



"""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_ )










"""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))




