


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









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() = }')





