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This approach leverages the property that reversing subarrays can reorder elements but will not affect the element counts. Thus, if the sorted versions of the arrays are the same, then the arrays can be made equal.
Time complexity: O(n log n) due to sorting.
Space complexity: O(1) for in-place sort.
1def canBeEqual(target, arr):
2 return sorted(target) == sorted(arr)This Python solution sorts the arrays and directly compares them using the equality operator.
Instead of sorting, we can simply count the occurrences of each element in both arrays. If the frequency distributions are the same, the arrays can be made equal.
Time complexity: O(n) for a single pass through the arrays.
Space complexity: O(1) as the frequency array size is constant.
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This solution uses an array to count occurrences of each element, incrementing for elements of target and decrementing for arr.