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_  (       snake_case_ ):
  
      '''simple docstring'''
      UpperCamelCase									=				['''image_processor''', '''tokenizer''']
      UpperCamelCase									=				'''OneFormerImageProcessor'''
      UpperCamelCase									=				('''CLIPTokenizer''', '''CLIPTokenizerFast''')
  
  
      def __init__(	self				,	A=None				,	A=None				,	A = 77				,	A = 77				,	**A )	->     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`.""" )
  
          _SCREAMING_SNAKE_CASE             =    max_seq_length
          _SCREAMING_SNAKE_CASE             =    task_seq_length
  
          super().__init__(A				,	A )
  
  
      def     snake_case_(	self				,	A=None				,	A=77 )	->     List[Any]:
          if text_list is None:
              raise ValueError("""tokens cannot be None.""" )
  
          _SCREAMING_SNAKE_CASE             =    self.tokenizer(A				,	padding="""max_length"""				,	max_length=A				,	truncation=A )
  
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    tokens["""attention_mask"""], tokens["""input_ids"""]
  
          _SCREAMING_SNAKE_CASE             =    []
          for attn_mask, input_id in zip(A				,	A ):
              _SCREAMING_SNAKE_CASE             =    torch.tensor(A ) * torch.tensor(A )
              token_inputs.append(token.unsqueeze(0 ) )
  
          _SCREAMING_SNAKE_CASE             =    torch.cat(A				,	dim=0 )
          return token_inputs
  
  
      def __call__(	self				,	A=None				,	A=None				,	A=None				,	**A )	->     str:
  
          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.""" )
  
          _SCREAMING_SNAKE_CASE             =    self.image_processor(A				,	A				,	A				,	**A )
  
          if isinstance(A				,	A ):
              _SCREAMING_SNAKE_CASE             =    [task_inputs]
  
          if isinstance(A				,	A ) and all(isinstance(A				,	A ) for task_input in task_inputs ):
              _SCREAMING_SNAKE_CASE             =    []
              for task in task_inputs:
                  _SCREAMING_SNAKE_CASE             =    f'the task is {task}'
                  task_token_inputs.append(A )
              _SCREAMING_SNAKE_CASE             =    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""" ):
              _SCREAMING_SNAKE_CASE             =    encoded_inputs.text_inputs
  
              _SCREAMING_SNAKE_CASE             =    []
              for texts in texts_list:
                  _SCREAMING_SNAKE_CASE             =    self._preprocess_text(A				,	max_length=self.max_seq_length )
                  text_inputs.append(text_input_list.unsqueeze(0 ) )
  
              _SCREAMING_SNAKE_CASE             =    torch.cat(A				,	dim=0 )
  
          return encoded_inputs
  
  
      def     snake_case_(	self				,	A=None				,	A=None				,	A=None				,	**A )	->     str:
  
          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.""" )
  
          _SCREAMING_SNAKE_CASE             =    self.image_processor.encode_inputs(A				,	A				,	A				,	**A )
  
          if isinstance(A				,	A ):
              _SCREAMING_SNAKE_CASE             =    [task_inputs]
  
          if isinstance(A				,	A ) and all(isinstance(A				,	A ) for task_input in task_inputs ):
              _SCREAMING_SNAKE_CASE             =    []
              for task in task_inputs:
                  _SCREAMING_SNAKE_CASE             =    f'the task is {task}'
                  task_token_inputs.append(A )
              _SCREAMING_SNAKE_CASE             =    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""" ):
              _SCREAMING_SNAKE_CASE             =    encoded_inputs.text_inputs
  
              _SCREAMING_SNAKE_CASE             =    []
              for texts in texts_list:
                  _SCREAMING_SNAKE_CASE             =    self._preprocess_text(A				,	max_length=self.max_seq_length )
                  text_inputs.append(text_input_list.unsqueeze(0 ) )
  
              _SCREAMING_SNAKE_CASE             =    torch.cat(A				,	dim=0 )
  
          return encoded_inputs
  
  
      def     snake_case_(	self				,	*A				,	**A )	->     List[Any]:
          return self.image_processor.post_process_semantic_segmentation(*A				,	**A )
  
  
      def     snake_case_(	self				,	*A				,	**A )	->     Tuple:
          return self.image_processor.post_process_instance_segmentation(*A				,	**A )
  
  
  
  
  
      def     snake_case_(	self				,	*A				,	**A )	->     str:
          return self.image_processor.post_process_panoptic_segmentation(*A				,	**A )
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					int = 10**6					)		->int:
  
      if n <= 0:
          raise ValueError("""Please enter an integer greater than 0"""					)
  
      _SCREAMING_SNAKE_CASE             =    list(range(n + 1					)					)
      for number in range(2      , n + 1					):
          if phi[number] == number:
              phi[number] -= 1
              for multiple in range(number * 2      , n + 1      , __lowerCamelCase					):
                  _SCREAMING_SNAKE_CASE             =    (phi[multiple] // number) * (number - 1)
  
      _SCREAMING_SNAKE_CASE             =    1
      for number in range(1      , n + 1					):
          if (answer / phi[answer]) < (number / phi[number]):
              _SCREAMING_SNAKE_CASE             =    number
  
      return answer
  
  
  if __name__ == "__main__":
        print(solution())
  
  
  
  