

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a:      str								=     logging.get_logger(__name__)

_a:      Optional[Any]								=     {
    """MBZUAI/swiftformer-xs""": """https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json""",
}

class   __UpperCamelCase      (   lowercase  ):
	SCREAMING_SNAKE_CASE__      			=       'swiftformer'




	def __init__(						self		:  Any						,		lowerCAmelCase		:  int=3						,		lowerCAmelCase		:  Dict=[3, 3, 6, 4]						,		lowerCAmelCase		:  Optional[int]=[48, 56, 112, 220]						,		lowerCAmelCase		:  str=4						,		lowerCAmelCase		:  List[str]=[True, True, True, True]						,		lowerCAmelCase		:  Any="gelu"						,		lowerCAmelCase		:  List[Any]=3						,		lowerCAmelCase		:  Dict=2						,		lowerCAmelCase		:  Any=1						,		lowerCAmelCase		:  List[Any]=0.0						,		lowerCAmelCase		:  List[Any]=True						,		lowerCAmelCase		:  Union[str, Any]=1e-5						,		lowerCAmelCase		:  Optional[int]=1e-5						,		**lowerCAmelCase		:  Union[str, Any]						,		):


		'''simple docstring'''







		super().__init__(**lowerCAmelCase							)
		UpperCAmelCase_    			=     num_channels
		UpperCAmelCase_    			=     depths
		UpperCAmelCase_    			=     embed_dims
		UpperCAmelCase_    			=     mlp_ratio
		UpperCAmelCase_    			=     downsamples
		UpperCAmelCase_    			=     hidden_act
		UpperCAmelCase_    			=     down_patch_size
		UpperCAmelCase_    			=     down_stride
		UpperCAmelCase_    			=     down_pad
		UpperCAmelCase_    			=     drop_path_rate
		UpperCAmelCase_    			=     use_layer_scale
		UpperCAmelCase_    			=     layer_scale_init_value
		UpperCAmelCase_    			=     batch_norm_eps


class   __UpperCamelCase      (   lowercase  ):
	SCREAMING_SNAKE_CASE__      			=       version.parse('1.11'  )




	@property
	def  __A   (						self		:  Optional[Any]							):


		'''simple docstring'''







		return OrderedDict(
		    [
		        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
		    ]							)




	@property
	def  __A   (						self		:  Any							):


		'''simple docstring'''







		return 1e-4


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



def  __lowerCAmelCase (  A    ,       A=3      ):
	UpperCAmelCase_    			=     int(mask / 2      )
	# copy image size
	UpperCAmelCase_    			=     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
			UpperCAmelCase_    			=     ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
			# calculate mask median
			UpperCAmelCase_    			=     sort(A      )[inta(divide((multiply(A    ,       A      ))    ,       2      ) + 1      )]
			UpperCAmelCase_    			=     median
	return median_img


if __name__ == "__main__":
				# read original image
				_a:      Optional[int]								=     imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a:      Dict								=     cvtColor(img, COLOR_BGR2GRAY)

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

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