code: 
  
  
  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)
  
  
  
  
  
  
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() = }')
  
  
  
  
  
  