
import numpy as np
from PIL import Image


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  np.ndarray:
				__a										=    np.array(a__  )
				if arr.shape[0] != arr.shape[1]:
								raise ValueError('''The input array is not a square matrix'''  )
				__a										=    0
				__a										=    0
				__a										=    0
				__a										=    0

				# compute the shape of the output matrix
				__a										=    (arr.shape[0] - size) // stride + 1
				# initialize the output matrix with zeros of shape maxpool_shape
				__a										=    np.zeros((maxpool_shape, maxpool_shape)  )

				while i < arr.shape[0]:
								if i + size > arr.shape[0]:
												# if the end of the matrix is reached, break
												break
								while j < arr.shape[1]:
												# if the end of the matrix is reached, break
												if j + size > arr.shape[1]:
																break
												# compute the maximum of the pooling matrix
												__a										=    np.max(arr[i : i + size, j : j + size]  )
												# shift the pooling matrix by stride of column pixels
												j += stride
												mat_j += 1

								# shift the pooling matrix by stride of row pixels
								i += stride
								mat_i += 1

								# reset the column index to 0
								__a										=    0
								__a										=    0

				return updated_arr


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  np.ndarray:
				__a										=    np.array(a__  )
				if arr.shape[0] != arr.shape[1]:
								raise ValueError('''The input array is not a square matrix'''  )
				__a										=    0
				__a										=    0
				__a										=    0
				__a										=    0

				# compute the shape of the output matrix
				__a										=    (arr.shape[0] - size) // stride + 1
				# initialize the output matrix with zeros of shape avgpool_shape
				__a										=    np.zeros((avgpool_shape, avgpool_shape)  )

				while i < arr.shape[0]:
								# if the end of the matrix is reached, break
								if i + size > arr.shape[0]:
												break
								while j < arr.shape[1]:
												# if the end of the matrix is reached, break
												if j + size > arr.shape[1]:
																break
												# compute the average of the pooling matrix
												__a										=    int(np.average(arr[i : i + size, j : j + size]  )  )
												# shift the pooling matrix by stride of column pixels
												j += stride
												mat_j += 1

								# shift the pooling matrix by stride of row pixels
								i += stride
								mat_i += 1
								# reset the column index to 0
								__a										=    0
								__a										=    0

				return updated_arr


# Main Function
if __name__ == "__main__":
					from doctest import testmod

					testmod(name='avgpooling', verbose=True)

					# Loading the image
					A     :	str    = Image.open('path_to_image')

					# Converting the image to numpy array and maxpooling, displaying the result
					# Ensure that the image is a square matrix

					Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

					# Converting the image to numpy array and averagepooling, displaying the result
					# Ensure that the image is a square matrix

					Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  tuple[int | None, int | None, float]:
				if not arr:
								return None, None, 0
				if low == high:
								return low, high, arr[low]

				__a										=    (low + high) // 2
				__a							,     __a							,     __a										=    max_subarray(a__						,    a__						,    a__  )
				__a							,     __a							,     __a										=    max_subarray(a__						,    mid + 1						,    a__  )
				__a							,     __a							,     __a										=    max_cross_sum(a__						,    a__						,    a__						,    a__  )
				if left_sum >= right_sum and left_sum >= cross_sum:
								return left_low, left_high, left_sum
				elif right_sum >= left_sum and right_sum >= cross_sum:
								return right_low, right_high, right_sum
				return cross_left, cross_right, cross_sum


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__  )       ->  tuple[int, int, float]:
				__a							,     __a										=    float('''-inf'''  ), -1
				__a							,     __a										=    float('''-inf'''  ), -1

				__a										=    0
				for i in range(a__						,    low - 1						,    -1  ):
								summ += arr[i]
								if summ > left_sum:
												__a										=    summ
												__a										=    i

				__a										=    0
				for i in range(mid + 1						,    high + 1  ):
								summ += arr[i]
								if summ > right_sum:
												__a										=    summ
												__a										=    i

				return max_left, max_right, (left_sum + right_sum)


def     __lowerCAmelCase			(			a__  )       ->  float:
				__a										=    [randint(1						,    a__  ) for _ in range(a__  )]
				__a										=    time.time()
				max_subarray(a__						,    0						,    input_size - 1  )
				__a										=    time.time()
				return end - start


def     __lowerCAmelCase			(			)       ->  None:
				__a										=    [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
				__a										=    [time_max_subarray(a__  ) for input_size in input_sizes]
				print('''No of Inputs\t\tTime Taken'''  )
				for input_size, runtime in zip(a__						,    a__  ):
								print(a__						,    '''\t\t'''						,    a__  )
				plt.plot(a__						,    a__  )
				plt.xlabel('''Number of Inputs'''  )
				plt.ylabel('''Time taken in seconds'''  )
				plt.show()


if __name__ == "__main__":
					from doctest import testmod

					testmod()