


import numpy as np
from PIL import Image

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : np.ndarray      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int    ):
     __UpperCamelCase   							=np.array(SCREAMING_SNAKE_CASE__    )
     if arr.shape[0] != arr.shape[1]:
          raise ValueError('The input array is not a square matrix'    )
     __UpperCamelCase   							=0
     __UpperCamelCase   							=0
     __UpperCamelCase   							=0
     __UpperCamelCase   							=0

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

     return updated_arr

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : np.ndarray      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int    ):
     __UpperCamelCase   							=np.array(SCREAMING_SNAKE_CASE__    )
     if arr.shape[0] != arr.shape[1]:
          raise ValueError('The input array is not a square matrix'    )
     __UpperCamelCase   							=0
     __UpperCamelCase   							=0
     __UpperCamelCase   							=0
     __UpperCamelCase   							=0

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

     return updated_arr


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

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

   # Loading the image
   _A			=						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 ....utils import logging


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_=None ,       A_=2048   )  ->	Any:
         __UpperCamelCase   							=config.__dict__
         __UpperCamelCase   							=modal_hidden_size
         if num_labels:
              __UpperCamelCase   							=num_labels





