sentence-transformers How to use AI4free/JARVIS-tool-search-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("AI4free/JARVIS-tool-search-v2")
sentences = [
"Solve: Once Volodya was at the museum and saw a regular chessboard as a museum piece. And there were only four chess pieces on it: two white rooks, a white king and a black king. \"Aha, blacks certainly didn't win!\", — Volodya said and was right for sure. And your task is to say whether whites had won or not.\n\nPieces on the chessboard are guaranteed to represent a correct position (every piece occupies one cell, no two pieces occupy the same cell and kings cannot take each other). Thus, your task is only to decide whether whites mate blacks. We would remind you that it means that the black king can be",
"<think>\nOkay, let's tackle this problem. So, the task is to find the capital city such that the number of road inversions needed is minimized. The roads are unidirectional, and the country forms a tree structure if we ignore the directions. Hmm.\n\nFirst, I need to understand the problem correctly. The country has n cities connected by n-1 roads, forming a tree when directions are ignored. The capital needs to be a city from which all other cities are reachable via the directed roads. The goal is to choose the capital (or capitals) that require the least number of road reversals.\n\nSo, for each possible city as the capital, I need to calculate how many roads would need to be reversed. Then, pick the minimum and all cities that achieve that minimum.\n\nBut calculating this directly for each city would be O(n^2), which is not feasible since n can be up to 2e5. So we need a smarter approach.\n\nWait, since the underlying structure is a tree, maybe there's a way to compute this efficiently with a traversal. Like, using a post-order traversal and dynamic programming.\n\nLet me think. Let's imagine that the tree is rooted at some node. For each node, the number of inversions required would be the sum of inversions needed in each subtree plus the inversions from the parent to the child. Wait, but the direction of the edges matters. For example, if the tree is rooted at u, and there's an edge from u to v, then that edge is in the correct direction (no inversion needed). But if there's an edge\n```\nimport sys\nfrom collections import deque\n\ndef main():\n n = int(sys.stdin.readline())\n adj = [[] for _ in range(n + 1)]\n for _ in range(n - 1):\n s, t = map(int, sys.stdin.readline().split())\n adj[s].append((t, 0))\n adj[t].append((s, 1))\n \n parent = [0] * (n + 1)\n direction = [0] * (n + 1)\n sum_inv = 0\n \n stack = [(1, -1, False)]\n while stack:\n \n```",
"<think>\nOkay, let's see. I need to solve this programming problem. Hmm. Let's read the problem carefully.\n\nSo, the problem is about tracking artifacts owned by Kostya and his allies. There are basic and composite artifacts. Basic ones are bought, and composite ones are made by combining certain numbers of basic artifacts. When you create a composite, the components are consumed. Each time after a purchase, we check if a composite can be made, and if so, it's done immediately. The output is the count of each artifact each ally has, sorted lexicographically.\n\nThe input gives k allies, n basic artifacts, m composite recipes, and q purchases. Each purchase is a friend (ai) buying a basic artifact. Then, after each purchase, check if the composite can be made. If yes, create it, consume the components, and add the composite to the inventory. And this process might cascade, like making a composite could allow another composite to be made, but the problem states that after the i-th purchase, no more than one opportunity arises. So we don't have to handle multiple steps here. Wait, the problem says: \"It is guaranteed that after the i-th purchase no more than one opportunity to collect the composite artifact appears.\" So after each purchase, there's at most one possible composite that can be crafted. That simplifies things. So for each purchase, after adding the artifact, check if any composite can be crafted, and if exactly one can be (as per the problem's guarantee), then do it.\n\nWa\n```\nfrom collections import defaultdict\n\nk, n, m, q = map(int, input().split())\n\nbasics = set()\nfor _ in range(n):\n basics.add(input().strip())\n\ncomposites = []\nfor _ in range(m):\n line = input().strip()\n name_part, components_part = line.split(': ')\n comp_name = name_part\n components = {}\n for item in components_part.split(', '):\n art, cnt = item.split()\n components[ar\n```",
"<think>\nOkay, so I need to determine if the black king is in checkmate based on the given positions of two rooks, the white king, and the black king. Let's break down the problem step by step.\n\nFirst, I remember that checkmate occurs when the black king is currently under attack (in check) and has no legal moves to escape. So my plan is to check two things: 1) Is the black king in check right now? 2) Does every possible move of the black king lead to a position where it's still in check?\n\nHmm. Let's think about how to model the chessboard. Each position is given as a two-character string like 'a6'. So I'll need to convert these into coordinates. Maybe represent the board as 8x8 grid with rows 1-8 and columns a-h. Let's map the letters a-h to 0-7 and numbers 1-8 to 0-7 as well? Or maybe 1-based to 8-based? Wait, let's see. For example, 'a1' would be (0,0) if we use zero-based, but that's up to how I parse it. The exact coordinates need to be handled correctly.\n\nSo the first step is to parse all the input positions into their x and y coordinates. For example, 'a6' would be x=0 (since a is first), y=5 (since 6 is the sixth row, but if rows are 1-8, maybe 1 is the first row). Wait, in chess, rows (ranks) go from 1 to 8, with 1 being the bottom for white. So when converting 'a6' to coordinates, the letter is the file (a-h, columns) and the number is the rank (1-8, rows). So for example, 'a1' is the bottom-left for white's perspective. So maybe to model this as (x, y) where x is 0 \n```\ndef pos_to_coords(s):\n x = ord(s[0]) - ord('a')\n y = int(s[1]) - 1\n return (x, y)\n\ndef is_rook_attacking(rook_pos, target_pos, occupied):\n rx, ry = rook_pos\n tx, ty = target_pos\n if rx != tx and ry != ty:\n return False\n if rx == tx:\n start = min(ry, ty)\n end = max(ry, ty)\n for y in range(start + 1, end):\n if (rx, y) in occupied:\n \n```"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
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