


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__				:    Tuple         =    {'configuration_regnet': ['REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RegNetConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__				:    Any         =    [
     'REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RegNetForImageClassification',
     'RegNetModel',
     'RegNetPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__				:    int         =    [
     'TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFRegNetForImageClassification',
     'TFRegNetModel',
     'TFRegNetPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__				:    Any         =    [
     'FlaxRegNetForImageClassification',
     'FlaxRegNetModel',
     'FlaxRegNetPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_regnet import (
      REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      RegNetForImageClassification,
      RegNetModel,
      RegNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_regnet import (
      TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRegNetForImageClassification,
      TFRegNetModel,
      TFRegNetPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_regnet import (
      FlaxRegNetForImageClassification,
      FlaxRegNetModel,
      FlaxRegNetPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE__				:    Optional[int]         =    _LazyModule(__name__, globals()['__file__'], _import_structure)









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() = }')





