
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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase								=	{
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	["""MobileViTFeatureExtractor"""]
     UpperCamelCase								=	["""MobileViTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MobileViTForImageClassification""",
         """MobileViTForSemanticSegmentation""",
         """MobileViTModel""",
         """MobileViTPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFMobileViTForImageClassification""",
         """TFMobileViTForSemanticSegmentation""",
         """TFMobileViTModel""",
         """TFMobileViTPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_mobilevit import MobileViTFeatureExtractor
          from .image_processing_mobilevit import MobileViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mobilevit import (
              MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              MobileViTForImageClassification,
              MobileViTForSemanticSegmentation,
              MobileViTModel,
              MobileViTPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mobilevit import (
              TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFMobileViTForImageClassification,
              TFMobileViTForSemanticSegmentation,
              TFMobileViTModel,
              TFMobileViTPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




