feat: implement Monotonic Queue and sliding window maximum function with test cases

data_structures/queue/Monotonic_Queue.py

@@ -0,0 +1,51 @@
+"""
+A Monotonic Queue is a data structure that supports efficient insertion, deletion,
+and retrieval of elements in a specific order, typically in increasing or decreasing order.
+"""
+
+from collections import deque
+
+
+class MonotonicQueue:
+    def __init__(self):
+        self.deque = deque()
+
+    def push(self, value):
+        while self.deque and self.deque[-1] < value:
+            self.deque.pop()
+        self.deque.append(value)
+
+    def max(self):
+        return self.deque[0]
+
+    def pop(self, value):
+        if self.deque and self.deque[0] == value:
+            self.deque.popleft()
+
+
+def sliding_window_max(nums, k):
+    if not nums or k == 0:
+        return []
+    if k >= len(nums):
+        return [max(nums)]
+    if k == 1:
+        return nums
+
+    q = MonotonicQueue()
+    result = []
+    for i in range(len(nums)):
+        if i < k - 1:
+            q.push(nums[i])
+        else:
+            q.push(nums[i])
+            result.append(q.max())
+            q.pop(nums[i - k + 1])
+    return result
+
+
+# Test cases
+print(sliding_window_max([], 3))  # Edge case: Empty list
+print(sliding_window_max([1, 2], 3))  # Edge case: k > len(nums)
+print(sliding_window_max([1, 3, 2, 5, 4], 1))  # Edge case: k == 1
+print(sliding_window_max([1, 3, 2, 5, 4], 5))  # Edge case: k == len(nums)
+print(sliding_window_max([8, 5, 10, 7, 9, 4, 15, 12, 90, 13], 3))  # Normal case