



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey







class 	_snake_case							:






   '''simple docstring'''






   def __init__(  self:			Any   ,lowerCamelCase_:			str   ,lowerCamelCase_:			int   ,lowerCamelCase_:			int				)     ->	Any:



       if dst_width < 0 or dst_height < 0:
           raise ValueError("""Destination width/height should be > 0"""				)

       UpperCAmelCase_			:    Tuple   						= img
       UpperCAmelCase_			:    List[Any]   						= img.shape[1]
       UpperCAmelCase_			:    Any   						= img.shape[0]
       UpperCAmelCase_			:    Optional[int]   						= dst_width
       UpperCAmelCase_			:    int   						= dst_height

       UpperCAmelCase_			:    Any   						= self.src_w / self.dst_w
       UpperCAmelCase_			:    List[str]   						= self.src_h / self.dst_h

       UpperCAmelCase_			:    Any   						= (
           np.ones((self.dst_h, self.dst_w, 3)   ,np.uinta				) * 255
       )


   def 	A__  (  self:			List[str]				)     ->	Optional[Any]:



       for i in range(self.dst_h				):
           for j in range(self.dst_w				):
               UpperCAmelCase_			:    int   						= self.img[self.get_y(a__				)][self.get_x(a__				)]


   def 	A__  (  self:			Union[str, Any]   ,lowerCamelCase_:			int				)     ->	str:
       return int(self.ratio_x * x				)






   def 	A__  (  self:			List[Any]   ,lowerCamelCase_:			int				)     ->	int:
       return int(self.ratio_y * y				)


if __name__ == "__main__":
      UpperCamelCase_						,UpperCamelCase_										=					800, 600
      UpperCamelCase_										=					imread('''image_data/lena.jpg''', 1)
      UpperCamelCase_										=					NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          F"Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}", n.output
      )
      waitKey(0)
      destroyAllWindows()







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


snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[Any]							=    'vit_msn'


      def __init__(self       :      Union[str, Any]				,			a__       :      Optional[Any]=768				,			a__       :      Optional[Any]=12				,			a__       :      Optional[int]=12				,			a__       :      Optional[int]=3072				,			a__       :      Union[str, Any]="gelu"				,			a__       :      str=0.0				,			a__       :      int=0.0				,			a__       :      Optional[Any]=0.0_2				,			a__       :      List[Any]=1E-06				,			a__       :      Optional[int]=224				,			a__       :      str=16				,			a__       :      Optional[Any]=3				,			a__       :      int=True				,			**a__       :      List[Any]				,			):





         """simple docstring"""



         super().__init__(**a__      )

         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   initializer_range
         __snake_case							       =   layer_norm_eps
         __snake_case							       =   image_size
         __snake_case							       =   patch_size
         __snake_case							       =   num_channels
         __snake_case							       =   qkv_bias

