






"""simple docstring"""



from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like



def 						lowercase   (    __snake_case    :			List[str]		, __snake_case    :			List[Any]=3     ):
		lowercase_			:			List[str]								=       int(mask / 2     )
		# copy image size
		lowercase_			:			Tuple								=       zeros_like(_A     )
		for i in range(_A		, gray_img.shape[0] - bd     ):
				for j in range(_A		, gray_img.shape[1] - bd     ):
						# get mask according with mask
						lowercase_			:			Optional[Any]								=       ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]     )
						# calculate mask median
						lowercase_			:			Optional[Any]								=       sort(_A     )[inta(divide((multiply(_A		, _A     ))		, 2     ) + 1     )]
						lowercase_			:			Union[str, Any]								=       median
		return median_img


if __name__ == "__main__":
						# read original image
						__A		:	Optional[Any]      =		imread('''../image_data/lena.jpg''')
						# turn image in gray scale value
						__A		:	Union[str, Any]      =		cvtColor(img, COLOR_BGR2GRAY)

						# get values with two different mask size
						__A		:	Tuple      =		median_filter(gray, 3)
						__A		:	str      =		median_filter(gray, 5)

						# show result images
						imshow('''median filter with 3x3 mask''', medianaxa)
						imshow('''median filter with 5x5 mask''', medianaxa)
						waitKey(0)











"""simple docstring"""



from typing import TYPE_CHECKING

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


__A		:	Any      =		{
    '''configuration_realm''': ['''REALM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RealmConfig'''],
    '''tokenization_realm''': ['''RealmTokenizer'''],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	Optional[int]      =		[
						    '''REALM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RealmEmbedder''',
						    '''RealmForOpenQA''',
						    '''RealmKnowledgeAugEncoder''',
						    '''RealmPreTrainedModel''',
						    '''RealmReader''',
						    '''RealmScorer''',
						    '''load_tf_weights_in_realm''',
						]
						__A		:	Union[str, Any]      =		['''RealmRetriever''']


if TYPE_CHECKING:
						from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
						from .tokenization_realm import RealmTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_realm import RealmTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_realm import (
												    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RealmEmbedder,
												    RealmForOpenQA,
												    RealmKnowledgeAugEncoder,
												    RealmPreTrainedModel,
												    RealmReader,
												    RealmScorer,
												    load_tf_weights_in_realm,
												)
												from .retrieval_realm import RealmRetriever


else:
						import sys

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



