 3579366, upload-time = "2024-07-01T09:48:36.527Z" }, + { url = "https://files.pythonhosted.org/packages/37/ae/2dbfc38cc4fd14aceea14bc440d5151b21f64c4c3ba3f6f4191610b7ee5d/pillow-10.4.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:cfdd747216947628af7b259d274771d84db2268ca062dd5faf373639d00113a3", size = 2554652, upload-time = "2024-07-01T09:48:38.789Z" }, +] + +[[package]] +name = "pillow" +version = "11.3.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.9.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/f3/0d/d0d6dea55cd152ce3d6767bb38a8fc10e33796ba4ba210cbab9354b6d238/pillow-11.3.0.tar.gz", hash = "sha256:3828ee7586cd0b2091b6209e5ad53e20d0649bbe87164a459d0676e035e8f523", size = 47113069, upload-time = "2025-07-01T09:16:30.666Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/4c/5d/45a3553a253ac8763f3561371432a90bdbe6000fbdcf1397ffe502aa206c/pillow-11.3.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:1b9c17fd4ace828b3003dfd1e30bff24863e0eb59b535e8f80194d9cc7ecf860", size = 5316554, upload-time = "2025-07-01T09:13:39.342Z" }, + { url = "https://files.pythonhosted.org/packages/7c/c8/67c12ab069ef586a25a4a79ced553586748fad100c77c0ce59bb4983ac98/pillow-11.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:65dc69160114cdd0ca0f35cb434633c75e8e7fad4cf855177a05bf38678f73ad", size = 4686548, upload-time = "2025-07-01T09:13:41.835Z" }, + { url = "https://files.pythonhosted.org/packages/2f/bd/6741ebd56263390b382ae4c5de02979af7f8bd9807346d068700dd6d5cf9/pillow-11.3.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7107195ddc914f656c7fc8e4a5e1c25f32e9236ea3ea860f257b0436011fddd0", size = 5859742, upload-time = "2025-07-03T13:09:47.439Z" }, + { url = "https://files.pythonhosted.org/packages/ca/0b/c412a9e27e1e6a829e6ab6c2dca52dd563efbedf4c9c6aa453d9a9b77359/pillow-11.3.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cc3e831b563b3114baac7ec2ee86819eb03caa1a2cef0b481a5675b59c4fe23b", size = 7633087, upload-time = "2025-07-03T13:09:51.796Z" }, + { url = "https://files.pythonhosted.org/packages/59/9d/9b7076aaf30f5dd17e5e5589b2d2f5a5d7e30ff67a171eb686e4eecc2adf/pillow-11.3.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f1f182ebd2303acf8c380a54f615ec883322593320a9b00438eb842c1f37ae50", size = 5963350, upload-time = "2025-07-01T09:13:43.865Z" }, + { url = "https://files.pythonhosted.org/packages/f0/16/1a6bf01fb622fb9cf5c91683823f073f053005c849b1f52ed613afcf8dae/pillow-11.3.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4445fa62e15936a028672fd48c4c11a66d641d2c05726c7ec1f8ba6a572036ae", size = 6631840, upload-time = "2025-07-01T09:13:46.161Z" }, + { url = "https://files.pythonhosted.org/packages/7b/e6/6ff7077077eb47fde78739e7d570bdcd7c10495666b6afcd23ab56b19a43/pillow-11.3.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:71f511f6b3b91dd543282477be45a033e4845a40278fa8dcdbfdb07109bf18f9", size = 6074005, upload-time = "2025-07-01T09:13:47.829Z" }, + { url = "https://files.pythonhosted.org/packages/c3/3a/b13f36832ea6d279a697231658199e0a03cd87ef12048016bdcc84131601/pillow-11.3.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:040a5b691b0713e1f6cbe222e0f4f74cd233421e105850ae3b3c0ceda520f42e", size = 6708372, upload-time = "2025-07-01T09:13:52.145Z" }, + { url = "https://files.pythonhosted.org/packages/6c/e4/61b2e1a7528740efbc70b3d581f33937e38e98ef3d50b05007267a55bcb2/pillow-11.3.0-cp310-cp310-win32.whl", hash = "sha256:89bd777bc6624fe4115e9fac3352c79ed60f3bb18651420635f26e643e3dd1f6", size = 6277090, upload-time = "2025-07-01T09:13:53.915Z" }, + { url = "https://files.pythonhosted.org/packages/a9/d3/60c781c83a785d6afbd6a326ed4d759d141de43aa7365725cbcd65ce5e54/pillow-11.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:19d2ff547c75b8e3ff46f4d9ef969a06c30ab2d4263a9e287733aa8b2429ce8f", size = 6985988, upload-time = "2025-07-01T09:13:55.699Z" }, + { url = "https://files.pythonhosted.org/packages/9f/28/4f4a0203165eefb3763939c6789ba31013a2e90adffb456610f30f613850/pillow-11.3.0-cp310-cp310-win_arm64.whl", hash = "sha256:819931d25e57b513242859ce1876c58c59dc31587847bf74cfe06b2e0cb22d2f", size = 2422899, upload-time = "2025-07-01T09:13:57.497Z" }, + { url = "https://files.pythonhosted.org/packages/db/26/77f8ed17ca4ffd60e1dcd220a6ec6d71210ba398cfa33a13a1cd614c5613/pillow-11.3.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:1cd110edf822773368b396281a2293aeb91c90a2db00d78ea43e7e861631b722", size = 5316531, upload-time = "2025-07-01T09:13:59.203Z" }, + { url = "https://files.pythonhosted.org/packages/cb/39/ee475903197ce709322a17a866892efb560f57900d9af2e55f86db51b0a5/pillow-11.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9c412fddd1b77a75aa904615ebaa6001f169b26fd467b4be93aded278266b288", size = 4686560, upload-time = "2025-07-01T09:14:01.101Z" }, + { url = "https://files.pythonhosted.org/packages/d5/90/442068a160fd179938ba55ec8c97050a612426fae5ec0a764e345839f76d/pillow-11.3.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7d1aa4de119a0ecac0a34a9c8bde33f34022e2e8f99104e47a3ca392fd60e37d", size = 5870978, upload-time = "2025-07-03T13:09:55.638Z" }, + { url = "https://files.pythonhosted.org/packages/13/92/dcdd147ab02daf405387f0218dcf792dc6dd5b14d2573d40b4caeef01059/pillow-11.3.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:91da1d88226663594e3f6b4b8c3c8d85bd504117d043740a8e0ec449087cc494", size = 7641168, upload-time = "2025-07-03T13:10:00.37Z" }, + { url = "https://files.pythonhosted.org/packages/6e/db/839d6ba7fd38b51af641aa904e2960e7a5644d60ec754c046b7d2aee00e5/pillow-11.3.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:643f189248837533073c405ec2f0bb250ba54598cf80e8c1e043381a60632f58", size = 5973053, upload-time = "2025-07-01T09:14:04.491Z" }, + { url = "https://files.pythonhosted.org/packages/f2/2f/d7675ecae6c43e9f12aa8d58b6012683b20b6edfbdac7abcb4e6af7a3784/pillow-11.3.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:106064daa23a745510dabce1d84f29137a37224831d88eb4ce94bb187b1d7e5f", size = 6640273, upload-time = "2025-07-01T09:14:06.235Z" }, + { url = "https://files.pythonhosted.org/packages/45/ad/931694675ede172e15b2ff03c8144a0ddaea1d87adb72bb07655eaffb654/pillow-11.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:cd8ff254faf15591e724dc7c4ddb6bf4793efcbe13802a4ae3e863cd300b493e", size = 6082043, upload-time = "2025-07-01T09:14:07.978Z" }, + { url = "https://files.pythonhosted.org/packages/3a/04/ba8f2b11fc80d2dd462d7abec16351b45ec99cbbaea4387648a44190351a/pillow-11.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:932c754c2d51ad2b2271fd01c3d121daaa35e27efae2a616f77bf164bc0b3e94", size = 6715516, upload-time = "2025-07-01T09:14:10.233Z" }, + { url = "https://files.pythonhosted.org/packages/48/59/8cd06d7f3944cc7d892e8533c56b0acb68399f640786313275faec1e3b6f/pillow-11.3.0-cp311-cp311-win32.whl", hash = "sha256:b4b8f3efc8d530a1544e5962bd6b403d5f7fe8b9e08227c6b255f98ad82b4ba0", size = 6274768, upload-time = "2025-07-01T09:14:11.921Z" }, + { url = "https://files.pythonhosted.org/packages/f1/cc/29c0f5d64ab8eae20f3232da8f8571660aa0ab4b8f1331da5c2f5f9a938e/pillow-11.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:1a992e86b0dd7aeb1f053cd506508c0999d710a8f07b4c791c63843fc6a807ac", size = 6986055, upload-time = "2025-07-01T09:14:13.623Z" }, + { url = "https://files.pythonhosted.org/packages/c6/df/90bd886fabd544c25addd63e5ca6932c86f2b701d5da6c7839387a076b4a/pillow-11.3.0-cp311-cp311-win_arm64.whl", hash = "sha256:30807c931ff7c095620fe04448e2c2fc673fcbb1ffe2a7da3fb39613489b1ddd", size = 2423079, upload-time = "2025-07-01T09:14:15.268Z" }, + { url = "https://files.pythonhosted.org/packages/40/fe/1bc9b3ee13f68487a99ac9529968035cca2f0a51ec36892060edcc51d06a/pillow-11.3.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:fdae223722da47b024b867c1ea0be64e0df702c5e0a60e27daad39bf960dd1e4", size = 5278800, upload-time = "2025-07-01T09:14:17.648Z" }, + { url = "https://files.pythonhosted.org/packages/2c/32/7e2ac19b5713657384cec55f89065fb306b06af008cfd87e572035b27119/pillow-11.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:921bd305b10e82b4d1f5e802b6850677f965d8394203d182f078873851dada69", size = 4686296, upload-time = "2025-07-01T09:14:19.828Z" }, + { url = "https://files.pythonhosted.org/packages/8e/1e/b9e12bbe6e4c2220effebc09ea0923a07a6da1e1f1bfbc8d7d29a01ce32b/pillow-11.3.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:eb76541cba2f958032d79d143b98a3a6b3ea87f0959bbe256c0b5e416599fd5d", size = 5871726, upload-time = "2025-07-03T13:10:04.448Z" }, + { url = "https://files.pythonhosted.org/packages/8d/33/e9200d2bd7ba00dc3ddb78df1198a6e80d7669cce6c2bdbeb2530a74ec58/pillow-11.3.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:67172f2944ebba3d4a7b54f2e95c786a3a50c21b88456329314caaa28cda70f6", size = 7644652, upload-time = "2025-07-03T13:10:10.391Z" }, + { url = "https://files.pythonhosted.org/packages/41/f1/6f2427a26fc683e00d985bc391bdd76d8dd4e92fac33d841127eb8fb2313/pillow-11.3.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:97f07ed9f56a3b9b5f49d3661dc9607484e85c67e27f3e8be2c7d28ca032fec7", size = 5977787, upload-time = "2025-07-01T09:14:21.63Z" }, + { url = "https://files.pythonhosted.org/packages/e4/c9/06dd4a38974e24f932ff5f98ea3c546ce3f8c995d3f0985f8e5ba48bba19/pillow-11.3.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:676b2815362456b5b3216b4fd5bd89d362100dc6f4945154ff172e206a22c024", size = 6645236, upload-time = "2025-07-01T09:14:23.321Z" }, + { url = "https://files.pythonhosted.org/packages/40/e7/848f69fb79843b3d91241bad658e9c14f39a32f71a301bcd1d139416d1be/pillow-11.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:3e184b2f26ff146363dd07bde8b711833d7b0202e27d13540bfe2e35a323a809", size = 6086950, upload-time = "2025-07-01T09:14:25.237Z" }, + { url = "https://files.pythonhosted.org/packages/0b/1a/7cff92e695a2a29ac1958c2a0fe4c0b2393b60aac13b04a4fe2735cad52d/pillow-11.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:6be31e3fc9a621e071bc17bb7de63b85cbe0bfae91bb0363c893cbe67247780d", size = 6723358, upload-time = "2025-07-01T09:14:27.053Z" }, + { url = "https://files.pythonhosted.org/packages/26/7d/73699ad77895f69edff76b0f332acc3d497f22f5d75e5360f78cbcaff248/pillow-11.3.0-cp312-cp312-win32.whl", hash = "sha256:7b161756381f0918e05e7cb8a371fff367e807770f8fe92ecb20d905d0e1c149", size = 6275079, upload-time = "2025-07-01T09:14:30.104Z" }, + { url = "https://files.pythonhosted.org/packages/8c/ce/e7dfc873bdd9828f3b6e5c2bbb74e47a98ec23cc5c74fc4e54462f0d9204/pillow-11.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:a6444696fce635783440b7f7a9fc24b3ad10a9ea3f0ab66c5905be1c19ccf17d", size = 6986324, upload-time = "2025-07-01T09:14:31.899Z" }, + { url = "https://files.pythonhosted.org/packages/16/8f/b13447d1bf0b1f7467ce7d86f6e6edf66c0ad7cf44cf5c87a37f9bed9936/pillow-11.3.0-cp312-cp312-win_arm64.whl", hash = "sha256:2aceea54f957dd4448264f9bf40875da0415c83eb85f55069d89c0ed436e3542", size = 2423067, upload-time = "2025-07-01T09:14:33.709Z" }, + { url = "https://files.pythonhosted.org/packages/1e/93/0952f2ed8db3a5a4c7a11f91965d6184ebc8cd7cbb7941a260d5f018cd2d/pillow-11.3.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:1c627742b539bba4309df89171356fcb3cc5a9178355b2727d1b74a6cf155fbd", size = 2128328, upload-time = "2025-07-01T09:14:35.276Z" }, + { url = "https://files.pythonhosted.org/packages/4b/e8/100c3d114b1a0bf4042f27e0f87d2f25e857e838034e98ca98fe7b8c0a9c/pillow-11.3.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:30b7c02f3899d10f13d7a48163c8969e4e653f8b43416d23d13d1bbfdc93b9f8", size = 2170652, upload-time = "2025-07-01T09:14:37.203Z" }, + { url = "https://files.pythonhosted.org/packages/aa/86/3f758a28a6e381758545f7cdb4942e1cb79abd271bea932998fc0db93cb6/pillow-11.3.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:7859a4cc7c9295f5838015d8cc0a9c215b77e43d07a25e460f35cf516df8626f", size = 2227443, upload-time = "2025-07-01T09:14:39.344Z" }, + { url = "https://files.pythonhosted.org/packages/01/f4/91d5b3ffa718df2f53b0dc109877993e511f4fd055d7e9508682e8aba092/pillow-11.3.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:ec1ee50470b0d050984394423d96325b744d55c701a439d2bd66089bff963d3c", size = 5278474, upload-time = "2025-07-01T09:14:41.843Z" }, + { url = "https://files.pythonhosted.org/packages/f9/0e/37d7d3eca6c879fbd9dba21268427dffda1ab00d4eb05b32923d4fbe3b12/pillow-11.3.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:7db51d222548ccfd274e4572fdbf3e810a5e66b00608862f947b163e613b67dd", size = 4686038, upload-time = "2025-07-01T09:14:44.008Z" }, + { url = "https://files.pythonhosted.org/packages/ff/b0/3426e5c7f6565e752d81221af9d3676fdbb4f352317ceafd42899aaf5d8a/pillow-11.3.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2d6fcc902a24ac74495df63faad1884282239265c6839a0a6416d33faedfae7e", size = 5864407, upload-time = "2025-07-03T13:10:15.628Z" }, + { url = "https://files.pythonhosted.org/packages/fc/c1/c6c423134229f2a221ee53f838d4be9d82bab86f7e2f8e75e47b6bf6cd77/pillow-11.3.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f0f5d8f4a08090c6d6d578351a2b91acf519a54986c055af27e7a93feae6d3f1", size = 7639094, upload-time = "2025-07-03T13:10:21.857Z" }, + { url = "https://files.pythonhosted.org/packages/ba/c9/09e6746630fe6372c67c648ff9deae52a2bc20897d51fa293571977ceb5d/pillow-11.3.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c37d8ba9411d6003bba9e518db0db0c58a680ab9fe5179f040b0463644bc9805", size = 5973503, upload-time = "2025-07-01T09:14:45.698Z" }, + { url = "https://files.pythonhosted.org/packages/d5/1c/a2a29649c0b1983d3ef57ee87a66487fdeb45132df66ab30dd37f7dbe162/pillow-11.3.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:13f87d581e71d9189ab21fe0efb5a23e9f28552d5be6979e84001d3b8505abe8", size = 6642574, upload-time = "2025-07-01T09:14:47.415Z" }, + { url = "https://files.pythonhosted.org/packages/36/de/d5cc31cc4b055b6c6fd990e3e7f0f8aaf36229a2698501bcb0cdf67c7146/pillow-11.3.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:023f6d2d11784a465f09fd09a34b150ea4672e85fb3d05931d89f373ab14abb2", size = 6084060, upload-time = "2025-07-01T09:14:49.636Z" }, + { url = "https://files.pythonhosted.org/packages/d5/ea/502d938cbaeec836ac28a9b730193716f0114c41325db428e6b280513f09/pillow-11.3.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:45dfc51ac5975b938e9809451c51734124e73b04d0f0ac621649821a63852e7b", size = 6721407, upload-time = "2025-07-01T09:14:51.962Z" }, + { url = "https://files.pythonhosted.org/packages/45/9c/9c5e2a73f125f6cbc59cc7087c8f2d649a7ae453f83bd0362ff7c9e2aee2/pillow-11.3.0-cp313-cp313-win32.whl", hash = "sha256:a4d336baed65d50d37b88ca5b60c0fa9d81e3a87d4a7930d3880d1624d5b31f3", size = 6273841, upload-time = "2025-07-01T09:14:54.142Z" }, + { url = "https://files.pythonhosted.org/packages/23/85/397c73524e0cd212067e0c969aa245b01d50183439550d24d9f55781b776/pillow-11.3.0-cp313-cp313-win_amd64.whl", hash = "sha256:0bce5c4fd0921f99d2e858dc4d4d64193407e1b99478bc5cacecba2311abde51", size = 6978450, upload-time = "2025-07-01T09:14:56.436Z" }, + { url = "https://files.pythonhosted.org/packages/17/d2/622f4547f69cd173955194b78e4d19ca4935a1b0f03a302d655c9f6aae65/pillow-11.3.0-cp313-cp313-win_arm64.whl", hash = "sha256:1904e1264881f682f02b7f8167935cce37bc97db457f8e7849dc3a6a52b99580", size = 2423055, upload-time = "2025-07-01T09:14:58.072Z" }, + { url = "https://files.pythonhosted.org/packages/dd/80/a8a2ac21dda2e82480852978416cfacd439a4b490a501a288ecf4fe2532d/pillow-11.3.0-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:4c834a3921375c48ee6b9624061076bc0a32a60b5532b322cc0ea64e639dd50e", size = 5281110, upload-time = "2025-07-01T09:14:59.79Z" }, + { url = "https://files.pythonhosted.org/packages/44/d6/b79754ca790f315918732e18f82a8146d33bcd7f4494380457ea89eb883d/pillow-11.3.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:5e05688ccef30ea69b9317a9ead994b93975104a677a36a8ed8106be9260aa6d", size = 4689547, upload-time = "2025-07-01T09:15:01.648Z" }, + { url = "https://files.pythonhosted.org/packages/49/20/716b8717d331150cb00f7fdd78169c01e8e0c219732a78b0e59b6bdb2fd6/pillow-11.3.0-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:1019b04af07fc0163e2810167918cb5add8d74674b6267616021ab558dc98ced", size = 5901554, upload-time = "2025-07-03T13:10:27.018Z" }, + { url = "https://files.pythonhosted.org/packages/74/cf/a9f3a2514a65bb071075063a96f0a5cf949c2f2fce683c15ccc83b1c1cab/pillow-11.3.0-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:f944255db153ebb2b19c51fe85dd99ef0ce494123f21b9db4877ffdfc5590c7c", size = 7669132, upload-time = "2025-07-03T13:10:33.01Z" }, + { url = "https://files.pythonhosted.org/packages/98/3c/da78805cbdbee9cb43efe8261dd7cc0b4b93f2ac79b676c03159e9db2187/pillow-11.3.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:1f85acb69adf2aaee8b7da124efebbdb959a104db34d3a2cb0f3793dbae422a8", size = 6005001, upload-time = "2025-07-01T09:15:03.365Z" }, + { url = "https://files.pythonhosted.org/packages/6c/fa/ce044b91faecf30e635321351bba32bab5a7e034c60187fe9698191aef4f/pillow-11.3.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:05f6ecbeff5005399bb48d198f098a9b4b6bdf27b8487c7f38ca16eeb070cd59", size = 6668814, upload-time = "2025-07-01T09:15:05.655Z" }, + { url = "https://files.pythonhosted.org/packages/7b/51/90f9291406d09bf93686434f9183aba27b831c10c87746ff49f127ee80cb/pillow-11.3.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:a7bc6e6fd0395bc052f16b1a8670859964dbd7003bd0af2ff08342eb6e442cfe", size = 6113124, upload-time = "2025-07-01T09:15:07.358Z" }, + { url = "https://files.pythonhosted.org/packages/cd/5a/6fec59b1dfb619234f7636d4157d11fb4e196caeee220232a8d2ec48488d/pillow-11.3.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:83e1b0161c9d148125083a35c1c5a89db5b7054834fd4387499e06552035236c", size = 6747186, upload-time = "2025-07-01T09:15:09.317Z" }, + { url = "https://files.pythonhosted.org/packages/49/6b/00187a044f98255225f172de653941e61da37104a9ea60e4f6887717e2b5/pillow-11.3.0-cp313-cp313t-win32.whl", hash = "sha256:2a3117c06b8fb646639dce83694f2f9eac405472713fcb1ae887469c0d4f6788", size = 6277546, upload-time = "2025-07-01T09:15:11.311Z" }, + { url = "https://files.pythonhosted.org/packages/e8/5c/6caaba7e261c0d75bab23be79f1d06b5ad2a2ae49f028ccec801b0e853d6/pillow-11.3.0-cp313-cp313t-win_amd64.whl", hash = "sha256:857844335c95bea93fb39e0fa2726b4d9d758850b34075a7e3ff4f4fa3aa3b31", size = 6985102, upload-time = "2025-07-01T09:15:13.164Z" }, + { url = "https://files.pythonhosted.org/packages/f3/7e/b623008460c09a0cb38263c93b828c666493caee2eb34ff67f778b87e58c/pillow-11.3.0-cp313-cp313t-win_arm64.whl", hash = "sha256:8797edc41f3e8536ae4b10897ee2f637235c94f27404cac7297f7b607dd0716e", size = 2424803, upload-time = "2025-07-01T09:15:15.695Z" }, + { url = "https://files.pythonhosted.org/packages/73/f4/04905af42837292ed86cb1b1dabe03dce1edc008ef14c473c5c7e1443c5d/pillow-11.3.0-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:d9da3df5f9ea2a89b81bb6087177fb1f4d1c7146d583a3fe5c672c0d94e55e12", size = 5278520, upload-time = "2025-07-01T09:15:17.429Z" }, + { url = "https://files.pythonhosted.org/packages/41/b0/33d79e377a336247df6348a54e6d2a2b85d644ca202555e3faa0cf811ecc/pillow-11.3.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:0b275ff9b04df7b640c59ec5a3cb113eefd3795a8df80bac69646ef699c6981a", size = 4686116, upload-time = "2025-07-01T09:15:19.423Z" }, + { url = "https://files.pythonhosted.org/packages/49/2d/ed8bc0ab219ae8768f529597d9509d184fe8a6c4741a6864fea334d25f3f/pillow-11.3.0-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:0743841cabd3dba6a83f38a92672cccbd69af56e3e91777b0ee7f4dba4385632", size = 5864597, upload-time = "2025-07-03T13:10:38.404Z" }, + { url = "https://files.pythonhosted.org/packages/b5/3d/b932bb4225c80b58dfadaca9d42d08d0b7064d2d1791b6a237f87f661834/pillow-11.3.0-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2465a69cf967b8b49ee1b96d76718cd98c4e925414ead59fdf75cf0fd07df673", size = 7638246, upload-time = "2025-07-03T13:10:44.987Z" }, + { url = "https://files.pythonhosted.org/packages/09/b5/0487044b7c096f1b48f0d7ad416472c02e0e4bf6919541b111efd3cae690/pillow-11.3.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:41742638139424703b4d01665b807c6468e23e699e8e90cffefe291c5832b027", size = 5973336, upload-time = "2025-07-01T09:15:21.237Z" }, + { url = "https://files.pythonhosted.org/packages/a8/2d/524f9318f6cbfcc79fbc004801ea6b607ec3f843977652fdee4857a7568b/pillow-11.3.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:93efb0b4de7e340d99057415c749175e24c8864302369e05914682ba642e5d77", size = 6642699, upload-time = "2025-07-01T09:15:23.186Z" }, + { url = "https://files.pythonhosted.org/packages/6f/d2/a9a4f280c6aefedce1e8f615baaa5474e0701d86dd6f1dede66726462bbd/pillow-11.3.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7966e38dcd0fa11ca390aed7c6f20454443581d758242023cf36fcb319b1a874", size = 6083789, upload-time = "2025-07-01T09:15:25.1Z" }, + { url = "https://files.pythonhosted.org/packages/fe/54/86b0cd9dbb683a9d5e960b66c7379e821a19be4ac5810e2e5a715c09a0c0/pillow-11.3.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:98a9afa7b9007c67ed84c57c9e0ad86a6000da96eaa638e4f8abe5b65ff83f0a", size = 6720386, upload-time = "2025-07-01T09:15:27.378Z" }, + { url = "https://files.pythonhosted.org/packages/e7/95/88efcaf384c3588e24259c4203b909cbe3e3c2d887af9e938c2022c9dd48/pillow-11.3.0-cp314-cp314-win32.whl", hash = "sha256:02a723e6bf909e7cea0dac1b0e0310be9d7650cd66222a5f1c571455c0a45214", size = 6370911, upload-time = "2025-07-01T09:15:29.294Z" }, + { url = "https://files.pythonhosted.org/packages/2e/cc/934e5820850ec5eb107e7b1a72dd278140731c669f396110ebc326f2a503/pillow-11.3.0-cp314-cp314-win_amd64.whl", hash = "sha256:a418486160228f64dd9e9efcd132679b7a02a5f22c982c78b6fc7dab3fefb635", size = 7117383, upload-time = "2025-07-01T09:15:31.128Z" }, + { url = "https://files.pythonhosted.org/packages/d6/e9/9c0a616a71da2a5d163aa37405e8aced9a906d574b4a214bede134e731bc/pillow-11.3.0-cp314-cp314-win_arm64.whl", hash = "sha256:155658efb5e044669c08896c0c44231c5e9abcaadbc5cd3648df2f7c0b96b9a6", size = 2511385, upload-time = "2025-07-01T09:15:33.328Z" }, + { url = "https://files.pythonhosted.org/packages/1a/33/c88376898aff369658b225262cd4f2659b13e8178e7534df9e6e1fa289f6/pillow-11.3.0-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:59a03cdf019efbfeeed910bf79c7c93255c3d54bc45898ac2a4140071b02b4ae", size = 5281129, upload-time = "2025-07-01T09:15:35.194Z" }, + { url = "https://files.pythonhosted.org/packages/1f/70/d376247fb36f1844b42910911c83a02d5544ebd2a8bad9efcc0f707ea774/pillow-11.3.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:f8a5827f84d973d8636e9dc5764af4f0cf2318d26744b3d902931701b0d46653", size = 4689580, upload-time = "2025-07-01T09:15:37.114Z" }, + { url = "https://files.pythonhosted.org/packages/eb/1c/537e930496149fbac69efd2fc4329035bbe2e5475b4165439e3be9cb183b/pillow-11.3.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ee92f2fd10f4adc4b43d07ec5e779932b4eb3dbfbc34790ada5a6669bc095aa6", size = 5902860, upload-time = "2025-07-03T13:10:50.248Z" }, + { url = "https://files.pythonhosted.org/packages/bd/57/80f53264954dcefeebcf9dae6e3eb1daea1b488f0be8b8fef12f79a3eb10/pillow-11.3.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c96d333dcf42d01f47b37e0979b6bd73ec91eae18614864622d9b87bbd5bbf36", size = 7670694, upload-time = "2025-07-03T13:10:56.432Z" }, + { url = "https://files.pythonhosted.org/packages/70/ff/4727d3b71a8578b4587d9c276e90efad2d6fe0335fd76742a6da08132e8c/pillow-11.3.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:4c96f993ab8c98460cd0c001447bff6194403e8b1d7e149ade5f00594918128b", size = 6005888, upload-time = "2025-07-01T09:15:39.436Z" }, + { url = "https://files.pythonhosted.org/packages/05/ae/716592277934f85d3be51d7256f3636672d7b1abfafdc42cf3f8cbd4b4c8/pillow-11.3.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:41342b64afeba938edb034d122b2dda5db2139b9a4af999729ba8818e0056477", size = 6670330, upload-time = "2025-07-01T09:15:41.269Z" }, + { url = "https://files.pythonhosted.org/packages/e7/bb/7fe6cddcc8827b01b1a9766f5fdeb7418680744f9082035bdbabecf1d57f/pillow-11.3.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:068d9c39a2d1b358eb9f245ce7ab1b5c3246c7c8c7d9ba58cfa5b43146c06e50", size = 6114089, upload-time = "2025-07-01T09:15:43.13Z" }, + { url = "https://files.pythonhosted.org/packages/8b/f5/06bfaa444c8e80f1a8e4bff98da9c83b37b5be3b1deaa43d27a0db37ef84/pillow-11.3.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a1bc6ba083b145187f648b667e05a2534ecc4b9f2784c2cbe3089e44868f2b9b", size = 6748206, upload-time = "2025-07-01T09:15:44.937Z" }, + { url = "https://files.pythonhosted.org/packages/f0/77/bc6f92a3e8e6e46c0ca78abfffec0037845800ea38c73483760362804c41/pillow-11.3.0-cp314-cp314t-win32.whl", hash = "sha256:118ca10c0d60b06d006be10a501fd6bbdfef559251ed31b794668ed569c87e12", size = 6377370, upload-time = "2025-07-01T09:15:46.673Z" }, + { url = "https://files.pythonhosted.org/packages/4a/82/3a721f7d69dca802befb8af08b7c79ebcab461007ce1c18bd91a5d5896f9/pillow-11.3.0-cp314-cp314t-win_amd64.whl", hash = "sha256:8924748b688aa210d79883357d102cd64690e56b923a186f35a82cbc10f997db", size = 7121500, upload-time = "2025-07-01T09:15:48.512Z" }, + { url = "https://files.pythonhosted.org/packages/89/c7/5572fa4a3f45740eaab6ae86fcdf7195b55beac1371ac8c619d880cfe948/pillow-11.3.0-cp314-cp314t-win_arm64.whl", hash = "sha256:79ea0d14d3ebad43ec77ad5272e6ff9bba5b679ef73375ea760261207fa8e0aa", size = 2512835, upload-time = "2025-07-01T09:15:50.399Z" }, + { url = "https://files.pythonhosted.org/packages/9e/8e/9c089f01677d1264ab8648352dcb7773f37da6ad002542760c80107da816/pillow-11.3.0-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:48d254f8a4c776de343051023eb61ffe818299eeac478da55227d96e241de53f", size = 5316478, upload-time = "2025-07-01T09:15:52.209Z" }, + { url = "https://files.pythonhosted.org/packages/b5/a9/5749930caf674695867eb56a581e78eb5f524b7583ff10b01b6e5048acb3/pillow-11.3.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:7aee118e30a4cf54fdd873bd3a29de51e29105ab11f9aad8c32123f58c8f8081", size = 4686522, upload-time = "2025-07-01T09:15:54.162Z" }, + { url = "https://files.pythonhosted.org/packages/43/46/0b85b763eb292b691030795f9f6bb6fcaf8948c39413c81696a01c3577f7/pillow-11.3.0-cp39-cp39-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:23cff760a9049c502721bdb743a7cb3e03365fafcdfc2ef9784610714166e5a4", size = 5853376, upload-time = "2025-07-03T13:11:01.066Z" }, + { url = "https://files.pythonhosted.org/packages/5e/c6/1a230ec0067243cbd60bc2dad5dc3ab46a8a41e21c15f5c9b52b26873069/pillow-11.3.0-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6359a3bc43f57d5b375d1ad54a0074318a0844d11b76abccf478c37c986d3cfc", size = 7626020, upload-time = "2025-07-03T13:11:06.479Z" }, + { url = "https://files.pythonhosted.org/packages/63/dd/f296c27ffba447bfad76c6a0c44c1ea97a90cb9472b9304c94a732e8dbfb/pillow-11.3.0-cp39-cp39-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:092c80c76635f5ecb10f3f83d76716165c96f5229addbd1ec2bdbbda7d496e06", size = 5956732, upload-time = "2025-07-01T09:15:56.111Z" }, + { url = "https://files.pythonhosted.org/packages/a5/a0/98a3630f0b57f77bae67716562513d3032ae70414fcaf02750279c389a9e/pillow-11.3.0-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cadc9e0ea0a2431124cde7e1697106471fc4c1da01530e679b2391c37d3fbb3a", size = 6624404, upload-time = "2025-07-01T09:15:58.245Z" }, + { url = "https://files.pythonhosted.org/packages/de/e6/83dfba5646a290edd9a21964da07674409e410579c341fc5b8f7abd81620/pillow-11.3.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:6a418691000f2a418c9135a7cf0d797c1bb7d9a485e61fe8e7722845b95ef978", size = 6067760, upload-time = "2025-07-01T09:16:00.003Z" }, + { url = "https://files.pythonhosted.org/packages/bc/41/15ab268fe6ee9a2bc7391e2bbb20a98d3974304ab1a406a992dcb297a370/pillow-11.3.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:97afb3a00b65cc0804d1c7abddbf090a81eaac02768af58cbdcaaa0a931e0b6d", size = 6700534, upload-time = "2025-07-01T09:16:02.29Z" }, + { url = "https://files.pythonhosted.org/packages/64/79/6d4f638b288300bed727ff29f2a3cb63db054b33518a95f27724915e3fbc/pillow-11.3.0-cp39-cp39-win32.whl", hash = "sha256:ea944117a7974ae78059fcc1800e5d3295172bb97035c0c1d9345fca1419da71", size = 6277091, upload-time = "2025-07-01T09:16:04.4Z" }, + { url = "https://files.pythonhosted.org/packages/46/05/4106422f45a05716fd34ed21763f8ec182e8ea00af6e9cb05b93a247361a/pillow-11.3.0-cp39-cp39-win_amd64.whl", hash = "sha256:e5c5858ad8ec655450a7c7df532e9842cf8df7cc349df7225c60d5d348c8aada", size = 6986091, upload-time = "2025-07-01T09:16:06.342Z" }, + { url = "https://files.pythonhosted.org/packages/63/c6/287fd55c2c12761d0591549d48885187579b7c257bef0c6660755b0b59ae/pillow-11.3.0-cp39-cp39-win_arm64.whl", hash = "sha256:6abdbfd3aea42be05702a8dd98832329c167ee84400a1d1f61ab11437f1717eb", size = 2422632, upload-time = "2025-07-01T09:16:08.142Z" }, + { url = "https://files.pythonhosted.org/packages/6f/8b/209bd6b62ce8367f47e68a218bffac88888fdf2c9fcf1ecadc6c3ec1ebc7/pillow-11.3.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:3cee80663f29e3843b68199b9d6f4f54bd1d4a6b59bdd91bceefc51238bcb967", size = 5270556, upload-time = "2025-07-01T09:16:09.961Z" }, + { url = "https://files.pythonhosted.org/packages/2e/e6/231a0b76070c2cfd9e260a7a5b504fb72da0a95279410fa7afd99d9751d6/pillow-11.3.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:b5f56c3f344f2ccaf0dd875d3e180f631dc60a51b314295a3e681fe8cf851fbe", size = 4654625, upload-time = "2025-07-01T09:16:11.913Z" }, + { url = "https://files.pythonhosted.org/packages/13/f4/10cf94fda33cb12765f2397fc285fa6d8eb9c29de7f3185165b702fc7386/pillow-11.3.0-pp310-pypy310_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e67d793d180c9df62f1f40aee3accca4829d3794c95098887edc18af4b8b780c", size = 4874207, upload-time = "2025-07-03T13:11:10.201Z" }, + { url = "https://files.pythonhosted.org/packages/72/c9/583821097dc691880c92892e8e2d41fe0a5a3d6021f4963371d2f6d57250/pillow-11.3.0-pp310-pypy310_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d000f46e2917c705e9fb93a3606ee4a819d1e3aa7a9b442f6444f07e77cf5e25", size = 6583939, upload-time = "2025-07-03T13:11:15.68Z" }, + { url = "https://files.pythonhosted.org/packages/3b/8e/5c9d410f9217b12320efc7c413e72693f48468979a013ad17fd690397b9a/pillow-11.3.0-pp310-pypy310_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:527b37216b6ac3a12d7838dc3bd75208ec57c1c6d11ef01902266a5a0c14fc27", size = 4957166, upload-time = "2025-07-01T09:16:13.74Z" }, + { url = "https://files.pythonhosted.org/packages/62/bb/78347dbe13219991877ffb3a91bf09da8317fbfcd4b5f9140aeae020ad71/pillow-11.3.0-pp310-pypy310_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:be5463ac478b623b9dd3937afd7fb7ab3d79dd290a28e2b6df292dc75063eb8a", size = 5581482, upload-time = "2025-07-01T09:16:16.107Z" }, + { url = "https://files.pythonhosted.org/packages/d9/28/1000353d5e61498aaeaaf7f1e4b49ddb05f2c6575f9d4f9f914a3538b6e1/pillow-11.3.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:8dc70ca24c110503e16918a658b869019126ecfe03109b754c402daff12b3d9f", size = 6984596, upload-time = "2025-07-01T09:16:18.07Z" }, + { url = "https://files.pythonhosted.org/packages/9e/e3/6fa84033758276fb31da12e5fb66ad747ae83b93c67af17f8c6ff4cc8f34/pillow-11.3.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:7c8ec7a017ad1bd562f93dbd8505763e688d388cde6e4a010ae1486916e713e6", size = 5270566, upload-time = "2025-07-01T09:16:19.801Z" }, + { url = "https://files.pythonhosted.org/packages/5b/ee/e8d2e1ab4892970b561e1ba96cbd59c0d28cf66737fc44abb2aec3795a4e/pillow-11.3.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:9ab6ae226de48019caa8074894544af5b53a117ccb9d3b3dcb2871464c829438", size = 4654618, upload-time = "2025-07-01T09:16:21.818Z" }, + { url = "https://files.pythonhosted.org/packages/f2/6d/17f80f4e1f0761f02160fc433abd4109fa1548dcfdca46cfdadaf9efa565/pillow-11.3.0-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fe27fb049cdcca11f11a7bfda64043c37b30e6b91f10cb5bab275806c32f6ab3", size = 4874248, upload-time = "2025-07-03T13:11:20.738Z" }, + { url = "https://files.pythonhosted.org/packages/de/5f/c22340acd61cef960130585bbe2120e2fd8434c214802f07e8c03596b17e/pillow-11.3.0-pp311-pypy311_pp73-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:465b9e8844e3c3519a983d58b80be3f668e2a7a5db97f2784e7079fbc9f9822c", size = 6583963, upload-time = "2025-07-03T13:11:26.283Z" }, + { url = "https://files.pythonhosted.org/packages/31/5e/03966aedfbfcbb4d5f8aa042452d3361f325b963ebbadddac05b122e47dd/pillow-11.3.0-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5418b53c0d59b3824d05e029669efa023bbef0f3e92e75ec8428f3799487f361", size = 4957170, upload-time = "2025-07-01T09:16:23.762Z" }, + { url = "https://files.pythonhosted.org/packages/cc/2d/e082982aacc927fc2cab48e1e731bdb1643a1406acace8bed0900a61464e/pillow-11.3.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:504b6f59505f08ae014f724b6207ff6222662aab5cc9542577fb084ed0676ac7", size = 5581505, upload-time = "2025-07-01T09:16:25.593Z" }, + { url = "https://files.pythonhosted.org/packages/34/e7/ae39f538fd6844e982063c3a5e4598b8ced43b9633baa3a85ef33af8c05c/pillow-11.3.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:c84d689db21a1c397d001aa08241044aa2069e7587b398c8cc63020390b1c1b8", size = 6984598, upload-time = "2025-07-01T09:16:27.732Z" }, +] + +[[package]] +name = "pillow" +version = "12.3.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.15'", + "python_full_version >= '3.12' and python_full_version < '3.15'", + "python_full_version == '3.11.*'", + "python_full_version == '3.10.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/1c/3d/bb7fca845737cf9d7dbde16ed1843984665ff2e0a518f5db43e77ec540b9/pillow-12.3.0.tar.gz", hash = "sha256:3b8182a766685eaa002637e28b4ec8d6b18819a0c71f579bf0dbaa5830297cce", size = 47025035, upload-time = "2026-07-01T11:56:38.965Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/25/c2/669d88644cddb1485bd9534e63e8cf476c8e51cb3c3a1297677023505c0e/pillow-12.3.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:6c0016e7b354317c4e9e525b937ac8596c38d2d232b419529b9cd7a1cd46e39a", size = 5392418, upload-time = "2026-07-01T11:53:27.808Z" }, + { url = "https://files.pythonhosted.org/packages/6b/ba/3762f376a2948e3036488d773a146e0ae6ecc2ca03ac20e2615bd0b2ba02/pillow-12.3.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:bcc33feacfaefce60c12fd500a277533bdc02b10a19f7f6d348763d8140bbba7", size = 4785287, upload-time = "2026-07-01T11:53:29.761Z" }, + { url = "https://files.pythonhosted.org/packages/07/50/b5d688cc9c52d4482f3d5bcab6ce20bc2a74a85d2343841c907444a3be2c/pillow-12.3.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5594fc43d548a7ed94949d139aa1341b270f1863f11cfd37f5a6c8b778a6b67f", size = 6253754, upload-time = "2026-07-01T11:53:32.298Z" }, + { url = "https://files.pythonhosted.org/packages/4e/89/36f4cd76cf4baf05c50ababb976249153f18c959171c7f6ba09a6f217260/pillow-12.3.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:f0606c8bf2cdefea14a43530f7657cbbb7ecf1c4222512492ef4a4434a9501ec", size = 6925605, upload-time = "2026-07-01T11:53:34.487Z" }, + { url = "https://files.pythonhosted.org/packages/eb/c0/4de58cf6633b9e3a6061ef4be6fb91fc3c90b812ece886f531e3c523d777/pillow-12.3.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:85f998ea1848bc6757289e739cfbdda3a04adfd58b02fc018ce54d754a5ce468", size = 6327788, upload-time = "2026-07-01T11:53:36.433Z" }, + { url = "https://files.pythonhosted.org/packages/87/3c/14d53682a19550dbbaf3b598f807d5457646c510805a44c7d7891cd1cd1a/pillow-12.3.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:25b9b82bb22e6e2b3cd07b39c68b7b862001226cb3dff7130d1cb914121b39ed", size = 7036288, upload-time = "2026-07-01T11:53:38.712Z" }, + { url = "https://files.pythonhosted.org/packages/38/1d/36279e3c77efe034e4cc2b0393ee74ffdb5a62391dacbf9b916154f5f0b8/pillow-12.3.0-cp310-cp310-win32.whl", hash = "sha256:37dc8f7bbb66efe481bb60defacef820c950c24713fb44962ed6aa2a50966de1", size = 6472396, upload-time = "2026-07-01T11:53:40.781Z" }, + { url = "https://files.pythonhosted.org/packages/48/7c/8fa0039574c476d7c6fa57dd7c32a130436877c6ec1e5ce1cc8ec44878c1/pillow-12.3.0-cp310-cp310-win_amd64.whl", hash = "sha256:300557495eb45ebb8aec96c2da9c4be642fbf7cd937278b4013ba894ea8eb0eb", size = 7226887, upload-time = "2026-07-01T11:53:42.764Z" }, + { url = "https://files.pythonhosted.org/packages/fa/17/e324be141d173c1c919428066c3259f21c1b8982e564e01a4a81e96dbdcf/pillow-12.3.0-cp310-cp310-win_arm64.whl", hash = "sha256:514435a37670e3e5e08f3945b68718b6ed329bb84367777e16f9f4dfe1e61a0f", size = 2568039, upload-time = "2026-07-01T11:53:45.372Z" }, + { url = "https://files.pythonhosted.org/packages/fb/c8/0a78b0e02d7ac54bc03e5321c9220da52f0c2ea83b21f7c40e7f3169c502/pillow-12.3.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:00808c5e14ef63ac5161091d242999076604ff74b883423a11e5d7bbb38bf756", size = 5392415, upload-time = "2026-07-01T11:53:47.162Z" }, + { url = "https://files.pythonhosted.org/packages/b2/5b/a02d30018abd97ced9f5a6c63d28597694a00d066516b9c1c6de45859fc9/pillow-12.3.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:37d6d0a00072fd2948eb22bce7e1475f34569d90c87c59f7a2ec59541b77f7a6", size = 4785266, upload-time = "2026-07-01T11:53:49.079Z" }, + { url = "https://files.pythonhosted.org/packages/c8/98/766667a4be768150a202836acd9fad19c06824ca86c4286d3cf6b274964e/pillow-12.3.0-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:bcb46e2f9feff8d06323983bd83ed00c201fdcab3d74973e7072a889b3979fcd", size = 6263814, upload-time = "2026-07-01T11:53:51.32Z" }, + { url = "https://files.pythonhosted.org/packages/3b/2d/ede717bc1144f63886c21fd349bb95860b0d1a21149ff16f2bb362b612b6/pillow-12.3.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:23d27a3e0307ec2244cc51e7287b919aa68d097504ebe19df4e76a98a3eea5bd", size = 6934408, upload-time = "2026-07-01T11:53:53.487Z" }, + { url = "https://files.pythonhosted.org/packages/a3/48/9c58b685e69d49c31af6c8eb9012055fab7e665785165c84796e2c73ce72/pillow-12.3.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:4f883547d4b7f0495ebe7056b0cc2aea76094e7a4abc8e933540f3271df27d9c", size = 6337160, upload-time = "2026-07-01T11:53:55.457Z" }, + { url = "https://files.pythonhosted.org/packages/ff/fa/dc2a5c0ba6df93f67c31d34b808b7ce440b40cdbf96f0b81cde1d1e6fa93/pillow-12.3.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:236ff70b9312fb68943c703aa842ca6a758abfa45ac187a5e7c1452e96ef72b5", size = 7045172, upload-time = "2026-07-01T11:53:57.736Z" }, + { url = "https://files.pythonhosted.org/packages/86/a5/444817a4d4c4c2417df00513086ca196f388d8f9ef40c2e4ccd1ad1af54b/pillow-12.3.0-cp311-cp311-win32.whl", hash = "sha256:10e41f0fbf1eec8cfd234b8fe17a4caac7c9d0db4c204d3c173a8f9f6ef3232b", size = 6472232, upload-time = "2026-07-01T11:53:59.767Z" }, + { url = "https://files.pythonhosted.org/packages/63/c6/4bad1b18d132a50b27e1365e1ab163616f7a5bb56d330f66f9d1d9d4f9d4/pillow-12.3.0-cp311-cp311-win_amd64.whl", hash = "sha256:8e95e1385e4998ae9694eeaa4730ba5457ff61185b3a55e2e7bea0880aef452a", size = 7233653, upload-time = "2026-07-01T11:54:02.066Z" }, + { url = "https://files.pythonhosted.org/packages/fd/16/00f91ab7760dc842f5aad55217e80fc4a7067a0604535249bc8a2d6d9870/pillow-12.3.0-cp311-cp311-win_arm64.whl", hash = "sha256:ebaea975e03d3141d9d3a507df75c9b3ec90fa9d2ffd07567b3a978d9d790b26", size = 2568195, upload-time = "2026-07-01T11:54:04.622Z" }, + { url = "https://files.pythonhosted.org/packages/37/bf/fb3ebff8ddcb76aac5a01389251bbbb9519922a9b520d8247c1ca864a25d/pillow-12.3.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:ba09209fbe443b4acccebe845d8a138b89a8f4fbaeedd44953490b5315d5e965", size = 5345969, upload-time = "2026-07-01T11:54:06.397Z" }, + { url = "https://files.pythonhosted.org/packages/d8/66/9a386a92561f402389a4fc70c18838bf6d35eb5eb5c6850b4b2dc64f5048/pillow-12.3.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ffd0c5368496f41b0944be820fcb7a838aa6e623d250b01acf2643939c3f99d7", size = 4780323, upload-time = "2026-07-01T11:54:09.351Z" }, + { url = "https://files.pythonhosted.org/packages/25/27/ac8f99618ffd3dde21db0f4d4b1d2ab00c0880595bfd17df103f7f39fd0c/pillow-12.3.0-cp312-cp312-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d9c7f76c0673154f044e9d78c8655fb4213f6ca31a836df48b40fe5d187717b9", size = 6266838, upload-time = "2026-07-01T11:54:11.71Z" }, + { url = "https://files.pythonhosted.org/packages/84/21/a35af28dcc61f37ed850a2d64c65c701321dfbf25085e469d5559360cbbf/pillow-12.3.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:78cb2c6865a35ab8ff8b75fd122f6033b92a62c82801110e48ddd6c936a45d91", size = 6940830, upload-time = "2026-07-01T11:54:13.732Z" }, + { url = "https://files.pythonhosted.org/packages/eb/51/8b08617af3ad95e33ce6d7dd2c99ed6c8298f7fb131636303956be022e25/pillow-12.3.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:e491916b378fba47242221bb9ead245211b70d504f495d105d17b14a24b4907c", size = 6344383, upload-time = "2026-07-01T11:54:15.756Z" }, + { url = "https://files.pythonhosted.org/packages/1d/72/cf78ac9780bb93c28328f408973845a309d4d145041665f734572ced1b52/pillow-12.3.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:0dd2064cbc55aaec028ef5fbb60fa47bb6c3e7918e07ff17935284b227a9d2df", size = 7052934, upload-time = "2026-07-01T11:54:17.721Z" }, + { url = "https://files.pythonhosted.org/packages/20/20/25e0f4dc178a6bc0696793720055519a0de89e7661dae886992decbd2f81/pillow-12.3.0-cp312-cp312-win32.whl", hash = "sha256:dbce0b29841537a2fa4a214c2bbf14de3587c9680caa9b4e217568472490b28f", size = 6472684, upload-time = "2026-07-01T11:54:19.839Z" }, + { url = "https://files.pythonhosted.org/packages/45/89/da2f7971a317f83d807fdd4065c0af40208e59e692cc43d315a71a0e96d1/pillow-12.3.0-cp312-cp312-win_amd64.whl", hash = "sha256:a2b55dd6b2a4c4b7d87ffa56bdb33fdc5fdb9a462173861a7bc097f17d91cb09", size = 7227137, upload-time = "2026-07-01T11:54:22.025Z" }, + { url = "https://files.pythonhosted.org/packages/de/47/4845a0a6c0dbf1db8456bd9fc791f13c5ced7ced20606d08a0aacfd25b49/pillow-12.3.0-cp312-cp312-win_arm64.whl", hash = "sha256:331b624368d4f1d069149002f25f44bc61c8919ce8ddb3c45bdad8f6e2d89510", size = 2568267, upload-time = "2026-07-01T11:54:24.051Z" }, + { url = "https://files.pythonhosted.org/packages/9d/ac/31fb64e1e7efb5a4b50cd3d92049ba89ac6e4d8d3bb6a74e15048ca3353e/pillow-12.3.0-cp313-cp313-ios_13_0_arm64_iphoneos.whl", hash = "sha256:21900ce7ba264168cd50defae43cd75d25c833ad4ad6e73ffc5596d12e25ac89", size = 4161684, upload-time = "2026-07-01T11:54:25.934Z" }, + { url = "https://files.pythonhosted.org/packages/87/b4/9805e23d2b4d77842b468513841fda254ee42f0289d25088340e4ff46e2d/pillow-12.3.0-cp313-cp313-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:4e8c2a84d977f50b9daed6eeaf3baef67d00d5d74d932288f02cb94518ee3ace", size = 4255487, upload-time = "2026-07-01T11:54:27.935Z" }, + { url = "https://files.pythonhosted.org/packages/df/39/ecf519435a200c693fe053a6ee4d835b41cf963a4dfc2551c4e637cb2a71/pillow-12.3.0-cp313-cp313-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:ae26d61dfa7a47befdc7572b521024e8745f3d809bd95ca9505a7bba9ef849ec", size = 3696433, upload-time = "2026-07-01T11:54:29.813Z" }, + { url = "https://files.pythonhosted.org/packages/42/92/2fc3ffad878ae8dd5469ec1bc8eb83b71f48e13efdf68f02709003982a32/pillow-12.3.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:7a743ff716f746fc19a9557f60dab1600d4613255f8a7aeb3cdde4db7eb15a66", size = 5345889, upload-time = "2026-07-01T11:54:31.97Z" }, + { url = "https://files.pythonhosted.org/packages/10/76/8803c13605b763d33d156c4678fc77f8443389c0c51c8aef707bb02015f4/pillow-12.3.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d69141514cc30b774ceea5e3ed3a6635c8d8a96edf664689b890f4089111fb35", size = 4780109, upload-time = "2026-07-01T11:54:34.026Z" }, + { url = "https://files.pythonhosted.org/packages/1f/01/e18aff37cb0b4aac47ac90f016d347a49aca667ef97f190b06ac2aabc928/pillow-12.3.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f7401aebd7f581d7f83a439d87d474999317ee099218e5ad25d125290990ba65", size = 6263736, upload-time = "2026-07-01T11:54:36.131Z" }, + { url = "https://files.pythonhosted.org/packages/f7/62/de5bdd77d935331f4f802edc11e4d82950f642caad6cb2f949837b8560e2/pillow-12.3.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0847a763afefb695bc912d7c131e7e0632d4edc1d8698f58ddabec8e46b8b6d3", size = 6937129, upload-time = "2026-07-01T11:54:38.216Z" }, + { url = "https://files.pythonhosted.org/packages/70/4d/105627a13300c5e0df1d174230b32fd1273062c96f7745fd552b945d1e1d/pillow-12.3.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:571b9fcb07b97ef3a492028fb3d2dc0993ca23a06138b0315286566d29ef718a", size = 6339562, upload-time = "2026-07-01T11:54:40.354Z" }, + { url = "https://files.pythonhosted.org/packages/6b/1d/f13de01a553988ab895ba1c722e06cf3144d4f57656fd5b81b6d881f1179/pillow-12.3.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:756c768d0c9c2955feb7a56c37ea24aea2e369f8d36a88da270b6a9f19e62b5e", size = 7049439, upload-time = "2026-07-01T11:54:42.489Z" }, + { url = "https://files.pythonhosted.org/packages/c9/f9/066794cca041b969964f779ee5fa66a9498bbf34248ac39c5d7954e4198f/pillow-12.3.0-cp313-cp313-win32.whl", hash = "sha256:a876864214e136f0eb367788dbd7df045f4806801518e2cfe9e13229cfe06d8f", size = 6473287, upload-time = "2026-07-01T11:54:44.9Z" }, + { url = "https://files.pythonhosted.org/packages/a6/9b/7a58e61d62be561da3a356fe2384d4059a6345fc130e23ef1c36a5b81d24/pillow-12.3.0-cp313-cp313-win_amd64.whl", hash = "sha256:1cca606cd25738df4ed873d5ad46bbdb3d83b5cbca291f6b4ff13a4df6b0bbe8", size = 7239691, upload-time = "2026-07-01T11:54:47.141Z" }, + { url = "https://files.pythonhosted.org/packages/aa/b0/c4ed4f0ef8f8fa5ee8351537db6650bb8189f7e118842978dd6589065692/pillow-12.3.0-cp313-cp313-win_arm64.whl", hash = "sha256:b629de27fda84b42cde7edef0d85f13b958b47f6e9bbcbba9b673c562a89bd8b", size = 2568185, upload-time = "2026-07-01T11:54:49.137Z" }, + { url = "https://files.pythonhosted.org/packages/dc/01/001f65b68192f0228cc1dbbc8d2530ab5d58b61037ba0587f946fea607cd/pillow-12.3.0-cp314-cp314-ios_13_0_arm64_iphoneos.whl", hash = "sha256:9cf95fe4d0f84c82d282745d9bb08ad9f926efa00be4697e767b814ce40d4330", size = 4161736, upload-time = "2026-07-01T11:54:51.156Z" }, + { url = "https://files.pythonhosted.org/packages/1a/d2/0219746d0fd16fc8a84498e79452375be3797d3ce4044596ce565164b84f/pillow-12.3.0-cp314-cp314-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:8728f216dcdb6e6d555cf971cb34076139ad74b31fc2c14da4fafc741c5f6217", size = 4255435, upload-time = "2026-07-01T11:54:53.414Z" }, + { url = "https://files.pythonhosted.org/packages/c8/02/8d0bc62ef0302318c46ff2a512822d2610e81c7aa46c9b3abe6cbaca5ad0/pillow-12.3.0-cp314-cp314-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:a45650e8ce7fafffd731db8550230db6b0d306d181a90b67d3e6bca2f1990930", size = 3696262, upload-time = "2026-07-01T11:54:55.739Z" }, + { url = "https://files.pythonhosted.org/packages/85/e2/73c77d218410b14f5f2d565e8a998d5317b7b9c75368d29985139f7a46f0/pillow-12.3.0-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:ba54cfebe86920a559a7c4d6b9050791c20513650a1952ebe3368c7dc70306f8", size = 5350344, upload-time = "2026-07-01T11:54:57.657Z" }, + { url = "https://files.pythonhosted.org/packages/c7/da/32c752228ae345f489e3a42499d817b6c3996da7e8a3bc7a04fc806b243b/pillow-12.3.0-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:e158cb00350dc278f3b91551101aa7d12415a66ebf2c91d8d5ac14e56ddd3ad0", size = 4780131, upload-time = "2026-07-01T11:54:59.713Z" }, + { url = "https://files.pythonhosted.org/packages/b1/9d/8b2c807dbef61a5197c047afe99823787eb66f63daf9fb2432f91d6f0462/pillow-12.3.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e9aeb04d6aef139de265b29683e119b638208f88cf73cdd1658aa07221165321", size = 6263757, upload-time = "2026-07-01T11:55:01.778Z" }, + { url = "https://files.pythonhosted.org/packages/5c/44/c85361f65dbe00eea8576ee467c768d25129989efb76e94f205e9ca9bb46/pillow-12.3.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:251bf95b67017e27b13d82f5b326234ca62d70f9cf4c2b9032de2358a3b12c7b", size = 6936962, upload-time = "2026-07-01T11:55:03.93Z" }, + { url = "https://files.pythonhosted.org/packages/18/7e/e483414b35800b86b6f08dbbc7803fb5cd52c4d6f897f47d53ea2c7e6f65/pillow-12.3.0-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:fe3cca2e4e8a592be0f269a1ca4835c25199d9f3ce815c8491048f785b0a0198", size = 6339171, upload-time = "2026-07-01T11:55:05.989Z" }, + { url = "https://files.pythonhosted.org/packages/f0/f4/68c491844841ede6bed70189546b3ee9731cf9f2cbad396faff5e1ccba45/pillow-12.3.0-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:23aceaa007d6172b02c277f0cd359c79492bbb14f7072b4ede9fbcaf20648130", size = 7048116, upload-time = "2026-07-01T11:55:08.131Z" }, + { url = "https://files.pythonhosted.org/packages/a3/34/77f3f793fed8efc7d243f21b33c5a3f0d1c97ee70346d3db855587e155ff/pillow-12.3.0-cp314-cp314-win32.whl", hash = "sha256:af8d94b0db561cf68b88a267c5c44b49e134f525d0dc2cb7ed413a66bc23559a", size = 6467209, upload-time = "2026-07-01T11:55:10.408Z" }, + { url = "https://files.pythonhosted.org/packages/f1/e0/492879f69d94f91f60fc8cd05ba03650e9520afebb2fb7aa12777d7c7f38/pillow-12.3.0-cp314-cp314-win_amd64.whl", hash = "sha256:fdafc9cce40277e0f7a0feabce0ee50dd2fa1800f3b38015e51296b5e814048d", size = 7237707, upload-time = "2026-07-01T11:55:12.745Z" }, + { url = "https://files.pythonhosted.org/packages/c9/ac/6b11f2875f1c2ac040d84e1bbf9cf22a88038f901ca1037898b280b38365/pillow-12.3.0-cp314-cp314-win_arm64.whl", hash = "sha256:e91206ee562682b51b98ef4b26a6ef48fd84e15fd4c4bc5ec768eb641d206838", size = 2565995, upload-time = "2026-07-01T11:55:14.736Z" }, + { url = "https://files.pythonhosted.org/packages/52/69/c2208e56af9bfc1913afb24020297a691eb1d4ef688474c8a04913f65e04/pillow-12.3.0-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:164b31cd1a0490ab6efae01aa5df49da7061be0af1b30e035b6e9a1bfe34ee6e", size = 5352503, upload-time = "2026-07-01T11:55:17.076Z" }, + { url = "https://files.pythonhosted.org/packages/07/70/e5686d753e898a45d778ff1718dba8516ead6ab6b95d85fc8c4b70650cf2/pillow-12.3.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:5afb51d599ea772b8365ae807ae557f18bccfe46ab261fd1c2a9ed700fc6eb17", size = 4782956, upload-time = "2026-07-01T11:55:19.448Z" }, + { url = "https://files.pythonhosted.org/packages/d5/37/25c6692f06927ee973ff18c8d9ee98ad0b4d84ee67a09610c2dd1447958e/pillow-12.3.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:3edce1d53195db527e0191f84b71d02022de0540bf43a16ed734ed7537b07385", size = 6322855, upload-time = "2026-07-01T11:55:21.613Z" }, + { url = "https://files.pythonhosted.org/packages/cc/91/420637fcb8f1bc11029e403b4538e6694744428d8246118e45719f944556/pillow-12.3.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:bf16ba1b4d0b6b7c8e534936632270cf70eb00dbe09005bc345b2677b726855c", size = 6989642, upload-time = "2026-07-01T11:55:24.006Z" }, + { url = "https://files.pythonhosted.org/packages/10/08/b94d7811281ccf0d143a1cf768d1c49e1e54af63e7b708ab2ee3eb87face/pillow-12.3.0-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:24870b09b224f7ae3c39ed07d10e819d06f8720bc551847b1d623832b5b0e28d", size = 6391281, upload-time = "2026-07-01T11:55:26.252Z" }, + { url = "https://files.pythonhosted.org/packages/d2/87/24233f785f55474dc02ce3e739c5528a77e3a862e9333d1dd7a25cc31f70/pillow-12.3.0-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:30f2aa603c41533cc25c05acd0da21636e84a315768feb631c937177db558931", size = 7096716, upload-time = "2026-07-01T11:55:28.318Z" }, + { url = "https://files.pythonhosted.org/packages/23/26/fcb2f6e37175b04f53570b59937867e2b80ee1685e744023153028fc14f9/pillow-12.3.0-cp314-cp314t-win32.whl", hash = "sha256:4b0a7fe987b14c31ebda6083f74f22b561fd3739bc0ac51e019622e3d72668c7", size = 6474125, upload-time = "2026-07-01T11:55:30.956Z" }, + { url = "https://files.pythonhosted.org/packages/90/de/3634abee5f1c9e13c56787b7d5517b0ba8d6de51700b95578cf338349c9f/pillow-12.3.0-cp314-cp314t-win_amd64.whl", hash = "sha256:962864dc93511324d51ddbb5b9f8731bf71675b93ca612a07441896f4688fb8c", size = 7242939, upload-time = "2026-07-01T11:55:34.044Z" }, + { url = "https://files.pythonhosted.org/packages/ce/2a/fd13f8eb24de5714a6eb444a3d67e2842c6c576e159a43793adf23051351/pillow-12.3.0-cp314-cp314t-win_arm64.whl", hash = "sha256:0740a512dc522224c77d9aa5a8d70d8b7d73fb91f2c21125d8d025d3b8990e45", size = 2567506, upload-time = "2026-07-01T11:55:35.988Z" }, + { url = "https://files.pythonhosted.org/packages/5d/dc/8fdce34ec725a33c81c6ba122b904d6b9024e50ea9ac7bede62fab54506c/pillow-12.3.0-cp315-cp315-ios_13_0_arm64_iphoneos.whl", hash = "sha256:0feb2e9d6ad6c9e3c06effe9d00f3f1e618a6643273576b016f591e9315a7139", size = 4162063, upload-time = "2026-07-01T11:55:37.941Z" }, + { url = "https://files.pythonhosted.org/packages/76/66/2044b9a63d3b84ff048228dfcb7cd9bf0df983e8470971bf7d4c57b693de/pillow-12.3.0-cp315-cp315-ios_13_0_arm64_iphonesimulator.whl", hash = "sha256:9e881fca225083806662a5c43d627d215f258ff43c890f831966c7d7ba9c7402", size = 4255549, upload-time = "2026-07-01T11:55:40.022Z" }, + { url = "https://files.pythonhosted.org/packages/52/7e/1f67e6f4ece6b582ee4b539decbcc9f848dc245a93ed8cd7338bafef72f1/pillow-12.3.0-cp315-cp315-ios_13_0_x86_64_iphonesimulator.whl", hash = "sha256:4998562bf62a445225f22e07c896bb04b35b1b1f2eb6d760584c9c51d7a5f78c", size = 3696331, upload-time = "2026-07-01T11:55:41.98Z" }, + { url = "https://files.pythonhosted.org/packages/12/40/d306fc2c8e4d45d7f175c77edca7063be7b86fe7fe6e68f4353bf71d808c/pillow-12.3.0-cp315-cp315-macosx_10_15_x86_64.whl", hash = "sha256:dc624f6bc473dacdf7ef7eb8678d0d08edf15cd94fad6ae5c7d6cc67a4e4902f", size = 5350370, upload-time = "2026-07-01T11:55:44.028Z" }, + { url = "https://files.pythonhosted.org/packages/dd/44/668fb1437e8ce420f62d6106eb66e44a5971602a4d794615bdf79315d82d/pillow-12.3.0-cp315-cp315-macosx_11_0_arm64.whl", hash = "sha256:71d6097b330eea8fd15097780c8e89cb1a8ce7838669f48c5bacd6f663dd4701", size = 4780147, upload-time = "2026-07-01T11:55:46.073Z" }, + { url = "https://files.pythonhosted.org/packages/0c/08/93fa2e70e30a2d81547e481b6ee2bb9522117221fb1e0ce4b5df70967677/pillow-12.3.0-cp315-cp315-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:28ce87c5ab450a9dd970b52e5aca5fe63ed432d18a2eaddd1979a00a1ba24ace", size = 6273659, upload-time = "2026-07-01T11:55:48.264Z" }, + { url = "https://files.pythonhosted.org/packages/f8/6d/043e96ff814fc31a33077e4cba86082167db520c93632afdf2042febbb0c/pillow-12.3.0-cp315-cp315-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6b02afb9b97f65fbca5f31db6a2a3ba21aa93030225f150fa3f249717e938fb4", size = 6947439, upload-time = "2026-07-01T11:55:50.503Z" }, + { url = "https://files.pythonhosted.org/packages/af/92/ba71d2ee2ac0edf3fa33bd9d5ee9ee080da70b1766f3ca3934f9938ddac9/pillow-12.3.0-cp315-cp315-musllinux_1_2_aarch64.whl", hash = "sha256:1182d52bc2d5e5d7d0949503aa7e36d12f42205dc287e4883f407b1988820d39", size = 6353577, upload-time = "2026-07-01T11:55:52.697Z" }, + { url = "https://files.pythonhosted.org/packages/0f/ce/e63064e2122923ff687c8ad792d0d736a7b3920a56a46982e81a7fdd25d6/pillow-12.3.0-cp315-cp315-musllinux_1_2_x86_64.whl", hash = "sha256:e795b7eb908249c4e43c7c99fac7c2c75dab0c43566e37db472a355f63693d71", size = 7060394, upload-time = "2026-07-01T11:55:55.149Z" }, + { url = "https://files.pythonhosted.org/packages/54/76/a09cc3ccc8d773a7283d34c38bec1708f9e3cc932093cbc4c5e71ac4060b/pillow-12.3.0-cp315-cp315-win32.whl", hash = "sha256:57b3d78c95ba9059768b10e28b813002261d3f3dfc55cc48b0c988f625175827", size = 6467375, upload-time = "2026-07-01T11:55:57.769Z" }, + { url = "https://files.pythonhosted.org/packages/3e/03/1846c49ba3b1d5550392a4bbd06d6fb4578e1cd91a803198b5c90f5f7d53/pillow-12.3.0-cp315-cp315-win_amd64.whl", hash = "sha256:fa4ecea169a355be7a3ade2c783e2ed12f0e40d2c5621cda8b3297faf7fbb9f5", size = 7237048, upload-time = "2026-07-01T11:55:59.975Z" }, + { url = "https://files.pythonhosted.org/packages/fb/bb/89f35dcc79610423f9f195504d7def7f0d1416a711541b42867e25fe3412/pillow-12.3.0-cp315-cp315-win_arm64.whl", hash = "sha256:877c3f311ff35410f690861c4409e7ccbf0cd2f878e50628a28e5a0bb689e658", size = 2566006, upload-time = "2026-07-01T11:56:02.143Z" }, + { url = "https://files.pythonhosted.org/packages/30/88/707027ba09942dfa2c28759b5c222d769290a41c6d20ea60ec250801941f/pillow-12.3.0-cp315-cp315t-macosx_10_15_x86_64.whl", hash = "sha256:e9871b1ffbfa9656b60aeee92ed5136a5742696006fa322b29ea3d8da0ecc9cf", size = 5352509, upload-time = "2026-07-01T11:56:04.2Z" }, + { url = "https://files.pythonhosted.org/packages/b0/6d/00352fa25332c2569cd387851f568cc5a4b75a9adbfb37ac4fbce4c02eec/pillow-12.3.0-cp315-cp315t-macosx_11_0_arm64.whl", hash = "sha256:53aa02d20d10c3d814d536aa4e5ac9b84ca0ff5a88377963b085ad6822f93e64", size = 4783167, upload-time = "2026-07-01T11:56:06.631Z" }, + { url = "https://files.pythonhosted.org/packages/13/4f/9e049dfa21af7c22427275720e2490267ba8138120add5c4c574deb69782/pillow-12.3.0-cp315-cp315t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:446c34dcc4324b084a53b705127dc15717b22c5e140ae0a3c38349d4efec071e", size = 6329237, upload-time = "2026-07-01T11:56:08.868Z" }, + { url = "https://files.pythonhosted.org/packages/36/16/cf6eeaae8d0fce8dd390a33437cf68c5d5bd73834a2bc6e2f14efda0ab45/pillow-12.3.0-cp315-cp315t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cf1845d02ad822a369a49f2bb9345b1614744267682e7a03527dc3bf6eea1777", size = 6997047, upload-time = "2026-07-01T11:56:11.379Z" }, + { url = "https://files.pythonhosted.org/packages/1e/69/dbf769bdd55f48bf5733cac28edc6364ffaa072ec9ba336266e4fe66be55/pillow-12.3.0-cp315-cp315t-musllinux_1_2_aarch64.whl", hash = "sha256:186941b6aef820ad110fb01fb06eb925374dc3a21b17e37ec9a53b250c6fe2d1", size = 6400440, upload-time = "2026-07-01T11:56:13.908Z" }, + { url = "https://files.pythonhosted.org/packages/a0/e1/ffc9cfc2eea0d178da8018e18e959301ad9d6bc9f3edb7181e748a474b97/pillow-12.3.0-cp315-cp315t-musllinux_1_2_x86_64.whl", hash = "sha256:f13c32a3abd6079a66d9526e18dad9b6d280384d49d7c54040cd57b6424041d9", size = 7105895, upload-time = "2026-07-01T11:56:16.575Z" }, + { url = "https://files.pythonhosted.org/packages/18/f0/a5595c1e8c3ae44b9828cb2f0fa8155e5095ef04d6327b8f61cf44a3df85/pillow-12.3.0-cp315-cp315t-win32.whl", hash = "sha256:1657923d2d45afb66526e5b933e5b3052e6bdea196c90d3abb2424e18c77dae8", size = 6474384, upload-time = "2026-07-01T11:56:18.855Z" }, + { url = "https://files.pythonhosted.org/packages/e4/04/62bcd9f844984c5938d3b05264a61d797a29d3e0812341a8204af70bbdee/pillow-12.3.0-cp315-cp315t-win_amd64.whl", hash = "sha256:8cd2f7bdda092d99c9fc2fb7391354f306d01443d22785d0cbfafa2e2c8bb418", size = 7243537, upload-time = "2026-07-01T11:56:21.214Z" }, + { url = "https://files.pythonhosted.org/packages/3d/68/1f3066acedf37673694a7141381d8f811ae97f30d34413d236abe7d489f1/pillow-12.3.0-cp315-cp315t-win_arm64.whl", hash = "sha256:06ff022112bc9cbf83b60f8e028d94ad87b60621706487e65f673de61610ab59", size = 2567491, upload-time = "2026-07-01T11:56:23.506Z" }, + { url = "https://files.pythonhosted.org/packages/75/18/2e8b40223153ccbc60df07f9e8928dc0c76202aa4e55ae9f53962b6510d6/pillow-12.3.0-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:b3c777e849237620b022f7f297dd67705f9f5cf1685f09f02e46f93e92725468", size = 5302510, upload-time = "2026-07-01T11:56:25.736Z" }, + { url = "https://files.pythonhosted.org/packages/46/3e/51fabf59d5ab801ceab709453d3ab6b180083496579549de4c45ced6528a/pillow-12.3.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:b343699e8308bdc51978310e1c959c584e7869cc8c40780058c87da7781a1e94", size = 4736058, upload-time = "2026-07-01T11:56:28.041Z" }, + { url = "https://files.pythonhosted.org/packages/bf/20/22fe9384b7949e25fb1293bcfc84fb82590ff4ea6b37c95b24d26d793d86/pillow-12.3.0-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:fbd139c8447d25dd750ab79ee274cc5e1fe80fc56340ab10b18a195e1b6eca3e", size = 5237776, upload-time = "2026-07-01T11:56:30.263Z" }, + { url = "https://files.pythonhosted.org/packages/08/14/f6ba68107680ffa74b39985f3f30884e41318fbc4250caa423c79b4788bb/pillow-12.3.0-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e7e480451b9fa137494bccd3a7d69adbe8ac65a87d97be61e11f1b1050a5bac3", size = 5860358, upload-time = "2026-07-01T11:56:32.68Z" }, + { url = "https://files.pythonhosted.org/packages/36/54/0169bc772ec491108b62f644f8ecf1fe5d8ae5ebafde2ee2142210166903/pillow-12.3.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:04f01d28a6aaff387bf842a13be313df23ba0597a44f1a976c9feb3c6ff4711a", size = 7231786, upload-time = "2026-07-01T11:56:35.046Z" }, +] + [[package]] name = "platformdirs" version = "4.3.6" @@ -1228,6 +2911,95 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/d5/6f/9ac2548e290764781f9e7e2aaf0685b086379dabfb29ca38536985471eaf/pylint-4.0.5-py3-none-any.whl", hash = "sha256:00f51c9b14a3b3ae08cff6b2cdd43f28165c78b165b628692e428fb1f8dc2cf2", size = 536694, upload-time = "2026-02-20T09:07:31.028Z" }, ] +[[package]] +name = "pymupdf" +version = "1.24.11" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.8.1' and python_full_version < '3.9'", + "python_full_version < '3.8.1'", +] +sdist = { url = "https://files.pythonhosted.org/packages/d4/a3/3edbb6be649e311107b320141cae0353d4cc9c6593eba7691f16c53c9c71/PyMuPDF-1.24.11.tar.gz", hash = "sha256:6e45e57f14ac902029d4aacf07684958d0e58c769f47d9045b2048d0a3d20155", size = 51199098, upload-time = "2024-10-03T20:59:54.415Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f5/75/b059d603530d99926de2b6a64314f3534e2149ee5496142de550c66907ac/PyMuPDF-1.24.11-cp38-abi3-macosx_10_9_x86_64.whl", hash = "sha256:24c35ba9e731027ff24566b90d4986e9aac75e1ce47589b25de51e3c687ddb73", size = 18895391, upload-time = "2024-10-03T20:58:18.04Z" }, + { url = "https://files.pythonhosted.org/packages/16/f8/8396ca7218622cb3600c919b320a24f05b7c14bd81eea03f3f2182844a06/PyMuPDF-1.24.11-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:20c8eb65b855a33411246d6697a3f3166727fe2d8585753cf0db648730104be6", size = 18152689, upload-time = "2024-10-03T20:58:31.823Z" }, + { url = "https://files.pythonhosted.org/packages/55/3d/84bd559129d2ff07267baae0bde0c6f4f49232408b547971f7a2e1534cb9/PyMuPDF-1.24.11-cp38-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:32fd013e3c844f105c0a6a43ee82acc7cd0c900f6ff14f5eed9492840bbcbdd9", size = 19033047, upload-time = "2024-10-04T07:59:52.645Z" }, + { url = "https://files.pythonhosted.org/packages/ca/21/ad66778ad2485f87ef1d5a36f17ec8d4aee8ce247c8e46c673eff776a877/PyMuPDF-1.24.11-cp38-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2efb793644df99db0fe2468149048175cf25c5803997828efc9152aca838f5f2", size = 19563533, upload-time = "2024-10-03T20:58:58.548Z" }, + { url = "https://files.pythonhosted.org/packages/6a/92/9ff020892560f80433876ec904c0f2669d1d69403adf412565e54a946615/PyMuPDF-1.24.11-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:9b7ac5b8ec3daec17f2e830962ed091610e576a5e531d2fe28c437fbd69b1969", size = 20691324, upload-time = "2024-10-03T20:58:46.085Z" }, + { url = "https://files.pythonhosted.org/packages/28/6b/a0247598f06585d84ae9927d6ed191d89d38686ad6bf0dadc0ed699a77e7/PyMuPDF-1.24.11-cp38-abi3-win32.whl", hash = "sha256:6fda6c7ed7e6ad74d9cfac5c3837ef42efd58c506440e2513a0a200bc3c4dbc0", size = 14685566, upload-time = "2024-10-03T20:59:21.644Z" }, + { url = "https://files.pythonhosted.org/packages/f6/03/99895f003d7ff59c83d524aeccecff4e1ee1f39a7724f88acfda4f67b8bc/PyMuPDF-1.24.11-cp38-abi3-win_amd64.whl", hash = "sha256:745ce77532702d6ddeeecb47306d3669629aa5ff82708318cd652881f493b0ba", size = 15984328, upload-time = "2024-10-03T20:59:11.654Z" }, +] + +[[package]] +name = "pymupdf" +version = "1.26.5" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.9.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/8d/9a/e0a4e92a85fc17be7c54afdbb113f0ade2a8bca49856d510e28bd249e462/pymupdf-1.26.5.tar.gz", hash = "sha256:8ef335e07f648492df240f2247854d0e7c0467afb9c4dc2376ec30978ec158c3", size = 84319274, upload-time = "2025-10-10T14:04:51.826Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/dd/3f/7fc927fd66922ce838d4c974ff9a685c5f5aba108a5d94914dc05c9371f5/pymupdf-1.26.5-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:2bfb58f07ad631e5f71ad0bd6f1ff52700f7ba7ebb4973130e81e75b721beae1", size = 23065601, upload-time = "2025-10-10T13:58:43.98Z" }, + { url = "https://files.pythonhosted.org/packages/c1/e2/e87e62284ba98d59f1fd4fc7542ef2ed0002525754a485fa4077b3bbddae/pymupdf-1.26.5-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:d58599479bc471d3ae56c3d68d9160d0b7de8a3bd40221ddc3a4eaae2d281b86", size = 22412612, upload-time = "2025-10-10T13:59:04.846Z" }, + { url = "https://files.pythonhosted.org/packages/df/c2/af93c6367f79e9b5435f803bde51c1dc8225f054f8238162dda80b44986d/pymupdf-1.26.5-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:7dfea81fdd73437a6a6ce83e1fcf556faee9327a6540571e58bf04fa362bb0cd", size = 23457410, upload-time = "2025-10-10T22:45:26.355Z" }, + { url = "https://files.pythonhosted.org/packages/5b/5a/1292a0df4ff71fbc00dfa8c08759d17c97e1e8ea9277eb5bc5f079ca188d/pymupdf-1.26.5-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:caad0ffeb63dcc4a29ca40f3c68d7b78d32a932e834b0056b529cc0bdbaaffc9", size = 24064941, upload-time = "2025-10-10T13:59:48.544Z" }, + { url = "https://files.pythonhosted.org/packages/28/90/87b7fdfc9cd6991a3eb69a5752f6343374c34f258c511c242f4d60791eea/pymupdf-1.26.5-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:e24e7a7d696bd398543cc5c147869edb2026d5d5a21b7f8e35db2f20170b389e", size = 24268203, upload-time = "2025-10-10T14:00:28.791Z" }, + { url = "https://files.pythonhosted.org/packages/2c/99/9d4b36485538e29df0a013fb02bbf6b5b0743a428fa07515e36631c43363/pymupdf-1.26.5-cp39-abi3-win32.whl", hash = "sha256:a2a42f5911d153a47bf5c3e162a0bfe8745eb9bec3e59fbaf87617b4003d8270", size = 17130722, upload-time = "2025-10-10T14:00:51.377Z" }, + { url = "https://files.pythonhosted.org/packages/c6/96/fd59c1532891762ea4815e73956c532053d5e26d56969e1e5d1e4ca4b207/pymupdf-1.26.5-cp39-abi3-win_amd64.whl", hash = "sha256:39a6fb58182b27b51ea8150a0cd2e4ee7e0cf71e9d6723978f28699b42ee61ae", size = 18747258, upload-time = "2025-10-10T14:01:37.346Z" }, +] + +[[package]] +name = "pymupdf" +version = "1.28.0" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.15'", + "python_full_version >= '3.12' and python_full_version < '3.15'", + "python_full_version == '3.11.*'", + "python_full_version == '3.10.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/8e/e9/6d6c5d6c0a3551bffd47681a6240caf941727f195b45593cf20ab36f018f/pymupdf-1.28.0.tar.gz", hash = "sha256:e53f3567403a92da15caa9e7ae0164327fff48817e9f40175367fb9de524258d", size = 87637751, upload-time = "2026-06-29T09:08:47.547Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/c8/b7/88043e38cc7529de070f0c9bd267fa258035cca0b4ad5260536b994594a7/pymupdf-1.28.0-cp310-abi3-macosx_10_15_x86_64.whl", hash = "sha256:892b89ba88e8f98b53133b62877a9dc9b5e7dc6a4aeb837b612db56a8d2e03ac", size = 24597385, upload-time = "2026-06-29T09:03:30.608Z" }, + { url = "https://files.pythonhosted.org/packages/33/f4/23775bbda0781b61fc398cc75079a2b0e64696d8fcf93271748883e9627e/pymupdf-1.28.0-cp310-abi3-macosx_11_0_arm64.whl", hash = "sha256:4d692dcf44d3566ae96bc6f6346c6ad432274a29ba617bf7a9fe18009e24adb4", size = 23828292, upload-time = "2026-06-29T09:03:46.129Z" }, + { url = "https://files.pythonhosted.org/packages/1c/f5/bf75fc7a415722f8b33662054f82d88520c0cbfd4c36d0e08aeaec605e49/pymupdf-1.28.0-cp310-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:47a5c29ed4eb0744de9c4e37bb49b1259b18d4d75fcc8a7c130f7c9fa15956f6", size = 25045507, upload-time = "2026-06-29T09:04:03.86Z" }, + { url = "https://files.pythonhosted.org/packages/58/69/5d12c9f1f2d76f28383d6110a069c79fbfced5a4f97bb1ee6e8354f52bb7/pymupdf-1.28.0-cp310-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:44f0973f5e5edbaec95bc34b64e71d1959d4ee90b1328de1b4f4f5b4fa78673f", size = 25716599, upload-time = "2026-06-29T09:04:19.367Z" }, + { url = "https://files.pythonhosted.org/packages/4d/b4/ec0e017bc42857cc86bd651441dbc41cc18be48d4698ecd27aac491e0c9a/pymupdf-1.28.0-cp310-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:4d61ec323a706e153a12e262e51febfb43eeaa20977785ace135d18d48bcdc83", size = 25940489, upload-time = "2026-06-29T09:04:36.624Z" }, + { url = "https://files.pythonhosted.org/packages/06/86/f831fef09013f33b3c9c09fb3923f2ff53e1e437f6ace14b8ae46392f558/pymupdf-1.28.0-cp310-abi3-win32.whl", hash = "sha256:caea2b3b67347fd79e5d15ed7929b0e886aac594ea228073b6d39de0078189da", size = 18489703, upload-time = "2026-06-29T20:50:30.599Z" }, + { url = "https://files.pythonhosted.org/packages/2e/5d/1a03f53eb0449900469335fcfc742ca28e3ba159b7d650e0921d50b8b308/pymupdf-1.28.0-cp310-abi3-win_amd64.whl", hash = "sha256:e01e90fd86abfeb37ceb921eddb951f988a11d45ff6ce6b7664f2039849068ec", size = 19773102, upload-time = "2026-06-29T09:04:49.773Z" }, + { url = "https://files.pythonhosted.org/packages/72/f6/1e52ce243ca792254f6223b4017c5667194c146ce9b88baf37bc5eb3d1c9/pymupdf-1.28.0-cp313-abi3-pyemscripten_2025_0_wasm32.whl", hash = "sha256:74c6d00ba2a9aad3a635db73b07c15db462b480741d831a34a75a56535ebc22b", size = 18357011, upload-time = "2026-06-29T20:50:50.353Z" }, + { url = "https://files.pythonhosted.org/packages/62/b1/46b5b3d8ef3cc71114667cf10c4d8b33f39af97253af32e9a0986775b638/pymupdf-1.28.0-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:b3e1399c7a64c6914239116a369efcdaac4cfb9e838bde2656d7accc4a85c72d", size = 25753599, upload-time = "2026-06-29T09:05:09.398Z" }, +] + +[[package]] +name = "pyparsing" +version = "3.1.4" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.8.1' and python_full_version < '3.9'", + "python_full_version < '3.8.1'", +] +sdist = { url = "https://files.pythonhosted.org/packages/83/08/13f3bce01b2061f2bbd582c9df82723de943784cf719a35ac886c652043a/pyparsing-3.1.4.tar.gz", hash = "sha256:f86ec8d1a83f11977c9a6ea7598e8c27fc5cddfa5b07ea2241edbbde1d7bc032", size = 900231, upload-time = "2024-08-25T15:00:47.416Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/e5/0c/0e3c05b1c87bb6a1c76d281b0f35e78d2d80ac91b5f8f524cebf77f51049/pyparsing-3.1.4-py3-none-any.whl", hash = "sha256:a6a7ee4235a3f944aa1fa2249307708f893fe5717dc603503c6c7969c070fb7c", size = 104100, upload-time = "2024-08-25T15:00:45.361Z" }, +] + +[[package]] +name = "pyparsing" +version = "3.3.2" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.15'", + "python_full_version >= '3.12' and python_full_version < '3.15'", + "python_full_version == '3.11.*'", + "python_full_version == '3.10.*'", + "python_full_version == '3.9.*'", +] +sdist = { url = "https://files.pythonhosted.org/packages/f3/91/9c6ee907786a473bf81c5f53cf703ba0957b23ab84c264080fb5a450416f/pyparsing-3.3.2.tar.gz", hash = "sha256:c777f4d763f140633dcb6d8a3eda953bf7a214dc4eff598413c070bcdc117cbc", size = 6851574, upload-time = "2026-01-21T03:57:59.36Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/10/bd/c038d7cc38edc1aa5bf91ab8068b63d4308c66c4c8bb3cbba7dfbc049f9c/pyparsing-3.3.2-py3-none-any.whl", hash = "sha256:850ba148bd908d7e2411587e247a1e4f0327839c40e2e5e6d05a007ecc69911d", size = 122781, upload-time = "2026-01-21T03:57:55.912Z" }, +] + [[package]] name = "pytest" version = "8.3.5" @@ -1294,6 +3066,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/d4/24/a372aaf5c9b7208e7112038812994107bc65a84cd00e0354a88c2c77a617/pytest-9.0.3-py3-none-any.whl", hash = "sha256:2c5efc453d45394fdd706ade797c0a81091eccd1d6e4bccfcd476e2b8e0ab5d9", size = 375249, upload-time = "2026-04-07T17:16:16.13Z" }, ] +[[package]] +name = "python-dateutil" +version = "2.9.0.post0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "six" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/66/c0/0c8b6ad9f17a802ee498c46e004a0eb49bc148f2fd230864601a86dcf6db/python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3", size = 342432, upload-time = "2024-03-01T18:36:20.211Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427", size = 229892, upload-time = "2024-03-01T18:36:18.57Z" }, +] + [[package]] name = "pytokens" version = "0.4.1" @@ -1461,6 +3245,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/82/3b/64d4899d73f91ba49a8c18a8ff3f0ea8f1c1d75481760df8c68ef5235bf5/rich-15.0.0-py3-none-any.whl", hash = "sha256:33bd4ef74232fb73fe9279a257718407f169c09b78a87ad3d296f548e27de0bb", size = 310654, upload-time = "2026-04-12T08:24:02.83Z" }, ] +[[package]] +name = "six" +version = "1.17.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/94/e7/b2c673351809dca68a0e064b6af791aa332cf192da575fd474ed7d6f16a2/six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81", size = 34031, upload-time = "2024-12-04T17:35:28.174Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050, upload-time = "2024-12-04T17:35:26.475Z" }, +] + [[package]] name = "textual" version = "0.73.0" @@ -1665,10 +3458,26 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/61/73/d21edf5b204d1467e06500080a50f79d49ef2b997c79123a536d4a17d97c/uc_micro_py-2.0.0-py3-none-any.whl", hash = "sha256:3603a3859af53e5a39bc7677713c78ea6589ff188d70f4fee165db88e22b242c", size = 6383, upload-time = "2026-03-01T06:31:26.257Z" }, ] +[[package]] +name = "zipp" +version = "3.20.2" +source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version >= '3.8.1' and python_full_version < '3.9'", + "python_full_version < '3.8.1'", +] +sdist = { url = "https://files.pythonhosted.org/packages/54/bf/5c0000c44ebc80123ecbdddba1f5dcd94a5ada602a9c225d84b5aaa55e86/zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29", size = 24199, upload-time = "2024-09-13T13:44:16.101Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/62/8b/5ba542fa83c90e09eac972fc9baca7a88e7e7ca4b221a89251954019308b/zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350", size = 9200, upload-time = "2024-09-13T13:44:14.38Z" }, +] + [[package]] name = "zipp" version = "3.23.1" source = { registry = "https://pypi.org/simple" } +resolution-markers = [ + "python_full_version == '3.9.*'", +] sdist = { url = "https://files.pythonhosted.org/packages/30/21/093488dfc7cc8964ded15ab726fad40f25fd3d788fd741cc1c5a17d78ee8/zipp-3.23.1.tar.gz", hash = "sha256:32120e378d32cd9714ad503c1d024619063ec28aad2248dc6672ad13edfa5110", size = 25965, upload-time = "2026-04-13T23:21:46.6Z" } wheels = [ { url = "https://files.pythonhosted.org/packages/08/8a/0861bec20485572fbddf3dfba2910e38fe249796cb73ecdeb74e07eeb8d3/zipp-3.23.1-py3-none-any.whl", hash = "sha256:0b3596c50a5c700c9cb40ba8d86d9f2cc4807e9bedb06bcdf7fac85633e444dc", size = 10378, upload-time = "2026-04-13T23:21:45.386Z" },