






from random import randint
from tempfile import TemporaryFile

import numpy as np




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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

										_A							,_A								= _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      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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


__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)











__A      =				{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__A      =				{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= []

					for neighbour in graph[vert]:
										if not visited[neighbour]:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					order.append(_lowercase )

					return order




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= [vert]

					for neighbour in reversed_graph[vert]:
										if not visited[neighbour]:
															component += find_components(_lowercase					,			_lowercase					,			_lowercase )

					return component




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= len(_lowercase ) * [False]
					_A								= {vert: [] for vert in range(len(_lowercase ) )}

					for vert, neighbours in graph.items():
										for neighbour in neighbours:
															reversed_graph[neighbour].append(_lowercase )

					_A								= []
					for i, was_visited in enumerate(_lowercase ):
										if not was_visited:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					_A								= []
					_A								= len(_lowercase ) * [False]

					for i in range(len(_lowercase ) ):
										_A								= order[len(_lowercase ) - i - 1]
										if not visited[vert]:
															_A								= find_components(_lowercase					,			_lowercase					,			_lowercase )
															components_list.append(_lowercase )

					return components_list



