






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





class       __a  :







 def __init__(		self :       Optional[int]							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






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

      UpperCamelCase__      :       Union[str, Any]	    =  img
      UpperCamelCase__      :       Dict	    =  img.shape[1]
      UpperCamelCase__      :       int	    =  img.shape[0]
      UpperCamelCase__      :       Union[str, Any]	    =  dst_width
      UpperCamelCase__      :       Any	    =  dst_height

      UpperCamelCase__      :       str	    =  self.src_w / self.dst_w
      UpperCamelCase__      :       Optional[int]	    =  self.src_h / self.dst_h

      UpperCamelCase__      :       int	    =  (
          np.ones((self.dst_h, self.dst_w, 3)							,       np.uinta						) * 2_55
      )







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      for i in range(self.dst_h						):
           for j in range(self.dst_w						):
                UpperCamelCase__      :       Any	    =  self.img[self.get_y(SCREAMING_SNAKE_CASE						)][self.get_x(SCREAMING_SNAKE_CASE						)]







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      return int(self.ratio_x * x						)







 def  __lowercase (		self :       Union[str, Any]							,       SCREAMING_SNAKE_CASE :       int						):


      '''simple docstring'''






      return int(self.ratio_y * y						)


if __name__ == "__main__":
 lowerCamelCase    ,      lowerCamelCase     :							str    =800, 600
 lowerCamelCase     :							Optional[int]    =imread('''image_data/lena.jpg''', 1)
 lowerCamelCase     :							str    =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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase     :							List[Any]    =logging.get_logger(__name__)

lowerCamelCase     :							Optional[Any]    ={
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}





class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Union[str, Any]				=			'''bit'''
 _lowerCAmelCase	:	List[str]				=			['''preactivation''', '''bottleneck''']
 _lowerCAmelCase	:	Any				=			['''SAME''', '''VALID''']







 def __init__(		self :       str							,       SCREAMING_SNAKE_CASE :       Tuple=3							,       SCREAMING_SNAKE_CASE :       str=64							,       SCREAMING_SNAKE_CASE :       List[Any]=[2_56, 5_12, 10_24, 20_48]							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=[3, 4, 6, 3]							,       SCREAMING_SNAKE_CASE :       str="preactivation"							,       SCREAMING_SNAKE_CASE :       Any="relu"							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       List[Any]=32							,       SCREAMING_SNAKE_CASE :       Tuple=0.0							,       SCREAMING_SNAKE_CASE :       Dict=False							,       SCREAMING_SNAKE_CASE :       int=32							,       SCREAMING_SNAKE_CASE :       str=1							,       SCREAMING_SNAKE_CASE :       List[str]=None							,       SCREAMING_SNAKE_CASE :       Optional[int]=None							,       **SCREAMING_SNAKE_CASE :       int							,       ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      if layer_type not in self.layer_types:
           raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types						)}'						)
      if global_padding is not None:
           if global_padding.upper() in self.supported_padding:
                UpperCamelCase__      :       Any	    =  global_padding.upper()
           else:
                raise ValueError(F'Padding strategy {global_padding} not supported'						)
      UpperCamelCase__      :       Dict	    =  num_channels
      UpperCamelCase__      :       Dict	    =  embedding_size
      UpperCamelCase__      :       Tuple	    =  hidden_sizes
      UpperCamelCase__      :       Any	    =  depths
      UpperCamelCase__      :       Optional[int]	    =  layer_type
      UpperCamelCase__      :       int	    =  hidden_act
      UpperCamelCase__      :       str	    =  global_padding
      UpperCamelCase__      :       Any	    =  num_groups
      UpperCamelCase__      :       str	    =  drop_path_rate
      UpperCamelCase__      :       Optional[Any]	    =  embedding_dynamic_padding
      UpperCamelCase__      :       Tuple	    =  output_stride
      UpperCamelCase__      :       List[str]	    =  width_factor

      UpperCamelCase__      :       Any	    =  ["stem"] + [F'stage{idx}' for idx in range(1							,       len(SCREAMING_SNAKE_CASE						) + 1						)]
      UpperCamelCase__    ,					UpperCamelCase__      :       Union[str, Any]	    =  get_aligned_output_features_output_indices(
          out_features=SCREAMING_SNAKE_CASE							,       out_indices=SCREAMING_SNAKE_CASE							,       stage_names=self.stage_names						)