





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class     SCREAMING_SNAKE_CASE       (__lowercase				):
   _UpperCamelCase   :		List[str]			 =	["image_processor", "tokenizer"]
   _UpperCamelCase   :		List[Any]			 =	"AutoImageProcessor"
   _UpperCamelCase   :		str			 =	"AutoTokenizer"


   def __init__(   self      :    Dict      ,					a      :    str=None      ,					a      :    Union[str, Any]=None      ,					**a      :    List[Any]	)->       List[str]:





     """simple docstring"""







     lowercase__           =  None
     if "feature_extractor" in kwargs:
       warnings.warn(
           'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
           ' instead.'      ,					snake_case_      ,					)
       lowercase__           =  kwargs.pop('feature_extractor'	)

     lowercase__           =  image_processor if image_processor is not None else feature_extractor
     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`.'	)

     super().__init__(snake_case_      ,					snake_case_	)
     lowercase__           =  self.image_processor
     lowercase__           =  False


   def __call__(   self      :    int      ,					*a      :    Any      ,					**a      :    List[str]	)->       int:





     """simple docstring"""







     if self._in_target_context_manager:
       return self.current_processor(*snake_case_      ,					**snake_case_	)

     lowercase__           =  kwargs.pop('images'      ,					snake_case_	)
     lowercase__           =  kwargs.pop('text'      ,					snake_case_	)
     if len(snake_case_	) > 0:
       lowercase__           =  args[0]
       lowercase__           =  args[1:]

     if images is None and text is None:
       raise ValueError('You need to specify either an `images` or `text` input to process.'	)

     if images is not None:
       lowercase__           =  self.image_processor(snake_case_      ,					*snake_case_      ,					**snake_case_	)
     if text is not None:
       lowercase__           =  self.tokenizer(snake_case_      ,					**snake_case_	)

     if text is None:
       return inputs
     elif images is None:
       return encodings
     else:
       lowercase__           =  encodings['''input_ids''']
       return inputs


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					*a      :    List[str]      ,					**a      :    int	)->       Optional[int]:





     """simple docstring"""







     return self.tokenizer.batch_decode(*snake_case_      ,					**snake_case_	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					*a      :    Optional[Any]      ,					**a      :    Any	)->       str:





     """simple docstring"""







     return self.tokenizer.decode(*snake_case_      ,					**snake_case_	)


   @contextmanager
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Dict:





     """simple docstring"""







     warnings.warn(
         '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
         'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
         'your images inputs, or in a separate call.'	)
     lowercase__           =  True
     lowercase__           =  self.tokenizer
     yield
     lowercase__           =  self.image_processor
     lowercase__           =  False


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    Optional[int]      ,					a      :    Tuple=False      ,					a      :    List[str]=None	)->       Tuple:





     """simple docstring"""







     if added_vocab is None:
       lowercase__           =  self.tokenizer.get_added_vocab()

     lowercase__           =  {}

     while tokens:
       lowercase__           =  re.search(R'<s_(.*?)>'      ,					snake_case_      ,					re.IGNORECASE	)
       if start_token is None:
         break
       lowercase__           =  start_token.group(1	)
       lowercase__           =  re.search(Rf"""</s_{key}>"""      ,					snake_case_      ,					re.IGNORECASE	)
       lowercase__           =  start_token.group()
       if end_token is None:
         lowercase__           =  tokens.replace(snake_case_      ,					''	)
       else:
         lowercase__           =  end_token.group()
         lowercase__           =  re.escape(snake_case_	)
         lowercase__           =  re.escape(snake_case_	)
         lowercase__           =  re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""      ,					snake_case_      ,					re.IGNORECASE	)
         if content is not None:
           lowercase__           =  content.group(1	).strip()
           if r"<s_" in content and r"</s_" in content:  # non-leaf node
             lowercase__           =  self.tokenajson(snake_case_      ,					is_inner_value=snake_case_      ,					added_vocab=snake_case_	)
             if value:
               if len(snake_case_	) == 1:
                 lowercase__           =  value[0]
               lowercase__           =  value
           else:  # leaf nodes
             lowercase__           =  []
             for leaf in content.split(R'<sep/>'	):
               lowercase__           =  leaf.strip()
               if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                 lowercase__           =  leaf[1:-2]  # for categorical special tokens
               output[key].append(snake_case_	)
             if len(output[key]	) == 1:
               lowercase__           =  output[key][0]

         lowercase__           =  tokens[tokens.find(snake_case_	) + len(snake_case_	) :].strip()
         if tokens[:6] == r"<sep/>":  # non-leaf nodes
           return [output] + self.tokenajson(tokens[6:]      ,					is_inner_value=snake_case_      ,					added_vocab=snake_case_	)

     if len(snake_case_	):
       return [output] if is_inner_value else output
     else:
       return [] if is_inner_value else {"text_sequence": tokens}


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       List[str]:





     """simple docstring"""







     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,					snake_case_      ,					)
     return self.image_processor_class


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Dict:





     """simple docstring"""







     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,					snake_case_      ,					)
     return self.image_processor









def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       int:

  if exponent == 1:
    return base
  if exponent % 2 == 0:
    lowercase__           =  _modexpt(_SCREAMING_SNAKE_CASE		,						exponent // 2		,						_SCREAMING_SNAKE_CASE		) % modulo_value
    return (x * x) % modulo_value
  else:
    return (base * _modexpt(_SCREAMING_SNAKE_CASE		,						exponent - 1		,						_SCREAMING_SNAKE_CASE		)) % modulo_value




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE = 1777		,						_SCREAMING_SNAKE_CASE = 1855		,						_SCREAMING_SNAKE_CASE = 8		)    ->       int:
  lowercase__           =  base
  for _ in range(1		,						_SCREAMING_SNAKE_CASE		):
    lowercase__           =  _modexpt(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						10**digits		)

  return result


if __name__ == "__main__":
      print(f'''{solution() = }''')


