{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 19\n",
    "\n",
    "Find 200 and 239 in the 55 collection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>code_show=true; function code_toggle() { if (code_show){ $('div.nbinput').show(); } else { $('div.nbinput').hide(); } code_show = !code_show} $( document ).ready(code_toggle);</script><form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import IPython; IPython.display.HTML('''<script>code_show=true; function code_toggle() { if (code_show){ $('div.nbinput').show(); } else { $('div.nbinput').hide(); } code_show = !code_show} $( document ).ready(code_toggle);</script><form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
   ]
  },
  {
   "attachments": {
    "f183a599-8db2-4401-baeb-98718597f1dc.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 51. N-Queens\n",
    "\n",
    "Hard\n",
    "\n",
    "The n-queens puzzle is the problem of placing n queens on an n×n chessboard such that no two queens attack each other.\n",
    "\n",
    "Given an integer n, return all distinct solutions to the n-queens puzzle.\n",
    "\n",
    "Each solution contains a distinct board configuration of the n-queens' placement, where 'Q' and '.' both indicate a queen and an empty space respectively.\n",
    "\n",
    "![image51.png](attachment:f183a599-8db2-4401-baeb-98718597f1dc.png)\n",
    "\n",
    "Example:\n",
    "\n",
    "    Input: 4\n",
    "    Output: [\n",
    "     [\".Q..\",  // Solution 1\n",
    "      \"...Q\",\n",
    "      \"Q...\",\n",
    "      \"..Q.\"],\n",
    "\n",
    "     [\"..Q.\",  // Solution 2\n",
    "      \"Q...\",\n",
    "      \"...Q\",\n",
    "      \".Q..\"]\n",
    "    ]\n",
    "    Explanation: There exist two distinct solutions to the 4-queens puzzle as shown above.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['.Q..', '...Q', 'Q...', '..Q.'], ['..Q.', 'Q...', '...Q', '.Q..']]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clean solution from LeetCode\n",
    "# res = list of solutions,  [[1, 3, 0, 2], [2, 0, 3, 1]] for n=4, \n",
    "# Each solution is a list of j indices of the queen placement at each row. \n",
    "\n",
    "# cols, diag, and off_diag: lists of indices that have already been occupied. \n",
    "# dfs 需要知道整個 board 當前的狀態。cols, diag, and off_diag 是這個狀態抽象化的結果\n",
    "\n",
    "n = 4\n",
    "\n",
    "def solveNQueens(n):\n",
    "    def dfs(cols, diag, off_diag):\n",
    "        i = len(cols)\n",
    "        if i == n:\n",
    "            res.append(cols)\n",
    "            return\n",
    "        for j in range(n):\n",
    "            if j not in cols and j-i not in diag and j+i not in off_diag:\n",
    "                dfs(cols+[j], diag+[j-i], off_diag+[j+i])\n",
    "                \n",
    "    res = []\n",
    "    dfs([], [], []) \n",
    "    return [['.'*j + 'Q' + '.'*(n-j-1) for j in sol] for sol in res]\n",
    "\n",
    "solveNQueens(n)"
   ]
  },
  {
   "attachments": {
    "99ba6e78-b876-4073-a837-4940c8ea0cb5.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 986. Interval List Intersections\n",
    "Medium\n",
    "\n",
    "Given two lists of closed intervals, each list of intervals is pairwise disjoint and in sorted order.\n",
    "\n",
    "Return the intersection of these two interval lists.\n",
    "\n",
    "(Formally, a closed interval [a, b] (with a <= b) denotes the set of real numbers x with a <= x <= b.  The intersection of two closed intervals is a set of real numbers that is either empty, or can be represented as a closed interval.  For example, the intersection of [1, 3] and [2, 4] is [2, 3].)\n",
    "\n",
    "![image986.png](attachment:99ba6e78-b876-4073-a837-4940c8ea0cb5.png)\n",
    "\n",
    "    Example:\n",
    "\n",
    "    Input: A = [[0,2],[5,10],[13,23],[24,25]], B = [[1,5],[8,12],[15,24],[25,26]]\n",
    "    Output: [[1,2],[5,5],[8,10],[15,23],[24,24],[25,25]]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2], [5, 5], [8, 10], [15, 23], [24, 24], [25, 25]]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# my solution，如果要改開區間，把 no overlap 兩個條件都改成 <=\n",
    "\n",
    "A = [[0, 2], [5, 10], [13, 23], [24, 25]]\n",
    "B = [[1, 5], [8, 12], [15, 24], [25, 26]]\n",
    "\n",
    "def intervalIntersection(A, B):\n",
    "    i = j = 0\n",
    "    res = []\n",
    "    while i<len(A) and j<len(B):\n",
    "        if A[i][1] < B[j][0]:  # no overlap\n",
    "            i += 1\n",
    "        elif B[j][1] < A[i][0]:  # no overlap\n",
    "            j += 1\n",
    "        else:                   # there is overlap\n",
    "            \n",
    "            s = max(A[i][0], B[j][0])\n",
    "\n",
    "            if A[i][1] <= B[j][1]:\n",
    "                res.append([s, A[i][1]])\n",
    "                i += 1\n",
    "            else:\n",
    "                res.append([s, B[j][1]])\n",
    "                j += 1\n",
    "                \n",
    "    return res\n",
    "\n",
    "intervalIntersection(A, B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2], [5, 5], [8, 10], [15, 23], [24, 24], [25, 25]]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 花花醬解答，如果要改成開區間就用 s < e\n",
    "\n",
    "A = [[0, 2], [5, 10], [13, 23], [24, 25]]\n",
    "B = [[1, 5], [8, 12], [15, 24], [25, 26]]\n",
    "\n",
    "def intervalIntersection(A, B):\n",
    "    i = j = 0\n",
    "    res = []\n",
    "    while i<len(A) and j<len(B):\n",
    "        s = max(A[i][0], B[j][0])\n",
    "        e = min(A[i][1], B[j][1])\n",
    "        if s <= e:\n",
    "            res.append([s, e])\n",
    "            \n",
    "        if A[i][1] < B[j][1]:\n",
    "            i += 1\n",
    "        else:\n",
    "            j += 1\n",
    "        \n",
    "    return res\n",
    "            \n",
    "intervalIntersection(A, B) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 969. Pancake Sorting\n",
    "Medium\n",
    "\n",
    "Given an array of integers arr, sort the array by performing a series of pancake flips.\n",
    "\n",
    "In one pancake flip we do the following steps:\n",
    "\n",
    "    Choose an integer k where 1 <= k <= arr.length.\n",
    "    Reverse the sub-array arr[1...k].\n",
    "\n",
    "For example, if arr = [3,2,1,4] and we performed a pancake flip choosing k = 3, we reverse the sub-array [3,2,1], so arr = [1,2,3,4] after the pancake flip at k = 3.\n",
    "\n",
    "Return the k-values corresponding to a sequence of pancake flips that sort arr. Any valid answer that sorts the array within 10 * arr.length flips will be judged as correct.\n",
    "\n",
    "\n",
    "    Example:\n",
    "    \n",
    "    Input: arr = [3,2,4,1]\n",
    "    Output: [4,2,4,3]\n",
    "    Explanation: \n",
    "    We perform 4 pancake flips, with k values 4, 2, 4, and 3.\n",
    "    Starting state: arr = [3, 2, 4, 1]\n",
    "    After 1st flip (k = 4): arr = [1, 4, 2, 3]\n",
    "    After 2nd flip (k = 2): arr = [4, 1, 2, 3]\n",
    "    After 3rd flip (k = 4): arr = [3, 2, 1, 4]\n",
    "    After 4th flip (k = 3): arr = [1, 2, 3, 4], which is sorted.\n",
    "    Notice that we return an array of the chosen k values of the pancake flips.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 2, 3, 1, 2]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# code will be slightly cleaner if 1 is allowed in the flips\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "arr = [3, 2, 4, 1]\n",
    "\n",
    "def pancakeSort(arr):\n",
    "    n = len(arr)\n",
    "    res = []\n",
    "\n",
    "    for i in range(n, 1, -1):\n",
    "        k = np.argmax(arr[:i]) + 1\n",
    "        arr[:k] = arr[:k][::-1]    \n",
    "        arr[:i] = arr[:i][::-1]\n",
    "        res += [k, i]\n",
    "\n",
    "    return res\n",
    "\n",
    "pancakeSort(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 560. Subarray Sum Equals K\n",
    "Medium\n",
    "\n",
    "Given an array of integers and an integer k, you need to find the total number of continuous subarrays whose sum equals to k.\n",
    "\n",
    "\n",
    "    Example:\n",
    "    \n",
    "    Input:nums = [1,1,1], k = 2\n",
    "    Output: 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "# nums = [-1, -1, 1]\n",
    "# k = 0\n",
    "\n",
    "nums = [1, 1, 1]\n",
    "k = 2\n",
    "\n",
    "def subarraySum(nums, k):\n",
    "    cumsumCount = defaultdict(int)\n",
    "    cumsumCount[0] = 1\n",
    "    cumsum = 0\n",
    "    res = 0\n",
    "\n",
    "    for i, n in enumerate(nums):\n",
    "        cumsum += n\n",
    "        res += cumsumCount.get(cumsum-k, 0)  # 一定要放在 update 字典之前不然遇到 k=0 的情況會把自己算進去\n",
    "        cumsumCount[cumsum] += 1\n",
    "\n",
    "    return res\n",
    "\n",
    "subarraySum(nums, k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 64. Minimum Path Sum\n",
    "Medium\n",
    "\n",
    "Given a m x n grid filled with non-negative numbers, find a path from top left to bottom right which minimizes the sum of all numbers along its path.\n",
    "\n",
    "Note: You can only move either down or right at any point in time.\n",
    "\n",
    "    Example:\n",
    "    \n",
    "    Input:\n",
    "    [\n",
    "      [1,3,1],\n",
    "      [1,5,1],\n",
    "      [4,2,1]\n",
    "    ]\n",
    "    Output: 7\n",
    "    Explanation: Because the path 1→3→1→1→1 minimizes the sum.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = [\n",
    "    [1, 3, 1], \n",
    "    [1, 5, 1], \n",
    "    [4, 2, 1]\n",
    "]\n",
    "\n",
    "def minPathSum(grid):\n",
    "    m = len(grid)\n",
    "    n = len(grid[0])\n",
    "    \n",
    "    for i in range(1, m):\n",
    "        grid[i][0] += grid[i-1][0] \n",
    "        \n",
    "    for j in range(1, n):\n",
    "        grid[0][j] += grid[0][j-1] \n",
    "    \n",
    "    for i in range(1, m):\n",
    "        for j in range(1, n):\n",
    "            grid[i][j] += min(grid[i-1][j], grid[i][j-1])\n",
    "        \n",
    "    return grid[-1][-1]\n",
    "    \n",
    "minPathSum(grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 47. Permutations II\n",
    "Medium\n",
    "\n",
    "Given a collection of numbers that might contain duplicates, return all possible unique permutations.\n",
    "\n",
    "\n",
    "    Example:\n",
    "    \n",
    "    Input: [1,1,2]\n",
    "    Output:\n",
    "    [\n",
    "      [1,1,2],\n",
    "      [1,2,1],\n",
    "      [2,1,1]\n",
    "    ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[3, 3, 5], [3, 5, 3], [5, 3, 3]]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums = [3, 3, 5]\n",
    "\n",
    "def permuteUnique(nums):\n",
    "    if not nums:\n",
    "        return [nums]\n",
    "    else:\n",
    "        idx = {}\n",
    "        for i, n in enumerate(nums):\n",
    "            if n not in idx:\n",
    "                idx[n] = i\n",
    "\n",
    "        res = []\n",
    "        for n, i in idx.items():\n",
    "            for l in permuteUnique(nums[:i] + nums[i+1:]):\n",
    "                res.append([n]+l)\n",
    "\n",
    "        return res\n",
    "\n",
    "permuteUnique(nums)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15. 3Sum\n",
    "Medium\n",
    "\n",
    "Given an array nums of n integers, are there elements a, b, c in nums such that a + b + c = 0? Find all unique triplets in the array which gives the sum of zero.\n",
    "\n",
    "Notice that the solution set must not contain duplicate triplets.\n",
    "\n",
    "\n",
    "    Example:\n",
    "    \n",
    "    Input: nums = [-1,0,1,2,-1,-4]\n",
    "    Output: [[-1,-1,2],[-1,0,1]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[-1, -1, 2], [-1, 0, 1]]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums = [-1, 0, 1, 2, -1, -4]\n",
    "\n",
    "def threeSum(nums):\n",
    "    nums = sorted(nums)\n",
    "    n = len(nums)\n",
    "    i = 0\n",
    "    j = 1\n",
    "    k = n-1\n",
    "    \n",
    "    res = []\n",
    "    for i in range(n-2):\n",
    "        if i > 0 and nums[i] == nums[i-1]:    # skip duplicate numbers\n",
    "            continue\n",
    "            \n",
    "        j = i+1\n",
    "        k = n-1\n",
    "        while j<k:\n",
    "            s = nums[i] + nums[j] + nums[k]\n",
    "            if s > 0:    k -= 1\n",
    "            elif s < 0:  j += 1\n",
    "            elif s == 0: \n",
    "                res.append([nums[i], nums[j], nums[k]])\n",
    "                while j < k and nums[j] == nums[j+1]:     # skip duplicate numbers\n",
    "                    j += 1\n",
    "                while j < k and nums[k] == nums[k-1]:     # skip duplicate numbers\n",
    "                    k -= 1\n",
    "                j += 1\n",
    "                k -= 1  \n",
    "    return res\n",
    "\n",
    "threeSum(nums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# solution making use of two sum; slow but will pass\n",
    "\n",
    "nums = [-1, 0, 1, 2, -1, -4]\n",
    "\n",
    "def twoSum(nums, target):\n",
    "    '''\n",
    "    This is a stronger version of twoSum that can handle multiple solutions as well as no solution\n",
    "    '''\n",
    "    allSln = set([])\n",
    "    s = set([])\n",
    "    for n in nums:\n",
    "        if target - n in s:\n",
    "            allSln.add((n, target-n))\n",
    "        s.add(n)\n",
    "\n",
    "    return allSln\n",
    "\n",
    "def threeSum(nums):\n",
    "    res = set([])\n",
    "    for i, a in enumerate(nums):\n",
    "        for b, c in twoSum(nums[i+1:], -a):\n",
    "            res.add(tuple(sorted([a, b, c])))\n",
    "\n",
    "    return [list(sln) for sln in res]\n",
    "\n",
    "# twoSum([1, 7, 5, 5, 4, 4, 4, 4, 5, 3, 3], 8)\n",
    "# twoSum([1, 2], 8)\n",
    "\n",
    "threeSum(nums)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 104. Maximum Depth of Binary Tree\n",
    "Easy\n",
    "\n",
    "Given a binary tree, find its maximum depth.\n",
    "\n",
    "The maximum depth is the number of nodes along the longest path from the root node down to the farthest leaf node.\n",
    "\n",
    "Note: A leaf is a node with no children.\n",
    "\n",
    "\n",
    "    Example:\n",
    "    \n",
    "    Given binary tree [3,9,20,null,null,15,7],\n",
    "\n",
    "        3\n",
    "       / \\\n",
    "      9  20\n",
    "        /  \\\n",
    "       15   7\n",
    "\n",
    "    return its depth = 3.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definition for a binary tree node.\n",
    "# class TreeNode:\n",
    "#     def __init__(self, val=0, left=None, right=None):\n",
    "#         self.val = val\n",
    "#         self.left = left\n",
    "#         self.right = right\n",
    "\n",
    "def maxDepth(root):\n",
    "    if root==None:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1 + max(maxDepth(root.left), maxDepth(root.right))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 344. Reverse String\n",
    "Easy\n",
    "\n",
    "Write a function that reverses a string. The input string is given as an array of characters char[].\n",
    "\n",
    "Do not allocate extra space for another array, you must do this by modifying the input array in-place with O(1) extra memory.\n",
    "\n",
    "You may assume all the characters consist of printable ascii characters.\n",
    "\n",
    "\n",
    "    Example:\n",
    "    \n",
    "    Input: [\"h\",\"e\",\"l\",\"l\",\"o\"]\n",
    "    Output: [\"o\",\"l\",\"l\",\"e\",\"h\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['o', 'l', 'l', 'e', 'h']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = ['h', 'e', 'l', 'l', 'o']\n",
    "\n",
    "def reverseString(s):\n",
    "    n = len(s)\n",
    "    for i in range(n//2):\n",
    "        s[i], s[-i-1] = s[-i-1], s[i]\n",
    "\n",
    "reverseString(s)\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 53. Maximum Subarray\n",
    "Easy\n",
    "\n",
    "Given an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum.\n",
    "\n",
    "Follow up: If you have figured out the O(n) solution, try coding another solution using the divide and conquer approach, which is more subtle.\n",
    "\n",
    "\n",
    "    Example:\n",
    "    \n",
    "    Input: nums = [-2, 1, -3, 4, -1, 2, 1, -5, 4]\n",
    "    Output: 6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# curMax 是以當前數字結尾的所有 subarray 中產生最大的 sum\n",
    "# 實測在 LeetCode 上把 for n in nums[1:] 換成 for i in range(1, len(nums)) 速度沒有明顯的差別\n",
    "\n",
    "nums = [-2, 1, -3, 4, -1, 2, 1, -5, 4]\n",
    "\n",
    "def maxSubArray(nums):\n",
    "    res = curMax = nums[0]\n",
    "    for n in nums[1:]:\n",
    "        curMax = max(n, curMax + n)\n",
    "        res = max(curMax, res)\n",
    "        \n",
    "    return res\n",
    "\n",
    "def maxSubArray_(nums):\n",
    "    '''Can use solution of 121 Best Time to Buy and Sell Stock because profit is sum of daily PnLs from consecutive days'''\n",
    "    \n",
    "    import numpy as np\n",
    "    return maxProfit(np.cumsum(nums))\n",
    "\n",
    "maxSubArray(nums)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Two Sum\n",
    "Easy\n",
    "\n",
    "Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target.\n",
    "\n",
    "You may assume that each input would have exactly one solution, and you may not use the same element twice.\n",
    "\n",
    "You can return the answer in any order.\n",
    "\n",
    "\n",
    "    Example:\n",
    "    \n",
    "    Input: nums = [2,7,11,15], target = 9\n",
    "    Output: [0,1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums = [2, 7, 11, 15]\n",
    "target = 9\n",
    "\n",
    "def twoSum(nums, target):\n",
    "    d = {}\n",
    "    for idx, n in enumerate(nums):\n",
    "        if target - n in d:\n",
    "            return [d[target - n], idx]\n",
    "        else:\n",
    "            d[n] = idx\n",
    "\n",
    "twoSum(nums, target)"
   ]
  }
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