


from random import randint
from tempfile import TemporaryFile

import numpy as np



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     List[str]										=    0
     if start < end:
          a_    :     Any										=    randint(__UpperCamelCase  ,  __UpperCamelCase )
          a_    :     List[str]										=    a[end]
          a_    :     str										=    a[pivot]
          a_    :     str										=    temp

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

     a_    :     Tuple										=    a[new_pivot_index + 1]
     a_    :     str										=    a[end]
     a_    :     Any										=    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)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase        =     {
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''PegasusXForConditionalGeneration''',
           '''PegasusXModel''',
           '''PegasusXPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_pegasus_x import (
                  PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PegasusXForConditionalGeneration,
                  PegasusXModel,
                  PegasusXPreTrainedModel,
              )


else:
       import sys

       __lowerCamelCase        =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



