





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







class 		_lowerCAmelCase:

  """simple docstring"""







  def __init__(   self    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   Tuple:
     __A    =  0
     # max greyscale value for #FFFFFF
     __A    =  int(self.get_greyscale(2_55    ,							2_55    ,							2_55      )      )

     if not self.min_threshold < threshold < self.max_threshold:
        __A    =  f"Factor value should be from 0 to {self.max_threshold}"
        raise ValueError(lowercase_      )

     __A    =  input_img
     __A    =  threshold
     __A   ,		__A    =  self.input_img.shape[1], self.input_img.shape[0]

     # error table size (+4 columns and +1 row) greater than input image because of
     # lack of if statements
     __A    =  [
         [0 for _ in range(self.height + 4      )] for __ in range(self.width + 1      )
     ]
     __A    =  np.ones((self.width, self.height, 3)    ,							np.uinta      ) * 2_55

  @classmethod
  def 						SCREAMING_SNAKE_CASE__			(   cls    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase      )->   float:
     return 0.114 * blue + 0.587 * green + 0.2126 * red





  def 						SCREAMING_SNAKE_CASE__			(   self      )->   None:






     for y in range(self.height      ):
        for x in range(self.width      ):
           __A    =  int(self.get_greyscale(*self.input_img[y][x]      )      )
           if self.threshold > greyscale + self.error_table[y][x]:
              __A    =  (0, 0, 0)
              __A    =  greyscale + self.error_table[x][y]
           else:
              __A    =  (2_55, 2_55, 2_55)
              __A    =  greyscale + self.error_table[x][y] - 2_55
           self.error_table[y][x + 1] += int(8 / 32 * current_error      )
           self.error_table[y][x + 2] += int(4 / 32 * current_error      )
           self.error_table[y + 1][x] += int(8 / 32 * current_error      )
           self.error_table[y + 1][x + 1] += int(4 / 32 * current_error      )
           self.error_table[y + 1][x + 2] += int(2 / 32 * current_error      )
           self.error_table[y + 1][x - 1] += int(4 / 32 * current_error      )
           self.error_table[y + 1][x - 2] += int(2 / 32 * current_error      )


if __name__ == "__main__":
    # create Burke's instances with original images in greyscale
    _UpperCamelCase       :				Optional[int]           =			[
        Burkes(imread("""image_data/lena.jpg""", 1), threshold)
        for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
    ]

    for burkes in burkes_instances:
        burkes.process()

    for burkes in burkes_instances:
        imshow(
            F"""Original image with dithering threshold: {burkes.threshold}""",
            burkes.output_img,
        )

    waitKey(0)
    destroyAllWindows()




import copy

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


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   def __init__(							self,       lowercase_=256,       lowercase_=1024,       lowercase_=16,       lowercase_=16,       lowercase_=4,       lowercase_="gelu",       lowercase_=1E-6,       **lowercase_,       )				->       int:






     """simple docstring"""






     super().__init__(**lowercase_			)
     a__    =hidden_size
     a__    =image_size
     a__    =patch_size
     a__    =image_size // patch_size
     a__    =mask_input_channels
     a__    =num_point_embeddings
     a__    =hidden_act
     a__    =layer_norm_eps







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   def __init__(							self,       lowercase_=256,       lowercase_="relu",       lowercase_=2048,       lowercase_=2,       lowercase_=8,       lowercase_=2,       lowercase_=3,       lowercase_=3,       lowercase_=256,       lowercase_=1E-6,       **lowercase_,       )				->       int:






     """simple docstring"""






     super().__init__(**lowercase_			)
     a__    =hidden_size
     a__    =hidden_act
     a__    =mlp_dim
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =attention_downsample_rate
     a__    =num_multimask_outputs
     a__    =iou_head_depth
     a__    =iou_head_hidden_dim
     a__    =layer_norm_eps







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   def __init__(							self,       lowercase_=768,       lowercase_=256,       lowercase_=12,       lowercase_=12,       lowercase_=3,       lowercase_=1024,       lowercase_=16,       lowercase_="gelu",       lowercase_=1E-06,       lowercase_=0.0,       lowercase_=1E-10,       lowercase_=True,       lowercase_=4.0,       lowercase_=True,       lowercase_=True,       lowercase_=14,       lowercase_=[2, 5, 8, 11],       lowercase_=128,       lowercase_=None,       **lowercase_,       )				->       Union[str, Any]:






     """simple docstring"""






     super().__init__(**lowercase_			)

     a__    =hidden_size
     a__    =output_channels
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =num_channels
     a__    =image_size
     a__    =patch_size
     a__    =hidden_act
     a__    =layer_norm_eps
     a__    =attention_dropout
     a__    =initializer_range
     a__    =qkv_bias
     a__    =mlp_ratio
     a__    =use_abs_pos
     a__    =use_rel_pos
     a__    =window_size
     a__    =global_attn_indexes
     a__    =num_pos_feats
     a__    =int(hidden_size * mlp_ratio			) if mlp_dim is None else mlp_dim







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Optional[int]        =  'sam'
   lowerCamelCase__					:      str        =  True



   def __init__(							self,       lowercase_=None,       lowercase_=None,       lowercase_=None,       lowercase_=0.02,       **lowercase_,       )				->       Tuple:






     """simple docstring"""






     super().__init__(**lowercase_			)
     a__    =vision_config if vision_config is not None else {}
     a__    =prompt_encoder_config if prompt_encoder_config is not None else {}
     a__    =mask_decoder_config if mask_decoder_config is not None else {}

     if isinstance(lowercase_,       lowercase_			):
       a__    =vision_config.to_dict()
     if isinstance(lowercase_,       lowercase_			):
       a__    =prompt_encoder_config.to_dict()
     if isinstance(lowercase_,       lowercase_			):
       a__    =mask_decoder_config.to_dict()

     a__    =SamVisionConfig(**lowercase_			)
     a__    =SamPromptEncoderConfig(**lowercase_			)
     a__    =SamMaskDecoderConfig(**lowercase_			)
     a__    =initializer_range



   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =copy.deepcopy(self.__dict__			)
     a__    =self.vision_config.to_dict()
     a__    =self.prompt_encoder_config.to_dict()
     a__    =self.mask_decoder_config.to_dict()
     a__    =self.__class__.model_type
     return output
