From a6b89dfbc012b8bdef392e5a0405a1bf31b5aa59 Mon Sep 17 00:00:00 2001 From: Peter Norvig Date: Sun, 14 Dec 2025 23:25:26 -0800 Subject: [PATCH] Add files via upload --- ipynb/Advent-2025.ipynb | 416 +++++++++++++++++++++++----------------- 1 file changed, 239 insertions(+), 177 deletions(-) diff --git a/ipynb/Advent-2025.ipynb b/ipynb/Advent-2025.ipynb index af0f860..9e74ad6 100644 --- a/ipynb/Advent-2025.ipynb +++ b/ipynb/Advent-2025.ipynb @@ -158,7 +158,7 @@ { "data": { "text/plain": [ - "Puzzle 1.1: .0005 seconds, answer 1182 correct" + "Puzzle 1.1: .0006 seconds, answer 1182 correct" ] }, "execution_count": 5, @@ -192,7 +192,7 @@ { "data": { "text/plain": [ - "Puzzle 1.2: .1418 seconds, answer 6907 correct" + "Puzzle 1.2: .1639 seconds, answer 6907 correct" ] }, "execution_count": 6, @@ -242,7 +242,7 @@ { "data": { "text/plain": [ - "Puzzle 1.2: .0009 seconds, answer 6907 correct" + "Puzzle 1.2: .0013 seconds, answer 6907 correct" ] }, "execution_count": 8, @@ -387,7 +387,7 @@ { "data": { "text/plain": [ - "Puzzle 2.1: .0028 seconds, answer 23560874270 correct" + "Puzzle 2.1: .0030 seconds, answer 23560874270 correct" ] }, "execution_count": 12, @@ -471,7 +471,7 @@ { "data": { "text/plain": [ - "Puzzle 2.1: .0029 seconds, answer 23560874270 correct" + "Puzzle 2.1: .0039 seconds, answer 23560874270 correct" ] }, "execution_count": 15, @@ -493,7 +493,7 @@ { "data": { "text/plain": [ - "Puzzle 2.2: .0037 seconds, answer 44143124633 correct" + "Puzzle 2.2: .0039 seconds, answer 44143124633 correct" ] }, "execution_count": 16, @@ -602,7 +602,7 @@ { "data": { "text/plain": [ - "Puzzle 3.1: .0006 seconds, answer 17085 correct" + "Puzzle 3.1: .0007 seconds, answer 17085 correct" ] }, "execution_count": 20, @@ -679,7 +679,7 @@ { "data": { "text/plain": [ - "Puzzle 3.1: .0006 seconds, answer 17085 correct" + "Puzzle 3.1: .0007 seconds, answer 17085 correct" ] }, "execution_count": 23, @@ -701,7 +701,7 @@ { "data": { "text/plain": [ - "Puzzle 3.2: .0021 seconds, answer 169408143086082 correct" + "Puzzle 3.2: .0020 seconds, answer 169408143086082 correct" ] }, "execution_count": 24, @@ -800,7 +800,7 @@ { "data": { "text/plain": [ - "Puzzle 4.1: .0572 seconds, answer 1569 correct" + "Puzzle 4.1: .0610 seconds, answer 1569 correct" ] }, "execution_count": 27, @@ -851,7 +851,7 @@ { "data": { "text/plain": [ - "Puzzle 4.2: 1.2620 seconds, answer 9280 correct" + "Puzzle 4.2: 1.3428 seconds, answer 9280 correct" ] }, "execution_count": 29, @@ -900,7 +900,7 @@ { "data": { "text/plain": [ - "Puzzle 4.2: .1446 seconds, answer 9280 correct" + "Puzzle 4.2: .1437 seconds, answer 9280 correct" ] }, "execution_count": 31, @@ -1040,7 +1040,7 @@ { "data": { "text/plain": [ - "Puzzle 5.1: .0111 seconds, answer 635 correct" + "Puzzle 5.1: .0118 seconds, answer 635 correct" ] }, "execution_count": 35, @@ -1205,7 +1205,7 @@ { "data": { "text/plain": [ - "Puzzle 6.1: .0025 seconds, answer 5877594983578 correct" + "Puzzle 6.1: .0021 seconds, answer 5877594983578 correct" ] }, "execution_count": 41, @@ -1288,7 +1288,7 @@ { "data": { "text/plain": [ - "Puzzle 6.2: .0065 seconds, answer 11159825706149 correct" + "Puzzle 6.2: .0061 seconds, answer 11159825706149 correct" ] }, "execution_count": 43, @@ -1678,7 +1678,7 @@ { "data": { "text/plain": [ - "Puzzle 8.1: .6055 seconds, answer 24360 correct" + "Puzzle 8.1: .6053 seconds, answer 24360 correct" ] }, "execution_count": 56, @@ -1709,7 +1709,7 @@ "outputs": [], "source": [ "def last_connected(boxes) -> Tuple[Point, Point]:\n", - " \"\"\"Go through tall the pairs of boxes, in closest first order. \n", + " \"\"\"Go through all the pairs of boxes, in closest first order. \n", " Return the last two boxes that finally make it all one big circuit.\"\"\"\n", " circuits = {B: (B,) for B in boxes} # A dict of {box: circuit}\n", " for (A, B) in closest_pairs(boxes, len(boxes) ** 2):\n", @@ -1731,7 +1731,7 @@ { "data": { "text/plain": [ - "Puzzle 8.2: .6371 seconds, answer 2185817796 correct" + "Puzzle 8.2: .6392 seconds, answer 2185817796 correct" ] }, "execution_count": 58, @@ -1749,7 +1749,7 @@ "id": "fa988909-1a8b-4e8c-aca6-c53af99bc0b6", "metadata": {}, "source": [ - "Today's puzzles had the slowest run times yet. I could perhaps make them faster by mutating circuits rather than forming a new tuple for each new circuit, or by using a Union-Find data structure, but I think gains from that would be small, and since the run time is still under a second, I'll leave the code as is." + "Today's puzzles had the slowest run times yet. I could perhaps make them faster by mutating circuits rather than forming a new tuple for each new circuit, or by using a Union-Find data structure (optimized for unions, not finds). If we had 100,000 unions I think that would be worth it, but for just 1000 I think gains would be small, so I'll leave the code as is." ] }, { @@ -1759,7 +1759,7 @@ "source": [ "# [Day 9](https://adventofcode.com/2025/day/9): Movie Theater \n", "\n", - "The Elves are redecorating the movie theater floor by switching out some of the square tiles in the big grid they form. Some of the tiles are red; their coordinates form the day's input file:" + "The Elves are redecorating the movie theater floor by switching out some of the square tiles in the big grid they form. Some of the tiles are red; their *x*, *y* coordinates form the day's input file:" ] }, { @@ -1812,7 +1812,7 @@ "\n", "### Part 1: What is the largest area of any rectangle you can make?\n", "\n", - "The Elves would like to find the largest rectangle that uses red tiles for two of its opposite corners. That's easy; we can try all combinations of two corners and take the corners with the maximum area. The only tricky part is remembering that we have to add one to the delta-x and delta-y values before multiplying them; the area of a square with corners (0, 0) and (1, 1) is 4 tiles, not 1." + "The Elves would like to find the largest rectangle that uses red tiles for two of its opposite corners. That's easy; we can try all combinations of two corners and take the corners with the maximum area. (I'll define a pair of corner points to be a type called `Rectangle`.) The only **tricky part** is remembering that we have to add one to the delta-x and delta-y values before multiplying them; the area of a square with corners (0, 0) and (1, 1) is 4 tiles, not 1. Initially I had a **bug** and forgot this on my first submission." ] }, { @@ -1822,11 +1822,11 @@ "metadata": {}, "outputs": [], "source": [ - "Corners = Tuple[Point, Point] # Type representing a rectangle as specified by two corners\n", + "Rectangle = Tuple[Point, Point] # Type: a rectangle is defined by two opposite corners\n", "\n", - "def tile_area(corners: Corners):\n", - " \"\"\"Area, in tiles, of a rectangle formed by tiles at these corner positions.\"\"\"\n", - " (x1, y1), (x2, y2) = corners\n", + "def tile_area(rect: Rectangle):\n", + " \"\"\"Area, in tiles, of a rectangle defined by two opposite corner positions.\"\"\"\n", + " (x1, y1), (x2, y2) = rect\n", " return (abs(x1 - x2) + 1) * (abs(y1 - y2) + 1)" ] }, @@ -1835,7 +1835,7 @@ "id": "271f2f81-dc40-41db-9912-0c45f4d75dbb", "metadata": {}, "source": [ - "Now we just maximize the area over all combinations of two corners:" + "That's all there is to Part 1; just maximize the area over all combinations of two corners:" ] }, { @@ -1847,7 +1847,7 @@ { "data": { "text/plain": [ - "Puzzle 9.1: .0278 seconds, answer 4772103936 correct" + "Puzzle 9.1: .0262 seconds, answer 4772103936 correct" ] }, "execution_count": 61, @@ -1867,13 +1867,11 @@ "source": [ "### Part 2: What is the largest area of any rectangle that has only red and green tiles?\n", "\n", - "In Part 2 we pay attention to the **green** tiles on the floor. Every red tile is connected to the red tile before and after it (in the input list order) by a straight line of green tiles. (It is guaranteed this will always be a straight horizontal or vertical line.) The first red tile is also connected to the last red tile. This forms a closed polygon, and the interior of the polygon is also all green. (The color of the tiles outside of the polygon is not stated, but I'm going to say \"white.\") The elves want to know: What is the largest area of any rectangle that consists of only red and green tiles?\n", + "In Part 2 we pay attention to the **green** tiles on the floor. Every red tile is connected to the red tile before and after it (in the input list order) by a straight line of green tiles. (It is guaranteed this will always be a straight horizontal or vertical line.) The last red tile loops around and is connected to the first one. This forms a closed polygon, and the interior of the polygon is also all green. (The color of the tiles outside of the polygon is not stated, but I'm going to say **white**.) The elves want to know: What is the largest area of any rectangle that consists of only red and green tiles?\n", "\n", - "**This is a tough one!** More difficult than all the previous puzzles. There are only 496 red tiles, so enumerating all pairs of them in Part 1 was easy. But there are roughly 100,0002or 10 billion total tiles, so doing something like a [flood fill](https://en.wikipedia.org/wiki/Flood_fill) for all the green tiles and checking against them for each pair of corners would be too slow. \n", + "**This is a tough one!** More difficult than all the previous puzzles. There are only 496 red tiles, so enumerating all pairs of them in Part 1 was easy. But the *x* and *y* coordinates range from 0 to 100,000, so there are roughly 10 billion total tiles to consider. I need some way of finding the biggest rectangle without having to consider tiles one by one for every candidate rectangle. \n", "\n", - "I really want to see what the red and green tiles look like! \n", - "\n", - "I'll plot the border tiles, but not the interior tiles:" + "I really think it will help to see what the red and green tiles look like:" ] }, { @@ -1884,9 +1882,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, @@ -1894,9 +1892,11 @@ } ], "source": [ - "def plot_tiles(red_tiles, figsize=(8, 8)) -> None:\n", - " \"\"\"Plot the red-and-green border tiles.\"\"\"\n", - " plt.figure(figsize=figsize)\n", + "def plot_tiles(red_tiles: Sequence[Point], figsize=(7, 7)) -> None:\n", + " \"\"\"Plot the red tiles (as dots) and the green border (as lines), but not the green interior.\"\"\"\n", + " red_tiles = red_tiles + red_tiles[:1] # Close the loop\n", + " plt.figure(figsize=figsize) \n", + " plt.axis('equal')\n", " plt.plot(*T(red_tiles), 'g-')\n", " plt.plot(*T(red_tiles), 'r.')\n", "\n", @@ -1905,7 +1905,7 @@ }, { "cell_type": "markdown", - "id": "ef662323-d014-4c64-88cf-b6e777aee3d8", + "id": "cd555e6f-897d-4d0f-bdb7-e946b79a9cc9", "metadata": {}, "source": [ "**Very Interesting!** Here's what I'm thinking:\n", @@ -1913,20 +1913,29 @@ "- A maximal-area red-and-green rectangle can't cross the two equator lines, because there are white tiles between them.\n", "- Therefore one of the corners of the maximal rectangle has to be one of the two points on the east end of the equator lines, and the other corner has to be somewhere on the left side of the circle.\n", "- The points are all roughly in a circle, so we're looking for a rectangle roughly inscribed in the circle.\n", - "- A roughly correct way to check if a candidate rectangle is all red-and-green is to see if **the rectangle contains a red tile** in its interior.\n", - "- To be more precise, consider the diagram below, in which the two large red circles mark the corners of a rectangle depicted with small purple squares. I have filled in the green tiles that connect the red tiles to form a polygon, and used light green for the interior of the polygon. Is the purple rectangle valid? It is ok that there are two other red tiles on the bottom border; they don't let a white square in. It is ok that there are two red tiles at the top that extend one square into the rectangle; they still don't let a white square in. But the red tile near the bottom right corner is two squares in from the border, and any red tiles in that position let in white squares (here three of them in the bottom right of the rectangle. The only exception is when there are two adjacent red tiles that form a 180 degree turn (as in the left of the rectangle); they do intrude into the rectangle, but because they are adjacent there are no white squares between them.\n", - " \n", - "        \n", + "- A roughly correct way to check if a candidate rectangle is all red-and-green is to make sure **the rectangle contains no red tile** in its interior. If the red tile is in the middle of the rectangle, then there must be white tiles on at least one side of the red tile, and thus in the rectangle (with some caveats).\n", + "- The red tiles are serving two purposes: as potential **corners** of the maximal rectangle, and as **obstacles** that disqualify a candidate rectangle.\n", + "- To be more precise, consider the diagram below, in which the two large red circles mark the corners of a rectangle depicted with small purple squares. I have filled in the green tiles that connect the red tiles to form a polygon, and used light green for the interior of the polygon.\n", "\n", - "- To deal with the two-adjacent-red-tile problem, I will verify that there are no adjacent red tiles in *my* input. I bet Eric Wastl designed it so that nobody gets two adjacent tiles, but they aren't explicitly forbidden in the rules.\n", - "- This red-tile-in-interior heuristic by itself isn't enough to stop a candidate rectangle from crossing over the long equator lines. But if we assume that any maximal-area rectangle has sides at least *d* units long, we can fix the problem by inserting an extra red tile every *d* steps along the path. Then, any rectangle with sides greater than *d* that crosses the equator (or any other long line) will contain a red tile in the interior.\n", + "        " + ] + }, + { + "cell_type": "markdown", + "id": "279fc8b1-bcf6-4b09-a9b7-ed5875242f76", + "metadata": {}, + "source": [ + "- Does the purple rectangle contain only red and green tiles? Let's consider red tiles that are potential obstacles:\n", + " - The two **corners** are obviously **ok**; they define the rectangle and they're red.\n", + " - The two red tiles **on the border** at the bottom right are **ok**; they don't let a white square in.\n", + " - The two red tiles that **extend one square in** to the rectangle at the top middle are **ok**; they don't let a white square in.\n", + " - The two red tiles that are **adjacent** to each other the left side are **ok**, even though they extend three squares in, because there is no room for any white squares to fit between them. This only applies when the two adjacent tiles form a 180° U-turn like this.\n", + " - The red tile that **extends two squares in** from the lower right corner **disqualifies** the rectangle; it lets in the three white squares in the lower right corner.\n", + "- Besides red tile obstacles inside the rectangle, we also have to worry about two red tiles that are both outside the rectangle, but whose connecting green line passes through the interior of the rectangle. But if we assume that the largest rectangle has width and height of at least *d* spaces, then we can lay down an additional obstacle every *d* spaces, and that means there will always be an obstacle inside the rectangle. I call such obstacles \"breadcrumbs.\" \n", "- If there were more red tiles we could put them into a data structure like a [quadtree](https://en.wikipedia.org/wiki/Quadtree).\n", - "- Normally in problems like this we have to check if the rectangle we are considering is inside the polygon or outside of it. But for polygons that are anything like mine (i.e., mostly convex), only very small rectangles can be on the outside. Any sufficiently large rectangle must be on the inside, so I didn't bother checking.\n", + "- Normally in problems like this we have to check if the rectangle we are considering is inside the polygon or outside of it. But for polygons that are anything like mine (i.e., mostly convex), and if we are only considering rectangles with corners on tiles, any sufficiently large rectangle must be on the inside, so I didn't bother checking.\n", "\n", - "I'm ready to start coding! I'll start with this:\n", - "- `find_2_corners` will return a list of the two candidate corner points at the east end of the equator lines.\n", - "- `breadcrumbs` will leave \"breadcrumbs\" (that is, red tiles) along the trail at least every `d` spaces.\n", - "- An assertion to show that no two of my red tiles are in adjacent squares." + "I'm ready to start coding! I'll start with some functions that manipulate the sequence of red tiles: finding obstacles, laying down breadcrumbs, finding adjacent tiles, and finding the 2 corners on the equator." ] }, { @@ -1936,23 +1945,35 @@ "metadata": {}, "outputs": [], "source": [ - "def find_2_corners(red_tiles, d=10000) -> Optional[List[Point]]:\n", + "GAP = 10000 # How big a gap between red tiles to put in a \"breadcrumb\" red tile\n", + "\n", + "def find_obstacles(red_tiles: Sequence[Point], d:int=GAP) -> Set[Point]:\n", + " \"\"\"Obstacles are tiles that, when found inside a rectangle, disqualify that rectangle.\n", + " They are the set of red tiles with breadcrumbs added, minus the adjacent tiles with 180° turns.\"\"\"\n", + " return (set(red_tiles) | set(breadcrumbs(red_tiles, d))) - adjacent_180_tiles(red_tiles)\n", + " \n", + "def breadcrumbs(red_tiles: Sequence[Point], d:int=GAP) -> Iterator[Point]:\n", + " \"\"\"Extra obstacles to lay down along the trail when the gap between red tiles is too big.\"\"\"\n", + " for (p, q) in sliding_window(red_tiles, 2):\n", + " if distance(p, q) > d: \n", + " dx, dy = d * sign(X_(p) - X_(q)), d * sign(Y_(p) - Y_(q))\n", + " for i in range(1, distance(p, q) // d + 1):\n", + " yield (X_(q) + i * dx, Y_(q) + i * dy)\n", + "\n", + "def adjacent_180_tiles(red_tiles: Set[Point]) -> Set[Point]:\n", + " \"\"\"Yield all adjacent points that form a 180 degree U-turn.\"\"\"\n", + " return union({q, r} for (p, q, r, s) in sliding_window(red_tiles, 4)\n", + " if distance(q, r) == 1 and direction(p, q) == direction(s, r))\n", + "\n", + "def find_2_corners(red_tiles, d:int=GAP) -> Optional[List[Point]]:\n", " \"\"\"Find two adjacent corners, separated on each side by a gap of at least `d`.\"\"\"\n", - " return first([B, C] for [A, B, C, D] in sliding_window(red_tiles, 4)\n", - " if distance(A, B) > d and distance(C, D) > d)\n", + " return first([q, r] for [p, q, r, s] in sliding_window(red_tiles, 4)\n", + " if distance(p, q) > d and distance(r, s) > d)\n", "\n", - "def breadcrumbs(points, d=10000) -> List[Point]:\n", - " \"\"\"Leave extra points along the trail on long lines, every `d` spaces.\"\"\"\n", - " trail = [points[-1]]\n", - " for p in points:\n", - " while distance(trail[-1], p) > d: \n", - " q = trail[-1]\n", - " dx, dy = sign(X_(p) - X_(q)), sign(Y_(p) - Y_(q))\n", - " trail.append((X_(q) + d * dx, Y_(q) + d * dy)) # Leave a breadcrumb\n", - " trail.append(p)\n", - " return trail\n", - "\n", - "assert not any(distance(p, q) == 1 for (p, q) in sliding_window(red_tiles, 2))" + "def direction(p: Point, q: Point) -> Point:\n", + " \"\"\"A unit vector in the orthogonal direction from p to q.\"\"\"\n", + " ((x1, y1), (x2, y2)) = p, q\n", + " return (sign(x2 - x1), sign(y2 - y1))" ] }, { @@ -1960,7 +1981,7 @@ "id": "4425bb14-d9ca-4afb-8f1c-5787bf3e17a4", "metadata": {}, "source": [ - "Let's visualize the output of these two functions:" + "Let's visualize the breadcrumbs (blue squares) and the two corners (black diamonds):" ] }, { @@ -1971,9 +1992,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -1981,8 +2002,9 @@ } ], "source": [ - "plot_tiles(breadcrumbs(red_tiles))\n", - "plt.plot(*T(find_2_corners(red_tiles)), 'bD');" + "plot_tiles(red_tiles)\n", + "plt.plot(*T(breadcrumbs(red_tiles)), 'bs');\n", + "plt.plot(*T(find_2_corners(red_tiles)), 'kD');" ] }, { @@ -2002,24 +2024,23 @@ "metadata": {}, "outputs": [], "source": [ - "def biggest_rectangle(red_tiles, second_corners=None, d=10000) -> Corners:\n", - " \"\"\"Find the biggest rectangle that stays within the interior-and-border of the tiles.\"\"\"\n", - " if second_corners is None: # You can pass in a hint for second corners, or it will try them all\n", - " second_corners = red_tiles\n", - " tiles = breadcrumbs(red_tiles, d)\n", - " corner_pairs = cross_product(second_corners, red_tiles)\n", - " for corners in sorted(corner_pairs, key=tile_area, reverse=True):\n", - " if not any_intrusions(tiles, corners):\n", - " return corners\n", + "def biggest_rectangle(red_tiles, second_corners=None, d:int=GAP) -> Rectangle:\n", + " \"\"\"Find the biggest rectangle that stays within the interior-and-border of the red tiles.\n", + " You can pass in a hint for `second_corners`, or they can default to `red_tiles`.\"\"\"\n", + " obstacles = find_obstacles(red_tiles, d)\n", + " rectangles = cross_product(second_corners or red_tiles, red_tiles)\n", + " for rect in sorted(rectangles, key=tile_area, reverse=True):\n", + " if not any_intrusions(obstacles, rect):\n", + " return rect\n", " raise ValueError('No rectangle') # Shouldn't get here unless there are no corners\n", "\n", - "def any_intrusions(red_tiles: List[Point], corners: Corners) -> bool:\n", - " \"\"\"Does any red tile intrude inside the rectangle defined by the corners?\"\"\"\n", - " # OK for a red tile to be on border or just one square in, but not 2 squares in\n", - " xlo, xhi = min(Xs(corners)) + 2, max(Xs(corners)) - 2\n", - " ylo, yhi = min(Ys(corners)) + 2, max(Ys(corners)) - 2\n", + "def any_intrusions(obstacles: List[Point], rect: Rectangle) -> bool:\n", + " \"\"\"Does any ostacle point intrude inside the rectangle?\"\"\"\n", + " # OK for an obstacle to be on border or just one square in, but not 2 squares in\n", + " xlo, xhi = min(Xs(rect)) + 2, max(Xs(rect)) - 2\n", + " ylo, yhi = min(Ys(rect)) + 2, max(Ys(rect)) - 2\n", " return any(xlo <= x <= xhi and ylo <= y <= yhi\n", - " for (x, y) in red_tiles)" + " for (x, y) in obstacles)" ] }, { @@ -2039,7 +2060,7 @@ { "data": { "text/plain": [ - "Puzzle 9.2: .0156 seconds, answer 1529675217 correct" + "Puzzle 9.2: .0068 seconds, answer 1529675217 correct" ] }, "execution_count": 66, @@ -2057,7 +2078,7 @@ "id": "53ef7a00-aef7-4c20-8a2b-d735df8f28dd", "metadata": {}, "source": [ - "Let's see what the biggest rectangle looks like:" + "Let's see what the biggest rectangle looks like (at a bigger scale to see more detail):" ] }, { @@ -2068,7 +2089,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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pUu4BAMXB6zV75oerEGeUVPKq8ipVhIdS+ocn9xgfs187UGjGkZz/oRNK+/NRdzLx3FgrXULhUMptoL1Njs8nrVhhQv8CgdGv17ZNYB8p/ACQE8zQZ8AMPQDk38ie99peaddYe96zSBEnRAxFZTzJ+aOsYDm7d48iV1+ZcVzyTL5kZvNPPJI4Q+S4XOrvfjXl8VnGDwDZY4YeAFBUejzSnsuUUiQkyKLHOP3aUVSy+J6PrmCpWyarMbE7hNXYqIq6ZeNe6TLi2pOpf0xakYga71mk6s3V0duie6tVtq419kJaJGLS/pmpB4AJIRQPADA5ctg3flyyCRIbR9gYMO2N5/v5ySel1lbppZekurr0Kfd+v5lBT5r137Upzaz/d78nPZO+hWTCXY611SXd9bInHwCywpL7DFhyDwDnYRL6xtNmDphCgYCZQR8aiq1iSbc83ralSy6R4v6cdCxL/W+8llD8W3aPKmrfIyt+HEvzAWBU9KHPEQp6AEiSaeZsnH3js9l/O/R4h6q+cKdpM5dNn20AE2fb2c36BwJSc7OZ0R+tALdtOZdcklDQD0nyfSnxeYEuFABg0IceAJB7Wcy8uw7/hyqy7Bs/1v7bZ5aY95P/wLdG9t42NPAHPjCZvN7sfsayWfLf3Z1QzEuSW6nPC+Nams+yfACgoAcAZMm2o8W8ZApr97pWXX6kNeEP8qtsab9Se10/vzYxSVvKbv/tuPfeAph6mYr/LPflW3aPnKfSLM2/ZL6ccCh6jmX5AGBQ0AMAsjNGv+v4gr76XPpe1xXnIlJ5VeIHlq+QLCtl/23nA6/pybg2c872NG3mamtz8EUBmBIjafxx+/JH0vgTlFcq+fW7SCSihdsWRZ9nRlbtWE50AKt2AJQsCnoAgJFp+arfb/avZzPDlm0B7vVKjz2WsP/W6uhI/CN/wcKUQoA2c0ABytHSfFbtAEAMoXgZEIoHoCRkmUrv+qu/0qwdT8uSWUZvNTaaNlhp7i+rhOwR2YRvZRvQBaBw2bbk86Usze9/PfbCoWX3qMKf5kVDgvMAFBFS7nOEgh5AQcsmNCrLVPpxp09TgAM4H1m8IDjQ3ib3ulaVOabgtzK9aAgABYaUewAoddnOumeZSj/uZa7ZJmQDQLwsluYPrmnUh37WqrqT0ub7/k0VdcvS3xdJ+ACKHDP0GTBDD6AgjaMX/FW2tP/x1FT6s3sTU+lZ5gpguhhob1PZula5HY36giVJ+AAKGTP0AFDKskykl8aRSk84HYDpwLZNoT48JZWuhSZJ+ABKBQU9ABSjLBPppXGm0meTUg0AkymLFyxJwgdQKijoAaAYeb0Kt21NCY1K6fksjX/mnb3xAPLJ7zdL6Md4wXJcL1QCQAFz5fsCAACTY3BNoy7dINU3Sv2vHx5772hTk9kL39Vl3rLPFMB05fWaFyHdbvN+3AuWVeVVqiqvUuWChWZf/fB+IoctQgCKFKF4GRCKB6BQhcIhVW+uliT1bexTVfx+eAAodBlaY4bCIdW3Vg8n4e8ZPQk/3oED0osvStddJ1199diPTXo+gElEKB4AAACKV4btP2VPdGpfwOyld35wvQZGad05ovy2FpVt3yFLptPH4OpbFH68Pe39kp4PYLpghj4DZugBFCpm6AGUrHG07pRGb9/5gdukn8W9ZjCSnp8QuEf7TgCTINs6lD30AAAAKC5jJOGn86ET6dt31p1MPDdmej4A5AFL7gGg0I2xl7MmaP4Atewek2YPAKUgiyT8eK4DB+XsXp4yQ7/5vj36xtVXRs+Rng9gumGGHgAKWSAgx+eTVqyQ4/NpoL1NoXBIoXBIQ4936PgWqatTqvAvlgKBfF8tAEyNLJLw428VdctkNSbur7caG1VRtyxhXEp6vsuVmJ5v26ZbiG1P4RcLoJSxhz4D9tADyItsEpTH2CMqsc8TADIl4ac4cEB66SWprm7UlPuB9jaVfb5VbkmOZSn86DYNrmkkLA9ATmVbh1LQZ0BBD2DKBQJymptlRSJyXC4zG5Qmmdn1oz2qaPh4yvn6RrP3s6szzX13dUn19Tm/ZAAoCaO8kLq0SdFE/SheRAUwAbStA4BCZNvRYl6SrEhE7nWtuvxIa0oy81W2tF+pqczPr90j5w/msc8TAHJtlLC9upNjhOVR0AOYROyhB4DpZBzJzNXn0qcyV5yLqHLBQllJ+0cT9nkCAMZvJGwvjuN2a/N9e8x++ni8iApgCjBDDwDTid8vx+VKKOpHS2bOmLbc1CQ1NIxv/ygAYHQjYXstLWYGfjhsr6JumQbatsq9rlVljnnetngRFcAUYA99BuyhB5BzGQLvBtrb5P58q8pkEpStsYKVAoGEPyzV3k4IEwBMtjRhe6FwSIvurVZtr7Rr0xFV0ioUwASwhx4ApqMsAu+GhgZUZclsiM+EWXgAmHpeb9rn2x6PuaXrdQ8Ak4EZ+gyYoQeQM2O0mRsJvKsJ0m4OAApRKBxS9eZqSVLfxj5VlVfl+YoAFLJs61BC8QBgqmQReOfvHSMpGQBQfGzbtBS17dyMA1BSWHIPAFMli8C7jEF3AIBpqyZoXpi17B4pmz30gYCctWtlOY4cy1L40W0p27AkqeyJTpWvv938bnC5TDAfeSkAxJL7jFhyDyCXBtrbUlOQk/8oI+gOAArOQHubyta1yu1o1IyUeJbdo4ra98iK+1N8SJLvS7FtWBJbsYBSlW0dSkGfAQU9gHHJkGAfCodU31qtuhPS5k17VFG3bPT7IegOAApDFhkpyW4+JH33mdTzn7pZemZJ7P36o1JXZ5o76OqS6usnctUApjFS7gFgqmWTYP94h/YFzEyL86/Xj75scpQEZQDANDRGRspoBb01yl1t/+RTevLmP4+Ny7QVK8MLyQCKGzP0GTBDDyArJNgDQOmybcnnk5IyUvpfPzxqC7t0S+7lcknHj6f8Tjj3l59T2fYdGuloOrj6FoUfb2dvPVDESLkHgKlEgj0AlC6v1xTTbrd5fzgjpXLBQlWVV6W9VS5YKOuxxxI+Rx0dqS/w2rbKdvxjdEbfkmR9e4fqW6tVtq419rsnEjH5K6TgAyWFJfcAkI1MSxpJsAeA0tbUJDU0jC//JJvPGeUF47qTY7xIzKovoGQwQw8AmQQCcnw+acUKOT6fBtrbFAqHEm9z5+jsZ2/WyN9WjiTrc59LmJ2pXLBQVtIMjtrb+cMLAIqF12uC6sbzvJ7pc/x+s5w+juN2a/N9e+QknedFYqD0sIc+A/bQAyUuy+Tice2PJ8EeADAeo7QzzaoVKoCCRMo9AGQj01L6LJOLx9wfn3y/JNgDAMZjlKX5g2sadfmRVtX2Srs2HVblgoV5vlAAU42CHkDpyqLNnOWrUYVlJaQQO5alXZteS0guZn88AGBSjfJicI/H3EZL0wdQ3CjoAZQm244W85JkRSJyr2vV5UdaU5bSn3AS+wVblqXK8kqpvCp2csFCk06cvCSSmXgAAABMEgp6AKVpHEvpU9JDI5H0S+nPJ+EYAAAAOE8U9ABKUxZt5qTzWErP/ngAwBSqCZoXny27x6wWG02mzBgABYm2dQCKk21LXV3mbTper9kzP/yu43LJam9PaDNHqzkAwHRW9kSnjm+RujqlCv/i9G1VwyENtLdF26/K5zOp+QCKAjP0AIpPFmF3kjQ0NKAqS1Km5p0spQcATDe2rfL1t8sa/h02VhbM8S2KjlMkYvJeGhr4fQYUAQp6AMVlHGF38X3jrUx/4LCUHgAwnUxGW1UABYeCHkBx4Q8cAEAp8Psll8vMuA/LSRYMgIJCQQ+guExW2B0AANOJ15vSLnUkCybBgoUaaNsq97pWlTmxzJhRX7wmPA8oKITiASg8YwXeEXYHACgVTU3SsWPmd+KxY+b9UUT30DuOBgYHCM8DigQz9AAKSxaBd4TdAQBKRqaMl5HwvOF3LcchPA8oIhT0AApHFoF3hN0BABCHbBmgqFHQAygcWfxRwh8kAADEmazwPPbaA9MCe+gBFI7hwLt45o+SI+rb2Ke+jX16btORlDGE3QEAStZIeF5cZsxo2TKDt3w2ulvNkXRu1V8oNHcOe+2BacxyHCfTLtOSdubMGXk8HgWDQc2ePTvflwOUvIH2tlhS7/AfJSkhQIFAQuqv0o0BAKCU2PbYmTG2LcfnS5ihH7SkSzco7V77hNVwbrcJ5WOmHsiZbOtQltwDKCiDaxp1+ZFW1fZKuzYdTm3PIxF2BwBAskyZMey1BwoSBT2A6SeLfXlW2rNxCLsDACB7udhrz756YMqxhx7A9BIIRPflOT6fBtrbEvbtDT3eoeNbpK5OqcK/mH17AADkwgT32g8898/sqwfygD30GbCHHphCGfbvsW8PAIBJdp577S1HcseP4/czMCHZ1qHM0AOYPsbYvydl2LcHAAAmzuuV6utHL8RH+V3tTh7H72dgSrCHHsD0MdyWzhpl/964e+QCAIDcSrfX3uWSHEdW/MJffj8DU4IZegDTh9ercNtWDQ4n3jlJ+/cqFyyUlbS/T+3tLOcDAGCqpNtr39Gh8KPbEn5/8/sZmBrsoc+APfRAjmVIwA2FQ6pvrVbdCWnzpj2qqFuW/j5oSQcAQP4k/S4OhUNadG/1cFvZI+nbygLIGn3oAUw/gYCc5mZZkYgcl8vMxq9pTBgy9HiH9gXMXnnnX683swBNTYn3Q0s6AADyK83v4h6PucW3uQMwuSjoAUwN244W85JkRSJyr2vV5Uda1eMxQ5JT7K1IRGppkRoaKOABAACAJOyhBzA1MiTYS6TYAwAAAOPBDD2AqZEhwV4SKfYAAADAODBDD2BqZEiwJ8UeAAAAGB9m6AFMmcE1jbr8SOtwAu7h9Am4TU1mzzwp9gAAFJSaoNk+Z9k9Urrf8Rk63QAYPwp6AFMqqwRcUuwBACgoZU90RoNtne2LNZDUyabsiU6Vr7/dbKtzudJ3sQEwbhT0AAAAAM6fbZtiPa5LTXwnm5EuNiMfF11sgJxhDz0AAACA85ehkw1dbIDJwww9gMkxyj65jPvrAABAYfH7zTL6UTrZ0MUGmDzM0APIvUBAjs8nrVghx+fTQHubQuGQhh7v0PEtUlenVOFfLAUC+b5SAAAwUV6v2RMf16UmvpNN5YKFKZ1u6GID5IblOE7yAhjEOXPmjDwej4LBoGbPnp3vywGmP9uW4/MlvAo/aElLm6R9gaQld263dOwYv9ABACgGtj1ql5pQOKT61mrVnZQ237dHFXXLUj+XBHwgKts6lBl6ALk1yj66upPsnwMAoKh5vVJ9fdqCvOyJTu0LSFv+RZq17Pro6r1QOKSB9rboyj75fKzgA8aBGfoMmKEHxinNDL3jduvsnn/TrGXXp+6fY4YeAIDiNsrqvUs3mOORdndR/H0AMEMPIE+83pR9clZ7uyrqlslK2l/H/jkAAErAGCn4JOADE8MMfQbM0APjFwqHtOjeatX2Srs2HVFlfJr9GPvrAABAEbJts5Q+afVe/+uHJZmgXFbwAYmYoQcwuWxb6uoyb0dhpTs5xv46AABQhMZIwScBH5gYZugzYIYeSCMQkNPcLCsSkeNymV/EaxqjHx56vENVX7hTbkdyXC6z1L6pKY8XDAAA8m6UVXpjruwDSlS2dSgFfQYU9ECSMYJtejxSTZBwGwAAkL1QOKTqzdWSpL6Nfaoqr8rzFQH5x5J7AJNjjGAbiXAbAAAwfjVBqf6oZNk9mQdnse0PKBVl+b4AAAXG7zfL6JOCbXZtOizHWyPL7pGzPU24TW1tHi4WAABMd2VPdEZX9znbF2sgaStf8tjy9bebvzNcLrM3n219KGEsuc+AJfcoObYtdXdLfv+oS+QH2tvk/nyryjTKHvlAQGppMTPzI+E2/LIFAADJMmzli8e2PpQSltwDGL9AQI7PJ61YIcfn00B7m0LhUMptYGhAVtoI+2FNTeaXa1eXeUsxDwAA0smwlS8e2/qAVMzQZ8AMPUpGlq+Q8+o4AADImTF61DvemoShlt2TXc/6LFYbAtMdM/QAYrIJj8nyFXJeHQcAADkzRo/6qvKqhFvlgoUavOWzGvkzxJF0btVfKDR3TmwVYXtbdLWhfD6zDRAoYszQZ8AMPQpehp7xIyy7RxW175EV95TgWJb633gt4RXyrF8dBwAAyNYoPeqTx9A6F6Ui2zqUlHugmNl2tJiXJCsSkXtdqy4/0po2aOaEI8VvjbcsS5XllVJ8P9gFC80r6cmhd/yiBAAA58vrzfy3xBirCXs8GVYR8ncKihQFPVDMMvzii+fvTbMHJxJJ/0uwqUlqaMj8SjoAAECu+P2mVR2tc4EoCnqgmGXoGR9v3L8Es3klHQAAIFdG9tvHrRIc2W8vSVqwUANtW+Ve16oyx/zNY7GKEEWOUDygmHm9Zs/88Dp6J0PQjJUUSsNSegAAMK1kaI07uKZRS5ukDQ3S2T3/lr51bjZhwUCBIBQvA0LxUOhC4ZAW3Vut2l5p16YjsVexR5NNKA0AAMA0NNDeprJ1rXI7ShsGXPZEp8rX325WJLpcZsY/XdEP5Fm2dSgFfQYU9Ch0oXBI1ZurJUl9G/tUFR9wBwAAUCxIwUcRoQ89AAAAgNIxRhiwlCEFHyhQhOIBAAAAKHyk4KMEMUMPAAAAoPCNpODHBfzGhwFXLliYEhZMADAKHTP0AAAAAIpDU5PU0DBqwO/gmkZdfqR1OCz4cOawYGCao6AHipVtm71kvhrVBM2+McvukfjFBQAAipnXO+ase4/H3BxvzRReFDA5KOiBYhQIyGlulhWJqMKydMIx+2uc7YtpzwIAAEramBMdwxMi8vtZio+CwB56oNjYdrSYlyTLcaI/6FYkIrW0mF9WAAAAJabsiU4d3yJ1dUoV/sUaaG9TKBxSKBzSQHubHJ9PWrFC8vmkQCDflwtkRB/6DOhDj4LT1WV+EWUaU18/JZcDAAAwLYzRp16iRz2mF/rQA6XK75fjGuNHm/YsAACgFI3Rp54e9ShUFPRAsfF6E1uyuFySNfwO7VkAAECpGulTH8f0qT+i5zYdSZ0QYRIEBYCCHihCg2sadekGqb5R6u9+VTpxwiyzP3aMQDwAAFCaxuhTT496FCpS7oFClUUKqzVykKF9CwAAQEkYo089PepRiCjogUIU15bOcbnMK8prGqMfHnq8IxrsQqs6AACAOGNMdNCjHoWGgh4oNMlt6SIRude16vIjrerxmN6q8Smt0VZ1DQ3M0gMAAIxhzB718ehXj2mCPfRAoRkjoVUipRUAAOB8jNWjPv5Gv3pMJ/Shz4A+9Jh20vRQddxu9b9+WI63Rpbdowr/4sSinz6qAAAAoxujR32PJzYseSWkJP7OwqSgDz1QqGzbJNLbdvqPj7SlG37XcbmiCa1V5VWqXLBQVlKCKymtAAAAY8iwAnIEKyEx3bCHHphOMoTdjRgaGlCVJWm09TVjJLgCAAAgyUiP+qQVkLs2HU4IyLPsHjnb06yEpF898oQl9xmw5B5ThqVeAAAA+RMImCDhoaHYCsc0XYIG2tvkXteqMmd4peRINyGC8pBDLLkHCg1LvQAAAPKnqclMkHR1mbdjtPy1Rv4WcxwNDA4QlIe8YYY+A2boMWUyhN2NIPQOAAAgT0ZZUWk5kjt+HH+bYYKYoQcKzUjYnWXeddzuhLC7kRuhdwAAAHkyyopKd/I4Vk9iijBDnwEz9JhKoXBIi+6tVm2vtGvTEVUuWDj6YNsm9A4AAGAq2bZZUh+/otLlkhxHVnxZxQw9JogZeqBA9XikPZcpYZl9Wl6vVF/PLwoAAICp4vVKSSslrY4OhR/dlrDKktWTmCq0rQOmWoYE1JqgCb6z7B5prBl6AAAATL007YEHwyFdfqR1eJXl4bFXWQI5REEPTKUMfeaHHu+ItqRzti82rwCPkbAKAACAPPB6UyZmejzmlnGVJZBDFPTAVLHtaDEvSVYkIve6Vl1+pFU9ntT+8lYkYnqhNjSwZAsAAABACvbQA1MlQ595+ssDAAAAGA9m6IGp4vfLcblS+szv2mT6zFt2j5ztafrL19bm4WIBAAAwHmPmIGXIUALOFzP0wFTJ0Gee/vIAAACFqeyJTh3fInV1ShX+xRpob1MoHFIoHNJAe5scn09ascK0vAsE8n25KCL0oc+APvTIpaz6zNNfHgAAoHDYthyfL2GV5aAlXbrBHMdnJEmiRz2ykm0dypJ7YIplTEBNk5oKAACAaWqMnCRLY2Qk8fcecoCCHgAAAADOl98vuVxSmpwkSWQkYVKxhx4AAAAAzpfXKyXlII3kJFUuWJiSoURGEnKJGXoAAAAAmIimJqmhIW0O0uCaRl1+pHU4Q+lw+gwl4DxR0AMAAADARI2Rg5QxQwk4TxT0wFSxbbkO/4eusqXqc6P0KAUAAEDRSehRX15JT3rkDAU9MBUCATnNzaqIRLRfJvHU2b7Y7Ldqasr31QEAAGCSjPSodzuS89R75EiyHMcE6fG3ICaIPvQZ0IceE5amN2kUfUgBAACK11h/B0r8LYhRZVuHknIPTLY0vUmjRvqQAgAAoPiM9XegxN+CmDAKemCy+f1yXKP8qNGHFAAAoHiN9KgfDX8LYoIo6IHJ5vUm9h8dOU8fUgAAgOKW1KPecbk0NPwhetIjF3Je0A8ODupv/uZvdNlll6miokILFizQ3//93ysSt9TEcRx97Wtf0/z581VRUaH6+nr96le/SrifgYEB3X777brwwgtVVVWlm266SbZtJ4w5ffq0Vq9eLY/HI4/Ho9WrV+u3v/1twpgTJ07oE5/4hKqqqnThhRfqjjvuUDgczvWXjVJn21JXl3mbxuCaRi1tkjbcKJ39f79rxh47RggKAABAsWtqMn/3dXWpv/tV+b4k1TdK/a8f5m9BTFjOC/oHHnhA//2//3dt27ZNr776qh588EF985vf1NatW6NjHnzwQT388MPatm2bDhw4oHnz5umGG27Q7373u+iYDRs26Nlnn9XOnTu1d+9e9fX1aeXKlRoaGoqOWbVqlV555RW98MILeuGFF/TKK69o9erV0Y8PDQ3p4x//uEKhkPbu3audO3fqe9/7nu66665cf9koZYGAHJ9PWrFCjs+ngfY2hcKhhNvQ4x3aF5C27JZm3fwZ6X//b16NBQAAKBVer1RfL8dbox6PtOcyetIjN3Kecr9y5UpddNFFCgQC0XP/5b/8F1VWVmr79u1yHEfz58/Xhg0bdM8990gys/EXXXSRHnjgAbW0tCgYDOrd7363tm/frs985jOSpDfffFMXX3yxdu3apYaGBr366qu64oortG/fPl1zzTWSpH379mnp0qV67bXXtGjRIj3//PNauXKlTp48qfnz50uSdu7cqVtvvVWnTp1KmxY4MDCggYGB6PtnzpzRxRdfTMo90kuTXDpoSZdukHo85v2aoKKtSqJINAUAACg5oXBI1ZurJUl9G/tUVV6V5yvCdJW3lPsPfehD+uEPf6jXX39dkvTv//7v2rt3rz72sY9Jko4ePaq3335bN954Y/RzZs6cqeXLl+vll1+WJB08eFDnzp1LGDN//nwtWbIkOuYnP/mJPB5PtJiXpA9+8IPyeDwJY5YsWRIt5iWpoaFBAwMDOnjwYNrr37x5c3QJv8fj0cUXX5yLfxYUqzTJpWWOVNsbe9/fm1TMSySaAgAAAJiwslzf4T333KNgMKj3vOc9crvdGhoa0je+8Q199rOflSS9/fbbkqSLLroo4fMuuugiHT9+PDqmvLxcc+bMSRkz8vlvv/225s6dm/L4c+fOTRiT/Dhz5sxReXl5dEyyjRs36s4774y+PzJDD6Q1nGAfX9Q7brd2bTocXUZl2T1yti9OLPxJNAUAAAAwQTmfof+f//N/6tvf/ra+853v6Oc//7k6Ozv10EMPqbOzM2GcZVkJ7zuOk3IuWfKYdOPPZ0y8mTNnavbs2Qk3YFTJCfZut6z2dlUuWKiq8ipVlVepcsFCWXHppqTbAwAAlK6aoFR/1Ez6JMgQsgykk/OC/stf/rLuvfde/cVf/IXe+973avXq1frSl76kzZs3S5LmzZsnSSkz5KdOnYrOps+bN0/hcFinT58ec8yvf/3rlMf/zW9+kzAm+XFOnz6tc+fOpczcA+drcE2jLt2QIa00Lt2UdHsAAIDSVPZEp45vkbo6pQr/4miY8kB7WzRkWT6fFJdHBowl5wX9O++8I5cr8W7dbne0bd1ll12mefPm6Qc/+EH04+FwWHv27NG1114rSbryyis1Y8aMhDFvvfWWDh06FB2zdOlSBYNB7d+/Pzrmpz/9qYLBYMKYQ4cO6a233oqO2b17t2bOnKkrr7wyx185SllWaaXD6abMzAMAAJQg21b5+tuj2UpWJCL3ulbVt1arbF1rbHtmJCK1tDBTj6zkfA/9Jz7xCX3jG9/QJZdcoj/8wz/UL37xCz388MP6q7/6K0lmCfyGDRt0//33y+/3y+/36/7771dlZaVWrVolSfJ4PGpqatJdd92ld73rXfr93/993X333Xrve9+rj3zkI5KkxYsX66Mf/ajWrl2r9vZ2SVJzc7NWrlypRYsWSZJuvPFGXXHFFVq9erW++c1vqre3V3fffbfWrl3LUnoAAAAAU2eUMOW6k2MEKDMRhAxyXtBv3bpV9913n9avX69Tp05p/vz5amlp0aZNm6JjvvKVr6i/v1/r16/X6dOndc0112j37t264IILomMeeeQRlZWV6dOf/rT6+/v14Q9/WE8++aTcI/uQJe3YsUN33HFHNA3/pptu0rZt26Ifd7vdeu6557R+/XrV1dWpoqJCq1at0kMPPZTrLxulwral7m7J7094gq0JmjR7y+6RFizM4wUCAABgWvL7JZfLzMAPc9xubb7v3+T84HoClHFect6Hvthk2/8PJSAQkNPcLCsSkeNymTC8NY0aerxDVV+4U25HJvG+o4M98gAAAEgVCJjl9ENDsaDkpiYNtLfJva5VZU4sZJm/J0tbtnUoBX0GFPSQJNm2HJ8v4ZXTQUta2iTtCyQtk3K7TfAdS6QAAACQzLbNcvra2ujfi6FwSIvurVZtr7Rr0xFVsuKz5GVbh+Z8yT1QlNjzBAAAgFzwetP+ndjjMbcxQ5aBJDlPuQeKkt8vJ6l7g9nztCflPHueAAAAAEwFCnogG16v2TNvmXdH9jZV1C0ze+ZHwhpH9kIxOw8AAIBxqAlK9UeHQ5bj2bbU1UUbO6TFHvoM2EOPEWPubUqzFwoAAADIxkB7m8rWtUZDlkfCl8ue6FT5+tvN1k+XSyJ8uWQQipcjFPQYEQqHVL25WpLUt7FPVeVVeb4iAAAAFDzCl5FGtnUoS+4BAAAAIF/OJ3wZGEZBD4wmzX6lUfc2AQAAAOfD7zfL6eMQvoxsUdAD6QQCcnw+acUKOT6fBtrbNPR4h45vkbo6pQr/YikQyPdVAgAAoNB5vWZvfFzI8kj4cnIoM+HLSMYe+gzYQ1+CRtnHZDmSO34ce5gAAACQK2lClscMZUZRy7YOLZvCawIKwyj7mFKM7GGioAcAAMBEeb1p/67s8Zib463Jw0VhumPJPZDM70/Zr+S4XHIsK3Ece5gAAAAwycbMcKJHfcmjoAeSeb0p+5Wsjg5Zjz2WsLeJPUwAAACYTGVPdCZkOA20tykUDikUDmmgvS2a+SSfj3ynEsUe+gzYQ1+aRt2vlGZvEwAAAJBzo+Q6XbrBHB/fQo/6YsYeemCC0u5XGmVvEwAAAJBTo+Q61fZKlsboUc/fqiWFgh6lzbal7m7T/zPpya8mKPl7h/crkSgKAACAqTTSnz6uqHfcbu3adNgcb1+cWPCT71SS2EOP0pWm1/zIniR6zgMAACCvRulPX7lgoSoXLKRHPSSxhz4j9tAXKfYkAQAAoBCMkuFEj/rixh56YCzsSQIAAEAhGCPDiR71oKBHaRruNW+xJwkAAABAgWIPPUpTul7zcXuSrKT9SuxJAgAAwHRTE5Tqjw6HOI/FtqWuLvMWRYU99Bmwh754Zdx3RM95AAAATFMD7W0qW9cqtyM5LpeZrFrTmDKu7IlOla+/3aw+dblM0F5TUx6uGOORbR1KQZ8BBX3xCoVDqt5cLUnq29inqvKqPF8RAAAAkIUxAp57PLFhNUHCngtVtnUoS+4BAAAAoJCMEfAcz987RtgzigKheAAAAABQSPx+s3w+TcBzfOK9ZfcQ9lzkmKFH6UgTBpJ1kAgAAAAwXXi9Zi98XIjzSMBzVXlV9Fa5YGFKEDRhz8WFPfQZsIe+SAQCcpqbZUUi0dCQgaEBVX3hzmiQiEVACAAAAApJFiHOoXBI9a3Vqjspbb5vjyrqlpnP6+42M/0U99MSoXg5QkFfBEYJDbEcyR0/joAQAAAAFJnkNPzBWz6rsh3/SOr9NEcoHjBilNAQd/I4AkIAAABQTGxb5etvjwbjWZGIyrbviP1tHIlILS30py9gFPQofn6/HFfit7rjcsmxrMRxBIQAAACgmKSZ2LKSxzCpVdAo6FH8vN6UMBCro0PWY48lBIkQEAIAAICiMpKGHydlvzWTWgWNgh6FL016fbLBNY1a2iRtuFE6u+ffzD6hpiazZ76ry7xl7xAAAACKSZo0/MHVt5B6X0QIxcuAULxpLk16/eCaxpRhQ493kGgPAACA0hSXhh+aO0eL7q1Wba+0a9MRVS5YmO+rQxqk3OcIBf00Nkp6/aUbpB5PbFhNUDq+RdEwEEkk2gMAAKAkhcIhVW+uliT1bexTVXlVnq8I6ZByj+I3Snp9bW/iMH9vUjEvEf4BAAAAoOCV5fsCgPM2nF4fX9Q7brd2bTosx1sTPWfZPXK2L04s/gn/AAAAAFDgmKFH4UqXXt/ersoFC1VVXhW9VS5YaPbMk2gPAAAAqCYo1R+VXAcOZgyXxvTGHvoM2EM/vYXCoexDPeLCQCjmAQAAUIoG2ttUtq7VhEVruC+9y2XS8AmNnjayrUNZco+C1+Mxt/hl9ml5vRTyAAAAKF22rfL1t8santK1Rs5HIlJLi9TQwN/LBYYl9wAAAABQCtKESkcRGl2QKOgBAAAAoBT4/WZ5fTqERhckCnoUFttOCe4YCfWw7J48XhgAAAAwzXm9Zq/8cFj0SJiaQ2h0wWIPPQpHICCnuVlWJCLH5VK4bauGhgZ0fIvpM+9sX0yYBwAAADCWpiapoUH9r/5Sy57+mKrOSbs2HR47XBrTFin3GZByP03YthyfL2HPz6AlWY7kjh/ndkvHjvHqIgAAADCGUDik6s3VkqS+jX2qKq/K8xUhXrZ1KEvuURjSBHiUJRfzEmEeAAAAAEoGBT0Kg98vJynAw3G55FhW4jjCPAAAAACUCAp6FAavV+G2rRocrt8dt1tWR4esxx6LhnqIMA8AAAAAJYRQPBSMwTWNuvxIq2p7k4I7GhrMMvvaWop5AAAAIEs1QcnfO9wtKj4Uz7al7m7T5o6/r6c1ZuhRUHo80p7LJMdbEzvp9Ur19TzZAAAAAFkqe6JTx7dIXZ1ShX+xBtrbFAqHNNDeJsfnk1askHw+KRDI96ViDBT0AAAAAFBKbFvl62+Xe7jfmRWJyL2uVfWt1Spb1xoLo45EpJYWM2OPaYmCHgAAAABKySgdpOpOKlrkR9FFalqjoAcAAACAUuL3S8kdpNxubb5vT0pnKbpITW8U9AAAAABQSrxeqaMjoVuU1d6uirplKZ2l6CI1vZFyj4JylS196ITkOnBQqluW78sBAAAAClNTU9puUaN2lsK0REGPglF+W4v2b5csSc7u5VJjo/Tkk/m+LAAAAKAweb1pZ997POaW0FkK0xJL7lEYDhxQ2fYdGl79Y952dkoHDuTxogAAAAAgfyjoURhefDFazCd46aWpvhIAAACgqNUEpfqjkmX3jD7ItqWuLlra5RkFPQrDddcpuYOGJKmubqqvBAAAAChaZU906vgWqatTqvAv1kB7m0LhUMJtoL1Njs8nrVgh+XxSIJDvyy5ZluM4aeskGGfOnJHH41EwGNTs2bPzfTkl7dxffi667N6RZLGHHgAAAMgd25bj8yX0qB+0pEs3mD31kpm9P74lqV+92y0dO0Yafg5lW4cyQ4+CEX68XR+4TdrQIJ3du4diHgAAAMil7u6EYl6Syhyptjf2vr83qZiXpKEhk5aPKUfKPQrKz7zm9o2rr8z3pQAAAADFxe+XXC4prqh33G7t2nQ4mnhv2T1yti9OLPzdbtP6DlOOGXpMT6OEbGQV0AEAAABg/LxeqaPDFOiS5HbLam9X5YKFqiqvUlV5lSoXLFS4basGhxOrHbdbam9nuX2esIc+A/bQ50EgIKe5WVYkIsflMk8Yaxo19HiHqr5wp9yO5Lhcsjo6pKamfF8tAAAAUFxs2yyhr61NW6iHwiEturdatb3Srk1HVLlgYR4usrhlW4ey5B7Ti21Hi3lJsiIRude16kM/a9W+QGy/jhWJSC0tUkMDrwYCAAAAueT1jvk3tmX3aOlJpW8rjSnFkntML6MEcdSdJHwDAAAAyLtAQBW179F3n5GefkaqqH0PbevyiIIe04vfL8eV+G3puN3afN+elPOEbwAAAABTyLblrF0rK27XtuU4ZuVsUvYVpgYFPaYXrzclZMNqb1dF3TKzZz4uoIPwDQAAAGAKdXcnFPNRrJzNG0LxMiAUb+qNGbKRIaADAAAAwCSxbTmXXJJa1Lvd0rFj/H2eQ9nWoczQY1rq8Uh7LlO032WU1yvV1/NkAQAAAEw1r1fhR7dpKP6cy8XK2Twi5R4AAAAAkJXBNY26/Eirlp6Utn/yKc1adj3FfB4xQw8AAAAAyFqPR3pmiTR0859TzOcZBT3yz7alrq6EZMyaoFR/1PS4BAAAAACkYsk98isQkNPcLCsSkeNyKdy2VUNDAzq+xfSdd7Yvljo6pKamfF8pAAAAAEwrFPTIH9uOFvOSZEUicq9rVZUjDTenMx9raZEaGljOAwAAAABxWHKP/OnujhbzI8riivko+loCAAAAQAoKeuSP3y/Hlfgt6LhcciwrcZzbbfrOAwAAAMi7ceVdpcnLQu5Q0CN/vF6F27ZqcLh+d9xuWR0dsh57zBTxknlLX0sAAABgWih7olPHt0hdnVKFf7EG2tsUCofS3gba2+T4fNKKFZLPJwUC+b78omM5juPk+yKmszNnzsjj8SgYDGr27Nn5vpyiEwqHtOjeatX2Srs2HVHlgoXmA7ZtltnX1lLMAwAAANOBbcvx+RK2zQ5a0qUbTCu7eDVBRYOuo9xu6dgx/r7PQrZ1KKF4mFy2LXV3S37/mD+4VvIJr5cfdAAAAGA6GSUDq7Y3taD39yYV81IsG4u/83OGgh6TJ01LusE1jQlDhh7voEUdAAAAUAj8fsnlkuKKesft1q5Nh+V4axKGWnaPnO2LE18AIBsr51hynwFL7s9TFstxWIYDAAAAFJhAwLSVHhqK5V2NMiE30N4m97pWlTnDeVljjEUiltwjv7JYjsMyHAAAAKDANDVJDQ1Z5V0NrmnU5Udah/OyDsfyspAzFPSYHMMt6awxluOwDAcAAAAoQOPMu5obkqyf7JPKK5m4yzHa1mFypGtJ196uygULVVVeparyKlUuWCiro4MWdQAAAEARKnuiUycekZ5+Rqr4XKN0ySW0rssx9tBnwB768zdqS7pktKgDAAAAikuaTC1JJlTv+HH+7s+APfSYFno85pacepmAFnUAAABAcUmTqSXJJOSTmZUzLLkHAAAAAOTWSIu7ZC4XmVk5REEPAAAAAMgtr1fq6JATV9Q7liV1dDA7n0MU9AAAAACA3GtqUn/3q/rUzdKnbpb633iNPvQ5xh56AAAAAMCkcLw1emaJOX5yrFwtnBdm6DGpaoLSzYck93e/Z9LsAQAAAAA5wQw9Js1I30mXJD3TKFmW9NhjLLMBAAAAgBxghh6Tw7ZVvu4Lid9gjiO1tDBTDwAAAAA5QEGPydHdLctxUs8PDZm+kwAAAABKQk1Qqj8qWXZP7KRtS11dTPZNEEvuMTn8frPEPrmod7vpOwkAAACUiLInOnV8i+R2JGf7Yg20bZUkla+/XVYkYvrSd3SwLfc8WY6TbhoVI86cOSOPx6NgMKjZs2fn+3IKSyAgZ+3a6Ey943LJ4ocVAAAAKA22LcfnM4X7sEFLshzJHT/O7ZaOHaM/fZxs61CW3GPyNDWp/43XYn0nu1+lmAcAAABKRXd3QjEvSWXJxbzEttwJYMk9JhV9JwEAAIAS5febJfVxRb3jckmOk5i3xbbc88YMPQAAAAAg97xesz/ePTwn73bL6uhQ+NFtGrTMKcftltrbWW5/npihR+7YttTdbV6Ji/uBrAlK/t7hVMsFC/N4gQAAAACmVFOT1NBgltTX1kperwbDIV1+pFW1vdKuTYdVSY1w3ijokRuBgJzmZlmRiByXS7/7h2/p7C2r5dr+eDTVMvLUYoW2/Q+psVGVlSYEX5LCYencOWnGDKm83JxzHOmdd8xxurFlZdLMmbGHD4XM24oKs6pnvGPPnTPj3W5p1qzY2HfeMdcya1bshcXxjB0clAYGzONUVMTG9veblUfjGTtzpvlaJLPN6OxZ8+9SWXl+Y8+eNR8rLzf/9pL53P5+c1xVdX5jBwbM1zLa/+d4xo7n//58v09G+/+c6PfJaP+f5/N9ku7/cyLfJ+n+P3PxfZLu//N8v094juA5IpuxPEfwHCHxHMFzBM8RBfEccZFXM4Yn/CIR8+/TM1vq8ZgtupgAB2MKBoOOJCcYDOb7UqavkyediMvlOOb5xXEk5/Pa5kiOc5/+NnrutDzRIeFw7NPvvtucu/vu2LlwOHZ3p0/Hzv/t8N2tX594CWVl5rxtx849+KA519iYONbjMedffz12bts2c+7mmxPHzp9vzv/iF7FzTzxhzn3sY4lja2vN+b17Y+eeftqcW748cewf/7E5v3t37Nz3v2/OXXVV4thrrzXnn302dq6ry5y74orEsR/5iDn/7W/Hzu3fb875fIlj//RPzfmOjti5Q4fMuQsvTBy7apU5/8gjsXNHj5pzlZWJY2+7zZz/+tdj506div1/xvviF825r341dq6vLza2ry92/qtfNee++MXE+xgZe+pU7NzXv27O3XZb4tjKSnP+6NHYuUceMedWrUoce+GF5vyhQ7FzHR3m3J/+aeJYn8+c378/du7b3zbnPvKRxLFXXGHOd3XFzj37rDl37bWJY6+6ypz//vdj53bvNuf++I8Txy5fbs4//XTs3N695lxtbeLYj33MnH/iidi5X/zCnJs/P3HszTeb89u2xc69/ro55/Ekjm1sNOcffDB2zrbNubKyxLHr15vzf/u3sXOnT8f+P3mO4DnCcXiOGMFzhMFzRAzPEQbPEUahPkfo4hcd/a2cvoE+B6myrUOZocfEpUmvdCW9BQAAAICR1QvWqSX6UHelXAcOSuGhlG27yA596DOgD30WbFt9lyzWHKdXkvRb/Z7cLkd9/7JbF9z4Ec10BiRJjqR3XBdIr76qSn8NS+VYKsdSuUJaKsdyWp4jxHMEzxE8R4w2lucIc47niPMbW4rPEb/d1iHPhha5HVMjWJK5qI4O2lwPy7YOpaDPgII+O73/T4fe9cVmSdLvXLNV3fGI+WEMBKSWFvOTPJJgyQ8pAAAAUJpsW47Pl7LCV5KpF44dY6Ze2dehLLlHTsxovkXzDtXo0tOSNv9Mqh1OqkyTagkAAACgRKXZrhs1NGTqBmqGrFHQIydcLuntmjf1do1kXZKUVOn18kMJAAAAQAOX+NWiJyU5aleLZioc+6DbbSYBkTUyywAAAAAAU2JwnledalSnbtWgyjSy/9sZ2Z7LROC4MEOP8bFtqbs7JYUyHJZm//BuXfiOdO4zPdJ7FubxIgEAAABMRzNmSA8+KCn4Ww1e+z194Nn/S1XnpF2bDqtyATXEeFHQI3uBgJzmZlmRiByXS+G2rRpc0yhJOvvY/9CZF7+pM5LKrrhAemwL4XcAAAAAEpSXS1/+siT9nkLh6/SzA+a8460Z69MwCgp6ZMe2o8W8JFmRiNzrWnX5kVZJ0uuPlKtRF0iSZjhhk2zf0MCSGQAAAACYJBT0yE6aNMoyR6rtNX0jKxXWk1oT++CQSKgEAAAAkCASkd56yxzPfld+r6UYUNAjO36/HJcroah33G7t2nTYHG9fnFjwk1AJAAAAIEl/f2zO79e9+b2WYkDKPbLj9Zo985Z513G7ZbW3q3LBQlUuWCiro8MU8ZJ5S0IlAAAAgDTKysxNkmqCUv1RyXXgoNTVZUK4kTXLcRwn87DSdebMGXk8HgWDQc2ePTvfl5NXoXBIi+6tVm2vtGvTkYQUylBIqpkfkYYi6vn5r1W1kFALAAAAAKMbaG9T2bpWuR3JkdnKK5dL6ugo+YDtbOtQZugxLj0eac9l6VMog2dcCobKpBqKeQAAAABjsG2Vr79d7uHpZWvkfCRiAraZqc8Ke+iRExUV0uuvx44BAAAAYFRpQrejhoYI2M4SBT1ywuWS/P58XwUAAACA6WxgQLrzTkl9V+tha5ZmOmdTBxGwnTWW3AMAAAAApsTgoNTWJrU9Va3Bbf89Gqw9EuzmELA9LszQIzu2Ldfh/9BVtlR9TrLsHikuFO/cOZNdIUnNzdKMGXm6TgAAAADT1owZ0t/+7fDxbY3STR9W/6u/1LKnP6aqc9KuTYcTwrcxNlLuMyDlXlIgIKe5WVYkEk2fdFwu06puOH0yFJKqq83wvj6pqipvVwsAAACggITCIVVvNsVE38Y+VZVTTGRbhzJDj7HZdrSYl2Lpk9ZI+mRDg+T1yu2Wbr7ZfGykHT0AAAAAYPJQ0GNsWaZPzpolffe7U3tpAAAAAAqL40jBoDn2eCTLGns8xkZBj7H5/WZ5fbqinvRJAAAAAOPwzjvSnDnmmK26E0fKPcbm9SrctlWDw6+cRQMXSJ8EAAAAkCM1Qan+6HD4djzblrq6zFukoKBHRoNrGnXpBqm+UTq7d4/5gTp2LBqIJ5lX2mpqzO2dd/J2qQAAAACmscpKKRw2t8pKc67siU4d3yJ1dUoV/sUaaG9TKBzSQHubHJ9PWrFC8vmkQCCv1z4dkXKfASn32aVOknIPAAAAYNxsW47Pl7DFd9CSljZJ+wKSO75adbvNxGIJrBIm5R5TatYs6Re/iB0DAAAAQEZpQrjLHKnuZFIxLyWEcsNgyT1ywu2W3vc+c6NtHQAAAIB0wmHpy182t3BYkt8vuRLLUsft1ub79shJOk8odyoKemRk2T361CHp5kNpQioAAAAAIEvnzkkPPWRu587JzLZ3dMRmBd1uWe3tqqhblhjOTSh3Wuyhz6Dk99AHAnLWrpU1/G3iWJasxx5LCMSTzA/jjh3m+JZbpBkzpvpCAQAAAEx34bD0139tjr/xDam8fPgDtm2W09fWRov2UDikRfdWq7ZX2rXpiCoXLMzPRedBtnUoBX0GJV3QpwmokGSWxBw/nvDqGKF4AAAAAHIpm3DuYkUoHiYuTUCFJCkSSQmjcLulj30sdgwAAAAAmFwU9Bid3y/H5Uo/Q58URjFrlvTcc1N4bQAAAAAKjuNIg4PmuKxMsqyxx9cEpWtPSq7vfk9avoI99EkIxcPovF6F27ZqKP6HzLJMaAU/SAAAAADG6Z13zL758nJzPJayJzp14hHp6Wekis81SpdcIgUCU3OhBYI99BmU9B56xYIolp6UnvrkU6pYdj3FPAAAAIDzknX21jjyvIpRtnUoM/TIqMcjPbNEitz856P+4Lzzjmkh6fdnfqUNAAAAQGmqrJROnza3ysoxBmbK84Ik9tAjRxwn9nPFmg8AAAAA6ViW9Hu/l8VAv9/MxmeR51XKmKFHTsyaJe3da26zZuX7agAAAAAUNK9X6uiQ44qVrA55XimYoUcq25a6u82rYnPnqCYo+Xsly+6RFixM+ylut1RXN8XXCQAAAKCghMPS/feb469+1YTjjaqpSf3XX6fGexZJkjofeE2Vo9QjpYpQvAxKLhQvEJDT3CwrEpHjcunsZ29W+XeeltuRaWHX0SE1NeX7KgEAAAAUoKxD8UbGh0Oq3mw+oW9jn6rKM3xCkci2DmWGHjG2HS3mJcmKRDRrx9Ma6VpnRSJSS4vU0JCyzGVwUHr2WXP8yU+anpIAAAAAEK+sTFq/PnaMieGfEDFpkiSt5DFDQyb9LqmgHxiQPv1pc9zXxw8nAAAAgFQzZ0rf+la+r6J4UHYhxu83y+rjinpHSUW92502VdLlkpYvjx0DAAAAACYXpRdivF6F27ZqcLiCd9xuWY2NpoiXzNv29rSpkhUV0o9+ZG4VFVN2xQAAAACKXE1Qqj8quQ4clLq6TIg3JBGKl1GpheKFwiEturdatb3Srk1HTIqkbZtl9rW1tIgAAAAAcN5CoVgf+t/+NnMo3kB7m8rWtZqQbg2vHna5TPu6Ig7rJhQP563HY26Ot8ac8Hop5AEAAADkxOBglgNtW+Xrb5c1PAUd3Qo8Rlh3qWHJPXKiv1963/vMrb8/31cDAAAAYDqqqDALgG07i626aUK7o0bCukscM/TIiUhE+vd/jx0DAAAAQDKXS6qpyXKw328+IV2BMUpYd6lhhh45MWuWtHu3uc2ale+rAQAAAFDwvF6zV344pHsk/M0ZI6y71DBDjxRX2dKHTgynSNYty+pz3G7phhsm+cIAAAAAFLRwWPqHfzDHX/yiVF6e4ROamqSGBvW/+kste/pjqjon7dp02IR3g5T7TEot5f7cX35OZdt3yNJwimRjo/Tkk3m+KgAAAADFIBSSqqvNcV9f5pT76OeFQ6rebD6xb2Ofqsqz/MQClW0dypJ7xBw4EC3mpeEUyc5O6cCBjJ86OCg995y5ZZ1aCQAAAKCklJVJjY3mVsZ68QnjnxAxL74YawUR76WXpKuvHvNTBwaklSvNcV8fP5wAAAAAUs2cyQLgXKLsQsx115ll9snn6+oyfqrLJV11VewYAAAAAHKpJij5eyXL7pHS7aG3bam726Tjl0hgHqUXYq6+WoOrb4mlR0pmLUyG2XnJ9JA8cMDcMvaTBAAAAIBxKHuiU8e3SF2dUoV/sQba2xQKh6K3gfY2OT6ftGKF5PNJgUC+L3lKEIqXQamF4oXCIdW3VqvupLT5vj2qyDLlHgAAAAAyCYVifeh7erIMxbNtOT6frLh+9IOWdOkGqcdjZu6Pb5Hc8ZWt2y0dO1awM/WE4uG8/cwr/cNSKXL1lfm+FAAAAABFJhg0t6x1dycU85JU5ki1vebY35tUzEvS0JD0xhsTus5CwB565ER/v/SRj5jjf/1Xlt0DAAAASFVRIb3+euw4K36/CeqKK+odt1u7Nh2W462RZffI2b44seh3u6Xa2txd+DTFDH2psm2pq8u8TVITlOqPDodNZCkSkV5+2dySXjwDAAAAAEmmLvf7YzV6VrxeqaPDFOmS5HbLam9X5YKFqiqvUuWChQq3bdXgcLq343ZL7e0Fu9x+PNhDn0FR7qEPBOQ0N8uKROS4XOabf02jJGno8Q5VfeFOuR3JcblkdXRITU0Z73JwUPr+983xypW0rQMAAACQY7ZtltHX1qYU66FwSIvurVZtr7Rr0xFVpkvBLyDZ1qEU9BkUXUE/RqCEVHxhEgAAAACmj3PnzGS7JDU3SzNm5OZ+Q+GQqjdXS5L6NvapqjybtL3pK9s6lHnUUjNGoISlMcIkKOgBAAAATFA4LH3hC+b41ltzV9CXqknZQ9/T06PPfe5zete73qXKykq9733v08GDB6MfdxxHX/va1zR//nxVVFSovr5ev/rVrxLuY2BgQLfffrsuvPBCVVVV6aabbpKdtN/79OnTWr16tTwejzwej1avXq3f/va3CWNOnDihT3ziE6qqqtKFF16oO+64Q+FweDK+7MLg98tJ2qxiAiWO6LlNR1I+lm2YxNCQ9KMfmdvQUO4uFwAAAEDxcLulm282t5Et8ectKRfsfLLACl3OC/rTp0+rrq5OM2bM0PPPP6/Dhw/rv/23/6bf+73fi4558MEH9fDDD2vbtm06cOCA5s2bpxtuuEG/+93vomM2bNigZ599Vjt37tTevXvV19enlStXaiiuWly1apVeeeUVvfDCC3rhhRf0yiuvaPXq1dGPDw0N6eMf/7hCoZD27t2rnTt36nvf+57uuuuuXH/ZhcPrTQmMGAmUqFyw0OyZjwubyDZM4uxZ6frrze3s2Um8fgAAAAAFa9Ys6bvfNbdZsyZwR4GAHJ9PWrFCjs8n11/9lY5vkbo6pQr/YikQyNUlT2s530N/77336qWXXtKLL76Y9uOO42j+/PnasGGD7rnnHklmNv6iiy7SAw88oJaWFgWDQb373e/W9u3b9ZnPfEaS9Oabb+riiy/Wrl271NDQoFdffVVXXHGF9u3bp2uuuUaStG/fPi1dulSvvfaaFi1apOeff14rV67UyZMnNX/+fEnSzp07deutt+rUqVNZ7Ykvuj30yiIwYoywidG884509dXm+MABqbIyxxcNAAAAAFLaXDBHZgtxVIFngWVbh+Z8hv6f//mfddVVV+lTn/qU5s6dqz/5kz/RY489Fv340aNH9fbbb+vGG2+Mnps5c6aWL1+ul19+WZJ08OBBnTt3LmHM/PnztWTJkuiYn/zkJ/J4PNFiXpI++MEPyuPxJIxZsmRJtJiXpIaGBg0MDCRsAYg3MDCgM2fOJNyKUY9H2nOZ5HhrUj/o9Ur19eP65q+slH71K3OjmAcAAAAwadLkglnJY0aywIpczgv6//zP/9Sjjz4qv9+vf/mXf9HnP/953XHHHXrqqackSW+//bYk6aKLLkr4vIsuuij6sbffflvl5eWaM2fOmGPmzp2b8vhz585NGJP8OHPmzFF5eXl0TLLNmzdH9+R7PB5dfPHF4/0nAAAAAACk8c47Uk2Nub3zznneSbpcsOQxWWaBFbqcF/SRSETvf//7df/99+tP/uRP1NLSorVr1+rRRx9NGGdZia+hOI6Tci5Z8ph0489nTLyNGzcqGAxGbydPnhzzmgAAAAAA2XEc6c03ze28N3+nywVrbDyvLLBCl/OC/g/+4A90xRVXJJxbvHixTpw4IUmaN2+eJKXMkJ86dSo6mz5v3jyFw2GdPn16zDG//vWvUx7/N7/5TcKY5Mc5ffq0zp07lzJzP2LmzJmaPXt2wg2Z9fdLN9xgbv39+b4aAAAAANPRrFnSL35hbhMJxRtc06hLN0j1jVL/64elJ580e+a7uszbpqbcXPA0l/OCvq6uTkeOHEk49/rrr8vn80mSLrvsMs2bN08/+MEPoh8Ph8Pas2ePrr32WknSlVdeqRkzZiSMeeutt3To0KHomKVLlyoYDGr//v3RMT/96U8VDAYTxhw6dEhvvfVWdMzu3bs1c+ZMXXnllTn+ygvLVba04WXJdSB9lsB4RSLSv/6ruSVtZwEAAAAASWby/H3vM7eJtq1LyQU7jyywQleW6zv80pe+pGuvvVb333+/Pv3pT2v//v3q6OhQR0eHJLMEfsOGDbr//vvl9/vl9/t1//33q7KyUqtWrZIkeTweNTU16a677tK73vUu/f7v/77uvvtuvfe979VHPvIRSWbW/6Mf/ajWrl2r9vZ2SVJzc7NWrlypRYsWSZJuvPFGXXHFFVq9erW++c1vqre3V3fffbfWrl1b0jPv5be1aP92Exzh7F4uNTaaV7QmYOZM6dvfjh0DAAAAACZXztvWSdL3v/99bdy4Ud3d3brssst05513au3atdGPO46jv/u7v1N7e7tOnz6ta665Rt/61re0ZMmS6JizZ8/qy1/+sr7zne+ov79fH/7wh9XW1pYQUtfb26s77rhD//zP/yxJuummm7Rt27aEnvcnTpzQ+vXr9W//9m+qqKjQqlWr9NBDD2lmllVn0bWtO3BAzgc+kJoCuX9/rO8cAAAAAEyCc+ekHTvM8S23SDNmnN/9hMIhVW+uliT1bexTVXlVjq5wesi2Dp2Ugr6YFF1B//DD0l13pZ5/5BFpw4YpvxwAAAAApSMUkqpNHa6+PqnqPOtwCnoj50vuMc1dd50cpenTWFc3obsdGpJ+/nNz/P73T3w/DAAAAIDi43ZLH/tY7BgTQ0Ffaq6+WoOrb1HZ9h1mD71kWjxMcLn92bPSBz5gjifyShsAAACA4jVrlvTcc7m5r5qg5O8dDvoOD0l+f0kF4kkU9CUp/Hi7rp25Q3Unpc337VFF3bIJ36dlScONDGSlTP8DAAAAQO6UPdGp41sktyM5ncvNSZdL6ugomZZ1EnvoMyq6PfQq/v0mAAAAAIqYbcvx+WSl65ftdps+9AU+U59tHZrzPvQAAAAAAKTzzjtmZbzfb47PS3d3+mJeMuFeb7xx3tdXaFhyDwAAAACYEo4Tq7fPe62432+W1482Q19be97XV2iYoUdOnD0r/dmfmdvZs/m+GgAAAADT0axZ0t695jZr1nneiddr9soPx+SPvC7guN1Se3vBL7cfD2boS1SuEyGHhqR/+qfYMQAAAAAkc7sn3DHbaGqSGhrU/+ovtezpj6nqnLRr02FVLliYgzsvHBT0JWgyEiHLy82njxwDAAAAwKTyehWZO0c/e9m863hr8ns9eUBBX2psW+Xrb5c1vC4l2mEuEpFaWqSGhvOaqZ8xQ1q7NmdXCQAAAKAIDQ5Kzz5rjj/5SamMinRC+OcrNdkkQpbQnhMAAAAAU2dgQPr0p81xXx8F/UTxz1dqJikRMhKRXn3VHC9ebB4CAAAAAOK5XNLyuF2/uTCSD2bZPVJ5pdTdPeGMsEJBQV9qRhIhW1qkoSE5MsvuHbdb1gQSIfv7pSVLzHFfn1RVlbMrBgAAAFAkKiqkH/0od/eXkA/21HtMfeM4E84IKxSW45x397+ScObMGXk8HgWDQc2ePTvfl5M7tp2UCHlkQomQoZB06aXm+NgxCnoAAAAAk8y25fh8o28pdrtNcVKAM/XZ1qHM0JeqHCdCVlVJv/lNDq4LAAAAALIxVj6YVBIZYex0BgAAAABMif5+6X3vM7f+/gne2Ug+2GgmkBFWKCjoAQAAAABTIhKR/v3fzW2syfWsjOSDud2SJMfl0tDwhxy3W5pARlihYMl9KbDtUZMeExIhJ7CH/uzZWN5EICDNmjWRCwYAAABQjGbNknbvjh1PWFOT1NAgvfGG+i+Zr4XbFqm2V9q16fCEMsIKBaF4GRR8KF4gIKe5WVYkIsflUrhtqwbXNEqShh7vUNUX7jSJkC6XrAmkQIZCUnW1OSblHgAAAMBUC4VDqt5sipK+jX2qKi/cooRQPJjUx+FiXpKsSETuda26/EirJEXbO4x8TC0t5tWt81iWUl4uPfJI7BgAAAAAMLko6ItZmtTHMkeq7TW9593JazMmkAI5Y4a0YcN5XykAAACAEjA4KP3Lv5jjhgapjIp0QvjnK2Z+v1lKH1fUO263dm06bI63L04s+EsgBRIAAABA/gwMSCtXmuO+Pgr6iSLlvph5vWbPvGXeddxuWe3tqlywUJULFpo988OJkJpgCmQkIh07Zm4TTqsEAAAAUJRcLumqq8xtrI5z56smKNUfHQ79jmfbUleXeVtECMXLoNBD8ULhkBbdWz2c9HgkNenRts0y+9raCbV0IBQPAAAAQD4NtLepbF1rNPR7JBC87IlOla+/3axOdrlMq7vzDAOfKtnWoRT0GRRDQT8VSY+hkDR3rjk+dYqCHgAAAMAUsm05Pl/CluJBS1raJO0LJOWHud1mafE07lGfbR3KknvkRFWVKepDIYp5AAAAAFNslEDwupNjhIEXAQp6AAAAAMCU6O+X6urMrb8/h3fs96dsynfcbm2+b4+c5M36RRQGTkFfjJICH0YNhgAAAACAKRSJSC+/bG45DdP2es3e+LjQb6u9XRV1y1KCwicSBj7dsIc+g4LbQx8IyGlulhWJyHG5dPazN6v8O09HgyGsSQqAGBiQvvAFc7xtmzRzZs4fAgAAAECBGxyUvv99c7xy5SS0rUsT+p0xKHwaIhQvRwqqoE8TBOFIsuLHTFIABCn3AAAAAKajqQoKz6Vs69Bcvx6CfEoTBGEljxkJgMhxQT9jhvT1r8eOAQAAAACTi4K+mPj9Zll9phn6SQiAKC+X/vqvc363AAAAAIrI0JD04ovm+LrrYlvecX4IxSsmXm9K4IPV2JgQDFFMARAAAAAACsvZs9L115vb2bNT97hX2dKGlyXXgYNT96BTgBn6IjO4plGXH2kdDnw4bAIfvv71lGCIXHMc6f/8H3N84YWSlbLWHwAAAECpsyzpiitix1Oh/LYW7d9uVi47u5dLjY3Sk09OzYNPMkLxMiioUDzlL/CBUDwAAAAA086BA3I+8IHUbLH9+6Wrr87HFWUl2zqUJfcAAAAAgOL04oupxbwkvfTSVF/JpGDJPXKiqsosuwcAAACAaeO661KDwiWpri4PF5N7zNADAAAAAKZEf790ww3m1t8/BQ949dUaXH2LRuYeHcnsoZ/Gy+3Hgxn6ImPZPfrUoeF2dXaPtGBhvi8JAAAAACRJkYj0r/8aO54K4cfbde3MHao7KW2+b48q6pZNzQNPAQr6YhIIqGLtWj09/PKT8733SI89JjU1TfpDDwxI99xjjh94QJo5c9IfEgAAAECBmTlT+va3Y8dT5Wdec/vG1VdO3YNOAVLuMyiYlHvbluPzyUp+mcvlko4fn/Te86TcAwAAAJiO8tUJbCKyrUOZoS8W3d2pxbxk1rG88cakF/QzZkhf/WrsGAAAAAAwuSjoi4XfL8flSj9DX1s76Q9fXi594xuT/jAAAAAACtjQkPTzn5vj979fcrsn8cFs20x8+mpUE5T8vcWXM0bKfbHwehVu26qh+H4MliV1dEz67DwAAAAAZOPsWekDHzC3s2cn8YECATk+n7RihSpq36MTj0hdnVKFf7EUCEziA08t9tBnUDB76GX2hiy6t1pLT0pPffIpVSy7fsqKeceR3nnHHFdWmtcSAAAAACDeO+9IV1xhjg8fNrVDzo2WLzbC7ZaOHZvWE5/Z1qHM0BeZHo/0zBIpcvOfT+k36DvvmFC86upYYQ8AAAAA8SorTS197NgkFfPS6PliI4aGTM5YEaCgBwAAAAAUj+F8sVG53VOSMzYVKOiRE5WVpl1dX98kvtIGAAAAAJkM54sNDm8DdizLhIVLpphvb5/Wy+3Hg5T7YmHbch3+D11lS9Xnpj690bLoPQ8AAABgbGfPSn/xF+Z4505p1qzJeZzBNY26/EiranulXZteU2V5pVlmX1tbNMW8REFfHAIBOc3NqohEtF+SJcnZvtgk3Dc15fvqAAAAAECS2b7+T/8UO55MPR5zc7w1UnlVURXyIyjoC51ty2lujoY+jITLW5GI1NIiNTRMyTduOCz93d+Z47/9W9OXHgAAAADilZebeceRY0wMBX2hGyvBcSS9cQoK+nPnpPvvN8df/So/nAAAAABSzZghrV2b76soHhT0hW44wTFtUT+F6Y1lZdIXvxg7BgAAAABMLlLuC11yguPI+SlOb5w5U9qyxdxmzpyShwQAAABQYCIR6Ve/MrexWsXnQk1Qqj86HBg+wralri7ztggwl1oE4hMcn1+7RxXnIkWX3ggAAACg8PX3S0uWmOO+vsnrlFX2RKeOb5HcjgkMH2jbKkkqX3+7Wd3schVFiLjlOI6TeVjpOnPmjDwej4LBoGbPnp3vy0krFA6penO1JKlvY5+qyukfBwAAAGD6CYWkSy81x8eOTVJBb9tyfL6EbcmDlmQ5kjt+nNttLmIaToRmW4ey5B45EQqZXvSWZY4BAAAAIFlVlfSb35jbZM3OpwsOL0su5qVYiHgBo6AHAAAAABQPv98sqY/juFxyLCtx3BSGiE8WCnrkRGWldOqUuVVW5vtqAAAAAJQsr9fsj3cPz8m73bI6OhR+dFssTHyKQ8QnC6F4RaImKPl7hxMcFyxM/KBtS93d5pWqSfqGtSzp3e+elLsGAAAAUCTOno3l0AUC0qxZk/RATU1SQ4NZUj8cGD4YDkXDxHdtOqzK5LqpADFDXwRGEhy7OqUK/2INtLcpFA4pFA5poL1Njs8nrVgh+XzmpwYAAAAA8mBoSPrOd8xtaGiSH8zrlerrEyY1ezzSnsskx1szyQ8+NZihL3S2bVovDPcqsCIRude16vIjrZKk41sU/ZgiEamlxbxSleOZ+nBY+uY3zfGXvyyVl+f07gEAAAAUgfJy6ZFHYseYGAr6QjdKgmNtr2TJ9F1MMJLkmOOC/tw56W/+xhxv2MAPJwAAAIBUM2aYegG5QUFf6EYSHOOKesft1q5Nh83x9sWJBf8kJTmWlUm33RY7BgAAAABMLvbQF7p0CY7t7apcsFCVCxYq3LZ1SpIcZ86UHnvM3GbOzPndAwAAACgCkYh07Ji5JS00nly2LdeP9ugqW6o/OhwmXgQsx3GSF2UjzpkzZ+TxeBQMBjV79ux8X87obDshwXFEKBzSonurh5McjxRFkiMAAACAwhQKSdXV5rivT6qqmoIHDQTkNDfLikTkyGxNdlwuWR0dscj9aSbbOpTF0cXC6x115r3HY27FkuQIAAAAoHBVVk7hg9l2tJiXTDEvmTDxyQoMn0osuUdOhELm1bWqKnMMAAAAAMlG6oWR+mHSpQkRjxoJDC9gzNAjZ955J99XAAAAAABx/H6zvD5dUT9JgeFTiRl65ERFhXT0qLlVVOT7agAAAABAktebGBQ+cn4SA8OnEjP0Rc6ye/SpQ+Yb17J7pEkKxXO5pEsvnZS7BgAAAFAkBgakL3zBHG/bNjUdsgbXNOryI62q7ZWeX7tHFeciKWHihYqU+wwKJuU+nUBAztq1sob/ix3LkvXYY9M2yREAAABAcctHyn0oHFL1ZvOgfRv7VFU+FZv3J4aU+1I3kuYY93qN5ThSc/OkJDmeOyd961vmuLVVmjEjp3cPAAAAoAjMmCF9/euxY0wMBX2xGi3NMRIxSY45LujDYelLXzLHa9fywwkAAAAgVXm59Nd/ne+rKB4U9MVqtDRHl2tSkhzdbmnVqtgxAAAAAOSFbUvd3ZLfH53IvMqWPnRCch04KNUty/MF5g4FfbEaTnMs+3yrovW1ZUkdHZMS/jBrlrRjR87vFgAAAEARcRzp//zHm9J//qcuvOpSWRfnuDYJBMzW40hEjsulcNtWuV7co/07JEuSs3u51NgoPflkbh83Tyjoi50lyTGBeOH7/28Nrv4LKRyatIcLDd91RYVZDCCZ/fXhsJm5nzUrNvadd8wP9KxZsVn9wUGTfOlyJba/G8/Y/n6zs2DmTKls+Dt8aEg6e3Z8Yy1LqqyMjT171nysvDy2pWA8YyMR83hSYvjHwID5WmbMMOPHO9ZxzL+PZK7BGm7JEQ6bf/vxjC0rS0waTff/OZ6x4/m/z8X3ycj/50S/T5L/Pyf6fTLa/+dEv0/i/z8n+n0y2v/n+X6fjPb/yXPE6GN5joiN5Tli/GN5jjDneI4Y/1ieI2JjeY7IbuxEnyOGntip+Xc1SZqvPusCVT22JXeh3SM5YsOrlK1IRO7Pt6pcpizSyNvOThP8dfXVuXncPKKgL1a2rfL1t8sazsSzHEfur/6NLj/1N+rxTOLjPnhKeufd0vo/lOYeNucO3ib9/x6TFv0v6bOfjI195KgUvFRae7VU8zNz7j9WSf/vDmnBD6S/vDE29luHpN/8odRYL122x5x79U+l//m/pItfkpo+FBvbsV9682pp1celhbvMuf/9EWn7D6SLXpHW/Uls7BNd0vF66VOfkv7wGXPuxLXS/3hJ+v1u6Y64Nn87vi91f1z601ulP+k05976Y6n9FemCHumuuFcXn35aOvwp6WOt0gfazLn/f620tVua+Vtp45zY2GefkP79VumGL0t1D5lzZ+ZLD/dIrnPSpvLY2Oe2SQdapeVfk67/O3Ou3yM98FtzfN8MyT1ojnc/KL38Zenab0o3fsWcGyqT/u9z5vie35Mqgua462+lPV+Trv6W9PEvxB7v78NSZIZ0Z400+01z7qW7pR98U/rjJ6VPromN3XxaGvg96Xa/9K43zLn966Vd35Ku+K706U/Hxv43W/pdjdTyPukP/t2c+0Wj9E9PSv7npFtWxsb+P69LvX7pr+qkS1425351s/Td70q+H0lrro+NffQX0q/fJ62+Qbr8X8251z8mfec5af4BqfkDsbGBvdLJOukzfyYt/idz7uhyqfNH0rt/JbUuiY19arf0nzdIf36L9EffMed6rpIeOyB5jklfuiw29h+flY78mfSJtdKVj5tzp66Q2n4lVf5G+src2NjvfVv65S1SwwZp6T+Yc6d90j8ck2aEpL+ujo395w7p52ulFX8tLbvfnAtdKH3zN+b4a1Zs7POPSD/dIF33DenDf2POhSul+4d/u361Siof/q38w69LL/61dM0W6f/6Uuw+vjb85PHld0tV/8cc//ir0r99Q3r/Y9JNzbGx3+iTzlVJX7xUmnPcnPvJF6V/2SK9d4f0Xz4XG8tzhMFzhDnHc4Q5x3NEbCzPEQbPEeYczxHmXJE9R/yF4l4ZcSJSS0vuQrvT5IiNWvC+9FJRFPSufF8AJkm6b2ZHqu3N0/UAAAAAKHnVCsmRJUeWqvSOWWbwxhu5ufPhHLF4jmUpbZ/2urrcPGae0Yc+g4LtQ2/bks9n1t0Mc9xu9b9+WI63ZtIelqVyo49lqVxsLEvlxj92Oi2VYzktzxEjeI4Y/1ieI8wxzxHjH8tzRGwszxHjHztdniMGj72pC/7ojzTLORsb7HZLx47lLOdroL1N7nWtKnNM/WO1t0svvmiW2Y8ogD302dahFPQZFGxBL0mBgFnCMjRkflDa23O3PwUAAAAAxiltwZ3DGiUUDmnRvdWq7ZV2bTqiygXDW18OHDDL7OvqCmKpPQV9jhR0QS+Zmfo33jCt6iYh3R4AAAAAsjVqwZ3D+6/ebPID+jb2qaq8KsNnTE/Z1qGE4hU7r5dCHgAAAMC00eMxt8ncClwqCMUDAAAAAKAAUdADAAAAAFCAWHIPAAAAACgaNUHJ3yu5DhyUwkOS31+025Ap6AEAAAAARaHsiU4d3yK5HcnpXG5OulxSR0dRdvxiyT0AAAAAoPDZtsrX3y73cB83a+R8JGLaedt2vq5s0lDQAwAAAAAKX3e3rEgk/ceGhkw77yJDQQ8AAAAAKHx+v1len47bLdXWTu31TAEKegAAAABA4fN6zV55t1uSNLzyXo7bLbW3F2UwHgV9KbBtqaurKPeMAAAAACgsNUGp/qhk2T25v/OmJunYMfXv3qUP3CbVN0r9rx8uykA8iZT74hcIyGlulhWJyHG5FG7bqsE1jfm+KgAAAAAlaOjxjlgK/fbFk5M+7/UqMneOfvayedfx1uT2/qcRCvpiZtvRYl6SrEhE7nWtuvxIq3o8eb42AAAAACWlJqhoMS+Z+kQtLVJDQ1Euh58KLLkvZmlSHsscqbY3T9cDAAAAoGT5e2PFfFSRps9PFWboi5nfL8flSijqHbdbuzYdLuplJwAAAACmH8vukbN9ceKkY5Gmz08VCvpi5vUq3LZV7nWtKnNMMW+1t6tywcJ8XxkAAACAUrNgodkz39JiZuYnOX2+JmhWBVh2j3ls25a6u017uyJZ4m85jpO86AFxzpw5I4/Ho2AwqNmzZ+f7csYtFA5p0b3Vqu2Vdm06QjEPAAAAIL9s2yyzr62dtMJ6oL1NZetaTfiey6XBWz6rsh3/aFYHuFyTE8aXQ9nWoRT0GRRDQV+9uVqS1LexT1XlVXm+IgAAAACYRLYtx+dL3HosyYof43ZLx45N25n6bOtQQvEAAAAAAMUjTTi4lTymSML4KOgBAAAAAMXD7zfL6uOkLEsvkjA+CnoAAAAAQPHwes0eebfbvO92a3D1LRocnqZ3JjmMbyqRcl+M4tMb585JTXcEAAAAgGLW1CQ1NETD98Jz52jRhTuGw8IPF01YOAV9sQkE5DQ3y4pE5Lhccn32Zh3/jky64/bF0z7NEQAAAABywuuNzcKHQ+rxSD0eyfHW5Pe6cogl98XEtqPFvCRZkYhm7Xha7uENI1YkYno+2nYeLxIAAAAAkAsU9MWkhNIcAQAAAKDUUdAXE79fTomkOQIAAABAqaOgLyZer8JtWxPSG63GxoR0x2JJcwQAAACAMdm21NUV3XJcE5Tqjw6HhRcJy3GclElcxJw5c0Yej0fBYFCzZ8/O9+VkFAqHtOje6uH0xiMmvdG2o+mOFPMAAAAAil5SWPjZz96s8u+YfDHH5ZI1zcPCs61DKegzKMSCvnpztSSpb2Ofqsqr8nxFAAAAADCFbFuOz5eQL+YoKV/M7ZaOHZu2E57Z1qEsuQcAAAAAFI8SCgunoAcAAAAAFI8SCgunoAcAAAAAFI8SCgunoC9CV9nShpcl14GD+b4UAAAAAJhyg2sadekGqb5R6n/9sPTkk2bPfFeXeTuNA/HGoyzfF4DcKr+tRfu3mz0izu7lUmOj+eYFAAAAgBLS4zE3x1tjTni9RTErH48Z+mJy4IDKtu+IBj5YktTZKR04kMeLAgAAAABMBgr6YvLii6npjZL00ktTfSUAAAAAgEnGkvtict11qf0VJamuLg8XAwAAAAD5Ydk9uvmQqY0su0dasDDflzQpmKEvJldfrcHVt0RbMjiS2UN/9dV5vCgAAAAAmEKBgCpq36PvPiM9/YxUUfseKRDI91VNCstxnJSWfIg5c+aMPB6PgsGgZs+ene/LySgUDqm+tVp1J6XN9+1RRd2yfF8SAAAAAEwN25ZzySWykstct9uk2xdIKF62dSgz9EXoZ17pH5ZKkauvzPelAAAAAMDU6e5OLeYlaWhIeuONqb+eSUZBDwAAAAAoDn6/HCtNVLjbLdXWTv31TDIKegAAAABAcfB6FX50m4biz7lcUnt7wSy3Hw9S7ouMZffoU4dMIF4xpzkCAAAAQDqDaxp1+ZFWLT0pbf/kU5q17PqiLOYlZuiLy3Ca49PPSN8t8jRHAAAAABhNj0d6Zok0dPOfF20xL1HQFw/bltPcnBAAYTmO1Nws2XYeLwwAAAAAMBko6ItFd7esSCT1fCRSlGmOAAAAAFDqKOiLhd8vx5Xmv9PlKso0RwAAAAAodRT0xcLrVbhtq4biOzRYltTRUdR7RgAAAABAti11dUW3G9cEpfqjw0HhRYyU+yISn+b41CefUkURpzkCAAAAgCQpEDB5YpGIHJdLrs/erOPfkdyO5GxfbCY5m5ryfZWTwnKcuBQ1pDhz5ow8Ho+CwaBmz56d78sZUygcUvXmaklS38Y+VZVX5fmKAAAAAGAS2bYcny8hT8yRFL9wWW63dOxYQU12ZluHsuQeAAAAAFCY0oSDW8ljhoaKNiicgh4AAAAAUJjShIOnLEF3u4s2KJyCHgAAAABQmIbDwQeHp+Udt1tWY6Mp4iXztr29oJbbjweheEWmJij5e4fTHMsrpe5uye8v2m9gAAAAAKVtJBy8tlfatemwKhcslL7+dbPMvra2qGshCvoiUvZEp45vGU5zfOo9JgzCcUwv+iJOdgQAAABQ2no85uZ4a8wJr7eoC/kRLLkvFrat8vW3yz28YcRyHFPMS1IkIrW0RHsyAgAAAAAKHwV9sUiT7pigiJMdAQAAAKAUseS+WPj9Zmn9aEV9ESc7AgAAAChtpZolxgx9sfB6zT754TRHx7I0FJf0WMzJjgAAAABK10iWWFenVFH7HjmXXCKtWCH5fFIgkO/Lm1SW4zgpbfoQc+bMGXk8HgWDQc2ePTvfl5OZbUtvvKF3LpmvhdsWDSc9HjFJjwAAAABQTGxbjs83+vZjt1s6dqzgJjezrUNZcl9shtMcnXAoNekRAAAAAIpJtlliBVbQZ4sl9wAAAACAwjSSJTaaIs8So6AHAAAAABSm5Cwxl0tDwx8qhSwxCvoiVhOU6o8OJz1KZn99Vxf96AEAAAAUj6Yms0++q0v93a/K9yWpvlHqf/2w+VgRYw99kRpJenQ7krN9sc7d8lmV7fhHs7/E5TKvYhX5NzcAAACAElGiWWLM0Bcj21b5+tvlHu5fYEUiKtu+IxYWEYlILS3M1AMAAABAAaOgL0Zpkh6t5DEjaY8AAAAAgIJEQV+M0iQ9OsljijztEQAAAACKHQV9MUpKepTbrcHVt2hweJq+FNIeAQAAAJSIpPDvlHDwIkYoXrFqapIaGsyy+tpahefO0aILd6i2V9q16bAqFyzM9xUCAAAAwMQEAnKam2VFInJcLrk+e7OOfycWDl7sYeCW4zgpq7ERc+bMGXk8HgWDQc2ePTvfl3PeQuGQqjdXS5L6Nvapqrwqz1cEAAAAABNg23J8voT8MEdJ+WFut2lpV2Crk7OtQ1lyDwAAAAAoPISBU9ADAAAAAAqQ3y+nxMPAKehLyEg4hOvAwYTQCAAAAAAoOF6vwm1bE8K/rcbGhHDwYg8DZw99BsWyh36gvU1l61pNOISGl6K4XEUfEgEAAACgeIXCIS26t3o4/PuICf+27Wg4eKEW89nWoaTclwLbVvn622UNv3QT3VcSiUgtLSYNv0C/0QEAAACUth6PuTneGnPC6y2Z+oYl96UgTVhEVJGHRAAAAABAsaKgLwV+v1len06Rh0QAAAAAQLGioC8FXq/ZKz8cDjESmuCUQEgEAAAAgOJl2T26+ZD0qUPmuNQQipdBsYTiSZJsW/2v/lLLnv6Yqs7FhUYAAAAAQKEJBOSsXStruKR1LEvWY48VReg3oXhI5fUqMneOfvayeTcaGgEAAAAAhcS2E4p5Sea4xEK/WXIPAAAAACgs3d0JxXxUiYV+U9ADAAAAAAqL3y/HslLPl1jo96QX9Js3b5ZlWdqwYUP0nOM4+trXvqb58+eroqJC9fX1+tWvfpXweQMDA7r99tt14YUXqqqqSjfddJNs204Yc/r0aa1evVoej0cej0erV6/Wb3/724QxJ06c0Cc+8QlVVVXpwgsv1B133KFwODxZX25BqAlK9UfjQiNsW+rqMm8BAAAAYLrzehV+dJuG4s+5XCUX+j2pBf2BAwfU0dGhP/qjP0o4/+CDD+rhhx/Wtm3bdODAAc2bN0833HCDfve730XHbNiwQc8++6x27typvXv3qq+vTytXrtTQUOy/bNWqVXrllVf0wgsv6IUXXtArr7yi1atXRz8+NDSkj3/84wqFQtq7d6927typ733ve7rrrrsm88ue1sqe6NTxLVJXp1ThX6xzf/k5OT6ftGKF5PNJgUC+LxEAAAAAMhpc0yjfl6RP3Syd3fGUdPx4UQTijcekpdz39fXp/e9/v9ra2vT1r39d73vf+7RlyxY5jqP58+drw4YNuueeeySZ2fiLLrpIDzzwgFpaWhQMBvXud79b27dv12c+8xlJ0ptvvqmLL75Yu3btUkNDg1599VVdccUV2rdvn6655hpJ0r59+7R06VK99tprWrRokZ5//nmtXLlSJ0+e1Pz58yVJO3fu1K233qpTp05llVpfbCn3js8nKxKJnnIkJSxUcbulY8dK6lUtAAAAAIUnFA6penO1JKlvY5+qyqvyfEW5k20dOmkz9K2trfr4xz+uj3zkIwnnjx49qrfffls33nhj9NzMmTO1fPlyvfyyiV8/ePCgzp07lzBm/vz5WrJkSXTMT37yE3k8nmgxL0kf/OAH5fF4EsYsWbIkWsxLUkNDgwYGBnTw4MG01z0wMKAzZ84k3IpGd3dCMS8lFfNSyYVIAAAAAEChmpS2dTt37tTPf/5zHThwIOVjb7/9tiTpoosuSjh/0UUX6fjx49Ex5eXlmjNnTsqYkc9/++23NXfu3JT7nzt3bsKY5MeZM2eOysvLo2OSbd68WX/3d3+XzZdZePx+s68k0wx9CYVIAAAAAEChyvkM/cmTJ/XFL35R3/72tzVr1qxRx1lJiYSO46ScS5Y8Jt348xkTb+PGjQoGg9HbyZMnx7ymguL1Sh0dpmiXJLdbg6tv0eDwP4XjdpdciAQAAAAAFKqcz9AfPHhQp06d0pVXXhk9NzQ0pB//+Mfatm2bjhw5IsnMnv/BH/xBdMypU6eis+nz5s1TOBzW6dOnE2bpT506pWuvvTY65te//nXK4//mN79JuJ+f/vSnCR8/ffq0zp07lzJzP2LmzJmaOXPm+XzphaGpSWpoMMvqa2sVnjtHiy7codpeademw6pcsDDfVwgAAAAAGVl2j24+ZFYcW3aPVIK1TM5n6D/84Q/rl7/8pV555ZXo7aqrrtItt9yiV155RQsWLNC8efP0gx/8IPo54XBYe/bsiRbrV155pWbMmJEw5q233tKhQ4eiY5YuXapgMKj9+/dHx/z0pz9VMBhMGHPo0CG99dZb0TG7d+/WzJkzE15wKDler1RfH52J7/FIey6THG9Nfq8LAAAAALIRCKii9j367jPS089IFbXvKcmOXTmfob/gggu0ZMmShHNVVVV617veFT2/YcMG3X///fL7/fL7/br//vtVWVmpVatWSZI8Ho+ampp011136V3vepd+//d/X3fffbfe+973RkP2Fi9erI9+9KNau3at2tvbJUnNzc1auXKlFi1aJEm68cYbdcUVV2j16tX65je/qd7eXt19991au3Zt4SfWAwAAAEApsm05a9fKimvYZjmO1NJiViOX0BbiSQnFy+QrX/mK+vv7tX79ep0+fVrXXHONdu/erQsuuCA65pFHHlFZWZk+/elPq7+/Xx/+8If15JNPyj2y/1vSjh07dMcdd0TT8G+66SZt27Yt+nG3263nnntO69evV11dnSoqKrRq1So99NBDU/fFAgAAAAByp7s7oZiPGunYVUIF/aT1oS8WRdWHPo1i7t0IAAAAoAjZtpxLLkkt6t1u6dixoijo896HHoWjJijVH5VcBw5KXV2Sbef7kgAAAAAgPa9X4Ue3aSj+nMtVkh278rLkHtNH2ROdOr5FcjuS07ncnHS5THu7pqa8XhsAAAAApDO4plGXH2nV0pPS9k8+pVnLri+5Yl5ihr602bbK198u9/BKFWvkfCRiAiWYqQcAAAAwTfV4pGeWSEM3/3lJFvMSBX1p6+6WFYmk/9hIoAQAAAAAYFqioC9lfr9ZXp+O2y3V1k7t9QAAAAAAskZBX8q8XrNXfrgV4EhGpON2l2SgBAAAAAAUEkLxSl1Tk9TQoP5Xf6llT39MVeekXZsOq3LBwnxfGQAAAAAksm2zddhXo5qg5O+VLLtHKtH6hYIekteryNw5+tnL5l3HW5Pf6wEAAACAZIGAnOZmWZGIKixLJxyz5NzZvrhku3Sx5B4AAAAAML3ZdrSYlyTLcaLFrFXCXboo6BFVE5Tqjw4vWZHMD0RXV0n+YAAAAACYRsbq0CWVbJcuCnpIksqe6NTxLVJXp1ThX6xzf/k5OT6ftGKF5PNJgUC+LxEAAABAqfL75YzWoUsq2S5dFPSQbFvl62+Xezjm3opEVLZ9R+wVsBJewgIAAABgGvB6FW7bqkHLvOtYVqwFdwl36SIUD2mXr1jJY0aWsJTgDwkAAACA/Btc06jLj7Sqtlfatek1VZZXmhqltrZk6xQKekh+v3l1K66od5RU1JfoEhYAAAAA00ePx9wcb41UXlWyhfwIltzD/BB0dJiiXZLcbg2uviW2nKWEl7AAAAAAwHTFDD2MpiapoSG6ZCU8d44WXbhjeDnLYVUuWJjvKwQAAAAAxKGgR4zXG5uFD4cSl7MAAAAAwFSxbam722wPjlspXBOU/L3DrbaZdGTJPQAAAABgGgkEoi20HZ9PA+1tCoVDGnq8I6HVNq21KegBAAAAANOFbctpbo524bIiEbnXtaq+tVpVX7gzodU2rbUp6AEAAAAA00WaltpljlR3QtFiPmqktXYJo6AHAAAAAEwPfr8cV2KZ6rjd2rxpT8p5WmtT0AMAAAAApguvV+G2rQkttK32dlXULZOV1Gqb1tqk3CNefJLk3DkkSAIAAACYcoNrGnX5kdbUFtpJrbZLvZiXKOgxIhCIhk84Lpdcn71Zx79j9qk42xdLHR3mBwgAAAAAJtmoLbTjW22DJfdQ2iTJWTueJkESAAAAAKYxCnqkTZK0kseQIAkAAABgilxlSxtellwHDub7UqY1CnqkT5JMHkOCJAAAAIApUH5bi/Y/Lj2yW5r1oeXSrbfm+5KmLQp6pE+SbGwkQRIAAADA1DpwQGXbd0RXDFuS1NkpHTiQx4uavijoIckkSV66QapvlPpfPyw9+aR07JjU1WXeEogHAAAAYLK9+GLq9l9Jeumlqb6SgkDKPaJSkiRJkAQAAAAwla67To7SZHrV1eXhYqY/ZugBAAAAANPD1VdrcPUt0UwvR5IaG6Wrr87jRU1fFPSIqglK9Ucly+5J/aBtm+X3tK4DAAAAMInCj7frA7dJGxqks3v3mO3ASIuCHpKksic6dXyL1NUpVfgXa6C9TaFwSKFwSAPtbXJ8PmnFCsnnkwKBfF8uAAAAgCL2M6/0D0ulyNVX5vtSpjX20EOybZWvv13W8LoWKxKRe12rLj/SKkk6vkXRjykSkVpapIYG9tcDAAAAQB4xQw+pu1tWJJJwqsyRanslf6/kTm5KPzQkvfHG1F0fAAAAACAFM/SQ/H7J5TKz78Mct1u7Nh02x9sXJxb8brdUWzvVVwkAAAAAiMMMPczS+Y4OU6hLktstq71dlQsWqnLBQoXbtmpwuG+E43ZL7e0stwcAAACAPGOGHkZTk9kX/8YbZvY9rmAfXNOoy4+0qrZX2rXpsCoXLMzjhQIAAAAoKrYtdXeblcPDdUhN0Gz/teweifpjVBT0iPF6R5157/GYm+OtmeKLAgAAAFC0AgE5zc2yIhE5LpfCbVs1NDSg41tMlpezfbFZTdzUlO8rnZYo6AEAAAAAU8+2o8W8NNxt6/OtqrJiwdwWXbbGxB56AAAAAMDUS9dtS3TZGg8KegAAAADA1PP75bgSS1LH5Uo5R5et0VHQIyPL7tGnDkk3HxoOpQAAAACAifJ6UzpqWR0dspI6cNFla3SW4zjJCxoQ58yZM/J4PAoGg5o9e3a+L2fqBQJy1q6VNfxt4liWrMceI5QCAAAAwISFwiEturd6uKPWkVhHLdtO24GrVGRbhxKKh9GNhFTEveZjOY7U3EwoBQAAAICcSNtRa4wOXIhhyT1GlyakQpIUiRBKAQAAAAB5RkGP0aUJqZAkuVyEUgAAAAAYP9uWurrM22E1Qan+KHld54OCHqMbDqkYsuLOWZbU0cHyFwAAAADjEwjI8fmkFSvk+HwaaG/T0OMdOr5F6uqUKvyLpUAg31dZUAjFy6DUQ/FGQiqWnpSe+uRTqlh2PcU8AAAAgPGxbTk+X8KW3kGZ+cKEvvNut3TsWMnXHNnWoczQI6Mej/TMEily85+X/A8WAAAAgPOQJp+rTEnFvCQNDZHXNQ4U9Mgo6z0tafbDAAAAAEC6fC7H5UrN7HK7yesaBwp6jKnsic6EPS0D7W0KhUMpt4H2tuh+GPl87H0BAAAAEDOczzU4nM/luN2yOjpkdXSYIl4yb9vbWRU8Duyhz6Ck99Cn2+diSZduMMvwR9QEpeNb2PsCAAAAYHQj+Vy1vdKuTUdUuWCh+YBtm2X2tbXUD8OyrUPLpvCaUGjS7XNxpNrexILe3zvG3hd+IAEAAAAM6/GYm+OtiZ30eqkbzhMFPUbn95ue83FFveN2a9emwwk/gJbdI2f74sTin70vAAAAADCp2EOP0Xm9pud83J4Wq71dlQsWqqq8KnqrXLAwZT8Me18AAAAARNm2XD/ao6vsLAO3kRX20GdQ0nvoR2Sxp2XU/TAAAAAASlsgIKe5WVYkIkeSJZNwb3V0SE1N+b66aYk99MidLPe0pN0PAwAAAKB02Xa0mJdMMS/JvN/SIjU0sLJ3AlhyDwAAAACYHGmCtqNGgrRx3ijoAQAAAACTw++X4xql7CRIe8Io6AEAAAAAk8PrTQzQHjlPkHZOsIceAAAAADBpBtc06vIjrartlZ5fu0cV5yJjBm4jexT0mDjbluvwf+gqW6o+N9yCgpR7AAAAAMNGArQjV18plVfl+3KKBgU9Jma4BUVFJKL9Gm5BsX2x6V9PCwoAAAAAmDTsocf5y9SCwrbzd20AAAAAUOQo6HH+aEEBAAAAAHlDQY/zRwsKAAAAAPFsW+rqSlmtWxOU6o8O520hZyjocf5oQQEAAABgRCAgx+eTVqyQ4/NpoL1NoXBIQ4936PgWqatTqvAvlgKBfF9p0bAcx3EyDytdZ86ckcfjUTAY1OzZs/N9OdNOKBzSonuraUEBAAAAlDLbluPzJWzJHbSkpU3SvoDkjq863W7p2DFqhjFkW4eSco8JowUFAAAAUOLS5GuVOVLdiaRiXorlbVHQTxhL7gEAAAAAE5MmX8txu7V5057U3C3ytnKGgh4TNmbAxSihGAAAAACKSHK+ltstq71dFXXLZHV0mCJeIm8rx9hDnwF76Mc20N6msnWtcjuS43KZH+I1jZKksic6Vb7+drP0xuWSOjqkpqY8XzEAAACAyRCfr7Vr0xFVLlgY+6Btm2X25G1lJds6lII+Awr6MYwSfHHpBnN8fAvhFwAAAECpCIVDqt5cLUnq29inKvK1zlu2dShL7nH+Rgm+qO2V/P9fe3cfHeVd5///dc2kCbmB2VYWMJl2SpoJxUV3ldIWoZCiS1xbPX499mhvMMUISAMtW3VLVxddv2p0j8flt9W4SZlDI61Wbeue7op+izaLrS2CtF1Bapqe5e6KICp0UiJNSObz++PKDJmbzExCkplr8nycM4cy80lypb06c72vz/vmdJrmFwAAAACAi0aXe4xdMOik0g8L6o3Xq51bDjn/vGN+fMBP8wsAAACgcNi21NXlxAVDWbhVYWdzz7K7peEp95gQ7NBj7Px+py5+WIMLq7VVZdW1KquuTWqKQfMLAAAAoECEQjKBgLRihUwgoL7WFg1ua9PRrVJHu1QanC+FQrk+yoJHDX0G1NBnYYQGF2mbYgAAAABwp1S9tCRZFj20xku2cSgp97h4fv+I/5N2+5yH8VdN8kEBAAAAmBCpemlJ0kg9tAjoJwwp9wAAAACA7AWDMp74UNJ4PEnP0UNr4hHQY0JVhaW6w0NNMYazbamjw/kTAAAAgHv4/Un9sqy2NlkJ/bXooTXxSLnHhCna3h6bRW92zFdfywMaWN2gou3tKr5ro5Om4/E4jfUaG3N9uAAAAACyNLC6QVd1Ng31yzp0oV9WfX3K/lqYGDTFy4CmeGOUqlGGJS1ulPaEaJYBAAAAuFlvf68qmiskSWfvP6vy4vIcH1FhyTYOJeUeEyNVowwjLTmWEMxLF5plAAAAAACyRkCPiREMOun0wxivV81bdtMsAwAAAHCLNL2vRuyXhUlDQI+J4fc7tfHDmmJYra0qXbIsqYEGzTIAAACAPBQKyQQC0ooVMoGA+lpb1Nvfq97+Xg1ua9PRrVJHu1QanC+FQrk+2imJGvoMqKG/SLad1BSjt79X8zZXDDXQ6LzQQAMAAABAfhihJ9aVm5x/jja/jqEv1rjKNg6lyz0mlt+f8n/qbp/zMP6qHBwUAAAAgLRG6IlVc1qylKYvFgH9pCKgR05UhaXg6aF6m8QdetuWurqcOnzeEAAAAIDJFwzKeDxxQb3xerVzyyHnn3fMjw/46YuVE9TQY9JF59NH622G1+L0tbbE6nQUCFCLAwAAAOSC35/U+8pqbVVZda3KqmtlJfTLoi9WblBDnwE19OOMWhwAAADAFTL2vkrRLwvjgxp65CdqcQAAAADXSNv7aoR+WZg8BPSYXNH59NTiAAAAAMBFoYYek2uE+fTRWhxm1AMAAAB5wLbl+e/dusaW6g4PNbNG3qGGPgNq6CfICPU2zKgHAAAAciwUklm7VlYkIiOnNNZ4PE4jvMbGXB/dlEANPfJbmnobZtQDAAAAOWLbsWBecoJ5Sc7f162T6uvJoM0jpNwDAAAAABwpmljHRBtWI28Q0AMAAAAAHMGgjGeEMJGG1XmHgB4AAAAA4PD74xtVR5+nYXVeooYeAAAAABAzsLpBV3U2qea09OM1u1V6PpLUzBr5gYAeeacqLC0+Lnl/8Li0fAVvHAAAAMAkizaqjixaKBWX5/pwMAICeuSVou3tOvavQ7UgjzVIliU9+CDjMQAAAAAgATX0yB+2reL1G+JPSmOc8Ri2naujAgAAAIC8RECP/NHVJcuY5OcZjwEAAAAASQjokT+CQSfFPhHjMQAAAIDxYdtSR0fGDNiqsFR3WLLs7kk6MIwFAT3yh98vPfigzLCg3ng8jMcAAAAAxkMoJBMISCtWyAQC6mttUW9/b9JjcFubjm6VOtql0uB8KRTK9ZFjBJYxqXKcEdXT0yOfz6dwOKwZM2bk+nCmhD//7ytquG+eJKn9q50qq67N8REBAAAALmfbMoGArEgk9tSAJV25yelmH1UVlo5ulbzDo0SvVzpyhE22SZRtHMoOPfKO8VfpsQXSYwucfwYAAABwkbq64oJ5SSoyUs3p+GXB0wnBvERPqzzG2Drkpaqw82Zi2d1S4g69bUtdXU7NPXcJAQAAgMyCQRmPJy6oN16vdm45FLeJZtndMjvmxwf/9LTKW+zQI+8UbW+Pq9kZXtvT19oSq/tRIEA9DwAAAJANv1/9LQ9oYKhdlfF6ZbW2qqy6VuXF5bFHWXWtrLY2J4iXnD/paZW3qKHPgBr6SZamtkeingcAAAAYq97+Xs3bXKGa09LOLRl6Vdm2k2ZfU8O1dg5kG4eSco/8kqa2x1Kaeh7eZAAAAIC0LLtbi48719UZ+f1cY7sAKffIL8Gg5Ik/LZ3ank79aEunM8ZuOOp5AAAAgMxCIZXWXK0fPCZ9/zGptOZqylcLAAE98ovfLyXU7ERre8qqa5PqfqjnAQAAADKwbZk1a2QNq7a2jJHWrXNS6+FapNwj/zQ2SvX1KWt2BlY36KrOpqG6n0PMqAcAAAAy6eqKC+ZjKF91PQJ65Kc0NTvdPufBjHoAAAAgC8GgjGUlB/WUr7oeKfcAAAAAUMj8fvV/6xsaHP6cx0P5agEgoIfrVIWlusNOl05JTt1PRwf1PwAAAMAIBlY3KPD30i0fkt545NvS0aNOqStcjYAerlK0vV1Ht0od7VJpcL7Of/QOmUBAWrFCCgTo1AkAAACMoNsnPbZAGvzQB9mZLxAE9HAP21bxXRtjs+itSERFOx65MLc+EqFTJwAAAIApg4Ae7tHVdSF4H2Ilrol26gQAAACAAkdAD/cIBp3mHcMkDd+gUycAAACAKYKAHu7h90ttbU7QLklerwZW3a6BoW164/XSqRMAAABTUxaNopOaS8P1mEMPd2lslOrrnbT6mhr1z7pU82Y+oprT0s4th1RWXZvrIwQAAAAmVygks3atrEhExuNRf8sDGljdELdkcFubjm6VvEYyO+Y7G2V0uXc9yxiTlLWMC3p6euTz+RQOhzVjxoxcHw4S9Pb3qqK5QpJ09v6zKi8uz/ERAQAAAJPItmUCgbheUwOWdOUmp6u95OzMR4P5GK9XOnKE7NY8lW0cSso9XC+r1CFm1QMAAKAQpWgcXWSkmtMX/h48nRDMSzSTLhCk3MPVonPpo6lDfSnSi4q2t6v4ro3OG53HQ3oRAAAACkcwKOPxxAX1xuvVzi2HZPxVkpyNL7NjfnzgTzPpgkDKfQak3Ocx0osAAAAA9bW2yLu+SUXGCeat1tbkDaxQSFq3ztmZjzaTZpMrb2Ubh7JDD/dKk14UDejTphcR0AMAAKAADKxu0FWdTekbRSc0l+ZauDAQ0MO9onPpSS8CAADAFPfm16W3n5CsEyelkSY/+f0E8gWGpnhwrxRz6a3WVpVV16q8uFzlxeUqq651xnYwqx4AAAAFqvjj67R3m/SvT0nTli6X7rwz14eESUINfQbU0LuAbadNHert79W8zRVDKUidzKoHAABA4di3T+baa2UlPr93r7RoUS6OCOOAGnpMHVmkDnX7nEc0FR8AAAAoCM88kxzMS9IvfkFAPwWQcg8AAAAAbnXDDUqZcr1kyWQfCXKAgB4AAAAA3GrRIg2suj0W1BtJamhgd36KIKAHAAAAABfr39aqaz8ubaqX3nh2t/TQQ7k+JEwSAnoAAAAAcLlf+aX/b7EUWbQw14eCSURADwAAAACAC9HlHgXPsrt1y0GnnsiyuyXG1gEAAAAoAOzQo7CFQiqtuVrff0z6wWNSac3VUiiU66MCAAAAgItGQI/CZdsya9fKMhcGeVjGSGvXSradwwMDAAAAgItHQI/C1dUlKxJJfj4SkV59dfKPBwAAAADGEQE9ClcwKONJcYp7PFJNzeQfDwAAADAati11dGSVXVoVluoOD/WMwpRBQI/C5ferv+UBDQ5/zrKktjbJ70/9NaN40wQAAAAmTCgkEwhIK1bIBALqa21Rb39vysfgtjYd3Sp1tEulwfn0jJpC6HKPwmdJMpKxLPV/+f9qYNVHpP7epGVF29tVfNdGJ03f43EC/8bGyT9eAAAATG3RXlBD5aNWJCLv+iZd1dmkbl/80qqwdHSr5B1qG2VFItK6dVJ9/cibWCgYBPQoXLbtBOjRNzdj5P3Hz+qqU58d8Y0wula8EQIAACBXUvSCKjJSzWklXccGT18I5mMGB52eUVzHFjwCehQu3ggBAADgRkO9oIZfyxqvVzu3HJLxV8UttexumR3z4697vV56Rk0RBPQoXMGgkzrPGyEAAADynW1LXV3ONexQLyjv+iYVGTnBfWuryqprk7+uutYpFV23ztmQ8nql1lY2paYImuKhcPn9zpub1+v83euNvRGWF5fHPcqqa9Xf8oAGLGep4Y0QAAAAkyVFA7y+wb4L5aAmMZU0QWOjdOSI09z5yBH6QE0hljGZzo6praenRz6fT+FwWDNmzMj14WAsbNtJna+pSRug9/b3at7mCtWclnZu6Ux9BxQAAAAYT7YtEwjEZYoOyBnOFFcS6vU6wTobTlNCtnEoKfcofH5/1m983T7nkZiSDwAAAEyIVH2fJIn+TsgCKfcAAAAAkCtDDfCGMx5P0nP0d0IqBPQAAAAAkCtDDfCG93Ky2tpkJfSCor8TUiHlHgAAAAByaGB1g67qbBrq5XToQi+n+vqsekFh6iKgBwAAAIAcS9nLaRS9oDA1EdADw1SFpeBpybNvv9Q/GJsDCgAAAAD5hoAeGFK0vV1HtzrjQUz7cudJj8eZZc8sTwAAAAB5hqZ4gCTZtorv2hib9WlFn49EpHXrnFn2AAAAwHixbamjI3adWRWW6g5Llt2d4wODmxDQA1LK+Z8x0ZmfAAAAwHgIhWQCAWnFCplAQJ6PfUxHt0od7VJpcL4UCuX6COESljHG5Pog8llPT498Pp/C4bBmzJiR68PBRLFtKRBwduQTeb3SkSPU0gMAAODi2bZMIBC3mWQ0LENU4voTWceh7NADkvNmOWzWZ/Qul2HmJwAAAMZTisxQK3ENGaLIEk3xgKjGRqm+XudePqBl33+vys8nzAEFAAAALlYwKOPxZN6hr6mZ7CODC7FDDwzn9yuyfJl+5Zd2z02YAwoAAABcLL9f/S0PaGAogjder6yGhlimqMgQxSgQ0AMpZOwymtCVFAAAAMjWwOoGXblJqmuQzr1ySHroIadmvqPD+ZORycjSuAf0zc3NWrRokaZPn65Zs2bpAx/4gDo7O+PWGGP0+c9/XpWVlSotLVVdXZ1+85vfxK3p6+vTxo0bNXPmTJWXl+v973+/7ITg6cyZM1q1apV8Pp98Pp9WrVql1157LW7NsWPH9L73vU/l5eWaOXOm7r77bvX394/3r40CEp1HH+0y2tfaot7+3tijr7Ul1pVUgQBdSAEAADBq3b6EjFC/X6qrY2ceozLuAf3u3bvV1NSkPXv2aNeuXRoYGNDKlSvV29sbW/Mv//Iv+vrXv65vfOMb2rdvn+bMmaO//du/1euvvx5bs2nTJv3whz/Uo48+qmeffVZnz57VzTffrMHBwdia2267TS+99JJ+8pOf6Cc/+YleeuklrVq1Kvb64OCgbrrpJvX29urZZ5/Vo48+qscff1yf/OQnx/vXRqFInEcfici7vknzNleoorlC8zZXqGh904WaJ+bUAwAAAMiRCR9b94c//EGzZs3S7t27tWzZMhljVFlZqU2bNum+++6T5OzGz549W1/96le1bt06hcNh/eVf/qV27NihD3/4w5Kk3/3ud7r88su1c+dO1dfX6+WXX9Zb3vIW7dmzR9ddd50kac+ePVq8eLF++9vfat68efrxj3+sm2++WcePH1dlZaUk6dFHH9Wdd96pU6dOZTWGjrF1U0xHh7PznqCuwbmDWnfY2blP+XV1dRN+eAAAAHC/3v5eVTRXSJLO3n9W5cXlOT4i5Ju8GVsXDoclSZdddpkk6fDhwzp58qRWrlwZW1NSUqLly5frueeekyTt379f58+fj1tTWVmpBQsWxNY8//zz8vl8sWBekq6//nr5fL64NQsWLIgF85JUX1+vvr4+7d+/P+Xx9vX1qaenJ+6BKSQYlDzx/1sYr1c7t3Tq7P1n9aMtnTIJr9OFFAAAACMaofdSxp5NQBYmNKA3xujee+/V0qVLtWDBAknSyZMnJUmzZ8+OWzt79uzYaydPnlRxcbEuvfTStGtmzZqV9DNnzZoVtybx51x66aUqLi6OrUnU3Nwcq8n3+Xy6/PLLR/trw80S5tHL65XV2qqy6lqVF5errLo2qSspXUgBAACQUigU671kAoFYb6bBbW1xPZvoyYSxmtCAfsOGDfr1r3+t7373u0mvWVbcpEUZY5KeS5S4JtX6sawZ7v7771c4HI49jh8/nvaYUIAaG9N2GU3qSkoXUgAAACSybZm1a2O9l6K9meqaKlS+4d64nk30ZMJYTVhAv3HjRj355JPq6OiQf9ju5Zw5cyQpaYf81KlTsd30OXPmqL+/X2fOnEm75ve//33Sz/3DH/4Qtybx55w5c0bnz59P2rmPKikp0YwZM+IemIIydBlN6koKAAAADNfVdaGR8pAiIy05plgwHzM4KL366uQdGwrGuAf0xhht2LBBTzzxhJ5++mnNnTs37vW5c+dqzpw52rVrV+y5/v5+7d69W+985zslSQsXLtQll1wSt+bEiRM6ePBgbM3ixYsVDoe1d+/e2Jpf/vKXCofDcWsOHjyoEydOxNY89dRTKikp0cKFC8f7VwcAAAAARzCY1HvJeL1q3rKbnkwYN+Me0Dc1Nenhhx/Wd77zHU2fPl0nT57UyZMnde7cOUlOCvymTZv05S9/WT/84Q918OBB3XnnnSorK9Ntt90mSfL5fGpsbNQnP/lJ/exnP9OLL76oO+64Q29961v17ne/W5I0f/58vec979GaNWu0Z88e7dmzR2vWrNHNN9+sefPmSZJWrlypt7zlLVq1apVefPFF/exnP9OnPvUprVmzhp13AAAAABPH70/qvWS1tqp0yTJZCT2b6MmEsRr3sXUj1aZv375dd955pyRnF/+f//mf1draqjNnzui6667TN7/5zVjjPEl644039OlPf1rf+c53dO7cOb3rXe9SS0tLXJO606dP6+6779aTTz4pSXr/+9+vb3zjG/qLv/iL2Jpjx47prrvu0tNPP63S0lLddttt+trXvqaSkpKsfh/G1iFRb3+v5m2uUPC09KMtnSqrrnVqnrq6nC75vBkDAABAF64ba05LO6PXjVG27aTZ19Rw/Ygk2cahEz6H3u0I6JGor7VFReub5DWS8Xg0cPutKnrku06NlMfjdMmnUR4AAMCUx7x5jFXezKEHCoptq/iujXFdSYt2PHKh4QldSgEAAABMEgJ6YDRSdCtNKjKhSykAAMDUYtvOyOMUmzpVYanusGTZ3Tk4MBQ6AnpgNIJBJ61+mKSalUxdStO84QMAAMBlQiGZQEBasUImEFBfa4t6+3vV29+rwW1tOrpV6miXSoPzpVAo10eLAkNAD4yG3+/UyA/rSjp4/XWxoN5IOn/bR9Q769LYG/nwR19rS+wNX4EAb+oAAABuZtsya9fGMjitSETe9U2at7lC8zZXqHzDvXGlmpRmYrwV5foAANdpbJTq6520+vJyea+/PpZ2b0myHn5E82Y+om5f/JdVhaWjWyUrGv1H39Tr6+lsCgAA4EYpyjGLjFRz2rku9CamckZLM7n2wzhhhx4YC79fqquTzp4d8U08UfB0mjd1AAAAuE8wKJNYjun1aueWTv1oS2fSaxlLM4FRYoceuBjRmvphQb3zJn5Ixl8Vt9Syu2V2zI+/AcCbOgAAgHv5/epveUDe9U0qMs51oNXaemHefFubk5E5OOhc97W2sjuPccUOPXAxUtTUR9/Ey4vL4x5l1bXqb3lAA0P5+YY3dQAAANcbWN2gKzdJdQ3SuVcOOeWZUY2N0pEjTkPkI0fiXwPGgWWMSWrSjQt6enrk8/kUDoc1Y8aMXB8O8pVtO6nzNTVpA/Te/l7N21yhmtPSzi2dF+7eAgAAwJV6+3tV0VwhSTp7/1mVF5fn+IhQCLKNQ0m5B8aD35/1Tnu3z3kkpuQDAAAgD9m21NXllFqOcL1XFXb6JVl2t8SGDSYRKfcAAAAAkEqaGfPMmkc+YIcemGRp7+BmcQcYAAAAk2CEGfNXdTbFxhNHxxInzZpnLDEmCTv0wCQq2t4edwd3+F3evtaW2B1gBQLc3QUAAMilNDPmoxhLjFyjKV4GNMXDuLFtmUAg7oNhwJKu3OT88/C7u5KczvlHjnB3FwAAIBdSXLsZr1fnXrkwntiyu1UaTDGWmGs4XKRs41B26IHJkuYuL3d3AQAA8szQjPnhI4cTxxOXVdfKShhhzFhiTCZ26DNghx7jxradVPoUd3klcXcXAAAgz2Q9cjjLEcZAttihB/KN3y8l3MGN3uUtq65NugPM3V0AAIBJYttSR4fzZwpWpq/3+6W6Oq7dMOnYoc+AHXqMuxHu4GZ9BxgAAADjJxSKdbM3Ho+zybK6QZI0uK1N5RvulddIxuNx0usbG3N8wJgKso1DCegzIKDHZOnt71VFc4Uk6ez9Z1VeXJ7jIwIAAChwNC1GniLlHgAAAADSoWkxXK4o1wcA4IKqsPPhYdndUjTl3ralri4pGORuMAAAwHgKBp1U+oSmxTu3OE2LzY4UTYtraib7KIERsUMP5Imi7e06ulXqaHc63ve1tqivtUUmEJBWrHA65IdCuT5MAACAwpFmNB0j6eAG1NBnQA09JkWq+i1JlkXdFgAAwETK2JiYkXTIgWzjUFLugXyQqn5Lkkaq2+LDBAAAYNx0+5yH8Vclv+j3c+2FvEXKPZAPgkHJE/+/o/F4ZBKeo24LAAAgCxnmyieqCkt1h4f6GAEuQkAP5AO/X0qo0bLa2pJquqjbAgAAyCAUivUgMoGA+lpb1NvfO+JjcFtbXB8jehbBTaihz4AaekyqhBqtjDVdAAAAuDAVqKJC5vrrU86V7/Ylf1lVmFnzyE/U0ANuNEKNlpXuaxhrBwAAprJQSGbtWlmRiIxlyUrYr4zOlU8V0KedNc91FVyAgB7IY9FRdl7jzEHta3lAA6sb4l4vvmujcxfa43HS9hsbc3fAAAAAk8m2Y8G8JFnGyCh+M8RI+vGa3YosWpj05Zbdzax5uBoBPZCvbNsJ1ofuGluRiLzrm3RVZ5O6fRdSxKKvKxKR1q2T6uu5owwAAKaGFJOCEjMbLUml5yNScXny11fXOhsi69Y5O/P0LILLENAD+SrVKLthKWOkiAEAgCkvGJTxeOKumRJ36DPuuDc2OhsizJqHC9HlHshXqUbZeb3auaVTZ+8/qx9t6WSsHQAAmNr8/qSpQFZDQ9zkoKx23P1+qa6OYB6uQ0AP5KtUo+xaW1VWXavy4nKVVdc6H2BDy43Hk/4Da5TzWAEAANxgYHWDrtwk1TVI5145JD30kNOlvqPD+ZP+QihgpNwD+SyLFDDLkpNbJqlvoE8D/b1Ja2ieBwAAClm3z3kYf5XzxAiTg4BCwxz6DJhDj7xl2zKBQMY5q8xXBQAAhay3v1cVzRWSpLP3n1V5quZ3gMtkG4eScg+4VZqmecOlbZ4HAAAAwLVIuQfcKto0b3hXV69XO7ccupBuJuarAgAAAIWKHXrArTI0zYs+Ys3zhnV/Zb4qAAAA4H7s0ANuluXc1IHVDbqqs0k1p6WdWw6prLp2kg8UAABglGxb6upyshIzbERUhZ0yQ8vulrjOwRRCQA+4XZZdXJO6vwIAAOSrUEhm7VpZkYiMx+NkG65uSLl0cFtbrAGw2TGfaT6YUgjogSki7Z3rUdwBBwAAmFC2HQvmJcmKRORd36SrOpviJvlIydN8rEhEWrfOyWDkmgZTADX0wBRQtL1dR7dKHe1SaXC++lpb1Nvfq97+XvW1tsgEAtKKFVIgIIVCuT5cAAAwldi21NHh/CllPclHYpoPwBz6DJhDD9dLM69eYkY9AADIoRSp9YN/+26V1lwta1iYYjwenet6Oal00LK7VRpMMc2Haxm4HHPoATjS3OXmrjYAAMiZEVLrlzfPU+Keo2WMyorL4ib5RKf5WAlTf5jmg6mEGnqg0KWZVy9p9DPqqbcHAADjYYRNhyXHUuw6GuNsOKS69shy6g9QiNihBwpdmnn1ZdW1Grj9VkXvgRtJ52/7iHpnXRqrsR/+oN4eAACMm2BQxhMfjhivV81bdic9n3HDwe+X6uoI5jHlUEOfATX0KBi2nXznOk19faYuspKoUQMAABelr7VF3vVNKjJOMG+1tjo77qGQ061+cPBCGj2j6DCFZBuHknIPTBWp5tWnqa9PDOjT1tsT0AMAgDEYWN2gqzqbVHNa2rnlkMqio3VJoweyQkAPTGVp6utTdZEddb09AABABt0+55F47ZFyMwJAHGrogaksTX19qi6y/S0PaMBylhq6yAIAAAA5xQ49MNWNIqVtxLQ4AAAAAJOOgB7AqFLaRkyLAwAASCXDyNuqsNOrx7K7JTYLgFEh5R4AAADAxAiFYiNvTSCgvtaWuJG4g9vadHSr1NEulQbnMxIXGCV26AEAAACMP9uWWbs21lDXikTkXd+kqzqb1O1LHolrRSLOqLr6enr0AFlihx4AAADA+EszHlfKMBIXQFbYoQcAAAAw/oJBGY8nLqgfPh6XkbjAxWOHHgAAAMD48/uTRt4OH49bVl0rK2F8LiNxgdFhhx7AqMR1oi0uS9u1FgAATG0ZR96OYnwugGQE9ACyVrS9Pda8xnz7ahlJljGSxyO1tTkfygAAYOrIMJIuykr3PUYxPhdAPFLuAWTHtlV818YLnWiNcYJ5SYp2pbXt3B0fAACYXBlG0jGWDph4ljEmsbckhunp6ZHP51M4HNaMGTNyfThA7nR0SCtWZF5TV5f6tSzv4AMAABewbZlAIK6h3YAlXblJ6vY5f08cSyfJqZM/coRrASCDbONQdugBZCcYdFLrR2AsS3++ojLpznxvf6/6Wltid/AVCHB3HgAAt8swkk5iLB0wGdihz4AdemCYUMhJrR8cdOrnh700aEmBTRfuykdxdx4AgAKUYofeeL0694ozkk5yGuiWBlOMpeMaAMiIHXoA46+x0fkQ/vrXk5rbeBPuykdxdx4AAJezbaesbnivnMSRdB5P3Eg6xtIBk4Mu9wBGx++XbrlF+tSnnGZ4Q4zXq51bLtyVj7LsbpkdKe7O19RM1hEDAICxCoVk1q6VFYnIeDxOEL+6QZI0ONin8uhN+5GSfhlLB0woUu4zIOUeGMGw9PvYHfcRxtb1tbbIu75JRcYJ/K00awEAQJ5I0/hOoqQOmEjZxqHs0AMYm1HccR9Y3aCrOptUc1raueWQyqprJ/FAAQDAmKRpfGcpTUkdAT0waQjoAYyd3z+qD+3Euvs4jLUDACC/BINObXyKEjtJlNQBeYCmeAAmXNH2dh3dKnW0S6XB+eprbWGsHQAA+S6x8d1Q2VxZdS0N74A8QQ19BtTQAxcpTf1dt4+xdgAA5LPe/l7N21wxVDbXmVw2Z9s0vAMmADX0APJDmvq7bl+GsXZcGAAAkHPdPueROMlG0qjL7wCMLwJ6ABMrGJQ8nhFH3DHWDgCAHMmyf01V2LkBb9ndEo1tgbxCDT2AieX3Swk1dtH6u/LicpVV1ybV51GDBwDABAuFYv1rTCCQ1N8m+hjc1hbXB4c+N0B+oYY+A2rogXGSpsYuY30eAAAYPxn620TR5wbIHWroAeSXLGrsGGsHAMAkyNDfJoo+N0D+I6AHkHPRsXZe48y07Wt5QAOrG+JeL75ro3Px4fE4KfyNjbk7YAAA3CzNfPnhje/ocwPkP1LuMyDlHphgEzXWjh19AMBUlOXnX19ri7zrm1Rk5AT3I90sD4WkdeucnflonxtuqgMTjpR7AO4wxrF2514+oMisS1N+S3b0AQBTUigks3atrEhExuNxms4Oy3gbbnCwT+XRz9d0+3uNjVJ9PbPmgTzFDn0G7NADE8y2pUAgaazduVectD/Pvv2atnR5XH29kXTtx6VfpbimoIEPAGBKyrLRncRnJeAG2cahjK0DkFsZxtqV9g8mNcuzJJWfT/3t0jbwAQDAzWxb6uhw/kyUJuMtEZ+VQOFghz4DduiBSTLSWLsMO/iJLLtbpcEUDXzYdQAAuFmGdHrL7lZpzdWyhl3aG49H57peTvq85LMSyH/U0ANwl5HG2kV38Ic15Inu4KdUXau+lgcuNPoZWs8FCgDAtWw7FsxLkhWJyLu+SVd1NsXS6avC0jETPwLWMkZlxWVScXn896uuTfpsFZ+VgCsR0APIf6NsyDOwukFXdTap5rS0c8uhkYN/AADcIIu58cHTKWppjRl5ZjzN7oCCQEAPwB1G2sEfQbfPeaRKywcAwFWymBs/ppnxo/xsBZB/aIoHAAAA5DO/36mZH8qnNwkNZMuLy1VWXevMkh/WZJY0eqDwEdADKEhVYanusLNjESddh2AAAHIpzWfUwOoGLW6UNtVLb+x+2kmZT9TY6DS26+hw/ky1BkBBIaAHUHCKtrfr6Fapo10qDc5XX2uLevt71dfaIhMISCtWOJ3zQ6FcHyoAAI5QKPYZZQKB2GdX9DG4rU17QtLW/ydNW3bjyJ9hfr9UV8fOPDBFMLYuA8bWAS5j2zKBQFwN4YAlLW6U9oQS5u4yogcAkA9G+Oy6cpPTD6YqLB3dymcYMJVkG4eyQw+gsIzQCXjJsYQLIckZ1fPqq5N3bAAApJKmi73kdLDnMwxAKgT0AApLMCh54t/ajNer5i27ZRKez9j9FwCAyTDUxX44p4t9p87ef1Y/2tLJZxiAlAjoARQWv19K6PJrtbaqdMmypA7BdP8FAOSFDF3s6WAPYCTU0GdADT3gUrbtpCLW1MQueHr7ezVvc4VqTks7t3SqrLo2xwcJAIAjq8+oFJ9tAApTtnFo0SQeEwBMHr9/xIsdK9PX2rbU1eWk73PBBAAYL1l8vqT9jErz2QZgaiLlHsCUMdI4u+EPRtsBACZEFmPphn9G8fkDIBuk3GdAyj1QIDKMBJLGOBaI3XwAQCaMpQMwSoytA4DhMowEkkYeC3Tu5QNJO/ns5gMAssZYOgAThB36DNihBwqEbTtB97ALKuP16twrh2T8VZIkz779mrZ0eVz9opF07celXyVskLCbAgDIWood+uGfQZbdrdLg/Pign88UYEpjhx4AhhthnF10JFB5cblK+weTmhFZksrPJ387dlMAAFljLB2ACcIOfQbs0AMFJt3Inyx28aNGvZtCrT0AFKYs3997+3tV11ShJcel5n/ardIly1J/L8bSARA79ACQmt8v1dWlvlDKYhc/+iirrtXA7bcqekfUSDp/20fUO+tSau0BYKrI0Lk+sYv9npC09f9J05bdmPqzIN1nFACkwA59BuzQA1NQNjskWXTNl6i1B4CCleXngMRnAYDRY4ceAMYqmx2SLLrmS9TaA4Dr2bbU0eH8OVyWnwMSnwUAJk5Rrg8AAFwpGJQ8nqR6+51b4uvtLbtbZkeKWvuamsk8WgDAWIRCMmvXyopEZDwep7Hd6gZJkhWoUqllyRqW7Go8Hu3c8nLKvit8FgCYCOzQA8BYZFlvX1Zdm9TZmM7FAOACth0L5iXJikTkXd+keZsrVNFcodpvzFNi5apljMqKy1L2XaGLPYCJwA49AIxVY6NUX5+x3n5gdYOu6mxSzWlp55ZDKquuneQDBQCMWpqU+m6fk0aftDNmjPOZkOrzIMvPDAAYDQJ6ALgYfn9WF2XdPueRmIYJAMhTwaCMxxMX1A8vrRpTGn2WnxkAkC1S7gEg10ZquDRe6wEAo+f3J5VMDS+tIo0eQD4goAeASVAVluoOO42R4oxihjEz7QFgcg2sbtCVm6S6BuncK4ectPnhGhud0XMdHc6fia8DwAQj5R4AJljR9vbY/GGzY776hrokW3a3StesiXVItiIRFa1v0lWdTUkzjKULc4ytaA+mSERat86pyWRHCAAusG2pq8uZSJL4/rhvn/TMM9INN0iLFmX17ax0L5JGDyCH2KEHgIlk2yq+a2Ns/vDwLsl33jcvbtyR5AT9i4+n/lbMMQaALKTJfDr/0Ttkrr1W+uQnZa69Vuc/ekfarKjBbW06ulXqaJdKg/PJigKQdyyTOG8DcXp6euTz+RQOhzVjxoxcHw4At+nocNLjE9Q1SH/ZK/3gseQvOffItxX50AeTnrfsbpUGUzRgOnKE3SEAkJxRc4FA3PvkgCVduUl68+vS3m3xu+1G0rUfl36V4i00mhUVdyOV91wAkyTbOJQdegCYSMGg5Il/q3W6JHeq/audMlZCIqdlqXTZjUkzjJlpDwBZSDNqbumx5NR5S9ISsqIAuBg79BmwQw/gooVCTq374OCFLsjRxkmhkLR2rVMP7/FIbW1pmyr19veqrqlCS45JzVt2q3TJstQL09WPAkChSrFDb7xenXvlkKwTJzVt6fKkHfo3nt2tyKKFSd+KrCgAucQOPQDki3RdkBsbpaNHndeOHs3YIbloe7v2hKStT0nTlt2Ysis+nfABTFlpRs2VLlkmq6EhbrnV0KDSJctGzIpiLB2AfMcOfQbs0APIG2lqQ6Nd8an5BDDV9fb3at7mCtWclnZu6VRZdW38gn37pF/8QlqyJLsu97btpNnX1PA+CmDSZBuHMrYOANwiTW1oNKBPW/PJhSiAKaLb5zyMvyr5xUWLsh5XJ4mxdADyGgE9ALhFtMFeQm3ozi2HYhetlt0tsyNFzWdNzWQfLQAAACYYNfQA4BZ+v9M0b1g9Z7Q2NKkT/tCXGI9n5JpP23Zq92170n4FAAAAjB8CegBwk3QN9oYZPg2vb6CPxnkA8tNobizu2yd9/evOnxlUhaW6w07WEgAUMpriZUBTPACuQuM8APkgm9GZoZDM2rWyIhEZj8fJLlrdkHJp8cfXqWjHI7LkjJobWHW7+re1plw7uK1N5Rvuldc4WUpWhnGgAJCPaIoHAFPRRDTOY6Y9gNHIIlC37G6Vrlkja2hfyYpEVLS+SVd1NsXeq6KusaW9OxSbH29JKtrxiN5Z8oh+lfCWlHjD0opEpHXrpPp63r8AFCQCegAoJNk2zvv21bELaUkylqVzV1TK9PfGfbui7e0qvmujc1Hs8Tg1/Ox0ARiJbceCeckJqL0pAvVbDkrfT7ix6DXS4uPSYwkB/dJjF4L5KEvSkuNKCuiZ9AFgqiGgB4BCEm2ct26dcxE7rHFeTHGZEq93IzKq/ca8uAvu6E6XFV3MTheATLLIEpKU9B4U9e3/82099KEPxj3n2bdf5qnlcUG9kdT8T7v1pUUL49Yy6QPAVENADwCFprHRCbpffdW5iE0Mvru64nbnJWdHK/GCm50uAEkyleAEg07deposIWko8H48PlNIlqXSZTdKxeXx33PJMqmhQWpvv7C0oUGlS5Yl//zq2qSbmiNO+gCAAkBTvAxoigeg4Ni209k+4YL73CvJF9ylwRQ7XTTPA6amLJvYeT72MU175PuxBnZWQ4P00EMpv5/WrnXei7Ip6dm3T/rFL6QlS6RFi9Ifq22PfFMTAFwg2ziUgD4DAnoABSkUSt7BSnEh3dfaIu/6JhUZJ+i3RlgHoMBlMUFDGsMUDQJvAEiJLvcAgJFlSssfMrC6QUt/1aQlx6TmLU+nTnGNohs+ULiyrI0fdamO38/7BQBcBAJ6AJiqsriQLtrerj0h5wLd/PRG9Y2QYks3fMDlxrM2nqZ0ADBpSLnPgJR7AFPWRKbYspMP5I+JqI3PoqQHADAyUu4BABfnIlNsz718QJFZl8Z/PTv5QH7Jcm58VVg6+p0L8+AtSXr4YemLX0y+MZdlSQ8A4OIR0AMAUgsGnaA7Q4qtZ99+mfbkGdHLvv9e/eq5C8+Naa79vn3SM89IN9yQXVdrdv6B0aE2HgBcjYAeAJCa3580z9lqbVVZdW38uv7BpC+1JJWfj39uNDv5klT88XUq2vFILL13YNXt6t/WmvJQ2fkHRkBtPAAUNGroM6CGHsCUl2msVJZz7T379mva0uSd/Gs/Lv0q4dteY0t7tymrtaOu4QemCmrjAcC1qKEHAIyPTKmz47yTL0lLj8UH89G1S44nB/SjTgUGpgJq4wFgSiCgBwBcvGwu9LOsyZeG6vKfSt7Nb/6n3frSooVxa0kFBlKgNh4ApgQCegDA+BivnXxJWrJMamiQ2ttjT1kNDSpdsix5bXWt+loekHd9k4qMc5PAam0l6EBhozYeACBq6DOihh4Axlmmmvzh9u2TfvELacmStF3ue/t7NW9zhWpOSzu3dKa+SQAUCmrjAaDgZRuHEtBnQEAPAPmvt79XFc0VkqSz959VeXF5jo8ImCC2LRMIxO2oD1jSlZuUXBu/dRTNIkdzow0AMOFoigcAAOA2mVLpqY0HAAxDQA8AKAhVYSeIsexuKVXKfaZACRiN0ZxP2a7NIpXeClSp1LJkDUuwNB6Pdm55mdp4AJiCCOgBAK5XtL09ll5sdsxXX0IgVLS9XcV3bXSCG4/Hac5HjTDGKssadmkU594oxswdM/FjHS1jVFZcJg0vNamuTWpCKZpFAkDBoYY+A2roASDPZagpHnUt8dD3ZDd/isr03z7LGnZplOdeR4e0YkXSj6trkHbPHfb3w1JHe9Iy5+vr6lL/PtTGA4DrUEMPAJgaMtQUj1RLfO7lA4rMujTp2xVtb1fx+g1OSrNlSQ8+OPJuPoG/e2Tz3yqLnXfPoV+rNIsadml0596EpdJTGw8ABY0d+gzYoQeAPGfbUiAgJczbPveKM2/bs2+/pi1dHpeibCRd+3HpVwlxTlVYOvavkmf4WsvSuVd/GxdUSaTxu0o2tel2t0prro4LqActKbApPlC/xpb2blPS+fTGs7sVWbQw7nte7Lkny5KOHUsOyBkzBwAFjx16AMDU4Pcn1Qpbra0XZtH3DyZ9iSWp/Hzyt1p8PCGgklOf3HDfPD224MJz0VRqKxr7RSLOz6+vZzc032RZm37LQen7CVscXuOcE48NW1dxPj6Yl5y/l56PxNewS6M694Knk889GZO6K31jo3OukUoPAFMeAT0AwP3SBTjBoLODnrCDv3PLoaRdd+8PHpceS93cbLhRjwRD7mQ55m2kdMVv/59v66EPfTD291GlvI/i3COVHgAwFgT0AIDCMFKAk2kHf7jlK5w054Q65vavvqyHLnYk2L590jPPSDfcIC1alP53yXZtIdbwj/c4uGBQxuOJ+2+VKqi27G6Zx+NT7mVZKl1249i7x4/m3KMrPQBgDKihz4AaegAoENl2+86yPvn8R+9Q0Y5HZMnZ3R1Ydbv6t7Wm/JbFH1837mtH1bwv+vuPd/A/3sH3RIyDk9TX2iLv+iYVGSeYt0aqOQ+FpLVrnR31TH0RRtM9fqLWAgAKVrZxKAF9BgT0ADAFZQqqRjG6bKQmaqkao2W7djTN+6RRNvDLNkgfbfCd6ebDRI2Dk9Tb36u6pgotOS41/9NulS5ZNvLvRUANAMgDNMUDAGCsMtUnZ1mXLUlLj6VuorbkeHJAn+3abJv3SaNs4JdlkG7Z3SpdsyaWnm5FIipK0Wgu+vOP/euw38sYmTVrdO7GG+JuPkzUODhJGtzWpj0h52vMrhvT39CgNh0A4CLs0GfADj0AIEmGUXnDjTS6bDRjzhLXen/wuKbdkRxo3/IhJQX0dYeljvYUv0NHh1RXF/c7ZbtDfstB6fuPJX/LVD//QwelH2SxdiLHwY1mNx8AgHzADj0AABNlNM3OliyTGhqk9gtRtdXQkDrtO9u1WTbvk4aavX07vtmbsSydu6JSpr839txodsiz7QgvZT85YCLHwTGRAABQqNihz4AdegDAiEZTb71vn/SLX0hLlmTX5T7T2iyb98m2Za64Ii6gH7SkwKb4QH00O+SW3a3SmuSO8Dp2LPnfg21LV1yRdPPhXNfLSV3mS4MpJgek2kkfRYbEqL4vAAB5Its4NLEEDwAAZMvvd9LWswkMFy2SNm3KHMxnu7ax0QlKOzqcP0eqCe/qig+85exY15yOX5Zuh7y8uDzuUVZdK+vBB50Ge5Lz54MPjjy67cEHnSBacrIZ2tpUVl2b/D3b2uLWZRwHN/x7DmVIpDzWbL8vAAAuww59BuzQAwBcLcvd7DHtZE/EODbGwQEAwNi68UJADwBwvWzT87NdBwAAJhQB/TghoAcAFISJ2CEHAAATgi73AADggmznqzOHHQAA16ApHgAAAAAALkRADwAAAACACxHQAwAAAADgQgT0AAAAAAC4EAE9AAAAAAAuREAPAAAAAIALTYmAvqWlRXPnztW0adO0cOFCPfPMM7k+JAAAAAAALkrBB/Tf+973tGnTJn3mM5/Riy++qBtuuEF/93d/p2PHjuX60AAAAAAAGDPLGGNyfRAT6brrrtM73vEOfetb34o9N3/+fH3gAx9Qc3Nz0vq+vj719fXF/t7T06PLL79c4XBYM2bMmJRjBgAAAABMXT09PfL5fBnj0ILeoe/v79f+/fu1cuXKuOdXrlyp5557LuXXNDc3y+fzxR6XX375ZBwqAAAAAACjUtAB/R//+EcNDg5q9uzZcc/Pnj1bJ0+eTPk1999/v8LhcOxx/PjxyThUAAAAAABGpSjXBzAZLMuK+7sxJum5qJKSEpWUlEzGYQEAAAAAMGYFvUM/c+ZMeb3epN34U6dOJe3aAwAAAADgJgUd0BcXF2vhwoXatWtX3PO7du3SO9/5zhwdFQAAAAAAF6/gU+7vvfderVq1Stdcc40WL16strY2HTt2TJ/4xCdyfWgAAAAAAIxZwQf0H/7wh/WnP/1JX/jCF3TixAktWLBAO3fuVCAQyPWhAQAAAAAwZgU/h/5iZTv/DwAAAACA8cAcegAAAAAAChgBPQAAAAAALkRADwAAAACACxHQAwAAAADgQgT0AAAAAAC4EAE9AAAAAAAuREAPAAAAAIALEdADAAAAAOBCBPQAAAAAALgQAT0AAAAAAC5EQA8AAAAAgAsR0AMAAAAA4EIE9AAAAAAAuBABPQAAAAAALkRADwAAAACACxHQAwAAAADgQkW5PoB8Z4yRJPX09OT4SAAAAAAAU0E0/ozGoyMhoM/g9ddflyRdfvnlOT4SAAAAAMBU8vrrr8vn8434umUyhfxTXCQS0e9+9ztNnz5dlmVN+M/r6enR5ZdfruPHj2vGjBkT/vOA8cK5CzfivIVbce7CrTh34Ua5OG+NMXr99ddVWVkpj2fkSnl26DPweDzy+/2T/nNnzJjBmxxciXMXbsR5C7fi3IVbce7CjSb7vE23Mx9FUzwAAAAAAFyIgB4AAAAAABcioM8zJSUl+tznPqeSkpJcHwowKpy7cCPOW7gV5y7cinMXbpTP5y1N8QAAAAAAcCF26AEAAAAAcCECegAAAAAAXIiAHgAAAAAAFyKgBwAAAADAhQjoAQAAAABwIQL6PNLS0qK5c+dq2rRpWrhwoZ555plcHxIKVHNzsxYtWqTp06dr1qxZ+sAHPqDOzs64NcYYff7zn1dlZaVKS0tVV1en3/zmN3Fr+vr6tHHjRs2cOVPl5eV6//vfL9u249acOXNGq1atks/nk8/n06pVq/Taa6/FrTl27Jje9773qby8XDNnztTdd9+t/v7+CfndUViam5tlWZY2bdoUe45zF/mqu7tbd9xxh970pjeprKxMf/M3f6P9+/fHXufcRT4aGBjQZz/7Wc2dO1elpaWqrq7WF77wBUUikdgazl3k2s9//nO9733vU2VlpSzL0n/8x3/EvZ5v5+iBAwe0fPlylZaWqqqqSl/4whc05uFzBnnh0UcfNZdccol58MEHzaFDh8w999xjysvLzdGjR3N9aChA9fX1Zvv27ebgwYPmpZdeMjfddJO54oorzNmzZ2NrvvKVr5jp06ebxx9/3Bw4cMB8+MMfNm9+85tNT09PbM0nPvEJU1VVZXbt2mVeeOEFc+ONN5q//uu/NgMDA7E173nPe8yCBQvMc889Z5577jmzYMECc/PNN8deHxgYMAsWLDA33nijeeGFF8yuXbtMZWWl2bBhw+T8y4Br7d2711x55ZXmbW97m7nnnntiz3PuIh+dPn3aBAIBc+edd5pf/vKX5vDhw+anP/2pefXVV2NrOHeRj774xS+aN73pTea//uu/zOHDh80PfvADU1FRYbZu3Rpbw7mLXNu5c6f5zGc+Yx5//HEjyfzwhz+Mez2fztFwOGxmz55tPvKRj5gDBw6Yxx9/3EyfPt187WtfG9PvTkCfJ6699lrziU98Iu65q6++2mzevDlHR4Sp5NSpU0aS2b17tzHGmEgkYubMmWO+8pWvxNa88cYbxufzmX//9383xhjz2muvmUsuucQ8+uijsTXd3d3G4/GYn/zkJ8YYYw4dOmQkmT179sTWPP/880aS+e1vf2uMcd6APR6P6e7ujq357ne/a0pKSkw4HJ64Xxqu9vrrr5tgMGh27dplli9fHgvoOXeRr+677z6zdOnSEV/n3EW+uummm8zHPvaxuOc++MEPmjvuuMMYw7mL/JMY0OfbOdrS0mJ8Pp954403Ymuam5tNZWWliUQio/59SbnPA/39/dq/f79WrlwZ9/zKlSv13HPP5eioMJWEw2FJ0mWXXSZJOnz4sE6ePBl3TpaUlGj58uWxc3L//v06f/583JrKykotWLAgtub555+Xz+fTddddF1tz/fXXy+fzxa1ZsGCBKisrY2vq6+vV19cXl4oKDNfU1KSbbrpJ7373u+Oe59xFvnryySd1zTXX6JZbbtGsWbP09re/XQ8++GDsdc5d5KulS5fqZz/7mV555RVJ0v/8z//o2Wef1Xvf+15JnLvIf/l2jj7//PNavny5SkpK4tb87ne/05EjR0b9+xWN+isw7v74xz9qcHBQs2fPjnt+9uzZOnnyZI6OClOFMUb33nuvli5dqgULFkhS7LxLdU4ePXo0tqa4uFiXXnpp0pro1588eVKzZs1K+pmzZs2KW5P4cy699FIVFxdz/iOlRx99VC+88IL27duX9BrnLvLV//7v/+pb3/qW7r33Xv3jP/6j9u7dq7vvvlslJSX66Ec/yrmLvHXfffcpHA7r6quvltfr1eDgoL70pS/p1ltvlcT7LvJfvp2jJ0+e1JVXXpn0c6KvzZ07d1S/HwF9HrEsK+7vxpik54DxtmHDBv3617/Ws88+m/TaWM7JxDWp1o9lDSBJx48f1z333KOnnnpK06ZNG3Ed5y7yTSQS0TXXXKMvf/nLkqS3v/3t+s1vfqNvfetb+uhHPxpbx7mLfPO9731PDz/8sL7zne/or/7qr/TSSy9p06ZNqqysVENDQ2wd5y7yXT6do6mOZaSvzYSU+zwwc+ZMeb3epDuLp06dSrrDA4ynjRs36sknn1RHR4f8fn/s+Tlz5khS2nNyzpw56u/v15kzZ9Ku+f3vf5/0c//whz/ErUn8OWfOnNH58+c5/5Fk//79OnXqlBYuXKiioiIVFRVp9+7d+rd/+zcVFRXF3eEejnMXufbmN79Zb3nLW+Kemz9/vo4dOyaJ913kr09/+tPavHmzPvKRj+itb32rVq1apb//+79Xc3OzJM5d5L98O0dTrTl16pSk5CyCbBDQ54Hi4mItXLhQu3btint+165deuc735mjo0IhM8Zow4YNeuKJJ/T0008npfbMnTtXc+bMiTsn+/v7tXv37tg5uXDhQl1yySVxa06cOKGDBw/G1ixevFjhcFh79+6NrfnlL3+pcDgct+bgwYM6ceJEbM1TTz2lkpISLVy4cPx/ebjau971Lh04cEAvvfRS7HHNNdfo9ttv10svvaTq6mrOXeSlJUuWJI0HfeWVVxQIBCTxvov89ec//1keT3zI4PV6Y2PrOHeR7/LtHF28eLF+/vOfx42ye+qpp1RZWZmUip+VUbfRw4SIjq0LhULm0KFDZtOmTaa8vNwcOXIk14eGArR+/Xrj8/nMf//3f5sTJ07EHn/+859ja77yla8Yn89nnnjiCXPgwAFz6623phzv4ff7zU9/+lPzwgsvmBUrVqQc7/G2t73NPP/88+b55583b33rW1OO93jXu95lXnjhBfPTn/7U+P1+RtAga8O73BvDuYv8tHfvXlNUVGS+9KUvma6uLvPII4+YsrIy8/DDD8fWcO4iHzU0NJiqqqrY2LonnnjCzJw50/zDP/xDbA3nLnLt9ddfNy+++KJ58cUXjSTz9a9/3bz44ouxEeD5dI6+9tprZvbs2ebWW281Bw4cME888YSZMWMGY+sKwTe/+U0TCARMcXGxecc73hEbIQaMN0kpH9u3b4+tiUQi5nOf+5yZM2eOKSkpMcuWLTMHDhyI+z7nzp0zGzZsMJdddpkpLS01N998szl27Fjcmj/96U/m9ttvN9OnTzfTp083t99+uzlz5kzcmqNHj5qbbrrJlJaWmssuu8xs2LAhbpQHkE5iQM+5i3z1n//5n2bBggWmpKTEXH311aatrS3udc5d5KOenh5zzz33mCuuuMJMmzbNVFdXm8985jOmr68vtoZzF7nW0dGR8tq2oaHBGJN/5+ivf/1rc8MNN5iSkhIzZ84c8/nPf35MI+uMMcYyZqgCHwAAAAAAuAY19AAAAAAAuBABPQAAAAAALkRADwAAAACACxHQAwAAAADgQgT0AAAAAAC4EAE9AAAAAAAuREAPAAAAAIALEdADAAAAAOBCBPQAAAAAALgQAT0AAAAAAC5EQA8AAAAAgAv9/wPLHAt3QanYAAAAAElFTkSuQmCC\n", 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" ] @@ -2078,10 +2099,13 @@ } ], "source": [ - "((x1, y1), (x2, y2)) = biggest_rectangle(red_tiles, find_2_corners(red_tiles))\n", - "\n", - "plot_tiles(breadcrumbs(red_tiles), figsize=(12, 12))\n", - "plt.plot([x1, x1, x2, x2, x1], [y1, y2, y2, y1, y1], 'b:');" + "def plot_rect(rect: Rectangle, fmt='b:') -> None:\n", + " \"\"\"Plot a rectangle.\"\"\"\n", + " ((x1, y1), (x2, y2)) = rect\n", + " plt.plot([x1, x1, x2, x2, x1], [y1, y2, y2, y1, y1], fmt)\n", + " \n", + "plot_tiles(red_tiles, figsize=(12, 12))\n", + "plot_rect(biggest_rectangle(red_tiles, find_2_corners(red_tiles)))" ] }, { @@ -2104,8 +2128,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 2.33 s, sys: 166 ms, total: 2.5 s\n", - "Wall time: 1.6 s\n" + "CPU times: user 1.3 s, sys: 139 ms, total: 1.44 s\n", + "Wall time: 547 ms\n" ] }, { @@ -2123,16 +2147,52 @@ "%time tile_area(biggest_rectangle(red_tiles, red_tiles)) == tile_area(biggest_rectangle(red_tiles, find_2_corners(red_tiles)))" ] }, + { + "cell_type": "markdown", + "id": "b7161525-d572-455f-b6d0-cfe9e86405c8", + "metadata": {}, + "source": [ + "Yes, it gets the correct answer, and yes, it is slower.\n", + "\n", + "In my puzzle (and if Eric Wastl is being fair (which he always is), then in everybody's puzzle) there were no adjacent points, and thus no `adjacent_180_tiles`. So here is a test case with five pairs of adjacent points. We can see that `adjacent_180_tiles` coorectly found the ten adjacent points (the magenta \"o\"s), that it correctly did not eliminate obstacles for the adjacent points that do not form 180° U-turns, and that `biggest_rectangle` finds what appears to be the right answer." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "437bd73a-6c8f-4196-8095-3299e5f8ef9d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "points = [(-16, 3), (-16, 24), (-17, 24), (-17, 39), (-17, 32), (-5, 32), (-5, 33), (-17, 33), \n", + " (-17, 39), (30, 39), (30, 38), (41, 38), (41, -9), \n", + " (40, -9), (40, 27), (18, 27), (18, 26), (34, 26), (34, 3), (16, 3), (16, 15), \n", + " (30, 15), (30, 16), (10, 16), (10, -3), (2, -3), (2, 27), (1, 27), (1, 3)]\n", + "\n", + "plot_tiles(points) # Green dots and red lines\n", + "plt.plot(*T(adjacent_180_tiles(points)), 'mo') # The eliminated obstacles are magenta dots\n", + "plot_rect(biggest_rectangle(points)) # Rectangle is blue dotted line" + ] + }, { "cell_type": "markdown", "id": "b4e35203-687a-434e-88f5-0afea9f31c57", "metadata": {}, "source": [ - "Yes, it finds the same maximal-area rectangle, but it takes a lot longer to find it.\n", - "\n", "**Three final remarks**:\n", "1) This was the first puzzle of the year that was **difficult**; we had to work to find an efficient solution.\n", - "2) My solution is **unsatisfying** in that it works for *my* input, and I strongly suspect that it would work for *your* input, because Eric Wastl probably created them all to be similar. But it does not work on every possible input allowed by the rules. \n", + "2) My solution is **unsatisfying** in that it works for *my* input, and I strongly suspect that it would work for *your* input, because Eric Wastl probably created them all to be similar. But it does not work on every possible input (particularly a highly non-convex shape).\n", "3) In retrospect, I could have done the standard edge-edge intersection algorithm for determining if the rectangle intersects the polygon formed by the red tiles. I was scared away from that because the geometry is tricky for general polygon intersection, when the edges can have any slope. But the geometry is actually easy when all lines are axis-aligned." ] }, @@ -2152,7 +2212,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 70, "id": "84cd63a7-4d50-4f14-9807-3cb439f80c88", "metadata": {}, "outputs": [ @@ -2210,7 +2270,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 71, "id": "179e62b1-e4cb-43e4-8dc7-b87a28d21e8d", "metadata": {}, "outputs": [], @@ -2231,17 +2291,17 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 72, "id": "d1368c2f-d792-4353-82e7-b0161ece784f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Puzzle 10.1: .0535 seconds, answer 441 correct" + "Puzzle 10.1: .0840 seconds, answer 441 correct" ] }, - "execution_count": 71, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -2267,7 +2327,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 73, "id": "1d2683fa-5126-4d2a-9dad-6a0e155f3449", "metadata": {}, "outputs": [ @@ -2277,7 +2337,7 @@ "7.181818181818182" ] }, - "execution_count": 72, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" } @@ -2296,7 +2356,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 74, "id": "a714c2c2-d433-41d0-8d1e-01832b35a1a9", "metadata": {}, "outputs": [ @@ -2306,7 +2366,7 @@ "114.81498043610085" ] }, - "execution_count": 73, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -2353,7 +2413,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 75, "id": "713a1503-ae14-445f-91ea-714fcd618ad0", "metadata": {}, "outputs": [], @@ -2383,7 +2443,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 76, "id": "46d0d274-bd4c-44af-83e9-35c791a8e96b", "metadata": {}, "outputs": [ @@ -2393,7 +2453,7 @@ "Puzzle 10.2: .1304 seconds, answer 18559 correct" ] }, - "execution_count": 75, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -2420,7 +2480,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 77, "id": "b9f82fef-612b-48a5-9de1-6ff0b137f7fb", "metadata": {}, "outputs": [ @@ -2430,7 +2490,7 @@ "Counter({-1: 68, 1: 65, 0: 32})" ] }, - "execution_count": 76, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -2449,7 +2509,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 78, "id": "e536441d-64d4-410d-bb40-2343f6e01d88", "metadata": {}, "outputs": [ @@ -2459,7 +2519,7 @@ "Counter({-1: 32, -2: 31, 1: 34, 2: 31, 0: 32, -3: 5})" ] }, - "execution_count": 77, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -2486,7 +2546,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 79, "id": "b72544c8-6069-4310-a6dc-b4acd77981b4", "metadata": {}, "outputs": [], @@ -2513,7 +2573,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 80, "id": "11e17f6a-acba-44c4-b704-7e4ff7471e7e", "metadata": {}, "outputs": [ @@ -2566,7 +2626,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 81, "id": "7540a982-988a-4822-af0d-6581f6f848c6", "metadata": {}, "outputs": [], @@ -2593,7 +2653,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 82, "id": "0c2d68a5-843b-49d6-aff6-23045968207f", "metadata": {}, "outputs": [ @@ -2603,7 +2663,7 @@ "Puzzle 11.1: .0003 seconds, answer 574 correct" ] }, - "execution_count": 81, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -2625,7 +2685,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 83, "id": "0294e044-c7ef-418a-9c02-71615a453002", "metadata": {}, "outputs": [], @@ -2645,17 +2705,17 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 84, "id": "677a97b3-183b-474e-87ba-7db0d6b763d8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Puzzle 11.2: .0017 seconds, answer 306594217920240 correct" + "Puzzle 11.2: .0020 seconds, answer 306594217920240 correct" ] }, - "execution_count": 83, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -2677,7 +2737,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 85, "id": "a1b304e8-339e-4e32-a462-3ba88103c415", "metadata": {}, "outputs": [ @@ -2754,12 +2814,12 @@ "source": [ "### Part 1: How many of the regions can fit all of the presents listed?\n", "\n", - "There have been Tetris-like puzzles in past AoC years. Is this another search problem? If so, will the searches be trivial or difficult? I want to get a feel for it. First, how many regions?" + "There have been Tetris-like puzzles in past AoC years. Is this another search problem? If so, how big will the search trees be? I want to get a feel. First, how many regions?" ] }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 86, "id": "194cbece-0104-4934-b335-13a4e7c720e0", "metadata": {}, "outputs": [ @@ -2769,7 +2829,7 @@ "1000" ] }, - "execution_count": 85, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -2788,7 +2848,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 87, "id": "388d13ab-b5db-47e4-9f33-50f7062424b9", "metadata": {}, "outputs": [ @@ -2798,7 +2858,7 @@ "1822.223" ] }, - "execution_count": 86, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -2817,7 +2877,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 88, "id": "5d982737-cbca-4739-be1c-fa1dce319ef4", "metadata": {}, "outputs": [ @@ -2827,7 +2887,7 @@ "240.488" ] }, - "execution_count": 87, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -2841,12 +2901,12 @@ "id": "af3fe830-aa75-469b-9046-3f36ff3a03e2", "metadata": {}, "source": [ - "Next I want to get a feel for the variation in how tight the packing is. Each present can definitely fit into a 3x3 square, so what's the ratio of the total quantity of presents to the number of 3x3 squares? I'll make a histogram of that ratio, which I'll call the occupancy ratio, for each region:" + "Next I want to get a feel for the variation in how tight the packing is. Each present can definitely fit into a 3x3 **box**, so what's the ratio of the total quantity of presents to the number of 3x3 boxes? I'll make a histogram of that ratio, which I'll call the occupancy ratio, for each region:" ] }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 89, "id": "b8cf45b6-5513-426a-86c2-425d0d74d781", "metadata": {}, "outputs": [ @@ -2862,11 +2922,14 @@ } ], "source": [ - "def squares(width, length) -> int: \n", - " \"Number of full 3x3 squares in a region.\"\n", - " return (width // 3) * (length // 3)\n", + "def boxes(region_W, region_L, shape_W=3, shape_L=3) -> int: \n", + " \"\"\"Number of shape-sized boxes that can fit into a WxL region. By default 3x3 boxes.\"\"\"\n", + " # Our presents all fit in a 3x3 box, but if they were diffferent, say 4x3,\n", + " # then we try to fit the boxes into the region both ways and take the most.\n", + " return max((region_W // shape_W) * (region_L // shape_L),\n", + " (region_W // shape_L) * (region_L // shape_W))\n", " \n", - "occupancy_ratios = [sum(quantities) / squares(W, L) \n", + "occupancy_ratios = [sum(quantities) / boxes(W, L) \n", " for (W, L, quantities) in regions]\n", "\n", "plt.hist(occupancy_ratios, bins=100);" @@ -2877,30 +2940,29 @@ "id": "54f2eaa0-b8b6-4a60-bcde-28eeefa26e8c", "metadata": {}, "source": [ - "**Very interesting!** There's a real split. About half the regions have an occupancy ratio below 1.0 and thus are trivially easy to fit into the region: just plop onee present into each 3x3 square, without worrying about rotations. The rest of the regions with occupancy ratios above 1.35 may well be impossible to fit. I say that because, just looking at the shapes, I estimate that the average overlap of one shape with another would be to save two \".\" squares; so I could see getting to an occupancy rato of 1 + 2/9 = 1.22, but I don't think it is possible to get to 1.35. I can prove it is impossible to fit all the presents in a region if the total area of the solid parts of the presents (the '#' squares) is more than the total area of the region (the width times length). \n", + "**Very interesting!** There's a real split. About half the regions have an occupancy ratio below 1.0 and thus are trivially easy to fit into the region: just plop one present into each 3x3 box, without worrying about rotations. The rest of the regions with occupancy ratios above 1.35 may well be impossible to fit. Just looking at the shapes, I estimate that the average overlap of one shape with another would be to save two \".\" squares; so I could see getting to an occupancy rato of 1 + 2/9 = 1.22, but I don't think it is possible to get to 1.35; almost certainly not 1.4 or more. I can prove it is impossible to fit all the presents in a region if the total area of the solid parts of the presents (the '#' squares) is more than the total area of the region (the width times length). \n", "\n", "I can do **triage** on the regions to classify each one as a trivial fit, an impossible fit, or an uncertain fit (for which we would have to do a search):" ] }, { "cell_type": "code", - "execution_count": 89, - "id": "58b879f2-19ad-4e3f-a804-8c22151e5865", + "execution_count": 90, + "id": "0cd74af7-a078-4e63-975a-ee459f3eb26d", "metadata": {}, "outputs": [], "source": [ - "shape_area = [cat(shape).count('#') for shape in shapes] # List of total number of '#' in each shape\n", - "\n", - "def triage(region) -> str:\n", + "def triage(regions, shapes) -> str:\n", " \"\"\"Decide if a region's presents trivially fit, or are impossible to fit, or it is uncertain.\"\"\"\n", - " width, length, quantities = region\n", - " presents_area = sum(q * shape_area[i] for (i, q) in enumerate(quantities))\n", - " if sum(quantities) <= squares(width, length):\n", - " return 'fit' # The total number of presents is no more than the number of 3x3 squares\n", - " elif presents_area > width * length:\n", - " return 'impossible' # The '#' area of all the presents is greater than the area of the region\n", - " else:\n", - " return 'uncertain' # We would need to do a search to see if the presents fit" + " shape_area = [cat(shape).count('#') for shape in shapes] # List of total number of '#' in each shape\n", + " counts = Counter()\n", + " for (width, length, quantities) in regions:\n", + " presents_area = sum(q * shape_area[i] for (i, q) in enumerate(quantities))\n", + " result = ('fit' if sum(quantities) <= boxes(width, length) # The presents all fit in their own 3x3 box\n", + " else 'fail' if presents_area > width * length # The presents are bigger than the region\n", + " else '????') # Uncertain; would need a search\n", + " counts[result] += 1\n", + " return counts" ] }, { @@ -2913,23 +2975,23 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 91, "id": "6768fa83-2af7-4aab-a930-9106da3859bd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Counter({'impossible': 546, 'fit': 454})" + "Counter({'fail': 546, 'fit': 454})" ] }, - "execution_count": 90, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Counter(map(triage, regions))" + "triage(regions, shapes)" ] }, { @@ -2942,24 +3004,24 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 92, "id": "0ec553c4-85eb-40a6-8bed-7adddf75e512", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Puzzle 12.1: .0018 seconds, answer 454 correct" + "Puzzle 12.1: .0021 seconds, answer 454 correct" ] }, - "execution_count": 91, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "answer(12.1, 454, lambda:\n", - " quantify(triage(region) == 'fit' for region in regions))" + " triage(regions, shapes)['fit'])" ] }, { @@ -2974,7 +3036,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 93, "id": "4d512a50-c6ae-4803-a787-b8f6e0103e31", "metadata": {}, "outputs": [ @@ -2982,31 +3044,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Puzzle 1.1: .0005 seconds, answer 1182 correct\n", - "Puzzle 1.2: .0009 seconds, answer 6907 correct\n", - "Puzzle 2.1: .0029 seconds, answer 23560874270 correct\n", - "Puzzle 2.2: .0037 seconds, answer 44143124633 correct\n", - "Puzzle 3.1: .0006 seconds, answer 17085 correct\n", - "Puzzle 3.2: .0021 seconds, answer 169408143086082 correct\n", - "Puzzle 4.1: .0572 seconds, answer 1569 correct\n", - "Puzzle 4.2: .1446 seconds, answer 9280 correct\n", - "Puzzle 5.1: .0111 seconds, answer 635 correct\n", + "Puzzle 1.1: .0006 seconds, answer 1182 correct\n", + "Puzzle 1.2: .0013 seconds, answer 6907 correct\n", + "Puzzle 2.1: .0039 seconds, answer 23560874270 correct\n", + "Puzzle 2.2: .0039 seconds, answer 44143124633 correct\n", + "Puzzle 3.1: .0007 seconds, answer 17085 correct\n", + "Puzzle 3.2: .0020 seconds, answer 169408143086082 correct\n", + "Puzzle 4.1: .0610 seconds, answer 1569 correct\n", + "Puzzle 4.2: .1437 seconds, answer 9280 correct\n", + "Puzzle 5.1: .0118 seconds, answer 635 correct\n", "Puzzle 5.2: .0002 seconds, answer 369761800782619 correct\n", - "Puzzle 6.1: .0025 seconds, answer 5877594983578 correct\n", - "Puzzle 6.2: .0065 seconds, answer 11159825706149 correct\n", + "Puzzle 6.1: .0021 seconds, answer 5877594983578 correct\n", + "Puzzle 6.2: .0061 seconds, answer 11159825706149 correct\n", "Puzzle 7.1: .0011 seconds, answer 1681 correct\n", "Puzzle 7.2: .0020 seconds, answer 422102272495018 correct\n", - "Puzzle 8.1: .6055 seconds, answer 24360 correct\n", - "Puzzle 8.2: .6371 seconds, answer 2185817796 correct\n", - "Puzzle 9.1: .0278 seconds, answer 4772103936 correct\n", - "Puzzle 9.2: .0156 seconds, answer 1529675217 correct\n", - "Puzzle 10.1: .0535 seconds, answer 441 correct\n", + "Puzzle 8.1: .6053 seconds, answer 24360 correct\n", + "Puzzle 8.2: .6392 seconds, answer 2185817796 correct\n", + "Puzzle 9.1: .0262 seconds, answer 4772103936 correct\n", + "Puzzle 9.2: .0068 seconds, answer 1529675217 correct\n", + "Puzzle 10.1: .0840 seconds, answer 441 correct\n", "Puzzle 10.2: .1304 seconds, answer 18559 correct\n", "Puzzle 11.1: .0003 seconds, answer 574 correct\n", - "Puzzle 11.2: .0017 seconds, answer 306594217920240 correct\n", - "Puzzle 12.1: .0018 seconds, answer 454 correct\n", + "Puzzle 11.2: .0020 seconds, answer 306594217920240 correct\n", + "Puzzle 12.1: .0021 seconds, answer 454 correct\n", "\n", - "Time in seconds: sum = 1.710, mean = .074, median = .003, max = .637\n" + "Time in seconds: sum = 1.737, mean = .076, median = .004, max = .639\n" ] } ],