

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class      lowerCamelCase_ (			_lowerCAmelCase):
 _lowerCAmelCase									=	['''image_processor''', '''tokenizer''']
 _lowerCAmelCase									=	'''EfficientNetImageProcessor'''
 _lowerCAmelCase									=	('''BertTokenizer''', '''BertTokenizerFast''')




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   super().__init__(_UpperCAmelCase  ,  _UpperCAmelCase							)




 def __call__(			self  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase=None  ,  _UpperCAmelCase="max_length"  ,  _UpperCAmelCase=64  ,  _UpperCAmelCase=None  ,  **_UpperCAmelCase							):
   if text is None and images is None:
     raise ValueError('You have to specify either text or images. Both cannot be none.'							)

   if text is not None:
     __UpperCAmelCase	:  Union[str, Any]		=     self.tokenizer(
         _UpperCAmelCase  ,  padding=_UpperCAmelCase  ,  max_length=_UpperCAmelCase  ,  return_tensors=_UpperCAmelCase  ,  **_UpperCAmelCase							)

   if images is not None:
     __UpperCAmelCase	:  Union[str, Any]		=     self.image_processor(_UpperCAmelCase  ,  return_tensors=_UpperCAmelCase  ,  **_UpperCAmelCase							)

   if text is not None and images is not None:
     __UpperCAmelCase	:  List[Any]		=     image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**_UpperCAmelCase							)  ,  tensor_type=_UpperCAmelCase							)




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   return self.tokenizer.batch_decode(*_UpperCAmelCase  ,  **_UpperCAmelCase							)




 def     a       (			self  ,  *_UpperCAmelCase  ,  **_UpperCAmelCase							):
   return self.tokenizer.decode(*_UpperCAmelCase  ,  **_UpperCAmelCase							)




 @property
 def     a       (			self							):
   __UpperCAmelCase	:  Tuple		=     self.tokenizer.model_input_names
   __UpperCAmelCase	:  List[Any]		=     self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)








from __future__ import annotations





def  _snake_case		(snake_case_					:     int  )							->		int | None:
  for r in range(1    ,       snake_case_  ):
    __UpperCAmelCase	:  List[str]		=     []
    for x in range(n - 1  ):
      __UpperCAmelCase	:  Union[str, Any]		=     pow(snake_case_    ,       snake_case_    ,       snake_case_  )
      if val in li:
        break
      li.append(snake_case_  )
    else:
      return r
  return None


if __name__ == "__main__":
      _SCREAMING_SNAKE_CASE							 =			int(input('''Enter a prime number q: '''))
      _SCREAMING_SNAKE_CASE							 =			find_primitive(q)
      if a is None:
            print(F'''Cannot find the primitive for the value: {a!r}''')
      else:
            _SCREAMING_SNAKE_CASE							 =			int(input('''Enter private key of A: '''))
            _SCREAMING_SNAKE_CASE							 =			pow(a, a_private, q)
            _SCREAMING_SNAKE_CASE							 =			int(input('''Enter private key of B: '''))
            _SCREAMING_SNAKE_CASE							 =			pow(a, b_private, q)

            _SCREAMING_SNAKE_CASE							 =			pow(b_public, a_private, q)
            _SCREAMING_SNAKE_CASE							 =			pow(a_public, b_private, q)

            print('''The key value generated by A is: ''', a_secret)
            print('''The key value generated by B is: ''', b_secret)





