






import numpy as np
from PIL import Image
def      a_ (      _A      ,   _A      ,   _A  )			->			np.ndarray:







				"""simple docstring"""
				snake_case__									=     np.array(_A  )
				if arr.shape[0] != arr.shape[1]:
								raise ValueError('The input array is not a square matrix'  )
				snake_case__									=     0
				snake_case__									=     0
				snake_case__									=     0
				snake_case__									=     0

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

				return updated_arr
def      a_ (      _A      ,   _A      ,   _A  )			->			np.ndarray:







				"""simple docstring"""
				snake_case__									=     np.array(_A  )
				if arr.shape[0] != arr.shape[1]:
								raise ValueError('The input array is not a square matrix'  )
				snake_case__									=     0
				snake_case__									=     0
				snake_case__									=     0
				snake_case__									=     0

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

				return updated_arr


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

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

							# Loading the image
							__UpperCamelCase					:		int	  =						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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase					:		int	  =						{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""XGLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Optional[int]	  =						["""XGLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		List[str]	  =						[
							    """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XGLMForCausalLM""",
							    """XGLMModel""",
							    """XGLMPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Any	  =						[
							    """FlaxXGLMForCausalLM""",
							    """FlaxXGLMModel""",
							    """FlaxXGLMPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						[
							    """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXGLMForCausalLM""",
							    """TFXGLMModel""",
							    """TFXGLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)




