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     __UpperCamelCase				(     _A		):
  		SCREAMING_SNAKE_CASE              =					["image_processor", "tokenizer"]
  		SCREAMING_SNAKE_CASE              =					"OneFormerImageProcessor"
  		SCREAMING_SNAKE_CASE              =					("CLIPTokenizer", "CLIPTokenizerFast")
  
  
  		def __init__(self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=None  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	int = 7_7  ,  __SCREAMING_SNAKE_CASE				:	int = 7_7  ,  **__SCREAMING_SNAKE_CASE				:	int):
  							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__(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	str=None  ,  __SCREAMING_SNAKE_CASE				:	Any=7_7):
  							if text_list is None:
  												raise ValueError("tokens cannot be None.")
  
  							A    =      self.tokenizer(__SCREAMING_SNAKE_CASE  ,  padding="max_length"  ,  max_length=__SCREAMING_SNAKE_CASE  ,  truncation=__SCREAMING_SNAKE_CASE)
  
  							A    ,					A    =      tokens["attention_mask"], tokens["input_ids"]
  
  							A    =      []
  							for attn_mask, input_id in zip(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  												A    =      torch.tensor(__SCREAMING_SNAKE_CASE) * torch.tensor(__SCREAMING_SNAKE_CASE)
  												token_inputs.append(token.unsqueeze(0))
  
  							A    =      torch.cat(__SCREAMING_SNAKE_CASE  ,  dim=0)
  							return token_inputs
  
  
  		def __call__(self				:	int  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	Tuple=None  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=None  ,  **__SCREAMING_SNAKE_CASE				:	List[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(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)
  
  							if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  												A    =      [task_inputs]
  
  							if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE) and all(isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE) for task_input in task_inputs):
  												A    =      []
  												for task in task_inputs:
  																	A    =      F"""the task is {task}"""
  																	task_token_inputs.append(__SCREAMING_SNAKE_CASE)
  												A    =      self._preprocess_text(__SCREAMING_SNAKE_CASE  ,  max_length=self.task_seq_length)
  							else:
  												raise TypeError("Task Inputs should be a string or a list of strings.")
  
  							if hasattr(__SCREAMING_SNAKE_CASE  ,  "text_inputs"):
  												A    =      encoded_inputs.text_inputs
  
  												A    =      []
  												for texts in texts_list:
  																	A    =      self._preprocess_text(__SCREAMING_SNAKE_CASE  ,  max_length=self.max_seq_length)
  																	text_inputs.append(text_input_list.unsqueeze(0))
  
  												A    =      torch.cat(__SCREAMING_SNAKE_CASE  ,  dim=0)
  
  							return encoded_inputs
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=None  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=None  ,  **__SCREAMING_SNAKE_CASE				:	Tuple):
  
  							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(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)
  
  							if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  												A    =      [task_inputs]
  
  							if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE) and all(isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE) for task_input in task_inputs):
  												A    =      []
  												for task in task_inputs:
  																	A    =      F"""the task is {task}"""
  																	task_token_inputs.append(__SCREAMING_SNAKE_CASE)
  												A    =      self._preprocess_text(__SCREAMING_SNAKE_CASE  ,  max_length=self.task_seq_length)
  							else:
  												raise TypeError("Task Inputs should be a string or a list of strings.")
  
  							if hasattr(__SCREAMING_SNAKE_CASE  ,  "text_inputs"):
  												A    =      encoded_inputs.text_inputs
  
  												A    =      []
  												for texts in texts_list:
  																	A    =      self._preprocess_text(__SCREAMING_SNAKE_CASE  ,  max_length=self.max_seq_length)
  																	text_inputs.append(text_input_list.unsqueeze(0))
  
  												A    =      torch.cat(__SCREAMING_SNAKE_CASE  ,  dim=0)
  
  							return encoded_inputs
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  *__SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  **__SCREAMING_SNAKE_CASE				:	List[Any]):
  							return self.image_processor.post_process_semantic_segmentation(*__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple  ,  *__SCREAMING_SNAKE_CASE				:	Tuple  ,  **__SCREAMING_SNAKE_CASE				:	List[Any]):
  							return self.image_processor.post_process_instance_segmentation(*__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  *__SCREAMING_SNAKE_CASE				:	str  ,  **__SCREAMING_SNAKE_CASE				:	Dict):
  							return self.image_processor.post_process_panoptic_segmentation(*__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      1
  					while b > 0:
  										if b & 1:
  															res *= a
  
  										a *= a
  										b >>= 1
  
  					return res
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      1
  					while b > 0:
  										if b & 1:
  															A    =      ((res % c) * (a % c)) % c
  
  										a *= a
  										b >>= 1
  
  					return res
  
  
  
  
  
  