diff --git "a/python_zeroshot_test.jsonl" "b/python_zeroshot_test.jsonl" --- "a/python_zeroshot_test.jsonl" +++ "b/python_zeroshot_test.jsonl" @@ -50,8 +50,8 @@ {"pair_id": "python_6108_reversed", "problem_id": "p02789", "language": "python", "direction": "reversed", "speedup": 4.4572, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfrom sys import stdin\n\n\ndef func_1():\n var_2, var_1 = list(map(int, stdin.readline().split()))\n if var_2 == var_1:\n print(\"Yes\")\n else:\n print(\"No\")\n\n\nfunc_1()\n\n```\n\n## Code B:\n```python\nimport math\nimport statistics\nimport numpy as np\nimport queue\n\n\ndef func_1(var_10):\n if var_10 == 1:\n return 1\n else:\n return var_10 * func_1(var_10 - 1)\n\n\ndef func_2(var_1, var_2):\n if var_1 == 0:\n return var_2\n elif var_2 == 0:\n return var_1\n else:\n return func_2(var_2, var_1 % var_2)\n\n\ndef func_3(var_1, var_2):\n return var_1 * var_2 / func_2(var_1, var_2)\n\n\ndef func_4(var_11):\n if len(var_11) < 2:\n return var_11\n else:\n var_3, var_5 = 0, len(var_11) - 1\n var_9 = (var_3 + var_5) // 2\n var_12 = [var_4 for var_4 in var_11 if var_11[var_9] > var_4]\n var_6 = [var_4 for var_4 in var_11 if var_11[var_9] == var_4]\n var_7 = [var_4 for var_4 in var_11 if var_11[var_9] < var_4]\n return func_4(var_12) + var_6 + func_4(var_7)\n\n\nvar_10, var_8 = list(map(int, input().split()))\nif var_10 == var_8:\n print(\"Yes\")\nelse:\n print(\"No\")\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "from sys import stdin\n\n\ndef func_1():\n var_2, var_1 = list(map(int, stdin.readline().split()))\n if var_2 == var_1:\n print(\"Yes\")\n else:\n print(\"No\")\n\n\nfunc_1()\n", "code_b": "import math\nimport statistics\nimport numpy as np\nimport queue\n\n\ndef func_1(var_10):\n if var_10 == 1:\n return 1\n else:\n return var_10 * func_1(var_10 - 1)\n\n\ndef func_2(var_1, var_2):\n if var_1 == 0:\n return var_2\n elif var_2 == 0:\n return var_1\n else:\n return func_2(var_2, var_1 % var_2)\n\n\ndef func_3(var_1, var_2):\n return var_1 * var_2 / func_2(var_1, var_2)\n\n\ndef func_4(var_11):\n if len(var_11) < 2:\n return var_11\n else:\n var_3, var_5 = 0, len(var_11) - 1\n var_9 = (var_3 + var_5) // 2\n var_12 = [var_4 for var_4 in var_11 if var_11[var_9] > var_4]\n var_6 = [var_4 for var_4 in var_11 if var_11[var_9] == var_4]\n var_7 = [var_4 for var_4 in var_11 if var_11[var_9] < var_4]\n return func_4(var_12) + var_6 + func_4(var_7)\n\n\nvar_10, var_8 = list(map(int, input().split()))\nif var_10 == var_8:\n print(\"Yes\")\nelse:\n print(\"No\")\n", "expected": "A"} {"pair_id": "python_13903_forward", "problem_id": "p03343", "language": "python", "direction": "forward", "speedup": 2.1419, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\n\ndef func_9():\n return sys.stdin.readline().strip()\n\n\ndef func_10(var_9, var_11, var_12):\n return [[var_12] * var_11 for var_15 in range(var_9)]\n\n\ndef func_11(var_9, var_11, var_12, var_13):\n return [[[var_13] * var_12 for var_16 in range(var_11)] for var_15 in range(var_9)]\n\n\ndef func_12(var_9, var_11, var_12, var_13, var_14):\n return [\n [[[var_14] * var_13 for var_16 in range(var_12)] for var_16 in range(var_11)]\n for var_15 in range(var_9)\n ]\n\n\ndef func_8(var_20, var_21=1):\n return int(-(-var_20 // var_21))\n\n\ndef func_1():\n return int(eval(func_9()))\n\n\ndef func_3():\n return list(map(int, func_9().split()))\n\n\ndef func_2(var_7=None):\n return list(func_3()) if var_7 is None else [func_1() for var_15 in range(var_7)]\n\n\ndef func_7():\n print(\"Yes\")\n\n\ndef func_5():\n print(\"No\")\n\n\ndef func_6():\n print(\"YES\")\n\n\ndef func_4():\n print(\"NO\")\n\n\nsys.setrecursionlimit(10**9)\nvar_4 = 10**18\nvar_6 = 10**9 + 7\nvar_7, var_5, var_8 = func_3()\nvar_1 = func_2()\nvar_10 = var_4\n\nfor var_20 in var_1:\n\n var_2 = []\n var_19 = []\n for var_15 in range(var_7):\n if var_1[var_15] >= var_20:\n var_19.append(var_1[var_15])\n else:\n var_2.append(var_19)\n var_19 = []\n var_2.append(var_19)\n\n var_3 = []\n for var_17 in var_2:\n var_18 = len(var_17)\n if var_18 - var_5 + 1 >= 1:\n var_17.sort()\n var_3 += var_17[: var_18 - var_5 + 1]\n\n var_3.sort()\n if len(var_3) < var_8:\n continue\n var_21 = var_3[var_8 - 1]\n var_10 = min(var_10, var_21 - var_20)\nprint(var_10)\n\n```\n\n## Code B:\n```python\nvar_4, var_3, var_5 = list(map(int, input().split()))\nvar_1 = list(map(int, input().split()))\nvar_2 = set()\nfor var_9 in range(var_4):\n var_2.add(var_1[var_9])\nvar_2 = list(var_2)\nvar_8 = float(\"inf\")\nfor var_7 in var_2:\n var_6 = []\n var_11 = []\n for var_10 in range(var_4):\n if var_1[var_10] >= var_7:\n var_11.append(var_1[var_10])\n else:\n if len(var_11) >= var_3:\n var_11 = sorted(var_11)\n var_6 += var_11[: len(var_11) - var_3 + 1]\n var_11 = []\n if len(var_11) >= var_3:\n var_11 = sorted(var_11)\n var_6 += var_11[: len(var_11) - var_3 + 1]\n if len(var_6) >= var_5:\n var_6 = sorted(var_6)\n var_6 = var_6[:var_5]\n var_8 = min(var_8, var_6[-1] - var_6[0])\nprint(var_8)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\n\ndef func_9():\n return sys.stdin.readline().strip()\n\n\ndef func_10(var_9, var_11, var_12):\n return [[var_12] * var_11 for var_15 in range(var_9)]\n\n\ndef func_11(var_9, var_11, var_12, var_13):\n return [[[var_13] * var_12 for var_16 in range(var_11)] for var_15 in range(var_9)]\n\n\ndef func_12(var_9, var_11, var_12, var_13, var_14):\n return [\n [[[var_14] * var_13 for var_16 in range(var_12)] for var_16 in range(var_11)]\n for var_15 in range(var_9)\n ]\n\n\ndef func_8(var_20, var_21=1):\n return int(-(-var_20 // var_21))\n\n\ndef func_1():\n return int(eval(func_9()))\n\n\ndef func_3():\n return list(map(int, func_9().split()))\n\n\ndef func_2(var_7=None):\n return list(func_3()) if var_7 is None else [func_1() for var_15 in range(var_7)]\n\n\ndef func_7():\n print(\"Yes\")\n\n\ndef func_5():\n print(\"No\")\n\n\ndef func_6():\n print(\"YES\")\n\n\ndef func_4():\n print(\"NO\")\n\n\nsys.setrecursionlimit(10**9)\nvar_4 = 10**18\nvar_6 = 10**9 + 7\nvar_7, var_5, var_8 = func_3()\nvar_1 = func_2()\nvar_10 = var_4\n\nfor var_20 in var_1:\n\n var_2 = []\n var_19 = []\n for var_15 in range(var_7):\n if var_1[var_15] >= var_20:\n var_19.append(var_1[var_15])\n else:\n var_2.append(var_19)\n var_19 = []\n var_2.append(var_19)\n\n var_3 = []\n for var_17 in var_2:\n var_18 = len(var_17)\n if var_18 - var_5 + 1 >= 1:\n var_17.sort()\n var_3 += var_17[: var_18 - var_5 + 1]\n\n var_3.sort()\n if len(var_3) < var_8:\n continue\n var_21 = var_3[var_8 - 1]\n var_10 = min(var_10, var_21 - var_20)\nprint(var_10)\n", "code_b": "var_4, var_3, var_5 = list(map(int, input().split()))\nvar_1 = list(map(int, input().split()))\nvar_2 = set()\nfor var_9 in range(var_4):\n var_2.add(var_1[var_9])\nvar_2 = list(var_2)\nvar_8 = float(\"inf\")\nfor var_7 in var_2:\n var_6 = []\n var_11 = []\n for var_10 in range(var_4):\n if var_1[var_10] >= var_7:\n var_11.append(var_1[var_10])\n else:\n if len(var_11) >= var_3:\n var_11 = sorted(var_11)\n var_6 += var_11[: len(var_11) - var_3 + 1]\n var_11 = []\n if len(var_11) >= var_3:\n var_11 = sorted(var_11)\n var_6 += var_11[: len(var_11) - var_3 + 1]\n if len(var_6) >= var_5:\n var_6 = sorted(var_6)\n var_6 = var_6[:var_5]\n var_8 = min(var_8, var_6[-1] - var_6[0])\nprint(var_8)\n", "expected": "B"} {"pair_id": "python_13903_reversed", "problem_id": "p03343", "language": "python", "direction": "reversed", "speedup": 2.1419, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_4, var_3, var_5 = list(map(int, input().split()))\nvar_1 = list(map(int, input().split()))\nvar_2 = set()\nfor var_9 in range(var_4):\n var_2.add(var_1[var_9])\nvar_2 = list(var_2)\nvar_8 = float(\"inf\")\nfor var_7 in var_2:\n var_6 = []\n var_11 = []\n for var_10 in range(var_4):\n if var_1[var_10] >= var_7:\n var_11.append(var_1[var_10])\n else:\n if len(var_11) >= var_3:\n var_11 = sorted(var_11)\n var_6 += var_11[: len(var_11) - var_3 + 1]\n var_11 = []\n if len(var_11) >= var_3:\n var_11 = sorted(var_11)\n var_6 += var_11[: len(var_11) - var_3 + 1]\n if len(var_6) >= var_5:\n var_6 = sorted(var_6)\n var_6 = var_6[:var_5]\n var_8 = min(var_8, var_6[-1] - var_6[0])\nprint(var_8)\n\n```\n\n## Code B:\n```python\nimport sys\n\n\ndef func_9():\n return sys.stdin.readline().strip()\n\n\ndef func_10(var_9, var_11, var_12):\n return [[var_12] * var_11 for var_15 in range(var_9)]\n\n\ndef func_11(var_9, var_11, var_12, var_13):\n return [[[var_13] * var_12 for var_16 in range(var_11)] for var_15 in range(var_9)]\n\n\ndef func_12(var_9, var_11, var_12, var_13, var_14):\n return [\n [[[var_14] * var_13 for var_16 in range(var_12)] for var_16 in range(var_11)]\n for var_15 in range(var_9)\n ]\n\n\ndef func_8(var_20, var_21=1):\n return int(-(-var_20 // var_21))\n\n\ndef func_1():\n return int(eval(func_9()))\n\n\ndef func_3():\n return list(map(int, func_9().split()))\n\n\ndef func_2(var_7=None):\n return list(func_3()) if var_7 is None else [func_1() for var_15 in range(var_7)]\n\n\ndef func_7():\n print(\"Yes\")\n\n\ndef func_5():\n print(\"No\")\n\n\ndef func_6():\n print(\"YES\")\n\n\ndef func_4():\n print(\"NO\")\n\n\nsys.setrecursionlimit(10**9)\nvar_4 = 10**18\nvar_6 = 10**9 + 7\nvar_7, var_5, var_8 = func_3()\nvar_1 = func_2()\nvar_10 = var_4\n\nfor var_20 in var_1:\n\n var_2 = []\n var_19 = []\n for var_15 in range(var_7):\n if var_1[var_15] >= var_20:\n var_19.append(var_1[var_15])\n else:\n var_2.append(var_19)\n var_19 = []\n var_2.append(var_19)\n\n var_3 = []\n for var_17 in var_2:\n var_18 = len(var_17)\n if var_18 - var_5 + 1 >= 1:\n var_17.sort()\n var_3 += var_17[: var_18 - var_5 + 1]\n\n var_3.sort()\n if len(var_3) < var_8:\n continue\n var_21 = var_3[var_8 - 1]\n var_10 = min(var_10, var_21 - var_20)\nprint(var_10)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_4, var_3, var_5 = list(map(int, input().split()))\nvar_1 = list(map(int, input().split()))\nvar_2 = set()\nfor var_9 in range(var_4):\n var_2.add(var_1[var_9])\nvar_2 = list(var_2)\nvar_8 = float(\"inf\")\nfor var_7 in var_2:\n var_6 = []\n var_11 = []\n for var_10 in range(var_4):\n if var_1[var_10] >= var_7:\n var_11.append(var_1[var_10])\n else:\n if len(var_11) >= var_3:\n var_11 = sorted(var_11)\n var_6 += var_11[: len(var_11) - var_3 + 1]\n var_11 = []\n if len(var_11) >= var_3:\n var_11 = sorted(var_11)\n var_6 += var_11[: len(var_11) - var_3 + 1]\n if len(var_6) >= var_5:\n var_6 = sorted(var_6)\n var_6 = var_6[:var_5]\n var_8 = min(var_8, var_6[-1] - var_6[0])\nprint(var_8)\n", "code_b": "import sys\n\n\ndef func_9():\n return sys.stdin.readline().strip()\n\n\ndef func_10(var_9, var_11, var_12):\n return [[var_12] * var_11 for var_15 in range(var_9)]\n\n\ndef func_11(var_9, var_11, var_12, var_13):\n return [[[var_13] * var_12 for var_16 in range(var_11)] for var_15 in range(var_9)]\n\n\ndef func_12(var_9, var_11, var_12, var_13, var_14):\n return [\n [[[var_14] * var_13 for var_16 in range(var_12)] for var_16 in range(var_11)]\n for var_15 in range(var_9)\n ]\n\n\ndef func_8(var_20, var_21=1):\n return int(-(-var_20 // var_21))\n\n\ndef func_1():\n return int(eval(func_9()))\n\n\ndef func_3():\n return list(map(int, func_9().split()))\n\n\ndef func_2(var_7=None):\n return list(func_3()) if var_7 is None else [func_1() for var_15 in range(var_7)]\n\n\ndef func_7():\n print(\"Yes\")\n\n\ndef func_5():\n print(\"No\")\n\n\ndef func_6():\n print(\"YES\")\n\n\ndef func_4():\n print(\"NO\")\n\n\nsys.setrecursionlimit(10**9)\nvar_4 = 10**18\nvar_6 = 10**9 + 7\nvar_7, var_5, var_8 = func_3()\nvar_1 = func_2()\nvar_10 = var_4\n\nfor var_20 in var_1:\n\n var_2 = []\n var_19 = []\n for var_15 in range(var_7):\n if var_1[var_15] >= var_20:\n var_19.append(var_1[var_15])\n else:\n var_2.append(var_19)\n var_19 = []\n var_2.append(var_19)\n\n var_3 = []\n for var_17 in var_2:\n var_18 = len(var_17)\n if var_18 - var_5 + 1 >= 1:\n var_17.sort()\n var_3 += var_17[: var_18 - var_5 + 1]\n\n var_3.sort()\n if len(var_3) < var_8:\n continue\n var_21 = var_3[var_8 - 1]\n var_10 = min(var_10, var_21 - var_20)\nprint(var_10)\n", "expected": "A"} -{"pair_id": "python_7377_forward", "problem_id": "p03044", "language": "python", "direction": "forward", "speedup": 3.4449, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n\n```\n\n## Code B:\n```python\nfrom sys import stdin\nfrom collections import deque\n\n\ndef func_1():\n\n var_7 = stdin.readline\n var_1 = int(var_7())\n var_3 = [[] for _ in range(var_1)]\n for _ in range(var_1 - 1):\n var_8, var_9, var_10 = list(map(int, var_7().split()))\n var_8 -= 1\n var_9 -= 1\n var_3[var_8].append((var_9, var_10))\n var_3[var_9].append((var_8, var_10))\n\n var_2 = [0] * var_1\n var_4 = [False] * var_1\n var_4[0] = True\n var_6 = deque([0])\n while len(var_6) >= 1:\n var_5 = var_6.popleft()\n for var_11 in var_3[var_5]:\n if var_4[var_11[0]] == False:\n var_4[var_11[0]] = True\n var_2[var_11[0]] = var_2[var_5] + var_11[1]\n var_6.append(var_11[0])\n for var_11 in var_2:\n if var_11 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n", "code_b": "from sys import stdin\nfrom collections import deque\n\n\ndef func_1():\n\n var_7 = stdin.readline\n var_1 = int(var_7())\n var_3 = [[] for _ in range(var_1)]\n for _ in range(var_1 - 1):\n var_8, var_9, var_10 = list(map(int, var_7().split()))\n var_8 -= 1\n var_9 -= 1\n var_3[var_8].append((var_9, var_10))\n var_3[var_9].append((var_8, var_10))\n\n var_2 = [0] * var_1\n var_4 = [False] * var_1\n var_4[0] = True\n var_6 = deque([0])\n while len(var_6) >= 1:\n var_5 = var_6.popleft()\n for var_11 in var_3[var_5]:\n if var_4[var_11[0]] == False:\n var_4[var_11[0]] = True\n var_2[var_11[0]] = var_2[var_5] + var_11[1]\n var_6.append(var_11[0])\n for var_11 in var_2:\n if var_11 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "B"} -{"pair_id": "python_7377_reversed", "problem_id": "p03044", "language": "python", "direction": "reversed", "speedup": 3.4449, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfrom sys import stdin\nfrom collections import deque\n\n\ndef func_1():\n\n var_7 = stdin.readline\n var_1 = int(var_7())\n var_3 = [[] for _ in range(var_1)]\n for _ in range(var_1 - 1):\n var_8, var_9, var_10 = list(map(int, var_7().split()))\n var_8 -= 1\n var_9 -= 1\n var_3[var_8].append((var_9, var_10))\n var_3[var_9].append((var_8, var_10))\n\n var_2 = [0] * var_1\n var_4 = [False] * var_1\n var_4[0] = True\n var_6 = deque([0])\n while len(var_6) >= 1:\n var_5 = var_6.popleft()\n for var_11 in var_3[var_5]:\n if var_4[var_11[0]] == False:\n var_4[var_11[0]] = True\n var_2[var_11[0]] = var_2[var_5] + var_11[1]\n var_6.append(var_11[0])\n for var_11 in var_2:\n if var_11 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\n## Code B:\n```python\nimport sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "from sys import stdin\nfrom collections import deque\n\n\ndef func_1():\n\n var_7 = stdin.readline\n var_1 = int(var_7())\n var_3 = [[] for _ in range(var_1)]\n for _ in range(var_1 - 1):\n var_8, var_9, var_10 = list(map(int, var_7().split()))\n var_8 -= 1\n var_9 -= 1\n var_3[var_8].append((var_9, var_10))\n var_3[var_9].append((var_8, var_10))\n\n var_2 = [0] * var_1\n var_4 = [False] * var_1\n var_4[0] = True\n var_6 = deque([0])\n while len(var_6) >= 1:\n var_5 = var_6.popleft()\n for var_11 in var_3[var_5]:\n if var_4[var_11[0]] == False:\n var_4[var_11[0]] = True\n var_2[var_11[0]] = var_2[var_5] + var_11[1]\n var_6.append(var_11[0])\n for var_11 in var_2:\n if var_11 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "import sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n", "expected": "A"} +{"pair_id": "python_7377_forward", "problem_id": "p03044", "language": "python", "direction": "forward", "speedup": 3.4449, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n```\n\n## Code B:\n```python\nfrom sys import stdin\nfrom collections import deque\n\n\ndef func_1():\n \n var_7 = stdin.readline\n var_1 = int(var_7())\n var_3 = [[] for _ in range(var_1)]\n for _ in range(var_1 - 1):\n var_8, var_9, var_10 = list(map(int, var_7().split()))\n var_8 -= 1\n var_9 -= 1\n var_3[var_8].append((var_9, var_10))\n var_3[var_9].append((var_8, var_10))\n \n var_2 = [0] * var_1\n var_4 = [False] * var_1\n var_4[0] = True\n var_6 = deque([0])\n while len(var_6) >= 1:\n var_5 = var_6.popleft()\n for var_11 in var_3[var_5]:\n if var_4[var_11[0]] == False:\n var_4[var_11[0]] = True\n var_2[var_11[0]] = var_2[var_5] + var_11[1]\n var_6.append(var_11[0])\n for var_11 in var_2:\n if var_11 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n func_1()\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n", "code_b": "from sys import stdin\nfrom collections import deque\n\n\ndef func_1():\n \n var_7 = stdin.readline\n var_1 = int(var_7())\n var_3 = [[] for _ in range(var_1)]\n for _ in range(var_1 - 1):\n var_8, var_9, var_10 = list(map(int, var_7().split()))\n var_8 -= 1\n var_9 -= 1\n var_3[var_8].append((var_9, var_10))\n var_3[var_9].append((var_8, var_10))\n \n var_2 = [0] * var_1\n var_4 = [False] * var_1\n var_4[0] = True\n var_6 = deque([0])\n while len(var_6) >= 1:\n var_5 = var_6.popleft()\n for var_11 in var_3[var_5]:\n if var_4[var_11[0]] == False:\n var_4[var_11[0]] = True\n var_2[var_11[0]] = var_2[var_5] + var_11[1]\n var_6.append(var_11[0])\n for var_11 in var_2:\n if var_11 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "B"} +{"pair_id": "python_7377_reversed", "problem_id": "p03044", "language": "python", "direction": "reversed", "speedup": 3.4449, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfrom sys import stdin\nfrom collections import deque\n\n\ndef func_1():\n \n var_7 = stdin.readline\n var_1 = int(var_7())\n var_3 = [[] for _ in range(var_1)]\n for _ in range(var_1 - 1):\n var_8, var_9, var_10 = list(map(int, var_7().split()))\n var_8 -= 1\n var_9 -= 1\n var_3[var_8].append((var_9, var_10))\n var_3[var_9].append((var_8, var_10))\n \n var_2 = [0] * var_1\n var_4 = [False] * var_1\n var_4[0] = True\n var_6 = deque([0])\n while len(var_6) >= 1:\n var_5 = var_6.popleft()\n for var_11 in var_3[var_5]:\n if var_4[var_11[0]] == False:\n var_4[var_11[0]] = True\n var_2[var_11[0]] = var_2[var_5] + var_11[1]\n var_6.append(var_11[0])\n for var_11 in var_2:\n if var_11 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n func_1()\n```\n\n## Code B:\n```python\nimport sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "from sys import stdin\nfrom collections import deque\n\n\ndef func_1():\n \n var_7 = stdin.readline\n var_1 = int(var_7())\n var_3 = [[] for _ in range(var_1)]\n for _ in range(var_1 - 1):\n var_8, var_9, var_10 = list(map(int, var_7().split()))\n var_8 -= 1\n var_9 -= 1\n var_3[var_8].append((var_9, var_10))\n var_3[var_9].append((var_8, var_10))\n \n var_2 = [0] * var_1\n var_4 = [False] * var_1\n var_4[0] = True\n var_6 = deque([0])\n while len(var_6) >= 1:\n var_5 = var_6.popleft()\n for var_11 in var_3[var_5]:\n if var_4[var_11[0]] == False:\n var_4[var_11[0]] = True\n var_2[var_11[0]] = var_2[var_5] + var_11[1]\n var_6.append(var_11[0])\n for var_11 in var_2:\n if var_11 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "import sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n", "expected": "A"} {"pair_id": "python_16488_forward", "problem_id": "p03796", "language": "python", "direction": "forward", "speedup": 7.0096, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1 = int(eval(input()))\nfrom math import factorial\n\nvar_3 = int(1e9 + 7)\nvar_2 = factorial(var_1) % var_3\nprint(var_2)\n\n```\n\n## Code B:\n```python\nvar_1 = 10**9 + 7\nvar_2 = int(eval(input()))\nvar_3 = 1\nfor var_4 in range(2, var_2 + 1):\n var_3 *= var_4\n var_3 %= var_1\nprint(var_3)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1 = int(eval(input()))\nfrom math import factorial\n\nvar_3 = int(1e9 + 7)\nvar_2 = factorial(var_1) % var_3\nprint(var_2)\n", "code_b": "var_1 = 10**9 + 7\nvar_2 = int(eval(input()))\nvar_3 = 1\nfor var_4 in range(2, var_2 + 1):\n var_3 *= var_4\n var_3 %= var_1\nprint(var_3)\n", "expected": "B"} {"pair_id": "python_16488_reversed", "problem_id": "p03796", "language": "python", "direction": "reversed", "speedup": 7.0096, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1 = 10**9 + 7\nvar_2 = int(eval(input()))\nvar_3 = 1\nfor var_4 in range(2, var_2 + 1):\n var_3 *= var_4\n var_3 %= var_1\nprint(var_3)\n\n```\n\n## Code B:\n```python\nvar_1 = int(eval(input()))\nfrom math import factorial\n\nvar_3 = int(1e9 + 7)\nvar_2 = factorial(var_1) % var_3\nprint(var_2)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1 = 10**9 + 7\nvar_2 = int(eval(input()))\nvar_3 = 1\nfor var_4 in range(2, var_2 + 1):\n var_3 *= var_4\n var_3 %= var_1\nprint(var_3)\n", "code_b": "var_1 = int(eval(input()))\nfrom math import factorial\n\nvar_3 = int(1e9 + 7)\nvar_2 = factorial(var_1) % var_3\nprint(var_2)\n", "expected": "A"} {"pair_id": "python_3650_forward", "problem_id": "p02786", "language": "python", "direction": "forward", "speedup": 5.9301, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\nimport math\nimport os\nimport itertools\nimport string\nimport heapq\nimport _collections\nfrom collections import Counter\nfrom collections import defaultdict\nfrom collections import deque\nfrom functools import lru_cache\nimport bisect\nimport re\nimport queue\nimport copy\nimport decimal\n\n\nclass Class_1:\n @staticmethod\n def int():\n return int(sys.stdin.readline().rstrip())\n\n @staticmethod\n def string():\n return sys.stdin.readline().rstrip()\n\n @staticmethod\n def func_4():\n return [int(var_6) for var_6 in Class_1.string().split()]\n\n @staticmethod\n def func_6(var_4):\n return [Class_1.string() for var_3 in range(var_4)]\n\n @staticmethod\n def func_2(var_4):\n return [Class_1.map_int() for var_3 in range(var_4)]\n\n @staticmethod\n def func_1(var_4):\n return [Class_1.int() for var_3 in range(var_4)]\n\n\ndef func_5():\n var_1 = Class_1.int()\n var_2 = 0\n var_5 = 1\n while var_1 > 1:\n var_2 += var_5\n var_5 *= 2\n var_1 //= 2\n var_2 += var_5\n print(var_2)\n\n\ndef func_3():\n\n func_5()\n\n\nif __name__ == \"__main__\":\n func_3()\n\n```\n\n## Code B:\n```python\nimport math\n\nvar_2 = int(eval(input()))\nvar_1 = 1\nwhile var_2 > 1:\n var_2 = var_2 // 2\n var_1 += 1\nprint((2**var_1 - 1))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\nimport math\nimport os\nimport itertools\nimport string\nimport heapq\nimport _collections\nfrom collections import Counter\nfrom collections import defaultdict\nfrom collections import deque\nfrom functools import lru_cache\nimport bisect\nimport re\nimport queue\nimport copy\nimport decimal\n\n\nclass Class_1:\n @staticmethod\n def int():\n return int(sys.stdin.readline().rstrip())\n\n @staticmethod\n def string():\n return sys.stdin.readline().rstrip()\n\n @staticmethod\n def func_4():\n return [int(var_6) for var_6 in Class_1.string().split()]\n\n @staticmethod\n def func_6(var_4):\n return [Class_1.string() for var_3 in range(var_4)]\n\n @staticmethod\n def func_2(var_4):\n return [Class_1.map_int() for var_3 in range(var_4)]\n\n @staticmethod\n def func_1(var_4):\n return [Class_1.int() for var_3 in range(var_4)]\n\n\ndef func_5():\n var_1 = Class_1.int()\n var_2 = 0\n var_5 = 1\n while var_1 > 1:\n var_2 += var_5\n var_5 *= 2\n var_1 //= 2\n var_2 += var_5\n print(var_2)\n\n\ndef func_3():\n\n func_5()\n\n\nif __name__ == \"__main__\":\n func_3()\n", "code_b": "import math\n\nvar_2 = int(eval(input()))\nvar_1 = 1\nwhile var_2 > 1:\n var_2 = var_2 // 2\n var_1 += 1\nprint((2**var_1 - 1))\n", "expected": "B"} @@ -256,8 +256,8 @@ {"pair_id": "python_10708_reversed", "problem_id": "p03767", "language": "python", "direction": "reversed", "speedup": 9.4636, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1 = int(eval(input()))\nprint((sum(sorted(map(int, input().split()))[::-1][1 : var_1 * 2 : 2])))\n\n```\n\n## Code B:\n```python\nfrom collections import defaultdict, deque\nfrom heapq import heappush, heappop\nimport sys\nimport math\nimport bisect\nimport random\n\n\ndef func_9():\n return [int(var_15) for var_15 in sys.stdin.readline().split()]\n\n\ndef func_7():\n return int(sys.stdin.readline())\n\n\ndef func_11():\n return [list(var_15) for var_15 in sys.stdin.readline().split()]\n\n\ndef func_13():\n return list(sys.stdin.readline())[:-1]\n\n\ndef func_8(var_12):\n return [func_7() for var_8 in range(var_12)]\n\n\ndef func_10(var_12):\n return [func_9() for var_8 in range(var_12)]\n\n\ndef func_14(var_12):\n return [func_13() for var_8 in range(var_12)]\n\n\ndef func_12(var_12):\n return [func_11() for var_8 in range(var_12)]\n\n\nsys.setrecursionlimit(1000000)\nvar_11 = 1000000007\n\n\ndef func_1():\n var_12 = func_7()\n var_1 = func_9()\n var_1.sort(key=lambda var_15: -var_15)\n var_2 = 0\n for var_8 in range(var_12):\n var_2 += var_1[2 * var_8 + 1]\n print(var_2)\n return\n\n\ndef func_2():\n def func_15(var_5, var_15, var_4):\n if var_5 <= var_6[var_15]:\n return\n var_6[var_15] = var_5\n if not var_7[var_15]:\n var_7[var_15] = var_4\n for var_16 in var_14[var_15]:\n func_15(var_5 - 1, var_16, var_4)\n\n var_12, var_10 = func_9()\n var_7 = [0] * var_12\n var_14 = [[] for var_8 in range(var_12)]\n for var_8 in range(var_10):\n var_1, var_3 = func_9()\n var_1 -= 1\n var_3 -= 1\n var_14[var_1].append(var_3)\n var_14[var_3].append(var_1)\n var_6 = defaultdict(lambda: -1)\n var_13 = func_7()\n var_9 = func_10(var_13)\n var_9 = var_9[::-1]\n for var_15, var_5, var_4 in var_9:\n var_15 -= 1\n func_15(var_5, var_15, var_4)\n for var_8 in var_7:\n print(var_8)\n return\n\n\ndef func_3():\n var_12 = func_7()\n return\n\n\ndef func_4():\n var_12 = func_7()\n return\n\n\ndef func_5():\n var_12 = func_7()\n return\n\n\ndef func_6():\n var_12 = func_7()\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1 = int(eval(input()))\nprint((sum(sorted(map(int, input().split()))[::-1][1 : var_1 * 2 : 2])))\n", "code_b": "from collections import defaultdict, deque\nfrom heapq import heappush, heappop\nimport sys\nimport math\nimport bisect\nimport random\n\n\ndef func_9():\n return [int(var_15) for var_15 in sys.stdin.readline().split()]\n\n\ndef func_7():\n return int(sys.stdin.readline())\n\n\ndef func_11():\n return [list(var_15) for var_15 in sys.stdin.readline().split()]\n\n\ndef func_13():\n return list(sys.stdin.readline())[:-1]\n\n\ndef func_8(var_12):\n return [func_7() for var_8 in range(var_12)]\n\n\ndef func_10(var_12):\n return [func_9() for var_8 in range(var_12)]\n\n\ndef func_14(var_12):\n return [func_13() for var_8 in range(var_12)]\n\n\ndef func_12(var_12):\n return [func_11() for var_8 in range(var_12)]\n\n\nsys.setrecursionlimit(1000000)\nvar_11 = 1000000007\n\n\ndef func_1():\n var_12 = func_7()\n var_1 = func_9()\n var_1.sort(key=lambda var_15: -var_15)\n var_2 = 0\n for var_8 in range(var_12):\n var_2 += var_1[2 * var_8 + 1]\n print(var_2)\n return\n\n\ndef func_2():\n def func_15(var_5, var_15, var_4):\n if var_5 <= var_6[var_15]:\n return\n var_6[var_15] = var_5\n if not var_7[var_15]:\n var_7[var_15] = var_4\n for var_16 in var_14[var_15]:\n func_15(var_5 - 1, var_16, var_4)\n\n var_12, var_10 = func_9()\n var_7 = [0] * var_12\n var_14 = [[] for var_8 in range(var_12)]\n for var_8 in range(var_10):\n var_1, var_3 = func_9()\n var_1 -= 1\n var_3 -= 1\n var_14[var_1].append(var_3)\n var_14[var_3].append(var_1)\n var_6 = defaultdict(lambda: -1)\n var_13 = func_7()\n var_9 = func_10(var_13)\n var_9 = var_9[::-1]\n for var_15, var_5, var_4 in var_9:\n var_15 -= 1\n func_15(var_5, var_15, var_4)\n for var_8 in var_7:\n print(var_8)\n return\n\n\ndef func_3():\n var_12 = func_7()\n return\n\n\ndef func_4():\n var_12 = func_7()\n return\n\n\ndef func_5():\n var_12 = func_7()\n return\n\n\ndef func_6():\n var_12 = func_7()\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "A"} {"pair_id": "python_4903_forward", "problem_id": "p03598", "language": "python", "direction": "forward", "speedup": 3.8472, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_19 = sys.stdin\nvar_3 = 1 << 60\nvar_5 = 1000000007\nvar_15 = lambda: int(var_17())\nvar_16 = lambda var_21: [var_15() for _ in range(var_21)]\nvar_7 = lambda: list(map(int, var_19.readline().split()))\nvar_8 = lambda var_21: [var_7() for _ in range(var_21)]\nvar_11 = lambda: float(var_17())\nvar_14 = lambda var_21: [var_11() for _ in range(var_21)]\nvar_12 = lambda: list(map(float, var_19.readline().split()))\nvar_13 = lambda var_21: [var_12() for _ in range(var_21)]\nvar_17 = lambda: var_19.readline().rstrip()\nvar_18 = lambda var_21: [var_17() for _ in range(var_21)]\nvar_10 = lambda var_21: [list(var_17()) for _ in range(var_21)]\nvar_9 = lambda: var_19.readline().split()\nvar_6 = var_15()\nvar_4 = var_15()\nvar_20 = var_7()\nvar_1 = 0\nfor var_2 in range(var_6):\n var_1 += min(abs(var_20[var_2]) * 2, abs(var_20[var_2] - var_4) * 2)\nprint(var_1)\n\n```\n\n## Code B:\n```python\nvar_4 = int(eval(input()))\nvar_3 = int(eval(input()))\nvar_5 = list(map(int, input().split()))\nvar_1 = 0\nfor var_2 in var_5:\n var_1 += min(abs(var_2 - 0), abs(var_2 - var_3)) * 2\nprint(var_1)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_19 = sys.stdin\nvar_3 = 1 << 60\nvar_5 = 1000000007\nvar_15 = lambda: int(var_17())\nvar_16 = lambda var_21: [var_15() for _ in range(var_21)]\nvar_7 = lambda: list(map(int, var_19.readline().split()))\nvar_8 = lambda var_21: [var_7() for _ in range(var_21)]\nvar_11 = lambda: float(var_17())\nvar_14 = lambda var_21: [var_11() for _ in range(var_21)]\nvar_12 = lambda: list(map(float, var_19.readline().split()))\nvar_13 = lambda var_21: [var_12() for _ in range(var_21)]\nvar_17 = lambda: var_19.readline().rstrip()\nvar_18 = lambda var_21: [var_17() for _ in range(var_21)]\nvar_10 = lambda var_21: [list(var_17()) for _ in range(var_21)]\nvar_9 = lambda: var_19.readline().split()\nvar_6 = var_15()\nvar_4 = var_15()\nvar_20 = var_7()\nvar_1 = 0\nfor var_2 in range(var_6):\n var_1 += min(abs(var_20[var_2]) * 2, abs(var_20[var_2] - var_4) * 2)\nprint(var_1)\n", "code_b": "var_4 = int(eval(input()))\nvar_3 = int(eval(input()))\nvar_5 = list(map(int, input().split()))\nvar_1 = 0\nfor var_2 in var_5:\n var_1 += min(abs(var_2 - 0), abs(var_2 - var_3)) * 2\nprint(var_1)\n", "expected": "B"} {"pair_id": "python_4903_reversed", "problem_id": "p03598", "language": "python", "direction": "reversed", "speedup": 3.8472, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_4 = int(eval(input()))\nvar_3 = int(eval(input()))\nvar_5 = list(map(int, input().split()))\nvar_1 = 0\nfor var_2 in var_5:\n var_1 += min(abs(var_2 - 0), abs(var_2 - var_3)) * 2\nprint(var_1)\n\n```\n\n## Code B:\n```python\nimport sys\n\nvar_19 = sys.stdin\nvar_3 = 1 << 60\nvar_5 = 1000000007\nvar_15 = lambda: int(var_17())\nvar_16 = lambda var_21: [var_15() for _ in range(var_21)]\nvar_7 = lambda: list(map(int, var_19.readline().split()))\nvar_8 = lambda var_21: [var_7() for _ in range(var_21)]\nvar_11 = lambda: float(var_17())\nvar_14 = lambda var_21: [var_11() for _ in range(var_21)]\nvar_12 = lambda: list(map(float, var_19.readline().split()))\nvar_13 = lambda var_21: [var_12() for _ in range(var_21)]\nvar_17 = lambda: var_19.readline().rstrip()\nvar_18 = lambda var_21: [var_17() for _ in range(var_21)]\nvar_10 = lambda var_21: [list(var_17()) for _ in range(var_21)]\nvar_9 = lambda: var_19.readline().split()\nvar_6 = var_15()\nvar_4 = var_15()\nvar_20 = var_7()\nvar_1 = 0\nfor var_2 in range(var_6):\n var_1 += min(abs(var_20[var_2]) * 2, abs(var_20[var_2] - var_4) * 2)\nprint(var_1)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_4 = int(eval(input()))\nvar_3 = int(eval(input()))\nvar_5 = list(map(int, input().split()))\nvar_1 = 0\nfor var_2 in var_5:\n var_1 += min(abs(var_2 - 0), abs(var_2 - var_3)) * 2\nprint(var_1)\n", "code_b": "import sys\n\nvar_19 = sys.stdin\nvar_3 = 1 << 60\nvar_5 = 1000000007\nvar_15 = lambda: int(var_17())\nvar_16 = lambda var_21: [var_15() for _ in range(var_21)]\nvar_7 = lambda: list(map(int, var_19.readline().split()))\nvar_8 = lambda var_21: [var_7() for _ in range(var_21)]\nvar_11 = lambda: float(var_17())\nvar_14 = lambda var_21: [var_11() for _ in range(var_21)]\nvar_12 = lambda: list(map(float, var_19.readline().split()))\nvar_13 = lambda var_21: [var_12() for _ in range(var_21)]\nvar_17 = lambda: var_19.readline().rstrip()\nvar_18 = lambda var_21: [var_17() for _ in range(var_21)]\nvar_10 = lambda var_21: [list(var_17()) for _ in range(var_21)]\nvar_9 = lambda: var_19.readline().split()\nvar_6 = var_15()\nvar_4 = var_15()\nvar_20 = var_7()\nvar_1 = 0\nfor var_2 in range(var_6):\n var_1 += min(abs(var_20[var_2]) * 2, abs(var_20[var_2] - var_4) * 2)\nprint(var_1)\n", "expected": "A"} -{"pair_id": "python_17849_forward", "problem_id": "p02586", "language": "python", "direction": "forward", "speedup": 2.7429, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n\nfrom collections import deque, defaultdict\n\n\n\nfrom collections import Counter \n\n\nfrom itertools import accumulate, combinations, permutations, product \n\n\nfrom heapq import heapify, heappop, heappush\n\n\nfrom functools import reduce, lru_cache \n\n\nfrom decimal import Decimal\n\n\ndef input():\n x = sys.stdin.readline()\n return x[:-1] if x[-1] == \"\\n\" else x\n\n\ndef printe(*x):\n print(\"\n\n\ndef printl(li):\n _ = print(*li, sep=\"\\n\") if li else None\n\n\ndef argsort(s, return_sorted=False):\n inds = sorted(range(len(s)), key=lambda k: s[k])\n if return_sorted:\n return inds, [s[i] for i in inds]\n return inds\n\n\ndef alp2num(c, cap=False):\n return ord(c) - 97 if not cap else ord(c) - 65\n\n\ndef num2alp(i, cap=False):\n return chr(i + 97) if not cap else chr(i + 65)\n\n\ndef matmat(A, B):\n K, N, M = len(B), len(A), len(B[0])\n return [\n [sum([(A[i][k] * B[k][j]) for k in range(K)]) for j in range(M)]\n for i in range(N)\n ]\n\n\ndef matvec(M, v):\n N, size = len(v), len(M)\n return [sum([M[i][j] * v[j] for j in range(N)]) for i in range(size)]\n\n\ndef T(M):\n n, m = len(M), len(M[0])\n return [[M[j][i] for j in range(n)] for i in range(m)]\n\n\ndef binr(x):\n return bin(x)[2:]\n\n\ndef bitcount(x): \n x = x - ((x >> 1) & 0x5555555555555555)\n x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)\n x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F\n x += x >> 8\n x += x >> 16\n x += x >> 32\n return x & 0x7F\n\n\ndef main():\n mod = 1000000007\n \n \n R, C, K = map(int, input().split())\n \n \n base = 5000\n S = []\n for i in range(K):\n r, c, v = map(int, input().split())\n r -= 1\n c -= 1\n S.append((r * base + c, v))\n S.sort(key=itemgetter(0))\n p = 0\n dp = [[0] * 4 for _ in range(C)]\n dpi = [[0] * 4 for _ in range(C)]\n for i in range(R):\n for j in range(C):\n cv = 0\n if p < K and i * base + j == S[p][0]:\n cv = S[p][1]\n p += 1\n if j > 0:\n dpi[j] = copy(dpi[j - 1])\n else:\n dpi[0] = [0] * 4\n if i > 0:\n dpi[j][0] = max(dp[j], dpi[j][0])\n if cv != 0:\n for k in range(2, -1, -1):\n dpi[j][k + 1] = max(dpi[j][k] + cv, dpi[j][k + 1])\n \n dp[j] = max(dpi[j])\n print(dp[-1])\n\n\nif __name__ == \"__main__\":\n main()\n\n```\n\n## Code B:\n```python\nvar_7, var_8, var_9 = range, max, input\nvar_5, var_1, var_4 = list(map(int, var_9().split()))\nvar_3 = [[0] * -~var_1 for var_10 in var_7(var_5 + 1)]\nfor var_10 in \"_\" * var_4:\n var_11, var_6, var_12 = list(map(int, var_9().split()))\n var_3[var_11][var_6] = var_12\nvar_2 = [[[0] * -~var_1 for var_10 in var_7(var_5 + 1)] for var_10 in var_7(4)]\nfor var_11 in var_7(1, var_5 + 1):\n for var_13 in var_7(1, 4):\n for var_6 in var_7(1, var_1 + 1):\n var_2[var_13][var_11][var_6] = var_8(\n var_2[var_13 - 1][var_11][var_6],\n var_2[var_13 - 1][var_11][var_6 - 1] + var_3[var_11][var_6],\n var_2[var_13][var_11][var_6 - 1],\n (var_13 < 2)\n * (\n var_3[var_11][var_6]\n + var_8(\n var_2[1][var_11 - 1][var_6],\n var_2[2][var_11 - 1][var_6],\n var_2[3][var_11 - 1][var_6],\n )\n ),\n )\nprint((var_2[3][var_5][var_1]))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n\nfrom collections import deque, defaultdict\n\n\n\nfrom collections import Counter \n\n\nfrom itertools import accumulate, combinations, permutations, product \n\n\nfrom heapq import heapify, heappop, heappush\n\n\nfrom functools import reduce, lru_cache \n\n\nfrom decimal import Decimal\n\n\ndef input():\n x = sys.stdin.readline()\n return x[:-1] if x[-1] == \"\\n\" else x\n\n\ndef printe(*x):\n print(\"\n\n\ndef printl(li):\n _ = print(*li, sep=\"\\n\") if li else None\n\n\ndef argsort(s, return_sorted=False):\n inds = sorted(range(len(s)), key=lambda k: s[k])\n if return_sorted:\n return inds, [s[i] for i in inds]\n return inds\n\n\ndef alp2num(c, cap=False):\n return ord(c) - 97 if not cap else ord(c) - 65\n\n\ndef num2alp(i, cap=False):\n return chr(i + 97) if not cap else chr(i + 65)\n\n\ndef matmat(A, B):\n K, N, M = len(B), len(A), len(B[0])\n return [\n [sum([(A[i][k] * B[k][j]) for k in range(K)]) for j in range(M)]\n for i in range(N)\n ]\n\n\ndef matvec(M, v):\n N, size = len(v), len(M)\n return [sum([M[i][j] * v[j] for j in range(N)]) for i in range(size)]\n\n\ndef T(M):\n n, m = len(M), len(M[0])\n return [[M[j][i] for j in range(n)] for i in range(m)]\n\n\ndef binr(x):\n return bin(x)[2:]\n\n\ndef bitcount(x): \n x = x - ((x >> 1) & 0x5555555555555555)\n x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)\n x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F\n x += x >> 8\n x += x >> 16\n x += x >> 32\n return x & 0x7F\n\n\ndef main():\n mod = 1000000007\n \n \n R, C, K = map(int, input().split())\n \n \n base = 5000\n S = []\n for i in range(K):\n r, c, v = map(int, input().split())\n r -= 1\n c -= 1\n S.append((r * base + c, v))\n S.sort(key=itemgetter(0))\n p = 0\n dp = [[0] * 4 for _ in range(C)]\n dpi = [[0] * 4 for _ in range(C)]\n for i in range(R):\n for j in range(C):\n cv = 0\n if p < K and i * base + j == S[p][0]:\n cv = S[p][1]\n p += 1\n if j > 0:\n dpi[j] = copy(dpi[j - 1])\n else:\n dpi[0] = [0] * 4\n if i > 0:\n dpi[j][0] = max(dp[j], dpi[j][0])\n if cv != 0:\n for k in range(2, -1, -1):\n dpi[j][k + 1] = max(dpi[j][k] + cv, dpi[j][k + 1])\n \n dp[j] = max(dpi[j])\n print(dp[-1])\n\n\nif __name__ == \"__main__\":\n main()\n", "code_b": "var_7, var_8, var_9 = range, max, input\nvar_5, var_1, var_4 = list(map(int, var_9().split()))\nvar_3 = [[0] * -~var_1 for var_10 in var_7(var_5 + 1)]\nfor var_10 in \"_\" * var_4:\n var_11, var_6, var_12 = list(map(int, var_9().split()))\n var_3[var_11][var_6] = var_12\nvar_2 = [[[0] * -~var_1 for var_10 in var_7(var_5 + 1)] for var_10 in var_7(4)]\nfor var_11 in var_7(1, var_5 + 1):\n for var_13 in var_7(1, 4):\n for var_6 in var_7(1, var_1 + 1):\n var_2[var_13][var_11][var_6] = var_8(\n var_2[var_13 - 1][var_11][var_6],\n var_2[var_13 - 1][var_11][var_6 - 1] + var_3[var_11][var_6],\n var_2[var_13][var_11][var_6 - 1],\n (var_13 < 2)\n * (\n var_3[var_11][var_6]\n + var_8(\n var_2[1][var_11 - 1][var_6],\n var_2[2][var_11 - 1][var_6],\n var_2[3][var_11 - 1][var_6],\n )\n ),\n )\nprint((var_2[3][var_5][var_1]))\n", "expected": "B"} -{"pair_id": "python_17849_reversed", "problem_id": "p02586", "language": "python", "direction": "reversed", "speedup": 2.7429, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_7, var_8, var_9 = range, max, input\nvar_5, var_1, var_4 = list(map(int, var_9().split()))\nvar_3 = [[0] * -~var_1 for var_10 in var_7(var_5 + 1)]\nfor var_10 in \"_\" * var_4:\n var_11, var_6, var_12 = list(map(int, var_9().split()))\n var_3[var_11][var_6] = var_12\nvar_2 = [[[0] * -~var_1 for var_10 in var_7(var_5 + 1)] for var_10 in var_7(4)]\nfor var_11 in var_7(1, var_5 + 1):\n for var_13 in var_7(1, 4):\n for var_6 in var_7(1, var_1 + 1):\n var_2[var_13][var_11][var_6] = var_8(\n var_2[var_13 - 1][var_11][var_6],\n var_2[var_13 - 1][var_11][var_6 - 1] + var_3[var_11][var_6],\n var_2[var_13][var_11][var_6 - 1],\n (var_13 < 2)\n * (\n var_3[var_11][var_6]\n + var_8(\n var_2[1][var_11 - 1][var_6],\n var_2[2][var_11 - 1][var_6],\n var_2[3][var_11 - 1][var_6],\n )\n ),\n )\nprint((var_2[3][var_5][var_1]))\n\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n\nfrom collections import deque, defaultdict\n\n\n\nfrom collections import Counter \n\n\nfrom itertools import accumulate, combinations, permutations, product \n\n\nfrom heapq import heapify, heappop, heappush\n\n\nfrom functools import reduce, lru_cache \n\n\nfrom decimal import Decimal\n\n\ndef input():\n x = sys.stdin.readline()\n return x[:-1] if x[-1] == \"\\n\" else x\n\n\ndef printe(*x):\n print(\"\n\n\ndef printl(li):\n _ = print(*li, sep=\"\\n\") if li else None\n\n\ndef argsort(s, return_sorted=False):\n inds = sorted(range(len(s)), key=lambda k: s[k])\n if return_sorted:\n return inds, [s[i] for i in inds]\n return inds\n\n\ndef alp2num(c, cap=False):\n return ord(c) - 97 if not cap else ord(c) - 65\n\n\ndef num2alp(i, cap=False):\n return chr(i + 97) if not cap else chr(i + 65)\n\n\ndef matmat(A, B):\n K, N, M = len(B), len(A), len(B[0])\n return [\n [sum([(A[i][k] * B[k][j]) for k in range(K)]) for j in range(M)]\n for i in range(N)\n ]\n\n\ndef matvec(M, v):\n N, size = len(v), len(M)\n return [sum([M[i][j] * v[j] for j in range(N)]) for i in range(size)]\n\n\ndef T(M):\n n, m = len(M), len(M[0])\n return [[M[j][i] for j in range(n)] for i in range(m)]\n\n\ndef binr(x):\n return bin(x)[2:]\n\n\ndef bitcount(x): \n x = x - ((x >> 1) & 0x5555555555555555)\n x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)\n x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F\n x += x >> 8\n x += x >> 16\n x += x >> 32\n return x & 0x7F\n\n\ndef main():\n mod = 1000000007\n \n \n R, C, K = map(int, input().split())\n \n \n base = 5000\n S = []\n for i in range(K):\n r, c, v = map(int, input().split())\n r -= 1\n c -= 1\n S.append((r * base + c, v))\n S.sort(key=itemgetter(0))\n p = 0\n dp = [[0] * 4 for _ in range(C)]\n dpi = [[0] * 4 for _ in range(C)]\n for i in range(R):\n for j in range(C):\n cv = 0\n if p < K and i * base + j == S[p][0]:\n cv = S[p][1]\n p += 1\n if j > 0:\n dpi[j] = copy(dpi[j - 1])\n else:\n dpi[0] = [0] * 4\n if i > 0:\n dpi[j][0] = max(dp[j], dpi[j][0])\n if cv != 0:\n for k in range(2, -1, -1):\n dpi[j][k + 1] = max(dpi[j][k] + cv, dpi[j][k + 1])\n \n dp[j] = max(dpi[j])\n print(dp[-1])\n\n\nif __name__ == \"__main__\":\n main()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_7, var_8, var_9 = range, max, input\nvar_5, var_1, var_4 = list(map(int, var_9().split()))\nvar_3 = [[0] * -~var_1 for var_10 in var_7(var_5 + 1)]\nfor var_10 in \"_\" * var_4:\n var_11, var_6, var_12 = list(map(int, var_9().split()))\n var_3[var_11][var_6] = var_12\nvar_2 = [[[0] * -~var_1 for var_10 in var_7(var_5 + 1)] for var_10 in var_7(4)]\nfor var_11 in var_7(1, var_5 + 1):\n for var_13 in var_7(1, 4):\n for var_6 in var_7(1, var_1 + 1):\n var_2[var_13][var_11][var_6] = var_8(\n var_2[var_13 - 1][var_11][var_6],\n var_2[var_13 - 1][var_11][var_6 - 1] + var_3[var_11][var_6],\n var_2[var_13][var_11][var_6 - 1],\n (var_13 < 2)\n * (\n var_3[var_11][var_6]\n + var_8(\n var_2[1][var_11 - 1][var_6],\n var_2[2][var_11 - 1][var_6],\n var_2[3][var_11 - 1][var_6],\n )\n ),\n )\nprint((var_2[3][var_5][var_1]))\n", "code_b": "import sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n\nfrom collections import deque, defaultdict\n\n\n\nfrom collections import Counter \n\n\nfrom itertools import accumulate, combinations, permutations, product \n\n\nfrom heapq import heapify, heappop, heappush\n\n\nfrom functools import reduce, lru_cache \n\n\nfrom decimal import Decimal\n\n\ndef input():\n x = sys.stdin.readline()\n return x[:-1] if x[-1] == \"\\n\" else x\n\n\ndef printe(*x):\n print(\"\n\n\ndef printl(li):\n _ = print(*li, sep=\"\\n\") if li else None\n\n\ndef argsort(s, return_sorted=False):\n inds = sorted(range(len(s)), key=lambda k: s[k])\n if return_sorted:\n return inds, [s[i] for i in inds]\n return inds\n\n\ndef alp2num(c, cap=False):\n return ord(c) - 97 if not cap else ord(c) - 65\n\n\ndef num2alp(i, cap=False):\n return chr(i + 97) if not cap else chr(i + 65)\n\n\ndef matmat(A, B):\n K, N, M = len(B), len(A), len(B[0])\n return [\n [sum([(A[i][k] * B[k][j]) for k in range(K)]) for j in range(M)]\n for i in range(N)\n ]\n\n\ndef matvec(M, v):\n N, size = len(v), len(M)\n return [sum([M[i][j] * v[j] for j in range(N)]) for i in range(size)]\n\n\ndef T(M):\n n, m = len(M), len(M[0])\n return [[M[j][i] for j in range(n)] for i in range(m)]\n\n\ndef binr(x):\n return bin(x)[2:]\n\n\ndef bitcount(x): \n x = x - ((x >> 1) & 0x5555555555555555)\n x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)\n x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F\n x += x >> 8\n x += x >> 16\n x += x >> 32\n return x & 0x7F\n\n\ndef main():\n mod = 1000000007\n \n \n R, C, K = map(int, input().split())\n \n \n base = 5000\n S = []\n for i in range(K):\n r, c, v = map(int, input().split())\n r -= 1\n c -= 1\n S.append((r * base + c, v))\n S.sort(key=itemgetter(0))\n p = 0\n dp = [[0] * 4 for _ in range(C)]\n dpi = [[0] * 4 for _ in range(C)]\n for i in range(R):\n for j in range(C):\n cv = 0\n if p < K and i * base + j == S[p][0]:\n cv = S[p][1]\n p += 1\n if j > 0:\n dpi[j] = copy(dpi[j - 1])\n else:\n dpi[0] = [0] * 4\n if i > 0:\n dpi[j][0] = max(dp[j], dpi[j][0])\n if cv != 0:\n for k in range(2, -1, -1):\n dpi[j][k + 1] = max(dpi[j][k] + cv, dpi[j][k + 1])\n \n dp[j] = max(dpi[j])\n print(dp[-1])\n\n\nif __name__ == \"__main__\":\n main()\n", "expected": "A"} +{"pair_id": "python_17849_forward", "problem_id": "p02586", "language": "python", "direction": "forward", "speedup": 2.7429, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n \nfrom collections import deque, defaultdict\n\n \n \nfrom collections import Counter \n\n \nfrom itertools import accumulate, combinations, permutations, product \n\n \nfrom heapq import heapify, heappop, heappush\n\n \nfrom functools import reduce, lru_cache \n\n \nfrom decimal import Decimal\n\n\ndef func_6():\n var_29 = sys.stdin.readline()\n return var_29[:-1] if var_29[-1] == \"\n\" else var_29\n\n\ndef func_11(*var_29):\n print(\"## \", *var_29, file=sys.stderr)\n\n\ndef func_12(var_19):\n _ = print(*var_19, sep=\"\n\") if var_19 else None\n\n\ndef func_3(var_26, var_25=False):\n var_16 = sorted(range(len(var_26)), key=lambda var_18: var_26[var_18])\n if var_25:\n return var_16, [var_26[var_15] for var_15 in var_16]\n return var_16\n\n\ndef func_2(var_10, var_11=False):\n return ord(var_10) - 97 if not var_11 else ord(var_10) - 65\n\n\ndef func_10(var_15, var_11=False):\n return chr(var_15 + 97) if not var_11 else chr(var_15 + 65)\n\n\ndef func_8(var_1, var_2):\n var_4, var_6, var_5 = len(var_2), len(var_1), len(var_2[0])\n return [\n [sum([(var_1[var_15][var_18] * var_2[var_18][var_17]) for var_18 in range(var_4)]) for var_17 in range(var_5)]\n for var_15 in range(var_6)\n ]\n\n\ndef func_9(var_5, var_28):\n var_6, var_27 = len(var_28), len(var_5)\n return [sum([var_5[var_15][var_17] * var_28[var_17] for var_17 in range(var_6)]) for var_15 in range(var_27)]\n\n\ndef func_1(var_5):\n var_22, var_20 = len(var_5), len(var_5[0])\n return [[var_5[var_17][var_15] for var_17 in range(var_22)] for var_15 in range(var_20)]\n\n\ndef func_4(var_29):\n return bin(var_29)[2:]\n\n\ndef func_5(var_29): \n var_29 = var_29 - ((var_29 >> 1) & 0x5555555555555555)\n var_29 = (var_29 & 0x3333333333333333) + ((var_29 >> 2) & 0x3333333333333333)\n var_29 = (var_29 + (var_29 >> 4)) & 0x0F0F0F0F0F0F0F0F\n var_29 += var_29 >> 8\n var_29 += var_29 >> 16\n var_29 += var_29 >> 32\n return var_29 & 0x7F\n\n\ndef func_7():\n var_21 = 1000000007\n \n \n var_7, var_3, var_4 = map(int, func_6().split())\n \n \n var_9 = 5000\n var_8 = []\n for var_15 in range(var_4):\n var_24, var_10, var_28 = map(int, func_6().split())\n var_24 -= 1\n var_10 -= 1\n var_8.append((var_24 * var_9 + var_10, var_28))\n var_8.sort(key=itemgetter(0))\n var_23 = 0\n var_13 = [[0] * 4 for _ in range(var_3)]\n var_14 = [[0] * 4 for _ in range(var_3)]\n for var_15 in range(var_7):\n for var_17 in range(var_3):\n var_12 = 0\n if var_23 < var_4 and var_15 * var_9 + var_17 == var_8[var_23][0]:\n var_12 = var_8[var_23][1]\n var_23 += 1\n if var_17 > 0:\n var_14[var_17] = copy(var_14[var_17 - 1])\n else:\n var_14[0] = [0] * 4\n if var_15 > 0:\n var_14[var_17][0] = max(var_13[var_17], var_14[var_17][0])\n if var_12 != 0:\n for var_18 in range(2, -1, -1):\n var_14[var_17][var_18 + 1] = max(var_14[var_17][var_18] + var_12, var_14[var_17][var_18 + 1])\n \n var_13[var_17] = max(var_14[var_17])\n print(var_13[-1])\n\n\nif __name__ == \"__main__\":\n func_7()\n```\n\n## Code B:\n```python\nvar_7, var_8, var_9 = range, max, input\nvar_5, var_1, var_4 = list(map(int, var_9().split()))\nvar_3 = [[0] * -~var_1 for var_10 in var_7(var_5 + 1)]\nfor var_10 in \"_\" * var_4:\n var_11, var_6, var_12 = list(map(int, var_9().split()))\n var_3[var_11][var_6] = var_12\nvar_2 = [[[0] * -~var_1 for var_10 in var_7(var_5 + 1)] for var_10 in var_7(4)]\nfor var_11 in var_7(1, var_5 + 1):\n for var_13 in var_7(1, 4):\n for var_6 in var_7(1, var_1 + 1):\n var_2[var_13][var_11][var_6] = var_8(\n var_2[var_13 - 1][var_11][var_6],\n var_2[var_13 - 1][var_11][var_6 - 1] + var_3[var_11][var_6],\n var_2[var_13][var_11][var_6 - 1],\n (var_13 < 2) * (var_3[var_11][var_6] + var_8(var_2[1][var_11 - 1][var_6], var_2[2][var_11 - 1][var_6], var_2[3][var_11 - 1][var_6])),\n )\nprint((var_2[3][var_5][var_1]))\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n \nfrom collections import deque, defaultdict\n\n \n \nfrom collections import Counter \n\n \nfrom itertools import accumulate, combinations, permutations, product \n\n \nfrom heapq import heapify, heappop, heappush\n\n \nfrom functools import reduce, lru_cache \n\n \nfrom decimal import Decimal\n\n\ndef func_6():\n var_29 = sys.stdin.readline()\n return var_29[:-1] if var_29[-1] == \"\\n\" else var_29\n\n\ndef func_11(*var_29):\n print(\"## \", *var_29, file=sys.stderr)\n\n\ndef func_12(var_19):\n _ = print(*var_19, sep=\"\\n\") if var_19 else None\n\n\ndef func_3(var_26, var_25=False):\n var_16 = sorted(range(len(var_26)), key=lambda var_18: var_26[var_18])\n if var_25:\n return var_16, [var_26[var_15] for var_15 in var_16]\n return var_16\n\n\ndef func_2(var_10, var_11=False):\n return ord(var_10) - 97 if not var_11 else ord(var_10) - 65\n\n\ndef func_10(var_15, var_11=False):\n return chr(var_15 + 97) if not var_11 else chr(var_15 + 65)\n\n\ndef func_8(var_1, var_2):\n var_4, var_6, var_5 = len(var_2), len(var_1), len(var_2[0])\n return [\n [sum([(var_1[var_15][var_18] * var_2[var_18][var_17]) for var_18 in range(var_4)]) for var_17 in range(var_5)]\n for var_15 in range(var_6)\n ]\n\n\ndef func_9(var_5, var_28):\n var_6, var_27 = len(var_28), len(var_5)\n return [sum([var_5[var_15][var_17] * var_28[var_17] for var_17 in range(var_6)]) for var_15 in range(var_27)]\n\n\ndef func_1(var_5):\n var_22, var_20 = len(var_5), len(var_5[0])\n return [[var_5[var_17][var_15] for var_17 in range(var_22)] for var_15 in range(var_20)]\n\n\ndef func_4(var_29):\n return bin(var_29)[2:]\n\n\ndef func_5(var_29): \n var_29 = var_29 - ((var_29 >> 1) & 0x5555555555555555)\n var_29 = (var_29 & 0x3333333333333333) + ((var_29 >> 2) & 0x3333333333333333)\n var_29 = (var_29 + (var_29 >> 4)) & 0x0F0F0F0F0F0F0F0F\n var_29 += var_29 >> 8\n var_29 += var_29 >> 16\n var_29 += var_29 >> 32\n return var_29 & 0x7F\n\n\ndef func_7():\n var_21 = 1000000007\n \n \n var_7, var_3, var_4 = map(int, func_6().split())\n \n \n var_9 = 5000\n var_8 = []\n for var_15 in range(var_4):\n var_24, var_10, var_28 = map(int, func_6().split())\n var_24 -= 1\n var_10 -= 1\n var_8.append((var_24 * var_9 + var_10, var_28))\n var_8.sort(key=itemgetter(0))\n var_23 = 0\n var_13 = [[0] * 4 for _ in range(var_3)]\n var_14 = [[0] * 4 for _ in range(var_3)]\n for var_15 in range(var_7):\n for var_17 in range(var_3):\n var_12 = 0\n if var_23 < var_4 and var_15 * var_9 + var_17 == var_8[var_23][0]:\n var_12 = var_8[var_23][1]\n var_23 += 1\n if var_17 > 0:\n var_14[var_17] = copy(var_14[var_17 - 1])\n else:\n var_14[0] = [0] * 4\n if var_15 > 0:\n var_14[var_17][0] = max(var_13[var_17], var_14[var_17][0])\n if var_12 != 0:\n for var_18 in range(2, -1, -1):\n var_14[var_17][var_18 + 1] = max(var_14[var_17][var_18] + var_12, var_14[var_17][var_18 + 1])\n \n var_13[var_17] = max(var_14[var_17])\n print(var_13[-1])\n\n\nif __name__ == \"__main__\":\n func_7()\n", "code_b": "var_7, var_8, var_9 = range, max, input\nvar_5, var_1, var_4 = list(map(int, var_9().split()))\nvar_3 = [[0] * -~var_1 for var_10 in var_7(var_5 + 1)]\nfor var_10 in \"_\" * var_4:\n var_11, var_6, var_12 = list(map(int, var_9().split()))\n var_3[var_11][var_6] = var_12\nvar_2 = [[[0] * -~var_1 for var_10 in var_7(var_5 + 1)] for var_10 in var_7(4)]\nfor var_11 in var_7(1, var_5 + 1):\n for var_13 in var_7(1, 4):\n for var_6 in var_7(1, var_1 + 1):\n var_2[var_13][var_11][var_6] = var_8(\n var_2[var_13 - 1][var_11][var_6],\n var_2[var_13 - 1][var_11][var_6 - 1] + var_3[var_11][var_6],\n var_2[var_13][var_11][var_6 - 1],\n (var_13 < 2) * (var_3[var_11][var_6] + var_8(var_2[1][var_11 - 1][var_6], var_2[2][var_11 - 1][var_6], var_2[3][var_11 - 1][var_6])),\n )\nprint((var_2[3][var_5][var_1]))\n", "expected": "B"} +{"pair_id": "python_17849_reversed", "problem_id": "p02586", "language": "python", "direction": "reversed", "speedup": 2.7429, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_7, var_8, var_9 = range, max, input\nvar_5, var_1, var_4 = list(map(int, var_9().split()))\nvar_3 = [[0] * -~var_1 for var_10 in var_7(var_5 + 1)]\nfor var_10 in \"_\" * var_4:\n var_11, var_6, var_12 = list(map(int, var_9().split()))\n var_3[var_11][var_6] = var_12\nvar_2 = [[[0] * -~var_1 for var_10 in var_7(var_5 + 1)] for var_10 in var_7(4)]\nfor var_11 in var_7(1, var_5 + 1):\n for var_13 in var_7(1, 4):\n for var_6 in var_7(1, var_1 + 1):\n var_2[var_13][var_11][var_6] = var_8(\n var_2[var_13 - 1][var_11][var_6],\n var_2[var_13 - 1][var_11][var_6 - 1] + var_3[var_11][var_6],\n var_2[var_13][var_11][var_6 - 1],\n (var_13 < 2) * (var_3[var_11][var_6] + var_8(var_2[1][var_11 - 1][var_6], var_2[2][var_11 - 1][var_6], var_2[3][var_11 - 1][var_6])),\n )\nprint((var_2[3][var_5][var_1]))\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n \nfrom collections import deque, defaultdict\n\n \n \nfrom collections import Counter \n\n \nfrom itertools import accumulate, combinations, permutations, product \n\n \nfrom heapq import heapify, heappop, heappush\n\n \nfrom functools import reduce, lru_cache \n\n \nfrom decimal import Decimal\n\n\ndef func_6():\n var_29 = sys.stdin.readline()\n return var_29[:-1] if var_29[-1] == \"\n\" else var_29\n\n\ndef func_11(*var_29):\n print(\"## \", *var_29, file=sys.stderr)\n\n\ndef func_12(var_19):\n _ = print(*var_19, sep=\"\n\") if var_19 else None\n\n\ndef func_3(var_26, var_25=False):\n var_16 = sorted(range(len(var_26)), key=lambda var_18: var_26[var_18])\n if var_25:\n return var_16, [var_26[var_15] for var_15 in var_16]\n return var_16\n\n\ndef func_2(var_10, var_11=False):\n return ord(var_10) - 97 if not var_11 else ord(var_10) - 65\n\n\ndef func_10(var_15, var_11=False):\n return chr(var_15 + 97) if not var_11 else chr(var_15 + 65)\n\n\ndef func_8(var_1, var_2):\n var_4, var_6, var_5 = len(var_2), len(var_1), len(var_2[0])\n return [\n [sum([(var_1[var_15][var_18] * var_2[var_18][var_17]) for var_18 in range(var_4)]) for var_17 in range(var_5)]\n for var_15 in range(var_6)\n ]\n\n\ndef func_9(var_5, var_28):\n var_6, var_27 = len(var_28), len(var_5)\n return [sum([var_5[var_15][var_17] * var_28[var_17] for var_17 in range(var_6)]) for var_15 in range(var_27)]\n\n\ndef func_1(var_5):\n var_22, var_20 = len(var_5), len(var_5[0])\n return [[var_5[var_17][var_15] for var_17 in range(var_22)] for var_15 in range(var_20)]\n\n\ndef func_4(var_29):\n return bin(var_29)[2:]\n\n\ndef func_5(var_29): \n var_29 = var_29 - ((var_29 >> 1) & 0x5555555555555555)\n var_29 = (var_29 & 0x3333333333333333) + ((var_29 >> 2) & 0x3333333333333333)\n var_29 = (var_29 + (var_29 >> 4)) & 0x0F0F0F0F0F0F0F0F\n var_29 += var_29 >> 8\n var_29 += var_29 >> 16\n var_29 += var_29 >> 32\n return var_29 & 0x7F\n\n\ndef func_7():\n var_21 = 1000000007\n \n \n var_7, var_3, var_4 = map(int, func_6().split())\n \n \n var_9 = 5000\n var_8 = []\n for var_15 in range(var_4):\n var_24, var_10, var_28 = map(int, func_6().split())\n var_24 -= 1\n var_10 -= 1\n var_8.append((var_24 * var_9 + var_10, var_28))\n var_8.sort(key=itemgetter(0))\n var_23 = 0\n var_13 = [[0] * 4 for _ in range(var_3)]\n var_14 = [[0] * 4 for _ in range(var_3)]\n for var_15 in range(var_7):\n for var_17 in range(var_3):\n var_12 = 0\n if var_23 < var_4 and var_15 * var_9 + var_17 == var_8[var_23][0]:\n var_12 = var_8[var_23][1]\n var_23 += 1\n if var_17 > 0:\n var_14[var_17] = copy(var_14[var_17 - 1])\n else:\n var_14[0] = [0] * 4\n if var_15 > 0:\n var_14[var_17][0] = max(var_13[var_17], var_14[var_17][0])\n if var_12 != 0:\n for var_18 in range(2, -1, -1):\n var_14[var_17][var_18 + 1] = max(var_14[var_17][var_18] + var_12, var_14[var_17][var_18 + 1])\n \n var_13[var_17] = max(var_14[var_17])\n print(var_13[-1])\n\n\nif __name__ == \"__main__\":\n func_7()\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_7, var_8, var_9 = range, max, input\nvar_5, var_1, var_4 = list(map(int, var_9().split()))\nvar_3 = [[0] * -~var_1 for var_10 in var_7(var_5 + 1)]\nfor var_10 in \"_\" * var_4:\n var_11, var_6, var_12 = list(map(int, var_9().split()))\n var_3[var_11][var_6] = var_12\nvar_2 = [[[0] * -~var_1 for var_10 in var_7(var_5 + 1)] for var_10 in var_7(4)]\nfor var_11 in var_7(1, var_5 + 1):\n for var_13 in var_7(1, 4):\n for var_6 in var_7(1, var_1 + 1):\n var_2[var_13][var_11][var_6] = var_8(\n var_2[var_13 - 1][var_11][var_6],\n var_2[var_13 - 1][var_11][var_6 - 1] + var_3[var_11][var_6],\n var_2[var_13][var_11][var_6 - 1],\n (var_13 < 2) * (var_3[var_11][var_6] + var_8(var_2[1][var_11 - 1][var_6], var_2[2][var_11 - 1][var_6], var_2[3][var_11 - 1][var_6])),\n )\nprint((var_2[3][var_5][var_1]))\n", "code_b": "import sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n \nfrom collections import deque, defaultdict\n\n \n \nfrom collections import Counter \n\n \nfrom itertools import accumulate, combinations, permutations, product \n\n \nfrom heapq import heapify, heappop, heappush\n\n \nfrom functools import reduce, lru_cache \n\n \nfrom decimal import Decimal\n\n\ndef func_6():\n var_29 = sys.stdin.readline()\n return var_29[:-1] if var_29[-1] == \"\\n\" else var_29\n\n\ndef func_11(*var_29):\n print(\"## \", *var_29, file=sys.stderr)\n\n\ndef func_12(var_19):\n _ = print(*var_19, sep=\"\\n\") if var_19 else None\n\n\ndef func_3(var_26, var_25=False):\n var_16 = sorted(range(len(var_26)), key=lambda var_18: var_26[var_18])\n if var_25:\n return var_16, [var_26[var_15] for var_15 in var_16]\n return var_16\n\n\ndef func_2(var_10, var_11=False):\n return ord(var_10) - 97 if not var_11 else ord(var_10) - 65\n\n\ndef func_10(var_15, var_11=False):\n return chr(var_15 + 97) if not var_11 else chr(var_15 + 65)\n\n\ndef func_8(var_1, var_2):\n var_4, var_6, var_5 = len(var_2), len(var_1), len(var_2[0])\n return [\n [sum([(var_1[var_15][var_18] * var_2[var_18][var_17]) for var_18 in range(var_4)]) for var_17 in range(var_5)]\n for var_15 in range(var_6)\n ]\n\n\ndef func_9(var_5, var_28):\n var_6, var_27 = len(var_28), len(var_5)\n return [sum([var_5[var_15][var_17] * var_28[var_17] for var_17 in range(var_6)]) for var_15 in range(var_27)]\n\n\ndef func_1(var_5):\n var_22, var_20 = len(var_5), len(var_5[0])\n return [[var_5[var_17][var_15] for var_17 in range(var_22)] for var_15 in range(var_20)]\n\n\ndef func_4(var_29):\n return bin(var_29)[2:]\n\n\ndef func_5(var_29): \n var_29 = var_29 - ((var_29 >> 1) & 0x5555555555555555)\n var_29 = (var_29 & 0x3333333333333333) + ((var_29 >> 2) & 0x3333333333333333)\n var_29 = (var_29 + (var_29 >> 4)) & 0x0F0F0F0F0F0F0F0F\n var_29 += var_29 >> 8\n var_29 += var_29 >> 16\n var_29 += var_29 >> 32\n return var_29 & 0x7F\n\n\ndef func_7():\n var_21 = 1000000007\n \n \n var_7, var_3, var_4 = map(int, func_6().split())\n \n \n var_9 = 5000\n var_8 = []\n for var_15 in range(var_4):\n var_24, var_10, var_28 = map(int, func_6().split())\n var_24 -= 1\n var_10 -= 1\n var_8.append((var_24 * var_9 + var_10, var_28))\n var_8.sort(key=itemgetter(0))\n var_23 = 0\n var_13 = [[0] * 4 for _ in range(var_3)]\n var_14 = [[0] * 4 for _ in range(var_3)]\n for var_15 in range(var_7):\n for var_17 in range(var_3):\n var_12 = 0\n if var_23 < var_4 and var_15 * var_9 + var_17 == var_8[var_23][0]:\n var_12 = var_8[var_23][1]\n var_23 += 1\n if var_17 > 0:\n var_14[var_17] = copy(var_14[var_17 - 1])\n else:\n var_14[0] = [0] * 4\n if var_15 > 0:\n var_14[var_17][0] = max(var_13[var_17], var_14[var_17][0])\n if var_12 != 0:\n for var_18 in range(2, -1, -1):\n var_14[var_17][var_18 + 1] = max(var_14[var_17][var_18] + var_12, var_14[var_17][var_18 + 1])\n \n var_13[var_17] = max(var_14[var_17])\n print(var_13[-1])\n\n\nif __name__ == \"__main__\":\n func_7()\n", "expected": "A"} {"pair_id": "python_5180_forward", "problem_id": "p03734", "language": "python", "direction": "forward", "speedup": 55.1596, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1, var_2 = list(map(int, input().split()))\nvar_14 = []\nfor var_8 in range(var_1):\n var_12, var_11 = list(map(int, input().split()))\n var_14.append((var_12, var_11))\n\nvar_13 = var_14[0][0]\nvar_6 = [[[-1] * (330) for var_8 in range(110)] for var_10 in range(110)]\nvar_6[0][0][0] = 0\nfor var_8 in range(var_1):\n var_12, var_11 = var_14[var_8]\n var_7 = var_12 - var_13\n for var_9 in range(var_8 + 1):\n for var_10 in range(310):\n\n if var_6[var_8][var_9][var_9] == -1:\n continue\n var_6[var_8 + 1][var_9][var_10] = max(\n var_6[var_8 + 1][var_9][var_10], var_6[var_8][var_9][var_10]\n )\n var_6[var_8 + 1][var_9 + 1][var_10 + var_7] = max(\n var_6[var_8][var_9][var_10] + var_11,\n var_6[var_8 + 1][var_9 + 1][var_10 + var_7],\n )\nvar_5 = var_6[var_1]\nvar_4 = 0\nfor var_9 in range(var_1 + 1):\n if var_2 - var_9 * var_13 >= 0:\n var_3 = max(var_5[var_9][: var_2 - var_9 * var_13 + 1])\n var_4 = max(var_4, var_3)\nprint(var_4)\n\n```\n\n## Code B:\n```python\nimport sys\n\nvar_11 = sys.stdin.readline\nfrom itertools import accumulate\n\nvar_2, var_3 = list(map(int, var_11().split()))\nvar_8 = [[int(var_10) for var_10 in var_11().split()] for _ in range(var_2)]\nvar_1 = var_8[0][0]\n\nvar_4 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1])\nvar_5 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1 + 1])\nvar_6 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1 + 2])\nvar_7 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1 + 3])\n\nvar_4 = tuple(accumulate(var_4))\nvar_5 = tuple(accumulate(var_5))\nvar_6 = tuple(accumulate(var_6))\nvar_7 = tuple(accumulate(var_7))\nvar_9 = 0\nfor var_15 in range(len(var_4)):\n for var_16 in range(len(var_5)):\n for var_17 in range(len(var_6)):\n for var_18 in range(len(var_7)):\n\n var_12 = (len(var_4) - (var_15 + 1)) * var_1\n var_12 += (len(var_5) - (var_16 + 1)) * (var_1 + 1)\n var_12 += (len(var_6) - (var_17 + 1)) * (var_1 + 2)\n var_12 += (len(var_7) - (var_18 + 1)) * (var_1 + 3)\n\n if var_12 <= var_3:\n\n var_12 = var_4[-1] - var_4[var_15]\n var_12 += var_5[-1] - var_5[var_16]\n var_12 += var_6[-1] - var_6[var_17]\n var_12 += var_7[-1] - var_7[var_18]\n var_9 = max(var_9, var_12)\nprint(var_9)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1, var_2 = list(map(int, input().split()))\nvar_14 = []\nfor var_8 in range(var_1):\n var_12, var_11 = list(map(int, input().split()))\n var_14.append((var_12, var_11))\n\nvar_13 = var_14[0][0]\nvar_6 = [[[-1] * (330) for var_8 in range(110)] for var_10 in range(110)]\nvar_6[0][0][0] = 0\nfor var_8 in range(var_1):\n var_12, var_11 = var_14[var_8]\n var_7 = var_12 - var_13\n for var_9 in range(var_8 + 1):\n for var_10 in range(310):\n\n if var_6[var_8][var_9][var_9] == -1:\n continue\n var_6[var_8 + 1][var_9][var_10] = max(\n var_6[var_8 + 1][var_9][var_10], var_6[var_8][var_9][var_10]\n )\n var_6[var_8 + 1][var_9 + 1][var_10 + var_7] = max(\n var_6[var_8][var_9][var_10] + var_11,\n var_6[var_8 + 1][var_9 + 1][var_10 + var_7],\n )\nvar_5 = var_6[var_1]\nvar_4 = 0\nfor var_9 in range(var_1 + 1):\n if var_2 - var_9 * var_13 >= 0:\n var_3 = max(var_5[var_9][: var_2 - var_9 * var_13 + 1])\n var_4 = max(var_4, var_3)\nprint(var_4)\n", "code_b": "import sys\n\nvar_11 = sys.stdin.readline\nfrom itertools import accumulate\n\nvar_2, var_3 = list(map(int, var_11().split()))\nvar_8 = [[int(var_10) for var_10 in var_11().split()] for _ in range(var_2)]\nvar_1 = var_8[0][0]\n\nvar_4 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1])\nvar_5 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1 + 1])\nvar_6 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1 + 2])\nvar_7 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1 + 3])\n\nvar_4 = tuple(accumulate(var_4))\nvar_5 = tuple(accumulate(var_5))\nvar_6 = tuple(accumulate(var_6))\nvar_7 = tuple(accumulate(var_7))\nvar_9 = 0\nfor var_15 in range(len(var_4)):\n for var_16 in range(len(var_5)):\n for var_17 in range(len(var_6)):\n for var_18 in range(len(var_7)):\n\n var_12 = (len(var_4) - (var_15 + 1)) * var_1\n var_12 += (len(var_5) - (var_16 + 1)) * (var_1 + 1)\n var_12 += (len(var_6) - (var_17 + 1)) * (var_1 + 2)\n var_12 += (len(var_7) - (var_18 + 1)) * (var_1 + 3)\n\n if var_12 <= var_3:\n\n var_12 = var_4[-1] - var_4[var_15]\n var_12 += var_5[-1] - var_5[var_16]\n var_12 += var_6[-1] - var_6[var_17]\n var_12 += var_7[-1] - var_7[var_18]\n var_9 = max(var_9, var_12)\nprint(var_9)\n", "expected": "B"} {"pair_id": "python_5180_reversed", "problem_id": "p03734", "language": "python", "direction": "reversed", "speedup": 55.1596, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_11 = sys.stdin.readline\nfrom itertools import accumulate\n\nvar_2, var_3 = list(map(int, var_11().split()))\nvar_8 = [[int(var_10) for var_10 in var_11().split()] for _ in range(var_2)]\nvar_1 = var_8[0][0]\n\nvar_4 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1])\nvar_5 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1 + 1])\nvar_6 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1 + 2])\nvar_7 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1 + 3])\n\nvar_4 = tuple(accumulate(var_4))\nvar_5 = tuple(accumulate(var_5))\nvar_6 = tuple(accumulate(var_6))\nvar_7 = tuple(accumulate(var_7))\nvar_9 = 0\nfor var_15 in range(len(var_4)):\n for var_16 in range(len(var_5)):\n for var_17 in range(len(var_6)):\n for var_18 in range(len(var_7)):\n\n var_12 = (len(var_4) - (var_15 + 1)) * var_1\n var_12 += (len(var_5) - (var_16 + 1)) * (var_1 + 1)\n var_12 += (len(var_6) - (var_17 + 1)) * (var_1 + 2)\n var_12 += (len(var_7) - (var_18 + 1)) * (var_1 + 3)\n\n if var_12 <= var_3:\n\n var_12 = var_4[-1] - var_4[var_15]\n var_12 += var_5[-1] - var_5[var_16]\n var_12 += var_6[-1] - var_6[var_17]\n var_12 += var_7[-1] - var_7[var_18]\n var_9 = max(var_9, var_12)\nprint(var_9)\n\n```\n\n## Code B:\n```python\nvar_1, var_2 = list(map(int, input().split()))\nvar_14 = []\nfor var_8 in range(var_1):\n var_12, var_11 = list(map(int, input().split()))\n var_14.append((var_12, var_11))\n\nvar_13 = var_14[0][0]\nvar_6 = [[[-1] * (330) for var_8 in range(110)] for var_10 in range(110)]\nvar_6[0][0][0] = 0\nfor var_8 in range(var_1):\n var_12, var_11 = var_14[var_8]\n var_7 = var_12 - var_13\n for var_9 in range(var_8 + 1):\n for var_10 in range(310):\n\n if var_6[var_8][var_9][var_9] == -1:\n continue\n var_6[var_8 + 1][var_9][var_10] = max(\n var_6[var_8 + 1][var_9][var_10], var_6[var_8][var_9][var_10]\n )\n var_6[var_8 + 1][var_9 + 1][var_10 + var_7] = max(\n var_6[var_8][var_9][var_10] + var_11,\n var_6[var_8 + 1][var_9 + 1][var_10 + var_7],\n )\nvar_5 = var_6[var_1]\nvar_4 = 0\nfor var_9 in range(var_1 + 1):\n if var_2 - var_9 * var_13 >= 0:\n var_3 = max(var_5[var_9][: var_2 - var_9 * var_13 + 1])\n var_4 = max(var_4, var_3)\nprint(var_4)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_11 = sys.stdin.readline\nfrom itertools import accumulate\n\nvar_2, var_3 = list(map(int, var_11().split()))\nvar_8 = [[int(var_10) for var_10 in var_11().split()] for _ in range(var_2)]\nvar_1 = var_8[0][0]\n\nvar_4 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1])\nvar_5 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1 + 1])\nvar_6 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1 + 2])\nvar_7 = sorted([0] + [var_13 for var_14, var_13 in var_8 if var_14 == var_1 + 3])\n\nvar_4 = tuple(accumulate(var_4))\nvar_5 = tuple(accumulate(var_5))\nvar_6 = tuple(accumulate(var_6))\nvar_7 = tuple(accumulate(var_7))\nvar_9 = 0\nfor var_15 in range(len(var_4)):\n for var_16 in range(len(var_5)):\n for var_17 in range(len(var_6)):\n for var_18 in range(len(var_7)):\n\n var_12 = (len(var_4) - (var_15 + 1)) * var_1\n var_12 += (len(var_5) - (var_16 + 1)) * (var_1 + 1)\n var_12 += (len(var_6) - (var_17 + 1)) * (var_1 + 2)\n var_12 += (len(var_7) - (var_18 + 1)) * (var_1 + 3)\n\n if var_12 <= var_3:\n\n var_12 = var_4[-1] - var_4[var_15]\n var_12 += var_5[-1] - var_5[var_16]\n var_12 += var_6[-1] - var_6[var_17]\n var_12 += var_7[-1] - var_7[var_18]\n var_9 = max(var_9, var_12)\nprint(var_9)\n", "code_b": "var_1, var_2 = list(map(int, input().split()))\nvar_14 = []\nfor var_8 in range(var_1):\n var_12, var_11 = list(map(int, input().split()))\n var_14.append((var_12, var_11))\n\nvar_13 = var_14[0][0]\nvar_6 = [[[-1] * (330) for var_8 in range(110)] for var_10 in range(110)]\nvar_6[0][0][0] = 0\nfor var_8 in range(var_1):\n var_12, var_11 = var_14[var_8]\n var_7 = var_12 - var_13\n for var_9 in range(var_8 + 1):\n for var_10 in range(310):\n\n if var_6[var_8][var_9][var_9] == -1:\n continue\n var_6[var_8 + 1][var_9][var_10] = max(\n var_6[var_8 + 1][var_9][var_10], var_6[var_8][var_9][var_10]\n )\n var_6[var_8 + 1][var_9 + 1][var_10 + var_7] = max(\n var_6[var_8][var_9][var_10] + var_11,\n var_6[var_8 + 1][var_9 + 1][var_10 + var_7],\n )\nvar_5 = var_6[var_1]\nvar_4 = 0\nfor var_9 in range(var_1 + 1):\n if var_2 - var_9 * var_13 >= 0:\n var_3 = max(var_5[var_9][: var_2 - var_9 * var_13 + 1])\n var_4 = max(var_4, var_3)\nprint(var_4)\n", "expected": "A"} {"pair_id": "python_11192_forward", "problem_id": "p03816", "language": "python", "direction": "forward", "speedup": 46.1393, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport functools\n\nvar_2 = int(eval(input()))\nvar_1 = list(map(int, input().split()))\nvar_4 = [0 for var_5 in range(10**5 + 1)]\nfor var_3 in var_1:\n var_4[var_3] = 1\nvar_6 = sum(var_4)\nprint((var_6 - (0 if var_6 % 2 else 1)))\n\n```\n\n## Code B:\n```python\nvar_2 = int(eval(input()))\nvar_1 = list(map(int, input().split()))\nvar_3 = {}\nfor var_5 in range(var_2):\n if var_1[var_5] in var_3:\n var_3[var_1[var_5]] += 1\n else:\n var_3[var_1[var_5]] = 1\nvar_4 = 0\nfor var_5 in list(var_3.values()):\n var_4 += var_5 - 1\nprint((len(set(var_1)) - var_4 % 2))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import functools\n\nvar_2 = int(eval(input()))\nvar_1 = list(map(int, input().split()))\nvar_4 = [0 for var_5 in range(10**5 + 1)]\nfor var_3 in var_1:\n var_4[var_3] = 1\nvar_6 = sum(var_4)\nprint((var_6 - (0 if var_6 % 2 else 1)))\n", "code_b": "var_2 = int(eval(input()))\nvar_1 = list(map(int, input().split()))\nvar_3 = {}\nfor var_5 in range(var_2):\n if var_1[var_5] in var_3:\n var_3[var_1[var_5]] += 1\n else:\n var_3[var_1[var_5]] = 1\nvar_4 = 0\nfor var_5 in list(var_3.values()):\n var_4 += var_5 - 1\nprint((len(set(var_1)) - var_4 % 2))\n", "expected": "B"} @@ -298,8 +298,8 @@ {"pair_id": "python_3337_reversed", "problem_id": "p03695", "language": "python", "direction": "reversed", "speedup": 2.8011, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_2 = [0] * 13\neval(input())\nfor var_3 in input().split():\n var_2[int(var_3) // 400] += 1\nvar_5 = sum(var_2[8:])\nvar_4 = sum([1 for var_1 in var_2[:8] if var_1 > 0])\nprint((max(1, var_4), var_4 + var_5))\n\n```\n\n## Code B:\n```python\nvar_5 = int(eval(input()))\nvar_4 = list(map(int, input().split()))\nvar_2 = {}\nfor var_3 in range(var_5):\n if 1 <= var_4[var_3] <= 399:\n var_1 = 0\n elif 400 <= var_4[var_3] <= 799:\n var_1 = 1\n elif 800 <= var_4[var_3] <= 1199:\n var_1 = 2\n elif 1200 <= var_4[var_3] <= 1599:\n var_1 = 3\n elif 1600 <= var_4[var_3] <= 1999:\n var_1 = 4\n elif 2000 <= var_4[var_3] <= 2399:\n var_1 = 5\n elif 2400 <= var_4[var_3] <= 2799:\n var_1 = 6\n elif 2800 <= var_4[var_3] <= 3199:\n var_1 = 7\n else:\n var_1 = 8\n if var_1 in var_2:\n var_2[var_1] += 1\n else:\n var_2[var_1] = 1\nif not (8 in var_2):\n print((len(list(var_2.keys())), len(list(var_2.keys()))))\nelse:\n if len(list(var_2.keys())) == 1:\n print((len(list(var_2.keys())), len(list(var_2.keys())) - 1 + var_2[8]))\n else:\n print((len(list(var_2.keys())) - 1, len(list(var_2.keys())) - 1 + var_2[8]))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_2 = [0] * 13\neval(input())\nfor var_3 in input().split():\n var_2[int(var_3) // 400] += 1\nvar_5 = sum(var_2[8:])\nvar_4 = sum([1 for var_1 in var_2[:8] if var_1 > 0])\nprint((max(1, var_4), var_4 + var_5))\n", "code_b": "var_5 = int(eval(input()))\nvar_4 = list(map(int, input().split()))\nvar_2 = {}\nfor var_3 in range(var_5):\n if 1 <= var_4[var_3] <= 399:\n var_1 = 0\n elif 400 <= var_4[var_3] <= 799:\n var_1 = 1\n elif 800 <= var_4[var_3] <= 1199:\n var_1 = 2\n elif 1200 <= var_4[var_3] <= 1599:\n var_1 = 3\n elif 1600 <= var_4[var_3] <= 1999:\n var_1 = 4\n elif 2000 <= var_4[var_3] <= 2399:\n var_1 = 5\n elif 2400 <= var_4[var_3] <= 2799:\n var_1 = 6\n elif 2800 <= var_4[var_3] <= 3199:\n var_1 = 7\n else:\n var_1 = 8\n if var_1 in var_2:\n var_2[var_1] += 1\n else:\n var_2[var_1] = 1\nif not (8 in var_2):\n print((len(list(var_2.keys())), len(list(var_2.keys()))))\nelse:\n if len(list(var_2.keys())) == 1:\n print((len(list(var_2.keys())), len(list(var_2.keys())) - 1 + var_2[8]))\n else:\n print((len(list(var_2.keys())) - 1, len(list(var_2.keys())) - 1 + var_2[8]))\n", "expected": "A"} {"pair_id": "python_13355_forward", "problem_id": "p03636", "language": "python", "direction": "forward", "speedup": 3.0544, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_5 = sys.stdin\nsys.setrecursionlimit(10**7)\nvar_1 = lambda: int(var_3())\nvar_2 = lambda: list(map(int, var_5.readline().split()))\nvar_3 = lambda: var_5.readline().rstrip()\nvar_4 = var_3()\nprint((var_4[0] + str(len(var_4) - 2) + var_4[-1]))\n\n```\n\n## Code B:\n```python\nvar_1, *var_2, var_3 = input()\nprint(var_1, len(var_2), var_3, sep=\"\")\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_5 = sys.stdin\nsys.setrecursionlimit(10**7)\nvar_1 = lambda: int(var_3())\nvar_2 = lambda: list(map(int, var_5.readline().split()))\nvar_3 = lambda: var_5.readline().rstrip()\nvar_4 = var_3()\nprint((var_4[0] + str(len(var_4) - 2) + var_4[-1]))\n", "code_b": "var_1, *var_2, var_3 = input()\nprint(var_1, len(var_2), var_3, sep=\"\")\n", "expected": "B"} {"pair_id": "python_13355_reversed", "problem_id": "p03636", "language": "python", "direction": "reversed", "speedup": 3.0544, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1, *var_2, var_3 = input()\nprint(var_1, len(var_2), var_3, sep=\"\")\n\n```\n\n## Code B:\n```python\nimport sys\n\nvar_5 = sys.stdin\nsys.setrecursionlimit(10**7)\nvar_1 = lambda: int(var_3())\nvar_2 = lambda: list(map(int, var_5.readline().split()))\nvar_3 = lambda: var_5.readline().rstrip()\nvar_4 = var_3()\nprint((var_4[0] + str(len(var_4) - 2) + var_4[-1]))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1, *var_2, var_3 = input()\nprint(var_1, len(var_2), var_3, sep=\"\")\n", "code_b": "import sys\n\nvar_5 = sys.stdin\nsys.setrecursionlimit(10**7)\nvar_1 = lambda: int(var_3())\nvar_2 = lambda: list(map(int, var_5.readline().split()))\nvar_3 = lambda: var_5.readline().rstrip()\nvar_4 = var_3()\nprint((var_4[0] + str(len(var_4) - 2) + var_4[-1]))\n", "expected": "A"} -{"pair_id": "python_7361_forward", "problem_id": "p03044", "language": "python", "direction": "forward", "speedup": 4.8235, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n\n```\n\n## Code B:\n```python\nfrom collections import defaultdict\n\nvar_2 = int(eval(input()))\nvar_1 = defaultdict(list)\nfor _ in range(var_2 - 1):\n var_8, var_9, var_10 = list(map(int, input().split()))\n var_8 -= 1\n var_9 -= 1\n var_1[var_8].append((var_9, var_10))\n var_1[var_9].append((var_8, var_10))\nvar_3 = [-1] * var_2\nvar_6 = [0]\nvar_3[0] = 0\nwhile var_6:\n var_5 = var_6.pop()\n for var_7, var_10 in var_1[var_5]:\n if var_3[var_7] != -1:\n continue\n var_3[var_7] = var_3[var_5] ^ (var_10 & 1)\n var_6.append(var_7)\nfor var_4 in var_3:\n print(var_4)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n", "code_b": "from collections import defaultdict\n\nvar_2 = int(eval(input()))\nvar_1 = defaultdict(list)\nfor _ in range(var_2 - 1):\n var_8, var_9, var_10 = list(map(int, input().split()))\n var_8 -= 1\n var_9 -= 1\n var_1[var_8].append((var_9, var_10))\n var_1[var_9].append((var_8, var_10))\nvar_3 = [-1] * var_2\nvar_6 = [0]\nvar_3[0] = 0\nwhile var_6:\n var_5 = var_6.pop()\n for var_7, var_10 in var_1[var_5]:\n if var_3[var_7] != -1:\n continue\n var_3[var_7] = var_3[var_5] ^ (var_10 & 1)\n var_6.append(var_7)\nfor var_4 in var_3:\n print(var_4)\n", "expected": "B"} -{"pair_id": "python_7361_reversed", "problem_id": "p03044", "language": "python", "direction": "reversed", "speedup": 4.8235, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfrom collections import defaultdict\n\nvar_2 = int(eval(input()))\nvar_1 = defaultdict(list)\nfor _ in range(var_2 - 1):\n var_8, var_9, var_10 = list(map(int, input().split()))\n var_8 -= 1\n var_9 -= 1\n var_1[var_8].append((var_9, var_10))\n var_1[var_9].append((var_8, var_10))\nvar_3 = [-1] * var_2\nvar_6 = [0]\nvar_3[0] = 0\nwhile var_6:\n var_5 = var_6.pop()\n for var_7, var_10 in var_1[var_5]:\n if var_3[var_7] != -1:\n continue\n var_3[var_7] = var_3[var_5] ^ (var_10 & 1)\n var_6.append(var_7)\nfor var_4 in var_3:\n print(var_4)\n\n```\n\n## Code B:\n```python\nimport sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "from collections import defaultdict\n\nvar_2 = int(eval(input()))\nvar_1 = defaultdict(list)\nfor _ in range(var_2 - 1):\n var_8, var_9, var_10 = list(map(int, input().split()))\n var_8 -= 1\n var_9 -= 1\n var_1[var_8].append((var_9, var_10))\n var_1[var_9].append((var_8, var_10))\nvar_3 = [-1] * var_2\nvar_6 = [0]\nvar_3[0] = 0\nwhile var_6:\n var_5 = var_6.pop()\n for var_7, var_10 in var_1[var_5]:\n if var_3[var_7] != -1:\n continue\n var_3[var_7] = var_3[var_5] ^ (var_10 & 1)\n var_6.append(var_7)\nfor var_4 in var_3:\n print(var_4)\n", "code_b": "import sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n", "expected": "A"} +{"pair_id": "python_7361_forward", "problem_id": "p03044", "language": "python", "direction": "forward", "speedup": 4.8235, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n```\n\n## Code B:\n```python\nfrom collections import defaultdict\n\nvar_2 = int(eval(input()))\nvar_1 = defaultdict(list)\nfor _ in range(var_2 - 1):\n var_8, var_9, var_10 = list(map(int, input().split()))\n var_8 -= 1\n var_9 -= 1\n var_1[var_8].append((var_9, var_10))\n var_1[var_9].append((var_8, var_10))\nvar_3 = [-1] * var_2\nvar_6 = [0]\nvar_3[0] = 0\nwhile var_6:\n var_5 = var_6.pop()\n for var_7, var_10 in var_1[var_5]:\n if var_3[var_7] != -1:\n continue\n var_3[var_7] = var_3[var_5] ^ (var_10 & 1)\n var_6.append(var_7)\nfor var_4 in var_3:\n print(var_4)\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n", "code_b": "from collections import defaultdict\n\nvar_2 = int(eval(input()))\nvar_1 = defaultdict(list)\nfor _ in range(var_2 - 1):\n var_8, var_9, var_10 = list(map(int, input().split()))\n var_8 -= 1\n var_9 -= 1\n var_1[var_8].append((var_9, var_10))\n var_1[var_9].append((var_8, var_10))\nvar_3 = [-1] * var_2\nvar_6 = [0]\nvar_3[0] = 0\nwhile var_6:\n var_5 = var_6.pop()\n for var_7, var_10 in var_1[var_5]:\n if var_3[var_7] != -1:\n continue\n var_3[var_7] = var_3[var_5] ^ (var_10 & 1)\n var_6.append(var_7)\nfor var_4 in var_3:\n print(var_4)\n", "expected": "B"} +{"pair_id": "python_7361_reversed", "problem_id": "p03044", "language": "python", "direction": "reversed", "speedup": 4.8235, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfrom collections import defaultdict\n\nvar_2 = int(eval(input()))\nvar_1 = defaultdict(list)\nfor _ in range(var_2 - 1):\n var_8, var_9, var_10 = list(map(int, input().split()))\n var_8 -= 1\n var_9 -= 1\n var_1[var_8].append((var_9, var_10))\n var_1[var_9].append((var_8, var_10))\nvar_3 = [-1] * var_2\nvar_6 = [0]\nvar_3[0] = 0\nwhile var_6:\n var_5 = var_6.pop()\n for var_7, var_10 in var_1[var_5]:\n if var_3[var_7] != -1:\n continue\n var_3[var_7] = var_3[var_5] ^ (var_10 & 1)\n var_6.append(var_7)\nfor var_4 in var_3:\n print(var_4)\n```\n\n## Code B:\n```python\nimport sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "from collections import defaultdict\n\nvar_2 = int(eval(input()))\nvar_1 = defaultdict(list)\nfor _ in range(var_2 - 1):\n var_8, var_9, var_10 = list(map(int, input().split()))\n var_8 -= 1\n var_9 -= 1\n var_1[var_8].append((var_9, var_10))\n var_1[var_9].append((var_8, var_10))\nvar_3 = [-1] * var_2\nvar_6 = [0]\nvar_3[0] = 0\nwhile var_6:\n var_5 = var_6.pop()\n for var_7, var_10 in var_1[var_5]:\n if var_3[var_7] != -1:\n continue\n var_3[var_7] = var_3[var_5] ^ (var_10 & 1)\n var_6.append(var_7)\nfor var_4 in var_3:\n print(var_4)\n", "code_b": "import sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n", "expected": "A"} {"pair_id": "python_2319_forward", "problem_id": "p02700", "language": "python", "direction": "forward", "speedup": 4.033, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfrom math import floor, ceil, sqrt, factorial, log\nfrom collections import Counter, deque\nfrom functools import reduce\n\n\ndef func_11():\n return eval(input())\n\n\ndef func_2():\n return int(eval(input()))\n\n\ndef func_10():\n return list(map(str, input().split()))\n\n\ndef func_9():\n return list(map(int, input().split()))\n\n\ndef func_1():\n return [int(var_5) for var_5 in input().split()]\n\n\ndef func_8():\n return list(func_10())\n\n\ndef func_3():\n return list(func_9())\n\n\ndef func_6():\n return [list(map(str, var_6.split())) for var_6 in eval(input())]\n\n\ndef func_4():\n return [list(map(int, var_6.split())) for var_6 in eval(input())]\n\n\ndef func_7(var_7: int):\n return [func_8() for _ in range(var_7)]\n\n\ndef func_5(var_7: int):\n return [func_3() for _ in range(var_7)]\n\n\nvar_1, var_2, var_3, var_4 = func_9()\nvar_8 = -(-var_1 // var_4)\nvar_9 = -(-var_3 // var_2)\nif var_8 >= var_9:\n print(\"Yes\")\nelse:\n print(\"No\")\n\n```\n\n## Code B:\n```python\nvar_1, var_2, var_3, var_4 = list(map(int, input().split()))\nvar_5 = 0\nvar_6 = 0\nif var_3 % var_2 == 0:\n var_5 = var_3 // var_2\nelse:\n var_5 = var_3 // var_2 + 1\nif var_1 % var_4 == 0:\n var_6 = var_1 // var_4\nelse:\n var_6 = var_1 // var_4 + 1\nif var_5 <= var_6:\n print(\"Yes\")\nelse:\n print(\"No\")\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "from math import floor, ceil, sqrt, factorial, log\nfrom collections import Counter, deque\nfrom functools import reduce\n\n\ndef func_11():\n return eval(input())\n\n\ndef func_2():\n return int(eval(input()))\n\n\ndef func_10():\n return list(map(str, input().split()))\n\n\ndef func_9():\n return list(map(int, input().split()))\n\n\ndef func_1():\n return [int(var_5) for var_5 in input().split()]\n\n\ndef func_8():\n return list(func_10())\n\n\ndef func_3():\n return list(func_9())\n\n\ndef func_6():\n return [list(map(str, var_6.split())) for var_6 in eval(input())]\n\n\ndef func_4():\n return [list(map(int, var_6.split())) for var_6 in eval(input())]\n\n\ndef func_7(var_7: int):\n return [func_8() for _ in range(var_7)]\n\n\ndef func_5(var_7: int):\n return [func_3() for _ in range(var_7)]\n\n\nvar_1, var_2, var_3, var_4 = func_9()\nvar_8 = -(-var_1 // var_4)\nvar_9 = -(-var_3 // var_2)\nif var_8 >= var_9:\n print(\"Yes\")\nelse:\n print(\"No\")\n", "code_b": "var_1, var_2, var_3, var_4 = list(map(int, input().split()))\nvar_5 = 0\nvar_6 = 0\nif var_3 % var_2 == 0:\n var_5 = var_3 // var_2\nelse:\n var_5 = var_3 // var_2 + 1\nif var_1 % var_4 == 0:\n var_6 = var_1 // var_4\nelse:\n var_6 = var_1 // var_4 + 1\nif var_5 <= var_6:\n print(\"Yes\")\nelse:\n print(\"No\")\n", "expected": "B"} {"pair_id": "python_2319_reversed", "problem_id": "p02700", "language": "python", "direction": "reversed", "speedup": 4.033, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1, var_2, var_3, var_4 = list(map(int, input().split()))\nvar_5 = 0\nvar_6 = 0\nif var_3 % var_2 == 0:\n var_5 = var_3 // var_2\nelse:\n var_5 = var_3 // var_2 + 1\nif var_1 % var_4 == 0:\n var_6 = var_1 // var_4\nelse:\n var_6 = var_1 // var_4 + 1\nif var_5 <= var_6:\n print(\"Yes\")\nelse:\n print(\"No\")\n\n```\n\n## Code B:\n```python\nfrom math import floor, ceil, sqrt, factorial, log\nfrom collections import Counter, deque\nfrom functools import reduce\n\n\ndef func_11():\n return eval(input())\n\n\ndef func_2():\n return int(eval(input()))\n\n\ndef func_10():\n return list(map(str, input().split()))\n\n\ndef func_9():\n return list(map(int, input().split()))\n\n\ndef func_1():\n return [int(var_5) for var_5 in input().split()]\n\n\ndef func_8():\n return list(func_10())\n\n\ndef func_3():\n return list(func_9())\n\n\ndef func_6():\n return [list(map(str, var_6.split())) for var_6 in eval(input())]\n\n\ndef func_4():\n return [list(map(int, var_6.split())) for var_6 in eval(input())]\n\n\ndef func_7(var_7: int):\n return [func_8() for _ in range(var_7)]\n\n\ndef func_5(var_7: int):\n return [func_3() for _ in range(var_7)]\n\n\nvar_1, var_2, var_3, var_4 = func_9()\nvar_8 = -(-var_1 // var_4)\nvar_9 = -(-var_3 // var_2)\nif var_8 >= var_9:\n print(\"Yes\")\nelse:\n print(\"No\")\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1, var_2, var_3, var_4 = list(map(int, input().split()))\nvar_5 = 0\nvar_6 = 0\nif var_3 % var_2 == 0:\n var_5 = var_3 // var_2\nelse:\n var_5 = var_3 // var_2 + 1\nif var_1 % var_4 == 0:\n var_6 = var_1 // var_4\nelse:\n var_6 = var_1 // var_4 + 1\nif var_5 <= var_6:\n print(\"Yes\")\nelse:\n print(\"No\")\n", "code_b": "from math import floor, ceil, sqrt, factorial, log\nfrom collections import Counter, deque\nfrom functools import reduce\n\n\ndef func_11():\n return eval(input())\n\n\ndef func_2():\n return int(eval(input()))\n\n\ndef func_10():\n return list(map(str, input().split()))\n\n\ndef func_9():\n return list(map(int, input().split()))\n\n\ndef func_1():\n return [int(var_5) for var_5 in input().split()]\n\n\ndef func_8():\n return list(func_10())\n\n\ndef func_3():\n return list(func_9())\n\n\ndef func_6():\n return [list(map(str, var_6.split())) for var_6 in eval(input())]\n\n\ndef func_4():\n return [list(map(int, var_6.split())) for var_6 in eval(input())]\n\n\ndef func_7(var_7: int):\n return [func_8() for _ in range(var_7)]\n\n\ndef func_5(var_7: int):\n return [func_3() for _ in range(var_7)]\n\n\nvar_1, var_2, var_3, var_4 = func_9()\nvar_8 = -(-var_1 // var_4)\nvar_9 = -(-var_3 // var_2)\nif var_8 >= var_9:\n print(\"Yes\")\nelse:\n print(\"No\")\n", "expected": "A"} {"pair_id": "python_14580_forward", "problem_id": "p03603", "language": "python", "direction": "forward", "speedup": 7.4677, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1 = int(input())\nvar_2 = list(map(int, input().split()))\nvar_3 = [0] + list(map(int, input().split()))\nvar_10 = [[] for _ in range(var_1 + 1)]\nfor var_8, var_13 in enumerate(var_2, 2):\n var_10[var_13].append(var_8)\n\n\ndef func_1(var_13):\n var_11 = len(var_10[var_13])\n if var_11 == 0:\n return var_3[var_13], 0\n var_7 = [[float(\"inf\")] * 5001 for _ in range(var_11 + 1)]\n var_7[0][0] = 0\n for var_8, var_6 in enumerate(var_10[var_13], 1):\n var_4, var_5 = func_1(var_6)\n for var_9 in range(var_4, 5001):\n var_7[var_8][var_9] = min(\n var_7[var_8][var_9], var_7[var_8 - 1][var_9 - var_4] + var_5\n )\n for var_9 in range(var_5, 5001):\n var_7[var_8][var_9] = min(\n var_7[var_8][var_9], var_7[var_8 - 1][var_9 - var_5] + var_4\n )\n var_12 = min(var_7[-1][: var_3[var_13] + 1])\n if var_12 == float(\"inf\"):\n print(\"IMPOSSIBLE\")\n exit()\n elif var_12 > var_3[var_13]:\n return var_12, var_3[var_13]\n else:\n return var_3[var_13], var_12\n\n\nprint(\"POSSIBLE\") if min(func_1(1)) <= var_3[1] else print(\"IMPOSSIBLE\")\n\n```\n\n## Code B:\n```python\nvar_3 = int(eval(input()))\nvar_4 = list(map(int, input().split()))\nvar_5 = list(map(int, input().split()))\n\nvar_11 = [[] for _ in range(var_3 + 1)]\nfor var_14 in range(2, var_3 + 1):\n var_11[var_4[var_14 - 2]].append(var_14)\n\n\nvar_12 = [0] + var_5\nvar_13 = [0] * (var_3 + 1)\n\n\ndef func_1(var_1, var_2):\n var_16 = sum([min(var_12[var_15], var_13[var_15]) for var_15 in var_1])\n if var_16 > var_2:\n return -1\n else:\n var_6 = var_2 - var_16\n var_8 = set([0])\n for var_15 in var_1:\n var_7 = max(var_12[var_15], var_13[var_15]) - min(\n var_12[var_15], var_13[var_15]\n )\n var_9 = set(var_8)\n for var_18 in var_8:\n if var_18 + var_7 <= var_6:\n var_9.add(var_18 + var_7)\n var_8 = var_9\n var_19 = sum([var_12[var_15] + var_13[var_15] for var_15 in var_1])\n return var_19 - max(var_8) - var_16\n\n\nvar_17 = \"POSSIBLE\"\nfor var_14 in range(var_3, 0, -1):\n if len(var_11[var_14]) == 0:\n pass\n elif len(var_11[var_14]) == 1:\n var_15 = var_11[var_14][0]\n if min(var_12[var_15], var_13[var_15]) > var_5[var_14 - 1]:\n var_17 = \"IMPOSSIBLE\"\n break\n elif max(var_12[var_15], var_13[var_15]) > var_5[var_14 - 1]:\n var_13[var_14] = max(var_12[var_15], var_13[var_15])\n else:\n var_13[var_14] = min(var_12[var_15], var_13[var_15])\n else:\n var_10 = func_1(var_11[var_14], var_5[var_14 - 1])\n if var_10 < 0:\n var_17 = \"IMPOSSIBLE\"\n break\n else:\n var_13[var_14] = var_10\nprint(var_17)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1 = int(input())\nvar_2 = list(map(int, input().split()))\nvar_3 = [0] + list(map(int, input().split()))\nvar_10 = [[] for _ in range(var_1 + 1)]\nfor var_8, var_13 in enumerate(var_2, 2):\n var_10[var_13].append(var_8)\n\n\ndef func_1(var_13):\n var_11 = len(var_10[var_13])\n if var_11 == 0:\n return var_3[var_13], 0\n var_7 = [[float(\"inf\")] * 5001 for _ in range(var_11 + 1)]\n var_7[0][0] = 0\n for var_8, var_6 in enumerate(var_10[var_13], 1):\n var_4, var_5 = func_1(var_6)\n for var_9 in range(var_4, 5001):\n var_7[var_8][var_9] = min(\n var_7[var_8][var_9], var_7[var_8 - 1][var_9 - var_4] + var_5\n )\n for var_9 in range(var_5, 5001):\n var_7[var_8][var_9] = min(\n var_7[var_8][var_9], var_7[var_8 - 1][var_9 - var_5] + var_4\n )\n var_12 = min(var_7[-1][: var_3[var_13] + 1])\n if var_12 == float(\"inf\"):\n print(\"IMPOSSIBLE\")\n exit()\n elif var_12 > var_3[var_13]:\n return var_12, var_3[var_13]\n else:\n return var_3[var_13], var_12\n\n\nprint(\"POSSIBLE\") if min(func_1(1)) <= var_3[1] else print(\"IMPOSSIBLE\")\n", "code_b": "var_3 = int(eval(input()))\nvar_4 = list(map(int, input().split()))\nvar_5 = list(map(int, input().split()))\n\nvar_11 = [[] for _ in range(var_3 + 1)]\nfor var_14 in range(2, var_3 + 1):\n var_11[var_4[var_14 - 2]].append(var_14)\n\n\nvar_12 = [0] + var_5\nvar_13 = [0] * (var_3 + 1)\n\n\ndef func_1(var_1, var_2):\n var_16 = sum([min(var_12[var_15], var_13[var_15]) for var_15 in var_1])\n if var_16 > var_2:\n return -1\n else:\n var_6 = var_2 - var_16\n var_8 = set([0])\n for var_15 in var_1:\n var_7 = max(var_12[var_15], var_13[var_15]) - min(\n var_12[var_15], var_13[var_15]\n )\n var_9 = set(var_8)\n for var_18 in var_8:\n if var_18 + var_7 <= var_6:\n var_9.add(var_18 + var_7)\n var_8 = var_9\n var_19 = sum([var_12[var_15] + var_13[var_15] for var_15 in var_1])\n return var_19 - max(var_8) - var_16\n\n\nvar_17 = \"POSSIBLE\"\nfor var_14 in range(var_3, 0, -1):\n if len(var_11[var_14]) == 0:\n pass\n elif len(var_11[var_14]) == 1:\n var_15 = var_11[var_14][0]\n if min(var_12[var_15], var_13[var_15]) > var_5[var_14 - 1]:\n var_17 = \"IMPOSSIBLE\"\n break\n elif max(var_12[var_15], var_13[var_15]) > var_5[var_14 - 1]:\n var_13[var_14] = max(var_12[var_15], var_13[var_15])\n else:\n var_13[var_14] = min(var_12[var_15], var_13[var_15])\n else:\n var_10 = func_1(var_11[var_14], var_5[var_14 - 1])\n if var_10 < 0:\n var_17 = \"IMPOSSIBLE\"\n break\n else:\n var_13[var_14] = var_10\nprint(var_17)\n", "expected": "B"} @@ -400,8 +400,8 @@ {"pair_id": "python_1228_reversed", "problem_id": "p02889", "language": "python", "direction": "reversed", "speedup": 137.8701, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_17 = lambda: sys.stdin.readline().rstrip()\nvar_16 = lambda: list(map(int, var_17().split()))\n\n\ndef func_1(\n var_7, var_13, var_20=None, var_26=0, var_22=lambda var_4, var_11: var_4 + var_11\n):\n \"\"\"\"\"\"\n var_23 = [[var_20] * var_7 for _ in range(var_7)]\n for var_15 in range(var_7):\n var_23[var_15][var_15] = var_26\n for var_15 in range(var_7):\n for var_18, var_11 in var_13[var_15]:\n if 0 <= var_18 < var_7:\n var_23[var_15][var_18] = var_22(var_26, var_11)\n for var_19 in range(var_7):\n for var_15 in range(var_7):\n if var_15 == var_19:\n continue\n for var_18 in range(var_7):\n if (\n var_18 == var_19\n or var_23[var_15][var_19] == var_20\n or var_23[var_19][var_18] == var_20\n ):\n continue\n var_11 = var_22(var_23[var_15][var_19], var_23[var_19][var_18])\n if var_23[var_15][var_18] == var_20 or var_11 < var_23[var_15][var_18]:\n var_23[var_15][var_18] = var_11\n return var_23\n\n\nvar_7, var_6, var_5 = var_16()\nvar_24 = [[] for _ in range(var_7)]\nfor var_15 in range(var_6):\n var_1, var_2, var_3 = var_16()\n if var_3 <= var_5:\n var_24[var_1 - 1].append((var_2 - 1, var_3))\n var_24[var_2 - 1].append((var_1 - 1, var_3))\nvar_8 = int(eval(var_17()))\nvar_9 = []\nvar_14 = [set() for _ in range(var_7)]\nfor _ in range(var_8):\n var_25, var_27 = var_16()\n var_25 -= 1\n var_27 -= 1\n var_9.append((var_25, var_27))\nvar_12 = func_1(var_7, var_24)\nvar_21 = [set() for _ in range(var_7)]\nfor var_15 in range(var_7):\n for var_18 in range(var_7):\n var_11 = var_12[var_15][var_18]\n if var_11 is not None and var_11 <= var_5:\n var_21[var_15].add((var_18, 1))\nvar_10 = func_1(var_7, var_21)\nfor var_25, var_27 in var_9:\n if var_10[var_25][var_27] is None:\n print((-1))\n else:\n print((var_10[var_25][var_27] - 1))\n\n```\n\n## Code B:\n```python\nimport numpy as np\nfrom scipy.sparse import csr_matrix\nfrom scipy.sparse.csgraph import floyd_warshall\n\nvar_12 = np.int64\nvar_11 = np.int32\n\n\ndef func_1():\n var_17 = open(\"/dev/stdin\")\n var_14 = np.fromstring(var_17.read(), var_12, sep=\" \")\n var_7 = var_14[0]\n var_6 = var_14[1]\n var_5 = var_14[2]\n var_2 = var_14[3 : var_6 * 3 + 3].reshape((-1, 3))\n var_1 = var_2[:, 0] - 1\n var_3 = var_2[:, 1] - 1\n var_4 = var_2[:, 2]\n var_8 = var_14[var_6 * 3 + 3]\n var_16 = (var_14[var_6 * 3 + 4 : 2 * var_8 + var_6 * 3 + 4] - 1).reshape((-1, 2))\n var_15 = var_16[:, 0]\n var_18 = var_16[:, 1]\n var_10 = csr_matrix((var_4, (var_1, var_3)), shape=(var_7, var_7))\n var_9 = np.array(floyd_warshall(var_10, directed=False))\n var_9[var_9 > var_5 + 0.5] = 0\n var_9[var_9 > 0] = 1\n var_13 = np.array(floyd_warshall(var_9))\n var_13[var_13 == np.inf] = 0\n var_13 = (var_13 + 0.5).astype(int)\n var_19 = var_13[var_15, var_18] - 1\n print((\"\\n\".join(var_19.astype(str).tolist())))\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_17 = lambda: sys.stdin.readline().rstrip()\nvar_16 = lambda: list(map(int, var_17().split()))\n\n\ndef func_1(\n var_7, var_13, var_20=None, var_26=0, var_22=lambda var_4, var_11: var_4 + var_11\n):\n \"\"\"\"\"\"\n var_23 = [[var_20] * var_7 for _ in range(var_7)]\n for var_15 in range(var_7):\n var_23[var_15][var_15] = var_26\n for var_15 in range(var_7):\n for var_18, var_11 in var_13[var_15]:\n if 0 <= var_18 < var_7:\n var_23[var_15][var_18] = var_22(var_26, var_11)\n for var_19 in range(var_7):\n for var_15 in range(var_7):\n if var_15 == var_19:\n continue\n for var_18 in range(var_7):\n if (\n var_18 == var_19\n or var_23[var_15][var_19] == var_20\n or var_23[var_19][var_18] == var_20\n ):\n continue\n var_11 = var_22(var_23[var_15][var_19], var_23[var_19][var_18])\n if var_23[var_15][var_18] == var_20 or var_11 < var_23[var_15][var_18]:\n var_23[var_15][var_18] = var_11\n return var_23\n\n\nvar_7, var_6, var_5 = var_16()\nvar_24 = [[] for _ in range(var_7)]\nfor var_15 in range(var_6):\n var_1, var_2, var_3 = var_16()\n if var_3 <= var_5:\n var_24[var_1 - 1].append((var_2 - 1, var_3))\n var_24[var_2 - 1].append((var_1 - 1, var_3))\nvar_8 = int(eval(var_17()))\nvar_9 = []\nvar_14 = [set() for _ in range(var_7)]\nfor _ in range(var_8):\n var_25, var_27 = var_16()\n var_25 -= 1\n var_27 -= 1\n var_9.append((var_25, var_27))\nvar_12 = func_1(var_7, var_24)\nvar_21 = [set() for _ in range(var_7)]\nfor var_15 in range(var_7):\n for var_18 in range(var_7):\n var_11 = var_12[var_15][var_18]\n if var_11 is not None and var_11 <= var_5:\n var_21[var_15].add((var_18, 1))\nvar_10 = func_1(var_7, var_21)\nfor var_25, var_27 in var_9:\n if var_10[var_25][var_27] is None:\n print((-1))\n else:\n print((var_10[var_25][var_27] - 1))\n", "code_b": "import numpy as np\nfrom scipy.sparse import csr_matrix\nfrom scipy.sparse.csgraph import floyd_warshall\n\nvar_12 = np.int64\nvar_11 = np.int32\n\n\ndef func_1():\n var_17 = open(\"/dev/stdin\")\n var_14 = np.fromstring(var_17.read(), var_12, sep=\" \")\n var_7 = var_14[0]\n var_6 = var_14[1]\n var_5 = var_14[2]\n var_2 = var_14[3 : var_6 * 3 + 3].reshape((-1, 3))\n var_1 = var_2[:, 0] - 1\n var_3 = var_2[:, 1] - 1\n var_4 = var_2[:, 2]\n var_8 = var_14[var_6 * 3 + 3]\n var_16 = (var_14[var_6 * 3 + 4 : 2 * var_8 + var_6 * 3 + 4] - 1).reshape((-1, 2))\n var_15 = var_16[:, 0]\n var_18 = var_16[:, 1]\n var_10 = csr_matrix((var_4, (var_1, var_3)), shape=(var_7, var_7))\n var_9 = np.array(floyd_warshall(var_10, directed=False))\n var_9[var_9 > var_5 + 0.5] = 0\n var_9[var_9 > 0] = 1\n var_13 = np.array(floyd_warshall(var_9))\n var_13[var_13 == np.inf] = 0\n var_13 = (var_13 + 0.5).astype(int)\n var_19 = var_13[var_15, var_18] - 1\n print((\"\\n\".join(var_19.astype(str).tolist())))\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "A"} {"pair_id": "python_13995_forward", "problem_id": "p02283", "language": "python", "direction": "forward", "speedup": 3.3536, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nclass Class_1:\n var_3 = 0\n var_4 = None\n var_6 = None\n\n def __init__(self, var_3):\n self.key = var_3\n\n\nvar_1 = [None] * 5000005\n\n\ndef func_2(var_10):\n if var_10 == 0:\n return\n var_9 = 0\n var_8 = 0\n while var_8 is not None:\n var_9 = var_8\n var_8 = (\n var_1[var_8].l if var_1[var_10].key < var_1[var_8].key else var_1[var_8].r\n )\n if var_1[var_10].key < var_1[var_9].key:\n var_1[var_9].l = var_10\n else:\n var_1[var_9].r = var_10\n\n\ndef func_1(var_8):\n if var_8 == None:\n return\n func_1(var_1[var_8].l)\n print(\"\", var_1[var_8].key, end=\"\")\n func_1(var_1[var_8].r)\n\n\ndef func_3(var_8):\n if var_8 == None:\n return\n print(\"\", var_1[var_8].key, end=\"\")\n func_3(var_1[var_8].l)\n func_3(var_1[var_8].r)\n\n\nvar_5 = int(input())\nfor var_2 in range(var_5):\n var_7 = input()\n if var_7 == \"print\":\n func_1(0)\n print()\n func_3(0)\n print()\n else:\n var_8 = int(var_7.split()[1])\n var_1[var_2] = Class_1(var_8)\n func_2(var_2)\n\n```\n\n## Code B:\n```python\nimport sys\n\n\nclass Class_1:\n __slots__ = [\"key\", \"left\", \"right\"]\n\n def __init__(self, var_2):\n self.key = var_2\n self.left = self.right = None\n\n\nvar_4 = None\n\n\ndef func_2(var_2):\n global root\n var_5, var_6 = var_4, None\n while var_5 is not None:\n var_5, var_6 = var_5.left if var_2 < var_5.key else var_5.right, var_5\n if var_6 is None:\n var_4 = Class_1(var_2)\n elif var_2 < var_6.key:\n var_6.left = Class_1(var_2)\n else:\n var_6.right = Class_1(var_2)\n\n\ndef func_1(var_3):\n return func_1(var_3.left) + f\" {var_3.key}\" + func_1(var_3.right) if var_3 else \"\"\n\n\ndef func_3(var_3):\n return f\" {var_3.key}\" + func_3(var_3.left) + func_3(var_3.right) if var_3 else \"\"\n\n\neval(input())\nfor var_1 in sys.stdin:\n if var_1[0] == \"i\":\n func_2(int(var_1[7:]))\n else:\n print((func_1(var_4)))\n print((func_3(var_4)))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "class Class_1:\n var_3 = 0\n var_4 = None\n var_6 = None\n\n def __init__(self, var_3):\n self.key = var_3\n\n\nvar_1 = [None] * 5000005\n\n\ndef func_2(var_10):\n if var_10 == 0:\n return\n var_9 = 0\n var_8 = 0\n while var_8 is not None:\n var_9 = var_8\n var_8 = (\n var_1[var_8].l if var_1[var_10].key < var_1[var_8].key else var_1[var_8].r\n )\n if var_1[var_10].key < var_1[var_9].key:\n var_1[var_9].l = var_10\n else:\n var_1[var_9].r = var_10\n\n\ndef func_1(var_8):\n if var_8 == None:\n return\n func_1(var_1[var_8].l)\n print(\"\", var_1[var_8].key, end=\"\")\n func_1(var_1[var_8].r)\n\n\ndef func_3(var_8):\n if var_8 == None:\n return\n print(\"\", var_1[var_8].key, end=\"\")\n func_3(var_1[var_8].l)\n func_3(var_1[var_8].r)\n\n\nvar_5 = int(input())\nfor var_2 in range(var_5):\n var_7 = input()\n if var_7 == \"print\":\n func_1(0)\n print()\n func_3(0)\n print()\n else:\n var_8 = int(var_7.split()[1])\n var_1[var_2] = Class_1(var_8)\n func_2(var_2)\n", "code_b": "import sys\n\n\nclass Class_1:\n __slots__ = [\"key\", \"left\", \"right\"]\n\n def __init__(self, var_2):\n self.key = var_2\n self.left = self.right = None\n\n\nvar_4 = None\n\n\ndef func_2(var_2):\n global root\n var_5, var_6 = var_4, None\n while var_5 is not None:\n var_5, var_6 = var_5.left if var_2 < var_5.key else var_5.right, var_5\n if var_6 is None:\n var_4 = Class_1(var_2)\n elif var_2 < var_6.key:\n var_6.left = Class_1(var_2)\n else:\n var_6.right = Class_1(var_2)\n\n\ndef func_1(var_3):\n return func_1(var_3.left) + f\" {var_3.key}\" + func_1(var_3.right) if var_3 else \"\"\n\n\ndef func_3(var_3):\n return f\" {var_3.key}\" + func_3(var_3.left) + func_3(var_3.right) if var_3 else \"\"\n\n\neval(input())\nfor var_1 in sys.stdin:\n if var_1[0] == \"i\":\n func_2(int(var_1[7:]))\n else:\n print((func_1(var_4)))\n print((func_3(var_4)))\n", "expected": "B"} {"pair_id": "python_13995_reversed", "problem_id": "p02283", "language": "python", "direction": "reversed", "speedup": 3.3536, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\n\nclass Class_1:\n __slots__ = [\"key\", \"left\", \"right\"]\n\n def __init__(self, var_2):\n self.key = var_2\n self.left = self.right = None\n\n\nvar_4 = None\n\n\ndef func_2(var_2):\n global root\n var_5, var_6 = var_4, None\n while var_5 is not None:\n var_5, var_6 = var_5.left if var_2 < var_5.key else var_5.right, var_5\n if var_6 is None:\n var_4 = Class_1(var_2)\n elif var_2 < var_6.key:\n var_6.left = Class_1(var_2)\n else:\n var_6.right = Class_1(var_2)\n\n\ndef func_1(var_3):\n return func_1(var_3.left) + f\" {var_3.key}\" + func_1(var_3.right) if var_3 else \"\"\n\n\ndef func_3(var_3):\n return f\" {var_3.key}\" + func_3(var_3.left) + func_3(var_3.right) if var_3 else \"\"\n\n\neval(input())\nfor var_1 in sys.stdin:\n if var_1[0] == \"i\":\n func_2(int(var_1[7:]))\n else:\n print((func_1(var_4)))\n print((func_3(var_4)))\n\n```\n\n## Code B:\n```python\nclass Class_1:\n var_3 = 0\n var_4 = None\n var_6 = None\n\n def __init__(self, var_3):\n self.key = var_3\n\n\nvar_1 = [None] * 5000005\n\n\ndef func_2(var_10):\n if var_10 == 0:\n return\n var_9 = 0\n var_8 = 0\n while var_8 is not None:\n var_9 = var_8\n var_8 = (\n var_1[var_8].l if var_1[var_10].key < var_1[var_8].key else var_1[var_8].r\n )\n if var_1[var_10].key < var_1[var_9].key:\n var_1[var_9].l = var_10\n else:\n var_1[var_9].r = var_10\n\n\ndef func_1(var_8):\n if var_8 == None:\n return\n func_1(var_1[var_8].l)\n print(\"\", var_1[var_8].key, end=\"\")\n func_1(var_1[var_8].r)\n\n\ndef func_3(var_8):\n if var_8 == None:\n return\n print(\"\", var_1[var_8].key, end=\"\")\n func_3(var_1[var_8].l)\n func_3(var_1[var_8].r)\n\n\nvar_5 = int(input())\nfor var_2 in range(var_5):\n var_7 = input()\n if var_7 == \"print\":\n func_1(0)\n print()\n func_3(0)\n print()\n else:\n var_8 = int(var_7.split()[1])\n var_1[var_2] = Class_1(var_8)\n func_2(var_2)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\n\nclass Class_1:\n __slots__ = [\"key\", \"left\", \"right\"]\n\n def __init__(self, var_2):\n self.key = var_2\n self.left = self.right = None\n\n\nvar_4 = None\n\n\ndef func_2(var_2):\n global root\n var_5, var_6 = var_4, None\n while var_5 is not None:\n var_5, var_6 = var_5.left if var_2 < var_5.key else var_5.right, var_5\n if var_6 is None:\n var_4 = Class_1(var_2)\n elif var_2 < var_6.key:\n var_6.left = Class_1(var_2)\n else:\n var_6.right = Class_1(var_2)\n\n\ndef func_1(var_3):\n return func_1(var_3.left) + f\" {var_3.key}\" + func_1(var_3.right) if var_3 else \"\"\n\n\ndef func_3(var_3):\n return f\" {var_3.key}\" + func_3(var_3.left) + func_3(var_3.right) if var_3 else \"\"\n\n\neval(input())\nfor var_1 in sys.stdin:\n if var_1[0] == \"i\":\n func_2(int(var_1[7:]))\n else:\n print((func_1(var_4)))\n print((func_3(var_4)))\n", "code_b": "class Class_1:\n var_3 = 0\n var_4 = None\n var_6 = None\n\n def __init__(self, var_3):\n self.key = var_3\n\n\nvar_1 = [None] * 5000005\n\n\ndef func_2(var_10):\n if var_10 == 0:\n return\n var_9 = 0\n var_8 = 0\n while var_8 is not None:\n var_9 = var_8\n var_8 = (\n var_1[var_8].l if var_1[var_10].key < var_1[var_8].key else var_1[var_8].r\n )\n if var_1[var_10].key < var_1[var_9].key:\n var_1[var_9].l = var_10\n else:\n var_1[var_9].r = var_10\n\n\ndef func_1(var_8):\n if var_8 == None:\n return\n func_1(var_1[var_8].l)\n print(\"\", var_1[var_8].key, end=\"\")\n func_1(var_1[var_8].r)\n\n\ndef func_3(var_8):\n if var_8 == None:\n return\n print(\"\", var_1[var_8].key, end=\"\")\n func_3(var_1[var_8].l)\n func_3(var_1[var_8].r)\n\n\nvar_5 = int(input())\nfor var_2 in range(var_5):\n var_7 = input()\n if var_7 == \"print\":\n func_1(0)\n print()\n func_3(0)\n print()\n else:\n var_8 = int(var_7.split()[1])\n var_1[var_2] = Class_1(var_8)\n func_2(var_2)\n", "expected": "A"} -{"pair_id": "python_17824_forward", "problem_id": "p02586", "language": "python", "direction": "forward", "speedup": 3.4453, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n\nfrom collections import deque, defaultdict\n\n\n\nfrom collections import Counter \n\n\nfrom itertools import accumulate, combinations, permutations, product \n\n\nfrom heapq import heapify, heappop, heappush\n\n\nfrom functools import reduce, lru_cache \n\n\nfrom decimal import Decimal\n\n\ndef input():\n x = sys.stdin.readline()\n return x[:-1] if x[-1] == \"\\n\" else x\n\n\ndef printe(*x):\n print(\"\n\n\ndef printl(li):\n _ = print(*li, sep=\"\\n\") if li else None\n\n\ndef argsort(s, return_sorted=False):\n inds = sorted(range(len(s)), key=lambda k: s[k])\n if return_sorted:\n return inds, [s[i] for i in inds]\n return inds\n\n\ndef alp2num(c, cap=False):\n return ord(c) - 97 if not cap else ord(c) - 65\n\n\ndef num2alp(i, cap=False):\n return chr(i + 97) if not cap else chr(i + 65)\n\n\ndef matmat(A, B):\n K, N, M = len(B), len(A), len(B[0])\n return [\n [sum([(A[i][k] * B[k][j]) for k in range(K)]) for j in range(M)]\n for i in range(N)\n ]\n\n\ndef matvec(M, v):\n N, size = len(v), len(M)\n return [sum([M[i][j] * v[j] for j in range(N)]) for i in range(size)]\n\n\ndef T(M):\n n, m = len(M), len(M[0])\n return [[M[j][i] for j in range(n)] for i in range(m)]\n\n\ndef binr(x):\n return bin(x)[2:]\n\n\ndef bitcount(x): \n x = x - ((x >> 1) & 0x5555555555555555)\n x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)\n x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F\n x += x >> 8\n x += x >> 16\n x += x >> 32\n return x & 0x7F\n\n\ndef main():\n mod = 1000000007\n \n \n R, C, K = map(int, input().split())\n \n \n base = 5000\n S = []\n for i in range(K):\n r, c, v = map(int, input().split())\n r -= 1\n c -= 1\n S.append((r * base + c, v))\n S.sort(key=itemgetter(0))\n p = 0\n dp = [[0] * 4 for _ in range(C)]\n dpi = [[0] * 4 for _ in range(C)]\n for i in range(R):\n for j in range(C):\n cv = 0\n if p < K and i * base + j == S[p][0]:\n cv = S[p][1]\n p += 1\n if j > 0:\n dpi[j] = copy(dpi[j - 1])\n else:\n dpi[0] = [0] * 4\n if i > 0:\n dpi[j][0] = max(dp[j], dpi[j][0])\n if cv != 0:\n for k in range(2, -1, -1):\n dpi[j][k + 1] = max(dpi[j][k] + cv, dpi[j][k + 1])\n \n dp[j] = max(dpi[j])\n print(dp[-1])\n\n\nif __name__ == \"__main__\":\n main()\n\n```\n\n## Code B:\n```python\nvar_3, var_1, var_2 = list(map(int, input().split()))\nvar_12 = [[0] * var_1 for var_8 in range(var_3)]\nfor var_8 in range(var_2):\n var_4, var_5, var_6 = list(map(int, input().split()))\n var_4 -= 1\n var_5 -= 1\n var_12[var_4][var_5] = var_6\nvar_7 = [0] * var_1\nfor var_8 in range(var_3):\n var_13 = [0] * var_1\n var_11 = [0] * 4\n var_11[0] = var_7[0]\n var_11[1] = var_12[var_8][0] + var_7[0]\n var_13[0] = var_11[1]\n for var_9 in range(1, var_1):\n var_11[0] = max(var_11[0], var_7[var_9])\n for var_10 in range(2, -1, -1):\n var_11[var_10 + 1] = max(\n var_11[var_10 + 1], var_11[var_10] + var_12[var_8][var_9]\n )\n var_13[var_9] = max(var_11)\n var_7 = var_13\nprint((var_7[var_1 - 1]))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n\nfrom collections import deque, defaultdict\n\n\n\nfrom collections import Counter \n\n\nfrom itertools import accumulate, combinations, permutations, product \n\n\nfrom heapq import heapify, heappop, heappush\n\n\nfrom functools import reduce, lru_cache \n\n\nfrom decimal import Decimal\n\n\ndef input():\n x = sys.stdin.readline()\n return x[:-1] if x[-1] == \"\\n\" else x\n\n\ndef printe(*x):\n print(\"\n\n\ndef printl(li):\n _ = print(*li, sep=\"\\n\") if li else None\n\n\ndef argsort(s, return_sorted=False):\n inds = sorted(range(len(s)), key=lambda k: s[k])\n if return_sorted:\n return inds, [s[i] for i in inds]\n return inds\n\n\ndef alp2num(c, cap=False):\n return ord(c) - 97 if not cap else ord(c) - 65\n\n\ndef num2alp(i, cap=False):\n return chr(i + 97) if not cap else chr(i + 65)\n\n\ndef matmat(A, B):\n K, N, M = len(B), len(A), len(B[0])\n return [\n [sum([(A[i][k] * B[k][j]) for k in range(K)]) for j in range(M)]\n for i in range(N)\n ]\n\n\ndef matvec(M, v):\n N, size = len(v), len(M)\n return [sum([M[i][j] * v[j] for j in range(N)]) for i in range(size)]\n\n\ndef T(M):\n n, m = len(M), len(M[0])\n return [[M[j][i] for j in range(n)] for i in range(m)]\n\n\ndef binr(x):\n return bin(x)[2:]\n\n\ndef bitcount(x): \n x = x - ((x >> 1) & 0x5555555555555555)\n x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)\n x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F\n x += x >> 8\n x += x >> 16\n x += x >> 32\n return x & 0x7F\n\n\ndef main():\n mod = 1000000007\n \n \n R, C, K = map(int, input().split())\n \n \n base = 5000\n S = []\n for i in range(K):\n r, c, v = map(int, input().split())\n r -= 1\n c -= 1\n S.append((r * base + c, v))\n S.sort(key=itemgetter(0))\n p = 0\n dp = [[0] * 4 for _ in range(C)]\n dpi = [[0] * 4 for _ in range(C)]\n for i in range(R):\n for j in range(C):\n cv = 0\n if p < K and i * base + j == S[p][0]:\n cv = S[p][1]\n p += 1\n if j > 0:\n dpi[j] = copy(dpi[j - 1])\n else:\n dpi[0] = [0] * 4\n if i > 0:\n dpi[j][0] = max(dp[j], dpi[j][0])\n if cv != 0:\n for k in range(2, -1, -1):\n dpi[j][k + 1] = max(dpi[j][k] + cv, dpi[j][k + 1])\n \n dp[j] = max(dpi[j])\n print(dp[-1])\n\n\nif __name__ == \"__main__\":\n main()\n", "code_b": "var_3, var_1, var_2 = list(map(int, input().split()))\nvar_12 = [[0] * var_1 for var_8 in range(var_3)]\nfor var_8 in range(var_2):\n var_4, var_5, var_6 = list(map(int, input().split()))\n var_4 -= 1\n var_5 -= 1\n var_12[var_4][var_5] = var_6\nvar_7 = [0] * var_1\nfor var_8 in range(var_3):\n var_13 = [0] * var_1\n var_11 = [0] * 4\n var_11[0] = var_7[0]\n var_11[1] = var_12[var_8][0] + var_7[0]\n var_13[0] = var_11[1]\n for var_9 in range(1, var_1):\n var_11[0] = max(var_11[0], var_7[var_9])\n for var_10 in range(2, -1, -1):\n var_11[var_10 + 1] = max(\n var_11[var_10 + 1], var_11[var_10] + var_12[var_8][var_9]\n )\n var_13[var_9] = max(var_11)\n var_7 = var_13\nprint((var_7[var_1 - 1]))\n", "expected": "B"} -{"pair_id": "python_17824_reversed", "problem_id": "p02586", "language": "python", "direction": "reversed", "speedup": 3.4453, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_3, var_1, var_2 = list(map(int, input().split()))\nvar_12 = [[0] * var_1 for var_8 in range(var_3)]\nfor var_8 in range(var_2):\n var_4, var_5, var_6 = list(map(int, input().split()))\n var_4 -= 1\n var_5 -= 1\n var_12[var_4][var_5] = var_6\nvar_7 = [0] * var_1\nfor var_8 in range(var_3):\n var_13 = [0] * var_1\n var_11 = [0] * 4\n var_11[0] = var_7[0]\n var_11[1] = var_12[var_8][0] + var_7[0]\n var_13[0] = var_11[1]\n for var_9 in range(1, var_1):\n var_11[0] = max(var_11[0], var_7[var_9])\n for var_10 in range(2, -1, -1):\n var_11[var_10 + 1] = max(\n var_11[var_10 + 1], var_11[var_10] + var_12[var_8][var_9]\n )\n var_13[var_9] = max(var_11)\n var_7 = var_13\nprint((var_7[var_1 - 1]))\n\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n\nfrom collections import deque, defaultdict\n\n\n\nfrom collections import Counter \n\n\nfrom itertools import accumulate, combinations, permutations, product \n\n\nfrom heapq import heapify, heappop, heappush\n\n\nfrom functools import reduce, lru_cache \n\n\nfrom decimal import Decimal\n\n\ndef input():\n x = sys.stdin.readline()\n return x[:-1] if x[-1] == \"\\n\" else x\n\n\ndef printe(*x):\n print(\"\n\n\ndef printl(li):\n _ = print(*li, sep=\"\\n\") if li else None\n\n\ndef argsort(s, return_sorted=False):\n inds = sorted(range(len(s)), key=lambda k: s[k])\n if return_sorted:\n return inds, [s[i] for i in inds]\n return inds\n\n\ndef alp2num(c, cap=False):\n return ord(c) - 97 if not cap else ord(c) - 65\n\n\ndef num2alp(i, cap=False):\n return chr(i + 97) if not cap else chr(i + 65)\n\n\ndef matmat(A, B):\n K, N, M = len(B), len(A), len(B[0])\n return [\n [sum([(A[i][k] * B[k][j]) for k in range(K)]) for j in range(M)]\n for i in range(N)\n ]\n\n\ndef matvec(M, v):\n N, size = len(v), len(M)\n return [sum([M[i][j] * v[j] for j in range(N)]) for i in range(size)]\n\n\ndef T(M):\n n, m = len(M), len(M[0])\n return [[M[j][i] for j in range(n)] for i in range(m)]\n\n\ndef binr(x):\n return bin(x)[2:]\n\n\ndef bitcount(x): \n x = x - ((x >> 1) & 0x5555555555555555)\n x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)\n x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F\n x += x >> 8\n x += x >> 16\n x += x >> 32\n return x & 0x7F\n\n\ndef main():\n mod = 1000000007\n \n \n R, C, K = map(int, input().split())\n \n \n base = 5000\n S = []\n for i in range(K):\n r, c, v = map(int, input().split())\n r -= 1\n c -= 1\n S.append((r * base + c, v))\n S.sort(key=itemgetter(0))\n p = 0\n dp = [[0] * 4 for _ in range(C)]\n dpi = [[0] * 4 for _ in range(C)]\n for i in range(R):\n for j in range(C):\n cv = 0\n if p < K and i * base + j == S[p][0]:\n cv = S[p][1]\n p += 1\n if j > 0:\n dpi[j] = copy(dpi[j - 1])\n else:\n dpi[0] = [0] * 4\n if i > 0:\n dpi[j][0] = max(dp[j], dpi[j][0])\n if cv != 0:\n for k in range(2, -1, -1):\n dpi[j][k + 1] = max(dpi[j][k] + cv, dpi[j][k + 1])\n \n dp[j] = max(dpi[j])\n print(dp[-1])\n\n\nif __name__ == \"__main__\":\n main()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_3, var_1, var_2 = list(map(int, input().split()))\nvar_12 = [[0] * var_1 for var_8 in range(var_3)]\nfor var_8 in range(var_2):\n var_4, var_5, var_6 = list(map(int, input().split()))\n var_4 -= 1\n var_5 -= 1\n var_12[var_4][var_5] = var_6\nvar_7 = [0] * var_1\nfor var_8 in range(var_3):\n var_13 = [0] * var_1\n var_11 = [0] * 4\n var_11[0] = var_7[0]\n var_11[1] = var_12[var_8][0] + var_7[0]\n var_13[0] = var_11[1]\n for var_9 in range(1, var_1):\n var_11[0] = max(var_11[0], var_7[var_9])\n for var_10 in range(2, -1, -1):\n var_11[var_10 + 1] = max(\n var_11[var_10 + 1], var_11[var_10] + var_12[var_8][var_9]\n )\n var_13[var_9] = max(var_11)\n var_7 = var_13\nprint((var_7[var_1 - 1]))\n", "code_b": "import sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n\nfrom collections import deque, defaultdict\n\n\n\nfrom collections import Counter \n\n\nfrom itertools import accumulate, combinations, permutations, product \n\n\nfrom heapq import heapify, heappop, heappush\n\n\nfrom functools import reduce, lru_cache \n\n\nfrom decimal import Decimal\n\n\ndef input():\n x = sys.stdin.readline()\n return x[:-1] if x[-1] == \"\\n\" else x\n\n\ndef printe(*x):\n print(\"\n\n\ndef printl(li):\n _ = print(*li, sep=\"\\n\") if li else None\n\n\ndef argsort(s, return_sorted=False):\n inds = sorted(range(len(s)), key=lambda k: s[k])\n if return_sorted:\n return inds, [s[i] for i in inds]\n return inds\n\n\ndef alp2num(c, cap=False):\n return ord(c) - 97 if not cap else ord(c) - 65\n\n\ndef num2alp(i, cap=False):\n return chr(i + 97) if not cap else chr(i + 65)\n\n\ndef matmat(A, B):\n K, N, M = len(B), len(A), len(B[0])\n return [\n [sum([(A[i][k] * B[k][j]) for k in range(K)]) for j in range(M)]\n for i in range(N)\n ]\n\n\ndef matvec(M, v):\n N, size = len(v), len(M)\n return [sum([M[i][j] * v[j] for j in range(N)]) for i in range(size)]\n\n\ndef T(M):\n n, m = len(M), len(M[0])\n return [[M[j][i] for j in range(n)] for i in range(m)]\n\n\ndef binr(x):\n return bin(x)[2:]\n\n\ndef bitcount(x): \n x = x - ((x >> 1) & 0x5555555555555555)\n x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)\n x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F\n x += x >> 8\n x += x >> 16\n x += x >> 32\n return x & 0x7F\n\n\ndef main():\n mod = 1000000007\n \n \n R, C, K = map(int, input().split())\n \n \n base = 5000\n S = []\n for i in range(K):\n r, c, v = map(int, input().split())\n r -= 1\n c -= 1\n S.append((r * base + c, v))\n S.sort(key=itemgetter(0))\n p = 0\n dp = [[0] * 4 for _ in range(C)]\n dpi = [[0] * 4 for _ in range(C)]\n for i in range(R):\n for j in range(C):\n cv = 0\n if p < K and i * base + j == S[p][0]:\n cv = S[p][1]\n p += 1\n if j > 0:\n dpi[j] = copy(dpi[j - 1])\n else:\n dpi[0] = [0] * 4\n if i > 0:\n dpi[j][0] = max(dp[j], dpi[j][0])\n if cv != 0:\n for k in range(2, -1, -1):\n dpi[j][k + 1] = max(dpi[j][k] + cv, dpi[j][k + 1])\n \n dp[j] = max(dpi[j])\n print(dp[-1])\n\n\nif __name__ == \"__main__\":\n main()\n", "expected": "A"} +{"pair_id": "python_17824_forward", "problem_id": "p02586", "language": "python", "direction": "forward", "speedup": 3.4453, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n \nfrom collections import deque, defaultdict\n\n \n \nfrom collections import Counter \n\n \nfrom itertools import accumulate, combinations, permutations, product \n\n \nfrom heapq import heapify, heappop, heappush\n\n \nfrom functools import reduce, lru_cache \n\n \nfrom decimal import Decimal\n\n\ndef func_6():\n var_29 = sys.stdin.readline()\n return var_29[:-1] if var_29[-1] == \"\n\" else var_29\n\n\ndef func_11(*var_29):\n print(\"## \", *var_29, file=sys.stderr)\n\n\ndef func_12(var_19):\n _ = print(*var_19, sep=\"\n\") if var_19 else None\n\n\ndef func_3(var_26, var_25=False):\n var_16 = sorted(range(len(var_26)), key=lambda var_18: var_26[var_18])\n if var_25:\n return var_16, [var_26[var_15] for var_15 in var_16]\n return var_16\n\n\ndef func_2(var_10, var_11=False):\n return ord(var_10) - 97 if not var_11 else ord(var_10) - 65\n\n\ndef func_10(var_15, var_11=False):\n return chr(var_15 + 97) if not var_11 else chr(var_15 + 65)\n\n\ndef func_8(var_1, var_2):\n var_4, var_6, var_5 = len(var_2), len(var_1), len(var_2[0])\n return [\n [sum([(var_1[var_15][var_18] * var_2[var_18][var_17]) for var_18 in range(var_4)]) for var_17 in range(var_5)]\n for var_15 in range(var_6)\n ]\n\n\ndef func_9(var_5, var_28):\n var_6, var_27 = len(var_28), len(var_5)\n return [sum([var_5[var_15][var_17] * var_28[var_17] for var_17 in range(var_6)]) for var_15 in range(var_27)]\n\n\ndef func_1(var_5):\n var_22, var_20 = len(var_5), len(var_5[0])\n return [[var_5[var_17][var_15] for var_17 in range(var_22)] for var_15 in range(var_20)]\n\n\ndef func_4(var_29):\n return bin(var_29)[2:]\n\n\ndef func_5(var_29): \n var_29 = var_29 - ((var_29 >> 1) & 0x5555555555555555)\n var_29 = (var_29 & 0x3333333333333333) + ((var_29 >> 2) & 0x3333333333333333)\n var_29 = (var_29 + (var_29 >> 4)) & 0x0F0F0F0F0F0F0F0F\n var_29 += var_29 >> 8\n var_29 += var_29 >> 16\n var_29 += var_29 >> 32\n return var_29 & 0x7F\n\n\ndef func_7():\n var_21 = 1000000007\n \n \n var_7, var_3, var_4 = map(int, func_6().split())\n \n \n var_9 = 5000\n var_8 = []\n for var_15 in range(var_4):\n var_24, var_10, var_28 = map(int, func_6().split())\n var_24 -= 1\n var_10 -= 1\n var_8.append((var_24 * var_9 + var_10, var_28))\n var_8.sort(key=itemgetter(0))\n var_23 = 0\n var_13 = [[0] * 4 for _ in range(var_3)]\n var_14 = [[0] * 4 for _ in range(var_3)]\n for var_15 in range(var_7):\n for var_17 in range(var_3):\n var_12 = 0\n if var_23 < var_4 and var_15 * var_9 + var_17 == var_8[var_23][0]:\n var_12 = var_8[var_23][1]\n var_23 += 1\n if var_17 > 0:\n var_14[var_17] = copy(var_14[var_17 - 1])\n else:\n var_14[0] = [0] * 4\n if var_15 > 0:\n var_14[var_17][0] = max(var_13[var_17], var_14[var_17][0])\n if var_12 != 0:\n for var_18 in range(2, -1, -1):\n var_14[var_17][var_18 + 1] = max(var_14[var_17][var_18] + var_12, var_14[var_17][var_18 + 1])\n \n var_13[var_17] = max(var_14[var_17])\n print(var_13[-1])\n\n\nif __name__ == \"__main__\":\n func_7()\n```\n\n## Code B:\n```python\nvar_3, var_1, var_2 = list(map(int, input().split()))\nvar_12 = [[0] * var_1 for var_8 in range(var_3)]\nfor var_8 in range(var_2):\n var_4, var_5, var_6 = list(map(int, input().split()))\n var_4 -= 1\n var_5 -= 1\n var_12[var_4][var_5] = var_6\nvar_7 = [0] * var_1\nfor var_8 in range(var_3):\n var_13 = [0] * var_1\n var_11 = [0] * 4\n var_11[0] = var_7[0]\n var_11[1] = var_12[var_8][0] + var_7[0]\n var_13[0] = var_11[1]\n for var_9 in range(1, var_1):\n var_11[0] = max(var_11[0], var_7[var_9])\n for var_10 in range(2, -1, -1):\n var_11[var_10 + 1] = max(var_11[var_10 + 1], var_11[var_10] + var_12[var_8][var_9])\n var_13[var_9] = max(var_11)\n var_7 = var_13\nprint((var_7[var_1 - 1]))\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n \nfrom collections import deque, defaultdict\n\n \n \nfrom collections import Counter \n\n \nfrom itertools import accumulate, combinations, permutations, product \n\n \nfrom heapq import heapify, heappop, heappush\n\n \nfrom functools import reduce, lru_cache \n\n \nfrom decimal import Decimal\n\n\ndef func_6():\n var_29 = sys.stdin.readline()\n return var_29[:-1] if var_29[-1] == \"\\n\" else var_29\n\n\ndef func_11(*var_29):\n print(\"## \", *var_29, file=sys.stderr)\n\n\ndef func_12(var_19):\n _ = print(*var_19, sep=\"\\n\") if var_19 else None\n\n\ndef func_3(var_26, var_25=False):\n var_16 = sorted(range(len(var_26)), key=lambda var_18: var_26[var_18])\n if var_25:\n return var_16, [var_26[var_15] for var_15 in var_16]\n return var_16\n\n\ndef func_2(var_10, var_11=False):\n return ord(var_10) - 97 if not var_11 else ord(var_10) - 65\n\n\ndef func_10(var_15, var_11=False):\n return chr(var_15 + 97) if not var_11 else chr(var_15 + 65)\n\n\ndef func_8(var_1, var_2):\n var_4, var_6, var_5 = len(var_2), len(var_1), len(var_2[0])\n return [\n [sum([(var_1[var_15][var_18] * var_2[var_18][var_17]) for var_18 in range(var_4)]) for var_17 in range(var_5)]\n for var_15 in range(var_6)\n ]\n\n\ndef func_9(var_5, var_28):\n var_6, var_27 = len(var_28), len(var_5)\n return [sum([var_5[var_15][var_17] * var_28[var_17] for var_17 in range(var_6)]) for var_15 in range(var_27)]\n\n\ndef func_1(var_5):\n var_22, var_20 = len(var_5), len(var_5[0])\n return [[var_5[var_17][var_15] for var_17 in range(var_22)] for var_15 in range(var_20)]\n\n\ndef func_4(var_29):\n return bin(var_29)[2:]\n\n\ndef func_5(var_29): \n var_29 = var_29 - ((var_29 >> 1) & 0x5555555555555555)\n var_29 = (var_29 & 0x3333333333333333) + ((var_29 >> 2) & 0x3333333333333333)\n var_29 = (var_29 + (var_29 >> 4)) & 0x0F0F0F0F0F0F0F0F\n var_29 += var_29 >> 8\n var_29 += var_29 >> 16\n var_29 += var_29 >> 32\n return var_29 & 0x7F\n\n\ndef func_7():\n var_21 = 1000000007\n \n \n var_7, var_3, var_4 = map(int, func_6().split())\n \n \n var_9 = 5000\n var_8 = []\n for var_15 in range(var_4):\n var_24, var_10, var_28 = map(int, func_6().split())\n var_24 -= 1\n var_10 -= 1\n var_8.append((var_24 * var_9 + var_10, var_28))\n var_8.sort(key=itemgetter(0))\n var_23 = 0\n var_13 = [[0] * 4 for _ in range(var_3)]\n var_14 = [[0] * 4 for _ in range(var_3)]\n for var_15 in range(var_7):\n for var_17 in range(var_3):\n var_12 = 0\n if var_23 < var_4 and var_15 * var_9 + var_17 == var_8[var_23][0]:\n var_12 = var_8[var_23][1]\n var_23 += 1\n if var_17 > 0:\n var_14[var_17] = copy(var_14[var_17 - 1])\n else:\n var_14[0] = [0] * 4\n if var_15 > 0:\n var_14[var_17][0] = max(var_13[var_17], var_14[var_17][0])\n if var_12 != 0:\n for var_18 in range(2, -1, -1):\n var_14[var_17][var_18 + 1] = max(var_14[var_17][var_18] + var_12, var_14[var_17][var_18 + 1])\n \n var_13[var_17] = max(var_14[var_17])\n print(var_13[-1])\n\n\nif __name__ == \"__main__\":\n func_7()\n", "code_b": "var_3, var_1, var_2 = list(map(int, input().split()))\nvar_12 = [[0] * var_1 for var_8 in range(var_3)]\nfor var_8 in range(var_2):\n var_4, var_5, var_6 = list(map(int, input().split()))\n var_4 -= 1\n var_5 -= 1\n var_12[var_4][var_5] = var_6\nvar_7 = [0] * var_1\nfor var_8 in range(var_3):\n var_13 = [0] * var_1\n var_11 = [0] * 4\n var_11[0] = var_7[0]\n var_11[1] = var_12[var_8][0] + var_7[0]\n var_13[0] = var_11[1]\n for var_9 in range(1, var_1):\n var_11[0] = max(var_11[0], var_7[var_9])\n for var_10 in range(2, -1, -1):\n var_11[var_10 + 1] = max(var_11[var_10 + 1], var_11[var_10] + var_12[var_8][var_9])\n var_13[var_9] = max(var_11)\n var_7 = var_13\nprint((var_7[var_1 - 1]))\n", "expected": "B"} +{"pair_id": "python_17824_reversed", "problem_id": "p02586", "language": "python", "direction": "reversed", "speedup": 3.4453, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_3, var_1, var_2 = list(map(int, input().split()))\nvar_12 = [[0] * var_1 for var_8 in range(var_3)]\nfor var_8 in range(var_2):\n var_4, var_5, var_6 = list(map(int, input().split()))\n var_4 -= 1\n var_5 -= 1\n var_12[var_4][var_5] = var_6\nvar_7 = [0] * var_1\nfor var_8 in range(var_3):\n var_13 = [0] * var_1\n var_11 = [0] * 4\n var_11[0] = var_7[0]\n var_11[1] = var_12[var_8][0] + var_7[0]\n var_13[0] = var_11[1]\n for var_9 in range(1, var_1):\n var_11[0] = max(var_11[0], var_7[var_9])\n for var_10 in range(2, -1, -1):\n var_11[var_10 + 1] = max(var_11[var_10 + 1], var_11[var_10] + var_12[var_8][var_9])\n var_13[var_9] = max(var_11)\n var_7 = var_13\nprint((var_7[var_1 - 1]))\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n \nfrom collections import deque, defaultdict\n\n \n \nfrom collections import Counter \n\n \nfrom itertools import accumulate, combinations, permutations, product \n\n \nfrom heapq import heapify, heappop, heappush\n\n \nfrom functools import reduce, lru_cache \n\n \nfrom decimal import Decimal\n\n\ndef func_6():\n var_29 = sys.stdin.readline()\n return var_29[:-1] if var_29[-1] == \"\n\" else var_29\n\n\ndef func_11(*var_29):\n print(\"## \", *var_29, file=sys.stderr)\n\n\ndef func_12(var_19):\n _ = print(*var_19, sep=\"\n\") if var_19 else None\n\n\ndef func_3(var_26, var_25=False):\n var_16 = sorted(range(len(var_26)), key=lambda var_18: var_26[var_18])\n if var_25:\n return var_16, [var_26[var_15] for var_15 in var_16]\n return var_16\n\n\ndef func_2(var_10, var_11=False):\n return ord(var_10) - 97 if not var_11 else ord(var_10) - 65\n\n\ndef func_10(var_15, var_11=False):\n return chr(var_15 + 97) if not var_11 else chr(var_15 + 65)\n\n\ndef func_8(var_1, var_2):\n var_4, var_6, var_5 = len(var_2), len(var_1), len(var_2[0])\n return [\n [sum([(var_1[var_15][var_18] * var_2[var_18][var_17]) for var_18 in range(var_4)]) for var_17 in range(var_5)]\n for var_15 in range(var_6)\n ]\n\n\ndef func_9(var_5, var_28):\n var_6, var_27 = len(var_28), len(var_5)\n return [sum([var_5[var_15][var_17] * var_28[var_17] for var_17 in range(var_6)]) for var_15 in range(var_27)]\n\n\ndef func_1(var_5):\n var_22, var_20 = len(var_5), len(var_5[0])\n return [[var_5[var_17][var_15] for var_17 in range(var_22)] for var_15 in range(var_20)]\n\n\ndef func_4(var_29):\n return bin(var_29)[2:]\n\n\ndef func_5(var_29): \n var_29 = var_29 - ((var_29 >> 1) & 0x5555555555555555)\n var_29 = (var_29 & 0x3333333333333333) + ((var_29 >> 2) & 0x3333333333333333)\n var_29 = (var_29 + (var_29 >> 4)) & 0x0F0F0F0F0F0F0F0F\n var_29 += var_29 >> 8\n var_29 += var_29 >> 16\n var_29 += var_29 >> 32\n return var_29 & 0x7F\n\n\ndef func_7():\n var_21 = 1000000007\n \n \n var_7, var_3, var_4 = map(int, func_6().split())\n \n \n var_9 = 5000\n var_8 = []\n for var_15 in range(var_4):\n var_24, var_10, var_28 = map(int, func_6().split())\n var_24 -= 1\n var_10 -= 1\n var_8.append((var_24 * var_9 + var_10, var_28))\n var_8.sort(key=itemgetter(0))\n var_23 = 0\n var_13 = [[0] * 4 for _ in range(var_3)]\n var_14 = [[0] * 4 for _ in range(var_3)]\n for var_15 in range(var_7):\n for var_17 in range(var_3):\n var_12 = 0\n if var_23 < var_4 and var_15 * var_9 + var_17 == var_8[var_23][0]:\n var_12 = var_8[var_23][1]\n var_23 += 1\n if var_17 > 0:\n var_14[var_17] = copy(var_14[var_17 - 1])\n else:\n var_14[0] = [0] * 4\n if var_15 > 0:\n var_14[var_17][0] = max(var_13[var_17], var_14[var_17][0])\n if var_12 != 0:\n for var_18 in range(2, -1, -1):\n var_14[var_17][var_18 + 1] = max(var_14[var_17][var_18] + var_12, var_14[var_17][var_18 + 1])\n \n var_13[var_17] = max(var_14[var_17])\n print(var_13[-1])\n\n\nif __name__ == \"__main__\":\n func_7()\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_3, var_1, var_2 = list(map(int, input().split()))\nvar_12 = [[0] * var_1 for var_8 in range(var_3)]\nfor var_8 in range(var_2):\n var_4, var_5, var_6 = list(map(int, input().split()))\n var_4 -= 1\n var_5 -= 1\n var_12[var_4][var_5] = var_6\nvar_7 = [0] * var_1\nfor var_8 in range(var_3):\n var_13 = [0] * var_1\n var_11 = [0] * 4\n var_11[0] = var_7[0]\n var_11[1] = var_12[var_8][0] + var_7[0]\n var_13[0] = var_11[1]\n for var_9 in range(1, var_1):\n var_11[0] = max(var_11[0], var_7[var_9])\n for var_10 in range(2, -1, -1):\n var_11[var_10 + 1] = max(var_11[var_10 + 1], var_11[var_10] + var_12[var_8][var_9])\n var_13[var_9] = max(var_11)\n var_7 = var_13\nprint((var_7[var_1 - 1]))\n", "code_b": "import sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n \nfrom collections import deque, defaultdict\n\n \n \nfrom collections import Counter \n\n \nfrom itertools import accumulate, combinations, permutations, product \n\n \nfrom heapq import heapify, heappop, heappush\n\n \nfrom functools import reduce, lru_cache \n\n \nfrom decimal import Decimal\n\n\ndef func_6():\n var_29 = sys.stdin.readline()\n return var_29[:-1] if var_29[-1] == \"\\n\" else var_29\n\n\ndef func_11(*var_29):\n print(\"## \", *var_29, file=sys.stderr)\n\n\ndef func_12(var_19):\n _ = print(*var_19, sep=\"\\n\") if var_19 else None\n\n\ndef func_3(var_26, var_25=False):\n var_16 = sorted(range(len(var_26)), key=lambda var_18: var_26[var_18])\n if var_25:\n return var_16, [var_26[var_15] for var_15 in var_16]\n return var_16\n\n\ndef func_2(var_10, var_11=False):\n return ord(var_10) - 97 if not var_11 else ord(var_10) - 65\n\n\ndef func_10(var_15, var_11=False):\n return chr(var_15 + 97) if not var_11 else chr(var_15 + 65)\n\n\ndef func_8(var_1, var_2):\n var_4, var_6, var_5 = len(var_2), len(var_1), len(var_2[0])\n return [\n [sum([(var_1[var_15][var_18] * var_2[var_18][var_17]) for var_18 in range(var_4)]) for var_17 in range(var_5)]\n for var_15 in range(var_6)\n ]\n\n\ndef func_9(var_5, var_28):\n var_6, var_27 = len(var_28), len(var_5)\n return [sum([var_5[var_15][var_17] * var_28[var_17] for var_17 in range(var_6)]) for var_15 in range(var_27)]\n\n\ndef func_1(var_5):\n var_22, var_20 = len(var_5), len(var_5[0])\n return [[var_5[var_17][var_15] for var_17 in range(var_22)] for var_15 in range(var_20)]\n\n\ndef func_4(var_29):\n return bin(var_29)[2:]\n\n\ndef func_5(var_29): \n var_29 = var_29 - ((var_29 >> 1) & 0x5555555555555555)\n var_29 = (var_29 & 0x3333333333333333) + ((var_29 >> 2) & 0x3333333333333333)\n var_29 = (var_29 + (var_29 >> 4)) & 0x0F0F0F0F0F0F0F0F\n var_29 += var_29 >> 8\n var_29 += var_29 >> 16\n var_29 += var_29 >> 32\n return var_29 & 0x7F\n\n\ndef func_7():\n var_21 = 1000000007\n \n \n var_7, var_3, var_4 = map(int, func_6().split())\n \n \n var_9 = 5000\n var_8 = []\n for var_15 in range(var_4):\n var_24, var_10, var_28 = map(int, func_6().split())\n var_24 -= 1\n var_10 -= 1\n var_8.append((var_24 * var_9 + var_10, var_28))\n var_8.sort(key=itemgetter(0))\n var_23 = 0\n var_13 = [[0] * 4 for _ in range(var_3)]\n var_14 = [[0] * 4 for _ in range(var_3)]\n for var_15 in range(var_7):\n for var_17 in range(var_3):\n var_12 = 0\n if var_23 < var_4 and var_15 * var_9 + var_17 == var_8[var_23][0]:\n var_12 = var_8[var_23][1]\n var_23 += 1\n if var_17 > 0:\n var_14[var_17] = copy(var_14[var_17 - 1])\n else:\n var_14[0] = [0] * 4\n if var_15 > 0:\n var_14[var_17][0] = max(var_13[var_17], var_14[var_17][0])\n if var_12 != 0:\n for var_18 in range(2, -1, -1):\n var_14[var_17][var_18 + 1] = max(var_14[var_17][var_18] + var_12, var_14[var_17][var_18 + 1])\n \n var_13[var_17] = max(var_14[var_17])\n print(var_13[-1])\n\n\nif __name__ == \"__main__\":\n func_7()\n", "expected": "A"} {"pair_id": "python_289_forward", "problem_id": "p02714", "language": "python", "direction": "forward", "speedup": 3.7935, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7)\n\n\ndef func_1():\n return int(sys.stdin.readline().rstrip())\n\n\ndef func_6():\n return list(map(int, sys.stdin.readline().rstrip().split()))\n\n\ndef func_2():\n return list(map(int, sys.stdin.readline().rstrip().split()))\n\n\ndef func_3():\n return list(map(int, sys.stdin.readline().rstrip()))\n\n\ndef func_7():\n return sys.stdin.readline().rstrip()\n\n\ndef func_4():\n return list(sys.stdin.readline().rstrip().split())\n\n\ndef func_5():\n return list(sys.stdin.readline().rstrip())\n\n\nvar_3 = func_1()\nfunc_7 = [\"\"] + func_5()\nvar_4 = [0] * (var_3 + 1)\nvar_2 = [0] * (var_3 + 1)\nvar_1 = [0] * (var_3 + 1)\nfor var_6 in range(1, var_3 + 1):\n var_8 = func_7[var_6]\n var_4[var_6] = var_4[var_6 - 1]\n var_2[var_6] = var_2[var_6 - 1]\n var_1[var_6] = var_1[var_6 - 1]\n if var_8 == \"R\":\n var_4[var_6] += 1\n elif var_8 == \"G\":\n var_2[var_6] += 1\n else:\n var_1[var_6] += 1\nvar_5 = 0\nfor var_6 in range(2, var_3):\n if func_7[var_6] == \"R\":\n var_5 += var_2[var_6 - 1] * (var_1[-1] - var_1[var_6])\n var_5 += var_1[var_6 - 1] * (var_2[-1] - var_2[var_6])\n for var_7 in range(1, var_3 + 1):\n if 1 <= var_6 - var_7 <= var_3 and 1 <= var_6 + var_7 <= var_3:\n if (func_7[var_6 - var_7] == \"G\" and func_7[var_6 + var_7] == \"B\") or (\n func_7[var_6 - var_7] == \"B\" and func_7[var_6 + var_7] == \"G\"\n ):\n var_5 -= 1\n elif func_7[var_6] == \"G\":\n var_5 += var_4[var_6 - 1] * (var_1[-1] - var_1[var_6])\n var_5 += var_1[var_6 - 1] * (var_4[-1] - var_4[var_6])\n for var_7 in range(1, var_3 + 1):\n if 1 <= var_6 - var_7 <= var_3 and 1 <= var_6 + var_7 <= var_3:\n if (func_7[var_6 - var_7] == \"R\" and func_7[var_6 + var_7] == \"B\") or (\n func_7[var_6 - var_7] == \"B\" and func_7[var_6 + var_7] == \"R\"\n ):\n var_5 -= 1\n else:\n var_5 += var_2[var_6 - 1] * (var_4[-1] - var_4[var_6])\n var_5 += var_4[var_6 - 1] * (var_2[-1] - var_2[var_6])\n for var_7 in range(1, var_3 + 1):\n if 1 <= var_6 - var_7 <= var_3 and 1 <= var_6 + var_7 <= var_3:\n if (func_7[var_6 - var_7] == \"R\" and func_7[var_6 + var_7] == \"G\") or (\n func_7[var_6 - var_7] == \"G\" and func_7[var_6 + var_7] == \"R\"\n ):\n var_5 -= 1\nprint(var_5)\n\n```\n\n## Code B:\n```python\nfrom collections import defaultdict\n\n\ndef func_1():\n _ = int(eval(input()))\n var_1 = input().rstrip()\n var_8 = defaultdict(set)\n for var_7, var_2 in enumerate(var_1):\n var_8[var_2].add(var_7)\n var_6 = len(var_8[\"R\"]) * len(var_8[\"G\"]) * len(var_8[\"B\"])\n for var_3, var_4, var_5 in [\"RGB\", \"GBR\", \"BRG\"]:\n var_6 -= sum(\n (var_7 + var_9) % 2 == 0 and (var_7 + var_9) // 2 in var_8[var_5]\n for var_7 in var_8[var_3]\n for var_9 in var_8[var_4]\n )\n print(var_6)\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(10**7)\n\n\ndef func_1():\n return int(sys.stdin.readline().rstrip())\n\n\ndef func_6():\n return list(map(int, sys.stdin.readline().rstrip().split()))\n\n\ndef func_2():\n return list(map(int, sys.stdin.readline().rstrip().split()))\n\n\ndef func_3():\n return list(map(int, sys.stdin.readline().rstrip()))\n\n\ndef func_7():\n return sys.stdin.readline().rstrip()\n\n\ndef func_4():\n return list(sys.stdin.readline().rstrip().split())\n\n\ndef func_5():\n return list(sys.stdin.readline().rstrip())\n\n\nvar_3 = func_1()\nfunc_7 = [\"\"] + func_5()\nvar_4 = [0] * (var_3 + 1)\nvar_2 = [0] * (var_3 + 1)\nvar_1 = [0] * (var_3 + 1)\nfor var_6 in range(1, var_3 + 1):\n var_8 = func_7[var_6]\n var_4[var_6] = var_4[var_6 - 1]\n var_2[var_6] = var_2[var_6 - 1]\n var_1[var_6] = var_1[var_6 - 1]\n if var_8 == \"R\":\n var_4[var_6] += 1\n elif var_8 == \"G\":\n var_2[var_6] += 1\n else:\n var_1[var_6] += 1\nvar_5 = 0\nfor var_6 in range(2, var_3):\n if func_7[var_6] == \"R\":\n var_5 += var_2[var_6 - 1] * (var_1[-1] - var_1[var_6])\n var_5 += var_1[var_6 - 1] * (var_2[-1] - var_2[var_6])\n for var_7 in range(1, var_3 + 1):\n if 1 <= var_6 - var_7 <= var_3 and 1 <= var_6 + var_7 <= var_3:\n if (func_7[var_6 - var_7] == \"G\" and func_7[var_6 + var_7] == \"B\") or (\n func_7[var_6 - var_7] == \"B\" and func_7[var_6 + var_7] == \"G\"\n ):\n var_5 -= 1\n elif func_7[var_6] == \"G\":\n var_5 += var_4[var_6 - 1] * (var_1[-1] - var_1[var_6])\n var_5 += var_1[var_6 - 1] * (var_4[-1] - var_4[var_6])\n for var_7 in range(1, var_3 + 1):\n if 1 <= var_6 - var_7 <= var_3 and 1 <= var_6 + var_7 <= var_3:\n if (func_7[var_6 - var_7] == \"R\" and func_7[var_6 + var_7] == \"B\") or (\n func_7[var_6 - var_7] == \"B\" and func_7[var_6 + var_7] == \"R\"\n ):\n var_5 -= 1\n else:\n var_5 += var_2[var_6 - 1] * (var_4[-1] - var_4[var_6])\n var_5 += var_4[var_6 - 1] * (var_2[-1] - var_2[var_6])\n for var_7 in range(1, var_3 + 1):\n if 1 <= var_6 - var_7 <= var_3 and 1 <= var_6 + var_7 <= var_3:\n if (func_7[var_6 - var_7] == \"R\" and func_7[var_6 + var_7] == \"G\") or (\n func_7[var_6 - var_7] == \"G\" and func_7[var_6 + var_7] == \"R\"\n ):\n var_5 -= 1\nprint(var_5)\n", "code_b": "from collections import defaultdict\n\n\ndef func_1():\n _ = int(eval(input()))\n var_1 = input().rstrip()\n var_8 = defaultdict(set)\n for var_7, var_2 in enumerate(var_1):\n var_8[var_2].add(var_7)\n var_6 = len(var_8[\"R\"]) * len(var_8[\"G\"]) * len(var_8[\"B\"])\n for var_3, var_4, var_5 in [\"RGB\", \"GBR\", \"BRG\"]:\n var_6 -= sum(\n (var_7 + var_9) % 2 == 0 and (var_7 + var_9) // 2 in var_8[var_5]\n for var_7 in var_8[var_3]\n for var_9 in var_8[var_4]\n )\n print(var_6)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "B"} {"pair_id": "python_289_reversed", "problem_id": "p02714", "language": "python", "direction": "reversed", "speedup": 3.7935, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfrom collections import defaultdict\n\n\ndef func_1():\n _ = int(eval(input()))\n var_1 = input().rstrip()\n var_8 = defaultdict(set)\n for var_7, var_2 in enumerate(var_1):\n var_8[var_2].add(var_7)\n var_6 = len(var_8[\"R\"]) * len(var_8[\"G\"]) * len(var_8[\"B\"])\n for var_3, var_4, var_5 in [\"RGB\", \"GBR\", \"BRG\"]:\n var_6 -= sum(\n (var_7 + var_9) % 2 == 0 and (var_7 + var_9) // 2 in var_8[var_5]\n for var_7 in var_8[var_3]\n for var_9 in var_8[var_4]\n )\n print(var_6)\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7)\n\n\ndef func_1():\n return int(sys.stdin.readline().rstrip())\n\n\ndef func_6():\n return list(map(int, sys.stdin.readline().rstrip().split()))\n\n\ndef func_2():\n return list(map(int, sys.stdin.readline().rstrip().split()))\n\n\ndef func_3():\n return list(map(int, sys.stdin.readline().rstrip()))\n\n\ndef func_7():\n return sys.stdin.readline().rstrip()\n\n\ndef func_4():\n return list(sys.stdin.readline().rstrip().split())\n\n\ndef func_5():\n return list(sys.stdin.readline().rstrip())\n\n\nvar_3 = func_1()\nfunc_7 = [\"\"] + func_5()\nvar_4 = [0] * (var_3 + 1)\nvar_2 = [0] * (var_3 + 1)\nvar_1 = [0] * (var_3 + 1)\nfor var_6 in range(1, var_3 + 1):\n var_8 = func_7[var_6]\n var_4[var_6] = var_4[var_6 - 1]\n var_2[var_6] = var_2[var_6 - 1]\n var_1[var_6] = var_1[var_6 - 1]\n if var_8 == \"R\":\n var_4[var_6] += 1\n elif var_8 == \"G\":\n var_2[var_6] += 1\n else:\n var_1[var_6] += 1\nvar_5 = 0\nfor var_6 in range(2, var_3):\n if func_7[var_6] == \"R\":\n var_5 += var_2[var_6 - 1] * (var_1[-1] - var_1[var_6])\n var_5 += var_1[var_6 - 1] * (var_2[-1] - var_2[var_6])\n for var_7 in range(1, var_3 + 1):\n if 1 <= var_6 - var_7 <= var_3 and 1 <= var_6 + var_7 <= var_3:\n if (func_7[var_6 - var_7] == \"G\" and func_7[var_6 + var_7] == \"B\") or (\n func_7[var_6 - var_7] == \"B\" and func_7[var_6 + var_7] == \"G\"\n ):\n var_5 -= 1\n elif func_7[var_6] == \"G\":\n var_5 += var_4[var_6 - 1] * (var_1[-1] - var_1[var_6])\n var_5 += var_1[var_6 - 1] * (var_4[-1] - var_4[var_6])\n for var_7 in range(1, var_3 + 1):\n if 1 <= var_6 - var_7 <= var_3 and 1 <= var_6 + var_7 <= var_3:\n if (func_7[var_6 - var_7] == \"R\" and func_7[var_6 + var_7] == \"B\") or (\n func_7[var_6 - var_7] == \"B\" and func_7[var_6 + var_7] == \"R\"\n ):\n var_5 -= 1\n else:\n var_5 += var_2[var_6 - 1] * (var_4[-1] - var_4[var_6])\n var_5 += var_4[var_6 - 1] * (var_2[-1] - var_2[var_6])\n for var_7 in range(1, var_3 + 1):\n if 1 <= var_6 - var_7 <= var_3 and 1 <= var_6 + var_7 <= var_3:\n if (func_7[var_6 - var_7] == \"R\" and func_7[var_6 + var_7] == \"G\") or (\n func_7[var_6 - var_7] == \"G\" and func_7[var_6 + var_7] == \"R\"\n ):\n var_5 -= 1\nprint(var_5)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "from collections import defaultdict\n\n\ndef func_1():\n _ = int(eval(input()))\n var_1 = input().rstrip()\n var_8 = defaultdict(set)\n for var_7, var_2 in enumerate(var_1):\n var_8[var_2].add(var_7)\n var_6 = len(var_8[\"R\"]) * len(var_8[\"G\"]) * len(var_8[\"B\"])\n for var_3, var_4, var_5 in [\"RGB\", \"GBR\", \"BRG\"]:\n var_6 -= sum(\n (var_7 + var_9) % 2 == 0 and (var_7 + var_9) // 2 in var_8[var_5]\n for var_7 in var_8[var_3]\n for var_9 in var_8[var_4]\n )\n print(var_6)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "import sys\n\nsys.setrecursionlimit(10**7)\n\n\ndef func_1():\n return int(sys.stdin.readline().rstrip())\n\n\ndef func_6():\n return list(map(int, sys.stdin.readline().rstrip().split()))\n\n\ndef func_2():\n return list(map(int, sys.stdin.readline().rstrip().split()))\n\n\ndef func_3():\n return list(map(int, sys.stdin.readline().rstrip()))\n\n\ndef func_7():\n return sys.stdin.readline().rstrip()\n\n\ndef func_4():\n return list(sys.stdin.readline().rstrip().split())\n\n\ndef func_5():\n return list(sys.stdin.readline().rstrip())\n\n\nvar_3 = func_1()\nfunc_7 = [\"\"] + func_5()\nvar_4 = [0] * (var_3 + 1)\nvar_2 = [0] * (var_3 + 1)\nvar_1 = [0] * (var_3 + 1)\nfor var_6 in range(1, var_3 + 1):\n var_8 = func_7[var_6]\n var_4[var_6] = var_4[var_6 - 1]\n var_2[var_6] = var_2[var_6 - 1]\n var_1[var_6] = var_1[var_6 - 1]\n if var_8 == \"R\":\n var_4[var_6] += 1\n elif var_8 == \"G\":\n var_2[var_6] += 1\n else:\n var_1[var_6] += 1\nvar_5 = 0\nfor var_6 in range(2, var_3):\n if func_7[var_6] == \"R\":\n var_5 += var_2[var_6 - 1] * (var_1[-1] - var_1[var_6])\n var_5 += var_1[var_6 - 1] * (var_2[-1] - var_2[var_6])\n for var_7 in range(1, var_3 + 1):\n if 1 <= var_6 - var_7 <= var_3 and 1 <= var_6 + var_7 <= var_3:\n if (func_7[var_6 - var_7] == \"G\" and func_7[var_6 + var_7] == \"B\") or (\n func_7[var_6 - var_7] == \"B\" and func_7[var_6 + var_7] == \"G\"\n ):\n var_5 -= 1\n elif func_7[var_6] == \"G\":\n var_5 += var_4[var_6 - 1] * (var_1[-1] - var_1[var_6])\n var_5 += var_1[var_6 - 1] * (var_4[-1] - var_4[var_6])\n for var_7 in range(1, var_3 + 1):\n if 1 <= var_6 - var_7 <= var_3 and 1 <= var_6 + var_7 <= var_3:\n if (func_7[var_6 - var_7] == \"R\" and func_7[var_6 + var_7] == \"B\") or (\n func_7[var_6 - var_7] == \"B\" and func_7[var_6 + var_7] == \"R\"\n ):\n var_5 -= 1\n else:\n var_5 += var_2[var_6 - 1] * (var_4[-1] - var_4[var_6])\n var_5 += var_4[var_6 - 1] * (var_2[-1] - var_2[var_6])\n for var_7 in range(1, var_3 + 1):\n if 1 <= var_6 - var_7 <= var_3 and 1 <= var_6 + var_7 <= var_3:\n if (func_7[var_6 - var_7] == \"R\" and func_7[var_6 + var_7] == \"G\") or (\n func_7[var_6 - var_7] == \"G\" and func_7[var_6 + var_7] == \"R\"\n ):\n var_5 -= 1\nprint(var_5)\n", "expected": "A"} {"pair_id": "python_764_forward", "problem_id": "p02642", "language": "python", "direction": "forward", "speedup": 586.3373, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_8 = int(eval(input()))\nvar_1 = sorted(map(int, input().split()))\nvar_2 = 10**6\nvar_7 = [1] * (var_2 + 1)\nvar_4 = set()\nvar_6 = set()\nvar_5 = [0] * (var_2 + 1)\nfor var_3 in var_1:\n var_5[var_3] += 1\n if var_5[var_3] == 2 and var_7[var_3]:\n var_6.add(var_3)\n continue\n if 2 < var_5[var_3]:\n continue\n if var_7[var_3]:\n var_4.add(var_3)\n for var_9 in range(2 * var_3, var_2 + 1, var_3):\n var_7[var_9] = 0\nprint((len(var_4 - var_6)))\n\n```\n\n## Code B:\n```python\nvar_4 = int(eval(input()))\nvar_2 = list(map(int, input().split()))\nvar_3 = max(var_2) + 1\nvar_6 = [0 for _ in range(var_3)]\nfor var_1 in var_2:\n if var_6[var_1] != 0:\n var_6[var_1] = 2\n continue\n for var_7 in range(var_1, var_3, var_1):\n var_6[var_7] += 1\nvar_5 = 0\nfor var_1 in var_2:\n if var_6[var_1] == 1:\n var_5 += 1\nprint(var_5)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_8 = int(eval(input()))\nvar_1 = sorted(map(int, input().split()))\nvar_2 = 10**6\nvar_7 = [1] * (var_2 + 1)\nvar_4 = set()\nvar_6 = set()\nvar_5 = [0] * (var_2 + 1)\nfor var_3 in var_1:\n var_5[var_3] += 1\n if var_5[var_3] == 2 and var_7[var_3]:\n var_6.add(var_3)\n continue\n if 2 < var_5[var_3]:\n continue\n if var_7[var_3]:\n var_4.add(var_3)\n for var_9 in range(2 * var_3, var_2 + 1, var_3):\n var_7[var_9] = 0\nprint((len(var_4 - var_6)))\n", "code_b": "var_4 = int(eval(input()))\nvar_2 = list(map(int, input().split()))\nvar_3 = max(var_2) + 1\nvar_6 = [0 for _ in range(var_3)]\nfor var_1 in var_2:\n if var_6[var_1] != 0:\n var_6[var_1] = 2\n continue\n for var_7 in range(var_1, var_3, var_1):\n var_6[var_7] += 1\nvar_5 = 0\nfor var_1 in var_2:\n if var_6[var_1] == 1:\n var_5 += 1\nprint(var_5)\n", "expected": "B"} @@ -698,8 +698,8 @@ {"pair_id": "python_6216_reversed", "problem_id": "p00040", "language": "python", "direction": "reversed", "speedup": 49.4135, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport string\n\nvar_1 = \"abcdefghijklmnopqrstuvwxyz\"\nvar_3 = list(range(26))\n\n\ndef func_3(var_9, var_2):\n var_13 = \"\"\n for var_6 in var_9:\n var_13 += var_2[var_1.index(var_6)] if \"a\" <= var_6 <= \"z\" else var_6\n return var_13\n\n\ndef func_1(var_9, var_4, var_5):\n var_2 = \"\".join([var_1[(var_8 * var_4 + var_5) % 26] for var_8 in var_3])\n var_11 = func_3(var_9, var_2)\n return var_11\n\n\ndef func_4(var_9, var_4):\n var_4 = var_4 % 26\n var_2 = var_1[var_4:] + var_1[:var_4]\n var_11 = func_3(var_9, var_2)\n return var_11\n\n\ndef func_2(var_11):\n var_7 = \"t\"\n for var_8 in var_3:\n var_4 = 0\n var_13 = func_1(var_11, var_8, 0)\n var_6 = var_1.index(var_13[0])\n if var_6 != 19:\n var_4 = (19 - var_6) % 26\n var_13 = func_4(var_13, var_4)\n if var_13 in [\"this\", \"that\"]:\n return var_8, var_4\n return -1, -1\n\n\nvar_10 = eval(input())\nwhile var_10:\n var_10 -= 1\n var_11 = input()\n for var_12 in var_11.split():\n if len(var_12) == 4:\n var_4, var_5 = func_2(var_12)\n if var_4 != -1:\n break\n print(func_1(var_11, var_4, var_5))\n\n```\n\n## Code B:\n```python\nvar_7 = [\n \"a\",\n \"b\",\n \"c\",\n \"d\",\n \"e\",\n \"f\",\n \"g\",\n \"h\",\n \"i\",\n \"j\",\n \"k\",\n \"l\",\n \"m\",\n \"n\",\n \"o\",\n \"p\",\n \"q\",\n \"r\",\n \"s\",\n \"t\",\n \"u\",\n \"v\",\n \"w\",\n \"x\",\n \"y\",\n \"z\",\n]\nvar_11 = [1, 3, 5, 7, 9, 11, 15, 17, 19, 21, 23, 25]\nvar_12 = int(input())\nfor var_13 in range(var_12):\n var_4 = input()\n for var_1 in var_11:\n var_9 = 0\n for var_2 in range(26):\n var_6 = \"\"\n for var_3 in var_4:\n if var_3 != \" \":\n var_8 = var_7.index(var_3)\n for var_10 in range(26):\n if (var_1 * var_10 + var_2) % 26 == var_8:\n var_5 = var_7[var_10]\n break\n var_6 += var_5\n else:\n var_6 += \" \"\n if var_6.count(\"that\") > 0 or var_6.count(\"this\") > 0:\n var_9 = 1\n break\n if var_9 == 1:\n break\n print(var_6)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import string\n\nvar_1 = \"abcdefghijklmnopqrstuvwxyz\"\nvar_3 = list(range(26))\n\n\ndef func_3(var_9, var_2):\n var_13 = \"\"\n for var_6 in var_9:\n var_13 += var_2[var_1.index(var_6)] if \"a\" <= var_6 <= \"z\" else var_6\n return var_13\n\n\ndef func_1(var_9, var_4, var_5):\n var_2 = \"\".join([var_1[(var_8 * var_4 + var_5) % 26] for var_8 in var_3])\n var_11 = func_3(var_9, var_2)\n return var_11\n\n\ndef func_4(var_9, var_4):\n var_4 = var_4 % 26\n var_2 = var_1[var_4:] + var_1[:var_4]\n var_11 = func_3(var_9, var_2)\n return var_11\n\n\ndef func_2(var_11):\n var_7 = \"t\"\n for var_8 in var_3:\n var_4 = 0\n var_13 = func_1(var_11, var_8, 0)\n var_6 = var_1.index(var_13[0])\n if var_6 != 19:\n var_4 = (19 - var_6) % 26\n var_13 = func_4(var_13, var_4)\n if var_13 in [\"this\", \"that\"]:\n return var_8, var_4\n return -1, -1\n\n\nvar_10 = eval(input())\nwhile var_10:\n var_10 -= 1\n var_11 = input()\n for var_12 in var_11.split():\n if len(var_12) == 4:\n var_4, var_5 = func_2(var_12)\n if var_4 != -1:\n break\n print(func_1(var_11, var_4, var_5))\n", "code_b": "var_7 = [\n \"a\",\n \"b\",\n \"c\",\n \"d\",\n \"e\",\n \"f\",\n \"g\",\n \"h\",\n \"i\",\n \"j\",\n \"k\",\n \"l\",\n \"m\",\n \"n\",\n \"o\",\n \"p\",\n \"q\",\n \"r\",\n \"s\",\n \"t\",\n \"u\",\n \"v\",\n \"w\",\n \"x\",\n \"y\",\n \"z\",\n]\nvar_11 = [1, 3, 5, 7, 9, 11, 15, 17, 19, 21, 23, 25]\nvar_12 = int(input())\nfor var_13 in range(var_12):\n var_4 = input()\n for var_1 in var_11:\n var_9 = 0\n for var_2 in range(26):\n var_6 = \"\"\n for var_3 in var_4:\n if var_3 != \" \":\n var_8 = var_7.index(var_3)\n for var_10 in range(26):\n if (var_1 * var_10 + var_2) % 26 == var_8:\n var_5 = var_7[var_10]\n break\n var_6 += var_5\n else:\n var_6 += \" \"\n if var_6.count(\"that\") > 0 or var_6.count(\"this\") > 0:\n var_9 = 1\n break\n if var_9 == 1:\n break\n print(var_6)\n", "expected": "A"} {"pair_id": "python_10722_forward", "problem_id": "p03767", "language": "python", "direction": "forward", "speedup": 8.9104, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfrom collections import defaultdict, deque\nfrom heapq import heappush, heappop\nimport sys\nimport math\nimport bisect\nimport random\n\n\ndef func_9():\n return [int(var_15) for var_15 in sys.stdin.readline().split()]\n\n\ndef func_7():\n return int(sys.stdin.readline())\n\n\ndef func_11():\n return [list(var_15) for var_15 in sys.stdin.readline().split()]\n\n\ndef func_13():\n return list(sys.stdin.readline())[:-1]\n\n\ndef func_8(var_12):\n return [func_7() for var_8 in range(var_12)]\n\n\ndef func_10(var_12):\n return [func_9() for var_8 in range(var_12)]\n\n\ndef func_14(var_12):\n return [func_13() for var_8 in range(var_12)]\n\n\ndef func_12(var_12):\n return [func_11() for var_8 in range(var_12)]\n\n\nsys.setrecursionlimit(1000000)\nvar_11 = 1000000007\n\n\ndef func_1():\n var_12 = func_7()\n var_1 = func_9()\n var_1.sort(key=lambda var_15: -var_15)\n var_2 = 0\n for var_8 in range(var_12):\n var_2 += var_1[2 * var_8 + 1]\n print(var_2)\n return\n\n\ndef func_2():\n def func_15(var_5, var_15, var_4):\n if var_5 <= var_6[var_15]:\n return\n var_6[var_15] = var_5\n if not var_7[var_15]:\n var_7[var_15] = var_4\n for var_16 in var_14[var_15]:\n func_15(var_5 - 1, var_16, var_4)\n\n var_12, var_10 = func_9()\n var_7 = [0] * var_12\n var_14 = [[] for var_8 in range(var_12)]\n for var_8 in range(var_10):\n var_1, var_3 = func_9()\n var_1 -= 1\n var_3 -= 1\n var_14[var_1].append(var_3)\n var_14[var_3].append(var_1)\n var_6 = defaultdict(lambda: -1)\n var_13 = func_7()\n var_9 = func_10(var_13)\n var_9 = var_9[::-1]\n for var_15, var_5, var_4 in var_9:\n var_15 -= 1\n func_15(var_5, var_15, var_4)\n for var_8 in var_7:\n print(var_8)\n return\n\n\ndef func_3():\n var_12 = func_7()\n return\n\n\ndef func_4():\n var_12 = func_7()\n return\n\n\ndef func_5():\n var_12 = func_7()\n return\n\n\ndef func_6():\n var_12 = func_7()\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\n## Code B:\n```python\nprint(\n sum(\n sorted(list(map(int, exec(\"N=int(input())\") or input().split())))[\n -2 : -2 * var_1 - 1 : -2\n ]\n )\n)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "from collections import defaultdict, deque\nfrom heapq import heappush, heappop\nimport sys\nimport math\nimport bisect\nimport random\n\n\ndef func_9():\n return [int(var_15) for var_15 in sys.stdin.readline().split()]\n\n\ndef func_7():\n return int(sys.stdin.readline())\n\n\ndef func_11():\n return [list(var_15) for var_15 in sys.stdin.readline().split()]\n\n\ndef func_13():\n return list(sys.stdin.readline())[:-1]\n\n\ndef func_8(var_12):\n return [func_7() for var_8 in range(var_12)]\n\n\ndef func_10(var_12):\n return [func_9() for var_8 in range(var_12)]\n\n\ndef func_14(var_12):\n return [func_13() for var_8 in range(var_12)]\n\n\ndef func_12(var_12):\n return [func_11() for var_8 in range(var_12)]\n\n\nsys.setrecursionlimit(1000000)\nvar_11 = 1000000007\n\n\ndef func_1():\n var_12 = func_7()\n var_1 = func_9()\n var_1.sort(key=lambda var_15: -var_15)\n var_2 = 0\n for var_8 in range(var_12):\n var_2 += var_1[2 * var_8 + 1]\n print(var_2)\n return\n\n\ndef func_2():\n def func_15(var_5, var_15, var_4):\n if var_5 <= var_6[var_15]:\n return\n var_6[var_15] = var_5\n if not var_7[var_15]:\n var_7[var_15] = var_4\n for var_16 in var_14[var_15]:\n func_15(var_5 - 1, var_16, var_4)\n\n var_12, var_10 = func_9()\n var_7 = [0] * var_12\n var_14 = [[] for var_8 in range(var_12)]\n for var_8 in range(var_10):\n var_1, var_3 = func_9()\n var_1 -= 1\n var_3 -= 1\n var_14[var_1].append(var_3)\n var_14[var_3].append(var_1)\n var_6 = defaultdict(lambda: -1)\n var_13 = func_7()\n var_9 = func_10(var_13)\n var_9 = var_9[::-1]\n for var_15, var_5, var_4 in var_9:\n var_15 -= 1\n func_15(var_5, var_15, var_4)\n for var_8 in var_7:\n print(var_8)\n return\n\n\ndef func_3():\n var_12 = func_7()\n return\n\n\ndef func_4():\n var_12 = func_7()\n return\n\n\ndef func_5():\n var_12 = func_7()\n return\n\n\ndef func_6():\n var_12 = func_7()\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "print(\n sum(\n sorted(list(map(int, exec(\"N=int(input())\") or input().split())))[\n -2 : -2 * var_1 - 1 : -2\n ]\n )\n)\n", "expected": "B"} {"pair_id": "python_10722_reversed", "problem_id": "p03767", "language": "python", "direction": "reversed", "speedup": 8.9104, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nprint(\n sum(\n sorted(list(map(int, exec(\"N=int(input())\") or input().split())))[\n -2 : -2 * var_1 - 1 : -2\n ]\n )\n)\n\n```\n\n## Code B:\n```python\nfrom collections import defaultdict, deque\nfrom heapq import heappush, heappop\nimport sys\nimport math\nimport bisect\nimport random\n\n\ndef func_9():\n return [int(var_15) for var_15 in sys.stdin.readline().split()]\n\n\ndef func_7():\n return int(sys.stdin.readline())\n\n\ndef func_11():\n return [list(var_15) for var_15 in sys.stdin.readline().split()]\n\n\ndef func_13():\n return list(sys.stdin.readline())[:-1]\n\n\ndef func_8(var_12):\n return [func_7() for var_8 in range(var_12)]\n\n\ndef func_10(var_12):\n return [func_9() for var_8 in range(var_12)]\n\n\ndef func_14(var_12):\n return [func_13() for var_8 in range(var_12)]\n\n\ndef func_12(var_12):\n return [func_11() for var_8 in range(var_12)]\n\n\nsys.setrecursionlimit(1000000)\nvar_11 = 1000000007\n\n\ndef func_1():\n var_12 = func_7()\n var_1 = func_9()\n var_1.sort(key=lambda var_15: -var_15)\n var_2 = 0\n for var_8 in range(var_12):\n var_2 += var_1[2 * var_8 + 1]\n print(var_2)\n return\n\n\ndef func_2():\n def func_15(var_5, var_15, var_4):\n if var_5 <= var_6[var_15]:\n return\n var_6[var_15] = var_5\n if not var_7[var_15]:\n var_7[var_15] = var_4\n for var_16 in var_14[var_15]:\n func_15(var_5 - 1, var_16, var_4)\n\n var_12, var_10 = func_9()\n var_7 = [0] * var_12\n var_14 = [[] for var_8 in range(var_12)]\n for var_8 in range(var_10):\n var_1, var_3 = func_9()\n var_1 -= 1\n var_3 -= 1\n var_14[var_1].append(var_3)\n var_14[var_3].append(var_1)\n var_6 = defaultdict(lambda: -1)\n var_13 = func_7()\n var_9 = func_10(var_13)\n var_9 = var_9[::-1]\n for var_15, var_5, var_4 in var_9:\n var_15 -= 1\n func_15(var_5, var_15, var_4)\n for var_8 in var_7:\n print(var_8)\n return\n\n\ndef func_3():\n var_12 = func_7()\n return\n\n\ndef func_4():\n var_12 = func_7()\n return\n\n\ndef func_5():\n var_12 = func_7()\n return\n\n\ndef func_6():\n var_12 = func_7()\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "print(\n sum(\n sorted(list(map(int, exec(\"N=int(input())\") or input().split())))[\n -2 : -2 * var_1 - 1 : -2\n ]\n )\n)\n", "code_b": "from collections import defaultdict, deque\nfrom heapq import heappush, heappop\nimport sys\nimport math\nimport bisect\nimport random\n\n\ndef func_9():\n return [int(var_15) for var_15 in sys.stdin.readline().split()]\n\n\ndef func_7():\n return int(sys.stdin.readline())\n\n\ndef func_11():\n return [list(var_15) for var_15 in sys.stdin.readline().split()]\n\n\ndef func_13():\n return list(sys.stdin.readline())[:-1]\n\n\ndef func_8(var_12):\n return [func_7() for var_8 in range(var_12)]\n\n\ndef func_10(var_12):\n return [func_9() for var_8 in range(var_12)]\n\n\ndef func_14(var_12):\n return [func_13() for var_8 in range(var_12)]\n\n\ndef func_12(var_12):\n return [func_11() for var_8 in range(var_12)]\n\n\nsys.setrecursionlimit(1000000)\nvar_11 = 1000000007\n\n\ndef func_1():\n var_12 = func_7()\n var_1 = func_9()\n var_1.sort(key=lambda var_15: -var_15)\n var_2 = 0\n for var_8 in range(var_12):\n var_2 += var_1[2 * var_8 + 1]\n print(var_2)\n return\n\n\ndef func_2():\n def func_15(var_5, var_15, var_4):\n if var_5 <= var_6[var_15]:\n return\n var_6[var_15] = var_5\n if not var_7[var_15]:\n var_7[var_15] = var_4\n for var_16 in var_14[var_15]:\n func_15(var_5 - 1, var_16, var_4)\n\n var_12, var_10 = func_9()\n var_7 = [0] * var_12\n var_14 = [[] for var_8 in range(var_12)]\n for var_8 in range(var_10):\n var_1, var_3 = func_9()\n var_1 -= 1\n var_3 -= 1\n var_14[var_1].append(var_3)\n var_14[var_3].append(var_1)\n var_6 = defaultdict(lambda: -1)\n var_13 = func_7()\n var_9 = func_10(var_13)\n var_9 = var_9[::-1]\n for var_15, var_5, var_4 in var_9:\n var_15 -= 1\n func_15(var_5, var_15, var_4)\n for var_8 in var_7:\n print(var_8)\n return\n\n\ndef func_3():\n var_12 = func_7()\n return\n\n\ndef func_4():\n var_12 = func_7()\n return\n\n\ndef func_5():\n var_12 = func_7()\n return\n\n\ndef func_6():\n var_12 = func_7()\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "A"} -{"pair_id": "python_5151_forward", "problem_id": "p02409", "language": "python", "direction": "forward", "speedup": 2.6577, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfloor = [\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n]\ns = \"\nn = int(input())\nfor c in range(n):\n (b, f, r, v) = [int(x) for x in input().split()]\n if 1 > b > 5 and 1 > f > 4 and 1 > r > 10:\n break\n floor[b - 1][f - 1][r - 1] += v\nfor z in range(4):\n for i in range(len(floor[z])):\n for v in floor[z][i]:\n print(end=\" \")\n print(v, end=\"\")\n print()\n if not z == 3:\n print(s)\n\n```\n\n## Code B:\n```python\nI = input\nr = range\na = [[[0 for i in r(10)] for j in r(3)] for k in r(4)]\nfor i in r(int(I())):\n b, f, r, v = list(map(int, I().split()))\n a[b - 1][f - 1][r - 1] += v\nf = 0\nfor i in a:\n if f:\n print((\"\n else:\n f = 1\n for j in i:\n print((\"\", *j))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "floor = [\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n]\ns = \"\nn = int(input())\nfor c in range(n):\n (b, f, r, v) = [int(x) for x in input().split()]\n if 1 > b > 5 and 1 > f > 4 and 1 > r > 10:\n break\n floor[b - 1][f - 1][r - 1] += v\nfor z in range(4):\n for i in range(len(floor[z])):\n for v in floor[z][i]:\n print(end=\" \")\n print(v, end=\"\")\n print()\n if not z == 3:\n print(s)\n", "code_b": "I = input\nr = range\na = [[[0 for i in r(10)] for j in r(3)] for k in r(4)]\nfor i in r(int(I())):\n b, f, r, v = list(map(int, I().split()))\n a[b - 1][f - 1][r - 1] += v\nf = 0\nfor i in a:\n if f:\n print((\"\n else:\n f = 1\n for j in i:\n print((\"\", *j))\n", "expected": "B"} -{"pair_id": "python_5151_reversed", "problem_id": "p02409", "language": "python", "direction": "reversed", "speedup": 2.6577, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nI = input\nr = range\na = [[[0 for i in r(10)] for j in r(3)] for k in r(4)]\nfor i in r(int(I())):\n b, f, r, v = list(map(int, I().split()))\n a[b - 1][f - 1][r - 1] += v\nf = 0\nfor i in a:\n if f:\n print((\"\n else:\n f = 1\n for j in i:\n print((\"\", *j))\n\n```\n\n## Code B:\n```python\nfloor = [\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n]\ns = \"\nn = int(input())\nfor c in range(n):\n (b, f, r, v) = [int(x) for x in input().split()]\n if 1 > b > 5 and 1 > f > 4 and 1 > r > 10:\n break\n floor[b - 1][f - 1][r - 1] += v\nfor z in range(4):\n for i in range(len(floor[z])):\n for v in floor[z][i]:\n print(end=\" \")\n print(v, end=\"\")\n print()\n if not z == 3:\n print(s)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "I = input\nr = range\na = [[[0 for i in r(10)] for j in r(3)] for k in r(4)]\nfor i in r(int(I())):\n b, f, r, v = list(map(int, I().split()))\n a[b - 1][f - 1][r - 1] += v\nf = 0\nfor i in a:\n if f:\n print((\"\n else:\n f = 1\n for j in i:\n print((\"\", *j))\n", "code_b": "floor = [\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n]\ns = \"\nn = int(input())\nfor c in range(n):\n (b, f, r, v) = [int(x) for x in input().split()]\n if 1 > b > 5 and 1 > f > 4 and 1 > r > 10:\n break\n floor[b - 1][f - 1][r - 1] += v\nfor z in range(4):\n for i in range(len(floor[z])):\n for v in floor[z][i]:\n print(end=\" \")\n print(v, end=\"\")\n print()\n if not z == 3:\n print(s)\n", "expected": "A"} +{"pair_id": "python_5151_forward", "problem_id": "p02409", "language": "python", "direction": "forward", "speedup": 2.6577, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_4 = [\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n]\nvar_8 = \"####################\"\nvar_6 = int(input())\nfor var_2 in range(var_6):\n (var_1, var_3, var_7, var_9) = [int(var_10) for var_10 in input().split()]\n if 1 > var_1 > 5 and 1 > var_3 > 4 and 1 > var_7 > 10:\n break\n var_4[var_1 - 1][var_3 - 1][var_7 - 1] += var_9\nfor var_11 in range(4):\n for var_5 in range(len(var_4[var_11])):\n for var_9 in var_4[var_11][var_5]:\n print(end=\" \")\n print(var_9, end=\"\")\n print()\n if not var_11 == 3:\n print(var_8)\n```\n\n## Code B:\n```python\nvar_1 = input\nvar_8 = range\nvar_2 = [[[0 for var_5 in var_8(10)] for var_6 in var_8(3)] for var_7 in var_8(4)]\nfor var_5 in var_8(int(var_1())):\n var_3, var_4, var_8, var_9 = list(map(int, var_1().split()))\n var_2[var_3 - 1][var_4 - 1][var_8 - 1] += var_9\nvar_4 = 0\nfor var_5 in var_2:\n if var_4:\n print((\"#\" * 20))\n else:\n var_4 = 1\n for var_6 in var_5:\n print((\"\", *var_6))\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_4 = [\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n]\nvar_8 = \"####################\"\nvar_6 = int(input())\nfor var_2 in range(var_6):\n (var_1, var_3, var_7, var_9) = [int(var_10) for var_10 in input().split()]\n if 1 > var_1 > 5 and 1 > var_3 > 4 and 1 > var_7 > 10:\n break\n var_4[var_1 - 1][var_3 - 1][var_7 - 1] += var_9\nfor var_11 in range(4):\n for var_5 in range(len(var_4[var_11])):\n for var_9 in var_4[var_11][var_5]:\n print(end=\" \")\n print(var_9, end=\"\")\n print()\n if not var_11 == 3:\n print(var_8)\n", "code_b": "var_1 = input\nvar_8 = range\nvar_2 = [[[0 for var_5 in var_8(10)] for var_6 in var_8(3)] for var_7 in var_8(4)]\nfor var_5 in var_8(int(var_1())):\n var_3, var_4, var_8, var_9 = list(map(int, var_1().split()))\n var_2[var_3 - 1][var_4 - 1][var_8 - 1] += var_9\nvar_4 = 0\nfor var_5 in var_2:\n if var_4:\n print((\"#\" * 20))\n else:\n var_4 = 1\n for var_6 in var_5:\n print((\"\", *var_6))\n", "expected": "B"} +{"pair_id": "python_5151_reversed", "problem_id": "p02409", "language": "python", "direction": "reversed", "speedup": 2.6577, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1 = input\nvar_8 = range\nvar_2 = [[[0 for var_5 in var_8(10)] for var_6 in var_8(3)] for var_7 in var_8(4)]\nfor var_5 in var_8(int(var_1())):\n var_3, var_4, var_8, var_9 = list(map(int, var_1().split()))\n var_2[var_3 - 1][var_4 - 1][var_8 - 1] += var_9\nvar_4 = 0\nfor var_5 in var_2:\n if var_4:\n print((\"#\" * 20))\n else:\n var_4 = 1\n for var_6 in var_5:\n print((\"\", *var_6))\n```\n\n## Code B:\n```python\nvar_4 = [\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n]\nvar_8 = \"####################\"\nvar_6 = int(input())\nfor var_2 in range(var_6):\n (var_1, var_3, var_7, var_9) = [int(var_10) for var_10 in input().split()]\n if 1 > var_1 > 5 and 1 > var_3 > 4 and 1 > var_7 > 10:\n break\n var_4[var_1 - 1][var_3 - 1][var_7 - 1] += var_9\nfor var_11 in range(4):\n for var_5 in range(len(var_4[var_11])):\n for var_9 in var_4[var_11][var_5]:\n print(end=\" \")\n print(var_9, end=\"\")\n print()\n if not var_11 == 3:\n print(var_8)\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1 = input\nvar_8 = range\nvar_2 = [[[0 for var_5 in var_8(10)] for var_6 in var_8(3)] for var_7 in var_8(4)]\nfor var_5 in var_8(int(var_1())):\n var_3, var_4, var_8, var_9 = list(map(int, var_1().split()))\n var_2[var_3 - 1][var_4 - 1][var_8 - 1] += var_9\nvar_4 = 0\nfor var_5 in var_2:\n if var_4:\n print((\"#\" * 20))\n else:\n var_4 = 1\n for var_6 in var_5:\n print((\"\", *var_6))\n", "code_b": "var_4 = [\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n [\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n ],\n]\nvar_8 = \"####################\"\nvar_6 = int(input())\nfor var_2 in range(var_6):\n (var_1, var_3, var_7, var_9) = [int(var_10) for var_10 in input().split()]\n if 1 > var_1 > 5 and 1 > var_3 > 4 and 1 > var_7 > 10:\n break\n var_4[var_1 - 1][var_3 - 1][var_7 - 1] += var_9\nfor var_11 in range(4):\n for var_5 in range(len(var_4[var_11])):\n for var_9 in var_4[var_11][var_5]:\n print(end=\" \")\n print(var_9, end=\"\")\n print()\n if not var_11 == 3:\n print(var_8)\n", "expected": "A"} {"pair_id": "python_1347_forward", "problem_id": "p03037", "language": "python", "direction": "forward", "speedup": 51.2533, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\ndef func_3(var_5, var_2, var_1, var_6) -> int:\n var_11 = 0\n var_3 = max(var_1)\n var_4 = min(var_6)\n for var_7 in range(1, var_5 + 1):\n if var_3 <= var_7 and var_4 >= var_7:\n var_11 += 1\n return var_11\n\n\ndef func_2():\n var_10 = 1\n\n\ndef func_1():\n var_5, var_2 = list(map(int, input().split()))\n var_1 = []\n var_6 = []\n for _ in range(var_2):\n var_8, var_9 = list(map(int, input().split()))\n var_1.append(var_8)\n var_6.append(var_9)\n print((func_3(var_5, var_2, var_1[:], var_6[:])))\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\n## Code B:\n```python\nvar_7, var_6 = list(map(int, input().split()))\nvar_5 = [list(map(int, input().split())) for var_3 in range(var_6)]\nvar_1 = 0\nvar_2 = var_7\nfor var_4, var_8 in var_5:\n if var_1 < var_4:\n var_1 = var_4\n if var_8 < var_2:\n var_2 = var_8\nprint((max(var_2 - var_1 + 1, 0)))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "def func_3(var_5, var_2, var_1, var_6) -> int:\n var_11 = 0\n var_3 = max(var_1)\n var_4 = min(var_6)\n for var_7 in range(1, var_5 + 1):\n if var_3 <= var_7 and var_4 >= var_7:\n var_11 += 1\n return var_11\n\n\ndef func_2():\n var_10 = 1\n\n\ndef func_1():\n var_5, var_2 = list(map(int, input().split()))\n var_1 = []\n var_6 = []\n for _ in range(var_2):\n var_8, var_9 = list(map(int, input().split()))\n var_1.append(var_8)\n var_6.append(var_9)\n print((func_3(var_5, var_2, var_1[:], var_6[:])))\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "var_7, var_6 = list(map(int, input().split()))\nvar_5 = [list(map(int, input().split())) for var_3 in range(var_6)]\nvar_1 = 0\nvar_2 = var_7\nfor var_4, var_8 in var_5:\n if var_1 < var_4:\n var_1 = var_4\n if var_8 < var_2:\n var_2 = var_8\nprint((max(var_2 - var_1 + 1, 0)))\n", "expected": "B"} {"pair_id": "python_1347_reversed", "problem_id": "p03037", "language": "python", "direction": "reversed", "speedup": 51.2533, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_7, var_6 = list(map(int, input().split()))\nvar_5 = [list(map(int, input().split())) for var_3 in range(var_6)]\nvar_1 = 0\nvar_2 = var_7\nfor var_4, var_8 in var_5:\n if var_1 < var_4:\n var_1 = var_4\n if var_8 < var_2:\n var_2 = var_8\nprint((max(var_2 - var_1 + 1, 0)))\n\n```\n\n## Code B:\n```python\ndef func_3(var_5, var_2, var_1, var_6) -> int:\n var_11 = 0\n var_3 = max(var_1)\n var_4 = min(var_6)\n for var_7 in range(1, var_5 + 1):\n if var_3 <= var_7 and var_4 >= var_7:\n var_11 += 1\n return var_11\n\n\ndef func_2():\n var_10 = 1\n\n\ndef func_1():\n var_5, var_2 = list(map(int, input().split()))\n var_1 = []\n var_6 = []\n for _ in range(var_2):\n var_8, var_9 = list(map(int, input().split()))\n var_1.append(var_8)\n var_6.append(var_9)\n print((func_3(var_5, var_2, var_1[:], var_6[:])))\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_7, var_6 = list(map(int, input().split()))\nvar_5 = [list(map(int, input().split())) for var_3 in range(var_6)]\nvar_1 = 0\nvar_2 = var_7\nfor var_4, var_8 in var_5:\n if var_1 < var_4:\n var_1 = var_4\n if var_8 < var_2:\n var_2 = var_8\nprint((max(var_2 - var_1 + 1, 0)))\n", "code_b": "def func_3(var_5, var_2, var_1, var_6) -> int:\n var_11 = 0\n var_3 = max(var_1)\n var_4 = min(var_6)\n for var_7 in range(1, var_5 + 1):\n if var_3 <= var_7 and var_4 >= var_7:\n var_11 += 1\n return var_11\n\n\ndef func_2():\n var_10 = 1\n\n\ndef func_1():\n var_5, var_2 = list(map(int, input().split()))\n var_1 = []\n var_6 = []\n for _ in range(var_2):\n var_8, var_9 = list(map(int, input().split()))\n var_1.append(var_8)\n var_6.append(var_9)\n print((func_3(var_5, var_2, var_1[:], var_6[:])))\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "A"} {"pair_id": "python_14252_forward", "problem_id": "p03558", "language": "python", "direction": "forward", "speedup": 7.2343, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfrom collections import *\n\nvar_5 = var_1([(1, 1)])\nvar_2 = eval(input())\nvar_3 = {}\nwhile var_5:\n var_4, var_6 = var_5.pop()\n if (var_4 in var_3) - 1:\n var_3[var_4] = var_6\n var_5 += ((var_4 * 10 % var_2, var_6),)\n var_5.appendleft((-~var_4 % var_2, var_6 + 1))\nprint(var_3[0])\n\n```\n\n## Code B:\n```python\nfrom collections import deque\n\n\ndef func_1(var_1):\n var_2 = [99999999999] * (var_1 + 1)\n var_4 = deque()\n var_4.append((1, 1))\n var_2[1] = 1\n while var_4:\n var_5, var_3 = var_4.popleft()\n if var_5 == 0:\n return var_3\n _ = (var_5 * 10) % var_1\n if var_3 < var_2[_]:\n var_4.appendleft((_, var_3))\n var_2[_] = var_3\n _ = (var_5 + 1) % var_1\n if var_3 + 1 < var_2[_]:\n var_4.append((_, var_3 + 1))\n var_2[_] = var_3 + 1\n return None\n\n\nvar_1 = int(eval(input()))\nprint((func_1(var_1)))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "from collections import *\n\nvar_5 = var_1([(1, 1)])\nvar_2 = eval(input())\nvar_3 = {}\nwhile var_5:\n var_4, var_6 = var_5.pop()\n if (var_4 in var_3) - 1:\n var_3[var_4] = var_6\n var_5 += ((var_4 * 10 % var_2, var_6),)\n var_5.appendleft((-~var_4 % var_2, var_6 + 1))\nprint(var_3[0])\n", "code_b": "from collections import deque\n\n\ndef func_1(var_1):\n var_2 = [99999999999] * (var_1 + 1)\n var_4 = deque()\n var_4.append((1, 1))\n var_2[1] = 1\n while var_4:\n var_5, var_3 = var_4.popleft()\n if var_5 == 0:\n return var_3\n _ = (var_5 * 10) % var_1\n if var_3 < var_2[_]:\n var_4.appendleft((_, var_3))\n var_2[_] = var_3\n _ = (var_5 + 1) % var_1\n if var_3 + 1 < var_2[_]:\n var_4.append((_, var_3 + 1))\n var_2[_] = var_3 + 1\n return None\n\n\nvar_1 = int(eval(input()))\nprint((func_1(var_1)))\n", "expected": "B"} @@ -730,8 +730,8 @@ {"pair_id": "python_7255_reversed", "problem_id": "p02683", "language": "python", "direction": "reversed", "speedup": 119.8165, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_12, var_11, var_14 = list(map(int, input().split()))\nvar_3 = [0] * var_12\nvar_1 = [[0 for _ in range(var_11)] for var_6 in range(var_12)]\nfor var_6 in range(var_12):\n var_10 = [int(_) for _ in input().split()]\n var_3[var_6] = var_10[0]\n for var_8 in range(var_11):\n var_1[var_6][var_8] = var_10[var_8 + 1]\nvar_7 = float(\"inf\")\nvar_2 = var_7\nfor var_6 in range(2**var_12):\n var_4 = 0\n var_5 = [0] * var_11\n for var_8 in range(var_12):\n if var_6 >> var_8 & 1:\n var_4 += var_3[var_8]\n for var_9 in range(var_11):\n var_5[var_9] += var_1[var_8][var_9]\n var_13 = True\n for var_15 in range(var_11):\n if var_5[var_15] < var_14:\n var_13 = False\n if var_13:\n var_2 = min(var_2, var_4)\nif var_2 == var_7:\n print((-1))\nelse:\n print(var_2)\n\n```\n\n## Code B:\n```python\nvar_11, var_9, var_13 = list(map(int, input().split()))\nvar_7 = [list(map(int, input().split())) for _ in range(var_11)]\nvar_3 = [var_7[_][0] for _ in range(var_11)]\nvar_2 = [var_7[_][1:] for _ in range(var_11)]\nvar_1 = 1e9\nfor var_10 in range(2**15):\n var_5 = 0\n var_4 = [0 for _ in range(var_9)]\n for var_6 in range(var_11):\n if ((1 << var_6) & var_10) == 0:\n continue\n var_5 += var_3[var_6]\n for var_8 in range(var_9):\n var_4[var_8] += var_2[var_6][var_8]\n var_12 = 1\n for var_6 in range(var_9):\n if var_4[var_6] < var_13:\n var_12 = 0\n if var_12:\n var_1 = min(var_1, var_5)\nif var_1 == 1e9:\n var_1 = -1\nprint(var_1)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_12, var_11, var_14 = list(map(int, input().split()))\nvar_3 = [0] * var_12\nvar_1 = [[0 for _ in range(var_11)] for var_6 in range(var_12)]\nfor var_6 in range(var_12):\n var_10 = [int(_) for _ in input().split()]\n var_3[var_6] = var_10[0]\n for var_8 in range(var_11):\n var_1[var_6][var_8] = var_10[var_8 + 1]\nvar_7 = float(\"inf\")\nvar_2 = var_7\nfor var_6 in range(2**var_12):\n var_4 = 0\n var_5 = [0] * var_11\n for var_8 in range(var_12):\n if var_6 >> var_8 & 1:\n var_4 += var_3[var_8]\n for var_9 in range(var_11):\n var_5[var_9] += var_1[var_8][var_9]\n var_13 = True\n for var_15 in range(var_11):\n if var_5[var_15] < var_14:\n var_13 = False\n if var_13:\n var_2 = min(var_2, var_4)\nif var_2 == var_7:\n print((-1))\nelse:\n print(var_2)\n", "code_b": "var_11, var_9, var_13 = list(map(int, input().split()))\nvar_7 = [list(map(int, input().split())) for _ in range(var_11)]\nvar_3 = [var_7[_][0] for _ in range(var_11)]\nvar_2 = [var_7[_][1:] for _ in range(var_11)]\nvar_1 = 1e9\nfor var_10 in range(2**15):\n var_5 = 0\n var_4 = [0 for _ in range(var_9)]\n for var_6 in range(var_11):\n if ((1 << var_6) & var_10) == 0:\n continue\n var_5 += var_3[var_6]\n for var_8 in range(var_9):\n var_4[var_8] += var_2[var_6][var_8]\n var_12 = 1\n for var_6 in range(var_9):\n if var_4[var_6] < var_13:\n var_12 = 0\n if var_12:\n var_1 = min(var_1, var_5)\nif var_1 == 1e9:\n var_1 = -1\nprint(var_1)\n", "expected": "A"} {"pair_id": "python_16968_forward", "problem_id": "p02804", "language": "python", "direction": "forward", "speedup": 9.0247, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfrom collections import defaultdict\n\n\nclass Class_1:\n \"\"\"\"\"\"\n\n def __init__(self, var_13, var_10=10**9 + 7):\n self.mod = var_10\n self.modinv = self.make_modinv_list(var_13)\n self.fac, self.facinv = self.make_factorial_list(var_13)\n\n def __call__(self, var_12, var_14):\n return (\n self.fac[var_12]\n * self.facinv[var_14]\n % self.mod\n * self.facinv[var_12 - var_14]\n % self.mod\n )\n\n def func_2(self, var_12):\n\n var_7 = [1]\n var_8 = [1]\n for var_9 in range(1, var_12 + 1):\n var_7.append(var_7[var_9 - 1] * var_9 % self.mod)\n var_8.append(var_8[var_9 - 1] * self.modinv[var_9] % self.mod)\n return var_7, var_8\n\n def func_3(self, var_12):\n\n var_11 = [0] * (var_12 + 1)\n var_11[1] = 1\n for var_9 in range(2, var_12 + 1):\n var_11[var_9] = (\n self.mod - self.mod // var_9 * var_11[self.mod % var_9] % self.mod\n )\n return var_11\n\n\ndef func_1():\n\n var_3, var_2 = list(map(int, input().split()))\n var_1 = list(map(int, input().split()))\n var_1.sort()\n var_5 = 0\n var_10 = 10**9 + 7\n var_6 = Class_1(1000000)\n\n for var_9, var_4 in enumerate(var_1):\n if (var_3 - 1 - var_9) >= var_2 - 1:\n\n var_15 = var_6(var_3 - 1 - var_9, var_2 - 1)\n var_5 -= var_4 * var_15\n var_5 %= var_10\n else:\n break\n var_1.sort(reverse=True)\n for var_9, var_4 in enumerate(var_1):\n if (var_3 - 1 - var_9) >= var_2 - 1:\n\n var_15 = var_6(var_3 - 1 - var_9, var_2 - 1)\n var_5 += var_4 * var_15\n var_5 %= var_10\n else:\n break\n print(var_5)\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\n## Code B:\n```python\nvar_5, var_2, *var_1 = list(map(int, open(0).read().split()))\nvar_3 = 10**5 + 1\nvar_4 = 10**9 + 7\n\nvar_7 = [0] * (var_3 + 1)\nvar_7[0] = 1\nvar_7[1] = 1\nfor var_9 in range(2, var_3 + 1):\n var_7[var_9] = var_7[var_9 - 1] * var_9 % var_4\n\nvar_8 = [0] * (var_3 + 1)\nvar_8[var_3] = pow(var_7[var_3], var_4 - 2, var_4)\nfor var_9 in reversed(list(range(1, var_3 + 1))):\n var_8[var_9 - 1] = var_8[var_9] * var_9 % var_4\n\n\ndef func_1(var_11, var_10):\n if var_11 < var_10 or var_10 < 0:\n return 0\n return (var_7[var_11] * var_8[var_10] * var_8[var_11 - var_10]) % var_4\n\n\nvar_1.sort()\nvar_6 = 0\nfor var_9 in range(var_5):\n if var_9 >= var_2 - 1:\n var_6 += func_1(var_9, var_2 - 1) * var_1[var_9]\n if var_9 + var_2 - 1 < var_5:\n var_6 -= func_1(var_5 - var_9 - 1, var_2 - 1) * var_1[var_9]\n var_6 %= var_4\nprint(var_6)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "from collections import defaultdict\n\n\nclass Class_1:\n \"\"\"\"\"\"\n\n def __init__(self, var_13, var_10=10**9 + 7):\n self.mod = var_10\n self.modinv = self.make_modinv_list(var_13)\n self.fac, self.facinv = self.make_factorial_list(var_13)\n\n def __call__(self, var_12, var_14):\n return (\n self.fac[var_12]\n * self.facinv[var_14]\n % self.mod\n * self.facinv[var_12 - var_14]\n % self.mod\n )\n\n def func_2(self, var_12):\n\n var_7 = [1]\n var_8 = [1]\n for var_9 in range(1, var_12 + 1):\n var_7.append(var_7[var_9 - 1] * var_9 % self.mod)\n var_8.append(var_8[var_9 - 1] * self.modinv[var_9] % self.mod)\n return var_7, var_8\n\n def func_3(self, var_12):\n\n var_11 = [0] * (var_12 + 1)\n var_11[1] = 1\n for var_9 in range(2, var_12 + 1):\n var_11[var_9] = (\n self.mod - self.mod // var_9 * var_11[self.mod % var_9] % self.mod\n )\n return var_11\n\n\ndef func_1():\n\n var_3, var_2 = list(map(int, input().split()))\n var_1 = list(map(int, input().split()))\n var_1.sort()\n var_5 = 0\n var_10 = 10**9 + 7\n var_6 = Class_1(1000000)\n\n for var_9, var_4 in enumerate(var_1):\n if (var_3 - 1 - var_9) >= var_2 - 1:\n\n var_15 = var_6(var_3 - 1 - var_9, var_2 - 1)\n var_5 -= var_4 * var_15\n var_5 %= var_10\n else:\n break\n var_1.sort(reverse=True)\n for var_9, var_4 in enumerate(var_1):\n if (var_3 - 1 - var_9) >= var_2 - 1:\n\n var_15 = var_6(var_3 - 1 - var_9, var_2 - 1)\n var_5 += var_4 * var_15\n var_5 %= var_10\n else:\n break\n print(var_5)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "var_5, var_2, *var_1 = list(map(int, open(0).read().split()))\nvar_3 = 10**5 + 1\nvar_4 = 10**9 + 7\n\nvar_7 = [0] * (var_3 + 1)\nvar_7[0] = 1\nvar_7[1] = 1\nfor var_9 in range(2, var_3 + 1):\n var_7[var_9] = var_7[var_9 - 1] * var_9 % var_4\n\nvar_8 = [0] * (var_3 + 1)\nvar_8[var_3] = pow(var_7[var_3], var_4 - 2, var_4)\nfor var_9 in reversed(list(range(1, var_3 + 1))):\n var_8[var_9 - 1] = var_8[var_9] * var_9 % var_4\n\n\ndef func_1(var_11, var_10):\n if var_11 < var_10 or var_10 < 0:\n return 0\n return (var_7[var_11] * var_8[var_10] * var_8[var_11 - var_10]) % var_4\n\n\nvar_1.sort()\nvar_6 = 0\nfor var_9 in range(var_5):\n if var_9 >= var_2 - 1:\n var_6 += func_1(var_9, var_2 - 1) * var_1[var_9]\n if var_9 + var_2 - 1 < var_5:\n var_6 -= func_1(var_5 - var_9 - 1, var_2 - 1) * var_1[var_9]\n var_6 %= var_4\nprint(var_6)\n", "expected": "B"} {"pair_id": "python_16968_reversed", "problem_id": "p02804", "language": "python", "direction": "reversed", "speedup": 9.0247, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_5, var_2, *var_1 = list(map(int, open(0).read().split()))\nvar_3 = 10**5 + 1\nvar_4 = 10**9 + 7\n\nvar_7 = [0] * (var_3 + 1)\nvar_7[0] = 1\nvar_7[1] = 1\nfor var_9 in range(2, var_3 + 1):\n var_7[var_9] = var_7[var_9 - 1] * var_9 % var_4\n\nvar_8 = [0] * (var_3 + 1)\nvar_8[var_3] = pow(var_7[var_3], var_4 - 2, var_4)\nfor var_9 in reversed(list(range(1, var_3 + 1))):\n var_8[var_9 - 1] = var_8[var_9] * var_9 % var_4\n\n\ndef func_1(var_11, var_10):\n if var_11 < var_10 or var_10 < 0:\n return 0\n return (var_7[var_11] * var_8[var_10] * var_8[var_11 - var_10]) % var_4\n\n\nvar_1.sort()\nvar_6 = 0\nfor var_9 in range(var_5):\n if var_9 >= var_2 - 1:\n var_6 += func_1(var_9, var_2 - 1) * var_1[var_9]\n if var_9 + var_2 - 1 < var_5:\n var_6 -= func_1(var_5 - var_9 - 1, var_2 - 1) * var_1[var_9]\n var_6 %= var_4\nprint(var_6)\n\n```\n\n## Code B:\n```python\nfrom collections import defaultdict\n\n\nclass Class_1:\n \"\"\"\"\"\"\n\n def __init__(self, var_13, var_10=10**9 + 7):\n self.mod = var_10\n self.modinv = self.make_modinv_list(var_13)\n self.fac, self.facinv = self.make_factorial_list(var_13)\n\n def __call__(self, var_12, var_14):\n return (\n self.fac[var_12]\n * self.facinv[var_14]\n % self.mod\n * self.facinv[var_12 - var_14]\n % self.mod\n )\n\n def func_2(self, var_12):\n\n var_7 = [1]\n var_8 = [1]\n for var_9 in range(1, var_12 + 1):\n var_7.append(var_7[var_9 - 1] * var_9 % self.mod)\n var_8.append(var_8[var_9 - 1] * self.modinv[var_9] % self.mod)\n return var_7, var_8\n\n def func_3(self, var_12):\n\n var_11 = [0] * (var_12 + 1)\n var_11[1] = 1\n for var_9 in range(2, var_12 + 1):\n var_11[var_9] = (\n self.mod - self.mod // var_9 * var_11[self.mod % var_9] % self.mod\n )\n return var_11\n\n\ndef func_1():\n\n var_3, var_2 = list(map(int, input().split()))\n var_1 = list(map(int, input().split()))\n var_1.sort()\n var_5 = 0\n var_10 = 10**9 + 7\n var_6 = Class_1(1000000)\n\n for var_9, var_4 in enumerate(var_1):\n if (var_3 - 1 - var_9) >= var_2 - 1:\n\n var_15 = var_6(var_3 - 1 - var_9, var_2 - 1)\n var_5 -= var_4 * var_15\n var_5 %= var_10\n else:\n break\n var_1.sort(reverse=True)\n for var_9, var_4 in enumerate(var_1):\n if (var_3 - 1 - var_9) >= var_2 - 1:\n\n var_15 = var_6(var_3 - 1 - var_9, var_2 - 1)\n var_5 += var_4 * var_15\n var_5 %= var_10\n else:\n break\n print(var_5)\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_5, var_2, *var_1 = list(map(int, open(0).read().split()))\nvar_3 = 10**5 + 1\nvar_4 = 10**9 + 7\n\nvar_7 = [0] * (var_3 + 1)\nvar_7[0] = 1\nvar_7[1] = 1\nfor var_9 in range(2, var_3 + 1):\n var_7[var_9] = var_7[var_9 - 1] * var_9 % var_4\n\nvar_8 = [0] * (var_3 + 1)\nvar_8[var_3] = pow(var_7[var_3], var_4 - 2, var_4)\nfor var_9 in reversed(list(range(1, var_3 + 1))):\n var_8[var_9 - 1] = var_8[var_9] * var_9 % var_4\n\n\ndef func_1(var_11, var_10):\n if var_11 < var_10 or var_10 < 0:\n return 0\n return (var_7[var_11] * var_8[var_10] * var_8[var_11 - var_10]) % var_4\n\n\nvar_1.sort()\nvar_6 = 0\nfor var_9 in range(var_5):\n if var_9 >= var_2 - 1:\n var_6 += func_1(var_9, var_2 - 1) * var_1[var_9]\n if var_9 + var_2 - 1 < var_5:\n var_6 -= func_1(var_5 - var_9 - 1, var_2 - 1) * var_1[var_9]\n var_6 %= var_4\nprint(var_6)\n", "code_b": "from collections import defaultdict\n\n\nclass Class_1:\n \"\"\"\"\"\"\n\n def __init__(self, var_13, var_10=10**9 + 7):\n self.mod = var_10\n self.modinv = self.make_modinv_list(var_13)\n self.fac, self.facinv = self.make_factorial_list(var_13)\n\n def __call__(self, var_12, var_14):\n return (\n self.fac[var_12]\n * self.facinv[var_14]\n % self.mod\n * self.facinv[var_12 - var_14]\n % self.mod\n )\n\n def func_2(self, var_12):\n\n var_7 = [1]\n var_8 = [1]\n for var_9 in range(1, var_12 + 1):\n var_7.append(var_7[var_9 - 1] * var_9 % self.mod)\n var_8.append(var_8[var_9 - 1] * self.modinv[var_9] % self.mod)\n return var_7, var_8\n\n def func_3(self, var_12):\n\n var_11 = [0] * (var_12 + 1)\n var_11[1] = 1\n for var_9 in range(2, var_12 + 1):\n var_11[var_9] = (\n self.mod - self.mod // var_9 * var_11[self.mod % var_9] % self.mod\n )\n return var_11\n\n\ndef func_1():\n\n var_3, var_2 = list(map(int, input().split()))\n var_1 = list(map(int, input().split()))\n var_1.sort()\n var_5 = 0\n var_10 = 10**9 + 7\n var_6 = Class_1(1000000)\n\n for var_9, var_4 in enumerate(var_1):\n if (var_3 - 1 - var_9) >= var_2 - 1:\n\n var_15 = var_6(var_3 - 1 - var_9, var_2 - 1)\n var_5 -= var_4 * var_15\n var_5 %= var_10\n else:\n break\n var_1.sort(reverse=True)\n for var_9, var_4 in enumerate(var_1):\n if (var_3 - 1 - var_9) >= var_2 - 1:\n\n var_15 = var_6(var_3 - 1 - var_9, var_2 - 1)\n var_5 += var_4 * var_15\n var_5 %= var_10\n else:\n break\n print(var_5)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "A"} -{"pair_id": "python_7371_forward", "problem_id": "p03044", "language": "python", "direction": "forward", "speedup": 3.9029, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n\n```\n\n## Code B:\n```python\nimport sys\n\nvar_5 = sys.stdin.readline\nsys.setrecursionlimit(10**5)\n\n\ndef func_1(var_9, var_7, var_1):\n var_2[var_9] = var_1\n for var_3 in var_4[var_9]:\n if var_3[0] == var_7:\n continue\n if var_3[1] % 2:\n func_1(var_3[0], var_9, 1 - var_1)\n else:\n func_1(var_3[0], var_9, var_1)\n\n\nvar_6 = int(eval(var_5()))\nvar_2 = [-1] * var_6\nvar_4 = [[] for _ in range(var_6)]\nfor _ in range(var_6 - 1):\n var_8, var_9, var_10 = [int(var_11) for var_11 in var_5().split()]\n var_8, var_9 = var_8 - 1, var_9 - 1\n var_4[var_8].append((var_9, var_10))\n var_4[var_9].append((var_8, var_10))\nfunc_1(0, -1, 1)\nfor var_1 in var_2:\n print(var_1)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n", "code_b": "import sys\n\nvar_5 = sys.stdin.readline\nsys.setrecursionlimit(10**5)\n\n\ndef func_1(var_9, var_7, var_1):\n var_2[var_9] = var_1\n for var_3 in var_4[var_9]:\n if var_3[0] == var_7:\n continue\n if var_3[1] % 2:\n func_1(var_3[0], var_9, 1 - var_1)\n else:\n func_1(var_3[0], var_9, var_1)\n\n\nvar_6 = int(eval(var_5()))\nvar_2 = [-1] * var_6\nvar_4 = [[] for _ in range(var_6)]\nfor _ in range(var_6 - 1):\n var_8, var_9, var_10 = [int(var_11) for var_11 in var_5().split()]\n var_8, var_9 = var_8 - 1, var_9 - 1\n var_4[var_8].append((var_9, var_10))\n var_4[var_9].append((var_8, var_10))\nfunc_1(0, -1, 1)\nfor var_1 in var_2:\n print(var_1)\n", "expected": "B"} -{"pair_id": "python_7371_reversed", "problem_id": "p03044", "language": "python", "direction": "reversed", "speedup": 3.9029, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_5 = sys.stdin.readline\nsys.setrecursionlimit(10**5)\n\n\ndef func_1(var_9, var_7, var_1):\n var_2[var_9] = var_1\n for var_3 in var_4[var_9]:\n if var_3[0] == var_7:\n continue\n if var_3[1] % 2:\n func_1(var_3[0], var_9, 1 - var_1)\n else:\n func_1(var_3[0], var_9, var_1)\n\n\nvar_6 = int(eval(var_5()))\nvar_2 = [-1] * var_6\nvar_4 = [[] for _ in range(var_6)]\nfor _ in range(var_6 - 1):\n var_8, var_9, var_10 = [int(var_11) for var_11 in var_5().split()]\n var_8, var_9 = var_8 - 1, var_9 - 1\n var_4[var_8].append((var_9, var_10))\n var_4[var_9].append((var_8, var_10))\nfunc_1(0, -1, 1)\nfor var_1 in var_2:\n print(var_1)\n\n```\n\n## Code B:\n```python\nimport sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_5 = sys.stdin.readline\nsys.setrecursionlimit(10**5)\n\n\ndef func_1(var_9, var_7, var_1):\n var_2[var_9] = var_1\n for var_3 in var_4[var_9]:\n if var_3[0] == var_7:\n continue\n if var_3[1] % 2:\n func_1(var_3[0], var_9, 1 - var_1)\n else:\n func_1(var_3[0], var_9, var_1)\n\n\nvar_6 = int(eval(var_5()))\nvar_2 = [-1] * var_6\nvar_4 = [[] for _ in range(var_6)]\nfor _ in range(var_6 - 1):\n var_8, var_9, var_10 = [int(var_11) for var_11 in var_5().split()]\n var_8, var_9 = var_8 - 1, var_9 - 1\n var_4[var_8].append((var_9, var_10))\n var_4[var_9].append((var_8, var_10))\nfunc_1(0, -1, 1)\nfor var_1 in var_2:\n print(var_1)\n", "code_b": "import sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n", "expected": "A"} +{"pair_id": "python_7371_forward", "problem_id": "p03044", "language": "python", "direction": "forward", "speedup": 3.9029, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n```\n\n## Code B:\n```python\nimport sys\n\nvar_5 = sys.stdin.readline\nsys.setrecursionlimit(10**5)\n\n\ndef func_1(var_9, var_7, var_1):\n var_2[var_9] = var_1\n for var_3 in var_4[var_9]:\n if var_3[0] == var_7:\n continue\n if var_3[1] % 2:\n func_1(var_3[0], var_9, 1 - var_1)\n else:\n func_1(var_3[0], var_9, var_1)\n\n\nvar_6 = int(eval(var_5()))\nvar_2 = [-1] * var_6\nvar_4 = [[] for _ in range(var_6)]\nfor _ in range(var_6 - 1):\n var_8, var_9, var_10 = [int(var_11) for var_11 in var_5().split()]\n var_8, var_9 = var_8 - 1, var_9 - 1\n var_4[var_8].append((var_9, var_10))\n var_4[var_9].append((var_8, var_10))\nfunc_1(0, -1, 1)\nfor var_1 in var_2:\n print(var_1)\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n", "code_b": "import sys\n\nvar_5 = sys.stdin.readline\nsys.setrecursionlimit(10**5)\n\n\ndef func_1(var_9, var_7, var_1):\n var_2[var_9] = var_1\n for var_3 in var_4[var_9]:\n if var_3[0] == var_7:\n continue\n if var_3[1] % 2:\n func_1(var_3[0], var_9, 1 - var_1)\n else:\n func_1(var_3[0], var_9, var_1)\n\n\nvar_6 = int(eval(var_5()))\nvar_2 = [-1] * var_6\nvar_4 = [[] for _ in range(var_6)]\nfor _ in range(var_6 - 1):\n var_8, var_9, var_10 = [int(var_11) for var_11 in var_5().split()]\n var_8, var_9 = var_8 - 1, var_9 - 1\n var_4[var_8].append((var_9, var_10))\n var_4[var_9].append((var_8, var_10))\nfunc_1(0, -1, 1)\nfor var_1 in var_2:\n print(var_1)\n", "expected": "B"} +{"pair_id": "python_7371_reversed", "problem_id": "p03044", "language": "python", "direction": "reversed", "speedup": 3.9029, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_5 = sys.stdin.readline\nsys.setrecursionlimit(10**5)\n\n\ndef func_1(var_9, var_7, var_1):\n var_2[var_9] = var_1\n for var_3 in var_4[var_9]:\n if var_3[0] == var_7:\n continue\n if var_3[1] % 2:\n func_1(var_3[0], var_9, 1 - var_1)\n else:\n func_1(var_3[0], var_9, var_1)\n\n\nvar_6 = int(eval(var_5()))\nvar_2 = [-1] * var_6\nvar_4 = [[] for _ in range(var_6)]\nfor _ in range(var_6 - 1):\n var_8, var_9, var_10 = [int(var_11) for var_11 in var_5().split()]\n var_8, var_9 = var_8 - 1, var_9 - 1\n var_4[var_8].append((var_9, var_10))\n var_4[var_9].append((var_8, var_10))\nfunc_1(0, -1, 1)\nfor var_1 in var_2:\n print(var_1)\n```\n\n## Code B:\n```python\nimport sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_5 = sys.stdin.readline\nsys.setrecursionlimit(10**5)\n\n\ndef func_1(var_9, var_7, var_1):\n var_2[var_9] = var_1\n for var_3 in var_4[var_9]:\n if var_3[0] == var_7:\n continue\n if var_3[1] % 2:\n func_1(var_3[0], var_9, 1 - var_1)\n else:\n func_1(var_3[0], var_9, var_1)\n\n\nvar_6 = int(eval(var_5()))\nvar_2 = [-1] * var_6\nvar_4 = [[] for _ in range(var_6)]\nfor _ in range(var_6 - 1):\n var_8, var_9, var_10 = [int(var_11) for var_11 in var_5().split()]\n var_8, var_9 = var_8 - 1, var_9 - 1\n var_4[var_8].append((var_9, var_10))\n var_4[var_9].append((var_8, var_10))\nfunc_1(0, -1, 1)\nfor var_1 in var_2:\n print(var_1)\n", "code_b": "import sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n", "expected": "A"} {"pair_id": "python_4974_forward", "problem_id": "p03606", "language": "python", "direction": "forward", "speedup": 580.7195, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_3 = int(eval(input()))\nvar_6 = [0] * (10**5 + 1)\nfor var_1 in range(var_3):\n var_2, var_4 = list(map(int, input().split()))\n var_6[var_2 - 1] += 1\n var_6[var_4] -= 1\nfor var_1 in range(1, 10**5):\n var_6[var_1] += var_6[var_1 - 1]\nvar_5 = 0\nfor var_1 in range(10**5):\n if var_6[var_1] == 1:\n var_5 += 1\nprint(var_5)\n\n```\n\n## Code B:\n```python\nvar_1 = int(eval(input()))\nvar_2 = 0\nfor var_3 in range(var_1):\n var_4, var_5 = list(map(int, input().split()))\n var_2 += var_5 - var_4 + 1\nprint(var_2)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_3 = int(eval(input()))\nvar_6 = [0] * (10**5 + 1)\nfor var_1 in range(var_3):\n var_2, var_4 = list(map(int, input().split()))\n var_6[var_2 - 1] += 1\n var_6[var_4] -= 1\nfor var_1 in range(1, 10**5):\n var_6[var_1] += var_6[var_1 - 1]\nvar_5 = 0\nfor var_1 in range(10**5):\n if var_6[var_1] == 1:\n var_5 += 1\nprint(var_5)\n", "code_b": "var_1 = int(eval(input()))\nvar_2 = 0\nfor var_3 in range(var_1):\n var_4, var_5 = list(map(int, input().split()))\n var_2 += var_5 - var_4 + 1\nprint(var_2)\n", "expected": "B"} {"pair_id": "python_4974_reversed", "problem_id": "p03606", "language": "python", "direction": "reversed", "speedup": 580.7195, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1 = int(eval(input()))\nvar_2 = 0\nfor var_3 in range(var_1):\n var_4, var_5 = list(map(int, input().split()))\n var_2 += var_5 - var_4 + 1\nprint(var_2)\n\n```\n\n## Code B:\n```python\nvar_3 = int(eval(input()))\nvar_6 = [0] * (10**5 + 1)\nfor var_1 in range(var_3):\n var_2, var_4 = list(map(int, input().split()))\n var_6[var_2 - 1] += 1\n var_6[var_4] -= 1\nfor var_1 in range(1, 10**5):\n var_6[var_1] += var_6[var_1 - 1]\nvar_5 = 0\nfor var_1 in range(10**5):\n if var_6[var_1] == 1:\n var_5 += 1\nprint(var_5)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1 = int(eval(input()))\nvar_2 = 0\nfor var_3 in range(var_1):\n var_4, var_5 = list(map(int, input().split()))\n var_2 += var_5 - var_4 + 1\nprint(var_2)\n", "code_b": "var_3 = int(eval(input()))\nvar_6 = [0] * (10**5 + 1)\nfor var_1 in range(var_3):\n var_2, var_4 = list(map(int, input().split()))\n var_6[var_2 - 1] += 1\n var_6[var_4] -= 1\nfor var_1 in range(1, 10**5):\n var_6[var_1] += var_6[var_1 - 1]\nvar_5 = 0\nfor var_1 in range(10**5):\n if var_6[var_1] == 1:\n var_5 += 1\nprint(var_5)\n", "expected": "A"} {"pair_id": "python_8056_forward", "problem_id": "p03078", "language": "python", "direction": "forward", "speedup": 2.9328, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport bisect\nimport heapq\nimport math\nimport random\nimport sys\nfrom collections import Counter, defaultdict, deque\nfrom decimal import ROUND_CEILING, ROUND_HALF_UP, Decimal\nfrom functools import lru_cache, reduce\nfrom itertools import combinations, combinations_with_replacement, product, permutations\nfrom operator import add, mul, sub\n\nsys.setrecursionlimit(100000)\n\n\ndef func_6():\n return int(input())\n\n\ndef func_7():\n return list(map(int, input().split()))\n\n\ndef func_4():\n return float(input())\n\n\ndef func_5():\n return list(map(float, input().split()))\n\n\ndef func_8():\n return input().strip()\n\n\ndef func_9():\n return list(map(str, input().split()))\n\n\ndef func_1(*var_8):\n print(*var_8, file=sys.stderr)\n\n\ndef func_3(var_11):\n import time\n\n def func_12(*var_8, **var_15):\n func_10 = time.time()\n var_16 = var_11(*var_8, **var_15)\n var_10 = time.time()\n func_1(var_10 - func_10, \"sec\")\n return var_16\n\n return func_12\n\n\n@func_3\ndef func_11(var_1, var_2, var_3, var_4):\n var_1.sort(reverse=True)\n var_2.sort(reverse=True)\n var_3.sort(reverse=True)\n\n def func_10(var_12, var_13, var_14):\n return var_1[var_12] + var_2[var_13] + var_3[var_14]\n\n var_18 = set()\n var_9 = []\n if 1 < len(var_1):\n heapq.heappush(var_9, (-func_10(1, 0, 0), (1, 0, 0)))\n var_18.add((1, 0, 0))\n if 1 < len(var_2):\n heapq.heappush(var_9, (-func_10(0, 1, 0), (0, 1, 0)))\n var_18.add((0, 1, 0))\n if 1 < len(var_3):\n heapq.heappush(var_9, (-func_10(0, 0, 1), (0, 0, 1)))\n var_18.add((0, 0, 1))\n print(func_10(0, 0, 0))\n for _ in range(var_4 - 1):\n var_19, var_12 = heapq.heappop(var_9)\n print(-var_19)\n var_17 = (var_12[0] + 1, var_12[1], var_12[2])\n if var_17[0] < len(var_1) and var_17 not in var_18:\n heapq.heappush(var_9, (-func_10(*var_17), var_17))\n var_18.add(var_17)\n var_17 = (var_12[0], var_12[1] + 1, var_12[2])\n if var_17[1] < len(var_2) and var_17 not in var_18:\n heapq.heappush(var_9, (-func_10(*var_17), var_17))\n var_18.add(var_17)\n var_17 = (var_12[0], var_12[1], var_12[2] + 1)\n if var_17[2] < len(var_3) and var_17 not in var_18:\n heapq.heappush(var_9, (-func_10(*var_17), var_17))\n var_18.add(var_17)\n\n\ndef func_2():\n\n var_5, var_6, var_7, var_4 = func_7()\n var_1 = func_7()\n var_2 = func_7()\n var_3 = func_7()\n (func_11(var_1, var_2, var_3, var_4))\n\n\nif __name__ == \"__main__\":\n func_2()\n\n```\n\n## Code B:\n```python\nvar_9, var_10, var_11, var_6 = list(map(int, input().split()))\nvar_1 = list(map(int, input().split()))\nvar_2 = list(map(int, input().split()))\nvar_3 = list(map(int, input().split()))\nvar_7 = []\nfor var_4 in var_1:\n for var_5 in var_2:\n var_7.append(var_4 + var_5)\nvar_7.sort(reverse=True)\nvar_7 = var_7[:var_6]\nvar_8 = []\nfor var_4 in var_7:\n for var_5 in var_3:\n var_8.append(var_4 + var_5)\nvar_8.sort(reverse=True)\nfor var_4 in range(var_6):\n print((var_8[var_4]))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import bisect\nimport heapq\nimport math\nimport random\nimport sys\nfrom collections import Counter, defaultdict, deque\nfrom decimal import ROUND_CEILING, ROUND_HALF_UP, Decimal\nfrom functools import lru_cache, reduce\nfrom itertools import combinations, combinations_with_replacement, product, permutations\nfrom operator import add, mul, sub\n\nsys.setrecursionlimit(100000)\n\n\ndef func_6():\n return int(input())\n\n\ndef func_7():\n return list(map(int, input().split()))\n\n\ndef func_4():\n return float(input())\n\n\ndef func_5():\n return list(map(float, input().split()))\n\n\ndef func_8():\n return input().strip()\n\n\ndef func_9():\n return list(map(str, input().split()))\n\n\ndef func_1(*var_8):\n print(*var_8, file=sys.stderr)\n\n\ndef func_3(var_11):\n import time\n\n def func_12(*var_8, **var_15):\n func_10 = time.time()\n var_16 = var_11(*var_8, **var_15)\n var_10 = time.time()\n func_1(var_10 - func_10, \"sec\")\n return var_16\n\n return func_12\n\n\n@func_3\ndef func_11(var_1, var_2, var_3, var_4):\n var_1.sort(reverse=True)\n var_2.sort(reverse=True)\n var_3.sort(reverse=True)\n\n def func_10(var_12, var_13, var_14):\n return var_1[var_12] + var_2[var_13] + var_3[var_14]\n\n var_18 = set()\n var_9 = []\n if 1 < len(var_1):\n heapq.heappush(var_9, (-func_10(1, 0, 0), (1, 0, 0)))\n var_18.add((1, 0, 0))\n if 1 < len(var_2):\n heapq.heappush(var_9, (-func_10(0, 1, 0), (0, 1, 0)))\n var_18.add((0, 1, 0))\n if 1 < len(var_3):\n heapq.heappush(var_9, (-func_10(0, 0, 1), (0, 0, 1)))\n var_18.add((0, 0, 1))\n print(func_10(0, 0, 0))\n for _ in range(var_4 - 1):\n var_19, var_12 = heapq.heappop(var_9)\n print(-var_19)\n var_17 = (var_12[0] + 1, var_12[1], var_12[2])\n if var_17[0] < len(var_1) and var_17 not in var_18:\n heapq.heappush(var_9, (-func_10(*var_17), var_17))\n var_18.add(var_17)\n var_17 = (var_12[0], var_12[1] + 1, var_12[2])\n if var_17[1] < len(var_2) and var_17 not in var_18:\n heapq.heappush(var_9, (-func_10(*var_17), var_17))\n var_18.add(var_17)\n var_17 = (var_12[0], var_12[1], var_12[2] + 1)\n if var_17[2] < len(var_3) and var_17 not in var_18:\n heapq.heappush(var_9, (-func_10(*var_17), var_17))\n var_18.add(var_17)\n\n\ndef func_2():\n\n var_5, var_6, var_7, var_4 = func_7()\n var_1 = func_7()\n var_2 = func_7()\n var_3 = func_7()\n (func_11(var_1, var_2, var_3, var_4))\n\n\nif __name__ == \"__main__\":\n func_2()\n", "code_b": "var_9, var_10, var_11, var_6 = list(map(int, input().split()))\nvar_1 = list(map(int, input().split()))\nvar_2 = list(map(int, input().split()))\nvar_3 = list(map(int, input().split()))\nvar_7 = []\nfor var_4 in var_1:\n for var_5 in var_2:\n var_7.append(var_4 + var_5)\nvar_7.sort(reverse=True)\nvar_7 = var_7[:var_6]\nvar_8 = []\nfor var_4 in var_7:\n for var_5 in var_3:\n var_8.append(var_4 + var_5)\nvar_8.sort(reverse=True)\nfor var_4 in range(var_6):\n print((var_8[var_4]))\n", "expected": "B"} @@ -748,8 +748,8 @@ {"pair_id": "python_8129_reversed", "problem_id": "p03352", "language": "python", "direction": "reversed", "speedup": 10.508, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_4 = int(eval(input()))\nvar_1 = 1\nif var_4 == 1:\n print((1))\nelse:\n var_2 = 2\n while var_2**2 <= var_4:\n var_3 = 2\n while var_2**var_3 <= var_4:\n var_1 = max(var_2**var_3, var_1)\n var_3 += 1\n var_2 += 1\n print(var_1)\n\n```\n\n## Code B:\n```python\nvar_1 = int(eval(input()))\nvar_2 = 1\nfor var_3 in range(1, 1000):\n for var_4 in range(2, 1000):\n var_5 = var_3**var_4\n if var_5 > var_1:\n break\n var_2 = max(var_2, var_5)\nprint(var_2)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_4 = int(eval(input()))\nvar_1 = 1\nif var_4 == 1:\n print((1))\nelse:\n var_2 = 2\n while var_2**2 <= var_4:\n var_3 = 2\n while var_2**var_3 <= var_4:\n var_1 = max(var_2**var_3, var_1)\n var_3 += 1\n var_2 += 1\n print(var_1)\n", "code_b": "var_1 = int(eval(input()))\nvar_2 = 1\nfor var_3 in range(1, 1000):\n for var_4 in range(2, 1000):\n var_5 = var_3**var_4\n if var_5 > var_1:\n break\n var_2 = max(var_2, var_5)\nprint(var_2)\n", "expected": "A"} {"pair_id": "python_3597_forward", "problem_id": "p02934", "language": "python", "direction": "forward", "speedup": 3.9774, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport math, string, itertools, fractions, heapq, collections, re, array, bisect, sys, random, time, queue, copy\n\nsys.setrecursionlimit(10**7)\nvar_3 = 10**20\nvar_5 = 10**9 + 7\nvar_1 = [(-1, 0), (0, 1), (1, 0), (0, -1)]\nvar_2 = [(-1, 0), (-1, 1), (0, 1), (1, 1), (1, 0), (1, -1), (0, -1), (-1, -1)]\n\n\ndef func_2():\n return [int(var_8) for var_8 in sys.stdin.readline().split()]\n\n\ndef func_3():\n return [int(var_8) - 1 for var_8 in sys.stdin.readline().split()]\n\n\ndef func_1():\n return int(sys.stdin.readline())\n\n\ndef func_4():\n return sys.stdin.readline().split()\n\n\ndef func_5():\n return eval(input())\n\n\ndef func_6():\n var_6 = func_1()\n var_4 = func_2()\n var_7 = 0\n for var_8 in var_4:\n var_7 += 1 / var_8\n return 1 / var_7\n\n\nprint((func_6()))\n\n```\n\n## Code B:\n```python\nvar_1 = int(eval(input()))\nvar_3 = list(map(int, input().split()))\nvar_2 = 1 / sum(1 / var_4 for var_4 in var_3)\nprint(var_2)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import math, string, itertools, fractions, heapq, collections, re, array, bisect, sys, random, time, queue, copy\n\nsys.setrecursionlimit(10**7)\nvar_3 = 10**20\nvar_5 = 10**9 + 7\nvar_1 = [(-1, 0), (0, 1), (1, 0), (0, -1)]\nvar_2 = [(-1, 0), (-1, 1), (0, 1), (1, 1), (1, 0), (1, -1), (0, -1), (-1, -1)]\n\n\ndef func_2():\n return [int(var_8) for var_8 in sys.stdin.readline().split()]\n\n\ndef func_3():\n return [int(var_8) - 1 for var_8 in sys.stdin.readline().split()]\n\n\ndef func_1():\n return int(sys.stdin.readline())\n\n\ndef func_4():\n return sys.stdin.readline().split()\n\n\ndef func_5():\n return eval(input())\n\n\ndef func_6():\n var_6 = func_1()\n var_4 = func_2()\n var_7 = 0\n for var_8 in var_4:\n var_7 += 1 / var_8\n return 1 / var_7\n\n\nprint((func_6()))\n", "code_b": "var_1 = int(eval(input()))\nvar_3 = list(map(int, input().split()))\nvar_2 = 1 / sum(1 / var_4 for var_4 in var_3)\nprint(var_2)\n", "expected": "B"} {"pair_id": "python_3597_reversed", "problem_id": "p02934", "language": "python", "direction": "reversed", "speedup": 3.9774, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1 = int(eval(input()))\nvar_3 = list(map(int, input().split()))\nvar_2 = 1 / sum(1 / var_4 for var_4 in var_3)\nprint(var_2)\n\n```\n\n## Code B:\n```python\nimport math, string, itertools, fractions, heapq, collections, re, array, bisect, sys, random, time, queue, copy\n\nsys.setrecursionlimit(10**7)\nvar_3 = 10**20\nvar_5 = 10**9 + 7\nvar_1 = [(-1, 0), (0, 1), (1, 0), (0, -1)]\nvar_2 = [(-1, 0), (-1, 1), (0, 1), (1, 1), (1, 0), (1, -1), (0, -1), (-1, -1)]\n\n\ndef func_2():\n return [int(var_8) for var_8 in sys.stdin.readline().split()]\n\n\ndef func_3():\n return [int(var_8) - 1 for var_8 in sys.stdin.readline().split()]\n\n\ndef func_1():\n return int(sys.stdin.readline())\n\n\ndef func_4():\n return sys.stdin.readline().split()\n\n\ndef func_5():\n return eval(input())\n\n\ndef func_6():\n var_6 = func_1()\n var_4 = func_2()\n var_7 = 0\n for var_8 in var_4:\n var_7 += 1 / var_8\n return 1 / var_7\n\n\nprint((func_6()))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1 = int(eval(input()))\nvar_3 = list(map(int, input().split()))\nvar_2 = 1 / sum(1 / var_4 for var_4 in var_3)\nprint(var_2)\n", "code_b": "import math, string, itertools, fractions, heapq, collections, re, array, bisect, sys, random, time, queue, copy\n\nsys.setrecursionlimit(10**7)\nvar_3 = 10**20\nvar_5 = 10**9 + 7\nvar_1 = [(-1, 0), (0, 1), (1, 0), (0, -1)]\nvar_2 = [(-1, 0), (-1, 1), (0, 1), (1, 1), (1, 0), (1, -1), (0, -1), (-1, -1)]\n\n\ndef func_2():\n return [int(var_8) for var_8 in sys.stdin.readline().split()]\n\n\ndef func_3():\n return [int(var_8) - 1 for var_8 in sys.stdin.readline().split()]\n\n\ndef func_1():\n return int(sys.stdin.readline())\n\n\ndef func_4():\n return sys.stdin.readline().split()\n\n\ndef func_5():\n return eval(input())\n\n\ndef func_6():\n var_6 = func_1()\n var_4 = func_2()\n var_7 = 0\n for var_8 in var_4:\n var_7 += 1 / var_8\n return 1 / var_7\n\n\nprint((func_6()))\n", "expected": "A"} -{"pair_id": "python_17518_forward", "problem_id": "p02615", "language": "python", "direction": "forward", "speedup": 3.9809, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(1 << 25)\nread = sys.stdin.readline\nra = range\nenu = enumerate\n\n\ndef exit(*argv, **kwarg):\n print(*argv, **kwarg)\n sys.exit()\n\n\ndef mina(*argv, sub=1):\n return list(map(lambda x: x - sub, argv))\n\n\n\ndef a_int():\n return int(read())\n\n\ndef ints():\n return list(map(int, read().split()))\n\n\ndef read_col(H):\n \"\"\"\"\"\"H is number of rows\"\"\"\"\"\"H is number of rows\"\"\"\n ret = []\n for _ in range(H):\n ret.append(list(map(int, read().split())))\n return ret\n \n\n\nMOD = 10**9 + 7\nINF = 2**31 \n\nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef lcm(a, b):\n \n g = gcd(a, b)\n return a // g * b\n\n\n\n\nN = a_int()\nA = ints()\nA.sort(reverse=True)\nans = A[0]\ncnt = 1\nfor a in A[1:]:\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\nprint(ans)\n\n```\n\n## Code B:\n```python\ndef func_1():\n import sys\n\n var_5 = sys.stdin.readline\n var_2 = int(eval(var_5()))\n var_1 = list(map(int, var_5().split()))\n var_1.sort(reverse=True)\n var_3 = var_1[0]\n for var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\n print(var_3)\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(1 << 25)\nread = sys.stdin.readline\nra = range\nenu = enumerate\n\n\ndef exit(*argv, **kwarg):\n print(*argv, **kwarg)\n sys.exit()\n\n\ndef mina(*argv, sub=1):\n return list(map(lambda x: x - sub, argv))\n\n\n\ndef a_int():\n return int(read())\n\n\ndef ints():\n return list(map(int, read().split()))\n\n\ndef read_col(H):\n \"\"\"\"\"\"H is number of rows\"\"\"\"\"\"H is number of rows\"\"\"\n ret = []\n for _ in range(H):\n ret.append(list(map(int, read().split())))\n return ret\n \n\n\nMOD = 10**9 + 7\nINF = 2**31 \n\nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef lcm(a, b):\n \n g = gcd(a, b)\n return a // g * b\n\n\n\n\nN = a_int()\nA = ints()\nA.sort(reverse=True)\nans = A[0]\ncnt = 1\nfor a in A[1:]:\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\nprint(ans)\n", "code_b": "def func_1():\n import sys\n\n var_5 = sys.stdin.readline\n var_2 = int(eval(var_5()))\n var_1 = list(map(int, var_5().split()))\n var_1.sort(reverse=True)\n var_3 = var_1[0]\n for var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\n print(var_3)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "B"} -{"pair_id": "python_17518_reversed", "problem_id": "p02615", "language": "python", "direction": "reversed", "speedup": 3.9809, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\ndef func_1():\n import sys\n\n var_5 = sys.stdin.readline\n var_2 = int(eval(var_5()))\n var_1 = list(map(int, var_5().split()))\n var_1.sort(reverse=True)\n var_3 = var_1[0]\n for var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\n print(var_3)\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(1 << 25)\nread = sys.stdin.readline\nra = range\nenu = enumerate\n\n\ndef exit(*argv, **kwarg):\n print(*argv, **kwarg)\n sys.exit()\n\n\ndef mina(*argv, sub=1):\n return list(map(lambda x: x - sub, argv))\n\n\n\ndef a_int():\n return int(read())\n\n\ndef ints():\n return list(map(int, read().split()))\n\n\ndef read_col(H):\n \"\"\"\"\"\"H is number of rows\"\"\"\"\"\"H is number of rows\"\"\"\n ret = []\n for _ in range(H):\n ret.append(list(map(int, read().split())))\n return ret\n \n\n\nMOD = 10**9 + 7\nINF = 2**31 \n\nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef lcm(a, b):\n \n g = gcd(a, b)\n return a // g * b\n\n\n\n\nN = a_int()\nA = ints()\nA.sort(reverse=True)\nans = A[0]\ncnt = 1\nfor a in A[1:]:\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\nprint(ans)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "def func_1():\n import sys\n\n var_5 = sys.stdin.readline\n var_2 = int(eval(var_5()))\n var_1 = list(map(int, var_5().split()))\n var_1.sort(reverse=True)\n var_3 = var_1[0]\n for var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\n print(var_3)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "import sys\n\nsys.setrecursionlimit(1 << 25)\nread = sys.stdin.readline\nra = range\nenu = enumerate\n\n\ndef exit(*argv, **kwarg):\n print(*argv, **kwarg)\n sys.exit()\n\n\ndef mina(*argv, sub=1):\n return list(map(lambda x: x - sub, argv))\n\n\n\ndef a_int():\n return int(read())\n\n\ndef ints():\n return list(map(int, read().split()))\n\n\ndef read_col(H):\n \"\"\"\"\"\"H is number of rows\"\"\"\"\"\"H is number of rows\"\"\"\n ret = []\n for _ in range(H):\n ret.append(list(map(int, read().split())))\n return ret\n \n\n\nMOD = 10**9 + 7\nINF = 2**31 \n\nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef lcm(a, b):\n \n g = gcd(a, b)\n return a // g * b\n\n\n\n\nN = a_int()\nA = ints()\nA.sort(reverse=True)\nans = A[0]\ncnt = 1\nfor a in A[1:]:\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\nprint(ans)\n", "expected": "A"} +{"pair_id": "python_17518_forward", "problem_id": "p02615", "language": "python", "direction": "forward", "speedup": 3.9809, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(1 << 25)\nvar_15 = sys.stdin.readline\nvar_14 = range\nvar_11 = enumerate\n\n\ndef func_2(*var_8, **var_13):\n print(*var_8, **var_13)\n sys.exit()\n\n\ndef func_5(*var_8, var_17=1):\n return list(map(lambda var_18: var_18 - var_17, var_8))\n\n\n \ndef func_1():\n return int(var_15())\n\n\ndef func_3():\n return list(map(int, var_15().split()))\n\n\ndef func_6(var_2):\n\\\n\\\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return tuple(map(list, zip(*var_16)))\n\n\ndef func_8(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(tuple(map(int, var_15().split())))\n return var_16\n\n\ndef func_7(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return var_16\n \n\n\nvar_4 = 10**9 + 7\nvar_3 = 2**31 \n \nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef func_4(var_6, var_9):\n \n var_12 = gcd(var_6, var_9)\n return var_6 // var_12 * var_9\n\n\n \n \nvar_5 = func_1()\nvar_1 = func_3()\nvar_1.sort(reverse=True)\nvar_7 = var_1[0]\nvar_10 = 1\nfor var_6 in var_1[1:]:\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\nprint(var_7)\n```\n\n## Code B:\n```python\ndef func_1():\n import sys\n\n var_5 = sys.stdin.readline\n var_2 = int(eval(var_5()))\n var_1 = list(map(int, var_5().split()))\n var_1.sort(reverse=True)\n var_3 = var_1[0]\n for var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\n print(var_3)\n\n\nif __name__ == \"__main__\":\n func_1()\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(1 << 25)\nvar_15 = sys.stdin.readline\nvar_14 = range\nvar_11 = enumerate\n\n\ndef func_2(*var_8, **var_13):\n print(*var_8, **var_13)\n sys.exit()\n\n\ndef func_5(*var_8, var_17=1):\n return list(map(lambda var_18: var_18 - var_17, var_8))\n\n\n \ndef func_1():\n return int(var_15())\n\n\ndef func_3():\n return list(map(int, var_15().split()))\n\n\ndef func_6(var_2):\n\\\n\\\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return tuple(map(list, zip(*var_16)))\n\n\ndef func_8(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(tuple(map(int, var_15().split())))\n return var_16\n\n\ndef func_7(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return var_16\n \n\n\nvar_4 = 10**9 + 7\nvar_3 = 2**31 \n \nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef func_4(var_6, var_9):\n \n var_12 = gcd(var_6, var_9)\n return var_6 // var_12 * var_9\n\n\n \n \nvar_5 = func_1()\nvar_1 = func_3()\nvar_1.sort(reverse=True)\nvar_7 = var_1[0]\nvar_10 = 1\nfor var_6 in var_1[1:]:\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\nprint(var_7)\n", "code_b": "def func_1():\n import sys\n\n var_5 = sys.stdin.readline\n var_2 = int(eval(var_5()))\n var_1 = list(map(int, var_5().split()))\n var_1.sort(reverse=True)\n var_3 = var_1[0]\n for var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\n print(var_3)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "B"} +{"pair_id": "python_17518_reversed", "problem_id": "p02615", "language": "python", "direction": "reversed", "speedup": 3.9809, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\ndef func_1():\n import sys\n\n var_5 = sys.stdin.readline\n var_2 = int(eval(var_5()))\n var_1 = list(map(int, var_5().split()))\n var_1.sort(reverse=True)\n var_3 = var_1[0]\n for var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\n print(var_3)\n\n\nif __name__ == \"__main__\":\n func_1()\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(1 << 25)\nvar_15 = sys.stdin.readline\nvar_14 = range\nvar_11 = enumerate\n\n\ndef func_2(*var_8, **var_13):\n print(*var_8, **var_13)\n sys.exit()\n\n\ndef func_5(*var_8, var_17=1):\n return list(map(lambda var_18: var_18 - var_17, var_8))\n\n\n \ndef func_1():\n return int(var_15())\n\n\ndef func_3():\n return list(map(int, var_15().split()))\n\n\ndef func_6(var_2):\n\\\n\\\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return tuple(map(list, zip(*var_16)))\n\n\ndef func_8(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(tuple(map(int, var_15().split())))\n return var_16\n\n\ndef func_7(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return var_16\n \n\n\nvar_4 = 10**9 + 7\nvar_3 = 2**31 \n \nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef func_4(var_6, var_9):\n \n var_12 = gcd(var_6, var_9)\n return var_6 // var_12 * var_9\n\n\n \n \nvar_5 = func_1()\nvar_1 = func_3()\nvar_1.sort(reverse=True)\nvar_7 = var_1[0]\nvar_10 = 1\nfor var_6 in var_1[1:]:\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\nprint(var_7)\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "def func_1():\n import sys\n\n var_5 = sys.stdin.readline\n var_2 = int(eval(var_5()))\n var_1 = list(map(int, var_5().split()))\n var_1.sort(reverse=True)\n var_3 = var_1[0]\n for var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\n print(var_3)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "import sys\n\nsys.setrecursionlimit(1 << 25)\nvar_15 = sys.stdin.readline\nvar_14 = range\nvar_11 = enumerate\n\n\ndef func_2(*var_8, **var_13):\n print(*var_8, **var_13)\n sys.exit()\n\n\ndef func_5(*var_8, var_17=1):\n return list(map(lambda var_18: var_18 - var_17, var_8))\n\n\n \ndef func_1():\n return int(var_15())\n\n\ndef func_3():\n return list(map(int, var_15().split()))\n\n\ndef func_6(var_2):\n\\\n\\\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return tuple(map(list, zip(*var_16)))\n\n\ndef func_8(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(tuple(map(int, var_15().split())))\n return var_16\n\n\ndef func_7(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return var_16\n \n\n\nvar_4 = 10**9 + 7\nvar_3 = 2**31 \n \nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef func_4(var_6, var_9):\n \n var_12 = gcd(var_6, var_9)\n return var_6 // var_12 * var_9\n\n\n \n \nvar_5 = func_1()\nvar_1 = func_3()\nvar_1.sort(reverse=True)\nvar_7 = var_1[0]\nvar_10 = 1\nfor var_6 in var_1[1:]:\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\nprint(var_7)\n", "expected": "A"} {"pair_id": "python_9381_forward", "problem_id": "p02675", "language": "python", "direction": "forward", "speedup": 9.2065, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\nimport math\nimport collections\nimport itertools\nimport bisect\n\nvar_2 = 10**13\nvar_4 = 10**9 + 7\n\n\ndef func_12():\n return sys.stdin.readline().strip()\n\n\ndef func_11(var_7, var_8):\n return var_7 if var_8 == 0 else func_11(var_8, var_7 % var_8)\n\n\ndef func_13(var_7, var_8):\n return (var_7 * var_8) // func_11(var_7, var_8)\n\n\ndef func_1():\n return int(func_12())\n\n\ndef func_2():\n return [int(var_7) for var_7 in func_12().split()]\n\n\ndef func_7(var_5):\n return [int(func_12()) for _ in range(var_5)]\n\n\ndef func_3(var_5):\n return [[int(var_7) for var_7 in func_12().split()] for _ in range(var_5)]\n\n\ndef func_5():\n return func_12().split()\n\n\ndef func_8(var_5):\n return [func_12() for _ in range(var_5)]\n\n\ndef func_4(var_5):\n return [func_12().split() for _ in range(var_5)]\n\n\ndef func_6(var_3):\n print(*var_3, sep=\"\\n\")\n\n\ndef func_10(var_1):\n print(\"Yes\" if var_1 else \"No\")\n\n\ndef func_9(var_1):\n print(\"YES\" if var_1 else \"NO\")\n\n\nvar_5 = func_12()\nvar_6 = int(var_5[-1])\nif var_6 in [2, 4, 5, 7, 9]:\n print(\"hon\")\nelif var_6 in [0, 1, 6, 8]:\n print(\"pon\")\nelse:\n print(\"bon\")\n\n```\n\n## Code B:\n```python\nvar_1 = input()[-1]\nif var_1 == \"3\":\n print(\"bon\")\nelif var_1 in \"0168\":\n print(\"pon\")\nelse:\n print(\"hon\")\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\nimport math\nimport collections\nimport itertools\nimport bisect\n\nvar_2 = 10**13\nvar_4 = 10**9 + 7\n\n\ndef func_12():\n return sys.stdin.readline().strip()\n\n\ndef func_11(var_7, var_8):\n return var_7 if var_8 == 0 else func_11(var_8, var_7 % var_8)\n\n\ndef func_13(var_7, var_8):\n return (var_7 * var_8) // func_11(var_7, var_8)\n\n\ndef func_1():\n return int(func_12())\n\n\ndef func_2():\n return [int(var_7) for var_7 in func_12().split()]\n\n\ndef func_7(var_5):\n return [int(func_12()) for _ in range(var_5)]\n\n\ndef func_3(var_5):\n return [[int(var_7) for var_7 in func_12().split()] for _ in range(var_5)]\n\n\ndef func_5():\n return func_12().split()\n\n\ndef func_8(var_5):\n return [func_12() for _ in range(var_5)]\n\n\ndef func_4(var_5):\n return [func_12().split() for _ in range(var_5)]\n\n\ndef func_6(var_3):\n print(*var_3, sep=\"\\n\")\n\n\ndef func_10(var_1):\n print(\"Yes\" if var_1 else \"No\")\n\n\ndef func_9(var_1):\n print(\"YES\" if var_1 else \"NO\")\n\n\nvar_5 = func_12()\nvar_6 = int(var_5[-1])\nif var_6 in [2, 4, 5, 7, 9]:\n print(\"hon\")\nelif var_6 in [0, 1, 6, 8]:\n print(\"pon\")\nelse:\n print(\"bon\")\n", "code_b": "var_1 = input()[-1]\nif var_1 == \"3\":\n print(\"bon\")\nelif var_1 in \"0168\":\n print(\"pon\")\nelse:\n print(\"hon\")\n", "expected": "B"} {"pair_id": "python_9381_reversed", "problem_id": "p02675", "language": "python", "direction": "reversed", "speedup": 9.2065, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1 = input()[-1]\nif var_1 == \"3\":\n print(\"bon\")\nelif var_1 in \"0168\":\n print(\"pon\")\nelse:\n print(\"hon\")\n\n```\n\n## Code B:\n```python\nimport sys\nimport math\nimport collections\nimport itertools\nimport bisect\n\nvar_2 = 10**13\nvar_4 = 10**9 + 7\n\n\ndef func_12():\n return sys.stdin.readline().strip()\n\n\ndef func_11(var_7, var_8):\n return var_7 if var_8 == 0 else func_11(var_8, var_7 % var_8)\n\n\ndef func_13(var_7, var_8):\n return (var_7 * var_8) // func_11(var_7, var_8)\n\n\ndef func_1():\n return int(func_12())\n\n\ndef func_2():\n return [int(var_7) for var_7 in func_12().split()]\n\n\ndef func_7(var_5):\n return [int(func_12()) for _ in range(var_5)]\n\n\ndef func_3(var_5):\n return [[int(var_7) for var_7 in func_12().split()] for _ in range(var_5)]\n\n\ndef func_5():\n return func_12().split()\n\n\ndef func_8(var_5):\n return [func_12() for _ in range(var_5)]\n\n\ndef func_4(var_5):\n return [func_12().split() for _ in range(var_5)]\n\n\ndef func_6(var_3):\n print(*var_3, sep=\"\\n\")\n\n\ndef func_10(var_1):\n print(\"Yes\" if var_1 else \"No\")\n\n\ndef func_9(var_1):\n print(\"YES\" if var_1 else \"NO\")\n\n\nvar_5 = func_12()\nvar_6 = int(var_5[-1])\nif var_6 in [2, 4, 5, 7, 9]:\n print(\"hon\")\nelif var_6 in [0, 1, 6, 8]:\n print(\"pon\")\nelse:\n print(\"bon\")\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1 = input()[-1]\nif var_1 == \"3\":\n print(\"bon\")\nelif var_1 in \"0168\":\n print(\"pon\")\nelse:\n print(\"hon\")\n", "code_b": "import sys\nimport math\nimport collections\nimport itertools\nimport bisect\n\nvar_2 = 10**13\nvar_4 = 10**9 + 7\n\n\ndef func_12():\n return sys.stdin.readline().strip()\n\n\ndef func_11(var_7, var_8):\n return var_7 if var_8 == 0 else func_11(var_8, var_7 % var_8)\n\n\ndef func_13(var_7, var_8):\n return (var_7 * var_8) // func_11(var_7, var_8)\n\n\ndef func_1():\n return int(func_12())\n\n\ndef func_2():\n return [int(var_7) for var_7 in func_12().split()]\n\n\ndef func_7(var_5):\n return [int(func_12()) for _ in range(var_5)]\n\n\ndef func_3(var_5):\n return [[int(var_7) for var_7 in func_12().split()] for _ in range(var_5)]\n\n\ndef func_5():\n return func_12().split()\n\n\ndef func_8(var_5):\n return [func_12() for _ in range(var_5)]\n\n\ndef func_4(var_5):\n return [func_12().split() for _ in range(var_5)]\n\n\ndef func_6(var_3):\n print(*var_3, sep=\"\\n\")\n\n\ndef func_10(var_1):\n print(\"Yes\" if var_1 else \"No\")\n\n\ndef func_9(var_1):\n print(\"YES\" if var_1 else \"NO\")\n\n\nvar_5 = func_12()\nvar_6 = int(var_5[-1])\nif var_6 in [2, 4, 5, 7, 9]:\n print(\"hon\")\nelif var_6 in [0, 1, 6, 8]:\n print(\"pon\")\nelse:\n print(\"bon\")\n", "expected": "A"} {"pair_id": "python_6737_forward", "problem_id": "p03198", "language": "python", "direction": "forward", "speedup": 2.2972, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport collections, atexit, math, sys, bisect\n\nsys.setrecursionlimit(1000000)\n\n\ndef func_2():\n return list(map(int, input().split()))\n\n\ntry:\n\n import numpy\n\n def func_1(*var_2, **var_10):\n\n print(*var_2, file=sys.stderr)\n\n func_1(\"debug mode\")\nexcept Exception:\n\n def func_1(*var_2, **var_10):\n pass\n\n\nvar_9 = 0\nvar_13 = 0\nif var_9 > 0:\n func_1(\"use input\", var_9)\n sys.stdin = open(\"input\" + str(var_9) + \".txt\", \"r\")\nif var_13 > 0:\n func_1(\"use output\", var_13)\n sys.stdout = open(\"stdout\" + str(var_13) + \".txt\", \"w\")\n atexit.register(lambda: sys.stdout.close())\nif True:\n (var_1,) = func_2()\n\n var_20 = func_2()\nelse:\n var_1 = 100\n import random\n\n var_20 = [random.randint(1, 1000000000) for var_8 in range(var_1)]\nfunc_1(\"begin\")\nvar_21 = [0 for var_8 in range(var_1 + 1)]\nvar_23 = [0 for var_8 in range(var_1 + 1)]\n\n\ndef func_3(var_22, var_20):\n var_22[0] = 0\n var_15 = []\n for var_8 in range(var_1):\n if var_8 % 10000 == 0:\n func_1(\"---\", var_8)\n var_12 = [[var_20[var_8], 0], [var_20[var_8], 0], 1]\n var_15.append(var_12)\n var_11 = var_22[var_8]\n\n while len(var_15) > 1:\n var_5 = var_15[-2][1][1]\n var_3 = var_15[-1][0][1]\n var_7 = min(var_3, var_5)\n var_3 -= var_7\n var_5 -= var_7\n var_4 = 4**var_3\n var_6 = 4**var_5\n var_17 = var_15[-2][1][0] * var_6\n var_16 = var_15[-1][0][0] * var_4\n if var_17 < var_16:\n var_11 += 2 * var_15[-2][2]\n var_15[-2][0][1] += 1\n var_15[-2][1][1] += 1\n elif var_17 < var_16 * 4:\n var_19 = [var_15[-2][0], var_15[-1][1], var_15[-2][2] + var_15[-1][2]]\n var_15[-2] = var_19\n var_15.pop()\n else:\n break\n\n var_22[var_8 + 1] = var_11\n\n assert var_11 < 1000000000000000\n func_1(var_15)\n\n\nfunc_3(var_21, var_20)\nfunc_1(\"!!!!\")\nfunc_3(var_23, var_20[::-1])\n\n\nvar_14 = var_23[var_1]\nfor var_8 in range(var_1 + 1):\n var_18 = var_8 + var_21[var_8] + var_23[var_1 - var_8]\n var_14 = min(var_14, var_18)\nprint(var_14)\nfunc_1(\"end\")\n\n```\n\n## Code B:\n```python\nfrom sys import stdin\nfrom itertools import repeat\n\n\ndef func_1(var_1):\n var_8 = len(var_1)\n var_7 = [0] * (var_8 + 1)\n var_13 = [(0, 0, 0, 1)]\n var_10 = var_13.append\n var_9 = var_13.pop\n for var_5 in range(1, var_8):\n var_2 = 0\n var_12 = var_5\n var_14 = 0\n var_6 = 1\n while var_13:\n var_16 = var_1[var_12] << var_14\n var_15 = var_1[var_13[-1][0]]\n if var_15 >= var_16:\n break\n _, var_12, var_14, var_11 = var_9()\n var_4 = 0\n while var_15 < var_16:\n var_15 *= 4\n var_4 += 2\n var_2 += var_4 * var_11\n var_6 += var_11\n var_14 += var_4\n var_10((var_5, var_12, var_14, var_6))\n var_7[var_5 + 1] = var_7[var_5] + var_2\n return var_7\n\n\ndef func_2():\n var_8 = int(stdin.readline())\n var_1 = list(map(int, stdin.readline().split(), repeat(10, var_8)))\n var_7 = func_1(var_1)\n var_12 = func_1(var_1[::-1])[::-1]\n var_3 = 10**12\n for var_5 in range(var_8 + 1):\n if var_3 > var_7[var_5] + var_5 + var_12[var_5]:\n var_3 = var_7[var_5] + var_5 + var_12[var_5]\n print(var_3)\n\n\nfunc_2()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import collections, atexit, math, sys, bisect\n\nsys.setrecursionlimit(1000000)\n\n\ndef func_2():\n return list(map(int, input().split()))\n\n\ntry:\n\n import numpy\n\n def func_1(*var_2, **var_10):\n\n print(*var_2, file=sys.stderr)\n\n func_1(\"debug mode\")\nexcept Exception:\n\n def func_1(*var_2, **var_10):\n pass\n\n\nvar_9 = 0\nvar_13 = 0\nif var_9 > 0:\n func_1(\"use input\", var_9)\n sys.stdin = open(\"input\" + str(var_9) + \".txt\", \"r\")\nif var_13 > 0:\n func_1(\"use output\", var_13)\n sys.stdout = open(\"stdout\" + str(var_13) + \".txt\", \"w\")\n atexit.register(lambda: sys.stdout.close())\nif True:\n (var_1,) = func_2()\n\n var_20 = func_2()\nelse:\n var_1 = 100\n import random\n\n var_20 = [random.randint(1, 1000000000) for var_8 in range(var_1)]\nfunc_1(\"begin\")\nvar_21 = [0 for var_8 in range(var_1 + 1)]\nvar_23 = [0 for var_8 in range(var_1 + 1)]\n\n\ndef func_3(var_22, var_20):\n var_22[0] = 0\n var_15 = []\n for var_8 in range(var_1):\n if var_8 % 10000 == 0:\n func_1(\"---\", var_8)\n var_12 = [[var_20[var_8], 0], [var_20[var_8], 0], 1]\n var_15.append(var_12)\n var_11 = var_22[var_8]\n\n while len(var_15) > 1:\n var_5 = var_15[-2][1][1]\n var_3 = var_15[-1][0][1]\n var_7 = min(var_3, var_5)\n var_3 -= var_7\n var_5 -= var_7\n var_4 = 4**var_3\n var_6 = 4**var_5\n var_17 = var_15[-2][1][0] * var_6\n var_16 = var_15[-1][0][0] * var_4\n if var_17 < var_16:\n var_11 += 2 * var_15[-2][2]\n var_15[-2][0][1] += 1\n var_15[-2][1][1] += 1\n elif var_17 < var_16 * 4:\n var_19 = [var_15[-2][0], var_15[-1][1], var_15[-2][2] + var_15[-1][2]]\n var_15[-2] = var_19\n var_15.pop()\n else:\n break\n\n var_22[var_8 + 1] = var_11\n\n assert var_11 < 1000000000000000\n func_1(var_15)\n\n\nfunc_3(var_21, var_20)\nfunc_1(\"!!!!\")\nfunc_3(var_23, var_20[::-1])\n\n\nvar_14 = var_23[var_1]\nfor var_8 in range(var_1 + 1):\n var_18 = var_8 + var_21[var_8] + var_23[var_1 - var_8]\n var_14 = min(var_14, var_18)\nprint(var_14)\nfunc_1(\"end\")\n", "code_b": "from sys import stdin\nfrom itertools import repeat\n\n\ndef func_1(var_1):\n var_8 = len(var_1)\n var_7 = [0] * (var_8 + 1)\n var_13 = [(0, 0, 0, 1)]\n var_10 = var_13.append\n var_9 = var_13.pop\n for var_5 in range(1, var_8):\n var_2 = 0\n var_12 = var_5\n var_14 = 0\n var_6 = 1\n while var_13:\n var_16 = var_1[var_12] << var_14\n var_15 = var_1[var_13[-1][0]]\n if var_15 >= var_16:\n break\n _, var_12, var_14, var_11 = var_9()\n var_4 = 0\n while var_15 < var_16:\n var_15 *= 4\n var_4 += 2\n var_2 += var_4 * var_11\n var_6 += var_11\n var_14 += var_4\n var_10((var_5, var_12, var_14, var_6))\n var_7[var_5 + 1] = var_7[var_5] + var_2\n return var_7\n\n\ndef func_2():\n var_8 = int(stdin.readline())\n var_1 = list(map(int, stdin.readline().split(), repeat(10, var_8)))\n var_7 = func_1(var_1)\n var_12 = func_1(var_1[::-1])[::-1]\n var_3 = 10**12\n for var_5 in range(var_8 + 1):\n if var_3 > var_7[var_5] + var_5 + var_12[var_5]:\n var_3 = var_7[var_5] + var_5 + var_12[var_5]\n print(var_3)\n\n\nfunc_2()\n", "expected": "B"} @@ -758,8 +758,8 @@ {"pair_id": "python_13608_reversed", "problem_id": "p00447", "language": "python", "direction": "reversed", "speedup": 2.23, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfor var_3 in iter(input, \"0\"):\n var_1 = sorted(\n [list(map(int, input().split())) for _ in [0] * int(var_3)],\n key=lambda var_8: var_8[0],\n )\n var_2 = sorted(\n [list(map(int, input().split())) for _ in [0] * int(eval(input()))],\n key=lambda var_8: var_8[0],\n )\n for var_4, var_5 in var_2:\n var_6, var_7 = var_1[0]\n var_8, var_9 = var_4 - var_6, var_5 - var_7\n for var_6, var_7 in var_1:\n if [var_6 + var_8, var_7 + var_9] not in var_2:\n break\n else:\n print((var_8, var_9))\n\n```\n\n## Code B:\n```python\nvar_1 = [[False] * 1000001] * 1000001\nwhile True:\n var_7 = int(eval(input()))\n if var_7 == 0:\n break\n var_9 = tuple(tuple(map(int, input().split())) for var_5 in range(var_7))\n var_11, var_12 = var_9[0]\n var_8 = int(eval(input()))\n var_10 = [tuple(map(int, input().split())) for var_5 in range(var_8)]\n for var_5 in range(var_8):\n var_1[var_10[var_5][0]][var_10[var_5][1]] = True\n for var_5 in range(var_8):\n var_4 = True\n for var_6 in range(len(var_9)):\n var_2 = var_10[var_5][0] + var_9[var_6][0] - var_11\n var_3 = var_10[var_5][1] + var_9[var_6][1] - var_12\n if (\n 0 <= var_2 <= 1000000\n and 0 <= var_3 <= 1000000\n and var_1[var_2][var_3] == False\n ):\n var_4 = False\n break\n if var_4 == True:\n print(\n (\n \"{} {}\".format(\n str(var_10[var_5][0] - var_11), str(var_10[var_5][1] - var_12)\n )\n )\n )\n break\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "for var_3 in iter(input, \"0\"):\n var_1 = sorted(\n [list(map(int, input().split())) for _ in [0] * int(var_3)],\n key=lambda var_8: var_8[0],\n )\n var_2 = sorted(\n [list(map(int, input().split())) for _ in [0] * int(eval(input()))],\n key=lambda var_8: var_8[0],\n )\n for var_4, var_5 in var_2:\n var_6, var_7 = var_1[0]\n var_8, var_9 = var_4 - var_6, var_5 - var_7\n for var_6, var_7 in var_1:\n if [var_6 + var_8, var_7 + var_9] not in var_2:\n break\n else:\n print((var_8, var_9))\n", "code_b": "var_1 = [[False] * 1000001] * 1000001\nwhile True:\n var_7 = int(eval(input()))\n if var_7 == 0:\n break\n var_9 = tuple(tuple(map(int, input().split())) for var_5 in range(var_7))\n var_11, var_12 = var_9[0]\n var_8 = int(eval(input()))\n var_10 = [tuple(map(int, input().split())) for var_5 in range(var_8)]\n for var_5 in range(var_8):\n var_1[var_10[var_5][0]][var_10[var_5][1]] = True\n for var_5 in range(var_8):\n var_4 = True\n for var_6 in range(len(var_9)):\n var_2 = var_10[var_5][0] + var_9[var_6][0] - var_11\n var_3 = var_10[var_5][1] + var_9[var_6][1] - var_12\n if (\n 0 <= var_2 <= 1000000\n and 0 <= var_3 <= 1000000\n and var_1[var_2][var_3] == False\n ):\n var_4 = False\n break\n if var_4 == True:\n print(\n (\n \"{} {}\".format(\n str(var_10[var_5][0] - var_11), str(var_10[var_5][1] - var_12)\n )\n )\n )\n break\n", "expected": "A"} {"pair_id": "python_2678_forward", "problem_id": "p02630", "language": "python", "direction": "forward", "speedup": 5.8907, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_3 = lambda: [*list(map(int, input().split()))]\nvar_3()\nvar_7, *var_5 = [0] * 100002\nfor var_4 in var_3():\n var_7 += var_4\n var_5[var_4] += 1\nvar_6 = var_3()[0]\nfor _ in range(var_6):\n var_1, var_2 = var_3()\n var_7 += (var_2 - var_1) * var_5[var_1]\n var_5[var_2] += var_5[var_1]\n var_5[var_1] = 0\n print(var_7)\n\n```\n\n## Code B:\n```python\nfrom collections import *\n\neval(input())\nvar_6 = sum(var_1 := list(map(int, input().split())))\nvar_1 = var_2(var_1)\nfor var_5 in range(int(eval(input()))):\n var_3, var_4 = list(map(int, input().split()))\n var_6 += (var_4 - var_3) * var_1[var_3]\n var_1[var_4] += var_1[var_3]\n var_1[var_3] = 0\n print(var_6)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_3 = lambda: [*list(map(int, input().split()))]\nvar_3()\nvar_7, *var_5 = [0] * 100002\nfor var_4 in var_3():\n var_7 += var_4\n var_5[var_4] += 1\nvar_6 = var_3()[0]\nfor _ in range(var_6):\n var_1, var_2 = var_3()\n var_7 += (var_2 - var_1) * var_5[var_1]\n var_5[var_2] += var_5[var_1]\n var_5[var_1] = 0\n print(var_7)\n", "code_b": "from collections import *\n\neval(input())\nvar_6 = sum(var_1 := list(map(int, input().split())))\nvar_1 = var_2(var_1)\nfor var_5 in range(int(eval(input()))):\n var_3, var_4 = list(map(int, input().split()))\n var_6 += (var_4 - var_3) * var_1[var_3]\n var_1[var_4] += var_1[var_3]\n var_1[var_3] = 0\n print(var_6)\n", "expected": "B"} {"pair_id": "python_2678_reversed", "problem_id": "p02630", "language": "python", "direction": "reversed", "speedup": 5.8907, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfrom collections import *\n\neval(input())\nvar_6 = sum(var_1 := list(map(int, input().split())))\nvar_1 = var_2(var_1)\nfor var_5 in range(int(eval(input()))):\n var_3, var_4 = list(map(int, input().split()))\n var_6 += (var_4 - var_3) * var_1[var_3]\n var_1[var_4] += var_1[var_3]\n var_1[var_3] = 0\n print(var_6)\n\n```\n\n## Code B:\n```python\nvar_3 = lambda: [*list(map(int, input().split()))]\nvar_3()\nvar_7, *var_5 = [0] * 100002\nfor var_4 in var_3():\n var_7 += var_4\n var_5[var_4] += 1\nvar_6 = var_3()[0]\nfor _ in range(var_6):\n var_1, var_2 = var_3()\n var_7 += (var_2 - var_1) * var_5[var_1]\n var_5[var_2] += var_5[var_1]\n var_5[var_1] = 0\n print(var_7)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "from collections import *\n\neval(input())\nvar_6 = sum(var_1 := list(map(int, input().split())))\nvar_1 = var_2(var_1)\nfor var_5 in range(int(eval(input()))):\n var_3, var_4 = list(map(int, input().split()))\n var_6 += (var_4 - var_3) * var_1[var_3]\n var_1[var_4] += var_1[var_3]\n var_1[var_3] = 0\n print(var_6)\n", "code_b": "var_3 = lambda: [*list(map(int, input().split()))]\nvar_3()\nvar_7, *var_5 = [0] * 100002\nfor var_4 in var_3():\n var_7 += var_4\n var_5[var_4] += 1\nvar_6 = var_3()[0]\nfor _ in range(var_6):\n var_1, var_2 = var_3()\n var_7 += (var_2 - var_1) * var_5[var_1]\n var_5[var_2] += var_5[var_1]\n var_5[var_1] = 0\n print(var_7)\n", "expected": "A"} -{"pair_id": "python_17834_forward", "problem_id": "p02586", "language": "python", "direction": "forward", "speedup": 3.0979, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n\nfrom collections import deque, defaultdict\n\n\n\nfrom collections import Counter \n\n\nfrom itertools import accumulate, combinations, permutations, product \n\n\nfrom heapq import heapify, heappop, heappush\n\n\nfrom functools import reduce, lru_cache \n\n\nfrom decimal import Decimal\n\n\ndef input():\n x = sys.stdin.readline()\n return x[:-1] if x[-1] == \"\\n\" else x\n\n\ndef printe(*x):\n print(\"\n\n\ndef printl(li):\n _ = print(*li, sep=\"\\n\") if li else None\n\n\ndef argsort(s, return_sorted=False):\n inds = sorted(range(len(s)), key=lambda k: s[k])\n if return_sorted:\n return inds, [s[i] for i in inds]\n return inds\n\n\ndef alp2num(c, cap=False):\n return ord(c) - 97 if not cap else ord(c) - 65\n\n\ndef num2alp(i, cap=False):\n return chr(i + 97) if not cap else chr(i + 65)\n\n\ndef matmat(A, B):\n K, N, M = len(B), len(A), len(B[0])\n return [\n [sum([(A[i][k] * B[k][j]) for k in range(K)]) for j in range(M)]\n for i in range(N)\n ]\n\n\ndef matvec(M, v):\n N, size = len(v), len(M)\n return [sum([M[i][j] * v[j] for j in range(N)]) for i in range(size)]\n\n\ndef T(M):\n n, m = len(M), len(M[0])\n return [[M[j][i] for j in range(n)] for i in range(m)]\n\n\ndef binr(x):\n return bin(x)[2:]\n\n\ndef bitcount(x): \n x = x - ((x >> 1) & 0x5555555555555555)\n x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)\n x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F\n x += x >> 8\n x += x >> 16\n x += x >> 32\n return x & 0x7F\n\n\ndef main():\n mod = 1000000007\n \n \n R, C, K = map(int, input().split())\n \n \n base = 5000\n S = []\n for i in range(K):\n r, c, v = map(int, input().split())\n r -= 1\n c -= 1\n S.append((r * base + c, v))\n S.sort(key=itemgetter(0))\n p = 0\n dp = [[0] * 4 for _ in range(C)]\n dpi = [[0] * 4 for _ in range(C)]\n for i in range(R):\n for j in range(C):\n cv = 0\n if p < K and i * base + j == S[p][0]:\n cv = S[p][1]\n p += 1\n if j > 0:\n dpi[j] = copy(dpi[j - 1])\n else:\n dpi[0] = [0] * 4\n if i > 0:\n dpi[j][0] = max(dp[j], dpi[j][0])\n if cv != 0:\n for k in range(2, -1, -1):\n dpi[j][k + 1] = max(dpi[j][k] + cv, dpi[j][k + 1])\n \n dp[j] = max(dpi[j])\n print(dp[-1])\n\n\nif __name__ == \"__main__\":\n main()\n\n```\n\n## Code B:\n```python\nimport sys\n\nvar_15 = sys.stdin.read\nvar_16 = sys.stdin.readline\nvar_17 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_4 = 1000000007\n\n\ndef func_1():\n var_5, var_1, var_3, *var_6 = list(map(int, var_15().split()))\n var_11 = [[0] * var_1 for _ in range(var_5)]\n for var_14, var_7, var_18 in zip(*[iter(var_6)] * 3):\n var_11[var_14 - 1][var_7 - 1] = var_18\n var_8 = [0] * (var_1 + 1)\n for var_10 in range(var_5):\n var_8, var_9 = [0] * (var_1 + 1), var_8\n var_18 = [0] * 4\n for var_12 in range(var_1):\n for var_13 in range(4):\n if var_18[var_13] < var_9[var_12 + 1]:\n var_18[var_13] = var_9[var_12 + 1]\n else:\n break\n if var_11[var_10][var_12] > 0:\n for var_13 in range(2, -1, -1):\n if var_18[var_13 + 1] < var_18[var_13] + var_11[var_10][var_12]:\n var_18[var_13 + 1] = var_18[var_13] + var_11[var_10][var_12]\n var_8[var_12 + 1] = var_18[3]\n print((var_8[var_1]))\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n\nfrom collections import deque, defaultdict\n\n\n\nfrom collections import Counter \n\n\nfrom itertools import accumulate, combinations, permutations, product \n\n\nfrom heapq import heapify, heappop, heappush\n\n\nfrom functools import reduce, lru_cache \n\n\nfrom decimal import Decimal\n\n\ndef input():\n x = sys.stdin.readline()\n return x[:-1] if x[-1] == \"\\n\" else x\n\n\ndef printe(*x):\n print(\"\n\n\ndef printl(li):\n _ = print(*li, sep=\"\\n\") if li else None\n\n\ndef argsort(s, return_sorted=False):\n inds = sorted(range(len(s)), key=lambda k: s[k])\n if return_sorted:\n return inds, [s[i] for i in inds]\n return inds\n\n\ndef alp2num(c, cap=False):\n return ord(c) - 97 if not cap else ord(c) - 65\n\n\ndef num2alp(i, cap=False):\n return chr(i + 97) if not cap else chr(i + 65)\n\n\ndef matmat(A, B):\n K, N, M = len(B), len(A), len(B[0])\n return [\n [sum([(A[i][k] * B[k][j]) for k in range(K)]) for j in range(M)]\n for i in range(N)\n ]\n\n\ndef matvec(M, v):\n N, size = len(v), len(M)\n return [sum([M[i][j] * v[j] for j in range(N)]) for i in range(size)]\n\n\ndef T(M):\n n, m = len(M), len(M[0])\n return [[M[j][i] for j in range(n)] for i in range(m)]\n\n\ndef binr(x):\n return bin(x)[2:]\n\n\ndef bitcount(x): \n x = x - ((x >> 1) & 0x5555555555555555)\n x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)\n x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F\n x += x >> 8\n x += x >> 16\n x += x >> 32\n return x & 0x7F\n\n\ndef main():\n mod = 1000000007\n \n \n R, C, K = map(int, input().split())\n \n \n base = 5000\n S = []\n for i in range(K):\n r, c, v = map(int, input().split())\n r -= 1\n c -= 1\n S.append((r * base + c, v))\n S.sort(key=itemgetter(0))\n p = 0\n dp = [[0] * 4 for _ in range(C)]\n dpi = [[0] * 4 for _ in range(C)]\n for i in range(R):\n for j in range(C):\n cv = 0\n if p < K and i * base + j == S[p][0]:\n cv = S[p][1]\n p += 1\n if j > 0:\n dpi[j] = copy(dpi[j - 1])\n else:\n dpi[0] = [0] * 4\n if i > 0:\n dpi[j][0] = max(dp[j], dpi[j][0])\n if cv != 0:\n for k in range(2, -1, -1):\n dpi[j][k + 1] = max(dpi[j][k] + cv, dpi[j][k + 1])\n \n dp[j] = max(dpi[j])\n print(dp[-1])\n\n\nif __name__ == \"__main__\":\n main()\n", "code_b": "import sys\n\nvar_15 = sys.stdin.read\nvar_16 = sys.stdin.readline\nvar_17 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_4 = 1000000007\n\n\ndef func_1():\n var_5, var_1, var_3, *var_6 = list(map(int, var_15().split()))\n var_11 = [[0] * var_1 for _ in range(var_5)]\n for var_14, var_7, var_18 in zip(*[iter(var_6)] * 3):\n var_11[var_14 - 1][var_7 - 1] = var_18\n var_8 = [0] * (var_1 + 1)\n for var_10 in range(var_5):\n var_8, var_9 = [0] * (var_1 + 1), var_8\n var_18 = [0] * 4\n for var_12 in range(var_1):\n for var_13 in range(4):\n if var_18[var_13] < var_9[var_12 + 1]:\n var_18[var_13] = var_9[var_12 + 1]\n else:\n break\n if var_11[var_10][var_12] > 0:\n for var_13 in range(2, -1, -1):\n if var_18[var_13 + 1] < var_18[var_13] + var_11[var_10][var_12]:\n var_18[var_13 + 1] = var_18[var_13] + var_11[var_10][var_12]\n var_8[var_12 + 1] = var_18[3]\n print((var_8[var_1]))\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "B"} -{"pair_id": "python_17834_reversed", "problem_id": "p02586", "language": "python", "direction": "reversed", "speedup": 3.0979, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_15 = sys.stdin.read\nvar_16 = sys.stdin.readline\nvar_17 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_4 = 1000000007\n\n\ndef func_1():\n var_5, var_1, var_3, *var_6 = list(map(int, var_15().split()))\n var_11 = [[0] * var_1 for _ in range(var_5)]\n for var_14, var_7, var_18 in zip(*[iter(var_6)] * 3):\n var_11[var_14 - 1][var_7 - 1] = var_18\n var_8 = [0] * (var_1 + 1)\n for var_10 in range(var_5):\n var_8, var_9 = [0] * (var_1 + 1), var_8\n var_18 = [0] * 4\n for var_12 in range(var_1):\n for var_13 in range(4):\n if var_18[var_13] < var_9[var_12 + 1]:\n var_18[var_13] = var_9[var_12 + 1]\n else:\n break\n if var_11[var_10][var_12] > 0:\n for var_13 in range(2, -1, -1):\n if var_18[var_13 + 1] < var_18[var_13] + var_11[var_10][var_12]:\n var_18[var_13 + 1] = var_18[var_13] + var_11[var_10][var_12]\n var_8[var_12 + 1] = var_18[3]\n print((var_8[var_1]))\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n\nfrom collections import deque, defaultdict\n\n\n\nfrom collections import Counter \n\n\nfrom itertools import accumulate, combinations, permutations, product \n\n\nfrom heapq import heapify, heappop, heappush\n\n\nfrom functools import reduce, lru_cache \n\n\nfrom decimal import Decimal\n\n\ndef input():\n x = sys.stdin.readline()\n return x[:-1] if x[-1] == \"\\n\" else x\n\n\ndef printe(*x):\n print(\"\n\n\ndef printl(li):\n _ = print(*li, sep=\"\\n\") if li else None\n\n\ndef argsort(s, return_sorted=False):\n inds = sorted(range(len(s)), key=lambda k: s[k])\n if return_sorted:\n return inds, [s[i] for i in inds]\n return inds\n\n\ndef alp2num(c, cap=False):\n return ord(c) - 97 if not cap else ord(c) - 65\n\n\ndef num2alp(i, cap=False):\n return chr(i + 97) if not cap else chr(i + 65)\n\n\ndef matmat(A, B):\n K, N, M = len(B), len(A), len(B[0])\n return [\n [sum([(A[i][k] * B[k][j]) for k in range(K)]) for j in range(M)]\n for i in range(N)\n ]\n\n\ndef matvec(M, v):\n N, size = len(v), len(M)\n return [sum([M[i][j] * v[j] for j in range(N)]) for i in range(size)]\n\n\ndef T(M):\n n, m = len(M), len(M[0])\n return [[M[j][i] for j in range(n)] for i in range(m)]\n\n\ndef binr(x):\n return bin(x)[2:]\n\n\ndef bitcount(x): \n x = x - ((x >> 1) & 0x5555555555555555)\n x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)\n x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F\n x += x >> 8\n x += x >> 16\n x += x >> 32\n return x & 0x7F\n\n\ndef main():\n mod = 1000000007\n \n \n R, C, K = map(int, input().split())\n \n \n base = 5000\n S = []\n for i in range(K):\n r, c, v = map(int, input().split())\n r -= 1\n c -= 1\n S.append((r * base + c, v))\n S.sort(key=itemgetter(0))\n p = 0\n dp = [[0] * 4 for _ in range(C)]\n dpi = [[0] * 4 for _ in range(C)]\n for i in range(R):\n for j in range(C):\n cv = 0\n if p < K and i * base + j == S[p][0]:\n cv = S[p][1]\n p += 1\n if j > 0:\n dpi[j] = copy(dpi[j - 1])\n else:\n dpi[0] = [0] * 4\n if i > 0:\n dpi[j][0] = max(dp[j], dpi[j][0])\n if cv != 0:\n for k in range(2, -1, -1):\n dpi[j][k + 1] = max(dpi[j][k] + cv, dpi[j][k + 1])\n \n dp[j] = max(dpi[j])\n print(dp[-1])\n\n\nif __name__ == \"__main__\":\n main()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_15 = sys.stdin.read\nvar_16 = sys.stdin.readline\nvar_17 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_4 = 1000000007\n\n\ndef func_1():\n var_5, var_1, var_3, *var_6 = list(map(int, var_15().split()))\n var_11 = [[0] * var_1 for _ in range(var_5)]\n for var_14, var_7, var_18 in zip(*[iter(var_6)] * 3):\n var_11[var_14 - 1][var_7 - 1] = var_18\n var_8 = [0] * (var_1 + 1)\n for var_10 in range(var_5):\n var_8, var_9 = [0] * (var_1 + 1), var_8\n var_18 = [0] * 4\n for var_12 in range(var_1):\n for var_13 in range(4):\n if var_18[var_13] < var_9[var_12 + 1]:\n var_18[var_13] = var_9[var_12 + 1]\n else:\n break\n if var_11[var_10][var_12] > 0:\n for var_13 in range(2, -1, -1):\n if var_18[var_13 + 1] < var_18[var_13] + var_11[var_10][var_12]:\n var_18[var_13 + 1] = var_18[var_13] + var_11[var_10][var_12]\n var_8[var_12 + 1] = var_18[3]\n print((var_8[var_1]))\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "import sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n\nfrom collections import deque, defaultdict\n\n\n\nfrom collections import Counter \n\n\nfrom itertools import accumulate, combinations, permutations, product \n\n\nfrom heapq import heapify, heappop, heappush\n\n\nfrom functools import reduce, lru_cache \n\n\nfrom decimal import Decimal\n\n\ndef input():\n x = sys.stdin.readline()\n return x[:-1] if x[-1] == \"\\n\" else x\n\n\ndef printe(*x):\n print(\"\n\n\ndef printl(li):\n _ = print(*li, sep=\"\\n\") if li else None\n\n\ndef argsort(s, return_sorted=False):\n inds = sorted(range(len(s)), key=lambda k: s[k])\n if return_sorted:\n return inds, [s[i] for i in inds]\n return inds\n\n\ndef alp2num(c, cap=False):\n return ord(c) - 97 if not cap else ord(c) - 65\n\n\ndef num2alp(i, cap=False):\n return chr(i + 97) if not cap else chr(i + 65)\n\n\ndef matmat(A, B):\n K, N, M = len(B), len(A), len(B[0])\n return [\n [sum([(A[i][k] * B[k][j]) for k in range(K)]) for j in range(M)]\n for i in range(N)\n ]\n\n\ndef matvec(M, v):\n N, size = len(v), len(M)\n return [sum([M[i][j] * v[j] for j in range(N)]) for i in range(size)]\n\n\ndef T(M):\n n, m = len(M), len(M[0])\n return [[M[j][i] for j in range(n)] for i in range(m)]\n\n\ndef binr(x):\n return bin(x)[2:]\n\n\ndef bitcount(x): \n x = x - ((x >> 1) & 0x5555555555555555)\n x = (x & 0x3333333333333333) + ((x >> 2) & 0x3333333333333333)\n x = (x + (x >> 4)) & 0x0F0F0F0F0F0F0F0F\n x += x >> 8\n x += x >> 16\n x += x >> 32\n return x & 0x7F\n\n\ndef main():\n mod = 1000000007\n \n \n R, C, K = map(int, input().split())\n \n \n base = 5000\n S = []\n for i in range(K):\n r, c, v = map(int, input().split())\n r -= 1\n c -= 1\n S.append((r * base + c, v))\n S.sort(key=itemgetter(0))\n p = 0\n dp = [[0] * 4 for _ in range(C)]\n dpi = [[0] * 4 for _ in range(C)]\n for i in range(R):\n for j in range(C):\n cv = 0\n if p < K and i * base + j == S[p][0]:\n cv = S[p][1]\n p += 1\n if j > 0:\n dpi[j] = copy(dpi[j - 1])\n else:\n dpi[0] = [0] * 4\n if i > 0:\n dpi[j][0] = max(dp[j], dpi[j][0])\n if cv != 0:\n for k in range(2, -1, -1):\n dpi[j][k + 1] = max(dpi[j][k] + cv, dpi[j][k + 1])\n \n dp[j] = max(dpi[j])\n print(dp[-1])\n\n\nif __name__ == \"__main__\":\n main()\n", "expected": "A"} +{"pair_id": "python_17834_forward", "problem_id": "p02586", "language": "python", "direction": "forward", "speedup": 3.0979, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n \nfrom collections import deque, defaultdict\n\n \n \nfrom collections import Counter \n\n \nfrom itertools import accumulate, combinations, permutations, product \n\n \nfrom heapq import heapify, heappop, heappush\n\n \nfrom functools import reduce, lru_cache \n\n \nfrom decimal import Decimal\n\n\ndef func_6():\n var_29 = sys.stdin.readline()\n return var_29[:-1] if var_29[-1] == \"\n\" else var_29\n\n\ndef func_11(*var_29):\n print(\"## \", *var_29, file=sys.stderr)\n\n\ndef func_12(var_19):\n _ = print(*var_19, sep=\"\n\") if var_19 else None\n\n\ndef func_3(var_26, var_25=False):\n var_16 = sorted(range(len(var_26)), key=lambda var_18: var_26[var_18])\n if var_25:\n return var_16, [var_26[var_15] for var_15 in var_16]\n return var_16\n\n\ndef func_2(var_10, var_11=False):\n return ord(var_10) - 97 if not var_11 else ord(var_10) - 65\n\n\ndef func_10(var_15, var_11=False):\n return chr(var_15 + 97) if not var_11 else chr(var_15 + 65)\n\n\ndef func_8(var_1, var_2):\n var_4, var_6, var_5 = len(var_2), len(var_1), len(var_2[0])\n return [\n [sum([(var_1[var_15][var_18] * var_2[var_18][var_17]) for var_18 in range(var_4)]) for var_17 in range(var_5)]\n for var_15 in range(var_6)\n ]\n\n\ndef func_9(var_5, var_28):\n var_6, var_27 = len(var_28), len(var_5)\n return [sum([var_5[var_15][var_17] * var_28[var_17] for var_17 in range(var_6)]) for var_15 in range(var_27)]\n\n\ndef func_1(var_5):\n var_22, var_20 = len(var_5), len(var_5[0])\n return [[var_5[var_17][var_15] for var_17 in range(var_22)] for var_15 in range(var_20)]\n\n\ndef func_4(var_29):\n return bin(var_29)[2:]\n\n\ndef func_5(var_29): \n var_29 = var_29 - ((var_29 >> 1) & 0x5555555555555555)\n var_29 = (var_29 & 0x3333333333333333) + ((var_29 >> 2) & 0x3333333333333333)\n var_29 = (var_29 + (var_29 >> 4)) & 0x0F0F0F0F0F0F0F0F\n var_29 += var_29 >> 8\n var_29 += var_29 >> 16\n var_29 += var_29 >> 32\n return var_29 & 0x7F\n\n\ndef func_7():\n var_21 = 1000000007\n \n \n var_7, var_3, var_4 = map(int, func_6().split())\n \n \n var_9 = 5000\n var_8 = []\n for var_15 in range(var_4):\n var_24, var_10, var_28 = map(int, func_6().split())\n var_24 -= 1\n var_10 -= 1\n var_8.append((var_24 * var_9 + var_10, var_28))\n var_8.sort(key=itemgetter(0))\n var_23 = 0\n var_13 = [[0] * 4 for _ in range(var_3)]\n var_14 = [[0] * 4 for _ in range(var_3)]\n for var_15 in range(var_7):\n for var_17 in range(var_3):\n var_12 = 0\n if var_23 < var_4 and var_15 * var_9 + var_17 == var_8[var_23][0]:\n var_12 = var_8[var_23][1]\n var_23 += 1\n if var_17 > 0:\n var_14[var_17] = copy(var_14[var_17 - 1])\n else:\n var_14[0] = [0] * 4\n if var_15 > 0:\n var_14[var_17][0] = max(var_13[var_17], var_14[var_17][0])\n if var_12 != 0:\n for var_18 in range(2, -1, -1):\n var_14[var_17][var_18 + 1] = max(var_14[var_17][var_18] + var_12, var_14[var_17][var_18 + 1])\n \n var_13[var_17] = max(var_14[var_17])\n print(var_13[-1])\n\n\nif __name__ == \"__main__\":\n func_7()\n```\n\n## Code B:\n```python\nimport sys\n\nvar_15 = sys.stdin.read\nvar_16 = sys.stdin.readline\nvar_17 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_4 = 1000000007\n\n\ndef func_1():\n var_5, var_1, var_3, *var_6 = list(map(int, var_15().split()))\n var_11 = [[0] * var_1 for _ in range(var_5)]\n for var_14, var_7, var_18 in zip(*[iter(var_6)] * 3):\n var_11[var_14 - 1][var_7 - 1] = var_18\n var_8 = [0] * (var_1 + 1)\n for var_10 in range(var_5):\n var_8, var_9 = [0] * (var_1 + 1), var_8\n var_18 = [0] * 4\n for var_12 in range(var_1):\n for var_13 in range(4):\n if var_18[var_13] < var_9[var_12 + 1]:\n var_18[var_13] = var_9[var_12 + 1]\n if var_11[var_10][var_12] > 0:\n for var_13 in range(2, -1, -1):\n if var_18[var_13 + 1] < var_18[var_13] + var_11[var_10][var_12]:\n var_18[var_13 + 1] = var_18[var_13] + var_11[var_10][var_12]\n var_8[var_12 + 1] = var_18[3]\n print((var_8[var_1]))\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n \nfrom collections import deque, defaultdict\n\n \n \nfrom collections import Counter \n\n \nfrom itertools import accumulate, combinations, permutations, product \n\n \nfrom heapq import heapify, heappop, heappush\n\n \nfrom functools import reduce, lru_cache \n\n \nfrom decimal import Decimal\n\n\ndef func_6():\n var_29 = sys.stdin.readline()\n return var_29[:-1] if var_29[-1] == \"\\n\" else var_29\n\n\ndef func_11(*var_29):\n print(\"## \", *var_29, file=sys.stderr)\n\n\ndef func_12(var_19):\n _ = print(*var_19, sep=\"\\n\") if var_19 else None\n\n\ndef func_3(var_26, var_25=False):\n var_16 = sorted(range(len(var_26)), key=lambda var_18: var_26[var_18])\n if var_25:\n return var_16, [var_26[var_15] for var_15 in var_16]\n return var_16\n\n\ndef func_2(var_10, var_11=False):\n return ord(var_10) - 97 if not var_11 else ord(var_10) - 65\n\n\ndef func_10(var_15, var_11=False):\n return chr(var_15 + 97) if not var_11 else chr(var_15 + 65)\n\n\ndef func_8(var_1, var_2):\n var_4, var_6, var_5 = len(var_2), len(var_1), len(var_2[0])\n return [\n [sum([(var_1[var_15][var_18] * var_2[var_18][var_17]) for var_18 in range(var_4)]) for var_17 in range(var_5)]\n for var_15 in range(var_6)\n ]\n\n\ndef func_9(var_5, var_28):\n var_6, var_27 = len(var_28), len(var_5)\n return [sum([var_5[var_15][var_17] * var_28[var_17] for var_17 in range(var_6)]) for var_15 in range(var_27)]\n\n\ndef func_1(var_5):\n var_22, var_20 = len(var_5), len(var_5[0])\n return [[var_5[var_17][var_15] for var_17 in range(var_22)] for var_15 in range(var_20)]\n\n\ndef func_4(var_29):\n return bin(var_29)[2:]\n\n\ndef func_5(var_29): \n var_29 = var_29 - ((var_29 >> 1) & 0x5555555555555555)\n var_29 = (var_29 & 0x3333333333333333) + ((var_29 >> 2) & 0x3333333333333333)\n var_29 = (var_29 + (var_29 >> 4)) & 0x0F0F0F0F0F0F0F0F\n var_29 += var_29 >> 8\n var_29 += var_29 >> 16\n var_29 += var_29 >> 32\n return var_29 & 0x7F\n\n\ndef func_7():\n var_21 = 1000000007\n \n \n var_7, var_3, var_4 = map(int, func_6().split())\n \n \n var_9 = 5000\n var_8 = []\n for var_15 in range(var_4):\n var_24, var_10, var_28 = map(int, func_6().split())\n var_24 -= 1\n var_10 -= 1\n var_8.append((var_24 * var_9 + var_10, var_28))\n var_8.sort(key=itemgetter(0))\n var_23 = 0\n var_13 = [[0] * 4 for _ in range(var_3)]\n var_14 = [[0] * 4 for _ in range(var_3)]\n for var_15 in range(var_7):\n for var_17 in range(var_3):\n var_12 = 0\n if var_23 < var_4 and var_15 * var_9 + var_17 == var_8[var_23][0]:\n var_12 = var_8[var_23][1]\n var_23 += 1\n if var_17 > 0:\n var_14[var_17] = copy(var_14[var_17 - 1])\n else:\n var_14[0] = [0] * 4\n if var_15 > 0:\n var_14[var_17][0] = max(var_13[var_17], var_14[var_17][0])\n if var_12 != 0:\n for var_18 in range(2, -1, -1):\n var_14[var_17][var_18 + 1] = max(var_14[var_17][var_18] + var_12, var_14[var_17][var_18 + 1])\n \n var_13[var_17] = max(var_14[var_17])\n print(var_13[-1])\n\n\nif __name__ == \"__main__\":\n func_7()\n", "code_b": "import sys\n\nvar_15 = sys.stdin.read\nvar_16 = sys.stdin.readline\nvar_17 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_4 = 1000000007\n\n\ndef func_1():\n var_5, var_1, var_3, *var_6 = list(map(int, var_15().split()))\n var_11 = [[0] * var_1 for _ in range(var_5)]\n for var_14, var_7, var_18 in zip(*[iter(var_6)] * 3):\n var_11[var_14 - 1][var_7 - 1] = var_18\n var_8 = [0] * (var_1 + 1)\n for var_10 in range(var_5):\n var_8, var_9 = [0] * (var_1 + 1), var_8\n var_18 = [0] * 4\n for var_12 in range(var_1):\n for var_13 in range(4):\n if var_18[var_13] < var_9[var_12 + 1]:\n var_18[var_13] = var_9[var_12 + 1]\n if var_11[var_10][var_12] > 0:\n for var_13 in range(2, -1, -1):\n if var_18[var_13 + 1] < var_18[var_13] + var_11[var_10][var_12]:\n var_18[var_13 + 1] = var_18[var_13] + var_11[var_10][var_12]\n var_8[var_12 + 1] = var_18[3]\n print((var_8[var_1]))\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "B"} +{"pair_id": "python_17834_reversed", "problem_id": "p02586", "language": "python", "direction": "reversed", "speedup": 3.0979, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_15 = sys.stdin.read\nvar_16 = sys.stdin.readline\nvar_17 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_4 = 1000000007\n\n\ndef func_1():\n var_5, var_1, var_3, *var_6 = list(map(int, var_15().split()))\n var_11 = [[0] * var_1 for _ in range(var_5)]\n for var_14, var_7, var_18 in zip(*[iter(var_6)] * 3):\n var_11[var_14 - 1][var_7 - 1] = var_18\n var_8 = [0] * (var_1 + 1)\n for var_10 in range(var_5):\n var_8, var_9 = [0] * (var_1 + 1), var_8\n var_18 = [0] * 4\n for var_12 in range(var_1):\n for var_13 in range(4):\n if var_18[var_13] < var_9[var_12 + 1]:\n var_18[var_13] = var_9[var_12 + 1]\n if var_11[var_10][var_12] > 0:\n for var_13 in range(2, -1, -1):\n if var_18[var_13 + 1] < var_18[var_13] + var_11[var_10][var_12]:\n var_18[var_13 + 1] = var_18[var_13] + var_11[var_10][var_12]\n var_8[var_12 + 1] = var_18[3]\n print((var_8[var_1]))\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n \nfrom collections import deque, defaultdict\n\n \n \nfrom collections import Counter \n\n \nfrom itertools import accumulate, combinations, permutations, product \n\n \nfrom heapq import heapify, heappop, heappush\n\n \nfrom functools import reduce, lru_cache \n\n \nfrom decimal import Decimal\n\n\ndef func_6():\n var_29 = sys.stdin.readline()\n return var_29[:-1] if var_29[-1] == \"\n\" else var_29\n\n\ndef func_11(*var_29):\n print(\"## \", *var_29, file=sys.stderr)\n\n\ndef func_12(var_19):\n _ = print(*var_19, sep=\"\n\") if var_19 else None\n\n\ndef func_3(var_26, var_25=False):\n var_16 = sorted(range(len(var_26)), key=lambda var_18: var_26[var_18])\n if var_25:\n return var_16, [var_26[var_15] for var_15 in var_16]\n return var_16\n\n\ndef func_2(var_10, var_11=False):\n return ord(var_10) - 97 if not var_11 else ord(var_10) - 65\n\n\ndef func_10(var_15, var_11=False):\n return chr(var_15 + 97) if not var_11 else chr(var_15 + 65)\n\n\ndef func_8(var_1, var_2):\n var_4, var_6, var_5 = len(var_2), len(var_1), len(var_2[0])\n return [\n [sum([(var_1[var_15][var_18] * var_2[var_18][var_17]) for var_18 in range(var_4)]) for var_17 in range(var_5)]\n for var_15 in range(var_6)\n ]\n\n\ndef func_9(var_5, var_28):\n var_6, var_27 = len(var_28), len(var_5)\n return [sum([var_5[var_15][var_17] * var_28[var_17] for var_17 in range(var_6)]) for var_15 in range(var_27)]\n\n\ndef func_1(var_5):\n var_22, var_20 = len(var_5), len(var_5[0])\n return [[var_5[var_17][var_15] for var_17 in range(var_22)] for var_15 in range(var_20)]\n\n\ndef func_4(var_29):\n return bin(var_29)[2:]\n\n\ndef func_5(var_29): \n var_29 = var_29 - ((var_29 >> 1) & 0x5555555555555555)\n var_29 = (var_29 & 0x3333333333333333) + ((var_29 >> 2) & 0x3333333333333333)\n var_29 = (var_29 + (var_29 >> 4)) & 0x0F0F0F0F0F0F0F0F\n var_29 += var_29 >> 8\n var_29 += var_29 >> 16\n var_29 += var_29 >> 32\n return var_29 & 0x7F\n\n\ndef func_7():\n var_21 = 1000000007\n \n \n var_7, var_3, var_4 = map(int, func_6().split())\n \n \n var_9 = 5000\n var_8 = []\n for var_15 in range(var_4):\n var_24, var_10, var_28 = map(int, func_6().split())\n var_24 -= 1\n var_10 -= 1\n var_8.append((var_24 * var_9 + var_10, var_28))\n var_8.sort(key=itemgetter(0))\n var_23 = 0\n var_13 = [[0] * 4 for _ in range(var_3)]\n var_14 = [[0] * 4 for _ in range(var_3)]\n for var_15 in range(var_7):\n for var_17 in range(var_3):\n var_12 = 0\n if var_23 < var_4 and var_15 * var_9 + var_17 == var_8[var_23][0]:\n var_12 = var_8[var_23][1]\n var_23 += 1\n if var_17 > 0:\n var_14[var_17] = copy(var_14[var_17 - 1])\n else:\n var_14[0] = [0] * 4\n if var_15 > 0:\n var_14[var_17][0] = max(var_13[var_17], var_14[var_17][0])\n if var_12 != 0:\n for var_18 in range(2, -1, -1):\n var_14[var_17][var_18 + 1] = max(var_14[var_17][var_18] + var_12, var_14[var_17][var_18 + 1])\n \n var_13[var_17] = max(var_14[var_17])\n print(var_13[-1])\n\n\nif __name__ == \"__main__\":\n func_7()\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_15 = sys.stdin.read\nvar_16 = sys.stdin.readline\nvar_17 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_4 = 1000000007\n\n\ndef func_1():\n var_5, var_1, var_3, *var_6 = list(map(int, var_15().split()))\n var_11 = [[0] * var_1 for _ in range(var_5)]\n for var_14, var_7, var_18 in zip(*[iter(var_6)] * 3):\n var_11[var_14 - 1][var_7 - 1] = var_18\n var_8 = [0] * (var_1 + 1)\n for var_10 in range(var_5):\n var_8, var_9 = [0] * (var_1 + 1), var_8\n var_18 = [0] * 4\n for var_12 in range(var_1):\n for var_13 in range(4):\n if var_18[var_13] < var_9[var_12 + 1]:\n var_18[var_13] = var_9[var_12 + 1]\n if var_11[var_10][var_12] > 0:\n for var_13 in range(2, -1, -1):\n if var_18[var_13 + 1] < var_18[var_13] + var_11[var_10][var_12]:\n var_18[var_13 + 1] = var_18[var_13] + var_11[var_10][var_12]\n var_8[var_12 + 1] = var_18[3]\n print((var_8[var_1]))\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "import sys\n\nsys.setrecursionlimit(10**7) \nimport math\nfrom copy import copy, deepcopy\nfrom copy import deepcopy as dcp\nfrom operator import itemgetter\nfrom bisect import bisect_left, bisect, bisect_right \n\n \nfrom collections import deque, defaultdict\n\n \n \nfrom collections import Counter \n\n \nfrom itertools import accumulate, combinations, permutations, product \n\n \nfrom heapq import heapify, heappop, heappush\n\n \nfrom functools import reduce, lru_cache \n\n \nfrom decimal import Decimal\n\n\ndef func_6():\n var_29 = sys.stdin.readline()\n return var_29[:-1] if var_29[-1] == \"\\n\" else var_29\n\n\ndef func_11(*var_29):\n print(\"## \", *var_29, file=sys.stderr)\n\n\ndef func_12(var_19):\n _ = print(*var_19, sep=\"\\n\") if var_19 else None\n\n\ndef func_3(var_26, var_25=False):\n var_16 = sorted(range(len(var_26)), key=lambda var_18: var_26[var_18])\n if var_25:\n return var_16, [var_26[var_15] for var_15 in var_16]\n return var_16\n\n\ndef func_2(var_10, var_11=False):\n return ord(var_10) - 97 if not var_11 else ord(var_10) - 65\n\n\ndef func_10(var_15, var_11=False):\n return chr(var_15 + 97) if not var_11 else chr(var_15 + 65)\n\n\ndef func_8(var_1, var_2):\n var_4, var_6, var_5 = len(var_2), len(var_1), len(var_2[0])\n return [\n [sum([(var_1[var_15][var_18] * var_2[var_18][var_17]) for var_18 in range(var_4)]) for var_17 in range(var_5)]\n for var_15 in range(var_6)\n ]\n\n\ndef func_9(var_5, var_28):\n var_6, var_27 = len(var_28), len(var_5)\n return [sum([var_5[var_15][var_17] * var_28[var_17] for var_17 in range(var_6)]) for var_15 in range(var_27)]\n\n\ndef func_1(var_5):\n var_22, var_20 = len(var_5), len(var_5[0])\n return [[var_5[var_17][var_15] for var_17 in range(var_22)] for var_15 in range(var_20)]\n\n\ndef func_4(var_29):\n return bin(var_29)[2:]\n\n\ndef func_5(var_29): \n var_29 = var_29 - ((var_29 >> 1) & 0x5555555555555555)\n var_29 = (var_29 & 0x3333333333333333) + ((var_29 >> 2) & 0x3333333333333333)\n var_29 = (var_29 + (var_29 >> 4)) & 0x0F0F0F0F0F0F0F0F\n var_29 += var_29 >> 8\n var_29 += var_29 >> 16\n var_29 += var_29 >> 32\n return var_29 & 0x7F\n\n\ndef func_7():\n var_21 = 1000000007\n \n \n var_7, var_3, var_4 = map(int, func_6().split())\n \n \n var_9 = 5000\n var_8 = []\n for var_15 in range(var_4):\n var_24, var_10, var_28 = map(int, func_6().split())\n var_24 -= 1\n var_10 -= 1\n var_8.append((var_24 * var_9 + var_10, var_28))\n var_8.sort(key=itemgetter(0))\n var_23 = 0\n var_13 = [[0] * 4 for _ in range(var_3)]\n var_14 = [[0] * 4 for _ in range(var_3)]\n for var_15 in range(var_7):\n for var_17 in range(var_3):\n var_12 = 0\n if var_23 < var_4 and var_15 * var_9 + var_17 == var_8[var_23][0]:\n var_12 = var_8[var_23][1]\n var_23 += 1\n if var_17 > 0:\n var_14[var_17] = copy(var_14[var_17 - 1])\n else:\n var_14[0] = [0] * 4\n if var_15 > 0:\n var_14[var_17][0] = max(var_13[var_17], var_14[var_17][0])\n if var_12 != 0:\n for var_18 in range(2, -1, -1):\n var_14[var_17][var_18 + 1] = max(var_14[var_17][var_18] + var_12, var_14[var_17][var_18 + 1])\n \n var_13[var_17] = max(var_14[var_17])\n print(var_13[-1])\n\n\nif __name__ == \"__main__\":\n func_7()\n", "expected": "A"} {"pair_id": "python_11472_forward", "problem_id": "p03504", "language": "python", "direction": "forward", "speedup": 4.1084, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_8, var_1 = list(map(int, input().split()))\nvar_2 = [tuple(map(int, input().split())) for _ in range(var_8)]\nvar_7 = 2 * 10**5 + 2\nvar_3 = [0] * var_7\nfor var_11 in range(1, var_1 + 1):\n var_4 = [0] * var_7\n for var_9, var_10, var_5 in var_2:\n if var_5 == var_11:\n\n var_4[2 * var_9 - 1] += 1\n var_4[2 * var_10] -= 1\n for var_6 in range(1, var_7):\n var_4[var_6] += var_4[var_6 - 1]\n for var_6 in range(var_7):\n if var_4[var_6] > 0:\n var_3[var_6] += 1\nprint((max(var_3)))\n\n```\n\n## Code B:\n```python\nimport sys\nimport math\nimport collections\nimport bisect\nimport itertools\n\nsys.setrecursionlimit(10**7)\nvar_1 = 10**16\nvar_2 = 10**9 + 7\n\nvar_13 = lambda: int(sys.stdin.readline().rstrip())\nvar_14 = lambda: list(map(int, sys.stdin.readline().rstrip().split()))\nvar_11 = lambda: list(map(int, sys.stdin.readline().rstrip().split()))\nvar_12 = lambda: list(\n [int(var_19) - 1 for var_19 in sys.stdin.readline().rstrip().split()]\n)\n\n\ndef func_1():\n var_10, var_4 = var_14()\n var_9 = 10**5\n var_17 = [[0 for _ in range(var_9 + 10)] for __ in range(var_4)]\n for _ in range(var_10):\n var_15, var_16, var_5 = var_14()\n var_5 -= 1\n var_17[var_5][var_15] += 1\n var_17[var_5][var_16] -= 1\n var_3 = 0\n var_18 = 0\n for var_7 in range(var_9 + 10):\n var_6 = 0\n for var_8 in range(var_4):\n if var_17[var_8][var_7] > 0:\n var_18 += var_17[var_8][var_7]\n else:\n var_6 += var_17[var_8][var_7]\n var_3 = max(var_3, var_18)\n var_18 += var_6\n print(var_3)\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_8, var_1 = list(map(int, input().split()))\nvar_2 = [tuple(map(int, input().split())) for _ in range(var_8)]\nvar_7 = 2 * 10**5 + 2\nvar_3 = [0] * var_7\nfor var_11 in range(1, var_1 + 1):\n var_4 = [0] * var_7\n for var_9, var_10, var_5 in var_2:\n if var_5 == var_11:\n\n var_4[2 * var_9 - 1] += 1\n var_4[2 * var_10] -= 1\n for var_6 in range(1, var_7):\n var_4[var_6] += var_4[var_6 - 1]\n for var_6 in range(var_7):\n if var_4[var_6] > 0:\n var_3[var_6] += 1\nprint((max(var_3)))\n", "code_b": "import sys\nimport math\nimport collections\nimport bisect\nimport itertools\n\nsys.setrecursionlimit(10**7)\nvar_1 = 10**16\nvar_2 = 10**9 + 7\n\nvar_13 = lambda: int(sys.stdin.readline().rstrip())\nvar_14 = lambda: list(map(int, sys.stdin.readline().rstrip().split()))\nvar_11 = lambda: list(map(int, sys.stdin.readline().rstrip().split()))\nvar_12 = lambda: list(\n [int(var_19) - 1 for var_19 in sys.stdin.readline().rstrip().split()]\n)\n\n\ndef func_1():\n var_10, var_4 = var_14()\n var_9 = 10**5\n var_17 = [[0 for _ in range(var_9 + 10)] for __ in range(var_4)]\n for _ in range(var_10):\n var_15, var_16, var_5 = var_14()\n var_5 -= 1\n var_17[var_5][var_15] += 1\n var_17[var_5][var_16] -= 1\n var_3 = 0\n var_18 = 0\n for var_7 in range(var_9 + 10):\n var_6 = 0\n for var_8 in range(var_4):\n if var_17[var_8][var_7] > 0:\n var_18 += var_17[var_8][var_7]\n else:\n var_6 += var_17[var_8][var_7]\n var_3 = max(var_3, var_18)\n var_18 += var_6\n print(var_3)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "B"} {"pair_id": "python_11472_reversed", "problem_id": "p03504", "language": "python", "direction": "reversed", "speedup": 4.1084, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\nimport math\nimport collections\nimport bisect\nimport itertools\n\nsys.setrecursionlimit(10**7)\nvar_1 = 10**16\nvar_2 = 10**9 + 7\n\nvar_13 = lambda: int(sys.stdin.readline().rstrip())\nvar_14 = lambda: list(map(int, sys.stdin.readline().rstrip().split()))\nvar_11 = lambda: list(map(int, sys.stdin.readline().rstrip().split()))\nvar_12 = lambda: list(\n [int(var_19) - 1 for var_19 in sys.stdin.readline().rstrip().split()]\n)\n\n\ndef func_1():\n var_10, var_4 = var_14()\n var_9 = 10**5\n var_17 = [[0 for _ in range(var_9 + 10)] for __ in range(var_4)]\n for _ in range(var_10):\n var_15, var_16, var_5 = var_14()\n var_5 -= 1\n var_17[var_5][var_15] += 1\n var_17[var_5][var_16] -= 1\n var_3 = 0\n var_18 = 0\n for var_7 in range(var_9 + 10):\n var_6 = 0\n for var_8 in range(var_4):\n if var_17[var_8][var_7] > 0:\n var_18 += var_17[var_8][var_7]\n else:\n var_6 += var_17[var_8][var_7]\n var_3 = max(var_3, var_18)\n var_18 += var_6\n print(var_3)\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\n## Code B:\n```python\nvar_8, var_1 = list(map(int, input().split()))\nvar_2 = [tuple(map(int, input().split())) for _ in range(var_8)]\nvar_7 = 2 * 10**5 + 2\nvar_3 = [0] * var_7\nfor var_11 in range(1, var_1 + 1):\n var_4 = [0] * var_7\n for var_9, var_10, var_5 in var_2:\n if var_5 == var_11:\n\n var_4[2 * var_9 - 1] += 1\n var_4[2 * var_10] -= 1\n for var_6 in range(1, var_7):\n var_4[var_6] += var_4[var_6 - 1]\n for var_6 in range(var_7):\n if var_4[var_6] > 0:\n var_3[var_6] += 1\nprint((max(var_3)))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\nimport math\nimport collections\nimport bisect\nimport itertools\n\nsys.setrecursionlimit(10**7)\nvar_1 = 10**16\nvar_2 = 10**9 + 7\n\nvar_13 = lambda: int(sys.stdin.readline().rstrip())\nvar_14 = lambda: list(map(int, sys.stdin.readline().rstrip().split()))\nvar_11 = lambda: list(map(int, sys.stdin.readline().rstrip().split()))\nvar_12 = lambda: list(\n [int(var_19) - 1 for var_19 in sys.stdin.readline().rstrip().split()]\n)\n\n\ndef func_1():\n var_10, var_4 = var_14()\n var_9 = 10**5\n var_17 = [[0 for _ in range(var_9 + 10)] for __ in range(var_4)]\n for _ in range(var_10):\n var_15, var_16, var_5 = var_14()\n var_5 -= 1\n var_17[var_5][var_15] += 1\n var_17[var_5][var_16] -= 1\n var_3 = 0\n var_18 = 0\n for var_7 in range(var_9 + 10):\n var_6 = 0\n for var_8 in range(var_4):\n if var_17[var_8][var_7] > 0:\n var_18 += var_17[var_8][var_7]\n else:\n var_6 += var_17[var_8][var_7]\n var_3 = max(var_3, var_18)\n var_18 += var_6\n print(var_3)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "var_8, var_1 = list(map(int, input().split()))\nvar_2 = [tuple(map(int, input().split())) for _ in range(var_8)]\nvar_7 = 2 * 10**5 + 2\nvar_3 = [0] * var_7\nfor var_11 in range(1, var_1 + 1):\n var_4 = [0] * var_7\n for var_9, var_10, var_5 in var_2:\n if var_5 == var_11:\n\n var_4[2 * var_9 - 1] += 1\n var_4[2 * var_10] -= 1\n for var_6 in range(1, var_7):\n var_4[var_6] += var_4[var_6 - 1]\n for var_6 in range(var_7):\n if var_4[var_6] > 0:\n var_3[var_6] += 1\nprint((max(var_3)))\n", "expected": "A"} {"pair_id": "python_9946_forward", "problem_id": "p03806", "language": "python", "direction": "forward", "speedup": 271.5731, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_4, var_2, var_3 = list(map(int, input().split()))\nvar_1 = 10000\nvar_11 = [[var_1 for var_8 in range(401)] for var_5 in range(401)]\nvar_11[0][0] = 0\nfor var_12 in range(1, var_4 + 1):\n var_6, var_9, var_10 = list(map(int, input().split()))\n for var_5 in range(400, -1, -1):\n for var_8 in range(400, -1, -1):\n if var_5 - var_6 >= 0 and var_8 - var_9 >= 0:\n var_11[var_5][var_8] = min(\n var_11[var_5][var_8], var_11[var_5 - var_6][var_8 - var_9] + var_10\n )\nvar_7 = var_1\nfor var_13 in range(1, 401):\n if var_2 * var_13 > 400 or var_3 * var_13 > 400:\n break\n var_7 = min(var_7, var_11[var_2 * var_13][var_3 * var_13])\nprint((var_7 if var_7 != var_1 else -1))\n\n```\n\n## Code B:\n```python\nimport sys\nfrom collections import deque\nimport copy\n\n\ndef func_3(var_15):\n if var_15 == None:\n return var_22\n else:\n return var_15.readline\n\n\ndef func_1(var_6, var_3, var_4):\n\n var_13 = {}\n var_13[(0, 0)] = 0\n var_13[(var_6[0][0], var_6[0][1])] = var_6[0][2]\n\n var_19 = sys.maxsize\n var_21 = copy.deepcopy(var_13)\n for var_16 in range(1, len(var_6)):\n for var_12 in var_13:\n var_10 = var_13[var_12]\n var_20 = (var_12[0] + var_6[var_16][0], var_12[1] + var_6[var_16][1])\n if var_20 not in var_13:\n var_21[var_20] = var_10 + var_6[var_16][2]\n else:\n var_21[var_20] = min(var_13[var_20], var_10 + var_6[var_16][2])\n if var_20[1] * var_3 == var_20[0] * var_4:\n if var_21[var_20] < var_19:\n var_19 = var_21[var_20]\n var_13 = copy.deepcopy(var_21)\n return var_19\n\n\ndef func_2(var_6, var_3, var_4, var_1, var_2):\n var_11 = [\n [sys.maxsize for var_16 in range(var_1 + 1)] for var_18 in range(var_2 + 1)\n ]\n var_11[0][0] = 0\n for var_16 in range(len(var_6)):\n for var_7 in range(var_1, -1, -1):\n for var_8 in range(var_2, -1, -1):\n if var_11[var_7][var_8] >= sys.maxsize:\n continue\n if (\n var_7 + var_6[var_16][0] <= var_1\n and var_8 + var_6[var_16][1] <= var_2\n ):\n var_11[var_7 + var_6[var_16][0]][var_8 + var_6[var_16][1]] = min(\n var_11[var_7 + var_6[var_16][0]][var_8 + var_6[var_16][1]],\n var_11[var_7][var_8] + var_6[var_16][2],\n )\n var_19 = sys.maxsize\n for var_7 in range(0, var_1):\n for var_8 in range(0, var_2):\n if var_7 * var_4 == var_8 * var_3 and var_7 != 0 and var_8 != 0:\n if var_11[var_7][var_8] < var_19:\n var_19 = var_11[var_7][var_8]\n return var_19\n\n\ndef func_4():\n if len(sys.argv) > 1:\n var_14 = open(sys.argv[1])\n else:\n var_14 = None\n var_23 = func_3(var_14)\n var_17 = var_23().strip().split()\n [var_5, var_3, var_4] = [int(var_17[0]), int(var_17[1]), int(var_17[2])]\n var_6 = []\n for var_16 in range(var_5):\n var_17 = var_23().strip().split()\n [var_7, var_8, var_9] = [int(var_17[0]), int(var_17[1]), int(var_17[2])]\n var_6.append((var_7, var_8, var_9))\n\n var_10 = func_2(var_6, var_3, var_4, var_5 * 10, var_5 * 10)\n if var_10 < sys.maxsize:\n print(var_10)\n else:\n print(-1)\n\n\nif __name__ == \"__main__\":\n func_4()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_4, var_2, var_3 = list(map(int, input().split()))\nvar_1 = 10000\nvar_11 = [[var_1 for var_8 in range(401)] for var_5 in range(401)]\nvar_11[0][0] = 0\nfor var_12 in range(1, var_4 + 1):\n var_6, var_9, var_10 = list(map(int, input().split()))\n for var_5 in range(400, -1, -1):\n for var_8 in range(400, -1, -1):\n if var_5 - var_6 >= 0 and var_8 - var_9 >= 0:\n var_11[var_5][var_8] = min(\n var_11[var_5][var_8], var_11[var_5 - var_6][var_8 - var_9] + var_10\n )\nvar_7 = var_1\nfor var_13 in range(1, 401):\n if var_2 * var_13 > 400 or var_3 * var_13 > 400:\n break\n var_7 = min(var_7, var_11[var_2 * var_13][var_3 * var_13])\nprint((var_7 if var_7 != var_1 else -1))\n", "code_b": "import sys\nfrom collections import deque\nimport copy\n\n\ndef func_3(var_15):\n if var_15 == None:\n return var_22\n else:\n return var_15.readline\n\n\ndef func_1(var_6, var_3, var_4):\n\n var_13 = {}\n var_13[(0, 0)] = 0\n var_13[(var_6[0][0], var_6[0][1])] = var_6[0][2]\n\n var_19 = sys.maxsize\n var_21 = copy.deepcopy(var_13)\n for var_16 in range(1, len(var_6)):\n for var_12 in var_13:\n var_10 = var_13[var_12]\n var_20 = (var_12[0] + var_6[var_16][0], var_12[1] + var_6[var_16][1])\n if var_20 not in var_13:\n var_21[var_20] = var_10 + var_6[var_16][2]\n else:\n var_21[var_20] = min(var_13[var_20], var_10 + var_6[var_16][2])\n if var_20[1] * var_3 == var_20[0] * var_4:\n if var_21[var_20] < var_19:\n var_19 = var_21[var_20]\n var_13 = copy.deepcopy(var_21)\n return var_19\n\n\ndef func_2(var_6, var_3, var_4, var_1, var_2):\n var_11 = [\n [sys.maxsize for var_16 in range(var_1 + 1)] for var_18 in range(var_2 + 1)\n ]\n var_11[0][0] = 0\n for var_16 in range(len(var_6)):\n for var_7 in range(var_1, -1, -1):\n for var_8 in range(var_2, -1, -1):\n if var_11[var_7][var_8] >= sys.maxsize:\n continue\n if (\n var_7 + var_6[var_16][0] <= var_1\n and var_8 + var_6[var_16][1] <= var_2\n ):\n var_11[var_7 + var_6[var_16][0]][var_8 + var_6[var_16][1]] = min(\n var_11[var_7 + var_6[var_16][0]][var_8 + var_6[var_16][1]],\n var_11[var_7][var_8] + var_6[var_16][2],\n )\n var_19 = sys.maxsize\n for var_7 in range(0, var_1):\n for var_8 in range(0, var_2):\n if var_7 * var_4 == var_8 * var_3 and var_7 != 0 and var_8 != 0:\n if var_11[var_7][var_8] < var_19:\n var_19 = var_11[var_7][var_8]\n return var_19\n\n\ndef func_4():\n if len(sys.argv) > 1:\n var_14 = open(sys.argv[1])\n else:\n var_14 = None\n var_23 = func_3(var_14)\n var_17 = var_23().strip().split()\n [var_5, var_3, var_4] = [int(var_17[0]), int(var_17[1]), int(var_17[2])]\n var_6 = []\n for var_16 in range(var_5):\n var_17 = var_23().strip().split()\n [var_7, var_8, var_9] = [int(var_17[0]), int(var_17[1]), int(var_17[2])]\n var_6.append((var_7, var_8, var_9))\n\n var_10 = func_2(var_6, var_3, var_4, var_5 * 10, var_5 * 10)\n if var_10 < sys.maxsize:\n print(var_10)\n else:\n print(-1)\n\n\nif __name__ == \"__main__\":\n func_4()\n", "expected": "B"} @@ -1022,8 +1022,8 @@ {"pair_id": "python_404_reversed", "problem_id": "p03160", "language": "python", "direction": "reversed", "speedup": 3.1569, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_4 = int(eval(input()))\nvar_2 = list(map(int, input().split()))\nvar_1 = [float(\"inf\")] * var_4\nvar_1[0] = 0\nvar_1[1] = var_1[0] + abs(var_2[1] - var_2[0])\nfor var_3 in range(2, var_4):\n var_1[var_3] = min(\n var_1[var_3 - 1] + abs(var_2[var_3] - var_2[var_3 - 1]),\n var_1[var_3 - 2] + abs(var_2[var_3] - var_2[var_3 - 2]),\n )\nprint((var_1[var_4 - 1]))\n\n```\n\n## Code B:\n```python\ndef func_2(var_3, var_4):\n return var_4 if var_3 > var_4 else var_3\n\n\ndef func_1(var_3, var_4):\n return var_4 if var_3 < var_4 else var_3\n\n\ndef func_3(var_2, var_6):\n var_1 = 10**9\n var_5 = [var_1] * (10**5 + 10)\n var_5[0] = 0\n var_5[1] = abs(var_6[1] - var_6[0])\n for var_7 in range(2, var_2):\n var_5[var_7] = func_2(\n var_5[var_7], var_5[var_7 - 1] + abs(var_6[var_7] - var_6[var_7 - 1])\n )\n var_5[var_7] = func_2(\n var_5[var_7], var_5[var_7 - 2] + abs(var_6[var_7] - var_6[var_7 - 2])\n )\n return var_5[var_2 - 1]\n\n\nif __name__ == \"__main__\":\n var_2 = int(eval(input()))\n var_6 = list(map(int, input().split()))\n print((func_3(var_2, var_6)))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_4 = int(eval(input()))\nvar_2 = list(map(int, input().split()))\nvar_1 = [float(\"inf\")] * var_4\nvar_1[0] = 0\nvar_1[1] = var_1[0] + abs(var_2[1] - var_2[0])\nfor var_3 in range(2, var_4):\n var_1[var_3] = min(\n var_1[var_3 - 1] + abs(var_2[var_3] - var_2[var_3 - 1]),\n var_1[var_3 - 2] + abs(var_2[var_3] - var_2[var_3 - 2]),\n )\nprint((var_1[var_4 - 1]))\n", "code_b": "def func_2(var_3, var_4):\n return var_4 if var_3 > var_4 else var_3\n\n\ndef func_1(var_3, var_4):\n return var_4 if var_3 < var_4 else var_3\n\n\ndef func_3(var_2, var_6):\n var_1 = 10**9\n var_5 = [var_1] * (10**5 + 10)\n var_5[0] = 0\n var_5[1] = abs(var_6[1] - var_6[0])\n for var_7 in range(2, var_2):\n var_5[var_7] = func_2(\n var_5[var_7], var_5[var_7 - 1] + abs(var_6[var_7] - var_6[var_7 - 1])\n )\n var_5[var_7] = func_2(\n var_5[var_7], var_5[var_7 - 2] + abs(var_6[var_7] - var_6[var_7 - 2])\n )\n return var_5[var_2 - 1]\n\n\nif __name__ == \"__main__\":\n var_2 = int(eval(input()))\n var_6 = list(map(int, input().split()))\n print((func_3(var_2, var_6)))\n", "expected": "A"} {"pair_id": "python_14573_forward", "problem_id": "p03647", "language": "python", "direction": "forward", "speedup": 43.9436, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_2, var_1 = list(map(int, input().split()))\nvar_5 = [False] * var_2\nvar_7 = [False] * var_2\nfor _ in range(var_1):\n var_3, var_4 = list(map(int, input().split()))\n if var_3 == 1:\n var_5[var_4 - 1] = True\n elif var_4 == var_2:\n var_7[var_3 - 1] = True\nfor var_6 in range(1, var_2):\n if var_5[var_6] and var_7[var_6]:\n print(\"POSSIBLE\")\n break\nelse:\n print(\"IMPOSSIBLE\")\n\n```\n\n## Code B:\n```python\nimport sys\n\nvar_4, var_3 = list(map(int, sys.stdin.readline().split()))\nvar_5, var_6 = set(), set()\nfor _ in range(var_3):\n var_1, var_2 = list(map(int, sys.stdin.readline().split()))\n if var_1 in [1, var_4] or var_2 in [1, var_4]:\n if var_1 == 1:\n var_5.add(var_2)\n if var_1 == var_4:\n var_6.add(var_2)\n if var_2 == 1:\n var_5.add(var_1)\n if var_2 == var_4:\n var_6.add(var_1)\nif var_5 & var_6:\n print(\"POSSIBLE\")\nelse:\n print(\"IMPOSSIBLE\")\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_2, var_1 = list(map(int, input().split()))\nvar_5 = [False] * var_2\nvar_7 = [False] * var_2\nfor _ in range(var_1):\n var_3, var_4 = list(map(int, input().split()))\n if var_3 == 1:\n var_5[var_4 - 1] = True\n elif var_4 == var_2:\n var_7[var_3 - 1] = True\nfor var_6 in range(1, var_2):\n if var_5[var_6] and var_7[var_6]:\n print(\"POSSIBLE\")\n break\nelse:\n print(\"IMPOSSIBLE\")\n", "code_b": "import sys\n\nvar_4, var_3 = list(map(int, sys.stdin.readline().split()))\nvar_5, var_6 = set(), set()\nfor _ in range(var_3):\n var_1, var_2 = list(map(int, sys.stdin.readline().split()))\n if var_1 in [1, var_4] or var_2 in [1, var_4]:\n if var_1 == 1:\n var_5.add(var_2)\n if var_1 == var_4:\n var_6.add(var_2)\n if var_2 == 1:\n var_5.add(var_1)\n if var_2 == var_4:\n var_6.add(var_1)\nif var_5 & var_6:\n print(\"POSSIBLE\")\nelse:\n print(\"IMPOSSIBLE\")\n", "expected": "B"} {"pair_id": "python_14573_reversed", "problem_id": "p03647", "language": "python", "direction": "reversed", "speedup": 43.9436, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_4, var_3 = list(map(int, sys.stdin.readline().split()))\nvar_5, var_6 = set(), set()\nfor _ in range(var_3):\n var_1, var_2 = list(map(int, sys.stdin.readline().split()))\n if var_1 in [1, var_4] or var_2 in [1, var_4]:\n if var_1 == 1:\n var_5.add(var_2)\n if var_1 == var_4:\n var_6.add(var_2)\n if var_2 == 1:\n var_5.add(var_1)\n if var_2 == var_4:\n var_6.add(var_1)\nif var_5 & var_6:\n print(\"POSSIBLE\")\nelse:\n print(\"IMPOSSIBLE\")\n\n```\n\n## Code B:\n```python\nvar_2, var_1 = list(map(int, input().split()))\nvar_5 = [False] * var_2\nvar_7 = [False] * var_2\nfor _ in range(var_1):\n var_3, var_4 = list(map(int, input().split()))\n if var_3 == 1:\n var_5[var_4 - 1] = True\n elif var_4 == var_2:\n var_7[var_3 - 1] = True\nfor var_6 in range(1, var_2):\n if var_5[var_6] and var_7[var_6]:\n print(\"POSSIBLE\")\n break\nelse:\n print(\"IMPOSSIBLE\")\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_4, var_3 = list(map(int, sys.stdin.readline().split()))\nvar_5, var_6 = set(), set()\nfor _ in range(var_3):\n var_1, var_2 = list(map(int, sys.stdin.readline().split()))\n if var_1 in [1, var_4] or var_2 in [1, var_4]:\n if var_1 == 1:\n var_5.add(var_2)\n if var_1 == var_4:\n var_6.add(var_2)\n if var_2 == 1:\n var_5.add(var_1)\n if var_2 == var_4:\n var_6.add(var_1)\nif var_5 & var_6:\n print(\"POSSIBLE\")\nelse:\n print(\"IMPOSSIBLE\")\n", "code_b": "var_2, var_1 = list(map(int, input().split()))\nvar_5 = [False] * var_2\nvar_7 = [False] * var_2\nfor _ in range(var_1):\n var_3, var_4 = list(map(int, input().split()))\n if var_3 == 1:\n var_5[var_4 - 1] = True\n elif var_4 == var_2:\n var_7[var_3 - 1] = True\nfor var_6 in range(1, var_2):\n if var_5[var_6] and var_7[var_6]:\n print(\"POSSIBLE\")\n break\nelse:\n print(\"IMPOSSIBLE\")\n", "expected": "A"} -{"pair_id": "python_7379_forward", "problem_id": "p03044", "language": "python", "direction": "forward", "speedup": 3.0324, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n\n```\n\n## Code B:\n```python\nimport sys\nfrom heapq import heappush, heappop\n\nvar_13 = sys.stdin.read\nvar_14 = sys.stdin.readline\nvar_15 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_3 = 1000000007\n\n\ndef func_1():\n var_4, *var_5 = map(int, var_13().split())\n var_1 = [[] for _ in range(var_4)]\n for var_16, var_17, var_18 in zip(*[iter(var_5)] * 3):\n var_1[var_16 - 1].append((var_17 - 1, var_18))\n var_1[var_17 - 1].append((var_16 - 1, var_18))\n var_9 = [var_2] * var_4\n var_9[0] = 0\n var_10 = [(0, 0)]\n while var_10:\n var_8, var_17 = heappop(var_10)\n if var_8 > var_9[var_17]:\n continue\n for var_12, var_7 in var_1[var_17]:\n if var_9[var_12] > var_9[var_17] + var_7:\n var_9[var_12] = var_9[var_17] + var_7\n heappush(var_10, (var_9[var_12], var_12))\n var_6 = [0] * var_4\n for var_11 in range(var_4):\n var_6[var_11] = 0 if var_9[var_11] % 2 == 0 else 1\n print(*var_6, sep=\"\\n\")\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n", "code_b": "import sys\nfrom heapq import heappush, heappop\n\nvar_13 = sys.stdin.read\nvar_14 = sys.stdin.readline\nvar_15 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_3 = 1000000007\n\n\ndef func_1():\n var_4, *var_5 = map(int, var_13().split())\n var_1 = [[] for _ in range(var_4)]\n for var_16, var_17, var_18 in zip(*[iter(var_5)] * 3):\n var_1[var_16 - 1].append((var_17 - 1, var_18))\n var_1[var_17 - 1].append((var_16 - 1, var_18))\n var_9 = [var_2] * var_4\n var_9[0] = 0\n var_10 = [(0, 0)]\n while var_10:\n var_8, var_17 = heappop(var_10)\n if var_8 > var_9[var_17]:\n continue\n for var_12, var_7 in var_1[var_17]:\n if var_9[var_12] > var_9[var_17] + var_7:\n var_9[var_12] = var_9[var_17] + var_7\n heappush(var_10, (var_9[var_12], var_12))\n var_6 = [0] * var_4\n for var_11 in range(var_4):\n var_6[var_11] = 0 if var_9[var_11] % 2 == 0 else 1\n print(*var_6, sep=\"\\n\")\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "B"} -{"pair_id": "python_7379_reversed", "problem_id": "p03044", "language": "python", "direction": "reversed", "speedup": 3.0324, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\nfrom heapq import heappush, heappop\n\nvar_13 = sys.stdin.read\nvar_14 = sys.stdin.readline\nvar_15 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_3 = 1000000007\n\n\ndef func_1():\n var_4, *var_5 = map(int, var_13().split())\n var_1 = [[] for _ in range(var_4)]\n for var_16, var_17, var_18 in zip(*[iter(var_5)] * 3):\n var_1[var_16 - 1].append((var_17 - 1, var_18))\n var_1[var_17 - 1].append((var_16 - 1, var_18))\n var_9 = [var_2] * var_4\n var_9[0] = 0\n var_10 = [(0, 0)]\n while var_10:\n var_8, var_17 = heappop(var_10)\n if var_8 > var_9[var_17]:\n continue\n for var_12, var_7 in var_1[var_17]:\n if var_9[var_12] > var_9[var_17] + var_7:\n var_9[var_12] = var_9[var_17] + var_7\n heappush(var_10, (var_9[var_12], var_12))\n var_6 = [0] * var_4\n for var_11 in range(var_4):\n var_6[var_11] = 0 if var_9[var_11] % 2 == 0 else 1\n print(*var_6, sep=\"\\n\")\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\n## Code B:\n```python\nimport sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\nfrom heapq import heappush, heappop\n\nvar_13 = sys.stdin.read\nvar_14 = sys.stdin.readline\nvar_15 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_3 = 1000000007\n\n\ndef func_1():\n var_4, *var_5 = map(int, var_13().split())\n var_1 = [[] for _ in range(var_4)]\n for var_16, var_17, var_18 in zip(*[iter(var_5)] * 3):\n var_1[var_16 - 1].append((var_17 - 1, var_18))\n var_1[var_17 - 1].append((var_16 - 1, var_18))\n var_9 = [var_2] * var_4\n var_9[0] = 0\n var_10 = [(0, 0)]\n while var_10:\n var_8, var_17 = heappop(var_10)\n if var_8 > var_9[var_17]:\n continue\n for var_12, var_7 in var_1[var_17]:\n if var_9[var_12] > var_9[var_17] + var_7:\n var_9[var_12] = var_9[var_17] + var_7\n heappush(var_10, (var_9[var_12], var_12))\n var_6 = [0] * var_4\n for var_11 in range(var_4):\n var_6[var_11] = 0 if var_9[var_11] % 2 == 0 else 1\n print(*var_6, sep=\"\\n\")\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "import sys\n\ninput = sys.stdin.readline\n\n\ndef ii():\n return int(eval(input()))\n\n\ndef mi():\n return list(map(int, input().rstrip().split()))\n\n\ndef lmi():\n return list(map(int, input().rstrip().split()))\n\n\ndef li():\n return list(input().rstrip())\n\n\n\n\nfrom queue import Queue\n\n\ndef bfs(field, sy, sx, wall, dir):\n h = len(field)\n w = len(field[0])\n dp = [[-1 for _j in range(w)] for _i in range(h)]\n q = Queue()\n dp[sy][sx] = 0\n q.put((sy, sx))\n dy = [1, -1, 0, 0, 1, 1, -1, -1]\n dx = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def isin(x, y):\n return 0 <= y and y < h and 0 <= x and x < w\n\n while not q.empty():\n y, x = q.get()\n for k in range(dir):\n ny, nx = y + dy[k], x + dx[k]\n if not isin(nx, ny) or field[ny][nx] == wall:\n continue\n elif dp[ny][nx] != -1:\n continue\n dp[ny][nx] = dp[y][x] + 1\n q.put((ny, nx))\n return dp\n\n\n\ndef ABC088_D():\n h, w = mi()\n s = [[\"\" for _j in range(w)] for _i in range(h)]\n for i in range(h):\n s[i] = li()\n dp = bfs(s, 0, 0, \"\n if dp[h - 1][w - 1] < 0:\n print((-1))\n else:\n cnt = 0\n for i in range(h):\n for j in range(w):\n cnt += s[i][j] == \".\"\n print((cnt - (dp[h - 1][w - 1] + 1))) \n\n\n\n\n\n\ndef ATC002_A():\n R, C = mi()\n sy, sx = mi()\n gy, gx = mi()\n s = [[\"\" for _j in range(C)] for _i in range(R)]\n for i in range(R):\n s[i] = li()\n dp = bfs(s, sy - 1, sx - 1, \"\n print((dp[gy - 1][gx - 1])) \n\n\n\ndef ABC126_D():\n N = ii()\n G = [[] for i in range(N)]\n for i in range(N - 1):\n a, b, w = mi()\n a -= 1\n b -= 1\n G[a].append((b, w))\n G[b].append((a, w))\n is_bipartite = True\n dist = [-1 for _ in range(N)]\n que = Queue()\n for v in range(N):\n if dist[v] != -1:\n \n continue\n dist[v] = 0\n que.put(v)\n while not que.empty():\n v = que.get()\n for nv, nw in G[v]:\n if dist[nv] == -1:\n dist[nv] = dist[v] + nw\n que.put(nv)\n for i in dist:\n if i % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n ABC126_D()\n", "expected": "A"} +{"pair_id": "python_7379_forward", "problem_id": "p03044", "language": "python", "direction": "forward", "speedup": 3.0324, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n```\n\n## Code B:\n```python\nimport sys\nfrom heapq import heappush, heappop\n\nvar_13 = sys.stdin.read\nvar_14 = sys.stdin.readline\nvar_15 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_3 = 1000000007\n\n\ndef func_1():\n var_4, *var_5 = map(int, var_13().split())\n var_1 = [[] for _ in range(var_4)]\n for var_16, var_17, var_18 in zip(*[iter(var_5)] * 3):\n var_1[var_16 - 1].append((var_17 - 1, var_18))\n var_1[var_17 - 1].append((var_16 - 1, var_18))\n var_9 = [var_2] * var_4\n var_9[0] = 0\n var_10 = [(0, 0)]\n while var_10:\n var_8, var_17 = heappop(var_10)\n if var_8 > var_9[var_17]:\n continue\n for var_12, var_7 in var_1[var_17]:\n if var_9[var_12] > var_9[var_17] + var_7:\n var_9[var_12] = var_9[var_17] + var_7\n heappush(var_10, (var_9[var_12], var_12))\n var_6 = [0] * var_4\n for var_11 in range(var_4):\n var_6[var_11] = 0 if var_9[var_11] % 2 == 0 else 1\n print(*var_6, sep=\"\n\")\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n", "code_b": "import sys\nfrom heapq import heappush, heappop\n\nvar_13 = sys.stdin.read\nvar_14 = sys.stdin.readline\nvar_15 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_3 = 1000000007\n\n\ndef func_1():\n var_4, *var_5 = map(int, var_13().split())\n var_1 = [[] for _ in range(var_4)]\n for var_16, var_17, var_18 in zip(*[iter(var_5)] * 3):\n var_1[var_16 - 1].append((var_17 - 1, var_18))\n var_1[var_17 - 1].append((var_16 - 1, var_18))\n var_9 = [var_2] * var_4\n var_9[0] = 0\n var_10 = [(0, 0)]\n while var_10:\n var_8, var_17 = heappop(var_10)\n if var_8 > var_9[var_17]:\n continue\n for var_12, var_7 in var_1[var_17]:\n if var_9[var_12] > var_9[var_17] + var_7:\n var_9[var_12] = var_9[var_17] + var_7\n heappush(var_10, (var_9[var_12], var_12))\n var_6 = [0] * var_4\n for var_11 in range(var_4):\n var_6[var_11] = 0 if var_9[var_11] % 2 == 0 else 1\n print(*var_6, sep=\"\\n\")\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "B"} +{"pair_id": "python_7379_reversed", "problem_id": "p03044", "language": "python", "direction": "reversed", "speedup": 3.0324, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\nfrom heapq import heappush, heappop\n\nvar_13 = sys.stdin.read\nvar_14 = sys.stdin.readline\nvar_15 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_3 = 1000000007\n\n\ndef func_1():\n var_4, *var_5 = map(int, var_13().split())\n var_1 = [[] for _ in range(var_4)]\n for var_16, var_17, var_18 in zip(*[iter(var_5)] * 3):\n var_1[var_16 - 1].append((var_17 - 1, var_18))\n var_1[var_17 - 1].append((var_16 - 1, var_18))\n var_9 = [var_2] * var_4\n var_9[0] = 0\n var_10 = [(0, 0)]\n while var_10:\n var_8, var_17 = heappop(var_10)\n if var_8 > var_9[var_17]:\n continue\n for var_12, var_7 in var_1[var_17]:\n if var_9[var_12] > var_9[var_17] + var_7:\n var_9[var_12] = var_9[var_17] + var_7\n heappush(var_10, (var_9[var_12], var_12))\n var_6 = [0] * var_4\n for var_11 in range(var_4):\n var_6[var_11] = 0 if var_9[var_11] % 2 == 0 else 1\n print(*var_6, sep=\"\n\")\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n```\n\n## Code B:\n```python\nimport sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\nfrom heapq import heappush, heappop\n\nvar_13 = sys.stdin.read\nvar_14 = sys.stdin.readline\nvar_15 = sys.stdin.readlines\nsys.setrecursionlimit(10**9)\nvar_2 = 1 << 60\nvar_3 = 1000000007\n\n\ndef func_1():\n var_4, *var_5 = map(int, var_13().split())\n var_1 = [[] for _ in range(var_4)]\n for var_16, var_17, var_18 in zip(*[iter(var_5)] * 3):\n var_1[var_16 - 1].append((var_17 - 1, var_18))\n var_1[var_17 - 1].append((var_16 - 1, var_18))\n var_9 = [var_2] * var_4\n var_9[0] = 0\n var_10 = [(0, 0)]\n while var_10:\n var_8, var_17 = heappop(var_10)\n if var_8 > var_9[var_17]:\n continue\n for var_12, var_7 in var_1[var_17]:\n if var_9[var_12] > var_9[var_17] + var_7:\n var_9[var_12] = var_9[var_17] + var_7\n heappush(var_10, (var_9[var_12], var_12))\n var_6 = [0] * var_4\n for var_11 in range(var_4):\n var_6[var_11] = 0 if var_9[var_11] % 2 == 0 else 1\n print(*var_6, sep=\"\\n\")\n return\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "import sys\n\nvar_18 = sys.stdin.readline\n\n\ndef func_5():\n return int(eval(var_18()))\n\n\ndef func_9():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_8():\n return list(map(int, var_18().rstrip().split()))\n\n\ndef func_7():\n return list(var_18().rstrip())\n\n\n \n \nfrom queue import Queue\n\n\ndef func_4(var_13, var_30, var_29, var_33, var_8):\n var_16 = len(var_13)\n var_32 = len(var_13[0])\n var_10 = [[-1 for _j in range(var_32)] for _i in range(var_16)]\n var_26 = Queue()\n var_10[var_30][var_29] = 0\n var_26.put((var_30, var_29))\n var_12 = [1, -1, 0, 0, 1, 1, -1, -1]\n var_11 = [0, 0, 1, -1, 1, -1, 1, -1]\n\n def func_6(var_34, var_35):\n return 0 <= var_35 and var_35 < var_16 and 0 <= var_34 and var_34 < var_32\n\n while not var_26.empty():\n var_35, var_34 = var_26.get()\n for var_21 in range(var_8):\n var_25, var_24 = var_35 + var_12[var_21], var_34 + var_11[var_21]\n if not func_6(var_24, var_25) or var_13[var_25][var_24] == var_33:\n continue\n elif var_10[var_25][var_24] != -1:\n continue\n var_10[var_25][var_24] = var_10[var_35][var_34] + 1\n var_26.put((var_25, var_24))\n return var_10\n\n\n \ndef func_1():\n var_16, var_32 = func_9()\n var_28 = [[\"\" for _j in range(var_32)] for _i in range(var_16)]\n for var_17 in range(var_16):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, 0, 0, \"#\", 4)\n if var_10[var_16 - 1][var_32 - 1] < 0:\n print((-1))\n else:\n var_7 = 0\n for var_17 in range(var_16):\n for var_20 in range(var_32):\n var_7 += var_28[var_17][var_20] == \".\"\n print((var_7 - (var_10[var_16 - 1][var_32 - 1] + 1))) \n\n\n \n \n \n \ndef func_3():\n var_4, var_1 = func_9()\n var_30, var_29 = func_9()\n var_15, var_14 = func_9()\n var_28 = [[\"\" for _j in range(var_1)] for _i in range(var_4)]\n for var_17 in range(var_4):\n var_28[var_17] = func_7()\n var_10 = func_4(var_28, var_30 - 1, var_29 - 1, \"#\", 4) \n print((var_10[var_15 - 1][var_14 - 1])) \n\n\n \ndef func_2():\n var_3 = func_5()\n var_2 = [[] for var_17 in range(var_3)]\n for var_17 in range(var_3 - 1):\n var_5, var_6, var_32 = func_9()\n var_5 -= 1\n var_6 -= 1\n var_2[var_5].append((var_6, var_32))\n var_2[var_6].append((var_5, var_32))\n var_19 = True\n var_9 = [-1 for _ in range(var_3)]\n var_27 = Queue()\n for var_31 in range(var_3):\n if var_9[var_31] != -1:\n \n continue\n var_9[var_31] = 0\n var_27.put(var_31)\n while not var_27.empty():\n var_31 = var_27.get()\n for var_22, var_23 in var_2[var_31]:\n if var_9[var_22] == -1:\n var_9[var_22] = var_9[var_31] + var_23\n var_27.put(var_22)\n for var_17 in var_9:\n if var_17 % 2 == 0:\n print((0))\n else:\n print((1))\n\n\nif __name__ == \"__main__\":\n \n \n func_2()\n", "expected": "A"} {"pair_id": "python_7890_forward", "problem_id": "p02708", "language": "python", "direction": "forward", "speedup": 114.4401, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_2, var_1 = list(map(int, input().split()))\nvar_3 = 0\nfor var_4 in range(var_1, var_2 + 2):\n var_6 = var_4 * (var_4 - 1) // 2\n var_5 = var_4 * var_2 - var_6\n var_3 = var_3 + (var_5 - var_6 + 1)\nprint((var_3 % (10**9 + 7)))\n\n```\n\n## Code B:\n```python\nvar_2, var_1 = list(map(int, input().split()))\nprint(\n (\n (\n var_2 * (var_2 + 1) * (var_2 + 2) // 6\n - (3 * var_2 - 2 * var_1 + 4) * (var_1 - 1) * var_1 // 6\n - var_1\n + var_2\n + 2\n )\n % (10**9 + 7)\n )\n)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_2, var_1 = list(map(int, input().split()))\nvar_3 = 0\nfor var_4 in range(var_1, var_2 + 2):\n var_6 = var_4 * (var_4 - 1) // 2\n var_5 = var_4 * var_2 - var_6\n var_3 = var_3 + (var_5 - var_6 + 1)\nprint((var_3 % (10**9 + 7)))\n", "code_b": "var_2, var_1 = list(map(int, input().split()))\nprint(\n (\n (\n var_2 * (var_2 + 1) * (var_2 + 2) // 6\n - (3 * var_2 - 2 * var_1 + 4) * (var_1 - 1) * var_1 // 6\n - var_1\n + var_2\n + 2\n )\n % (10**9 + 7)\n )\n)\n", "expected": "B"} {"pair_id": "python_7890_reversed", "problem_id": "p02708", "language": "python", "direction": "reversed", "speedup": 114.4401, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_2, var_1 = list(map(int, input().split()))\nprint(\n (\n (\n var_2 * (var_2 + 1) * (var_2 + 2) // 6\n - (3 * var_2 - 2 * var_1 + 4) * (var_1 - 1) * var_1 // 6\n - var_1\n + var_2\n + 2\n )\n % (10**9 + 7)\n )\n)\n\n```\n\n## Code B:\n```python\nvar_2, var_1 = list(map(int, input().split()))\nvar_3 = 0\nfor var_4 in range(var_1, var_2 + 2):\n var_6 = var_4 * (var_4 - 1) // 2\n var_5 = var_4 * var_2 - var_6\n var_3 = var_3 + (var_5 - var_6 + 1)\nprint((var_3 % (10**9 + 7)))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_2, var_1 = list(map(int, input().split()))\nprint(\n (\n (\n var_2 * (var_2 + 1) * (var_2 + 2) // 6\n - (3 * var_2 - 2 * var_1 + 4) * (var_1 - 1) * var_1 // 6\n - var_1\n + var_2\n + 2\n )\n % (10**9 + 7)\n )\n)\n", "code_b": "var_2, var_1 = list(map(int, input().split()))\nvar_3 = 0\nfor var_4 in range(var_1, var_2 + 2):\n var_6 = var_4 * (var_4 - 1) // 2\n var_5 = var_4 * var_2 - var_6\n var_3 = var_3 + (var_5 - var_6 + 1)\nprint((var_3 % (10**9 + 7)))\n", "expected": "A"} {"pair_id": "python_18283_forward", "problem_id": "p02831", "language": "python", "direction": "forward", "speedup": 6.9271, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1, var_2 = list(map(int, input().split()))\nvar_3, var_4 = var_1, var_2\nwhile True:\n if var_3 > var_4:\n var_4 += var_2\n elif var_3 < var_4:\n var_3 += var_1\n if var_3 == var_4:\n print(var_3)\n break\n\n```\n\n## Code B:\n```python\nvar_2, var_3 = list(map(int, input().split()))\nimport math\n\n\ndef func_1(var_2, var_3):\n for var_1 in range(min(var_2, var_3), 0, -1):\n if var_2 % var_1 == 0 and var_3 % var_1 == 0:\n return var_1\n\n\nprint((int(var_2 * var_3 / func_1(var_2, var_3))))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1, var_2 = list(map(int, input().split()))\nvar_3, var_4 = var_1, var_2\nwhile True:\n if var_3 > var_4:\n var_4 += var_2\n elif var_3 < var_4:\n var_3 += var_1\n if var_3 == var_4:\n print(var_3)\n break\n", "code_b": "var_2, var_3 = list(map(int, input().split()))\nimport math\n\n\ndef func_1(var_2, var_3):\n for var_1 in range(min(var_2, var_3), 0, -1):\n if var_2 % var_1 == 0 and var_3 % var_1 == 0:\n return var_1\n\n\nprint((int(var_2 * var_3 / func_1(var_2, var_3))))\n", "expected": "B"} @@ -1072,8 +1072,8 @@ {"pair_id": "python_3490_reversed", "problem_id": "p02922", "language": "python", "direction": "reversed", "speedup": 5.0088, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1, var_3 = list(map(int, input().split()))\nvar_4 = 1\nvar_2 = 0\nwhile var_4 < var_3:\n var_4 += var_1 - 1\n var_2 += 1\nprint(var_2)\n\n```\n\n## Code B:\n```python\nimport math, string, itertools, fractions, heapq, collections, re, array, bisect, sys, random, time, queue, copy\n\nsys.setrecursionlimit(10**7)\nvar_7 = 10**20\nvar_8 = 10**9 + 7\nvar_5 = [(-1, 0), (0, 1), (1, 0), (0, -1)]\nvar_6 = [(-1, 0), (-1, 1), (0, 1), (1, 1), (1, 0), (1, -1), (0, -1), (-1, -1)]\n\n\ndef func_2():\n return [int(var_9) for var_9 in sys.stdin.readline().split()]\n\n\ndef func_3():\n return [int(var_9) - 1 for var_9 in sys.stdin.readline().split()]\n\n\ndef func_1():\n return int(sys.stdin.readline())\n\n\ndef func_4():\n return sys.stdin.readline().split()\n\n\ndef func_5():\n return eval(input())\n\n\ndef func_6():\n var_1, var_3 = func_2()\n var_4 = 1\n var_2 = 0\n while True:\n if var_4 >= var_3:\n return var_2\n var_2 += 1\n var_4 -= 1\n var_4 += var_1\n\n\nprint((func_6()))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1, var_3 = list(map(int, input().split()))\nvar_4 = 1\nvar_2 = 0\nwhile var_4 < var_3:\n var_4 += var_1 - 1\n var_2 += 1\nprint(var_2)\n", "code_b": "import math, string, itertools, fractions, heapq, collections, re, array, bisect, sys, random, time, queue, copy\n\nsys.setrecursionlimit(10**7)\nvar_7 = 10**20\nvar_8 = 10**9 + 7\nvar_5 = [(-1, 0), (0, 1), (1, 0), (0, -1)]\nvar_6 = [(-1, 0), (-1, 1), (0, 1), (1, 1), (1, 0), (1, -1), (0, -1), (-1, -1)]\n\n\ndef func_2():\n return [int(var_9) for var_9 in sys.stdin.readline().split()]\n\n\ndef func_3():\n return [int(var_9) - 1 for var_9 in sys.stdin.readline().split()]\n\n\ndef func_1():\n return int(sys.stdin.readline())\n\n\ndef func_4():\n return sys.stdin.readline().split()\n\n\ndef func_5():\n return eval(input())\n\n\ndef func_6():\n var_1, var_3 = func_2()\n var_4 = 1\n var_2 = 0\n while True:\n if var_4 >= var_3:\n return var_2\n var_2 += 1\n var_4 -= 1\n var_4 += var_1\n\n\nprint((func_6()))\n", "expected": "A"} {"pair_id": "python_13497_forward", "problem_id": "p02406", "language": "python", "direction": "forward", "speedup": 24.3469, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\ndef func_1():\n def func_2():\n var_2 = eval(input())\n var_1 = 1\n var_3 = \"\"\n\n while True:\n var_4 = var_1\n if var_4 % 3 == 0:\n var_3 += \" \" + str(var_1)\n\n var_1 += 1\n if var_1 <= var_2:\n\n continue\n else:\n\n break\n\n while True:\n if var_4 % 10 == 3:\n var_3 += \" \" + str(var_1)\n\n var_1 += 1\n if var_1 <= var_2:\n\n break\n else:\n\n return var_3\n\n else:\n var_4 /= 10\n if var_4:\n\n continue\n else:\n\n var_1 += 1\n if var_1 <= var_2:\n\n break\n else:\n\n return var_3\n\n return var_3\n\n print(func_2())\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\n## Code B:\n```python\nvar_2 = int(input())\nfor var_1 in range(1, var_2 + 1):\n var_3 = str(var_1)\n if var_1 % 3 == 0 or \"3\" in var_3:\n print(\"\", var_1, end=\"\")\nprint()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "def func_1():\n def func_2():\n var_2 = eval(input())\n var_1 = 1\n var_3 = \"\"\n\n while True:\n var_4 = var_1\n if var_4 % 3 == 0:\n var_3 += \" \" + str(var_1)\n\n var_1 += 1\n if var_1 <= var_2:\n\n continue\n else:\n\n break\n\n while True:\n if var_4 % 10 == 3:\n var_3 += \" \" + str(var_1)\n\n var_1 += 1\n if var_1 <= var_2:\n\n break\n else:\n\n return var_3\n\n else:\n var_4 /= 10\n if var_4:\n\n continue\n else:\n\n var_1 += 1\n if var_1 <= var_2:\n\n break\n else:\n\n return var_3\n\n return var_3\n\n print(func_2())\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "var_2 = int(input())\nfor var_1 in range(1, var_2 + 1):\n var_3 = str(var_1)\n if var_1 % 3 == 0 or \"3\" in var_3:\n print(\"\", var_1, end=\"\")\nprint()\n", "expected": "B"} {"pair_id": "python_13497_reversed", "problem_id": "p02406", "language": "python", "direction": "reversed", "speedup": 24.3469, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_2 = int(input())\nfor var_1 in range(1, var_2 + 1):\n var_3 = str(var_1)\n if var_1 % 3 == 0 or \"3\" in var_3:\n print(\"\", var_1, end=\"\")\nprint()\n\n```\n\n## Code B:\n```python\ndef func_1():\n def func_2():\n var_2 = eval(input())\n var_1 = 1\n var_3 = \"\"\n\n while True:\n var_4 = var_1\n if var_4 % 3 == 0:\n var_3 += \" \" + str(var_1)\n\n var_1 += 1\n if var_1 <= var_2:\n\n continue\n else:\n\n break\n\n while True:\n if var_4 % 10 == 3:\n var_3 += \" \" + str(var_1)\n\n var_1 += 1\n if var_1 <= var_2:\n\n break\n else:\n\n return var_3\n\n else:\n var_4 /= 10\n if var_4:\n\n continue\n else:\n\n var_1 += 1\n if var_1 <= var_2:\n\n break\n else:\n\n return var_3\n\n return var_3\n\n print(func_2())\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_2 = int(input())\nfor var_1 in range(1, var_2 + 1):\n var_3 = str(var_1)\n if var_1 % 3 == 0 or \"3\" in var_3:\n print(\"\", var_1, end=\"\")\nprint()\n", "code_b": "def func_1():\n def func_2():\n var_2 = eval(input())\n var_1 = 1\n var_3 = \"\"\n\n while True:\n var_4 = var_1\n if var_4 % 3 == 0:\n var_3 += \" \" + str(var_1)\n\n var_1 += 1\n if var_1 <= var_2:\n\n continue\n else:\n\n break\n\n while True:\n if var_4 % 10 == 3:\n var_3 += \" \" + str(var_1)\n\n var_1 += 1\n if var_1 <= var_2:\n\n break\n else:\n\n return var_3\n\n else:\n var_4 /= 10\n if var_4:\n\n continue\n else:\n\n var_1 += 1\n if var_1 <= var_2:\n\n break\n else:\n\n return var_3\n\n return var_3\n\n print(func_2())\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "A"} -{"pair_id": "python_17497_forward", "problem_id": "p02615", "language": "python", "direction": "forward", "speedup": 5.0504, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(1 << 25)\nread = sys.stdin.readline\nra = range\nenu = enumerate\n\n\ndef exit(*argv, **kwarg):\n print(*argv, **kwarg)\n sys.exit()\n\n\ndef mina(*argv, sub=1):\n return list(map(lambda x: x - sub, argv))\n\n\n\ndef a_int():\n return int(read())\n\n\ndef ints():\n return list(map(int, read().split()))\n\n\ndef read_col(H):\n \"\"\"\"\"\"H is number of rows\"\"\"\"\"\"H is number of rows\"\"\"\n ret = []\n for _ in range(H):\n ret.append(list(map(int, read().split())))\n return ret\n \n\n\nMOD = 10**9 + 7\nINF = 2**31 \n\nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef lcm(a, b):\n \n g = gcd(a, b)\n return a // g * b\n\n\n\n\nN = a_int()\nA = ints()\nA.sort(reverse=True)\nans = A[0]\ncnt = 1\nfor a in A[1:]:\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\nprint(ans)\n\n```\n\n## Code B:\n```python\nimport sys\n\nvar_5 = sys.stdin.readline\nvar_2 = int(eval(var_5()))\nvar_1 = list(map(int, var_5().split()))\nvar_1.sort(reverse=True)\nvar_3 = 0\nfor var_4 in range(1, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(1 << 25)\nread = sys.stdin.readline\nra = range\nenu = enumerate\n\n\ndef exit(*argv, **kwarg):\n print(*argv, **kwarg)\n sys.exit()\n\n\ndef mina(*argv, sub=1):\n return list(map(lambda x: x - sub, argv))\n\n\n\ndef a_int():\n return int(read())\n\n\ndef ints():\n return list(map(int, read().split()))\n\n\ndef read_col(H):\n \"\"\"\"\"\"H is number of rows\"\"\"\"\"\"H is number of rows\"\"\"\n ret = []\n for _ in range(H):\n ret.append(list(map(int, read().split())))\n return ret\n \n\n\nMOD = 10**9 + 7\nINF = 2**31 \n\nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef lcm(a, b):\n \n g = gcd(a, b)\n return a // g * b\n\n\n\n\nN = a_int()\nA = ints()\nA.sort(reverse=True)\nans = A[0]\ncnt = 1\nfor a in A[1:]:\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\nprint(ans)\n", "code_b": "import sys\n\nvar_5 = sys.stdin.readline\nvar_2 = int(eval(var_5()))\nvar_1 = list(map(int, var_5().split()))\nvar_1.sort(reverse=True)\nvar_3 = 0\nfor var_4 in range(1, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n", "expected": "B"} -{"pair_id": "python_17497_reversed", "problem_id": "p02615", "language": "python", "direction": "reversed", "speedup": 5.0504, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_5 = sys.stdin.readline\nvar_2 = int(eval(var_5()))\nvar_1 = list(map(int, var_5().split()))\nvar_1.sort(reverse=True)\nvar_3 = 0\nfor var_4 in range(1, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(1 << 25)\nread = sys.stdin.readline\nra = range\nenu = enumerate\n\n\ndef exit(*argv, **kwarg):\n print(*argv, **kwarg)\n sys.exit()\n\n\ndef mina(*argv, sub=1):\n return list(map(lambda x: x - sub, argv))\n\n\n\ndef a_int():\n return int(read())\n\n\ndef ints():\n return list(map(int, read().split()))\n\n\ndef read_col(H):\n \"\"\"\"\"\"H is number of rows\"\"\"\"\"\"H is number of rows\"\"\"\n ret = []\n for _ in range(H):\n ret.append(list(map(int, read().split())))\n return ret\n \n\n\nMOD = 10**9 + 7\nINF = 2**31 \n\nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef lcm(a, b):\n \n g = gcd(a, b)\n return a // g * b\n\n\n\n\nN = a_int()\nA = ints()\nA.sort(reverse=True)\nans = A[0]\ncnt = 1\nfor a in A[1:]:\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\nprint(ans)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_5 = sys.stdin.readline\nvar_2 = int(eval(var_5()))\nvar_1 = list(map(int, var_5().split()))\nvar_1.sort(reverse=True)\nvar_3 = 0\nfor var_4 in range(1, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n", "code_b": "import sys\n\nsys.setrecursionlimit(1 << 25)\nread = sys.stdin.readline\nra = range\nenu = enumerate\n\n\ndef exit(*argv, **kwarg):\n print(*argv, **kwarg)\n sys.exit()\n\n\ndef mina(*argv, sub=1):\n return list(map(lambda x: x - sub, argv))\n\n\n\ndef a_int():\n return int(read())\n\n\ndef ints():\n return list(map(int, read().split()))\n\n\ndef read_col(H):\n \"\"\"\"\"\"H is number of rows\"\"\"\"\"\"H is number of rows\"\"\"\n ret = []\n for _ in range(H):\n ret.append(list(map(int, read().split())))\n return ret\n \n\n\nMOD = 10**9 + 7\nINF = 2**31 \n\nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef lcm(a, b):\n \n g = gcd(a, b)\n return a // g * b\n\n\n\n\nN = a_int()\nA = ints()\nA.sort(reverse=True)\nans = A[0]\ncnt = 1\nfor a in A[1:]:\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\nprint(ans)\n", "expected": "A"} +{"pair_id": "python_17497_forward", "problem_id": "p02615", "language": "python", "direction": "forward", "speedup": 5.0504, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(1 << 25)\nvar_15 = sys.stdin.readline\nvar_14 = range\nvar_11 = enumerate\n\n\ndef func_2(*var_8, **var_13):\n print(*var_8, **var_13)\n sys.exit()\n\n\ndef func_5(*var_8, var_17=1):\n return list(map(lambda var_18: var_18 - var_17, var_8))\n\n\n \ndef func_1():\n return int(var_15())\n\n\ndef func_3():\n return list(map(int, var_15().split()))\n\n\ndef func_6(var_2):\n\\\n\\\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return tuple(map(list, zip(*var_16)))\n\n\ndef func_8(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(tuple(map(int, var_15().split())))\n return var_16\n\n\ndef func_7(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return var_16\n \n\n\nvar_4 = 10**9 + 7\nvar_3 = 2**31 \n \nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef func_4(var_6, var_9):\n \n var_12 = gcd(var_6, var_9)\n return var_6 // var_12 * var_9\n\n\n \n \nvar_5 = func_1()\nvar_1 = func_3()\nvar_1.sort(reverse=True)\nvar_7 = var_1[0]\nvar_10 = 1\nfor var_6 in var_1[1:]:\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\nprint(var_7)\n```\n\n## Code B:\n```python\nimport sys\n\nvar_5 = sys.stdin.readline\nvar_2 = int(eval(var_5()))\nvar_1 = list(map(int, var_5().split()))\nvar_1.sort(reverse=True)\nvar_3 = 0\nfor var_4 in range(1, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(1 << 25)\nvar_15 = sys.stdin.readline\nvar_14 = range\nvar_11 = enumerate\n\n\ndef func_2(*var_8, **var_13):\n print(*var_8, **var_13)\n sys.exit()\n\n\ndef func_5(*var_8, var_17=1):\n return list(map(lambda var_18: var_18 - var_17, var_8))\n\n\n \ndef func_1():\n return int(var_15())\n\n\ndef func_3():\n return list(map(int, var_15().split()))\n\n\ndef func_6(var_2):\n\\\n\\\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return tuple(map(list, zip(*var_16)))\n\n\ndef func_8(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(tuple(map(int, var_15().split())))\n return var_16\n\n\ndef func_7(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return var_16\n \n\n\nvar_4 = 10**9 + 7\nvar_3 = 2**31 \n \nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef func_4(var_6, var_9):\n \n var_12 = gcd(var_6, var_9)\n return var_6 // var_12 * var_9\n\n\n \n \nvar_5 = func_1()\nvar_1 = func_3()\nvar_1.sort(reverse=True)\nvar_7 = var_1[0]\nvar_10 = 1\nfor var_6 in var_1[1:]:\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\nprint(var_7)\n", "code_b": "import sys\n\nvar_5 = sys.stdin.readline\nvar_2 = int(eval(var_5()))\nvar_1 = list(map(int, var_5().split()))\nvar_1.sort(reverse=True)\nvar_3 = 0\nfor var_4 in range(1, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n", "expected": "B"} +{"pair_id": "python_17497_reversed", "problem_id": "p02615", "language": "python", "direction": "reversed", "speedup": 5.0504, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_5 = sys.stdin.readline\nvar_2 = int(eval(var_5()))\nvar_1 = list(map(int, var_5().split()))\nvar_1.sort(reverse=True)\nvar_3 = 0\nfor var_4 in range(1, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(1 << 25)\nvar_15 = sys.stdin.readline\nvar_14 = range\nvar_11 = enumerate\n\n\ndef func_2(*var_8, **var_13):\n print(*var_8, **var_13)\n sys.exit()\n\n\ndef func_5(*var_8, var_17=1):\n return list(map(lambda var_18: var_18 - var_17, var_8))\n\n\n \ndef func_1():\n return int(var_15())\n\n\ndef func_3():\n return list(map(int, var_15().split()))\n\n\ndef func_6(var_2):\n\\\n\\\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return tuple(map(list, zip(*var_16)))\n\n\ndef func_8(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(tuple(map(int, var_15().split())))\n return var_16\n\n\ndef func_7(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return var_16\n \n\n\nvar_4 = 10**9 + 7\nvar_3 = 2**31 \n \nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef func_4(var_6, var_9):\n \n var_12 = gcd(var_6, var_9)\n return var_6 // var_12 * var_9\n\n\n \n \nvar_5 = func_1()\nvar_1 = func_3()\nvar_1.sort(reverse=True)\nvar_7 = var_1[0]\nvar_10 = 1\nfor var_6 in var_1[1:]:\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\nprint(var_7)\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_5 = sys.stdin.readline\nvar_2 = int(eval(var_5()))\nvar_1 = list(map(int, var_5().split()))\nvar_1.sort(reverse=True)\nvar_3 = 0\nfor var_4 in range(1, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n", "code_b": "import sys\n\nsys.setrecursionlimit(1 << 25)\nvar_15 = sys.stdin.readline\nvar_14 = range\nvar_11 = enumerate\n\n\ndef func_2(*var_8, **var_13):\n print(*var_8, **var_13)\n sys.exit()\n\n\ndef func_5(*var_8, var_17=1):\n return list(map(lambda var_18: var_18 - var_17, var_8))\n\n\n \ndef func_1():\n return int(var_15())\n\n\ndef func_3():\n return list(map(int, var_15().split()))\n\n\ndef func_6(var_2):\n\\\n\\\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return tuple(map(list, zip(*var_16)))\n\n\ndef func_8(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(tuple(map(int, var_15().split())))\n return var_16\n\n\ndef func_7(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return var_16\n \n\n\nvar_4 = 10**9 + 7\nvar_3 = 2**31 \n \nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef func_4(var_6, var_9):\n \n var_12 = gcd(var_6, var_9)\n return var_6 // var_12 * var_9\n\n\n \n \nvar_5 = func_1()\nvar_1 = func_3()\nvar_1.sort(reverse=True)\nvar_7 = var_1[0]\nvar_10 = 1\nfor var_6 in var_1[1:]:\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\nprint(var_7)\n", "expected": "A"} {"pair_id": "python_9757_forward", "problem_id": "p02554", "language": "python", "direction": "forward", "speedup": 4.1747, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_3 = 10**9 + 7\nvar_1 = int(eval(input()))\nvar_5, var_4, var_6, var_2 = 1, 0, 0, 0\nfor _ in range(var_1):\n var_5, var_4, var_6, var_2 = (\n 8 * var_5,\n var_5 + var_4 * 9,\n var_5 + var_6 * 9,\n var_4 + var_6 + var_2 * 10,\n )\n var_5 %= var_3\n var_4 %= var_3\n var_6 %= var_3\n var_2 %= var_3\nprint(var_2)\n\n```\n\n## Code B:\n```python\nvar_2 = int(eval(input()))\nvar_1 = 10**9 + 7\nprint(((10**var_2 - 2 * 9**var_2 + 8**var_2) % var_1))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_3 = 10**9 + 7\nvar_1 = int(eval(input()))\nvar_5, var_4, var_6, var_2 = 1, 0, 0, 0\nfor _ in range(var_1):\n var_5, var_4, var_6, var_2 = (\n 8 * var_5,\n var_5 + var_4 * 9,\n var_5 + var_6 * 9,\n var_4 + var_6 + var_2 * 10,\n )\n var_5 %= var_3\n var_4 %= var_3\n var_6 %= var_3\n var_2 %= var_3\nprint(var_2)\n", "code_b": "var_2 = int(eval(input()))\nvar_1 = 10**9 + 7\nprint(((10**var_2 - 2 * 9**var_2 + 8**var_2) % var_1))\n", "expected": "B"} {"pair_id": "python_9757_reversed", "problem_id": "p02554", "language": "python", "direction": "reversed", "speedup": 4.1747, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_2 = int(eval(input()))\nvar_1 = 10**9 + 7\nprint(((10**var_2 - 2 * 9**var_2 + 8**var_2) % var_1))\n\n```\n\n## Code B:\n```python\nvar_3 = 10**9 + 7\nvar_1 = int(eval(input()))\nvar_5, var_4, var_6, var_2 = 1, 0, 0, 0\nfor _ in range(var_1):\n var_5, var_4, var_6, var_2 = (\n 8 * var_5,\n var_5 + var_4 * 9,\n var_5 + var_6 * 9,\n var_4 + var_6 + var_2 * 10,\n )\n var_5 %= var_3\n var_4 %= var_3\n var_6 %= var_3\n var_2 %= var_3\nprint(var_2)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_2 = int(eval(input()))\nvar_1 = 10**9 + 7\nprint(((10**var_2 - 2 * 9**var_2 + 8**var_2) % var_1))\n", "code_b": "var_3 = 10**9 + 7\nvar_1 = int(eval(input()))\nvar_5, var_4, var_6, var_2 = 1, 0, 0, 0\nfor _ in range(var_1):\n var_5, var_4, var_6, var_2 = (\n 8 * var_5,\n var_5 + var_4 * 9,\n var_5 + var_6 * 9,\n var_4 + var_6 + var_2 * 10,\n )\n var_5 %= var_3\n var_4 %= var_3\n var_6 %= var_3\n var_2 %= var_3\nprint(var_2)\n", "expected": "A"} {"pair_id": "python_5934_forward", "problem_id": "p03693", "language": "python", "direction": "forward", "speedup": 5.5314, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nfrom collections import defaultdict, deque\nfrom heapq import heappush, heappop\nimport sys\nimport math\nimport bisect\nimport random\n\n\ndef func_3():\n return [int(var_8) for var_8 in sys.stdin.readline().split()]\n\n\ndef func_1():\n return int(sys.stdin.readline())\n\n\ndef func_5():\n return [list(var_8) for var_8 in sys.stdin.readline().split()]\n\n\ndef func_7():\n return list(sys.stdin.readline())[:-1]\n\n\ndef func_2(var_5):\n return [func_1() for var_3 in range(var_5)]\n\n\ndef func_4(var_5):\n return [func_3() for var_3 in range(var_5)]\n\n\ndef func_8(var_5):\n return [func_7() for var_3 in range(var_5)]\n\n\ndef func_6(var_5):\n return [func_5() for var_3 in range(var_5)]\n\n\nsys.setrecursionlimit(1000000)\nvar_4 = 1000000007\n\n\ndef func_9():\n var_6, var_2, var_1 = func_3()\n var_7 = var_6 * 100 + var_2 * 10 + var_1\n if var_7 % 4 == 0:\n print(\"YES\")\n else:\n print(\"NO\")\n return\n\n\nif __name__ == \"__main__\":\n func_9()\n\n```\n\n## Code B:\n```python\nvar_4 = input().split()\nvar_2 = \"\"\nfor var_3 in var_4:\n var_2 = var_2 + var_3\nvar_1 = int(var_2)\nif var_1 % 4 == 0:\n print(\"YES\")\nelse:\n print(\"NO\")\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "from collections import defaultdict, deque\nfrom heapq import heappush, heappop\nimport sys\nimport math\nimport bisect\nimport random\n\n\ndef func_3():\n return [int(var_8) for var_8 in sys.stdin.readline().split()]\n\n\ndef func_1():\n return int(sys.stdin.readline())\n\n\ndef func_5():\n return [list(var_8) for var_8 in sys.stdin.readline().split()]\n\n\ndef func_7():\n return list(sys.stdin.readline())[:-1]\n\n\ndef func_2(var_5):\n return [func_1() for var_3 in range(var_5)]\n\n\ndef func_4(var_5):\n return [func_3() for var_3 in range(var_5)]\n\n\ndef func_8(var_5):\n return [func_7() for var_3 in range(var_5)]\n\n\ndef func_6(var_5):\n return [func_5() for var_3 in range(var_5)]\n\n\nsys.setrecursionlimit(1000000)\nvar_4 = 1000000007\n\n\ndef func_9():\n var_6, var_2, var_1 = func_3()\n var_7 = var_6 * 100 + var_2 * 10 + var_1\n if var_7 % 4 == 0:\n print(\"YES\")\n else:\n print(\"NO\")\n return\n\n\nif __name__ == \"__main__\":\n func_9()\n", "code_b": "var_4 = input().split()\nvar_2 = \"\"\nfor var_3 in var_4:\n var_2 = var_2 + var_3\nvar_1 = int(var_2)\nif var_1 % 4 == 0:\n print(\"YES\")\nelse:\n print(\"NO\")\n", "expected": "B"} @@ -1580,8 +1580,8 @@ {"pair_id": "python_5687_reversed", "problem_id": "p03039", "language": "python", "direction": "reversed", "speedup": 360.6653, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\ndef func_1(var_13, var_10):\n var_15, var_16 = 1, 1\n var_10 = min(var_10, var_13 - var_10)\n for var_8 in range(var_10):\n var_15 *= var_13 - var_8\n var_15 %= var_3\n var_16 *= var_8 + 1\n var_16 %= var_3\n return var_15 * pow(var_16, var_3 - 2, var_3) % var_3\n\n\ndef func_2(var_5):\n var_7 = var_3\n var_15, var_11 = (0, 1)\n var_16, var_12 = (1, 0)\n while var_7 != 0:\n var_14 = var_5 // var_7\n var_5, var_7 = (var_7, var_5 % var_7)\n var_15, var_11 = (var_11 - var_14 * var_15, var_15)\n var_16, var_12 = (var_12 - var_14 * var_16, var_16)\n return var_11 % var_3\n\n\nvar_3 = 10**9 + 7\nvar_4, var_2, var_1 = list(map(int, input().split()))\nvar_15 = 0\nfor var_8 in range(1, var_4):\n var_15 = var_15 + var_8 * (var_4 - var_8)\nvar_15 = (var_15 * var_2 * var_2) % var_3\nvar_16 = 0\nfor var_9 in range(1, var_2):\n var_16 = var_16 + var_9 * (var_2 - var_9)\nvar_16 = (var_16 * var_4 * var_4) % var_3\nvar_6 = func_1(var_4 * var_2 - 2, var_1 - 2) * (var_15 + var_16) % var_3\nprint(var_6)\n\n```\n\n## Code B:\n```python\nvar_3 = 2 * 10**5 + 100\nvar_4 = 10**9 + 7\nvar_8 = [0] * var_3\nvar_11 = [0] * var_3\nvar_9 = [0] * var_3\nvar_8[0] = 1\nvar_8[1] = 1\nvar_9[0] = 1\nvar_9[1] = 1\nvar_11[1] = 1\nfor var_10 in range(2, var_3):\n var_8[var_10] = var_8[var_10 - 1] * var_10 % var_4\n var_11[var_10] = var_4 - var_11[var_4 % var_10] * (var_4 // var_10) % var_4\n var_9[var_10] = var_9[var_10 - 1] * var_11[var_10] % var_4\n\n\ndef func_1(var_12, var_13):\n if var_12 < var_13:\n return 0\n if var_12 < 0 or var_13 < 0:\n return 0\n return var_8[var_12] * (var_9[var_13] * var_9[var_12 - var_13] % var_4) % var_4\n\n\nvar_5, var_2, var_1 = list(map(int, input().split()))\nvar_6 = 0\nfor var_7 in range(1, var_5):\n var_6 += var_7 * (var_5 - var_7) * var_2**2\n var_6 %= var_4\nfor var_7 in range(1, var_2):\n var_6 += var_7 * (var_2 - var_7) * var_5**2\n var_6 %= var_4\nvar_6 *= func_1(var_5 * var_2 - 2, var_1 - 2)\nvar_6 %= var_4\nprint(var_6)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "def func_1(var_13, var_10):\n var_15, var_16 = 1, 1\n var_10 = min(var_10, var_13 - var_10)\n for var_8 in range(var_10):\n var_15 *= var_13 - var_8\n var_15 %= var_3\n var_16 *= var_8 + 1\n var_16 %= var_3\n return var_15 * pow(var_16, var_3 - 2, var_3) % var_3\n\n\ndef func_2(var_5):\n var_7 = var_3\n var_15, var_11 = (0, 1)\n var_16, var_12 = (1, 0)\n while var_7 != 0:\n var_14 = var_5 // var_7\n var_5, var_7 = (var_7, var_5 % var_7)\n var_15, var_11 = (var_11 - var_14 * var_15, var_15)\n var_16, var_12 = (var_12 - var_14 * var_16, var_16)\n return var_11 % var_3\n\n\nvar_3 = 10**9 + 7\nvar_4, var_2, var_1 = list(map(int, input().split()))\nvar_15 = 0\nfor var_8 in range(1, var_4):\n var_15 = var_15 + var_8 * (var_4 - var_8)\nvar_15 = (var_15 * var_2 * var_2) % var_3\nvar_16 = 0\nfor var_9 in range(1, var_2):\n var_16 = var_16 + var_9 * (var_2 - var_9)\nvar_16 = (var_16 * var_4 * var_4) % var_3\nvar_6 = func_1(var_4 * var_2 - 2, var_1 - 2) * (var_15 + var_16) % var_3\nprint(var_6)\n", "code_b": "var_3 = 2 * 10**5 + 100\nvar_4 = 10**9 + 7\nvar_8 = [0] * var_3\nvar_11 = [0] * var_3\nvar_9 = [0] * var_3\nvar_8[0] = 1\nvar_8[1] = 1\nvar_9[0] = 1\nvar_9[1] = 1\nvar_11[1] = 1\nfor var_10 in range(2, var_3):\n var_8[var_10] = var_8[var_10 - 1] * var_10 % var_4\n var_11[var_10] = var_4 - var_11[var_4 % var_10] * (var_4 // var_10) % var_4\n var_9[var_10] = var_9[var_10 - 1] * var_11[var_10] % var_4\n\n\ndef func_1(var_12, var_13):\n if var_12 < var_13:\n return 0\n if var_12 < 0 or var_13 < 0:\n return 0\n return var_8[var_12] * (var_9[var_13] * var_9[var_12 - var_13] % var_4) % var_4\n\n\nvar_5, var_2, var_1 = list(map(int, input().split()))\nvar_6 = 0\nfor var_7 in range(1, var_5):\n var_6 += var_7 * (var_5 - var_7) * var_2**2\n var_6 %= var_4\nfor var_7 in range(1, var_2):\n var_6 += var_7 * (var_2 - var_7) * var_5**2\n var_6 %= var_4\nvar_6 *= func_1(var_5 * var_2 - 2, var_1 - 2)\nvar_6 %= var_4\nprint(var_6)\n", "expected": "A"} {"pair_id": "python_7277_forward", "problem_id": "p02881", "language": "python", "direction": "forward", "speedup": 3.1691, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_4 = int(eval(input()))\nvar_1 = [1, var_4]\nfor var_2 in range(1, int(var_4**0.5) + 1):\n var_3 = var_4 // var_2\n if var_4 % var_2 == 0 and var_1[0] + var_1[1] > var_2 + var_3:\n var_1 = [var_2, var_3]\nprint((var_1[0] - 1 + var_1[1] - 1))\n\n```\n\n## Code B:\n```python\nvar_2 = int(eval(input()))\nprint(\n (\n min(\n var_1 + (var_2 // var_1) - 2\n for var_1 in range(1, int(var_2**0.5) + 1)\n if var_2 % var_1 == 0\n )\n )\n)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_4 = int(eval(input()))\nvar_1 = [1, var_4]\nfor var_2 in range(1, int(var_4**0.5) + 1):\n var_3 = var_4 // var_2\n if var_4 % var_2 == 0 and var_1[0] + var_1[1] > var_2 + var_3:\n var_1 = [var_2, var_3]\nprint((var_1[0] - 1 + var_1[1] - 1))\n", "code_b": "var_2 = int(eval(input()))\nprint(\n (\n min(\n var_1 + (var_2 // var_1) - 2\n for var_1 in range(1, int(var_2**0.5) + 1)\n if var_2 % var_1 == 0\n )\n )\n)\n", "expected": "B"} {"pair_id": "python_7277_reversed", "problem_id": "p02881", "language": "python", "direction": "reversed", "speedup": 3.1691, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_2 = int(eval(input()))\nprint(\n (\n min(\n var_1 + (var_2 // var_1) - 2\n for var_1 in range(1, int(var_2**0.5) + 1)\n if var_2 % var_1 == 0\n )\n )\n)\n\n```\n\n## Code B:\n```python\nvar_4 = int(eval(input()))\nvar_1 = [1, var_4]\nfor var_2 in range(1, int(var_4**0.5) + 1):\n var_3 = var_4 // var_2\n if var_4 % var_2 == 0 and var_1[0] + var_1[1] > var_2 + var_3:\n var_1 = [var_2, var_3]\nprint((var_1[0] - 1 + var_1[1] - 1))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_2 = int(eval(input()))\nprint(\n (\n min(\n var_1 + (var_2 // var_1) - 2\n for var_1 in range(1, int(var_2**0.5) + 1)\n if var_2 % var_1 == 0\n )\n )\n)\n", "code_b": "var_4 = int(eval(input()))\nvar_1 = [1, var_4]\nfor var_2 in range(1, int(var_4**0.5) + 1):\n var_3 = var_4 // var_2\n if var_4 % var_2 == 0 and var_1[0] + var_1[1] > var_2 + var_3:\n var_1 = [var_2, var_3]\nprint((var_1[0] - 1 + var_1[1] - 1))\n", "expected": "A"} -{"pair_id": "python_17493_forward", "problem_id": "p02615", "language": "python", "direction": "forward", "speedup": 5.4, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(1 << 25)\nread = sys.stdin.readline\nra = range\nenu = enumerate\n\n\ndef exit(*argv, **kwarg):\n print(*argv, **kwarg)\n sys.exit()\n\n\ndef mina(*argv, sub=1):\n return list(map(lambda x: x - sub, argv))\n\n\n\ndef a_int():\n return int(read())\n\n\ndef ints():\n return list(map(int, read().split()))\n\n\ndef read_col(H):\n \"\"\"\"\"\"H is number of rows\"\"\"\"\"\"H is number of rows\"\"\"\n ret = []\n for _ in range(H):\n ret.append(list(map(int, read().split())))\n return ret\n \n\n\nMOD = 10**9 + 7\nINF = 2**31 \n\nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef lcm(a, b):\n \n g = gcd(a, b)\n return a // g * b\n\n\n\n\nN = a_int()\nA = ints()\nA.sort(reverse=True)\nans = A[0]\ncnt = 1\nfor a in A[1:]:\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\nprint(ans)\n\n```\n\n## Code B:\n```python\nvar_2 = int(eval(input()))\nvar_1 = list(map(int, input().split()))\nvar_1.sort(reverse=True)\n\nvar_3 = var_1[0]\nfor var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(1 << 25)\nread = sys.stdin.readline\nra = range\nenu = enumerate\n\n\ndef exit(*argv, **kwarg):\n print(*argv, **kwarg)\n sys.exit()\n\n\ndef mina(*argv, sub=1):\n return list(map(lambda x: x - sub, argv))\n\n\n\ndef a_int():\n return int(read())\n\n\ndef ints():\n return list(map(int, read().split()))\n\n\ndef read_col(H):\n \"\"\"\"\"\"H is number of rows\"\"\"\"\"\"H is number of rows\"\"\"\n ret = []\n for _ in range(H):\n ret.append(list(map(int, read().split())))\n return ret\n \n\n\nMOD = 10**9 + 7\nINF = 2**31 \n\nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef lcm(a, b):\n \n g = gcd(a, b)\n return a // g * b\n\n\n\n\nN = a_int()\nA = ints()\nA.sort(reverse=True)\nans = A[0]\ncnt = 1\nfor a in A[1:]:\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\nprint(ans)\n", "code_b": "var_2 = int(eval(input()))\nvar_1 = list(map(int, input().split()))\nvar_1.sort(reverse=True)\n\nvar_3 = var_1[0]\nfor var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n", "expected": "B"} -{"pair_id": "python_17493_reversed", "problem_id": "p02615", "language": "python", "direction": "reversed", "speedup": 5.4, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_2 = int(eval(input()))\nvar_1 = list(map(int, input().split()))\nvar_1.sort(reverse=True)\n\nvar_3 = var_1[0]\nfor var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(1 << 25)\nread = sys.stdin.readline\nra = range\nenu = enumerate\n\n\ndef exit(*argv, **kwarg):\n print(*argv, **kwarg)\n sys.exit()\n\n\ndef mina(*argv, sub=1):\n return list(map(lambda x: x - sub, argv))\n\n\n\ndef a_int():\n return int(read())\n\n\ndef ints():\n return list(map(int, read().split()))\n\n\ndef read_col(H):\n \"\"\"\"\"\"H is number of rows\"\"\"\"\"\"H is number of rows\"\"\"\n ret = []\n for _ in range(H):\n ret.append(list(map(int, read().split())))\n return ret\n \n\n\nMOD = 10**9 + 7\nINF = 2**31 \n\nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef lcm(a, b):\n \n g = gcd(a, b)\n return a // g * b\n\n\n\n\nN = a_int()\nA = ints()\nA.sort(reverse=True)\nans = A[0]\ncnt = 1\nfor a in A[1:]:\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\nprint(ans)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_2 = int(eval(input()))\nvar_1 = list(map(int, input().split()))\nvar_1.sort(reverse=True)\n\nvar_3 = var_1[0]\nfor var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n", "code_b": "import sys\n\nsys.setrecursionlimit(1 << 25)\nread = sys.stdin.readline\nra = range\nenu = enumerate\n\n\ndef exit(*argv, **kwarg):\n print(*argv, **kwarg)\n sys.exit()\n\n\ndef mina(*argv, sub=1):\n return list(map(lambda x: x - sub, argv))\n\n\n\ndef a_int():\n return int(read())\n\n\ndef ints():\n return list(map(int, read().split()))\n\n\ndef read_col(H):\n \"\"\"\"\"\"H is number of rows\"\"\"\"\"\"H is number of rows\"\"\"\n ret = []\n for _ in range(H):\n ret.append(list(map(int, read().split())))\n return ret\n \n\n\nMOD = 10**9 + 7\nINF = 2**31 \n\nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef lcm(a, b):\n \n g = gcd(a, b)\n return a // g * b\n\n\n\n\nN = a_int()\nA = ints()\nA.sort(reverse=True)\nans = A[0]\ncnt = 1\nfor a in A[1:]:\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\n if cnt == N - 1:\n break\n ans += a\n cnt += 1\nprint(ans)\n", "expected": "A"} +{"pair_id": "python_17493_forward", "problem_id": "p02615", "language": "python", "direction": "forward", "speedup": 5.4, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nsys.setrecursionlimit(1 << 25)\nvar_15 = sys.stdin.readline\nvar_14 = range\nvar_11 = enumerate\n\n\ndef func_2(*var_8, **var_13):\n print(*var_8, **var_13)\n sys.exit()\n\n\ndef func_5(*var_8, var_17=1):\n return list(map(lambda var_18: var_18 - var_17, var_8))\n\n\n \ndef func_1():\n return int(var_15())\n\n\ndef func_3():\n return list(map(int, var_15().split()))\n\n\ndef func_6(var_2):\n\\\n\\\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return tuple(map(list, zip(*var_16)))\n\n\ndef func_8(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(tuple(map(int, var_15().split())))\n return var_16\n\n\ndef func_7(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return var_16\n \n\n\nvar_4 = 10**9 + 7\nvar_3 = 2**31 \n \nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef func_4(var_6, var_9):\n \n var_12 = gcd(var_6, var_9)\n return var_6 // var_12 * var_9\n\n\n \n \nvar_5 = func_1()\nvar_1 = func_3()\nvar_1.sort(reverse=True)\nvar_7 = var_1[0]\nvar_10 = 1\nfor var_6 in var_1[1:]:\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\nprint(var_7)\n```\n\n## Code B:\n```python\nvar_2 = int(eval(input()))\nvar_1 = list(map(int, input().split()))\nvar_1.sort(reverse=True)\n \nvar_3 = var_1[0]\nfor var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nsys.setrecursionlimit(1 << 25)\nvar_15 = sys.stdin.readline\nvar_14 = range\nvar_11 = enumerate\n\n\ndef func_2(*var_8, **var_13):\n print(*var_8, **var_13)\n sys.exit()\n\n\ndef func_5(*var_8, var_17=1):\n return list(map(lambda var_18: var_18 - var_17, var_8))\n\n\n \ndef func_1():\n return int(var_15())\n\n\ndef func_3():\n return list(map(int, var_15().split()))\n\n\ndef func_6(var_2):\n\\\n\\\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return tuple(map(list, zip(*var_16)))\n\n\ndef func_8(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(tuple(map(int, var_15().split())))\n return var_16\n\n\ndef func_7(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return var_16\n \n\n\nvar_4 = 10**9 + 7\nvar_3 = 2**31 \n \nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef func_4(var_6, var_9):\n \n var_12 = gcd(var_6, var_9)\n return var_6 // var_12 * var_9\n\n\n \n \nvar_5 = func_1()\nvar_1 = func_3()\nvar_1.sort(reverse=True)\nvar_7 = var_1[0]\nvar_10 = 1\nfor var_6 in var_1[1:]:\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\nprint(var_7)\n", "code_b": "var_2 = int(eval(input()))\nvar_1 = list(map(int, input().split()))\nvar_1.sort(reverse=True)\n \nvar_3 = var_1[0]\nfor var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n", "expected": "B"} +{"pair_id": "python_17493_reversed", "problem_id": "p02615", "language": "python", "direction": "reversed", "speedup": 5.4, "difficulty": "medium", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_2 = int(eval(input()))\nvar_1 = list(map(int, input().split()))\nvar_1.sort(reverse=True)\n \nvar_3 = var_1[0]\nfor var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n```\n\n## Code B:\n```python\nimport sys\n\nsys.setrecursionlimit(1 << 25)\nvar_15 = sys.stdin.readline\nvar_14 = range\nvar_11 = enumerate\n\n\ndef func_2(*var_8, **var_13):\n print(*var_8, **var_13)\n sys.exit()\n\n\ndef func_5(*var_8, var_17=1):\n return list(map(lambda var_18: var_18 - var_17, var_8))\n\n\n \ndef func_1():\n return int(var_15())\n\n\ndef func_3():\n return list(map(int, var_15().split()))\n\n\ndef func_6(var_2):\n\\\n\\\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return tuple(map(list, zip(*var_16)))\n\n\ndef func_8(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(tuple(map(int, var_15().split())))\n return var_16\n\n\ndef func_7(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return var_16\n \n\n\nvar_4 = 10**9 + 7\nvar_3 = 2**31 \n \nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef func_4(var_6, var_9):\n \n var_12 = gcd(var_6, var_9)\n return var_6 // var_12 * var_9\n\n\n \n \nvar_5 = func_1()\nvar_1 = func_3()\nvar_1.sort(reverse=True)\nvar_7 = var_1[0]\nvar_10 = 1\nfor var_6 in var_1[1:]:\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\nprint(var_7)\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_2 = int(eval(input()))\nvar_1 = list(map(int, input().split()))\nvar_1.sort(reverse=True)\n \nvar_3 = var_1[0]\nfor var_4 in range(2, var_2):\n var_3 += var_1[var_4 // 2]\nprint(var_3)\n", "code_b": "import sys\n\nsys.setrecursionlimit(1 << 25)\nvar_15 = sys.stdin.readline\nvar_14 = range\nvar_11 = enumerate\n\n\ndef func_2(*var_8, **var_13):\n print(*var_8, **var_13)\n sys.exit()\n\n\ndef func_5(*var_8, var_17=1):\n return list(map(lambda var_18: var_18 - var_17, var_8))\n\n\n \ndef func_1():\n return int(var_15())\n\n\ndef func_3():\n return list(map(int, var_15().split()))\n\n\ndef func_6(var_2):\n\\\n\\\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return tuple(map(list, zip(*var_16)))\n\n\ndef func_8(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(tuple(map(int, var_15().split())))\n return var_16\n\n\ndef func_7(var_2):\n \n var_16 = []\n for _ in range(var_2):\n var_16.append(list(map(int, var_15().split())))\n return var_16\n \n\n\nvar_4 = 10**9 + 7\nvar_3 = 2**31 \n \nfrom collections import defaultdict, Counter, deque\nfrom operator import itemgetter, xor, add\nfrom itertools import product, permutations, combinations\nfrom bisect import bisect_left, bisect_right \nfrom functools import reduce\nfrom math import gcd\n\n\ndef func_4(var_6, var_9):\n \n var_12 = gcd(var_6, var_9)\n return var_6 // var_12 * var_9\n\n\n \n \nvar_5 = func_1()\nvar_1 = func_3()\nvar_1.sort(reverse=True)\nvar_7 = var_1[0]\nvar_10 = 1\nfor var_6 in var_1[1:]:\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\n if var_10 == var_5 - 1:\n break\n var_7 += var_6\n var_10 += 1\nprint(var_7)\n", "expected": "A"} {"pair_id": "python_2893_forward", "problem_id": "p02559", "language": "python", "direction": "forward", "speedup": 2.9378, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport bisect\nimport heapq\nimport math\nimport random\nfrom collections import Counter, defaultdict, deque\nfrom decimal import ROUND_CEILING, ROUND_HALF_UP, Decimal\nfrom fractions import Fraction\nfrom functools import lru_cache, reduce\nfrom itertools import (\n combinations,\n combinations_with_replacement,\n product,\n permutations,\n accumulate,\n)\nfrom operator import add, mul, sub, itemgetter, attrgetter\nimport sys\n\nvar_19 = sys.stdin.readline\nvar_2 = 2**62 - 1\n\n\ndef func_7():\n return int(var_19())\n\n\ndef func_8():\n return list(map(int, var_19().split()))\n\n\ndef func_5():\n return float(var_19())\n\n\ndef func_6():\n return list(map(float, var_19().split()))\n\n\ndef func_9():\n return var_19().strip()\n\n\ndef func_10():\n return var_19().strip().split()\n\n\ndef func_1(*var_6):\n print(*var_6, file=sys.stderr)\n\n\ndef func_3(var_9):\n import time\n\n def func_13(*var_6, **var_13):\n var_21 = time.perf_counter()\n var_20 = var_9(*var_6, **var_13)\n var_8 = time.perf_counter()\n func_1(var_8 - var_21, \"sec\")\n return var_20\n\n return func_13\n\n\nclass Class_1:\n def __init__(self, var_7, var_15, var_11):\n _len = len(var_7)\n self.__op = var_15\n self.__size = 1 << (_len - 1).bit_length()\n self.__tree = [var_11] * self.__size + var_7 + [var_11] * (self.__size - _len)\n self.__ie = var_11\n for var_10 in range(self.__size - 1, 0, -1):\n self.__tree[var_10] = var_15(\n self.__tree[var_10 * 2], self.__tree[var_10 * 2 + 1]\n )\n\n def func_12(self, var_10, var_23):\n var_10 += self.__size\n self.__tree[var_10] = var_23\n while var_10:\n var_10 //= 2\n self.__tree[var_10] = self.__op(\n self.__tree[var_10 * 2], self.__tree[var_10 * 2 + 1]\n )\n\n def func_4(self, var_14, var_18):\n \"\"\"\"\"\"\n var_14 += self.__size\n var_18 += self.__size\n var_20 = self.__ie\n while var_14 < var_18:\n if var_14 & 1:\n var_20 = self.__op(var_20, self.__tree[var_14])\n var_14 += 1\n if var_18 & 1:\n var_18 -= 1\n var_20 = self.__op(var_20, self.__tree[var_18])\n var_14 //= 2\n var_18 //= 2\n return var_20\n\n def __getitem__(self, var_12):\n return self.__tree[var_12 + self.__size]\n\n\n@func_3\ndef func_11(var_3, var_1, var_4):\n var_22 = Class_1(var_1, add, 0)\n var_5 = []\n for var_17 in var_4:\n if var_17[0] == 0:\n _, var_16, var_24 = var_17\n var_22.update(var_16, var_22[var_16] + var_24)\n else:\n _, var_14, var_18 = var_17\n var_5.append(var_22.query(var_14, var_18))\n return var_5\n\n\ndef func_2():\n var_3, var_4 = func_8()\n var_1 = func_8()\n var_4 = [func_8() for _ in range(var_4)]\n print(*func_11(var_3, var_1, var_4), sep=\"\\n\")\n\n\nif __name__ == \"__main__\":\n func_2()\n\n```\n\n## Code B:\n```python\ndef func_3():\n def func_1(var_3, var_11):\n while var_3 <= var_5:\n var_2[var_3] += var_11\n var_3 += var_3 & -var_3\n\n def func_2(var_3):\n var_8 = 0\n while var_3:\n var_8 += var_2[var_3]\n var_3 -= var_3 & -var_3\n return var_8\n\n (var_5, var_6, *var_10), var_1, *var_9 = [\n list(map(int, var_9.split())) for var_9 in open(0)\n ]\n var_2 = [0] * -~var_5\n *list(map(func_1, list(range(1, var_5 + 1)), var_1)),\n for var_6, var_4, var_7 in var_9:\n if var_6:\n var_10 += (func_2(var_7) - func_2(var_4),)\n else:\n func_1(var_4 + 1, var_7)\n print((\" \".join(map(str, var_10))))\n\n\nfunc_3()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import bisect\nimport heapq\nimport math\nimport random\nfrom collections import Counter, defaultdict, deque\nfrom decimal import ROUND_CEILING, ROUND_HALF_UP, Decimal\nfrom fractions import Fraction\nfrom functools import lru_cache, reduce\nfrom itertools import (\n combinations,\n combinations_with_replacement,\n product,\n permutations,\n accumulate,\n)\nfrom operator import add, mul, sub, itemgetter, attrgetter\nimport sys\n\nvar_19 = sys.stdin.readline\nvar_2 = 2**62 - 1\n\n\ndef func_7():\n return int(var_19())\n\n\ndef func_8():\n return list(map(int, var_19().split()))\n\n\ndef func_5():\n return float(var_19())\n\n\ndef func_6():\n return list(map(float, var_19().split()))\n\n\ndef func_9():\n return var_19().strip()\n\n\ndef func_10():\n return var_19().strip().split()\n\n\ndef func_1(*var_6):\n print(*var_6, file=sys.stderr)\n\n\ndef func_3(var_9):\n import time\n\n def func_13(*var_6, **var_13):\n var_21 = time.perf_counter()\n var_20 = var_9(*var_6, **var_13)\n var_8 = time.perf_counter()\n func_1(var_8 - var_21, \"sec\")\n return var_20\n\n return func_13\n\n\nclass Class_1:\n def __init__(self, var_7, var_15, var_11):\n _len = len(var_7)\n self.__op = var_15\n self.__size = 1 << (_len - 1).bit_length()\n self.__tree = [var_11] * self.__size + var_7 + [var_11] * (self.__size - _len)\n self.__ie = var_11\n for var_10 in range(self.__size - 1, 0, -1):\n self.__tree[var_10] = var_15(\n self.__tree[var_10 * 2], self.__tree[var_10 * 2 + 1]\n )\n\n def func_12(self, var_10, var_23):\n var_10 += self.__size\n self.__tree[var_10] = var_23\n while var_10:\n var_10 //= 2\n self.__tree[var_10] = self.__op(\n self.__tree[var_10 * 2], self.__tree[var_10 * 2 + 1]\n )\n\n def func_4(self, var_14, var_18):\n \"\"\"\"\"\"\n var_14 += self.__size\n var_18 += self.__size\n var_20 = self.__ie\n while var_14 < var_18:\n if var_14 & 1:\n var_20 = self.__op(var_20, self.__tree[var_14])\n var_14 += 1\n if var_18 & 1:\n var_18 -= 1\n var_20 = self.__op(var_20, self.__tree[var_18])\n var_14 //= 2\n var_18 //= 2\n return var_20\n\n def __getitem__(self, var_12):\n return self.__tree[var_12 + self.__size]\n\n\n@func_3\ndef func_11(var_3, var_1, var_4):\n var_22 = Class_1(var_1, add, 0)\n var_5 = []\n for var_17 in var_4:\n if var_17[0] == 0:\n _, var_16, var_24 = var_17\n var_22.update(var_16, var_22[var_16] + var_24)\n else:\n _, var_14, var_18 = var_17\n var_5.append(var_22.query(var_14, var_18))\n return var_5\n\n\ndef func_2():\n var_3, var_4 = func_8()\n var_1 = func_8()\n var_4 = [func_8() for _ in range(var_4)]\n print(*func_11(var_3, var_1, var_4), sep=\"\\n\")\n\n\nif __name__ == \"__main__\":\n func_2()\n", "code_b": "def func_3():\n def func_1(var_3, var_11):\n while var_3 <= var_5:\n var_2[var_3] += var_11\n var_3 += var_3 & -var_3\n\n def func_2(var_3):\n var_8 = 0\n while var_3:\n var_8 += var_2[var_3]\n var_3 -= var_3 & -var_3\n return var_8\n\n (var_5, var_6, *var_10), var_1, *var_9 = [\n list(map(int, var_9.split())) for var_9 in open(0)\n ]\n var_2 = [0] * -~var_5\n *list(map(func_1, list(range(1, var_5 + 1)), var_1)),\n for var_6, var_4, var_7 in var_9:\n if var_6:\n var_10 += (func_2(var_7) - func_2(var_4),)\n else:\n func_1(var_4 + 1, var_7)\n print((\" \".join(map(str, var_10))))\n\n\nfunc_3()\n", "expected": "B"} {"pair_id": "python_2893_reversed", "problem_id": "p02559", "language": "python", "direction": "reversed", "speedup": 2.9378, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\ndef func_3():\n def func_1(var_3, var_11):\n while var_3 <= var_5:\n var_2[var_3] += var_11\n var_3 += var_3 & -var_3\n\n def func_2(var_3):\n var_8 = 0\n while var_3:\n var_8 += var_2[var_3]\n var_3 -= var_3 & -var_3\n return var_8\n\n (var_5, var_6, *var_10), var_1, *var_9 = [\n list(map(int, var_9.split())) for var_9 in open(0)\n ]\n var_2 = [0] * -~var_5\n *list(map(func_1, list(range(1, var_5 + 1)), var_1)),\n for var_6, var_4, var_7 in var_9:\n if var_6:\n var_10 += (func_2(var_7) - func_2(var_4),)\n else:\n func_1(var_4 + 1, var_7)\n print((\" \".join(map(str, var_10))))\n\n\nfunc_3()\n\n```\n\n## Code B:\n```python\nimport bisect\nimport heapq\nimport math\nimport random\nfrom collections import Counter, defaultdict, deque\nfrom decimal import ROUND_CEILING, ROUND_HALF_UP, Decimal\nfrom fractions import Fraction\nfrom functools import lru_cache, reduce\nfrom itertools import (\n combinations,\n combinations_with_replacement,\n product,\n permutations,\n accumulate,\n)\nfrom operator import add, mul, sub, itemgetter, attrgetter\nimport sys\n\nvar_19 = sys.stdin.readline\nvar_2 = 2**62 - 1\n\n\ndef func_7():\n return int(var_19())\n\n\ndef func_8():\n return list(map(int, var_19().split()))\n\n\ndef func_5():\n return float(var_19())\n\n\ndef func_6():\n return list(map(float, var_19().split()))\n\n\ndef func_9():\n return var_19().strip()\n\n\ndef func_10():\n return var_19().strip().split()\n\n\ndef func_1(*var_6):\n print(*var_6, file=sys.stderr)\n\n\ndef func_3(var_9):\n import time\n\n def func_13(*var_6, **var_13):\n var_21 = time.perf_counter()\n var_20 = var_9(*var_6, **var_13)\n var_8 = time.perf_counter()\n func_1(var_8 - var_21, \"sec\")\n return var_20\n\n return func_13\n\n\nclass Class_1:\n def __init__(self, var_7, var_15, var_11):\n _len = len(var_7)\n self.__op = var_15\n self.__size = 1 << (_len - 1).bit_length()\n self.__tree = [var_11] * self.__size + var_7 + [var_11] * (self.__size - _len)\n self.__ie = var_11\n for var_10 in range(self.__size - 1, 0, -1):\n self.__tree[var_10] = var_15(\n self.__tree[var_10 * 2], self.__tree[var_10 * 2 + 1]\n )\n\n def func_12(self, var_10, var_23):\n var_10 += self.__size\n self.__tree[var_10] = var_23\n while var_10:\n var_10 //= 2\n self.__tree[var_10] = self.__op(\n self.__tree[var_10 * 2], self.__tree[var_10 * 2 + 1]\n )\n\n def func_4(self, var_14, var_18):\n \"\"\"\"\"\"\n var_14 += self.__size\n var_18 += self.__size\n var_20 = self.__ie\n while var_14 < var_18:\n if var_14 & 1:\n var_20 = self.__op(var_20, self.__tree[var_14])\n var_14 += 1\n if var_18 & 1:\n var_18 -= 1\n var_20 = self.__op(var_20, self.__tree[var_18])\n var_14 //= 2\n var_18 //= 2\n return var_20\n\n def __getitem__(self, var_12):\n return self.__tree[var_12 + self.__size]\n\n\n@func_3\ndef func_11(var_3, var_1, var_4):\n var_22 = Class_1(var_1, add, 0)\n var_5 = []\n for var_17 in var_4:\n if var_17[0] == 0:\n _, var_16, var_24 = var_17\n var_22.update(var_16, var_22[var_16] + var_24)\n else:\n _, var_14, var_18 = var_17\n var_5.append(var_22.query(var_14, var_18))\n return var_5\n\n\ndef func_2():\n var_3, var_4 = func_8()\n var_1 = func_8()\n var_4 = [func_8() for _ in range(var_4)]\n print(*func_11(var_3, var_1, var_4), sep=\"\\n\")\n\n\nif __name__ == \"__main__\":\n func_2()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "def func_3():\n def func_1(var_3, var_11):\n while var_3 <= var_5:\n var_2[var_3] += var_11\n var_3 += var_3 & -var_3\n\n def func_2(var_3):\n var_8 = 0\n while var_3:\n var_8 += var_2[var_3]\n var_3 -= var_3 & -var_3\n return var_8\n\n (var_5, var_6, *var_10), var_1, *var_9 = [\n list(map(int, var_9.split())) for var_9 in open(0)\n ]\n var_2 = [0] * -~var_5\n *list(map(func_1, list(range(1, var_5 + 1)), var_1)),\n for var_6, var_4, var_7 in var_9:\n if var_6:\n var_10 += (func_2(var_7) - func_2(var_4),)\n else:\n func_1(var_4 + 1, var_7)\n print((\" \".join(map(str, var_10))))\n\n\nfunc_3()\n", "code_b": "import bisect\nimport heapq\nimport math\nimport random\nfrom collections import Counter, defaultdict, deque\nfrom decimal import ROUND_CEILING, ROUND_HALF_UP, Decimal\nfrom fractions import Fraction\nfrom functools import lru_cache, reduce\nfrom itertools import (\n combinations,\n combinations_with_replacement,\n product,\n permutations,\n accumulate,\n)\nfrom operator import add, mul, sub, itemgetter, attrgetter\nimport sys\n\nvar_19 = sys.stdin.readline\nvar_2 = 2**62 - 1\n\n\ndef func_7():\n return int(var_19())\n\n\ndef func_8():\n return list(map(int, var_19().split()))\n\n\ndef func_5():\n return float(var_19())\n\n\ndef func_6():\n return list(map(float, var_19().split()))\n\n\ndef func_9():\n return var_19().strip()\n\n\ndef func_10():\n return var_19().strip().split()\n\n\ndef func_1(*var_6):\n print(*var_6, file=sys.stderr)\n\n\ndef func_3(var_9):\n import time\n\n def func_13(*var_6, **var_13):\n var_21 = time.perf_counter()\n var_20 = var_9(*var_6, **var_13)\n var_8 = time.perf_counter()\n func_1(var_8 - var_21, \"sec\")\n return var_20\n\n return func_13\n\n\nclass Class_1:\n def __init__(self, var_7, var_15, var_11):\n _len = len(var_7)\n self.__op = var_15\n self.__size = 1 << (_len - 1).bit_length()\n self.__tree = [var_11] * self.__size + var_7 + [var_11] * (self.__size - _len)\n self.__ie = var_11\n for var_10 in range(self.__size - 1, 0, -1):\n self.__tree[var_10] = var_15(\n self.__tree[var_10 * 2], self.__tree[var_10 * 2 + 1]\n )\n\n def func_12(self, var_10, var_23):\n var_10 += self.__size\n self.__tree[var_10] = var_23\n while var_10:\n var_10 //= 2\n self.__tree[var_10] = self.__op(\n self.__tree[var_10 * 2], self.__tree[var_10 * 2 + 1]\n )\n\n def func_4(self, var_14, var_18):\n \"\"\"\"\"\"\n var_14 += self.__size\n var_18 += self.__size\n var_20 = self.__ie\n while var_14 < var_18:\n if var_14 & 1:\n var_20 = self.__op(var_20, self.__tree[var_14])\n var_14 += 1\n if var_18 & 1:\n var_18 -= 1\n var_20 = self.__op(var_20, self.__tree[var_18])\n var_14 //= 2\n var_18 //= 2\n return var_20\n\n def __getitem__(self, var_12):\n return self.__tree[var_12 + self.__size]\n\n\n@func_3\ndef func_11(var_3, var_1, var_4):\n var_22 = Class_1(var_1, add, 0)\n var_5 = []\n for var_17 in var_4:\n if var_17[0] == 0:\n _, var_16, var_24 = var_17\n var_22.update(var_16, var_22[var_16] + var_24)\n else:\n _, var_14, var_18 = var_17\n var_5.append(var_22.query(var_14, var_18))\n return var_5\n\n\ndef func_2():\n var_3, var_4 = func_8()\n var_1 = func_8()\n var_4 = [func_8() for _ in range(var_4)]\n print(*func_11(var_3, var_1, var_4), sep=\"\\n\")\n\n\nif __name__ == \"__main__\":\n func_2()\n", "expected": "A"} {"pair_id": "python_349_forward", "problem_id": "p03164", "language": "python", "direction": "forward", "speedup": 314.9517, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\ndef func_1():\n import sys, copy\n\n var_2, var_3 = list(map(int, sys.stdin.readline().split()))\n var_1 = tuple([tuple(map(int, sys.stdin.readline().split())) for _ in range(var_2)])\n\n var_5 = [[10**11 + 1 for _ in range(10**5 + 1)] for _ in range(var_2)]\n var_5[0][var_1[0][1]] = var_1[0][0]\n\n for var_6 in range(1, var_2):\n\n var_5[var_6] = copy.copy(var_5[var_6 - 1])\n var_10, var_8 = var_1[var_6][0], var_1[var_6][1]\n\n var_5[var_6][var_8] = min(var_10, var_5[var_6][var_8])\n\n for var_7 in range(10**5 + 1 - var_8):\n if var_5[var_6 - 1][var_7] != 0:\n var_5[var_6][var_8 + var_7] = min(\n var_5[var_6][var_8 + var_7], var_10 + var_5[var_6 - 1][var_7]\n )\n var_4 = 0\n for var_9, var_11 in enumerate(var_5[-1]):\n if var_11 <= var_3:\n if var_4 < var_9:\n var_4 = var_9\n\n print(var_4)\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\n## Code B:\n```python\n\"\"\"\"\"\"\n\nvar_1 = 2000000000\nvar_8, var_3 = list(map(int, input().split()))\nvar_10 = [0] * var_8\nvar_9 = [0] * var_8\nfor var_6 in range(var_8):\n var_10[var_6], var_9[var_6] = list(map(int, input().split()))\nvar_2 = sum(var_9)\nvar_5 = [[var_1 for var_6 in range(var_2 + 1)] for var_7 in range(var_8 + 1)]\n\nvar_5[0][0] = 0\nfor var_6 in range(var_8):\n for var_7 in range(var_2 + 1):\n if var_7 < var_9[var_6]:\n var_5[var_6 + 1][var_7] = var_5[var_6][var_7]\n else:\n var_5[var_6 + 1][var_7] = min(\n var_5[var_6][var_7], var_5[var_6][var_7 - var_9[var_6]] + var_10[var_6]\n )\nvar_4 = 0\nfor var_6 in range(var_2 + 1):\n if var_5[var_8][var_6] <= var_3:\n var_4 = var_6\nprint(var_4)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "def func_1():\n import sys, copy\n\n var_2, var_3 = list(map(int, sys.stdin.readline().split()))\n var_1 = tuple([tuple(map(int, sys.stdin.readline().split())) for _ in range(var_2)])\n\n var_5 = [[10**11 + 1 for _ in range(10**5 + 1)] for _ in range(var_2)]\n var_5[0][var_1[0][1]] = var_1[0][0]\n\n for var_6 in range(1, var_2):\n\n var_5[var_6] = copy.copy(var_5[var_6 - 1])\n var_10, var_8 = var_1[var_6][0], var_1[var_6][1]\n\n var_5[var_6][var_8] = min(var_10, var_5[var_6][var_8])\n\n for var_7 in range(10**5 + 1 - var_8):\n if var_5[var_6 - 1][var_7] != 0:\n var_5[var_6][var_8 + var_7] = min(\n var_5[var_6][var_8 + var_7], var_10 + var_5[var_6 - 1][var_7]\n )\n var_4 = 0\n for var_9, var_11 in enumerate(var_5[-1]):\n if var_11 <= var_3:\n if var_4 < var_9:\n var_4 = var_9\n\n print(var_4)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "\"\"\"\"\"\"\n\nvar_1 = 2000000000\nvar_8, var_3 = list(map(int, input().split()))\nvar_10 = [0] * var_8\nvar_9 = [0] * var_8\nfor var_6 in range(var_8):\n var_10[var_6], var_9[var_6] = list(map(int, input().split()))\nvar_2 = sum(var_9)\nvar_5 = [[var_1 for var_6 in range(var_2 + 1)] for var_7 in range(var_8 + 1)]\n\nvar_5[0][0] = 0\nfor var_6 in range(var_8):\n for var_7 in range(var_2 + 1):\n if var_7 < var_9[var_6]:\n var_5[var_6 + 1][var_7] = var_5[var_6][var_7]\n else:\n var_5[var_6 + 1][var_7] = min(\n var_5[var_6][var_7], var_5[var_6][var_7 - var_9[var_6]] + var_10[var_6]\n )\nvar_4 = 0\nfor var_6 in range(var_2 + 1):\n if var_5[var_8][var_6] <= var_3:\n var_4 = var_6\nprint(var_4)\n", "expected": "B"} @@ -1768,8 +1768,8 @@ {"pair_id": "python_306_reversed", "problem_id": "p02899", "language": "python", "direction": "reversed", "speedup": 2.3178, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_4 = int(input())\nvar_1 = var_3(map(int, input().split()))\nvar_3 = [0] * var_4\nfor var_2 in range(var_4):\n var_3[var_1[var_2] - 1] = var_2 + 1\nfor var_2 in range(var_4):\n print(var_3[var_2], end=\" \")\n\n```\n\n## Code B:\n```python\nimport sys\n\nvar_6 = sys.stdin.readline\n\n\ndef func_1():\n return int(eval(var_6()))\n\n\ndef func_3():\n return list(map(int, var_6().split()))\n\n\ndef func_2():\n return list(map(int, var_6().split()))\n\n\ndef func_4():\n var_7 = 10**9 + 7\n var_3 = func_1()\n var_1 = func_2()\n var_2 = []\n for var_5 in range(var_3):\n var_2.append([var_1[var_5], var_5])\n var_2.sort()\n var_4 = []\n for var_5 in range(var_3):\n var_4.append(var_2[var_5][1] + 1)\n print((\" \".join(map(str, var_4))))\n\n\nfunc_4()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_4 = int(input())\nvar_1 = var_3(map(int, input().split()))\nvar_3 = [0] * var_4\nfor var_2 in range(var_4):\n var_3[var_1[var_2] - 1] = var_2 + 1\nfor var_2 in range(var_4):\n print(var_3[var_2], end=\" \")\n", "code_b": "import sys\n\nvar_6 = sys.stdin.readline\n\n\ndef func_1():\n return int(eval(var_6()))\n\n\ndef func_3():\n return list(map(int, var_6().split()))\n\n\ndef func_2():\n return list(map(int, var_6().split()))\n\n\ndef func_4():\n var_7 = 10**9 + 7\n var_3 = func_1()\n var_1 = func_2()\n var_2 = []\n for var_5 in range(var_3):\n var_2.append([var_1[var_5], var_5])\n var_2.sort()\n var_4 = []\n for var_5 in range(var_3):\n var_4.append(var_2[var_5][1] + 1)\n print((\" \".join(map(str, var_4))))\n\n\nfunc_4()\n", "expected": "A"} {"pair_id": "python_735_forward", "problem_id": "p03945", "language": "python", "direction": "forward", "speedup": 2.3594, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_4 = sys.stdin.readline().rstrip()\n\n\ndef func_1():\n var_3 = \"$\"\n var_2 = -1\n for var_1 in var_4:\n var_2 += var_1 != var_3\n var_3 = var_1\n print(var_2)\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\n## Code B:\n```python\nprint((sum(map(input().count, [\"WB\", \"BW\"]))))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_4 = sys.stdin.readline().rstrip()\n\n\ndef func_1():\n var_3 = \"$\"\n var_2 = -1\n for var_1 in var_4:\n var_2 += var_1 != var_3\n var_3 = var_1\n print(var_2)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "code_b": "print((sum(map(input().count, [\"WB\", \"BW\"]))))\n", "expected": "B"} {"pair_id": "python_735_reversed", "problem_id": "p03945", "language": "python", "direction": "reversed", "speedup": 2.3594, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nprint((sum(map(input().count, [\"WB\", \"BW\"]))))\n\n```\n\n## Code B:\n```python\nimport sys\n\nvar_4 = sys.stdin.readline().rstrip()\n\n\ndef func_1():\n var_3 = \"$\"\n var_2 = -1\n for var_1 in var_4:\n var_2 += var_1 != var_3\n var_3 = var_1\n print(var_2)\n\n\nif __name__ == \"__main__\":\n func_1()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "print((sum(map(input().count, [\"WB\", \"BW\"]))))\n", "code_b": "import sys\n\nvar_4 = sys.stdin.readline().rstrip()\n\n\ndef func_1():\n var_3 = \"$\"\n var_2 = -1\n for var_1 in var_4:\n var_2 += var_1 != var_3\n var_3 = var_1\n print(var_2)\n\n\nif __name__ == \"__main__\":\n func_1()\n", "expected": "A"} -{"pair_id": "python_736_forward", "problem_id": "p03436", "language": "python", "direction": "forward", "speedup": 2.0111, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\nimport queue\n\nsys.setrecursionlimit(1000000)\n\n\ndef s(m, v, q):\n if q.empty():\n return -1\n k, i, j = q.get()\n if i == len(m) - 1 and j == len(m[i]) - 1:\n return k\n if i > 0 and m[i - 1][j] == \".\" and (i - 1, j) not in v:\n q.put((k + 1, i - 1, j))\n v.add((i - 1, j))\n if i < len(m) - 1 and m[i + 1][j] == \".\" and (i + 1, j) not in v:\n q.put((k + 1, i + 1, j))\n v.add((i + 1, j))\n if j > 0 and m[i][j - 1] == \".\" and (i, j - 1) not in v:\n q.put((k + 1, i, j - 1))\n v.add((i, j - 1))\n if j < len(m[i]) - 1 and m[i][j + 1] == \".\" and (i, j + 1) not in v:\n q.put((k + 1, i, j + 1))\n v.add((i, j + 1))\n return s(m, v, q)\n\n\nh, w = list(map(int, input().split()))\nm = [input() for _ in range(h)]\nq = queue.PriorityQueue()\nq.put((1, 0, 0))\nv = set((0, 0))\nc = s(m, v, q)\nif c == -1:\n print(-1)\nelse:\n print(h * w - c - sum([r.count(\"\n\n```\n\n## Code B:\n```python\nimport sys\n\nvar_9 = sys.stdin.readline\nfrom collections import deque\n\n\ndef func_1() -> None:\n var_12 = [(0, 0)]\n while var_12:\n var_15, var_16 = var_12.pop(0)\n for var_6, var_8 in var_7:\n var_10, var_11 = var_15 + var_6, var_16 + var_8\n if (\n 0 <= var_10 < var_1\n and 0 <= var_11 < var_3\n and var_2[var_10][var_11] == \".\"\n and var_5[var_10][var_11] == 0\n ):\n var_12.append((var_10, var_11))\n var_5[var_10][var_11] = var_5[var_15][var_16] + 1\n\n\ndef func_2():\n global H, W, S, dist, dxy\n var_1, var_3 = tuple(map(int, var_9().split()))\n var_2 = tuple(var_9().rstrip() for _ in range(var_1))\n var_5 = [[0] * var_3 for _ in range(var_1)]\n var_5[0][0] = 1\n var_7 = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n func_1()\n var_14, var_15 = sum(var_13.count(\".\") for var_13 in var_2), var_5[-1][-1]\n var_4 = var_14 - var_15 if var_15 else -1\n print(var_4)\n\n\nif __name__ == \"__main__\":\n func_2()\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\nimport queue\n\nsys.setrecursionlimit(1000000)\n\n\ndef s(m, v, q):\n if q.empty():\n return -1\n k, i, j = q.get()\n if i == len(m) - 1 and j == len(m[i]) - 1:\n return k\n if i > 0 and m[i - 1][j] == \".\" and (i - 1, j) not in v:\n q.put((k + 1, i - 1, j))\n v.add((i - 1, j))\n if i < len(m) - 1 and m[i + 1][j] == \".\" and (i + 1, j) not in v:\n q.put((k + 1, i + 1, j))\n v.add((i + 1, j))\n if j > 0 and m[i][j - 1] == \".\" and (i, j - 1) not in v:\n q.put((k + 1, i, j - 1))\n v.add((i, j - 1))\n if j < len(m[i]) - 1 and m[i][j + 1] == \".\" and (i, j + 1) not in v:\n q.put((k + 1, i, j + 1))\n v.add((i, j + 1))\n return s(m, v, q)\n\n\nh, w = list(map(int, input().split()))\nm = [input() for _ in range(h)]\nq = queue.PriorityQueue()\nq.put((1, 0, 0))\nv = set((0, 0))\nc = s(m, v, q)\nif c == -1:\n print(-1)\nelse:\n print(h * w - c - sum([r.count(\"\n", "code_b": "import sys\n\nvar_9 = sys.stdin.readline\nfrom collections import deque\n\n\ndef func_1() -> None:\n var_12 = [(0, 0)]\n while var_12:\n var_15, var_16 = var_12.pop(0)\n for var_6, var_8 in var_7:\n var_10, var_11 = var_15 + var_6, var_16 + var_8\n if (\n 0 <= var_10 < var_1\n and 0 <= var_11 < var_3\n and var_2[var_10][var_11] == \".\"\n and var_5[var_10][var_11] == 0\n ):\n var_12.append((var_10, var_11))\n var_5[var_10][var_11] = var_5[var_15][var_16] + 1\n\n\ndef func_2():\n global H, W, S, dist, dxy\n var_1, var_3 = tuple(map(int, var_9().split()))\n var_2 = tuple(var_9().rstrip() for _ in range(var_1))\n var_5 = [[0] * var_3 for _ in range(var_1)]\n var_5[0][0] = 1\n var_7 = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n func_1()\n var_14, var_15 = sum(var_13.count(\".\") for var_13 in var_2), var_5[-1][-1]\n var_4 = var_14 - var_15 if var_15 else -1\n print(var_4)\n\n\nif __name__ == \"__main__\":\n func_2()\n", "expected": "B"} -{"pair_id": "python_736_reversed", "problem_id": "p03436", "language": "python", "direction": "reversed", "speedup": 2.0111, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\n\nvar_9 = sys.stdin.readline\nfrom collections import deque\n\n\ndef func_1() -> None:\n var_12 = [(0, 0)]\n while var_12:\n var_15, var_16 = var_12.pop(0)\n for var_6, var_8 in var_7:\n var_10, var_11 = var_15 + var_6, var_16 + var_8\n if (\n 0 <= var_10 < var_1\n and 0 <= var_11 < var_3\n and var_2[var_10][var_11] == \".\"\n and var_5[var_10][var_11] == 0\n ):\n var_12.append((var_10, var_11))\n var_5[var_10][var_11] = var_5[var_15][var_16] + 1\n\n\ndef func_2():\n global H, W, S, dist, dxy\n var_1, var_3 = tuple(map(int, var_9().split()))\n var_2 = tuple(var_9().rstrip() for _ in range(var_1))\n var_5 = [[0] * var_3 for _ in range(var_1)]\n var_5[0][0] = 1\n var_7 = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n func_1()\n var_14, var_15 = sum(var_13.count(\".\") for var_13 in var_2), var_5[-1][-1]\n var_4 = var_14 - var_15 if var_15 else -1\n print(var_4)\n\n\nif __name__ == \"__main__\":\n func_2()\n\n```\n\n## Code B:\n```python\nimport sys\nimport queue\n\nsys.setrecursionlimit(1000000)\n\n\ndef s(m, v, q):\n if q.empty():\n return -1\n k, i, j = q.get()\n if i == len(m) - 1 and j == len(m[i]) - 1:\n return k\n if i > 0 and m[i - 1][j] == \".\" and (i - 1, j) not in v:\n q.put((k + 1, i - 1, j))\n v.add((i - 1, j))\n if i < len(m) - 1 and m[i + 1][j] == \".\" and (i + 1, j) not in v:\n q.put((k + 1, i + 1, j))\n v.add((i + 1, j))\n if j > 0 and m[i][j - 1] == \".\" and (i, j - 1) not in v:\n q.put((k + 1, i, j - 1))\n v.add((i, j - 1))\n if j < len(m[i]) - 1 and m[i][j + 1] == \".\" and (i, j + 1) not in v:\n q.put((k + 1, i, j + 1))\n v.add((i, j + 1))\n return s(m, v, q)\n\n\nh, w = list(map(int, input().split()))\nm = [input() for _ in range(h)]\nq = queue.PriorityQueue()\nq.put((1, 0, 0))\nv = set((0, 0))\nc = s(m, v, q)\nif c == -1:\n print(-1)\nelse:\n print(h * w - c - sum([r.count(\"\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\n\nvar_9 = sys.stdin.readline\nfrom collections import deque\n\n\ndef func_1() -> None:\n var_12 = [(0, 0)]\n while var_12:\n var_15, var_16 = var_12.pop(0)\n for var_6, var_8 in var_7:\n var_10, var_11 = var_15 + var_6, var_16 + var_8\n if (\n 0 <= var_10 < var_1\n and 0 <= var_11 < var_3\n and var_2[var_10][var_11] == \".\"\n and var_5[var_10][var_11] == 0\n ):\n var_12.append((var_10, var_11))\n var_5[var_10][var_11] = var_5[var_15][var_16] + 1\n\n\ndef func_2():\n global H, W, S, dist, dxy\n var_1, var_3 = tuple(map(int, var_9().split()))\n var_2 = tuple(var_9().rstrip() for _ in range(var_1))\n var_5 = [[0] * var_3 for _ in range(var_1)]\n var_5[0][0] = 1\n var_7 = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n func_1()\n var_14, var_15 = sum(var_13.count(\".\") for var_13 in var_2), var_5[-1][-1]\n var_4 = var_14 - var_15 if var_15 else -1\n print(var_4)\n\n\nif __name__ == \"__main__\":\n func_2()\n", "code_b": "import sys\nimport queue\n\nsys.setrecursionlimit(1000000)\n\n\ndef s(m, v, q):\n if q.empty():\n return -1\n k, i, j = q.get()\n if i == len(m) - 1 and j == len(m[i]) - 1:\n return k\n if i > 0 and m[i - 1][j] == \".\" and (i - 1, j) not in v:\n q.put((k + 1, i - 1, j))\n v.add((i - 1, j))\n if i < len(m) - 1 and m[i + 1][j] == \".\" and (i + 1, j) not in v:\n q.put((k + 1, i + 1, j))\n v.add((i + 1, j))\n if j > 0 and m[i][j - 1] == \".\" and (i, j - 1) not in v:\n q.put((k + 1, i, j - 1))\n v.add((i, j - 1))\n if j < len(m[i]) - 1 and m[i][j + 1] == \".\" and (i, j + 1) not in v:\n q.put((k + 1, i, j + 1))\n v.add((i, j + 1))\n return s(m, v, q)\n\n\nh, w = list(map(int, input().split()))\nm = [input() for _ in range(h)]\nq = queue.PriorityQueue()\nq.put((1, 0, 0))\nv = set((0, 0))\nc = s(m, v, q)\nif c == -1:\n print(-1)\nelse:\n print(h * w - c - sum([r.count(\"\n", "expected": "A"} +{"pair_id": "python_736_forward", "problem_id": "p03436", "language": "python", "direction": "forward", "speedup": 2.0111, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nimport sys\nimport queue\n\nsys.setrecursionlimit(1000000)\n\n\ndef func_1(var_6, var_9, var_7):\n if var_7.empty():\n return -1\n var_5, var_3, var_4 = var_7.get()\n if var_3 == len(var_6) - 1 and var_4 == len(var_6[var_3]) - 1:\n return var_5\n if var_3 > 0 and var_6[var_3 - 1][var_4] == \".\" and (var_3 - 1, var_4) not in var_9:\n var_7.put((var_5 + 1, var_3 - 1, var_4))\n var_9.add((var_3 - 1, var_4))\n if var_3 < len(var_6) - 1 and var_6[var_3 + 1][var_4] == \".\" and (var_3 + 1, var_4) not in var_9:\n var_7.put((var_5 + 1, var_3 + 1, var_4))\n var_9.add((var_3 + 1, var_4))\n if var_4 > 0 and var_6[var_3][var_4 - 1] == \".\" and (var_3, var_4 - 1) not in var_9:\n var_7.put((var_5 + 1, var_3, var_4 - 1))\n var_9.add((var_3, var_4 - 1))\n if var_4 < len(var_6[var_3]) - 1 and var_6[var_3][var_4 + 1] == \".\" and (var_3, var_4 + 1) not in var_9:\n var_7.put((var_5 + 1, var_3, var_4 + 1))\n var_9.add((var_3, var_4 + 1))\n return func_1(var_6, var_9, var_7)\n\n\nvar_2, var_10 = list(map(int, input().split()))\nvar_6 = [input() for _ in range(var_2)]\nvar_7 = queue.PriorityQueue()\nvar_7.put((1, 0, 0))\nvar_9 = set((0, 0))\nvar_1 = func_1(var_6, var_9, var_7)\nif var_1 == -1:\n print(-1)\nelse:\n print(var_2 * var_10 - var_1 - sum([var_8.count(\"#\") for var_8 in var_6]))\n```\n\n## Code B:\n```python\n \nimport sys\n\nvar_9 = sys.stdin.readline\nfrom collections import deque\n\n\ndef func_1() -> None:\n var_12 = [(0, 0)]\n while var_12:\n var_15, var_16 = var_12.pop(0)\n for var_6, var_8 in var_7:\n var_10, var_11 = var_15 + var_6, var_16 + var_8 \n if 0 <= var_10 < var_1 and 0 <= var_11 < var_3 and var_2[var_10][var_11] == \".\" and var_5[var_10][var_11] == 0:\n var_12.append((var_10, var_11))\n var_5[var_10][var_11] = var_5[var_15][var_16] + 1\n\n\ndef func_2():\n global H, W, S, dist, dxy\n var_1, var_3 = tuple(map(int, var_9().split()))\n var_2 = tuple(var_9().rstrip() for _ in range(var_1))\n var_5 = [[0] * var_3 for _ in range(var_1)] \n var_5[0][0] = 1\n var_7 = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n func_1()\n var_14, var_15 = sum(var_13.count(\".\") for var_13 in var_2), var_5[-1][-1]\n var_4 = var_14 - var_15 if var_15 else -1\n print(var_4)\n\n\nif __name__ == \"__main__\":\n func_2()\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "import sys\nimport queue\n\nsys.setrecursionlimit(1000000)\n\n\ndef func_1(var_6, var_9, var_7):\n if var_7.empty():\n return -1\n var_5, var_3, var_4 = var_7.get()\n if var_3 == len(var_6) - 1 and var_4 == len(var_6[var_3]) - 1:\n return var_5\n if var_3 > 0 and var_6[var_3 - 1][var_4] == \".\" and (var_3 - 1, var_4) not in var_9:\n var_7.put((var_5 + 1, var_3 - 1, var_4))\n var_9.add((var_3 - 1, var_4))\n if var_3 < len(var_6) - 1 and var_6[var_3 + 1][var_4] == \".\" and (var_3 + 1, var_4) not in var_9:\n var_7.put((var_5 + 1, var_3 + 1, var_4))\n var_9.add((var_3 + 1, var_4))\n if var_4 > 0 and var_6[var_3][var_4 - 1] == \".\" and (var_3, var_4 - 1) not in var_9:\n var_7.put((var_5 + 1, var_3, var_4 - 1))\n var_9.add((var_3, var_4 - 1))\n if var_4 < len(var_6[var_3]) - 1 and var_6[var_3][var_4 + 1] == \".\" and (var_3, var_4 + 1) not in var_9:\n var_7.put((var_5 + 1, var_3, var_4 + 1))\n var_9.add((var_3, var_4 + 1))\n return func_1(var_6, var_9, var_7)\n\n\nvar_2, var_10 = list(map(int, input().split()))\nvar_6 = [input() for _ in range(var_2)]\nvar_7 = queue.PriorityQueue()\nvar_7.put((1, 0, 0))\nvar_9 = set((0, 0))\nvar_1 = func_1(var_6, var_9, var_7)\nif var_1 == -1:\n print(-1)\nelse:\n print(var_2 * var_10 - var_1 - sum([var_8.count(\"#\") for var_8 in var_6]))\n", "code_b": " \nimport sys\n\nvar_9 = sys.stdin.readline\nfrom collections import deque\n\n\ndef func_1() -> None:\n var_12 = [(0, 0)]\n while var_12:\n var_15, var_16 = var_12.pop(0)\n for var_6, var_8 in var_7:\n var_10, var_11 = var_15 + var_6, var_16 + var_8 \n if 0 <= var_10 < var_1 and 0 <= var_11 < var_3 and var_2[var_10][var_11] == \".\" and var_5[var_10][var_11] == 0:\n var_12.append((var_10, var_11))\n var_5[var_10][var_11] = var_5[var_15][var_16] + 1\n\n\ndef func_2():\n global H, W, S, dist, dxy\n var_1, var_3 = tuple(map(int, var_9().split()))\n var_2 = tuple(var_9().rstrip() for _ in range(var_1))\n var_5 = [[0] * var_3 for _ in range(var_1)] \n var_5[0][0] = 1\n var_7 = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n func_1()\n var_14, var_15 = sum(var_13.count(\".\") for var_13 in var_2), var_5[-1][-1]\n var_4 = var_14 - var_15 if var_15 else -1\n print(var_4)\n\n\nif __name__ == \"__main__\":\n func_2()\n", "expected": "B"} +{"pair_id": "python_736_reversed", "problem_id": "p03436", "language": "python", "direction": "reversed", "speedup": 2.0111, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\n \nimport sys\n\nvar_9 = sys.stdin.readline\nfrom collections import deque\n\n\ndef func_1() -> None:\n var_12 = [(0, 0)]\n while var_12:\n var_15, var_16 = var_12.pop(0)\n for var_6, var_8 in var_7:\n var_10, var_11 = var_15 + var_6, var_16 + var_8 \n if 0 <= var_10 < var_1 and 0 <= var_11 < var_3 and var_2[var_10][var_11] == \".\" and var_5[var_10][var_11] == 0:\n var_12.append((var_10, var_11))\n var_5[var_10][var_11] = var_5[var_15][var_16] + 1\n\n\ndef func_2():\n global H, W, S, dist, dxy\n var_1, var_3 = tuple(map(int, var_9().split()))\n var_2 = tuple(var_9().rstrip() for _ in range(var_1))\n var_5 = [[0] * var_3 for _ in range(var_1)] \n var_5[0][0] = 1\n var_7 = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n func_1()\n var_14, var_15 = sum(var_13.count(\".\") for var_13 in var_2), var_5[-1][-1]\n var_4 = var_14 - var_15 if var_15 else -1\n print(var_4)\n\n\nif __name__ == \"__main__\":\n func_2()\n```\n\n## Code B:\n```python\nimport sys\nimport queue\n\nsys.setrecursionlimit(1000000)\n\n\ndef func_1(var_6, var_9, var_7):\n if var_7.empty():\n return -1\n var_5, var_3, var_4 = var_7.get()\n if var_3 == len(var_6) - 1 and var_4 == len(var_6[var_3]) - 1:\n return var_5\n if var_3 > 0 and var_6[var_3 - 1][var_4] == \".\" and (var_3 - 1, var_4) not in var_9:\n var_7.put((var_5 + 1, var_3 - 1, var_4))\n var_9.add((var_3 - 1, var_4))\n if var_3 < len(var_6) - 1 and var_6[var_3 + 1][var_4] == \".\" and (var_3 + 1, var_4) not in var_9:\n var_7.put((var_5 + 1, var_3 + 1, var_4))\n var_9.add((var_3 + 1, var_4))\n if var_4 > 0 and var_6[var_3][var_4 - 1] == \".\" and (var_3, var_4 - 1) not in var_9:\n var_7.put((var_5 + 1, var_3, var_4 - 1))\n var_9.add((var_3, var_4 - 1))\n if var_4 < len(var_6[var_3]) - 1 and var_6[var_3][var_4 + 1] == \".\" and (var_3, var_4 + 1) not in var_9:\n var_7.put((var_5 + 1, var_3, var_4 + 1))\n var_9.add((var_3, var_4 + 1))\n return func_1(var_6, var_9, var_7)\n\n\nvar_2, var_10 = list(map(int, input().split()))\nvar_6 = [input() for _ in range(var_2)]\nvar_7 = queue.PriorityQueue()\nvar_7.put((1, 0, 0))\nvar_9 = set((0, 0))\nvar_1 = func_1(var_6, var_9, var_7)\nif var_1 == -1:\n print(-1)\nelse:\n print(var_2 * var_10 - var_1 - sum([var_8.count(\"#\") for var_8 in var_6]))\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": " \nimport sys\n\nvar_9 = sys.stdin.readline\nfrom collections import deque\n\n\ndef func_1() -> None:\n var_12 = [(0, 0)]\n while var_12:\n var_15, var_16 = var_12.pop(0)\n for var_6, var_8 in var_7:\n var_10, var_11 = var_15 + var_6, var_16 + var_8 \n if 0 <= var_10 < var_1 and 0 <= var_11 < var_3 and var_2[var_10][var_11] == \".\" and var_5[var_10][var_11] == 0:\n var_12.append((var_10, var_11))\n var_5[var_10][var_11] = var_5[var_15][var_16] + 1\n\n\ndef func_2():\n global H, W, S, dist, dxy\n var_1, var_3 = tuple(map(int, var_9().split()))\n var_2 = tuple(var_9().rstrip() for _ in range(var_1))\n var_5 = [[0] * var_3 for _ in range(var_1)] \n var_5[0][0] = 1\n var_7 = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n func_1()\n var_14, var_15 = sum(var_13.count(\".\") for var_13 in var_2), var_5[-1][-1]\n var_4 = var_14 - var_15 if var_15 else -1\n print(var_4)\n\n\nif __name__ == \"__main__\":\n func_2()\n", "code_b": "import sys\nimport queue\n\nsys.setrecursionlimit(1000000)\n\n\ndef func_1(var_6, var_9, var_7):\n if var_7.empty():\n return -1\n var_5, var_3, var_4 = var_7.get()\n if var_3 == len(var_6) - 1 and var_4 == len(var_6[var_3]) - 1:\n return var_5\n if var_3 > 0 and var_6[var_3 - 1][var_4] == \".\" and (var_3 - 1, var_4) not in var_9:\n var_7.put((var_5 + 1, var_3 - 1, var_4))\n var_9.add((var_3 - 1, var_4))\n if var_3 < len(var_6) - 1 and var_6[var_3 + 1][var_4] == \".\" and (var_3 + 1, var_4) not in var_9:\n var_7.put((var_5 + 1, var_3 + 1, var_4))\n var_9.add((var_3 + 1, var_4))\n if var_4 > 0 and var_6[var_3][var_4 - 1] == \".\" and (var_3, var_4 - 1) not in var_9:\n var_7.put((var_5 + 1, var_3, var_4 - 1))\n var_9.add((var_3, var_4 - 1))\n if var_4 < len(var_6[var_3]) - 1 and var_6[var_3][var_4 + 1] == \".\" and (var_3, var_4 + 1) not in var_9:\n var_7.put((var_5 + 1, var_3, var_4 + 1))\n var_9.add((var_3, var_4 + 1))\n return func_1(var_6, var_9, var_7)\n\n\nvar_2, var_10 = list(map(int, input().split()))\nvar_6 = [input() for _ in range(var_2)]\nvar_7 = queue.PriorityQueue()\nvar_7.put((1, 0, 0))\nvar_9 = set((0, 0))\nvar_1 = func_1(var_6, var_9, var_7)\nif var_1 == -1:\n print(-1)\nelse:\n print(var_2 * var_10 - var_1 - sum([var_8.count(\"#\") for var_8 in var_6]))\n", "expected": "A"} {"pair_id": "python_6759_forward", "problem_id": "p02938", "language": "python", "direction": "forward", "speedup": 153.6463, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1, var_4 = list(map(int, input().split()))\nvar_9 = 10**9 + 7\n\n\ndef func_1(var_1, var_4):\n if var_1 > var_4:\n return 0\n var_4 = bin(var_4)[2:]\n var_3 = len(var_4)\n var_11 = func_1(var_1, int(\"0\" + \"1\" * (var_3 - 1), 2))\n var_1 = bin(var_1)[2:]\n if len(var_1) != var_3:\n var_1 = \"1\" + \"0\" * (var_3 - 1)\n for var_6 in range(var_3):\n if var_4[var_6] == \"0\":\n continue\n var_5 = var_4[:var_6] + \"0\" + \"?\" * (var_3 - var_6 - 1)\n if var_6 == 0:\n var_5 = var_4\n for var_7 in range(var_3):\n if var_1[var_7] == \"1\" and var_7 != 0:\n continue\n var_2 = var_1[:var_7] + \"1\" + \"?\" * (var_3 - var_7 - 1)\n if var_7 == 0:\n var_2 = var_1\n var_12 = 1\n for var_10, var_8 in zip(var_5, var_2):\n if var_10 == \"0\" and var_8 == \"1\":\n var_12 *= 0\n break\n elif var_10 == \"?\" and var_8 == \"?\":\n var_12 = var_12 * 3 % var_9\n elif var_10 == \"?\" and var_8 == \"0\":\n var_12 = var_12 * 2 % var_9\n elif var_10 == \"1\" and var_8 == \"?\":\n var_12 = var_12 * 2 % var_9\n var_11 += var_12\n var_11 %= var_9\n return var_11\n\n\nprint((func_1(var_1, var_4)))\n\n```\n\n## Code B:\n```python\nvar_1, var_3 = list(map(int, input().split()))\nvar_2 = 10**9 + 7\nvar_11 = \"{:060b}\".format(var_1)[::-1]\nvar_14 = \"{:060b}\".format(var_3)[::-1]\nvar_12 = [\n [[[-1 for var_11 in range(2)] for var_10 in range(2)] for var_9 in range(2)]\n for var_8 in range(60)\n]\n\n\ndef func_1(var_13, var_5, var_6, var_7):\n if var_13 == -1:\n return 1\n if var_12[var_13][var_5][var_6][var_7] != -1:\n return var_12[var_13][var_5][var_6][var_7]\n var_15 = 0\n\n if var_11[var_13] == \"0\" or var_5:\n var_15 += func_1(\n var_13 - 1, var_5, 1 if var_6 or var_14[var_13] == \"1\" else 0, var_7\n )\n\n if (var_11[var_13] == \"0\" or var_5) and (var_14[var_13] == \"1\" or var_6) and var_7:\n var_15 += func_1(var_13 - 1, var_5, var_6, var_7)\n\n if var_14[var_13] == \"1\" or var_6:\n var_15 += func_1(\n var_13 - 1, 1 if var_11[var_13] == \"0\" or var_5 else 0, var_6, 1\n )\n var_15 %= var_2\n var_12[var_13][var_5][var_6][var_7] = var_15\n return var_15\n\n\nvar_4 = func_1(59, 0, 0, 0)\nprint(var_4)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1, var_4 = list(map(int, input().split()))\nvar_9 = 10**9 + 7\n\n\ndef func_1(var_1, var_4):\n if var_1 > var_4:\n return 0\n var_4 = bin(var_4)[2:]\n var_3 = len(var_4)\n var_11 = func_1(var_1, int(\"0\" + \"1\" * (var_3 - 1), 2))\n var_1 = bin(var_1)[2:]\n if len(var_1) != var_3:\n var_1 = \"1\" + \"0\" * (var_3 - 1)\n for var_6 in range(var_3):\n if var_4[var_6] == \"0\":\n continue\n var_5 = var_4[:var_6] + \"0\" + \"?\" * (var_3 - var_6 - 1)\n if var_6 == 0:\n var_5 = var_4\n for var_7 in range(var_3):\n if var_1[var_7] == \"1\" and var_7 != 0:\n continue\n var_2 = var_1[:var_7] + \"1\" + \"?\" * (var_3 - var_7 - 1)\n if var_7 == 0:\n var_2 = var_1\n var_12 = 1\n for var_10, var_8 in zip(var_5, var_2):\n if var_10 == \"0\" and var_8 == \"1\":\n var_12 *= 0\n break\n elif var_10 == \"?\" and var_8 == \"?\":\n var_12 = var_12 * 3 % var_9\n elif var_10 == \"?\" and var_8 == \"0\":\n var_12 = var_12 * 2 % var_9\n elif var_10 == \"1\" and var_8 == \"?\":\n var_12 = var_12 * 2 % var_9\n var_11 += var_12\n var_11 %= var_9\n return var_11\n\n\nprint((func_1(var_1, var_4)))\n", "code_b": "var_1, var_3 = list(map(int, input().split()))\nvar_2 = 10**9 + 7\nvar_11 = \"{:060b}\".format(var_1)[::-1]\nvar_14 = \"{:060b}\".format(var_3)[::-1]\nvar_12 = [\n [[[-1 for var_11 in range(2)] for var_10 in range(2)] for var_9 in range(2)]\n for var_8 in range(60)\n]\n\n\ndef func_1(var_13, var_5, var_6, var_7):\n if var_13 == -1:\n return 1\n if var_12[var_13][var_5][var_6][var_7] != -1:\n return var_12[var_13][var_5][var_6][var_7]\n var_15 = 0\n\n if var_11[var_13] == \"0\" or var_5:\n var_15 += func_1(\n var_13 - 1, var_5, 1 if var_6 or var_14[var_13] == \"1\" else 0, var_7\n )\n\n if (var_11[var_13] == \"0\" or var_5) and (var_14[var_13] == \"1\" or var_6) and var_7:\n var_15 += func_1(var_13 - 1, var_5, var_6, var_7)\n\n if var_14[var_13] == \"1\" or var_6:\n var_15 += func_1(\n var_13 - 1, 1 if var_11[var_13] == \"0\" or var_5 else 0, var_6, 1\n )\n var_15 %= var_2\n var_12[var_13][var_5][var_6][var_7] = var_15\n return var_15\n\n\nvar_4 = func_1(59, 0, 0, 0)\nprint(var_4)\n", "expected": "B"} {"pair_id": "python_6759_reversed", "problem_id": "p02938", "language": "python", "direction": "reversed", "speedup": 153.6463, "difficulty": "easy", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_1, var_3 = list(map(int, input().split()))\nvar_2 = 10**9 + 7\nvar_11 = \"{:060b}\".format(var_1)[::-1]\nvar_14 = \"{:060b}\".format(var_3)[::-1]\nvar_12 = [\n [[[-1 for var_11 in range(2)] for var_10 in range(2)] for var_9 in range(2)]\n for var_8 in range(60)\n]\n\n\ndef func_1(var_13, var_5, var_6, var_7):\n if var_13 == -1:\n return 1\n if var_12[var_13][var_5][var_6][var_7] != -1:\n return var_12[var_13][var_5][var_6][var_7]\n var_15 = 0\n\n if var_11[var_13] == \"0\" or var_5:\n var_15 += func_1(\n var_13 - 1, var_5, 1 if var_6 or var_14[var_13] == \"1\" else 0, var_7\n )\n\n if (var_11[var_13] == \"0\" or var_5) and (var_14[var_13] == \"1\" or var_6) and var_7:\n var_15 += func_1(var_13 - 1, var_5, var_6, var_7)\n\n if var_14[var_13] == \"1\" or var_6:\n var_15 += func_1(\n var_13 - 1, 1 if var_11[var_13] == \"0\" or var_5 else 0, var_6, 1\n )\n var_15 %= var_2\n var_12[var_13][var_5][var_6][var_7] = var_15\n return var_15\n\n\nvar_4 = func_1(59, 0, 0, 0)\nprint(var_4)\n\n```\n\n## Code B:\n```python\nvar_1, var_4 = list(map(int, input().split()))\nvar_9 = 10**9 + 7\n\n\ndef func_1(var_1, var_4):\n if var_1 > var_4:\n return 0\n var_4 = bin(var_4)[2:]\n var_3 = len(var_4)\n var_11 = func_1(var_1, int(\"0\" + \"1\" * (var_3 - 1), 2))\n var_1 = bin(var_1)[2:]\n if len(var_1) != var_3:\n var_1 = \"1\" + \"0\" * (var_3 - 1)\n for var_6 in range(var_3):\n if var_4[var_6] == \"0\":\n continue\n var_5 = var_4[:var_6] + \"0\" + \"?\" * (var_3 - var_6 - 1)\n if var_6 == 0:\n var_5 = var_4\n for var_7 in range(var_3):\n if var_1[var_7] == \"1\" and var_7 != 0:\n continue\n var_2 = var_1[:var_7] + \"1\" + \"?\" * (var_3 - var_7 - 1)\n if var_7 == 0:\n var_2 = var_1\n var_12 = 1\n for var_10, var_8 in zip(var_5, var_2):\n if var_10 == \"0\" and var_8 == \"1\":\n var_12 *= 0\n break\n elif var_10 == \"?\" and var_8 == \"?\":\n var_12 = var_12 * 3 % var_9\n elif var_10 == \"?\" and var_8 == \"0\":\n var_12 = var_12 * 2 % var_9\n elif var_10 == \"1\" and var_8 == \"?\":\n var_12 = var_12 * 2 % var_9\n var_11 += var_12\n var_11 %= var_9\n return var_11\n\n\nprint((func_1(var_1, var_4)))\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_1, var_3 = list(map(int, input().split()))\nvar_2 = 10**9 + 7\nvar_11 = \"{:060b}\".format(var_1)[::-1]\nvar_14 = \"{:060b}\".format(var_3)[::-1]\nvar_12 = [\n [[[-1 for var_11 in range(2)] for var_10 in range(2)] for var_9 in range(2)]\n for var_8 in range(60)\n]\n\n\ndef func_1(var_13, var_5, var_6, var_7):\n if var_13 == -1:\n return 1\n if var_12[var_13][var_5][var_6][var_7] != -1:\n return var_12[var_13][var_5][var_6][var_7]\n var_15 = 0\n\n if var_11[var_13] == \"0\" or var_5:\n var_15 += func_1(\n var_13 - 1, var_5, 1 if var_6 or var_14[var_13] == \"1\" else 0, var_7\n )\n\n if (var_11[var_13] == \"0\" or var_5) and (var_14[var_13] == \"1\" or var_6) and var_7:\n var_15 += func_1(var_13 - 1, var_5, var_6, var_7)\n\n if var_14[var_13] == \"1\" or var_6:\n var_15 += func_1(\n var_13 - 1, 1 if var_11[var_13] == \"0\" or var_5 else 0, var_6, 1\n )\n var_15 %= var_2\n var_12[var_13][var_5][var_6][var_7] = var_15\n return var_15\n\n\nvar_4 = func_1(59, 0, 0, 0)\nprint(var_4)\n", "code_b": "var_1, var_4 = list(map(int, input().split()))\nvar_9 = 10**9 + 7\n\n\ndef func_1(var_1, var_4):\n if var_1 > var_4:\n return 0\n var_4 = bin(var_4)[2:]\n var_3 = len(var_4)\n var_11 = func_1(var_1, int(\"0\" + \"1\" * (var_3 - 1), 2))\n var_1 = bin(var_1)[2:]\n if len(var_1) != var_3:\n var_1 = \"1\" + \"0\" * (var_3 - 1)\n for var_6 in range(var_3):\n if var_4[var_6] == \"0\":\n continue\n var_5 = var_4[:var_6] + \"0\" + \"?\" * (var_3 - var_6 - 1)\n if var_6 == 0:\n var_5 = var_4\n for var_7 in range(var_3):\n if var_1[var_7] == \"1\" and var_7 != 0:\n continue\n var_2 = var_1[:var_7] + \"1\" + \"?\" * (var_3 - var_7 - 1)\n if var_7 == 0:\n var_2 = var_1\n var_12 = 1\n for var_10, var_8 in zip(var_5, var_2):\n if var_10 == \"0\" and var_8 == \"1\":\n var_12 *= 0\n break\n elif var_10 == \"?\" and var_8 == \"?\":\n var_12 = var_12 * 3 % var_9\n elif var_10 == \"?\" and var_8 == \"0\":\n var_12 = var_12 * 2 % var_9\n elif var_10 == \"1\" and var_8 == \"?\":\n var_12 = var_12 * 2 % var_9\n var_11 += var_12\n var_11 %= var_9\n return var_11\n\n\nprint((func_1(var_1, var_4)))\n", "expected": "A"} {"pair_id": "python_2252_forward", "problem_id": "p03862", "language": "python", "direction": "forward", "speedup": 2.0879, "difficulty": "hard", "system_prompt": "You are a senior Performance Engineer with 10+ years of experience in Python optimization.\n\nYour task is to determine which code runs faster based on algorithmic complexity, data structure efficiency, and implementation details.", "user_prompt": "Compare the following two functionally equivalent Python implementations and determine which one is more efficient.\n\n## Code A:\n```python\nvar_2, var_3 = (int(var_8) for var_8 in input().split())\nvar_1 = [int(var_8) for var_8 in input().split()]\nvar_4 = []\nfor var_5 in range(var_2 - 1):\n var_4.append(var_1[var_5] + var_1[var_5 + 1] - var_3)\nvar_6 = 0\nfor var_5 in range(var_2 - 1):\n var_7 = min(var_4[var_5], var_1[var_5 + 1])\n var_6 += var_7\n var_4[var_5] -= var_7\n if var_5 + 1 < len(var_4):\n var_4[var_5 + 1] -= min(var_7, var_4[var_5 + 1])\n var_6 += var_4[var_5]\nprint((max(0, var_6)))\n\n```\n\n## Code B:\n```python\nvar_2, var_7 = list(map(int, input().split()))\nvar_1 = list(map(int, input().split()))\nvar_6 = 0\nvar_4 = 0\nfor var_3 in var_1:\n var_5 = max(0, var_6 + var_3 - var_7)\n var_6 = var_3 - var_5\n var_4 += var_5\nprint(var_4)\n\n```\n\nBased on algorithm complexity, data structures, loop efficiency, and other performance metrics, determine which implementation runs faster.\n\nYou must output ONLY the identifier of the faster implementation:\n- Output \"A\" if Code A is faster\n- Output \"B\" if Code B is faster\n\nYour response must be exactly one character: A or B\n\nNo explanation or additional text.", "code_a": "var_2, var_3 = (int(var_8) for var_8 in input().split())\nvar_1 = [int(var_8) for var_8 in input().split()]\nvar_4 = []\nfor var_5 in range(var_2 - 1):\n var_4.append(var_1[var_5] + var_1[var_5 + 1] - var_3)\nvar_6 = 0\nfor var_5 in range(var_2 - 1):\n var_7 = min(var_4[var_5], var_1[var_5 + 1])\n var_6 += var_7\n var_4[var_5] -= var_7\n if var_5 + 1 < len(var_4):\n var_4[var_5 + 1] -= min(var_7, var_4[var_5 + 1])\n var_6 += var_4[var_5]\nprint((max(0, var_6)))\n", "code_b": "var_2, var_7 = list(map(int, input().split()))\nvar_1 = list(map(int, input().split()))\nvar_6 = 0\nvar_4 = 0\nfor var_3 in var_1:\n var_5 = max(0, var_6 + var_3 - var_7)\n var_6 = var_3 - var_5\n var_4 += var_5\nprint(var_4)\n", "expected": "B"}