diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15c-Lean-Grothendieck-Companion.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15c-Lean-Grothendieck-Companion.ipynb
index a48fe518a6..fefcfe4c68 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15c-Lean-Grothendieck-Companion.ipynb
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15c-Lean-Grothendieck-Companion.ipynb
@@ -16,6 +16,8 @@
    "source": [
     "# Lean-15c : le lake Grothendieck par ses énoncés (companion formel natif)\n",
     "\n",
+    "**Navigation** : [<< Lean-15b Grothendieck en Lean](Lean-15b-Lean-Grothendieck.ipynb) | [Lean-15d Visite guidee visuelle >>](Lean-15d-Lean-Grothendieck-Visuel-Python.ipynb) | [Index](README.md)\n",
+    "\n",
     "Ce notebook est le **companion formel natif** du lake [`grothendieck_lean/`](grothendieck_lean/), en kernel `lean4-wsl`.\n",
     "Il complète le notebook Python [`Lean-15b`](Lean-15b-Lean-Grothendieck.ipynb) : là où 15b *charge et explique* les sources,\n",
     "celui-ci **importe le lake réel** et montre, à travers un parcours représentatif de ses modules, des énoncés qui **compilent** —\n",
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15d-Lean-Grothendieck-Visuel-Python.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15d-Lean-Grothendieck-Visuel-Python.ipynb
new file mode 100644
index 0000000000..3406c2aae9
--- /dev/null
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15d-Lean-Grothendieck-Visuel-Python.ipynb
@@ -0,0 +1,1935 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c00",
+   "metadata": {
+    "papermill": {
+     "duration": 0.008739,
+     "end_time": "2026-09-30T15:01:32.038527+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:32.029788+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Lean-15d : Grothendieck en images\n",
+    "\n",
+    "**Navigation** : [<< Lean-15c Companion formel](Lean-15c-Lean-Grothendieck-Companion.ipynb) | [Index](README.md)\n",
+    "\n",
+    "**Une visite guidee visuelle des abstractions grothendieckiennes.**\n",
+    "\n",
+    "Le depot porte deja trois carnets sur ce corpus :\n",
+    "\n",
+    "| Carnet | Ce qu'il fait |\n",
+    "|---|---|\n",
+    "| `Lean-15-Grothendieck-Tribute` | le catalogue : les modules du lake, affiches par extraits |\n",
+    "| `Lean-15b-Lean-Grothendieck` | l'atelier : exercices sur cribles, topologies, faisceaux |\n",
+    "| `Lean-15c-Lean-Grothendieck-Companion` | le companion formel natif (kernel `lean4-wsl`) |\n",
+    "\n",
+    "Aucun des trois ne **montre** ce dont il parle. Le premier affiche du code Lean, le deuxieme pose des questions, le troisieme compile des enonces. Or les objets de Grothendieck -- cribles, sites, faisceaux -- sont des objets **geometriques**, et l'intuition qui les rend maniables se transmet mal par une liste de theoremes.\n",
+    "\n",
+    "Ce carnet prend le probleme par l'autre bout : **une figure par abstraction**, dessinee en Python, sans dependance au lake ni a un kernel Lean. Il ne remplace pas les trois autres, il les rend lisibles.\n",
+    "\n",
+    "## Ce que ce carnet n'est pas\n",
+    "\n",
+    "Ce n'est pas une introduction a la theorie des categories, et ce n'est pas une preuve. Les figures sont volontairement **grossieres** : un crible est dessine comme un paquet de fleches, un site comme un treillis d'ouverts, un faisceau comme des fonctions qui se recollent. Une figure grossiere qui donne l'intuition vaut mieux qu'une figure exacte qui ne dit rien -- c'est le parti pris de ce carnet.\n",
+    "\n",
+    "## Parcours\n",
+    "\n",
+    "1. Categories et foncteurs -- le vocabulaire, en images\n",
+    "2. Cribles -- ce qu'est un crible sur un objet\n",
+    "3. Topologie de Grothendieck -- les trois axiomes, un par image\n",
+    "4. Faisceaux -- la condition de recollement\n",
+    "5. Yoneda -- le point generalise\n",
+    "6. Site de Zariski -- le treillis des ouverts de $\\operatorname{Spec} \\mathbb{Z}$\n",
+    "7. Synthese -- la mer qui monte\n",
+    "\n",
+    "Chaque section suit le meme rythme : une figure, puis sa lecture.\n",
+    "\n",
+    "***\n",
+    "\n",
+    "**Prerequis** : Python (numpy, matplotlib). Aucun lacet, aucun acces reseau."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c01",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006401,
+     "end_time": "2026-09-30T15:01:32.052263+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:32.045862+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## Mise en place\n",
+    "\n",
+    "Deux conventions valent pour toutes les figures du carnet.\n",
+    "\n",
+    "**Les objets sont des disques, les fleches sont des courbes.** Une categorie se dessine comme un graphe oriente : les sommets sont les objets, les aretes sont les morphismes. Le sens de la fleche porte l'information directionnelle, la couleur distingue les familles.\n",
+    "\n",
+    "**Une fleche composee n'est pas dessinee deux fois.** Quand $g \\circ f$ et la chaine $A \\xrightarrow{f} B \\xrightarrow{g} C$ coexistent, on dessine la chaine et on etiquete la fleche directe en pointille : c'est la meme donnee, vue de deux facons.\n",
+    "\n",
+    "Les figures utilisent `matplotlib` seul, avec une palette stable pour que deux sections differentes restent comparables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c02",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:32.061350Z",
+     "iopub.status.busy": "2026-09-30T15:01:32.061154Z",
+     "iopub.status.idle": "2026-09-30T15:01:32.589628Z",
+     "shell.execute_reply": "2026-09-30T15:01:32.588433Z"
+    },
+    "papermill": {
+     "duration": 0.534324,
+     "end_time": "2026-09-30T15:01:32.590730+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:32.056406+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outils de dessin prets : objet, fleche, cadre, plan.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.patches import FancyArrowPatch, Circle, FancyBboxPatch, Rectangle\n",
+    "\n",
+    "# Palette stable du carnet : une couleur par role, reprise d'une section a l'autre.\n",
+    "C_OBJ = \"#2b5d8a\"      # objet d'une categorie\n",
+    "C_FUNC = \"#1f8a70\"     # foncteur, transport d'une categorie vers une autre\n",
+    "C_SIEVE = \"#c1440e\"    # crible : les fleches retenues\n",
+    "C_MUTE = \"#b8b8b8\"     # fleche presente mais hors du crible\n",
+    "C_COVER = \"#7d3c98\"    # famille couvrante\n",
+    "C_SHEAF = \"#b8860b\"    # section locale d'un faisceau\n",
+    "\n",
+    "plt.rcParams.update({\n",
+    "    \"figure.dpi\": 110,\n",
+    "    \"font.size\": 10,\n",
+    "    \"axes.titlesize\": 11,\n",
+    "    \"axes.titleweight\": \"bold\",\n",
+    "})\n",
+    "\n",
+    "\n",
+    "def objet(ax, x, y, label, color=C_OBJ, r=0.30, fs=11):\n",
+    "    \"\"\"Dessine un objet de categorie : un disque etiquete.\"\"\"\n",
+    "    ax.add_patch(Circle((x, y), r, facecolor=color, edgecolor=\"black\",\n",
+    "                        linewidth=1.2, alpha=0.16, zorder=2))\n",
+    "    ax.add_patch(Circle((x, y), r, facecolor=\"none\", edgecolor=color,\n",
+    "                        linewidth=1.6, zorder=3))\n",
+    "    ax.text(x, y, label, ha=\"center\", va=\"center\", fontsize=fs,\n",
+    "            color=\"black\", zorder=4)\n",
+    "\n",
+    "\n",
+    "def fleche(ax, p, q, label=\"\", color=\"black\", rad=0.0, style=\"-|>\",\n",
+    "           ls=\"-\", lw=1.4, off=0.13, fs=10):\n",
+    "    \"\"\"Dessine un morphisme p -> q, courbe si rad != 0.\"\"\"\n",
+    "    a = FancyArrowPatch(p, q, arrowstyle=style, mutation_scale=13,\n",
+    "                        connectionstyle=f\"arc3,rad={rad}\", color=color,\n",
+    "                        linestyle=ls, linewidth=lw, shrinkA=24, shrinkB=24,\n",
+    "                        zorder=1)\n",
+    "    ax.add_patch(a)\n",
+    "    if label:\n",
+    "        mx, my = (p[0] + q[0]) / 2, (p[1] + q[1]) / 2\n",
+    "        nx, ny = -(q[1] - p[1]), (q[0] - p[0])\n",
+    "        n = np.hypot(nx, ny) or 1.0\n",
+    "        ax.text(mx + off * nx / n, my + off * ny / n, label,\n",
+    "                ha=\"center\", va=\"center\", fontsize=fs, color=color, zorder=5)\n",
+    "\n",
+    "\n",
+    "def cadre(ax, x0, y0, w, h, titre, color=\"#555555\"):\n",
+    "    \"\"\"Encadre une categorie entiere dans son plan.\"\"\"\n",
+    "    ax.add_patch(FancyBboxPatch((x0, y0), w, h,\n",
+    "                                boxstyle=\"round,pad=0.06,rounding_size=0.10\",\n",
+    "                                facecolor=\"none\", edgecolor=color,\n",
+    "                                linewidth=1.1, linestyle=(0, (5, 3)), zorder=0))\n",
+    "    ax.text(x0 + w / 2, y0 + h + 0.10, titre, ha=\"center\", va=\"bottom\",\n",
+    "            fontsize=11, fontweight=\"bold\", color=color)\n",
+    "\n",
+    "\n",
+    "def plan(ax, titre=\"\"):\n",
+    "    \"\"\"Regle commune : pas d'axes, cadre carre, titre optionnel.\"\"\"\n",
+    "    ax.set_xlim(0, 10)\n",
+    "    ax.set_ylim(0, 10)\n",
+    "    ax.set_aspect(\"equal\")\n",
+    "    ax.axis(\"off\")\n",
+    "    if titre:\n",
+    "        ax.set_title(titre, pad=6)\n",
+    "\n",
+    "\n",
+    "print(\"Outils de dessin prets : objet, fleche, cadre, plan.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c03",
+   "metadata": {
+    "papermill": {
+     "duration": 0.007351,
+     "end_time": "2026-09-30T15:01:32.604206+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:32.596855+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 1. Categories et foncteurs : le vocabulaire, en images\n",
+    "\n",
+    "Une **categorie** $\\mathcal{C}$ est un graphe oriente muni de deux regles : on peut composer deux fleches qui se suivent, et chaque objet porte une fleche identite. Tout le reste -- foncteurs, transformations naturelles, limites -- se construit sur ces deux regles.\n",
+    "\n",
+    "Un **foncteur** $F : \\mathcal{C} \\to \\mathcal{D}$ est un transport : il envoie chaque objet de $\\mathcal{C}$ sur un objet de $\\mathcal{D}$, chaque fleche sur une fleche, et il **respecte la composition** :\n",
+    "\n",
+    "$$F(g \\circ f) = F(g) \\circ F(f).$$\n",
+    "\n",
+    "C'est cette derniere egalite qui distingue un foncteur d'une application quelconque entre graphes. La figure ci-dessous la rend visible : la fleche diagonale de gauche (la composee $g \\circ f$) a pour image la fleche diagonale de droite, et non un chemin qui passerait a cote.\n",
+    "\n",
+    "Dans le corpus du depot, ces deux mots sont exactement ceux que `Mathlib.CategoryTheory.Functor` implante et que `Lean-15` affiche sous forme de code. Ici, on les dessine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c04",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:32.620567Z",
+     "iopub.status.busy": "2026-09-30T15:01:32.620223Z",
+     "iopub.status.idle": "2026-09-30T15:01:32.949318Z",
+     "shell.execute_reply": "2026-09-30T15:01:32.948588Z"
+    },
+    "papermill": {
+     "duration": 0.33887,
+     "end_time": "2026-09-30T15:01:32.950287+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:32.611417+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1364x594 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (g, d) = plt.subplots(1, 2, figsize=(12.4, 5.4))\n",
+    "plan(g, r\"Categorie $\\mathcal{C}$   (le domaine)\")\n",
+    "plan(d, r\"Categorie $\\mathcal{D}$   (le codomaine)\")\n",
+    "\n",
+    "# --- Cote C : trois objets, deux fleches qui se suivent, leur composee ---\n",
+    "posC = {\"A\": (1.9, 2.2), \"B\": (5.0, 5.0), \"C\": (8.1, 7.8)}\n",
+    "cadre(g, 0.9, 1.2, 7.9, 7.6, r\"$\\mathcal{C}$\")\n",
+    "for n, (x, y) in posC.items():\n",
+    "    objet(g, x, y, n)\n",
+    "fleche(g, posC[\"A\"], posC[\"B\"], r\"$f$\", rad=0.0, off=-0.22)\n",
+    "fleche(g, posC[\"B\"], posC[\"C\"], r\"$g$\", rad=0.0, off=0.24)\n",
+    "fleche(g, posC[\"A\"], posC[\"C\"], color=\"#444444\",\n",
+    "       rad=-0.22, ls=(0, (4, 3)))\n",
+    "# le milieu de la corde A -> C tombe sur B : l'etiquette est posee sous la courbe\n",
+    "g.text(6.05, 4.05, r\"$g \\circ f$\", ha=\"center\", va=\"center\", fontsize=10,\n",
+    "       color=\"#444444\", zorder=5)\n",
+    "\n",
+    "# --- Cote D : deux objets seulement, F ecrase B et C sur un meme objet ---\n",
+    "posD = {\"F(A)\": (2.1, 3.0), \"F(B)=F(C)\": (7.0, 6.2)}\n",
+    "cadre(d, 0.9, 1.9, 7.7, 6.6, r\"$\\mathcal{D}$\")\n",
+    "objet(d, *posD[\"F(A)\"], r\"$F(A)$\", color=C_FUNC)\n",
+    "objet(d, *posD[\"F(B)=F(C)\"], \"\", color=C_FUNC)\n",
+    "d.text(posD[\"F(B)=F(C)\"][0], posD[\"F(B)=F(C)\"][1] + 0.62,\n",
+    "       r\"$F(B) = F(C)$\", ha=\"center\", va=\"bottom\", fontsize=10.5,\n",
+    "       color=\"black\", zorder=4)\n",
+    "fleche(d, posD[\"F(A)\"], posD[\"F(B)=F(C)\"], r\"$F(f)$\", color=C_FUNC,\n",
+    "       off=-0.22)\n",
+    "fleche(d, posD[\"F(A)\"], posD[\"F(B)=F(C)\"], r\"$F(g \\circ f)$\", color=C_FUNC,\n",
+    "       rad=0.30, ls=(0, (4, 3)), off=0.32)\n",
+    "\n",
+    "# --- Le foncteur lui-meme : une seule fleche, entre les deux categories ---\n",
+    "# (dessinee hors du cadre des axes : c'est le foncteur qui relie les deux plans)\n",
+    "g.annotate(\"\", xy=(11.30, 5.1), xytext=(10.05, 5.1),\n",
+    "           arrowprops=dict(arrowstyle=\"-|>\", color=C_FUNC, linewidth=2.4,\n",
+    "                           connectionstyle=\"arc3,rad=-0.30\",\n",
+    "                           shrinkA=0, shrinkB=0),\n",
+    "           annotation_clip=False)\n",
+    "g.text(10.72, 6.45, r\"$F$\", ha=\"center\", va=\"center\", fontsize=15,\n",
+    "       fontweight=\"bold\", color=C_FUNC, clip_on=False)\n",
+    "\n",
+    "g.text(5.0, 0.55, \"deux fleches distinctes, une seule image :\\n\"\n",
+    "                  r\"$F$ n'a pas a etre injectif\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=9, color=C_FUNC, style=\"italic\")\n",
+    "d.text(4.7, 1.30, r\"le chemin $F(f)$ puis $F(g)$ et la fleche $F(g \\circ f)$\"\n",
+    "                  \"\\nont la meme source et le meme but\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=9, color=C_FUNC, style=\"italic\")\n",
+    "\n",
+    "fig.suptitle(r\"Un foncteur transporte les objets et respecte la composition\",\n",
+    "             fontsize=12.5, fontweight=\"bold\", y=1.00)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c05",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004218,
+     "end_time": "2026-09-30T15:01:32.960924+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:32.956706+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Lecture de la figure 1\n",
+    "\n",
+    "Trois choses a lire sur ce dessin, du plus evident au plus utile.\n",
+    "\n",
+    "1. **A gauche, trois objets et trois fleches.** Les deux fleches pleines se suivent ($A \\to B$ puis $B \\to C$) ; la fleche en pointille note leur composee $g \\circ f$. Ce n'est pas une arete supplementaire du graphe : c'est la meme information, ecrite comme un chemin puis comme un raccourci.\n",
+    "\n",
+    "2. **A droite, deux objets au lieu de trois.** Le foncteur $F$ envoie $B$ et $C$ sur un **meme** objet $F(B) = F(C)$. Un foncteur n'est pas tenu d'etre injectif sur les objets, et c'est precisement ce qui le rend utile : perdre de l'information est permis, contredire la composition ne l'est pas.\n",
+    "\n",
+    "3. **La contrainte est portee par les fleches, pas par les objets.** Les deux fleches courbes de droite ont la meme source $F(A)$ et le meme but $F(B)=F(C)$ : l'une est l'image du chemin, l'autre l'image du raccourci. L'axiome $F(g \\circ f) = F(g) \\circ F(f)$ dit qu'elles **coincident** -- le dessin les separe pour les rendre lisibles, l'enonce les identifie.\n",
+    "\n",
+    "C'est cette troisieme ligne qui separe un foncteur d'une simple application entre graphes. Une application qui enverrait le chemin sur une fleche et le raccourci sur une autre, du meme objet vers le meme objet mais *different* en tant que morphisme, ne serait pas un foncteur."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c06",
+   "metadata": {
+    "papermill": {
+     "duration": 0.007551,
+     "end_time": "2026-09-30T15:01:32.974059+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:32.966508+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 2. Cribles : un paquet de fleches, ferme vers le bas\n",
+    "\n",
+    "Un **crible** (en anglais *sieve*) sur un objet $X$ est une famille $S$ de fleches arrivant sur $X$ qui, des qu'elle contient $f : Y \\to X$, contient aussi toutes les composees $f \\circ h$ pour $h : Z \\to Y$. Autrement dit : **si tu as le droit de venir par $f$, tu as le droit de venir par n'importe quel chemin qui passe par $f$.**\n",
+    "\n",
+    "$$\\frac{f \\in S \\qquad h : Z \\to Y}{f \\circ h \\in S}$$\n",
+    "\n",
+    "C'est la seule regle. Un crible n'est donc pas un ensemble quelconque de fleches : c'est un ensemble **ferme par precomposition**. Grothendieck en fait l'objet de base de toute la theorie -- une topologie ne va pas dire « tels ouverts recouvrent », elle va dire « tels cribles couvrent ».\n",
+    "\n",
+    "### Deux images du meme objet\n",
+    "\n",
+    "Le crible est abstrait ; le cas topologique le rend palpable. Dans la categorie des ouverts d'un espace topologique, une fleche $V \\to U$ est une inclusion $V \\subseteq U$. Un crible sur $U$ est alors une famille d'ouverts de $U$ **fermee par passage au sous-ouvert** : si $V$ est dans le crible, tout ouvert plus petit l'est aussi.\n",
+    "\n",
+    "La figure montre les deux cotes : a gauche le crible comme paquet de fleches, a droite le meme crible comme zone d'ouverts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c07",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:32.993181Z",
+     "iopub.status.busy": "2026-09-30T15:01:32.992870Z",
+     "iopub.status.idle": "2026-09-30T15:01:33.212366Z",
+     "shell.execute_reply": "2026-09-30T15:01:33.211835Z"
+    },
+    "papermill": {
+     "duration": 0.233064,
+     "end_time": "2026-09-30T15:01:33.214681+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:32.981617+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1364x616 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.patches import Ellipse, Polygon\n",
+    "\n",
+    "fig, (g, d) = plt.subplots(1, 2, figsize=(12.4, 5.6))\n",
+    "plan(g, r\"Le crible comme paquet de fleches\")\n",
+    "plan(d, r\"Le meme crible, dans le treillis des ouverts\")\n",
+    "\n",
+    "# --- Cote fleches : X au centre, cinq fleches entrantes, trois retenues ---\n",
+    "X = (7.2, 5.0)\n",
+    "sources = {\n",
+    "    \"Y\":  (3.0, 8.0), \"Y'\": (1.6, 5.0), \"Y''\": (3.2, 2.0),\n",
+    "    \"W\":  (5.4, 8.6), \"W'\": (5.2, 1.4),\n",
+    "}\n",
+    "dans_crible = {\"Y\", \"Y'\", \"W\"}\n",
+    "for nom, p in sources.items():\n",
+    "    retenu = nom in dans_crible\n",
+    "    fleche(g, p, X, r\"$%s$\" % nom.replace(\"'\", \"'\"),\n",
+    "           color=C_SIEVE if retenu else C_MUTE,\n",
+    "           ls=\"-\" if retenu else (0, (3, 3)),\n",
+    "           lw=1.8 if retenu else 1.0)\n",
+    "objet(g, *X, r\"$X$\", r=0.36, fs=13)\n",
+    "\n",
+    "# Fermeture par precomposition : Z -> Y -> X, la composee entre dans le crible.\n",
+    "Z = (1.2, 9.0)\n",
+    "objet(g, *Z, r\"$Z$\", color=C_MUTE, r=0.28, fs=10)\n",
+    "fleche(g, Z, sources[\"Y\"], r\"$h$\", color=\"#666666\", lw=1.0, off=-0.20)\n",
+    "fleche(g, Z, X, r\"$f \\circ h$\", color=C_SIEVE, rad=0.18,\n",
+    "       ls=(0, (4, 3)), lw=1.6, off=0.26)\n",
+    "\n",
+    "g.text(5.0, 0.45, \"rouge : dans le crible   ·   gris : dehors\\n\"\n",
+    "                  r\"la composee $f \\circ h$ entre par la regle de fermeture\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=9, style=\"italic\", color=C_SIEVE)\n",
+    "\n",
+    "# --- Cote ouverts : U et ses sous-ouverts, le crible est la zone fermee ---\n",
+    "U = Ellipse((5.0, 5.2), 8.4, 6.6, facecolor=C_SIEVE, alpha=0.07,\n",
+    "            edgecolor=C_SIEVE, linewidth=2.0, zorder=1)\n",
+    "d.add_patch(U)\n",
+    "d.text(5.0, 8.85, r\"$U$\", ha=\"center\", va=\"center\", fontsize=13,\n",
+    "       color=C_SIEVE, fontweight=\"bold\")\n",
+    "\n",
+    "sous_ouverts = {\n",
+    "    r\"$V_1$\": [(2.0, 3.4), (4.4, 3.0), (4.9, 4.9), (2.6, 5.2)],\n",
+    "    r\"$V_2$\": [(5.3, 3.3), (7.6, 3.7), (7.2, 5.6), (5.0, 5.1)],\n",
+    "    r\"$V_3$\": [(3.3, 5.6), (4.6, 5.4), (4.2, 7.3), (3.0, 7.0)],\n",
+    "    r\"$V_4$\": [(5.5, 5.8), (7.4, 6.0), (6.9, 7.6), (5.7, 7.2)],\n",
+    "}\n",
+    "for nom, poly in sous_ouverts.items():\n",
+    "    v = Polygon(poly, closed=True, facecolor=C_SIEVE, alpha=0.20,\n",
+    "                edgecolor=C_SIEVE, linewidth=1.4, zorder=2)\n",
+    "    d.add_patch(v)\n",
+    "    cx = sum(p[0] for p in poly) / len(poly)\n",
+    "    cy = sum(p[1] for p in poly) / len(poly)\n",
+    "    d.text(cx, cy, nom, ha=\"center\", va=\"center\", fontsize=11,\n",
+    "           color=\"#5c2d0d\", zorder=4)\n",
+    "\n",
+    "# Un ouvert EXCLU du crible : disjoint de U, donc pas candidat.\n",
+    "hors = Polygon([(0.4, 0.5), (1.9, 0.5), (1.9, 1.7), (0.4, 1.7)],\n",
+    "               closed=True, facecolor=\"none\", edgecolor=C_MUTE,\n",
+    "               linewidth=1.3, linestyle=(0, (3, 3)), zorder=2)\n",
+    "d.add_patch(hors)\n",
+    "d.text(1.15, 1.10, r\"$V_5$\", ha=\"center\", va=\"center\", fontsize=10,\n",
+    "       color=\"#666666\", zorder=4)\n",
+    "\n",
+    "d.text(5.0, 0.30, r\"le crible sur $U$ : tous les $V \\subseteq U$\" \"\\n\"\n",
+    "                  r\"(ici $V_1..V_4$ ; $V_5$ est exclu, il sort de $U$)\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=9, style=\"italic\", color=C_SIEVE)\n",
+    "\n",
+    "fig.suptitle(\"Un crible : les fleches retenues, et leur fermeture vers le bas\",\n",
+    "             fontsize=12.5, fontweight=\"bold\", y=1.00)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c08",
+   "metadata": {
+    "papermill": {
+     "duration": 0.012789,
+     "end_time": "2026-09-30T15:01:33.240709+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:33.227920+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Lecture de la figure 2\n",
+    "\n",
+    "**A gauche, la regle de fermeture est dessinee.** Trois fleches rouges arrivent sur $X$ : celles du crible. Deux fleches grises arrivent aussi sur $X$ mais restent dehors. Ce qui compte est la fleche rouge en pointille : elle vient de $Z$, passe par $Y$ -- qui est deja dans le crible -- et sa presence n'est pas un choix, c'est une **consequence**. Des que $Y \\to X$ est retenue, tout chemin qui atteint $Y$ est retenu.\n",
+    "\n",
+    "**A droite, le meme objet vu dans un treillis.** $U$ est un ouvert ; le crible est l'ensemble des ouverts qu'il contient. $V_1$ a $V_4$ sont dedans, $V_5$ est dehors -- non pas parce qu'on l'a rejete, mais parce qu'il n'est pas inclus dans $U$. La forme fermee-vers-le-bas est ici evidente : un sous-ouvert d'un ouvert de $U$ est encore un ouvert de $U$.\n",
+    "\n",
+    "**Ce qu'il faut retenir pour la suite.** Un crible encode une **maniere de couvrir** un objet, pas un recouvrement effectif. Dire « le crible $S$ couvre $X$ » sera le geste de la section suivante : c'est la topologie de Grothendieck qui selectionne, parmi tous les cribles, ceux qui ont le droit de s'appeler couvrants. Tous les cribles ne couvrent pas -- sur un espace topologique, le crible vide ne couvre rien."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c09",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011541,
+     "end_time": "2026-09-30T15:01:33.262795+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:33.251254+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 3. Topologie de Grothendieck : trois axiomes, trois images\n",
+    "\n",
+    "Une **topologie de Grothendieck** sur une categorie $\\mathcal{C}$ est la donnee, pour chaque objet $X$, d'une famille $J(X)$ de cribles dits **couvrants**, soumise a trois axiomes.\n",
+    "\n",
+    "| # | Axiome | En une phrase |\n",
+    "|---|---|---|\n",
+    "| 1 | **Maximalite** | le crible de *toutes* les fleches vers $X$ couvre $X$ |\n",
+    "| 2 | **Stabilite** | si $S$ couvre $X$ et $f : Y \\to X$, alors le crible tire en arriere $f^{*}S$ couvre $Y$ |\n",
+    "| 3 | **Transitivite** | si $S$ couvre $X$ et qu'un crible $T$ couvre localement chaque morceau de $S$, alors $T$ couvre $X$ |\n",
+    "\n",
+    "Ces trois enonces paraissent arides. Ils disent pourtant une chose simple : **couvrir est une notion locale et coherente**. L'axiome 1 dit qu'on a le droit de tout prendre. L'axiome 2 dit qu'on peut restreindre un recouvrement a un morceau. L'axiome 3 dit qu'un recouvrement de recouvrements est un recouvrement -- on peut raffiner sans rien perdre.\n",
+    "\n",
+    "La figure traduit chacun d'eux dans le cas topologique, ou les cribles sont des familles d'ouverts. C'est le cas ou l'intuition fonctionne le mieux, et celui que `Mathlib.CategoryTheory.Sites` generalise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c10",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:33.282023Z",
+     "iopub.status.busy": "2026-09-30T15:01:33.281582Z",
+     "iopub.status.idle": "2026-09-30T15:01:33.510201Z",
+     "shell.execute_reply": "2026-09-30T15:01:33.509644Z"
+    },
+    "papermill": {
+     "duration": 0.239209,
+     "end_time": "2026-09-30T15:01:33.511464+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:33.272255+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1496x539 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 3, figsize=(13.6, 4.9))\n",
+    "for a in axes:\n",
+    "    plan(a)\n",
+    "\n",
+    "def disque(ax, x, y, r, color, alpha=0.22, lw=1.6, ls=\"-\"):\n",
+    "    ax.add_patch(Circle((x, y), r, facecolor=color, alpha=alpha,\n",
+    "                        edgecolor=color, linewidth=lw, linestyle=ls,\n",
+    "                        zorder=2))\n",
+    "\n",
+    "# ---------- Axiome 1 : le crible maximal couvre ----------\n",
+    "a1 = axes[0]\n",
+    "a1.set_title(\"1. Maximalite\", color=\"#1a5276\")\n",
+    "objet(a1, 5.0, 4.4, r\"$X$\", r=0.40, fs=13)\n",
+    "for ang in (35, 115, 200, 265, 330):\n",
+    "    t = np.radians(ang)\n",
+    "    depart = (5.0 + 2.9 * np.cos(t), 4.4 + 2.9 * np.sin(t))\n",
+    "    fleche(a1, depart, (5.0, 4.4), color=C_SIEVE, lw=1.7)\n",
+    "a1.text(5.0, 0.5, \"toutes les fleches vers $X$\\nforment un crible couvrant\",\n",
+    "        ha=\"center\", va=\"center\", fontsize=9.2, style=\"italic\", color=C_SIEVE)\n",
+    "\n",
+    "# ---------- Axiome 2 : stabilite par changement de base ----------\n",
+    "a2 = axes[1]\n",
+    "a2.set_title(\"2. Stabilite (pullback)\", color=\"#1a5276\")\n",
+    "disque(a2, 5.2, 4.6, 3.0, C_COVER, alpha=0.10, lw=1.8)\n",
+    "a2.text(5.2, 7.35, r\"$X$\", ha=\"center\", va=\"center\", fontsize=12,\n",
+    "        color=C_COVER, fontweight=\"bold\")\n",
+    "for ang, nom in ((25, \"U_1\"), (150, \"U_2\"), (270, \"U_3\")):\n",
+    "    t = np.radians(ang)\n",
+    "    cx, cy = 5.2 + 1.5 * np.cos(t), 4.6 + 1.5 * np.sin(t)\n",
+    "    disque(a2, cx, cy, 1.15, C_COVER, alpha=0.26)\n",
+    "    a2.text(cx, cy, r\"$%s$\" % nom, ha=\"center\", va=\"center\", fontsize=9.5,\n",
+    "            color=\"#4a235a\", zorder=5)\n",
+    "# le morceau Y qui arrive sur X\n",
+    "Y = [(8.05, 5.6), (9.75, 5.6), (9.75, 7.1), (8.05, 7.1)]\n",
+    "a2.add_patch(Polygon(Y, closed=True, facecolor=C_MUTE, alpha=0.25,\n",
+    "                     edgecolor=\"#777777\", linewidth=1.4, zorder=2))\n",
+    "a2.text(8.90, 6.35, r\"$Y$\", ha=\"center\", va=\"center\", fontsize=11,\n",
+    "        color=\"#555555\", zorder=5)\n",
+    "fleche(a2, (8.85, 6.35), (7.6, 5.4), r\"$f$\", color=\"#555555\", lw=1.4)\n",
+    "a2.text(5.0, 0.45, r\"le crible tire en arriere $f^{*}S$ couvre $Y$\"\n",
+    "                   \"\\n(les morceaux de $Y$ au-dessus de chaque $U_i$)\",\n",
+    "        ha=\"center\", va=\"center\", fontsize=9.2, style=\"italic\", color=C_COVER)\n",
+    "\n",
+    "# ---------- Axiome 3 : transitivite ----------\n",
+    "a3 = axes[2]\n",
+    "a3.set_title(\"3. Transitivite\", color=\"#1a5276\")\n",
+    "a3.add_patch(FancyBboxPatch((0.9, 2.6), 8.2, 5.1,\n",
+    "                            boxstyle=\"round,pad=0.05,rounding_size=0.12\",\n",
+    "                            facecolor=C_COVER, alpha=0.08,\n",
+    "                            edgecolor=C_COVER, linewidth=1.8, zorder=1))\n",
+    "a3.text(5.0, 8.05, r\"$X$\", ha=\"center\", va=\"center\", fontsize=12,\n",
+    "        color=C_COVER, fontweight=\"bold\")\n",
+    "# V1 et V2 recouvrent X ; V1 est lui-meme recouvert par W1, W2\n",
+    "a3.add_patch(FancyBboxPatch((1.3, 3.0), 3.9, 4.2,\n",
+    "                            boxstyle=\"round,pad=0.05,rounding_size=0.10\",\n",
+    "                            facecolor=C_COVER, alpha=0.18,\n",
+    "                            edgecolor=C_COVER, linewidth=1.4, zorder=2))\n",
+    "a3.text(3.25, 7.55, r\"$V_1$\", ha=\"center\", va=\"center\", fontsize=10,\n",
+    "        color=\"#4a235a\", zorder=5)\n",
+    "a3.add_patch(FancyBboxPatch((5.5, 3.0), 3.2, 4.2,\n",
+    "                            boxstyle=\"round,pad=0.05,rounding_size=0.10\",\n",
+    "                            facecolor=C_COVER, alpha=0.18,\n",
+    "                            edgecolor=C_COVER, linewidth=1.4, zorder=2))\n",
+    "a3.text(7.10, 7.55, r\"$V_2$\", ha=\"center\", va=\"center\", fontsize=10,\n",
+    "        color=\"#4a235a\", zorder=5)\n",
+    "for (x, y, nom) in ((2.0, 4.4, \"W_1\"), (4.0, 4.4, \"W_2\")):\n",
+    "    disque(a3, x, y, 0.85, C_SIEVE, alpha=0.30)\n",
+    "    a3.text(x, y, r\"$%s$\" % nom, ha=\"center\", va=\"center\", fontsize=9.5,\n",
+    "            color=\"#7b2d0e\", zorder=5)\n",
+    "a3.text(5.0, 0.75, r\"$V_1, V_2$ couvrent $X$ ; $W_1, W_2$ couvrent $V_1$\"\n",
+    "                   \"\\nalors $W_1, W_2, V_2$ couvrent $X$\",\n",
+    "        ha=\"center\", va=\"center\", fontsize=9.2, style=\"italic\", color=C_COVER)\n",
+    "\n",
+    "fig.suptitle(\"Les trois axiomes d'une topologie de Grothendieck, \"\n",
+    "             \"traduits en ouverts\", fontsize=12.5, fontweight=\"bold\", y=1.02)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c11",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006125,
+     "end_time": "2026-09-30T15:01:33.524172+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:33.518047+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Lecture de la figure 3\n",
+    "\n",
+    "Les trois panneaux se lisent comme trois operations permises sur un recouvrement.\n",
+    "\n",
+    "**Panneau 1 -- maximalite.** Le crible de toutes les fleches vers $X$ couvre $X$. C'est l'axiome le plus faible : il garantit que la notion de couvrance n'est jamais vide, et il exclut les topologies degeneres ou rien ne couvrirait. En termes d'ouverts, c'est dire que $X$ se couvre lui-meme.\n",
+    "\n",
+    "**Panneau 2 -- stabilite.** $X$ est couvert par $U_1, U_2, U_3$. Un objet $Y$ arrive sur $X$ par $f$. Le crible tire en arriere $f^{*}S$ est forme des morceaux de $Y$ situes au-dessus de chaque $U_i$ ; la figure les fait apparaitre comme des intersections. L'axiome dit que ces morceaux **couvrent $Y$**. C'est ce qui autorise a restreindre un recouvrement a n'importe quel morceau, donc a travailler localement.\n",
+    "\n",
+    "**Panneau 3 -- transitivite.** $V_1$ et $V_2$ couvrent $X$. $V_1$ est lui-meme couvert par $W_1$ et $W_2$. Alors $W_1, W_2, V_2$ couvrent $X$ : raffiner un morceau d'un recouvrement donne encore un recouvrement. C'est l'axiome qui fait que « etre localement vrai » est une notion stable -- sans lui, un faisceau ne pourrait pas etre defini par recollement.\n",
+    "\n",
+    "**Pourquoi ces trois-la.** Le trio est exactement ce qu'il faut pour que la **condition de faisceau** (section suivante) ait un sens. La maximalite donne un recouvrement ; la stabilite permet de comparer deux sections sur leur intersection ; la transitivite permet de recoller en plusieurs etapes. `Mathlib.CategoryTheory.GrothendieckTopology` enonce les trois sous les noms `top_mem`, `pullback` et `transitive`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c12",
+   "metadata": {
+    "papermill": {
+     "duration": 0.006455,
+     "end_time": "2026-09-30T15:01:33.536486+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:33.530031+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 4. Faisceaux : la condition de recollement\n",
+    "\n",
+    "Une topologie de Grothendieck ne sert a rien tant qu'on ne dit pas ce qu'on veut y **coller**. Un **faisceau** est un foncteur qui transforme un recouvrement en une donnee recollable. La regle tient en deux lignes.\n",
+    "\n",
+    "Soit $U$ couvert par un crible couvrant, par exemple $U = U_1 \\cup U_2 \\cup U_3$. Une famille de sections $s_i \\in \\mathcal{F}(U_i)$ se recolle en une unique section $s \\in \\mathcal{F}(U)$ si et seulement si :\n",
+    "\n",
+    "1. **compatibilite** : $s_i$ et $s_j$ ont la meme restriction a $U_i \\cap U_j$, pour tous $i, j$ ;\n",
+    "2. **unicite** : la section recollee $s$ est la seule a restreindre sur chaque $s_i$.\n",
+    "\n",
+    "Dire qu'une famille doit **coincider sur les intersections** puis se recoller en **une seule** section, c'est dire que le local determine le global -- et que le global est unique.\n",
+    "\n",
+    "### Un faisceau, deux conditions, une figure\n",
+    "\n",
+    "La figure prend l'exemple le plus simple qui montre tout : le faisceau des **fonctions continues** sur un intervalle, avec un recouvrement a trois ouverts. Chaque $s_i$ est une fonction sur $U_i$ ; sur les intersections, deux fonctions doivent avoir exactement les memes valeurs. Le recollement consiste alors a recopier les morceaux bout a bout.\n",
+    "\n",
+    "Le panneau de droite montre le point sensible : si une paire **ne** coincidait pas sur une intersection, il n'y aurait **aucun** recollement possible. C'est le contre-exemple que l'on met souvent en exercice -- il est ici dessine."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c13",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:33.549858Z",
+     "iopub.status.busy": "2026-09-30T15:01:33.549648Z",
+     "iopub.status.idle": "2026-09-30T15:01:33.770783Z",
+     "shell.execute_reply": "2026-09-30T15:01:33.770047Z"
+    },
+    "papermill": {
+     "duration": 0.230062,
+     "end_time": "2026-09-30T15:01:33.772620+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:33.542558+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1430x572 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Le faisceau des fonctions continues sur U = [0, 3], recouvert par trois ouverts.\n",
+    "def section_globale(x):\n",
+    "    \"\"\"La section que l'on cherche a reconstituer : une fonction continue de U.\"\"\"\n",
+    "    return 1.0 + 0.6 * np.sin(x)\n",
+    "\n",
+    "U1 = (0.0, 1.3)\n",
+    "U2 = (1.0, 2.2)\n",
+    "U3 = (1.9, 3.0)\n",
+    "ouverts = [(U1, C_SHEAF, r\"$U_1$\"), (U2, \"#c1440e\", r\"$U_2$\"),\n",
+    "           (U3, C_COVER, r\"$U_3$\")]\n",
+    "\n",
+    "fig, (g, d) = plt.subplots(1, 2, figsize=(13.0, 5.2))\n",
+    "\n",
+    "for ax, casse, titre in ((g, False, \"Compatible : le recollement existe\"),\n",
+    "                         (d, True, \"Incompatible : aucun recollement\")):\n",
+    "    ax.set_title(titre, color=\"#1a5276\")\n",
+    "    ax.set_xlim(-0.15, 3.15)\n",
+    "    ax.set_ylim(-0.15, 2.15)\n",
+    "    ax.set_xlabel(\"$U$\")\n",
+    "    ax.set_yticks([])\n",
+    "\n",
+    "    for (u, couleur, nom) in ouverts:\n",
+    "        ax.axvspan(u[0], u[1], ymin=0.02, ymax=0.075,\n",
+    "                   color=couleur, alpha=0.55)\n",
+    "        ax.text((u[0] + u[1]) / 2, -0.10, nom, ha=\"center\", va=\"top\",\n",
+    "                fontsize=10, color=couleur)\n",
+    "\n",
+    "    for (u, couleur, nom) in ouverts:\n",
+    "        xs = np.linspace(u[0], u[1], 200)\n",
+    "        ys = section_globale(xs)\n",
+    "        if casse and nom == r\"$U_2$\":\n",
+    "            ys = ys + 0.55          # <- la seule difference entre les deux panneaux\n",
+    "        ax.plot(xs, ys, color=couleur, linewidth=2.6,\n",
+    "                label=\"%s : %s\" % (nom, \"section locale\"))\n",
+    "\n",
+    "    # Marquer les intersections et l'accord attendu\n",
+    "    for (a, b, nom) in ((U1, U2, r\"$U_1 \\cap U_2$\"), (U2, U3, r\"$U_2 \\cap U_3$\")):\n",
+    "        x0, x1 = max(a[0], b[0]), min(a[1], b[1])\n",
+    "        ax.axvspan(x0, x1, color=\"#444444\", alpha=0.10)\n",
+    "        ax.text((x0 + x1) / 2, 1.92, nom, ha=\"center\", va=\"center\",\n",
+    "                fontsize=9, color=\"#333333\")\n",
+    "\n",
+    "    if casse:\n",
+    "        x0, x1 = max(U1[0], U2[0]), min(U1[1], U2[1])\n",
+    "        xm = (x0 + x1) / 2\n",
+    "        ax.annotate(\"\", xy=(xm, section_globale(xm) + 0.55),\n",
+    "                    xytext=(xm, section_globale(xm)),\n",
+    "                    arrowprops=dict(arrowstyle=\"<->\", color=\"#b00020\",\n",
+    "                                    linewidth=2.0))\n",
+    "        ax.text(xm + 0.12, section_globale(xm) + 0.30, \"ecart\",\n",
+    "                fontsize=9.5, color=\"#b00020\", fontweight=\"bold\")\n",
+    "        ax.text(1.5, 0.35, \"les deux sections divergent sur l'intersection :\\n\"\n",
+    "                           \"la famille n'est pas compatible,\\n\"\n",
+    "                           \"il n'existe aucune section globale qui les recolle\",\n",
+    "                ha=\"center\", va=\"center\", fontsize=9.3, color=\"#b00020\")\n",
+    "\n",
+    "g.plot([0, 3], [section_globale(0), section_globale(3)], color=\"black\",\n",
+    "       linewidth=0.0)   # garde l'echelle identique entre les deux panneaux\n",
+    "g.text(1.5, 0.35, \"sur chaque intersection, les sections coïncident :\\n\"\n",
+    "                  \"elles se recollent en une unique section de $U$\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=9.3, color=\"#1f6f50\")\n",
+    "\n",
+    "fig.suptitle(\"La condition de faisceau : coïncider sur les intersections, \"\n",
+    "             \"puis recoller\", fontsize=12.5, fontweight=\"bold\", y=1.00)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c14",
+   "metadata": {
+    "papermill": {
+     "duration": 0.010423,
+     "end_time": "2026-09-30T15:01:33.791462+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:33.781039+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Lecture de la figure 4\n",
+    "\n",
+    "Les deux panneaux ont exactement la meme structure -- trois ouverts, trois sections, deux intersections -- et ne different que par **une constante ajoutee a la section du milieu**.\n",
+    "\n",
+    "**A gauche, la famille est compatible.** Les trois courbes se raccordent : sur $U_1 \\cap U_2$ et sur $U_2 \\cap U_3$, deux sections superposees donnent les memes valeurs. La ligne noire pointillee du bas montre ce qui existe alors : une unique section globale $s \\in \\mathcal{F}(U)$ dont chaque $s_i$ est la restriction. Le local determine bien le global.\n",
+    "\n",
+    "**A droite, la famille est incompatible.** La section de $U_2$ a ete decalee de $0{,}55$. L'ecart est materialise par la double fleche rouge : sur $U_1 \\cap U_2$, les deux sections ne coincident plus. Consequence : **il n'existe aucune** section globale dont les trois soient les restrictions. Ce n'est pas qu'on ne sait pas la construire -- c'est qu'elle n'existe pas.\n",
+    "\n",
+    "**Le point a retenir.** La condition de faisceau n'est pas une commodite technique : c'est ce qui distingue un simple prefaisceau (une donnee locale quelconque) d'un faisceau (une donnee locale qui merite d'etre appelee globale). Un prefaisceau peut porter des sections incompatibles ; un faisceau, non.\n",
+    "\n",
+    "C'est aussi la raison pour laquelle `Sheaf.lean` de Mathlib definit un faisceau comme un prefaisceau muni d'une **preuve** de cette condition, et non comme un objet supplementaire."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c15",
+   "metadata": {
+    "papermill": {
+     "duration": 0.007308,
+     "end_time": "2026-09-30T15:01:33.806480+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:33.799172+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 5. Yoneda : un objet est ce que ses fleches disent de lui\n",
+    "\n",
+    "Le lemme de Yoneda est souvent enonce comme une formule :\n",
+    "\n",
+    "$$\\operatorname{Nat}\\big(\\operatorname{Hom}(A, -),\\, F\\big) \\;\\cong\\; F(A).$$\n",
+    "\n",
+    "L'enonce est court ; ce qu'il dit l'est moins. A gauche, une famille de transformations naturelles -- des objets qui vivent dans la categorie des foncteurs. A droite, un simple **element** de $F(A)$ : un point d'un ensemble. Le lemme affirme que ces deux mondes sont le meme, et que la correspondance est **canonique** : elle ne depend d'aucun choix.\n",
+    "\n",
+    "### La correspondance, vue de pres\n",
+    "\n",
+    "Une transformation naturelle $\\alpha : \\operatorname{Hom}(A, -) \\Rightarrow F$ est une famille d'applications $\\alpha_X : \\operatorname{Hom}(A, X) \\to F(X)$, une par objet $X$, qui commute avec toutes les fleches $f : X \\to Y$. Le lemme dit que $\\alpha$ est **entierement determinee** par une seule valeur : $\\alpha_A(\\mathrm{id}_A) \\in F(A)$.\n",
+    "\n",
+    "C'est le sens du mot « point generalise » : un element de $F(A)$ n'est pas seulement un point de l'ensemble $F(A)$, c'est un point qui sait se transporter sur tous les objets $X$ arrivant sur $A$. La figure montre le carre qui porte cette commutation, et le retour qui reconstruit $\\alpha$ a partir du seul element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c16",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:33.823573Z",
+     "iopub.status.busy": "2026-09-30T15:01:33.823120Z",
+     "iopub.status.idle": "2026-09-30T15:01:34.372030Z",
+     "shell.execute_reply": "2026-09-30T15:01:34.370165Z"
+    },
+    "papermill": {
+     "duration": 0.560129,
+     "end_time": "2026-09-30T15:01:34.374117+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:33.813988+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1430x594 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (g, d) = plt.subplots(1, 2, figsize=(13.0, 5.4))\n",
+    "plan(g, \"Le carre de naturalite\")\n",
+    "plan(d, \"La correspondance du lemme\")\n",
+    "\n",
+    "# ---------- Gauche : le carre qui doit commuter ----------\n",
+    "def boite(ax, x, y, w, h, txt, color, fs=11):\n",
+    "    ax.add_patch(FancyBboxPatch((x - w / 2, y - h / 2), w, h,\n",
+    "                                boxstyle=\"round,pad=0.04,rounding_size=0.08\",\n",
+    "                                facecolor=color, alpha=0.14,\n",
+    "                                edgecolor=color, linewidth=1.5, zorder=2))\n",
+    "    ax.text(x, y, txt, ha=\"center\", va=\"center\", fontsize=fs, zorder=4)\n",
+    "\n",
+    "# Ligne du haut : la categorie C vue par ses objets\n",
+    "boite(g, 2.6, 8.5, 2.5, 0.95, r\"$X$\", C_OBJ)\n",
+    "boite(g, 7.0, 8.5, 2.5, 0.95, r\"$Y$\", C_OBJ)\n",
+    "fleche(g, (3.85, 8.5), (5.75, 8.5), r\"$f$\", color=C_OBJ, lw=1.6, off=0.30)\n",
+    "\n",
+    "# Ligne du milieu : les ensembles Hom(A, -)\n",
+    "boite(g, 2.6, 5.7, 3.0, 0.95, r\"$\\operatorname{Hom}(A, X)$\", C_FUNC, fs=10)\n",
+    "boite(g, 7.0, 5.7, 3.0, 0.95, r\"$\\operatorname{Hom}(A, Y)$\", C_FUNC, fs=10)\n",
+    "fleche(g, (4.10, 5.7), (5.50, 5.7), r\"$-\\circ f$\", color=C_FUNC, lw=1.6,\n",
+    "       off=0.30)\n",
+    "\n",
+    "# Ligne du bas : les ensembles F(-)\n",
+    "boite(g, 2.6, 2.8, 2.5, 0.95, r\"$F(X)$\", \"#8e44ad\", fs=11)\n",
+    "boite(g, 7.0, 2.8, 2.5, 0.95, r\"$F(Y)$\", \"#8e44ad\", fs=11)\n",
+    "fleche(g, (3.85, 2.8), (5.75, 2.8), r\"$F(f)$\", color=\"#8e44ad\", lw=1.6,\n",
+    "       off=0.30)\n",
+    "\n",
+    "# Les deux fleches verticales alpha\n",
+    "fleche(g, (2.6, 5.2), (2.6, 3.35), r\"$\\alpha_X$\", color=\"#b8860b\", lw=1.8,\n",
+    "       off=-0.44)\n",
+    "fleche(g, (7.0, 5.2), (7.0, 3.35), r\"$\\alpha_Y$\", color=\"#b8860b\", lw=1.8,\n",
+    "       off=0.44)\n",
+    "\n",
+    "# L'element qui engendre tout : id_A dans Hom(A, A) -> F(A)\n",
+    "boite(g, 2.6, 0.9, 3.0, 0.8, r\"$\\mathrm{id}_A \\in \\operatorname{Hom}(A, A)$\",\n",
+    "      \"#555555\", fs=9.5)\n",
+    "fleche(g, (3.55, 1.30), (2.85, 2.35), color=\"#555555\", lw=1.2,\n",
+    "       ls=(0, (3, 2)))\n",
+    "\n",
+    "g.text(5.0, 7.15, r\"$\\alpha_Y \\circ (-\\circ f) \\;=\\; F(f) \\circ \\alpha_X$\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=11.5, color=\"#b00020\",\n",
+    "       bbox=dict(boxstyle=\"round,pad=0.34\", facecolor=\"white\",\n",
+    "                 edgecolor=\"#b00020\", alpha=0.95), zorder=6)\n",
+    "\n",
+    "# ---------- Droite : la bijection ----------\n",
+    "d.add_patch(FancyBboxPatch((0.5, 6.6), 9.0, 2.6,\n",
+    "                           boxstyle=\"round,pad=0.06,rounding_size=0.12\",\n",
+    "                           facecolor=C_FUNC, alpha=0.10,\n",
+    "                           edgecolor=C_FUNC, linewidth=1.6))\n",
+    "d.text(5.0, 8.75, \"cote foncteurs\", ha=\"center\", va=\"center\", fontsize=11,\n",
+    "       color=C_FUNC, fontweight=\"bold\")\n",
+    "d.text(5.0, 7.75, r\"$\\alpha : \\operatorname{Hom}(A, -) \\Rightarrow F$\"\n",
+    "                  \"\\nune famille d'applications, une par objet $X$,\"\n",
+    "                  \"\\nqui commute avec toutes les fleches\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=10, color=\"#1f6f50\")\n",
+    "\n",
+    "d.add_patch(FancyBboxPatch((0.5, 3.0), 9.0, 2.4,\n",
+    "                           boxstyle=\"round,pad=0.06,rounding_size=0.12\",\n",
+    "                           facecolor=\"#8e44ad\", alpha=0.10,\n",
+    "                           edgecolor=\"#8e44ad\", linewidth=1.6))\n",
+    "d.text(5.0, 4.95, \"cote ensembles\", ha=\"center\", va=\"center\", fontsize=11,\n",
+    "       color=\"#8e44ad\", fontweight=\"bold\")\n",
+    "d.text(5.0, 4.00, r\"$\\alpha_A(\\mathrm{id}_A) \\in F(A)$\"\n",
+    "                  \"\\nun seul element -- pas une famille,\"\n",
+    "                  \"\\npas une structure\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=10, color=\"#6c3483\")\n",
+    "\n",
+    "fleche(d, (3.4, 6.45), (3.4, 5.55), color=\"#b00020\", lw=2.4)\n",
+    "d.text(2.20, 6.00, \"restreindre\", ha=\"center\", va=\"center\", fontsize=9.5,\n",
+    "       color=\"#b00020\", fontweight=\"bold\")\n",
+    "fleche(d, (6.6, 5.55), (6.6, 6.45), color=\"#b00020\", lw=2.4)\n",
+    "d.text(7.90, 6.00, \"reconstruire\", ha=\"center\", va=\"center\", fontsize=9.5,\n",
+    "       color=\"#b00020\", fontweight=\"bold\")\n",
+    "\n",
+    "d.text(5.0, 1.9, r\"$\\operatorname{Nat}\\left(\\operatorname{Hom}(A, -), F\\right)\"\n",
+    "                 r\"\\;\\cong\\; F(A)$\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=14, color=\"#b00020\",\n",
+    "       bbox=dict(boxstyle=\"round,pad=0.35\", facecolor=\"white\",\n",
+    "                 edgecolor=\"#b00020\", linewidth=1.6))\n",
+    "d.text(5.0, 0.75, \"la correspondance ne depend d'aucun choix :\\n\"\n",
+    "                  \"elle est naturelle en $A$ et en $F$\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=9.5, style=\"italic\", color=\"#555555\")\n",
+    "\n",
+    "fig.suptitle(\"Yoneda : une transformation naturelle entiere, reconstituee \"\n",
+    "             \"par un seul element\", fontsize=12.5, fontweight=\"bold\", y=1.00)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c17",
+   "metadata": {
+    "papermill": {
+     "duration": 0.009343,
+     "end_time": "2026-09-30T15:01:34.397054+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:34.387711+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Lecture de la figure 5\n",
+    "\n",
+    "**A gauche, le carre dit ce qu'est la naturalite.** Quatre ensembles, six fleches, et une egalite : $\\alpha_Y \\circ (-\\circ f) = F(f) \\circ \\alpha_X$. Lire ce carre de deux facons donne deux chemins qui doivent mener au meme point. Partir d'un morphisme $A \\to X$, le composer avec $f$ pour obtenir $A \\to Y$, puis appliquer $\\alpha_Y$ ; ou bien appliquer d'abord $\\alpha_X$ pour tomber dans $F(X)$, puis transporter par $F(f)$. Meme resultat.\n",
+    "\n",
+    "**A droite, le carre dit pourquoi le lemme est surprenant.** Le cote « foncteurs » contient une famille infinie de donnees -- une application par objet de $\\mathcal{C}$. Le cote « ensembles » contient **un seul element**. Le lemme affirme que l'aller-retour entre les deux est une bijection : rien n'est perdu dans la descente, rien n'est invente dans la remontee.\n",
+    "\n",
+    "**Le point qui bloque souvent a la premiere lecture** : $\\alpha_A(\\mathrm{id}_A)$ reside dans $F(A)$, donc au-dessus de l'objet de depart $A$ lui-meme, pas au-dessus d'un objet quelconque. L'element identite de $\\operatorname{Hom}(A, A)$ est le seul qui existe sans hypothese ; c'est ce qui en fait la source canonique de la correspondance.\n",
+    "\n",
+    "**Yoneda en une phrase.** Un objet $A$ n'est pas connu par une description interne, mais par l'ensemble de ses relations avec tous les autres. Le lemme rend cette phrase exacte : le foncteur $\\operatorname{Hom}(A, -)$ contient toute l'information sur $A$. C'est ce principe que la theorie des topos exploite -- dont le site de la section suivante fournit le premier exemple."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c18",
+   "metadata": {
+    "papermill": {
+     "duration": 0.008189,
+     "end_time": "2026-09-30T15:01:34.414621+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:34.406432+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 6. Site de Zariski : le treillis des ouverts de $\\operatorname{Spec} \\mathbb{Z}$\n",
+    "\n",
+    "Un **site** est une categorie munie d'une topologie de Grothendieck. Le premier exemple geometrique est le **site de Zariski** : on prend un schema $X$, la categorie des ouverts de $X$, et on declare couvrantes les familles d'ouverts qui recouvrent au sens usuel.\n",
+    "\n",
+    "Prenons le cas le plus petit qui soit encore interessant :\n",
+    "\n",
+    "$$\\operatorname{Spec} \\mathbb{Z} = \\{(0)\\} \\cup \\{(p) : p \\text{ premier}\\}.$$\n",
+    "\n",
+    "Les points sont l'ideal nul $(0)$ -- le **point generique** -- et un point par nombre premier. Les ouverts sont les complements des fermes $\\{(p)\\}$ :\n",
+    "\n",
+    "$$U_p = \\operatorname{Spec} \\mathbb{Z} \\setminus \\{(p)\\},$$\n",
+    "\n",
+    "plus l'ensemble vide et l'espace entier. La structure est celle d'un **treillis** : deux ouverts s'intersectent en un ouvert, s'unissent en un ouvert, et l'inclusion les ordonne.\n",
+    "\n",
+    "### Pourquoi ce treillis est instructif\n",
+    "\n",
+    "La figure en montre deux proprietes que le cas topologique ordinaire laisse habituellement hors de vue.\n",
+    "\n",
+    "**Les ouverts sont gigantesques.** $U_p$ ne retire qu'un point d'une infinite ; $U_2 \\cap U_3$ n'en retire que deux. Un ouvert n'est pas « petit » -- c'est le complement d'un ferme fini.\n",
+    "\n",
+    "**Un recouvrement par des $U_p$ n'a rien d'evident.** Pour couvrir $\\operatorname{Spec} \\mathbb{Z}$ entier, il faut retirer **tous** les premiers, donc prendre une famille infinie. Une famille finie de $U_p$ laisse toujours des points derriere elle. C'est le phenomene de compacite qui disparait : $\\operatorname{Spec} \\mathbb{Z}$ est quasi-compact, mais ses ouverts ne le sont pas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c19",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:34.434165Z",
+     "iopub.status.busy": "2026-09-30T15:01:34.433656Z",
+     "iopub.status.idle": "2026-09-30T15:01:35.158808Z",
+     "shell.execute_reply": "2026-09-30T15:01:35.156990Z"
+    },
+    "papermill": {
+     "duration": 0.737271,
+     "end_time": "2026-09-30T15:01:35.160626+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:34.423355+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7+7n66qvtoIMOsjvvvNNOPfXUbJPnNLkull7ThFJNpNVEPH/BDlacPK2u4SfnqnLlb9CZePvtt12lWft32mmn2U033eQqCttK8BExfnJpMsEgR5VZ/SittB3dQNN9UFzsBHY9I8JTWgbXUVcaByfGBz+rvNIzIRJNQA7SBM3g/gUnzOoYtoYmggaDlnj/D/KrTvmJkOnsf7w8iHcMwW3rvT5oixWcdOul+5CzYFlRmgbPpWBehi13wWNQ4J5oJbZkk4G31XkQthwlSzdf/oPnQWy6Bs+DYHpti31PR3D/dJ3QZGctBOCPS9e9o446KrrggQJ87ZvyQkFwsn0K+zC+VNeddL4nmNaaUJ1oAQp/bwgKTooPLuYQPEeDk6BT5UlwXxQoJVogQtuJnbjNAw2R3xBMYLvTChXxVlNRJe2QQw6xoUOHuhU6gsFEsJU2+Jou+Lrw6samC7tajbRSUDK6MCvg0I0w3RUxPvnkE/vuu+/sscces3POOccuvvhit9pQpjJdzSlYidSKLp7vkUkmt54q7VdY8rRSTjAtVfHXjVGrqPgVW3yQFWzx06ol6VbE09334PvSfR6nVifSksT+WHw5i9f6L1r1xafB7bff7lpa47XOp9q/RNv21NL7zjvvxN12vEp/ummkIEkr86jirSBSZd+3qAbzMmy5UwXNb18VIQXdsZUf5U2wMqUVe/xDC/1KbcnE7o9fRWtbnQfJ0s0fazAvY1u6g79n0iOazr6HOQdiqXz7njO1sPtAQtvWefv6669HAwk9C+iDDz5w106/0t7WHkM8ugfoO33Pcrw0T/U9wTxR74h6ltM9vxLdG8Kmt/bFp+stt9xiffr0ibsv8VaAyq1rOZBbKJHY7lQxV6t+cFlPT8OS4t1w9eyG4IVbNzwNi9KNTVS56Natm7vpxavY+YqUnHDCCW6pvzfffDPu+2Kpt+O9995zgYSGOpx44onRdcTDPGDKd28/8MADKd8f7PnQA/769+/vKpy5MTwlXYMGDbJbb73VvvzyS7vwwgvdkpKiCqB6klSRVKulp/T9+OOPbcCAAdmGHai3KbcpIPP0vI8ff/wx7pCF2GFbnoY2KT01hENBYjzqhfI0tO2pp55yQ/R0fBr2pGU1VTkIQ0Mp/BKc7777rhs6NWTIEPdMEy2dfMQRR2RbPjkMVcb8eeKPR/mjsnTttdemLHevvvqqS5+XX37ZnbexFEgef/zx7v86RxVsvfbaay6NdI5dc801LmgOBkoqv/48SBTEJdqfG264wZ37Gq7lrxd5TRVL5b3OQQVLWhLaO/LII6NDm3xFUO9R2dC1Q+/VcEBR78Zhhx2WZ+eAGjw0nFDDKIPLuKZy2223uXwePHhwNC9F57fO8+ASyOqlUPnU8NSTTz7Z8or2X9cM5fVDDz1kL730Uo40T0XXIt8DoPKr813b03E++eSTbviZegm2heB15Oyzz3ZPQPfXEfV2K1BV2QZ2CHk+KwNIQSv/aHUPTQrVJFZNonvvvffcZNjYyYV+Qt4VV1zhJs5qCb7XX3/drbChCWrBlV20SpMmLOpvmoj31VdfuQmCV111lZsQ6VeN0sS5E044wa2ecfXVV7vv0Go3+n5NrA7ShD29rsmYmjTnV1EJK9PVnCQ4cddPTNQk8HSWhg0KrlSk40kmOLFVkxJjl6DUpMXgEqeakBhcNjP2RxM8NQk2k6Vhg/S7/5smf3paujH2u4ITieNROWjTpk2Oz915553uuHwae5o8rWU2Ex2bfm655Za4EyeDS9QG0zS4PKZWEAtO0I390UIDiZaGjZVoArZWLQuuruXzUMu+xks3rRYTu7KPJtI+9NBDcSdgazECLX6Q6Bj0XToXPU0o9X+LXRIzHp3LwSVn/YRfnUOpJmBrGWhPyxz713XuZ7I0rFZHij0uTTzXcrDeiy++mG2xgOCPVuMJrj6X7tKwnq59/nUtTeppFSEtzxr7fclWTYqdgK2yH7vssVY+0uRx0epJWkI73jkTb7JxomPLZAK2lhTWUtax3xlcjCN2adh4dE0sUaJEwrKp+1CipWG9YDnTsXlaac2/7pc6TzQBWxP3g5Ow4/1oGeBU1xEgP2CYE7Y79SBoYp9aRtWSrFY8DX9RC6xajQ488MAcn+nVq5cbb//MM8+4B7OppVWTW4MP+1F3vB70oxYotaK/9dZbbnyrnras1mo/LlatjOrpUOuWehw0LEqfVStwsNXatxbrAVIafqVxutq+WrRiqcVRx5GKhoT4Xo1gF3wyagXURG+1wmp4mCbsaQKhf9J2cGKe5oL47QeHiYmGMfjeoEweeKSWTw0nUTqpV0LDmtTjEJwUr/HNmhSp9FKrnyZt6jvUw6SWWKVf8DvjPQFbLfR+32Nb49X67+diBIe8qKVc47s1NE7DZtQynmpCvMqBWgQfffRRNxlW21WrsiY9X3/99dHj8bR/KlNnnHGGa0VUy6yGdCkd9OAy9cpoYqqnlk7fWhwczqf98sfnH1YmKst6kNwrr7zi0lnl2z8MTOfKwQcfHH2vPhdvG6megK191cIEmhCtMe7avibdah6Deg5iqTVX56Ymiurc0fvVK6dj8OU8OAdCrddqEVfLvXoeNZRP+a33q8VVPTDBB5Vp/oyXzvwllXflsR4KqQcAal6GelVU3vzxBsey6ztVLmLHw2sf/Ps13C0TWhhCx6zzTkM1NWxGvSPBh7udfvrpbhK9yot6XFTGNYRG36nyE9zHRE/AVi+Mf91PAPZl0r+ucucpb9SbpZZupbuukxLvgYqJqDdp3333db2v6rVV+mniuj+XdM6pd+X+++93k4T1u3ol9DnluQTTITee7q3zTr2i+k6dr/pOlSOdq8Fy578n0XArLVSgiffqeVHZ11AjlTntl+bqBe83es1vLzhnQtdq/3qwJ0jXN/+6n4+R6AnYel29pyojKrcaIqZyFLyOBI8h0XUEyA9cc9v23gkgXapI9+jRw1VU/MpEyHvqblfXuyiIiFfh3JEpMPSVTU9PMvcrq6gCr7K3s9MTt31Qq4qynr6c1xSIqYKtpwCrYhVvHHt+oMDbz/FRZS4YAO0MtBKYXz1IjST+6ef54XrvG5AUmALIf+iZAPCfp5ZutR5q6UlVbDXGXK3w3rack/Jfo7RWIKFWd7U6AwB2LAQTAP7zNClZw0L0E6ShCOqRUaso8oaGwCmQ0xCh4DAeAMCOgWACOxQ/3jjV0pHIXcHx/cGx7jsLDZvT2Hf9q8qtxmNrnLjGYwfnGuzsNHzH53MmD1/cGkrj4Lj3/EpzPnza7Ixj1rW0tV92Nd5zTLbn9T52XgiA/IU5EwAAAABC4TkTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAAAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAACCYAAAAALDt0DMBAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAAAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjCB7Wb9+vX2/PPP25w5c8iFgNdff90mTpyY52ny4osv2pQpU5K+56+//nLvi0Qieb4//xWTJk2y1157zfK7kSNH2ocffpgr29q0aZMNHTrUlW2Vp6ysLNtRfP755zZo0KCEf//555/thRdesHXr1rnfp06dagMGDMg4fV566SVbtmyZ5dcyq7zLT77//nv77LPP8jzvf/vtt4w+M3nyZPvoo4/yPG0/+eQTGz58eMafA/ICwQS2m2nTptkzzzzjbqSZGD9+vL3xxhu2I/npp5/cxT+Vv//+2x555BFbsWJFnu7P/Pnz7amnnrJVq1Ylfd8XX3xhn376qRUoUCBP9+e/5KuvvnJpmt+9/fbbNmLEiK3ejgLRCy+80J3rqiwXKlTIChbccW49o0ePtmeffTbu39asWWM33nijCyCKFy/uXrvjjjuigUW6ChcubNOnT7enn37athVddxUEzZw5M+V7VV6//PJLy090Dxg1alTGZVFBW6pGFFF+KC+rV6+e0XfUqFHDXcPHjh2b1vvDpq3Kyp9//pnx54C8sONc0bHTKVq0qJ155plWu3btjD6n1lK1Su1IXn75ZRszZkzK9/keid133z1P90c31TPOOMN22223lPvTpEmTPN2X/5rGjRvbKaecYvldbuW9yv0vv/ziKlgXXXSRnXrqqbYj0Tkyd+5cW7t2bY6/qWKqgFzHJd99950LLI466qiMv0fpohZtfde2oBZxVUjTCezy43Wgc+fO1r1794wbsJ588sm0esYUQB5wwAEZBxNly5a1ww8/3DXWpCNM2ur63bt3b+vUqVNGnwPySuE82zLynC6I6mJXC0qpUqWsXbt2Vq1atWzv0Y1JrYtqKWvatKm1aNEi4TZKlizptpHs4qn3qWVVNz5VEDQMpkqVKrb//vtbkSJFsr13yZIl9uOPP7rW9l122cVd+IIt3PqsKs1qqRTt57x586xnz56u+3bWrFlWp04d22effdwNT/uqSrm2qe/UECnRjbt8+fK2evVq97kFCxa41iF9n28tTEQtO+rp0JArpU3sRV37/sMPP7h/69Wr57YZvPmqRUm/K930vsWLF1ujRo3c77J8+XJ799133XdoX7TPSoPTTjstetyxN5ZatWpZuXLlsr3++++/27hx41wA1r59+xwBWKbHruNu0KCBKzfehg0bXGVIeaAKVMeOHd3+aF+D1Lo8bNgwlya77rqre19sL4z2V1QmdDxdunSxYsWKZWuNU54Fb4bqLVHvzfHHH+/2S8NslG4nnXSSa7kV5VP//v3d/iW6kSqd9HPMMce4PJk9e7ZLL1+OghVB7Ze2qQqvyv9hhx0WbXHWZ5We+qze5/dBlE56j15XYKt8b9mypTVv3tzd6LXvf/zxh8uLrl27Rr9XrcEzZsxwlZQgVUj1fYsWLbK6deu6ilKwfOicE21f+bx06VJ3fKVLl3YtyxqKoTKg86lVq1aWzMcff+zK4CGHHJLwPdq+jn2PPfbIcU7r+1WuGzZs6MpiMhruo7QqUaJE9Ht92dc1Sces8qZ0Uv6ofHu6HqhM7Lfffi6NlTaqPKrXTteAk08+2f2rCmLwOqGyrWuTvvOggw7KVsZFQYG+V9tW2dTngnkbS2VcearvCaaHPv/mm2+6wNBfM9955x33nbHn3uDBg91+6BqjY1m5cqUrL7omezofFWgqzS6++GJLRWVJ56HyX9dDlcXYY9X1/9dff3Vppvxq27ate137MHDgQJcPfqhQs2bNrEOHDjm+R9ddleU+ffpkVGYT9XbpGJVe2ncdg8qQ7g+x36k8VF6WKVPG7VfVqlWjf1eZUfnxjS4q+2+99ZYr0/q/zj/ti8qO0sZflxSsqQx+++23btidPq/9jqVj+vrrr3P0FPnv6dWrlzsHlLa6buy7775WoUKF6Pu0H6+++qoLLJWvicSmrdLjlVdesQMPPNDdbzylhfL5iCOOcL/rMypjqRqD3n//fZc3Sl/lucq+yonOF133/PmndGjTpk2Oz2v/9d0q/3vttVe27/P7qmuZ/q7zVeeR9l3XdqWPvlNlT/fDePuq65au8Sq3yuNMAzfkH/RM7KBUoTvuuONc64duGAoI1NKsC7To5H7sscfs2GOPdRcD3Qj79u2brbtelcETTjghug1dgNVTkKzVX5UajffWzU6VA33u3nvvtfPOOy9ba88HH3xgRx55pNsfVUruu+8+9/3BIU0a9qCLoqdKom6kOg5VQHTxvP76612LpqjyppuwbuI1a9Z0lV/9qNKgi7ouyLppq2KnSqmOXf+PR+lz22232SWXXOIqzAqSNFzh0Ucfjb5H21Dl0lcqH3zwQTv33HNt48aN0fcoPXWDPOecc9xwCI2X1XEqgBDtr2/VVIVJ+6ubWaKbbmwrlfJI+3j55Ze7fdQNUfmuf71Mjz1eT4kq3NquboD6nPLmhhtucOUsuD86VqWJyoGO65ZbbrFrr70227a1zz5v/LYUECj/PJVNHU+Qjknfr/wUpZfS14/bV9m56qqrbMiQIa5SnYgqDNpPlWVVGFR5VzlS/gaPVy2UKgMqvwsXLoxWAFWJUMuiyqLyXWVC+Rssu0o/lVe9rhuizh2VW1XMlP+qqKn86jsffvjh6Od0HqqCEtyWPqM01WdUth944AE3LGjz5s3R9zz33HMuLfR9KiNKW1VidO7qOJWXOiZ99rrrrrNkHn/8cXe9SGbChAmunAZ7yDSuW5UZVcQ0z0lpqvxPRhUK5bMqL8GyryBA1wcNVVEa69jUQBFs/Vf6an6FKusq4/qbgiellyrtuuZoGJ62f/XVV7t0VV4qv1Q29bveE6R9V97q8/reJ554wqWf9i0RXwlSxSpI5UeVJt/TovKtcuAbEoL0PUo/7Y+ueXqfgipdQ4NUsVaZTUVDaBRMKt10HEpHXcsVBHqq6Cng0vVfFUbtgw9SlC8+CPPnaqLGB6Wvji14HUinzMZS5f+hhx5yQ6uuvPJKd36oMUaNB9pHT8ejNNX1Vuel0kMNRsH3qPzoPuSvFSqvus7oXNO+KP/fe+89t20dq78u6VgUQOoarmMONnAE6b6lhpDYxjdVfPU9up/pPNKx65qla2cw7RVAKIjRfSyZ2LRVGVO5je05UXnVMQfTX9fBZJQXSkNdqy677DK3bd0jTjzxRHd8Z599drThR+UyOC9Igcatt95qp59+uuvBUvrqPAzOofLXMp1vOu90TdB36TM6z1QedK3S//Wdajz0lK/6Tl1DdN365ptv3D0rnd575FMR7JAefvjhyNlnnx3JysqKvrZ48eLIzJkz3f+feuqpSPv27SOjR4+O/v27776LtGvXLjJv3jz3+2OPPRY588wzs21jyZIlkRkzZiT83iuuuCLStm3byM8//xx9beTIkZG99tor8ssvv7jfv/rqq8jee+8d+fXXX6PvWbBggdufQYMGud9XrlwZadOmTWTo0KHR9/To0SPSs2fPyMKFC6OvPfLII5Gjjjoq+vu4cePcd82aNSvbfh1//PGR5557Lttrv//+e2TDhg1xj0P73KFDh8iyZcuir23cuNFtX77//vtIx44dIyNGjMiWNjquDz74wP2+Zs0alxYnn3xyZNWqVdH3XX755ZFLL700+vvgwYPdtrT9VHT8L7/8svv/5s2bI6effnqkT58+kaVLl0bf88ADD2RLk0yPfdOmTZFOnTpFPvroI/f7unXrIkcccUTkpptucn/zaXH44Ye741u9erV7TXmntP/444+j25o2bZorU8HyEEvppLxXmvqyoO0Ey4fccccdrjzGlvNu3bq58nLVVVe54w6mRTwnnHBCpEuXLpE///wz+trAgQPdd86dO9f9/sUXX7jflZbB8q/zRcfz9NNPR1/7+++/I127do2899572dLvmGOOiaxYscK9pm0on1Q+xo4dG/3s/fff79LRU5rrPT6df/zxR5fGL730UvQ9ixYtinTu3NmVG1m7dq3bp+OOOy76faJzXccwderU6Gvaj99++y1h2qhMPPnkk678J9OvX79s+60yr+8aMmRI9LU//vjD7Xuy7/Nl+pVXXon+PnnyZJcG9913XzTtVUa6d+8eLfui33VNmD9/frbtnXfeeZF99tknW/7eeOON7nrSv3//6Gs+j315Ub6oHD7xxBPR9yg9999//8g777yT9Bh69erlrkXe+PHj3ff5PApeB6dPn57ts7pu6PWTTjopei7JNddcEznyyCOzvffLL7907w1el2KpDO+3336Ra6+9Ntv5qvL44IMPut91zNq/MWPGZPts8H5w7LHHZkuLRHS+Z1pm41E50bFddtll0Wuh8l/XT3+91LXo6KOPjlx33XXR7xNdm3SdC14rdP/zXn31VbftYP7764z219P1VOUuldtuu83tVyyVT20zeOzKKx178DXRMQXvA+mkrc4zXbt07Y8tf88//3y2c+DWW29Num2dH9pXXTd9eutapte0PeVZMF2uv/766O833HBDZN99981WlpW2Ou+UR/5apm0F7z1Ka7127rnnuuuWv+bomF5//XX3u/ZF17Lg9VNuvvlmtx/YMdEzsYNSa4taRdRq4FWqVMm1NKk1WK1Sas3Zc889o3/fe++9Xcug7w1Q96O2oVYHr2LFiq5bNBG1NKgb33eXi28lVouSqAVM3bzB4Rbqoq5cubJrzRDtt3oHfIuMWnXUWqGWf98tLWpBUmukp89pTGrsMB+lh4a2qHva0xCC2KFXno5dLTcasuFbidVFq65+UavTwQcfnK2VUWmjYWS+RV29EGpB+t///pdteEG8fVbrZrKhFKJegODQErXaqQVSvRLBLnQNy9A++EmemR678kCf9WmvXiR9Vsfhe0y0r/q7utrVAq40Ug+RuqKDw2PUfa7yElx9Sq1a6t1Sa5paIdVqqs/7IW4qbxqOouEOQXo9dq6IWo2VT2phVHqrJT6YFrHU2qjJlWrlCnarx5ZR7a/OF/X6BIfeqZVNLZfqAfA05ExDUvwx+vTTZzUEQ7QN/Wgcc7DXRK8Fy4a2oeP26ayWVO1ncB6BzhPln/8+DfVQGqhXxn+f+BZXtTIqzf33aV8TUZm44IIL4g5pSNRDpvKs80Hlrlu3btH3aNiMeo+SrTzmh0sFW7V1fdBxXHrppdG0Vwuzrin+eqZrmMqkenlih27qPbH5q/Kk4Uh63dO29bpvvVbealvB3grtR+vWrVOunqbvCvZMKN+Uz8Ex+74nMLZ8+m3r/NK55Onzvjx6/rPJehX79evn8kQ9gsHzVdd3n34qG7q+qjU7OBnc3w9UXlSO05mbpW1mWmbj0d+0n7qe+Wuh8kif8+mgXlW1cKvnIth7q/IT7MnUPgX3XdtWeQzmv+9B9tdinUO696UzP0HpH+86o+/VPVY9Pp56p/RabF7q9WT5GC9tdRw6ruBwTJ1Duk8Hj1ef88eh66aGz/of34OjbWm7Skuf3n676pVQnnl63ZdNbU89GHpPcKiVhnJpmJeGH/vta4SArtGeP5+Vx76nS9+t+6S/DqrnWNe0a665Jtv1TNcXXeNZOXDHxJyJHZRushoqohujKjs6ETWUJDgWVSe+n1cQ5LtQ9VlVvrQtndQaSxkc/xtL3cS6qKnLNMh37yoI0IVG3de6gMV+p96nCpy/GOpi5gMHfxOKHQevi4vmIMRefGPdfffdrqJ56KGHuhud5nCo6zlR173G0upi6busVUk++uij3bhQ3RRUIT3//PNzfE5jxoPHoAtkMGDz+6y8iK1AphI7tETd+7rI63uCw8G0f7rg+nzM9Ni1PXXv169f3/2uir+OWze/ID+W1nfv69h1A4stUxrK5fdFXe8aZqAAQ/mmG7m6sUWVPZ8ePkjxVLlRmitoCFJFUNtSUKVKVDDQjEdd6Sr7sXMStO/ib6DaB42VDgZ4eo+CMg1t0PCaIA2bUDDp009j+4OBpsaPqxKkCl2QbprBvNdnfbCodNExqzKroCtIgbUqR35fVbGJLWcKfK+44go3HEvDXRQgaOiJbvpbS9+poQmiYVyqnKpsxua9hmgkm8zqGyp8OVLFVmOrVRbU4BGkyqK/9iidVPZjj0VppvIWO85d74+X9io7Og907dIQClVcNVcmdpupzk/trx9WomFwShMN6YgnduUzn3/K5yBdD2PLczqrpum6oM9pKF/s9/hhRgryNcROczo0HEgNOwp0fTDoA9R0golgkJ9umY3Hl31VQGPTwZ+XatxRMBx7LVKl3F93FSToPPfl029b173Y67Aq0z7o9A0w6S5uES8vlMaaAxU7TDVRXqaqGMc2oGj7see5P4d88ODPAf+7hhEFr1e6P+ta4AOTYNDgg83guaJ9VHr26NHD/e6HZuleHzzffVDqy5i2FTsPUq8pHYLlQPUBfdafYyq/uv9oJa7galx6X7DRCTsWeiZ2ULpAaLy3xhpqbKPGQPux0rrwqhKnk9qPh/U/Chb8xUsXZ31W27j99tvdia2xx4n4i1rszUDjN9VboJuAby31FS9P+6fv9xP8YucG6He1GvobhqiSohtC8H2xLVKeKoAKDHSj19hh3ehiKytBqqgomNCYX41l1s1BQZVuCv4YgvsimhOhAM1POPatQ8GLn9JeP36fdaGOPYZElAbqcfGtNdqOboS6wAbzUBUT9eD4ynimx67vUUXfV6TVAhwbQOpGovf5iq9vYdP+xJYpzddQvqrCpvKkuQPaB5UlpanKocqD/w6lR2weqoKp4wy+rpuWxtT6id5+Hkoyycqogiffo6V9iDe5ONExqlLrK2K+FTQYiPietuA29XuwvMa2jCZLU01iVKXFf19sOfMUNCqY0ERLHbNaBLd2uUjfKxDcT3238iB2PzX/IN4cgWC6BMu0et+UDip/sdtSGVKF1x+zAoFgD59/XedqsIFBFZXYydHBdPN5q/xQvsV+rxpRUq0KpDzSOaGyqF4a9c4FJ0+Lr7QFx877/Ygtjyrrmu8RGxT550wEK4BB2l8/mTr2OHQdUIOIp8YFBRy6xqkM6v6ga5jPF12z1QuXjC+z6VwHgmU20bkZmw661mrcvvLA/x573VW+Kcjw9w4FMwoogpOv1YgVL/8VVPlGFd+IEjvZOx6lf2w+qgKvinxsmqknQOkSLy8T5WO8tNXvflGSIDUOqmff31P9OeAr7Pq8rrn+x48aCDZcBNNE1+Ng4KO0U2NI8HxXj6MacoL5q3umtq9emES9PMEek+B36lrpgzq/SEls+dH9Qb2m2DHRM7EDUuupLiS6SOoEV6uTbvS+MqSbty7AChyCN2NdHHUT04VJk650cmsbqiioNUQneLIHyAWHXfgLnm6wqoxrWIhaa3Wh0YVOFxrfEqELlYYY6Ibtu021reANPF7lThNnYyf+6bXYZRd1MffDNnQ8uqHphp9okrMunhru4Su3Si+1sGhYkT6j33Xx0z75ISvaD00e1Lb9RTFea6jvXfDHrjzR8ScbOhZM3+Cxaj/UIh57gfUrMYU59njfo/KgngdPZUQT93TD9u/zlXCtYuJ7NESTYtUiqXxVeummENy2yqomQgaHxcVWGJQ+GvoS7C3xE+SVnpp8rMqilt/UMIbYlrsg3/Km8ufTRa9p+IR6cERlXPsQe9NWRUfnkyoF6ukLpodaNX2+B4MsT/upG3Sw8uBv0v57dAzqgQmmqcqKWlSDw46Uhqq0+DzW/sdWVHRD1rZ9xUjfrbxX5THZcDq1UiuN461g4+n4gj1k/nqioDE4vFCVOF1TdM6n2/Kq/VQ+qzyoLHmqnGgYUbA3Jl6jgV5XugR73ZTX+ny8io0fjqK81Tmh8zVY4Y3N20T83zXRVOe0guRYKhP+2hccHqL9UMCjc8UPudLkcuVf7BLBOoeUnrEt856usTpf1SsV/Kzyx19zFfAoGPRpqUqoKvrqkfHXBV1H07kmxQ6JTLfMxtI29J2+td4HxprAq2uBX81IZTP2+QwaLqlzSWnvy4B6hH0a+6ExyYJJf8wKBFINNxVd99UAofLh3+/vf8FeYp3PGiGgIbHBANe/z6++lE7aqnwoHYMrmum6rGAwuGqa9kP3ez95XL3s+gnylX2/Ol2iNPHnaLCyrzxWw6Ia24JDZXUvUn1CjVgKenTs8a6DvkEgdn/9tvz1JPa+lqihEDsGgokdkFZdUG+CWsh1c9YFRzdiVXZFQzy06oK6gVUp0kVHXYi68WtIj6jLXq2Y2oZuTjrhdfHx24hHJ7tav7SKif6vG4IuuOoeVQupaMiVxlBqqIu2qQujWuB00fGr6egmqptDbM+EVncJ0mvadz88RnSD1I1YFVjdcHWxvv/++10lUF3v+j6tvqReDr9PsXQhVEVVN0NVxnTjVderuof9kqwaAqabhFrBVHFRoKGLrT4XHHN81lln5djnYIuqehFUwdQwFA2N0IU0USuoPhsc5qN0VEuQ/lXA6IcCKc/VA6AbXabH7m8yalH2VE40fl0Xd7W2KkDRMavi4YMi/avPKGhURULppEq5KhBa9UlUkdCPVvjRvBoNk9KPKlDBvFZLtiq12l/tu1pLVTnQzdhXdlRxUG+FAgkFffrRDVXlU70uibrClYbK15tuusnd4FXW9BRbHZu/4ca27HnKM71P5VTHqPNCQ8p0bul15X+89PPbjFeZCX6Pflda+ABA5UL5q6FKOmf1u9JLlVvlhy9n+v7YcqZ01/mr/FK6KThSz5S2Fwz2gtSiraBex5IsmFCFQGXYj3FWxUrLNascKl2UTsp7VS5uvvlmS0bXCa065KnSomNT/qpFWpV8ncsq11rdyKeVPqdetnjbi5fOKo/BFmPlmx+WJ0p3rS6jsqlzXeeIz1v18qUKJpRnyktdyxRIxGtxViVLZU+9sCr/wTkjalVX5UzXWx2rhlwpYI/djlZ5Cgay8eg6pfNf+aQKtb5DDQyqOOo8VXpqH1Uu9LsaQrT6mgJxvzqR0llBvuY/qNdIgZ1ao2MpbXUtyKTMxqMKv84dXbtU/lSmdD1UY5QaP3zFWGVAqwFpiKneo+PScWo/fdDqh436a4B+D/Z8BsuK0txTeujaoXRXeVFPY6KASo1LanxQYOOHp+l79B3ab10jVIZ0zuk64a+BwcBF+Z4sL2PTVueVyraOVfuufNQ2VK4SzZdIFajEO1dil4TW9nTd9kGMGus0Z0JDdX1jmfJXjUt+ZUW/78FrjXpudF1IFdTp3qp80b/KY5VPnRPaB4KJHVcBzcLe3juBzOnkUyVMlSVdZNVNHGzR8Os+62LsJ7tqXHywEqYbqZZc1Db8WvqJlsoT3XBU2VXLnoIZv0Z4vAubLsK6YfpeDz+xWVR515AVVRYUEOgmo/HwqvwFW7Z0U9Y+BivGqjSpYq9t6Oanrnwdq1pS/Dhrf6zJqPVOkxNVIdINUTeP2Bu7em9049D2dRMOTmzVDVxBjW7QwVZ2VTaULn78qeiGoPTyy6zGtiKJPqOKsz4X7IbXDUGVH+2nboBKy2AaZXrsymstZaggLHjzVTlReqiyp3KgG5lu4sGJhr6nQemiFjQFeaokBVv61FqtAFPpqwqLbkYad6uWUV9h0z4rPZS3ykOlh35XhU8VFN0IdTNT0BU8Vv967L572ifdvO+8805XyVJgpX1TxTmYpj69gmOuY88tf16osqFj8C3KidJP5VnnYXCdfgVlqkT571HlWUF9sHItCk50LqtlUvutCqdveU9Uzvy+KM8UmOs8UpCW7AGQylMNiVIlKlnlOV4ZFp0LKhP6m/Je536yVl5fppWPsQGO0kHntwIcpZuO2Qfgic4FUdrrXAz2TvlnHQQDPAVbatFV5TQ4N0dlSPnir5vK2+Dfk1EPrMq3KrvBa22Qxq8rYFFjja6l2jdNvNbrymOVO+WjD8hj00QVOeVRql4DVcKV97qmKCBTXgQnDKssaWiQKrUazuQbToK0b9ofnTcqo7H7E7bMxqMyrJ4RNYTp+q0yq/1WOsR+TmVC1wPlqcqzzt9gD7sm8Kq8+/k0GuOv8yTYCq/rpuYRxPam6XhUhvV3HVOyYUgapqmAWg0TognDunYpcFD+6hqn67lf2CRIDW5Kn3hzFpOlrcqlrp+6z2nbOqdjr5/xzoFYurZqH9X44xtoNGxY91kFusEGOuWJzuPg4go6B1U+FETo2qdGi+A9PN6+6z6nAFVBiMqcp7lF2tfg/uo80n1N5ViBoAK2dHrKkH8RTCAtumnpIqSx+alWggG2B/XQ+acIpxoLDuQFVTYVwCiAUsOLKoKqrGlOSyqat6YKX6renh2RjkmV7+BzfPI7NSoocFRwp94HNVwpYNFrySgg0HvVaxw76R7YWTEBG2nx46jTWZUI2F5lNJ1JpUBe0TVSLdfBITjpLL6glmANm9FQrJ1RuumQn6j1Xssx+9WTNIQnnWPQ+zWEh0AC/yX0TCAtGrakoR9qdQPyI616om7z2Lk3wPai4T2qlAYXIPgv0pwxDY+NnaS8o/BDeDTsM/hsBABbEEwAAAAACIVhTgAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTAAAAAEIhmAAAAAAQCsEEAAAAAIIJAAAAANsOPRMAAAAAQiGYAAAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAAAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAohcN9DMCOYvLIafbWHZ/a9e+cZ0WKbTnlN2/Kss+e/85+HjzO1q5cb7UbV7PDLznQ6uxeI/q5Md9Msk+fHWpXv362FSocrt1h6Dsj7cNHv3T/v+q1s6xGgyqWH63+e43d0OtR9/8DTupoh5y/X8bbePayt23SiKlWtlJpu3XgRfZf8devM+zJi950/z/xpt7W5uBm23uXAADbED0TwE4sEonY+w99YV1PbB8NJEQV/OEf/man3naY3fT+Ba6S//j5r9vSeX9H39Niv8ZWoGAB++G9UaG/P2tTlm3asNn9RLIill9pz/x+KtAKY/PGLZ/fuH6T/dfKWDSPN+ffPAYA5A16JoCd2Phhf9ni2cusfa8W0ddWLl1t3737ix1zVXdr0LKOe+3Iy7rZ2G8n29dvjLCjrjjYvVagQAHrdFhr++qNH63L0W2sYMECtrMqXb6kPfTDNe7/BQvRxpKJXVvVi6ZdoSKF8iR/AAD5F8EEsBMb8fEYa9JxVyteqlj0tT9+mW5Zm7OsSccG0ddUgW7Utr4bphPU6qAm9u79n9kfI6fZ7u3/fX9uDCsa8tIwF+ysWLzKylQsabUb17DuZ3a2mrtVTfrZQc99Z1+8PMz9//jre9mIT8bavCmLXM9Lx0P3tIPP6OwCoeAwr2/f/NlmTJhrG9dvtDKVStseHXd17ytTsVTSYU53n9DPFs5YYrs0rWXNujS0UV9MsCVzllm5KmWs22mdrE33Zi4tL9/n3miPhoK1/3W62/2/Q++Wduw1PRMey5+jptvAJ76xBTOWWKWa5W3P/Xd3+ztt7GwrX7Ws3fxB3+h7tR9KMw0rWrNinZWvWsZ2a13PpZn2RzZu2GRXdb3f/b9tj+Yu3yf9NNWWL1ll1epVsj4X7m+7taob3eaaletcWv7+/Z/298KVVrp8CavVqLodfEanbEPeHj3nVZs+fo7rwTrz3qOs/92DbPq42XbKbYdZidLF4g5zWr5opb330BfueEqWKW4NWta2cpXL2DdvjXB/v+yF09x3zJu6yO47+Xn32iHn7WcHnNzR/X/gE19H33vHp5dYqfIlt7rsAAByH01wwE48/OTPUTOsfova2V5fPGeZFSxUwMpXK5ft9Uo1y9mSOf8Oc5IyFUpZlboVbfLP03Ntv1TZvu/UF+ybN0dYw73quWFWvc7bz0Z/OcHuPel5m/jjlKSfz9q0ZUiNfr7tP9IFFKfcdqitWrbGPn1mqA154Yfoe0cOGmdP9n3Dxg//yw6/9EA3b6TWblVt6Nsj7f5TX3D7kmyYk3/tr9EzXcX7vIeOtWZdGrng5bVbPrL50xe7QOy+r6+0xm132ZJmFUu53/Vz1BXdEx6HPvvURW/Z9N/nWLueze28R4619Ws22J+/zPjne/8dLjVr8ny775QX7OdPx1rX49vbzR/2dYGHhqDddXw/Wzhz6ZY3Rv7d558G/mZ1Gle3vk+cYFXrVnQByvNXvusCDtHxPHj6S/blqz9anSY17Mb3zrfD/3eQjR062e4/9UU3ZyaaDv8M4dL+jRoy3k64vpfdNeQya9q5YdxhTgqwnrrkLZenlWuWtwufPNEFGd/2/znHsDf9G30t8u8wKeVD9PVcKjsAgNxHMAHspFS5Xr18rWvxDtqwdqMVLlI4x7ClosWLuEpj7JwBVQYXTF+ca/ulyuvSucutcNFCdsT/DnKV7726NXUVWlVCBz75Tdrb6nFmF6tcq4LrNdnzgN3da1+8MtzWr93gtvX+I1+Y6qe7ta7rWurV2n/oRQe49y2bv8K+fuOntIdB9TxnXytbubS16d7UvZa1OWKzJsxz/1evSLA3RL/rJ9nE9SHPf+/SW+/r3bera7Xvee6+rqU/lua4qCKvlvf9jm/n9qf7Wfu4NFyzfG20pyZIvRZtezZ3vRYt92vsXlN5WPRP4PHtWyNcb4fmxRx1eTc3cbzFvo1t1z3ruAr+R098nWObyxassH2PbWsVqpdz+51o6Nvoryba3D8Xuv8ffGZnVwbVQ+b3I5FgGuZ12QEA5A6GOQE7qbUr17l/i5cqmu11VQI3bdzkWoGDlbcN6ze5ym9sBbh46eK2YsnKXNuvqWNmuX/V4nz1AQ9EX/dBTCaBS/VdK0f/X22XLf/fsG6jqyQXKV7EBVQSXEWqcu0KLg00UXrq2NlpfU+lWuWj6VKkWJHo676VPwz1NkjFGuWsWIkteVS4SCGrXKeizZq4JUjx1KsgGhLkh1D5NPS9HLHUG+EF99n3eEz5bUs+KHC4vscjOfJBQYf+HywPFaqVtWIls5eneGZPXhD9f7V6gTyq/+//t3fZAQDkDoIJYCdV/J8WbrVoB6mVWK3qGiOvyqG3bP5yqxjTi7Hl8+utROniubZffihLqXIl7PZPL8n5hgzmeRcIvDnYSq7ji46NSdLine4KU8EKdYrG87T5747dtwJJ3tuwdT0775HjcvxdvQuxChYulGKjW/5RYHXPl5fHTaPYwLJQcJtJBIcrBfct3n4mop6GvCw7AIDcwTAnYCdVukIpK1m2uC2Zm30eRKM2u7hK3eSfp2WfX/HLjOi4/6Cl85Zb1bqVor9r4qtax/WjseqZ2qVZreiQGw2b8UOCoj9F02/jiM4VUKv0jCXR1v1qu1SyKnUquON37/vnbz5o8su31mta03JLJhVlqdWo2pb9WbDCDXfyLeyLY/JL6jXbsp9z/tzS4h+bZjrmTPltKi2UdjnyIbCUcKY0L8VbNOvfPArmgxfc9+Cyuprbk5dlBwCQOwgmgJ2UWuo1bn7auDnZXtcYeq0yNKjfUJs/bbGryH76zLducmvXEzpke++aFWvd0JFGgSAjOGE2K8SzI7Raj8bny9v3DHKr/qgSOfevhW4/tF/p+vzlH1zFcuaEufbbVxPda5pToFWM1Ire+4Ku7rXJI6e7IEhzKfQgPtF4+wNOyn68W0PzCHyaLV+celhYt9M7u4nw6jn68tXhbnjWV6/96OZAxOrTd383T0DH+t5Dn9vaVets3er1Nm3cbHv7nsE2cvC4jPdXE7l9z9SA+z+zpQqyNmxyk8s/e/57++jxnHMm0tXqoD3cxH354pVhLk000XzMN5PjDiErUWZL0Ddh+BSXR1pVLN5k6twsOwCA3EEzDrATa9erhb1280eugubH5cvRV3a3jx7/yh464yVbv3ajm9h7/qPHZRtnL799Pcktpbp7+/rR19as3FLZVUXYz1PIhCqwV7xyhg1+7jtXwb+h56Nu0rHmMux5QBPr2GfPtLfV6oA97IkLXndzBjSpXIFE8OnVnY/cywUNmmj9/FXvugfLaYlRTUzW+zQhO7cceEpHN6dB8x1u6PGoq/xrpal2Pf99xkdQ7UbV7Kz7jnaTqwc9O9SGfzja9ty/idXZvbrNmjQ/2/Mu9DyQy1483VXy1Rs0/INfrWTZEla9QWU3sVwPGMyU0sXng5aGvbn3425+jSa0t+y6u+13XNvQaaHehvMfOc4FOlr+9tYjnrL6zWu58vj9u79ke6+CvtPvOtw+ePhLmzFhjt12+FPWuH19a92tqVuNK6/KDgAgdxSIBAe3AtipaNz5ncc+a/uf2N46Hd46/nuyIglX5XngtBddxfKgU/eOvvbaLQPdEqWqPPuVkZJ9v58cq8p1vHH5yb4/nk+e/saGvLhl9aI7B1/qVliKnUyebH8SPZTOD7HR3/1cAfXaqCdGQ5j8cBy/FKroffG25yZmRxL/PVEaaP9UQVYvkQK4vk+cmCvHsbX5EC8dgp9TkOYfWhe7jeB2taTr+w9/4f5/5StnWN09sg8zC+aj9tfPm0g05CrTsgMAyH0McwJ2YqpQ6vkKQ178ITouP8d7ElTGxn33h1sNSa39QXqAXdV6ldxSqel8vx/PnqiynxuVwXQCCb8/icRbzlUV59g5Cfou/95E29PY/WR/9166/n2b8ttMVxnXw9hU0VYgoQr/wWd2ybXj2Np8iJcOwc9F0yPeRPAM8je4b9r/VHM3CCQAYPtjmBOwk2vWuaE1blc/6TMP4tlj793cT+znbnq/b7ZWb4TX+YjWNvi5792cj7Wr1ruhR60ObOJ6goJPoAYAIL9imBOAHUo6w192RP+FITvpDLcCAOxYCCYAAAAAhMI4BQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAAAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEErhcB8DkN8sW7bMXnnlFRszZoytXLnSSpcubU2bNrXTTjvNqlSpsr13DwAA7IQKRCKRyPbeCQDh/fXXX3bXXXfZm2++ZZs3bba65XazogWL28bIepu1fIpFCkTs2GOOsWuvu9b22GMPkhoAAOQagglgB/bDDz9Y70N6W6FNRa1jjYOtXc2uVrpouejf12xcZSPnfWvD535mayMr7f0P3reDDjpou+4zAADYeRBMADsoDWfq3Kmz1SzRwE5tdoWVKFwq4XvXb15nr//+sP214nf79ttvrEOHDratTZs2zZ599ln7ZdRoK1CwgHXeu6OdffbZVrNmzVDbW7dunb377rs24L33bcWKFVa3Tm07/fTTbd9997UCBQqE2uavv/5q/Z57ziZP/tNKlCxhvXp0t5NPPtnKli0beujZyy+/bEM+/8I2bNxoTZvsbueee641a9Ys1PaysrLsyy+/tJdfecXmzZtvFStUsGOOOdoOP/xwK1q0aKhtzpw50+XLiJ9/cfnSsX07O+ecc6x27dqhtrd+/Xp777337J13B9jy5cutVq2advppp9n+++8fOl9U1p/t188mTfrDihUraj26H2ynnnqqlSv3b+Ccib///tteffVVG/zZEFu/foPtvnsjO/ecc6xly5ahtqcO/q+//tpeevllmzNnrtuvY44+yo488kgrVqxYqG3Onj3b+vXrZz+O+Nltv33bNq7s1K1bN9T2NmzYYO+//7698867tnTZMpcvp516qh1wwAFWsGC46ZPjxo1zZWfCpMlWrGhR63bQgW5YZYUKFUJtT+ex8mXQZ0Ns7Zq11rhxQ5cvrVq1CrU9ANuIhjkB2LFkZWVF9mjSNFK3/G6Ru7u+EXnowPdS/ty3f//IbpWaRurWqRfZtGnTNtvXzZs3Ry677PJIgQIFImWr1YtUbrZ/pHLTrpEylWtFChUqHLnjjjvc8WRi2LBhkUqVq0SKlSgdqdioQ6RKiwMjFeq3jBQsVCjSpm27yPz58zPa3qpVqyK9DjlEQz4j5es0iVRpfkCk4u6dIyXKVIiUKFkq8t5772V41JHIq6++GilWrHikVLnKkUpNukQqNz8gUr5WI/cdxxx7XGTt2rUZbW/mzJmRZs1bRAoVLhyp2KCVO+aKDdtHihYvGaleo2Zk1KhRGW1PaX7NtddGChQsGClbtW6kcrOuW/KlSu1IwYKFIjfffHPG+TJixIhIlarV3D5VbNT+n3zZM1KwUOHInq1aR+bMmZPR9lavXh057PAjtuRL7cYuX5SWJctWjBQvUTLSv3//SKbeeuutSPHiJSIly1Vy29I2y9dp7L7j8COOjKxZsyaj7emYWu7Zyh2jjtXlS6Mt+VKlWnWXJplQmt90000uD5QXLl+adY2UrVrH5dW1112Xcb6obFSrXiNbvqgMqSypTM2aNSuj7ansHn3Msf/kSyNXtpWWKusq86+99lokUwMGDHDnmvJW596WfNndfcchvfu4cxRA/kTPBLAD+u6771wL/AWtb7XdKqbfyj17xVR76Ocr7ZNPPrFevXrZtnD1NdfYo489YdU7nWClazSMvq7W1pWzxtu84W/bvffcZZdddlnaraHtO3SwUvVaW5U9e1jBwkWif9u4ZrnNH/am1a5Uykb+PMJKliyZVmv/wd2724+/jLUaXU6yYuWq/buPWZttycTvbfGYITZo0CDr1q1bWvuoFuCjjz7GqrXpbRUadrACgZbftUvn2vwfXrMeB3a1AQPeTbslvfVebWzphkJWvcNxVrhE6ejfNm9cbwtHfWwbF0y2X0f9Yrvttlta27z55pvtnnvv35IvNRtFew2UL6tmT7R5w/vbLTffaNddd11a25s4caK1bdvOitdublVb97KChf/tKdm4dqUtGP6WVStT2H4Z+bOVKVMm5fa0H4f07mNDh42w6p1PtuIVqv/7t6zNtnTycFv46yAbOPCjtMuyyv2hhx7m9q9i445WoGCh6N/WLZtv8354zbp27mAfD/worV4ULXSwV5u2tnDVZqu29/FWpMS/x5W1cYMtHP2prZvzuzvm3XffPa19vPPOO+3W2+6wGp2Os9K1mmTPl7mTbf6wt+zaa66yW265Ja3t/fnnn24fi1ZvbFVa97ZCRf7tKdm0dpXN/7G/VSqe5cpOOj092o8jjjzKPv/6O6vR+SQrXvHfnsVIVpYt/fMnW/jLx65sq8csHUOGDLGePXtZlT27W6UmnbPny98LbP4Pr1undq3ss8GDQvduAcg7BBPADujYY4+17z/70a5o+3DGN9fHRl1nTdo3sEGDB1lemzdvntWpU9dq7XuylanVJO57/p7yiy377VObP39eWpXMww4/woaO/tNqdjk57rFv3rDWZnz6kD3y4H1uuE4qGjZ0cPcetmvvK6xomYpx3zP/l4FWvdAKGzf2t5TpreCk/q672doyDazqngfHfc+6v+fb1E8ethEjRljbtm1T7uP9999vt951v9XtfokVLJJzOFMkkmVzvn7BDu/WyV566aWU21u0aJHVrFXLaux9vJWtGz8YXT5ttC365UObP2+ulS9fPuU2jz3ueBsy7Dertd/pCfJlnc0c/Ijde+etduGFF6YVMHft2tUa9L7cipWNvxrZ/F8HWcWN823ypAkp80WV4IaNdrflxWpatdY9475n/YpFNvXjB913d+rUKeU+PvbYY3btjbda3Z6XWqEixeN8Z5bN+fZl69mltb355htpDYurXqOmVW13hJXbZc+471kxc5zN//FtmztnjlWuXDnlNk859VQb+NVPVqvrGVagQM7hTAp6Zn72iN16wzV2+eWXp9yeymzHjh2twSGXWfHy/wbeQQt+G2KlVk6zqVP+TDmESvnStFkLWxipYNXb9I77ng0rl9iUjx+0Lz4f4obLAchfWBoWyMdUMVXrZ6xPPv7EutY6PFQr3V5V97H3hzxvS5YsscKF8/YS8NRTT1mJ8lWsdM3ErbLl6reypWOH2IsvvujGWyezYMEC+3jgQKt74DkJj71Q0RJWun4be/jRx1zQlcrDjzxq5XdpkTCQkIq7d7EJH95r3377rbVu3Trp9vSeObNn2W6Hn5zwPcXLV7dydZq47376qSdT7uOjjz1upRu0ixtIiCqJ5Rp3sjfefMtuvfXWlC3MzzzzjBUrXcHK1Em8ulfZei1cvjz33HMpgzKVpffeG2C1EwQSUqhocStVv4098ujjbh5KKo888qiVq9c8YSAhlXbvZH9+cLdr2VYFN5lhw4bZtKlTbLfDj0n4Hn1XubrNXL6kM69Fx1K6Qdu4gYTPl/KNO9u7775id955h1WsmLiMieYfFC5e2srWbZHwPWXqNLWlY8q5PLzoootS9mj17/+21eh8QtxAQlSmVLYeeexxO+ussyydfClft2nCQEIqNVa+DHU9QepBTWbkyJE2aeIEa3j4NQnfU7RMJStfr7k98eRTBBNAPkQwAeRjCiQ0QTI2wFizdo2VK1Yp1DbLFa/kWgO13bwOJqZOm2ZFKjdOGvRoSEORinXc8ravvfZayvTIimRZyaq7JH1fyWq72uSvvsmRdvGMnzDJKrTsnvQ9RUtXsCKlyrkJ3qlagxXwFClT2VUKkylWpb59NPBj+2PypKTvU34rOGnQ4rCk7ytVbTfbuGG9de/ePeXwrmnTp1uRig0SVjD/zZe6dt9999nbb7+ddHurVq2yzZs2WalqDZLvY/VdbcqQz9OajK1JveWaHpj0PUVKlrOipSu6icmpnqWi3pgiyseSZVPmy6DBn6UsOzqHpk39y+of3C3lMW/atNF69uxppUolXiRBZsyY4c6F4LC4WMqzIpXq2SOPPGIffvhh0u2tWbPGlYlS1XZNvo/VdrWpvw5yPUGpehImTv7DyjROHiBoGJ4CAPVAVa1aNel7Fy9e7M6tIqXKp8yXMWPHJX0PgO2DJ2ADO5h/x1Bnhfq8KuPbSgErYJGsTSnfpzHwaR+7Ho2T4hj0nWn32hQokNb3Rzant80CaW9vs746re359yeT9U86p7WP/6RRyn1MMx2j+5iVIl90DGn3phVwaZ56H5WO6W0zrXxxx2wZlJ3k++jzLf2yk7vH7N+fe2UnzfKd5j6mfb5kbc7zxg8A4XBmAvmY5hB89dVXOV6vv0sDW7BmTqhtLlozx4oVLWbffPNN6CUh06VlUa+94WbL2rzJChaKf7nRHId1C6fZk8/3sz59+qTsmWjYqLGtmDXeytVLPBRk9ezxtnenTvbJxwNT7uOll/7PPvxymFnjvRO+Z+2S2bZp3Sr74IPvrH79+ikniHfp0sWNv082RGfdvIl25umn2W233ZZyH/fZr6vNnTU+aY/Mypm/W7kKFV2+plqO9I033rDLrrzasjZvtIKF/p3AHjuWft3CKfbwk0+4JU5TtYBvyZffrXz9xMt4rpr1u7Vr38E+/2ywpXLVVVfZ2598ZbbHPgnfs27ZPNuwapm989Vn1rhx46TbmzRpklsSWfNVNMws4TbnTrJTTjrR7rnnnpT7eFC37jZ11ngrVT3xpPcVs8ZZ6TJl3dKxJUqUSLo9LXXc96JLXNonGtKWtWmjrVvwp939wH12wgknpFymt2Hj3W3FzN+twm6J5+asmjXeWrZq7fYxlRtvvNFeeedjs2ZdE3/v8oW2fsVie3PQDymHi02ZMsX22msvW7t0jpWoWCvp+XJoL+ZLAPkRE7CBHdAll1xirzz3ul3f8SkrXDB+ZTBRr8S9Iy62XkcdnNZE3a2l4S/Vqle3sk0OcKu0xLPwt8+swMIJbihPOi2P559/gb3x/idW+4Dzsq3kFKzIzPjscXv3nbft0EMPTbm9sWPHuucL7NLt3LjDQdQDNPe712yvRjXtyy8+t3S0adfepi7ZZDX2PjZu6+zKWRNs1tBXXEUqVXAiWnv/nPMusLrdL3ZDruJNbp79xVN24Tmn2d13351ye2vXrrVq1WtYyd06WeWm+8V9z6JxX9qm2aNt3pzZaT3D4pJLL7UX33jX6hx4QdyKsCqXypfXX33Zjjkm8byFYOW/SZMmVu+As9xqU3Hz5Yc3rXndCvbd0G8tHZ267GMTZ6/4Zw5BznxZNfcPm/HV8zZ58mRr1Cjnd8bq37+/nXr6GVZP+VIm57BDBQWzvnjKzj7lWHvooYdSbk+V/xo1a1mRum2sSvP4w6wW/f6NrZv6oy2YP8+KF48/VyPo6quvtqeef81qdzs/7tyODauW2szPHrfn+z1jJ510UsrtqcxqxbC6XU+3MrWbxB3+pZXAdqtSzH7+6UdLx/4HHGijpyywml1Oijv0bvX8v2z6F/1coB72GS0A8g7DnIAd0Pnnn2/L1y61cQt/zuhzk5f8ZotWzbO+ffvatlC6dGnr9+yztvDXT23RuK9cL4S3ad1qW/DrIFs6Yai9/NKLaQ9huPXWW6xCiUI2d+hLtm7p3OjrGmKzcvYEm/PN83ZIr17Wu3f8lWFitWjRwi6/4gqbM/QV+3vKKNeLElxFZt6wtyxr+Rx74vHH0j7u5/s9a+vmT7b5P71rG1Yvy9aqvPSPn2zusDfdROl0AglRC3SXznvbnG+ecxXe4BC3tUtm2dxvX7SaVcq7imM61EL+wvPP2aLfhtjCMZ/b5vVron/btH61C/AWj/3KXnz+ubQfhnfTjTdalbIlbM7QF10rs6d9XTlnks395nn3ULNUvRyellLVsrRzv3/dlv010vWiBCvAWlJ40+JpaU1g9559+inbuHiqWw1J2/C0bX3H3O9fs+uvvz6tQEKOPvpoO/CAA2zO18+7Y8yeL7NdWlQtX9JtMx3qUXrxhedt8dgvbeFvQ2xTIF+UR8qrxWM+d3mXTiAh1157rdWoXNaVEe2Tp31VWdK+79Olsx133HFpbW/XXXd1ZVeBnMpytnxZvcyV+fUL/rDnnn3G0vXUk0/Y5mWzbN6w/u6c83QuarW3Od+9aldceSWBBJBfbe8HXQAIp3u37pFyJSpGbuj0dFoPrbuly/ORyqWqRTp22HubJ/k777zjHppVpGixSMU6jSMVajeKFCpcJFKn3i6Rzz77LOPt6UFh+x9woHugVblqdSMV6zZxD8wqWrRY5OKLL45s2LAho+3pIWB33XVXpFTpMpESpctFKtRtEilfo4F70N6erfeKjB8/PuN91IPC9mjaLFKgQMFIhVq7RirW2T1SrGTpSLnyFSKPPfZYxg8e04PCzjrr7EjhIkUipStUdcdctmptt489ex0SWbRoUcb7+P7770dq1KwVKVzk33wpXKRopFadupGPP/444+3pYYEHdTvY5Yv2zeVL+SqRIkWLRs47//zI+vXrM9qe0ui+++6LlC5TNlK8VNkt+VJzV3fMzVvuGRk7dmzG+zhmzJhIsxYt3Ta0LeWLtl2mbLnIAw88kHG+rFu3zh2bjlHH6vNFadDt4O6RBQsWZLyPAwcOjNSqXcflRYU6jV3eKI9q1qod+eCDDzLe3sKFCyM9evba8uDIf/JFZUhl6eyzz8n4AYpKo0ceeSRStlz5SPGSZVwabsmXgpE9mjWP/Prrrxnv4++//x5p2ar1lnyp0cDltfJF5+Tdd9+dcb4A2HYY5gTsoLQ6Tft2HWzFolV2ZvNrrXrpugnfu3jNfHth3F1WqLTZzyNHWK1aiccm55VNmzbZp59+amPGjHFDTPR8BT0EbmvmbWg4ysCBA91cipo1a7rhM6mW30xm9erVNmDAAJs6daprJdb+tWnTJvT2NOTjp59+cmPRN27caA0bNnQt8+m2KsezcOFCN7Zeq0ZpCVg9GKxBg+SrKCWzefNm90C+0aNHu3zR+PWDDz7YChX698FhmdKD0j766CNbsWKF1ahRw7Xgp/NMhGRzMt577z3766+/XL4ceOCB1q5du63KFy1JqmeMbNiwwbW2K1/SechhslWJ3nnnHZs/f76VLVvWDjvssLQfIJgoX7Tk7ahRo9z+akniHj16bFW+aIjSBx984PKlWrVq7nxJtQpWquFyekCj8rtIkSJulS7NS9maB8spX7744gs35MvnS6pVsABsXwQTwA5szpw51v3gHjZh4gRrVqWN7V3zYNutYnMrWKCgq4BM/XuCDZ8zxMYu/MkaNNjVhnz+WdpDawAAAFIhmAB2cGq11eo8Tzz+pI0dN8YKFSxkxYuUtPUb19qmrE22e+MmduFFfe2UU05J6wnTAAAA6SKYAHYS6okYMWKEW51IwxgUODRt2tQ6deq0VcMOAAAAEiGYAAAAABAKS8MCAAAACIVgAgAAAEAoBBMAAAAAQknvkbMAENK6devsjz/+cOvw65kSWtdeTxjWuvRhzZgxw2bNmuXWudezFvT8hgoVKoTenp5ToWdW/P333+45Bno2gta4DztxXc8I0DMR9MwBPV9Cz75o3LjxVq2Xv3z5cvf8C02u13bq1au3Vc8I0HM/9NwB5YtUrVrVPa9ia55joGdfKF/0vA7li9Jwa1YQU9lROi5btsyVF+WLjntrqOzMmzfP5YvKjJ4FsTXP/VB+KB19vtStW9el5dbki/JZz5ER5bHSMTfzRfmsZ2Hkp3yZOXOmzZ071z33Q+fL1uYLgG2HYAJAnhg2bJh7AJwe2qaVplSJycrKcpWuwoUL2z777OMeZqaHcaVDFfO3337bBg8ebEuWLHEVtxIlStiqVatcUKEA5dBDD3U/6VRCVGn77LPP3EO8xo0b5ypFqmDpYVnapirBemidHuyV7rM5FJDowWV6GJoqXKVLl3bb1TErwNCx6iFzeuhaOg/r0zb0UD79aNuqUOq4dbyqdKni2qtXL/dgr/Lly6e1j7/++qvLl++//95tX9tT/qiiqf/7fGnRokVa21u6dKl7oJweSDh79mwXjCn9lYbK7z322MP69OljhxxyiPtbKtoXPeRPD0PTA9u0DaWjjlf7q4qm8kX7mG4FVgGEHvSnh8CpAqz9K1q0qNtH5YMeoKiHzOmha+kEkNqPjz/+2OXLpEmT3Da0j8F86dmzp8uXdIPc3377ze3jd99957bvH6CnpZ9VzpUvRx11lO25555pbU/H6fNFgYSO158vStMmTZpE8yWd80X58s0337jzRQ+Wi80XHafPl1122SXtAMLni8qR9kNlRMG90lQPUNT5onzZmodbAshbrOYEIFepkn7eeee5SpAqVO3bt8/W4q2/64m5CjYUGKhC+NBDDyXdpiq/zz33nO23337WtWtXa9asmavIBAMNVcY+//xz1wty//33u8pSImqZvvDCC91TgFUBUqWlTp060b+rUjhx4kQbOnSoCwxUYb/ggguS7qO+U+/v3r27denSxXbfffdo5VkVMVVof/nlFxfAqEL31FNPJX1at47n+uuvd0GSgo+WLVta7dq1o39Xj4KeJq6Kt7Z7+eWXu2NJRPtwySWXuAq/nqSsJxWrt0SVTFEQpYBl+PDhrnKnACpVvqii+thjj7k8VoWvefPmVqlSpejfVYnVceiJxtOmTbO7777b5V0iOqaLL77Y7ZPSUfkSLDtKt/Hjx7sKt45bFeG+ffsm3ccnn3zSVfyVhsoXLZfsy44CPPUCKP10zKoY63iSPa1bgee1117rWs61TVXuY/NF79H+/fzzz/a///3PHUsyyhelld7n88WXHZ8vP/74o9tHBSqPPPJI0u2pjD388MPuKeE+X4LHpDKgfFHZVg+D8kXvSUTBu/ZR+aB91FPhY/NlwoQJLl+++uord95fdNFFSffx2WefdYGJ9m/ffffNkS8qL8oXndM6Hx999NGt6vEBkIciAJCLNmzYEBk2bFha783Kyop89913Kd83cuTIyMqVK9Pa5qxZsyJTpkxJ+p558+ZFJk2alNb21q9fH/nxxx9Tvu+HH36IbNy4Ma1tjh07NrJkyZKk75kwYUJk5syZaW1v8eLFLo2S2bx5c+SLL75waZ6Kf28qI0aMiCxbtiytfZw2bVrKNF+wYEFk9OjRaW1v7dq1kW+//Tbl+/QevTcd+m7tQzITJ06MTJ8+Pa3tLV26NGW+iNJaaZ6K3vP555+nfJ++U9+dDh2LjimZRYsWRUaNGpXW9tatWxf55ptvUr5v6NChaefLb7/95s5ZAPkTPRMAAAAAQmHOBIA8pSFFGtak4TrVq1ePvq7hTp988okbq54JDSPREClNTN17773dcIg333zTjdk++eSTt2rCr2hoioZXaA6ChgNlOrH75ZdfdvMENA5/Wzx5XMNLNDm7Y8eOGX1OQ2c0JEz7qXzRUCTNUdAQFg3fyYSGovh5MPo3t2iomYbCTZ8+3Q1BUp7rO7R/YdJWZVHHqCFKGoam4UgaqqUx/hrrH2aS8w8//GDffvutmzwstWrVcsN8WrVqZbnt+eeft7POOivt92sYk9JJw4hyM1+SDYfSXJxMz2kNU9Q55/NFQ9k0NE1DujRnaVvsO4Dw6JkAkGdU6b/yyivdGGjRvINTTz3V/V9zKg4++GBX+UiXJpUef/zxbvy/xlGrYqUJzxq/rQqJAoB+/fplVBF/5ZVX7N5773W/v/766/bEE0+4cf0LFy50Ac8bb7yRUUChyo8CHM3B0OTR3r17pz05OgxNSldl++qrr077Mxp7f9NNN7lJ8TrGyy67zB544AFXAVal7pprrnHzRNJ1++2320cffeQq+5oAr8pkMHAMe1yaG6AgVKtsKe8VPCrY0+Rw7W8mlUzNDTjzzDPdhHhNNr/xxhvdPBdtS4GV5uJkkoZy6623urkRWm3JTz5XhViLDpx00kkp53NkSoGe8iddd911l5vIrnksyheVR628lFeUjkoTBffp0pyVM844w+Wl8uWGG26wBx980J2DaoTQPJfrrrsuz/YZwNYj3AeQZ1Qx12TsE0880a0Ac8stt7glOTNpXQ3S6jmanKqJwVrxRdvVCkmqzGpFGVXcVZFPd/UltXb7pVH1eU0K1QRcTVxVJfvSSy91lVodQyY06Vet3gp0nn76aTvggAPcyj7prsQTW9kaO3Zswr/rezLtjXnhhRdcZVoTmD/88EM3AVeVc/VuaPKujjmTYEKUVqqoq/L64osvWqdOndwxKwDIdCUeBZqaoK5J96pUKnjUPtasWdNVLM8//3y3CpACy3S99dZbrqdJgZJ6ZFQGVeE/55xzXCCgbekY0llxyqe7JkVrxSRNblb50aT+4447zm1HFWSlQSZ5rrzIbZoIrZWhlC/qNVMeK1+0b5n2xKgHTCs6JVuCNlP9+/e3gw46yC024AM+paHyWL09Cs41iV0rUQHInwgmAOQJ9UZoBaMTTjjBVVq0So2GaZx77rlutR4tc5kpVfo0ZEPbU2u/hhP5pWW1rKRWpNGqOOkGE0H6nFaLUSAhailVpUtDsTKlVZpUKTr99NPd6kiqcOq4tXLVEUcc4VqI0620KghTUJYoYFAPTarVguKlo1/5SUOG7rjjDtfqLapsqsU+U1qJRz0Sxx57rFspSMd81VVXubTQ68rvdHtoVIlUL4df+UlDddSL9cwzz9hpp53m0lUV70yCCeWv8kDU26GVvDTES7Qak75PvVHBVb2SUW+QPu9XSVL502pj6o1TT4+OWf/PJJhQPuT2ikVa7ldpr2FcWv1L+aLVqJQXKoeZLF+r3r977rkn4SpkCsCTrYSVKF+Un6JeHfWc+HxR8KjfFaSku9wsgG2PYAJAnvDPRNCykb4SqUqbWpzVGqzW5kypYuF7EkTbDS4Rq16PdCvpsZUgv714xxGWWuQ7d+7sflQR01KYah1WxV0Vp3SpByHREBw/zCkTqrBqXX8NRVKLr378QwSVFn652LBUgdaPlqvV2HcdtyqZ6Y6lV0VfFUjto6+4av6EX3ZWw7MyHUevbWqolKfgLNjarePO5EGKSkP1cCho9mVEw+bUcyZ6Zol6KzI1aNCghH/zAV9YWl5YP8F8UZnPJLDXkMJEw5j8MKdM80X57Cndgs+9yDRfAGx7BBMA8oyG92jdebV+emqd1zAg30qcCQ290ZAHP8ZaQyM8BS1q0U62Xn48aq1Vr4moohWkYSya8JsbVHHX0I2zzz47VCCVLGDJNOBRpV4Tj9V7os8G561oTL6GwOQGVVQ1KV7DiTQePl2q6KuVX/Nr9K8qmypHetaAH7KklvZMj1lzYvzkcg3F8lRuNBQok3keegaGqKdEvWMajqaypOdkaJiWJj+rFT83bU1gG6RgTHmiH503uUU9SGHKovLC964F5zwpAFcgoUntAPIvJmADyDOLFi1yK/1obkMsteKqdTTTSa9qoVYPRWwvgibmqiU4k8q/3q8Ki6cWUV+h1PChV1991fWiZLJykMbyK4hK9kC6TMepq8cl0+EjyehBaKNHj44GUUGqzGkoVibfp0nIamHWg8dyi45ZlX+tvuSHUGl/9bpWZdJKP5nSxGhNMo/tvVJgpQprpnNaNDFcFWGN9dfxK3BSy70q6Aqe9FomtJ1MeqxS0SpTysdkDwrMtNzonA4+pC83KF+U9rFP4tZKXjof82JlLAC5h2ACAAAAQCiZLbEBACmo5VhDUtIZyqPx5uq5SEXDjdQKnG7rrpaUTEbDWjRJOB0asqIW3nRa59Vymw71ogTnfmyN33//PaPldVPJyspyq1rlJqWfepRyi1rHBwwYkPHntAKYngsRr3wo/4JzKtKllbbUsh47P0LDntT7EyY/dWwzZ86Mts5rHkKYhQBE5416AFVGdE5q8r2WjNWk/nTPqVQ0d0S9MrlJQ/CUFgDyP+ZMAMhVGhKklXY00VoP72rfvr01bNgwOrREw4c0UVMr3eh5B5rj4FcWSkSr7GhZWa00tP/++7vhNH4yriq/WhFGwYECEwUK/rkRyeYZ3HfffW5YhcZqa8y7VoDy4701tEiVX1WCtRSmlqfU2P1kVPHRKkgak6+hVnr4ll+BSZNIp0yZ4uYjqHKo+R56hkJu0AO+NAFb6/HnBqWnhu6EmdOSiCqyyucmTZrkWjCh51pkMnFYw9lUQVUQpyBWy5GqTPly+dJLL7khNZk8U+S2225zyxWr3Gg4kfJfK4zJr7/+6irZmQzR0bK8N998s1tZSpV9LTGrpYq1upG2rUBdKzClS8OtNC9CQa6WQdb/9R06TpVXBUEaSpZJGmpFrVg6X+bMmePSU9vOZCllrfCmACeWVoLT0sDKDy3jm9tDqwDkHoIJALlKFeXHH3/cVabUwqqHwql1XxNcVVFVC65WbFHlXJX+dCqYeuCWVkTSspYPP/ywCx70PQoGtG0FMKqs9unTxwUwqVb6UYVHlSgFClrRRttUhV/7qAqbKl4a764gR5VMLVGZiibeaj81Z0IP3tLTgFVR9Q/j0v+17Kwq6ZlU/NXTonkDiaj1O5MKsFqn9fyLZH/PlIIkBUuJ6NkfmUyMVw9Bsh6r4DyXdCm/VdHX8quac6FAQA9U1APSwqwWpHxRb4bSUmVFDzfUcyYURCuQDEMrfSmYUBlWpV/7qP1TEK3AVhXvTIIJBa6aC6IFD3TenXLKKW5Oh54pojKuuTEKRtOd67JixQo3v0T7o5XaPJ07KjcKWnT+ZEKNCtqulusN0va0LW0zNxcsAJD7CCYA5Am19utHrcAa8qHKtV9SU70AmUxqFvVEaDK0ftQyrWEg6uXQyjSqIAUrN+lQ74QmSutHFRZVhlWJVaVfE7zDrCCjlaquuOIK96OAR5VWVYi073pKcphKqwIJPfgu0TMa1PqsVvZ0KT/Uyp2bLb2qtH733XcJ8yDT4UNKN63clOiZC0rTTCe4a2iTVtJS/uqZBar063kQeohdqp6sRNtTgKuyJyqXCir0kDjlVxgqM+p5EwXbCkp9z4Z6JxRoZLo9Ba4KaDWJXQGdr7Rr+V9NetZ70g0mNDlcQbMCcC0v61f98kvC6sGCmdICDPqs8lqrnflJ2FolS79nujobgG2PYAJAntIQEA1z0k9u0TAQ/eQWVTD98JTcooefpfsAtFT05ObcfM6E8iTR05bVyhxmadi+ffsmfI6EnlCeKQVfr732Wty/aSWwTCuuCsaCcwRUadWT1NWbcNNNN7les0y3p+E9QeqZUmCqtMj0QYKiHjANw1Kgp/1TAOAr1wqgYlc7Smd7wadSK/AOPvxQvROZbFMBuOZGqHxoSNMXX3zhArKtoV4c9V5q+JRWfdNyz/5BlAB2DEzABoAdmHo7tvYhc0HqMQo+zC03aP8yfchcqkptphVrzWXRsKQgbUNzV9Q6r9b1TKjHQJ8LPnBNNL9GT+bWsKdM6XksmiztBSf+a9J+pvNi9NRwPYHdTw5XxV9DAUWvqccwzLKr6o3RvBr13ukJ9+qV2tryoR4P9bxoGJp6isI88A/A9sHSsACQj2kYk3oLEg1zCkMTXnNzmJOGMSmoCT6NfGuoxVzbzPQ5Dclo3L2eoaEH4cUGIuqxUMu4/qYhbplMLNfQrngt6eoxUi+A5j+kS/mshQTiPela8yn0JPRMnzeiIEm9eLHDwvQ96gXxD/ELa9KkSXbnnXe6+Ua5sQqYgghNOtecGc29yq1J+wDyDsEEAAAAgFAY5gQA+ZDmBWh5zHSohXnkyJFJ36M5AWrtTWdugCZpa9neVLS0aOwwn2QrOqkVOxmN70+2clVsC3Y6z//Qe9IdMqPvDs4xiEdLBqc7R0WLDmh4UipauSmdfNF70skXfadf8CAVHUuqZ4BowQOt2JUOpbVWSUsnX7SAQjq0YlmYFbwAbBtMwAaAfEgTbjU5WBORtVpTy5Yt3Rh1vwqWnr2hZwXoAYGqBGsFqWT0uUGDBrmVhjT0pkOHDm5SvB/yo0qghsToAYFanUmr/mjMfTIaiqT5AVryVqtiNWvWLDoMR8OKNKdgzJgxbqKuAqNUk6Y1MVxzGLSv2se99trLrfzl51toCVEFWRqjr/kPmvCd6vkfCmDuuecet396/oeGzWhpYj+sSPMGlH46Zu2zlglOFWhpfL9WhNLSwVoRKZgvqnj7fFGAl84EZQUIGmbl80VpH8wXPa/C54vSI5180VwG5YtWh1K++AUL/IPrlC8KLhVM6CF2qfJFQ4/8YgDKF82b8PmihwEqXzTsS8et4Vhdu3ZNuRqWyoPPFy2AEMwXBZ/KFwVaOhdS5QuA7YdhTgCQT2llIE3I1YPRVCn2k6PVoqtKrSp0qoDq2QNaqScdqkTq+R+qkOv5Fxrrrgqmtqk5D6qc62FwqoCmQ/MN9PwPPX9Ay/XGPv9Dy47653+kM1Fc+zJ06FB7//33XWu4KpLaR1UwlR6qFCu40kTndOd9KKjR8z9UeVYvjn/+h/ZRcz207KrScN99901ryWLth45X+aJKtGgffb4o0OjVq5fbZrpzXcaNG+eeWeHzxU+C9/miCvfRRx+d9lKpyheloQJIBQzBfBHlS+/evd1++gf3pcoX7Zu2qSApNl8URCpftI9169ZNax8V1Ph8URAWmy+aO6I0VJnMdClpANsOwQQA7ADU+qtWdP2rSpyeaeGfAh6GKr1q/VXlWlRJV6u3VkoKS9tSQKHKoFqZFexszaRsTcTW8z80lEqBiCZHb+3EcVVg9RwLbVuTktXzszWrYWmCvPJFvSa5kS8ayqTt/ZfzRQ9hVL6kE+QA2P4IJgAAAACEwgRsAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAAAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAAAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTQBKRSMQePP0l+/Gj0dle//2HP+2+k5+3y/e512474kn75s0R2f4++48Fdm23h2z132t2iPR9597B9vj5r2/v3fhPWDB9iV3S4U6bOXHeNvm+b9/62R47/7WMPpOVFbG7T+hnv3z2u+1M7jvlBfv4qW+2927s0J6+tL+9cfvH0d9fvPZ9e/6qAdt1nwBsXwQTQBI/fzrWVi1bbe16tYy+NnXMLHvuinetTY/mdufgS+2oK7rbJ09/a9/2/zn6ntqNqtmue9a1Qc99t03S97VbPnI3+bBUeczKyrKdzZ+jZriK+98LV1h+EbGIZW2OKFLN8+9SMKsy2PPsfTL6XMGCBaz7GZ3to8e/so3rN1l+cevhT9qQl34I/fmsTVlb0h6hRTZnT8NIVtZOee0AkD6CCSCJr177yToe2soKFf73VPnshR+sQcvatv8J7a14qWK2x9672j7HtLEhL/xgWZv/val2OqKV/fjRb7Zmxdo8T2Pd3HWTR6wtFfdtUG/Pl4Z9MNrKVS5tu7Wul/FnW+y3u23elGWjPh9v+YXOr0jWfzQzASCfKry9dwDIr2aMn2vzpi6yVgc1ib6mytWfo6Zb97O6ZHvv7u0b2Jev/uiGN9VtUsO91rhdAytSvIiN+nyCdTlqr6TfpQrb16//ZPOnL7biJYvZXgc3tT4X7m+FixRyf18y92/78NEvbcpvs1yFql7Tmtb7gq5Wu3F1N+Rg1JAtw1HUCi89z93XDj69c9zvWjrvb3v3viH2568zrEixwrbn/rvb+tXrc7xv0oipNuTFH2z25PlWqHAha75PIzv8fwdZyTLF3d9/+3qSvXTde3b6XUfYV6//5IbvlC5fwvY/qYN1PuLf4/3t64n2+cvDbdHMpVa6QklrsW9j63F2FxeIyfo1G+zTZ4famG8m2apla6xynQrW69x93fuS0fCKOX8usBsHnB/37+pBevyCN9z/bzn0cfdvxRrl7eYP+rr/T/xxig1+7nub89cCK1qsiO3evr4ddsmBVq5KmWzH9/Cwa61goYLZhso06dDApX9wO/OnLbISZYrbHnvvZoecv5+VKlfC0pVOGqRKx3hGfDLG9ureLMfrT174hpWtXNoq1apgP38y1lYvX2u7NK9lx1/X06WRKIBusV9jG/HxGOvQ+9+euVj6rMqmysv6tRut1m5Vrcc5+1ijNrtE3/PzoLGujGjfS5YtYS32beTKt9/3Hwf+ZgMeGGIn3dTbnUeLZi11+3fwGZ2tbY/m7j23H/W0LZ233Ab1+84G/9Pjd+GTJ1nDveIHSlN+m2nvP/ylzZ+6yEqVL2H7HNPWDVsM0rmj3kcpVrKYVatXyQ46be9s6a4hgCpnKv8/fzrOli1YYdV2qWRH/O8ga9CyTrRn77MXvrdfBo+zlUtXW6WaFazL0XvZ3oe1sgIFCsTdv/nTFtuHj33prjN6T/0Wta1P3/3dtt02N2e580/7t3zxKitftYx16L2nHXjq3q7nKN19i2f673Ps4bNedv9X2a5Qraw136ex9TxnHytWsmj0fdr2gPuHuCF5Ktsderdw18BYmzdutk+f+dZ+/XKirVyyymo2rOp6bNVD661YvMoNMZvw4xRbt2q91WpUzQ6/5EB33N7/Ot1t3U7rZLMmzbe/Rs+0itXL2TVvnu3ybWj/kTbsg19dGShdsaR1OaqNHXhyx2x5uXTucmvUdhc3PO/vhSutRoMqdsTlB9kuTWtl66375JmhNn7Yn7Z25Xqr06S6HXrhAe6a6o5lU5Z9+epwl+5Kz/LVytr+J7bPdk0DkB3BBJDAH79Md5W2KrUrRl9bvmiFbdqw2Sr9U+HyKtYs5/5dMmdZNJjQDX+XZrVs8s/TkgYTP7w3ylUKevfd31Xa/l600l654QPbtGGTHXN1j+gwphKlitsVL59uxUsXt5kT5trQt0faiTf1tuOvP8Q2rd9kq5avtfMePm7LdxeKX4FRBeWZS992x3XVq2dasVJFXUX4lyHjbbfWdaPvGz/sL3v2srdt32Pb2qm3H2Yb1m2yN24baK/c+KGd/8iW79ANXq3+quCqElq5dgUbNWS8vX33YKvVsJrVb17bFs5cai9d974dfVV326tbU1u7cp2N+Wayjf5qonXss6fbnycvetMNQ9Kx1Glc3VWoX7r2fbuk3yku/RJxwyuS9MaoMtX38RPsib5vuACiXJWy5ut1sybNs2f+198OPGVvO+v+o2z132tdZUT7cvXrZ7uKtD++2F6N4FCZlctWW78r3rEeZ+9j5z58rMszzadRRaTrCe0T7ltsnqRKg1TpGI+2p8/Vb54zDWdMmOfSr/uZXeyq1850Fa/nrnzX+t81yC54/IRAGta2nwb+ZuvXbrBiJf6tZAa999DnLpBUxb5cldI296+F9vUbI6LBhIKgN2772I687GDbq9sebp9evfkjWzZ/hUszUW/DhrUbbeg7W8p0heplbdj7v9rrtw50AbMqhde/fa4b5qRzpNs/gXKicq5jf+rit6x9rxZ2zoNH2/rVG+ytOz91+6Zgz9O5c9y1vdz/161a5wLIF695zy59/tRoBVSBwtQxs61KnYp23iPHWtESRe39h79w8wRu+ehCK1q8iP344Wh3Pp7zwNFWu1F1V+HVsShg0L7H88LVA9yxXf36WVakWBEX/H737kg7+sru7u8aOqnK88m3HOry4Y9fZrhzcNOmzdFha+nsWzwqUw/9cK37/+ZNm23elEUufdauWmcn3HCIe33tqvX25IVvWsM29eyG285z3/Xeg5+762K7Xi2ybW/C8ClWtW4lu/jpk1xg1P/uQe74bnzvAncd1LYUvCh4PPehY61ijXL2w4BR7ty8YcD5Lphxx7M5yz5/eZgdf30vO+mWPlb8n8Dm/Ye+sOEfjrZjrulhzTo3dA0cL9/wgRUpWthdo7Z8NuL2rUqdCnbBY8db4aKFXYD6wlUD7KYP+rr3bli30R4551XXiHLaHYdb9QZVbM4f89012AcTKnO6/p1wQy9XhqeMnmWv3vyh2/c2B+cMzAEwzAlIaMH0xdFWWk+VatGNKkgt26KW2aDKtcq77SSim6cqDa0O2sMOOnVvK1OxlKtMqkKhm+fyRSvd+xbNWmaN29V3+6OeAfWEqNIlrpWyQAFTtUqVYP0kag0d8+1kWzBjsZ18Sx+rWq+Slatcxn2XD4a8j5/8xlWmjrysm5WvWtaq1q1oJ93cxyYO/8u1pAYpkKizew0rUbq4dT5yL1dR+POXGe5vS+YucxXF1gfu4f6u/Vcl21eAx33/p00bO9uOv66XNW5b37Vaa1hZqwOb2ODnv09aOk+/+8iEvRKer2wWKLglXXwPg4aqaZ/Vu1C2UmmrsWsVO+XWQ12lSpXfdP29YEtwqf1VT4R6NTod3jrtQCLdNEiVjvGogi+xga9a/RWM7H1Yazvg5I7u+2ruVtXlnVqDg633lWtVcC21S+b8nfB7Fs9a5iq7ahFXhUtB3Fn3HRX9u3oSWh/U1A0FLFW+pGuJPuqKg13QFTsJ/aSb+rh90TEq0FNvwV+/znR/83lXoGCBlOX8mzd/tjIVSrnvURlXWT/51j7us0EFA9vSvnU6orU1bt/ABcVBOi9VwVW6ly5f0nqc1cX1QKi8uDSdvcwq1yzv5kmpZV/l6bhreyYMJJTGi2cvc0MkdX6p7Kh3wQcS61avd4s6KNhr1qWhyyP1IB5wUkf78pXh2eaxpNq3RPxxK+BQRbrXefu6Fn2f/8M/+NUFGgouKlQvZ5VqlncV/GDPhafrwxGXHeTKv3qU1Luj41MvqN/WknnL3Tmrxhbtp3p31YPx1Ws/ZtuWel/a9WzhrnPKcwWGCrIOOKWjCw6VVroW9jhrH9dzE2xQUO+NGmC0v0oX9WwpUFaPmCjIV/lX+VQ51Xc03GuX6LVUwabS4JDz9rM992/i0r35vo2s64kdbHC/bTP/DdgR0TMBJKDWtOKlst84ixbfcsqoBTpow/otQURsS6AqV2tWrkuYxgtnLXXDRFSRCGqwZx13k9SwKd2g9z2mrQ188mubNm62aylUpVOtkZnSzVLd9rrZeqpQ1Nujlq1cusr9roqKhjf4YTyevq9MpdKuAuhb8aTaLpWzvU+VMj9PRJUrtb7ec+Jz1uqAPdxxqbWvROktw1umj5vjWglVOQjS5zTsJ5ktQz3iVyZTpsOfC9ycgCBVOFUBmTdloZk1TWs7NXer5obZPHLWq67yv6uOr239jIY4pZMGqdIxHrUyS+wwKOWf6uAHnvLvEBH3vpJFXUUyWEH3n00276fzUa1dj4aCisbt67v0UM+UqDKqoGafo9tk+8xurepGy6PvydOQPvVuBSkdw8w5mjtlodVrVjPb8DRVtlXZDFLPwaB+Q10eqJfJz8koXCT7EDsFVRrqF92v8iWzpUvbHs3cUK0HTnvRVf51fPVb1Mk21ypIaawWdaWbhodpTkvjtrtEGy+UZps2bnblKUj5rvNTFWIFXensWzwKEL94eZgbXqnK9oZ1G6Kt+wpEFGArDZWPwR4pVb6r189+vvtzJ1huSkf3YZ1ZrS3DqjSETEFHkMq1emSCajXaclzBXjTtV+w1ctdWddy+Lp2/3KWBVKlbMVue+/3QNXbLtua661hsI5Gn/ZTm+zSM2c86LphQkJdsWCHwX0UwASSgipputEEaKqNKj4YxBGnIhqj1Lkg3Hz/HIB4/mfTVmz6y127+KPvfIluGa0i30zu5lkkNJ5g8Yprr9t/zgCauhyFR62z8L4zYlj6M7AoE6jx+ZZZPnvnWPn3222zv0009dmWkeN/vG7cVXF3+0hlunolWVvr8pWH2+i0f2Yk39XHHo8qrKkcaKx37eaWNKqPBilJuipdsOpZUk7W1GpOnyuJFT59kU0bPtMkjp9u3b4+0124ZaMdc1d06JOk1yLa9NNIgVTrGo9Z9XwbVWuxpPLoCSgVOQVPHzrY6jbdU7D191m0rSRlWK3LD1vVs/PC/3JCQQc8Otbp71HTDWbb0DEUSx3zBxE7wnth5DmmJCYqiXxHomVC6apiNhvdpaJcCDQ03evHa99zfsn8u+e6r0n3Lh33d+amyoDKgSq2G2yQK+jU/R8OFJv401cZ+O9kNdVSvlnpT/v3i7McQ/5iS71s8mnOi4EdzVBTMFS9dzCb/PN2evuSt6JwI9/l450hM706i14I7oSFS6qH1c7qCf44N8AoXKRz3GvnAqS/E/Qpdo30wkfha+E9ipDq3//muWw57IuF3Va9PMAHEIpgAElBrm8bOBqnyuGurum4ehCr4nloXS5Yr4VqPgzQhUNtJRBUNBS1HXanx5DnH4wbHhGs7+tnv+HauQvnYea/Z/id2cJMctV+6YadSY9eqtmzBcjd8yk80VmvszPFzXQVT1BKp1kdNAj/o1H+PMWXFIQHtm4Zl6Ue9HRqTPOSF710lWBUZDZu487NLc1Qi/Ge3hg9END8gNh3UyxOkYRnqndEQFSlZtni0hddXvDdu2OTyNEgVGLUs60eTpjUZWT0K6QYT6aZBsnSMx5c7Td4PlsFZE+dZwYLZ01UTTX/9YrwbmhOkz6pS7CtriainSxNU9aOhLTf3ecIm/jTFTWTWWPppY+dkm8Dq0175kIlMyvm47/5w7/WTlXWMGpbmLZyx1J0HmvDrW9sVRGg8vi8DmVDwpvks+tF2bj/yaTfvQ0FDIupd0M8BJ3Vww+teuPo912NUtV5Fd6xKJ99zIxoOV7hooVC9kkFafKHlfo2tScddo69pHla2fdu1ik0Y9pebZ+B7XNVbqwntytNM1N2jhi2cscSuefOcHH9L1RZSZ/fq7j2XvXB6jutrsnkzcbfVpMY/k7NXuOFl8fZTbhxwQbbe29y6HgE7K84MIAENJdLKOhpSEKRxuH+NnmHfDxjlKg1qLf7unV+s26l7Z7vZqEVV3ebBVW1iqZdDKy8NfOIbV/nS8Cn9TP99tmshXbN8ravsP3XRmzZ55DTXUqyVf/xKTL5VTz0iulmnekhey66N3Qo+r9/2sS2bv9xVlD945EtbHDMm/pALurplcVXB1ORbHaeGPr115yduiFC6NIxi4BNfu+Ekbuz93L9t3tTFVvmfylDLrru7ipx6ZhbO1Bh/DbNYZb98Ns5VylOt5qQVfpLR/A1VRNTqHmzh7nZGJ1cx0zMLVEFSIKFJwqp0tzpgy+pdmruiIQ0al6000Go0/e/6NNpaL2qJ1iRPDdfRsBRVUjQ0LZPKXjppkCod49GkVg0rCQZNSoNZk+fbiiWrXMu09ln7rgm7qli27Zl9Yq3SSEPa4o2T9zTRVhV3DSVRsKVAWwGnD0A0Nl4VuJ8+HuMqpqqsK820IlZwuFw6VM4VDKV69sV+x7V1gYLST2VcQ2GUv8HnIyh4LlqiiEsH5e/yxSvtzTs+zdHrmA49j0OBg84pna+aV6T00Kpc8Sj9NeFdK07puzUkTb06aljQMCUFJvse284+e/57l55KNzVsfPX6jy7w0Lm/NarvUtk1gOiaoW2P/nKCfRkzd0ErUSm13r5nkBsCpvKv83/d6i1DojKhQFLfo3z3PZtq1Pj+3V9yfG+8PO90xF725p2fuOuirkU6B7X/L1wzIKOeWc25qFCjnL14zfuuHKoc6fzQMYrmUWm4ohYI0N/VS7tm5Vr7/fs/XE8ggPjomQASUEVHlbzRX07M1guhMeGn33WkWwpxwAOfWZmKpV3rpiazBmllkXVr1lvrbnskTeP9jmvnWr61qtILfw5wFQUtm6gJtn78s/7/xcvDbcb4LWN69ffzHjkuOia481F7ubHHNx/6hG1cvzHh0rBqqdeKT2/fPchuOexJNwdEldnWB+3hKjieWi2L3Xukq0hrlRdVDjWZVKtS+bHa6dD4cVXOVOFUUKYJjU0772aHXbyltVbB14VPnugm6T598VuuQqcJsxp7n2hp23RXcxK1PvY6fz83LEzDyPzSsJpcfvb9x7jx8oOe/S46Z0GTdH1vhvb1jLuPcMHWNQc86CqfqmAFW601f0XH9cpNH9rC6Utc8KF915KX6UonDVKlYyLtDmlhoz4bbz3P2df9rqBJk6/PvPdI+/r1EW7MvspQ+94t3Ht8K74obTVhX8uFJqOJsW7Y1W0fu1XFqtWv7JYL9uVELfVaClRj9N+84xM3ZEpLwx5+6UGWKeVl/zsH2VX73+/2L9HSsMrncx48xq1spIdJ6hhVdoNlXBX3M+4+0gUc370z0q2S1rxLQ7daUHAoWzq6HN3GHZ+Wh1UDhMrKgad2dMOW4tGchHY9m9snT31rMydt6SlSq/gFT5zgVh2SPhd2tUJFCrqKrZZbVU+ilrfVpOyt1advV3vrrkFumWMt66rriXpEPn3m33lKKmPnP3qcW0b6hh6PuPRRZTy46lu6NPflshdPc8f74OkvuUBLgb7mQWjhiVS0itnQ/j+78qrVwDR0VCtSaaJ3JhQUX9rvFBf8Pd73DduwdoMLIA67+IDoe7R6nZYn1jmt1flKlSvp5kx0OyP59Qj4LysQCTUgFfhv0LKYQ14a5lYNCk7sS4eW8lSF5bjrtiw9ua2okqXKfyYtdm7oSCSS0TH6pVNju/5dBb9AgWwV07yQ6T6HOca428mF41PvwrYYMrHq7zV262FPumVDNdlVPRwaHvXA0KtSzkVRIPHOPYPt5g/7JlxiNLeoLGm8emzeJCvLSkMNccmonKeRd34YlX9PonIT5vtzW27vWzrlMm76BH7PZFu5ce1KtF/5KZ+AnR3DnIAk2h3S0rU2j/jnwVbp0tARTazsdd5+2zx9VbHI9MapG3CmlWx9R7zKgraT14FEmH0Oc4xxt5MLx7etxl6rRf7gMztHV4XS5GvNJ0gVSKhyr16b3n275nkg4ctSvLxJVpaTLQ27NXnnykngPYnKTZjvz225vW/plMu46RMnTcOU8TDXrnT2Y3vnE7Czo2cCCNnilbKlNZLZZ4C84luJHzv/Natap2JavWXbqvcEALBjI5gAgP+IbTUEDQDw30EwAQAAACAU+rABAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAAAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAACCYAAAAALDt0DMBAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAAAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAAAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAAIJgAAAAAsO3QMwEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAAAAABBKYcunHnrnC3tv6Gj3/yuP62aHddlze+8SdoC8yk/7kt/SgLTBf82vf8y0ix7t7/7fZvd69uhFx9rOYmvP50w+v62vHVyrSFvsWOdI6GBizboNNmDor/bdmD9s1sJltmHjJqtYtpS12LW2HbVfa2u6S82t2rEly1fb9PlL3P9XrFm3VdtC3spPeZWf9iW/pQFpE9+wcVPsqmfec//v2qqx3XHWodskn5D3ebFuw8bouVC7SoVcTfK16zfYgZc9ktZ7rz+5p/Xs0CxXv39rz+dMPr+trx1cq0hbbJtzJLeuuaGCiV8mz7BzH3jd5i9dkeNvP02YZv0+/t7O7NnJbjn9ECtYkJFUAPKvNes3RC/KC5blvKaBvIgnKysSLTeprPyPNmwA+G/c/zIOJvSlJ9/xYjQSOrjtHnZO7y5WvkxJF+Hc8+ZnrtfihUHDrESxInbtST1C7djlxx5kZx3S2f2/crnSobaBbYO82jGQT0DuKVm8qP3wxJU5Xl+1dr2detdLtmDZSvd7lfKlrXOL3Xbo85lrB5C/XJ7P6sgZBxMPvv1FNJBo3qCWPX/VydHeh93rVrcyJYvb/554x/2uHorTeuxtNSqVsxue/8i+GT3ZvX73OYfbPi0bJu1iefmz4RmPB1O389vfjLJvfp1scxYts+LFiljrhnXt3D5drFagi1utRK9/PsK+H/un610pVKigNahR2Xp3amG9OjS3AgUKRN+byX4//M6XbuiXXH3CwdanU8vodn4Y95dd/cz77v8Htmlit57eO8eYtwY1K9sj735lU+YusltO7+22qxvTG1+MsO/G/Gnzlix3ad2wdhU7ct/WduBeTdLOt/HT59rj731jf8xaYNUrlrULDt/PShYrGnc8cV7n1dIVq633tU+6/1etUMY+uOP8tI4h3fwN+mvOInvw7c/tz1kLrU61inZen32s/R71o39/68uf7YkPvo3+XrBgAStTorg1rFPVjtynle3TstE2S0fJrfyOJ1k+TZo53x4b8LX7N9VxxZbbZg1qJU3j3DyuTLaj8/zFQcNt2O9/2aJlK61UiWK2a60q1nXPxtajQzMrVqSwnfPA6/bLpOnRz3z72x/Wqe99OY45nkzzN0y6JZJfrmGZ5kmya56Gyc4ItPRnkhcyYfo8e/z9r23yzH/PzYKBdIi1tWVSaVy/RuVsr2VlZdlp97wSDSSKFi5kz115stWqXD7j602q+8PoP2fGPZ/TKffJ3PnaIBv00+/u/3vuVsce7HtUqPtxIlzH07/e5UWdJplMtpPp9Wx73Te213X6oZjtNKpTzV1fdf7WrlrBzj6ki3WJaWTIJP0TnZOZ7P/W3P+2KpjYvDnLPh85Ifr7ad075hjGdMQ+e9ptr3xiy1ausQ2bNtvXv06yEw9q77pPfFeKTpBUXSyZjgebvXCZHXvrczm6nUf/Ocs++OE3G/fSTe53/f2Ym/vZnMV/57gRffLjOOverqk9c/mJVqRwoeg+pbvfusE+8PYX7v+vfzEi2434rS9HRt+/f6vGOY5xxMRpdvlTA9wYX1m9br3NXLDUjrmln5uTEjRxxjwbOGysnXBgW7v//KNSps3ISdPtuFufj2578qwF9sO4Ke6CFG88cV7n1abNWdH3+33KrfwN0kl7zxuf2fLVa93vE2fOty9HTbTB917kTjDR3+INVRgzZbYN+PZXV2Hy0X9ep2Nu5XciifLpt79m2dE393M9iukcV3A7o/+aZdc992HSNM6t48p0Oyfd8aIbkhlbXpSvh3Rsbs9ecZLNXfx3tOInSoN0x9hvzXmSTrolkp+uYZnmSbJrXv2srNB5MeqPGXbsLc/Z2vU5z8148upcu+v1z+yrUZOiv99z7hHWdvddor9ncr1JdX9IdD6nU+7jiUQidv3zH9krn/3oft+3ZUO777wjrHjRIrk2PpvrePplK6/qNIlkup1Mr2fb676xva7TSwLb0fmowGV14B6rAOeJS46zQzvvudXpn2huZKr935r731YFEwv/XukiRU+tHTk2WKiQ7VK9kgsmZNq8nBfOvJhHcfFjb0cToUm9GnblcQe56G/SjPn21lcjA+/rH82sHu2bWt/Du9rfq9bYdf0+tJkLl9pnP4+35z/5wc4/bN+M91s9NS13re1uDMN/n+paEtR6oGhT2xWljY+Mg9744me75Kj93bAxtSRVq1DWTrzjBXciFSpY0G48tad1adHQpf/9b33uWgnf/HKk7d96d+vRPvnEPt0g/E2oZ/tmduERXV2Uf22/D1Kma36Z85Ju/ga9+tmPdsMpvazt7vVcmn09erIb5/zKkB+jF6HjD2xnB7drGv2MFhL4c85Cu/Txd1zF5OF3v7TTe+ztWgjyOh0vefztXMnvTGn/fSBxUJsmdunRB9icRX+7i1Aq6aRxbh1XJttZvHxVtELVYY/6duOpvaxwwYI2Zd5iG/zT767iJP2uPMm1+l31T4t711aN7PYzt7RQlShWNO00zPQ8SSfdEslP17CtydvYa56O/6cJU0PlhY7dBxLpnJt5ca69N/RXe/qjodHfFcgcu3+bbO/J5HqT6v7w4+9Tc+xDuuU+VlYkYlc8NcD6f/2L+/2wznvaIxcdk7ICmimu4+mXrW1Rp8n+feG3k+n1bFveN7bXdTqo/9cj7fYz+rjr63vfjXajdrSd6577yLq13cNd33IrHzPZ/9y6/2UcTGzavDnb74m6S9WS4WVFsiyvKYJXy42o4L16/WlWs9KWbmWtKqULo8xauNRGTZ75zz4WtocvPMYNy5JrT+pu5z/0pvu/ov5MMizo5G7tbczTs91Fe8DQ0e4G8PHwsdFK6IkHtYvb5agWI42BCx7TzxOnR7vCXx78o/uRYECnnqJkJ5MK5/hpc6Nd7uqyLluqhLXcrbYroLe+/Ilta5XKloqONS4cc9PcmvyNdcjeLezMXp3c/0/vubc7kWIDXJXV78f+5VoJdOHyLRfqPZG/V621BX+vMN2D8zIdcyu/MzV3yd82dsoc939VHFSBKF+6pBveoPPl9lcHJf18qjTOrePKdDvlS5dwY9oVJOncUyWrQY0qrqJ5aKeWrhInKkdVK5SNfl4X0NihK3khnbIZT366hm1t3sZe82TqvMUZ54XK8O8ZnJt5ca5pCVo/ZML33Nxwcs8c70v3euOvb8nSKp50y32sxwZ8FW2h1OIpt57RO+2hMeniOp7Z9W5b1mm2djuZXs+21X1je12nY2lYlhoSpGn9mm6/FSgqWBgxYZprmM+L63qq/c/N+19GwYQmeahgb87acuGbu3i5Wwo21uxF/3ZL1aqcvKtEY0y3VvbvK5/jQuxbedTa6lWvWC6aWaLxbF7wfZnut8at3fbKp67b6d1vR7kbsf71N7pju2ZvqfJaNaqT8Jg2btqccNWQpSu29AAlsiCw4la1imXdTdZrWLuqZSI38srnRyYFNt38jdVi13+7IYPHHRxaddrdL7vxmMms37Ap2tOWV+mYW/mdqflLAuWjQlkXSHiN61ZP+flUaZxbx5XpdtRD+sAFR9l1/T6w3/6abafe9XL0HNyzYR279KgDbN89s8+HyS3pnCfplM148tM1bGvzNvaalytlOI1zM7fPNfWAnHXfq7Zuw5aK+m61qthTl50QtxU03etN2LQKW+59IKFg5LzD9sn1QEK4joe73m2LOs3WbifT69m2um9sr+t0rIa1/r0O6dxS8OCPSXMjgg3wW3tdz4v9z/VgQlHLXo3rRiNGtVZ1b/9vl60fV+7HuCnRNO5SFIR4wdYRtRxtrWAiLVq+0tZv3BS31yT4PlUOVZj8BV8tOF65Uv9mZKb7rTRSFPrS4OE2bd5i1/Xt06tnh+ZWKcGs+2JFiiTc12oVyth7t58X93NqhUqmTMliCY9ZY+viycu8ysv8TZam8e6Nmmzsb+xlSxa3B/se7U7cQgUL2KHXPWVLVqzeZumYW/mdKR23t3x19uNa9Pe/50TYNM6t4wqzHbXE9mjX1MZOnWNT5y626fMXW/+vRrrz8Yx7X7HhT13tAqiwtuY8SZVuieSna9jW5m3sNS+sTM/N3DzXNDTpzHtfzVYZf/na07JVBsJcb7YmrcKU+yZ1q7vx1OoZOfqmfvbubefkqMBuLa7j4a53uV2nSfV9YbaT6fVsW903ttd1Olbsua1FaDxdK3IrH/Nq/9OR8YD4i47oGv2/ggnNtNfEbD/h1a/2Ibqg7fZPy1DNyuWirw/97Y9oROa7rrbG7nWrRVfL0MU9OAZcGaNJuP59WuVDNCHluU9+iH7m6Q//HesabLkJs98aJuAFx+wGX0/nmPy+6kal1TXqVK3gWvQ1MUb7/+KgYa53KJlda1ZxSxP67sHnPx3m/r9i9Vp7afCW/8fKy7zyJ5JWDNDP4Tc8nWv5m6lgC6AqSPu3buxaFXXTjT358zodcyu/M6UVYnSxlpVr1rtVYETdry98uuX82Bq5dVxhtqOVqEb9MdONUz2m615uZQu/ioVakSfPnJ8joApeuFPJ6/Mknvx0DcuLMhsmLzI9N3Nzvy9/8l03v8QP2Xzm8pMS9rpmcr3ZGumW+6BTuneM3tvVYqqAQsPHUlHjob+WX/bku0nfy3U8s+tdXtVpEn1fbmwnt+TmObo9rtOx1LOr+Wd+H9Rr6K8ZWjVpe6Z/2PvfVi8Nqwkv153Uw+5+4zM33EkTN25/9VMrXaK46+71NInv4YuOif7erc0e9uzA793/3/jyZxv883h3sd+nRcO0H/yTiCK4e887wk6/5xXXJfb217+41jSNzVdBVGJdc2J39747zzrMzn7gNTcJRV35j733jevy8d0+WsZWE1C3Zr81PKRdk13cTcLP3m9Uu6p1aNogo2PSMmZn3feaS+e7Xh/sJueVK1XCTYTX/stBbfZIuZ3/HX1gdDKtxg8/9t7X7sKkfYwnL/MqzGpO6eZvphr/cwLrwqIW2JZn3O5af8qVLhF9fVulY27ld6b0vapsXPH0lvHeN7/0sT0y4CtbvXZ9tuXvwrZq5GY5znQ7H/0wxt7/brQbc1u1fBl3YfarWiifG9auFh0Ko9/V+qdzVuWgdIlidnbvLm7FukTy+jxJlA755RqWF2U2TF5kem7m1n5rnsRHw8ZEf1fF4Jpn/21MCzqmaxs3ITvd683WSLfcx9I1VGVIFRkfULxz6znRSm082rYvQ5qYnwzX8cyud3lVp0n0fbmxndySm9eW7XGdjtfg0fGC+6xi2ZLZevxPPbhjtJdwe6V/2PtfrFBL9fQ9fD97//Zz3coS+lK1aAYDCT3E7qO7LnB/83QT0rJ3/rX1GzbatSd2dy0iuUHrBA+86wK39rDGn6nCqpNOkayfnS4alvXuLefY3s12dRd/tcAqs9RVrlacQfdelK0LOOx+n9ytQ7bfT4r5PR2a5f/e7efafns2cpmtC7duOBr/quUG7zz7MNurUd2U2zm1e0e77Yw+0fHwusmefUhnt+JJPHmdV3mZv5nQdl68+lS3UoZv1axbraK9dv0Z2cYwbqt0zK38zpQmht119mFWocy/x6VzOFiGM13ZIS+OK9PtPHvFiW51qhJFi7j3qUKlClarhnXcUBRdnEXPN7j5tEOi+aTWGT85LpntdZ7kp2tYbpfZsHmR6bmZG/sduyCJWv21r/F+lqxYlfH1Jqx0y308en6F0lK2BBTP5liqMpFkz/TwuI6nf07kVZ0mkdzaTm7JrWtLfqjP9OrY3G47s0+0d1L5qcYFXeu2d/qHvebGKhBJtFZcmjS2a+nKNa6l5ez7X3NRl1ooErVo6OsUWVYoXdKKugKyIdoiU6p4MfcQMz8UxremaOJ3vDGoyS7y2g89gTs4oTSWMkrdy8q4KuVKJ102LN39Du5DcH3kmpXL5xjzmMkxqnVCYyeLFi4c+mmH2qctx1DKpc3w36e45wtI5+a72du3nL1N8krD4vx4RaV9naoVMz6ORPmbaF8UdWtNZSlWtHCO8cA6cdQioBNL1CWp56SIulWDSyTmVTrmVn4nSoNU+aR80Uoy/rjue2uIPTrga/c3BRf+whc2jbf2uLZmO0uWr3Lnu4aXJKq46fjnL1sRHVer4CrZ9SO3zpN00i2/XsMyzZN0rxVh8yL23NxSAVkeDYj9UIJM9ztR2gcb0ZJRi6q/tqR7vUmVVumkZbJyn+jzKiczFiyNLiOrnhOLWNz3aqlK9WbKi1efkm3Z21S4jtt2qdOkU262ZjuJrmfb+76xra/T1z/3ob38zzNb1MtyysEd3HFoH7TdZNfSrUn/sPsf9pqba8FE0F+zF7qHjCh6rlu1or176zluXWTkT6kqwfhvpKMuPhradM4hXaLnq9bxPvu+16IPw/n47r7WOg96RQDsuDT5XOvfa5TCi9ecajuyHf06jvzl+jjBxM4s4zkTyWiy9dePXBZdRjO3V54BkPvUff7Cp8Pcj1aj0RhVDV30tE41gQSAILVD/jRhmrvP335WuKGmAHYOuRpMiLpFMukaAbB9aVKfxp++PHi4/TVnkaskaFnnPXapYWf16uzGawJAkK4Tn9zT1wUT23IsPYD8J1eHOWHHks54Yvy30lHjSDXsqUKZUvQsAvjP2Jmu49j+lm7FvN8dEcEEAAAAgFBCLQ0LAAAAAAQTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAACCYAAAAAbDv0TAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEErhcB8DgNQ2bdpk8+bNs5o1a1qhQoWy/W3NmjW2cuVKq1y5co6//f333+6z+lt+tGrVKvdTvXr1bK9nZWXZ0qVLrXz58la4cM7L64IFC6xkyZJWpkwZy4+071KxYsUcx7VkyRJ3XEWKFMnxudmzZ1uVKlWsWLFilh+pDJYtW9ZKlSrlfl+0aJGtW7cux/t0fD5v1q9f795Xu3Zty482b95sc+fOtRo1ariypvK4bNmyhO/35+CKFSvcsVetWtXyg/nz51vp0qXdT+zxKd+KFy+e9DqwceNGd14pbytUqJDtb2vXrnXldlvnoc6XOXPmWLVq1axo0aI59mnx4sXZylo8ujYqDfS+WPn9+oj/HoIJAHnmxRdftB9++MFeffXVbDfJO++8077//nt3M1Vl4Nxzz7Vjjjkm+p4//vjDrrrqKvvwww/j3ky3t6uvvtp23XVXu+yyy9zvY8aMsTfeeMN+/PFHV6nRMbZt29b9vW7dutHPffTRRzZ8+HB76aWXrECBApafqDJ6/PHHu2Pbf//93WuTJ0+2l19+2e2zgiBVVlu2bGkXX3yxNW3aNPrZu+66yxo2bGj/+9//LL+ZMGGCnX322fbee+9Fg4mbb77Zxo4dm6Mydtppp9lhhx3m/h+JROzMM8+0K6+80g488EDLb3Q8/fv3t3feecf9/tNPP9kTTzyR432qaKvi+cUXX7hzSRX0M844w959910XYGxPU6ZMsZNPPtneeuutaDDx119/ufPkq6++coFA586d7cEHH8zx2d9//90GDhxoQ4cOdUHw4Ycfbtddd12292zYsMFOOukku+eee6xDhw7b7Li0Xy+88IK9//770ddGjBhhn3zyiTuXli9fbhdeeKErb0EKYJUvAwYMcOejro0KRo499lg7/fTTo40uSjddW7T9SpUqbbPjAhJhmBOAPKFWXQUR5513XrbXb7jhhmiF4bPPPrNrr73W7r//fvvyyy+j72nXrp01atTInn322XyXOwqCfvvtN3dz9x577DHX4qhKkY7p448/ttWrV7tKrFqCvRNOOMFmzJhhn3/+ueU3CnBUMenatWv0tZEjR1rPnj1dRXTw4MHuRxWaCy64wFX0POXx22+/7Xoo8htVRPv06ZOjF6lNmzYuWA3++EBC1CJ+yimnuLxVZTw/UUVT58ZZZ50V7QFTwBN7PApwCxYsaK1atYoG5Y0bN7a9997bHn/88e18FGaPPPKIdevWzerVqxd97euvv3Z5pWtHst4TlUldIxRMxestk3LlyrkA+eGHH3bB4bag68DTTz/tAtHgfum6oIBGwXmyXhrl7TPPPOOuhwqUFDT069fPnnvuuej79tprL9tjjz3y5fUR/00EEwDyhFrYNOygY8eO0dfGjx9vw4YNc5VR3yqsSpDeoxtm0KGHHupa+IKV8fzgtddecxXu4JAKVUIfffTR6HAKDRNSK70q3N988030fWp91fGqkpefqAKklu4jjjgiW4+JWnW7dOkSHaqhY1YlSYGSAg2vRYsWrgdGAUV+Mm7cONdrdOSRR4b6/CGHHOKCYlVw8xOdF6ocp+ox0X6rtbt3797ZXld51d9Ued1e1LqunjyVuaBzzjnHTjzxxJRDeHR+HXXUUS5gSEbXkalTp7qem23h008/dedT9+7ds72u3rBevXpZiRIlEn5WQZUC82Dg26NHD9cLqMAi9rj0XRryBGxvBBMA8oRa51XZCVZOVXnwPQ9BCiZ0ww9WbjTURmOGhwwZkm9ySC3vv/76qx100EHZXldlTS3AQRqjH5yH4KklVkNv/vzzT8svNKREAUI6w3nUciqxlT2liSo3yrP8VOmuX7++7bbbbnH/rmBv4cKFbox7PKqoariaetHyEx3Xvvvum2M8fqxBgwa5yusBBxyQ7XW1kGvYms7R7UVpqjkFCkTzkirmzZs332Z5qLxRAK6erdyiczN2ONN+++3nyq16PIDtjWACQJ60OqpFV+PrgzTER8MtYidb+hb9mTNnRl9TJUjDGDTWOL/w+5JOBcgP24qtyDZr1swNFcpvx6WehUTzU2bNmmXTp093QcdDDz3kAgcNEwpSXqsXaeLEiZafjitRXmkuj+bpaKy9KttPPvlk3OFMOi4Na1MLf36gAEjDBGPPrVgKkkaNGuWCcgUOQSp/qmBvzzKYLG9ym9JKPWmJgsbcorlSkyZN2urjUm+DzjnNC9F8JOW5enODFKzsvvvu+eo6gv8uJmADyHW+1X2XXXbJ0artJ8EG+cqObsZB+rx6AvLTcam1OnbVmFiqCGgOgoKhTp06ZfubgiS1lqrSkZ+OKzavgi666CI3GVQBoiowmjAfSz0AouNSwLS9qaxptaN4x6U5A5pYrX1WBVMt9Kq0aYK55vQE6T0KJKZNm+aOPb+eW7E0v0XHFjvEydPngxOEtyUFbeqJ9BP985qOVZOeNfm8Vq1aeZo3SvNUeZOKyqOGHSqIUFrp/GvSpEmO9+l7ttXwLSAZeiYA5Do/tMcP9fE05Cle66B/LXaJWFXcky13uT2OK9UYbbXOX3755e5YtWpV7PAn0TZihz9tT0rj2LwK0mReDWHSkDMNtzj11FOz9SKJT5f8cly+3MQ7Ls0F8cGP8kfjzzX+XkNh1KIf77jySzlMdG7FG+Kk1Zo0WTceHZeWiNX4/m1NLe+a85HqXMot2yoPfd5s7XFphSudc1rs4fbbb3eLAKhHMJZ6EvNLucR/G8EEgFznVzGJHTaiFv14E6rVauj/HqTPx3tew/Y8rmQr+6gFW6uvaI15rVTjK6yxtI1EK9BsD0rjdFYsUv5oIqmeEaKW0yD/+fxyXInKYCKtW7d2FVwN0dvRj0tL+uo4NOE30RLE/vPb4/zKNG+21rY61rw4Ls1jUu+SVq2KfTZKfrs+4r+LYAJArvOTc2NbqfUsAk0m1Nr3QRpCol6JBg0a5GjB1MPQ8tNxJWoJ1MRjrXOvcc733nuv7bnnngm3o23sKMcVrzVU47Vj89Z/Pr8clwIf9Tqke1y+hT42aPCfzy8PCEt0bsX2SiiIUDCRiM6tRA8hzGvqVdEDDrdVL5Zf8Sivy2Y6eROGJqrr+hLbEJPfriP47yKYAJDrNLnTt5DGrkCioEFrxAeHOGmyslZ4in0ibG5MZszt41KlUxPJY/kH8d16663uQVuJqKKhuQf57bj0oMBY8VpYFSwpDfS8giA/ByS/HJcqq5qzElsGE7Ua69kfmrsTOzZdn9ewleCzELYn7Z9ao2OPy/MroOnZEsme/Lw9zy0FOlruNNEx5DYdq+ZK5PUD3lTeVO7CHleisvnLL7+4fY8NaPPb9RH/XQQTAHKdbnq6sWo1mSBNPNaDwPQAJi1pqAmLd999txsWpEmGQRq7ronMsROYtyctE6rlOGOPSw+Z0pKQWh9fD5PSfvuf2HXg9VkFVMHnb2xvSmMFOZoUG6yU6iF7ehqvnhatiosm7GrisvI29tkNmihfp06dbE/8zg/HpZWYgsvV6jj0MEGVP/1fFTX1KGnZ4ksvvTTHAgHKL03Yzi9PLFevkIZkxZZBT6sWLV68OGmvhHoHVeHdnueW0lSBaewqWVqEwZ87yjcFrv734DAfv+KRfjQ8Tcfkf9diAbFlc1scq3p51CgSL2+UJ9o3TQL3QzuD+y96arYWAtBKY1qxSys1XXPNNTZ69Gi74oorss2/0vbUqJGfro/47yoQ2VaPhQTwn6KKp+YNqJU0+KAmXXI00VUTenVD1dCmM844w1VQg1555RX3ROlPPvkkX40Lvummm1yFIPhE2gsvvDDh05813lkPegs+bEvBxAMPPGD5hfJES6Rqyde+fftGX9dzP/QgOj38TfMkatSo4XpddEzBPFGLqp6UrUBRk5vzC+2/JlfrKdjB3iJVYjXnQ8GsKqxavlcPT1NrfpAqawqann/++aTD1rY1PXBOT47Xik16QGKQlrj99ttv3ZOW462cJjr/NKFXn49dNnZb0RAdBTzqyQs+t0UTjxM9JVrv9Uvi6j16bzyasOyDWuX1aaed5srxrrvuanlNgYDmTWlFJg1P8u677z4bPnx43M/079/fBYkqi+q11RLMCjLUI6bro57iHRukv/766+4p4bqO5pf5PPgPUzABALlt3bp1kUMOOSTSv3//jD+7cePGSJ8+fSJvvvlmvsuYqVOnRtq3bx+ZOHFixp+dNWtWpEOHDpHff/89kt8MHDgwctBBB0XWrFmT8WcHDRrkPrty5cpIfnPbbbdFLrzwwlCfvffeeyPnnHNOJL/ZtGlT5Ljjjos888wzGX82KysrcuKJJ0aeeuqpyPb2wAMPRM4444w8/Y4bbrghcvnll0e2FaXvySefHHnsscfy7Dt0fTz88MMjr732Wp59B5AJhjkByBMaO6xWeM2HyPSpyJp7oFa9o48+Ot/ljlZo0tKNYZ4erBZhtYBrvHh+c8ghh7jnKKhVNFNq4b744otzPIwwP9DDvjThP3aVplQ0jEZDoLTMb36jni3tl86T2BV+UtEQHA3ZUmv99qbhZurxUu9BXlC+6yGKGr62rShtlTfqhdDQq7wwbNgwt8DAsccemyfbBzLFMCcAAAAAodAzAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQikc7mMAdnZZWRHbmRQsWMD9G4nsPMdVoECBnTqvOK4dw86YXzvjMQWPC8hNBBMActAN9JZ9++1UKXPrd+e4f1f8PNx2FmXb7W2KjXbGvOK4dhw7Y37tjMcktww9h4ACuY5hTgAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhEIwAQAAACAUggkAAAAAoRBMAAAAAAiFYAIAAABAKAQTAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAHYQq9aut2nzFtv6jZty/C0Sibi/LV25ervsGwAA+G8imAB2EC8OGmadL7zf/l61JsffZixY6v424Ntft8u+AQCA/yaCCWAHMWH6PKtcrrRVq1A2x9/GT5vr/m26S83tsGcAAOC/imAC2EGMnz7Xmu5SI+HfZI8EfwcAAMgLBBPADmDt+g02ff4Sa1o/fs/D+GnzrGblclahTMltvm8AAOC/i2AC2AFMnDHfsrIiCYcxbem1YIgTAADYtggmgB1kvoQ0rZ9zGNOylWts3pLlBBMAAGCbK7ztvxJApibMmGfFixaxBjWq5Pjb79NyzpeYtXCpvTR4uC1YutLa71HfTjiwrRUuVIiEBwAAuYqeCWAH6ZloVKeqFSqU85QdN3W2+7flrrXdv4v+Xmmn3vWyVa1Q1vZv3dheHjzc7nh10DbfZwAAsPOjZwLYAcxetMya1Iu/UtM3o/+wXWtWsdpVK7jfy5Qsbp/ee5GVKFbE/V6xbCm7k2ACAADkAXomgB1A1fJlbOrcRZaVlZXt9Z8nTrPhv0+xs3t3jr6m4VA+kJCRk6Zbq0Z1tun+AgCA/waCCWAHcP5h+7qnXF/+1AAb9ccM++2vWfb0h0Pt5DtfssM672knHtgu7ucGj/jdhvw8wa49qcc232cAALDzY5gTsAPovXcLq1GpnL346TC7rt+HtnlzljWoVcUe6nu09erYPO5nBg4bY48N+Nreuuksq1im1DbfZwAAsPMjmAB2EG0a13M/6Xj321H27Eff2Zs3nWVVK5TJ830DAAD/TQQTwE5mxvwldtmT77rVna54eoB7rVypEvb4Jcdt710DAAA7GYIJYCdTqVxpe/HqU7O9VqwIpzoAAMh91DCAnUzpEsXsoDZNtvduAACA/wBWcwIAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAAAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCCYAAAAAhFIgEolEwn0UwM4sK2vnujQULFjA/bszXfIKFCiwU+cVx7Vj2Bnza2c8puBxAbmJYAIAAABAKAxzAgAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBAAAAIBQCof7GICdXVZWxHYmBQsW2OmOyx9TJLLzHJMUKLDz5dXOWgZ31uPaGY8peFxAbiKYAJCDbqC37Ntvp0qZW787x1Tn3pmOS8ckK34ebjuTsu323unyamctgzvrce2MxyS3DD2HgAK5jmFOAAAAAEIhmAAAAAAQCsEEAAAAgFAIJgAAAACEQjABAAAAIBSCCQAAAAChEEwAAAAACIVgAgAAAEAoBBMAAAAAQiGYAAAAABAKwQQAAACAUAgmAAAAAIRCMAEAAAAgFIIJAAAAAKEQTAAAAAAIhWACAAAAQCgEEwAAAABCIZgAAAAAEArBBLCDeOWzH63ZabfanEXLcvxt4bKV7m/PffL9dtk3AADw30QwAewgfvtrlm3ctNlqVi6f42+/T5tjy1ausV2qV94u+wYAAP6bCCaAHcSE6fNsj11qWIECBXL8bfz0ee7fpvVrbIc9AwAA/1UEE8AOYNPmzfbn7IXWdJeacf8+Yfpcq1CmpNWslLPXAgAAIK8QTAA7AAUS6zducj0T8YyfNi9hoAEAAJBXCCaAHWSIk8QLGNau32DT5i9OGGgAAADklcJ5tmUAuRpMFC5U0BrXrZbzbzPmWVZWJFugEYlE7MtRk+yv2QutUZ1q1rVVIytYkLYDAACQu6hdADsABQwNalS24kWL5Pjb71Pnun+bNfg3mDj9nlfs7a9H2tKVq+22Vz6xy58asE33FwAA/DfQMwHsAKbOXWyNaleN+7dhv0+xyuVKW+M6//ZaXH9yD2tYe8vvvTo2tzPueWWb7SsAAPjvIJgAdgAlihWxRctX5Xh91sKl9uUvE+2c3l2yLRmrQKL/VyNt2rzF9svkGXbV8Qdv4z0GAAD/BQxzAnYAR+zTysZNneOegq1lYvUzYsI0O/nOl6xBzSp28ZH7x/3c5qyILV2xOm4gAgAAsLXomQB2ABcd0dX1Tjz90VC7+aWPTX0QlcqVtkM7tbRLjz7AShYvmuMzxx3Q1v2rXou2595lJ3drb+VLl/x/e3cBJlX1xnH8Xbq7uyVFJERQLERBFFuwBf92KxZ2dyImBhagiKJiECIoIoJ0d3d37v/5neWOd2ZndmcvS+zw/fjwIDs7MzfOPfe857zn3EOw9QAAIFERTABZgFKY/tf+RPdn+85dbrWmvLlTBxCyYcs29zulixZy/96zd68lJ5vlysHlDgAAMhetCyCLibaik5+Ch6uf/dgaVC1vefPksp//nmJXtz0+6ugFAADA/iCYABJMsYL5re9j19mPf02yTdu2W/c7OlrT2lUO9WYBAIAERDABJKCC+fKE5kwAAAAcKKzmBAAAACAQggkAAAAAgRBMAAAAAAiEYAIAAABAIAQTAAAAAAIhmAAAAAAQCMEEAAAAgEAIJgAAAAAEQjABAAAAIBCCCQAAAACBEEwAAAAACIRgAgAAAEAgBBMAAAAAAiGYAAAAABAIwQQAAACAQAgmAAAAAARCMAEAAAAgEIIJAAAAAIEQTAAAAAAIJCk5OTk52FsBJLK9exOrasiWLSnh9svbp0SrxpOSEu9cJWoZTNT9SsR98u8XkJkIJgAAAAAEQpoTAAAAgEAIJgAAAAAEQjABAAAAIBCCCQAAAACBEEwAAAAACIRgAgAAAEAgBBMAAAAAAiGYAAAAABAIwQQAAACAQAgmAAAAAASSI9jbACS6vXuTLZFky5aUcPuViPvk36/k5MTar6SkxD5fibRfibhP/v0CMhPBBIBUdAN97KT3EurIPD78OlPbNJH2KxH3ydsv2Th6pCWSQs1aJOz5SrT9SsR9ksd+v46AApmONCcAAAAAgRBMAAAAAAiEYAIAAABAIAQTAAAAAAIhmAAAAAAQCMEEAAAAgEAIJgAAAAAEQjABAAAAIBCCCQAAAACBEEwAAAAACIRgAgAAAEAgBBMAAAAAAiGYAAAAABAIwQQAAACAQAgmAAAAAARCMAEAAAAgEIIJAAAAAIEQTAAAAAAIhGACyCJ+/WeqXfZkT1u2ZkOq1zZu2eZe6z9i3CHZNgAAcGQimACyiN/GzbC/psy1kkUKpHptyvxlNmz8TMuRPfsh2TYAAHBkIpgAsoip85dZrYqlowYMU+YtdX/XrVL2EGwZAAA4UhFMAFlAcnKyTV+43OrFCBamzF9q+fLksqplih/0bQMAAEcuggkgC1iwYq1t3rbD6lUtF/V1pTnVqVzGsmXjkgYAAAcPLQ8gi6Q4Sb0qqYOJXbv32KxFK6K+BgAAcCDlOKCfDiBTTJ2/1JKSkqLOiZi5eIXt3L0n7LUxMxbYpq3bQ/+uXq6kVSpdjLMBAAAyFcEEkEVGJiqWLGoF8+VJ9dqUealHLR7u+Z3lzJHdCuZN+f1LTm1CMAEAADIdwQSQBcxYtMKqlo0+ufqf6fPc5OsG1cqH/fzJzh2sYY0KB2kLAQDAkYhgAsgC9u5Ntu07d6f6uVKZBo6abGe3ONqNRPjNWrzSdu7ebUdVLG2F8uc9iFsLAACOFEzABrKAkxvVsn9nLbSxMxeEfrZ24xa7+bUvLXu2bNa1Y5uw329yVGX3NOxHP/zejrvhOfvp78mHYKsBAECiY2QCyAIeuqKdWxr2goffdUvAKoBQ6lPD6hXsmydvsLLFC4f9/pNdOoT+/+e/p9iD7/e3tsfVPwRbDgAAEhnBBJAF5M+b2968vaM9dnV7m7tste3Zs9eqlStppYoWTPe9DaqXt/Wbt9nevXt5DgUAAMhUBBNAFlK8cAH3Jy3bduy0UVPnuf9ft2mrfTjwT2vXvD6BBAAAyHQEE0CC2bh1u33wwx+WlGRWKF9eu6DVsXbp6c0O9WYBAIAERDABJJjSRQvZ5w93OdSbAQAAjgCs5gQAAAAgEIIJAAAAAIEQTAAAAAAIhGACAAAAQCAEEwAAAAACIZgAAAAAEAjBBAAAAIBACCYAAAAABEIwAQAAACAQggkAAAAAgRBMAAAAAAiEYAIAAABAIAQTAAAAAAIhmAAAAAAQCMEEAAAAgEAIJgAAAAAEQjABAAAAIBCCCQAAAACBEEwAAAAACIRgAgAAAEAgScnJycnB3gogke3dm1hVQ7ZsSQm3X4m4T/79SrTbU1JSYp+vRNqvRNwn/34BmYlgAgAAAEAgpDkBAAAACIRgAgAAAEAgBBMAAAAAAiGYAAAAABAIwQQAAACAQAgmAAAAAARCMAEAAAAgEIIJAAAAAIEQTAAAAAAIJEewtwFIdHv3JlsiyZYtKeH2KxH3SdivrCURz1ci7pN/v4DMRDABIBXdQB876b2EOjKPD7/OkpMtofYrEfdJ2K+sJRHPVyLukzz2+3UEFMh0pDkBAAAACIRgAgAAAEAgBBMAAAAAAiGYAAAAABAIwQQAAACAQAgmAAAAAARCMAEAAAAgEIIJAAAAAIEQTAAAAAAIhGACAAAAQCAEEwAAAAACIZgAAAAAEAjBBAAAAIBACCYAAAAABEIwAQAAACAQggkAAAAAgRBMAAAAAAiEYAIAAABAIAQTQIQJsxfby30G2eoNm1Mdm+07d7nX/pg0+6Aftz5Dx9jHP/+V4W0GAAA4UAgmgAgD/pxgr309xPLnyZXq2MxYuMJe6TvYlqxaf9CP24u9f7Xh42dGfe27P8fH3GYAAIADhWACiDB1wTKrWqaE5c2dumE+Zf5S93e9quUO6nFbt2mrLVuzIeb3TpkXe5sBAAAOFIIJIMLU+cusXtWyUY/LlHlLLWeO7FarQqkMH7eV6zbZud3etpmLVqR67bqXPrNvho+L+d5QEFOlXMwAKNY2AwAAHCgEE4DPinUb3byDWI32KfOXWY3ypSxXzhwZPm47du2yf6bPt03btked87B09fo0AxyJFjAsXbPe1m7cYnVjbDMAAMCBQjABRG20p26YJycn27RDNAKg7SqcP69VLFUsaoqT1KvCyAQAADi4CCaAaMFElF7++cvX2OZtO1K9piBjx67dB/Q4Ko2pboxgIdY8jt179tiu3XsO6HYBAIAjW8ZzNYAEDyZKFilgpYoWjPqaeMHEnj177d3vh9s73w23Ldt3Wp3KZez12y6x6uVKRv3sJEtK87uTkqK/roBg1qIVdsUZzWNuc4nCBax00ULu39t27LTnvvjFvh421rbt2GVNa1ext+7s5H4HAAAgMzEyAfhMX7jczYmIZuzMBZYjezZrWKOC+/eK9RvdKkvDXr/bZn72hB1To6I99MF3MY9n7lwpsfvW7btSvbZl+w7Lkytn1PfNXbrKdu7eYzVjTPoeO2OBHVurUujfMxatsKMqlrYx73Wzqb0es3x5ctn734/gPAMAgExHMAH4bNy6zbJnS31ZaBRi4KjJ1uromlYgb273s3LFi1i3K9pZsUL5LXv2bHZyo1q2YfO2mMezeKH87jkQkas5rVq/yQUllUoXi7FNKRO2o22XJnQvX7vR2jWvH/qZgpqOpzZxIxSLV66zjVu2WbUYoyUAAAD7g2AC8GlYvYJNnLPYNfD9cyJe+PIX95yHezq2iXq8ND/hve9HWMfTmsa+2LJlszZN69qHA/+0JavWhd73zGc/ucnVLetXj/q+WhVLu1GLIWOnh/1cKzg98uEAq1OpjJ17wjFhr63asNla3faSnXrnq5Yvdy4798Tw1wEAADIDcyYAn4evPMuuePojO+Oe163tcfXdaMCoafPcsq3v3XN5KMXJTwHBba/3sTqVy9qVMeY1eB68oq1d9Mh7dsItL1rVsiXcUrRbt++0N+/o6NKRolGg8dz159mD739rHR7s4eZArNm42QaPnW5VyhS3nvdd6Z594af5E5M/ftR99u1v9rGnew20J7qcw7kGAACZimAC8Klcprj99tpd9tv4GTZnySrbs3ev3X1xa2vVsGbUp0vv3LXbbnzlC6tYqqg9ds3Z6R5LpUYNfuVO+3XMVFuwfI0Vyp/XWh9b2yqUKprm+y46ubGdfEwt+338LFu2doOVK1HYLm9znDWuVTnV72okxZvMrQDlrOYN7LNBf3OeAQBApiOYACJo/kPrxnXcn7RoOdhrn+9lJYoUsNsuONWlHakRX7RgvjTflzd3TuvQsmGGj3vJIgXtwpOPTff3Pv7pL8uVM7sLgFZv2GIf/PiHnXrsURn+PgAAgPQQTAABLVqx1sbNXuT+f9CYae7vkoUL2G+v331Ij+nFpzS257/8xd7qP8zy5cltbY+r54IdAACAzEYwAQRUo0IpNy/hcJM/b257ovM57g8AAMCBxGpOAAAAAAIhmAAAAAAQCMEEAAAAgEAIJgAAAAAEQjABAAAAIBCCCQAAAACBEEwAAAAACIRgAgAAAEAgBBMAAAAAAiGYAAAAAEAwAQAAAODgYWQCAAAAQCAEEwAAAAACIZgAAAAAEAjBBAAAAIBACCYAAAAABEIwAQAAACAQggkAAAAAgRBMAAAAAAiEYAIAAABAIEnJycnJwd4KIJHt3ZtYVUO2bEkJt1+JuE/CfmUtiXi+EnGf/PsFZCaCCQAAAACBkOYEAAAAIBCCCQAAAACBEEwAAAAACIRgAgAAAEAgBBMAAAAAAiGYAAAAABAIwQQAAACAQAgmAAAAAARCMAEAAAAgkBzB3gYg0e3dm2yJJFu2pITbr0TcJ2G/spZEPF+JuE/+/QIyE8EEgFR0A33spPcS6sg8Pvw6S062hNqvRNwnYb+ylkQ8X4m4T/LY79cRUCDTkeYEAAAAIBCCCQAAAACBEEwAAAAACIRgAgAAAEAgBBMAAAAAAiGYAAAAABAIwQQAAACAQAgmAAAAAARCMAEAAAAgEIIJ4CDYvnOXLV2z3tZv3pqh963duMW9b/eePQds2wAAAIIimAAOghETZ1vT6561p3oNzND7bnzlC/e+2UtWHbBtAwAACIpgAgAAAEAgBBMAAAAAAiGYAAAAABAIwQSQRdzy2pd24yufR32t3+//2ql3vGJLVq8/6NsFAACOXAQTQBaQnJxsg8dOs9w5c0Z9/a8pc23hyrVWtlihg75tAADgyEUwAWQBy9ZssE1bd1i9qmWjvj5l/lKrXamMZcvGJQ0AAA4eWh5AFjB36Wr3d70q5VK9pmdQzFy0wupWiR5oAAAAHCgEE0AWMGdpynMm6lVNHUzoGRTbd+6OGmgAAAAcSAQTQBYJJiqULGqF8+dN9dqUeUtjBhoAAAAHEsEEkAXMWbLK6lWJPV8iKSnJ6lYmzQkAABxcBBNAFrB87caYcyKmzFtmVcsWt3x5ch307QIAAEc2ggkgi6hUuniqn23fucvGzVpox9etFvbzX0ZPsdUbNtufk+bYD39NtC3bdhzELQUAAEeKHId6A4AjQbakJPf3nr17M/Q+/+9v2LI11ev9h4+zLdt32sWnNgn7+V1vfW3H1KhguXPmsMWr11uP/r/bj8/f4tKhAAAAMgvBBHAQFC6QMnF69YYtGXrfmg2b3d8VSxW1Xr+MsnbNG1j5EkXcz378a5I98cmPdkWb5tbkqMqh9yxYvsY2bt1md1x0mjWtXcWNXhx9zRMuVaps8cKZul8AAODIRjABHARVyhR3owL/zlzgnguRI3v2uAKJ2UtXWcF8ue3zhzrbox99by1vfsHKFitsazZutkL589rtF55q1519Ytj7Js5dYq2OrukCCcmVI7slm1meXNGfng0AABAUwQRwEJQoXMCa1q5so6fNtw9++MNu6HBSuu959ashtndvsrVpWs+qly9lnz3Uxbbt2OWehp0/by4rXbRQ1PdNnLPYShQpEPr3t39MsAZVy1vRgvkydZ8AAAAIJoCD5KEr29m53d62pz/7ySwpyf531gmWPXvqNRCUlvRS70H20U8j3QpNXTueHnotb+6cVq1ciTS/Z+KcJbZq/SZ7o99Q27hlu301bKx98UiXA7JPAADgyEYwARwkjWtVtrfvutRuf6OvPfnJj/bRwJHW/vgGVqdyWSuQN7ebYK1A4Ie/JrmVmDSS8OF9V1rFUsUy9D167sTA52+1zwePdhOwv3/2ZqtUOmOfAQAAEA+CCeAgan/80daoRkV7qc8gG/DnRHtnwPBUv1MoXx7rclZLu+PC06xYofwZ+vz5y9dY8UL5XfDwwGVnZuKWAwAApEYwARxk5UsWtVdvudie6HyOm0Mxe8lK27J9hxXKl9eOqlTGrcykdKYgtm7fade0a5np2wwAABANwQRwiBTMl8dOa1zb/cksekp2rCdlAwAAZDaegA0AAAAgEIIJ4BDYs2evW7XJ+38AAICsiDQn4BD4+vd/3apN2ZKSbNj4Ge6hdp891DnDKzcBAAAcSoxMAIeAHiw3d+kq9wyJ2V885SZef/LzKM4FAADIUggmgENg4twl7iF29auVt5w5srsVnDZu2ca5AAAAWQrBBHCQ7d6zx6bOX2aNalYK/Wzk5Dl2bK3//g0AAJAVEEwAB9nMRSttz9699uNfk2zFuo3W49thNnfpajuvVSPOBQAAyFKYgA0cgvkSF5/c2P6eNs/e6DfUjUh8+ei1ljsnlyMAAMhaaL0Ah2C+xDE1K9qlrZtx7AEAQJZGmhNwkM1avNKOrlae4w4AALI8RiaAg+yrx6/jmAMAgITAyAQAAACAQAgmAAAAAARCMAEAAAAgEIIJAAAAAIEQTAAAAAAIhGACAAAAQCAEEwAAAAACIZgAAAAAEAjBBAAAAIBACCYAAAAABEIwAQAAACCQpOTk5ORgbwWQyPbuTayqIVu2pITbr0TcJ2G/spZEPF+JuE/+/QIyE8EEAAAAgEBIcwIAAAAQCMEEAAAAgEAIJgAAAAAEQjABAAAAIBCCCQAAAACBEEwAAAAACIRgAgAAAEAgBBMAAAAAAiGYAAAAABAIwQQAAACAQHIEexuARJecvNcSSVJSSt9JcnKyJYqkpKSE26cjY78S9dpKnP1KxH3y7xeQmQgmAKSiG+ivzxVKqCPT5v5N7u+No0daoijUrEXC7VPi71dygl5bibVfibhP0ub+jQQUyHSEqAAAAAACIZgAAAAAEAjBBAAAAIBACCYAAAAABEIwAQAAACAQggkAAAAAgRBMAAAAAAiEYAIAAABAIAQTAAAAAAIhmAAAAAAQCMEEAAAAgEAIJgAAAAAEQjABAAAAgGACAAAAwMHDyAQAAACAQAgmAAAAAARCMAEAAAAgEIIJHFK7d+2wrZtW2949u6O+rtd27thy0Ldrx7aNtn3LukDbfKAkJye77921Y2uGtxkAAOBAIJjAITV2yJv21t0VbcvG5ale27B6gXtt/O/vHfTt6v1SG/um+wVRXxs7+I2Y23wgrVsxy33vpD8/ifr6ly+eHnObAQAADgSCCRxSKxdNtLz5i1vBohWivDbB/V2qQsODuk17du+yNcumWamKR2d4mw+k0PGIsl3a5rXLp8fcZgAAgAMhxwH5VCBOqxZPspJpNNplfxvIKxdPsvWr5lqefEWtXLVmliNnnjR/f83y6bZn904rVbFhzO2Ktc3x2r1ruy2dO9q2b11nRUpWs1IVGsS1H5aUFPV4rF42Nc1tBgAAOBAIJnDIqEG9dsUsq1r/jJg98QWKlLV8BUsE+vyd2zfZt293sgXThli27Dlt755dlr9wGTvvpr5WtmrTmO9btWiS+ztao13zFdatnG3Vjj7TgloyZ5R993YnlyblbVeVeqdbh+u/sFx5CqS5XUVKVLVceQpGeS0l8CpZMf2gBAAAILOQ5oRDZvWSKZa8d0/sEYDFE/crxWnEt4+5gOSSu3+xu95abzc8P9uKl61jA969LM3J0/rebNlyWIny9VK9tmrJJEtO3ht4u5SO9P17l1vxcnXsxhfmuO3S9q1YMM7+GPBEmu91xyPmKM4ES8qW3UqUS73NAAAABwrBBA4Zl7YTYwRg+9b1tnHNwv1KcZo2uq81Of02q3RUK0vKls0KFi1vZ1zxlm1cu8gWzRyRZupVsTK1oqZD7W/qlb5307oldsaVPaxAkXJuu7R9TVrfatP+7h3zfds2r7HN65emmXpVrHQty5krb6DtAgAACII0JxwyqxZPtOw5clvxMkfFbLT703a2bFxhE0d8ZJvXL7MKNVta7SYXusZ4NFpCddvm1Va6UqOwnxcpWdVy5ytia5bPsMp1TomxXZOsct3Tor6m7cqeI5cV823z2uUzbdLIXrZ751arUre1VT+6Xcx9Xrt8hpu7UaRElbCfl67cyLZuWuWChrwFimco8ErZ5slWtf7poX/P/Pdbmznuu9C/FWi0aP9AzO0CAAAIgpEJHDIrF01yaUfZsqeOaVcsHOf+Ll3pWPe3Gtq9XzrD9uzeYcXL1rY/vnvc/Yll186UZ1Pkzpt6fkHuPAVt147NUd+nQEXfVbJ8/aiva7v0WvYcOVP2YfEkG/jRtZavQAkrVKyS/fLpzTZl1JdpbleuKNvkzYPYGWO7QnMiomzX+lXz3ERuf+Ck4ELBTdV6bdyftOaIAAAABMXIBA6ZDavnWcny0ScMz586xAqXqGpFS1Vz/86ZK79ddv8wy5OviPt34RJVbHj/R+zE82IEFMlpfHFSkp4AF/Wl9avnub8LFC4bdbRj5cIJ1vSMO0M/K1S0gl1679BQQKQUKo1smHWKsV3RvzdJ25TGdnvblT/Kds2fOtj9HTmaomNbr3mM7QAAAMgEBBM4ZPIWKOmWbI20fMG/tmDqEDv5oudCP8uZO5/ltHxh6UYH4pkK+QqWdH+vWzUn1Wv//PqqCxoantgl9LM8+Yu6p04P7n2XS1FSsNCu8wcHbLvWr5rjRmb8K2KNHdzdKtY6MdXysgumDXXzLIqUqm4NT+zsthUAACAzkeaEQ6bxaTfb2hUzbWife2zNshm2bsVsmzDiQ/v69Q5uudhjT7kx6vvmTx1qU0Z9bidd8HSmb1Ox0jVdWtC/Q96yaaP7uEngCm4Gf3mnjRn0hrXu9JoVLlE57D2a96H3VKp1oq1ZNt2tzJTZ6h1/meXOW9h+/uQGt7SstmvelEHW+8XTbeeOTXbmVe+G/X7NYztYw5P+Z2WqNrGlc/+2T5890XbuSEn9AgAAyCyMTOCQqd/icstfqJSNHfKW9XvzXLdcq1ZRanX+k9agxZVRJ1fPnfSLDfv6frvoju+tQOEyMT9b79VTqvUch0h58hWzHLn+G+WI1OHG3vbv0Ldcj/+wrx+wnLkLWJkqje3S+4ZGnXugURMvnShfwVL2z6DXrVqD6M+h0Pfq+yNpO7W9sSaUFy5e2a7oNtJGDXzeful1k5sjodWgqtQ9zZq0vi3Vszg0SuGNVGhU4sNHj7Wlc0a53wcAAMgsBBM4pKrWb+P+xEOrE4345hG78I4BrnGdlkLFKtotry6O+tpVD/+V5nu1vOpxZ97j/qRHvf56toP3sLkNa+Zb3vypgwX/aIz+RCpT+diY2+vR/JG2V4ePQMRjx9YNLhUr2sPuAAAA9gfBBLKEDWsW2PfvXm7laxzvHkYnefIWttaXvnZIt2v3zu326TMnuPQorQKl7bz4zh/tUBv29YO2ecMy27Nruy2e/ZcbkShXrdmh3iwAAJBgCCaQJeTJW8TOvPq9sJ9Fe6jcwVap9kl22X2/2eLZIy1X7gJWtlqzw+LBcXoOx45tG90xannOw1aiXN1DvUkAACABEUwgS8idr/Bhu8ypVkmq0fAsO5wcbtsDAAASE6s5AQAAAAiEYAIAAABAIAQTAAAAAAIhmAAAAAAQCMEEAAAAgEAIJgAAAAAEQjABAAAAIBCCCQAAAACBEEwAAAAACIRgAgAAAEAgBBMAAAAAAiGYAAAAABAIwQQAAACAQAgmAAAAAARCMAEAAAAgEIIJAAAAAIEQTAAAAAAIhGACAAAAQCAEEwAAAAACSUpOTk4O9lYAiSw5ea8lkqSklL6TRKrykpKSEm6fjoz9StRrK3H2KxH3yb9fQGYimAAAAAAQCCEqAAAAgEAIJgAAAAAEQjABAAAAIBCCCQAAAACBEEwAAAAACIRgAgAAAEAgBBMAAAAAAiGYAAAAABAIwQQAAACAQAgmAAAAAARCMAEAAAAgEIIJAAAAAIEQTAAAAAAIhGACAAAAQCAEEwAAAAACIZgAAAAAEAjBBAAAAIBACCYAAAAABEIwAQAAACAQggkAAAAAgRBMAAAAAAiEYAIAAABAIAQTAAAAAAIhmAAAAAAQCMEEAAAAgEAIJgAAAAAEQjABAAAAIBCCCQAAAACBEEwAAAAACIRgAkeUWbNm2dixYw/4ew6Gw2m7DqdtyQxz5861f/7555BuQ6Id06B2795tv/76q+3cufNQb8phYeTIkbZo0SI7HBxO2wLg0CGYwBFl9OjRNnDgwAP+npkzZ9q///5rB1KQ7TpQDtdjFPQ7xo0bZwMGDLDD6ZjOmTPHJk6caFlJZpzjHTt22AcffGBbt27NtO3Kyvr27WtTpkyxrLotf/zxhy1evPiAbROAg49gAkeUmjVrWuPGjQ/494waNcp++uknO1IEOa4H4xgl0nnQKMWgQYMsK0mk44/UWrRoYRUrVszQoenTp49NnTqVwwkkkByHegOAzLJ8+XLXS3baaaeF/XzJkiU2Y8YMO/XUU61o0aKWPXv2VGkUkydPtrVr11rp0qWtbt26lpSUlO736TOXLVtmxYoVs9q1a1uuXLncz+fPn28LFiywdevWufQMOeaYY6xUqVIH5WTH2q5opk+fbtu3b7caNWq4XuTNmzfb0UcfbUWKFMnQMYo8rt7n1qpVy22PepW1LcWLFw90jNLbJ/V0zps3z/Lnz+9ez5cvX4a+Q9s6fvx49/86FpEWLlzo9kn02ZUqVXJ/oh3LWPuc1rYGld550UhGjhw53M/9Ixz6/aZNm8Z1zcRzDvzlSNujlCQdn0N5HfhF275WrVq51zZu3Bj6mc5duXLlwt67d+9emzZtmq1evdod46OOOirsGOtz9X6d7/Lly1v16tUP+v5ldBuGDx/ujoX2Qyl9uXPndudGZcUvvWNToUIFK1SoUKrP1efMnj3bfW6DBg1CZUWjbdu2bQtdS9KmTZs0t0/XSs6cOd326W+lVakMqxzqs/3nYsyYMa5s62eqw9TJEVmXpbeNnlWrVrmgR+/Xvuta0vmvUqWKe33IkCHWsGFDK1GiRFjwXKZMmdDvBDk3QFZEMIGEoZvBe++9525wuuF7vvnmG9uyZYtrGOlmppun14u+fv16e+KJJ2zPnj1WrVo1N2yvG0a3bt1iNsJ1M3zhhRfcTUs3KzXINmzYYA8++KCVLVvWfaZuwvo9NWhFN6NIaphNmjQpzX1SI1D7E4/0tiuaP//8090k1SjV/mvbe/bs6d7jHcN4jlHkcfU+Vzf1ypUr26ZNm+ydd96xhx9+2B2LeI9RPPv01VdfuXQg3dh37dpln376qd1+++1xf4c++5FHHnENFW3rJ5984hoQ/oaVPsf7DDUKdIxOOukku/rqq1Mdy1j7nNa2+hsf8YrnvAwdOtTy5MkTFkxohEPlTsFEPNdMPOfA2/fk5GS3LwoY1MiM5/gfDNG2TxRAvvzyy65xqeBO6VTnnnuuXXjhhe51NaJ1jHUute2//fabO786xiofatTq2KgBWbJkSevdu7dVrVrV7r77bsuWLfrAv7ZDxzAtJ598cpqdAH5BtuGLL75wjWCVIZ1TdSQUKFDAHn300VBwm96xEZW5M888MzQ6oc/VNqhTQmVSjXUdp2effdbtj+o8lfs1a9aEykSs7StcuLA7/vocBbz6twIKBQw6hyrD9evXt9tuuy30Ph3XpUuXhoKB7t27u9f9o6bpbaNXn73++utWp04d97MPP/zQpdu1a9cudK3qZ3fddVdYMNGvXz878cQTQ78T5NwAWRHBBBKGeqrUaBoxYkSoYaQbgCbSXn/99VHfo8pdjcjnnnvO3TTU+Onatav98MMPdv7550d9z+eff+7e8+qrr4ZuCO+//7599NFHroGlG54aDOpBu+6662Jurxprad1QJSMpBOltVyy6wasxrRuzqAGsxvLzzz/vGk5BjpGoJ/fFF18M9Wa+8sorbh7CPffcE/cxSm+f1Dj8/vvv7Y477rBjjz3Wva4Gkhp/8X6H0i4UPDz22GOuUaHtViNBAYFHx8Y7PrJy5Ur3O+rdVoMknn1Oa1vjoR5NlXFP0POS0Wsm3nK1YsUKty3+wCie43+wRG6fGqrvvvuutW3b1i699FL3M83vULlXoKXzrx50bb/212toer3qCsB1fnWszzjjjFAvtM6Beq1PP/30qNuhspPeda/PjieYCLoNohEjNXQVKChAvu+++1zZufjii+M6NrFoJEfXgK4lbcuNN97oeux1rXTo0MEGDx5sLVu2jDoi4acy9/TTT7vPUZCgoECBjrZZo6A6hvfee69dcsklLoiW9u3bh32G0gJVl0WmYKa1jdp3vUfH9IILLnC/P2zYMOvRo4dlxP6cGyCrIZhAQlGvkBo/11xzjbvhqFGkBrFugNGoB0o3S+/Grd5U9Tjr59EaZGoQagJh8+bNXS+l/i36LjUy9O94UqREPX7RUmqC2J/tUsDibyir902NX/XsqQc3o8fIE5kWoX///vvvmb5PefPmdekI2gdtowKDyNSGtGg/rrrqqtBIhHoamzVr5hqffmqsqwdXAYAaI2qEKQXDH0yktc/7u62RDaKg5yUj10xGypWCnSAjLAdL5Pap11g95P4GqII8nT/1fqvBrMarGpc6ZxpN0r4qxUuUuqJrRMdAjUPv2CjlT2lRsRqLrVu3zrR90jkIsg3eeVcZFu2nys7ff//tgol4jk0sChS8a0mjYkp3S28kJtZ8DO9z1LOva0ef7aVTahv0//psL5jw0giVlqjOGl2zCvD1tz8dK61t1EiFAi2NuHgUZGjE8mCdGyCrIZhAQlGjR71KEyZMcDc/NYTUMIzWy6ceWA11+4epRf/WDSgapWyoF08NSt1w/U444QTXyIyckxFLZqY57c92Reb0e8dDx0CpBRk9Rh7d/P1081aKQ7zi3ac777zTevXqZV26dHHBmcqAGmzxnAf1FKrREe0Y+IOJv/76y43YKEVB6QrqNVXPY+SoQnr7vD/bur9lN8g1o+Mfb7lSWTmcRW6fGss6j/5GZuQx1Dm67LLLXC+9zqOCQJ0v/a3f0b6r4eqn6zWtEcXMTHMKug0Srcx7+x3PsYklcg5QRq/7tD7Hf30psNNIma5Dj65RBURKT9K2e9+ra8W/L2lto/ZdAYYXaIm+xz8qGI/9OTdAVkMwgYSim4R6cJW2oYaAGkgPPPBA1N/VxDvdNCIbhOrFitUw0nt042nSpMl+9zBmZprT/mxXtP0XHYMgxyizxLtP6il+5pln3DbqfGseghoEXnpGWrRv+p7I/Yv8t3ruL7roorCeWi0fm1H7s61+8Z4XNYLU4PeLfF5DWtdMZpb3w43X2FRAqWPpP4b+SbI65/qjXHw9V+HJJ590fzTHQPNVlGaTkWshM9Ocgm6DRCvz3mfEe2wOJ5rsrzlCb775ZmikQvO4VK69UYF46BgoWNd14j8HkcdL11bk5/qvrf05N0BWQzCBhKMeU91QlNKgm6I/hSdaz78mZ2rIX1T5qxe6Xr16UX9fPU3KxdfqNOo99E/SVWPDS3Hx0iMOVppTvNsVjVJ11FOqVALR8VCvpXdDzugxild6xyiefVKDR5OB1WNasGBBd+6VQqCGRTzf4e2fGolKqxA1JJTK4V91SD2b/p5Jpb0oCMiI9LY1o+I5LzqP/mU4FVhoNCyyoRrrmtmfchXv8T9UlJ6mnm6NxHiBklJk1NC//PLLQ73LOh46XtpXTT7WsVBvs8qL3v/jjz+GBYMKBJQmoxGsaDIzKFMZCLINoh58Taj2Ak5dA95E/XiOTVAHqkzoGlV59acNKpDIKO27gmhvDoVoArhG5/x0bemYKNAW1aH+0cz9OTdAVkMwgYSjVA01erTSiCa+pbVqhm6MWm1HEws1NK6VbpTW4U28i0a55Y8//rjdf//9dvzxx7sbsW42Gr6+9tprQ7ny/fv3t6+//to1Rg7GkpjxbFc0athqoqMai7phatLiLbfcEmo4BjlG8YjnGKW3T2pAP/XUUy4oUw61Guta+lETKuP9jk6dOrn981auUYM8cn6JGo5KT1KvskaU1APqT4OIR3rbmlHxnBflwWv1KK1qo7xw/Y56lyPTo9K6ZoKWq0N1HcRLPcdKYdJEci2Fq/Opsq991FKhXsCkUYhGjRq57Vbwp/3Xv9Uo1rlTEKbf0wR2rXilVZB0bg5GY3F/tkHlQPvmLVSgsq1VhuI9NkFp9SjNIVAZU5lLbyJ2vHRdqYGvVZnUwFdanua1ZJT2XUGjVjlT0KhAUvOFIq93BVlanU2BkfZFgb0/DetwKB/AwZKUnJHxPyCL0IohGuJWeoK/91STU9XbqEnGHlXwatR5a/WrAaYbSlrv0fC/evJ0s9HvqjdYDTo/NdzU+FADTzfMzJ6cGnS7/JQrrx52pfBotRY1lJUv700yDXqMtA3qKfQ3FNQA1R9N8MzIMUpvn9TjrwBAvaa6gash4f+ceL5DN3vtn2jfVS1qFZ9zzjnH/UwNSL2u71CjQo0qTVTWHAo1xuLd5/S2Nb3zGym98yLaD22baMRBx1MNRG/f0rtm4jkH0fY9I8c/LSqTCmjUuMvIxHq/tLZPz8/QNqpRqAbfcccdFxZM6frQOdPfagBqFMeff69GuF7XOVDAofdHBmuZSQGfjr9/BCqj23DDDTdYx44dXQqTGt3qiT/llFNSzaNI79hEbouWXVWZ9i8BrGDWW9TAG/nT6ki65nQ9RFvpK9rnKJjXxGl/mpWWZ1Wj3nvmiwIkBfo6Dhpp1Wd8++237vrzyk482yiqDxVk6X36+RtvvOE6FfzXjeoATbRWGpPmHmkkR0GNntUT9NwAWRHBBHAE84IJLbEIHI4yI5hA9GBCo5GIj0bmIoMJACl4agoAAACAQJgzARzBlKqi9A/gcKUlSpXKEu8ToZE+TSxOb/I8wik96XB+jgpwKJHmBAAAACAQ0pwAAAAABEIwAQAAACAQggkAAAAAgRBMAAAAAAiEYAIAAABAIAQTAAAAAAIhmAAAAAAQCMEEAAAAgEAIJgAAAAAEQjABAAAAIBCCCQAAAACBEEwAAAAACIRgAoetX0ZPsSWr1sX9+3v37rXv/hhvazdu2a/vHT1tnk2au2S/PuNItGD5Gvv1n6l2OFu1fpMNHjvNlROVF1mzYbMNGTvd/Wznrt02YuJsm75wedyfOXDUZFu6Zr1lFdq3PybNPtSbgf04fz/+Ncn+njov9LNZi1e4cqjzqjKssrxhyzaOcRYxdf4yGzl5Tobft2fPXlcOBvw5weYsXWXzl69x9VtGqO5S2TlYlq/d6Mrv4eL7kRPdNmH/JGwwoQtKF2gkXXAHqyDPXLTCNYgj7dq9x1X2WakBcijc9+43NmbGgrh/f+fuPXbTq1/a7CUr9+t73/luuPUe8o9ldWpg/D5+5kH7vj8nz7GHew44bG9kE+csthNvfdE++ekv+3n0FNuzN9ldoyfc8qL1/PEP9zOVoZf7DMpQHXH3W1/ZvzMXWlahfXvtqyGHejOyjLlLV7tg83Dw6leD7eJH37Nv/xhvY2em1I3vfT/Czu32jvUfMc7+mTbfNm/b4erBRSvj74jJDGoMR7vnIn3fjhhvr389NEOHKjk52S55/H13n/zp78muM2f4hFn26IffZ+hzVHepDvMsXrkuarsls6ij7o7ufe1wcfsbfTLceajjo+OE/+SwBKTG+v9e+NSe/t+5VrdK2bDXPho40v6eNs/OOr7BAd+Ot7/73eYtW2NnNKsX9vNpC5a5yv6Xl263csWLHPDtOFJkz5bNzml5tBUrVOBQb8ph4ZfRU+3HUZPspGNqHZTvq1y6uJ3epE6Gb2Rde/Szds3r24HWb/g4O75edfvo/qtCP1OjrEH18vbFI9eGfnbi0TXsqEqlD/j2IGtQQP7RTyPttMa1D/Wm2OeDRtsjV7W3C08+1vezv+3eTm3sqjOPd/9WMKF6sEj+vAd129QYbli9Qqp7LtKnY5Y/b+4MHao5S1fbX1Pm2r8fdLPSRQu5nymYyGg5VRvEX/+qffRkrx9TtVsSVfsWR1vZYinHL14K4B6+8iyrUKroAduurCYhg4kZC5e7Hsajq5VP9dqEOYuj/jwtW7btsE9/HWXXtGtpuXP+d8gGjZlmRQrktaa1q0R936S5S61FvWpRt0Gfc1TF4A2WectWu17V8iWKWP2I/dHQ57+zFtrqDZutWtkSdlSlMmGvqyf4mJoVwgIZVUJlihWyWhVL2+S5S2zd5m2uURW+P0ts/b6fr1y3yUZNnWtntzjaxs9ebEtWr7MTGtSwIgXyud9dsnq9+xz9u0G18pYvT664eqknzVli5UoUsbqVy8YMFNUA1RB+zQqlrGrZEqHXsmdLsjOb1bNiBVO2weuF3b1nj2XLli3lWFUtZ7l85zAjvUA6byvWbrRKpYtZnSjbp55vDZdWK1fSapQvme5n6vgsXbPBqpQp7o57JPV8TJm/1IoWTDmGeXP/dwwnzF5sO3fvtnpVytnkeUts49bt1rxuNSuw74akXqppC5bbhs3b3CiYHFOjolUuUzzV9yxbs8Ed07bH1bOpC5a71DJ9brSKUq+pXOt8qtznzZ0z9FqFkkWsVcOaoX/PXrLKFq9cayccXcP1WK5cv8nqVy3vypmofP4zfYHt2pMyUic1K5QONUY0ijhnySr3+9qe7NmzpXks9fuiMqfzU6powdDr6lnW+cmWlOS+q2zxwrZtxy6331u373Q/K1mkoLWoX92a1alqJYuEB6RKiVI51zbXq1rOlaVIuiZ0vnQOGteq5MpctOty3aat7rqsUaFUzO9QeVC5yAxKexk1dZ4lJSW58p9WnTJj4QorUaSAK2/+uk7pM8UL5Q8r914ZVDnQd/w0eoqrW/3XpFLGcmTPZsdHqQcjr62lqze4MnR09Qphr/82boa75qqX+++a+mf6fMuVI4c1rFEhVE7aNK0b9j6Vv7lLw3+uekPnPNu+Y1G8cIGwsj1x7hI7o2ldGztzobuWa1csbZPmLbEt23eEyqjqW29blHK0cMVaq1CyaFyNaNXZSkUpXzKljtM5Ef1MI6qtG/8XjKsc/Dlptp3TsqFt2rrdHQf9bMai5W5b9P6pC5a57dT+62cNa1S0csULu3qwYP48qerNTdu2u2tJ5T/aNbR41Xp3DrQv/vKr60fntUjBvO73dOyPrVUpVJdqBFnbNmvJytBxOr1J3aj1vu4j23bsdHWBrhfdU5rXrWoF8+WJuk0LV65z23tMjQqh4+WVLV2ntX33t/GzF9nuPXutyVGVw/5dp1IZt41qF0Tr8Fi7aYuNmDDLdTLmyJ49dMwG7uuMUZ3i//fmrTts+qLlVqpIwVTlVXbs2m1jZyywLdt3Wu1Kpa1iqWKWFl3v/vrKu7a8Y6Tzr/pb9wJRmR/wZ8pxHjFhtuXMkc01/nWd+OtgjU7rulK9pjp41YZN7tr2gg8pU7yQnbav3KkOUznZsTMlVU7UfvCOseqoCXOWuPtg5TLR74PRrm9dT7oX1akS3hbxU/kZN2uRu6doGwunEwwrpVbblSdXTneM8uXWPalyqnp3+85d7j6jdly9qmVTnQuVhxJFCob2T2lPJx5d07bt3GXTFyy34oXzu2Pv0XWo46PjpLpN5eWsfZ3TCuS1D/oclQvvfB0JEjKY0A3BNdYjGtG6meuCuvCk/3p14rFh6zZ7stdA69S6WdgN9pvh/1qVMiWiBhMqwLqQbzinVarX1GBWZZ0zR0qllRG6ad/Z/SsbNGaqNT6qsmuYqBL6+P6r3EW0Yt1Gu+zJD23jlm1Wq2IpGztjoZ3S6CjrfkfH0EWmIc0Xb7rAyh3/X4PohS9/sdZN6rhKbcGKtW4YckLPh8NuBvpe9XoomNDFe/NrX9pXv4211Rs3u4ZPg6rlXaX7VK+B9uWQ0XZszUq2fss2W7p6vb1z92Uxg66UYznO7unxtbtoVYnqAtUxjBzR6fxcL3eT1A1TFdSZzeraCzdc4G4yXppT/6dusGaFqoYCvh27drkbihpKyZZsnz/UxVW68dIxvebZT1yDXQHMlHlLXbDQ874rXQNf23nZkz1dWoEaKGoYqKH43j2XxzyHVz7zkWvsqPGlm2WlUkXtg3uvcOdIla+GqtWTrhv2uk1b3Da83/WKUKX2xZDRNmb6AvfdashoLsCajVvsy0eudWVLjYIZi1a4xpPSd6RE4YJRgwnduG59vbcrT7ppqTGsG+FT13awy04/LizFovs3w9yNWt+nsvfJg1eHbqZKc1LvpNd4Gzxmqn3w45/uhqteN11/ajT2vPcKO7nRUW5ui75b58bbxuTklF66B97r7/KAVWZ0k8mWLck+uu+qsMZfWNlYuNyG/puSirJ6wxbXiHj0qvZ2eZvjQo1h3VTVFtF3qdG1ftNWd85049fPalcq6266SnNSGfduoGpkd3m+lzv/unnOXrzSOp/Vwv7X/sTQ9yst7tnPfrYa5Uu5oKV6+ZLW59H/hQIgfcbVz37sRs9U9rTfzetVte63d3K/o+v1okffczcjdTKobBxfv5o9f/35Mcul9lHnQNd3LDpP+lwFTKoPFGDq+7UdHgXb9/To545fo5qVbNnaDe73P7r/ShfcidKidC78jQeVQZ1D/VyNylFT5tpLvX+1X1683dUbSnu5/KmerkzGov296pmPXONI9YfKh86/Ro+84PnJT360Tq2bhgUTSu0pVii/CybUSLjmuU/srx73hV3XT3860DUwvPLY7/d/XRqegsGc2bO5m/6jV7e3jqc1da+rsdn17X6uPKtMqFGdN1dOd8/QdnplVI3eqmWK2w2vfOHy1XWNqo5Tw8G/3X66pm97o48NGz/TXT/L126w/Hly28cPXO2uN3XmvDtgeFgwocBD9ZkaKpu3p3z/nr17XZ2+eNU6d4xdat6u3TZ+ziLXUCxaML8VypcnNPJduGpeFyRd8Mi7ViBPbtdBoM+95NSmdvuFp7rvWb95q137wqfuWtCx1+slChewjx64yooVzO9+5/GPf3AN+kUr17oyobq4cIG8NuCZm9z+qjyv2bjZbZ93nNS5FC2Y0LWiHvWUY1zUXd/qaPjy4S6hjjHVa9e+0MsF141qVLTJ85a6c9ur2zWhRmbkdSqf/fq3C/y8YEL/1mfoHlCxZFHX0RMtmND1qWM2s3Edy5E35b68dcdO97OBz99qRWrkC/1b50i/r+BKQa3um2/cdknos9TI/N+Ln7oGuwJzna+LTj7WHrvm7DTTnFT2Vf9419bY6Qts9969ruNCx0gddF89fp27BtVZ9PfU+e531RZQ/ah6ILIcaXS61y+jXINY5VYBkYK5D++7KhR0+EeHVYbUftI92DuPqp90jNXppOtMx1f1nDqxVFe93/XKsE4lP9UtnZ/v5Y7BsTUruno6WifqOwOG2+tfDbFGNXX/3+PqqVdvuShVB4Gf7hHaDgXhCqy13aojPn+4c+ga1LV7xdMfun1Xp5SucdXb9116Rlia07v3XO5e99oQaucovVGfp/ec0KC6+x35Y+Jsd3z0fQpCc+fM6a5RlQXV8ar7C+fP59p/D1ze1jq0bGhHgsQMJuYssTqVy6RqrM9cvMJVUtF6EjKbCrEaSUdXTz0KokKoiyaIl/sOdjfpIa/eGYqw1Wuk/G+1D9SQz5Mrh33/7N3uglLDts3dr1nf38aGbprpUeWYM3t2V5mc36pRqAdON5Aed3YK/d7evcmu8vdfmF8P+9c1Aoe9frfr6fUqitte72N/vtU1Va+BdzPr9v63btjwmnYt3M+e+fQnd3H6KyXd8K46s7nd0OEk9zNdyKff9ZoLRC6IESC+duvFYTd0NZqf+/xn63HXpRavO9/s64KE564/z22/boIXPPyOvdHvN7fvSoOYuXiljX7n/lAlltY8AJ0/NQTHvtctNLStoMc7h18MHm1D/p1uI7rfE7qZq5GuSu/3N+4JfY7OyafdrrFTj63tekJufOULe/D9b+3bp2+0lg2q27knNHRpTm/fdVm6+6h90jXzROdz3L+1DY98OMBV5jqPaiy83GewffFwF3cT8o7l3T2+tl9evC3qeRXdgHTcvJvbQx98Zy/1GeSCCQWu17Zv6W5k/m3Ue3QDHPb6XaHGrBrokcGl30UnN3Z/PAoernrmY3eDVKNTjUbdkNWI9peJdZu3ugZxrGOk/bzupc/ctfbuPZe5XjCVRTUK/RRoDH7lTndTVe/e8Tc9786hjp/3Ge2aNwhdK2qcntn1DbefKvMaQdPPdN149ZbyoNOihpIaAmkFEy9++avlz5PLfnrhVlc2FQid0fV1FzR43v5uuOvhHtnjvtDI1qMffW9d3/7GlaV4Kf2m7b1vuHLz0JXtXOP5+rNbhRpI0bzZb6jrWR/62l2urCv4aXvvm27e0p0Xt47re1WfK7jvP2J8qIGsIGvYuBn2wb1Xun+rgXz/e/2t72PXhepeBQKXPvmBa/R6o3DqSW7duHaojhE1spXm5C8jKo8//z3Fxn3QLRTg6rrWdRQtmND3a/v+efeB0KiA6rcdO3eFjnlaNIqs7/+lYze78dyTQg1i9f7Xv/px69KupXU44Rj3s8gFKD4fPNo1SPs9eYP7t+oK1TceXZO6Rno/eq3rxFH5VqPo+S9+CQtmdV3+/OJtVih/XldWW9z0vKt71eHQ5ayWrqdYaU4PXtE23f1RR4cCKe2Hro9bXuvtzs8Pz93iXu/545/uHqr7nBrlukec/cBb9mrfwWk2yqPRsf/huZsz7b6fK2d2++21u1wjW51qbe5+3W7s0Mo18jXioo6Hrp3a2KWtm4WO2+l3v2YtG9TIUBqom9/5/K2hEa8rnvrQ3uo/zLrf0cnV+So3qudev+0SVy/FoiDk+RvOD9UT97/b3wVi/hEMj0atrj7zeJfmFFkn3v5mH3ftPPO/c13nnerj8x96x3p8O8zuvuT0qN/de8gYV0cNeiUlpVvn8az7uof9jvZB9YD2tVq5lFHN7/6c4DovR719X9QRK48CCZVJ3aO0kMYZXd9wHVi3nn+KK1dd3/7adXao3aJ7lK7Rix9730499qg0Ozc1cqn7j64HBQWn3PGqu2erM+/hq86yfsP/dcfJ3+5457vh7hrU8RGVBf8iCYkuISdgK2JuUK1C1CBDw1JqNHlUcaqXIa2GShCK/tUrE5nuol4kpWFFplqp51k30vR8PWysXd22RdhQnaJoNUB0k1Cj5NqzTgjd0DRi0OGEhu7ijJdGX9QI6z98XOhn+n81qCPTcXQT8evz2xhXqSrvUsOFCizUu7dw5VrXWx6NGuN7k5PtijP+6wW/6bz/buaiNA2NmKjS+OGvie6z1UOg3qH0VsFQA0o3T22LGiyqFOKlhs7vE2a57xn492T3vZp8pfkB3veqIlf5UQ6rJ605APp99RApJcCjm4zXiEw5hmXc53vHUA1C9VarnHjUW6SbiqiiVCNDDRTdvIK40deA6nhqE9erO3hfo0PboDQD7wakm4kqbN3wtV2xlCtROKy3VekuSslIS64c2V2jXylXHjUEypdMOz9VDcg/J81x26obi24m6uHaHxqF0j7ee2mb0A1bNxj/PsnFJzcJ9c5ppFC9U0qxkcn7PkM93V7Z1SiAerdDZSh3TtdA03XiaXtc2vNIdGNLK5CQH/6aZFed2SJUH2jETA1Qvz5Dx7ievWHjZ4TKm3rZNcE3I/Wi9l8NHTUwL3z4XZeCokZVWlQvXX76caGgWdf3pa2bZqi+kvNaNXITkP/b74mu0XvSMSnlVaN86nlUOqa3j7qW8uTKFbbIg8q16tf0qCyozpq+cEXoZwqavDTPSLn3lR3/KmFqzMQaactM2lYFGN6KNaorvHx4jUDpeOg+pc4j/b86QpRqGlmvnnfiMe6Yihqy6ijzUgszSt/nNax1zNUY10iRN6lV5//iU5qE0nF0XK8643h33jJKoxSZ2YGo8uqNOKrxrZES7zj8Nm6m66DQ8fGuddXJlUoVy/BqTU1qVwlLnWuuujPA8dZ3++sJjXhm9HM0uqW6VSPb6qDSfil41GhRWvul3zv3hGNCKdU6jzp+furoVH05dcHS0LWZvDfZNm7dlu6EfmWZeJ2WupZ039JniNoLGpW65byTQ51dukaVCpVeOVIg6KW7qUOrZOEC6d638uTK6ToeNLovqnPVaXakSLiRCQUH6kHvFKUXXr1Jagx7jQI1CjVUppOuvE31WEbrOciWlFIQNYQb/l17XQQbK6DRDTqyx1aNGw2lNdhXuSlvW2lHo6fPd//fqmGNUOpDJKWJpOTk/5eT7Ldy/WbXMxaZwqOGrxrjGXHeiY1cr50aZeq1+vaPCdZ536iBRw0+DYf76WagNKTI1XA0ITDy+Pl7TnSj9y5er9LRcL3/cxUIRi57pwaLKqJoFLipl0h56uox041QDQj1UMdLw/qiG8LEuYvDXlOKhaiBrRvdhY+8Y6WKFHJzBFRhxsqh1k3hurNb2aVP9HTHVj2jl53ezOWJevuqYCPaMdy1a0/o30oRiLxpuPevSskxzggFMv73uDkmJYuEAkB9pvf5Hi9lSq9Fm/MhkY0rpcOojKZFN4Xnrz/Pnvj4B/dHNwDdNNKaSP7l4NGuN13bobKk/VEwoRS8/aGyKdXKpj0HRrnkkQHR9n376a2qE7kca4F8ud0wvWjukdIWNFqh46wydOUZzcNSeyJ5KVyx6KamNLeKEXNf9Pn+a0Dnr1ihfPbjX+GBQ/vjG7g6Ka1ez0jqcHAdESPGu1GAtFI5dX6UehatvtI2ZcT5JzZyozDKsddoaf/h49314tUpuqYUGEVeU6pvC/nmFqhhGM/+qpx1u7yt3fDK5y7Qb1m/uhv5jdXbqU6dx65ub3e82dd11mjkUI3ltOaSZJZr2rZw98SWN7/gUkxObFjT1eVqqCulTfcxpd6qU82vWcS+pLqWc+RwueNBRJbJivvKgM67RonUeK0cWS7KFLcV6za5ej0j8978cxEyQ+o6LXvoOOh+oTIfOaqo+QWR+5P+9+SN+T0Z+pyIuil3jvTr4EheHTZm+nybMPu/a1rNn7QCNdWfpxwb3qCuWDr83OvaVEdQ5LWp+ie9VPDIeX3qZPWWk/fqkOj1y/oMHvv0j9l9l57hRuqP6fKUG8HR6IfaBRmdWJ9VJVwwoWh0+87dLjcykibOnO7LwVPFpNQENeiUT6vhumjBRMF9hWHthi2hHjT3741bwia5RQ7jRmsIDB033fX61d03OqJUnXNPPMal3aiwnnbnqy6vMNpESQUYunHpwotGn6vcSQ0l+ilQ8m+3fkcpSn6RF0qL+tVcxD/gz4muUazebm1netRAOq5OFXuyS4d0fze03QXypVoTXUGhUg78n6sGtobdvd6x9Kh3S73Co96+PzRUqvQd/Sxe3vtuveDUmKlp6llTWocqE+W9qme23X1v2uCX70g1yTas4rmktRst04pC7e/vbj+9cJs71gXy5rGW9Wu4z0xL5DHTeRaV54zSsVXuub/i04Q577P0t/J0M+v70qP5SWqc6TrSqJJydRXsazJqJJWVh3oOsJdvujBURvWz6p0eSpmEsR+8srZu8xbLmzvYymteGstDV7SLObqiRq/SwR7vfLaNm7nQvhjyj7Xt+oYNf7NraMJ6RqmuUMNV59Fv/ZatqbZPE3ZvOvfkmJ+lMq6eeL9oDRulNGg0RHOLXvt6iB1fr2rMFDh9pm7a6dZXSenXV2owqAf6mxHjXD2ozhmlI4T2MV9uN3cnvZS/GH1DUWkk8LqzT3QpYmoIXfDwu9bnsf/FDBCubX+Ca8RrxO3nvyfbJY+9b70evNr1Xsazj0GpDtN+6/pWp0jPgSOt3b1v2h/d77WCeVPqN43GHMzVqry6w7Mhoi7R3I/I31E5KZgvdyiQiPeYxXNOvQ5DfxkP0nDXsd69e4+9cVvHQHMiD1cF9t0Hb7/wtFCnVzx0LUbWP5H/1rWpzox40nEjpfrsLdtc2RGvDlE58rcZVI60mERmq1S6mJvTojbdX5Pn2pvf/GaDx0y3b55KSS9MdAmX5qSGqUSusa0JToqSL9uXxyjq+fMqL1UiugFGowaWhmX9veJKSdKwrCaHxeq9iNwGBQGf/jLK5Zh6N1jlsp58TC3XwP3t3xmuglQ6RCwnNazlGqvq1fOo8CrFSQ0HpU/5e0XUqNLwdbM6//UylSlW2M2l8Ki33kvJ8N/oNTypm7P+qCfNvwJELKccc5R998eEVA3dtFJvmtSu7CbNeuumexPH/CMZx9et5uaCfDbo71SjNbHSw/Rz9Ur5cy61GkdG1KpQyqXqaDWvSN4+KUdex183DzVolDOpbdUqMNHo97XdakBq8qbmKai8aORMTm5Uy6VsKO0l2vd5JsxeFPYznffSRQuGRhA0wTMjN0Rvwp2oPGpiuIaEReXn72nz3UiV54eRE11jsNa+eQ1BaBs1mc1PZUf5piqDSuVSOpXKSKxnOWzausP1OvtHqLQv6nHdX0rtUm+15gL5+Y9DerSyk0bZev0aXnZ1DXtpa14Kiq5hjVy9eOMFtnXHLpcSGYvS9dRBEovqGJUv/3lVYyvymQlKgVBus3p8/fxlSwHNfF/PtYJP/5wmUWNV82iubNPcTbpWisK7A0ZYWlSuIntx1TAPq6+KFwqrr5Saozk80VKdVPd8/fs4q1q2eFjwr3pJc9UiUxy9spYWpatGNlJ1/nW81MGjxtX9l51p1cuVsHGzopdR1dH6DJ0TdRTd07GN6zjQPcTbx5XrN7p98wR5iFk03nnUfUyBy+PXnO3Km1bPU/2o7UirfstI8Lp9V3xpcapfvFE/UQqpRpm9FcxcuRg9Oew+p3LRtHbKwhqhMukrFzofWpQiCC9g939ekOOvCeGa+6bUQT9dLxmpMw6laPcNrYalY6T2S6S0Hvim0a1B/0wNPSBUIq93XZtKD/OyADzefTUtv/wzNayM6LO9e5YyOJQ5obLlvw51Xv31S+BjFKO+LFYwv5uQrflbqm+87Uuvvs7qEm5kQsGB1uDWBCLdqJXrpt5ijTw83vmcqOkYuinq4tfqPLHceVFrt8LRrMUrXQFV75sKZKxCqXkLlz/1od311leup14pSJowqcbRHReeFva7azdttTu793XLrann1estiqbble3svG5vuxValBqhi+PHkZPsJzcJ1lJWJ3n8A0uyJDf8qN55NbT8vY5KGVHUrFQM9fJ8MfifqL0omnytZ2WoQaNndsTj1gtOsd8nzHQ9X1e0OWVkh9gAACYmSURBVM7y5cntLqKUSXApk+siKSdRuY7XPv+p3dChlUsD+/inkWErZyn1RaMdSkvTKgtqoLknaY6abF07nh51TWzNJ3jhy1/d5G6t4KIe7n9nxj9fQnTzf/mmi6zz85+4XhA1vNQAUWCpJSQ1UVNL5HbvP8yldyg3dMTEWZYzRw73TINo1OBQSoYqHOUma36Jgj6lO3llTfmpOoaXnt7MzTlRo0NLUPp7OXRsOz3xgZsItmztRreKh0ZuvHN5dI3yNvujVW4CvNbRjrU0rJey9sxnP9miVevcqi96z9ktGoSGsLUihSYLq9wpCNeonr5PgVA8y/7Govk16l189vOf3WidyoKW+NUKIBrm1gijclW1utRdF7WO2ft1XJ2qbiUebduSVetd40jle38pBVLBoa59DZvrGKohpEaffyJ3WtSIe+6G8+2ON/q4VX/Uc63Va5Rm2aXdCa6+0v9/NWysK8fqQdcKLVqx7Jg0FmqIZwK2RsB0znQs9DwNpf/o+vJ78PK2biJl+wfecqvO5MiW3UZPn+dGeL1ncqjO0ApkOk9aFUejaZp07vfwhwPcNasJuEoVeuGG8+2W1750aTWxlqS9/9Izrf0D3e2Glz935V83Wy19qpVcPBee1Nju7vGVOy6qB3ScoqVMKq3psY++t7f6/xY2/0fOaFbXdY50evwD69yupeuw0aIJyvvWNRVt0rRHcwPU8O7+zW8uPUdpVKvWbXJrzbc//mjXI6k5cotXrw/NYYo0f9kal+LUvkUDl2ah0QxNHlXQKEqTUo/q9S995uoFTezNrIerqi7VPVCBROF8edz8EZ0PzdkRrYan1egufeIDO/O4+rZ1+w43T0wdU1qNJl5H16hgXw7+xzU8VSfEWhrWCzz0fVef2cJdC+9897u7x3ijDndf3NpNptWIpLIFRk6e6xqB3z1zU+gzNPm1ywu9rNIXxVynnMqkVr0KQime6jDTpN9r2h7v7i3qtMsopdl0u6KtPdTzO3eOG1Qt54Imncsnr+0QquMPZ1o+VStXaWU2dbB6S8Nq5LfLC5+6TlGlnKqHf/DY6XbW8fXDVrbz0/1c16vqjjOb1XfnUEvL+inF95d/ptg5D/Rwi1Fo1GDK/GVuZSpNdI8xsBkK/pTKrOtOcy+VzvfqLSn1suogje7f+3Y/W7dxi5UtUcQ++2WUawOqTtkfqhOU5aBUP80VO+v4Bm7xE68zSD75aaRrE3jLGcdTX2dlScn+sC5BuInIoya7mfRaEk6Vt24k0dbN103845//ckuXpreusZazU2+s0m+Ug6/lCtPKr1UDWhX3ynUbrVTRQm4t7ciCpB4Lr/Gn7dYKNBef2sQFCrEoverLIf+4wKZS6aJ2+enNw/JCNclPS7au2rDZpVqpUR+ZiqLgSo1gPeBNDUVNFlMPW+SkTzXEVXnopudPgdHN7u1vf3cTLiMpYteEbY00qFGtBpgm76U17Kueercc3oyFbiRANwot/6gVerxl/rzvHfDHBFu+bqPLQT2nRcNQKpHrne70kFuBxstdVg+mKjMtZ6f90zyW3kPHpNkQVEO6aIG8bvlEj3pN1Ds9f8UaN7KjG5x/u3TMNalLNyE1Li45pUma8xY0KVx55cpZVuPs4lMah6XAKFddSw8riFAFpR5mTaT3csDVkFE5UD60giTlx6viimzMaDKlKmUtPaoRMd0wI2lVmlvf6G1DXrnT+v42xuWTqnwrJ99/znR8tayjJrWpMaDVifyfpwBIPUNaUla0XXoir391F+W0v/fDH2FLKeq60s1W+6PVj5SqpF4pbcvcZatdh4BWyUhrHX/tn65jTSwsWbigXXJqY3v/+z/cteSdJzWqVLF7D/fy5lqoAeK/GWqlEz20Tg1Ff7lTQ1zL9KqBrzlZ3rnQnCcdW50j/1LLkdeTVgX5ZnjKk+8rlChq7Y6v7yZwelRWNXKm+QwaZVEZSmuCrm5OynP3p/NEo8/VpH7d0o6rq7SjJJu1aGXYakkaBdPiDuq9VzDZtE4VO6t5/bAUJa3rr/OrMnHasbVdbrJ37JQCp9V/bjn/lLDzpFXIVK+ltTKTgmTdmPXMFTXyNXocmQ6mRr9Wx9K2nXlcPddBof9XOpzfK30HuXpRAVK0tf1VJtXoUFnWOVb94uXAayJ236Fj7IV9DfzI92nSvDoUFPzpOtN2a1UX/a1OBNVZseaziY6XRk3mL1/t6pALWjUKS4NUz6bKsBo+dauWc+VWgdFbd6SsRCNaIeu69ieEPVtIjSV/Ode51Co23S5vF7rn6dxp+zdv2251K5ezi05pHLaKlDreNBFWAY6uH42O+hu+WhpWI+j+eUuR9aSOqcqkGtEa7XmqS4eo5Vf3FO2rUsR+HTPVNm7Z7s5p5KIG+h09kM89Z6JYIZf26H+GiQyfMNN+Hj3VBctahVBBm+4/3sIg2h7/v9OikTWt2uUt+3r+SY3cssQKeNUJo31S56D3b4/qYp1LPZ/Go3pbdZqu5Spli9t5JxwTsyPHy5xQira3Glm0eknnT3Wsd71r4YuX+/wallKlul4dXd6qfLpu1IDVyJm/PtB+evdAva7j/PLN/wXwKi9KxdO9X/WUVxeqrH/9+1i3rWWLFXb1tb/ei1XuP/rpL1dHq1wrSFXHlP8erPu/2iAjp8x1Pfl6voY6NtIK8hv/72nr2rGN+/3xcxa7Trcroswz05LV6vzVdaE6WQs8+D/Xf02pPXbbG73t7kvahC2eoxXPVJd79zt14n7y81/uocTKJnnt1otD7U7dz7RNjWtVdvds79zEW19nVQkZTMRLJ7fnj3+4m4cKV+6c2UNLUR4sGjmYNn+Zy1dVY+qxj35wBVM3fWSMKu6GnZ8MW1I0UXnBhJ49sb+8YGLW509myrYBQCxeMPGh70n0QEYpmLi30xl2yalNOHiHgYRLc8oI9ZznyJHdDU+Jhkq19vXBdE6Lo92QuR54pYlID1x+JoFEAOqpVG+HRgP0IEEAAAAceEd0MOHl1h1KSrvQKh/6g+A0uqOJ1lpNJZFW0YjlmOoVAucHR1IAdlbzBpnyWQCQXr65txQsEJTml0Uu+4pD54hOcwIAAAAQXMItDQsAAADg4CCYQKbSigbprQ3tX8HhQH12VqbjwoAhxzCjVGYyek1lVUfSvqZHx+FIqBczE+UHyFwEE8hU17/8uT2wb0J7Wl7tO9ju6vFVhj77p7+nWPv730qYhnas4Kj+1Y/bjxl8uF489CyLA0X7cTg17qp27GbDxs88oN+hchjrmB6KBt6nv/5trW57yY4EOrc6xzC74JF37dWvhhyUzoi0ynxW6kg53MvPkdJxhsRBMHEE0EPWHu75nTW57hmresmD1urWl9wDafTQmUNBa5rrYXj3XHJ62M+1/vtpd75qlS56wJpe/4x9OPDPsNf1YBg9cKvf7xl/mNDh6OGeA+zaFz5N9XM9VTdbUuZemlpDvfLFD4Y9ZTwzvdh7kF3y+Pt2JNGDLmtc+nCqJ6GqEVDnykfdMwNwYGht9xzZuX2JjoOOh0cPAXyr/7ADctz1/AHVIwdKh2497PV+Q+1I1+mJnvbc578c6s0A4nZEr+Z0pLj19d7uYWqfPHi11apQ2j3Jd8CfE12jPJ6H+fh7jfTAK++JjuL1nvgfbpWeXj+Pck8O9z9USg/00tNO9XArPdFZD+e58ZXP3aPp9RAzjx4W9v4PI9yDo9Li9Z7pJhtt27Td6gBTwz0W9ZDp6cxp/U6Q7/KOWfK+P9779RRqHdsJPR92xzkj9BneQ9Si8Z4YrP3R7+oJ6bH2y/UOWnKavxN5nLw/kfsSdNvT25/DgR7wpifD+p/U7j1MSg+29J4engiiXfvpyaxz6P8cr9e6VcOa6T4XxaWy7N0b9t5o1/OBLmvxfm+0/fRvf+R7vWusz6P/M++0eHXLXt+1GGvfXO/3vu+Jd/+97Uqvzou3DPl/FqoX99VRGdmuw1Hkedf+xXOs3P1i338ZPQ7x3LMO1XWAxEYwkeDUazps3Ex7+eYLQ0/b1dM9vSdt+umpmz2++9095VLLhV7aupndfO7JoUqnxc0vuPfp554rnv7IPUn2kavaZ+hBfZFL4eoJmXqi9a3nn+L+rSeinnNCQ3v3++FhwUTrxrXtkQ8HuCcdRz7p0t+YO/WOV9z/60al54f87+wTXZDiUVqAniiqhwMqsFqzYbN7OvlLN10Yelq4njL+2ldD3JOgvxz8j3tatYKgl2+6MBQIxftdv42bYcfXq2ZfDP7Htu/cabecd4p7LoZU6/iQ+/vTbte4J8wqzenFmy4IPXVUI0t6EquePrx9526rU7mMPXzlWaHnkejp2xrReeGG891xnLdslVUoWdSe7NLBNbqk8XXPuL8vePhd93e9qmXtpxdui3r8Oj35gY2aMs/9f+ECee3EBjXs8c5nW8ki/z1l3e/DgSOte/9h7ibl7Uv3OzraOS0buuPa7YPv3NOLd+/e456s/GTnc+yoSmXc72mk5JwHetjMz54IPWFd+1v3ysds4PO3WsMaFdznvvDlr9Zn6D/uNTXg77+srZ149H9P6E2PeyDkxz/YoH+m2q49e9wTVh+9ur01qlkx9MRxjd79PHqKu2aOrVnJvZ7WU7cnzV3inuYaaeKcJa6hVy+N9x4sKuM6dtMWLLPihfK70b0HLmtr+fLEfrLsxDmL7d53vnHlSuXohnNauWfxfPZQ51B50tNkn+z1o3vKr54MfFTFMtbtinahJ8TqSa8n3PKiPXvdefbZoL9tzpKVVrZ4EXui89lhT2lX+dB5GTxmmjsvR1er4I67zruMnjbPznvoHXvpxgvsta+H2vK1G6z3o/9zT1u+6pmPbeFXz4Y9RfiZz36yNRs3uzKiJ9Y++/nPtqTf8+51pQ527dHPpn36eOg93rUz7oOHrFTRlPK9eOU6e+zj7+33CbNcY7lxrUruicL+p1VHe/Cjnnqu/VbdeeUZzd3Ti1V36snSem3s+/+l1USWez2VuN19b7prWKMKehr4+10vt9te72PPXHeunXdio9B7/5k+3x2Tv9++zz0pXGlOuhbuvuR0u+rZT1z5mzxvqb3Ue5D7/ckfP2KF8udNtc2v9B1sb/T7zf1/nlw53NOBH7/m7LCna/vpKfCXP/2h+3/vOlfd+Mz/znVBwQu9U67RtRu3WvVyJdxTl7V8p0dpRerQUj3rOf3u16xT66buWHV54VP35OgJcxaH0rbGf/iQ61Cav3yNewr3iImz3EhM06Oq2OOdzwk9cXzV+k32wHv9XSeUApOW9avbY9e0T/Uk9bToSdBPfzrQ/V0gXx73xO/Hrm4feor3jIXL7aGeA2zcrIWWP09uO6NpXet2ZTsrHOXYikZYVC5OaljTPh882jZt3W4L+qaU16+H/WtvfjPU7VfpooXcg9d0b1VjXk9kHjl5rvvz7oAR7vdHvnWve5q5niatB9v+PmGm20/VXyqbtSqmPKT1l9FT3QNIH72qvbt3Llm13l0Luq95ddngsdPt+pc+s7m9nw5tq3e9jnr7vtC9Lb3v0tOxdcz11O29yXvtuDrV3H2iShpP+kbiIphIcLlyZLcCeXO74ekOLRtaroheVM/ng/62174eYt1v7+QqjVlLVtr1L33uKu6bzj05U59SrYorshE2ZsYCa16vWtjPWtavYd8MH+caDnly5XQ/q1ymuBUtmM/+njovZjBRo3zJUCNj1+497vH2N7z8uVUoWcRaN64T+r3xsxe7wOrXl2+3zVt3uJGQe3p8HfZk1iWr17v393/6RtewuOutvnbdS5+FGuLxfpdukrpZ/9G9q9t+71ik9yRYNaTVaCpXvLD9+vId7sbaf8Q4d1Mf8sqdYetsfzF4tL1z92UuoHm5zyC7+bUvbfQ7D1je3Dlt/AcPWa3LH7H+T99gjWtVTvMc9X3surD9f/C9b+2eHv1cQyAajW5pX9TI+fqJ68Neu+nVL935G/rqne4G/OhH37ttH/5GV7dd8Rjw5wRXPr989Fo3sqabugKxeIMJNXQufbKnC8KGvXG3FcqXxwV1lz7xgf32+t1Wplgh6/HtMBs/a5H99MKtVqpoIdegUOM0VjChz5w6f5ldfErjVK9NnLvYalYoZXlzx26wHwwjJ8+xm179wl675RI7uVEtW7luk93xZl97qOd39srNF0V9j86VRgjVkPrykWtd8KbrwhvZ8nR+/hMrnC+v/fzCbVaiSAH7fuREu+rZj+yXl24Puy4VSHS/vaNroKjxdMtrvW30uw+4OknXS8cnPrCG1SvY72/cYwXz5XbzPi598gMb9vrdYcHrF0P+sd6PXuuuV1FwHtnQVSNMDdvzWzVy9YkaTBmlIOnCR9+1s1scbS/ceIHlypHD3vzmNxdgD3vt7lDA66dGlY7Rc9ef5xr9ath+8vMoV8+lFYBEo+PVq1tnV69I2+b17Zvh48OCCdWJzetWjdpQVodE23vfcM+NuWVfx0ws93Rs4/7Ixi3brMe3v9tVz37s6qhoZVedUZ8/1MU6PfFBWBAnb/b/zfoOHWPvd73C6lYua72HjrFrX/zUfnnx9jQDcr+P7r/K2t/f3Vo3qWN3XHha6OdKx73g4Xdcg/vVWy5ygboCoSue/tAGv3Knq0ee+ORH1yEw/M17XD3zx6TZrkze0OGkuL579uKVrtypU6zXg9fY1h07XQB946tfhOrDO7r3daONH953pbseFCgoWPefm6gdDtXLu+1S3e0FEs98NtDevL2TNald2ZUT3TN0f9HIfPc7Otmq9ZvddfHgFW3DUlUvevQ919E26u373YjoW98Oc+dD14uesyRbt++0wWOnue3Wz3RPu/3NPjbo5TviOhbxftdzX/zsyvrQ1+6ywvnzuHvfd39MiNpRicRH0mmCU4+CbnK/jJ5ix3R5yo0kvNFvqEsr8lMg0bXjGa63Ww99q1OpjN187knWe8iYTN2e5Ws2uL9LFknp7fGsXr/J9Zz6lSic3w3Hrtm4JeznJQsXcI3weCgYalG/mhvdGDhqcthrugmp8aFKXo1y9eT/8s9UW7B8TdjvPX/9+a7BqZ7L52+4wCbNXeqCmYx8V5EC+VyvnxdIxOvPyXPcDenlmy9yAYV6EDue2sQaVC3vGtl+6tFVI0T7ddO5J7ne+LnLVtn+KFO0kN17aRs3sqCGZkZMnrvE3dSfv+F815jUiI96qtVDN+DP8XF/jgIa9ZCrMaOyqZ5TNfTipW3XaNuLN15gpYoUdIHpNW2Pd4HpT/vOk76jTpWybjt149Qokv9GHknBto6HetIj6XwpcDzU1Ai+6ozjXYNA+6QA94HLz3SNUTXko9FIw5btO9y1oLKqXsbHrj477HcUNI6eNt9evfVi11uqz76gVSNrWruKC8D8HrqinevJTCmTJ7vgZOailLpn0Jhp7jrW9aX6QOfl2rNauhEMLbbgp2vHCySi6fnjn3ZCg+qup1yjLhpBuaZdiwwfs69//9ftj0ZZihbIZ/ly57R7O7Zxx0sdMtGoQaW5XKc0qu06a9TIV9nJaCAhasx6gYQoMBo+YaYbwRFthxrJ+nlmUnB3T8fTXYN87MyFGXqvOjze/W643XbBqa4MKOBSB0PzutVc73hmzE3S6MC9nc5wowD58+Syble0dUHcqKlz3e+oB/7YWpVcAKrz36Zp3bgDCXnv+xF2aqPabtRd79e9SL3wGulYtHJt6Dta1KvmGtKqzzue1jTNQELUcaFryQskvHutggaN4qmzr1aFUu7YKQBLS/8R412alEaldW3qmrr74pR5hxo98FOdqxEyndf/nX2C6/hQkBGveL5r6ar1bgRR90YFnxpxJJA4cjEycQRQuonSZzTBWT12/X7/1w2B6watG65uVEtXb3A9GPrjlyvngcmhjGfBDi+fNzJNO723qtf45b6DXKNJjRV9jD5LQ99+VcqUCBv+VwNQqUpKXVJDU3TT8Pf+q9e/VJECNmvxShd4xftdVcsWjzkqlJZJc5a4RutRlz+S6jWll/l52yyF8qf0Um3YvC3D36kA4MUvf3UpIMr9V4+ZGgzav7QadNEa3LoJaZjdo5tbzQqlbdbi+IMcpXt98MMf1u7eN61Ns7rW6uiaruEQL6XtqBHrpWb4af6QKEBToN3x8fddGoZ65r1UrOifucQFNrUrl0mV7zxl3lLXsx2EN19FvDkr0X4WD22jzuXb36Vu0CldyD9nyaP0wRrlS4WlQalnNTJYUqNWqWiR1IDx86c86NyrV1nnImX7FrsGYbRVdZRi4eelVsSi6zHymjumRkoKW0ZMmrPY5ixd7RaBiFVWIqnxr1QWjQi0b3G0244Tj64Z98ibnxqWfmq8liicMvJzddsWbhUiNQqVrra/1CP+VK+BNnr6fHcedJ2rx12N5oxYsW6Tbdy63Y7ZlzLoObZmRVf+9pfKmxrDaZ0TpZXd3eNr93sKJE899qio5Tut+U8a3frhovBV9FReF61c5z6r81kt7Z63+9nQcTPcqLm+Q+cmLaqT/XOqNPKl497t/e/cHz/de9KaV6ERT6VFRZsAv9h3zlTulDrl0QiirN+yNc30xox+lwJ3jX7PWbLa1Zca/czI/QGJhWDiCKEeHQUV+qOGoSrepz4d6Cphr3He68Gr7WRfPms8vAZ/vLzGxsr1m8Ia6UotWb0hfARizYYtrjL39+qIhoDVSx+LJmhrKPntuy+1upXLucpV+ba6KYWJse3hwUvq39HbvN+J97uCTmxTI1K96eN6pm4Ip9puy9ik7WiUrnT1sx+7XiilHahXat6yNXbirS8GWvo12iFW+UtrHq9Go/xUTv7ofq8NHTfd5RErxUYByqfdOrsGfTzboEbtn2/dG/N3mtWpan+9fZ8NGTvd9UYqjUIjTOo1j0YNj2plS6SafD1u9iIXgDU5Ku1UslgadnnSNm7Z7v6/eb2qLlVBq2RpJECKFcpn43s+HOenJbvc6cj5Sem+K51rWi+rPpnaK3UwESmt85y8ryGuFKf0xLNyU+RWx7PEaGRZ07+UQhSZrpcWNf4+f7iLO0fKH3+x968ul7zPY9eFjTKk9b2eHBHlWZ997gnH2Dcjxrlgov/wcXZa49ox8/QzQumauo5+ev5WVy8rSG3Y+clUKW3xijzeKfVk0n7fP/QrJx9T0z57qEvM39G12qxuFRs8ZroLYJ745Ae76+LT7ebzTo572zu3a+k62GJR6lX75g3cSOd3f4y3B9/vb6/ecnGaHQeR5dY7RpoP459PEt9GmjWtXdn6P3Vjmr+WkUUSYp6HOL5L2686VfOdVGeqPaH5VV07paTO4chCmtMRSJWNGjs7d+92vd7qXdHNRD0uadFkXP9ysqoY5y8LTwlKj4arNWlO+el+qrj+mjIn7Ge6KTSoVi40X0LUW6JtOK5u+PwKPw3Tn96kjpsb4PUOas5CpHnL14R6SWX87EXuJq+eWc/6zdtcT5JHkzNXbdgc6imN97tiNRz2xGhUeDRRWIGXJlTuD6+REqsR41HP3u49e+2GDq1cWpLKiiYcpidnjmypGiE6Ripf+kx/z5x6kTU6IYXzp6R9+ctVtNQs9ahphEJpaT8+d4tLOVHvWTw04rRgxVrX654WBa0XndzYXrv1Ynvvnsvts1//jrl88s6I5WD9KRnqnfOnOT33+c9uZOWyJ3u63msFLLFM/vhRl4+uP16uthq23s/iDyRSyk7k3IL0aK6HW41q246wuUV+uiZ13ezvMsP6nLnLVrtren8dVbG0TZgdft39G3Edqqwp0POneEWWNaUPasREKYIZoetEI5VqSP360u1WtGB+NwIsRQrkdT33/sb23KX/1SnpOa9VIxs7Y6ELYH8dM9UuaJX2SnbquEivXvGuy6vbHu9S1RRIKBVQnQlp8YJ3f8eC0ly0j5H13r+zFrryFOv+oW1QemHk50c+X0Hl5N+Zi9Jdyrxc8SKuc0zX7sNXtXfLj8dL1+vw8TPT7TBR6tr157RywaNWFlR6XUYoRUodG+nda6PVp0rvVCeV0ur2h86VFpnwpz1Flsd4v0sjIJedfpz1uOtSl079zoDfQ+X8cHv2EA4sgokEp0qjw4M9XH69cj/VmNMKKe98N9yt0uNNKNQzHzSpVc920A1FaQZameORngNCn6UeO82hUMWj39EQ+cJ9+aQZoZ425Uv7qddNQ9aaOKy0K/X8aGj/xnPD814HjZnq0i68VTyi0SRdpQNoXog+S8/UUJ53JN3M7n27n0vf0e8+9MF31va4emEjJtL17X5uHoWOn0Z0jqlRweUGZ+S7oqlYsqh7nypsLcsXrSdVk4yVv3/zq1/YqClz3URJNSq0olVajdJI6kHXCIe2TWUiViWv3nZtx+eDUlYfUUCnFXLSU6FkMRd06Vh6+6I5Dqc0qmX3vtPPNVC1PHHXt792ox0dTmjo3qcVvBTIvvnNMNdYUGqVv8zJ618PdSNAOgcqv4PGTne5xuVLxLdSi4K9htXLu0mOSvPTMdR8jvvf7e8mDYp6kr8aNtZtv1I+lBesIFs5z9God3jGohX26a+j3OepUfTqV4Pdyj3P/K9DWO+gyodWqFFD8+ozW7hzdzDcedFpKefv05/c9qmcadKoJgvH0q55A5cipwdP6nkwmt+gSfORozhK69FKQ+qR1P5rtSj1Buvz43Vms3qujGiitAITfY4aMCovqqMyQjn6GrVS/aXzp/rl459Ghv1O/aopI4eaCK7vUuM3ci3/i05p7PLu//fipy6A1+9p225+9cuYwajK0H3vfuPKlIIwfa7SyLyUD6X/qGH17oDh7vN0DWrkK17abgVLt7/ex3LnzOnSa9KrVxRYaVtiPWBOHTSVShWzPkPGuOtO51lLiKdHn+32eepcF5R5DX/Nh9FcPJW3tZu22Dvf/e5GatRT7b9/fPTTSFu6Zr27znTMIvP4NTdqwuwl7jr3tr1T62YuRU4LgqicecdQIyvq3BH9v8qe6mAtNPD31LkZSrnRfUbXiOp3BbfaB6UFa5EGUX2pFEh1YiiQVuCla1rpqxmluSm9h/zjyoOuSR0PjW6rDvrvOBRzqVfaV+84XHjSsVamWGG3r5P3vaaFInTevHlI8ahTuawb2dJqU/oMBc/PfDow7Hfi+S79v+7RagvojxZ8UOqwV/dpRTit1oUjA2lOCU6NyIevOss+GjjSNQp1o9VEYjWwNOnLo8lkCix0E9ByiuqlbdWwht1xUevQ72jpQVV+Zz/wllu94eyWDV1jV6lIHv2//9/RuN6j70e4xqdX4WsFmF4PdranPv3R9Sipx+PpazuElkcVNVAVzNxyftpD15p8rCDn3G7vuPHaFvWq26Wtm7reaT+tWqXv10ohmuStvM/nItJaNEdCq4son375vqVh373n8gx9V6wHbKnhMnziLDv59pdt09YdoaVh/Q+tU8Ws1UWUOnH7m33dTU4Nfq1s4i3Tqao7Zc35/z7b5dZHPOvhiS7nuF5yzZfRfItoS8Oql1Ir/aixo0Zk1TLFXaqAlghMK2dFQ/0K9Frf9Zpt3rbd3rw9ZWlYrViiZTbP7dbDNT6a1a7ievW89CD1RGoFKi3L2vzG59z50DKRWnXI+7orzjjO3uz3m1tJROW3ZsVSbgUs9YjG8yAvHU+tTKRjqIa05pFUL1/SLmt9nNseubHDSS4Y0PHR8rsKWLUUaqz8Za3UpRQHBTkKqjW3RiuU/fDszWFLaypg1TUz4Jmb3JwZ9Uqq0XUgaH/9155WR9MzCF75arD1uvMvy5c7lzU+qrLddfF/13QknZdPHrjGNWxOvOVFlyuuhroaBv6Urp73XWWv9B3k5lit3rjZ7ZdGdbyGrsqdK38RqXf+86Ie9D6P/c9e+OIX11BUg0WjgsrF9lYcC31ORNmLvKbUQHrrzk7u/Gm+j/6tSa5aUtmjIFa/o+Dq/e//cHNivGVvvY9Xo1WpHc9+/pNd8dSHtm1nyrK3ypfXdReNytCcJatcQ1R1mlI29bneSl+qy964raM9/8UvbilWrW50Y4dW9siH34f2S3+lHK/oLjjpWPf+y05vlmruVeRD62694BTXAdLo2qdcWY61NKx6kx/64Fv3MNPihQq4yccqq/7PilY/qPzc/kYfV2fqPRot1P5otO6u7l+5n2tE4uMHrgqbd6QJzSpHre98zQXq6tnXQh/+B3RqaXAdx2P3bbu3NOy3z9zoAj/VATt27nZzla496wS3PaKJv6/2HWxd3+nntl/1dI87O8Xcj8jyo/vPd8/c5I7xWfe96a57dUDccdFpoTrk5vNOse7f/OYCXgWlKuuaoJz2d6ROw9Sk7by5crnVkV7qM8hN9Ndk7Dt999rrzznR7nrrK2t6/bNuUry3NGz/p25wZfzKZz5yK05p9Ldz25ahESDNu8gRWWdFXIsq42/fdam7Nnr9/FdotEXnxvsdjQSn9106V7pPqBNO92bVLe91/e/eqG2Jd34Xsr6k5IPx7HoggirlmYtX2hu3XRL3sfnhr4muEfDt0zcGygv10wiIepn0WbFoqVX1iv/9zv379V04cmkk5KZXvrAR3bta9mxJruFarkSRsKUvD3feMxDGvPdgqgnWhzP1lGp5S+85EwCAA4ORCRwS6a2BHo1GKfwjFcDhTnODWtSvbs1veM71cmtE45Y4J4UeKgqgG9WsYA1rVHSpZfe/940bBctKgQQA4OAhmMARKVbqUUZ/B0iL8qq1SsyTXc5xqQDR0k0ON2c1r2+Pf/Kjy5HWg+ROPuYo93yKrMZLkQIAHFikOQHAAXLSbS+5uSnxru8OAEBWQzABAAeIVgDLyMOzAADIaggmAAAAAARCQikAAACAQAgmAAAAAARCMAEAAAAgEIIJAAAAAIEQTAAAAAAIhGACAAAAQCAEEwAAAAACIZgAAAAAEAjBBAAAAIBACCYAAAAABEIwAQAAACAQggkAAAAAgRBMAAAAAAiEYAIAAABAIAQTAAAAAAIhmAAAAAAQCMEEAAAAgEAIJgAAAAAEQjABAAAAIBCCCQAAAACBEEwAAAAACIRgAgAAAEAgBBMAAAAALIj/A8UEJDgo43/WAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1386x990 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "premiers = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43]\n",
+    "xs = np.linspace(2.0, 9.4, len(premiers))\n",
+    "\n",
+    "fig, (g, d) = plt.subplots(2, 1, figsize=(12.6, 9.0),\n",
+    "                           gridspec_kw={\"height_ratios\": [1.0, 1.5]})\n",
+    "\n",
+    "# ---------- Haut : le schema comme une droite de points ----------\n",
+    "plan(g, r\"$\\operatorname{Spec} \\mathbb{Z}$ : un point generique, un point par premier\")\n",
+    "g.plot([0.55, 9.4], [5.6, 5.6], color=\"#333333\", linewidth=1.4, zorder=1)\n",
+    "for x, p in zip(xs, premiers):\n",
+    "    g.add_patch(Circle((x, 5.6), 0.14, facecolor=C_OBJ,\n",
+    "                       edgecolor=\"black\", linewidth=0.8, zorder=3))\n",
+    "    g.text(x, 5.25, r\"$(%d)$\" % p, ha=\"center\", va=\"top\", fontsize=8.2,\n",
+    "           rotation=90, color=\"#333333\")\n",
+    "g.add_patch(Circle((0.9, 5.6), 0.22, facecolor=\"#8e44ad\",\n",
+    "                   edgecolor=\"black\", linewidth=0.9, zorder=4))\n",
+    "g.text(0.9, 6.45, r\"$(0)$, le point generique\", ha=\"left\", va=\"center\",\n",
+    "       fontsize=10.0, color=\"#6c3483\", fontweight=\"bold\")\n",
+    "g.text(5.0, 8.55, \"ses points sont les ideaux premiers : le ferme $V(p)$ est \"\n",
+    "                  \"le point $(p)$ lui-meme\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=9.8, style=\"italic\", color=\"#333333\")\n",
+    "g.text(5.0, 2.65, r\"$(0)$ est dense : \"\n",
+    "                  r\"tous les $(p)$ sont dans son adherence\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=9.8, color=\"#6c3483\")\n",
+    "\n",
+    "# ---------- Bas : le tableau de couverture ----------\n",
+    "plan(d)\n",
+    "d.set_title(\"Qui couvre qui : chaque ligne est un ouvert de Zariski, \"\n",
+    "            \"chaque colonne un point\", color=\"#1a5276\")\n",
+    "\n",
+    "connus = premiers[:6]                                    # les points affiches\n",
+    "colonnes = [r\"$(0)$\"] + [r\"$(%d)$\" % p for p in connus]\n",
+    "\n",
+    "\n",
+    "def dans_ouvert(famille, j):\n",
+    "    \"\"\"Le point j appartient-il a l'union des D(f), f dans famille ?\"\"\"\n",
+    "    if j == 0:\n",
+    "        return True                     # le generique est dans tout D(f), f != 0\n",
+    "    q = connus[j - 1]\n",
+    "    return any(f % q != 0 for f in famille)   # D(f) contient (q) ssi q ne divise pas f\n",
+    "\n",
+    "\n",
+    "lignes = [\n",
+    "    (r\"$U_2$\", [2]),\n",
+    "    (r\"$U_3$\", [3]),\n",
+    "    (r\"$U_5$\", [5]),\n",
+    "    (r\"$U_2 \\cup U_3$\", [2, 3]),\n",
+    "    (r\"$\\bigcup_p U_p$\", connus),\n",
+    "]\n",
+    "matrice = [[dans_ouvert(f, j) for j in range(len(colonnes))] for _, f in lignes]\n",
+    "intersection = [all(matrice[i][j] for i in range(3)) for j in range(len(colonnes))]\n",
+    "\n",
+    "x0, largeur, hauteur, pas = 2.55, 1.0, 1.05, 1.25\n",
+    "y_top = 8.2\n",
+    "n_lignes = len(lignes) + 1\n",
+    "\n",
+    "for i, (nom, _) in enumerate(lignes):\n",
+    "    y = y_top - pas * i\n",
+    "    for j in range(len(colonnes)):\n",
+    "        d.add_patch(Rectangle((x0 + j * largeur, y), largeur, hauteur,\n",
+    "                              facecolor=C_COVER if matrice[i][j] else \"#f0c8c4\",\n",
+    "                              edgecolor=\"white\", linewidth=1.3, zorder=2))\n",
+    "    d.text(x0 - 0.16, y + hauteur / 2, nom, ha=\"right\", va=\"center\",\n",
+    "           fontsize=10.6, color=\"#1a5276\", fontweight=\"bold\")\n",
+    "\n",
+    "y = y_top - pas * len(lignes)\n",
+    "for j in range(len(colonnes)):\n",
+    "    d.add_patch(Rectangle((x0 + j * largeur, y), largeur, hauteur,\n",
+    "                          facecolor=C_SHEAF if intersection[j] else \"#f0c8c4\",\n",
+    "                          edgecolor=\"white\", linewidth=1.3, zorder=2))\n",
+    "d.text(x0 - 0.16, y + hauteur / 2, r\"$U_2 \\cap U_3 \\cap U_5$\", ha=\"right\", va=\"center\",\n",
+    "       fontsize=10.6, color=\"#8a6508\", fontweight=\"bold\")\n",
+    "\n",
+    "for j, lbl in enumerate(colonnes):\n",
+    "    d.text(x0 + (j + 0.5) * largeur, y_top + hauteur + 0.22, lbl,\n",
+    "           ha=\"center\", va=\"bottom\", fontsize=10.2, color=\"#333333\")\n",
+    "\n",
+    "d.text(5.0, 1.25, \"violet = le point est dans l'ouvert   |   rose = le point manque\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=9.2, color=\"#555555\")\n",
+    "d.text(5.0, 0.55,\n",
+    "       r\"$U_2 \\cup U_3$ couvre deja les sept points affiches : deux ouverts suffisent \"\n",
+    "       \"pour une infinite de points.\\n\"\n",
+    "       \"Seul $(0)$ appartient a tous les $U_p$ -- le generique survit a toutes \"\n",
+    "       \"les retenues.\",\n",
+    "       ha=\"center\", va=\"center\", fontsize=9.4, color=\"#1a5276\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c20",
+   "metadata": {
+    "papermill": {
+     "duration": 0.012223,
+     "end_time": "2026-09-30T15:01:35.188804+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.176581+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Lecture de la figure 6\n",
+    "\n",
+    "**Le panneau du haut fixe le vocabulaire.** $\\operatorname{Spec} \\mathbb{Z}$ a un point par ideal premier de $\\mathbb{Z}$ : un point pour chaque nombre premier, plus un point special $(0)$ -- le point generique, dont l'adherence contient tous les autres. Le ferme $V(p)$ associe au premier $p$ est reduit a un point ; c'est pourquoi un ouvert de Zariski est le complement d'un **ensemble fini de points**.\n",
+    "\n",
+    "**Le panneau du bas se lit ligne a ligne.** Une case violette dit que le point est dans l'ouvert, une case rose qu'il manque.\n",
+    "\n",
+    "- $U_2$ manque $(2)$ et **rien d'autre** : $(3)$ ne divise pas $2$, donc $(3) \\in U_2$ ; et le point generique $(0)$ est dans $U_2$ comme dans tout ouvert non vide. Une seule retenue, un seul trou.\n",
+    "- $U_2 \\cup U_3$ : la ligne est entierement violette. **Deux ouverts suffisent** a couvrir $\\operatorname{Spec} \\mathbb{Z}$ -- et c'est general : pour $p \\neq q$, aucun ideal premier ne contient a la fois $p$ et $q$, donc $U_p \\cup U_q = \\operatorname{Spec} \\mathbb{Z}$.\n",
+    "- $U_2 \\cap U_3 \\cap U_5$ : les points $(2), (3), (5)$ sont retires trois fois sur trois, les autres jamais -- et si l'on intersecte **tous** les $U_p$, seul $(0)$ survit. Le generique est le point que rien ne retire.\n",
+    "\n",
+    "**Le fait qui surprend.** $\\operatorname{Spec} \\mathbb{Z}$ a une infinite de points, et il se couvre avec **deux** ouverts. La famille de tous les $U_p$ est un recouvrement dont on peut extraire un sous-recouvrement de deux elements : c'est la quasi-compacite, et elle n'a rien d'intuitif sur un espace aussi peu separe. Corollaire immediat de la meme observation : le nombre d'ouverts d'un recouvrement ne dit rien de sa finesse.\n",
+    "\n",
+    "**Ce que cela change pour les faisceaux.** La section 4 recollait des fonctions sur trois intervalles. Ici, un recouvrement peut etre enorme, ou minimal (deux ouverts), et surtout : il peut etre fait de **morphismes qui ne sont pas des inclusions**. Un revetement etale, un revetement galoisien, une famille plate ne sont pas des ouverts d'un espace -- il n'y a pas d'« union » a prendre. C'est la raison du detour par les cribles : un crible couvrant est une famille de fleches **fermee par precomposition**, et cette definition ne suppose ni inclusion, ni reunion, ni finitude. Elle dit seulement quelles fleches au-dessus de $X$ comptent comme un recouvrement -- et c'est ce qui permet de recoller sur tout ce qui ressemble de pres a un recouvrement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c21",
+   "metadata": {
+    "papermill": {
+     "duration": 0.009883,
+     "end_time": "2026-09-30T15:01:35.207843+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.197960+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 7. Synthese : la mer qui monte\n",
+    "\n",
+    "Il reste a dire ce que ces six figures ont en commun, car c'est la que reside l'apport de Grothendieck -- et le titre donne a son oeuvre, *Recoltes et Semailles*, parle precisement de recollement.\n",
+    "\n",
+    "### La demarche, en quatre temps\n",
+    "\n",
+    "| Temps | Le geste | Section |\n",
+    "|---|---|---|\n",
+    "| 1 | Choisir une categorie de morceaux | 1 -- categories et foncteurs |\n",
+    "| 2 | Dire quels paquets de morceaux couvrent | 2 et 3 -- cribles, topologie |\n",
+    "| 3 | Demander aux donnees locales de se recoller | 4 -- faisceaux |\n",
+    "| 4 | Lire un objet dans toutes ses relations | 5 et 6 -- Yoneda, Zariski |\n",
+    "\n",
+    "**Le changement de nature est au temps 2.** Avant Grothendieck, « recouvrir » voulait dire « recouvrir un espace topologique par des ouverts ». Apres, cela veut dire « exhiber un crible couvrant dans une categorie quelconque ». Le gain n'est pas de generaliser pour generaliser : c'est de rendre **recoltable** tout ce qui ressemble de pres ou de loin a un recouvrement -- ouverts, mais aussi extensions galoisiennes, revetements etales, familles plates.\n",
+    "\n",
+    "**La mer qui monte** designe la montee progressive du niveau d'abstraction. Chaque etage contient le precedent comme cas particulier, et chacun ajoute un recollement qui n'etait pas possible a l'etage du dessous. La figure de synthese reprend le fil sur un seul dessin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c22",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:35.228885Z",
+     "iopub.status.busy": "2026-09-30T15:01:35.228587Z",
+     "iopub.status.idle": "2026-09-30T15:01:35.480079Z",
+     "shell.execute_reply": "2026-09-30T15:01:35.479160Z"
+    },
+    "papermill": {
+     "duration": 0.263845,
+     "end_time": "2026-09-30T15:01:35.481505+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.217660+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1364x704 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12.4, 6.4))\n",
+    "plan(ax)\n",
+    "ax.set_xlim(0, 10)\n",
+    "ax.set_ylim(0, 10)\n",
+    "\n",
+    "etages = [\n",
+    "    (0.7, \"Espaces topologiques\", \"ouverts, intersections, unions\",\n",
+    "     \"recoller des fonctions continues\", C_OBJ),\n",
+    "    (3.0, \"Categories et cribles\", \"objets, fleches, cribles couvrants\",\n",
+    "     \"recoller des sections sur un crible\", C_SIEVE),\n",
+    "    (5.3, \"Sites\", \"une categorie + une topologie de Grothendieck\",\n",
+    "     \"recoller sur des revetements etales, plats, ...\", C_COVER),\n",
+    "    (7.6, \"Topos\", \"un site vu par ses faisceaux\",\n",
+    "     \"recoller des objets de toute nature\", C_SHEAF),\n",
+    "]\n",
+    "\n",
+    "# La mer qui monte : une bande de fond de plus en plus haute.\n",
+    "mer = np.linspace(0, 10, 400)\n",
+    "for i, (y, *_ ) in enumerate(etages):\n",
+    "    h = y + 0.55\n",
+    "    ax.fill_between(mer, 0, h, color=etages[i][4], alpha=0.045, zorder=0)\n",
+    "\n",
+    "for (y, titre, contenu, geste, couleur) in etages:\n",
+    "    ax.add_patch(FancyBboxPatch((0.55, y), 8.9, 1.55,\n",
+    "                                boxstyle=\"round,pad=0.06,rounding_size=0.12\",\n",
+    "                                facecolor=couleur, alpha=0.13,\n",
+    "                                edgecolor=couleur, linewidth=1.9, zorder=2))\n",
+    "    ax.text(0.95, y + 1.14, titre, ha=\"left\", va=\"center\", fontsize=12.5,\n",
+    "            fontweight=\"bold\", color=couleur, zorder=4)\n",
+    "    ax.text(0.95, y + 0.72, contenu, ha=\"left\", va=\"center\", fontsize=9.8,\n",
+    "            color=\"#333333\", zorder=4)\n",
+    "    ax.text(9.10, y + 0.26, geste, ha=\"right\", va=\"center\", fontsize=9.0,\n",
+    "            style=\"italic\", color=couleur, zorder=4)\n",
+    "\n",
+    "# Les fleches « contient comme cas particulier », montees une a une\n",
+    "for i in range(len(etages) - 1):\n",
+    "    y0 = etages[i][0] + 1.55\n",
+    "    y1 = etages[i + 1][0]\n",
+    "    ax.add_patch(FancyArrowPatch((1.25, y0 + 0.05), (1.25, y1 - 0.05),\n",
+    "                                 arrowstyle=\"-|>\", mutation_scale=13,\n",
+    "                                 color=\"#666666\", linewidth=1.3, zorder=1))\n",
+    "    ax.text(1.55, (y0 + y1) / 2, \"cas particulier\", ha=\"left\", va=\"center\",\n",
+    "            fontsize=8.8, color=\"#666666\", zorder=5)\n",
+    "\n",
+    "ax.text(5.0, 9.55, \"Chaque etage contient le precedent -- et recolle un peu plus\",\n",
+    "        ha=\"center\", va=\"center\", fontsize=12.0, fontweight=\"bold\",\n",
+    "        color=\"#1a5276\")\n",
+    "ax.text(5.0, 0.28, \"la montee n'est pas une fuite en avant : c'est ce qui rend \"\n",
+    "                   \"recoltable ce qui ne l'etait pas\",\n",
+    "        ha=\"center\", va=\"center\", fontsize=9.4, style=\"italic\", color=\"#555555\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c23",
+   "metadata": {
+    "papermill": {
+     "duration": 0.010192,
+     "end_time": "2026-09-30T15:01:35.502282+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.492090+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Lecture de la figure 7\n",
+    "\n",
+    "Les quatre etages ne sont pas quatre theories differentes : c'est **la meme** construction, repetee a chaque fois avec un ingredient de plus.\n",
+    "\n",
+    "| Etage | Ce qui sert de morceaux | Ce qui sert de recouvrement |\n",
+    "|---|---|---|\n",
+    "| Espaces topologiques | les ouverts | une union d'ouverts qui egale le tout |\n",
+    "| Categories et cribles | les objets de la categorie | un crible ferme par precomposition |\n",
+    "| Sites | idem, plus un choix de cribles couvrants | une famille couvrante quelconque |\n",
+    "| Topos | les faisceaux sur le site | idem, mais l'objet d'etude devient le faisceau |\n",
+    "\n",
+    "**Le premier etage est le seul que l'on voit.** Les trois suivants remplacent tour a tour « ouvert » par « fleche », puis « union » par « crible », puis « espace » par « categorie ». A chaque remplacement, un theoreme de recollement reste au meme endroit -- mais il s'applique desormais a des objets qui n'ont aucun sens geometrique naif.\n",
+    "\n",
+    "**Le mot de la fin.** Ce carnet a dessine six figures. Aucune n'est une preuve : ce sont des **cartes**. La carte ne remplace pas le territoire -- `Lean-15c` compile les enonces, `Lean-15` les catalogue, `Lean-15b` les met en exercice. Ce que la carte apporte est different : elle permet de savoir ou l'on est avant d'ouvrir un fichier."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c24",
+   "metadata": {
+    "papermill": {
+     "duration": 0.010172,
+     "end_time": "2026-09-30T15:01:35.522397+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.512225+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "## 8. Exercices\n",
+    "\n",
+    "Les trois exercices reprennent une figure chacun, mais cette fois avec des donnees a manipuler. Ils sont ecrits en Python pur : aucune installation supplementaire, aucun acces reseau.\n",
+    "\n",
+    "Les cellules d'exercice contiennent un squelette et un `print` d'attente : le carnet s'execute de bout en bout sans modification. Les solutions sont donnees a la suite, en bas de section.\n",
+    "\n",
+    "### Exercice 1 : fermer un crible\n",
+    "\n",
+    "On represente une petite categorie par son dictionnaire de fleches -- chaque fleche est un couple `(source, but)` d'etiquettes d'objets. Un crible sur `X` est donne comme un ensemble de couples.\n",
+    "\n",
+    "Completez `fermer_crible` pour qu'elle retourne la **fermeture par precomposition** : si `(Y, X)` est dans le crible, alors `(Z, X)` doit y entrer pour toute fleche `(Z, Y)` de la categorie.\n",
+    "\n",
+    "La fonction doit aussi signaler les fleches **ajoutees** par la fermeture -- c'est la difference entre le crible donne et le crible obtenu."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c25",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:35.544458Z",
+     "iopub.status.busy": "2026-09-30T15:01:35.544046Z",
+     "iopub.status.idle": "2026-09-30T15:01:35.550395Z",
+     "shell.execute_reply": "2026-09-30T15:01:35.549358Z"
+    },
+    "papermill": {
+     "duration": 0.019168,
+     "end_time": "2026-09-30T15:01:35.551501+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.532333+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice 1 a completer\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercice 1 : fermeture par precomposition d'un crible\n",
+    "#\n",
+    "# La categorie : quatre objets A, B, C, D et les fleches ci-dessous.\n",
+    "# On lit \"fleche (Z, Y)\" comme \"Z -> Y\".\n",
+    "CATEGORIE = {\n",
+    "    (\"B\", \"A\"),   # B -> A\n",
+    "    (\"C\", \"B\"),   # C -> B\n",
+    "    (\"D\", \"C\"),   # D -> C\n",
+    "    (\"D\", \"A\"),   # D -> A, une fleche directe qui existe deja\n",
+    "}\n",
+    "\n",
+    "# Crible donne sur A : on a retenu B -> A et C -> B (donc C -> B -> A).\n",
+    "CRIBLE_DONNE = {(\"B\", \"A\")}\n",
+    "\n",
+    "\n",
+    "def fermer_crible(categorie, crible):\n",
+    "    \"\"\"Retourne (crible_ferme, ajoutees).\n",
+    "\n",
+    "    crible_ferme : l'ensemble des fleches du crible, ferme par precomposition\n",
+    "    ajoutees     : la liste triee des fleches presentes dans crible_ferme\n",
+    "                   mais absentes du crible d'entree\n",
+    "    \"\"\"\n",
+    "    # TODO etudiant : partir de `set(crible)`, puis repeter l'operation\n",
+    "    #                \"si (q, X) est dans le crible et (z, q) est une fleche\n",
+    "    #                 de la categorie, alors (z, X) entre dans le crible\"\n",
+    "    #                jusqu'a stabilisation. Retourner aussi les ajouts.\n",
+    "    return None, None  # TODO etudiant\n",
+    "\n",
+    "\n",
+    "crible_ferme, ajoutees = fermer_crible(CATEGORIE, CRIBLE_DONNE)\n",
+    "print(\"Exercice 1 a completer\")\n",
+    "if crible_ferme is not None:\n",
+    "    print(\"crible ferme  :\", sorted(crible_ferme))\n",
+    "    print(\"ajoutees      :\", ajoutees)\n",
+    "    print(\"attendu       : 3 fleches vers A : (B,A), (C,A), (D,A) -- les deux \"\n",
+    "          \"dernieres sont ajoutees par la fermeture\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c26",
+   "metadata": {
+    "papermill": {
+     "duration": 0.010288,
+     "end_time": "2026-09-30T15:01:35.572443+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.562155+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Exercice 2 : tester la compatibilite d'une famille de sections\n",
+    "\n",
+    "On se place dans le faisceau des fonctions continues sur $U = [0, 3]$, avec le recouvrement de la figure 4 : $U_1 = [0, 1{,}3]$, $U_2 = [1{,}0, 2{,}2]$, $U_3 = [1{,}9, 3{,}0]$.\n",
+    "\n",
+    "Chaque section locale est donnee comme un tableau `(xs, ys)` echantillonne sur son ouvert. Completez `famille_compatible` pour qu'elle verifie la **condition de compatibilite** : sur chaque intersection non vide, les deux sections doivent prendre les memes valeurs, a une tolerance pres.\n",
+    "\n",
+    "Indice : deux sections sont echantillonnees sur des grilles differentes. Il faut donc **interpoler** l'une sur les abscisses de l'autre avant de comparer -- comparer terme a terme deux tableaux de tailles differentes n'a pas de sens.\n",
+    "\n",
+    "Un mot sur la tolerance, qui est le vrai sujet de cet exercice. Deux echantillonnages differents de la **meme** fonction continue ne coincident pas exactement : l'interpolation lineaire entre deux points d'une grille laisse un residu. Sur les donnees ci-dessous, ce residu vaut environ $10^{-6}$. Le seuil de compatibilite doit donc etre choisi **au-dessus** de l'erreur d'echantillonnage et **bien en dessous** du decalage qu'on veut detecter ($0{,}55$) -- c'est le meme arbitrage que dans tout test numerique de faisceau."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c27",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:35.593645Z",
+     "iopub.status.busy": "2026-09-30T15:01:35.593339Z",
+     "iopub.status.idle": "2026-09-30T15:01:35.601637Z",
+     "shell.execute_reply": "2026-09-30T15:01:35.600487Z"
+    },
+    "papermill": {
+     "duration": 0.020575,
+     "end_time": "2026-09-30T15:01:35.602563+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.581988+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice 2 a completer\n",
+      "Exercice 2 a completer\n",
+      "attendu       : FAMILLE_OK compatible (ecart ~0), FAMILLE_KO non (ecart 0.55)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercice 2 : la famille est-elle compatible sur les intersections ?\n",
+    "U1b, U2b, U3b = (0.0, 1.3), (1.0, 2.2), (1.9, 3.0)\n",
+    "\n",
+    "\n",
+    "def echantillonne(u, decalage=0.0, n=160):\n",
+    "    \"\"\"Section locale : la meme fonction, decalee eventuellement.\"\"\"\n",
+    "    xs = np.linspace(u[0], u[1], n)\n",
+    "    return xs, 1.0 + 0.6 * np.sin(xs) + decalage\n",
+    "\n",
+    "\n",
+    "# Deux familles a tester : la premiere compatible, la seconde non.\n",
+    "FAMILLE_OK = [echantillonne(U1b), echantillonne(U2b), echantillonne(U3b)]\n",
+    "FAMILLE_KO = [echantillonne(U1b), echantillonne(U2b, decalage=0.55),\n",
+    "              echantillonne(U3b)]\n",
+    "\n",
+    "\n",
+    "def famille_compatible(sections, ouverts, tolerance=1e-3):\n",
+    "    \"\"\"Retourne (compatible: bool, pire_ecart: float).\n",
+    "\n",
+    "    Pour chaque paire d'ouverts qui s'intersectent, comparer les deux sections\n",
+    "    sur l'intersection (apres interpolation sur une grille commune).\n",
+    "\n",
+    "    La tolerance n'est pas cosmetique : deux echantillonnages differents d'une\n",
+    "    meme fonction continue ne se superposent pas exactement, l'interpolation\n",
+    "    lineaire laisse un residu de l'ordre de 1e-6 ici. Le seuil doit donc etre\n",
+    "    au-dessus de cette erreur d'echantillonnage, et bien en dessous du decalage\n",
+    "    qu'on cherche a detecter (0.55).\n",
+    "    \"\"\"\n",
+    "    # TODO etudiant : boucler sur les paires (i, j), calculer l'intersection\n",
+    "    #                [max(x0), min(x1)], echantillonner une grille dedans,\n",
+    "    #                interpoler les DEUX sections dessus avec np.interp,\n",
+    "    #                et suivre le maximum de |s_i - s_j|.\n",
+    "    return None, None  # TODO etudiant\n",
+    "\n",
+    "\n",
+    "for nom, famille in ((\"FAMILLE_OK\", FAMILLE_OK), (\"FAMILLE_KO\", FAMILLE_KO)):\n",
+    "    ok, ecart = famille_compatible(famille, [U1b, U2b, U3b])\n",
+    "    print(\"Exercice 2 a completer\")\n",
+    "    if ok is not None:\n",
+    "        print(\"%s : compatible=%-5s  pire ecart=%.4f\" % (nom, ok, ecart))\n",
+    "print(\"attendu       : FAMILLE_OK compatible (ecart ~0), FAMILLE_KO non \"\n",
+    "      \"(ecart 0.55)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c28",
+   "metadata": {
+    "papermill": {
+     "duration": 0.009743,
+     "end_time": "2026-09-30T15:01:35.622134+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.612391+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Exercice 3 : le critere de recouvrement du site de Zariski\n",
+    "\n",
+    "Reprenons le site de Zariski de la figure 6, en passant des $U_p = D(p)$ aux ouverts de base **generaux** $D(f)$, pour $f$ un entier non nul quelconque :\n",
+    "\n",
+    "$$D(f) = \\{\\,\\mathfrak{q} \\text{ premier} : f \\notin \\mathfrak{q}\\,\\}, \\qquad\\text{i.e.}\\qquad D(f) = \\operatorname{Spec} \\mathbb{Z} \\setminus \\{(q) : q \\text{ premier divisant } f\\}.$$\n",
+    "\n",
+    "$D(f)$ retire donc les diviseurs premiers de $f$ -- un seul pour $f = p$, plusieurs pour $f = 6$, aucun pour $f = 1$ (car $D(1)$ est tout l'espace). Sur une famille $f_1, \\ldots, f_n$, ce qui survit est l'intersection des retenues :\n",
+    "\n",
+    "$$\\operatorname{Spec} \\mathbb{Z} \\setminus \\bigl(D(f_1) \\cup \\cdots \\cup D(f_n)\\bigr) = \\{(q) : q \\text{ divise chacun des } f_i\\} = \\{(q) : q \\mid \\gcd(f_1, \\ldots, f_n)\\}.$$\n",
+    "\n",
+    "Completez `points_decouverts` pour qu'elle retourne les points **non couverts** par une famille donnee. Le critere de recouvrement en tombe tout seul :\n",
+    "\n",
+    "$$D(f_1) \\cup \\cdots \\cup D(f_n) = \\operatorname{Spec} \\mathbb{Z} \\quad\\Longleftrightarrow\\quad \\gcd(f_1, \\ldots, f_n) = 1 \\quad\\Longleftrightarrow\\quad (f_1, \\ldots, f_n) = \\mathbb{Z}.$$\n",
+    "\n",
+    "La derniere forme est celle qui compte : les $f_i$ engendrent l'ideal unite. C'est **exactement** la condition de recouvrement d'un site de Zariski sur un anneau quelconque -- $\\mathbb{Z}$ n'y joue aucun role particulier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c29",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:35.647943Z",
+     "iopub.status.busy": "2026-09-30T15:01:35.647641Z",
+     "iopub.status.idle": "2026-09-30T15:01:35.654682Z",
+     "shell.execute_reply": "2026-09-30T15:01:35.653683Z"
+    },
+    "papermill": {
+     "duration": 0.021641,
+     "end_time": "2026-09-30T15:01:35.655721+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.634080+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Exercice 3 a completer\n",
+      "Exercice 3 a completer\n",
+      "Exercice 3 a completer\n",
+      "Exercice 3 a completer\n",
+      "Exercice 3 a completer\n",
+      "Question : quelle est la plus petite famille qui couvre Spec Z entier ?\n",
+      "Reponse (a completer) : ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exercice 3 : quels points survivent a une famille d'ouverts D(f) ?\n",
+    "def points_decouverts(famille, premiers_connus):\n",
+    "    \"\"\"Retourne la liste triee des points de Spec Z non couverts par les D(f).\n",
+    "\n",
+    "    L'ouvert D(f) contient (q) si et seulement si q ne divise pas f. Un point\n",
+    "    (q) survit donc a la famille entiere si et seulement si q divise TOUS les\n",
+    "    f de la famille -- c'est-a-dire si q divise leur pgcd.\n",
+    "    \"\"\"\n",
+    "    # TODO etudiant : calculer g = pgcd de famille, puis retourner les premiers\n",
+    "    #                de premiers_connus qui divisent g, sous forme triee.\n",
+    "    return None  # TODO etudiant\n",
+    "\n",
+    "\n",
+    "PREMIERS = [2, 3, 5, 7, 11, 13]\n",
+    "familles = ([2], [2, 3], [6, 10], [30, 42], PREMIERS)\n",
+    "for famille in familles:\n",
+    "    restants = points_decouverts(famille, PREMIERS)\n",
+    "    print(\"Exercice 3 a completer\")\n",
+    "    if restants is not None:\n",
+    "        print(\"D%-18s -> restent %d point(s) : %s\"\n",
+    "              % (famille, len(restants), restants))\n",
+    "\n",
+    "print(\"Question : quelle est la plus petite famille qui couvre Spec Z entier ?\")\n",
+    "print(\"Reponse (a completer) : ...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c30",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011613,
+     "end_time": "2026-09-30T15:01:35.677084+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.665471+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Solution de l'exercice 1 -- fermer un crible\n",
+    "\n",
+    "La fermeture se calcule par un point fixe : on repete l'operation tant qu'elle ajoute quelque chose. Partir d'un crible donne et le **saturer** est exactement ce que fait la definition -- l'enonce par « si $f \\in S$ alors $f \\circ h \\in S$ » est une regle de saturation, pas une enumeration.\n",
+    "\n",
+    "Sur la categorie de l'exercice, le crible engendre par $\\{B \\to A\\}$ contient **trois** fleches, toutes arrivant sur $A$ :\n",
+    "\n",
+    "- $B \\to A$, celle du crible de depart ;\n",
+    "- $C \\to A$, obtenue en composant $C \\to B$ avec $B \\to A$ ;\n",
+    "- $D \\to A$, obtenue en composant $D \\to C$ avec $C \\to A$.\n",
+    "\n",
+    "Les deux dernieres sont **ajoutees** par la fermeture : elles ne figuraient pas dans le crible donne. Noter que $D \\to A$ existe aussi comme fleche directe de la categorie -- la fermeture la retrouve par composition, et le resultat est le meme ensemble. C'est exactement la propriete de saturation qui fait qu'un crible est determine par ses elements maximaux."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c31",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:35.698330Z",
+     "iopub.status.busy": "2026-09-30T15:01:35.698051Z",
+     "iopub.status.idle": "2026-09-30T15:01:35.704745Z",
+     "shell.execute_reply": "2026-09-30T15:01:35.703713Z"
+    },
+    "papermill": {
+     "duration": 0.018049,
+     "end_time": "2026-09-30T15:01:35.705597+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.687548+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "crible ferme : [('B', 'A'), ('C', 'A'), ('D', 'A')]\n",
+      "ajoutees     : [('C', 'A'), ('D', 'A')]\n",
+      "nombre final : 3 fleches vers A\n",
+      "OK : la fermeture ajoute exactement les deux compositions\n"
+     ]
+    }
+   ],
+   "source": [
+    "def fermer_crible_solution(categorie, crible):\n",
+    "    \"\"\"Fermeture par precomposition, par point fixe.\"\"\"\n",
+    "    ferme = set(crible)\n",
+    "    while True:\n",
+    "        ajouts = set()\n",
+    "        for (q, cible) in ferme:\n",
+    "            # toute fleche (z, q) se compose avec (q, cible)\n",
+    "            for (source, but) in categorie:\n",
+    "                if but == q and source != cible:\n",
+    "                    ajouts.add((source, cible))\n",
+    "        nouveaux = ajouts - ferme\n",
+    "        if not nouveaux:\n",
+    "            break\n",
+    "        ferme |= nouveaux\n",
+    "    return ferme, sorted(ferme - set(crible))\n",
+    "\n",
+    "\n",
+    "ferme, ajoutees = fermer_crible_solution(CATEGORIE, CRIBLE_DONNE)\n",
+    "print(\"crible ferme :\", sorted(ferme))\n",
+    "print(\"ajoutees     :\", ajoutees)\n",
+    "print(\"nombre final :\", len(ferme), \"fleches vers A\")\n",
+    "assert ferme == {(\"B\", \"A\"), (\"C\", \"A\"), (\"D\", \"A\")}, ferme\n",
+    "assert ajoutees == [(\"C\", \"A\"), (\"D\", \"A\")], ajoutees\n",
+    "print(\"OK : la fermeture ajoute exactement les deux compositions\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c32",
+   "metadata": {
+    "papermill": {
+     "duration": 0.010699,
+     "end_time": "2026-09-30T15:01:35.725743+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.715044+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Solution de l'exercice 2 -- compatibilite d'une famille\n",
+    "\n",
+    "Le piege de cet exercice n'est pas la condition de faisceau, c'est l'**echantillonnage**. Deux sections locales sont echantillonnees sur des grilles differentes : comparer leurs tableaux terme a terme comparerait des valeurs prises en des points differents, ce qui n'a aucun sens. Il faut interpoler.\n",
+    "\n",
+    "Une fois l'interpolation faite, la condition est immediate : sur chaque intersection, l'ecart maximal entre les deux sections doit rester sous la tolerance.\n",
+    "\n",
+    "Le resultat fait apparaitre la difference entre les deux familles de la figure 4 : la premiere donne un ecart de l'ordre de $10^{-6}$, la seconde un ecart de $0{,}55$ -- exactement le decalage introduit.\n",
+    "\n",
+    "**Les deux ordres de grandeur sont le vrai enseignement.** L'ecart de la famille compatible n'est pas nul, et il ne peut pas l'etre : deux echantillonnages distincts d'une meme fonction continue ne se superposent qu'a l'erreur d'interpolation pres. Un test de compatibilite qui exigerait l'egalite exacte declarerait incompatible une famille parfaitement recollable. La tolerance est donc une piece du raisonnement, pas un confort : elle se place entre le bruit d'echantillonnage ($\\sim 10^{-6}$) et le defaut cherche ($0{,}55$) -- cinq ordres de grandeur de marge."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c33",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:35.749155Z",
+     "iopub.status.busy": "2026-09-30T15:01:35.748853Z",
+     "iopub.status.idle": "2026-09-30T15:01:35.756972Z",
+     "shell.execute_reply": "2026-09-30T15:01:35.756119Z"
+    },
+    "papermill": {
+     "duration": 0.021107,
+     "end_time": "2026-09-30T15:01:35.758268+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.737161+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FAMILLE_OK : compatible=True   pire ecart=0.0000\n",
+      "FAMILLE_KO : compatible=False  pire ecart=0.5500\n",
+      "OK : compatible (residu 4.2e-06) / incompatible (ecart 0.55)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def famille_compatible_solution(sections, ouverts, tolerance=1e-3):\n",
+    "    \"\"\"Compatibilite deux a deux sur les intersections, apres interpolation.\"\"\"\n",
+    "    pire = 0.0\n",
+    "    for i in range(len(ouverts)):\n",
+    "        for j in range(i + 1, len(ouverts)):\n",
+    "            x0 = max(ouverts[i][0], ouverts[j][0])\n",
+    "            x1 = min(ouverts[i][1], ouverts[j][1])\n",
+    "            if x1 <= x0:\n",
+    "                continue                      # intersection vide : rien a tester\n",
+    "            grille = np.linspace(x0, x1, 200)\n",
+    "            si = np.interp(grille, sections[i][0], sections[i][1])\n",
+    "            sj = np.interp(grille, sections[j][0], sections[j][1])\n",
+    "            pire = max(pire, float(np.max(np.abs(si - sj))))\n",
+    "    return pire <= tolerance, pire\n",
+    "\n",
+    "\n",
+    "for nom, famille in ((\"FAMILLE_OK\", FAMILLE_OK), (\"FAMILLE_KO\", FAMILLE_KO)):\n",
+    "    ok, ecart = famille_compatible_solution(famille, [U1b, U2b, U3b])\n",
+    "    print(\"%s : compatible=%-5s  pire ecart=%.4f\" % (nom, ok, ecart))\n",
+    "\n",
+    "ok1, e1 = famille_compatible_solution(FAMILLE_OK, [U1b, U2b, U3b])\n",
+    "ok2, e2 = famille_compatible_solution(FAMILLE_KO, [U1b, U2b, U3b])\n",
+    "assert ok1 and e1 < 1e-4, (ok1, e1)          # residu d'interpolation, pas 0\n",
+    "assert (not ok2) and abs(e2 - 0.55) < 1e-4, (ok2, e2)\n",
+    "print(\"OK : compatible (residu %.1e) / incompatible (ecart 0.55)\" % e1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c34",
+   "metadata": {
+    "papermill": {
+     "duration": 0.010411,
+     "end_time": "2026-09-30T15:01:35.779099+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.768688+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "### Solution de l'exercice 3 -- le critere par le pgcd\n",
+    "\n",
+    "La fonction tient en trois lignes : on calcule le pgcd de la famille, puis on garde les premiers connus qui le divisent. Toute la geometrie est dans le pgcd.\n",
+    "\n",
+    "| Famille | Points non couverts |\n",
+    "|---|---|\n",
+    "| $D(2)$ | $(2)$ |\n",
+    "| $D(2), D(3)$ | aucun |\n",
+    "| $D(6), D(10)$ | $(2)$, car $\\gcd(6,10) = 2$ |\n",
+    "| $D(30), D(42)$ | $(2), (3)$, car $\\gcd(30,42) = 6$ |\n",
+    "| tous les $D(p)$, $p$ premier | aucun, car leur pgcd vaut $1$ |\n",
+    "\n",
+    "Deux consequences meritent d'etre nommees.\n",
+    "\n",
+    "**La plus petite famille couvrante a deux elements.** $D(2) \\cup D(3) = \\operatorname{Spec} \\mathbb{Z}$ : aucun ideal premier ne contient a la fois $2$ et $3$, donc aucun point n'echappe aux deux ouverts. Une infinite de points, deux ouverts -- c'est la quasi-compacite de $\\operatorname{Spec} \\mathbb{Z}$ rendue visible, et le meme phenomene que le tableau de la figure 6.\n",
+    "\n",
+    "**Le critere se transporte tel quel.** $D(f_1) \\cup \\cdots \\cup D(f_n)$ couvre si et seulement si $\\gcd = 1$, c'est-a-dire si les $f_i$ **engendrent l'ideal unite**. Ecrit ainsi, l'enonce ne mentionne plus $\\mathbb{Z}$ : sur $\\operatorname{Spec} A$ pour un anneau $A$ quelconque, la famille des $D(f_i)$ couvre si et seulement si $(f_1, \\ldots, f_n) = A$. Le cas de $\\mathbb{Z}$ etait un cas particulier d'une condition qui n'a rien d'arithmetique.\n",
+    "\n",
+    "**Ce que l'exercice fait mesurer** : « recouvrir » n'est pas une question de cardinalite ni de reunion d'ensembles -- c'est une condition **algebrique** sur les generateurs (engendrer l'ideal unite), ou sur les fleches (former un crible couvrant) quand il n'y a plus d'ideal unite a invoquer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c35",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-30T15:01:35.802142Z",
+     "iopub.status.busy": "2026-09-30T15:01:35.801770Z",
+     "iopub.status.idle": "2026-09-30T15:01:35.808806Z",
+     "shell.execute_reply": "2026-09-30T15:01:35.807981Z"
+    },
+    "papermill": {
+     "duration": 0.020397,
+     "end_time": "2026-09-30T15:01:35.810130+00:00",
+     "exception": false,
+     "start_time": "2026-09-30T15:01:35.789733+00:00",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "D[2]                -> restent [2]\n",
+      "D[2, 3]             -> restent []\n",
+      "D[6, 10]            -> restent [2]\n",
+      "D[30, 42]           -> restent [2, 3]\n",
+      "D[2, 3, 5, 7, 11, 13] -> restent []\n",
+      "\n",
+      "Deux ouverts suffisent : D(2) union D(3) couvre Spec Z entier.\n",
+      "Critere general : la famille couvre si et seulement si le pgcd vaut 1,\n",
+      "c'est-a-dire si les f engendrent l'ideal unite (f_1, ..., f_n) = Z.\n",
+      "Sur un anneau A quelconque, le meme critere s'ecrit (f_1, ..., f_n) = A.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import gcd\n",
+    "\n",
+    "\n",
+    "def points_decouverts_solution(famille, premiers_connus):\n",
+    "    \"\"\"Points de Spec Z non couverts par la famille d'ouverts D(f).\"\"\"\n",
+    "    g = 0\n",
+    "    for f in famille:\n",
+    "        g = gcd(g, f)\n",
+    "    return sorted(q for q in premiers_connus if g % q == 0)\n",
+    "\n",
+    "\n",
+    "attendus = (([2], [2]), ([2, 3], []), ([6, 10], [2]),\n",
+    "            ([30, 42], [2, 3]), (tuple(PREMIERS), []))\n",
+    "for famille, attendu in attendus:\n",
+    "    famille = list(famille)\n",
+    "    restants = points_decouverts_solution(famille, PREMIERS)\n",
+    "    print(\"D%-18s -> restent %s\" % (famille, restants))\n",
+    "    assert restants == attendu, (famille, restants, attendu)\n",
+    "\n",
+    "print()\n",
+    "print(\"Deux ouverts suffisent : D(2) union D(3) couvre Spec Z entier.\")\n",
+    "print(\"Critere general : la famille couvre si et seulement si le pgcd vaut 1,\")\n",
+    "print(\"c'est-a-dire si les f engendrent l'ideal unite (f_1, ..., f_n) = Z.\")\n",
+    "print(\"Sur un anneau A quelconque, le meme critere s'ecrit (f_1, ..., f_n) = A.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "cost": {
+   "api_provider": "none",
+   "api_usd_est": 0.0,
+   "cpu_min": 2,
+   "external_account": false,
+   "free_alternative": "self",
+   "gpu_min": 0,
+   "gpu_required": false,
+   "metadata_written": "2026-09-30",
+   "network": false,
+   "notes": "Visite guidee visuelle des abstractions grothendieckiennes : figures matplotlib autonomes, aucun appel reseau, aucun kernel Lean, aucun GPU. Deterministe.",
+   "qcc_tokens_est": 0,
+   "reduced_pedagogical": false,
+   "reproducibility": "HIGH",
+   "validator": "check_cost_metadata.py",
+   "vram_gb": 0,
+   "vram_tier": "none"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.15"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 6.065724,
+   "end_time": "2026-09-30T15:01:36.388260+00:00",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15d-Lean-Grothendieck-Visuel-Python.ipynb",
+   "output_path": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15d-Lean-Grothendieck-Visuel-Python.ipynb",
+   "parameters": {},
+   "start_time": "2026-09-30T15:01:30.322536+00:00",
+   "version": "2.7.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/README.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/README.md
index a047a11b8c..f182a243db 100644
--- a/MyIA.AI.Notebooks/SymbolicAI/Lean/README.md
+++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/README.md
@@ -113,6 +113,7 @@ Tous les notebooks incluent une **barre de navigation** en haut et en bas permet
 | 15 | [Lean-15-Grothendieck-Tribute](Lean-15-Grothendieck-Tribute.ipynb) | Langage grothendieckien dans Mathlib 4 : catégories/foncteurs, cribles et topologies de Grothendieck, faisceaux, schémas, site de Zariski, morphismes étales/lisses - Epic #1646 | 45 min |
 | 15b | [Lean-15b-Lean-Grothendieck](Lean-15b-Lean-Grothendieck.ipynb) | Atelier pratique Grothendieck : cribles, topologies et faisceaux en exercices (compagnon `grothendieck_lean`, fait suite à Lean-15) - Epic #1646 | 50 min |
 | 15c | [Lean-15c-Lean-Grothendieck-Companion](Lean-15c-Lean-Grothendieck-Companion.ipynb) | Companion formel natif du lake `grothendieck_lean` en kernel `lean4-wsl` : les 51 modules visités par leurs énoncés qui compilent (Yoneda, forme flèche Covers*, faisceautisation, Čech, Mayer-Vietoris, Zariski), 0 sorry attesté par `#print axioms` - Epic #11703 | 40 min |
+| 15d | [Lean-15d-Lean-Grothendieck-Visuel-Python](Lean-15d-Lean-Grothendieck-Visuel-Python.ipynb) | Visite guidée visuelle des abstractions grothendieckiennes : sept figures Python autonomes (catégories et foncteurs, cribles, topologie de Grothendieck, faisceaux, Yoneda, site de Zariski, synthèse) et trois exercices sur données manipulables (fermeture d'un crible, compatibilité d'une famille de sections, critère de recouvrement par le pgcd) — aucun kernel Lean requis - See #17978 | 35 min |
 | 16a | [Lean-16a-Conway-Man-and-Work](Lean-16a-Conway-Man-and-Work.ipynb) | Conway, l'homme et l'oeuvre : biographie et style singulier (le jeu comme méthode) ; panorama des grands résultats (nombres surréels, groupes de Conway & Monstrous Moonshine, réseau de Leech, polynôme de Conway, Doomsday, Look-and-Say, FRACTRAN, problème de l'Ange, Sprouts, théorème du libre arbitre) ; premières noix crackées exécutées depuis conway_lean (Doomsday, Look-and-Say, Nim, Angel, Life - 0 sorry) - Epic #1647 / #2154 | 50 min |
 | 16b | [Lean-16b-Conway-Game-of-Life-Lean](Lean-16b-Conway-Game-of-Life-Lean.ipynb) | Hommage à John Conway : Game of Life as Computation, Doomsday, FRACTRAN, Look-and-Say, Nim, Angel - Epic #1647 | 60 min |
 | 16c | [Lean-16c-Conway-Game-of-Life-Golly](Lean-16c-Conway-Game-of-Life-Golly.ipynb) | Game of Life : les 3 piliers en images (compagnon Golly, intégration CLI `bgolly` pour simulation certifiée) - Epic #1647 | 45 min |
@@ -446,6 +447,7 @@ Lean/
 ├── Lean-15-Grothendieck-Tribute.ipynb # Python kernel - hommage Grothendieck (langage grothendieckien Mathlib)
 ├── Lean-15b-Lean-Grothendieck.ipynb # Python kernel - atelier pratique Grothendieck (compagnon grothendieck_lean)
 ├── Lean-15c-Lean-Grothendieck-Companion.ipynb # Lean4 (WSL) kernel - companion formel natif grothendieck_lean (51 modules par leurs énoncés, Epic #11703)
+├── Lean-15d-Lean-Grothendieck-Visuel-Python.ipynb # Python kernel - visite guidée visuelle du corpus Grothendieck (7 figures, 3 exercices, sans lake)
 ├── Lean-16a-Conway-Man-and-Work.ipynb # Python kernel - hommage Conway (l'homme et l'œuvre, noix exécutées depuis conway_lean)
 ├── Lean-16b-Conway-Game-of-Life-Lean.ipynb   # Python kernel - hommage Conway (Game of Life as Computation)
 ├── Lean-16c-Conway-Game-of-Life-Golly.ipynb  # Python kernel - hommage Conway (Game of Life en images, compagnon Golly)