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


_SCREAMING_SNAKE_CASE:						Optional[int]    	=       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE:						List[str]    	=       {
    '''MCG-NJU/videomae-base''': '''https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json''',
}

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      '''videomae'''



		def __init__(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Tuple=224  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=16  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=3  ,					__SCREAMING_SNAKE_CASE    :				Any=16  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=2  ,					__SCREAMING_SNAKE_CASE    :				Dict=768  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=12  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=12  ,					__SCREAMING_SNAKE_CASE    :				int=3_072  ,					__SCREAMING_SNAKE_CASE    :				List[Any]="gelu"  ,					__SCREAMING_SNAKE_CASE    :				str=0.0  ,					__SCREAMING_SNAKE_CASE    :				str=0.0  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=0.0_2  ,					__SCREAMING_SNAKE_CASE    :				str=1e-12  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=True  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=True  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=6  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=384  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=4  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=1_536  ,					__SCREAMING_SNAKE_CASE    :				Dict=True  ,					**__SCREAMING_SNAKE_CASE    :				Union[str, Any]  ,					):






					"""simple docstring"""
					super().__init__(**__SCREAMING_SNAKE_CASE   )

					_snake_case              =      image_size
					_snake_case              =      patch_size
					_snake_case              =      num_channels
					_snake_case              =      num_frames
					_snake_case              =      tubelet_size

					_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              =      qkv_bias
					_snake_case              =      use_mean_pooling

					_snake_case              =      decoder_num_attention_heads
					_snake_case              =      decoder_hidden_size
					_snake_case              =      decoder_num_hidden_layers
					_snake_case              =      decoder_intermediate_size
					_snake_case              =      norm_pix_loss


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

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      0
					# max greyscale value for #FFFFFF
					_snake_case              =      int(self.get_greyscale(255  ,					255  ,					255   )   )

					if not self.min_threshold < threshold < self.max_threshold:
								_snake_case              =      F'Factor value should be from 0 to {self.max_threshold}'
								raise ValueError(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      input_img
					_snake_case              =      threshold
					_snake_case       , _snake_case              =      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
					_snake_case              =      [
					    [0 for _ in range(self.height + 4   )] for __ in range(self.width + 1   )
					]
					_snake_case              =      np.ones((self.width, self.height, 3)  ,					np.uinta   ) * 255



		@classmethod
		def 				_a				(			cls    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






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







		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					for y in range(self.height   ):
								for x in range(self.width   ):
											_snake_case              =      int(self.get_greyscale(*self.input_img[y][x]   )   )
											if self.threshold > greyscale + self.error_table[y][x]:
														_snake_case              =      (0, 0, 0)
														_snake_case              =      greyscale + self.error_table[x][y]
											else:
														_snake_case              =      (255, 255, 255)
														_snake_case              =      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
			_SCREAMING_SNAKE_CASE:						Tuple    	=       [
			    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
			    for threshold in (1, 126, 130, 140)
			]

			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()

