prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Concatenate/consolidate all algorithms with different implementations
  ### Feature description
  
  There are lots of algorithms with the same concept but different implementations/methods in different files. All these should be moved into one file
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `dynamic_programming/longest_sub_array.py`
  Content:
  ```
  1 """
  2 Author  : Yvonne
  3 
  4 This is a pure Python implementation of Dynamic Programming solution to the
  5     longest_sub_array problem.
  6 
  7 The problem is  :
  8 Given an array, to find the longest and continuous sub array and get the max sum of the
  9     sub array in the given array.
  10 """
  11 
  12 
  13 class SubArray:
  14     def __init__(self, arr):
  15         # we need a list not a string, so do something to change the type
  16         self.array = arr.split(",")
  17 
  18     def solve_sub_array(self):
  19         rear = [int(self.array[0])] * len(self.array)
  20         sum_value = [int(self.array[0])] * len(self.array)
  21         for i in range(1, len(self.array)):
  22             sum_value[i] = max(
  23                 int(self.array[i]) + sum_value[i - 1], int(self.array[i])
  24             )
  25             rear[i] = max(sum_value[i], rear[i - 1])
  26         return rear[len(self.array) - 1]
  27 
  28 
  29 if __name__ == "__main__":
  30     whole_array = input("please input some numbers:")
  31     array = SubArray(whole_array)
  32     re = array.solve_sub_array()
  33     print(("the results is:", re))
  34
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/dynamic_programming/longest_sub_array.py b/dynamic_programming/longest_sub_array.py
  deleted file mode 100644
  --- a/dynamic_programming/longest_sub_array.py
  +++ /dev/null
  @@ -1,33 +0,0 @@
  -"""
  -Author  : Yvonne
  -
  -This is a pure Python implementation of Dynamic Programming solution to the
  -    longest_sub_array problem.
  -
  -The problem is  :
  -Given an array, to find the longest and continuous sub array and get the max sum of the
  -    sub array in the given array.
  -"""
  -
  -
  -class SubArray:
  -    def __init__(self, arr):
  -        # we need a list not a string, so do something to change the type
  -        self.array = arr.split(",")
  -
  -    def solve_sub_array(self):
  -        rear = [int(self.array[0])] * len(self.array)
  -        sum_value = [int(self.array[0])] * len(self.array)
  -        for i in range(1, len(self.array)):
  -            sum_value[i] = max(
  -                int(self.array[i]) + sum_value[i - 1], int(self.array[i])
  -            )
  -            rear[i] = max(sum_value[i], rear[i - 1])
  -        return rear[len(self.array) - 1]
  -
  -
  -if __name__ == "__main__":
  -    whole_array = input("please input some numbers:")
  -    array = SubArray(whole_array)
  -    re = array.solve_sub_array()
  -    print(("the results is:", re))
  
verification_info: {"golden_diff": "diff --git a/dynamic_programming/longest_sub_array.py b/dynamic_programming/longest_sub_array.py\ndeleted file mode 100644\n--- a/dynamic_programming/longest_sub_array.py\n+++ /dev/null\n@@ -1,33 +0,0 @@\n-\"\"\"\n-Author  : Yvonne\n-\n-This is a pure Python implementation of Dynamic Programming solution to the\n-    longest_sub_array problem.\n-\n-The problem is  :\n-Given an array, to find the longest and continuous sub array and get the max sum of the\n-    sub array in the given array.\n-\"\"\"\n-\n-\n-class SubArray:\n-    def __init__(self, arr):\n-        # we need a list not a string, so do something to change the type\n-        self.array = arr.split(\",\")\n-\n-    def solve_sub_array(self):\n-        rear = [int(self.array[0])] * len(self.array)\n-        sum_value = [int(self.array[0])] * len(self.array)\n-        for i in range(1, len(self.array)):\n-            sum_value[i] = max(\n-                int(self.array[i]) + sum_value[i - 1], int(self.array[i])\n-            )\n-            rear[i] = max(sum_value[i], rear[i - 1])\n-        return rear[len(self.array) - 1]\n-\n-\n-if __name__ == \"__main__\":\n-    whole_array = input(\"please input some numbers:\")\n-    array = SubArray(whole_array)\n-    re = array.solve_sub_array()\n-    print((\"the results is:\", re))\n", "issue": "Concatenate/consolidate all algorithms with different implementations\n### Feature description\n\nThere are lots of algorithms with the same concept but different implementations/methods in different files. All these should be moved into one file\n", "before_files": [{"content": "\"\"\"\nAuthor  : Yvonne\n\nThis is a pure Python implementation of Dynamic Programming solution to the\n    longest_sub_array problem.\n\nThe problem is  :\nGiven an array, to find the longest and continuous sub array and get the max sum of the\n    sub array in the given array.\n\"\"\"\n\n\nclass SubArray:\n    def __init__(self, arr):\n        # we need a list not a string, so do something to change the type\n        self.array = arr.split(\",\")\n\n    def solve_sub_array(self):\n        rear = [int(self.array[0])] * len(self.array)\n        sum_value = [int(self.array[0])] * len(self.array)\n        for i in range(1, len(self.array)):\n            sum_value[i] = max(\n                int(self.array[i]) + sum_value[i - 1], int(self.array[i])\n            )\n            rear[i] = max(sum_value[i], rear[i - 1])\n        return rear[len(self.array) - 1]\n\n\nif __name__ == \"__main__\":\n    whole_array = input(\"please input some numbers:\")\n    array = SubArray(whole_array)\n    re = array.solve_sub_array()\n    print((\"the results is:\", re))\n", "path": "dynamic_programming/longest_sub_array.py"}], "after_files": [{"content": null, "path": "dynamic_programming/longest_sub_array.py"}]}