


'''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_  )







'''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()



