import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 		_lowerCamelCase		( UpperCamelCase_     ):
    __a						=   ["image_processor", "tokenizer"]
    __a						=   "AutoImageProcessor"
    __a						=   "AutoTokenizer"
    def __init__(	self			,			lowerCAmelCase=None			,			lowerCAmelCase=None			,			**lowerCAmelCase )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: List[Any]=   None
     if "feature_extractor" in kwargs:
      warnings.warn(
          '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
          ''' instead.'''			,			__A			,			)
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   kwargs.pop('''feature_extractor''' )

     SCREAMING_SNAKE_CASE__: Optional[int]=   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__(__A			,			__A )
     SCREAMING_SNAKE_CASE__: List[Any]=   self.image_processor
     SCREAMING_SNAKE_CASE__: Optional[Any]=   False
    def __call__(	self			,			*lowerCAmelCase			,			**lowerCAmelCase )							->       List[str]:
     # For backward compatibility
     if self._in_target_context_manager:
      return self.current_processor(*__A			,			**__A )

     SCREAMING_SNAKE_CASE__: List[str]=   kwargs.pop('''images'''			,			__A )
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   kwargs.pop('''text'''			,			__A )
     if len(__A ) > 0:
      SCREAMING_SNAKE_CASE__: str=   args[0]
      SCREAMING_SNAKE_CASE__: Any=   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:
      SCREAMING_SNAKE_CASE__: List[Any]=   self.image_processor(__A			,			*__A			,			**__A )
     if text is not None:
      SCREAMING_SNAKE_CASE__: Tuple=   self.tokenizer(__A			,			**__A )

     if text is None:
      return inputs
     elif images is None:
      return encodings
     else:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   encodings['''input_ids''']
      return inputs
    def     UpperCamelCase_					(	self			,			*lowerCAmelCase			,			**lowerCAmelCase )							->       Union[str, Any]:
     return self.tokenizer.batch_decode(*__A			,			**__A )
    def     UpperCamelCase_					(	self			,			*lowerCAmelCase			,			**lowerCAmelCase )							->       Any:
     return self.tokenizer.decode(*__A			,			**__A )
    @contextmanager
    def     UpperCamelCase_					(	self )							->       Tuple:
     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.''' )
     SCREAMING_SNAKE_CASE__: Any=   True
     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.tokenizer
     yield
     SCREAMING_SNAKE_CASE__: str=   self.image_processor
     SCREAMING_SNAKE_CASE__: List[str]=   False
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase=False			,			lowerCAmelCase=None )							->       Optional[Any]:
     if added_vocab is None:
      SCREAMING_SNAKE_CASE__: Union[str, Any]=   self.tokenizer.get_added_vocab()

     SCREAMING_SNAKE_CASE__: Tuple=   {}

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

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

     if len(__A ):
      return [output] if is_inner_value else output
     else:
      return [] if is_inner_value else {"text_sequence": tokens}
    @property
    def     UpperCamelCase_					(	self )							->       List[str]:
     warnings.warn(
         '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,			__A			,			)
     return self.image_processor_class



    @property
    def     UpperCamelCase_					(	self )							->       Optional[Any]:
     warnings.warn(
         '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,			__A			,			)
     return self.image_processor



def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int | float | str				)			->						tuple[int, int]:
       try:
              SCREAMING_SNAKE_CASE_							:				int		=float(UpperCAmelCase_				)
       except ValueError:
              raise ValueError('''Please enter a valid number'''				)
       SCREAMING_SNAKE_CASE_							:				Any		=decimal - int(UpperCAmelCase_				)
       if fractional_part == 0:
              return int(UpperCAmelCase_				), 1
       else:
              SCREAMING_SNAKE_CASE_							:				Any		=len(str(UpperCAmelCase_				).split('''.'''				)[1]				)
              SCREAMING_SNAKE_CASE_							:				str		=int(decimal * (1_0**number_of_frac_digits)				)
              SCREAMING_SNAKE_CASE_							:				Any		=1_0**number_of_frac_digits
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=denominator, numerator
              while True:
                     SCREAMING_SNAKE_CASE_							:				Any		=dividend % divisor
                     if remainder == 0:
                            break
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=divisor, remainder
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=numerator / divisor, denominator / divisor
              return int(UpperCAmelCase_				), int(UpperCAmelCase_				)


if __name__ == "__main__":
 print(F"{decimal_to_fraction(2) = }")
 print(F"{decimal_to_fraction(89.0) = }")
 print(F"{decimal_to_fraction('67') = }")
 print(F"{decimal_to_fraction('45.0') = }")
 print(F"{decimal_to_fraction(1.5) = }")
 print(F"{decimal_to_fraction('6.25') = }")
 print(F"{decimal_to_fraction('78td') = }")
