code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
  					import torch
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							List[Any]							= ['''image_processor''', '''tokenizer''']
  			A	:							Dict							= '''OneFormerImageProcessor'''
  			A	:							str							= ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
  			def __init__(     self       : Any ,						lowerCAmelCase_       : List[str]=None ,						lowerCAmelCase_       : Optional[Any]=None ,						lowerCAmelCase_       : int = 77 ,						lowerCAmelCase_       : int = 77 ,						**lowerCAmelCase_       : List[Any]  )			->		Union[str, Any]:
  						if image_processor is None:
  									raise ValueError('You need to specify an `image_processor`.'  )
  						if tokenizer is None:
  									raise ValueError('You need to specify a `tokenizer`.'  )
  
  						__A=  max_seq_length
  						__A=  task_seq_length
  
  						super().__init__(lowerCAmelCase_ ,						lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : str ,						lowerCAmelCase_       : Union[str, Any]=None ,						lowerCAmelCase_       : int=77  )			->		List[Any]:
  						if text_list is None:
  									raise ValueError('tokens cannot be None.'  )
  
  						__A=  self.tokenizer(lowerCAmelCase_ ,						padding='max_length' ,						max_length=lowerCAmelCase_ ,						truncation=lowerCAmelCase_  )
  
  						__A,  __A=  tokens['attention_mask'], tokens['input_ids']
  
  						__A=  []
  						for attn_mask, input_id in zip(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  									__A=  torch.tensor(lowerCAmelCase_  ) * torch.tensor(lowerCAmelCase_  )
  									token_inputs.append(token.unsqueeze(0  )  )
  
  						__A=  torch.cat(lowerCAmelCase_ ,						dim=0  )
  						return token_inputs
  			def __call__(     self       : Dict ,						lowerCAmelCase_       : int=None ,						lowerCAmelCase_       : Dict=None ,						lowerCAmelCase_       : str=None ,						**lowerCAmelCase_       : Optional[Any]  )			->		Any:
  
  						if task_inputs is None:
  									raise ValueError('You have to specify the task_input. Found None.'  )
  						elif images is None:
  									raise ValueError('You have to specify the image. Found None.'  )
  
  						if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs  ):
  									raise ValueError('task_inputs must be semantic, instance, or panoptic.'  )
  
  						__A=  self.image_processor(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  									__A=  [task_inputs]
  
  						if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ) and all(isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ) for task_input in task_inputs  ):
  									__A=  []
  									for task in task_inputs:
  												__A=  F"""the task is {task}"""
  												task_token_inputs.append(lowerCAmelCase_  )
  									__A=  self._preprocess_text(lowerCAmelCase_ ,						max_length=self.task_seq_length  )
  						else:
  									raise TypeError('Task Inputs should be a string or a list of strings.'  )
  
  						if hasattr(lowerCAmelCase_ ,						'text_inputs'  ):
  									__A=  encoded_inputs.text_inputs
  
  									__A=  []
  									for texts in texts_list:
  												__A=  self._preprocess_text(lowerCAmelCase_ ,						max_length=self.max_seq_length  )
  												text_inputs.append(text_input_list.unsqueeze(0  )  )
  
  									__A=  torch.cat(lowerCAmelCase_ ,						dim=0  )
  
  						return encoded_inputs
  			def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : Optional[int]=None ,						lowerCAmelCase_       : str=None ,						lowerCAmelCase_       : int=None ,						**lowerCAmelCase_       : Union[str, Any]  )			->		Union[str, Any]:
  
  						if task_inputs is None:
  									raise ValueError('You have to specify the task_input. Found None.'  )
  						elif images is None:
  									raise ValueError('You have to specify the image. Found None.'  )
  
  						if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs  ):
  									raise ValueError('task_inputs must be semantic, instance, or panoptic.'  )
  
  						__A=  self.image_processor.encode_inputs(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  									__A=  [task_inputs]
  
  						if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ) and all(isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ) for task_input in task_inputs  ):
  									__A=  []
  									for task in task_inputs:
  												__A=  F"""the task is {task}"""
  												task_token_inputs.append(lowerCAmelCase_  )
  									__A=  self._preprocess_text(lowerCAmelCase_ ,						max_length=self.task_seq_length  )
  						else:
  									raise TypeError('Task Inputs should be a string or a list of strings.'  )
  
  						if hasattr(lowerCAmelCase_ ,						'text_inputs'  ):
  									__A=  encoded_inputs.text_inputs
  
  									__A=  []
  									for texts in texts_list:
  												__A=  self._preprocess_text(lowerCAmelCase_ ,						max_length=self.max_seq_length  )
  												text_inputs.append(text_input_list.unsqueeze(0  )  )
  
  									__A=  torch.cat(lowerCAmelCase_ ,						dim=0  )
  
  						return encoded_inputs
  			def 				lowerCAmelCase    (     self       : Dict ,						*lowerCAmelCase_       : Union[str, Any] ,						**lowerCAmelCase_       : Tuple  )			->		Optional[Any]:
  						return self.image_processor.post_process_semantic_segmentation(*lowerCAmelCase_ ,						**lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Tuple ,						*lowerCAmelCase_       : Tuple ,						**lowerCAmelCase_       : Union[str, Any]  )			->		int:
  						return self.image_processor.post_process_instance_segmentation(*lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  			def 				lowerCAmelCase    (     self       : List[Any] ,						*lowerCAmelCase_       : Optional[Any] ,						**lowerCAmelCase_       : int  )			->		List[Any]:
  						return self.image_processor.post_process_panoptic_segmentation(*lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import numpy as np
  from cva import destroyAllWindows, imread, imshow, waitKey
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Dict ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : int  )			->		Optional[int]:
  						__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(lowerCAmelCase_  )
  
  						__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  ) * 255
  			@classmethod
  			def 				lowerCAmelCase    (     cls       : Tuple ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int  )			->		float:
  						return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red
  
  			def 				lowerCAmelCase    (     self       : str  )			->		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=  (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
  					UpperCAmelCase__      =							[
  					    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()
  
  
  
  