From 1209aceb03c665375b8d52b9d3c53cd9adfcfd8c Mon Sep 17 00:00:00 2001
From: Aditya <161931434+AdityaPatadiya@users.noreply.github.com>
Date: Mon, 21 Oct 2024 13:03:24 +0530
Subject: [PATCH 1/2] feat: implement Monotonic Queue and sliding window
 maximum function with test cases

---
 data_structures/queue/Monotonic_Queue.py | 48 ++++++++++++++++++++++++
 1 file changed, 48 insertions(+)
 create mode 100644 data_structures/queue/Monotonic_Queue.py

diff --git a/data_structures/queue/Monotonic_Queue.py b/data_structures/queue/Monotonic_Queue.py
new file mode 100644
index 000000000000..d45c2358bc00
--- /dev/null
+++ b/data_structures/queue/Monotonic_Queue.py
@@ -0,0 +1,48 @@
+"""
+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

From 4793aec30e43616d07c41022c6ba40b574e014f8 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Mon, 21 Oct 2024 14:55:22 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 data_structures/queue/Monotonic_Queue.py | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/data_structures/queue/Monotonic_Queue.py b/data_structures/queue/Monotonic_Queue.py
index d45c2358bc00..c645b9a4d779 100644
--- a/data_structures/queue/Monotonic_Queue.py
+++ b/data_structures/queue/Monotonic_Queue.py
@@ -1,10 +1,11 @@
 """
-A Monotonic Queue is a data structure that supports efficient insertion, deletion, 
+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()
@@ -21,6 +22,7 @@ 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 []
@@ -28,7 +30,7 @@ def sliding_window_max(nums, k):
         return [max(nums)]
     if k == 1:
         return nums
-    
+
     q = MonotonicQueue()
     result = []
     for i in range(len(nums)):
@@ -40,9 +42,10 @@ def sliding_window_max(nums, k):
             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([], 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