

from random import randint
from tempfile import TemporaryFile

import numpy as np




def 					_lowercase     (				a_				:					Dict					,a_				:					Union[str, Any]					,a_				:					Tuple				)				->	str:




					'''simple docstring'''







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

										__magic_name__,	__magic_name__							=    _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 					_lowercase     (				a_				:					Any					,a_				:					Any					,a_				:					Any				)				->	List[Any]:




					'''simple docstring'''







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

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


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


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


outfile.seek(0)  # using the same array
A__						=							np.load(outfile)
A__						=							len(M) - 1
A__						=							_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, is_vision_available


A__						=							{"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						=							[
						    "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "VanForImageClassification",
						    "VanModel",
						    "VanPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

						A__						=							_LazyModule(__name__, globals()["__file__"], _import_structure)




