





import numpy as np
from PIL import Image






def 							_lowerCAmelCase (  _a   :					Optional[int]					,   _a   :					Any					,   _a   :					Tuple			)	->			List[str]:
     lowerCAmelCase_					:       Optional[int]    =		np.array(lowerCamelCase_			)
     if arr.shape[0] != arr.shape[1]:
          raise ValueError("""The input array is not a square matrix"""			)
     lowerCAmelCase_					:       Tuple    =		0
     lowerCAmelCase_					:       Optional[Any]    =		0
     lowerCAmelCase_					:       str    =		0
     lowerCAmelCase_					:       str    =		0

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

     return updated_arr






def 							_lowerCAmelCase (  _a   :					List[str]					,   _a   :					int					,   _a   :					List[Any]			)	->			List[Any]:
     lowerCAmelCase_					:       Any    =		np.array(lowerCamelCase_			)
     if arr.shape[0] != arr.shape[1]:
          raise ValueError("""The input array is not a square matrix"""			)
     lowerCAmelCase_					:       Optional[Any]    =		0
     lowerCAmelCase_					:       int    =		0
     lowerCAmelCase_					:       int    =		0
     lowerCAmelCase_					:       Union[str, Any]    =		0

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

     return updated_arr


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

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

   # Loading the image
   UpperCAmelCase_					:   List[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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_					:   int 							=						{"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Optional[int] 							=						["""PLBartTokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						[
          """PLBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PLBartForCausalLM""",
          """PLBartForConditionalGeneration""",
          """PLBartForSequenceClassification""",
          """PLBartModel""",
          """PLBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_plbart import PLBartTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_plbart import (
                PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                PLBartForCausalLM,
                PLBartForConditionalGeneration,
                PLBartForSequenceClassification,
                PLBartModel,
                PLBartPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   List[str] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
