

"""simple docstring"""





from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean




def   _A  (  UpperCamelCase_     :       int)	->						Optional[int]:
   '''simple docstring'''


   def is_in_circle(UpperCamelCase_     :       float,			UpperCamelCase_     :       float) -> bool:
      __lowercase								=						sqrt((x**2) + (y**2))
      # Our circle has a radius of 1, so a distance
      # greater than 1 would land outside the circle.
      return distance_from_centre <= 1

   # The proportion of guesses that landed in the circle
   __lowercase								=						mean(
       int(is_in_circle(uniform(-1.0,			1.0),			uniform(-1.0,			1.0)))
       for _ in range(UpperCamelCase_))
   # The ratio of the area for circle to square is pi/4.
   __lowercase								=						proportion * 4
   print(F"""The estimated value of pi is {pi_estimate}""")
   print(F"""The numpy value of pi is {pi}""")
   print(F"""The total error is {abs(pi - pi_estimate)}""")




def   _A  (  UpperCamelCase_     :       int,			UpperCamelCase_     :       Callable[[float], float],			UpperCamelCase_     :       float = 0.0,			UpperCamelCase_     :       float = 1.0,			)	->						float:
   '''simple docstring'''


   return mean(
       function_to_integrate(uniform(UpperCamelCase_,			UpperCamelCase_)) for _ in range(UpperCamelCase_)) * (max_value - min_value)




def   _A  (  UpperCamelCase_     :       int,			UpperCamelCase_     :       float = 0.0,			UpperCamelCase_     :       float = 1.0)	->						None:
   '''simple docstring'''


   def identity_function(UpperCamelCase_     :       float) -> float:
      return x

   __lowercase								=						area_under_curve_estimator(
       UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)
   __lowercase								=						(max_value * max_value - min_value * min_value) / 2

   print("******************")
   print(F"""Estimating area under y=x where x varies from {min_value} to {max_value}""")
   print(F"""Estimated value is {estimated_value}""")
   print(F"""Expected value is {expected_value}""")
   print(F"""Total error is {abs(estimated_value - expected_value)}""")
   print("******************")




def   _A  (  UpperCamelCase_     :       int)	->						None:
   '''simple docstring'''


   def function_to_integrate(UpperCamelCase_     :       float) -> float:
      return sqrt(4.0 - x * x)

   __lowercase								=						area_under_curve_estimator(
       UpperCamelCase_,			UpperCamelCase_,			0.0,			2.0)

   print("******************")
   print("Estimating pi using area_under_curve_estimator")
   print(F"""Estimated value is {estimated_value}""")
   print(F"""Expected value is {pi}""")
   print(F"""Total error is {abs(estimated_value - pi)}""")
   print("******************")


if __name__ == "__main__":
   import doctest

   doctest.testmod()







'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase :      List[str]					 =     logging.get_logger(__name__)

lowerCamelCase :      List[Any]					 =     {
    "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}







class 							A__    (       A__     ,   A__  ):
  A__		 =				'convnextv2'





  def __init__(		self      :	Tuple							,					_a      :	Optional[int]=3							,					_a      :	Any=4							,					_a      :	int=4							,					_a      :	Union[str, Any]=None							,					_a      :	List[str]=None							,					_a      :	Optional[Any]="gelu"							,					_a      :	Any=0.02							,					_a      :	Any=1e-12							,					_a      :	Tuple=0.0							,					_a      :	int=224							,					_a      :	Any=None							,					_a      :	Optional[int]=None							,					**_a      :	List[str]							,					)	->    Optional[Any]:





       '''simple docstring'''






       super().__init__(**_a )

       _SCREAMING_SNAKE_CASE							    =num_channels
       _SCREAMING_SNAKE_CASE							    =patch_size
       _SCREAMING_SNAKE_CASE							    =num_stages
       _SCREAMING_SNAKE_CASE							    =[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
       _SCREAMING_SNAKE_CASE							    =[3, 3, 9, 3] if depths is None else depths
       _SCREAMING_SNAKE_CASE							    =hidden_act
       _SCREAMING_SNAKE_CASE							    =initializer_range
       _SCREAMING_SNAKE_CASE							    =layer_norm_eps
       _SCREAMING_SNAKE_CASE							    =drop_path_rate
       _SCREAMING_SNAKE_CASE							    =image_size
       _SCREAMING_SNAKE_CASE							    =['stem'] + [f"stage{idx}" for idx in range(1							,					len(self.depths ) + 1 )]
       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =get_aligned_output_features_output_indices(
           out_features=_a							,					out_indices=_a							,					stage_names=self.stage_names )
