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 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     ['image_processor', 'tokenizer']
    SCREAMING_SNAKE_CASE							=     'OneFormerImageProcessor'
    SCREAMING_SNAKE_CASE							=     ('CLIPTokenizer', 'CLIPTokenizerFast')
  
  
  
    def __init__(self       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase = 77       ,					_lowerCamelCase = 77       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          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__ :       Tuple						=						task_seq_length
  
          super().__init__(_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase=None       ,					_lowerCamelCase=77  ):
  
  
          """simple docstring"""
  
          if text_list is None:
                raise ValueError("""tokens cannot be None."""  )
  
          UpperCAmelCase__ :       Any						=						self.tokenizer(_lowerCamelCase       ,					padding="""max_length"""       ,					max_length=_lowerCamelCase       ,					truncation=_lowerCamelCase  )
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						tokens["""attention_mask"""], tokens["""input_ids"""]
  
          UpperCAmelCase__ :       str						=						[]
          for attn_mask, input_id in zip(_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       List[str]						=						torch.tensor(_lowerCamelCase  ) * torch.tensor(_lowerCamelCase  )
                token_inputs.append(token.unsqueeze(0  )  )
  
          UpperCAmelCase__ :       Tuple						=						torch.cat(_lowerCamelCase       ,					dim=0  )
          return token_inputs
  
  
  
    def __call__(self       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          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__ :       Dict						=						self.image_processor(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					**_lowerCamelCase  )
  
          if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       str						=						[task_inputs]
  
          if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) and all(isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) for task_input in task_inputs  ):
                UpperCAmelCase__ :       str						=						[]
                for task in task_inputs:
                      UpperCAmelCase__ :       Optional[Any]						=						F"""the task is {task}"""
                      task_token_inputs.append(_lowerCamelCase  )
                UpperCAmelCase__ :       Tuple						=						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"""  ):
                UpperCAmelCase__ :       str						=						encoded_inputs.text_inputs
  
                UpperCAmelCase__ :       Optional[Any]						=						[]
                for texts in texts_list:
                      UpperCAmelCase__ :       int						=						self._preprocess_text(_lowerCamelCase       ,					max_length=self.max_seq_length  )
                      text_inputs.append(text_input_list.unsqueeze(0  )  )
  
                UpperCAmelCase__ :       List[str]						=						torch.cat(_lowerCamelCase       ,					dim=0  )
  
          return encoded_inputs
  
  
  
    def    _a			(self       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					_lowerCamelCase=None       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          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__ :       str						=						self.image_processor.encode_inputs(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					**_lowerCamelCase  )
  
          if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       Tuple						=						[task_inputs]
  
          if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) and all(isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) for task_input in task_inputs  ):
                UpperCAmelCase__ :       Dict						=						[]
                for task in task_inputs:
                      UpperCAmelCase__ :       Union[str, Any]						=						F"""the task is {task}"""
                      task_token_inputs.append(_lowerCamelCase  )
                UpperCAmelCase__ :       Dict						=						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"""  ):
                UpperCAmelCase__ :       Tuple						=						encoded_inputs.text_inputs
  
                UpperCAmelCase__ :       str						=						[]
                for texts in texts_list:
                      UpperCAmelCase__ :       Union[str, Any]						=						self._preprocess_text(_lowerCamelCase       ,					max_length=self.max_seq_length  )
                      text_inputs.append(text_input_list.unsqueeze(0  )  )
  
                UpperCAmelCase__ :       Union[str, Any]						=						torch.cat(_lowerCamelCase       ,					dim=0  )
  
          return encoded_inputs
  
  
  
    def    _a			(self       ,					*_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.image_processor.post_process_semantic_segmentation(*_lowerCamelCase       ,					**_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					*_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.image_processor.post_process_instance_segmentation(*_lowerCamelCase       ,					**_lowerCamelCase  )
  
  
  
  
    def    _a			(self       ,					*_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.image_processor.post_process_panoptic_segmentation(*_lowerCamelCase       ,					**_lowerCamelCase  )
  
  
style_context: 
  
  """simple docstring"""
  from __future__ import annotations
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   set[int]:
        UpperCAmelCase__ :       List[Any]						=						{1}
        UpperCAmelCase__ :       Optional[int]						=						[1]
        for _ in range(2 ,  depth + 1						):
              UpperCAmelCase__ :       Tuple						=						[*previous_coefficients, 0]
              UpperCAmelCase__ :       List[str]						=						[0, *previous_coefficients]
              UpperCAmelCase__ :       Dict						=						[]
              for x, y in zip(lowerCAmelCase ,  lowerCAmelCase						):
                    coefficients.add(x + y						)
                    previous_coefficients.append(x + y						)
        return coefficients
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   set[int]:
        UpperCAmelCase__ :       List[str]						=						set()
        for number in unique_coefficients:
              UpperCAmelCase__ :       int						=						2
              UpperCAmelCase__ :       List[Any]						=						number
              while divisor**2 <= copy_number:
                    UpperCAmelCase__ :       Tuple						=						0
                    while copy_number % divisor == 0:
                          copy_number //= divisor
                          multiplicity += 1
                    if multiplicity >= 2:
                          non_squarefrees.add(lowerCAmelCase						)
                          break
                    divisor += 1
  
        return unique_coefficients.difference(lowerCAmelCase						)
  
  
  
  
  def 				a__	(	lowerCAmelCase = 51						)  ->   int:
        UpperCAmelCase__ :       Any						=						get_pascal_triangle_unique_coefficients(lowerCAmelCase						)
        UpperCAmelCase__ :       Dict						=						get_squarefrees(lowerCAmelCase						)
        return sum(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      print(f'''{solution() = }''')
  
  