Dynamic programming approach with Python
Intuition\n> We can iterate through nums in reverse order to find the maximum OR reachable from each index. After this we will iterate through nums from each in
# Intuition\n> We can iterate through nums in reverse order to find the maximum OR reachable from each index. After this we will iterate through nums from each index until we will reach the target OR. We will use dynamic programming to improve time efficiency.\n<!-- Describe your first thoughts on how to solve this problem. -->\n\n# Approach\n<!-- Describe your approach to solving the problem. -->\n\n# Complexity\n- Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n\n- Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n\n# Code\n```\nimport sys\n\nclass Solution:\n    def smallestSubarrays(self, nums: List[int]) -> List[int]:\n\n        def helper(i, item, target):\n\n            #Dynamic programming\n            if (i, item, target) in dp:\n                return dp[i, item, target]\n            \n            #Has target been reached in the current index\n            temp = item | nums[i]\n            if temp == target:\n                dp[i, item, target] = 1\n            #Recursion call\n            else:\n                dp[i, item, target] = helper(i+1, temp, target) + 1\n\n            return dp[i, item, target]\n\n        #Increase recursion limit\n        sys.setrecursionlimit(10**6)\n        n = len(nums)\n\n        #Find the maximum or reachable from each index\n        item = 0\n        max_or = []\n        for i in range(n):\n            item |= nums[-i-1]\n            max_or.append(item)        \n        max_or.reverse()\n\n        #Iterate through the array\n        dp = {}\n        output = []\n        for i in range(n):\n            output.append(helper(i, 0, max_or[i]))\n\n        return output\n```
['Dynamic Programming', 'Bit Manipulation', 'Python3']