Simple || Sliding Window Solution || Python
\n# Approach\nThe approach is to find the longest substring in string s that can be transformed into a substring in string t while keeping the total transformat
\n# Approach\nThe approach is to find the longest substring in string `s` that can be transformed into a substring in string `t` while keeping the total transformation cost within a specified limit.\n\nIt does this by comparing characters in both strings and calculating the cost of transforming each character. Then, it moves through the string, adjusting the window of characters being considered, until it finds the longest substring that meets the cost constraint. Finally, it returns the length of this longest substring.\n\n# Complexity\n- Time complexity: O(N)\n\n- Space complexity: O(1)\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n\n# Code\n```\nclass Solution:\n    def equalSubstring(self, s: str, t: str, maxCost: int) -> int:\n        def getCost(char1, char2):\n            return abs(ord(char1)-ord(char2))\n        \n        l, r = 0, 0\n        n = len(s)\n        ans = 0\n        while r<n:\n            maxCost -= getCost(s[r], t[r])\n            while l<=r and maxCost<0:\n                maxCost += getCost(s[l], t[l])\n                l += 1\n            ans = max(ans, (r-l+1))\n            r += 1\n        return ans\n\n\n\n```
['Two Pointers', 'Sliding Window', 'Python3']