
"""simple docstring"""






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class 							snake_case       (     SCREAMING_SNAKE_CASE_		):
   a_					:		Union[str, Any]     					=     ["""image_processor""", """tokenizer"""]
   a_					:		List[str]     					=     """AutoImageProcessor"""
   a_					:		str     					=     """AutoTokenizer"""







   def __init__( self       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=None       ,							**__UpperCAmelCase)   ->List[str]:
      a_						   =     None
      if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."       ,							__UpperCAmelCase       ,							)
         a_						   =     kwargs.pop("feature_extractor")

      a_						   =     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__(__UpperCAmelCase       ,							__UpperCAmelCase)
      a_						   =     self.image_processor
      a_						   =     False







   def __call__( self       ,							*__UpperCAmelCase       ,							**__UpperCAmelCase)   ->Any:
      # For backward compatibility
      if self._in_target_context_manager:
         return self.current_processor(*__UpperCAmelCase       ,							**__UpperCAmelCase)

      a_						   =     kwargs.pop("images"       ,							__UpperCAmelCase)
      a_						   =     kwargs.pop("text"       ,							__UpperCAmelCase)
      if len(__UpperCAmelCase) > 0:
         a_						   =     args[0]
         a_						   =     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:
         a_						   =     self.image_processor(__UpperCAmelCase       ,							*__UpperCAmelCase       ,							**__UpperCAmelCase)
      if text is not None:
         a_						   =     self.tokenizer(__UpperCAmelCase       ,							**__UpperCAmelCase)

      if text is None:
         return inputs
      elif images is None:
         return encodings
      else:
         a_						   =     encodings["input_ids"]
         return inputs







   def    UpperCAmelCase__	( self       ,							*__UpperCAmelCase       ,							**__UpperCAmelCase)   ->List[str]:
      return self.tokenizer.batch_decode(*__UpperCAmelCase       ,							**__UpperCAmelCase)







   def    UpperCAmelCase__	( self       ,							*__UpperCAmelCase       ,							**__UpperCAmelCase)   ->Tuple:
      return self.tokenizer.decode(*__UpperCAmelCase       ,							**__UpperCAmelCase)







   @contextmanager
   def    UpperCAmelCase__	( self)   ->Dict:
      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.")
      a_						   =     True
      a_						   =     self.tokenizer
      yield
      a_						   =     self.image_processor
      a_						   =     False







   @property
   def    UpperCAmelCase__	( self)   ->str:
      warnings.warn(
          "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,							__UpperCAmelCase       ,							)
      return self.image_processor_class







   @property
   def    UpperCAmelCase__	( self)   ->List[str]:
      warnings.warn(
          "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,							__UpperCAmelCase       ,							)
      return self.image_processor

"""simple docstring"""






def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase = 1_000     )						->str:

   """simple docstring"""



   if (not isinstance(UpperCAmelCase ,   UpperCAmelCase     )) or (digit_position <= 0):
      raise ValueError("Digit position must be a positive integer"     )
   elif (not isinstance(UpperCAmelCase ,   UpperCAmelCase     )) or (precision < 0):
      raise ValueError("Precision must be a nonnegative integer"     )

   # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
   # accurate
   a_						   =     (
       4 * _subsum(UpperCAmelCase ,   1 ,   UpperCAmelCase     )
       - 2 * _subsum(UpperCAmelCase ,   4 ,   UpperCAmelCase     )
       - _subsum(UpperCAmelCase ,   5 ,   UpperCAmelCase     )
       - _subsum(UpperCAmelCase ,   6 ,   UpperCAmelCase     )
   )

   # return the first hex digit of the fractional part of the result
   return hex(int((sum_result % 1) * 16     )     )[2:]
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->float:

   """simple docstring"""



   a_						   =     0.0
   for sum_index in range(digit_pos_to_extract + precision     ):
      a_						   =     8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
         # if the exponential term is an integer and we mod it by the denominator
         # before dividing, only the integer part of the sum will change;
         # the fractional part will not
         a_						   =     pow(
             16 ,   digit_pos_to_extract - 1 - sum_index ,   UpperCAmelCase     )
      else:
         a_						   =     pow(16 ,   digit_pos_to_extract - 1 - sum_index     )
      total += exponential_term / denominator
   return total


if __name__ == "__main__":
      import doctest

      doctest.testmod()