



'''simple docstring'''



import numpy as np
from PIL import Image



def   __lowerCAmelCase					(							lowerCamelCase   :						np.ndarray				,		lowerCamelCase   :						int				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	__lowerCAmelCase												=  np.array(lowerCamelCase					)
	if arr.shape[0] != arr.shape[1]:
		raise ValueError("The input array is not a square matrix"					)
	__lowerCAmelCase												=  0
	__lowerCAmelCase												=  0
	__lowerCAmelCase												=  0
	__lowerCAmelCase												=  0

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

	return updated_arr



def   __lowerCAmelCase					(							lowerCamelCase   :						np.ndarray				,		lowerCamelCase   :						int				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	__lowerCAmelCase												=  np.array(lowerCamelCase					)
	if arr.shape[0] != arr.shape[1]:
		raise ValueError("The input array is not a square matrix"					)
	__lowerCAmelCase												=  0
	__lowerCAmelCase												=  0
	__lowerCAmelCase												=  0
	__lowerCAmelCase												=  0

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

	return updated_arr


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

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

	# Loading the image
	lowerCAmelCase	:		Optional[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()




'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase	:		Union[str, Any]				=   {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Tuple				=   ['''DistilBertTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Tuple				=   [
	    '''DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DistilBertForMaskedLM''',
	    '''DistilBertForMultipleChoice''',
	    '''DistilBertForQuestionAnswering''',
	    '''DistilBertForSequenceClassification''',
	    '''DistilBertForTokenClassification''',
	    '''DistilBertModel''',
	    '''DistilBertPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		List[str]				=   [
	    '''TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFDistilBertForMaskedLM''',
	    '''TFDistilBertForMultipleChoice''',
	    '''TFDistilBertForQuestionAnswering''',
	    '''TFDistilBertForSequenceClassification''',
	    '''TFDistilBertForTokenClassification''',
	    '''TFDistilBertMainLayer''',
	    '''TFDistilBertModel''',
	    '''TFDistilBertPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase	:		Optional[int]				=   [
	    '''FlaxDistilBertForMaskedLM''',
	    '''FlaxDistilBertForMultipleChoice''',
	    '''FlaxDistilBertForQuestionAnswering''',
	    '''FlaxDistilBertForSequenceClassification''',
	    '''FlaxDistilBertForTokenClassification''',
	    '''FlaxDistilBertModel''',
	    '''FlaxDistilBertPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_distilbert import (
	    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    DistilBertConfig,
	    DistilBertOnnxConfig,
	)
	from .tokenization_distilbert import DistilBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_distilbert_fast import DistilBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_distilbert import (
		    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DistilBertForMaskedLM,
		    DistilBertForMultipleChoice,
		    DistilBertForQuestionAnswering,
		    DistilBertForSequenceClassification,
		    DistilBertForTokenClassification,
		    DistilBertModel,
		    DistilBertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_distilbert import (
		    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDistilBertForMaskedLM,
		    TFDistilBertForMultipleChoice,
		    TFDistilBertForQuestionAnswering,
		    TFDistilBertForSequenceClassification,
		    TFDistilBertForTokenClassification,
		    TFDistilBertMainLayer,
		    TFDistilBertModel,
		    TFDistilBertPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_distilbert import (
		    FlaxDistilBertForMaskedLM,
		    FlaxDistilBertForMultipleChoice,
		    FlaxDistilBertForQuestionAnswering,
		    FlaxDistilBertForSequenceClassification,
		    FlaxDistilBertForTokenClassification,
		    FlaxDistilBertModel,
		    FlaxDistilBertPreTrainedModel,
		)

else:
	import sys

	lowerCAmelCase	:		List[str]				=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)