


'''simple docstring'''






from random import randint
from tempfile import TemporaryFile

import numpy as np






def 		_A			(lowerCAmelCase__					:Optional[int]			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:str						)				->					List[Any]:




     '''simple docstring'''



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

          _a ,				_a      =							_in_place_partition(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__						)
          count += _in_place_quick_sort(lowerCAmelCase__			,  lowerCAmelCase__			,  p - 1						)
          count += _in_place_quick_sort(lowerCAmelCase__			,  p + 1			,  lowerCAmelCase__						)
     return count






def 		_A			(lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:List[Any]						)				->					List[Any]:




     '''simple docstring'''



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

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


a_   :      List[Any]				    =      TemporaryFile()
a_   :      Union[str, Any]				    =      1_0_0  # 1000 elements are to be sorted


a_    ,							a_   :      Dict				    =      0, 1  # mean and standard deviation
a_   :      Tuple				    =      np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
a_   :      Optional[int]				    =      np.load(outfile)
a_   :      List[Any]				    =      len(M) - 1
a_   :      str				    =      _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'''






import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


a_   :      Optional[Any]				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							None:
      warnings.warn(
          'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use CLIPImageProcessor instead.' ,			__magic_name__ ,			)
      super().__init__(*__magic_name__ ,			**__magic_name__  )

