code: 
  
  
  
  import numpy as np
  from cva import destroyAllWindows, imread, imshow, waitKey
  
  
  
  
  
  
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def __init__(			self			:		str		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		int  )    ->				Tuple:
  							UpperCAmelCase_=     0
  							# max greyscale value for #FFFFFF
  							UpperCAmelCase_=     int(self.get_greyscale(255		,						255		,						255  )  )
  
  							if not self.min_threshold < threshold < self.max_threshold:
  													UpperCAmelCase_=     F"""Factor value should be from 0 to {self.max_threshold}"""
  													raise ValueError(__UpperCAmelCase  )
  
  							UpperCAmelCase_=     input_img
  							UpperCAmelCase_=     threshold
  							UpperCAmelCase_,     UpperCAmelCase_=     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
  							UpperCAmelCase_=     [
  							    [0 for _ in range(self.height + 4  )] for __ in range(self.width + 1  )
  							]
  							UpperCAmelCase_=     np.ones((self.width, self.height, 3)		,						np.uinta  ) * 255
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		Tuple		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		int  )    ->				float:
  							return 0.114 * blue + 0.587 * green + 0.2_126 * red
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				None:
  							for y in range(self.height  ):
  													for x in range(self.width  ):
  																			UpperCAmelCase_=     int(self.get_greyscale(*self.input_img[y][x]  )  )
  																			if self.threshold > greyscale + self.error_table[y][x]:
  																									UpperCAmelCase_=     (0, 0, 0)
  																									UpperCAmelCase_=     greyscale + self.error_table[x][y]
  																			else:
  																									UpperCAmelCase_=     (255, 255, 255)
  																									UpperCAmelCase_=     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       					=		[
  	    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()
  
  
  
  
  
style_context: 
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __A       					=		logging.get_logger(__name__)
  
  __A       					=		{
      '''ZinengTang/tvlt-base''': '''https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json''',
  }
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Tuple	     =							"tvlt"
  
  
  
  
  	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		List[str]=224		,						__UpperCAmelCase			:		List[str]=2_048		,						__UpperCAmelCase			:		Tuple=128		,						__UpperCAmelCase			:		Union[str, Any]=[16, 16]		,						__UpperCAmelCase			:		str=[16, 16]		,						__UpperCAmelCase			:		str=3		,						__UpperCAmelCase			:		List[str]=1		,						__UpperCAmelCase			:		Tuple=8		,						__UpperCAmelCase			:		Optional[Any]=768		,						__UpperCAmelCase			:		List[Any]=12		,						__UpperCAmelCase			:		Tuple=12		,						__UpperCAmelCase			:		List[str]=3_072		,						__UpperCAmelCase			:		List[Any]="gelu"		,						__UpperCAmelCase			:		str=0.0		,						__UpperCAmelCase			:		str=0.0		,						__UpperCAmelCase			:		List[Any]=0.02		,						__UpperCAmelCase			:		Optional[int]=1E-6		,						__UpperCAmelCase			:		Tuple=True		,						__UpperCAmelCase			:		List[Any]=False		,						__UpperCAmelCase			:		Union[str, Any]=16		,						__UpperCAmelCase			:		Any=512		,						__UpperCAmelCase			:		Optional[Any]=8		,						__UpperCAmelCase			:		Dict=2_048		,						__UpperCAmelCase			:		Dict=0.75		,						__UpperCAmelCase			:		Union[str, Any]=0.15		,						__UpperCAmelCase			:		Any="frame-level"		,						__UpperCAmelCase			:		int=True		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Union[str, Any]="classification"		,						**__UpperCAmelCase			:		Any		,						)    ->				Tuple:
  							super().__init__(**__UpperCAmelCase  )
  
  							if audio_mask_type not in ("frame-level", "patch_level"):
  													raise ValueError(
  													    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
  													    F"""got {audio_mask_type}"""  )
  
  							UpperCAmelCase_=     image_size
  							UpperCAmelCase_=     spectrogram_length
  							UpperCAmelCase_=     frequency_length
  							UpperCAmelCase_=     image_patch_size
  							UpperCAmelCase_=     audio_patch_size
  							UpperCAmelCase_=     num_image_channels
  							UpperCAmelCase_=     num_audio_channels
  							UpperCAmelCase_=     num_frames
  
  							UpperCAmelCase_=     hidden_size
  							UpperCAmelCase_=     num_hidden_layers
  							UpperCAmelCase_=     num_attention_heads
  							UpperCAmelCase_=     intermediate_size
  							UpperCAmelCase_=     hidden_act
  							UpperCAmelCase_=     hidden_dropout_prob
  							UpperCAmelCase_=     attention_probs_dropout_prob
  							UpperCAmelCase_=     initializer_range
  							UpperCAmelCase_=     layer_norm_eps
  							UpperCAmelCase_=     qkv_bias
  							UpperCAmelCase_=     use_mean_pooling
  
  							UpperCAmelCase_=     decoder_num_attention_heads
  							UpperCAmelCase_=     decoder_hidden_size
  							UpperCAmelCase_=     decoder_num_hidden_layers
  							UpperCAmelCase_=     decoder_intermediate_size
  							UpperCAmelCase_=     pixel_mask_ratio
  							UpperCAmelCase_=     audio_mask_ratio
  							UpperCAmelCase_=     audio_mask_type
  
  							UpperCAmelCase_=     task_matching
  							UpperCAmelCase_=     task_mae
  							UpperCAmelCase_=     loss_type
  
  
  
  
  