code: 
  
  
  import warnings
  
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding
  
  
  class 		_SCREAMING_SNAKE_CASE    (	lowercase__    ):
  					__SCREAMING_SNAKE_CASE    				=						["""image_processor""", """tokenizer"""]
  					__SCREAMING_SNAKE_CASE    				=						"""AutoImageProcessor"""
  					__SCREAMING_SNAKE_CASE    				=						"""AutoTokenizer"""
  
  
  					def __init__(     self       ,				A_=None       ,				A_=None       ,				**A_						):
  							_UpperCAmelCase		:       Dict       =    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       ,				)
  									_UpperCAmelCase		:       Union[str, Any]       =    kwargs.pop("""feature_extractor"""						)
  
  							_UpperCAmelCase		:       str       =    image_processor if image_processor is not None else feature_extractor
  							if image_processor is None:
  									raise ValueError("""You have to specify an image_processor."""						)
  							if tokenizer is None:
  									raise ValueError("""You have to specify a tokenizer."""						)
  
  							super().__init__(_A       ,				_A						)
  							_UpperCAmelCase		:       str       =    self.image_processor
  
  
  					def __call__(     self       ,				A_=None       ,				A_=None       ,				A_=None       ,				**A_						):
  
  							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		:       Optional[Any]       =    self.tokenizer(_A       ,				return_tensors=_A       ,				**_A						)
  
  							if images is not None:
  									_UpperCAmelCase		:       Any       =    self.image_processor(_A       ,				return_tensors=_A       ,				**_A						)
  
  							if text is not None and images is not None:
  									_UpperCAmelCase		:       int       =    image_features.pixel_values
  									return encoding
  							elif text is not None:
  									return encoding
  							else:
  									return BatchEncoding(data=dict(**_A						)       ,				tensor_type=_A						)
  
  
  					def 					__snake_case(     self       ,				*A_       ,				**A_						):
  							return self.tokenizer.batch_decode(*_A       ,				**_A						)
  
  
  					def 					__snake_case(     self       ,				*A_       ,				**A_						):
  							return self.tokenizer.decode(*_A       ,				**_A						)
  
  
  					@property
  					def 					__snake_case(     self						):
  							_UpperCAmelCase		:       Union[str, Any]       =    self.tokenizer.model_input_names
  							_UpperCAmelCase		:       Any       =    self.image_processor.model_input_names
  							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)
  
  
  					@property
  					def 					__snake_case(     self						):
  							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 					__snake_case(     self						):
  							warnings.warn(
  							    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,				_A       ,				)
  							return self.image_processor
  
  
  
  
  
  
style_context: 
  
  
  from itertools import count
  from math import asin, pi, sqrt
  
  
  
  
  def       a__						(		snake_case__				:       float    ):
    return (
        (1 - 2 * point) * sqrt(point - point**2    ) + 2 * point + asin(sqrt(1 - point    )    )
    ) / 4
  
  
  
  
  def       a__						(		snake_case__				:       int    ):
    _UpperCAmelCase		:       int       =    (circles_number + 1 - sqrt(2 * circles_number    )) / (
        2 * (circles_number**2 + 1)
    )
    _UpperCAmelCase		:       Union[str, Any]       =    circles_number * intersection_y
  
    _UpperCAmelCase		:       Tuple       =    intersection_x * intersection_y / 2
    _UpperCAmelCase		:       str       =    circle_bottom_arc_integral(
        1 / 2    ) - circle_bottom_arc_integral(snake_case__    )
  
    return triangle_area + concave_region_area
  
  
  
  
  def       a__						(		snake_case__				:       float = 1 / 1000    ):
    _UpperCAmelCase		:       Dict       =    (1 - pi / 4) / 4
  
    for n in count(1    ):
      if concave_triangle_area(snake_case__    ) / l_section_area < fraction:
        return n
  
    return -1
  
  
  if __name__ == "__main__":
   print(F'{solution() = }')
  
  
  
  
  
  