from random import randint
from tempfile import TemporaryFile

import numpy as np



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


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

  __a       ,     __a       =  _in_place_partition(_A							,				_A							,				_A					)
  count += _in_place_quick_sort(_A							,				_A							,				p - 1					)
  count += _in_place_quick_sort(_A							,				p + 1							,				_A					)
 return count



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  0
 __a       =  randint(_A							,				_A					)
 __a       =  a[end]
 __a       =  a[pivot]
 __a       =  temp
 __a       =  start - 1
 for index in range(_A							,				_A					):
  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


SCREAMING_SNAKE_CASE :	Any							     =		TemporaryFile()
SCREAMING_SNAKE_CASE :	Optional[int]							     =		100  # 1000 elements are to be sorted


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


outfile.seek(0)  # using the same array
SCREAMING_SNAKE_CASE :	Any							     =		np.load(outfile)
SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		len(M) - 1
SCREAMING_SNAKE_CASE :	Optional[Any]							     =		_in_place_quick_sort(M, 0, r)

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






print(z)



import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset



@pytest.mark.parametrize("dataset_size"							,				[None, 400 * 2**20, 600 * 2**20]					)
@pytest.mark.parametrize("input_in_memory_max_size"							,				["default", 0, 100 * 2**20, 900 * 2**20]					)
def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 if input_in_memory_max_size != "default":
  monkeypatch.setattr(datasets.config							,				"IN_MEMORY_MAX_SIZE"							,				_A					)
 __a       =  datasets.config.IN_MEMORY_MAX_SIZE
 if input_in_memory_max_size == "default":
  assert in_memory_max_size == 0
 else:
  assert in_memory_max_size == input_in_memory_max_size
 if dataset_size and in_memory_max_size:
  __a       =  dataset_size < in_memory_max_size
 else:
  __a       =  False
 __a       =  is_small_dataset(_A					)
 assert result == expected


