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dsa-solutions/lc-solutions/1600-1699/1652-defuse-the-bomb.md

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+---
+id: defuse-the-bomb
+title: Defuse the Bomb Solution
+sidebar_label: 1652-Defuse the Bomb
+tags:
+  - Circular Array
+  - Sliding Window
+  - LeetCode
+  - Java
+  - Python
+  - C++
+description: "This is a solution to the Defuse the Bomb problem on LeetCode."
+sidebar_position: 2
+---
+
+In this tutorial, we will solve the Defuse the Bomb problem using a circular array and sliding window approach. We will provide the implementation of the solution in C++, Java, and Python.
+
+## Problem Description
+
+You have a bomb to defuse, and your informer will provide you with a circular array `code` of length `n` and a key `k`. To decrypt the code, you must replace every number as follows:
+
+- If `k > 0`, replace the `i-th` number with the sum of the next `k` numbers.
+- If `k < 0`, replace the `i-th` number with the sum of the previous `k` numbers.
+- If `k == 0`, replace the `i-th` number with 0.
+
+As `code` is circular, the next element of `code[n-1]` is `code[0]`, and the previous element of `code[0]` is `code[n-1]`.
+
+### Examples
+
+**Example 1:**
+
+```
+Input: code = [5,7,1,4], k = 3
+Output: [12,10,16,13]
+Explanation: Each number is replaced by the sum of the next 3 numbers. The decrypted code is [7+1+4, 1+4+5, 4+5+7, 5+7+1]. Notice that the numbers wrap around.
+```
+
+**Example 2:**
+
+```
+Input: code = [1,2,3,4], k = 0
+Output: [0,0,0,0]
+Explanation: When k is zero, the numbers are replaced by 0. 
+```
+
+**Example 3:**
+
+```
+Input: code = [2,4,9,3], k = -2
+Output: [12,5,6,13]
+Explanation: The decrypted code is [3+9, 2+3, 4+2, 9+4]. Notice that the numbers wrap around again. If k is negative, the sum is of the previous numbers.
+```
+
+### Constraints
+
+- `n == code.length`
+- `1 <= n <= 100`
+- `1 <= code[i] <= 100`
+- `-(n - 1) <= k <= n - 1`
+
+---
+
+## Solution for Defuse the Bomb Problem
+
+### Intuition and Approach
+
+The problem can be solved using a sliding window approach to handle the circular nature of the array. Depending on the value of `k`, we sum the appropriate elements and use modular arithmetic to handle the circular behavior.
+
+<Tabs>
+<tabItem value="Brute Force" label="Brute Force">
+
+### Approach 1: Brute Force (Naive)
+
+The brute force approach involves iterating through each element and calculating the sum of the next or previous `k` elements based on the value of `k`.
+
+#### Code in Different Languages
+
+<Tabs>
+<TabItem value="C++" label="C++" default>
+<SolutionAuthor name="@ImmidiSivani"/>
+
+```cpp
+class Solution {
+public:
+    vector<int> decrypt(vector<int>& code, int k) {
+        int n = code.size();
+        vector<int> result(n, 0);
+
+        if (k == 0) return result;
+
+        for (int i = 0; i < n; ++i) {
+            int sum = 0;
+            for (int j = 1; j <= abs(k); ++j) {
+                if (k > 0) {
+                    sum += code[(i + j) % n];
+                } else {
+                    sum += code[(i - j + n) % n];
+                }
+            }
+            result[i] = sum;
+        }
+
+        return result;
+    }
+};
+```
+
+</TabItem>
+<TabItem value="Java" label="Java">
+<SolutionAuthor name="@ImmidiSivani"/>
+
+```java
+class Solution {
+    public int[] decrypt(int[] code, int k) {
+        int n = code.length;
+        int[] result = new int[n];
+
+        if (k == 0) return result;
+
+        for (int i = 0; i < n; ++i) {
+            int sum = 0;
+            for (int j = 1; j <= Math.abs(k); ++j) {
+                if (k > 0) {
+                    sum += code[(i + j) % n];
+                } else {
+                    sum += code[(i - j + n) % n];
+                }
+            }
+            result[i] = sum;
+        }
+
+        return result;
+    }
+}
+```
+
+</TabItem>
+<TabItem value="Python" label="Python">
+<SolutionAuthor name="@ImmidiSivani"/>
+
+```python
+class Solution:
+    def decrypt(self, code: List[int], k: int) -> List[int]:
+        n = len(code)
+        result = [0] * n
+
+        if k == 0:
+            return result
+
+        for i in range(n):
+            sum_val = 0
+            for j in range(1, abs(k) + 1):
+                if k > 0:
+                    sum_val += code[(i + j) % n]
+                else:
+                    sum_val += code[(i - j + n) % n]
+            result[i] = sum_val
+
+        return result
+```
+
+</TabItem>
+</Tabs>
+
+#### Complexity Analysis
+
+- Time Complexity: $O(n \cdot |k|)$ due to nested loops.
+- Space Complexity: $O(n)$ for the result array.
+- Where `n` is the length of the code array.
+- The time complexity is $O(n \cdot |k|)$ because we iterate through each element and calculate the sum of `k` elements.
+- The space complexity is $O(n)$ because we store the result in a new array.
+
+</tabItem>
+<tabItem value="Optimized" label="Optimized">
+
+### Approach 2: Sliding Window (Optimized)
+
+The sliding window approach uses a more efficient way to calculate the sum by maintaining a running sum and updating it as the window slides over the array.
+
+#### Code in Different Languages
+
+<Tabs>
+<TabItem value="C++" label="C++" default>
+<SolutionAuthor name="@ImmidiSivani"/>
+
+```cpp
+class Solution {
+public:
+    vector<int> decrypt(vector<int>& code, int k) {
+        int n = code.size();
+        vector<int> result(n, 0);
+
+        if (k == 0) return result;
+
+        int start = k > 0 ? 1 : k;
+        int end = k > 0 ? k : -1;
+
+        int sum = 0;
+        for (int i = start; i != end + 1; ++i) {
+            sum += code[(i + n) % n];
+        }
+
+        for (int i = 0; i < n; ++i) {
+            result[i] = sum;
+            sum -= code[(start + i + n) % n];
+            sum += code[(end + 1 + i + n) % n];
+        }
+
+        return result;
+    }
+};
+```
+
+</TabItem>
+<TabItem value="Java" label="Java">
+<SolutionAuthor name="@ImmidiSivani"/>
+
+```java
+class Solution {
+    public int[] decrypt(int[] code, int k) {
+        int n = code.length;
+        int[] result = new int[n];
+
+        if (k == 0) return result;
+
+        int start = k > 0 ? 1 : k;
+        int end = k > 0 ? k : -1;
+
+        int sum = 0;
+        for (int i = start; i != end + 1; ++i) {
+            sum += code[(i + n) % n];
+        }
+
+        for (int i = 0; i < n; ++i) {
+            result[i] = sum;
+            sum -= code[(start + i + n) % n];
+            sum += code[(end + 1 + i + n) % n];
+        }
+
+        return result;
+    }
+}
+```
+
+</TabItem>
+<TabItem value="Python" label="Python">
+<SolutionAuthor name="@ImmidiSivani"/>
+
+```python
+class Solution:
+    def decrypt(self, code: List[int], k: int) -> List[int]:
+        n = len(code)
+        result = [0] * n
+
+        if k == 0:
+            return result
+
+        start, end = (1, k) if k > 0 else (k, -1)
+
+        sum_val = sum(code[i % n] for i in range(start, end + 1))
+
+        for i in range(n):
+            result[i] = sum_val
+            sum_val -= code[(start + i) % n]
+            sum_val += code[(end + 1 + i) % n]
+
+        return result
+```
+
+</TabItem>
+</Tabs>
+
+#### Complexity Analysis
+
+- Time Complexity: $O(n)$ due to the sliding window.
+- Space Complexity: $O(n)$ for the result array.
+- Where `n` is the length of the code array.
+- The time complexity is $O(n
+
+)$ because we iterate through each element once with a running sum.
+- The space complexity is $O(n)$ because we store the result in a new array.
+
+</tabItem>
+</Tabs>
+
+---
+
+<h2>Authors:</h2>
+
+<div style={{display: 'flex', flexWrap: 'wrap', justifyContent: 'space-between', gap: '10px'}}>
+{['ImmidiSivani'].map(username => (
+ <Author key={username} username={username} />
+))}
+</div>