code: 
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
  
  
  SCREAMING_SNAKE_CASE__				:    Any         =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE__				:    str         =    {
      'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
      # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
  }
  
  
  class 		_SCREAMING_SNAKE_CASE    (	lowerCAmelCase_       ,  lowerCAmelCase_    ):
  					__SCREAMING_SNAKE_CASE    				=						'convnext'
  
  
  					def __init__(     self       ,				A_=3       ,				A_=4       ,				A_=4       ,				A_=None       ,				A_=None       ,				A_="gelu"       ,				A_=0.0_2       ,				A_=1e-12       ,				A_=1e-6       ,				A_=0.0       ,				A_=2_24       ,				A_=None       ,				A_=None       ,				**A_       ,				):
  							super().__init__(**lowerCamelCase_						)
  
  							_UpperCAmelCase		:       Any       =    num_channels
  							_UpperCAmelCase		:       List[Any]       =    patch_size
  							_UpperCAmelCase		:       Any       =    num_stages
  							_UpperCAmelCase		:       Tuple       =    [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
  							_UpperCAmelCase		:       str       =    [3, 3, 9, 3] if depths is None else depths
  							_UpperCAmelCase		:       Dict       =    hidden_act
  							_UpperCAmelCase		:       List[str]       =    initializer_range
  							_UpperCAmelCase		:       Tuple       =    layer_norm_eps
  							_UpperCAmelCase		:       int       =    layer_scale_init_value
  							_UpperCAmelCase		:       Dict       =    drop_path_rate
  							_UpperCAmelCase		:       Tuple       =    image_size
  							_UpperCAmelCase		:       str       =    ["""stem"""] + [F'''stage{idx}''' for idx in range(1       ,				len(self.depths						) + 1						)]
  							_UpperCAmelCase,_UpperCAmelCase		:       List[str]       =    get_aligned_output_features_output_indices(
  							    out_features=lowerCamelCase_       ,				out_indices=lowerCamelCase_       ,				stage_names=self.stage_names						)
  
  
  class 		_SCREAMING_SNAKE_CASE    (	lowerCAmelCase_    ):
  					__SCREAMING_SNAKE_CASE    				=						version.parse('''1.11'''    )
  
  
  					@property
  					def 					__snake_case(     self						):
  							return OrderedDict(
  							    [
  							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
  							    ]						)
  
  
  
  
  					@property
  					def 					__snake_case(     self						):
  							return 1e-5
  
  
  
  
  
  
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() = }')
  
  
  
  
  
  