

'''simple docstring'''
from random import randint
from tempfile import TemporaryFile

import numpy as np


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Dict				)      ->			Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =0
     if start < end:
          _SCREAMING_SNAKE_CASE							    =randint(_UpperCamelCase							, _UpperCamelCase				)
          _SCREAMING_SNAKE_CASE							    =a[end]
          _SCREAMING_SNAKE_CASE							    =a[pivot]
          _SCREAMING_SNAKE_CASE							    =temp

          _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =_in_place_partition(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase				)
          count += _in_place_quick_sort(_UpperCamelCase							, _UpperCamelCase							, p - 1				)
          count += _in_place_quick_sort(_UpperCamelCase							, p + 1							, _UpperCamelCase				)
     return count


def        _lowerCAmelCase					(						_UpperCamelCase						:     List[str]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Tuple				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =0
     _SCREAMING_SNAKE_CASE							    =randint(_UpperCamelCase							, _UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =a[end]
     _SCREAMING_SNAKE_CASE							    =a[pivot]
     _SCREAMING_SNAKE_CASE							    =temp
     _SCREAMING_SNAKE_CASE							    =start - 1
     for index in range(_UpperCamelCase							, _UpperCamelCase				):
          count += 1
          if a[index] < a[end]:  # check if current val is less than pivot value
               _SCREAMING_SNAKE_CASE							    =new_pivot_index + 1
               _SCREAMING_SNAKE_CASE							    =a[new_pivot_index]
               _SCREAMING_SNAKE_CASE							    =a[index]
               _SCREAMING_SNAKE_CASE							    =temp

     _SCREAMING_SNAKE_CASE							    =a[new_pivot_index + 1]
     _SCREAMING_SNAKE_CASE							    =a[end]
     _SCREAMING_SNAKE_CASE							    =temp
     return new_pivot_index + 1, count


lowerCamelCase :      str					 =     TemporaryFile()
lowerCamelCase :      Dict					 =     1_0_0  # 1000 elements are to be sorted


lowerCamelCase ,    lowerCamelCase :      int					 =     0, 1  # mean and standard deviation
lowerCamelCase :      List[str]					 =     np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
lowerCamelCase :      Tuple					 =     np.load(outfile)
lowerCamelCase :      Dict					 =     len(M) - 1
lowerCamelCase :      Tuple					 =     _in_place_quick_sort(M, 0, r)

print(
    "No of Comparisons for 100 elements selected from a standard normal distribution"
    "is :"
)







print(z)



'''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 )
