code: 
  
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
   import torch
  
  
  class 		_SCREAMING_SNAKE_CASE    (	__lowerCamelCase    ):
       __SCREAMING_SNAKE_CASE    				=						['image_processor', 'tokenizer']
       __SCREAMING_SNAKE_CASE    				=						'OneFormerImageProcessor'
       __SCREAMING_SNAKE_CASE    				=						('CLIPTokenizer', 'CLIPTokenizerFast')
  
  
       def __init__(     self       ,				A_=None       ,				A_=None       ,				A_ = 77       ,				A_ = 77       ,				**A_						)     ->   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`."""						)
  
         _UpperCAmelCase		:       List[Any]       =    max_seq_length
         _UpperCAmelCase		:       Dict       =    task_seq_length
  
         super().__init__(A_       ,				A_						)
  
  
       def 					__snake_case(     self       ,				A_=None       ,				A_=77						)     ->   Any:
  
  
  
  
  
  
         if text_list is None:
           raise ValueError("""tokens cannot be None."""						)
  
         _UpperCAmelCase		:       List[Any]       =    self.tokenizer(A_       ,				padding="""max_length"""       ,				max_length=A_       ,				truncation=A_						)
  
         _UpperCAmelCase,_UpperCAmelCase		:       Tuple       =    tokens["""attention_mask"""], tokens["""input_ids"""]
  
         _UpperCAmelCase		:       Optional[Any]       =    []
         for attn_mask, input_id in zip(A_       ,				A_						):
           _UpperCAmelCase		:       List[str]       =    torch.tensor(A_						) * torch.tensor(A_						)
           token_inputs.append(token.unsqueeze(0						)						)
  
         _UpperCAmelCase		:       Tuple       =    torch.cat(A_       ,				dim=0						)
         return token_inputs
  
  
       def __call__(     self       ,				A_=None       ,				A_=None       ,				A_=None       ,				**A_						)     ->   Dict:
  
  
  
  
  
  
         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."""						)
  
         _UpperCAmelCase		:       List[Any]       =    self.image_processor(A_       ,				A_       ,				A_       ,				**A_						)
  
         if isinstance(A_       ,				A_						):
           _UpperCAmelCase		:       Any       =    [task_inputs]
  
         if isinstance(A_       ,				A_						) and all(isinstance(A_       ,				A_						) for task_input in task_inputs						):
           _UpperCAmelCase		:       List[Any]       =    []
           for task in task_inputs:
             _UpperCAmelCase		:       List[Any]       =    F'''the task is {task}'''
             task_token_inputs.append(A_						)
           _UpperCAmelCase		:       Any       =    self._preprocess_text(A_       ,				max_length=self.task_seq_length						)
         else:
           raise TypeError("""Task Inputs should be a string or a list of strings."""						)
  
         if hasattr(A_       ,				"""text_inputs"""						):
           _UpperCAmelCase		:       int       =    encoded_inputs.text_inputs
  
           _UpperCAmelCase		:       List[Any]       =    []
           for texts in texts_list:
             _UpperCAmelCase		:       Optional[int]       =    self._preprocess_text(A_       ,				max_length=self.max_seq_length						)
             text_inputs.append(text_input_list.unsqueeze(0						)						)
  
           _UpperCAmelCase		:       Optional[Any]       =    torch.cat(A_       ,				dim=0						)
  
         return encoded_inputs
  
  
       def 					__snake_case(     self       ,				A_=None       ,				A_=None       ,				A_=None       ,				**A_						)     ->   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."""						)
  
         _UpperCAmelCase		:       Optional[int]       =    self.image_processor.encode_inputs(A_       ,				A_       ,				A_       ,				**A_						)
  
         if isinstance(A_       ,				A_						):
           _UpperCAmelCase		:       Optional[Any]       =    [task_inputs]
  
         if isinstance(A_       ,				A_						) and all(isinstance(A_       ,				A_						) for task_input in task_inputs						):
           _UpperCAmelCase		:       Any       =    []
           for task in task_inputs:
             _UpperCAmelCase		:       Dict       =    F'''the task is {task}'''
             task_token_inputs.append(A_						)
           _UpperCAmelCase		:       str       =    self._preprocess_text(A_       ,				max_length=self.task_seq_length						)
         else:
           raise TypeError("""Task Inputs should be a string or a list of strings."""						)
  
         if hasattr(A_       ,				"""text_inputs"""						):
           _UpperCAmelCase		:       Optional[int]       =    encoded_inputs.text_inputs
  
           _UpperCAmelCase		:       Any       =    []
           for texts in texts_list:
             _UpperCAmelCase		:       Optional[int]       =    self._preprocess_text(A_       ,				max_length=self.max_seq_length						)
             text_inputs.append(text_input_list.unsqueeze(0						)						)
  
           _UpperCAmelCase		:       Union[str, Any]       =    torch.cat(A_       ,				dim=0						)
  
         return encoded_inputs
  
  
       def 					__snake_case(     self       ,				*A_       ,				**A_						)     ->   Dict:
         return self.image_processor.post_process_semantic_segmentation(*A_       ,				**A_						)
  
  
       def 					__snake_case(     self       ,				*A_       ,				**A_						)     ->   List[Any]:
         return self.image_processor.post_process_instance_segmentation(*A_       ,				**A_						)
  
  
  
  
       def 					__snake_case(     self       ,				*A_       ,				**A_						)     ->   Optional[int]:
         return self.image_processor.post_process_panoptic_segmentation(*A_       ,				**A_						)
  
  
  
  
  
  
style_context: 
  
  
  from typing import Any
  
  
  class 		_SCREAMING_SNAKE_CASE    :
  
  
       def __init__(     self						):
         _UpperCAmelCase		:       Union[str, Any]       =    []
         _UpperCAmelCase		:       Dict       =    0
  
  
       def __str__(     self						):
         _UpperCAmelCase		:       Union[str, Any]       =    """<""" + str(self.stack						)[1:-1] + """>"""
         return printed
  
  
       def 					__snake_case(     self       ,				A_						):
         self.stack.append(A_						)
         _UpperCAmelCase		:       Optional[Any]       =    self.length + 1
  
  
       def 					__snake_case(     self						):
         self.rotate(1						)
         _UpperCAmelCase		:       List[Any]       =    self.stack[self.length - 1]
         _UpperCAmelCase		:       Optional[Any]       =    self.stack[:-1]
         self.rotate(self.length - 1						)
         _UpperCAmelCase		:       Union[str, Any]       =    self.length - 1
         return dequeued
  
  
       def 					__snake_case(     self       ,				A_						):
         for _ in range(A_						):
           _UpperCAmelCase		:       Dict       =    self.stack[0]
           _UpperCAmelCase		:       List[Any]       =    self.stack[1:]
           self.put(A_						)
           _UpperCAmelCase		:       Dict       =    self.length - 1
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Tuple       =    self.get()
         self.put(A_						)
         self.rotate(self.length - 1						)
         return front
  
  
       def 					__snake_case(     self						):
         return self.length
  
  
  
  
  
  