





'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     0
      if start < end:
            _snake_case		:	List[Any]					     =     randint(lowerCAmelCase_			,      lowerCAmelCase_	)
            _snake_case		:	Any					     =     a[end]
            _snake_case		:	List[str]					     =     a[pivot]
            _snake_case		:	Optional[int]					     =     temp

            _snake_case     ,							_snake_case		:	List[Any]					     =     _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_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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

      _snake_case		:	Any					     =     a[new_pivot_index + 1]
      _snake_case		:	str					     =     a[end]
      _snake_case		:	Optional[int]					     =     temp
      return new_pivot_index + 1, count


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


UpperCAmelCase,       UpperCAmelCase					:		str		=							0, 1  # mean and standard deviation
UpperCAmelCase					:		Optional[Any]		=							np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
UpperCAmelCase					:		int		=							np.load(outfile)
UpperCAmelCase					:		Optional[int]		=							len(M) - 1
UpperCAmelCase					:		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 pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class lowerCamelCase						(unittest.TestCase					):







  def 				UpperCAmelCase_				(		self		)		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	Any					     =     torch.nn.Linear(10     ,     10		)
        _snake_case		:	Optional[int]					     =     torch.optim.SGD(model.parameters()     ,     0.1		)
        _snake_case		:	List[str]					     =     Accelerator()
        _snake_case		:	Optional[Any]					     =     accelerator.prepare(lowercase__		)
        try:
              pickle.loads(pickle.dumps(lowercase__		)		)
        except Exception as e:
              self.fail(F'''Accelerated optimizer pickling failed with {e}'''		)
        AcceleratorState._reset_state()


