





from random import randint
from tempfile import TemporaryFile

import numpy as np




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     Tuple	=		0
       if start < end:
              _lowerCamelCase							:     int	=		randint(lowercase_ ,    lowercase_      )
              _lowerCamelCase							:     Optional[int]	=		a[end]
              _lowerCamelCase							:     Optional[Any]	=		a[pivot]
              _lowerCamelCase							:     Optional[Any]	=		temp

              _lowerCamelCase,  _lowerCamelCase							:     str	=		_in_place_partition(lowercase_ ,    lowercase_ ,    lowercase_      )
              count += _in_place_quick_sort(lowercase_ ,    lowercase_ ,    p - 1      )
              count += _in_place_quick_sort(lowercase_ ,    p + 1 ,    lowercase_      )
       return count




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     Tuple	=		0
       _lowerCamelCase							:     int	=		randint(lowercase_ ,    lowercase_      )
       _lowerCamelCase							:     Dict	=		a[end]
       _lowerCamelCase							:     Union[str, Any]	=		a[pivot]
       _lowerCamelCase							:     Union[str, Any]	=		temp
       _lowerCamelCase							:     int	=		start - 1
       for index in range(lowercase_ ,    lowercase_      ):
              count += 1
              if a[index] < a[end]:  # check if current val is less than pivot value
                     _lowerCamelCase							:     Dict	=		new_pivot_index + 1
                     _lowerCamelCase							:     List[str]	=		a[new_pivot_index]
                     _lowerCamelCase							:     Optional[Any]	=		a[index]
                     _lowerCamelCase							:     Optional[Any]	=		temp

       _lowerCamelCase							:     Dict	=		a[new_pivot_index + 1]
       _lowerCamelCase							:     Tuple	=		a[end]
       _lowerCamelCase							:     Tuple	=		temp
       return new_pivot_index + 1, count


_lowerCamelCase			       =      TemporaryFile()
_lowerCamelCase			       =      100  # 1000 elements are to be sorted


_lowerCamelCase       ,	_lowerCamelCase			       =      0, 1  # mean and standard deviation
_lowerCamelCase			       =      np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


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

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



print(z)








import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCamelCase			       =      logging.get_logger(__name__)







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def __init__(		self      ,							*a__      ,							**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DonutImageProcessor instead.'''      ,							a__      ,							)
             super().__init__(*a__      ,							**a__)

