import numpy as np
from PIL import Image
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							np.array(_lowercase						)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('''The input array is not a square matrix'''						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							0
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							0
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							0
						SCREAMING_SNAKE_CASE							:    List[str]							=							0

						# compute the shape of the output matrix
						SCREAMING_SNAKE_CASE							:    Tuple							=							(arr.shape[0] - size) // stride + 1
						# initialize the output matrix with zeros of shape maxpool_shape
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							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
																		SCREAMING_SNAKE_CASE							:    Dict							=							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
												SCREAMING_SNAKE_CASE							:    List[Any]							=							0
												SCREAMING_SNAKE_CASE							:    str							=							0

						return updated_arr
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							np.array(_lowercase						)
						if arr.shape[0] != arr.shape[1]:
												raise ValueError('''The input array is not a square matrix'''						)
						SCREAMING_SNAKE_CASE							:    Dict							=							0
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							0
						SCREAMING_SNAKE_CASE							:    str							=							0
						SCREAMING_SNAKE_CASE							:    Dict							=							0

						# compute the shape of the output matrix
						SCREAMING_SNAKE_CASE							:    Tuple							=							(arr.shape[0] - size) // stride + 1
						# initialize the output matrix with zeros of shape avgpool_shape
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							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
																		SCREAMING_SNAKE_CASE							:    int							=							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
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							0
												SCREAMING_SNAKE_CASE							:    str							=							0

						return updated_arr


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

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

					# Loading the image
					__UpperCamelCase		:       Union[str, Any]									=     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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase		:       List[Any]									=     {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     ['GLPNFeatureExtractor']
					__UpperCamelCase		:       List[str]									=     ['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase		:       Optional[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





