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Intuition of Code :
The problem focuses on finding the maximum sum of a subarray of size k where all elements are distinct. A naive approach would involve checking every possible subarray, but given the constraints, this is computationally expensive. Instead, we can leverage the sliding window technique to process the array efficiently in O(n).

The challenge lies in ensuring that the current window of size k maintains the distinct property. To achieve this, we track the elements in the current window using a hash map. This map keeps the count of each element, allowing us to efficiently detect duplicates.

When adding a new element to the window, if it causes a duplicate, we must adjust the window by removing elements from the left until the distinct condition is restored. Additionally, as we slide the window, we dynamically update the sum of the elements, ensuring we always have the sum of the valid window at hand.

By combining the sliding window approach with a hash map for duplicate detection and sum tracking, we can efficiently find the maximum sum of all valid subarrays that meet the criteria.
APPROACH:
1:Initialize Data Structures:
A hash map to track element counts within the current window.
A variable to store the current window sum and another for the maximum sum.
2:Set Up Initial Window:
Populate the first k elements of the window.
Calculate their sum and ensure all elements are distinct.
3:Iterate Through the Array:
For each new element added to the window, update its count in the hash map and adjust the sum.
Remove the element that exits the window, adjusting the hash map and sum accordingly.
Check if the window contains k distinct elements. If so, update the maximum sum.
4:Handle Edge Cases:
If no valid window exists (e.g., all elements are duplicates), return 0.
This approach ensures O(n) time complexity by processing each element at most twice—once when it enters the window and once when it exits.

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