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				):
     lowercase       :  Dict      =				['image_processor', 'tokenizer']
     lowercase       :  int      =				'OneFormerImageProcessor'
     lowercase       :  str      =				('CLIPTokenizer', 'CLIPTokenizerFast')
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_ = 77    ,      SCREAMING_SNAKE_CASE_ = 77    ,      **SCREAMING_SNAKE_CASE_ ):
  
  
  
  
  
  
         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				:   Tuple          =							max_seq_length
         UpperCamelCase				:   Dict          =							task_seq_length
  
         super().__init__(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=77 ):
  
  
  
  
  
  
         if text_list is None:
             raise ValueError("""tokens cannot be None.""" )
  
         UpperCamelCase				:   Tuple          =							self.tokenizer(SCREAMING_SNAKE_CASE_    ,      padding="""max_length"""    ,      max_length=SCREAMING_SNAKE_CASE_    ,      truncation=SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase       ,							UpperCamelCase				:   str          =							tokens["""attention_mask"""], tokens["""input_ids"""]
  
         UpperCamelCase				:   Dict          =							[]
         for attn_mask, input_id in zip(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   int          =							torch.tensor(SCREAMING_SNAKE_CASE_ ) * torch.tensor(SCREAMING_SNAKE_CASE_ )
             token_inputs.append(token.unsqueeze(0 ) )
  
         UpperCamelCase				:   Optional[Any]          =							torch.cat(SCREAMING_SNAKE_CASE_    ,      dim=0 )
         return token_inputs
  
  
  
  
  
  
  
     def __call__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      **SCREAMING_SNAKE_CASE_ ):
  
  
  
  
  
  
         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				:   Tuple          =							self.image_processor(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
         if isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
             UpperCamelCase				:   int          =							[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 ):
             UpperCamelCase				:   Tuple          =							[]
             for task in task_inputs:
                 UpperCamelCase				:   int          =							f'the task is {task}'
                 task_token_inputs.append(SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   List[str]          =							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""" ):
             UpperCamelCase				:   List[Any]          =							encoded_inputs.text_inputs
  
             UpperCamelCase				:   Optional[int]          =							[]
             for texts in texts_list:
                 UpperCamelCase				:   int          =							self._preprocess_text(SCREAMING_SNAKE_CASE_    ,      max_length=self.max_seq_length )
                 text_inputs.append(text_input_list.unsqueeze(0 ) )
  
             UpperCamelCase				:   Dict          =							torch.cat(SCREAMING_SNAKE_CASE_    ,      dim=0 )
  
         return encoded_inputs
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      **SCREAMING_SNAKE_CASE_ ):
  
  
  
  
  
  
         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				:   Tuple          =							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_ ):
             UpperCamelCase				:   Dict          =							[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 ):
             UpperCamelCase				:   Optional[int]          =							[]
             for task in task_inputs:
                 UpperCamelCase				:   Any          =							f'the task is {task}'
                 task_token_inputs.append(SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   int          =							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""" ):
             UpperCamelCase				:   Dict          =							encoded_inputs.text_inputs
  
             UpperCamelCase				:   Optional[int]          =							[]
             for texts in texts_list:
                 UpperCamelCase				:   Tuple          =							self._preprocess_text(SCREAMING_SNAKE_CASE_    ,      max_length=self.max_seq_length )
                 text_inputs.append(text_input_list.unsqueeze(0 ) )
  
             UpperCamelCase				:   List[str]          =							torch.cat(SCREAMING_SNAKE_CASE_    ,      dim=0 )
  
         return encoded_inputs
  
  
  
  
  
  
  
     def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         return self.image_processor.post_process_semantic_segmentation(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         return self.image_processor.post_process_instance_segmentation(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         return self.image_processor.post_process_panoptic_segmentation(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]						,snake_case_     :       Optional[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      assert b != 0, "This cannot accept modulo that is == 0"
      if n == 0:
          return 1
  
      if n % 2 == 1:
          return (bin_exp_mod(snake_case_						,n - 1						,snake_case_    ) * a) % b
  
      UpperCamelCase				:   List[Any]          =							bin_exp_mod(snake_case_						,n / 2						,snake_case_    )
      return (r * r) % b
  
  
  if __name__ == "__main__":
    try:
      __A :		Any      							=    int(input('''Enter Base : ''').strip())
      __A :		Any      							=    int(input('''Enter Power : ''').strip())
      __A :		List[Any]      							=    int(input('''Enter Modulo : ''').strip())
    except ValueError:
      print('''Invalid literal for integer''')
  
    print(bin_exp_mod(BASE, POWER, MODULO))
  
  
  
  
  