
"""simple docstring"""


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



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase=3       )				-> Any:
	_lowerCAmelCase								=int(mask / 2       )
	# copy image size
	_lowerCAmelCase								=zeros_like(UpperCamelCase_       )
	for i in range(UpperCamelCase_    , gray_img.shape[0] - bd       ):
		for j in range(UpperCamelCase_    , gray_img.shape[1] - bd       ):
			# get mask according with mask
			_lowerCAmelCase								=ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]       )
			# calculate mask median
			_lowerCAmelCase								=sort(UpperCamelCase_       )[inta(divide((multiply(UpperCamelCase_    , UpperCamelCase_       ))    , 2       ) + 1       )]
			_lowerCAmelCase								=median
	return median_img


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

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

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










from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase				       = logging.get_logger(__name__)

_UpperCAmelCase				       = {
    'MCG-NJU/videomae-base': 'https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json',
}



class 							_UpperCamelCase    (		lowerCAmelCase_   ):
       _UpperCamelCase :    List[Any]   			=      '''videomae'''

       def __init__(						self:       Optional[Any]    ,      _SCREAMING_SNAKE_CASE:       List[str]=224    ,      _SCREAMING_SNAKE_CASE:       Dict=16    ,      _SCREAMING_SNAKE_CASE:       List[Any]=3    ,      _SCREAMING_SNAKE_CASE:       Tuple=16    ,      _SCREAMING_SNAKE_CASE:       Tuple=2    ,      _SCREAMING_SNAKE_CASE:       Optional[Any]=768    ,      _SCREAMING_SNAKE_CASE:       str=12    ,      _SCREAMING_SNAKE_CASE:       List[Any]=12    ,      _SCREAMING_SNAKE_CASE:       Dict=3072    ,      _SCREAMING_SNAKE_CASE:       Tuple="gelu"    ,      _SCREAMING_SNAKE_CASE:       List[Any]=0.0    ,      _SCREAMING_SNAKE_CASE:       List[Any]=0.0    ,      _SCREAMING_SNAKE_CASE:       Tuple=0.02    ,      _SCREAMING_SNAKE_CASE:       Optional[int]=1e-12    ,      _SCREAMING_SNAKE_CASE:       Dict=True    ,      _SCREAMING_SNAKE_CASE:       Dict=True    ,      _SCREAMING_SNAKE_CASE:       List[str]=6    ,      _SCREAMING_SNAKE_CASE:       Dict=384    ,      _SCREAMING_SNAKE_CASE:       Any=4    ,      _SCREAMING_SNAKE_CASE:       Dict=1536    ,      _SCREAMING_SNAKE_CASE:       Optional[int]=True    ,      **_SCREAMING_SNAKE_CASE:       str    ,      )				->       Dict:




              """simple docstring"""


              super().__init__(**_SCREAMING_SNAKE_CASE						)

              UpperCamelCase_      				=     image_size
              UpperCamelCase_      				=     patch_size
              UpperCamelCase_      				=     num_channels
              UpperCamelCase_      				=     num_frames
              UpperCamelCase_      				=     tubelet_size

              UpperCamelCase_      				=     hidden_size
              UpperCamelCase_      				=     num_hidden_layers
              UpperCamelCase_      				=     num_attention_heads
              UpperCamelCase_      				=     intermediate_size
              UpperCamelCase_      				=     hidden_act
              UpperCamelCase_      				=     hidden_dropout_prob
              UpperCamelCase_      				=     attention_probs_dropout_prob
              UpperCamelCase_      				=     initializer_range
              UpperCamelCase_      				=     layer_norm_eps
              UpperCamelCase_      				=     qkv_bias
              UpperCamelCase_      				=     use_mean_pooling

              UpperCamelCase_      				=     decoder_num_attention_heads
              UpperCamelCase_      				=     decoder_hidden_size
              UpperCamelCase_      				=     decoder_num_hidden_layers
              UpperCamelCase_      				=     decoder_intermediate_size
              UpperCamelCase_      				=     norm_pix_loss

