[**ONLY WORKS IN PYTHON**] DP + Sliding Window
Note: This DP + Sliding Window solution works only in Python and not in other languages. See below for more details.\n\n# Intuition\nThe problem involves partit
**Note**: This DP + Sliding Window solution works only in Python and not in other languages. [See below](#additional-insight-on-why-this-is-not-tle) for more details.\n\n# Intuition\nThe problem involves partitioning the target string into the minimum number of valid substrings, where each substring must match the prefix of any word in the given words list.\n\nThe dynamic programming (dp) array helps track the minimum number of substrings needed to form the target up to each position. \n\nA sliding window (left and right) is used to explore all possible substrings within the target, ensuring that each substring is valid according to the list of words.\n\n# Approach\n- Sliding Window: Use two pointers (left and right) to explore substrings in target. Adjust left until the substring target[left:right + 1] is a valid prefix in words.\n\n- Update DP: For each valid substring found, update the dp array to track the minimum number of valid substrings needed to reach the current position.\n\n- Expand Window: Increment right to continue expanding the window and check the next substring in the target. Return dp[-1] as the result.\n\n# Complexity\n- Time complexity: $$O(N^2M)$$ where N is the length of target and M is the number of words. The nested loop results from checking substrings within the target, and the prefix search in words contributes to the overall complexity.\n\n- Space complexity: $$O(N)$$ where N is the length of target\n\n# Additional Insight on Why This is not TLE\n\nThe solution\'s time complexity of $$O(N^2M)$$, might suggest it could run into a Time Limit Exceeded (TLE) error. However, the code works efficiently in Python due to the language\'s optimized string matching.\n\nThanks to **@lone17** for this insight:\n> The code works in Python because string matching in Python is absurdly well optimized, it basically has a bunch of caching under the hood for quick string matching. (Side fact, the authors of Python did this because they observed that string matching is one of the most used operation in Python)\n\nAs a result, this DP + Sliding Window approach is feasible in Python but might not perform as well in other languages that lack such string matching optimizations.\n\n\n# Code\n```python3 []\nclass Solution:\n    def minValidStrings(self, words: List[str], target: str) -> int:\n        dp = [0] * len(target)\n\n        def search(prefix):\n            for word in words:\n                if word.startswith(prefix): return True\n            return False \n\n        left, right = 0, 0\n        \n        for i in range(len(target)):\n            while not search(target[left:right + 1]) and left <= right: left += 1\n            \n            if left > right: return -1\n\n            substrLen = right - left + 1\n            dp[i] = 1 if i < substrLen else dp[i - substrLen] + 1\n\n            right += 1\n\n        return dp[-1]\n```
['Dynamic Programming', 'Sliding Window', 'Python3']