



"""simple docstring"""







import numpy as np
from PIL import Image

def       _lowerCamelCase				(							UpperCAmelCase_ :       np.ndarray,	UpperCAmelCase_ :       int,	UpperCAmelCase_ :       int     )	-> np.ndarray:






	"""simple docstring"""





	A__											=					np.array(UpperCAmelCase_     )
	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				(							UpperCAmelCase_ :       np.ndarray,	UpperCAmelCase_ :       int,	UpperCAmelCase_ :       int     )	-> np.ndarray:






	"""simple docstring"""





	A__											=					np.array(UpperCAmelCase_     )
	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
			UpperCamelCase     =							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()






"""simple docstring"""

from __future__ import annotations

from collections.abc import Iterator
class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       value
          UpperCAmelCase          =       None
          UpperCAmelCase          =       None

class   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_				)    ->       None:
          UpperCAmelCase          =       tree




      def      a_   (				self ,							lowercase_				)    ->       int:
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left				) + self.depth_first_search(node.right				)
          )




      def __iter__(				self				)    ->       Iterator[int]:
          yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



