



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



class 		_lowercase		:

   def __init__(   self      :							int				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							int				)							->					Tuple:


    """simple docstring"""






    a							=		0
    # max greyscale value for #FFFFFF
    a							=		int(self.get_greyscale(255				, 255				, 255				)				)

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

    a							=		input_img
    a							=		threshold
    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				) * 255

   @classmethod
   def 		A				(   cls      :							List[str]				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							int				)							->					float:


    """simple docstring"""






    return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red




   def 		A				(   self      :							List[str]				)							->					None:


    """simple docstring"""






    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							=		(255, 255, 255)
       a							=		greyscale + self.error_table[x][y] - 255
      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
     A_		:  Union[str, Any]										=  [
         Burkes(imread('''image_data/lena.jpg''', 1), threshold)
         for threshold in (1, 1_26, 1_30, 1_40)
     ]

     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()








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


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"nystromformer"






						def __init__(   self   :   List[str]		, _A   :   Optional[Any]=30000		, _A   :   str=768		, _A   :   Optional[int]=12		, _A   :   Dict=12		, _A   :   Any=3072		, _A   :   str="gelu_new"		, _A   :   Optional[Any]=0.1		, _A   :   int=0.1		, _A   :   Dict=510		, _A   :   List[Any]=2		, _A   :   Optional[Any]=64		, _A   :   Union[str, Any]=64		, _A   :   List[Any]=65		, _A   :   Dict=False		, _A   :   str=0.0_2		, _A   :   Dict=1E-5		, _A   :   List[Any]=1		, _A   :   Optional[int]=0		, _A   :   List[Any]=2		, **_A   :   int		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      str           =						max_position_embeddings
							snake_case_		:      Tuple           =						hidden_size
							snake_case_		:      Union[str, Any]           =						num_hidden_layers
							snake_case_		:      str           =						num_attention_heads
							snake_case_		:      Optional[Any]           =						intermediate_size
							snake_case_		:      str           =						hidden_act
							snake_case_		:      str           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Union[str, Any]           =						initializer_range
							snake_case_		:      Union[str, Any]           =						type_vocab_size
							snake_case_		:      Dict           =						segment_means_seq_len
							snake_case_		:      Tuple           =						num_landmarks
							snake_case_		:      Any           =						conv_kernel_size
							snake_case_		:      List[str]           =						inv_coeff_init_option
							snake_case_		:      str           =						layer_norm_eps
							super().__init__(pad_token_id=_A		, bos_token_id=_A		, eos_token_id=_A		, **_A						)

